climate-dt/climate-dt-earthkit-fe-timeseries.ipynb

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    "## Polytope Climate-DT Time Series example notebook\n",
    "\n",
    "This notebook shows how to use earthkit-data and earthkit-plots to pull destination-earth data from LUMI and plot it using earthkit-plots.\n",
    "\n",
    "Before running the notebook you need to set up your credentials. See the main readme of this repository for different ways to do this or use the following cells to authenticate.\n",
    "\n",
    "You will need to generate your credentials using the desp-authentication.py script.\n",
    "\n",
    "This can be run as follows:"
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    "%%capture cap\n",
    "%run ../desp-authentication.py"
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    "This will generate a token that can then be used by earthkit and polytope."
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    "output_1 = cap.stdout.split('}\\n')\n",
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    "# Requirements\n",
    "To run this notebook install the following:\n",
    "* pip install earthkit-data\n",
    "* pip install earthkit-plots\n",
    "* pip install earthkit-regrid  (Optional for spectral variables)\n",
    "* pip install cf-units         (Optional for unit conversion in maps)\n",
    "\n",
    "If you do not have eccodes installed please install eccodes using conda as it is a dependency of earthkit, or install earthkit via conda\n",
    "\n",
    "* conda install eccodes -c conda-forge\n",
    "* conda install earthkit-data -c conda-forge"
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    "# This request matches multiple parameter of the climate DT\n",
    " \n",
    "import earthkit.data\n",
    "\n",
    "LOCATION = ((38, -9.5))\n",
    "\n",
    "\n",
    "request = {\n",
    "    \"activity\": \"scenariomip\",\n",
    "    \"class\": \"d1\",\n",
    "    \"dataset\": \"climate-dt\",\n",
    "    \"experiment\": \"ssp3-7.0\",\n",
    "    \"generation\": \"1\",\n",
    "    \"levtype\": \"sfc\",\n",
    "    \"date\": \"20200101/to/20210101\",\n",
    "    \"model\": \"ifs-nemo\",\n",
    "    \"expver\": \"0001\",\n",
    "    \"param\": \"167\",\n",
    "    \"realization\": \"1\",\n",
    "    \"resolution\": \"high\",\n",
    "    \"stream\": \"clte\",\n",
    "    \"type\": \"fc\",\n",
    "    \"time\": \"0000/to/2300\",\n",
    "    \"feature\": {\n",
    "        \"type\" : \"timeseries\",\n",
    "        \"points\": [[LOCATION[0], LOCATION[1]]],\n",
    "        \"time_axis\": \"date\",\n",
    "    }\n",
    "}\n",
    "\n",
    "ds = earthkit.data.from_source(\"polytope\", \"destination-earth\", request, stream=False, address='polytope.lumi.apps.dte.destination-earth.eu')\n"
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    "ds._json()"
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    "def location_to_string(location):\n",
    "    \"\"\"\n",
    "    Converts latitude and longitude to a string representation with degrees\n",
    "    and N/S/E/W.\n",
    "    \"\"\"\n",
    "    (lat, lon) = location\n",
    "    lat_dir = \"N\" if lat >= 0 else \"S\"\n",
    "    lon_dir = \"E\" if lon >= 0 else \"W\"\n",
    "    return f\"{abs(lat):.2f}°{lat_dir}, {abs(lon):.2f}°{lon_dir}\""
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       "    number:         0\n",
       "    Forecast date:  2021-01-01 00:00:00Z</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-a46d764c-6ef9-42e4-8899-57172f33635c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a46d764c-6ef9-42e4-8899-57172f33635c' 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'>x</span>: 1</li><li><span class='xr-has-index'>y</span>: 1</li><li><span class='xr-has-index'>z</span>: 1</li><li><span class='xr-has-index'>number</span>: 1</li><li><span class='xr-has-index'>datetime</span>: 1</li><li><span class='xr-has-index'>t</span>: 8808</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d77bf4b6-914f-4a6f-9f5a-98d12c84515e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d77bf4b6-914f-4a6f-9f5a-98d12c84515e' class='xr-section-summary' >Coordinates: <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-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>37.97</div><input id='attrs-276a63a8-7b6d-4a48-8fbf-6342385678d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-276a63a8-7b6d-4a48-8fbf-6342385678d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36bae54a-67d5-49b4-bf9e-8bdd85ee4932' class='xr-var-data-in' type='checkbox'><label for='data-36bae54a-67d5-49b4-bf9e-8bdd85ee4932' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([37.968952])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>350.5</div><input id='attrs-fb02671d-9953-4b16-85c2-aeb3a665a38e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb02671d-9953-4b16-85c2-aeb3a665a38e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be369137-42f9-47dc-ae6d-d587de6e818c' class='xr-var-data-in' type='checkbox'><label for='data-be369137-42f9-47dc-ae6d-d587de6e818c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([350.507812])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-74f3c9de-bdfc-4bf7-b331-7f2b357e59bf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74f3c9de-bdfc-4bf7-b331-7f2b357e59bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-674514b5-3985-41d9-9c87-fbefb7d66f2d' class='xr-var-data-in' type='checkbox'><label for='data-674514b5-3985-41d9-9c87-fbefb7d66f2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>number</span></div><div class='xr-var-dims'>(number)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-c00a1659-18fd-4050-ae26-35f9b6fb4ee0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c00a1659-18fd-4050-ae26-35f9b6fb4ee0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84765d70-1a38-495a-be6b-7dce4e8d3c06' class='xr-var-data-in' type='checkbox'><label for='data-84765d70-1a38-495a-be6b-7dce4e8d3c06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>datetime</span></div><div class='xr-var-dims'>(datetime)</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>&#x27;2021-01-01 00:00:00Z&#x27;</div><input id='attrs-cfbba4fc-9aae-4e19-9711-aca04338e18f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cfbba4fc-9aae-4e19-9711-aca04338e18f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0baf08fc-d0ff-4da2-8c65-fb5487cf7053' class='xr-var-data-in' type='checkbox'><label for='data-0baf08fc-d0ff-4da2-8c65-fb5487cf7053' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-01-01 00:00:00Z&#x27;], dtype=&#x27;&lt;U20&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-01 ... 