SST_bias_NOAA_OISSTv2.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "8bfb94a9-e573-406d-b140-86af797d9523",
   "metadata": {},
   "source": [
    "# Sea Surface Temperature Bias - NOAA OISSTv2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb7a4fa0-db98-49a6-85c4-94096b405a03",
   "metadata": {},
   "source": [
    "This notebook plots the SST bias of a model simulation vs. climataology derived from NOAA OISSTv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "68078c46-4b7b-4b2f-b8c1-2d2538f46ebc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50093a3b-2193-4041-8409-de3dc594cf7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {\n",
    "    \"startyr\": \"1993\",\n",
    "    \"endyr\": \"2017\",\n",
    "    \"dora_id\": \"odiv-319\",\n",
    "    \"pathPP\": \"/archive/Brandon.Reichl/fre_om5/FMS2023.01_om5_20230927/\"\n",
    "    + \"om5_b02_minthick1em3_MLErestart/gfdl.ncrc5-intel23-prod/pp/\",\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3f810d8-4fac-4b1b-afc3-14077762b34a",
   "metadata": {},
   "source": [
    "MAR will pass for environment variables to the script when running via the web engine:\n",
    "\n",
    "* `MAR_STARTYR`: Beginning year of analysis from model\n",
    "* `MAR_ENDYR`: Ending year of analysis from model\n",
    "* `MAR_DORA_ID`: The experiment ID in the database\n",
    "* `MAR_PATHPP`: The top-level path to the post-processing experiment directory of the experiment\n",
    "\n",
    "The block below will use values passed in by Dora but default to the values defined above in `config`. This is useful for interactive usage and debugging.\n",
    "\n",
    "If executed from Dora, there will also be a `DORA_EXECUTE` variable that is set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3ed173d4-cc6d-4693-934a-ff02dd96e632",
   "metadata": {},
   "outputs": [],
   "source": [
    "for k, v in config.items():\n",
    "    config[k] = (\n",
    "        os.environ[f\"MAR_{k.upper()}\"]\n",
    "        if f\"MAR_{k.upper()}\" in os.environ.keys()\n",
    "        else v\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74366b78-6b75-4210-9ed3-24cef6af4550",
   "metadata": {},
   "source": [
    "### Import Python Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dfcdc828-ad45-4ef0-92bc-05df9c036270",
   "metadata": {},
   "outputs": [],
   "source": [
    "import doralite\n",
    "import glob\n",
    "import momlevel\n",
    "import subprocess\n",
    "\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "from matplotlib.colors import ListedColormap, BoundaryNorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "806241bd-ebea-4640-ab69-cea6edba62de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# momgrid will use a directory of pre-computed weights\n",
    "import momgrid\n",
    "os.environ[\"MOMGRID_WEIGHTS_DIR\"] = \"/nbhome/John.Krasting/grid_weights\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ddbddd-eed1-4ac4-9123-6c7df5fcab03",
   "metadata": {},
   "source": [
    "### Definie Local Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "04229c22-3781-400f-a390-8c5eaaef5189",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define some local variables. These are taken from the doralite object\n",
    "# or they can be defined locally\n",
    "\n",
    "experiment =  doralite.dora_metadata(config[\"dora_id\"])\n",
    "pathPP = experiment[\"pathPP\"]\n",
    "expName = experiment[\"expName\"]\n",
    "\n",
    "# Define start and end years\n",
    "start = int(config[\"startyr\"])\n",
    "end = int(config[\"endyr\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8642873-93e4-4e1f-9486-435d6070fc43",
   "metadata": {},
   "source": [
    "### Determine What Files to Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "382835f3-a3d7-419f-a92f-e65808230c59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/archive/Brandon.Reichl/fre_om5/FMS2023.01_om5_20230927/om5_b02_minthick1em3_MLErestart/gfdl.ncrc5-intel23-prod/pp/ocean_monthly/ts/monthly/5yr/ocean_monthly.199301-199712.tos.nc\n",
      "/archive/Brandon.Reichl/fre_om5/FMS2023.01_om5_20230927/om5_b02_minthick1em3_MLErestart/gfdl.ncrc5-intel23-prod/pp/ocean_monthly/ts/monthly/5yr/ocean_monthly.199801-200212.tos.nc\n",
      "/archive/Brandon.Reichl/fre_om5/FMS2023.01_om5_20230927/om5_b02_minthick1em3_MLErestart/gfdl.ncrc5-intel23-prod/pp/ocean_monthly/ts/monthly/5yr/ocean_monthly.200301-200712.tos.nc\n",
      "/archive/Brandon.Reichl/fre_om5/FMS2023.01_om5_20230927/om5_b02_minthick1em3_MLErestart/gfdl.ncrc5-intel23-prod/pp/ocean_monthly/ts/monthly/5yr/ocean_monthly.200801-201212.tos.nc\n",
      "/archive/Brandon.Reichl/fre_om5/FMS2023.01_om5_20230927/om5_b02_minthick1em3_MLErestart/gfdl.ncrc5-intel23-prod/pp/ocean_monthly/ts/monthly/5yr/ocean_monthly.201301-201712.tos.nc\n"
     ]
    }
   ],
   "source": [
    "# Determine what files are needed (leave this up to the developer for flexibility)\n",
    "# This is an example of what someone might do:\n",
    "\n",
    "component = \"ocean_monthly\"\n",
    "static = f\"{component}/{component}.static.nc\"\n",
    "varname = \"tos\"\n",
    "\n",
    "chunk = \"5yr\"\n",
    "filelist = sorted(glob.glob(f\"{pathPP}{component}/ts/**/{chunk}/{component}.*.{varname}.nc\", recursive=True))\n",
    "\n",
    "def is_in_range(file,start,end):\n",
    "    start = int(start)\n",
    "    end = int(end)\n",
    "    target = set(list(range(start,end+1)))\n",
    "    fname = os.path.basename(file)\n",
    "    times = fname.split(\".\")[1]\n",
    "    times = times.split(\"-\")\n",
    "    times = [int(x[0:4]) for x in times]\n",
    "    candidate = set(list(range(times[0],times[1]+1)))\n",
    "    return len(candidate.intersection(target)) > 0\n",
    "\n",
    "filelist = [x for x in filelist if is_in_range(x,start,end)]\n",
    "staticfile = f\"{pathPP}/{static}\"\n",
    "\n",
    "_ = [print(x) for x in filelist]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bfd8545-cb3b-43ee-b1fb-e71a99fe3262",
   "metadata": {},
   "source": [
    "### DMgetting Files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "736dbdaa-f8ed-4ad8-a641-eee616041458",
   "metadata": {},
   "source": [
    "Dora cannot issue calls to dmget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7096dc5b-559b-4221-a189-0836d77afd12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling dmget on files ...\n"
     ]
    }
   ],
   "source": [
    "if not \"DORA_EXECUTE\" in os.environ.keys():\n",
    "    print(\"Calling dmget on files ...\")\n",
    "    cmd = [\"dmget\"]+filelist+[staticfile]\n",
    "    _ = subprocess.check_output(cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b32a4d6d-e5a7-4477-af8f-9e5468e3babc",
   "metadata": {},
   "source": [
    "### Load model data and grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "70b1b444-20e4-4e63-b8a6-4d076d977eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = momgrid.Gridset(filelist, force_symmetric=True, return_corners=True)\n",
    "model_type = ds.model\n",
    "ds = ds.data.sel(time=slice(f\"{str(start).zfill(4)}-01-01\",f\"{str(end).zfill(4)}-12-31\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "676c8308-6c0b-4448-9335-be668cb99e82",
   "metadata": {},
   "outputs": [
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       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (time: 300, nv: 2, xh: 1440, yh: 1080, yq: 1081, xq: 1441)\n",
       "Coordinates:\n",
       "  * time        (time) object 1993-01-16 12:00:00 ... 2017-12-16 12:00:00\n",
       "  * nv          (nv) float64 1.0 2.0\n",
       "  * xh          (xh) float64 0.5 1.5 2.5 3.5 ... 1.438e+03 1.438e+03 1.44e+03\n",
       "  * yh          (yh) float64 0.5 1.5 2.5 3.5 ... 1.078e+03 1.078e+03 1.08e+03\n",
       "    geolon      (yh, xh) float32 -299.7 -299.5 -299.2 -299.0 ... 60.0 60.0 60.0\n",
       "    geolat      (yh, xh) float32 -79.81 -79.81 -79.8 -79.8 ... 64.33 64.22 64.11\n",
       "    areacello   (yh, xh) float32 3.436e+07 3.434e+07 ... 2.686e+06 1.503e+06\n",
       "  * xq          (xq) float64 1.0 2.0 3.0 4.0 ... 1.439e+03 1.44e+03 1.441e+03\n",
       "  * yq          (yq) float64 1.0 2.0 3.0 4.0 ... 1.079e+03 1.08e+03 1.081e+03\n",
       "Data variables:\n",
       "    average_DT  (time) timedelta64[ns] dask.array&lt;chunksize=(60,), meta=np.ndarray&gt;\n",
       "    average_T1  (time) object dask.array&lt;chunksize=(60,), meta=np.ndarray&gt;\n",
       "    average_T2  (time) object dask.array&lt;chunksize=(60,), meta=np.ndarray&gt;\n",
       "    time_bnds   (time, nv) object dask.array&lt;chunksize=(60, 2), meta=np.ndarray&gt;\n",
       "    tos         (time, yh, xh) float32 dask.array&lt;chunksize=(60, 1080, 1440), meta=np.ndarray&gt;\n",
       "    geolon_c    (yq, xq) float32 -299.8 -299.6 -299.3 -299.1 ... 60.0 60.0 60.0\n",
       "    geolat_c    (yq, xq) float32 -79.84 -79.84 -79.84 ... 64.28 64.17 64.06\n",
       "Attributes:\n",
       "    title:               om5_b02_minthick1em3_MLErestart\n",
       "    associated_files:    areacello: 19930101.ocean_static.nc\n",
       "    grid_type:           regular\n",
       "    grid_tile:           N/A\n",
       "    external_variables:  areacello</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-ebf4dfff-ce0b-4298-9177-e973a4fd9bbc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ebf4dfff-ce0b-4298-9177-e973a4fd9bbc' 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>: 300</li><li><span class='xr-has-index'>nv</span>: 2</li><li><span class='xr-has-index'>xh</span>: 1440</li><li><span class='xr-has-index'>yh</span>: 1080</li><li><span class='xr-has-index'>yq</span>: 1081</li><li><span class='xr-has-index'>xq</span>: 1441</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b3f9a7d8-154f-400b-b27c-d161e73e306c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b3f9a7d8-154f-400b-b27c-d161e73e306c' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1993-01-16 12:00:00 ... 2017-12-...</div><input id='attrs-b78b6a02-24f9-4dbb-8c00-8ca0eeda1849' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b78b6a02-24f9-4dbb-8c00-8ca0eeda1849' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b08ffd1f-cbea-4ad1-b41d-07e4947f0d9f' class='xr-var-data-in' type='checkbox'><label for='data-b08ffd1f-cbea-4ad1-b41d-07e4947f0d9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>JULIAN</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeJulian(1993, 1, 16, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(1993, 2, 15, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(1993, 3, 16, 12, 0, 0, 0, has_year_zero=False),\n",
       "       ...,\n",
       "       cftime.DatetimeJulian(2017, 10, 16, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2017, 11, 16, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2017, 12, 16, 12, 0, 0, 0, has_year_zero=False)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>nv</span></div><div class='xr-var-dims'>(nv)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0</div><input id='attrs-2d7fff89-a4b1-42f9-a858-3e5e6b5145fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d7fff89-a4b1-42f9-a858-3e5e6b5145fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8a4c4e8-378b-4644-95f4-5d522f16d75d' class='xr-var-data-in' type='checkbox'><label for='data-f8a4c4e8-378b-4644-95f4-5d522f16d75d' 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>vertex number</dd></dl></div><div class='xr-var-data'><pre>array([1., 2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xh</span></div><div class='xr-var-dims'>(xh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 1.438e+03 1.44e+03</div><input id='attrs-44aaa038-b40e-4c8f-acae-0ed71c5451e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44aaa038-b40e-4c8f-acae-0ed71c5451e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31a93c90-4722-41d0-91f9-b3c10bb6ec99' class='xr-var-data-in' type='checkbox'><label for='data-31a93c90-4722-41d0-91f9-b3c10bb6ec99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>h point nominal x-index</dd></dl></div><div class='xr-var-data'><pre>array([5.0000e-01, 1.5000e+00, 2.5000e+00, ..., 1.4375e+03, 1.4385e+03,\n",
       "       1.4395e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yh</span></div><div class='xr-var-dims'>(yh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 1.078e+03 1.08e+03</div><input id='attrs-c93285c3-9114-4c8a-be78-ef4298ba4363' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c93285c3-9114-4c8a-be78-ef4298ba4363' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e952c341-e561-418b-9bf1-11752721b3f1' class='xr-var-data-in' type='checkbox'><label for='data-e952c341-e561-418b-9bf1-11752721b3f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>h point nominal y-index</dd></dl></div><div class='xr-var-data'><pre>array([5.0000e-01, 1.5000e+00, 2.5000e+00, ..., 1.0775e+03, 1.0785e+03,\n",
       "       1.0795e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon</span></div><div class='xr-var-dims'>(yh, xh)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-299.7 -299.5 -299.2 ... 60.0 60.0</div><input id='attrs-eded7f46-894f-429e-a4d4-278dfd0dd11a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eded7f46-894f-429e-a4d4-278dfd0dd11a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ae29c0b-01ba-4481-bd35-ff49240420fa' class='xr-var-data-in' type='checkbox'><label for='data-1ae29c0b-01ba-4481-bd35-ff49240420fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>comment :</span></dt><dd>Longitude of tracer (T) points</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([[-299.71835 , -299.47037 , -299.2224  , ...,   59.537766,\n",
       "          59.78573 ,   60.03369 ],\n",
       "       [-299.73    , -299.4819  , -299.23376 , ...,   59.52564 ,\n",
       "          59.773754,   60.02187 ],\n",
       "       [-299.74112 , -299.49286 , -299.2446  , ...,   59.514084,\n",
       "          59.76234 ,   60.010605],\n",
       "       ...,\n",
       "       [-299.99744 , -299.9923  , -299.9872  , ...,   59.9872  ,\n",
       "          59.99232 ,   59.99744 ],\n",
       "       [-299.99847 , -299.9954  , -299.9923  , ...,   59.99232 ,\n",
       "          59.99539 ,   59.998463],\n",
       "       [-299.99948 , -299.99847 , -299.99744 , ...,   59.99744 ,\n",
       "          59.998463,   59.99949 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat</span></div><div class='xr-var-dims'>(yh, xh)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-79.81 -79.81 -79.8 ... 64.22 64.11</div><input id='attrs-a4f63d12-1b2a-4bf3-9373-3a9f89b8fa7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4f63d12-1b2a-4bf3-9373-3a9f89b8fa7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c8832aa-c3e3-4fb9-bc19-3098f1af7300' class='xr-var-data-in' type='checkbox'><label for='data-0c8832aa-c3e3-4fb9-bc19-3098f1af7300' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude of tracer (T) points</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([[-79.80674 , -79.80527 , -79.80382 , ..., -79.81127 , -79.809746,\n",
       "        -79.808235],\n",
       "       [-79.743546, -79.74212 , -79.74071 , ..., -79.747925, -79.746445,\n",
       "        -79.74499 ],\n",
       "       [-79.68049 , -79.67911 , -79.67774 , ..., -79.68472 , -79.6833  ,\n",
       "        -79.681885],\n",
       "       ...,\n",
       "       [ 64.11358 ,  64.22249 ,  64.33097 , ...,  64.33097 ,  64.22249 ,\n",
       "         64.11358 ],\n",
       "       [ 64.11359 ,  64.22251 ,  64.33101 , ...,  64.33101 ,  64.22251 ,\n",
       "         64.11359 ],\n",
       "       [ 64.11359 ,  64.22252 ,  64.33103 , ...,  64.33103 ,  64.22252 ,\n",
       "         64.11359 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>areacello</span></div><div class='xr-var-dims'>(yh, xh)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.436e+07 3.434e+07 ... 1.503e+06</div><input id='attrs-6292746b-e1fd-4e6b-a299-f3321a32fc10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6292746b-e1fd-4e6b-a299-f3321a32fc10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2638aff6-22c7-4934-964b-c6f932879b91' class='xr-var-data-in' type='checkbox'><label for='data-2638aff6-22c7-4934-964b-c6f932879b91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m2</dd><dt><span>long_name :</span></dt><dd>Ocean Grid-Cell Area</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd></dl></div><div class='xr-var-data'><pre>array([[34357848.  , 34342084.  , 34321268.  , ..., 34427748.  ,\n",
       "        34405976.  , 34384440.  ],\n",
       "       [34473324.  , 34457312.  , 34436164.  , ..., 34544340.  ,\n",
       "        34522220.  , 34500340.  ],\n",
       "       [34625628.  , 34609280.  , 34587688.  , ..., 34698124.  ,\n",
       "        34675540.  , 34653204.  ],\n",
       "       ...,\n",
       "       [       0.  ,   903446.56,  2092422.6 , ...,  3848782.2 ,\n",
       "         2685177.2 ,  1503193.2 ],\n",
       "       [       0.  ,   903517.06,  2092583.2 , ...,  3849071.5 ,\n",
       "         2685382.5 ,  1503310.1 ],\n",
       "       [       0.  ,   903559.4 ,  2092679.6 , ...,  3849244.8 ,\n",
       "         2685505.8 ,  1503380.2 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xq</span></div><div class='xr-var-dims'>(xq)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 ... 1.44e+03 1.441e+03</div><input id='attrs-fdb1d89a-4b4c-4593-a249-2e719d02b39e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdb1d89a-4b4c-4593-a249-2e719d02b39e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e11c27e-fb94-4b94-a79b-bd69f37d098a' class='xr-var-data-in' type='checkbox'><label for='data-4e11c27e-fb94-4b94-a79b-bd69f37d098a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>q point nominal x-index</dd></dl></div><div class='xr-var-data'><pre>array([1.000e+00, 2.000e+00, 3.000e+00, ..., 1.439e+03, 1.440e+03, 1.441e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yq</span></div><div class='xr-var-dims'>(yq)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 ... 1.08e+03 1.081e+03</div><input id='attrs-53000ea8-285e-489d-8cfc-60b81bca9f0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53000ea8-285e-489d-8cfc-60b81bca9f0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b814180-1c45-4687-9e8a-93ad84bfc253' class='xr-var-data-in' type='checkbox'><label for='data-8b814180-1c45-4687-9e8a-93ad84bfc253' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>q point nominal y-index</dd></dl></div><div class='xr-var-data'><pre>array([1.000e+00, 2.000e+00, 3.000e+00, ..., 1.079e+03, 1.080e+03, 1.081e+03])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f3e119e2-8158-45ba-a943-e76ac90f28dc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f3e119e2-8158-45ba-a943-e76ac90f28dc' class='xr-section-summary' >Data variables: <span>(7)</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>average_DT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(60,), meta=np.ndarray&gt;</div><input id='attrs-4887bd29-ffe0-43a6-9140-614ad9675a9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4887bd29-ffe0-43a6-9140-614ad9675a9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf2bf725-43fd-44ba-a516-9776114dd44e' class='xr-var-data-in' type='checkbox'><label for='data-cf2bf725-43fd-44ba-a516-9776114dd44e' 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>Length of average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.34 kiB </td>\n",
       "                        <td> 480 B </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (300,) </td>\n",
       "                        <td> (60,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 5 chunks in 11 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> timedelta64[ns] numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>average_T1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(60,), meta=np.ndarray&gt;</div><input id='attrs-ba0ade52-fe14-4fe1-ac70-501c2d4b7ed1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba0ade52-fe14-4fe1-ac70-501c2d4b7ed1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64491162-aab1-4e16-bfed-73525fe8bd49' class='xr-var-data-in' type='checkbox'><label for='data-64491162-aab1-4e16-bfed-73525fe8bd49' 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>Start time for average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.34 kiB </td>\n",
       "                        <td> 480 B </td>\n",
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       "                    <tr>\n",
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       "                        <td> (60,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 5 chunks in 11 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>average_T2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(60,), meta=np.ndarray&gt;</div><input id='attrs-2c52b872-30c6-4c7b-975d-4ea0f74ef28a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c52b872-30c6-4c7b-975d-4ea0f74ef28a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7669cd1-1ad1-442a-a80c-aa04648f9f82' class='xr-var-data-in' type='checkbox'><label for='data-a7669cd1-1ad1-442a-a80c-aa04648f9f82' 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>End time for average period</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.34 kiB </td>\n",
       "                        <td> 480 B </td>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> 5 chunks in 11 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nv)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(60, 2), meta=np.ndarray&gt;</div><input id='attrs-8c271282-e86e-46ed-a672-516ea63277fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c271282-e86e-46ed-a672-516ea63277fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adecf009-e562-4901-a259-1134b5438c28' class='xr-var-data-in' type='checkbox'><label for='data-adecf009-e562-4901-a259-1134b5438c28' 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 axis boundaries</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.69 kiB </td>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> 5 chunks in 11 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tos</span></div><div class='xr-var-dims'>(time, yh, xh)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(60, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-0e7904b9-467a-47ee-921d-0735da21dc39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e7904b9-467a-47ee-921d-0735da21dc39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e20d79f-6d0c-43ce-8ec3-6192f18a04db' class='xr-var-data-in' type='checkbox'><label for='data-7e20d79f-6d0c-43ce-8ec3-6192f18a04db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degC</dd><dt><span>long_name :</span></dt><dd>Sea Surface Temperature</dd><dt><span>cell_methods :</span></dt><dd>area:mean yh:mean xh:mean time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacello</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 5 chunks in 11 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_c</span></div><div class='xr-var-dims'>(yq, xq)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-299.8 -299.6 -299.3 ... 60.0 60.0</div><input id='attrs-95c50530-44d7-4d87-b70a-42ea88af98ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95c50530-44d7-4d87-b70a-42ea88af98ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23d99726-79d2-4137-aa5d-dc7ebb7726f2' class='xr-var-data-in' type='checkbox'><label for='data-23d99726-79d2-4137-aa5d-dc7ebb7726f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>comment :</span></dt><dd>Longitude of corner (Bu) point</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([[-299.83624 , -299.58835 , -299.34045 , ...,   59.667988,\n",
       "          59.91587 ,   60.16376 ],\n",
       "       [-299.84827 , -299.60022 , -299.35217 , ...,   59.65565 ,\n",
       "          59.90369 ,   60.151733],\n",
       "       [-299.85974 , -299.61154 , -299.36334 , ...,   59.643887,\n",
       "          59.892075,   60.140266],\n",
       "       ...,\n",
       "       [-300.      , -299.9959  , -299.99182 , ...,   59.99181 ,\n",
       "          59.995907,   60.      ],\n",
       "       [-300.      , -299.99796 , -299.9959  , ...,   59.995903,\n",
       "          59.99795 ,   60.      ],\n",
       "       [-300.      , -300.      , -300.      , ...,   60.      ,\n",
       "          60.      ,   60.      ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_c</span></div><div class='xr-var-dims'>(yq, xq)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-79.84 -79.84 ... 64.17 64.06</div><input id='attrs-b27d9309-d24a-4809-bfea-e3d0839fb835' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b27d9309-d24a-4809-bfea-e3d0839fb835' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-617cfae0-3b21-4083-86a5-d9177764bf71' class='xr-var-data-in' type='checkbox'><label for='data-617cfae0-3b21-4083-86a5-d9177764bf71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>comment :</span></dt><dd>Latitude of corner (Bu) point</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([[-79.83915 , -79.83765 , -79.83616 , ..., -79.84222 , -79.840675,\n",
       "        -79.83915 ],\n",
       "       [-79.775856, -79.7744  , -79.77296 , ..., -79.778824, -79.777336,\n",
       "        -79.775856],\n",
       "       [-79.71271 , -79.711296, -79.7099  , ..., -79.715576, -79.714134,\n",
       "        -79.71271 ],\n",
       "       ...,\n",
       "       [ 64.05896 ,  64.16809 ,  64.2768  , ...,  64.2768  ,  64.16809 ,\n",
       "         64.05896 ],\n",
       "       [ 64.05896 ,  64.168106,  64.276825, ...,  64.276825,  64.168106,\n",
       "         64.05896 ],\n",
       "       [ 64.05896 ,  64.168106,  64.27683 , ...,  64.27683 ,  64.168106,\n",
       "         64.05896 ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-34574a62-a27f-4cb0-9a6c-641aa00a005e' class='xr-section-summary-in' type='checkbox'  ><label for='section-34574a62-a27f-4cb0-9a6c-641aa00a005e' class='xr-section-summary' >Indexes: <span>(6)</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-8059e5ed-c9cf-49d0-9c83-bf89ee2b4653' class='xr-index-data-in' type='checkbox'/><label for='index-8059e5ed-c9cf-49d0-9c83-bf89ee2b4653' 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(CFTimeIndex([1993-01-16 12:00:00, 1993-02-15 00:00:00, 1993-03-16 12:00:00,\n",
       "             1993-04-16 00:00:00, 1993-05-16 12:00:00, 1993-06-16 00:00:00,\n",
       "             1993-07-16 12:00:00, 1993-08-16 12:00:00, 1993-09-16 00:00:00,\n",
       "             1993-10-16 12:00:00,\n",
       "             ...\n",
       "             2017-03-16 12:00:00, 2017-04-16 00:00:00, 2017-05-16 12:00:00,\n",
       "             2017-06-16 00:00:00, 2017-07-16 12:00:00, 2017-08-16 12:00:00,\n",
       "             2017-09-16 00:00:00, 2017-10-16 12:00:00, 2017-11-16 00:00:00,\n",
       "             2017-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=300, calendar=&#x27;julian&#x27;, freq=&#x27;None&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>nv</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-33b49ada-3c25-4f4c-acc7-989d16c17747' class='xr-index-data-in' type='checkbox'/><label for='index-33b49ada-3c25-4f4c-acc7-989d16c17747' 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(Index([1.0, 2.0], dtype=&#x27;float64&#x27;, name=&#x27;nv&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xh</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a3c699a1-5ac2-4e63-b7a4-c8990397430d' class='xr-index-data-in' type='checkbox'/><label for='index-a3c699a1-5ac2-4e63-b7a4-c8990397430d' 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(Index([   0.5,    1.5,    2.5,    3.5,    4.5,    5.5,    6.5,    7.5,    8.5,\n",
       "          9.5,\n",
       "       ...\n",
       "       1430.5, 1431.5, 1432.5, 1433.5, 1434.5, 1435.5, 1436.5, 1437.5, 1438.5,\n",
       "       1439.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xh&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yh</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-68d4c58a-0616-48b2-88bd-5a99af832087' class='xr-index-data-in' type='checkbox'/><label for='index-68d4c58a-0616-48b2-88bd-5a99af832087' 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(Index([   0.5,    1.5,    2.5,    3.5,    4.5,    5.5,    6.5,    7.5,    8.5,\n",
       "          9.5,\n",
       "       ...\n",
       "       1070.5, 1071.5, 1072.5, 1073.5, 1074.5, 1075.5, 1076.5, 1077.5, 1078.5,\n",
       "       1079.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;yh&#x27;, length=1080))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xq</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-229d00cd-494f-40f8-9fbd-3fa552c7486a' class='xr-index-data-in' type='checkbox'/><label for='index-229d00cd-494f-40f8-9fbd-3fa552c7486a' 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(Index([   1.0,    2.0,    3.0,    4.0,    5.0,    6.0,    7.0,    8.0,    9.0,\n",
       "         10.0,\n",
       "       ...\n",
       "       1432.0, 1433.0, 1434.0, 1435.0, 1436.0, 1437.0, 1438.0, 1439.0, 1440.0,\n",
       "       1441.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xq&#x27;, length=1441))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yq</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-67cd947d-f632-4726-8697-6969cf0353d6' class='xr-index-data-in' type='checkbox'/><label for='index-67cd947d-f632-4726-8697-6969cf0353d6' 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(Index([   1.0,    2.0,    3.0,    4.0,    5.0,    6.0,    7.0,    8.0,    9.0,\n",
       "         10.0,\n",
       "       ...\n",
       "       1072.0, 1073.0, 1074.0, 1075.0, 1076.0, 1077.0, 1078.0, 1079.0, 1080.0,\n",
       "       1081.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;yq&#x27;, length=1081))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2ce02105-9721-4bac-befd-c541447358bd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2ce02105-9721-4bac-befd-c541447358bd' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>om5_b02_minthick1em3_MLErestart</dd><dt><span>associated_files :</span></dt><dd>areacello: 19930101.ocean_static.nc</dd><dt><span>grid_type :</span></dt><dd>regular</dd><dt><span>grid_tile :</span></dt><dd>N/A</dd><dt><span>external_variables :</span></dt><dd>areacello</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (time: 300, nv: 2, xh: 1440, yh: 1080, yq: 1081, xq: 1441)\n",
       "Coordinates:\n",
       "  * time        (time) object 1993-01-16 12:00:00 ... 2017-12-16 12:00:00\n",
       "  * nv          (nv) float64 1.0 2.0\n",
       "  * xh          (xh) float64 0.5 1.5 2.5 3.5 ... 1.438e+03 1.438e+03 1.44e+03\n",
       "  * yh          (yh) float64 0.5 1.5 2.5 3.5 ... 1.078e+03 1.078e+03 1.08e+03\n",
       "    geolon      (yh, xh) float32 -299.7 -299.5 -299.2 -299.0 ... 60.0 60.0 60.0\n",
       "    geolat      (yh, xh) float32 -79.81 -79.81 -79.8 -79.8 ... 64.33 64.22 64.11\n",
       "    areacello   (yh, xh) float32 3.436e+07 3.434e+07 ... 2.686e+06 1.503e+06\n",
       "  * xq          (xq) float64 1.0 2.0 3.0 4.0 ... 1.439e+03 1.44e+03 1.441e+03\n",
       "  * yq          (yq) float64 1.0 2.0 3.0 4.0 ... 1.079e+03 1.08e+03 1.081e+03\n",
       "Data variables:\n",
       "    average_DT  (time) timedelta64[ns] dask.array<chunksize=(60,), meta=np.ndarray>\n",
       "    average_T1  (time) object dask.array<chunksize=(60,), meta=np.ndarray>\n",
       "    average_T2  (time) object dask.array<chunksize=(60,), meta=np.ndarray>\n",
