clusters_tavg_prcp_sum_post1950_publish.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "baea5838",
   "metadata": {},
   "source": [
    "# Calculation of alternate climate divisions from nClimGrid data using hierarchical clustering, specifically Ward's method"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f113a01b",
   "metadata": {},
   "source": [
    "### imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bacec1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "from metpy.plots import USCOUNTIES\n",
    "from matplotlib.offsetbox import AnchoredText\n",
    "\n",
    "import pickle\n",
    "\n",
    "from scipy.cluster.hierarchy import single, complete, average, ward, dendrogram\n",
    "from scipy.cluster.hierarchy import fcluster\n",
    "from scipy.spatial.distance import squareform\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cdda070",
   "metadata": {},
   "source": [
    "### read in the nClimGrid data, subset down in space and time to Colorado and the period of interest\n",
    "#### nClimGrid originally downloaded from: https://www.ncei.noaa.gov/thredds/catalog/data-in-development/nclimgrid/catalog.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a33736ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "lonmin=-109.096667\n",
    "lonmax=-102.0\n",
    "latmin=36.95\n",
    "latmax=41.05\n",
    "\n",
    "### note -- nClimGrid can change after the fact! \n",
    "### use these provided files for reproducible results, otherwise the results may not be exactly the same\n",
    "### these are based on the nClimGrid files released in April 2022.\n",
    "tavg = xr.open_dataset(\"nclimgrid_tavg_CO_forclusters.nc\").sel(lon=slice(lonmin,lonmax), lat=slice(latmax,latmin))\n",
    "prcp = xr.open_dataset(\"nclimgrid_prcp_CO_forclusters.nc\").sel(lon=slice(lonmin,lonmax), lat=slice(latmax,latmin))\n",
    "\n",
    "tavg = tavg.sel(time=slice('1950-1-1','2021-12-31'))\n",
    "prcp = prcp.sel(time=slice('1950-1-1','2021-12-31'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f64f90a",
   "metadata": {},
   "source": [
    "### calculate climatology over the 1950-1999 period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2babacd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "tavg_monmean = tavg.sel(time=slice('1950-1-1','1999-12-31')).groupby(\"time.month\").mean(\"time\")\n",
    "prcp_monmean = prcp.sel(time=slice('1950-1-1','1999-12-31')).groupby(\"time.month\").mean(\"time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6317096e",
   "metadata": {},
   "source": [
    "### and the monthly anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba6a1d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "tavg_anom = tavg.groupby(\"time.month\") - tavg_monmean\n",
    "prcp_anom = prcp.groupby(\"time.month\") - prcp_monmean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06c9367c",
   "metadata": {},
   "source": [
    "### inspect the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f83af9d6",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 864, lat: 98, lon: 170)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1950-01-01 1950-02-01 ... 2021-12-01\n",
       "  * lat      (lat) float32 41.02 40.98 40.94 40.9 ... 37.1 37.06 37.02 36.98\n",
       "  * lon      (lon) float32 -109.1 -109.0 -109.0 -108.9 ... -102.1 -102.1 -102.0\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    prcp     (lat, lon, time) float32 8.678 9.086 -2.089 ... -16.54 -9.079</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-91d6df85-65f3-4b18-811e-57ffede85557' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-91d6df85-65f3-4b18-811e-57ffede85557' 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>: 864</li><li><span class='xr-has-index'>lat</span>: 98</li><li><span class='xr-has-index'>lon</span>: 170</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-beda1661-1250-4fec-98db-a5e0d3393fea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-beda1661-1250-4fec-98db-a5e0d3393fea' class='xr-section-summary' >Coordinates: <span>(4)</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'>1950-01-01 ... 2021-12-01</div><input id='attrs-e2a96993-c121-4593-9998-d21c53d2c21f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2a96993-c121-4593-9998-d21c53d2c21f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daaf7f70-0121-47e4-80e8-d3576fcdb966' class='xr-var-data-in' type='checkbox'><label for='data-daaf7f70-0121-47e4-80e8-d3576fcdb966' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time, in monthly increments</dd><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;1950-01-01T00:00:00.000000000&#x27;, &#x27;1950-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1950-03-01T00:00:00.000000000&#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 class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>41.02 40.98 40.94 ... 37.02 36.98</div><input id='attrs-21c724c4-526f-4614-8f56-2015f1c838a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21c724c4-526f-4614-8f56-2015f1c838a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43d563e8-343d-4c38-a35b-dfcfa2ebc5f4' class='xr-var-data-in' type='checkbox'><label for='data-43d563e8-343d-4c38-a35b-dfcfa2ebc5f4' 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><dt><span>valid_min :</span></dt><dd>24.562532</dd><dt><span>valid_max :</span></dt><dd>49.3542</dd></dl></div><div class='xr-var-data'><pre>array([41.020866, 40.9792  , 40.937534, 40.895866, 40.8542  , 40.812534,\n",
       "       40.770866, 40.7292  , 40.687534, 40.645866, 40.6042  , 40.562534,\n",
       "       40.520866, 40.4792  , 40.437534, 40.395866, 40.3542  , 40.312534,\n",
       "       40.270866, 40.2292  , 40.187534, 40.145866, 40.1042  , 40.062534,\n",
       "       40.020866, 39.9792  , 39.937534, 39.895866, 39.8542  , 39.812534,\n",
       "       39.770866, 39.7292  , 39.687534, 39.645866, 39.6042  , 39.562534,\n",
       "       39.520866, 39.4792  , 39.437534, 39.395866, 39.3542  , 39.312534,\n",
       "       39.270866, 39.2292  , 39.187534, 39.145866, 39.1042  , 39.062534,\n",
       "       39.020866, 38.9792  , 38.937534, 38.895866, 38.8542  , 38.812534,\n",
       "       38.770866, 38.7292  , 38.687534, 38.645866, 38.6042  , 38.562534,\n",
       "       38.520866, 38.4792  , 38.437534, 38.395866, 38.3542  , 38.312534,\n",
       "       38.270866, 38.2292  , 38.187534, 38.145866, 38.1042  , 38.062534,\n",
       "       38.020866, 37.9792  , 37.937534, 37.895866, 37.8542  , 37.812534,\n",
       "       37.770866, 37.7292  , 37.687534, 37.645866, 37.6042  , 37.562534,\n",
       "       37.520866, 37.4792  , 37.437534, 37.395866, 37.3542  , 37.312534,\n",
       "       37.270866, 37.2292  , 37.187534, 37.145866, 37.1042  , 37.062534,\n",
       "       37.020866, 36.9792  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-109.1 -109.0 ... -102.1 -102.0</div><input id='attrs-d9d90ce9-5872-4c56-b452-beca124d4a71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9d90ce9-5872-4c56-b452-beca124d4a71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4ba6c84-6da7-43b9-9bf4-54f38549c542' class='xr-var-data-in' type='checkbox'><label for='data-e4ba6c84-6da7-43b9-9bf4-54f38549c542' 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><dt><span>valid_min :</span></dt><dd>-124.6875</dd><dt><span>valid_max :</span></dt><dd>-67.020836</dd></dl></div><div class='xr-var-data'><pre>array([-109.0625  , -109.020836, -108.979164, -108.9375  , -108.895836,\n",
       "       -108.854164, -108.8125  , -108.770836, -108.729164, -108.6875  ,\n",
       "       -108.645836, -108.604164, -108.5625  , -108.520836, -108.479164,\n",
       "       -108.4375  , -108.395836, -108.354164, -108.3125  , -108.270836,\n",
       "       -108.229164, -108.1875  , -108.145836, -108.104164, -108.0625  ,\n",
       "       -108.020836, -107.979164, -107.9375  , -107.895836, -107.854164,\n",
       "       -107.8125  , -107.770836, -107.729164, -107.6875  , -107.645836,\n",
       "       -107.604164, -107.5625  , -107.520836, -107.479164, -107.4375  ,\n",
       "       -107.395836, -107.354164, -107.3125  , -107.270836, -107.229164,\n",
       "       -107.1875  , -107.145836, -107.104164, -107.0625  , -107.020836,\n",
       "       -106.979164, -106.9375  , -106.895836, -106.854164, -106.8125  ,\n",
       "       -106.770836, -106.729164, -106.6875  , -106.645836, -106.604164,\n",
       "       -106.5625  , -106.520836, -106.479164, -106.4375  , -106.395836,\n",
       "       -106.354164, -106.3125  , -106.270836, -106.229164, -106.1875  ,\n",
       "       -106.145836, -106.104164, -106.0625  , -106.020836, -105.979164,\n",
       "       -105.9375  , -105.895836, -105.854164, -105.8125  , -105.770836,\n",
       "       -105.729164, -105.6875  , -105.645836, -105.604164, -105.5625  ,\n",
       "       -105.520836, -105.479164, -105.4375  , -105.395836, -105.354164,\n",
       "       -105.3125  , -105.270836, -105.229164, -105.1875  , -105.145836,\n",
       "       -105.104164, -105.0625  , -105.020836, -104.979164, -104.9375  ,\n",
       "       -104.895836, -104.854164, -104.8125  , -104.770836, -104.729164,\n",
       "       -104.6875  , -104.645836, -104.604164, -104.5625  , -104.520836,\n",
       "       -104.479164, -104.4375  , -104.395836, -104.354164, -104.3125  ,\n",
       "       -104.270836, -104.229164, -104.1875  , -104.145836, -104.104164,\n",
       "       -104.0625  , -104.020836, -103.979164, -103.9375  , -103.895836,\n",
       "       -103.854164, -103.8125  , -103.770836, -103.729164, -103.6875  ,\n",
       "       -103.645836, -103.604164, -103.5625  , -103.520836, -103.479164,\n",
       "       -103.4375  , -103.395836, -103.354164, -103.3125  , -103.270836,\n",
       "       -103.229164, -103.1875  , -103.145836, -103.104164, -103.0625  ,\n",
       "       -103.020836, -102.979164, -102.9375  , -102.895836, -102.854164,\n",
       "       -102.8125  , -102.770836, -102.729164, -102.6875  , -102.645836,\n",
       "       -102.604164, -102.5625  , -102.520836, -102.479164, -102.4375  ,\n",
       "       -102.395836, -102.354164, -102.3125  , -102.270836, -102.229164,\n",
       "       -102.1875  , -102.145836, -102.104164, -102.0625  , -102.020836],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 6 7 8 9 10 11 12</div><input id='attrs-8c7f2add-03ff-4cbc-83b1-b488125a9ee1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c7f2add-03ff-4cbc-83b1-b488125a9ee1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30af72e0-b4cc-4a16-bc8c-1dc9b79c5c6b' class='xr-var-data-in' type='checkbox'><label for='data-30af72e0-b4cc-4a16-bc8c-1dc9b79c5c6b' 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'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3, ..., 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0b482835-49c7-4da9-aeb2-1f5d75186bbb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0b482835-49c7-4da9-aeb2-1f5d75186bbb' 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>prcp</span></div><div class='xr-var-dims'>(lat, lon, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>8.678 9.086 ... -16.54 -9.079</div><input id='attrs-38bc3f22-5955-4954-8b64-af88062d8984' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-38bc3f22-5955-4954-8b64-af88062d8984' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b86d1d1e-6d21-460c-9431-eb94802697b3' class='xr-var-data-in' type='checkbox'><label for='data-b86d1d1e-6d21-460c-9431-eb94802697b3' 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'></dl></div><div class='xr-var-data'><pre>array([[[ 8.67839813e+00,  9.08628845e+00, -2.08923721e+00, ...,\n",
       "          2.35703888e+01, -2.13778114e+01,  2.05376358e+01],\n",
       "        [ 1.06773033e+01,  1.20706253e+01, -2.05799103e-01, ...,\n",
       "          2.79889259e+01, -2.69247055e+01,  2.53394737e+01],\n",
       "        [ 7.88697433e+00,  1.15331841e+01,  1.50865173e+00, ...,\n",
       "          2.59052925e+01, -2.38668938e+01,  2.27580261e+01],\n",
       "        ...,\n",
       "        [-2.38281250e+00, -2.62058640e+00, -8.13240242e+00, ...,\n",
       "         -1.03629494e+01, -6.24236298e+00, -7.47689438e+00],\n",
       "        [-2.32357407e+00, -1.98380852e+00, -8.53751945e+00, ...,\n",
       "         -1.02433205e+01, -6.06138706e+00, -7.64828110e+00],\n",
       "        [-2.27380848e+00, -1.01208973e+00, -9.26558685e+00, ...,\n",
       "         -1.01713276e+01, -5.89167976e+00, -7.85113239e+00]],\n",
       "\n",
       "       [[ 1.38097839e+01,  5.77658081e+00, -6.98476410e+00, ...,\n",
       "          2.54139633e+01, -2.65224228e+01,  2.68461723e+01],\n",
       "        [ 1.31548615e+01,  6.65242004e+00, -5.38281250e+00, ...,\n",
       "          2.64871292e+01, -2.70410538e+01,  2.68639450e+01],\n",
       "        [ 1.21695518e+01,  7.06669998e+00, -3.16130829e+00, ...,\n",
       "          2.60999794e+01, -2.57707825e+01,  2.57243347e+01],\n",
       "...\n",
       "         -1.29881248e+01, -1.60257626e+01, -8.54132843e+00],\n",
       "        [-8.66431618e+00, -8.26972675e+00, -2.21372261e+01, ...,\n",
       "         -1.24885540e+01, -1.62285347e+01, -8.59470749e+00],\n",
       "        [-8.86058617e+00, -8.55542946e+00, -2.25269337e+01, ...,\n",
       "         -1.21747665e+01, -1.63084373e+01, -8.85206985e+00]],\n",
       "\n",
       "       [[-7.64220715e+00,  3.01914215e-01, -9.59052753e+00, ...,\n",
       "          4.79617119e+00, -9.24919891e+00,  1.26643553e+01],\n",
       "        [-6.82861328e+00,  3.32968712e-01, -9.52302742e+00, ...,\n",
       "          4.45265579e+00, -9.07861328e+00,  1.28909569e+01],\n",
       "        [-6.79675770e+00,  3.17226410e-01, -1.00939846e+01, ...,\n",
       "          4.02789116e+00, -1.02311716e+01,  1.46298637e+01],\n",
       "        ...,\n",
       "        [-8.67115211e+00, -8.46679688e+00, -2.22463284e+01, ...,\n",
       "         -1.26567001e+01, -1.62124023e+01, -8.72816372e+00],\n",
       "        [-8.89585972e+00, -8.63164043e+00, -2.26242771e+01, ...,\n",
       "         -1.21814461e+01, -1.63344135e+01, -8.85654259e+00],\n",
       "        [-9.07439423e+00, -8.72001934e+00, -2.28532219e+01, ...,\n",
       "         -1.16671295e+01, -1.65399017e+01, -9.07915974e+00]]],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8b56020f-beb5-4972-a402-b78d1f3c4aa2' class='xr-section-summary-in' type='checkbox'  ><label for='section-8b56020f-beb5-4972-a402-b78d1f3c4aa2' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-11bafa60-9dbf-43f5-9013-0df24fb8503d' class='xr-index-data-in' type='checkbox'/><label for='index-11bafa60-9dbf-43f5-9013-0df24fb8503d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1950-01-01&#x27;, &#x27;1950-02-01&#x27;, &#x27;1950-03-01&#x27;, &#x27;1950-04-01&#x27;,\n",
       "               &#x27;1950-05-01&#x27;, &#x27;1950-06-01&#x27;, &#x27;1950-07-01&#x27;, &#x27;1950-08-01&#x27;,\n",
       "               &#x27;1950-09-01&#x27;, &#x27;1950-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-03-01&#x27;, &#x27;2021-04-01&#x27;, &#x27;2021-05-01&#x27;, &#x27;2021-06-01&#x27;,\n",
       "               &#x27;2021-07-01&#x27;, &#x27;2021-08-01&#x27;, &#x27;2021-09-01&#x27;, &#x27;2021-10-01&#x27;,\n",
       "               &#x27;2021-11-01&#x27;, &#x27;2021-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=864, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d1e61ff9-b979-4e20-a1ae-4f5e47bb6449' class='xr-index-data-in' type='checkbox'/><label for='index-d1e61ff9-b979-4e20-a1ae-4f5e47bb6449' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([41.02086639404297, 40.97919845581055, 40.93753433227539,\n",
       "              40.89586639404297, 40.85419845581055, 40.81253433227539,\n",
       "              40.77086639404297, 40.72919845581055, 40.68753433227539,\n",
       "              40.64586639404297, 40.60419845581055, 40.56253433227539,\n",
       "              40.52086639404297, 40.47919845581055, 40.43753433227539,\n",
       "              40.39586639404297, 40.35419845581055, 40.31253433227539,\n",
       "              40.27086639404297, 40.22919845581055, 40.18753433227539,\n",
       "              40.14586639404297, 40.10419845581055, 40.06253433227539,\n",
       "              40.02086639404297, 39.97919845581055, 39.93753433227539,\n",
       "              39.89586639404297, 39.85419845581055, 39.81253433227539,\n",
       "              39.77086639404297, 39.72919845581055, 39.68753433227539,\n",
       "              39.64586639404297, 39.60419845581055, 39.56253433227539,\n",
       "              39.52086639404297, 39.47919845581055, 39.43753433227539,\n",
       "              39.39586639404297, 39.35419845581055, 39.31253433227539,\n",
       "              39.27086639404297, 39.22919845581055, 39.18753433227539,\n",
       "              39.14586639404297, 39.10419845581055, 39.06253433227539,\n",
       "              39.02086639404297, 38.97919845581055, 38.93753433227539,\n",
       "              38.89586639404297, 38.85419845581055, 38.81253433227539,\n",
       "              38.77086639404297, 38.72919845581055, 38.68753433227539,\n",
       "              38.64586639404297, 38.60419845581055, 38.56253433227539,\n",
       "              38.52086639404297, 38.47919845581055, 38.43753433227539,\n",
       "              38.39586639404297, 38.35419845581055, 38.31253433227539,\n",
       "              38.27086639404297, 38.22919845581055, 38.18753433227539,\n",
       "              38.14586639404297, 38.10419845581055, 38.06253433227539,\n",
       "              38.02086639404297, 37.97919845581055, 37.93753433227539,\n",
       "              37.89586639404297, 37.85419845581055, 37.81253433227539,\n",
       "              37.77086639404297, 37.72919845581055, 37.68753433227539,\n",
       "              37.64586639404297, 37.60419845581055, 37.56253433227539,\n",
       "              37.52086639404297, 37.47919845581055, 37.43753433227539,\n",
       "              37.39586639404297, 37.35419845581055, 37.31253433227539,\n",
       "              37.27086639404297, 37.22919845581055, 37.18753433227539,\n",
       "              37.14586639404297, 37.10419845581055, 37.06253433227539,\n",
       "              37.02086639404297, 36.97919845581055],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1e4027bc-42fb-450b-b66c-c8b0c8364893' class='xr-index-data-in' type='checkbox'/><label for='index-1e4027bc-42fb-450b-b66c-c8b0c8364893' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([          -109.0625, -109.02083587646484, -108.97916412353516,\n",
       "                        -108.9375, -108.89583587646484, -108.85416412353516,\n",
       "                        -108.8125, -108.77083587646484, -108.72916412353516,\n",
       "                        -108.6875,\n",
       "              ...\n",
       "              -102.39583587646484, -102.35416412353516,           -102.3125,\n",
       "              -102.27083587646484, -102.22916412353516,           -102.1875,\n",
       "              -102.14583587646484, -102.10416412353516,           -102.0625,\n",
       "              -102.02083587646484],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=170))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a4d708c0-b3b9-41ff-a381-eff080b083eb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a4d708c0-b3b9-41ff-a381-eff080b083eb' 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.Dataset>\n",
       "Dimensions:  (time: 864, lat: 98, lon: 170)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1950-01-01 1950-02-01 ... 2021-12-01\n",
       "  * lat      (lat) float32 41.02 40.98 40.94 40.9 ... 37.1 37.06 37.02 36.98\n",
       "  * lon      (lon) float32 -109.1 -109.0 -109.0 -108.9 ... -102.1 -102.1 -102.0\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    prcp     (lat, lon, time) float32 8.678 9.086 -2.089 ... -16.54 -9.079"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prcp_anom\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c6a6ded",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1269ab590>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prcp_anom.prcp[0,0].plot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555e2e2c",
   "metadata": {},
   "source": [
    "### now reshape these into numpy arrays that are x*y, t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e34926c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16660, 864)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var1 = prcp_anom.prcp\n",
    "prcp_anom_reshape = var1.to_numpy().reshape(var1.shape[0]*var1.shape[1],var1.shape[2])\n",