2021-01-01T23:00:00</div><input id='attrs-01d8abef-5313-45c0-853d-b0411f452bab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01d8abef-5313-45c0-853d-b0411f452bab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c47434de-2c75-4db0-b44a-109aa7fe9a26' class='xr-var-data-in' type='checkbox'><label for='data-c47434de-2c75-4db0-b44a-109aa7fe9a26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-01T02:00:00.000000000&#x27;, ..., &#x27;2021-01-01T21:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-01T22:00:00.000000000&#x27;, &#x27;2021-01-01T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-219b3b3f-e8ac-4f1e-b529-336d79d893af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-219b3b3f-e8ac-4f1e-b529-336d79d893af' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>2t</span></div><div class='xr-var-dims'>(x, y, z, number, datetime, t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>287.7 287.7 287.5 ... 286.6 286.5</div><input id='attrs-537bc465-e176-4872-a5dd-12c554575978' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-537bc465-e176-4872-a5dd-12c554575978' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f02931ad-e794-431f-96a7-f4c443d15a8e' class='xr-var-data-in' type='checkbox'><label for='data-f02931ad-e794-431f-96a7-f4c443d15a8e' 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>type :</span></dt><dd>Parameter</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2t</dd></dl></div><div class='xr-var-data'><pre>array([[[[[[287.71446228, 287.6860199 , 287.54673004, ...,\n",
       "            286.66054535, 286.63554382, 286.4747467 ]]]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f0ea9d5e-3b19-4dbb-b084-ac29da3bbbc1' class='xr-section-summary-in' type='checkbox'  ><label for='section-f0ea9d5e-3b19-4dbb-b084-ac29da3bbbc1' 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>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3be69575-12ae-4357-b5a1-e50f5b9f06be' class='xr-index-data-in' type='checkbox'/><label for='index-3be69575-12ae-4357-b5a1-e50f5b9f06be' 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([37.968952367503], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-abcf302a-c512-4716-b6b8-d042e99794da' class='xr-index-data-in' type='checkbox'/><label for='index-abcf302a-c512-4716-b6b8-d042e99794da' 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([350.5078125], dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-129ef2d1-6cf4-4911-a64f-fd092dbcdae8' class='xr-index-data-in' type='checkbox'/><label for='index-129ef2d1-6cf4-4911-a64f-fd092dbcdae8' 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], dtype=&#x27;int64&#x27;, name=&#x27;z&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>number</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0124fd5d-50f6-4891-ab90-1283ec865872' class='xr-index-data-in' type='checkbox'/><label for='index-0124fd5d-50f6-4891-ab90-1283ec865872' 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], dtype=&#x27;int64&#x27;, name=&#x27;number&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>datetime</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c5bd385b-46f6-4d56-b92d-945fd6657843' class='xr-index-data-in' type='checkbox'/><label for='index-c5bd385b-46f6-4d56-b92d-945fd6657843' 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([&#x27;2021-01-01 00:00:00Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;datetime&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-64d0b24a-6b48-42e5-9a70-2fd763523e11' class='xr-index-data-in' type='checkbox'/><label for='index-64d0b24a-6b48-42e5-9a70-2fd763523e11' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-01-01 00:00:00&#x27;, &#x27;2020-01-01 01:00:00&#x27;,\n",
       "               &#x27;2020-01-01 02:00:00&#x27;, &#x27;2020-01-01 03:00:00&#x27;,\n",
       "               &#x27;2020-01-01 04:00:00&#x27;, &#x27;2020-01-01 05:00:00&#x27;,\n",
       "               &#x27;2020-01-01 06:00:00&#x27;, &#x27;2020-01-01 07:00:00&#x27;,\n",
       "               &#x27;2020-01-01 08:00:00&#x27;, &#x27;2020-01-01 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-01-01 14:00:00&#x27;, &#x27;2021-01-01 15:00:00&#x27;,\n",
       "               &#x27;2021-01-01 16:00:00&#x27;, &#x27;2021-01-01 17:00:00&#x27;,\n",
       "               &#x27;2021-01-01 18:00:00&#x27;, &#x27;2021-01-01 19:00:00&#x27;,\n",
       "               &#x27;2021-01-01 20:00:00&#x27;, &#x27;2021-01-01 21:00:00&#x27;,\n",
       "               &#x27;2021-01-01 22:00:00&#x27;, &#x27;2021-01-01 23:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;t&#x27;, length=8808, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a45cbfe2-2d22-4971-80b4-7ab3f426f978' class='xr-section-summary-in' type='checkbox'  ><label for='section-a45cbfe2-2d22-4971-80b4-7ab3f426f978' class='xr-section-summary' >Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>activity :</span></dt><dd>scenariomip</dd><dt><span>class :</span></dt><dd>d1</dd><dt><span>dataset :</span></dt><dd>climate-dt</dd><dt><span>experiment :</span></dt><dd>ssp3-7.0</dd><dt><span>expver :</span></dt><dd>0001</dd><dt><span>generation :</span></dt><dd>1</dd><dt><span>levtype :</span></dt><dd>sfc</dd><dt><span>model :</span></dt><dd>ifs-nemo</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>resolution :</span></dt><dd>high</dd><dt><span>stream :</span></dt><dd>clte</dd><dt><span>type :</span></dt><dd>fc</dd><dt><span>number :</span></dt><dd>0</dd><dt><span>Forecast date :</span></dt><dd>2021-01-01 00:00:00Z</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 141kB\n",
       "Dimensions:   (x: 1, y: 1, z: 1, number: 1, datetime: 1, t: 8808)\n",
       "Coordinates:\n",
       "  * x         (x) float64 8B 37.97\n",
       "  * y         (y) float64 8B 350.5\n",
       "  * z         (z) int64 8B 0\n",
       "  * number    (number) int64 8B 0\n",
       "  * datetime  (datetime) <U20 80B '2021-01-01 00:00:00Z'\n",
       "  * t         (t) datetime64[ns] 70kB 2020-01-01 ... 2021-01-01T23:00:00\n",
       "Data variables:\n",
       "    2t        (x, y, z, number, datetime, t) float64 70kB 287.7 287.7 ... 286.5\n",
       "Attributes: (12/14)\n",
       "    activity:       scenariomip\n",
       "    class:          d1\n",
       "    dataset:        climate-dt\n",
       "    experiment:     ssp3-7.0\n",
       "    expver:         0001\n",
       "    generation:     1\n",
       "    ...             ...\n",
       "    realization:    1\n",
       "    resolution:     high\n",
       "    stream:         clte\n",
       "    type:           fc\n",
       "    number:         0\n",
       "    Forecast date:  2021-01-01 00:00:00Z"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da = ds.to_xarray()\n",