       "    time_bnds   (time, nv) object dask.array<chunksize=(60, 2), meta=np.ndarray>\n",
       "    tos         (time, yh, xh) float32 dask.array<chunksize=(60, 1080, 1440), meta=np.ndarray>\n",
       "    geolon_c    (yq, xq) float32 -299.8 -299.6 -299.3 -299.1 ... 60.0 60.0 60.0\n",
       "    geolat_c    (yq, xq) float32 -79.84 -79.84 -79.84 ... 64.28 64.17 64.06\n",
       "Attributes:\n",
       "    title:               om5_b02_minthick1em3_MLErestart\n",
       "    associated_files:    areacello: 19930101.ocean_static.nc\n",
       "    grid_type:           regular\n",
       "    grid_tile:           N/A\n",
       "    external_variables:  areacello"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3281e9a1-7d5d-4b51-afd8-867b973e2e10",
   "metadata": {},
   "source": [
    "### Load observational data (if needed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cd28781-a0f9-44b5-9ef7-fc8251601eca",
   "metadata": {},
   "source": [
    "Note that Dora only mounts `/archive`, `/nbhome`, and `/home`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f322d2e-1c88-479f-86c7-5910d71cf3f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "if \"om4\" in model_type:\n",
    "    dsobs = xr.open_dataset(\"/archive/John.Krasting/NOAA_OISST_v2_annual_mean_1993-2017_OM4.nc\", use_cftime=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20efeacb-e03c-4ddb-8f12-1cb67dee8a4e",
   "metadata": {},
   "source": [
    "### Define some helper function for the plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c4fa45cd-e682-4aac-a98c-a0af350de2a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_levs_and_cmap(start,end,delta,cmap=\"RdBu_r\"):\n",
    "    \"\"\"Generates a difference colormap centered on white\"\"\"\n",
    "    boundaries = np.arange(start,end,delta)\n",
    "    levels = (boundaries[0:-1] + boundaries[1:]) / 2.\n",
    "    base_cmap = plt.get_cmap(cmap)\n",
    "    colors = base_cmap(np.linspace(0, 1, len(levels)))\n",
    "    colors[[int(len(colors) / 2) - 1]] = [1, 1, 1, 1]\n",
    "    colors[[int(len(colors) / 2)]] = [1, 1, 1, 1]\n",
    "    cmap = ListedColormap(colors)\n",
    "    norm = BoundaryNorm(boundaries, cmap.N, clip=True)\n",
    "    return (cmap, norm, boundaries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "294f6a30-47b8-4938-8fa4-f6390e841234",
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_annotaions(ax):\n",
    "    _ = ax.set_xticks([])\n",
    "    _ = ax.set_yticks([])\n",
    "    _ = ax.text(0.0,1.06, \"SST Bias Relative to NOAA OISSTv2 (1993-2017)\", weight=\"bold\", fontsize=12, transform=ax.transAxes)\n",
    "    _ = ax.text(0.0,1.02, expName, style=\"italic\", fontsize=10, transform=ax.transAxes)\n",
    "    _ = ax.text(1.0,1.05, str(starttime.values), ha=\"right\", fontsize=8, transform=ax.transAxes)\n",
    "    _ = ax.text(1.0,1.02, str(endtime.values), ha=\"right\", fontsize=8, transform=ax.transAxes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "574269bd-afb5-437f-ad30-703b70489d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_stats_box(ax, stats_str, x=0.015, y=0.8):\n",
    "        # Adding the text box annotation\n",
    "    props = dict(\n",
    "        boxstyle=\"round,pad=0.3\", edgecolor=\"black\", linewidth=1.5, facecolor=\"white\"\n",
    "    )\n",
    "    ax.text(\n",
    "        x,\n",
    "        y,\n",
    "        stats_str,\n",
    "        transform=ax.transAxes,\n",
    "        fontsize=8,\n",
    "        verticalalignment=\"top\",\n",
    "        bbox=props,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "80be5109-0e21-450c-827f-2bed52263b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_stats(model,obs,areacello):\n",
    "    diff = model - obs\n",
    "    stats = {}\n",
    "    stats[\"min\"] = float(diff.min())\n",
    "    stats[\"max\"] = float(diff.max())\n",
    "    stats = {**stats, **momgrid.xr_stats.xr_stats_2d(model,obs,ds.areacello,fmt=\"dict\")}\n",
    "    # Limit to 4 significant digits\n",
    "    stats = {k:f\"{v:.4g}\" for k,v in stats.items()}\n",
    "    # Stats string\n",
    "    stats_str = str(\"\\n\").join([f\"{k} = {v}\" for k,v in stats.items()])\n",
    "    return (stats, stats_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aadf7139-8861-4a4a-8a2a-1a6c6244cc37",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_colorbar(fig, cb, boundaries):\n",
    "    cbar_ax = fig.add_axes([0.16, 0.06, 0.7, 0.03])\n",
    "    fig.colorbar(\n",
    "        cb, cax=cbar_ax, orientation=\"horizontal\", extend=\"both\", ticks=boundaries[::4]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "64a9c87a-dc17-4c3c-ba71-db76f86dad3e",
   "metadata": {},
   "outputs": [
    {
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       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (xq: 1441, yq: 1081, time: 25, xh: 1440, yh: 1080)\n",
       "Coordinates:\n",
       "  * xq         (xq) float64 1.0 2.0 3.0 4.0 ... 1.439e+03 1.44e+03 1.441e+03\n",
       "  * yq         (yq) float64 1.0 2.0 3.0 4.0 ... 1.079e+03 1.08e+03 1.081e+03\n",
       "  * xh         (xh) float64 0.5 1.5 2.5 3.5 ... 1.438e+03 1.438e+03 1.44e+03\n",
       "  * yh         (yh) float64 0.5 1.5 2.5 3.5 ... 1.078e+03 1.078e+03 1.08e+03\n",
       "    geolon     (yh, xh) float32 -299.7 -299.5 -299.2 -299.0 ... 60.0 60.0 60.0\n",
       "    geolat     (yh, xh) float32 -79.81 -79.81 -79.8 -79.8 ... 64.33 64.22 64.11\n",
       "    areacello  (yh, xh) float32 3.436e+07 3.434e+07 ... 2.686e+06 1.503e+06\n",
       "  * time       (time) object 1993-07-02 12:00:00 ... 2017-07-02 12:00:00\n",
       "Data variables:\n",
       "    geolat_c   (time, yq, xq) float64 -79.84 -79.84 -79.84 ... 64.28 64.17 64.06\n",
       "    geolon_c   (time, yq, xq) float64 -299.8 -299.6 -299.3 ... 60.0 60.0 60.0\n",
       "    tos        (time, yh, xh) float64 dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    title:               om5_b02_minthick1em3_MLErestart\n",
       "    associated_files:    areacello: 19930101.ocean_static.nc\n",
       "    grid_type:           regular\n",
       "    grid_tile:           N/A\n",
       "    external_variables:  areacello</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-fb57fc6d-4355-4e50-8291-34df28a52b4a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fb57fc6d-4355-4e50-8291-34df28a52b4a' 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'>xq</span>: 1441</li><li><span class='xr-has-index'>yq</span>: 1081</li><li><span class='xr-has-index'>time</span>: 25</li><li><span class='xr-has-index'>xh</span>: 1440</li><li><span class='xr-has-index'>yh</span>: 1080</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-13d252f8-661b-44ea-8993-0c7e0005ee9c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-13d252f8-661b-44ea-8993-0c7e0005ee9c' class='xr-section-summary' >Coordinates: <span>(8)</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'>xq</span></div><div class='xr-var-dims'>(xq)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 ... 1.44e+03 1.441e+03</div><input id='attrs-75f6f7e3-b140-4232-b7a7-5e9555cd6534' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75f6f7e3-b140-4232-b7a7-5e9555cd6534' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f47fbbd6-d65d-4dd7-b88c-ff5436290fb2' class='xr-var-data-in' type='checkbox'><label for='data-f47fbbd6-d65d-4dd7-b88c-ff5436290fb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>q point nominal x-index</dd></dl></div><div class='xr-var-data'><pre>array([1.000e+00, 2.000e+00, 3.000e+00, ..., 1.439e+03, 1.440e+03, 1.441e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yq</span></div><div class='xr-var-dims'>(yq)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 ... 1.08e+03 1.081e+03</div><input id='attrs-5d9e81b8-0450-4671-9323-d2311acc46ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d9e81b8-0450-4671-9323-d2311acc46ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79c8bb7e-4594-4e9b-a58a-8711b12cec1e' class='xr-var-data-in' type='checkbox'><label for='data-79c8bb7e-4594-4e9b-a58a-8711b12cec1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>q point nominal y-index</dd></dl></div><div class='xr-var-data'><pre>array([1.000e+00, 2.000e+00, 3.000e+00, ..., 1.079e+03, 1.080e+03, 1.081e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xh</span></div><div class='xr-var-dims'>(xh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 1.438e+03 1.44e+03</div><input id='attrs-ce57d337-c481-46b1-8aae-42f3abea7364' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce57d337-c481-46b1-8aae-42f3abea7364' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc1ae5e4-9469-4bbd-ba96-518c66362d6b' class='xr-var-data-in' type='checkbox'><label for='data-cc1ae5e4-9469-4bbd-ba96-518c66362d6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>h point nominal x-index</dd></dl></div><div class='xr-var-data'><pre>array([5.0000e-01, 1.5000e+00, 2.5000e+00, ..., 1.4375e+03, 1.4385e+03,\n",
       "       1.4395e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yh</span></div><div class='xr-var-dims'>(yh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 1.078e+03 1.08e+03</div><input id='attrs-1aad2a8c-3147-4986-a8e0-d789115d90df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1aad2a8c-3147-4986-a8e0-d789115d90df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-413a39ca-e312-43a1-ad0b-b90c2cc66c02' class='xr-var-data-in' type='checkbox'><label for='data-413a39ca-e312-43a1-ad0b-b90c2cc66c02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>h point nominal y-index</dd></dl></div><div class='xr-var-data'><pre>array([5.0000e-01, 1.5000e+00, 2.5000e+00, ..., 1.0775e+03, 1.0785e+03,\n",
       "       1.0795e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon</span></div><div class='xr-var-dims'>(yh, xh)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-299.7 -299.5 -299.2 ... 60.0 60.0</div><input id='attrs-d564cbf9-c99f-4a62-a25a-80df1d894d97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d564cbf9-c99f-4a62-a25a-80df1d894d97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13c1682b-be3f-4bcd-9578-6ed1145036f5' class='xr-var-data-in' type='checkbox'><label for='data-13c1682b-be3f-4bcd-9578-6ed1145036f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>comment :</span></dt><dd>Longitude of tracer (T) points</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([[-299.71835 , -299.47037 , -299.2224  , ...,   59.537766,\n",
       "          59.78573 ,   60.03369 ],\n",
       "       [-299.73    , -299.4819  , -299.23376 , ...,   59.52564 ,\n",
       "          59.773754,   60.02187 ],\n",
       "       [-299.74112 , -299.49286 , -299.2446  , ...,   59.514084,\n",
       "          59.76234 ,   60.010605],\n",
       "       ...,\n",
       "       [-299.99744 , -299.9923  , -299.9872  , ...,   59.9872  ,\n",
       "          59.99232 ,   59.99744 ],\n",
       "       [-299.99847 , -299.9954  , -299.9923  , ...,   59.99232 ,\n",
       "          59.99539 ,   59.998463],\n",
       "       [-299.99948 , -299.99847 , -299.99744 , ...,   59.99744 ,\n",
       "          59.998463,   59.99949 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat</span></div><div class='xr-var-dims'>(yh, xh)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-79.81 -79.81 -79.8 ... 64.22 64.11</div><input id='attrs-5b832e05-1592-45e8-ab9d-3f81dcefb685' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b832e05-1592-45e8-ab9d-3f81dcefb685' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e42c3eea-b178-49fd-aea9-47b77fb74abe' class='xr-var-data-in' type='checkbox'><label for='data-e42c3eea-b178-49fd-aea9-47b77fb74abe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude of tracer (T) points</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([[-79.80674 , -79.80527 , -79.80382 , ..., -79.81127 , -79.809746,\n",
       "        -79.808235],\n",
       "       [-79.743546, -79.74212 , -79.74071 , ..., -79.747925, -79.746445,\n",
       "        -79.74499 ],\n",
       "       [-79.68049 , -79.67911 , -79.67774 , ..., -79.68472 , -79.6833  ,\n",
       "        -79.681885],\n",
       "       ...,\n",
       "       [ 64.11358 ,  64.22249 ,  64.33097 , ...,  64.33097 ,  64.22249 ,\n",
       "         64.11358 ],\n",
       "       [ 64.11359 ,  64.22251 ,  64.33101 , ...,  64.33101 ,  64.22251 ,\n",
       "         64.11359 ],\n",
       "       [ 64.11359 ,  64.22252 ,  64.33103 , ...,  64.33103 ,  64.22252 ,\n",
       "         64.11359 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>areacello</span></div><div class='xr-var-dims'>(yh, xh)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.436e+07 3.434e+07 ... 1.503e+06</div><input id='attrs-b8d67cbc-6fa2-4984-ab67-fdd0b34b85a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8d67cbc-6fa2-4984-ab67-fdd0b34b85a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed4afe2e-d360-4b51-80a2-e1eee5a05c2e' class='xr-var-data-in' type='checkbox'><label for='data-ed4afe2e-d360-4b51-80a2-e1eee5a05c2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m2</dd><dt><span>long_name :</span></dt><dd>Ocean Grid-Cell Area</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd></dl></div><div class='xr-var-data'><pre>array([[34357848.  , 34342084.  , 34321268.  , ..., 34427748.  ,\n",
       "        34405976.  , 34384440.  ],\n",
       "       [34473324.  , 34457312.  , 34436164.  , ..., 34544340.  ,\n",
       "        34522220.  , 34500340.  ],\n",
       "       [34625628.  , 34609280.  , 34587688.  , ..., 34698124.  ,\n",
       "        34675540.  , 34653204.  ],\n",
       "       ...,\n",
       "       [       0.  ,   903446.56,  2092422.6 , ...,  3848782.2 ,\n",
       "         2685177.2 ,  1503193.2 ],\n",
       "       [       0.  ,   903517.06,  2092583.2 , ...,  3849071.5 ,\n",
       "         2685382.5 ,  1503310.1 ],\n",
       "       [       0.  ,   903559.4 ,  2092679.6 , ...,  3849244.8 ,\n",
       "         2685505.8 ,  1503380.2 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1993-07-02 12:00:00 ... 2017-07-...</div><input id='attrs-56fe6409-eb68-4881-9a54-b9ae63b66d15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56fe6409-eb68-4881-9a54-b9ae63b66d15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-380a08b2-4bee-4b32-a8b5-d7b8b01bfaec' class='xr-var-data-in' type='checkbox'><label for='data-380a08b2-4bee-4b32-a8b5-d7b8b01bfaec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>JULIAN</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeJulian(1993, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(1994, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(1995, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(1996, 7, 2, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(1997, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(1998, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(1999, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2000, 7, 2, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2001, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2002, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2003, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2004, 7, 2, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2005, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2006, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2007, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2008, 7, 2, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2009, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2010, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2011, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2012, 7, 2, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2013, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2014, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2015, 7, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2016, 7, 2, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(2017, 7, 2, 12, 0, 0, 0, has_year_zero=False)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-931163bb-5b35-4546-b02d-983f86d03fe7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-931163bb-5b35-4546-b02d-983f86d03fe7' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>geolat_c</span></div><div class='xr-var-dims'>(time, yq, xq)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-79.84 -79.84 ... 64.17 64.06</div><input id='attrs-d9f7bd8a-45e3-4672-bd63-ea6b8cc7e8fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9f7bd8a-45e3-4672-bd63-ea6b8cc7e8fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd48877a-aabd-4893-bb47-05bae9ef7ff4' class='xr-var-data-in' type='checkbox'><label for='data-fd48877a-aabd-4893-bb47-05bae9ef7ff4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>comment :</span></dt><dd>Latitude of corner (Bu) point</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([[[-79.83914948, -79.83764648, -79.83615875, ..., -79.84221649,\n",
       "         -79.84067535, -79.83914948],\n",
       "        [-79.77585602, -79.7743988 , -79.77295685, ..., -79.77882385,\n",
       "         -79.77733612, -79.77585602],\n",
       "        [-79.71270752, -79.71129608, -79.7098999 , ..., -79.71557617,\n",
       "         -79.71413422, -79.71270752],\n",
       "        ...,\n",
       "        [ 64.05895996,  64.16809082,  64.27680206, ...,  64.27680206,\n",
       "          64.16809082,  64.05895996],\n",
       "        [ 64.05895996,  64.16810608,  64.27682495, ...,  64.27682495,\n",
       "          64.16810608,  64.05895996],\n",
       "        [ 64.05895996,  64.16810608,  64.27683258, ...,  64.27683258,\n",
       "          64.16810608,  64.05895996]],\n",
       "\n",
       "       [[-79.83914948, -79.83764648, -79.83615875, ..., -79.84221649,\n",
       "         -79.84067535, -79.83914948],\n",
       "        [-79.77585602, -79.7743988 , -79.77295685, ..., -79.77882385,\n",
       "         -79.77733612, -79.77585602],\n",
       "        [-79.71270752, -79.71129608, -79.7098999 , ..., -79.71557617,\n",
       "         -79.71413422, -79.71270752],\n",
       "...\n",
       "        [ 64.05895996,  64.16809082,  64.27680206, ...,  64.27680206,\n",
       "          64.16809082,  64.05895996],\n",
       "        [ 64.05895996,  64.16810608,  64.27682495, ...,  64.27682495,\n",
       "          64.16810608,  64.05895996],\n",
       "        [ 64.05895996,  64.16810608,  64.27683258, ...,  64.27683258,\n",
       "          64.16810608,  64.05895996]],\n",
       "\n",
       "       [[-79.83914948, -79.83764648, -79.83615875, ..., -79.84221649,\n",
       "         -79.84067535, -79.83914948],\n",
       "        [-79.77585602, -79.7743988 , -79.77295685, ..., -79.77882385,\n",
       "         -79.77733612, -79.77585602],\n",
       "        [-79.71270752, -79.71129608, -79.7098999 , ..., -79.71557617,\n",
       "         -79.71413422, -79.71270752],\n",
       "        ...,\n",
       "        [ 64.05895996,  64.16809082,  64.27680206, ...,  64.27680206,\n",
       "          64.16809082,  64.05895996],\n",
       "        [ 64.05895996,  64.16810608,  64.27682495, ...,  64.27682495,\n",
       "          64.16810608,  64.05895996],\n",
       "        [ 64.05895996,  64.16810608,  64.27683258, ...,  64.27683258,\n",
       "          64.16810608,  64.05895996]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_c</span></div><div class='xr-var-dims'>(time, yq, xq)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-299.8 -299.6 -299.3 ... 60.0 60.0</div><input id='attrs-9f451283-4a9f-4d2c-8421-2bac8e55ae23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f451283-4a9f-4d2c-8421-2bac8e55ae23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57495af6-8222-4098-890f-0d5725122cde' class='xr-var-data-in' type='checkbox'><label for='data-57495af6-8222-4098-890f-0d5725122cde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>comment :</span></dt><dd>Longitude of corner (Bu) point</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([[[-299.83624268, -299.58834839, -299.3404541 , ...,\n",
       "           59.66798782,   59.91587067,   60.16376114],\n",
       "        [-299.8482666 , -299.60021973, -299.35217285, ...,\n",
       "           59.65565109,   59.90369034,   60.1517334 ],\n",
       "        [-299.85974121, -299.61154175, -299.36334229, ...,\n",
       "           59.64388657,   59.89207458,   60.14026642],\n",
       "        ...,\n",
       "        [-300.        , -299.99591064, -299.99182129, ...,\n",
       "           59.99180984,   59.99590683,   60.        ],\n",
       "        [-300.        , -299.99795532, -299.99591064, ...,\n",
       "           59.99590302,   59.99795151,   60.        ],\n",
       "        [-300.        , -300.        , -300.        , ...,\n",
       "           60.        ,   60.        ,   60.        ]],\n",
       "\n",
       "       [[-299.83624268, -299.58834839, -299.3404541 , ...,\n",
       "           59.66798782,   59.91587067,   60.16376114],\n",
       "        [-299.8482666 , -299.60021973, -299.35217285, ...,\n",
       "           59.65565109,   59.90369034,   60.1517334 ],\n",
       "        [-299.85974121, -299.61154175, -299.36334229, ...,\n",
       "           59.64388657,   59.89207458,   60.14026642],\n",
       "...\n",
       "        [-300.        , -299.99591064, -299.99182129, ...,\n",
       "           59.99180984,   59.99590683,   60.        ],\n",
       "        [-300.        , -299.99795532, -299.99591064, ...,\n",
       "           59.99590302,   59.99795151,   60.        ],\n",
       "        [-300.        , -300.        , -300.        , ...,\n",
       "           60.        ,   60.        ,   60.        ]],\n",
       "\n",
       "       [[-299.83624268, -299.58834839, -299.3404541 , ...,\n",
       "           59.66798782,   59.91587067,   60.16376114],\n",
       "        [-299.8482666 , -299.60021973, -299.35217285, ...,\n",
       "           59.65565109,   59.90369034,   60.1517334 ],\n",
       "        [-299.85974121, -299.61154175, -299.36334229, ...,\n",
       "           59.64388657,   59.89207458,   60.14026642],\n",
       "        ...,\n",
       "        [-300.        , -299.99591064, -299.99182129, ...,\n",
       "           59.99180984,   59.99590683,   60.        ],\n",
       "        [-300.        , -299.99795532, -299.99591064, ...,\n",
       "           59.99590302,   59.99795151,   60.        ],\n",
       "        [-300.        , -300.        , -300.        , ...,\n",
       "           60.        ,   60.        ,   60.        ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tos</span></div><div class='xr-var-dims'>(time, yh, xh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-94f39c23-c593-476e-84d8-9d7ab733fe53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94f39c23-c593-476e-84d8-9d7ab733fe53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2690be2-51ba-441b-83bd-b36ed881b521' class='xr-var-data-in' type='checkbox'><label for='data-d2690be2-51ba-441b-83bd-b36ed881b521' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degC</dd><dt><span>long_name :</span></dt><dd>Sea Surface Temperature</dd><dt><span>cell_methods :</span></dt><dd>area:mean yh:mean xh:mean time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacello</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 296.63 MiB </td>\n",
       "                        <td> 11.87 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (25, 1080, 1440) </td>\n",
       "                        <td> (1, 1080, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 25 chunks in 389 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"196\" height=\"156\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"26\" y2=\"16\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"90\" x2=\"26\" y2=\"106\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"90\" />\n",
       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"91\" />\n",
       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"91\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"93\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"93\" />\n",
       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"94\" />\n",
       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"95\" />\n",
       "  <line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"96\" />\n",
       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"97\" />\n",
       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"98\" />\n",
       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"99\" />\n",
       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"99\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"101\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"101\" />\n",
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       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"106\" style=\"stroke-width:2\" />\n",
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       "        </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-cbb0b29b-8532-479e-b6fa-eb6784334ecd' class='xr-section-summary-in' type='checkbox'  ><label for='section-cbb0b29b-8532-479e-b6fa-eb6784334ecd' class='xr-section-summary' >Indexes: <span>(5)</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>xq</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f1487850-a626-4fbf-ac62-5f6af0e489bd' class='xr-index-data-in' type='checkbox'/><label for='index-f1487850-a626-4fbf-ac62-5f6af0e489bd' 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(Index([   1.0,    2.0,    3.0,    4.0,    5.0,    6.0,    7.0,    8.0,    9.0,\n",
       "         10.0,\n",
       "       ...\n",
       "       1432.0, 1433.0, 1434.0, 1435.0, 1436.0, 1437.0, 1438.0, 1439.0, 1440.0,\n",
       "       1441.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xq&#x27;, length=1441))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yq</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c86de18d-7de1-4db2-b0e5-577c45331098' class='xr-index-data-in' type='checkbox'/><label for='index-c86de18d-7de1-4db2-b0e5-577c45331098' 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(Index([   1.0,    2.0,    3.0,    4.0,    5.0,    6.0,    7.0,    8.0,    9.0,\n",
       "         10.0,\n",
       "       ...\n",
       "       1072.0, 1073.0, 1074.0, 1075.0, 1076.0, 1077.0, 1078.0, 1079.0, 1080.0,\n",
       "       1081.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;yq&#x27;, length=1081))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xh</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b8798128-5aa8-4e82-af50-f5a6d7ebd6db' class='xr-index-data-in' type='checkbox'/><label for='index-b8798128-5aa8-4e82-af50-f5a6d7ebd6db' 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(Index([   0.5,    1.5,    2.5,    3.5,    4.5,    5.5,    6.5,    7.5,    8.5,\n",
       "          9.5,\n",
       "       ...\n",
       "       1430.5, 1431.5, 1432.5, 1433.5, 1434.5, 1435.5, 1436.5, 1437.5, 1438.5,\n",
       "       1439.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xh&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yh</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c45e470e-6b93-494d-a56d-d7b335701d57' class='xr-index-data-in' type='checkbox'/><label for='index-c45e470e-6b93-494d-a56d-d7b335701d57' 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(Index([   0.5,    1.5,    2.5,    3.5,    4.5,    5.5,    6.5,    7.5,    8.5,\n",
       "          9.5,\n",
       "       ...\n",
       "       1070.5, 1071.5, 1072.5, 1073.5, 1074.5, 1075.5, 1076.5, 1077.5, 1078.5,\n",
       "       1079.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;yh&#x27;, length=1080))</pre></div></li><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-90909987-3bec-4bb8-beee-30e17a8bdb57' class='xr-index-data-in' type='checkbox'/><label for='index-90909987-3bec-4bb8-beee-30e17a8bdb57' 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(CFTimeIndex([1993-07-02 12:00:00, 1994-07-02 12:00:00, 1995-07-02 12:00:00,\n",
       "             1996-07-02 00:00:00, 1997-07-02 12:00:00, 1998-07-02 12:00:00,\n",
       "             1999-07-02 12:00:00, 2000-07-02 00:00:00, 2001-07-02 12:00:00,\n",
       "             2002-07-02 12:00:00, 2003-07-02 12:00:00, 2004-07-02 00:00:00,\n",
       "             2005-07-02 12:00:00, 2006-07-02 12:00:00, 2007-07-02 12:00:00,\n",
       "             2008-07-02 00:00:00, 2009-07-02 12:00:00, 2010-07-02 12:00:00,\n",
       "             2011-07-02 12:00:00, 2012-07-02 00:00:00, 2013-07-02 12:00:00,\n",
       "             2014-07-02 12:00:00, 2015-07-02 12:00:00, 2016-07-02 00:00:00,\n",
       "             2017-07-02 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=25, calendar=&#x27;julian&#x27;, freq=&#x27;None&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a2e8b86b-916b-49d2-b4e3-59e9a317cb3c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a2e8b86b-916b-49d2-b4e3-59e9a317cb3c' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>om5_b02_minthick1em3_MLErestart</dd><dt><span>associated_files :</span></dt><dd>areacello: 19930101.ocean_static.nc</dd><dt><span>grid_type :</span></dt><dd>regular</dd><dt><span>grid_tile :</span></dt><dd>N/A</dd><dt><span>external_variables :</span></dt><dd>areacello</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (xq: 1441, yq: 1081, time: 25, xh: 1440, yh: 1080)\n",
       "Coordinates:\n",
       "  * xq         (xq) float64 1.0 2.0 3.0 4.0 ... 1.439e+03 1.44e+03 1.441e+03\n",
       "  * yq         (yq) float64 1.0 2.0 3.0 4.0 ... 1.079e+03 1.08e+03 1.081e+03\n",
       "  * xh         (xh) float64 0.5 1.5 2.5 3.5 ... 1.438e+03 1.438e+03 1.44e+03\n",
       "  * yh         (yh) float64 0.5 1.5 2.5 3.5 ... 1.078e+03 1.078e+03 1.08e+03\n",
       "    geolon     (yh, xh) float32 -299.7 -299.5 -299.2 -299.0 ... 60.0 60.0 60.0\n",
       "    geolat     (yh, xh) float32 -79.81 -79.81 -79.8 -79.8 ... 64.33 64.22 64.11\n",
       "    areacello  (yh, xh) float32 3.436e+07 3.434e+07 ... 2.686e+06 1.503e+06\n",
       "  * time       (time) object 1993-07-02 12:00:00 ... 2017-07-02 12:00:00\n",
       "Data variables:\n",
       "    geolat_c   (time, yq, xq) float64 -79.84 -79.84 -79.84 ... 64.28 64.17 64.06\n",
       "    geolon_c   (time, yq, xq) float64 -299.8 -299.6 -299.3 ... 60.0 60.0 60.0\n",
       "    tos        (time, yh, xh) float64 dask.array<chunksize=(1, 1080, 1440), meta=np.ndarray>\n",
       "Attributes:\n",
       "    title:               om5_b02_minthick1em3_MLErestart\n",
       "    associated_files:    areacello: 19930101.ocean_static.nc\n",
       "    grid_type:           regular\n",
       "    grid_tile:           N/A\n",
       "    external_variables:  areacello"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "momlevel.util.annual_average(ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9fb9c99-0918-484c-9bf8-3d32d7afc082",
   "metadata": {},
   "source": [
    "### Perform some calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0f1309d7-1411-47ce-9aa0-440c4ece8724",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time-average the model data\n",
    "model = ds.tos\n",
    "starttime = model.time[0]\n",
    "endtime = model.time[-1]\n",
    "model = momlevel.util.annual_average(model).mean(\"time\", keep_attrs=True).load()\n",
    "\n",
    "# Obs data is already a climatology\n",
    "obs = dsobs.tos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bd979de-61bb-449e-b447-9e959e640406",