    "\n",
    "var2 = tavg_anom.tavg\n",
    "tavg_anom_reshape = var2.to_numpy().reshape(var2.shape[0]*var2.shape[1],var2.shape[2])\n",
    "\n",
    "prcp_anom_reshape.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b94c1ee8",
   "metadata": {},
   "source": [
    "Now follow the steps here, using our data: https://towardsdatascience.com/how-to-apply-hierarchical-clustering-to-time-series-a5fe2a7d8447\n",
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c7200ec",
   "metadata": {},
   "source": [
    "### reshape the data and put into a pandas dataframe. Result is a pandas dataframe with the time series for each gridpoint in a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0540bfe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>16650</th>\n",
       "      <th>16651</th>\n",
       "      <th>16652</th>\n",
       "      <th>16653</th>\n",
       "      <th>16654</th>\n",
       "      <th>16655</th>\n",
       "      <th>16656</th>\n",
       "      <th>16657</th>\n",
       "      <th>16658</th>\n",
       "      <th>16659</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-3.371211</td>\n",
       "      <td>-3.331758</td>\n",
       "      <td>-3.300664</td>\n",
       "      <td>-3.243301</td>\n",
       "      <td>-3.182266</td>\n",
       "      <td>-3.109746</td>\n",
       "      <td>-3.014356</td>\n",
       "      <td>-2.895098</td>\n",
       "      <td>-2.755176</td>\n",
       "      <td>-2.583906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.670117</td>\n",
       "      <td>0.689668</td>\n",
       "      <td>0.689746</td>\n",
       "      <td>0.733242</td>\n",
       "      <td>0.775664</td>\n",
       "      <td>0.833594</td>\n",
       "      <td>0.899570</td>\n",
       "      <td>0.967773</td>\n",
       "      <td>1.032559</td>\n",
       "      <td>1.115098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.643750</td>\n",
       "      <td>-2.599785</td>\n",
       "      <td>-2.553594</td>\n",
       "      <td>-2.504082</td>\n",
       "      <td>-2.440820</td>\n",
       "      <td>-2.358769</td>\n",
       "      <td>-2.274375</td>\n",
       "      <td>-2.149277</td>\n",
       "      <td>-2.012988</td>\n",
       "      <td>-1.844121</td>\n",
       "      <td>...</td>\n",
       "      <td>2.839805</td>\n",
       "      <td>2.850156</td>\n",
       "      <td>2.851856</td>\n",
       "      <td>2.854785</td>\n",
       "      <td>2.868027</td>\n",
       "      <td>2.885176</td>\n",
       "      <td>2.889922</td>\n",
       "      <td>2.896992</td>\n",
       "      <td>2.915957</td>\n",
       "      <td>2.917305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.323398</td>\n",
       "      <td>-1.333066</td>\n",
       "      <td>-1.332441</td>\n",
       "      <td>-1.343457</td>\n",
       "      <td>-1.334297</td>\n",
       "      <td>-1.320234</td>\n",
       "      <td>-1.312910</td>\n",
       "      <td>-1.275254</td>\n",
       "      <td>-1.236953</td>\n",
       "      <td>-1.180605</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.243047</td>\n",
       "      <td>-0.241738</td>\n",
       "      <td>-0.239785</td>\n",
       "      <td>-0.239395</td>\n",
       "      <td>-0.245390</td>\n",
       "      <td>-0.249336</td>\n",
       "      <td>-0.254414</td>\n",
       "      <td>-0.267735</td>\n",
       "      <td>-0.268125</td>\n",
       "      <td>-0.278301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.234023</td>\n",
       "      <td>-0.255801</td>\n",
       "      <td>-0.277734</td>\n",
       "      <td>-0.296992</td>\n",
       "      <td>-0.319277</td>\n",
       "      <td>-0.328652</td>\n",
       "      <td>-0.346621</td>\n",
       "      <td>-0.347285</td>\n",
       "      <td>-0.346015</td>\n",
       "      <td>-0.333555</td>\n",
       "      <td>...</td>\n",
       "      <td>0.499804</td>\n",
       "      <td>0.482325</td>\n",
       "      <td>0.454590</td>\n",
       "      <td>0.444629</td>\n",
       "      <td>0.436309</td>\n",
       "      <td>0.440215</td>\n",
       "      <td>0.440801</td>\n",
       "      <td>0.439981</td>\n",
       "      <td>0.439883</td>\n",
       "      <td>0.451211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.613906</td>\n",
       "      <td>-2.625215</td>\n",
       "      <td>-2.626406</td>\n",
       "      <td>-2.638614</td>\n",
       "      <td>-2.642852</td>\n",
       "      <td>-2.649140</td>\n",
       "      <td>-2.654707</td>\n",
       "      <td>-2.660898</td>\n",
       "      <td>-2.669434</td>\n",
       "      <td>-2.680176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.412500</td>\n",
       "      <td>0.411211</td>\n",
       "      <td>0.398359</td>\n",
       "      <td>0.395840</td>\n",
       "      <td>0.402306</td>\n",
       "      <td>0.400078</td>\n",
       "      <td>0.404200</td>\n",
       "      <td>0.408575</td>\n",
       "      <td>0.411427</td>\n",
       "      <td>0.439903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>0.564648</td>\n",
       "      <td>0.585996</td>\n",
       "      <td>0.607246</td>\n",
       "      <td>0.655293</td>\n",
       "      <td>0.673456</td>\n",
       "      <td>0.688515</td>\n",
       "      <td>0.699726</td>\n",
       "      <td>0.698692</td>\n",
       "      <td>0.698027</td>\n",
       "      <td>0.694355</td>\n",
       "      <td>...</td>\n",
       "      <td>0.449160</td>\n",
       "      <td>0.489552</td>\n",
       "      <td>0.518867</td>\n",
       "      <td>0.535761</td>\n",
       "      <td>0.564259</td>\n",
       "      <td>0.590157</td>\n",
       "      <td>0.606426</td>\n",
       "      <td>0.620743</td>\n",
       "      <td>0.632656</td>\n",
       "      <td>0.646757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>1.747070</td>\n",
       "      <td>1.769941</td>\n",
       "      <td>1.774239</td>\n",
       "      <td>1.787637</td>\n",
       "      <td>1.787207</td>\n",
       "      <td>1.816015</td>\n",
       "      <td>1.834199</td>\n",
       "      <td>1.847832</td>\n",
       "      <td>1.871407</td>\n",
       "      <td>1.904433</td>\n",
       "      <td>...</td>\n",
       "      <td>2.177383</td>\n",
       "      <td>2.215078</td>\n",
       "      <td>2.240450</td>\n",
       "      <td>2.264179</td>\n",
       "      <td>2.288067</td>\n",
       "      <td>2.308046</td>\n",
       "      <td>2.326288</td>\n",
       "      <td>2.325390</td>\n",
       "      <td>2.331133</td>\n",
       "      <td>2.336798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>-0.132168</td>\n",
       "      <td>-0.113633</td>\n",
       "      <td>-0.096445</td>\n",
       "      <td>-0.068301</td>\n",
       "      <td>-0.052344</td>\n",
       "      <td>-0.024160</td>\n",
       "      <td>-0.000215</td>\n",
       "      <td>0.028887</td>\n",
       "      <td>0.059414</td>\n",
       "      <td>0.081387</td>\n",
       "      <td>...</td>\n",
       "      <td>0.691875</td>\n",
       "      <td>0.741777</td>\n",
       "      <td>0.802071</td>\n",
       "      <td>0.862247</td>\n",
       "      <td>0.911563</td>\n",
       "      <td>0.953632</td>\n",
       "      <td>1.002460</td>\n",
       "      <td>1.041523</td>\n",
       "      <td>1.094063</td>\n",
       "      <td>1.138437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>3.976621</td>\n",
       "      <td>3.994512</td>\n",
       "      <td>3.993516</td>\n",
       "      <td>4.024805</td>\n",
       "      <td>4.054062</td>\n",
       "      <td>4.079766</td>\n",
       "      <td>4.106582</td>\n",
       "      <td>4.140390</td>\n",
       "      <td>4.175820</td>\n",
       "      <td>4.209434</td>\n",
       "      <td>...</td>\n",
       "      <td>2.648164</td>\n",
       "      <td>2.671640</td>\n",
       "      <td>2.703184</td>\n",
       "      <td>2.726309</td>\n",
       "      <td>2.761562</td>\n",
       "      <td>2.787129</td>\n",
       "      <td>2.809512</td>\n",
       "      <td>2.826621</td>\n",
       "      <td>2.851855</td>\n",
       "      <td>2.876348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>2.430391</td>\n",
       "      <td>2.477812</td>\n",
       "      <td>2.506895</td>\n",
       "      <td>2.552481</td>\n",
       "      <td>2.578281</td>\n",
       "      <td>2.603242</td>\n",
       "      <td>2.640078</td>\n",
       "      <td>2.676641</td>\n",
       "      <td>2.679453</td>\n",
       "      <td>2.692402</td>\n",
       "      <td>...</td>\n",
       "      <td>4.166973</td>\n",
       "      <td>4.250449</td>\n",
       "      <td>4.322773</td>\n",
       "      <td>4.393750</td>\n",
       "      <td>4.470019</td>\n",
       "      <td>4.544063</td>\n",
       "      <td>4.619336</td>\n",
       "      <td>4.685293</td>\n",
       "      <td>4.767617</td>\n",
       "      <td>4.852207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>864 rows × 16660 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0         1         2         3         4         5         6      \\\n",
       "0   -3.371211 -3.331758 -3.300664 -3.243301 -3.182266 -3.109746 -3.014356   \n",
       "1   -2.643750 -2.599785 -2.553594 -2.504082 -2.440820 -2.358769 -2.274375   \n",
       "2   -1.323398 -1.333066 -1.332441 -1.343457 -1.334297 -1.320234 -1.312910   \n",
       "3   -0.234023 -0.255801 -0.277734 -0.296992 -0.319277 -0.328652 -0.346621   \n",
       "4   -2.613906 -2.625215 -2.626406 -2.638614 -2.642852 -2.649140 -2.654707   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "859  0.564648  0.585996  0.607246  0.655293  0.673456  0.688515  0.699726   \n",
       "860  1.747070  1.769941  1.774239  1.787637  1.787207  1.816015  1.834199   \n",
       "861 -0.132168 -0.113633 -0.096445 -0.068301 -0.052344 -0.024160 -0.000215   \n",
       "862  3.976621  3.994512  3.993516  4.024805  4.054062  4.079766  4.106582   \n",
       "863  2.430391  2.477812  2.506895  2.552481  2.578281  2.603242  2.640078   \n",
       "\n",
       "        7         8         9      ...     16650     16651     16652  \\\n",
       "0   -2.895098 -2.755176 -2.583906  ...  0.670117  0.689668  0.689746   \n",
       "1   -2.149277 -2.012988 -1.844121  ...  2.839805  2.850156  2.851856   \n",
       "2   -1.275254 -1.236953 -1.180605  ... -0.243047 -0.241738 -0.239785   \n",
       "3   -0.347285 -0.346015 -0.333555  ...  0.499804  0.482325  0.454590   \n",
       "4   -2.660898 -2.669434 -2.680176  ...  0.412500  0.411211  0.398359   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "859  0.698692  0.698027  0.694355  ...  0.449160  0.489552  0.518867   \n",
       "860  1.847832  1.871407  1.904433  ...  2.177383  2.215078  2.240450   \n",
       "861  0.028887  0.059414  0.081387  ...  0.691875  0.741777  0.802071   \n",
       "862  4.140390  4.175820  4.209434  ...  2.648164  2.671640  2.703184   \n",
       "863  2.676641  2.679453  2.692402  ...  4.166973  4.250449  4.322773   \n",
       "\n",
       "        16653     16654     16655     16656     16657     16658     16659  \n",
       "0    0.733242  0.775664  0.833594  0.899570  0.967773  1.032559  1.115098  \n",
       "1    2.854785  2.868027  2.885176  2.889922  2.896992  2.915957  2.917305  \n",
       "2   -0.239395 -0.245390 -0.249336 -0.254414 -0.267735 -0.268125 -0.278301  \n",
       "3    0.444629  0.436309  0.440215  0.440801  0.439981  0.439883  0.451211  \n",
       "4    0.395840  0.402306  0.400078  0.404200  0.408575  0.411427  0.439903  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "859  0.535761  0.564259  0.590157  0.606426  0.620743  0.632656  0.646757  \n",
       "860  2.264179  2.288067  2.308046  2.326288  2.325390  2.331133  2.336798  \n",
       "861  0.862247  0.911563  0.953632  1.002460  1.041523  1.094063  1.138437  \n",
       "862  2.726309  2.761562  2.787129  2.809512  2.826621  2.851855  2.876348  \n",
       "863  4.393750  4.470019  4.544063  4.619336  4.685293  4.767617  4.852207  \n",
       "\n",
       "[864 rows x 16660 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsers_tavg = pd.DataFrame(tavg_anom_reshape.transpose())\n",
    "tsers_prcp = pd.DataFrame(prcp_anom_reshape.transpose())\n",
    "\n",
    "tsers_tavg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1e7e4f0",
   "metadata": {},
   "source": [
    "### calculate the correlation matrix of both variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bd6318bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9min 34s, sys: 3.4 s, total: 9min 38s\n",
      "Wall time: 9min 39s\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>16650</th>\n",
       "      <th>16651</th>\n",
       "      <th>16652</th>\n",
       "      <th>16653</th>\n",
       "      <th>16654</th>\n",
       "      <th>16655</th>\n",
       "      <th>16656</th>\n",
       "      <th>16657</th>\n",
       "      <th>16658</th>\n",
       "      <th>16659</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.999891</td>\n",
       "      <td>0.999584</td>\n",
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       "      <td>0.998483</td>\n",
       "      <td>0.997630</td>\n",
       "      <td>0.996823</td>\n",
       "      <td>0.995950</td>\n",
       "      <td>0.994878</td>\n",
       "      <td>0.993423</td>\n",
       "      <td>...</td>\n",
       "      <td>0.615758</td>\n",
       "      <td>0.613609</td>\n",
       "      <td>0.611559</td>\n",
       "      <td>0.609202</td>\n",
       "      <td>0.606739</td>\n",
       "      <td>0.604186</td>\n",
       "      <td>0.601344</td>\n",
       "      <td>0.598325</td>\n",
       "      <td>0.595584</td>\n",
       "      <td>0.591566</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.999891</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999884</td>\n",
       "      <td>0.999537</td>\n",
       "      <td>0.999135</td>\n",
       "      <td>0.998465</td>\n",
       "      <td>0.997790</td>\n",
       "      <td>0.997029</td>\n",
       "      <td>0.996062</td>\n",
       "      <td>0.994721</td>\n",
       "      <td>...</td>\n",
       "      <td>0.616916</td>\n",
       "      <td>0.614767</td>\n",
       "      <td>0.612733</td>\n",
       "      <td>0.610387</td>\n",
       "      <td>0.607941</td>\n",
       "      <td>0.605408</td>\n",
       "      <td>0.602587</td>\n",
       "      <td>0.599580</td>\n",
       "      <td>0.596858</td>\n",
       "      <td>0.592851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.999584</td>\n",
       "      <td>0.999884</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999845</td>\n",
       "      <td>0.999593</td>\n",
       "      <td>0.999093</td>\n",
       "      <td>0.998540</td>\n",
       "      <td>0.997888</td>\n",
       "      <td>0.997023</td>\n",
       "      <td>0.995777</td>\n",
       "      <td>...</td>\n",
       "      <td>0.618174</td>\n",
       "      <td>0.616023</td>\n",
       "      <td>0.614003</td>\n",
       "      <td>0.611663</td>\n",
       "      <td>0.609227</td>\n",
       "      <td>0.606708</td>\n",
       "      <td>0.603896</td>\n",
       "      <td>0.600889</td>\n",
       "      <td>0.598172</td>\n",
       "      <td>0.594160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.999044</td>\n",
       "      <td>0.999537</td>\n",
       "      <td>0.999845</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999910</td>\n",
       "      <td>0.999589</td>\n",
       "      <td>0.999177</td>\n",
       "      <td>0.998651</td>\n",
       "      <td>0.997903</td>\n",
       "      <td>0.996776</td>\n",
       "      <td>...</td>\n",
       "      <td>0.619270</td>\n",
       "      <td>0.617125</td>\n",
       "      <td>0.615119</td>\n",
       "      <td>0.612788</td>\n",
       "      <td>0.610368</td>\n",
       "      <td>0.607869</td>\n",
       "      <td>0.605072</td>\n",
       "      <td>0.602076</td>\n",
       "      <td>0.599376</td>\n",
       "      <td>0.595373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.998483</td>\n",
       "      <td>0.999135</td>\n",
       "      <td>0.999593</td>\n",
       "      <td>0.999910</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999846</td>\n",
       "      <td>0.999569</td>\n",
       "      <td>0.999173</td>\n",
       "      <td>0.998556</td>\n",
       "      <td>0.997573</td>\n",
       "      <td>...</td>\n",
       "      <td>0.620533</td>\n",
       "      <td>0.618405</td>\n",
       "      <td>0.616427</td>\n",
       "      <td>0.614116</td>\n",
       "      <td>0.611721</td>\n",
       "      <td>0.609251</td>\n",
       "      <td>0.606482</td>\n",
       "      <td>0.603503</td>\n",
       "      <td>0.600828</td>\n",
       "      <td>0.596847</td>\n",
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       "      <td>0.610158</td>\n",
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       "      <td>0.613683</td>\n",
       "      <td>0.615918</td>\n",
       "      <td>0.618871</td>\n",
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       "      <td>0.997085</td>\n",
       "      <td>0.998189</td>\n",
       "      <td>0.998942</td>\n",
       "      <td>0.999500</td>\n",
       "      <td>0.999855</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999829</td>\n",
       "      <td>0.999442</td>\n",
       "      <td>0.998633</td>\n",
       "      <td>0.997377</td>\n",
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       "    <tr>\n",
       "      <th>16656</th>\n",
       "      <td>0.601344</td>\n",
       "      <td>0.602587</td>\n",
       "      <td>0.603896</td>\n",
       "      <td>0.605072</td>\n",
       "      <td>0.606482</td>\n",
       "      <td>0.607400</td>\n",
       "      <td>0.608989</td>\n",
       "      <td>0.610960</td>\n",
       "      <td>0.613226</td>\n",
       "      <td>0.616208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.995741</td>\n",
       "      <td>0.997070</td>\n",
       "      <td>0.998036</td>\n",
       "      <td>0.998821</td>\n",
       "      <td>0.999423</td>\n",
       "      <td>0.999829</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999867</td>\n",
       "      <td>0.999360</td>\n",
       "      <td>0.998446</td>\n",
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       "    <tr>\n",
       "      <th>16657</th>\n",
       "      <td>0.598325</td>\n",
       "      <td>0.599580</td>\n",
       "      <td>0.600889</td>\n",
       "      <td>0.602076</td>\n",
       "      <td>0.603503</td>\n",
       "      <td>0.604431</td>\n",
       "      <td>0.606025</td>\n",
       "      <td>0.608011</td>\n",
       "      <td>0.610298</td>\n",
       "      <td>0.613303</td>\n",
       "      <td>...</td>\n",
       "      <td>0.994290</td>\n",
       "      <td>0.995823</td>\n",
       "      <td>0.996979</td>\n",
       "      <td>0.997973</td>\n",
       "      <td>0.998796</td>\n",
       "      <td>0.999442</td>\n",
       "      <td>0.999867</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999776</td>\n",
       "      <td>0.999168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16658</th>\n",
       "      <td>0.595584</td>\n",
       "      <td>0.596858</td>\n",
       "      <td>0.598172</td>\n",
       "      <td>0.599376</td>\n",
       "      <td>0.600828</td>\n",
       "      <td>0.601770</td>\n",
       "      <td>0.603377</td>\n",
       "      <td>0.605378</td>\n",
       "      <td>0.607688</td>\n",
       "      <td>0.610712</td>\n",
       "      <td>...</td>\n",
       "      <td>0.992257</td>\n",
       "      <td>0.994028</td>\n",
       "      <td>0.995397</td>\n",
       "      <td>0.996627</td>\n",
       "      <td>0.997707</td>\n",
       "      <td>0.998633</td>\n",
       "      <td>0.999360</td>\n",
       "      <td>0.999776</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999755</td>\n",
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       "    <tr>\n",
       "      <th>16659</th>\n",
       "      <td>0.591566</td>\n",
       "      <td>0.592851</td>\n",
       "      <td>0.594160</td>\n",
       "      <td>0.595373</td>\n",
       "      <td>0.596847</td>\n",
       "      <td>0.597795</td>\n",
       "      <td>0.599404</td>\n",
       "      <td>0.601417</td>\n",
       "      <td>0.603748</td>\n",
       "      <td>0.606795</td>\n",
       "      <td>...</td>\n",
       "      <td>0.989673</td>\n",
       "      <td>0.991700</td>\n",
       "      <td>0.993304</td>\n",
       "      <td>0.994789</td>\n",
       "      <td>0.996144</td>\n",
       "      <td>0.997377</td>\n",
       "      <td>0.998446</td>\n",
       "      <td>0.999168</td>\n",
