Bd_pr_month-vs-pr-ensemble-average.ipynb

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    "#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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c5954180",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2020-01-16T12:00:00 ... 2020-12-16T12:00:00\n",
       "    lon      float64 0.0\n",
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       "    tp       (time) float32 2.866e-05 4.734e-05 ... 7.179e-05 2.966e-05\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg...\n",
       "    Conventions:  CF-1.6\n",
       "    history:      Wed Jul 13 15:33:26 2022: cdo -L fldmean -ymonavg -remapnn,...\n",
       "    frequency:    mon\n",
       "    CDO:          Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg...</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-63cfc829-ec10-4611-812c-dd2bb755adf0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-63cfc829-ec10-4611-812c-dd2bb755adf0' 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'>time</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7f7327b6-508d-4b2f-a8ee-79e3628a5244' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7f7327b6-508d-4b2f-a8ee-79e3628a5244' 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'>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'>2020-01-16T12:00:00 ... 2020-12-...</div><input id='attrs-09825520-8aa3-409a-bd75-3c2f28df0983' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09825520-8aa3-409a-bd75-3c2f28df0983' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d02f657e-272c-4987-8e3d-cad8f486a4f4' class='xr-var-data-in' type='checkbox'><label for='data-d02f657e-272c-4987-8e3d-cad8f486a4f4' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-16T12:00:00.000000000&#x27;, &#x27;2020-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-03-16T12:00:00.000000000&#x27;, &#x27;2020-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-05-16T12:00:00.000000000&#x27;, &#x27;2020-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-07-16T12:00:00.000000000&#x27;, &#x27;2020-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-09-16T00:00:00.000000000&#x27;, &#x27;2020-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-11-16T00:00:00.000000000&#x27;, &#x27;2020-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-901017bd-6dfe-436e-920c-ef8e1e1e2515' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-901017bd-6dfe-436e-920c-ef8e1e1e2515' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1ffa6a2-080d-4223-bf21-a57896c2ae22' class='xr-var-data-in' type='checkbox'><label for='data-e1ffa6a2-080d-4223-bf21-a57896c2ae22' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-b3b1155e-c9ec-43f8-aba6-f9d965e9e13a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3b1155e-c9ec-43f8-aba6-f9d965e9e13a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfe9efc2-ac50-4204-806b-620931db165f' class='xr-var-data-in' type='checkbox'><label for='data-dfe9efc2-ac50-4204-806b-620931db165f' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0acd7ac8-04e9-4711-b8f0-1b117d3aec71' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0acd7ac8-04e9-4711-b8f0-1b117d3aec71' 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)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3219bd02-320f-4bd1-b9ff-4178417e16a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3219bd02-320f-4bd1-b9ff-4178417e16a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20accae8-9d2a-4bfe-8a04-e414e2e7baae' class='xr-var-data-in' type='checkbox'><label for='data-20accae8-9d2a-4bfe-8a04-e414e2e7baae' 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>Total precipitation</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([2.866126e-05, 4.734449e-05, 7.192317e-05, 1.499279e-04, 3.003384e-04,\n",
       "       6.153994e-04, 6.786717e-04, 5.672286e-04, 4.397557e-04, 2.273886e-04,\n",
       "       7.179099e-05, 2.965603e-05], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4a7a5a53-2230-4d11-8c12-01bf5a2f58d7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4a7a5a53-2230-4d11-8c12-01bf5a2f58d7' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>Wed Jul 13 15:33:26 2022: cdo -L fldmean -ymonavg -remapnn,grid.des pr_198001_202012_mon.nc era5_yearly.nc\n",
       "Thu Jun 16 11:44:48 2022: cdo monavg ../daily/pr_1980_01_day.nc pr_1980_01_mon.nc\n",
       "Thu Jun 16 11:41:54 2022: cdo -L -b F32 --timestat_date middle shifttime,1h -dayavg -selmonth,1 -mergetime ../hourly/pr_1980_01.nc ../hourly/pr_1980_02.nc pr_1980_01_day.nc\n",
       "2019-05-21 12:33:12 GMT by grib_to_netcdf-2.10.0: /opt/ecmwf/eccodes/bin/grib_to_netcdf -o /cache/data1/adaptor.mars.internal-1558441888.9574902-16326-3-5452902c-0664-4951-a53d-562b897ee9cc.nc /cache/tmp/5452902c-0664-4951-a53d-562b897ee9cc-adaptor.mars.internal-1558441888.9579558-16326-2-tmp.grib</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2020-01-16T12:00:00 ... 2020-12-16T12:00:00\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    tp       (time) float32 ...\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg...\n",
       "    Conventions:  CF-1.6\n",
       "    history:      Wed Jul 13 15:33:26 2022: cdo -L fldmean -ymonavg -remapnn,...\n",
       "    frequency:    mon\n",
       "    CDO:          Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = xr.open_dataset('/home/mkibria/Academic/pr_of_thesis/reanalysis/ERA5/era5_yearly.nc')\n",
    " \n",
    "\n",
    "data=data.squeeze()\n",
    "\n",
    "\n",
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a5346964",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data['tp']\n",
    "x = x*1000*24\n",
    "\n",
    "\n",
    "#data=data.to_array()\n",
    "\n",
    "\n",
    "#plt.plot(x,data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c472d839",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mon=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov','Dec']\n",
    "#mon=['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N','D']\n",
    "\n",
    "mon\n",
    "\n",
    "plt.title('Seasonal cycle of Bangladesh')\n",
    "plt.xlabel('months')\n",
    "plt.ylabel('precipitation in mm/day')\n",
    "plt.plot(mon,x)\n",
    "plt.savefig(\"/home/mkibria/Academic/pr_of_thesis/figures/month-vs-pr-era5.png\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0bf7ae5c",
   "metadata": {},
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) object 2005-01-16 12:00:00 ... 2005-12-16 12:00:00\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    pr       (time) float32 1.536e-05 1.142e-05 2.925e-05 ... 4.33e-05 1.959e-05\n",
       "Attributes: (12/165)\n",
       "    CDI:                                             Climate Data Interface v...\n",
       "    Conventions:                                     CF-1.7\n",
       "    history:                                         Sat Jul 16 11:48:15 2022...\n",
       "    source:                                          RegCM Model output file\n",
       "    institution:                                     Oak Ridge National Labor...\n",
       "    project_id:                                      CORDEX\n",
       "    ...                                              ...\n",
       "    experiment_id:                                   historical\n",
       "    driving_experiment:                              MIROC-MIROC5, historical...\n",
       "    driving_model_id:                                MIROC-MIROC5\n",
       "    driving_experiment_name:                         historical\n",
       "    model_id:                                        ICTP-RegCM4-7\n",
       "    CDO:                                             Climate Data Operators v...</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-785fa968-379e-46b8-88af-f35cfc6e23bb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-785fa968-379e-46b8-88af-f35cfc6e23bb' 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'>time</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9db25804-fd62-4195-9955-b214d249d5d9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9db25804-fd62-4195-9955-b214d249d5d9' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2005-01-16 12:00:00 ... 2005-12-...</div><input id='attrs-1e05f174-287e-4be1-8edd-00691b4bf547' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e05f174-287e-4be1-8edd-00691b4bf547' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5199f21-1ef7-4b71-ba07-6072b06a873e' class='xr-var-data-in' type='checkbox'><label for='data-e5199f21-1ef7-4b71-ba07-6072b06a873e' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2005, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-27c1d5a1-76e0-4f8c-8acb-2d448a3a8518' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27c1d5a1-76e0-4f8c-8acb-2d448a3a8518' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c091dfaa-1766-4325-83e4-db95155eb523' class='xr-var-data-in' type='checkbox'><label for='data-c091dfaa-1766-4325-83e4-db95155eb523' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-ba702411-3e2b-4770-80f2-d63f9b31a5ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba702411-3e2b-4770-80f2-d63f9b31a5ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ab80bd5-0995-47ce-bb49-c78aa4921c34' class='xr-var-data-in' type='checkbox'><label for='data-2ab80bd5-0995-47ce-bb49-c78aa4921c34' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-491132ec-ff1b-4c11-8dba-67af00b89dda' class='xr-section-summary-in' type='checkbox'  checked><label for='section-491132ec-ff1b-4c11-8dba-67af00b89dda' 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>pr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-93d79791-0d8f-4f11-be31-3a76d11fb393' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93d79791-0d8f-4f11-be31-3a76d11fb393' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28453560-3040-4fdd-a27c-f1a1f406485a' class='xr-var-data-in' type='checkbox'><label for='data-28453560-3040-4fdd-a27c-f1a1f406485a' 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>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([1.536402e-05, 1.142338e-05, 2.925245e-05, 6.558516e-05, 9.592339e-05,\n",
       "       1.224795e-04, 1.384992e-04, 1.378350e-04, 8.467238e-05, 5.878526e-05,\n",
       "       4.330435e-05, 1.959261e-05], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e8cd0433-4bfa-4467-99f4-4a8011d68537' class='xr-section-summary-in' type='checkbox'  ><label for='section-e8cd0433-4bfa-4467-99f4-4a8011d68537' class='xr-section-summary' >Attributes: <span>(165)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>history :</span></dt><dd>Sat Jul 16 11:48:15 2022: cdo -L fldmean -ymonavg -remapnn,grid.des miroc5_pr_197001-200512.nc miroc5_yearly.nc\n",
       "Mon Jun 27 15:34:28 2022: cdo mergetime pr_WAS-22_MIROC-MIROC5_historical_r1i1p1_ICTP-RegCM4-7_v0_mon_197001-198012.nc pr_WAS-22_MIROC-MIROC5_historical_r1i1p1_ICTP-RegCM4-7_v0_mon_198101-199012.nc pr_WAS-22_MIROC-MIROC5_historical_r1i1p1_ICTP-RegCM4-7_v0_mon_199101-200012.nc pr_WAS-22_MIROC-MIROC5_historical_r1i1p1_ICTP-RegCM4-7_v0_mon_200101-200512.nc miroc5_pr_197001-200512.nc\n",
       "2018-05-11 16:23:33 : Created by RegCM RegCM Model program</dd><dt><span>source :</span></dt><dd>RegCM Model output file</dd><dt><span>institution :</span></dt><dd>Oak Ridge National Laboratory</dd><dt><span>project_id :</span></dt><dd>CORDEX</dd><dt><span>ipcc_scenario_code :</span></dt><dd>HISTORICAL</dd><dt><span>institute_id :</span></dt><dd>ORNL</dd><dt><span>note :</span></dt><dd>The domain is larger than WAS-22</dd><dt><span>comment :</span></dt><dd>RegCM CORDEX SAsia25 run</dd><dt><span>experiment :</span></dt><dd>WAS-22</dd><dt><span>driving_model_ensemble_member :</span></dt><dd>r1i1p1</dd><dt><span>creation_date :</span></dt><dd>2019-09-12T16:23:22Z</dd><dt><span>CORDEX_domain :</span></dt><dd>WAS-22</dd><dt><span>rcm_version_id :</span></dt><dd>v0</dd><dt><span>ICTP_version_note :</span></dt><dd>Model run at ORNL on Titan</dd><dt><span>contact :</span></dt><dd>mashfaq@ornl.gov</dd><dt><span>product :</span></dt><dd>output</dd><dt><span>tracking_id :</span></dt><dd>635194b8-8309-11e9-827b-f04da273f023</dd><dt><span>title :</span></dt><dd>ICTP Regional Climatic model V4</dd><dt><span>references :</span></dt><dd>http://gforge.ictp.it/gf/project/regcm</dd><dt><span>model_revision :</span></dt><dd>tag-4.7.0</dd><dt><span>projection :</span></dt><dd>ROTMER</dd><dt><span>grid_size_in_meters :</span></dt><dd>25000.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>13.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>70.0</dd><dt><span>grid_north_pole_latitude :</span></dt><dd>13.0</dd><dt><span>grid_north_pole_longitude :</span></dt><dd>70.0</dd><dt><span>grid_factor :</span></dt><dd>0.0</dd><dt><span>landsurface_model :</span></dt><dd>clm4.5</dd><dt><span>model_icbc_data_source :</span></dt><dd>MI_85</dd><dt><span>model_sst_data_source :</span></dt><dd>MI_85</dd><dt><span>boundary_nspgx :</span></dt><dd>30</dd><dt><span>boundary_nspgd :</span></dt><dd>30</dd><dt><span>boundary_high_nudge :</span></dt><dd>8.0</dd><dt><span>boundary_medium_nudge :</span></dt><dd>6.0</dd><dt><span>boundary_low_nudge :</span></dt><dd>4.0</dd><dt><span>boundary_nm :</span></dt><dd>-1.0</dd><dt><span>boundary_dm :</span></dt><dd>-1.0</dd><dt><span>model_is_restarted :</span></dt><dd>No</dd><dt><span>model_simulation_initial_start :</span></dt><dd> 1970-01-01 00:00:00 UTC</dd><dt><span>model_simulation_start :</span></dt><dd> 1970-01-01 00:00:00 UTC</dd><dt><span>model_simulation_end :</span></dt><dd> 1975-01-01 00:00:00 UTC</dd><dt><span>atmosphere_time_step_in_seconds :</span></dt><dd>40.0</dd><dt><span>surface_interaction_time_step_in_seconds :</span></dt><dd>480.0</dd><dt><span>convection_time_step_in_seconds :</span></dt><dd>240.0</dd><dt><span>radiation_scheme_time_step_in_seconds :</span></dt><dd>1800.0</dd><dt><span>absorption_emission_time_step_in_seconds :</span></dt><dd>64800.0</dd><dt><span>dynamical_core :</span></dt><dd>1</dd><dt><span>asselin_filter_nu_1 :</span></dt><dd>0.0625</dd><dt><span>asselin_filter_nu_2 :</span></dt><dd>0.0625</dd><dt><span>diffusion_hgt_factor :</span></dt><dd>1</dd><dt><span>upstream_mode :</span></dt><dd>Yes</dd><dt><span>off_centering_max :</span></dt><dd>0.25</dd><dt><span>stability_enhance :</span></dt><dd>Yes</dd><dt><span>temperature_extreme_gradient :</span></dt><dd>5.0</dd><dt><span>cloud_extreme_gradient_fraction :</span></dt><dd>0.5</dd><dt><span>vapor_extreme_gradient_fraction :</span></dt><dd>0.2</dd><dt><span>tracer_extreme_gradient_fraction :</span></dt><dd>0.5</dd><dt><span>lateral_boundary_condition_scheme :</span></dt><dd>5</dd><dt><span>semi_lagrangian_advection_scheme :</span></dt><dd>0</dd><dt><span>boundary_layer_scheme :</span></dt><dd>2</dd><dt><span>cumulus_convection_scheme_lnd :</span></dt><dd>4</dd><dt><span>cumulus_convection_scheme_ocn :</span></dt><dd>5</dd><dt><span>moisture_scheme :</span></dt><dd>1</dd><dt><span>ocean_flux_scheme :</span></dt><dd>2</dd><dt><span>zeng_ocean_roughness_formula :</span></dt><dd>1</dd><dt><span>zeng_ocean_roughness_method :</span></dt><dd>1</dd><dt><span>pressure_gradient_scheme :</span></dt><dd>0</dd><dt><span>surface_emissivity_factor_computed :</span></dt><dd>0</dd><dt><span>lake_model_activated :</span></dt><dd>0</dd><dt><span>chemical_aerosol_scheme_activated :</span></dt><dd>0</dd><dt><span>diurnal_cycle_sst_scheme :</span></dt><dd>0</dd><dt><span>ocean_precipitaion_cooling_effect :</span></dt><dd>0</dd><dt><span>ocean_albedo_from_whitecapping :</span></dt><dd>0</dd><dt><span>simple_sea_ice_scheme :</span></dt><dd>0</dd><dt><span>seasonal_desert_albedo :</span></dt><dd>0</dd><dt><span>convective_lwp_as_large_scale :</span></dt><dd>0</dd><dt><span>large_scale_cloud_fraction_scheme :</span></dt><dd>0</dd><dt><span>ocean_stratiform_clouds_scheme :</span></dt><dd>1</dd><dt><span>rrtm_radiation_scheme_activated :</span></dt><dd>0</dd><dt><span>climatic_ozone_input_dataset :</span></dt><dd>0</dd><dt><span>climatic_aerosol_input_dataset :</span></dt><dd>0</dd><dt><span>static_solar_constant_used :</span></dt><dd>0</dd><dt><span>cumulus_cloud_model :</span></dt><dd>1</dd><dt><span>sun_year_offset :</span></dt><dd>0.0</dd><dt><span>fix_solar_parameters :</span></dt><dd>0</dd><dt><span>cloud_bottom_level_with_no_clouds :</span></dt><dd>1</dd><dt><span>cloud_rh_with_fcc_one :</span></dt><dd>1.01</dd><dt><span>cloud_rh_threshold_for_land :</span></dt><dd>0.8</dd><dt><span>cloud_rh_threshold_for_ocean :</span></dt><dd>0.9</dd><dt><span>cloud_rh1_limit_temperature :</span></dt><dd>238.0</dd><dt><span>cloud_cloud_fraction_maximum :</span></dt><dd>0.75</dd><dt><span>cloud_condensation_threshold :</span></dt><dd>1.0</dd><dt><span>cloud_cloud_fraction_max_for_convection :</span></dt><dd>1.0</dd><dt><span>cloud_cloud_liqwat_max_for_convection :</span></dt><dd>0.0003</dd><dt><span>cloud_shallow_convective_cf_param :</span></dt><dd>0.07</dd><dt><span>cloud_deep_convective_cf_param :</span></dt><dd>0.14</dd><dt><span>cloud_k2_convective_cf_param :</span></dt><dd>500.0</dd><dt><span>cloud_surface_radiation_hack :</span></dt><dd>No</dd><dt><span>cloud_arctic_cloud_correction :</span></dt><dd>Yes</dd><dt><span>subex_auto_conversion_rate_for_land :</span></dt><dd>0.00025</dd><dt><span>subex_auto_conversion_rate_for_ocean :</span></dt><dd>0.00025</dd><dt><span>subex_gultepe_factor_when_rain_for_land :</span></dt><dd>0.4</dd><dt><span>subex_gultepe_factor_when_rain_for_ocean :</span></dt><dd>0.4</dd><dt><span>subex_land_raindrop_evaporation_rate :</span></dt><dd>0.0002</dd><dt><span>subex_ocean_raindrop_evaporation_rate :</span></dt><dd>0.0001</dd><dt><span>subex_land_raindrop_accretion_rate :</span></dt><dd>3.0</dd><dt><span>subex_ocean_raindrop_accretion_rate :</span></dt><dd>3.0</dd><dt><span>mit_lowest_convection_sigma :</span></dt><dd>0.95</dd><dt><span>mit_autoconversion_threshold_mixing_over_ocean :</span></dt><dd>0.00011</dd><dt><span>mit_autoconversion_threshold_mixing_over_land :</span></dt><dd>0.00011</dd><dt><span>mit_autoconversion_threshold_temperature :</span></dt><dd>-55.0</dd><dt><span>mit_mixing_coefficient_in_entrainment :</span></dt><dd>1.5</dd><dt><span>mit_fractional_area_unsaturated_downdraft :</span></dt><dd>0.05</dd><dt><span>mit_fractional_precip_outside_cloud :</span></dt><dd>0.12</dd><dt><span>mit_pressure_velocity_of_rain :</span></dt><dd>50.0</dd><dt><span>mit_pressure_velocity_of_snow :</span></dt><dd>5.5</dd><dt><span>mit_rain_evaporation_coefficient :</span></dt><dd>1.0</dd><dt><span>mit_snow_evaporation_coefficient :</span></dt><dd>0.8</dd><dt><span>mit_momentum_transport_coefficient :</span></dt><dd>0.7</dd><dt><span>mit_downdraft_velocity_coefficient :</span></dt><dd>10.0</dd><dt><span>mit_max_parcel_neg_temp_perturbation :</span></dt><dd>0.9</dd><dt><span>mit_approach_rate_quasi_eq_coeff_a :</span></dt><dd>0.2</dd><dt><span>mit_approach_rate_quasi_eq_coeff_d :</span></dt><dd>0.1</dd><dt><span>mit_maximum_land_precipitation_efficiency :</span></dt><dd>0.999</dd><dt><span>mit_maximum_ocean_precipitation_efficiency :</span></dt><dd>0.999</dd><dt><span>tiedtke_actual_scheme :</span></dt><dd>4</dd><dt><span>tiedtke_entrainment_rate_downdraft :</span></dt><dd>0.0003</dd><dt><span>tiedtke_entrainment_rate_deep_land :</span></dt><dd>0.00075</dd><dt><span>tiedtke_entrainment_rate_deep_ocean :</span></dt><dd>0.00075</dd><dt><span>tiedtke_penetrative :</span></dt><dd>Yes</dd><dt><span>tiedtke_midlevel :</span></dt><dd>Yes</dd><dt><span>tiedtke_shallow :</span></dt><dd>Yes</dd><dt><span>tiedtke_cumulus_downdraft :</span></dt><dd>Yes</dd><dt><span>tiedtke_prognostic_cloud :</span></dt><dd>Yes</dd><dt><span>tiedtke_cumulus_friction :</span></dt><dd>Yes</dd><dt><span>tiedtke_ke_dissipation :</span></dt><dd>Yes</dd><dt><span>tiedtke_tracer_transport :</span></dt><dd>Yes</dd><dt><span>tiedtke_tracer_smooth_massflux :</span></dt><dd>No</dd><dt><span>tiedtke_shallow_wstar_closure :</span></dt><dd>No</dd><dt><span>tiedtke_detrainment_rate_deep_land :</span></dt><dd>7.5e-05</dd><dt><span>tiedtke_detrainment_rate_deep_ocean :</span></dt><dd>7.5e-05</dd><dt><span>tiedtke_shallow_entrainment :</span></dt><dd>2.0</dd><dt><span>tiedtke_cloud_cover_evap_over_land :</span></dt><dd>0.05</dd><dt><span>tiedtke_cloud_cover_evap_over_ocean :</span></dt><dd>0.05</dd><dt><span>tiedtke_coeff_evap_over_land :</span></dt><dd>5.55e-05</dd><dt><span>tiedtke_coeff_evap_over_ocean :</span></dt><dd>5.55e-05</dd><dt><span>tiedtke_critical_rh_over_land :</span></dt><dd>0.9</dd><dt><span>tiedtke_critical_rh_over_ocean :</span></dt><dd>0.9</dd><dt><span>tiedtke_cloud_water_conv_over_land :</span></dt><dd>0.03</dd><dt><span>tiedtke_cloud_water_conv_over_ocean :</span></dt><dd>0.0014</dd><dt><span>tiedtke_cape_adjustment_timescale :</span></dt><dd>2400.0</dd><dt><span>uwpbl_advection_scheme :</span></dt><dd>0</dd><dt><span>uwpbl_cloud_evap_entr_incr_efficiency :</span></dt><dd>15.0</dd><dt><span>uwpbl_eddy_LS_stable_PBL_scaling :</span></dt><dd>1.5</dd><dt><span>uwpbl_czero :</span></dt><dd>5.869</dd><dt><span>uwpbl_nuk :</span></dt><dd>5.0</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>driving_experiment :</span></dt><dd>MIROC-MIROC5, historical, r1i1p1</dd><dt><span>driving_model_id :</span></dt><dd>MIROC-MIROC5</dd><dt><span>driving_experiment_name :</span></dt><dd>historical</dd><dt><span>model_id :</span></dt><dd>ICTP-RegCM4-7</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) object 2005-01-16 12:00:00 ... 2005-12-16 12:00:00\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    pr       (time) float32 ...\n",
       "Attributes: (12/165)\n",
       "    CDI:                                             Climate Data Interface v...\n",
       "    Conventions:                                     CF-1.7\n",
       "    history:                                         Sat Jul 16 11:48:15 2022...\n",
       "    source:                                          RegCM Model output file\n",
       "    institution:                                     Oak Ridge National Labor...\n",
       "    project_id:                                      CORDEX\n",
       "    ...                                              ...\n",
       "    experiment_id:                                   historical\n",
       "    driving_experiment:                              MIROC-MIROC5, historical...\n",
       "    driving_model_id:                                MIROC-MIROC5\n",
       "    driving_experiment_name:                         historical\n",
       "    model_id:                                        ICTP-RegCM4-7\n",
       "    CDO:                                             Climate Data Operators v..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miroc5_data = xr.open_dataset('/home/mkibria/Academic/pr_of_thesis/regcm driven by gcm/MIROC5/miroc5_yearly.nc')\n",
    "\n",
    "miroc5_data = miroc5_data.squeeze()\n",
    "\n",
    "miroc5_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b89bbd4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x2 = miroc5_data['pr']\n",
    "x2 = x2*86400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1003bcde",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) object 2012-01-16 12:00:00 ... 2012-12-16 12:00:00\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    pr       (time) float32 6.06e-06 7.278e-06 1.45e-05 ... 2.578e-05 8.278e-06\n",
       "Attributes: (12/29)\n",
       "    CDI:                    Climate Data Interface version 1.9.9rc1 (https://...\n",
       "    Conventions:            CF-1.4\n",
       "    history:                Sat Jul 16 11:54:49 2022: cdo -L fldmean -ymonavg...\n",
       "    source:                 MIROC5 2010 atmosphere: MIROC-AGCM6 (T85L40); oce...\n",
       "    institution:            AORI (Atmosphere and Ocean Research Institute, Th...\n",
       "    institute_id:           MIROC\n",
       "    ...                     ...\n",
       "    title:                  MIROC5 model output prepared for CMIP5 historical\n",
       "    parent_experiment:      pre-industrial control\n",
       "    modeling_realm:         atmos\n",
       "    realization:            1\n",
       "    cmor_version:           2.5.6\n",
       "    CDO:                    Climate Data Operators version 1.9.9rc1 (https://...</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-0845d146-09d7-4a1e-b2a6-17c9b83c0142' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0845d146-09d7-4a1e-b2a6-17c9b83c0142' 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'>time</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-395c3ee1-ffed-402a-ba1d-69ee88dedac2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-395c3ee1-ffed-402a-ba1d-69ee88dedac2' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2012-01-16 12:00:00 ... 2012-12-...</div><input id='attrs-406c7b08-8b1e-492d-9281-d99bd8eea504' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-406c7b08-8b1e-492d-9281-d99bd8eea504' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8537fe57-3a68-4978-8438-8cf5e7ae13a2' class='xr-var-data-in' type='checkbox'><label for='data-8537fe57-3a68-4978-8438-8cf5e7ae13a2' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2012, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-63cf5236-fa5d-4235-a2a2-33c27dc0f3a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63cf5236-fa5d-4235-a2a2-33c27dc0f3a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de51dbb0-cc76-4c46-895d-5a3bf53eb58d' class='xr-var-data-in' type='checkbox'><label for='data-de51dbb0-cc76-4c46-895d-5a3bf53eb58d' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-2a82dd72-3a4b-4a62-ab7e-c8f9832f4dc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a82dd72-3a4b-4a62-ab7e-c8f9832f4dc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a4d83a4-6b3a-4092-80eb-7403dcbd3f43' class='xr-var-data-in' type='checkbox'><label for='data-0a4d83a4-6b3a-4092-80eb-7403dcbd3f43' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-587ebc6e-8e42-418d-9441-bad2dc2c6f57' class='xr-section-summary-in' type='checkbox'  checked><label for='section-587ebc6e-8e42-418d-9441-bad2dc2c6f57' 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>pr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-828f43c0-6977-4a3a-9555-b936597c5676' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-828f43c0-6977-4a3a-9555-b936597c5676' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e31848b-ab70-4c6e-a700-7ab1a032ed02' class='xr-var-data-in' type='checkbox'><label for='data-2e31848b-ab70-4c6e-a700-7ab1a032ed02' 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>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>comment :</span></dt><dd>at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)</dd><dt><span>original_name :</span></dt><dd>PRCP</dd><dt><span>original_units :</span></dt><dd>kg/m**2/s</dd><dt><span>history :</span></dt><dd>2011-04-23T09:11:46Z altered by CMOR: Converted units from &#x27;kg/m**2/s&#x27; to &#x27;kg m-2 s-1&#x27;. 2011-04-23T09:11:46Z altered by CMOR: replaced missing value flag (-999) with standard missing value (1e+20). 2011-04-23T09:11:46Z altered by CMOR: Inverted axis: lat.</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>associated_files :</span></dt><dd>baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_MIROC5_historical_r0i0p0.nc areacella: areacella_fx_MIROC5_historical_r0i0p0.nc</dd></dl></div><div class='xr-var-data'><pre>array([6.059618e-06, 7.278179e-06, 1.449820e-05, 3.358752e-05, 1.203325e-04,\n",
       "       2.401624e-04, 2.125506e-04, 1.464726e-04, 1.142946e-04, 7.744579e-05,\n",
       "       2.578253e-05, 8.278077e-06], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e02f1f91-e25c-429e-b314-c0cd008b1201' class='xr-section-summary-in' type='checkbox'  ><label for='section-e02f1f91-e25c-429e-b314-c0cd008b1201' class='xr-section-summary' >Attributes: <span>(29)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>history :</span></dt><dd>Sat Jul 16 11:54:49 2022: cdo -L fldmean -ymonavg -remapnn,grid1.des pr_Amon_MIROC5_historical_r1i1p1_185001-201212.nc miroc5_yearly.nc\n",
       "2011-04-23T09:11:46Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.</dd><dt><span>source :</span></dt><dd>MIROC5 2010 atmosphere: MIROC-AGCM6 (T85L40); ocean: COCO (COCO4.5, 256x224 L50); sea ice: COCO (COCO4.5); land: MATSIRO (MATSIRO, L6); aerosols: SPRINTARS (SPRINTARS 5.00, T85L40)</dd><dt><span>institution :</span></dt><dd>AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba, Japan), NIES (National Institute for Environmental Studies, Ibaraki, Japan), JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan)</dd><dt><span>institute_id :</span></dt><dd>MIROC</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>model_id :</span></dt><dd>MIROC5</dd><dt><span>forcing :</span></dt><dd>GHG, SA, Oz, LU, Sl, Vl, SS, Ds, BC, MD, OC (GHG includes CO2, N2O, methane, and fluorocarbons; Oz includes OH and H2O2; LU excludes change in lake fraction)</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_experiment_rip :</span></dt><dd>r1i1p1</dd><dt><span>branch_time :</span></dt><dd>150015.0</dd><dt><span>contact :</span></dt><dd>Masahiro Watanabe (hiro@aori.u-tokyo.ac.jp), Seita Emori (emori@nies.go.jp), Masayoshi Ishii (ism@jamstec.go.jp), Masahide Kimoto (kimoto@aori.u-tokyo.ac.jp)</dd><dt><span>references :</span></dt><dd>Watanabe et al., 2010: Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J. Climate, 23, 6312-6335</dd><dt><span>initialization_method :</span></dt><dd>1</dd><dt><span>physics_version :</span></dt><dd>1</dd><dt><span>tracking_id :</span></dt><dd>a906be44-c850-4f0c-b8c4-2b165051669a</dd><dt><span>product :</span></dt><dd>output</dd><dt><span>experiment :</span></dt><dd>historical</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>creation_date :</span></dt><dd>2011-04-23T09:11:46Z</dd><dt><span>project_id :</span></dt><dd>CMIP5</dd><dt><span>table_id :</span></dt><dd>Table Amon (31 January 2011) 53b766a395ac41696af40aab76a49ae5</dd><dt><span>title :</span></dt><dd>MIROC5 model output prepared for CMIP5 historical</dd><dt><span>parent_experiment :</span></dt><dd>pre-industrial control</dd><dt><span>modeling_realm :</span></dt><dd>atmos</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>cmor_version :</span></dt><dd>2.5.6</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) object 2012-01-16 12:00:00 ... 2012-12-16 12:00:00\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    pr       (time) float32 ...\n",
       "Attributes: (12/29)\n",
       "    CDI:                    Climate Data Interface version 1.9.9rc1 (https://...\n",
       "    Conventions:            CF-1.4\n",
       "    history:                Sat Jul 16 11:54:49 2022: cdo -L fldmean -ymonavg...\n",
       "    source:                 MIROC5 2010 atmosphere: MIROC-AGCM6 (T85L40); oce...\n",
       "    institution:            AORI (Atmosphere and Ocean Research Institute, Th...\n",
       "    institute_id:           MIROC\n",
       "    ...                     ...\n",
       "    title:                  MIROC5 model output prepared for CMIP5 historical\n",
       "    parent_experiment:      pre-industrial control\n",
       "    modeling_realm:         atmos\n",
       "    realization:            1\n",
       "    cmor_version:           2.5.6\n",
       "    CDO:                    Climate Data Operators version 1.9.9rc1 (https://..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcm_miroc5_data = xr.open_dataset('/home/mkibria/Academic/pr_of_thesis/GCM/MIROC5/miroc5_yearly.nc')\n",
    "\n",
    "gcm_miroc5_data = gcm_miroc5_data.squeeze()\n",
    "\n",
    "gcm_miroc5_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "78afc30a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x3 = gcm_miroc5_data['pr']\n",
    "x3 = x3*86400\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8cbc2847",
   "metadata": {},
   "outputs": [
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       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  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",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) object 2005-01-16 12:00:00 ... 2005-12-16 12:00:00\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    pr       (time) float32 1.6e-05 1.797e-05 2.372e-05 ... 3.501e-05 1.981e-05\n",
       "Attributes: (12/165)\n",
       "    CDI:                                             Climate Data Interface v...\n",
       "    Conventions:                                     CF-1.7\n",
       "    history:                                         Mon Aug 15 13:07:36 2022...\n",
       "    source:                                          RegCM Model output file\n",
       "    institution:                                     Oak Ridge National Labor...\n",
       "    project_id:                                      CORDEX\n",
       "    ...                                              ...\n",
       "    experiment_id:                                   historical\n",
       "    driving_experiment:                              MIROC-MIROC5, historical...\n",
       "    driving_model_id:                                MIROC-MIROC5\n",
       "    driving_experiment_name:                         historical\n",
       "    model_id:                                        ICTP-RegCM4-7\n",
       "    CDO:                                             Climate Data Operators v...</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-381b2960-a291-49e8-b9c1-8d1bb1a98b5f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-381b2960-a291-49e8-b9c1-8d1bb1a98b5f' 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'>time</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ef3f5e97-259a-418d-9550-2153e222f45a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ef3f5e97-259a-418d-9550-2153e222f45a' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2005-01-16 12:00:00 ... 2005-12-...</div><input id='attrs-82191c57-b0bb-4df6-a8a4-cd9b10070894' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82191c57-b0bb-4df6-a8a4-cd9b10070894' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2e6a51a-39ac-460a-9823-e865bf5bfc2e' class='xr-var-data-in' type='checkbox'><label for='data-b2e6a51a-39ac-460a-9823-e865bf5bfc2e' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2005, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-e92f73e9-7926-48c8-adb8-c6c93b977711' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e92f73e9-7926-48c8-adb8-c6c93b977711' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e6e293f-d8b7-4bea-9764-815b785be256' class='xr-var-data-in' type='checkbox'><label for='data-5e6e293f-d8b7-4bea-9764-815b785be256' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-8b72f04b-7b1e-4e23-a5eb-673c1792366a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b72f04b-7b1e-4e23-a5eb-673c1792366a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b68d1eaa-e4da-489c-95f9-4eec9a26f732' class='xr-var-data-in' type='checkbox'><label for='data-b68d1eaa-e4da-489c-95f9-4eec9a26f732' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-777fa8b6-9e78-4b78-8aee-4c6c32c28549' class='xr-section-summary-in' type='checkbox'  checked><label for='section-777fa8b6-9e78-4b78-8aee-4c6c32c28549' 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>pr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-17992843-8de9-48ab-a93c-155a69f5c8df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17992843-8de9-48ab-a93c-155a69f5c8df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6156710-b3de-4b40-8669-1c52d5529f6b' class='xr-var-data-in' type='checkbox'><label for='data-c6156710-b3de-4b40-8669-1c52d5529f6b' 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>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([1.600220e-05, 1.797207e-05, 2.371708e-05, 5.375583e-05, 8.277963e-05,\n",
       "       1.284784e-04, 1.519986e-04, 1.368925e-04, 8.638496e-05, 5.049224e-05,\n",
       "       3.500918e-05, 1.981301e-05], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-43eb81b9-ffde-4c53-b71a-db5de49266ca' class='xr-section-summary-in' type='checkbox'  ><label for='section-43eb81b9-ffde-4c53-b71a-db5de49266ca' class='xr-section-summary' >Attributes: <span>(165)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>history :</span></dt><dd>Mon Aug 15 13:07:36 2022: cdo -L fldmean -ymonavg -remapnn,grid.des regcm_ensmean_pr_197001-200512.nc regcm_ensmean_pr_197001-200512_yearly.nc\n",
       "Mon Aug 15 13:06:47 2022: cdo ensmean miroc5_pr_197001-200512.nc mpiesm_pr_197001-200512.nc noresm_pr_197001-200512.nc regcm_ensmean_pr_197001-200512.nc\n",
       "Mon Jun 27 15:34:28 2022: cdo mergetime pr_WAS-22_MIROC-MIROC5_historical_r1i1p1_ICTP-RegCM4-7_v0_mon_197001-198012.nc pr_WAS-22_MIROC-MIROC5_historical_r1i1p1_ICTP-RegCM4-7_v0_mon_198101-199012.nc pr_WAS-22_MIROC-MIROC5_historical_r1i1p1_ICTP-RegCM4-7_v0_mon_199101-200012.nc pr_WAS-22_MIROC-MIROC5_historical_r1i1p1_ICTP-RegCM4-7_v0_mon_200101-200512.nc miroc5_pr_197001-200512.nc\n",
       "2018-05-11 16:23:33 : Created by RegCM RegCM Model program</dd><dt><span>source :</span></dt><dd>RegCM Model output file</dd><dt><span>institution :</span></dt><dd>Oak Ridge National Laboratory</dd><dt><span>project_id :</span></dt><dd>CORDEX</dd><dt><span>ipcc_scenario_code :</span></dt><dd>HISTORICAL</dd><dt><span>institute_id :</span></dt><dd>ORNL</dd><dt><span>note :</span></dt><dd>The domain is larger than WAS-22</dd><dt><span>comment :</span></dt><dd>RegCM CORDEX SAsia25 run</dd><dt><span>experiment :</span></dt><dd>WAS-22</dd><dt><span>driving_model_ensemble_member :</span></dt><dd>r1i1p1</dd><dt><span>creation_date :</span></dt><dd>2019-09-12T16:23:22Z</dd><dt><span>CORDEX_domain :</span></dt><dd>WAS-22</dd><dt><span>rcm_version_id :</span></dt><dd>v0</dd><dt><span>ICTP_version_note :</span></dt><dd>Model run at ORNL on Titan</dd><dt><span>contact :</span></dt><dd>mashfaq@ornl.gov</dd><dt><span>product :</span></dt><dd>output</dd><dt><span>tracking_id :</span></dt><dd>635194b8-8309-11e9-827b-f04da273f023</dd><dt><span>title :</span></dt><dd>ICTP Regional Climatic model V4</dd><dt><span>references :</span></dt><dd>http://gforge.ictp.it/gf/project/regcm</dd><dt><span>model_revision :</span></dt><dd>tag-4.7.0</dd><dt><span>projection :</span></dt><dd>ROTMER</dd><dt><span>grid_size_in_meters :</span></dt><dd>25000.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>13.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>70.0</dd><dt><span>grid_north_pole_latitude :</span></dt><dd>13.0</dd><dt><span>grid_north_pole_longitude :</span></dt><dd>70.0</dd><dt><span>grid_factor :</span></dt><dd>0.0</dd><dt><span>landsurface_model :</span></dt><dd>clm4.5</dd><dt><span>model_icbc_data_source :</span></dt><dd>MI_85</dd><dt><span>model_sst_data_source :</span></dt><dd>MI_85</dd><dt><span>boundary_nspgx :</span></dt><dd>30</dd><dt><span>boundary_nspgd :</span></dt><dd>30</dd><dt><span>boundary_high_nudge :</span></dt><dd>8.0</dd><dt><span>boundary_medium_nudge :</span></dt><dd>6.0</dd><dt><span>boundary_low_nudge :</span></dt><dd>4.0</dd><dt><span>boundary_nm :</span></dt><dd>-1.0</dd><dt><span>boundary_dm :</span></dt><dd>-1.0</dd><dt><span>model_is_restarted :</span></dt><dd>No</dd><dt><span>model_simulation_initial_start :</span></dt><dd> 1970-01-01 00:00:00 UTC</dd><dt><span>model_simulation_start :</span></dt><dd> 1970-01-01 00:00:00 UTC</dd><dt><span>model_simulation_end :</span></dt><dd> 1975-01-01 00:00:00 UTC</dd><dt><span>atmosphere_time_step_in_seconds :</span></dt><dd>40.0</dd><dt><span>surface_interaction_time_step_in_seconds :</span></dt><dd>480.0</dd><dt><span>convection_time_step_in_seconds :</span></dt><dd>240.0</dd><dt><span>radiation_scheme_time_step_in_seconds :</span></dt><dd>1800.0</dd><dt><span>absorption_emission_time_step_in_seconds :</span></dt><dd>64800.0</dd><dt><span>dynamical_core :</span></dt><dd>1</dd><dt><span>asselin_filter_nu_1 :</span></dt><dd>0.0625</dd><dt><span>asselin_filter_nu_2 :</span></dt><dd>0.0625</dd><dt><span>diffusion_hgt_factor :</span></dt><dd>1</dd><dt><span>upstream_mode :</span></dt><dd>Yes</dd><dt><span>off_centering_max :</span></dt><dd>0.25</dd><dt><span>stability_enhance :</span></dt><dd>Yes</dd><dt><span>temperature_extreme_gradient :</span></dt><dd>5.0</dd><dt><span>cloud_extreme_gradient_fraction :</span></dt><dd>0.5</dd><dt><span>vapor_extreme_gradient_fraction :</span></dt><dd>0.2</dd><dt><span>tracer_extreme_gradient_fraction :</span></dt><dd>0.5</dd><dt><span>lateral_boundary_condition_scheme :</span></dt><dd>5</dd><dt><span>semi_lagrangian_advection_scheme :</span></dt><dd>0</dd><dt><span>boundary_layer_scheme :</span></dt><dd>2</dd><dt><span>cumulus_convection_scheme_lnd :</span></dt><dd>4</dd><dt><span>cumulus_convection_scheme_ocn :</span></dt><dd>5</dd><dt><span>moisture_scheme :</span></dt><dd>1</dd><dt><span>ocean_flux_scheme :</span></dt><dd>2</dd><dt><span>zeng_ocean_roughness_formula :</span></dt><dd>1</dd><dt><span>zeng_ocean_roughness_method :</span></dt><dd>1</dd><dt><span>pressure_gradient_scheme :</span></dt><dd>0</dd><dt><span>surface_emissivity_factor_computed :</span></dt><dd>0</dd><dt><span>lake_model_activated :</span></dt><dd>0</dd><dt><span>chemical_aerosol_scheme_activated :</span></dt><dd>0</dd><dt><span>diurnal_cycle_sst_scheme :</span></dt><dd>0</dd><dt><span>ocean_precipitaion_cooling_effect :</span></dt><dd>0</dd><dt><span>ocean_albedo_from_whitecapping :</span></dt><dd>0</dd><dt><span>simple_sea_ice_scheme :</span></dt><dd>0</dd><dt><span>seasonal_desert_albedo :</span></dt><dd>0</dd><dt><span>convective_lwp_as_large_scale :</span></dt><dd>0</dd><dt><span>large_scale_cloud_fraction_scheme :</span></dt><dd>0</dd><dt><span>ocean_stratiform_clouds_scheme :</span></dt><dd>1</dd><dt><span>rrtm_radiation_scheme_activated :</span></dt><dd>0</dd><dt><span>climatic_ozone_input_dataset :</span></dt><dd>0</dd><dt><span>climatic_aerosol_input_dataset :</span></dt><dd>0</dd><dt><span>static_solar_constant_used :</span></dt><dd>0</dd><dt><span>cumulus_cloud_model :</span></dt><dd>1</dd><dt><span>sun_year_offset :</span></dt><dd>0.0</dd><dt><span>fix_solar_parameters :</span></dt><dd>0</dd><dt><span>cloud_bottom_level_with_no_clouds :</span></dt><dd>1</dd><dt><span>cloud_rh_with_fcc_one :</span></dt><dd>1.01</dd><dt><span>cloud_rh_threshold_for_land :</span></dt><dd>0.8</dd><dt><span>cloud_rh_threshold_for_ocean :</span></dt><dd>0.9</dd><dt><span>cloud_rh1_limit_temperature :</span></dt><dd>238.0</dd><dt><span>cloud_cloud_fraction_maximum :</span></dt><dd>0.75</dd><dt><span>cloud_condensation_threshold :</span></dt><dd>1.0</dd><dt><span>cloud_cloud_fraction_max_for_convection :</span></dt><dd>1.0</dd><dt><span>cloud_cloud_liqwat_max_for_convection :</span></dt><dd>0.0003</dd><dt><span>cloud_shallow_convective_cf_param :</span></dt><dd>0.07</dd><dt><span>cloud_deep_convective_cf_param :</span></dt><dd>0.14</dd><dt><span>cloud_k2_convective_cf_param :</span></dt><dd>500.0</dd><dt><span>cloud_surface_radiation_hack :</span></dt><dd>No</dd><dt><span>cloud_arctic_cloud_correction :</span></dt><dd>Yes</dd><dt><span>subex_auto_conversion_rate_for_land :</span></dt><dd>0.00025</dd><dt><span>subex_auto_conversion_rate_for_ocean :</span></dt><dd>0.00025</dd><dt><span>subex_gultepe_factor_when_rain_for_land :</span></dt><dd>0.4</dd><dt><span>subex_gultepe_factor_when_rain_for_ocean :</span></dt><dd>0.4</dd><dt><span>subex_land_raindrop_evaporation_rate :</span></dt><dd>0.0002</dd><dt><span>subex_ocean_raindrop_evaporation_rate :</span></dt><dd>0.0001</dd><dt><span>subex_land_raindrop_accretion_rate :</span></dt><dd>3.0</dd><dt><span>subex_ocean_raindrop_accretion_rate :</span></dt><dd>3.0</dd><dt><span>mit_lowest_convection_sigma :</span></dt><dd>0.95</dd><dt><span>mit_autoconversion_threshold_mixing_over_ocean :</span></dt><dd>0.00011</dd><dt><span>mit_autoconversion_threshold_mixing_over_land :</span></dt><dd>0.00011</dd><dt><span>mit_autoconversion_threshold_temperature :</span></dt><dd>-55.0</dd><dt><span>mit_mixing_coefficient_in_entrainment :</span></dt><dd>1.5</dd><dt><span>mit_fractional_area_unsaturated_downdraft :</span></dt><dd>0.05</dd><dt><span>mit_fractional_precip_outside_cloud :</span></dt><dd>0.12</dd><dt><span>mit_pressure_velocity_of_rain :</span></dt><dd>50.0</dd><dt><span>mit_pressure_velocity_of_snow :</span></dt><dd>5.5</dd><dt><span>mit_rain_evaporation_coefficient :</span></dt><dd>1.0</dd><dt><span>mit_snow_evaporation_coefficient :</span></dt><dd>0.8</dd><dt><span>mit_momentum_transport_coefficient :</span></dt><dd>0.7</dd><dt><span>mit_downdraft_velocity_coefficient :</span></dt><dd>10.0</dd><dt><span>mit_max_parcel_neg_temp_perturbation :</span></dt><dd>0.9</dd><dt><span>mit_approach_rate_quasi_eq_coeff_a :</span></dt><dd>0.2</dd><dt><span>mit_approach_rate_quasi_eq_coeff_d :</span></dt><dd>0.1</dd><dt><span>mit_maximum_land_precipitation_efficiency :</span></dt><dd>0.999</dd><dt><span>mit_maximum_ocean_precipitation_efficiency :</span></dt><dd>0.999</dd><dt><span>tiedtke_actual_scheme :</span></dt><dd>4</dd><dt><span>tiedtke_entrainment_rate_downdraft :</span></dt><dd>0.0003</dd><dt><span>tiedtke_entrainment_rate_deep_land :</span></dt><dd>0.00075</dd><dt><span>tiedtke_entrainment_rate_deep_ocean :</span></dt><dd>0.00075</dd><dt><span>tiedtke_penetrative :</span></dt><dd>Yes</dd><dt><span>tiedtke_midlevel :</span></dt><dd>Yes</dd><dt><span>tiedtke_shallow :</span></dt><dd>Yes</dd><dt><span>tiedtke_cumulus_downdraft :</span></dt><dd>Yes</dd><dt><span>tiedtke_prognostic_cloud :</span></dt><dd>Yes</dd><dt><span>tiedtke_cumulus_friction :</span></dt><dd>Yes</dd><dt><span>tiedtke_ke_dissipation :</span></dt><dd>Yes</dd><dt><span>tiedtke_tracer_transport :</span></dt><dd>Yes</dd><dt><span>tiedtke_tracer_smooth_massflux :</span></dt><dd>No</dd><dt><span>tiedtke_shallow_wstar_closure :</span></dt><dd>No</dd><dt><span>tiedtke_detrainment_rate_deep_land :</span></dt><dd>7.5e-05</dd><dt><span>tiedtke_detrainment_rate_deep_ocean :</span></dt><dd>7.5e-05</dd><dt><span>tiedtke_shallow_entrainment :</span></dt><dd>2.0</dd><dt><span>tiedtke_cloud_cover_evap_over_land :</span></dt><dd>0.05</dd><dt><span>tiedtke_cloud_cover_evap_over_ocean :</span></dt><dd>0.05</dd><dt><span>tiedtke_coeff_evap_over_land :</span></dt><dd>5.55e-05</dd><dt><span>tiedtke_coeff_evap_over_ocean :</span></dt><dd>5.55e-05</dd><dt><span>tiedtke_critical_rh_over_land :</span></dt><dd>0.9</dd><dt><span>tiedtke_critical_rh_over_ocean :</span></dt><dd>0.9</dd><dt><span>tiedtke_cloud_water_conv_over_land :</span></dt><dd>0.03</dd><dt><span>tiedtke_cloud_water_conv_over_ocean :</span></dt><dd>0.0014</dd><dt><span>tiedtke_cape_adjustment_timescale :</span></dt><dd>2400.0</dd><dt><span>uwpbl_advection_scheme :</span></dt><dd>0</dd><dt><span>uwpbl_cloud_evap_entr_incr_efficiency :</span></dt><dd>15.0</dd><dt><span>uwpbl_eddy_LS_stable_PBL_scaling :</span></dt><dd>1.5</dd><dt><span>uwpbl_czero :</span></dt><dd>5.869</dd><dt><span>uwpbl_nuk :</span></dt><dd>5.0</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>driving_experiment :</span></dt><dd>MIROC-MIROC5, historical, r1i1p1</dd><dt><span>driving_model_id :</span></dt><dd>MIROC-MIROC5</dd><dt><span>driving_experiment_name :</span></dt><dd>historical</dd><dt><span>model_id :</span></dt><dd>ICTP-RegCM4-7</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
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      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) object 2005-01-16 12:00:00 ... 2005-12-16 12:00:00\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    pr       (time) float32 ...\n",
       "Attributes: (12/165)\n",
       "    CDI:                                             Climate Data Interface v...\n",
       "    Conventions:                                     CF-1.7\n",
       "    history:                                         Mon Aug 15 13:07:36 2022...\n",
       "    source:                                          RegCM Model output file\n",
       "    institution:                                     Oak Ridge National Labor...\n",
       "    project_id:                                      CORDEX\n",
       "    ...                                              ...\n",
       "    experiment_id:                                   historical\n",
       "    driving_experiment:                              MIROC-MIROC5, historical...\n",
       "    driving_model_id:                                MIROC-MIROC5\n",
       "    driving_experiment_name:                         historical\n",
       "    model_id:                                        ICTP-RegCM4-7\n",
       "    CDO:                                             Climate Data Operators v..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#RegCM ensemble average\n",
    "\n",
    "#ea_data = xr.open_dataset('/home/mkibria/Academic/pr_of_thesis/regcm driven by gcm/ensemble-average/regcm_ensmean_pr_197001-200512_yearly.nc')\n",
    "\n",
    "\n",
    "ea_data = xr.open_dataset('/home/mkibria/Academic/pr_of_thesis/regcm driven by gcm/ensemble-average/regcmensble_yearly.nc')\n",
    "\n",
    "ea_data = ea_data.squeeze()\n",
    "\n",
    "ea_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "be7d35a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x4 = ea_data['pr']\n",
    "x4 = x4*86400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6db7df54",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 12)\n",
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       "Attributes: (12/29)\n",
       "    CDI:                    Climate Data Interface version 1.9.9rc1 (https://...\n",
       "    Conventions:            CF-1.4\n",
       "    history:                Fri Jul 22 11:03:45 2022: cdo -L fldmean -ymonavg...\n",
       "    source:                 MIROC5 2010 atmosphere: MIROC-AGCM6 (T85L40); oce...\n",
       "    institution:            AORI (Atmosphere and Ocean Research Institute, Th...\n",
       "    institute_id:           MIROC\n",
       "    ...                     ...\n",
       "    title:                  MIROC5 model output prepared for CMIP5 historical\n",
       "    parent_experiment:      pre-industrial control\n",
       "    modeling_realm:         atmos\n",
       "    realization:            1\n",
       "    cmor_version:           2.5.6\n",
       "    CDO:                    Climate Data Operators version 1.9.9rc1 (https://...</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-1afd6b9d-c765-444d-90a2-093eec47e730' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1afd6b9d-c765-444d-90a2-093eec47e730' 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'>time</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-93d83656-2485-4bf6-943d-8f0753d7a569' class='xr-section-summary-in' type='checkbox'  checked><label for='section-93d83656-2485-4bf6-943d-8f0753d7a569' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2005-01-16 12:00:00 ... 2005-12-...</div><input id='attrs-f0241d15-60b0-4231-a762-9f980f1d13d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0241d15-60b0-4231-a762-9f980f1d13d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbaa855b-1178-477d-8ce8-bc92fd33868b' class='xr-var-data-in' type='checkbox'><label for='data-bbaa855b-1178-477d-8ce8-bc92fd33868b' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2005, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-9d2bf339-68e2-4123-8041-6137b2c931f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d2bf339-68e2-4123-8041-6137b2c931f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28c4e990-bebf-4ead-bda5-7774fd0733d3' class='xr-var-data-in' type='checkbox'><label for='data-28c4e990-bebf-4ead-bda5-7774fd0733d3' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-145832f9-e8fc-48ed-9203-4ab4a64de0e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-145832f9-e8fc-48ed-9203-4ab4a64de0e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf6fe506-8f5d-4ed7-a0b9-6cff82bd4c81' class='xr-var-data-in' type='checkbox'><label for='data-bf6fe506-8f5d-4ed7-a0b9-6cff82bd4c81' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a77e73fa-c0d2-41c4-9ed5-d81b9cf7a449' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a77e73fa-c0d2-41c4-9ed5-d81b9cf7a449' 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>pr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-87c2ba5c-5ccc-4556-8187-222e612a1e20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87c2ba5c-5ccc-4556-8187-222e612a1e20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9e8ee8b-7e06-4553-9f6e-bccf777370ef' class='xr-var-data-in' type='checkbox'><label for='data-c9e8ee8b-7e06-4553-9f6e-bccf777370ef' 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>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>comment :</span></dt><dd>at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)</dd><dt><span>original_name :</span></dt><dd>PRCP</dd><dt><span>original_units :</span></dt><dd>kg/m**2/s</dd><dt><span>history :</span></dt><dd>2011-04-23T09:11:46Z altered by CMOR: Converted units from &#x27;kg/m**2/s&#x27; to &#x27;kg m-2 s-1&#x27;. 2011-04-23T09:11:46Z altered by CMOR: replaced missing value flag (-999) with standard missing value (1e+20). 2011-04-23T09:11:46Z altered by CMOR: Inverted axis: lat.</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>associated_files :</span></dt><dd>baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_MIROC5_historical_r0i0p0.nc areacella: areacella_fx_MIROC5_historical_r0i0p0.nc</dd></dl></div><div class='xr-var-data'><pre>array([7.539265e-06, 7.261881e-06, 9.902473e-06, 2.127659e-05, 6.616465e-05,\n",
       "       1.572796e-04, 1.713749e-04, 1.304397e-04, 1.030866e-04, 5.800313e-05,\n",
       "       2.067896e-05, 7.568090e-06], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4211c780-a191-40de-91b8-3f7f87055e93' class='xr-section-summary-in' type='checkbox'  ><label for='section-4211c780-a191-40de-91b8-3f7f87055e93' class='xr-section-summary' >Attributes: <span>(29)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>history :</span></dt><dd>Fri Jul 22 11:03:45 2022: cdo -L fldmean -ymonavg -remapnn,grid1.des gcmensemble.nc gcmensemble_yearly.nc\n",
       "Fri Jul 22 11:00:06 2022: cdo divc,3 final.nc gcmensemble.nc\n",
       "Fri Jul 22 10:53:46 2022: cdo -add gcm_miroc_remap1.nc -add gcm_mpi_remap.nc gcm_noresm_remap.nc final.nc\n",
       "Fri Jul 22 10:53:11 2022: cdo selyear,1850/2005 gcm_miroc_remap.nc gcm_miroc_remap1.nc\n",
       "Thu Jul 07 18:16:03 2022: cdo remapnn,grid1.des pr_Amon_MIROC5_historical_r1i1p1_185001-201212.nc gcm_miroc_remap.nc\n",
       "2011-04-23T09:11:46Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.</dd><dt><span>source :</span></dt><dd>MIROC5 2010 atmosphere: MIROC-AGCM6 (T85L40); ocean: COCO (COCO4.5, 256x224 L50); sea ice: COCO (COCO4.5); land: MATSIRO (MATSIRO, L6); aerosols: SPRINTARS (SPRINTARS 5.00, T85L40)</dd><dt><span>institution :</span></dt><dd>AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba, Japan), NIES (National Institute for Environmental Studies, Ibaraki, Japan), JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan)</dd><dt><span>institute_id :</span></dt><dd>MIROC</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>model_id :</span></dt><dd>MIROC5</dd><dt><span>forcing :</span></dt><dd>GHG, SA, Oz, LU, Sl, Vl, SS, Ds, BC, MD, OC (GHG includes CO2, N2O, methane, and fluorocarbons; Oz includes OH and H2O2; LU excludes change in lake fraction)</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_experiment_rip :</span></dt><dd>r1i1p1</dd><dt><span>branch_time :</span></dt><dd>150015.0</dd><dt><span>contact :</span></dt><dd>Masahiro Watanabe (hiro@aori.u-tokyo.ac.jp), Seita Emori (emori@nies.go.jp), Masayoshi Ishii (ism@jamstec.go.jp), Masahide Kimoto (kimoto@aori.u-tokyo.ac.jp)</dd><dt><span>references :</span></dt><dd>Watanabe et al., 2010: Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J. Climate, 23, 6312-6335</dd><dt><span>initialization_method :</span></dt><dd>1</dd><dt><span>physics_version :</span></dt><dd>1</dd><dt><span>tracking_id :</span></dt><dd>a906be44-c850-4f0c-b8c4-2b165051669a</dd><dt><span>product :</span></dt><dd>output</dd><dt><span>experiment :</span></dt><dd>historical</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>creation_date :</span></dt><dd>2011-04-23T09:11:46Z</dd><dt><span>project_id :</span></dt><dd>CMIP5</dd><dt><span>table_id :</span></dt><dd>Table Amon (31 January 2011) 53b766a395ac41696af40aab76a49ae5</dd><dt><span>title :</span></dt><dd>MIROC5 model output prepared for CMIP5 historical</dd><dt><span>parent_experiment :</span></dt><dd>pre-industrial control</dd><dt><span>modeling_realm :</span></dt><dd>atmos</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>cmor_version :</span></dt><dd>2.5.6</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) object 2005-01-16 12:00:00 ... 2005-12-16 12:00:00\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    pr       (time) float32 ...\n",
       "Attributes: (12/29)\n",
       "    CDI:                    Climate Data Interface version 1.9.9rc1 (https://...\n",
       "    Conventions:            CF-1.4\n",
       "    history:                Fri Jul 22 11:03:45 2022: cdo -L fldmean -ymonavg...\n",
       "    source:                 MIROC5 2010 atmosphere: MIROC-AGCM6 (T85L40); oce...\n",
       "    institution:            AORI (Atmosphere and Ocean Research Institute, Th...\n",
       "    institute_id:           MIROC\n",
       "    ...                     ...\n",
       "    title:                  MIROC5 model output prepared for CMIP5 historical\n",
       "    parent_experiment:      pre-industrial control\n",
       "    modeling_realm:         atmos\n",
       "    realization:            1\n",
       "    cmor_version:           2.5.6\n",
       "    CDO:                    Climate Data Operators version 1.9.9rc1 (https://..."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#GCM ensemble average\n",
    "\n",
    "\n",
    "gcm_ea_data = xr.open_dataset('/home/mkibria/Academic/pr_of_thesis/GCM/ensemble-average/gcmensemble_yearly.nc')\n",
    "\n",
    "gcm_ea_data = gcm_ea_data.squeeze()\n",
    "\n",
    "gcm_ea_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d6cac035",
   "metadata": {},
   "outputs": [],
   "source": [
    "x5 = gcm_ea_data ['pr']\n",
    "x5 = x5*86400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6619ec97",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'x1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_8963/704679869.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'precipitation in mm/day'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Reanalysis(Era5)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Observation(Chirps)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'RegCM(Miroc5)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'GCM(Miroc5)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'x1' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mon=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov','Dec']\n",
    "#mon=['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N','D']\n",
    "\n",
    "mon\n",
    "\n",
    "plt.title('Seasonal cycle of Bangladesh')\n",
    "plt.xlabel('months')\n",
    "plt.ylabel('precipitation in mm/day')\n",
    "plt.plot(mon,x,label='Reanalysis(Era5)')\n",
    "plt.plot(mon,x1,label='Observation(Chirps)')\n",
    "plt.plot(mon,x2,label='RegCM(Miroc5)')\n",
    "plt.plot(mon,x3,label='GCM(Miroc5)')\n",
    "\n",
    "\n",
    "\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "\n",
    "plt.savefig(\"/home/mkibria/Academic/pr_of_thesis/figures/month-vs-precipitation.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cd296d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "mon=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov','Dec']\n",
    "#mon=['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N','D']\n",
    "\n",
    "mon\n",
    "\n",
    "plt.title('Seasonal cycle of Bangladesh')\n",
    "plt.xlabel('months')\n",
    "plt.ylabel('precipitation in mm/day')\n",
    "plt.plot(mon,x,label='Reanalysis(Era5)')\n",
    "plt.plot(mon,x1,label='Observation(Chirps)')\n",
    "#plt.plot(mon,x2,label='RegCM(Miroc5)')\n",
    "#plt.plot(mon,x3,label='GCM(Miroc5)')\n",
    "plt.plot(mon,x5,label='GCM(ensemble avg)')\n",
    "\n",
    "plt.plot(mon,x4,label='RegCM(ensemble avg)')\n",
    "\n",
    "\n",
    "\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "\n",
    "#plt.savefig(\"/home/mkibria/Academic/pr_of_thesis/figures/month-vs-EA_precipitation.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8b0b266",
   "metadata": {},
   "source": [
    "#################################################################################\n",
    "\n",
    "Now I will plot the graph of 3 observations vs Era-int and Regcm(Era-int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "72e2d2fd",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2018-01-01T06:00:00 ... 2018-12-01T06:00:00\n",
       "    lon      float64 0.0\n",
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       "    CDI:          Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg...\n",
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       "    history:      Wed Jul 27 14:32:43 2022: cdo -L fldmean -ymonavg -remapnn,...\n",
       "    frequency:    day\n",
       "    NCO:          netCDF Operators version 4.8.1 (Homepage = http://nco.sf.ne...\n",
       "    CDO:          Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg...</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-6299c818-d756-42d5-8a76-36f687c719fc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6299c818-d756-42d5-8a76-36f687c719fc' 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'>time</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0c0b5075-6e09-47bb-bda9-5288f9a62260' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c0b5075-6e09-47bb-bda9-5288f9a62260' 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'>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'>2018-01-01T06:00:00 ... 2018-12-...</div><input id='attrs-47530007-cbf3-4143-8c82-811707ab4cca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47530007-cbf3-4143-8c82-811707ab4cca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1d3e54c-f99d-4b39-8c85-3debbf97fcb2' class='xr-var-data-in' type='checkbox'><label for='data-f1d3e54c-f99d-4b39-8c85-3debbf97fcb2' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-01T06:00:00.000000000&#x27;, &#x27;2018-02-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2018-03-01T06:00:00.000000000&#x27;, &#x27;2018-04-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2018-05-01T06:00:00.000000000&#x27;, &#x27;2018-06-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2018-07-01T06:00:00.000000000&#x27;, &#x27;2018-08-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2018-09-01T06:00:00.000000000&#x27;, &#x27;2018-10-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-01T06:00:00.000000000&#x27;, &#x27;2018-12-01T06:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-81dd2273-d344-4447-baa7-4e32dbedae4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81dd2273-d344-4447-baa7-4e32dbedae4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aae29d35-e068-48c2-ab7d-5df3d2d41b6a' class='xr-var-data-in' type='checkbox'><label for='data-aae29d35-e068-48c2-ab7d-5df3d2d41b6a' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-a53ea0ea-c87b-428c-85fe-da4fcda733a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a53ea0ea-c87b-428c-85fe-da4fcda733a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-653ac115-ee6b-44ef-b8b3-7c665dc85e1e' class='xr-var-data-in' type='checkbox'><label for='data-653ac115-ee6b-44ef-b8b3-7c665dc85e1e' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5b455d25-de1a-4bc1-9123-c6dca84f1995' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5b455d25-de1a-4bc1-9123-c6dca84f1995' 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)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2ce56467-6072-4c31-bb92-a3e78cd6c0be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ce56467-6072-4c31-bb92-a3e78cd6c0be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5993cbb9-dada-455b-a2d1-91a1458c9a0f' class='xr-var-data-in' type='checkbox'><label for='data-5993cbb9-dada-455b-a2d1-91a1458c9a0f' 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>Total precipitation</dd><dt><span>units :</span></dt><dd>m/h</dd><dt><span>cell_methods :</span></dt><dd>time: sum</dd></dl></div><div class='xr-var-data'><pre>array([1.995966e-05, 3.723454e-05, 5.826737e-05, 1.292804e-04, 2.772571e-04,\n",
       "       5.405791e-04, 5.941652e-04, 5.165552e-04, 4.084145e-04, 2.040582e-04,\n",
       "       6.384267e-05, 2.389247e-05], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1bca03c5-c1a8-4a2e-b39f-d0847fb64446' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1bca03c5-c1a8-4a2e-b39f-d0847fb64446' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>Wed Jul 27 14:32:43 2022: cdo -L fldmean -ymonavg -remapnn,grid.des pr_197901-201812.nc pr_197901-201812_yearly.nc\n",
       "Tue Jul 26 14:21:52 2022: cdo divc,48.0 pr_197901-201812.nc tmp.nc\n",
       "Tue Jul 19 13:50:47 2022: ncrcat pr_197901.nc pr_197902.nc pr_197903.nc pr_197904.nc pr_197905.nc pr_197906.nc pr_197907.nc pr_197908.nc pr_197909.nc pr_197910.nc pr_197911.nc pr_197912.nc pr_198001.nc pr_198002.nc pr_198003.nc pr_198004.nc pr_198005.nc pr_198006.nc pr_198007.nc pr_198008.nc pr_198009.nc pr_198010.nc pr_198011.nc pr_198012.nc pr_198101.nc pr_198102.nc pr_198103.nc pr_198104.nc pr_198105.nc pr_198106.nc pr_198107.nc pr_198108.nc pr_198109.nc pr_198110.nc pr_198111.nc pr_198112.nc pr_198201.nc pr_198202.nc pr_198203.nc pr_198204.nc pr_198205.nc pr_198206.nc pr_198207.nc pr_198208.nc pr_198209.nc pr_198210.nc pr_198211.nc pr_198212.nc pr_198301.nc pr_198302.nc pr_198303.nc pr_198304.nc pr_198305.nc pr_198306.nc pr_198307.nc pr_198308.nc pr_198309.nc pr_198310.nc pr_198311.nc pr_198312.nc pr_198401.nc pr_198402.nc pr_198403.nc pr_198404.nc pr_198405.nc pr_198406.nc pr_198407.nc pr_198408.nc pr_198409.nc pr_198410.nc pr_198411.nc pr_198412.nc pr_198501.nc pr_198502.nc pr_198503.nc pr_198504.nc pr_198505.nc pr_198506.nc pr_198507.nc pr_198508.nc pr_198509.nc pr_198510.nc pr_198511.nc pr_198512.nc pr_198601.nc pr_198602.nc pr_198603.nc pr_198604.nc pr_198605.nc pr_198606.nc pr_198607.nc pr_198608.nc pr_198609.nc pr_198610.nc pr_198611.nc pr_198612.nc pr_198701.nc pr_198702.nc pr_198703.nc pr_198704.nc pr_198705.nc pr_198706.nc pr_198707.nc pr_198708.nc pr_198709.nc pr_198710.nc pr_198711.nc pr_198712.nc pr_198801.nc pr_198802.nc pr_198803.nc pr_198804.nc pr_198805.nc pr_198806.nc pr_198807.nc pr_198808.nc pr_198809.nc pr_198810.nc pr_198811.nc pr_198812.nc pr_198901.nc pr_198902.nc pr_198903.nc pr_198904.nc pr_198905.nc pr_198906.nc pr_198907.nc pr_198908.nc pr_198909.nc pr_198910.nc pr_198911.nc pr_198912.nc pr_199001.nc pr_199002.nc pr_199003.nc pr_199004.nc pr_199005.nc pr_199006.nc pr_199007.nc pr_199008.nc pr_199009.nc pr_199010.nc pr_199011.nc pr_199012.nc pr_199101.nc pr_199102.nc pr_199103.nc pr_199104.nc pr_199105.nc pr_199106.nc pr_199107.nc pr_199108.nc pr_199109.nc pr_199110.nc pr_199111.nc pr_199112.nc pr_199201.nc pr_199202.nc pr_199203.nc pr_199204.nc pr_199205.nc pr_199206.nc pr_199207.nc pr_199208.nc pr_199209.nc pr_199210.nc pr_199211.nc pr_199212.nc pr_199301.nc pr_199302.nc pr_199303.nc pr_199304.nc pr_199305.nc pr_199306.nc pr_199307.nc pr_199308.nc pr_199309.nc pr_199310.nc pr_199311.nc pr_199312.nc pr_199401.nc pr_199402.nc pr_199403.nc pr_199404.nc pr_199405.nc pr_199406.nc pr_199407.nc pr_199408.nc pr_199409.nc pr_199410.nc pr_199411.nc pr_199412.nc pr_199501.nc pr_199502.nc pr_199503.nc pr_199504.nc pr_199505.nc pr_199506.nc pr_199507.nc pr_199508.nc pr_199509.nc pr_199510.nc pr_199511.nc pr_199512.nc pr_199601.nc pr_199602.nc pr_199603.nc pr_199604.nc pr_199605.nc pr_199606.nc pr_199607.nc pr_199608.nc pr_199609.nc pr_199610.nc pr_199611.nc pr_199612.nc pr_199701.nc pr_199702.nc pr_199703.nc pr_199704.nc pr_199705.nc pr_199706.nc pr_199707.nc pr_199708.nc pr_199709.nc pr_199710.nc pr_199711.nc pr_199712.nc pr_199801.nc pr_199802.nc pr_199803.nc pr_199804.nc pr_199805.nc pr_199806.nc pr_199807.nc pr_199808.nc pr_199809.nc pr_199810.nc pr_199811.nc pr_199812.nc pr_199901.nc pr_199902.nc pr_199903.nc pr_199904.nc pr_199905.nc pr_199906.nc pr_199907.nc pr_199908.nc pr_199909.nc pr_199910.nc pr_199911.nc pr_199912.nc pr_200001.nc pr_200002.nc pr_200003.nc pr_200004.nc pr_200005.nc pr_200006.nc pr_200007.nc pr_200008.nc pr_200009.nc pr_200010.nc pr_200011.nc pr_200012.nc pr_200101.nc pr_200102.nc pr_200103.nc pr_200104.nc pr_200105.nc pr_200106.nc pr_200107.nc pr_200108.nc pr_200109.nc pr_200110.nc pr_200111.nc pr_200112.nc pr_200201.nc pr_200202.nc pr_200203.nc pr_200204.nc pr_200205.nc pr_200206.nc pr_200207.nc pr_200208.nc pr_200209.nc pr_200210.nc pr_200211.nc pr_200212.nc pr_200301.nc pr_200302.nc pr_200303.nc pr_200304.nc pr_200305.nc pr_200306.nc pr_200307.nc pr_200308.nc pr_200309.nc pr_200310.nc pr_200311.nc pr_200312.nc pr_200401.nc pr_200402.nc pr_200403.nc pr_200404.nc pr_200405.nc pr_200406.nc pr_200407.nc pr_200408.nc pr_200409.nc pr_200410.nc pr_200411.nc pr_200412.nc pr_200501.nc pr_200502.nc pr_200503.nc pr_200504.nc pr_200505.nc pr_200506.nc pr_200507.nc pr_200508.nc pr_200509.nc pr_200510.nc pr_200511.nc pr_200512.nc pr_200601.nc pr_200602.nc pr_200603.nc pr_200604.nc pr_200605.nc pr_200606.nc pr_200607.nc pr_200608.nc pr_200609.nc pr_200610.nc pr_200611.nc pr_200612.nc pr_200701.nc pr_200702.nc pr_200703.nc pr_200704.nc pr_200705.nc pr_200706.nc pr_200707.nc pr_200708.nc pr_200709.nc pr_200710.nc pr_200711.nc pr_200712.nc pr_200801.nc pr_200802.nc pr_200803.nc pr_200804.nc pr_200805.nc pr_200806.nc pr_200807.nc pr_200808.nc pr_200809.nc pr_200810.nc pr_200811.nc pr_200812.nc pr_200901.nc pr_200902.nc pr_200903.nc pr_200904.nc pr_200905.nc pr_200906.nc pr_200907.nc pr_200908.nc pr_200909.nc pr_200910.nc pr_200911.nc pr_200912.nc pr_201001.nc pr_201002.nc pr_201003.nc pr_201004.nc pr_201005.nc pr_201006.nc pr_201007.nc pr_201008.nc pr_201009.nc pr_201010.nc pr_201011.nc pr_201012.nc pr_201101.nc pr_201102.nc pr_201103.nc pr_201104.nc pr_201105.nc pr_201106.nc pr_201107.nc pr_201108.nc pr_201109.nc pr_201110.nc pr_201111.nc pr_201112.nc pr_201201.nc pr_201202.nc pr_201203.nc pr_201204.nc pr_201205.nc pr_201206.nc pr_201207.nc pr_201208.nc pr_201209.nc pr_201210.nc pr_201211.nc pr_201212.nc pr_201301.nc pr_201302.nc pr_201303.nc pr_201304.nc pr_201305.nc pr_201306.nc pr_201307.nc pr_201308.nc pr_201309.nc pr_201310.nc pr_201311.nc pr_201312.nc pr_201401.nc pr_201402.nc pr_201403.nc pr_201404.nc pr_201405.nc pr_201406.nc pr_201407.nc pr_201408.nc pr_201409.nc pr_201410.nc pr_201411.nc pr_201412.nc pr_201501.nc pr_201502.nc pr_201503.nc pr_201504.nc pr_201505.nc pr_201506.nc pr_201507.nc pr_201508.nc pr_201509.nc pr_201510.nc pr_201511.nc pr_201512.nc pr_201601.nc pr_201602.nc pr_201603.nc pr_201604.nc pr_201605.nc pr_201606.nc pr_201607.nc pr_201608.nc pr_201609.nc pr_201610.nc pr_201611.nc pr_201612.nc pr_201701.nc pr_201702.nc pr_201703.nc pr_201704.nc pr_201705.nc pr_201706.nc pr_201707.nc pr_201708.nc pr_201709.nc pr_201710.nc pr_201711.nc pr_201712.nc pr_201801.nc pr_201802.nc pr_201803.nc pr_201804.nc pr_201805.nc pr_201806.nc pr_201807.nc pr_201808.nc pr_201809.nc pr_201810.nc pr_201811.nc pr_201812.nc pr_197901-201812.nc\n",
       "Tue Jul 19 12:21:22 2022: ncatted -a units,tp,m,c,m/h monthly/pr_197901.nc\n",
       "Tue Jul 19 12:21:22 2022: cdo -b f32 daysum tmp.nc monthly/pr_197901.nc\n",
       "2022-07-19 10:21:22 GMT by grib_to_netcdf-2.12.5: grib_to_netcdf -k 4 -d 3 -s -u time -o tmp.nc pr_197901.grib</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.8.1 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2018-01-01T06:00:00 ... 2018-12-01T06:00:00\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    tp       (time) float32 ...\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg...\n",
       "    Conventions:  CF-1.6\n",
       "    history:      Wed Jul 27 14:32:43 2022: cdo -L fldmean -ymonavg -remapnn,...\n",
       "    frequency:    day\n",
       "    NCO:          netCDF Operators version 4.8.1 (Homepage = http://nco.sf.ne...\n",
       "    CDO:          Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg..."
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eraint_data1 = xr.open_dataset('/home/mkibria/Academic/pr_of_thesis/reanalysis/Eraintrim/pr_197901-201812_yearly.nc')\n",
    " \n",
    "\n",
    "eraint_data1=eraint_data1.squeeze()\n",
    "\n",
    "\n",
    "eraint_data1\n",
    "\n",
    " \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2ce108e7",
   "metadata": {},
   "outputs": [
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       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
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       "  width: 15px;\n",
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       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "\n",
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       "  display: none;\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
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       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
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       "\n",
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       "\n",
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       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
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       ".xr-array-data {\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-var-dtype {\n",
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       "  text-align: right;\n",
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       "\n",
       ".xr-var-preview {\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tp&#x27; (time: 12)&gt;\n",
       "array([ 0.4790318 ,  0.893629  ,  1.398417  ,  3.102729  ,  6.6541696 ,\n",
       "       12.973898  , 14.259966  , 12.397324  ,  9.801949  ,  4.8973966 ,\n",
       "        1.5322242 ,  0.57341933], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2018-01-01T06:00:00 ... 2018-12-01T06:00:00\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0</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'>time</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e87a518c-e0ba-4506-a7ae-17ef0e0b495b' class='xr-array-in' type='checkbox' checked><label for='section-e87a518c-e0ba-4506-a7ae-17ef0e0b495b' 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>0.479 0.8936 1.398 3.103 6.654 12.97 ... 12.4 9.802 4.897 1.532 0.5734</span></div><div class='xr-array-data'><pre>array([ 0.4790318 ,  0.893629  ,  1.398417  ,  3.102729  ,  6.6541696 ,\n",
       "       12.973898  , 14.259966  , 12.397324  ,  9.801949  ,  4.8973966 ,\n",
       "        1.5322242 ,  0.57341933], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-147a90fb-dab1-45e9-91c8-aa7f6cbe0f87' class='xr-section-summary-in' type='checkbox'  checked><label for='section-147a90fb-dab1-45e9-91c8-aa7f6cbe0f87' 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'>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'>2018-01-01T06:00:00 ... 2018-12-...</div><input id='attrs-c7364229-2d26-4167-b4e1-92e7ed1f86a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7364229-2d26-4167-b4e1-92e7ed1f86a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-716f5d1d-1182-4dfc-8de6-e47c7507d045' class='xr-var-data-in' type='checkbox'><label for='data-716f5d1d-1182-4dfc-8de6-e47c7507d045' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-01T06:00:00.000000000&#x27;, &#x27;2018-02-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2018-03-01T06:00:00.000000000&#x27;, &#x27;2018-04-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2018-05-01T06:00:00.000000000&#x27;, &#x27;2018-06-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2018-07-01T06:00:00.000000000&#x27;, &#x27;2018-08-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2018-09-01T06:00:00.000000000&#x27;, &#x27;2018-10-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-01T06:00:00.000000000&#x27;, &#x27;2018-12-01T06:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-420d910b-c94c-4503-8def-02eb1ab1e12d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-420d910b-c94c-4503-8def-02eb1ab1e12d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a843dc0b-e9af-4717-a281-cc5fbc27f553' class='xr-var-data-in' type='checkbox'><label for='data-a843dc0b-e9af-4717-a281-cc5fbc27f553' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-01510c94-91e7-4606-abd8-89486b6e445c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01510c94-91e7-4606-abd8-89486b6e445c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9f90983-10fc-4e8c-9c60-cc01440da897' class='xr-var-data-in' type='checkbox'><label for='data-a9f90983-10fc-4e8c-9c60-cc01440da897' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d29f7b2d-2932-472e-8bd5-dee62a8cb84c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d29f7b2d-2932-472e-8bd5-dee62a8cb84c' 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' (time: 12)>\n",
       "array([ 0.4790318 ,  0.893629  ,  1.398417  ,  3.102729  ,  6.6541696 ,\n",
       "       12.973898  , 14.259966  , 12.397324  ,  9.801949  ,  4.8973966 ,\n",
       "        1.5322242 ,  0.57341933], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2018-01-01T06:00:00 ... 2018-12-01T06:00:00\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xrea = eraint_data1['tp']\n",
    "xrea = xrea*24000\n",
    "xrea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a6e71d62",
   "metadata": {},
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\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",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  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.Dataset&gt;\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2017-01-16T12:00:00 ... 2016-12-16T12:00:00\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    pr       (time) float32 6.188e-06 1.016e-05 ... 2.902e-05 9.846e-06\n",
       "Attributes: (12/165)\n",
       "    CDI:                                             Climate Data Interface v...\n",
       "    Conventions:                                     CF-1.7\n",
       "    history:                                         Fri Jul 22 23:50:34 2022...\n",
       "    source:                                          RegCM Model output file\n",
       "    institution:                                     International Centre for...\n",
       "    project_id:                                      CORDEX\n",
       "    ...                                              ...\n",
       "    experiment_id:                                   evaluation\n",
       "    driving_experiment:                              ECMWF-ERAINT, evaluation...\n",
       "    driving_model_id:                                ECMWF-ERAINT\n",
       "    driving_experiment_name:                         evaluation\n",
       "    model_id:                                        ICTP-RegCM4-7\n",
       "    CDO:                                             Climate Data Operators v...</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-18c95eb2-bae3-4f61-a929-882160d39a4b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-18c95eb2-bae3-4f61-a929-882160d39a4b' 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'>time</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-198440ba-d902-4a2c-8699-44e55c0ba64d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-198440ba-d902-4a2c-8699-44e55c0ba64d' 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'>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'>2017-01-16T12:00:00 ... 2016-12-...</div><input id='attrs-e4622f95-094c-4af0-b30e-546b586b513b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4622f95-094c-4af0-b30e-546b586b513b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-064cfe7e-8b14-47da-8cb2-eaede76473c1' class='xr-var-data-in' type='checkbox'><label for='data-064cfe7e-8b14-47da-8cb2-eaede76473c1' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-16T12:00:00.000000000&#x27;, &#x27;2017-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-03-16T12:00:00.000000000&#x27;, &#x27;2017-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-05-16T12:00:00.000000000&#x27;, &#x27;2017-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-07-16T12:00:00.000000000&#x27;, &#x27;2017-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2017-09-16T00:00:00.000000000&#x27;, &#x27;2017-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2017-11-16T00:00:00.000000000&#x27;, &#x27;2016-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-96268dfc-5358-41b9-ad1d-057bf7ee1be8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96268dfc-5358-41b9-ad1d-057bf7ee1be8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb65d86a-2bc8-4567-aee0-27a0371546cd' class='xr-var-data-in' type='checkbox'><label for='data-cb65d86a-2bc8-4567-aee0-27a0371546cd' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-07da63a9-825a-457f-b5c3-44c202de3d3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07da63a9-825a-457f-b5c3-44c202de3d3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4758d2ea-de9c-4868-9cdc-beabc1139578' class='xr-var-data-in' type='checkbox'><label for='data-4758d2ea-de9c-4868-9cdc-beabc1139578' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a3978eb3-146c-4b75-abc2-02cb29419177' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a3978eb3-146c-4b75-abc2-02cb29419177' 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>pr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4c2f10a9-592e-47d0-9687-8f085acc08dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c2f10a9-592e-47d0-9687-8f085acc08dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daee9fbc-857d-4662-86ab-63e864521453' class='xr-var-data-in' type='checkbox'><label for='data-daee9fbc-857d-4662-86ab-63e864521453' 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>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([6.188297e-06, 1.016173e-05, 1.862352e-05, 5.164883e-05, 8.379136e-05,\n",
       "       1.523521e-04, 2.198203e-04, 1.613780e-04, 1.197672e-04, 6.952466e-05,\n",
       "       2.902059e-05, 9.845583e-06], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a2fef41e-0af5-4248-b72a-f34588226026' class='xr-section-summary-in' type='checkbox'  ><label for='section-a2fef41e-0af5-4248-b72a-f34588226026' class='xr-section-summary' >Attributes: <span>(165)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>history :</span></dt><dd>Fri Jul 22 23:50:34 2022: cdo -L fldmean -ymonavg -remapnn,grid.des pr_was-22-ecmwf_197901-201711.nc pr_was-22-ecmwf_197901-201711_yearly.nc\n",
       "Wed Jul 13 17:38:34 2022: cdo mergetime pr_WAS-22_ECMWF-ERAINT_evaluation_r1i1p1_ICTP-RegCM4-7_v0_mon_197901-198012.nc pr_WAS-22_ECMWF-ERAINT_evaluation_r1i1p1_ICTP-RegCM4-7_v0_mon_198101-199012.nc pr_WAS-22_ECMWF-ERAINT_evaluation_r1i1p1_ICTP-RegCM4-7_v0_mon_199101-200012.nc pr_WAS-22_ECMWF-ERAINT_evaluation_r1i1p1_ICTP-RegCM4-7_v0_mon_200101-201012.nc pr_WAS-22_ECMWF-ERAINT_evaluation_r1i1p1_ICTP-RegCM4-7_v0_mon_201101-201711.nc pr_was-22-ecmwf_197901-201711.nc\n",
       "2018-02-13 16:42:58 : Created by RegCM RegCM Model program</dd><dt><span>source :</span></dt><dd>RegCM Model output file</dd><dt><span>institution :</span></dt><dd>International Centre for Theoretical Physics</dd><dt><span>project_id :</span></dt><dd>CORDEX</dd><dt><span>ipcc_scenario_code :</span></dt><dd>EVALUATION</dd><dt><span>institute_id :</span></dt><dd>ICTP</dd><dt><span>note :</span></dt><dd>The domain is larger than WAS-22</dd><dt><span>comment :</span></dt><dd>RegCM CORDEX India25 run</dd><dt><span>experiment :</span></dt><dd>WAS-22</dd><dt><span>driving_model_ensemble_member :</span></dt><dd>r1i1p1</dd><dt><span>creation_date :</span></dt><dd>2019-09-12T15:13:03Z</dd><dt><span>CORDEX_domain :</span></dt><dd>WAS-22</dd><dt><span>rcm_version_id :</span></dt><dd>v0</dd><dt><span>ICTP_version_note :</span></dt><dd>none</dd><dt><span>contact :</span></dt><dd>esp@ictp.it</dd><dt><span>product :</span></dt><dd>output</dd><dt><span>tracking_id :</span></dt><dd>f1ed9934-3b9a-11e9-8719-0894ef50c218</dd><dt><span>title :</span></dt><dd>ICTP Regional Climatic model V4</dd><dt><span>references :</span></dt><dd>http://gforge.ictp.it/gf/project/regcm</dd><dt><span>model_revision :</span></dt><dd>tag-4.7.0</dd><dt><span>projection :</span></dt><dd>ROTMER</dd><dt><span>grid_size_in_meters :</span></dt><dd>25000.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>13.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>70.0</dd><dt><span>grid_north_pole_latitude :</span></dt><dd>13.0</dd><dt><span>grid_north_pole_longitude :</span></dt><dd>70.0</dd><dt><span>grid_factor :</span></dt><dd>0.0</dd><dt><span>landsurface_model :</span></dt><dd>clm4.5</dd><dt><span>model_icbc_data_source :</span></dt><dd>EIN75</dd><dt><span>model_sst_data_source :</span></dt><dd>EIN75</dd><dt><span>boundary_nspgx :</span></dt><dd>30</dd><dt><span>boundary_nspgd :</span></dt><dd>30</dd><dt><span>boundary_high_nudge :</span></dt><dd>8.0</dd><dt><span>boundary_medium_nudge :</span></dt><dd>6.0</dd><dt><span>boundary_low_nudge :</span></dt><dd>4.0</dd><dt><span>boundary_nm :</span></dt><dd>-1.0</dd><dt><span>boundary_dm :</span></dt><dd>-1.0</dd><dt><span>model_is_restarted :</span></dt><dd>No</dd><dt><span>model_simulation_initial_start :</span></dt><dd> 1979-01-01 00:00:00 UTC</dd><dt><span>model_simulation_start :</span></dt><dd> 1979-01-01 00:00:00 UTC</dd><dt><span>model_simulation_end :</span></dt><dd> 2016-01-01 00:00:00 UTC</dd><dt><span>atmosphere_time_step_in_seconds :</span></dt><dd>40.0</dd><dt><span>surface_interaction_time_step_in_seconds :</span></dt><dd>480.0</dd><dt><span>convection_time_step_in_seconds :</span></dt><dd>240.0</dd><dt><span>radiation_scheme_time_step_in_seconds :</span></dt><dd>1800.0</dd><dt><span>absorption_emission_time_step_in_seconds :</span></dt><dd>64800.0</dd><dt><span>dynamical_core :</span></dt><dd>1</dd><dt><span>asselin_filter_nu_1 :</span></dt><dd>0.0625</dd><dt><span>asselin_filter_nu_2 :</span></dt><dd>0.0625</dd><dt><span>diffusion_hgt_factor :</span></dt><dd>1</dd><dt><span>upstream_mode :</span></dt><dd>Yes</dd><dt><span>off_centering_max :</span></dt><dd>0.25</dd><dt><span>stability_enhance :</span></dt><dd>Yes</dd><dt><span>temperature_extreme_gradient :</span></dt><dd>5.0</dd><dt><span>cloud_extreme_gradient_fraction :</span></dt><dd>0.5</dd><dt><span>vapor_extreme_gradient_fraction :</span></dt><dd>0.2</dd><dt><span>tracer_extreme_gradient_fraction :</span></dt><dd>0.5</dd><dt><span>lateral_boundary_condition_scheme :</span></dt><dd>5</dd><dt><span>semi_lagrangian_advection_scheme :</span></dt><dd>0</dd><dt><span>boundary_layer_scheme :</span></dt><dd>2</dd><dt><span>cumulus_convection_scheme_lnd :</span></dt><dd>4</dd><dt><span>cumulus_convection_scheme_ocn :</span></dt><dd>5</dd><dt><span>moisture_scheme :</span></dt><dd>1</dd><dt><span>ocean_flux_scheme :</span></dt><dd>2</dd><dt><span>zeng_ocean_roughness_formula :</span></dt><dd>1</dd><dt><span>zeng_ocean_roughness_method :</span></dt><dd>1</dd><dt><span>pressure_gradient_scheme :</span></dt><dd>0</dd><dt><span>surface_emissivity_factor_computed :</span></dt><dd>0</dd><dt><span>lake_model_activated :</span></dt><dd>0</dd><dt><span>chemical_aerosol_scheme_activated :</span></dt><dd>0</dd><dt><span>diurnal_cycle_sst_scheme :</span></dt><dd>0</dd><dt><span>ocean_precipitaion_cooling_effect :</span></dt><dd>0</dd><dt><span>ocean_albedo_from_whitecapping :</span></dt><dd>0</dd><dt><span>simple_sea_ice_scheme :</span></dt><dd>0</dd><dt><span>seasonal_desert_albedo :</span></dt><dd>0</dd><dt><span>convective_lwp_as_large_scale :</span></dt><dd>0</dd><dt><span>large_scale_cloud_fraction_scheme :</span></dt><dd>0</dd><dt><span>ocean_stratiform_clouds_scheme :</span></dt><dd>1</dd><dt><span>rrtm_radiation_scheme_activated :</span></dt><dd>0</dd><dt><span>climatic_ozone_input_dataset :</span></dt><dd>0</dd><dt><span>climatic_aerosol_input_dataset :</span></dt><dd>0</dd><dt><span>static_solar_constant_used :</span></dt><dd>0</dd><dt><span>cumulus_cloud_model :</span></dt><dd>1</dd><dt><span>sun_year_offset :</span></dt><dd>0.0</dd><dt><span>fix_solar_parameters :</span></dt><dd>0</dd><dt><span>cloud_bottom_level_with_no_clouds :</span></dt><dd>1</dd><dt><span>cloud_rh_with_fcc_one :</span></dt><dd>1.01</dd><dt><span>cloud_rh_threshold_for_land :</span></dt><dd>0.8</dd><dt><span>cloud_rh_threshold_for_ocean :</span></dt><dd>0.9</dd><dt><span>cloud_rh1_limit_temperature :</span></dt><dd>238.0</dd><dt><span>cloud_cloud_fraction_maximum :</span></dt><dd>0.75</dd><dt><span>cloud_condensation_threshold :</span></dt><dd>1.0</dd><dt><span>cloud_cloud_fraction_max_for_convection :</span></dt><dd>1.0</dd><dt><span>cloud_cloud_liqwat_max_for_convection :</span></dt><dd>0.0003</dd><dt><span>cloud_shallow_convective_cf_param :</span></dt><dd>0.07</dd><dt><span>cloud_deep_convective_cf_param :</span></dt><dd>0.14</dd><dt><span>cloud_k2_convective_cf_param :</span></dt><dd>500.0</dd><dt><span>cloud_surface_radiation_hack :</span></dt><dd>No</dd><dt><span>cloud_arctic_cloud_correction :</span></dt><dd>Yes</dd><dt><span>subex_auto_conversion_rate_for_land :</span></dt><dd>0.00025</dd><dt><span>subex_auto_conversion_rate_for_ocean :</span></dt><dd>0.00025</dd><dt><span>subex_gultepe_factor_when_rain_for_land :</span></dt><dd>0.4</dd><dt><span>subex_gultepe_factor_when_rain_for_ocean :</span></dt><dd>0.4</dd><dt><span>subex_land_raindrop_evaporation_rate :</span></dt><dd>0.0002</dd><dt><span>subex_ocean_raindrop_evaporation_rate :</span></dt><dd>0.0001</dd><dt><span>subex_land_raindrop_accretion_rate :</span></dt><dd>3.0</dd><dt><span>subex_ocean_raindrop_accretion_rate :</span></dt><dd>3.0</dd><dt><span>mit_lowest_convection_sigma :</span></dt><dd>0.95</dd><dt><span>mit_autoconversion_threshold_mixing_over_ocean :</span></dt><dd>0.00011</dd><dt><span>mit_autoconversion_threshold_mixing_over_land :</span></dt><dd>0.00011</dd><dt><span>mit_autoconversion_threshold_temperature :</span></dt><dd>-55.0</dd><dt><span>mit_mixing_coefficient_in_entrainment :</span></dt><dd>1.5</dd><dt><span>mit_fractional_area_unsaturated_downdraft :</span></dt><dd>0.05</dd><dt><span>mit_fractional_precip_outside_cloud :</span></dt><dd>0.12</dd><dt><span>mit_pressure_velocity_of_rain :</span></dt><dd>50.0</dd><dt><span>mit_pressure_velocity_of_snow :</span></dt><dd>5.5</dd><dt><span>mit_rain_evaporation_coefficient :</span></dt><dd>1.0</dd><dt><span>mit_snow_evaporation_coefficient :</span></dt><dd>0.8</dd><dt><span>mit_momentum_transport_coefficient :</span></dt><dd>0.7</dd><dt><span>mit_downdraft_velocity_coefficient :</span></dt><dd>10.0</dd><dt><span>mit_max_parcel_neg_temp_perturbation :</span></dt><dd>0.9</dd><dt><span>mit_approach_rate_quasi_eq_coeff_a :</span></dt><dd>0.2</dd><dt><span>mit_approach_rate_quasi_eq_coeff_d :</span></dt><dd>0.1</dd><dt><span>mit_maximum_land_precipitation_efficiency :</span></dt><dd>0.999</dd><dt><span>mit_maximum_ocean_precipitation_efficiency :</span></dt><dd>0.999</dd><dt><span>tiedtke_actual_scheme :</span></dt><dd>4</dd><dt><span>tiedtke_entrainment_rate_downdraft :</span></dt><dd>0.0003</dd><dt><span>tiedtke_entrainment_rate_deep_land :</span></dt><dd>0.00075</dd><dt><span>tiedtke_entrainment_rate_deep_ocean :</span></dt><dd>0.00075</dd><dt><span>tiedtke_penetrative :</span></dt><dd>Yes</dd><dt><span>tiedtke_midlevel :</span></dt><dd>Yes</dd><dt><span>tiedtke_shallow :</span></dt><dd>Yes</dd><dt><span>tiedtke_cumulus_downdraft :</span></dt><dd>Yes</dd><dt><span>tiedtke_prognostic_cloud :</span></dt><dd>Yes</dd><dt><span>tiedtke_cumulus_friction :</span></dt><dd>Yes</dd><dt><span>tiedtke_ke_dissipation :</span></dt><dd>Yes</dd><dt><span>tiedtke_tracer_transport :</span></dt><dd>Yes</dd><dt><span>tiedtke_tracer_smooth_massflux :</span></dt><dd>No</dd><dt><span>tiedtke_shallow_wstar_closure :</span></dt><dd>No</dd><dt><span>tiedtke_detrainment_rate_deep_land :</span></dt><dd>7.5e-05</dd><dt><span>tiedtke_detrainment_rate_deep_ocean :</span></dt><dd>7.5e-05</dd><dt><span>tiedtke_shallow_entrainment :</span></dt><dd>2.0</dd><dt><span>tiedtke_cloud_cover_evap_over_land :</span></dt><dd>0.05</dd><dt><span>tiedtke_cloud_cover_evap_over_ocean :</span></dt><dd>0.05</dd><dt><span>tiedtke_coeff_evap_over_land :</span></dt><dd>5.55e-05</dd><dt><span>tiedtke_coeff_evap_over_ocean :</span></dt><dd>5.55e-05</dd><dt><span>tiedtke_critical_rh_over_land :</span></dt><dd>0.9</dd><dt><span>tiedtke_critical_rh_over_ocean :</span></dt><dd>0.9</dd><dt><span>tiedtke_cloud_water_conv_over_land :</span></dt><dd>0.03</dd><dt><span>tiedtke_cloud_water_conv_over_ocean :</span></dt><dd>0.0014</dd><dt><span>tiedtke_cape_adjustment_timescale :</span></dt><dd>2400.0</dd><dt><span>uwpbl_advection_scheme :</span></dt><dd>0</dd><dt><span>uwpbl_cloud_evap_entr_incr_efficiency :</span></dt><dd>15.0</dd><dt><span>uwpbl_eddy_LS_stable_PBL_scaling :</span></dt><dd>1.5</dd><dt><span>uwpbl_czero :</span></dt><dd>5.869</dd><dt><span>uwpbl_nuk :</span></dt><dd>5.0</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>experiment_id :</span></dt><dd>evaluation</dd><dt><span>driving_experiment :</span></dt><dd>ECMWF-ERAINT, evaluation, r1i1p1</dd><dt><span>driving_model_id :</span></dt><dd>ECMWF-ERAINT</dd><dt><span>driving_experiment_name :</span></dt><dd>evaluation</dd><dt><span>model_id :</span></dt><dd>ICTP-RegCM4-7</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2017-01-16T12:00:00 ... 2016-12-16T12:00:00\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    pr       (time) float32 ...\n",
       "Attributes: (12/165)\n",
       "    CDI:                                             Climate Data Interface v...\n",
       "    Conventions:                                     CF-1.7\n",
       "    history:                                         Fri Jul 22 23:50:34 2022...\n",
       "    source:                                          RegCM Model output file\n",
       "    institution:                                     International Centre for...\n",
       "    project_id:                                      CORDEX\n",
       "    ...                                              ...\n",
       "    experiment_id:                                   evaluation\n",
       "    driving_experiment:                              ECMWF-ERAINT, evaluation...\n",
       "    driving_model_id:                                ECMWF-ERAINT\n",
       "    driving_experiment_name:                         evaluation\n",
       "    model_id:                                        ICTP-RegCM4-7\n",
       "    CDO:                                             Climate Data Operators v..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RegCM Era-int\n",
    "\n",
    "eraint_data = xr.open_dataset('/home/mkibria/Academic/pr_of_thesis/regcm driven by gcm/Era_intrim/pr_was-22-ecmwf_197901-201711_yearly.nc')\n",
    "\n",
    "eraint_data = eraint_data.squeeze()\n",
    "\n",
    "eraint_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3f515e26",
   "metadata": {},
   "outputs": [],
   "source": [
    "xera = eraint_data['pr']\n",
    "\n",
    "xera = xera*86400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e55b041c",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2022-01-01 2022-02-01 ... 2021-12-01\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    precip   (time) float32 10.33 17.45 41.05 106.9 ... 301.9 152.2 20.93 7.059\n",
       "Attributes: (12/17)\n",
       "    CDI:               Climate Data Interface version 1.9.9rc1 (https://mpime...\n",
       "    Conventions:       CF-1.6\n",
       "    history:           Mon Jul 11 11:59:05 2022: cdo -L fldmean -ymonavg -rem...\n",
       "    institution:       Climate Hazards Group.  University of California at Sa...\n",
       "    title:             CHIRPS Version 2.0\n",
       "    version:           Version 2.0\n",
       "    ...                ...\n",
       "    comments:           time variable denotes the first day of the given month.\n",
       "    acknowledgements:  The Climate Hazards Group InfraRed Precipitation with ...\n",
       "    ftp_url:           ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/products/CH...\n",
       "    website:           http://chg.geog.ucsb.edu/data/chirps/index.html\n",
       "    faq:               http://chg-wiki.geog.ucsb.edu/wiki/CHIRPS_FAQ\n",
       "    CDO:               Climate Data Operators version 1.9.9rc1 (https://mpime...</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-8f40c15d-0e09-4b91-a333-0bf8fa2c326f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8f40c15d-0e09-4b91-a333-0bf8fa2c326f' 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'>time</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a91c29c0-e9d2-497c-bba5-de0b7f6d157a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a91c29c0-e9d2-497c-bba5-de0b7f6d157a' 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'>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'>2022-01-01 ... 2021-12-01</div><input id='attrs-39378c3f-1b0a-4b6b-ac23-45107b2a2070' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39378c3f-1b0a-4b6b-ac23-45107b2a2070' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1831558a-c21b-461b-8f96-82f7b7b256cd' class='xr-var-data-in' type='checkbox'><label for='data-1831558a-c21b-461b-8f96-82f7b7b256cd' 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>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-03-01T00:00:00.000000000&#x27;, &#x27;2022-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-05-01T00:00:00.000000000&#x27;, &#x27;2021-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-07-01T00:00:00.000000000&#x27;, &#x27;2021-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-01T00:00:00.000000000&#x27;, &#x27;2021-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-00dbd9e5-a13f-4547-a196-e6f0e5b1dfb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00dbd9e5-a13f-4547-a196-e6f0e5b1dfb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-375854ea-8139-47bb-8ebc-2407654ed6e1' class='xr-var-data-in' type='checkbox'><label for='data-375854ea-8139-47bb-8ebc-2407654ed6e1' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-f5f107ea-5694-42d0-b3dc-e6bd9b66c8d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5f107ea-5694-42d0-b3dc-e6bd9b66c8d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b23c90c-832b-40a3-83f6-abfc84e26268' class='xr-var-data-in' type='checkbox'><label for='data-6b23c90c-832b-40a3-83f6-abfc84e26268' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c42d198-a010-44fa-8fbd-63cdd871a207' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c42d198-a010-44fa-8fbd-63cdd871a207' 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>precip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1a2b2171-9732-4ed9-8d42-4b6193261972' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a2b2171-9732-4ed9-8d42-4b6193261972' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e7918f5-63a6-4507-9959-c4e6b9cccf90' class='xr-var-data-in' type='checkbox'><label for='data-7e7918f5-63a6-4507-9959-c4e6b9cccf90' 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>standard_name :</span></dt><dd>convective precipitation rate</dd><dt><span>long_name :</span></dt><dd>Climate Hazards group InfraRed Precipitation with Stations</dd><dt><span>units :</span></dt><dd>mm/month</dd><dt><span>time_step :</span></dt><dd>month</dd><dt><span>geostatial_lat_min :</span></dt><dd>-50.0</dd><dt><span>geostatial_lat_max :</span></dt><dd>50.0</dd><dt><span>geostatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geostatial_lon_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><pre>array([ 10.330193,  17.454016,  41.05213 , 106.907524, 229.25429 , 377.52768 ,\n",
       "       431.01114 , 368.0943  , 301.93417 , 152.1763  ,  20.928537,   7.058659],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-798a5e8f-9b93-451c-ba01-7df091964fb7' class='xr-section-summary-in' type='checkbox'  ><label for='section-798a5e8f-9b93-451c-ba01-7df091964fb7' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>Mon Jul 11 11:59:05 2022: cdo -L fldmean -ymonavg -remapnn,grid.des chirps-v2.0.monthly.nc chirps_yearcicle.nc\n",
       "created by Climate Hazards Group</dd><dt><span>institution :</span></dt><dd>Climate Hazards Group.  University of California at Santa Barbara</dd><dt><span>title :</span></dt><dd>CHIRPS Version 2.0</dd><dt><span>version :</span></dt><dd>Version 2.0</dd><dt><span>date_created :</span></dt><dd>2022-05-16</dd><dt><span>creator_name :</span></dt><dd>Pete Peterson</dd><dt><span>creator_email :</span></dt><dd>pete@geog.ucsb.edu</dd><dt><span>documentation :</span></dt><dd>http://pubs.usgs.gov/ds/832/</dd><dt><span>reference :</span></dt><dd>Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p., http://dx.doi.org/110.3133/ds832. </dd><dt><span>comments :</span></dt><dd> time variable denotes the first day of the given month.</dd><dt><span>acknowledgements :</span></dt><dd>The Climate Hazards Group InfraRed Precipitation with Stations development process was carried out through U.S. Geological Survey (USGS) cooperative agreement #G09AC000001 &quot;Monitoring and Forecasting Climate, Water and Land Use for Food Production in the Developing World&quot; with funding from: U.S. Agency for International Development Office of Food for Peace, award #AID-FFP-P-10-00002 for &quot;Famine Early Warning Systems Network Support,&quot; the National Aeronautics and Space Administration Applied Sciences Program, Decisions award #NN10AN26I for &quot;A Land Data Assimilation System for Famine Early Warning,&quot; SERVIR award #NNH12AU22I for &quot;A Long Time-Series Indicator of Agricultural Drought for the Greater Horn of Africa,&quot; The National Oceanic and Atmospheric Administration award NA11OAR4310151 for &quot;A Global Standardized Precipitation Index supporting the US Drought Portal and the Famine Early Warning System Network,&quot; and the USGS Land Change Science Program.</dd><dt><span>ftp_url :</span></dt><dd>ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/products/CHIRPS-latest/</dd><dt><span>website :</span></dt><dd>http://chg.geog.ucsb.edu/data/chirps/index.html</dd><dt><span>faq :</span></dt><dd>http://chg-wiki.geog.ucsb.edu/wiki/CHIRPS_FAQ</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2022-01-01 2022-02-01 ... 2021-12-01\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    precip   (time) float32 ...\n",
       "Attributes: (12/17)\n",
       "    CDI:               Climate Data Interface version 1.9.9rc1 (https://mpime...\n",
       "    Conventions:       CF-1.6\n",
       "    history:           Mon Jul 11 11:59:05 2022: cdo -L fldmean -ymonavg -rem...\n",
       "    institution:       Climate Hazards Group.  University of California at Sa...\n",
       "    title:             CHIRPS Version 2.0\n",
       "    version:           Version 2.0\n",
       "    ...                ...\n",
       "    comments:           time variable denotes the first day of the given month.\n",
       "    acknowledgements:  The Climate Hazards Group InfraRed Precipitation with ...\n",
       "    ftp_url:           ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/products/CH...\n",
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       "    CDO:               Climate Data Operators version 1.9.9rc1 (https://mpime..."