   "metadata": {},
   "source": [
    "### Establish a Dictionary to Store Scalar Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c885c9d6-b164-47b8-a473-f9f6851795db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'metadata': {'expName': 'om5_b03_c5',\n",
       "  'created': '2024-02-08T11:51:18.777557',\n",
       "  'starttime': '1993-01-16 12:00:00',\n",
       "  'endtime': '2017-12-16 12:00:00'},\n",
       " 'results': {}}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_dict = {}\n",
    "stats_dict[\"metadata\"] = {\n",
    "    \"expName\": str(expName),\n",
    "    \"created\": datetime.datetime.now().isoformat(),\n",
    "    \"starttime\": str(starttime.values),\n",
    "    \"endtime\": str(endtime.values),\n",
    "}\n",
    "stats_dict[\"results\"] = {}\n",
    "stats_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "77df75fe-0b2a-4659-aff8-f23708e1003d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup plot\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "ax = plt.subplot(1,1,1, facecolor=\"lightgray\")\n",
    "\n",
    "# Definie geolon and geolat for plotting (use corners!)\n",
    "x = ds.geolon_c\n",
    "y = ds.geolat_c\n",
    "\n",
    "# Get colormap\n",
    "cmap, norm, boundaries = gen_levs_and_cmap(-4.5,4.75,0.25)\n",
    "\n",
    "# Run pcolormesh\n",
    "cb = plt.pcolormesh(x,y,model-obs, cmap=cmap, norm=norm)\n",
    "\n",
    "# Clean up figure and add labels\n",
    "set_annotaions(ax)\n",
    "\n",
    "# Add statistics\n",
    "stats, stats_str = calculate_stats(model, obs, ds.areacello)\n",
    "add_stats_box(ax, stats_str)\n",
    "\n",
    "# Add colorbar\n",
    "add_colorbar(fig, cb, boundaries)\n",
    "\n",
    "# Save stats\n",
    "stats_dict[\"results\"][\"global\"] = stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "622f21c0-9ae1-49ba-9bc9-2c170a9d092c",
   "metadata": {},
   "source": [
    "### North Atlantic Region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6ce86514-5edc-4ff8-b8e6-c99420ff82ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xrange = (-80.,-0.)\n",
    "yrange = (30.,60.)\n",
    "\n",
    "_model = momgrid.geoslice.geoslice(model,x=xrange,y=yrange)\n",
    "_obs = momgrid.geoslice.geoslice(obs,x=xrange,y=yrange)\n",
    "\n",
    "xq = list((_model.xh.values - 0.5).astype(int))\n",
    "yq = list((_model.yh.values - 0.5).astype(int))\n",
    "\n",
    "xq = xq + [xq[-1] + 1]\n",
    "yq = yq + [yq[-1] + 1]\n",
    "\n",
    "x = ds.geolon_c.values[yq,:][:,xq]\n",
    "y = ds.geolat_c.values[yq,:][:,xq]\n",
    "\n",
    "# Get colormap\n",
    "cmap, norm, boundaries = gen_levs_and_cmap(-4.5,4.75,0.25)\n",
    "\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "ax = plt.subplot(1,1,1, facecolor=\"lightgray\", projection=ccrs.Miller(central_longitude=-60))\n",
    "cb = ax.pcolormesh(x,y,_model-_obs,transform=ccrs.PlateCarree(), cmap=cmap, norm=norm)\n",
    "ax.coastlines()\n",
    "\n",
    "# Clean up figure and add labels\n",
    "set_annotaions(ax)\n",
    "\n",
    "# Add statistics\n",
    "stats, stats_str = calculate_stats(_model, _obs, _model.areacello)\n",
    "add_stats_box(ax, stats_str)\n",
    "\n",
    "# Add colorbar\n",
    "add_colorbar(fig, cb, boundaries)\n",
    "\n",
    "# Save stats\n",
    "stats_dict[\"results\"][\"north_atlantic\"] = stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c20fce81-1c8e-409f-a6e8-23d033b1d187",
   "metadata": {},
   "source": [
    "### Arctic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "814e39fe-6b31-4051-9aae-9b44862c89fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WDSZKT4SFyPh9Yffu3aJSpUqS6z+lKFy7dk3juFTbabrWqKgoaVqkSJEiQgj1Oeu04jDmzp0rPeS4ubnpPOesnMctVapUmu3SE2EhNAfdpXxduXJFCKH4LrNmzZrhv6+UIqx8TZkyRafrTcnp06eFra2tdL/cu3evtC+lCCcmJkr7Dh8+LG0vUKCAxr4zKsKqUy3//POP1nYtWrRIdU/NTDK8RMnT05PNmzcTGhrKuXPnmDhxopob5t9//5Xem5mZMW7cOJ48ecLjx49ZuXIlderUkfZfvnw5zSUZH8uKFSsQQhAdHc2aNWuk7ZMnTyY0NFTrccnJyVSvXp3Zs2dz//59NZeTktjYWOn97NmzqVChAvHx8SxdupTevXtTu3ZtnJycNLo4MkJcXFwqV1JQUJBOx6bl9r948SJVq1Zl9+7dBAQEpJpOADRed0bQ09Pj999/l8ayb98+1q9fT2JiIgB//PGH1FaXa0rrO9MF1XO4u7tL7w0NDXFzc8vQWFSpU6cOFStWBGDYsGEa26ieLywsTOPyKdW/BQsLC+zt7QF1t3Lx4sXx9fXF19eXMmXKSNtV2wCsXr1ael+4cGF8fX25e/eudIxIw42txMHBAQsLC43jU/L27Vvev3+v8Tq1kdH7Qv369Tl58iShoaEcOHCAvn37SuNKTExk69at6Z5TE+bm5rRp0waAmzdvcuPGDbX7hOrvU4kQgkGDBtG7d2+Sk5PJly8fp06dSnfp5KdgyZIlTJ06lfz582NsbIybmxvdu3cnW7ZsUhvlvdnZ2ZkLFy7Qpk0bHBwcMDU1pWjRovTs2VNqq+m7u3DhAomJibx69YqxY8dK20eMGCFNr2ha+qNpueeWLVuoWbMmYWFhWFpasnv3burVqyftt7e3x9DQUPq/6t+I6m/M2dk5A5+SZp48ecKuXbsAcHFxkX4HX4SMKPa7d+80bj9z5kyqp5TIyEi1JxlVlFGegLhw4UKq/Z8iMEsIoWbtKM+ryRJWdQs6OzuLW7duicTERBEeHq72pJaSoKAgcfToUbF06VJRqlQpqd358+fTHHNKd3R8fLzYvXu39NQM6kEcqpawtmABVbeJJktYNSCjTZs2IjQ0VAihHsGr6hlYuXJlmk+Z2izQoKAgycpu1qyZ5Lp1dnYW8fHxUjtVS1jb56WLK0jpOko5DiG0W8KJiYkfZQkLof43oGqxaQvMmjFjRqp+lW56+C8wS9WtnNZLNdhK1c2W1kuTGzslqtZCgwYNUu2fOnWqtF+XwKyM3he03XPGjx8vtVX12Kiieq3aLFVV70OfPn2kKFzV6SYlcXFxonXr1lL78uXLS383upJZ7mht3L9/X4qE9/b2Trf90KFDpfPMnj073faqXg/ld6Rp6U/K5Z6zZs2SfsdZs2bV6r0oWbKk1Ieqd0f1HJkRmNWjRw+p7fjx49Nsq7ynGBkZfRJ3dIYs4V69elG3bl3Wrl3L8+fPSUhIIDg4mPXr10ttChUqBMD58+fJly8fEyZM4OrVq0RFRRETE8OePXt49eoVoLBA8ufPn5EhfBCxsbGsXbtWzTLMmjWr1vaqT2MGBgZYWlry/v17rcEIU6dOZd26dYSHh1OmTBlatmxJ0aJFpf0vXrzI0HiNjIyoX78+EyZMkLb16dOHuLg4ABo3bqx27j179hAVFUVkZCQXLlygT58+NG3aNM1zqF6jqakpZmZm3Lhxgzlz5mhsr7TKAO7du0d8fLxO1+Lk5ESjRo0ARUCWMnCnY8eOGBkZSe2UQVwAPXr04OrVq8TFxUkWdIsWLZg0aVK651Md540bN9QCT5SBSQCzZs3C19eX8PBwRo4cKf02PD09yZ07t07Xpkr58uWlp/qkpKRU+w0NDRk5cqT0/9GjR7N+/Xqio6MJCQlh+PDh7Nu3D1B8/yNGjABg1apVOiWCUA22SmkVa+PZs2ecOnUqzTYjRoyQvqfdu3czcuRIQkNDiY6OZt26dWpJEUaNGqX2u9JERu8LJUuW5LfffmPfvn0EBweTkJDAo0eP2L9/v9Sn8p7zIXh7e1OiRAkAFixYQHBwMPBfQJaS9+/fU7t2bSkgs0WLFhw5ckTt96YLymAwPz+/VN6Q3r17kyNHDvT09Lh69aq0PTk5mcOHD1OqVCm8vLzw9vbm2LFj7N+/n+PHj3P58mWqVatG7ty5KVKkiPSbHzZsGAsWLKBw4cJ4e3uTLVs2hg0bRkxMDIGBgcyaNYtp06YBivthx44dGT9+PLlz58bGxobmzZvj5+dHfHw8ly9fJmfOnJJFamBgQM6cOQGFJSwUU5sIIYiIiCAgIAAHBwccHBz4888/6devH8nJyRQqVIiDBw8yffp08ubNS8GCBRkyZIh0rarBf3379qVEiRKYmZkxefJkQPHb6NixI8eOHaN06dIULFiQggUL0q9fPzWPYXR0NKGhoYSGhhIXF0evXr3InTs3efLkYfr06dLfiLm5OVFRUeTOnZvcuXOr/Y2CwvOhDBYrXrw4v/zyC3ny5CFfvnxs27ZN7TtSPcfChQt1/1FkRLFVLQlNLzMzM2kOQtWHr+2l7YnmU88Jq67P02QJJyYmSusTVV/58uVT+7+S6tWraz1XlixZxOvXr9Mcs7YlSvHx8dKcKqgHj6g+wWp6qX5umizhc+fOabSwVK9R9WnW399fY1Cesr+05mIPHTqkdoxqQJaS2NhYNStW00uXeZ4ZM2akOk5pSSQnJ4umTZtq7d/c3FycPXs23XNosoSFUFhVKZMnpEzW0a9fvzSv0dTUVG2pi6p1uHPnzlRjSbn+UzWICzTP46paDLqsGdYlWUe/fv3UjtFmCWf0vuDq6ppm29y5c2tdPqTaLq0525TBX5aWlqn61CWhkC6/z3Xr1kntUy5ROnnyZLrX2759eymgStWaS/lSLvtR9SQo579TvqytrcWZM2fEyZMnhaenp4iMjFRbO6vpNWLECK3XGBsbK44cOSKuX78uzf+m9SpTpox0rK7JOq5duyaePHmS7tJEQAwZMkRUq1ZNJCYmijdv3qgFEjZp0kS65tjYWFG8eHG1IFjVJUrVq1eX7olPnz4Vzs7OUl6MVatWqZ3D3d091d++NjIkwleuXBHDhg0TFStWFDly5BDm5ubCyMhIZM+eXfzyyy9qf/Bv3rwRc+fOFU2bNhX58uUTtra2wsDAQNja2opKlSqJFStWaDXtM1uE9fT0hJWVlShVqpSYPn26iIuLk9prC8x69uyZaNy4sbC1tRVWVlaiWbNm4tWrV2r9KlmzZo1o2LChcHd3FxYWFsLAwEBkzZpVNGvWTPj6+qY7Zm0iLIR6RhgbGxu1wIA9e/aIevXqCUdHR2FoaCgcHR2Fj4+P6NevnxQVmbJ/1cCsrVu3iiJFighTU1Ph7u4uJk6cKJYvX67x8xBCiC1btojChQur3ZB1EeHk5GS1IJwaNWpo/BwSEhLEvHnzRNmyZYWVlZUwNjYWbm5uolKlSmL8+PFqCTG0ERMTI3r06CFcXV2lh4yU0bmLFy8WZcqUEVmyZBGGhoYie/bson379uLBgwfp9p/yWlVFWAghfv75Z7XfiKY/xJMnT4qff/5ZZM+eXRgbGwtzc3NRsGBB0atXLzVX+OnTp6V+nJ2dJVevKu/evZO+Dz09PbWALOUa15Q8ePBATXB0WTP8+PFj0bt3b1GwYEFhbm4ujI2NRfbs2cXPP/8sTp48maq9NhHO6H1h586donv37sLHx0dkzZpVGBsbCzMzM1GwYEHRv3//NBMo6CrCERERag8umtYvZ5YIx8XFSS7vVq1apdqf1pp81b9JLy8vMWDAAGkaR5nHoG7duqJUqVIapzvat28vDA0NhZmZmTA2Nha5cuUS3bt3F8+fPxdCKNbuKkVu0qRJIm/evFJb5fddoUIFkSdPnnSvUwjFPVQXEU55j4+LixNTp04VhQsXlvIXODg4pMrPIET6+QEAUbp0aSlgMD4+XkoUoq+vL9q0aaOWhWvBggVq9z1lsg5jY2ORP39+cenSJWlfixYtpCnPevXqSecQQoiBAwfqvFY5w9HRMjIyMjIfzrRp0wQovB6avGTu7u7i1q1bats8PDzEli1bhBBCXLhwQRgbG0tCW6VKFen948ePhZ2dnejVq5d07ObNm4Wnp6cwNjZOc3lR/fr1xcaNG6X/7927V1StWlWtzfHjx7U+3KXk2bNnwt7eXm3bzp07Rfny5UXnzp1FsWLFRM2aNbXODytZsWKFaNasmdb9AQEBwtnZWS3LXNGiRYW/v78QQvHAcvHiRWnfggULRMeOHYUQaV/z48ePpfiOnj17CktLSxEUFCS1HThwoBg7dmy650gPuYqSjIyMzGekT58+5MuXj9jYWCZOnKjTMTt37mTZsmX4+PiwcOFCKlSoIM3Vr1y5kvPnz+Pt7c3AgQOpUaOGWrxF8+bNuXPnDg8ePGD16tU8ePBA63n09PSk9+IDCn2kR0JCAufPn6d169Zcu3aNP//8kwYNGkgrJjJKeHg4DRo0YNCgQfj4+EjbfX191aLE07oubfu2b99OUlIS9vb2UmT4h/STHrIIy8jIyHxGjIyMePDgAUII5s2bp9MxRYoUYf/+/Vy7do1Vq1bx+vVrPD09AcXSos2bN+Pr68u2bdt4//69tE8VDw8PSpcuzZ49ewAoV64c3t7eUrBYjhw51CqgPX/+XKcsYL1798bb2xtvb29u3bqVZlt3d3dcXV2pWrUqoMi8GB8fLwXlZYSIiAjq1KlDw4YN08zgldZ1pbVvwIABCCEIDQ3Fzs7ug/tJF53sZRkZGRmZz4Imd3RAQID0fsmSJaJ48eLS3HlgYKD0/sCBAyJ79uxSvua7d+9KxwUHB4s8efKIQ4cOaTzv8ePHRaFChdSClFLGPHysOzo5OVkUKlRIih+6fPmycHR0VFuumBJN7uiIiAhRrly5VNWftB1fvXp1KWgqR44c0ueiyzUrGT16tFpglpOTk3jz5k2650gPWYRlZGRkvgK6d+8uXF1dhYGBgXB2dha5c+eW9o0ZM0bkzZtX5MmTRzRo0EC8ePFC2rd06VKRO3dukT9/flGlShU1Ae/WrZvw9PQURYsWFUWKFBELFixIcwxjx44VOXPmFDlz5hRDhw6VtsfGxgpXV1fh4OAgjIyMhKurqxgyZIjWfooVKyZcXFyEvr6+cHV1Fb/++qu07/Lly6JkyZKicOHComTJkuLUqVMa+3j8+LFwdXUVtra2wszMTLi6ukrjHz9+vDA0NBRFixaVXqrrfVXnhBMTE0X37t1Frly5RK5cudSyOqZ1zf7+/qJo0aLS/yMjI0XLli1F7ty5Rd68ecXmzZulfemdIy30hPgEjn8ZGRkZGRmZdJHnhGVkZGRkZL4QsgjLyMjIyMh8Ib4bEW7VqhXdunX70sOQkZGRkZHRma9GhMePH5+qEoe5ubnGPLya8PX1VcvXrImwsDB+//13HBwccHJyonv37mrVkJ48eULNmjXJmjUrJiYmuLq6MmDAgA9ew5aStWvXprpGPT09AgICMqV/GRkZGZlvi69GhC9dusTvv/9OQECA9Hrx4gUGBgbpHhsdHc3jx4/x9vZOs02VKlV48OABR48eZdu2bWzbto2ZM2dKbaKiomjfvj2HDx/m4cOHTJw4kTlz5vD3339nxiVy6dIlateurXaNgYGBaRaTkJGR+f6JjY2lcePG5MuXD29vb+rUqaO27jQ4OJg6deqQN29evLy8OHPmjLRv+fLlFC5cGENDQ+bPn6/Wr+oaXm9vb0xNTZk7d67GMVy+fJly5cphbm5O8+bN1fYpCyZ4enri5eXF8OHDtSakiIyMpHbt2lIBh5SEhYXRpk0bjQUcPsV40iuuoCxaoamAgyrR0dG0bt36yxZwUHLr1i1Rt25dkSVLFuHs7Cz69+8v5WNu3769+OWXX8TIkSOFi4uLsLKyEhMnThTR0dGib9++wsHBQTg4OIjly5er9eni4iLWrVv3IcORihHs2LFDFCtWTJibm4sqVaoIPz8/qc3QoUNFjhw51PLk/vnnn2rJwzWRLVs2MWHCBJ3HsnXrVuHj4yNMTEyEo6OjWoL80qVLZ6gvGRmZH4OYmBixd+9eab3vvHnzRM2aNaX9HTt2lHIRX7p0SeTIkUPKJe7r6yvu3r0r2rZtm+bSmICAAGFqaqq25liVly9fiosXL4rFixenWperLJigHGv58uW13q9VCzikXCcshKI8pmoxGm0FbjJrPGkVV1AtWqGpgIMqY8eO/fIFHIRQXHyWLFnE8OHDxaNHj8SJEydE1qxZxbhx44QQivVZdnZ2YurUqeLhw4di1KhRQk9PT9StW1csWLBAPHr0SPTq1UtYWlpKP7jnz58LQLi5uQl7e3tRpkwZrQumNbFw4UJhbGwsqlWrJq5cuSKuXr0qvLy8RO3atYUQii/J2tpazJ8/X+24CRMmiFy5cmnsMzw8XIwbN064ublp/dGmZOjQodIDxsOHD8XZs2elnK7x8fHCxMREZMuWTdjZ2Qlvb2+xZs0ana9RRkbmx+Hy5ctq64QtLCzUilWULFlSrRiLEAoDKC0RnjJlimjUqFG6504vV7MQinq8f/31V5ptNCXrePTokciRI4dISkpKdxyZNZ60iiuoFq0QInUBB1U8PT0/SQGHDLujO3fuTNu2bRk/fjx58uShcuXKdO7cmT179pCQkMC9e/fo0aMHAwcOJG/evPz2228IIahbty7du3cnT548tGnThsjISCIjIwFF+rFly5axc+dOdu/ejaOjI40aNeL+/fs6jcnX1xc7Ozt2795N8eLF8fHxYejQoZw6dQohBMePH+f9+/c0a9ZM7bjg4GBsbW3VtvXr1w9LS0usrKykNHEuLi7pjuHixYtMnjyZjRs30rFjR/LmzUu5cuWkdGqhoaFMmzaNbdu2cejQIYoXL07btm05evSoTtcoIyPz4zB37lwaNGgAwJs3b0hOTsbR0VHa7+HhkeE65cuXL1er1/uhBAYGsmXLFql+dka4e/cu2bNnp2vXrvj4+FCrVi2uX7+e6ePx9vbm9evXgKKeu7u7u7RP9bNLa9/r16/Vpjg/tJ/0yJAI379/n6tXr9KrVy+17cbGxsTFxXH37l3i4+P57bff1AZuZGREhw4d1LbZ29uTJUsWQFGUu1OnTvj4+FC2bFn+/fdfkpOTOXTokE7j8vX1pUuXLpibm0vbLCwsSEpKQk9PD19fX7JmzZpKTK9cuZIqmGvw4MFcvXqV7du38/z5c/766y+dxvD333/ToEEDqlWrpnF/1qxZ6dWrF6VLl6Z48eIsWbIENzc39u7dq1P/MjIyPwYTJ07k0aNHTJgwQdqmWhwAMl5c4ezZs4SHh3+QcKqirWCCrnxLBRyyZcuGr6+vTm3T25cWGRLhO3fuYGRkRL58+dS23717l8KFC3Pjxg2cnZ3x8PCQ9l2/fp2CBQtKggtw48aNNIOojIyMMDAw0CkoKzk5mdu3b6fq79q1a9K2sLAwtadIgICAAC5cuCA9bSpxcXEhf/78NG7cmDZt2nDgwIF0xwCK6yxTpoxObQH09fWl65SRkZEBmD59Otu2bWP//v2SUWFvbw9ASEiI1C5DBQKAf/75h/bt26vdb1IWcEgPbQUTvtcCDp+qn5RkSISzZMlCUlISCQkJ0rYXL16wZcsWfvnlF3x9fSlWrJjaMdevX0+1LT0R3rhxI0lJSTo9tT148IDo6Gi1pUwxMTEsWbJEssgdHR15+/at2nETJkwgZ86c1K9fX2O/ycnJnDt3jjx58qQ7BlA8OLx//16ntgBnzpzh2bNnNGrUSOdjZGRkvl9mzpzJhg0bOHz4MDY2Nmr7WrRowYIFCwBF1HBgYCAVKlTQqd/IyEi2bNmi5qEEOHfuHL6+vly8eFGnPurUqUPt2rVTRRDPnTsXX19ffH19KVy4cJr9FC9eHCsrK27evAkovJEArq6uOl2LLuNJSYsWLfj7779JSkri7du3bNy4kVatWkn7Vq1aRVRUFHFxcSxfvpyff/5Zaz/K7+DZs2ecPHmShg0bpnuOdNFp5vj/vHv3Tjg4OIi+ffuKJ0+eiKNHjwpPT0/Rpk0bIYQQ1apVE8OGDVM7xtvbW8yaNUttW/bs2aWgpA0bNog1a9aIu3fvitu3b4spU6YIc3NzMXnyZJ3GtH79epElSxZRqlQpcfv2bXHjxg1RsWJFUbFiRSl68Pbt20JfX1/MnTtXPHnyRAwfPlyYmJiI06dPCyEUAQSDBw8WZ8+eFc+ePROnTp0SjRo1EhYWFuLKlSs6jWPkyJHC3t5e7Ny5Uzx79kwcO3ZMLFq0SAghxMGDB8Xff/8tbt68KR48eCD+/vtvYW9vL7p27apT3zIyMt83L1++FIDIlSuXVJCgVKlS0v7AwEBRs2ZNkSdPHuHp6SlOnDgh7VuzZo1wdXUV5ubmwsbGRri6uopr165J+5ctWyYqVaqU7hg+pmBCSuQCDroXcMhwdPTZs2dFiRIlhJmZmciVK5eYNGmSSExMFEIIYW9vrzaw+Ph4YWxsrBbF9/btWwFIlT5mzZol8ubNK0xNTYWjo6OoWrWq2L17t87jGTx4sPjpp5/E3LlzhYODg7C2thbdu3cXUVFRau1WrlwpcuXKJczNzUXVqlXFxYsXpX137twRVatWFXZ2dsLExERkz55dtGvXTudSVEIowvL79+8vXF1dhYmJicibN69YtmyZEELxoOHl5SXMzc2Fra2tKFOmjFi9erUUHS4jIyMj82MiV1GSkZGRkZH5Qnw1GbNkZGRkZGR+NL56ET5x4gSWlpYaX7qs380sfv/9d63j+PPPPz/bOGRkZGRkvh++end0TEwM/v7+Gvfp6+uTK1euzzKOoKAgIiIiNO6zsbHRmCNVRkZGRkYmLb56EZaRkZGRkfle+erd0TIyMjIyMt8rsgjLyMjIyMh8IQx1bRgbG0t8fPynHIuMjIyMjMx3g7GxMaampmm20UmEY2NjyZ49O6GhoZkyMBkZGRkZme8dFxcXnj17lqYQ6yTC8fHxhIaGcvjwYSwtLTNtgDJflrJly6baNmXKFCpVqpRq+8SJE9m9ezdDhw6V8qUq8ff3p3nz5syePVvnZPCq3Lt3j1u3blG2bFmmTJnC1atXAUXZzJT5br9FbLK6p9qWzdZCY9uEkOcA6MeEk2xmJW3Xjwn/r1H0O/WDzG00b88kHiTbf5J+P5bJkydz69YtunXrhr+/P7Nnz6ZGjRpS5bNnz54xfvx47t69S926dRk6dChGRkYffD4/Pz927dpFoUKFKF++PLt27WLv3r14e3tz8eJFihUrxuDBgzPr8r47du/ezcSJE1m9ejV58+bV2MbPz4/hw4fz7Nkz2rRpw9q1axkyZMg3mWM/MjKSmjVrEh8f//EirES5Llbm2yc4OFjj9lGjRjFo0CBatmwpbXvw4AEHDhzA3d2dX375JdUxefPmxdramhs3blC9evUMj2Xs2LE8f/6cTZs2Sd6WAgUK8Mcff2BiYpLh/j43tq45M3xMRBK4ahJiq8IkBPuBStUxAH3DZJX//FdABQuVethZVP42o8IyPCZtlCQ2zf33kjUvz4uNjcXExCRVGb7MwszMjKdPnzJw4EBMTU1xcnKiZcuWWFpaMmPGDNasWYO7uzt///03ZcuW/aBxCCF4+vQpO3bsYMOGDcTFxQFga2tLWFgYVapUYfv27SQkJODh4SHfH9Ngz549VKpUKVVBHyUPHz6kR48e2NrasmnTJvLly8eWLVuIjIz8rj/XDImwzPfB0aNH6du3r/T/1q1bY2ZmRuvWrZk3bx7jx4+nRo0a2NnZAXDs2DGSkpJYu3atxv709fWpWrUqp06dSlVSLCAggLlz51K5cmXq1KmT6tikpCSeP1dYf/7+/nTv3p29e/dibW39VQmwkZN6WTJLI/USlJEJSVr3udpa4B8WlapP/7AojUJs5OQBoBDj/5NsZg2AfoyOlbpUxVmbIKu2SYt0BL2g/n/TVElvAgF4FQflmnbB2dmZRYsWfZLEOl26dMHQ0JCGDRtSsGBBafv69etZuXIl1atXZ9KkSZiZmWW47/DwcNatW8fu3bt5+fIl1tbW/PLLL3Tq1ImQkBDmzJlD9erVady4MVevXuXixYupyqLK/Mf79++5desWY8eO1bj/1atXdO7cGWdnZ5YsWUJwcDA//fQTsbGxOley+1aRRfgHIyYmRk2AGzduzObNm1m6dCkuLi7Url2bXbt2ERUVhZ2dHQkJCRw4cAAfH59U5dWUnDlzhh07dgAKy0HV4vj777/Zs2cPe/bs4cqVKwwePFjNJZiUlIShoSFJSUkULVqU9u3bY2VlxeTJk5kxYwb9+vVDX//Dg/iTkpJ4/fo12bNn1/kYLy8vADXhVBVZ5f+VYnvx6lW2bliLibkVNWrVpHjpsqmEOFOxsFUIYwoRTTaz1izSyvYpt2X0fBpIehOIgb2LJL5KxqzeR0REBIaGhkwfMZDtwzpK+57kqqb7udPA0dExlft33bp1TJ48GQcHB3r37v1BAnzu3Dn++usvQkNDqVevHoMHD6ZMmTLSQ6G1tTXz5s2T2hcvXpzixYt/3MV855w5cwYhhMYpsJiYGHr06IG5uTmLFy9GCEH37t2xsbFhzZo1aZa9/R6QRfgH4/Xr19L7vHnzUrNmTXbs2EHHjh0ZMGAABw4cIFu2bGTNmhWA1atX8+LFizTnui5fviy9P3bsmJpL2svLi61bt1K3bl22bt3Ko0ePqFmzJkuXLsXDw4PmzZvj6OhItmzZWLhwIebm5rx58waAlStXki9fvo+yMFavXs3MmTNxcnLCzMyMatWqUbFiRby9vTEyMiLGyhUrUwPyO1ml31kKlEL88vFD1i9fCsCK+TOoWa8+s+YvwsbGVrPLWUeMnDzUrGFQEVoVEVVayarvU4lxGkL6oSiFN6UARxhZsnv3btq1a4eVlRUrV65E38ZRejjL/fRYqr5SCnNCQgJr1qwhJiaG7t276+RKFkKwZs0aGjZsyIQJEzJ8PcnJyUyePJkNGzZQrFgxli5dipubW4b7kUnNkSNH8PLyIjk5mVatWvH27VtGjhxJpUqVWLt2rVSX3s7OjhkzZhAREcHatWs/a2riL4W8TvgHw9zcHCcnJ0xNTVm/fj2VKlVi1KhRAEyfPp27d+8yfvx4DA0Vz2fbtm2jQYMGlCtXTmuf/fr149atWxQoUIBjx46RkJDAqFGjqFy5MkIIfHx8ePnyJcuXLyc0NJQpU6YghMDIyIhhw4YRGBjIX3/9hbm5OaDI033o0CEKFizIqVOnPug6o6OjGT58OHPmzAGgYcOGlCpVim3btvHbb7/xc/tOXFfRpJTuYk3uY200a/kzJy9eo0PXngAc3reHMydPqLX5EDFWFWCli1pJspm19DJy8lB7KfenwsL2v1dGSCHeSW8CUwkvgIG9C1cMctBt5mqioqJo27YtefLkITIyklvmedHLXwa9/GUwLNMQA1snxTG2ThjYOpEv7Lb0yhblR7du3Zg1axaLFy8mMjJSOsezZ884fPgwiYmJqc7/4MED/P39qV27dsauD4WAT5s2jX///ZcRI0awatUqWYAzicDAQI4cOcKdO3eoXbs2d+/exdDQkLlz57J//34WLlxI27ZtiYyMZNKkSaxbt47o6GgsLD78AfZbQraEfzBOnz4tBWXNnj2bIUOGoJq5dNq0aZQsWRKAs2fPEhAQoNOcTGJiIkFBQZQrV465c+eye/duihUrxrhx46Q2hQsXZufOnRw/fhxPT0+yZcvG0aNHcXFxUXMXm5ubY25uTlhYGBUqVNB4PiEEFy9exNfXl2LFilGqVCk1aykwMJBdu3ZhbGzMyZMnsbZWiFLz5s1ZvHgxx48fZ8HwXsxbu43wWIWrWTlHm1KAU7qiU+6zNDIgd958jJ84meI+PmzZsI6KVaqmaqttblgXUs4Pq4pyyrllpQWt1T39EaiKr0EOxTxs0ot7hFm6MHHOGhYuX01iYiLt2rXDxcUFBwcH7OzsGDNmDJMmTUJPT4/8TpYY5CgoHa+8Jv1QP16+j6Pmrz0JCgoid+7chIVHULp0afT19Tl37hzt2rXj3bt3DBo0iLZt25KUlISBgcL1v2LFClxcXDS6PNMiOjqaxYsXs3btWkaMGEGrVq0y4ZOSAcXfoTJORHmfmTlzJpGRkYwbN45BgwZRvHhx6tSpwy+//IKLiwsJCYrAw3fv3pElRYDi94gswj8Qr1+/ZsqUKdL/69WrR3BwsGQtAlJ08okTJ+jVqxelS5fWaXnA3r17CQsLIz4+nrVr1zJw4ECaN2/O77//zq1btwC4c+cORYsWpWbNmtJxNWrU0NqnjY0Nr169SrX9xo0bTJ06lZs3b2Jubs6CBQsYPHgwzborXOZBUfHo53WkessOnN2zWZoXjI+Pp1+/frx+/RrXnHmo1ryt1KeqEKcluppQtrc0MqBJ85Y0ad5Sq+X7MUKc0hpWHas2Ic4s1MTX3gUDzyqAIgJ60vJtzJzZGSEEXbt2pV69etJDlaGhIX379mXUqFHEx8fTvHlzwnxKkyt3HsL8nwGQ3dKAAL/nnL1whWGTZmKor8f48ePp168fEydOJCn0BX6RyYwcN4F3796RNWtWDh06xKpVqwgKCuLy5cts2LCBffv2MXLkSJ2XIUVHR7Nu3TpWr15NZGQk/fr1kwU4E0lOTqZr166SJ6N27dpMnjxZ8rJVq1aN9+/f4+bmxt69e0lKSmLx4sVMnTqV06dPf9B8/reILMI/EPfv3yc+Pp6GDRvSpk0b/vnnH+7fv094uGINasmSJalVqxavX79mwoQJGBgYMHv2bJ2WB6xevRqAf//9l6ZNm9K2bVs6duwoCXCbNm0oUqRIhsbbpEkTJk2aRO3ataV55kOHDjFkyBDy5s0rLT3p0qULU6ZM4f6zF/QcPh5nG1uCouKpVrc+RzetZPjw4djY2HDx4kWCQ0KYs2oTtevUloRXlYwKcMpjMxqQpS1CGtIWUk0PC5r6yog1rKltspk1+iruaKUA37x5k6VLl7Js2TJiY2Np3Lgx/fr1kyLqVWnSpAmBgYEsXLiQw4cPA5CjYFFyu9jx5s0b7t69K7mXK1euzOjRo/n777/R09Pj7du35CpekVevXmFsbEzLli0pWrQow4cPl/qvXbs279+/p3Xr1jRv3jzd6wwMDOTp06dMmjQJf39/mjZtSqdOnaQ4CJnM4eXLlzx58oRZs2bRr18/KleuLAkwKALclB6qKlWqAIoH9VmzZuHv7//DVKaTRfgHwtPTk7Jly7Jr1y527dqltk9PT4+2bdvi4ODApUuXCAwMpEWLFjqvz6tYsSIPHz6kdOnSjBw5Ej09PQYNGsT06dPJnj07Xbp0yfA6zdatW3PlyhWGDBnCsmXLePHiBaNGjaJatWpMnjxZsnjmz5/P1q1bWbhwIedPHKVW45ZUbPwLjvmKkM+7JKfOnCUuNgYPd3fmr92GT5nyGgU4PDYJK1MDaZ+VqYHa9oyQlriqoutccUorOCXKBwBtQgzqwVpKwU05d5zy/0ZOHuDkAc9vkGxmjYGTBxfuPaN7hw7cuHGDFi1aUKdOHUqUKJHm+Lp160br1q2Jjo7m6tWrnD59moiICHLmzEmdOnXIly8fbm5uuLq6EhkZycaNGwFYsGABtWrVonfv3lSuXFmKG8iZMyeOjo5s376dGzduMGDAgHSnTcLCwpgzZw5bt24FIF++fGzbtg0PD480j5P5MO7fvw/Ahg0bACTB1YQy8jwoKAgTE5PPVqL2a0AW4R8IFxcXlixZIomWm5sbvr6+gGK+RpmIQBmRqG1JkiaUAtuqVSvpadfT05Ply5d/8Hj19PSYNGkSXbp0oXfv3lIe1lGjRqm5HE1MTPjll18oU6YMY8eOZfu6FaxdMp/W/UfRZ9Zycjg7kpSYSDZr83TPqSrOmoQ6rWOsTA1SWcMpRVHVFf0xkdOaSCnEqsKtnCNWRWPwlgb8w6JwdS9KYFgUdw4dYuDAgdy8eZOJEydmKHLdxsYGGxsbsmXLluZxRkZG/Prrr0RFRdGnTx/s7VNn7CpcuDCgEHdduHDhAoMGDSIxMZFBgwZRtmxZcuXK9VHL32TSJjZWkeTl0qVLAGkGuhkZGZEzZ05u3779Wcb2NSGL8A9Iz549admyJdWrV8fExITBgwczbtw4yZqJilIIRUbW5ymfZB0dHTN1rCYmJkybNo3GjRvz9u1bQJFZRxk8pkquXLlYtWoV9+/fp0WLFmyYOY5szs5YVFfc8A2i4nG2MP6gcaS0jlNuV0VVDEFzpPXHWsDKuWVt65c/dI5Y0/kiE5I4+yiQ5fOms3zeDExMTOjVqxf169fX6RoyivI3mRncvn2bdevWsW/fPkqXLs2kSZM0irpM5tOgQQPy5s2Lq6srRkZGkhdDE3p6ejRs2FBaJ/ypsqx9jcgi/AMSEBBArVq1AIiLi2PcuHGULFlSmoNRzhFrmt/TRocOHXB0dKRQoUKZPl4nJyf++usvKcnIlStX1ET40aNHZM+enb59+2JmZsasWbNo1qwZ+w4cpH79+jyPAXvzDxPflGgTY+W+jLqtPyXarO6UFnLK7SnF/XVwGD3at+TeNYVFM2XKlA9KT/q5OXLkCAMGDMAgixPWJX7hiXMxavZdBcC1Nf3TOVrmY9HX18fT01Pn9s7OzsTFxREXF5du5aHvCVmEf0Dmz58vvS9cuDC3bt2SxDMpKYlx48aRL18+tVSA6WFqakqzZs0yfaxKqlevjr6+PsnJydIDBChcXZ06dcLQ0FAK7jlx4gTHjx+naJmKmFtYYq+XoK1bALV5YF3RJrjK7emlsfxUWBoZSP2ntMA1WbkplzpFJiQRHptEUFQ8L4JCeHj9EruXzyfk+WPatm1LdHQ0VaumXn71NZGUlMSpU6cYMmQIdoUrUfDXEbwJiCbyXRSx70OJCQukfO/1AGT3Kce/HTy+7IBlACRLOTIyUhZhme8b1dD/W7duYWBgIFk2t27d4sWLF6xatUpaf/m1kJysKGLg7v5fVSJl0Idq8oZevXoBcOHofpbNmUqdevXI7+lFdGJqF5dSSD9UiLVtVxVo1SVMoHvQli5YGhmk6l+J0mWt7Vyqoqu8/sSEBHb8u4aw5w/Ytm2blL1s7dq1FC1aNFPGnJlERkby7NkzsmXLxu7duzl85gJP79wgMjKSHEXLULL7WF4GqBegMLNVz8LUaVsA5laK6ZR5NXT3/qQkMTGRuXPn4uXlRa5cub77nMeZzenTp3FxccHWNoPJZL5xZBH+Aenbty8FCxYkNjaWyZMnk5SUJLmeletyTUxMpKUcRkZGGudgPyePHj2S3qsuc/D29ubIkSPS/xs2bEh0dLS0bemsKSydNYVWbdrx71qFK/JBcHiGBTcoKh4gzTllVSHXZCl/yBImTaS0cFX71FXc/cOieB0RT1BUPG+i40lMTCD42knmTxrL65fPcXBwoHTp0jRv3hxLS8sMeUU+B0IIevTowenTp6VtRkZGVKtVm+q9+2ORpxiWHp5ce/4OcytFkojId1GY2TpL7d2LemJuZUJ0eJy07a8r0dL7kSXSD+RT5ciRI6xYsUL6f926dbG0tKRo0aI0bNjwh5rn/BDOnTtHrVq1vrqH/0+NLMI/IJaWlpLr+N27d9jZ2ZEjh6JKkJeXF5aWlvz8889qx6xcufKLJqkPCAjA2NiYSZMmqW1v3749BgYGTJkyhblz50qu0q1btzJmzBip3cZ1q7EwMWTJkiVqx6uKpeqSpJQ4WxgTFKUQLW1CnPK4zzlHrE18IxOSNGYAC49N4m5IJMGRcby8f5O98/7i6R1fcubMyZQpU6hXr97nGPZHcfHiRbJkycLevXt5/vw52Qp6kz9XTunh4l5wBK62CiF1zWnHWSDKRvE5Rb6LwtzKBCcbU5RFPZ1sFC5QV1tz/MOiWf5Esf233LqN5/bt2xgaGrJhwwamT5/O/v37yZYtG5s3b8bR0VFK/ern50dYWJjWkn4/KjY2NlI8yo+EnlDNWaiF8PBwrK2tOX/+/Hdd11FGQUREBA8ePCAqKgoPDw8aNmxIjx496NKly5ceWoZISEhgwoQJ2Nvb4+fnx6FDh6hfvz7Dp83DwPy/5Tm6RDwrURXhtARZW9+gPm+bUdLKthXw+jV7dmyjX8+uWFtb4x8WxfXASGmMSks9KCqe26/fYnLzAJs3b+b8+fPY2dkxYcIErWlCvyZevnzJhAkTOHv2LIsWLaJr166p5rOV3AuOwMnShFVb9xH+2o/X/uEkRL7BsXQrnD3sJeFNiVK8AbL+v01d+7TrKj98+JBWrVrRtGlTOnTogJmZGfb29jRo0ABbW1usrKx49OgRgYGBUqUgXZdY/QiMHj2ae/fusWnTJo37nzx5wsGDB6W15V+7ZyEyMpKyZcvy/v17rKy0F4iRLWGZVGTJkkVarhQdHU1ycrJaEv1vBSMjI8kavn37NocOHWLPnj1YOrvR5s8xGgVUFze16k0+LSHWJsCQufPCyjnh1atXMm/qRKZPnUSr9r9TrVVHEk2teBMdj725MVamZgRFxXPs/GXWD/udoKAgihYtysSJE6lXr9434QaMjY2lU6dO6Ovr8/fKtVSqU196MLE0Mkjl7k+yM+Pc2rmcnD5diikAyF//V4rltMM/LBpd2f/mP8HWJMj58uVjxIgRjBkzhk2bNlGzZk1mzpzJoEGDmDRpEjdv3qRUqVJ06NABf39/Fi9ezJkzZ3j16hWlS5emffv2n2R1wbeCs7Mz27ZtY8qUKTRr1kyaU4+OjmbAgAFqUw+JiYnfTf1mWYRl0uTQoUPo6+unck9/a3h5eWFjY8O7d+8IDlUEG/0npsY6uY1VxfdDyKwawykDsSyNDLh07RrL5s2iSJEiFC1alA3LFrJ+6QLq169PuXLlMHZxYfOTJ9y+fZsdO3bg7u7OggULyJ8/f6aM6XMRHh5OQEAALdp3pljVemQxVtzCVCPCs2UxJjk5mTUbN7Nw2kT8/Z7Qpk0bqlevzm+//YZblZa4ujr9/zjzdIU44J1CcJUWccC7WPaj2Tpu1qwZpqamDBkyRCqUUqlSJcqXL09MTIzkSYyOjubevXu8e/eOGjVqcOnSJVq3bs3QoUNp3bp1ZnxU3xxt27YlKiqKvXv3snnzZtatW0e2bNlo27YtT548UWt7+/ZtfHx8cHV1/UKjzTxkEf6BSU5O5tKlS2TJkoX8+fOrBTwpuXnzJsnJyTg7O2vo4dshMTFRyo7kYpE6wX96FrA2Af7Q5B8ZRWntqS59UgZ6JSQk0LvDL+TKlYslS5ZgYWFBly5dWL9+Pbt27WLLli1qfXXq1Ik//vjjm0yQr/yNGqCYbw94/ZoieRXR8gnBfmRzdOfgwYP079+fe/fuUaFCBWZOmYinp6c0zx1y9TBPcuTEtemvWs+jFGZVt7RSjFVJaR37+fmxYcMGsmbNyuzZs6V9BgYGalN558+fl+pwu7m5MW/ePEaPHs3p06d/WBHOkiULAwcOpFevXrRu3ZoJEyZgZWWlJsAeHh5YWVmxc+dONm7cSO/evenYseNX75pOC1mEf1ASEhKYOXMma9euBRQ1fPv06aPWZsaMGWzevBkAHUIHvmpevHghZdyq33Okzsd9rPX7sehScSkw4DWvXr2iT58+Ug1WGxsbunfvTvfu3fH396dOnToAlClTRkp68q0RFxdH69atsbW1pd+fA1kycwozp0xk4MCBtGvXjhs3brBkSXtOnTpFsWLFUgUTtmrViunTpxMXEYapUAhqelawJjHOqmUeedyuq+wa9wcJCQksWrQozQIEOXPmlN5fuXKFBg0aoKenR40aNXjz5g3v37//ofInq2JqakqvXr2k+9H8+fPR09NDT09P8ii8ffuWDh06MGvWLO7cucOMGTO+8Kg/HFmEf0BevnxJ/fr1SU5OliymwED1Iu3Pnz9n5cqVAEydOlWjlfwtkStXLlq2bMmFS5fJYvPfOsT0LFllVLQ2lHPCqm2UfapGR2tyRX9IHmlN64KzZnPFwcmFyZMnU758+VQ1WLNly0adOnW4cuUKHTp00Ok8XyP79u0jICCAO3fu4OTkyMwpEwFFDezNmzfj5+dH/vz5WbBgARUrVkxlHbVv355du3aRaJONhr/1IPB9nKbTpIkmAU6Ii2XD4vnc27uSYkUKM3/+/HQL0ufKlYudO3fSqFEj+vbti4WFBUFBQdjY2EgVhfbu3SutWvjRqFKlCuXLl8fNzY3KlStL2+fMmcOyZcukxD2gyAv+Lae6/LbvrDIfxKJFi0hOTqZ8+fLExcURGRlJw4YN1dqo5nktX7785x7iJ+Hdu3fY2tmT196MR29idDpGF0s4ZRtdoqZToilQS5sVnFLQDQ0NGTz6Lwb26Mz169epVKmS2n49PT2mTZuWofF8jRw8eJAqVapg5ZKDJ+FxeHp6cvfuXby9vUlOTmbIkCG0bt1aa1GGNWvW8PDhQzoM+899qbRw07KIlW1UBdjPP4To57d4fuc6vkd28z40iHZtf6Vbt25p5khWJVeuXDg5OfH69WvJO/H69Wty585N4cKFcXJy0qmf7xF9fX0WL16canvx4sVZtmwZZcuWpUWLFrx48YL69et/swIMsgj/kPj7+1O1alXGjRtHxYoVAfjnn3949OgR7dq1AxR1gU1MTJg+fXqa4fXfCgkJCZw/f54O3Xqpzf9+iGCmh6b+dEnUoRRdZaarjPDy6UNA3c35LZOcnIwQQorYvn//PufOnZNuzAFP7/Hw4UO6dOkiZUhLi2XLljFnzhzqtetKpcatCY1OIKuNqTTPq+puVuIfFo2rrblauyC/R/iumcGJk6dISkzAwcGBsqVK0a1btw8qiViiRAm2b99Ovnz5uHv3Lq1bt2bHjh0Z7udH4PLly5w4cQKAs2fPcvbsWUCRQ6BLly7fbP1huY7XD0ZSUhLXrl3D2dlZLQtVTEwM06ZNIzAwEJ+2M1m65ThOTk7kzp37m58PBjh16hQRERHU+qmhRtexNlT3x8XGkJSUsUxbqqSseKSNjAowQM2GzbC1taVt27a8ePEiw8d/TVy+fJmiRYvStGlT7t27R0hICH/99Rc5c+akY8eOAKxZuhhnZ2f++OOPdPs7f/48c+bMoVHnPrTsPQx9fX2cLE3SPa5ETjvJ+s1qY8rdc0eZ+0dTHj16xJ/9+7Fv3z6OHTvGlClTPrgm8cCBA3FycmLw4MGsWrWKOnXqEBAQ8EF9fe+MHTtWqjMNULZsWcqUKcOGDRs4evToFxzZxyGL8A9EcnIyI0aMANQDrWrXrs28efMAqPP7eAD07fPy8uVL6tWrR5UqVb7JdcKgsIA7depE3759KVTUG2ePfB/UT3xcLD3qlmFKv868iY6XXppQFfmUUde6CnFGKVbYi6PnrvDmzRt++uknrl279knO8zlQjv3p06e0bNmSWrVq8ezZM2YuWEJwZDzHz19h746t1K9fH2PjtB+ihBBMmjQJQ0ND3M0E/4wdwMtH96T9WW1M1YRW9b3y+IAn91k9YQirR3SncsUKbN26lbZt25I9e/aPdoM6ODiwfv16/v33X6mvsLCwj+rze+HVq1f4+vpy9+5dhBDMmDGD3LkV6ctMTEyoU6cOL168wM7Ojho1anzh0X44sjv6B+L+/fvs2bOH9u3bM2DAAOLj4+nevTutWrXC1tYWPT09zO2ciRWgZ/Df/Fft2rW/yeUsoBBhZVHxWfMWaW2XVspKgIe3rhMUFERQ0D4Y2YcS1etRvEptSYijA/wwMjHBxU2xXEbVzf0xeaQ1VUTS5q52cnbG3MKS6KhIlixZwsKFC7/JovVdunTB29ubMWPG8OrVK/oOHELLjp2xsrYhMiGJsX92x8nRUacgs4SEBAIDA0lMTOSff/4hKiqKRAMjmvQdo9ZOdb5X+f7evUccn9IdPz8/HB0d6d+/P7/++mumJzUxMjKiUKFCHDt2jAYNGjB9+nSWLFmCoaEhQgji4+OZMWMGvr6+/PzzzzRp0uSbngNNj61bt3Lz5k22b98uGQuLFi2iQoUK7Nixg+joaM6fP8/QoUPJkycPU6dO/aZrRMsi/AOxbNkyAEqVKgWAsbGxlDYvNDQUIQQ22fOQIMwIv76RRBT5eXUNNPkaefjwofTeLpsbyWm0hdTVlE4fPcjYgb0JfxOCvas7rRr9xPbt27l4cCcNeo6gSp36nNu3jc3zJ6Ovr0+H4VNo0rotoF2IldZwWuUOQbMAp9ynRNlm1bbdtKhdlbNnzxISEvJNru/W09OjdOnSLFq0iCZNmrBzy0bOnjlNdFQU79+/x+/JI0aNGqVTCl1jY2O2b99OeHg4efPmZfTo0Rw+to937yMwEglYuxekTj57InKVxS5rdum4c3decG7BaAzi4li8eDElS5ZM1+r+WBwcHJg/fz6dOnViwYIF9OnThw0bNqjlSx89ejR2dnZSBPX3RkJCgpTlrnfv3sTExLB06VLJ3Z+cnMw///zD8uXLKVeuHNOmTfum708gi/APw40bNzh8+DCAxkAr5VynlYM5d1aMAqGQK19fXynx/LdIoUKF6NKlC0uWLOHmtas4epWR9ukSkLVzy0bC34QAIGIiCAoKYtSoUezatYvd88ezc+44ADp27MizZ8/Yu2YJlRq1wt5c9xu2NotYF/FNidLyHTly5DcpwKp4eHiwaNEitm3bhhCCrI72mJub07pl8wzVrnZ1dZUyKymzL+mHPsPM3Jzz6+ZyOjGRLFmy0KVLF46+Tibo2SNCrx4kMTGR2bNnf9bVAcWLF6dXr17Mnj2be/fucfHiRWmfp6cnL1++5NSpU/j4+PD+/Xvc3Ny+K6vYyMiILl26sHbtWlq3bs3EiYplaG3btuXgwYOsXLmSJUuW0K1bNzp37oyRUerEO98asgj/IKgKr6enZ6r9Tk5OuLq6EvbgMgXrt8f/9mXCn93G2to6VdtvCQMDAyliWD8pQadjVK3hBk2akxgdibWdPe8jI9m1axdbt26lQYMGHD16lIYNGxIREcHGjRuJjo7Gximr1n6VfX6KykpKF7W9gyOgSGzxPVCmTBnKlCmTfkMdKVCgAP/++6/0/7dv35KYmMiMGTOYNWsW+vr6uLi40KVLFxo2bEi2bNky7dy60rFjR6ytrTl48CCVK1emVq1aAFSuXJn58+ezdu1aKYlO5cqVmTt37jc57aCNFi1asGTJEsqWLStty5UrFwYGBjx9+lT6//cgwCBXUfruEULw5MkTNm3axIYNG2jZsiUjR2rOGDVr1iz+/fdfqoxei82NLezatYvTp09/c4k6YmJiuHTpEo8ePaJChQq0aNECgKtP/UkyMNNYG1jXSkpGb5+zdu1aZsyYgZ2dHWFhYbT8oy8vnvtx5dh+fCrXYsTsJan6T4nq+bRZwinnjdOzgv3DongfG0+FQrmJjY3l3Llz39x39yV58+YNZmZmX71788mTJ9y8eRMhBGPGjKFfv35S1Pj3wuzZs3n06BFJSUnSUqSePXvSvn17Ro8ezYEDBxgyZAg///zzV+sJkKso/cAkJCRgZGREQkICgwYNUit6Hx+vPfnE77//zoEDB7i6ajKRgX442Nt/czfxffv2MXbsWKKjFckXlFVpKletjoWFpVZx1aV6EkCCnTuVfxuAcQ5PXl05QY0aNRCFFDWM+46diomZuUbxTWs9ckaWLqUnxNaminn+SZMmcfbsWdzd3T94+cyPxrcS3JM7d24pStjPz49Zs2ZhaGhIs2bN2L17N05OTlSuXPmbto6VyUtWrVolifCmTZv4448/mDRpEnZ2dkycOJGrV68yZcqUb6ICmDZkS/g7Y926dcyaNYsNGzZw8OBB/v77b0aPHo2Pjw9OTk4YGhpiaqo5921MTAyNGjXCKnteiudx4+jRo2oC/rXz5s0batSoQalSpXjz5g0PHjwAIGvWrCxbu5HcXkWlerMfkqAjvexZ2sRXGUHt6aj9b0ebi1qblZyWGF+9elUqRWlgYMDUqVMll6bM90VycjITJ05k48aNkmdGCMG4ceNo0qTJlx5epvD+/XtevnyJi4uLWkKOvXv3MmTIEBYsWJAqS9zXgK6W8Ec/KindnUlJSSQnJ0s/ApnPT2xsLLNnzyYuLo5ly5bx999/A4olRrly5cLS0lKrAIPiSTP0zRt69u7DgQMHpCjqb4W///4bQ0MDduzYwf3793n0KpgtW7Zw6spNihbzkdp9ispHqn2eePaGuyGRnHj2hm0nr3F0znCiLu5m8+mrBEZqnqvV1RLXhcJudpTyLkyOrM40rVmRkcOH8fz580zrX+brQV9fn169euHl5UXBggXZtm0boPCGfS9YW1vj5eWVKiNWvXr1yJcvH1u3bv1CI8scPtrXePDgQQYOHKjozNCQxMREbGxsyJEjB4GBgRgbG2NqakpSUhJVq1alRo0aPHjwgOrVq2NoaJgq2bzMhxMXF0dsrCK9nqrbOSgoCEtLyzTnThISEvj333+pV78h29auwMTEhP79+3/yMWcWiYmJ7Nq1i0GDBuPoqAhOMjM3p0y1OhrbZ9Qa1mQFv4mOV4uCVrYJeBfLxYeB3Dx+lEcbxgCKvMUA/+QqxMx/1mLv/F8Al+oyJvi4wC0hBGcvXubvyaPI62RFfMhrXI+f48SJE7Rv3x5QfNdJSUlpPpDJfDtYW1uzYcMGQFGn2MjIiLt370rTUl8zcXFxvHv3DiMjI0JCQrCyskIIgYWFBVZWVtI9KzY2FiGEWr4CPT09mjZtyuTJk3n+/Dnu7u5f6jI+io8W4UqVKmFubk50dDSJiYmAIlH+u3fvAMiRIwePHz8G4NmzZyxfvhxQpCADmDlzJk5OThQpUuSrnWD/Vrhv+N+NXelGdnBwYMmSJfj7++Pl5cXQoUPVjnn27BkDBw7k6dOnJCQkMGNGC7p27Urz5s2/iVyscXFxHDp0iAMHDhAVFUXN0t7SvrRyMH+sNax0MSuFWCnAb6Lj8Q+L5uQ/cwm9uFntmIYNG3Lx4kVGd23LlNXbsMiicFHpOl+cVnIP/7Aont+7wahRozh69Ch6eno0ql6B0nlzEBMXz71792jUqBGGhoY8fPiQ6tWrq9W7lfk+MDc3Z8CAAUyZMoWbN28yfPhwtXKOXwO+vr7s27ePmzdv8uDBA0k3UmJlZUXBggWJiYnh7t27JP5/KZmrqyvVqlXDyMiIc+fOYWZm9k1Pk2bKnLAQgrCwMJYvX87Jkyfx8PAgKSmJ06dPq7UrUqQIN2/e1HgOT09PKlSoQLt27b75ZTFfgqtx1py7/YQlHRRBQpZu+Yh89VCt5FepUqX4559/ePv2LStWrJBKFYIiD2vlypV59eoVa9euZevWreTL92EpHj8XR48eZdy4cVKdYDMzM06cOCG50VMKsFLQPsT1q0s1pTfR8QRHxrHv+msCrp3lwZph0r4GDRowevRo/Pz86NixI+bm5nTq1AkfHx8iIiLw8fHhhZFzmhHb8J8Qq84Jv3wTwYQxI1m/SvGAO3LkSAICAjh16hRXr17VONY+ffrw6NEj9u3bp7b95s2b8sPwd8Ddu3eZMGECN2/epEGDBvz555+fJfAsKioKY2NjjIyMCAoKIjo6GgsLCx4+fMiNGze4fv06Fy9exNXVleLFi1O4cGFsbGzQ19fH2dmZyMhI9PT0iIyM5OnTpzx8+BATExOKFi2KhYUFISEh3Lt3jzNnzmBoaIiDgwP9+vX7pueEP1lglr+/P4sXL8bW1pYVK1bQtm1bsmTJwsKFCylbtiw5cuQgICCAnDlzIoQgNDSUU6dOYWFhwd9//y1F/8mkTYyVK3dDIklOSmLmkg3c/kdx48/Z6E+e7ZyBhYUFUVEKMTIzM2PAgAH4+/tLHglNNGjQQFok/7USGBjITz/9RLly5RgwYAD2bjlxtctC9uz/ZT3SJsJKMirG6QmxUoQD3sXiHxZNK6tX/Prrr8TExHDixAnpJujv78/s2bM5fPiwlCSlcePGtB87D1eb1AFXKcXY0shALZvWlMkTmT9tEh07duTnn38ma9b/PCLnzp3TqcgBQLdu3ejWrZsswt8JycnJ7Nixg1mzZpElSxZWrFiRKQlcQkJCmDp1Ko8fP8bT0xNjY2OOHz+OkZERwcHBGBsb4+DgwKtXr9SOs7W1pUiRIlSrVo3GjRt/09HbuvDFRVhJQkICgwcPlrI1GRsbU6ZMGebOncvjx49p3rw5efLkYfv27QQFBdG1a1ciIyPZsmWLbBGnQYyVIgOQUhgG9P+TBwfWYZO/LBhZkrVaF+LevsLI2oUpNc0YMmQI9+/fp0aNGkyYMIFz585RpEgRnJycWL9+PZMmTaJQoUIMGDCAokWLfvVzSStXrmT+ggXcePAMSx3iCtJbBvSxgVGqAn0vOIK69rGEhIRQrVo1AP744w969uypdkxoaCgPHz7k+fPnTJ06lZo1azJ28Tqt51CKsdIaTk5OZs6CRcwYPVhj/0r+/PNPDh06lGr78OHDadSoEaamprLwfse8evWKjh07IoSga9euVKpU6YNrFR84cIDx48djZGREhQoVePToEe/fv6dmzZoAZM+enaioKAICAvDx8cHOzo7IyEhy586dKQUvviW+mnXCRkZGzJgxg0ePHhEaGsq7d+8YNmwYAwcOlCL4lBmN4uPj0dPTIzAwkMDAQFmE/094eDgzZ86kb9++2NjYYOSUg5j/L7VZc/E54a+f8eCA4uYdFfSCfJ0WE/kuithoPYgOYuvWq9y/fx9QzBWPHTtWrepIcHAwoLCAlUtbvnbu3LmDdzEfSYB1KYigul/57Kmnp0eY/zPMACOnHGrtVYU5rQIPin0KN3JQVDwFnbJw4nUUk9u3xcrKijx58vDLL7+kOs7BwQEHBwfKlSuHv78/u3btSnP8yvzTymsdM6gvK1asoGfPnnTq1EnrcRMmTCAwMFCaCmrUqBH9+vX7ZtbFynwcbm5urF27ltGjR/PXX39hYmLCvHnzKF26dKq2CQkJHD58mFOnTvHq1StiY2NxcXEhS5YsPHjwgEePHlG7dm1GjBiBjY3N57+Y75DPkolBT0+PfPnySXOMZmZm9O/fX5qrPHz4MPv27WPw4MGYmZkxf/588ufP/zmG9k1w5MgRtm7dSunSpWnY/g8uvHxPcZP3rLmiSEjx7Ox/83pZCtUl4m04ce9DEMlJRNzawZon6nPzqgk4oqKi+OeffwC+ugCOtPDw8ODEypXcffQIz7x5gf+sXU1irNwmhGDckP6sWbOG5ORkzMzMSEpKQk9Pj6pVqzJu7hJM/x+BqWlOVtfI5c3r1uLv78/Bgwd1Sn3o7u5OeHg4hkmxmFukdkmrFoCwNDIgKjKS9evX07lz53Tdzaampqxbp93Clvn+cXZ2ZvHixbx//54BAwbw+++/4+bmRunSpcmZMyfJycn4+/tz9OhRQkNDKVSoELlz58bU1JTXr1/z8uVLPD096devHxUrVvzSl/Nd8UXSIVWtWpW+ffsyY8YMadvgwYPx8PBg+fLl0hITGQVKC0ZYu3AvMBxzEU/xak2oVNSD4OBgHty8Sc2aNXF1dWXlymWIu1sxqjSUfmWSGLpDXYA3bNiglpYvMjISJycnOnToQIECBT7rdX0MHTt2ZPfu3Yz+szfLt+wmi7H6T/nyhfMEBQYQn5BAaW8vSpUqxdGjR1m4cCHbtm3Dx8eHWrVqERMTg5GREVFRUSxZsgR7+6EMnjxbrS9VUdfm1lbNN/0mOh4Hd8WDQe3atZk1a1a69U5Lly5NUlIS/65bx29dupAQ/AIjpxwE3L3GzZs3CQ8PJyEhgWrVqlG5cmWmrlpBbGzsd5OQQebzYG1tzcKFCzl16hSXLl3i0qVLHDhwAAAXFxeqV69O69at5Zicz8gXy5iVmJjITz/9xOvXrwH49ddf6dy5M3Z2dlKbe/fuce3aNWxsbChYsCA5c+ZET0+PpKQkwsPDsbW1zZSxfM1s2bJFWs5VunRptaoqAD/99BN79+7VeryHhwdbtmxh+fLlLFy4kFu3bn3S8X5OTp06RY8ePZi7fA1NGzeRliRlszFPFfSRJ28+Hj96iJGREd26daNjx46pUnI2bdqU6Ohodp++nMoaVQpxWnPLymxcADFRUbQsqbiRbdy4UWPRjJSMHj2abdu2UbBgQapWrYqFiwfzJ40mJiZGalO8eHGuXLmCd/ES2FplYd68een2KyMj8/n5agKz0mLNmjVMnToVCwsLLly4kGp/uXLliIiIkP6fPXt2vLy8CAgIwNfXl6lTp1KtWjVMTEwybUxfE6dOnaJnz57Y2NhgamqKo6MjNWvW5ObNmxw+fBhnZ2eOHDnC/PnzAWjWrBkRERFcuXJFqkF67do1jIyMSExMRAjx1QdcZQQhBL169eLMmTPUa9CIP37/jZ9++omzZ89SoUIFtbY///wz3t7eVKhQQWuswfr165k2bRqJiYnsPH6O/J6FNLazNDLQGGkdEBGL0QtfYmNj2bdvn1S/2dfXV6fctomJiRw9epTDhw9z7tw5IiIiqFWrFgMHDsTe3p4FCxawfv16Ll68yKB+vbnz8AnLly/HxcVFl49LRkbmM/JJRPjs2bNYWVnx6NEjQkJCsLCwIH/+/GqZd+Li4rh27RqGhoaEhIQQEhJCfHw8v/zyCxYprIvk5GQmT56MjY0N3bt3V9v+7t07Fi5cyMaNG9Mc2++//06fPn1SbY+Li+PmzZt4eHh8k+7tixcv0rNnT8qUKcOcOXPULLvQ0FCqVlWsB9Zk2Q4YMICDBw/SsGFDJkyY8NnG/CVISEigadOm+Pn5Ua9ePbb9M4/mHf7A9859du7cyd69e7GxsZE+r/Ro0qQJjx8/pnLlyqzbvjfNeWZVIQ6PTWLJX4Mk4VXF3d2d9evXp/mHmJLExETevXunljBl8+bNjBs3jinD+tGoVlWqtv4DV1dXVqxYoXO/MjIyn4dPEh1dvXp1EhMTSU5OloKqQFHpIn/+/JibmzNjxgwphZoqBgYGFC9enNDQUKpVq4aenh76+voMGzYsVdtevXpx6tQpHB0d1da5KnF2diYoKAgACwsLTpw4QZUqVQC4cuUKbm5u7N69m7lz5wJQpUoV+vfvL0VhK4mMjPzqMq1EZ8nGns3rmTZqMD4+PkyfPj2Va9XBwQFTU1MaNGigsY+mTZvi4+NDq1atPseQvyjv37/n1atX1K5cnuG9OtO6+wD2HjrKjBkzMDY2ztCc6YgRI6TsbiYmJkybMI4cHjkpXaww+TUUdvcPiyIyIYnQ99F0b15bmrv38vJi/vz5nDp1ilGjRvH8+XNevXqlk0taiTIRgSotWrTg4sWLDJ44i9/qV2VYp5b0njCXpKSkb7qKjIzMj0yGRFiZc1Z1rZeenh4zZsyQbkCabjR6enqcOnWKWbNmATB58mR++uknred58+YNAPny5cPCwoI7d+4AMHToUHr27CkJMMCcOXMAhQVTvXp1aa3kiBEjpDZnzpzhxIkTFChQgMjISPr06cOrV6+YM2eOzvN1n5rExEROnjzJP6vXcevaZRo2bMjIkSO1utrPnDmDsbHmVIflypWjXLlyn3K4Xw3v378nMTGRtl7Z6D5kDDdu3ODXX3+V1i1mBNXMWw8fPuTmzZsEBwejr69PzZo12blzp0Z3fkiQYvmPj48P165d4/bt28THx9OkSRMaN25McnJypomksjLUkfNXWLRxN0KI7z7pgYzM90yG3NGaqFixIufPn0+V/9PW1pawsDAKFixInjx52L17t7Svc+fO9O7dG1AIu6GhoZqwP336lO7duxMfH8/YsWOJiIigbt263L17lzNnzuDn58e+ffto3bq12tIL5U0QFBbypEmTGD9+vLQOVpWsWbMSEBCAoaEhM2bMkJIqfC6SkpJ48uQJgYGBPHv2jDVr1hAUFISPjw+dO3dONacpo5nk5GTatWvHjRs3AMWDWuPGjTOtMHtoaCg1atQgKSmJAwcOULt2bW7fvo2tq8KrEpmQxIuAEGp552HIkCHUrFmTw4cP07Jly0yff1edhlBiaGjI9evXM/U8MjIyH88nmRNOSe/evencuTP//vuvNPdoYmJCq1atGDhwIKGhoVK0c7ly5SS38siRI2nZsiWgSBDh5ubGggUL0NfX58iRI0RFRWFhYcGiRYt4+PAhAAULFuTevXsAnDx5ElNTU8zMzGjQoIFUpi1LlizkyJGDFy9eSAFd5cuX58qVKxgaGjJ16lTMzc3p2LGj2nW4ublhZmZGo0aN+OmnnzA1NcXAwECtYkdm4efnJ7kqVaNe69SpQ4cOHaQi9DK6Ex0dzdy5c9HX12fgwIGZmpVHGTwIilSZzs7O7LkXRDEXSyLiE1k4YSQHDx4kMDCQbdu26ZQW8OLFi1y5coUePXpkaCwnT55Uy4pVuXJlBg8erJaqU0ZG5uvgs4gwQK5cuciePTsnT56UShmCIrm+amq0iRMnsmHDBjw8PLC1tcXR0ZHevXtTv359QFGMXk9PT2NmoWLFiqk97Tdt2pSOHTtiamrKsmXL2LhxI/3792fmzJlYWlpibm5OcHAwpUuX5smTJ7x9+5Y8efLw8OFDatWqRcOGDXn16hXXrl2T0vnlz5+fx48fS7l8DQwMaNiwIePGjUs1Hl1KhL1//55nz54RGhpKaGgodevW5dGjR3Tr1g17e3uaN29O4cKF+f333wF0Wksq8/kQQvDXX3+xfft26jZpycLFf+Nm91/8gH9YFA/u3aV6eUWxiM6dO9O9e/dUy5409VukSBEAChcuzOLFi3UO2Lpw4QKdO3emb9++HD58mPnz538Tla5kZH5EPokIT5kyhYMHD3Ls2DEAsmXLJq3zBciTJ48U2GJjY6NWRcnX15e2bdsCimLMly9fxtLSkkaNGnHr1i0ePHiAl5cX9+/fx8/PT32QenqYmJgQGxuLpaUlcXFxqYpWm5qaEhsbi42NjVRG0c3NjVatWjF37lyqVKnCxYsXiYuLIy4ujkqVKuHg4MDBgweJiorC3d2dhQsX8vjxY+Lj47l+/Trr169nzJgxvH79mkePHvHs2TOCg4OJjo7Gzs4ONzc3KlWqRPHixYmPj2fDhg28ffuW4OBgAgMD1cY3cuRI1q1bh42NDYsXL5as7Ldv33LkyBHq1KmToehZmU/LlStX6NixI127dqV1n+E421mR30n9+3ka+JafqpaXUoKCwspNyxUeGBioNl89bty4DAWPRUdHZ5qrXUZG5tPxydYJ6+vrU6tWLfr160fjxo05deoUT548YeHChamEUXVd8aBBg9i/fz/dunWje/fuPH36lI4dO0rBMEWKFCEoKIjg4GCyZcuGv78/oJhbDg8Px9DQEGNjYyIiIvD29sbX1xcAfX19jIyMiIuLkx4KOnTowOrVq6UIbj09PRo2bMjOnTsBRZT369evefDggdTGwcGBY8eOoaenR3R0NJUrVyY2NlaKzi5dujT58uXD2dmZpKQkIiIipDRv8fH/Je/PkSMH1atXp0CBAri5uTF79mwuX75M8+bN2bJlC8uWLdOYs1Xm6yA6OpqoqCh69OhBVFQUe/bswdhZUSw8pQiDYonYs2fPaNSoEQCdOnWid+/eWoOlkpKS+P3337ly5Yq0rWLFiixcuPATXI2MjMyX4pMVcDA3N+f06dPo6ekhhMDHx4f9+/enEmBQFAZQinDFihWJioqic+fOgMKNXbduXdatW4etrS2rVq0iOTmZdevWMXPmTEAh3I6Ojuzdu1dKXgBIAtyuXTs2bNhAXFwcgLR/5cqVVKtWjVevXvHw4UOEEJLlWadOHU6ePEnv3r2ZMmWKNNYsWbLg6+tLnz59GD58OLGxsYDipjx27FiaNm2q8fOIjo4mODgYPT097OzssLCwkG7Ab9++5fLly4Ai8xVA3v/nOZb5+hgxYoT0oGZpacnaXQckAbY0MsA/LEqtjm9CsB8FnLNQuHBDkpOTGTRoEDNmzOD8+fOMHj1aY9S9gYEBc+bMoXz58rQuV4Qd1x/x8uXLz3OBMjIyXx0ftLZBGfhSpUoVKlSowP79+wFFhqfZs2dL7Tw8PKT3DRo0YMGCBWpzqZUrVwYgLCyM69evY2xsjKurq3SOEydOUKNGDaZMmULBggVTzbfZ2tpSunRpDAwMMDAwUMuudezYMby8vLCwsMDJyUmKfv7555/Jli0bU6ZMUQu8Klu2LA8ePCAsLIwBAwZI2y0sLHBwcKBr167cvHmTmzdv0rRpU2mO2tzcHA8PD9zd3cmSJYuaBaSaglOJvJ7zyyGE4MmTJ2zZskUK8lNF1TodM2YMBQoVxtLIIN3qTAnBfiSGPGfatGmcPHkSY2Nj2rdvn+ocd+7cYeLEiZKXp1n9mhR1d5EfzGRkfmA+Km3lpk2bWLBgAblz52bs2LFkz56dq1ev0qFDBypUqMCiRYvSHUCbNm2kNcbHjx9HX1+fNWvWsGPHDkJDQ3F3d6dr167s3LmTS5cu0b59e40ZglSDwkBRtzg+Ph43NzcqV65MlSpV6Ny5M6VKlWLx4sU8evSIZcuWcfjwYUxMTHB0dOTdu3dERkZSsWJFAgICpPltTRgZGXH16tV0I3F79uzJyZMnAahQoQILFy78oWpqfkmEELx+/ZrLly9z6NAh/P39efr0qbRfNdtaUlISs2bNYtWqVXgVLcbmg8fR09NLJcCqlrCShGA/6b2RkwcxMTGUKFGCkJAQxo8fz71791izZo20/n3hwoVMmTKFxOgI3ryPwMjElMOHD6fKKCcjI/Pt8kVzR2ckg8/58+fp0qULoEhOv3LlSkDhyu3Vq5ck0EpGjBjB+PHjAUUuaTMzM8LCwggJCdHYf/bs2Xn58iXNmzfH3t6ev//+G4AyZcpQq1YtIiIimDt3Lubm5ri6ukpBNnp6eri7u6cKElOiutZZ5utk9uzZUpnGkiVLom9pR63mbfAqWZa2FQpRokQJ/v77b0JDQ2nXrh0vX76kT58+dBumiIjXZgGnFGJVEQaFEAcFBVGnTh1p6kSVwoULp0o3unDhQrlEnIzMd8QnmxPWhYy4XMuUKSO9v3r1KoULF2blypUUL16ctWvXcuPGDY4fP87y5csBqFmzJrGxsfz8889SNqnk5GTCw8M5cuQIxYoV4+TJk2zZsoWhQ4dSoUIFNm7cyJw5c6R5XlAs97hw4QIWFhbkypWLR48eSQJsZ2fHiBEjqFGjBocPH+bPP/+Ujps6dSo1a9ZMdymKzJcjISGB5ORkTp48Sfny5RkxYgRubm4A+Bko8ogXr1yTpPBQAgMD+fPPP4mJiWHt2rUULVpUrXavLhg5eSjO+38xTgj2w9nZgytXrnD79m0MDAzYsmULxw8fwNTEhCdPn9GwZhXKFi9KVidHOvQfwYMHD/D09MTe3j5TPwsZGZmvmy9aRUnJ2rVr1YKkQFEvtn///tL/Dx8+TGRk5AfVT3306JHWwCpV9PX1MTMzk5KKdOvWjU6dOjFnzhzWrFkDQL9+/fjtt98yPAaZz8fgwYPZt28foFg2N2zYMEqWLEmMlatUarCB53+Vh+wcHJm+bB1exUpI26xM/5sLVlq+KQOzNJEQ7CeJsqZ9SvRj3gPw2O8FBao0BBR1tpX5zmVkZL5tdLWEv4qks7Vq1ZLeK4ssrFixQm39Zc2aNT+4gPn79+9TbTM3Nydr1qyptiktJoBFixZRokQJSpYsyaVLl7h8+XKqbFsyXxcRERFqUwiPHz/mt99+I0ioi2fhUorc2v1HT2TLictqAqwkZbnC9AQY0CrAyn3KV7KZNclm1uTxyMGAPzoAyJmvZGR+QL4KEXZycmLkyJGsX7+eVatWSVZMyvngD6VEiRLcunWLxYsXS9sOHDjAoUOHpEpDBQsW5NSpU2zZsoV///2X4sWLS2179+5Nr169CAwMlIOqvmKUHo+7d++m2lejSC62LZpKdKQign7iym3svhtI1Va/EaVvSlBUvGQlfw6UYp1sZs3EcaOpXbs2Bw4ckCKnZWRkfgy+Cne0JiIiIjAzM8vUudeEhARu3bpFsWLFJDFNSEjAx8cHSF2bNzo6mhkzZrBp0yZp282bN2Uh/krZvn07o0aNokKFCty5c4ewsDCMjIzU1rCXrlaHEfNXau3D2UJRmUo5J2xpZKCTBfyhKF3Uu05fpUePHoSHh9OnTx9at24tV0eSkfmG+abc0ZrIkiVLpgc/GRkZ4ePjoyaiRkZGTJkyRUoiooq5uTkjR46UEjgAai5yma+LWrVqkTt3bnx9fRFC0KxZM65evYqjo6PU5uKxA8zq+QvjOzbirz6dpapb8J8Af06U7umGFYtz5+xh2jerz+TJk+nQoYPacioZGZnvk6/WEv7aiI+PZ8eOHdStW5csWbJ86eHIaCE5OZnk5GS1B7gaNWqo1aB2d3enYMGCPHjwgICAAKZu2EfO/J5qIqxqCYNu88Efi9IqPnPiGB0HjeP169f89ttvdOzYUV5DLCPzjfFF1wnLyHxNLF26lPPnz2Nra4uFhQVjx45FT0+P2NhYWrdujYGFNVPX7UZfX/+TC7F/WFSqbSlTYQLExMQyfsZc5i5ZTlxcHM7OzjRu3JgePXrI0yEyMt8AsgjLyOiAMsNbg19/p+OAUbjZ/vf7TinC8HFCrEmA0+o3IdiP/RduSsUhAE6cOCGvJZaR+Qb45ueEZWQ+B8WLF2fYsGHsWfcPPeuV4cCq+WrpT1X5VAKsbb+RkwcNGzbkme95aVuVKlV0SgcrIyPzbSCLsMwPT+vWrdm2bRuFChVi1KhR/FK9FCc3/UNcoB/w33ph/7Ao6ZURdG2vrX/XrC68e/bfsquFCxcSGhqaoTHIyMh8ncgiLCODIrPWzJkz+ffff3Fzc2PgwIFUrFiRLi0acOngTsL8n6VK3vGxRCYkae1TVYiNnDwwNzcjPugZ44YqKnwNGTKEhw8fZup4ZGRkPj/ynLCMjAZu3brFmTNnOH78OPfv30cIQWFvHzYdOKZWXUkXF7UmS1iT+GoqGKGpWMSc1VuYP38+/v7+/Prrr/z222/Y2trqemkyMjKfATkwS0YmE0hMTGT48OFSFrdLz9+q5ZVWklIs03JB62JRpxUMlhDsx9uwd/QaPJLTF69gZmbGvHnzcHd3T7ffD0VZizkuLg5ra2tcXFzkIiYyMmkgB2bJyGQChoaGVK1aFYBFe89I21O6klXncz9WgFP2r6k/O1sb1i2Zx6WdqzHWE/Ts2VNjjvTMQAjBtGnTaNKkCT///DN169ala9eun+RcMjI/GrIIy8ikg5mZGQDmlookLeGx/wlpRuaJP2ROWSnGquKuzLIF4OKRl+3rVxAWFkbTpk05c+ZMGr19GPPmzWPNmjU0NXVkml1O2jll49KlS4SFhWX6uWRkfjRkEZaRSQMhhJR7+k1QgMY2qlar8r2m18egySpWCnGenB6c2rMFKwtzBg8ezIoVKzI15eXhw4dp1KgRPfJ6UNHHnZpF3RFCyMUmZGQyAVmEZWQ08P79e2bNmkXNmjXp168fAG+D/0t9qWoNK8mo0IbHJml8aSMtIS6YLw8Ht66jcuXKzJ07l0aNGjFo0CDevn2boTFpws7OjpADxwFw9MpK6fJeZLe3ZsCAATx48OCj+5eR+ZGRRVhGRoX4+HhmzpxJzZo12bBxE16V6tBr+lKWHrxI6Wq1de4nLXFNT2zTEmdV97QSpXs6u2s2Jk6cSMjDGyydPZXzZ89QtWpVfv/9d16+fPkBn4YCHx8fbsdHYeFsgUVWe5xzuHBs8XgsLS1p2bIlBw4c+OC+U5KYmMiIESP4888/iY6OzrR+ZWS+VmQRlpFRYeLEiaxbt45ff/2VfXt203fsFIpXqY1Ldt0jj7UJbHrim1FSBmwZOXng5eWFhYU57Vu34Pa5YyyYOh5/f39atWrF1atXP+g87u7uhIskhK0RAKZ5PMnh7MDatWvJZmfFhQsXPvpalLRt25adO3dy6NAh4uM/X31nGZkvhbzGQEbm/9y/f5+tW7cycuRIWrZsiZ+BI2+itQtBeGySlF9addunQtm3lalBKte06jImpYvaCejUtjXNGv5Ey45d6dq1K6VLl+bVq1cYGxuTM2dOPD09cXNzIylJ0V+tWrVSndfT0xOAJofOc7NZTeZvOcCGc3foXN0HC1NjzM3NAcX8eUxMjPT/jNK5c2du374NwIULF+TKUTI/BLIIy8j8ny1btpA1a1Zade3Ls/cJOh2jFGJN4hsUpV3AM6t2cWRCEpZGBviHRaVaT6wUYxv82LH2H/6aPIUbdx9Qp0JJ4qMjuXbrLiePHyUqJk46pmPHjnTq1Alra2tpm4ODAwCWpsa8jYhi0II12Nra8ceUK9hZmhMUFMSDBw+YOHEi165do3z58rRu3ZqKFSuir6+bs+3JkyeSRX3w4EFZgGV+GGQRlpH5Pxs3bqR+/fr/T0Lxnwjbm6ctmEoBTkt0UxIUFf/BQqxqEUPaQgwKMbYGpi9eSUKwH/oxKuuJo8I4e+0Wc3ecxMPKkDlr1rB69WrKlClDhw4dKF26NBs2bCCLqTEHJ/dl7v4LJCUls3z5cu7du8eQIUM4dOgQhw4dInfu3AyrU4a9tx/Rs2dPSpcuTfPmzalYsWK6onru3Dnp/du3b8mWLdsHfTYyMt8asgjLyIC0DClX/oLSNqUr+k10vCTEmsRTKb5pua5VSasvTf0qSdlWkztc1T2tzVWtrFkMgJk15SvaUr5iJQDqtunMyZMn2bJlC507dyZPnjw8fvyYyoXzktXOmvJlS7Fg2yF69+7NsGHDKFy4MLdu3WJ8y+q0K5oXKxcHBreEbSeuMvvYFQYOHIixsTE1atTgr7/+wthY8/VevnxZeq+tipWMzPeILMIyMoCRkRFOTk7ExWXMmoX/xDc4Mi6t5gA4WZqoiXpGUJ5Pk3CnXB6lGrSV0kJWuqlVUQpzlTxQJU8zRrWqyuFzV1i3+zABAQGULuqJga0TTWsVYbNHUcaNG0eXLl0A6NGgMv1+aYCBrRNJYcEAtGpWm6ZVivM89B07r9xj7LZD6OvrU65cOcqVKyfVRI6Li2PRokUcP65YAmVoaEihQoUy/NnIyHyryCIsI/N/smfPjv/r1zoFV6Xleg54FwtAVhtT6b3y/8GRcWpCrLSGU1rFurq2U7qmlShFWZmDWpNVrIrSQk42U8wF66MI0qpVqxZEqWfGapDPhp9WT2fr7WCyhT2kfOVKJL0JBMDA1kmtbR57B3o72BCVqwTTp09nz549GBoaYm5uTrZs2QgICCA8PFxqnyNHDoyMjHS6dhmZ7wFZhGVk/k9oaCgFvUuk2q7JalUKZ1qoCrDy/9qEGDI2pwyoBYSpPjioCrJyvliJtnljULeQU7qsU84j6+vr06KIC+ACgIG94l8sbFOJtoGtE6Psgxm2aSrvomJYfC+KqKgoXr9+TZnYaG4kJXM9MgITExPKlCmTgU9ARubbRxZhGRkgODiY58+f069M2Y/uK6UFrLr9Y1FayxZG8MLvKa7Z3Xn66CFXL55j786d5M5fgP6jJ6UpxLqgFGSlGCerCrGFbaptySmFGoUwq1rIBoCDQTAjSluS8CaUF7//jvk/E3l07Tl6tiaMOH+DnTt3UrZsWapUqZKh8crIfKvIIiwjA1y6dAmAAj7qIvwhc7eayIgAK+eYU547a/Qrbl64ydmzZ9m1Zy8hQYF4eHjw+vVrkpKSSEpK4va1y4wcPynN/tOyhlOiKsZKV7UqqttUXdlKlNKvKsZJYcEY2TuQ4/ImKJIHhyJ5CL35mLlDJjJs2DD69OnDggULqFChgk5jlJH5lpEzZsn88Dx9+pShQ4diaWmJnYOjtF2bAOviNlYV3bQEOGVEter/1d4/vk2NGjX47bff2LRpE9WrVmHy5MkULVqUzp07c/HiRSZNmkRMTDShIcHpji+tcouaUIqxMkWmtheoC7PSapbc1fw3b2xk74CRvWINskORPHje28esWbOwtbVlz549GRqfjMy3ygdbws+fP2f//v08fPhQWt4h8/ViZmZGyZIlqVWrlloihh8dPz8/GjVqBEC5cuUA3UQ2o0L8Iag+BPw1sBeOjo4sXbqUPHny/H8tM/z000+AouDE33//TfGSpXFydtHYX0q0CbEuc8YfglKIk94ESkKstIqVPNm6kDdv3sjuaJkfhg8S4VOnTtG3b19ZfL8x9u/fz7Jly1i+fDmurq5fejhfnBs3bkjLbADatWv3BUeTNrb2jty7cZW3b99KAqwkIiKCDh068O7dOxavXIeent5HnSu9SGpdSDWHrBKslXKuWMmbl34M3nAMLy8v6tSp88HnlpH5ltATQoj0GoWHh2Ntbc358+cxNDSkcuXKREdHU758eVq0aIGlpeXnGKvMByKEIDAwkGXLlvH8+XPKly/P4sWLv/SwvgiBgYHUrFmT2rVrExYWRnh4OKtWrSJLjnzo6+trzX6V0m2c0lX9Jjpep3XCSpwsTTT2pXoe1e0WSbGM+OMXbt26xahRoyhbtizv3r3j6dOnTJw4kaioKC5evIiVSw6tJRUzGpwFqYVYl/lk1cjqlFHVqiiFWMmElVuZtmEvGzZsIHfu3Bkeq4zM10RkZCRly5bl/fv3WFlZaW2XYUv43LlzREdHkyNHDo4fPy6v6fuGaNq0KYUKFeLixYuEh4en+cP4XjEzMwMU+YkBRo8ejbVHgVTtlFHIuridlcLpZGmSISHWhL25cSrBV4zFmNkrN9CrbUsGDhyotr9cuXJMmDE3TQGGD4uS1uSyzkhgV3oWsSqnrt+lQoUKsgDL/FBkWIRfv34NQNmyZWUB/sbw9PTE3t6eN2/eEBwc/EOKcEoPQPtufVCVPG3FGFKia4pKVVSt35R96RKFbWVtw8qdB3n2+BGXz5/FzsEBl6zZ8PL2IYuxoSTA2tYMw4cJ8ceiafmSJh4Fv6du8XKfYUQyMl8PGY6OVpY8+1ABvnLlCm3atPmgYz+W4cOHU7hwYby9vfH29mbjxo1a2zZv3pxs2bKhp6dHZGSk2r7evXvj4eGBnp6eVHpNyZUrVyhbtizFihWjYMGCTJ069ZNcy4ei/N6U3+OPRsmSJSlfvjwALVu2xNjYWBIl1Xq/QVHxGU6eoc0KdrI00SrASnQR9fDYJCLiksmVNx+t2nWkZr0GFC5WHD09PV5HxEtt0iMyISlNi/ljUY2UViJFTP8/Wlr57+odB2jUfRhh7yOwsLAgODj9yG4Zme+Jz75EqUSJEqxbt+5znxaAgQMHcuvWLXx9fdm3bx+dO3cmLCxMY9uuXbvi6+urcV/z5s05c+YM7u6pC7137tyZoUOHcv36dc6ePcv06dO5e/duZl6GzAcSExPDwYMHOXv2LKAoIA+p8y5nFrqIr5KUlnBalrHyYUH1pdyeET6lEEMa0dRKIQbO+95h78nzXLp1jxo1anD48GGtf5MyMt8jmSbCenp6TJo0iVKlSpErVy6OHDnC0KFDKVasGIUKFeLOnTsAnDhxghIlFKkB/fz8cHBwYNSoURQvXpw8efKwb9++zBpSKmxsbKT3ERER6OnpkZycrLFtjRo1cHJy0rivUqVKuLm5aT3Pu3fvAIiKisLY2Bg7O7sPHrNM5nDnzh3q1Kkj/b6aNm1K/fr1pf0ZFbD00FV8lWgLyPrUfGqrWJVUyT4sbJk0YhB9f29L5ZLeDG9VHRMTE8aNG4cO8aIyMt8FmWoJW1lZcenSJaZMmUKjRo2oUKEC169fp3379kyYMEHjMW/evKF48eJcvXqV+fPn069fP43tjhw5IrmRU76GDx+u8xjnzp1L/vz58fHxYcmSJVI1l8xixYoVjBw5khw5cpAvXz4mTZqEi4tu6zZlPg3nzp3j559/xszMjJUrV7JmzRo2bNjwyc6XUQH+VKScD/4SaHVL/x8bayumj/gTUxNjnB3sWDK6L0eOHOGff/75jKOUkflyZGraylatWgHg4+ODvr6+lEigePHibNu2TeMxFhYWUrKEsmXL8uTJE43tatSoodU9nBF69+5N7969uXHjBr/++is1atTIVCGeNm0a06ZNo2XLljx9+pQqVapQqlQp8ufPn2nnkEmb0NBQbG1tMTBQiNC8efPw8fFh+fLlFC1aVK1tZEKSRis4ZYEGTVHLKflaxBcyLsCfMmArZQ1jjYFa/3dRN61VmW7dujFnzhxiYmL4448/tNYglpH5HshUS9jUVJEhyMDAABOT/25IBgYGWgt1K49RttMWMJRRS/ju3bvS/h49eqTaX7RoUVxdXTlx4kRGLjFNQkND2b59Oy1btgQgV65clC5dmnPnzmXaOWTS5syZM1StWpWWLVsSHR2Nn58ft2/fpl27dmoC7B8WxYPgcJ3d0B8SDa3E3txY60u1TWbxNVjA6ZFsZq0xFzUWtszt0YIxPTuyfPlymjRpwt69e3/YQEKZ759vpoBDRi1hT0/PVO3v3btHwYIFAXjy5AnXr1/H09Mz08Zoa2uLqakpJ0+epHLlyoSGhnLhwgUGDRqUaef4UXnx4gWxsbHky5dPbXtiYiJLly7l1atXFC5cmAsXLgDw8OFDDA0NefToEQC//vqr2nHKedCUgpXZc8Pp8TWI7+dYspSyKpMSTVaxnqUdI7q1o1H1CgyfvZQhQ4awcOFCWrVqhZ2dHZUrVyZLliyffMwyMp+Db0aEM4MhQ4bw+PFjjIyMMDQ0ZP78+ZIoL168mNevXzNu3DgAGjZsyLVr1wDInz8/efPmlazmHj16sHPnTgIDA6lRowaWlpY8fvwYAwMDNm3aRP/+/UlMTCQhIYEBAwZQsmTJL3K93wNhYWH079+fK1euAGBpaUnv3r1p3bo1ycnJrFq1ioULFwKwa9cu9PUVzh1HR0fmzZvH/v37MTAwwNbWNsNFCzKDjxFZZcKQ9PgYyzczsmhlhJSuaa1Y2FI4Xy52LZzEhdNnmLVxDzNmzCA5ORkzMzOKFCmCq6srRkZG5MyZkwYNGvyQ695lvn0ynLZy69atTJ8+nV9//ZU1a9Z8jjHKZCJZs2YlMDCQLVu2fNXz1FFRUezatYvdu3dz7949ypUrx+XLl9HT0yM6OpqxY8dy6NAhzp49S6tWrfj9998ZNGiQFHEfHR2NEEJKLiOE0CjCKSODVS1h1TlhTe7o9OaJP4cAw+e1gD9GgJVoEmGNyTxSpLmMfP2CsIhIZp14wL179wgICCA56BUP37xHCCharBjdunWjTJkyHz1GGZmP5ZOlrZSR+dQIIRg/fjx79uzBzMyMBQsWUK5cOQpmSWLI/LVMnz6d0aNHkz17dsaOHUvTpk3x8vLi2rVraslTYmNjGTduHPXr108lwJqW5aTlitYmqJqE+GNdzJ9SgD/G9ZwZAqwNqRaxqhgr1xP/X4wts+XA7E0g05qXBcry0NaLHJc38fTxS47c92P9+dv07t2bXbt2ySsSZL4ZZBGW+WLEx8dz/vx5TE1N2bZtG4aGhpiYmHDlyhWePXvGyJEjadq0KYaGhhTMkkSymTVj+3ahcnEvEk2tyZMnDwBeXl5Sn8r3StFdtGoDrrYW+IdFfbL1sBkV3YyIbFp8zgCszBTgtFzSWiOn/y/EqhWYcj89BvYO5AK65MlOuVyulJ++jt69e7Np06ZMG6+MzKdEFmGZL8KmTZuYM2cO4eHh0jYTExNy5MiBu7s7AwYMoFKlSuR3UlToUqZUMTIyona1yqnWn6Y13/sl5oK1kVkC/CF87pzRaaEtUAt0E+KUJLwJxS9W4ZGwsPh0FruMTGbzw4jwggULWLx4sbQMqnPnzvTu3TvNY8aOHcuYMWO4deuWZGFduXKFXr16ERsbS2xsLB07dpSin4cPH86uXbuk9alDhw6V1k7/6ERHRxMWFoafnx+bNm3i2LFjNGnShDZt2vDmzRusrKzULFog1f+18SlENqN5o3XhQwRYae2mVZQhPT5WfD+lGzqtqGlI2z2t1o+9A0b//7ubNGlSpo9TRuZT8cOI8K+//iqtFw4PD8fLy4sqVapQpEgRje2vXbvGhQsXyJEjh9r2zp07M3bsWBo2bMjbt28pUKAA9evXx9PTk4EDB0qZwV6/fk2BAgWoVasWtra2mk7xQ/D06VOmTp0q5WsGyJ49O5MnT6ZevXoUcNa81ERr3mEVdBFfpQs6o0KUMlmHcpsS5T5NJQ8zw9pNKbRf8/KjT4lWMY4Kk1zTBrZOJIUFk8dFkR72woULNG7c+AuMVkYm42SaCOvp6TFx4kS2b99OaGgoS5Ys4ejRoxw4cID4+Hg2bdpEoUKFCAwMpHXr1oSHhxMbG0v16tWZM2cOenp6dO7cGVNTU+bNm8fbt28pWbIkq1atokKFCh89Pmvr/xIDREdHk5iYiJ6ensa2cXFx9OjRg/Xr11O1atVU+7Xlhs5IbuofAeUSrbdv39K/f3/y5s2Lvr4+ZcuWRU9PT83SVbWE0hNgXS3fj50DTktMU+77UOFVlk78WhNsfEorWBXV71zXJUwphbiAJzQuUZDRo0czcuRIAEaOHCklz5GR+RrJVEtYmTt68+bNNGrUiE2bNjFp0iSmTp3KhAkTWL9+PTY2NuzevRtLS0uSkpJo1KgRW7dupXnz5sybN4+yZcuyefNmVq9eTefOnTUK8N27d/nll180jqFYsWKsWLFC474tW7YwevRoHj9+zOTJkylcuLDGdqNGjeLXX38lZ86cqfatWLGCRo0aMWLECEJCQliyZIlaJObcuXNZsGABr169Yvny5Zmem/pbYtKkSfj5+bF48WJF6k4nS60C+7GWry6Cq2oVamqvay3hzEBVdD+VAH/Nbui00BS4pdEi1sDcDvXZceWe9H9Nlc5kZL4mPnvu6OTkZAYPHsyZM2cQQhAcHIy3tzfNmzfH1NSUzZs3U7x4ccqWLcvgwYM1nkdTNixdaN68Oc2bN8fPz48mTZpQr169VGtlz58/z+XLl5k8ebLGPtLLDf2pc1N/bTx//hxHR0devXrFixcv8PT0JDk5mcmTJ3Py5EmGDh1KqVKldJ7f1YaqAGdGlLNSoD5XBSFVvlar92tCWwS1WtBWCmsYwCG7G+XKlePcuXN0796d0qVLf8ZRy8hknEwVYV1yR8+cOZM3b95w8eJFTE1N6d+/P7GxsVLb+/fvS8W94+Pj1fpR8qGWsBIPDw9Kly7Nnj17UonwyZMnuX//vmQFv3r1itq1a7Ns2TJKlizJ9u3bpXrIqrmhU/ajmpu6WbNmaY7nWyQ0NJSlS5eyfv16rW0GDRpE69atP0qAleKrrdBCeigFL6XYKkXY0shAbZ+qQKZ3voxazp9TfL/1uWDIQHat/6OcGz40sAX1RgZz5MgR/vjjDymLmozM18hnD8wKCwvDxcUFU1NTgoKC2Lx5s2RBv3jxgu7du3PkyBHmzp1L3759WbRoUao+PsQSVs0bHRISwtGjRzWK45AhQxgyZIj0fw8PD/bs2YOXlxdJSUlp5ob+1Lmpvxb27NnDuHHj0NfXp1WrVhQpUoSsWbOSNWtWnj59Cii+IwcHh3QFWJuLWVUYlUKnLWI5rfnYlCKpSZRTCnHKtmmhTbS/dWv3S7miU6IpelrjEiYVjt98SA7vMpxau5ZJkyYxdOhQWYhlvlo+uwj37t2bFi1a4O3tjaurKzVq1AAUifh//vln/vrrLzw9PZk9ezZly5Zl48aNmbLMZ968eZw8eRIjIyOEEPTr14+aNWsCipzDu3btYtmyZWn2kV5u6LRyU39LhIaGcvfuXcLCwkhMTCQqKgpXV1cSExM5cOAAx48fp27dugwZMgRra+sPsnTTci9rSx2pjYyIs7JvVZHMLJf0ty68XzO6WsVXH72gwej/Htz//fdfWrRokarwh4zM10KmibBqCmoPDw9CQ0Ol/1epUkVKwO/u7s6lS5c09qFa8s/U1JTr169n1vCkJP+aaNiwIQ0bNtS4z8/PT+3/NWrU4OrVqxrb7ty584PH96UJCQlh9+7dbNq0CX9/f2m7np4exsbGxMXFAZAnTx4GDBhA69atMTAwyJAA6zKvm1EB/lC+5ojkL83XYgV/CA/fRgOwevVqjhw5QrZs2cibN+8XHpWMjHZ+mHXCMtq5e/cuY8eO5fHjx9SsWZP+/ftTqFAh7O3tMTAwwNDQkPHjx7Np0yZ++uknfv31V2xdU0eOw39Cq7yRp5ezWdOcqibxVc3PnFnl/zRZxdrQ5rLO7GNSHpfyvSqRCUk6bfseULWCNbmilcFZuVydAUVK1IEDB36u4cnIfDCyCP+gXL16lZ49exIYqIgqLVWqFPv27cPZWXETe1SjFWUnNcM0jycGOQqyYsUKhg4dmmpttbY5XW1Wr7ZAJm1W74eI76cogJCWuKWcX1YlvWVRuhyn7byatn+PAgzq7ui05oTLFMpLoUKFmDZtGuvXr8fY+MulCZWR0YVMi1bQ09MjMjJS4z5vb29iYmIy61QfzcWLF/H29iZfvnxUr16dgIAAje02btxIsWLF8PLyonDhwsybN0/at3r1ary9vaWXg4MDTZs2lfbv2bOHAgUKkCdPHpo1ayZ9Nq9fv6Z27drkz5+fIkWK0LJlS96+fftpL1gD586dkwQY4Pr165hOHAeTBsOkwZTrXw34L0+vubk53t7eFC1aNF0XdGRCktorPPa/V1BUvMZXSt5Ex0sCbG9urJMAO1sYf5HczJZGBtIrvXYZ2f4l+Fpd0elZwqD4rerr6zNy5EiePn3KmDFjSEhI+K+PhAS2bdtG586dqVu3LgMGDODy5cufeugyMmnyWUIGfX19MTMz+xynShchBG3atGH27Nk8fPiQunXr0r9/f41t3dzc2L9/P7dv3+bMmTPMmTNHSr/Yrl07fH19pVfWrFlp06YNoKgj2alTJ3bs2MHjx4/JmjWrlM7SwMCAkSNH8uDBA27evIm7u7taNPbnolevXqxbt47OnTsDihvUngCFKDsWzYWZoy2meTxJdvDAwL1ouv1pE92UwqsLadXo/ZJk5ppiXYX7c/M1FbvQCYvUKWEbmgWxpNfP7N+/n99//50HDx4wd+5cfHx8GD16NBcuXODVq1ccPHiQLl26EB0d/QUGLiOjIFPd0dOnT+fw4cOEhIQwduxYWrduDSis5IiICCwtLRk4cCAnTpwgISEBa2trli1bRt68eQkJCaFNmzYEBASgp6dH8eLF013v+yFcuXIFExMTqlSpAsAff/yBk5MTCQkJGBkZqbUtX7689N7a2poCBQrw7Nkzte0Aly5dIigoSAru2r9/PyVKlKBAgQIAdO/enXr16jFp0iScnZ0lly9A6dKlWbx4caZfpy4UKVKEIkWK0KVLF0qWLMkeA2PaAFEBb7AqkBcDexcM/r9ERNdoZkh/Tjcj6GoB60JmpIj82DnXr010U/I1WsIZWiv8f89Nq8olyO5oy6+Tl9O8eXOt7X18fKT8BjIyX4JMFWE9PT3Onj3L06dPKVWqFBUqVCB79uxqbQYPHsy0adMAxfKBfv3+x95Zh0XRdnH4XrpDEEUkxAJb7Fawu7u7u1870dfu7hZbbMDuxnoVFQlFumuB/f7Yb8ddGsXe+7q8XHaemXkGduc35zwnxnD69Gn27NmDjY0NFy5cAMjQRTty5EiuXr2a7raNGzdmWSHHx8dHoZSdvr4++vr6fPr0KU2zBnlevHjBrVu32LRpU5ptW7dupUePHoKIpz6HjY0N/v7+pKSkKOQrJicns3bt2p9ebP758+eANIc7b9m6gLTwQXrIBDir3F34OuH9Hr15f0SJyD8F/7CYX1KIM0WuzSF8EeLqJeDBun/Y9Dqe2bNnK+yyceNGLl++zLBhw5Q5xEp+Krkqwv379weklaRq1qzJtWvX0lS2unDhAqtXryYqKoqUlBShn2zVqlVZvnw548aNo06dOjRq1Cjdc6xateqb55k6uEg+vSo9/Pz8aNWqFRs2bKBAgQIK22JjYzl48KBCelV650iNRCJh6NChGBkZMWLEiBzMPvcIDg5GW1sbe3t7VFVVqVChAjGfQtA1/3+ZzXRcfTIMtFR5EyJd588N9/HXRDz/zN68Sn4OWdWOlkfV2AxTY5ikF4h7rVpcu3ZN2Hb79m0qVqyIvn76XbyUKPlRfNfo6NRC5OPjw8iRI7l79y62trY8ffoUR0dpAFC1atV4/Pgxly5d4siRI0ybNo1Hjx4JvXll5NQSdnZ25sCBAwAsWrQIKysrhdzfqKgooqKiMDc3T/eYHz9+pH79+kybNo0OHTqk2e7i4oK9vb1CZSwrKyvc3d2Fn729vbGwsFB44h45ciS+vr4cP378pz2Jt27dGrFYTOXKlUlOTqZly5bEVqlCwbBnqJrkJ0XbkNR2Y+q0m28V4O8hvkpr99v4lazhnLii5ZGvJ61qbEbdunUVRFi21LVo0SKaNm36zfNUouRryVUR3rZtG9OnT8fb25vr168rRBMDREREoKGhQf78+ZFIJKxZs0bY9v79eywsLOjYsSONGzfGzMyM6OhohRaEkHNLOHUZypSUFOLj47l8+TJ169Zl48aNtG7dOs16MMCnT59wcnJi0qRJ9OrVK8Nr7tevn8J7jRs3ZtiwYbx69Qo7OzvWrVtH586dhe0jR47Ey8uL48eP/7QUiuTkZCIipFbF5cuXATA2NqZY2DPpAF1jhc5GFsa6CuvC39pt6HtZvkoBzh1+JSHOFqlc0vDFLZ0cEsCUiqbcdrDn4sOX5M+fH2NjYwIDA//o5ipKfg9y1QTT1NSkRo0aNGzYkNWrV6dZDy5dujQdOnSgZMmS1K1bV2EN9vLly1SoUIFy5cpRo0YN/v333zQCnBuoqKiwZ88eRo0aRbFixXB1dWXp0qXC9qZNmwrVvWbMmIGPjw8rV64UUpHkg8Xevn3LgwcP0pTV1NfXZ8uWLbRu3ZoiRYrg7+/P1KlTAbhx4warV6/G29ubKlWqUK5cOdq0aZPr15kVt2/fTvPe58+fSQ4LlAZkyUVE+4fFpNtIISdWsCzNKLvpRjJkaUd/qgD/jC5O2eVPiJQGqRirGpux8Z/hWOY1RkdHhx07dnD58uVsd1m6ePEivXr14uHDh1kuXylRkhNEkmx8oiIjIzE0NOTWrVscOXKEJUuW0L17d3bv3v0j5qgkFzE3NycgIIA6derg7e3NkSNHsPz8mIcqFhQsWJDiZnqoxEUIIpxeVLQs7Si1CMuLq3yOb07Ip6vB55jEr1rv/Z4i/KtHNX9PfpZFnJ4rOss14VTWsDzJIQG8+uBPlX5TqF6zFkuWLEnXA5YeXbt2xdPTE4Bx48bRrVs3oqOj2bBhA02aNKFcuXLZOo6Sv4fo6GiqVatGREQEBgYGGY5ThgX+pZQtW5YPHz4QcWwdRvq6NLDSwl5fKrKpBViW/wtfBFie9Kzbr7V4Za9zgoGWaq4LsHyrw79ZgOHnWcTyyyHZJpNgQlWT/NhZW7BvzmiuXr3KwoULSUlJyfKQt2/f5uXLl1TDGDU1Nfz8/Dhy5Ai1a9dm3759zJw5M+fzVKLk/yjLVv6lvHz5EoA4sVjqgrayJ0XbULjxpXfjTb0O/LXpRDIR/1pr93s1X8isdKSSP4dm1R2YNGkSCxYsIDw8nPnz56dbTEgsFhMZGcmAAQMwQI2S6PM0KZKDBw8q5BanXnZToiQnKEX4L+XixYsAiOp0RsNGRxDgjBouZCcQK7ui+q2u5h8hwEr+TGTBWjMaQd68y5kyZQp9+vRh69atghCrqKjw6tUrhWwII9Sx0FJn2859jBs3Tug0pqamhqmp6Y+/ECV/DH+tO/rNmzdUr16dYsWKUblyZV68eJHp+Pj4eEqUKEHFihWF9zw9PalduzZ2dnaULl2agQMHCi3/QJqiVaZMGSGoSz5F4ty5c1SsWJEyZcpQtWpVnjx5kvsXmQW9evXCysqKFFMbBdeffAnK7PI9cnZlbubv5WpW8oeSgUs6KSmZoxeuEBQaDsAQh3zs2LGDN2/esH37dvr370/ZsmUZOnSoQtaCkUiNLpr5KGuoifrI2awSG9Bex+T/x0zi7du33/2SlPy5fBcRTkpK+h6HzVUGDRrEwIEDef36NRMnTkyTZpSaf/75h2rVqim8p6WlxZo1a3j16hWPHz8mIiJCIdIapI0SZPWla9WqBUgrU8kC254+fcqiRYuEutM/ksKFC1PcTA/4EgEtb/nK/5MnteB+LwHOTVLXas5Ol6K/GVkwloWx7u+VqpQJ8zfupuOYWTh0HMqnYGkAV4kSJWjWrBkbN27k3r17lDI0wMvLi/79+9O9e3cA9FVUqWyojaGZLoZm0t9FFz0zluWxxdHRkbdv32ZrbVmJkvTI1S5KS5cupW7dukyZMoXevXszePBgnJycsLa2ZtSoUXh4eFC7dm1sbGxYtmwZIM3bHT58OHZ2dpQtW5YKFSoQHx8PwPnz56lZsyYVKlSgSpUqGRbpyCmBgYE8fPhQ+JK1a9eO9+/fKxTxkOfatWu8efOGHj16KLxftGhRypQpA0gbM1SqVIl3795lef63b99iZmaGvb09AHXq1OHDhw88fPjwG64q5xgbSy0GmRX8NekyuSnAP9ryVQZdKSITXHkB/tnIe2hkkdEp2tlIXUxlDXt98GfBxj20bt2a5ORkqg2bS2BYBMXCntGkSRNh3NaOjuzbtw9LS0uOHDlCxTxGLKlcFqN8eujm00U3n64gxmUtTKj/6B1RUVF4eHjkyvUq+fvI1TXhhIQEofBD7969efbsGW5ubiQnJ2NjY0NUVBSXL1/m06dPFC9enIEDB/LmzRvc3Nx48eIFKioqQkGPd+/eMXv2bM6dO4eBgfTpVJZWkzqtQL4qVmpmzpyZJg/X19eXAgUKoKYmvXyRSISVlRU+Pj7Y2NgojI2JiWH06NGcPHmSN2/eZHjtMTExbNmyhUWLFim8X7duXcRiMU5OTsydOxddXV2hYcXt27epWrUqx44dIzo6Gm9vbxwcHLLzq84V7OzshHXg7PT8ledXSyHKCKXI/r5IJBKmT5/OiaMuJKekYF2oMMsmDqFIyTKCEGe3jOWdW7dJTk5m2rRphISE0KFDBzos3IHbguH0stNHZXR33r3ywrpIQWJObGKnXX50ahcHIDYw7TliPsegm0+XyhIJFcShjB49GmNjYxo0aICamhrDhg3LNC1FiRIZuSrCffv2Vfi5devWaGpqAlC8eHGaNm2KiooKFhYWGBsb4+fnh62tLWKxmL59+1KvXj2aNWuGiooK586dw8vLi9q1aysc09fXF1tbW4X3UlfFyg7ZrR89YcIEhg0bhoWFRYYiLBaL6dSpEw0bNqRVq1bC+x8+fMDKyoqYmBgGDx7MhAkTWLduHYaGhhw5coTJkycTFRVFzZo1KVGiRLZzFnMLX1/fNO99ayWs1PysAhpK8f396d27N7t27aJX5w4kqWpw+fJlHJp2Yu7cuYzqmXFnpPR4ryoNnlJTU6NAgQL8+++/DBo0iPN3a9GsugM92jYjOSwQAO28xsQFhSnWUf8/sYER6JrpCD+LRCIW2BTlSkQYryqV4+bNm/j5+fHo0SN2794t3P+UKMmIXBVhPT09hZ/lw/hVVVXT/JyUlIShoSHPnz/nypUreHh4MGXKFK5evYpEIqFx48bs2rUry/Pm1BK2tLTEz8+PpKQk1NTUkEgk+Pr6pttF6fr165w5c4Y5c+YQHx9PWFgYJUuWFDoPicViOnbsiLm5OStXrlTYV3Y8XV1dhg4dysCBA4VttWvXFrwGCQkJ5M+fX3BP/wi0tbXTPMzktgD/LJQCnHN+tutZFpUvK49qYazLwYMHAWjYojVlypRh1KhRzJw5kwkTJlCjdFEqli9LirZhxtawXClL2b1JVgq3WrVqFClShA3HLtC4SjlUVVVQNTYjOSwQdZMv0c7aedMGecmEOCYwFt18usR8jsHJyISKN17RHQPu6Fqz6OVL3N3dFVzdSpSkx0+Pjg4KCiImJoaGDRuyYMECbGxsePHiBQ0bNuTcuXM8e/ZMGHv37t10jzF58mQh+Cn1v/RKQpqZmVG+fHn27NkDwJEjR7CxsUnjigZ4+vQp3t7eeHt7c+DAAUqXLi0IcFJSEp07dyZPnjxs2rRJwboOCwsTmoWnpKRw8OBBypcvL2z/9OmT8Hru3Lk4OjpSpEiRHPzmvo3Vq1d/97q5P9oK/llrvH9S8NLPQD4tTvbaPywGTy8f9PX1WbRokdDxy9nZGV1dXS5duS7sk501Ytl3+8GDB4C0dnr+/Pm5cOcJOvW60WTsfFJSUoQWnuompoIApxZiHTPp+XTNdKT/Uq0VV9HTpXDhwmliPCQSCY8fP2bBggWMGTNGSBNU8nfz00XY19eXBg0aUKZMGUqXLk2pUqVo0qQJRYsWZc+ePULagL29fRpL81vYuHEjGzdupFixYjg7O7N161ZhW//+/Tl58mSWxzh48CBHjx7l/v37lC9fnnLlyjFs2DAAXr16RdWqVSlbtiylS5cmJCSEFStWCPtOnz4dOzs7ihQpwocPHxTO/yNwsDAQIqNl/I61l+HXCbBSCnHOyawal46uLuvXr8ff35969eqxatUqVFRUqFy5MkvXbSYwKDjb52lll4cKFSowfvx4xo8fT5cuXbh+/ToqKtIH53svvqQZyQuxzCqWCbHMPS0TYkDBPQ1QoLQZMT6+Qt5xYmIiy5Yto127dvTo0YMz+w9z6dKlXGnLquT3R1k7+i9DVjv6vvsZKtSTuspkgVnpuaNlFapyyo8Q9J8pvJkJ7m/X+ICf9wCR3u8qWpys8LdVS4wmf/782NracuLECQICAmjQoAH7Nq+hfctmQBYBWv93ST+I1mHNmjUcPXoUa2Nd1vRuTo2GDfgYHEpsfCJFCuZX2E22RgwgDgkmLkh6nJhPIYBiwFZMoNTrFfM5hhhDNVpev8W///6Lnp4ey5Yt482bN5RT06O6hiFGKmosiP4AINSjVvLnoawdrSRbpI6MTs2vuE78q1i+GZE61edX52fOc+Tg/sycMjHN+/LFYpI09DA3NycsLIwrV66QnJyMffGi7HM5DmQjQlrXGHSNSXp3n6Muh4mJieGFXyAG+aRrwAVM86QRYJBaxDKrGNK3huVd0wC6+XQ5WkQaVb1ixQqGDBlCyjs/JutZM8PEilLqehiJ1NEVqaCjo2hBK/k7UYrwX4q6iYWCBZybYpsTKzg9MZUvrJHev59NTkTrV8q5/dVISkri4tkznD55LMuxW7duJW/evAwfPpwmTZrw/oMvN+/eF7IakpKSSE7O/DN88IwHMXHxQhaHvlkBBZHNjIzc0kAaIb5yQBpQZh4UwTQjSxab2FBZV/r3z6+lip6KKvVbtqBgwYLZOreSPxtl7ei/lM+RcWhm4IL+UaRXvepX5muFND0h/h1d1rmNmpoaNx8/S/O+nrpqGu9Mmaq1efLkCTVr1uTBgwfEx8dzePsGHj97wVznfzl/5SbGxsasWbOGoNdP8P0UQJdWTbAvWwGVuAhStA2xKlsV9QMn2LZtGyN7tKdwqdIZzi05JEB4LYuazi5bqlYgOjAGPVU1IgLT/zu/db2EkUPJbB9TyZ/LX28Jz5o1i/Hjx+fa8U6fPi0EXLVr147o6Oh0x33+/Jm2bdtSpkwZ7OzsFIK24uPj6d27txCo1rJlS4KDpUEoly9fRkdHR6hHXa5cOeLi4gB4//49FSpUoFy5cpQuXZoOHToQFpZxf1VZW8LUrQm/hexYwb+KRZsdvpdrWWkZSylla4mxcZ4076f3+dDS0hKK2ezfv5/QsHC69h/GjQdPGT58OCKRiI4dOzJs2nyc125l/FxpCVlZ9PTIHu14esGFE9tWsXjm5CzbHmaEvDWcbqCWmhp6qmntm4D4ZALikwlMTuSlJD5NDQQlfyd/XO3on3nu6Oho+vXrx/Hjx/Hy8sLc3Jz58+enO3bs2LGULl2ap0+fcv/+fbZt28a9e/cAaeR2dHQ0T58+5dmzZ+TLl4/FixcL+5YoUUIhDUsWhVmgQAGuX7/O48eP8fT0xMLCgrlz56Z7/lfBsQrim5tCnBm/i/jC9xdKpRBnTurPin9YDJs2bUIsFnP79m16DR1NaEQkixcvpm/fvgoP02XLluXe0+ccOn4asVgsvF+4RBmaNGuBiv7/3cn/Xy9OT5BTC7F8/nBGyEdKy1vBAXIeJ9eEYExNTencuXOWx1Py5/Pdakffvn1bsMpKlSrF+vXrAfD398fJyYkyZcrQqlUrmjdvzpo1awBphRzZa4Dx48cza9YsANzc3KhWrRrly5enVKlSbN++XRhXt25d/vnnH5ycnGjUqBEAS5YsoXLlyjg4ONC0aVOhOlRERATt27enRIkSNGrUCC8vr9z6FXD27FkqVqyInZ0dAEOHDmX//v3pjn3y5AnNmkkjO/X09KhTp45CtHlsbCxisZikpCSio6OztX6kqakpCHJycjLR0dGoqPw4Z0dWVrBSgNM/z88U41/hQSAnc/APi0FNTQ1DQ6nl6eLiQtWqVQFpzQGQ5gR369aNPCamdB80guKV67B603aSk5O5c/8R0TFScUyTX6xrTExsHDceen5VQwZ5axgQmj0ExCfzOSGJp3ExvE6K5X5SNH369FFW01ICfMfa0a1atWLcuHF07doVQHCLjhw5ktq1azNz5kzevXtH2bJlady4cZbHdnBw4Pr166iqqhIaGoqDgwONGzfG3NwcgMePH3Pu3DnU1dXZt28fr1+/5tatW6iqqrJ7926GDx/OiRMnmDNnDgYGBrx48YLg4GAcHBzo2LFjuuds3759hiJ96tSpNM28fXx8sLa2Fn62sbHB39+flJSUNGJYqVIl9u3bR8WKFQkODub8+fOCeA8aNIhbt25hZmaGqqoqVapUYfjw4cK+//33Hw4ODqiqqtKnTx+GDh0qbEtMTKRy5cp8+PCBsmXLZpnv/C0NGOTTl37XHOP0+BnCJKsU9TfyNdc9YsQI5s+fj7u7O506deLmzZts2LABgFq1atGkSROaNGnCf//9x/Llyxk3fQ4bt+/k9bsPdGjVnL2bVgOkqbhlWKkpAG8v7se6QOYuaVnKkq65iZC2BF+s4ZjP0uv6nJBEgiSFDYl+8H+Hk9IVrUTGd6sdXa9ePebNm4eXlxeOjo7UrFkTAA8PDyFJ3dbWFicnp2wdOyQkhH79+vH69WvU1NQIDg7m+fPnggj36NFDqL18/Phx7t+/T4UKFQCpVaiqqiqcf/Vq6RfQ1NSUtm3bZnhOFxeXnFw+kLYmdUYsXbqU8ePH4+DgQP78+XF0dBSe5C9duoRIJCIgIAAVFRV69+7NnDlzmDVrFg4ODvj5+WFoaIifnx9NmzbF1NRUeJDQ0NDg8ePHJCYmMmLECDZs2MDEiWlTQGR8jkn8pmYM2RXf38EK/tlWoez8P0qMf/b1pubF82e0buxEm/Yd+Wf2PAwM0q+E5R8WA6raVK1alZUrV/Lhwweh+h2gUCqyePHiDB8+nBs3bhASEY2TkxOnL7hx940/lYtaAF+EODA4VNivYL68X3UNOmaGCvnDKRIJUaqJuMR8KSxibW0t3LeUKMlVX6V87ejRo0dz+vRpzM3NmTp1qoK1lhFqamoKaQayloYAgwcPpk6dOnh6evL48WOKFSumsF3+3BKJhGnTpglrprJ9ZNuyS/v27RUCoOT/pdf8wMrKSqEdore3NxYWFum6hPPkycO2bdsECx6ka70AGzZsoE2bNmhpaaGhoUG3bt2EVmkGBgaCK65gwYJ06dKFa9eupTm+hoYGffr0yVZBFVlwVuo14szWiX/F/OFv4VcSpNQuavngsJzO82e7u3PCg7u3iY2JYe/O7Zw6djTL8aNHjyYuLk5BgNu1a0fp0opRzyVLlmTv3r2cPn2aadOmkZCQwOOblxXGpGgbcuO91JrV0FAXHtozQ76cpXyQlgxxSgqr4gNYFuOLD9LgSTMzM44fPy50cFOi5LstGP7333/Y2toyYMAApk6dyu3btwFwdHRk27ZtgFSk3NzchH0KFy7MnTt3AKnle+bMGWFbWFgY1tbWiEQirl69ypMnTzI8d8uWLVm3bh2hodInW7FYzKNHjwBwcnIS1pNDQ0M5dizjHEUXF5cMa1KndkUDNG7cmHv37vHq1SsA1q1bl2HwRUhIiBAw8vDhQ44fPy48qNja2nL+/HkkEgkSiYTTp09TqlQpQFpzWrZeFRUVxenTp4Wa1D4+PsTI1rtSUjh06JDQ7zi7yItvbvUK/tWt4F9VpL5VQDPa91cV5vadu7F1zwG27ztEp249shxfzakJz9/5kSdPHkqXLs3kyZPp3r07q1evJiDgS4qRSCSiTJky6OnpkZSUREpKCvqpms0A1C4itX5H98v43KmDs7TzGqdbW1rXTIcT8aHcjI2iX79+wraePXsqBViJAt/t07B69Wo8PDzQ0NBAVVWVpUul6QIrV66kZ8+eHD58mGLFilG/fn1hn0GDBtG+fXtKly5N4cKFqVKlirDN2dmZoUOH4uzsTIkSJRS2paZHjx6EhIRQt25dRCIRSUlJ9OvXj/LlyzN9+nT69u1LiRIlsLa2pkGDBrl2zfr6+mzZsoXWrVuTlJRE6dKl2blzp7C9XLlynDlzhgIFCnD37l1GjBiBuro6+vr6HDp0SHBRzZo1i4EDB1KyZElEIhElSpRg48aNgLTZxPr161FTUyMpKYkOHTrQp08fAJ49eya0dExJScHBweG71af9nd3Q8p16fkeyclund12/w7Vqa2vTqGnzHO2jp69PgQIFKFasGK1bt6Zz5854e3sjkUgYOXJkmvGyDIT6dWqm2WZcwJqwZzfQ19MVSl3KkM8VlgmxOOSLi1m2RqxrbkKkfxCeweGcS5GWiS1atKgwLrPlLyV/Jz+9dnTv3r2pWLGiQuCRku+HrHb03nPXKGpfMlOXc1aW8K8eDf27i212kBfi3/U6c7IGLl/EQ/b5atOoHvfu3aNIkSJCIOXMmTNp3z5tz+ELFy4wbtw4fD3vks9Mavmqm9kgDvQG5Epg/l+E5Yt2AOkW7ZAX47igMKa9iWfLli3Ce7Vr1+bq1avUqVNHIftDyZ9NdmtH59gSlgU/RUVFff3slPwUUlJSBHe17O+YWy7nn0lmYvu7ClN2+dOvLzs4Ojry33//UdI6P5MmTWLAgAHpphgdPXqUmTNnYmlRAIuSFRWCKOWFGBB6Eaua5Cc5JODL/6nKXMr3H04MDuKCeWW2TOpJ8eLFGTVqFKdPnxbiUGTpk0qUyJNjES5eXFqc/PLlywQEBJA/f8Zh/Nlhx44d37S/kuxz5swZoqKi0NM3oHiRQmj+35L19/Xh/ds31KwrjVRPHXQls3h/pWCs1OKjFKO/l+7du+Ps7Ezyhydc9pJapfL3JYlEwr1795g5cyYAs+fOyzCLQSFlSU6I4UvxjvREOTkskP2P3zJk9TxAmipYq1YtatWqRVBQEHXq1KFp06a5f/FKfntyLMLly5fH0tISX19f7OzscHR0RF9f/3vMTUkuIZFI+PjxI1euXAGgSas2mOhLRcvr9X/0bNcSf39/Fi5dQY8+/YX95EU3PQGOjE/+4fnBSrH9s/haV7TsZz11VYwtCgGgal2WanmlLQVldQk8PT3p2bMnSUlJODk5sW3bNubOnStkHciTxhoGQYiFilpyogwIYgxw6q1UvE+cOIGp6ZcArrx58wqFeZQoSU2O14T19PTw9fVl4MCB+Pn5/Yg5KslFqlWrxqyFS/A4dwoXFxe8vLzQ1NTE2NiYgIAA7t27h3lhe4UbXlYWcHpC/D3Wg5UC/OeR05zo1EIs/zmzMNbl3rPXVC5dHA0NDezs7Pj06RNBQUE0atRIqOgnq8Pu+daHUrZpsxzkhVhmFcuqa6VeM5aRHBKAw5B5vH79msuXL2e6Bqjk7+C7rQkDWFpacvr0aR49esR///2nUJtVSe7x4MEDLl++TKVKlahWrRqWlpZExEoDqRKyUVYvIjSUTauXU6RIEVJSUvD19eXWrVs0qlsDNTU16tWrx8iRI6lYsSIJCQl0796dXr168fTpUwIi44UbnnxlrOygFGAl2SE3ipLIrGHZ8cwLFGDy5Mk8f/OOsM8fMTO3YNGCeeQrVJx/JowRBHjqXGeMjfNkGEugbmYDfBFk+Z9V4iIUa03/X5Dz5MmDSCRS9glWkiO+yhJW8n2Jj4/nyJEjODs706dPH8aOHSu43CCtNZAR6ilJFLfMx4QJE+jatSsJCQn4+/sTFBRE4cKFFVxmAPfv36dPnz64urrStGlTod+wjPSEWGkFK/lavlaEs/v5l/8cRouTscsntWa3HjpGjTqOCmNy8vlK7bKWWcdF67WhTJkyzJs3L9vHUvLn8l0tYSXfh9jYWNavX8/hw4eJiYmhc+fOjBkzRmFMdm5AX24+qqiqqgqVxTQ1NbG1tcXW1jbd/SpUqICNjQ09e/bk7NmzVKpUSRDijCzh3FwXVgrt38O3WMHp9RtOj9QPkCt3Hca2mB1FC1kJn1uZJZ2dVDbZGJlVLEMmypUqVeLQoUNoampiZWVFr169cnxtSv4+lJbwL0JwcDCDBw/Gx8eH7t2707x5c2xtbSlVqpTCDUv+xiIO9EHdzCrNseQtgDaN6hEZGZntOthv3rxh+vTpeHl58eLFCzSN8wnnzEiIU4vw11jCSgH+u/hWV3R2RDi7sQypP6+pm2nIu7xl29Pj8OHD9OnTR0gDfPTokbI61l+M0hL+jfDz86Nfv34kJSWxd+9eocKOvACnd9NJT4DlCQkO4t69e+jo6Cg0sciMokWL4uzsTIsWLWjatCmbNm2icOkK2XYByuaaEyFWCvDfxfduUJHd+AV5a1iGzCqW9/6kHpN6/rLPb4cOHUhOTqZLly6AtDRtvnz5vulalPz5/Lhms38hSUlJnDp1il27dmU4Jjk5mXnz5hEXF8euXbsoWrQopUqVEmpFZxdxoA+g+FR/++YNAHbt2pUtAZZhYWFB7dq1ef/+PZ07d8ZIQ3rc75WO9Le28FPy80kt2NHiZEGAZdXkIuOTFf7JxsiE2T8sRvgM29vb07JlSwChMYsSJZmhFOHvyOzZs5k6dSr//vuv0MAiNStWrODGjRssXLgQCwsLipt9nbtf3cwKPXVVwvzfC+999nkHkG6ziUyPpa7O2rVrOX78OOHh4fTs2ZMCRjqCEGdHjHNiOSv5e8itiOjcJLXIygtwet3EZGPSm4uqqipVq1YFSLdPeXR0dLrVvJT8vShF+Dtx5MgRTpw4waBBgyhcuDDDhw8nNjZWYczHjx+FimGampoUN9MjXtuER48e8ejRI1atWoX7xQtER0Wl695N7z1ZFHWY/3u6dOmCpqYmI0eOFHoV5wRLS0vmzZvH0aNHuX79OhbGusI55cVY/ualRMnP5ms8NvLtPNOrp56RGIOiEBc30yMiIgItLS20tbWF9xctWoSTkxPVqlWjQYMGeHp65niOSv5MlCL8Hbh//z6zZs2iQ4cODB06lDlz5pCQkIC7u7vCOHNzc+GL2qZycS54XMHe3h4HBwccHByYOXMmPTu1pbydLf9MHIuWSIKeuqrwD0BXTYXk2Ch01RT/lKVKlSI+Pp5169bh4/2edu3aCR1kckL9+vWxsLCgf//+RL26jYWxLsXNvgQZ/OiKWUr+bnLDCk4tuJk1McloXxmpHzyf+oZw/Phx4uPjOXDgAP7+/ri6urJnzx6s1CRUqFCBwMBAunbtmm5PciV/H0oRzmXi4+OZMmUKZcuWZerUqaioqGCjI3U/xcXFCeNk0dBxcXGMGzeO/Vef0KpbP2xsbNi2bRt79+7l6NGjuLq60rJlS3Zu2cTL58+EIJB9u3bQvW1zyhWzoWShgvTt1kk4tmxMqVKl6Nu3LwcOSdtGDh48mEuXLuXoelRUVFi1ahXv3r3DpkYzSpewY9KkSUR4v0JHVVp/NyMhVrqkleQW8muw30JOBPdrOHPmDP7+/tjb2zN//nwaN27M5MmTae9QnG3n3IR7A0jr7ytRooyOzmXOnj3L58+f2bx5M6qqqhQ306P3wrMAaGlpcezYMZ48ecKZM2fQ0tJi3bp1lCtXjlatWlGtWjXWrFmTJoiqdu3aHDx4ELWkOJ49e8aZM2eYNGkSlSpVokOHDkRHR7Nnzx5EcREUKFAAgPDwcHbv3k3R/MY41q6B+67V1OoymLFjx3LhwoUcNd4oVqwYkydP5vr16xgaGrJ582YWL16MsbExB0+exaqY/Tf9zpTR0Uoy4nd7kJs6dSr58+enSpUquLq64n5oF01L29KnjgN+IhENIp9gtXkztWvXxtvb+2dPV8kvgFKEc5HY2FjWrl1LnTp1sLGxESKce/bsyf79+5k6dSoikQhra2t69uxJjx49MDQ0ZNu2bURERDBz5sx0o5hfv36Njo4OefPmRSwWs3jxYho2bMjSpUsB+PDhA3v27GHnzp1cuHCBlJQUPn/+zH///QdAywZ12bvaGUdHRzw9PTExMcnxtXXq1IlOnaTWdlJSEkuXLmXPnj2opOpGk9PiHUoBVpIR3yLAOS21mhPSa/8ZLU4Winn0btUAlbgISraqxriahYQexIXfuSMGSn26xfDhw1myZAkODg7K5g5/OUoRzkVOnz5NUFAQ27dvB75U0nEsXxxfX1/iP3tTwN4BtajPQseWZ/5hHDx4kEaNGmVonT558oQyZcqgqqrKmTNnCAkJYfDgwcL2gIAA4fw3b94U3ndxccHX15cJEyawZPUGapcqzhKxmNOnT9OmTZuvvk41NTVCQkIoUqQIBYvaZTo2pznDSv5c/oRUtPQEWPbgKXugVDezIfnDE9A1JqNPfq9evXB1deXcuXNKEf7LUa4J5yL379/Hzs4OS0tLwQpWN7MhTisPAwYMwKltNywtLSlauS5z5szhrfcH1qxZQ0BAAH369En3mCkpKTx+/JiyZcsSEBDArFmzqFu3rlDQA+Dz588ABAYGUr16dQDKlClD8eLFqV+/Pl27dmXR1gPUK2ZB3xaOzJ49m2vXrn31dQYHB+Pu7q68eSj57UhPRLMzPp+uRpb7yj9kyLouoWuMqkn+L//+3384ODiYoKAgLl++zNWrV3M0JyV/FkpLOBcJCgrCzEz6JZMv8r5y5Uo8PDzo2rUr+vr6+Pv7M3/+fKHJ+Pjx4xVEVZ7//vuP8PBwqlSpwpEjR9BQEeHs7KwwJjIyEgAvLy9GjhzJokWL0ND4csOoXbs2u3btwqz5IFpUssexQkmcnZ2pVavWV13n0aNHUVVVpUOHDtLzp3L7/Yw+w0p+XX6GBZyZKzq1mMqCtTIS2azEN73PukJvYm1DocmDbGTgp0Cho9OtW7eoXbt2pudQ8ueiFOFcIi5OGjQ1bNgw4T1ZoffTp09Ts2ZNhWYM48aN48GDB6iqqlKzZs0Mj3vjxg20tbUpW7YssyePp1Gz5ujqKq6jhoaGAqCtrU3t2rXTrCtXrlyZyZMnExgYyPbt2+hWoywXfXyIjo7+qlrgrq6u1KlTB9OipXO8rxIl2SW7jRrSQz6HPStyah2nJrOHTpkYp8iE+P8u6qJ6hnTt2pVDhw7lKEhSyZ+H0h2dSwQGBhIfH4+VlbSes0yAHzx4wLNnzyhRooTCeD09PerUqZOpAMfFxeHi4kLt2rXx8/PjQ2AoDRo0SDNOFoAVFxeXbmCXSCSiW7dujBkzBkdHJy57hwDSgK6c8vbtW969e0fdunUzHSd/8/vdIlyVKMmMzHKL5a1+2T1A9r/MRZ0cEoCd7zWmTJmCoaEhUVFR33W+Sn5tlCKcS8hy/kxNTRXqPl+/fp3Y2FicnJxyfMzFixcTGhrKkCFDePdOWoKyWLFiacbJylJWrlw5y2O2b98ePz8/QOq+zoqUlBSSk5M5ceIEHTt2pHXr1lhYWODUvluW+yoraCn52eT2skh28ozTc78L7Q9jwrh07ykVRyxk69athISEZLgUpeTvQCnCuYSBgQEqKipYW1vz7Nkz4X0PDw+KFCmSYQ/fjPjw4QNHjx5l5MiRFC5cmCc712NiYoKpqWmasRYWFpiamjJ16tQsj1utWjVMTEwwMjLi4cOHGY4Ti8U8f/6csmXLUq5cOaZNm0a+fPlYuWELF2/cRU9PP1vXkVGNXVCmJynJmNwqzgFfhFhWalX+X07ISoAz67QkoGuMy7MAXvgEsGLFCsqXL//VsRlK/gyUIpxLVK1alZSUFO7cuaNgCYeHh6Ovnz3BkiGRSJg3bx558+alQ4cOeHh4sNvzLW3btkWUKi8XoEePHnh4eFC4cOEsj62qqoqdnR2ampqZuqPv3LlD586dhZ937drF6tWradexMyra395TWinAfw+/wt86I8HNiRhnZ+04KyFO0TakePHiAEyfPp3t27ejo6OTrfMr+TNRivA3kpiYyN69e5k0aRIAERERgiX89OlT7t69S7ly5XJ0zFu3bnH79m1mzZrF6dOnGTNqFI6OjgwdOjRX5lyoUCFiY2N5//49Eokk3TGVKlUSXh85cgTH5m1RN7MizP+90DYRMnY5x8fFsnfzWh5cvYiv9/t0xyhRkprctIBzQk6s4vTSlVLHQMi3OUzNqHZOFCxYkNu3b+eoxaiSPxOlCH8l8fHxJCUlsX//fpydnYmOjqZz584KgVO6urrExcUpCFp2ePv2LQAPHz5k1qxZtO/YkX///Rc1tdwJZi9QoAAJCQmEhoby/n36AqmpqcmgQYPQ09Ojbdu2wvvqZlaom1khDvRREGMZnz/5Ex4Wyk2PS6ycN50hPTrRoEo5Pn/6qDDuTyjcoCT3+dULu3xLJLVsXVjVwJTu3btz6dIlhXrySv5OlCKcBRKJhEePHjFx4kSGDx9OTEwMDx8+pFKlSpQvX541a9ZQr149jI2NOXDgACKRSOgJfO3aNUQikVCwPbvcvXsXgM2bNzN27Fj++eefr35iTkhISPNF19LSIjFRur4VHh6eZh+JRMKtW7fYv38/jo6OaQQzMj6ZOAML4gws0I70V9g2oF0TOjlV5cSujYC0CxPA0tnTFMb9Ci5KJb8mv7oQp0dO15dLliyJRCLBxyftg6ySvwtlnnA6BAcH4+rqysGDB9O0G/v06RNHjx4Vfq5fvz4PHz7k48ePVCxbEk1NTWHb+/fvyZs3LwYGBuSEChUqEB0dzYgRI3BwcPimaxk5ciRv377lyJEjGBpKUyTk+5yGh4eTmJjI3bt3efHiBa9eveLJkycEBgZSvnx5Jk6cKLjW0nM9ywtxcnIyYrGYsJBgbt0Kpnv37kyaNInp06dz7+7tb7oOJX8XshxhmSD/CBd1TnKLsyKrB4nChQujrq7O7du3hTViJX8nShFOxdWrVxUKbsijoqJC27ZtkUgkaGpq0rhxY2bPns2zY9tIEIsp06Yv169fR1ylCtXMbIiNjVUQvOzSu3dvevfu/Y1XIuXNmzcEBQUxYcIEVqxYITSCAMibNy9z584lMTGRyMhI9PX1sbe3p1mzZtSoUYPKlSujkc860+OrBL1j5e6DeL99Q58uHQgJ/CxsO3z4MGPHjuXRo0dp8qSVKMkKeSHLStQyE2l5Qc9KzL9GgFNbwdmx5PX19alfvz5HjhyhZ8+e6QZcgjRLwdvbW5nG9AejFOFUFCxYkGbNmlGqVClq1KhB//79CQyUdkFJSUkRxiUkJHDy5EkKFSpEnz79ePHiBR3btOS93yfsbK1wv34bsVhMaGgoKSkpqKj8eM9/REQEQUFBANy7d4/mzZvTqlUrjIyMAJg4cSJhYWHEJ0OtqpWoWLs+IpEozY0q9Y1JnJiISCRCTV2dOXPmcPLkSQAKWhcCpIFfsoYU6urqNGzYkB07dhAT5I9uXgulK1pJriMvfLLPb3oinpUQ58Qazo4LOvVnXVZBq7iZHh06dKBv376MHDmSefPmCZ4qefbs2cOyZctwcXFRWsx/KEoRToWtra1CbWZ597KNjQ1GRkY8f/4cVVVVihYtyooVK3j8+DHi0E8EhUexZs0a5s6dS/Xq1Zk4cSJRUVFcuXKFevXq/fBrMTAwoGXLlpw8eZJixYpRo0YNDh8+THh4OOXLl6du3bqYF5b2Ao4WJxOTlJLmGKlvRi89HzO2VwfEScks33EQMapYWlpib2/PWZd9ODZpgfvZU2zYsIEpU6YAMGjQII4ePYqzszNzl64WWr4pUfI9yMoSzY5FLC+wsrKU2amJLn/urD7jlSpVYvny5cyYMYPly5czatQoLl++TFhYGMuXL1cYGx8fn+mxlPy+KEU4C1xdXRGJRISHh2NoaIhIJEIsFqOqqoqKigobN25kzZo1wvj8+fMzb948BgwYgJ2FCeXLlOLMmTM/TISDg4NRV1cX5jp//nwqVqzIjBkzmDlzJhs2bCAgIIBkTX3BOk/vhpRafFNSUnh87zZjenfCxtoKVVVVZo3sz7Itu+javCHGxsZ8/vyZzhUq4372FAAnTpyga9euaGpqUq5cOa5evUpcbCzaOjpKIVby2yBf7CMzciLAAPb6ydi3qUdg4HAWLlzIkSNH0h3Xrl27HAd3Kvl9UEZHZ4FsrcbIyEh4ra6uLghYv379UFVVFQpluLu7k5SUBIBYy4iI6Nh03Uzfgzdv3lCvXj2huxHAx48f2bZtG2ZmZhQqVIhPEXFItA1RUVHJdk5mcnIyw9o3YnDH5iTExzFmzBjs7e3x9fVlaPeODBw1jqdPnwIQ9fE9zdp3AcDQ0FBw5ffv3x9vb292bt38Ha5ciZLs8z2CvHIqwAIxYbRs2VLhrfr163Pnzh08PT3x9PRk1qxZuTRLJb8iShH+RlRVVQWrGMDR0ZHk5GRhm7+/PzY2Nj9kLjKX1adPnzh27BiXL1+me/fuiMVitm/frhAkltmNSCKR4PfmGb6vPTHQUuXtfy958OABIK2kVaJECQ4fPgxI2zc+u3eLsdNmAdJCIzq6esLr1atXI5FIKFWqFG3atGHlEmei/t96UZkr/Hfwp/+dvyalStbaMCQ8gk6dOgHStKVBgwaxbNkyZRWtvwilCH8jIpEIMzMz3rx5A8C7d++ETkpv375FS0uL169f/5C5yK9fz5gxgxEjRmBnZ8euXbsoW60OxhbSwKnMBNjz0QPa1qtG2/q1adegDm5nXTm1Yy0g7UusqalJdHS0MF5fX58rV65waNd2Rk2S5jO77NpCnjx5ADh+/LiQ0jV48GDi4uJYv8yZMH9lFS0lvz+pBTgzK1gc6E3cRy/OXHRn7zFXPnl7UbHzCCFXePz48QwfPjzDSGklfyZKEc4F5KMWt23bhpWVFdra2rx+/ZqFCxdy7NixDNd7cpNixYoxYMAARCIREydO5NSpU6xevZri5asIYzIT4LCQEAZ374RIJGL9+vUUKlSIi8cPUaaatOG4iooKfn5+Cv1PZW3Y1FRE7Ny4lrKVqyORSKhRo4ZQqOPVq1cA5MuXj1atWnH48GFKlSpFmP97hWYXSpT8TuTEAhYHegOweNV6WnfvR68x/2BZtz0m6hKaNm1KmTJlWLx48XeaqZJfGaUI5wKLFy8W+uu+evUKT09PChYsyPv376lZsyaduvZg1qxZbNy48bvPpVevXkgkEjZu3EiLFi2oUaMGvj7SRg1ZrYWdPuZCRHgYa9eupWbNmhQoUICIiAgObN0ASNs1Ll68GFVVVYW2iU2bNsXe3p7w8HBqVCxHo0aNOH36NIsXL2bBggUKOc+amppoaGjw7NkzSpUqRalSpXj27JlSjP9Q/nRXtDxZrQWrxEXw0lu6PHXx4kUKFixIXiN9XAY1okqVKoSEhPygmSr5lVCKcC6goaFBmzZthJ89PT0xNDTEx8eHS5cu8erlcwA2btyYYcOE3MLQ0JChQ4diamqKiYkJMTExma4vRcYnC/9evXyFra2t0C4xf/78eHh44OnpKRzj8uXLxMbGsmnTJg4fPoyZmRktWrTg7NmzAIwbN46SFaugoqLC06dPqVSpEhYWFoB0jfj06dNUrFhRodOU7LVSiJX8rsgEOCEhgZEjR2JjY0ORIkVo0aIF08aPIvDDW15GqWJiYoK3tzeHDx8mJCQEQx0t6szbw+bNm8mfP7+wrKXk70GZovSNSCQSduzYwe7du4X31qxZQ2xsLHPmzGHKlCkUL16cEiVK0KBBg29e73F3d+fixYtYWlqSP39+jI2NsbS0xMrKiqSkJGnOslhMREQEsbGxjB49GpWEaCBvlsdWTYxWKCoi3wN506ZNjBs3jtq1awuCbGdnh5ubG3v27AFg8uTJODs7k89AFyPjPIIFPHDgQBwdHRk4cCAAw8dPESwk2c0rtRDLi7QSJblJdvKEs3MMGZGREUweORhXV1fCwsIAaN26NQYGBrx7946VG7exbM0GurdsiE25aoD0+wRQzd4Wl83Spapnz57Rtm1bOnTowKBBg8iXL983zVHJ74FShL8SiUTClStXWLlyJV5eXhQoUAAbGxvKli2LpqYmRYsWJSgoCLFYjLOzM5aWlt98zsePHzNq1CgA8uTJQ2hoqLBNJBKhqqpKUlISampqdOzYkTZt2mBnZ4exRaFs5QJbWFhw6tQpxGIx6urquLu7C9s+fPjAxYsX051XcnIyjRo1ws/PD5B2aVq4ah0eF86xf8dWNm3ahJubGzY2Nnh7e7NmyUJWbt6Z7rGUYqzkV0degFNSUhg3pL/gCZIxffp0NDSkHZciIiLYvXs3F0648OHIGUY3qU4pEwMi4xNpoaPO5RYtOHXqFG3atOHIkSMcPnyY48ePs3Tp0p9S5EfJj0Upwl/Jvn37hMpaVlZWQoTjopVruX/nNvPnzwWka7S5IcAA9vb2lC9fnjdv3tCuXTvy5s2L76fPxCeK0dDUQlNLkxKly+FQvBDFylUWhDe7T/3FqzoSt3w5Xl5e2Nvb4+XlBUDDhg1p0aJFulZ8fHw8S5YsAaQPAm3btmXt2rXcvn2bNdv3Mnn2Ao4e2MPsSeMoWLAgAMbGxsL+GRXtUIqxkl8ReQGOjYmhUqliRERE4ODgwOvXr4mOjqZVq1aCAIN0iWj48OEMGTKEtWvXsnbHDvT09JgwYQJRPg/o06cPp06dEnLtW7VqxdOnTxk5ciRbtmyhSpUqaeah5M9BJMnGImVkZCSGhobcunULPT29HzGvX55OnTphZWXF7NmzOXTsJEud51OjUQvmLFlF88olMDAwoESJEixYsCBXf2deXl5CEwkzMzNSUlIIDg4Wtuvo6lG4aDFsixWnmJ09LTt0Jq9Zxm4teWs4MSGBDnUcyJcvH7t372bBggXs37+fxo0b8++//wrj9u7di4mJCQkJCVy7do3z58+ze/duajVqwdPHj2jZUPr0Pn36dLoNH8/ngE+MG9QXUzMznBo3p1GLVuTR/ZKznFVAi1KIf09+5aCsr3VHy4vw1XMn6dq1KyAVWmtra7S1tdm8eXOmy07BwcFMnTqVW7duAdKHV5FEQqUqVRA/fEE1kRFBkkSeFTXh/fv3rF+/ngoVKnzVfJX8PKKjo6lWrRoRERGZdtJTWsJfSUREBObm5ujo6NBx8FjKNOmIsakZni+fERcXx7Bhw+jVq1eun7dIkSLcv3+fhIQE9PX1AelDkpeXF4GBgXz69Im3b9/y/tVzThzaz79zZrB+90HqNWyc5bE1NDUZPHEmM8cMJiIigmHDhvHq1SuKFCmCu7s7q1atIjExUWjvKBKJKFy4MJMnT6ZeM2lgmt//I7F19fSpWbMmAIUtC7LnxBd3XU6LGyitYiW/CrJuTC67trBs2TIA6tatS9++fSlZsiSqqqpZxn2YmpqyceNG3r9/z9atW7GysmLv3r34+Phgm6KKX7KYfGiyY8cOunTpwsCBA1m5cqXwfVLyZ6EU4RySlJTEqVOn8Pf358MHqeC8DI4BXamLdeOCfyhcuDCtW7f+bnPQ0NBQcHcZGBik23d4w4YNrF27lr0bV2dLhAHE4kTy5s2LtrY2Ghoa7Nq1C4lEQocOHRCJRDg1boo4MZFSdsWoWrUqRctWUti/RZt25LMuRL78BTA1MwO+WB2JiYnMmTSWm1c8SEoSs3H7HipVrZbtOtJKMVbyvchJ3+JTB3czevRo4eelS5cqfB+zg0gkwtbWlvnz5wPQoEEDtm/fzrnjJ4knHEPUiKzcmi0ee5k8eTLDhg3j9OnTuba0peTXQSnCOeDDhw8MHz4cb29vGjduzIQJExS2+189zsuHd9m4ceMPqxedEa9fv2b9+vUAjB07Ntv7GRgZExQUxJs3byhZsiQgLVXp7+/PokWLaNUlc+teJBJRpUIFhZtZwEd/rnu4oauvj8u+3djY2PDR3489WzdQqWq1HDdySJ1brBRjJd9C6paHWQmxubm58Lpv376oq6t/8xxsbW2ZO3cus2bN4vHjx8yaNQs3748Y1OvF6rsuVKtWDXd39+/iXVPyc1HmCWeTqKgoITL54MGD/Pvvv5iZmXE2RIvA6AQCoxN4+/YtahqaVK9enfDwcN6+fSvse+bMGaG6VGZIJBJu3rzJ4cOHiYn5ujW15ORkbt26RUpKCg0bNmTevHl4XDiXrX11dKWCuHattFRlSkoKy5Yto3Tp0hQqVIjp/0zG4+pVosXJvPXx5d1brwyPJRaLmTF+FI2rOTBt7AjGDuyDiooK3t7eAOTNK02b+pq1Q1mhD1DmF/+K/MrrwVmhp66a6ZJJ3rx5UVNTY8SIEYwZMyZXy0yqqqpSoUIFRo0aRRCJ7NYOon///ujq6rJnzx7u3btH5P9rryv5M1Bawtnk5s2bvH37lj179lCiRAnOhmilHeMVgLGhdAF+586dbNmyRWH7+PHjM3ySffjwIQcPHuT58+eCm3v16tUMHz6cMmXKkJyczLt37/j06RMg/bKqqamhra1NgQIFMDY2xsDAgLdv3/Lvv//i4+ND3rx5uXDhAmpqagzr1YXdx89QoUo1hfPK+qTK2LZ6KQCNGjUCwNvbm5cvX1KoUCHatm1LfHw8p1wO0axtB25fvsR///3HklXr6Ny9Z5prenD7Jod276B79+507NiRu3fvsnXrVnR0dHC9+ZDwAH8++vlR4P9R01+D0kX96/E7CPC35ApHR0eTlJSEvb19Ls/qC05OTmzdupUXL15w/PhxIiMjiYyMpG/fvgCMHj2avn37KutM/wEoRTibnDx5ElNTU8FFK8/DO3c4vXgctpXqEBQUREREBL1791YQYUtLSxo3Trsum5KSwoIFCzh48CC2trZUrlyZmTNnYmlpyfI165k3b55ClS1jY2NEIhHJyckkJSWRkJAgtE6Ux8jICCsrK4YOHUqrVq2oVq0aj65eSiPC8kRHRvLpwzvq1q1Lq1atALCxsWHo0KGcO3eOjh070rx5c4YMGcK+bZsYMGAA5cuXZ8u6VVSuWg3bIkUVjqetI7WqW7ZsSaFChShUqBBBQUEcOXIEcaAPjWrXIiIinMu3H2BRJe2adk5QuqiVfC0yMU5t/Wa0TmxmZkaePHnYtm0bNWvW/C5CKBKJqFy5MpUrV6ZevXrMmTOH169fEx4eDsCKFSvQ0dGhS5cuuX5uJT8WpQhng+TkZK5fv86IESNQU1P8lSWJEznpPJrYsGBeekib2d+6dYtChQoxadIkFi1aBICvry9Xr15V6PULUuv44sWLVKlShU2bNqGZ30bYtmxTZcbM+Zcg37ckSlSxtLZFP9Vac1JSEkEBnwgNDsT93Cl2r19FjRo1WLNmjTBXsVgsRFOLA31QN7MS9pe3grevXUZ4eLjgdgdp04YhQ4Zgb29PbGwskZGRtGvXjl69emFgYEBoaCh9+vShY+vmHDrnjm1BaYlKPXVVwgL8AWn6RlxcHMnJyejr60tfa+gSEREOwM6Na6hTZVuO/y6pkbeKlWL8c/gdrOCMyEiM5dFTV0XPuiijR49mxowZxMTEfPe0TWtra7Zu3QpIv8uenp5MmDAhV9ailfx8lCKcDVRVVbG0tBTycR8kGNLEJIKzIVqoqqljX7kWD84fQ0tPn8KFrPnnn39ITEwU9u/evTvu7u4sW7ZMIZH/5s2bQhWq2rVrK5SMBKlAGhoZY2hUMcO5qampYV7QEvOClpQo6wAS2L1hFTNnzmTChAkYGRlx8+ZNQLqWlZEAA+j833ItUKBAmvPs3r2bu3fvCj97eHiQnJwstHK8ffs2u1cvYdy8JcJNzMpG2jpxy5YtuLu7o6mpSePGjdHS0qJNYycAjIzzCGvEuYVSjJV8C6nFOD3Xdbly5RCJRJw8+SVX+Eegrq6Og4MDbm5uP+ycSr4vShHOJoUKFRICrQBhTVgkEjFi/kqiJsxEW0+f2MgIbh3fS1R4GIVLlsEwIZTPyZp8/LiHqlWroqamRnJyMosWLWL//v3UqFGDGTNmUKBAAQWB/BpEIhEjpsyiUJFiLJ05ibNnz1KyZEkeP35MmTJlqFSpUqb7N6ldhY3L4rh7967QFSowMBAnJydhTKlSpWjdoQsP7t3BwNAIDQ11Du7djZqaGmXKlFGwIkqXLceIiVPZvn4N0VHSYJKbN29StGhRbt++LS1oYm9HaGhottOUckJqMVYK8ffld7aC00OWE5weLVq0oFGjRixevJiPHz/SsmVLihUr9oNnqORPQCnC2URFRYW4uLh0t0kkEpLEiZjqaqFqoEuh4RN49/IZo9rVVxhXunRp3rx5w6xZs3j27BmDBg1iyJAhaJkXSnPM1FZqTmjeoSvV6zXg/AkX3jx7TOM2Henapjka+awz3c+8XC1KlnNg8uTJdO3ala5du2JsbIyampqw7tyvXz869BqgcIMaNX4yIpGIfHJ9hkH6UDBp8lQGDB/N7etXGdW/JyrqmuTJkwc1NTXmzZuHWCxm3LhxvHjmiUWtql99zZmhXC/+/vwpAhwZn4yB1hfhlX3O07OGR3Vrxf3799m/fz83btzg2LFjP3q6Sv4AlClK2SQ0NFRIqZHHTE+T7fMnMaZJZdqWtWTNzAnk09WgmHXaiF+xWMyWLVt49uwZzZs3Z9CgQd8swBKJhM+f/ElOVtwnj2leuvQbwpL1W+g9eFgaAU7vHIkJCQybNJNWXXqxe89eGjRowJo1azh69Cg7d+5EU1OTy5cvp6lJnd/cPI0AK8xFT4cmjRoxZsoMylaoSEhICOXKlUNbW5tXr14B8Pb542xf89eQOqVJmdaUe/wpAixD/rshbwlHBweycM5MRHERTJkyhe4jp1C6sCXGxsZ4eXkpXcRKvgqlJZwN/Pz8ePz4sVDdBqTiK8sPjo+JBqSRzu7HDzJ87ATyFSjI+YeviY+PY+Xcadxwv8COHTsAaYH2efPmpet+zokAnzt2mA1LF/DR9wP5LQoy8p851G/WGkDhaT47JPq+oGuXLvj6+lK+SnXWHzzJTY+L7Fq3km3bpEFTRkZG9O/fH8h56UmRSMTQYcMB2LR2NQtmT2fAgAFoa2szefpsBgwYkKPjfS1KF7WS7JDaIgY4uHcXa1csRZIYx6ZNm7CwsECibcjnz48AiI2N/RlTVfKbo2zgkAUSiYQRI0bg6enJxov3iZFIn1sCoxMAqRjHx8Zwadc6XLasBuD06dNYW1sTZ2AhHCcpKUlYF7XMbwrA9g1reOn5lGHjJ2NjWzhLAf4ck4hEIuH4hiVcOXOct2/f0qBBAxo3boyrqyvu7u5sOHgKh6o1shTh1Oc67bKfOeOGMXXqVHbu3Im6ujqHrjwkKiKCl56PCA0OxtGxnlCKEnIuxPKEhYXi/e4ddvYlKGKRda/j74XSRf1t/M5WcEZ5wv89f8aOTevo3m8gVVI1Tjjjsg8bGxtat25NnTp1WLx4McHBwbx8+ZIaNWqkCa5U8veS3QYOyk9MFnz48IErV64wadIktLR10h2jrqGJBAlmFpZUqFYTgyLlFQQYpFHMRsZ5MDLOQ1RCCm5u7iyeNY1TRw5x47J7pgL8OSaRzzHSaGv/q8fZtnopmpqaLF++nNmzZ6Onp0dCgvShIPz9c7Qj/XN8nUXtpPnPLi4uQmNy7Uh/9A0NKVepGlaFbDl19BDhYaEkxMcjkUiIFicL/0B6U4tKTEIsFgv/MnrGMzbOQ/P6dX6qAEP6kdRK/m42rV7OsQN7ObBtEyDt8mVhrMv754+4cPoExmpiYmNjOXv2LIGBgZiamlKrVi2lACv5KpSWcBaEhYVRt25djIyMyJcvH71798agagvECfEY5JFatPfdz7Jm4iDymOUnNDCALUfPUaZC5UyPGxEexqwxQyhqX4oRfbuRbJp2bRgQxBfgw5uXjO/UhJSUFA4fPoyZmRlNmzYlLCyMwoUL06xZM3r37o1fvBr/Pb6LU+OmQhlKeTIS/BMnT3LH/Rwm+c2p37oz+QpaIQ72p1/bxoSHSNOzbGwL4/1OGiXepGUblm/eAUBwYCCjenfG89kzEv//QADQtmNnVm1QrByW21HQuYW8CCst46z5lazg1J+prOaWkRX80c+XFnWqERMdRYvWbTl57AggjecwMTFJU3r2yZMnSvFVki5KSziXMDY2JiUlhdDQUF6+fMmkSZMYUqcEk1vXJCU5mfLqYexZMAmAFk2kpR5jfP/L8riGRsYs336AoROnZSjAAGY66nhePsPUTg0Y3qoeefLk4fjx49ja2vLp0yfCwsKYM2cOp67fp/u4WZy4fJvODaozYWh/5kwZn+Z4GQnw55hEqjo1ZtT8FbTuOYjbbmfZt2YJ7epUFAS4TJkyfHj/jsqVpQ8YZ08eI9r3DQDLF8zmwYMHCgIMoKWW9iP2K9285VHWo84+v9LfMDcf6nw/eBMTLRXaPj27C++fP3+eqKgoFi9ezKxZs4T3lWUjlXwrysCsTJBIJArBWCAtZPHx40fEYjHlNcI5f/4i4eHhdOjQgdGjR/Po0SMWLFiA+fbtFC1VlmnzF5NAzirbxMXG8P7Nf9y67Ibb2ZN4vXxO1apVWbp0KbVr10ZLS5qjbGlpiZqaGjNmzODqrbuEhUfw4NY1wVtx/OA+3jx7wsrt+yhobZPt87tvW8rWzZuFoiI6OjqsXLmSK1eu8PTpU6Foh5OTE5qamgDUb9qCW9evEhEaQocefTAwNKRchUpUqVlbweqQrSPL38R/NctYWY/69yG9z87XWsHiqHBUk5PYeeAIKuJYhXakmzdvJk+ePNSoUQMDAwPu3r3LmTNnSEhIEL6PSpR8DUp3dCYcOnSIuXPn8s8//7Bp0yYcHR2ZNm0agwYN4ubNm5QsWZK3b99iYmLC/PnzqVChAp8+fWL48OG8fv0agCLF7Zi6aBWlymdc9Uqezx/96NywBjFRUejo6FCzZk06d+6cYaGNs2fPMnHixEyPOWXKFHqNngxkbgnLuLz1X5YuXYqpqalQJUyGqakpVlZWzJgxA7tq9bJ1TQBRkREsmD6F29euUKCgJau27kY9JYmoRDFHdm+nYuUq9OjYNk1Z0J+N0kWdPj/bEs7swS0nIqynrkpycjJzp09lx5aNQj78lClTWLBgAQDiQG8KOdTEwMCAnTt3oq2tDUiDLX+1z6uSXwelOzoXCAwMREVFBU9PT4KCgqjw/0hJWZrOq1evsLGx4dixY8I2c3Nzli1bJhxDkiRmdM/2iALfpj1BOiyYPIYkcRKbN2/Gw8ODpUuXZlrpqkmTJuzZs4eVK1cK723dupXz58+zcOFCABYuXJhhgJR80BdIrf8EA2m/1NQCDKCho8uSzbtyJMAAmxbP5diBveQxNeXBnVvs3b6ZymXscKpYmnUrl9G3WyfKV6iYbjOKn4nSRZ2WnynAsiCpryU9K3jq6KFs2bCWgQMHcuLECZo0acLSpUvZsGGDMGbm+JG8fPmSpUuXCp/R9AQ4OTkZsVj81fNT8vehFOFM6NGjB5UqVeLatWtMnDiRJk2acOrUKZYulbb7MzQ0ZMuWLcKTsQxra2tu377NvHnzMDU1JTIyMl1BS83nmETixEkYGujz7Nkz6tWrR4MGDYT2hRlRtmxZHB0dOXXqFPfu3aNy5coUKFAAPz8/YUz8p/fp7hsfF8vqGeOY2K0FLUrkp2s1e14/uKkwxtbWVnj90ecDI3t15sThAxnORyKRcP/2TR7fv0dEXBIjB/Tl5OkzAKhKUgBYt3SRMH78+PHUrFmTwIBPv2yQi0yMlVHUPx6Z8ObGsoV8Wp2euipXPdzZu3cvFhYW9OrVC1tbW+bOnUv58uWZMWMG8fHxqJvZ0LtrR6aNH8WhQ4do27YtJ0+eTFdsO3bsqFDmVYmSrMjRHW/16tVcvnyZmJgY4uLiCA0N/V7z+qE8f/4cBwcHmjVrRmJiImKxGHd3d169esXmzZu5evUqsbGxVK5cmalTpxIbG4uxsTE1a9bEMFVXIxmPHj1i2rRp3Llzh/Hjx1Mwi565Mmt0xOwlGBkZsXLlSmJjYwkICODChQsZ7hcbG4uHhwexsbHY2NgI61Pe3t6sXbsWgFq1aqFdwDbd/V+67uGCy15ePb4PQHRkOBdOHgVg8uTJHDlyJI0l/uzZMyYNH0REeFi6x7xy6QLdWzWhc7P6eJw9RVRkBJ8/SdOmqlevrjC2Vq1adOjQAX19fQoWLPjLirAMZUrT74+sDKW/ny89OrZBW1ubo0ePoqMjTUHU1NSkb9++BAUFKXQUmzFhNPv376ektTn//PMPLVq0YMWKFVy7dg03NzeGDRvG69evhRQ/JUqyQ44WNM5dcmffvn2oqKqCREJKSgrXr1/PUIh+da5cucL48eOJj48HpGs8s2fP5uTJk8KYsmXLUqVKFV6+fCnUjtbV1eX9+/cMHjw43eOePn2aqVOnYmVlxfTp06latWqavOGMMLOwZOnxqywaO5Aw37ds27YtjaUtz549e1i9ejV58+bl2LFjwt9CXsyuXbvG2/vXyVsqbS/hR4+k1X5cXV0Ri8V4e3sLN578+fPTrl07hfHydaTdjuyjbb9haY55/oyr8FozPozuA4dz6/IlqlevzpAhQxgwYABbt27l9OnTmJmZsXr1ai5evEiPvv2z9Tv62fzNXZp+tCv6ewXtffT3Z/r4USQnJxMXF0eVKlUYOXKkULmtevXqFC1alMePHyvs176qHe1Lz+fpf29ZvmkXx44dE9oMlrfMJ4yLjo4mJSWFffv2cebMGSpUqMCECRMEoVeiREaOzI7DbrdwuXyPyfOXUMWpCQANGjRg+fLlQrPp3wmZu0mGtbW1IMAuLi6sXLkSDQ0NDh8+zLVr14RxzZo1A+DEiRPCe/Hx8dy/f58lS5YwdepUWrRowalTp6jVsiMxeuZcczvPGufZ7Fy3gpCgQGE/2VptPl0NhbnJbg5isTjTNIiWLVsCEBQURPfu3QW3d8GCBVFV/eJ6e/LkifBatg78OSYRVVVVTE1NKVCgALa2tjg6Ogp9SlP/TfX19Vm8eDEgFWh7e/s084mMT8Ykr7SqVkHrQgQnqjJlaB9sbGxYsWIFKioqaGhoUKJECXx8fDhy5AjuHpfp0KED0+cs+OkBPznhb1sv/h3+NhnNUb6oDMC0iWPTeJhevHih8HObNm24e/cuN27cEN5L0TYEXWPKFC/MlqlD8Nk6nUv/9OHG7IF4zBrAgQPSZRp3d3dq1KjB2rVref/+PS4uLhw8eDC3LlPJH0SOoqPdn3mjp/8lyuuTny+Hd23h6J7tqKqIGDRoEK1atUJXV/e3aDidkJBAUFAQvXr1IjDwizBaW1vTpaw1Jx695pXfZ2pVLEOZsuUxzGeBj48PkxetZO0SZ9YtW8ykSZN48eIFFy5cENIV9PT0cHBwYOLEiew+epoLxw7y6dMnzM3NCQ8PR11dnUaNGvHS6z0vnjzEpkgxitiVwL50Oao274ieoRFeL54yo087HBwcaN68OdbW1hgYGKClpUVSUhLTpk0jOTmZe/fuoaKiwtSpU1m1ahWRkZFoaWnh6urKhAkTePToERKJhNZdejLVeQUv3n1Az9AITS2pdT2nVwvi4uJYv349RkZGANy6dYubN28yduxYEhISOHHiBOXKlePp06fMmTMHADc3NyxKKUZ8yyKv/Xy8Gda1DZ98PwDSVKo1a9YIa8uXL1/G1dWVO3fuEB4ezqNXbzHN+6Uc5q+WspRd/nSr+GeIcE4+C5nNL3WP4LqVy+Hl5QXAjh070NLSIjAwkDx58lC2bFkAQkJCGDZsGK9evWLq1KlMH94XAJW4CIiRupyTQwJIDvty70hKTsag7ViFc584cYLQ0FCKFCkifMeU/PlkNzr6q0VYVps4Mj6ZsJBgNi+YyvHjxwVX5fjx42nXrt1vkdLk4+MjWLcy8uTJQ5kyZahatSoeHh7cuXOHDRs20LyjNIE/NCaOAd06cueqBwCjR4+mYqO2dG9SWzhGwYIF+fjxIx06dKBly5aUKVOGsLAwRo0aJbiBJ02axOvXr7l//z6+vr5YWNmwdt9xClhasX/FXJYvX64wr+LFi9OxY0fmzp0LSJsqyCzWZr2H4rpjHQB16tTBy8sLXV1dYmJi6DzqHyrWdqJHzVIkJYnZddUTY9O8LBnSiStXrgDQvXt3WrVqhZ2dHUFBQWm6Rk2ePBlXV6mr+fzth1gXKqywXSbCVw5tZcKECWzbto1ixYoRGRnJ8+fPKVq0KGZmZrRu3ZrAwEAKWBRk5ozpNOvQTTjG7yrAMv7UlKY/QYBl6KmropkcR/UaNXnzOm1hnerVqyORSFBVVaVGjRqcP3+ex48f8+K2B0UK2QD/F2JII8ZehepRpkwZ4VhWVlacPn1aWdTjL+S7inBGzQECPvpzcPtm1q+SCkfx4sXZvHkzxsbGX3kZP56IiAjU1dUV1m5iY2OpUqUKgwcPZtiwYUL3o0uX3BjerS1F7Ety7NABvFXzMrJVHd6/kX6xVVRUaNq0qZAqJMPNzY3Ro0dTqVIloUMRSJ+Yp02bBsCgsVPoPXwsn/x8kEgkPHt4n/s3r3LD4yKhwUGAVPiPn3LF++0bDIzzcGDvHgK1zbl8+ggnd23GqkhxBv4zH0vbooDU9T1rUFceXvdgwY4jlK5cA7/3XgxpVlNhfvXq1cPDw4MOHTpQvHhxqlatiq+vL8nJyQwfLu2EdPToUQpWqKvwWZCJ8PPHD+jTqgGVKlVCW1ubGzduKLRaFIlETJ+zgIHDRqT7N/jdhRj+LDH+2SlJmZHduckL8csHdylctCh5TEzRSonnwoUL7Ny5k6S4aLS1tFETpaCiokJkdDTuN+6SkiKN6H/78AaWFgWE48gLcXJIAADJYYHce+hJXn1djsbnZdq0aZQsWVKI21Dy9/DDLOHU6KmrEh8fT1zIJ+rUqUPRokXR1dVFU1NTWE/MKWKxmJiYGEQiEXp6egprnT+CqKgonJycKF26NHuPuZKsJl2/DQsJ4eyZMzhUrUkBS6kwx8fFcu3SeZbMmIi1lSXbtm3Ltmt+/vz5wpoSQLnK1ZizYiP5Lb5EVicnJxP8+ROa2jqoq6uTkpyMv88HbIoWQ0srbQCXfA4wQHJSEuGhwZiY5ScqPIzr509RskIVxImJTOrekoT4OFRV1RCJROQraMVnvw8KubsFCxZk48aNCkFe8l4RkIr90b07OHv0IBqaWtRp2JQGjRrifv4MEomERs1aYldE0YqWZ+fWTdy87Ea1atWYOHHib7G0kRF/gov6VxXhnM7ruedTZv4zmdvXr6KhoYGFhQXv30tT92xtbQkPDydfvnx4nt0PSNd/g4JDePbyP3R1tKlcobzC8dKzhp037+WZ90dWd2tIvDiJle+SWb58Ofb29hw6dChH81Xye5NdEf7qci/p9duUrbdoaWlRuFQp1q1bR+fOnYXtJUqUoFevXvj5+WFpaZmt8wQFBdGwYUNBCJydndO4jr83+vr6rFq1ihEjRtCtTTPWbN2NgVl+xg/owY0bN9DR0WXemq3YWBXAqpAtMWFBhIWGUKlihRwJyKhRozA3N+f9+/c0atSIefPm0belI0VKlMG8oCW6+gYgkeDv483zh3eFdWwDI2PyFbDAwNAYXT09dPUNKO1QiXbd+5BPV0NBiD/7+RAjUUU9PJRu1UsAUou9Xr16JMRLo7+bN29G/4UbAQgLCuSp51NMzQuSEhWChU1hzl1zp5JEh8LF7VHX0EhThUskEtG8fRcMjY1JSU7myoUzbF+1GIlIRJnyFQn6/Jm58xemcdGJxWJGDurHqeNHKVOuPK7/X/eeMWNGzv9ovwjKEpjfhn9YjCDE3/Iw8MH7PR1aNMbAwABbW1vy5MlDsWLFeP/+Paampuzfv5/r168zbtw4jtz5jzZ1pfXR85qaUK9W9SyOLhXg2PgEnA+eJy5RTP/GNWj0z2r09fWxsrLi5cuXuLu74+jo+NXXoOTP5JtqrqUnxPJ06tQJ/zfPGTddun65dOlStm3bRlhYGAcPHqREiRJZnsPNzU3BEqtXL2eVmnKLqlWrsm7dOiZPnkzjOtU4ft4dkAp0VFQUY/t2VhhfunRp+vXrl6Nz6Onp0bdvX+HnFStW0KFDB4KvumNvb09sbCwikYi8efPSokULihYtikQi4ePHj3z+/JmoqChiY2O57XGbs0cPEuDvS6NW7TGwkrqjgz75M6hpdTS0tDEraA1At27diI6O5sSJE4hUVJCkpPD0lRc2yUE8SDAEXSPqONZHIpEQGvSZ/euWce7QLkAaIX34ykM05WrnSiQSVi+cxaXTxwjwlxYL0dTUpGHDhtK171vXuXLpPBMnT8HAQDG1LfBzAGdOSSPOnz6WrpkXL148R7/DX5XUhT5+FzH+FSKiv3UOFsa6zJ4kDVps0aIF48aNEx6OO3ToQMGCBdHS0sLaWvqdyKo4jjwvvd7hcuwkrevXooSJDp/PbEUikXAzyQwNjY1ERUUJnZfu3bunFGElafim6GgZ8kIss4ZlT6+RkZEM69ONWuXsWLBuBx8CpCk0Li4u2brBXrhwgXHjxgH8EjnJYWFhdO3aVaEaFYC2traQRwzSeZubm3/1edzc3Jg4cSKJiYmIRCIePnyY7Tq1ZcqUUShTuWrVKgrV78iN86dwHjMAW1tb3r17x8aNG4XiGT4+PowbNw5vb2+WuVxEJ7+1sH9yUhK75o7niqu0iIeamhply5blwYMHrFixguptehL8OYDDu7YSHxfL8QO7iYuJpnDhwgQEBLBnzx6KFClCZGQk3bp1Q0dHhwvX76Y794/+/jx6cI9PH/0pUrQ4dZ3q/xFrxPL8TuvFv4IIfysFjHSoWrWq0HjE1NQUV1dXhbgPiUTC8uXL2bFjBy9vX8bWxirTY8pc0d0mLRRSj9auXUv/0nmEMR2W7FeoOWBpacmZM2dy7bqU/Nr80NrRmTWkNzAwYPeRU/Tr2Z2yJb6Ibp8+fXB1dSUuLi7dusaypvCy5gRjx4796QIM0taGs2fPBqSFPJycnBgwYECaL1fqnMOccOPGDWbMmIGmpiY1atRAIpEwbdo0nj9/Dkhd9PXq1ePSpUvp7t+4cWMGDx6Mh4cHVapUYfv27QBUa9CMjiOm8u7dO7p37061al/Wda2srFi2bBkJCQl4uu7j/L4tPLx8gaQkMdO7NhIEuHr16jx69IjNmzejp6fHy5cvAVg7ewLb1yzl1MHdGBnoU7RoUVxcXDh//jxFihRh69attG7dmuDgYIXYAFn+puxffnNzyjtUpH6jJlSoVPmPE2BIm1/8N+QY/ygiIyNo0aAedlbmHD9yiB3rVmBvb8/du3eFqnV58uRJU5nt+fPnbN++nYlD+mQpwPKsnfqlWM3Tp08VtmlESZeLVFRUWLFihXDfUKJEnlxrASJzTUeLk6Ul4eTWckAa5NC/R2ccqzpQom4L2rVrx+TJ0s4+7du3Z+bMmQrHGzJkCHfu3BF+7tGjR25N9ZupXLky165dw8DAQOHL7ObmxoYNG3BwcKBs2bIkJyfnKIgsMTGR7du3s2bNGoyNjenUqROdB43k4K7tnD12mG7dutGgQQMePXpEcHAw169fp379+mmOIy9y5cqV49ixY9gkB+Gtmpe4mCjMzMyYNGmSwj4SiYSXL18ikUjYsmWLUBf34sWLBHi/pW3btgo3kX379hETE0Pjxo0B+PDhA23atCExMRE3Nzc+ffrE/v37+fTpEw0aNGD9+vXY2tqyZstOittLlyGixckkJSXhfv4MltY23Lt1k+3rV/NJzo09eMRopowfg4mJyR/XsUbmogapGP+KVrGFse5vYw3HxsQwYdhAHj24B8DwAX3R1NQkISEBFRUV7O3tOXv2bLr73rsn3adnu5Y5OqeRWjKXd62iz9SFDGyomDe/fuIA9Kzt2LFjByYmJpQuXforrkrJn06u3tXkhRi+uLIsjHVRN7OhcdPmqAR7k+j1FCM1iPr/fufPn2fatGkKghUSEiK8njRp0i93A04v6d7MzIwZM2YQFxdHzZo1qVKlCitXrhTmLh+IFBAQwMSJExGLxRQsWJDExEQ8PT0JDg5m4MCBjJmzmKgEaWpEn9FT6DFsPHu3rOPKeVcq1Xbkptt5YmKyvjmqq6sTGBhIZGQkIZpST0JwcDBHjx5FU1NTqLDl4eHBgwcPqFevHp+jEwn5/BHbkuXwFBuSkpIiFDAA8Pf3Z82aNXTp0gULhzoAfPz4kdq1a/P27VuKFi1KdFyC8DCwe/dudHR0mDt3riDAAJqkMLRvD9zPf/EiNG/enFI9uuPs7ExCQgIrlyxi5RJps4eqVavi5ub2R5X++9VLYP4uAnzq2BEmjRlBbGws8+fPx8nJiTdv3lCsWDG8vb3p1KkTFy9Ke3+n9929dOY0APms06+xLkOIiJaha0zNCqV5c36fkKYkbEqMpnHjxuzYsUMwIh49evTL3cuU/Fxy/dMgH6yV2ipWN7MhOS4CjSJl6FK/Gscev0dPT4+wsLA07qHNmzcLQVg5CZT4FQgNDSUxMZFr164xfvx4dHV1uXTpEsuXL6dChQrcuXOHqVOnEh8fT40aNQgLC0NDV5/GrdrRvkUTzMvVEgRYRkiihF5DRtFryChe+37k7NFDVKyYdY9iU1NTQJrrjCY41G3IffezgudBW1ubpKQkSlSoyj+rd1DFsREikYiQWGlE9UdvaVUhZ2dnzp49y4IFC9i7dy/x8fH07NkTgKDPnwgNDcXc3JwrV65QpEgR5q3dyounT8hfoACux49Qx6khZUuWICUlBdcTx9DS1qZ6zdoKAgzQqlUrhb+3sbGx8CBx+/ZtdHV1adu2LRs3bhSu7U/gVxfjX5n7d24zpF8vzMzMOHDggJB5Ua5cOUCalSEratOnTx+2bNmCiYkJAJ6enhw4cICnr15TuXJlDPT1Mz1Xivb/a7PLi7GusZCmJI+qsRl21nYK7509e5YWLVp87aUq+QP57o9k8laxhbGu9EOsbciCfxfTI0qVdu3aUaFCBQUrUSKRsHr1auHnESPSL+rwK5KSkiI0QBg4cCA7d+4kISEBgMGDBwvusRIlSrBp0yZMiyq6qDJbX5elGt12P09ycjINGzbMdC6y4Ks6dergbyxdjy9UoiwLXdxJTIgnWSxGXUsLXYl0/d3IxFT4O5joaOD33ouH544BEBcXx+3bt4Xozl69epHHXprGoa6ugZq6OtOnTwegQgVpalbZCtKHhL5DRpASF83c6VPZuHaVML/Ro0cLr3fu3EmvXr34+PEjZmZfSlheunSJGTNmCJW6qlatytGjR7GwsBA+O79DVbbsklqMlUKcNXaFpGu9gYGBfPr0Kd30x+7du7NmzRq8vLyoW7cudevWZeDAgXTv3h1jI0NmThrD2KmzUJcLoBEHemd/ErrGyC88yaxi+8iXCsNcXFyUIqxEgVyJjs4M+cjp4maKH/D/AqPp06cPHz9+ZO/evYJlc+7cOSZMmABA7969hejor+HZs2eMHDmSf//9lwoVKnz1cbKLrLpWkyZNWLx4MS/9gujYRDEt4ejRo9hXd0yTJ5sdAQa4e/kCc4f2xMnJidGjR2NjY5PuPj169OD58+ecPn2aTyZfmi18fP+Gx9fc8PN6RXjwZ14/vEtSkpgC1rY06dQTDS0t7rif5+F1DwwMDHBycqJjx44MGDCA6OhoNhw8hUPVGgrn+vD2Dc/uXefSWVdmLVqGpU0h6TVFhLPCeR7HD+wjNjZGmNfHjx+5fv06IhUV4uPiePjwoUJOdUBAAK9evaJu3bo4Ozuzd+9eaRu5kiWpU6eO0C6ub9++QhebP42fHUX9u7iiQRqQ1aVNC8JCgtm3bx/Pnz+ncuXKgus3OTkZJycnYZlLXV1diHuIjo5GIyYIdTObDI+fniCncU2nYw0nhwRw+9lrxizbQnCiCB8fHw4cOEDJkiW/7kKV/DZ894pZOcVASzVN+hJIS8GVq9OYrl27MmKEdE2nd+/evHz5kjx58uDq6vrVlo5EIhHquE6ZMoWuXbt+1XGyg5+fHz179mTEiBFCcQkPDw/MSzjw6pYH02fNoWqtOvQbNhJdcbRQ+lJGZgIMaStf3Th/ik0LpxMW9JnSpUtTtGhRZsyYoeDWnzlzJk+fPuXYsWPSnF/A4/wZ9s4cjrq6Ora2tsTExNCoUSOKFy/OhQsXuHjxIgC2JUrTp0tHGjVqhIaGtELYrFmzuHnzJkeuP0l3XSt1znhkRDjdmtXnzZs3AFhYWLB8+XLs7Ox4/Pix4M4GaaBXRoErEokEiUQiXNvEiROFAJt69erh7u6e6e/ud+dniPHvJMCyZa9HD+7TqpGjUGYSpFkYY8aMEdqLAgwdOpR169bRrFkzKpQsxuxFy7J9rtRirCDEGYiwjBf6dtSsWZM2bdqkCYxU8ufx3Stm5ZTI+GRhvVg+crqwQw2qVavG5s2bcXFxITo6GjU1NfT19TEzM0NTU/ObzisrppEnT56sB2cTsVgsWG1isZhbt24xbJg0VUEmwKampujr6yMO9MGuWj0On5cvMpLzuaSufFWjUQtsK9fh1tljHN+0nKdPn/LkyRM6d+5Mx44dEYlE0rXm/wtoYLTUJX7lwBaSkpJYv349VatWVThHgwYNSEhIQE1NDVVVVUJCQujYUSrEFy5cICQkhLCwMEJe3CFfmS+W8LNH9/nwzouC5vmoUddREMtVixbw6dMntm/fTp8+fWjXrp3Q/rB8+fLMmTOHGTNmoKamlm5bRBkikUjwGnz48IHixYtz9epVYmNj+eeff/j8+TNBQUF/rOv2R68X/24CLPvfOE8eDh47zfYtG7lx7Sqt2rZj+7Yt+Pn54eHhQZ48eShcuLCQEtm7d2/at2+fo/PJrGWZGKdoG6a1iOVQNckvCHGJqFf06NGDDRs2MHDgwN+qpr6S78cPD9OTWXz/BUYC0uIe67bv5cRRFwI/B6CtrYNDaXuOHTvGjh07eP/+PcWKFfuqc4lEIqFaTXaCmLLC29sbZ2dnbty4gYmJCSkpKcTFxREfH4+hoSEREV++jK6urmhqamJsUShNF5f0MNBSzdIaTi3EGppa1GndhTqtu3D31k2uHNjCvHnzOHv2LK1bt+b+/fsKFbgAvD3vA9I0pqNHj6Y5h/xDj7OzM2/fvuXly5dC2zcDAwMCAwORtS933bZSIXVp1uJlNO3UGx01CccP76dQoUIULFiQW7duoaurmPPbvHlzZsyYQZ8+fTKNGI2Pj+fhw4ds2rSJBw8eoK6uTvVadRg8fBTlypWjUKFCREVF0bt3b7Zs2fLDa4v/KFKnNMne+5vRU5dmYzz2OEvnzp0pUao0F67eErYXMDNlxYoVVKlSBVNTU06cOMG9e/eYO3cu7dq1++rzqpvZpHVRZxCgJU+NGjXYsGED/v7+ShFWAvwEEYYvQiykM6lr0ryTtJ2dnroqFsa6qKiosGPHDlavXo2Xlxdqamo0btyYoUOHZrstmI+Pj/A6M3dAdkhMTKR9+/YkJCTQo0cPjI2NUdXS5XNwMG07d+PBnVssnD4FgCpVqqCjo4OxRaEcncNAS5WE+Hj8fD4QGPCJD+/fgkhEw6YtiU5MISEhnrz5zAmKk5bxNNHREKKYbUpXwKZ0BV7fu8a5LcuZPn06RvkK0Lp1a86GSMtK+r+RFvvo3r17liU1Hz16xLlz5wBFYY6MjKRfv35sPl8KcyNdnJ2dadKkCY6OjtJ1/Fjpw1VgWBTRkZF4enoyePBgqlSpQrNmzRTavKmrq3Pnzp1M62snJyczdOhQ7t27RzH7kizbuA37YkXx9fEhLi5WIUJ6165dlCtXTgiM+xNJXYs6t4O3ficrWIaeuqqQ5xsdFaWwbfjEaQyb8A8F8+jh7+9PmTJlqFixIi1atMi19oIK1nA6Qqxqkl94XVLPEH19fc6ePfvXP0ApkZKjNeH7Xr4Ka8JZWW7ZJb1GEAWMdNi1axcLFizg9evXwrZDhw5l6rqUIRaLmTx5MhcuXACkwV4WFhbfNM+jR4+ycuVKQkNDAbCzs8O6aHEKW1rQbehYXr3wxO/DB5q1bY+eXuapDulx8cwpJgzpT3x8PIBg0cm3AcyT14yGLdrQrntftPJbCyIsczfLCPnog65RHrR0vqynv9m7GBcXF9zc3NIVvv379xMVFYWurlRc1dTUhLrdenp6REdHC2P3nr+GsbEJHR0rk5iYSGJiIo1atWfa4lVCLenK1opu927dugkFWrJCVvTjwYMHHDx4kNatW9N31ES2rlvFicMH0oxv2bIlAQEBREVFcf369VxdfviVye314l9ZhGVrv+lxytWVIT06MXLcBCb+MzPdMbldfU0c6M3KXS5s37SeM7vWU9A835eNmVjEI9ceZt26daxevZq6devm6pyU/Dp8l8Cs1CIsbM8FMU5df1r+CzNo0CA2bdpEuXLlWLp0qUIKS3p8/vyZiRMn8vTpU0FEbt68iX4WOYDZITExkfv37xMSEsLdu3cJCAjg6dOnSCQS+vbty/Dp87P1hP3kwX1OHz3M9ctuREdGsmXzJjp37kz16tXp3r07ZmZm5M+fn8jISJYuXYqNjQ12dnbcuXMHV1dXQkNDyWtugb6hMSUcKlOncz/yFrBMI8afwuMxN9LiU3g8T88fxm39HIyMjFi2bBmVKlUSxvn6+tK0aVPh52LFirF161aaNm1GVFSkkGeZP39+wsPDKVioMKt2HiYyIpxrl85RwMoap6athPVgiURCFRsT4XgVK1ZkzJgxCpZwZrRr107h4Wvxpt3MHTdUWF6wt7dHVVVVECF3d3dev37N+PHjMTc35/nz5791C8Sckhvu6V9ZgLNDXGwsWtraGX7/vkcJ1BJ2xXj53xu2LZlDz/aZVNuSE+UXySY4OjpSunRpVq1alfE+Sn5rfjsRhi9CnDqKetiwYaxbt47BgwcLAVAZ8fr1awYMGICamhp169bl0KFDjB8/nl69euXKHNMjLCyMQYMG8fLlS86cOYNthRoZjn334Ab/rliNx4WzmJubC4Up2rVrx9WrVzl9+nSWFaESExO5cOEC7969IzQ0FA8PD+Lj46nVphutBowiGqn7+FN4vMJ+n/57woFJ3YWfPT09hdf//vsvu3ZJuyM1adIEFxcXwhIkvPN6w5Vr15g+bqQwtmLFinh7e5OQkMC+0xfJZyNdsw/6/Ik3L55RrnI1dHT1WDBjCsd3bkz3GkaMGMHAgQPT3fbo0SOFyOkOHTpw79491NTUqFixIgcOHGDs2LH06dOHXr168fDhQy5dukS+fPkYP34858+f59WrV39MB6ac8DVi/LuLb3b5HiL87tFNbt17SJtmjVBXV880SEteiKfvucDChQuZPn06HTt2zPV5Kfn5/NAGDrlFajGX3Rzc3NwAhKIXmTFnzhxMTEzYunUrp06dokWLFgo39O/Bnj17ePnyJXXqN8S6XNVMx44ePZpXno9xdnbm3LlzDB48GENDQ27dukWjRo2yVZJRQ0OD5s2bM3LkSGbNmoWrqysNGzbkxon9LOjeGL/X0rVfcyMthf3cl44VXpuYmCg0zujTp490H3NzJk6cKMzDtkhRdFIFVLVu3ZqjR4+ira3Nwc1rePXwFkM6t6J5lVKM7t2JQR2aEx8fRwl7e9r37I+Dg0Oaa1i9erXgdo+NjWXWrFksW7aMnTt3MmfOHGGcqakpTbsPwtvbm5CQEME6vnv3LhKJhBEjRjBq1Cjy5ctHREQE58+fB8DJyUlYJ/ybUDaG+LHYlq9Ot4HDBa9LirahUFUrM2Y0KkWnTp2YN28e9+/f/97TVPIL882BWbllBcsjiyaWlbxs1qwZXl5eDBkyJNP9xGIxz58/Z/To0UycOJHk5GQGDBjw1QEYsbGx3L9/H09PTxISEvjw4QONGjUS3LYSiYRt27axadMmRo8ezaDJszI915Z/5/L69WvWrVtHrVq1SEpKwsXFhVq1anHu3DmsrLLfvUUePT095s6dy8CBAxk/fjybRnZm3bp1eOb54m6WpKQQFhbGuHHjqFSpEiYmJgpzleVWBgUFCetUsuL9oiSxMG7VqlVCOdGCBQty5MgRDhw4QOHChZkzZw758uVj4MCBuKxdxKpVq9DR0RGWDzQ0NBCLxUgkEmrUqCEEfMXGxnLkyBEAVNXUUFVVQ1VNDTU1dRq368KT+7cBqcdBVqTj+vXrzJgxgzlz5giR7/IBZP7+/lSvXp358+cLnbj+FrKb0vS3WMA/YB7K+AABAABJREFUgvRSlyDj8pYqKipsH9ySN2/eMGrUKGbOnEmNGjXSZBDISElJ4dOnT98c16Lk1+OrLWFZ3m9uI39MWXu7Bu27oaauzpIlSzLd9+3btyQlJbFhwwa8vb3ZsWMHhQrlLEIZpOJ66NAhGjZsyLBhwzh8+DBnzpzB3d2dSZMm4erqyuLFi+nUqRMrVqxg5LgJDJ4yO1MBPrxpFUuWLKF79+7UrFkTkDY9CA4ORlNTk6SkJAoUKJDjucpjaWnJzp07KVWqFDNnzqSXzZffpX9YLGZmZnh7e1OyZEny58+vsK9sLTcpKUnBgrQw1qVciS8pYvJruo6OjsTFxWFnZ8e2bdto3bq1kKYls6RjY2NZtGgRR48epX///mhoaFCyZElu3LghNHhYs2aNMAeTPHlo1LABo0eNQktTgwdXL3Hnkit58uRJ8xB2/PhxBStCVVWVvHnzoqWlhZubG0lJScyaNUuIC/jbkBfe1Jbx3yjAP+Ka1c1sFCpvpbGMdaVpSaom+dEwK4CqqiqRkZGMGzeOhg0bCp/n06dPs3v3bkDq1nR2dqZx48YsX75coW+5kt+fX7Kdh3wKU2R8MoUKF+GfeYuYMX4UEomEli1bUrZs2TSiFxgo7d8ZHR3N5s2bv6p1WHR0NDNmzODixYu07dydASPHYGNbmNjYGOqUKU50dDSTJ0/GxrYwZcqWY/q8RdSuJy1LmVE+8L37j0jQMgKgQIECX+oz/7+I/JEjR2jUqJEgzt+ClpYW48ePp3Pnznh4eEAhaatDkYoKoaGhnDhxgunTp6fJpX3y5AkA06dPp5x1XoVt8mNPnTpF7969AejSpQv29vZUrFiR58+fs27dOq5fv07JkiW59DpQ2KdTp054enpStGhR3r9/z9OnT+k3cTZbF88kIiKCU6dOAdKn/VWrVgkl/W7fvo26ujofPnygbt26DB06FCcnJ6HAQr169YQesSBNeTp2TFrr+tChQ4hEIubMmfNXd61JL6Upp6lzfxLpCfH3WCtOnUecJo0JICaM+uWLCw+9kZGR9OnTBycnJ2EJ7smTJ1y5ckVYutm2bRsqKipCGl5CQsI3FzRS8nP5Kkv4e1jAmZ0nMj6Zxh16MH78eC5evEiPHj3o378/L168SHe/qlWrpqkGlRUxMTEsWrSINm3acPv2HVZt282ClWspVLgIIpEIXV09ztx8wM6jp7n37DXX7z9h3dadggAD6aZP3H/4hB7N6rJqnrS5weLFi4X2gbq6ulSpUgVLS0sWLFiQrSITsbGxbNiwgU6dOtG9e3d27NiBn5+fwpiSJUtSsmRJIc9XRlxcHElJSQpl/WTInrq1tbXTbJM9eTdu3Jg2bdoI72toaFC5cmX8/Pzo2rUr169fB6QN0u8f3aZwjNKlS+Pj40OXLl3w9/fnwYVjlKlRj4tu7qipqaGjo8PkyZMVauqqqakhFouxs7Pj3LlzrF69mqJFi1KtWjUA2rZti7m5ucJ5DA0NGTVqFKtWrUIikbBw4UIKFizI2LFj073uv4VSpUoJ4hvm/54w//c/eUbfjsxTJvv3tfiHxQj/cpPUtajTs4rnjurH7c3zFcbJBBjg/v37aXL6z549i0QiwdnZmYoVK3L37t1cnbeSH8tXiXDqvN5v4XNMovBP/r30xvWfMJ3g4GBWr15NSEgInTp1YvLkyXh7ewMIeaxfUzHp4cOH7Nmzh4CAAE6cd6Nt6zZIJBJuX7vC6aMuLJ03izvXrlK+YmXMs+k2joxP5uTBPdK5RUWSN6/UwpRZ7CAVNlNT03QF4t27d6xbt45BgwbRqlUrGjduTN26ddm8eTOFCxfGrKAVq1evpkmTJrRr1479+/eTmCj93RUpUoSAgAD6Fv5yvJYtpSkUssL18lSpUgWQ1of29AtV2Obs7Ezp0qVZvHgxhoZpg05kAg7Qpk0b5syZI9Tplefjx49CitHjx495esODhLhYOnXqxIULF+jWrZswdv/+/Vy5coU3b94waMI/xMbGsmnTJjw8PJg7dy5Vq1YVfp+pef/+i8BYWVnh7+/P8uXLKV68OI8fPyYbCQG/PfKCIi8wxhaFfmsxzkx05d//WmH+HkKcnhjLU7F6dTzWzBQ+zzVr1sTa2hqQPrQPHjwYMzMzOnfuLJ3j/4uO7N27F+CvzAL4k/jqNeHcEOL0xFbGi3cfWLJgDgf278Mn5Etwg6qqKnXr1sXFxYX1SxZw+/ZtWrRoQe/evYUP5Y0bN5gzZw7h4eFpjiuRSPjvv/+EbioyatSowdChQ1FTU2PmlIl4v3jM6oVz6N2+JeOH9GPHhjVMGDaAdcsWZ/vLbaClSqUatQHQ1NYhKCgIgO3btwtjPn78yKNHjxg6dKggDklJSSxdupRWrVqxfv16bt68ScuWLenRowczZszgn9nzsC1Rlio1anPc4yb9+/fHwrYYzs7OtG7dmlevXhEbG5sm0lrmup02bVqauQ4ZMgQXFxdSUlLYsGGDwjZtbW3i4uJ4/vw5586dE4RUhryo9+7dmzZt2tClSxeWL1+OiooKV69exdPTk6pVq3Lt2jWFfV1cXJg8ebIg7ikpKWzZsoUFCxYA0geWHq2aCOP37dtHvnz52Lx5c7qdaFJSUoRiKjNmzGDXrl00aNAAAC8vL8qXLy80f/jTycy6Sy3GvzLywpqT8bLXORXk77F2nKEQ/981Xb2MHc93OjNz5kzmFTXm2LFjXOrVnJL7dxMzZgRlypTBzc0tTbGbGzdupPtgrOT34ZsWy7JT7/hryKerwd7dGzi0TZpjumPpHOas2EDR+k7Al3WukgWM6N7ckZMXLrNyz1E+f/7MjBkzSExMZO3atbi5ufHPP/8o9N2dPn06J06cQE1NjStXrgj5WyoqKgwZMoQSJUowatQohfVZDQ0NwcL88O5thlV74EstWxmNq5blSseuXDl7ivXr12Nubq7wpenXrx+bN2/m3r17QqrR2LFjuXLlisJxHz16xNixYxk7dixv374lT548hIeHCxZ0AUsrOnfuLHQnkqio0uj/vy8ZshrcGQWQFS9enFatWrF7925q165N9+7SnOJhw4bRvHlzunTpAsCECRMU0r4cHR2F6OauXbsya9YsatasSf369YW1Zhmy6GaQrvnKR4MGBwczYcIEITildevWlC9fnpkzv1RAmjJlSrpzl+Hs7AxI/2bt27dHJBKxbNkyhfiAO3fuKBQm+ZPIqYAYWxRSsIh/tfXinAqwRCLJlXKU8k1mZK/lf7dfs46c2Tqxqkl+DAlggGYQzS/eodjTrvzbth6aJVSJ+RTC2IY9aNq0KUlJSVy/fp2AgABsbW3/qoI0fyo/rVhHais4n66024/Mwl4+YyI7d+7E2dmZ/fv38+rVK05evUO+/OboqqmgLo4hJSUFY2JRT4yGmDCSQwJIDpO6egPCIhm77RTHr96lcePGTJ06lejoaJo2bUqRIkXw8vLi7NmzCoE9Mry9vXnz5g3+/v4cPnwYHx8fLC0tKVu5GnWcGlCgoCXxkeFY2xTCrkRaayw8LgGv/14SHBRIcGAgB7ZuQCwWs2/fPoVWgzJu377NgAEDAKlFfuPGDUDa+m/69On4+fkxb948QLpOum/fPuzt7enYsSMvX76kffv2xMTEcPbsWWbNmsWt1368ef6UliOmITKV3lT9w2L5+OQ611eM4/jx4xQuXDjNPAAiIiLo2bMnPj4+uLm5Ubt2bSQSCT179mTPnj1MnDiRLl26KAQ7SSQSDh8+zNy5cxWO1aNHjzTpQcHBwUKK05EjRxSac8gKbwAMHDiQ4cOHk5SURJMmTQgNDSUpKQlHR0emT58uBLXJI5FIqFq1KrGxsVy+fFlhzKFDhwgPD2f16tUAvHjxIlvlT38nvtWCk7eIfwUxzqk7+ci+3cybOpH5K9bQtLVic4bMHpy/ha8R4wx7E8eEkfDZH11H6cOvz+75GIqlAVlHKETfvn1Zu3YttWvX/qY5K/kx/NCKWfDtQiwvwnrqqhhpSGsz16xZk2HDhtGuXTtMTEwoYFuM21fciPn/+q+GhgaFrC3Jl8cIDQ11VFKSEcXHoK+tiU0+EzT09FlzzA01NTXy5s1LUFAQCxcuZNCgQRw9epSiRYtmOkeJRMK1a9fYvXs3b968SePGHjBgADMXLSckOIhpE8dx+8Y1wsPDFVy0EyZM4N9//xWqOskTGxsrrMfKMDU1JTg4WJjftm3bWL58OSBtzXjz5k1CQ0OpU6cOIF3PVlNTY+jQoVy/fp2LFy/ib1ycwOgEhapZLQ2DadasGS1atKBly5bcuHGDXr16pXFbr1q1ih07dnDr1i0qVKgASNd9e/bsiYeHB8+fP6dmzZrC2nt0dDQqKipoa2vz+PFjXFxcOHnyJDNnzky3VdyhQ4fYsGEDS5YsEQp5xMfHC2U0N2zYQI0aX6qOXbt2jWHDhqVZy7W0tGTNmjXY2toK7338+JG8efOmsRA+f/5M/fr1hZ9r1qyZxjX+u5NbbtRfQYy/Zj23U8PaPHnyhJYtW7J48+4021MLcWa1qL8Gecs5vfflybAvcUwY95/9R9hHXwrW74ztWzeSQkN4WMiRevXqsXz5coXPsZJflx/eT/hbXdOfYxIFIQZp5LCdnR3+/v4YGRmxcuVKZs2aRZDve3r17EmxYsVQU1PD398fX19fwVJKSUkhWUOPD9HRXLn6hKCgIEpam2NbpiIfH9xiXLkilLhwGIBbt25lKcIikYjatWsLT58RERGEhISgp6dHp06d2Lx5MxEREQQGBnL58mUGDhyIoaEhNjY2uLq6cubMGWJipF/K9Ky3mJgYDAwMiIyMFN4LDg6mXLlyWFlZkZiYKAjwtGnTKFKkCICwvrxo0SJBcBo2bMj169dp2bIlJ0+eJFDdRuFcJyNMKVK7OQcPHuTgwYOAtJ1gahE2MDAgJSUFPz8/QYSrV68OIFixJ06cwNbWlqCgIBo2bEihQoUwNzenb9++zJ8/n/nzFSM+5enYsWOaUn3yoikvwAC1atVi69atLFu2TCHX1dfXl1atWmFjY8PRo0dRV1dPN9daXoC1tLTo0aMH27dvzzXX5a9Abq5jylzUIBXkHy3EXyPAkfHJDJg4k+Hd2qJhmFchzTGz4+emEOfkbyC/RiwO9P5S3AOoWKUqxBTnzvUTGA+fxdIB7ajlILXsZfEOSv4ccjWBMqdCnLo/roxocTLvAkIpWLAg+/fvJz4+nlKlSuHi4pKj+UgkEnbt2sWSJUtoaRfEio4NSJFIWJpgBFxCIpEgkUhwd3enePHi6bqmU2NoaCis6TZs2JB9+/Zx6NAhQFr6ccSIEcJYKysrzpw5w5kzZ6hRo0a6+ap58+YV3M8SiYSkpCREIpEwViKRMHXqVKpXry5ETIJ0/Va+9jOAg4MD/fv35+TJk0ybNo1NmzZxHh0Fa1hd64vgjhgxAktLyzRz6tq1K3v27OHgwYNUrF0fC2NdBWvTzs5O+Fn28PPmzRvevHlDq1atsvwdpoeqqirXr19XiByXp1KlSmzfvl1oa5gnTx569uzJihUr8Pb2ZuLEicLDSmr09fVp0qQJkZGRODs706dPH5KSkoiNjc2wQtHfTnoR1D9CjL9WgAEq16zL+Yev0Tc0UtgmE+JvSWPKLRISEli/fj3q6up06tQJU1NThWpb8mJ86v5LEsVJ+EbGo6qqiqGhYRpPnJLfn1yvYvAtFvFr349cOnaAl9cv8vjJExLi4ylUqFC666iZERYWxsiRI9HQ0MDT0xN1dXVWXntK+X5jcXd3Z+PGjQwbNoxu3brh4+PD6NGjgS/WXXYZMmQINWvWpGTJkoSEhKRZZ5W1IAwJCclWIJBIJErjRhWJREJAVFZYW1szatQoypcvz7Bhwzh27Bg6dbspjEn58ISaNWuyfv36DI+joaGBhoaGMJcPQREEfv5MgwYNuHz5smBFg/RhwMbGBm9vbxwdHYVI5OyQkpJCUlISGhpSD4j8A056aGlpMX/+fBo2bEhSUhL9+vWjV69eLF26lBIlSmS4n46OjlCdq2nTpvj6+gLSfOp9+/YJVr6StPwsMf5ajE1Msx6UitTinJ5lnJ6Ay4Iws7Kk5S3k6EA/xowZA8Dw4cOJj49Pt9hGirYh525IAxSd6tZCLBYTGRmZZQe59HBzc8PV1ZWAgADKli1Lnz59FI4jFov58OEDnz9/xtraOlvGiJLc46eXEpK3hpdOHcOjqxepU6cOI4YPp3LlyhQrVixHeb+y9VNNTU3U1NSEQhNisRivM3vZvv8c/fv3Z/DgwYBitO7r169zJMJGRkbUqlULIN3+tStXrqRgwYIEBgaip6eXZvv3onbt2lSoUIEbN27QQE6EzY208PLyol27dpnsDevWrcPX15cSJUrwMSqROWMGcfqo1IVfvnx5hYeip0+fCnna3bt3z5F7d/78+Vy4cAEPD49sV7UyNzdn+fLlwpq7mpoakyZNyvY5ZQKspqbGhw8fhAh5ZYP1zPndxFgeeaPAQEuVD+/f4ufzgRp1HNMdn9NUKPnxWQmybl4LdHV1hSUq2QMopK0/3aJBXWLi4ihnX4THQUFIJJI05Waz4ubNm4KRAdLOaQcOHKBBgwYUK1aM69ev8+zZM4XmONnpVqck98j1Lkrfsi5sUdASfX195s+fT69evYSesTlB5qJVF0mED3o+E2MGDRrEujPXSEhIwMjIiJSUFC5cuECPHj0AsLGxwcnJKcPjfg1Pnz6lUaNGFChQgOfPn+fqsbPC1NSUsLAwmpgotjMsX748R48e5ePHjxnu6+bmRqFChajeRvq7EalKLeIJEyawceOX1oQSiUShyMjt27ezPb+AgAAhYlm2zvXw4UP++++/LPetX78+TZo0yXJcehw9epQLFy4I9aT37NnzVcf5W5EX3l8lvzgnNQseP31Oo6oO9OvYJuvBX0FWeckikQjPt74cOimNFcnsoXX61Mm89DiJiYUNnz9/BsiWCEskEvz9/bly5YrgAdLS0hJiLWTLRytXruTBgwckJCSgr69Pq1atqF69Ohs2bGDv3r0ZLg0pyV1+qVaGHQeNBmDSpEnZaluYHgsXLqRIkSJExyeyaGg31kwfTYcm9di4cSN+gaGIRCLWrl1L165dGTduHCBtEH/w4MFcz7nT/n+DcZlV+iP4+PEjW7Zs4fLly5QuXRpv1bxCS0MzPU0WLlxIQkICFy9eJCkpiUePHrFr1y4hMGzq1Km8fv2a8uXL85+3H+5Xr9F5zHQMDQ25efOmQoTy8uXL6dGjB+rq6ujr6+Pq6kpMTAyhoaGcP3+eNWvWZFhSz8jISHh99+5dgoKC6NWrl+Cq+14ULVoUMzMzVq1ahZmZGSkpKUorOIf8ilW3ZEKcmSBLJBKGd2sLgKFxnhwZDPINa7K7X0ZlNTU0NKheszZhCZJ0C6qkLuwBCA+NWRklslKtjRs3Zvjw4Whra1OjRg0MDAzYsGGDUOyjdevW/Pvvv8J+pqamuLm5cfPmTUCab59ZcKWS3OOnuqNTB2UZ5zVjwvItzB/Wk0aNGlGrVi0GDBiQoxZ/gwYNwsvLi+UzJzKkR0e0ilQUtolEIiQSCbGxsYJlWq5cOVavXp2tPr7Z5X/snXV0FGfbxn/JRjbZuBFBgntwd3cpRYprcYp7cfdSHIoXh6LF3d0hOEFCQogT32zy/THMZHazGyO0vF9znbMn2bGdnZ15rue263737h1Lly4lMTGRt2/fcuXKFZo1a5Zpx9eHxMRExo8fz8GDBzE2NiabqxvX79yn3MtnYO0mEfFz8xwULVqUxYsXa3WlOnToEEOHDpWaKVTuPoLhPzUiPESwUnv06MHWrVtp1qwZCxYsoGTJkhLBbtmyBdts2fmhcT0qV66sZR2fO3eOXbt2JTtfpVLJb7/9xpAhQ5gwYYIUTxezm9VqNRqNBqVSmWzfr0VsbCw1a9Zk+vTpWnXKWUgfvjcXdWoWsZGREUXLVebZvRv8vklI8kyv506XiNNjhctbtKYGXWEPsbTx48ePeHp6GtzvwoULbNu2jWHDhvF+4TZ+3bqVXbt2cfnyZdRqtaTxrtuR7vXr1zg5OVGgQAGeP39OfHy8lshRFr4dMo2E03szG5KsLFW5BpsPn+PAjj85tn8Pd+/eZfPmzVqWkyF8+vSJ27dvM3XqVAZ1ac7JG9pNzefPn4+nlRFt+49Eo9EwbNgwunbtmu7Er8TERAICApLV/IJQG7ZgwQJJhL1gwYJ8/vw5mQh7RnDy5EnJUhRriOPj4zl37hw3b96UCNQzdx6U5mY8unuLB4e3oWzQXzqGi5U5TXoOwXzvdq6cPCwt9/b2pnfv3tg5OjNq4SoK5s1DvqIluH3xDABnL1xk7969/Prrr/To0YNatWpJLu2QkBC8ajTgzJWb3L1yHhsbG6pVq8aff/7JlClTiIuLk2JfLVq0oHr16gwfPlxS2YqPj5fc0CLpzp49m4MHD3LgwIF0x8FSQlhYGFWrVsXBwYHY2Fj8/Pwy7dj/VfwbZKwb59VdZggj5i2X/tcti/ya8/haMpYrdMmRYGGLcaAPrq6uKBQKevXqxejRo8mZMydnzpzh/v371KlTh6pVqxIVFcWKFSuww4TIhXuwx4w7d+6wbt06ChUqJHn6li9fzocPH7h58yZqtRofHx82btxIYGAggYGBXLlyBXNz8yw1rn8IGRbr+NqaYLE8yBABZlOZ4fviEd1aNcXU1JS+ffvi5ORE2bJlDWbQ7t27l4kTJ3LgwAEae+Vk3d5j9B4qJO28u36CUAtX/lq9iElLhA4/NWvWZObMmVhbW6f53B88eMD8+fO5ffs2M2fOTGbhNmnShLdv39K4cWMqlypGvuKl+emnn5gwYQIqlYr69eun6+ZOSEigd+/eXLt2Ldm6KlWq4O/vz8uXL1EqlRgZGVG2bFmWLVvGmzdvaNasmZDEVKopfqExlMguXDdHS2HgMY+LYHiP9ri4uTN+9mJuP3xMnsJFCY83wtHSjJioSOICPxAdGYFHrtw4Orugjotj/bKF3L56CVNTU1p17E6tRs0kkZUItYaCLjY8DQjHx/shTevWoHr16rRr147ExEQpIe7evXvUqlVLq+5RVAvbsGEDvXr1Ij4+no0bN0qCHpkBjUZDyZIlpffW1tZaNdr/q/he+gN/jdBHekqIMkOlL6MwRNxpJWJDlrA+Eta8uQeRIUReO83fhy+z5MwdrgQJz4wJRtSsW4ezJ08RjzCMu7q6Ut4/kYozB7Nv3z5u3LiBl5cXs2bN0luOKCI+Pp7Q0FBMTEzSZPBkIXV8E7GO8BgNCaZfV2sXHx/PgxuXWTx+CBGfw+nQfzi1mrXG2s4+2bYe+Ypy4Pw1po8dwYwZM0hMTMTNzY1du3YlI2KRgFs1qkuj4jlIsLClcZUyNKxZhfoVS+KmMsGNQPw/CXV2Y8eO5bfffmP58uWMGjUq1azeR48esWTJEi5duiTVlh45coRmzZqxZs0a9uzZw/r163FyciIuLo5965dJ+9atUVWSdLS2tk6X7JxGo0nWMAEEkY2nT5/i7+8vZVt6eXnRp08fjIyMpIzlQoUK8QAkdzRAUFQcjpZmxJpZsXbvMUAYoAqWEMjO8csYo7RUocyZn/yyQcfUzIzeQ7VF5EWIg+jTgHDCYzR4Fi7Gzz//zIoVKzh9+rTWtq3bdyQ4OJghI8ewce1qcmTPzpjJMxjarxf9+vWTYmA5c+bkxYsXvH37ltq19WezpgcKhYLLly/z+++/s337dj5//syJEyfSVVqVBcP4p6zi9JZCZhYBpwR5TXJKSI9ASIKFLcaAqaMTtcsVpriZOWcOveB6eCSmGGN28jnZ8SCceEwxxtrfhILzBjFy5EhKlCjB6NGj+emnn1KNJZuYmODklP7yrix8Pf7xmPCkIX04cXAvRYoUoVxJL9bOncy6eVMpV6MuHQaMIHchbS3mvDmys/7P7diZwYkTJ/jhhx/YsWMHvXv3lrYJCwtj8eLFNGzYkO3LZmJkZIRRdBiuLk4cWjZd63jzR/fnl5a1iC9ah/DwcJYtW4afnx9jxozR6/ZMTExk8eLFrF27Vlq2dd54xi/fyuPHjwkKCuLNmzf4+vrSunVroqKi6NChg7TtoyfPOHnuovQ+KioqXdfL1NSUHTt28Pz5czp37ixlfBsZGbF9+3YaNWpEnjx5mDx5Mh4eHnrPX0RARCwuVkJNokjEaRmcUtsmm8pMa0B8/ClC+r9Cz7G4NOyOKjGWF/dv4ZGnAPfP/M3dKxfo3PcX+o4Yy4ixQlcnD3sVduvXs3jxYnbv3k3RokVxcHBg2LBh3LlzhyVLlqBSqRgxYgRLly7VasoAQu/VsLAwateuneKkytramvHjx7N9+3aAFGNs/wv4XqxgOdJDxhkV0fhWDWREBEUJ973oOcpMpMclDaCwd8HCORBLF1uK5rIloWd/li5div+L1zhjThXsGf3gKJcvX2bkyJHUq1ePBQsW/L9RhPv/jH+chEtXrMqJg3sZO3YsJUuWZPjw4Rw9epS58+ah0cQzcbm25qs4a7S0tKRfv34AyWKx8+bNQ6OOY9Xkodru7cgQdGFuZkb+4iXQBD1kYbuq1MjnTNdxc6hXrx4VKlRg+fLlWrV7K1euZO3atcwZ0ZchXVqjUdpiZmZK/kJFqdq6O02bNkWj0VC9QhlyeXry+IUP4wf2AgRXctvugvtVqVQSExOToWJ7ELJ6p0+fzpw5c/D390ehUBAfH09wcDAtW7ZMRsDlypXDxMSEixcv0r59Do4EZX6Ck4iPkXHSgAVJZH/vvdiC0oJwLMhXvQkAzfqNptvwCZJbTz7wVK1alapVqzJhwgRmzJhBzZo1pVpuuRqZrsssPj6e7t27A0IcvlevXhQrVsyg8IDcu5CR/tPfE3Q7/HxPSEkC859UsDKkzieH/B7WXf4tiBjSbhUrHF0xDQlA5RaCZx3otXgxr1+/pmLFily9epV+cydz4sQJhg0bRsWKFZk4cWIWAf+PIN0krO9GTk9yg9hf19/fHxBS4zt16sTGbbswMTUzeLxz127j7+/PvHnzaNiwobRclJ3s3LkzHq7JE6UAoWenDiErHAWrt1V9V2pXLMPsPeeYP38+s2bNolixYiiVSh4/fsymTZuYPLA7w7u3A5U94uOSr6gX3t7e/PbbbyiVSgYNGoSVOkyrvOD27ds8fyUMQF26dGH16tXpyvSWIywsTGphBkK9r6WlJZ06dWLdunVUr16dEiVKJH1llYp8+fLh7e0tEbBoBacV8kHJ0CCkS75+oTG42SklAvYNSbL849VxWCdG4nP1IbUqlMWmSEEKuuiPlUyZMoXo6GhOnjwJxsZYWqqwsrHj6aP7gFBi4e7uTtmyZSXi3rhxI127duXp06eMHDkSBwcHRowYQdOmTbUGpDt37kitGM+fP58ugZYspB/2HrmJUGtQB7zNVBd1ZlrBhgg4vchIolZ6ERUQRl3fOLZYWnL16lUcHR2pVasW48aNo1ixYqxatSrdyaZZ+PeQLhL+FKXG0iq5RZXWLMOPkXHY2jvg7OzMwoULyZ07NwULFgSgcuNW/LVyPkrfB1BAaBpgo1SgMjGmW/s2nDx2BEtLS62kGkBqMi8nIAmq5HFmaZmMlO1srJjVrTFRUVFs2/KnpFFtaaFk+pBejB48AIyMkhpxf4GLi4vUfB5AHRAmlRWYunhKzQ8AHt28gkqlkpo4xMXFkZCQkKYSnOjoaK3+xgCTJk1iwYIFtG3blgIFCrBo0SI2bNggrY+MjOTly5c0a9Ys3eQLyQclXWtAl3wBSaNarlUtwvzzB9YO7kFUuHDdVxob0717d5YuXar3GhgbG0tlFE8DwrEyVfDhcxzBgZ+4ev40n8NCefLsGTcvn2H37t1YW1tTtmxZevTowdGjR/nw4QPBwcGMGzeOp0+fMmDAACwsLIAkwQMLCwtJ8SwL3w6ixWvqIkxA5WQsLksJmUW2abGGDUF+/2dGVvXXopSFFYnRcBMTcgWZMH/+fK5cucJPP/2UJgKWVyxk4d9FuqZLOTWBGf4g8eaPMVWx7sAprO2d6Ny5M0uXLiU2NpaBbQVt5fPnz2vtFxkRwcljR2jVqhW7d+/m+vXrxMTE8ObNG6ZOncqoUaNo1KgRlSpVSvkEVPbapKzzv5GREb8PaMOny/uJe3CKcO8rhDy8xKix40m0tEtGwJAkL6cO8EnWmkz3/f3nPtSoUQMjIyM+f/5MgwYNqFChApMnT5Y0pg3h8ePHAJQvX15SwAFo3rw569atw9ramtu3b0vx5oSEBDZs2IBarc7UtmdBUXHSS4QuAcvhZqfEw94SZyUcmz8cN2cHfv31Vw4ePEjfvn3ZuHEjv/zyi9b109drtaCLDR72KtytzXBwcqZI1frkKV2Z9v2Hs+TwVZYfPE/b7n2Iik9k69atUulUgwYNaNOmDVu2bKFGzVoMGTKElStXSn2k8xcslGnX5t/E9+iKTkk5ytQlpxYhixAFMeSvU7v/ZM74EURHCd/xc1gYh3Zv4/rFs1r16N8bMjJx0I0HqwN8pD7DmiB/rXUFc9pQ2sKKzQ8uEVytADt27CAiIkIq91u9erVBoRxfX1/KlCnDypUr032OWch8ZFpMOKXZYbLZp60La/YcZsPciaxbt47Tp0/z008/kTNnTk6ePEnbtm2lh/Spt0BAP/74I8+ePWP8+PGMHz8eEBJsxowZw9TebTE2zsADaYCUjQFL0Eu8utBHGiD0B/186yjtR8/i0MlzGCVqCAgIEKztbdsIDBQmNAcOHGDo0KEpNi4oXbo0Fy5cwM7OTlK5ad++PaNHj8bR0ZElS5aQmJhIXFwclpaWrF27lpUrV9K9e3ficpTAkfS72xwtzVLcRyRfEW52SomIX14/y40dy2g+8FcqVavOjVOXCHj7iuW7d0uej379+uHv78+aNWsIDQ1lw6IZoEPEuspBTpYmTBzxCxs3bpQmLrnzF6R1h07kK1SUIiXLMOW3Vbx69oRP/n6UqVwVF1d3OvwyjpOH9nL+8F5u3bmLJj6eQoWLsGXzxnRdkyykjvTEeU1dcgpkJRKxjXZew6eHVyQN42p1G1C5Vj0mD+gi9YEuVLwkf+w5gpmeBgjpQUZc0ZllDaclHpyQkMCcNVvwsDSlpoXgNVJlU1EQgVAvXLhA48aNOXXqFBs2bJA8YkqlknPnzklCRO/fv+fMmTPcvn0bgL///lsqGczCv4dMTcwydGPqcwOFJ5rSb/ICSjXrxIa5EyWJtDY9+hFt40H0l5nkob8P4eDgQNGiRbWSaebMmUP/FjWwUCpJsLAlAVljbJIIVFyWYGGLcXRYsuX6IG6j2/NTDt39/9i2hwnzllDGqyjTBnalVNnyxMapGde2Ph2qlUShMKbj5CVUqlRJmsE7OTmxfPnyFAkYhExoMRFJjG2K2eHlypWTjqdWq/ntt99Yt24d7dq1Y9iwYfiQtkFG329kiIh1CVgX949ux+/lE3xunGVg3RIsO7KHnDlzJlOn+vXXXylSpAizZ8/myZMn9PyxMT26d0epFAZVdYCPlqj9xi07WLduHUOGDKFEiRIEBQWxd+9efp89XZI5dXBwYMCAAZSq15J8njkIj9FgYWmJQqGgSpUq1G7RlsoVygP66zKzkDF8TQvCaBsPLMJ9sQj3ld4DXHop6CVXr9+YXKWETlc5ChaHCxcoVaoU9+/f5+CurfzYqXtmfAUg6d7WF8LRDcmkRsRpLVkC/VYwAJEhDJ08l2V7jtGsTCFqVislbaPKpiJixK/Ur1+fI0eOaFVClC5dmtu3b/P69WuKFi1KREQEnTp1IiwsTCr/a968eZrOLQvfFukmYX2DsjhYiyUv8hszpRjMx8g4chcqypR1e7A3SSQhMQGl0kJan5CQwNF9u6levToKhQI7OzvKly9PXFwcw9s1kMhX2l6P5Spfpvu/PtJODcbRYcTFqdl/8CCNqlXESiWc78UrV/kUHMrRs5c4d/Um+XJ64Ghnw9nrd+lWrxLTujTjzK/dOX/nKccevebq24+sXLlSsgzTisGDB9O7d2+p+Fve8KBZ8+ZERkTQvXt3Bg8ejI/C2eBx0prtKW5niMj1uaGLlyqDz+1L5MzmiEaj4cbZEwCEhoZib5/kcRB7qtrZ2TFixAiGPHjAyu37Wbt2LRU8VCRY2EqDkXF0GM5fBsaSJUtK8fYGDRqQmJhIWFgYkyZN4vTp00ybNo3cf/7J+Zv3OXhoJzMnjiU6MgKFQsHKlStp2LAhNWrUYOTIkf/zmdHfAumRV5Rvnx7oumujbTz4GBmHp+YTisDXmJiYULFOI5YdOEfOfMIz8jEyjp+GTuDY3h3Y2tqSLVs2Pj5/oO/wGYJ8cikv55NDt2zpa4g4pesrylbeirBk2R6hln9+x4bw9qP2MVytmKyJJdzBjqtBIfz0009s376d27dvU6tWLWnie+nSJa1exI0bN6Zr164GPz8L/xwyJYVOd4D+GBknvdKKkHgjwjQKrX2fPLjLu3fvpO4fAAEBAdy9exefoKRaVFMXTy2rVf5enxi6iAQLW+mlu6/ufvL3k+YspP3wqbhUaU6U3zs0Qf7M692WKl7CYBEdE8uDZ684e/0uABtOXCFH53EUdHOiU/mihFraY2JikqH6VFNTUy31lR07dlCnTh3y5cuHqakZ85b/wc+/zuGdmZB8lFYrODXoI21dAv7w5C5bh7Ti1cM7zF6xgVHDh7PlrwPS+lu3buk9doMGDbhw4QI7d+7k7du3dO3aldjYOIyjw6QXkSEosglZzN26dePly5fS/qKnQHTxN2rUiNevX9OoRiVGDviZcmVKc+TIESmMcfToUalE7sWLF6l+9/8C9MVw5e8NEW1mlRmJY8VTtRU1a9Zk4sSJ5E4IlAgY4MWjezy9d4s8xctw4cIFPnz4QPYiX6emllrIRf5K636ZCS/zJDU3BytLVG6OWLrYonJJ0ro3NjJiRvGiTC9eRJLHHTp0KL///jumpqacPn1aq93n+vXrmTNnTlZi1neCTHVHZ2ZxezaVGSbu2XFzc2Py5MkoFAo+fvyIj48PhQoVwt01W4pEKX+fEhHLXZ5pQmQINYt48qeDLdWK5UUREcyzvHXIG3KafQN/5Panz5y6epeNF+9hZqKgR/WSTNlzhnzOdgxfd4A9D14TFRVF5cqVM8UKS0hIwNjYmJ07d2LinIOYBEW6M0DTEt9KbdDxf/aAHWM6kydPHvyeP+LUX1uoXaYIi2ZOpW7dugwaNIhcuXIZ3N/Ozg47OzvWrVtHhw4dmLX5AL/WK8L7gGAuP3jC8Wv3OHj5trT9mjVrmD17ttYxNm3ahEKh4O+//+b+/ftkz56d4cOHU7ZsWRITExk3bhwACxYswMXFhXHjxlG6dGl++eUXxowZ84/2fP5ekBYSTY2I0wu5Fax7r966cJrPnz+zf/9+uk9fju1Hb0xNTfmoys7Yzi2IiYnBxMQEjUZD/oKFqdGgSaacU2ohFt1tXKzMM7V+2JBQh6mpCXtmjeDkxeuYmSgI12gI7zqKOyP6cjVvCYIP/42bhTlO5ubkUamwsLDAxMSEW7du4e7uzosXL/jjjz+oVauWUO4H/1jXsCdPnnD8+HEGDhyYVTKVAtKlHX3lyhWemiZXZUoLUrtZ5TFJOSEYxUUwcfhgjhzYKy0LDAyUSn3+KejLVNSECP021UHJs8YTEhKJVccTEx9PZGwcoRHRVJm8hsqVKzNixAjy58+f5s+OiYlhwoQJnDt3jgoVKjBs2DBy585NaGgozZs3p2nTpowaNUqKpckHtrTU+qYFqWVER/z9O2vWrMHBwYGePXsyb948ZsyYwfjx42nfvj39+vXTckWnhO7du3Pz5k1MTRSo44UBO3/+/NSqVYt69erh4+NDkSJF0lVz/eDBAzp06ICJiQm3b9/GyMgIPz8/Bg8ejLe3Ny1btmTv3r2pH+g7RHqzo/9JkQxd6LqhdUn4w5vX9G9WjRz5i+Do6sGtM0dwdHRk//79WmV6xYoV4+HDh+w5cY4cBbSV0wxB3+RUvK9TI2Gx/l38CwIRi89UapPYtLikU8uOfvzgAaUGzKROnTpSgxg5TIyM6JUtO09DwzkTI4TaTExM6Ny5M61bt6ZJkyb06tWLwYMHp3iuqSEiIoLY2FiUSiXR0dGo1WpsbGwkOV8RoqLd+fPn0/zs/3/CN9GOBihjHsat2LTFTyF9A7++G9naxpYFq9ZRoWJF9u7awe6dO9JMwOLg9LUJOPIkiWTrdAjY1NGJ6Ng4ei/eyl+X72KqMGZih8asOHgOEMqM0kPAIFi7Dx8+JDo6mrNnz3L27FmcnJykBIuOHTtKBKyLb6X0IydgNzsln74kjgUHB5OYmIhCoWDjRiH7eNu2bdja2kqZrqlh9uzZXLhwgbi4OFxdXfHy8tLStS1UKP2lRYsWLQJg4cKFUnKbm5sbGzZsoG7duuzbt4+rV69SsWLFdB/730R6CPjfJF990EeK5s4erDx8mZ/rl8fHWxBmcStYnMNP/KlevTqXLl1Co9GwfPlyqlevzt7tW/ll4qwMfX5qBCze43Lddfn/aUVKyVlyxayUZCsB1jwKBeDUqVMYG0GCjvkUn5jISv93eCjM6GzlwqvYWFwwZ/DgwcyYMQMLCwt69eqV7vOX4/Xr17Rt25aYmOS5IFOnTuWHH34A4Pnz59LyrIYQKeObyFamNPDHxkTj+/olcXGxeOYvhNLS8E0n3rzGxsb06juAXn3TNoiD9uCkO1BlhJTFJC5dK1iEqWMSSVx89JI9l+4AEBevYe3xy/iGhGNsbJyhHp2WlpbkzZuXGjVqUL58eQYPHkxgYCCdOnWiR48euBctI1kYXytUb0jQQDdTWrQKwp5ew9zOhtu3b1OqVCkWLVoktQk8d+6ctH1ISAjh4eEpzgilc8iWjdatW3/V95BDrVZz48YNAK3mGe/evaNz585S1r3Yd9XOzo4ePXpI2/mGRBIdHU1CRPD/ZP/h74V8U6udFe8vjdIaa3tHjBPiCQsL4+Wda8zsdRYAE1NTrCwsJEvw+bOnhg6nBd17OqXwim6ug/z9p3eveXH7MraWSqzrNQPLzPXIpUTEhRKTJvwbuzahgKkpCaGRvA6L5FL+UmzYsAErKytMlEo2v3mDq6srl/z9ud6kCb6+vlInt4zi6dOn9O7dG3Nzc8rFWJIIRKGh6cTBLF++nKVLl2JmZkalSpWkUsrs2bNnyWemgnS7o62srPRawikRr2jhzho7lAM7/pRqPJVKJZXqN6Xn6CkUyO6mtY++2aO+bEJ9N+zTgHCD26dlfzlEl1BiRDBvvB8Tq1aT3dkBpbnwneRkrNEk4B8azsXr99h++T5Xn78jLDppll2yZEk2b96c7DP0ISwsjBEjRuDv7y91RAIYOHAgz58/58WLF+zbt0+SBPxaEtbnhTA0cImWw5Nr51g3OmlmrVKpuHLlivTQaTQafvjhB16/FtSRrKys2LlzZ4ot1b4FNBoNU6ZMoUWLFlJW9aZNm6SBQkSLFi3Yv38/ANu3b6ddu3YAfPjwgWLFihESEkL58uVp3749efLkoVmzZv/6AJMWS/h7JOGUXMMAY1rVxP/tK0BIvJMPU3Z2doSGhgJw5coVFO6pVxikdi+DNtnK5VY97C0pkd2WoHO7GDNmjNSG1UJlzZjf/qBExaoZdkeLMOSWlruk417c592nYGzUsRiFC4mpkX5BRAWEERkQxd+VqjFjxgwOHz6Mo6MjSqWSkydPsm7dOho2bChJtWYEz549o0ePHigUCgYMGMD169c5ceJEqoIpbdq0YeLEiRn+3P9lfDN3tC7S6u48c+Qge7dupGfPntSqVQszMzMuXbrEipWryJk9OwVGCz+U7s2qS6QpCZ77hkSmq01YalAH+JDwOYhN23cyd9M+nj17BoBCYUyBAgVp4JWHXp3akt/WjA+vX1D2lzkEhQkPR/Ec2Xjwaw/6bT/OkyioVKmSloxlaoiIiODq1avJli9duhSAnj17Zkh/Nz0CA6nJ/D3YvYrChQszdOhQevfuTWRkJC9evJDc7QqFgjp16vDHH39IvYJTUwf7FlAoFEydOlV6/+rVK4mAa9WqhUaj4fz585KS186dOzl58iShoaGsWLGCqlWrSk0kgoODGTlyJPHx8axcuZLmzZvj5uam93OzkIS0WsEiPPIWkEjY3Nxccn/Wrl2b6tWrM3nyZAAuX77Mtm2DqFivKV36/pLsuGmdlIoELCdfEW52ShI0GiZMmEDVqlWZO3cuISEhDB8+nEUj+7D5yAVQJe/AJkdqmtIpuaUTLGwxjgzBLJ8Xnvb+aEICUJsHEnhfO7O/adOmTJs2jRs3btCyZUsA6tat+9WqeQEBAfTv35+wMMEbOG3aNHLmzMno0aMpVaoUnz594t69e9y+fZubN29K+/30009fHX/+LyBdJCzGIA3V0BnCS79PTBjcB2tra5o3b46npyfGxsYUKlSITZs2cf+Wfnk1fWQqNo6X37D69tG1cA1ZDIbcP+oAH/wDPvFDh27cevCYmjVr0q9fP6ytrXnz5g337t3jz1OXWXv4PLd2rSRRaUtQmFCL2rZtW3bs2MGRR6+48voDTVu1YeTIkalfKBk8PDy4d+8e+/fvTzaT3L9/P2Wq1QHQsoIN4WuUfeRELHdJB7x9xY0bN5gzZw6VKlVi//79nDp1KlnZlVibeP/+fYYNG/ZdtA3MlSsXa9asoVChQlhbW0slcE5OTvTu3ZujR48SFhFNv379SExM1IpvLViwAFdXVypUqEDfvn3p27cvP/74I58+fcLZ2ZkePXrQuHHjf+urfZfIiIRj76mLmdPvJ149vEOZMmVo3bo1kydPxtLSUiJggOsPn3Hz5k1u3rxJ11ZNSHTJm+FuSXICDpBZxX6hMSQmmqNQKPDy8kKpVOLm5sbvv/9O27ZtmTpyIL9vEvTm5SSr73unRcBDPh6J9cKiot9TjSOHLr7m1YkjVLJRUdrCgtjAGCI/RuJiaYmHhwdnzpyhWbNmmVYDv3DhQhISEmjcuDFXrlxh7969ODg4SF6gwoULS2GeqKgoTp48iaenJ15eXpny+f/fkS4S7tq1K15eXpTsMZ4A7NJMxOZKJXmLFMf7zg1atGiBk5MTBQoUIDg4mJCQECb2FWZL4s2ZkiWrz7UWodZw7NB+ls6dRVBgAEZAkSJFWDRlLGVqNQK0b+y0JrNs2fUX97yfsWXLFq0bqkKFCrRt25aIiAhq1arFH3/uZFrvn1i0aBETxo7h77//JiEhgT5bj5ErVy46d+6cps/ThbGxMXXr1tUi4Y4dO9K8efM0f4fMkNYTjyEf3N4+EmLetWrVAiBPnjx6uxH16NGDZs2aUa5cua8+j8yCQqGQErASExOpU6cOBw4coG7dupiYmODn58fLZ944OTlRr149tm7dKu3bqVMnzp07R+HChVGr1bx48YI9e/ZI6/fs2UOTJk3YtGkTDg4O//h3+19AWuKz5hYWmJkLSVANGzZk9+7dhIWFJfOkPPF+RPHS5XDPkQs/y+zkDvfFEwwK1aSW5R+gR3wGBJe4q6ur1oTMycmJdu3aCbFYM6NkZThf2+84ODiY69ev8+7+VVo1rIOfsT2LF//G2rVrsbe354+QEJzMzGhu70wLRxfC6rajYXgCG86dY+jQocyZMwelUsnHjx8JDw/H39+f169f4+PjQ1BQELVq1aJly5aphlROnz5Nt27dUKlUnDlzRouAdWFpaZmlxJVOpIuEP378yL59+3B3d8ez7VAgScotpZo5UzNz5m45SEjgJ94+f8LNC6f4+P4tubNlp/eoyVSpXc/g7PDG1SsM+Lk7TZq3ZNKM2ZIlLCfjP1auZP6k0dSoUYOG9etiZGTE4cOHadi2C0unjOL+y3cULVORDh06AGnvv5rHMxfx8fFS9x1dWFlZUb9+fbYfv0DfH+pjF/ScNUM60nX+RqysrNi2bRvu7u5fVRT/8WOSQo6xsTEeHh5SVyF9VrAh0tV3ffUNEIZm8roDZ1UPS3YCo0ePpnDhwlKvZ114enp+F9avIRgZGTF+/HhKlChBw4YNiY+PJzY2lri4OJycnDhxQlD7WrlyJX379uXz589s2bKFnTt3EhgYKE1CTp48ydOnT7l58ybr16/H0dGR+vXrs2nTpmT9r/8L0HdvpTdfocuY6YxrU4cJEybg4OBAzVYdqdK2G0eOHJG28cidj34TkurFRfL11HzSeg/6CViEPje0iDuvg3Er5U62Sk05sW0pvXr1kkIu5cuXZ+nSpdy5cY0yFVJpIvMFhlzToofPNySS32dPZf78+VLM9bL3G3LkyMHatWv5+eefGTRoEN7e3uzatYs/Dxxg20c/miidqGlmR3R8InuvXqVh+SpkKyy0MxVhYWFBrly5CHvymjNnznDz5k2mTJmCiYlhKsiVKxfXr1/nxx9/JDo6WipPykLmIF0V1H/99ReDBw+WYgyikkxa1WPsnZwpUaka46bMZPHaP5ny20qq1qmvlyAi1BpevHlL907t+OD7nj9WLsMkToi3vnx4j9plinHx7ClevfHlt2njadeuHUuWLKF37978/PPPrFmzBmNjY34aMIqZC5fQs2dPYv1fScdPi2XcrGpZbG1ttR56XZiZmWFu60ilXmPpPn053RduIi5eQ0REBM2aNfvqGOiVK1cAQSUqISGBjRs3anWeSQk2SoX0Sm29vu3ky3TJPdBWiEefOXOG5cuXEx4ezv8qLC0tadu2LTY2NqjVaoyMjPjw4QPe3t58+vQJLy8vKlWqJLWtFLOpxe5WxYoVw8XFherVqzNs2DDWrVvHiBEjuHHjBoULF9aSFv3/DrH7UVohHzsSExN5ePU84SFCCMM9d34Gzl1FvZ+6Exkdy5snD3nx4JaWFda+cR29x5WTsaHOX6BfdlUXLl8qAUo0aoeVswe9evVCrVYDUKJECVQqFXduXEvzdxaR0rXav38/lStXFpIv7e0xMzNj5syZNG7cmN69e2NkZESRIkWYNGkSR48epXm7NuyOCWBBxDscjcxoFC0kz4Z5v6I+zrTElY540DnamRpPommOK7Nnz+bw4cMMHDhQ6lOuDy1btuTWrVtSl7Z/I6/j/zPSRcI2NjZas0BIf6N4+WAuH+StTBXSS7Twpo4fg6lCwapVq0hMTOTEiRN42KtQoubD+3csnD6ZiCB/4uPjqVu3rtbD6eTkxJYtW1iyZAmDBw/GxMSE54FRWo0YUiJiUxdPzMxMqVevHrt378bX11daFx8fz5UrV9i2bRtXrlwhLi6OjyECCSlMk66HvLY1o+jcuTP3799n+PDhNG/eXOqIklIsOCXizQy4WJlTvGgRCpcuT97igmygqEb1v44nT55IWtQAHTp04I8//sDY2JhmzZpx/Phxqd65fPnyzJs3j02bNmnde+XKlaNr167s27cPW1tbKcs6s/E9ZEbrth40hNSyoQGe3LrC/IGdWDdtFEH+H0jQaChbuxH5qzYkNjqS14/vcf/yWcwsVNg6udCvXz9Kliyp1WJT/roVa8utWFvKmIdRxlz4PfURsGgFG3JFe9gLz1wuN0cmjhxMcHCwNB4YGxvj5ubGu2ePk4XR0joZkV87SZ0sIgJLS0vc3NxQKBR8+PABjUZDv379klmhzs7O/Prrr6xatYpYV3tWx73H1cyUNrhTDnt8ieENUfgRQ7RMbf/1mBXUj3fg9qUrNG3alCVLlhAZKdxTMTExaDQaEhIS2LVrF06YSSElQ/KzWcgYMpwdbYh83zx/godnXkxMTZOtEwnYRqkgNCSY7Tv2cv3SRUIiY+g3YAC1a9TQ2v7ZowfkyJGDM2fOAMKg9+JDILVr12b0r5OwsLTk3kWhXrBkyZLJPs/V1RVXV1f8/PyIiYmhkNEnjKK1H4yUSpQSLASBiWvXrtG5c2caNGhAUFAQ165dIzg4GCMjI/LmzUtJawUfPyopWrQot27dokKFCkybNi3DWbNHjx5l5MiRDB48mJYtW0pkbmNjQ2oVZfJJTWYNwPLYVlLIwYy5fx4gV3wAZcqU+X+jQ/vqleAt0Wg0zJgxI1l8S/6bKpVKGjZsqLU+ISGB58+fkz9/fhwcHBg4cCAjRoxg2bJl1KhR4x+TDPyWEJOLvibeqc975lnYCzfPvLx5cJPhTYWYffWWP9Hj17nsdMtOoN97nt26yty957B1FCzdW6mrTXIr1paAiFgaOYaBORwJSp8r1TckSiLitzb5AKFsTQyzZMuWjfDI6HQdUx/E6xqh1jBq1CgGDx7MxYsXiYqK4uHDh7i7u6co/Vq5cmX27NlD27Zt2afR4ORkz/3793FzcyMxMZE7/v7CpCHBjLxYkh8rcmDBT3hwNzaMtavXsG3bNqytrfnw4QO2trY0bNiQly9fUg8n/m4xiAIFCjBixAh++OEHKlasyNu3bwkPD8fa2ppixYpRsmTJFF3bWUiOTBX0/PDmNQNb1GRS7/bJ1okEHBMTzeSxo6jmVZCpY0fi9/Y1ob6v6fZjM5bMmYaHvQorU8GSGzRuCvcfPGDPX38xbvpsKlWuTJ3K5bhw4y6Dho2kV98BUrw2pXq1t2/fkjObY1L96pt7BvsAixDXV8ttz4UNC6hUqRIXL14k+PkDWpXITXYHgRAj/D8wtUoJrv9QS0rMePr0aartCVOCmNCzePFiatWqxfnz51Gr1Vy9etVg1yXR+hW9Cd8C+uLNRkZG1GrUjJMnT9K1a1fu3r37TT77n0LRokVxdXVl9OjRGUow2bRpE61bt6ZEiRIUL16c6OhoKlSowNixY7W62PyvIz0EnNZYsIXKivZDJxISEsLgwYOpUKECj65dBJKe74jwUBSKtA3yus0XRPJt5BhDI0dtq9eQFayLbLkLYG1tzf3796Vl1tbWhIYmV9PLCMTr2uCnbjx//pymTZtibm6Oj49PmurSraysmD9/PkWLFiV79uzMnDmTo0ePcuLECc6ePcuECRPIXqEEF41DOYg/H4jBCCiPPe1wp8BnY1w+RFATR7KHJbBrxw7MMcYFc4wwovKzaApFm/LX1u388ssvLFu2jK2r17JixQq6d+9OkyZNpIlsFtKGTJ2yuOYQZmn3r13k8PYNNGzbBTfrpFlnYmIi00cO4tyxv+nbpw+tWrXCyckJjUbDb7/9xpw5cyhbtiyV6ggZzS1atqBa1SpoEjT8PmcGwUFBBAcF8cT7MXnyCS7xwoULA7Bv3z4p8UoXt27domBOdzRB/ihASvc31DxeF9ldnflr1lDiXggP3oTzb/l48T7jx49nxowZPH/tz22fAG48egIILfvi4uIk13FaERAQgEqlonz58uzYsYMVK1Zw9uxZBg4ciJeXFz4+Pvy2cq0k0KGLb0W+KSHaxoPJi1aSM3de/lg8j6VLl7J8+fL/Wcu4cOHCUjJWRqA7SE6YMEH6//jx49TQ8fb8r+BrrF5dGMohSUxM5OldoVzRxMQEpVKJRi1sW6Vpa26dPkz1Fu2xsktdh9iQFOW6L823euQVXgDTkkpbiQpP2s/SRvD2BYTG4GFviV9oDJ1zqvk1OlpLC9nR0ZEXL15oPZNfc72kxC0bF6YuWcOxY8eIjY2lZs2aadq/SJEiLFy4MNlyR0dHWrduTevWrXn06BH9+/fnYPBHTExMcIg3xh0lObAgG+YoMKIgUA47jAHTL/aaEZAdC7ywQUMiFtEKjDEiMSqRGltmMWHCBEaMGMG2bdswN09fqPK/igxbwmKMRetgxsb8vldwD6+YOoaBTauyYvZEFk0aSccGVWlUrjDHD/zFtGnT6N27t+RmVSgUDBs2jAIFCrB1526tYzo4OeHsko2jB/YBYGNrS/kadaQbvnzN+tSqVYslS5bw5MmTZOfk7e2Nt7c3R67cISom6QGTWuSRZBnLX/L1IhT2LsI5OTigVqslt4upsRFTHj3Bzc2NIUOGsG/fvgzppTZv3pyKFSvSsGFD8ufPT7Vq1QBhcDJXGLFv3z5Kli6TLB4sWsByfItYoK5FI1rGJiYmNGvbCQ8PD65du8bKlSsz/bP/V9C1a1cePHjAgwcPtNrHgRBmyCz8E/Hg9CZZ6UNareDExETm9mvP3+uXAUI99rlz5yicPy/+b19TplZDarfuQtkvE/SUoI+A/UJjtGLA024KL4AJZYXJspyA5e9d7JRS3Hj57SDi4+O1whJmZmbExqbBL55OiNd/1IwFmJiY8Ntvv0miMV+LokWLcuzYMXbv3s2YMWMo3bA2bx1MOMhHNvGeK4TwmXjMMcYUYwbcP0i2iV3Zhi8H+chhAjDDGGOESacRRpzvOI65c+fi4+PDkiVLMuU8/wv4Kne0o6WZ1ivoox/H/hBmYE5OTpTyKsbxA3u5fOYk+XLnIjI8jFq1atGoUfIHycjICEdHR+IM3Mx1GzelevXqLNu4DaWsZMjIyIiZM2fi7u5O3759OX/+PCC4hOvVq0fbtm2lbT+FJs/gFSXhjAN9hFd0GMaBPkkb6Gna0M5NGJjWrVsHwPO6rYTjf/pEly5dyJs3b0qXLRnevn3LsGHDpKQIX19fatasKWXhAsyev5AmTZK3bfuWCVi6yKYy03rJ4ZY9B3su3sPExISIiAgDR/hvoVOnThw+fFgSzTfVkyfxvUJOvv+EFRyvjsP75uVky2/cvMWYVjVYOqIXf86byOgfahCv1n8MfX1/5eQL2klYAaExDDoZTM+//FhS14G1rQzncIgx4epmfoD2b2liYiIJGUHar5e8d3pKk5V6zVqxdMtenjx7TsuWLaWWhF8LpVJJwYIFadeuHfPmzePMmTNs27aNjt278sIynq34cq2kPaHtKtKtWzemTp1K/eZNmTVrFqGoOUwAEcRrHfNE6+EMHjyYjRs3MmHCBN6+TVslx38ZGdKOFiGWAbx98ZTlU0fz6GaSzGKZMmXYsGGD1nGio6NRKBQGXZVt27alYMGCzF61Uf95GFCbKehiw6ZNm+jatSsgNLrfvXs3u3btAgRxjWvXrpHd2YEbf63FOccXucfIEKJjYpm7dA3mtvYMaFwVaw/9iQ/yxg3qoEDqrjjKjRs3KGJjjX90DC558zJp0qQMJd7MmjVLSxBCF4MHD2bhwoUYGxtL0py6tYZyS/hrLSB9SV2pDSxios6A1g2ApAlKFgQ0bNgQY2PjTBmUvpUVnFlkmxKhGCJhzb0TUkN6exdXHG2s8PPzkyamIhp26s1PQ35Ntn9qnZBAIGB57DcqPJZIHet3ezdPAHr+JZCtpY05pXILORqNVf4MGjSIkJAQjh49KnnCpk+fzo3bdzh8UWgSYqhGWnfimhYvgVyxThEZwuzxwzl//DA1a9Zk7NixuLu7p3qMjCAqKopTp05x+vRp3rx5g4ODA926dZPaSd67d49ffvmF4OBg3NzccPOLohS2mGBEIok8JgJvRyNCQkLo3bt3mjuo/X/CN9GOrl+/PtmzZ6dWrVr069ePB9cvMXvoz4SHBJM7d27KlSuHg4MDM2fO1FtLZkj0AoSyn+fPn0utsNIKsbi9dOnSkqBCbGwsP/74I6VKlaJIkSLkzZuXO3fu0KVLF45dvE6n9rkhMgT1pw90+HURR67excjIiA0797OoT2vq1ayGiZObRLwi5M0aVpbJwe6ESObd8aaYVwkWLVqU4ZKk0aNH06NHD8zNzbGwsMDIyIgjR44we/ZsPD09mTNnTpqbYmeUgL82nixOkIqUq8Kxv7Z/1bH+P8LR0ZH79++zevVqevfu/U0/Kz33wNdmOsuJJDV1NkMEHBARy9rVm5IWqGN58SLp2ctbvDRdB4/hY1AwZWo20Lt/atAV4xAJOCJUm+R/2uDD9m6erG3lRs+//HCRtS6cNWsWwcHBLF++XCsD2M7OjigD3h/59UnPtdK3j0Zlz7zVmzl9+AC/TRtPixYtGDRoEJ06dUrz+JBWWFpa0qxZM5o1a6Z3fYkSJTh06BAXL17k9u3b/PXXXyjLFqXY5fcYYURRrCkYlIBZv7asWLGCvHnzJqskyIKAdJFw8eLFuXz5Mt7e3jx+/JizZ88CglD3sGHDUiTZ1ODn5yfEWoqkvcmBXCfaw74YFy5cAAS5tyFDhmBtbc3BgwcBoYTJxcWFSSu3EmWbg57F7Fm88zCHLt1iyZIl5MiRg8mTJ9Ns0gpKlrxCzzql6VCvGhaxSS5sdVAg0Z9CUGs0rL/zlGW3vWnYsCEzZsz4KlejsbFxMlWlFi1aEBsby7Rp03j58iVFihQBSNUKTi/Ssm9aBmjxXLLnyo2/vz9RUVHpTkz7X4darUahUOgdEH/77TdGjx7N8OHD8fLyynDf4vT0DzYE3d8zvQSckgWX0S5eSsskD5tdXi9Cbia1wWwzcAw5S1Ykp579UiJgQ3XAhggYICI0kp82+ABJVrGYzOXv7090dHSyXrpKpZLYmOQlSpl1ncTJi6OlGQFRauo0aUHFGrVZ/9ts5s2bx507d5g1a9Y/rmJlbW1No0aNaNSoEZUqVWLw4ME4kQ03hPMwwZh+/frx7NkzfvvtN+rXr5/pk4X/D0gXCY8ePZoWLVoA8OLFC5o1a0a+fPm0eq9mBImJiVJpS/ZcyfWHQf9AEaHWUNBFMPPVAT50qlWKyS4uDBkyBBCUjcLDw3F0FMqTli1bxqxZsxgxYgQlFo1j3pb9tGnTRspY3bhxIxcuXGDTpk0MXbyJWduOkt9RhY2lkjbVStOiSE6u3HnGmJPXeBESQf/+/endu/dX31hRUVGMHj2a3Llz0717dynz0sPDQ/juX9So0jMA/xuZ0lamCoxjI1Faqr5qQva/iMePH9OrVy/MzMyoWbMmderUoUKFClLoRRRUaNGiBbdu3cowCX8tMsPizeg2uj2pIYlEO076jQ4TF2FkZMT7wM+EP7uOmYUKG0cXChUpLDWNSYvVmx7EhGh7u5T2rhI5/7TBB5WNuRQrztGrF8OGDWP//v2UKFFC2sfa2prw8HASEhK+KcmI0sAfI+PAyJyBv86gYtXqDOvTnVGjRrF48eJ/rbVmzZo1yZcvH2devKYp2bBBMEqWeTWj47pJ9OjRA29vb4oWLfqvnN/3jHSRsIuLC6dOncLZ2TlTf+wLFy5IikuuHtnTvJ/oihYFN5QOrqxdu5ZRo0bh7e3N+vXryZ07qd1foUKFGDp0KJ07d6ZKn1/JlSsXffv2ldYbGRlRvXp1qlevztOnT1m4cCFKa2veffxIl/kbqVfYk1NP3lCwUCG2LpssWadfi7i4OM6ePcvZs2dZv349GzdupHTp0pJLX2zELXczGrKCM5t80zNoR6g1BPm/x1pl+a/32f2nsWbNGimRbs+ePezZsweVSkW1atWoXr06pqambNq0CTMzM/Lly5ehz/iaWPC3JN/MgnjPKExMsS9SBbcvrmCReNNKwIb6AqcXIhmveym0MwwOEMJRoiEiIm/evGg0Gh7dv0vxkqUz/HmGoKvJL5/I1G7YmNmzZzNkyBDOnTuX5jKmzIaxsTErVqygR48enFGrWbt2OQea9AcEL6S1tTVnz57NImE9SPe0zcXFJdMH2LJly0r/v3xwM4UtU4ffpUOSYLm+LOWcOXPi6elJu3bt2LFjB87O+rutFCxYkFWrVjF//nw2bdpEv379eI8FDRo2ZMuWLZlGwCDElHbv3i11auratSsvXrzgwYMHuLi4UKxYsUxxQ34tdLM59Q3Qr/2D/pP9datXr06pUqXImVNwmrq7u9OiRQvevHnDuHHjGDlyJGq1mnPnztGgQfK4phz6fut/4vcXf8+0ZOymB3IpSV3oU95zs1NKBJxZ0CfGYWVnWC1PH0IKC1Ud9+7d01pubW0NwKMr5zI1k9wQxGoUECZXDdt3p1SpUskSYf9puLq6smHDBpRKJd26dSMEQV/b1NSUqlWrcuzYMUlzOwtJ+C70xSwtLbl27RoVKlTg/ds3lK1YGRDc1LqEr5shLZedNI4OI8Q+yZ199+5dqcuNCAcHBylOnFYYGRnRv39/+vfvn679UoLogs+fPz9WVlacPn1aq865VatWKJVK6tWrp3UN/omHXLSmxM9KLbYlTzIJev8aV9eUG5z/f8QPP/wgJRWePn2ayZMnc/r0aQ4cOMC7d+8wNTU1mOSiDxkh3fRawfp+1/RkNhvqmibfNqVtIO3WbVpgqBmDPgJW2ZgTGR6L0j7pXtVHyiobc0pkF2QvO+UxZhWCISJH3rx5yZcvH8dPn+WnfkP/kWdUDitTBa1bt2b8+PEEBwf/qy00XVxcWL9+PT///DOnEkJoEmTM0uJNMSKaN8aBNG7cmKlTp1KpUto6Tv0X8N1EyV+/fg2A4xfL9OnjR1QsnJubV4XawbTUw3p/FvrE1q5dG4Dr169/o7P9erx+/ZouXbpQqVIlPn78iK+vL3Fx2h1lBg4cyNq1a9NUlvQ9QGVizIMHDwxKa/5XULt2bX7//Xf8/f2ZNWsWP/74I82bN6dJkyZcu+/NyUuGO+78Ux4PQ2QrJ1rdhghpge62qe2X3gYwInQJV/e9b0iUQVe0qIQFAvGKLxEqG3Pp5ZnTVpoo7H8l/Da62sgmJib88MMPXD5/lsvnzmTo+3wNItQaaQJ48+bXeRIzA05OTsyfP5+goCBO8omPxDLnwSn++usvnJ2d9ap5/Zfx3ZDw3LlzAXj8RSv22MF9hIWE8PjBfa3tUiKgwtYa1q1bx+nTpzEzM8uULkaZAY1Gw/jx43n8+DHr16/n4cOH5M6dm0KFCgGC+7lnz55MnDhRy4ocP348sQrtBKd/UpxDhG45hXxgzaYykxS7oiIjiY2NRaH4viYH/wbE+ta9e/fSvHlzvLy8uHHjBnWqlKde1YpafaIzA+mxguUELP6WcvJMK+nq61yUXqTXEpaLbxiyfOXkm5ImtJxs5S85POwtcbEyp7CLNc458+Dh4cGNGzeSHUuUz+3RtiXBH3zS9Z0yAypnD8qVK8fw4cNp3Lgx48ePp3jx4vz88896zzclaDQaTpw4IbUuzAjEZNIPxLIPf16/fo2VlRWfP39Oll3+X8d3Q8KtWrUif/78LFq0iK3r10jLTU21Z53yQUV0RYsyk/vPXWfjxo0MGzaMW7duScX//zZevXrFgQMH+P3331m4cCHt27fnzz//ZNasWYCgkNW6dWvKlCnDjh07cHBwoGHTZlITCHGAlRPwt7KC9Q3mqbWhi4qM5KcfW1CrYmmUSqXUb/q/DDErukyZMowfP57w8HDq169P5Jd6UlFV7OHDh9I+/3TcX066ItKbBJVR6FO30oWu2pU+0tWVo0wrAVvaGLbALW3MsfxiBUOSS71INhvy5MnD3r17pVaXIsqWLcvvv/8OwLb1fwD6J69fM2HRh4+RcYTHaIhOgK37DlOyZEnevXvHgQMHsLS0JCQkhB49ejBkyBBevXqlt9FNfHy81vIzZ84wbNgw2rVrh4+PT4bOq1SpUty6dYsVK1YAQgXI8OHDiY6OZsGCBRk65v9XfDck3KJFC/bs2UOnTp2YOmYE3nduUK12XSrX0I7pikQUodZoDVohYeH0HDyKWrVq0a1bt3/y1FOFn5+gvuPj48OqVasAwfKXN3tPSEhg9+7dDB06lMTERDb8sUbvsf4p6LOg5IOHo2WSdOWV82e5eOYUOXLkYOnSpSm2W/uvoFy5cjx48IANGzZw/fp1fHx86NmzJ8VLlASEJJYHDx7w8eNHHjx4IO2XEbGVtFrBhpKtdLsN/VNEnBJ0yVcfAaeUvJWWrkgi2aYGOWHW6T2a6Ohotm/XFqQxMjKSMpM3rl5OTLS2FZlZpGsI4TEaNCZmHDp9kRcvXuDo6EhUVBRBnz4xb/xwvB/ep0WLFjRq1Ij3798DgoLhypUrqVSpErVq1WL48OEMGDBAqlSJ/eBHu3btOHXqVIbO6erVq/z999/Y29szY8YM7t27x5QpUyhQoECmfe//D/huSBiEG3nUqFH079+fM2fOMHrcBHLl1q/DbGWqkCzh4EQL6nQeSFRUFL/++ut3Vx4jpuX7+vri6OgoLX/x4gVKpZIGDRrg4eHB5s2befPmDYcOHSJbtmxSLFiOzGxVGKHWJHtB+rRvX/kHMn3scEqWLMmaNWukxt9ZEBAeHs7MmTMBIVYcHx/P8fNXuH79Ou3ataNu3bp07txZS7ZU/lukhJS200fAcoikIBKtSHTyl3y9LvQRd1qs27RsAykTrL7sad34r0sq2dXy9frIWFzvZqckICJWul5lypTF1taWpUuXSnksIvz9k2qO7VTmWIT74qn5lOJ5ZAbE3zY8RrgflA6uzJ8/H4DA4GBK1GjA81c+7Nmzhw8fPtCvXz/evHlDmzZtWLliOQ0bNqRLhUIEBQVh+v4lP3m6salCGVbnK0rBOGNGjhzJnTt30n1eixcv5tChQ4SEhPDgwQOsrKyoUKFCpn73/w/4rkgYBCL++PEj2dzcKFy0WDLC0UcQDRs25P79+4wePTpZ5uL3gM2bN0v/y610Ozs7qlevzqdPn1ixYgXjxo3jzp07VK5c2SABZxbSMnjLLSdDM/nzh/cREBDAnDlzvrvJz7+N0NBQBg4cKHlCALwfPeT9C28GDx5MYGAgI0eOxN7eno4dOzJ/3hzkUu6GfqPUSDqlSZTozZCToV9ojOTKTamu1hDR6nsvXya6OjPLstaNCes7Z0OWsIudUiJY+f+pfZYcMTExuLu7kyNHDq3lrq6utGzZEoAbVy4SbeNBtI2HQSLOTOtYTsQfPsdRqXEr3gdH8D44giJlKxMSm0irVq2YOnUqb968oWnTprx58wZNQiJmwW+Z2bcdpwc0ZkufH/i1QQUKWFvjuWMjqy6fRq1WM2LECNLQZkBC//79efbsGSC46vPmzUu5cuWSJbVl4TskYV9fX/7++286dRW0lMWsYN2BRXRHv337VlLbymj84ltCrVazfv16qlYsR4fWLalSpQobli2ifPnynDx5kvfv31OwYEEaNWrEjBkzyJEjxzePDaZEwLo1oobiV+Ly96+e4+7u/s2E5P9X8e7dOzp06CBZEJUrV2bnzp14enrSo0cPnj9/zpIlS+jSpQtLliyhf//+/DZrGkf2/6V1HEPeisyAaPHqkpj8fUazl0Eg3egbf9OjvCcHtm5I97l9zXp9SI1w04pWrVrx4cOHZK0pjYyMpB66qxbOJThQIF8fhTNlzMP0tn/NzDix/LkVyVh+3/iGRDJhwgTu3btHxbKCqEihPDn5qXEdFPYumDo6YeFsT1TPccRPns6uXbskoyF//vxpPo/AwEBJQhiEfu6vX7/mp59++qrv9/8V3920ZPny5VhZWdG6RdMUBxxzEti5YS2dOnXC2dmZwoULM3To0H/wTNMGU1NTSpcuTVhkDBuWLZKWRxsr6devHwAzZsyQlosELP/uhixg0R2fHtJOqTuSIZclaFsxonygc8Jnzh/clar4xH8NT58+pXXr1tL7n3/+mT59+mBubs6aNWuYMWMGbdu21VIP6tevH7dv32bT2jU0bvljhj87pWxouQtal3zllqOcrBwtzfAOSGqpKcIvNCZVQY2o8FBGfbnHs3lmTCUsLRDbDGZUHcvFTpnMcg4IjZG6J+ni0yeBXMeOHYunp6dW57R+/frh5ubGypUrmTd+GLNWCV6wW7FCkpdIxOJ7XRgi4tTqreUQf+9sKrNk94NvSCTFixfnyo1bqAN8ADAO9EET5M/LPLXZcH4DNzcO5Pr168TFxVGkSBG2bNkiCQmlBWL3OhEmJibMnDmTypUrp/kY/yV8NyR8/vx5/P39OXPmDBUqVyVHweLJmtfL8ceKZcyY/Cu3L55GqVRiaWn5XfZrTUxMJCEhATMzM54GRFDQRRCq79KlC35+fpQsWVJyYekjYF3IxUlSWiY/nghDBJxW8gW09HvnTZuMmZmZNJnIggCRgAsVKsTkyZO1yNbV1VVvw/NXr17x+vVrjE3Nk9WDpxVpieWLBCy9D43RamYvxkZ9Q6IkknWxMufe+zAt0pX/b4iQzx7YA4BXrcbk9kqeK5AZWtBudsoMWcX6YGljrnUtfEOiJIKHJO3mz5qk32X37t1aJOzo6EjPnj2xsrJi+vTpUp9hUTdbTsaGiDg1pCSaIk+clJOxCNEi9rBXYeriiTrAB+8EJ+Kt7VizYgXLly+nWLFidO/enTZt2mSozFMefjE1NWXFihVZseAU8N2Q8JQpUwj4os3avF0Hg0Rko1Rw/fJFZk+bBMCffx2S9v/eEBsby/Lly7l9+zYAtkQDVpi6eGJK0jmnZMnKO0V9DTKDgBOCfLl98wUmDm4cWjGbZzcu0LJlS61ksyxAjRo1qF69Om3btk3T9u/evaNfv36YqmyYt2pDuj8vJetXHIDFOLAI3d668r66AQjWoV9ojETAkLL1q7vOLzSGXKUqU7p5F9oOGS8tl7u309IDODWIn+sXGoOHvaVkDcut+YDQmAy5og3FlYfOXsLN86e4sHsDFy5ckGrj5fFOsYPYnwsm02HIBELijbQaWNyKtdVrFYuNKnSRkqs6I25s35BIQnyFxLK7d+8yY8YMnjx5woABA+jTp89X5XcMGzaMevXqERYWRokSJZLFzrOgDaPENETbw8PDsbW15cqVK1hZWaW2eYZw+fJl+vTpw8Yde6hQU6gz1R1cbJQKEhMT+bFuNZRmpqxdu5YLFy7w5s0bOnXqlOlB/4MHD2Jra0v16tXTve/79+9p1KiR9L5F/VpsWLkUlcoSUxdPrW1Tcyenl4BTsoD1uZ/1PcT6Bsg9C37l2sEdgNAb2tjYmLlz52bo+mQBjh8/zoYde3jz5CEWdo7MXLcLF3ehgYnceknJIs5IFrScgOWN7SNCI8mWU3DBWtqY42KnpGxuBxo5xkjt/CziQnj56g2FChfE2FiBhbUNtVShrLoXisreGWM9Qi1ychYJJi0tCNMK8fgpJWrpg5yc5dcDkr6/3BIWP8vFyhxHSzPev37BoBY1iY+PR6VScfr0aYl8IyIi6NSpEy9fvqR37940GzIVSP6sBUTE0sgxhiNBhicJ6Y3Lyy1jffeRlamCEN/XqNVq5s+fz9atW3F3d2fBggVaVn0Wvg4RERFUqlSJsLAwbGxsDG73r1vCgYGBdOrUCV9fX1xdXalavSaGJL4DPvpzcPMfPH74AAcHBxQKRTJt6MyEWC/3999/S8L8aUFCQoKWDjRAz/atUKmS99f9twhYF2lxCzb6eYREwqdOnZKE67OQMRw9epQH1y9Tv31PmvUchIu7fs1tXb10+XJD0PVo6CMofX11I8NjJdWogNAYDt/5wGEEy9LdzoJlv3QkzP+dtL2FhQXj4+NRq9Xkr1yPJiMX/GtZ8vos4tTqhVNbL7qk9RF79tz56D5yEmtmTSAyMlKrn6+VlRVFihTh5cuXrF69mmbdB4Kti952jiIBN3KM0XovneOXZzM1Mk4tbqwOeAvAHR8f/vzzT3bsEJ7lUaNG0aZNm3+8H3EWBPzrJBwTE4Ovry8AI0aMQG0snJI+K7hf227cunYFgODgYIKCgr6JNKVarebPP/+U3ms0Gtq1a0eDBg3S1Dv5ypUrWkliBfLkIqd78s5C34MFnJ6YXCXzYKytralQoUIWAWcACQkJrF69mocPH/L06VP8/f1p2LYLPw0XQitizFEf0hLv1ZeAJSI9je0jZdZg4MNLhJ9YSakff+Rt4VZEB2vLbf78889ER0ejVCpZsmQJr29dIE9ZwTOizwL+1tCNEetaumlFVHis5JY3ZFkHRcXRvPPPPL51jed3rxMVFaXlKZwyZQodO3akS5cuHN23m4ZdU24AIydjfZZxWskYkqt1gTCuLV26lE2bNmFrayv1gk9PY5EsZD7+VRLWzSL19PQ0uO3po4clAnZxceHAgQNSn93Mxrx589i2bRsAVapUYcCAAbx79474+Hhy5cpFnTp1Uty/ZMmS1K5dm9OnTwOwdckcihXMRwIkc0Ubwj/lgk7J9ScOopfuvyL03mnGrJ+Hi4sLkyZNSte5/Zfx6dMnyWvj6+vLsmXLAKjZoCkdhzWmUNV6evfT7U6VEvRpQeuzfuVJWIYIOCI0Eis7FZHhsYS9usP7U5uJfP+WRYsWoVKtlpqMiOIyYitQtVrNsuUrJCs5RTWrb6zEZUjIQ18CmhzyayMiQLZel5BFMuw2YgJDfqjNvHnztHJTTE1NKVq0KE2bNmXd7/No+EMbsHHWaw1DUnz7SJDSoFUM2nFj3UmbvnvmY2QcVh+9GT58OPfv36dv37507do1y/L9TvCv1QmHh4dLrQEdHR3p0bsfRasa1hz+dfgv0v/u7u7fhIA/fvzIlClTJAI2NTWlQIECvHsnDCzPnj1jyJAhWuIb+qBSqbQyhh3skmdBGrKCPexV/5gFLCbc6BNqEAeyxMREzkzrzrm1cyRrx87OLl3n919EYmIiCxcupHbt2gwZMgS1Wo27uzvm5uaULFueuas30a7dT7jaZZ5HQdcClv+mIgFHpmABi4gIjSQiNJKnG0cT+V7ozW1vb6/Vfm7jxo1avbifPXtGgiaebPmKamVViy8R34KADUlapjQRiAqPlUhX/r+cgCNly0HbmvYLjZGUtEwd3Bg4cCD79u3j1atXyT6rTZs2REdH4//gaponVkeClMnc1BmBRbgv1gFP6Nq1K+/evWPt2rX06dMni4C/I/xrJLx3715CQ0M5duwYd5/5MGrabIyMjPQKc1iE+5IQL0SKjY2NqVKlyjc5p8uXL7N7924A2rVrJwltlCtXjjwe2aTt5IXo+hAZGUmbNm2k9wVqNOPDx4AU9hCQkQzor3FB61NIEt+LA9vjS6cI//SBcePGce3aNanzUxZSxubNm1m/fj0NGzbk4sWLVKtWjTp16hAbG0v9ps21thWtGV2rJqXevrrb6FrAkuxkaIwWAYsEK0dMiD8xIf7S/yLMsyX91j///DOLFi3i4sWLrFixgmzZsmkd482bNwAUKSaUY/1T7mcRKXl0dJOrImWEG6VDuiLE66S7DehP/GrdujXW1tasXbtWa/mTJ0/o3r07RkZGLF66nPgvTe0NhR306WTLLWPQvrYpZUZbhPuiVqsZOHAgkZGRbNy4kdKlSxvcPgv/Dv41ElYohEznfF5lU03kiLbxoGvXrpQpV4FcuXJx9uxZNBrDMTKxNi+9aNasGYUKFSJXrlwMHjyYmjVrUiJ/LjYM7cCGbbvw8PAgf/78khawISQkJNC8edJAGx8fT0hYuOSK1iXOjFi/+qCPgEUVHblcoe6DLp/he9hb4manRB0Thc2TUwReEUrAvqat2X8N8fHxrF27lnr16uHt7U18fDwWFhZUqlSJVVt20aPfoEz5HN2Jla4LWiTfT+/D+fg2mIjQSIlw5WQLSM3tlfauxAW/ZVTpIMzdkzJl27dvT2BgINHR0VStWjXZuYhWcViAUCOqz+L91m7olIjYUIlSpMw7IEKfl0Ak4mSiHl+u+1ZfJU5OTlJ2dHx8PDNnzqRjx47ExsaSO3duHt67w+Lxv2CrSHpO5YSqW+Ilf04NWcWGyNwiXMiz2bBhA97e3ixbtiyrVOg7xb8WE3Z0dEStVhMXGytJvYGQgKUvKWvkpBn8Zq9i8eLFDBkyBB8fH/LmTd7cYd26dSxevJjp06enK+Hg8ePHtGvXDoDJkydT3jaWk8smoQkSBqs5f/xBeHg469atSzUZzNramhkzZhATE8Px48fZumQOhUuU0bttZpCvLnRrgNOSqBMQGoNJ8Cv8npzH0qseN9bPIPjVIwC8vLy0YvdZSBl+fn4EBwdz9+5dLC0tyZ8/Py9evGDe0tUYWQi/t+59nh5FJNBfXqZLwKBt3enCyk6ll3CCLq5iwF6hJsnIVImlmYI9e/Ywffp0AC5evCi12QQhHixm2sbHq7WI5dG1C+zfsBKVhZLW4xdiav7PuUF1SVkU49DtGQz6iVdcLm4fFR6LpY25FhHLidMsWy4OHRakLKOiojhw4ADGxsY0b96ciRMnsmPHDubOnUtIdDzTlwhd0oKi4rRES/SJj4jvxXgxCGVN+sQ+JO+JwpnYxxdZtWoVXbp00RKMycL3hX+NhENCQjAxMcFKJ8tWHwGDQFaRkZFs2LABa2trvQIRV69eZdEiQRry3LlzaSZhtVrNH38IPUArVKjA2L5dICYcIkNQOLqiCfLn+vXrlCtXLlWN5FOnTuHv70/Hjh1xcHBApVJRt1pFrYQsD3uVpFrztRCPZQiG6kR14WKn5M6urTy/fAKL4/vRRAv9bpcuXUrVqlVR6Kn/zIJ+eHh4ULhwYby9vaVlQ4cOxfpLrWB69J/TkqBlyALWjf3GhPgTHSJkN9vnKSEjI1cSNfFAIkYKU0xUjsR9eomLiwsBAQFoFEqJgEEoS/r06RMLFy6kUKFCZM+enWPHjlG3XTdKlSolbffyzXvmDegovW+dpNmRaZCTlqEYsPya6ENK8XExSU2XiEWIyVRudkpqdejDxc1LOHriFKFBgrTl8OHD6dKlCwCdO3fmr7/+4s6V85InT0zSktdP69Y964Mo9uGDc7J1nppP3LlzhwEDBpAnTx769u1r8DhZ+Pfxr5Fw/vz5iY+P59qZY1Ss3RArU4XBwUlUjfr55595+vQpf/zxR7LkoPj4eBYsWED5vNnxD/2cruShkSNHcurUKebNmyeUSX3RVBVx8OJNnjx5wpMnT3j27FmK/TCHDBkCCLHr7du3k8vDDTu35DXG38ICNnQN5QSc9PmC26zsF31cO9NEHqoFco4OC5a2c3V1zSLgDCA2NpZaVStTv0kz8ubNy48//ohfWHSy7bKpzFKN/eoSsaEM9zuvhd9Nl4B1Xc8W9kI8NzExkVFewTx48ICFS9eQECMk6hmbW1OgQAH27NlDs2bN8PHxwb5AWcJePyBBHUvt2rUJ//yZxIQEDh06hIWFBQ4ODnQaOVXrc94+uQ9A/jKVaTVsapqs4LQKdRiS0JQfR19WuBwqG3MtT4F4nZT2rljZpfx8itawPN5cvmIlGtWugUaj4c8pg9m9e3ey3tq9e/dm1KhRHNz5Jy3bd9Vap88qFr+LbrmXo6WZRMCemk/4KJwJiopDhZrHLx8zZswYcuTIwdq1ayUXeRa+T3xTEk5ISMDYWH/YOSwsDGNjY06cOU/F2g0l8hAtX1GgQCRgHx8ftm/fzvDhw/WKiS9ZsoSnT58yrkV1Zuw7x4c9e6hcuTK1a9c2eH6xsbHUqFGDyMhIvLy8GDFiBADG0V+6najsITKEA7eEllxOTk6ptvMqWLAgT58+leLGXdu0SHH71CC3clMjbjkBi3FgQwQsV/65cGQfo4YLs2WVSoWZmRkhISE0bNgwqwF3BnDr1i1evXrFihUrqF+/frL1KU04DSE1opaX4egSi2j9ymFtHsaNyT/RNdkaSIj9zLNnnwkODpZyAUKe3aRUx+G425jy8fUzipUpj2fh4uxfsxiHbO5UatRS2j8uNoapXZphqrSg46TFFKtWD4VJcl33tKhnpQUicWXW8QQy1ibi1KxhOQIiYgkODqZw4cLJ1OQaNWrE6tWrWT57CkqlJdXrNwL0J+aJx9JHwAA51R85++ITp589xizUl7Nnz3L//n2io6MxMzPDzMzsmykcZiHz8M0Ss27dukWJEiWknpK6mDhxIk5OTnTvrd9VIndDe9irePz4MYmJiXo79jx+/Jh169YxdOhQ9lwX4pjx8fF8/Jh88JHj0aNHREYKJNe6dWvUAT7JrOBT7+O48fgFAOXKlSMxMZGwsOQtyUSMHj1a+n/DskWMHTUixXMwBN+QyDR1R9LdJi2iDiWy20oPc+TncOYOT/oNIiMjyZ49O7/++itz587N6hGcAciTc741RMKRu6ABKQlLJODoL1aefZ4SOOXOSz4H/efm5OQkTZx3794t5QK4u7tzZ8sC/Hx9SUxMpHpeFzwLFWfwgj/oPGoq+YqXlshBoTDh/cunvH50l3h1nF4CTg2iize1UiOReOVxaLk0p2ixyknT0sZceq+yMU/V6hUhT+ASLWt5hYFYtuRiZU5ggjmBgYGcOHECtVpbA3DFihUUKVKEiUP60KVxDZyNo8mmMpO8HY6WZtJLXublYmVOEWcrsqkEF/bSpUsZ/GNdFo39hfnz52NkZET//v1ZuXIlcXFx+Pj48OjRI2JiYkhMTBT6B6eQ0JqFfwffjIQDAwMBkqXsx8fHExoaStmyZQkICKBiiSKcOHpYq12flalgARd0SdLbvHv3LiqVSm9S1M2bNwHo2LEj0fFCA/FcuXKl2r/SwSGpVVmZMkLilGQFA+eu3qRJkyYSmR85coQ2bdpQtWpVFi5cqHWsoKAg1Go15cqVY+XKlQB8CgxCoVCkWaADDJOvPivYNyRSsqgMWVa6pSJudkoSExPx8b7PhgXT6F2vrNb6uXPnsnXrVtq1a5dFwBnAwYMHGThwIEZGRuTJkydN+6S1dlQOuSs6Na3kaB13dNFiLjSoJ8i9Tpkyhd9//10qOQoMDGTYsGGA4F3q1KkTp06domfPngDcPrCZW8f20qdPH+zMFVqEAQKBKExMWHryHr2n/oZXjYYGz0ufxZoa6YrbgLZVKD+moeshJ185UooJpwSR5ANkv4FoFTfuMxKlUsnw4cPZvn271n6urq6sWrWKnTt3EhYSzA+1KvPXhpVEBftLZCy+5NfXxiSRkKBAySviEyE885cvX+bOnTtMnDiR69evS3kxCoWCn376iYoVK+Ll5UXTpk2pWrUqDx8+zND3zcK3wTcj4WrVqgFw+PBhEhISePToEb1796Z8+fJUq1aNK1euSNuamgoPkki++kp2Xrx4gZ2dnd4mDfPmzQPA4eFh6pQqhJWVFevXr0+VRMLDw6X/9+3bJ/2fYGHL6VuPadbjF0qXLs2JEyfYvXs3FStWxMXFBXt7e8zMtB/+mjVrUrp0adq1a8f48UIGSh7P5LFgQ9Ztapav7jr5e5GADXVGAm3xja2zxzK5c1OObt9Aq1at2LlzJydPnuT69et4e3tz7Ngxg+eRBf0ICgpi69atzJo1iwIFCnD58mWDNdXpdUUbQnrdrvZ5SpC7mBuNi7uR3dmBshUrs3v3biIjI9m6dStjxozB1dWVxo0bY2FhAcD69etxcXGhbdu2nDhxgtWrV1OrVi3UajV96pbm2f3byT7H0dIMKzt7KjduhYdj6u36dAU99C3XtYrlBKzXjZuG2LJIyFZ2KqzsVCjtXaVSLTBMzqJFHCV7+bwN487rYHxDorj3PgwHtxySYE+hQoX0ekUKFy7Mhg0bsLCwYMH0SdQqXZRujaqydPp49m7dwLnjh/l09TDPj27l0OJJdK1WjAalCxD25hmOlmY0bCloETRt0ZJBgwbRuHFjrl27homJCd27d+f99RNs2bKFxX1b071mafI62eHu7s7YsWOTWedZ+PfwTbsolSxZEo1GQ+PGjTl37hx58+alR48eWNo7c/vOHV4/e0KP3n2pUr0moN/aE93Dm3bsodcvI7h8+bKWbnFMTAzlypWjXbt2rO9QGcsWg2natCmzZs1K0zk+fPiQ9u3bs3TudHp37YipiyefP3+mQL685Mqdh99//z1NiQ0bNmxgwYIFgCCrWa5kcdb8Nhe3wknF8frqg9Pick5pH92eyymVJQEEet9kbr/2jB07lhYtWmgpj/3yyy+cOXMGgHv37hmM52dBG7dv32bIkCGEhIRQoEABLl68qKUmJUK3X7S+3y016JOlvPM6OFksWO6OFhOxnHLnxTm7DSs7lMTKVMGpg3vo2lWICrdq1UpLclEuKavvXjh79iyDBg2iTJky1GzWmrbdekuTXn3lU4YmDKmJeujrZy1CJF/559x7H6ZlCadGxoaum6HkLFHWU/d/EFzbljbmlMrtIGRtv3zCop5ChUbNmjVZtGiRwU5vERERnDx5kitXrvDo0SPevn2rlX9iZWVF7dq1OXDgAEMmTKdO+14APLp1jetnjvH2xVNM1VF06D2Agd2EjHR1gA/G0WFo3noT8+IxACdsStC6dWsWLFigN18hC5mHf7WLUmJiIt7e3tjb2xMYGMjdu3dp1qwZf/zxByqVQCKNm2knLMkJWCRe4+gwsBBm0qW9BOGAJ0+eUK5cUoPwgwcPAtClSxf+CgkBSFXSMioqSrJcxEYN7X5IKmcaPXo0oeGf2Tx9epozC7t160bVqlX54YcfCAgI4O/jp5g0ewGrN24xuE96CVi+T0rWlCEC9guN4cntWxgZGdGqVSst6bq3b99KBHzkyJEsAk4H9u3bR1hYGH5+fqhNVQRrEvUUjnw99MlSQnIiESG36kSUyu0geZvaVSkqJWa9fPlSK5GyYMGCLFu2jJMnTya7Fw4fPszUqVMxMTHh1q1b3Lp1CxuVBY3aCqU4Ysa3nCTlWb+gncyUVnUt0fqVN7owdK/Ls5ZTImLRGhavn0CqwnUTLWE56cr/6v4vQvxd3PIWYuis3zm8bQNnz55lzZo1WnK2clhZWdGyZUtatmwJJIXtIiIisLOzY9u2bSxfvhyAAm4OeAd8xsXKHNfCpWheuBSOloL72kapkMofTV080by5h8LRFdOQAF7mqY3ihZDfklXx8P0gQyNtQkICw4cPZ//+/VrLN2zYQMOGDSlVqhTt2rXD0dGRU6dO4evry7Zt2/SSo67rWYuAZShepQ4WFhY8fvxYWhYTE8OqVauoU6cOOXLkoFOnToCQFJYSZs+eTePGjSUCbtKkCbZfZirv3r1jxYoVxMTEUK9ePQ4dOiR954SEBEnAXh/y5csn9eMsXrw4nXsJCU9pTbJKCfLYb2rSlPogDnrx2UuQmJjI+fPn8ff3Z/jw4UybNo0mTZoA8Pvvv5M9e/avOtf/Eh4/fszBgwfpM3AwGnNrojT6HUupTZ5SiwvryhOKv6fP2zCtZCwRInGILlan3HlR2ZjjZqfEw16F5vFZEh5dkjxG9+7dY82aNdL+3t7e7N69m0GDBrF582aCg5PK1rZv345SqSQ+Pp6xY8fSsmVLJo0cQvfG1Vg4dRzx8fGpfh/duG9qYiW68V+5ApxuDbyuTKWLndKgYpYIlU6ilnj90pq0BUgylz5vBWvcLzSGEvVaMn/7Yfr378/y5cu5f/9+mo5lYmKCk5MTnp6e2NnZSQTs4JZdCvXJJx5BUXF6n/8EC1v2PwnmSHx2lDf+4sRvQhnZlStX+Pz5c5q/Wxa+HTJkCUdGRnL8+HGOHz/OihUrWL58OY6OjqxYsQJjY2PGjBmDg4MDtWrVwtQ05czI1EQrjKPDSLCwRaFQYG5urpWZ/OTJEz5+/Mi+Tau14r9yt5ouwsLCOH78uPS+WLFi7F82A758jr29vbTOy8uLEiVKEBkZSf369QkPDydv3rxa8ePw8HAOHz5M69atCQsLY+zYsQwePJgHDx6QPXv2ryZfIFnylb4M6LQQMEBRrxLcKlSeESNGYmZmSmxs0oPs4eHxTfsz/39DYmIic+bMIXfu3PQbOY49e/9i+dwZQgMQl1Ip7puWLHZd6JJNQGhMMgLWdZGKUNmY45lTyIr3DYnEFfj70GnGrkmaSJcsWRIQXNFt27YFhKYmjx8/xs7OThK/8fPzo0KFChw+fJhTp04xZcoUTp06xaNHj3j06BH1m7WiYFEvXCxNCYgyHHsUy+QgZQ1kfVrJ8mshF+zQJWB5L2AXO6Veq1hXSUv+VyxNigyP1bqu+vS3lfauksKW/HNKZLeldOdhWG/ezO7du/WWWKaEDRs2SP//Mm/VF6Us/e593fvq0YdQfvzxR+l98ZzZ6F6pODsPHcLPz0/q6pWFfw8ZIuGYmKQbzNfXl/79++Pn50dCQgJ169ZNMSv5/v37vHr1ikRzFTlz5cbVLanPriErGARLNDQ0VMtKO3r0KObmZhRwc8DWWqO1rS6CgoKwsrKiR48eREZGMnPmTGxsbGhTSjhewhe3t3lUIA72dgSHhLJp0yaioqJYs2aNlMQlPkCBgYHcvHmTkSNHAsLMddq0aVSqVAl3d3dy5cqFp6cnH0INZ66KpCpmhouTkZSSriA54epaHfJWabo1jAmaeDp078GFY87kslJw4MABad369esNnmsWkuP06dPcvn2bZcuWERVvxOCegjtWLsOq77fUh5QmUbr13roKULqEoEvEVnYqLG3MtQhKE+SPfZx2spBYviJPIhI9T+PGjUOlUlG7dm1iY2MpWrIMtVt1ZFTvzuzevZsjR47w9u1bOnTowJAurUkAlJbWjJi7DNci2k0D5G34UiJfOeTbyuPh4jVJCeL39g2JSpGIIakGWLy2usSsC11FMqW9qzQxkrdsqZbwnMjIyGQlQjdv3mTy5Ml06dIFhULB+/fvKVWqlFRfHBAQwO+//46ZhYqhf+zH0kMQ/0jNhS8aN35+gpZ3165diYiIYM+ePRQoVxXr159SLeHMwj+DDJGws7MzY8aMYfbs2YBAxAC9evWSXMJyPHz4kGLFinH//n1KlCghLS9YuAiHTp6DNKhHJQS9w8LCQnKhHD16lC1btjBq1ChsbYRELU9PT3x8fChevLjWvseOHWPEiBEMGjRIqluuU6cOlpaW2IU8hMgQjBGIOD4+nqho4SFdsmQJO3fuJDY2FiMjI6lO+erVq/z8889an/HhwwcSEhK4dOkSAPv379dLwLoDsS4Bi//LB+/wGI3enrFy95yuopK4zi80hhdXTxH+yY9C1Rrx4tROTm0WXFuKBj9onYufnx9usklRFgzD39+fiRMnUqxYMapVq8a9x0llH0WKFEm2fUYzonW1oVOTYASSEbBzdiHU4manTKrlzVmYbOpEStjZci80DDMzM/r27cvu3bsNllaJFQGenp5cuXKZhX2G0nnIWNYunMGZM2dYtWoVq1atYvfu3Vy9epVAf1/evniajIR1++HqI2LdBvYp6Z+L0LWCdaG7PiUy1lfKJEJuNRvqySx9xpe/vz/xJyEhQUtKNzY2liVLlvDmzRumTZsmLTcxMeHOnTts3LiR+fPn4+joyKD1R7G0sUvx+4kQY/4Az58/B4QM7UaNGmFtbc2mTZsoUqQIM2bMSNPxsvBtkeHsm44dO3Lq1CmKFy+OiYkJVapUoWfPnno1nUV4eHiQM6dQtqNQKHj39k2ypA99VjCA38cAoqOjcXV1Ra1WM3nyZNq2bMbUoX2kbYKDgylTpoyUdPDs2TMmT54sKWHlz59f2jYqKooCIQ9ROGonrwQEBkmW/u7du2nRogVHjhzh9OnTAPTt21eLgGvXro21tbVWPC1v3ryUqpLk1hXjuIYGYl13vFj/q5v5DMnddnLyFQVO5EX/7x7c4ODsIZxbO4f9U/vg4eEBgJm5ktvH9molnnXt2pWnT5/qPccsaGPKlCmEh4fz8OFDli9fzoNzhwEYOHCgtI2hOHBGXNFyAhahm4wlxjJVNuaobMzJltNBIotSuR1wsTJPul8iQ3AukYe5NQSCjIuLw8LCgsGDB3P7tlB2JLbj9PLy4uTJk1StWpX4+Hh8fX15/kTQxW7daxCDZ/zGq1evePbsGZUrV2bhwoXUr18ft+w5qd+mk0ElKBHiejG+K1+n739dMQ7xmqRWLy1CJGMxTpyWmLEc8npjKzuVliyorkSoOFnyKlcRG488TJ06VSLGiRMnStcakMrC4uPjmTx5MvPnzweg8+x1WgScWmlahFqDb0gkDx8+lLJyo6OjUSgUDB8+nMuXL7Nly5Y017Fn4dviq7KjXVxc6Nu3LwMGDKBPnz6pli85Ojpy9+5dRo79FW/vxwwePlLLdZcSXr95CwjlIHPnziUyMpJendtjEvuZBAtbIs3siIuLw93dnVevXnH16tVkZUq//PILFuZmHJ8xiPIK2cOiSooDu7tm49Pzezx4F0SVXLYoYgQ3tHeCE3fOHePuqw+EhIRQKa8LM1dsZGzfLlg5jWPuyg2ULFmSuLg4KtdugLGxcarWj3zGKkLXAk52DXWyQnV1hVWmsGTuTM6eOkmu8rV4/OABuXPnZvLkyXTt2pVES1s6jpjMkwf3uHVsL0uXLsXd3Z2ePXvi6+vLw4cPKViwYIrn/V+HRqPh4sWLgCD4snLlSvr164etrS0DBgzg06dPekuUIH0ErGv9iWQjd5VGhseSLaeDlqyi6E61lBFwiey20r3jYa9CE26PhbM97ioLFpYszrC7D4iKisLSKFES/L9+/TqWlpaMHDmSbNmyER0djbGxMSEhITRq1EiSRs1dRmhtqFarWbRoEZ8/f8bExASV0gx3Gwstz4ycQMQMXzlJ6+oki/uIFrE+b4DY2SgtRCrGj0UiTourOjUo7V2TkS8IkyTn7DbSMVtPXMa+6QNp3bo1tWrV4vLly9jY2EihrujoJG3xPXv2MGrUKKLLtcPdWbu8RZ8rWn4NRZEjF7UVEV9q1eW6BqlVj2Thn8VXlyhVrVoVGxsbzp49q9VBRReiSzoKM6bMmpuuzzCODqNA3jw4OzuzdetW8ubNS/2aVSlXqgQJFpaYungyqndvYmJiOHjwIKdOnUqm8WxmZoa7uztTpkyhfO4vLjkdK1iEvWki1fM4aC0rbK0B62wUz5XUzHzPxtUAmLp4srlKHSBtJURphb62jiLEh04kYBulguuXLzK4VxdCgoKoU6cOJ9YvxtLSktatW1OyZEly5crF1c2LGb35KFWatsbnwS12797NnDlzOHr06Fef738FCoWCESNGsHXrVj58+AAIim6XL1+mcOHCeHp68vr1a0B/j+f0QDf+KXdD67pOPXPaSqQikk2J7LYERMRKJSzu1kmDsamjE84l8lAPWBSuZlnAO0YVys+dcpVYsWIFb968Yc2aNahUKubNm8emTZtYtWoVVapUwS8wKVta9GZNmjRJSpw0MjKiQYvWX7wzSZ+pa8Xps+rk31ckZPE6+IZE4fNW+Az5xCMtEI+l23lJJGWRiNOCgNAYLHUaQIiQa0/LJ0MRZg50XLiTB8d28fjiUSpXroxpnpK8OneQEiVKcObqTZyze5KvdCWKVKmDo3tO6XrIRUoMQff3BaH0KWfOnFkeru8Y6SLhsLAwLWtXo9GwefNmwsPDJdnH1JARgQoAJ0cH7ty5w+vXrymf30PKhjZ18SQ2NlYr4zkqKopiLg5UK5abjY98aN68OX379sXR0ZECIULsTiJgmRWs1xUuWy9CTOLSlaNMKwGLzSl0Ib8uuiL/ItnqS+CxNjdmZP+fObhnp7Tst99+Y9iwYVy+fJm7d+9ibGzMvHnzaNu2LTdO/I3vq6dYWllx+PBhevTokWX9phNdu3bl+PHjfP78WXqJSXqlS5f+6qx43bCD3NVqaWOeRBZ2Sqkhh0i6gKQ7XMTZiiLOVtgotWVgEyxsMcvnhUVQIJYuttStmIdKAa6oXCzJ4xDDCgRFpzt37kjlMSB4osqXL89vv/2Go5kRQVHAl2cxLCyMkiVLcvfuXRITE/llvFAOI59MFnax1ko0g+RELH5XD3tLLQIy1CdZTnTyfeXQta51uzDpS+6SZ1brLgeBiFU25mmSvUyysJWUatqRUk07Sp9du2Nf/EJj6JzU9RHHVLpEySFOyOVjipjkGh0djUaj0Soxy8L3hXSRcMOGDblx4wZKpZKQkBDmzZvHwYMH6dKli1S7lhJEa1ieBZyeln4eHh64ubnh4+NDdhWYZcuNRqOhSJEivHnzhln9OlA3uy2/7TvHqPoV8Mybnf7lhFKLAiEPIcRP2/rVIViRXMFAhrYB8hW/C+gnYEMWkPjd1QE+mLp46s2OTk7WSa3vxOL8UH9fDu7ZyZAhQ7C3t5fqpOvWrcuJEye4e/cugYGBFC5c+Evv1+P4XDwkHfH333/PKlXIACwsLHB1dWXcuHG0bNmSp0+fkj9/fnoPEcg4M61gfclYIvkCWgL/4j1TytXqy3baz5iopASgzFcEJyD6UwiWfkEAVCzrRamihbjzyFvqiezi4sKCBQsoUaIEa9euJS4ujgB/PxwdXAFHxv+xB4yMyOdVhr8WT6NkbjecXLIlSypMD+Q1vyllQGtNSnSQGoEZgq672hDEyYAhl7Qu5O5ucZKhD/rc8iL0xdjFSbqHvUoi4Jsv/ejQoQMBAQE0bdo01XPLwr+DdLuj5WpVIDSqFi2A9EJ3cBAVXnSRmJhI94HDuHbnAbVr12b16tVMmDCBqVOnEh8fz6tXrwDYd/4G/Yf/xILWtbFwFghWtHyBFAlYFyLhinXK8nOUIzWLR3fwFQckG6WQhKHbtQmSvAX6Wt7J3Xtiq0dTl2xYWVnh7+9Pz549adWqFaDdoOLdu3c4OTmh0WioXCQvYQFlCHkmkHUalEuzoAcDBw6kd+/ebNq0ibi4OCZMmEDZ6nXTfRxdkkrJCtYHXfKVW7yQRLoJFrZaKnSo7FEAvHhMpF8QUQFhWLrYEvHsJXt6N6Hv3zk5fvw4FStWZPny5VLN/8GDgvvU1SM7n6LjhQYDlatI59173PRk55iWUiQXK3O9Pa+1trFT4pNCZrgudF25cqtbt/WhaPnKJzfy48ghntun9+GybHT94S1diPFr+e8qj00bIn5DrmhxMm5lqpDGk8d+YQwcOJDAwEB27dpF3rx503RuWfjnka7s6Lp1kw8wFSpUSNcHptTBQx8hAbzwecuWXXt58eIFq1cLcVixm5K5uTl9ugllUR1aNEKZrwj2lapg6uiEqWNSxyUt93MqLmaRaE1dPFHkKpFsuQjdGlBdy8cQAYvrDR0XkiYoYlMLOWyUCumh87BXsWnl70RERLB9+3ZCQ0Ol7U6dOiWJj4hZ0J8/fybi/XM++77GyMiIMmXKSMIMWTCMuLi4ZApDJUuWpHPnznh7ezNp0iTOnz+foWPrq/NOK0QCBpJ1HlMH+KB5c0+yenX/ilDmK4KTVz6cSwjZspF+QVjGxPL0S3OGChUqaInuxMfHU7xi9WSVDfJuSvpQxjzpc0XLvbCLdbJGDXLI47giOVl+yf42BL3kLWt5qK9hhPg58l7b8vOTN4+Q4tRf3NEidEVSRDe1vnIy3QQweUvEtGZ5gzYBu6g/YRwdhnGgDx8+fOD27dv8+uuvWQT8nSNdlvC0adOoW7cuY8aMkZblypUrU07EEAEDXLj7BBB6/u7evZvatWtrlYIsmTONJROHSYOLJsgfhb0LkDz2K1oD8r8i5GRo6H99lm9akrB0Re3zO1qkuo88fi63inVJ+fDhw9L/clnNixcvkqdQMW5duSD9TgqFgsOHD5MnTx5+/31Tpv1+/5/x/v17unbtSnR0NCNHjmTVqlUkJCRQr149hg4dSsWKFenRo4eWFvfXJObptik0VBcskoG89zYkWb7GAJEh0vaaIP/kuRBfrGGVoytcO42Fsz3Rn0KI9AuiVLHCaExe06FDB63Ptba2JiJcfylhSnKVgtKTADFRTNzn8acIQCBJ3VisaMHKyU8OFzslZXM7aMXE77033PNbfg4i5MeXZ2zLv4+8REoOORHLG0BAUvKYPJNbPOf0QF5XLcfHyDgswj9x5NQRHCxMaFrYjWf2xbhx4y+MjY21dBmy8H0iXSSckJAg9aoEwd0p1p2mBw8fPqSgi3Y5k6H6YICL124AQt1u6bLl2bT7AH5hSen8mDrjkdtTInKFOMBEhmiRrwhFrhIkBPhIf9PS7ze95Ksvs1lMSEmrYD0YTmSTx4/j4uKwtbWlX79+uLi4SNsoFAosvtQX3r59m8qVK1OhQgWOHz9OuXLlsgg4jXj16hUBAYLswsSJE/H09KR+5bL88eefkuchW7ZsuOUtLO2jL5RgCPpipqnVgnrYW2qRhTgx07y5p0W+miADcUrZsyH+VVWoTdwLQds40i+IESVz0uDiNTp27EifPn1o0KABRkZGxMbGEhoRlapymxyGRDnECUQRZyup5Ekf5CVKui5p0fKtXUAoCwuKitNKttLXeUn33IBkz2Zq+tcudkotVaxP78O11os12yJ0CdhQ4ldaIV6rt9fOM2DIKIrm86TuuM7k5DExMfEoFAqpT3QWvl+ki4Q1Gg0lSpTg+PHjNGzYkBMnTtC5c2fGjx+fTKUqLUiJeEUkWNgyacYcrLLlJEfOnNRp9gMRak0ya9A3JBKPL2QqyV+i7WZWywhX9690jFSyt8XPNpSAJU+kEv8Pj9EkywjNSKKOPnUtgF/nLGJ4727MmzePkJAQOnfujJmZmZBBHhlC4cKFmTVrFlOnTqVx48YcP35cq5dyFlJGpUqVyJMnD69evWLdunWULVuWIjYJFMzryfBpgqDCpOmztTrTpMc7kh6ISUii1SZPyJEQGYImyB9NSIC+QxgszUNlL3mQnLyA+y84PG0g0w9cYeTIkXz69InOnTvj5eXFlZtJTVLErkby7yPP5tclVjkRis+M8BK8Q2468VL59m52Su68Tsr09cxpK7mPxfOQb5/ahFdOtLoELT6/HyP1T4rS2qkJkitwyRO/UlP+0q2lhiQCLmMeRvEBAwCwRiN5MSp8DEatVnPo0CF++EFbGS8L3xfSRcKmpqZSz1wQdKCnT59Ot27dmDx5Mra2tri7u5MvX75Uj2VIH9rIyAgjIyMt8szjAuMmCeUO8mYGIhmKf0Xy9JARq5yqRcKVk60u6aZF6zelDGhxUBEJU9+2jpZmekuUdCHPmk4pk7xS+fLs3buXycs28Me6lWzatAm1Wk18fDzFGrcn/vYZXr16xeDBgzl79ixHjx5NV1/o/zpMTU1xdnbG09NTKzHx0PFTALRt25YeffS3qMssiAIccjemeB+J95o6wAdjPQSsDgrUyo+Q3NJ6PEWKnEnWvFstJ7Lbu/B3l0GMmTqLefPmSXXA8eo4PkXE4Gyl1CIIXetRPvk05OaF1D0H4vZBVsIk5JHM6jQkXqHP+haJWvfzdd/LJ9AiDJUx3XkdzKf34ZIrGgx3X9LnhtYXw05LVndARCzIvvrVF++Y+iaBNm1642JuDtuOMXHiRAoWLKhXSjUL3we+SqzDy8uLP//8k0GDBjFu3Dhp+ZQpU6QMXTkiIiJYuXIlT58+pXbt2lTI58qrN+/5FBxCXIIxm3fspmWD2gwcM5GCFtpWakrNDXT/SmQsIy1dwtX9K9/fEPTV9+patPL1HvYqngaE8zEyjiLOVpK7K5vKLFkGqwjRihcTtnTLl8T3ugStccpN7xHjqdO2K08vn+Tp/dtEWzhzaHmSalhsbCyDBg3KKkfKABITE7l16xYrVqygX79+hIV/5sy1OwDMnDmTnE42Bj0o6el6lVIWsWhNlZVJUIbH6HiFVPbwxQWtDgqUFotErAkJkKxdQCJieX6EImdhFI6uSWQd6MPsvu0wtnHht3mzpQYP4W+f41xE8IDpc90a+o5iMlFKSKk0SEyIsrQxp3FxQetcdyIg/2x9kwARKQniiEjpN9G1YuU9nOWuaLGWWZ+yl654iBz63OPy7yY2hJg8eTLXr19n+fLlrF69WuqclC9fPq0QVRa+P3y1YpaZmRkrV67Ex8eHN2/eMGjQICZNmsTq1aupWrUqoaGhHDt2TNre1NQUtVrN1atXpWUuLi5SzO3vc1dY+WexFBO15NCdQcsfqKcB2i7XG29DpIcurX91kRY3slyOsqCLjTRIjqyRj6cB4RR0sTH4/Qy56MXt40ICeHL/NmsPnUehjqLP4BEUKFAAEAaU/I75aVgqP8+DohnQvEay40RGfn1rxf8i+vfvz6RJk1i+fDmRkZF0alBVWidq8GZUiEYXuipZ8kFbTk4fI+PSlOCXHkileQheJHlMeXaf1rT9oTl1GjVFZWWNW67c5He0kJ4J+TMjJwp5UpE8m1ff9cqmMiPoS6mSLiHZKBWCCIlOLNaQJe4dIGSz6x5LrjIn/wspS8WKx4Ik4tSXNCfGguWdmVKC7vmlNWfExcqcmBihttvS0pLZs2dTs2ZNRo0axY4dO6hZsyZLlixJ07Gy8O/hq0kYBIm6w4cPs3LlSmmZr68vO3bsSLZt+fLlcXZ2Zt++fSxcuJBhw4YRHR0tdSmaMGECoF8QA5Jbt3LIXcKGoLtNan9FbWZdjWZ90JelCuCCQK4avxDyOHmiefM6qVbzCyTy/WKZyOPXYrJNQGAw+Ws0IzIyEgsLCywsLFj6x0a2bdum5XmIjIxk6+r1xH4OBYQSk4iICOrWrUuvXr1S/A5Z0I8yZcpw8OBBJk+ezJYtW6hSuigAnTp10uplrYuvtYINldvoc6mmB1qZ0nqs4QQLW4mIxWQtgJIFK3Li5mMsP3/AIburlodKnAyLcWDdbkki5Ja7fH8r0yQSNyTTmE1lRtncDhy+I8iFipKc4jr5dkFW5lKWtL5rZki5Li2hIvkkSVe6UuxaJU6e9EXm5fXAqUlS6vutxe+cGCbU+P/999/ky5dPklG1trZmwJdYcRa+b2QKCQNSjWTHjh0ZOnQonz9/pl69esTHx3Py5El27NjBmjVraNeuHdWqVWPs2LGSSMTnz5+xsLBg9uzZ9OjRI9mxda1G0R0rxn5FizctriVIvR+v7nrxfUpErCuUIFclEhNlFI6uyWo1JQEF+XZf4nSaN/eIjIjk4o07vPX1Y9PBk5Ilu2fPHpydnWnYsCFXTxyiWVWhG86x0+do1r4bAK6urmzevFlq1p6Fr4ORkREfP36kcIF83HkiaEOnN+klNQLWlwCkO0jrDsq6uQLyxCu5S9rQNoBhIo4M0XJfG0eHUdoSTD2Lo3lzD004Ui29qI4ln0zIiVi0gnUnqfJwS4Rao5WtrGs9gyB9SSl3rdPX103M0dJMunaGJi3yHI4ItSZVq1g8nlg+Ju/AJGpa67qbJTJOIXkrPRUTcne7mNtx7tw5Ll26xJo1ayhdujSjRo2i0JfmDVn4vpFpJKxr9Zqbm3P16lX8/f3Jli0bvXr1onTp0lSpUgUjIyNMTEw4d+4cNWrU4Ny5c9y+fVvvTSNlOstIS5eU01IOkhaCFrfRjSmJMJS8IZ5DMqRWJgLJCFhMqIk0t2b5tn0s3PQXwcHByWr+3r17R44cOVCpVCQmJvL2vS9jp85m1/4kOcro6OgsAs5khIWFkb9gYQoXFhKY9AnYpAe6hAXJGxjoJjQlqa4lF3IRoQkJ0EvAWvFgXeghYklV68v/8MWr8/istJs4If7wOS7ZZEJOwIB2EtmXe1+tp0xQnuWs+z3zO1pItcWiUIn8+RbHA9FVn9okW1Sg002mlB/T0dJMa4KUWuKUXAFLhL6wgvj7yiGKmiQmJuLj48ODhw+5a+VMzQZNMP9Siy4mmEVEREj7xcfHky1bNjZu3JjiuWXh+0KmkbA+mJubS7WolpaWVK2aFEd79uyZJLixbt06g7O2lMqYJDI2daagS1JijJxsRaIUHzCRZOXQ7Twi318eP9J1X+lrRSiel3GgcG6iK88sn5f2RvI6TfnimFgWr9vN9A17pGUlS5ZkyJAhlClThsmTJ7Nnzx7Wrl1L8eLFcXR0ZPfhk8xftQEQGsrPnDmT1q1bZ2VAfwNky5aN8KAABg0axKBBg1LcNiXFNDkM1QS72SkFqw/t+1As5TFIwClM+lLFl4mjWN4nuqXl1Qqax2e1PkP03NgoFRRxtuLxpwi9tbnis6PrJTJG8PoocpXAylQhxZn1TTLkFqtIwPLnV9xe3C4lAhaJTBf6mqd8jIxLJnMJ2paxbuggtRpgfUQeEBFLolkiO3bsYPny5YSEhGitf+wXQkRcokDUGjh/O0nVzMHBAVfXtElnZuH7wTclYUOIjY3F19cXgAYNGlCuXDmt+K6W8k8ajpdaEwiRLJ8GhCdLvHK3NtMbZ5YPdPK6ZPFY6gAfTO21a4/lVnvci/uSJWLq6KRdFvIFmrfeWm7BW09e0nL0XII/R1GiRAnu3RN0tO/evUu3bt3o2LEjFStWZM+ePVy/fp2aNWtqKWSB0MEnb968XL9+PUsT+hvgyZMnejP/If1tLFPKupVbSPJsYn3xVJBNSCO1B+20QotUv9ynciKGpDCQsSwDW7x/1QE+FHTx5GlAuEFhjqRn55P2ucryIDxcPKX4sL7vKV8nn+SklhSnbwIkEvDHyDhslBZ69Qd0reGgqLhkCl6QvBOTIQLWVwMsPxeAs2ePMGPGDOrUqUObNm3w8vKicuXKAMSr1YAJt2JtKWMexocPHzA2NmbNmjUp5iZk4fvFP07CV69e5eeffwagevXqzJkzx+C20kNvwBoW3Wa65TpyyK1V+QMmWs668SnRqn4aEC5tL2Yzu0aHkYAtmjcvwMJWSJiKDEEtOx/NW280JI/FiW5msfRDXKYJCcAsnxeaIH/GrtiKjaMLm7YtxcPDg/j4eN6+fcvt27eZMmUKW7ZsoWHDhlhbW/P582eJgMePH0/NmjWxsbGRNKLlWr9ZSB8SEhJ48+YNuXPnBuDt27e8e/eOAgUK4OvrS5UqVZLtk1pWtKEmDYZiwHIXtLCvtrWnb+Kp+5yYOjrpd0kbEuv4AvmEUSRiubiNbpMVUXkOkoRpQLvntTwWDCQL1SgA0y+x5ZQm1fJnXLc9o74ksbRCn5fLUI2/vglGSgScljpguVXu5+eHiYkJixYtkoi1bdu2nDp1ChNTU2xkmt1Xrlwhb968WQT8P4x/jIRnzJiBh4eHVJpUtmxZxowZI6kMhfi+xt4jt9599ek8yxOcxOW6Osu6s1pdy8EF0DwWJDGNVfYkWNhK2+SLfk0CthgH+qAJt5dEEOT1k5L1IHsvF0mI/hQidXMCtNbJ/xdd1pcfPqd79+6SFKiJiQl58uQhT548/P3339y8eZN79+4xa9YsNm/eTNmyZSlZsiTlypXTUmvKwtfh/v37dO7cmdq1axMcHMyjR49Qq9WULVsWIyMjXr16xbhx4yhfvjwtW7ZM9/FTsoDlblx97lJdAtZX6iaSrCHFrLRAK3taBtE7pXB0hS/PjAKBnA95f0x2zrpJVZD07Oq6zXVjw/LELX0w5I4Xoa9cKr0wlEci78gkEnB6ydeQyEj27NmJj4/n/v37Uh5ImTJl2LlzJ6HBwTg4OeFoaUZMZAynT5/W0vLPwv8e/jES3r59O5DUXq9nz57kyJEj1f1MddSvUrKMU5pBy0Uu4MtAICNWAONAH9Rf/qKyJ/7qAdRBgSjzFZFEDkQ3c/SVS9KxLZztMf0y4ImWhygfZ+Fsr6VYpDswRsfG8cDnAyGJJmTLlo1t27bRs2dPyaIV0ahRI27evMn8+fPp3r07q1evTtbFJguZg/z58wNw+vRpQGhuHxsby82bN8mWLZskTDNkyBBatmyZquCLmDEskpM+a8pQdqx8u2wqM0kZTrzXtbxFMle0LoEaypLWB/Fel2fq60uekrZ/fJYEJ09KuTpz8lWwdK5yV66L+hOaNy+kGLP4OfLzNY4O07ayv0yuU/psQ9BHwPL48NcQs/z3M0TAIgwlcKXUljC+anPyFlnF4MGDWbVqFQULFuT+/fu4urpinZD0OeLEW6VKe0/2LHx/+MdG8T/++AOA4OBgqlevTqVKlZJtE+L7On0H/TLopPaAioOU5s09jAOFpCnNW+8kt3CQv/Q+8elVIq+dJu7eOcKfPBfO68olwp88J+TKJYGAP4Xw6d4rIv2C+HRP6GWsDgrE74xg5YsELP4vrpe/Yj4FsPTAWfL0mETNUYv4YfQ83r9/z+fPn/nll1+SfYeWLVtSurRQhrR+/Xr27duXvmuVhTRDpVIxffp0PD09AfD29iYiIoLu3bsTEBAgJWSJEo76oI8EgqLipJcIfW31RAsrICLWgGaxAUtYJ9FP4eiKwt4Fhb0LynxFtDOjdVp6yklbV1FLnsWsBdk640AfXMNf0LVMDholelMupz0FXWxwt05Sh4u7d47Ep0kiPeFPnkuTA02Qv/A8y+LExoH6J9we9irppQtDYQFxUiC+vqbGGpILqnjYWyZ7pUV6UoQ89OBhp2Lzrr04OzvTvn17Ro0axZ49e6hXrx6mLjml76NWC4EwE5N/JbUnC5mEf+zXE4vIAZYuXZqmGIau/KQ4CIgJTZogf4ycPOHNPa24lQjd8iZ5HEprFm7volXSEXj/BfCCqIAwIgOiULkkWaWWLrZEBQjHE9e/PZkkZi//39JFW5BDjg4HHnH0wD7atGnDDz/8gJ2dHQ8ePGD06NFcu3aNDx8+4O6eVAtpZmZG165duf0lG7JYsWIpXbosZBCJiYmEhYXRvHlzrl69io+PDwDmpiZMmTKF9evXS+V4Bw8e1Dvop8cNKi/lkROuoYxpXRes3LsDJCdilb3WfZ/MxSxbr5uvIFnDaDdDEaEJ8pcStDQhAfiduYqTVz4C778geyt/zKr9JD27xoE+KOxdiHnxGNWXzxEnqnJdayDDyWUidDObdZPaxPW6seuUICdtse2iIaQkvpFaFzXpfJVOHDx1gXWrV7B31w7Kly/PqMkztGLVYuvM6Ohog8fLwvePb0LCYWFhLF68mNDQUAYNGkTu3LlZtWoVABs2bEiRgEN8X+NhX8zgjNY4OgzkurZfYsL6YklimZB8YNEHOQGLlqtIsACRAVFEfozEpbiz9vKPwjl+eBCArYsw2KiyffkrI27xmCC4rsNMlRw9sA+AevXqYWtri5ubGzly5KB48eJcvnxZb6mBo6MjAE2bNpWkKrOQudi1axfTpk1j/vz5HDok1Fw3adKEh/fvkiNHDpYtW8aePXs4ffo0ZcpXBAy7oEXoup71Eay+/rf6ZBt13dEiJLlJPc+D6OKVCFm3NE5GxCnBUBgo5sVj6f+3J29RoG9nLbLXvLmHJsifmBePif4UgumL+5KXCYRJsBhjliC3iEmbSzqlEkUREWqNJCoih77t0gN9sV6R3HU9H3IYamgBYGFhwYDBwxgweFiydTZKBYGfBVlOW1vDk/0sfP/4JiQcHR3Nrl27ADhx4gTNmjUjODiY4sWLU6ZMmXQdS3wYdONeyWJeMmvAEPmKsVl98TG5C1kk2siPkYQFREoEKyffsADtSYL4XiThyIAog5bwrMvvpf979+6dbL1SqeT9+/d4eXlRt25dadLi5eXFuHHj9GbnZiFtiImJkSwIXRw9epRp06YBULx4cYYNG0atWrXYsWMHmkQjHj58SIv2XWnRviufw8NJNLdIcbBOKQM6vdm0YrMGfZKKum06dbdQ5CqR9HwYkkvV3UdX1EOXvL+IeMiTq9xqVST8yXMUjq4kOHlKORzRp/doJSmGP3lOpF8QKjdHLJztkxPwl+MbqqVPDYaEeXR/K/F3cbTUvrby7Qwdy+WLvjXoVzQD/R2lUjtvEaklnakD3uLn5yd8vptbittm4ftGpseEL1y4wLt377h27Zo0Qzt48CDu7u7MmzcvTcd4+PCh9CCI8aRkg4UspmUcHaYVm4Ikl7No5YrEK5+BixaqPgKWIyxAIN3Ij5HJCNhf5wEVtxEt4Ui/IOnYcfEaBq3ey4oVK7C0tGTVqlW0b9+evHnzah0jJiaGDRs2MGzYMC5evCgtNzIyon379uTMmTPVa5iF5Dh27BjlypXD3z+5mIVGo2HkyJEAVKtWDXd3d7p3746npyd58uTh/fv3mNokuU2NLFTJEuPCY/RbWboQJQ99Q6K48zqYgC+NAMSXlsrSl1IloZbV8MCsayX6hkRqvQJMnaXOXPJt5cpYoL98KTXxD1NHJ5T5imCWzwubQvlBZc/+8zepWa8BL4NjJHdz4P0X0vOgcnPEplB+VBVqGz5wGglY/I4Rao3eSVFqhCYnx5QmVR8j4/gYmZRg5/alr7MY1y/ibEURZytJ0UuEofhzSm0dU4KVqYL8+fOzfPlyALJnz57i9ln4vpGplnBCQgL9+/eX3g8ePJjFixfj4uLCrl27Mly7KvZJTRGRIQZ7qYqQky5oE6QY503J2gWBkP1jNLgqFRIBi39dlQoty1mEeOzhR69w4NlbRowYQatWrbC2tpaK8EHwIJiamuLr68uTJ09Qq9VUrFgxlauTBX0ICwujadOmzJ8/nwoVKmit27p1Ky1atCBnzpzSPfnXX39J6xMSEnj+/DmLFy/GysoKtVqNs7MzFpbJLVRdiAQs76MLybNoRR1heYcd3cbvIsTMatFa0+qdreOW1hUMkVt4YgcvIFn4JiVlOl0k67T0ZbIrInDHHyzafpbLdx6yceNGpjQugYVz0nNo4WyPMp+sv20qZKsbizYEebxUPmExRKz6hDJSgq6cbUqqYHLo6mnrg+75pkbEp06d4syZM4BQUlejRvKOaVn430CmkrCxsTE7d+6kbdu2ACxevBhIKvlID9QBb8Ejt+EBQnRVpUGfWR6TFUlXDt04r66l6/rlAREJWFz+MVboq5rNPPlllFvT0rFz5CfhiQ8VK1bE2to62T4WFoJKV65cuSS5zyykH58+feLBgweEhobSq1cvLl68iK2trZTtvH79etavX6/V99rcXBhQXV1duXTpEpcuXdI65pRZc9Nci61LwCJ8Q6L0ku/XQLdPNiTFPUU8D4qWrK0bb0Mkmcdk2cUGYsNy61hOiFKnpS+JjSBMeiP9gphcPBeLrFXUtomUYsEA9pWqJDum1mcaIOSU4sGG1LIMCW4IhKfd0MGQl0F+HQ15OOQkbkiqVFdLW4Q8cUzc9+zrIMmylp+bnJhz5szJ8OHDWbBgQYpZ+ln4/pHpMeHChQtz+/ZtmjRpgkqlYseOHZiZZawcQEviTh/0DBganXpdrc1lBKxr+YJ+Apb/BSTileNjbDzZzE0kwpbiwy6WRAZE8eFBAO7FXWjfoQsnTpwgMDCQggULGv5eWfgqDB8+nDt37kjvO3bsSHR0tNSzWoRcl7dp06YEBwezefNmAEaOHEmfPn1wc3MjICAAU1tng5+nO/AasoDF3rO6re/kzd91O/DIoaucJUJUd9MlXzlpPP4UoUUWNkqFROC6ynRaQjQYFu4QochZWPpfma8Iqk8h5AXWF83Np3sveftQqBRw8sqnJfKhW9sMaMWB02oByyEn3vQmV4lITfNbHut3sTKXPBXpaVGpr62iRbgg5Zs36BnXDl8jsXBhoQGLex6JgMWJU4iv0LmuTJkyNG/ePEPfMwvfB75JYtarV68ICAjg559/zjABgxAbLmyt0U+2eizfr1EIEjOdIXmcV4ScgP1jhP9dlSbSOrlF/PRtuPTXVangbWSU1Kaxb9++0nazZs2iadOmGT7vLCRHzZo1tUj4zZs3WutF2c+nT59Ky4yNjenWrRtt2rShZ8+e/PHHH0ycOBFLS0s8PT0NCnLoliPpG3DvvBYELDLD+hXbaYqfqy+ZKCUySIovW3zVecj10o2jwySSFtXfogLCJA+TczE3nLzyCS7oLwRsEGmIA+vLDheRVuLVTbhKrazMkDtZTsSQ3LUtLtfnupZ/tkW4L1FRUUyfPp2DBw+iUCjQaDSoVCpKlChBzZo1adeuHR72QiOYHDlycPPmTapXr56m75uF7xeZkpgVERHBnj17pIYBT58+RaPR0KRJk8w4fDJ8VZcY9Md/Qdvd/DE2Xut1L0ywaPxj4iUCFt/L4R+jkVzVT9+GS8S96uVrvTHxsWPHMnHixK/6PlnQRo8ePXjw4AGXLl3i8OHDLF26lIULF3L16lXOnDkjaUKfOHFCyjAVoVKppFCAvgYYqWVDy6UM/UJjtAg4MhUrGLR7zvqFxkjHk5OAnChE8hCTwlKDeJzwGI1WbDklpKY1DUiJkmJylnOJPFi62OJZrxhOXvmk5CyxfCq1ciM5Uae1NEn3t7EyVUgvfZC7oMWkKxFyYZXU4rlyz4d8W7nFrC88IX7m408RJCT8X3vnHd9E/cbxd7r3pIsWaNm7ArJB9lRAkCEoQwVRcPtDFAfLgWwXQ5kOUBkqKKDIEtkge4+WQqENtOnebX5/hLteLpc0KS1t4d6vV18kl2/uvrmE+9zzfJ9RwMSJE9m2bRtTp07l4MGDrFq1itGjR+Pg4MAXX3xBZGQkR3ZsJv/qcdyv/8eQIUPYtWuXWNlNpWJSIpbwDz/8wJdffsmhQ4d44YUX2LBhAwApKSl3ve+zqfbUs+JWoahgrKKQuqGVLF75YylxWXkEuzgouqqDnB0InfQY//xvHn379mXKlCnixT0uLo7u3btz6dIlq+aoYh2pqals2LCB1NRU9Ho9KSkpHD58mF9//ZU5c+bw0ksv8fzzz5OXl8f48eMZPnw4e/fuJTk5mZo1a7Jp0ya++OILk3V7ay1gwQVdkhawcAxpZSXBnWntuqUwx8K5OhUZACRH2jRFwKimO4Y1Yt/WgXjdyUxw9K+EU83GFFQKF8dZ2yFNCaWuZ0UhL9RhjqIEV8DaYC5LxxD6Bofa5fLZZ1+ya9cuvvzyS9G6bdSoEY0aNQJgxowZ/PDDD7hl3oZ0VwoS41n6ZCuuXbvGxx9/TIMGDQgKCir2nFTKDo3ein53KSkpeHt7s2/fPsUetTExMUZWr5ubGx07duTjjz8ukcYC9ewKRbUoN7Q5AbYUBQ2FIixYseYE1xKCa1ogyNkBO8d8JiVdJahWTZYsWYKXl5fRmOzsbPLz801qRasUn23btvHaa68pvrZ79268vb0ZOHAgFy5cwM/Pj8TERJo1a0ZgYCB///03VapU4ezZs+JSipKlJU1Fklo8NyUpRubWgKVILWEhOlpYF5aWPrTkzjTXM1dpu9RtWsvf1aghRP7V44qBjqIlbGGt1qQ4iHwJScENbbImrLB/pdQrMA3GsjY1SWmcuRKjULTYykVbOl4pQM+oQUfcDea8MpwbVwxpk3369GHKlCkmS3inT59m6NChvPrqq3w6oKVR3MsNXQqdZv2Eh4cHq1evNpsDr3LvSUtLo3Xr1iQnJ5tc96XYfDN66tQpGjVqxF9//SVu27Vrl1H90h9//JFPP/20xDr7nC0ozM+0yjWG9RYwGAtwSaJ3yGeK7ipuwUEsWLBA/CKys7PFNoTOzs6qAJcwHTp04NVXX2XMmDG8/vrrNG7cWHzNx8cHjUbD8OHD0Wg0zJ8/nwsXLnD48GE2bdpEUlISZ8+e5VZ6rph/CsYpP4IAC65Kwd0oLcBxtwIsIOzTXBlLJYLcC4N+pC7XIHcn/N2cRKGQWtBK3ZisRS6UBa7eimu70iwHcwJsab9S0bVkAZtzQRdlBQvnTX6elDDnppZ6G+RdlqDQNa1Ny0Z7/aoowGCopzBo0CCuXbtmtM958+ZRo0YNpj1S1aTuQWVfL2bNmsWlS5c4ePCgxc+nUj6x2R09d+5cwCC03bt3Jzk5mZkzZxbu0MFBbMVXkpwtqEQ9u9smlrA1wVjpNxNEC7g0EdzSANnOObydocUvrDKLFy+mUiXDjURSUhLt27fniSeeYMqUKaU+pwcRBwcHRo8eLT5PTEzkxIkTdOnSRdzWq1cvFi1axKRJk7h8+bK4XbwhSs/FWpRygG1xQZvLD7YGc4UgpOIrTYGxJi3nbhF7DktTnu5EPRulGypUxVKqAW+NxWsuHUnpfdasnSudV0vBdwJKwh3i42IkxIJ3o97DrZm/5TAO2alUdrcnQ5fIlHf+x7PPPsvy5cvFIhy3b9+mefPm6JMKe5cLxDQfzIWff8bOzo7q1asX+blUyh82W8JCK8JnnnkGAA8PDypVqsTjjz/OzJkz2bJly11FRFtCahErIXQ4spQXnK7NMHFFlySR3s40fak1M1Ou06hJM9566y1WrlxJy5Yt2bFjB+3btwcMqVwqpcuRI0do1KgRK1euBDByCTk7OzNo0CBu3LhBerrx70BuZUmtYCh060oDbpQE2JwV7O7lbBKQBcZWsFLpSgFzIiCIqrR/r9QqFDoIlSRKQVO52miTSlxmo54lLuiiUpLMiay5SllK7ytKgKWdlqRYI8DC6/L4AKkAS7cdv56MT6VAPEJr4Fi5Nh27dGXHjh24ODrw/vvvi7EjwcHBJBz6l5jmg432E9N8MPn5+axYsYIuXbqolbMqKDZZwps3b+bPP/8E4NChQ7Rv357MzExSUlKoWbMmvXr1KpVJmsPcWjAo5wQrIXdFKwVXWUuktwuVGwXy+h97KNDr2bt3L3v37hVfl7YoHDJkSLGPo2IdQgckgdOnT6PX68Va3Nu3b6d7p0fw9TWfFmPu4i51NRbXAobiWcHycodyq1cqwALSwCTpeLHLkcRCNfE2FZEnrISJxWsBQXyVBN1c5LMl5BWnlKxkeYEM6Tbpc7loy3tBK1m+cqGWliHVJmUZrfkfv55MZJjh899IzaF51TC+mv0Rjw4ZycaNG+nbty9hYWHsuHiR1atXc/z4cZycnBgyZAg1s7N58cUXuXbtmpE3UqViYZMlvGLFCvHxb7/9xsKFC+nXrx8FBQV069atpOemyAVfQws/SwIsRSrAUgvYXFlKMB8FbYlIbxfqVPUioGEIF6MK2zYqBbIJrfBUSpcnnniCvXv3ihXbLly4IDYWOXHiBCdOnODZsePMvt9cNHRCRo5ozQhFOLQya8ccShawEuaaxFvquiPHUt9dKeJ6sJnSsOYqZintR2ltWbByzf2BZQG2Bmk6ktJ6sNQbIMWc5StHer6F9eL6AR5G6+/Cv3JhFrwawk1adEyyUY1wbVq2KNzntSl06/gIvXr1Ys6cOWRkZDB48GDy8vKYMWMGUVFRHDhwgCeffJL27dtz/Phxxo0bp7Y2rcDYZAnHxMQAhTWhFyxYQN++fRk0aJBR79uyQloTGswLsJySCMiKz85jwNMt2XfsArezDNbQ1KlT6d+/PxcuXGDp0qVs3rwZgAMHDlC/fn1Lu1Oxkry8PLZv306bNm0Ub3g8PT15+eWXeeWVV2hctzYzPvmEgIAAPv30U+rXry+WrQTzF30lN7QS5qxga4XXGqRpSrYgRBNLLWIlsVLMPjBjCSu1DxWwFOEsHVuccpRypNWkrBlvi0teag1buvEx95q0xaH0Ri0jJRt5NIu/mxNeLq44Bobz2bRJ1G/dScwV3rZtG2CId8jLy2Pv3r1izWghjUmlYlKsPOG2bdvSrl07nJycyiQY4IJvQ2roDAnqlqxgMC++Slbw3biiBw6ow3cnLjFzU6H7efLkycybN4+BAwfy0ksviSLcoEGDYh9HxZgNGzYwefJk6taty4cffqhYDrRjx47UrFkTe3s7wqpU4ZVXXsHFxYVff/1VjOC3Zh0YjKOUi6oFbav4Sl2VRSHPE7ZGWKQiZWt+cHFREtmiim8oIbiUleYtWPryAC5rcoOVzp2151OOvHmH1FMCcOu6oW6Cud9FQkYOtfxdidWlU+vhdowd+RTffPMNzs7OJCQkULduXbp164aDgwOPPPKIWi3rPsFmER41ahR169YV19XKisvVO1PjSmGlmOIU5QBTK7g4rmiAvTdvMWHHIfr27cvLL7/M3r17mTx5MklJSSxZskQMVhsyZAgtWrQo1jFUTBFuaM6dO8fAgQOZOHEiTz/9tNEYjUbDe++9x6hRo+jYsSNRUVG8/uJoHn74YcV9mhNgwWUoD7QpqWIccHdCLMXDsbA2tDmUXpPXjTaHNUJaHLGVI79xUJpzrC4dXWyU+PxItjfNnJOZcyfo/dk7nUKj7Q31v4WmCdJqY3KKCuBSyimW3qAdvlOo5fQpLWlJ6eiuHMfVNwgXX4NHISDMy+h7FlpWpmQV3jxMefsNtu3ew8yZMwkICGDp0qV06NCBOXPmiA1HVCo+Nomwm5sbAwYMKHMBtoSSK9ocJRGM1SPIncqNAvkQPc0a1efDDz9Eo9EwYMAAevbsKbbRe/rpp6lRowadO1von6pikbS0NJYvX07nzp1F8a1Tpw4bNmxgzpw57Nq1i08//ZROnTqZpMk1a9aMTp06ceTIEdauWEy/p55VPobMalKybsD8mq05hEhpuRWUkZJtc3CWvHKWgDXrxAJSMbMUlAXW5eYLTSCk5GqjiyXEUovW0pq20EMYDCVGd+3axYkTJziT4UrIiEdh1xbq1KnDPq0df/75J/369aNy5crEV24ImG+4AKZu/7PaVPHxyo3njEQ0OiYZNy9nok7dJKiqH/ExiQRV9ePcTkOrwUxdHP999wYAj80/Ddy5cfNxKVwvTssWjyeUEw2t2ZiDW3/n6vXr1KlZgz+372LgqLGMGDGCRYsWWQwoVKk42CTCGRkZREdHi7V3y5rL1TtTNeHnEttfca1gp+bhbJn5Hf+bMMHoBsXNzY2TJ0+Kz7t3737Xc3yQuX79Ol9//TVff/0148ePFxthREREMGvWLNHDsG3bNkaMGGHy/qeffpodO3bgX622UXEZpXVEeUUsJQG2NhirKIoS4ptJWYT4uBi1w1OK0C0UFcNza13OYkcjM1gbHa0kxKXFqVOnyPQqvNE6tHs3L7/8Mh4eHmRkZdPjh89N3rNu3TrAkB74xmcrCA6rxvYLt3i2Biy7YzWH+LgQ6OHM8evJRvm9gjWdlZVF5SbJpKXFkpuWS35+Pu187bC3t0fTTIOPTwF1Hqtj8HwNGCYe+/z584wbN44BAwbQpVUXateuyvXr1/niWCqd2zYzaXEoCnHVOjSsWodcbTQ9Ondg18Y1dBswjJUrV5qtCqdSsbBJhFu1akW7du1Kay53hWuAr5FL2i3Q26I1XFLVsbwD3flsx2E8PD3p169fiexTRZmAgMJ2gl999RUXLlygV69e6HQ6Nm7ciIODAyHVa7Nq1SoGDhxoUonMx8cHgNdee41jx46Z7F/qhpaWeLSlWpW5Bg2W1oetsYQFIRbmY1T+0EzKjFKDBrlVKRdNa9zRJeFmNod8vlKXumD55mpjOHbsGLM2LqbAszLjh/dj5/ELgCEOY+HChVy5coWePXsycOBArl27hp+fH1euXCE0NJS33nqL31YsZuAbU4jVZXDmTDTVbt7E19eXdQeTCa3fjLO38ni2qx/4F87l888/Z/ny5eTlWb5Zd3R0pF69eqSnp3Pt2jU8PDxITDS4pxctWsSiRYtwdXUlMzMTgKzRbzPqhcIofematLzdZNPIRvQfMIA1a9Ywfvx4xaYwKhULm0S4Tp065e5Lj2k+mKqHCq1h9xB/oxxhoaeve5B7qRTn0Pg7s2bvMUaNHqMYnatyd6xcuZLZs2fj7+9PZmam2OJNo9Gwbds2tm7dChgufNOmTWPAQ1Vp0MeQYynNxT527BjDhw8H4MUXXxS3yy/6wlqgvB60HKWALEslKotDrC7DqGiH0jwEBFGWNmfwcjEfzFRaSK1ha7sfWUJJgBMTE8XvEmDS+lmkphn2NWHCBHH7li1b+PTTT8Uloc6dO4u/l24tI+/s340hjxvn7Nv7VOXoP7+LzT++/fZbjhw5wpEjR+jQoQPjxo3D19cXR0dH7O3tKSgoEP+0Wi0nTpzgv//+4/Tp03To0IG6deuSmpqKm5sbTzzxBFeuXOHUqVNERESwZ88e1iyZgfO1ozz93mxq+lVBo9GIgWjBznp0Op2R6/n1557i22+/5ddff2XQoEFFnkOV8o1NIvzQQw+V0jTuDkGIBWtYEGIla9g70J1kbTrBLvYlYg3/dSmWzNxc+vfvf9f7UjFFsDoSEhLQaDTo9XqWLFlCSEgIq1ev5vLly3h6evLXX3/hlnaTmvV783DDOhw7dsxIhKUX57g4U0svLbewFeCZW2mAqQVsaR24pAVYfkxBjAVhFgTZnHUsxWohvlNGUm4N21qoAywLsC3iK+ViQib2167x3XffSQ7kjk5X6AEbMGAAYWFhfP65wR0dGxtLlSpVAENf6SVLltCgQYM7sRnJNKsB82THyU+KoXHjxjg4OODp6UlOTg5t2rTh3Xff5YknnrBoiAQFBdGoUSOeeuops2MCAwNp1aoVAF26dOGhhx5i2rRp/PlnI554bjyfzfoUgOsxV6nbvDGenp7EHN/Pjfh41q9fz5Xb6VQPDWLatGm4ubmVWstYlXuDTSKckJBQ9KAyQi7EUgRruKQJdnFgx81EvLy8CQ62/UKlUjTPPfccQ4cOZciQIWIFrJdffplHHnmEF154gZo1a6LX6w0XtYw08q6eIUaro1YjH6P9SIVX6DQjr8YkDdRRsoBDfd0U05IslacsCiVXtLB/afSsuRsAqRjLG8wL1nDh/C0X7ZBibZS0OYoblCXnvNaQ1uPhaE+QuxOfLl0qru0aDiTLdoiLY+rUqVy6dIlNmzZx5MgRoqKi+Oabbzh27BgajYbZs2fz77//smfPHk6cOAEYhFGrNc7czcvLQ6fTsXXr1lL9/923b19q167N/PnzWbf0K4YN6EvL1m2pFhJMz8f64OXmyuotu5g4cSL5+flkZBT+Ft5++21VhCs4NlXM6t27d2nNo8Rw9K+Ea4Av7iH+Jq+5BxkuQt6BxhejIGfb06UjvV3o2TaMs34OJCcn06dPH/7555/iTVrFIm5ubqxcuZLBgwdjZ2dHZmYmf/75J/3792fYsGG0a9eOjIwMBjWvzfnTp7l58yZt2rQx2sfXX38tNsxQumgJVvBZbapZF7SSEJaWBQyIlbjkf0ejEonVZRCry+DZGoZ5meu2JE15UcJEKCUlJu39g03aGELRHZeEfRY3SEt6s7D/WjIfbDrHqhM3cU2J5cyZM9SvX5+lS5fiFFBLHCeUzLWzM1zSJk2aBEB8fDzjx48XYwDs7e158803efHFF/n++++pVq0azzzzDOvXr+eHH35g5MiRfPfdd3z00UfivuV9pUuDunXrsmDBAho3bszUdwxem5qhAUx4/TXWrl3LuHHjCA8PZ9u2bSxatIiqVasyf/58Y6+ASoXEJvUpz6lJYLCGhdxh14DCi0aGNhn3QIM7T1gX9g50B226kUs62MXBpgjp3V4abt26BRjqFI8fP56jR48aRd6qlAyXL19m7969FBQUGG339vNnZMeOtKrqQ6Uq4ew/Z0gLEUpVgsGD07p1a8aOHUvLli1p0KCBkRUsD8QqbhqSrVgTkCVETgtWt/AewVqeftgwTj5XIUBLui5sKW/YKEJaEFwzZSytQVgbVrKIra1sBXD8ejIvhmfQb/AAtjSsQ3R0NJmZmQQFBdHhoXC2bjW0Ahw3bhxXrlxh7NixAHh7e3Po0CGcnZ1p3rw5N27cwMXFhf/973/ivqdMmcITTzwhPm/cuLHY9vKhhx5i9erVnDt37p61GrWzs+Pxxx9n2rRp5OTkEKuD3QcOU1BQwLJly4iMjMTJyYm2bdvyxx9/3JM5qZQ+951aXK7emRpsFytpuYf4G60LSwO0pEIc5OxgU77weq8k/v7lPN27d2f06NE4Ozvj4uKiCnApoNfref3110lONl7fDw8PJ+rSRYYMfILBnRpQkBhPlr3Bhevq6goYWm8uX76cxYsXs3//fhYtWiTeTEpzgktDgNNTsm2qmqVU9CNd1pNYuk83L2ej4h6xugwejvATx8an5+Dl4io+L8odbZKqZKbxgrVuZktCbG5OUnE+r01hWGgWo0a9RIo2lgMHksSI4kqVKhEUFAQYumOFh4ezdu1ao30Jyw5NmzaladOmgOH3sGnTJsaMGaNYXU3K0qVLgXtrfISEhACgjY8jrEpVwmsbuq35+/uXWnc6lbLlvlQMQYjBUEnLLdBQx1bJIgZsDtLKpYC/r8XTu3dvZsyYUe49BBUdjUbDiBEj2Lt3L++99x4ODg789ttv5ObmkpCQwMsvv8ymXr1Y/8kbNKhuCMLZsmULffr0Yfny5YAhx7igoMDI8oHCdCR5OUpbSUsq/D15+BQKiyUhNldpS+7iTktKJ0sXJ1ZbAsN6s/D+6JRswqsafuM3k7LErjxB7oYKTJU9DRdvQeDkwieNaJbWfJbnDhfValAJc0Js7oZAul138zqd2rcnK8twYyS0nHRwcMDJyYk2bdrw/fffk5KSQkZGhlUWa+fOna0umHOvLGApgYGBAERdiyWsSlUa1K0LGH6/ar/g+5P7UoQFHP0L+w/L05bAVIjjs/Oscknr7/zboUMHVYDvEc8//zzPP/+8+PzVV18VH3fo0IF33nmH4e7ufPDBB4weuJMZM2YwZ84cwFAt6+DBgzRr1gw/Pz8x3UVemtBSChAUBmZJcfdyJj0lGw8fd1GI05LSTYRYjiDMSoIr7CtLVxgYlamLJ1MXj6tvEFk6SPMNFo/hfscibiKxgo32KYuOVnJLKxXaKI7oWsLWYK1vF38pCrAQGQ8waNAgHB0dqVSp8P93eUudLC5CLnxqgiFILC8vFyhc61a5/7hvRVhaW1q+PiwgBGqZa2loDuG/w5EjRypEsNr9Ts+ePcnMzOSDDz6geoAXc99+ibp16zL9s8VkYviewBCsI1+LlLYmtAZpWzoBc0IsIBVkASVhFt5zO8pQvinxynGLc8nSIVrHgiUcIqs7LURHC653QYytFeKSQLpfc0Is3x6rS2f8hElkZmby33//cebMGTFdrWPHjoAhmGnIkCFUq1btvhFhId2qdrVQQn3d+emHf9BoNNSrV6+MZ6ZSWty3IgymZS2FiGnBLS2kLQm5w9asC+dSwK8YLJTc3NxSmvn9w+HDh/nuu++YO3eu2LGoNOjfvz8pKSnMnD2bnfsO8b9XxxN75gg3s+x494OpJCXpeHzoSAATK9gaV7S0aAbcSR/ycRHFWBBVQXClIiw8VhJj+VjdHeHN1CmnB8m3S13UgEmusLwjUFE5w5Ys1ZJKOzIn9NL9p+XmU5CmIyQkREwjEkhJMaQtCY057ieOHzd8//Xq1SM7O5u5Mz6kTZs2+PubZnuo3B/c9z6OmOaDTbYJa8RQaA0LFOWKziCfRHJxdHQkODjYJFpXpZDk5GSeeeYZtm/fzn///Vfqxxs5ciQrV64EYPAzLxBYpwkXL15k7leLWPbDT1QODTUKxpJbwdauBT8c4Ueorxuhvm4E+rgQ6ONCQJgX7l7OopvZw8ddFF3pY1DOH87SxRUpwFIydXFk6uLI0sWJ7mhpfWkpKVnGNx1pufliSUtro5Th7spVKr1XuubsGBiOY2A4sbp0Tp06xYXbGQx98Q0++eQTwFA57dlnDU03ZsyYUex5lGf0ej0//vgjbdu2JdfRnZs3bxIbG8vgwabXMJX7h/vaEpZiqYiHe5C71S7pGxgu2Lm5uSxcuJCQkBC1WpYZpGkUp0+fpnnz5qVynLy8PJYtW0ZBQQHHjh3j3JWrgKHhyKHjp2jcytB3Vd6i0Fakrl652xcwatIuBGQFhHkZpRnp9XpcPZ2M0o6klrA1AizgKrOCpfWlpfWk5e0O5b1yi2p5WNo4BoaTkZHBxK++59mBfXhyxHPYZaaQlmaoXBYZGUlERARNmjShZs2a9+2N79GjRzlz5gyLf1gDQJUqhhKWQt1plfuTB0KEpfWllUpapsen4x3oTlxMiuUdOeXzT04iHTp04OGHH2b16tX3RUrStGnT0Ov1vP/++yUSAHL79m0uXLjAjh07qFy5MlWqVGHv3r2MGjWKlJQUvLy8SmDWBtasWcO0adOMtoWEhPDWW2/RuHFjmrTtpNjUXbCCBcvRkhUc6utmIrqBHs5o07KNOu0ICLm84VW9jYK5cpPO8sfst6hUK5LaIz/EzctZbPR+tyj1IJYKsYAQMS1gaY24pJGvORe4euMYGM7XX3/N/PnzOXv2LF++NVZ8vWfPnly/fp3jx4/z/PPPs2bNGvr06VOqcywr0tPTmTx5MnUbNKJXjx6E+rqzb98+9Hq9mpp0n1PxFcRKBCFWsoahMFLaUnT0qpybgKFsYp06dRg1alTpTbgY5Ofnk5+fL/6nFdJ3evfuzVNPPWUUyZ2cnExubi7+/v6sWWO48w4PD2fkyJHFOvatW7eYPHky+/btEwNoPD09mTp1Kp7pN3n+g9lizu64ceMICwujefPmxSoH+M0337Bnzx4yMzM5c+YMYEjtWLt2LQUFBTRr1gwPDw+jfrNQaAlKy1NaCsgS1oEFAZavtwpCLD6XrBELgVIAXWv78tV7r3N8h8EzkJOeTKCPC9ExhpvALF3cnehn28tEVoqowdIBIUw/XHgTYa7Lkr9bYQ9dJTG2RYiL6s5kLbnaaHbv3s3Zs2fFbbVq1SIhIYEtW7aI227evFms/VcEUlNTefvtt9Fqtazdula8ERaCtCIjI8tyeiqlzAMjwmAQ4oBNi006LVlDHnoyKKB3795FJvmXBXq9ntGjR6PT6Zg7dy7Vq1cnOzubkydPcvLkSf7++2/effddsZLU0KFDuXbtGm3bthX3MXv2bDp37iwWvLeWefPm8dNPP+Hq6sqHH36Io6MjvXv3Jjw8HMf48+Ql3+abjbvEnN0FCxYA4OHhwQsvvECNGjXw9fXF19eXkJAQs2lfR48eZdmyZfz777/k5eUx9Il+vPzs0zRs2R4fHx8aNmwIGAQiWSLA0uAkQYSkTdqLwlxjBPlrsboMIyF+OMKPQA9nfvzsI1GAa7bqSq1+Y4mOSSY9JZvbUZeLLcCuvoZiFS//bXBXCo0d5GvD8g5LghgLQiyvqiUg7WAk3aa0jmwuB9kc0vVgaYBjgwYNOH3a0PjexcWFhQsX8tBDD4k5wvcLsbGxYselZcuWkZmZydy5c+nVppk45kL0dcBwc61y//JAibAcqUvaPcidYG262ejoA+jQoxeDQ8obR44c4fBhQw3DESNG8Pvvv4s1b8PCwrh9+zYDBw5kxIgRjBs3jmvXrgGwZ88ecR+RkZFilSFrSElJYe7cuaxbt46Bw4bz2buvElDJH7vMZApcXbCLPw/pOhwc7Fm8eDHnz5/n4VBPom+nUDPYjzEfzGbOnDli/idAcHAw4eHhBAQE4OfnR+XKlUlLS+Py5cvs3r0bHx8fxo58iv+9/AKhIcEmAT+CGNxINXbFXkwwVFqSumhvJmUpuqGlkdCCFezv5kRCRo5Rv15hf5ZEGqB1j37Y29vT4onnSMp3Ys/xQquu+AIcbBIZXVRgmWm7Q1OLGIxd1HKKCuSy1a1d4OpNfHw8Dg4O5OXliQIMkJWVRf369XFwcMDbu2RzlsuKbdu2sWDBAi5cMPQ/dnB0pM8Tgxk/+hnqNm1lNLbTo/2oMutTnn32WZ5//nn69OlzT+pYq9xbNHrpFdAMKSkpeHt7s2/fvvuiZ27ApsWiJSyIsPbkLZLvlLA8nmzqovwTLTpvJ3bs2FEucxI3bdrExIkTqVy5MikpKdjZ2ZGRkUFeXh49evTgk08+4a+//uL9998nPz/fKLhl7NixDBo0SCwDCFBQUMC4ceN48sknxbxMKVu2bGHGjBlkZWUxYcIE3hv1eGGtYWm5Q0n94bMFlajnaXzBLygoICb2JrrkVG5otazZfpD4+HiSE26hS04m6uo1nJyceKhhfVq0acdbzw+nkr+fYrRtrC7dRHwFy1faHxiMBVjetUjqhg70cDYRXoEgdycj17Z0jVlAED6hKUSsLoPomGTiYxLRXTlebAH2rR6Jh487P44K57n1N3HzcjZaF5anVEnXtOU3Df5uTgS5O5kEbN1NH2JzQpyrjTayguPT8wht8LDZ/fTq1YuZM2cWex5lRU6O4Xfh5ORERkYGW7du5ZdffuHIkSO0bduWQYMGEdmyHYHBwXh7+wDG50xYSrkSc4P5099j2x+/4uDoRGTjRjz77LNGHiyV8klaWhqtW7cmOTnZYhzMA2kJ3+o91kiIATFCWkmAARrgyR/JBvdRs2bNFMfcSzZv3kybNm1EC6F37958/vnnhIaGMmHCBE4e2kuAvx/Nm0TSpEMPnJycaBjqy8CBA1mxYgUffvghAMOGDaOgoIDo6GgKCgpIT0+nZs2aXL58mT179pCcnCyK8JUrV9i+fTsnT55k+/btdOvWjeXLlxMaGkr+1TuFJczUGwaoZ3cb0o3H2NnZEV4llPAq0IS6PNr5EaNKTRnOhubplkoIKlXAsiS+hvdkGBXckCO1gKUIkcbmngsWszm0SVmkp2QbVcOyFcENDfDc+pssHRDCy38nGt1MyPsQy1segpJlXNj0AVAMaBMoSqCtsojTdfy4fofRpoceekjseASG/sDljfPnz7NmzRr+/fdfRowYwbBhw8jNzSUuLo6dO3dy8uRJNm/eDCD2I87OzqZly5bMnTuX1157DY1GY9HTIJz7SoFBfPjFNwx74312bVzHwS3reeGFF5g4cSJPP/30vfvQKqXGAynCUCjEAunaDLwD3QlOzjYJzNKj5ziGKNawsLB7Ok8l8vPzeeuttwA4cOAAbm5uXLt2jdjYWN6d+hH961eif/2+5CfEYe/vCTfPkg/g6k2NGjWYPn06v27YyKkTx1m1ahVgCHYSmDVrllgM//z58yxcuJCEhAQ2bNhAZmYmHh4ezJo1i1dHDMQu8zb5Zy7eOZZykJXJa9LuPBJBlpdJdAwMx5ITMla27gsYdUMCZcsXMCvAgmhJrUW50MotxsLtrkY3AnIxjtVlkJGSTVpSerHd0Eo8t97g3pZ3WLJGjKWfU7pObPg85oVWKtDWWsxSK7ggNYE35yxh0869RmOGDh1qJMKtWhm7aMuaJUuW8Nlnn4nPBQ9TbGwscXFxODo6imvczz//PDVq1MDJyYnHH3+c8PBwwLxL39z2+PQc/AODGfDceG7cvMnp06fv2zStB5EHVoSlSEtZKpFOPtfv5AfbsmZaWtjb2+Pm5kZGRgYjR46kc+fOYkRlS/t48hNM1yjtq0UiXCrPa1P45qdfWfDxB4r9SCdMmIC7hwd9+/bl6NGjfPvttwQHB/PEE0/wzvhniYhshaurq8H6vSOoUpEVxFS44GrqtILMZKtb41kqCiFYWEKzd3nEsyW3syUCfVxMoqHBWIAtiZLxmML3yLsz3b0VbHyjI20QIW0IIXRYAstiLCAXZGnQVmmwYNtJvlix2mR7Tk4O27Zt4+LFi2JHofKEv78/7u7u1KlTRyxAI5RFnThxImO6PIS7fxAHr6fSt29fo/fKo/UFpDcx5rwPwlLHrXP/0bFjR0aMGFFSH0mljHmgRfhW77G4Lf0YuFO4I175TnQj8eLjd999ly+//PKezM8SgwcPZsWKFZw7d45z584B8FCL1tSPKLTU7f2Dwd2XAldv7DG+0/b18yOiTXccfvyR516ZwOwp7/Dvv/+i0Who0aIF3t7e2NnZcerUKQAx8jhXGw2p8eTLb1zcfSFdZ1r0/46la18tstBlLUPaQs+cAEvddOe1KUWKLygIjRnrN9BMEQ5pT97iIAi4MDchJQlsK8ohJ1MXj4tvMGlJ6fz+WgOeXBFtUoVLKBAiR3pDIkRTA2arbcld1MVFXqpyzervAUMd6J07d4rbw8LCCAwMFLsJlTf69+9P//79yc/PZ8OGDezevZt///2XzMxMFixYwPLlrvww/yPIymTHjh2EVK+L5531QLnXxsvF3sSL4OFobyLEjpnJXN+yhh3nb3D27FmbsxdUyjcPtAgDZDw3CbelHxvqSQe5ExSTYuKO1ni7w51etuUlIGLkyJGsWLECZ2dnNh85z42kNAbVdMM+4WrhoDsCLAibNOUkLTef5h26sf5YDMEeznh4eNCzZ0/xrYLoCeIrRxTOO+IrHE8+BgqFVWoh21cz5D5KW+iZi3SWX5SESGdQDoYqDtKCHEIwltwNbQ7hQirM08vF3iTiOMTHBbc7jR4M3ZAMFm2mLs7ocVFk6uLwqx4ptjZ8ckU0P44K58kV0eIYqWUs7zksRWodS6ttKWGrVSxfD7bLTCY/P59jR/+jcZ3q5GSkYW9vT2xsLGAosPLXX39Zvf+yxt7eXhRkgN9//52FCxcSExNDt2FjxHGurq707t2bd6bNwKNS4Y2F/FxePH6ItWvXcj1Oi6+fH9WrhBKfksFP364g8bYWvV5PYGAgtWvXZvTo0ffmQ6rcEx54EQaDEPPJRNLj0wl2see4zMibOHEikyZNAhAtw9ImJSWFQ4cO0aVLF8XXP1+wCG9vH1b/shEPTy+ealANgPyswgpMUqtUGiiTlpvPxYRMo5Qb6etSi9lSgI24f4W1XCgU2PPaFDwc7QlVsHKVLF9hvDzPtyjxlVq+lgQFzFuK8mjoooTH3HqoIMRB7k6c1aaKc0tLSsfVN4hMncG7Igiwq2+Q0Xaw1MQhXgzOSktKNxJgUO5hLPUCyAU5VpchCrFUjKX5xGC9EMt/L7naaOyAN+Yu46uvvmLBhNHUDq/CoZPnuHjxIgD9+vUrcr/lmccee4x69eoxc+ZMhg4dSp06dcjMzGTr1q2sWrWKQ492Z+f+I+Ro7BXP4fTp09m+fTtNmzbl+JFDrLx6Fb1eT9OmTXlx7Bi6du2Kn59yq0qVio0qwgLvfIr76y8r1pC+OmkxIwjjulM6GzZswN/fn06dOlG9evUSy1/8559/qFu3ruiGmzp1Kn/99Rdbt24Vq0r9/vvvXLt2jYsXL7J161amzJxLr07tAEmxhGqF1XUKZF1vBDeuPHgJCt28gqhIizLICzEI5QelZQjNuZEdA8NBmyKmDkm7+Mgv1lLxl0Y7x6fnIF0pEAptSCOdhfKQSmuewmtCIQ1h7VT4V6j5bG4t2BxFuRIFIQ70cOam5LgC0ihn+TapGJtDsIaltafB0DAiXbY+LEVJkKXnDjC5wbFViAXEYKx0HYf3G4KwbmUVcDvHEMPg4uKCn58f48ePt3qf5ZUaNWqwePFio21jx46lQ4cODBo0iL//3EzvPsY3G+L/KUdHatasyYoVK9BoNGRkZJCdnY2vr/lsA5X7A1WEJbjP+wLvYc8qRki7Yk94jge1yGb58uUsX74cBwcHunfvjru7O3Z2dkycOLFYOcS5ubniRWjz5s2EhYURExMDQHR0NJ6enrz59iT27DT0R/YJqsxjTz1H4+4DRWEVLoxSy1XrGACyCOIztwxF8aWpNGdupYnP6wd44OVib2SNXkzIpJa/q8GNjR8eunRC73S8UbJuBYSuOILwyxGCq+RILV4wiG69QE/RopTn98ofC0hFRVrDGRQCmnxcOByVyMMRfmbzgqXIbySEGwi5a1pAOLaHjztZukKxlRfcUEJuEQvPXX2DRCGWkpaUbtS1SRB/Jctfft6UXNPC78QaIVbymhQUFLDrwFEOHDkKQI5XCMOGdeDvc9kMeqQKGzZsUNzX/ULdunVp1KgRq5Z/w5gRwwBDreghQ4bQrVs3Xn31VQICAvjzzz/Jzc3FyckJNzc3i2l5KvcPqgjLCFy1jKB2AxXrRztix0N4cxHDBTUvL49NmzaJr0+YMKFYx3R0dMTHx4ekpCTGjBnDJ598ItZ/HjNmDI6Ojuj1et77eBYeVWqT41+VU/E5fL4jit5NKtMxwtBrVGhZJ1iRwoVSqWYyQP0AD+LTDW5dITp2Z1QC9QI9qeXvamSNXkzIFItT1PJ3FcVfeqw6gcoJ6cJxBbGXBvuYczWDcYSzVHxPn9Lifmd9Vf6vIDjhVb1NIoLlCNaiu2zdNCEjx+r1YAF5OUepVezv5kSorxvapCziYxJNRFMqmIJV6yKuExvWj6VC7Gr0mqk1LexHsIjNRU/LsZQ3LWDpvCgJsF1mMj/8vJaRb38sbvvoo48A+OCDD/j+++/LZRnYkqZFixZ89913nDx5krpBnny7ZCl//PEHf/zxBy3C/fG3M5z7WbNm0aVLF1q2bEleXh7z58/nxo0bjB49mgYNGpTxp1ApDVQRNkOkt4ti4Q4/jC9CAQEBdOjQgbFjx+LsbLl8oSWmT5/Oyy+/zPXr1xk+fDgANWvW5JFHHsHLP5DHBg3lup0vx68ns+fQTcKrenN291EyUgyVmDrXDhD3deZWjpHYQaEQylNRzmpTORxVWHtYns4jdVsLa8j7ryUblXEU0lkEy7ZOoJdJAQ1hP9L9xacbLuoXEzI5q00l0MOZ49cNC/LS7kRCmUdBNG9HXSbNNxjdleOiCN3G2Ko8fUpLQJiX+LmkpEtaCEoFUHtH7OsFehqthcox505XWlOXfuaMlGw8fNxNjmsJYZ1YmpokDeYC+P21BnSdvK1Ii1iK3DUuzTGWpjQJVcPMYSlHOFcbTeatG0YCLOWTTz7B1dWV8ePHc+XKFapXr252XxWd4OBgcnJyePrpp/nmo7d5ouPDrG/fir9370cbe40PXx1Nsp0HW7du5ccff6RatWp4e3tz6sQJAoKCGDNmDFOnTqVbt25l/VFUSpgHsmyltXzZ6DHF7VfJYIdzCj169GDKlCl3XcYyMzOT119/nT179vB8g5p06dKMh6uHUu3xEdhXi+S8NoX915LZdPImp08ZVjBvR10GDMIT0TCEQB8XsWEAmFZ6gsJgJsHdKHQB2nT0hknJRigUQvmFWBBKYR0xMsywLi64ssFwcV514ib1Aj3ZfuGW2bKJ8pQiubULEB9juEm4ceRPs+fQr7phLVywDl18g/Hwccfdy1nsaCSsB6ffKZgBhVaoME74PPLoaKn71ZwIC0ijuoUAuOPXkzkalSgeX0Cw4AWk67vSfGJLa8Rya1gqxpbEXh68JSAtgSlEjQvfWf0Aw/9/eXqNYjBWZjI/Ll/G01M/BwwlHIVyjgJhYWFcv25oVCCNf7gfOXjwIDNnzuTKlSssmTmFJ5tVJ1+nxd43kKxLZ8i8pSPtxm0Oxd7i53wvru7cwUCvABq4ezA/yIddu3bx559/3tfn6H5CLVtZigTiTHZ2NvXr179rAb527Rrjxo0jPvY683q2pqufLzW6tMGpZmMTARYQKi7V7dgJNy9nmkT4WbRgtWnZHI5KNErDUcqhlQfpKLX583dzIjLMW7whiPZy5q/d0TRoGAiNQsQxCRk5bDp6g8N3gqFi77Tuk/bYlf771+5o8bMJwhEfk2hReKUkXjHOQc7UxUP1SNKS0klPyRatYgFpUJNwPKk7VrixKG5AkvAeAen3I3Whm0Ne0EMeOS1F7paWrhNLRV0uyPKbASnm0pqgaAGGwkIt9vaF/amFFpdSBAEeNGgQAQEBJq/fT7Ro0YJVq1Yxbdo0nnnzPbRDe9C/ko8hEEubjFugNxqNhoZOzkRodbj3akO6NoP0+HQan7/CjoICMjKKLjyjUrFQRdgCL538XdEadsWeLl26sH79ep566qm7OsbKlSvJTNDy29AeNG4QYfSa4N49fj2ZUF83jkYl4u7lTIOGgYT2acCe4zcZ1T5CbC4PyjWSBes2VpchjovVZShWkjoalSi6IwXRrhfoKYpRfHpO4RpnmBdRp27i4eMuWuiCtXlu5w4xpzXxynEqN+tBUFU/Tp/Sog3z4tb1FKP3C5Z94pXjJmugtiIGLukEYSq0HNy9nEVhkoqVKEh3PrutkdICSrnN2rRs8TuIlqxDy5FHORt/JsvR0kpCDMZWsdz6lyJdH5emb8nPhRwlAc7VRmN3J3d8QMeWrF69mtWrVxsFYLm7u6PX6xk1ahQPPfQQrVu3tvj57hecnJyYOnUqhw8fZsIPW/CsWZ9aru6k5edx1uEqIYGeNKxZGeGsuga4cjMzi9XnbtK1a9f72mX/oKKKcBGYE2I/Pz+xLZmTkxO1a9fmkUcesWqfZ86cYerUqXTv3p0/Nm6gZ92q1PAzWGpedWsZrOD6Hdmw65L4HjHFBhje0pATLFhrHSP8jfrkyi1YQVjl1q00XUepqIMgwLX8XUXLp3lVX85rU0gI8ybEx4WvTt3k2n97cfUNItrLGTcvZ9F9DIUW6o0jf3LDUN2Pa3dE9vYdgZaLbknVVBb2bcDYhScXJyGIKTomuXB99M4NjdwalqZZFYU8FUweECVfmwVTK7gwp9g0fWnP58No+8oqswFaSlgSY+m8tBjnFPu7OYlWsKUuSQL5CYbP8d/fG4wEWNozODIy8oERYAF7e3vWrl3LwIEDmZ6ZTONr8RzNTSWuwPBbcdjpwBMRlbFv3o5t27cRH2/4vnv16lWW01YpJVQRtgK5EEeRwV9r1gCwcOFCwFAZ58CBA2Yb0ks5fPgwZ86c4cyZMwR6ufPJ8N6EVA8XxTdWl07onbFrd0WJa3QPRxgn67eqYhBhD0d7owhjaVDTwxF+im5lKeFVvUUBNjdeetEVoqAvJmTSsl0E0VX9uHr8jNXuY0FkBYEuKdG1dCxAMacWjMVIsAg3Hb1Bkztr7EJUt7yespAOJg/EklrBwtr8zaQs0RoW3N4ZCmvB5gTYHG1fWWWyzVLaU1EBYUW5ya3BLjOZ/IQ4PvxxC9MXrBTrmjs5OTFp0iSmTJkijp0/fz5t2rS5q+NVRDw8PFiwYAGff/45h44fx98/ghnvvIOXlxc7duxg2bJleO3YQadOnWjfvj21a9dW14LvU1QRtpKXTv4OGIK1ou6kKE2bNo3o6GiCgoLo0aNHkQKcm5vL4sWL+ebrr6kWXImvpkygqT4R/wYPiU0Ofj8bTy1/V2btusTaXVGkyyo7SV3DUjwc7anl72o0DowbCMjXg6Vryb2bVBbfKw1MElKP5H1g61SL5GJCJg9H+BEdk0yliBqiS7m8kangmpWLkXRtWHrOpdHk0ohpwRouqsm9gNylq03Kwu2O69cS1hT0kI6xlP5kDUoCLC3jaZUHIF3HgZjbTF+w0jAnZ2e6tGrCtFGP02SkoftX9erVuXLlCmfPnrVpfvcT1atXZ/78+Sbba9WqxejRo9FoNFbd1KtUbFQRLgYNHd25mJtO27ZtxdqxAIcOHWLLli28+eabJon258+f5/333+fCuXO82qohH340EU1qAk6RQ7GvFsmhGB1ern6QkcnifTFiWo6wRhca4ScKq4DUPSpQy9/VJC840MNZjIQWHneuHUBCRo4ouALStV9pAI5jYLhRA4ZcbTSP1QsnyN2JjhH+zN5+kV1Rl8U14PKI0jqpgFysMlKyORqVCBLvg3Ce5OddWqBDKlLyjkqCe1tuERcXa6Oii2PZCt6XUF83oxsyMB8VDgYrOC8vn+FvfSRu2/brjzQP82LHtu3itp49e9KxY0cuXbqktJsHHsF7oHL/o4qwjbx08ne+fbgPdnZ2zJs3D41GQ0pKCu+99x7btm3j559/JiMjg08++UR8zy+//MK0yZOp7ufFppcGERkWiIODPZpWfYlzDBBdzxvOajkclUh0TLJRoEzbyBAiw7xN1heFHFtzqTTCdqHIhpBeIlh0wnbpWAGpVS0g74SUq42medVwDsXoxHXqhyP8+Oq7YAb0acBfu6O5sG29Tee3tDDk1hqLli35ugJK+cPm2s8JBLk7GX13lloruvgGW2x1aO51QYClKVcCSoU5zCFPVQvxcREFuCgrWKgRfSH6GldvGOb4y5LPaF6nGqTr2HPhujjW3d2devXqUa9ePavnpqJyP6KKcDEYcXgju8eM4ffffxe3vfTSSwwaNIgffviB33//nTFjxpCVlcXGjRv5/vvvGdqoJh90bIpvWCBedWuhqdOKY1nenLmWCCRy/HoysboMI+soIMyLQB8XE1emUDv5LIU5t9LgISWkwmHusYBUyNNy841qRwvdjwokATiVPZ2o7OlEUOdaAAS+2AZ/NyfD2nT7N1i/8bTVqUaW+O+7N2g6fG6x3msI/ooXSz0KgVqWUnjk1rBxoRPzLf7kYpWSlW+yNlyUEAOKJSmlr5scVzZ/QXwFYbXUxEFA3lM50MPZKgEWKHD15naSYdli9NAB9OnaQXytXv364uO5c+cSFxfHW2+9ZdV+VVTuV1QRLib169dn//79tG3blr59+1K3bl0A2rVrx7///it2hXG2t+Pt9g/x/pujyEtMwKVmfZzaP8mhmMIG90LxCwE3SeEIKfLCFsKFUslCLiwRaUCpdrP8PeaQrn9KmzgICNuEICUvF2/xon0zKYuIhiFk6SLJ1MUbpS0JCNHR5lKTVr3bm/Hjx9Onzw7efPJJZm+OK9ZambUpPMLjoKqFrmh5PWXBIpb2hpWeD3NrxdKgOSlCJS0p1tSV/v21Bjw2/7TRNvc7UepgLLZNIky78MiLqAi/MXk3qaKQRkW3aNmaTye9zrgRQwrbVwKPjRwH4wylXfPz8/nuu+9UEVZ54FFFuJi0a9eOZcuWsWfPHvbs2UOPHj3Izs4m/tRxgt1cGPPmBGrs20qLulVxdrBHo9Hg+uizaCXu551RCWLJSAHhQiktGwimXW0Ak9ZzAtISkUI7OltQKkxhbVqOIECCMAviHujTmuiYZOJjEgmq6idWtLr2316qNG1TKEBXDP9kJN6g4PZZmodpmD79CFqtIQ95xowZ2NfshcbD+khRVwtiZs7ahMJI6T3Hb9I20lCIRPge5G3+lM4DFOYNx1sRv2XJNW4ph1j6XqkbWirA8kpo4hhJ9TJ/NydxyULAFitYwMk3kDefH2nUShPgwIEDALz//vtMnz4dAL1erwYfqTzQqCJcTBo1akRoaKjYlHzOnDnE/ruN66kZTPeuSrt9u3EP9MPZwZ6wAX3R1GnFFfwgN5/fz8az/cItI5ek3A2oVCDBUqqR4CqV14aWWsm2WDZyiqoRbM4yvpFqCP6K1WUQXtUbNy9nejepTKBHXTadvIm7l6Hql1i+8U7D+pybx8mKPciBWNPjdaupZ5vMYDZnRVsSYAFzQiwIn7uXs+iWtlS4wlL+rCXkpSsV961gKQM8Nv80v7/WwKinsHQNWEl85bWghd+FNPhKPK6NAixQ4Opt1Fe6wNWb9euXAIgCHB4efs8F+MaNGwwbNow333yTPn363NNjq6gooYpwMXFxcWH16tWsWrWK1atX89133+FoZ8eE0HD6P9GSkE6tSDl3kUqPPYF9/Y6G6GcXQ/CVXEyl5STlCJZuUbm+oGwtSxEsYyUsWXXWIO0vLBVkLxd7/N2c6N0oxOhif+ZWGr0bhXBcUs3rwL9RJF44SOa5TeTdOgeAs7OhRKjApEmT+Pjjj3GoNxCNsyFaXBBaayptSStLSR9LhVgqeMK/6SnZRmvD1t7QhPq6Gy09mPselUpZyoVZsHbNWcWWoqDlAmxNbWxbMddTGgzu55P/HRafd+rUiTFjxhTrOMXhv//+Y8OGDaxbtw4w/I5WrlxJzZo1eeaZZx6ITk4q5RO1gUMJoNfruX79OsErP6dGj+bcPnGJkE6tcGjV1+B+vnMh3hmVYFJOUi6+0ou0teKrJOCWOt8IyIXEmouyUqF+cwgX5fPaFOoEehn9C4X9jYUmEuc2fE/cjm+M9jFr1iw+/vhjdDqDkB04cID27duT59cA++CHTCxduQhbYwkXjjXf41doBtE2MkRsaCAVMqnbVnqOhG5SQqcoKGxUoVS0QwklK1kpoEy+FqwUZAWYzNvoc1q55GALudpo1m3cxNDR4+nRoweTJ0/G09Oz6DfeBbm5ufz999+cPXuWuLg4Nm/ejIODA4MHD6Zbt2788ccfuDnAjt17KUDD5s2bVbe4SolibQMHNRmtBNBoNFSpUgXH92YBUOWdj3Ht+wrHsrxJu+N+Xn001khQ5QIsrO9KKU0BBtOALiWKuihbsn7k+xAqbdUJ9KJOoBdeLva0quJNvUBP/N01aP/91uS9er2e6dOnY29vz7Rp08T864K4o6LApl87Tn76bcAguq6+wfz33RtWCbBxuUxD8QtpClCWLo4sXZzYCOLonTV8bVo2CRk54nq7EKBli0ApRSgrpRO533HXS/HwcRf/rDqWGQvYaJ/FtICLQuMXxqSPZhMeHs7s2bNLXYABlixZwltvvcXy5cvZvHkzw4cP5+DBgzz0/FQqtezFh18tZeXqNSz9fDaxsbEcOXKk1OekoqKEKsIlTEzzwZzXpvH7WcMFff+1ZLZfuGU0JkSWdiQXW6G5gqU0FnPvLWmkObDmivUXRaivuzhOOl4Q5Vr+roxqV5tGz38qvhYSEkLTpk1Zt24djo6OHDt2jP79+3P58mVq1TKkQvlWj8QuJ4n8S5tIP7SMjFtXSY/5j+xrh1iyZAkZiTfY8/kwk/mkXz/Jo8HRpJ3dgj4/1+g1QwR3vEn0tPA8I8XQkepmUpaREMen5xilcylh7ubI7Y4FKwiw9LEUJTGWvqaEpTVsW7tCFYdDMToavbGeqKgooqOjuXLlSqkfEwzub4Aff/yRKd//QYNR73KioBJgfPP5SJuWhIcG88UXX5Cbm6u4LxWV0kQV4VJkZ1QCYHwhtOR+VhLeooRYHulqrRUMdxeoVRykQiw8FqziWv6u1Hm4NbWfms6QIUPo2LEj/v7+HDt2jLFjx7J8+XJyc3N5/PHHOX36NEGPPIOHjzs5N48b3Ij6fJq5Xyb/8p9kX/qbzz77jLzzvzFx4kS+e60Vmbo4ds9/EoCC2P2sW7eOgrijjO/sb/X8BWtYe8edfDMpi+PXk0U3c0pWvpEQC/9Kc7flN03m8nXBtiIb1oyXft/3QoDPa1N4a/1JetjtBSAiIgI/P9M0qdJg165dAEQ7BhNetxENQnyoH+Bh8Lq4GVLL/ou6waRZX2Hn7MZ///3Hr7/+ek/mpqIiRRXhUiI8/xa9/LPEYCm59QvWW7HWWMTmMCe0ltaDzc/DujrJ1iIX42m96/L9x+OJqv8c+d1fw3fYhxw8eJCmTZsyd+5cPvqosBSiviCfiz+8TVb0fkaPHo2HhwcHDhygb9++7N+/n7CwMMhKYtOmTYwZM4b8W2do1aoVT9VNwtMhh6FDh9K9e3e+WLqajIRYcm9dID/lBtIQCcEVLX2elpRuaMN4R4jB8D1KXdNSITZXTUsatRzo4yL+yTFnEQv8OCocMF03lueYlyTW/g48HO35b8MWPvvsM3r16sWGDRvw8fEptXlJOXfOENin8ats5IIXKsUBbNv8B3PnzhWt82PHjt2TuamoSFFFuJTp5Z8lWqdSK/Vu3chyQVdKOxGEVngs3VaW2GUmGzWDyNVGc/3UIf7duAYnXQzvD2rM8JbVKMjPY9q0aeLF8ejRo+J7tP9+S07caQoKChg2bBidOnUCYMCAAbi7u9O0aVMAunbtilarpSD2AAUFBSxcuJDc3Fy6du1KcHAwdlmJcGYVmafWkX5kJfkpNwDT7kXy51IhjtVliAVXLiZkkpKVz41Ug3s6JSuf+PQcEjJyjPo+g7JQWrKMLSHUGC8uRa0Hh/q6mwScFcXfVxKp3rQxgNgBSKvViq7i0uLrr78GYMSIERaDrRq2aE+dyGYA1K5dm86dO5fqvFRUlFBTlO4BzZyTOYJ30QPNYM6qMbfeV5TQFmUFl0S6CpjmD0uRF3Lo+eQznD59GhcXF9zc3HB1daV27drs2LEDgI4dOzJhwgQeffRRAF588UXs7e1p1qwZlSpVYurUqYwePVpset64cWM2bNhAy5Yt8fT0pHHjxvTr148zZ85Qp04dXFxcCAsL4+bNm2zduhUAp/C22HtVNulKlCWpOy2UvPTwcTfquRury2D7hVtijW9DMY+iPQxKJSwDfVyKjJxWyi2WWs1C9L0UpRQ1c9+vueIstgSeadOyade8OscwrNG++OKL/Pvvv7z//vsMHjzY6v3YyhdffAGApsUTQOEasDQPOiUrn0qBQVSvHMj547BmzRq1aYJKmaCK8D2imXMyR7ItC3GoJGdWvk2KpWCb8oqQQwymAgyFgTRZWVlkZWXh5+cnCjDAzp07efzxx2nfvj1Xrlxh3Lhxxvt3dBQFGKBu3brUq1ePrl278uSTT4rbIyMjxceVKlVi4sSJbNu2DY1Gg1f9Hti7GILFzFXRkm4XRFAreT1Wl2HS97moKHSl790a3L2ceW79TX4cFc5z62/a9F5LFNWQwlq6eKZw/MppWrRowbffFka+l3b/4DfffJM5c+YQdXg3DRrWN7rxEKrBebnYG9bwcaJ58+aqAKuUGaoI30OaOSeDM2xOsCyiUutIbtFYI8CWinLIMZcbXFJIi3iYwzEwnClTpvD333+Tm5vLmjVr0Ov1PP/88zRv3pzNmzfj7+9Pp06d6Nixo1X5nJGRkfz8889FjgsKCuJ///sfM2fOJC/5hijCRSHtwJRxp/+wYL0ejkq0WIDF2vxvW93LGbLe02Ba87o42GL9Sr/n1atXM3v2bPF5r169GDt2rGG9vhQZMWIECxYsQI/epMGJ/Dfurs8ms+hSCSoqpYZ6+1dGFHVhFIS3OAE2tqz7WqorLXdHFqcko7UMGTKEb775hv/9739s3LiRTp068fXXXxMXF8fUqVN55ZVXsLOzw97evkStFr1eL3bDyk65bbL2K1BUfWmpYGqTsozyiaX/CigF6pVmMJVw/KKscqkVbKtFLAiwsN5/+PBho9dnzJhBjRo1bNpncTh//jyZmZnUf6hJkWNPnjxJzZo1S31OKirmUEW4DOjln0Uv/6ItIflF2ZJwWypZKY0INbe9qMpJtuQIS4OuLI2Tvi78NWzYkDpNWtKxvyG/98yZM/z222+lmsMpXIT1F39HqYCcIMCWuhopRTEflgmxEkpifDdIbwYsubfNtby8W4TvXusYwCXP1uJ2az0YJYHQ7GPn/pNmx6Rk5VNQUEBsbOw9uTFQUTGHKsJlyLP36P++UExCii11os1ZwNYGXRWFcOG2y0wmVxvNjdQcqvsahGn16tW89957HDx40KZ9WotGo+GFF14ADOvRaQeXkJEQa5SaZImAMC/cvJxpEuEn/kmxNQpeeuNlbZ6w0nqwtgSKuNxNUF5abj7V23eha9euODo6MmfOnHsmwm3atKFLly5cWDuPH/7YpVjZDOD6kZ0AVK1a9Z7MS0VFCVWEyxi5EFtbKetuuJtGDVIsWcJya9jcOOljofOOl4s9lxwKI5Q7depEq1at7nq+5ggODuaDDz7gnXfeQZ9xm9yonUavK5WHFJ4L+b3mLFqhqEdZURK/JVvywwtcvYnzqslbEyZwZOZwLl68SOfOnXFyunepcY6OjsyYMYO6tWqw4+PnOXPugvia4PHxcrHn4LloALV5g0qZoopwOeDZGsZiXJyIaFsqZZUnpEKcqEui07Dnmbv5P/YcOSVu37FjR6laUY6OjgwaNIjQUEOnZ3s3P4uuZzBEJgeEGYK4hGjoQA9nQ3OHO3/apCzxuzSplCX7vsx9v7ZWzYJCl7Q5a1jqFTG37ltcK9guM5nglEts/OUP0m/f5OrVq2WSf+vi4sKkSZPIy87k3JmzJmvhKVn5nNm/E09Pz3t6g6CiIkcV4XLEszWsE2Cl4h9FtTEUsMYKttQ1qSRQspQLXL35bOXP7Fz9DStff5IT384A4I033mDbtm33JIXkr7/+AsC7QXdA2QIWtgsI35f0uxC33ckfNoelGydp0Q55bWlrkK4NCzcA1v5GlIjVpVttERe4evPiojXYVzecx8qVKzNs2DA++eSTYh+/OPzxxx+4+FTisUd7mKQpRWkT+Xfffvr163dPGkqoqJhDFeFyhtw9XZQAC/WipRd0cxGw5gTYUnEOW0tVyl3Mll6X4hgYzu7duwHIS7lNUFAQy5cv55lnniEwMNCmORQXIfjLIyDA7BglUS6uF6IoUbS2jKXFYxTDFW7JChbE2NLvwjEwnPDQABr2Gw7A8OHDOXnyJPv377d5LnfD7t27ad+zDw+FVaKWv6uYG3zmVho/rFxOekoyTz311D2dk4qKHFWEyyFy97SAtRd7W1KUisoNNmcJK60HFyW65gRYCOQaPnw41atXJz8/n3PnzrFr1y7eeOMNfvrpJ4tzLAm2b9/O5s2bsXdxx9PX02yLQGkfXzDcJMnF1Np1WHMNN4pKVZIK8dIBIaSnZIt/UgRrWLouLcw1Pj3HbIT03RbrOK9N4WZSFmGVjS1Mac7wvSA/Px9XD+O8by8Xezzs8rm4bT0tW7Ys9ZxlFZWiUEW4HFOa0dNC1SAppZEXLI2UNhc1LURFP//881y6dInf/txO18f68euGDWzdupUPP/yQvLy8u56LEmfPnmXDhg18//332Nvb0+2D5XgG+pltDShH2tZQm5YtpiXZgiDESnWlzTV3MGcRmxNia/FwtL+rqGj7apFcTMgkIzmR01+9Bhi6J7377rtiC8p7hb29PU4UiM9Tsgy1vDf/uo6zZ8+qVrBKuUCtmFXO6eWfVWSFLWuwNSK6pARYiHq2NEZAsK77du9E3+6dOPvRTHbt2s2LT/alSZMmvP322yV64czNzWXYsGHk5eXh6upKuzc+p179esTqMsRSlOkp2UbWr4ePuxiQJSCPaBdcwIE+LjYX4JBW05LXlZbWlLaEVIjdJZW8hP3LK6qZqxMtJdTX3cQFHatLN/md7N3yK2+9+wnZSbdJSUnhjz/+KJMUoGPHjhEfH49fYNCdQLTCz/vn94sBaNKk6GIeKiqljWoJVwCE4h7WBNZY44pWsoBtCcay1hUNptZvgat3kXnEwv7rBfvQqVMHhgwZAsDatWstvs8WcnJy2LBhA3l5eYwZM4bNmzfz6KOGQCJtUhYZd9y7UgGOaBhCQJiXaJVm3OktrE3KIjommeiY5LsSYAFLnZYsdVlKS0o3cZenp2SL87Q1XUronCT8HpR+F1JhdgwMZ+Tr73L2v4NcuXKFxMRELl68aPXxSgq9Xs+8efOoUaMGPQcPN3otPj2HmzFR4jgVlbJGtYQrEL38sziSreyG9HdzUgzIKsoCtjUS2hoBFixgS9hS0OPl6fP5ffMWqlatSlJSks09abOysti5cyd79+7l9OnT5Obmkp6ejlarxadOa46FDCA5ypWHIwxWrdyFm6WLo1pkfRNh1QJRp26K68dCO8Hwqsaf7e5rN5t2WrKEXIhF7sxDm5Zd5M1acT0hTz87mnOnT/HjymVAYQvDe8lHH33Ef//9xzMzviElT4O/5KNWSo6ie7dubN68maSkJDw8PO75/FRUpKgiXMEw141J7mIMcndSrAttLhK6pFORbK2aJUcQ+zqB4Xg42vPehzOY/L9XefTRR/nss894+OGHTd6Tn59Pbm4uzs7O5OfnY29vz/bt2/n444/RarU4V6pGn46Gjjk7LqXS9YUhNGzSlMq+riYBUoJLN6JhCBkpfoRX9TZpyhAdY7jRkIqesJYsiLW1Ahzo4XxXKURmhVfymjRvWcrdrAHLmTzhdcaNG4dGo+GTTz6hQYMGJbZva9iyZQs//fQTj42fxIC+fQDIz8tj+oQ32fjzD3h6epKamsqYMWPUoCyVcoEqwhWQZs6Gi79UjJUsG0t9gqUU1wIWsGbtV46143O10YQGhvP2yy/QunE9Jk6cyNixY3njjTcYPHgwjo6OZGdn8++//zJp0iQyMjKws7NDr9fj5OREdnY2zTt2442F7xJv78/6jacJqurH08NCGNoklPj0HCMPQqivm+hSbhsZYii+4eHM8evJRIYZ5iuIpZuXMx4+7qQlpaO7cpws3yDcI+uL77fkjtamZZsIv1SIre20pIRyyc1g3L2cidVlWNE8xPobMvnY77//nm+++YZx48aJvZ/vFdu2bWPChAkAdOhhfOyNP/8AQGpqKt26deOVV165p3NTUTGHKsIVGCUxBmUXtJIFrBRsU1xstXxtGS/cAHTo0IGdO3fy6quv8umnn7Jy5UoiIiLYu3cvAFWqVuO9F8aSmZkJQGyWHTXqN2JAr654OjmQlpvP0CahRvuu5e9q9DwyzJuhTUI5cyuNVlW8xXPVtbofR+PSxHE3k7II9HEh4866caYuDlffIOJjEgmq6icGdpkTvKLSzaQCbIsrWi7AQlcoF99gk8jpu0UuwJk3LvHmm2/SuHFjRowYUaLHsobjx48DMPT9uQRUriJut3dwYM7v+8k7vYuJEycSF2ddXXAVlXuBKsL3Ac2ck4lGucBEcfOApRTVBelekauNxh74cvo7ohCfOmUob9m7d28++GQ2YVWqip8pVpdOWm4+dQK9xOcpWflU9jS+SWkS7EFg7i0AHOuFA9C8qq/RmFhduujiF9sT3hFKQfgSrxwHjgM9RCG+236+cgEWjildtxbczcI8BOHNlAhy5pE4/KpHEu1VWOSlODXELf1elv/6F1qtli+++AI3t9Jry6jEhg0bWL58OfXadKZJF4MbWrpEU7t6OKE1Apg4caKamqRSrlBF+D4hPP8W0fYBZlsTCkitYGsoLwIsxS4zmVqVvFkyaypgiMoV5uno6y4+Dg0MN7L0pZG+AvLPJ+7nzntvpOZQ2dOJtNx84tNzOKtN5eadSOOMlGzO7dxhJHYAN478CfQgomGIKKJSITZnBQvibs4NLU9PUrJslQRY+lp6Sg3xWPUD7j4oKVcbjWNgOFk3L7NkyRJq165NvXr1SE9P5/XXX8fZ2ZnZs2fj7Fx6tc11Oh3vvvsuAC2GvQoUnkvpMs3BfXsAaNmyZanNRUXFVlQRvo8Iz78FKZDpFWrymoej/V1XQrIVaatD4WJdUqIujb7O1UabrDELBUACgVztLauPLcxZEGAhuE0Q4MNRiaIYxseYL8yhu2JwjdIwhFBfN9EiLkqApZhzQysV4JC6oZUEWNh+O+oy0V7OhPq6iS73kmDPmWiOHj3KkiVL0Gg0TJ06lX379gGGdLDSFOEDBw4A0PO1j2nQsL64XTjXggfjTKLhuxSWK1RUygOqCN+HuKbEkpsCjoFVjcTXVisYTIVU6XVBYK3Zj9K4uxVmaT/i/KvHxeR3qVBLt5tbjxas37TcfKOSjmdupaFNyzZUxkrK4tb1FNKS0sm6sw4MpsKXqYuDK4bHGSnZtI0MAZQDsqQCLFjBSsU/hH2BcRGRLF0cmbp4s+IrJz0lm1hdhhhoJsWW34b0e/vll19wd3OjadOmAFy+fLlwzhkZpdok4Y8//gCgU0ih9yfQw9kkWLFe8zZ4+QfwxRdfMGPGjHvSFERFpSjUX+F9TK42Bii5FBTHwHCTP2F7SexX+twSd5v+ZE2/Y8GdH+TuJF7MpTnEHj7uYrtDV98gXBVaH2bq4tBdOU58TCJH75S3BMQSl8IfGMTX2mhoQYCla8DWCrAUbVq22frRRZGfn89v/xxG71OZq1ev8tVXX/Hk0KE4Ojpy/vx5Llww9PDt1q0bARYaYtwt06dPZ+fOnQBEuxZW5tKmZZOQkSMuz9Tyd6VZeDCjXnuHzZs389JLL93zYh25ubn88MMPnD59+p4eV6V8o1rC9zm6WEN1IN/QiFJpS1iSWCPEgvVlqQ61NcjfLxxPiBhPy80Xu+4kZOQYuaGFXOCAMC9uXTec09uYW4eNw5dIbl1PYU9KNm5ezjS5039YjrkgLAGpG9oa97McpRsFsK5spZSrV68SHh4OwIcffkiHDh0AWLp0KV27dmXo0KGAIVhu+vTppWpx/vzzzwDUad+LiMYPi14GqRUs3FB5udgz7vnnSM8tYOm0/3Hu3Dnq1atXanOTs3r1ambNmgXArl278PMz/A6Sk5PZvn07NWrUoHHjxvdsPirlA1WEHxB0sVHoYqFhw4ZlNgelWsO2UtTariUr2ZZcZkGUUrLy8Xdz4uEIP4MQU1g6MtTXDSL8OBqViLtXfVx8g9FdOa4YqOXqG4xv9Ug8fNzZo1BVS8BcbWhpGc0sXdydSGxoOvy4VZ/HHPHpOXi5uBY9UMLnn38uPn7vvfeMXhMEGKBGjRo4OdkegW0LCxcu5MUXX8TV08eQNnZHhOXFazwc7cUbrBFDh7Bs+oR7KsLbtm3jyy+/FJ+PHj2a3imDH9cAACW/SURBVL17ExERwdy5c4mJMXitatWqxRNPPKFGcD9AqCL8gHHq1KkSFWJb84yF8XcjxoLVKl2Llj9WwpIAm1vXNlhRThDoSb1AT7ZfuCVGOkvXdmN1GWLxjmv/7VVcI848EieKcXpKtkkjCCXka8BSAbYFV99gXH2DcPENVuwQJU3lskRiYiIFBYbORJUrVyYjI4OkpCSmTZvGBx98AEDdunWpU6cOPXv2tHmetnD58mX+/PNPAOwDw8XvxVxJTulNYL3GTZg1axbVqlUT17FLgxs3bvDvv/8yffp0unXrxuTJk1m9ejV79uxh8eLFZGVlERYWxvDhw/nuu++4ePEiX375pSrCDxAavRULIykpKXh7e7Nv3z611up9RHHFWLiYlVShDynCfkvCdW5LwJeSAEs/nzSy/GKCIbpWmmcrBG8djkrk9CktF7atN3ssqVUMFNk2USrAxVn/FQQYoFJEDdy9nAmv6s3DEX7UC/Sklr+raClaIjc3l9q1axMdHS1ue++996hUqRJdunQhOTmZrVu3MmDAgHsS9PS///2PP//8k4ceeoga47+kT5OqogBLvxsvF3uToMR9Z67w8nMjuXjmBD/++CNVqlQxPUAx+fvvv0lPTyczM5OPPvoIgI4dO/LZZ59xNNcXbVo2vfyzyMjIYM4vO1k/+130ej0tWrSgYcOGtGjRglatWpXYfFTKhrS0NFq3bk1ycjJeXuZvcFVL+AFGKHRhixgLwiSsm8qjr6XbpP8WZ/8CxRVkW1KilCxh+XGFzyyvsgVQP8ADfzcnAj2cOX1Ka/K6FCFyOuuOZSpEOQuiLKe4AuwqCRwDxEAyNzMFO4q6+bG3tye4WnVRhD09PWnXrh2hoYaUOG9vbwYOHGj1/O6WTp068eeff3Il04tHfNw4fj2ZzrUDrCpC0rp+df43fylDW9Vh0qRJfPfddyU2r9dffx0AJycnunXrRr9+/XBq0p2juYW54ob2pC5kZ6SRl5fHzp078ff3L7E5qFQcVBFWURRjJStXLrrCNvljc/9C8SK1lQS5pKxlKeZSsJSOLcfD0R4vF3sSMnLo3j4cd6+hXD1+xqzrWElMs3SGf10kAVTFjYCWWr/C/jx83HH3cjbbZrGo86nT6Rj55CD279oOwG+//Vaqkc9F0bt3bxYsWEDM+X+I1aXRsnaIWAdcLsRKN4Pta4cSEBLKsWPHyMnJuev167S0NKN13w4dOvDI67PwCCwMxBNS3a4npuO9dwlH//oLHx8ffH19lXap8gCgirCKiCDGvqERJq8pCWpxkAuykrDLkV48pQJYEtZycbDkipdWoXLzcuZWRA1uR11WFFFzopp45bhRJLOt7me/6pGAsfgCohs61NeNyDBv/N2cRFdtUedv48aNTJgwgfPnz4vbOnfuzNatW8ukXSGARqNh6NChzJwzD3vHwqhouSsalG/+Qn3dee/T+bw6YhBHjhyhdevWdzWfCxcu8MMPhkYRHYeOofNzb2DvUHiJFQR4eNVcNhzbwpQlS3B2dua7775Tc5YfYNRvXsUEIa0pLTdf/CsNrBV2+TyU5hSrS1cUxrvNYTbnzpaXwBTm4+Vij7+bE5Fh3jSJ8KNBw0DqtW9ClaZtTPZhLrdY+Ff4sxaD9RuMy50/Dx93PHzcCQjzokHDQNpGhojtGP3dnKxeC/5s0RL69+9PcnIya9euZf/+/WJU8YABA6yeX2ng7e2NPi+Hm/E6cZtQ5cySAIPhu3356SeoFh7B9u3b72oe6enpvP/++wC8/PLL9B77FiE+7kbBe4EezjxbA/bs2cOUKVMAmDx58j1Nk1Ipf6iWsIoighA7BlY12p6SVZg/C0U3iChpzLnChQutkpv6bstlWlMRTIpgiUWGeYstCWN9XAgIGwVA1KmbgLmWg4UUx/3s4htMUFW/wnXfO65naUS3LQJ8Lj6Zz+bMxM3NjfHjx7Njxw6++uorIiIiWLx4MSkpKVbPsTSoXr06AFkX90CrGmJqUlECLKDRaKjfsJFRsJmt5OXl8dZbbxETE0Pz5s0ZPXo0R3ONg+2EjmcAjRo1wt3dHV9f33ve7lGl/KGKsIpFhKpbUFiTurhVloqDIPryx3Kka35yIS6JetVFCbFwbKHIBxS6RAM9nAvFWJdBYLsIomOSSZcEYgnBWYILWSiJaW4tWCq6wlovYCK+gFizWnDVWuuCjtWlE310H1GXLgKIlh5AcHAwLVq0wMGhbC8hQhvLuIN/EfjiGCN3tFyAlW7OAAL8fLh47gx6vR6NRmPT8fPz8/nmm2/4559/mDFjBsFdh3E01/CaVHil+Pn50bdvX3755RfVDa2iirCK9bimxALGDSIsCWNJIRV96WNrj1sSjSPMCbB8fVi6vi2IgRAsZBjvRqwug/Cq3miTssQ8YHmakiDQab7BuOqCxO5I0mArQXylwis9jjnxFeZtiVhdOp9/tZCvZkwxea1p06Z8/fXXFt9/rxBaJjYZPQVQ7qUtYC6Yr9tj/fn222/5999/ad++vU3H/+CDD9iwYQOPPTOe4K7DAPPim52dzbp161izZg2XLl2ibdu2Nh1L5f5EFWEVm3FNib0rIZZbKObWhIuyuOXHtTUdyhakLQ7lSC/sUkGOT88xEmDBGhaEONDHBXkyU6CPC9qkLNy8nMlIycbdy5l0H3fSZBHOYLB6jap3SY4jLd9oi/UrcCEqmpnvTxCfL1u2DGdnZw4ePMhjjz1m1T7uBS1atADANf4U0ID49MJ60Uq/B7kQx+rSeWpgP75s2ZLPP/+cs2fP4u3tTZ8+fbCzs+P69evUrFnT5Lh6vZ7Vq1cbBHjcOwx89kWgUICTk5M5fvw4x44dY8eOHdy6ZehXnZqaSteuXXn99ddp08Y0TkDlwUMt1qFy12R6hVotwpZEUi7G1rq95cc2Z+2VVBvFotaHz129yStjRpKQmUdKSgqNWneg9dAXxdcFt7Q5BJEG0zKW5kTXaIzE+gVsEmC9Xs/06dOZPHmy0fby+n9fr9fTv39/7Dx8eXfpeqObDrA+Ja5BRCjJycoW7JQpUwgICOCRRx4BDF2h3nvvPbZu3UrLli2ZN2+e2CUqPz+fmTNn8uOPP1JQUICfnx/NmjWjVq1a5OXl0adPH7Hutsr9jVqsQ+WeIW2daI7iWKjSADBLY5SOU1YCnJiYyKT/vcbWrVvFbZdOHDESYcP83EyEWCqswmNzYitvhygv1Wir+1ngiy++YPLkyfTu3Zu0tDQeeeQR+vXrh4uLS9FvLgM0Gg2DBg1i9uzZRF2/Sf0mtWxeHgn1dadGeFX+O36SZs2aceTIEaPXhUjmHj164OPjw86dO0lNTWXevHl07drVaOzVq1dZtWoVTk5O/Prrr4SFhdm8zqzyYKGKsEqJIQRxWRJjAaXCF0r5wtYIsRJyt2NJrAuD+QAtYd/xCZn8svZno9deWrDGRDTlbQulQVRgLLLy3FcllITH1huf2Z8vYMKrrxIWFsYnn3xSYYKGevXqxezZs7mw6w8erj0OcLIqgA8Kf4f//L6WYydP0zSyEVNnzWPVuo1cu3bN6L1CnepWrVrxzjvviJHZAFlZWUydOpVNmzbh5+fH/PnzS7QUpsr9iyrCKiWONKJaSZCl4mipCpWA9IJ6N6lRJSXE5rDLTKa2G6yc9xHzVq7h2LFjVI6oRY+OhmAf6fowWF7HBYq1livF2mYZwrjDB/YDMGjQIGJiYqhatSp2dnZih5+qVYu+uSoL/Pz86NChA3//vIInhj8DkhsWpXiDS1dj+G31d1QLj+D5UU/j6uqKk5MTLZo1Yd7Cb5j1+SIKCgq4du0ab7zxBufOneOFF15g/PjxAMybN8/INX/69Gnmzp3LiRMneOedd3j88cfLredApfyhirBKqSIKspX9jIuqnnU3kdglJcDyIC2j/abreKp7G27Eazl27Bh1GzchyN1JLCABpq5kYZvSeqZwzuRztyZv2ZomG8Lrubm5/LTKUD953rx5zJs3z2hcREQEGzZsKPKYZcWLL77IwIEDWThjKoMHDeJa1GUe7dMHX7/CkpHCb+vjye/z14Z1AKz5YSU71n8vjmnSpiMtWmzn7bffJiwsTOwMlZuby7Bhw+jatauRAGdkZPDKK6/g6OjI3LlzbY6uVlFRRVjlnlBUP+OS6Mqk5GaUUtKWsHxfBa7ehhJ06Tpq12tItYgIgv0KAzL83ZxEa1hpTdec5Zt/9bhJabui6lzbyuWLFwBwcXGhWbNm7Nmzx+j1adOmFXvf94LatWvTqFEjtv28gm0/rwBg/YpFfPj5ImrVrY+vm8EyzcnJ4e/ffxHfp9PpqNeyI0+NGMnUqVPp1i2cbt26cerUKbGMK4CjoyPvvPOOyXHffvtttFotP/74Iw0aNCjdD6lyX6KKsMo9RXphMyfIRVnD5RFB4AtcvcHVmydGdgLg990zcHS0J/7O/YVSr1tL1q9dpnLErtLx5dhyU+MdFIK9gwOdOnVixowZ2NnZcfv2bby9vXF0dDQam5+fj739va2UVhQajYaVK1dy7do11q5di6+vL+vWrWNA10cIDg5my5YtREZG4kaO2A8ZDPWvV65cSW5urtFv0xp+++03duzYQUREhNH6sIqKLagpSirlAkGQBeGwRYTNFedXorTWhAURtstMpsDVG6cgQxOMnUfPEFw5lJSsfCOXNGCSQiSdtyUBLnD1NjquOaxpcCE9399+s4iP35vIkCFDePfddxWjen/++WdmzpzJ9u3bLaZdlAcyMzM5dOgQ06ZNQ6PRMHHiRF599VXOHtjJ+VPHCQ8LxTGseHWbk5OT6dKlC02aNGHBggUmNyoqKtamKFWM8EeV+x7B/SfUrLYGD0d7mwTYVixZoUUJ5N69e3mkSze8fQwt6qRr2UHuha5nW9OIhP07Bobb5H42ZxVLj5mSnATAsWPHjMbcvn2bn382RHxPnz6d7OxsLly4YPWxywpXV1ceeeQRpk6dSrVq1Xj99deZPHkyBR4B1GrVtdgCDODh4YGvry/u7u6qAKvcFao7WqXcIRVipejq4lbFKikrWCrA5oSwdevWrFpjWHsUrPpa/q5G0d225DRba/0KKIluUT2YU7U3sLOzY/Xq1eTn53PlyhViYmL46quvuHTpEtOnTxfHfvDBB2zatKnIeZQH2rZtS9u2bVm4cCHTp0+nZcuWVKtWzeb9pKamMnXqVEJCQujVqxdt2rRh/fr1xMbGUrlyZTUfWKVYqCKsUq6RpzuVVllKS0hFVxDDAlfvIsVQCDaTr3GbE2DFQK877m1bsLQWrFS2ESAjPZ1ff/2VgoICOnbsaLE7Uu3atU2ipysCvr4Gr0RGhvlqZZZYuXKlmCu8YsUKPvroI9avX0/Pnj1Zs2YNdevWLbG5qjw4qO5olQpDrjbmTpS19S7r4qIkvKbziTb6s+S+FlznlT2drBJgc8e+2/7IoCzS2VlZYtlGqQD36dOHFi1asGLFCho3bgzA119/XW5zhs2h1+tZsWIFrVq1Klb/3k8//ZTFixcDhfWqb926JQZkJSYmAlBQUMDPP//Mli1bSmjmKvc7qiWsUiGRCrFvaIT4uKTWgwXkwip9biK66TqLd7XWBIspCbmtlrA1yC1iX39/Pv/8c1555RXGjh3LwIEDCQ4ONnrPsmXLuHTpEv7+/iU+n9Jm7dq1xMbGcvPmzWK9PzAwUHx88OBBABYtWkRWVhYeHh6MHTuWiRMnUlBQwKxZswBDbnWdOnXufvIq9zWqJaxS4RGsY11slM1pJkpYkxZkdzsa0nXGf1D47x1Cfd3FP1spcPU2EWC9Xs/cuXO5evWqzfuTIy8Z2qRTTwDq1KlDRkaGSfCVs7NzhcyF3b17t5jnHBwcbJSiZC3PPPMMJ0+eZMKECSblPN3c3OjatSuffvqpKMAAZ8+evbuJqzwQqJawyn2HXIiF9CfFClcWtotIhDU/Ic7sce39g8HdF/tqkTbNV6AoF7hjYDgLFizgzTff5MCBA/z000/FOo45AgKDCA0NZfLkyaSmplK/fn3xGHq9nn379nHixAmefvrpCpWqKC00kpaWVuz9bNu2jVmzZlG1alW++OILjh07xuTJk5k4cSJdu3YlKiqKzMxMwsLCeOaZZzh16hSPP/54CXwClfsZVYRV7nuURNlcxSl5CUoplgRYitDkwZpSk8KYogQ4Ue/K8/36iaUjX3/9dcVjK633ynOuLQW3DRs2TLTm3njjDcBQG/nJJ58Ux3Tv3r1CifDbb79NbGwss2bNwtfXl7y8PJycLDfEkHPx4kVee+01AN5//31cXFyYPXs2HTp0oHv37gDUqFFDHB8QEEBCQkKJfQaV+xfVHa3ywHHq1CnOa9PEP7lIK4mgXIDzdVqj5/b+xuunUnEVBFYeyCUEc1kTBDZixAhRgKuFR9CqVSuTMdYIsLltAIm3b4sCvGzZMlq2bEl0dLSRAC9durTCVYfq0qULp06dwt7enqtXr5Kbm2vzPqpXr85DDz0EGALXHn30UfLy8hgzZozi+Pr163P48GGLUeYqKqCKsIoKUFgsRBDms6mWU6HsfQMtvi5HEFup6MrXns0JsGNgODpdoVXerkNHm45tDWm5+bi4uorPT506RXJyskm95ObNm5f4sUubefPmsXnzZt58801WrFiBu7vt6/P29va0bdsWgDfffJO8vDx27NhBZKTy0sPgwYPJz8/nww8/vKu5q9z/qCKsomKGswWVxL8Lvg3Fv6KQi625MVIsRUDv2rWLQ4cOic/btHvEitnbjpu7Ox5ePgDMnTuXdu3aiV6C/v3706VLl7sqSDFlyhSjz3EvWLt2LcuWLWPIkCE8/fTTNGvWrNj7euGFF1i5cqX43JKYV65cmQkTJrB58+YSCRZUuX9RRVhFxUakgiz8CWJtK0oR0HLqBnsb9ad9rHtnq/dva3GTjfuOm6Qmubq6MnXqVObPn2/TvqTk5OSwbt06nn322WLvw1Zu377NF198QZ8+fczWwrYVQXibNGlCbm4u27dvJycnR3Fs3759qVmz5l2dN5X7H5sCs+4mslBF5UHgkJX/Rep45FPg6gWpqRbH2WWm4ARcPLSbKo0MrmBvb2+TtcYbZqpkpecpr/+mAe4O9ibjXFJu4O3tTUJCgrh26u7uzsKFCxkxYoQ1H02R6Oho8XFqamqpl3iMjY3lnXfeQaPRMGrUKNLT765NpkBoaCiLFy8mKCiIF154gYMHD9KsWTNeffVVatWqZTJ+4MCBzJgxg7i4uAoVzKZy91irl1Z1UcrKyiIiIoK4OOuiQ1VUVFRUVB50goODiYqKMvJkybFKhMEgxObcLioqKioqKirGODk5WRRgsEGEVVRUVFRUVEoWNTBLRUVFRUWljFBFWEVFRUVFpYxQRVhFRUVFRaWMUEVYRUVFRUWljFBFWEVFRUVFpYxQRVhFRUVFRaWMUEVYRUVFRUWljFBFWEVFRUVFpYywqXZ0eUOv17N37161prWKiorKA4SnpyetW7cu9Rrk94IKKcJ5eXn89NNPPD36JcjNBEfXot8kwc7O+o+tsbftFNnZPN62H5GdnW3OC42dbft3sHE+Dva2zcfRxv3b2zh/exv/UzrYun8bx9vqarL1fNqCPt+2ZvaaAuXmD2axcbw+P8+2/eeX7v4L8gps23+ejfvPt23/ts7H9vG2FUvMs7G4oi3jbZwKGeTjjj1f/LCCwYMH4+BQIaUMqGBlKzMzM/Go3ZkC7SlAg11gQ+z8aqKxQVQBXH2Dix4kjg2yad8uNuwbwMPHtgbj7l7ONo13s3F8oI/lOqdyQn3dbBofYuP+Az1sm7+/m5NN44PcbRvv5WJba0BbWwmG+trecN5acrXRNo231A9ZkXSdTcPzE2xrCJOv09o0Pjfhtk3jM2/ZNv/0mwk2jc/Q2nY+07UZto2Pt61TVLLWtvFxWbbdBMVnW3+TEpdl2w1NHnoukMZxUtADny74nGeeeabIOs3lkQqxJpycnIx95Ydx8/Kn4PZ57EOa4VBvAPaV6toswCoqKioqFRsHNNTHkyFUpgU+vDfuVfxc3Wmp8SU52cabxzKmXCtYfHw8lRt3o+D2OTSufthXbYvGM+y+WAdQUVFRUbk77NBQE3dq4EYMmRwjhUAfX+rjyZa4CwQF2ebJLAvKpSUcFRWFfaV6BIeEos/SYV+9Gw61emPnVUUVYBUVFRUVIzRoqIYb/QimJ4EkkktYcAgNNJ5ERUWV9fQsUq5E+OTJk9j51aB6jZro83NwqP0YDtW7YedR/u9mVFRUVFTKnhBc6EUgjxNMDgXUrF6dWhoPTp06VdZTU6RciPDevXux865K48gmaOwccag7AIfwDmhc/cp6aioqKioqFRB/nOhCAIOpjCMaHmrUmGoaN/bu3VvWUzOizERYr9ezefNm7DyCadu+IxoXbxzqD8S+Shs0zp5lNS0VFRUVlfsIbxx5BH+GUhlfHOnYth2VNS5s3ryZ8pAcVCYivHHjRuzcKtG77wA0XqE41B+EfeXmaBxtS3dRUVFRUVGxBnccaIUvTxFGGK4M6P0YAXbO/P7772U6rzIR4X5Pj4csHXZBjbELaIjGwbZcUBUVFRUVleLgjB2ReNEUbxLJ5eU+g8t0PmUiwtm3L7Ny5QoKEi6Qd2YN+dqTNlfzUVFRUVFRsYUcCjhGMquI5SxprPz2Wy7klG1ecZmIsKOjIyNGjCA/PYFf165Gn3SVvDM/k3/zP/R5WWUxJRUVFRWV+5RM8jlEEj8QSxQZrP5tPbfzsxg+fDiOjo5lOrcyjY62s7OjX79+5KfFs+PvP9Fn3DJYxtf3o89RmzKoqKioqBSfVPLYQyKriEVLNn/t3E5cQRZ9+/a1uQ5/aVEuZqHRaOjYsSMFKbEcOXQAfV4meWfXk3d1N/qspLKenoqKiopKBUJHDju4zY/EkkE+B44c5po+kw4dOpS7gk/lQoSlNG3alAJdFOfPnQGNHXnnN5AXtY2C9FtlPTUVFRUVlXKMlmz+RMs6bmKHhnMXLnBZn07Tpk3LempmKbe1o2vXrk1BwnliY2Op2qQH+Ze3UOAWgF1QYzQeIeXubkZFRUVF5d6jR08sWRwlhVtkUw9Prl6/SGhoaFlPzSrKrQgLhIaGkq89RUJCAoENu5EfvRONkyd2QY3QeFdTxVhFRUXlAaQAPdFkcJQU0sijIZ6cTIjFz69iVVqsUP2EAdLT0/Gqc6ensJ0jdgH10TjZVmHL2cPX6rFOnj427dvJw7YfgKuHbf0vnd1si+RzsXG8j439df09bcvx9rNx/z6uts3fy9m2+0ofF9vGuznZtoLjam9bP+FKnqXXDzUvybb+vZps2/rZkpVq0/CCVNv69xakJtk0Pi81xabxOUm2zT8r0bbx2Um29e/NSs62bbzOtsyS9CTbxutyCmwbn2t9/2FbxoIh4OokKeSi56P5cxg9ejTu7qXXi7s0qXAiLJCTk8OqVat4ZvwEAtw1VKpUyWqrODEx0eq7JVvG2jper9dz+/btUpt7aY9X51+2422df0Wee2nPx9bx6vxLdrytc485e4F5Xy9k5MiRODnZdmNf7tBXcJKTk/WAPjk52er31KtXr1TG2jq+tOde2uPV+ZfteFvnX5HnXtrzsXW8Ov+SHV/acy/PlLvoaBUVFRUVlQeFB1KEx48fXypjizPeVkp7Pur8S3b/5Wn+FXnuxdm/Ov+SpTTnU9pzL89U2DVhgZSUFLy9vUlOTsbLy6usp2MTFXnuoM6/rKnI86/Icwd1/mVJRZ67EhXeEnZ2dmby5Mk4O1e8TkwVee6gzr+sqcjzr8hzB3X+ZUlFnrsSFd4SVlFRUVFRqahUeEtYRUVFRUWloqKKsIqKioqKShmhirCKioqKikoZoYqwioqKiopKGVGhRDg6OprnnnuOiIgIXF1dqVGjBpMnTyYnJ8fi+/R6PVOmTKFy5cq4urrSsWNHTp8+fY9mbcxHH31EmzZtcHNzw8fHx6r3jBo1Co1GY/TXqlWr0p2oGYoz//Jy/nU6HcOHD8fb2xtvb2+GDx9OUlKSxfeU5blfsGABERERuLi40KxZM3bv3m1x/K5du2jWrBkuLi5Ur16dRYsW3ZN5msOW+e/cudPkPGs0Gs6dO3cPZ2zgn3/+oU+fPlSuXBmNRsOvv/5a5HvK07m3df7l6dx/8sknNG/eHE9PTwIDA3n88cc5f/58ke8rT+ffViqUCJ87d46CggIWL17M6dOnmTdvHosWLWLSpEkW3zdz5kzmzp3Ll19+yaFDhwgODqZbt26kptpWgL0kyMnJYdCgQbz44os2va9nz57cvHlT/Nu0aVMpzdAyxZl/eTn/w4YN49ixY2zZsoUtW7Zw7Ngxhg8fXuT7yuLc//TTT7z22mu8++67HD16lPbt29OrVy9iYmIUx0dFRdG7d2/at2/P0aNHmTRpEq+88grr1q0r9bkqYev8Bc6fP290rmvVqnWPZlxIeno6kZGRfPnll1aNL2/n3tb5C5SHc79r1y7Gjx/P/v372bp1K3l5eXTv3p30dPPNL8rb+beZsqyZWRLMnDlTHxERYfb1goICfXBwsH7GjBnitqysLL23t7d+0aJF92KKiixfvlzv7e1t1diRI0fq+/XrV6rzsRVr519ezv+ZM2f0gH7//v3itn379ukB/blz58y+r6zOfYsWLfQvvPCC0ba6devq3377bcXxb731lr5u3bpG28aOHatv1apVqc3RErbOf8eOHXpAr9Pp7sHsrAfQ//LLLxbHlLdzL8Wa+ZfXc6/X6/VarVYP6Hft2mV2THk+/9ZQoSxhJZKTky1234iKiiIuLo7u3buL25ydnenQoQN79+69F1MsEXbu3ElgYCC1a9dmzJgxaLXasp6SVZSX879v3z68vb1p2bKluK1Vq1Z4e3sXOY97fe5zcnI4cuSI0TkD6N69u9m57tu3z2R8jx49OHz4MLm5uaU2VyWKM3+BJk2aEBISQpcuXdixY0dpTrPEKE/n/m4oj+c+OTkZwOI1vqKf/wotwpcvX+aLL77ghRdeMDsmLs7QQzUoKMhoe1BQkPhaeadXr1788MMPbN++nTlz5nDo0CE6d+5MdrZt/UbLgvJy/uPi4ggMDDTZHhgYaHEeZXHub9++TX5+vk3nLC4uTnF8Xl4et2/fLrW5KlGc+YeEhPD111+zbt061q9fT506dejSpQv//PPPvZjyXVGezn1xKK/nXq/X88Ybb9CuXTsaNmxodlxFP//lQoSnTJmiGBgg/Tt8+LDRe27cuEHPnj0ZNGgQo0ePLvIY8p6Zer3e6j6apTF/WxgyZAiPPvooDRs2pE+fPmzevJkLFy7wxx9/VIj5Q+mdf1vmrnS8ouZR2ufeEraeM6XxStvvFbbMv06dOowZM4amTZvSunVrFixYwKOPPsrs2bPvxVTvmvJ27m2hvJ77l156iRMnTrB69eoix1bk8+9Q1hMAw8l+8sknLY4JDw8XH9+4cYNOnTrRunVrvv76a4vvCw4OBgx3SyEhIeJ2rVZrcvdUXGyd/90SEhJCtWrVuHjxYonsrzTnX9rn39q5nzhxgvj4eJPXbt26ZdM8SvrcK1GpUiXs7e1NrEZL5yw4OFhxvIODA/7+/qU2VyWKM38lWrVqxffff1/S0ytxytO5LynK+ty//PLLbNiwgX/++YewsDCLYyv6+S8XIlypUiUqVapk1djY2Fg6depEs2bNWL58OXZ2lo35iIgIgoOD2bp1K02aNAEMa1a7du3i008/veu5g23zLwkSEhK4du2akajdDaU5/9I+/9bOvXXr1iQnJ3Pw4EFatGgBwIEDB0hOTqZNmzZWH6+kz70STk5ONGvWjK1bt9K/f39x+9atW+nXr5/ie1q3bs3GjRuNtv311188/PDDODo6ltpclSjO/JU4evRoqZ7nkqI8nfuSoqzOvV6v5+WXX+aXX35h586dREREFPmeCn/+yyoirDjExsbqa9asqe/cubP++vXr+ps3b4p/UurUqaNfv369+HzGjBl6b29v/fr16/UnT57UDx06VB8SEqJPSUm51x9Bf/XqVf3Ro0f1U6dO1Xt4eOiPHj2qP3r0qD41NVVx/qmpqfo333xTv3fvXn1UVJR+x44d+tatW+tDQ0MrxPz1+vJz/nv27Klv3Lixft++ffp9+/bpGzVqpH/ssceMxpSXc//jjz/qHR0d9UuXLtWfOXNG/9prr+nd3d310dHRer1er3/77bf1w4cPF8dfuXJF7+bmpn/99df1Z86c0S9dulTv6OioX7t2banOs6TmP2/ePP0vv/yiv3Dhgv7UqVP6t99+Ww/o161bd8/nnpqaKv6uAf3cuXP1R48e1V+9elVx7uXt3Ns6//J07l988UW9t7e3fufOnUbX94yMDHFMeT//tlKhRHj58uV6QPFPCqBfvny5+LygoEA/efJkfXBwsN7Z2Vn/yCOP6E+ePHmPZ29g5MiRivPfsWOHOEY6/4yMDH337t31AQEBekdHR33VqlX1I0eO1MfExFSI+ev15ef8JyQk6J966im9p6en3tPTU//UU0+ZpGWUp3P/1Vdf6atVq6Z3cnLSN23a1ChNY+TIkfoOHToYjd+5c6e+SZMmeicnJ314eLh+4cKF92Se5rBl/p9++qm+Ro0aehcXF72vr6++Xbt2+j/++KMMZl2YsiP/GzlypOLc9fryde5tnX95Ovfmru/S60l5P/+2orYyVFFRUVFRKSPKRXS0ioqKiorKg4gqwioqKioqKmWEKsIqKioqKiplhCrCKioqKioqZYQqwioqKioqKmWEKsIqKioqKiplhCrCKioqKioqZYQqwioqKioqKmWEKsIqKioqKiplhCrCKioqKioqZYQqwioqKioqKmWEKsIqKioqKiplxP8BKybqfQB45gcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xrange = (-298,61)\n",
    "yrange = (60.,91.)\n",
    "\n",
    "_model = momgrid.geoslice.geoslice(model,x=xrange,y=yrange)\n",
    "_obs = momgrid.geoslice.geoslice(obs,x=xrange,y=yrange)\n",
    "\n",
    "xq = list((_model.xh.values - 0.5).astype(int))\n",
    "yq = list((_model.yh.values - 0.5).astype(int))\n",
    "\n",
    "xq = xq + [xq[-1] + 1]\n",
    "yq = yq + [yq[-1] + 1]\n",
    "\n",
    "x = ds.geolon_c.values[yq,:][:,xq]\n",
    "y = ds.geolat_c.values[yq,:][:,xq]\n",
    "\n",
    "# Get colormap\n",
    "cmap, norm, boundaries = gen_levs_and_cmap(-2.0,2.125,0.125)\n",
    "\n",
    "fig = plt.figure(figsize=(6,6))\n",
    "ax = plt.subplot(1,1,1, facecolor=\"lightgray\", projection=ccrs.NorthPolarStereo())\n",
    "cb = ax.pcolormesh(x,y,_model-_obs,transform=ccrs.PlateCarree(), cmap=cmap, norm=norm)\n",
    "ax.coastlines()\n",
    "\n",
    "# Clean up figure and add labels\n",
    "set_annotaions(ax)\n",
    "\n",
    "# Add statistics\n",
    "stats, stats_str = calculate_stats(_model, _obs, _model.areacello)\n",
    "add_stats_box(ax, stats_str)\n",
    "\n",
    "# Add colorbar\n",
    "add_colorbar(fig, cb, boundaries)\n",
    "\n",
    "# Save stats\n",
    "stats_dict[\"results\"][\"arctic\"] = stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6d78292-72df-488d-a638-0628000cc7d3",
   "metadata": {},
   "source": [
    "### Write Stats to a File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "53578224-09ad-4f27-b203-e2464682ff4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "metadata:\n",
      "  created: '2024-02-08T11:51:18.777557'\n",
      "  endtime: '2017-12-16 12:00:00'\n",
      "  expName: om5_b03_c5\n",
      "  starttime: '1993-01-16 12:00:00'\n",
      "results:\n",
      "  arctic:\n",
      "    bias: '-0.2702'\n",
      "    max: '3.483'\n",
      "    min: '-3.118'\n",
      "    rmse: '0.4259'\n",
      "    rsquared: '0.9853'\n",
      "  global:\n",
      "    bias: '0.1311'\n",
      "    max: '5.79'\n",
      "    min: '-4.073'\n",
      "    rmse: '0.3156'\n",
      "    rsquared: '0.9989'\n",
      "  north_atlantic:\n",
      "    bias: '-0.05753'\n",
      "    max: '5.79'\n",
      "    min: '-4.073'\n",
      "    rmse: '0.5332'\n",
      "    rsquared: '0.9885'\n",
      "\n"
     ]
    }
   ],
   "source": [
    "if not \"DORA_EXECUTE\" in os.environ.keys():\n",
    "    import yaml\n",
    "    yml = yaml.dump(stats_dict)\n",
    "    with open('oisst_stats.yaml', 'w') as file:\n",
    "        file.write(yml)\n",
    "    file.close()\n",
    "    print(yml)"
   ]
  }
 ],
 "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": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}