       "      <td>0.999755</td>\n",
       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>16660 rows × 16660 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6      \\\n",
       "0      1.000000  0.999891  0.999584  0.999044  0.998483  0.997630  0.996823   \n",
       "1      0.999891  1.000000  0.999884  0.999537  0.999135  0.998465  0.997790   \n",
       "2      0.999584  0.999884  1.000000  0.999845  0.999593  0.999093  0.998540   \n",
       "3      0.999044  0.999537  0.999845  1.000000  0.999910  0.999589  0.999177   \n",
       "4      0.998483  0.999135  0.999593  0.999910  1.000000  0.999846  0.999569   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "16655  0.604186  0.605408  0.606708  0.607869  0.609251  0.610158  0.611733   \n",
       "16656  0.601344  0.602587  0.603896  0.605072  0.606482  0.607400  0.608989   \n",
       "16657  0.598325  0.599580  0.600889  0.602076  0.603503  0.604431  0.606025   \n",
       "16658  0.595584  0.596858  0.598172  0.599376  0.600828  0.601770  0.603377   \n",
       "16659  0.591566  0.592851  0.594160  0.595373  0.596847  0.597795  0.599404   \n",
       "\n",
       "          7         8         9      ...     16650     16651     16652  \\\n",
       "0      0.995950  0.994878  0.993423  ...  0.615758  0.613609  0.611559   \n",
       "1      0.997029  0.996062  0.994721  ...  0.616916  0.614767  0.612733   \n",
       "2      0.997888  0.997023  0.995777  ...  0.618174  0.616023  0.614003   \n",
       "3      0.998651  0.997903  0.996776  ...  0.619270  0.617125  0.615119   \n",
       "4      0.999173  0.998556  0.997573  ...  0.620533  0.618405  0.616427   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "16655  0.613683  0.615918  0.618871  ...  0.997085  0.998189  0.998942   \n",
       "16656  0.610960  0.613226  0.616208  ...  0.995741  0.997070  0.998036   \n",
       "16657  0.608011  0.610298  0.613303  ...  0.994290  0.995823  0.996979   \n",
       "16658  0.605378  0.607688  0.610712  ...  0.992257  0.994028  0.995397   \n",
       "16659  0.601417  0.603748  0.606795  ...  0.989673  0.991700  0.993304   \n",
       "\n",
       "          16653     16654     16655     16656     16657     16658     16659  \n",
       "0      0.609202  0.606739  0.604186  0.601344  0.598325  0.595584  0.591566  \n",
       "1      0.610387  0.607941  0.605408  0.602587  0.599580  0.596858  0.592851  \n",
       "2      0.611663  0.609227  0.606708  0.603896  0.600889  0.598172  0.594160  \n",
       "3      0.612788  0.610368  0.607869  0.605072  0.602076  0.599376  0.595373  \n",
       "4      0.614116  0.611721  0.609251  0.606482  0.603503  0.600828  0.596847  \n",
       "...         ...       ...       ...       ...       ...       ...       ...  \n",
       "16655  0.999500  0.999855  1.000000  0.999829  0.999442  0.998633  0.997377  \n",
       "16656  0.998821  0.999423  0.999829  1.000000  0.999867  0.999360  0.998446  \n",
       "16657  0.997973  0.998796  0.999442  0.999867  1.000000  0.999776  0.999168  \n",
       "16658  0.996627  0.997707  0.998633  0.999360  0.999776  1.000000  0.999755  \n",
       "16659  0.994789  0.996144  0.997377  0.998446  0.999168  0.999755  1.000000  \n",
       "\n",
       "[16660 rows x 16660 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "corr_matrix_tavg = tsers_tavg.corr()\n",
    "corr_matrix_prcp = tsers_prcp.corr()\n",
    "corr_matrix_tavg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daf598a6",
   "metadata": {},
   "source": [
    "### pickle these for potential time savings later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fab73192",
   "metadata": {},
   "outputs": [],
   "source": [
    "###now pickle them\n",
    "#pickle.dump( corr_matrix_tavg, open( \"corr_matrix_tavg_post1950_pub.pkl\", \"wb\" ) )\n",
    "#pickle.dump( corr_matrix_prcp, open( \"corr_matrix_prcp_post1950_pub.pkl\", \"wb\" ) )\n",
    "\n",
    "### or read pickles in\n",
    "\n",
    "#with open(\"corr_matrix_tavg_post1950_pub.pkl\", 'rb') as pickle_file:\n",
    "#    corr_matrix_tavg = pickle.load(pickle_file)\n",
    "    \n",
    "#with open(\"corr_matrix_prcp_post1950_pub.pkl\", 'rb') as pickle_file:\n",
    "#    corr_matrix_prcp = pickle.load(pickle_file)    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8db5bd7b",
   "metadata": {},
   "source": [
    "### convert correlation into a squared euclidean distance, based on discussion in this post: https://stats.stackexchange.com/questions/165194/using-correlation-as-distance-metric-for-hierarchical-clustering\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9c7f58db",
   "metadata": {},
   "outputs": [],
   "source": [
    "distance_matrix_tavg = 2*(1 - corr_matrix_tavg)\n",
    "distance_matrix_prcp = 2*(1 - corr_matrix_prcp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a51c43b9",
   "metadata": {},
   "source": [
    "### now sum the distance matrices of the two variables to get a 'total distance' based on both temperature and precip anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "22ffaf2d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.577482</td>\n",
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       "      <td>2.533384</td>\n",
       "      <td>2.534335</td>\n",
       "      <td>2.537401</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084221</td>\n",
       "      <td>0.059879</td>\n",
       "      <td>0.035516</td>\n",
       "      <td>0.016506</td>\n",
       "      <td>0.006371</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003159</td>\n",
       "      <td>0.016052</td>\n",
       "      <td>0.032239</td>\n",
       "      <td>0.057015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16656</th>\n",
       "      <td>2.565708</td>\n",
       "      <td>2.564983</td>\n",
       "      <td>2.567279</td>\n",
       "      <td>2.556803</td>\n",
       "      <td>2.556320</td>\n",
       "      <td>2.554632</td>\n",
       "      <td>2.546583</td>\n",
       "      <td>2.541178</td>\n",
       "      <td>2.542056</td>\n",
       "      <td>2.545301</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116064</td>\n",
       "      <td>0.087378</td>\n",
       "      <td>0.057650</td>\n",
       "      <td>0.032093</td>\n",
       "      <td>0.016858</td>\n",
       "      <td>0.003159</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005819</td>\n",
       "      <td>0.016700</td>\n",
       "      <td>0.036014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16657</th>\n",
       "      <td>2.576387</td>\n",
       "      <td>2.575355</td>\n",
       "      <td>2.577482</td>\n",
       "      <td>2.567315</td>\n",
       "      <td>2.566689</td>\n",
       "      <td>2.565177</td>\n",
       "      <td>2.557394</td>\n",
       "      <td>2.551976</td>\n",
       "      <td>2.552847</td>\n",
       "      <td>2.556297</td>\n",
       "      <td>...</td>\n",
       "      <td>0.166021</td>\n",
       "      <td>0.131833</td>\n",
       "      <td>0.094821</td>\n",
       "      <td>0.060708</td>\n",
       "      <td>0.038613</td>\n",
       "      <td>0.016052</td>\n",
       "      <td>0.005819</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003486</td>\n",
       "      <td>0.015160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16658</th>\n",
       "      <td>2.586220</td>\n",
       "      <td>2.584960</td>\n",
       "      <td>2.586835</td>\n",
       "      <td>2.576897</td>\n",
       "      <td>2.576101</td>\n",
       "      <td>2.574746</td>\n",
       "      <td>2.567099</td>\n",
       "      <td>2.561849</td>\n",
       "      <td>2.562788</td>\n",
       "      <td>2.566541</td>\n",
       "      <td>...</td>\n",
       "      <td>0.207758</td>\n",
       "      <td>0.169613</td>\n",
       "      <td>0.127816</td>\n",
       "      <td>0.087741</td>\n",
       "      <td>0.061063</td>\n",
       "      <td>0.032239</td>\n",
       "      <td>0.016700</td>\n",
       "      <td>0.003486</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16659</th>\n",
       "      <td>2.592085</td>\n",
       "      <td>2.590648</td>\n",
       "      <td>2.592049</td>\n",
       "      <td>2.581881</td>\n",
       "      <td>2.580933</td>\n",
       "      <td>2.579305</td>\n",
       "      <td>2.571541</td>\n",
       "      <td>2.566093</td>\n",
       "      <td>2.566960</td>\n",
       "      <td>2.570472</td>\n",
       "      <td>...</td>\n",
       "      <td>0.255902</td>\n",
       "      <td>0.214175</td>\n",
       "      <td>0.169271</td>\n",
       "      <td>0.122693</td>\n",
       "      <td>0.091644</td>\n",
       "      <td>0.057015</td>\n",
       "      <td>0.036014</td>\n",
       "      <td>0.015160</td>\n",
       "      <td>0.004840</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16660 rows × 16660 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6      \\\n",
       "0      0.000000  0.003715  0.009233  0.021233  0.033400  0.048470  0.066503   \n",
       "1      0.003715  0.000000  0.004176  0.016305  0.025952  0.041092  0.060209   \n",
       "2      0.009233  0.004176  0.000000  0.007137  0.013174  0.024614  0.040508   \n",
       "3      0.021233  0.016305  0.007137  0.000000  0.002257  0.007496  0.017072   \n",
       "4      0.033400  0.025952  0.013174  0.002257  0.000000  0.002935  0.009856   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "16655  2.557671  2.557148  2.559602  2.548854  2.548594  2.546724  2.538770   \n",
       "16656  2.565708  2.564983  2.567279  2.556803  2.556320  2.554632  2.546583   \n",
       "16657  2.576387  2.575355  2.577482  2.567315  2.566689  2.565177  2.557394   \n",
       "16658  2.586220  2.584960  2.586835  2.576897  2.576101  2.574746  2.567099   \n",
       "16659  2.592085  2.590648  2.592049  2.581881  2.580933  2.579305  2.571541   \n",
       "\n",
       "          7         8         9      ...     16650     16651     16652  \\\n",
       "0      0.085971  0.106571  0.137126  ...  2.520336  2.525171  2.537289   \n",
       "1      0.079728  0.098952  0.129654  ...  2.520435  2.525294  2.537065   \n",
       "2      0.057219  0.074288  0.102207  ...  2.523637  2.528485  2.540303   \n",
       "3      0.028680  0.042518  0.066026  ...  2.511314  2.516383  2.528644   \n",
       "4      0.019104  0.030773  0.051903  ...  2.511840  2.516809  2.528878   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "16655  2.533384  2.534335  2.537401  ...  0.084221  0.059879  0.035516   \n",
       "16656  2.541178  2.542056  2.545301  ...  0.116064  0.087378  0.057650   \n",
       "16657  2.551976  2.552847  2.556297  ...  0.166021  0.131833  0.094821   \n",
       "16658  2.561849  2.562788  2.566541  ...  0.207758  0.169613  0.127816   \n",
       "16659  2.566093  2.566960  2.570472  ...  0.255902  0.214175  0.169271   \n",
       "\n",
       "          16653     16654     16655     16656     16657     16658     16659  \n",
       "0      2.540605  2.547954  2.557671  2.565708  2.576387  2.586220  2.592085  \n",
       "1      2.540126  2.547341  2.557148  2.564983  2.575355  2.584960  2.590648  \n",
       "2      2.543029  2.550030  2.559602  2.567279  2.577482  2.586835  2.592049  \n",
       "3      2.531397  2.538591  2.548854  2.556803  2.567315  2.576897  2.581881  \n",
       "4      2.531381  2.538378  2.548594  2.556320  2.566689  2.576101  2.580933  \n",
       "...         ...       ...       ...       ...       ...       ...       ...  \n",
       "16655  0.016506  0.006371  0.000000  0.003159  0.016052  0.032239  0.057015  \n",
       "16656  0.032093  0.016858  0.003159  0.000000  0.005819  0.016700  0.036014  \n",
       "16657  0.060708  0.038613  0.016052  0.005819  0.000000  0.003486  0.015160  \n",
       "16658  0.087741  0.061063  0.032239  0.016700  0.003486  0.000000  0.004840  \n",
       "16659  0.122693  0.091644  0.057015  0.036014  0.015160  0.004840  0.000000  \n",
       "\n",
       "[16660 rows x 16660 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_matrix = distance_matrix_tavg + distance_matrix_prcp\n",
    "distance_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f21b09",
   "metadata": {},
   "source": [
    "### condense the distance matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6f7e09ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(138769470,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condensed_dist_matrix = squareform(distance_matrix)\n",
    "condensed_dist_matrix.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8f8d578",
   "metadata": {},
   "source": [
    "### calculate the linkage matrix with ward's method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2d9dd8ad",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min, sys: 7.33 s, total: 1min 7s\n",
      "Wall time: 1min 8s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "def hierarchical_clustering(dist_mat, method='ward'):\n",
    "    if method == 'complete':\n",
    "        Z = complete(dist_mat)\n",
    "    if method == 'single':\n",
    "        Z = single(dist_mat)\n",
    "    if method == 'average':\n",
    "        Z = average(dist_mat)\n",
    "    if method == 'ward':\n",
    "        Z = ward(dist_mat)\n",
    "    \n",
    "    fig = plt.figure(figsize=(16, 8))\n",
    "    dn = dendrogram(Z)\n",
    "    plt.title(f\"Dendrogram for {method}-linkage with correlation distance\")\n",
    "    plt.show()\n",
    "    \n",
    "    return Z\n",
    "\n",
    "#linkage_matrix = hierarchical_clustering(distance_matrix)\n",
    "linkage_matrix = hierarchical_clustering(condensed_dist_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a711152",
   "metadata": {},
   "source": [
    "### pickle the linkage matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0472a08a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump( linkage_matrix, open( \"linkage_matrix_tavg_prcp_sum_post1950_pub.pkl\", \"wb\" ) )\n",
    "\n",
    "#with open(\"linkage_matrix_tavg_prcp_sum_post1950_pub.pkl\", 'rb') as pickle_file:\n",
    "#    linkage_matrix = pickle.load(pickle_file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66c6952e",
   "metadata": {},
   "source": [
    "### plot pdf version of dendrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "47c799b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "\n",
    "#dn = dendrogram(linkage_matrix)\n",
    "dn = dendrogram(linkage_matrix, 5, truncate_mode='level')\n",
    "\n",
    "plt.tick_params(\n",
    "    axis='x',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    labelbottom=False) # labels along the bottom edge are off\n",
    "\n",
    "plt.tick_params(axis='y', labelsize=14)\n",
    "plt.ylabel('distance', fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "fig.savefig('dendrogram.pdf', bbox_inches='tight', transparent=False, facecolor='white', dpi=250)\n",
    "\n",
    "plt.close('all')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0010fcd9",
   "metadata": {},
   "source": [
    "### now establish the actual clusters, either with a number of clusters, or by using a cutoff on the linkage matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9d3cb140",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10 11]\n"
     ]
    }
   ],
   "source": [
    "# select maximum number of clusters\n",
    "nclusters=11\n",
    "cluster_labels = fcluster(linkage_matrix, nclusters, criterion='maxclust')\n",
    "\n",
    "### or use a cutoff value\n",
    "### for example, a cutoff value of 27.5 (this could be visualized from the dendrogram)\n",
    "#cluster_labels = fcluster(linkage_matrix, 27.5, criterion='distance')\n",
    "\n",
    "print(np.unique(cluster_labels))\n",
    "nclusters = len(np.unique(cluster_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d464bb8",
   "metadata": {},
   "source": [
    "### now reshape the cluster labels back to the original shape - assigning grid points to each cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f4f9c37a",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters = cluster_labels.reshape(prcp_anom.prcp.shape[0],prcp_anom.prcp.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "121640be",
   "metadata": {},
   "source": [
    "### and write those back into a variable in the original xr dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fee540ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "prcp_anom[\"clusters_tavg_prcp_sum\"]=(['lat', 'lon'],  clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb3b027e",
   "metadata": {},
   "source": [
    "### make a map of the clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "92c35958",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rschumac/mambaforge/envs/ats641_2023/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n",
      "  return lib.buffer(\n",
      "/Users/rschumac/mambaforge/envs/ats641_2023/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n",
      "  return lib.buffer(\n",
      "/Users/rschumac/mambaforge/envs/ats641_2023/lib/python3.11/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects\n",
      "  return lib.intersects(a, b, **kwargs)\n",
      "/Users/rschumac/mambaforge/envs/ats641_2023/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n",
      "  return lib.buffer(\n",
      "/Users/rschumac/mambaforge/envs/ats641_2023/lib/python3.11/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects\n",
      "  return lib.intersects(a, b, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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DDzwQmRcMBrlhGG328dOf/pQD4B9//HFk3rnnnstHjx7d4bH9fj+vqKjgU6ZMabPf8PHDvxGllPfr148XFhby48ePR9YLBAJ8/PjxHEDUd9YeN954IwfAX3vttaj5DzzwAAfAH3zwwci88Hkybtw4HgwGI/OPHDnCNU3j1113XafHO3DgQLu2/fvf/+YA+J/+9Keo+YZh8PHjx/OBAwdyxljUfjweD//yyy8j61ZXV3On08klSeKPPfZY1H4WLlzIFUXhTU1NkXnf+MY3OAC+cOHCyL4553zDhg1ckiQ+a9asqHkA+E9+8pM2tl955ZU8Pz+fNzY2RuYB4AD4ihUr2qzv8/mijhfm61//OieE8GPHjkXmrVq1igPgzz77bJv1E7lOO7IpkXOvI5qbm9vM27FjB9c0jd96661t7APA//GPf0TN//73v88B8J07d0bmXX/99RwAf/vtt6PWve222ziANp8zFuHvMS8vL6qdCQaDfMKECVyWZX7w4MHI/GnTpnEA/Oc//3mbfSXSXt16660cAH/66afb7KdlOxzrtwzPmzhxYtQ1d/jwYe7xePiwYcNinkddwePxxPU9cs75//zP/8R9ToRhjPExY8Zwh8PBt23b1mZ5y+9i2rRpfMCAAVHLY53PnMe+PoqLi/n8+fM7tam9fR47dow7HI6Ybdodd9zBCSF83759UfsBwJ955pk268drSyxWrlzJAfDf/va3bZY99dRTHAD/8Y9/HDX/5ZdfjlznLYl1bXq9Xj58+HA+atSoqPkdtSter7fNPNM0+bRp03hBQQHXdb3TzxW+tu68886o+a+88goHwL/zne9E5j399NMcAL/rrrui1v3ggw84AD579uzIvIULF/KCgoKY9+WWhNv97nDZZZe1u4/a2louyzI/99xz21yfd955JwfAzz777Kj58+fP57/85S/5P//5T/6Pf/yD33XXXdzhcPDCwsKo66WmpoYD4Ndee23MY0+YMIGXl5e3mf/+++9zAPzXv/51oh+Vd6kL/oUXXkBhYSF++tOftlkWjxs5XsIu5pdffjnurrqW1NfX46233sLll18Op9OJmpqayDRw4EAMHTo0yhsS5s4774Qsy0m1BQAKCgrauLa///3vo6CgIKq7QtO0SMwWpRR1dXWoqanBrFmzAEQ/4RQVFeHIkSMdlkJYvnw5Tp06hZtvvhn19fVR38P8+fMBIPI9bNq0CYcPH8aiRYvQq1evyD4cDkckJqQzGGN44403cPbZZ+PKK6+MWnbPPfcgLy8Pr7zySpvtvv/970PTtMj/ffr0wfDhw7F37964jtseL7zwAjweD6666qqoz15fX48rrrgCBw8ebHOMq666Cv369Yv8X1ZWhuHDh4MQgltuuSVq3SlTpoBSGrO7+kc/+lFU99v48eMxe/ZsrFy5Eo2NjQCAF198EYDljWppX01NDRYsWICmpqZIGEiYcePGYebMmW2O53K5IsfTdR2nT59GTU0N5s6dC8YYNmzYkMA3lxixbErk3OuIcO8A5xyNjY2oqalBeXk5RowYEfOJv6qqCtdee23UvLBt4a44xhiWLFmCsWPH4tJLL41a97/+67/i/NRnuPHGG6O6njVNw9133w3TNLFkyZI267e+nhJprxhjeOmllzBy5Eh8+9vfbrPveNvhu+++O+qa69u3L2688Ubs3bs3odCidLJ582Zs374dixYtitkTlex70rZt27B169Yubf+vf/0LwWAQ3/zmN9tc61dccQUYY1ixYkXUNqWlpVE9Csmwpbq6GgBQUlLSZtnrr78OSZLwox/9KGr+1VdfjeHDh7dZP3xtAlaXdm1tLXw+H2bOnImdO3dG2rnOaOnVCwQCqK2txenTpzFnzhw0NjZi165dce0HAH7yk59E/b9w4UKMGDECr732WmTeq6++CkmS2uiYiy++GDNnzsSKFSsithcVFcHr9eLNN9/scsZ3MigpKcGiRYvw2WefYdGiRdiyZQv27duHRx55JBKq0Lrn5q233sLPfvYzXHPNNbj22mvx6KOP4t1330VTUxPuvvvuyHrh7doLh3E6nTF7hUpLSwEgZohQZ3Tpyty7dy9GjBgBp9PZlc3j5vbbb8f48eNx2223oaSkBPPmzcPvf//7NjGT7bFnzx4wxvDcc8+hvLy8zbR79+6Y+2rdnZcMWwBEYhtb4nA4MHjwYOzfvz9q/pNPPolzzjkHDocDJSUlKC8vj8Qp1dXVRdZ76KGH4Ha7MWXKFFRVVeGGG27A3//+dwSDwcg6O3fuBAB897vfbfMdjBw5EgAinyNsR6xug3gzVKurq9HU1BTzZuByuTBkyJCYMXaDBw9uM6+0tBS1tbVxHbc9du7cCa/Xi169erX5/OGSGa1/x9ZB3ABQXFyM3r17t/kNi4uLASCmne19j4wxHDhwIGJfeH5r+8LiorV9sc5RwHpgeeCBByJxiKWlpSgvL8dNN90EIPrcSTaxbErk3OuITZs24fLLL4/Ec4b3sXXr1pifqb1zCTjzO506dQrNzc0xf6Oqqqo2oTmd0dE10/r6Li8vb7P/RNqrmpoa1NXV4ZxzzulWXcZEbM5Uwg+PY8eO7fFj/f73v0d9fT3OOeccDB48GN/+9rfx6quvgjEW1/bh62Hu3Lltft/Zs2cDaHs9DB48OKaI7o4t4XMmlpjav38/KisrY4rTWOfLqVOn8L3vfQ+VlZXweDwoKytDeXl5JKY13oz35uZm/PCHP0T//v3hcrki+wmHMcTbdhUVFUU5T1raXlNTE4lV/OKLL1BZWRlpF1py9tlngzEWcSrcd999GDRoEBYuXIjy8nIsXLgwEtIT72c7ceJE1KTrelzbtuYPf/gDvvOd7+CFF17AuHHjMGzYMDzyyCORWq4FBQWd7mPatGmYOnUqVq5cGckVCT8AtNQOLfH7/TG7/sPnUFfaoZRnwXdkZGvPYklJCT755BN88MEHWL58OdauXYsf/OAH+PnPf47XX3+90yLR4S/m+uuvx7e+9a2Y67SOdQBix1d01xag/c/OOY9a9tvf/hY//OEPMWfOHNxxxx2oqqqCpmk4evQoFi1aFNXATJo0Cfv27cOyZcuwatUqrFq1Cv/3f/+HX/7yl3j//fdRWVkZ+R5+/etftxsLFY6L6czWeOjsCbG95a29zvHuLx57SkpK8I9//KPddc4666y4bGlvfvg48doDtL0JLF26tN1yKK3FfKxzFLA8Wo8//ji+9rWv4b777kNFRQVUVcWmTZvw4x//OO4bZSLXaUc2deXca82XX36JqVOnorCwED/72c8wYsQIeDweSJKEu+66KxIT15JEfqdkFdaOtZ/2GueOvqt42qtkeWESsTlT6e530d7njHWeh3tM3n77baxevRorV67EM888g0mTJmHVqlUx7yexbH322WfbTdRq/fDU3rXeHVvKy8sBtC/q4v3tGWOYPXs2du3ahTvuuAMTJkxAYWEhZFnGs88+ixdffDHuNuf666/HW2+9he9973uYOnUqSkpKoCgKli5dikcffbTbbVd77W5H64YZMmQItm/fjlWrVuG9997DmjVr8B//8R/4xS9+gRUrVnTqnPnNb37TpjzTqlWrujTQhcvlwtNPP41HHnkEO3bsgKqqGDduXKRnJ/xg3xkDBw7E6tWrUVdXB5fLheLiYrhcrnYz8I8ePRrznD19+jSAM+dUInRJgA4fPhy7d+9GIBBI2Asafqo6ffp01IgUgUAAx48fx9ChQ6PWJ4Rg6tSpmDp1KgDrCXL8+PH4xS9+gTVr1gBo/4QbOnQoJElCMBiMdF93h3hs6Yj9+/dD1/WoLq9gMIgDBw5EeY/+/ve/Y+DAgXj77bejnnzfeeedmPv1eDxYuHAhFi5cCMDKCv/mN7+JJ598Evfff3+k28Ttdnf6PYSDzHfs2NFmWax5saioqEB+fn7MLrxAIIAvvvgi7oskXjpqMMPna7hxTCU7d+7EBRdc0GYeISRy/g8fPhzvvPMO+vbtG8lw7Sp///vfMXXq1KgsaAAxM0A7+s4SvU7bI5Fzrz1effVVeL1eLFmyJJK5H6a2trbLGdQVFRXIy8uLeV4fO3Ys4azOWPsJe7xieWRbk0h7VV5ejuLiYmzZsqXNA2yiNrdMhkvU5kxgxIgRANDl8k0lJSWRm2hL2quEUFxcjBtuuCGSXHP//fdj8eLFeOmllyIF3dv7PcLXQ2lpaVLuSfHYEosxY8ZAkqSY7cKQIUPw9ttv4/Tp0228oOFzI8zWrVvx+eef4+c//3kbgfWXv/ylzb7b+17C4Sc33XRTm2oA7WXft0ddXR1OnDjRxgu6a9culJWVRTyE4c9ZW1vbxgu6ffv2qDYasEJq5s6di7lz5wKwkqZmzJiBhx9+OCr7PRY333xzVPkioPse++Li4qjk3HAtznBoU2fs3bsXqqpGPjshBOPHj8fGjRvh9/ujHmAOHjyI48eP44orrmizn/A51NqJEw9d6oK/8cYb0dDQgAceeKDNss6eRsMXYOuTKtYTTk1NTczt8/PzoxqMcE201k9zpaWlmD9/Pl5//fWYGdyc80gsTGfEa0tHNDY24sknn4ya9+STT6KxsTEiHgHLeyNJUtR3SSnFr3/967jsCnuawnbNnTsXFRUVeOSRR2Ku7/f70dTUBAA477zz0K9fP/z1r3+NKlkTDAbxu9/9Lq7PSQjBggULsHXrVrz55ptRyx577DE0NzdHMtWTRXvnAGBlwHPOce+998Y8PxMJo0iURx55JOqYmzZtwnvvvYeZM2dGGsJwfbb/+q//iul1SSS2RpblNp/R6/Xi0UcfbbNuR99ZItdpRyRy7rVH2JvZ+nM9/fTTMcsqxUv4PN2yZUubh7v2Cjp3xAsvvBDlPdB1HY8++ihkWY7ZcLcmkfaKEILrr78eu3fvxv/+7//GXDceHn300aiuwCNHjuDFF1/E8OHD46rskQmMHTsWY8aMwV//+teYD73x3JN27dqFo0ePRuYFg0E88cQTUeuZphmzO/m8884DgDb3pFjX1TXXXAOHw4HFixfHjKdraGhotwu0q7bEory8HKNHj46Z1XzVVVeBc45HHnkkav6//vWvNuWJ2rs2t23b1qYME9B+m9Pefo4fPx5TyHZG63vlq6++it27d+Oqq66KzFu4cCE453jooYei1l23bl1kpKFwGx2r7Tr33HNBCIlLiwwePBizZs2KmsKhW8ngwIEDePjhhzF8+HBcc801kfnV1dUx2+s33ngDH374IebMmRP1AH/DDTfA7/fjj3/8Y9T64Xt/rFqi69evByGkjcCOhy55QO+8804sWbIEDz74IDZs2IA5c+bA6XRi+/bt2L17d4dPLLNmzcLIkSPx85//HLW1tRg0aBA++OADrF+/PlKWIcx3v/tdHDlyBHPmzMGAAQMQDAbx8ssv49SpU/jP//zPyHqTJk0CYJXxuf766+FwODBp0iQMGjQIf/zjHzF58mTMmDEDN910E8477zwwxvDFF1/g9ddfx8033xzX0Fnx2tIRQ4YMwf33349t27ZFnjSeeeYZjBw5EnfddVdkvauvvhr33nsv5s2bh6985StobGzEiy++GLN7dtSoUbjgggswceJE9OnTBydPnsTTTz8NRVEiBYbdbjf+9re/4aqrrsLIkSPxrW99C8OGDUN9fT127dqFV155Ba+++iqmT58OWZbx+9//HldffTUmTpyI733ve/B4PHjhhRcS6up66KGH8N577+GrX/1qpAzT+vXr8be//Q1jx47FnXfeGfe+4qG0tBRDhgzBSy+9hKFDh6K8vBwVFRWYOXNmpPTSH//4R2zevBlXXHEFysrKcOTIEaxbtw779u1LqO5jIhw6dAhz587FggULcPz4cTz++ONwuVz47W9/G1lnwoQJuP/++/GLX/wC48aNw7XXXouqqiocP34cGzduxNKlS+OOF7r66qvx1FNP4Wtf+xpmzZqFkydP4plnnokZ5zR69Gjk5eXhySefhMfjQUFBAQYNGoRJkyYldJ12RCLnXnvMmzcPbrcbN910E26//XYUFxfjww8/xNKlSzFkyJAuJwUCVhmzd955BwsXLsRtt90WKcO0YcOGhD4nYAmZSZMm4ZZbbkF+fj5efPFFfPrpp/jZz34W9yhEibRXDzzwAFauXInvfve7WL58OSZPngzOOT777DNQSqNKwbQHpRRTpkzB9ddfj6amJvzpT3+C3+/HH/7whyhvzuLFi3H//ffj2WefjdSK7YjHH388IpAMw8ChQ4ciDosBAwZEYpIBq3TYG2+8AQAR4f38889HEitvuummDr8/SZLw7LPP4pJLLsHEiRPx7W9/G2eddRbq6+uxZs0aXHrppfh//+//tbv97bffHhld5pZbboGu63j++efbdH03NTWhd+/eWLBgAcaNG4fKykocOnQIf/rTn5CXlxf1UD1p0iQ888wzWLx4MUaMGAFJknDdddehb9+++OMf/4jvfOc7GDVqFG6++WYMGDAA1dXV2Lp1K1577TXs2LGj0/HqE7GlPa655hr893//N44fPx5VC3TRokX4y1/+gocffhiHDh3C1KlTsWvXLvzlL3/B2WefHZX0NGrUKIwZMwaPPPIIfD4fRowYgT179uCpp57CWWedhU2bNkUds6N79Zw5c/D3v/8dLpcLEyZMwKFDh/DUU09h0KBBCeUBlJWV4ZVXXsGxY8cwffr0SBmmysrKKC/tokWL8Pzzz+O3v/0tDh48iJkzZ0bKMBUUFOCxxx6LrDtnzhwUFhZi6tSp6NevHxoaGvC3v/0NjDHcfPPNUZ8vXDZv3rx5UFUVM2fOREVFRYc2L1myBFu2bAFwxqPY0sHXMlHqqaeewptvvokpU6agrKwMu3btitzzX3755ShB+cILL+Cxxx7DV7/6VQwaNAicc6xfvx4vvfQSKioqoj4jYBWdf+655/CjH/0IBw8exNixY7F27Vr87W9/w0033RQp1xeGc4633347UqM2YRLOmw/h9/v5Aw88wEePHh1J6T///PP5E088EVknVhkmzjnfvXs3nzt3Lne5XLywsJBfc801/MiRI21KV/z73//mV1xxBe/Tpw/XNI2XlZXxyZMn8xdffLGNPQ8++CDv378/l2W5TemM6upq/sMf/pAPGzYsYutZZ53F77jjDr59+/bIeh2ViEjElliEP9vGjRv5jBkzuNvt5kVFRfzrX/86P3HiRNS6lFL+q1/9ig8ZMoRrmsb79+/P//M//5Pv2LGjTamhhx56iE+ZMoWXl5dzVVV5nz59+MKFC/m6deva2LB161Z+44038qqqKq6qKq+oqOAXXngh/+Uvf8lra2uj1l2yZAk/77zzuMPh4JWVlfz222/n27Zti7sME+ecHzx4kN988828srKSq6rK+/fvz++++25eX18ftV575wnnscumtMdHH33EJ02axJ1OZ8wSOn/729/45MmTeX5+Pnc4HHzAgAF84cKF/KWXXoqs01E5p/ZsiWV/uBzHqVOn+Ne//nVeUlLCXS4XnzFjBt+wYUNM+998800+Z84cXlxczDVN43379uWXXnopf/LJJ6PWA8C/8Y1vxNyH1+vlP/zhD3n//v25w+HgQ4cO5Q899BB/7733YpZceuONN/g555zDNU1rs994r9PObOI8sXMvFmvWrOEXX3wxz8vL44WFhXz+/Pl869at3S6rw7lVzmn+/Pnc4/HwgoICvmDBAr5///5299PRfn//+9/zoUOHck3T+NChQ9uU7OK883M63vaKc87r6ur4f/7nf/IhQ4ZwVVV5SUkJnzx5clQJqo7KMG3bto3ffvvtvLKykjscDj5hwgS+bNmyNjbdc889HEDMZbEIlw+KNbX+TsPXT3tTvCWZdu3axW+88cZIe9O7d29+5ZVX8o0bN0bWae+7f+655/jw4cO5qqp84MCB/OGHH+YrVqyIOl+CwSD/yU9+widOnMhLSkoibfNNN93Ed+zYEbW/48eP8yuvvJIXFhbGLF30wQcf8KuuuirSbvfu3ZtPnz6d/+Y3v+F+vz/qe4x1DiZiS3scPXqUK4rCf/Ob37RZVldXx//jP/6Dl5WVcafTySdNmsSXL18es8zQwYMH+dVXX83Lysq4y+XiEyZM4K+88kq799P27tXV1dX829/+Nu/duzd3OBz8rLPO4n/+8587vD+0Jvz77t+/ny9YsIDn5+fzvLw8vmDBAr53794263u9Xv5f//VfkeuntLSUX3vttXzXrl1R6z399NN89uzZvFevXlzTNF5ZWcnnzJnD33nnnaj1KKX8rrvu4r169eKEkLjtDn+v7U0tWbt2LZ82bRovKyuL/O633norP3r0aJv9fvDBB3zBggW8f//+3OVyRe4Jd9xxR1RJvpbU19fzO++8k1dVVUXasQcffDBmCapw27dkyZJOP2MsJM7TWFNAILApixYtwl//+te0luwQpIZwLFi83sFMIOzRPHDgQKfeNsDq2s3Pz48r1l2QPdxyyy1YtmwZdu/enbLx4HuS6dOn4+DBgzFL4gmSz5VXXomjR4/i008/7VIcevIKpAkEAoHAdpw6dQpbtmyJChsR2INf/vKXqK2tjQwzLBDEy8aNG7FkyRI8+uijXU6CTHkZJoFAIBBkDxUVFTBNM91mCHqAioqKLo3hLRCMHz8+oYTUWAgPqEAgEAgEAoEgpYgYUIFAIBAIBAJBShEeUIFAIBAIBAJBShECVCAQCAQCgUCQUuJOQgoEAnEXwxYIBAKBQCAQ5B4OhyOuIZLjEqCBQAADB/bHyZPxDVspEAgEAoFAIMhNAoFApyI0LgGq6zpOnqzGtm1rkZ+flxTjBLnFyy8vxzXXzE63GYIs4t//Xo4FC2ZCVeV0m5IS3nnnA4wbNwq9erUdNjXTYYzjzTfXoG/fSlhprRyctxzb23rlnEctb7tex4RXk6Qz78PEO68lN9zwLUyaNB7//OfTcR0/07FzO9vU5MPKlR/jyitnpNuUbpFtv1Gi9jY1NeOss6YiGAwmR4CGyc/PQ0FBfiKbCAQAAJfLKc4dQUK4XC4UFuZBUXKjXLHT6UJ+vidrr5Orr54DxjgkCZHC1Nar1GoeYsyLfk0VkiSBMZa133lr7NzOapoDmubI+s/ncjmRl+cBIdmRgtOT51RutOwCgSDryMUCcZKUHTelWDidncd8ZRphASrIfDRNscWACLIsQ9d1OJ3OdJuSdrK3tRMIBAIbwTlHih2AAgCUZr+oyQWyxWPYGYoiIxAw0m1GRmCPX1QgEAgEggSRJAmU0nSbIcghFEVGMJg9FYUkSeoxz7MQoAKBQJABcM5BiHCBphLr5iq64AWpQ1GUrBKglse2Z+wVAlQgEGQsdul2ix8hQFOJFQNqny544dHNfFRVQTCYPV3wQoAKBIKcI97SPHYhxz5uxmCHxJYwhBAR05rhqKoCXc8eAWoJ5mCP7FsIUIFAIMgIRBd8qrE8oPZR/oQQGIYQoJmMqiowjOwRoFbIQM/YKwSoQCAQZACWB1QI0FRitzJMskxAafaIm1xE07LRAyq64AUCgcDW5FzIa5qRJNhKgBIi2bYLnjFmi7AcTVOh69kTp6tpao8JUFGIXiAQZCy5lYTEITygqcZeXfCyLMMwEhc3jDEYBo1Mum6AUhOUtnxvRua1/N80TZgmg2mG/2dgjEXmJ0s0SpIEny+QlH2lE1VVYRhN6TYjbiyPbcfnVPj8CQaDqK6ui3vfQoAKehw7eRgEgp6C89QPRZnrECKBc/u0T6ZpYtmyddA0NeFtZZmAEAJZliHL0a+KIodeCRRFhqapcLudUBQZqqpAUaxJVa3/rUmFpimQZTlp5/WLLy5Nyn7SiaapXXpISBTGGCilCAR0BIM6/H4dwWAQum5A1w0Eg9brmQcPA4Zx5gEjTDCogzGOvXsPdXg8QghOn25ASUn8w3YKASrocXTdOrm9Xh88Hne6zREIMhahP1ONvWJANU3FzJkT0b9/73SbImgHq0s7iLq6RgQCOnTdEoiBgBEShzoMwwjdN00YxhlvdKI1awkhkYeElpOmqZGHCE1T4XQ64HCocDo1OBwOOJ1al3qfXnxxKebPnxL3+kKACnocTVNQUVGK5cvX46qrZqbbHIEgI7FDfFu2QYi9uuDFYAaZxbJl63DiRA0URY7MC1/nq1Z9EvIaR4tCp1NDQUFe5L3DocHp1OB0OqCqiq3CkoQAFfQ4hBCcffYwbN68K92mCAQZjSTZ5+aSHdirC55zbvtzKNwNf8MN89NsSecEAkHMmXMRevUqTbcpGYkQoIKUEAwaUBRxugkEgszByoLPTA8oY+FknpaTGUn2CcfwWa86dN1ATU29rcM4wqIzW2JBFUWBrmfPsJupRigCQUrQdR2qKk43u/OnP72MggJPh+tYyTad78vv75nRNzIVkYSUeiSJwDAoXnttJRjjkVI/jHFwztIuTiVJAiFS6JVEvT+T9GMl+2iaCsYYevUqS6vNqYIxlvHd0YoiZ1XJpVQjFIEgJRgGFQI0Bygo8GRF11hmwm3tvcpEJEmCJAEXX3xuKNObhCYlJPJIxoucluzd+2VW2dtVZNl6cHA4tHSb0iGKImfVqEfdRZKkhIa2FYpAkBJ03ehSaRCBIJcQAjS1hJOQysuL022KIAGsMe8zX4Bm27jv3UVRFPj98Ycc2P9RSZAR6LohPKACQQfkQgJJpmGFPGRmDKigfSwPaOaP+GSN+547XfCqKiMYjD90SrR2gpRgdcELD6hAIMgcJEkS5a+yEEJIVgi73BOgiY0bLwSoICUYBoWmCQ+oQNAenIux4FONEKDZiSxbXfCZjjXsZubbmSxUVUUgEH/IgWjuBCkhGwLGBYL0wiGa5NRiCdB0WyFIFGvM+8zvgtc0JWpYS7tjjRsvuuAFGYYVMC664AWC9oi3PJUgeYgY0OyEEJJQtnW6yDUPqKapCZWdEgJUkBIoNYUH1ObYaUztdCEEaGohRHTBZyOKkh0xoA5HrnlAVQSDogtekGEIAWp/rMLQQkF1HdEFn2okiQgBmoUQImeNBzQbYlWThaZpCdU9Fa2dICVQasLpFALUzpgmEyP5dAORhJR6LA9ouq0QJIqiyFnhWXQ4tKwQysnC6RRd8IIMhHMuxoK3OUKACrINkQWfnVhZ8Jkv7DRNzQo7k4XDoSUUGiEEqEAgSAqMCQHaHTjnOTGMYiYhkpCyE1nODg+opikwzdyJjXc4HAmFHIjWTiAQJAUhQAXZhuiCz04UJTtiQBVFySkPu9OZmAdU9IkKBIKkYJoiCam7CAGfWqyhT3NHINgFWZYTGnEnWTDGYBgUwaABwzAir7pugFITum7AMExQSmEYNPSaO2PBu1yJeUCFABUIBElBeEC7j/j+UosoRJ+dKArB6dON2LPnEAzDEn1nBB8FpZYIpJSBUgrTNEEpg2ma3S4XJ8sEsiyHJgJVVSDLMhTFmlRVgaoqcDodUFUFR46cStKnznxUVYFpxn9BCQEqEAiSghCggmxD1AHNTiSJoLa2Hl9+eQyybAk+RZGhaSo8HldIBKrQNGuZpqnQNA0OhwJFUVIaa71ly+6UHSvdJPq9CgEqEAiSAmMiiUaQXVjnqxCg2YbDoaKqqhyzZl2YblME3UDcLQQCQVIQZZgE2QYhRHTBZyFWeaPcyS63K0KACgSCpCC64AXZhiVAhQLNNqxYw8zPghd0jBCgAoEgKYihOAXZhh1DRrqbZJMNqKoqBKgNsN/VJxAI0gJjXHhABVmFLNvLAyrLJKE6jNlKrhV4tytCgAoEgqRg1QEVTYogeyBETrcJSYWQ3BCgwgNqD8TdQtDj5EKXkABgzBQeUEFWYbcHpmwZI727CA+oPbDX1SfISHSd2q6hF7TFGstcCFBB9mC3Lvhc8YDm2hCXdkWoAkGPo+s6FMVeXV2Ctlh1QIUAFWQPsmyvdokQktBQiAJBOhECVNDjBINCgOYCpslDY2sLBNmBLNvrfJVlGYZh/y74bEOEocXGXlefICMJBnWoqhh0y+5wziAiLQTZhN26cmWZiOScDEOWZeh67nilFSX+m4C4XQh6nGDQgKIIAWp3rEL0okkRZA92i023kpByR+xkA4oiIxgMptuMlJHIvd5eV58gI9F1Q3hAcwCrEL1oUgTZgx274HMhCz6bkGUCXTfSbUbKSORen5AquOeen6GsrBTFxUUoLS1GaWkJKivLUF5ehsrKchQU5CdsrMD+CA9obiBGQhJkG3Z7YLK64EW8YSahqgqCwdwRoEOH9ot73YRUwb///Van60gSIEkEiiJDURRomgaHQ4Pb7YLb7YLH40FengcFBXkoLCxAQUF+SNAWoaSkGGVlpSFRW4q8vLxEzOsWu3bVRP0/cmRZyo5tdwzDgKYJAWp3rCx4e93QBfbGbiEjlgdUdMFnEoqSawK0f9zrJqQK9u37GIFAECdOnEJ1dS1qa0/j1KkaVFfXoqamDnV1DWhsbEJzczN8Pj/8/gB0XUdzsw/19Y0wTROMsYQzwgghIIRAlmWoqgJVVaFpGlwuJ1wuJ9xuN/LzPSgoyEd+fh7y8z0hr2wZSkqKUVxchPLyEpSXlyEvzxPXTbK1IE0muSZuDYPC6dTSbYaghxFDcQqyDbt1wSuKDEqFBzSTUFUFgYCebjMykoQEqKqqKC0tQZ8+vbt9YMYYmpu9OHmyGjU1p1FdXYvTp+tx+vRp1Nc3oqGhEQ0NTWhoaEBTkxderw8+nx+BQBC6rqOpqRl1dfURUZtoJqMkSSFRawlbQqwnx/z8fLhcLjidTjidTng8HrjdbhQUFCAvLy8yFRQUoLCwEAUFBSguLkZhYSGcTmdcx+5JcduSTBG6hmGgoMCTbjMEPYyVBW+vG7rA3tjtfLW64O3vAV2//vOsiassKPCgsbEp3WZkJGnrFyWEoKAgHwUF+Rg2bHBS9skYQ11dfcgja3ll6+sbUVdXj7q6ejQ1NUem5mZL1Hq9Pvj9ATQ2NiMYDMI0TTQ0NEQ8tV0p0REWt2GvreW5VSOeW8t7a4lct9sNt9uNvLw8uN1ueDweeDyeiOAtKCiIiN28vLyEYilTJXTDtCd4DYNC09SU2iJIPYzxhEpwCATpxo4eUJ8vO4RZdzh48Biuv35eus2Ii+LiAuzbdzjdZsSNYVAEAjoCgSCCQR3BoAFdP/Oq6xS6bsAwKCiloVcTlFrOQL8/EPexbBWYRwhBaWkJSktLAAxLaNvnnnsTd999N2677TZcd911Ucssb20zGhoaUFdXh4aGBjQ2NqKpqQlNTU3w+Xzwer2RKRAIwO/3IxAIIBgMwjAMGIYREbgtxW1Xa9DFErmKokBRlIjYdTgccDgccDqdcLlcEYHrcrkint2w4A17dj0eD/Lz81FQUJBw4lB7gre21genMwDGel4QZ4rXNxcRSUiCbMNuISPBoAFNs3+4k1Vwn8Lh6NpnPX7BFUm2qH1Mjwe1Eyfi+C8eAgD0Xr8k4X3oOm0hCPUoYajrRpQoDAvDlqIwESRJCmmJcMjjmUnTVHg8LhQXF8LhUOFwqHA6tZDOUKEoCpqbvfjhD38c17FsJUC7Q11dHQAgP79tJr/lrbU8kf36xZ/hFS+MMTQ1NaGxsRH19fVobGxEc3MzGhsb4fV6IwLX7/fD5/PB7/dHBK71ZHJG5AYCAXi93jZCtztIkhSZWopeQkhE9LYUvlbimSMyUUpRXFyMgoKCKMEbfg17gMPiV9O0LneNJdvrW1b2URL3tSBp+8pErBhQe3mUUgkhEgIBXcRLpxC7CdCmJi8GDKhKtxkAelbk0enTceypZ5Df3Nxjx2gPxhiaGUNTaPIyE40caJQkNEoSmgE0SxJ84PAyBr/PC/PzLZCnTQWVFcgvvR0aNS7+YxIiQZYVqKocuteeEYSqqiAvzwVVLYTTqYaEoQMOhwanU4OqKhl7ngsBGiIsQAsKClJ+bEIICgsLUVhY2CMCN0zYkxsWu83NzZGptcht7cUNBoOhpy09InatpywKXddhmiZM0wTnPCmiN0xL8dtSALcWwS1DHVqL4fDU0iPscDjgcrkinuGwVzgshMPe4YICmrQM/pqaN5Kyn0yhtaDmXGTBdwdFUaDrQoCmkkw6X5Mh2GqnTUPfzZtxvKEhCRZlBowx+BizBJ3J4GUM+44cQaPXC8nngxccXg74iAQ/B3wS4OccwVaTwRh0xmFwBsNkMDkDZQxmeArduxjnMFv0UDLOAc7RrTva4cO40+dDX0jov/j/iaowIcS3EKIhdMEWFRWl15AepKUnt0+fPik5JmMMgUAgInTDYQphwdvSo9vaq9tS9IaFrxVzckb8Ukrh9/sj3t6w+E2mCG6J9SDZWhRLkCQCQsICuaWnuKVQliHL4RJlcqSLQ1FkaJr1dKuqCpxOBQ6HHHqSVeB2a3A4ZLhcClwuDS6XArdbDf3vCHWDKHA6NTidauhV6fGba2tB7fUGoGkENTU7e/S4mULfvbPxxHP3Yv6W/bj23u43pdNqpuGp5c8goMQfQ5Ut/POhzEyMCRw7CiC1XbI9ie7QkN/UfsKLzhh0xuAHEGAMQcYQ4BxBzhDg1rwAAJ8kwS9JCEhAQJIsEYfQqwQYoX0ZAHTOQRmDwRgoByi3xJwREnU01B6brV5ZC4HHW05A5233l4cS/m6k8CRJIABI6FWWJMgAHABUSYIqSVAkCRqRoUoSNCLBIUnQQpNbInASCU5C4JYIXITATSR4CIGHyHARggJCUCDLyCMEfz19Gn+pOw1eVw+P0ynEZwvENxGiKXTR2lmApgNCSCTRqqKiIm12UEojgre16A0L35aTrus4ePAgqqoogkEairMxoes0FGdjQtetGBvDMCPxNqbJQ95gBtOkMAwG0ww1uCzU4LJwA8uRymGow90wlnAGzghpqx7iGTEtRb1vKaplmYS6gwgUhUCWpVC3EEEwCDidMvLyFMiyFPJIk5D4DgvvM/9b4pxAVa31zwhzKRTTLIeWh1/PiPfw8S0RL0deCSERe2K/b7mvM950QfZCGQMFYAIwW77nDAYHGGCJIwCUc5gIiySgNjRu+qqmJlDwkICyRBMFIgLKCC8DEFRkGBygkvU/hdTi/ZnJ5JZQY7D2YXIOk1v/myHhZfLo9wwAC/3PWoizyPvQct6OaON790S8dake4z4s8BASdhLOiDwSEnkyJKgAZAlQJAkKAIUQS/jBEn5qSOipkiX8HBERSHB8yGCMPHgQzpAIdIVe3RKBm1iThxB45JAYRPq93AWyDADwMjFCVWuEAA0RFqDFxcVptkTQEyiKEglziJdPPlmO+fNTVz7KCjTX4fcb8PmsIHO/X0cgYGDHDh+amnQMGiQhEKChoHNLGAeDZ4SxrlNQykL/W+LYNFlELJumCUoZKGWR95ZYtuaFhbJpWq/WewrD4JH/OWcREW1NbQV1qm9+qSA6jOrMPz/iHPybUstZrVdpsY9YM62XXXwXuMTBwSGFZ3YWupWM0K7OfqpYy9vbJvz7t1yBA2cBaP201WYXoeXpOHNuC3lCexqp5askRXnlIgKOWA+DCmMREScD0EKvhPOI104mxHqVJMiQoISEnRwSc4oEqDjj1VMkhMQdgSYhIvY0iUSEngbLu+eQJDgIgVMCHBKBM/S/m1hePydSL+6WXXwx5jR7U3rM7pIX+o6axAhVbRACNITP5wMAuN3uNFsiyFU0TYGmKWgdhvzJJwH06WNi8mQX8vKy21vHGMNrrzVi9mwnDMPyLlteZEs4n/nfhN+vg1IeEdJn3jMYBoVpstD7sKgOe6DPCGrr/Zl5jJ353xLbaOGdPiO6w97qlpNpspBGspbLhhMNTafh0imqCwCEhThv8R4txHhrcc5DQi2suFopr6hlZ2aeEXddVGoKU0AJjSmGOTi4xAEpWkRyyRLFXArNCy+XEDUv8hrWz5IlzIce52fEVkhehxZZIgsSiHRm+RkPmrVMavG/HF4eWiaHtlOiulatbWQJUELrK5J1DCW0XAntz+AceUQOCTdLlMnhrtjwPiQS8tZFe+osz13IiwdADb1q3RBmu0cMR1DTcM7WbV3ehyBzcIfOhWYuBGhrhACFlTkdFqDpdtcLMoNkZr93l9paE5dc4oKmZf+5acUha2BMRUlJfAM3ZCrJjgFNCQyYeWImVlatTOlh/5GhMaCZCGEcXNyHbEPYA+pNsBxSLiDO8hB+vz9jSxUIUksmiU8AKCuTsW+ffYpL9+4t44sv7PN5BIJkQkwTXJQzsw1hAdosuuDbIM7yEMFgUHg/BRknPgHg3HM1HDxoHw/SoEEqTp0SAfm5RNZ4iDMAiTMwm43QlMuEk5D8ogu+DeIsDxEMBiGHThSBIJNQFALGAJ/PHg0YY60TegS5gBCh8SEzBi4uENvgCTm2/KILvg0ZL0C//PI4Dh06hurq02hq8oHSnvEEGYYhBKggY5kwwYGVK/344gsDy5dnVxZoa+rrTWiauMEKBLEgjIGJ3jjbUBgWoKHkQ7vUnE0GGf9I+s47H6Jv316hQuQmDCO+2DFJkjBr1gUoL4+vrJJhGFBVtTumCgQ9RmWlgv79TZw4QREMAqdOUVRUZPzlG5O+fVVs2aLj5EmKysrs/AzZCkmzz+Hae5WMLUqfKRCTgYkYUNsQSUISMaBtyOjWn1KK/HwP5s+fnPC2a9ZswIkTNXELUEopnM7szsoV2JtzznEAAJqbGdas8eOyyxQcOWKgpESG251dN6wZM1xYtsyHCy90ChGaQwjx2TmS6IK3FeHcEvuNb9Z9evyuRSnF8uXrcOTISQBAIKBj3bot2LfvcKfb+nwBaFrXvJKGQeFwxL8tpRQOh6NLxxIIUkleHoFhcCxd6sXOnTqWL/dB17Pr6drtJpgzx42NG4P45JMAmIiPsj1CfMaHzBgYEQI0FhKska2yEZ/4SduQdNfD8ePVWLXq08j/hkExYsRAvPPOh3A6NRBCMHhwX3z88ecYOrRfh/vy+YJQ1a6ZaAlQLe71TdMUHtAcJxMz4NtjxgwXCJGQn09w4ICB997zQ9MkzJplDaSg6wx1dSyjvYtuN8H8+R6sXu3Dvn0Ghg8XD4CpgKdhrCEhPuOHMCbqgLYDMRmookDroVyQnkICELDf4HDdJqG7E2Mczz77GhwODWVlxTh5shayTOB0OjBlynlYvfpTcM4xb95kFBdHD+cyZEg/lJYWRtzR+/YdBmOsw9JHfr+/Wx7QRLZljAkPaA6TTeITAAoLzyTMDRqkYsAAGW+/7QdgncvvvedHMMgxdaoTpaWZK0IBYMwYDdu26Rg+PN2WJMbmYi8umDMPK4tHAbgz3eZkFEJwdh2rDqhwl8WCmCZ0Tcs+ASpJCKZlkNnMJqE70yefbMXQof0xduxwbNu2D7NmzQchBPv3H8GKFesxefJ56Nu3Mua2rWMxKypKsHv3QYwaNbjd4/l8ekJezJZQasLpjF9Qcs7hcrm6dCxBdpNt4jMWhBA4HBLeessLSQIGDFBQUSFjw4YgZs8mGV3jtrSUwOfLvsa5rOwj9K2ajfV19+KfD9GsKDMUGWNekLGILPj2kZkJIwuThYkkIcCzr43raRI6y0+dOo2LLx6HgoI8XHTRuMhNbciQvrjuunntis9YeL0+uFztd3kzxnDkyAk4nV0VoIl1wQsBKsh2Zs1y47LLPJg/34MxYxwoL1dQVCRj7drMDn8nhIAQa8x7SrMrwuvIsOW4bdFDWDp2SFZ4/VIhQLPhe8hkCGNgwgMaE5nSrBWguhCgbUhIgC5YMD1pnhSv14+BA6vazG9s9OL111fh739/C6qqYPz4MV3aP6Umjhw5iSNHTqCurrHDdcMJEB6Pp0vHEmQ3NTUXpduEHmPSJCd8Po7jxzNbFMyd64Kuc+zYkX1DdB4Zttzqip/+RLpN6RwO9KQGFeKz+8hiKM52kU0TtItheemESBIMIUDbkLazvD0hu2bNpxg8uC9uvvkKzJw5EZrWtW6tSZPOxsGDx7Blyx784x/vdrhuU1MTACFABfZE0yRk+hgLhBAMGaJmvFBuD3nMeIybsBO3rPt9uk3plHQkIQnih4gyTO2iUBOGkvmhLq2RhQCNSUY8ZjHGcOzYKezZcxA1NfUYNWpQt/c5ZEg/zJlzIS67bCoKC/M6HEGpvr4egBCguYydvaDBIM+KovW9eysoKCDYsCGzQwZiUVb2EcZXTUWg7nfCCyjoFlYWvBCgsVAoBc3CLnhZkkBFqbk2pE2AlpYW4dVXV8DnC+Dll5fj00+34/Dhkxg7djiUJD/hcM47DB0IC9D8/PykHleQXdhVhLpcEpYu9aKuzky3KZ1y4YUuNDYyrF7tE/GgPYFwwmQ8Yiz49lEozUoPqCJJoMID2oa0/ZJz516EffsO4+WXl2HQoD6YOnV8jx6vIwHa0NAAQAhQQbQIpfSjdkVpNmXOz5zpRn29ifXrA7j00sz38s+c6caePUG8844f8+e7MjqDvzWReNAMLc0ksuAzH5lSIUDbQaEUppJ9HlBVkuAXHtA2pPVRYujQfp0Wo08FYQFaUFDQyZoCQXaJzzBFRTKyqf0bPtwBwwA++CCAqVPd6TYnIeQx4zEOG3HLut/jTxdmlggVAjTzIYAQoO0gUwPBLKzXrUoSmPCAtiF7XAs9SLgLvrCwML2GCAQ9BKUM2db+jRnjQGNjlhkNEQ8qEPQUKqWgWdgFLwRobIQAxRkBWlxc3PGKgpylrOyjyJSNrF4dwPDh2dd15fFIOHIkO0szZXw8aA+QDcX4BdmLqhswlQwv6REDB4QAjYVoLQA0Nlp1QouKitJriCBjYC36q7NVdIb54AM/VBUYNqxrgzqkkwsvdOLdd32oqpKzKhYUyPx4UIEg21AoBU1yTTmTEBiKAqooMFQVhqaCygqoqoKqivVeUUAVGaaiwJSt94zIMBUZJpFhyjKYTNoNnQj+619gx31JtdsOCAEKoLm5GYDoghecgTEGyQZxWNXVFIEAx6xZ2RVHGcbptOqDbtigY+LE9kdOy1QyMR60p+uAXnuvklNeX0HqUAwDNeXleH/qFJiEgMkyGCHdGrpU4hyEMRCTQWYmZEohmwyySSGbJhRqQjYpHLoOxeuDQg0o1IRqGJAphWYYUAwDqmFAaSfQ/qWGRhwUHtA2CAEKwOeznkySXf5JkL0wxkAIyXrvp6JIoDS7G77Ro1W8+abwHiQDkqKoq3i74oVQFSSCputw+v0YvXWbJfgohabrloBMt3Ed4ISogBaLnFBcjDG8+OJSAECfPuWYNm1CZNmuXTXw+Xy28HYJkodpmtC0RgCZX7aoIw4fpigqyr6YqZYQQlBSIuOLL3QMHpx9YQQCgSA5qIYBmTGU1tWl25SEcGWBvtB1ikAgiGBQRzAYRDBoIBAwoOs6dN2ArhswDBo1UUrh9weinHd+f/wDieSEAP361y8HAPh8Abz55po2ywOBgBCggihM0wSxwWgkBw8auPzy7Ox+b8l55zmwbJkPvXopcLsz2dchEAh6Ck3XwTJ9XOEYOEP3kgBjcCYQLkApRSCgIxjUEQjo0HU99L8BwzBCwrClKDRAqQlKzag8hnggRIKiKFAUGaqqQFUVKIoCh0OFqqrIz/fA4VChaRqcTuvV5XLg9ddX4YYb5kf209jYhB/+8MdxHTMnBGgYwzBQV9eEN99cA1VVoaoKmpspfD4f5Cw8qQU9hxUDmm4ruo+q2uBDAHC7CS66yIkVK3y44oq8dJsjEAjSAEF2dmW7QqLzzalTUODxQA31yHaGJElQFDkiChVFgaapIWGowO12oahIgcPhgKapcLk0OBwanE4NiqKkJHGzO867nBKghYX5uPbaOfD5AggEwk8Up6HruhCggigsD2i6reg+2VR8vjMqKhQoigRdZ9A0G/w4AoEgbhiAN76yEIP2f5FuUxLGLVnt1bgVKzHU4UDv+5ek2aLMIKcEKAAUFxeguPjMiEeyXADTNEUCkiAKKwnJHt5DO1FeLmPvXgNjxmTfaCiZQk9nwQu6ByUE3A5Pv0mGAHB7vRi7ZUu6TUkYl2z9nl47eQRCEEKg6zo0LfH4fHGWw4qzEAJU0BJLgKbbiuSg6+m2IHmMG6dh3z4D9fVmuk3JSiQuQYzGmdnsGj0KhXX16TZDkETcoW7qZhsKUFVV4PcHu7StTW6x3YNS2iX1LrAvdhCg69cHUFxM4HRm+QdpgaIQzJjhxvr18WdaCgTZRlF9dmV5Czomj1ghfl5mvwdnVVXg8wkB2mUopXA4RJee4AyMMeh6ZbrN6BbV1RQXXGC/87qggICK8pFdQhLuz4xH4mh3RB1BduIOeTO8pv08oJqmIhAQArTLMMbgcrnSbYYggwgXoq+puSjdpnQJXbdiWLNt+Mp4kSQgELBfY55riLHj2yJxJgSozci3cQyoEKDdhDEGpzP7hvkT9BxWFrx1eWSjCLWp7owwapSKDRu61ujlNBmYfyREaDSEcXDJ5hdwjuEJNcg+Gw7H6XAIAdotOOdwu7O/WLcgeWT7WPCEADZs6yIMHqzh9OnEiy0LMjMLXojQlnDhAbUZ+VK4C95+MaAOhwOBQNcyXYUADSG64AUt4ZxHdV9nmxd09eoAhgyx9029d28Fu3cb6TYjqxAxoJmPxDiY+JlsRYFiJSH5Q16B4xdckU5zkorTqUHvYqkVe9+h4iAQsLJpPZ7sHvNbkFxadsGHqam5CGVlH6XJovjZvTsIQoBRo+yXgNSSAQMU7NihY9SodFsiSAatvaD/fCg3M80I56IOqM0IZ8EHuP16bFwuB4LBrjkCcv4sr6uzyl3k5Ynh/XKZAwcOYOvWrdi+fTt27dqF2tramAk82eAJPXrUxJgx9i8rVlJC4PNlXndyJpNNHtBc7ZaXGIMo1movwlnwPhuGDDmdDui6EKBdor6+HoAQoLnOsWPHUFlZieLiYng8HlRWVqJfv34x162puSgyZSJ9+8rYs8f+XdOKQkAph67br1EXWOSiCJU4BxOjsNkSvw0D891uB3S9a70VOS9Aa2trAQD5+flptkSQTiRJQkVFBaqqqtCvXz8MHDgwa0fHGj7cgdOnc0OUjRih4t13fek2Q9CD5JoIFWWY7Isdh89wuZwwDCFAu8Tp06cBAAUFBZ2sKRBkB4EAg6qm24rUMGKEA4RIGZsNP67Og/XL3sbM1bel25QImZgFLzgD4aIMkx2RJMmWAlTTVJhdzO7PrUfLGIQFaGFhYZotEQiSQyDAIcvptiJ1uN0SamsZysvTf9Puu3d21P8bj62Fs/geXHvvnWmyKJpsigFtybX3KjmTlCQx4QG1I0SSELDhs193BjvJeQHa0NAAQAhQgX0oKpIRDFqjIWla+kVZT9O7t4ydOw2Ul6e3OaupuQgnj62Nmrf501H404WZIT6znVwRoYQDXOhP2yFJEoKi9yGKnBegjY2NAIQAzSWqq6uxe/fuqCe3TO3C7Sr9+yvYscPAuHH2LsUEACNHOnDokBfHjlFUVaWnSaupuQjm9o3Y/Gl0TSghPpNLTohQxkQZJhtCABg2TELqDjkvQH0+K4GhtLQ0zZYIUsXp06fRr18/DBgwIN2m9BijR6tYssSH4cNVuN32v5n16aOgpsZMmwAtK/sIJ2GJ/UwXnVwSN8FMhnAOnqWhEoL2kQmBLgRoFPa/M3WC1+sFADEWfA5hmmbWZrjHi6IQXHihEx984E+3KSmhtFRGQ0N6vdjymPEYN2Enbln3+7Ta0RGS6NvNeEQMaPtInMPMUu+wLEnCA9oKe9+FO2HXrpqIB1SQO5imCTUH0sSLiggM+5cDBQAUFhL4fOkVoGVlH6FmzEUILHsA/3yI5lz5IEH7MACMEDBCwIkEJoXeS1KbeU35+fC7nDhRWQkmSWCybG0rW8vD6w08cAAatXk4QisIY6CKDDkLa/8qkgQqBGgUOd9CBgKBbmVxCbIPSmnSBGgmD8/pdBIUFBBs3hy0fSxoYyOD203Q2Mhw7JiBykoFxcWpLwVQVvYRblv0EJ547l7886H9GSdCszULvrt8dPFFaE7jYCMSAHAOKTSBI/I+MiG8jIMwhgODB0PiHIQzEJOF1mOQTYaasjIwmWDkrt1p+0zpgDAGQ1Hh6OLIO+lEkST4zewTzj1JZrWOaSAYDAoBmmPkQhd8mIsvduDtt/22F6CMAadPm/jkkwCKiwnWrvVj1iw3PB5xbQuA5rw8zHl3WbrNSBq7Ro6EKedGG9YSYjKYSnbWmFMlCc02HAu+O+TeGdwKXddzRowILJLdBZ/JXlAAyIVen6oqBVdeecbDNXAgxQcf+DF3rieNVgkEPYNiGAg67P1QGQuZmaBKdoZPaYSA2bgxZowl7MzLefeAYRhCgOYYjDFompbUfWbquPCHD5soKcm9y1ySAFXNze7mzhAjIWU/CqWgOXjfIqaZtZ9bBWwrQGWZdGk8+Ny7M7UimfGAguyAc54zYRe7dukYMSK3zm+fj2HTpiDq6xnWrfOjqUl0e4XJ1RhQu6EaRtYKse5gJSFl5+d2SJJtBaiqqvD7Ex9oNDt/ySRCKYXb7U63GQJBj1BeLuPwYROlpfa/1GtrKT74IACnU8LAgQqGDVNx8CDFRx/5oevAZZe5cubBQ2BvVMOAmUvj7YZQqJm2z80A6JoGQ1VBNRW6qoEqCgxVhaEoMFUVVFFAFRlUUUEVGUyWQWUZpqwguOQN8FDZR7uhqgr8/gCKiwsS2s7+d6VOME0z6d2xAkGmMHKkFqoFav94sU2bgpg2zYWiojM3qMGDNQwerGHZMi8CAaCnnzXDw3Fm0vjvLRF1QO2BSg3QLE3G6Q6EmQg4nWh2u2GExKChqqAhEUhVNSQAFZiKAiorMGUZVLFEIJMJmCx3KQhFghUCIIcnakI2KRRqQqE09N6Ay++DQikUw4BqGFB167W8qQn7bOoB1TQVfr+e8HY5L0AZY6IIvcC2OJ1AU5M9G73WGAaixGdLFEWCrvMeF6Dj6jz43wwf/13EgGY/im70SBa8SYgl5BQFZsSTF3ovKyEhJ8NUFJiyAlMmYESGGZ5PLIFnEmt+pHZpHD0PYbHo0IPtrsMIQW1pKY727RsRf7JpQjEpZEot4UdNOP0BqNSAYlBLBFIDalC3XnUjLbGHHpuKTwBwOFQEAqILPmGEABXYGUIIDIPjzTe9uOgiB0pKcvOSNwwOWe5Z71/fvbPxxHP3Yv6W/fjThbn5PQtSg6brqC8pxopZl0QK2icDKVSDlDCr9qjMTBCTgbCQ14+xkOfPBDFNOAwj5P2zvIEqNS0haFIoRktRSDsVfYf79sHxPn0w8eNPkvJZMg1XKP66K9nimY6mqQgEhAc0YTjnQoAKbE1BAcHFFzuxcqUfCxemrxh3T9Oeg4ExBsMA8vN7rtGvqbkIr4fEZ6YVnxfYD1OWkd/YiIvXvg+VUsgs+xPtZGqCSfYSZi1xE0uA+hhDns0EqNPpQCDQvue6Pez1LXQBzrlIQhLYnsJCGZqWm/F/+/fTHi1FVVNzEcztG0Nxn0J8CnoeQ1WhGgacum4L8QkAiknBZPtKEmdIdDbY5PdqicPhQDCY+OhUOd1a0tA4uh6PKFYtiI9MLjjfGR6PhGPHKKqqOr7sN20KoKiIYPDg7ErOa88DOmCAgh07dPh81nCdycbcvhGbMzzuU2AvqKpCNs10m5FU5DRmuKcCd8i722RDAepyadC7MDxqTgvQhoYGAEKACnKD0aM1bN+udyhAP/00gECA48QJA7IsYcCA7KghumNHEG53bA+vphFceKEVgjB/fnJLMfXdOxuvL7sX87c8nhVxn6IOqD0wNA0ytZcAVUwaV7JStuIOfbamLHhwoJQiENDh9wcRDOoIBnUEAgZ0XQ9NFIZBoesGDIPC6/WjoaEJzz33Bkwz/oL0md9i9iDV1dUAhAAVxEc2ez8BoKJCwaefWnE6us6gaWcae8YY3n8/gGCQY84cDz77LJAVBdwpZVizJgBJAi64oP1Y7ooKBUVFBg4dMjFoUHJuci2TjrKp651L9s3GzRUMVYGSwI0+G7B7DKgnJEC93fCAUkpDYvDMpOs6gkEjIgzDotAwrFdKTVBqgieQhS9JEhRFgarKUFUl9F6Bw6FC01QUFHjgcGiRyeXSoGkaXn99FS6/fAZ++MMfx3Wc7Gk1e4Da2loAQEFBYsVTBblHtovPMC6XhFdeaYbTKYExQJaBadNcOH3aBOfAnDnWw9iYMRrefdePs87K3PqhlDIsWeLD2WdrGDq083CBvDyChobkieqNGVzvsz1EHVB7YCgKZGovAapQe3tAw4lHG4cOgTRyJOSXl4NS2gVhKLcQhypUVYGmqdA0BXl5bjgcKpxOSxA6ndbkcGgZOeR45lmUQmpqagAAeXn2zQwWREMphZRgyRK7iE8AmDkzOuHu0CEDK1daw1XOmOGKzNc0gqIigi++0NMeC7p3r47mZoaxY7Wo7vNgENA0KS7xCQBnnaXirbd8GD1ajfL+doWamosAbOzWPgSCrkIVFYrduuCpvZOQwl3whUePYmwggH4/uw1OZ2YKw1Rh3187DsIxoEVFRek1RJAyKKUJxQDaSXzGYsAAFZdf7sHChR706hXdEE6a5MDWrTpYjC6j5maGPXuCOHQoOvCcMYYvvtBRXU2xeXPiZTlacuoUxVtveXHypInTpxneftuPd97xor7euvEeOmQgkZwFRSE47zwH1qzxd8suQXrJpnCHnoKqChSaeNJHpkIJwZKFV6Hq6NF0m9Jj5IfuO1JDI4oaGpGX585p8QnkuAe0sbERgOiCzyU2bNiA0tLSdJuRUhizBGNeXvvC2+lsu0zTCAYOVPHWW36Enca9eskIBDjq6xkqK2XU1urwehlGj7a66j/9VEdTE4NpciiKhD17ghg+PL5u/IYGE4cOUQQCHNXVJhwOCVOmuFBQYNmm6wxeL8fq1X4MH26N837ppa5O9hpNv34qjh41sXSpFxde6EBxce41gWIkpOyHyjIceuKFvzMVwhg8zc0Ys31Huk3pMfJDT8s+G2bBh7EGPon/wSj3Wt8WhAWo8IDmDoQQjBo1Kq517eL9LC4mWLbMh/HjHQlntY8d68CwYSrcbgLGGLZtM+BySbjoIgcIsea99ZYfzc0c552noamJ4ZxzNFRUKAgEGJYt82HoUDXK69zczLB2rR8VFTLOP9+JxkaGNWv8cLkkVFbKKC+XcdZZWpuSSZpGoGnA9OkubN2qY8qUrmW0X3CBE3V1JtatC6BPHxNjx2ZunGuiXHTyIrjM9kW5zGSccJ1IoUWCnoBwJG30o0wgF7piwx5QL7evAFVVBX5//D1fOS1AvV4vAKC4uDjNlggyAbsIztZcfLELH33kT6i7uiVhIUgIwTnnRIs1QgjmzXNhzx4Db77pi6rF6XQS9OmjYONGHWPHaqipYSgpIVi71o9x4xz48kuKt9/2IhDgmD7dheLi+AwsKpIxZUpins/WFBfLuPRSF5Ys8aFXLxmVlfZoCl3UhRV9VqTbDEEPI3Fm64xxO1IYEqDBkAf0+AVXoPf6Jek0KemoqoJAQHhA48Ln8wEQSUgC+4rPMIbBe2wkJEUhGD3agaFDLe9qywSfc8/VsHp1ACtW+OHxEPh8DEOHKqiqsqbqagqPh/RIgfjOIIRg5kw31q/3wzSDmDXLBUXJ8pu6fZxiHXLtvQr++ZC9ssATgTAGRuxbtN2OhJOQfMy+ITCWABUe0LgIC9BkFqYWZB92F58AQCngcPSsOomVWR4Wee1RXp7eJig/n2D2bA/27tWxbl2w255VQerIZRFKGIOR4wks2UZYZwRs3AWvaSqCwfgFaM4qr127ahAIBBIuySOwF7kgPgGAUt7jAjSbGTZMQ309i5nxL8hccjUjnjAOLhwnWUkggbqf2YamqQl1wef0GRwIBIT3M4fJFfEJAKYJaNk1tHvKKSoiOHFCCFBB5kNME1zEgGYdkiQhYGOnl8OhIhiMvzpDbj4+htB1HZIk4aOP2hcikiRBlq2RB8Kv4ckahSB60rTcLiwryFzEw1bH9Okj4/Bhiqoqcf0KMhuJM1sXbbcrEuztAXU4NJw+3RD3+jnd0hqGAU3TcNFFF8VczpjVJRcMBqHremhsVQOUWuOs+nw+UEpBKYVpmpEp3qG1wuKWEAJZliPvw2K3pfBVVTVKAKuqCkVRhKgQCJJE374Kdu8WReoFmY/MRBZ8NkIkCUH76k84HJqoAxovlFKoavt1EQkhEUHo8Xh65Pi6rkdEbMspLGZ9Ph9M0wRjDJRSMMYi/3c1Xq2l8G0pfluL3pavYcEbfi+Er8BuaBqBaQIbNgRwzjlat4frjJuWl7G4rARxQBgDJ/btyrUrkiQhaGMPqMvlgK7HnxiY8wLU5Upf1mtY0KWSsHANe3HbE7+GYUR5dVsKX9Ps2hjEkiRFBG9r4Rv+v2WIQ0vvb/h/Sik0TRMCWNAjzJ/vws6dBl591QuPRwJp5yavBtejpvYYChx7cMmxS+Lat8QlcCn2zYeDQ0pCDSWPkfwHZUHmQUzhAc1GZEmCYeORyJxOh/CAxktYzOQSLb26TqczpcduKXLDAre18A0Gg/D7/e2KX0JIhwJYkqQ28bhh4StJEnw+Hz7++GNIkgSHwwdCAEkCZFmCJAGEnHkvy9b/hEih+WeWW++tV1k+szz8v6JIkfUVRcRfZguEEGiaBFWVMGyYihEj2o6SVFNzET589AFcvWV//FnYDCnxbsYrhgXZjcQYmPCAZh2EEBg294AahvCAxgVjDA6HfYbhy3RS4fGllMLv9yMQCCAQCIBSCs45GGPgnGPs2LEoKCiAaZooLNwI0+QwTStLnDHANDkoBTgPz+eh+YBhAIzxyDxravue89bvLdskCZg3T3ioMp2DBw3Mnu1Gfn5bxVhTcxHM7RvhLL4H1957Z/w7Fc8fgiQiMyay4LMQRZJAbSxAnU4nKBUCNC4YYyn3Agp6FkVRkJ+fj/z8/E7XLSxM7UgiS5d6U3o8QdfQdcQUnwBgbt+IzZ+Owp8uTEB8CgRJhpimiAHNQmRJAjXtW+pN0xQkkpqS049QjLG0xoAKBILMglKGjsr0yWPGY9yEnbhl3e9TZ5RA0AprKM6cvn1nJSohoDYeCSlRcvoM5pwLD6hAIIiwe7eB4uL2m8Wyso8wvmoqAnW/y9lhIAXphzAGbuOC5nZFlSQwG3fBJ0rOCtBwCSO3u/1xqgX2pqYmdv3XnkAM8ZgdfPEFxaRJHceFHxm2HLcteghLxw5JkVUCQTSyaYos+CxEEwI0ipw9g30+HwAhQAWpgVIrIz4dCPEbP6pqxYB2xuZiLy6YM090xQvSgvCAZidCgEaTswK0trYWAOJKVhHYl1R5QRlDh7GFdj12tlFYSHDyZNfq3AoEqcKuhejt/qjskCTbf8ZEEAJUCNCcJxUi1PKApueGkU7va7ZRXi4LASrIeGRm2q4ME2EMZooHZkk1TkmKe6juXMBeZ3AChAVoXl5emi0R5AKmydPYBS8EaLxUVSk4epSioUGIUEHmIlPTdoXoCWMw7C5A021AhpGzt6W6ujoAQEFBQZotEeQCpsnT1g1OqTVClKBz3G6CyZOd+OijAN57zyfiZwUZCbFhXE0ueEDdSRhu107krABtaGgAABQVFaXXEEFOYJrp80KaJhMe0AQoL1cwb54HbreEL74QpZYymURKYdnpUYIAthtRnJgmqJzawUFSjSf0qwXEgy0AIUBRWFiYZksEuYAlQNPz9Cu64LvGeec5sGePkW4zBO2QiPiUOBeF2zMcmTFQVU23GT2KKxS328jsG+KTiGM+Z6/I5uZmAEBxcXGaLRHkAukUgSIJqWucOGHC4xFdZnZA4hxcXAQZDTFN23fBu0LqrMnGw3EqSvxebHv/2h3g9VrjcgsPqACwMuHLyj4CALz+ejNUtfvCQ5KsSZaBYJCjoCA9N8B0el+zmX37DEyY0HFRekF6SHQUKonbr8vabsiMwUhAvGQjnlCIQbONu+BVNX5ZmbMCNFyIXtO0NFsiyBTCIlRVJcyf7+nWvjjnME0O07QE4JEjFKdOpSeekLH0ZeBnM6bJ4XAI4W4LRBd8xiObJkzZ3pLEHfaA2rgLXlHiD6PIySty164aBAIBkRks6DEkSYKiEDgcBG43QUEBAefpOd9M03YJsymBMcDmPYI5g4gBzXxkasK0eRKSO3QOem3cBa9p8TeaOXtFCgEqiEVPFaVXFMujlg5Mk0OWxbmeKJyL2NlM5dp7E3sykCAEaKZDmAlq8yc+T+gctHMXvBCgcRAMBiHb/GlLkDiUUjCW/NGxZFlCutocG5YM7HEaGxko5SBCtGQsiYhQiXMxdnqGI5vM9h7QvNDn89lagMYf1mjvx40O0HVdCFBBG8IPJjU1F0SSkpKBoqRPgFoe0PQcO1t5/30/pkwR45ZkOtfeq8SXkCS64DMe2aSgCcQPZiN5YQ8oz1wB6vP5UFfXgLq6BtTX16OurgG1tXU4fboe9fWNaGxsRENDM5qbvfB6ffD7/fD5AggGg9B1HX5/IO5j5awANQwDqs1rjgkSp6ceTFQ1nV3wIgs+UVwuCT4fh6jSlvnEI0IJ5+DiGshoZEoRdNj7oa8gJED9CcSAUkpRV1ePxsamkABswunTdaitbQiJwSY0NjajqakZXq8PPp8ffr8fgUAAwaAOXTeg6zoMwwClJiilME0TjLHIxDnv8hj1kiSBEAJCCBRFTuj+mbMClFIqMuAFbTAMA0ooDqllaabuoihIaxe8cP4kxvjxDnz4YQB9+tjjIbUoWIQLT12IbcXbcDjvcLrNST2iDmjGI5smTJuXYQp3wT/f3ISX9SDMqnNgAjHFYFcFYRhCJEgSgSzLkGUCRVGgqgqcTjccDg2apsHh0OByueByOeByueDxuOHxuJGXl4f8fA8KCvJRUJCHgoICFBcXoqioEEVFBSguLsIbb6zB179+eZvjNjY2YcCA8+KyMWcFqGmawgMqaENLAZrMLnhCrKSWdMAYRBd8gqxfH8CoUTZ4QGXApJpJ6OfrhzqtDiqzZ5vXmReUcA4mxuHOaFRqwrT5Q0JBKA7ZNAxIjMEJwNGrAg6HGhKEDjgcDrhc1qvb7YLb7YLL5UJenhsejwd5eWGBaE1FRQWhqRCFhQWR+1dPk4z4+JwWoA6HKDItiMYwDMiynFTxCSTnYu0qpsmhaeLm2xmHDxuor2eorjZRXCxj0KDsF2ulwVJ4qAdL+yzF0Mah6TanR+lQhIoY0Ixn87njMGzP3nSb0aM4Qp6AWXn5+F2fPgCA3uuXpNOktCIEqEDQAs55j8SAsjRmPRYWEnz+uY7Bg1VomrgJt8fGjUEMGaKif38FQ4fawPsJoNZVC7VWhU/2gUscDJmb/JAM2suMn3QKeOF8BbWuM8vbE6sMACMkMpmybL2XCZhEwML/EwKTEPDIMgmMyNb/khS1DScEPDTPUFVInIFLLfZJJOt/Ilnbk9B2kgROpNAriRrNyW5F2x3BIEbu2pVuM1KCL4OTkFKJvc7gBOCcw+m0d8CzIHFM00Re3mEAyX040fX0dYMPHqxh/34DlAIi7Ll9ZBk4++zsfihVmIJpx6YBEiBBgspUNKlNmFA7AeWBclCJYoB3AKRW3dESpMhYlTGX9RQcSEXPuGqqOE8/D4ycufEvm9t+TIzEuTUxHnlPODvznrHQMhZaxkHM8HJmLQ+tR0LrySaHxBgODRyAMdu2gZgMMmMgpmmtF3pVmGm9Nxnk8DwzNK/nvypBDyMBCIhxYQHksABljMHlcqXbDEGGwRjrEaGo61YppnQhRvXpHEWRcOIERa9e2ftFNagN+NLzJU47T4OBgRLLyzfz+Eys7LUSkNDGC8rArLtiq2VR69lR+VQlPqZ8Mjg0aCAGf3Eg5ccVZAaSJMEPoUABezYrceN2u9NtgiDDYIzB7x+S9P0Ggz0jbONFZMJ3DmNARUV2f0kSJATlIAJKAIOaB6HSX4kKfwUa1UYE1AACSgC6okdNVKGgMgUlFIywyASCM5NNSXREJYGgu0iShGC6jcgQcvLq03UdAODxeNJsiSDTsDygclJLMAGAYQCqmj4PKOeAothYSSQBa+jN7P2Oph+bjjK9DB+XfwwA6OvtC13WUagXYkfRjjRbl7m0FqHp8IoKcgciSQimqyRKhpG9rW03qK+vByAEqKAtjLGICEnmuPDBIE9rF7wgPtKZLJYwDHBSJxRTwVcPfhVlehmCUhAk3KxLwPsV7+PDig9xMO9gWk3NJoRXVNCTyIRAFwIUQI56QE+fPg1ACFBBW8Ie0GSj6wyi7Gzmky0e0P5N/TGxdiJOa6fxQcUHOOU8haJgEY54jrSJ8axz1qXJSoFA0BpZkmAIAQogRwVoTU0NACA/Pz/NlghSSWNjIz7//POIyFAUJTQ6hApVtQoBe73eKBES9oLG2x3f1GTVkZRlq8tdUazkn+ZmBkmSEAgwEIIWU6oED48cW1GyR2ilCq+XQdet78jp7P5306+5H6qd1Qgo8Y+LnAiFRiFMmFhfvh6jGkbBTd3YUbwD+wv2R9bhItGhy8Q9xrxAkCCKEKARclKAhj2gBQUFabZEkEoaGxtRUlKC0aNHgzEWGis3iGAwCF3Xoes6ioqKUFRU1GbbeGNC16/3w+MhkCRrDHbT5DBNwDA4OAeqq/3gHJGpJ7HEphQSwBxr1ljHTqSXmXPA72dwuy1RJsvA3Ln26znweAgmTHDgjTe8yMvrXIBS+hEQ8MFXugmXHLskapnEJTSpTRhVPwpBOQgP9eBA3gHsLN6ZFFvPPn02ygPlqNfqccnxS7CjaAeWFy+PKjEUhoDYvvZnTyFEqKAnUAhBkNrjvGoZstYVclKA1tVZXVJCgOYWpmlGutcJIXC73QlVQognJjQYXI8xY85DVdWGLtuZDBhjoBQIBoFAgOHss4HS0sQvd11neO89P+bPt0TnW295sXSpt816hACSZE1hYS1JwIUXupCfnx3e1pMnTYwapcVVC7Sm5iKY2zdi86ej8I/hf429EgOK9WIU68Vw0+RV3KjwV2BFnxVQTAVj6sdEeT0BqxbogOYBST1mriJEqCDZqBKB1wYeUEWREQjocLu7Xk89JwVoY2MjAMT0dAnsS0sB2lNwzlM2Fm9HEEKgaVbh+e4IQE0j4ByglEFRCC67rK33kzEGxgBKz5R7IsQKR1i50ofJk51dEr+phDGGY8coLr88iaKNWPGXZYEyDGgegMpAZbd2pzAFhBN4TA9mHZ0FLnHITIbCFKveJwPmHpsLEyZOuU5hbeXamF7RZDL1xFT4ZB92Fe4CBweXOHyyz1bprUKECpKJRiQwGwhQWZYRDAoBmjBNTU0AhADNNUzTTFgcjqtLrLv5MyqBEJL0Mk7ppLCQ4MQJE337xlYVhJBIbGlLSksJZs92Y/lyHy67zJ3RZaB27zZQWan0SGwsIwyH8g5hW8m2bu2nf1N/lOglqNPqUOWrwrrKdejj7YMZx2egxlGD0mApJC5hY/nGHk08ytfz0dvXG27qBpUovIoX406Pi4y8BFgF8SVI2FW4C81ac4/ZIhBkG5pkDwGqKDKCQaN7+0iSLVlFc7PVIBYXF6fZEkEqMU0TDkf8Qy0mKj7tSlMTQ1lZ1zzHbjfByJEq1q0LYsqUzB157IsvKObO7Zp9/3yIdlq6h0vdv+EE5AD6ePsg38hHabAUHt2Do56jMIgBh+nAwbyDGNUwqtvH6Yxxp8ehUW1EUA7i85LP23hZK3wVULmKoY1DUWAU2EKACi+oIFk4bCJAVVVBMNi9kvo5KUC9XiuGTYyElFt0N2A6V+Ec3coMHzHCgf37vaiupigvz8wmR5atEIKWXtzWMb8tPdrj6jx4YtnbcBZ3LvgIT845d8p9CsurlkNjGhymA17FasdOuU6dWakhOWK3IyRI2Ju/Fz7N166dADC8YThOOU/FXCcbESJUkAycEgG3gQBVFAW63j0PaM7djXftqoHf7wcgStHkGonU+Oyq99OOpeaT0VZOnerCunUBUJqZGdnFxTKOHTMj/9fUXIRxdZ6oqe/e2ZFp47G1cBbfE1m/M2GSrJJIuqKjWWtGrau23dY7JeWXOjnRZxybAcIJqGwvwSaK1Au6iwuwRYE0Ve2+AM3JqykQCECS7CgV0s/WrVsRDAZDcYEEsizHnMI1OAGAUgpKKQzDiLw3TTOSNNR6m5ZTuIZn+P+OHioK/BKG+/MwRHStJ0RenoRjxyiqqrreXOTlEZx1loalS32YNcsdKeuUKVRUyDhxgmLwYCuGcVydBxuPrW13/c2fnvF8rpz+BGauvq3drniJp66tkVLwCCRxCUzq+EGCgGBFnxU9bks6EJ7QnoPB/l4xty3kZ1iAdu86yEkBGhZImU53YxA3F7ctl9PTNDY2YuzYsTBNMyIow2LSNE1QShEMBmGaJhhj4JxHxKUsy3C5XJBlGaqqQpblyDYtRarf74/MD+8n/NoeLpPAZwRxYdWIHv38HNbvtrnYa5tEpBEjNOzcqXdLgALA4MEa8vMJli/3YfhwFSNGqDh2zERRkRxX7c2epKSEYMMGiqVLvdCCBfAo7+DE+vdB2qmh2brrvaUIbc2ukQymbOL+BT1iuiANiPHjkw9hDKaigNikRmZ7eEIPiT7G4M4CHdIemiY8oF1C1/WMKJXTEclIgOloHz0lTiVJQl5eXo/su6ukI5koLELtQK9eCj75JJCUUYLKyxXMnevG1q06li71Iz9fQn19ELNnp88r+vnnQXz5JcW4cQ4UF09HcOun+HDLPpjDL0RB7TlQWHzJSWER2hoW6m2JJ1kpGSSzC54wgmknpp0ZXx6AzGToRE/aMQQCAJAYB5VlqHYXoKH2oNGkcBMtzdZ0HVVV4fX6u7WPzFZhPYSu6z1eD7I7pEIw2UkgZTJ28YKec44Dn3wSxNSp3c9kdzoJJkw4UzuutpZi+XIf5s51J2UYzEQ4dMjA8eMU55/vgKJMtZKL3nsXhcX3INhYg6binSisHRclwDpi5fQn2szzeb4EJI4x27f3uAhNdne/kzphEhOreq1K6n7thuiW7z4yM0FV1Ro9w8a4SViAMvRS02xMN9A0BfX1ogs+YSilUNUs/uUFWYGduuIHDlSxbVvHXq/3+XWYIr2U8L5LSxVMmODEsmU+zJ+f2nqhn3+uY/ZsF5qbJ1vi87l7I8lFjmAZGKGoL98INVgEqjXB3TgQDr0ksYNIHOBSux7SZJPsLPiwR3XKiSnQWHweG4Xl3q1FiNDuQUwTNIMdQ8nCQ6zP2MTMTtbMbDRNhWEIAZowlFJ4PJmRiJLOWpPJ9oJ2FIMpyH7cbgnHj1P07t222XifXxf12prOhGlVlYKxYx1YtsyP/Hnf7tI+EmXnziBKSwmcToKhhz1tMtsBwOXvBS1YBN1RB4nLMBUfkKAA5YhODkpVV3y89PH2waj6Uahx1mBz6ebohdIZ2zWmYUWVPROLkkW8IrSj3z9XRazMmOUBtTmekAe0yczu+6WmaaDdDJfInFYwhZimCU3L3tiLZJJMESrqbNqbCROcWLvWj8sui2422hOdrdfpTEAOGKDi4+0eeLp4HtXXmzhyhEJRrLJKlZWWnYGANZ49APTqJaNXLxnbt+swTWDOHBdqai7CyWNrsb6dup4yc8Ll742gdhrewi+gBcqgsK4NP9dZxnx3STQLPk/PQ//m/thduBuj60ej3F8etdxpnvmcClPQy9sLQTnYoyMtZTud/a6XHOv4N8pVTyoxTZgZnpuRDPJCHtDmLHfYOBya8IB2hURHxOkp7DbSjmEYGSdA7fYdp5P8fGt8+bo6E8XFViMaj/gME48IJaoDtLEBWlHbUco62379+gB69VJgGBw7dwYwZIiK/HyCzz8P4oILnCgtJdizx8D+/QYmTnSiuFhGTc1FMLdvDInPe9rdNwA49BKQOgWNpVvhbhwAZ7Aivg8OHlX4rydFKGEEZ9WdBYO0zU51MAdc1AUucdRqtdhcthlTT05FraMW1c5qnHCdwOCmwW2225e/DwBwxHME/Xz9UBGowFv93kqq3YJoclGEyozBUOzfBZ8XukfaQYBS2r0wgpwUoIyxpAnQsMBJ1IuYScIoWV5QSmnGCdBUY/fqsn36KDh0iKK4WE5IfIbpSES+z69D/shDqN/8CSqmz014e0qBc85RQQjBoEGWN/TECYrJk50oLbWautGjz1z34WLzT8QhPsOotABF1eeioWxzAgIUaH1m9FQ8KJMYTjhPIKAEWhzZOrZf9qPOWQfCCKafmI5Ljl2CIAni44qPAQBbSrd0uO/txdsBAJccuyTpdgvakmsiVKa54QHNt40AVWGaQoAmDOccLldyx6XOJEHZFZIhQimlGVVdIBN+E7tVGxg4UMV77/nQMOZGkC62Hh0JV3ffAajf/AmYroO0EybT3vZm5UfYs2cvRo50oKhIRlFRx+diuNh8vOIzDIECgKCpYC88jYNC/yfOyulP4J8P3ZZcL6gEVLuqOxx/nRGGlVUrYywA3KYbuqxj6ompUd35HBxBOYhmpRmaKcKXBMlHNk2YGXT/6CnyQ5/Rl+VJSE6n8IB2iWQJ0EwQOMmk9edJVDgZhpExAjRdv017+cd2yIQHALebYNQoDZ+9+xqI0wVumug1J7kV1j2DhqJh+2coPndSQtsVnHUePl99EiNHdl7GpbO4z84orBmHoPs4Gsq2gDAVzua+kE0HiOloI0g5eIdFnDIlKcnJnJh7dC6a1CbsLNqJo56jZxYyoMgoQlGwCP787tX+EwhiQUwKmgMe0MKIBzS7R0RKRm+n/X/tVoQztbubBW838RmLRL13pmlmRBd8pv02dirHBAAnht6MorwjYAZF8Phh1H32CYrPnZi0/Tt79UXD5xsT3o5oWnIGro/nWCBw+frA5esDXW1AwHMMXKLgxAQPjZ7EiQmnrxKWBI0dnNHTSUnxQBjBrGOzYBITlFCsrVwLXWlVcosA9Y561Dvq02KjwP4oOeIBLQiJbH+We0CTQc4J0MFHrRvUAKUw44TK5lMH8OHRnXCr8cenSgBkiUAhcmRSiQyVKFBlBRpRoMmhiShwKCqcsgpNVuBUNLgUFUpX+1JbQSnN+BGm0oVduuLD3d+uXn2t1z79ceLNfyJ/xBgo7uRcT2pxKcyAL+HtjNPVIA4n3udfSXrJpo7QjEJo9YVt5lPiQ1PJTjBiwOWtanf7VNUHbY8hjUOgEx3H3ccBAIObB0cVtA93xUdeefT/4Xlc4jAlE1SikLkMCRI4rHlc4mASgwkTTGKQIEHiEggIJEggnFj/89D/sP5vuaz1NpF5Lee3mtfyfWub04XHyKz7TqYgmyaCOVCdJtz36s9yD2gyyDm1cKT2JAAgKDHsqzsOl6LBpTrgVjRoREmrB68p6Mf5lUMxqWp43NswxhBgFDrVETANBKmBoGkgaFLojEI3DfhpEI26D4ZJYTATBjNBGQVlDJSZYJ0M3eeX2wZLS5IEQghkWY68+v1+lJaWJvy5k0mmPVS0ZFydB5uR+V7QRJKLCCEouXA6Ti5fgsKzz0Pe4PjP3Y72mQiMMZxetwp6/WmUXjANQNeL4icThblRXDMezfn7IbGO6xv2SDxonJjEhMIUEE7AwcGl0NSiXQgXtw8vD7+PzAOHBAkqUyFzGaZ0xrsjczlKXMpMBiSAgUWOwyQWeWVgoBI9Mw/Mei/x2PNC78NTZD+hfbV8H+eAVj2KSOKKjUIp/EnOzchEwu2bP9Rbc/yCK9B7/ZJ0mpQ2ck6AHqs9BQDgMsHO00egm5ZIM5gJxs8IrZaSrMxVgCuGTOhx24LMQL6WWH1BQgjcRINb6dknx9beO0opKKUwDCPyapomysvL29lDz5PJ4rMlmdwV35XMdmd5L/S+7GqcXP4GJEWFp/+gbtuh5Bei+v3lKL34kk4FacPnG0BUDVWXXZPwcRJNQOpp0tEV/0XeFxjSNAQ7i3em9LiCjsmlDHggnISUG5JEAuDn2Z0Fnwxy49duwfGGWgDAhP4j4xaVT3y2FM9uOzMCiCwRqESGQhQ45BZd3LIKl6zCqWhwyKrlXVU0OFUHnHF4Vw3ThFPJzJEgOhV3amhqSmy/yeqWzgTxyRjrtIsvk+NBuyI+wxBFQfnUOahe/Q4UTx5kjweK093l/ZVPvgR1m9ahfsOHKJk4pd31GGPwHz6AXp2Iz5qai6L+P1P7M/EEpMThiMf1lqx4UN5Jj0YbCGAQA27dDZ+WeOiDIPnkmvgEAJlSmHIGuKhTgCRJ8GdAOEi6yTkBWt1gjeBRWtA2Zqs9bjt3fuQ9Yww6owhQHT6qw091BKmBgKkjQA006X7U+JugMwO6aYa6vSlMHrujW8IZb2uzHsA55QO6/uGykFjCMR5RmgmCszU6oyBS541KJsaDdkd8hlE8eXD1G4i6TevBAn5UXXFtt/ZXOG4STr7zasxlTNcBRcHp9WuglVV2+HAXrvfZkkRqf3YXLlnDwcdDupKSDuQdwKiGUdhYnnjyl0CQDGSaG0lIgCVAA4k+KNqQ3BOgjacBACX5RV3anhACJ9HgVDR0bQ+C7pKJ4hMADGZCluJ/gs9EL2h3KTrnfADA8bf+1e19EUKgFhbh+DuvomDUOZBdbjTv3QGjvg6cMTA9CEgSSkJxn7EIi8+Nx9ZG5sUz6lFysUaDj5dUJyXNOzwPOtHRpCbYfSHoOgztOsVz0fsJACqlYDkiQIkkIdD5arYn5wRofXMjAKCssO1Qf4LMoCMPYaaKTwAwTAo5A8pQZQLOqv449ubLcPXpn3A9z5aUXXwJ9LpaNO3aCjMYgGfwCJRcOACEEDDGIl3+7TGuzoMnnrs32rZUx31KPG4PaEu67QXtQOSEydfzwSWOFX1WdLyiIGlYdWGJlRTVilwVnwCgUANmjrSfsiTBSFHJuEwm5wRog9cSNmUFQoBmMpnYTd0ZBqMJeUDtTPG5E1F87kQcf+tfYGePB+lGeS6tuBSlF05vM58QAtnhgO/QfmghzysA6HW10IrPVGO4YM48bP40FbGe7RFfDGh3KAxaIUUtY5BnnJiBgBzAuvJ17R7+4pMXY0/hHlR5q0AlilPuUz1qp6B9AZrL4hMAFGrmjgeUECFAkYMCtNFnDVFXVlCUXkMEnZLJ3s5YGIwJAdqK/JFn48Tbr0B2eeDsXQXP4BFQXF1PTmpNyYUzULN2GY6//QocZZUI1pyE6WtG2dS5QCJDtWc5554+N1LmKCgHAQaoTMUJ1wlccuISgAOUUKypXBMlRhu1RvT19YVX9qI8WI53nO9kRKkiO8MlDsKI+J5boRgGWI54QBXhAQWQgwLU67eyPEXBdEGyMVh6uuATTSBKZX3MvCEjkDdkBPT6OngP7cOp997sdnJSSwghqJh+KRil8B7YC8/g4ZAUFbUfrQTmd759auAA79nzYmfhToxqGIVDnkOgEsWlxy5FtaMaO4t3Rsorjakbg9nHZ8Mv+1HrqMXO4p34qPJMDPLFJy/Gwi8XYknfJaBKbnvjepKwB7Qlue79BKyRkHJJgOqmGAkpN37tEM51fvgCPkhxZCoLBIlimIklISWDZGSvpwKtqBjFYydAKylF8xd7kr5/oijIHzYKjtJyaIVF0ErKsHSpF09/vgzV/iBCJcmTftx44FJ7A3Emj5POkyjUC9Hf2z9SVql1Rvv24u34sOJDbCnZgkKjEOfWnAsAUEwFk09MhspUvN33bSE+exgu8aiRpgCkbRjWTEKmNGcEqCoRmDbxgIaHN+8KOXfW+4NBENFNKugBKDOhkNTFMHVVfKZzlCD3gKHwfflFUkZM6ojSSVMxRTqGvK0X4m/b34NeuhmcmCiqHQvC0jHcX/wSNJwBH48omXlspiVmJGBL8RY0qU2o8FW0WwvUp1o9QOsr1mPukbkAgLPrzo54RAU9DwcHieERD//eHXlD4xWq3fWodnScnvLW5tJdWSOSLQSoLMsIBnW4XIkNoBMm5wRoUA9CyZFAZ0FqSbQMUzpJlwiVFBk8hV1PRc48XNy7BJs/HQWf+wh8niPIaxqcsuNbcKsYaALEKzScphNBEoTCFHhMDwY1D4IpmTjuOt7ptjrRccmxS+Cmbnxc/nFC9gm6Tssu+K0HvozMP3tQfwDJ8YZee6/SbaHY0raWnH1vfxEy0E0ckgSWgQKUMYZAQEcgEEQgoEPXDei6jmDQQDBowDAM6Lo18qFhUNTXN2HJkrXQdR2SJFkF9v3xF5jKOQGqG4YQoIIeweRd84Cmqxs9lcdlegDNX+xF4PgROHv3TdlxW+L09UJD6da0HDteD2ii9T+X9luKKw9dCZWrGNVgZfq/2edNBNTObwKreq068092PDfZAi5ZHtDWAq89wdcVwmK2K1x7r5JUW3IZBsBUFAQ1DYamIqg5YGgqpJpqMG/nVV4YY6CUIhDQEQzqodewGNRDrxS6br1aw2JTUGqCUrNL3eOKIkNRFCiKDFVVIpOmqVBVFR6PG06nCk3TcM45w+B2O/Huu+tw+eVTkZfnRmNjE374wx/Hd6yErctyDMOAlqHDXQqyG8pMKFL8AtRuReg74tSa5VALCuEoq0Te8DFpsaGpZAe0YEnqDxwj5i8WiXS9t+T1Aa8DsLpGV8y6BH/+tw6n3rGH6tp7FSE60wQHxz+OWeWuBgZe7JFjHDxwQ5c8lWHx2Z5dB503RNbLZi9oWBjqqgrdoUHXHNA1FYamwVBVGKoKqqigqgJDUWAqCqiiwJSVhIcLJYxBNk3IlEKhFAo1IW/fDg5g2dw5AAD1xaXtb09ISBSeEYSKYglCTVOQl+eCqhbC6VThcKhwODQ4HBqcTg2K0vkQ4MlA01QEgwby2i/JHJOcE6CUUrg1R7rNENgQykxRiL4dODVQOmlqSo9ZU3MRTh5bi/XL3kZwsAlZz4enOfVD3VodbfF5QLvb/colCSQOr0eqh/oUnGGGzwsTpMfEJ2AJ27AITYRM8HwyAEyWLXHYUhC2eKUhQUgjwlAGVWSYsgImy3GJxFjCUKEGFMOAZlC4/H6ougHNMKDqQWi6AU0PQtWNbj+7lXm92MU55ry7DADQ+/4l3dxjelEUKxY04e16wJaMhpomHGo6khAEdsdgJlxKYg832ZLF3h2YHkAiSTjJYIr0EvrWzcbvP/wIxrDpUIMu5DUPSqkNZ+BRBeJ7grA3qrFgINZO/i1IJ017OsabF1gQcFyjL+7x40REaHvd8aHnFAUKCCOQuYwaFGF24DcoRTNkwkHAwCFBhQkNJjbgbDx1QMVjReXYMlayPIOyDDMyWWKQyaRLGe2GouC9uXNAGAMxQwLRDItDGnpvTW5vACqlUHUDCqXQDB1qUIdKjaSIxJ4keZWQMwNVVaDrRsLb5Uzr4/MFoDEG06RwCg+ooAcwOUtpFnzWoGjgZuq666ZIL6Hv3tn4n9V/g9xrIvJPD4XCXAnvxyrbZE2cWHdrickA5DZ1HDsmsbHgEyUsPldOfwIcnyGevvXwePNZL0JDIoqAgPDQBAKJS5C4ZL2HFFkmocV8Lp2Z13p+eB+hP8LP/B/OYG+5DoCodcL7a/1+ttePL1GF85XD0KGAgEMCBwFCrzypp8pTuBG3H2h/uXVMFpn+i12P8doRcEhgXEK+FAQBQw33gEJGLYqhgsKUTLh9huUxpBSqQaEYlvdQDU0KpRktAtOJO1QKMsAYnDboNVNVGbouPKAx+eyznVi/fis8kgaTMbgcXSsZIBB0BGUMapyNybg6DzbjIkwpe8n2XlBCCJS8AjTu3oaCEWf16LGmSC9Fut7dgfPRXLQHTV0sL2QJCyk6g13isKL44s9gZXIA8a7e1bi6ldOfCB1LR335xpBQ7ph/f/XbyG8+jO9sAg706r7qcZgOaxSmFMPDv4d05j2XeNQyLvGE3zOJRfbBJBb5zcPvqUQj7yPbgEW9hv+YxPCXkychwcTH/Gys1wfAhAQTBCYIKCQwSLAkW/IEyRKM7tb2o+STcEkGNlErcfAleS0A4M6i/hi2N3tjQNONO3SfaGSmTQSogmCwhz2gL7+8PFLvSVHkUBCsFgl8dTo1uFwOOJ0OuFxOeDxOeDyutI46RCnFli17MG7cCMzAcNzFfgSPI3FviEDQGSYzoQoPaAS9rhaB40dQMHosiKYhcPxojwtQwBL3Tyx7G87ie2B6vWDEQH7jsB4/bns0lGxNuAxTIoTFZ3P+fqh6PvLqRybgoT0PM1fflhQv6CXHLsGKqhXd3o8dCcdWDgy8iCsc21GL7BhmmEGCnKYBHOyMO1Sur5GaqLBBUrSqqjCMHhag11wzGwUF+ZFaUV6vH35/AD5fIFI3qqamDoGAVTsqXC+Ksc4f/8MZXmFBq2kaXC4tImZdLgfcbkvUalr8MZwbN+7E4MF9ceGFY4F1fnDO4XYKASpIPpQzKCT+S8rOXlBGKarXLgPRHFBLymHUn0bv+V9NuR1qsBiBvCMpP25XCHeLJ7oNADBQ6M46FFWfl2B4gEXWd8VnAT2ZdNRTUE4gkzP374GBFyOZ8IKu45HDHlB7iHtVVaF3UnkjFl1qcQghcLudcLuT05XNGIOu6/B6A/B6LTHr91uvdXWNOH68JiRorSKo8dS2kmUCRVHg8wVw/fWXRi3Ld2fH06cgu2CcJewBtasI9R7YC1dVfxSMHovqtcugFqWu/NHW6kMwNSf8rpPw532JvPr0eT8TJSwoEyXoqoEaLOyS+LRNPGiG0jazPHuGgjZBrLhUQVLJC3W7N9lkPHhNU9DQkHj4TUa0NoQQOJ1OOJ1OlJZ2f3+MMRgGhc/nR36+JxICENCt4sz5LiFABcnHigFNTIBuLvairOwjW4lPAFDcHvgOH4TiyUPveV9J6bHXHduFYJ/BkKVTKKo+t9OMcDtgOOrg8FV2eXshQnuGll3v2YgJCUR0wSediADl9vhuNU0FpYl7QLM/+jUGhBA4HBqKiwuj4k+P19UCAAo9+ekyTWBjTGYm1AVvV/EJAK4+/WH6mlN+XMYYTM7hPLALhXVn54T4BABT8UPVi9JthqAF2S4+AeEB7SnyQo4Kr0264DVNg2EIAdohR2tPAgAKPQmW6xcI4sDkDGqcw7yGxaddqdu0Hs6K3mk5NpEkGGW90nLsdNKV7ndBz5LN4hMQArSnyA95QJtNewhQh0PtkgDNDfdAiBP1NQCA4ryCNFsisCMm51AT8IAC9i1ELztd8B05lPLjrl8fxDnlw7Hps9VAccoPnxao0gyJC/GZChIZKSjbxScAmFyCLAkBmmwKQo4Kn008oA6HJrrgO+NEvdUFX5RfmGZLBHaEcQYtDgFq5673MHlDR6W0+Hyg+gSOLfkHfD6OC6tGpuy48dEzN3AGHc35+9FYvBN5dSN65BiCM2TCMJWpxuTRHtCDTmtkpWweBz4TCHtAvTaJAbUEaOIJVTnlAT3dWA8AKCsQAlSQfKyRkDp+pssF8QkAen0t5BQm+9V9+gEqZsyDkleAzbVf4oI587D505QdPuU0Fe4GVbxw+MtRVD0+Kd3vXSkDlWvYwauZCCYIpJAAFeIzeRSG7hN+m3hAnU61SwI0pzygtU0NAIDigqL0GiKwLaQDAZor4hMAtJJy0MZ6UL+vx44ROHUc/hNH0LhjC5SCIiih0Jqyso8wvmoqxk3o2ghI2YChNaKw9hy4ff2SHvspBEZbctH7CQAUEgi4EJ9JJjwSkl264BVFias8ZmtySoDWe5sAABWFqatJKMgdWlf321zsjZrsnHTUGqIoKBgzDvWb1vXYMWo/Wo3mfbvgP3EUpRdMj8x/n1+HI8OWY3zVVATqftdjx08nruY+8BbuT/p+w3VIhdCw2HrgS1tks3cVKpKQegQS8YDa47vtyPHS4XZJtiOjaQqVhSkvzJHsBEHa2FzsjTk/F7yfYfKGjIDRUI/AyWNJ3a/34D7UfLQKSkEhyifPQuXM+SCthvt9n1+HzcVe3LbooQwRocktPu7wV8BU/EndZ5hcFKFhodl6CpOL4hOwBOi5ZF+6zbAtvuwZk6BHyKkY0AavJUD/sW8dJEhQiAyVyFCJAk2WockqNFmBQ1bgkDU4ZRUORYVL0SKTRpQuq31BbtCetzOXxGcYz6Bh8B0+CGdlVdL2WbdpPYrGTYS778AO1ysr+wiom52042YWDLwHPVO5FA/aWfd6ropPwBoLnuWWnyplSJKEQOj98QuuQO/1S9JqTzrIGQHqXOdHIGj93N886xIwxuCjOvw0iIBpIEB1BKiBoKkjYFLUB73QTQO6SaEzCmqaMBiFGcpaa9n0Sy3+Dz/QcAASpJDAlaHKClQiwxESuZqsWgJXVuFUVDgVDW5Fg1OIXNuQi4KzNf6jX6J4wsVJ3Wfe0FHQ62qQN3h4h+vV1FyEk8fWwll8T1KPn25MEkRD6efIaxjao8dZOf0J/POh22w9MlIud6/Hh7gP9RREkhDg9uiC7yr2bVli4A34I8KOEII8zYk8LTnj2ceCMoZASOT6WwpcaiBoUtTRZkvghiaDUVBmgoZEbkth2/p/qcXcliJXkxVoRAl5cjU4ZAUuRYVD0eCUNbgUFW7FCYUQIXKTDEduJRrFQ96Qkahe/S4q514Fxdn9a02vr4N+ugamP3aIQ5hRtV/C3L4Rmz8d1e1jZhLNeQehu6qRVz8cmpGaah52H55TiM+O2cYHZdPw9VkDkSQEhQDNHQK6DjmFoktJicil8FMjJHR1y5NrGghSA0HTgJcGoHstcRs0KSijMJgJxlkMMWvRWvSG3ykSgUwIFCJHJrXFqxby8qpEhUOWoYY8vCpR4JQVOBTV1t5dv8yE+GyFZ9BQEJcbp5a/gaorru3yfvT6OtR+uAKSqqHwrHPhqurX7rqjar/EuDoPnlj2NpzF9hGg9SVbIFNP0souxYOdx4jP1cx2QWZAJAl6uo1IM/ZqUTohqAehyPb6yApRkK8pyNdcPXocyhh0U0fQpNBNI/RKIyEKRsiD22wEYZheUMZAOYVhMtBQ6AJlDCY/E7vWUvjGCmFovQ4gQSEEiiRDJrL1PhTDq8oyVEmGKsvQiBLxBqtECYlgGU5ZtZbLWqf1OhNlc7EXiuLF+/zbSd2vHXD1qkI9IWB6AKQLD2N6Qx2q17yLipnzoHYyiMQU6SX0rZuNJ56713Zd7xJX4AiUpnzITTvGg4qud0G6kSUJuk3KMHUVe6mxTjAMA5rNBGiqsMSeE241fTZQZkKnBoKMImga0ClFkBkwTNOK12UmdEbRpPthMBOUmZHXL5tqUOkuhNlKBAPxj1PTthdKsuJ4NMDhaERzM0Pg/eWQZAWSLIdeFUiKAiLLkFQVkqxCUhUQWYWkqiCKAqJq1jJVy1jvcOOubWjesw2yOw+lF8+E4nLHtZ338AE07dgCo6kBp9YshyRJAJEgSQScM0hEhuLJh+x0wdVvELSi6AoVjFJUr34X5TPiFJ97Z2OjDeM+AYAwBaYc6HxFQYcI8ZkYEjgGBl7EwQM34Ox7RS3QZKFIEqjogs8dDGrAoaZRQQm6hUJkKJqM+KRPNM9uW4HrR01Nqj2MMWwqbAKlFPn5n+JTfRa4aYIZBrhJwQwdnJrgpgHT0IGAL/Q/BTNN8NAEFnrPGLo7bKNEZICQkAAOTUQBUWRIigLIITGsqJBkBURVrfeKCtnhAHE4IBFLQEOWQQhB3eZPEDx1Ar0uvxb+wwdw+uO1qJh+aae26HW1aPjsY1TMuQrg3PpspglmmjD9PtSuW4micy8ETArT70XtulVQC4pQcuH0iBAPnjwGR1k5tBijl02RXkJNzUWR/2tgJR1lZtxn94PoDK0J+Q1iyM1kIMRn4ggRmlwUQmAID2juQKmJAk/qhgcUZAa7ag+DSMn3LBJCoCgKFEVBYaEMB69M+jESgTEGUB3MoJYIpjoYNcAMClADjFJwaoBTCjPgBzcpOKURIcwpBaOGJRY5A2cczNAhAaha8DUAgGfAEDTt2oqaj1ZBdrkROHHUOjjnUItKUHbRDNRt+RT+Lw+AOF0omzY3ZvLRyY0fofi8i+AZMDgyr/Cs89C4YwtOvPkyXP0Hgfn9CFafQN7Is9psHxaf4+rOXM8bM1R8JqtcEmEyTBKAzHouptzubD3wZQaIz+zyevEWD09ChCYPRZLgFx7Q3MFkJhyalm4zBCnkpLcOa4/swKIxl/TYMTJlhCNCCKA5QZJ4ile/vxyeQdHljnrNvQpNe3eAeptROfeqiLeybtN6HHn1Bbj79Eevy67uMJzAM3Ao/EcORglQACgYPRaewSPQvHcHtKq+KDhrXMyu95qai2Bu34gnlr0dNT9Tk46SkUTMpdz2lnQXkXQkyCQckoR64QHNHUzThEtzpNsMQYrwGQG8uvdjXDdyMjQlp071pEGbGuHuO6DN/Pxho9vMKz7vAhSfd0Fc+80bPByN2zfHXCY7nSg8+7x2t40kGi17O4tiPbsnQX3uI1AMT9q8n9leE1TEfQoyDU2SwGzmAU10PPjMzHjoIRhjcDt7NltckDnsPH0UkiTBzPGnzG