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chirps_data = xr.open_dataset('/home/mkibria/Academic/pr_of_thesis/observation/chirps/chirps_yearcicle.nc')\n",
    "\n",
    "chirps_data = chirps_data.squeeze()\n",
    "\n",
    "chirps_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "664f44ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = chirps_data['precip']\n",
    "x1 = x1/30.4167"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c11d0960",
   "metadata": {},
   "outputs": [
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       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\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",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  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.Dataset&gt;\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2021-01-16 2021-02-15 ... 2021-12-16\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    pre      (time) float32 11.18 19.13 39.4 102.8 ... 294.8 152.6 30.54 6.455\n",
       "    stn      (time) float64 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 7.0 6.0 6.0 6.0\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg...\n",
       "    Conventions:  CF-1.4\n",
       "    history:      Fri Jul 22 23:56:44 2022: cdo -L fldmean -ymonavg -remapnn,...\n",
       "    source:       Run ID = 2205201912. Data generated from:pre.2204291209.dtb\n",
       "    institution:  Data held at British Atmospheric Data Centre, RAL, UK.\n",
       "    title:        CRU TS4.06 Precipitation\n",
       "    references:   Information on the data is available at http://badc.nerc.ac...\n",
       "    comment:      Access to these data is available to any registered CEDA user.\n",
       "    contact:      support@ceda.ac.uk\n",
       "    CDO:          Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg...</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-97fbca97-54a0-4573-a69a-a4a448595f86' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-97fbca97-54a0-4573-a69a-a4a448595f86' 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'>time</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-13a1ba88-c433-4015-9099-a25169a04872' class='xr-section-summary-in' type='checkbox'  checked><label for='section-13a1ba88-c433-4015-9099-a25169a04872' 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'>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'>2021-01-16 ... 2021-12-16</div><input id='attrs-30e60e6c-7d71-4f36-b4a3-c45407afa0c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30e60e6c-7d71-4f36-b4a3-c45407afa0c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caa19896-4fc9-481a-9d70-e854d0ab3f61' class='xr-var-data-in' type='checkbox'><label for='data-caa19896-4fc9-481a-9d70-e854d0ab3f61' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-01-16T00:00:00.000000000&#x27;, &#x27;2021-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-03-16T00:00:00.000000000&#x27;, &#x27;2021-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-05-16T00:00:00.000000000&#x27;, &#x27;2021-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-07-16T00:00:00.000000000&#x27;, &#x27;2021-08-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-16T00:00:00.000000000&#x27;, &#x27;2021-10-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-16T00:00:00.000000000&#x27;, &#x27;2021-12-16T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-cd589aec-f8ee-4492-a5fe-d3f783e1a8d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd589aec-f8ee-4492-a5fe-d3f783e1a8d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42db57ea-8c73-48a2-85fb-a55a50908859' class='xr-var-data-in' type='checkbox'><label for='data-42db57ea-8c73-48a2-85fb-a55a50908859' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-c2cba602-1ae8-4863-aa07-93d1609fd719' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2cba602-1ae8-4863-aa07-93d1609fd719' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-827566db-e5e1-4709-8198-3f982093db44' class='xr-var-data-in' type='checkbox'><label for='data-827566db-e5e1-4709-8198-3f982093db44' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-905f7502-d9d4-4a51-b68b-de3d2487daf8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-905f7502-d9d4-4a51-b68b-de3d2487daf8' class='xr-section-summary' >Data variables: <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>pre</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bac23719-9441-4a93-8f36-e0eafc620444' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bac23719-9441-4a93-8f36-e0eafc620444' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cdf3d23-2aa5-4b84-87ff-bb5ac25e6ece' class='xr-var-data-in' type='checkbox'><label for='data-4cdf3d23-2aa5-4b84-87ff-bb5ac25e6ece' 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>precipitation</dd><dt><span>units :</span></dt><dd>mm/month</dd><dt><span>correlation_decay_distance :</span></dt><dd>450.0</dd></dl></div><div class='xr-var-data'><pre>array([ 11.182276,  19.129562,  39.39875 , 102.79315 , 218.03304 , 426.303   ,\n",
       "       482.51096 , 424.24908 , 294.84567 , 152.57097 ,  30.536295,   6.455163],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stn</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9cbc925e-acfd-4fc8-8c80-167d5628c3f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9cbc925e-acfd-4fc8-8c80-167d5628c3f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-740b31f3-4db3-4a20-898c-e70804b099f7' class='xr-var-data-in' type='checkbox'><label for='data-740b31f3-4db3-4a20-898c-e70804b099f7' 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>description :</span></dt><dd>number of stations contributing to each datum</dd></dl></div><div class='xr-var-data'><pre>array([6., 6., 6., 6., 6., 6., 6., 6., 7., 6., 6., 6.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-29fd9356-98fc-4724-91fd-44b9134d937f' class='xr-section-summary-in' type='checkbox'  ><label for='section-29fd9356-98fc-4724-91fd-44b9134d937f' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>history :</span></dt><dd>Fri Jul 22 23:56:44 2022: cdo -L fldmean -ymonavg -remapnn,grid.des cru_ts4.06.1901.2021.pre.dat.nc cru_ts4.06.1901.2021.pre.dat_yearly.nc\n",
       "Fri 20 May 21:52:05 BST 2022 : User f098 : Program makegridsauto.for called by update.for</dd><dt><span>source :</span></dt><dd>Run ID = 2205201912. Data generated from:pre.2204291209.dtb</dd><dt><span>institution :</span></dt><dd>Data held at British Atmospheric Data Centre, RAL, UK.</dd><dt><span>title :</span></dt><dd>CRU TS4.06 Precipitation</dd><dt><span>references :</span></dt><dd>Information on the data is available at http://badc.nerc.ac.uk/data/cru/</dd><dt><span>comment :</span></dt><dd>Access to these data is available to any registered CEDA user.</dd><dt><span>contact :</span></dt><dd>support@ceda.ac.uk</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2021-01-16 2021-02-15 ... 2021-12-16\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    pre      (time) float32 ...\n",
       "    stn      (time) float64 ...\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg...\n",
       "    Conventions:  CF-1.4\n",
       "    history:      Fri Jul 22 23:56:44 2022: cdo -L fldmean -ymonavg -remapnn,...\n",
       "    source:       Run ID = 2205201912. Data generated from:pre.2204291209.dtb\n",
       "    institution:  Data held at British Atmospheric Data Centre, RAL, UK.\n",
       "    title:        CRU TS4.06 Precipitation\n",
       "    references:   Information on the data is available at http://badc.nerc.ac...\n",
       "    comment:      Access to these data is available to any registered CEDA user.\n",
       "    contact:      support@ceda.ac.uk\n",
       "    CDO:          Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg..."
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cru_data = xr.open_dataset('/home/mkibria/Academic/pr_of_thesis/observation/cru/cru_ts4.06.1901.2021.pre.dat_yearly.nc')\n",
    "\n",
    "cru_data = cru_data.squeeze()\n",
    "\n",
    "cru_data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d30a1aa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "xcru = cru_data['pre']\n",
    "xcru = xcru/30.4167\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "771a107d",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2008-01-01 2008-02-01 ... 2008-12-01\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    date     (time) int32 20030101 20030201 20030301 ... 20031101 20031201\n",
       "    datesec  (time) timedelta64[ns] 1 days 1 days 1 days ... 1 days 1 days\n",
       "    precip   (time) float32 0.01188 0.02228 0.04768 ... 0.2224 0.05015 0.0123\n",
       "Attributes:\n",
       "    CDI:                       Climate Data Interface version 1.9.9rc1 (https...\n",
       "    history:                   Fri Jul 22 23:58:08 2022: cdo -L fldmean -ymon...\n",
       "    Conventions:               None\n",
       "    creation_date:             Thu Nov  5 16:19:03 CET 2009\n",
       "    source_html:               \\nhttp://disc.sci.gsfc.nasa.gov/data/datapool/...\n",
       "    title:                     TRMM: 3B43\n",
       "    nco_openmp_thread_number:  1\n",
       "    CDO:                       Climate Data Operators version 1.9.9rc1 (https...</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-3e895231-4537-4af3-ad0a-01b273eb061d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3e895231-4537-4af3-ad0a-01b273eb061d' 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'>time</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c173c522-0c8f-4af7-9bcc-34bfa03c289c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c173c522-0c8f-4af7-9bcc-34bfa03c289c' 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'>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'>2008-01-01 ... 2008-12-01</div><input id='attrs-ae8bc661-fe7d-433f-8143-fa68265fd938' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae8bc661-fe7d-433f-8143-fa68265fd938' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a0bed17-402f-4195-b6ad-fe061293c390' class='xr-var-data-in' type='checkbox'><label for='data-6a0bed17-402f-4195-b6ad-fe061293c390' 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>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2008-01-01T00:00:00.000000000&#x27;, &#x27;2008-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-03-01T00:00:00.000000000&#x27;, &#x27;2008-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-05-01T00:00:00.000000000&#x27;, &#x27;2008-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-07-01T00:00:00.000000000&#x27;, &#x27;2008-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-09-01T00:00:00.000000000&#x27;, &#x27;2008-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-11-01T00:00:00.000000000&#x27;, &#x27;2008-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-50d31558-b4b5-4dbd-b9bd-3bb16dbefa0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50d31558-b4b5-4dbd-b9bd-3bb16dbefa0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98c65b68-dee6-4591-aacd-2ecc3f5ac90d' class='xr-var-data-in' type='checkbox'><label for='data-98c65b68-dee6-4591-aacd-2ecc3f5ac90d' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-7bb3e210-bced-4b15-a950-af02f36d4e43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bb3e210-bced-4b15-a950-af02f36d4e43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7723db50-e6cb-4022-b9b7-60adb538356e' class='xr-var-data-in' type='checkbox'><label for='data-7723db50-e6cb-4022-b9b7-60adb538356e' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1834c063-4e21-43c3-9a67-6796f7c502fd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1834c063-4e21-43c3-9a67-6796f7c502fd' class='xr-section-summary' >Data variables: <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>date</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-03ad9bcc-1827-4feb-9010-d231ccbdd983' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03ad9bcc-1827-4feb-9010-d231ccbdd983' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fc9402c-65bc-4bf5-94c6-af2571d44255' class='xr-var-data-in' type='checkbox'><label for='data-7fc9402c-65bc-4bf5-94c6-af2571d44255' 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>current date</dd><dt><span>units :</span></dt><dd>yyyymmdd</dd></dl></div><div class='xr-var-data'><pre>array([20030101, 20030201, 20030301, 20030401, 20030501, 20030601, 20030701,\n",
       "       20030801, 20030901, 20031001, 20031101, 20031201], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>datesec</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f635a098-2eb9-40e5-9231-214853c5e0cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f635a098-2eb9-40e5-9231-214853c5e0cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eca3640-19dc-4849-97d0-78bb156c2acd' class='xr-var-data-in' type='checkbox'><label for='data-5eca3640-19dc-4849-97d0-78bb156c2acd' 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>current seconds of current date</dd></dl></div><div class='xr-var-data'><pre>array([86400000000000, 86400000000000, 86400000000000, 86400000000000,\n",
       "       86400000000000, 86400000000000, 86400000000000, 86400000000000,\n",
       "       86400000000000, 86400000000000, 86400000000000, 86400000000000],\n",
       "      dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>precip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d040b713-d8c9-46aa-933e-3330821278d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d040b713-d8c9-46aa-933e-3330821278d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f14602f-c83a-4b9b-875c-7741e489cd3c' class='xr-var-data-in' type='checkbox'><label for='data-8f14602f-c83a-4b9b-875c-7741e489cd3c' 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>mm/h</dd><dt><span>avg_period :</span></dt><dd>0000-00-00 01:00:00</dd><dt><span>delta_t :</span></dt><dd>0000-00-00 03:00:00</dd><dt><span>hdf_name :</span></dt><dd>precipitation</dd></dl></div><div class='xr-var-data'><pre>array([0.011881, 0.022275, 0.047683, 0.112677, 0.275681, 0.489794, 0.571396,\n",
       "       0.486421, 0.371685, 0.222441, 0.050148, 0.0123  ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b46dbbb5-e003-4797-9636-56c9fcde4873' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b46dbbb5-e003-4797-9636-56c9fcde4873' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg.de/cdi)</dd><dt><span>history :</span></dt><dd>Fri Jul 22 23:58:08 2022: cdo -L fldmean -ymonavg -remapnn,grid.des 3B43.1998_2008.nc 3B43.1998_2008_yearly.nc\n",
       "Thu Nov  5 16:26:10 2009: ncrcat 3B43.1998.nc 3B43.1999.nc 3B43.2000.nc 3B43.2001.nc 3B43.2002.nc 3B43.2003.nc 3B43.2004.nc 3B43.2005.nc 3B43.2006.nc 3B43.2007.nc 3B43.2008.nc 3B43.1998_2008.nc\n",
       "Thu Nov  5 16:26:03 2009: ncrcat 3B43.19980101.nc 3B43.19980201.nc 3B43.19980301.nc 3B43.19980401.nc 3B43.19980501.nc 3B43.19980601.nc 3B43.19980701.nc 3B43.19980801.nc 3B43.19980901.nc 3B43.19981001.nc 3B43.19981101.nc 3B43.19981201.nc 3B43.1998.nc</dd><dt><span>Conventions :</span></dt><dd>None</dd><dt><span>creation_date :</span></dt><dd>Thu Nov  5 16:19:03 CET 2009</dd><dt><span>source_html :</span></dt><dd>\n",
       "http://disc.sci.gsfc.nasa.gov/data/datapool/TRMM/01_Data_Products/02_Gridded/06_3-hour_Gpi_Cal_3B_42/index.html\n",
       "</dd><dt><span>title :</span></dt><dd>TRMM: 3B43</dd><dt><span>nco_openmp_thread_number :</span></dt><dd>1</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 12)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2008-01-01 2008-02-01 ... 2008-12-01\n",
       "    lon      float64 0.0\n",
       "    lat      float64 0.0\n",
       "Data variables:\n",
       "    date     (time) int32 ...\n",
       "    datesec  (time) timedelta64[ns] ...\n",
       "    precip   (time) float32 ...\n",
       "Attributes:\n",
       "    CDI:                       Climate Data Interface version 1.9.9rc1 (https...\n",
       "    history:                   Fri Jul 22 23:58:08 2022: cdo -L fldmean -ymon...\n",
       "    Conventions:               None\n",
       "    creation_date:             Thu Nov  5 16:19:03 CET 2009\n",
       "    source_html:               \\nhttp://disc.sci.gsfc.nasa.gov/data/datapool/...\n",
       "    title:                     TRMM: 3B43\n",
       "    nco_openmp_thread_number:  1\n",
       "    CDO:                       Climate Data Operators version 1.9.9rc1 (https..."
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "trm_data = xr.open_dataset('/home/mkibria/Academic/pr_of_thesis/observation/trm/MonthlyTRMM_3B43/3B43.1998_2008_yearly.nc')\n",
    "\n",
    "trm_data = trm_data.squeeze()\n",
    "\n",
    "trm_data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3473b9d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrm = trm_data['precip']\n",
    "xtrm = xtrm*24\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "31dea29f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HLQKaB2lbB1geoI/vQn2P9dBDDz30ODMfMZ+AHnrooYceeuihR7QfON/6dP8P8Jggx/cKcPyICMbtHKCf048nQ+yjXYAbNJtCbDsxi7GTgKYh9vFOJHPP4rV3jvD92+LqZ2KI7crm4JwZ4RozA2gQYtuR7teeR+f5JwF+5tuBJhH0dV6Q58rjf8PuswjGmO3q4yjZByheCPAaDwJdIhi/CtkHpsK+IYsTKPrT1W5POO8DzsqYVa4+9gMlc/kcCvR5/wOoEUJbQ+AbqnuB4iG0rxOg7e25+XpDnMO8bNoUxgleXQIMxfkmfqDr7j1hzqVsDl7HbQHG7xZi24lZzD8vfm/UxD94mQbcGOK4HTKvI4HmH/R99Ooj7M98jN+v9wO0nR/iZ64qzpcVAv28LKEFYZ4L0G5YmK9/ZIA+bgly/IuuY1OBuhH+7IP+DsjinDj9+BooFkL7Cjgrnb3bpgO1Ij1n9NBDDz30KPiPs3bPXhERETmj3RmgLth2JV/h3Nj1NiCK+Q2+sNaGtCe4tfYX4DNXdV1jzAWBjg/RAGvtquwPA5xv7p901XXPwdh5xka+ZRvAKJwVF6cZnC3t8iVjTAOcRMPeTgJXW2vXhNuftfbPIM8l46yW8Xa9MeacUPs3xtQDLnNVT7LWHg7SpgJOvgGf6QC9rLVzQx3b09Davdba4+G2C0F/nJv5p2UAN4TzPmSeuzfg3Fw8rSL+27dFTWYeDnc+inTgb9baHdm1t9ZanDxE7tdZOUC/BUknY4zN6oHzHiXhrDx4Ff9ttHbgvP9vhzNoTq5f1toPgM9d1Tk9d/Li98bd+Odpetla+0Uog1pnK8PhoRwbbbF4vzI/s31c1ck4n9kTIYy7B+c6cyrEqbrHLw8MdlW/Za0dE04/1tqROFumeXssSJPzXeV51trN4YzpNXakvwO2AP2ste7zPNAYB4BnXNVxONtPiojIWUpBGBERETmjGGOq4ORU8LbCWrs2qzbW2hT8c8DUxj9HSyQygEfDbPNRgLoWEY7/o7V2eqgHZ948+K+rutmZnnA784byh67q9rGYS4j+gf/f8s9EEoAJ0XhXOVB+l2DuxT9/wVvZtLkfJ/eEt3estTPDGDdXGWMMznvhbZK1dkG4fVlr/8D/HLwh0rmFYCD+uQvettb+GmoH1tpT+N+UhYKZRyqnfgZuxFlV9mUMxnd/0SAn169c/72R+Zz7GnIAJyAejtdx8o8UNJG8XwPwz8U00lq7L9RBM/8WejPU413c1+SjwLAI+3K/z82MMXWyOLa0q5wU4Zg58UyYgbdPcILa3iL9O05ERM4AZ/T/TIuIiMhZ6TZ8E98CfBBCu0ArZQKtqAnXD9ba38NsEyh5a/0Ix383gjZLXOVS/C8h7Zlsk6vc3BjjPpfyix6u8lEiv7GWLWvtSpxky97uMsZkm+zYGFMM53PpbXEIN/uvdU8DeCm78fJYU+BCV91/ctDft65ya2OM+6ZrtAT6VnbY55C19kfAHeRuYoypFtGsCq72wBvAU8aYijEY3339OscYUyvCvvLi90ZDnBwh3j4Jd6WCtTYDZ0u1giaS9+sqVzmF4Kt8sxLWKi0vN7nKnwVbzZiNBfivQO6UxbHuoEtrY0yhCMeNxDH8V4MGlbmC1P0eR/p3nIiInAHy8heXiIiISF5wbyF1Cvg4u0bW2gXGmE343lC93hhTIfNbvpH6MdwG1tq9xphjQEmv6rJ5NT5Owlu3sjj5RgoMY0wpnGTEFwEJONs7lcH5uQb6MpJ71UVRnD30s92aKS8ZYxrh5DfxNj2H27GFYjxOzqLTauKsOsvuG/O34OyR7y3ozX5jTBngYlf1wki3oMlF7puG6fjfjA6He1u4Yjg3q1fmoE8/mcGzS1zV6621iRF2+RnQyFXXFghpW6l85hhOAvNgSuHkSnKf19VwEs0PMsbcYa39JtJJZAbf2uME+hrjbPNWJnPsQMFP97ZeALWAbREMnxe/Ny4NUOdeTROqb4DnI2wbFbn9fmWuHGrpqp5rrT0Y7lyttWuNMRuBeqG2ydyKrImrOuwVf15zyDDGbAXKeVVfTOAvzSwGenuVzwPeNcbcn0tbTLotstamZn+Yn9+BBl7lSP+OExGRM4CCMCIiInLGMMa0w/d/eAFmhLFVx/8Bz3qViwJ9gddyMC33NyFDdYicB2FOhpLbIYux3QrMzQNjTAucLaKuBYrnsLty5LMgDNAmQJ17lUpumArsArxzwQwi+yDMIFf5AP7b/7ldgv+Ny7x4jeFq5ypbYJGzS1lEAt2YrRRpZ0HUxz/ouCwH/S0NUNecghmEWWat7RzKgZl5kS7D2W7PezupysCXxpj+1tqwvkFvjKmLkx/jJnJ+3S0XQZu8+r3hvqEPsCKCcQHW4+SkcW+vl+vy8P2qj/+2XMtzMNZywgjC4PzecX95YbgxJidbD9Z1lbO61n0CPIfv7/PbgauNMRNxrjNLM1dF5Yac/B3nrcD8HSUiItGnIIyIiIicSQJtHxbKVmSnfYizT7n3HdQ7yVkQJtJVNGmuciTbYkVr7EjHz1OZW4e9inPTP1rb7ubHmyZVA9TlVi4YD2ttmjHmHWCEV3V3Y8x51lr3Cg4AjDHNgNau6okhJDeOyWuMQA1XuRD+idpzKje2tgp0s3NdDvr7LcQxzijW2l3AJGCSMWYwMJb//f6IB943xqwOdYWRMeZp4HH8835EKpLrV1793nCf16mZP8+wWWtPGWO24781YK7K4/fLvfoRcpYLZ32Yx7uvdQDn52D8QAJe66y1u40xj+P8fvdWBSfn3qPAQWPMApxVM4uABdbao1GaVyz/jhMRkTOEcsKIiIjIGSFz66lerupknG1KQmKt3QrMc1U3zVxZEalAN6bySizHzlOZAZjPcBIHR/Nv3Px408S9BRI453peeBvf88oA9wQ53r0KxhJaPoJYvsZw5EXuj5yu5gqkfIC6gznoL9B7E+g9PGNZa18DXnRVFwXGhdLeGPM68AzRu6EPkV2/8ur3hvsczOl2irm9HaOPGLxf5QLU5eQ1h9s2ptc6a+2/cVa4nsrikHLA1TjvyfdAsjHmJ2PMA8aYnAaEz5q/pUREJPcoCCMiIiJnit74bt8FMMVamxJmP4FWzgRaYSP5yzDgugD1O3GSZffD2U6lJs7NmmLWWuP9ALrk1WRzqEyAumh94zeozG+qT3NV32GM8dtGKzOvSx9X9Rxr7cYQhorZawxToGBGQeDe1gicXCiRCtQ20BhnuucAd46KLsaYQFtveRhj+gH3BXjqAPAeTq6zDkAdnHOueIDr13k5nXwecwcvIsm54S3c3/URi9H7lRef2WBifq2z1r6Ek29nEs72c8EUwtki8DVgqzHmRWOM+29EERGRPKPtyERERORMEShQcq8x5t4o9H2rMeZha+2JKPQlUWaMqQIMd1WfwvnW7HhrbVbfnHXLjRUHueFwgDp3fo/cNB7fVWeVcfIhuHNf/D/85/VWiGPE+jWGyn1NWGmtvTgmMwnPkQB1OblBGahtoDHOaNbaQ8aYn4Durqe6kcV2epmr+P4V4KkXgFFh/N4pKNev09wrMXIatAsUuI26GL5fefGZDSbQ62pmrV2VgzmEzVq7AehnjLkfuAbnyxPtcXLmZJWMqwTwCHCtMaZb5qpnERGRPKWVMCIiIlLgGWMSgEtzcYhywI252L/4CncLnWtxbrJ4G2at/XcYARgoONsnJQWoy7NvKVtrfwLcN97c246Bk6zc2y7gqxCHielrDMN+V7mgrEYItH1YuRz0F6htpHkUCrpASbyD5QnqBJzjqnvNWjs8zMB/Qbl+neY+B0sHWlEXhrzYLgti934dDFCXk5xl4bZ1X+sghtc7a+0ha+1ka+1d1tqGOO9/D2AMWecPqwd8m8PzTEREJCIKwoiIiMiZIC+2C9OWZMGFm5Q5mHBvTl3hKifjrNYIV7STDOeW3QHqLsrjObzuKrc3xjQ+XTDGtMfZNsbbu2EExfLDawzFHle5rDGmVkxmEp59Aeoa5qC/hAB1gW7ang0CreIKlpPCff3KAEZHMG5BuX6dtt1VNkCjSDoyxlQEzs3xjEITq/drb4C6+hGMe1qDMI93X+sgH12TrbXJ1tpvrbWPWWsvwvnZvAmkuw5thP6eExGRGFAQRkRERAq0zK1B/p+rOhXnm/o5ebi/xd3ZGFPQbnLlpUA3HsPeHsYYUwMoFmazmq7yYmttJPkF2kTQJhYWBqhrl8dzmIT/N7O9V764V8akA++G0f9S/BMw5/VrDMWSAHVX5fkswrcR/xw7LXPQX6sAdctz0F9BFmiFgftGsDf39WujtTbQDe/sFJTr12mBPjuRrmjNzZWwbrF6vzbg/5ltEcG4kbZdHKAu317rrLUbrbX3Af0DPP23vJ6PiIiIgjAiIiJS0F2Lk5PC2zRrbbOcPIAnXX0aYEBevKAC6mCAukiCVp0iaOP+lnnY2yAZYyrh7C2f71lr1+H/reSexpicbE0T7hyOAxNc1f/PGFMy82fpvsn1tbV2Rxj9HwFWuKovNcZcGP5sc9WsAHX98nwWYbLWpuN/E7yBMSailQjAzQHqFkTYV0EXaIVBoJVdp0Xj+lUYuD7cdjG2GLCuuj4R9tU3h3MJR0zeL2ttBk5w2lsXY0y5CMZPwNmaK5zxtwKbXdWXGGPC6ievWWsnAytd1flmBY+IiJw9FIQRERGRgi7QthIfRaHfKTgrarzdbozR30+B7cT/W7qXRNDP3RG0OeYqB9v6Jyv3E/4KnFj60lUuBdyXx3N4Hd+bqGVwbqLeARR1HftmBP1Pc5UNTnLl/GQx/gGx9saYrrGYTJi+D1DnzuOTLWNMB/y3nlsd4eqAAs0YU57AK7bWBWkWjetXH/zzlORr1tpkYKarur0xJqwVIsaYC8jbnG2xfL9muMpF8V8JHIpIfs+Cf06vOODpCPvKS+td5Tz7woKIiMhpuokgIiIiBZYxpjrQzVW9D/gup31baw/gf8OjBtA9p32fiTK/pbvSVX1NOKszjDHXAh0jGH6Xq9zWGFMyjHEbAcMjGDeWXsJ/i6OnjTFN8moC1trf8f+s3Qfc46r7ncArRrLzJv7b3N1ljHF/5mMmM8fNmABPvWeMca/Qy2/eB0666u41xoT8LXFjTCHgtQBPBao7GzxF4GDu9CBt3NevesaYOqEOaIypinM9KIgCBWffMsaUCKVx5vn3Nv5B39wUy/drApDiqhuRmRMn1PETiDxg/zL+14y+xphbIuwvr7gDXoFyYomIiOQqBWFERESkIBsAxLvqpoSR/Ds7gVbUKKFr1txBq+LAP0NpmHnj1729Vah+cpVLASNCHLcOzg3SvLyJl2PW2s3AZFd1MeC/xhj3qoRshXMT0WW8q9wM/23o3rbWurcdypa19hAw1lVtgCnGmM7h9meMqRLqzd0wvQm4t1qrBczIzHEUNmNMZWPMP40x7iTgUWOt3Q/8n6u6EDDVGJPtt/SNMQb4D9DU9dReorMasUAxxgwF/h7gqR8yP69ZcV+/IHBgL9CYFYBviGw1Rn7wDf7bDl4EfJ1dAN8YUwznPLs8l+aWlZi9X5mfWfd1vyLweebPI7vxqwBTgcIRjr8LZwWk2/vGmIjyrBhj4o0xtxhjRgc55uXM4FEk/TcHOriqV0XSl4iISE4oCCMiIiIFUuYNwEA5WqJ58+9r4JCr7toC8A33WJmI/+qMB4wxz2R+Y9lP5g2YO4GfgQo421u5t4HLzlQgw1X3D2PMs1mNmzn2rThJ7k8HDdyrLvK7IcAfrroawC/GmGHZBRyMMXHGmC7GmI+ATRHOYQbOSpespBB5cA2cIN4iV1054HtjzAuZ2z8FZYy5xBjzBrAFqJKDuQRkrT0J9ML/G+otgBXGmAeMMcVDmGcRY0wPY8z/AVuBJ8j9bXOG4x9AqotzDmW5pVrmKsSvgNsCPH135s/kjGeMOccY088Y8wvwaoBD0oDB2XTzHXDEVdfLGPOfYCv6MleELQJaZlYVtOvX6dxEA3B+Tt4uA9YbY+7LDBx4GGPKGWNuAxKB0yswDuK/5VRuifX7NQzY76rrDPxgjGkYZPz2OL9nT+csOhHh+E/in0+qBE4g6D+Z28NlyxjT2BgzCtgIfIJ/MNfbncBaY8wsY8xd7nMiyBg9cH5Hue97nXVBYhERib0s/6dUREREJJ/rgv837jdZaxdHawBrbYox5jNgoFd1YZzE24FuuJ3VrLV/GWNeA4a6nnoaZ8uSqTi5EY7jfHu3CXANzqqB08YAtwK1wxh3Y2Ygob/rqSdx8vh8DqzGyVlTAagPXAt43yw6jnNzK5LcJTFhrT2Y+e3j+UBpr6fKAC8Ajxtj5uDc+NuL8/rLAucCF+PkrshRUMJaa40xb5L19jqfZX57O9L+U40xN+Pc9PNenVEE5/0aYoz5Eefb6btxbmyWBqrirMppC9SMdPww5rnQGHM3TiDSeD1VGWdrrmcz53n6vTiMs2KrHFAHJ2BzMRDyNnrRYK09YIzpj5Obw/v/Dc8DZhljVuAEo7fgbEN0Ds6WgVcSeNut16217rwRBU1LY8zKbI4piXMtqRDkmFNAX2vtb8E6stYmG2NexT+3xp3A9Zm/g1YAyTjny/lAD5zr52npwIPkLOAZE9ba1caYB4C38P3sVMNZdTHeGLMXSALK41yzvFfAWpxcRoP4X4AB/L8QEK35xvT9stbuM8bcC3yG78+rDbDaGPMDMBsnT1shnN+v1wCtvY4/irO1WEgrRl3jnzTG3IBzLXNfW+/E+Z27DPgR57pxAOf9Kofz3jXDCURVD3dsoGvm4y1jzFrgV+A3nHPjYOY4FYCGwBX4ng+n/YST809ERCRPKQgjIiIiBVWgbcFy49uNH+EbhDk9toIwgT2Bc5PEvSXWBcCj2bSdktn+1gjGHQJcgv9Nlxr4B4Xc0oCbcQIxBYq1dqUxph3OjXJ34KoMcEPmIze9D4zC+Ta0W46DWtbaHcaY1jiv0f1t6eI4AYErczpOTllr/88YkwR8iHOz2Fs54LrMR75irZ2bmY/pM/yDQM0zH6EIFIAtiEoS/Fv5odgN3GWt/SbE4/8JdMp8eKuIE2AIxuLk+JgXzgTzE2vtO8YYixOIca9aMDhB1aqBmgJDrbVTjDHuFUe5uTIopu+XtXaqMWYQzvXVOxBTCCdPXrC8WaeAPplzjXT8v4wxl+D8znbncYvHCfi0jrT/EMThBLXCzYGWCPTOzGEnIiKSp7QdmYiIiBQ4xphywI0BnpqUC8PNB7a56hpl3hQWF2vtcZytUdzblQRthrOSok+kN0cy84d0xX/rquz8BXS11v43knHzA2vtGpwA1Ef4b8sWqogTFVtrk/HPUwCw2lq7INJ+XWNsx1m58xr+236F6jD+SaWjylr7Lc6qlmk453WkTuHkjlgdjXllx1o7A+dm6rIImicBg6y1Q3Rzk/0417IGYQRgsNam4QToQm6T6SDQy1r7Tpjt8h1r7bs4N+6Xh9jkD+BKa+24zLI78OneSjRq8sP7Za19G+iLs9IkVMnAddbar6Mw/m6cfDxPhjmHQNYBnwZ5fncO+7fAB0B7a+1fOexLREQkIgrCiIiISEHUF/+tcBZaa4PlpohIZkLxQMGdQCtxBLDWJuHcMB9E8Hwh6Tj7tbez1v4jpzdwrbU7cW4kP4B/rhS3rcBTODdL5+dk3PzAWrvXWvv/cFYgvQtsD6HZIeALnJVHESWQ9xLo5n1Ut3az1h6z1g4BLsRZiRZKHpvjOOfYXcC5mTcOc5W19k9r7Y1AI5xv9od6XTqAk9/oXqCGtbantXZjLk3Tj7V2BU4w7xac7YyyC3b9hrMC6gJr7Vu5PL38JhUn4PI78D3wPNAT5xz7R2ZQOCyZba7F+f2WXfBtL/AiUN9a+3m4Y+VX1tplOOfgZcAbwEpgD05Q8gjOOfchcBNQz1o706t5NVd3OQ0MZDfXmL9f1tqPcVZ/jid4IP0QTgC7YTS/cGCtPWWtHY2zCvNhnJwzoeR0OwUswLl+XGKtTbDW/l+QcRrgBLefAmYR+iqnvTjnUXNr7e2RfC5FRESixTj3FUREREREcocxph7ODZQqOPk6juDcvFxgrc21G2WZ416Ck5ejJHAMJwn5amvthtwaN78wxjTA2Ru/UubD4vzs/8JJYr0xMzF2NMZagJOT4LQjQHVrrTuBdVQZY2rjbB1VGWd7nfjMsfcAG4D11tpQbgrmKmNMDZx5VsKZZ3GcvAyHcVbarc8MIuYbmUnGL8XJA1MFJw/Pfpwbm79mrk6SXGKMqYXzmaqKs7XgSZzP7lqca5j+Rz6TMeZCnATv3u601r6fh3OI6ftljInHyYF1Hs5n1uB8XtcCyzJX7+Q6Y0wJnJwv5+Jc68rhBHSP4Fw7NgCbc3JdNsbE4eTSugAnAFQGZzvMFJxr6i6cn/mWSMcQERGJNgVhREREREQkYsaYi4BVruq3rLWDYjEfETm7GGOG4p+nrYm1NjEG0xERERHxo+3IREREREQkJx4IUPdGns9CRM46xpgiwIOu6v04W5eJiIiI5AsKwoiIiIiISEQyt9nq76r+wVq7JhbzEZGzzqs4W1N5ey+nOcZEREREoklBGBERERERidRYoKir7pVYTERECiZjTE9jzI2ZeU1CbVPEGPMGcJ/rqVPAW1GdoIiIiEgOKQgjIiIiIiJhMcZUN8Z8ANzoemqhtfbbWMxJRAqsRsBUYIsxZpwx5kpjTFX3QcaYOGPMRcaYYcDvQKC8UyOVkF1ERETyG2OtjfUcREREREQkHzPG/Bc4N7NYDfC7QQqkA+2ttYvybGIiUuAZYx4Dng/w1EHgAHAcKANUBooH6WoWcKW2IhMREZH8plCsJyAiIiIiIvleAlA7m2NGKwAjIlFULvMRiveBQQrAiIiISH6k7chERERERCQnLPCitXZErCciIgXSr8BvEbZdAlxnrb3TWpsaxTmJiIiIRI22I4uRzKSDdXG+VXguUBZIAZJx9rddZq09FqWxyuPss3shUAEohrO0ex+w3Fr7ezTGEREREZEzkzFmC74rYY4B24D5wDvW2hWxmJeInDmMMRcCHYFLcf7ftTZQHiiBE+xNxtme7HfgJ2CutXZpbGYrIiIiEjoFYfKQMaYWTvLSrkAHnH1ts5KOs6ft+HCTmxpjCgOXAT2BzjgBmGD+At4D3rDW7g5nrEgYYzoDc3PQxVZrbZ2oTEZEREREREREREREJJcoCJNHjDGTgVsjbP4NMNBauyeEcVoDM3C+MRSug8Bga+1HEbQNmYIwIiIiIiIiIiIiInI2KBTrCZxF6mVRvxPYBOzBeT/OB5rim6+nBzDfGNMphJUqlQkcgEkF1gC7gUNARaBl5n9PKwd8aIypYq19JZtxREREREREREREREQkCAVhYuNX4H1gRqB8LMaY6sDTwN1e1fWAz4wxHW3oy5eOAp8Ck4EF1toTrnEMcD3wb6CW11MvG2PWWGtnhThOTo3NnEOoTuXSPEREREREREREREREokZBmLxjgW+BkdbaZUEPtHYncI8xZhXwutdT7YFbgE+yGWsv8C/gLWvtsSDjWGCaMWY+TmLDhl5PjzPGJIQR8MmJg9baLXkwjoiIiIiIiIiIiIhInonL/hCJkputtT2yC8B4s9a+AUx1Vf+/bJotBs631r4cLADjGicJJ19Nhld1A5ztykREREREREREREREJAIKwuSRHKz0eN1V7pLNOPtCDb642q0Cfg5nLBERERERERERERERyZqCMPnfr65ycWNMuTwa69xcGkdERERERERERERE5IynIEz+FygJfZE8Giu3xhEREREREREREREROeMpCJP/1XWVTwH782isXbk0joiIiIiIiIiIiIjIGa9QrCcg2brJVV5mrc2I9iDGmDLAFa7qJdEeJwtdjDEXAc2AKkA8cADYDSwE5gLTrbVpeTQfEREREREREREREZEcUxAmHzPGlALudFVPy6Xh7gFKeJUP4QQ/8kLHAHXnZj6aA/cDO4wxLwBvWGttHs1LRERERERERERERCRiCsLkb88D1bzKB4H/RHsQY0wd4ClX9VhrbWq0x8qBGsB44CpjTD9r7cFodm6MqQJUDrNZKaAlcBgnaLUdyE8/MxERERERERERERFx8p/X9Cr/aK09lBcDKwiTTxljbgAecFU/Ya09EOVxigBTgNJe1VuAf0VznCwcBmYDPwJrgb3ACaA8UA9ne7RbgGJeba4BvjTGdItykOg+YEQU+xMRERERERERERGR/Ok6YHpeDKQgTD5kjGkK/J+reibwZi4M9x/gEq9yOnCbtfZYLox12m5gAPCJtfZkFscsBSYZYx4D3geu8nquE/AC8FAuzlFEREREREREREREJEfiYj0B8WWMqQV8i7PV1WlbgX7RzoVijHkW+H+u6uHW2vnRHMfNWrveWjsxSADG+9jdOKtfPnM9db8x5rxcmaCIiIiIiIiIiIiISBQY5TjPPzLzkvyEsxXXabuBjtbaTVEeayjwqqv6FWvtw9EcJ1qMMSWAzcA5XtWjrbVPRqn/SHLCNAA+P1348ssvqVu3bjSmIyIiIiIiIiIiIiJRsnnzZq6//nrvqubW2l/zYmxtR5ZPGGMq4ORH8Q7A7Ae65kIA5i7gFVf1m/k1AANgrT1ujBkHPO9VfSUQlSCMtXYvTk6akBljfMp169alUaNG0ZiOiIiIiIiIiIiIiOSeaOYbD0rbkeUDxpiyODlfmnhVJwNXWGvXRnms/we8BXhHECYA90dznFzynavcJOBRIiIiIiIiIiIiIiL5gIIwMWaMKY0TXGjhVX0YuNJauzLKY/XGCbh4v++TgIHRzjeTS7a4ykUyA1giIiIiIiIiIiIiIvmOgjAxZIwpCfwXuNSr+ihwlbV2SZTH+hvwIRDvVf0ZcJu1NiOaY+WiEwHqiuf5LEREREREREREREREQqAgTIwYY4oD3wDtvaqPA9dYaxdEeaxrgY/xzQH0JdDHWpsezbFyWaUAdUl5PgsRERERERERERERkRAoCBMDxphiwHSgs1f1SeBaa+38KI91Nc6Kl8Je1d8Ct1hrT0VzrDzQ2lXeZ61Ni8lMRERERERERERERESyoSBMHjPGFAG+ALp6VacA11tr50R5rCuAqUARr+qZwN+stanRHCuP9HGV58ViEiIiIiIiIiIiIiIioVAQJg8ZYwoBnwJXeVWnATdZa7+P8lidgK+AYl7VP+AEe1KiOVZeMMZ0Bm50VX+V9zMREREREREREREREQmNgjB5xBgTD0wCrvOqPoWzLdg3UR6rDU6+Ge+k9fOBntbaQMntczLWSGOM9XrMy+b4bsaYpmGO0RpnRY/xqt4ATAl7wiIiIiIiIiIiIiIieaRQ9odIlLwP9HLVPQ78aoypE2Zfu621JwM9YYy5GJgBlPKq3gDcD1QxxgRqlpWT1trdYc4tO22Bp40xM4FPgP9aa/cGOtAYUxMYAjyIb06bNOC+ApjTRkRERERERERERETOIgrC5J3+Aer+lfkIVxeyzodyHVDWVVcfWBPBOD8CnSNolx0DdM98YIzZiRMoOgicwJl/vcyHWzpwh7X2h1yYl4iIiIiIiIiIiIhI1CgII/lB9cxHdv4A+ltrf8nl+YiIiIiIiIiIiIiI5JiCMJLXpgOVgQ5AAhCfzfGngCXA28AUa21K7k5PRERERERERERERCQ6FITJI9basJKx5GCckcDIvBgrkvGstSuAFQDGmGI4gZjawDlAaZzcL0eBZOBPYJm19nhUJy0iIiIiIiIiIiIikgcUhJGYsdaexAnIrIj1XEREREREREREREREok1BGJECxFpLRkYG1tpYT0VERETkjGWMIS4uDmPyZDG7iIiIiIicwRSEEcnH0tPTOXbsGEeOHOHYsWOkp6fHekoiIiIiZ40iRYpQunRpSpcuTbFixRSUERERERGRsCkII5IPpaens2vXLo4cORLrqYiIiIictVJTU0lKSiIpKYnChQtz7rnnUqJEiVhPS0RERERECpC4WE9ARHylpaWxdetWBWBERERE8pG0tDS2bdvG8ePHYz0VEREREREpQBSEEclHUlJS2LJlCykpKbGeioiIiIi4WGsViBERERERkbBoOzKRfGTPnj2cOnXKp84YQ4kSJShdujTFixcnPj5e+5GLiIiI5CJrLWlpaRw9epTDhw+Tlpbm89xff/3FBRdcoL/JREREREQkWwrCiOQTaWlpHDt2zKeuSJEi1KxZkyJFisRoViIiIiJnp8KFC1OiRAkqV67Mzp07fbaKTUtLIyUlhWLFisVwhiIiIiIiUhBoOzKRfOLQoUM+5bi4OGrXrq0AjIiIiEgMGWOoXr06hQsX9qk/fPhwjGYkIiIiIiIFiYIwIvmEOwhTpkwZChXSYjURERGRWDPGUKZMGZ8675UxIiIiIiIiWVEQRiQfsNaSmprqU+f+H30RERERiZ1SpUr5lFNTU7HWxmg2IiIiIiJSUCgII5IPZGRk+NW5t7wQERERkdgJtEI50N9wIiIiIiIi3hSEEckHAn2LMi5OH08RERGR/CLQ32ZaCSMiIiIiItnRXV4REREREREREREREZFcoCCMiIiIiIiIiIiIiIhILlAQRkREREREREREREREJBcoCCMiIiIiIiIiIiIiIpILFIQRERERERERERERERHJBQrCiIiIiIiIiIiIiIiI5AIFYURERERERERERERERHKBgjAiIiIiIiIiIiIiIiK5QEEYERERERERERERERGRXFAo1hMQEZHoO378OCtWrGDTpk3s37+fkydPUrx4capWrUq9evW4+OKLKVKkSER9T5w4kQEDBnjKEyZM4Pbbb4/SzEUC69y5Mz/++KOnbK2N4Wxi78CBA9SvX5/9+/cDMGPGDK688sqI+8vIyGD9+vWsX7+enTt3cvToUQDKly9P+fLlSUhIICEhgfj4+JD6mzdvHl26dPGUR4wYwciRIyOeXzA6N8KzZcsWGjRoQEpKCnFxcSxZsoQWLVrEeloiIiIiIiJnLAVhRETOEOnp6Xz66adMmDCBuXPncurUqSyPLVasGN27d2fgwIH06NEjD2cpItEwbNgwTwDmiiuuiDgAM2vWLCZOnMi3337LoUOHgh5bokQJ2rdvT9++fbnhhhsoXbp0RGNKbNWpU4cHHniAl19+mYyMDO677z4WLlxIXJwWyIuIiIiIiOQG/d+WiMgZ4IcffiAhIYE+ffowa9asoAEYgJMnT/LVV1/Rs2dPWrVqxYoVK/JopnI22rJlC8YYz0Mrp3Jm2bJlvPfee57ymDFjwu5j4cKFtGzZkm7dujF58uRsAzDgrLCbOXMmt912GzVq1GDUqFGeFTNSsDzxxBOUKVMGgCVLljBx4sTYTkhEREREROQMpiCMiEgB98wzz9C1a1c2btzoU2+MISEhgW7dunHrrbfStWtX6tWr59d+2bJltGnThnfffTevpiwiOfDYY495tty65ppruPjii8NqP2rUKNq3b8/y5cv9nqtQoQKXXnopPXv25NZbb+Xyyy+ncePGFC5c2Oe4w4cPM2LECG666abIX4jETPny5bn//vs95ZEjR3Ly5MkYzkhEREREROTMpe3IREQKsKFDhzJ27FifutKlSzN8+HD69u1LrVq1/Nps3ryZiRMn8tJLL5GSkgJAamoqd999N8eOHWPo0KF5MXURicCcOXOYM2eOpzx8+PCw2t977728/fbbPnVFihThnnvuoW/fvrRq1SrgtlSHDh3i+++/55NPPuHLL7/0BIF0477geuihh3j11Vc5efIk27dv54033uChhx6K9bRERERERETOOFoJIyJSQH3wwQd+AZj27dvz22+/MXz48IABGIC6devyz3/+k9WrV9O4cWOf5x5++GHmzZuXW1MWidi8efOw1noeZ6vnnnvO8++mTZvSrl27kNuOGzfOLwDTrl071q9fz7hx42jdunWWeUHKli1Lr169+OKLL1i2bBldu3aN7AXkAp0bkalUqRK9evXylF955RXS0tJiOCMREREREZEzk4IwIiIF0MaNG3nggQd86tq2bcuMGTOoUaNGSH3Uq1ePOXPm0LBhQ09dRkYG/fr18yT8FpH8Y+XKlfzwww+e8r333hty28WLF/PII4/41F1zzTXMmjWL8847L6x5NG/enJkzZ/LPf/6T+Pj4sNpK/uJ9Du3cuZOPP/44hrMRERERERE5MykIIyJSAD3yyCM+CbHLlSvH1KlTKVWqVFj9VKlShc8//5wiRYp46nbu3Mmzzz4btbmKSHS8+eabnn8XLlyYW265JeS2gwcP9lnlcN555/HJJ59QvHjxiOZijOGJJ57gnXfeiai95A9t2rTh/PPP95S9zzERERERERGJDuWEEREpYNavX88333zjU/fCCy9QrVq1iPpLSEjgkUce8dnm6L333mPkyJGUL18+7P7S0tL45Zdf2Lp1K7t27aJMmTLUqFGDzp07U6ZMmYjmCHDq1CkSExNJTExk//79HD16lMKFC1OqVCmqV6/OBRdcQEJCQo6+mf/HH3+wYsUK9u3bR3JyMmXLlqVatWq0adOGc889N+J+A0lLS2PBggVs27aN3bt3k5GRQaNGjejRo0dUxwnXgQMH+O2339i0aRMHDhzg5MmTlClThooVK9KsWTMaNmyIMSamc4zUX3/9xaJFi9izZ4/n/a1cuTKtWrUKezVIKLZv387SpUvZsWMHJ06coFKlSjRp0oSWLVtmue1XVk6ePMmnn37qKV9++eUhfz7nzJnD0qVLferee++9sIO2gVxwwQURt129ejVr165l586dGGOoUqUKbdq0oW7dujmeV6QyMjJYsmQJf/zxB7t37yYlJYXatWvTp0+fsPpYtGgRf/zxB3/99RfFihWjevXqdOzYkcqVK0c8N2st69atY82aNezZs4cjR44QHx9PyZIlOeecczj//PNp3LixT1A9FDfffDNjxowBYNGiRWzYsIH69etHPE8RERERERFx8d5DWw899Aj9ATQC7OlHYmKijVRaWpr97bfffB5paWkR9ydntnvvvdd6n3uVKlWyKSkpOepz7969tnDhwj79jhkzJuCxEyZM8DluwoQJ1lprk5OT7cMPP2wrV67s8/zpR9GiRW3v3r3ttm3bwprb7t277eDBg23FihUD9uv9KFGihO3WrZtnTqE4evSofe6552zdunWD9t2sWTP76aef2oyMjJD6HTFihE/7uXPnWmutTUpKsvfdd5+tUKGC3xhNmza11lrbo0cPn/pZs2aF9TOz1tqMjAx7wQUXePqIi4uz27dvD3jsokWL7EMPPWSbNGlijTFBfw4VK1a0w4YNs7t27cp2DrVr1872PQv0CPT+derUyeeYUKWnp9uPPvrINm3aNOiY9erVs+PHj7epqakh9+3dvlOnTp76X375xXbt2tXGxcUFHKtq1ap23LhxNj09PeSxpk6d6tPH66+/HnLbq6++2qdt8+bNQ24bqblz5/qMOWLECM9zEydOtI0bN87yvWjatKmdMWNGyGOFc25kdf06fvy4HT58uD333HP95lO2bNmQXtuJEyfsqFGjbM2aNQO+rvj4eHvVVVfZtWvXhvzarLX20KFD9oknnrA1atTI9rNTpEgR2759e/vyyy+HfK365ZdffPp4+umnw5rf2UR/r4mIiIiIFFyJiYnu/4dqZPPoPrK2IxMRKWC+++47n3L//v3D/uazW+XKlenZs2fQcYLZsGEDzZo14+WXX2bfvn0Bj0lJSeGTTz4hISGB6dOnh9TvDz/8QP369XnttddISkrK9vjjx48zc+ZMnnzyyZD6//777zn//PN5/PHH2bx5c9BjV65cSa9evejWrRuHDh0KqX+3X375hYsuuog33niDAwcOZHncwIEDfcrvvfde2GP9+OOP/P77755y9+7dA+YL+uKLL7j00kt55ZVXWLNmzekgc5aSkpIYM2YMjRs3ZtasWWHPKy/t2rWLNm3a0K9fP1atWhX02NN5lpo0acKmTZsiHvO5556jY8eOzJ49m4yMjIDH7NmzhyFDhnDTTTeRmpoaUr/ffvutT7lLly4htUtNTWXu3Lk+dXfffXdIbaPt2LFj3Hzzzdx+++0kJiZmedyqVau46qqr+Ne//pUn81q3bh3Nmzfn+eef56+//oqoj927d9OmTRuefvpptm/fHvCY9PR0ZsyYQbNmzXjrrbdC6nf16tU0bNiQ0aNHs2PHjmyPT01N5eeff+bhhx8mPT09pDFatWpFyZIlPWX3SksRERERERHJGW1HJnIGOJWewa5DJ2M9jTPeOWWLUSg+trHrHTt2sGXLFp+6bt26RaXvbt268cUXX3jKixYtIi0tjcKFCwdtl5SURLdu3di2bRvg5Kpo3bo11atX5+jRo6xZs8bzHMDRo0e56aab+OKLL4JuvbVp0yZ69OjBiRMnfOpr165Nw4YNPVsxHT58mD///JNNmzb55LzIzrvvvsugQYP8blTWqVOHRo0aUbZsWQ4fPsyqVat8bqrOnj2b9u3bs2DBAkqXLh3yeH/88Qf/+Mc/PMGXkiVL0qpVK6pVq8bRo0d9bvxfc801VKtWjd27dwMwbdo0Dhw4QIUKFUIezx24ufPOOwMe5w4UxMfHc+GFF3LeeedRpkwZjDEkJSWxZs0az3zAed+vueYafvzxR9q0aRPyvPLK1q1b6dSpE1u3bvWpL126NJdccglVqlThwIEDLFu2zCfAt2HDBtq2bcvs2bNp2rRpWGO+9NJLPPHEE55y/fr1qV+/PiVLlmTXrl0sWrSIkyf/d62eNm0aTz31lGcrqGC+//57z78rVapEw4YNQ5rTkiVL/D5Dl112WUhtoykjI4M+ffp4ArDx8fG0aNGCmjVrEh8fz+bNm/n11199goDDhg2jSZMmXHXVVbk2rwMHDtCjRw/++OMPAIoUKUKrVq2oXr06qamp/PHHHz7Xr0BSUlK45pprWLlyJQBxcXG0aNGCOnXqkJqayrp169i4caPn+LS0NAYNGoQxhnvuuSfo3Lp168aePXt86qtVq0bjxo2pWLEi8fHxHDlyhO3bt7N+/Xqf8ytUhQsXpk2bNsyePRuAX3/9lb1791KlSpWw+xIRERERERF/CsKInAF2HTpJh3/Nzf5AyZGfHu1CzQolYjqHX375xa+uZcuWUem7RYsWPuUTJ06wcuVKWrVqFbTd6NGjSU5OxhjD4MGDGTFihF+wYNasWQwaNMizMiMtLY3bb7+dtWvXUrVq1YD9Pv300z43jy+77DJeeeWVLG+Mp6Sk8OOPPzJt2jTmzZsXdM4//PCDXwCmb9++PP744yQkJPgdP2/ePAYPHuz59n5iYiJ33303H3/8cdBxvA0dOpQjR45Qvnx5nnvuOQYMGEDRokV9jvnzzz8BKFSoELfddpvn5nxKSgqTJk1i8ODBIY116NAhpk6d6ilXrlyZa6+9Nsvjy5UrR79+/ejZsycdO3akWLFiAY9buHAhTzzxhGdlRVpaGr1792bTpk0BV2P9/PPPnDp1ih07dtChQwdP/d/+9jdeeumlLOdTqVKlbF9jMKdOneLWW2/1CcCUKlWK0aNHc/fdd/u8vlOnTvHJJ5/w97//nf379wOwf/9+evXqxfLly0POm7JmzRp++uknAK6//nqef/55GjRo4HNMcnIyDz30EBMnTvTUvfzyywwaNIg6depk2ff27dvZuXOnpxxOcMh9zShXrlxMcq68+eab7N+/n/j4eP7xj3/wyCOPULFiRZ9jNmzYQP/+/VmyZImnbsiQIWzcuDHX8hCNHDmSI0eOUKxYMZ5++mkGDx7s956f/lxm5e233yY5ORmAW2+9lRdffJHq1av7HLN06VIGDRrE8uXLPXVDhgyhffv2NGrUKGC/L774ok8AplmzZowfP5527doFPP7UqVMsXLiQ6dOnM2HChKBzdmvWrJknCGOtZdGiRUGvGSIiIiIiIhI6bUcmIlKAuLejqVq1qt+NzEg1btw42/ECOX3zcdy4cYwdOzbgao0rrriChQsX+tyUTkpK4pFHHgnYp7WWr7/+2lOuV68e//3vf4PefC5atCjdunXjzTffZMWKFVked/ToUfr06eMJwBhjeO+99/joo48CBmAAOnfuzMKFC31WfHzyySfMnz8/y3Hcjhw5QoUKFZg/fz733nuvXwAG8EkO7165Es6WZJMnT/YJYPXv3z/LFU2dOnVi586dvPbaa3Tr1i3LAAxAmzZtmDNnDgMGDPDUbdu2jcmTJwc8vkaNGtSpU8dvG7RSpUpRp06dLB85TRj/xhtvsHDhQk+5ZMmSzJw5kyFDhvi9vkKFCtGvXz/mz5/vE/zZuHEjzzzzTMhjHjhwgIyMDB599FGmTZvmF4ABKF++PBMmTOC6667z1KWnp2f73i5btsyn3KRJk5Dn5R28AWjQoEGuBTSCOR2A+fLLL3n++ecDXrfq16/PrFmzqFmzpqdu8+bN/PDDD7k2ryNHjlCkSBFmzJjB8OHDA5573p/LQE5fAx9++GEmT57sF4ABZ8uv+fPn+wQjU1NTuffee7Ps96uvvvL8u0KFCsyZMyfLAAw453KHDh148cUX2bFjB4UKhf5dq4suusin7B0IExERERERkZxREEZEpABx5xE5vSVXNBQrVswvMBAsb4m366+/ngceeCDoMZUrV+ajjz4iLu5/v3qmTJnis8XVafv37+fYsWOecs+ePQMGLbJSvHjxLJ976623fL5d/o9//IM77rgj2z5LlSrFlClTfOYRbDVHIP/+978DBrsCufDCC+nYsaOnvGrVqqDBJW/vv/++TzmrrcjAeV9KlAh9hZcxhvHjx/tsVTRp0qSQ2+e2jIwM/v3vf/vUvfDCC9lumdawYUO/PB3vvPMOR44cCXns9u3b8/zzz2d73OjRo33K2QUZNmzY4FOuXbt2yHNyf4bLlSsXcttoGzZsWNAtCAHKlCnDsGHDfOpyMwgD8OSTT9K5c+cc9dGiRYtsc9iUKFGCyZMn+wR6fv755yw/194rubp06RLWdoTBgqmBuM8p7+3TREREREREJGcUhBERKUBy+4aquz/vXBnBjBo1KqTjWrRowfXXX+8pp6WlZbmKwtvevXtD6j8Ur732muffpUqV4qmnngq5bc2aNenVq5enPHPmTFJSUkJu27dv39AnSmSrYVavXu2zcqJt27Yh5w8JVYkSJXzydCxevDjLJPR5bf78+T7bR9WoUYP77rsvpLZ/+9vffLb3O3z4MNOmTQt57CeeeMInyJiVRo0a+Ww/djqXSFbceaDcK4uCcX+GYxWEKV68OA8//HBIx7oDNb/++mtuTAlwzuUhQ4bkuJ+RI0eG9N7XqFGDu+++26fOe3u6rETzGhiI+5xyn3MiIiIiIiISOQVhRETEI5Jtipo0aRLW9kh9+vTxKf/8889+x1SqVMlna6jPP//ck28jJzZs2OCTZPvqq68Oe+urLl26eP6dkpIS8rY91157bUg3ab3dfPPNlC1b1lOePHlytom33YGagQMHhjWmt5MnT7Jv3z62bt3Kli1bfB6lS5f2HHfkyJGQtq7LC+7z6dZbbw3r596/f/+g/WWlePHiYSW89w6MHT9+nKNHj2Z5rPsGfDRXwOWVNm3ahLySo3bt2j6rs3IzANGlSxefz1gkypcvz5VXXhny8aFcAwGfLe1++eUXPv3008gmGAL3OZXbQR8REREREZGziYIwIiIFiPsm5qFDh6La/8GDB4OOF0jr1q3DGsN9/NKlS/2OMcbQu3dvT/nEiRN06dKF3r178+WXXwa9YR2M+2ZnrVq1/IIL2T3cN/R///33kMa++OKLw55v8eLFufXWWz3lgwcPMnXq1CyPT01N9dkarHTp0j4rd7KzePFiHnnkEdq1a0e5cuUoXrw4VapUoU6dOpx33nk+j/Hjx/u0PZ0XI9bc+VPatm0bVnv38YHOz0AuuOACihQpEvI47pvewT7Lx48f9ykH227PLbevGaHKKt9SVrxX7OTmnCP5XLq1aNEirPwrTZs29dkubPXq1aSmpvod5x2sycjI4JZbbuHqq69m8uTJIW8VGSr3loTe20GKiIiIiIhIzoT+f4wikm+dU7YYPz3aJfsDJUfOKRveHvu5wX1DNZo3vk+ePOm3yiJQ8my3evXqhTVOjRo1KFasmGesrL5xPXLkSGbMmOEJcqSnpzNlyhSmTJlCfHw8zZo1o02bNrRv356OHTtyzjnnZDv29u3bfcovvfRS2Hld3EK9GeqdQyUcAwcO9MlV8v7772e5rdmXX37ps/1U7969KVmyZLZjJCYmcv/99zN//vyI5gixu7nv5j6fwj0/vVcfBOovK+GuTilcuLBPOS0tLctjT5065VMO54a/+zPsDrTmlZz8fIL9bHIq0s+lt3DPsUKFClGnTh3Wr18PONe2pKQkv2vYAw88wOeff86iRYs8dTNmzGDGjBkYY2jUqBFt2rShXbt2dOrUyWeLu3C5z6nc/JmLiIiIiIicbRSEETkDFIqPo2aF0BNrS8FVvXp1n/Lu3btJSkoKKViSnbVr12Y7XiBlypQJe6yyZct6gjCpqakcO3bML1hQsWJFFi5cyKBBg/xWf6Snp7N8+XKWL1/O+PHjMcZw6aWXcuedd9K/f3+/G9ynhZrjJhyhJm733r4rHC1atKBp06asWrUKgLlz5/LHH39w/vnn+x3r3orMnVMmkJ9//pmrr746rAT0geSXnDDuwGS4W02VLFmSQoUKeQIfoQbZwt1qLhzulS8nTpwIue25557rUz594z+v5ebPJyci/Vx6i/Qa6O3AgQN+QZiiRYsye/ZsHnroId577z3S09M9z1lrSUxMJDExkXfffReAiy66iNtvv5177rnHb2VLdtznVCjBWxEREREREQlN/vw/YhERCSjQ1kru7Zci5e6nePHiNGvWLNt2keSRCbWPypUr8/nnn7NmzRoeffRRmjRpEvBYay0LFy5k4MCBNGnSxBOwcAu05U9OWWuj3qebdzDFWsuECRP8jtm2bRuzZ8/2lBs3bpztVnGHDx+mV69ePgGYsmXLcs899/Dxxx/z66+/smfPHo4dO0Z6ejrWWs9jxIgRUXhl0ed+P3J6fkbj/M4p903+cLaKateunU85OTmZTZs2RWVe4sjNa2DJkiV5++232bRpE8888wytWrXKciXU6tWreeihh7jwwguZO3duWOO7z6lIAksiIiIiIiISmIIwIiIFSK1atahVq5ZP3cyZM6PS96xZs3zKrVu3DinHRSTbUHm3KVKkSLbf2m7cuDFjxoxh9erVHDhwgO+++46nnnqKDh06+N2Q3LBhA5dffjl//vmnXz+VKlXyKU+ePNknsBDJY+TIkWG//nD169fPJ4fExIkT/VaeTJgwwaculFUwb731Frt27fKUW7duzaZNm3jrrbfo3bs3zZo1o0qVKpQoUcJvJcPhw4cjfTm5Kqc5UI4dO+az/Ve422jlhpo1a/qUvd+z7LRu3drn3AHCvkEvweX0GgjZn2fnnXceTz/9NEuWLOHgwYPMnTuXf/7zn3Tr1o2iRYv6HPvXX39x9dVXs3jx4pDn89dff/mU3eeciIiIiIiIRE5BGBGRAubKK6/0KX/44Yc53r9/3759TJ8+Peg4Wdm4cWNYY+3YscMn90y4ORnKlStH9+7dGTVqFPPnz2fPnj28+OKLPtv7JCUl8dRTT/m1rVq1ao7mHivly5fnhhtu8JR37NjhE3yz1jJx4kRPuUiRIvTr1y/bfr/66ivPv40xTJ48mcqVK4c0J/dN2/zCfT6F+x5v2LAhaH+x4N56bseOHSG3LVKkCF26+OYMe+edd6IyL3GEe46dOnWKLVu2eMrx8fF+wcNgSpYsSefOnXniiSf4/vvvSUpK4p133vHZzuzkyZM8/PDDIfe5c+dOn/J5550XclsREREREREJTkEYEZEC5sEHH/TZumbfvn0Bt6cKx9ixY30COSVLluSuu+4Kqa130uhQuL+d3apVq7Dau1WoUIFHHnmE77//3ufnMn36dL/VIu7t3KK1iigvDBw40Kfsnf9lzpw5Pjd1r7/+er9VP4F4b0vVsGHDgHlmsrJw4cKQj83LLb1atmzpU16wYEFY7d3H5/T8jIamTZv6lMPN6/LQQw/5lJcvX868efNyOi3JtHz5cp/VU9lZtWqVTyD6oosu8lvNEo7T1+sFCxb45HJZsGABe/fuDamPdevW+ZRD2YpSREREREREQqMgjIhIAZOQkMBVV13lUzds2DD27NkTUX+//fYbL774ok/dgAEDQv5mdmJiImvWrAl5vMmTJ/uU27dvH3LbYFq3bk3jxo095SNHjrB//36fY5o3b+4TnFi4cCGJiYlRGT+3denSxSdIMn36dM/r8w7IQGhbkQEcPHjQ8+9wEtj/8MMPbNu2LeTj3TeYU1JSQm4bLvf59PHHH/sF44L58MMPg/YXC82aNaNw4cKeclY5j7LStWtXWrRo4VM3cODAsHLLZOX333/PcR8FXXJyMt99913Ix+fWNbBOnTpcdtllnrK11ic4G8zq1at9ypdccklU5iQiIiIiIiIKwoiIFEgvv/yyTx6VgwcPcuONN3L06NGw+tm3bx833XSTT8L6c845h6effjqsfkI9fvny5Xz55ZeecuHChenTp09YYwXjzg/jvvkfFxfHfffd5ylbaxkyZEhY32KPFWMMd9xxh6ecmprKhx9+SHJyMtOmTfPU165dm65du4bUp3ceik2bNoUUrEhLS2P48OFhzNzZQs5bODlNwtWxY0efrZS2b9/O22+/HVLbadOmsWTJEk+5TJkyXH/99dGeYtiKFi1K69atPeWNGzeGnYdk3LhxPp+P33//nT59+visyAiHtZbnnnuOu+++O6L2Z5qRI0eG9PnZsWOH33Zwt912W9Tmkd01MCve5325cuW0EkZERERERCSKFIQRESmAGjRowGuvveZTt2DBAq666qqQ80Vs2rSJyy+/3Gcbmri4OD788MOQ84Kc9uWXX/L6668HPWbfvn3069fP50Zlr169qFatmt+xK1euZNq0aaSnp4c8h9WrV/usEDjnnHMCru54+OGHfVbDzJ07lwEDBoR9M3rbtm3MmDEjrDY5dfvttxMfH+8pv//++0yaNMlnZcmAAQOIiwvt17v3Nlf79+/nP//5T9Dj09PTueeee3xu2IaiWLFi1KlTx1NeunSpzyqcaIqLi+PBBx/0qRs2bFi2c96wYQP33nuvT91dd91FmTJloj7HSFx99dWef6enpzN//vyw2rdt25Z//etfPnXTp0+ne/fubN26Nay+VqxYQbdu3XjiiSfC+oyeyZYvX86wYcOCHnPixAn69OnjEyxv166d3yolcK4vH3zwQVirxnbt2sXs2bM95SJFioS0xeBff/3lk9eme/fuPtcZERERERERyRkFYURECqg77riD+++/36fu559/JiEhgRdeeIHt27cHbLd582aefPJJmjRp4reN2JgxY7j88svDmsfp1RSDBw9m6NChJCcn+x0ze/Zs2rZt65PLokKFCrz00ksB+9yyZQs33ngjF1xwAY8++igLFizwyVnj7dSpU3z++ed0797dJ8CT1bfLy5Qpw5QpU3y+Mf7RRx/RvHlzJk2aFDQYs2vXLiZMmMC1117L+eefz5QpU7I8NjdUr16dK6+80lNOTExk1KhRnnJcXBwDBgwIub9bbrnFp/zAAw8wbtw4n5VRpy1dupTLLrvMk38o3ECdd3L448ePc+WVVzJlyhQSExP5888/2bJli+cR7oout/vvv99n5ciRI0e44ooreOONN/xuap86dYpJkybRoUMHn/wZdevWZcSIETmaRzS5V+TMmjUr7D7+/ve/+21VN3/+fOrXr8/QoUNZunRplqs5Dh8+zGeffcaNN95Iy5YtfW72n+1OXwNfeukl+vbt65fkHpzPT4cOHfjpp588dUWKFOGtt94K2OeBAwe4/fbbqVWrFg888ABz5szhxIkTAY+11jJr1iw6d+7MkSNHPPU33ngjpUuXznb+7vcyP6z+EhEREREROZMUyv4QERHJr8aPH0/58uUZPXo01lrAueE8fPhwHn/8cRISEqhZsybly5cnKSmJrVu3smHDBr9+ChcuzNixYxk0aFDYc3j88ccZN24c27dvZ+zYsbzxxhtceumlVK9enWPHjrF69Wq/b9oXLlyYDz74IOAqGG9bt27lxRdf5MUXX6RIkSIkJCRQvXp1ypUrR3p6Ort37+bXX3/125qpQYMGPPnkk1n2e9lllzFx4kTuvPNOz035devW0a9fP+644w6aNWvGueeeS6lSpTh69CgHDhxg/fr1ISe5zk133nkn3377rae8b98+z7+vuOIKatWqFXJft912G+PGjfPkg0hLS+PBBx9k5MiRtG7dmooVK3Lo0CESExN9ckt06tSJ9u3bM3r06JDHGjJkCB9++KFn67fFixfTu3fvgMdOmDCB22+/PeS+3QoVKsTHH39Mp06dPMHIw4cPc//99zN8+HBat25NpUqVSE5OZtmyZX65gypUqMCnn34a0g3svNKwYUNatWrF0qVLAWfrtLFjx2KMCaufd999l3PPPZfRo0d7Ai4pKSmMHTuWsWPHUrFiRerXr0+lSpUoWbIk+/btY/fu3WzYsCFgINR7W8Sz1T333MN3333HypUrmTx5Mp988gmtWrWidu3apKamsm7duoDX3XHjxvnksQpk7969vP7667z++uvEx8fToEEDatWq5dnib9++faxatcrnOgBQpUoVXnnllZDm//nnn3v+XbZsWQVhREREREREokxBGBGRAu7ZZ5+lU6dO3HfffWzatMlTb61l7dq1rF27Nmj75s2b8/bbb9OyZcuIxq9UqRLff/893bt3Z/v27aSlpfl829utZMmSTJo0iR49eoQ1TmpqKitXrmTlypVBj2vXrh1ffPEFJUuWDHpc3759qV+/PrfeeiubN2/2GSfU7ba8c6rklZ49e1K1alX27Nnj99zAgQPD6qtQoUJ8/fXXXHbZZT4J1oMlGu/atStTp04N+Qbvac2aNeOdd97hvvvuizgPSTjOO+88Fi1axLXXXsvy5cs99YcPHw66iuTCCy/k66+/pn79+rk+x3DdcccdniDMjh07WLhwIW3btg2rD2MMo0aNolu3bgwZMoRff/3V5/mkpCQWLFiQbT/ly5dn2LBhDBkyJKzxz0RFixblv//9L927d2fNmjVkZGSwePFiFi9eHPD4woULM27cOO65556wxklPTw/pmp6QkMBXX33FOeeck22fhw4dYubMmZ7yrbfeSrFixcKal4iIiIiIiASn7chERM4AXbt25bfffmPSpElcfvnlfsmZ3YoWLUrPnj356quvWLZsWcQBmNMaNmzIypUrefDBB6lYsWKWY95yyy2sW7eO6667Lmh/3bp144svvmDgwIHUrVs32/GNMXTo0IFJkybx008/UaVKlZDm3bJlS9avX89HH31EmzZtsv25xcXFcfHFF/Poo4+yYsUKXn311ZDGiaZChQrRv39/v/pKlSpx7bXXht1frVq1WLFiBUOGDAm6quHiiy/m7bff5vvvv484T8qAAQNYv349o0aNomvXrtSoUYOSJUuGvZojVOeeey5Llizhgw8+4KKLLgp67IUXXsi4ceNITEzMlwEYgP79+/t8vrLL4RNM+/btWbFiBd999x29e/cOmD/JrWTJklx99dV89NFH7Nixg2HDhlG8ePGI53AmOeecc1i0aBFPP/001atXD3hMfHw8V155JStXrvTLP+TWqFEjZsyYweDBg2nUqFFIeZ6aN2/O66+/zqpVq0K6bgJ8+OGHntWAxhi/fEoiIiIiIiKSc+b09jUiEh5jTCMg8XQ5MTGRRo0aRdTXqVOnfFYwgHNDMLsbwiJZOXbsGMuXL2fz5s3s27eP1NRUihYtStWqValXrx7NmzenaNGiuTJ2amoqP//8M1u3bmXPnj2UKlWKmjVr0qVLl4hv3iclJbF27Vr+/PNPkpKSOH78OEWLFqVMmTLUrVuXZs2aZRn8CceRI0dYtGgRO3bs4MCBA5w8eZJSpUpRsWJFLrzwQho2bJhvErXnhmPHjrFgwQI2bNjA4cOHKVu2LNWqVaNp06Yh39TNz3bu3MmiRYvYs2cPBw8epHTp0lSpUoVWrVqFlMA8P3j66ad59tlnAWcrsJ07d3q2psqJjIwM1q1bx7p169i5cydHjx4lLi6OcuXKUbFiRRISEkhISAgpGHCmmzdvnk+OoxEjRjBy5EhPOSMjg4ULF/LHH3/w119/UaxYMapXr07Hjh1DDhC7HT582HMN3LNnD8eOHaNw4cKUKVOGOnXq0KxZs5BWvrg1adKExETnT5mePXsyffr0iOZ3ttDfayIiIiIiBdfatWvdW0I3ttYG32ogShSEEYmQgjAiIpLXkpOTOf/88zl48CAAzz//PI899lhsJ3WWyS4IU1DMnDmT7t27A84qmKVLl9KiRYsYzyp/099rIiIiIiIFVyyDMPo6o4iIiEgBUb58eR599FFP+dVXX+XEiRMxnJEUVP/85z89/7755psVgBEREREREcklCsKIiIiIFCBDhw6lTp06AOzdu5c33ngjthOSAmfOnDn89NNPABQrVoznnnsuxjMSERERERE5cykIIyIiIlKAFC9enHHjxnnKo0eP5sCBAzGckRQkGRkZPPLII57yY489xgUXXBDDGYmIiIiIiJzZtIGxiIiISAHTs2dPxo8fz759+wDYvHkzl1xySYxnJQXBjh07uO6667juuuuIj4/32d5OREREREREok9BGBEREZEC6P7774/1FKQAqlWrFiNHjoz1NERERERERM4a2o5MREREREREREREREQkFygIIyIiIiIiIiIiIiIikgu0HZmIiIiISIg6d+6MtTbW0xAREREREZECQithREREREREREREREREcoGCMCIiIiIiIiIiIiIiIrlAQRgREREREREREREREZFcoCCMiIiIiIiIiIiIiIhILlAQRkREREREREREREREJBcoCCMiIiIiIiIiIiIiIpILCsV6AmcrY0w8UBdIAM4FygIpQDLwO7DMWnssymMWBtoBtYBzgKPAX8Cv1tot0RwrEsaYckBboDpQCdgP7AQWWGsPxm5mIiIiIiIiIiIiIiLhUxAmDxljagE3Al2BDkCZIIenG2NmAeOttd/mcNzKwDPALUCFLI5ZALxirZ2ak7EiYYy5GHgauBooEuCQFGPMDOAZa+3KvJybiIiIiIiIiIiIiEiktB1ZHjHGTAa2Aq8C1xA8AAMQD1wJfGOM+doYUzXCca8CEoFBZBGAydQW+NwY85ExpmQkY0XCGPMYsBi4nsABGICimc8vNsY8mjczExERERERERERERHJGa2EyTv1sqjfCWwC9uC8H+cDTfENkPUA5htjOllrd4c6oDGmM/AlvsENC6wA/gDKARfjbP11Wl+gjDHmemttRqhjRcIY8zgw2lV9AlgK7MLZpq0VUCzzuSLAGGOMtda+mJtzExERERERERERERHJKa2EiY1fgcFAXWttDWttF2ttb2vtTdba5jg5W95xtakHfGaMMaEMYIypAXyBbwDmF6CRtbaltbaXtbYbUAN4EEjzOq4n8M+IXlmIjDE9AozxDlDLWtsp8+fREedn8R/XcWOMMVfm5vxERERERERERERERHJKQZi8Y4FvgVbW2ubW2vHW2t8DHmjtTmvtPcD9rqfa4+R1CcUzQHmv8gKgq7V2nWusFGvtOKCXq/1DxpjaIY4VFmNMPPAS4B1QetVae4+1dr9rfvustXcB//buAng5sx8RERERERERERERkXxJQZi8c7O1toe1dlmoDay1bwBTXdX/L7t2xpgLgdu8qlKB2621J4OM9SXwgVdVUWBEqHMNU3+gvld5AzA8mzaPZR53WgLO1mkiIiIiIiIiIiIiIvmSgjB5xFq7JcKmr7vKXUJo0wfwXiXyhbV2UwjtxrjKvYwxxQIemTP9XeVXrbUpwRpkPj82m35ERERERERERERERPINBWHyv19d5eLGmHLZtLnBVZ4QykCZW5Ut9qoqCXQLpW2ojDEVgQ5eVanA5BCbT8I3d00nY0yFaM1NRERERERERERERCSaFITJ/04FqCuS1cHGmGpAU1f7X8IYb56rfFUYbUNxBb6rdJZba4+E0tBaexhY4VVVKLM/EREREREREREREZF8R0GY/K+uq3wK2B/owEyNXeXV1tpjYYy3wFVuFEbbULjntzDM9rk9PxERERERERERERGRqFAQJv+7yVVeZq3NCHJ8gqu8Oczxfs+mv5zK7/MTEREREREREREREYkKBWHyMWNMKeBOV/W0bJq5V85sC3PYra5yRWNM+TD7CCan83Mff2EO5iIiIiIiIiIiIiIikmsKxXoCEtTzQDWv8kHgP9m0Kecq7w1nQGvtUWPMSaCYV3VZIDmcfoIo5yqHNb8Ax5eNfCr/Y4ypAlQOs9kF0RhbRERERERERERERM5MCsLkU8aYG4AHXNVPWGsPZNO0lKt8IoLhT+AbhCkdQR9Zyen83MdHa273ASOi1JdIzB0/fpwVK1awadMm9u/fz8mTJylevDhVq1alXr16XHzxxRQpUiSividOnMiAAQM85QkTJnD77bdHaeYigXXu3Jkff/zRU7bWxnA2sXfgwAHq16/P/v1OmrgZM2Zw5ZVXRtxfRkYG69evZ/369ezcuZOjR48CUL58ecqXL09CQgIJCQnEx8dHZf75SXp6OhdddBG//fYbAK+++ipDhw6N7aRERERERETkjKEgTD5kjGkK/J+reibwZgjN3UGOkxFM4QTgvQWZu8+cyOn83EGYaM5NpEBLT0/n008/ZcKECcydO5dTp05leWyxYsXo3r07AwcOpEePHnk4SxGJhmHDhnkCMFdccUXEAZhZs2YxceJEvv32Ww4dOhT02BIlStC+fXv69u3LDTfcQOnS0fyORuzEx8czZswYevbsCcCIESO45ZZbOOecc2I8MxERERERETkTKCdMPmOMqQV8i29wYSvQz0b2td+8ahOpcMc6u7/6LJKFH374gYSEBPr06cOsWbOCBmAATp48yVdffUXPnj1p1aoVK1asyKOZytloy5YtGGM8D62cyplly5bx3nvvecpjxowJu4+FCxfSsmVLunXrxuTJk7MNwICzwm7mzJncdttt1KhRg1GjRnlWzBR0PXr0oEOHDgAcPnyYYcOGxXhGIiIiIiIicqbQSph8JDMvySygulf1buAKa+2+ELtx3w0pHsFU3G2ieYflKL6rbMKdX27N7Q3gszDbXAB8FaXxRSL2zDPP8Mwzz/htz2SMoWHDhtSoUYOKFSuyb98+tm3bxsaNG32OW7ZsGW3atGH8+PHcddddeTl1EYnAY4895vm8X3PNNVx88cVhtR81ahTPPPMMGRkZfs9VqFCBevXqUblyZUqVKsXevXvZs2cPGzZsIC0tzXPc4cOHGTFiBAsWLOC7777L2QvKJ5588km6d+8OwKRJk/jHP/5BkyZNYjwrERERERERKegUhMknjDEVgNlAPa/q/UBXa+2mMLpSECYC1tq9wN5w2hhjojG0SI4MHTqUsWPH+tSVLl2a4cOH07dvX2rVquXXZvPmzUycOJGXXnqJlJQUAFJTU7n77rs5duyYciGI5GNz5sxhzpw5nvLw4cPDan/vvffy9ttv+9QVKVKEe+65h759+9KqVSvi4vwXSh86dIjvv/+eTz75hC+//NITBDp5MpJdT/Onbt260bJlS5YtW0ZGRgZPPPEE06dPj/W0REREREREpIDTdmT5gDGmLE7OF++vWybjrIBZG2Z37v1EKoc5l1L4BzoOhjmHYHI0P6CKq3ww8qmIFGwffPCBXwCmffv2/PbbbwwfPjxgAAagbt26/POf/2T16tU0btzY57mHH36YefPm5daURSI2b948rLWex9nqueee8/y7adOmtGvXLuS248aN8wvAtGvXjvXr1zNu3Dhat24dMAADULZsWXr16sUXX3zBsmXL6Nq1a2QvIJ+77777PP/+5ptvWLs23D/DRERERERERHwpCBNjxpjSwHdAC6/qw8CV1tqVEXTpXjVTO8z27uMPWGuTI5hHVqI9v3BWCYmcMTZu3MgDDzzgU9e2bVtmzJhBjRo1QuqjXr16zJkzh4YNG3rqMjIy6Nevnyfht4jkHytXruSHH37wlO+9996Q2y5evJhHHnnEp+6aa65h1qxZnHfeeWHNo3nz5sycOZN//vOfxMfHh9U2v+vduzflypUDwFrLyy+/HNsJiYiIiIiISIGnIEwMGWNKAv8FLvWqPgpcZa1dEmG361zlumG2P99V/i3CeWQl2vNz9ydyVnjkkUd8EmKXK1eOqVOnUqpUqbD6qVKlCp9//jlFihTx1O3cuZNnn302anMVkeh48803Pf8uXLgwt9xyS8htBw8e7JPT5bzzzuOTTz6hePFIdi11tuR84okneOeddyJqn18VL16cG264wVP++OOPOXTIvYhXREREREREJHTKCRMjxpjiwDdAe6/q48A11toFOeg60VW+yBhTwlp7PMT27n1N3P3llLu/NmG2z+35ieR769ev55tvvvGpe+GFF6hWrVpE/SUkJPDII4/4bHP03nvvMXLkSMqXLx+kZWBpaWn88ssvbN26lV27dlGmTBlq1KhB586dKVOmTERzBDh16hSJiYkkJiayf/9+jh49SuHChSlVqhTVq1fnggsuICEhIUffzP/jjz9YsWIF+/btIzk5mbJly1KtWjXatGnDueeeG3G/gaSlpbFgwQK2bdvG7t27ycjIoFGjRvTo0SOq44TrwIED/Pbbb2zatIkDBw5w8uRJypQpQ8WKFWnWrBkNGzYssDmx/vrrLxYtWsSePXs872/lypVp1apV2KtBQrF9+3aWLl3Kjh07OHHiBJUqVaJJkya0bNkyy22/snLy5Ek+/fRTT/nyyy8P+fM5Z84cli5d6lP33nvvhR20DeSCCy6IuO3KlSvZsGEDu3fv5tixY1StWpX+/ftTuHDhHM8rJ3r16sWECRMA5+f+ySefcM8998R0TiIiIiIiIlKAee+vrkfePIBiwCzAej1OAJdHqf9Vrr67hdF2kavttVF+7ZWAU179pwClQ2xbGkj1apsGVIjh+9jI+2eVmJhoI5WWlmZ/++03n0daWlrE/cmZ7d577/X+jNpKlSrZlJSUHPW5d+9eW7hwYZ9+x4wZE/DYCRMm+Bw3YcIEa621ycnJ9uGHH7aVK1e2ruuIBWzRokVt79697bZt28Ka2+7du+3gwYNtxYoVA/br/ShRooTt1q2bZ06hOHr0qH3uueds3bp1g/bdrFkz++mnn9qMjIyQ+h0xYoRP+7lz51prrU1KSrL33XefrVChgt8YTZs2tdZa26NHD5/6WbNmhfUzs9bajIwMe8EFF3j6iIuLs9u3bw947KJFi+xDDz1kmzRpYo0xQX8OFStWtMOGDbO7du3Kdg61a9fO9j0L9Aj0/nXq1MnnmFClp6fbjz76yDZt2jTomPXq1bPjx4+3qampIfft3b5Tp06e+l9++cV27drVxsXFBRyratWqdty4cTY9PT3ksaZOnerTx+uvvx5y26uvvtqnbfPmzUNuG6m5c+f6jDlixAhrrbWnTp2y//rXv7L8vCUnJ3v6yOpaE6qs3p/spKam2pIlS3raduzYMaxx5cylv9dERERERAquxMRE9/+DNrJ5dB9Z25HlMWNMEeALwDujbQpwvbV2TpSGmeYqDwhxbg2A1l5Vx4CZUZoTANba/cDPXlVFgD4hNu8LeH89dr619kC05iZSUHz33Xc+5f79+/tsJxaJypUr07Nnz6DjBLNhwwaaNWvGyy+/zL59+wIek5KSwieffEJCQgLTp08Pqd8ffviB+vXr89prr5GUlJTt8cePH2fmzJk8+eSTIfX//fffc/755/P444+zefPmoMeuXLmSXr160a1bt4i3J/rll1+46KKLeOONNzhwIOvL18CBA33K7733Xthj/fjjj/z++++ecvfu3QPmC/riiy+49NJLeeWVV1izZs3pIHOWkpKSGDNmDI0bN2bWrFlhzysv7dq1izZt2tCvXz9WrVoV9NjTeZaaNGnCpk2Rpxt77rnn6NixI7NnzyYjIyPgMXv27GHIkCHcdNNNpKamhtTvt99+61Pu0qVLSO1SU1OZO3euT93dd98dUtto27VrFx06dODRRx/N9vMWS4ULF6Zdu/8tvP3ll184ePBg7CYkIiIiIiIiBZq2I8tDxphCwKfAVV7VacBN1trvozjUJOBJ4PSePDcaYy601mZ3V2mYq/yptfZkFOd12v8BnbzKfzfGTLTWpmTVwBhTFBjqqv4gF+ZWMKWfgsM7Yz2LM1+Z6hAf28vmjh072LJli09dt27dotJ3t27d+OKLLzzlRYsWkZaWlu3WQElJSXTr1o1t27YBzg3M1q1bU716dY4ePcqaNWs8zwEcPXqUm266iS+++CLo1lubNm2iR48enDhxwqe+du3aNGzY0LMV0+HDh/nzzz/ZtGmTT86L7Lz77rsMGjSI9PR0n/o6derQqFEjypYty+HDh1m1ahXbt2/3PD979mzat2/PggULKF26dMjj/fHHH/zjH//wBF9KlixJq1atqFatGkePHvW58X/NNddQrVo1du/eDcC0adM4cOAAFSpUCHk8d+DmzjvvDHicO1AQHx/PhRdeyHnnnUeZMmUwxpCUlMSaNWs88wHnfb/mmmv48ccfadMm3J0lc9/WrVvp1KkTW7du9akvXbo0l1xyCVWqVOHAgQMsW7bMJ8C3YcMG2rZty+zZs2natGlYY7700ks88cQTnnL9+vWpX78+JUuWZNeuXSxatIiTJ//3a3XatGk89dRTjBkzJtu+v//+f38mVKpUiYYNG4Y0pyVLlvh9hi677LKQ2kZTSkoKN9xwA4sXLwac86xFixbUrFkTgG3btrF8+fI8n1dWOnbsyMyZzvdQ0tPTmTVrFjfffHOMZyUiIiIiIiIFkYIwecQYE48THLnOq/oUcIu19pvArSJjrd1kjPkAuCOzqggw0RhzeVZBFWPMdcDtXlWpwDPZjWWMGQmM8Kr60VrbOZtmHwCPAvUzy/WB54CHg7R53ut4gN9wfp4CTgBm7EWxnsWZ78HVUL52TKfwyy+/+NW1bNkyKn23aNHCp3zixAlWrlxJq1atgrYbPXo0ycnJGGMYPHgwI0aM8AsWzJo1i0GDBnlWZqSlpXH77bezdu1aqlatGrDfp59+2ufm8WWXXcYrr7yS5Y3xlJQUfvzxR6ZNm8a8efOCzvmHH37wC8D07duXxx9/nISEBL/j582bx+DBg0lMdNJQJSYmcvfdd/Pxxx8HHcfb0KFDOXLkCOXLl+e5555jwIABFC1a1OeYP//8E4BChQpx2223eW7Op6SkMGnSJAYPHhzSWIcOHWLq1KmecuXKlbn22muzPL5cuXL069ePnj170rFjR4oVKxbwuIULF/LEE094VlakpaXRu3dvNm3aFHA11s8//8ypU6fYsWMHHTp08NT/7W9/46WXXspyPpUqVcr2NQZz6tQpbr31Vp8ATKlSpRg9ejR33323z+s7deoUn3zyCX//+9/Zv38/APv376dXr14sX7485Lwpa9as4aeffgLg+uuv5/nnn6dBgwY+xyQnJ/PQQw8xceJET93LL7/MoEGDqFOnTpZ9b9++nZ07/xdoDyc45L5mlCtXjrp164bcPlpef/11jhw5QlxcHH//+98ZPnw4FStW9Dlm+/btUclTEw3NmjXzKS9cuFBBGBEREREREYmItiPLO+8DvVx1jwO/GmPqhPkIfHfM1wgg2avcFpidueWYhzGmqDFmMPCZq/3L1tqt5AJrbTrwCM7ee6c9ZIx52xjjc0fGGFPJGPMO8HfvLoCHM/sROavs2LHDp1y1alW/G5mRaty4cbbjBZKc7Fxqxo0bx9ixYwOu1rjiiitYuHChz03ppKQkHnnkkYB9Wmv5+uuvPeV69erx3//+N+jN56JFi9KtWzfefPNNVqxYkeVxR48epU+fPp4AjDGG9957j48++ihgAAagc+fOLFy40GfFxyeffML8+fOzHMftyJEjVKhQgfnz53Pvvff6BWAAn+Tw7pUr4WxJNnnyZJ8AVrBk5506dWLnzp289tprdOvWLcsADECbNm2YM2cOAwb8b5fLbdu2MXny5IDH16hRgzp16vhtg1aqVCnq1KmT5SOnN+LfeOMNFi5c6CmXLFmSmTNnMmTIEL/XV6hQIfr168f8+fN9gj8bN27kmWey/S6Cx4EDB8jIyODRRx9l2rRpfgEYgPLlyzNhwgSuu+5/38dIT0/P9r1dtmyZT7lJkyYhz8s7eAPQoEEDjDEht4+WI0eOAPDhhx/y0ksvBbxu1axZk0KF8sf3gy66yPeLDUuWLInRTERERERERKSgUxAm7/QPUPcv4M8IHpdmN5i1dgdwI86KltPaAb8ZY5YaY6YYY74DtgPj8M218g3wVDgvLlyZq3/cSRvuBrYbY+YaYz42xswDtgF3uY57zFoberIKkTOIO4/I6S25oqFYsWJ+gYFgeUu8XX/99TzwwANBj6lcuTIfffQRcXH/+9UzZcoUny2uTtu/fz/Hjh3zlHv27BkwaJGV4sWLZ/ncW2+9xZ49ezzlf/zjH9xxxx1ZHn9aqVKlmDJlis88gq3mCOTf//53wGBXIBdeeCEdO3b0lFetWhU0uOTt/fff9ylntRUZOO9LiRIlQuoXnKDV+PHjqVKliqdu0qT8szAxIyODf//73z51L7zwQrZbpjVs2JC33nrLp+6dd97xBA9C0b59e55//vlsjxs9erRP+Ycffgh6/IYNG3zKtWuHviLP/RkuV65cyG2j7Y477qBPn1DTwMVW9erVfQJCGzdujOFsREREREREpCBTEOYMZq2dB9wAeGfJNkBLnFU53YHKrmYfA73zYpWJtfY5nNVA3kkcigOdgd44eWO876Sm4QRg/pXbcxPJr3L7hqq7P+9cGcGMGjUqpONatGjB9ddf7ymnpaVluYrC2969e0PqPxSvvfaa59+lSpXiqadCjznXrFmTXr3+t6hx5syZpKRkmc7Kr23fvn1DnyiRrYZZvXq1z8qJtm3bhpw/JFQlSpTgqqv+l95s8eLFWSahz2vz58/3bOsGzmqc++67L6S2f/vb33y29zt8+DDTpk0LeewnnnjCJ8iYlUaNGvlsP7Zy5cqgx7vzQLlXFgXj/gzHMgjz2GOPxWzscMXFxXHOOed4yvv27fMJDIuIiIiIiIiESkGYM5y19r9AY+AtfLcnc1sE3GSt7WOtzbO7DNba54HWwFf4rtrxlpr5/CXW2uyzF4tIxCLZpqhJkyZhbY/k/ib8zz//7HdMpUqVfLaG+vzzzz35NnJiw4YNbNu2zVO++uqrw976qkuXLp5/p6SkhLxN0bXXXhvSDXpvN998M2XLlvWUJ0+e7JPYPRB3oGbgwIFhjent5MmT7Nu3j61bt7JlyxafR+nSpT3HHTlyJKSt6/KC+3y69dZbw/q59+/vu3A10PkZSPHixcNKeO8dGDt+/DhHjx7N8lh3EDKaK+DySqNGjbjwwgtjPY2wuH/O+/bty+JIERERERERkazlj423zwLW2rzfgP1/Y+8FBhljHsTZkqw2UA04BuwEfrXW/hmki2B9jwRG5nB+vwLXG2PK4+SuqQ5UBJIy57fAWhssgCRy1nDnWzl06FBU+z948GDQ8QJp3bp1WGO4j1+6dKnfMcYYevfuzfjx4wE4ceIEXbp04aabbqJ379507do1orwh7hvqtWrV8ltlkB33Df3ff//dJ+l8Vi6++OKwxgHnxv6tt97q2Sbr4MGDTJ06NcsVNampqT5bg5UuXdpn5U52Fi9ezGeffcbChQtZu3ZtWOdXcnIytWrVCvn43OLOn9K2bduw2ruPD3R+BnLBBRdQpEiRkMdx3+A/dOhQluf08ePHfcrBtttzy+1rRqgiOf9jzb1Nn1bCiIiIiIiISCQUhDmLWGtTgbmxnkdWMgMt38Z6HgVSmerw4OpYz+LMV6Z6rGfgd0M1OTl68cmTJ0/6rbIIlDzbrV69emGNU6NGDYoVK+YZK6utxkaOHMmMGTP4/fffASeB+ZQpU5gyZQrx8fE0a9aMNm3a0L59ezp27OizdVBWtm/f7lN+6aWXws7r4hZq3hzvHCrhGDhwoE+ukvfffz/LIMyXX37ps/1U7969KVmyZLZjJCYmcv/99zN//vyI5gixu7nv5j6fwj0/GzRoELS/rIS7OqVw4cI+5bS0tCyOhFOnTvmUw0le7/4MuwOteSXS8z+W3D/nYO+RiIiIiIiISFYUhBE5E8QXgvKhJ2qWgqt6dd9A0O7du0lKSgopWJKdtWvXZjteIGXKlAl7rLJly3qCMKmpqRw7dswvWFCxYkUWLlzIoEGDmDp1qs9z6enpLF++nOXLlzN+/HiMMVx66aXceeed9O/f3+8G92mh5rgJR6iJ27237wpHixYtaNq0KatWrQJg7ty5/PHHH5x//vl+x7q3InPnlAnk559/5uqrrw4rAX0g+SUnjDsw6b2dWyhKlixJoUKFPIGPUINs4W41Fw73ypcTJ06E3Pbcc8/1Ka9fvz4qcwpXpOd/LLl/zqEENEVERERERETclBNGRKQACbS1knv7pUi5+ylevDjNmjXLtl0keWRC7aNy5cp8/vnnrFmzhkcffZQmTZoEPNZay8KFCxk4cCBNmjTxBCzcUlOzSj0VOWtt1Pt08w6mWGuZMGGC3zHbtm1j9uzZnnLjxo2z3Sru8OHD9OrVyycAU7ZsWe655x4+/vhjfv31V/bs2cOxY8dIT0/HWut5jBgxIgqvLPrc70dOz89onN855Q50hrMtVrt27XzKycnJbNq0KSrzOtO5f86RBJxFREREREREFIQRESlAatWq5Zd3Y+bMmVHpe9asWT7l1q1bh5TjIpJtqLzbFClSxC/3glvjxo0ZM2YMq1ev5sCBA3z33Xc89dRTdOjQwW/LoA0bNnD55Zfz55/+qa4qVarkU548ebJPYCGSx8iRI8N+/eHq168fxYoV85QnTpzot/JkwoQJPnWhrIJ566232LVrl6fcunVrNm3axFtvvUXv3r1p1qwZVapUoUSJEn4rPQ4fPhzpy8lVOc2BcuzYMZ/tv8LdZiw31KxZ06fs/Z5lp3Xr1j7nDjirqc506enpOe7jr7/+8vy7aNGiBXJLNREREREREYk9BWFERAqYK6+80qf84Ycf5jhXwb59+5g+fXrQcbKycePGsMbasWOHT+6ZcG9slitXju7duzNq1Cjmz5/Pnj17ePHFF322nUpKSuKpp57ya1u1atUczT1Wypcvzw033OAp79ixwyf4Zq1l4sSJnnKRIkXo169ftv1+9dVXnn8bY5g8eTKVK1cOaU7eN6jzE/f5FO57vGHDhqD9xYJ767kdO3aE3LZIkSJ06dLFp+6dd96JyrxymzvA6s6NE0xO82UdPXrUJ9BYu3btfLEqSkRERERERAoeBWFERAqYBx980Odm4L59+wJuTxWOsWPH+gRySpYsyV133RVS20WLFoU11uLFi33KrVq1Cqu9W4UKFXjkkUf4/vvvfX4u06dP91st4t7OLVqriPLCwIEDfcre+V/mzJnDli1bPOXrr7/eb9VPIN7bUjVs2DBgnpmsLFy4MORj8/LmdcuWLX3KCxYsCKu9+/icnp/R0LRpU59yuHldHnroIZ/y8uXLmTdvXk6nlevc238dPHgw5LaBclyFY926dT7lULZmFBEREREREQlEQRgRkQImISGBq666yqdu2LBh7NmzJ6L+fvvtN1588UWfugEDBvht65SVxMRE1qxZE/J4kydP9im3b98+5LbBtG7dmsaNG3vKR44cYf/+/T7HNG/e3Cc4sXDhQhITE6Myfm7r0qWLT5Bk+vTpntfnHZCB0LYiA9+b2uEksP/hhx/Ytm1byMcXLVrUp5ySkhJy23C5z6ePP/7YLxgXzIcffhi0v1ho1qwZhQsX9pSzynmUla5du9KiRQufuoEDB4aVWyYrv//+e477yIp7FdJvv/0Wcttvv/02R2OvXr3ap3zJJZfkqD8RERERERE5eykIIyJSAL388ss+eVQOHjzIjTfeyNGjR8PqZ9++fdx0000+CevPOeccnn766bD6CfX45cuX8+WXX3rKhQsXpk+fPmGNFYx7+yL3zf+4uDjuu+8+T9lay5AhQ8La5ihWjDHccccdnnJqaioffvghycnJTJs2zVNfu3ZtunbtGlKf3vlONm3aFFKwIi0tjeHDh4cxc2cLOW/h5DQJV8eOHTnvvPM85e3bt/P222+H1HbatGksWbLEUy5TpgzXX399tKcYtqJFi9K6dWtPeePGjWHnuhk3bpzP5+P333+nT58+PlsDhsNay3PPPcfdd98dUftQXHTRRcTHx3vKM2bMCCnXy549e3K85Zr3eQDQuXPnHPUnIiIiIiIiZy8FYURECqAGDRrw2muv+dQtWLCAq666KuR8EZs2beLyyy/32XYnLi6ODz/8MOS8IKd9+eWXvP7660GP2bdvH/369fO50d+rVy+qVavmd+zKlSuZNm1aWMm1V69e7bNC4Jxzzgm4uuPhhx/2WQ0zd+5cBgwYEPbN6G3btjFjxoyw2uTU7bff7nNT+v3332fSpEk+K0sGDBhAXFxov969t7nav38///nPf4Ien56ezj333ON3gzo7xYoVo06dOp7y0qVLw9paKhxxcXE8+OCDPnXDhg3Lds4bNmzg3nvv9am76667/LbEipWrr77a8+/09HTmz58fVvu2bdvyr3/9y6du+vTpdO/ena1bt4bV14oVK+jWrRtPPPFEWJ/RcJUoUYI2bdp4yrt37/a77rkdPXqUXr16hR2kcps7d67n39WqVaN58+Y56k9ERERERETOXgrCiIgUUHfccQf333+/T93PP/9MQkICL7zwAtu3bw/YbvPmzTz55JM0adLEbxuxMWPGcPnll4c1j9OrKQYPHszQoUMDJsSePXs2bdu29cllUaFCBV566aWAfW7ZsoUbb7yRCy64gEcffZQFCxb45KzxdurUKT7//HO6d+/uE+C57bbbAh5fpkwZpkyZ4rMq4KOPPqJ58+ZMmjQpaDBm165dTJgwgWuvvZbzzz+fKVOmZHlsbqhevTpXXnmlp5yYmMioUaM85bi4OAYMGBByf7fccotP+YEHHmDcuHE+K6NOW7p0KZdddpkn/1C4gTrv5PDHjx/nyiuvZMqUKSQmJvLnn3+yZcsWzyPcFV1u999/v8/KkSNHjnDFFVfwxhtv+G2FdurUKSZNmkSHDh3Yu3evp75u3bqMGDEiR/OIJveKnFmzZoXdx9///ne/rermz59P/fr1GTp0KEuXLs1yNdThw4f57LPPuPHGG2nZsiWzZ88Oe/xIuHNTPfLII4wdO9Zv9Zq1llmzZtGmTRvmz5/vs8orXFu3bvXJl3TdddflaV4jERERERERObMUyv4QERHJr8aPH0/58uUZPXo01lrAueE8fPhwHn/8cRISEqhZsybly5cnKSmJrVu3smHDBr9+ChcuzNixYxk0aFDYc3j88ccZN24c27dvZ+zYsbzxxhtceumlVK9enWPHjrF69Wq/b9oXLlyYDz74IOAqGG9bt27lxRdf5MUXX6RIkSIkJCRQvXp1ypUrR3p6Ort37+bXX3/1+9Z7gwYNePLJJ7Ps97LLLmPixInceeednpvy69ato1+/ftxxxx00a9aMc889l1KlSnH06FEOHDjA+vXrfW7Sx8qdd97pk+9i3759nn9fccUV1KpVK+S+brvtNsaNG+fJf5GWlsaDDz7IyJEjad26NRUrVuTQoUMkJiayZcsWT7tOnTrRvn17Ro8eHfJYQ4YM4cMPP/TcPF+8eDG9e/cOeOyECRO4/fbbQ+7brVChQnz88cd06tTJE4w8fPgw999/P8OHD6d169ZUqlSJ5ORkli1b5pc7qEKFCnz66aeULl064jlEW8OGDWnVqhVLly4FnK3Txo4dG3Zw4N133+Xcc89l9OjRnoBLSkoKY8eOZezYsVSsWJH69etTqVIlSpYsyb59+9i9ezcbNmwIGAj13hYxN/Tt25c333yTRYsWAc4qoKFDh/Lss89y6aWXUq5cOZKTk1m+fLknL1ahQoWYNGmSz+qhcEydOtWnnFVAV0RERERERCQUCsKIiBRwzz77LJ06deK+++7z+fa2tZa1a9eydu3aoO2bN2/O22+/TcuWLSMav1KlSnz//fd0796d7du3k5aWxk8//ZTl8SVLlmTSpEn06NEjrHFSU1NZuXIlK1euDHpcu3bt+OKLLyhZsmTQ4/r27Uv9+vW59dZb2bx5s884oW63lZNv20eqZ8+eVK1a1XPD2dvAgQPD6qtQoUJ8/fXXXHbZZT4J1pOTk/nuu+8CtunatStTp07llVdeCWusZs2a8c4773DfffdFnIckHOeddx6LFi3i2muvZfny5Z76w4cPB11FcuGFF/L1119Tv379XJ9juO644w5PEGbHjh0sXLiQtm3bhtWHMYZRo0bRrVs3hgwZwq+//urzfFJSEgsWLMi2n/LlyzNs2DCGDBkS1vjhio+P59NPP6Vbt24+K+mSkpJ8gpGnlShRgg8//JCrrroq4jE//fRTz78bNGjgsyWaiIiIiIiISLi0HZmIyBmga9eu/Pbbb0yaNInLL7/cL0G9W9GiRenZsydfffUVy5YtizgAc1rDhg1ZuXIlDz74IBUrVsxyzFtuuYV169Zx3XXXBe2vW7dufPHFFwwcOJC6detmO74xhg4dOjBp0iR++uknqlSpEtK8W7Zsyfr16/noo49o06ZNtj+3uLg4Lr74Yh599FFWrFjBq6++GtI40VSoUCH69+/vV1+pUiWuvfbasPurVasWK1asYMiQIUFXNVx88cW8/fbbfP/99xHnSRkwYADr169n1KhRdO3alRo1alCyZMlc2+rp3HPPZcmSJXzwwQdcdNFFQY+98MILGTduHImJifkyAAPQv39/n89Xdjl8gmnfvj0rVqzgu+++o3fv3gHzJ7mVLFmSq6++mo8++ogdO3YwbNgwihcvHvEcQlWzZk0WLFjA0KFDsxyvWLFi3HrrraxatYobb7wx4rHWrl3L4sWLPeW///3vEfclIiIiIiIiAmBOb18jIuExxjQCEk+XExMTadSoUUR9nTp1ymcFAzg3BLO7ISySlWPHjrF8+XI2b97Mvn37SE1NpWjRolStWpV69erRvHlzihYtmitjp6am8vPPP7N161b27NlDqVKlqFmzJl26dIn45n1SUhJr167lzz//JCkpiePHj1O0aFHKlClD3bp1adasWZbBn3AcOXKERYsWsWPHDg4cOMDJkycpVaoUFStW5MILL6Rhw4b5JlF7bjh27BgLFixgw4YNHD58mLJly1KtWjWaNm0aUjAsv9u5cyeLFi1iz549HDx4kNKlS1OlShVatWrF+eefH+vpheTpp5/m2WefBZxVHzt37qRcuXI57jcjI4N169axbt06du7cydGjR4mLi6NcuXJUrFiRhIQEEhISiIuL7fd3Tpw4wc8//8ymTZs4ePAg5cuXp2bNmnTo0CGkQFJ2Bg8ezPjx4wEn79G2bdsoVqxYjvuVM4P+XhMRERERKbjWrl1L48aNvasaW2uDbx8TJQrCiERIQRgREclrycnJnH/++Rw8eBCA559/nsceeyy2kzpDHDhwgNq1a3P06FEAXn75ZR566KEYz0ryE/29JiIiIiJScMUyCKPtyEREREQKiPLly/Poo496yq+++ionTpyI4YzOHP/+9789AZhatWpx//33x3hGIiIiIiIiciZQEEZERESkABk6dCh16tQBYO/evbzxxhuxndAZICkpiddee81Tfv7553Nty0YRERERERE5uygIIyIiIlKAFC9enHHjxnnKo0eP5sCBAzGcUcE3atQozxZvXbp0oU+fPrGdkIiIiIiIiJwxtIGxiIiISAHTs2dPxo8fz759+wDYvHkzl1xySYxnVTClp6dTtWpVRowYAUDfvn1jPCMRERERERE5kygIIyIiIlIAKWdJdMTHx/P444/HehoiIiIiIiJyhtJ2ZCIiIiIiIiIiIiIiIrlAQRgREREREREREREREZFcoCCMiIiIiIiIiIiIiIhILlAQRkREREREREREREREJBcoCCMiIiIiIiIiIiIiIpILFIQREREREREREREREfn/7N13fI3XHwfwz8kQZEsQm5iJFatG7V2rqNqzlKK0SkuriraoFv0ZrdESau9KtdTesfeeGXZIiER2zu+PK0/vc0fuvVk34fN+vZ6Xe557xveOXMnzveccokzAJAwREREREREREREREVEmYBKGiIiIiIiIiIiIiIgoEzAJQ0RERERERERERERElAmYhCEiIiIiIiIiIiIiIsoETMIQERERERERERERERFlAiZhiIiIiIiIiIiIiIiIMgGTMERERERERERERERERJmASRgiIiIiIiIiIiIiIqJMwCQMERERERERERERERFRJmAShoiIiIiIiIiIiIiIKBMwCUNERERERERERERERJQJmIQhIiIiIiIiIiIiIiLKBEzCEBERERERERERERERZQImYYiIiIiIiIiIiIiIiDIBkzBERERERERERERERESZgEkYIiIiIiIiIiIiIiKiTMAkDBERERERERERERERUSZgEoaI6DX08uVLHDp0CP7+/vjpp5/w3XffYcaMGVi+fDmOHTuG+Pj4NPe9dOlSCCGUY+nSpRkXOJERjRs3Vr3v3nTh4eHInz+/8nxs377d2iFRFmrVqpXy2n/66afWDoeIiIiIiIhSYWftAIiIKGMkJSVh3bp18Pf3x969e5GYmGi0bu7cudGqVSsMGjQI7dq1y8IoiSgjjB07Fk+ePAEAtGjRAq1bt1bu27dvH5o0aZLpMUycOBGTJk3K9HFI308//YRdu3YhOTkZ8+bNw4ABA1C1alVrh0VEREREREQGcCYMEdFrYM+ePfD19UXPnj2xc+fOVBMwABAbG4stW7agffv2qFWrFk6fPp1FkdKbKCgoSDWLpX///tYOKUc7efIkFi9erJSnT59uxWjIGqpUqYJevXoB0CTgR4wYYeWIiIiIiIiIyBgmYYiIcrjJkyejefPmuH79uuq8EAK+vr5o2bIlevTogebNm6NcuXJ67U+ePIm6devit99+y6qQiSgdxo0bByklAKBt27aoVq2alSMia/jqq69gY6P5Vf7gwYP4+++/rRwRERERERERGcLlyIiIcrBPP/0Us2fPVp1zdnbGl19+iV69eqF48eJ6bW7evImlS5dixowZiIuLAwDEx8dj8ODBiI6O5v4CRNnY7t27sXv3bqX85Zdf6tWpU6cO7ty5Y1Z///vf/1SfIe+99x5mzJhhVls3Nzez6lHmqFChAjp16oSNGzcC0CRl2rRpwz2TiIiIiIiIshkmYYiIcqhly5bpJWDq16+P1atXo2jRokbblSlTBt9//z369u2L9957DxcvXlTuGz16NPz8/NC4cePMCpsoTfbt22ftELKFqVOnKrerVq2Kt99+W69O7ty5UbJkSbP6002kODk5md2WrG/48OFKEub8+fPYtm0b2rRpY+WoiIiIiIiISBuXIyMiyoGuX7+Ojz/+WHWuXr162LZtW6oJGG3lypXD7t274ePjo5xLTk5G7969lQ2/iSj7OHv2LPbs2aOUP/roIytGQ9lBkyZNUL58eaVs7iwmIiIiIiIiyjpMwhAR5UBjxoxBVFSUUnZzc8PGjRvh5ORkUT8FChTAhg0bkCtXLuXcvXv38N1332VYrESUMebPn6/ctre3R7du3awYDWUXPXv2VG7v3bsX165ds2I0REREREREpIvLkRER5TBXr17F1q1bVed++OEHeHl5pak/X19fjBkzRrXM0eLFizFp0iS4u7tb3F9CQgIOHz6M4OBgPHjwAC4uLihatCgaN24MFxeXNMUIAImJibh48SIuXryIJ0+eICoqCvb29nByckKRIkVQunRp+Pr6wtbWNs1j3L59G6dPn0ZYWBgiIiLg6uoKLy8v1K1bF4ULF05zv4YkJCTgyJEjCAkJwcOHD5GcnIyKFSuiXbt2GTqOpcLDw3H58mXcuHED4eHhiI2NhYuLCzw8PODn5wcfH58cu+fE/fv3cfToUTx69Eh5ffPnz49atWqhVKlSGT5eaGgoTpw4gbt37yImJgaenp6oXLkyatasqWyobq7Y2FisW7dOKTdr1ixNP59ZITg4GGfOnMGDBw8QHh4ONzc3dOrUyeyfoRcvXuDgwYO4d+8ewsLC4OrqCj8/P9StW9fk8xYcHIzAwECEhoYiKSkJBQsWRN26dVGhQoWMeGiKc+fO4cqVK7h37x6SkpJQvHhxNGrUCIUKFUq1XXx8PAIDA3Hp0iVERETAxcUF3t7eaNKkCfLmzZumWLp27YqJEycq5aVLl2LatGlp6ouIiIiIiIgygZSSBw8eaTgAVAQgU46LFy/KtEpISJCXL19WHQkJCWnuj15vH330kdR+73l6esq4uLh09fn48WNpb2+v6nf69OkG6/r7+6vq+fv7SymljIiIkKNHj5b58+dX3Z9yODg4yO7du8uQkBCLYnv48KEcMWKE9PDwMNiv9pE3b17ZsmVLJSZzREVFyalTp8oyZcqk2refn59ct26dTE5ONqvfiRMnqtrv3btXSinl06dP5bBhw2S+fPn0xqhataqUUsp27dqpzu/cudOi50xKKZOTk2Xp0qWVPmxsbGRoaKjBukePHpWfffaZrFy5shRCpPo8eHh4yLFjx8oHDx6YjKFEiRImXzNDh6HXr1GjRqo65kpKSpIrVqyQVatWTXXMcuXKyXnz5sn4+Hiz+9Zu36hRI+X84cOHZfPmzaWNjY3BsQoWLCjnzJkjk5KSzB5r48aNqj5++eUXs9umRvd92q9fP5NtjD3uzZs3y7p16xp8D23evFmp169fP9V9d+7ckVJKeevWLdm7d2+ZJ08eg89b8eLF5dq1aw3GdPDgQdmgQQOjr2+tWrXksWPHzHpO7ty5Y/A5SU5OlgsXLpQVK1Y0OIatra3s1auXfPTokV6f0dHRcvz48dLNzc1g2zx58sivv/5axsTEmBWjrrJly6qeJ8oc/H2NiIiIiCjnunjxou7fYhVlFl1H5nJkREQ5zPbt21Xlvn37qpYTS4v8+fOjffv2qY6TmmvXrsHPzw8zZ85EWFiYwTpxcXFYs2YNfH19ERAQYFa/e/bsQfny5TF37lw8ffrUZP2XL19ix44d+Prrr83q/99//4W3tze++uor3Lx5M9W6Z8+eRdeuXdGyZUs8f/7crP51HT58GFWqVMGvv/6K8PBwo/UGDRqkKi9evNjisfbv349bt24p5VatWhncL2jTpk2oU6cOZs2ahQsXLqQkmY16+vQppk+fjkqVKmHnzp0Wx5WVHjx4gLp166J37944d+5cqnVT9lmqXLkybty4keYxp06dioYNG2LXrl1ITk42WOfRo0cYOXIkunTpgvj4eLP6/fvvv1XlJk2apDnGjJaYmIhBgwahU6dOCAwMNPkeMmT79u2oWrUqVqxYgZiYGIN1QkJC0K1bN0yePFl1ftq0aWjUqBEOHjxotP8TJ06gQYMGerMIzRUTE4MOHTpgyJAhuHTpksE6SUlJWLlyJd566y0EBwcr54ODg1GjRg1MmTIFz549M9r/999/j3bt2hl9/KnRfj+EhITgwoULFvdBREREREREmYPLkRG9BhKTE/Ho5SNrh/HaK5i3IOxsrPuxeffuXQQFBanOtWzZMkP6btmyJTZt2qSUjx49ioSEBNjb26fa7unTp2jZsiVCQkIAaPaqqF27NooUKYKoqChcuHBBuQ8AoqKi0KVLF2zatCnVpbdu3Lhh8IJkiRIl4OPjoyzFFBkZiTt37uDGjRtISEgw+/H+9ttvGDp0KJKSklTnS5YsiYoVK8LV1RWRkZE4d+4cQkNDlft37dqF+vXr48iRI3B2djZ7vNu3b+Pzzz9Xki+Ojo6oVasWvLy8EBUVpbrw37ZtW3h5eeHhw4cAgM2bNyM8PBz58uUzezzdxM3AgQMN1tNNFNja2qJs2bIoVaoUXFxcIITA06dPceHCBSUeQPO6t23bFvv370fdunXNjiurBAcHo1GjRqqL4QDg7OyMt956CwUKFEB4eDhOnjypSvBdu3YN9erVw65du1C1alWLxpwxYwbGjx+vlMuXL4/y5cvD0dERDx48wNGjRxEbG6vcv3nzZkyYMAHTp0832fe///6r3Pb09ISPj49FsWWmMWPGqN5v5cuXR7ly5ZTHfeLEiVTbHzt2DP3791eem6JFi8LPzw/Ozs548OABAgMDERcXp9SfNGkSqlatio4dO2LGjBn46quvlPsqVqyIsmXLwsHBAbdu3cKpU6eUpFB8fDx69OiBy5cvo1ixYhY9xt69eysJHHt7e9SqVQvFihVDfHw8zp07h9u3byt1g4OD8d577+HYsWOIiIhA48aNlc9tZ2dn1K5dG/nz58eLFy8QGBioev/t3r0bX375Jf73v/9ZFF/Dhg2xaNEipbx9+3ZUrlzZoj6IiIiIiIgoczAJQ/QaePTyEVpvbG3tMF5729/bjiJORawaw+HDh/XO1axZM0P6rlGjhqocExODs2fPolatWqm2mzJlCiIiIiCEwIgRIzBx4kS9ZMHOnTsxdOhQZWZGQkIC+vfvj0uXLqFgwYIG+/3mm29UCZimTZti1qxZRi+Mx8XFYf/+/di8eTP27duXasx79uzRS8D06tULX331FXx9ffXq79u3DyNGjMDFixcBABcvXsTgwYOxevXqVMfR9umnn+LFixdwd3fH1KlTMWDAADg4OKjq3LlzBwBgZ2eHfv36KRfn4+LisHLlSowYMcKssZ4/f46NGzcq5fz586NDhw5G67u5uaF3795o3749GjZsiNy5cxusFxgYiPHjx2Pv3r0ANK9j9+7dcePGDYOzsQ4dOoTExETcvXsXDRo0UM6/9957mDFjhtF4PD09TT7G1CQmJqJHjx6qBIyTkxOmTJmCwYMHqx5fYmIi1qxZg1GjRuHJkycAgCdPnqBr1644deoUnJyczBrzwoULykyMjh07Ytq0aXr7kEREROCzzz7D0qVLlXMzZ87E0KFDUbJkSaN9h4aG4t69e0rZ0uRQZjp9+jT2798PQJM8/Omnn/QSRC9evFAlUXQNHToUsbGx8PHxwdy5c9GsWTPV/WFhYfjwww+xZcsW5dzo0aNRoEABjBs3DgDQrl07zJgxA+XLl1e1vXjxIrp27YorV64A0CSBJ0yYoHoNTAkICFA+48aMGYMvv/xSbz+ezZs3o2/fvoiKigIAnDp1CitWrMD69esRFBQENzc3TJ8+HQMGDFAlthMSEjBt2jTVni5z587FiBEjULp0abNj9PPzU5UDAwPNbktERERERESZi8uRERHlIHfv3lWVCxYsCA8Pjwzpu1KlSibHMyQiIgIAMGfOHMyePdvgbI0WLVogMDBQdVH66dOnGDNmjME+pZT466+/lHK5cuXwzz//pHrx2cHBAS1btsT8+fNx+vRpo/WioqLQs2dPJQEjhMDixYuxYsUKgwkYAGjcuDECAwNVMz7WrFmDAwcOGB1H14sXL5AvXz4cOHAAH330kV4CBoBqc3jdmSuWLEm2atUqVQKrb9++Rmc0NWrUCPfu3cPcuXPRsmVLowkYAKhbty52796NAQMGKOdCQkKwatUqg/WLFi2KkiVL6i2D5uTkhJIlSxo9zE18GPPrr7+qLkI7Ojpix44dGDlypN7js7OzQ+/evXHgwAFV8uf69et6y16lJjw8HMnJyfjiiy+wefNmgxvBu7u7w9/fH++++65yLikpyeRre/LkSVU5O81wePHiBQBgyJAh+OuvvwzO0HF2dk41sRYREYEaNWogMDBQLwEDaJKIGzZsUD3u27dvo127dkhKSsKQIUMQEBCgl4ABNJ9r27ZtQ548eZRz69evV+I2R8pn3LJly/Djjz/qJWAAoFOnTnqv46hRo/D333/D3d0dhw8fxuDBg/V+Du3t7fHNN99g6NChyrnk5GSLkkSAZvaRdiL0+PHjFrUnIiIiIiKizMMkDBFRDqK7j4ihi4FplTt3br3EQGr7lmjr2LEjPv7441Tr5M+fHytWrICNzX//9axdu1a1xFWKJ0+eIDo6Wim3b9/eYNLCGO0LrroWLFiAR4/+W77v888/xwcffGCyTycnJ6xdu1YVR2qzOQz53//+ZzDZZUjZsmXRsGFDpXzu3LlUk0valixZoiobW4oM0LwuefPmNatfQJO0mjdvHgoUKKCcW7lypdntM1tycrLeUk4//PCDySXTfHx8sGDBAtW5RYsWWXSxvn79+pg2bZrJelOmTFGV9+zZk2r9a9euqcolSpQwO6as4OPjgzlz5kAIkab29vb2WLNmDVxdXY3WsbOzw+eff646FxERAV9fX5NjlyhRAt27d1fKL1++tHimSP/+/dGnT59U63Tt2hXe3t6q+ABNgtpYgjfFuHHjVI9h9+7dFsVnZ2eHIkX+m6l57949vHz50qI+iIiIiIiIKHMwCUNElIPoJkXc3NwytH/d/rT3KkjNt99+a1a9GjVqoGPHjko5ISHB6CwKbY8fPzarf3PMnTtXue3k5IQJEyaY3bZYsWLo2rWrUt6xY0eqyyzptu3Vq5f5gSJts2HOnz+vmjlRr169DN8/JG/evHjnnXeU8rFjx4xuQp/VDhw4oCzrBmhm4wwbNsystu+9955qeb/IyEhs3rzZ7LHHjx+vSjIaU7FiRdXyY2fPnk21vu4+ULozi6zts88+M7gcnbnef/99lClTxmS9Fi1apHls3b2zTD3nurT3nUmNbowlSpRAz549TbYrXrw4ypUrp5TPnTun7GVjLt33he77hoiIiIiIiKyDSRgiIlKk5ZvslStXtmh5JN0LkocOHdKr4+npqVq+aMOGDcp+G+lx7do1hISEKOU2bdpYvPRVkyZNlNtxcXFmL/vToUMHsy7Qa3v//fdVswNWrVql2tjdEN1EzaBBgywaU1tsbCzCwsIQHByMoKAg1eHs7KzUe/HihVlL12UF3fdTjx49LHre+/btm2p/xuTJkwdNmzY1exztxNjLly+VvUQM0U1CZuQMuIygvbxaWmgn9FLj5eWl9/PaurV5+6GVLVtWVdaeDWdK6dKl9dqbO06LFi3Mfv9ptzX1njBE932RkclrIiIiIiIiSjs7awdARETm091v5fnz5xna/7Nnz1Idz5DatWtbNIZu/RMnTujVEUKge/fumDdvHgAgJiYGTZo0QZcuXdC9e3c0b948TfuG6F5QL168uMXfFte9oHrr1i3VpvPGVKtWzaJxAM2F/R49eijLZD179gwbN240OqMmPj5etTSYs7OzauaOKceOHcP69esRGBiIS5cuWfT+ioiIQPHixc2un1l090+pV6+eRe116xt6fxpSunRpi2aD6F4wf/78udH3tO6yUqktt5fVihYtivz586erD0tmarm6uirJCWdnZ9USXKbaaYuMjMy0+DKqbWRkpCrZaYrusoLaSzoSERERERGR9TAJQ/QaKJi3ILa/t93aYbz2CuYtaO0Q9JIiKXsOZITY2Fi9WRYeHh4m22kvoWOOokWLInfu3MpYxr6tPWnSJGzbtg23bt0CoNnAfO3atVi7di1sbW3h5+eHunXron79+mjYsCEKFSpkcuzQ0FBVecaMGRbv66LL3H1ztPdQscSgQYNUe5UsWbLEaBLmzz//VC0h1717dzg6Opoc4+LFixg+fDgOHDiQphiBjE8IppXu+8nS92eFChVS7c8YS2en6G7QnpCQYLRuYmKiqmxnl31+fUvr+1qbJc+d9mO3ZDlG3ecstedbV1rjA7IuxoxoT0RERERERJkj+/wVT0RpZmdjhyJO5n0bmHI23W99P3z4EE+fPjUrWWLKpUuXTI5niIuLi8Vjubq6KkmY+Ph4REdH6yULPDw8EBgYiKFDh2Ljxo2q+5KSknDq1CmcOnUK8+bNgxACderUwcCBA9G3b1+9C9wpzN3jxhLmbtxuyTfatdWoUQNVq1bFuXPnAAB79+7F7du3VRuAp9Bdikx3TxlDDh06hDZt2li0Ab0h2WVPGN3EZGqbvRvi6OgIOzs7JfFhbpLN0qXmLKE78yUmJibTxrJUWt/X2tL63GXmc55R42RVjID++8KcBCwRERERERFlPu4JQ0SUgxhaWkl3+aW00u0nT5488PPzM9kuLfvImNtH/vz5sWHDBly4cAFffPEFKleubLCulBKBgYEYNGgQKleurCQsdMXHx6c7VkNjZzbtZIqUEv7+/np1QkJCsGvXLqVcqVIlk0vFRUZGomvXrqoEjKurK4YMGYLVq1fjzJkzePToEaKjo5GUlAQppXJMnDgxAx5ZxtN9PdL7/syI93d66SY6ucwUGaL7vkhLgpyIiIiIiIgyHpMwREQ5SPHixfX23dixY0eG9L1z505VuXbt2mbtcZGWZai02+TKlUtvLwNdlSpVwvTp03H+/HmEh4dj+/btmDBhAho0aKC3BM+1a9fQrFkz3LlzR68fT09PVXnVqlWqxEJajkmTJln8+C3Vu3dv5M6dWykvXbpUb+aJv7+/6pw5s2AWLFiABw8eKOXatWvjxo0bWLBgAbp37w4/Pz8UKFAAefPm1ftGvyV7amSl9O6bFB0drVr+y9JlxjJDsWLFVGXt14woxf3791Vl3fcNERERERERWQeTMEREOUzr1q1V5eXLl6d77f+wsDAEBASkOo4x169ft2isu3fvqvaesXRPCTc3N7Rq1QrffvstDhw4gEePHuGnn35SLTv19OlTTJgwQa9twYLqfX0sjd1a3N3d0alTJ6V89+5dVfJNSomlS5cq5Vy5cqF3794m+92yZYtyWwiBVatWmb3Juu4F3+xC9/1k6Wt87dq1VPuzBt2l5+7evWulSCg7u3fvnnI7d+7cZu2TRURERERERJmPSRgiohzmk08+US2RFBYWZnB5KkvMnj1blchxdHTEhx9+aFbbo0ePWjTWsWPHVOVatWpZ1F5Xvnz5MGbMGPz777+q5yUgIEBvtojucm4ZNYsoKwwaNEhV1t7/Zffu3QgKClLKHTt21Jv1Y8iNGzeU2z4+Pgb3mTEmMDDQ7LpZuaRXzZo1VeUjR45Y1F63fnrfnxmhatWqqvLVq1etFAllV0+ePFHteVWlSpVssZQeERERERERMQlDRJTj+Pr64p133lGdGzt2LB49epSm/i5fvoyffvpJdW7AgAF6yzoZc/HiRVy4cMHs8VatWqUq169f3+y2qalduzYqVaqklF+8eIEnT56o6lSvXl2VnAgMDMTFixczZPzM1qRJE1WSJCAgQHl82gkZwLylyADg2bNnym1LNrDfs2cPQkJCzK7v4OCgKsfFxZnd1lK676fVq1frJeNSs3z58lT7swY/Pz/Y29srZWN7HtGbS/c98dZbb1kpEiIiIiIiItLFJAwRUQ40c+ZM1T4qz549Q+fOnREVFWVRP2FhYejSpYtqw/pChQrhm2++sagfc+ufOnUKf/75p1K2t7dHz549LRorNbr7w+he/LexscGwYcOUspQSI0eOVO0Bkl0JIfDBBx8o5fj4eCxfvhwRERHYvHmzcr5EiRJo3ry5WX1q73dy48YNs5IVCQkJ+PLLLy2IXLOEnLbM3NOkYcOGKFWqlFIODQ3FwoULzWq7efNmHD9+XCm7uLigY8eOGR2ixRwcHFC7dm2lfP369TTtxUSvL+33LQA0btzYOoEQERERERGRHiZhiIhyoAoVKmDu3Lmqc0eOHME777xj9n4RN27cQLNmzXDlyhXlnI2NDZYvX272viAp/vzzT/zyyy+p1gkLC0Pv3r1VF/q7du0KLy8vvbpnz57F5s2bkZSUZHYM58+fV30bvFChQgZnd4wePVo1G2bv3r0YMGCAap8ac4SEhGDbtm0WtUmv/v37w9bWVikvWbIEK1euVM0sGTBgAGxszPvvXXuZqydPnuD3339PtX5SUhKGDBmid8HXlNy5c6NkyZJK+cSJE6pZOBnJxsYGn3zyierc2LFjTcZ87do1fPTRR6pzH374IVxcXDI8xrRo06aNcjspKQkHDhywYjSU3ezdu1e5bW9vjxYtWlgxGiIiIiIiItLGJIyVCCG8hRDdhBA/CSH2CSEihRBS6whKQ58ldfpI79E/Ex5343TGZPHzQvS6+uCDDzB8+HDVuUOHDsHX1xc//PADQkNDDba7efMmvv76a1SuXFlvGbHp06ejWbNmFsWRMptixIgR+PTTTxEREaFXZ9euXahXr55qL4t8+fJhxowZBvsMCgpC586dUbp0aXzxxRc4cuSIas8abYmJidiwYQNatWqlSvD069fPYH0XFxesXbtWNWtmxYoVqF69OlauXJlqMubBgwfw9/dHhw4d4O3tjbVr1xqtmxmKFCmC1q1bK+WLFy/i22+/Vco2NjYYMGCA2f1169ZNVf74448xZ84c1cyoFCdOnEDTpk2V/YcsTdQ1adJEuf3y5Uu0bt0aa9euxcWLF3Hnzh0EBQUph6UzunQNHz5cNXPkxYsXaNGiBX799Ve9pdASExOxcuVKNGjQAI8fP1bOlylTBhMnTkxXHBlJd0bOzp07rRMIZTsxMTE4fPiwUm7atGm2SR4SERERERERYGe6CmUUIURjAF8CqAnAvM0WrCvG2gEQUermzZsHd3d3TJkyBVJKAJoLzl9++SW++uor+Pr6olixYnB3d8fTp08RHByMa9eu6fVjb2+P2bNnY+jQoRbH8NVXX2HOnDkIDQ3F7Nmz8euvv6JOnTooUqQIoqOjcf78eQQHB+uNt2zZMoOzYLQFBwfjp59+wk8//YRcuXLB19cXRYoUgZubG5KSkvDw4UOcOXNGb2mmChUq4Ouvvzbab9OmTbF06VIMHDhQuSh/5coV9O7dGx988AH8/PxQuHBhODk5ISoqCuHh4bh69arqIr21DBw4EH///bdSDgsLU263aNECxYsXN7uvfv36Yc6cOTh//jwAzVJjn3zyCSZNmoTatWvDw8MDz58/x8WLFxEUFKS0a9SoEerXr48pU6aYPdbIkSOxfPlyZem3Y8eOoXv37gbr+vv7o3///mb3rcvOzg6rV69Go0aNlGRkZGQkhg8fji+//BK1a9eGp6cnIiIicPLkSb29g/Lly4d169bB2dk5zTFkNB8fH9SqVQsnTpwAoFk6bfbs2dx8nbBt2za8fPlSKRtLQBMREREREZF1MAmTtfwAtLR2EGZ6CSBr19khojT57rvv0KhRIwwbNgw3btxQzkspcenSJVy6dCnV9tWrV8fChQtRs2bNNI3v6emJf//9F61atUJoaCgSEhJw8OBBo/UdHR2xcuVKtGvXzqJx4uPjcfbsWZw9ezbVem+//TY2bdoER0fHVOv16tUL5cuXR48ePXDz5k3VOOYut6W9p0pWad++PQoWLIhHjx7p3Tdo0CCL+rKzs8Nff/2Fpk2b4tatW8r5iIgIbN++3WCb5s2bY+PGjZg1a5ZFY/n5+WHRokUYNmyYxUu/pUWpUqVw9OhRdOjQAadOnVLOR0ZGpjqLpGzZsvjrr79Qvnz5TI/RUh988IGShLl79y4CAwNRr149K0dF1rZu3Trltru7Ozp16mTFaIiIiIiIiEgXlyPLHuIA3DJZy7S7AEql8Tis09d6KWVkBsRkymwL46yfBTER5TjNmzfH5cuXsXLlSjRr1kxvg3pdDg4OaN++PbZs2YKTJ0+mOQGTwsfHB2fPnsUnn3wCDw8Po2N269YNV65cwbvvvptqfy1btsSmTZswaNAglClTxuT4Qgg0aNAAK1euxMGDB1GgQAGz4q5ZsyauXr2KFStWoG7duiafNxsbG1SrVg1ffPEFTp8+jZ9//tmscTKSnZ0d+vbtq3fe09MTHTp0sLi/4sWL4/Tp0xg5ciTy5s1rtF61atWwcOFC/Pvvv2le6mjAgAG4evUqvv32WzRv3hxFixaFo6Njps3mKFy4MI4fP45ly5ahSpUqqdYtW7Ys5syZg4sXL2bLBAwA9O3bV/XzZWoPH3r9PX36FH/++adS/uijj5A7d27rBURERERERER6RMryNZT5hBCfAvgRwCUAJwGcePXvBQBvA9irVT1YSlkyi+IqAiAYgK3W6YZSSuNfZU/7WI2hfpyTpZSTMnqcrCCEqAjgYkr54sWLqFixYpr6SkxMVM1gADQXBE1dECYyJjo6GqdOncLNmzcRFhaG+Ph4ODg4oGDBgihXrhyqV68OBweHTBk7Pj4ehw4dQnBwMB49egQnJycUK1YMTZo0SfPF+6dPn+LSpUu4c+cOnj59ipcvX8LBwQEuLi4oU6YM/Pz8jCZ/LPHixQscPXoUd+/eRXh4OGJjY+Hk5AQPDw+ULVsWPj4+r/VeC9HR0Thy5AiuXbuGyMhIuLq6wsvLC1WrVjUrGZbd3bt3D0ePHsWjR4/w7NkzODs7o0CBAqhVqxa8vb2tHZ5ZvvnmG3z33XcAgLx58+LevXtwc3OzblBkNTNnzsSYMWMAALly5UJQUBAKFSpk5aheX/x9jYiIiIgo57p06RIqVaqkfaqSlDL15WMyCJMwWUgI4Q4gRkqptw6LgeREViZhxgP4XuvUDSlluUwaqzGYhNHDP+qJiMgcERER8Pb2xrNnzwAA06ZNw7hx46wbFFlFQkICypQpg5CQEADAiBEjMGfOHCtH9Xrj72tERERERDmXNZMwXI4sC0kpIwwlYKxJaNaAGaBzerE1YiEiIqLUubu744svvlDKP//8M2JiYqwYEVnL8uXLlQSMk5MTvv76aytHRERERERERIYwCUONAJTWKicCWGalWIiIiMiETz/9FCVLlgQAPH78GL/++qt1A6IsFx8fj6lTpyrlL7/80uy9sIiIiIiIiChrMQlDA3XKf0spH1olEiIiIjIpT548qmWnpkyZgvDwcCtGRFntl19+wa1btwAA5cuXV/aFISIiIiIiouyHCxi/wYQQrgDe0znNpciIiIiyufbt22PevHkICwsDANy8eRNvvfWWlaOirGJra4uJEycCANq2bYtcuXJZOSIiIiIiIiIyhkmYN1sPAHm0yg8AbMviGJoIIaoA8ANQAIAtgHAADwEEAtgLIEBKmZDFcREREWVrw4cPt3YIZCUjR460dghERERERERkJiZh3my6S5Etk1ImZnEMDQ2cK/zqqA5gOIC7QogfAPwqpZRZGRwRERERERERERERUVoxCfOGEkJUBlBT53R2XYqsKIB5AN4RQvSWUj7L6AGEEAUA5LewWemMjoOIiIiIiIiIiIiIXh9Mwry5dGfB7JdS3szC8SMB7AKwH8AlAI8BxABwB1AOQAsA3QDk1mrTFsCfQoiWUsr4DI5nGICJGdwnEREREREREREREb3BmIR5AwkhcgHorXM6q2bBPAQwAMAaKWWskTonAKwUQowDsATAO1r3NQLwA4DPMjVKIiIiIiIiIiIiIqJ0srF2AGQV7wLw0Co/B7AhKwaWUl6VUi5NJQGjXfchNLNf1uvcNVwIUSpTAiQiIiIiIiIiIiIiyiCcCfNm0l2KbLWUMsYqkZggpZRCiP4A6gMo9Op0Lmgew9cZONSv0E/2mFIawJYMjIGIiIiIiIiIiIiIXiNMwrxhhBDFoNlvRdvv1ojFXFLKl0KIOQCmaZ1ujQxMwkgpH0OzL43ZhBAZNTwRERERERERERERvYa4HNmbpz/Ur/s5KeUpK8Viie065cpWiYKIiIiIiIiIiIiIyEzZNgkjhFgrhGhk7TheJ0IzdWOAzunF1oglDYJ0yrmEEK7WCISIiIiIiIiIiIiIyBzZNgkD4H0Ae4QQV4QQI4UQbtYO6DXQFID2hvZxAFZaKRZLGdqzJk+WR0FEREREREREREREZKbsnIRJUR7AzwDuCSEWCyHesnZAOdgHOuVNUspwq0RiOU8D555meRRERERERERERERERGbKCUkYCUBAM+uhP4BAIcQpIcQgIUReq0aWg7yaSdRZ53ROWYoMAGrrlMOklAlWiYSIiIiIiIiIiIiIyAzZOQnTAcA2aJIw0PpXAKgGYCGA+0KIuUKISlaIL6fpBSC3VjkIwB7rhJImPXXK+6wRBBERERERERERERGRubJtEkZKuVVK2Q6aPUymAngITQIG+G92jAuAYQDOCSEOCiF6CCHsrRJw9qe7FNkSKaU0WDObEUI0hv4sni1ZHwkRERERERERERERkfmybRImhZQyVEr5NYDiALoC2K1996t/BYB6AFZAs3fMD0II76yNNPsSQvgBqK51KhnA0gzod5IQQmod+0zUbymEqGrhGLUBbMR/CTgAuAZgrcUBExERERERERERERFloWyfhEkhpUySUm6QUrYAUA7ALADhUF+cF9Bs4P45gOtCiO1CiHeFENnmcQohigohSuoeALx0qtoZqvfqMLRJfWoG6pT/lVKGpvlBpF09AGdevS79hRAFjFUUQhQTQvwE4CCAfFp3JQAYJqVMzORYiYiIiIiIiIiIiIjSxc7aAaSFlPIWgDFCiK+gmR0zBMDbKXe/+tcGQItXx30hxG8AfpdS3s/qeHUcAlDCjHpFANwxct8yAP3NGUwI4QD9/VQWm9M2kwgArV4dEELcg2ZmyzMAMQBcoUmylTPQNgnAB1LKnLSXDRERERERERERERG9oXJkEiaFlDIemiXIVgghfKHZH6YXNBfygf9myRQBMBHA10KIvwDMl1Luyup4raQT1DNJwgAEWCkWQ4q8Oky5DaCvlPJwJsdDRERERERERERERJQhss0yXeklpbwspfwYQGEAgwGcSrnr1SGgSTp1BPCvEOK6EGKkEMLZGvFmId2lyJZLKROsEokm+fMrgAvQzGoxJRHAEQD9APgyAUNEREREREREREREOUmOngljiJQyRgixDMBLAFOgWforJRGTQgAoA+BnABOFED8CmJUVyQkpZcnMHkNnvBaZ2PckAJMsqH8awGkAEELkBuALzetTCIAzAHsAUQAioFmK7aSU8mWGBk1ERERERERERERElEVeqySMEKI0NLNg+gNI2bw+JfkidKqnzI5xBzAVQE8hxLtSyqDMj5SklLHQJGROWzsWIiIiIiIiIiIiIqLMkOOXIxNC2AohOgshdkCzwfsYAPnxX9JFvDr2AXgfQEsAG6BZ6gr4LxlTGcDeN2B5MiIiIiIiIiIiIiIiygI5NgkjhCgmhPgOQAiA9QCa4b/HkzL7JQqaPUgqSimbSik3Sil3SSm7AigO4CcAsVr1iwP4JKseAxERZU93797FP//8gwULFmD69OmYMmUK5s6dixUrVuDIkSOIiYmxdogZbtmyZRBCQAiBkiVLIi4uztoh0WuiZMmSqvdWZtm3b58yjhACkyZNyrSxKHsZPHiw8rp37NjR2uEQERERERGp5KjlyIQQAkBbAEMAtIYm6ZIy40V72bFL0CRflkspowz1JaV8BGCsEGIJgL0ACr5q2xHA95n0EIiIMkTJkiURHBycah0bGxs4OzvD1dUVZcuWhZ+fH9q2bYvGjRtD83FK2i5fvoxFixZh8+bNCAkJSbWura0t/Pz80K1bN/To0QNFixZNtX7//v2xbNkyvfPdunXDmjVr0hTv7du3UaZMGUgp9e67c+eORRe7nz17hi+++EIpT506FQ4ODmmKi4goq3377bdYvXo1oqKisGXLFvz9999o27attcMiIiIiIiICkENmwgghvIQQX0OzWfsWAG0A2L66O+XqUxI0M2KaSCkrSynnG0vAaJNSXoMm6ZJyRbJchgZPRGQlycnJeP78OUJCQrB7927MnDkTTZs2RdmyZfHPP/9YO7xsIzQ0FN26dUOlSpUwe/ZskwkYAEhKSsKpU6fwxRdfoESJEujfv7/JpJghf/75JyIiItISNpYsWWIwAZMW33zzDR4/fgwA8PPzQ48ePTKkXyKirODl5YVRo0Yp5U8++QQJCQlWjIiIiIiIiOg/2ToJI4RoIYTYCCAYwGRolgvT/vq2APDw1X0lpJTdpJT70zBUoNZtx7TGS0SUE9y6dQtt27bF5MmTrR2K1f3111+oUqUK1q1bp5fQyJ07N2rUqIHWrVujZ8+eeOedd1CtWjU4O6u3DktOTsayZctQvnx5i8ePi4vDqlWrLG6XnJyMP/74w+J2hty+fRsLFixQyuPHj+dMKSLKcT799FM4OTkB0Pw/t2jRIitHREREREREpJFtlyMTQtwA4J1ShGbGi3x1WwDYD82SY5ullInpHC781b8Z85ViIqIstnr1atSpU0d1LikpCc+fP8elS5ewadMmBAQEIDk5Wbl/0qRJKFeu3Bs762HFihXo378/kpKSVOfbtWuHYcOGoXHjxsiTJ49eu4SEBBw4cAB//vknFi9erOwPY8keKvb29sq3tP39/TF8+HCLYt+xYwdCQ0MN9mepiRMnKm3LlSuHzp07p6kfIiJrypcvH4YMGYKZM2cCAL7//nsMGDAAefPmtXJkRERERET0psvOM2FKa91OSb5EA5gPoJKUsomUcn0GJGC08au/RJQjeXl5oWTJkqqjdOnSqF69Ovr06YPNmzdj165dyreEU4wdO/aN3ID95MmTGDRokCoBU6hQIezbtw9//fUX3nnnHYMJGECT8GjWrBnmzp2Lmzdv4qOPPoKNjWX/nbZr1065ferUKVy4cMGi9kuWLFFuly9fHuXKpW0lzaCgIKxevVopDx061OLHQkSUXQwbNkyZyffw4UMsXbrUugEREREREREheydhUggAVwCMAFBYSjlcSnk5g8d4CKDJq6NpBvdNRJQtNGnSBPPmzVOdCw0NxZ49e6wUkXVERETg/fffVyWfSpcujcOHD6NRo0YW9VW4cGHMnz8ff/31F9zc3Mxu17lzZ1V97aSKKeHh4QgICFDKAwYMMLutrv/9739KIip37tzo169fmvsiIrI2b29vtGjRQin//PPPqhmgRERERERE1pCdkzCJADYBaCqlrCSl/EVKGZUZA0kp46SU+1OOzBiDiCg76NOnDzw9PVXn3rQkzNSpUxEUFKSU7e3tsX79epQqVSrNfbZp0wYnT540u37u3LlVy8CtXLnS7OXEVqxYoSSQbG1t0adPH8uCfSU2Nlb1LfG2bdvC3d09TX0REWUXvXr1Um7fvHkTO3futGI0RERERERE2XhPGAAlpZT3rR0EEdHrxMbGBrVq1cK2bduUc9p7i1ji9u3bOH36NMLCwhAREQFXV1d4eXmhbt26KFy4cLpjff78Ofbv34+7d+8iMjIShQoVQvHixVG/fn3Y29unqc9nz55h4cKFqnNffvklqlWrlu54S5cubbqSlgEDBmD+/PkAgLCwMGzduhWdOnUy2c7f31+53bp16zQ/15s3b8bz58+V8vvvv5+mfrRFRUXh8OHDuH//PsLCwmBra4sCBQrAx8cH1atXz/Clzq5fv45z587h7t27SExMRP78+VGjRg1Urlw5zX1KKXHlyhVcuHABjx49wosXL2BrawtHR0cUKlQI3t7eqFSpEnLlypXmMR48eIBjx47h8ePHePr0KZycnFCgQAHUqlUL3t7epjuwQGJiIo4cOYI7d+7gwYMHyJUrFypUqIBGjRrB0dEx1bbPnj3DoUOHcOPGDcTExMDDwwOVKlVC3bp1M/S1DAoKwunTp3Hv3j3ExMTAy8sLVapUgZ+fX4aNYYnw8HAcOXIEDx8+xJMnT5A7d27kz58ffn5+qFixolViCg0NxZUrV3D79m1ERkYiKSkJ7u7uKFCgAGrXro0iRYpYJa60uHr1Kk6dOoUHDx7Azs4ORYoUQcWKFeHr65sh/b/77rvIlSsX4uPjAQBLly5Fq1atMqRvIiIiIiKiNJFS8uDBIw0HgIrQ7FckAciLFy/KtEpISJCXL19WHQkJCWnuj15/JUqUkNrvv71795rdtmfPnqq2LVq0MLttVFSUnDp1qixTpoyqD93Dz89Prlu3TiYnJ1v82G7duiU7d+4s7e3tDfbt6ekpP/vsMxkVFSWllNLf3191v7+/v9G+f/zxR1VdBwcH+fjxY4tjtFS/fv1U465fv15KKWXlypWVc+3atTPZz8mTJ1X9bNiwQUopZcWKFVXn79y5Y7Kv9u3bK/VtbW3l8+fP0/z4tm/fLps0aWL0NUt53caOHSvDw8PN6vPOnTuq9v369VPu++uvv2SdOnWMjuXt7S1XrFhh0WN4/vy5HD9+vCxatGiq720AMleuXLJ+/fpy5syZZr/H4+Pj5fz581WvuaGjbNmycuHChWb/H2Ds/R8ZGSnHjRsnCxYsaHAcZ2dnOXHiRIPjBAUFyV69ehl9PQsXLiyXL19u9nOr/XlVokQJ5fy2bdtko0aNpBDC4DilS5dO9edZ1969e1XtJ06caHbb5ORkuXr1alm7dm1pY2Nj9PUpUqSI/OGHH+TLly/N7jst4uLiZEBAgOzXr58sVqyYyfdkuXLl5C+//CJjYmJM9q37Hrx+/brF8UVFRUlnZ2elD1dXV5PPyfr166Wvr6/Rx1C9enW5du1apX6jRo1U91uiRYsWSrvcuXPL6Ohoix+jIfx9jYiIiIgo57p48aLu3yEVZVZdR86qgXjweN0OMAlDVpSeJEy7du1Ubdu3b29Wu+3bt8sCBQqYvBiofTRv3lw+e/bM7NhWr14tc+fObVbfPj4+8tq1axYlYZo2baqq2717d7NjSw9jSZhZs2Yp5+zs7OSDBw9S7WfYsGGqpEZcXJyU0vIkTGxsrMybN69S/6233krT4woLC5PNmze36D3h4eEh9+/fb7JvQ0mYxMRE+fHHH5s91vDhw81Kkpw7d04WLlzYoseRcpjzWX3y5Enp7e1tUb9+fn7y7t27Jvs29P6/deuWLFeunFnjtGzZUnkfSSnlv//+K11dXc1q+9VXX5mMT0rDSZhx48aZ/Vy0bt3arIvoaU3C3Lp1S1arVs2i16dkyZLp+n/flBEjRqTp/Vi1alV569atVPv+3//+p2ozbtw4i+PTfd8NHTrUaN34+HjZtWtXsx/D4MGDZWJiYrqSMFOnTlW1DQgIsPgxGsLf14iIiIiIci5rJmGy83JkRESUCU6fPq0qm7P80W+//YahQ4cqm7inKFmyJCpWrAhXV1dERkbi3LlzquXNdu3ahfr16+PIkSNwdnZOdYx169ahd+/eemN4e3vD19cXjo6OuH//Po4fP464uDhcuXIFHTp0wPDhw03GDwDx8fEIDAxUnWvatKlZbTNLnz59MHbsWCQkJCAxMRHLly/H559/brBuXFwcVq9erZR79+6d5iWxDhw4gJcvXyrlBg0aWNzHzZs30apVK9y+fVt13tnZGTVq1EDBggWRlJSkLDWVsjn206dP0aJFCwQEBFi8RNAnn3yCX375BQAghECVKlXg7e0NBwcHBAcH48SJE0hMTFTq//LLL6hYsSKGDh1qtM/w8HC0bNkSjx49Up338vJCpUqV4OHhAVtbW7x48QKhoaG4evUqYmNjzY5569at6Natm+r5BoBChQqhatWqyJcvH6Kjo3H58mXcuHFDuf/s2bOoXbs2jh49iqJFi5o9XkREBFq0aKG8Li4uLqhduzY8PT3x/PlzHD16FOHh4Ur9HTt2YOzYsfj5559x+PBhdOjQQdlzqHDhwqhWrRpcXV3x8OFDHDlyRPXYp06dilq1aqFjx45mxwcAM2bMwA8//KCUixcvjsqVK8PJyQn37t3DsWPHVHskbd++HW3btsW///6brmXgDDl+/Djatm2LJ0+eqM57eHigWrVq8PT0RFxcHG7cuIGLFy8q9wcFBeHtt9/Gvn37MmXZNN3N5PPmzQsfHx8UKlQILi4uiI+Px8OHD3Hu3Dm8ePFCqXfu3Dk0adIEZ86cQb58+Qz23bt3b4wdO1Z5nZctW4bvv/8etra2Zse3ePFiVXngwIEG60kp0aVLFwQEBKjO29raolatWihevDji4uJw/fp1XLlyBQCwaNEivb3LLNWwYUNVefv27Wjfvn26+iQiIiIiIkqzrMr28ODxuh3IRjNhkhMSZFzoXR6ZfCRno2+7pnUmzJYtW/S+dbx58+ZU2+zevVva2tqq2vTq1UteunTJYP29e/fKSpUqqeqbmnESEhIiXVxc9L7RffjwYb26kZGRcvz48dLOzk4Cmhkh2u2MzYQ5evSo3mM/c+ZMqnFlFGMzYaSUsnPnzsp5Hx8fo32sWrVK1ce5c+eU+yydCfPtt9+q6luytJSUUkZHR+u9xuXLl5cbNmyQiYmJevXv378vP/zwQ1V9T0/PVGd66M6E0X6dBw0aJENDQ/Xa3L17V7Zp00bVzsXFRVm6zhDdGRl+fn7y0KFDRusnJCTIAwcOyDFjxkgPD49UP6svXbokHR0d9WZ1HDt2zGD906dPywYNGqjq169f3+BzmkJ3RoK7u7sEIN3c3OSiRYtkfHy8qn58fLycNGmSqo2tra08deqUMhvI19dX7tq1S2+sJ0+eyE6dOqnalipVyuRsI+3PK0dHR5krVy4JQJYpU0bu2LFDr/7Tp0/lyJEj9ZYpGz9+fKrjWDoT5sGDB7JQoUKqNrVr15Y7duww+Jhu3LghO3bsqKpftmxZGRkZmeo4aTF8+HBZunRpOXnyZHny5EmZlJRksF58fLxct26d3kyrbt26pdp/t27d0jxT5Nq1a3o/M8bozrrBq1kzhpaBvHDhgjL7RQgh8+XLl+aZMC9evFC9f6pVq2ZRe2M4E4aIiIiIKOfiTBgzCSHqAqgHwAeAOwBXAJbsDCullM0yIzYia0p4+Ai3mje3dhivvdK7diFX0Zyz+bGuK1euYPDgwapzxYsXR5s2bYy2iYqKQs+ePZXZKUII/P777/jggw+MtmncuDECAwPRsmVLZebJmjVrMHToUL1vJ6f4/PPPERkZqZTr1q2LHTt2wMnJSa+us7Mzvv/+e1SuXBk9evTQ+wa7Mffu3dM75+PjY1bbzDRgwABs2rQJgOY1Onr0KOrUqaNXb8mSJcrtGjVqoEqVKmke8+TJk6qypRvZf/7556pZAe+88w42btyIPHnyGKxfqFAhLFq0CBUqVMDo0aMBAE+ePMGECRNUjys1Ka/zr7/+anRmS5EiRbBlyxbUqVMHp06dAgBERkZizZo1Rr+pv2XLFuV2vnz5sHv3bqMzCADAzs4ODRo0QIMGDfDdd9/Bzs7wr1LJycno1q0boqOjlXOTJk3CxIkTjfZdrVo17NmzB926dVPeE4cOHcLKlSvRt29fo+20RUREwNXVFQcPHkSlSpX07re3t8fEiRPx6NEjzJ8/HwCQlJSE5s2bIyIiArVq1cKuXbvg4uKi19bDwwPr1q1DjRo1cP78eQDAnTt3sGfPHjRrZt6vVynPR4UKFXDgwAHkz59fr06+fPkwe/ZslClTBiNHjlTOT58+HX369EH58uXNGsuUgQMH4sGDB6rywoULjc4IKVOmDDZv3oxPPvkEc+bMAQDcuHEDP//8M7755psMiSnFF198gTlz5sDGJvVfc+3t7fH++++jefPmaNq0Kc6ePQsAWL9+PaZOnWp0puPAgQOxdu1apbx48WKzZ4qYOwvm6dOn+Oqrr1TnZs2ahVGjRhmsX6lSJezatQvvvfceAgICVDO2LOXk5IRSpUopM8IuXLiA2NhY5M6dO819EhERERERpZUlCQyrEUIMFkLcAHAIwI8ABgDoCKAJgEZmHo1fHUREb4Tk5GSEh4fj0KFD+Oyzz1CzZk3Vkks2NjaYP39+qsv7LFiwQNXm888/TzUBk8LJyQlr166Fg4ODcm7GjBkG696/fx8bN25Uynnz5sWqVasMJmC0devWDYMGDTIZSwrdC3q5c+dWxWct77zzDgoVKqSU/f399eqEhIRgz549SnnAgAHpGvPatWuqcokSJcxue//+ffz+++9KuWTJktiwYYPRBIy2zz77TJX0W7lyJR4+fGj22D179kx1aTFAkySZPHmy6pz2c6crODhYud2kSZNUEzC6Urugu2nTJlWiqmvXrqkmYFLY2dlh2bJlKFCggHLO2M+OMbNnzzaYgNE2btw4CCGUckREBHLlyoU1a9YYTMBox5eSSEuxe/dui+KztbXFypUrDSZgtI0YMQIdOnRQyomJicpydOl18uRJ/PPPP0q5bt26WLRokVlLcs2aNUuVBJ03b56ytFdGKV68uMkEjDZ3d3fVz2VycrJq+UJdzZs3R8mSJZXy33//rbcknyGJiYn4448/lHLu3LnRq1cvg3WXLFmiWobvnXfeMZqASWFnZ4elS5eiYMGCJmMxRftzLTExUW/pRCIiIiIioqySrZMwQoi8QogtAOYDKA1AaN+tdRhsbuJ+IqLXRpMmTSCEUB22trbw8PBAgwYN8PPPP6suhuXJkwfLly9PdRYMAMydO1e57eTkhAkTJpgdU7FixdC1a1elvGPHDoMXKjds2KDax6NXr16qi4Op+frrr82+UPn06VNV2c3Nzax2mc3W1lY1y2HNmjWIiYlR1fH391f2iHBwcEDPnj3TNWZISIhy29HR0aLnYsGCBYiPj1fKEydORN68ec1ur30BPz4+Htu3bze7rbnvv5YtW6qSi2fOnDGr3ePHj82OxZSUmRKAZgaZ9h4opjg5OWHIkCFK+cKFCwgKCjKrbbFixdC7d2+T9YoXL45y5cqpznXt2tWsPaJatmypKqfMvjBXx44dUb16dbPqfv/996ryH3/8obdfSlpovz6AZn8bcz9LbG1t8cknnyjlsLAwvf2mrKFGjRqqxMORI0eM1hVCqJK5uskVY/755x9V4rRz585wd3c3WFc3CfT111+b7B/QJJQ+/vhjs+qmRncvJXN/hoiIiIiIiDJatk7CAPgdQHtoEiny1TmhVU45hIFD6rQhInrjubq6YujQobhy5YrJC/nXrl1TXaxv06aNydkpupo0aaLcjouLw/Hjx/Xq6F4o1E7cmFK8eHHUrl3bopiyI+3ZRZGRkaqZQVJKLF26VCl37NjR6EVPc0RGRqqSPJb2tXPnTuW2ra0tunTpYlH7+vXrq5bwOnjwoFntvL29UaFCBbPq2tvbo3Tp0ko5teSKdp+HDx/GunXrzBojNdHR0Th69KhSrlWrFkqVKmVRH9o/O4D5z1PLli3N3mC9bNmyqnLr1q3Naufl5aX6LDBnBoU2S5KIlStXVs3qef78uWqGUVrt2rVLue3l5YVGjRpZ1D6tr096SSkRFRWFBw8eICgoSO/Qnl2UstG9MQMGDFAlnnSXGTNEt46x2YjR0dHKknWAZqnAevXqmew/hSX/Dxij+9mWkUlWIiIiIiIiS2TbPWGEEG0BdMd/iZRIAN8B2AhN3DdenZdSShshhDOAwtDsGdMfQINXbR8D6Cul3JF10RMRZU8vX76EjY2NWUu9HDp0SFUuXry4xd8k1v1m+a1bt9CgQQPVuXPnzqnKNWvWtGiMWrVqmfUtdN1lpp4/f27ROJmpXLlyqFevnpKQ8vf3V2Yz7N69W/W8m7McXGq0Z0QBMGsZsRSxsbHKXiuAZtbFkydPzN6XJ4Wbm5vS5tatW2a18fX1tWgM7Quwqb3WPXv2xOnTpwH8t4/L0qVL0bt3b7Ru3dqi5clSHD16FAkJCUrZ29vb4p8d3dke5j5Pluxz5Orqmq62UVFRAKDaz8kcliZOa9eurUq8nDhxIl17It26dUu1F0yZMmVUy9KZQ3s2WEqfmSE+Ph47duzApk2bcPr0aVy9etXspc8iIiJSvb9YsWJo2bKlMhvt2rVrOHz4MN5++22D9R89eqRaws3b2xuNGzc2WPfixYvKXmKA5Z/rZcuWhaura7o+p3Vn6Gnvz0RERERERJSVsm0SBsDnr/4V0CRgGkkpzwOAEEJv8Xop5QsA114d/kKIdwEsAZAfwF9CiO5Sys1ZEjkRURZbvXq13mbu0dHRuH37NrZv3w5/f3/ExMQgISEBv/zyCy5cuIDt27enegE+NDRUVZ4xY4bFe1PoMrTRsvYFfBcXF4uXCStevLhZ9Tw8PFTlmJgYxMXFZYt9YQBNciUlCbN3714EBQWhZMmSqo3rixUrhubNm6drHO2l3wAY3VjekIcPH6qSC0FBQRbP8NBl7ubbls7Ysbe3V27rPmZtH3/8MTZs2KCaubJt2zZs27YNQghUrFgRdevWxdtvv41GjRqZtVSe7s/OmjVrsGbNGovi15UZz5Pua2/Jz552W+33hCl58+ZFkSJFzK4P6M/YSe+MBt3X59ChQ1n2PrbEli1bMGrUKNy5cydN7c1JYAwaNEi1JOCSJUuMJmGWLVum+lkaOHCgal8hbbqJWUv2nQI0y6UVK1YsXUkY3fe3Je9TIiIiIiKijJQtkzBCCBcA9fHfLJhvUxIw5pJSbhFCtAKwF4AjgOVCiMpSyrT9JUuUjdl7FURpraVVKHPYe6V/o+DM4uXlZfDicMWKFdG+fXt8+umnaNWqlXIx78CBA/joo4+wbNkyo33q7qGSEV68eKF37tmzZ8ptZ2dni/tMbRNxbYULF9Y7d/XqVVStWtXiMTNDt27d8MknnyA6OlpZguzTTz/F5s3/fX+gX79+Fm3WbYhu4k13/5nUZNV7wpD0Pm5jHBwcsGvXLnz22WdYvHix6tv7UkpcvHgRFy9exG+//QYAqFKlCvr3748hQ4YY3Qsnpz5PmfUcazP351Wb7oyd9CY8rPn6mOt///ufyU3sTZFSmqzToUMH5M+fH2FhYQCAdevWYfbs2QaXntROCNva2qJfv35G+9X+XAcy97PdGN3PNkdHx3T1R0RERERElFbZMgkDoDb+268mHoDpRaoNkFKeFEJ8B+AHAHkAfA1gYIZESJSNCDs75Cpq2TeL6c1StmxZbN26FW+99ZayJMsff/yBDh064L333jPYRne5nYxg6KKgg4OD8u3qtIxpbpvq1asjT548qgtzx48fzzZJGCcnJ7z//vvK/i9Lly5F/vz5ERsbC0B/I+20cnZ2hhBCeS0sWaInq94TWc3R0RELFy7EuHHjsHz5cmzduhVnzpwxOIPm/Pnz+OyzzzBjxgysWLFCb28Q4PV9njKCsZkTWdlHdn99jh07htGjR6vOlSxZEj179kS9evXg7e0NLy8v5MmTBw4ODqrno3Hjxti/f7/ZY9nb26NPnz6YNWsWACAqKgpr167FwIHqX5cPHTqEa9euKeV33nkn1RlNujMMM/Oz3Rjdz7b0JnWIiIiIiIjSKvO/8pg2KWvLSAAXpZSprkUghEgtmfQLgDholjXrLITIlTEhEhHlLL6+vvj2229V50aNGmV0JoSnp6eqvGrVKkgp03VMmjRJbxzdvTt098IwxdxvxefKlUtvyba9e/daNFZm006yBAcHY8KECUq5YcOG8Pb2TvcYuXLlQoECBZTykydPzF6mR/c90bJly3S/JyzdKyUzlSpVCt988w2OHz+OZ8+eYe/evfj+++/RsmVLvYvK9+/fR5s2bXDs2DG9fnSfp6lTp6b7eUpJzuV0aVleSreNpUvT6dJ9fQYPHpzu12ffvn3piknb5MmTVZ+DgwcPxo0bNzBlyhS0bdsWPj4+cHd3R+7cufUSUpbuzwNoliTTtnix/nefdM/pJml06b5GpvanMSS9M57u37+vKhcrVixd/REREREREaVVdk3CaP/lFmTgft2vpuY21pGUMhrA8VdFFwCGF7omInoDjBgxAmXKlFHKoaGhmDdvnsG6BQuql1+7fv16psSkvVdAfHy8xeNcuHDB7LqtW7dWlTdt2pQpSxOlVcOGDVX7X2hfuPzggw8ybBztZI6UUu9ipTFZ9Z7IDhwdHdG4cWOMHz8e//77L54+fYpFixahUKFCSp3Y2Fi9GQvAm/U8Werly5e4d++eRW1u3LihKmsnEdMiO78+0dHR2L17t1L29vbGL7/8YvbeTQ8ePLB4TB8fH9StW1cpBwYG4sqVK0r5xYsXWL9+vVIuWLAg2rVrl2qfunvAWPI5DWieh/QmaHXfZ+nd94eIiIiIiCitsmsSRvsvTUPrpOguvJ3fRH/aV5eKpikiIqLXgL29PSZPnqw6N336dIP7GdSrV09V3rFjR6bElJ7ZKUlJSTh48KDZ9T/88EPV3gRxcXGYO3eu2e2zQv/+/fXOOTs7o0uXLhk2hu4SbFevXjWrnYuLCypWrKiUg4KC9C6Qv64cHR3x4Ycf4siRI6q9JY4cOaK3UXzdunVVMxR27tz52iwnlhGOHj1qUX3d2Ua1atVK1/gVK1ZU7TMTGBiY4Xu6pFVwcLBqGa7WrVubnYC5ffs2Hj58mKZxdWfDaO//snbtWtXSXv369TMZU5kyZVQzjs6ePau3T0xq9u3bZ/GsSG1SStXyaQUKFFAlUImIiIiIiLJSdk3CaP8lrL8zKBAFQPsvM1PrC2iv1ZB9d9YmIsoC3bt3R/ny5ZXy06dPMWfOHL161atXV11ECwwMxMWLFzM8nubNm6vKv//+u9kXrAMCAiy66Oju7o7Bgwerzk2bNg3nz583uw9jbt26le4+AM0FTltbW9W5bt26Gd0EPi10L2KfO3fO7LatWrVSlVM2rH9TlCxZEk2bNlXKhpZUy58/P6pVq6aU7927h23btmVViNne6tWrza574cIF1eeOq6srKlWqlK7xbW1t0axZM6UcFxeH5cuXp6vPjKKbqNBOFpmSniXrunXrpkpQ//HHH8oyhbpLkZk7K0/3Of7jjz/MjmfRokVm1zXk9u3biIqKUspvvfVWuvojIiIiIiJKj+yahLmrddtT904pZTKA21qnapror7zWbX4VlYjeaDY2Nvj6669V52bOnKm374KNjQ2GDRumlKWUGDlypMHNytOjZcuWqmViTp8+bdaF/aioKINLQZny5ZdfqvYGiI+PR5cuXRASEmJxXym2bduGmjVN/VdkniJFiuDYsWM4ePCgckyZMiVD+k7RqFEjVfn48eNGauobOnSo6lvwc+fOxaVLlzIstpxAdxaA7n4xAPDxxx+rymPGjFFdFH6T/fnnnzh9+rRZdXU/q/r06QMbm/T/+jpixAhVefLkyWlayiuj6e6lYu4steDgYIPJdHM5OjqiW7duSvnx48fYunUrrly5opq51KBBA1USPzUfffSRqjx58mSzkub//PMPAgICzIzcMN3PtMaNG6erPyIiIiIiovTIrkmYlPUDBABfI3W0v7b8nrGOhBAVAFTBf8mXR+mOjogoh+vRowfKlSunlCMiIvDzzz/r1Rs9erRqNszevXsxYMAAxMbGWjReSEiI0ZkANjY2GD9+vOrcxx9/jDVr1hjt78mTJ2jTpg3u3LljURwA4OHhgfXr1yNXrlzKuRs3bqBevXo4dOiQRX09ePAAw4YNQ7t27SxaaseUGjVqoH79+sqR3j0wdJUuXVr1+u/bt8/s2UdlypTBgAEDlHJsbCzatGmDy5cvWxRDXFyc1TebDwkJwbJlyxAXF2d2mwcPHmDXrl1KOVeuXKo9dlL07dsXFSpUUMpXrlxBp06dLN6gPCwsDJs2bbKoTXaXlJSEXr164cmTJ6nWmzdvnupivK2tLYYPH54hMTRu3BgtWrRQyo8fP0abNm1w9+7dVFrpe/HiBVatWpUhMQGan00np/8mgW/dutXkDMQnT56gc+fOeol0Sw0cOFBVXrJkid4sGN06qWncuDEaNGiglMPDw9G8efNUE967d+9WJYPSSndZy7Zt26a7TyIiIiIiorTKzkmYZ69u5xNClDBQ5+9X/woAdYQQvXUrCCHyAFj8qk7KkmSWLURORPQasrW11fuG+c8//6x3gdjFxQVr165VffN/xYoVqF69OlauXJlqMubBgwfw9/dHhw4d4O3tjbVr1xqtO3DgQNUyVwkJCejRowdatWqFFStW4MyZM7h27Rr27duH8ePHo3z58jh48CCEEOjataulDx+1a9fGokWLVN+ov3fvHho0aIB3330X//77L2JiYgy2TUhIwJ49ezBixAiULl0a8+fPT9feBdbSqVMn5fbTp09x5swZs9vOmjULVapUUcohISGoWbMmxo8fj9DQUKPtYmJisGvXLowcORLFihVTJXOsITw8HP3790fx4sXx8ccfY/fu3UZfdykldu7cicaNG6v2D+ncubNqGacUtra22LBhA1xcXJRzu3btQpUqVTB//vxU9yAJDw/H2rVr0aNHDxQrVixdMxyyG0dHR9jb2+Pq1auoV6+eKqGVIjw8HJ9++ilGjhypOj927FhVYiu9li1bhqJF/9sq8OzZs6hSpQp+/PHHVBNEL168QEBAAAYOHIgiRYrgq6++yrCYcuXKhY4dOyrlhIQEtGrVCtu3b9erm5SUhI0bN6JWrVo4ffo0hBDw8PBI89h16tRR7fm0bds2+Pv7K2UXFxe8//77FvXp7++v2kPp0qVL8PX1xaeffoodO3bg6tWrOH/+PDZs2ICuXbuiRYsWiIqKQtmyZVG9evU0Pxbt91X58uUz9H1DRERERERkKfN2+sxiUkophDgAoMOrU20AzNepthnAbACO0CRYlgohmkKTnHkOoAKAjwGUxX+zYE5LKW9mcvhERDlCz5498d133ykbq0dGRmLGjBl6S181bdoUS5cuxcCBA5UZA1euXEHv3r3xwQcfwM/PD4ULF4aTkxOioqIQHh6Oq1ev6m1WbsqaNWvQunVr1SbcO3bswI4dO4y2mTBhAkqVKoV169Yp57Q3RE9Nv3794Orqiv79+6u+QR4QEICAgADkzp0blSpVQv78+eHu7o5nz57h4cOHuHHjhsEL6Hny5DFr3Oyib9++mD59ulLeuHGj2Rc9nZycsHXrVrRs2VJZLikmJgZTp07F1KlT4e3tjQoVKsDNzQ2JiYl4/vw5goKCcPPmTSQlJWXK40mPx48f45dffsEvv/wCW1tbVKhQAcWLF4ebmxsAzWyUc+fOISwsTNWuQIECmDVrltF+K1asiI0bN6JLly7Ke+zu3bsYNmwYRowYgcqVK6N48eJwcXHBy5cv8ezZM1y/ft3i2Rg5iaenJ4YNG4axY8fixo0baNGiBUqUKIEqVarA0dER9+7dw9GjR5X9SFI0atQIEydOzNBYChUqhK1bt6JNmza4f/8+AM2swLFjx2LcuHHw8fGBt7c3XF1dERcXh2fPnuHWrVsICgpSzRzLly9fhsY1adIk/Pnnn8rydffv38c777yDEiVKoFq1asibNy+ePHmCEydOqBLnX331FQ4dOoT9+/eneeyBAwfis88+A6BJ8oSHhyv39ejRw+K9qUqXLo0tW7agQ4cOePnyJQAgOjoas2fPxuzZsw22cXJywoYNG/SScOY6ffq0apZkv3790tQPERERERFRRsmWSZhXtuC/JEx36CRhpJTPhBBTAUyFJsliA6DfqyOFeHWfAJAMQL3eDRHRG8zW1hbjx49H//79lXNz5szBqFGjVEuQAUCvXr1Qvnx59OjRAzdv/pfLjo+PN3s/Ed29DnS5ublh586d+Oyzz7B48eJUl8fKkycP5s6di4EDB2L+fHWO3tCsBGM6duyIc+fOYfTo0di4caPqvtjYWJw8edJkH3Z2dvjggw8y/AJxZvP19UXt2rWVpNf69est2numWLFiOHHiBD766COsXLlSdd/t27dx+/ZtIy3/k5LkyE6SkpJw6dIlk/vc+Pr6YsuWLShUqFCq9Zo3b46TJ0+iR48eqvdTUlISzp49i7Nnz5qMydTPTk7zxRdfICwsDDNmzACg2c8kODjYaP1WrVph06ZNqiUEM0rVqlVx5swZ9OnTR5XwlVLi8uXLZi2zl9GvT+nSpbFhwwZ06dJFtY9Qas/T559/ju+//z7de5/07dsX48aNQ3x8vN59gwYNSlOfzZo1w759+zBo0CCcP38+1bpVqlTBunXrUL58edWsNEs+17WT8nZ2dujbt6/lQRMREREREWWg7LocGaCZ6XIRwGUA7kKI4gbq/AhgI/5LtgD/LT2mnYABgG+klMa/Tk1E9Abq3bs3ypQpo5SjoqLw448/Gqxbs2ZNXL16FStWrEDdunX1NifXZWNjg2rVquGLL77A6dOnDe45o8vZ2Rm//fYbzpw5gzFjxqBq1arw8PCAg4MDSpQogYYNG2LmzJm4c+eOsjeB7l4srq6uJsfRVqJECWzYsAEXLlxQlskyxc7ODm+99RZmzZqF0NBQLFy4EIULF7Zo3Oxg1KhRyu0bN27gwIEDFrV3cnLCihUrcO7cOfTu3dusi9GFCxdGr169sH79erM26c5MFStWxLZt2zBixAhUrFjRrA3fq1evjl9++QXnzp1T/eykpkyZMjh+/Dj++usvNG/eHA4ODibb+Pj4YMSIETh48OBrtycMAPz0008ICAjA22+/bbRO6dKlsWTJEmzfvt3iGRiWKFCgAP79918cOHAAHTt2VC2fZUypUqUwaNAgbNu2zaxkraVatWqFkydP4t133zU6u8/Ozg6tWrXC7t27jX5uW8rDwwPvvvuu3vkqVaqgZs2aae43Zcm0tWvXomvXrvD29kbevHnh4uICHx8fvP/++9iyZQtOnTqF8uXLA1B/tpv7uZ6YmIg//vhDKb///vsoUqRImuMmIiIiIiLKCMLcjXizKyGELYDPAHwFwNBfaMEAvpBSrs/SwOi1J4SoCE2iEABw8eJF1VrqlkhMTFSWhEpRtmxZkxe5iazpxYsXOHr0KO7evYvw8HDExsbCyckJHh4eKFu2LHx8fFT7YWSWPn36YMWKFUo5PT+LKUJCQnDhwgUEBwcjMjISiYmJcHV1hbu7O0qXLo1q1aohd+7c6Q3d6pKSklC2bFll6Z4ePXqka5Px5ORknD9/HpcvX0Z4eDiePXuG3Llzw8XFBSVLloSPj49ZSS5riYyMxKVLl3Dnzh08evQI0dHRsLe3V+L38/MzOfPFHLGxsTh27BiCg4Px9OlTREdHw9HREe7u7ihTpgx8fHzStbdHTnPnzh2cOnUK9+/fR0xMDLy8vFClShVUq1bNKvEkJibi5MmTuHnzJp4+fYoXL14gb968cHV1hbe3N3x8fODl5ZVl8Tx69AiHDh1CSEgIYmJikD9/fhQqVAh16tTRm7X4uoiNjYWLi4uyLF3NmjVx4sQJk+1Slv9LcfLkSdSoUSPD4uLva0REREREOdelS5dQqVIl7VOVpJSpL4ORQXJ8EiaFEMIeQCNo9oBxAxAB4ByAY1LKnLdjMmV7TMIQWZ+UEqVKlVKW6HF0dMTz589ha2tr5chyjiVLliiziuzt7XHr1q1snSghotff3r170bRpU6U8ZMgQLFiwwGS7evXqITAwEADQvn17BAQEZGhc/H2NiIiIiCjnsmYSJjsvR2YRKWWClHKXlHK+lHKalHKBlDKQCRgiotfXtm3bVHsk1KpViwkYC/Xr1w++vr4AgISEBPz0009WjoiI3nS6e33VrVvXZJs9e/YoCRgbGxtMnTo1U2IjIiIiIiKy1GuThCEiojfLixcvMHLkSNU5bsBsOVtbW2WDdAD47bffcPfuXStGRERvsn/++Qfr1/+3irCTkxM6d+5sst2kSZOU2wMHDtT9hhsREREREZHVMAlDRETZQmBgIIYNG4aQkBCTde/cuYPGjRvj1q1byrmCBQuie/fumRnia+udd95RLnLGxsZi/PjxVo6IiF4XEydOxKJFixAXF2ey7urVq/H++++rzn3wwQdwdnZOtd3mzZtx8OBBAICHhwemTZuW9oCJiIiIiIgyGBcwJiKibCEuLg7z58/HwoUL0bhxY7Rq1QrVq1eHl5cXcufOjYiICFy5cgX//vsv1q9fr2zYnMLf3x958uSxUvQ535w5c1C5cmUAmqV8YmNjkTt3bitHRUQ5XXBwML799luMHTsWnTp1Qv369eHr64t8+fIhMTERYWFhOH78ONauXYtTp06p2pYuXRpTpkwxOcaLFy8wceJEAMDbb78NDw+PTHksREREREREacEkDBERZSvJycnYs2cP9uzZY1Z9W1tbzJo1C++8804mR/Z6K1KkiGo5HyKijPTs2TP4+/vD39/frPolSpRAQEAAnJycTNblUpRERERERJSdWSUJI4RoaI1xAUBKecBaYxMRkXFOTk5wcHAwa8maFJUrV8bMmTPRokWLTIyMiIjSytPT06L6tra26NatG37++WcUKFAgk6IiIiIiIiLKOtaaCbMPgLTCuBKc/UNElC3VrFkTYWFh2LlzJw4ePIhz584hKCgIYWFhiImJgYODA9zd3VG0aFG8/fbbaNmyJVq2bAkhhLVDJyIiI2bMmIGPPvoIO3bsQGBgIK5evYrQ0FA8f/4c8fHxcHFxgYeHBypUqIBGjRqhc+fOKF26tLXDJiIiIiIiyjDWTkjwyhkRESmcnZ3RuXNnZZN4IiLK+cqUKYMyZcpg2LBh1g6FiIiIiIgoy1kzCWNpAkZ35oyx9ubWIyIiIiIiIiIiIiIiyjTWSsJMtqCuA4AhANzwX0LlGYDTAK4DeP7qnCuAcgCqv6oLaBIy4QAWAohPR7xEREREREREREREREQWsUoSRkppVhJGCFEQwN/4LwFzFsAkAH9LKZOMtLED0BbARAB+ANwBtAbQVkr5MH2RExERERERUY6WEAOcXQncOQg4FQBqfgAU8LF2VERERET0mrL2njBGCSEcAPwDoBo0M1pmAxgtpUxOrZ2UMhHAFiHEXwBmAvgEmmTMNiFEXSllbKYGTkRERERERNlT0GEgYAQQfuu/cyeXAA1GAw3GAHa5rBcbEREREb2WbKwdQCo+x38JmHVSylGmEjDapJTJUspRANZBM4umCoAvMiVSIiIiIiIiyr5iI4Gto4ClbdQJGABITgT2Twd+awo8OG+d+IiIiIjotZUtkzBCCAHgw1dFCWB0OrobDSAleTMoPXERERERERFRDnNtG/BLbc2Ml9Q8ugD81gTY9wOQlJA1sRERERHRay9bJmEA1AFQDJoEzEkp5f20diSlvAfgJDSzYYoIIeplTIhERERERESUbUWFAesHAKu7Ay/M/JMyORHYN02TjHl4IXPjIyIiIqI3QnZNwnhr3Q7KgP60+/A2VomIiIiIiIhyOCmBc2uAX2oBlzalrY+HF4BFjYF90zkrhoiIiIjSJbsmYQpr3c6InRG1+yiUAf0RERERERFRdvMsBFjxHrB5CBATkb6+khOBfVM1e8U8upQx8RERERHRGye7JmFitG5XyID+tPuIMVqLiIiIiIiIcp7kJODYQuCXOsCt3abruxUH3l8GVGhnuu7D88DCRsD+nzgrhoiIiIgsll2TMPde/SsAlBdCVEtrR0KI6lAnYe4Zq0tEREREREQ5zOOrwJLWwLYvgIRoE5UFUGc4MOwoULEj0G0F0Pl3II976s2SE4C93wO/NwMeXc6oyImIiIjoDZBdkzD7ASQCkNAkYn4VQli8LJkQwgHAr1qnEl/1TURERERERDlZYrxmz5aFDYC7x03XL+ALDNoFtJ4K5HLUnBMCqPI+MOwYUL6t6T4enAMWNgQOzACSEtMXPxERERG9EbJlEkZKGQ5gGzQJGAngLQD/CCG8zO3jVd1/XrWVr45tr/omIiIiIiKinOruKWBRI82eLUnxqde1sQcafwUM3g8UrWm4jnNBoPtKoPNvQG631PtLTgD2fAcsbg48vpKm8ImIiIjozZEtkzCvjAEQ9+q2ANAEwFUhxDQhRFUhhNBtIDT8hBDTAVwF0Bj/zaaJe9UnERERERER5UTx0cD2r14lQMxYFqxoLeCjg0DjsYCdicUVhACqdAWGHwPKvWO67/tnNLNiDs7irBgiIiIiMsrO2gEYI6W8IYQYDMBf67QLgC9eHTFCiDsAIqFJtLgCKAUgz6u6KbNoBIBkAB9KKW9mUfhERERERESUkW7tBf76BHgWbLquvSPQ7BvgrQ8BG1vLxnH2AnqsBs6v1ewzE/vceN2keGD3ZODqVuDdX4ECFYzXJSIiIqI3UrZNwgCAlHK5ECIRwEIATtAkVQBNYiUvgIo655Sm+C8BEw1gsJRydZYETURERERERBknJgL492vg7Arz6pduBrT7GXAvkfYxhQCqdgdKNQK2fgpc3556/XunNLNimnwJ1B0B2GbrP7WJiIiIKAtl5+XIAACvkieVAWx9dSol2ZKSaIGBckqdrQAqMwFDRERERESUw0gJXPoTmPeWeQmYPO5Ap4VA743pS8BocykE9FgDdJwPOLimXjcpDtg1CVjSCgi7njHjExEREVGOl+2TMAAgpQyWUnYAUBbAZAC7oVmGTOgcka/umwygrJSyg5QyyCpBExERERERUdpEPgDW9gbW9wOiH5uuX7EzMPyEZvaK/vah6SME4NcTGH4UKNvSdP17J4EF9YHDs4HkpIyNhYiIiIhynBw1R1pKeRuaBAsAQAiRC4D7q2KElDLeKoERERERERFR+kkJnP4D2DEBiEtlL5YUzoWBtjOBCm0yPzaXwkDPdcDZVcD2cUBcpPG6SXHAzm+AK1uBjr8CnmUzPz4iIiIiypZyxEwYY6SU8VLKR68OJmCIiIiIiIhyqqe3gGXtgb9GmpeAqTFAMzslKxIwKYQAqvUChh0FyjQ3Xf/ucc2smCNzOSuGiIiI6A1ltZkwQogJADZLKS9aKwYiotfZvXv3cOHCBYSGhuLZs2eIi4uDk5MTXF1d4enpicqVK6NkyZKZNv7jx49x7tw5BAUFISIiAvHx8XB2doa7uzuKFy+O6tWrw8XFJdPGt4b9+/ejcePGAABnZ2fcvHkTBQoUsG5Q9Fpo3Lgx9u/fr5SllKnUTrugoCCUKlVKKffr1w9Lly7NlLEoe5k6dSrGjx8PAKhevTpOnDgBG5sc/X0tykmSEoGjvwB7pwKJsabr5ysNdJgDlKyf+bEZ41oE6LUBOLMC+Per1GfFJMYCO74GrvwFvPsr4Fkm6+IkIiIiIquz5nJkkwFMEkLcBrAZmoRMoBXjISLK8c6ePYvFixcjICAAISEhJuu7urri7bffxnvvvYd3330XHh4e6Ro/ODgYixYtwsaNG3Ht2rVU6woh4OPjg/fffx+9evVC2bKpL9MxadIkTJ48We987dq1cfTo0TTFGxUVBS8vL0RHR+vdt3fvXiWhYo6EhAQMGzZMKY8bN44JGCLKMUaNGoUFCxYgNDQUp0+fxvz58zF8+HBrh0VvgocXgC0fAw/Omq4rbIG3RwKNxgL2eTI9NNPxCKB6H6B0EyBgBHBrT+r1Q48BC94Gmn0D1P4IsLHNmjiJiIiIyKqyw9fbSgMYDeCQEOK+EGKBEKKVECJH7VdDRGRNV65cQZs2bVCtWjXMmzfPrAQMADx//hz//PMPBg4ciEKFCmHw4MG4c+eOxeOHh4dj6NChKF26NKZOnWoyAQNovsl/+fJlTJ48GeXKlcO7776LS5cuWTz2sWPHcOXKFYvbAcCaNWsMJmDSYu7cubh8+TIAwMvLC6NGjcqQfomIskKePHkwadIkpfz1118jPDzcegHR6y8hFtj9LbCosXkJGK8qwOC9QPNJ2SMBo821KNB7E9B+DpDLOfW6ibGamTP+bTTLrxERERHRa8+aSZjnAMSr2+LV4QXgQwD/AAgTQqwUQnQRQjhaKUYiomxv0aJFqF69OrZt26Z3n729Pfz8/NC8eXN0794dnTp1wttvv61abihFQkICfvvtN5QrVw7Pnj0ze/yjR4+iatWqWLBgAZKS1Gud29nZoWrVqmjZsiV69OiBdu3aoWbNmsiXL59ePwEBAahSpQru3btn9tgplixZYnGb9LTT9fz5c0ydOlUpjx49GnnyZLMLREREJvTp0wclSpQAADx79gzTpk2zckT02go+otkn5eBMIDkx9bp2uTWJlw/3AIWqZkl4aSIEUKMfMCwQ8G5iun7oUWD+28DR+UBycubHR0RERERWY83ZJvkBNAbQCcC7AAq/Op+SmHEF0P3VESeE2A3NsmUBUsonWRsqEVH29O2332LixImqc0IIvPfeexg0aBDq168PR0fDeex79+5hx44d2LBhA/755x/lfGJiIhITTVwQeWX37t3o0KEDXr58qTpfv359jBo1Cs2aNYOrq6teu+TkZBw7dgwBAQFYuHAhIiIilPMJCQlmjW1vb6/UXb58OaZNmwY7O/P/W7t27RoCA/9bBVO7P0vNmDEDT58+BQC4ubnho48+SlM/RETWZG9vjzFjxmDEiBEAgHnz5mHUqFEoXLiwiZZEZoqNBHZPBk78bl79EvU1e794lM7cuDKSWzGgz2bg1FLNPjDxUcbrJsYA28cBlwOAd+flrMdJRERERGaz2kwYKWWilHKXlHK4lLIogLoAfgRwXataSkImN4A2AH4D8EAIsU8I8YkQokTWRk2ZRQjhJoRoI4T4UAjx5at/2wgh3KwdG1F2tX79er0EjK+vL86ePYv169ejVatWRhMwAFCkSBEMGDAAf//9N86dO4du3bpZNH5wcDC6du2qSsA4Oztjw4YNOHjwIDp37mwwAQMANjY2qFu3LqZNm4bbt2/jq6++goODg0Xjt2vXTrn96NEjgzOBUrN48WLltqurKxo0aGBR+xRRUVGYN2+eUu7Xrx+cnJzS1BcRkbVpf4bFxsbif//7n3UDotfH9X+BX+uYl4BxcAHa/Qz0+ytnJiaEAGoO0MyKKdXIdP2QI5pZMccWclYMERER0WsoO+wJAwCQUh6TUo6TUlYAUBHA1wBOQb1kGQDYAmgAYBaA20KI00KICUKISlkedDoIIbyFEN2EED+9SipFCiGk1hGUjr5lOo+SGfdITcZaTQixGcAjAH8DWARg6qt//wbwUAixWQjhl1UxEeUE165dwwcffKA617BhQxw+fBhVqlSxuL8qVapgzZo1CAgIMGsz+fj4eHTp0kW1X4Cnpyf27duH9957z6Kx3dzcMGXKFBw4cABFixY1u129evVQoUIFpWzJ0mKJiYlYvny5Uu7Ro0ealw/7/fffVcu3cRYMEeVkzs7O6Nmzp1JetGgRXrx4YcWIKMeLfgJsGAis6gpEmrHkaPk2wPBjQM0PAJts8+dq2rgVB/puAdrOAnKZ+IJGYgyw7QtgWXsg3PL9+YiIiIgo+8qWv9VKKa9IKadKKWsBKA7gEwD7AKR8LUg7MVMVwCQA54QQN4QQPwoh6mZxyGYRQjQWQvwrhHgK4BaANQDGAGgEwMQOjq8fIcQ4AMcAdASQy0g1h1f3HxNCfJE1kRFlf+PGjUNU1H/LWxQoUAAbNmyAm5tbuvpt3749Lly4AGfn1D+SfvvtN5w8eVJ1bunSpahevXqax37rrbdw6tQpeHh4mN1mwIAByu2///4bYWFhZrX7559/8PDhQ4P9WGrBggXK7Ro1aqgSQ0REOVGvXr2U28+fP8eqVausGA3lWFIC59cB82oBFzeYrp/XE+jiD3RfBbi8RkvgCQHUGggMPQKUami6fvAhYH494NgizoohIiIiek1Yc08Ys0gp7wKYC2CuECIfgPbQ7CPTAkAeaBIx8tW/pQGMBjBaCPEYwBZo9pHZLaU0b4ODzOUHoKW1g8gOhBBfAZiiczoGwAkAD6DZI6gWNEvRAZokzXQhhJRS/pRlgRJlQ1euXMGWLVtU5+bOnYv8+fNnSP+mZsIkJSVh5syZqnN9+/ZF27ZtM31sXX379sX48eORmJiIhIQErFixAqNGjTLZTnvWTMWKFfHWW29ZHCsABAYG4tq1a0r5/fffT1M/2mJjYxEYGIiQkBCEhYVBSon8+fOjTJkyqFOnjkX73pgjNDQUJ06cwN27dxETEwNPT09UrlwZNWvWhE06voF88+ZNnD9/Hvfu3cOLFy8ghEDevHlRsGBBlCpVCpUrV0bevHnT3H94eDiOHDmChw8f4smTJ8idOzfy588PPz8/VKxYMc39GiKlxPHjx3Hz5k3cu3cPNjY2KF26NBo3bgx3d/dU2758+RKHDh3C1atX8eLFC7i7u6N8+fJo2LAh7O3tMyzGhw8f4vjx47h37x6eP3+OggULokKFCqhTpw6EEKY7yGBRUVE4fPgw7t+/j7CwMNja2qJAgQLw8fFB9erV0/XeSqtHjx7h8uXLuHXrFp49e4b4+Hi4ubnB09MTNWrUQOnSOWfJo+DgYAQGBuLBgwdISkpCkSJFUK5cOdSoUSND+q9fvz4KFSqEBw8eANAk2YcMGZIhfdMb4lkosHUUcHOnefWr9gBaTQXy5svcuKzJvQTQZwtwcjGwcyKQEG28bsJLYNvnwJVXe8W4l8yyMImIiIgoE0gpc+QBIC+AzgCWAwiHZpaM7pH06ogAsBJAFwCOVoz5U2gSRrpHLICbOueC0jGOdj9HAZS08LDL5Oeh3avXRzvOhQA8derlh2YfIO16yQBaW/v99yq+itqxXbx4UaZVQkKCvHz5supISEhIc3/0ehs8eLDqM6RYsWIyMTExy8Zft26d3ufYuXPnMn3ciRMnqsb86aefpJRStmvXTjlXuXJlk/08fPhQ2tnZKW1mzJghpZSybdu2qv737t1rsq8RI0ao2ly6dCnNj+/o0aOyffv2Mk+ePIb+n5AApIuLixwyZIi8d++e2f1qt2/UqJFy/vDhw7J58+bSxsbG4FgFCxaUc+bMkUlJSWaPFRsbK3/88UdZtmxZo48h5bC1tZU1atSQ3333nYyOjjar/+TkZLl69WpZu3Zto3EDkEWKFJE//PCDfPnypVn97t27V9V+4sSJUkop4+Li5A8//CBLlixpcBwHBwf58ccfyxcvXuj1GRYWJocNGybz5s1rsK27u7v8+eefzX5+GzVqpGqf4ujRo7Jt27bS1tbW4DiFCxeWM2bMMPv/lDt37qja9+vXz6x2KbZv3y6bNGki7e3tjb4+np6ecuzYsTI8PNyivi2VmJgod+/eLYcOHWrWe7Jo0aJy6tSp8tmzZyb71v7cASB37txpcXzJycmydOnSSh82NjYyNDQ01TZ79uyRb731ltHHUK5cOfnrr78q9fv166e6/86dO2bH9+GHH6ra3rp1y+LH+Lrh72tmSEqS8tgiKacUlnKii+ljViUpb1j+85Pjhd+R0r+tec/R94U0z6kF/x8TERERkb6LFy/q/g1VUWbRdeRsuRyZOaSUL6WUm6SUfQAUgGaGyXwA97WqiVeHK4DuANYCCBNC/CWE+ODVzJqslgDgLIDfAQwBUAOapcgGZdJ4sVLKIAuPTJs1JISwBTAD/y0pBwA/SymHSCmfaNeVUoZJKT8E8D/tLgDMfNUP0Rtpx44dqvKAAQNga5t1PxLbt29XlevUqZOmfWgyivbeOBcuXNBbJk3XH3/8gcREzcecvb09+vTpk+ax//nnH+V2wYIF4evra3EfL1++RM+ePVGnTh389ddfiImJMVo3MjISCxcuRNmyZbFhgxlLuxgxdepUNGzYELt27UKykaVOHj16hJEjR6JLly6Ij4832WdoaCiqVauGL774Ajdu3DBZPykpCadOncKECRNw//59k/Vv376NGjVqoEePHjh27JjRuAHg3r17GDduHHx9fXHp0iWTfRvy9OlTNGzYEOPGjUNQUJDBOnFxcZg3bx4aNGig2hfozJkzqFq1Kn799Ve8fPnSYNuIiAiMGjUK/fv3T/WxpObXX39FvXr18PfffyMpKclgnfv372PMmDGoU6eO2cv1pcWTJ0/QokULtG7dGnv37kVCQkKqdadPn46yZcviwIEDmRbT7Nmz0axZM8yfP9+s9+Tdu3fx1Vdfwc/PD6dPn0617qBB6l/bFi9ebHF8+/fvx61bt5Ryq1atjO6LJaXEqFGj0LRpUxw/ftxon9evX8ewYcPw7rvvIjo6lW/Zm6FJkyaq8t9//52u/ugNEHYd8H8H+GcMEB9lorIAan+k2bi+TPMsCS9bcS8J9A0A2swA7E3MCk2I1jynf3QAIoKzJDwiIiIiyljZfjkyc7xKGux6dQwXQtSGZsmyjgDKvaqWsmxZbgBtXh1FAXybhaEuA7BAShmre4c1lgqxkr4AymuVrwH40kSbcQDe0WrnC6AXgD8yPLocKjkpGVERcdYO47Xn5O4AG1vr5q5DQkL0Lgg3atQoS2PYv3+/qty0adMsHV9Xu3btUKBAATx+/BgA4O/vj5o1axqt7+/vr9xu27atxUugpbhx44bqAmr9+vUt7iMsLAytW7fWu+CbJ08eVKtWDYULF4atra2yZFjKhe2XL1+ia9eu+P3331VJKHPMmDED48ePV8rly5dH+fLl4ejoiAcPHuDo0aOIjf3vv6nNmzdjwoQJmD59utE+4+Pj0bp1a1y5ckV1Pl++fKhcuTIKFiwIe3t7vHjxAg8ePMDly5ctukB8/PhxtG3bFk+eqHL18PDwQLVq1eDp6Ym4uDjcuHEDFy9eVO4PCgrC22+/jX379sHPz8/s8eLj49G2bVscO3YMgOb1qF27Nry8vPDy5UucPHlSlTg6mY6BuAABAABJREFUe/YsBgwYgM2bN+PGjRto1qwZIiIilBhr1aqFfPny4enTpzhy5Ihqk/Ply5ejevXq+PTTT82ODwDWr1+Pjz/+OGVmJgoUKIDq1avDzc0Njx49wtGjR1UJvVOnTqFJkyY4ePCgySXULHXz5k20atUKt2/fVp13dnZGjRo1ULBgQSQlJSEoKAinT59Wkk5Pnz5FixYtEBAQgFatWmVoTAD0klu5cuVChQoVULRoUbi6uiIpKQlhYWE4d+4cwsPDlXpBQUFo2rQpTp06ZXSJsrZt28LLy0vZW2rz5s0IDw9Hvnzmf79HN3EzcOBAo3VHjhyJefPmqc4JIeDn54dSpUpBCIHbt2/j7NmzkFIiICAAgwcPTteSdw0bqveu2L59O0aMGJHm/ug1lhgPHJ4NHPgRSDKdtEf+CkCHuUCxtC0F+tqwsQHe+hAo0wzY8jEQfDj1+kEHNXvFtPgWqPmBZq8ZIiIiIsoRXoskjC4p5TFoNnwfJ4TwgSYh0wmaWSfAfwmZrI4rIqvHzIb66pR/llKmmj2QUsYJIWYD+FWnHyZhXomKiMPyrwOtHcZrr8/3deHimceqMRw5ckRVFkKkmnDIaI8ePVIlHgCgVq1aWTa+Ifb29ujduzdmzZoFAFi9ejVmzpyJ3Llz69UNDAxUJQoGDBiQ5nGPHj2qKltykR/QXCDu0aOHKgFTuHBhfP/99+jZsyccHBxU9Z89e4Yff/wR06dPR3JyMqSUGD58OGrUqIGqVauaNeaFCxdw8OBBAEDHjh0xbdo0VKhQQVUnIiICn332GZYuXaqcmzlzJoYOHYqSJUsa7Nff3x+XL19WyiVLlsQvv/yC1q1bG9z7Q0qJU6dOYevWrSZnEDx8+BAdO3ZUJWBq166N7777Ds2bN9f7EsPNmzfx+eef488//wSg2VS8a9euOHXqFJydnVMdK8WCBQsQEREBBwcHTJ48GSNGjFDtXyOlxKJFi/Dxxx8rs6r+/PNP7Nq1C2PGjEFERASKFi2Kn3/+GZ07d1Y9B9HR0RgzZgwWLFignJswYQIGDhxodnwAMGTIEEgpUbBgQcyZMwfvvfeeakZcdHQ0Zs2ahe+//16ZyXTp0iWMGDECK1asMHscU16+fIlOnTqpEjDly5fHlClT0LFjR71Zeg8ePMDEiRPx22+/AdAkvHr37o2zZ8+iSJEiGRZXCi8vL/Tr1w/t2rUzuqdScnIydu3ahXHjxuHMmTMANO+bXr166f2cp7Czs0O/fv2U5GRcXBxWrlxpdpLi+fPn2Lhxo1LOnz8/OnToYLBuQECAXgKmS5cumDFjBkqUKKE6HxwcjM8++wybNm3CqlWr4OnpaVY8hhQpUgSenp7Kz56x54LecPdOAVtGAI/NmHVoYw80GA00+AywczBd/02RzxvotxU48Ruwa5JmPxhj4qOAvz8DLm/R7BXjVjzLwiQiIiKitMuxy5GZS0p5RUo5VUpZC0BxAJ8A2AvN3iKUhYQQHgAaaJ2KB7DKzOYroVnKLUUjKy0nR2RVuss2eXl5wcXFJcvGv3fvnt45Hx+fLBvfGO3ZIBEREcoFeF1LlixRbhcsWBBt2rRJ85i6y55VrlzZovYzZ87E7t27lXL16tVx/vx5DBgwQC8BAwBubm6YOnUq1q5dqyQeYmNjMWrUKLPHDA8PR3JyMr744gts3rxZLwEDAO7u7vD398e7776rnEtKSko1WbJlyxbltp2dHXbu3Ik2bdoY3Xw9JXk4adIkBAUF6V1I1jZw4EBlc/CU8uHDh9GiRQuDs0jLlCmDzZs3Y+TIkcq5Gzdu4OeffzY6hq6IiAjY29vjn3/+wdixY1UJmJT4hwwZgilTpqjOd+3aFefOnUOpUqVw7NgxdOnSRe85cHR0xPz581XvvaioKKxbt87s+FJi9PLywqFDh9C1a1e9ZIejoyMmTJiAtWvXqu5buXKl6n2XXp9//rlq9tE777yDM2fO6CWFUhQqVAiLFi3CzJkzlXNPnjzBhAkTMiymFD179kRISAh++OEH1K9f32ACBgBsbGzQsmVLBAYGqmbkHDt2DPv27TPav+7MFUuWJFu1apVqplLfvn0NzlpJTEzE8OHDVedGjhyJ9evXG/y5KVGiBDZu3Ihhw4YBgN7sMUtpLzUZHh6Omzdvpqs/eo3EvwT+HQ/83ty8BEyRGsCQA0CTL5mAMcTGBqg9BBh6GChez3T9O/uBX+sCJ5cAMsu/W0hEREREFnrtkzDapJR3pZRzpZTNABQEsNraMb1hWgDQviJzSkr5wlhlbVLKSADa6/XYveqP6I2ivWQOoLkwb83xrRGDIRUrVlTNyNFecizFy5cvsXbtWqXcp08foxdlzXHt2jVVObVEgq6YmBj89NNPStnV1RVbt26Fh4eHybZdunTB0KFDlfLevXtN7l+hrX79+pg2bZrJeroJhj179hitGxz83xr1VatWRZkyZcyOx9bW1uiSSSdPnlTtu1O3bl0sWrTIrD2QZs2apbqAPG/ePMTFmb9s4/jx400utTdixAg4OTkp5ZQlyP744w8ULlw41bZjx45VldOSGFmwYIHJ57pjx456F/Fnz55t8ViG3L9/H7///rtSLlmyJDZs2IA8eUzPGPzss89UiaiVK1cqS3tllMKFC1u0HJeDgwP8/f1VbVauXGm0ftmyZVVLdp07d87sn0XthDBgfCmyzZs34+7du0q5SpUqqgSWMf/73/9QsWJFs2JJje7n2vXr19PdJ70Gbu8D5tcFAucB0sT32uzzAq2mAQN3AgUt3zftjZPPG+j/N9D6B8DOxGdpfBSwdRSwvBPwLDRr4iMiIiKiNHmjkjDapJThUkrTu7RSRqqkU7Z0/awjOuX0X10gymGePn2qKru6ulrU3tPTE0IIk4exZad0xweyRxIGUM+G2bVrF0JD1Rck1q9fr9qLw9K9VHTp7s1jbENtQ1avXq3aJP3TTz9FoUKFzG4/evRoVTkgIMDstuPHjzc6Q0VbxYoVVe+Ds2fPmtV/yt48GWHOnDmq8tSpU82KHdAkdz755BOlHBYWhsBA8/7byZs3r6qtMXny5MHbb7+tOle/fn2z9geqX7++Kllh7vObokaNGqrZSqn55ptvkCtXLqX8999/q95/abVgwQJlqTMAmDhxot6sodRov4/j4+Oxffv2dMeUXoUKFUK9ev99C113CUhdaZkNc/78edVMunr16hmdUbh6tfr7QmPHjjUreWxvb49x48aZrGeK7uea7ucevWFiIoAtw4E/3gUigkzX924MDAsE6g4DbEwnz+kVGxugztBXs2Lqmq5/e69mVsyppZwVQ0RERJRNvbFJmDdIcSGEvxDikhAiQggRL4R49Kq8QggxOAuX9dL9+pula1rc0inz63REpOjRo4dyUTs5ORnLli1T3a/9zfPatWunexk13WSDJZud79y5U1Xu1q2bRWN7e3ujePH/1oFP2efFlDx58pic3aFN+zl6+fIloqKiDNbTXtYsNDQUM2bMMHuM1OzatUu57eXlhUaNGlnUvkmTJqqyuc9T3bp1zU4uli1bVlVu3bq1We1sbGxUm74/evTIrHYpevbsaXZdDw8PtGzZUiknJyebnZBKjfb72NbWFl26dLGove4SYea+Phnh5cuXePToEYKDgxEUFKQ6tH+Wr169CpnKRc33339flQxftWoVYmNjUx1bN1EzaNAgo3W1Xyd7e3t06tQp1b61de7cOV2z/QD9z7WMTLJSDnM5APilNnDGjD2lcrsB7/4K9PkTcC+ZyYG9xjxKa2bFtJoG2Onvc6cS/wL46xNgxXvA87up1yUiIiKiLJe+v8ysRAjhCMAFgPlrTLwipQzJ+IiytVKvDm0FXh2+AHoBmCWE+A3ABCml4StsGUN3zRRLXwvd+mUN1iJ6jeXLp86ZPn/+3Krjp8RQoECBLI3DEFdXV3Tq1AmrVmm2mlq6dCnGjx8PIQRu3ryJAwcOKHXTOwsG0FzETWFvb2/WElkpDh06pNzOlSsXHBwcLP6Geb58+RASovlYvHVLN0dtWOnSpVUzIkzRvQD7/Plz1fJbKXr27IlNmzYp5c8//xx//vknBgwYgDZt2lg0yyfFrVu3VHvBlClTRrXsmTm0Z2mk9GkOSxJ0urPR0to2MjLS7HaAJpFoaf2tW7cq5RMnThjdCN4csbGxOHXqlFIuVqwYnjx5YvEeJG5ubkobc18fSyUnJ2Pfvn3YsGEDTpw4gcuXL6t+fk21jYyMNDrrME+ePOjRowcWLFgAAHj27Bk2btyIXr16GawfHx+vWuLM2dkZXbt2NVj30aNHqiXafH19zVrqLUXevHnh6+uL8+fPm93GUB/aoqOj09wX5VAvHgL/jAGu/GVefd+OwDs/As4FMzWsN4aNrWYmUdmWwJZhQOix1Ovf2q2ZFdNqClCtD2Bg7zQiIiIiyno5IgkjhCgFoD+AZgCqAjB/rQs1iRzymLOYI4BPAbQRQnSWUpqxu2aauOmULf06pW59y9ZhSoUQogCA/BY2K226StZwcndAn+/NWK6A0sXJ3fobyeruGWJpEubMmTNISkrSO3/06FH06NHD4vEBzUXH7JCEATTJlZQkzK1bt3DgwAE0atRINQsmT5486N69e7rHSkxMVG5b8m3z5ORk3L9/XynHx8erZkSkhaG9egyxZLYOAL39NBISEgzW69y5Mzp37qxKxBw+fBiHDx8GoEmg1KtXD2+//TYaNGhgVqJCdzm5Q4cOoVQp3e8UWCYznifd196S5fm022q/n8xRrlw5i+rrzthJ74yGhw8fqt4PQUFBWfb6WOLgwYP4+OOP05WIeP78eapLPw4aNEhJwgCaWXfGkjB//vmnalnH7t27w9HR0WBd3YSWJftOpShevHi6Hrvu+9vYZwC9hqTUzHrZMR6INeN3DScvoO1MwKdd5sf2JvIsAwzYBhydD+z5DkhMZcZdXCQQMAK4vAVoPwdwLZJ1cRIRERGRQdk6ISGEcAAwDcAI/Ld0Gr/OY55EAIcA7AJwHsBdAC8AOAEoDqABgL7QzIhJUQ7ALiFEHSmlZV83No/u16djLGyvW985HbHoGgZgYgb2l6VsbG3g4mn+t2Mp59KdUfDw4UNERkbCxcXFrPbFihUzeN7cWRiGNhu/cuWKxReEM0vTpk1RsmRJ5fH4+/ujfv36+OOPP5Q67733ntnPV2ry5MmjLM9lavkhbREREUhONrGRsYW097pJjbn7qVhKCIG1a9di4sSJmDVrlt7zcfPmTdy8eVN5HcqUKYPevXtjxIgRBmdXAYb3H0qvrHieMus51mXpe1g3iZDehIc1Xx9zbdiwAT179kx34sDUz2uNGjVQtWpVnDt3DgCwd+9e3L59G97e3np1dZci091TRtuzZ89UZWdny3/tSe9nXUyM+lcvYwkjes2E39EsbXVnv3n1q/cDWnwL5HHL1LDeeDa2QL2PgXKtgD+HAXePp17/5i7NrJjWUwG/XpwVQ0RERGRF2XZPmFcJmL8AfALAFv8lX7jboGlfAygipWwipZwipfxLSnlGSnlTSnlWShkgpfwcQAkAP0D9nHoB2CREpvyWrpuEMf+qpYZuEkZ/TRyi15zuJuDJycmqJYEym5eXl96FxePHTVwEyEJCCPTv318pb9iwARs3bsS9e/eUcxmxFBmgvrgppTR7eSPdJbJeF3Z2dpgyZQqCgoIwY8YMNGjQAA4OhmeP3bx5E5MmTYK3tzfWrl1rsE5mPE+p7e3xpknvf/PZ/fUJDg5Gv379VAmYAgUKYNSoUdi0aRPOnz+PJ0+e4OXLl0hOToaUUjn69etn8XjayRQpJfz9/fXqhISEqPY5qlSpUqrLyun+/KTlOU/v66S7/FhGJLApG0tKBI7M1Vy4NycB414K6PcX0GEOEzBZybMs8MF2oMV3gK2JWdpxz4Etw4FVXYHI+6nXJSIiIqJMk22TMADGA2j+6rb2X+XXAWwFsBLAMguPP/AGeJV4MbnOiJQyVkr5JTQzjbRVB2B6XaL0s/RqC6+e0RuvePHiKFmypOrc/v1mflM1g+hujr53794sHd+U/v37KxeYo6Oj8dFHHyn3lSpVCo0bN86QcXRnFWnvX5Ia3SXdypUrp7oAnNYjuyhYsCBGjx6NAwcO4Pnz5zhy5AhmzJiBd999V28/mefPn6NHjx74888/9frx9PRUlQcPHpzu52jfvn2Z+MizlqV7yOguXWjp0nS6dF+fli1bpvv1sXRfpNRMnz5dlRht3749bt++jVmzZqFTp06oXLkyPDw8kCdPHr2ElKXPLQD07t0buXP/t3H20qVL9WbQ+Pv7q86lNgsG0H+NIiIiLI4rvTOetJdOBIzPpqTXwMOLwOLmwI6vgUQTk9WFDVBvJDD0CFCqYdbER2o2tsDbI4GPDgFFapquf2MH8Esd4OwqzVJzRERERJSlsmUSRgiRB8BoaC66i1fHfAAlpZQ+UsoOUso+UsoBlh7WfFzZlZTyFwABOqeHZcJQUTplS9fP0q2v2196/AqgkoXHuxk4PpHZWrZsqSr7+/sb3Ocls7Ru3VpVDgwMxMWLF7NsfFNKlCiBpk2bKmXtC5faCZr00p0RdPfuXbPa5cqVS3Vx9c6dO6/tPgsODg6oW7cuRo8ereyFsWbNGtXydVJKjBw5Uu+CdcGC6k2dr1+/niUx5xSWPh83btxQldO7j1N2f322bNmi3HZycsKKFSvMXkpLN/FgDnd3d3Tq1Ekp3717Fzt27FDKUkosXbpUKefKlQu9e/dOtc/ChQur9mS5cOGCxXGl97NZexYhgHTv+0PZUGIcsOd7YFEj4P4Z0/ULVgY+3AO0/A7IldZtOinD5C8HDNwBNJ9s3qyYP4cCq7oBkeZ9cYSIiIiIMka2TMIAaIL/LrhLAN9IKYdLKUNTaUPpM02nXEcI4ZbBY2TbJIyU8rGU8pIlB4BbGTU+kSU+/fRTVSIhJCREtSF6ZuvcubPebJwZM2Zk2fjmMLTkmBAiTcsMGVO1alVV+erVq2a3rVevnnI7ISHhtZqhkZpcuXKhW7duOHbsGIoU+W+j4NDQUL1l9SpWrKjaxyQwMDDD9wzJyY4ePWpR/WPHjqnKtWrVStf4Li4uqFixolIOCgrSS/RYy8uXL1WJlIYNG5q9jFZsbCzOnDHjQrQBgwYNUpW193/ZvXu3aqZPx44d9WYT6cqTJ4/qc+bRo0e4cuWK2fFcunQJjx+bnBidKu3xbGxsULly5XT1R9lMyFFgQX3gwE9AcmLqdW0dgGbfAIP3AoWrZU18ZB4bW6D+p8BHB4EiNUzXv/Ev8Gtt4NwazoohIiIiyiLZNQmT8vViAeAp9BMElPGOA9Be58IWgG8Gj/Fcp5zfwva6X9t9lvZQiHIuHx8fdOzYUXVuxIgRCAsLy5Lx7ezsMHr0aNW5ZcuWYdu2benu+/Hjxxlyob1z585wc3NTnWvWrBlKlCiR7r5T6F7ETtmU2xytWrVSlX/77bcMiSmncHNzQ+fOnVXn7ty5oyrb2tqiWbNmSjkuLg7Lly/PkvhygtWrV5td9+nTp6pZGTY2Nqhbt266Y8iu72PdDe21k3mmrFq1Ks37qDRp0kQ1Qy4gIABPnjwBoE7IAKaXIkvRvHlzVdmS53jRokVm1zUkLi5ONcPJ19dXb0lByqHiXgD/fA4saQ08MWMWW/G6wNDDQIPRgK195seXRaSUeBEei8T4rJtNnKnylwc+2AE0mwjY5kq9buxzYPMQYHWP/7N333FRXFsAx3+zLB0EEUEFexew99iNvbfYUjRqekwvlhRNTG+aGFOMiXkxGo29JvbeG4JdbKCggvTOzvsDNaCzBWFhwfP9fPg8d+6duQfEvN05c8+BhMjCiU8IIYQQ4gFmq0mY248rqsBuVVVLyDtj26WqqgG4dNfhvCZJzLn7Edm83g29e75tPHIrRBH45JNPcHd3v/M6KiqKwYMH39P3wVrGjRtH48aNcx0bNWpUnhIRd9u3bx9NmjQhOjo6v+Hh5OTEjh072L59+52vOXPm5Pu6ObVs2TJX4+x9+/ZZfO5jjz2WK0m0aNEiNm7cWJDh2bycZZbg3ibkkJ1czGnKlCkW994p6Q4ePJir5JYpU6dOzZVY6NmzJ2XL5v//4p999tlcf4/ffvstoaGh+b5uft3dS8XSXWpxcXF88MEH972uoii5duGlp6fzv//9j5s3b7J06dI7xytXrnxPcsWYp556Cp3uv7frM2fOtKjE2NGjR5k1a1Yeor/XoUOHyMz8b3dEQfXTEkXszPrs3iD7fsJsu0UHN+j1JYxak90MvoRIik1j74owfnt7J79P3MXPr2xj599nMGQZzJ9s6+z00PZVeHqbZTuWTq+FmS3g6F+yK0YIIYQQwopsNQmTc0eG1B4pPHd34cxruTBz7q6hUSOP51e767XlNTmEKGFq1aqVq78AwLZt22jTps199QDIa8NnR0dH/v7771yJhGvXrtGuXTvNJuumxMbGMmnSJNq1a2dxXxVLBAQE0KZNmztfBd1Q2sXFhfbt2995ffjw4XuewDfG09OTN954I9exwYMHs2PHjjzFkJWVxZIlS/LdfDs/kpOT+fbbb/O0gykxMfGeEnp169a9Z16HDh3o0qXLndfXrl2jZ8+eef49SUhI4M8//8zTOcXBM888w7lzpitjLlu2jJkzZ+Y69tJLLxXI+jVq1GD06P/a7aWmptKzZ0+OHz+ep+ukpaXd89+z/HB2dqZmzf9uGB8+fDjXTiAtycnJDBs2LFfJsPsxatQo7Ozs7ryeM2cO8+bNIy0t7c6x0aNH50qsmFKtWjVGjBhx53V6ejrdunUjODjY6DlHjx6le/fu+e41tXnz5lyve/Xqla/riSKWFA2Lx8G8wRBvwX9Da3aD5/dCs7Fg4e+rLVNVlatnY/lndgi/T9zFgTUXSI7LTk4bslSObLjMjoUl6Pkun7owZkN2CTmdmd1LqbGw9ClYMBISogolPCGEEEKIB42tvqPO+RhlQe/GEMbdXZz8RgFf/+47w3mthfKQmesJ8UAZOHAgH374Ya5jISEhNGjQgOHDh7N+/XpSUu7Orf4nIyODf//9l8cee4whQ4bkef2qVavy119/4ez8X742Pj6eAQMG0L59e5YuXUp8fLzmuQaDgT179jBx4kSqVavGRx99lOsmZXGRsxG3wWBg06ZNFp/75ptv0rVr1zuvY2Nj6dChA88//zynTp0yel5GRga7du3irbfeonr16gwaNMjoz7kwpKenM378ePz9/XnyySdZuXKlyYTMvn376Ny5MxcvXrxzrGXLltSqVUtz/ty5c/H397/z+siRI9SvX5/PPvvsTqknLQkJCaxYsYIxY8bg5+fHxIkT7+O7s12lS5cmMjKSNm3asHDhQgyG3E9wJyUl8eGHHzJ06FCysv7bUDxixAiLd2FY4quvvqJ+/fp3Xl+6dImmTZsyadIkLl823sovJSWFDRs2MH78eCpWrJgrmVMQhg4dmuv1kCFDmDdv3j0/J1VV2bBhA61atWLdunUA+dol5OfnR/fu3e+8DgkJYerUqXde63S6PH+v06dPp3z58ndeX7lyhSZNmjB27FhWrVpFaGgooaGhrFq1irFjx9K0aVMiIyPx9vbOVdIvrzZs2HDnzx4eHrITprhSVTj2N8xsBscWmp/vUgYG/QIj/gIPf/PzbVxmehYndl1h4Uf7WfLFIc4euIbBoL3j49jWCI7vvKI5VizZ6bNLyD29Dco3ND//1OrsXjHBi2RXjBBCCCFEAdObn1IkdpPd78MTaKEoip2UJLMuRVG8uXenSUF/ClkPZJHdbwagiaIo7qqqmn18WlEUdyBn7aPMW9cT4oE2adIkfH19GT9+/J2Ei8FgYMGCBSxYsABHR0cCAgIoW7YsXl5e2fXPExK4dOkSp0+f1kx8ODk5WXyTsGvXrqxfv55HHnkkVyPsbdu2sW3bNuzt7QkICMDX15cyZcqQkJBAZGQkYWFhmmXH9Ho9Dg5m6pjbkKFDh/Lyyy/f+TkuXrz4nl4nxuj1ehYuXEjPnj3ZtWsXkL2z5fvvv+f777/Hz8+PwMBAvLy8MBgMxMfHEx4ezsmTJ/P9hLs1xMfH8+uvv/Lrr7+iKAo1atSgWrVqeHp6otfriY6OJiQk5J5dLC4uLiZ7V5QvX55Vq1bRs2fPO79jN2/e5K233uLtt9+mbt26VKtWDQ8PD9LS0oiNjeXcuXNcuHABNcdNJC8vL+t840Vk1qxZDBs2jMjISIYOHYqvry9NmjTBw8ODqKgo9uzZQ3Jycq5zAgIC+Pbbbws0Djc3N1atWkXXrl3vlP1KSUnho48+4qOPPqJatWrUqVMHT09PMjMziYuL48KFC5w9ezZXcqigvfbaa8yZM+fO70x8fDyPPvoor7/+Ok2bNsXDw4OYmBiOHDmSq8TdyJEj0ev1zJ07977XHjNmDKtXr77zOme/ri5dulCpUqU8Xc/Ly4t169bx8MMP37lWZmYmv/zyyz29Zm6zt7dn3rx59+wAUxTFojWvX7/Otm3b7rweOnQoTk5OeYpb2IC4cFj1anYjdkvUHwrdPgbXMtaNqxAkxKQSsjWc4zuukppk+f9nbv3zFF7lXSlXzfJeUjbPtx6M3QA7v4Etn4LBxM8j5SYsGQvHl0Hvr8Ht7pacQgghhBDifthkEkZV1QxFUb4HJgLuwChA+1OmKCjDyL0zKooCLvelquoNRVF2ALfr9zgAI4AfLTh9JJBzL/02VVWLrv6OEDZk7NixtGnThldffZW1a9fmGktLS+PQoUMWXcfZ2ZmhQ4cyZcqUPN0kfOihhwgODmbChAnMmTMn143VjIwMjhw5YvYaiqIwePBgpk2bRoUKFSxeu6iVLl2a/v3789dffwGwatUqUlNTLb5Z6eHhwZYtW3j77beZPn16rp9dREQEERERZq/h4uKi2U+lKKmqypkzZzhzxnRpFz8/P5YsWUJQUJDJeQ0aNODw4cM89thjucpKqarK8ePHLSp/dXefkOJu6NChXLt2jZdffhmDwUBUVBRr1qwxOr9x48asW7fOKsmoihUrsn//fp555hnmzZuXaywsLIywsDCz18hZ2rAgeHp6smrVKrp37861a9fuHI+MjGTVqlWa54wcOZJff/2VcePG5WvtPn364OvrS1TUvWV9xo4de1/XrF+/Pjt37mTs2LG5kiNaKleuzF9//UWLFi3uSdLk7CVmyuLFi3P99yhnrxtRDBgMcOAX2DAF0i0oFVnKH/p8AzW7mJ1qy1RVJeJ0LMc2h3P+6PX72sxhyFJZ+8Mxhkxohltp2/r/1nyxs4d2b0DtnrDsWbhqpoffyVVwcRf0/BwCB4GFCVwhhBBCCKHNVsuRAUwDTgEK8JmiKLWLOJ4SS1EUX2DyXYdXqqpV9qH/ftfrVxRFMfkJ59b4y3cdvv9HVIUogerUqcOaNWs4fPgwL7zwgsVJFC8vL7p3787s2bOJjIzk119/zfNT2gBlypThp59+4uzZs0yYMMFoaamcdDodQUFBTJ06lXPnzrFw4cJcfRyKi1deeeXOn+Pj41m40IJyLznY29vz5Zdfcvr0aZ599ll8fX3NnlOmTBkGDhzIb7/9RlRUVK5SRYXNw8ODrVu38uabb9KkSZNcjdqNqV27Nh999BGnT5+mefPmFq3j4+PDP//8w7Zt2+jfvz+urq5mz6latSpjx45l7dq1HDhwwKJ1ipMXX3yRbdu20bVr11x9SHKqUKECn3/+OXv37s1XmS1z3Nzc+OOPPzh69CiPPvqoRUmvChUqMHLkSBYtWkRkZGSBx9SoUSOOHDnC448/jr29dk8ERVFo06YNixYt4o8//jA6Ly/0ej2PP/74Pce9vb3p27fvfV+3Zs2abN26ldWrV/PEE09Qq1Yt3N3dcXV1pWbNmvTu3Zt58+Zx4sQJWrRoAXBPnyoPD8ue7p89e/adP7ds2fLO9UQxcOMM/NYT1rxuQQJGgeZPwfN7inUCJiMti5BtESz4YB/Lvz5M2JH7S8DclhyfztofgsnMKIGFGHwDYOxG6DjJfK+YlBhYPAYWPgaJ103PFUIIIYQQJinWuc9eMBRFqQxsASoDMcCLqqrOL9KgrERRlA5Azg6oF1VVrZLHa9QGaqmqujIP55QDVgJNcxxOB+qqqmry0VVFUd4H3stxaKuqqh3MnGNHds+fnEm1r1RVfc3EOV8Br+Q4dByoX9Ql6hRFCSBHX5qQkBACAgLu61qZmZn3PDFes2ZNi25mCmFMeHg4x44d49KlS8TGxpKeno67uzuenp6UKVOGgIAAqlW7uwphwYmKiuLIkSNcvHiRmzdvkpGRcWf9qlWr0qhRI4ufyrZ17dq1Y/v27QC0atXqTnmx+3XixAmCg4OJjo4mNjYWvV5PqVKlqFixInXq1KFatWoWlxUqbCkpKYSGhnLu3DkiIyNJSkpCURRKlSpFpUqVqF+/PpUrV873OpmZmRw4cICzZ88SHR1NQkICLi4ueHh4UK1aNerWrUu5cuUK4DsqHq5evcrevXuJiIggISEBHx8f6tSpQ8uWLS1uAl+QDAYDwcHBHD9+nJiYGGJjY3FycqJUqVJUqVKFunXrUrFixUKLJzY2lh07dhAWFkZiYiJeXl6UK1eOpk2b5uo5VNJUrFjxTglAb2/vXKXRjDl48CBNm/73tjAvZRZLOpt+v5aVATunw9bPIMuCHmvetaDvd1Cp+CbY4q6ncGxrOCd3XSUtOTPP57t4OJAcl250vE7LcnR6oq7N/v9tvkWGwLJnIPKY+bkuZaDnFxAo/y0QQgghRPEVGhpKYGBgzkOBqqqGGptfkGw6CQN3epX8BvQEVOAi2b1ATpDdN8Zg7FwtqqrevROjUCmK4o92GbiWQM4EUwTQxshlElVVvacbcY5EzjHgD2CpqqqatWBu9Vh5guwdMHc/dv2uqqofGP8u7lzjffKYhLl1Xm9gBdm7nG77CZioqmp0jnnewEdAzrogKtBTVdV15taxNknCCCFu27x5M506dbrzes+ePfLkuBCiSJ0/fz5Xor1bt26sW2f+7dOIESOYPz/7LentUoAl9iZ0Htns+7Urh2H5ixBlwc10nR7avAJtXwf74tfnRzWoXD4RQ/CWcC6GRGd/MsijSgFeBHXwp3JAGTb/cZITu64andtmSE0adC68hHGhy8qA7V/Bts/AYEEiq14/6PUVuHpbPzYhhBBCiAJWlEmY4nCHNxpYCLQCSgNVgPsrqJ2tSJMwwA6yd/aY4wecNzI2l+w+OcYEAZ8CnyqKEkd2ouAGkAC4ARWBBmj//f9kSQImP1RVXaUoymSyS87d9hTwmKIoe4FIoDzQHHC+6/S3bSEBI4QQOXXs2JGuXbve6Vcybdo0VqxYUcRRCSEeZLNmzcr1ulWrVmbPOXv2bK6Sih9//LEkYGxZejJs+Rh2fweqBc+lVWiUvfulXKD5uTYmPSWTk3uucmxLBLFRyXk+397JjrqtyhPUwR9PX5c7x9sPr03M1SSizsdrnrdz8Vm8/FypWKfg+2nZBDt76PAW1O4By54zn8g7vhwu7IBeX0LAgMKJUQghhBCiBLDpJIyiKBWBxUCTW4dyPuuUl0+E6q35tr3txzo8gIcsmJcEvKKq6s9WjgcAVVU/UhRFBaYAtwsSOwMdjJySAbyjqupnhRCeEELk2RdffEHjxo3JzMxk5cqVHDhwIFdJHyGEKCxHjhxh+vTpd14risKjjz5q9rwpU6aQlZVd7bVLly706NHDajGKfDq/DVaMh5vGntnKQe8MnSZBi2fBzqY//t3jZmQSx7ZEcHL3VTLS8l6JuHQ5F4I6+FO7ZTkcnO793u3sdfR4JohFH+0nSaM0mWpQ+efnEB6Z0IxS3nc/G1aClK8P4zbB9i9h+xemd8UkR8OiUdkJmZ5fgmuZQgtTCCGEEKK4Kvwi4RZSFKUssI3sBEzOhItC3hIwt895EJwgu3zXTiDFwnNOAxOBKoWVgLlNVdWPgRbAcrL70GhJvzXeXFXVTwsrNiGEyKugoCDGjx9/5/Xrr79ehNEIIUqSmTNn8tlnn5GQYK7ROmzcuJEuXbqQnv7fW6s+ffpQvXp1k+cdPHiQefPmAeDg4MB3332Xv6CFdaTEwooXYW4fyxIwVdvBc7ug9YvFJgFjMKhcCL7BihlH+PP9vRzbEp63BIwCVep70/elhgx/rwVBHfw1EzC3uXo40v2ZIHR67Y+MaUmZrJkVTHpq3vvOFCt6B+g4ITsZ42NBieXQpTCzeXYyRgghhBBCmGTL78S/Irtsl8p/O1nCyO4HcwaIA4rdO2FVVatY8dpRwCQARVF0QE2gOtmlzTwBJ7KTMzeBq8B+VVXNd2g1vt77wPv5jPkw0F9RlNJA61uxliG7DF0EsEtV1Zv5WUMIIQrLlClTKFWqFLf7rV27dg0fH58ijkoIUdxdv36dKVOmMGXKFPr27Uu7du0ICgrC2zu7L0N0dDSHDh1i6dKlbN68Ode5Xl5efP/992bXiIiI4N133wUgICCAWrVqFfw3IvLnxCpY/RokRpqf6+gB3aZBo0ehmJSUS03K4OTuqxzbEk78jdQ8n+/ooqfuQxUIau+X510r5ap60GFEHTb9fkJzPDoiiU2/n6DbuMCSX6KvfAN4agts+zx7Z4xqIgGWfAMWPg6Bg6DH57IrRgghhBDCCOX2jSJboihKOeAy2Tt1FCARGKOq6qIiDUyIHBRFCSC73w4AISEhBARY8NSYBptt9CqEEEKIIvf+++8zZcqUPJ/n5eXFsmXLaNu2rRWievAU2fu1hChY+4blOw7q9oGeX4B7OevGVUCiIxIJ3hLO6b2RZKZb0NvmLmX8XAnq4E+tFuWwd7DLVyzbF54meFO40fEWfavRtGeVfK1RrFw5nN0r5tpx83Ndy0Lvr7N//4QQQgghbFBoaCiBgbn6IwaqqhpaGGvb6h3e9sDtd9Aq8JiqqrLPWQghhBBCPHC8vLxQFIW8PDzVs2dPpk+fTo0aNawYmbAqVYUjf8I/EyE11vx8N9/s5Eu9vlYPLb8MWQbOB9/g2OZwIk7H5vl8RadQraE39Tv6U76GZ4HtTnloUA2iI5KIOKW9EX/vyjDK+LtRtb53gaxn8yo0yt4Vs/Uz2PG16V0xSdfhr0chaAj0+AxcvAotTCGEEEIIW2erSZhKt/5XBcIkASOEEEIIIR5U48ePZ9CgQaxbt45du3Zx/PhxLl26RGxsLKmpqbi5ueHl5UWNGjVo164d/fr1o379+kUdtsiPmxdg5csQttnczGyNHoWuH4JzaWtGlW8piekc33GFkK0RJN5My/P5Tm72BLSpQEA7P9y9nAo8Pp2djm7jAlj08QESojVKoqmwfk4og99qild51wJf3ybpHaHzO1CnV/aumOvaJdvuOLYIwrZCn2+yzxFCCCGEEDabhMn5iM3RIotCCCGEEEIIG+Dn58eYMWMYM2ZMUYcirMmQBXt/gE0fQkay+fmlq0Cf6VCtg7Ujy5frlxII3nyZM/uvkZWZ95JjZSu5U7+jPzWa+qC3z1/JMXOc3Rzo+Wx9Fn92QLM8WkZqFmtmBTPk7aY4uthbNRab4tcYnt4KWz6Bnd+AauLvMekaLBgBQY9Aj09lV4wQQgghHni2moSJyPHn9CKLQgghhBBCCCEKQ9RxWPECRBw0P1fRQcvnoOMkcHCxfmz3ISvLQNih6wRvDicyLC7P5+t0CtWb+FC/oz++VUsVWMkxS3j7u9H5iXr883OI5njctRT+/eU4vZ6vj05XeHEVOb0jPPwe1O19a1fMSdPzjy2E81uzE4W1exROjEIIIYQQNshWkzA59zhXLLIohBBCCCGEEMKaMtNg+5ew/SswZJif7xMA/b4FvybWj+0+JMWlZZcc2xZBclzen6dzKeVAQNvskmOuHo5WiNAyNZr4cCO8MgfXXtQcvxQazd7lYbQaUL2QI7MBfk3gqa2w5WPYNcP0rpjEKJg/DOoPgx6f2HzJPCGEEEIIa7DJJIyqqsGKooQCAUAzRVE8VFXN++NTQgghhBBCCGGrLu+D5S/AjVPm59o5QLs34aGXQO9g/djyKPJ8HMc2h3P24DUMWWqezy9XrRRBHf2p3sgHO73OChHmXYs+1YgOT+TCsWjN8UP/XMTb342azXwLOTIbYO8EXaZA3T6w7Fm4cdr0/OAFELbl1q6Y7oUSohBCCCGErbDJJMwt3wI/APbA28CEog1HCCGEEEIIIQpAWiJsnAr7fgIsSFhUbAF9v4Wyta0eWl5kZRg4czCKY5vDuXYxIc/n2+l11GzqQ1BHf3wql7JChPmj6BQefjKAvz85QGyUdo+eTb+fwNPXhbKV3As5Ohvh3xSe3g5bPoJd35rZFRMJ84dCgxHQ/WNw9iy0MIUQQgghipLNJmFUVf1JUZQ+QC/gDUVRjqmq+mdRxyWEEEIIIYQQ9+3sBlj5CsRdMj/XwQ06vwfNxoLONnaHACTeTCVkWwTHd1whJcGCEmp3cSvtSEA7PwLaVMDZ3fZ29eTk6Kyn57NB/P3pQdJTMu8Zz8wwsOaHYB6Z0MzmvxersXeCLlOhzq1dMdFnTM8/+ieEbYY+M6BW18KJUQghhBCiCNlsEuaWIcAfwEDgf4qidAI+VlX1XNGGJYQQQgghhBB5kBwD6yZkl2WyRI0u0Ptr8LSNFpmqqnL1bBzBm8MJO3Id1ZD3kmMVanpSv6M/VRt4o7OznaSSOaXLudLlyXqs/j5Yc+NSYkwa634Koe/LDbErRt9XgavYDJ7ZDpunwa7vMLnLK+Eq/DkEGj4K3abJrhghhBBClGg2m4RRFGXOrT/GAzeB0sBoYLSiKOeAE7eOm9jvfA9VVdUxBRqoEAVAUZR7jqlq3j/YCiGEEEII69B6b6b1Hk7jRAhdAmvehOQb5uc7e0H3T6D+I2DJ9a0sIz2LM/uiCN4STnR4Yp7P19vrqNWiHEEd/PH2d7NChIWjSpA3LftVY8+yMM3xK2di2bnwDO2G21bJuEJn7wxdP8zeFbP8OYg+a3r+kT/g3Kbscns1Hy6cGIUQQgghCpnNJmGAUeR+dEYFbn8KqQFUz+P1lFvXkCSMsDk6jfISmZmZ2NvbF0E0QgghhBDibllZWfcc03oPl0tcBKx+DU6vtWyRwMHQ41Nw9b6PCAtW/I0UQrZGcHzXFdKS7i3DZY57GSeC2vtT96HyOLmWjPe0jbtV5kZ4ImcPXNMcP7Y1Au9K7tR7qEIhR2aDKrWAZ3bApg9h90xM74q5AvMGQaNHodtH4ORRaGEKIYQQQhQGW07CaJGtAaJEUhQFvV5PZuZ/H3CTkpJwdnYuwqiEEEIIIcRtycm5G7Pb2dkZ3wljMMDBX2H9e5BuQcP6Un7ZpcdqdSuASO+fqqqEn7rJsc3hXAi+wf1szPavU5r6Hf2pHOSNTlf0O3kKkqIodHqsLrFRydy4rL0raOufp/Aq70q5apJIwN45u9RY3T6w7DmIMVNV/PAfcG5z9q6YGp0LJ0YhhBBCiEJg60mYkvWuXQgT3NzciI2NvfM6ISEBb++ifwpSCCGEEEJAYmLum+5ubkZKa904CyvHw8Wdll246Rh4+H1wKpW/APMhPTWT03sjCd4Swc2rSXk+397RjjotyxHYwR+v8q5WiLBgqarKgYs32X0uGm83R/o2rICbo2Ufje0d7ejxTBCLPj5AamLGPeOGLJW1PxxjyIRmuJV2LOjQi6dKLf/bFbPne0w+WxkfAX8MhMaPQ9dpRfrvQgghhBCioCi22ndCUZTK1riuqqoXrXFd8eBRFCUACLn9OiQkhICAgPu+XlJSEpcuXcp1rGLFisY/4AshhBBCiEKRnp5OWFhYrr4wfn5+lCqV4wZxVgbs+ha2fAJZaeYvWqZG9hP/lVtbIWLLxF5LJmRLBCd2XyU9Je8lxzx8nAnq4E+dVuVxdLb15/uyXbiRxDvLQ9h+5r/+PN5uDnwxpAEdavtYfJ2IUzdZPv0IqkH787RPZXcGvN4Yvb1dvmMuUS7uguXPQ4x2b51cSvlDv2+heifrxyWEEEKIEi80NJTAwMCchwJVVQ0tjLVtNgkjhK0r6CSMqqqcOXMmV71xRVHw9/eXRIwQQgghRBHJzMzk4sWLpKen5zpeq1Yt7Oxu3WC/cgRWvACRx8xfULGDNi9DuzfB3qnA4zVHNahcOh5D8OZwLoVG5/0CClQOKENQR38q1fVCKSYlx9Iys/hhSxgzt5wlPdOgOWdsm6q80b02jnrLEifBm8PZ/tdpo+N1Wpaj0xN1jZete1ClJ8PGqbD3ByyqON5kFHT9EBzdrR2ZEEIIIUqwokzCFI/HlYR4ACiKgoeHBzExMXeOqarK5cuXcXJywt3dHVdXV+zs7Mw3gRVCCCGEEPfNYDCQkZFBfHw88fHxGAy5b9q7u7tnJ2AyUmDLx7DrO1CzjFwth/INoO93UL6+lSI3Li0lk5O7rnJsazhx11LyfL6Ds566rcsT2N4PTx8XK0RoPTvO3OCd5SGcv2G61NrsHefZHRbNjOGNqF7W/ENQQR38uBGewImdVzXHT+6JxLuiOw06V7yvuEssBxfo8Ul2r5jlz8HNC6bnH/wNzm7M3jlWvWNhRCiEEEIIUaBkJ4wQ96mgd8JAdtLlypUrxMfH5zc8IYQQQghhBY6OjlSuXBm7y7thxXjzzcYB9E7QcSK0fB7sCvc5uJirSRzbEs7JPZFkplmQKLpL6fKu1O/oT63mvjg4Fa9n+K7Fp/LB6hOsPHolT+c529vxft96PNK0otldLFkZBpZ9fYjIMO3374pOoc/4BlSs45WnGB4Y6UmwYQrs+9Gy+U2fhC5TZVeMEEIIIfJMypEJUQxZIwkDkogRQgghhLBV9vb2VC7nhf3mKdlP51uiSlvoMx3KVLdqbDkZDCoXgm9wbEs44Sdv5vl8RYEq9b2p39Efv9qli105rSyDyh97LvLFP6dISMt7r5vbetUvz0cDgvBwtjc5LykujUUf7ScpLl1z3NFVz5C3m+FR1vm+Yynxzm/P7hUTa0ELV49K0O87qNbe+nEJIYQQosSQJIwQxZC1kjCQnYi5du0acXFxuXrECCGEEEKIouHq6opv4nEc174MCdrlp3Jx9ICuU6HxE9lZjUKQmpTB8Z1XCNkaQUJ0ap7Pd3TVU++hCgS296NUmeKZMAgOj2XS0hCORcQVyPX8PJ2ZPqwhTauY3skSeT6OpV8ewpCp/fm6jJ8rA99oUux2ExWqtETY8D7s/9my+c3GwsNTwFH6ZwohhBDCPEnCCFEMWTMJc5uqqiQnJxMfH09iYiKZmff/JJ8QQgghhLCcoijY29vj4eGBh7s79v++BQd/tezkOr2h5xdQqrx1g7zlRngixzZf5vS+KDIztJvOm1LG3y275FgzX/QOljWltzVxKRl88c8p/th7EUs+4pZ2sefxVlWYt/cSNxLTTM7VKTC+c01e6FgDvZ3x3ownd19l49wTRserNy5Lt3GBxW5nUaE7v+3WrphL5ud6VoZ+M6FqW+vHJYQQQohiTZIwFlAUxRVoBTQEfIFSgOl94fdSVVUdU8ChiQdUYSRh7qaqKgaDgeLy71YIIYQQojjS6XQoivLfzfIjf8KyZ82f6Fo2O/lSr5/Vd78YsgyEHckuOXblTGyez1d0CtUblSWooz/lq3sU28SAqqosP3KFD1efMJtMuW1o04q81aMOXq4OXE9I442/j7Ll1HWz5zWrUppvhjXCz9P4LqHtC08TvCnc6HiLvtVo2rOKRXE+0NISYf27cOAXy+Y3fwoefh8cXK0alhBCCCGKL0nCmKAoih/wATAEcMnPpchOwhTPR7uEzSmKJIwQQgghhChkqgqzWsO146bnNRwJXT8EF+s2YE9JSCd0+xVCt0eQeNOypENOzu72BLT1I6CtH26lHa0QYeE5dz2Rd5aFsOtctEXza/u6M21A4D2lxQwGlV93XeDTtSdJzzK9k6iUk55PBtWnZ5D2LidDloEVM44Sccp4L56ez9Wnan1vi2J+4IVtheUvQJwFu2JKV8neFVOljdXDEkIIIUTxI0kYIxRF6QnMB9zITqIA3G/AkoQRBUqSMEIIIYQQD4ALO+G3nsbHPStBn+lQvZNVw7h2MZ7gzeGcORBltO+IKT6V3anf0Z8aTXyxszdeUqs4SM3I4vvNZ/lha5jZpAmAs70dr3SpyeiHqmJvopxYSEQc4xccJux6ktlrDmtWkXf71MPF4d4eLymJ6Sz6+IDRvjz2TnYMfqspXuVl14ZF0hLg33csKweo2MGg2RA40PpxCSGEEKJYkSSMBkVRmgHbAYdbh1T+S8TcL0nCiAIjSRghhBBCiAfAolEQulR7rOVz0Gmy1UogZWUaOHfoGsGbw4k6H5/n83V2CjWa+lC/Q0V8q5ayQoSFb8upa7y7PJRLMckWze9az5f3+gaYLCGWU3J6JlNXHmfB/stm51Yv68qM4Y0IqOBxz9iN8EQWf3aAzHTtJJGHjzND3m6Ko0teK2w/wM5thhUvQpyZvxt7F3h2J3hVK5y4hBBCCFEsSBJGg6Ioe4Fm/Jd8SQEWAP8CZ4A4IM9dylVVvViAYYoHmCRhhBBCCCFKuIRI+DoADBofO2p2g5ELrbJsUlwaodsiCN1+heT49Dyf7+rhQEC77JJjLqUczJ9QDETGpTJ1VShrjkVaNN/P05mp/QLoXNf3vtZbc+wqby8OJj7V9EdOBzsdb/Wow5MPVbmnr87Zg9f45+cQI2dCpYAy9Hq+Pjpd8ezHUyRS42H9O3DwN9PzKrWGUatBV7x3fQkhhBCi4BRlEubevdM2QFGU2vyXgAE4DvRUVdWCQrBCCCGEEEIIUQAO/qadgAFoPq5Al1JVlajz2SXHzh28hsGQ94flylf3IKijP9UalcXORNmt4iQzy8Bvuy7w9frTJKVnmZ2v1yk81a4aL3aqibPD/RdB6BlUngYVPXl5wWH2XzDe3yU9y8AHq46z/cx1vhjSAG+3//rs1Gjiw43wyhxcq/0c4KXQaPYuP0erATXuO84HjlOp7PJ/dfvCivEQH64979Iu2PcjtHy2cOMTQgghhNBgkzthFEUZBcy59dIA1FdV1UwnTCEKl+yEEUIIIYQowbIy4OtASNTYeVG6Krx4qECess/MyOLM/msc2xLO9UsJeT7fzl5HrWa+BHXwp2wl93zHY0sOXbrJpKUhnLhqWSm2FlW9+LB/IDV9C+7nkJll4LvNZ5mx8Qzm8mLebo58+UgD2tcqe+eYalBZMyuYC8eijZ7XdUwANZvd346dB1pqHKybAEfmaY/rneGZHeAtSS4hhBBCyE4YLbffgarAQUnACCGEEEIIIQrVyVXaCRiAZmPynYBJiEklZFsEx3dcITUxI8/nu3k5EtTen3oPVcDJrWT1FYlNTufTdadYsP8SljwzWMbVgYk96zKwsd89JcHyS2+n4+WHa/FQDW9eXnCEiNgUo3NvJKbxxJx9jG1TlTe618ZRb4eiU3j4yQD+/uQAsVHafWw2/X4CT1+XEpdEszonD+j7HcReggvb7x3PTIFlz8KT60AnrWGFEEIIUXRsdY96zn3m54osCiGEEEIIIcSDad9s7eN6J2g48r4uqaoqEadvsu7HY/xv8m4OrbuY5wSMX21PejwdxGMftKJxt8olKgGjqiqLD4bT+cutzN9nPgGjKDCiRSU2vtaeQU38CzwBk1OzKl6sGd+WXkHlzc6dveM8g2bt4tz1RAAcnfX0fDYIB2ftZyAzMwys+SGYlIS89/954Ol00O87sHfVHg/fB7tnFm5MQgghhBB3sdWdMJdz/NnR6CwhhBBCCCGEKGhRx+HiDu2xoMHg4pWny2WkZXF6XyTHtoQTHZGU53D0DjpqtyhHUAd/yvi55fn84uBMVAKTl4Ww93yMRfPrli/FtAGBNK5U2sqR/cfDxZ7vRjSi3QFv3l9xnJQM4z1qQiLi6T1jB1P6BjCkqT+ly7nS5cl6rP4++L/OpzkkxqSx7qcQ+r7csMT08yk0patA1w9g9ava45s+hJpdwadOoYYlhBBCCHGbrSZhduf4c80ii0IIIYQQQgjx4NlvZBcMQLNxFl8m/kYKx7aEc2LXVdKSM/McRilvJ4I6+FOnVXmcXEvOjpecUtKzmLHpDD9vCyPTXNMVwNXBjle71uaJVpXR5yNZoaoq0anRuOhdcLF3sfg8RVEY2qwSTat48eKfhzluol9NSkYWby4OZuuZ63w0IIgqQd607FeNPcvCNOdfORPLzoVnaDe8dp6/nwde0yfhxAoI23LvWFZadlmyMevBzlZvgQghhBCiJLPJdyCqql5SFGUH0AYIUBSlhqqqZ4s6LiGEEEIIIUQJlxoPwX9pj/k3gwoNTZ6uqirhJ24SvCWcC8duaO56MKdSPS+COvpTOaAMis56JbaK2sYTUby7PNRkn5WcegaV493eAZTzcMrXulsvb+Wrg18RFheGo50jg2sNZnyj8XlKxlQv68bS51vz2bpT/LLjvMm5q4OvcuRSLNOHNaRJt8rcCE/k7IFrmnOPbY3Au5I79R6qkKfv6YGnKNn9Yb5vBekJ945fOQS7pkPb1wo/NiGEEEI88GwyCXPLRGALoABfAP2LMhghhBBCCCHEA+DoAkhP1B4zsQsmPTWTU3uyS47djNRuwG6KvZMddVqVJ6i9H6XLGelvUUJExKYwZUUo/x6Psmh+JS8XpvYLoENtn3ytm5ieyGf7P2Pp2aV3jqVlpTHvxDx2Ruzkk7afEOAdYPH1HPV2vNO7Hm1revP6oqPcSDTe0yUiNoVHftzNS51r8fTIOsRGJXPjsvbv2dY/T+FV3pVy1Tws/+YEeFaE7h/Bihe1xzd/DLW6g6/lf8dCCCGEEAVBUc11OyxCiqJMAKaR/fzYD8BLqqrmfR+/EFagKEoAEHL7dUhICAEB8oZeCCGEEKLYUlWY2QJunLp3zMUbXj0O+twtK2OjkgneEs7J3VfJSDXeI8QYT1+XWyXHyuHgZMvPyOVfRpaBOTvO882GMyb7qdzmYKfjmfbVeK5jDZzs7fK19v7I/UzeMZkrSVeMztErel5o9AKjAkZhp8vbetcT0nh90VG2nr5udm7zKl5M61aXbTOPkZqYoTnHpZQDQyY0w620tEjNE1WFeYPh7Abt8XL1YdwmsCuZ5f2EEEIIYVxoaCiBgYE5DwWqqhpaGGvbdBIGQFGU14GPAR1wHPgS+EdV1atFGph44EkSRgghhBCihAnbCr/31R5r8yo8/B4AqkHlYmg0xzaHc+m4ZY3kc1GgSpA39Tv441+3NIpSckuO3bb/QgyTl4ZwKkqjVJSGh2qUYWq/QKqXdcvXuqmZqUw/NJ0/Tvxh8TnNyjXjozYfUc61XJ7WMhhU5uw8z6frTpKRZfpzdiknPe+3rE7kisuoRnrh+FR2Z8DrjdHnMwH1wImLyC5LlhanPd5hInR4q3BjEkIIIUSRkySMGYqitAJWAZ45DicAsYAhD5dSVVWtXnCRiQeZJGGEEEIIIUqYvx6FEyvvPa7o4KVg0hzKcWLXVY5tjSD+umV9THJydNFTt3V5Atv741HWuQACtn0xSel8svYECw+EWzTf282Rd3rXpW+DCvlOToXcCGHijomcjzPds0VLKYdSvNvqXbpV6Zb3dSPiGL/gMGHXk8zOHVvOm9Injc+r07IcnZ6o+0Ak6grUkfmw7BntMZ0exm2G8vULNyYhhBBCFClJwpigKEpfsne/VLt9KB+XU1VVlceIRIGQJIwQQgghRAkSFwHfBIGqUSardi+OV/qCHQvPkJGW95JjXhVcqd/Rn1rNy2Hv+GB8HDEYVBYdvMwna09yM1m75FZOigKPtazMa11r4+Gcv1JRGVkZ/Bj8I7OPzSZL6+8zD/rX6M/bzd/G1T5vfXqS0zOZsuI4fx24bHqiCkNUZ6rEG5/SZkhNGnSumKf1H3iqCvOHw+m12uO+gdmJGL1D4cYlhBBCiCJTlEkYmy46rCjKu8B7dx2+36yRPDokhBBCCCGE0HbwV+0EDBBRfiyb/3cyT5dTFKjasCz1O/hToZbnA7WT4WRkPJOXhnDg4k2L5gf5efBh/0AaVPTM99pnbp5h0o5JnIg5YXauu4M7Cemmy6MtO7uMg1EH+bTtpwSVDbI4DhcHPZ8Ork/bWt5MWHKMhFQjrU0VWEIKw/SOVMjUaU7ZufgsXn6uVKzjZfH6DzxFgT7fwMzdkBp773hUCGz7DDpNLuzIhBBCCPEAstmdMIqiDAH+uvVSJXcSJRGIA4y8kzVOVdWq+Y9OCNkJI4QQQghRYmSmwdcBkKTRVL1MDVaqv1jc+8XJ1Z56bSsQ2M4Pdy+nAg7UtiWlZTJ94xl+2XGeLCN9TnJyd9TzerfaPNqyMna6/CWpsgxZ/H78d749/C0ZBvM7b4bUGsLrTV9n0+VNfLjnQ5IyTJcOs1PseK7hc4wJHIOdLm+7mcJvJvPygiMmk1KuBngswQl3Vfvn4OiqZ8jbzR6YMnYF5tjfsHiM9phiB2M3gF/jwo1JCCGEEEVCypHdRcl+TOw8UIn/EjDHgM+B9aqqRhVheEIAkoQRQgghhCgxghfBkrGaQ0ntPmfuohqY+9hUtpI7QR38qdnM54FrpK6qKv+ERjF1ZShX4lItOqdvgwpM7lUXn1L5T1Rdjr/M5J2TOXTtkNm5Ps4+THloCm382tw5Fp4QzoTtEzhy/YjZ8xv7NObjth9Twa1CnmLMzDLw7aazfLvpDMbyU+UzFYYlOqI3UsShjJ8rA99ogoOTTRe0sC2qCgsf0+71BFC2Djy9DfSOhRuXEEIIIQpdUSZhtPc7F71W/JeAAVgGNFZV9Q9JwAghhBBCCCEK1P6ftY/bu3Imrb3RBIxOp1CzqQ8D32jCkAlNqdu6/AOXgLkck8yYuQd45o+DFiVgqnq78seYFswY3ijfCRhVVVl4aiGDVg6yKAHTo2oPlvRbkisBA+Dv7s+v3X/luQbPoVNMf0Q+dO0Qg1cMZu15I71GjNDb6XilSy0WPNWKCh7a3/dVvcq/LsZ38URHJLHp9xPY4oOUNktRoNfX4FJGe/z6Sdj8UeHGJIQQQogHjq0mYRre+l8FSAPGqWo+OyoKIYQQQgghxN2uBsPlvdpj9R/h1KE4zSGPss48/lFruo4NpHx1jweq5wtAeqaBmZvP0uXrrWw6ec3sfAe9jlcersXal9rSpqZ3vtePSori2Y3P8sGeD0jJTDE519PRky/af8Fn7T7Dw9FDc45ep+fZhs8yt/tc/Nz8TF4vISOBN7e9ycTtE0lMT8xT3M2rerH2pXb0DCqnOR7qkMUBB+NVt88dus7BtRfztOYDz60s9PrS+PiuGXB5f+HFI4QQQogHjq0mYW6/M1aB3aqqRhdlMEIIIYQQQogSytguGCC68mhuXNa+yV6nVTlcPR/MEkZ7wqLpOWM7n/9zitQMg9n5bWt68+/L7Xjp4Zo45XOnkKqqrA5bzYAVA9gZsdPs/Pb+7VnabyndqnSz6PoNfRryd5+/6VOtj9m5K8NWMnjlYI5cO2LRtW/zcLFn5ojGfDIwCGeNn8cW5wwu6o0/g7h3RRjng2/kac0HXsAACBioPaYaYNmzkGE6mSeEEEIIcb9sNQmTsyPm1SKLQgghhBBCCFFypdzM7gejpVJrTp11NXpqrebaOxlKshuJaby68AjDftrD2Wvmd4D4uDvy3YhG/P5kc6p4G/9ZWupm6k1e2/oab29/m4T0BJNzXe1dmdp6Kt92+hZv57ztvHFzcOOjth/xadtPcbd3Nzk3IjGCUetGMevILDINxnew3E1RFIY1r8TKF9tQr3ypXGOqAitd0onVGU9wrZ8TSszVJIvXE0DPL8C1rPZY9BnY9GHhxiOEEEKIB4atJmEicvzZ9LteIYQQQgghhLgfR/4EI6WsDE3HcnqfdjvK8jU8KOXtbM3IbIrBoDJv70U6f7mVJYcizM7XKTD6oSpsfK09vetXKJBSbVsub2HA8gGsv7je7Nxm5ZqxuO9iBtQckK+1e1bryd99/6axT2OT87LULL4/+j2j140mPCE8T2vU8HFj6fOtefKhqrmOp+hgmWs66Wj3f8lIzWLNrGDSko33kBF3cS0Dvb8xPr57JlzcXWjhCCGEEOLBYatJmO3A7U9DDYswDiGEEEIIIURJZDDA/tnaY26+RNi1JSk2TXO4dosHZxdM6JU4Bs7axaSlIcSlmL/h36CiJyteaMN7fQJwd7LP9/qJ6Ym8s/MdXtz0ItGppqtUO9o58lazt5jddbbZvi6WquBWgTnd5vBioxexU0yXUjty/QiDVw5m5bmVeVrDUW/Hu33q8evoZpRxdbhz/LqdylqXdKPnxV1L4d9fjmMwaCdqhIa6vaH+UCODKix/DtJlh5EQQgghCpZNJmFUVU0ElgAK4K8oSociDUgIIYQQQghRsoRtgpgw7bEmozi9X7vnhp1eR40mPlYMzDYkpmUydeVx+ny7gyOXY83OL+Wk58P+gSx5tjWBfh5m51ti39V9DFoxiGVnl5mdG1gmkIV9FvJovUfRKQX7MddOZ8dT9Z/i9x6/U9G9osm5SRlJTNwxkbe2vWW2ZNrdOtb2Ye3LbWlb87/yaacdDOx2NJ78uhQazd7l5/K0zgOv+yfgZiSRGhMGG6cWbjxCCCGEKPFsMglzy7vA7ULD3yqKImXJhBBCCCGEEAVjn5FdMIodGUGPc+7wdc3hKvW9cXTJ/w4PW6WqKquDr9L5yy3M2XkeSzZZDGzkx8bXOvBoy8rY6fJfeiw1M5VP933KmH/HcCXpism5ekXPCw1f4H89/0c1j2r5XtuU+mXrs6jPIvrX6G927przaxi8YjCHog7laQ0fdyfmjm7O5F51sbfL/lnucMrkrD7L6DmH/rnEmf3apfOEBhcv6DPd+PjeH+D89sKLRwghhBAlns0mYVRVPQ88AqQB9YANiqJUNX2WEEIIIYQQQphx8yKcXqc9Vrc358P0ZKRp3/Su3bLkliK7GJ3EE7/u5/k/DxEVr12KLafqZV35c1wLvhrakLLujgUSw7Hrxxiycgh/nPjD7NwanjWY12seTzd4Gr1OXyDrm+Nq78oHD33AF+2/wN3B9HOCV5KuMPqf0Xx7+FsyDJb3btHpFMa2rcbS5x6imrcrKLDaNZ1oncHoOet+DSX8XKzFazzwaneHhiONjy9/DtISjY8LIYQQQuSBzSZhFEWpBBwHRgGxQFPghKIo/1MUZYSiKI0URamiKEqlvHwV4bckhBBCCCGEsAUH5oCRhuc0G8epvZGaQ05u9lQK8LJeXEUkLTOL6RvO0OXrbWw7rb0DKCdHvY43utVm7UvtaF3d2+x8S2RkZfDt4W95bO1jXIi/YHKugsLogNEs6L2AemXqFcj6edWtSjeW9F1Cs3LNTM4zqAZ+Cv6JUWtHcTn+cp7WCPTzYOWLbRjSxJ90BZa6ppNq5PdWZ4D5Xx/iyBnTfXNEDt0+glJGegfFXoL17xZuPEIIIYQosRRVtc0mfoqiGMj9yej2vvb8BKyqqlo4j0iJEk9RlAAg5PbrkJAQAgICijAiIYQQQghhVkYqfFUXUmLuHStbl6SRW5g7YRdaH5OCOvjTblgt68dYiHaevcE7y0IIu2FZM/KOtcsytV8gFb1cCiyGMzfPMGnHJE7EnDA719/Nn2ltptHYt3GBrZ8fWYYsfg39lZmHZ5KpZpqc66J3YUKLCfSr3g9FyVvZtpVHrzBx6TG8EwwMSnJAQfv8cL2BmkOrMbpN1Tyv8UA6uwH+GGR8/LFlUL1joYUjhBBCCOsJDQ0lMDAw56FAVVVDC2Ntm90Jk0PO5Iua49j9fgkhhBBCCCEeVKFLtRMwAM3GcObANc0EDEDtFiWnFNm1hFReWnCYkbP3WpSAKe/hxA+PNmbOqGYFloDJMmQxJ2QOQ1cNtSgB80itR1jcd7HNJGAA7HR2jA0ayx89/6Byqcom5yZnJvPOznd4Y9sbxKXF5WmdPg0qsGZ8W7xqeLDNyXiyxz9Tx85FZxkz9wDRieZLyj3wajwMjZ8wPr7iRUiNL7x4hBBCCFEiFYckDEgiRQghhBBCCFEQ9v+sfdzBHRoMM1qKzNPXBZ8qpnuAFAdZBpXfd1+g85dbWX7EdNN7ADudwri2Vdnwanu6B5YvsN0Vl+MvM/qf0Xx98Guz/VJ8nH2Y9fAs3mn1Di72BbcDpyAFeAewsPdCBtU0saviln8u/MPglYPZH7k/T2tU9HLhr6da0rJnFU7aG0/ENE7Xc/1oNN2nb2f7GfPl5R543aaBh5HK5XGX4d9JhRuPEEIIIUocWy7NNbeoAxBCCCGEEEKUIBGHIOKg9liDYUTfULhxWbsZd+0WvsW+vFNweCyTl4UQHG7ZLowmlUvzYf9A6pYvVWAxqKrKotOL+OLAF6Rkppid36taLyY0n4CHo0eBxWAtLvYuvN/6fdr4teH93e+b3O0SmRTJmH/GMCZoDM81fA57nb1Fa+jtdLzatTa7Knux8btgvDK0fye7pNiz4GY6j/2yj6fbVeO1rrVx0BeXZzALmaM79PsWfu+nPX7od6jbD2o+XLhxCSGEEKLEsNmeMELYOukJI4QQQghRzCx7Do7M0x57bi+7dug5/O8lzeHHPmxFKW9nKwZnPfGpGXzxzyn+t+ei0VJrOXm62DOhRx2GNKmITldwiaeopCje2/UeO6/sNB+DoyfvtHyHrlW6Ftj6txmSkohbsYLEbduxL18et86dcG3dukCTbFFJUUzaOYm9V/eanRtQJoBP231qtpzZ3SLCE/j7kwPoM7X/UhMVlf+5p5KogyA/D2YMb0RVb9c8rfFAWf0a7J+tPeZeAZ7bDc6ehRqSEEIIIQqO9IQRQgghhBBCCGtKjoGQxdpjVdpi8K7N6X1RmsMVanoWywSMqqosPxJB5y+38vtuyxIwQ5r4s/HV9gxtVqnAEjCqqrIqbBUDVgywKAHTwb8DS/sttUoCJuXYMcIGDiRyylQSN2/m5p9/cnnMWM4PGEjcihWoGaZLo1nK19WXn7r8xGtNXkOvM12AIjQ6lCErh7DkzBLy8pCkn787g15sYLRgt5uq0D/JETsVjkXE0WvGdhYduJynNR4oD0+B0lW0xxKuwLoJhRqOEEIIIUoOScIIIYQQQgghSr7D/4PMVO2x5uOIOH2TpFjtRua1W5SzYmDWEXY9kUd/2ctLC45wPcF8g/Zavm4sfLoVnw9pQBk3xwKLIyY1hte2vsaE7RNISE8wOdfV3pWpracyo9MMvJ29CywGADUrixs//MCF4SPIuHjvbqe0kye58uZbnO3ajehffyMrUbssXV7oFB2jAkfxZ88/qepR1eTclMwU3tv1Hq9ueZXY1FiL1/Cv7UW7obWMjpfP0tE12R5USE7P4o2/gxm/4AjxqQWTbCpRHN2g3/fGx4/+CafWFl48QgghhCgxJAkjhBBCCCGEKNkMWbD/F+0x9wpQuxen90RqDtvpdVRvXNaKwRWs1Iwsvlp/mu7fbGfn2Wiz853t7Xi7Rx1Wj29L86peBRrL5kubGbB8AOsvrjc7t3m55izpu4QBNQcUeO+djIgILj7xBNe/mQ6ZxhvaA2Revcq1Tz/lbMdOXPviCzKiruV7/bpl6vJX7794pNYjZuduuLSBQSsHWVTG7LbA9n7Ufai88fEMPY3T7e68Xnn0Cj2nb+fgxZsWr/HAqPIQtHjW+PjKl7J31QkhhBBC5IEkYYQQQgghhBAl29kNEHtRe6zpaDKyFM4dvq45XKW+N44uljVNL2pbT1+n2zfbmLHxDOlZBrPzu9TzZf2r7XimfXXs7Qruo2FCegKTd0xm/ObxxKSavmHtaOfI283f5ueuP1PBrUKBxXBb3KrVhPXrT8qBg3k6z5CQQPTsXzj78MNcmTCR1NOn8xWHs96Zd1q9w4yOMyjtWNrk3GvJ1xj37zi+OvAVGVnmd6woikL7YbUpV62U0TkdU+yplPHf33H4zRQe+XE33248Q5ZBypPl0vld8KquPZYYBWvfLNx4hBBCCFHsSRJGCCGEEEIIUbLt+1n7uM4eGj/B+SPXyUjL0pxSu6XtlyKLjEvl+XmHeGLOPi5GJ5ud7+fpzOzHm/Lz403xL+1SoLHsvbqXQSsGsfzccrNzg7yDWNhnISPrjkSnFOxH06yEBCLeeJMrr7+OIT+lxTIyiFu6lPN9+3HpqadI2rM3Xz1VOlbqyOK+i2ldobXJeSoqv4b+ysg1IwmLCzN7XTt7Hd2fDsLVw0FzXIdC32QHPLL+22WUZVD5cv1pRvy8h6txKXn7RkoyBxfoPwujzXaOLYLjKwo1JCGEEEIUb5KEEUIIIYQQQpRc0eeyd8JoqdcX3H05tVe7FJmTmz2VAgq2RFdByswyMGfHeR7+aiurj101O1+vU3imfXXWv9qOh+v5FmgsKZkpfLLvE8b+O5arSaZj0St6Xmz0Ir/3+J1qHtUKNA6A5IMHOd+vP/ErVxbodZO2befSqFFcGDyEuNWrUc2UNjOmrEtZZj08izebvYm9zvQuqxMxJxi6cigLTy00m/xx9XCkxzP1sdNrf8x3VhUGJDlgf9dl9p6Pofs321kXov3v4IFUqQW0fsH4+KpXIOlG4cUjhBBCiGJNkjBCCCGEEEKIkuvAHMDIzevmT5EUl8bl49ols2o29cWuAMt0FaTDl27S97udTF11nMQ088mA5lW8WPNSW97uUQcXB32BxhJ8PZhHVj7CvBPzzM6t4VmDP3v9yVP1n0KvK9g41IwMrk2fzsXHHifjyhWTc3VublT4/HOqrVmD55AhKA7aO0i0pIaGcuW11znXtRsxv/+OISkpz7HqFB2P1XuM+b3mU93DSOmr2+tlpfLBng94afNL3Ew13cfFt2opOoysbXS8rEFHj2SHe/5JxKVk8MwfB5m49Bgp6dq7wh44HSeBdy3tseQbsPq1wo1HCCGEEMWWkp+t1EI8yBRFCQBCbr8OCQkhICCgCCMSQgghhBC5pCfDV3UgNe7eMd8geGY7RzZeZuffZzVPH/xWU3yrGu+zURTikjP49J+TzN93CUs+ynm5OjChRx0GN/Ev+Ib3WRnMOjqLX0J+waCa7kGjoDAqcBQvNHwBBzvLEx6WSr94kYg33iQ1ONjsXOcmTajw6ac4+PvdOZZ54wYx8+Zx88/5GOI0fl9M0Hl4UHrYMLweHYm+bNk8x56amcpXB79i/sn5Zud6O3szrc00s+XMti88TfCmcOPjThnscdJO3tX0cWPG8EbULW9bv/tFIvwA/NIFjP1+D/4VAgcWbkxCCCGEuC+hoaEEBgbmPBSoqmpoYaxtm491CSGEEEIIIUR+hfytnYABaD4WFMVoKTJPXxd8qrhbMbi8UVWVxQfD6fTlFv7ca1kCZnjzimx8tT1DmlYs8ATM6ZunGbFmBD8f+9lsAqaie0Xm9pjLq01eLfAEjKqqxC5eTNiAgeYTMHZ2lH35JSr/PjdXAgZA7+2Nz0svUXPzJnwnTcLez8/IRe5liIsj+scfOdupM1cmTybt3Lk8fQ9OeicmtpjIzM4z8XIyXf7uRsoNnl7/NJ/v/5z0rHSj8x4aVAO/2qWNjrdNtad6hvbtgDPXEuk3cye/7Tyfr/43JYJ/U3joZePjq1+DxGuFFo4QQgghiifZCSPEfZKdMEIIIYQQNkxV4cd2EKlxY97RA147QfR1lQUf7NM8vUXfqjTtWdXKQVrm7LUEJi8LYU+Ydtm0u9Up5860AUE0qWz8Jvz9yjJk8Vvob8w8MpMMQ4bZ+UNrD+XVJq/iYu9S8LHExnL13fdI+Pdfs3PtK1fC7/PPca5f36Jrq5mZJKxfT/Qvc0gNCTF/wl3cOnakzJOjcW7aNE8JsBspN3hn5zvsiNhhdm6t0rX4rN1nVPfULmeWmpjBok/2E38jVXM8DZU/3NOIsTN+T6BzHR8+G1yfMm6Oln0DJVFmGvzYHq6f0B6v0xuG/gEFnOgUQgghRMGSnTBCCCGEEEIIUZDC92snYAAajQQHV6O7YABqNS9npcAsl5KexWfrTtJj+naLEjCuDnZM7lWXVS+2sUoC5lL8JUb/M5pvDn1jNgHj4+LDjw//yOSWk62SgEnas4ewfv0tSsB4DB5EtSVLLE7AACh6PaV69KDKooVUmjsXt/bt8xRf4ubNXHzscS4MHUb8un9Qsyzrs+Lt7M33nb9nQvMJOOhM7xo6ffM0Q1cNZcHJBZo7Vpzc7OnxTH30Dtof+x1RGJDkgKOJjUwbT16jx/Tt7DjzADeh1zvCgFmg2GmPn1wFxxYVbkxCCCGEKFYkCSOEEEIIIYQoefb9bHys2VgMBpXT+6I0hyvU9KSUt7OVArPMppNRdPl6K99vOUdGlvnqBT0Cy7HhtfaMbVsNvV3BfsxTVZW/Tv7F4JWDOXztsNn5vav1ZknfJbT2M9235H4Y0tOJ+uxzLo0aTWaU9t/fbToPD/ymT6fChx+ic3W9r/UURcG1RXMq/vgD1VauwGPgQLC3t/j81OBgIl5+mXPdexAzbx6GlBSL1hxRdwQLei+gZumaJuemZaUxbe80Xtj0AtEp0feMe/u70fmJekbP9zLoGJTuhGLiV+xaQhqP/rKXj9ecID3TdOm5EqtCI2j3uvHxNW9A/NXCi0cIIYQQxYqUIxPiPkk5MiGEEEIIG5V4Hb6uB1o9M6p3gseWcvlkDCu+OaJ5esdH61CvTQXrxmjEldgUpqwM5Z9Q0wmG2yp6OTO1byAd6/hYJZ7IpEje2/Ueu67sMju3tGNp3mn1Dl0qd7FKLGnnzhHx+huknTBSFioHl1YtqfDJJ9j7+hZ4HBlR17j5xx/cXLAAQ0JCns618/Sk9IjhlB45En2ZMmbnp2Wl8c3Bb/jjxB9m55ZxKsOHbT6kjV+be8b2LD/HwbUXjZ57zc+RuUmxZteo7+/BjGGNqOJ9f0mtYi0zHX7uBFHHtMdrdYfhC6QsmRBCCGGjirIcmSRhSjhFUUoDAUBNwAtwAmKB68BBVVXz1jVS3CFJGCGEEEIIG7XtC9j0gfbYsPlQpycbfzvOyT33liOz0+sY/dlDOLpYvtuhIGRkGfh153m+2XCG5HTzpavs7RSebled5zvWwNnBSJmkfFBVlVVhq/h478ckZJhPNHSs2JF3W72Lt7O3VWK5OX8+1z79DDUtzfRke3t8XnkFr1FPoOisW/ghKzGJuMV/Ez13LplX8rYLQnF0xKN/f7xGPYFjVfO9h3ZE7GDyjslEp9672+VuI+uO5JUmr+Bo918fF9WgsuaHY1wINl5WzLW9L58cv0RqhundLq4OdkzpF8igxn556ndTIkQeg586gCFTe7zf99nlDoUQQghhcyQJ8wBSFKUa0Axoeut/GwPuOaZcVFW1yn1c1x7oBPQBOpCdgDHlCvAL8L2qqsaLYhcQRVE6AJvzcYn7+rlYgyRhhBBCCCFsUFYmTG8A8eH3jnlUgpeOkJEBv765g4y0e5Md1Rv70P2pwHvPtaIDF2KYvCyEk5GW7apoVa0MH/QPpIaPm1XiiUmN4YPdH7Dh0gazc93s3Xir+Vv0q97PKjfkM6OjuTpxEolbt5qd61C9On5ffI5T3boFHocpakYG8ev+IXrOHIt26eSiKLh17kSZJ8fg0riRyakxqTG8u/Ndtoab/1nU8KzBp+0+pVbpWneOpadk8venB7gZmax5jt5eR9MxdZi05bRFv4t9G1TgwwGBlHIq3IRlkdv6GWyepj3mWAqe2wMefoUbkxBCCCHMKsokjPSEKUSKonRQFOUfRVGigXPAAuB1oD25EzD3e/0WQBSwDnge8wkYgArAO8AJRVEezW8MQgghhBBCFKnT67QTMABNR4POjrAj1zUTMAC1W5azYnC53UxK562/gxn8w26Lbnp7uznwzdCG/DmuhdUSMJsubWLA8gEWJWBalGvBkr5L6F+jv1USMInbthHWt59FCZjSI0ZQ9e9FhZ6AAVDs7fHo05uqSxZT6dc5uLa5txyYUapK4oaNXBwxggvDRxC/fj1qlvbvppeTF992+pbJLSbn2uWi5WzsWYavGs68E/O4/eClg7Oens/Wx8FZr3lOZoaBkIXnmP94M0a1rmI29BVHr9BrxnYOXbppdm6J0uYVKN9QeywtHla8CPKwqxBCCCFykCRM4WoIdCW7LJg1lAVKaxxPBw4Cq4E/gX+Au/exewL/UxTlVSvFJoQQQgghhPXt/1n7uJ0DNH4cgNN7tTeAO7nZUynAWm/V/2MwqCw8cJlOX27hrwOXzc5XFHi0ZSU2vtqB/o2sUwIqIT2BSTsm8dLml4hJjTE518nOibebv81PXX+ivFv5Ao/FkJpK5Acfcvmpp8mKNl1+y65MGfx/mEW5d99B5+xc4LHkhaIouLZqRaXZP1N1+TI8+vUDvXbCQ0vK4cNEvDiesJ69uLngLwypqZprDK0zlIW9F1K7dG2T10s3pPPJvk94duOz3EjJLkPm6etClyfrgZFfocSYNLb8eoJ3etVlzqimeLk6mFzjckwKQ37YzXebzpBleEASD3b20H9W9n9TtJzbCId+L9yYhBBCCGHTJAljG9LI3hlTkBKBOcDDgKeqqk1VVe2tqupIVVW7k52wGQhcuuu8LxVFsU4nTW3Tgap5+MrDY2VCCCGEEOKBcuMMhG3RHgsYCK7eJMWlcfmEdpKhZlNf7Oys+xHpVGQCQ3/azZt/B3MzOcPs/IAKpVj63EN82D8IDyv1qdl7dS8DVwxkxbkVZufW967Poj6LGFl3JDql4H9WqSdPcn7wYG7Om2d2rmv7dlRbvgz3Dh0KPI78cqpdmwqffkKNDevxevJJdK6WN7JPv3iRyPff52ynzlyfOZPMm/fuNKnmWY0/e/3JE/WeMHu9nRE7GbRiEFsvZ+8oqhLkTav+1Y3Ov3Imlp0Lz9Cpji/rXmpLmxqm+/xkGVS++Pc0I2fv4Wpcitl4SgTfetBhgvHxfyZB7N0ftYUQQgjxoJIkTOHLAI4As4GngSZklyIbW0DXv0Z2ibNyqqqOUVV1o6qq97wTVrMtJbsXzd2Fi2cohddhMVZV1Qt5+DJSW0IIIYQQQjzw9s82PtZ8HABn9kcZrRRUu4X1SpElp2fy8ZoT9Jqxnf0XzJdvcnPU816feix//iEaVvS0SkwpmSl8vPdjxv47lsgk0+0h9To94xuNZ26PuVTxqFLgsagGA9G//saFIY+Qftb082mKoyO+70ym4g8/oPc2nSAoavblyuH75hvU2LIZnzfeQO/ra/G5WTEx3Pj2O8527ETk1A9Iv5T7pr6DnQOvN3udH7v8SFnnsiavFZMawwubXmDanmmkZqbSqGslajb1MTr/2NYIju+8gk8pJ35/sjkTetRBrzP9EXFPWAw9pm/nn1Crtxq1Da3Hg19T7bH0BFj+PBgMhRuTEEIIIWySohaTWqWKonQku+F8I8AH8ADy+iiYqqqq8Ud+rExRlNJAiqqq9+wr12hYn+cG9IqilAWSVVVNyuN5DYBD5E7KNVdVdX9ermPhWh3I/X1OUVX1/YJepzAoihIAhNx+HRISQkCAJW14hBBCCCFEgUtLhK/qZvdkuFv5hvDUFlAU/pq2jxuXE++Z4unrwoj3W1il1Ne/oZG8vyKUK3H3lpfS0rt+ed7pXQ/fUk4FHsttR68fZfKOyVyIv2B2bg3PGnzc9mPqeNWxSiwZUde4OuFtknbtNjvXsU4d/L74HMcaNawSi7Wp6enEr11L9C9zSDt9Om8n63S4d+lCmTFP4ly/fq6hm6k3eW/Xe2y+vNnIyf+p5lGNz9p9RjW3Giz5/KDmvwcAnZ1C/1cbU766BwDB4bGMn3+YC9HJZtcY2aISk3vVw9nBzoJvrBi7fhp+aANZadrjvb6EZgX1vKUQQggh8iM0NJTAwMCchwJVVQ0tjLVtfieMoijdFEU5BWwAJgI9gKZATaDKfXwVGVVVb2olYArw+tfzmoC5dd5RYMddhzsWTFRCCCGEEEIUgmMLtRMwkL0LRlGIjkg0esO5dotyBZ6AuRyTzNi5+3nqfwctSsBUKePC708257sRja2WgMnIymDGoRk8vvZxswkYnaLjycAn+av3X1ZLwMSvX8/5vn0tSsB4jR5NlYV/FdsEDIDi4IBHv35UXb6Mij//jGvrVpafbDCQ8M8/XHhkKBcffYyETZtRb+20KO1Umukdp/Neq/dw1pvujRMWF8bw1cP588wfdH86ECc37WcbDVkq6348RuLN7ARDfX9PVo1vy6DG/mZDnbf3En2/28HJSCP/JkuKsrWg8zvGx/99F2LOF148QgghhLBJNp2EURTlDWANUIPcrQPV+/gSph2+63WFIolCCCGEEEKIvFJV2GekFJlzaQgcBMCpvcbLJNVqbnmZKHPSMw18v+UsXb7eyoYT18zOd7DT8VLnmqx7uR3tapkuK5Ufp2JOMXz1cH4+9jMG1XSZpIruFZnbfS6vNHkFB2MNyPPBkJTE1XfeIeLF8WTFxZmcq/fxodKcX/B96010DgUfS1FQFAW3tm2oNGcOVZcsplTv3mBn+a6R5AMHCH/uOcJ69yH2778xpKWhKAqDaw3mr95/UderrsnzMwwZfHHgC9449DItH/dHZ6TUWHJ8Omt/CCYzIwvILpP35SMNmD6sIe6OepNrnLmWSN/vdjJ31wWKSwWO+9LyOajYUnssI0nKkgkhhBDCdpMwiqJ0Az7lv+SLeuvPCpACXCW7qbylXxe5twm9+E/mXa9LxqcbIYQQQghR8l3aDdeMVBJo9CjYO2MwqJzeF6U5pUJNT0p5m949YKk9YdH0mrGdz9adIjXD/I3XtjW9+eeVdrzSpRZO9tYp3ZRlyGL2sdkMWz2MUzdPmZ0/tPZQ/u7zNw19GlolnpRjxzg/cBCxi/42O9e9y8NUXb4M19atrRKLLXCqVw+/Lz6nxr//4PXEE+hcXCw+Nz0sjKuT3+Fs54e58cOPZMXFUdWjKvN6zmN04GgUTO/u2n11N88cf5yyDxtPkly7mMDWeadyJVL6NfRjzUttaVTJ03R8mQbeWxHKuN8PEJOUbvH3Vazo7KD/92BsB9LFnbDvp8KNSQghhBA2xWaTMMAnt/73dvIlHHgBqKKqqpuqqv6qqlbN61eRfTe27+49/VeLJAohhBBCCCHyat/PRgYUaDoGgIjTN0mK1e7bULtFuXyHEJ2YxmsLjzLspz2cuaZd8iwnH3dHvh3eiN+fbE5Vb9d8r2/MxfiLPLHuCaYfmk6m4e7nru6KycWHHx/+kcktJ+Nib3kiwFJqVhY3fviRC8NHkH7xosm5irMz5T/8AL8ZM9CXLl3gsdgiez8/fCe8TY3Nmyj76qvYlfW2+NysGze4/s03nOnYichpH6FevcarTV7l564/4+PiY/Lc2LRYpsS/RGpN4zvFTu6JJHhTeK5jFb1cWPh0K17oWANzlfw2nLhG92+2sfPsDYu/p2KlTHV4+H3j4xveh+hzhRWNEEIIIWyMTSZhFEWpDjTgvzJie8lulPO9qqqym6WAKYpSCuhy1+F9hbR8R0VRliiKEqYoSqKiKCmKokQoinJQUZTvFEUZpCiKdpFiIYQQQgghEiLhxArtsZpdwCv7OazTe7RvMNvpdVRvfP8lwAwGlT/3XqLTl1tZfCjc7HydAqNaV2HDa+3p06BCgfehuU1VVRacXMCQlUM4ev2o2fl9qvVhab+ltPazzo6TjIgILj7xBNe/+QYyTSeDnIKCqLZ0CZ6DB1vt52PL7Dw88H5qHDU2bqT8tGk41Khu8blqcjI3//c/znXrRsSrr1E/2o0lfZfQpfLdH/fuosDvXp9x0/OK0Sk7F5/l8smYXMfs7XS83q0288e1pLyH6T5G1xLSePSXvXyy9iTpmSWwPFfzp6ByG+2xzBRY9iwYsgo3JiGEEELYBJtMwgC3uxMqZCdiHldVNaEI4ynpngZyPuoWB2wupLXbAQOAqoAr4ER2P5rGwPPA30CYoijPKw/iJzAhhBBCCGHawblgbIdHs3EAZKRlcfbwdc0pVep74+hyf8/8HL8Sz+AfdjFx6THiUjLMzm/g78Hy59vwft8ASjlZ7zmjyKRInl7/NNP2TiMlM8Xk3NKOpfm6w9d81PYjSjmUsko8catWE9Z/ACkHDpqeqNNR5pmnqfLnPByqVLFKLMWJzsEBz0EDqbZiBf4/zMKleXPLT87KIn7NGi4MHkzsUy8xRenH1FZTcDZWMgsw6LJYWX0WSQ7aPXpUg8o/P4cQd/3e36mW1cqw9qW2dAsw3VtJVeGHrecY/MMuLtxIsvz7KQ50Ouj3Hdgb2dl2eS/s+b5wYxJCCCGETbDVJMzt/dIqcFhV1TNFGUxJpihKFeCduw5PV1XVlgr2+gPfASsVRfG0xgKKovgoihKQly/A8kfShBBCCCFEwcvKgIO/ao+VrgI1HgYg7Mh1MtO0n0Cv3TLvpcgS0zL5YNVx+ny3g0OXYs3Od3fS80G/AJY89xBB/h55Xs9Sqqqy8txKBi4fyO6ru83O71ixI0v6LeHhyg9bJZ6shAQi3nyTK6+/jiHB9DN1+grlqfz7XHxefhnFXjbC56TodLh36EDl3+dSZdEi3Ht0z77hb6HkvXsJf/oZGrz2K/OVcdT3qGd8rkM8/9SaTaainVRMS8pk7Q/BpKfem/j0dHHgh0ebMG1AII560/EFh8fRa8Z2lliwe6xY8aoKXacaH9/4AVw/XXjxCCGEEMIm6Is6ACNy7ng4W2RRlHCKojgAfwHuOQ5fAD4rhOXjgQ3AViAUuAakAKWBWmSXRxtK9s6Y23oByxRF6WqFJNFzwHsFfE0hhBBCCGFNJ1dDgpFWhs3G3rlRfXqvdikyJzd7KgV4WbycqqqsDYlk6srjRManWnRO/4YVmNirLj7upks15VdMagxTd09l46WNZue62bvxdvO36Vu9r9XKfSUfPMiVN94k44rx8la3lerdm3LvvoNdKevsxClJnIMC8f/6a9LDw4n5bS6xixejppje7XRb2pmzMPUr3vPx4UTnpnxc7gBJGr+W19wvsa3aX3Q696jmdaIjktg09wTdngq85/dHURRGtqhM8ypevDj/MCcjjSffktKzeHXhUbadvs4H/QNxt+LusELV5Ek4vgLOb713LCsNlj0DT/4LdrZ6O0YIIYQQBc1Wd8JE5PizXZFFUfLNBnLuac8CnlBV1Zr7wiOB0YCvqqqDVFWdoarqRlVVj6mqelZV1f2qqs5TVXUU2SXK1t51fnvgEyvGJ4QQQgghiot9P2sf1ztBw5EAJMWlcflEjOa0mk19sbOz7CPR1bgURv+2n+fmHbIoAVOtrCt/jm3BN8MaWT0Bs/HSRgYsH2BRAqZF+RYs6buEfjX6WSUBo2ZkcH3GDC4+9rjZBIzOzY0Kn3+G3xefSwImjxz8/Sk3eRI1Nm2k7EvjsStTxuJzs65do9b8Pcz5Uc8z25woE6/eM+e0z36Cy20xeo1zh69zcO0Fo+M1fd1Z9vxDjGpdxWw8y45coeeM7Ry6dNOC6IuB22XJHNy1xyMOwq4ZhRuTEEIIIYqUrSZhQnP8uWKRRVGCKYryAfDYXYcnqKq6zZrrqqp6UlXV31RVNfvJVVXVSLJ3vyy6a+h5RVGqWiVAIYQQQghRPEQdh4s7tMeCBoNL9g6XM/ujUO+9xwxYXorsWnwqw37aw5ZT2n1lcnLU63i9ay3WvtSW1jW8Lbr+/UpIT2DSjkm8vPllYlK1E023Odk5MaH5BH7q8hPl3cpbJZ70ixe58Oij3Ph+FhhMN153btyYqsuW4dGnj1VieVDoS5fG+9lnqbFpI+WmTsGhquUfk5TkVDrtTGTmLAMvrsiiclTufyi7qywnvJTx0ll7V5znfPANo+NO9na83zeAX55oiperg8lYLsekMOSH3czcfJYsg5F/sMWJZyXoNs34+JaPs/8bJoQQQogHgk0mYVRVPQaEkF2WrImiKKWLOKQSRVGUl4HJdx3+SlXVz4sgHJNUVVWBUUDOOhMOwJgCXup7IDCPX/0KOAYhhBBCCGGp/bONjzUbd+ePp4yUIvP0dcGnspEn1e/y3opQLkYnm53XoXZZ1r/Snhc61cRRb90N/Xuu7mHgioGsOLfC7Nz6ZeuzqM8iRtQdgU4p+I+AqqoSu3gJYQMGkno02PRkOzvKvjSeyr/PxcHfr8BjeVDpHB0p/cgjVFu9Cv/vZ+LcpInl5xpU2oaqfD4ni0nzs6gfZgBVRVUMrK/1K/GOxhMt6+eEEnPVdCGFznV9WftSW9qYSUpmGVQ+/+cUj87eS2ScZeX+bFrjx6F6Z+2xrHRY9mx2XyshhBBClHg2mYS55ctb/2sHvFaUgZQkiqKMA7666/AsVVVt9mesqmoycPd+7e4FvMY1VVVD8/IFnCvIGIQQQgghhIVS4yH4L+0x/2ZQoSEA0RGJ3LicqDmtdotyFpXj2nn2BmtDtBM5t5Ur5cSskY35dVQzKpVxMXvN/EjJTOGjvR8x7t9xRCaZjkuv0/NS45eY230uVTyqWCWerNhYIl5+hauTJqEmm05U2VeqRJU/5+H97LMoeumHYQ2KTod7p05UmfcHVRbMx71rV8hD2bkGF1Qm/2XgszlZtA0xkKlLYl3tX8jQpWnOz0jNYs2sYNKSTScTfEs58fuTzXm7Rx30OtPx7A6Lpvv0bfwbavr32+YpCvT9Fhw9tMevHoEdXxdqSEIIIYQoGjabhFFVdS6wmOzdMG8qitKjiEMq9hRFeQz4geyf6W2/As8XTUR5su6u10FFEoUQQgghhCh6RxdAunZyxZJdMAC1mvuaXSYjy8D7K0KNjtvpFMa0qcqG19rTI6i81Zrc33b0+lGGrBzC/JPzzc6tWbomC3otYGzQWPQ66yQ8kvbsIaxffxL++cfsXI9BA6m2dAnODRpYJRZxL+eGDfGfMZ3q69biOXwYiqOjxedWuQYvrjTw7awsWoeEs7PyPKNz466l8O8voRjMlBHT6RSeaV+dxc+2prKZZGVscgZP/e8gk5cdIzUjy+K4bY6HH/Qw0dJ066dw1czuMSGEEEIUezabhLnlCWAFoAeWK4oyVVEUz6INqXhSFGUY2QmXnH/n84Cxt0p+2boLd712UBTFyCNFQgghhBCixFJV46XIXLwhoD8ABoPK6X1RmtMq1PSklLez2aX+t/siZ65pJ3uqebuy8oU2vNO7Hm6O1t3VkZGVwfRD03l87eNcjL9ocq5O0TEmcAwLei2gtldtq8RjSE8n6vPPuTT6STKjtH/Gd+Lx8MBv+nQqTJuGztXVKvEI0xwqV6b8e+9RY/MmvF94AbvSllf79k6AxzcZePvPg6Rl3v1c3H8uhcawd7llhQIaVPRk9fi2DGxsvhzdH3su0fe7HZyMjLc4ZpvTYDjUMlLIwZAJy56DzPTCjUkIIYQQhcpm94ArivLurT8eBVoD3sAk4FVFUXYDx4GbgOmOj3dRVXVqQcZZHCiKMgj4H9ml3W5bBDyhqmqefn5FKEXjmDMQV9iBCCGEEEKIInR+G9w4pT3W+HHQZz/tH3HqJkmx2iWUarcoZ3aZG4lpfL3BeFPyTwfXp16FUubjzadTMaeYuGMip28aj+W2Su6VmNZmGg19GlotnrRz54h44w3Sjp8wO9elZUsqfPIx9uXM/7yF9em9vCj7wvOUGfMkccuWEf3bb2RcvGTRuS5p0H3HKo4GVSCmTH3NOYf+uYS3vzs1m5nfZebmqOerRxrSvlZZJi0NITEt0+jc01GJ9P1uJ5N71eWxlpWtvuOswCkK9P4Gvm8JqbH3jkcdg22fQ6dJhR2ZEEIIIQqJzSZhgPeBnDs0VLLLaLkAnW593Y8HKgmjKEpfYD65/66XASNUVS1O+7q1ujhGF3oUQgghhBCiaO3/Wfu4ooOmT955aawUmZ1eR/XGZc0u8/m6UySkat8Y7tewAs2qeJmPNR8yDZn8FvobM4/MJNNg/Ab1bcNqD+OVJq/gYm+dnjSqqhK7YAFRn36Gmmqmabq9PT6vvILXqCdQdLZefOHBo3N2pvTw4Xg+8ggJGzcS88scUo4eNXuegkrg8bkcaPwGya7aibV/5x6jlI8jvpU9LYqlX0M/GlUszfgFhzlyOdbovPRMA+8uD2Xb6Rt8Nrg+Xq4OFl3fZpQqDz0/hyXjtMe3fwl1ekKFRoUblxBCCCEKRXF7R6ySOzGTF8XscZn8UxSlJ9k7XuxzHF4NDFVV1fwnOdvS4q7X11VVNd39UQghhBBClCxxEXByjfZYrR7gWRGAjLQszh2+rjmtSn1vHF3sNcduO3o5loUHL2uOuTjYMaFHXctjvg8X4y8yat0oph+abjYB4+viy09dfmJSy0lWS8BkRkcT/uxzRE6ZajYB41C9OlX/WkCZJ0dLAsbGKXZ2lOralcoL5lP5z3m4de6cvWvDBH1WKvVDfkSfmaw9IVPHvG+2ERZpumxeTpXKuLDomVa80LGGueXZcCKKHtO3sevsDYuvbzOChkCd3tpjahYsfRYytXfvCSGEEKJ4s/V3xUoBfj1QFEXpAiwGcj4i9C8wSFXV4lhwdsRdr7cURRBCCCGEEKIIHfw1+2alluZj7/wx7Mh1MtO059Vuabo0lsGg8v7KUIx1TXyhUw3KeThZFG5eGVQD80/OZ8jKIRy9bn5nQt/qfVnSbwmtKrSySjwAidu2Eda3H4lbtpidW3rEcKr+vQinevWsFo8oeIqi4NK4MRVnfke11avxfOQRFAfjO01cUq5R7/hvYKSytWOKG799vYHVZ4wkTDXY2+l4vVtt5o1tQblSpv99RcWnMfKXvXy67iQZWcWluja3ypJ9Dc5GdtFdPwFbPincmIQQQghRKGy5HFnHog6guFIUpT2wHMj57nUT0F9V1WL3aI2iKB2AgXcdXl74kQghhBBCiCKTmQ4H52qPlakBVTvceXnaSCkyJzd7KgWYLiO25HAEhy/Fao5VKePCmDZVLQg27yKTInln5zvsubrH7FwvJy/ebfkunSt3tkosAIbUVK598SU3//jD7Fw7Ly/KT/sQ947yEa64c6xWlfJTp1B2/IvEzJtH7J/zyYq7tw2nd0wo1cNWcK56f83r+MZV5d9ft7Gjx3YmtpiIu4O7Reu3ru7N2pfa8ubiYNYfjzI6T1Vh1pZz7DoXzYxhDalcxtWi6xc5Nx/o9SX8PVp7fOc3UKcX+Dct1LCEEEIIYV02m4RRVXVrUcdQHCmK0gpYRXbT+tu2AX1UVdVqbp+ftd4H3stxaKuqqh1MzO8KRKmqav6xvv/OaUH2jp6cu5lOAX/lKVghhBBCCFG8nVgBSde0x5qNhVulr5Li0rh8IkZzWs1mvtjZGS8GkJCawSdrTxodf7dPPRz1dpbHbAFVVVkZtpJP9n5CQkaC2fmdK3XmnZbvUMa5TIHGkVPqyZNceeMN0s6cNTvXtV1bKnz0EXpvrRaOorjSe3vj89JLeI8bR+ySpcT89hsZ4eG55lS6vJ4EN3+u+WonDGrFtEP9aR7Ph/TnlUFf0MjHsn4npV0d+OmxJszbe4kPVh0nLdP4bpejl2PpNWMHH/QPYEAjf8u/waIUOBCOL4fjy+4dUw2w7Fl4ehvYO987LoQQQohiydbLkZU4iqL4K4pS5e4v4O66CHqtebe+ND/hKIrSCFgLuOU4fAp4HvAxcT2tL9N1Gu5Pa+CwoijrFEUZpSiKj7GJiqJUVBTlc2A7kPNxxQzguWLY00YIIYQQQuTHvp+1j9u7QIPhd16e2R9ltJRY7Ram3+J+u+ksNxK1N453rF2WTnV8LQrVUtEp0by8+WUm7ZhkNgHjZu/GtDbT+LrD11ZLwKgGA9G//caFIY+YTcAojo74Tp5MxR9/lARMCaZzccHr0ZFUX7cWv6+/wikw8M6YAtQ99QduCdr9kwB0zkN57ldHQp8cybz5k8jIsqytp6IoPNqyMitfbEOdcqZ30SSmZfLKX0d5ecFhElKLSdvQXl+Ba1ntsRunYfO0wo1HCCGEEFZlszthSrAdQGUL5vkB542MzQVGaRzvB3jcdaw2cMzS4HLYCnS4j/PMUYBut75QFCWC7ERRLJBCdvy1bn3dLQt4UlXVTVaISwghhBBC2KrIY3DZSJmu+o+As+edl6eMlCLz9HXBp7Lxm7lnryUyZ4f22297O4V3+wRYHK4lNl7cyNQ9U4lJ1d61k1PL8i354KEPKOdqjeeksmVEXePqhAkk7dpldq5j7dr4ffE5jjVrWi0eYVsUvZ5SPXrg3r07yfv3E/PLHBK3bsXOkEFQyE8caPImGRolx1SdnmMB42h28FMcpyxh25x1VHvuVar0HYZiZ35XWS1fd5Y9/xAfrznB3N0XTc5dduQKhy7FMnNEY4L87/5YbGNcy2T3h/nrUe3xXd9Bnd5QqWXhxiWEEEIIq5CdMKKo+QGdyO75MhLojXYCJgxor6qq+aLUQgghhBCiZDG2Cwag2bg7f4yOSOTG5UTNabVblENRFM0xVVWZsjKUTIP2FpoxbapR1btgek7Ep8czcftEXt7ystkEjJOdExNbTOTHLj9aNQETv3495/v1sygB4zV6NFUWLZQEzANKURRcmzen4o8/UG3VSjwGDcTFkEBg6GwUQ5bmOemOHgQHPkWWTk+Fy8mkTviQY53bEfPHPAzJyWbXdLK3Y0q/QGY/3pTSLvYm516KSWbQrF38b89FVGNb4mxF3T4QNMTIoJpdlizd/M9HCCGEELZPkjCiMK0Avid7Z472O/TcMoFdwBNAPVVVd1oxNiGEEEIIYYtSYuHYIu2xSq2g3H/lkYztggGo1dx4KbENJ66x/cwNzTEfd0de6FTDolDN2XVlFwOXD2Rl2EqzcxuUbcDfff9meJ3h6BTrfGwzJCVx9Z13iHhxPFmxsSbn6suWpeIvs/F96010Dg5WiUcUL441alBh2jSqb9xAjUc6Uivc+O91QqkqnKo1nNtpEfvIGKI+/JDTHTtyfcYMMqOjza73cD1f1r3cjodqmC7Hl55l4J1lIby04AiJaTZexbrHZ+Bm5L9NMWGwcWrhxiOEEEIIq1Bs/ukQUSIpiuIE1CO7NFt5wB2wBxKBm2SXYjugqqrNPvqjKEoAEHL7dUhICAEBBVumQgghhBDigbf7e/hngvbYoF8gaDAABoPK7xN2khSXfs+0CjU9GfBaY81LpGZk0fXrbVyK0X7b+c3QhvRv5Hd/sd+SnJHM1we/ZsGpBWbn6nV6nm/4PKMDRmOnM1+u6X6lHDvGldffIP2i6RJPAO5dHqbc1KnoS5e2Wjyi+MtMSGT955sIu+ZmdE7NM4uoGLHlnuOKoyMe/fvjNeoJHKtWNbmOwaDy47Ywvvz3lNHda7dVK+vKrJFNqG2mr0yROrUW5g8zPj5qNVRpU3jxCCGEECVUaGgogTn62wGBqqqGFsbaRZKEURSl0t3HVFW9ZG5OQbh7HSHulyRhhBBCCCGszGCA75pCzLl7x1x94JVQ0Gfvyrh8IoYV049oXqbjo3Wo16aC5th3m87wxb+nNceaVC7N38+0MlrGzBJHrh1h0o5JXEow/zGkVulafNTmI2p71b7v9cxRs7KInv0L17/9FjJN7xJQnJ0pN2kiHoMG5etnIB4cWRkGln51kKjzCZrjippFg6Mz8Yo9pX0BRcGtcyfKPDkGl8aNTK515HIsLy04zMVo08/tOdnr+LB/EIOb+Fv0PRSJpc/C0T+1xzwrw7O7wNF4cksIIYQQ5hVlEqaoypFdIHunw+2vMAvmFMSX1jpCCCGEEEIIWxS2WTsBA9Bk1J0EDBgvRWan11G9cVnNsSuxKczcrH19RYEpfQPuO/mQnpXONwe/4Yl1T5hNwOgUHeOCxrGg1wKrJmAyIiK49MQorn/9tdkEjFNgIFWXLMZz8GBJwAiL2dnr6PFMfVw9tEvWqYodIQFPkuJkpKSYqpK4YSMXR4zgwrDhxK9fj5qlXcm6YUVPVo9vS98G2gnW21IzDLy+6Chv/R1MaoYlVbGLQPePwd3I9xF7ETa8V7jxCCGEEKJAFXVPGCXHlyVzCuJLCCGEEEIIURzsn619XLGDpqPvvMxIy+Lc4euaU6vU98bRSDPvj9acIMXITdnhzSsR6OeRt3hvORVziuGrh/NLyC8YVIPJuZVLVWZu97mMbzweezvTTcfzI271asL6DyD5wAHTExWFMs88TZX5f5otCyWEFlcPR3o8Ux87vfbthkx7N4IDnybTztHkdVKOHCHixfGE9ezFzQULMKSm3jPHzVHP9GEN+bB/IA52pm9v/HXgMv1n7iTseqLl30xhcfaEvt8aH98/G8K2FFY0QgghhChgRZ2EEUIIIYQQQoh7xV6C0+u0x+r2hlL/PTUeduQ6mWnayZQ6LctpHt8TFs2q4KuaYx7O9rzeNe87UjINmcw+Npthq4dx+qZ2ibOchtcZzsLeC2no0zDPa1kqKzGRK2+9xZXXXseQoF0i6jZ9hfJU/n0uPi+/jGJvvYSQKPl8q5aiw0jj/4aS3Pw4UecxLCmOnn7xIpHvT+Fsp85cnzmTzJs3c40risKjLSuz5LnWVPRyNnmtk5EJ9P1uJ6uN/NsvUjUfhsaPGx9f/gKkxhdePEIIIYQoMPoiWnduAc0RQgghhBBClEQH5oCxXSTNxuV6edpIKTJnd3sqBnjdczwzy8D7K4yXf36tay28XLXLKRlzIe4Ck3ZOIvh6sNm55VzLMbX1VFpVaJWnNfIq+dAhrrzxJhkREWbnlurVi3LvvYtdqVJWjUk8OOq0Ks/1ywkEbwrXHL9ethEnanan3hkjyda7ZMXEcOPb74j+eTaeAwfgNWoUDpX+ayUb6OfBqhfb8saio/x7PMrodRLTMnn+z0PsO1+Zib3q4qi3y9s3Zk1dp8G5zRB3+d6xuMvw72ToO6Pw4xJCCCFEvhRJEkZV1dEFMUcIIYQQQghRAmWkwkEjz2SVrQtV2tx5mRSXxuUTMZpTazT1xU6jRNGf+y5xMlJ7V0idcu6MaF5Jc0yLQTWw4OQCvj74NalZ95ZLulvf6n15u/nbuDu4W7xGXqmZmdz4fhY3fvgBDKbLoenc3Cj33rt49OljtXjEg+uhQTWIjkgi4tRNzfFIvz7srX6FTgeDqaxdUfAeamoqN/+cz835C3Dv0oUyY57EuUEDIHsX24+PNeGXHef5ZO1JMg3G99rM3X2RI5dj+W5EYyp6ueT5e7MKp1LZZcn+1197/NBcqNcXajxcqGEJIYQQIn+kHJkQQgghhBDCtoQuhRTtxArNxkCORvFn9kehGrnPWrvFvaXIYpLS+fJf46XC3u8bgN5Mb4nbIpMieWr9U3y872OzCRgvJy++6fgN09pMs2oCJv3SJS6MHMmN7783m4BxbtyYqsuWSgJGWI3OTkf3cYGU8nYyOqd8xhNMG1GBD4fqCK6ShzauqkrCv/9yYegwLjz6KAmbNqMaDCiKwti21fjr6ZaU9zC+LsDR8Dh6f7uDjSeM75wpdNU7QtMxxseXvwgpsYUWjhBCCCHyT5IwQgghhBBCCNuy/2ft4w7u0GBYrkOnjJQi8/R1wafyvcmOL/49RVxKhuY5veuXp2W1MmbDU1WV5WeXM2D5APZe3Wt2fudKnVnSdwmdK3U2O/d+qapK7JKlnO8/gNSjZkqi2dnhPf5FKv8+Fwd/f6vFJASAk5s9PZ6pj95B+/aDQ5YTPU49xclKrnw43I43R9uxvZ5CVh7yMSkHDhL+3HNcGDyEjCtXAGhS2YvV49vSrlZZk+fGpWQwZu4BPl57gsws04nLQtNlKnga2ZGXcAX+mVi48QghhBAiXyQJI4QQQgghhLAdEYcg4qD2WINh4PhfYiU6IpEblxM1p9ZuUQ5FyX0XNyQijvn7LmnOd7LXMbFnXbPhRadE8/Lml5m8czKJGdpr3+Zu785HbT7i6w5fU8bZfHLnfmXFxhLxyqtcnTgRQ3Kyybn2lSpR5c95lH3uORR9UbUIFQ8ab383Oj9Rz+i4R2pZ+px/BkVVuFBO4dt+drzwrB2rmimk5KE9U+rx41x+4QXUjOxEq5erA7+NasZrXWqhM5PU+XFrGCN+3ktknPmyglbn6Ab9vjc+fmQenLKsl44QQgghip4kYYQQQgghhBC2Y/9s42PNxuZ6eWqP9i4YgFrNfXO9VlWV91eEGi1d9nyHGlTwdDYZ2oaLGxiwfACbLm8yOQ+gVflWLOm3hD7V+9yTDCpISXv2EtavPwnrzN+Q9Rg0kKpLltzpnyFEYarRxIemPasYHS97owpPJL5+53W0h8LvD9vx7PN2zOugI8bNsnXSjp8g+pdf7rzW6RRe7FyTP8a0wNvN0eS5+y7E0GvGdnacuWHZYtZUtS20eMb4+MqXINlI2UYhhBBC2BRJwgghhBBCCCFsQ3IMhCzWHqvSFnzq3HlpMKic3qedhKlQ05NS3rkTKsuPXOHARe3m4BW9nBnXrprRsFRV5asDX/HKlle4maZ9jduc9c5MajGJH7v8SDnXe3vSFBQ1PZ2ozz/n0ujRZEaZ7meh8/DA75tvqDBtGnZurlaLSQhzmveuSpX63kbHnUL8+dB3BqUdS985luyksLyVjheeteP7XjouGz/9jhszvyftzJlcx1rX8GbN+Da0qOpl8tzopHQem7OXbzacJstgJGtbWDq/C15G/tuUGAlr3yrceIQQQghxXyQJI4QQQgghhLANh/8HmUZKATUfl+tlxKmbJMWla06t3SJ38iMxLZOP1pwwuuw7verhZG9ndHzp2aX8Gvqr0fHbGpZtyKI+ixhWZ5hVd7+khYVxftgwYn6Zg9GtPbe4tGxJteXLKNW9m9XiEcJSik6hy+h6lC7nYnRO5GqF2Y3/oHWF1rmOZ+oVttTX8fpYOz4eoiOkkvF/Y2pGBlcmTUbNysp13KeUE/PGtuD5jtVNxqmq8M2GM4z6dR83EtMs+M6sxMEV+s8CjHyvxxbCiZWFGpIQQggh8k6SMEIIIYQQQoiiZ8iC/b9oj7lXgNq9ch06tVd7F4ydXkf1Jj65js3cfJZrCdo3UtvW9KZLPV/NMYDE9ESmH5puInCw19nzcuOX+a37b1QuVdnk3PxQVZWbCxZwfuAg0o4bTyplB2WPzxtvUGnOL9iXs96OHCHyysFZT89n6+PgrN2TKDPDwO7fLvNl8+m81ewt7HX2ucZVReFwDR1TR9ox4Qk7Ep2010kNDiZm7u/3HNfb6XijWx1+HdUMTxd7jTP/s/3MDXrN2M7+C0VY9qtSS2j1vPHxVa9AUnThxSOEEEKIPJMkjBBCCCGEEKLond0AsRe1x5qOBrv/bthmpGVx7vB1zalVG3jjmOPm7vkbSczeHqY5V69TeK9PgMldK3NC5hCTavwGbO3StVnQewFjgsZgpzO+mya/MqOjCX/ueSLfn4KaarpxuEO1alT9awFlxjyJopOPfML2ePq60HVMgNENHokxaayffZzhtUcwv9d8anjW0Jx3roLC3M7Gf8evT59O+oULmmMd6/iwenxbGlb0NBlrVHwaw37aw49bz6Ga2XlmNZ0mQ5ma2mNJ12HN69pjQgghhLAJ8o5cCCGEEEIIUfT2/ax9XGcPjZ/IdSjsyHUy07I0p99diuyDVcfJyNK+cTr6oSrU8DHe7TsyKZLfj9/7JD2ATtExLmgc83vNp1bpWkavURASt20jrF9/EjdvNju39IjhVF38N0716lk1JiHyq3JgGVr1N14W7MqZWHYuPENtr9rM7zWfEXVGaM7bGqRwuJp2NkdNS+PK5MmoBoPmuJ+nMwufbsWTD1U1GWuWQeXjtScZ9/sB4pIzTM61Cnvn7LJkipFbOKFLIHRp4cYkhBBCCItJEkYIIYQQQghRtGLCsnfCaKnXF9xzlws7baQUmbO7PRUD/mu6velkFJtOXtOc6+3myPjORp4sv2XGoRmkZWmXMXsy8EnGNx6PvZ3pckb5YUhNJfLDaVx+6mmybtwwOdfOywv/Wd9T7t130Tk7Wy0mIQpSo66VqNnUx+j4sa0RHN9xBSe9ExNaTGBm55l4OXnlnqQo/NRdR7KD9jVSDhzk5vz5Rtdw0Ot4t089Zo1sjLujdom02zacuEavb7dz9HKsyXlWUbEZtB5vfHzVq5Co/d87IYQQQhQtScIIIYQQQgghitb+XwAjZX6ajcv1MikujcsntMuD1Wjqi51d9kectMwspq48bnTJt3vUwd3JeALlePRxVoZpN7z2cvJiTOAYo+cWhNRTp7gwZAg3//jD7FzXdm2ptmI57h07WjUmIQqaoih0fLwu3hWN70jbOv8UV8/FAdDOvx2L+y6mrV/bXHOiPRT+6GT89sa1L78iPTzCZCw9gsqz8sU21CtfyuS88JspDPlhN7/vvlD45ck6ToSydbXHUmKy+8MUVck0IYQQQhglSRghhBBCCCFE0UlPhsNGEg2+gdlNqXM4sz/K6D3GnKXI5uy4wIXoZM15jSp5MrCRn9GQVFXliwNfGB1/rsFzuDkYv2mcH6rBQPRvv3Fh8BDSzpw1OVdxcMB38mQq/vgjem9vq8QjhLXZO9jR45kgnNy0k6KGLJV1Px4j8Wb2rjRvZ29mdp7JhOYTcND9t/1lY0OFkMpGypIlJxP57jtmkyZVvF1Z8lxrhjevZHJeepaBd5eH8uL8wySmZZqcW6D0jtD/e1CM9J86uQqO/V148QghhBDCIpKEEUIIIYQQQhSdkMWQGqs91mwsKLlvqp7co12KzNPXBZ/K7gBExqXy7aYzmvMUBd7vE4BOZ6QjOLDl8hb2R+7XHKvqUZVBtQYZPTc/MqKucXnsOK598ilqhum+E461a1N18d94PToSRTH+vQhRHJQq40z3pwKN/rtMjk9n7Q/BZGZk94JSFIURdUfwV++/8HfzB0BVFH7soSPVyAa3pF27iVu82GwsTvZ2fDwwiK+HNsDZ3kiy45ZVwVfp++0OTlyNN3vdAuPXGNq+anx8zeuQoP3fSSGEEEIUDUnCCCGEEEIIIYqGqsL+n7XHHD2g/iO5DkVHJBIdnqg5vXaLcneSEZ+sPUFyepbmvEeaVKRBRU+jIWUYMvjq4FdGx19r8hp6nem+EfcjYcMGzvfrR9KuXWbneo0aRZVFC3GsabqnjRDFiV+t0rR5xPjv9LWLCWyddyrXbpYapWvwW/ffqFKqCgBRpRXmtzd+myPqk0/JiIqyKJ4BjfxZ8cJD1PAxvest7EYS/WfuZOGByxZdt0C0ezN7p6CW1FhY+bKUJRNCCCFsiCRhhBBCCCGEEEUj/ABcPao91nAEOLjmOnTKyC4YgFrNfQE4cCGGZUeuaM5xd9LzRvfaJkP6+/TfXIi/oDnWolwL2vm3M3l+XhmSk7n6zruEv/AiWbGxJufqy5al4i+z8X37LXQORrqQC1GMBbb3o+5D5Y2On9wTSfCm8FzHfF19+bX7r9QsnZ3AWddE4aSRaoOGxEQi33vf4l4uNX3dWf78Q/RvWMHkvLRMA2/+Hcwbi46SYiQBXKD0DtB/FhhLCJ9eC0cXWD8OIYQQQlhEkjBCCCGEEEKIomFsFwxklyLLwWBQOb1POwlToaYnpbydyTKovLs81OglX3m4Ft5ujkbHE9ITmHVkluaYgsJrTV8r0NJfKcdCOD9wELGLFpmd697lYaquWI7bQw8V2PpC2BpFUWg/rDblqpUyOmfn4rNcPhmT65i3szdzus6hXpl6qDqFH3rZkW6kkljili3Er1plcUyujnq+HtqQjwYE4aA3fQtl0cFwBny/k3PXtXfsFajy9bN3xBiz9i2Ii7B+HEIIIYQwS5IwQgghhBBCiMKXeB1Cl2qPVesI3jVyHYo4dZOkuHTN6bVblgNgwf5LHDfSm6GmjxuPtapsMqTZx2ZzM+2m5lif6n2oW6auyfMtpWZlcePHn7gwfDjpFy6YnKs4O1Pug6n4zZiBvnTpAllfCFtmZ6+j+9NBuHpo7/ZSDSr//BxC3PWUXMc9nTyZ3XU2Dcs25EoZhUVtjd/uiPhgCpk3blgck6IojGhRiSXPtqZyGReTc09GJtD32x2sPKq9I69AtX0VyjfQHkuLg5XjpSyZEEIIYQMkCSOEEEIIIYQofId/hyztpArNn7rn0Km92rtg7Ox1VG/sQ2xyOl/8c8rocu/3DcDezsRN2cQI/jj+h+aYk50TLzZ60ei5eZFx5QqXnhjF9a+/hsxMk3OdAgOpumQxpYcMKdAdOELYOlcPR3o8Ux87IztP0pIyWftDMOmpuf8NuTu482OXH2lWrhkrWyicK6d9fSU+idCJL+c5rkA/D1a+2IbuAUYufEtSehYvzj/Mu8tDSMu0YnkyO3vo/wPo7LXHz26Aw/+z3vpCCCGEsIgkYYQQQgghhBCFy5AFB37VHvOoBLW65TqUkZbFucPXNadXre+No7Oer9af5mZyhuacHoHleKiGt8mQZhyaQbpBOyn0eMDjlHM1fdPVEvFr1hDWrz/JBw6YnqgolHn6aarM/xPHqlXzva4QxZFv1VJ0GGm8h1N0RBKb5p64p7+Li70L33f+nlYV2/B9Lzsyjdz1cNh2kN1/fpXnuEo52TPr0ca827seep3p5Ojvuy8y5IfdXI5JzvM6FvOtBx0nGB9fNxFiL1lvfSGEEEKYVWySMIqiVFMUZYyiKN8pirJQUZR/FEXZlMevjUX9fQghhBBCCPHAO70O4i5rjzUdDbrczRzCjlwnM037afLaLcpx4mo8f+y5qDnuqNcxsafpMmLHrh9jzfk1mmNlnMrwZOCTJs83JysxkStvvUXEq69hSEgwOVdfvjyVf5+Lzysvo9gbebpdiAdEnVbladCpotHxc4evc3DthXuOO+mdmNFxBjWbdGZJaxO3Pb78mfXBS/Icl6IoPNmmKgufaUUFDyeTc4PD4+g1Yzvrj0fleR2LtX4J/Jpoj6UnwPIXpCyZEEIIUYRsPgmjKEqgoijrgdPAT8CzwCDgYaB9Hr463PoSQgghhBBCFKV9P2sft3OAxo/fc/i0kVJkzu72+NcrzfsrQjEYub/4bIfqVPQy3sNBVVW+OPCF0fEXGr2Aq72r0XFzkg8d5nz/AcQtX2F2bqmePam2fBkuzZrd93pClDStB1XHv47xfkh7V5znfPC9/V0c7Bz4osMXpAzvwcWy2ud6JkHY1HdYeW7lfcXWuFJpVo9vS4faRha4JT41k3G/H+DjNSfIyDLc11om2emh/yywc9QeP78VDswp+HWFEEIIYRGbTsIo/2fvvqOrqtI+jn/3LamEEAhJ6L33DnYU7AWxjqOICnbFrljBXsDe+ziWcXytDIqKFRsd6b2HQKghhbR79/tHAqSccyGkAr/PWnflnv3ss8+TWSO59zxn723MP4AZwPEU5KqFkEVEREREDmZblsHKn5xjnYZCdPFlwzLTcli3aJtj99a9E/lmwSamrnKON6oTydXHtgqZzo9rf2RW6izn8eu0ZkjrISHPd2Pz89n8/Ausufhi8tavD9nXEx1NwyefoOH4cXhr1z6g64kcqjxeDyeN6EztePcZJ9+/vYBtKZml2v0eP48MfIKFVx1PwOVuwjHzg/zfu6P5ZOknB5RfXHQYb1/ah9tPasc+VifjtV9XctEbf7ExLfuArhVS/XZw/L3u8e/ug+2rK/66IiIisk81tghjjOkLvAuEUVB82f1smznAl4iIiIiIVLfpb7rH+o4s1bR02ibXVXSa9azPo18vch3u3tM6EOH3usbzAnk8PdN9T4hbe9+Kz+Nzz9dF7tq1rPnnxWx5+WUIhn7qPbJHD1p8+QWxZ56JMfraIuIkopafU67uii/M+RZGXnaAr1+ZS47DvlBej5eb/vkCy07t6Dr+yEkBnvppLP9eeGCb2Hs8husGtuaDEf2pH+MyG6XQ9NXbOfX5KUxZ5rzPVbkMuA4a93WO5WXCF9ft898kERERqXg1tggDPAX42Vt8yQDGUbCkWCIQZq31lPHl/g1MREREREQqV04GzPnQOdagu+OeBktcliKrkxjFJys2keLyRPmRretxcuekkOl8vORj1qY7b1g9oMEAjmx4ZMjzS7LWsuOzz1k15Gx2/f136M5eL/E33kCzf79HWOPGZbqOyOEovnEtBg13L6Skpe7iu7cWEHRYm9BjPJz5yAdkNIh1PLdeOlzyU5Anpz/Jm/NCFIr3YUCreky88SgGtKwXst+2zFyGvT2NZ75fSsBtLcUD4fEWLEvmi3SOr/kNprssBykiIiKVpkYWYYwxjYCjKSjAGGAZ0Nlae4e19ldr7WZrbX61JikiIiIiImUz77+Qs9M51ncklJgJsjU5g63rMxy7J3apy+tTVjnGvB7DA2d0CjmzJC0njVfnvuoYMxhu7X1rmWamBNLSSL75FlLuvptgVlbIvv6mTWn+wfvUv/ZajK/sM21EDleteibQ+9TmrvG1C7Yx+Z2F5OcFSsW8ERF0HP8y1uU/60FzLF1WBXlu1nO8MPsF7AFuZJ8QE8H7I/pxw/GtQ/azFp77YRmXvj2NLRk5B3QtR/GtYdAD7vHvH4CtKyrueiIiIrJPNbIIAxxV+HP3MmT/tNauq8Z8RERERESkPKyFaS5PmEfGQedzSjUv+ct5FgzAZ9t2kOuywfWwAc1omxgTMp035r5BWk6aY+zsNmfTrm67kOcXlfnXVFaeNYT0SZP22Td26FBafPYZkd277/f4IrJX39Nb0LxrvGt82fRNfD5+Npk7Shc2onv2pN6wYa7nXvVNkPBcy+tzX2f8jPEHXIjxegy3ntiOdy/rQ1yUP2Tf35Zv4dTnpjDNZW+rA9L3Kmh6hHMsfxd8cS0ESxeqREREpHLU1CLM7nUDLLDQWjujOpMREREREZFyWvsnpC5wjvW4GPzFl88JBi1LpzkXYaIaR/P1Suf9FOpFh3HToLYhU1mXvo4PFzsvixbpi+S67teFPH83m5tL6rhxrL3sMvI3uheMADyxsTR69lkaPvoI3lrR+zW+iJRmPIbBl3UkLinKtU/q6p188th0UteUnnlXf9Qo/E2aOJ6XkAYX/VxQ3P3Xwn/xyNRHCNoD30PluHYJTLzxaHo2rROyX2p6Dv944y9e/WWF43JqZebxwJCXwO/yv9G6v+CvV8p/HREREdkvNbUIE1bkvftOmyIiIiIicnCY5rYPgYHeV5RqTV6yncy0XMczpuS6L/d1x8ntiI0M/eT5c7OeIy9YegNvgMs6XUZCVELI8wFyVq5k9YX/YOubbxXM8gkhql8/Wn75BbVPPmmf44rIvoVF+jj1mq6ERbov55eZlstn42axbPqmYu2eqCgaPPSQ63mnzLS0X1fw3/THSz7m/t/vJ1COWSMN60Ty8VUDGHFUi5D9AkHL498sZuR7M9iR5fxvX5nUbQmDH3SP//gQbF5a/uuIiIjIPtXUIsyGIu81R1ZERERE5GCWvhEWfeUcazMY6pa+OblkqsvMEq/htxznIkzXxrGc18v5Cffd5qTO4dvV3zrG6kfW59JOl4Y831rL9v/8h1VDzyF74cKQffH7Sbj9Npq+8zb+pKTQfUWkTOokRnHylZ3x+t1vawTygnz31gL++nIFtsgMk+j+/ahzwQWu5109MYA/r6D/lyu+5K4pd7kWbveH3+vh3tM78urFvYiJCL0P1A+LUznt+d+Ys27HAV9vj95XQItjnGP52fDFNVqWTEREpArU1CLMsiLvG1VbFiIiIiIiUn4z/wXBfOdYn5GlmvJyAqyY7bzc2HJfgFyXjbXHnNkJj8clSEEBZdyMca7xG3rcQJTb8j1A/rZtrL/2OjaOGYvNznbtBxDWsiUtPv4P9a64AuOpqV+7RA5uTTrUZehtPYmODQvZb+Y3a/jmtXnkZu/9dyjh9tvwuRRHG26H86fsXYZs0upJ3PbzbeQGyjdD5eTOSfzvhqPo1LB2yH7JO3Zx3qt/8O7vqw54XxqgYFmyM1+EsFouF5oBfzx/4OOLiIjIfqmR3wastdOAVYABehtjIvdxioiIiIiI1ESBPJj5jnMsrjm0HlSqeeWczeTnOD+d/bfP+Wn0c3o2pmfTuJCpfLfmO/7e/LdjrG1cW85sdabruZl/TWXlmWeR8dNPIa8BUOcfF9Li0/8jomPHffYVkfJJaFab80b3IaF56MLGqr+38NlTs9i5ZRcA3lq1aPDgWNf+Z0yztNqwtwDy47ofufGnG8nOD12A3Zdm9aL59Joj+Ge/piH75QUsYyYs5PoPZ5OefeCzcIhrBic+7B7/6VFI1SrwIiIilalGFmEKvVT4Mxy4pjoTERERERGRA7R4IqSnOMd6X1HwpHYJbkuRZRrLal/pTbJrhfu485R2IdPIDeTyzMxnXOO39b4Nr8frGNs1Zw7rrrySwJYtIa/hrVuXxi+/TIMHHsATqefIRKpKdJ1wzr6lB237JobstzU5g08en8GGZTsAqHXMMcQOGeLY12PhmokBfPl7CzG/J//OdT9cR1ae+75U+yPC7+WRs7vw3IXdiQpz/ndnt4nzUjjzxd9ZuGHngV+w13BodbxzLJALn19dUDAXERGRSlGTizDPAL9SMBvmQWNMz2rOR0REREREymr6m87tvgjocXGp5sy0HNYv2uZ4ymJ/gKDDamOjTmhDQkxEyDQ+WvwRyRnJjrGjGh3FgIYDHGOB9HSSb70Nmxt6GaLoo4+m5ZdfEHP8wJD9RKRy+MK8DLqsI/2HtCy4i+AiOyOPL5+dzcLfCraiTbzrTrz14x37Nt0CQ/8oXvidtnEaV31/Fem56eXO+azujfjq+iNpm+iyXFihVVsyOfvl3/l4+toDW57MGDjzBQh3mS2UMgd+e7bs44qIiMh+qbFFGFvwyeI8YDoQBfxsjLncGFNjcxYRERERkSJSF8HqKc6xzudCVN1SzUunbcLtHuPCsNL7yrSsH82lRzQPmcaO7B28Nvc1x5jHeLi1162OMWstGx8YQ16yc/EGwISFkXjPPTR5/TV89euHzENEKpcxhl4nN+fUa7riD3efYRIMWH56fzFT/rsUE1ObBg884Np3yJ+WZpuK/6M0Z/McRnw3gh3ZO8qdc+uEGL647kiG9gy9HW5OfpA7P53HbZ/MJSvXZY+tUGIbw8mPucd/eQI2ziv7uCIiIrJPNbagYYwZBpwCvANsAWoBbwCrjTFvGGNuNsYMN8YMK8urOn8nEREREZHDitssGIC+Ixyb3ZYi2+oJstFbujoz5oxOhPlCf615be5rrk+tD20zlNZxrR1jaZ9/wc6vv3YdN7xdO5r/3yfUveRijAnx6L2IVKkWXeM5545e1I4PPUNu7o/r+d+Lf+MfcCy1Tz3FsY8vWLAsmTdQ/N+fhVsXcvl3l7NlV+hlCvdHVJiP8ed144lzuhC+j3/PPp21niEv/c7y1IyyX6j7P6HNSc6xYB58cQ3kh571JyIiImVnDmgqaxUwxgSBksnt/mZzwElba0MvuCqyn4wxnYD5u4/nz59Pp06dqjEjERERkRokeyc83QFyHW4UNuoNI38o1bw1OYP/PDTNcbhfI/KYGlH86e/BHRN5Y1jvkGms2bmGIV8MId+WfnI8yhfFxKETiY8svRRRzqpVrDrnXGyW894PMaecTMPHH8cTHh7y+iJSfXZl5DLptfl79oBxUycxipMuasq24ecS2L7dsc9Hx3r4/IjSBZLmtZvz5olvkhgdej+a/bVww06u/WAmq7eG3ncmKszLY0O7cFb30DNoStmZAi/3g+w05/ixd8HA0WUbU0RE5CCwYMECOnfuXLSps7V2QVVcu8bOhCmi6CNllr0FGFOGV8lxRERERESkMs392LkAA9B3pGPzkr+cZ8EALAoLFDsO83m477SO+0zj2ZnPOhZgAK7ocoVjASaYm0vyrbe6FmDCmjWj4cMPqwAjUsNF1grjzFHd6XR0w5D9dmzK4vNXlxO48j7XPuf+FqTRltLPg67euZrhk4a77jlVVh0b1mbCDUdxapekkP2ycgOM+s8c7v1iHtl5gZB9i6ndAE55yj0+ZRxsmLP/44mIiMg+1fQiTNECilNhpazjiIiIiIhIZbMWpr3hHIuqBx2HlGoOBi1LpzkXYdZ6A+z0FL/5edUxLWlaLypkGrM2zWLy2smOsYSoBC7peIljbPPTz5CzcJHzoH4/DZ8ejyc6OuS1RaRm8Po8HHtRO465sC3G435rIHdXPj9NCyd14JWOS2/4AwXLkplg6ej6jPUMnzScNTvXVEjOMRF+XrqoJ2PO6IjfG/p2xvt/reXcV/9g7T5mzhTT9Xxod5pzLJhfuCxZThkyFhERkVB81Z1ACJdVdwIiIiIiInIAVk+BLUucYz0vBX/pfRqSl2wnM815L4KFJWbBNIiN4JrjWoVMIWiDjJsxzjU+qucoIn2Rpdozpkxh27vvup6XcMstRGoJWpGDijGGLsc1pk5SFN++Pp+cLOfZcdbCfLrRsPNw2i74Nx5b/N+ethvg1BmWiX1LF0Y2Zm5k+KThvDH4Ddd9psqa8/AjW9CtSR2u/3A2yTt2ufadn7yT016YwrjzunFSp9AzaAoHhzOehbV/wq5tpeOpC+GXJ+CE+w/8FxAREZE9amwRxlr7r+rOQUREREREDoDbLBjjgd6XO4aWTHWeBZOHZUmJIszdp3YgKiz0V5lJqyYxb8s8x1iHuh04veXppdrzN29mw13ueyFEH3UUdS8dFvK6IlJzNWlfl3Pv7M3Xr8xl+0b3mSMb4vuQ2S2OLgveICyv+LKKF/1imdnasrFu6ULMll1buPzby3lt8Gt0qNehQnLu0TSOiTcexS3//ZsfF6e69kvPzueqf89k5NEtuOPk9vi9+1j4pFYCnDYO/s/532R+e6ZgtkzjXuXIXkRERKDmL0cmIiIiIiIHk7RkWDzROdb2FKjTpFRzXk6AFbM3O56ywh8gt8i9zn4t6nJ61wYhU8gJ5PDcrOdc47f1vg2PKf5VyAaDbLhrNIGtWx3P8darR8PHH8N49BVK5GBWJzGKc+7sTdNO9UL2S6vTmum97iQ9uvjG9/58yy3fR2Cs06JlsD1nO1d8dwVzN8+tuJyjwnhzWG/uPLk93hBLqgG8MWUVF77+Fylp7jNn9ug0FDqe5RyzQfjiasjLPoCMRUREpCh9gxARERERkYoz812wLptE9x3h2Lxyzmbyc5zPWVBkFozHwJgzO2FM6JuQHyz6gA2ZGxxjxzU+jr4N+pZq3/bOu2T+/rvrmA0ffxxffHzI64rIwSE80sdp13Wl++CmIfvlRNRlVs9b2RzfrVh785WZXLasoet56bnpjPxuJDM2zqiQfAE8HsM1x7XiwxH9SIgJD9l35prtnPb8b/y61Lm4vYcxcNrTEOXyb9uWpfDTIweYsYiIiOymIoyIiIiIiFSM/NyCIoyTeq2hxXGOIbelyDKNZbUvuOf44v7N6NCgdsgUtmVv4425zsuheY2Xm3vfXKp917z5pD77rOuYdS+7jFpHHxXyuiJycPF4DEee05oTLu2Ax+de2A14w5nX+UpWNTuZonNfTvl6Cyf4u7iel5WfxTWTr+HPDX9WYNbQr2U9Jt54NEe0Cj2TZ1tmLpe+M42nv1tCIOg8aweA6Hg4/Wn3+B8vwNqpB5itiIiIgIowIiIiIiJSURZ9BZkuexb0GQEOS3llpuWwfpHDxtDAYn+AYOG90bgoP7cMbrvPFF79+1UySuzhsNu5bc+lZWzLYm2BjEySb7sV8vIcz4no1ImEm2/a53VF5ODUfkADhtzck8gYf8h+q1qcwYIOlxHwFPSzu3Yx6vtwjmxwhOs52YFsrv/hen5Z90uF5lw/Jpx/X9GPG49vTaiJgdbC8z8u55K3prI5Pce9Y8ezoPO5bqPAF9dArvseOiIiIhLaQVeEMcb4jTH9jTE3GGMeMsa8ZIx5sfD9DYWx0J+eRERERESk4k1znoGCPwq6/cMxtHTaJly2VmBBWP6e97ed1I46UWEhL78qbRWfLPnEMRbtj+ba7teWat/00EPkrVnreI6JiqLR+HGYsNDXFZGDW4NWsZw3ug/xTWqF7Jea2JtZPW4mJywWgOw//+LhtOMZ2GSg6zm5wVxu+ukmvlv9XYXm7PUYbjmxHe9e1pe60aH/jfpjxVZOe34KU1c673kFwKlPQa1E59i2FfDjQ+XIVkRE5PB20BRhjDGdjDFvAjuA34FngbuBq4FrCt8/WxjbYYx50xjTuVqSFRERERE53GycB+v+co51PR8i6ziG3JYi2+oJsslbUJ3p1LA2F/YJvXcDwNMznybf5jvGRnQZQd2IusXa0iZMIO3LL13HS7rvPsKaN9/ndUXk4BdTN4Kht/WiVY/6IfulxzRjeq87SYtpDsDWJ8fzRIc7OKn5Sa7n5Nt8bv/1diasmFCRKQNwbNv6TLzxKHo1iwvZLzU9h3+88Rcv/7ycoNPyZFF14fRn3Qf46xVY7b5vloiIiLir8UUYY4zHGPMwMAe4DIgETOGrVPfCV2Rh3znGmEeMMd4qSldERERE5PDkNgsGoM9Ix+atyRlsXe+8dNiCsMCeT/xjz+yE1xNizR1g+sbp/LzuZ8dYg+gGXNLxkmJtuWvXsnHMWNfxap9+OrFDzgp5TRE5tPjDvZw0sjN9Tm8Rsl9ueCyze9zExsQ+BNPT2fLgozx+1OOc2epM13OCNsg9v93D/y39v4pOmwaxkfznyv6MPDp03kELT05awoj3ZrA9M7d0h/anus5aBAtfXgs5zv9mi4iIiLsaXYQpLJ5MAEYDXgq+htnCF+wtuhQtyhSNe4C7gP+pECMiIiIiUkl27YB5zsuA0XQAJDlPUF/yl/MsGIBFYQEAhnRvSO/mdV37QcHNzaemP+UaH9VzFOHe8D3HNi+P5NtuJ5iZ6djf37gxSWMewITabEFEDknGY+h7egtOGtkZn9/9lknQ42dhh+Esb3kW6T/9TOY33/LQkQ9xftvzXc+xWMb+OZYPFn1Q4Xn7vR7uOa0jr1/Si5gIX8i+Py5O5fQXfmP22u2lgyc/BjENnE/cvhomjyl3riIiIoebGl2EAV4CTmFv8YXC97OBF4HrgUsKX9cDLwCz2FuUsYU/TwReqcrERUREREQOG3M+hDyXTZv7jHBsDgYtS6c5F2HWegPs9FiiwryMPrXDPi8/ceVEFm1b5BjrVK8Tp7Q4pVjb5udfIHvuXOfBfD4ajR+Ht1bovSFE5NDWulcCQ2/vRa248JD91jY9kbmdryL50acIbtvOvf3vLTXzrqTHpz3OW/Peqsh09zixUxITbziaLo1iQ/ZL3rGL81/7k3d+X4UtujFXZByc+YL7idPfgJW/VFC2IiIih4caW4QxxvQFrmTvzBYD/A/obK3tba290Vr7srX2g8LXy9baUdbaPkAn4CuKF2KuMMb0q57fRkRERETkEBUMwvQ3nWPRCdDBeXme5CXbyUxzWA4HWFg4C+aG49uQWDsi5OWz87N5fvbzrvHb+9yOx+z92pP5559sfdMlX6D+jTcS2a1byGuKyOGhftMYzr2rN0kta4fstzW+C9NajWDFg09jjOH23rczsovzMoy7PTvrWV6a81LxAkgFaVovik+uHsAl/ZuF7JcXsIydsJDrPpzFzuy8vYE2g6FHiELSl9dDTnoFZSsiInLoq7FFGGBM4c/dawDcbq0901q7cF8nWmsXWWuHALdSfBbNAxWdpIiIiIjIYW3lT7BthXOs13DwhTmG3JYiy8OyJCxAi/hoLj+q+T4v/++F/2ZjpvNYJzQ9gV6JvfYc52/bxoY77gSXm55RA/pTb8QV+7ymiBw+omPDGXJzT9r3TwrZLzO6Ib9k9GPpB99jjOHGnjdyQ48bQp7z6t+v8vTMpyulEBPh9/LQkM48d2F3osJCr87+9byNnPnCbyzYkLa38aRHoHZj5xPS1sJ391ZgtiIiIoe2GlmEMcZEA8ezdxbMa9ba8WUdx1r7DAXLkO1enuz4wrFFRERERKQiuM2CMV7ofZljKC8nwIrZqY6xFf4AuQbuP70j4b7QNw637NrCm/Ocr+8zPm7qedOeY2stKaPvJn/zZsf+3jp1aPj4ExhPjfyKJCLVyOv3cPylHTjinNZ7HxN1kOevxeRfYd63ywC4suuV3N779pBjv7vgXR6Z+ghBG6zIlPc4q3sjvrr+KNolxoTst3prFme//Af/mba2oCgUEQtnhViWbOa7sPyHik1WRETkEFVTv2EcBYRR8PEmANxXjrHuB/IL3/sLxxYRERERkfLasRaWTnKOtT8Najd0DK2cs5n8XOcbjgvCAhzfPoGB7RP2eflX5rxCVr7zXjQXtL+A5rHN9xxv//f7ZPzivo9Bg8cexZ+472uKyOHJGEOPwU057dquhEW4F4it8fLr5+v49aMlBAJBhnUaxn39Q9/S+HjJx4z5YwyBYKCi0wagdUItvrjuSM7p6TKzpVBufpC7PpvHrf/9m6zcfGh1PPS+3P2Er26A7DT3uIiIiAA1twjTqPCnBaZZa7ce6ECF504r0hT6U4eIiIiIiOyfGW+D29Pbfd33Q5j963rH9kxj2RAO953ecZ+XXrFjBf+37P8cYzH+GK7qetWe4+zFi0l96inXseIuvpiYgQP3eU0RkeZd4jnnzt7E1A49U2/eL8lMeP5vsjPzOL/d+Tx85MPF9qcq6fPlnzP6t9HkBfNc+5RHZJiX8ed348lzuhLuC30r6LPZyZz14u8sT02HwQ9CnabOHXcmw6S7KyFbERGRQ0tNLcLUL/J+bQWMt67I+/gKGE9ERERE5PCWlw2z3nOO1W8PzY92DGXsyGbLip2OscX+AJcd04IW8fteQfjpmU+7Lt9zZdcriYuIAyCYlUXyLbdi85xvbIa3a0fC7bft83oiIrvVbRDN+Q8cQUJk6M3pk5ds55PHZ7AtJZOzWp/FE8c8gc/4XPt/s+obbv/ldvIClVOIATi/TxO+uO7Iff47uyw1gzNf/J0vF+2Es15y7zjnfVj6bQVnKSIicmipqUWYnCLvoypgvAiXsUVERERE5EAs/AKyXCas9xkBxnnjhIkTVrhuqbAxzsP1A1vv89J/pfzFr+t/dYw1qtWIizpctOd402OPkbtypWNfExFBo6fH4wkP3+c1RUSKioj2M+ThwTTdOTtkv52bd/HpEzNYPW8LJzc/mfHHjcfv8bv2/2HtD4z6aRTZ+dkVnfIeHRrU5qvrj+S0Lg1C9svKDTDqP3O4e04c+b3dZzfy1Y2wa3sFZykiInLoqKlFmKK7dO57LYJ961TkvfNOnCIiIiIisv+mveHcHlYLul7gGMrOC7Bs+ibH2FZPkCuHtCc63P0pcYBAMMC46eNc4zf1vIkwbxgAOydNYscnzkuWASTePZrwVq1CXk9ExI0/OooTbhtIu6UfYULs55KbHWDiy3OZ/d1aBjYZyAvHv0C41734OyV5Ctf/cD1Zec57XlWEmAg/L17Ug7FndsLvdSuNF/hw6lrOX3ESeUX22SomYyN8c1fFJykiInKIqKlFmMWFPw3QyhjT70AHMsb0BYo+TrfYra+IiIiIiOyHDbMheYZzrNuFEFHbMfTGhMXE5TqflpYQxlndGzkHi5iwcgJLti9xjHWt35WTmp8EQF5yMin33e86TsxJJ1HnvPP2eT0RkVCievak86AWdJ/7Ar68DPeOFv74bDk//msR/RMG8MqgV4j0Rbp2n7pxKldPvpr03NBLnpWHMYZLj2jO/119BI3quOcCMCsllxFpl2Hd5jLO/Q8snlgJWYqIiBz8amQRxlo7i4IZK5aCQsxLxpiI0GeVVnhO0cVLt1hrZ1ZMliIiIiIih6lpb7rH+oxwbN6wYxezpyS7nvaP89pjXJYw2y0rL4sXZr3gGr+99+0YY7D5+STfdjvBdOebl76GDWjw4Nh9Xk9EZH8k3HQTCbV20Wfmk0RnbgjZd/FfG/nimVl0jOzK64NfJ8Yf49p3dupsRn43krSctIpOuZhuTeow8cajOKF9Qsh+v2S34a38k907TBgFmS7LVIqIiBzGamQRptAHFBRgLNADmGSMSdzfk40xCcBEoFdhky0cU0REREREDlTWNpjvssRX86MhoYNj6NGJC2mb7XWM5cT56d0p9M0/gPcWvkfqrlTH2OBmg+me0B2ALS+/wq7ZLvs0eDw0GjcOb2zsPq8nIrI/PFFRNHj4ISKzt9Jr1njit8wN2X/jyp383+MzaJTdijdPepM64XVc+y7YuoDLv72crbsqt7hRJyqMN4b1ZvQp7fF63AvUT+VfwIqgy14ymZvhm9srKUMREZGDV00uwjwC7H50zQBHA4uNMQ8ZY9q7nWSMaWeMeZCCZceOo6D4ApABPFp56YqIiIiIHAZmvw9uG0b3dd64+c8VW5k3K5UY63xj7+jBzfZ52c1Zm3l7/tuOMZ/Hx809bwYgc9o0trz6qus48dddS1TPnvu8nohIWUT370+d88/HF8imy/zXabbm25D9M7bn8NlTMwlbHc/bJ71NvYh6rn2Xbl/KZd9eRmqWcxG6ong8hquObcVHI/uTWNt5z5ocwrgt72oCLv+eM/9TWPBF5SUpIiJyEKqxRRhr7VbgUgqKKLsLKbHA3cACY8xWY8xfxphvjTGTjDF/GmO2AguBe4A6hecYIABcZq3dUqW/hIiIiIjIoSQYhBlvOcdiGkK700o15weCjJ2wgE65zrNg8Bq6Dmi4z0u/NOclduXvcoxd1P4imtRuQmDHDjbccWdBng6ievcm/uqr93ktEZEDkXD7bfiSkjBYWq36io6L3sUTzHPtn58X5Ns35rP9Ny9vn/g2CVHuMwJXpa1i+KThbMgIvdxZRejboi4Tbzyao1rHO8Zn2za8ETjdfYCJt0DG5krKTkRE5OBTY4swANbaL4Argd2fWnbvEWOAOKAvMAgYXPg+rkh8d99c4Bpr7WdVmXtNY4zxG2OOM8YMM8bcaYy5zhhztjGmeXXnBmCMqWOMOdUYM9IYM7rw56nGmDrVnZuIiIiIFFo+Gbavdo71vgy8vlLNH0xdy4qUdNrkORdhWnaLJzyy9HlFLd2+lM+Xf+4Yqx1Wmyu7Xom1lpT77iN/40bHfp7YWBo+9STG61IMEhEpJ29MDA3GjtlznLRpOj1mP0vYPvZ0mT5xNUv/m8mbx79No1qNXPutS1/HpZMuZe3OtRWVsqv4WuH86/K+3DSoDU7bZz2Tfw5Lgy65Zm0tKMRY6xwXERE5zNToIgyAtfZtoD8wh4KiCuydHVPyL3rRNlN4zgBrbYidQ6uHMaalMeYCY8xTxpifjTE7jTG2yGt1BV2nvjHmZWAj8BPwL+Bx4EXgM2CVMeZ3Y8w5FXG9A8ivhzHmc2ATBXv4vE7BsnGvFx5vNMZ8bozpXh35iYiIiEgR099wbvf4oeelpZq3ZeYy/rsltMnzEobz0jUd+rvsLVDE0zOeJmidZ7dc3e1qYsNj2fHxx6R/P9l1jAYPPYi/wb6vJSJSHrWOPZbYs87acxybvpres54kJn1NyPNWzN7MtFc38XK/N2hW232Jxo2ZGxk+aTgrdqyosJzdeD2Gmwa15b3L+1IvOqxYbPeyZPnW5bbSoq8KliYTERGRml+EAbDWzrHW9gKOp6CIsJK9M15KvlYW9jnBWtvTWuuyI2fVK5yJ8m3hsmkrgP8AtwHHAjGVcL1TgPnANUDdEF2PAP7PGPO+MSa6ovNwY4y5C5gKDAHCXLqFF8anGmPuqJrMRERERKSUbatg2ffOsY5nQkxiqeZx3y1hZ3Y+HV2WIouM8dOkU6iPqfB78u/8vuF3x1iTmCZc2O5CcpYtY9Njj7uOUeeCC6h94okhryMiUlESR9+FN37vUl4ROTvoOfsZElJnhDxvy7oMfn5uNePavUTrOq1d+23etZnLJl3G4m2LKyznUI5uU5+JNx5Nn+Zxxdrn2la8EjjT/cSvb4P0TZWcnYiISM13UBRhdrPW/mytvcxa24aCokI7CmbJDCh8X9da26awz0/VmauL7sCJhC6IVAhjzHHAF0DRRWUtMBP4BPgeKLlHzj+Bj4wxlf7/C2PM3cBjgL9I8y7gV+BjYApQdMfXMOAJY8ztlZ2biIiIiDiY8RalJ6IX6jOyVNP85DQ+mraW6CA0y3f+eNmmdyJer/tHz0AwwLgZ41zjN/e6GW9egORbbsXm5Dj2CWvdisS77nQdQ0Skonnr1CHpgfuLtwXz6LTwHVqsmhDy3F3pefz08krujXuKDnU7uPbbnrOdy7+9nHmb51VIzvuSFBvBhyP7c9WxLYu1v5B/NouCTZxP2rUdJozSsmQiInLYO6iKMEVZa3dYa5dZa6dZa6cWvt9R3XkdoBwKZsZUCGNMYwqWGis6u+R3oJO1tre19nxr7YlAY2AUe/fcATgDeLiicnHJ73SHa7wONLXWHmutvdBaewzQFCi5lNwTxpiTKzM/ERERESkhNwtm/ds5ltgZmvYv1mSt5YGvFmAtdMj14XFZiqxd/6SQl/1i+Rcs37HcMdYjoQeDmg4i9cknyVm2zLGPCQuj0fin8URGhryOiEhFqz14MDGnFP/qaoAWaybRZf7reIO5rucG8y1/fbiWa3Y9QNf4bq790nPTGfn9SGZtmlVRaYfk93oYfUoH3hjWm9oRBXt55eLntrxryLMu+20t/Qbmflwl+YmIiNRUB20R5iCWR8FeNW8CVwG9KFiKbEQFXmMsUHSe8B/AIGvtoqKdrLU51trngfNLnH+LMcZ9EdpyMMZ4gXFQ7Jv4M9baq6y1xWbmWGs3W2tHAs8WHQIYXziOiIiIiFSF+Z9C9g7nWJ8RlNy1+cs5G5i5ZjsAnVyWIotLiqJ+U/cVebPysnhxzouu8dt630bGjz+y/cOPXPsk3HkHEe3ausZFRCpT0r334q1Tp1R7/S1/02vmU0SF5ZU+qYiFP27ivBU307duf9c+mXmZXD35av7c8Gd5091vgzsmMvHGo+naOBaABbY5L+YPce2fM+FWbFpyFWUnIiJS86gIU7X+BdS21vaw1o601r5urZ1lrQ39yasMjDFtgKK7ouYCw6212S6nYK39ojC33cKBByoqpxKGUbB03G5LgNH7OOeuwn67daRg6TQRERERqWzWwvQ3nGPhsdC1+PM8GTn5PPp1wbM/9QOGhKDzV462/ZIwxnmGDMA7C95hy66Sq+cWOKX5KXQIJJBy9z2u59c6/njiLrrINS4iUtl89eqReO+9jrFamRvo9dtYEhuFhxwjeWEaA2dczrExg1377MrfxfU/XM+v638tV75l0aRuFJ9cPYBLBxQ8v/lS4CwWBJ2f5QzPz2DBa8PZuct99o+IiMihTEWYKmSt3R6qGFJBLgKKPm74mbXWeX2G4p4ocXy+MSai4tLaY1iJ42estc4LeBcqjD+3j3FEREREpDKsnwEpfzvHul8EYdHFml78cTmp6QUf7zq6zIIBaNs30TW2KXMT785/1zHm9/i5sfv1bLj9DgJpaY59fImJNHjk4ZBFHhGRqlD7tFOpdfzxjjF/1nZ6zn+JDkeEXpoxbdMuuv82hFP957n2yQ3mMuqnUXy/5vty5VsW4T4vY8/qzAv/6EF4WDi35F1DrsuyZJ2zpvHKM2OZn+z877aIiMihTEWYQ8/ZJY7f2Z+TCpcqm1qkKRo4saKSAjDG1AOOLtKUC3y4n6d/QPG9a441xtStqNxERERExIXbLBgoWIqsiFVbMnnrt5UAmML9YJw0bFOH2vXc92l5cc6LZAecn126uMPFRHz0NVnTpzufbAwNn3gCX1ycc1xEpAoZY0h64AE8Mc7LL+b8PZuugWkcdV6bkis7Fu+XlU+z347i3NyR4LLPfX4wn9t/uZ3/rfxfBWS+/87o1pAJNxyFSezEs/nnuPa7JuctrnvlKz6cuhZrXX4JERGRQ5Dzt6JKZox5u0STtdZesY8+FaHUdQ4lxpgkoOiuffnA72UY4megX5HjU4Cvyp/ZHoMpPktnprU2fX9OtNbuNMbMKpKfr3A87fAnIiIiUlkyNsOCz51jLQdCfOtiTQ9OWEBeoODGWtN8DzHW+Y5iu/7uT30v3raYL5d/6RirE16HS4J92fzC1a7n17vqSqL793ONi4hUNX9iAol33UXKPc5LKG557jk6fDmQuOu78e2bC8jdle/YzwYhfmZnLmlzOx/UHU/QEyzVJ2AD3D3lbnIDuQxtM7RCf49QWtavxefXHsnYL6P5e94MunlWlupT2+ziIfMawz6PY/rqbTw8pDPR4dVyW0pERKRKVddfu+HsfXbDFL4vWRwp2qciuF3nUNK5xPFca21mGc7/o8Rxp3LmU1LJ/Mq6c+AfFC8SVXR+IiIiIlLU7Pcg4LKGf9+RxQ5/XLyJn5Zs3nPcyWUpMq/fQ6ueCY4xay3jZozDunwNuL71ZaTd8QAEAo7xyO7dqX/ddc75iohUo9ihZ7Pz66/J/L30c5I2O5uUe++j6b/e5dw7e/H1K/PYsSnLdazoZY25LHEMHzR5gmx/6a/8FssDfzxAdn42F3Wour2xIsO8PH5eLybFP0P7n88l3JTe/vYY7zwuCv7Ih7NPYF5yGq/8sydtEp1nCYmIiBwqtBzZoaVjiePlZTx/xT7GK6+anp+IiIiI7BYMwAyXlW1jm0Dbk/cc5uQHeHDCwj3Hfgtt8pyLMC26xRMe6fws2JTkKUxNmeoYax7TjCM+mEfehg2OcU+tWjQcNw7j9zvnLCJSjYwxNHhwLJ6oKMd41vTp7Pj4Y+KSojnnjl406Rh69W3/pliGLb6fuKwGrn0em/YYb8+vjEVGQjt54HGkDbjDNX6P730am1SWp2Zw5ou/8/ns9VWXnIiISDWoziKMKXztT5+KeB0OWpc4XlvG89eUOK5njKnIxbTLm1/J/m3KkYuIiIiIhLJ0EqStc471vgw8e4ssb/22itVb9z613SbPS5jLR/B2/ZyXIssP5jN+xnjXdO7e0p+Mbya5xhs8OJawxo1c4yIi1c3fqBEJt9/mGk99ahx5yclERPs5/bqudDu+ScjxPBkRnL/wdpptK7noxF7PzHyGl+e8XOV7sCSceCuBhr0dY9Emh6d8r2MIsisvwM0f/83oz+aRnec8y1FERORgV11FmBYlXi33o09FvJyucyipU+I4tSwnW2szgJI7oMaWJ6ES6pQ4LlN+Dv0rMjcRERERKWraG87t3jDoeemew41p2bz4Y/EJzh1dliKLjPG7Pt392bLPWJlWeg8BgMF0JO6Vz1xTjT1nKLVPPdU1LiJSU9S54AKi+vRxjAWzski5/wGstXi8Ho46vw0DL2mPx+v+XKnJ83LKkhF0Tx7kuqD7K3+/wjOznqnaQozHi3foq1hfhGN4gHchl3i/33P80bS1DH35D1ZvKcuK6iIiIgeHatkTxlpbcsbFAfWRUmqVON51AGPsAop+SqrIxVnLm1/J/hWWmzEmAahfxtNaVdT1RURERGqULcth5U/OsU5nQ3T8nsPHvllEVu7ep5ejg9As3/lZrza9E/F6S8cycjN4ac5Ljuf48i0jvtiJ3eX80TGseXOSXDa7FhGpaYzHQ4OHH2LlWUOw2SWfgYTM338n7bPPqXPOUAA6HtmQOglRfPPaPLIzSu+xUjgq/deeQd2sBvzS6iMCnvxSPd6Z/w7Z+dnc1fcuPKaKnseNb4M54X749m7H8F2+//BLsBtrbMEMyYUpOznjhd948tyunNLFfZk1ERGRg432hDm0lCxylP5Et28lv92WHLM8yptfZeZ2LTC/jK8vK/D6IiIiIjXH9DfdY31G7u22ehtfzim+R0uHXB8et6XI+jsvRfb2/LfZlr3NMXbv7OaYZasdY8bvp9HT4133WBARqYnCmjWj/k2jXOObnniCvE17F4Jo2KYO593Vm3qNokOO23ZLb85ccANRubUd4x8t/ogH/3yQQLAKl/3qdw00PcIxFGVyeMr/Gobgnrb0nHyu+WAWYycsIDc/6HieiIjIwUZFmEPbgcw1rsqFYst6rapdxFZERETkcJSbCXM+dI416AaNC9b4DwQtD3y5oFSXTi5LkcUlRVG/aemJzBszN/Lewvccz+m7ykvHyStcU61/6y1EdOzoGhcRqanqXnIJkd26OcaCO3eycezYYsuH1Y6PZOjtvWjRLd7xnN0SM5ozdN6t1M9w3k/m02Wfcs/v95AfLD1bplJ4PHDWi+B3Lpb39Szhcm/p/b7e+X01N/93DsGgbgOIiMjBT0WYQ0tGiePIAxij5DklxyyP8uZXmbmJiIiICMDc/0JOmnOsz0gwBbNcPpq2loUpO4uF6wcMCUHnrxht+yVhTOkZMs/Pep6cQE6p9joZlhu/dt8HIfqYo6k7bJhrXESkJjNeLw0efQTj9zvGM378kZ1ff12sLSzCxylXdaHXKc1Cjl0rtw5nLRhFqy09HOMTV07kjl/vIC/gtrxZBavXCgaNdQ3f7vuYlmZDqfaJc1P4Yk5yZWYmIiJSJWpsEcYYEyh85RtjmpZjnGZFx6rIHGsgFWEO3MtA5zK+zqrA64uIiIhUP2vdlyKLjIMu5wKwIyuXcd8tKdWlo8ssGIC2fRNLtS3YuoAJKyeUajfWcvNED2E7nVev9cbH0/CxxzCeGvt1RkRkn8JbtSL++utd45sefoT8bcWXajQeQ/+zWnHiFZ3w+t3/DfQF/QxeNpw+a08FW7qg/f2a7xn10yjHInil6DMCmh/tGIoweYzzv4qH0suPPf7NYjJyDvVbOSIicqiryd9aTJFXTRqrJiv5yGKZNpo3xtSidKFjR3kSKqFc+QEJJY53HHgqxVlrU621C8ryAtzXxhARERE5GK39CzbNd471uBj8BR8Vn/5+KTuyij9BbWzBfjBOGrapQ+16xT9mWmsZP2O8Y//Tp1o6rHR/Qrvh44/jq1fPNS4icrCod/llrssqBrZvZ9PDDzvG2vRJZOhtPYmODQs5fq/kkzhpyeX4AqX7TUmewnU/XEdWXlbZEy8rjwfOegnCnLd27elZzkjvxFLtqek5vPTT8srOTkREpFLV5CIMaA+QslpW4jj0HOXSSvbfZq3dXo58Sqro/EqOJyIiIiLlMf0Nl4CB3lcAsHDDTt7/a02pHk3zPcQ4PG0N0K5/Uqm2n9f9zPSN00u1t9pguegX982Y615xObWOOtI1LiJyMDF+Pw0efQR8zkXsnV9/w87vv3eMJTSrzXmj+5DQvHbIa7TY3pUh828iJrtuqdjUlKlcM/kaMnKrYLXvuGZw4kOu4Vt9n9DarC/V/taUVazeklmZmYmIiFSqml6EOdRnrlS0RSWOW5fx/JYljheWIxcnFZ1fyfFERERE5EClb4KFXznH2gyGui2w1jJmwgKc9knu5LIUmdfvoVXP4hOa84J5PD3z6VJ9I3Iso74M4HWpwUR07kzCqFEhfw0RkYNNRPv2xF95pWt844MPEtixwzEWXSecs2/p4bjkY1HxWY0YOu9WknaW/FoNs1JnMfK7kaS57QdWkXpdBi0HOobCTD7j/a/iJVCsPTcQ5OGJ+vovIiIHr5pehKkIRefc5lZbFlWj5NoRXY0xUWU4v+QjhS5rURywkuMNKOP5lZ2fiIiIyOFr1r8g6LIEWJ+RAPxvbgrTVm0rFfZbaJvv/BR3i27xhEcWj/3f0v9j9c7Vpfpe8V2QpB3OKXiiomg0fhwmLPTSOyIiB6P4q68ivE0bx1hg8xY2Pf6E67m+MC+DLutI/yEtQz7KGplfizMWXkf7Tf1LxeZvnc8V317BtuzS/8ZXKGPgrBch3Hn2TjfPSq7ylt4rbPKiTfy6dHPl5iYiIlJJDociTOMi79OrLYsqYK1NAeYWafIBR5VhiONKHH9T3pxK+B6KPdLSyxgTsz8nFvbrWaQpv3A8ERERESmvQD7MeMc5FtccWg8iKzefR792fhK5TZ4Xv8tCwu36FV+KLD03nVfmvFKq39Hzghw733014qQH7iesWVlXsxUROTiYsLCCZck8zrdp0r74goxff3U/3xh6ndycU6/pij/ceWYigNf6OG7lPzhi1dkYW/xaS7Yv4bJJl5GalXpgv8T+im0MJz3qGr7J9yntzdpS7Q/+byF5AfflKkVERGqqw6EIc3bhTwusrsY8qsrnJY4v25+TjDHtgX5FmjKB7yoqKQBr7RbgtyJNYcBF+3n6PwF/keNfrbWV/IiOiIiIyGFiyURI3+Ac630FeDy8/NMKUtKyHbsc4YtwbI+M8dOkY/E9CN6Y9wbbc4pvO5i4zTLiO/cba7XPPIPYs84K8QuIiBz8Irt0od7l7l/hU+5/gEBG6L1bWnSN55w7elE73vnf5d26bjyOUxddSVh+ZLH2lWkrGT5pOBsyXP4mVJQeF0ObEx1DYSbAeP8r+Mgv1r48NYN//1l6TzIREZGarlqLMMaYY9xeJbr2D9XX4TXYGDPMGPM+cG2RcWZW4a9XXT6g+GyTocYY5znNxd1Z4vi/1lrnb9nl816J45uNMeGhTiiM31Si+V8VmZSIiIjIYW3aG87tvgjocTFrtmby+q8rHbvUxhCX7lxAadM7Ea9371eO5IxkPlj4QbE+3oBl1FcBIl0WDvY3aULS/ffv+3cQETkExF9/PWHNmzvG8jduJPWpcfsco16jWpx7V28atqkTsl+TtA4MnXczsbuK79u1Ln0dwycNZ93OdfubdtkZA2c8BxGxjuFOnjVc5/2yVPszk5eyNSOn8vISERGpBNU9E+Zn4CeX124G+ChEP6fXJOAd4B8U/x3/W2m/SQ1hrV1G8QJFGPCuMcb1MRhjzFnA8CJNucDYfV3LGDPGGGOLvH7ejxT/BSwpctwOcJ+HXOCxwn67LaSg2CQiIiIi5ZW6CFZPcY51Phei6vLQ/xaR67IEzMVJ9QvmnDto17/4UmTPzXqO3GDxasuFvwZpneKSm89Ho/Hj8NaqFeo3EBE5ZHgiIgqWJTPOm7vs+PhjMv/6a5/jRNYK48xR3el0dMOQ/epkJzJ03s003tGuWHtKZgqXTrqUlTucC/AVonZDOOVJ1/D1vi9oa4oXgtKz8xn33dLKy0lERKQSVHcRZjfj8NpXfH9esPcr4VfW2qLFnWphjGlsjGle8gUklejqc+pX+Irfx2UeAIqu8XAEMLlwybGiuYQbY24APilx/nhrbaXM8bXWBoDbKP5V/RZjzGvGmHol8os3xrwO3Fx0CODWwnFEREREpLymv+ke6zuCn5ekMnnRJsdwvegwGu10Ls7EJUVRv+ne7f/mbZ7HN6uKbznYZVWQs/5y3wem/qgbiezaNUTyIiKHnqiePYm7+GLXeMq99xHMytrnOF6fh2MvascxF7bFeJyLOgDhgShOXXQ1XVKOLfZNffOuzVz27WUs2bbE9dxy63oBtDvVMeQ3AR7yv0PJSv9/pq9lfnJa5eUkIiJSwWpCEcb9k0DFjJ0JjAcuqMTrlMVvwCqH10cl+jVy6bcKCDn/2Fq7HhhKwYyW3Y4EFhpjphtjPjbGTALWAc9TfK+V/wH3HdBvtp+stf8D7i3RfCWwzhjzkzHmo8JZNWuBkSX63WWtnVSZ+YmIiIgcNrJ3wt//cY416k1uQjcenLDQ9fRb+7Zgx4ZMx1jbfkmYwie5rbWMm1H8I2ztTMv1E9z3gYk+YgD1rrhiH7+AiMihKeHmm/A3buwYy1u/ntRnn92vcYwxdDmuMWfc2I3wKJ9rPw8ejlw9lGNXXogn6N3Tvi17G5d/eznzt8wvU/77zRg4/VmIjHMM9/Ms5lzvr8XarIWxExZgrXsRX0REpCZx/wtcNUItefVA4U9LQaFgx36OaYGcwv6LgBnW2n0/InKIsdb+bIw5G3gXqF/YbIDehS8nHwEjq2KWibX2UWOMpeD/A7uLQJHAcS6n5AH3WWvd5yqLiIiISNnM/RhyXTZ57juSd/9YxcotzkWWbo1jaZpui02/Lqpt38Q9739Y+wOzUmftDVrLtRODxDkPjTcujgaPP47x1IRnxkREqp4nKooGDz3I2ssud4xv//f71D75ZKJ69tyv8Zq0r8u5d/bm61fmsn2j+y2SDqkDqLMrgW/bvU22v+Dvw87cnYz4bgQvn/AyPRP373plEpMIpzwFn41wDI/2fcj3gV6ksXdpyumrtzNhbgpndgu93JqIiEhNYGrqkwPGmCB755y2sNaurc58KooxZjXQrJzD/MtaO3w/r5dAQaHjAsD50RL4Cxhnrf20LEkYY8awt1gG8Iu19rgyjtGjcIxTKNi/pqRc4BtgjLV2TlnGrmzGmE7AnseB5s+fT6dOnaoxIxEREZEysBZe6gdbHJaZiapH6ohZDHz2LzJznZ/P+ezqAcx9ZSGZabmlYo3a1mHILQU36vICeQz5cghr0/d+nD9lepDLJrvPgmn86ivEHHdc2X4fEZFDUMp997Pjk5IriBcIa96cFl98jifCdQvYUnJ25fPdmwtYu2BryH7p4VuZ1O5NtkZv2NMW6YvkheNfoF+Dfvt9vf1mLfx7CKz82TH8Yf7x3J1fvEjTIDaCH249lqiw6n6+WEREDgYLFiygc+fORZs6W2sXVMW1a/qjZZW5VFm1sNY2t9aacr6Gl+F6qdbaayjYc+Z44DJgNHAjcA7Q0lo7oKwFmMKxx5TI67gDGGO2tXZIYX6nA1cBdxf+PB1IstYOqWkFGBEREZGD3uopzgUYgJ7DeHzyatcCzLm9GlM/C8cCDBQsRbbbx0s+LlaAab7RcvFP7gWYuGGXqAAjIlIo4Y7b8SUmOsZyV69my0svlWm88Egfp13Xle6Dm4bsF5NTjyHzb6L51r37cu3K38W1k6/l1/W/hjjzABkDp44Hr9OzmXCR70d6mGXF2lLSsnn1l5UVn4uIiEgFq8lFmIGFr+OBjdWcy0HPWptrrf3JWvuutfZxa+0L1trPrLWrqjs3AGvtdmvtRGvt69baxwp/TrTWuq1wISIiIiLlMe0N53bjYV6Dc/hsVrJjuFa4jztObseSqc4f0b1+D617JgCQlpPGq3Nf3RMLz7WM+jKA32Xx2/AOHUi47bb9/x1ERA5x3pgYksaOcY1vfettds2bV6YxPR7Dkee05oRLO+DxuT/76g+Gc/LSK+i5/sQ965TkBnMZ9dMofljzQ5muuV/iW8ORN7mGH/G/jZfif0Be+2UF67cfdivQi4jIQabGFmGstb8UeTk/YiciIiIiImWXlgyLJzqGbJuTuPvHNNdTbxrUhrgwPyvmbHaMt+gWT1hkwdIwb8x9g7ScvWMNnxyk0TbncU1kJI3Gj8MT5vwUtIjI4SrmuOOIPetM52AwSMrd92Bzy37bpP2ABgy5uSeRMf6Q/fquO40Tlg3DFyjolx/M59ZfbuXrlV+X+Zr7dPQtENfcMdTRs4Zh3u+KteXkB3n060UVn4eIiEgFqrFFGBERERERqSQz3wXrPB3ll9ghzEt2LsK0qh/NsAHNWTlnM/k5zue3K1yKbF36Oj5c/OGe9gELg5zwt/t+lEn33E14y5b7+QuIiBxeEkePxhsf7xjLWbaMLa+9fkDjNmgVy3mj+xDfpFbIfm229uLMBTcSnRMLQMAGuGvKXXy+7PMDuq4rfyScOs41fIvv/0ig+IIZX8/byB8rtlRsHiIiIhVIRRgRERERkcNJfm5BEcZBIK4lt86Mcz31gTM6EebzsOSvFMd4ZIyfJh3rAvDszGfJC+YBUH+H5apJ7vvAxJxyMrHnnLOfv4CIyOHHW6cOSfff5xrf8tprZC9x2edrH2LqRjD0tl606lE/ZL+EzKYMnXcrCenNALBY7v/jfj5a/NEBXddVm8HQwXnmT4zZxf3+f5dqf3DCQvID7n9nREREqpOKMCIiIiIih5NFX0FmqmPo++gz2JqV7xg7sWMix7StT+aOHNYvdt62r03vRLxeD3NS5/DdmoIlYzxBy41fBYjKcU7H37AhDcaOxRj3fQlERARqn3giMSef7BzMzydl9N3YfOd/w/fFH+7lpJGd6XN6i5D9ovNiOXPBDbTZ3GtP26NTH+Xd+e8e0HVdnfw4+KMdQ6d7/+Joz9xibYs3pvPRtLUVm4OIiEgFOWiKMMaYWsaYfxhjXjDGTDbGzDTGLDfGrCzDa0V1/x4iIiIiItVq+puOzUFfJHet7OIYC/N5uPe0jgAsnbYJ67KqWLv+SVhreWrGU3vazpsSpF2ySy5eLw3HjcNbu/Z+py8icjhLuvcevHXqOMayFy5k69vvHPDYxmPoe3oLThrZGZ/f/XaRz/o5Yfkw+q05A2MLCujjZ47nlb9fwbr9gSir2EYwcLRr+EHfO4RTfB+c8d8vZUeWthQWEZGap8YXYYwxfmPMo8A64H3gWmAg0ANoCTQv40tERERE5PC0cT6s/dMx9FPYQHYEoxxjVx3Tkqb1CmJLpm507BOXFEX9pjF8u+Zb5m4ueEK545ogZ//hfkOu/vXXEdWzR1l+AxGRw5ovPp7Ee+5xjW958UVyVpTv+dPWvRIYensvasWFh+zXY8MgTloyAn9+Qb+X57zMc7Oeq7hCTL+rIaGTY6iFZxNXeycUa9uRlcfT3y+tmGuLiIhUoBpdhDHGxAN/AHcCsUDJNQrsfrwo8lNERERE5PA1/Q3X0FPbj3FsbxgbwbXHtQZgy/oMtiZnOPZr2y+JvGAez858FoBaWZYbJgRdv3BE9elDvSuv3O/URUSkQO3TT6PWwIGOMZubS8o992IDgXJdo37TGM69qzdJLUPPVGy+vTNnz7+ZmOx6ALw1/y2emP5ExRRivH44/WnX8LW+r2hmij8Y8P5fa1i8cWf5ry0iIlKBamwRxhjjAf4D9KKg+LL7L3gesGl3t8Kfa4EdRdqKFmvSC+NrCn+KiIiIiBx+du2Auf91DP1tOrDYNnWM3X1aByLDvID7LBiAdv2S+GjxRyRnJIO1XPN1kHrpzn29sbE0fOpJjNdbpl9BRETAGEPSmAfwxMQ4xnfNmcP2998v93WiY8MZcnNP2vdPCtmv7q4GDJ13Cw3TCgr2Hyz6gLF/jiUQLF8hCICm/aHHJY6hcJPHg753KfrcbdDCgxMWVtxsHBERkQpQY4swwIXA8eyd0bIeOBeIAY4o2tFa28JaWw+IBk6gYNmyfAqKMT7gocI+oXeYExERERE5VP39EeRlOYbezDnBsb1/y7qc1qUBAMGgZdk05yJMo7Z1CERl89rc1wAYPNvSZ5n7DbAGjzyMPyn0TT0REXHnT0wk8a47XeOpzzxL7tryP4fq9Xs4/tIOHHFO69JrkxQRmV+L0xZdS4dNBbdrPl32Kff+fi/5wfxy58CgsRAZ5xg61juXUz1Ti7X9sWIr3y5wf2hARESkqtXkIswthT8NkAocaa39zFqbh8vyYtbabGvtT9baYcCRwGogEnjDGHNdFeQsIiIiIlLzBIMw/U3H0GYby6Rg31LtHgNjzuyEMQV33ZIXbyczzXnD47b9knht7muk56bTJNVy6eSgayp1/nEhMYMGHcAvISIiRcUOHUr0EUc4xmx2Nin33ocNuv97vL+MMfQY3JTTru1KWIT7DEav9XLsygs4auU5eIIe/rfyf9zx6x3kBfLKl0B0PRj8oGv4fv+/qUXxhwwenriI7LwKmIkjIiJSAWpkEaZwL5ie7J0Fc4+1dn1ZxrDWzgAGAZspKOQ8Y4zpXsGpioiIiIjUfKt+hq3LHUMfBo4nD1+p9kv6N6N90t69ANyWIvP6PYS1yeE/i/+DP88y6ssAYS73vcLbtCbxTvcnt0VEZP8ZY2jw0IN4oqIc41nTprHjv87LUB6I5l3iOefO3tSuHxmyX+dNx3Dq4qsJz4vi+zXfc/PPN5MTyCnfxbtfDE36OYaSzHZu8n1arG399l288evK8l1TRESkgtTIIgyw+y+rAXYBHx7IINbalcA9hYdeYHT5UxMREREROchMc54Fk289fJhfeimyuCg/twxut+c4NzufFXM2O47Rols8Ly14nnybz7AfgjTd4pyCCQ+n4fjxeCIiyp6/iIg48jdqRP3bbnWNpz41jrwNGyrsenUbRHPeXb1p3N55ebDdGqe1Y+j8W6iTlcgv63/h+h+uJ8tlScz94vHAaU+DcZ6JM9z7LR3MmmJtL/+8gpS0XQd+TRERkQpSU4swDQt/WmCutTY7VGdjjD9E+D0gg4KCzunGmOiKSVFERERE5CCwYx0s/cYx9F2wN5uoW6r99pPaExu19yP2qjmbyc9xnt7ibZfB5LWT6bMkyEmz3feBSbzrTiLati1j8iIisi9xF15IVO/ejrFgZiYpD4yp0I3qI6L9nH5DN7oc2yhkv9js+pw9/2aabu/IXyl/cc3ka8jMyzzwCyd1hv7XOIZ8JsjD/rcx7F1+bVdegMe+Xnzg1xMREakgNbUIU/SRCqdlyEouRu36OJ21NheYVqTfkeVLTURERETkIDLjbbDOewL8OzC4VFvnRrW5oE+TYm1uS5FF1PLz9vbnqbfTcs3X7vsO1Bp0AnUuvLAMSYuIyP4yHg8NHnkY4zLTMHPKFNK++LJCr+n1ejjmH+049qJ2eDzGtV94IJJTFo+k24aBzNo0i5HfjSQtJ+3AL3zcXRDT0DHUy7OM872/FGv76u8NTF+97cCvJyIiUgFqahGm6F9wp1kw6SWOE/cx3qYi753/WouIiIiIHGrysmHWvxxDS4ON+DPYsVT7mDM64S1yQy1zRw7rF293HCOs3S7mb53LDV8FqOUyd92XmEiDhx7CGPebdCIiUj5hzZpRf9Qo1/imxx4jLzW1wq/b+ZhGnDmqO+HRpfcW283gYcCaIQxccRELUhcy4rsRbMs+wMJIeAyc/Jhr+C7fR8Sxs1jb2AkLCAQrbiaQiIhIWdXUIkzRv5gxDvFMIL/IcfN9jBdW5H3CAeYkIiIiInJwWfgFZG11DBXMgileGDm7RyN6Ny++PNnSaZtwW8VmgvcDhv5h6bjO5frG0PCpJ/HFhd47QEREyq/usEuI6NbVMRbcuZONYx+s0GXJdmvULo7z7upD3YahV39vt7kfZy64gTUbk7l80uVsznLea2yfOp4FrQc5huJMBqN9HxVrm5+8k09muP2hEhERqXw1tQhTdDe1UkUTW/CpYVmRpr77GK9zkfd55chLREREROTgMe0Nx+YMG8HngaOKtUWHebnrlPal+rotRWbicglumMl5v7kvQxZ/zdVE993XR3UREakIxuul4SOPYPzO2+Zm/PAD6d847xFWXrH1Iznn9l407xofsl9SRguGzruVHRuyGT5pOCkZKWW/mDFw6lPgc15+7XzfL/Q2xfeCeerbJaTt0u0gERGpHjW1CLP7r6UBSq+RUGBOkfeuC0wbY/oB7Yo0bShXZiIiIiIiB4MNsyF5hmPos8DRZBBVrO2GE9qQWLv4Da0t6zPYmpzhOMa86B+58asAHpeHqiN79CD+2mvLnreIiByw8Natib/uOtf4xoceJn9b5eyREhbp45Sru9DzpGYh+8XkxjFk/ih8q+MYPmk469IPYJZK3ZZw9K2u4Yf97+ArsoDK1sxcnv9hmWt/ERGRylQjizDW2pXA7sVKaxtjSj+SB0V3letkjBldsoMxJgF4Byj61fD3CktURERERKSmmvama6hgKbK9WsRHc9mRzUv1c5sFA9Br1u/U3+kc88TE0PCppzA+9z0CRESkctS74nLCO3ZwjAW2b2fTw49U2rU9HsOAs1sx6LKOeH3ut5z8wXBOXHo5DRd3ZfjXw1mZtrLsFztyFNRt5Rhq71nHZd5Jxdr+9cdqlqeW3GJYRESk8tXIIkyhn4u8P8Uh/j9g9wKiBnjYGPOTMeZWY8wIY8w4YAEFs2AMBYWYn6216ysxZxERERGR6pe1Deb/n2Poz0BHltnGxdruP6Mj4T5vsbZg0LJsmnMRJsuzjCMXuj9J3eDBsYQ1blTGpEVEpCIYv5+GjzwCLoXwnV9/TfrkyZWaQ7t+SQy5tQdRtcNC9uu9/hS6/X0aIyZeyZJtS8p2EV84nDbeNXyT71MasHdftPyg5cH/LaqUfXFERERCqclFmM8KfxrgkpJBa20WcA97CywGOAZ4EngNuBmoV+SUPOCuSsxXRERERKRmmP0+5Gc7ht4rMQvmhPYJDGxXahtGkhdvJzMt13GMLkumul469txzqH2K0zNUIiJSVSI6dCD+ypGu8ZSxYwmkpVVqDkktYjlvdG/qN40J2a/Vtu4cO2MYN3x1Cwu2LCjbRVoNhM7nOIaiTQ73+98r1vbr0s38sCjVsb+IiEhlqclFmP8BE4CJQLIxpmnJDtbaN4Hn2FuI2c3s7lL4Ph+4ylo7vVIzFhERERGpbsEgzHjLMbTRxvF9sNee4zCvh/tOd96CcfFU582STTCXRhtnO8b8LZqTdPfdZUxYREQqQ72rrya8TWvHWGDzFjY9/kSl51ArLoKzb+tJ696li/1FxWc1ZvDMK7nrkweZkzqnbBc58REIcy70nOKdznGe4n+zHp64kJz8QNmuISIiUg41tghjrc2y1p5lrT2j8LXWpd/NwAXAYgoKLrsLMLvf/wYca639V1XkLSIiIiJSrZZPhu2rHUMf5p9APnuXpxlxdAuax0eX6pebnc/K2ZtLtQPU3zIXX6D0LBvr89L46afxREUdWN4iIlKhPGFhNHjkEfA43/pJ+/xzMqZMqfQ8/GFeTryiE/3ObBGyX1ReDIPnXsET77/KtJRp+3+B2g3g+Htdww/63iWCnD3Hq7dm8c7vq/d/fBERkXKqsUWYsrDWfmKt7QS0BU4D/gmcCjSy1h5jrf2zWhMUEREREakq099wbM6zXj4KDNxznFg7nOsGOj8hvWrOZvJzg46xpI3ON8YSb7+diA7OG0GLiEj1iOzalbqXDXeNp9z/AIGMjErPwxhD71NbcMpVXfCFud+K8lofRy09n3fenMhv637b/wv0GQFJXR1DTT2budb3ZbG2F35YRupO52U7RUREKtohUYTZzVq73Fr7jbX2I2vtJGut8xoKIiIiIiKHom2rYNn3jqFJwT5sJm7P8d2ndiA63HnT5iVTNzq2+3PTqbt9Uan2wIDu1B027AASFhGRylb/hhsIa9bMMZafkkLquHFVlkvLHvU5547e1KobHrJf5+Rj+fz1aSzZtmT/Bvb64PRn2Ls4SnFXeyfQ0mzYc5yZG+CJSfs5toiISDkdUkUYEREREZHD2oy3KL5V4l7v5Z+4532f5nGc2a2hY7/MHTmsX7zdMZaYOgOPLT5DJrN2GO3Hv4Qxzje+RESkenkiImjw6CPg8u/0jv98TOZfU6ssn/jGtTjvrj4ktaodsl/Lzd159l/vkZaTtn8DN+4NvYY7hsJMgId871D0b+Sns9Yze63z3zsREZGKVGOLMMaYpkVeB5ynMcZbdKyKzFFEREREpMbI2wWz33cMLQo2ZbptB4DHwJgzO7kWTZZO24R1ruOQtLH4TbogUPfRMfjq1j3gtEVEpPJF9epF3D//6RpPue8+gllZVZdP7TCG3NyT9kckhezXYemxPPDlYwSCgf0beNADEBXvGDrSu4AzPcVXqx8zYSHBoMsfPRERkQpSY4swwGpgFbASaFyOcRoXjrN7LBERERGRQ8/8T2GX8xO97wUGs3uJln/0bUqnhrGuw7gtRRaVmUJMxrpibStO60zLQWcfWL4iIlKlEm6+CX+jRo6xvHXr2Pzcc1Waj9fn4fhLOnDEua2wxrkQ4rU+Ev7owQtTX9q/QSPj4MSHXcP3+t8nhr3Fpr/X7eCz2cllyltERKSsanIRBgq+KVbEugaGihtLRERERKRmsRamveEY2mmj+CJwJACxkX5uO7Gd6zBb1mewNdl5g+akTdOKfZhe2dDL0WNeOeCURUSkanmio2nw8EOu8W3v/ZusWbOrMCMwxtBjUDPOuL4bQa/zbJfY7PqsmpjNpFXf7t+g3S6EZkc6hhLMDm71/bdY2xOTFpORk1+mvEVERMqiphdhNCdURERERGRfkmdCyhzH0P8FjmEXEQDcdmJb4qLDXIdxmwUDkLRp+p73WWGw8+4riItxXvJFRERqpugBA6hz3nnOQWtJuecegjk5VZsU0KxTPEdd0No13nZLb/71+ecs3b5034MZA6eNB4/PMXyJ93s6m70LpWxOz+HFH5eXOWcREZH9VdOLMJq5IiIiIiKyLy6zYAD+HRgMQIcGtbmoXzPXfsGgZek05yJMne1LicjZu9TZp2fV45yB1x1gsiIiUp0S7rgdX2KiYyx31Sq2vLifS39VsO5HN6dht1qu8X7Lz+Ke/40lLSdt34MldIAB1zuGvMbysP9tPAT3tL392ypWb8ksc84iIiL7o6YXYSpCeJH32dWWhYiIiIhIZcjcAgs+cwz9GujCKtsAgDFndMTrcX/Gaf3ibWSl5TrGkjZN2/P+l86GY664lzCv+4waERGpubwxMSSNecA1vvXtt9k1b34VZlTAGMNpw3sSVsc57g+G03n2Sdz102gCQeely4o59g6IbeIY6u5ZyUXeH/Yc5waCPDxx4QFkLSIism+HQxGmRZH3O6stCxERERGRyjDrPQg4F092z4I5o1tD+rWsF3IYt6XIPIFcEjYX7BGQEgdTL+rKSc1OKkfCIiJS3WIGDqT2mWc4BwMBUu65B5vr/LelMoVF+jjrmt7gcV6dPj6rMfyZwItzXtyPwaLhlCdcw3f4PiaevbNqJi9K5Zelm8ucs4iIyL4cDkWYSwp/WmBFdSYiIiIiIlKhggGY8Y5jaL2N54dgTyL9Xu4+tX3IYXKz81k52/nGU/zWufgC2eR74PkzvYw66i6M0arBIiIHu8TRo/HWcy7Q5yxdypbX3Ze6rEwJzWpzxNnu+8N03nQMk3+exnerv9v3YO1Pg7anOIZqmyxG+z8o1vbghAXkBYKO/UVERA6U8y5lVcQYM2w/u55rjNlShqHDgQbAicCAIu3TnLuLiIiIiByEln4LaWsdQx/mn0AQD9cf35oGsZEhh1k1ZzP5uc43nZI2FnyE/uhYD60GnET3hO7lSllERGoGX1wcSfffT/KoUY7xLa++SszgQUS0a1fFmUH3E5qydtFW1i/c4RgfuOIfPPbDOFoMbUGbuDahBzvlCVj5M+TvKhU6x/sbnwSO469gRwBWbM7kX3+sZsTRLcv5G4iIiOxVrUUY4F0KZqiEYoCnynGNoo/pfViOcUREREREapbpzk8p51gf/wkMpGndKK44qoVjn6IW/7bOsd2fu5O62xfxd3PDpAF+vux5U3myFRGRGqb2SSey86STSP/229LB/HxS7r6H5h//B+Or2ttHxmM48bLOfPDgn+Skl97/JTwQxVGLz+emH27iwzM+JDY81n2wuGZw7O3ww4OO4Yd873Bq7mPkFd4ie27yMob0aER8rXDH/iIiImVVU5YjMw6vfcX39SrpVWvtjMpIXkRERESkym1ZDit+dAxNDPZnG7W5//SORPi9IYfJ2LaL9cuct05MTJ1BemSQF8/w8I8O/6RJbecNjkVE5OCVdN+9eGOdixjZCxaw9R3nZS8rW2RMGCeP6OoaT0pvSeKiztw55U4CwdKFmmIG3ADxzjN62niSGeH9es9xek4+475dckA5i4iIOKkJRZjKWlB6dzFmJXCDtfa6SrqOiIiIiEjVm/GWa+jf+YM5tm19TuiQsM9h/n71azDOXwuSNk7j5dM82LqxXNn1ygNOVUREai5ffDyJ997jGt/ywovkrFxZhRnt1bhdHL1Pa+4a75k8mNULN/PSnJdCD+QLg9PGu4Zv9H1GY7N3b7SPZ6xjfnJaWdMVERFxVN3LkV3m0m6AtwvfW+AOYH/3hLFADrADWGStdV4kW0RERETkYJWbSWDW+zjNcZkXbM58TxsmndERY0I/77RrwQKWL8qC6LhSsajMFH7tsJ7Zrb3c0e3q0Eu9iIjIQa326aezc+LXZPz8c6mYzc0l5e57aPbB+xhv6NmVlaHPqc1JXrKdlOWliyIGD8cvu5j3I5+iQ70ODG422H2gFkdD1wtg7selQpEmlwd8/2Jk3m0AWAtjvlrAJ1cP2OffUhERkX2p1iKMtfZfbjFjzNvs3S/mExVTREREREQKzf0v3lznJcTeC5zI5Ue2pFX9WiGHCGZmsviuJ8hoNNwxHpY5jQ9P89AkpgkXtruwvBmLiEgNZowhaewYVp52OsGMjFLxXXPmsP2DD6g7bFiV5+bxehh8eSc+fngaOVn5peLRebEMXP5P7om8l+a1m9Mmro37YCc+DEsnQXbpgs5g7ywGB2bwfbA3ADPWbOervzdwVvdGFfa7iIjI4akmLEcWih43EBEREREpylrSp7ziGNpho/kz8jiuP771PofZ+MijrAs2do3/t98s8n2Gm3vdjN/rP+B0RUTk4OBPTCTxrjtd46nPPEvuunVVmNFeMXUjOOHSDq7xpmkdaLu2P6N+GkVaTohlxGolwAn3u4Yf8L9HJNl7jh/7ejFZuaULPyIiImVRk4swLQpfLYHkas5FRERERKRGyF31BzFpzhsG/zdwHLec2o2YiNBFk7SJE9nx2edsSujtGM/0LGVl0g56JvRkUNNB5c5ZREQODrHnnEP0EQMcY3bXLlLuvQ9rrWO8srXoVp+uA90fHui77nRyNni4a8pdBIIB94F6XQYNeziGGpst3Oj7fM/xxp3ZvPLzigPOWUREBGpwEcZau6bIK8RfTxERERGRw8eaSc85tgetYXbCUIbsY9mU3PXr2fjAGLbFtSM3vI5jn2nNZwBwa+9btRa+iMhhxBhD0oMPYaKiHONZU6ey47+fVHFWex0xtDXxTZyX2/RaL4OWXcq0NTN5ac5L7oN4vHD6M2Ccb4mN8H5NG7N+z/Frv65k3bascuUtIiKHtxpbhBERERERkeJSN6yl+abJjrFfgl25duhgPB73oonNy2PDrbcRzMhgY2I/xz75JpeV9eZwSvNT6Fq/a4XkLSIiB4+wxo1IuPUW13jqk0+Sl5JShRnt5fV7OGlEZ/zhXsd47Zx6HLvyAt6Y+wbfr/nefaCGPaDPCMeQ3wR42L93m+Lc/CCPTFxU3tRFROQwpiKMiIiIiMhBYvpnz+A3zpPE17S6iC6NY0Oev/nFl9j199/ke8PZXL+bY5/Vdedjwiyjeo0qd74iInJwivvHP4js3csxFszMJOWBB6ptWbI6iVEce1E713irrT3okDqAe367h+Xbl7sPNPAeiE5wDPXzLGaoZ8qe40kLNvLH8i0HnLOIiBzeVIQRERERETkITF+ZSs/NXzjG1pPAGUOHhTw/86+pbH39dQA2x3cj6A137Le0/nT+2fGfNKoVelkzERE5dBmPh4YPP4wJd/5bkfnrFNK+/LKKs9qrXb8k2vdPco0fuWooETvrMOqnUaTlpDl3iqwDJz3qOsbd/g+JJWPP8dgJC8kPBA80ZREROYxVSxHGGLOyxKvULmcOfSripd3UREREROSgEwhavvv0bRqYbY7xlDYXUa+28/r9APnbt7Phjjug8KnljYl9Hftl+dNJT9jIiC7OS7SIiMjhI6x5c+qPcp8VuenRx8hLTa3CjIo7+sK21El0/tvns2EMXnopG3akcNeUuwgEXbYa7nIutDjWMRRvdnK77+M9x0s2pfPhtLXlzltERA4/1TUTpjnQrPDn7tf+9KmIl4iIiIjIQeWjaWsZuNP5ieMcwuhx5g2u51prSbn7HvILb5TlhMWyPc55GZfl9WZydY+rqB1Wu/xJi4jIQa/upcOI6Oq8P1hw5042PvhgtS1LFhbh48QRnfD6nG9t1d3VgCNWD+W35N94ac5LzoMYA6eNB4/fMXyR90e6mb1Lmo3/binbM3PLnbuIiBxeqns5sv35S20r6CUiIiIictDZnpnLZ99O5gjvQsf4jlZn4ouJdz//ww/J+OmnPcebEnuDcf4akNE8mfPanVe+hEVE5JBhvF4aPvIwxu9cpMiY/APpkyZVcVZ71W8Sw5HntnaNd0w9glZbevDGvDeYvGayc6f4NnCk84wfj7E84n8bLwUzadJ25fH090vLnbeIiBxeqqsIs7bIa03hz1B9KuLldh0RERERkRrr6e+XclbeN67xxBOud41lL1lC6hNPFmtzW4psW+RGLh/4T/wuTwOLiMjhKbxNG+Kvu9Y1vvGhh8nf5rxcZlXofGwjWnRzfxjhmJUXEJNdj3t+u4cVO1xWqT/mNqjTzHl8z2ou8X6/5/iDqWtYlLKzXDmLiMjhpVqKMNba5tbaFkVf+9OnIl7V8fuKiIiIiByIhRt28sXUxQz1/uYYz0nqCQ17OMaCu3aRfOut2Ny9y6akRzcio1Zjx/5ZLVI4vunA8ictIiKHnHpXXEF4hw6OscC2bWx6xH2D+8pmjOH4YR2oFRfuGA8PRDJ46aVk5+Yw6qdR7Mx1KKD4I+HUca7XuNX3CfXZDkDQwtgJC6ptGTYRETn4VPdyZCIiIiIi4sBay5ivFnCW5zdizC7HPuEDrnI9f9PjT5C7vPgTvxuTnGfBAJx76mCMMQeWrIiIHNKM30/DRx8Bn88xvnPiRNJ/+KGKs9orItrPiVd0wnic/44lZDaj79rTWLNzDXf9ehdBGyzdqe2J0P50x/NjzC7u87+/5/ivldv4Zv7GCsldREQOfSrCiIiIiIjUQBPmpjBt9VaGeb9zjNuoetBxiGNs53ffsePjj4v3x7Apobdj/+zEbfRq7bzxsoiICEBEhw7UGznCNb5xzFgCaWlVmFFxDVrXoe/p7gugdE85gSbbOzAleQovzXnJudMpT4A/2jF0pvdPjvLM23P8yMRFZOcFypWziIgcHlSEERERERGpYbJy83l04iL6exbR1pPs2Mf0HAb+iFLteRs2kHLvfaXat8W1Ize8juNYRxzbqVz5iojI4SH+mmsIa93KMZa/eTObSuxDVtV6ntyMRu3iXOPHL7+YqNzavD73dSavmVy6Q2xjOO4u1/Mf9L1DGHkAJO/Yxeu/rix3ziIicuhTEUZEREREpIZ56aflbNyZzSVus2CMB3pfXro9P5/kO+4guLP0evcbE/s5j+UN0mtAm/IlLCIihwVPWBgNH3kEPM63k9I++4yMKc77mFUFj8cw+PKORMb4HeOR+bU4ftklGGu457d7WLFjRelO/a+BhI6O57f0bOQq74Q9xy//vJwNO5yXDBUREdlNRRgRERERkRpkzdZM3vh1FYls4yTPDMc+pu3JUKdpqfYtr77GrhkzS7Xne8PZXL+b41gtusUTFum8xr+IiEhJkd26UXf4cNd4yv33E8jIqLqESoiODeeE4c5FFIDGO9vSI3kQWflZjPppFDtzSzy44PXDaU+7nn+970uamk0AZOcFeeybxRWSt4iIHLoOqiKMMcZvjBlijBlnjPnFGLPIGLPJGLOx8P0vhbEhxhjnxx5ERERERGqwh/63iNxAkIt8P+IzDhsHA/QpvSZ/1syZbHn5Zcfum+O7EfSGO8Y6DWh8wLmKiMjhqf6NNxDWrJljLD8lhdTx46s4o+KadapHj8GlH1bYrfe6U0na2ZI1O9cwespogrbE39tmA6D7xY7nhps8HvS9C1gAJvy9gWmrtlVQ5iIicig6KD8ZZesAAQAASURBVIowxphwY8xYYD3wKXAzcBTQDqgPJBS+P6ow9imw3hgz1hhTeqFsEREREZEa6OclqUxetAk/+fzD+6Nzp7qtoOXAYk2BtDSSb7sdgs5Fm5XN+jq2R8b4adqxbrlyFhGRw48nIoIGjzzsGt/x0X/InDqtCjMqrd9ZLUloXtsx5sHDoGXDCM+L4tf1v/LyHIeHGAY/CJHO+8sc5/2bkz3T9xyP+WoBgaCtkLxFROTQU+OLMMaYnsDfwL0UFFxMkbAt8dpzWmHfe4E5hWOIiIiIiNRYuflBHpywEICTPNNJMDucO/YZUWwtfmstKffdT35KimP37TGxZEe1c4y16ZOIx1vjvxKIiEgNFNW7N3H//KdrPOXeewlmZVVhRsV5fR5OvKITYRFex3it3DiOW/EPsPDa3Nf4Yc0PxTtE14NBY1zHf8D/HtEU7AezMGUnH09fV1Gpi4jIIaZGf+MyxvQFfgDaUFBY2V1sMft4Fe3XFvjBGNOnqvMXEREREdlf7/6xipVbMgG4xPe9cyd/FHS/qFjTjk8+If2771zH/d+RfTEuH/vb9Us6sGRFRESAhFtuxt+woWMsb906Nj/3fBVnVFxs/UiOu7i9a7zF9q502ng0AHf/djcrdqwo3qHHMGjsPJu0gdnGTb5P9xyP+24JaVl55U9aREQOOTW2CGOMiQMmALGFTbuLKrOB24CjgSQgEogofH90YWw2xYsxscBXxpg6VfcbiIiIiIjsn9Sd2Tw3eRkA7c1a+nlcNvntch5E1tlzmLN8OZsefcx13MndDREe55tHcUlR1G8ac8A5i4iIeKKjafDwQ67xbe+9R9bs2VWYUWlteifS8WjnQhHAEWuGUC+zEVn5WYz6aRTpuel7gx4PnP40GOfbZ5d5J9HerAVgW2Yuz/2wrEJzFxGRQ0ONLcIAj1CwpNju4ksqMNRa29ta+7S19ndrbaq1Nsdam1v4/vfCWG/gHGBTkfESCscUEREREalRHp+0mMzcAADDvO6zWug7cs/bYE4Oybfcis3Oduy6vh58NaAx9bKcbzy165+EMcYxJiIisr+ijziCOued6xy0lpR77iWYk1O1SZVw1HltqNsw2jHmtT4GL70UXyCMNTvXMHrKaIK2yB5rSV2g39WO5/pMkIf9b2Mo6P/en6tZnpru2FdERA5fNbIIY4wJBy5mbwFmI3C0tfaL/R3DWvs5cAwFxZvd41xcOLaIiIiISI0wc802PpuVDEBtMhni/d25Y5P+BTeCCqU+NY6cpUsdu+Z54bmzvDTf4TwLBqBtXy1FJiIiFSPhjjvwJSQ4xnJXrmTLSw4b31chf5iXE0d0wud3vg1WJzuRo1edB8Av63/hlb9fKd5h4N0Q08Dx3N6epZzn/QWA/KBl7ISFWGsd+4qIyOGpRhZhgOOAWuxdUmyUtXZ5WQcpPGdU4TgUjnlcxaQoIiIiIlI+gaBlzFcL9xyf4/2VKOPytHCRWTDpP/7E9vffdx3338d7WJvgoc2WXo7xRm3rEFM34sCSFhERKcEbE0PS2DGu8a1vvcWu+QuqLiEH9RrW4qjz27jG223uS9vNBdsJv/r3q/yw9oe9wfAYONl9+c/Rvo+IYycAU5ZtYfKi1IpJWkREDgk1tQjTrMj7VOD/yjHW/1F8WbJmbh1FRERERKrSJzPWMS85DQBDkIu9k507RidAhzMByNuUSsrdd7uOObO1YVIvQ6O0tkTnxTr2addfs2BERKRixQwcSO0zznAOBgKk3HMPNje3apMqoeNRDWndy3nGDsDRK88jdld9AO6ecjcrd6wscvIQaHW843lxJoM7ff/Zc/zwxIXk5AcqJGcRETn41dQiTFzhTwvMsuWYx2mtDQIzHcYWEREREak2aVl5PPntkj3HR3oW0MqT4ty516XgC8MGAmy44w4CO3Y4dkuv7efl0zxgzJ6neUvy+j206uF+A0pERORAJd49Gm+9eo6xnCVL2PLGG1WcUXHGGI67uD21451ng/qD4QxeOhxP0EtWfhajfhpFem767pPh1HHgdV7l/kLfz/QyBX/X12zN4q3fVlXK7yAiIgefmlqEKTpvc0cFjJdW5P3mChhPRERERKRcnpm8lG2Ze58IHub9zrmj8UKvywDY+uZbZE2d6tjNGsMzpwVIjzL4AmG02NbVsV/LbvGERfrKl7yIiIgDX1wcSffd5xrf8uprZC9x3s+sqoRH+jjxis54PMYxHp/VmAFrzgJg9c7VjJ4ymqANFgTrtYKjb3Ed+2H/23gpmAHz4o/L2bQzu2KTFxGRg1JNLcKsL/I+sQLGKzrGugoYT0RERETkgC3ZmM6//1qz57ghWzjBM8u5c/tTIbYRu+bMYfPzz7uO+eMxscxvXvDxvsW2rviDzk/qtu2npchERKTy1D75JGJOPNE5mJdXsCxZfn7VJlVCYova9B/SyjXeZeOxNNvWGYBf1v/CK3+/sjd45E1Qt6XjeR086xjunQRAVm6AJ75ZXGE5i4jIwaumFmGmAOmAAfobYyIPdCBjTBTQv/AwA/it/OmJiIiIiBwYay1jvlpAILh3xd1/+ibjNS4r8Pa9kkB6Osm33Q4B5/XlM9o05M1+6XuO3ZYii4zx07Rj3QNPXkREZD8k3Xcv3ljnfcmy589n27vvVm1CDroPakLTTu5/EweuuIjonDoAvPr3q/y49seCgD+iYFkyFzf7PiWJrQB8NjuZWWu3V1jOIiJycKqRRRhrbTbwceFhJHBTOYa7CYiiYH+Z/7PW7ipXciIiIiIi5fDN/I38uXLrnuMw8rjA+7Nz5/rtsc2OYuOYseStX+/YxURH8fApmQS8BcuqROfE0jitrWPfNn0S8Xhr5FcAERE5hPjq1yfxnrtd45uff4GcldW7Z4rxGE64tCNRtcMc4xH50QxaNgxjC/5u3v3b3axMW1kQbH0CdDrb8bxaJpv7/f/eczz2qwUEgwe81bGIiBwCavI3sAeAbYXv7zfGnFHWAQrPuZ+CAsyOwveHPGPMu8YYW0Gv1ZWQ38/lzGl4ReckIiIiUhV25QZ4ZOKiYm2neqYSb3Y6n9BnBGlffsXOiRNdx5w5rA8rozP3HLfe0gvj8jG/nZYiExGRKlL7jDOodeyxjjGbm0vKvfdig8Eqzqq4qNphDLq8Y8E6LA4apLei17qTAMjMy2TUj6NIzy2ceXrSYxAW43jeqd5pHOeZA8Df69P4dJbzgxQiInJ4qLFFGGttCnAmkAaEA58aY8YZY+L2da4xJs4YMx74FAgrHOMMa21yZeZ8iNLMIREREZEK8sovK0jeUfzj1TDfd86dw2qREzuAjQ895Dqe77TBjKs7tVhb2y3OS5HFJUVRv6nzzSIREZGKZowhaewYPLVqOcZ3zZrF9g8+rOKsSmvSvi69Tm7mGu+VfCIN01oDsHrnau6ecjdBG4TaDWCg+2yfsb53CScXgCcmLSE9O69iExcRkYNGjS3CGGOOAXzA7cD2wvc3AxuMMV8YY0YbY84xxgw2xgwqfD/aGPMlsIGCZch8FMymuQPwGWOO2Z9XtfzCNden1Z2AiIiIyKFg3bYsXv1lRbG2TmYVPT3LHfsHO53HhtEPYLOyHOP+Zk15a7AhP7h3c+N6mQ2pl9XQsX+7/kkY4/Kor4iISCXwJyWRcOcdrvHUp58m12W5zarU9/QWNGjlvIeNwcMJy4YRkRcNwM/rf+bVv18tPPFKSOrieF4zTyrX+r4EYEtGDi/+6Pz3XkREDn2+6k4ghJ8pWEZsN0vBBNFw4IzCl5vd3y4tEAe8VobrWmr2/y774zZgzAGc1xv4pMixBd6uiIT2oUUZ+2+plCxEREREKtEjExeRm1982ZVh3u9d+2+eFU72woXOQb+fzHuvYuLKB4o1t9nsPAsGA237aikyERGpenXOPZedX39N1p9/lYrZXbtIufc+mr7zdrU+KODxehh8RSc+fngaOVn5peLRebEcv/xivm7/OhjLK3+/Qoe6HRjYdCCc9gy8Nchx3Ku9E/gicBSrbAPe/n0VF/RpQsv6zjODRETk0FVjZ8IUUfSvsGVvYca4vEr2C9U31BgHLWvtFmvt6rK+gJKfGn6y1q6sgnzLmmtGZeckIiIiUpF+W7aFSQs2FmuLJYOzvL879s8I9mLbx1+5jlf/5pt4Mr34hGVjDW229HLs36htHWLqRpQxaxERkfIzxtDgoYcwUVGO8ay//mLHJ584xqpSTN0Ijh/WwTXedEdHuqYct+d49G+jWZm2Epr0gZ6XOp4TbvJ50PcOYMkLWB4usS+ciIgcHmp6EcYU+bm/hZJDrqhSFYwxkcCFJZrfqo5cRERERA4leYEgYyYsKNV+nvcXIkzp9eHzsz1s+DbddbzoI49kxnFJzN08t1h7o7S2ROc5L6XSrp9mwYiISPUJa9yYhFtucY2nPvEkeSkpVZiRs5bd69PluMau8X5rz6B+RlMAMvMyGfXjKNJz02HQGIiq53jO0d75nOH5E4AfF6fy05LUCs9bRERqtpq87NbY6k7gMHMuUPRb+w7gs+pJRUREROTQ8a8/VrM8tfhEXkOQi72TS/W1FjbMSCSQ5jzx11uvHvGPPMizv19eKtbWZSkyr99Dqx4JB5C5iIhIxYm76B/s/OYbds2cWSoWzMwkZcwYmrz6arXvX3bEOa3YsHwHW9eX/lvstV4GLb2UT7s+Ra4vm9U7V3P3b3fz3MDn8Ax+CL681nHM+/zv83NOd9KJ4qH/LeTIVvGE+Wr6c9EiIlJRamwRxlqrIkzVuqLE8QfW2uxqyURERETkELE5PYfnJi8r1X6sZy7NPZtKtW9bEk3mevebTw0ff4xPtn5PckZysXZfIIwW27o6ntOyWzxhkTX2Y7+IiBwmjMdDg4cfYtWQs7E5OaXimb/8ys6vviL2rLOqIbu9fH4vJ43oxH8fm0F+TqBUPDYnnmNWXsDkNv8CAz+v+5nX/n6Na7pdBbP/DWv/LHVOgtnBLb5PGJt/KSs3Z/Len6sZcXTLKvhtRESkJlDZXTDGtAKOKdH8ZnXkIiIiInIoeerbxaTnlN7g9xLv96Xadm3zkzq3tutYdYcPJ79PF16b+1qpWMtt3fAHwx3Pa6ulyEREpIYIb9GC+jfe6Brf+Ohj5G/eXIUZOYtLiubYf7R1jbfe2pP2qf33HL/898v8tP4XOO1p8Dg/+DDM+x2dzCoAnpu8jM3ppQtRIiJyaFIRRgAup/jeObOstXOqKRcRERGRQ8KcdTv474z1pdobm1QGeuYUawvkGZL/iIOg8yyYiI4dqX/Lzbw699WCtedLcFuKLDLGT9OOdcuevIiISCWpO/xSIro6z94MpqWx8cEHsdZWcValte/fIOSeakeuPoe4rMQ9x6N/G83KiAjo77wkmddYHvG/jYcg6Tn5jPt2SYXnLCIiNZOKMIc5Y4wXuLRE81tVnMNzxphpxphUY0yuMWabMWaZMWaCMeYOY4z74yciIiIiNVB2XoA7/2+uY+xi72Q8pvjNpU0zY8nLcH5y1kRF0XD8ONZlp/Dx4o9LxaNzYmmU1sbx3DZ9EvF49ZFfRERqDuP10vCRh8Hvd4ynfz+Z9G+/reKsnB3zj7bEJkQ6xvzBMAYtHY43UPB7ZOZlctNPN5FxxPVQu7HjOd09K7jQ+xMA/525jnnr0yoncRERqVH0jUxOBhoVOd4FfFjFOdwI9AHqA34gDmgNnA48ASwyxnxWuGxapTDGJBhjOpXlBVRaPiIiInJwe/ybxSzZVHrGSji5/MP3S7G2tNWRpK2Och0r6d57CW/RgmdmPkO+Lb20WestvTAuH+tDPcErIiJSXcLbtKH+tde4xjc++BD527dXYUbOwiJ8nDSiMx6f80zVersacsSas/ccr0pbxehpDxM8+VHXMe/0fUQ90rAWxkxYUCNm/YiISOVSEUYuL3H8qbV2R3UkEoIHOBuYZYw5p5KucS0wv4yvLyspFxERETmI/bQklXf/WO0YO9P7B7HsLc7kZnjZOCPWdazap51G7NlDmLFxBj+s/cGxT8dtAxzb45KiqN80Zv8TFxERqUL1RowgvEMHx1hg27YasyxZ/aYxHDG0tWu806Yjabm1+57jn9f9zGu5G6DNSY79Y00Wd/sLnn2duWY7X87ZUJHpiohIDaQizGHMGFMfOKNEc1UuRTYPeBK4COgLtAW6ACcA9xbGi6oNfGyMObUKcxQRERHZb1sycrj9E+dlyMByfa2f9x4FIfmPOIL5zh/J/Y0akTTmASyW8TPGO/apl9mQ2IwEx1i7/kkY4/zkroiISHUzfn/BsmRer2M8/ZtJbH7uuSrOylnXgY1p3jXeNX7siguIyd67B9vLf7/Mzz3PBV+EY/9zvFPoZxYB8Ng3i8jMKT3TVUREDh3VUoQxxqws8VqxH30q4lXqOoe5YRQs/7XbCuAXl74V6UOgs7W2q7X2TmvtR9ba6dbaZdba+dbaH621j1hruwIXA0XX8vBSUIhp5DiyiIiISDWx1nLn/81lS0aOY/z4WmtplrN0z/HmeTFkbwtzHszrpdH4cXhjYvhm1TfM3zrfsVv/dOenbDHQtq+WIhMRkZotomNH6o0c4Rrf+uprbH333apLyIUxhhOGdaBWXLhjPDwQxaBll+IJ7r3NNnr2M6zqP9J1zIf8b+Mnn007c3jlZ92uEhE5lFXXTJjmQLPCn7tf+9OnIl6y12Uljt+2VTDX11r7urV2wX72/YCCmTFZRZprAQ9UcFovA53L+DqrgnMQERGRg9j7f63hh8WprvHHm0zd8z5zYxhbF9Vy7Vv/xhuJ7N6dnEAOz81yfgrYWEPLzT0cY43a1iGmrvPTtyIiIjVJ/LXXEtbKfcvV1MefYMcXX1RdQi4iavkZfHkn3CaZJmY0p8+60/YcZ+RlMCr9bzLqOS9l1taTzBXerwF4fcpK1m7NcuwnIiIHv+pejmx/bvjbCnpJEcaY/kCnIk0B4N3qySY0a+10CpYnK+pSY0x0BV4j1Vq7oCwvCmYOiYiIiLBsUzoPT1zkGr9pQBwJawtutORne9jwVxzgfBcnql8/6o24AoD3F75PSmaKY79Tws4jL8P5Y267fpoFIyIiBwdPWBgNH38ME+YyOxRIuede0n/8sQqzctawTR36nN7CNd5jwyAa72i/53jVztXc3aI9QZf+N/o+pxGbyc0P8sjXCys4WxERqSmqqwiztshrTeHPUH0q4uV2ncPVFSWOv7HW1uTd4F4GdhY5DgMGVlMuIiIiInvk5Ae48T9zyMl3vsXSsUFtrq/zJwRysRZSptUhP9t5/XtvnTo0fPIJjNfLtuxtvDnvTed+xkv/9BOdY34PrXo47xMjIiJSE0V26UKjp8eDx+U2VSBA8k03kzltWtUm5qDXKc1p1LaOa/yEZRcTlVt7z/FPW+fyersjHPtGmRwe8L8HwLcLNvH78i0VmquIiNQM1VKEsdY2t9a2KPranz4V8aqO37emKZxBckGJ5reqI5f9Za3NAX4q0dy1OnIRERERKWrct0tYlLLTMRbu8/D8BV3wzXoXgO3LosnY4L5MWINHH8WfmAjAK3NeISMvw7HfeS0uIHXBLsdYy27xhEX6yvAbiIiIVL+YQYNo8NBDrnGbm8v6a68je2H1zhjxeAyDL+9ERC2/YzwyP4bjl18Mdu+M15dzk/mldj3H/id6Z3KCZyYAYycsID/gNm9GREQOVtW9HJlUj/OAmCLHm4D/VVMuZbG6xHH96khCREREZLcpyzbzxpRVrvF7T+9I6x1/QNpasrf7SJ1T27Vv3D//SczxBRN9V6at5JOlnzj2q+Wvxcn2PPJznW/StNVSZCIicpCqc85QEm6/3TUezMhg7YiR5Kxy/9tbFaLrhHPCpR1c443T2tFjwwl7ji2Wu+rHscrv/JDEWP+/iCSbpZsyeP+vNRWer4iIVC8VYQ5PJZcie89am18tmZRNycc9I6slCxERERFgW2Yut/73b9f4oA4JXNyvKUx/g2C+IfnPOGzQeR+Y8LZtSbhj702nZ2Y8Q8AGHPuO6DKC9bPSHWORMX6adqxbht9CRESkZql3xeXUGznSNR7Yto11V4wgb9OmKsyqtOZd4uk+qIlrvM/aU0lMb77nOCOYy6iGjckwpT8LNDZbuN73BQBPf7+UbZm5FZ2uiIhUIxVhDjPGmLbAUSWaa/RSZEXElzjWYqkiIiJSLay13PnpXFLTcxzj8bXCeeKcrpitK2DFj2yaVZvcnc7LlpiICBo9PR5PeDgA01Km8fP6nx37NohuwNkNz2P9ku2O8TZ9EvF49RFfREQObvVvuZk6553rGs/bsIG1V1xB/nbnv4dVpf+QViQ0i3GMefAyaOmlhOXvfX50lSfIPfXr4TSXdaR3Iq1MMjuz83n6+yWVlLGIiFQHfUM7/Fxe4vg3a+3B8te9X4njDdWShYiIiBz2/jN9Hd8vdH8Cd9x5XalXKxxmvMXOdRHsWBnt2jdx9GjCW7cGIGiDjJsxzrXvqJ6jWD1zO1jnePv+DfbvFxAREanBjDEkjRlDzIknuvbJXb6CdVdfTTAzswozK87r83DiiE74I7yO8Zjcuhy34h/F/m7/GB3F63VKL08aZgI87HsHsHw4dS0LNzjvNyciIgcfFWEOI8YYLzCsRPNBMQvGGNMF6FKi+edqSEVEREQOcys2Z/DgBPdNgS87sjnHtUuA3EzypnxIyrQ6rn1jTjyROueft+f4fyv/x6Jtixz7dq7XmVNanMLSqRsd43ENoolvUmv/fgkREZEazni9NBz3FFED+rv2yf57LutvuJFgbvUt3xVbP4qB/2zvGm+5rRudNhVfkOTlOrH8EhlRqu8A70KGeH4naGHshAVY6/LUhYiIHFRqbBHGGNPCGPN2kVfCAYyRWGKMxpWR60HkVKDo45HpgPOOrzVIYfHomRLNy6217nc/RERERCpBbn6QUf+Zza485/1a2ifFcOfJBTdi7JyPSf7FSzDP+SO3r0EDGjz0IKZwbfhd+bt4ftbzrte+rc9tbEvOZGuy8xO/7fol7hlLRETkUOAJC6PxCy8S0aXkM5l7Zf7xBxvuuBMbcP7bXBXa9Emkw5Hus1EHrB5CvcyGe46tMdyVEM9qn69U33v871ObDKau2sbX85wfvBARkYNLjS3CAFcCw4FLgXbW2tSyDmCt3QS0LxzjUkpvSH+4Kfn7/8daW655u8YYW+J13D7632CMKf24h3v/MOAN4IQSobFlTlZERESknJ7+finzk52XBwnzeXjuwh5E+L0Et6wl+YEn2LUl3Hkgj4dGTz2JNzZ2T9O/F/6bTVnOS5yd0PQEeiX2YslfLjdjDLTtm1Sm30VERORg4K0VTZPXXyOsVSvXPumTJrHxwYeqdebI0ee3JS4pyjHms34GLR2OLxC2py3D42FUYn0ySjxAUd/s5HbffwF49OtF7MqtvuKSiIhUjJpchBla5P3b5RjnbcAUvs7bR99DljEmETitRPOb1ZDK88AqY8xTxph+xpjSj30AxhifMeYsYCpwWYnwZOCDSs5TREREpJg/VmzhtV9XuMbvPqU97ZJiCGzbxtoLzyJ9lftH7fhrryWqd+89x1t2beGtec6rxPqMj5t73UwwEGTpNOciTaO2dYipu9/PuYiIiBxUfHFxNH3rTXwN3Web7Pj4YzY/+1wVZlWcP9zLSSM74/U7//2Py07kqFXnFGtbGebnnvr1CJbo+0/vD3Q1K0jesSvkZw8RETk41MgiTOGyYW0KDy3weTmG+5y9W6B1KCxGHI6GAUULHvOttdOqKZck4DbgLyDDGDPTGPM/Y8wHxpj/GmN+BrYDXwDdS5w7AxhqtTCqiIiIVKEdWbnc8vHfuH0CObZtfS49ojm569ez+uxT2LU+23WsyG6diL/6qmJtL895maz8LMf+F7S/gGa1m7F+8Xaydjqved+un2bBiIjIoc2flETTN9/CW7eua5+tr73G1nferbqkSqjXqBZHndfGNd5+c3/abO5VrO3H6CjeqFO7WJvHWB72v42HIK/+UlCMERGRg1eNLMKwdwN2C6y01m470IGstVuBoo8NdCtPYgexy0scOz9qWfXCgZ4UzNK5iILZSscCJXeVtRTMojnaWptepRmKiIjIYc1ay92fz2PjTufCSr3oMJ46ryvZ8+ez+tyh5G5yXq4MwBPpp9Ezz2OKrAG/fPtyPl32qWP/mLAYru56NQBLpjovReb1e2jVo8zbJ4qIiBx0wlu2oMnrr+OJjnbtk/rEE+z4/IuqS6qETkc3pFXP+q7xo1eeT+1d8cXaXqoTy6+RxWe0dvWs4mLv92TnBXn060WVkquIiFSNmlqEaVHk/ZIKGG+py9iHBWPMkRTsjbNbLvB+NaVzO/A1sHU/+28GXgI6WmtHWWvdHysVERERqQSfzFwfcmPcJ8/tSuT0P1lzyTACO9yfFTF+Q+OXXsbfsGGx9qdnPk3QllyIpMBVXa+iTkQdcrPzWTlns2Oflt3iCYt0XOFVRETkkBPZuRONX34ZExbm2ifl3ntJ//HHKsxqL2MMAy9u77pMaFgwgsHLhuMJeve0WWO4q348a3zF/57f5vsv9dnOxLkpTF25v7dRRESkpqmpRZii8zB3VMB4RceIdet0qLLW/m6tNUVe4dbaLRU0tinx+nkf/cdZa0+z1sYDTYBBwHDgVuBeYDRwNXAu0Npam2Ctvd5au7gi8hUREREpi9VbMhnz1QLX+LABzeg558f/Z+++o6Mq2jCAP7MtvTdISKFXQakK0psdFLGAIl3EAqKI9CKKFEGqdLAhfDbsgApSpEon1ADpAdJ72TLfH6Hn3s2GTUKA53dOjtn7vjPzrp5j7u7cmUHsG29A5qk/K6J1siBs2Ty4tHz4huu74ndhe9x2xTZBrkF4sc6LAIBzhxJhKlCeqKnFrciIiOge49KiOYJmfwJoVL7WMpsRN/xtZO+9PbuwOzjr0WVgfQiNUIz7ZQfjweinbriWqdVgWIAvssW1Nu4iF2P1hUfiTvrlOMwW7sxORHQnqqiTMMbrfncuhf6crvtd+S8glTspZayU8m8p5edSytlSyg+llB9LKZdIKb+XUvL0OSIiIrptjGYLhq07hJwCs2K8pp8zhpz8AxcmTwEsyhMkAGBwN6Lqp+Pg+GCnG66bLWbM+m+WarvhTYbDoC18yvfUbuWVOE5ueoTUU98bn4iI6G7l1qkTKn/wgWpcFhQg9rWhyDt+vByruqZSNQ882K2aarxhQjuEptS/4dpZgwFj/Xxw/V1Fd+1OtNQcw4mEDKzdF11G1RIRUVmqqJMw16/SCCqF/q7vg+s3iYiIiKhYc/86g8MxaYoxF5gxN+IHpK9aabUPZ/98hL3fHfq2LxeJ/Xz2Z5xOPa3QCmjo1xBdQ7sCALJS8xF7KlUxr1azStBoK+otPRERUdny7PEM/N97TzVuyc5G9MBByD9/vhyruuaBziEItvKwRLuzveCSf+OGLX+7OGO5h/sN1z7QrYIBRszaeArpOUYQEdGdpaJ+You//E8B4AEhxM2HtNvsctvG111KsKcwIiIiIrr77TmXjIX/RCjGXAtysOL4lxBb/rLah3toDoJ7VYX2qY+KxHKMOZh/cL5q25FNR0Jc3o7k9L4LgMruI7Uf5FZkRER0b/Pp3w8+gwapxs0pKYgeMADGC+rnu5UVoRHo1LcenNyVz69xMrmi45k+EPLGTVsWeHlgm9O1M2WqaxIwSPsbUnOMmPOX8gMcRERUcVXUSZhdAEwo/LipA9Dfjr76Xe4Dl/vbZV9pRERERHQ3S881YsT/DkMqTHz4Z6fgs92fweP0Mat9+NTLRGB7Ac0LqwFd0S9ePg//HIm5iYptu4R2wf3+9199fXqP8pdGXpVd4Bt8y88qERER3TX8RrwNz549VeOm+AREDxwIU6ryytKy5OxuQOd+9VQ3xw/MrIHGsV1vuCaFwPt+vojS6a5ee1P3I4LFRXy5OwqnL2aWZclERFTKKuQkjJQyC8BeFP6JEgDGCSGqlLSfy23GoXDyRQLYL6VMKc1aiYiIiOjuIaXEuPXHEJeWWyRWMzUGc7fPh2+KlYXVQqJS0zT4N8yEeGYJ4BlcJCUxJxGrwlcpNtdpdBjeZPjV10mxmUiOy1bMrd0i4OpqGSIionuZEAKVJk2EW5cuqjkFEWcRM2QILNnKf1fLUnBdbzTuGqoabxLbFZXTq99wLVOrwbAAX2Rf/lvvKIyYrPscZosFU345Dqn0tAgREVVIFXIS5rK5l/8pAfgC2CCEqGprYyFEGIDfAfjh2vMG80qzQCIiIiK6u6w/FIdfDscXud78wnHM2LEInnnqT54KnQXBrVPgVSMHaDUcqNVVMW/BoQXINRWd5AGAXnV6Idjt2sTNqd0qW6cIoFZzbkVGRER0hdBqEThrJlxaPqSak3f4CGLffAuWgoJyrKxQ8yerolI1d8WYBhp0OtMHjkaXG66fNRgwzs/n6q6kHbSH0FXzH3ZEJOHP4xfLuGIiIiotFXkS5jsARy7/LgHUA3BICDFeCBGg1kgI4S+EGAfgIID6uLYKJhzAmrItmYiIiIjuVNHJORi/PrzI9UfP78KE3avgaFY/CFfraEZoh2S4BuYDIS2BDuMV806lnMKPZ35UjLkb3DG44eCrry1mC07vVf6CJaiWJ9y8HRVjRERE9yqNwYAq8+fDsWFD1ZzsnTsR/94oSLO5HCsDtFoNOg+oDwdnnWLcxeiJ9hG9i5wD95eLM5Z7XJu8maj/HM7Iw9TfTiDPWL7vgYiIbk2FnYSRhesqnwOQceUSADcAkwDECyGOCyG+FUIsFUIsufx7OIB4AJMBeFxuJwCkAeghuVaTiIiIiBSYzBYMX3cQWfmmq9eEtKBv+O946/D30N78jch1DO5GhHVKgpO3EXD2BZ5dAWiVv2CZvX82pEpfQxoNgYeDx9XXsSdTkZOh/KRu7RZcBUNERKRE4+KC4CWLYaheXTUnc8MGXJg8pdy39HL3cUL7l+uoxkPT6uO+C22LXJ/v5YFtToUPXwSKFAzTfY/olBys2HG+zGolIqLSU2EnYQBASnkawNMonEQBCidirpwTUwfAMwAGABh4+fe6KHxP4rrcZADdpZRnyrN2IiIiIrpzLNgSgQPRaVdf680mjNz/DZ4/s9lqO2e/fIR1SoLB1QxAAM8sBdwDFXN3xO3AzvidirEQtxC8UPuFG66d2qO8FZlWr0H1B/yt1kVERHQv03l5IWTFcugCK6vmpP3vf0j8dK5qvKxUf8AfDdoGqcYfjHoKvlk3HosshcD7fr6I1hU+5DFA+wdqi2gs3BKBC+l5ZVovERHZr0JPwgCAlPIfAM0A7Ma1s12ubDFWJP266wLANgBNpJTby7hMIiIiIrpD7Y9Kwby/rz2v41qQg6k7l6J97EGr7dxDchDcLhlaw+XbzzYjgRodFXNNFhM++e8T1b7ebvI29Fr91dcFeSacO5SomFutkS8MTsorbYiIiKiQvlIlhKxYAa23t2pO8pIlSF61uvyKuqzVszXgE+SqGNNKHTqf7gu9yeGG65laDYYF+CJbCOiEBVP1K5FbYMT0DSfLo2QiIrJDhZ+EAQAp5TkpZSsAXQH8iMKVMULlJxWF58m0l1K2k1JG35aiiYiIiKjCy8wzYvi6Q7Bcnkfxz0nBJ9sXoGHyOavtfOpmIvChNGi0ly+EtQbava+avz5iPSLSIhRjjf0bo2PIjZM35w4lwlRgUcyv/aD6U71ERER0jUPVqgheuhQaFxfVnEvTpyPtB+Xz2sqKTq9Fl4H1oTMofy3nke+HNuefK/L4cYTBgPF+PpAAmmlO41ntNvx4MA77o1LLvmgiIrpld8QkzBVSyj+llD0A+AJoAKAzgBcu/3QCUF9K6SulfE5KufU2lkpEREREd4CJP4UjJiUXAFAjLRZzts5HSOYl9QZColLTNPg3yoS4skbbxR/osQLXZmRulG3MxoKDC1S7fLfpuxBXOyt0arfyVmRO7gYE1/VSr4+IiIhu4NSgPqosWgRhMKjmJIwfj8y//y7HqgDvyi5o80It1XjNpKaondiiyPU/XZyxwsMdADBatwaeyMTkX8JhsfAYZCKiiuqOmoS5QhY6LqX8W0r5v8s/m6WUJ253bURERER0Z/jpUBx+OBgHAGh24QRmbF8E7/xM1XyhtaBK6xR41ci57qIGeHYF4Bag2m7VsVVIzktWjD1a9VHc53ffDdeyUvMRe0r5idZaTQOg0d6Rt/BERES3jUuL5giaMxvQqPwNNZsR9/YIZO/ZW6511XmoMmo2U7+HePh8D3jmFI3P8/LAdidHeIssvKdbiyOx6fhuf2xZlkpERHbgJzgiIiIiuufEpuZg3PpjAIBHI3dj4p5VcDIXqOZrHc0I7ZgMt8D8GwPtxgBV26i2u5B9AZ+Hf64YM2gMGNZ4WJHrp/ddUD79EEDtByupjkVERETq3Dp2ROWpU1XjsqAAsUOHIjc8vNxqEkKgXe/a8PBzUozrLQ7odOYVaM36G65LITDKzxfROh166bagsTiNGRtPIjPPWB5lExFRCXEShoiIiIjuKWaLxIh1h5GZa8Qrx//AW4e+g1Yqn78CAAZ3I8I6JcHJ+6YvNqp3BFq/Y3Ws+QfnI8+cpxjrXa83glyDbrgmpVTdisyrsgt8g5UP8SUiIqLieT7zNPxHjVKNW7KzETNoMPLPny+3mgyOOnQZWB8arVCM++YE4aGobkWuZ2o1GBbgixwhMFW/CqlZuZi/Wfn8OSIiur3u6EkYIYSnECJYCBFyu2shIiIiojvDZ/9E4ODZSxi5/xu8cNr6/u/OfvkI65gEg6v5xoBbIPDMUvVtTQCcSD6BX87+ohjzdPDEoPsGFbmeFJuFlPhsxTa1WwQUOTuGiIiISsanX1/4DB6sGjenpCB6wAAYLyg/FFEW/EPd0fKZGqrxBhdbo2pywyLXIwwGjPPzQV1NFF7RbsKqf8/jXGJWWZZKRES34I6ahBFCdBdCrBRCnBFCGAEkA4gEcE4lP0wI0ebyT5PyrJWIiIiIKp5DMWlY+vsRfLBrGTrEHrCa6x6Sg+B2ydA63LQ3mNACz64EXHxV2xrNRkzeNRlSZV+x1xq9BjeDW5Hrp/aofOEjgFrNuRUZERFRafB7ezg8n3tONW6KT0D0gIEwpSqf0VYWGnaogrD7fFTj7c6+CLc87yLX/3RxxgoPd4zQfQtvczI++PV4WZZJRES34I6YhBFCdBVCnALwPYBXAFQHoAUgrvtRUh3APwC2ANghhPAq+2qJiIiIqCLKzjdh0vK/MWPrfDRKOms116duJgIfSoNGqxDsOAEIfchq+7kH5iI8WXlP+TD3MPSs3bPIdYvZgjN7Lyq2CarlCTdvR6tjEhERkW2EEKg0cQLcunZVzSk4exYxrw6BOUt5hWpZ1NThlbpw8TAoxh3Mzuh4pg80lqJf5c3z8sAhZ2C8/ktsOZWILScvlXW5RERUAhV+EkYIMQHAbwBqoOhki8qRpZeDUv4N4MTldgYAz5dFjURERERU8c1f8ive/WkGQjOVJzoAABoNKjXPhn+jTCju/FWzK9DyLavjbI/djs+Pf64af7vJ29Br9EWux55MRU5GgWKb2i24CoaIiKg0Ca0WgTNnwKWl+oMVeUeOIO6tN2EpUP77XNqcXA3oPKC+8j0IgEpZVdE09rEi16UQeM/PFw0d9qON5jA++PU4Ckzq590REVH5qtCTMEKItwBMwo115gPYBuBXqK+Aud66635/vNSKIyIiIqI7xpYv1qPLkonwyctQzRGOjqjyqB5e1dKVEzyCgacXWz0HJjEnEeP+HacabxnYEu2D2yvG1LYi0+o1qP6Av2qfREREdGs0BgOqzJ8Px4ZFz1u5InvnLsSPfA/SbFbNKU1BtbzQ9PGqqvEH4jqiSlrtItcztRoMC/DFaP1qxCWlYvXO82VZJhERlUCFnYQRQtQEMAuFq10kCidf3gPgI6VsB+BNG7v6+UqXAFoLnmZKREREdE+J/HwN/KaNhZNZ/SlWrY8PQvvVgpubyhcWGh3w7CrAuehe7FdYpAWjd4xGSl6KYtzb0RtTW02F0u1oQZ4J5w4lKrardr8fDE461XGJiIjo1mlcXBC8ZDEM1aur5mRu3IgLkyZDSqsbspSapo+FIbCmp2JMQIMOES/BqaDo2XIRBgOWBZgxRPcT5v0dgUuZeWVcKRER2aLCTsIAmAJAh8LJkzwAHaWUs6SUuSXs58jl9gDgBqBm6ZVIRERERBWVlBIXP52L3GkfQCvVt+TQhYUhbGwPOKVuUO+s8wdAcDOr4608thJ7Evaoxj98+EP4Ofspxs4dSoSpQLlGbkVGRERUtnReXghZsRy6wMqqOWnffovEOZ+WSz0ajUDn/vXg6FJ0+1IAcDa6o0NEb0AWfbDjTxdnOHtvgW9BDGZtPFXWpRIRkQ0q5CSMEMIBwFO4tgpmnJRy1630JaW0oPBcmCvq2F8hEREREVVksqAACe+/j5TFi63mmes3QrW542DYP009qc4TwIOvWe3n0KVDWHBwgWq8b/2+eDjoYdX4qd3KW5E5uRsQXNfL6thERERkP32lSghZsQJab/VVr8lLlyJ55apyqcfVyxEdX6mrGg9Or4v74zsoxhZ6u+F592X4dn8MjsSmlVGFRERkqwo5CQOgFQAnFK6CyQGwyM7+4q/7PdDOvoiIiIioAjNnZCB60GCk//Sz1byExq1Qb+U8aDe+AZjzlZM8Q4FuC6F6Qi6AjIIMjNo2CmapvFd8A58GeOuBt1TbZ6XmI/ZUqmKsVtMAaLQV9ZadiIjo7uJQtSqCly2FxsVFNefSjBlI+/6HcqknrKEvGnUIVo03j3kCAZlhRa5LIfBlQBbaGzZj0s/h5baNGhERKauon+jCLv9TAtgrpVT5VGyz609gLbppJhERERHdFYwJCYjq3Rs5e9S3BQOAbY27ovXqz6DZ+C6Qck45SWsAnvsccPJU7UdKiUk7JyE+O14x7qJ3wYw2M6DXKm8nAgCn910ovOtVUPtBbkVGRERUnpzq10eVRYsgDAbVnITx45H511/lUs9DT1eHX4jyV1kaqUHnM6/AYHIqEsvUapBYZQNOx0Ri/aG4si6TiIisqKiTMNdvlq28N0PJaFR+JyIiIqK7RN6JE4h8/gXkn4lQzTFDYHnjHnhswUfQH1wFHP9JvcOuHwGBD1gd89vT3+LPqD9V4xMenIBgd/UnWKWUqluReVV2gW+wq9XxiYiIqPS5tGiOoDmzAa1WOcFiQdyId5C9Z2+Z16LVa9BlYH3oHZRrcc33RtuzLyg+0BFp0KJW0CJM++MEsvNNZVwpERGpqagTEtevfHEohf58rvtdea8HIiIiIrpjZW3fgajeL8F06ZJqTp5Wjw9a9MXDI4cgOPcEsHGMeof1nwGaDbQ65pnUM5ixb4ZqvHuN7nis2mNW+0iKzUJKfLZirHaLAAgr26ARERFR2XHr2BGVp05VjcuCAsQOHYrcY+FlXounvzPa9a6tGq+ecj/qXmqpGDvjlgGDfh0WblF/SIWIiMpWRZ2ESbzu9yql0F8jlb6JiIiI6A6X9t13iBkyBJacHPUcgwtGPfwaAh7pjO61XYBv+wIWo3Kyd3XgyblWz4HJNeXivW3vIV/lLJkw9zCMbj662NpP7VFZ9C2AWs25FRkREdHt5Pl0d/iPGqUat2RnI2bQIOSfO1/mtdRqXgl1WlZWjbeOfAbe2crxDL89WLn/D0QlKz/4QUREZauiTsJc2ZhbALhfCKF+IloxhBCNceP2ZgfsKYyIiIiIKgYpJRLnzUPCuPGA2ayaF+vqh7fbvoXsqrXxQbf6ED+9DqRFKyfrHAvPgXF0tzr2jH0zEJGm/ESpQWPAzLYz4ax3ttqHxWzBmb0XFWNBtTzh5u1otT0RERGVPZ9+feHz6quqcXNqKqIHDoDxQmnspm9dm+drwTNA+f5CY9Hj0dN9oTMXPctGCsC58hcY/+vWsi6RiIgUVNRJmL0AMlC4o6UeQH87+hpx3e9RUsooewojIiIiottPFhQg4f3RSFr0mdW8cO8wjGjzBi65+mDO8/fD49BS4NRv6g0enQFUus9qnxsjN+K709+pxt9p+g7qeNex2gcAxJ5MRU5GgWKsdgv1J12JiIiofPkNHwbP555TjZviExA9YCBMqWW7A77eQYuug+pDq1P+Os8trxLanH9aMWbUmnAwbxb+PhlTliUSEZGCCjkJI6U0A/gNhSthBIDJQgj1E01VCCGeBtALhZM5EsA3pVknEREREZU/c2Ymol99Fek//WQ1b1tgQ4xu9SoyDS4Y2q4GmusigL8mqjdo+DzQuI/VPuOy4jB552TVePvg9nixzotW+7hCbSsynV6D6o39FGNERERU/oQQqDRxAtweeUQ1p+DsWcQMfhXmrLLd8su3ihtaPVtDNV4rsSVqJDVWDjom4v1t41BgUl9BTEREpa9CTsJc9gEACwonTzwB/COEqG9rYyFEXwBrLrcXAPIAzC31KomIiIio3BgTEhDVqzdydu22mvd9jbb4uNlLMGr1aBTsiWEtfYBv+wEWk3ID31rA47OtngNjtBjx3rb3kGnMVIwHOAfgg1YfQFjp44qCPBPOHVI+qrDq/X4wOOqK7YOIiIjKj9BqEThjOlxatlTNyTt6FLFvvgFLgfJK19LSoG0Qqj2g/sBGx7PPwT3PRzGW53AAwzfMKavSiIhIQYWdhJFSngQwH4UTKBJAVQAHhBArhBBdAfjf3EYIESyEGCCE2AVgBQCH69pPlFJeKrc3QERERESlKu/kSUQ+/wLyz5xRzbFA4LP7umN5gychhQbOBi3mPtcQ+p9fAzJilRvpnIDnvgAcXK2Ov+jQIhxJPKIY0wgNpreZDg8HD5vey7lDiTAVWBRjtVtUsqkPIiIiKl8agwFV5s+DY8OGqjk5u3Yj/t2RkFbOq7OXEALtX6oDV28H5bjFCd1O9oHGolWMb0/6AhvO8nwYIqLyUmEnYS57B8CfuDaRogfQF8DvAHZfvgYAEEJkA4gEsBRA8+vaAMCPUspZ5VU0EREREZWurB3/Iqr3SzBdUn+mJk+rxwctXsHP1R++em3SU/URdnIZcGaTeudPzAb861odf1f8Lqw4ukI1PqTREDQJaGK1j+ud2q28FZmTuwHBdb1s7oeIiIjKl8bFBcFLFsNQo7pqTuamTbgwaTKklKo59nJ00aPLgAYQGuUVuC65YegY+ZhyYyExZscoxGTyfBgiovJQoSdhpJQWAN0ArMaNkypXzorBddecbrp+JW8lgBfKulYiIiIiKhtp3/+AmCFDYMlW32M9zeCC91sNwe7KDa5ee+y+SujpGwVsnqre+QMvAff3sjp+cm4yxuwYAwnlL1KaBjTF4PsGW38T18lKzUPsKeWDe2s1DYBGW6Fv0YmIiO55Oi8vhKxYAX1goGpO2rffInF22W77Vbm6B1o8VVU1Xv1iJ9RIVn7QxIhsDNn0JnKMOWVVHhERXVbhP+FJKfOklP0BPA8gHDdOstyQihsnac4C6C2lHCilVNn8m4iIiIgqKiklEucvQMLYsYBJ/XYu1sUXI9q+iVPeoVevVfZwxLQulSC+GwBIle1A/OsBj860WoNFWjD237FIyk1SjHs4eGBa62nQapS3+1Byeu9FqMznoPaD3IqMiIjoTqAPCEDwiuXQenur5iQvW4bkFSvLtI7GXUJRpY76KtouEb3gkq+8XWp01llM+HdCma7YISKiO2AS5gop5bdSyoYAOgL4GMAOANEAsgEYASQAOARgIYCnANSRUn5ze6olIiIiInvIggIkjBmLpIULreaFe4fhnTZvIsHF9+o1IYBPnm0Ajz+GAlnK237B4Fp4DozB2Wr/X4R/gX/j/lWNT201FZVcbJ84kVLi1B7lmrwqu8A32Pq5NERERFRxOFStiuBlS6FxcVHNuTRzJtK+/6HMahAagU796sHJTa8Yt1jc0evkixBS+ZnmjVEbsTp8dZnVR0REd9AkzBVSyi1SyjFSyjZSyqpSSncppaOUsoqUsomU8k0p5a9Sqj3ySEREREQVmTkrCzFDhiD9xx+t5u0IvA9jWr2KDIcbv/h4tU11tIxbDZz7R73xk3MB35pW+z+WdAxzD8xVjfeu2xvtgttZ7eNmSbFZSIlX3latdosACKG26JuIiIgqIqf69VHls0UQBoNqTsL48cj8668yq8HFwwGd+tVTjWtz6uLJyPaq8U8PfIqd8TvLojQiIkIFnYQRQjwghJh93U+V210TEREREZU944ULiOr9ErJ37rKa9331Nvio2cso0N741GeDIHe8UyMB+GeaeuOm/YH7nrXaf1ZBFkZuHQmTyq62dbzrYESTEVb7UKK2CgYCqNWcW5ERERHdiVyaN0fQp3MArcr2pBYL4ka8g+zde8qshpB6PmjcNUQ1HnThCTRICVWMWaQFI7eORExmTFmVR0R0T6uQkzAA2gIYDmAYCs+Cibut1RARERFRmcs7dQqRz7+A/FOnVHOkEPjsvm5Yft9TkOLGW1knvRYLngyEfv0gqB66Uqkh0NXKBA0KtwybsnsKYrNiFeNOOifMaDMDBq36E69KLGYLzuy9qBgLquUFN2/HEvVHREREFYdbhw6oPHWqalwWFCD29deReyy8zGpo/lQ1BFR1Vx4fWnSM6AOPfOX7jYyCDAzfMhw5xpwyq4+I6F5VUSdhrv+LcETyhDAiIiKiu1rWv/8iqldvmC4qT1IAgDQYMLVZH/xcvbVifOLjtRC25S0gO1G5Awd3oOdqQG99smN9xHr8cf4P1fjYFmNR1aOq1T6UxJ5MRU5GgWKsdguugiEiIrrTeT7dHf7vj1KNW7KzETNoEPLPnS+T8bVaDboMqA+Dk04xnm/2Rf+Tz6k+q3I69TQm7pwIfg1HRFS6KuokzKXrfk++bVUQERERUZlL++FHxLw6BJZs5bNSAEB4emFy+zewM/A+xXiXegF4PvtrIGqH+kDdFgA+1a3Wci79HKbtVV8p83i1x/FU9aes9qFGbSsynV6D6o39bqlPIiIiqlh8+vaFz6uvqsbNqamIHjAAxoSEMhnf3dcJ7V+qoxrPzWmC/uebq8Y3RG7A6vDVZVAZEdG9q6JOwsRf97vXbauCiIiIiMqMlBKJCxciYcwYwKR89goA6ENCsOz5MdjjFKgY93dzwCeNkyC2f6I+WIshQL1uVuvJN+dj5NaRyDXlKsZD3EIw/sHxEEJY7UdJQZ4J5w4pr9Cper8fDI7KT6wSERHRncdv+DB4Pv+8atyUkIDoAQNhSk0tk/FrNPFH/TZBqnHHi8+hXbL6AyCfHvgUO+N3lkVpRET3pIo6CbMTQP7l3x+4nYUQERERUemTRiMSxo5D0vwFVvOc7r8f+0bOxPcX1Sc+5j/hD7ffh0J1b43AxkDnD4qt6ZP/PsHp1NOKMZ1GhxltZ8BF71JsP0rOHUqEqcCiGONWZERERHcXIQQqTRgPt0ceUc0pOHcOMYNfhTlLfSWwPR5+tga8A5XvWyzQo9nZ/gjO0yrHpQXvbXsPsZnK5+MREVHJVMhJGCllBoCNAASAACFEx9tcEhERERGVEnNWFmJeHYL0H36wmufWuROMM+dh0tY41ZxXWwWjxX8jgRyVHWwdPQrPgdEZrI61OXozvjn5jWr87cZvo75Pfat9WHNqt/JWZE7uBgTX5cJvIiKiu43QahE4YzpcWrZUzck7ehSxb74BS4HymXH20Bm06DqwAXR65a/+Ms2BGHCqGxwsyg+JpOenY9iWYcgx5pR6bURE95oKOQlz2RgAeZd/nyOEcLudxRARERGR/YwXLyLqpZeRvdP6Fhfer/SB76xPMOyHk8g3KX85ULeyO0YavgVidqt31H0x4BVqdawL2Rcw/t/xqvHWQa3xcr2XrfZhTVZqHmJPKW83UqtpADTainxLTkRERLdKYzCgyvx5cGzUUDUnZ9duxL87EtJsLvXxvQNd0PqFWqrxCzlt8e75eqrx06mnMWnnJEipstqYiIhsUmE/8UkpjwMYcfllfQCbhBBVb2NJRERERGSHvFOnEfn8C8g/eVI9SQgEjH4fAaNHY/bfZ3E8IUMxzUGnwYoHk6DbNU+9r4feAOo8ZrUmk8WEUdtGIaNAeRw/Jz9MfXjqLZ0Dc8XpvRdVd0qr/SC3IiMiIrqbaVxcELx4MQw1qqvmZG7ahAuTymayo27LyqjZ1F81np7YF30uOavG/4j8A5+Hf17qdRER3Usq7CQMAEgpFwPoASALQHMA4UKIL4QQzwghqgohbm1TbiIiIiIqV9m7diGqd2+YLihvywUAwsEBQZ9+Cu9XXsGOM0lYuu2cau60Dh4I3DJcfcAqzYFOk4qta8mRJThw6YByPRCY1noavB29i+1HjZQSp/Yov2evyi7wDXa95b6JiIjozqDz8kLIihXQBwaq5qR9+x0SZ88p9bGFEGjXuw7cfR0V4ybphOCowXgw26Tax5wDc7Arflep10ZEdK+osJMwQgizEMIM4HsArig8H8YRQG8A3wKIAJBxJc/GH/W/KERERERUJtLWr0f0oMGwZGWp5mg9PRGyahXcu3ZBanYBRvzvkGpul9peePrsOCAvTTnByRvouQrQ6q3Wte/CPiw9slQ1PvC+gWhRuYXVPoqTFJuFlHjlA3frPFjJrhU2REREdOfQBwQgZOUKaL3VH+5IXrYMyStWlvrYBicdug5qAI1W+b4j2VQVPc8+jsom5a/NLNKCkdtGIjYzttRrIyK6F1TYSRgUTrpc+QEKN3GQN12/lR8iIiIiKgdSSiR99hkS3h8NqHyoBwB9SAjC1n4D58YPQEqJ9384gkuZ+Yq5vq4OmOu7HiJuv/rAzywFPKpYrS01LxXvb38fFql83sz9fvdj6P1DrfZhC7VVMBBAreYBdvdPREREdw5DWBhCli+DxlV9JeylmTOR9v33pT62f6g7HnpafUu0M9mPYez5MDhYlO+N0vPTMXzLcOSacku9NiKiu11FnoQBru2ebe8kCk8QIyIiIipH0mhEwvjxSJxr5cwWAI6NGiJs7TcwhIUBANbti8HG8Iuq+asfTIDT/iXqHT48AqjZ2XptUmLCvxNwKeeSYtzN4IbpbaZDp9FZ7ac4FrMFZ/Yqv5egWl5w9VLeFoSIiIjuXo716qHKooUQDg6qOQnjJyDzr79KfexGHYIR2sBHNX4meQjGJqh/9XYq9RQm7pxYJmfXEBHdzez7ZFm2toGTJ0RERER3HHNWNuKGD0f2jh1W81w7dUTQzJnQODkBAM4lZmHyL8dV899uokODfWPVOwxtBbS3Er9szck1+Cf2H9X4lJZTEOiqvme7rWJPpiIno0AxVrtFJbv7JyIiojuTS/PmCJozG7FvvgWYzUUTLBbEvT0CwcuWweVB+7ZGvZ7QCHR8pS7WTt2LnPSi9yj50g0y4TW85DILX3kqr9b54/wfqO9TH6/Uf6XU6iIiuttV2EkYKWW7210DEREREZWM8eIlxAwZgvwTJ6zmeb38MgLeHwWh1QIACkwWDFt7CLlGhS8iADTwd8CbyVOA/HTlDp19gR4rAK3129sTySfwyX+fqMafq/UcOoV2stqHLaSUOLo1TjGm02tQvbGf3WMQERHRncutQwdU/nBq4batCqTRiNihQxHy+edwuq9BqY3r5GZA5/718dOnBxUffU4w1kXr6Edw2uEv7HVSXrU7e/9s1PKqhYcCHyq1uoiI7mYVfTsyIiIiIrpD5J0+jcgXXrA+ASME/N8fhUpjx1ydgAGAOX+dxtE45QkWg06DL4N/gubCYbVOgR7LAffKVuvLMeZg5LaRMFqMivEanjUwstlIq33Y6sTOBEQeSVKMVb3fDwbHCvssFBEREZUTz+7d4f/+KNW4JScHMYMHI//c+VIdt0ptLzR9NEw1fjC7B96JqYzKKmf6WaQFI7eNRGxmbKnWRUR0t+IkDBERERHZLXv3bkT1fgmmhATVHGEwIGjOHPj07XvD9V1nk7F461nVdksfiIJX+Bfqg7cdBVRvX2yNH+75EFEZUYoxR60jZrWdBUed/ee0JMdnYfva06pxbkVGREREV/j07QufIa+qxs2pqYgeMABGK/dYt6LZ42GoXMNDJarBntS3MCM+Hw4Wi2JGen463v7nbeSacku1LiKiuxEnYYiIiIjILuk//4zoQYNhycxUzdF6eCBk9Sq4P9L1xrY5Roz43yGone/6XNV8tD31gfrgVdsCbd8rtsZfzv6Cn8/+rBp/v/n7qO5Zvdh+imMsMGPT8nCYjMpfWHgHuiC4nrfd4xAREdHdw2/YMHg+/7xq3JSQgOgBA2FKTS21MTVaDTr3rw8HF+XVuTkWb8QkDsWEJPUxT6acxMSdEyHVbuSIiAgAJ2GIiIiI6BZJKZG0eDHi3xsFGJW3+AIAfXAwQtd+A+fGjYu0H/PjUSSk5ym2q+ws8aFpJkRBlnLHrgGF25BptMrxy6IyojB191TVeNewrnim5jNW+7DVjv+dQUp8tmJMoy08DFejEaUyFhEREd0dhBCoNGE83B59RDWn4Nw5xAwaDHOW8n3GrXDzdkTHPnVV49EFjRF8qRNeSs9Qzfnj/B/44riVFctERHTnTsIIIXRCiAAhRF0hRJ3Lv+tvd11ERERE9wJpMuHChIlI/HSu1TzHhg0RtvYbOFStWiT23f5Y/HZUfWuN78LWQ594XDkoNMCzKwFXf6vjG81GvLftPeSYchTjQa5BmPjQRAhh/8TImf8u4viOeNV4y2dqwD/U3e5xiIiI6O4jtFoETZ8Ol1atVHPyjh1D7BtvwFJQUGrjVm3kh4btq6jGd2e+hJcu+KFZrvJDMwAwe/9s7E7YXWo1ERHdbe6oSRghxP1CiE+FEP8ByAEQD+AYgPDLv2cLIf67nPPA7ayViIiI6G5lzspGzGtDkfbtt1bzXDt2ROjnq6Hz8SkSi0zKxqSfw1Xbzqp1AkHnrPTffiwQ9nCxtX564FMcT1aeyNEJHaa3mQ43g1ux/RQnPTEHW746qRoPa+iLhh3Uv+AgIiIiEgYDqsybC6dGjVRzcnbvRvw770KazaU2bstnasA32FUxZoEOm9NH4KMLOahkMinnSAtGbh2JuKy4UquJiOhuckdMwlxe7bIVwH4AbwJoDEAHQNz0o7scexPAf0KIrUII9XWVRERERFQixkuXENXnZWRv3241z6t3b1SZNxcaJ6eifZgtGL7uELILlL886OCTgh4Jn6h3XqMT8PCIYmvdFrvN6vYYbzzwBhr5qX/JYSuzyYJNy8NhzFN+P65eDujYp26prLYhIiKiu5vGxQXBSxbDoWYN1ZzMP/9EwsTSO4tFq9eg68AG0Dkob/GaYa6EQ2lDMOdCIhwsyufepeWnYfiW4cg15ZZKTUREd5MKPwkjhBgE4ACAh1E40QIA8rofqFwTAFoDOCCEGFg+1RIRERHdvfLPnEHkCy8g//gJq3n+772HgHFjIbTKH+Tn/30Gh2LSFGPu2nwsMsyFMCpvHwb3IODppYDG+m3spZxLGLdjnGr8ocoPoV+Dflb7sNWuH8/iUlSmYkxoBDoPqA9HV+6aS0RERLbRenoiePly6AMDVXPSv/seibNnl9qYngHOaNertmr8bF4riPR2mJicoppzMuUkJu2cVGqTQ0REd4sKPQkjhHgVwGIADpcvSVxb9ZIP4BSAPZd/Tl2+diV+ZULGAcCSy5M5RERERHQLsnfvQWSv3jDFq5/hIgwGBH06Bz79+6mu+th7PgULtkSo9CDxXfD3cEw9ozKAtvAcGJei25tdz2wxY/T20UjNT1WMezt646PWH0Ej7L8VjjyShMN/x6jGmz9RFYE1PO0eh4iIiO4t+oAAhKxcAa3Ctq5XJC9bjuQVK0ptzNotKqHOg5VU49szBqBlqg9eSs9Qzfn9/O/48viXpVYTEdHdoMJOwlzeRmze5ZdXJl+yAcwG0BSAm5SynpTyocs/9QC4XY7NuZwrrms7XwihPqVPRERERIrSf/kV0YMGwZKpvNoDALQeHghZtRLujzyi3k+uEW+vOwSLysORYyrvR60Lv6oX0mkSEPJgsfWuOLYCey/sVY1Pe3gafJ18i+2nOFmpefjrc+XzZgCgSh0vNH4k1O5xiIiI6N5kCAtDyLKl0Lgqn9cCAJdmzkLa99+X2pitX6gFzwBnxZgZDtiU/i7eSs5B09w81T5m75+NPQl7Sq0mIqI7XYWdhAEwC8CVfRsEgO0A6kop35VSHpBSFtl0W0ppvhx7B0A9ADtwbSLGAMDK5uJEREREdD0pJZKWLEX8yJGA0aiap69SBaHffAPnJk2s9jfhp2OIS1PeJ7yZUzwGZSxSb1zrUaDlm8XWfPDSQSw6pN5Pv/r90DKoZbH9FMditmDTinDkZysfUOvkpkenfvWg0fAcGCIiIrp1jvXqIfizRRAODqo5CeMnIOPPP0tlPIOjDl0G1odGp3wPk2IKwe6M/ph1KQk+yrdBMEsz3t36LuKy4kqlJiKiO12FnIQRQvgC6IJr57v8B6CrlNLm/3tLKWMv93HgustdLvdNRERERFZIkwkXJk5C4pw5VvMc77sPYWu/gUO1qlbz1h+Mw0+H4hVjLsjFapcFEGaVJyo9QoDui4BiDrZPz0/HqG2jYC76rA4A4D7f+/DmA8VP5Nhi32+RSIhIV4136lcPLh7qX5YQERER2cq5WTMEzZkDqJy3B4sF8SPeQfbu0ll94hfshlY9aqrGj+d2RUr2g1h48QJ0UvmrxbT8NAzfMhy5JuUHcIiI7iUVchIGQBsAWhSuYgGA16SU6uscVVxu89p1/Wgv901EREREKizZ2Yh5/XWk/e9/VvNc27dH6OerofO1/oxLTEoOxq0/phKVWBOwBi5ZkcphjR7ouRpw9rY6hpQSk3ZOQkK28pk1rnpXTG8zHXqtXjFeErEnU/DfH5Gq8cZdQxFSz/q5NUREREQl4dahPSp/OFU1Lo1GxA4dityjavdcJXNfuyBUbaR+j/dPxlBUyfHC6MQ01ZyTKScxeddkSKmyFy0R0T2iok7CBF3+pwRwWkq5/1Y7klLuA3BKoW8iIiIiuokpMRFRL/dB9tZtVvO8er2IKgvmQ+OsvGf41f7MFgxfdwhZ+cr7VQzz2I5G6X+rd9BlKlDF+jZnAPDt6W/xV/RfqvEJD01AsFtwsf0UJyejAH+uPH5tvfZNKlVzR/OnrK8KIiIiIroVnt27I2D0+6pxS04OYgYPRv65c3aPJYRAhz514eqlvLK3QLpgU/oI9MjKQrssN9V+fjv3G748/qXd9RAR3ckq6iSM43W/nyiF/q7vw1E1i4iIiOgelh8RgcjnX0DecfXD5gHAf+RIBIwfD6G2JcZ1Fm45i/1RqYqxRtpIDDOuVG9c9ymgxavFjnE69TSm752uGn+m5jN4tOqjxfZTHGmR+Hv1ceRkFCjGHZx16DygPrTainqLTURERHc671degc8Q9fsjc2oqogcMhDFBeXVwSTi66NFlQH0IlTPuLhlrYU9WL8xODEdNfahqP7P3z8aBiwdU40REd7uK+gnx+g3DlT/llsz1fShvRk63lRCisRDieSHEiMs/zwshHrjddREREd0rsvfuRWSv3jDGq98qCb0eQbM/gc+A/hDFnM8CAPujUjFv8xnFmBty8IXbImgsKrd6XmFAtwXFngOTa8rFyK0jUaDST1WPqhjVbFSxtdri4J/RiD6eohrv0Kcu3H2cSmUsIiIiIjV+w4bB84XnVeOmhAREDxgIU6rygzAlUbmGJ5o/ob7K92D204jPfwCTIs4gwClAMccszRizYwyyjdl210NEdCeqqJMw16+bDCuF/q6fjrd/TWYFJ4SYJISQdvysLqc69UKI94UQEQD2A1gL4JPLP2sBHBBCnBFCjBJC2L+BOxERESlK//U3xAwYCEtGhmqOxsMDIatWwv2xx2zqMzPPiOHrDsJsUdq3S2K552p45MUqN9YagJ6fA44exY4zfe90nEtXvr0zaAyY2WYmnPXWt0yzxYVz6djzk/pt5H3tqqDa/X52j0NERERUHCEEKo0fD7dHH1HNKTh3DjGDBsOcZf/ER+NHQhFU20s1/nf6MFQ35qNvZlUYNAbFnLisOMzcN9PuWoiI7kQVdRJmN4AYAAJAEyFE5VvtSAgRCKAZCnfujpNS7iqdEskeQoiaKPzvPA1AdSupNQB8DGCXEKJGedRGRER0r5BSImnZMsS/+y6k0aiapw8KQtg3a+DctKnNfU/8ORwxKbmKsdcc/0KLvB3qjR/5GAi8v9gxNpzfgO/PfK8aH9lsJGp71y62n+LkZRuxaXk4LIoTSoBvsCta9rB2O0NERERUuoRWi6Dp0+HSqpVqTt6xY4h94w1Y8vPtGkujEejcrx6c3JSfj821eODP9OHoEbUeb1Tvr9rP92e+xz8x/9hVCxHRnahCTsJIKSWA5ZdfagDMsKO7Gbj2PlfYUxeVDiFEJQB/Amh8UygCwE8AfgZw9qZYEwCbhBD+ZV8hERHR3U+aTLgweTISP5ltNc+xQQOErVsLh2rVbO7758Px+OFAnGKskYjASPGVeuMGPYCm6h/er4jNjMXkXZNV4x1DOuL52urbdNhKSoktX51EZkqeYlznoEXXgQ2g0xd/Pg4RERFRaRIGA6rMnwenRo1Uc3J27y584MZksmssF08HdOxbTzUeV9AQ4TlPodn2H9G9enfVvIk7JyIlT317VyKiu1GFnIS5bAaAwyhcDdNLCKF+2qqKy216XX55DECJ+7hLvAigagl+3i2rQoQQGgDrceMWcQkAukopa0opu0spu0kpawB4FMCF6/KqAvhR2LIJPREREamyZGcj9vU3kLZ2ndU813btEPrF59D5+trcd1xaLsb+eFQx5oEsrHJdBI1UWXXjUwN4cm6x58AYLUaM2jYKWcYsxXgll0qY3HKyTefWFOfY1jicO5ioGm/XqzY8A+zf7oyIiIjoVmicnRG8ZDEcaqpvHpL5519ImDQJhc8837rQ+j54oHOIanxv1gvwTs9Ct5wQBLkGKeak5KVg8s7JdtdCRHQnqbCTMFLKfACPADiAwomYd4UQe4QQj17+Il+REEIjhHhMCLEX1yYTDgLoIqVUfoTx7ndBShlZgp+kMqylN4AW171OAdBSSrnp5kQp5QYALQFcf5JcSwD2P9ZKRER0jzIlJiKqzyvI2rrVap7niy+gyoL50DjbPsFgtki8ve4QMvOUnrSUWOSyDN7GCwoxADrHwnNgHNyKHWfBwQU4knREMaYRGkxvPR0eDsWfJ1OcxJhM/PtdhGq8zkOVULtFJbvHISIiIrKH1tMTwcuXQx+kPPEBAOnffY/ETz6xe6wW3arBP8xdMSahxZ9pb6PSv/MwqeloCCg/ELM5ZjN+OvuT3bUQEd0pdLe7ADVCiAmXf92EwhUQXig82+VXAKlCiP0ATgPIQOF5Lx4AaqFw26orp4UJFH7JvwHAq7Y+DSmlnFI674KuJ4TQArh535ARUspItTZSyvNCiBEAVl13eaoQ4n9SSksZlElERHTXyr98QKsxTnmrsCv8330H3gMGlHglyeKtZ7H3vPL2Eq/qfkcr8z71xo/NAio1KHaMnXE7sfLYStX4a41eQ+OAm3c8LbmCPBM2LQ+H2aR8u+FVyRltXrD/vBkiIiKi0qAPCEDIiuWI7P0SzMnJijnJy1dA6+kJn4EDb3kcrU6DLgPq438f7kVBnrlIPMvihyNpLyL4r/+hb6O+WBW+SqEX4OO9H6NZpWaqK2aIiO4mFXYSBsAkFE6uXCFROKkiAHgD6HT552bipjZeAN4v4dichCkbD6NwQu2KOABWNoW/6ksAUwFc+ctcHYUrYqyc6EtERETXy9m3DzFvvAlLerpqjtDrUXnaNHg88XiJ+z8Uk4Y5f55WjDURpzBKt1a9caMXgQdeKnaMpNwkjNkxRjXerFIzDLpvULH92GLb2tNIu5ijGNPqNOgysAH0DjwHhoiIiCoOQ1gYQpYtRVSfV2DJUt629dKsT6D19ITns8/e8jgefk5o91IdbFoerhg/n/8gAk8uw0ttX8YOrx04k3qmSE62MRtjd4zFii4roNXwnoqI7m4VdjsyFfK6H3ty1PCskbL19E2vv5BSFn1s4iaXc26erHmm1KoiIiK6y6X/9hui+w+wOgGjcXdHyMoVtzQBk51vwvC1B2GyFL398kIGljotgAYqf/L96gCPf1LsOTAWacHYHWORnKf8ZKengyemPTytVD7En9ydgFO7VbZNA/DwczXhW8XV7nGIiIiISptjvXoI/mwRhIODak7ChInI2FRkV/gSqdk0APUeDlSN78rsiwvfzMS0lh9Cp1F+Bnz/xf348viXdtVBRHQnqOiTMKKcf6hsPXLT639K0Pbm3EftqoSIiOgeIKVE8ooViH/nXUijUTVPHxiIsG/WwLlZs1saZ8ovxxGZXHTViIAF8x0Ww8eiPHECvXPhOTAGl2LH+Dz8c+yM36kan9pqKgJcAmyuWU3qhWxs/UZ5RQ8AVG/sh/qt1b9wICIiIrrdnJs1Q9CcOYBW5eEUiwXx77yL7N277Rrn4edqwt1febLHAj2OXugOx/3b8OYDb6r2Me/gPJxOVb/3IiK6G1Tk7cja3+4CqPQIIRwA1Ljpckn+2t/8rUtNIYRBSllgX2VERER3J2ky4eJHHyF1zTdW8xzr1UPwksXQ+fnd0jh/HE3Auv9iFGOvaX/Gw+KQeuMn5gD+dYod40jiEcw7ME81/lLdl9A2uG2x/RTHZDRj47JwmPKVV+24+Tii/Ut1SnxWDhEREVF5c+vQHoEffYj4Uco79EujEbFDX0fI55/D6b7iz+VTojdo8diQRlg3dTekpehz3mnmIJzYsB19PhiCrTFbceDSgSI5RosRY7aPwZrH18CgNdxSHUREFV2FnYSRUm693TXcRV4VQowDUBeADwAjgGQAUSg8V2WDlHJ7GddQG8D1j2BcklJm2NpYSpkhhEgC4Hv5khZALQDHSq9EIiKiu4MlJwdx77yLrC1brOa5tm2LoNmfQONS/EoUJQnpuXj/h6OKsRbiBN7Vf6feuHEfoNELxY6RWZCJ97a9B5M0KcbretfF203etqne4vz7XQSS45T3T9doBLoObAAHZ32pjEVERERU1jy6dYM5PR0XP5qmGLfk5CBm8GCEfv0VHKpVu6UxfAJd0eq52tixtui5LwBwNrc13BYtwYevfYgeP/dAjqno6ulTqaew8NDCUrunIyKqaCr6dmRUOl4A0BFAIAAHAK4AQgG0ATAGwDYhxD4hRKcyrOHmVTDRt9DHzW1q3mItREREdy1TUhKiXulb7ASM5/PPo8rCBbc8AWOxSIxYdxjpuUW3OfNFOhY5LIAGFuXGAQ2AR2cUO4aUElN2TUFcVpxi3EnnhBltZpTKU5NnD1zCsa3K4wDAg92rI6Cqu93jEBEREZUn7z594PPaENW4OTUV0f0HwBgff8tjNGxbBR5h6s95HzvfBJqjZzCq+SjVnFXHVuHAxaIrZYiI7gachKErmgLYJIT4UJTNHhueN72+dAt93NzG49ZKKUoI4S+EqF+SHwDVS2t8IiKi0pB/7jwiX3gReUeVV6dc4TdiBCpNmgihu/VF0cu2n8Ouc0XPetHAgrmGBfBBqnJDg2vhOTB6p2LH+DHiR2yI3KAaH//geIR5hNlasqqMpFxs/vKkajykvg/u7xRs9zhEREREt4PfW2/B84XnVeOmCxcQPWAgTCkpt9S/EALPvvUQtLps5f6lEzZ+nYJOzi3RLridYo6ExJgdY5BtVO6DiOhOxkmYu1scgGUABgF4GEA9AHUAtALwJoCNN+ULFK6M+agManG96XXuLfRxcxu3W6xFyVAUbm1Wkp+fSnF8IiIiu+Ts34+oF1+EMTZWPUmvR+DMmfAdPMiuc02OxaVj1qZTirE3tT+ilSZcvfFT8wDfmxfIFnUu7Rym7VHeOgMAnqz2JJ6s/mSx/RTHbLZg04pwFOQqb3fm4mFAp751ITQ8B4aIiIjuTEIIVBo/Hu6PPaqaU3D+PGIGDYY569YmQRyd9WjapyEElM/WyzF74edZOzCm0Th4O3or5sRlxWHmvpm3ND4RUUXGSZi7014AXQEESykHSymXSyn/lVKekFKeklLulFIukFI+AqAZgJs37nxfCNGtlGu6eRIm7xb6uHkS5uY+iYiI7kkZf/yB6H79YU5PV83RuLsjZPlyeDz5hF1j5RSY8NbagzCaZZFYK81RDNP/oN642UCgQY9ix8gz5WHktpHIMyvfLoS6h2Lsg2NtrtmaPT+dw8XzysfUCQF07l8fTm48JJaIiIjubEKrReDHH8OlVSvVnLzwcMS+8QYs+fm3NEbT5qEwVFU//jc91xu7FhzA+GYTVXO+P/M9tkRb31aXiOhOw0mYu5CU8ncp5SYpZdFvR4rm/gfgQQCnbwp9LITQlkmBl4cupzZERER3LSklklesRNzbIyALClTzdIGVEbbma7i0aG73mFN/O4FziUWfkPRHKubpF0Gj9ue6ciOgq22LbWf9NwunU2++NSmk0+gwo80MuOhv7Syb60WFJ+PgJvVj6po+Foag2l52j0NERERUEQiDAVXmz4NTo0aqOTm7dyP+3XchTcqrhIvz7BtPwsHxnGo8IcEB5j+c0b16d9WcSbsmISXv1rZGIyKqiDgJQ5BSpgB4ETdOctQB0L4Uh8m66XXxG8EXdXObm/u0xyIADUr4U9qrhYiIiGwmzWZcnPohLs20vmWDQ726CFu7Fg41it8CrDibwi9gzZ6ikxZamDHfMB8+QmUljoNH4TkwOodix/g76m+sO7VONT6iyQjU86lnc81qstPy8ffq46rxwJqeaPp4VbvHISIiIqpINM7OCF6yGA411e8NM//8CwkTJ8KGZ3uL8HQxILRPe3jq1CdiIo7moEPscwhyDVKMp+SlYPLOybc0PhFRRcRJGAIASCkPANh00+VHSnGICj0JI6W8JKUML8kPgLOlNT4REVFJWHJzEfvmW0j9+mureS5tWiPsyy+h9/e3e8xLGXkY9f0Rxdjbuu/QQqN+sD26LwS8i5/QSMhKwISdE1Tjbau0xUt1Xyq2n+JYLBJ/rgpHbqZRMe7oqkfn/vWh4TkwREREdBfSenoiePkK6IOUJ0EAIP37H5D4ySe31H/nxtWR0MgdHto41Zzj/yTiDe0ECCjfb22O2Yz1EetvaXwiooqGkzB0vQ03vW5Yin3f/Gis3y30cfM3SGm3VgoREdGdy5ScjKhX+iJr82areZ49eyJ40SJoXOzftstikXjn28NIzSk6adFOcwhv6H5Sb/zgUKDuk8WOYbKYMGr7KGQUKO8j7u/kjw9afQAh7J8Y2f9HJOJOpanGO75SF65exa/aISIiIrpT6QP8EbJyBbQ+Pqo5yctXIHn58lvqf0Cv7kj2OQonTapqTtSGPPR3GqYan75vOuKy1CdyiIjuFJyEoetF3vT6ViZK1Jy56XXoLfRxc5ub+yQiIrqr5Z8/j8gXXkTeEeUVKVf4DR+OSlMmQ+h0pTLuyn/PY/uZpCLXKyMZs/WL1BsGNQE6TbZpjMWHF+PgpYOKMQGBaa2nwcvR/vNZ4s+kYt+v51Xj93cKRth9vnaPQ0RERFTRGUJDEbJ8GTSurqo5l2Z9gtRvvy1x314uBtR5bjiquK+BXuSq5jlsrYZmlraKsWxjNsbuGAuzxVzi8YmIKhJOwtD1bv6reCtbhqk5BeD6v5r+Qgg3WxsLIdwBXP+NiBmchCEiontIzoEDiHrhRRhjYtST9HoEzpgO3yGvlsqKEQA4Hp+BGRtOFbmugwkLDPPgLVR2B3X0BHquBnSGYsfYm7AXS48sVY0PbjgYzSs3t7FidblZBdi04jjUthf3D3PHg92r2z0OERER0Z3CsW5dBC/+DMJBfRXwhYmTkLHp5h3si9e1QSC213sVrTzmQwOTYo7FLNHicA/45VVRjO+/uB9fHv+yxGMTEVUknISh69382GfRR15vkZQyH0XPUHmoBF20vOn1mct9EhER3fUyNmxEdN9+MKerHHwPQOPmhpBly+Dx1FOlNm6e0Yxhaw+iwGwpEhupW4cmGivPQzy9BPAMKXaM1LxUjN4+GhLKMyON/RtjSKMhNtesRkqJzZ+fQHaa8u2DwVGLLgPqQ6vj7TERERHdW5ybNkXQp3MArVY5wWJB/DvvInvXrhL3/XaPdljl/DDauauvnjblSTx75m245HsoxucdnIdTKUUfCiIiulPwUyZdr8VNr+NLuf+bz5xpV4K2N+f+YVclREREdwApJZJXrUbc229DFhSo5ukqV0bYmq/h8uDNf8rtM+33EzhzqehKl06a/XhV95t6w5ZvAbUfKbZ/KSXG/TsOl3IvKcbdDe74uPXH0Gns31bt8N8xiDyarBpv/3JdePiV5iJgIiIiojuHW/v2CPzoQ9W4NBoR+/obyD16tET9ersY0PHpAThoAFq4fq3ef5YOPSKGw2ByLBIzWowYs2MMCszq98NERBUZJ2EIACCEcATwzE2X/ynlYX686fXLQgiVxyyuuZzzUjF9ERER3VWk2YyLH36ES9OnQ3X/LAAOdesibO1aONSsWarjbzl5CZ/viipyvYpIxCf6z9QbBj8IdJxg0xhfnfgK22K3qcantJyCyq6VberLmktRGdj1480Lcq+p3zoQNZr42z0OERER0Z3Mo1s3BIwZrRq35OQgZtBg5J87V6J+H72vMvbVGQlPp39R32mjap5zhjcePT0YGkvRr4pOp57GwkMLSzQuEVFFwUkYumIUgKDrXpsBWHnE9ZZsB3D9SbhVUHRyRclLuLG2swD+LcW6iIiIKhRLbi5ihw1D6ldfWc1zad0aoV9+CX1A6U4gJGbmY+R3h4tc18OEBfq58BA5yg2dfYBnVwJafbFjHE8+jtn7Z6vGn6/9PDqGdrS5ZjX5uSZsXHYMFrPyRJZPkAse7lm6E1hEREREdyrvPn3gO/Q11bg5LQ3R/QfAGF+yzVPGdW+KifrheNBtBcIc9qrmVU6vjvYRvQFZ9HzDVcdW4cDFAyUal4ioIuAkzF1GCPGyECKghG0GAZh40+XVUsqij7/e2E7e9NPOWr6U0qwwzmwhRJiVMcIAzLnp8jgpZdHN6YmIiO4CppQURPXti6y//raa59nzWQQvWgitq0upji+lxMjvDiMpq+h2D2N0X+N+jdqTjwJ4eingEaQSvybbmI33tr0Hk0X5gNaaXjXxbtN3S1K2Iikl/vn6JDKS8hTjOoMGXQY2gM5Q7MJcIiIionuG75tvwvPFF1TjpgsXED1gIEwpKTb36ePqgF7du2GO+Vl08fwEAXr1M15qJjfBg9FPFrkuITFmxxhkG7NtHpeIqCLgJMzdZwCA80KIz4UQjwshVL+ZEUI0FUL8AGApgOsfMYgDMK6M6vsawJ7rXnsD2CmE6KJQX1cAuwB4XXd5J4B1ZVQbERHRbVUQG4uoF3sh7/ARq3l+w4eh0pQpEPriV5yU1Be7ovDPqcQi1x/R7EU/nfr2EWj9DlCzk01jfLj7Q0RlKD/r4aRzwqw2s+CoK7ofeEmd+DcBEf8pnzcDAG1eqAXvyqU7iUVERER0pxNCoNK4cXB/7FHVnILz5xEzaDDMWUXPD1TzeMPKiK07AHstNfG410fw0Kqvprk/viMaJLQpcj0uKw4z9s2weUwiooqAkzB3JycAfQD8CiBDCHFSCLFBCLFOCLFGCLFRCHEBwD4AT9/UNgXAI1LKC2VR2OUVLE8DiL7ucmUAG4UQp4UQPwoh1gshzgDYAKDSdXmRAJ6R0srG+ERERHeovJMnEfniiyiIsrIQVadD4PSP4TtkCIQoukWDvU5fzMSHv58ocj1EXMQM/RL1hmGtgXbq+4df75ezv+CXc7+oxt9v/j6qeVazqS9rkuOzsH3dadV4reYBqPOQ/efNEBEREd2NhFaLwI8/hsvDD6vm5IWHI/b1N2DJz7e538ndG2KK7k3kCzOe9JoCJ02aam6ryKdRNblRkes/nPkBW6K32DwmEdHtxkmYu58GQG0AXQE8B+BFAF0AKG1Z9jeARlLKY2VZkJQyAUBnAAdvCtUE0B1ANwA1boodANBZSnmxLGsjIiK6HbL37kXUSy/DnJikmqNxdUXIsqXw6NatTGrIM5rx1jcHUWC6ccdPBxRgkX4u3EWuckMXf6DHckCrK3aMyPRIfLD7A9X4o2GP4ukaNz8fUnLGAjM2LguHyai8e6mHvxPa9qpdJhNZRERERHcLYTCgyry5cGpUdCLkipw9exD3zjuQJuVtZm/m6+qAN7q3xSjjIHjoLuIJr6nQqdxnCmjQ8czLqJRR9AGdSbsmITk32bY3QkR0m3ES5u4zF8AaAFbPc7lONoAfAXSSUnaSUsaWWWXXkVKeBtACwGgAapvLA8DZyzkPSikjyqM2IiKi8pSxaRNiBg6CxcpWDrpKlRC65mu4PPRQmdUxY8MpnLyQWeT6eN2XaKCJVGklCidg3CqpxK8pMBfgvW3vIdek/CE7yDUI4x8aXyoTIzvWnUZqgvJe4RqdQNeBDWBwLH7SiIiIiOhep3F2RvCSxXCoWVM1J+uvv5EwcSJs3bjkiYaVgbpP4htTe/jrz+IRz1kQMCvm6qQej5wcCK+cG58lTslLweRdk20ek4joduKnz7uMlPJHFE6qQAjhCaA+gGAUrnxxRuHEWxqAVAAnAByRUir/pSt+LLu+JZFSGgF8DOBjIUQTALUABF4OxwM4LaXcb88YREREFVnq2nW4MGUKYFFesQEADrVqIXjZUugDlBaxlo6tpxOx8t/zRa4/pdmJl3R/qzdsNxqo1tamMebsn4MTKUW3OgMAndBhZpuZcDO42dSXNWf2XcTxfxNU46161IBfiP3jEBEREd0rtJ6eCF6+HFG9esEYF6eYk/79D9B6eiJg5Mhi+xNC4IPuDdBtdj+0MJ9ANYcDaO++CJsz3lTMdzS74LETQ/DjfXOQY8i4en1LzBasj1iPp2vav5KaiKgscRLmLialTAPw7+2uwxaXJ1s44UJERPcEKSWSFi1C0vwFVvOcmzZFlUULoXV3L7NakrPy8e63h4tcrybi8ZF+uXrDau2BNu/aNMbWmK346sRXqvG3Gr+F+/zus6kva9Iu5WDL1ydV41Ub+eK+dlXsHoeIiIjoXqMP8EfIyhWI7NUb5mTlbcBSVqyEzssLPgMHFtufv5sj3u/WFMPWvYEfDBNR13kzsiy+2Jv1omK+W4E3HjsxBD/Vnwuj7toZNNP3TUfzys0R5Bp0a2+MiKgccDsyIiIionIkzWZcmDKl2AkYt86dEbxieZlOwEgpMer7o0jMvPEwVUfkY6F+LlxFnkpxlYFnlgEabbFjXMy+iHH/jlONtwpshVfqv1KiupWYjRZsWh4OY57yAl9Xbwd06FOX58AQERER3SJDaChCli+DxtVVNefSrE+Q+u23NvX3VKNAVKr7ED4x9QQANHX5H+o5bVLN980JQtfTA6CxXLsHzTZmY8z2MTBbbmmTFyKicsFJGCIiIqJyYsnPR9zbI5D2zVqreZ7PPYegT+dA4+BQpvV8vScaf524WOT6ZN3nqKuJUW4ktECPFYCrX7H9my1mjN4xGmn5aYpxH0cfTH14KjTC/lvSXT+eRWJ00TNtAEBoBLoMaABHF73d4xARERHdyxzr1kXw4s8grNynXpg4CRkb1SdTrhBC4MPuDbBW/zR2mutBCKCt+xKEOexTbVMlvTbane0FXLdD/oFLB/DF8S9K9kaIiMoRJ2GIiIiIyoE5MxMxgwYjc5P1D6S+r7+OSpMnQWiLX2Vij4hLmZj62/Ei13totuF53T/qDTuMA8Ja2TTG8qPLse+C+ofoj1p/BF8nX5v6sub8kSQc3qwyaQSgxVNVUbm6h93jEBEREVHhlrlBn84B1O5XLRbEv/susnftKrYvf3dHTOzWACOMryFNukAjLOji8Qn89adV29RKaooW0U/ccG3+wfk4lXKqRO+DiKi8cBKGiIiIqIwZL11C1Mt9kLN3r3qSEKg0cQL83nyjzLfMyjeZ8dY3h5BntNxwvaaIxVT9SvWGNbsArYbbNMaBiwew6PAi1Xj/Bv3RMrClTX1Zk5mSh78/LzqZdEVwXS807hJq9zhEREREdI1b+/YInPaRalwajYh9/Q3kHj1abF/d7w9Cg7p18b5xEABAr8nH454fwV2boNrmgfhOqH/h4auvjRYjxuwYgwJzQQneBRFR+eAkDBEREVEZKoiMRFSv3sg/qX5gvNDrETRnDrxeVD6ItLTN3nQaxxMybrjmjDws0s+Fk1D54OpeBXh6CaAp/vYxPT8do7aPgkVaFOMNfRvijQfeKHHdN7OYLfhzZTjys02KcSd3Azr1qw+h4TkwRERERKXN46mnEDBmjGrckpODmEGDkR8RYbUfIQQ+fPo+7DS0xFpTOwCAszYdT3pNgZMmXbXdw+d7ICy54dXXp1NPY+GhhSV7E0RE5YCTMERERERlJPdYOCJ79YYxNlY1R+PqiuDly+H+SNdyqenfiCQs2XbupqsSU/UrUVMTp9xIowN6rgKcvYvtX0qJCf9OwIXsC4pxV70rpreZDr3G/vNZ9v0WiYQIlQ/mAujcrx6c3Q12j0NEREREyrz7vAzfoa+pxs1paTj/TA8kTJyEgqgo1bwAd0dMeLI+ppj64JylEgDAU3cBj3tNhU7kKbYR0KDTmZdRKaPq1Wurjq3C/ov7b/HdEBGVDU7CEBEREZWBrH//RXSfPjCnpKjmaP18EfrlF3Bp0bxcakrNLsCI/x0qcv157T94RrtDvWGnyUCwbTWuO7UOm2M2q8YntpyIKm5VbOrLmpiTKfjvj0jVeJOuoQiuW/ykERERERHZx/fNN+HVS31FtywoQNq6dTj76GOIfftt5IaHK+b1aByEFrWDMdz4Ooyy8LyZAH0EunrMgoBZsY1OGvDIyUHwzPUvHAsSY3eMRbYx2853RURUejgJQ0RERFTK0n/7DTFDXoMlJ0c1Rx8agrA1a+BYt2651CSlxPs/HMHFjPwbrtcVUZisW63esPbjwEOv2zTGqZRTmLlvpmq8R80eeCTsEZv6siYnowB/rTwOSOV45eoeaP5kVeUgEREREZUqIQQCxo2D+2OPWU+0WJD5xwZE9ngW0f0HIHv3bkh57YZOCIFpzzTEeYfamG3qefV6mON+tHNfrNqto9kFj58YAucCdwBAXFYcZuybYd+bIiIqRZyEISIiIipFKV98ifh33gWMRtUcx/r1EbZmDQzBweVW1//+i8HG8Is3XHNFDhbq58JRqNTqGQJ0XwiI4s9UyTHmYOS2kSiwKJ8pU82jGkY1H1Xium8mLRJ/rT6OnAzlcRycdeg8oD40Wt7mEhEREZUXodEg8ONpcGnd2qb87J07Ed23HyJ7PoeMjZsgzYUrXSp5OGL8E/WwxPwEdluuPaxUz/kvNHNZq9qfW74PHjvxKvQmBwDAD2d+wJboLXa8IyKi0sNPp0RERESlQEqJS7Pn4OJHH1nNc2n5EEI+/xw6H59yqgw4l5iFST8fv+mqxDT9clTTKJ/dAo0e6LkacPKyaYzp+6bjfPp5xZiD1gEz286Ek87J9qJVHPwzGjHH1bd46/hKXbh5O9o9DhERERGVjDAYUGXup3Bu0cLmNnnHjiFu2DCce/wJpH77LSwFBejZpApa1wrA2wVDkSZdruY2c12Huk5/qvblm1MFXU73h8ZSuJXZpF2TkJybfOtviIiolHAShoiIiMhO0mRCwrhxSF661Gqe+2OPIXjxYmhdXazmlaYCkwXD1x1CrvHGfbRf0v6FJ7W71Rt2/QgIamLTGH+c/wM/nPlBNf5es/dQy6uWTX1Zc+FcOnb/dE413rB9FVRt5Gf3OERERER0azTOzghZsRwBY8ZAV6mSze0KIiNxYfwEnO3YCSkrV+KjrlWR5RCAMcYBV3OEANq6L0GIYb9qP8HpddD23AuABFLyUjB51+QbtjwjIrodOAlDREREZAdLbi5i33wL6d+rT0IAgNfLLyNw1kwIg6GcKiv06V+ncSQ2/YZrDcQ5jNd9qd6oXneg+SCb+o/JjMHkXZNV451DO6NnrZ6qcVvlZRuxcfkxSIvyh2i/EDe0fKaG3eMQERERkX2ETgfvPi+jxqaNqDxtGgzVq9vc1pSYiEszZyGr++OYn7MbO3Pq43+mtlfjWmFGV89Z8NedUe2jdmJzNI95HACwJWYL1kesv+X3QkRUGjgJQ0RERHSLzGlpiB4wEFlbrO837TdiBALGjIbQlO+t1+5zyfhs69kbrrkjG4v0c+EgTMqNvKsBT82z6RwYo8WIUdtGIduYrRiv7FIZEx+aCGFDX9ZIKbHly5PISslXjOsdtOgyoD60et7aEhEREVUUwmCA59PdUe2Xn1Fl4QI4NWpkc1tLZiYCflmLL/76CHH7vXA2I+BqzKDJw+NeH8Jdq7KtLoDGcV1Q70IrAIXb5sZmxt76GyEishM/qRIRERHdAuOFC4h86SXkHjignqTRoPKHU+E7eJDdExEllZ5jxIh1h3Dj7gsSM/RLEaJJVG6kdQB6fg44etg0xvyD83E06ahyV0KL6W2mw8PBtr6sObY1DucOqdQMoF3v2vAMcLZ7HCIiIiIqfUKjgVvHjghd+w1Cv/wCLm3b2NxWbzLikfN7kfe7DjE7vZGXqgMAOGvT8aTXFDiKdNW2D59/FmEpDZBtzMbYHWNhtphVc4mIyhInYYiIiIhKKP/sWUS+2AsFEWdVc4SDA6osmA/PHj3KsbJCUkqMWX8U8el5N1zvr92AR7T71Bs+Oh2o3NCmMf6N+xerjq1SjQ+9fyge8H/Apr6sSYzJxI7v1LebqNuyMmo1t32/cSIiIiK6PYQQcG7WDCFLlqDq+h/h/sQTgFZrU1sNJLKiHXF+oz+it3oj+6IBHtoEPO71IXRQXi2tgQadTr+CgMwwHLh0AF8c/6I03w4Rkc04CUNERERUAjkHDyKqV2+YEhJUczTu7ghZtRJuHTqUY2XXfH8gDr8dubG+B8QZjNatUW90X0+gSV+b+k/KTcKYHWNU480rNceABgNU47YqyDNh0/JwWEzK58B4VXJG6+dr2T0OEREREZUvxzp1EDRrJqpv3ACvXr0gHBxsbpud4IjoLb6I/MsXzpdi0MVzFgSUV7nopAGPnhwMj1x/zD84H6dSTpXWWyAishknYYiIiIhslPnPP4ju1x/mdPVtD3QBAQj7+is4N25cjpVdE5WcjYk/HbvhmgeyMN8wH3qhsgWDby3giU9tOgfGIi0Ys30MUvJSFONeDl6Y1noatBrbnmq0Zts3p5F2MUcxptVr0HVQA+gd7B+HiIiIiG4PQ5UqqDRhPGps/hs+rw2Bxt3d5rZ5yQbE7fCGZXMUmuWrr9B2NLng8RNDoMtzxJgdY1BgLiiN0omIbMZJGCIiIiIbpK1fj9jX34DMy1PNMVSrhrBv1sChZs1yrOwao9mCYWsPIbvg2mSLgAWf6D9DFZGk3EjnVHgOjIOrTWOsOrYKuxJ2qcanPjwV/s7+JapbycldCTi1R/2w1dbP1YRPkG01ExEREVHFpvPxgf+wYaixeTP8R42CLiDA5rYFGXq47jqIanG/qea45/vgsZOv4nxSFBYcWlAaJRMR2YyTMERERERWSCmRvGIFEt4fDZjVD/N0atQIoV9/BX1gYDlWd6P5myNwKCbthmuDtb+hk/ageqPHPwEC6tnU/+HEw1hwUP1D68v1XkabKrYftKom9UI2tn6jvlVEjSb+qPfw7fv3TERERERlQ+vqAp9+fVHjz02o/OGHMFStanPb0DO/o5KVh4X8soPR+VQ/fHHkC+y/uL80yiUisgknYYiIiIhUSIsFl6bPwKWZs6zmubRtg5BVK6Hz8iqnyoraF5mCBZtvPMC+qTiJkbp16o3u7w080Num/jMKMjBq2yiYpEkxXs+nHoY3Hm5ruapMBWZsXBYOU4FFMe7u64h2L9WBsGHrNCIiIiK6MwmDAZ49nkG1335F0Px5cGzYsPg2AOqcXgPv5HDVnJD0umhz7nmM3T4W2cbsUqyYiEgdJ2GIiIiIFMiCAsSPeh8pq1dbzfPo1g3BCxZA4+xcPoUpyMgzYvjaQ7Bcd369NzIw37AAOqE8mQG/usBj1ieXrpBSYsquKYjLilOMO+ucMbPNTBi0hpKWXsS/30UgOS5LMabRCnQZ2AAOTjq7xyEiIiKiik9oNHDv3Blh69Yi5PPP4fLww1bzNdKCBsdXwC0zWjWndmILBJ5ohBn7ZpR2uUREijgJQ0RERHQTS3Y2Yoa+joxffrGa5zNwACp/PA1Cry+nypRNWH8McWm5V18LWDBHvwiVRYpyA70L8NzngMG2iaMfzvyAjZEbVePjHhyHEPeQEtWs5OyBSzi2TXmiBwAeero6AsJsP6yViIiIiO4OQgi4tGiOkOXLEPrddzhauznMUF4ZrTPno+HRz+CYq3ImIoAmcV1xYscFbI7eXFYlExFdxUkYIiIiouuYUlIQ1bcfsnfssJrnP2oU/N9997Zvi7X+YBzWH4q/4dpQ7c9oqz2i3ujJTwG/2jb1fzbtLD7e+7Fq/KnqT+HJ6k/a1Jc1GUm52PzlSdV4aAMfNOoQbPc4RERERHRnc25QHy1WLsIbj47Bb1UfglT4dtOhIAP3H1kIvVF5hTUAtDnXE8t++gbJucllWC0RESdhiIiIiK4qiI1DVK/eyDt6VD1Jp0PgzBnw6de33OpSE5OSg/Hrj91w7SFNOEbovlVv1KQv0PA5m/rPM+Xh3a3vIs+cpxgPcw/D2BZjbS1XldlswaYV4SjIVT5vxsXDgI5960JoeA4MEREREQGhPi7o+2wrLGjUAyO6vAnXunnQ6G/chtc59xIaHl0MjblAsQ8BDVof7YG1H0+AJT+/PMomonsUJ2GIiIiIAOSdOoWoF19EQWSkao5wdkbwZ5/B40n7V37Yy2S24O11h5CZf23iwg9pmKtfCK2Qyo0C7gMeUV/VcrNZ/81CRFqEYkyv0WNGmxlw1tt/Fs6e9edw8XyGYkwIoPOA+nBytf+8GSIiIiK6e/R5KAzNw7xx0jEU0+v2Ro2nLsL//nToHM1XczwyzqP+8VWAVD4nUWoM0Md0wb5Hn0HK55/Dkp1dXuUT0T2EkzBERER0z8vZtw9RL70MU2Kiao7W0xOhq1fBtbX1w0DLy6J/zuK/qNSrrzWwYK5+AfxFmnIDg1vhOTB6J5v6/zPqT6w7tU41/k7Td1DXp25JSlYUdSwZB/9UPzi16eNVEVTLy+5xiIiIiOjuotEIzHi2IRz1GvxqeQg/alrDp042qj95EZWapcHgVviwkl/yEdQ+o35fazS44VhwH8TMWoiIDh2ROG8+TKmpqvlERCXFSRgiIiK6p2X+9ReiBwyEJTNTNUcfGIjQNWvg1LBhOVam7kB0Kub+feaGa8N0P6Cl9rh6o27zAZ/qNvUfnxWPiTsnqsbbVWmHXnV62dSXNdlp+fhrtXrNQbU80fSxMLvHISIiIqK7U5ivC0Z2rQMAmGh8BVEWf2i0gFf1HFR79BKCWqXA0bsAQfE7EBq1QbWfPCc/HG74GvKz8pC0aBEi2nfAhakfwhgXV15vhYjuYpyEISIiontW6v/+h9i3hkEWKO8TDQAOtWoh9Jtv4FCtajlWpi4r34Thaw/BbLm25VhrzRG8qf1RvVHzwUD9p23q32QxYdS2UcgsUJ6U8nf2x5RWUyCEfeezWCwSf64KR16WUTHu6KpH5/71oeE5MERERERkRd+WYWga6oVsOGGY8Q2YZOHXnUIDuAfnIaxzEkLaJ+G+nG9R6cJu1X4y3UIRXm8ALEIDmZeH1K++QkSXrogfNQp5p0+X19shorsQJ2GIiIjoniOlROKiRbgwYSJgUd4fGgCcmjZB6FdfQh/gX47VWTfxp3BEp+RcfR2AFMzRL4JG7RyYwAeALlNt7n/RoUU4lHhIMaYRGnzc+mN4Odq/Pdj+PyIRdypNNd6pbz24eDrYPQ4RERER3d20l7clc9BpcEjWwKemHjfEhQBcAgoQ2j4Fj4TMh6vpvGpfyT71carWi7h6Z202I/2nn3H+qW6IGfIacvbvL7s3QkR3LU7CEBER0T1Fms24+MFUJM2bbzXPtVNHhCxfDq27ezlVVrxfDsfj+wOxV19rYcY8wwL4CuVD7eHgAfRcDehsm8zYk7AHy48uV40PbjgYzSo1K0nJiuJOp2Lfr+offu/vHILQBj52j0NERERE94Zqfq4Y2bU2AGCRuRv2Wmor5rl45+C5eouR56J+FmRC5ZY4H/Z4ketZ//yDqN4vIbJXb2Ru2QJp5WEuIqLrcRKGiIiI7hmWggLEjXgHqWvWWM3z7NkTVT79FBpHx3KqrHhxabkY++PRG669o/sWLTQn1Rt1XwR4hdnUf0peCkZvHw0J5RU1jf0b49WGr9parqrcrAL8uSIcUmXhjn+YOx7sVs3ucYiIiIjo3tKvVVU0DvGEBRq8XTAUGdJZMc8p4zS6NdmKLIdU1b4iwx5DXOWWirHcAwcQ+9pQnO/WDek//QRpVN5el4joCk7CEBER0T3BnJWFmEGDkblxo9U836GvodKUyRA6XTlVVjyzRWLEukPIyDNdvdZecxBDdT+rN3roDaDuEzb1b5EWjNsxDom5yk8EuhvcMb3NdOg09v07kVLi789PIDtd+Qweg5MOXQfWh1bHW1QiIiIiKhmtRmBmz0Yw6DSIgx/GGvur5tY4/TWqdjyFPF22as7pWi8gyaeBajz/TATiR72PiK5dkfLFl7Dk5KjmEtG9jZ9wiYiI6K5nSkxE1Mt9kLNnj3qSEAgYPw5+b71l96HzpW3JtrPYcz7l6utAJGG2/jP1BlWaAZ0m2dz/V8e/wva47arxD1p9gEoulWzuT83hv2MQdTRZNd7+pTpw93WyexwiIiIiujdV93PFu11qAQB+sbTE9+bWqrk9wxfjYsudMAnlB4Sk0OJYvf7IcAu1OqYpPgEXP/oIER06InHBQphS1VfYENG9iZMwREREdFcriIpCZK/eyD9xQjVH6PUImjMb3r17l2Nltjkck4bZm05ffa2HCQsM8+AlspQbOHkBz64CtHqb+g9PDsecA3NU4y/WeREdQjqUqGYlFyMzsOvHs6rx+m2CUKOJv93jEBEREdG9bcDD1XB/sCcAYKLxFURZlO8xRX46RuXtx576P0JC+XwXi9YBh+97DTlOfsWOa05LQ9KCBYjo0BEXPvoIxvj4W34PRHR34SQMERER3bVyw8MR2as3jDExqjkaFxcEL1sK90ceKcfKbJOdb8LwdYdgslw7QOU93Vo01kSoN3p6KeAZbFv/xmy8t/U9mCwmxXhtr9p4p+k7JapZSX6uCZuWH4PFrHwQjE+QKx5+tobd4xARERERaTUCs3o2hEGnQRacMdz4OkxS+StQn+g9GFTdATuqfq/an9HghiPNR6BA72rT+DI3F6lffImILl0R//5o5EdYuXcnonsCJ2GIiIjorpS9axeiX+4Dc7L69ldaX1+EfvkFXB58sBwrs90Hvx7H+aRr+1R31vyHQbrf1Ru0Gg7U6mJT31JKfLD7A0RnRivGnXROmNF2Bhy0DiUpWXGcf746iYykPMW4zqBB10H1oTNo7RqHiIiIiOiKGv5ueLtT4bZkB2VNzDU9o5rbfu9XqN1Y4EDgn6o5OcIdJ5/+BE5dHgNs3brYZEL6+vU498STiBn6OnIOHizReyCiuwcnYYiIiOiuk/HHH4ge/KrVwzH1ISEIW/M1HOvVK8fKbLfhWALW7ru2gqeKuIRP9IvVG4S0BDqMt7n/n8/+jN/O/aYaH918NKp5VLO5PzXHd8QjYv8l1XibF2rDq5KL3eMQEREREV1vUOuqaFTFAwCwyNwN+yy1lBOlGe8d/xdxdQ/gtO8+1f6SLplwrFYfhP36Kzx79oTQ27b9LwBkbd6MqBd7IfKll5C1dSukVF4hTkR3J07CEBER0V0l5cuvEDfiHcBoVM1xqFcXYWu+hiEkpBwrs92F9Dy8/8PRq68NMGKhfh7chcqkkrMv8OwKQKuzqf/I9Eh8uOdD1fijVR9F9xrdS1KyouS4LGz/3xnVeK0WAajzUCW7xyEiIiIiuplOq8HMno1g0GpghhZvG19HhnRSzHVJjcSHwhdbq69FrMcp1T6jjiVj9858VJoyGdX/+gveA/pD42L7A0W5/+1HzKtDcL5bd6T/8gukSXlbYCK6u3AShoiIiO4KUkpc+vRTXPzwQ8DKk2XODz2I0C++gM7Xtxyrs53FIjHif4eQlnNtEmmM7ms00pxTaSGAZ5YC7oE29V9gLsDIbSORa8pVjFdxrYIJD06AsHWbBRXGfDM2LjsGs1H5kFMPfye0fbG23eMQEREREampFeCGYZ1qAgBipR/GGQeo5jY59iteqfwgNtZagSTnWNW84/8m4L/fI6EP8EfAyJGosWUz/N5+G1ofH5vryj99GvEj38PZro8g5auvYclVvjcnorsDJ2GIiIjojidNJiSMH4/kxUus5rk9+giClyyB1tW2QzXLm9ki8fGGk9h59to5No9pdqOvbpN6ozYjgRodbR5j9v7ZOJlyUjGmEzrMbDsTrgb7//1sX3caqReUV+5odAJdBzWAwdG2lTtERERERLfq1TbVcF9Q4bZkP1ta4nvzw6q5rx/6DVW9A/F73SXINKSo5u395TyO/xsPANC6u8P31cGo8fdfqDRpIvTBwTbXZoyLw8WpUxHRoSOSPvsM5rQ0m9sS0Z2DkzBERER0R7Pk5SH2rWFI/+57q3levXsj6JNPoDEYyqmykknKyscrK/di6bZrK17CRAKm65epNwprDbR73+Yx/on5B1+f+Fo1PqzxMDTwbWBzf2pO772AEzsTVOOtetSEX7Cb3eMQERERERVHp9VgVs9G0GsLV2BPNPZFtMVPMdeQl45paTkwOubit7qLkafNVu33n69PIfJo0tXXGkdHeL3wAqr/8TuCZn8Ch7p1ba7RnJqKxLnzcKZDR1z8eDqMFy7Y3JaIKj5OwhAREdEdy5yejugBA5G1ebPVPL/hwxEwbiyEpmLe+uw5l4zH5m7HjohrH+IcUICF+nlwEypbE7j4Az1WABqtTWNcyL6A8f+OV423CmqFPvX7lKhuJWkXc/DP1+r7aFe73w/3tQuyexwiIiIiIlvVruSGtzoUbkuWBWcMN74Ok1T+bFArah/e9GiINOeL2FBnOUxC+axJaZHYuOwYLkVl3HBd6HRwf+wxVP3hewQvWwbnFi1srlPm5CBl9WpEdO6C+DFjkX/2rM1tiajiqpjfRBAREREVw3jxIqJeehm5+/erJ2k0qPTBFPgOebVCnj1isUgs3BKBF5ftxqXM/BtiE3VfoL4mSrmh0ADPrgDcAmwax2wxY/T20UjLT1OM+zr54sNWH0Ij7Ls1NBst2LQiHMZ8s2Lc1dsB7V+uUyH/WxARERHR3W1Iu+qoH+gOADgga2Ge6RnV3D6HfkETz9q44H4Of9f8EhLK5xyaCiz4dcFhpCcW3YZXCAHX1g8j9PPVCPvfOrh17gzYeh9sNCL9hx9w7vEnEPPGG8g9dMi2dkRUIXEShoiIiO44+efOIfLFF5F/5oxqjnBwQJX58+DVs2c5Vma71OwC9P98H2ZuPAWLvDHWTbMDvXRWVve0GwNUbWPzWEuPLsV/F/9TjAkIfPTwR/Bxsv0gUTU7f4hAYnSm8jgagS4DGsDRRW/3OEREREREJaW/vC2ZTlM4EbLQ3A37LLUUc7UWEz6MjoCLzhnnfQ7j37AfVPvNzTTil3mHkZtZoJrj1LAhqsyfh2q//QqPHs8AetvvibP++huRL7yIqJf7IGv7dkgpi29ERBUKJ2GIiIjojpJ7+DCievWGKV79zBGNuztCViyHW0fbD6wvT/ujUvDYvO3451RikVhNEYuP9CvUG1fvCLR+x/axLu7H4sOLVeMD7huAhwIfsrk/NecOJeLIlljVeIunqqJydQ+7xyEiIiIiulV1K7vjzcvbkpmhxdvG15EhnRRzg5LPY5S+CgDgWOXtOBT4t2q/6Ym5+G3RERgLlFeEX+FQrRoCP/wQNf76E979+kHj7Gxz7Tn79iFm0GCcf/oZpP/6G6TJZHNbIrq9OAlDREREd4ysbdsQ1bcfzGlpqjk6f3+EfvUlnJs2Lb/CbCSlxPLt5/D8kt1ISM8rEn9CswvfGybCReQrtAbgFgg8sxSw8WybtLw0jNo2ChapvH1CQ7+GGHr/UJvrV5OZkofNX5xQjQfX80bjLqF2j0NEREREZK+h7aujXuXCbclipR/GGfur5nY//hfae9YBAOwO+QVnfJVXlwPAxfMZ2LQ8HBaz8r339fQBAQgY9R5qbNkMv+HDoPX2trn+/JMnEf/uuzj7yKNI/eYbWPKKfq4gooqFkzBERER0R0j/6SfEDH0dMlfloHoAhqpVEfbNGjjWUt5W4HZKzzFi8Jf7MfW3EzDdtP+YE/LwsW4pFhjmw12ovD+hBZ5dCbj42jSelBITdk7AxZyLinE3vRtmtJkBvca+7cEsZgv+XBGO/BzlJ/Gc3Q3o1LcehIbnwBARERHR7afXajCzZ8Or25L9bGmFH82tFHMFgImn9sLbwRMQEluqr0Gs+2nVviOPJGHr2tM2bxmm9fCA75AhqLH5bwRMGA99UJDN78MYG4sLk6cgomMnJC1eAnNGhs1tiah8cRKGiIiIKrzkFSsRP+p9wMqSe8dGDRG65usSfXApL0di0/D4/O3483jRCZF6IhK/GsbiBd0/1jvpOAEItX3bsLWn1mJLzBbV+MSWExHkav+/q72/nEfC2XTloAA69a8HZ3eD3eMQEREREZWW+oEeeL19jauvJxj7Icbip5jrk5OGybk6AIBFY8am2iuQ5Byn2vfx7fHY/0dkierRODrCu1cvVN+4AYGzZsGhdm2b25qTk5H46aeIaNceF2fMhPHipRKNTURlj5MwREREVGFJiwUXp8/ApZkzrea5tGmN0FWroPPyKqfKbCOlxOc7I/HsZ7sQm3rzCheJvtoN+NEwAdU16ufbAABqdgVavmXzuKdSTmHWvlmq8WdrPYuuYV1t7k9NzIkU7N8YpRpv8kgoguvYvrUCEREREVF5eb19DdSp5AYAyIQzhhlfh1kqr95uF3UAz7gVniVToMvDH3WWINOQqtr3np/P48TOYu7xFQidDh5PPI6q639E8NIlcG7WzOa2lpwcpKxcibOdOiF+3Djknztf4vGJqGxwEoaIiIgqJGk0Iv7995GyapXVPPennkTwwoUlOtSyPGTmGfHGmoOY+HM4Cm7aF9oLGVim/wST9F/AQRRzoGbVNsCzK2w+BybHmIN3t76LAkuBYryGZw281+w9m/qyOk5GAf5cdRxQ2Wmhcg0PNH+iqt3jEBERERGVBYNOg1k9G0F7eVuyA7IW5pufVs1/L3wbgpwKV8tkO6Tj97qLka/NUc3f8tVJRIUn31JtQgi4tmmD0C+/QNjab+DaqaPNbaXRiPTvvse5xx9H7JtvIffo0VuqgYhKDydhiIiIqMKx5OQgZujryPj5F6t53v37I/DjjyH09p1rUtrC49Px5Pwd+O1o0affHtKE4w+H0eisPWC9E6EF2o8DXl4POLjZPPa0vdMQmRGpGHPQOmBmm5lw0jnZ3J8SaZH4a1U4cjOUJ3ocXHTo3L8+NFreahIRERFRxdUgyAND21W/+nq+6Wn8Z1E+X9LFbMRHiSkQKJy0SXW+gA11lsOs8lCVtEhsWHoMl6LsO6vF6f77EbxgAar99is8nn4a0OlsayglMv/8E5E9n0NU337I2vGvzWfVEFHp4idjIiIiqlBMqamI6tsP2du3W83zHzkSAe+NhLBxhUh5kFJizZ5oPL1oJyKTb3wqTgsz3tH9D1/rP0Ilob51AQDAIxjo9zvQdiSg0do8/u/nfsf6iPWq8feavYcaXjVU47Y6sCkKMSfU30PHPnXh5u1o9zhERERERGXtjQ41UDug8KEnM7QYbhyKTKn80FLjxPPo51Dl6usE97P4u+aXkLAo5pvyzfh14RFkJN28NXHJOVSvjsBpH6HGn5vg/corECXYCSBn927EDByI8z16IOP33yHNZrvrISLbVZxvLYiIiOieZ4yLQ1Sv3sg7ckQ9SadD4PSP4TOgf/kVZoPsfBPeXncIY348igLTjR/CqohE/M8wBW/q1kMjinn6rF43YMh2IOTBEo0fkxGDKbunqMY7h3ZGz1o9S9SnkoSINOz5WX1/6YYdqqBqI+VDTYmIiIiIKhoHnRYzeza8ui1ZrPTHeGM/1fzXT/6LWk4BV1+f8zmEnWHrVfNzMwrwy/zDyM1SXkVeUvrKlREw+n3U3Pw3fN96E1pPT5vb5h8/gbgR7+Dso48hde06WPLzS6UmIrKOkzBERERUIeSdPo3IF3uh4Lz6F/zCyQnBixbCo1u3cqyseKcuZOKpBTuw/lB8kdhjmt343TAaTTRnrHeicwKenAv0/Bxw8irR+EazESO3jUS2MVsxHugSiEktJ0EI5YNGbZWXbcSmFeGQFuWJJL8QN7R82v6VNkRERERE5alhFU8MaVvt6uv1loex3txSMdcAYFr0Weg117YFO1p5Kw5V3qzaf9rFHPy28AiMBaW3AkXr6Qm/oUNRY/PfCBg7FvrAQJvbGqOjcWHSJER07ISkpctgzswstbqIqChOwhAREdFtl/Pff4h66WWYLl1SzdF6eiJ09Sq4tmlTjpUV77v9sei2cAfOJt44AeKIfEzTLcMiwzy4C/UDOwEA/vWBwf8ATfoCtzBRMu/gPIQnhyvGtEKL6W2mw93gXuJ+ryelxOYvTiArVflpOb2jFl0G1odWz9tLIiIiIrrzvNWxJmr6u159Pd7YHzEW5RXetbJS8JbZ9YZru0N/xhmf/ar9XzyfgT9XhMOi8kDTrdI4O8P75ZdQfeMGBM6YDoeaNW1ua05KQuLs2Yho1x6XZs2C0crnMSK6dfyUTERERLdV5t9/I3rAQFgy1A+s1AVWRuiar+HUqFE5VmZdboEZI789jHe/PYw8443bj9UR0fjFMA4v6rYU31GzQcCgvwH/OrdUx464HVgdvlo1/sYDb+B+//tvqe/rHf0nFucPJ6nG2/WuDU9/2/elJiIiIiKqSBx0Wszq2QiXdyVDJpwx3DgUZqn8kNTLkUfQxLHStQtCYkuNrxHnrr4C/vzhJGxfexpSlu5EDAAIvR4eTz2Fqj//hCqLP4NTkyY2t7VkZyN5+Qqc7dgJCeMnoCAystTrI7qXcRKGiIiIbpvUb79F7JtvQVrZi9ihZk2EffMNHKpVU80pb2cTs9B94b/4dn/sTRGJl7Wb8JNhPGpq4qx34ugJPP818PgsQK988GdxEnMSMXbHWNV4i8ot0L+B/WfnJEZn4t/vI1TjdVtVRq1mlVTjRERERER3gkbBnhjcpvrV1/tlbSwwP62YqwXwYcRhuGgdr16zaMzYWHsFkp2KblN8xbFtcTiwMarUar6ZEAJu7doh7OuvELrma7i2b29zW2k0Iu3bb3H20ccQO2w4co8eK7M6ie4lnIQhIiKicielRNLixbgwfgJgsajmOTVpgtCvvoQ+IEA1p7z9dCgOT87fgVMXb9w32ROZWKafjQ/0q+EgjNY7CW0FvPYvUPeJW67DIi0Ys2MMUvJSFOPejt6Y9vA0aIR9t3sFeSZsXHYMFpPy03pelV3Q+vlado1BRERERFRRDO9UE9X9XK6+nmd6Gvstylt8BRnzMSrzxnv/Al0ufq+7GFmGVNUxdq8/h5O7E0qnYCucGzdG8GeLUO2XnwvP1dTpim8EAFIic+NGRPbsiej+/ZG9c2eZrN4huldwEoaIiIjKlbRYcHHqh0j8dK7VPNcOHRCyYjm0Hh7lVJl1eUYzxv54FMPWHkLOTQdqthAn8IfDaHTWqu8BDQAQGqD9WOCVXwCPKnbVs/LYSuxO2K0an9pqKvyclfewtpWUElvXnEJ6Yq5iXKvXoOvA+tAbtHaNQ0RERERUUTjqtZh53bZkZmgx3DgUmVJ59Xr3i+fRQe97w7Vsh3T8VncJ8rXqZ0Nu+eIkoo8nl1rd1jjUrInA6R+jxqaN8OrzMoST7Svxs3fuQnT/AYh8ticyNmyANJuLb0REN+AkDBEREZUbS0EB4t55B6lff201z7Pns6gyby40jo5W88pLZFI2eny2E1/vib7huhZmvK37Ft8YpqKyUF6RcpV7FaDv70Db9wCNfZMWhy4dwoKDC1Tjr9R7Ba2rtLZrDAA4uSsBp/deVI23fq4mfIJcVeNERERERHeixiFeGNT62nbIMTIAE4x9FXMFgIkRh+Ctc7nheqpzAjbWXgGzMCm2s1gkNiw5hsToTMV4WdAHBqLSmDGosflv+L7xRokeeMsLD0fc8Ldx9pFHkbJmDSy5yg9qEVFRnIQhIiKicmHOykLM4FeR+ccGq3k+rw1BpSlTIGxdKl/G/jiagCfn70B4fMYN14OQiLWGDzBM9yM0opil+XWfBIZsB0IfsruejIIMjNo2Cmap/ARafZ/6GNZ4mN3jpCRkY9va06rxGk39Ue/hQLvHISIiIiKqiN7uXAvVrtuW7EfLw/jJ3FIx19tiweRLiUWux3tEYHONr1THMOab8euCw8hIKt8JDZ2XF/zeeB01tmxGwJgx0FWubHNbY0wMLk75ABEdOiJx/gKYUop5GI2IOAlDREREZc+UlISoPn2Qs1t9+ywIgYCxY+E/bBiEEOVXnIoCkwWTfg7Ha18fQGb+jU+vPaLZi98dRqOZRn2SAgCgcwSemAM89yXg7G13TVJKTN45GfHZygd9uuhdMKPNDOi1ervGMRWYsXHZMZgKlM/rcfd1RPvedSrEfyciIiIiorLgqNdi5rONcO2WV2C8sR9ipa9ifrv0JPSQRVeJn/U9iJ2hP6qOk5NRgF/mH0ZeVjHnSpYBjbMzvPu8jBqbNqLyx9NgqFHd5rbm1FQkLVyIiA4dcWHKFBRERxffiOgexUkYIiIiKlMF0dGI7NUb+cdPqCfp9Qia/Qm8X36p/AqzIiYlBz0X78TqnZE3XHdEPj7SLcdiw6fwEOr7OwMA/OoCg/8BmvYHSmmy4rsz32FT1CbV+PgHxyPEPcTucXZ8ewYp8dmKMY1WoOugBjA4VYyVSkREREREZaVJqBcGPlz16usMuGB4wVCYpfL9/cioEwjSuRW5fiTwHxyuvEV1nLSLOfht0RGYCm7PeStCr4dn9+6o9vPPqLJoEZweeMDmtjIvD6lrvsHZRx5F7PC3kXv0aBlWSnRn4iQMERERlZm848cR2as3jFaeitI4OyNk6RK4P/poOVam7s/jF/H4vO04HJt+w/XaIho/G8ahl25z8Z00HQAM3gL41y21uiJSIzB973TVePca3fF4tcftH2f/JYRvV15pAwAPPV0d/qHudo9DRERERHQneKdLbVT1vbYt2X+yDhaYuyvmukiJj2LPQ6DoJM2u0J8Q4XNAdZwL59KxaUU4LJZitjouQ0KjgVuH9gj7Zg1Cv/4Krm3b2t7YYkHmhg2I7Pkcovq8gqytWyHl7XsvRBUJJ2GIiIioTGTv3o2ol/vAnJSkmqP18UHIl1/A5SH7z0qxl9FswUe/n8CgL/5DRt71249JvKT9Ez8ZxqOWJs56J46ewPNfAU/MBvROpVZbrikXI7eNRL45XzEe5h6G0c1H2z1OemIutnypvmIp7D4fNOoYbPc4RERERER3Cke9FjOebXjD4vZ5pmdwwFJDMb9xbg765SuslBESW2p8jQvu51THOn84CTvWna4QkxfOTZogeMliVP3pJ3h06waU4MzOnL17EfPqEJx/qhvSflwPWVBQhpUSVXychCEiIqJSl7FhA2IGDYYlW3lLKwDQBwcjbM3XcKpfvxwrU5aQnosXlu7G0m03fiDyQBaW6Odgqn4VHEUxezSHPAQM2QHUfbLU65u5byYi0iIUY3qNHjPbzoSz3tmuMcwmCzYtP4aCPOUtEFw8HdDhlbo8B4aIiIiI7jnNwrzRr+W1bcnM0GK48XVkSUfF/NfjI1FbW/R8GLPGhN9rL0WOW6rqWEe3xuHgpopzvopj7VoInP4xavy5Cd79+kHj4lJ8o8vyz5xBwujRiOjcBckrVsKclVWGlRJVXJyEISIiolKV8vXXiHt7BKRRfdLCoW5dhK35GobQ0HKsTNk/py7hsbnbsT/qxg9CzcUJ/OHwPrpq/7PegdAAbd8HXvkV8Cz9VSKbIjfh29Pfqsbfbfou6njXsXuc3evP4lJUpmJMCKDLgPpwcjXYPQ4RERER0Z1oZNfaCPW59uBTtAzABGNfxVwDgGlRZ6AX2iKxAl0uvq/5KeBiKhK7YtePZ3FqzwU7Ky5d+sqVETDqPdTYshl+74yAzs/P5ramixdxaeZMRLRrj0uzZsF48VIZVkpU8YiKsLyNyo4QQgugBoB6AAIBeADIB5AK4CyA/6SU6o8pkyohRH0Ax668PnbsGOpXgKe5iYhuFyklEufNQ/Jni63mObdogSoLF0DrWvTJsPJkMlvw6V9nsGDLjStMtDDjTd2PeFP7I7SimPsk9yDgmWVAWKsyqTEuKw49f+6JTKPy5Ej74PaY236u3atTIo8m4beFR1TjzZ+simaPV1WNExERERHdC/aeT8HzS3fh2tepEvP0C/CUdpdi/moff3zirrxaxi+3Cp478R7M+cqfOTQagSfebITgut6lUHnpsxQUIOOXX5G8ciUKzp4tWWO9Hh5PPAGf/v3gULNm2RRIdJPw8HA0aNDg+ksNpJTh5TE2J2HuQkKIEADPAOgEoDUAa6fnmgH8CWCBlPK3cigPQoh/AJTgZK8i+kkpV5dONbeOkzBERNdIkwkXJk9B2rfqKzYAwK1rVwTOnAGN4fauqLiUkYc3vzmIPedTbrgeiCTMMSxCC83J4jup8wTw1HzAuWw+FBktRvTb0A+HEw8rxgOcA/Ddk9/B09HTrnGyUvOxbupe5GUrr1wKqu2Fp4bdD42G25AREREREU36ORyrd0Zefe2ObPzuMBpVRNGzMM0ABlStif1QPtuxuaUdmv73DCxm5e9n9Y5aPP1OY/gFu5VG6WVCWizI2roVyStWIPe//SVu79q2LXwGDoBT06bc+pjK1O2chOF2ZHcZIcQaAFEA5gB4HNYnYABAC+ARAL8KIX4RQgSUcYlERHSXseTlIXb48GInYLx69ULQ7E9u+wTMvxFJeGze9iITMF01+/C7w+jiJ2C0/2fvvsPjKM4Hjn/nik69W5IlW26SccW4UQ0YixJ6TSiBYGoIISFAGoEEkpBfOr2kUEzooZfQbQw2xuDei+Ruy5YsybZ6ubv5/bEn+W51J92dTjqV9/M899g7uzM7upXudvfdeccBZ/8dLn2+2wIwAE+sfCJgAMaiLPzpxD91OQDjdms+eXpdwABMXJKd064dJwEYIYQQQgghPH7+rSPITz+clqyaBG5rvhmXbn/ObAX+sGsrCcrut61vLPNpOmlbwH21NLp479FVVFc2dLnf3UVZLCSdcgrDn3+e4S+/RNJppxn5jINU+/nn7Ljqe2y/9DKqP/wI7fI/R6UQfZkEYfqf0QHK9wDzgVeA14EVgNu0zTnAF0qpnG7rnRBCiH7FVV3Nzuuvp/bTuR1uN+jWH5P967tR1vY5kXuKy6156NNirnzqaypqm9vKHTTze9vT/DPmAVJVJxk6B42BGz+D6deHdGERqsV7F/PkmicDrr/pyJuYljOty/tZ+r9tlBYfDLi+aPY4ElIcXd6PEEIIIYQQ/UV8jI0/X3ykT9kSPYbHXOf73T7P6eKX+/cHbO/pxgfJPy3wOXf9oWbee2RVwAenepO4o45iyCMPM+qD90m97FJUCA/gNa5ezZ6f/IQtZ57FgZdewt3Y2I09FaJnSRCmf1sB/Ago0FoP0VqforW+TGt9idZ6CpAP/MtUZzTwqurZ8X8jQny91oN9E0IIEUBLWTk7rryq4yHnFgs5v/stmT/4QVSHllfUNnH109/wwKeb8c7EOlrt4p2Yu7nK9mnnjUy9Bm74DLK7N/VkRUMFdy64E43/lARTs6dy45E3dnk/ezYdYOn72wOun3x6PsPGZ3R5P0IIIYQQQvQ3x43K4HvHDfMpe9h5ESvcBX63P7+mmlkt/m/DajSPun/H2JMDJ6c5sK+e959YjbOlb4wSiRk+nMH33kvBZ/PIvPkHWFNSgq7bsnMn+377O0pmFbH/scdwHjjQjT0VomfInDD9jFJqCVAG3Ku1XhpknZuBx0zFl2utX450/zz7m4/XnDBa+xmv2QfInDBCiIGsaes2dl1/PS2lpQG3UTEx5N3/d5JOPbUHe9be11sr+dFLKyiv8c7DrPmudS6/tj1HrOrkibLYFGPul3H+n2yLlCZXEy9ueJF/r/k3Nc01frdJcaTw2rmvkZPQtUGrDTXNvHzfN9Qfava7PntEMhf+dApWqzyvI4QQQgghhD91TU7OePALdh84nCosX5XxfsydJKr2oziqLBYuHD6CKu3/+uOCkRdwwvrvsGVF4FEzoyYP4vQbJvS5dMHu+noOvv4GVXPm0LJnT0h1VWwsqRdfTPo1s4kZMqSbeigGApkTRkTSt7XW5wQbgAHQWj+OkaLM21WR7ZYQQoj+omH1anZccUWHARhLUhL5Tz0Z1QCM2615fH4JVzz5tU8AJoVanrA/yB/sT3cegBl6LNy0sFsDMC63i3e2vMO5b57L/cvuDxiAAfj98b/vcgBGuzWfztkQMADjiLdx+nXjJQAjhBBCCCFEBxIcNv5iSku2U2dzT8tsv9unu938dm/ga6i3tr6F7bT9DC4IPGpky4r9LHy1mL72UL0lPp70q65k1Ecfkvv3vxE7blzQdXVjIwdeeIEtp5/Bnttvp2Ftj9wzFyKi5Oq6n9Fabw+zqnkkzCld7IoQQoh+qHbBAnZcPRvXwYMBt7ENGsSw558nfvr0nuuYyYG6Zq57dgl/+XATLvfhC5TpaiPvO+7kTOuSjhtQFjj5FzD7f5Ca3y191Frz5Z4vufS9S7lr4V3srdvb4fZXjLmCU/K7/vW88tNd7FxXGXD9KVeNITkzrsv7EUIIIYQQor87viCTK4/1vV543X0i77qO9bv9zIYGLq73/zAUwO+X/JZjZueRlhMfcJs1n+1mxSc7w+twlCmbjZSzz2b466+RP+cZEmbMCL6y2031+x+w/ZJL2HH1bGoXLOhzwSgxcEkQRrRaYVqOU0qlRqMjQggheqdD777Lrh/cjG5oCLhNzPDhDHvpJWKPGN2DPfO1bMcBzn54AZ9tOjyM34KbH1vf4OWY35OnAgcgAEjKhavfhVN+BVZbt/RxQ+UGbvzkRm769CY2HdjU6fZj0sdw+7Tbu7zffdsOsfitLQHXTzg5j1GTs7q8HyGEEEIIIQaKX545lrxU74eYFHe1XMse7X9+xZ+Vl5GH/+uMqsYq/rTqD5zzo0nEpwSe1P6rN7aw+Zt9Xel2VCmlSDj2WPKf/Dcj3nqT5PPOBVvw1171X3/NrhtuZNv5F3Do7bfRLZ1kOBAiyiQII1o5/ZQF/rQXQggxoFQ+M4fSn/0cnP6+LgyxRx7JsBdfIGZIXg/27DCtNU8u2Mql//yK0kOHczDnUMlLMfdxu/01rKqTJ6WOOBt+8CUMD+GJrBDsqd3DLxf8ku+89x0W710cVJ1RKaN4+JSHcVgdXdp3U30LHz+5Drfb/3uQMSSREy7xP5GoEEIIIYQQwr9Eh42/XOKblqyaRG5rvhmXn2mQE7Tm/0r3EGhWl/m75/Np1Qec+6NJ2GOtAfc799kN7N5Y1ZWu9wqxY8aQ95e/UPDxR6RffTWW+MCjgMyaNm+m9Be/pOS006l8+hlctbXd2FMhwidBGNHKfNfFCVREoyNCCCF6D601ZX/9K+V//nOH2yXMmMGwZ57Glp7eQz3zdai+he8/t4z7/rcBp1eQ4XTLEj5w3Mkxlo0dN2B1wFl/g8tegPjI/wwHGw/y1yV/5dw3z+V/W/8XVJ0EewK3HHULL579IoMTB3dp/1prPnt+IzWV7ScIBbA5rJxx/Xhs9sAXeUIIIYQQQgj/TijI5PKjfdOSfaPH8rjL/9ySU5qauPZQ4IDBn7/5Mw0pBznzpolYrP7DNW6X5oN/rKFid/8IPNhzc8m+85cUfDaPQbffjnVQZtB1nfv2Uf6Xv1ByyizK//53WsrLu7GnQoROgjCi1SWm5aVaa3dP7Fgp9ZBS6hulVLlSqlkpVaWUKlZKvauU+rlSKno5bYQQYgDTLS3s/eWdVD31dIfbJZ97LkMffwxLQkIP9czX6t0HOfuRBXy8vqytzEEzv7M9w79iHiBNdXJRknkE3DAPjr4BVKDn0cLT6Gzk6bVPc9YbZ/Gf9f+hxd35MHmbsnH5mMv534X/4/uTvk+8PfgnwQJZt6CULcv3B1x/8uWjScuJzvETQgghhBCiP/jVWWPITYn1KXvIeREr3aP8bv/DqiqOcPm//qh31nP3wrvJHZ3CrO+NDbjP5kYX7z2ykpoq/w9b9UXWlBQyb7yBgrlzGXzf74kZOTLouu6aGir//SRbik6l9K67aNoSOBWzED1JyQRGQimVCBQDOV7Fv9Ba/6Wb9jcfODmEKm7gbeBnWute8+mplBoPrG1dXrt2LePHj49ij4QQInLc9fXsvu026j7/osPt0mfPJuvnP0NZev65Dq01zy3ewX3vbaDZdfi5gQK1m0fsjzDWsqvzRqZcDd/6I8RENgDhcrt4d+u7PLriUcrqyzqv4HH6sNO5dcqt5Cfnd75xkCp21/Lan5bicvp/tuKIY3M4dfa4iO1PCCGEEEKIgeqLzfv53tPf+JQNU/v4X8yvSFTtAyXFdjuXDsmlBf/3Z38y5SdcN/E6ln+0g6/eDHxLLG1wAhf9dAqxCfau/QC9kHa7qZ0/n8qnnqZh2bKQ6yeecgoZ111L3NSpqAg/dCf6lnXr1jFhwgTvogla63U9sW8ZCSMA/ohvAOYg8GR0uuKXBbgQWK6UujjanRFCiP7OeeAAO665ptMATNbPfkr2L38RlQBMTWMLt7y4gt+8vc4rAKO53DqXd2Pu7jwA40iBb8+B8x6OaABGa82C3Qv49nvf5tdf/jroAMzU7Km8cNYL/H3m3yMagGlpcvHxk2sDBmBSs+M56TIZcCqEEEIIIUQknDR6EJdNH+pTtkPncK/zar/bF7a08OPKwPO6PLryUTZVbWLy6flMnDkk4HYH9tbxwT/W4GxxhdfxXkxZLCTNmsXwF55n2EsvknTaqSFlMKj97DN2XHkVOy67nOqPP0a7+t97JHo/GQkzwCmlLgTeMBX/UGv9eDfucz7GSJg1wAfASqAEI/jjALKA44BLgYmm6i7gPK31+xHuUxYwKMRqozBG6AAyEkYI0T+0lJay8/obaN66NfBGViuD/3AfqRdc0GP98ra+tJqbX1jG9sr6trJkavmj/UnOtn7TQU2PocfAxU9CauSCHQDrKtfxwNIH+Hrf10HXGZUyitum3sZJQ07qlqey5j67no1f7fO7zmqzcMkvp5I5JCni+xVCCCGEEGKgqm5s4YwHvmDvIe+RL5pH7Q9zjrX9tYILuD4vj6Ux/udnLEwr5OWzX8am7Hz0r7VsXRk4zfCoKVmccf14lKV/j/ho2raNqmfmcOitt9DNzSHVjRk2jPRrriHlgvOxxMZ2XkH0G9EcCSNBmAFMKTUJWAgkehV/DHxLd+MvhlLqRuDLYH7JlVLfBZ4AvO8Q1QJjtNZ7Itine4F7utKGBGGEEH1dU3ExO6+/AWdZ4NEbKjaWIQ89SOLJoWSVjAytNS8v2cU976yj2WtkxxS1mYdjHmWIquikBQUn/RRO/iVYbRHr1+6a3Ty84mE+2PZB0HWy4rL44eQfct6o87BZItcXb5u+3senz6wPuP6ky0Z3+DSdEEIIIYQQIjzzN5Uz+5klPmXJ1PKB407yVGW77UttVi4aOoS6AGnJrhl/DbdPux1ns4t3HlrJ3i2HAu570qyhzPhOYdd+gD7CWVFB1QsvcODFl3AfCvye+GNNTyftyu+Sdvnl2NLSuqmHojeRIIzocUqpfGARkOdVvAOYrrUOHFKPAqXUdGA+4D0z8b+11jdGcB/3IkEYIcQAVr98Obtu+gHu6uqA21hTUhj6z38Qd9RRPdcxj7omJ3e/tZY3VxyOv1twc7P1bX5iex2b8p9uq03SYLjoXzDipIj16UDjAf61+l+8vOllnG5nUHUS7AlcN+E6rhx3JXG2uIj1xexgWT2v/N8SnE3+h9qPmjyIM26cIDmRhRBCCCGE6CY/f20V/12626fsGLWBl2Luw6La3499KzGBXw/K8NuWQvHUGU8xPWc6jbUtvPG3ZRzYV+93W4ATLingqFMjO/K/N3PX1XHw9TeomjOHltLSkOqquDhSL76Y9NmziRmS13kF0WfJnDCiR3lSb32CbwBmH3BabwvAAGitlwB3m4qvVkpFdhZlIYQYoGrmfcbOa67tMABjGzyYYS++EJUAzOayGs57dKFPACabKl6w/x8/tb/aeQBm9Jlw05cRC8A0Oht5cs2TnPXGWTy/4fmgAjA2i43vjv0u71/0PjcceUO3BmBcLW4+enJtwABMUnosp1w1RgIwQgghhBBCdKO7zh5HTrJvuquv9ViecJ3rd/vza+uYVd/od51Gc/fCu6ltriU20c45t0wiPjkm4L6/fK2E4iXBzU/ZH1gSEkj/3lWM+vgjcv/2NxxjxwZdVzc0cOD559lyxhnsuf0OGtb1yD15McDISJgBRimVjjGqxHuulQpgZk9F/sKhlHIA5UCyV/G5Wuv3ItS+zAkjhBiQDr7+Ont/cw90MDlhTMEo8p98EntOTg/2zPDast3c/dYaGlsOB1pOtSzjr/Z/kqZqO65sjYHT74Ojbwxp4sZAXG4X72x5h0dXPkp5fXnQ9b41/Fv8ePKPGZo8tPONI+CLVzaz5rPdftdZLIoLfzqFnJEpPdIXIYQQQgghBrLPNpZzzRzftGQ2nLwWcy9HWdrPw1llsXDh0CFUBXhs/oKCC/j9Cb8HYP+uGt7823JaAjx8ZbEpzvvRUeQdMfBSbWmtqf/qKyqfepq6L78MuX78cceSce11JMw4QR5e60ckHZnoEUqpFGAuMNWr+AAwS2u9MiqdCoFS6i3gfK+iu7TW/xel7qCUGg+sbV2WIIwQoi/RWlP5r3+z/4EHOtwubsoUhj7+GNbU1J7pmEdDs4t73lnrM3zfQTN32l5ktu3jzhvIHA2XPA05EzvfthNaaxbsWcADyx6g5GBJ0PWm50zn9qm3MyFzQucbR8jWlfv54B9rAq4/7sJRTDljWI/1RwghhBBCiIHujv+u4vXlvg9JDVd7+V/Mr0hQTe22/zwulltysgK29+ApD1KUXwTArvVVvPfoKtxu//d3Y+JsXPTTKWTkJfpdPxA0bthA5dPPUP3++x0+fOiPY8wYMq69huQzz0TZ7d3UQ9FTJB2Z6HZKqSTgQ3wDMNXAt/pCAMZju2k51JErQgghAO12U/Z/f+w0AJN4yinkP/Vkjwdgtuyv5YLHvvQJwIxSe3gr5jfBBWCmfA9unB+RAMzairVc9/F1/HDuD4MOwBSkFvBY0WM8dfpTPRqAqalqZN5/NgRcnz8uncmnDZy80EIIIYQQQvQGvzlnHFlJDp+y7Xow9zqv9rv9yQ2NXFwTeNT/bxf9loqGCgCGjktn1vfGBNy2ucHJu4+soqbKf5qzgSB27Fjy/voXCj7+iPSrv4eKj++8kkfTxo2U/vwXlJx+BpVz5uCqrevGnor+TIIwA4Bn7pT3gWO9imuBM7XW30SnV2FpMC13X0J9IYTop9zNzZT+9GcceO65DrdLufgihjzyMJa4nv2ofXvlHs57ZCGbymo8JZrvWD/j3Zi7GWvZ2XFlR7Ix+uW8RyCma9OG7arexc8+/xmX/+9yluxb0nkFICs+i98d/zteO/c1ThpyUo8OW3e53Hz85Dqa6v3PTxOfEkPR7HEoiwylF0IIIYQQoielxNv5vwvbPyD2qutk/uc62m+dn1UeIM/pf+7LA00H+O2i39Ka3eiIYwdz7AUjA+6/7mAT7z26iqb6ljB633/Y8/LIvvNOCj+bx6Cf/ARrZmbQdZ1791L+pz9TMmsW5fc/gHN/r5tSW/RyEoTp55RSccB7wAyv4nrgbK31ouj0KmzmT8eKqPRCCCH6KFdtHbtvuskYht2BjO9/n8H33Yey2XqoZ9DY4uKuN9dw68srqWs2hognU8ej9kf4i/3fxPsZpu9jyHS4aQFMuLhL/ahqrOJP3/yJ894+jw+3fxhUnUR7IrdOuZX3LnyPCwsvxGqxdqkP4fjm3W3s23rI/0oFp10zrsOJO4UQQgghhBDd59Rx2Vw0Oc9UqvhVy/WU6vR22ydozR/LywPeuJ2/ez5vlrzZtjzljGFMOMnc/mFVpXW8/8QaXC3+AzsDiTUlhcybvk/B3E/J+f3viBk+POi67upqKv/1L0pmFbH317+maWv7eX2E8EeCMP2YUioWeAeY6VXcCJyntf4iKp3qmmNMy6VR6YUQQvRBzspKdl59NXWLvupwu+xf/Yqs237So6M4dlTWcfETi3jh68MjXaaozbzvuJNzrIs7qa3gxDvgmg8gbXjYfWhwNvDv1f/mrDfO4oUNL+B0+x9R4s1msXHl2Ct5/6L3uX7i9cTZojNAc9f6KpZ/tCPg+mlnDmfImPYXdkIIIYQQQoie85tzxzHIlJbsEInc3nIzbt3++mtyUzPXHAzwoBXw52/+zK6aXQAopTjxstGMmBR4dEdp8UE+fXY9OsD8MQONxeEg7dvfZuT7/2PIY48SN3ly0HV1SwsHX32NrWedza4f3kL98uXd2FPRH0gQpp9SSsUAbwCnehU3ARdoredGp1fhU0pNBMxjN+dHoStCCNHnNO/axfYrrqBxXQfzzdnt5P79b6R/76qe6xjwwZq9nPPwQtaVVgNgwc3N1rf4b8zvGKI6GfCYmAPfexuKfgPW8CZJdLqdvFH8Bue8cQ4Pr3iYupbgcvyeOeJM3rngHX5x9C9Ii00La9+RUHeoiU+eWQcBrqMGF6Qw/ezhPdonIYQQQgghRHup8TF+05Itdo/jH65z/db54YFDHNHsP41YvbOeuxbehcttZBKwWBSnXzeenJHJAftQsrScL98Ibq7LgUJZLCQVFTH8pRcZ9uKLJJ5aBCE8lFg7dy47rvgu2y+7nOpPPkG7ZbSRaE+15g8U/YdSyga8BpzvVdwCXKS1fi86vQqfUsoKfAQUeRWXaK0Lo9QlAJRS44G1rctr165l/PjxUeyREEK017hhAztvuBFXReCAhiU+niGPPkLC8cf3WL+anW7++MEGnvlye1tZNlU8YH+c463rO2+g8Ay44HFICD6PrzetNV/s/oIHlj3AlkNbgq53dM7R3D71dsZnRv/zXrs17zy8kt0bD/hdH5tg59K7p5OYFtvDPRNCCCGEEEIEcuvLK3h7pW9yFztOXou5l0mW9umtiu12Ls0bTEuAuMBPpvyE6yZe17bcWNvC639dxsGy+oB9mPHtQiYVDQ3vBxgAmrZuo+qZZzj09tvo5uaQ6sYMH076NdeQcsH5WByOziuIHrNu3TomTJjgXTRBa93B06qRIyNh+hlPwOIFfAMwTuDS7gjAKKW06TWzk+1/5EmTFmz7McC/8Q3AAPw25M4KIcQAU7f4a3ZceVWHARhrejr5//lPjwZgdlXV8+1/fuUTgJllWc4Hjl92HoCxxsC3/gxXvBJ2AGbN/jVc89E13DLvlqADMIVphTxe9DhPnv5krwjAACz7aEfAAAxA0dVjJQAjhBBCCCFEL3PvuePJTPSdr7EFG7e2/JB63f6mfWFLC7dWBT7vf3Tlo2yq2tS2HJto59wfTSKugzkhF75WTMmy8jB6PzA4Ro5g8O9/R8HcT8n4/vexJAceXWTWvH07++65h5KiU6n4xz9wHTzYfR0VfYaMhOlnlFLPAt8zFf8ceDWM5vZprRs72Z/5F+gUrfX8TrbfBzyPMVpnmda6XeJ9z2ies4F7gaNMqz8FTtdR/uWVkTBCiN6s+sOPKP3Zz9At/oeuA9iHDCH/yX+HNBFhV326vow7Xl3FoQajXzG0cKftRa6xfdR55YwCuORpGDwprH3vrN7JQ8sf4uMdHwddJzs+m1sm38K5I8/FarGGtd/usLfkIG/evyJgPudJRUOZ8e2oDhgVQgghhBBCBPDh2n3c9PyyduXfsX7GX+z/blfuBq7LyWJpnP+HrArTCnnp7JdwWA8HcfbvrOHNvy+npcnlt47Fpjjvx0eRNzp66ZX7CldtHYdef43KZ5/FWbo3pLoqPp7USy4m4+qrsefldVMPRTCiORJGgjD9jJ+gSFd0GFAJsL9ggjDemoB1wF7gEGAHsoCpQKKfJpYCs7TWNR32vAdIEEYI0VsdeOkl9v3u99DBd7xjzBiG/uuf2LOyeqRPLS43f/toE//84vDw+lFqDw/bH2W8JfCk8m0mX2mMgHH4+2roWGVDJf9c/U9e3fQqzvZxf7+S7ElcN/E6vjv2u8Taetdoksa6Fl657xtqDzT5XZ81LImLfjYVq00GPAshhBBCCNFb/eilFby7qtRUqnnc/hBnWb9pt32pzcpFebnUWfznJZs9fjZ3TLvDp2znukr+99hq3AEe3nLE27jwp1PIyA39Omsg0i0tVH/4IZVPPU3Txo2hVbZaST7zTDKuu5bYsWO7p4OiQxKEERHTB4MwwdLAI8AvOhud01MkCCOE6G201lQ88igVjz/e4XbxRx/NkMcexZqU1CP92nuogVteXMGyHa1D6DXftn7Ob23PEq/8BxLaOJLhnAdg4iUh77e+pZ7n1j/HM+ueoa6lLqg6doudy8dczg0TbyA1NjXkfXY3rTXvP7GG7av9p5izx1q59K7ppAyK7+GeCSGEEEIIIUJRVdfMafd/TmWd75wjKdTyoeOXDFZV7eq8nZjA3YMy/LanUDx1xlNMz5nuU75h0V7m/WdDwH4kpjm4+OfTSEyT+UuCpbWmbtEiqp56irpFX4VcP+H440i/7joSjj8epQJM9iMiTuaEEQPJz4D3gcogt98PPAaM01rf2lsCMEII0Ztop5OmLVvY95t7Og3AJJ1+OkP//a8eC8DM31TOWQ8taAvAJFHPw/ZH+av9X50HYPKmwfe/CDkA43Q7eW3za5zz5jk8uvLRoAMwZ488m3cvfJefTf9ZrwzAAKz+bHfAAAzAKVeOkQCMEEIIIYQQfUB6Qgz3XTChXfkhErm95Qe4dfub8+fV1lFUV++3PY3m7oV3U9tc61M+9vjBHHPeyID9qD3QxHuPrqSpIbisAQKUUiSecAL5Tz/NiDdeJ/mcc8AafPrqukVfseu669l20cUceve9DtOIi/5BRsKIqFFKDQGOAIYAGUAc4AIOABXASq11cDMmR4GMhBFC9DStNc7SUhqLi2naXExTsfFq3rIlqJO21MsvI+fuu1EhnByGy+ly8+CnxTz6WUlb2WRVzMP2Rxlq2d9JbQUzfgKn3AVWe9D71Fozf9d8Hlz+IFsPbe10+1bHDD6G26fezriMcUHXiYb9O2t47S9LcTv9n7uNm5HLKVeO6eFeCSGEEEIIIbrihy8u53+r288z8gvbS/zA9m678iqLhYuGDKYywHXdBQUX8PsTfu9TprXm8xc3sW6BOf3ZYXlHpHLuLUdhtcsz++Fo3r2Hqv88y8FXX0M3NIRU15Y7mIzZs0m9+GIsCQnd1EMh6ciE6IMkCCOE6E7OqqrDgZbNm9sCLu664EZ1mGX+6BYyb765R4Y6l1c38uOXV7B4qzF8XuHmJut73GH7Lzbl7rhyYjZc+E8YdUpI+1y1fxX3L72f5eXLg64zOm00t0+9neNze/8Q8OZGJ//9wxIO7fd/Mp+em8Alv5yGPab7A2xCCCGEEEKIyKmsbeK0B76gypSWzI6T12Pu4UjLtnZ1Po+L5ZacwPN7PnjKgxTlF/mUuV1uPvjn2g5H1hdOz+a0a8ahAsw7IzrnOniQAy+/TNVzz+OqDDYRkMGSkkLa5ZeRfuWV2DIzu6mHA5cEYYTogyQII4SIBHddHU0lJTQVF9PYFmwpwVUR+MQ4JBYLOb/5DWmXXRqZ9jqxqKSCH7+8kopaI9XYIA7wgP1xZliDOK8pPB0ueAISgj/Z3H5oOw+veJhPdnwSdJ2chBx+NPlHnD3ibKyW3h+00FrzydPrKV5S5ne9zW7h23dOJz1XnpgSQgghhBCiL3pvdSm3vLiiXflIVcp7MXf5TeV8b0Y6rycn+m0vzZHGG+e/QWac77VVS7OLtx9YQdm26oB9mXxaPsdfXBDiTyDM3E1NHHrrbaqeeYbm7dtDqqtiYkg5/3zSr7kGx8gR3dPBAUiCMEL0QRKEEUKEQjc307Rte9uIltbRLS27d3fbPlVMDLl/+yvJp5/ebfto5XJrHp1XwoNzN9N6ajHTsoK/2/9BhqrpuLLFDqf9Do65CSzBDX2vaKjgH6v+weubX8epg8tdnBSTxA0Tb+CKsVfgsPadSSfXf1nKZ89tDLj+lKvGMO6E3B7skRBCCCGEECKStNbc/MJyPli7r926S62f8Wf7v9uV1yvFxXk57Lb7T+E8c8hMHp71cLtR/w21zbz+l2UcKg+cMmvGdwqZNGtoiD+F8Ee7XNTMm0fVU0/TsHJlaJWVIrFoFhnXXkf8lMnd0r+BRIIwQvRBEoQRQvij3W5a9uw5nEJsczFNxZtp2rYdnD030aElMZEhjz9GwtFHd/u+KmqbuO2VlSwoNkbvxNDCL2wvc53tg84rp4+CS56G3KOC2ld9Sz3Prn+WOWvnUO/0PyGlmd1i54oxV3DDkTeQ4kgJqk5vUVVax6t/XIKzxX8at8Lp2Zx27bhen05NCCGEEEII0bH9NU2c/sDnHKg3z/epecL+IGdal7Srs8IRw+zB2bgDXA/ce9y9XDz64nblh/Y38PpfltJQE2BuUQXfumECo6YETnkmQle/fDmVTz5F7bx5IdeNmzKFjOuuJfGUU1BBPrwofEkQRog+SIIwQgxsWmtcFRXt0og1lZSg64MLDkSc1UrM8OHET5lMxo03EjO0+59c+nprJT9+eQVl1cbw+BFqL4/YH2GCZXvnlSddAWf9FRz+h9B7c7qdvFH8Bk+seoKKhuBStSkUZ488m1sm30JeYl5QdXoTZ7OLV/+0lKpS//MAJQ+K49JfTScmztbDPRNCCCGEEEJ0h3dWlfLjl9qnJUulhg8dd5KjqtqtezAthadS/T9sFm+L57XzXmNoUvtrw/Id1bx5/wqcTS6/da02C+fdehS5hamh/RCiU01btlD5zDNUv/0OuiVAICyAmBEjSL/2GlLOOw+Lo+9keOgNJAgjRB8kQRghBg5XTY0RYPFKI9a0eTOugwej1idb7mBiC0fjGF2Io7AQx+jRxIwY0WMnYW635p9fbOVvH2/C5daA5hLrF/zWNocEP/mKfcQkwTn3w5Hf6XQ/Wmvm7ZrHQ8sfYtuh9hNSBnLc4OO4beptjM0YG3Sd3uazFzayfkGp33UWq+Lin08la1hyD/dKCCGEEEII0V201tz0/DI+Wtd+PsjjLOt4Ieb/sOB7L7cFuDw3h02OGL9tTs6azDNnPON3Pswdayv53+Or0W7/94cd8TYu+ulUmX+ym7SUl3Pguec58PLLuGs6SeNtYs3MJP2qq0i77FKsKX0r40O0SBBGiD5IgjBC9D/upiaat25tC7I0euZvcZbujVqfrKmpOEaPNl6FhUbQpaAAa1JS1Pp0oK6Z2/+7ks827QcgkXr+YH+a862LOq+cOwUueQrSR3a66cryldy/7H5WlLd/EiyQMeljuG3qbRyfe3zQdXqj4qVlfPxk4HPBGd8uZFKR5GgWQgghhBCivymvaeT0B77gYLu0ZPBL20vcZHu3XXmx3c6leTm0BEhL9pMpP+G6idf5XbdhUSnz/hN4DsrEdAeX/HwaCaky6qK7uGrrOPjaq1Q9+x+ce0O7/6Di40n79iWkX3019lyZK7QjEoQRog+SIIwQfZd2uWjZtetwGrHNRrCleccOcPkfit3dVHw8joICHIUFxLYFXEZjzcjoVfN9LN95gFteWE7poUYAJqkSHrY/yjBLeeeVT/gJnHIX2Pw/odVq26FtPLz8YT7d+WnQ/cpNyOWWybdw9sizsai+mx+3udHJtlUVfPHSJpob/f8uDj8yk7N+MLFX/V4IIYQQQgghIuetFXv4ySsr25XbcfKm4x4mqPZZAp5NTuJvGWl+27NZbLx09kuMSR/jd/2S/23jm3cDZx7IyEvkop9OkVTI3Uy3tFD9wQdUPvU0TZs2hVbZaiX5rLPIuO5aYsf4P84DnQRhhOiDJAgjRO+ntcZZXm6kEPMEWpo2b6ZpyxZ0Uycps7qLzYZjxIi2IEtrOjF7Xl6vnlxPa81TC7fxpw824nRrFG5utP6Pn9r+i111ErhKyIKL/gmjZnW4WUVDBU+sfILXi1/HpYMLhiXHJHPjkTdy2ZjLcFj75pNZzmYXO9ZWUry0nB1rKnC2uANum5jm4NK7jiY20d6DPRRCCCGEEEL0JK01N/xnGZ9uaJ+WbKQq5X3HXcTie03rBq7LyWJpXKzfNgtSC3j5nJf9XjdprZn/wibWL/SfDhlgyJg0zrllElZb771u7S+01tQt/JLKp5+i/qvFIddPOOEEMq6/jvhjj5WH97xIEEaIPkiCMEL0Lq5Dh2gqLj48uqW4mKbiEtyHDkWtT/ahQ41gS2sascJCHMOHo2I6HgnS2xxqaOFnr67i4/XGBcAgDnK//XFOtK7tpCZQcCpc8A9IHBRwk/qWeuasm8OcdXNocDYE1acYSwzfHftdrpt4HSmOvpf/1uV0s2tDFcVLy9i2soKWAJNhelMWxQW3Tya3ILX7OyiEEEIIIYSIqvLqRk69/3OqG53t1l1mncef7E+2Ky+1WbkobzB1AR7wmz1+NndMu8PvOrfLzQf/WMP2NZUB+zRqyiBOuKSQpHT/gR4ReQ3r1lH11NNUf/ghuAM/sOePY9xYMq67juQzzkDZZBSTBGGE6IMkCCNEdLgbGmjacnjeltZ/neVBpMPqJtbMTGJbgyytqcRGjcKS0PcnL1y9+yA/fHE5u6qM4MhMy0r+Zv8Hmaq644oWO5x6Lxx7MwS4AGhxt/DG5jd4YtUTVDYGPtH3plCcO+pcbjnqFgYnDg7lR4k6t8vNnuKDlCwpY8uK/TTVt7+Y6sgx541k2lnDu6dzQgghhBBCiF7njeW7uf2/q/ys0fwr5kFOtyxpt+btxATuHpThtz2F4qkznmJ6znS/61uaXLz1wArKtwe+3lMK8sdnMG5GLsMmZmC1ysiYntC8ezdVc57l4OuvoxuCe3ixlT0vj/Srryb1kouxxMd3Uw97PwnCCNEHSRBGiO6lnU6ad+wwBVuKad65E6L03WVJTDw8sqUt4FKALT09Kv3pTlprnlu8g/ve20Czy00MLfzM9go32N7vvHL6SLjkacidHLDteTvn8eDyB9levT3oPp2QewK3Tb2NI9KPCLpOtGm3Zu/WQ5QsKaNkeTkNNe0n1wzGkDFpnPvjo7BYZCi5EEIIIYQQA4XWmuufXcrcje0fOkylhrnxvyLD7ftAmwZuy8pkboL/m+25Cbm8ft7rJMYk+l1fX93M639dRvX+zm/0x6fEMPa4wYw9IZeUQXGd/0Ciy5wHDnDgpZc48PwLuKqqQqprSUkh7YrLSb/ySmwZ/gN1/ZkEYYTogyQII0RkaK1x7t17OI2YZ+6W5i1b0C3h3bDuKmW3EzNq1OEUYoWFxI4ejW3w4AGRT7WmsYVfvrGG/63eC8BwtZdH7I8w0bK988qTLoez/gqOJL+rV5Sv4P6l97Ny/8qg+zM2fSy3Tb2N43KPC7pONGmt2b+zhuIlZZQsK6f2QNfmH8otTOXMmyYSmyDzwAghhBBCCDHQlFU3clqAtGTHW9byQswfUfje362yWLgobzCVNqvfNs8fdT73zbgv4D4Pltfzxl+XhfQQ2ZAxaYybkcvIowbJvDE9wN3YyKG33qbymadp2bEzpLoqJoaUCy8k45rZxAwf3j0d7IUkCCNEHyRBGCFC5zxwgKZNXnO2eAIv7rq66HRIKWLy8z3BltHGv6NHE5OfP2Dzpa4vreaHLy5nW4VxTC6yfMHv7c+QoDoJJMQkwtn3w6RL/a7eemgrDy17iHm75gXdl7zEPH40+UecOeJMLKr3n8RX7qmleGkZxUvLg3pqrCNxyTEUTM2icGoWOaNSBkTwTwghhBBCCOHfa8t289NX/aUlg7vsL3GD9d125Z/HxXJLTlbANh885UGK8osCri/bXs1b9y/H2RzaPCRxSXaOOHYw42fkkpo9cFNf9RTtclEzdy6VTz1F46rVoVVWiqRTi8j+9a+xZwX+XekvJAgjRB8kQRghAnPX1dG0ZUtbkMUY5VKCq6Iian2yZWeb0ogV4hg1EkucDJkGY/TGy0t2cc8762h2ukmknt/Z53CRdWHnlXMnw8VPQcaodqv21+/niVVP8EbxG7h055PPA6Q4Urhx4o1cNuYyYqwxof4oPepgWX1b4OXA3q4FEx0JNkZNzqJwWha5o9Mk9ZgQQgghhBACMK7Xrp2zhM827W+3zo6TDxN/yyjnlnbr7s1I5/Vk/2nH0hxpvHH+G2TGZQbc757NB/jkqXXUHWoOq9+5hamMm5HLqCmDsNn9j8oRkaG1pmHZMiqfeprazz4Lup41PZ2CeXOxxMZ2Y+96BwnCCNEHSRBGCNDNzTRt3+6TRqxp82Zadu+OWp8sycltacRiW4MtBQVYU1Oj1qferq7Jyd1vreXNFXsAOFJt4WH7owy3lHVe+fgfwazfgM03WFLXUsecdXN4dt2zNDiDGxXisDr47tjvct3E60iOSQ755+gp1ZUNlCwrp2RpOft31nSpLXuslZGTBlEwLYuhY9Nl2L4QQgghhBDCr72HGjj9/i+oaWqflmyU2sNHcb/G5m70Ka9Xiovzctht95/a+OQhJ/PIrEc6HHnfVN/CxsX7WL+wlKrS8B48c8TbOOLYHMbNyCUj139QSEROU0kJlc88w6F33oVO0rxn/vhHDLr55h7qWXRJEEaIPkiCMGIg0W43LXv2HE4h1hpw2b690y/07qIcDhwFBb6jW0YXYsvKktRNIdhcVsPNLyynpLwWhZvrre/zc9sr2FUno1YSBsGF/4CCU32KW9wtvL75dZ5Y9QRVjcFNEqhQnDfqPG6ZfAs5CTnh/ijdqu5QkyfwUsa+rdVdastmtzD8yEwKp2WTPyFdnggTQgghhBBCBOW/S3bx89f9p5y6yj6P31ufbFe+whHD7MHZuANcJ9973L1cPPriTvettaZsWzXrFuyhZGk5zpbQ0pS1yhmZzLgZuRRMzcbukGuh7tRSVs6B55/jwMuv4K5p/wChioujYN5cbGlpUehdz5MgjBB9kARhRH+ktcZVUdE2Z0trGrGmkhJ0fX10OmW1EjNsmCeFWAGO0aOJLSzEPnQoyionbF3x2rLd/PqttTS0uBjEQf5uf4KTrGs6rzhqFlz4T0g8nDNWa80nOz7h4RUPs6N6R9B9mJE3g59M+QlHpB8Rzo/QrRpqm9m6Yj/FS8vYs/kgdOGUyWJT5I/LoHB6FsMnZhITOzDnHBJCCCGEEEKET2vN1c8s4YvN7dOSgeaV5Ec4pnlxuzUPpaXwZGqK3zbjbfG8dt5rDE0aGnQ/mhqcFH+zj3ULS6nYVRt0PW8xsVZGH22MjhmUnxRWGyI4rtpaDv73Var+8x+c+/a1laddeSU5d98VxZ71LAnCCNEHSRBG9CXupibcNTW4amq8/q3FXVuDq6bWGOXimb/FdeBA1Pppyx1MbOHotnRijtGjiRkxAovDEbU+9UcNzS7ueWct/11qpI07ybKKv9ufYJDqZISHxQZF98Bxt4DlcNqsZWXLuH/Z/azeH/wkgOMyxnH71Ns5ZvAxYf0M3aWpwcm2VfspXlLO7g1VuN3hnycpi2LomDQKpmUz8qhMHPH+UwAIIYQQQgghRLBKDzZw+gNfUOsnLVka1SxMvpuEZt/5WFuAK3Jz2OjwP+fm5KzJPHPGM1gtoT3oqLVm/84a1i0spfibMlqagpsH1CxrWBLjZuRSOD1bHljrRrq5meoPPqDyyado2rKFUR9/RMyQIdHuVo+RIIwQfZAEYURP0c3NuGprPcGT1sBJDe7qmrYgirumBletUeaq9QRYvIIuOkopwwKxpqZ60oeNPpxOrLAAa5I8/dLdtuyv5YcvLGfjvhrsOPmp7RW+b/tf5xXTRsAlT0He1LairQe38sDyB5i/a37Q+89LzOPHk3/Mt0Z8C4vqHfOftDS52L6mguIlZexcV4XLGd6wegAU5BakUjg9m1GTBxGX5P8iRwghhBBCCCHC9fI3O/nlG/6zGMy0r2OO9Q/tyovtdi7Ny6ElQFqyW6fcyvUTrw+7T82NTkqWlrNuYSnl28NL4WxzWCmclsX4GXlkDU+SVOPdRGtN08aNxI4dG+2u9CgJwgjRB0kQRgRDO524a2sPB1HaAideI1E6CaboxsbOd9RLqfh4z7wtBcS2BlxGj8aakSEnU1HwzqpS7nx9NXXNLoapfTxsf5RJlq2dV5z4HTj77xCbDEB5fTmPr3ycN0vexK2DC1ikOlL5/pHf5ztHfIcYa/QDE64WNzvWVVKytIxtqytwNnch8AJkj0imcFo2BVOzSEiVkVtCCCGEEEKI7qO15ntPf8OC4gq/6x9Of43z6t9oV/5schJ/y/A//4fNYuOls19iTPqYLvevYnct6xeWsunrfTQ3tB+xE4yMvETGzcjliGOyJauAiAgJwgjRB0kQpv/Tbjfuurr2abxqa3FVV/uk8/LZxmskijta86j0NJsNx4gRniBLYdsIF3teHsrSO0Y7DGSNLS7u+996nl+8E4ALLAu5z/40iaqTAJ89Ac65HyZdBkBtcy3PrHuG59Y/R4OzIah9O6wOrhp3FddOuJakmOiOdHK53OzeeICSpWVsXVkR9sVAq8yhiW2Bl+TMuAj1UgghhBBCCCE6t+dgA2cESEsWQwtfZf4fGbWbfMrdwPU5WSyJi/XbZkFqAS+f8zIOa2QeLGtpdrFleTnrF5ayt+RQWG3Y7BZGTc1i3IxcBo9KkQc6RdgkCCNEHyRBmN5Na427rh53bY1vGq9AI1FqarxSfhnL7ro6kM/IduxDhhxOI9Y6d8vw4aiY6I9uEO3tqKzj5heWs660mgQa+J19DhdbF3RecfAkuOQZyBhFi6uFVze/yj9X/5Oqxqqg9mtRFs4fdT43H3UzOQk5Xfwpwud2a/YWH6R4aRlblu+nsa5rqfnScuIpmJZN4bQs0nISItRLIYQQQgghhAjdC1/v4K431/pdN86+l/ccd2Fx+j58V2qzcnHeYGoDPDA5e/xs7ph2R8T7WrW3zhgds3hf2NdlaTnxjJuRy5hjBxObKKNjRGgkCCNEHyRBmO6jtUY3NraNOnFXV/vOhVJTi6umo5Eotbhra8EV3oRwwmDNzCS2NcjiSSPmGDUKS4LceO4rPly7l5+9upqaJicT1FYesT/CCEtZ5xWPuwWK7kFb7Xy842MeWv4Qu2p2Bb3fk4acxE+m/ITCtMIu9D58WmvKtlVTvLSMkmXl1B9q7lJ7yZmxbYGXjLxEefJKCCGEEEII0Storfnuk1+zaEul3/V3ZX3FDdWPtCt/OzGBuwdl+K2jUDx1xlNMz5ke0b62crW42bpyP+sWlrJn04Gw2rDYFKOOGsS4E/PIG50q12giKBKEEaIPkiBMYO7mZs/8J9VGEKV1JEpNte+8J97BFNNIFJxdSxMkOqEUlsRELEmJWBOTsKakENOaTswzwsWWnh7tXoowNTvd/PGDDTzz5XYUbq6zfsDPbS8TozoJTMZnwoX/gMLTWLJvCQ8se4A1Ff4ne/RnfMZ47ph2R7edrHdEa03Frloj8LK0nJqqrs2llJAS4wm8ZMuEkEIIIYQQQohea1dVPWc8+AX1zf6u9zRfDP03+fvnm0rh9qxMPk2I99tmbkIur5/3OokxiRHvr7eD5fVs+LKUDYv20lAT3uiYlEFxxuiY4wYTnywZOkRgEoQRog8aiEGY5p07OfTuu/5HolRXtwVRdHPXnjoXnbPEx2NJSsKanIQlMaktmGJJTsKa5FWWlGRs5ymzJiViSU7GEh8vc7X0U7sP1PPDF1ewatdBMjnE3+z/YKZ1VecVR54CF/6TEmcNDy5/kM93fx70PockDuHWKbdy+vDTsaie/b2q2lvXFng5WNa1OZjikuyMmpxF4fQsBo9KRVkk8CKEEEIIIYTo/Z5bvINfv+U/Ldlgex0Lku7CVl/uU37AYuHCvMFU2qx+650/6nzum3FfxPvqj8vpZvvqCtYvLGXnhiojShQii0UxYlIm42bkMnRsulzPiXYkCCNEHzQQgzB1X33FzmuujXY3+jwVF4c1MRFLkil4ktgaMEk0AibJxrIlsTWYkuxZl4iy+j9JEgPbp+vLuOPVVRxqaGGGZQ0P2B9nkOpk8kOLDYp+Q9mk7/D46n/wVslbuLU7qP2lOdL4/qTv853R38Fu7bl8vIf2N1CyrIziJeVU7qntUluOeBsjjxpE4bRs8o5IxWKV4KQQQgghhBCib3G7jbRkX231n5bs+tzt3F31q3blX8TF8sOcrIDtPjjzQYqGFUWsn8Gormhgw6K9bPiylLowU0snpccybsZgxhyXS2KaI8I9FH2VBGGE6IMGYhCmYe06tl9ySbS7EVUqJsYIlHiCKCGNRPHUU3aZPE5ExqGGFr7aUsGCYuO1s6oeO07usL3KTbZ3O28gbTg15z/KM1UreG79czS6gkvhFWuN5apxV3HNhGtIiknq4k8RnNoDjZQsK6d4SRnlO2q61JbNYWXEkZkUTs8mf2w6VrsEXoQQQgghhBB9W8dpyeB/R3zA+B3PtSu/NyOd15P9px1Lc6TxxvlvkBmXGdG+BsPtcrNjXRXrF5ayY00F4dzCVgqGTTRGxwwbny4P3Q1wEoQRog8aiEGY5h072HLGt6LdjfDZbEbwJDm585EoraNOfEaiJGFxyBMUInpaXG5W7jrIgs37WVBSwapdB3F7fY3nqzIetj/KUZYtnbc1/iL+e8QM/rF+DgebDga1f4uycEHBBdw86WayE7LD/CmCV1/dzJbl5RQvLWNvSScjejphtVkYPjGDgmnZDJuYgT1GRpMJIYQQQggh+pf/fLWd37zt/55ySoybJVl/JKbCd329Ulycl8PuAA+MnjzkZB6Z9UhU58msPdDoGR2zN+z5PxNSHYw9fjBjTxhMckZchHso+gIJwgjRBw3EIIyzqori40+Izs4tFt8RKK3BFE/qroDzn3iVqdhYmVxb9Claa7ZW1LGwuIIFxftZvLWK2iZn2/o4GjnKsoVpahPTLJs52rKRONXxcG1tT+Cj46/loapl7K7dHXRfZg6Zya1TbqUgrSDsnycYjXUtbF25n5KlZezeeCCsp51aWSyKoePTKZyWzYgjM4mJs0Wuo0IIIYQQQgjRy7jdmsv/vZivt1X5XX/J0Fr+euDHKKdvIGOlI4arB2fjDnDP5N7j7uXi0RdHvL+hcrs1uzdUsW5hKdtXVeB2hzM8BvLHpjPuxFyGH5mJVUbHDBgShBGiDxqIQRjd3MzGIyeFVddiDp74G4niEzzxHomShCUhXgIoYkCoqmvmy5IKFhZXsLCkgj0HG9rWDeIgUy2bmWbZxDTLJsarHdiV/6Hm/izJHcv9WYNZe6gk6DoTMydy+9TbmZYzLaSfIxTNjU62raqgZGkZO9dX4XaFf26iFOQdkUbhtGxGTh5EbIKk/xNCCCGEEEIMHDsq6zjjwS9obPE/1+crU9ZxzPo/tCt/KC2FJ1NT/NaJs8Xx+rmvMzR5aET72hV1h5rYtHgf6xaWUr2/ofMKfsQlxzD2uBzGnpBLalZ8hHsoehsJwgjRBw3EIAzAjqu+h4qN9Z08voP5TyxJSVgSEmQieSECaHK6WLbjAAuKjcDL2tJDntEfmlGqlOkWY5TLNLWJ4ZaysPZRbLfzwMhJLGguD7pOflI+P57yY04fdnq3BECdzS52rK2keGkZ29dU4gpwgRCswQUpFE7LZtSULOKTYyLUSyGEEEIIIYToe575chu/fXe933UJMRaWjnqGuG0f+ZS3AFfk5rDR4f96anLWZJ454xmslt51f0e7NXs2H2D9wlK2rNyP2xneve68I9IYPyOXkUcNknlD+ykJwgjRBw3UIIwQomu01mwuq2VB8X4WFFfwzbYqGlpcxNDCRLXVCLhYNjHVspl0Vdulfe2zWnlsUDbvxNlxE9z3fXpsOt8/8vt8e/S3sVsjO4rE5XSza30VxUvL2Laqgpam4Efx+JM1LImCadkUTM0iKT02Qr0UQgghhBBCiL7N7dZc9q/FfLPdf1qyb42w8kTNj1G1vg/6FdvtXJabQ7PF/4N4t065lesnXh/x/kZKQ20zmxbvY/3CUg7sqw+rjdgEO0ccl8P4Gbmk5SREuIcimiQII0QfJEEYIUSwymsa+bKkom20S3lNEynUMtWy2TPSZRNHqm04VEtE9lejFE+lJvN8aipNQQZf4mxxXDXuKq4Zfw2JMYkR6QeA2+Vmz+aDFC8tY+uK/TTVOzuv1IGMvIS2wIsMFxdCCCGEEEII/7ZV1HHmQ4HTkj09o4ZZS7/frvzZ5CT+lpHmt45CMTV7KkX5RRTlFzE4cXBE+xwpWmv2bjnE+gWllCwvDzvzwuCCFMbPyGXUlCxsMb1rBJAInQRhhOiDJAgjhAikscXFN9uq2ka7bNxXzVBVznS1yTOfy2ZGW/ZEfL/NwCvJSfwrLZWDAZ5cMrMoCxcWXMjNR91MVnxWRPqh3cYJb8nSMkqWl9NQ07XgUkpWHIXTsimYlkVGbuQCREIIIYQQQgjRnz21cBu/fy9QWjIri6fOJWnFv3zK3cD1OVksies828C4jHEU5Rdxav6pjEwdGYkuR1xjXQubvylj/cI9VO6pC6sNR7yN0UfnMG5GLplD5Jq0r5IgjBB9kARhhBCt3G7N+r3VLCwxRros276fAtc2pls2MdWyiemWzWSpgxHfb71SrHPEsDYhhTUpg1hucVLpCn5CwplDZ/KTKT9hVOqoLvdFa035jhqKl5ZRsrScuoNNXWovMd1B4dRsCqdnkzk0sVvmpRFCCCGEEEKI/szl1lz6z69YuuOA3/WnFCTzdMsvUGW+96FLbVYuzhtMrSX4uVGGJw9vGyEzIXNCr7uG01pTtr2a9QtLKV5ajjPM9NjZI5IZNyOXgqlZxMTaItxL0Z0kCCNEHyRBGCEGtr2HGtrSi60s3smwxvXGfC5qE5MtJcSrrgUhzFxAid3OWkcMa5IzWBMXT4m7Iei5XrwdmXkkt0+7nanZU7vUJ601VaV1FC8po3hpGdUVjV1qLz45hoKpWRROzyZ7eDIqyNE8QgghhBBCCCH827q/ljMfWkCT039KrsdPi+esry4Dp+/13DuJCdw1KCOsfWbFZ7UFZKZmT8Vm6V3BiuYGJ8VLy1i3oJT9O2vCasMea2X09GzGzcgla1hyhHsouoMEYYTogyQII8TAUtfk5OttlSwormDDpo0MqlreNqfLGLUTq4rc96kGyqxWVjtiWBsby+qkNNZbNQ26axPZD0sexq1TbuXU/FO79FTSwbJ6ipeWUby0nAN7wxvO3cqRYGPUlCwKp2WTW5iKRQIvQgghhBBCCBFR//5iK394f4PfdUkOGwtmbSH1szt9yjXwq0EZvJfYtcnpUxwpnDzkZIryizg+93hibZ2nOetJ+3fWsH5hKZu+2UdLY3jX3JlDExk/I5fCo3NwxPWugJM4TIIwQvRBEoQRon9zuTVr9hziy8372L5hGfFlS5nMRqZZNjNEVUR0XzWetGJrHA7WxMWzJi6OCroWcPGWHpvODyb9gItHX4zdYg+rjerKBkqWllO8tIyKXbVd6k9MrJURRw2icFo2Q8amYbUGP8RdCCGEEEIIIURoXG7NJf9YxIqdB/2uP7kwkzlxf0dt/sinvAV4LiWJ11JS2WXt+gNzcbY4ZuTNYFb+LE4achLJMb1nBElLk4uSZWWsX1jKvq3VYbVhi7FQMC2b8TNyyR6R3OtSsg10EoQRog+SIIwQ/c+uqnq+2rSLPWu/xLbnaya4NjDVUkyyqo/YPlqAzTF21jocxkiXuAS22VQYScU6F2eL4+rxVzN7/GwS7KE/vVR3qImSZeWULC0L+yS0lc1uYfikTAqnZZM/Ph2b3dql9oQQQgghhBBCBK+kvJazHl5Ac4C0ZA+dm8f5i74NdeXt1mlgc+FM5o0/g7k757HpwKYu98dmsXF0ztEU5RcxK38WmXGZXW4zUir31BqjY77eR1O9M6w20nMTGDcjlyOOySE2IbyHIUVkSRBGiD5IgjBC9H3VjS0sW7eZvWvmY939NaOb1zFBbcOuIjMKRQO7bVZjhItnpMtGh4Ombn4Yxm6xc37B+dw86WYGxQ8KqW5DbTNblu+nZGkZe4oP0pXokMWmGDY+g8Jp2QybmCGTFgohhBBCCCFEFP3z8y388YONftclxdqYf6GLjDcvD9zA2X+H6dezq2YX83bOY+7OuawsX4nu4mOFCsWkQZPa5pEZmjy0S+1FirPZxZYV+1m/sJTS4oNhtWG1WRg1dRDjZ+QyuCBVRsdEkQRhhOiDJAgjRN/jdLrYsG45e9d8jnX3YkY2rGGE2hex9g9aLKx1xLQFXNY6HBzogVRbyTHJTMycyITMCRw56EgmDZpEiiMl6PpNDU62rdxP8dIydm04gHaHf26gLIqhY9MonJbNiEmZOOLliR8hhBBCCCGE6A1cbs3FTyxi5a6DftefcsQgns55A/X1EwFaUJA3FQqKYNQsyJtGRfNBPtv1GXN3zuXrvV/jdIc3csTb6LTRbQGZ0Wmje0Xg4sC+OtZ/uZeNX+2lsbYlrDZSs+MZd0IuY47LIS4pJsI9FJ2RIIwQfZAEYYTo/bSzib0bFrN3zXwsuxczrG4N6aomIm03KdgUYwRcVjscrHXEsNPe/QEHu8XOmPQxPkGX/KT8kE9KW5pcbF9TQfGSMnasq8Tt7ML5gIK8wlQKpmUzasog4hLlZFIIIYQQQggheqOS8hrOenhhwLRk9180houWfQ/K1vpd78ORAiNPglFFUFBETXwaX+z+grk757Jwz0IanA1d7u+QxCFGQGZYEZMGTcKiojunqKvFzdZVxuiY3RsPhNWGxaoYedQgxs3IZcgRaShL9INMA4EEYYTogyQII0Qv1HCA2pJF7FszH8uer8mr24CD5i436wZ22G2s9Qq4bIyJwdkDT+MMTx7OhMwJTMycyMTMiRyRfgQx1vCCHM4WFzvXVVG8tIztqytwNvs/6Q5WzshkCqZmUzA1i4RUR5faEkIIIYQQQgjRM56Yv4U/fxg4LdlnV2WT+dIZ4GwMreGMgraATOOQaSyuXMOnOz5l/u75HGo61OV+Z8RmcEr+KRTlF3FMzjHYrdHNvHBof70xOmbRXuqrw7v3kJwZy9gTchl7/GASUuS6ujtJEEaIPkiCMEJEmdZwcCfO7Yuo3PA5ll3fMKhhS0SarrRYDs/jEhvD2hgHNT2QViw9Nr0t4HJk5pGMzxwfUloxf1wuN7s3HqBkSRlbV+6nubFr891kDk2kcJoReEnOjOtSW0IIIYQQQgghep7T5ebiJxaxarf/wEjRmCyeHLsC9cHPwt+JNQbyj4VRs3COmMlySzNzPfPIlNWXhd+uR5I9iROHnEhRfhEz8mYQb4/vcpvhcrnc7FhTyfqFpexYVxnW3KrKohg+MYNxM3LJH5+BRUbHRJwEYYTogyQII0QPczmhbC1651fUFn+JZfdiEpr2d7nZBqXYENM6j4sxl0upvfsnkHdYHYzLGGekFMs8kgmZE8hLzItIrlu3W1NafJDipWVsXb6fxrrw8tW2SsuJp3C6EXhJy0nocv+EEEIIIYQQQkTXpn01nPvIQppd/jMkPPCdI7mw+Few4Z3I7DAhC0bNQo88hXXpecytWM7cnXPZdmhbl5t2WB0cl3scRflFzBwyk9TY1K73N0w1VY1s+LKUDYv2UnugKaw2EtMcbaNjktJjI9zDgUuCMEL0QRKEEaKbNdXC7iWw62uat32JZc9SbM76LjXpArba7Z60YjGsdTgojrHj6ua0YgrFyJSRTBw0sS2tWEFaAXZL14ZOu1xuGmtbaKxtoaGmmYbaFvZtOUTJsvKwh0K3Ss6MNUa8TMsmIy+hV0yEKIQQQgghhBAich77rIS/frTJ77qUODuf/PhYslY9ASueg0O7IrvznCOhoIitg8cxt6WKubvns66y6/fDrcrK1OypFOUXMSt/FjkJORHobOjcbs3OdcbomO1rKtHu0O/BKwX5EzIYd0IuwydmYOmBDB39mQRhhOiDJAgjRIRV74Vdi2HnYtw7vkKVrUXprqXOKrNa2wIuaxwO1jliqLd0/0nLoLhBRrDFE3QZnzGexJjEDutorWlucNLQGlSpbaGxtpmGGs9yXQuNnkBL6zbNDc6I9jsh1UHBtCwKp2WTNSxJAi9CCCGEEEII0Y85XW4ufHwRa/b4T0t26ths/v29qSiAimLYMg+2zIXtC6Glaw9J+rAnwIgT2Zd/NHNjbcytWMWy8mW4ddfmMQWYkDGBomFGQGZkysgIdDZ0dQeb2LBoL+u/LKWmMsR5djziU2IYe9xgxs3IldTgYZIgjBB9kARhhOgCtxv2b2wLuuidi1EHd3SpyTqlWOeVUmyNI4ZyW/enFYuzxTEhc4JPWrGchBxcLW4jWFLnFUipbaGhttkzcsVY1/b/2hbcYTwZ0+X+J9kZNcUIvAwelYKSvLNCCCGEEEIIMWBs3FfNuY8spMXl/3r0ocuO4vyj8nwLnU2w8ysomQtbPoOyNZHtVOowDoyYwfy0LOY17WPRviU0u7uW7QFgZMpIivKLKMovYlzGuB5/8FC7Nbs3HmDdwlK2rdqPO8B73pmhY9MYNyOPEZMysdpkdEywJAgjRB8kQRghQtDSCKXLjZO0nV8bwZdG/0/aBMMJlMTYWe1wsNYTeNlit6O76wRKKxzOOOJdSRTEjqHAcQT5MSPIUoOJdybRVOf0GqFijFZpaezaKJ7u5Ii3MXLyIAqnZZM3OlWGNAshhBBCCCHEAPbI3GL+/slmv+tS4+18fNtJZCV1MDdJzT4jGLPFE5Spr4hc55SV+qHTWJBTyFxLMwuq1lLbUtvlZnMScpg1dBanDjuVyVmTsVm6/yFOb/XVzWxcvJf1C0s5VN4QVhtxSXbGHGuMjknNjo9wD/sfCcII0QdJEEaIDtRVto1yYediKF0B7vAmh9dAqc3aNrplrSOG9TExNHYhrZjVbSO2JZFYZwJxLYm+/3cmENuSSJwzgURXKvHOZGzNMaD79ggRu8PKiEmZFE7LZui4dHlaRgghhBBCCCEEAC0uNxc89iXrSqv9rj99XDb/vGpqcCNH3G7Yt9oIyJTMM+4NuCOXSrs5Lo1vhk1hbkI88+p2UNV0sMttpjpSmTl0JkX5RRyXexwOq6PrHQ2S1prSzQdZt7CUrSv243KGl4IttzCV8SfmMnLyIGx2a4R72T9IEEaIPkiCMEJ4aA1VWz2jXDxBl8risJurtijWegIurYGXKmsHJxBaEeuMbwuetAZQYtsCKgnEOX3L7e6eO6GKJqvdwvCJGRROy2bYhAxsMXIiJoQQQgghhBCivfWl1Zz36EKcAdJkP3z5ZM6blBt6w001xhwyJXONwEzV1i729DAXsCp7NHMzc5nrOsSepsoutxlni+PEvBMpyi/ixCEnkhST1PWOBqmxroVNi/ex/stSqkrrwmrDkWDjiGNyGDcjl4zcjuemHWgkCCNEHyRBGDFgOZuNp1p2LjYCL7u+hrr9YTXVAmyK8Z3HZbc1nlhnohE8aUk0/t82YiWhbV2sM5G4lkQczngsyKgOgJg4G3GJdjLyEhk5eRAjJmUSE9uzQ6qFEEIIIYQQQvRND366mQc/9f9QZVq8nU9uP5nMxC4+1Fi17XDasq2fQ3NN19rz0MDmuEQ+zSlgbgwUN1d1uU2bxcYxg4/h1PxTmTl0JplxmV3vaBC01uzbWs36hXsoWVqOsyW80TE5I1MYNyOXgmlZ2OWhTAnCCNEXSRBGDBiNh2DXksMjXfYsA2dw+Urd2kKjO4lGnUSDO5ldpLHNksZelUYlKdTrZBytgRbPyBW7O6abf6C+wWJTxCXGEJtoJ87zik2MIS7JTmyC3ShPivGUGy+rzO0ihBBCCCGEECJMzU435z/2JRv2+k9LdmJhJtecMJyCQUkMSYvDYuli2m5XC+xecniUTOlKjHBK1+202ZiXnsOnyamscvn/eUKhUEzOmsys/FkU5RcxJGlIBHrZuaYGJ8Xf7GPdwlIqdoU3F05MrJXRntExg4b23Mie3kaCMEL0QQMxCONyuqnYVYuyYOQBVca/SoGyeP5Vqt16i8VrW4u5zuF6eK/rrgnWRecO7vId5VK2DtBoDS06lkZ3Mg06mQZ3Mo2eV0O7f5Ood6fQrBNARqkAxpDguMQYYhPsRiAl0e4TZGldbl1nd1jl70AIIYQQQgghRI9aV3qI8x/9MmBaslYOm4WRgxIpyEpk1KAECrKM/4/ITMBhC3PURV0lbP0MtswzXjV7w2vHZL/Vwmfx8cxNz+YbixNnBAI9Y9LHtAVkClMLu/36XWvN/p01rFtYSvE3ZbQ0ucJqJ2tYEuNm5FI4PXvAZc6QIIwQfdBADMLUVDXyn18t6pmdKd+AjFKAJ9DjE9RpFwgyAj10sK6tPeWnPVMgqDW4hPIXbPLfP3/rWoNMliD71+7ns7Tf/+Egl6e9tr4H2NbcP2cDqvEgqvEArv1badyzhcayUhrqNQ3uJL/BFRcySgWMuVbaAidJnsBKYmtwxXd0ihF4sWGRUSpCCCGEEEIIIfqA+z/ZzMNzw5vr1aIgPz2eUa0BmqzWQE0iKXH24BvSGso3GCNkSubCjkXgagqrT94OWRRfxMUxLymZL+McNEQgIJOflE9RfhGz8mdx5KAjsajuvf5vbnRSsrScdQtLKd8e3igfm8PK6GlZjDsxj6xhSQPiIVAJwgjRBw3EIEx1ZQPP3fVVtLshRGQp2oIo/ken2In1SvsVlxiDLcYyIE5QhBBCCCGEEEIMPM1ON+c9upCN+yIzX0urQUkOCnxGzyRRkJVIdrKj82vs5nrYuQhK5hmBmf0bu9yfBqX4Ki6WufFxzE9IpDoCsZNBcYM4ZegpFA0rYnrOdOyWEAJPYajYXcP6BaVs+qaM5gZnyPUtVsXsP59AXGL/f+hWgjBC9EEDMQhzaH8Dz/9agjCid7M5rMS1zZnSPrDS9n/POke8vet5bIUQQgghhBBCiH5k7Z5DnP/Yl7g6SUsWCUkOGyO905p5AjX56fHYAmWVOLT7cNqyLZ9B48Eu9aEFWBbrYG5CPPPi4ykPN6Wal6SYJE4ecjJF+UUcn3s88fb4LrcZSEuziy3Ly1m/oJS9Ww4FXW/UlEF868aJ3dav3kSCMEL0QQMxCHOwvJ4XfrM42t0QA4hSeNJ6xXhNTu+VAsxPkMUW0/UTJSGEEEIIIYQQYqB74esd3PXm2s437CZ2q2J4xuH5ZlpTnI0clEB8jNd8Jm4XlK4w0pZtmQu7l4IOb84UADewLiaGTxPimBcfz/aYro9mibXGcnzu8RQNK+LkISeT4kjpcpuBVJXWsf7LUjYu3ktTXcejY8798STyx2V0W196EwnCCNEHDcggTFk9L9wjQRgRvmZrI422WnSsk9hEOykpCWSlZ5KTkUlCcqzX3CpGUMURZ0PJKBUhhBBCCCGEECIq1uw+xDur9rBxXw1bymspPdQY7S4BkJca5xOYaX2lJ8RAw0HY9oVnPpl5cGhn2PvRwFa7jbnx8XyaEM8GR9fTdlmVlWk504x5ZIbOIjshu8tt+uNscbF15X7WLyxlz6aD7dYnZcRy1e+PGzD3XSQII0QfNBCDMAf21fHivV9Huxuil3ApF422WhptdTTaa2m019FgM/5ttNXSYDfWxSc5GJk9jLF5ozkyZwLjMsaRYE+IdveFEEIIIYQQQggRoromJ1v311Gyv4aS8lpKymvZsr+O7RV1OHsgdVln0uLtPiNnRg1KYIy9jOz9i7BsmQfbF0BLfdjtl9qszIuPZ258HMtjHbgjMF/skZlHMit/FkX5RQxPGd7l9vw5WFZvjI75ai8NNS0AHHPeSKad1T37640kCCNEHzQQgzDarXE63Wi3Bg1aa7Tb82+75cP/97utW/vUo62s/brWMjS4vfet2++r3T6922vdxuVEN9VCYy26qRbdVOd51aOb69DN9ejmBmhuwO1sQaMAhcaCbv1X+y6j1eF1bdu0/t+zTev/tcVPWwqw4PaUO7EYL3X4/y7Py40FF6ptW40Fizb6qLCgtEKhUJ4yizb27V2uPNt6a7LW02irM4In9rrDwRVbLQ2ewIp3oKXZ2gCmc40EewITMiYwIXMCEwdNZGLmRLLis3rk91MIIYQQQgghhBDR0eJys6Oy3hOUqWVLeS0lnn/rmsNPDRYpcXYrIwclcERmDDMcW5jUtJy8qkXEVoR/D77KYmF+fBxzE+L5Ki6WlggEZApSC9oCMmPTx6Ii0KY3l9PN9tUVrP+ylFlXjSUh1RHR9nszCcII0QcNxCBMr+Zshrr9UFcOta2vMqOsdbl1XRcnawtHo1JUWS1UWaxUWi1UWa1UWq1UWS1t/xrrrBy0WnBF+EvWL01bUEYr0ModUnWrsjI6bTQTMycyIXMCRw46kuHJw7FaZE4WIYQQQgghhBBCGA/D7j3UyJb9tW0jZ1pHz1TUNkW7e2RbDnF+0iZm2dYwsWk5Cc4DYbVTpxQL4uOYGx/Hgvg46iyWzit1Ijchty0gMzlrstxv6SIJwgjRB0kQpge4WvwEUcqg1hRsqSuHhvC+JMPlBqotFt+AisUroOIVZKmyWiPy5RtteYl5TMw0RrdMHDSRMeljiLPFRbtbQgghhBBCCCGE6IMO1jf7BGe27K+jpLyWXQfqicYta4WbcWonJ1lWc5JlNdOsm7HT8cT2/jQDi+NimZcQz2fxcVRZux48SY9NZ+bQmRTlF3Hs4GOJsXZ9bpqBRoIwQvRBEoQJk6sF6io6GLHi9f+Gqh7tWpOCKovVE0AJEFDxjFY50FOjVaIkKSbpcMDFM9IlIy4j2t0SQgghhBBCCCFEP9fY4mLr/rrDARpPWrOtFXU0O0PL4tEV8TRyrGV9W1BmpGVfyG24gBWxDubGxzEvPp5Su63r/bLFc9KQkyjKL+LEISfKvLtBkiCMEH2QBGG8uJxQX9HBiBWv/9dX9li3NIdHq7SOVDGPUPH+f20/GK0SquSYZNJj08mIy2hLLTYxcyLDkodFPO+oEEIIIYQQQgghRLhcbs3uA/VeI2cOj6Kpbgx9xEqohqhyTras5kTLGo63rCVZNYRUXwMbY+x8Gh/PvIQ4SmK6PprFbrFzXO5xFOUXMXPoTNJj07vcZn8lQRgh+qABGYTZuxpWv9J+xEp9JcZXSfdrhraRKn4DKp5AS2uZc4AFEmwWmxFUic0gPc7zr9dy2/9jjf/brfZod1kIIYQQQgghhBAibFpr9tc2taU02+I198y+6sZu2acNJ0epEk6yruYkyxqOVFuxqNDuje2w2ZibEMfc+HhWxzq63CeLsjA5azJF+UUU5ReRm5jb5Tb7EwnCCNEHDcQgTPnXr5H1wXURbdMYraLaBVG8gyttk9dbrNRYB95olaSYJJ/ASUZc4P8n2ZNkBIsQQgghhBBCCCEEUNvkPByU8aQ1K9lfy47KelzuyN0XT6WGGZa1Ruoy62pyVGhzF5dZrXwWH8fchDiWxsZG5KHiselj2wIyo1JHDfj7RRKEEaIPGohBmK8/f59jPru80+1aoMP5VA6XD9DRKsoYreI9UqWj4IpMtiaEEEIIIYQQQggROc1ONzsq63zTmu2vZUt5HQ0tri62rilUezjJsoqTLas52rKRWNUSdO1DFsUX8XF8Gh/PorhYGiOQPn9Y8rC2gMyEzAlY1MB7yFmCMKLHKKVGAEcBuUAisBfYASzSWgf/adANlFJTgEIgz1O0B9istV4RvV4FNhCDMF+tWEDMhxe3H6HSGlDxBFkG5GgVe1Jbuq+A6b88QZfkmOQB//SBEEIIIYQQQgghRG/jdmv2Vje2pTNrDdJsKa+lsq45rDYdNHO0ZaMxSsaymiMsu4Ou26AUi+JimRsfz/z4uIjcc8uKy+KU/FMoyi9iWs407JaBkapegjCi2ymlLgFuB44LsEkV8ArwG611RQ/2yw7cAVwPjAqwWQnwJHB/tANF3gZiEOa55f/lL2t+H+1u9AirsgaV/isjNoO02DQc1q7n7hRCCCGEEEIIIYQQvdOBumZKPKNmWtOalZTXsudgA6HcYs+hkhOtazjJspoZlrWkqdqg6rUAS2JjmZcQx7z4OPbbbOH9IF6y4rL48JIPB0QgJppBmK4fKdGrKaUSgX8Dl3WyaTrwA+AipdTVWuuPeqBvhcDLwJRONi0A/gR8Wyl1mda6pLv7JvwbkjE02l3okgR7QqfpvzJiM4y5VWKSBuTQTCGEEEIIIYQQQgjRXlpCDNMT0pk+PN2nvKHZxdYK3+DMlvI6tlXU0exyt2tnHxm86prJq66ZWHAzUW1tm0tmsirBptrXAbADxzc2cnxjI7+qPMAaRwxz4+OZmxDHTnt4QZSxGWMHRAAm2iQI048ppawYo1vOMq3aD6wADmGMPpkMtOZGygbeVkqdqrVe2I19ywE+AYaZVpUA6zz9GY/v6JipwMdKqWO11uXd1TcRWH5yVrS74MOqrKTFpgVM/9U6UiU9Np202DRibbHR7rIQQgghhBBCCCGE6EfiYqyMz01hfG6KT7nT5WbXgQaftGatgZqaJicAbiys0gWschXwiOsikqjneMu6ttRlQy37/e7TAkxqamZSUzO3HThIid3O3IQ45sXHs8ER/NzC1ZVHhP1zi+BJEKZ/+xO+AZgWjJRk/9JatyUxVEqNw0j31ZqqzAG8pZSaqLXeG+lOKaUswFv4BmD2ArO11h+btv0W8AyQ4ykaAbyplJqhJZdej8uIy+j2fSTYE9pGpwRK/9UaaEl2JMtoFSGEEEIIIYQQQgjR69isFkZkJjAiM4HTxmW3lWutKa9p8klp1hqkKauGj9zT+cg9HdAMV/vaAjLHWdaToJra7UfKbIsAAABG7UlEQVQBhS0tFB5s4aaD1ey2WZnnGSGzwuFAB5iX2KI1P9z3MehbQOYu7lYShOmnlFIjgVtNxd/WWr9t3lZrvV4pVQTM5XAgJgO4B7ipG7r3XeAYr+Uq4Hit9XY/fftQKXU8sAxI8xQfD1yKkcpM9KBkRzJWZcWlXUHXsSgLaY60tknp/QZU4g6PVomzxXXjTyCEEEIIIYQQQgghRPQopchOjiU7OZbjCzJ91lU3thjBmba0Zjks2F/A85VnYNMtTLVs5kSLMZ/MBMt2v+0Pcbr4XnUN36uuodJiYX58HJ8mxPN1XCwtXsGWqY1NDLO0SACmB0gQpv+6ByNVYKs5/gIwrbTWDUqp2cAaoHXM2nVKqb9orbdGqlOeFGm/NRXf7i8A49W3bUqp2zFGxLS6Tyn1X621/ySJoltYlIX02HRqW2o7Tf/VupziSJHRKkIIIYQQQgghhBBCdCI51s7k/DQm56f5lDc5XWyvqGfL/mMoKa/lX+W1VOzbRd6BxRyrV3GSZQ2D1KF27WW43VxcW8fFtXXUKsWC+DjmxsexID6Oovp6Wkaf0lM/2oCmJKNT/6OUigMqgHiv4rFa641B1H0F+I5X0a+11vdFsG8nA/O9ivYAw7TueGiFJ3izA8jzKj6xO+et6YxSajywtnV57dq1jB8/Plrd6TFNriYcVke0uyGEEEIIIYQQQgghxIDmdmv2HGygpLyaA1tXELdzPkOrvuKI5rXYCXy7tUmBC4Vz9uckD5vUgz2OnnXr1jFhwgTvogla63U9sW8ZCdM/nYFvAOarYAIwHs/gG4S5CIhYEAa40LT8n84CMABaa5dS6nngF6a+RS0IM1BJAEYIIYQQQgghhBBCiOizWBRD0+MZmh4PY84EzjRWNNVSvXE+9Rs+Jn7X5yTXbfep59BQRhrZ+Uf2eJ8HIgnC9E/fMi3PD6HuAsDJ4d+NyUqpbK11WSQ6Rtf6Nh/fIMyZwO1d7I8QQgghhBBCCCGEEEL0H45EkiedQ/Kkc4zlAztgyzzYMhe99XNUUzV65CyZD6aHSBCmf5pgWv4q2Ipa6zql1BpgslfxeKDLQRillAMoMBUvDqGJRablQqVUjNa6uWs9E0IIIYQQQgghhBBCiH4qbRhMuwamXYNyOWHPUnIcSdHu1YAhs2X3T2NNyyUh1t9iWh7Xhb54OwKwei2Xa62rg63s2bbCq8gKjI5Q34QQQgghhBBCCCGEEKJ/s9og/1jI7v9zW/cWEoTpZ5RS6UC6qXhniM2Yty8Mv0c+zKNgQu2XvzqR6psQQgghhBBCCCGEEEIIEVGSjqz/STUt12ut60Jso9y0nBJ+d3ykdrKfYHRL35RSWcCgEKuN8V4oKQl1wJEQQgghhBBCCCGEEEKI7ubn3m1MT+1bgjD9T6JpuSGMNsx1IpUgsDf37Wbgnq40cMEFF0SmJ0IIIYQQQgghhBBCCCG601BgRU/sSNKR9T/mQEdjGG2YAx3mNsPVm/smhBBCCCGEEEIIIYQQQkSUBGH6P91DdcLRm/smhBBCCCGEEEIIIYQQQnSJpCPrf2pNy3FhtGGuY24zXL25b48Dr4ZYJxGYBlQDh4BdQHOE+tPbjQLe9lo+H9gSpb6IrpFj2b/I8ew/5Fj2H3Is+w85lv2LHM/+Q45l/yHHsn+R49l/yLHsPwb6sYzBSEHW6vOe2rEEYfqf3hzo6LV901qXA+VhVP06Evvva5RS5qItWut10eiL6Bo5lv2LHM/+Q45l/yHHsv+QY9m/yPHsP+RY9h9yLPsXOZ79hxzL/kOOJdBDc8CYSTqy/ueQaTleKZUQYhtZpuWD4XfHh7lvg8Joo7v6JoQQQgghhBBCCCGEEEJElARh+hmtdSVwwFScH2Izw0zLxeH3qMN2zPsJRnf1TQghhBBCCCGEEEIIIYSIKAnC9E8bTMsFIdYf2Ul74doEuLyWs5RSScFWVkolA5leRS4kCCOEEEIIIYQQQgghhBCil5IgTP+01rR8XLAVPanLjuykvbBorZtoP9lT0H0DjjctF3vaFEIIIYQQQgghhBBCCCF6HQnC9E8fmpZnhlD3RMDmtbxCa13W5R4d1pW+mbf9oEs9EUIIIYQQQgghhBBCCCG6kQRh+qePgAav5eOUUmOCrDvbtPxmRHoUuL2rlFLWzip5trmyk7aEEEIIIYQQQgghhBBCiF5DgjD9kNa6HnjNVPyLzuoppUYDF3oVOYEXI9g1gAXANq/lIbQPrvhzJZDntbwF+DKC/RJCCCGEEEIIIYQQQgghIkqCMP3XvUCL1/JspdR5gTZWSsUCzwAxXsVPaa3Nc7iY62nTa2ZH22utXcA9puL7lVLDO9jHcOABU/HdWmt3R/sSQgghhBBCCCGEEEIIIaJJgjD9lNZ6K/CQqfg1pdQtSinvQAtKqbHAXHwnvq8EfttN3XsB+NprOR1YpJQ63byhUuoM4Csgzat4EfBKN/VNCCGEEEIIIYQQQgghhIgIW+ebiD7sl8B44EzPsh14BPi1Umo5UAOMBKYAyqteM3Ch1npvd3RKa+1WSl0ILAbyPcWDgY+UUsXAOk9/xgMFpurbgYu01ro7+iaEEEIIIYQQQgghhBBCRIoEYfoxrbVLKfUd4EngUq9VWcC3AlQrB67WWi/o5r7tVUqdBrwMTPZaVeh5+bMcuFRrXdadfRNCCCGEEEIIIYQQQgghIkGCMP2c1roWuEwp9RpwB3BsgE2rMFJ83aO13t9DfduslDrG068bMEbl+LMFI5D0d611S4BtRM/aj2+6uh75nRHdQo5l/yLHs/+QY9l/yLHsP+RY9i9yPPsPOZb9hxzL/kWOZ/8hx7L/kGMZJUqyOg0sSqkRGOnHcoEEYB+wA/hSa90c5b5NBUZ7+gZQCmzWWi+LXq+EEEIIIYQQQgghhBBCiPBIEEYIIYQQQgghhBBCCCGEEKIbWKLdASGEEEIIIYQQQgghhBBCiP5IgjBCCCGEEEIIIYQQQgghhBDdQIIwQgghhBBCCCGEEEIIIYQQ3UCCMEIIIYQQQgghhBBCCCGEEN1AgjBCCCGEEEIIIYQQQgghhBDdQIIwQgghhBBCCCGEEEIIIYQQ3UCCMEIIIYQQQgghhBBCCCGEEN1AgjBCCCGEEEIIIYQQQgghhBDdQIIwQgghhBBCCCGEEEIIIYQQ3UCCMEIIIYQQQgghhBBCCCGEEN1AgjBCCCGEEEIIIYQQQgghhBDdQIIwQgghhBBCCCGEEEIIIYQQ3cAW7Q4IIYQQQgghhBB9kVJqNvCMV9E1Wus50emNEJGnlEoApgAFQCqQADQCNcBuYDuwWWvdEqUuii5SSsVjHONCIBOIBRqAMmAzsEJr3Ry9HgohRN8nQRgh+hml1HZgmFfRKVrr+dHpjejPTL9rO7TWw6PXGyF6nlLqdOAjU/GXWusZ0eiP8Psd2OoHWut/hNnmtcBTflbJ514vIuc/A4Mc575DviP7NqWUBbgUuAE4mc6zqDQppVYDX2Ac9y+01k3d20vRFUopK/Ad4BrgFDq+P9iolPoIeFJr/V5P9E8IIfobSUcmhBBRppTarpTSXXw9GO2fYyDr4Bje1IU2rw3Q5vYIdl10zbV+yk5QSo3p8Z6IzlzThbr+jrMQQoiOyXdkH6WUGgt8BbyIcXM+mPtGDmA6cAfwMXBmt3VQdJlSahawHuMYn0bnD2jHAucD7yqlliilpnRzF4OilJptuk6aHe0+RVKAa8ytSqmYCLSV2R19FoEFed+nUSl1yHOcv1JKPa+UulMpNVMpFRvtn0F0jQRhhBBCiO4jN377KaVUOnBBgNVy7Hqfo5VS40OtpJQaDZzQDf0RQoh+S74j+y6l1FHAQuBo0yo3sAl4D+PG/WvAPKC0J/snuk4pdQ/wKTDatEpjBGY+Bl7ybLPZTxPTgK+UUjd0Zz9FQCOAsB/0E72eA0jGOM7HAt8F/g/4DNivlHpRKSUjSvsoCcIIIYQQ3Udu/PZfV2KcJPvzPaWUpHztHbzz04cTFPW+WSi57oUQIjjyHdkHeeZ+eRdI9yquBu4C8rTWY7TW52qtv6u1/rbWukhrnQcMBq4G3gIkBVkv5smecC+gvIprgF8Bw7XW47XWZ2itr9Ban6a1PgJjnpg/4HtsY4B/KaV+0iMdF2Z3K6WSot0J0eMSgcuBBUqpT2Vkad8jQRghhOh9Lsd48iGU1++i0lMRiNz47f+u8/q/G/jAazkbOKdnuyMC8M5bfmUoN/48udK/51X0bsR6JYQQ/Zt8R/ZNPweGeC2XA8dqrf9Pa70vUCWt9T6t9X+01hcCQ4G7gYru7aoIlVLqauBWU/FCYJzW+o9a653+6mmtS7TWdwNHAmtNq/+ulJoZ6b6KTg0CfhrtTogu83ffZzTGSMTTMebkehLY4KduEbBMKfWdnumqiAQJwgghRO+zT2u9PcRXVbQ7LXzIjd9+TCk1DeNCtNVcjCcEvUm6ld7haa//ZwNnh1D3TIyne/21JYQQwg/5juzTrjAt36619nfzLyCt9X6t9R+01gsj2C/RRZ5R9o+aihcBZ2qtdwfThtZ6M8aNX+/fCQvwvMwvEhW3K6Wyo90J0SX+7vsUa62XaK0/0Vo/qbW+QWs9DiNLxuum+vHAS0qpy3q+6yIcEoQRQgghIk9u/PZv15mW52itvwSKvcrOUkoNRkTbYnxvFoQyMs37JuEG4OuI9EgIIfo3+Y7sgzzHo8CrqAVj3hfRP/wNI5VRq4PAxVrr2lAa0VqXA5cAzV7FecCvu9pBEZS3vP6fCPwmSv0QPUxrvUhrfQnGHDHef7cW4Gml1ITo9EyEQnKxCiHaeJ6kGAeMAlIxcr0exBhOvkxrvaUb9qkwhlsWArkYJ3T7gIXBPpUjRC/UeuN3rGf5GuDtIOvKjd9eTCkVhzF0vFU18Kbn/3M4/LSvFSM/+p8iuO8cjM/LPCAFKAM2Aou11jpS+/Hs6yjgCCAHSPDs6z9a676YHu8Z4C+e/5+tlMry3EQISCk1CN90OV0KhiqlLBjv5xEYqV6SACdwANgJfK21PtSVfYi+xzPy8QRgJMbfWj2wB/hCa70/mn0TIhzR/I7sTkqpeOAkYBjGfCnlwG5ggda6Ppp9i6Bc03KF1rpH53dRSiVifCbmYqRbcmG81xuA5Vprd4T3NxnjezkPI21eKbBIa70rkvuJNs+8EeYUgL/sKMVcR7TW65VSf8OYR6bVdUqpe7XWB0Ls2zhgIpABpAGNwH6MY766p38H+4B7gDOAOM/yDUqpB7TWJVHsk+hBWusXlVLlwEccHlgRBzwGnBy1jomgSBBGiAHMc/F/MsbTLKdiBEI62n438DjweLA3i5RSszFugLW6Rms9x3ND6ifALRi5L/3VXQD8TGstN6EjwJMS62iMmz2DMCZM3Q9sB77UWjdGeH/DgSkYFzZxGMG11VrrlZHcTy82YG/8KqVSgOMxLqKzMC6oPtdaL4/0vqLgEowASKtXtNYNnv//B/g9h0+IryWEG0xKqfl4nTxrrZWn/BiMJwy/hXHjyqxUKXU/8JDW2hnEfmYCn3kV/VZrfa/nO+F24EZ8n4Zt9TpGYL6v+Q/wfxjnvTbgKuDvndT5HmD3/N8JPBfqTpVSacCFwPkYN+9SO9jcrZRaCPxVa/1eB9uhlMrD+NxuPY/fChSEGohTSp2Db7rD17TW3w6ljb7I8920zavoWa317BDqz8fP32mAbe/FuGHS6hSt9XylVCzGRNfXYwRfzLRS6l3gF1rrjcH2TRzWk8dZ+OjO70jvz7jPtdYzQ6g7ByPo02qE1np7EPWygD8Cl2GkfTGrUUr9F7hTa70/0PdrsP2MMvO9oRSllFVr7eruHSulzgB+Aczg8HevWYVS6ingz8Hc6A/0GeA51/kBxjXoEX6qaqXUIuDnWutFIfwYvdmtgPdnWAW+1+fheBD4GYePVwLG/BV/CVShlVJqKMb8Q9/GyBgQSINS6jPgWeD11t9FP8fW2zNKqY5+tqD+9nuxUuAh4JeeZTtwH8ZnVLdSSiVhBEnzMO4lNHE4SLoi0g+EicC01p8qpX6P7znmSUqpU7TWnwWq549SaiTGPZtBGIHQQxj3bb7SWpdGor+eexJHYXzmZgLJGA8e7cP4/VnTE981vYLWWl7yklc/emHcmNFer5kdbHu7adtgX9uAKUH2Z7ap7myMm7Lzg9xXM3BFtN/X3nLMwmx/LPACxhdqoPe5HngZGB1mv7d7lX/Lc3zdAfZVAsyO9vvezccwE+OiosWr7I4g2rnDa/sWTxuZpra3d9JGGsaNjbcxgi0d/X25gM+Bc0L8eX3+fr3Kx2MMk2/0s68Ho32cInSszZ9dx5vWf2xaf1JX3lfgZs9xCubzcikwKIj9zDTVuxcjBd6iTtpPjfb7H8TP1u5v0VP+tlfZ2iDaWeu1/dueslD/FlcHedzMr5eAhE7afs1U54ww3qv3TG3Mivbxi/Cxnxlgu+Gm7eaEuJ92f6cdbHuvuU8YD7ysCvJ3obqvH5cBcpxnm/Y1O9rvUxSPz3zTexHJ70jvevND7NccU/3hQdQ5mc7Po1pfu4Fj8fP9Gu1jEsJ7NMLPzxXyd0uI+8wEPgnyPW59VQTze+PvMwAjfVOw+3MDv4/2cYnQ+7zN9LP9PULtvm5qd14n2yvgboyb96Ecc5+/WT/HNqx2+sIL/9eYKUCl6Xd1ajhtBdmH44EPMe7LBHpfyzBS3mV00pb5+ngnYAnjfTnJtP+Pon2swjyeM7vQVhLGw3He7b0UZN0E4E6MNKEd/b2swAiWqjD7OAYjiFrVyX4OAv/FmKMq6sepO18yJ4wQA5v5M6AZ46bR+xg3gf6L8TSXedL34cA8pdSoMPYZh3Hj52TPchPwpWdfrwPrTdvbgTlKqSMRIVFKWZVSD2DcTLwC44mDQOKAS4F1Sqlbu7DPPwIfYBzfQE+NjsJ4SukDT3qHfklrXYbxt9QqmLkovLd539NGqD4HngLOo+Mn78H4DDgJeFcp9ZJSKiGM/QGglLoJIwhwPsYoq35HKVWA8X61Ktbtn5KcY1oOe/JhpdS3MSZRbf2sLse4CHoZ47O5wVRlKvCZZwRGKBwY6WKO8yy7gG8wPpNfB5ZgXOD1Zd6jysYrpaYH2tAz8mh8gLqhMH/HVgELMQKVz2O85yswLka9XQa87EnXGchjpuWbQumYUiofY/6pVpvxfXpbdI9BGOkjWs9p6jDe95cxfh+2mrZPAl5TSpnTBAnR6/T0d2R3UkqdiHE+m2paVeopfwmYx+G8/HnAO55/+ySt9TaMp5K9/VMpNdbf9l3l+X35GiMbg7cajGDeKxgPHCzF9xwkA/jEM3omVC+Z9rcaeMPzWm3uInC3Uur/wthPr6GUGoJx7e7t4wg1b27nWKWU35FMnowMr2KMhosxrd6HERx7CeNYfInvnBfCizayGPzRq0gBf470fpRSds/osy8xUqAFGqUGxkO2dwBblFLnBtrIc23rPeJ7KHBaGN0zf3c8FUYbfZrWuob2I9rO9PytBeT57NyKkSXAX+YDb0dh3Kf72JPpIiie351HgHUY2QU6uzZNwQj2vN/Jdn1ftKNA8pKXvCL7IrSRMD8F9mKkApgB2AJsZwFOB5ab2l4cRH9mm+rs9/xbjzGEud3Tvhgps8xR+bnRfm97wzELoc04jBs95qcMmjFurr6BcaL7GcZNIPN294bY7+2e3yfvNnZgnGS9DCzA/9MznwEx0T4G3XAMW5++P99UPr2DNo4xbXu+pzzUp+/Xmrav9Lz/b2KkVHrD87fs73i8SxBPutD+SdcL8R35tA/jJsWLnt/DvfSDkTAYJ6veP/ddfraJw/eppDogOcj2ze9rldf7+R3Aato+ASNNmfmJwuc72c9M0/bVnn9dBHiKDeMiye93RG96dfC3aPO8j63lT3TQxj+9tisD7J7yUP8W13j+9n6Ikf4i0HZpGJ+f1ab2b+qk/XVe27YAuSG8T/eZ9nVbtI9dNxz7mQG2G27abk6I+/H5O+1k23tN+2o9B6rASNvS7vsP40ZHuaneU9F+f3vLq5ce59mmfc2O9vsUpWPT3d+R3m3PD7Fvc0z1h3ewbRLGOaz39luBszCdIwGxGKmeak1/462ve6N9XEJ8n/5q6r/GOMd4HiNdbmKE9hOP8R3pvZ+NwMWYznU82w8G/uXn8zSvg32YPwO8n8T+BDjCT50j8D9S5rRoH5suvNeX+vl5OhytEELb0/y07fdaByOFlnnb/3WwvQVjBMbDGOdHw73W2TzHdzjtrz9/6rXO36vXn8ua3oftpp+v9bzW4edzqsPf00BtBdjWTvuRi63nm4swgqTvYGS4MG/jBL7XQdvnmLb/b4jvSRK+9y8q6CP3E/wcg5ldbO8sP+//UR1sf4Pn+JjrbMO4b/MCxv2AnX62WQMkBXl8Fvip37qfDzz7eRPjvlSD9zbRPkbd/jsQ7Q7IS17yiuwrlA92jPka7CG07cB4CjvoLw7aX5hqjAuVgDejPfXy8b0h5cbIeR/19ziaxyyENp82tXkQYw6edl+cGBfEd5i+AN10MhzU1O9aDt8ILsbPSSDGRKYP0T5N2X3RPgbdcAz77Y1fT935pjqtbazDCNiab1JYgfxoH6cuHmMrxoTZrT+zCxgaYFvzjYIbg9yH+X3VGAGsDj/7gAtof0Jd1MH2M/3sR9MPUj8G+lv0rPubV/kBINZPffMNwr95rQv1b3F4iH2fZNr3FjpI0YCRz967P78Jcj82jKe5W+s1AOnRPnbdcOxnBjoupu3mhLgfn7/TTra918/fWSkwqpN600x/03UEceE7EF699DjPNu1rdrTfpygcl574jvSuMz/E/s0x1R/ewbZ/Mm27GcjppP2TMN1I8rzujfaxCfF9ysRIrebvHEF7PpdWAf/GmD/uSMJLJfSYqd33gbgg6plTaT/dwbbmz4DW1ysd9dnzu/xfU51t9LGb914/zx2mn2VfBNuO9fP+Xuhnu9P9bPeLEPaTip9zNs+6fv35S8fnteaffRkdPEzXUVt+tv2LaVu35++2XdpjjHlizCl4G4AjA7RtNX3ONHXUFz/1bzTt64FoH6cuHM+ZXWwvnfb3Vq4OsO0s2l8vPg+MC7D9TNoHyztMd4YxKutNP3/vzwFjA9SxYYyGehpojPYx6u6XpCMTYgDTWpdqrc1pUDravgkjXZJ3ne+GsevbtNZLOtnXToyb0q0UcEoY++qLPlNK6RBeM70re1IYeae12oHxRMSD2hi26kNr3aC1/jtQhDGXBxjv98OeSdSCkYAxtHwjRv7vT/zsp0prfSvGE4PefqGU8jcxZp+njUnSn/cquswzIbMPpVRrOrhWz4Xyt2lyrtb6RK31Y9pILRGobwe01n8DTsSYL6jVz0I47q2SMFJWHa+1/lh7zqi89uXy/E33ZWdiBK5bzdNa7wqw7TOm5eu6sN+btNYlHW2gtX6L9umpzH9nnXlaa/1iiHX6Gu+0YqkYI7jMzJNKm49l0HSIE79qrVdhpOloNZLDKeL8eRbfdB3XeyYc7swFGE8Vt3pFa21OOyq6z9Va6y0dbaC1XopxEdsqHmO+CSF6q2h9R0aU5xzteq8ijfFEtzlNlw+t9Rf4fn73SVrrCuBsjICaP1aMwMv1GNdpq4AKpdSrSqnzA6Wi8uZJr+j9Hm8HLtFam1Os+uvf/fimq/muUiqns3pedgPXaa0DpljVxuTQ1+L7HgzHSPPbF6Wblg9EqmGtdSPGDfSO9gfwG9PyP7TWQafP0lof9OxL+PoPRgaEVlMwUtp2iVJqEsaDet5u1Vr/UGu937y91vpLjFFLi72KY4En/bXv+Rt71qsoBrgyhC6avzMGXCqyVp7zd/MxyTdvp5RKxMhQ0XqdoDE+C6/UWpunA2htez7GdchXXsWXKaVO8re9xw0Y1xmtWoArtdZXaa03BNiPU2v9idb6WtqnTux3JAgjhAiJ1novxhDUVseH2MQegr+p9Z5peXKI+xpwPHMIeJ/oOoHzgrkZqI283d51C/D9Eu2MC/iuv5Mz034ewRi+3MqGMWqjv+pvN379acIYRXGo0y37LvMJ/5xAG2qtvwI2eRUdrZSaEMY+l2mt3w5y299hpJhrdbZSalAI+/pTCNv2SZ6LjK+9ivzNReBd9o3Wel339qqd103LAb9jPUH157yKhmLcPOuMef6YfwTXNREBX/t7SCEAOQcSfUk0viO7w+kYc460+lRrvTjQxiYPYsxn0qd5zgsnY9xAdQZRJQ3jPPYtYL1Syt95rreb8J0T5Lda6/oQuvh3r//HAN8Koe6ftdadzjXi2cYcJJgdwn56E3NQ5GCE2ze35/33g2de1xO8imqAX0S4DwOSJ5j4K1PxfcEEQztxO75zu77uuX7vqC+1GA8U1nkVT+/ghv1TGIGAVkHND6aUGo+Rur7V11rrtYG2HyDMgVV/1383Adley3/VWnc656XXcfUOtpoDdEDbvE93mop/rbV+obP9eO2vwwce+gMJwggh/FJKxSulspVSw5RSw71f+H7Qj+lk8mCzjzyjA4JhjpZnhbCfgeoUwPtC9gWttXmiyY48xuHRMBDaU19vaa2XB7nt3abl74Ux+qJP6G83fgN4rbPRGn2ZUioL35vb1Rhz63TkWdNyOJMPBz0yRWtdie8EqRaCD6at01oXh9KxPsz7gmOWZ4J6AJRSI4GTvdaHHQztiFLKqpRKUUoN8fP9ah7J0tmEyOYRUN/vZN8FGOkIWq0M4Qaj6Lr/hbCtnAOJPiGK35HdwXz+899gK3oCCebgaZ+ktd6vtb4BGIVxk3c5RsqbzhQAbyilOhpN7z0Jtwt4LcTuLcQ3OHRikPU0xjyVwXoJ35vEJ4R4zTtQ6E7WF5mWX9RaV3dXZwYarfW7GGmoW42kk3PBjiilHPhmZ4D2gZ5AfdkJPGEqnh1g260YaT9bTVRKTQ9iNzIKpj1zECbOzzY/8vp/LSGM3PSMavX+Ljzd83tidha+I1m2YswzJrz0yxteQojQKKUsSqlZSqnHlVJLlFJ1GE8x7MMYIr7N9LrAq7oFSA5hd36HOwZg/kJJ8btV/3M5MCKEl/cNtNPw9UooO/ZcQH7jVRTshQ2EdsN4Db7Dp1PwDR71N/3txq/ZWxHpZO/1PYwJKlv9N4i0Gf/B94bFVUqpmEAbB/B155t0uH0wFzMAK0LcT1/2MkaeajC+v672Wncth5/8a8C4AdNlSqk0pdS1SqnXlVLFGCOWDgK7aP/9ag5mpnXUtidg+7lX0beUUsM6qHITvk83yiiYniXnQKI/itZ3ZHeYZFpeGmL9DtMt9zVa651a6z9qradijKg4E+NBqtcxUnsF8iOMEbo+POnepnoV7QIyzeelHb0w0t4d9GpjVJA/zhZPurWgeLbd6lWUjnGDu68xpxuN9HdJaif7Mwc250d4/6L9yKJfe9JPhWM6xjzArZZorTeHUP8/puUZHWxrTlfWYWpKz3fEVV5FdYQWWO2vzPf1fQKjnrTv3inK3g9mRKDJZ17/d+A7GqmVOeD6745SPw5Utmh3QAgRXUqpE4FHMfL7hisF3zklOhJ0HlqtdYvpgaOuDq3tK/aFmlLKi/lEp8pzwRIK76eThiulLEF+gYZzw9g78DIdY1K//uhljDQVcRy+8dv6BEq33fjFSH12Nsbf90iCf/iiwxu/fvT3m/jmJ3TndFZBa71HKfUpRmoTMCa7PY/QnvgM5aIHwDyaJdgn58tD3E+fpbWuVkq9zuHc07OVUvdh/A1+z2vTN7qaXs8zP8vPgLuAcC+Gg7lZ8jiHA7kWjAlL7/LTHwe+TyTWAEGnCBAREUoufvO8YAPlHEj0PdH6juwOmablHSHW7+vz3wXk+U780PMC2kZXXooRdMk2VblTKfWiab6BHHw/y4ZjPIDQFf7mIPEn1HMqMM6rvIM8WUCHc3r1QuagSKjn+AF5gmrmuS4rTcuDTcs9Pdq/39Naf6WUehs431OUhZEy6t4wmptmWl7kd6vA1mLcS2h9SLdQKZUS4Jz6DYzzotbfycuVUrd1EMQ/D9/P6P/6m+92AEo1LZvfP/P9oZ1h3B8y3wsahe8ILJCAa1BkJIwQA5hS6hJgLl0LwEBonyUSDe9eQ03Li2n/pHVnr3O86lto/8XuT73WOtAknoGEe8O4z/EMu/dO+TVbGSx0w41fpdQvMW4EPAVchJEeIpS/01Cfkuu3N/GVUsfjOzKoxDMBZTC6OvlwqOkazL87wd6YGGgXMN4j01pHop2O7+dnl0akefIivwz8kfADMBDc3+2bwF6v5WsD5AP/Nr652p8P40k40TVyDiT6lSh/R3aHVNNyqN+PAyrNkta6RGv9B4zzTHPqNgtwm6ksg8hLCnK7cI5NuOdVvYn5+ixHKRWp4zA+iP2Z9xXKwwgieHdipPdrdYcnVWSozHVCCl5qrbWfOn77obVuxPdhoGSM+aUCMQf8zSNpBipzYNU8P6/5/tBPCf3+0BxTG/4+CyXgGgQJwggxQHnSlTyL79NI5cADGDdtj8R40iAesGitVeuL9nmcRe8RrYubgXphE4r+duO3TT9/Csl8U6hAKaWDedF+VNPpSqkhPdRv6DxP90A1H98nb6/B98JuBzCvi/u4jfYXkosxRsacgnHDKgVweH+/er5jQ6K1bgH+5VWUw+GnIb3dZFqWVGRCiK7qy9+R/jSZlkNNkdYbUqr1OE9A/7vAMtOq003L3fH+BPu9GYlzor54XuVvJIN5tEO4zO00ACs7qdMX38NeT2u9Ad8b5YnAr8NoynxDP5yHA0O5xjfP6eI3GO/5bjjDq2iD1jrUUTr9jlIqExhkKjaP4Oyp+0Pe+3H28/sDYZMgjBAD1y8wAiyt3gVGaq1v11q/qbVeo7Wu1Fo3eJ5o8BbKHDCiZ0Xr4magXtiEYj796MbvQODJp/ydCDZpIcAElQGE+llrHsEkTxv64flOm+NVdAlGioNWc/x87wXNk7PaOx2YBmZrrY/TWv9Naz1fa71Fa12ttW421Q33+/Vf+E5U7BNwUUqNB07wKlqkte6v6R+7m3kOLdE/yXHuRC/4jgxFsMfT/L0Zauqm/v5AUUBaayfwkKk4XynlPUm0eU6Wj83no2G8hgfZxXDmQunz51WeydLNafLMwbFwmeci/dp8XkP7Yz5g/0Z6wD34pqL6vlIq2DmTWpmvCbv1Gl9rvRJY7lV0sifNodlsfO9fm4M3A9WxfspWmZZ76v5Qf7+XExEShBFi4PJ+SrYWuFJrXRdk3dxu6I+IDO8T3UZMo5jCfG0PYr8D8sImFP30xm9/dyldG1Hkz7XKNNlVB0aH2HahabnfpomLgDkcTg0Vz+FJSM1/p+E4Gd/Pt+e01sGOIA3r+1VrXYqRlqzVLKWU9+/DD0xVnghnP/2E07Qc6hyZEcunL7qVHOfu19Pfkd6pdrrreJqfIJ4Y4n5C3b6/WemnzPu9LzOtC/U8pyvC2Vd/Oa/60LR8VYC0pUFTSg3C9zrG337AN10qwLiu7FcE5kkN/ohXkR24L8RmzHMI9cQ1vjmtmE/aMc93wjVeRS3Ac2H0qz8yB0IP0D4NmDkQekUE7g/d66cv3vNB2ZRSwaaKHFAkCCPEAKSUisf3Rs8XnjkrgqkbC0zulo6JSPC+uIkF8ntov/FKqbwQ6/SXC5tQzKEf3fgdAMxD4q8ERoTx+sKrjREYo5KC4e/ppo4cY1peEmL9AcPzZOhcP6s+CzLw3BHzZ9t7IdQ1T2oZise9/q+AG6HtO/9Kr3WVwKtd2E9fZz7fSQ22olLKge8kzaL3kuPc/Xr6O9L7mKaG2Fd/c1f4s9i0HOz3dbjb9zcuP2VtqYk815veNwiHmx4Y6E6jPKl7guLZdqRXURWwNeK96hkP4fuU+iB8b2qH41Z805rXAf/2s515jqiZXdyvmTx97+uP+AY9LlVKTQmhvvl6PKTgpSdgYv6bNs9RYvYiviN4rlZKeY9enInv3+K7WuuBcN+gQ56HKGebit/XWps/h3sq+C0B1yBIEEaIgSnVtBxKrs8rGKD5jvsIc27USA03D4bcMO5EP73x2y8ppcYCx3kVVQCvaK23h/oCnjc1H+zkw5eH0N8MfP/e3cBXwdYfoJ4OsixUqablUL5jZ4e7U631fHxvbs323Ey+At8A7TNaa/O8BwNJDb4X+6FcJBZhPOAgej85zt0oSt+R3jfdCoN9kt/T1+HB/WR8alq+yvMAWjD7mUzk5troq8x/Z4f8ZFr4yLR8Qzf2x5sCLgth+8vxTbnzZVdGrEeT1no98IGp+M9Kqexw2lNKjcNIdeztGa21eRQFtP+buiLCT8ibz2ccfrcaILTWBzECMa0U8KcQmlhqWg71GnE8vuecxZ4+BaS1PgS85lWUC3zLa9n8nWAeOTNQ3UH71NX/8rNdT90f6u6Aa78gQRghBibzkNAxwVRSSqUQ3gRvoueYL2yu78F9h3LDeCIwwavoELA24j3qnfrVjd9+zHzC/6on33k4XgO8U8BdpJRKDaLeVKWUvwnW/fkNvgHy97XWnT15NtC9AZxoer3WYY3ghPsde76nD13hPRomEyPtoff8MBr4Zxf30ad5bqKt9Coa4Zkzp0NKKQu+KR9FLybHudtF4zvSe96AGIK/kfSbYDuitd4IfO5VNDiY+kopG/BYsPvpjZRSSQHmYgjFjaZlf3MdPoFvusAfBfO3GSG/8Mxl1CHPNj83FQc7ury3ugOo91pOBd4I5v3w5klD9hq+55x7gd/5215rvQ7fv6lkfIMEXXXQtDw4gm33VY8Au72WT1NKnRpk3aX4BraODnG02lWm5YVB1jPP8XIdgOe74CKv8t20v98x4HiO592m4vla6y/8bL4c35RkxymlJvjZrqvMAdcbPOdUwou8IUIMQFrrBqDYq2iyUqrDCxlPOpOXCf5JMhEdHwBbvJaPVkpdG2jjCLsghOHO5vy0z2mt3X637H/6443ffsXzdK35IuLFcNvTWh/AN092LMbohGD8o7NJNZVSFwA/NBWbJ8cVJlrrZq31QtMrEiNEzBNi3qKUSuioglJqGvBMBPb9HMYIgFZ/AKZ6LX+qtS6JwH76OvOIxGBypv8FGTXY18hx7gZR/I40H897PcGPgJRSPyK00Q8Avzct36mUuiPQXDWez/eX8B0Z1BdlABuVUv8JJyiilLqX9vMTtPu98HwHeX/fxQLve0ZXhLI/h1JqdojdHAI81dGNQc+6pzzbttoOvB3ivnoVT4DxR6bi44EPlFJD/FRpx3Mzfi4w1qvYDVzVyYM/5gDND5VSdwSzT89+UzoYkbbBtNyTWSB6Ja11I3CPqfhP+J9M3V/d/5qKg5pXxvN7dLOpOKjgpdb6c3zvT52jlMrC+C6I8yp/ZgDdM/BLKXU5xjyQ3p9j9bR/7wHwvF/mlMUPd/b9GYYP8b0PNQq4PcL76PMkCCPEwPWKaflVpdR3zSelynAqRlqb1mGh8nR1L+V5CtH8xN4TSqmL/G3fEaXUqUqpkZ1v2cYKvNBZvmWl1C34TuTooo8/PRiKfnrjt785F8jyWt5J+yHWoTLfiAgmJdkBIAdYqJT6jp/P5wSl1N0Yn+feuZNf1Fqbn0YSPedrfCd3LgQ+8aTE8aGUSlRK/RyYjzF5cZe+X7XWNfhOVjrMtMk/utJ+P/I0h+fnAuMhgqeVUu0m71ZKjVVKvY3xFDG0f+pW9F5ynLtHtL4jX8F3XphpwFtKqXbz2imlhiqlngQe9hR1NjF0G631XNqPGPwbsEgpdaNSappSqlApdYJS6lcYN4Ev8Wz3crD76aWsGAG2tUqpJUqpHyulxncQgLIqpYqUUnNpf9P3c611oIeMbgdWey3nA0uVUn9QSg0N1DmlVJzn+uRhYBehncO2/g58ByPw0G5eBE/Zh55tvH2/CyO9eg2t9dO0v+aaAaxXSv0y0HuvlCpQSt0HrAEmmlb/wvM309F+5wF/NxX/TSn1jlJqqr86SimLUuo4pdRDGMc6J0DbO/Cdq+c4pdQLSqlvKaX+v717j5akqg89/v3BMDyC8nCG8FAYFEVdSzI4EYyiGaIkaJIrJKLyuGSMWbmorHh15WG8IYKIMT7iO9EYZUgANZqLUfFqWIJRFA2CSBgwwuiYAFd5DQ6DMMwwv/yxq6VOTXef7nO6Tp9z+vtZq9ZMVe9de5/a/aiqX+29D4+IFY1l1Dee56sLmDpE7SoGnyv2XUyda+clEfHKfhmqa85PAPWeVddUwZVB1UeG6AT76w+TJhN83Vp9Hj5J+b2sH+eHgZdnZjMgWfdOpvaGORY4v09ws1cdDo6IF3R7rZqL5rzG5rdExMAPQkRE18/5opKZLi4ui2ihPKmTtWV1j3R7A7c10ialO/FnKeMzfx64vfH6hZQJxOvbVvSpz5pG2jVD/j31vF8e9/EdZ5sNuc+PdGnbi4BVffLsDBxJuYi6cbq6NOq9mTKURALfA57fJf2+wLspN0Tq9Tpv3G3QQhsuG+G+lzX2vaFP+zXr8XXgKV3SdoZZ2Fylu2OYzxrlhvHP0o/7+LfQnpc2jsdbR7DP3Sk9FOr7XdnvuAIvbaz/qKrbxZQnEe/v8jm/Adh3mrqsbuQ5e9zHfIRtN/bPYpX2JV3aZjtleKRPUG7UfQV4sPb6VsrDDrP63aOMyd8sOym/+UvG3UZz2Parp0n/ti7H6AHK8DkXAZ+m3Fyd8ns1zPcfcPYwdWrkXdHIu3bcx3g+LPO0ndc08q8Z93FquQ3G8htZpXtVl/bcShny5mLgn4BvM/Vc8yKGuHapyllK6fnQ7bu013IBO/6+vnHc7TVEGzS/c+rLvZQH8j5LCfR/qjrmP+mRfh2w/zTlPa7LZ6+zrK/eZxdRfjO/AHyXMozZlLRD/D0XdHnvfhv4ZPX3XNejLm8Zd9u00NbnsuP1WOc85QbKyAoXU4Z9+m6P4/IQ8MohytyZ0sOi1/nJF6r27ry3Ng36mQXOHOJz2vezP98WZnFeS3nosd+x6LkvSuC5+d54L/CYLml/qfos1dM/SJfv8Gnquz/l+7yzj+b16WXjbo8W2vNl1XdVfTmMEjR7HiUI9SHKd2q3NtwMvHjAsn+lcXyTcs/nVGC3PvkOAF4OfIbyHbx2mnI+2eW9sxY4vEf6JcDzKUG4B8fdRq2/B8ZdARcXl9EuXb7Yn9Mn7ZHAj4c4abmQ8lTC2kFPZjAIM5M2Wz2Cfe5CGfaqWzveAVxGuQl4MWUC92spNyWaaXvWpVHvDZSb+tnY9hnKEA1f4ZEgzZQ2BZaOuw1aaMNFfeOXRRyEAQ5ix4v8I0a0739o7Pd90x1XytARD/f4LDeXa4DlA9RjdSPf2eM+7iNsv3nxWazS/xndb3J0W35KdRE1zGexT9lXdCnjnHG3zxy3fc/znyr9Unr/TnZb3lrlG/j7D4Mwk9LOaxr7WDPu49Ti8R/bb2SVJoD3D9Gef8+Q1y61snahzF3R7fy1vmyjzCMUwAsar71u3G02xPHfj9KLc9Bj22u5kAF/eykPBV04i7I29tn3ikbatcCjKAHYQfa9HXjzuNulxfZ+PuXBuZkc92uAX5xBmUF52G+6z1S3pednttrv38x2P/NxYZbntZSAVq9j0S8Is5TywFczTyfo/XHKQww3d0nzMPC7M/x7+wW/Xzbu9mihPWezXEaPwEaf8k9l6n2AzrKF0pP/Espv8SWUuZy63StcO00Ze1Z5u9X5+5QHvS+knJt9k8Y9qHG3UduLw5FJi0+zS+HmXgkz89vASsoFytZeySg/tCdl5mmZ2Sud5pGqnX4b+BPKD23dcsqJ90uBk4FfpwTkmu+drZQn7Qct822Up2Y6DqEMWfEyypwjuzSyfBF4YWY+hEYiM/8ROIvyue0I4BcoAZqXUtpi1+q1B4CTM7M+FvukW8PUob1uzMzre6QdVnO4lVOn6waeme8Dngv8C+WippvbgT8Cjs7+Y3JrDmXmm4EXMnUy6aZNlKdzn5a9h22ZieZwIw8DHx7h/uejgc9/AKrfnhdTAp0/6pWMciF5XGa+ftY11CjYzuO1hjH+RmZxJuVG0np6u4Zyw+70mV67ZObWzPxTSu/CNwFXUx5keojy5P43KDeUD8vM87Lcfdq7sZufzKTsccjMOzLzEODplKGNL2Pq8G/93EUZ7vKo6nrxrukyVGVuzszTKOepFzLYsHG3U3pMnESPIar6lHcfZc6Q1wG95kdLSk/yYzKzOfH1opFl2NqnUj5LX6IEE/vZQukJ9SJKAOZbMygzM/Mc4HDKOck902TZTLnRfwJl2MN++30lcDRlTsSrKJ/V5jXwJPqTmWSqfjtfQLlPVLcEeDblmvJFlF4bdZuA384y9N1M/F2P7fdQAgOTbjPlAdfnZOZxmfkfw2TOzIsoQxA2v/+WAkdRPmunVf8+l6lDj3b0/Z7OzM2Ue00fZOo9CYBDKe+rU4ETqzKHGhJtoYsqUiVpkYiI+5g6RuQTc4BJeCNib8oX8uOr/PdQLla/lZm3tlBVVSJiA1PH7T82M788wv3vD7yGEnA5ZJrk9wFfpXTX/0Rm3t1nvxtq+/thZq6otv8m5YTv2T2yrqcMQXb+gH/CvNelDZcPegE6wL6XMXWeiJ8d6z55jqcMp/L0Hkk2UU5kz83M9VWe+gnBv2bm6j77/zLwy531zJx2okdNb7rjGhEHUC4wD6I8zXkHZZiIb+SET1I531XjzD8T+HlKYPTHwK3A17JMgjrq8o6jBO46/jkzTxh1OfPJTM9/qrw7UeaXOILS4+kByk3Wq7OMN695wnZWXUQ8jdKmyyk3kW8DvpNlEvJx1OdcSi/Ijt/IzEvHUZdRqD4zKyhzmx0MPBrYg9J7cxPlWvH6UX1+qvKOoAQH9qUEtR6sytoA3JSZ/zXgvlYAP6htuiAz1zTSrKIEBA6k9Hy5Hfh6Zva84b9YVXN6rKLcVF9OuSm7hXK+8j3g2hzNHJb1MneiXKs8mfKdvCflAcDO+e31k/4A6CiuMav5zv5Hl5cG2ldEHEMJzK5mx4cqO+6kBFLfMptr4IjYmRJwa8719d7MfM1M9ztfdGnPbrZSvvc69+PWU4Yjuwq4ahTXDdVxfhnwauAZlOBaL9spc89eBny8epB70HKeRukp+utMPXdruocyJOFHc5o5phY6gzDSIlJNNFp/omQ78OjMHLg3gxa3iDiUcrK7nDIJ9HZK4OV2ysnuzVkmVRtVWasoJ1G788iF2sA/3Jqdub7xq9kxuKVRiYh/pDwl3HF8Zn5xXPVpm+c/k8F21nwXEf9KeXq444DM7NUDSy0aJAgjaXAR8SjKiAoHUYJmWyjBl5uAa9KbywtS1a7PBB5LCX7vRulxczdluLmbMnPQXpG9ytiF0uvlCZT3zu6UgOvtlPfPukl5oLBftEvSwrOysf59L0xVl5k/YOoFyaIoS91l5vcoT69JmhBV78cTapvWM7VXzGK0srHu+c/itLKxbjtr3oiIpzI1ALPBAIykxaIazu/z466HRqtq18taLmMr8LVqmWjOCSMtLi9urE/8l5wkSRPmtUwdLuIDE/B0ouc/k8F21rwUEUvYcd6t5lwKkiRpghmEkRaJiDgaeEVj80XjqIskSZp7EXE4ZQLyjnuBmU6OuiB4/jMZbGfNpYh4XERcXPVumS7tcspcis+qbX4A+FBb9ZMkSQuPw5FJC1D1tNVjKRPmHQT8BvBKYNdasm8Ci3pSK0mSJlVE7AbsX63uSxnP+SzKOMsdb8/Mn8x13dri+c9ksJ01D+wMnAycHBH/BnwWuAa4jTIp/V6Use2fB5xGmay+7nWZefvcVVeSJM13BmGkhemx9J9rYyPwikmZ3EqSpAn0TOCKPq+vA945R3WZK57/TAbbWfPJUdUyqLdn5gfbqowkSVqYHI5MWnz+HTgmM9eNuyKSJGks/gt4UWZuGXdF5pDnP5PBdtZc2AJsHjLPD4BTMvOPW6iPJEla4OwJIy1s2ynjvf8YuAr4HPDpCZiAV5IkTXU/8D3gn4F3L6ZhyLrw/Gcy2M4ai8z8/xGxjDLc2HOAVcChwH6Uoce2UXpk/Yjy3vwS8JnM3DaeGkuSpPkuPIeVJEmSJEmSJEkaPYcjkyRJkiRJkiRJaoFBGEmSJEmSJEmSpBYYhJEkSZIkSZIkSWqBQRhJkiRJkiRJkqQWGISRJEmSJEmSJElqgUEYSZIkSZIkSZKkFhiEkSRJkiRJkiRJaoFBGEmSJEmSJEmSpBYYhJEkSZIkSZIkSWqBQRhJkiRJkiRJkqQWGISRJEmSJEmSJElqgUEYSZIkSZIkSZKkFhiEkSRJkiRJkiRJaoFBGEmSJEmSJEmSpBYYhJEkSZIkSZIkSWqBQRhJkiRJkiRJkqQWGISRJEmSJM07EbE2IrK2rBh3nSRJkqRhGYSRJEmSJEmSJElqgUEYSZIkSdLIRcSGWi+WDeOujyRJkjQOBmEkSZIkSZIkSZJaYBBGkiRJkiRJkiSpBQZhJEmSJEmSJEmSWmAQRpIkSZIkSZIkqQUGYSRJkiRJkiRJklqwZNwVkCRJkqT5ICICOAo4DDgI2A6sB76cmRunybsHcAzwZOBRwEbgP4CvZObWWdbp6dV+9wN2Be4EbgOuzMzNM913n/KOAp4IHAg8BPyoKuvWUZY1WxGxG+WYH0I5NpuAH1KO+aZZ7HcfYCXlGOxFOeYPAPdW+78pM2+bTd0lSZI0OQzCSJIkSZoIEbEauKK26ZzMPDsilgKvBc4AVnTJuiUiPgz8aTPoERHLgHOANcAeXfJujIg3Ae/NzO1D1HUZ8AbgNGB5j2QPRcTlwNmZ+c0B97sGOL+26eWZuTYidgL+N3AmcGiPvF8F/qhfWRGxFvidLi8dEhHZp2oXZOaavpV/pIy9gXMpx2bvLkm2RcTFwBuGCZZExLHA64HnATtPk/Y24P8B78/M7wxahiRJkiaPw5FJkiRJmlgR8RjgK8Bb6R6AgdIT4kzgq1UAoJP3SOA7wKvoHoAB2Ad4F9AJdAxSpxOA71MCQ70CMABLgeOBb0TEhyJiRg/ZRcR+wOXAO+kRgKk8h3IMTplJOaMQEUcB11HaY+8eyZYApwNXR8QRA+wzIuI9lGPwq0wTgKkcBPwecNIAaSVJkjTB7AkjSZIkaVItBS4Fjq7WHwC+SRl+aw/gFylDcnWspPQiOTEingh8iRJkAbgbuBq4B3gM8CzKsGQd/xO4Fnh3vwpFxO8CH2bHB+bWAzcCPwUOpgwZVg8W/D5wcET8ZmZu61dGw+7A54BnVOtbgG9RhjvbGXgK8NRa+l0oAaUbMvP6IcoZhScBHwP2rdY3Uup6F/Bz7NheBwCXRMQRmXl/n/2+AfiDxrZtwPWU4cfupxynfSjDwh2IJEmSNKDI7NcjXJIkSZIWhy7DkW2k3FjfArwReF9m/rSWPijBjfcz9QG244B3AL8A3ErpsfJ/68ONRcTPVWnOqOXbDByYmff1qN9KShBoaW3zdcCrM/PrjbTLKUNy/a/Gbv4iM9/Qbf9VvjVMHY7sLmAZJQD1RuCvmwGLqvfJRZS5cjouz8znddn/MmDPavVKSo8RKEGdY3rVC9icmXc19rWWqUObder6Q+APgUsy8+Fa+gBOAf6WqT2T/jwzz+1WaETsRQm67VZtephyXN+Tmff2yHMApQfS6ZS5cs7q83dJkiRpwhmEkSRJkjQRugRhALYCx2fm5X3y/THwl7VNneDND4BjMvP2PnkvBV5Y2/R7mfmRHmmvAZ5e23Ql8Gv1wFCXPH8IvL22aTuwMjP/vUf6NUwNwkDp6XFsZl7dp5yDgRt4pHdPAk/KzFv65NkAHFKt/jAzV/RK2yP/WnacX2ZdVdc7++Q7Abiktuk/gRXZ5eI3Il4CfKK26ZzMPHuIOu6emQ8Mml6SJEmTxzlhJEmSJE2y8/oFYCrvo/Ri6egMQXZ6vwBM5S8b6zv0HoGfTQpfD8BsAl7aLwADkJnvoAwn1rETpWfOMF7bLwBTlfOfwIdqmwI4dshyZmsbcFK/AAxAZn6aMkxZx8FM7cVTd0hj/VPDVMgAjCRJkqZjEEaSJEnSpPop8J7pElU32r/W2HxlZl45QBlXUob66ljZI12zx8cHBgjwdLy+sX5yROw6YN7b2LFnTC+fa6wfOWC+UfmnzLxpwLQzret+Q9RHkiRJmpZBGEmSJEmT6qpe8350cXNj/QuDZKrmiVlf2/TzPZI250u5cMB6kZnrgGtrm3YDVg2Y/YuZuW3AtM0AyFwHLC4dIu2gdf1uY/28iNiza0pJkiRpBgzCSJIkSZpUg/aqAPjJiPI+uvliROwDPKG26d4h9w/w9cb6MwbMd+MQZWxsrO81RN5RaKOuXwLuqK0/E7g5It4cEasiwmtmSZIkzYonlJIkSZImVfNGfT/N3iL3zjDvki6vL2+s39xtEvlpNHt0DNpLZeBjkJlbG5t2GTTviAzTXgPVtZpz5wxge23z/sD/ocwrc3dEfD4izoqI1RGx2zAVliRJkgzCSJIkSZpU26dP0krepn0a681eN4No5tl3wHyj/Dva1kpdM/MS4Hjgli4v7w28AHgTcAVwZ0RcFBGD9jSSJEnShDMII0mSJEnjFY31YXvBdDOKfUyMzLwMeArwW8DHgR/3SLoncArwbxHx0YjYfY6qKEmSpAWqW1d4SZIkSdLcuaexPpO5Vpp5hhm6S0BmbgMuqRYi4jDgl4BnA8cBj29keTmlp8xvzV0tJUmStNDYE0aSJEmSxuvOxvqTZrCPwxvrd3RNpYFl5i2Z+Q+ZeUZmPgE4EvhYI9mJEfH8MVRPkiRJC4RBGEmSJEkao8zcCKyvbdo7Ip4y5G6e1Vi/ena1GolFNSRaZl6XmacAf9146cRx1EeSJEkLg0EYSZIkSRq/Kxvrpw6asQrYrKptehC4ZhSVmqUttf/vOrZajN5HGuuHjqUWkiRJWhAMwkiSJEnS+P19Y/3MiNh/wLx/0Vj/eGZu6Zpybt1b+/+yiNhlXBUZsW2N9flwrCVJkjRPGYSRJEmSpDHLzMuB62qb9gI+FhG798sXEa8FXlTfFfCukVdwZm6q/X8JcOy4KtJLRJw6g6HfTm+s39Q1lSRJkoRBGEmSJEmaL14BPFRbXw18NSKObiaMiGUR8QHgrxovvS0zr2+vikO5orF+fkS8KiJWRcTjI2JFbVk2lhrCScC6iLgiIl4dESt6JYyI5RHxDuB1tc3bgQtbrqMkSZIWsCXjroAkSZIkCTLz2og4E/ggjzwwtwr4RkTcAqyjzPfyOOAodryeuww4a46qO4hPAucBj63WDwQ+0CPtBcCaOahTN0EJeK0G3h8Rd1OO9d3A/cAewOOBpwE7N/K+JTNvnLOaSpIkacExCCNJkiRJ80RmfjgiNgLnA3vWXjqsWnr5KHBGZm5ts37DyMwHIuJE4NPAQWOuzjAeAzx3mjTbgHMz801zUB9JkiQtYA5HJkmSJEnzSGZ+CngC8B7grj5JtwL/Ajw7M18xnwIwHZn5LeCpwBmUYMwtwCbg4TFWq+41wB8Anwc2DpB+E6XXzhEGYCRJkjSIyMxx10GSJEmS1EVE7EQZkuzJwHJgV0pg5lbgysy8b4zVW1QiIii9jZ4IHAzsBewCbKYMTbYOuCEzH+q5E0mSJKnBIIwkSZIkSZIkSVILHI5MkiRJkiRJkiSpBQZhJEmSJEmSJEmSWmAQRpIkSZIkSZIkqQUGYSRJkiRJkiRJklpgEEaSJEmSJEmSJKkFBmEkSZIkSZIkSZJaYBBGkiRJkiRJkiSpBQZhJEmSJEmSJEmSWmAQRpIkSZIkSZIkqQUGYSRJkiRJkiRJklpgEEaSJEmSJEmSJKkFBmEkSZIkSZIkSZJaYBBGkiRJkiRJkiSpBQZhJEmSJEmSJEmSWmAQRpIkSZIkSZIkqQUGYSRJkiRJkiRJklpgEEaSJEmSJEmSJKkFBmEkSZIkSZIkSZJaYBBGkiRJkiRJkiSpBQZhJEmSJEmSJEmSWmAQRpIkSZIkSZIkqQUGYSRJkiRJkiRJklpgEEaSJEmSJEmSJKkFBmEkSZIkSZIkSZJaYBBGkiRJkiRJkiSpBQZhJEmSJEmSJEmSWmAQRpIkSZIkSZIkqQUGYSRJkiRJkiRJklpgEEaSJEmSJEmSJKkFBmEkSZIkSZIkSZJaYBBGkiRJkiRJkiSpBQZhJEmSJEmSJEmSWmAQRpIkSZIkSZIkqQUGYSRJkiRJkiRJklpgEEaSJEmSJEmSJKkFBmEkSZIkSZIkSZJa8N+Q64UcMvmn0QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mon=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov','Dec']\n",
    "#mon=['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N','D']\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "\n",
    "plt.title('Annual cycle of Bangladesh')\n",
    "plt.xlabel('months')\n",
    "plt.ylabel('precipitation in mm/day')\n",
    "#plt.plot(mon,xera,label='RegCM(Era-int)')\n",
    "#plt.plot(mon,xrea,label='Era-int')\n",
    "\n",
    "\n",
    "plt.plot(mon,x1,label='Observation(Chirps)')\n",
    "plt.plot(mon,xcru,label='Observation(Cru)')\n",
    "plt.plot(mon,xtrm,label='Observation(Trmm)')\n",
    "\n",
    "plt.ylim(0,20+2.5,2.5)\n",
    "\n",
    "\n",
    "plt.plot(mon,x4,label='RegCM(ensemble avg)')\n",
    "plt.plot(mon,x5,label='GCM(ensemble avg)')\n",
    "\n",
    "plt.legend(loc='best')\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig(\"/home/mkibria/Academic/pr_of_thesis/figures/month-vs-precipitation(Ensemble avg vs obs).png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b357d742",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b5b51cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bf4a93d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d750997d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a75ac563",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b568c220",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb4e6c95",
   "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": 5
}