4h9UwBQL2xwYozzRGkbghQBoaA5zg8DR0X3k8F2dztKsRn15CyLGQgW3DaTIASQmAYibUP2fk420U45/A4hADNFcZXDsH/z96fh0ly3ved4CfeuPKsu+8bjavRaOIiQZAiQBAkQZEiKdqSaRqiZyjN7Fp+MF7JejQr43lmd3zMLuznWXHpg6PRzGhF2xQFQyZFigckHhAIkAR4AATYABpHA32g77orrzjf2D8iM6uqu6q6siozIzPy/dSTT2RGREb8Misy4hu/93fsLozxV2/8lNFMgY8evANDDNQh3zIyCBCGgZQSb/ICYa3anNcOvNlppn/0t4Suw5b33t+WbQ4CkSa7Xn7pctpdE3S1IfEjB/ZuetuX76e3xKeq6q4Am36LBl4bw9BxXb+193TIlp4kiiLy6ywfo0gH2/Kj/OaR9/PcxTf5k6PfQxeCa4a3cd/et7V1P2koPl85fYLZZ38Ul4USGnp+iNE73tU28Sk9j8knv8O2938Ekcm1bbs9jxbBJpzwlaHjZKrb22fPJmhXTdBuxWOquE9Fr5Il1iRpIRagHra9/vP6gFwBIAhi1/BQTmXBDyK3b7uG27fFCS//xy++zbt33kjGUAlpDQLHYe7nP2bnx/5+x4ShO3URe2ILRmGoI9tPI5KAwFpgdOrtSZuyjM14Qa8mCo+eON0WL6iK+2wPkfLYdoR8yr5XwzBwXVcJ0JWYWpgDoJBVAnTQ+aWdh/jGmz/j169/99VXHhD86UtYYxMd9Ur65QX0Tf7+GsXm+6/T0caGzytDJ8hUd7TZlo3T8H5udgh+LVF4MvNA20SoEp+KXiUnYgEaSNn2znxJYJpGy0Pw/f+p18m52UsAjBSKCVuiSIIFr8q8U0FKydnyNLbqiLUMe8tW/IVZ5l98rmP7CCtl9Fxhw+9v1P5Ma6ejlfDteeweEqCwOfG5nnjMxvKNDJ8fPXG6+VDisz2oJKTOkK2LzoWUJMmapoHnqRjQFTkzUxegeSVAB5FHjj1FwcrihT7zbpX/yy0fStqknkJYGbZ/+Ne48I1HGb559RJIm8EaGce5eHbD72/U/uzFYvOdwMlcxPALiScfNbjvidbLMG00BnO/86WmJ1ShSCO5enOUkpSMJWxLOzCM1gVob5zZusDF2WkAhlX82cBR8mroQvDAoXv4zM3v5+PX3skXX/pbnMBL2rSeQgiBZnSuVW12z368+u+wVQ5NnyZ86VmeuazwfH+wMQ+Skz9HfuFAm23ZGBtJPFpJPLbildyMB1N5PxW9Tk6P5ddcGCZsSXuwLAPPU2WYVuTSwgwA40MjyRqi6DjfeOOnnK/MYmg6mqYRRCEf3HtLc/mB4W1omkbJq6lEpCWU33i1o7U5peugbSD0oT8Lz2+OSuEUoe5tqn5oO2m1BFO7EoCUkEwelYTUGQpND2h6BGit1ppTZ2AE6ExpHoDxFdr6KdKBlJLH3zoaD7G/La4xWQ08ciuITEPobMmpY2EpCy+/wPYPfaJj23enLqK1mOTUv0lHl7O+waa46PwZnNwFRi+9o2eG32H9JZhU9rlCcXXy9RjQUpiOGFDLslhYqLT0nt45u3WY2XIsQCdG0hBtobicQEq+8NLf8uLUaT584Lbm/JXEJ6hS0CuR3b2P0usvdWz7+X0HEYbJ7HNPr2v9QUw6Ko2+jBQew1Nv6ynx2SBuybl8mK2R9HPI9ZT4TCEqCakzDNVHmyopSUKyLLNZ7nK99N4ZrkMsVGJlPjE0mrAlinbzyswZ/sPPv8nt265hPFMkY169X7Y6pV6JME2iLtyNG8WRda2XlqSjVo610HAolK7FkL3dsa2fW3IqFL1Aoe4BLadkCN62TXy/tc8yMAK0VC0DMKaG4FPHE6df5Nevfze3bj1AGEky62i3KTTBxcpsF6zrH/yFeczR8Y5t35udJqiUKV7X34KyddYvQbWot0/Jl9cBXerxfDE6wH7nS8r7qVCsg6F6ElKaPKCqF/wKZJ6uUXFqQJzpq+h/3py7wPffegldCMazRXYXG8IpWtf/+GPXvJ2vvP4Mv3Xz+9UxUScoLWCPb+nY9sNqBaM4mGXQrjacLpHUCr1dcujyMkxquH0wUElInaEo4iH4akoEaCZjEQSteUAHQoACVF0HTVM/pH7HCTweO/EctcDjI9fcTtHKklvHkPvljGWLXDOynb947Yd8YN+tjGcHUxg1kFIifQ+jg61q7R27mfvFzzq2/X6mNPoyepBheOrWpE1ZkaXic2l5JSU+B4AIQDJAA6ZdYVikywNq23bLMaADI0BrjoOuPF19zQuXTvL0uVd425Z9vHvX5odx79t7hKOTp/jrE89RCz0Oj+/lXTtvaIOl/Uf5tZfIbNvZ0X0oT/PqSN1jePbmpM1YkYb4vLyupxKfg0GEhoEkQHAy8wBHDuxVMcBtoBED2lreePsIggDX9XCcxYfnebiuh+v6eF788P0A3/fx/ZAgCKjVXEzzSukYReA4Ln/xF99Ztw0DI0A938MQnatxqOgsX339x7ihz2duvq+ttTuPbNnHkS37kFLyzRM/4z+99Ld8+tB7B0YsySAAISgff4Xt938saXNSytojL76x0PNZcaq15eAi6wL0uBKfbaVxjaleNl9Kie8HOI5bF4YurusvEYYevu/jeUFdHMaPIAiRLXhTNU3DMHQMw8A044dlmVhWPB0ezmNZFpmMhW1bZDI2mYzFV77yPR544COrbndhocTv//4frMuGwRKgLdYgVCSPlJL/9PITTGSLfOK693RsP0IIPnbwTv7y9Wc4sXCJgyPbO7avXmD66SdwpycBkL5H4eANCKv1UAbFelhdXXrmPOWR1xieflsX7Vk/De9n6ZgSn4OKROOZzP8taTNSy9mREb79ofsxv/St5jwhRFMULheHJpmMxfBwAcsysW2bbNbCtm0yGRvL6rzGaWco48AossAPsDrYZlDRGf7Lqz/gbVv2cfu2g13Z39sm9nN08lSqBagMArzZaXZ+9O81Xwt1c9Z1JAHlkVcZmb4VIXuvI9fSoff9SRujSAyJho+uvJ8dQNM0MrNz3P8332bHv/h60uZ0ncEYZwSCUAnQfuPlqbcwhN418QlwYHgrM05p3evfOptnaurd3K090kGr2os3O4WeX0y66rb4bGWYKM1Uhk6QrezqffGpht4HGolGMDi+qq4iALfX4286yMAcVUEQkinYSZuhWCeT1XmePPMSv3XzB7q6XyEEo5kCf/ri96667tKBCMcUWNafsCC2owkdYVmMvfOe3vQsRhEkUPw4cKpIz13Xusv7v6ezbqhvz5Nf6N7NVaso8amwCNgqyhQpAb3dHKEfEZqGGykBmnpCGZKxlADtBwIp+fJrT/PJG34Jq0UB146f8t+57q6W1pdS8txwiZGRZ3javxvp+ziXznPx219j2y//nZ5LaMps3cHMT37Q1X3KIODSd77B+LvuXfH7aLTdbLB7Ng3932P0MMPs+HPL5kkREImA+fHnO7JPKXyE3PiIz7c/dD9P4vMx++V1vyeveauvv/SH2WoI2Ubfe/nJ4Crv1Yj4K/dwCzvYGP3U2jIC5mWGgnCSNiWVCE3Dq48Inb/rY+x4ZrCG4QdGgEopyWVUkkU3kVIy61bWXWMzkJIfnT3Ga7PneOeO6xnrk9qcQgjeXhrmeeM9vG/iWzwVfQqr3lHo0ne+ztYPfqxnRKgMAsrHX0HPdM+bIaXk4ne/wfDNt5HZuuOK5Xdrj7D79Q+ye8m8NPV/H5q76Yp5lcIptEiQq+zpyD5nx59jdPr2Db//vif+hEOfOsd+d3A8oK2I7c3QT4XdQwSijwRzv6FrGr7ygKYfKSU5WwnQbuAFAT+5+DovTZ2mYGaohR6jdoF7dt/EltzqrVD/7OUnODC8jV+7/l2MZgob2ncvndqHbriZKPC58M3/SvHGIz3RgvLS976BOTLG+Lvf17V9Vt54lbC8wMIrR3EunWf8nfc0lzXE57PnnuSZbz/WnJ8W8alQ9DMS0TypHj1xmiMPqUSkdmJoGv4Ax8QPjACNoohCtnNdXgaNt0pTPHH6RbKGhRf6uDI+KUURCA0ODG/jNw+/vzmE/trMWf76xM+JiPiVA3cwnhtqbuv7b73I+fIso5kC9+zp/BBYNxk+fBvFG45w8Tt/hchkyO85kKg9URAsE4DdoHjdoab4Pv+tLxM6Dnp9NGJq6t1cPPckz//0UGpjPVem970eRw7s5eSJB1QcqIL9zpc4mXkAYFk7VkAJ0k1gaAKvDz2gUsq2jOoNhAD16u2hCtlcwpb0P1JKvv7mT5l3q3zs4Duo+S55M0PRyq55QF4/tovrx3ZxsTLLl48/wzu3X88tW/fzzTd/xpxT4b17bl7Szz1dCMNg2/s/yoW//grm8BjW0Ope4I6TdDtaoeNNXyK7ay+Hpk8TvvQsz6Q40WhNevi68/i9n+fRhx/kyENKhCpi9jtf4uSJuBj9Uj75kKFEaIuEQuBaFoYuqLXYvjJpdF3geQGZzOardwyEAL1UmgGgqDygm2K6usBXjj/D4fG9/Oq17wRoeah8W36U37r5A/znl/6WZy8eZ2tumH9w4909EyPZCs+Prr+JmrAsRm5/F/Mv/JQtd3c3s/9y2nX32iqXnvgbMlu3kd2197IsdzXc3ossFaEcS9qabtDDdwQ9QkOELuVyQZomfMPAtSzcjI1r2Xi2jWdZ+JaFZ5n4pkVgGviGSWAYhMb6ui1qUYQehHDyBKHvd/hTtBdd1/E8XwnQ9XJm+hIAxZwSoBvlb078nLPlaT5x7TvXjONcD4YQ/OaR91P1HXJmf8blNsTnxMSPls1/KvrUqu/JbN/F3M9/3BF7pJTMH32W4cO3rV36KYKLf/M1hGmCEGhCR9MFWvO5DvV5CB3RmKcbCF1H0w0wdIRuoOk6mmGg6Ua8T91A6AYYxooCNygvsPXeDzXjPtOS5Z5mHr/388DvcDKjvKCKmKXHQWNYPkkk4FsmrmXjZjJ4dcHoWRaeaRI0poZBUJ/Kdd6ACynRgxAz8DH8ANP3MX0fy/MYmq9heS6262G7DrbjYnleS8XVP+e6zPTZELyuC/w2iebBEKAzFwEYLQxdZU3FSpyYv8i5ygy/daS9nrt2i89uFzhvRXxCnAA0dPjWtu2//OZrOBfOEZTmkJ6LOTLOhb/+SzLbdqJnsxRvunWZEJx86jsYxSFG3v5uNBkig5Ao9ImCgCgMkWEAYUgUBEgZQhAQyZDQ98B1CMIQZEgUhkQyJJKSKJREUsbzpYSo/noFojCuPdqI+1Tis5dS5lbn0YeDAfGC9sf/oxcQSH6R+b9y165D/NH/Jri4NRZ2vmkQGga+aRLqBoGhExoGga4T6gahLupTHamLdQvBq6GHEj3wMYMAoykSfWzPo1gqY3ketuvGD8fBCIKe6MJjaxpS9psAjT2g7WAgBOiFuWkARooJxt71IV4Q8MirT2HpBr923buSNueqBFKiaZ0/rTw/WmFi4kdXFZyXU7zpVuaf/wlBeYHsrn3Y41s2bIOUkvlf/IyRO95FZstdzaQed+oi/sI8tbOnmH/+J4zevljTNFiYZ8ev/PqG97lZzj/2FQKnmtj+Bw2JRGzyMqvacSrymlsvUdUQShpv097gocyvc/tMyMtHAvQgQA8lRhg/N4IQPQzIVT0M38cIfMwgrItDD9PzMYJgU0Lw8mSopfRLTGoGiPos9MMwdHy/Pd/vQAjQqdIcoDygreAFAV889n3evfN6bhzvTK3CduPJAL0DAvTyWM+NiE+A/J79mIUizvkzTP/wcXZ+/O9v2CZv+hJGYfiKrHp7Yhv2xDZy+6/lwjf+guFb71z0ggqRWPwngLBswnJJOZq6gBbpIAJosc3nfU88uOy1asepqEQ2X3eX17L995n/hSM7kivJtJb4bCzvBxGa1QRRnw3BG4aO5ykBum6m52cBmBgaTdiS/mDBrfLIK0/xnl039Y34BPBCH6PNArTh7VzKRsRnA2t0HGt0HOfSBc5/879ib93B2Dt+6Yr1ZBCsGssZOFVmnn6Crff9yqr7EUKQP3g9M898n4lGzc8ET3RSSsJaFXtiG0yfTsyOQUGLBFJ4LfeZX+nCPjjis7+EQFKczMSZ8EmLz6MnVj6P9ENSVOMznP73Ojy7+PqpJI1aJ4ZhqBjQVpivlAAYS7L8TR/xX197mo9feyfb8/0l2D0ZoLfRu7eS+GwXW+/9EAAzP/kBF/7mq2x930cQloWUknNf/XMgYudHfx1hLY+TlUHApe9+k7G73otRWLtT1PDh27j43W+w8OqLDN1wM0Bi3k/p1IgCn4PnvwhWd+uQDiatu5nve+JBHtx+7or5+9tgjSId9Lr47EXW8tZqdvw7TXJkqlXiIfiwPdtqy1Z6nIVKGVAe0PWiabA1239i3QsDDLG+MhhXY6Nxnq0ydud7qJx6M64ROjpOUKlgTWwht2sfMz/9IRO/9P5l61/63jcZOvS2FVtarsSW+z7ChW99Gef8GTTDQEpJ+dUXiWTI8OHbOvGRlnG39kj8JA/er8C3viV41+izPP/TAaz72VUiiNYvQhuxnqVjg+LtXAkVG7IWDfHZCa42pL6UfhKfV0PYseh86thpxnKCuWTNWReGoRMEygO6bsq1OPFhy4gSoFcjkBI38Pvmbmwpfhhi9KHd+X3XYG/dTlCaxygOI+wMQghKrx5l4eUXGLrpFgAmn/ou9tbtFA7esO5tCyHY9oGPUTt3mvIbr3Lxr/8Se8t2pO9x4dt/xdYPfLSj/+upqXcvez0mnuXx106iTcRxtaY7Sq60DzEYp6ImER2WO9qVAvTy+M6lqEQjUEPw66Pd3s80i8/G72o1/kfH4f8DzDow1id9cpQHtEWq9czbjNWfNSe7yVde+xHv2HFd0mZsCDf0MbT2eEC7jZHNYVzWqWvbL/9dZp7+W85/68ugaZjDo4ze9s6Wt61nMhSuuZ7CNdcvmz//4nNM/+hxtrynM4XxG52OljL9028zNPbfY4R5JBInd47Zrc8yeumOgROh7eC+Jx6s1+pcCW3Zemtd6FWikWItOlXvsxND6kdPnO6JOND1COtCPUR71pHQE4Whro5pGlSrTlu2lfozfubpGo7roiXdgrAPeO7iGwDcse1gwpZsDF+GbRuC7wWEEFcMwbeT4Ztv58J3vk7l1Jvk913T1m0v7XS0lNzo70H95lkgyFV3Exo13Owk2dr6wgoUMQ1R+ejDV4rQaImPdWkppdVQ4hPUEPzK3Kyd6Oj2O+HVPHriNEceai1WtRVPbCt2rEWxHgM63x491xUMwyAIlAd03Xieh+hCfch+peo7PH/pBC9Onea/P/LBpM3ZMIFsXwzooDB8+FYqJ15ruwCdmno3u+vPr1ZwPjTL5Mr9U22hPbRnuHfxQnulCBWI5XU8lci8CmoI/nJOZh7gj/mNZfM2U+KoEyKvl/a3lPUI6yE7ns46/XPsmaYSoC3heR66rgToavzHl57g0PhuPnP4/X0Z+9nAkyGmEqAtYQ6N4E1PrVn2qZNIJJEm0eXghce0y9+2VIQ2+N4HPsB7fvCnSnz2LP0z5LqUxvB2u4RdJ2M6NzsU341406GmB7S7Xfw2gxKgLeL5PoYYiI/aMs9dfJNhO8e9e25O2pRNE4Qhpq7+z61gFIoMHb6Vi9/9Btvu/3jXb0DiTj0RgXAQ0kIg8Mw5ArNMJELs6lYMme2qTf3GfudLnDzxQNwus877zmn8yT/VOXriTSU+e4woiqVnr0uO1eI+2xVj2Q2B1+tJS8N1AVryEjakBUzTIAyVAF03vu9jJuDd6VUW3Cp/9cZP8UKfsUyRT97wnqRNagt+FJLT7aTN6DsKB2/AX5hn+kd/y5b3dC7mdDUMb4TyyKtEIkCTBlL3sGtb0NBYGH+R4em3ocu0/V/bO+TWEKEN/nd8PnvqpBKfPYhEQ683Su1Xel3Y9Qtj2ViALrhqCD61BEFAxjSTNiNRAin56YXXOTZ9Bl0I7t19mH3DW5M2q60Eagh+w4zedidTP/wekz/4XlOElo+/Qun1lzEKRcbf+V68+RnM4kiz7/xSmvU+6+ye/SCf/8JDV43/BCguXNt87pnziNDGqA/Jm84I8+O/YGTythRmybc36WWp2PyY9TLXev+prdtXtIeoLkDbU0lR0c+MZPrPA2pZpvKArpdASoIwwMrmkzYlEd6Yu8Az517FCT0ODG/ngUN3kzFaa8/XLwQyxFJD8Btm4pfez+xzT3P+W/8VNIGeL7Dtgx9n7vmfcPF730TP5QlrFQhDsnsOMHTTLQjD4G7tkSvqfV489+S6xOflWP7yBghmMEx+4Rpmtz6LHloMzR5uub3kIKJpEf0YYzgISDSMnh+AV3SD0boHdN7rn9+qaZqEYXuO39RfrRfcCmEYkrXSNoS3Ogtele+/9RIXK3OMZPLcv/9WtuT6r7NRq8Qe0M0f0t3qgtSLjN7+rivmjb19ubiUQcD8Sz/n4re/xnhmnt17f7WZ8Q7w7Lkn29rpyHbHMS8NE5gVZid+zuil2xD0uQhVFX8GlsYQvEIxUh9MWugjAWpZphqCXy+zbgUpJZmUC1ApJT+7eJyXpt5CaBq3bzvIrxy4o6+z2lslkHLTHtBBFp/rRRgGo7e8A255B7Nff4Rnzz3JM5fV+8yMtrfVpsAgNEsYQbb/xWeDFlplKtJD7AHtn5g/RedodO4r+/1zLrCsuKVzO0i9AF1wq4RSkrPTmUl7emGSH549RiVw2T+0lX+Q4iH2qxFGIdYmYkCV+GyN0Zd+QtlzeP6nt7ZdcK5EqLuI0KZcfBOpu+QXrklhcpIi7chIQxey99PgFV2jnwRoO51a6RegXpUoiihk0yVAX5l+ix+cfYWileW9uw+zszietEmJE8eAbizZTInP1jh4/jjPHXsd/xfPkBu+qyv7zJX2UC2+hR7a6EE+xclJijSjYkAVS9E0qPiD6RFP/Zm74rtEUUQ+k7v6yn3ED869wn9z0/uwVHmpJmEksVS9145zt/YI3/uxjvv68xSGf7dr+xVYFEqLbWKd3DkCs3JF4tKgcoN+kV2iRIhGTlM51q2S1zw+Zr+8OGOxm+m60epD69EKb2ws04ko+f3judeI+rRsfn8gNA2njzyg7ST1V+v5WgWAoVwhYUvai64JJT4vI5ASW30nHeVu7RGe+JrJpcmTjFn/XWJ2VAqn0MOsEp9LuMGY4hl/LwaSl4N0lVjrBpXI5uvuTUmb0XMIJB4WGfqoVlAfITQNJxxMD2jqb2pmFuYAKOYGswzTIBERqY5XHebll68lY1jkTr2aqB1eZorC3PWJ2rA52n/BidC4JIuck8NMRsW2b18xmAgkQfqlQmLoAtxACdBUMlOeA2A4ny4PqELRLYJalcBxqJw4zquvvsov7789aZMQ0sS3Z5I2o6fQVGa1ogPEAnSwG7l0EkNoeAPqAU29u6hSqQIwWlBDdQpFq0gpufg3X4sj5aOIe975TgqVKzshdZvizE3Mbfk5+nRe9YpXKDqIjiRAdZjrFIau4dcF6Mjn7mDud59N2KLukX4BWo1jQIcLQwlb0j7aVYNLobga1ZPHMUfG2HrvhwCwpk9DJWGjiOuCDs0eYmH8RUYm70CkfzBHoUgEgcRXHtCOYQpw29RZqN9I/Vm7XPeATgyNJGtIG3FkgKGl/l+n6AGc82+R3bV32bxnzz2ZkDXLMYIC2fIeSqMvJW3KBtl85uuRA3s5mXmAk5kHOKyd5GTmgTbYpVAsoiMJ0y8VEsPSNQI5mEPwqT+qarX6EHwxPUPwbuBhbKLgukKxXsbecTfl13pX4IVGBd0fzPjux+/9PI8+HHDkwF6O7NvLSX2YT458iC9n/t/sFnPsFPPsFPNsX/LYJkqM94ILW9E36IQqBrSD2AaEKgY0nVSdGpAuD2gt8FS2t6IryDBAM3rz4iOFh5eZYWTyjqRNSYxYhD4IwJldP+XOYpE/fO8+DszMoF1WjTJ+HrHDKvFfnSP46T/9KzbBfudLnMw8oLLgO0xGBxkpAZpKao4DwNbR9HQKckMfS1ce0HahuiCtThT40MbWa+2kNPIK+YVrBj7+8/F7P7/s9dGx3+Ho/DfZ73xpxfV/RTvWDbMUKUFXWfAdJWtESDUEnz5c18NzXCBdhejdwMNUHtC2oMTn2jgXzmH14M2bZ84TaRLb7T3bkqYxLL9aPKhGJ6qQKtKKIFQxoB0kZ8iB9YCm+qgKfjiH68UCVPSoF2cjOGGApSsBulmU+Lw6kQyhB6su1IqnKMxdl7QZPcnS2NCVRWiEbEMClGIwMJAE6R8sTYycqRGlRIBKKXFdd93rp/qoqgYuvucjUpYx7oW+6nm+SZT4XB9hpYTZgx5QqQUYoeputhqN2NAjD+2FFUfclQBVrI/YA6pCvjpFrsvRDUEQUKt5OI5Lrebiui6u6+O6Hq7r4fs+nhfguj7VqoOmge8Hy7ZRLlf50pe+teL2PW/9LVtTrWKqvovne+gp8n4CeGGApauYnI3y/KjKAl4v0vUQE1cWns+M/l4C1ihaoSFCD33qgWXxoBrQez5tRa8SZ8ErAdopCmZ8MyilBCFwHI9azcFxXBwnFoWO4+F5Hp4X4Hk+nufj+z6+H+L7AWEYrnt/mqZhGAamqWOaJqZpYNsGpmli2xZDQwVs28R1fV588Tgf+9g9ZLOZdY8iLyyU+Kf/dH22pFqA1nwXz/PQU5aw48qAvJl8N5p+Rnk/14dfmqeg2th2gO4MuT1+7+f55IHf4eSJJSJUOT8VLRDXAU3XNbSXyFvxD/Lfen+PXCYDX/tbTFPHMAwsKxaIlmViWSaFQg7bNslkbGzbIpu1yWYtLMtqe5hhqVTl2LET5PO5tm53KekWoKGHHwSYKYuX9AIf1/BZ8KpkhIUhRKpiXDuJGnpfH1JKFl58DmFa2ONbkjZnGYGooUXqgrheHn044MhDe5eL0HSH/yvaiKE8oB2lUBegn6j9BQcygrm/3xutOG3bIgyDq6+4CdKlzC6jFvj4gY/Zo3UMN0o18Lg4c5Y35i4QRhIZbXRATcMQAkPoGJqOqetLnhtYQscUBqZuYOsGlm5gCxPLMLB1E1s3yOjtv/PqFEp8rp9L3/k6RnGIiffen7Qpy5BIFsZfojh7fdKm9A1L40FPnniAfxL9T0mbpOgjdEJVM7aDFKx4OutIDvTQjaFlGR0vD5Xqo8oNfIIgIGvaSZvSVjTgg/tuYVdxc8khgQxxAx839HHDAK8+9WUQT8MALwyoBA5+GOLLgEBKfBkSypAgCgmkJKoPJ642srf0EF66jtAEhlYXwELHFDqGMDD1+LkpDCxdxxQmtq5j6Sa2bsZC2DBb8v4q8dkaYa3C1vf/Ss/dXMyPvYDpjBCJANeeBiIiTQIRetCpoaJWT8LRVd8ldQ8iHd8oAaChAVr8pkhDyExb65suFaH3vtm2zSoGgJ9yCx/gqaTNSC1FO74qzjkJG5IA6RagYSxA7XwxaVPaii8DMoa16e0YQsewdPJ0P55USkkgJY706iI4wA19/NDHDUN86eOFIRXfxQ+r+DLAlyHB0scS728UQbiKJ7ghPhXrp3jDzcz+7IeM3/XeZfMnJn7E7p0f5JnZhxJJRJKGi2uVcfPnMZ0xRGRAFAs1NzvVln20LUQyWn1LkRYSmhWqZrUxh4ZklbqP4ecozt/YLkuARRH65KdO8HVuauu2FemjUcIri8PNvJawNellqO4fm3fSUYqpFVItQL3QJwhDcla6EnZ8Kcn0eRa8EAJLCCwM2LyWpuo7fOW1Z66YvzTjXXk/10/hhpu5+NhXrpj/VPQpDo2e5q77P8wz3/5s10Xo+KW7APCNBUqjxyhOviN1nZBc+xJeZq5j2z9mW5xk5SL1DVbroqQYDFZrYqBoP0NND6gSoKnCC+PyBFk7XQI0kCHZNnhA04QfhqsOF6uh99YRQoCuEzhVjMzyoe1j43s5dBjuAp7/aTL2mcEQIswQCQ9kun7fUoRosjNJH4/f+3kOXPgaR27fu/aKqlvnwKLEZ3cZrgvQBVcJ0FThRyGhlOQz2aRNaTNRz8XmJY0bBhiXNRxQcZ+bY+jwrVz6ztcZe+c9ZLbuWLasMRTPO57k+Z8eSsS+bGUXC2MvMzx1a6q8oJEI0KLOnZpLhT08+vDq2a2ffMjgZOYB5QVVKLrASCYWoKX1129PDakWoAAykuSznatjpegNfBmgi0WvkRKfmye/5wD2+BamnvouCJ2Jd92LUYjjqZ+KPsXd1z3CHSQXD2q7E/jWPNXiSQqla7q+/04htQC0qJ6gFMeGLoaTRqA1kpzqHhOtsY4kNGpIvRZ7UaM4sUmLBESi+VpqAY/f+/lV9780Y16J0PShPJwbRwIBBh4WLmZz6mPh1V/7GPiYeBgEmATo+BgEzYdOgE5Yf34+uwD8OSXlAU0Xst7DuphVLfvSjidDjLpXWInP9mHkCmz/0CeonTvD1NNPsP2DH2sua8SDPviZh/n8F5JKSnLIVHZ2fb+dxPJGqOXPUi2eXkyIairQpYlN8fNYj2oQCfQwg+VMIEI7rg6ghUSabD5Aotlrp1ldXrZpsESoRNVI7Q0aYs/Bwms+jDXFXkPo+UsE3lKxJ9vwvxXI+hbD5pbjvYdNyRlbGZCnhkGAhY+Jj123vvHI4DGX9fifgbKvBGiqcJ24rkExpwRo2vFDH10TKuO9Q2R37mbmJ0/iTk8uK0x/bHwvECclJREPGhg1fGsBwxtOzTC85Y1ieaMd276Tu3DVda7eSz59RFEsPdPcpnQj3s8IcLAokadCngpZqvVHjQwuNm5dDMaSTG+r2DPqW1wq+FYTeyZBXdz5S54vij2DoOfOEmP1EHY1BJ8ySpU4A3o4ZWWYFFfiyxBDLB7OyvvZfibu/iBTP/guO37l7yGMxe96YuJHTB1+N7fybNfjQUcmb6daOM3clp8yNH0EQ6pwm3axWi/5tBKhoSPbIpx6kY0OvZfJ80X+LjYeNh4ZXDK4ZKkxwQw5auRw6jLUw8TvWbHXizRG7kr+4H1bKRegcZFnJUDTjx8GzR+yojPY41soXn+YueeeYezO9zTnPxV9irsnHkmkPqhAUCjvx/VzVIdOMTSXTEJUWlmxl3xKkXUB6idtSAfYTNxnkQr/PY+00RrFSpS9wbt+pfoTV6txkefR4nDClig6TRCFGEL1K+40hRtuxpk8n7QZV2C6E4RG5eorKlrm0YcDjhzYy8nMA8seaSNCQ7Tc9Uqh2DyaBuUevfNp5NJ0gpY8oH/xF9+hWMxjWSaWZWHbJrZtkclYZLM2mUyGbNYmn8+Qz2cxjGQdrOX6EPxoYShROxSdxwuVAO0GQgiElcFbmMca6p0bOyd/FtPtXNzkILMsHnQJafOKRoCeQgGaxpuFtCE0jdrqldESQwhBEARYVmfqjrekEH/t1z6AZVlUKjWq1Rq1movjuDiOx+TkLK7r43kenhfgeT5RdPUfs2HoHRO01VoNgIlhdWFKO4EMyRj93R2qX7C3bsc5cxLrpluSNqWJm73E8FTv2JM2GiJ0KWnLkpdoiJSlICnx2R8ITcPpQQFqGDqO4/eGABVCI5fLkMtlgM2LOikljuN1SNCazJTmAThdm+HZC8exdJOsYZExLLKGRdYwyeiWKuqeAoJIKg9ol8hu30Xp1ZeSNmMZkRYh0h3SnjiX1w5NW6mmtA3BK/HZPwih4Qa9d+wZho7rukBnKgklesYWQnRM0FYqVTzXBWB0aIiS7+DWSngywA8DfBnghyFBFF6xndUOAw0NU+jxQzewdANbmPHUMLF1k4xhktWteGra5AxrWXa2ojMEMsAUOlf+NxVtRwgi2TvftEQCIZJAidAu0vSKpqTghIxA16LVLwAKRYfQhYbbO6fUJqZp4rqdqw+VqrP1UkFbLGYJ/din/eHr72xLPGogA2qBT9X3cAOPWujhBD5u6OEGASWvhhcGeHWB64UBgQyR6zyjaYCu6Zi6jiligWsJA1s3sOri1tZNlhejVgAEUmLqhhKgXaB64nVyu/cnbUaThfFfkKnuUOJTsSkkGkIJUEUCmELDD3vvwDPNeAi+U6T2jB3+cB7Pi5V7u5KhDGFQtAyKVmd6y0spcWRAzXdxAo9a4OGGPk59OudW8cKA9+xSpWYuJ4hCTGHgJG3IAODOTDFy6zuTNgOIvZ+RJslV9l59ZYViDSI0jBTFgO53vrSuYfjF9pIGHjY+xpJOQyYudueNHXAMnR4VoCauqwRoy1RDD8910bT+8RYKIcgJi5zRmYDfNBNKialiQLuCWRzCuXSe3O59SZuCFC5CpvY0pugiEg2tZfdnXLZeRzZ7/wgkgqj+OkJoEbq2uEwnQlu6nibrr+P3aIDQIvxIx4kMapjUpEkFC6++FYD3W6+T19YeHv1jfmNdn6LRXjJ+xN2HjHoTy0O81uJ3omgVU9dwgt67+bEso+nI6wSpPXPXfBfP99FVgtFAEMgQS0/t4dxTZLbvxjn31jIB2ugLf9f9H+aZb392+fodLEwfmhVEqDw0LaFJatlzoNWDg7QIiECL6vKrMZX1aJ9ocZ5WF2j19zxz1zt5mRr3mG+gARoRAtC0qB4oFDXnx9PF55cva2578x+QK8fRV5q3nDweW0UZj3Mt7S2K4k8SoSGhPtXq35qGjJYvi9AIm+tryKjxnjgFKow0/EjH1EKKwsUiwDbCuP2ktihSbAL+wl278sO/y/y/WvosimSwdY25ugd05HN3MPe7zyZsUYxlmXie8oC2TC2IM+h15RUbCEIpsVSyV1fI7TnAwrFfXDF/YuJH7N75Qe74zD3Nec+ee7JjPeIlEt9aQISZzuwgpUSaJDRqEAm0piwELdLi11GccBk/X1wOoq4RtfpU8KU7z/K/XrrEH4a/iiT2BzYFWaTV52lLRNmiOJP1okeyPq+xj6R4t3mCs+Ewp+RYYja0wsft3qpEodg4tgHhOqr8dBvTNCmVOtfgI7VXbCfw8H0fQ1cCdBAIojgJiRTFcPUqwrKwt2zj3NcfRc/mGX/XezHyhbgl53WPMDX17ua6d3BP29tzBkaZhdFjaNJARAb5uWvbtu1BQJMGhdLBTW3jvifimqC/N3ouFSWYYjSV3qlIhIwOUvaeALVtk5mZzhUoTa8ADWMBaqni5AOBjCS2MAE3aVMGgvF33kNQq+JOXmT66SfY9oGPAvFQPOOL601MPMKDn3mYz3+hPSLUN0qURl9haPoIhlSez07SEJmr8cmHDI6e6JIxXSBKYSF6RX+QM+NQjl7Dsix8XwnQlnHDAD8IsEwlQAcBGUkMFe/bVYxsDmPvARZefI7S6y9TvO6mK9a5PDZ0MyLUM+cpj7zG8PQRdCU+O84nH1r78nD0xOkUeT9V9SVFcuTNCNmDCjSTMZUA3Qhu6OOHAcVsZyr4K3oP1dEqGbZ94GOcf+wr5A/euOL/4Nj4Xg4dhrtgw/GgnjlHeeR1RqZvQUhVJaLT3PfEg/xv7zI4euL0quukSXxCLEDT1AlJ0T8UzN487pQHdIN4YUAYhmQ61MNUoVDECMuicM31XPrOX7H1fR9BrPCbayQo8Y4nef6nq9exDYVDtXgKiDDdMSxngsCaozz8BiOTtyBQv+dOc98TD9aH19Pl4bwakYoBVSREQ4BKKXvKkZLN2gRB59q79M4nbTNe6BOEATm7M0XjFQrFIsNHbqdw7SEuPfHXSHllHN1T0ac4c913uGPnPTizn11hCzFuZopIk1jOBL49y+yWn1IafoORyduU+OwCgyo+IY7BE20rBaVQrJ+CGd/6LHSu5OaGsG2LIFAe0JbxZYiUkpytYsUUySKlZOaZ70MUoQkdTReg62jCQNP1+iN+LgwDdAOh62iGET90M55v6AjDAiF66i65QeHgDfjlBc597c/Z/uFfw8gs/+2tJx7Uy0yTLe/B9saw3QlmJ37G0PQR1WazSwyq+IRGVdLeFKArdTT6Y35D1flMCUU7FqCzNRjpIclimgZRB2NTU3tWDyMZC9CM8oAqkkV6Hv7sNCO3vRMZBhCEyNAnCkMIQ2QYIv0qyJAoCImkJJIhURjWpzJeJiVEMp422VjR7eWra7EwFgKEaIpkTYi6SBYglgvlxWn8XNTFcm7XPoKFOSonXiW//1qEboJhXCGYVxKf1dwZhDSxvcU6jJnKbiojxxmaPbz+z7NJ1vLQXo1OFt3vNI24z0EUn9DohNR7rKedpqK/KdQHd2YdyYH0DkxfQWoFKMSX4GJOJSEpEibwEXaW7M49SVuyIjIIiIKAKAyQgd98HgXB1QVzKJcI5frU96mdPkntzOm6cI57wmQCjx85VSoLC2jjtSvsEJFBcebmZfOyte24uQvMjv+cfGk/ljfa0e9iM+Kz8f5+FaGP3/t5Hn34QQ596oGBFaEqCUmRBA0P6JyTsCFdJrUCtNG/VGXBK5JGBn7sRexR4uH9zp4KDk2f5tbZPJ//wkOMtSjQRqZvReIxt+UFirPXYwbDHbFxs+IzDTx+7+f55IHf4eSJwROhvZiEpLyfg8FQvZvwvDNYN0CpFaClchmA4XwxYUsUg04U+DDALWHv1h5h9+wHN1WMXmAxPP025sd/wdDsIYyg0FYb2yk++9kLCvDowwFHHto7cCI0gnqT0PagxKNivQzVPaALrhKgqaBcaQjQ9l6oFIpWicKwpz2g3eKu+z8MHFtx2VqlmRro0mZo+mYWxo8yMvl2RJtipTrh+dzoNntBuDaG4vlU0pZ0FxlpbROgSnwqWmFYCdB0Ua5UABgtDCVsiWLQkX6ANsAe0Eb2+x3cs/pKV6kP2sCQWQx/CC8zScbZ1kYre4N+9572MxEg2jAGr8SnolVGMvGBNz9gnaRTK0Ar1ViAjhQ7Ey+WBpzA42vHf4LQBKbQMYTAEAamrmNqOqauYwkDUzewhI6pm9i6ia0bWMLANkwscWWGs2I5Ueij6YMrQCHuhjQx8ciqy+/ggzwzu74h+vzCfkqjx/CyUxRmb9hUiaZejPtUIjQZ4hjQzfWCV+JTsREaArSkPKDpoFqLs2wnhpQAXQ1LGMy5FT5+8E78MMCVPl4Y4IcBngzxZEDJq+HLkECG9akkkAFBJAmlJIwkl5f8iWOplr9uoC17rqELgaHp6EJvimCzLoJH7AK3b7tmQ5/tbu0Rnop6YwwxCsOB9oA2WOv/cfd1j/DgZx5eV5yoLjOMTN9GLXue+YlfMDx164aG43tRfDboBRF65MDe1SImUkkvJiEpBoPRugAt+0qApoJqtQrA6NBIsob0MEIItmSH+OuTz7FvaCv37T3S1f0HMsANAtzQx60L34YI9sKAH59/rWUBeutsnud5NxMTP+qQ1a0ThcHAe0CvxnqK1F9OtrYDNMnC2FFGZm5paX+9LD4bXG5j+wSpklkrsdkkJOX9VGyUsVw8rXhKgKaCmlP3gBY7Wzew3/m7178LgD998Xtd37chDAzLIM/KrR9emDyxoe02ROjdE73hBY2CAJQAvSrHxvdy6DDcBTz/0/W9J1vdRSh8FkaOMTR39RhS6A/x2VmufpFrdkTqvDE9Q1yIfmMCIBnxqW4k0oJRD2Mr+4P1P01t8J7jxhVdtwyPXWVNhZSyJ09lfhjy/KUTvDz1Fq/PnuPU/CXOlWeYri6w4FVxAm/FvuMQi9CpqXdzt7Z63GG3iMIAoQToupiY+BF37LyHW9+x/rHfQnk/oVFF0rmexb1At4TzfU88OJDtOC8PHVovyXk+B8tbNgiUe6wXfIPVrrObJcUe0DidrJDLJWxJ7yOBebfGF19+gk/fdG/S5jQxhM7J+UtkDBNfhoRSEsiQMIpjUcOoEYMKB4a2rriNqankPaEyDNEtO7H99xNPRZ/i7useaSkpCcByJqgVzpAv719zvX73fnY6NvS+Jx6sez87tosepn1lmLrFRkWzovfQNKgEvecTFEIQBAGWZbV926kUoJmna7iuo36Y68QQgt+546N8IYFh+LX4wP5b+OHZY3ziundu6P23zuZ5frTSFKFL6aogDUO0DncaShNL40HXPRRf3sPclmfJlncvy4rvd8G5Ep0SoYvic/C8n1CvA9ri9T/JuE+DgBo2OQasdk9K0TSNaheG4KWUOI6H67o4jofn+biuh+v6uK6P7/t4no/vB/h+wPx8ma997QlcN3bPXq3qTa22/n6iqb0qup6nygP1OXuKE5R9Bz8MMPWNHapLRegyxttg4DqJC9Gn9qfWEwgE+fmDy7Li0yg+G3RChA6y+IT2d0LqNFkc5hgmx6WkTRk4AgQeFg42DjYuFi42LiYeFl596mPiYeJjEGDg1x8BBiE60RI3mab975RDwVm2MXXqXF0QxgIxfgT4/qIwDIKAIAgJgrBl+w1Drz8MTHPxYVkmpmlSKOSwbRPbtrjllhvI5TI8/vhP+KVfuoVt2ybW3PbCQonf//0/WJ8dLVveJ3hKgG4ArekFjYsyCwxNsDU3zAf335qIRTeN7eaps8c2laF/62z+inkTXRyWj2SosuC7gO2NISsOc1ueRZ96DRX0oGgF2WdlmPLUmGOInQMqQD0MnKbwWz71msJvUQB6mARNMag3ReBG0IjQCTEI6lsNms8bstPCo0AFCw8bH7tumY1LFhcLD2NJ3dnfEbAQWDzJXfDym2uKQssyyWQsbNsmk7G6onVs28J1/bZuM7UCNAgCdFV7sSU+c/N9y14HMqAW+Hz5taf5wovfQ0YRv3XkA1216a4dN/CnLz3O46cj7tv7tk1t69lzTzaf66Pdiw1VHtDuka3tJDr3CM6Bm/BliDk3lbRJHaMXaoWmjX7ygOapsECxY9uXsMSbF4u5WFoZ+E0xpzc9e0HzoS+ZNmSajkQQohPWp3LNHGiNlZKs5hhihAUABLK5R5NwiVWxZTY+OWqMsFAXgV5TAGbqYtDC65lMbF1oiKDGP+BrzH34/5m0OVdgmkZzGL5dpPaqGPgBhvI6bQpDGBQtoylM/+Tod3nu4ptcP7qTgrVy6aR2I4TgNw/fx7dOPMt3Tj7fNk/s0lJNl9NuURrJEM0w27pNxeoIIHPiZZxrDhPaOayLp3vmItNulAjdHEtjOF/ket5kL3+ufzdBi9ZPFodnuI1jXEeIQCKWDemGCHxMMhuMEY29fIuyUUfWfYYBBrIpM40lnkC76W8M63J1URCa+FjNh1e3uDX+mN/gH/FnG/o8vY4hNLywd2+ATNPA85QHdF0EYYCuvE5t5Z7dN3F08jQ/Pv8q147s4P1739YV178Qgvv23cKXX/1h27b57LknuYN7eJ7F2NBG8fp2l256Iqxy2LjAFs3oibqkaaYR9ymAzJsv4Y9vp3r9rQh/A3fuUQREEEVo0fLXRFHsLYtk/TX19WRzeWNZPI8lyySabCyvryPl4vIly9IqnHuByxOIBHKZgOt1NCKu4wR385P6sK+PvsRreJZtfJ+7eICvJWilYr0YukbQwwLUspQAXTdBEGCa7S8bMMhcN7qTg8PbuVCZ5ZXZs3zjzZ/x8Wvv7Mq+c4ZFELW3FllThI5WAK5MVGoTjvMzyuUb2LLlaEe2r4i5POlIAPb0BWS2gDl9EaNWWve2mkeaEPGDeBppGpEmQBPxDjRRf63Fy9GIRP01gK4TaQagIRvrLXlEjbTr5msNWLLOOqjpzzWfRyIEIjS5+qk9Elf+ju574kEe3H5uYBKQVspel4i+GoJ3yDDGPENUVlxuEGzAx6hICkuHctC7x59lmSoGdL2EYUg2q4bg240Qgp3FcUYyeb7y2jNJm7NpGiK0kxzzNI6UC5zsgZqk/cDd2iNM0drNwFoZ7+bsJN623YiTx9Z9OW6uJ2X86GEyo7/cfF7LnSUUHoXygTXfc98TDy57PUi1P1crnRSio9Pb/+ulxAJ0dtXlOqESoH2EpQsC2XpGe7ewLJP5+ZVvdjZKagVoEIaYpoq76xQ138PscoythoYTeGSM1j3bSxOQWlnWDhbcOY5d+hnvytzbUy1C+4GVhOXlMY9XK7dkVOYJs3mcAzeROfEKoo9ERstEGnG8wOo06n0uZZDLLzUIEX11bDhY5KmtutxUHtC+wtYhlL3rAbVtC9+fa+s2UytAQxkiDJ0n33oJWzexDZOMYZLVrXhq2ORMC0MYBDKgGnjkjPi14upUQw9TdFfg7yqMcWz6DLdtu6al93VaYF4NGcWjuEvjTldKflqJQRWqt87meXaVZRup72lPncN3q9QOHiZ76lVE0KM97zbA0u/Dj7YQWTbO7FdXXPcjL7zRrPe5lEERn2sVju8/D6hNbg0BahAQKgHaN2QMkHUBOvK5O5j73dXOgMlg2ya+3952x6lVW6GUZKwMGcPEDQJKfg0vDPBlEE/DAF+GyHrMjy0MfBkSrTsGSMMQAlPomMLE1PVY6OoGlm6S0U1s3SRrWmQNi6xukTVsrJR0xJmplbG67AG9eWIfT515qSUBmrT4BIiImpeBlZKfVmJpQlQaRehaiV5TU+/m4rkneebbj7V1n2ZpDuE61PbfgH3+JEZl/TGhaeFbtxzk0Yff4MhDe5XX8zLiGND+EaAeJnmqqy5XHtD+ImvU8xx7lNgDqgTouoiiiKFcnjt3XN+R7UspcUKPmu9RC31qgYsT+LihhxMEzAZl3CAWvG4YEMi64G0hkUbX9Fjg6gaWbmAJY1HgGiYZ3cI2zFjg1h+WMLqSmf7EW0f5xLUba5G5UXYURin7a5cU6QXBuRJL/yerxZ02kqFgeUJU2obs79YeWTPh69bZPJ9vs/hsoHsO2TdfonbN4TgmNGjvCTV5onoi0+osFaEc65JZfUCI6CsPqIdFYZUEJFBJSP1G3oyIeliB2rZF0ObzZSoFqKwnDeSzuY7tQwhBTmTImZ2phymlxJMBVd+lFng4gY8Tejihjxt4zLlVvHABL/TxZOzRDWRIuA6B2zjEda3hwa0L3Kb31sDWTTKGFXtx6+I2Y9pk6gJ32M6xb3hrRz77WmgaBFJi9HmXq6VC+Y6dsRhdqWMTkKq40Yb4XO2zQudvIoSU2GffxNlzPbkTL3d0X91mvUWEGiL0kw/9M0rH/nVHbeoXZJ8J0AhtWSedy+nvM+TgkTfXP/6aBJmMtaG2n2uRSgE6tRBnBg7lVr/I9TpCCDLC2lDCzXpoCFwn8KgGHs4SkeuGPiWvxlSthNcIWZABfhhSDVwW3GrLcZjtYtQu8ObcBa4f25nI/jvB1QRXc8i+i/3rO0VDfK71mds99L4SRq1C4FRwt+7CvnS24/vrGlHc0Xw9PH7v53n04Qc59KkH1FA8cQyooDezkCUQ1FtPevVHkM7L98BSNOObCSllT7YRV0Pw62RqPhagw/nOtSnrd5YK3JEW3/u14z9mdzEZNXRy4RK1wOWFyZMYQqALgaHpGEJgCIPJSgVd0xDErc10TYtbnKGhCzA0gS5AJ54K6Mkf+1KePfck+ugdVy2Q3+se0kPTp+Ph9S88lLQpAFjnT1G75jDG3DS65yRtTtdxZj/L4/d+nk8e+B1OnlAiVCIwaG+dw43yN9zDaxzEYjFZTizrRBRyDacStDAZNCICxJqe336lYMU3jmUPhrrTaLAljA7kr6RagI7mhxK2JJ1oaFgJVAt4eeotdhfHed+eI4vJZDKse2cDfCmZq2qEgBdJoiAuayEBGUXICCTxNKq/jlpIO4PVOhSDQENoIDQtrklef61rGl64+ZNlo3XomvSwh/Ru7RF2z36wp2J0BZB963Vqe65DeC72uZMI2ecxoS3GkDmzn+W3n/63HHmXEqHxEHxvDIKWyfHL/C3XcTJpU3oKgwAXC4P03TA2BOis05sCtBOkUoDOLMwBMDE0mqwhKSWQIa/PnmfOrWDrJpZuMGLn2ZIb7uh+dxZG+cHZlxnPruzZfvbck+wb6lzc72pIKeMhMhkRRvVH8znMtqF7xHoK5k/0aJzo3doj7H49Fp/dGF5vBeF75N98CX9oDGdfSmJC19lBqYEz+1kefTgY+KSkuAxTbwzB60T46bw8bwqdEBebfAoF6FA92m62Jtk30tujcu0ilUf4THkBgK3DSoB2gvfuPsyx2bPMu9VmfOiphUkevO0jHd3vSKaApa98yCbpWRNCIABjlXPGydLqpVJaYa3PeMfOe5jqwU5LjaSjTpRVaiea76NtpF98SlDxoFDD4iXeySl2AxEzjDYHvG/mGO+je53fdFXDc0UMQlzS2WK7aMc3jnNrF3pJFakUoLOleQB2jnU/S3sQGM8N8Z7c8vCGL7z4vYSsUcDaRe6TEqQN8Rm+9GxPi08AUSshrX1Jm7F5omj9qfBLaMSD8siDnEyxCN3vfGnVYvRZPN7Oz9nJJSQaezhPBg8J/Ad+kzJ5jvAqeznDD3gnHgZ7OMc+zjJPkQts4QivtEU26kSEqFbSl2MQ4JPODodFO57OO70RBtINUnmLNV8pA7BzZEvClijazUqdynoprjBJnj33JLfO5pmaeveyx9WSlzpFI+O918Un1JPRfJeg3xMXWxx+X0pDhB45sHfNjkH9zlriOoPHQU5zHafI1BOABPDb/Gd2c4FnuI0/5h8yR5ECVV7iBv4zv8bX+SAn2cvn+U1+xO04WLzI9RtKlYnfowToSsQxoOkUoMN1D+i8OzgCNJUe0LlKPAS/fWwiYUsU7WZLrsjRyZMc2bI/aVN6kpViRZMo4dTIeO+nmwNz8iz+ll193SFJ2lm0TWT0D0pS0kqeUA25auF2i4DbeInbeGnN7UrgP/JJXuRGhlng+7wLg9YS2zQiBJK380JL7xsETEK8lA7BDzUE6AB5QFMpQEvVuDvEWLGzSTGK7vP+vW/jT198nJ9dfIPrRnbynt2Hkjap57hc9N3BPV1NUFqa8d4P3s8GhlPF61Dd3W4RGQZ6ZfX+4OthUJOSNCKijcQvLEEAv8mj7TFIcQUGQWoF6Fg2PvYWBigGNHVD8Jmna5RrcdJHr9d3TAtx56nNnbjXS87M8OBtH+G/vel9nJi/wC8mT3Zlv/3Ms+ee3NBQ/N3aI81HK++Zmnp334nPJhoprDDYOnFSUjBQQ/GixZJsiu5jEqS2OsBIplEHdHCOwlT+J8vVCtomYqEUrbOeFqDtRAjBPzj0Xv79c1/jnp09XACzR2jUEV1vlvzl/doPcXpd+5miP5KOVkOfn8bdcx3Zt15P2pSN0aZr16AkJV3OZj2gis5i4qc2CWlUCdB0UHFrSoB2mW4LUIBfTJ6kaKXyEG47l2fJryVCG3U7d29wX5/vU/EJYE9fwLFsnB37yJwfvE4zSxm0TkkaUgnQHsfEx0upAB2rl7AuKQHa39RcRw2/d5EnzrzIodGNypWNMe9U+MGZX/CubSNd3W8/0xChZ9bIzVtat3Mj9KvncymZ86eo7b0ef2gMc2EmaXMSZZDiQeMuZ0qA9jIWPhXySZvREYy6Zqn0RjfYVWlnr/rUClBdVyUsusWlyjx37byhq/v84rHvcvN4Ud1otEgsQj/I3ddd6QVtiM9bZ/N97cVsB/aZ4zj7Dw28AAX41i0HefThN/jkQ/+MoydWD8U4cmAvAEdPnO4Lb+nl8a1xDKg6n/QyJj5BOmVLk2oPe0B1XeB5AZlMexLBUvmfdHxPCdAu8pFr7uDPX3mK9+05gkRi6yYHhrd1bH+NLO9hK51DMZ2mIUIPjS4XE1PUxecXHkrIst5BSAlhQJDJYTjt6WTVzzQ6JR1j56rrPFovN3SMnT0fN7pyctXms+AVncXCx09xfVRN06i2VrWrqxiGgeO4SoCuxrPnnqTsVNCFaLkG4R071+61rViZITvHfXuP8OrMWYQmeHPuAv/D7b/Stu03/o8Xqy6jGRODuOGLYuOs1lu+n+p2dhrr4lt42/ZgnHo1aVPWT4d+GM2kpPXSh8lLAkmQYnGTBiy8VHtANQ2qfu/eBBmGjuO0r2VxKv+TQRhgZ7Itv289F18lUlfmutGdXDcae0f+f0e/y3955QeMZQpszY2wszDKltzGarI2/icvz5SoBCFvLlQJpGT/UK5ttg8qKx3vaYjhbBeGU8UTBlLoCBkmbc766VACpjP72XWv28vJS6uVllIxoL2PlfIheKFpVMPePQZN08R121eoNJX/ySAMKRid+WjPnntSidCr8JnD9zHtlDhbnuFSdY5nLx7nxrHdvHvXjS1tpyGQKn7Agh9w17ZRAAIpmwHbis2hBOfamLMX8bbvIXPuZKJ2+ENj+OPbyZw5jvDb54HoFEuTl06eWC74ek2QNtDoXj1jxcaw8VJbBxRAFxpODw/Bm6aO67YvSyqV/8kwlJhm5+IDlQhdGyEEW3LDTa9nICVfePF7LQvQBrYuEEu8Ov0kPqWUqrh1HxPmh9DczXUW2izVfTeihQHW+VPU9t2AffYkRq3324U2kpeOPLR32fxe9IpCIwlJCdBexsYjTHGYhBCipwWoZRl4nhKgayKlxLbS2a6rHzGEYCyzdg/3pcPBXiA5U6khNA1daOgalL2Ao9ML3DSS76sEMwkIdVHrW8zJc7g7D8DU+UT2LwGEIHvqOADh/DRhvrC2AO2hGsiP3/t5fvvpxdf3PfFgT5d0kuq32tPEMaD9c/5vFUNoeGHvuiziIfj2jcD0jyupBWQUYdudFaAqWaM1fvnArbw09Rb/5y++w/ffenHNdaddj0nHJ4wiakHIvBewJWsx5/q8ttBfGckyAoj4+eQ8Pzw/w+vz5eYyJ5BUgwBPquaPvYruOWhSEuSKXd+3BNw916OX5xZnRhDmh/umXagz+9llj9grGlA89M+SNu0KhCpE3/NkcFPtATV0Db+HBahlmfh++1y0qfSAAmRsO2kTFEvImRk+dehupJR88dj3OVeeYWdhDLhSzJtCI2cIDg4vLzgspeT56RI/vjjLjSMFhu3kyzAFUsZCIZAcmy1TMHVuHMk365PKKKLkh9w4mmFH1uank/NcslzOV1yqQYili3gbEdw2MUTGSO/JtV+xz72Js+c6jBMvd3W/3o79CKeCPXlu0ZapczimSTC6BWt2coV39e7Fq0GcTf87nMx0fyh+rd72mqoD2vMI0p0oZuq9XYbJskyqVadt20udAK068ZeTyygB2osIIfiVA3fwjTd/xn97830repINIVjpJlAIwe1bhil7Ac9NzfdED/gXpktUgxBbCA6NFrhUc3nm4hxCaIQywhQaW7MWu/JxVYbbtgzz7KV59hQy3FIYam7n6PQCZT/oqgBVCUjrQwQ+kW7Uwyk6jwT8rbsICsPk3jh6xXJr8izO7utWEaCK1VhLfEIsQBWKJLF0jZLs3eNQeUCvwmwpHuLMb6AM0+VM1Vxen6+iaXEcXyMeUdc0dE1jPvgFlm6S1c14alhkDIusYZE1bTLCUJ16LqMhOEteadUwBlNoyDXqGRYsA0tP7nv1AsmzU/NoGhRNgzu2LJaYKloGe4pZBLFYOVNx2VtcPBYtIXjX9tErtrktZ/PmQo2JbHdunJT4bA1r8iy1gzdjnz+FtLMQRVhz7ReAEnCuOYy+MEvu9RdWFLwiCECIpiCWQiCtLIZTAfrBB9qbaESpjAHt1o2TYvNkDAh7WIBmMrZKQlqL+boADQ2DZy7OAssLayz914q6kNQ1DUPT0EUcBGxoAktovFVxuHmsSNEyCKTElxJPRvhhRCAlI3YBN/Qp+Q5urYQnA/wwwJMBQRgSROuvHahpAlMTmLqBpRtYwsAyTGxhYOsmtmGS0S2ypkVGN8kZNlnD6iuBu1RwrpUn8Va5hn0VgZlkIfpKEJAzdW4ZH1pxubXkf7JUfK7F1qzNnOvz0kyJw2OdjTdU4rN1zPlpNLeGv2UXwq0irSxOvkjm7Jtt3Y8/sRNRKWFPnVtzPb00h7P/EHp5nmBkHC2UBNUSWqRKCa3E1byf0BiCTxexqBaIvokavjpp9lRndK2nBahlmQSB8oCuykIl9gLsHR1q1o1cCVmP3fOkxAsj/LrA9MOIIIoo+5Ix26RoxV+RIQSGECyVE7dvu6ZtdntBgBN6VAMXJ/BxGtMwoBq4zLkV3NDHlyFeGODLgEBKlkrqCPDCgF+/7l2M51YWR0mxVHzOuz5eKHGCcNmQcyAlz08tkDP0q4qwjC44X3HYkc90zObV8GWE0YFM4+tHCjw7Oc/pUo3debttNxdKcLYHw6livPV687WzbS/Ojn1kzp9qeVuhlcHdsR/r4ltNzyVAMDxG9o21k/QA7MmzBOV5gqExssePIoDKwZvRS3NKfrI+wXk5cSH6/rmhXw86IR4mBu0rHp40aY4BzZpRT3f5y2YtfL99TTlSJ0DnyvHJfLhQWHM9IQSCWFjmNvgttLMeqGUYWIbBkL25Dj9vzl3gO6de4JM3/FLPeEeXik8pJS/PltlTyPLC9AIaGttzNpdqLr6M2F/MrktUHhkv8szFuYQEqOyIAAW4bbzI0Zky5+qB3hqwv5hjW07FNPcamYunqRy8uaUhTikM3J37iUwL6+JbuLuuwajHefrD4+hOdd3bMmpljNpiVQUt8In6qERZp9iI+ATqXsJ0iRtBlOqyRWkjb0REdQU68rk7mPvdZxO2aDm2bSsP6FqUq3GZnpFC/iprtodeK0p/zch2Xp05y1def4Zfv+HdidiwVomqV+YqjGdMDgzlODCUo+IHnC7VuH4kz5C1/qz2F2dK7EpAfAJ4MsIQnblQCSG4ZWLRex1IyY8vzlG0dHId6u6l2DjG3CTu7mvJnjm+5npxJ6MdQIQ5fQFzYSae7zkE2TxGrRJ3Ojr5yoZtEYFPmCsiEi6cnyQbFZ8x6YsBFYQEJF8tRLE+8mZvh4FkMhZB0D4PaG+4yNrIQiUWoBND3avb12s1QT98zR0seMlchFb7LrxA8pNLc4RRxPUji97pvGlwaKzYkvgEcELJgYT6wQdR5zygl2MIwdvGh3h+agGp6oX2HPb0RbRI4m7dteo67tZd+GNbyZw6Ru7Ey03xCWDOXMDbtocgP4wW+JvqOW+dP4UxN00wto0gP3z1N6SMzYnPRh3QdCGQqfOApjkGtGjG5/hePde3e1Q1dS6Vci0WXmNdFKBwpfBK2iuqafFBvNYBc7Eyz7xb5vqxKy+eb85dYM6tNH/qh8Z2kTPX9jiuJcSfnZrnuuFcW7K8pZQEMrrq5+sUoYwwre55SoqWwa5chh9fmmdvIcOFWtyJ4rbxYs+EWQwymbNvUtl/iHBPDi2KQAhEZQFz+hLurgMA5FbxbBqVEmG+jLttN9mTm2sPJGSIPX0ec3YSZ/8NGG/Ob2p7DSSAYSFNi8iMp+gGkWES6QaRbizPKtQ0RKWENXkOIXu4qOFlxDGg6fKA6kiC9F3mU0ux3j+n7MFQMgN8XSV1R2alGgvQieLaMaBpZyJb5BeTJ7l1lUSpU/OX+OuTP8fSDWadCoau8+Pzr/HOHdfz4tRpMrrJ1novdy8M+MvpM/zGTe9ddX9X8wJrGm0rMSSEYE8+wwszJW6b6L6nJ5Bgdln47RvKMZaxOF2uce1QjjnX56eT8+wtxGlxq8XCqgSk7qBXSyB0jLkphAzwhydwromz1O1LZ9Z8r33pzFXXaQ1JpAn84ihmabY+BxAG0opFZGRYRKZJpC8RkWsc01oYggzQggAt8NE8F71SQvNdNM9blmUtgWB0C86+60HT0EtzmNPnEXWvjjP7WX776X/LkXf9Tk/1hU9nDGj6BGjabhKWUqg7NmYdJUD7jpe+/DdUnTjbL5tJ9r+XdGzoh/bdzn98+XHGc0PsKU5wen6SkwuXsHUTXwYcmznDP7zpvWR0iy+//jQZw+If3nQvX37taT647xZ2FZcXef/PLz3BudI02/OjTa9bK6EHss0ey31DOc5dmG3LtloliKKuC1CIPaGN6gDDtomt68y4HjOuv6IAVeKze0SmRWRn8bJ5rItvYU+ehcmzidgipCT7xku4e6/Dn9ixuEBKtDCoP3w030fUKmi+h/A8kEFbYrIEYM1OYs1OIhH4E9tw9t0IgL4wjTl9EWf2szz6cNDWvvD7nS9tehg+beJGT+EQfJop1D2gszXJvpHeHN1yXY8vfvEbfPrTH930tlIlQAEqTvvaRG2WJEWoZRh85MAdfPX4j7F0g2Erx76hrXhhQBCF/Lc33YdVT2r5ezf8UvN9n7n5vhW395Fr7uC/vvYjTKGTM20+dePd67bl1EKVYdts+3Bxl8IwryCMIiw9+QvV9rzN9rzNjy8mI8QVi2Tr9UCl0KkduAnxxtFEA+wFkuzpVxO0YNEOe+o8TJ1HCoE/sRPnmsMgJd/c+Q945OH/hcOfap8XdDMiVBClUICG+Cm7zKc5BnTYjo+/uR6umvWbv/kJvvSlbxEEAcYmE2PTdWQCjuchklImPcaO/Cj37L6Jw+N7Ny3+xrNF/tEtHwLgr47/hM///GtY9daYa1HxA85UHN61bWRT+1+NJOJAwyhaVmy+F1Hez2QQMsSYvYS365q2F6nvd4SUcajBpTNIw8TdUuUvf/23eOLSSR7b+v/gw86/StQ+DZm6LHgdSag8oH1DsR6lNu/0tsg2DB3P27wA7e2r6AZwXBeth8RBkhnyQgiObNnfdoH28Wvv5K5to+QNnWMzpRXXuVBxefrCLC/PlrlptNARkbg1Y/GTyXl+dGGGt8rdy/qXUW+Vq/bCiJdmShybKfHaXJnj8xXc8e24Y9vwRibwh8aQPWVxurFnLhJmkqnQ0C+IwCdz/hSjU3dw+7M/40Vu5BP2i3zQeo2tYuVzynrZqDdVpNCzJggJU+RnkkCN9AZHNjyg825vH4uGYVCrbd5Nm7qrkuP56D0kQKH3yjS1ixtGC3gy4rnJK7Ntz1cdbhjJ846tI4xmrI7s/5rhPHdtG+WurSNcqLocn69c/U1tQKP95Sg2g9BgS9ZixDbJGjqmpsXxCYaBtLP4Y9vwx7cmbeZAofVoGZVeZGR+gb/HN/mqezPPBzt4m3GeT9gv8j7zOKNUN7TNjYnQNA7BpysG9HUOkN/gMdEPDDUEaI97QE1Tx3W9TW+nd66ibcLzfUQPdgNJqwi9ZWKIQEZ4l11whyyDKWfzB+h6EELwjq0jTNa6s79eOzUYQmNr1mZHPsOeQpZ9QznsqfPYl86SufgWerk95XgUik4zKYt817uer7o383o4zjut03zCfpH3mCfI01p8f6siNI0xoJOMs4WppM1oGwYhIywkbUbHGMnEx99CD8eAApim2RYBmh7ffB3P9zF6UICmme05m59dmqdo6hwZH8ILJBeqLu/sUNznamQNnRnHY6xDHte+RQNkr8nm9KJ8n+3hjBzljDcKSK4VM9xrnSCnuZyWo/zc34m3jg4/rSQlpTG5xcFmNxeTNqNtWHipKyu1lIYALXu9fSyapqEE6Er4QYhh9mbrsat5QZMuXr9R9haz7C1mefrCLD86P0sQSQ6PFjG6PEy9PWtztuIoAXoF6fLq9DqRlUEL/KTN6Auc2c/y+L2f55MH1qoJKjguJ9gWlahFBiVp8yHrdTQt4lQ4ytFgGzJFw8yK1bHwUxVScDmj2f4RoJ63+XNc6gRoEAbY2WzSZgwcgZSEUcS7617PbsdIeoHkfNVhVyG9AeobRtPQIuWX6wbe6Bb8se3YF04lbUrfsJ6aoO8zj+Oj87h/HQAvh9sxCDhiXORj9ivISOP1cJxXwi0sjSzbbF1QRW9h4ac6q3+sfvkq+b0tQC3LVAJ0JYJQYvWoB/RqJF28fr2s5MkVxNnhErru+fQCyQ8uzjBmm2xtU7el1NHb57PU4I9uJZtwDdB+5Fu3HOTRh9/gkw/9M0rH/nVzviDkw9ZrXJQFfhbsWfaeAIOfB7v4ebCLDB63muf5VeNl/Mjg5WArj1v/Q7c/hqLDmHiEKf51WUb82Ra8zn5GKSVBEOB5Aa7r43kevh+/9n2/+TwIAnw/fgRBSBiGBEHAzMwCruvx+uuniaLlF5dabf2x2qkToFJKLKs/BSj0vghdLYzgQs2lYBpdF58AAZJhy+TWBNpy9gORJlJZYqYX0Z0qYXEEUZpL2pS+4/F7P8+jDz/IoXph+mGqfMB+g5/5uzglx9Z8r4PFM/4+AIo43Gae44/4byhS4T38hGt4a102pC0JCeKY5LRItkzKY0AbnPWGeZw7KD/5bFP8xY+AMAwJQ0kQhFeIv1YQQqDrOobRmBoYhqhPDUxTxzQNslkb0zQxTR3LMjFNk7feOs/cXIn3v/+dV9QCXVgo8fu//wfrsiF1/0kZRdhWf8cA9roIXYnTZYe3X6UofacIZVyKaBAJpERbz0VzEycqxfqJDBPh9k43tn5iaTzo1078K/5j9Am+7V5HaR11H1cbaj/DNp7kLh7jfYwzx3t5mh1MrrhuGpOQNCIkot7nvv8RyFTeJCxF06AWRGRxMEeHME0D0zSx7XhqWSa2bWFZxqYLwW+UatVhfr6sOiFdThRFZDNqGHa9bLY81ILnM+v4+KHEkzIZD6iUiJSflFZDyvW1JPVHtxIUhuNvKYqWP4jQ6tN4HhDJ+utGTcv6Os2HRIsAZJxh35wXxRdyGS0uazwnPZ6Y1Yh0E91TAnSjOLOf5dNHf5f/Y+LnfM75N1zLFze1vd1c5AG+BsDr7OPbvJcyOXZykXt5mtEUl/SBWLAFGBh0p0Rdp0n7+QNA0zR0v8K7eI65I9clbc6KZDIWvh9sejupEqBBEH8hObv/BWivekEbrS/nXZ9jc2UMTWPYNrhuuEAuobuxMIq65gHtNbEbIK/aeta6eAaZzYIm6mpVIxJa/BpAiNiroMXD9U1Fq+kgNCJDI6q/Dy1ejyWvo+b8xvvq+9BY8p71f2fStNACvy6K+4uohxoU9CPSsvHM13jwC9/jtod2r5qUtJT1Jhpdxymu4xQSOMohvsJHcLE4wCney483Z3iPIpCqC1qfITQNJ0zairWxbQvPUwJ0GfOVuENCLpOOTOhOi9BWvJ9SSo7Olil7AZoGphAcGSuSN5M/hMKIq4qwduFJid5D4/2hXNkrcNf9H24+f+bbjyFq3ekS1Q6q+24gc/YNRLD5E1y3qR64KWkT+hpn10Gss2+umpR0ORvJchfALRzjFo4RIPgZt/BFfo0aNmPMbsL63kMjSnXZojSiCw2nx099uZzddPhthuTVQxuZK8U9hIu59PRhbojEdgvRVofez1fj1gy/tGPtZIAkCCPZNQ+oH0bovaM/CaKre0Dvuv/DPPPtx7pkUTvQVOH8AcQb2YLmuxhuDVielNQpDCR38XPu4ucskOe/8PGO7SsJGkPwiv5BF+AFvX3+s22LINi8mzZVR+Z8JfbyFHLpqwOadCtPN5QM9YC3cyXCCPQueUB9Kbu2r/Ww3gSsvhKhmkY/1o3yRiaSNqFvkYA/sZ3s8aPNeUuTki7n6InTbbdhiErqhqsFMtVli9KIITS8sLfPf4ZhbCoDv7mdNtjSM8yXYgE6nE+PB7RXKFgGF6u92aBWyqhrQ/C+jDB6SIDKqHufvavI/sradce3ExZHyJx4OWlT+hJvx36MmUtXSCVn9rP89tP/lvueeHDZ/CMP7e2ICE0bgkh5QPsMU9fwe1yAtotUHZmlugd0pFC46rqXai6Xah46ccyFrmkYmrb4XMSvDSEwRVxcXdD9Dj+9woRt8sZ8NWkzViSMoi56QCM0TWsmYyVN0EL4Qd94QftQUIdDY+SU+NwQUhiEuQL58ydXXN7whC7l0YcfVCJ0HSgPaP9h6hpOjw/Bt4tUCdCFahw7NFrMr7le2Qs4Plfh2pE8oYwIooigPnUDSSgjwigWNjKK4ims6HJe7TBZegkVmlZ/xM91TVtV+JpNAdxbwlcIgdDiJByrB4TXUkIibK07No3aJhdrLt8/P8P7diU/5Npq+EG/iNDeOsIUncTdfQ32hbULxTuzn132uhEfqkTo2sRZ8CoJqZ+wdZgbkBj4VAnQcjX20E0MDa26jpSSX0wv8LbxIQpW5z++lJKAuHxPIBeF7lLh60cRjh/3Uo9FL4vCNwK5gXg4jSuFr4CmsA5ltKykr9C0Je9prL/43AlCKn7IW6UaphDoAnQEhogFUENA61o8r5uiWUrQu3SOLVoG79g6wjMXk8+WrfgB825Axmjte+4XEapIP4GdJRI6RmU+aVNSiY5UWfBtRBK3gPWwcLDwsHCxcLHxMXEx8TDxl0wDDHyMJVOdEH3V0AhP/y+EcjB+D6kSoJV6D9Lx4upD8C/MlNhdyHZFfEIswizoutdwJeErowhTCExdwxKiWTS+sa6UkkDSFMJhUyhLLlZd9hczRIAbhoQByChYJpLjeuT1KdGa3uFoyfOlS5riV6MugDX0hudYgI62XPAKjWoQUjQH7yT74kyJommwI9f/dW8Vg4m38wCZs28mbUZqiYfg+//cKAG/LuhCdC4yjotdF3xW/bEo+nwMvLr4Wyr8AgxC9E0lmwlkfSvx1hp7MQmaktPCYwgXC79upY+N13xkcLHwVrTijwyHS8oD2n+Ua/EQ/FBh5SH406V4+d5i+rLkL6cV4dtYlxXWnay5vDHnsL+YZd9QZ5O7AimRMi6uHsr4dVj3EseCOBbH/tJQCaAShD2Vmd4tIuCmseKG3qu8oO1DCrHhAvSBnQUhMPqoTmu78IfG0HwPoTpHdQxxWR3QAFH34BlNcbaSSIu9dI3Xi8ItRNSnBkHzuUCiU8VGQ8PD7Eg1gcaeTHy+wz1N36JJ2JSdJgFZHIbwsfHr/smG+Iv9lBkcjB5uTZozVw73SyOpEaAvfflvqDrxiWylod+yF3CmXOOubSNdtqx/mXN9Xp+r8M5tI11psWmIeOzeavHk9YvpBQxdRQ22ihKh7cHbsR9r8lxL7wkyObzt++IWpkInrMxhXzrbIQt7Dwl4W3aS7bHELRebp3hH00sm0ZDohGhE9Wk8f+ljcV7UfK4RLVkWN6jt/k2yh8l5tvIkdwE0rYo/Xdh8rtc/pahP49cBRvN1iIXflJ5LPYAGAREa3+VuHuCr5Khh0eOV1HuYvBGPVg4CqRGgADXXQ1vBE7Y07jPpZJ5+YdbxeGm2zO1bhhLp794KMqLrpZHS4m9VInTzSCuLtbD2MLI0DNxte9ECH5ktQBRhnzuB7jmxGNu5n+q+G8mceg3Rw96ZduGPb0cvLyB6rNzWe/kR8wzX/XuyKUMXBVp4mViLh2OXzm8IuMZ8o+kvTC8S+Ft+iRFKSZvS9+TN3vpNdJKUCVB3RQHa7bjPNHCiVOPAUDax/u6tILtYhqmXaNcnViJ086wlLqQQ1PYdwpo8SyQE5uwk+pJhZwFkzp3EHxqjdu1h7LeOY7g1pNBBprOITjA8QfbN3vJ+AtzCK0mb0Jek8RhNirwZn9llj92cdYLeVxct4HjeFR7O06UaRIMR99lOPCnZle+P70xGES0mgisUbUGaFsgrhxslEBZH0GSEu20P9oVTGJWFNbdlLsygV8vU9l6PS4QWRXFNVCnJnHkd0Ybey72AXxhGeM5AeHoVilbJW7EArQYw8rk7mPvdZxO2aHU2Ww87VQLU83z0JfV4VNznYBCRfJ3Ufkd5QTeGPzyOvkRYSsA5cBMAwq0RGRbW1Lmris8GIvDIv/nisu3VDt4MQXrEmr9lF5m3Xk/aDIWiJynUBehMDQpWwsasgWHoOI5HLpfZ+DbaaE/ieH6AvqS0kIr7HAxkFKXrQFb0DTI3hH3+RPO1P74dUS2Rubh2YfX14o9vQ6+UUuMtDOwsRBIR+EmbolD0JA3ROVOV7B3uXe1imga1mrMpAdq7n24DeIGPUY9ZVHGfG6cfY08G8SZjMPIkexvhlPHGtgMQZPMEo1ux2iQ+pWXH27twqi3b6wW8ndeQOXfi6iuuQWb097jviQf55EOG6oKkSB3Fugd0zk3YkKtgmiaO421qG6lSZ0EQkslaA1XvsxOcq7rkjf4vXtwp+lGgKzqDfekslYM3U8ndhCZDsqdeadtdfW3vDdjnTqTGS+CNbkF4NYS3uSurEp+KNDNU7ysy7/S2i8E0DRxnc7/ltJzbAPDDEMM0OFOuccsGC3Qr4FSpxqHR1btJDTqBjDszJU3yFiggLhptnztJ7tSrCH9zHoGl2GeO4+7cT5Afbts2k0IShydYm+x6lBn9PQAlPhWpZSQTn9nn3d4WoJZl4jibC6VJlQANpUQ3TBX3uQm8QGLpoudrfyaJJ2Uqyz7ddf+Hkzah73B2HsAsz2O41bZv23CqZN98CXfH3rgkUx/j7jqIOXVhUxecpUPvCkVaGbLja8tCzwtQA2+ToxmpUhlSSnK2peI+N4GbUnHVToJIfUcKcLbvA03DvnSmY/sQUmKfP427+2DH9tFpgmyeyLKx5iY3vA0V96kYFBoe0FKPx4DatoXrbs4DmiqlFkURhezGM7IUMO/55FT855r4Mr2F71U5pvXhbNtDZJhkzxzv+L6Myjzett1I+s9jIA0Ld9c1ZN881tL7GkPtDZT4VAwKTQHq9boH1KJaXV95udVIjQBtJIbkMnbClvQ3MgKRTm3VNgIZoffAd9Sp05MSoWvjbtlFZGXJvvVa1/ap+R4yk0c4la7tc7NIIajtv4HM6dcRKxTrX42Gt3MpSnwqBoXRbHxxKfe4AM1kLDxPeUABqNazsQoZlfm+GQqmzuwm3eppJ4giDKXSBxJ3fDthrkDm1Ktd3a+olQnzRYw+EaASqO0/hH3+5LK2o+thpThPJT4Vg8JYfRC31wWobZt43ua6s6VGgM6XygAM5ZQA3QwLXkBW9bVck0BGzYYHSaIkcHdxx7YRFkfInGxfqaX1olfL+KNbu7zXjeEXR/C27saaOo9RKbX03szo7/HJh35HCU7FwGLVr7/VHvcDZTI2vq88oADMVWLPwHAhl7Al/Y0fyZ4oMdTLBJHE7vOsZEVreCNbCIfHyZx4OZE4zMgw0Xq8e5A0LJzdB9HCgOyJlxH1sKjL4znX4r4nHuR/e1dqLksKxYbpdQ9oNmvj+8oDCsBcwwOazydsSX8zVfN559b+qjvY7Z9pKCOM1PxyFFfDHxojGN1K5sRLiSUByUwO4dUS2vvaONv2EBaG0aTEunAKo7YYJrBSPOdaqFhPhQI0Dcp+8qNsa5HJZAiCcFPbSM1ltFT3gI4oAdoyl6oulSCk7AdkDKFqqF6FIELVSR0Q/OIo/sQOMm8mJz4BpJXBLM0maMHqyPwQ+TdevGL+Rup2KvGpUICmaVSD3h6JtCxj010BUyNAF6pxIehiXg3Bt8Ks4/H6fIUdOZuxjMmuvIqhvRqhjDBVmELqCfLDeFt3kX0jmWH3pUS6gXB7rzCgBKJVfguqdJJCsTGEplHb3Oh2X5D0ebVtlCqxAB0pqhaS62XW8Xh5tsytE0NcM5xX4nOdyChSHtCUE+SKuNv3xLGMbO4uvx1okQTD6AFLliNzRYR7ZWiAapk5eGhEPXd89itCaDgDIEBT4wEt1+KT4EhBCdD1IKXk5dkyd2wZIdPnWe++jDhdqqFr8Z2jLjR0bclDgIFAiPiOa7MhBmEUYaoyTKklyOZxd+wne+KlZiJN0ugLszi7riHSdfLHjyZtTpNI19HC5XFgy7sWJWSYousIJB4WGbykTel7dCVA+4tyLa41N1RQMaDrYdr1KVpG34tPiDtguWGIjGJxKIm9lDKKl8n6OjKCaEnKUuNZq1KyGoQ9UYhe0X6CTK7ZuadXxCeANTeJNTdJdd8NhFam5dqanUILQqS12H1OtcwcXAxCXEwlQNuAIQRe2N30Wiklvh/guj6u6+F5Pp7n4bo+nhfg+359uYfjxP/jhYUKf/mXjxMEAUEQEkURtdr6z02pEKAvfflvqDnxh85YVsLW9Adzrs+IZSZtxqYJpCSj61w30j3P9zMXZzF1VYYpbYRWBnf3QbInX2mpc083MWcn8Se2o587magdEnD2H4IowroYC00lPgcbnRAPC+iPZgm9jKFreGHERcaZOnupKQY9L8DzYiHoeQFBEOD7QVMANh5huLHsdCEEhqGj6zqmqWMYBqa5/DE1VcX3fd72thu46aZryGRsslmbTMbCMAwWFkr8/u//wfo+54as7EGqLajuQccJJJM1jxtH+z9cQcq4ZIVCsRmkaeHsvY7syVcRPVxvU1+YwZ/YkagN3ugW/PHtWJfOYi7MLFumxOfgohPi0/9OjV7A1DVKgeA73AM/P4ZhGHUxqNeFoMnwsI1lmcsemYyFbZtYltWxajYvvXScyclZDh7cveltpUaAVlxXFVBfgUBKLtZctmVtzpQdzlYcDKFxaKzAqN3/3uKAZArnPzs5j0Z9+F6j/jy2Q1v6WlvM9NNoiGWNRghpvK7W3JZW/yxi6XNtcT+isa6mUQ1Cnrk4ixdKjHrs69JBm8a3snSeqO9baPHU0ARbshZ6w476voJMHojQogiiCI0IZBRvrT4PotgVVk896NdgDmlY1PbdQPb064igt4cPBSCqZZyd+8kk4AUNMjn80a1kjx9d9v9ueD8f3H6u6zYpegOj6QFVbBZL10D6fJq/ZO6j/1PS5izDtq1Nt+BskBoB6nieql95GScWqpyrOAzbJmfKDrau865tI6n6nkIJ3c4HevuWYTwpiRqajDjmNCKONY1ozK9nhdbXixp/USwK4/UX37M4LyIEokjGeq+x3pLtAxwaKbI9b/PidImtOYutWfuqtgdSEkgII0kgI2qBZMrxkFG81ca+ml42TVs2XSy5o12xrF1EXTw+pWFQ238D9lvHET0SV7kWfnGESDfQ5OYKQK+FRAASmR8mKMRNKSI7Q6SboEH2zPEVxadKOhpsBBI/PZIiUWxdI5TR1VdMANu2Nt2Cs0FqjhbHVQJ0KRU/4ELVTZ3gvJwgkogud0U3hOipMkwREfo6v4PYdmj4K4dt2J5fQbhODPHMtx9rn5EtUD1wU1f2I4VObf8h7DNvYqxQSqjXcCd2EuaHMBamMWYnN7QNaVp4W3YSZuLwG71awijNISolvJ37kJlcfAeiaWiei1Evfq/NT2M4V8b2qbhPBcRjIMfZz/18P2lTUoFlaMgeFaBxD3jlAV2G5wcYKjGkyevzFW4YyadafEIyHtBeQ0ad+Q7uuv/DiYnQTiOFoHbgEPa5kysKq14kHBrdVEcmd3w7wcgE1qWzWBdOg5SEw+MEI+PIrbswZy9hnTvRt6EUimQZZZ4hlYDUFrLG4uhar6EE6Ap4gY9hKAHaIJARVsrFJ8RD34Me+xuhvoNWkAhqB27CvvAWRrWUtDnrJ4paFodSCJx9NwIgnArZN15ctg0xP405P902ExWDSfqvNN0lZ2pEUW8q0GzW2nQP+AapEaB+EJK1rx4DNwh4UuKGMhU1Pq9GEMmB94BGER0ToEl4QaVp4ezYz7IA2EZC1GXzaJyko8WEqXhdIKrX8axPNRkRIXG37cW+dAajMt/Vz9VtpGFS23dDLLRT/lkVijSRN5dWrO4tDMNomzhOjQANwxDTVCUgAE7MV9mdz/RUnGKnkBHoA+79k4Ceon915swbRLoOWv1DaVo9+SlO0Y80cVkClAZCI6qXClhcd1mpAUAjKAxjTl/ALM1190N1mSBfxN2xH/vsSYxaH3l5FQoFBTMWeLKHmmF0glQJUNtMzcfZFBEMTE/eUKrh5yiKup6I1Uk6OSxeufYI9vSFjm2/F5BCEAxPYMxe6or4VAlICkV7GbLiK3i1N/thtI3U+E1kFKkheOIyO9OOx55C5uorp4AQ5QGNADHocQjrRHgeQbZ/2/WudWMpAW9kC7VrDgNgzk511JZGBrwSnwpFexmuC9CZ3i/OsSlSI0AjKcmmoLD6ZpBS8tzkAvuK2YEYfoe4nuVgfNLViaIU/ZA7jH3hJN62vUmbsSG0MABz5XOcFILawZuR2TzZN18ic+4EooO1QpeWX1IoFO2lWP+ZzznpHstMzXUrAnKZwfD6rcbFmkfGEOwuZJM2pWtICfqAe/8iWs+OHlSE50IUEWRySZvSMpEm4rpjS5BAbdfBZlZ/5vxJRIfjxlTtT4WisxSt+Jo2U4ORz92RsDWdIxXXLS+IAyXymcEegj9RqnLjcP/3d28FGUUDPwQPpL7eazuxz5/E274vaTNaR9NALgaFScDdfS3Cd8m/8WJXM92V+FQoOsdQXcrMO72aC98eUjF+slAuA1DMDo7n73KOz1cYskysASi9tJQ4A1wJUMX60estN6VpIfze7v2+DK1e01PXIYqINA2jNIc9eTZpyxQKRRsZsuNrZ5SX8QAAPopJREFU2ryrBGjPM1eKuy8U8/03rNYO3irXmHF83r5lKGlTuk4Yrb8NpULRQOvD8ibG3CX88e3kjx9N2hSFQtFBhjPxNW1BCdDeZ64Ue0ALucHygE47Hq/NVcgagrdvGRrIYVgZKQ+oojVCK4O0+sz7CVizkwQjW5I2Q6FQdJjhuge05CZsSIdJhQCdr8QCdCjfv+VVWkVKybHZMnduGxmIlpurEakYUEULSMDZez3Zk68kbYpCoVCsyEg9n7rk9a4HVEq5aadXKpRLqRIXyxoaoCH4eS+gYBoDLT4hFhQDFvaq2AT++HaMhRlE0F/eT4VCMTiMZuKLWrlHBaiu63je5s+hqbh0l6pxDOhwYXAywPOGgRf2Xxxbu5FRhJ6Ow3jDRKS/ZVvbEDr0bJdlhUKhgLG6L61XBahp6tRqmxegqRiCL1VjD+hIcXCG4Ccdl6KlJ21G4shIeUC3Zm2euTjHvmKWXR2oAXvX/R9e9vqZbz/W9n10C+E5hNnBuVFVKBT9R6Z+Uav6CRuyCqZpUKu5jI5ubjupuHSXmwK0mLAl3aMahOSNVNw/bJJoIJOvlnLNUI47t40w7fo8fWGWqTbcmaaVMF9E72K9TIVCodgolZ71gJq4rhqC56Uv/w1VJ67rlx+gTkiBjAbe86dYxBCCt40PcdvEEKfKNX5ycY6SF1z9jRvgco9oXxFRH4ZXKBSK3kUDap05hW8a0zSUAG1QqQvQQfKEZQwdJ+zNu6Nuor6B5WQMnTu2DHNotMCLMyXOlGsd2U+/ilBr6hy+KmWkUCh6HE3TqPq9eYWzLBPHUQIUAMf10AasFI+tazhBmLQZih6laBncMJJnxu1cEFE/ilDhe0S6jkrZUigUvYwmtJ71gNq2qbLgG9Rcd6AEqBOEnFiosacDCSf9xuD811unYBm4qlLCFehOlXBoLGkzFAqFYlWEpuEEvXmFsywLtw3OjZQIUA9dT8VHWRdHZ0rcPFakaKkkJMXqWEIQRp0dwulHL2hkWGheyluMKBSKvkYXAqdHBzlt28T3N++eTYVqc30ffYASC8IoYtg2kzZD0Qd04/6530SoNC0Mp5K0GQqFQrEquq7h9ugQfCZj43nKAwqA5/vo+mAI0EBKpOzNwOQkUN9Eb9BPIlSLJNKwkjZDoVAoVsUQAq9nPaAWvr95AZqKMVw/CDAHpCbmsdkyu1Xsp2Kd2LrO+YrDjvzglChbC394HM33VCvOTfLowwFHHtq7bN7RE6cTskahSB+mrtGGPJ+OkM1a+P7m1XEqVFsQhGTswbjAlv2QI+NDSZvRM/RmiHbvcPNYgR9fnGNb1hqoMmWr4U3sIPvGi0mb0dc4s5/l8Xs/z6MPP7hs/pGH9ioRqlC0CdMQVJ3eHOPLZu22xICmQ4DKEMtKf0zkrONhD1CylWLzGEKwp5Dlxdkyb1M3LmhSpiPuKGEaInQpjz78oBKhCkWbsHSNXi1iYlkWYag8oADIUGIPgAA9U3HYkbOTNkPRZ+wtZjl30aHkBapywgCVa+s0zuxnl72OBenvJGOMQpEybEMj7NF8j3aNpqXCGSCjiKyVbmEWSEnJC1Qs32X05s+z9zgyVuTFmVLSZiSO5nv4w+NJm5FaHn044MiBvVdfUaFQrIltCGSHy+glTUvukN/+7f+R8fFRhoeLjI6OMjY2wvj4GBMTo2zZMsG2bVsYGip2ytZViaKIrJ1eARpIyY8vznHNUC5pUxR9St40GLIMTixUOTDAx5E0LfTKfNJmKBQKxZpkDY2U68/WBOhjj31v3esKIdB1gWEYGIaBbdvYtk0ulyGfz1EsFuqPPMPDQwwPDzE6Oszo6AgTE2OMjo6wZcsEW7eOk8ms7vWTMg6SKGTT6xl8ZbbCvmJWeT9XQA2orp9DI3mevjjHrkIGawATktyxbehOBRH0aHG9Pue+Jx7kkw8ZKgZUoWgDOSv9I3wtCdBTp55DCI3JyWkuXpxienqW6elppqfnmJ2dZ3p6ltnZWRYWypRKZSqVKrWag+u6eJ5HpVLl0qUAKSVSSqIW5b0QAiEEhqFjGCaWZSLqAvTZl4/x2//6s+Rsm1zGJpvJUMjY5LNZCrksxVyO4UKe4UKBoXye0WKeQi7X05nBUkp+MjmPF0puGs0nbY6izxFCcN1InqPTJe7YMpy0OV1FAsHYVrLHjyZtSirJjP4en3zod5T4VCjaRN6M3SsNJ1saaTkjoVAoUCgUOHBgX1sMCIKA6elZJienmZ6eYXJympmZOebnF5ibm2d2dp65uQUWFkqUyxWq1doyUVuu1QAo1xxefuPEhmImNE1D07TYaysEhq6j6zqWaWAaBrZpkrEtspZJ1s6QrYvcQjZDLpshn80ylMs1he5QPs9QIU8xn8PaRH3S81WXIdPgpm3dD2voB9L8w+wUW7M2p0s1pmouE9n0hq1cjrdjH8bspXQEvfcYmdHf474nHuTB7eeSNkWhSA0FM9Yy1RQP2CSeEmsYBtu2bWHbti0bev8j/+aP+Mf/+rP8o7/zMT79kfub813fZ75UZq5cZqFcZb5cZqFSoVyrUarWKFdrlGs1yo5LzXVxXA/X8/F8Hy/wCYKQquMShNVlHttWvbYNNE1D1EWu0AWG0GNPrm4sCl3LJGOaZCyLrG1RiTS2FfI8kc+Sy2brojdLIZuhkM9RyMaCt5jLkcvYPe3N7QSS+HtVtMbbxob42eQcY7bZ1mPmrvs/zDPffqxt22snWhgQqe5HbachPuOh96StUSSJRoQkJZnNPUDRjrXGTA06UUBPSsnCQpnZ2Vnm5mKH3/x8iVKp3HT4lUolKpUalUqVarVKtepQq9VwHJeLF6f4wz/8Q1zXx/M8fN/H94OW6oMmLkA3y2ypDEAhtzyxwjZNto6NsnVstO37lFJSddxY4FYqlKtVytW6sK3VqDo1KjWHSs2h6rpUXY+a6+J6Pm79n+QFAUHgU3NdyrUaMgyRUbSh0ITLWSZ2615dXdcx9Fj0mkYsei3TxDbMWPjWxW/WtmNvb8aOPb12hmzD25uJRXAxn62HNmzOw7tZAhmpk90GsAzBtpzNq3MVDo2117veqyI0zBawLqjh4XayXHyq73bQ0YgIMLBIscuuQziBZM6BOSdi3olYcCMuLsTL/t2PXbJ/+EeUy2Wq1YYYjEVgPHVwXQ/XdXHdWAh6XqwzgiB+hGFIGG7ekQZxKFdj1Fivjxbruo5hGORyNrouuHDh4rq21fcCdOjQAQAOv//dHP7Ehza9vZe+/DdXXUcIQSEXx5buYmOe2/UgpaTiOCyUKpRqNRYqFUrVal3c1qg6LlXHpeY4VFyXmutR8zy8utD1mkI3IAgD/CDA8TxCGSJlRCQlcpMH41KaoQxLxG8zGU3X0UV8kJqGiD2/S4WwaZCxrNgDbNuxILbtpje4KYxtm1wmDoNAN6i6IV5QSFQI9yMHh/M8fWGWahCQG4Dvzpybwt11DeLNYwhU6EY7UOJTsRSBJEDvKwHqBZIFD0oulDxJyYUFN6LiQ8WLqPgRFS9+Xg2g5kfUgoiqHz93AnACcMMINwA3BC+M8ELwJQQh+DIikBBGENanMoIoWl+S0R8+7cPTn11xWeOaq11xzdXrSeAmmUwW04yTwU3TxDTj/JlMJkMmkyWfz5LL5SgU8gwPF5dUORpiYmKcbdsmGB8fJZfLYa2jjvTCQol9+25f1/ff91eeubn4NmFsbKQt2zv8a5sXsVdjPSIXYqHbGGLvFo7rUa5VKVUa3lyHcq1GzfGo1GrUPI+q4+B6cciCs3S6RPT6foAfBvhBSBiG+H6AI71mOEND+G72bmwlNE1Da0yFaApiTdPQl4hiXeiL4ljXMfXYO2wYOpZhND3FDS+xbRqYholtGVimhW0aWIaJbVnxcstqCumsbWFbsZDO2haWZWEZRk+FSdw0WuDF6TJ3bhtp63Z70Qtqzk+DlLh7DpJ96/Wkzel7VNKR4nIEESE6EDtPnCCOX6x4sOBKfAnVurCr+lANIhw/ohZAzYdasCjonCDCCRZFnRtEuKGGG4IfyqbA88J4JMwPIZDxvPAywSejxUfE+oXfeokF4NKRR715zdF1Dd3QsevXGcMQmI2RyLrX0DJ1LMuMrzWmScbSyVo6OSu+PgW57Vx7+DDDw0OMjAwxPj5CsVjAtm0yGbM+tbFta10CsZfoL2tXYHY2ruk3MdE/xaW7IXIbrFfsNsjYFhnbYmJkpDMGXQWnLnBrNZeKE3t5K46L47pUHAen7uGNha+HUx9ucH0f1/O4VHHICAiCED8I6o+QIIwfUkpCGQtiV3rNsIfLBXG7RfFKaABLvMbNqaifyLRGCMXicyE0dL1eDaJxp7tkajRCLep3wKZhXDZPx9AXPdCXXJ+f57MUbasuvE3MephG/IjfF4txE10ILNNsCvVYhMd3141pr2KWZvG27qJ6zWHM6QuxKFW0jEo62jyBlHhNsRULJyeILvOkxcLLC2MPm7dkPa+xvO5x80PwJHhB43lUF2cNj5yGJ+uPEHyp1R91T139EcpoiacuQjanEVFE/Ty5OI2Ip0QREf8nv9XB72zxfAkaS8+TGkJozfOjMONzo90IPzPqORe6qAs/gWUsPjKmhm3oZE2t+ShYGgUL8pbGkBkxYsOQLRmxIkYzIUVDYuJj4ZHBwyBYRzhYWH/4LX3uP+YI/8j7/zL3G89u5GvraXr3arFOSqW4u8v4ePtjPdNAN8Xu5bQqfoF4GN6yNhR17UnJ85MLbffoSSnx/Dh8oZmw5vs4rovjefVHHIjtBUH83I8fDa9wsEQM+0HsHfbqwnjBqYdFhJKw4SGWDbEsCWSElCGR7zfF8RUe5Ciq391H0Lgo9BjNdLG6h7r+lPhysspzjUVv9pL3LvNya4vvWTYf7bJ5oPEUmqYRZbLorrN4MWtONYTWsG1xW43XYqldmhZfdOr7Es31uMzW5Z+lOb8+b+lnvjyprvGyMTc4dQljbrppz9Jli99qa0R1f1DjkGkcQ8vnxVPdvoPdb/06//Odgt94rNZcJ4pij1P9UERCU6As9TxJuXTZ4jSMiEUOsacqlDSfN4YrG9uRy4RQff3osnUb6zU8YPXP1dyGXLLOZftobp/l+73chghtmQiL6l9G89trs6etHSwXcYveO7FkKupTXQNDgC00DE1DF/FrU4Cpg6nFU1uPsEQ8tfUIU9ewdbCNuJ+5bWh87hmPI1sFn36bSdaIl+WMWOzlzLjkUNaErAFFW1C0oGDF8eobJ2Kjoq8TzP3uxgRk+KVvMfdA+sQnpECALiwoAdqrdFP8uq7HY4/9gC17d3D4o/dcsXwjYriBEKLpGR4pFjZj5oaQUhIQX6ChfsFj8WIfAZLYSyEj8KUkbFz6Ghd4KQlDieO6+EGAGwQEfojru9R8HxmE+EGI5/tLPMcBYShjb3IYEMq6JzkIY2EchAQyJJKSIIxDLSIZEcgQ33Vje5re5VhMRzJqvl58HiHrr5cJ7PjDx/Mgfk9EczlQXz9WK9HSefGTRQFw+fP50pXzm/SmgI9JOtW8vv9vw7/r0h61FV9oS58uzmmI/CXPlwn1pTcyS28+ltxkaGLJjYumoSEwtSj2uNEQatRHJpZMNQ0hiIVafb7RfA6miDDrIs7SJZYAU0hsEQu3jB5hi7D+vLFe/LB1DUOPxV/GiAVe1tCwDeLnphbPNyBnQMaI398rIT+fe8bj+nHB//2Xulf6baOCT9E9+l6AlssVgJ4e+lN0nq985XvceusNHDp0zYrLk/QEb4SlglkIgQVtqm/SnZqy7YgBrR64idyJl9tgTfeRUsapThEEUZz0JOuuMSnq07rIlRFEWkMIa0gE3vY9aJUSolxvG1pXxIt1jiPQNKSmEwmdSNPA0EHTQeix67Tphlsp6Spa9nRRgFG3YlHixcsirpla4Bt3avxWqVRfEqGJeF1daOhECBF7g00RkqdKRsRDk0KARhw6En8Ni7JSq7+KP3ljaYSoT+vluBH1dRrL4iFQvzk1OpBcVibDo3yc3+LRK7+3tvk3NRaVtd6mbXaGDYu6f3EdlX13M/e7/3t7DVL0NX2v2kqliqoFOeCcPn0B27ZWFZ/9SL8J5svZrP1/8o9+F6IIKQyE7J+s2gZCiOb9gtHinYM7tg2Zy5CdORuPcwJrN50N40njewpb2t26yIz+Hv/rB+pJR50oStijGMi69I1RXrWN43n99ztWdJa+F6CVSlUJ0AFn+/YxXNdL2gxFG/nv/vhzvPDCq0xP38Z9992ZtDld5ejR15mcnOW++/5J0qYs4zf4S+aSNqLLeF5A8JXvMPcpJTw3g6Zp+L46RyuW0xsBIpugWq32TJyLIhksy1JtOVPIkSPXcfbspaTN6Do33HCACxemkjZDARiGaMZeKzaOpml4XvKJQIreou+VW7VaQ9d7O25G0XniE5wa4kkTQggyGZvJydmkTekqlmU06+UqkkU5N9pD7AFVAlSxnL7/dTmOqxKQFGiahuO4SZuhaDO7d2/l1KnzSZvRdbZvH+fFF99I2gyFoi0oD6hiJfpegLquh2kqATqoTE7O8Bd/8W3y+SxDQ/mkzVG0maGhfLPSxSDxnvfcwXPPvay8+opUoATo5kjraEjfC1Df9zFNM2kzFAlw9OjrPPbYD7n77jv4+MfvTdocRQfYu3cnFy4MXseiTMbirrvexmOPPZW0KQrFphFCKAG6QYQQBEE6b0T73nXo+wGjo90rbqtIlnPnLjE7u4DjeLz44nF+4zc+okIwUkyxmCMIAqSUAxePl83a6PpgfWZFOhFCS62I6jS6ruO6AZZlJW1K2+n7s1sYhti2EqCDgJSSxx77IZcuzVKtOvydv/N+JT4HgC1bxjh+/HTSZnSdl19+kxtvTE9tW8XgommCMOxAgdoBwDB0PC+dJaz6XoBKKcnlskmboeggx469ieN4HDv2Jvv37+R973sHd999u4r5HBAOH76G1147lbQZXWd6ep5rrtmVtBkKxabR9fQOI3eaarVGJpM+7yekYAheSkkxgf7ciu7w/POv8ItfvM6PfvQCmYzNxz52ZZ93RboJQzlwpdY8z4t7jg9Y2IEinei6ThAoD+hG0DSNfD6XtBkdoe8FKEChoDxhaePMmQucOXOJN944w6c//SvqQjzAVKsOtp1OD8BqvPDCa+zfvzNpMxSKtiCEGoJXXElfX9UdxwFgeLiYsCWKdnLq1Dm++90f43k+f//v36/E54BTrTpks4MV5338+FvcdtuhpM1QKNqCrutKgCquoK89oJOTMwCMjY0ka4iirZw6dYFbb72BW2+9MWlTFD2A47jk84MV5x2Gklwuk7QZCkVbMAydMExnLUvFxulr19LUVFwfcGRkOGFLFO1ienqOS5em2b9fJV8oYmZmFhgbG6zfuKYlbYFC0T50XU9tMXXFxulrD+jMzBwAIyNDyRqiaBtf/erf4vsBIyMqrEIRMz9fYu/eHUmb0VU0TRvI2qeKdGIYBlGkBOhGSeu5oK8/0ezsHAAjIyOJ2qFoH9mszW//9t9L2gxFjzA9PU8mM1jxn1JKfF91jVGkB8MwkDJK2oy+JM2dkPpagDY8oOPjI4naoVAoOkO5XBm4+E/H8bBtO5UeD8VgYpomUaQE6EYwDB3HSecNaV+f4ebnFwAYHR1J1hCFQtERhoby1Gpu0mZ0lVwuo+LlFKnCsgwlQDeIYei4ruqE1HPMz88DMDExlrAlinagLrqKyykW86k9+SoUg4LygG6cuBWn8oD2HKVSBYDx8dGELVG0g2rVwTT7Oi9O0Wbi5IXBunCpGzFF2rAsa+B+x+3CMAwlQHuRhYUSAIWCasWZBsrl2sB1vFEoLkcIoS7WilShzusbxzD01CYl9rUALZcrSZugaCPlcpVMRp2oFIsob6BC0f9YljqvbxTTNHDddGbBb3q88623zjEzM9sOW1rm4sVJAF544aVE9q9oL6+9dpJyuab+ny0yNjbKnj3p7BteqzkYhp60GV1HU5XoFSmiIUDTWs+yk5imkVoP6KYE6FtvneOuu36ZarXWLns2xL33fiLR/SsUSZLLZXnmmb9OpQjNZjP4/uD1kA6CwfvMivTSqOXrOA65XC5ha/oLJUBXYWZmlmq1xhe/+EUOHTrULpsUCsU6OXbsGJ/+9KeZmZlNpQBNs7fE8zx+8YvjSBly551HmvO//e2nOXBAtaJVpIdsNgNApVJVArRFTNOkVKombUZHaEvK8aFDh7j99tvbsSmFQqFoIqVMZRyolJIvfvGbXHvtPmZm5vizP/smmqYRRRHDwwXuv/9dSZuoULSNTCYWoEmPlvYjpmmmthOSqnmjUCh6FiEEaQyHPHbsJHv2bOeeexZv3D0vwLLUKVmRPrLZeAi+UlECtFUsS8f3lQBVKBSKrqNpAs/z+j6T1vM8fvCD5zlz5iK1msNHP/reZcuV+OxNVA/zzZPLNTyg6RxK7iSWZaY2Jlyd8RQKRU+zc+cWjh07wS233JC0KRtGSsmf//ljHD58HQ88cDuGoU69/YIQKXTBd5lG3Kcagm8d27ZTOwTftQj/r371q+zfv79bu1Mo1s0//+f/nE984hPN14VCgaNHj3Zt/0899RS7d+9edfnzzz8/0GV5du3ayuTkTNJmbIrvf/9Z9uzZwdvffpMSn4qBI5vNAkqAbgTbNlLrAe3JFNOTJ0+iaRpzc3NJm5I4//yf/3M0TeM//If/sGz+rbfeyhe+8IXma9/3+Rf/4l9w8OBBstkse/bs4Z/+039KuVy+Ypv/6T/9JzRN44/+6I9W3e+//Jf/Ek3TeOyxx1Zc/md/9mdN0bZ//34KhQIXL15sLl9JNJ0+fZrPfOYz7Ny5k2KxyLXXXss/+Sf/hPPnzwNw7733Yts2hUKh+ZiYmGi+f//+/Xz1q19d1eZ2US6XOXLkyNVXbBN33303Z86c6dr++o2hoQKVipO0GZtC1wevlqlC0aCRBV+r9ffvOAksyyQMlQBVrEA3XOPj4+P8q3/1r1YUkw0eeOAB/vIv/5JHH32UcrnM9773PV544QXuv//+K2qI/cmf/AljY2P8yZ/8yYrbiqKIP/3TP11zna9//et87GMfa77OZDL8y3/5L1e17/Tp07zjHe/ANE1+9KMfsbCwwA9/+EN27NjB97///eZ6/+bf/BvK5XLzMTU1teo200Bah1baxQ9/+HO+9a0n2blza9KmbIp77rmdt966kLQZCkUiNASo67oJW9J/2LalPKCtcubMGe6//36Ghoa44447ePnll5ct/+xnP8t1111HsVjk4MGDyzx8d955JwC7d++mUCjwZ3/2Z5TLZX71V3+VrVu3Mjw8zD333MMLL7yw6v6fe+457rrrLoaGhpiYmFgmlo4fP86HPvQhxsbGOHjwIJ/73Oeayy4fjgUYGRnhiSeeaC7/6Ec/yj/+x/+YsbEx/uAP/gApJf/u3/07brzxRorFItdddx1//dd/DcRirrFsZGSEe++9l2PHjrX0Xb7nPe/h/9/evcdFUe//A38ty15Y2AVBuSkgpuYdBdEKFSUveUFNzfKK9cuHlqTmya/XREsxNT32EDM1887RPElKZWXeyLyGYimnPJ7wkoSiAssuLAu78/sD2dy4B7sD+Ho+Hvuo+ezszHtmhH3xmZnPPPnkk1izZk2Z7x8/fhwHDx5EQkICQkJCIJVK0bp1ayQkJODq1avYvXu31bYnJSXhk08+wYULF8rch0eOHMHt27exceNGHDx4EJmZmVbvFxYW4ttvv8WQIUMsbf/3f/+HrVu34n//+1+ZNcbExKBjx47YvHkzmjdvDolEAi8vL8yfPx8vvfRStfZHWbZt24bOnTvj3XffhaenJ7y8vKyOKwD861//QlBQEDQaDQICAqx6kB8lkUiQkpICoPh4R0ZGYurUqXB1dUVgYCCOHTuGhIQEtGzZEo0aNcKCBQusPr9u3Tr4+fnBw8MDCxcutOqtLqkzJiYG3t7eePHFF3H8+HG4ublZPp+dnY3Ro0fDzc0Nbdq0QVJSUo33T31VVFSEwsIi+Ph4iF1KjUkkEhQUGMUug8ju2AP69zk6OkIQGuaNcDYLoGPHjoWPjw8yMjKwe/dubN682er9gIAAHD16FFqtFh9//DFmz56NH374AQBw7tw5AMUhVqfTYdy4cTCbzRg7dizS0tJw584ddOnSBaNHjy73wERHRyMyMhLZ2dm4ffs2Zs+eDaD4C23IkCEICgpCeno6EhISsHLlSsTHx1d5277++mt0794dd+/exbvvvou4uDisXbsWu3fvhlarxZEjRxAQEAAA2LBhA7Zs2YLExETcu3cPI0aMQGRkJIzG4i+i9957zyrIlWfFihVYvXp1qTAIAN988w26d++OwMBAq3ZXV1cMHDgQ3377raVty5Yt6NKlC4YNG4aePXuW2cO5ZcsWDBkyBCNHjkTTpk2xc+dOq/eTkpLw5JNPwsvLy9LWunVrTJgwAQsXLiyz/m+++QZjxoypdDtr4sqVK1Aqlbh9+zb27t2Lt956yxKIExMTER0djX/+85/Izs7G+fPnERQUVKXlfvPNN+jbty8ePHiAcePGYfz48fj8889x6dIlnDx5Eu+//z4uXLgAoDi8L1q0CJ999hn++OMPODg44MoV60eLXr58GY6Ojrh582apfQsA06dPR3Z2Nq5fv46jR49ix44dNdwz9Vd4eCiCg9vh99/vil1KjbVtG4hTp8r/o5mooVKpeA0olWaTAHrr1i18//33WLVqFVQqFdq0aYOpU6dazTNy5Ej4+flBIpGgT58+GDBggKWXsSwajQYvvvginJ2doVQqsWTJEly9ehXp6ellzi+TyXDjxg2kp6dDoVCgV69eAICzZ8/ijz/+wNKlS6FUKtGpUydER0eX2xtWlg4dOmDSpElwdHSESqXChg0bsHjxYoSEhEAikcDf39/yZKj169fjnXfeQatWreDo6Ijp06cjPz8fZ8+eBQDMnTsXX3zxRaXrfPrppxEREYFly5aVeu/evXvw9S37KTi+vr6W0GoymbB9+3ZERUUBACZOnIjdu3dbnRbJyspCQkICoqKiIJFIMH78+FIh9a+n30ssXrwYiYmJuHjxYqn3MjMz0bRp5U93mTdvHtzc3Cyvfv36VfqZEh4eHpg9ezZkMhl69+6NwMBAS0/mhx9+iBkzZiAiIgIODg7w9PREly5dqrTc4OBgjBo1ClKpFGPHjkV6ejrmzZsHZ2dntG/fHkFBQZYAGh8fj3HjxqFbt26Qy+V4++234ezsbLU8V1dXLFiwAHK5vNRTQUwmE/bu3YulS5fCzc0Nvr6+lj+eHleeno3w4EGO2GXUiNlsxu+/34FWW/5lNEQNVUkA5Sl4epRNAmh6ejqUSiU8Pf+8bqukR7DE7t27ERwcjEaNGsHNzQ1fffVVhdf75efn4/XXX0fz5s2h0Wgsd9SX95lPPvkEBoMBISEhaNOmjeUU/++//w5fX1+rMQVbtGhRrZtA/P39raZv3LiBVq1alTnv9evXMX78eKtQlZWV9bduOomNjcXmzZtx/fp1q/bGjRuXG8TT09PRpEkTALDs47FjxwIAXnjhBeTn5yMhIcEy/65du6DRaDBo0CAAxSE1NTUVZ86cscyTmJiIoUOHllqXj48Ppk+fjrlz55Z6r3Hjxrh9+3al27h8+XJkZ2dbXocPH670MyW8vb2tpp2dnZGbmwug4mNUneWWBMa/tpVcn5ueng4/Pz/LezKZDD4+PlbLa9q0abmPmLx37x6MRqPVz8tff3YeN97ejet9cLt8+X9wdlZh2LA+YpdCZHclT0IyGBhA6U82CaC+vr4wGAy4e/fP02Y3b960+v+oqCisXLkSmZmZyM7OxqBBgyyn08v6cl69ejWSk5Nx8uRJaLVaSwgr7xT8E088gR07diAjIwMff/wx3nrrLSQnJ6NZs2ZIT0+3ujEnLS3NMgyOi4uL1WC5eXl50Gq1Vsv+a30BAQG4du1amXX4+flh3759VqEqLy/vb52Obtu2LcaMGYNFixZZtffr1w9nz55FWlqaVbtWq8WhQ4csvYhbtmyB2WxGx44d4e3tjdatW6OwsNCqh3PLli3IycmBn58fvL290bNnT0gkEss8V65cgSAI6NChQ5k1zpkzBz/++COOHj1q1T5gwADs2bOn2ttcWyo6RrXJ19cXt27dskwXFRVZ7vIvUdHzzRs3bmzpvS/x6M/O40ihkMNkqt/XQF2+/F/06FG1HneihsbZubgHlAGUHmWTAOrn54ewsDDMnTsX+fn5+PXXX7Fx40bL+zqdDoIgwNPTEw4ODvjqq6+srlNs0qQJHBwcrG5o0Wq1UCqVaNSoEXQ6HebPn19hDTt27MCdO3cgkUjQqFEjODg4wNHREd26dYOXlxcWLVqEgoICXL58GXFxcZbT0sHBwTh9+jR++eUXGAwGzJs3r9IxGKdMmYIlS5YgJSUFgiDg5s2blhuNpk2bhkWLFuHXX3+1bMeBAwcsPXPVtXjxYuzfv98qlERERGDQoEF4/vnnceHCBZhMJly9ehXPP/88nnjiCYwbNw537tzBl19+iR07diAlJcXySkxMxJEjR3D9+nUkJyfj0qVLOHz4sNU8GzduxJ49e6DX68vt/Szh6uqK+fPnIzY21qp9yZIluHTpEqZOnYqbN29CEARkZmZixYoV2Lt379/aF9UxZcoUfPDBBzhx4gTMZjPu3r1b5qUCNTVmzBjEx8fjxx9/RGFhIZYuXQq9Xl/lz0ulUowePRqLFi1CdnY20tPTsWrVqlqvs76p7xfhq9UqXL16XewyiERRcuaIp+DpUTa7CSk+Ph63bt2Cp6cnxo4di1deecXyXrt27bBgwQJERETAw8MDe/futQo1Tk5OiImJwcCBA+Hm5ob4+HjMmjULUqkUXl5e6NChA55++ukK1//dd98hKCgILi4uGDp0KFatWoWgoCDIZDJ88cUXSE5Ohre3N4YOHYpZs2ZZTktHRERgypQpeOaZZ9CyZUt07NgRarW6wnVNnz4dr732GkaPHg21Wo2+fftaAmJ0dDQmTZqEESNGQKPRoG3btlY3PMXGxmLgwIFV3q/NmjXDtGnTkJWVZdW+d+9eDBs2DKNGjYKzszP69OmDDh064PDhw5DL5di+fTv8/f3x0ksvwdvb2/J67rnnEBISgk8++QRbtmxB79690atXL6t5Jk2aBLVajb179+LgwYNlXv/5qOjo6FLXPQYEBOD8+fMwGAzo3r07NBoNnnrqKdy+fRvh4X8+knDOnDlW44C6uLjg/v37Vd4/5Rk+fDjWrFmDadOmwdXVFaGhoTYZbL5v376IiYnB8OHD4e3tjaKiIrRu3RoKhaLKy1i3bh1cXFwQEBCAiIgITJgwodbrrG9UKkW9Hox+8OBeSEn5lcNu0WOppAeUo0DQoyRCFboWtFotXF1dcePGBWg0f4axS5euoHfv4UhOTkZwcLBNCyXxZWZmok2bNsjIyIBMJhO7nHrBaDTCw8MDhw4dQo8ePWp9+RcuXEBISAiOH/8cQUHta335dcV//vMbbt++g759K/7Dsy47dSoFDg4OeOqpTmKXQtUQH/8Vxo4dJHYZ9ZrZbIaHx5MYM2YEPvxwhdjl1Dv16d+gVpuLgIBg5OTkQKPRVDgvB6KnKsvKysLatWsZPiuxf/9+5OfnQ6/XY86cOXB3d7eMbUt/T4sWzXDvXrbYZdRIt24d8NtvfOIVPX5KrnvnKXh6FB9KTFXWunVrtG7dWuwy6rydO3filVdegSAICAoKwoEDB6xGXaDqq+83ImVl5SA5ORVyOf94o8dXyfjXVP+YzWZotTrk5OTgwYNsaLW5yMnJRU6OFjqdDlqtDrm5udUaMo8BlKiWPTqsFREA/OtfX6NXrxD07h0qdilEouE1oLWnqKgIWVnFQTA7WwudTo+cnFzk5uZCp9NDp8uDXp8HvV4PvT4P+fkG5OcboNfnIS8vH3l5+SgoKIDBUIDCwsKHryKYTKaHLzMEQYDZbLbZNjCAElG9UMlgFHVa167tkJycimbNvODmVvFNjUQNkUQigdFYWPmM9YTRaERurg5abS5yc4tDXm6uDjqdDnp9/sPgl/8w8BUHwJLgl5eXj/x8AwyGAhQUFMBoLITRaLSEwKIiE0ymIphMZpjNxWHwjTfehCAINR4RRCKRQCKRwMHBAQ4OEkiljpBKHeDoKIOTkxPkchnkcjmUSgWUSiVUKic4OzshP9+IRo008PdvCrXaBWq1M1xcXODqqoGrqxoajRoeHu6QyaRo165nlWphACUisrFu3ToiMLAZDhw4hqio8ocxI2rIHh1/u6qKiooe9ubpkZeXD51Oj/z8kiBneBjsinv1DIaChz17xWFPrzfAYDBYwp7RWACDoTjoGY2FjwS+4tejvX9m85+vkuBXm8PBWQfB4pdUKoWjoxQymRROTgrI5fJHwqACKpUTVConODkpH/7XCS4uKjg7O8PFRQUXF5eH4dAFrq5qqNUucHNzhaur2vIwgL/r9OlLkMkc0bVrxTe7arVVH2KyVgJoyZiXRGRfj9PPXn0fC7RJk0ZQq1W4fz8bHh5uYpdDZFN37mRi9OjJyM7OgdFohCAIOHXqPPz8usBsNsFsFiAIZpjNglXIA2z3s14S+v4a/qTSkvDn+DD0yR7+/5+9gQqFAkqlHEql0hIIlUolnJwUUKlUcHJygrOzCi4uznBxcYZa7WwJgxqNGmq1M1QqleWGrGvXbuG3326hf/9nbLKttU0ul9X6JRQ1CqDu7o2gUjlh/PjxtVUPEVWTSuUEd/dGYpdhc3K5DJmZWWjSpP5ua8uWATh1KgWRkb3FLoXIpg4c+Bo//XQFMpkjZDI5XFyc4eDgAIVCAalU+rDdETKZddhTKIoDoFKpeDitKDMAOjkV9wQW9wyq4OKietgjWBwAnZ2doFa71NkbQOVyRxQVmcQuo8oUCjl0uqo/VKUqahRA/fx8cebM13jwIKvymWvZiy9ORlZWDr799lO7r5tqX3JyKjQaF7Rq5S92KfWOu3sj+Pn5il2GzXXu/CSSk1Px3HNhYpfyt3Xq1Appab/j7Nmf0L07xwOlhqtRI1cAwPr1K/DCC7zs5K/kcnm9CqByuQxGY+0+SKPGp+D9/HxF+fITBAFyubxBD779OMnMzIW/vzdat24udilUR3l5NcalS1fFLqPGIiPDsWVLAkJDO1hOxxE1NC4uxU/D0+vzRa6kblIo6lsPqAyFhbUbQOvtb7+CAiPH1GtACguL6uypEqobXFycGsRdtF9//QNatfJn+KQGreT3eUGBQeRK6iaZTAaTqf4EUKVSwQBaojiwMIA2FEVFPJ5UsYYQ2DIy7iM3V8/xQKnBUyiKf5/XdmhpKORyOUwm242xWduUytq/ZKBap+Bzc3W1uvKaKCwshEwmq9Yt/1R3FQ+Um8/jSeUqHmbFUK//jRw6dAK9enWt19vwuMnPr9//5sRSWFgcVrTaXO6/MpjNAvT6vHqzbwoLC6t0LKuTEyVCFcY7MBgMCAwMREZGRpUXTERERESPF29vb6SlpVU69miVAihQHEL5HFciIiIiKk/x2KmVD3xf5QBKRERERFQb6v9V/URERERUrzCAEhEREZFdMYASERERkV3V+ElItYU3ORERERHZVlVvErK1OhFADQYDXBr7wqS3/zPliYiIiB4XVR0mydbqRAA1Go0w6bPQ9LVtcFCoxC6n1l1W/j+xS7CZpwKaiV2CzWxf3XCf4HGi52qxS7CZguw4sUuwmdCIfmKXYDOdfcLELsFmfnbLE7sEm3F3Pyt2CTZxGiPFLsEm8nW5eKtXKIxGIwPooxwUqgYZQDUKidgl2IzUSSp2CTbjIm24I5Q5yZ3FLsFmJLKG+0hXZycnsUuwGY3KRewSbMbZueF+B6jV4p/KtQUnQS12CQ0eb0IiIiIiIrtiACUiIiIiu2IAJSIiIiK7YgAlIiIiIrtiACUiIiIiu2IAJSIiIiK7YgAlIiIiIrtiACUiIiIiu2IAJSIiIiK7YgAlIiIiIrtiACUiIiIiu2IAJSIiIiK7YgAlIiIiIrtiACUiIiIiu2IAJSIiIiK7YgAlIiIiIrtyFLuAR5kL8sQuwSa0EkHsEmzGlG8SuwSb0Zka7rblG/Vil2AzBYWFYpdgM/r8fLFLsBltnk7sEmxGL2+Y320AoFAYxC7BJvKRK3YJNpGvqzvbJREEQfR0lJOTg2bNmkGna7i/gIiIiIjE5u3tjbS0NCiVSlHrqBM9oBKJBDqdDrdu3YJGoxG7HKoirVYLPz8/Hrd6hsetfuJxq5943Oqfhn7M5HK56OETqCMBtIRGo2mQB7uh43Grn3jc6icet/qJx63+4TGzLd6ERERERER2xQBKRERERHZVJwKoQqFATEwMFAqF2KVQNfC41U88bvUTj1v9xONW//CY2UeduAueiIiIiB4fdaIHlIiIiIgeHwygRERERGRXDKBEREREZFcMoERERERkV6IH0A8//BCBgYFQKpUICQnB999/L3ZJVIHly5cjNDQUarUanp6eGD58OH799Vexy6JqWr58OSQSCWbOnCl2KVSJ27dvY/z48fDw8IBKpULnzp2RnJwsdllUgaKiIixcuBCBgYFwcnJCixYt8M4778BsNotdGj0iKSkJkZGR8PX1hUQiweeff271viAIWLx4MXx9feHk5ITevXvjypUr4hTbAIkaQPfu3YuZM2diwYIFuHjxInr27ImBAwfi5s2bYpZFFThx4gSmTZuGM2fO4PDhwygqKkL//v2h1+vFLo2q6Pz589i0aRM6deokdilUiaysLISFhUEmk+HQoUNITU3F6tWr4ebmJnZpVIEVK1bgo48+QlxcHP7zn/9g5cqVWLVqFdatWyd2afQIvV6PoKAgxMXFlfn+ypUrsWbNGsTFxeH8+fPw9vZGv379kJuba+dKGyZRh2Hq3r07goODsWHDBktb27ZtMXz4cCxfvlyssqgaMjMz4enpiRMnTqBXr15il0OV0Ol0CA4OxocffoilS5eic+fOWLt2rdhlUTnmzp2LH374gWeG6pkhQ4bAy8sLW7ZssbSNHDkSKpUKO3fuFLEyKo9EIkFCQgKGDx8OoLj309fXFzNnzsScOXMAAAUFBfDy8sKKFSswZcoUEattGETrATUajUhOTkb//v2t2vv3749Tp06JVBVVV05ODgDA3d1d5EqoKqZNm4bBgwejb9++YpdCVXDw4EF07doVL7zwAjw9PdGlSxds3rxZ7LKoEj169MCRI0dw9epVAMClS5dw8uRJDBo0SOTKqKrS0tKQkZFhlVEUCgXCw8OZUWqJo1grvnfvHkwmE7y8vKzavby8kJGRIVJVVB2CIGDWrFno0aMHOnToIHY5VIk9e/bgwoULOH/+vNilUBX99ttv2LBhA2bNmoX58+fj3LlzmD59OhQKBSZOnCh2eVSOOXPmICcnB23atIFUKoXJZMKyZcswZswYsUujKirJIWVllBs3bohRUoMjWgAtIZFIrKYFQSjVRnVTdHQ0fvrpJ5w8eVLsUqgSt27dwowZM/Dtt99CqVSKXQ5VkdlsRteuXREbGwsA6NKlC65cuYINGzYwgNZhe/fuxa5duxAfH4/27dsjJSUFM2fOhK+vL6KiosQuj6qBGcV2RAugjRs3hlQqLdXbeffu3VJ/cVDd88Ybb+DgwYNISkpCs2bNxC6HKpGcnIy7d+8iJCTE0mYymZCUlIS4uDgUFBRAKpWKWCGVxcfHB+3atbNqa9u2LT777DORKqKqmD17NubOnYuXXnoJANCxY0fcuHEDy5cvZwCtJ7y9vQEU94T6+PhY2plRao9o14DK5XKEhITg8OHDVu2HDx/GM888I1JVVBlBEBAdHY39+/fj6NGjCAwMFLskqoJnn30WP//8M1JSUiyvrl27Yty4cUhJSWH4rKPCwsJKDXN29epVBAQEiFQRVUVeXh4cHKy/XqVSKYdhqkcCAwPh7e1tlVGMRiNOnDjBjFJLRD0FP2vWLEyYMAFdu3bF008/jU2bNuHmzZuYOnWqmGVRBaZNm4b4+HgcOHAAarXa0oPt6uoKJycnkauj8qjV6lLX6To7O8PDw4PX79Zhb775Jp555hnExsZi9OjROHfuHDZt2oRNmzaJXRpVIDIyEsuWLYO/vz/at2+PixcvYs2aNXjllVfELo0eodPpcO3aNct0WloaUlJS4O7uDn9/f8ycOROxsbFo1aoVWrVqhdjYWKhUKowdO1bEqhsQQWTr168XAgICBLlcLgQHBwsnTpwQuySqAIAyX1u3bhW7NKqm8PBwYcaMGWKXQZVITEwUOnToICgUCqFNmzbCpk2bxC6JKqHVaoUZM2YI/v7+glKpFFq0aCEsWLBAKCgoELs0esSxY8fK/D6LiooSBEEQzGazEBMTI3h7ewsKhULo1auX8PPPP4tbdAMi6jigRERERPT4Ef1RnERERET0eGEAJSIiIiK7YgAlIiIiIrtiACUiIiIiu2IAJSIiIiK7YgAlIiIiIrtiACUiIiIiu2IAJSK7OH78OCQSCY4fP26T5aempmLx4sW4fv26TZZfX1y/fh0SiQTvv/++2KUQEZWLAZSIGoTU1FQsWbLksQ+gRET1AQMoEVEF8vLyxC6hTiosLERRUZHYZRBRPcUASkS14pdffsGYMWPg5eUFhUIBf39/TJw4EQUFBeV+pnfv3ujdu3ep9kmTJqF58+ZWbRs2bEBQUBBcXFygVqvRpk0bzJ8/HwCwbds2vPDCCwCAPn36QCKRQCKRYNu2bZbPf/fdd3j22Weh0WigUqkQFhaGI0eOWK1j8eLFkEgkuHDhAkaNGoVGjRrhiSeeKLf+bdu2QSKR4NixY3jttdfQuHFjeHh4YMSIEUhPT7eaVyKRYPHixaWW0bx5c0yaNKnUMo8ePYrJkyfDw8MDGo0GEydOhF6vR0ZGBkaPHg03Nzf4+PjgrbfeQmFhYanlms1mLFu2DP7+/lAqlejatWup7QWA//73vxg7diw8PT2hUCjQtm1brF+/3mqekssndu7ciX/84x9o2rQpFAoFrl27Vu6+ISKqCAMoEdXYpUuXEBoaijNnzuCdd97BoUOHsHz5chQUFMBoNNZ4+Xv27MHrr7+O8PBwJCQk4PPPP8ebb74JvV4PABg8eDBiY2MBAOvXr8fp06dx+vRpDB48GACwa9cu9O/fHxqNBtu3b8enn34Kd3d3DBgwoMxQNmLECLRs2RL79u3DRx99VGl9r776KmQyGeLj47Fy5UocP34c48ePr9E2v/rqq3B1dcWePXuwcOFCxMfHY/LkyRg8eDCCgoLw73//G1FRUVi9ejXWrVtX6vNxcXH4+uuvsXbtWuzatQsODg4YOHAgTp8+bZknNTUVoaGhuHz5MlavXo0vvvgCgwcPxvTp07FkyZJSy5w3bx5u3ryJjz76CImJifD09KzRNhLRY0wgIqqhiIgIwc3NTbh792658xw7dkwAIBw7dszSFh4eLoSHh5eaNyoqSggICLBMR0dHC25ubhXWsG/fvlLLFwRB0Ov1gru7uxAZGWnVbjKZhKCgIKFbt26WtpiYGAGAsGjRogrXVWLr1q0CAOH111+3al+5cqUAQPjjjz8sbQCEmJiYUssICAgQoqKiSi3zjTfesJpv+PDhAgBhzZo1Vu2dO3cWgoODLdNpaWkCAMHX11fIz8+3tGu1WsHd3V3o27evpW3AgAFCs2bNhJycHKtlRkdHC0qlUnjw4IEgCH8eu169elWyR4iIqoY9oERUI3l5eThx4gRGjx6NJk2a2GQd3bp1Q3Z2NsaMGYMDBw7g3r17Vf7sqVOn8ODBA0RFRaGoqMjyMpvNeO6553D+/HlLT2qJkSNHVqu+oUOHWk136tQJAHDjxo1qLedRQ4YMsZpu27YtAFh6dR9tL2s9I0aMgFKptEyr1WpERkYiKSkJJpMJBoMBR44cwfPPPw+VSmW1bwYNGgSDwYAzZ85YLbO6+4WIqDyOYhdARPVbVlYWTCYTmjVrZrN1TJgwAUVFRdi8eTNGjhwJs9mM0NBQLF26FP369avws3fu3AEAjBo1qtx5Hjx4AGdnZ8u0j49Pterz8PCwmlYoFACA/Pz8ai3nUe7u7lbTcrm83HaDwVDq897e3mW2GY1G6HQ66HQ6FBUVYd26dWWewgdQKuhXd78QEZWHAZSIasTd3R1SqRS///57tT+rVCqRk5NTqr2sHs6XX34ZL7/8MvR6PZKSkhATE4MhQ4bg6tWrCAgIKHcdjRs3BgCsW7cOTz31VJnzeHl5WU1LJJLqbEaVKBSKMm/Iun//fq2vCwAyMjLKbJPL5XBxcYFMJoNUKsWECRMwbdq0MpcRGBhoNW2L/UJEjycGUCKqEScnJ4SHh2Pfvn1YtmyZJfBVRfPmzbFv3z4UFBRYeg3v37+PU6dOQaPRlPkZZ2dnDBw4EEajEcOHD8eVK1cQEBBQbq9jWFgY3NzckJqaiujo6L+5lTXXvHlz/PTTT1ZtR48ehU6ns8n69u/fj1WrVllOw+fm5iIxMRE9e/aEVCqFSqVCnz59cPHiRXTq1MnSw0pEZA8MoERUY2vWrEGPHj3QvXt3zJ07Fy1btsSdO3dw8OBBbNy4EWq1uszPTZgwARs3bsT48eMxefJk3L9/HytXriwVPidPngwnJyeEhYXBx8cHGRkZWL58OVxdXREaGgoA6NChAwBg06ZNUKvVUCqVCAwMhIeHB9atW4eoqCg8ePAAo0aNgqenJzIzM3Hp0iVkZmZiw4YNtt1BD7f17bffxqJFixAeHo7U1FTExcXB1dXVJuuTSqXo168fZs2aBbPZjBUrVkCr1Vrd3f7BBx+gR48e6NmzJ1577TU0b94cubm5uHbtGhITE3H06FGb1EZExABKRDUWFBSEc+fOISYmBvPmzUNubi68vb0RERFRYc9aWFgYtm/fjvfeew/Dhg1DixYtEBMTg6+++srqkZ09e/bEtm3b8OmnnyIrKwuNGzdGjx49sGPHDsuNT4GBgVi7di0++OAD9O7dGyaTCVu3bsWkSZMwfvx4+Pv7Y+XKlZgyZQpyc3Ph6emJzp07W43BaUuzZ8+GVqvFtm3b8P7776Nbt2749NNPMWzYMJusLzo6GgaDAdOnT8fdu3fRvn17fPnllwgLC7PM065dO1y4cAHvvvsuFi5ciLt378LNzQ2tWrXCoEGDbFIXEREASARBEMQugoiIiIgeHxyGiYiIiIjsigGUiIiIiOyKAZSIiIiI7IoBlIiIiIjsigGUiIiIiOyKAZSIiIiI7IoBlIiIiIjsigGUiIiIiOyKAZSIiIiI7IoBlIiIiIjsigGUiIiIiOyKAZSIiIiI7Or/A7EmZ9B4FDc4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crs = ccrs.LambertConformal(central_longitude=-105.0, central_latitude=37.5)\n",
    "#crs = ccrs.LambertConformal(central_longitude=-100.0, central_latitude=42.0)\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(1,1,1,projection=crs)\n",
    "\n",
    "## Colorado versions\n",
    "lonmin=-109.5\n",
    "lonmax=-101.5\n",
    "latmin=36.4\n",
    "latmax=41.5\n",
    "#lonmin=-109.046667\n",
    "#lonmax=-102.046667\n",
    "#latmin=37.0\n",
    "#latmax=41.0\n",
    "\n",
    "#ax.set_extent([235., 290., 20., 55.])\n",
    "#ax.set_extent((-109.5,-101.5,36.4,41.5))\n",
    "ax.set_extent([lonmin,lonmax,latmin,latmax])\n",
    "ax.add_feature(cfeature.LAND)\n",
    "ax.add_feature(USCOUNTIES.with_scale('5m'), edgecolor=\"gray\", linewidth=0.4)\n",
    "ax.add_feature(cfeature.STATES)\n",
    "ax.add_feature(cfeature.BORDERS)\n",
    "\n",
    "## t rank\n",
    "cf1 = ax.contourf(prcp_anom.lon, prcp_anom.lat, prcp_anom.clusters_tavg_prcp_sum, \n",
    "                         np.arange(0,nclusters+1), \n",
    "                  cmap=plt.get_cmap('tab20'),\n",
    "                  transform=ccrs.PlateCarree())\n",
    "\n",
    "ax.set_title(\"clusters based on temperature and precip, \"+str(nclusters)+\" clusters (data post-1950)\", fontsize=13)\n",
    "cb1 = fig.colorbar(cf1, ax=ax, orientation='horizontal', aspect=30, shrink=0.65, pad=0.01)\n",
    "cb1.set_label('cluster number', size='large')\n",
    "\n",
    "# Add a text annotation \n",
    "text = AnchoredText(\"data source: NOAA/NCEI nclimgrid\",\n",
    "                    loc='lower left', prop={'size': 9}, frameon=True)\n",
    "ax.add_artist(text)\n",
    "\n",
    "fig.savefig(\"clusters_tavg_prcp_\"+str(nclusters).zfill(2)+\"clusters_post1950_pub.pdf\",\n",
    "           dpi=225,transparent=False, facecolor='white', bbox_inches='tight')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.close('all')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd2b2494",
   "metadata": {},
   "source": [
    "### if desired, write the calculated clusters out to a netcdf file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c185de5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "prcp_anom.clusters_tavg_prcp_sum.to_netcdf(\"clusters_tavg_prcp_sum_post1950_\"+str(nclusters)+\"_pub.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42ab0a07",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bee4945",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}