    "da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f12d8515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w/rryyht/k4GX1MnFA05sFmE0j99n8Bj1EOd8tf/CS3+Wcy+9UfDwvHTpMsF+U+qGyR4d2ze7POSbYjxMHLsy5QppTVMemiUt115bhvQ/QUk+Nj2G5z55q3oIGDbqDvV3RX8eRXgXfLhQDjtlaGEM6gvD96njQaerlC488LhtGOPz3nxbyazXduvkepl47+Py1rN3NcuzxVjvcsQAFQjoW3OY6lv8/UF0Fw+0EG2dP4zc++f/+Cd5BQK75hoqJ3ifowaqB9pWG6wvw0ffrj7DWy9siCj/asftVOoXNxIgARJIkkAqhbfYpDWInH7t+NuTL1aTKsyIQTFYpvBCaq6uu1dFPCA52CCwiD4i3+7IXsOaCS9+MB5/5mV59clb1R/32+97QiDIaI8IxUVXTVTpC8jnxSu8q8ZPbfaKEcLbdrON5dbRP0U+zGttsfbaq6RX4PNly79Tcok0DPM15l//8W81SeS6y86Www7qXBBe/ODhh09viDoh+gZmeuLKs9OuVxFDvSEKvt02bVUeH4Tk3wv+I7Puv06JNDZEag/uOUQO3X8PJYlaeHt030dGDTuzcJw9D+uvJBgSrrde541SxzEj3ZDE2Y/Uqdej2HTEGAKMSJFTeINck1u/Q+gQGTejgJCTa295QG66+rzCJKV5b/5D+p4/VjHC+NIPGM6JjYhsffCfRer1NLaza8cL0mp0Di844gHj0sGnyonVP73SveuBp1S09fkHxynBxoMaJjzidbaeXIXcUkRr9UPVdbdOUxIMTngVjO3jhZ9J95MuUnnqWniP6nOJegDCONTSgUjkhx9/Kuuu01JFr/WGSNt/PvlMPbhgi8rHjTXu57vvv1c5rnrTeaPzfn+b+l7oh0RE/4rl8Or2YDD5wZnq/+IhAw8tuCe94YFq4PAJgkmryJ9HhBEPB2edcqTbJap/g7Bi7KP/zU1HkPFAu2zZciW/GMcdfr5Ns/3QdxDx+2+9zPMc+ECPYfy9QM4xBBpj5bG7rlIP7FGEFw8Nv6k6S449vGszscXDDO7riEP28szjvWDkrfKXf/xbCfcf5/1F/U3Aw+XpJx0uh3bdQ90L8pHrZ74kkHRzA9edDzhNPShDXItt+k0T/iZjAt+bf3tHThl4jfrOYJ4F8nV1lF5HrDGp15njXPQk/JAESIAEIhBIpfAih/e+m1dGS53b+uu2lB3arayugPxdyICeROJ3/6bwdt5tJ5W+gGgoBPHf73+sJBcygEiSU3if/+OfBcJwx/UXqoghfsAgsIhoIdq0T4+BUnflueqVNCa7/OOdBc1mK/tVaUDEac9dV87wNjf9OvGKC/uoqKm54fUqZmhDcnWE15negR86RAufnDpaMHkEE3YwwQdRWKQ7tN9h62Yz2nfvfpaaDHPtZf2bnWvg8DoVucardC28zsoSB/e8QHbfZcdmFRiQE42cygcnrZzYBQ6bt9l4lQgbql3s0mF7qbvq3FWEN8g1ufU9hA5jyXxboKXfzJ3UwnDr6POl614dpf/QcSoiOvuR5pMUtSwjlxHjzim8OtfbOWkHYwGRdbxNQOoI7gcToa6pPaPZZSMHFGkSOD4eVNBfOJa54SEHM9pN4d1kow1W4YlzYoIT/htRUkjLl19/q6K9z02/QR0yKh831ph8NHHqDPWG4POmr+THH39UqQE4t37gCCu8qNCA9AR8N6c8OFMJ093jLlbRXMjjqeeOki3abCJnnXqkemjFgwveFiCCrVOPnNfqJbz6oQff76VLvYUXD3B4ONFvc7z+5jiFF3J3TN/LVNoTHuyiCC/OhRSKB5+YrcYOUoAwmRbfcUyqw9hyVpLR14e3L+gbpGh02e1X6uHkoSdmC3KaRwzprarJxCG8+FuIVC9UdYAcI2r8yO9flOcfWvkW7pjTL1MiPOzck+V3jz6rJsk5H8D9/o7zcxIgARKIQiC1whtk0hoif+/8+0N58bEbA927KbwQU0yWWPDRQpl22+Xqh3L2K29J/T1XF+TXTGn45tulqjzZycceIgNPO0b2OuJsufLivoWczuPPukJ+vdP2asLG3kedo/JBzdeLEL0NN1hXvf5z2/CKHT/kzg2T58648Dr16n81R2kjRJrwowdJ1MKL68ex9IYfQ8ibjkpCIpA7O/vVBvnPJ/9Vr2f71vxW5f798OOP6tUoSiitsUbziU/I0cXr6VeeuKUgvMPPO0W9RtcbhHev3X8lV13ct/BvEN5PFn1eiIKCwy93bNcslQQ74+EF8oIImxnhRTQuyDV5Ce9TL8xVkXe9aeHFD3DbTTdW/6yFV0ceEZFCZEqXs9NtV/y4Qr2ifezuq2XH7bdaRXjr7nxERf6d7dAefTX8vJNVJAv34zYrHZOwHn/mFXnzmTvk5HOulh9XrFCpJuaGB4//LPxvM+FFpG7ClQMLuyHaWn3aJbL1lpspscDkK4wfCC6k0xTeKHycrBFRPnng1UpOLx/Su5Azj3QSRGmjCq95no8/XSyH1lwg/U45SlULQMUB9CUeSsyoLyaX3vfos/Lio3Wu3ydE8sHV+VZIP9DiwQyiDrHVUUrzOiBsK9/U/DSZzW3sOYUX++jJXzMmXyPvfbAwUlkyPEAMG420gT+r0+LBq/bck+XKcVNUObYh/U4I9LcQO+EhqKb/laoE48z7r1WpTUjpevOZO5u9bUIO+F5HDFBVbfr0XDmHodiGCYD4noAlvksoJadFHOMBEWY8OPvlW/udh5+TAAmQQBgCmRbee6Y9LddPnF50hi+iCDtuv7WKKjiFV8skon34ITzl2EPVH3Qd7XXW+8U+mMCEeqBnXni9+rHVk8YwwQnCPHr4WXJCvyvUpA3ztW3UHF7kg+J4+LHZf6+fJtjpTkYuHMQtqPCagwMRW0SLwBE/SJjs9Jvf9pP9OncslCAy90f+NH68dIQ3qvAi7QGRNHPDZBfkSyJtw5nSEOSa3AY9BC+K0A0YNl7e+b+PVD6t24ZJUkgfcEZ49Wx5vHHY6H9pCGb7TVpvqCY07VF1lirpZKaDYD+8GUAZKKQn4GHu8yVfFsRWHwcyASEzI7xO4UUaB+591gPXNUsxwIMTIoJxCy/q4SK1Qo8hfa2QfzwEhBFeRG5RDaHLbr9U6R/mhrcAmOwIuT99yLWyeMkX6uHD3PQDjVs6AvYDF0Q1dZUB3faO3z2pcrTxOn/psu/Uq3ek7+BNiN4gcV0OP1tOOvpgX7F0E16kBh3VZ7jssO3m0vv4KvUgG6ZKg3mf+A6itBoeZhZ//qXKhb3+8rNVFYQwG8oqIj/9T7NuL/wNwcOxWe8Y5dcw7iaOvUClVvltEGdEdpGOc+Bx58uo4WcWJoTqyDb+duIh97jDu6q3bNxIgARIIGkCmRZe/NE/stdwlR94703DC/KpoeGH7fwRt0j/U1dGhZzCi8gUokZddv+VqiqAOrvIefQSXoghojSogYmZ3piwpjfMnoek4HXl3dOeVrOOMblIb1GFF6+v9+lxrhz9230FgmlueE0K2UEeaBDhRXTvyy+/abZoB46H3NF9O/9aLZABkUeOLYTBLN6PPMm2m26k8nFthRcF6Gc/PKGQI4zo3cEnDFF9dHavHqsIb5BrcvuiRBVeyA/qiWIypJkDi4L7mJGuc74hvMg/1jmxkFVEZiFkmKSmN7wdwKteyAk2jBPwRJ60XpAAMoWJkUjrwAQe5IBD3hBRx2Q4bIjc6jzfYsKrRVPnzqItKj0cceowVTVCT/KMysfJWudp3nTNIDlwn13Vx8g3Pabvpeq7pMVbpzTo2f5ufaZrtqIixYD/TZrEfmB/0AnnK9lE1HroNZPkDy+/KXMevbHZ9wwRXqSWQGjNvtPnQnQRKSuY/IbyYdgQ6cQbGuSw6wcxlNdab711VLktvWFSHt5YOEsQut2Hm/BiPzwUI7KJNyr3PvxMaOHF37T1112n2XcY1Wrwt+kPD49XfwudG+5v5A1TZDsl2isn0WJDasbRp18ma66xunqAQqQX1Snw9xJ/x/SGijWYVwGmeiy63bP+N93POAby4fEGTpdkg/TvdeQAVRkGpcow4RMPN9xIgARIIGkCqRRev4UnNt9s48IqaShXhcjYmmuuoXJxf/mLdgLBQFUBTKA4ZP/d5dpL+6vXzE7hBVxEoFCfcp/f7FKYee0lvDqSBeE5qvs+zdITIKaYydyy5dpq1rEzghlVeHGN+NG4bUq9iip13auTKsCP/DtMjsNrQ0ysCSK8+FG8+Z7HZOg5J6oVkrAhmobXw4gOIa8UOaQoH3XsYV3lxKMPUtUHwBJljS4eUKMmY9kILwrQYyLebzq1V2W4MGMekoJ+fGLKKDXpzRnhDXJNbl+UqEKnqzTgWob0O15F0Oe/+4HihPJ3evIgZqRDYG6/9gJV5gwTthC1wzjBwwlSNzChDFUukDqDPGoIAx6OsN8xh+0nvU+oku+//0FVDfn9c6/KlLrhSsRwz5gghRztfr2OUpUDkGOJXFlUZSgmvHoCFiZvoR4qZtPjQQ3CM/MPr0n93VfLlptvKmNvvj9SBNzJGrP4DzphiKpDfcUFfVQZNFQV2aLNz9SkQeSDY/EVPEjhe3DyMYeoe0elDrf0D6QazZrzunr42f3XOwrqw06693EVnX7ojpGqHR4ukHbQtUtHxQffSZRgQyULfEcwnrFhLE19+Bm5YcQAVWoL8tfrvNHqbwHGM6QY1THAHhKMldH096LfxTfIidUHqhJcH338Xxl7y/2qkolzwlsY4cW+mBD34qsN6rW/GeFF1PoX22/lmfaEtiir9uRzc1V6DN5cvd4wX/XtRWf3VFz1hvxm9AcqK2DD2MHbLqSE7LPnziq3elr9C6qCBMYzFq7Ahn7DmwqUk8NYf72hUZU5xBsmr5JnzvsHY8yPwLjGQ545iRX7Yv4DKsggzQfpRm5jIOkfPh6fBEig8gikUnj9lhY+5rD9m+WJInp178PPqtwwTN5YZ50WaiY7JnkdfnCXQqTSTXh1CTHzdaCX8GJ4IE8Vs+WdNXzxGSQGaRIQSkSBzc1GeHEcsw4vJtWh/BjKhumySUGEFz9EEKv6p19SS7aidivKWCEPF+kMeoOQ3TJ5ZR1ebHi9iZXp9KQ5G+FFFBOpHjgvRGTRf5fItlu3VT/Y+3X+dUFSnEsL+11TnMKLY2FMobICzosHKJSwwwMBIo+6GgLqsOINAiLWeEWNCDVmv+PV+DOzX5dFi5dI6w3XVxEslGXTEV4cf86rDeohpvHfHwpSRSDH55xW3Sza9eDjf5Dbf/ek/PezJaocHI6BFcEQVUbeOTakEjhTGvDviFKjisgXX36tjj104EmqFFS/i65XC6tMrRsmD9S/EIvw4nwvzm1Q+boffLxIif9ZpxwhVd32lNPOH6vkBg8AGGOou/r0C/NUasdjd13tWpoMD4+YIIgV0P6z8DOVi4vxfu7px6hJlnpDpRKMU0zMw+t9SDyWE8d3T/eRTnsyc9sRyUT9WNQ5RsQRD4yYYKVLhunjo1QZjo83Kai0gJKAWCXOfHPj9ZPhFeHF/vj7cWTvYercpvAi9x9VQswats7j42ER145FdZY0falqWPc+ofsqE1qRNoNygTq3G2+zJj/4tNTPfFmV5cNY2HGHrVU0Vy+HjHNhPyynjFQnLC2MHHqkepkLSQT5mUQZNlS1wXLpmNxqbvoNBN4G4K0ANxIgARIoBYHUCW8pbprnIIEsEEBkE5UJzJXTUD0EAotattzyRQAP7BBZc9Jnvu6Qd0MCJEAC5SNA4S0fe56ZBDwJYLEFVASortpXRSwRBUaqD+rSui2IQJTZJ4Cyhz2POlAtHcyNBEiABEggXgIU3nh58mgkEBsBpD0gdxUVIzC5bfttt1CpAsiH5UYCJEACJEACJBCcAIU3OCvuSQIkQAIkQAIkQAIkkEECFN4MdhovmQRIgARIgARIgARIIDgBCm9wVtyTBEiABEiABEiABEgggwQovBnsNF4yCZAACZAACZAACZBAcGzGdeIAACAASURBVAIU3uCsuCcJkAAJkAAJkAAJkEAGCVB4M9hpvGQSIAESIAESIAESIIHgBCi8wVlxTxIgARIgARIgARIggQwSoPBmsNN4ySRAAiRAAiRAAiRAAsEJUHiDs+KeJEACJEACJEACJEACGSRA4c1gp/GSSYAESIAESIAESIAEghOg8AZnxT1JgARIgARIgARIgAQySIDCm8FO4yWTAAmQAAmQAAmQAAkEJ0DhDc6Ke5IACZAACZAACZAACWSQAIU3g53GSyYBEiABEiABEiABEghOgMIbnBX3JAESIAESIAESIAESyCABCm8GO42XTAIkQAIkQAIkQAIkEJwAhTc4K+5JAiRAAiRAAiRAAiSQQQIU3gx2Gi+ZBEiABEiABEiABEggOAEKb3BW3JMESIAESIAESIAESCCDBCi8Gew0XjIJkAAJkAAJkAAJkEBwAhTe4Ky4JwmQAAmQAAmQAAmQQAYJUHgz2Gm8ZBIgARIgARIgARIggeAEKLzBWXFPEiABEiABEiABEiCBDBKg8Gaw03jJJEACJEACJEACJEACwQlQeIOz4p4kQAIkQAIkQAIkQAIZJEDhzWCn8ZJJgARIgARIgARIgASCE6DwBmfFPUmABEiABEiABEiABDJIgMKbwU7jJZMACZAACZAACZAACQQnQOENzop7kgAJkAAJkAAJkAAJZJAAhTeDncZLJgESIAESIAESIAESCE6AwhucFfckARIggVAEfvjhR7n34WfkwSf+IJ98uljabraxHHt4V+lzQpWsscbqoY7FnUmABEiABKIToPBGZ8eWJEACJFCUQN2dj8g905+WQacfK7vstL386S//lJvuflQu6HeCnFbzW9X2/seel7/N/7eMGnYmaZIACZAACSREgMKbEFgelgRIoLIJfP/DD7L3kefISUcfLIPPPK4AY8gVt8j7H30qD98xUv3bpWPvkh9//JHCW9nDhXdPAiSQMAEKb8KAeXgSIIHKJPDjjyvkw48/lVYbri+tNlivAOHaWx6QWbNfl+cfGie9B42WNxoaC589cueV0uHn21QmMN41CZAACSRIgMKbIFwemgRIgARMAoj6HnfGCPnF9lvKdZedLV9+9Y30HXKtbLtVG7lk0Cmy4frrMbeXQ4YESIAEEiBA4U0AKg9JAiRAAm4Errttmkyrf0GlM2y3zeZql5qzr5Ttt9mcKQ0cMiRAAiSQIAEKb4JweWgSIAES0ATGTXpQpj40S8ZfOVAO2HvXAhgKL8cICZAACSRPgMKbPGOegQRIoIIJIJf3ihvukadfeE1uvOpc2WuPXzWjQeGt4MHBWycBEigZAQpvyVDzRCRAApVI4KrxU+XpP8yTO6+/SH65Y7tVEFB4K3FU8J5JgARKTYDCW2riPB8JkEDFEJgx62W54obJcu9Nw2Xn9tu53jeEd7utN5fRw1mHt2IGBm+UBEig5AQovCVHzhOSAAlUAoGly5bLYacMlU6/+rnU9DholVvedeefy1prrSn9Lr5BFi76XAkvVmLbqNUGlYCH90gCJEACJSVA4S0pbp6MBEigUgjM/9f7cuwZl3ve7pxH6+RnG7eSP877iwy9ZpLaD6XK9vnNzpWCiPdJAiRAAiUjQOEtGWqeiARIgARIgARIgARIoBwEKLzloM5zkgAJkAAJkAAJkAAJlIwAhbdkqHkiEiABEiABEiABEiCBchCg8JaDOs9JAiRAAiRAAiRAAiRQMgIU3pKh5olIgARIgARIgARIgATKQYDCa0F9tdVWkxUrVlgcgU1JgARIgARIgARIgASSJkDhtSBM4bWAx6YkQAIkQAIkQAIkUCICFF4L0BReC3hsSgIkQAIkQAIkQAIlIkDhtQBN4bWAx6YkQAIkQAIkQAIkUCICFF4L0BReC3hsSgIkQAIkQAIkQAIlIkDhtQBN4bWAx6YkQAIkQAIkQAIkUCICFF4L0BReC3hsSgIkQAIkQAIkQAIlIkDhtQBN4bWAx6YkQAIkQAIkQAIkUCICFF4L0BReC3hsSgIkQAIkQAIkQAIlIkDhtQBN4bWAx6YkQAIkQAIkQAIkUCICFF4L0BReC3hsSgIkQAIkQAIkQAIlIkDhtQBN4bWAx6YkQAIkQAIkQAIkUCICFF4L0BReC3hsSgIkQAIkQAIkQAIlIkDhtQBN4bWAx6YkQAIkQAIkQAIkUCICFF4L0BReC3hsSgIkQAIkQAIkQAIlIkDhtQBN4bWAx6YkQAIkQAIkQAIkUCICFF4L0BReC3hsSgIkQAIkQAIkQAIlIkDhtQBN4bWAx6YkQAIkQAIkQAIkUCICFF4L0BReC3hsSgIkQAIkQAIkQAIlIkDhtQBN4bWAx6YkQAIkQAIkQAIkUCICFF4L0BReC3hsSgIkQAIkQAIkQAIlIkDhtQBN4bWAx6YkQAIkQAIkQAIkUCICFF4L0BReC3hsSgIkQAIkQAIkQAIlIkDhtQBN4bWAx6YkQAIkQAIkQAIkUCICFF4L0BReC3hsSgIkQAIkQAIkQAIlIlARwvvV19/KyHGT5eXX/iZrrbWm1FQfKGf36qEQN777gYy8YbL8672PZOstNpPRw8+SHbffSn328cLPpHbU7fL3xv9Tn1099Az5Vft2ha6h8JZolPI0JEACJEACJEACJGBBoCKE95q6e2Xxki/lmtozZPHnX8iJA66SG0YMkN122VEOP7VWTjn2EDmx+iB5+MnZct8jz8rjU0YppL3OGyUH7L2rnHLcofLI7+fIm397R8Ze0o/CazHg2JQESIAESIAESIAESk2gIoT3hZfflPY7bC1btv2Z4jtweJ0csM+ustfuv5Qep10irz01URCtxdb1mEFy5w0XScsWayvhff7B8bL66is/c26M8JZ6uPJ8JEACJEACJEACJBCeQEUIr4kF6Q1H9Bomd427WNZdp6Uc2atWXn96UkF4D625UIaec5Jqct+jz6hUhlde/5tstcVmctn5vWSHbbcoHI7CG37AsQUJkAAJpI3AkiVLpKGhQVq1aiWdOnVK2+XxekiABGIgUFHCu3TZchl8+U3y6512kAF9qmXFihVyZO/hKp2hpseBMnP2a3LJ6Dtk1LCzZNny5XLl+KkycewQ2bNTB5Xq8OhTL8pjd1/dTHi//vrrGLqBhyABEiABEigXgQ8++ECmTZsmW221lZx44onlugyelwSKElh33XVJyIJAxQjvF199I+cMGy977rqTnNv3mAIyTFq7avxU+WTRYjl4v92l4e13ZUDvalm+/Du5dUq9PHLnlWrfH39cIbsdeob84ZEJslGrDdS/IcK7dOlSC/xsSgIkQAIkUG4C77//vtx///2y9dZby8knn1zuy+H5ScCVQIsWLUjGgkBFCO+y5d/J6UOulaoD9lQT1Ly27777XuXw1t9zjXze9KUS5OceHKd2/+GHH2W3Q8+Ul2bcJBusv/IpiykNFiOPTUmABEggJQTeeustmTFjhrRs2VKGDh2akqviZZAACcRJoCKE99bJ9bJocZOMGNJ7FXao2DCgdw/Ze4+dZeLUGSrCe/t1F6r9ju57qZx63KFy9G/3k3sffkaeePZVeej2KwrHoPDGORR5LBIgARIoD4HZs2fLnDlz1MlHjBhRnovgWUmABBIlUBHCe/AJQ2TRZ02ymlFtATm7tQNPkrl/eluuHD9FPvv8C9llp+1l9LAzZdNNWivo//z3hzJ89B3y4ceL1GS1Ky/uy0lriQ5HHpwESIAESk+Awlt65jwjCZSaQEUIb1JQGeFNiiyPSwIkQAKlI0DhLR1rnokEykWAwmtBnsJrAY9NSYAESCAlBCi8KekIXgYJJEiAwmsBl8JrAY9NSYAESCAlBCZPniwLFixQV8Mc3pR0Ci+DBGImQOG1AErhtYDHpiRAAiSQEgIU3pR0BC+DBBIkQOG1gEvhtYDHpiRAAiSQEgIU3pR0BC+DBBIkQOG1gEvhtYDHpiRAAiSQEgIU3pR0BC+DBBIkQOG1gEvhtYDHpiRAAiSQEgJ1dXWyZMkSdTWDBg2S1q1XlqbkRgIkkB8CFF6LvqTwWsBjUxIggZIQwGSsVq1aUeKK0B45cmTh0969e0u7du1K0jc8CQmQQOkIUHgtWFN4LeCxKQmQQOIEPvnkE5k0aZI6D6sPeOOm8CY+FHkCEig7AQqvRRdQeC3gsSkJkEDiBObOnSuzZs1S5+nXr5+0bds28XNm8QQU3iz2Gq+ZBMIRoPCG49VsbwqvBTw2JQESSJyAORmLr+oZ4U18wPEEJJBiAhRei86h8FrAY1MSIIFECSxdulTGjh1bOAeF1x33e++9J1OmTCGnREcjD04C5SdA4bXoAwqvBTw2JQESSJTA/PnzZfr06YVzdO/eXbp06ZLoObN4cApvFnuN10wC4QlQeMMzK7Sg8FrAY1MSIIFECdTX10tDQ4O0aNFCli1bJl27dpVu3boles4sHpzCm8Ve4zWTQHgCFN7wzCi8FszYlARIoDQEkM6AtIb27dtLY2MjhdcDO4W3NOORZyGBchOg8Fr0ACO8FvDYlARIIDECuhwZ6u9WVVWp1AaIb01NTWLnzOqBzUoWuIcePXpIp06dsno7vG4SIAEPAhRei6FB4bWAx6YkQAKJEZg9e7bMmTNHOnfuLB06dFCTsrbddlvp06dPYufM6oE1K339TP3Iak/yukmgOAEKr8UIofBawGNTEqggAkgtaNmyZcnuGItNIMrbs2dPdV4Krzd6Cm/JhiVPRAJlJUDhtcBP4bWAx6YkUAEE3nrrLbXwA6QT8lmKhR90Tiomq9XW1qo8XuTz4hqGDh1aAdTD3SKFNxwv7k0CWSVA4bXoOQqvBTw2JYEKIDBz5kyZN2+eutNSCSeiuZBe89W8XkksK8sLQ9IXLlyoKkwk/ZCg+4jVLCrgC8lbrGgCFF6L7qfwWsBjUxKoAALmSme43aSF04zuDh48uJBGkTXh1fdRirxj3Uc414IFC1jNogK+l7zFyiRA4bXodwqvBTw2JYEKIKBzaXX0cNCgQdK6devE7tycrIbqDHqbMGGCNDU1Sb9+/RKPmMZxc2apsKQfEii8cfQYj0EC6SdA4bXoIwqvBTw2JYEKIKAjqzp6mPTyvlp4nZUGtNQlff64upTCGxdJHocESEAToPBajAUKrwU8NiWBnBNYsmSJ1NXVFfJQ8bo8aeHMo/AmHRVnhDfnX0TeHgn8jwCF12IoUHgt4LEpCeScgJmHilstp/DqZYa7d+8uXbp0ST15M8Kb9EOCU3hRu9hMB0k9LF4gCZBAIAIU3kCY3Hei8FrAY1MSyDkBvYJXx44dBdHecgqvV+Q3rV1QDuFFGggW6yjFRLm0cud1kUCeCVB4LXqXwmsBj01JIOcETMnEIhCNjY2qFi9WPktq8xLbrAkv6hfPmDFDYUp65TMd4aXwJjUqeVwSSAcBCq9FP1B4LeCxKQnknMC0adMKkgvhRfQwaXnTqQs9evSQTp06FQhnTXjNxSCSZkbhzfkXkbdHAv8jQOG1GAoUXgt4bEoCOSdgVkbAK/pSCK9XNYYsCy9SQqqrqxMbLRTexNDywCSQKgIUXovuoPBawGNTEsg5AV2SDMv5Ip+Xwhu8w80Ib9I5tRTe4P3CPUkgywQovBa9R+G1gMemJJBzAubqZqWKsHpFeOfPny/Tp0+X9u3bS01NTerJU3hT30W8QBLIHAEKr0WXUXgt4LEpCeSYgHNp3HILbymX6o2jW03hxfGSXG2NEd44eozHIIH0E6DwWvQRhdcCHpuSQI4JOCOqpRJOrwhvqc4fV5dSeOMiyeOQAAloAhRei7FA4bWAx6YkkGMCzohuqYQzr8Kb5GprjPDm+IvIWyMBgwCF12I4UHgt4LEpCeSYgHNls1IJ79ixY2Xp0qWCiXItW7YsEC7V+ePqUmeEN8nV1szJheCHLckUirgY8TgkQALhCFB4w/FqtjeF1wIem5JAjgk4I62lEk5zopyJt1Tnj6tLyyG8kFwvfnHdF49DAiRQPgIUXgv2FF4LeGxKAjkmUFdXp5YT1q/iSyWcFN7wg8pkRuENz48tSCArBCi8Fj1F4bWAx6YkkGMCTnFCmgFelyPNAOkGSW15FV7nynFx8qPwxkmTxyKB9BKg8Fr0DYXXAh6bkkBOCWAZ4UmTJkmrVq1k8ODBhbssRfQwL8KrU0Kw6MSCBQsSXZKZwpvTLyJviwQcBCi8FkOCwmsBj01JIKcEvNIXyim8QF2K88fVpRTeuEjyOCRAApoAhddiLFB4LeCxKQnklICecNW5c2epqqpKRYSXwus92BjhzekXkbdFAozwxjcGKLzxseSRSCDLBPRCE3gF365dO5kzZ84qr+GTjrD6TYxL+vxx9h8jvHHS5LFIgARAgBFei3FA4bWAx6YkkCMCOqoL4W3btq3MmzdPunfvLl26dCnc5YQJE6SpqalQuSHu26fwRiPKCG80bmxFAlkjQOG16DEKrwU8NiWBHBEw68bqiVbOxRK8VkGLC0Mehbdr164qWg6mffr0iQtVs+OYwjtx4kRZuHChJLnQRSI3wYOSAAn4EqDw+iLy3oHCawGPTUkgRwQovPF2ZimX+zWFN+mHkngp8WgkQAJhCFB4w9By7EvhtYDHpiSQIwKm8KLWrtvyvknLFCO80QYUhTcaN7YigawRoPBa9BiF1wIem5JAjghMmzZNGhsbm90Rlqo1Nwpv8A5nhDc4K+5JAiQQjACFNxgn170ovBbw2JQEckRAC5q+pRYtWkhtbW2zO9RS3LNnT+nQoUPsd68rRbRv315qampWOb6OZGKlN0Sh07xReNPcO7w2EsgmAQqvRb9ReC3gsSkJ5IiAU3jdJlnptAdMxOrWrVvsd+93fGeEGZPBsCocBDxtm75WVLqYNWuWtG7dWlW3SGJjSkMSVHlMEkgfAQqvRZ9QeC3gsSkJ5IhA1oQXpdPGjh2resCZepGGbqmrq5MlS5ZIv3791DLNSV4nhTcNPc5rIIHkCVB4LRhTeC3gsSkJ5IiAU3g7duwo1dXVze7QLwJri8Pv+GaEF+eaMmWKOiWkEgKcpq1UtXExudAU//r6emloaJAePXpIp06d0oSE10ICJGBJgMJrAZDCawGPTUkgRwS0oOlbcktb8BNSWxx+x/cS3jTWnC2V8DorW/gxtO0jticBEigfAQqvBXsKrwU8NiWBHBHIsvAmNYnOpnspvDb02JYESMCNAIXXYlxQeC3gsSkJ5IiAU3jdoqZJRw/9ju8V4UU0ervttpNWrVqpyWFp2Ci8aegFXgMJ5IsAhdeiPym8FvDYlARyRCANwuuXf+olvHopZHRHWiawuS33m0SuMVMacvQl5K2QgA8BCq/FEKHwWsBjUxLICQFUE0BVAURIUf8WE6EwCcxZ69ZvJTRbHH4LW3gJr3neNAqv333ZcHP2yVtvvSUzZswQr1rGNudiWxIggfISoPBa8KfwWsBjUxLICYGgIht0v6hY/MTQXPgCko76ts6t0oU36T6K2rdsRwIkYE+AwmvBkMJrAY9NSSAnBIJKktd+frm3QTH5Ca95HhwTC09QeN9T5dn0QiFB+zJon3A/EiCB9BCg8Fr0BYXXAh6bkkBOCASVJK/9zGV0bVZgo/CGH1DOPgnal+HPxBYkQALlJkDhtegBCq8FPDYlgZwQCCpJFN7gHW5OWjNTMTp06BD8IAH2pPAGgMRdSCAnBCi8Fh1J4bWAx6YkkBMCOlWgc+fOUlVV5XlXFN7gHW4Kb1wpH25nd/aJXnkNEw6HDh0a/IK5JwmQQOoJUHgtuojCawGPTUkgJwSCClnSwoslciFsgwYNcq2nGySHF5LnrC5R6m765JNPZNKkSdKmTRvp37+/BOUb5Trd+sSU7SjHZBsSIIF0EqDwWvQLhdcCHpuSQE4IBBWypIXXT9TM64QYz5s3T0klBBeSuWzZMknDMsOlrI1L4c3Jl5C3QQIBCFB4A0Dy2oXCawGPTUkgJwRshVdHZv1SIvxwhRFeiN6CBQsKgus34c3v3HF+TuGNkyaPRQIkoAlQeC3GAoXXAh6bkkBOCMycOVNFS7t37y5dunTxvCuvCK8WVV0aKyoWCm94cozwhmfGFiSQVQIUXoueo/BawGNTEsgJgTDRUTcpLZXw6lXEOnbsKFh4IisR3vnz58v06dMTWf3MTXjD9GdOhjBvgwQqggCF16KbKbwW8NiUBHJCIIwglVN4TbkD+qwIb9Cyb1GGE4U3CjW2IYFsEqDwWvQbhdcCHpuSQE4IUHjj7chS1sbVUe/27dtLTU2NupEw/RnvnfNoJEACSRKg8FrQpfBawGNTEsgJgTCC5IzwIrWgrq5OkbDJ4XWW8nJDywjvqlTcJhyG6c+cDGHeBglUBAEKr0U3U3gt4LEpCeSEAGrGQjiDlPRyCq+WUFvhDfLa39xn4cKFqmavrrub5GpmYbu5lBFeCm/Y3uH+JJBdAhRei76j8FrAY1MSyAkBv+oI5m2mRXiRv4ttxIgR6r+DllYrRZeVW3jTxKIUvHkOEqgUAhRei56m8FrAY1MSyAkBCm+8HUnhjZcnj0YCJLCSAIXXYiRQeC3gsSkJ5IRAXMJrRlzDogmb0pDmCK+zDJm+t7Zt20q/fv3Coim6v1s0lxHeWBHzYCSQGgIUXouuoPBawGNTEsgJARvh1XKnUegUg7Bo8iS8bsIZhnEYdhTeMLS4LwlkmwCF16L/KLwW8NiUBHJCIIyMOffVwlUK4cUkNSxj3LJlSzVhzYwopymqWW7hnTt3rsyaNUtsl3rOyfDmbZBAbghQeC26ksJrAY9NSSAnBMIIry55hVfzeEUfl/AGFVZ9rUDfokULqa2tVb0QtH0puqzcwhskWl4KDjwHCZBAvAQovBY8KbwW8NiUBHJAQNfRbdWqlQwePNj3jpw1XsspvGbdXwpvV+nWrZvqPwqv7zDmDiSQSQIUXotuo/BawGNTEsgBgbBylFbhTdNr/HJHeM3FQILUVs7BMOYtkEBFEKDwWnQzhdcCHpuSQA4IZE14x4wZI8uWLVPkzQhv2PtIsuvKLby4N30NrVu3VpUhkPfMjQRIINsEKkJ4v/r6Wxk5brK8/NrfZK211pSa6gPl7F49VM81vvuBjLxhsvzrvY9k6y02k9HDz5Idt9+qWa++9uZ8Oe38MfLE1NGy/TabFz6j8GZ78PPqScCWQFhRdEZ49Qpn+jqiVmkImpKgz0/hXUm8GLcJEyZIU1NToBX0bMcR25MACSRPoCKE95q6e2Xxki/lmtozZPHnX8iJA66SG0YMkN122VEOP7VWTjn2EDmx+iB5+MnZct8jz8rjU0YVyC9f/p3af9FnS2Ry3TAKb/JjkmcggcwQsBVeU0Bx04MGDRJEFcNuQYV35syZMm/ePHV4RniLC6/z4SRsn3B/EiCBdBGoCOF94eU3pf0OW8uWbX+m6A8cXicH7LOr7LX7L6XHaZfIa09NFERrsXU9ZpDcecNF8ovtVkZ5b7nnMVmxQuSZF9+QCVcOpPCma/zyakigrATiFt6oOaNaZLt37y5dunTxZKJzdbMmvDoVI+oDgReQYg8KFN6yfrV4chKInUBFCK9JDekNR/QaJneNu1jWXaelHNmrVl5/elJBeA+tuVCGnnOSHLTfbvLeB5/I4MtvlgcnjZDjzrrCVXh/+OGH2DuFByQBEsgGAaxYNnXqVNlmm23Uq2+/bcqUKfL+++9Lr169VIRV/3/dTv+733GcnzuP69VeXy8+33PPPQWCjC3sfYS9vjD7z5kzR1588UXZf//9pWvXrqpp0PsLcx7s63YufQzU4n3ttdfk0EMPVTV5uZFAuQmsvvrq5b6ETJ+/ooR36bLlMvjym+TXO+0gA/pUy4oVK+TI3sNVOkNNjwNl5uzX5JLRd8ioYWfJYQd1lr7nj5V+vY6SzrvuJEf1ucRVeDGjlxsJkEBlEnj11VdVigCEaK+99vKF8NBDD8lHH30kxx13nGy11Vai/79uqP/d90COHZzH9WqPCWu33Xab+ti85g8//FAefvhh2XLLLeX4448Pe/pY93djGvT+wl5Isf4L27dhz839SSAsAZQ/5BadQMUI7xdffSPnDBsve+66k5zb95gCMUxau2r8VPlk0WI5eL/dpeHtd2VA72r57PMmeaOhUa4eerra10t4Ic3cSIAEKpNA0NxZTcf5mryurk7w0IwfMpsJUmFev+v0AERP01h7Vk/k69Gjh3Tq1EmhC3N/YUai27l0+zB9q0uZtWvXLlCkP8w1cl8SIIF4CFSE8C5b/p2cPuRaqTpgTzVBzWv77rvvVQ5v/T3XyFUTpsqf//pPWeN/rxCWfPGVbLD+unLN0DOk294r/wizSkM8g5BHIYGsEggjRbhHvb/OtdUrnyG9AWkFUXN4wwih3jetwut2L2HuL8xYKnbcMH1rTgaMWmkjzHVzXxIggfAEKkJ4b51cL4sWN8mIIavm2KECw4DePWTvPXaWiVNnqAjv7ddduApJRnjDDy62IIG8EwgjRabwatksh/BCzubPn6+iuzqCimsLs0Rykv2aReEdO3asLF26VGGJe2Jdkqx5bBKoJAIVIbwHnzBEFn3WJKutvrISAzbk7NYOPEnm/ultuXL8FPns8y9kl522l9HDzpRNN1m1LBCFt5K+FrxXEghGIIvC63VnFN52zdDgoWD69OnSvn17qamp8RwQb731lsyYMaPwedQofbARx71IgASiEqgI4Y0Kx68dUxr8CPFzEsg3gbiEF1LV2NgYOaVB5wLbRBcpvM2FN2jJOT0G9Ein8Ob7O8+7yy4BCq9F31F4LeCxKQnkgEB9fb00NDSIOcGq2G05BVlLJlIcUCLLzKsNgycOWY3jGGGu2WvftKQ0BBVe5+IhFN44RgGPQQLxE6DwWjCl8FrAY1MSyAGBsJOpTOHt0KGDTJo0Sdq0aSP43xTelQPCjWnYB4ugQ6tY/wUVdwxiwAAAIABJREFU3rgqbQS9Zu5HAiQQjQCFNxo31YrCawGPTUkgBwRshBclrLCgAio04H9TeL2FN2zqSNChFYfwOice9uzZUz3AcCMBEkgXAQqvRX9QeC3gsSkJ5IAAhTf+TnRjWg7hxZ1BZlu2bClDhw51vVEzCmz70BI/SR6RBEjAJEDhtRgPFF4LeGxKAjkgQOGNvxPTJry4Q6/aurpCQ8eOHaV169ZWUfr4SfKIJEACFN6YxgCFNyaQPAwJZJRAnoQ37L0k1WVZEl694AQmG2KzSUtJiiePSwIksJIAI7wWI4HCawGPTUkgBwTCSqJ+BY4yZF26dIklh/eTTz4pTH7r379/ZKph7yXyiXwaZkl4zWtF31J4kxoVPC4J2BOg8FowpPBawGNTEsgBgbClvMycT6x0FsektaDVBPxwp0V43ZiWM4cX3LxSGvQKa6h/jPQGCq/fKOPnJFA+AhReC/YUXgt4bEoCOSBgI7yYyT9r1izp3LmzqtIQZFUvN2QU3ugDyU/y/T43+z/oymzRr5YtSYAEbAhQeC3oUXgt4LEpCeSAgI3wmrP6zRJlffr0CUWGwhsKV7Od/YTW73Oz/+Pqh+h3w5YkQALFCFB4LcYHhdcCHpuSQA4IUHjj70Q3proaAnKfa2pqYjupn9D6fU7hja0reCASSJwAhdcCMYXXAh6bkkAOCFB44+9EN6ZJRU/9hNbvcwpv/P3PI5JAUgQovBZkKbwW8NiUBHJAII/CW+6VwkopvPpcWFgCC0w4NwpvDr6kvAUS+B8BCq/FUKDwWsBjUxLIOIElS5ZIXV2dtGjRQmprawPdjdfKXGFyeFGGDIscaEGbO3duYfJbVVVVoOtw2ympSghhL6gcwutVhcGPiXmtujwc+gZVG7iRAAmkiwCF16I/KLwW8NiUBDJOIMprdlvh1e2Brnfv3qq6g5+UBcUc13GCns9rv6wKL+4nbMTflhXbkwAJBCdA4Q3OapU9KbwW8NiUBDJOwFZ4EaFtbGwUpBDgf+uavLpKAyLIiBbqbenSpWqBCfw7hTeeweMnqMUeAtAfqMNrRvj9jhfPVfMoJEACUQhQeKNQ+18bCq8FPDYlgYwTiCK8OgoIwW3Tpo0sWLBARWrx/yGzbdu2lX79+ikyWLYWn/fo0UP9u65UoLHpXNu4IrNxHce2W7MS4XXrfwqvbe+zPQkkR4DCa8GWwmsBj01JIOMEbIQXt77tttsWhBepCU5Z0hOmsG/Xrl1VZLehoUFFFJctW6b+Dau1xSWqWqg7duwo1dXVZesdCm/Z0PPEJJBrAhRei+6l8FrAY1MSyDiBuIV3zJgxSmQx4QmpDKbwIgKM/0B6UYsWqRBxC2/U+4mzG/VEwFatWsngwYMLh07q2vwissXOywhvnD3PY5FA8gQovBaMKbwW8NiUBDJOIKqEaclyRnidJbBQAQICCPlrampStBDd7dKli8yZMyeXwuvFVOfLQvpRQiyuLW7h9StjFtd18zgkQALhCVB4wzMrtKDwWsBjUxLIOAGdStC5c2cJUw7MS3iRsztv3ryCyOr9kMM7Y8YMRQuSbC5JHGdKQ1SBj7Mbi12Dn5xGuQ6/Y4aN8FJ4o/QC25BAaQhQeC04U3gt4LEpCWScQNTcWS/hdS6fay6KgGoA2JDGgAls06dPV6kNWGY36nU48VN4Vx2QFN6Mf0l5+SRgEKDwWgwHCq8FPDYlgYwTiCqaOlcXr+fxql6v8qXlCkKLqC6qNqCSQ//+/VWFBqQ3dOrUSf23WcKsvr5eTWZDG3weddNpA2jvtRBD1GMHbZe2CK9eTMKsnqHvxe1aGeEN2tPcjwRKT4DCa8GcwmsBj01JIOMEogqvORnNKZc6qotSZc66vF6iFadk+b3iT7rL0ia8uF8vJhTepEcDj08C8RKg8FrwpPBawGNTEsg4gSSEd+LEibJw4UKVuoCJacjZ1QtRUHhHKgRxRp+DCH4Y4dXR9u7du6vJhdxIgATSQ4DCa9EXFF4LeGxKAhknkITwamHSlRl06TETlS7dhdJlKGHGCG/0gWQjvG51i6OOieh3wJYkQAJBCVB4g5Jy2Y/CawGPTUkg4wR0VYWw0TwtRfr2zYil8zM34UU7U9TiFF6dX6zzikvdRVlKaXCTWwpvqUcMz0cCwQlQeIOzWmVPCq8FPDYlgYwTiCqaxYRXC59G4yXTSQlv1HuKqyvTKLxeDwFxCy8eoJDOgj7HJDluJEAC8RKg8FrwpPBawGNTEsg4gahyWEx4zUoJwIPJa6i769wqUXij8vYaZkGrUnidN27hjfv+Mv714uWTQOwEKLwWSCm8FvDYlAQyTiCqoMyfP1/V0cWmy46ZKHREkcLbp9kIicrba5gFrTscRnidtZTDDHG9sp7XQ06YY3FfEiCBVQlQeC1GBYXXAh6bkkDGCUQVMDNtwa0Kg1m2DJPSMDnNuZnnRqRxwYIFntHgMJij3lOYcxTbt9jqdXFfWxLCG/SYbgzMknRuUf24GPM4JFCpBCi8Fj1P4bWAx6YkkHECUQXMT3jNlAevElzmubHsMCo3eMlxGMxRJ+KFOUcQ4XWbrBeVdykjvHEIr+0CInH1BY9DAnkjQOG16FEKrwU8NiWBjBOIKmC6rBhu3y3Cq1+L4/MgwosFKortGwZzuasMFDt/VN62wjtt2jRpbGyUnj17SocOHQqHc7vWOITXqzJHmH7kviRAAqsSoPBajAoKrwU8NiWBjBMYO3Zss6WBw9yOfn3tJrxYzhYyhdfaXosXmPJH4Q1D/qd9g8qpl4RTeKNxZysSKBcBCq8FeQqvBTw2JYGMEwiyaIHXLRYT3iBYzBW9Zs2aVRERXq9IaxBebvvoyYPt27eXmpoaz8OEEV4cJMq4wEPOpEmT1DUwwhu1R9mOBIoToPBajBAKrwU8NiWBjBOIIjb6lnUlBrcIbxAspoRhCWJscSy5m+aUhrivLejxvPbTAu7MuY0yLsy8bgpvkG8A9yGB8AQovOGZFVpQeC3gsSkJZJxAFLHRt6xTEjp37ixVVVWhSSQlvEFf84e+4IANikloUEENeCqVNoKHBT/B9NrPK6c4yrig8AbtNe5HAtEJUHijsxMKrwU8NiWBjBOIIjZO4fWTLS9EFN5u1qMnqPDOnTtXkDbifDih8K7sAp1DjvrB3EggzQQovBa9Q+G1gMemJJBxAuUUXlPC5s2bp0jGkdLACO+qg9KLiZfwTpgwQZqamkKViTMXI4n6EFSur5PN96Bc18zzViYBCq9Fv1N4LeCxKQlkmIBelrZFixZSW1sb+k6CRhe9DmxKGBadoPCG7gLxysF1Hims8EYpn2bWXqbwhu9LtiCBIAQovEEoeexD4bWAx6YkkGECtpFQtMd/UHosyqpa+vytWrVS0UT89+DBg2MhWs6IXbEHgaBVFYJCCCqmFN7iRMs5XoL2NfcjARCg8FqMAwqvBTw2JYEME7AVXttbNyc54VhRqz24XcfEiRNl4cKFsSxVHPY+i630FjfztApvnH0Zln+U/Sm8UaixTTkIUHgtqFN4LeCxKQlkmEDc8hUWRZLCa9b49Vr4Iuz1Bt2/mIQWY47V67AaGiLd5mpoxc4bVHj1ynitW7dWebl682of9LjmtZkpDRTeoKOF+5FAOAIU3nC8mu1N4bWAx6YkkGEC5RZenUOsEcYpSXpCXMeOHaW6urqkvRRVeLUwtmzZUoYOHRromrHQAxZ8QHUBv7QStyim17XqKHWYXFydT4wLj7MvA4Gw3IkRXkuAbF4yAhReC9QUXgt4bEoCGSZQbuEFOi0acUuSvre2bdtKv379YuulIGIUVXh1VBoX61wIwusGglyPbhtGeKNMSNT3HXdfxtZ5RQ4UhmMprofnIAEvAhRei7FB4bWAx6YkkGECeRZeU6bjKHWG45lL5xY7ZlThNYURqQeI2uK/i21hRM1t37Fjxwoi7YgoI7Kst0oVXieHDH+9eek5JUDhtehYCq8FPDYlgQwT8FqMoJS3lFSEF/cQJQ+12L2bOcfFUgj8zuslqXV1dYJcW5SJW7ZsmboUv0ivrfB6ta9U4Q2SGlLK7wfPRQJOAhReizFB4bWAx6YkkGECUaQm7tvVCxzguHHnfeqc0p49ewaeBFZO4dXyiSgjrh21iZFDi9xc/G/wcebphhFet8UkvNpHeRjS+cRJ9GXc4855PM2Bwps0aR7flgCF14IghdcCHpuSQIYJpEF4k8z7jPv+3nrrLZkxY4bq8bgjvLqKgl4ExEw3QVpDQ0PDKssC6zZB6xe7RZ69hDdKukuS0fqkv2YU3qQJ8/hxEaDwWpCk8FrAY1MSyDCBuIUwCgpTeNu3by81NTVRDuPaJu77C7qSWJSUBqdgmv8fN6cjvH369Cnca1gppfB6Dy0Kb2xfOx4oYQIUXgvAFF4LeGxKAhkmELcQRkFhCm+YElhBzqXvr3PnzlJVVRWkSdF9khBeTBjDAhmYEIdSYFr6zbq5mEyGz50VJ8IKr1upsaQivM56v9bwEz4AhTdhwDx8bAQovBYoKbwW8NiUBDJMIO/CG1YI/bpSCyP2KybRfhHeMWPGqElpyNVFruycOXMKpzal30wR0DuY1SHC3p9bfyclvLjeuKpj+PVLHJ9TeOOgyGOUggCF14IyhdcCHpuSQIYJFFsCt1S3FTRqGuV6wgqh3zmC5hv7Ca/5Oa6xVMI7f/58mT59erPJgV7CqxcFCboIhi7Z1qZNGxWxzpLwBq2+4Tc++DkJlIIAhdeCMoXXAh6bkkCGCfiJWSluLY/C61XbVvM0ueP+kZ+rN3MynCnYbp+HjdC7PQAUq/IQpgKEeWx9P1mJ8FJ4S/FN5zniIkDhtSBJ4bWAx6YkkGECeRdedE0YafPrSlNA3VZwQ8Qc0U0IVLEIZ7mE11zKWcsohVdUf02ZMkX1GcuS+X0L+Hm5CVB4LXqAwmsBj01JIMMEKLzhOs+ZU+uMYJp1aIMKrxYt5O5i69atW+GizJxh/Y/du3eXLl26qP8bNsLrfADQooc0hP79+68CY+LEiUrgg0ggI7zhxhL3JoGoBCi8UcmJCIXXAh6bkkCGCaRBeHVeKTDGXaXBKXi2XeUnvH6f6/Ob3LXwur3+N9M9dFuTURThNSUWx8T5vRb80NcZZOEOCq/t6GJ7EghGgMIbjJPrXhReC3hsSgIZJpAG4TVfJychvFrw+vXrp8p62WxaaPXSv4MGDRKU38Kmy4iZx/fKYa2vr1cLSXTs2FH9t5dw6tXOcEw9GcysVayP47f8sHlNZp/7CW8Yoabw2owstiWB4AQovMFZrbInhdcCHpuSQIYJpE14zdf1cWGN6x7N1/+oXICJWearflPc9bV7Ca8WSYiu24ISur3zYQDVHEw5jnJvZi1eLFNcLMKrV5aDmFdXVxftEgpvXCOWxyGB4gQovBYjhMJrAY9NSSDDBKIIU9y3m/SEobju0W3lM1N4zWWHgwqv3s8rsm2yQRQXyxqbCzpEuTczausnvGHKurkJL+oM4+Eg7VvSYzDt98/ryxYBCq9Ff1F4LeCxKQlkmEBdXZ16FV9OMdH1W4ExyOSosLjjqjVsCh2kE6kIZiqBW76tX4TXT3jxuVlFwVlRIYrw6pxppEZg8luxCK9bVQcv/jr9AgtyoE+dEfCw/VbK/Sm8paTNc9kSoPBaEKTwWsBjUxLIMIE4S3bZYEhylasweajF7sEUOkQtkV5gRmanTZsmjY2NzQ4RVHiLTQrTJc4QjY1DeE1xR0WIYsJrCrffQ5HJGefIqvAGmaBnM9bZlgRsCVB4LQhSeC3gsSkJZJgAhTd455lCh1ZO4XVbJCKo8AaNbDv7K0r/hRXeoFHkvAhvEhMng48y7kkC/gQovP6MPPeg8FrAY1MSyDCBKMKUxO0mGeENM/Gq2L2ZQoeUBuTTmpO59D3oiWg4lpfwmqXYWrVqpWrgBsl1dcpnlP4LK7zOShBIV5g1a5ZCBVHXG4U3iW8Gj0kCqxKg8FqMCgqvBTw2JYEME4giTEncbpLCG2biVVDhdU72MnNdESFE9LeY8GJ/5BYjf7qmpiaQ7OJ4cQivLp8Gwcbre7+UBmdKiFkqzRR6Cm8S3wwekwQovLGOAQpvrDh5MBLIDIG0CG+YFb3Cwi2F8JrngAw7y4eFvWav/eMQXhxb97uW82Kv8c3c5aqqKjFXkzNTMSi8cfUyj0MCxQkwwmsxQii8FvDYlAQyTCAtwpskwriE15RNpDSgwgWipJjMFTZNIOr9ul0DUiIGDx4c6pBhhNe8N0Sjx44dWzgXhTcUdu5MArEQoPBaYKTwWsBj04olgNxQvVpWp06dMsmhEoRXv8I369dG6axi0VVTChEFRboCVnXD/45zC1NDt9h5owovxjlyl/Xmtcxxlqs0cNJanCOWx0qCAIXXgiqF1wIem1YsgbjKXZUTYCUIL/jGcZ9BhbdPnz6JdWncwouaufPmzWtWXs158WZ+sl4KGVHlpqYmQXst9UxpSKzbeWASaEaAwmsxICi8FvDYtGIJ6B/4IMuuphGSXvDBXKo2jdcZxzUlIbw677hfv34CKfSb/BXHfcQlvM4San5l0ZwVKHTurzl2zAU+EFWHSCexVHQcHJ3HcC7hjPrE3EggrQQovBY9Q+G1gMemFUtAy0dWhTGu3NYsDIAkhNeM+IKlsy5vElzKJbwTJkxQEV3kLEPuIcgQfK9ljkvFIy7GFN64SPI4pSBA4bWgTOG1gMemFUuAwpudrs+L8LpVg4iScxo2wuvcH+XIiq36RuHNzneDV5o9AhReiz6j8FrAY9OKJUDhzU7XxyG8uhwXUhgwIc18hY+oZykivEkJ76BBg1S01mszhVe/0dBRX82jHBHvuEYgI7xxkeRxSkGAwmtBmcJrAY9NK5YAhTc7XW/m20JWo2xOafZbajjKOfzaxCW8Wtb1+bxWhNOf63vF/2/fvr1aLMM5iY/C69d7/JwE4iFA4bXgSOG1gMemFUuAwpudrnfKWZQrT5PwQtohnlGjyqbAgkUY4dUpFM4lhym8UUYV25BAeAIU3vDMCi0ovBbw2LRiCUybNk0aGxuFk9bSPwSSEN758+fL9OnTVf9jW7BggVqqt0OHDokC0eKtS4T16NFDwtaBDiu8qDmt6+/q8znL8lF4E+12HpwEfnK2FStWrCCPaAQovNG4sVVlE9A/8BTe9I+DJITXTC/QwutX3isOUs4SYVHOaQpvmzZtpH///kUvzcxx1eej8MbRmzwGCYQnUBER3q++/lZGjpssL7/2N1lrrTWlpvpAObtXD0Wr8d0PZOQNk+Vf730kW2+xmYwefpbsuP1W6rMXXvqz3DDpQVn02RJpv8PWMvKivrL9Npszwht+nLEFCRQIaInC62VM3MnapiOUOicza9cf5nopvM1pzZ07V2bNmqX+McgDmym8Ov2BwhtmBHJfEoiPQEUI7zV198riJV/KNbVnyOLPv5ATB1wlN4wYILvtsqMcfmqtnHLsIXJi9UHy8JOz5b5HnpXHp4yShYs+l6P6DJdJ114gv95pB7np7kflrb+/I/eMr6Xwxjf+eKQKJGDOXPfLgUwjnjysFBeUaxLCi3PHEW0Neg96P31ORGYXLlyoauK2a9cu1GFMgQ0ivDg4xgs2vShDMeHFoiYQ6qwsyhKFRyjg3JkEYiRQEcL7wstvqgjtlm1/ptANHF4nB+yzq+y1+y+lx2mXyGtPTRSkJ2DreswgufOGi2TD9deTv/zjXTlk/z3Uv//jnQVyzvAJ8sJD4ym8MQ5AHqryCFB4s9PntnKPlcPq6uoES+oOHjy4cOPlEN6wNXTdeskUvKgRfp3Xq6UWfMAJJc7w36VYeS6uEUjhjYskj1MKAhUhvCZIpDcc0WuY3DXuYll3nZZyZK9aef3pSQXhPbTmQhl6zkly0H67NeN/1wNPSeO/3pdrL/spZ4s5vKUYojxH3ghQeLPTo7bC67UqnRZevQLZ0KFD1WpkSW5ui0CEPV8cdWedTMwqFllbxY/CG3YEcf9yEqgo4V26bLkMvvwmlaIwoE+1YL7ekb2Hq3SGmh4HyszZr8klo++QUcPOksMO6lzol5de+6tcNX6q3HvTJbLZz34qMg7h/eKLL8rZfzw3CWSOwIMPPigffvihuu4hQ4Zk7vpfeeUVQS5nly5dZO+9987c9Ye5YNt7RT+jv7faais54YQTCqc2x0CpxkEc59T3g2uO2v9OJuPGjSt8F7x4hemzUu5r8nD2cSmvo1LOtcEGG1TKrSZynxUjvF989Y2cM2y87LnrTnJu32MKMDFpDTL7yaLFcvB+u0vD2+/KgN7Vsl/nXdQ+Tz73qtw2ZYbcNuZ82WbLNs06AcK7fPnyRDqGByWBvBK477775P3331e3N3z4cN/b/Mtf/qIeLHfZZRf1arzc24svvigvvfSS7LvvvrL//vuX+3ISPb/tvaKf0d/bbLONnHLKKYVrNcdA0HFge6MPPfSQvPPOO4XDBBl7znNiZTgtqFH738lk1KhRhe+CFy/be0+qPb6bTz75pDq8s4+TOmclH3ettdaq5Nu3vveKEN5ly7+T04dcK1UH7KkmqHlt3333vcrhrb/nGhXJRZWGG+96VOX0/mzjVX9omdJgPf54gAokEDalQe+vC/eXG5nta/5yX3+Y89veq9crel2LWV9LKSYvhq2h68VJpyBErR2cp5QGk2nQSXxhxh/3JYE4CVSE8N46uV4WLW6SEUN6r8IOFRsG9O4he++xs0ycOkNFeG+/7kJp+vJrObrvpTL1xuGy1eabujKn8MY5FHmsSiGQJuFFxA6LIGDDrP0gm60EBjlHWvbRZbg6d+4sVVVVoS/LS3jjks8wF4Rrwf1g0RPnJLowx9HCG6XKA86jJ/K1bt1aTVTLcg4vhTfMyOG+5SZQEcJ78AlDZNFnTbLa6isrMWBDzm7twJNk7p/elivHT5HPPv9Cdtlpexk97EzZdJPW8tjTf5RLx96l6vaa2+yHJ0jrVuurf6Lwlnv48vxZJBBWeCdNmiQo15REhNesqxo0YldJwms7iSqI8NrIZ5TxD+HEeIq6sput8OKaTcml8EbpRbYhgfAEKkJ4w2MJ1oLCG4wT9yIBk8DYsWMFkVVsQaJkWgiiRhmL0dcyjX2C1j6tr6+XhoYGibI0bdZGgq3wekWIwy7gkCZueGDDhog3Fk+JslF4o1BjGxKwI0DhteBH4bWAx6YVS0D/2IcV3rhzBBHlg/C2aNFCli1bJvoVs1/HxLEYg9850vK5rfB6RcMrvZwVhTctI5zXUUkEKLwWvU3htYDHphVLIC3Cq2UMkWMsF9zU1KSWOvaL2lF4gw9dCq87Ky/hxd7mZ8FJl2dP5vCWhzvPGo0AhTcaN9WKwmsBj00rlkAY4dVRWMCKO8KrUxO6d++uJhLNmzdPgqRNUHiDD10v4U2yX4NfXfn2nDhxolreGGkxM2bMaDa2Kbzl6xeeOd8EKLwW/UvhtYDHphVLIIzwmq++EXlFBDauzRRXrPKF9IYgaQ2VJLzOigJh2Reb4KfHQdQlesNeS5r2N0vtzZkzh8Kbps7hteSWAIXXomspvBbw2LRiCUQVXgCLs16rU1wnTJgQKK2hkoQXzG0ijkGEN4nqG2n/clF4095DvL48EqDwWvQqhdcCHptWLIEwwovcWl0nN27h1dUihg4dKojwzpw5M1BaA4U3+NAtJrz6AYPCywhv8BHFPUkgOgEKb3R2zOG1YMemlUsgjPAmuUCBM3Kp80ohv5Bgr43CG3zsFmOVthX0gt+V/Z6M8Noz5BFIICwBCm9YYsb+jPBawGPTiiVgCi8mjHXp0sWThVN4sTIV8mxtN9QBRoQXJclqa2sLhwvy+p7CG5w+hdedlX6bgPxlrPxmTsgMMgaD90Cyeyb5QJrslfPolUiAwmvR6xReC3hsWrEETOH1e53t/EENslBFELBe9WWDyIYzFSLI+bK8TxAmUaLh6FtMiuvUqZO0a9cuy4hCX7se1xDdBQsWUHhDE2QDEghPgMIbnlmhBYXXAh6bViyBMMI7bdo0FQHTWxqE10YAs9jpuoSWWaMYfbJixQrf5XkrLRoetH8pvEFJcT8SiI8AhdeCJYXXAh6bViQBs/4qAPhFeLUwxS28Xkve6olUxVInKk14TWlFZBz1ipESEqQuMoXX/WteTHiDVgtJwx8QpjSkoRd4DUEJUHiDknLZj8JrAY9NK5KAWVc3jPC2atVKlQxDoX68ArfdvKoHBBG0ShbeWbNmCR5asFF4o49CPf7atGmjFqAwaxEHGYPRzxxvSwpvvDx5tGQJUHgt+FJ4LeCxaUUSCCu8dXV1Ks9TT+7xiwgHhaonDTknzQWRjUoW3ilTphQQBxFeLOYBQQ6yZHPQvsvDfs5ye+a4DjIG08KAwpuWnuB1BCFA4Q1CyWMfCq8FPDZNPYFiNVSjXnxY4dVyCSHAilRxCa+XVASRjUoTXj0OOnbsKA0NDaqyxbJlyyTIyneVxiro96LY9yDIGAx6nqT3o/AmTZjHj5MAhdeCJoXXAh6bpp4Ahde9ckClSZzOd9ZpJbqyAAaw38p3lcYq6JeawhuUFPcjgfgIUHgtWFJ4LeCxaeoJ6AoJQV5dB70Z5w89oobV1dWezbUw9ezZU624ZuY6Bj2n2366tJhzcloQya80iXPrM0R6KbzRRyCFNzo7tiSBqAQovFHJiXClNQt2bJp+AvrVapLCW+zYuqIDJvZUVVUJ8kfjuhYvaaXwrjou9SId+hOdXuInvGY7v0hw+r8N8V5hsWolTGmIlzWPRgKaAIXXYiwwwmsBj01TT0D/8PottRvmRpyRrWICay4O0a1bt1QIr9eCFWEYZHFfs3YyKmVg0h/yeIuVb6tUVkH716seta3w6omFqFnttkG2UW1DP0gGvV63/ZjDa0OPbUtNgMJrQZzCawGPTVNPwKyBG1eEjsKb+m53vUBIM/azAAAgAElEQVRzLECkIDpYIazYQiAU3uJ9bTKNq0oDKpqgskmx6LvulyCTDv1GK4XXjxA/TxMBCq9Fb1B4LeCxaeoJmD/IxSJ5YW4kqvDW1NQI8m7jiDZrKcAkrMGDBze7/LfeektmzJghXrnFlSpx5op3Q4cOFfx/P+HVMtS5c2eVksKtOYH6+npV9QKbKbyaNfLWO3ToEAqb+f3yKgVn7hPmQRZVUlq3bq2+G3qj8IbqHu5cZgIUXosOoPBawGPT1BNwRvXatXOvWhDmRsIIr3M1tLgmixWTVj+h9fs8DIss7WuKDSQpyGv3IPnQWWIQ97Xq8e0UXhtu5vfLK/pu1gAOKrxmPrb58BuH8CLFAg+ykGluJJAkAQqvBV0KrwU8Nk09AVN4o0Sb3G5QR1DNEld9+vRxZeH84U+D8FZq1FJLks651tHJYivfBdkn9V+CBC/QlFOTY9LC63x4CXKLXiIdh/DqiinF0mOCXCP3IQE/AhReP0JFPqfwWsBj09QT8MoxtLlw/QOpa7kWm7SWRuHVTOJ6ALBhWcq2iPDpSBxyP4NIWZAocCnvIY3n0g9xpuwFYet1L6aAeo3RKMJrRqPdrlVfT9CIsd7frFZB4U3jCM3XNVF4LfqTwmsBj01TTyBJ4dVLBRebOOP84Y9LoGxSGrSgII8Vr2ErdQsiZVxW2H90TJw4URYuXNhs8l8QtkGE12tVwijCa+YbFxPesEtImyJdaQ+R/qODe8RNgMJrQZTCawGPTVNPwBTeuOrfmj/mmASDzSsq5Jy8UwrhxfV4pU5oUUY5p/79+6e+/5K8wCBSFlcKSpL3Ue5jI8KJ6LmZHx+EbVzCG3Qyqlc+vzOlIWyU1pwMGdey4eXuU54/vQQovBZ9Q+G1gMemqSdQbuF1Cm5cwuucDOfsCL9FKVh1QMSvmkWxB4fUD/wyX6CN8HpVfjBvyZTMoIJq1gwuFuENejx9PTp/F/+fwlvmgVcBp6fwWnQyhdcCHpumnoD5IxdHOTDccJgIb1LC6ycUXsJbqfm7bgPVr1qF3+epH/xlvEC/8Vns0syHVK9luMNWX3GuCmdKrXksXFcY4S222lwZ8fPUOSZA4bXoXAqvBTw2TT0BU3hxsWEnpLjdoI3w2oiAeS1+x/ESXh2NqvT8XbAsJrQ6VQWc40qFSf2XJcYL9BufQYXXi31Y4dXRfH1eM9fWRnjN/F1GeGMcQDyUJwEKr8XgoPBawGPT1BPQ4teiRQvfZWSD3kwY4dWCqfMMbUQgjPCOGTNmlfvVi1WARW1tbdDbze1+xYTXfFDyWsAjt2BiuDGbcR4kDSms8Oo0Cf13wG1VOH3bYSK8OrUCOfGYuMdUoRgGDw9RlACF12KAUHgt4LFp6glocdElxML8mHndXBjhdUZabUQgjPC65QrzFX3zHtUPAFgsAA8k5mYKL/Myw3/NnTWPwxzBZO/WNziWrp6B/+31ncZEusbGRoHkzpo1S9DfurJKXMKrH2hRgxirG/JtQJie5r5RCFB4o1D7XxsKrwU8Nk09AafwxlE2yEZ4/SabBQU6c+ZMmTdvnnTv3l26dOmySjM34dXXzSjUT7i8Uj/0v6NElbMCQdA+quT9bB6ugqQhmft4LRyivyPI3UcfYqGYTp06CdJV4hBenb+L41ZXV8uUKVMovJU86Et07xReC9AUXgt4bJp6AvqHET9wzh+6qBfvJrxeObFOobIRAfN6/ao9uH3OVcNW7XE34dUiw9JtUb8hxfOj/Y4aVni9IvBmFBjnRGoKIsZxCa9+eMVxIdIUXr+e5edxEKDwWlCk8FrAY9PUE0hCeLU4IrqKV7cLFixwfa2qX5kjAjR48GDFqpzCqwUgjrSO1Hd8wAt0E964+ijgJeRyt6gMEYlFmoDOtQUct4mmfiknZr76smXLFGNEgvHvcQmvzt/FcSHSFN5cDuXU3RSF16JLKLwW8Ng09QT0DyNSGaZPnx7LK0czeopor5fwuv3oRxUBJ2i/CK9+nWtGv7iIwqrDdcKECdLU1KRyeCEtcT6UpP7LkeAFRh3nZjtcntd3y094dVUG5OzqBTEQhUVUNi7hNb+DSJvAA6VXznGCqHnoCiNA4bXocAqvBTw2TT0B/cOIqGZcERgb4dWvy4stRxwEqp/wOicNcYU1d6puHDU7rxqwQfqn0vdJUniD1L41o68QXb255dBHLUvmHDt8oKz0UV+a+6fwWnCm8FrAY9PUE9A/Qojg1dXVSRyLT9gIL4DF8cPoJ7z61TDOh1fCkH1ICCsONB+yxSb3kVX0r7ef8OJzRFqRJ11VVVU4UZAIr95HN3LrJ3zXkb6ASYd4uNSb23VReKP3M1uWngCF14I5hdcCHpummoAzhzYO0cQNBxVer6Vr47gO/YNuvop3dsbEiRNVbVDkGqMsE/IikUsM6ee2koBbmbi4SsdVMmM/4dWMwcgcw2YEFg9tDQ0NKvfWjNI6hRelwLbbbrtCFYZiD5VxCq/OiddSHcf3upLHDO89GAEKbzBOrntReC3gsWmqCTh/3NwWY4hyA0GF10uc4vhhDHIMUypwn1xAYdXepvBG+Qb4twkjvOa4NPsDZ3GrrOIUXn01ugZusQVW4hRe53cwyHfSnxz3IIHiBCi8FiOEwmsBj01TTcD54+aXBhD0ZmyF122iVNBz6/2C/LiauY6oFNGnT5/CxKyw58vr/ubkppqaGnWbOv8zjprNeeXmd1/FFvVAW+fDmK7EYAovUhHcJpp6CS+Oi+MUk+0khTeO77UfV35OAhReizFA4bWAx6apJuAlvLYiYyu8cYh3EOFF5+BHGK+Du3Xrluq+KtfFFRMglm+z65ViY1RXEdFn0GkBZnURjFu3vHstxXr1RPMqIbxuDzF6n2L9rZcH9lrMxUnDeX9xfK/tiLN1JRCg8Fr0MoXXAh6bppqA88ctrtxM84dNT75xmzhj1us1V0OL44cxqPAiD5I5u97D1K1qRhz9k+ovRokurtgY9Zoo5mTvlobkJ7zFvufFhFcLdNDJihTeEg0knqYZAQqvxYCg8FrAY9NUEyi38HqJUxxCFVR4U91BKbk4J0vnZKSUXGbmLiMO4dXfFfOtTDHhxYqHiBJjsptbpJbCm7lhxAt2EKDwWgwJCq8FPDZNNYG0Cq/tEr9+E4JS3SkpvDhOPkqmU8IIrxZa58NgsUmFmOwGsTU3pKEUWwwG+3pFZhnhTWYc8KjxEqDwWvCk8FrAY9NUE0hKeM0an1ikwG0mOcB4RXJtUysovPEOOy4gEC9PfbQgwuuUTCwrjDQcXarMuYAKjm0+MM6YMWMV4cW/udXg9bou3f9hhFenwiDvt3///urQXilMydDlUSuVAIXXoucpvBbwctYUrwJ13VazWHtWbzMp4TV/yIvJq9ercQpvukYUhTeZ/igmvPq7gdXsGhsbCwuiONvoag/mgjFmf2FBFWeEV/+brvzgvLs4IrxuD5223+tkeoFHzRsBCq9Fj1J4LeDlrGkcuaVpQlJu4fX6wbf9YWSEN95RZkbm2rVrJ5AxM3IX79kq52jFhFd/hgli5hsStzbFJoc5hVcfD4us1NbWusKm8FbOGMzjnVJ4LXqVwmsBL2dNKbzBOjRohNfrB1+/pkV0S9d+DXbmlXtReMPQ8t9XP4BAdvFmA6t96UUM/FtzDy8CQYQXq6ghBUF/F2yFV6clFOs/Ci/HbJYJUHgteo/CawEvZ03dZkRn+RbNZUqrqqpcl5GNcn+2wmsrrLbCHOWe89xGjxPzHim89j2uvyeonOAsjac/wyQzRGnBG7Wi9f/GIil6cy7oUCylAQusNDU1FQTa7S4ovPZ9yyOUjwCF14I9hdcCXs6a6h+SoHUo0377ztQB21QCfb/mD6bbpBrsh4k3mIDj9mrVVnjjuo+091+prg8TkJC/jm3BggXqv6NG30t1zVk4T7E3RmGE13kc8//reQdOHsX+hiUlvHwQzcKozP41Ungt+pDCawEvZ00pvME61PzB9JLXsMub4sxBJw1SeIP1U5S9EO2FAGOhkDxM3IzCIK42XsJrPgwipccvwltMeHUJMh3Z1ddebLW0iRMnqsm5enW3KFUa9GpuKI1WXV2tTmv7IBsXdx4n3wQovBb9S+G1gJezpnkX3rgiMLbCq3/wzZnnGEpBc6gpvDn74uX0drzGs1MM9fdJ5/OaEun2vXBb2tu5zHCxZaG9BFrX9XWe36173L6DFN6cDuSU3RaF16JDKLwW8HLWNO/CG9cPkq3wYti4Tc7RNUiL/VijLYU3Z1+8nN5OWOF1VmzQWJzj3U14dXkz3UZHb93QegmvPn+Q/G0Kb04HbQZui8Jr0UkUXgt4OWtK4Q3WoUGE1yu3V5+h2Gx0RLo6derkeTEU3mD9xL3KS6AUwos6vfhP69atVbUHvXnV4C0WMabwlne88OzBCFB4g3Fy3YvCawEvZ00pvME6NIjw+klpMeH1mzTod+xgd8G9SCBZAkGFV1dh0CkFzvFfLMKLUnLYdJoQ/nexGrxJCq9XqlKylHn0SiNA4bXocQqvBbycNaXwBuvQOITXTQbMYvwo0eS1TZs2Ta1O1bNnT+nQoUOwi+ZeJFBiAl7C68yl1/vpy3OOa+cEMa/j6u+PX0pCUikNuP5itYdLjJ+nyykBCq9Fx1J4LeDlrCmFN1iHllt4g05uC3Y33IsEkiHgNU69Irb6Kpw57F6T3JxpC/p76VdSjsKbTH/zqKUhQOG14EzhtYCXs6Z60pTfK/Ws3LbzhzWJSWterzH90g5sIrwU3qyMwMq+znIJr9/fL5T/mzdvnujSZfrvHiLL06dPD7TKnl+UuVgOcWWPCt69LQEKrwVBCq8FvJw1DfpKPSu37fxhi0N43RaUcHuN6Selzs/NHES/skh+x85K//A6803A+f3Td+u1IIz+3FlhIWiEV38vitXgxTmc53dbBMNc6c2tlyi8+R67ab47Cq9F71B4LeDlrGnehNf5oxSH8LodI4rwekWfMaTC5iDmbBjydnJCwOsth5/wukVHze+YV54sBBuLhmAZ8WKLhngJL5ZARrTXWR87jPA6F7XISVfyNlJEgMJr0RkUXgt4OWtK4fXvUAqvPyPuQQJukVT8G4QU6QSYiKYjsVpANbWowhuUupfw4rxBJ515RXj59iVoL3C/qAQovFHJiQiF1wJezpoGnfSRldt2/vigXmddXZ2q2Tlo0KBIt0HhjYSNjSqQgFMssWzzrFmzCiT05DRdS1d/oEuNmciCRHiDIqbwBiXF/dJIgMJr0SsUXgt4OWsatKxPVm672MSwqJNKggqvX6RI//h37txZvYLVxwVbv5QGPckGr2Dx+pUbCaSRgF/qgt+KguY96Vq9eFDFQyu2qN9hCm8aRwuvKSgBCm9QUi77UXgt4OWsKYXXv0PjEl7ncUzh9csh9JNp/7vgHiSQPIE4hdd8eJ0yZUrZhRfSjcg0BBxvjPTGlIbkx1Wln4HCazECKLwW8HLWlMLr36GlEF6/6BWF17+fuEf5CeRZeItNnDNLnpW/F3gFeSNA4bXoUQqvBbycNaXw+neom/A6Z2ZjYs6kSZOKLnHqPI4zv7HY61oKr38/cY/yE/ATXmf5sWJXHGeEV6/cptOJouQHe30H/epvl79XeAVZJ0DhtejBLAgvXxNZdHCIphRef1huwuscn/X19dLQ0CD6B9XtqFqKUT4JP/xBZqrr41B4/fuJe5SfgNeKaqjOgCWxzVQAv6s1jzVnzhy1e9Qc3mJ1fYN+t/yEF7W0N9poI2nTpg2X//brXH4eigCFNxSu5jtTeC3g5awphde/Q4MIr55U5hfBMn80Kbz+7LlHtgjotxZ6IRWbwEWphDdoHV0/4dU95TcBNVs9yqtNAwEKr0UvUHgt4OWsKYXXv0P9hBcrpmF5UkR2+vfvX/SAxYS3mCwHjUL53w33IIHkCDi/K1kQ3qDXSOFNbtzwyMUJUHgtRkgWhFfPiA1TxsYCScU2zZvwus2ktpVFP+HF53jl2rVrV+nWrVtk4fUa6zoVIohQV+xA5o2ngkASwouI6YIFC3xL9xUDUCylIYjwui0vrs+n84MZ4U3FEMzlRVB4Lbo1C8KbtxXALLor0aZ5E143uU1aePEat7GxUXr27Ombu2fWFsUPpc5NRCd7CW8cyyMnOoh4cBL4HwHnWMVETjyw+aX6uAHUKQ1pEN5i30GzvCDugykN/DrETSB1wvvxp4tl8802jvs+EzkehTcRrJk8aCUI75gxY2TZsmUSddEGvwgvfpgRgQryNsKMJs2fP18tuao3Cm8mv0K8aINAsUhqWFDlFl4due3UqZMgJxm1gN1klsIbtme5f1gCJRXe+x97Xk46+iDPa3zzb+/IoMtukhcfuzHsfZRlfwpvWbCn8qSVILz/396ZgFlRXG//uIIxBrJ8YtzgHxLBJLIYI2hCBFGZqAFcGRcQcWERZRFkABFRUUbZRoyCSwTBCCoKRg2oiYIxAhoVNQlINEFNBAnKIC648T2nyBlril6qu7rv7b737efJE5zbVV31O9XVb58+dcrmk2WQcdISvCKUIXgzeWugUTEIlILg5a81vBHM3LlziUMZ+OCXUVvBy+fHzSYRAzmKlAGBggre9icOoPPOOJ4uOOvEHdA++PunadykmXTyCUfRFUN65QI9BG8uzJR6I3nXINmyU1JlpX7RlC/gFb7gKnjF0yMrz7kLkoasW7duKizBawcmr67qbRHBy7G569ev940BRkhDyoMG1SdGIO+Cl+dBzrhiHhC8iQ0RVBSDQEEF7+rX36ILh0+kk37ZgQZfcKpq7hdffEkTp8+jexb8gcYOPUf9lpcj64JXFukwT5uFQHnhnrV2mp/iSsErkYbg9UosHzdlki6UWUjLYhz+f7+xDsGbtTsH7fEjkHfBy/1iT26DBg2IxS/fl3xwHuHFixd75tnWHQfCpRTmUozy7BAoqODlbr/573fp/Euvp6OOaE0D+5xMw6+6hf7xr39TzdWX0CEt/y87ZCxaknXBqwsxCF4Lg8Y8BYLXDlySglevi/lD8NrZAGflg4AueLt3766+IDVq1IgGDx4cuQNJxsYGCfFFixapWHoWtSxy9dAFWWTKX3d4Yxm/55G8aEPwRjYzClgQKLjg5Ta9+99NdMHwG+g/6zbSDw9qSpOvvIi+/c1vWDQ3W6dA8GbLHsVqDQSvHfkgwSsPQtuHuq3g5ThC/tLBO7fx4raFCxeqhTMsInCAQFYJ6MKSU/T5xb3atL9Qgle/J3knOP1ekxAkyRQBwWtjOZyTNIGiCF7uRO0HH1K/EZPpJ60OomH9eiTdr4LUB8FbEMyZvwgEr52JggRv1JRJtoJXPEa8HSvH+Nrm+bXrEc4CgfQIyNgNinu1uXqSglfy6LKgHTRoEHltAMNilg/9XoPgtbEUzkmbQEEFb/+qKfX68/EnW+m5l1bRz376Y9pll13qfrtlwpBE+73lw49p3OSZ9MyKV2m33Xalyu5HU/9e3dQ1OK6YF8txWMUB++5N1426kA763v7qt6eXv0zX3jiHNmzcRK1/9H2qHt2XvvOtRnVtg+BN1Ey5razUBK/E0pneVtdFa16CVxay8QOUr2ube5O9tbwrW4sWLVQ4Az+IeeEbe5WkDjMmMMy7lNsBiIaXJIEsCl4GrYvcqIJXDOWXOtAMaYiTd7gkBwM6lQiBggpeFpY2x9hLe9ucZn3O+JrZ9N6mD2h81fn03vub6YwBV9OksQPo0EMOohN6VtHZpxxLZ3TvTPc//BTNmf84PTTrWtq85SOqOGM4TRzbn37a5mCaeut99M67G1X4hRxZF7wiMLi9iOG1Hi6RTyw1weu3uCsNwWuyYwFbWVkZagO9jbIgxvSEmXVLpbgXQvHihAwQyLPglbh6EbYS3xsmeCXWN+y8DJgHTcghgYIK3mLx+eMzL1KL5gfQfvt8RzVh4Kga6vSztnTET35I3c4dTSsenU4sXpUwPHkQ3T5pOL3+r//QA48upVtvGKb+/sGWj9Rvyx6+mXbffTf1NwjeYlk0W9eF4LWzh5eHVz6RRhWjXoKXP7Hy4h753Mo7t/GKcPZU19bW1jUSgtfOXjiruARE8AZlNrBpYZIhDXw9Gw+vKXh15wvX4efh5S8+/GWG/5/vWZtNaGwY4BwQUJpt27Zt28oJBYc3nNhrJN0x+TL62h4N6Ve9qui538+oE7zHVQ6jERedqUIcNr5fS6MuOfurB+XJg+iuG0dR0/2bQPCW06AJ6SsEr91g8BK8+gNUvXAedRTxIp2ww0vwcgojr0+s7DXmxWtRRXVYG/A7CKRJQN8W3iX2PO5XFL++2QhezsSg59Q2BW9YqILr16Q07YK680ugrATvJ1s/pcFXTKNWBzenAb27E2v9X50zSoUzVHY7mhY9tYJGX3cbXTvyQnrtjbfo8y++qLeg7tjKYXTj1RfTwT9oWid4P/vss8xaf+nSpfT000+r9nXo0IF+8YtfZLateW4Yf1KfM2dOXRdGjx6d5+6omFjuz4EHHkg9e/as68tjjz1Gzz33XOyxJOPRHIuzZ8+mN998U13n2GOPpcMPPzyUH3uGJ02apHZykl2cmPttt91G7777Lp199tkqRdKaNWvotNNOo/vuu6+uTtwLoXhxQgYIjB8/vm7u5nk87rg156e49QgSaRffb/q/9ftbnjsyF+rPIq4nbI6UOYHvY469x7GdwK677goUDgTKRvByTO5FI6fQ4W0Ppov7nFyHjBetXT3lLlq34T06psNPaOXfXqcB53Snv6/5F72zfiPp8cRHnDiA5k4fW8/Du3nzZgf86RZ98skn6cUXX1QXad++PR155JHpXrBMa3/77bfp3nvvrev90KFDc0lCPv/vvvvuxB6Ztm3bUqdOner68uc//5k4TCDuWPIrzwvNXn/9dXWd008/nfbff/ui0bBj8uTJ9U5h7mwHtkfXrl3phRdeUP/mOnX78CfiH/3oR2HV43cQKCoBGd98v7ncd+b8FPf+FRjSLr7f9H/Ldfj+5X/zIXNh1DlS7uMo80FRjVWgi++1114FulJpXqYsBO/WTz+j84ZeTxWdDlcL1PyOzz77XMXpLrhzPL3013/Q3Q88TrNqRqrTOVNDxZmXqRhezvTAR9ZjeOWzELfVdvV7aQ7zdHtVCiEN5q58Xp9Q/UISbOn6ldc/d0aJ2fNKUu+1c9uIESNo7ty5dbs9RbmGbd9wHggkTSCpkAYzW4lt2JBff/xCGoJihaPOkfpOim3atEkaLeorUwJlIXhvnrmANrxXq7YuNg/O2DDgnG505GE/pul3LVQeXl6o9uFHn1DFmcOp+vK+9NPWLWnCTb+lLR99rFKTyQHBW6Z3jdHtqJN5FqnpfZCYV/PBmJbgldRkzCWKGNUFr7zQSV163C7H9uovf1GukUVboU3lQUDGt6TTcxm3+r1SbMFrs7mM61xTHiMEvYxKoCwE7zGnD6UNG2tpp523Z2Lgg2N2qwaeScv+8je6asos2vj+Zjrk4O/RdSMvoP/37cbqnGUv/I3GTZpFGza+T4ex6B11ITVu9HUI3qijrMTPNwVv2IKMLOLQvax++WpdH0J+5XV+LE5tD68vGH5eJj0tkotwsG0bzgMBVwKSoqtBgwa0devWSC+D5rWLIXj1FIP6FySbr42uc40re5QvTQJlIXjTMh08vGmRzVe9psjKo6CST4hMvtCCV1KT8U5o/fr1sza+l+A105zJQzdu2IR1Y3AiCCRMQB/fXLXLvJKG4OVQoerqamJBXlVVReY8aHqSdY91797BufYheBMeTKhOEYDgdRgIWRe8nJOU47dExIRNMg4oyrpoKQhe8+HKBu3RowfxlrxyuD6ERFSb9cYdPByXK+nGdK+RPFj5Qcxjfp999lF5PXlxnKtwiNtWlAOBqASyLnjDNnqB4I1qcZyfNgEIXgfCWRe8XjGODt1FUR8CpSB42VMj6b2km6ZHyVXwppFbkz+V8jbD3PaKigrVdLYHr2rnfL4sduVvs2bNguDFXZwbAqbg5Y1VeFOVOEeSHt7p06fT+vXrSTbEkJfNsNAufRFeWK5t17kmDiOUKX0CELwONobgdYBXQkVtdxHKcpfNjAdenlD9IcSeX05jxmEIIjTD+peG4A27pvyuxxC6CAfb6+E8EHAlYAreKPHt5rWTFLzSLvbgcjYXL8HrtTBNYpJtFs3JLont2rWznl9ceaN86ROA4HWwMQSvA7wSKpp3wWt6Zmw8vM2aNSP2mNosQJH6iil4uQ16OqUSGn7oSokSyJvg1e8xL6GqC+UwD6++kyJC8Up0gBehWxC8DtAheB3glVDRvAteiW+V1eB+glfOa926NXFuTBa8HDLAWSlsjmILXm4/fxJmsY4DBLJOQBe8Nqm8gvpTCA+vLni9FthJfzgUgje/CDogeLM+OvPZPgheB7tlWfDqn3C5i1E8cQ5IyrJo3gWvtF+yM4gRzU+o+kOIRSN/zuTD9lNrsQVvWQ5OdDq3BJLcOGjChAkqtRkfNiEFQdD8Qhq4DIcicDy9lweXXzh5ETW/LIfFIpuCF19ncjuMM9VwCF4Hc2RZ8AbteuPQZRT1IADBa5c7F4IXtw8I2BNIUvDqddl4WOMKXvveBZ/pJ3g5FVrDhg2TugzqKTMCELwOBofgdYBXQkUheCF4S2g4oysZIaCn3dM3cYjTvCR3Ggzy8MZpm1cZP8Hrkos4qbahnvwSgOB1sB0ErwO8EipaKoKXY3NXrlxZZxmENJTQIEVXckdAn1eSCkNgCK6isZCCl9cIdOvWjWbMmKHs59r23A0CNDhRAhC8DjgheB3glVDRUhG8kmaITSO7J+lm0r0u/FlRNn1ADG8JDWZ0JTMEylnwshEkblc2uIDgzczQzG1DIHgdTJcHwct5UjlJOAsUjn/CkTwBU/AmtZNY8i31rlHPrysL0bwWOeqCl2tau3atqjVWawIAACAASURBVBCCt1CWwnXKiYA+ryQVd5uEaCyEhxeCt5xGeuH6CsHrwDoPgldfeW8rTByQlGVRU/C6fn4sNERXwWu7kASL1gptWVwvzwT0ecX1U37eYnghePM8crPbdgheB9tA8DrAK6GipSR4eZte/iIQxcNr+zCG4C2hQY+upE4AgnecYqyHWtnONakbBxfIJQEIXgezQfA6wCuhoqUkeDlsgUMVggQvLyThfJqcbzPKJ1II3hIa9OhK6gSyKnjNvN1p5XiXGF4I3tSHWtlcAILXwdQQvA7wSqhoKQpevzRI+o5NYkJbrwsEbwkNenQldQL6vOIajpZkSAMEb+qmxwVSIgDB6wAWgtcBXgkVzbvglXyfnP6Hd0NiD69fHLKL4K2urlZeYdeHdwkNHXQFBHwJJCl4k/QWF0rwTp8+vS68ShbIui7ew3ArbwIQvA72h+B1gFdCRfMueHXPK4c0cKYGP8Gre4qienixPWgJDXp0JXUC5S54veaavC0ITn2Q4AKRCEDwRsJV/2QIXgd4JVS0lAQvm4VFb7NmzdT/zAOCt4QGLrqSaQK8gHTevHme8fRRG55HD++iRYto+fLl9boKwRvV8jhfJwDB6zAeIHgd4JVQ0QULFqgdyho1akS1tbW+3tGsdjlKbK3XQ8g2hhce3qyOALQriwTM7XVd2phHwWs6Erj/ELwuowBlIXgdxkCWBS/HYi5cuJD07WIRO+lg7ICiIhgl53HeJuUogtflIQTBm874Q62lSYAzofA83rhxY2rTpo1TJ/X7tm/fvsSZVuIehYrhlWeY3s68za1xGaNcOgQgeB24Zlnwem0mAMHrYGwIXkVAPrPGeQhB8KYz/lArCIQRSCMeWF7w00pLJh5ur7mGf+PnL187z0eSXvw8cyhU2yF4HUhD8DrAK6Gi5eThDXoIhZkUgjeMEH4HgXQI5FHwrlu3jmbMmFEPiHh49f6w95szzOTxEAcCe9zZ844jXQIQvA58IXgd4JVQ0XISvPyZtaamxvMhFGZSCN4wQvgdBNIhkEfByyTMNIhegpfPGzRokAr9yNuRpF3y1vditBeC14E6BK8DvBIqKoK3Xbt2alWx36YNWe1ylBjeoIdQWP8geMMI4XcQSIdAksKqUDG8TGLChAm0devWOig8x1ZUVJC5liCv+XmTtEs6I6e0aoXgdbBnXgQvB/9z9oC8vgU7mKggRUUwyhaYacW0pdWZqIJ36tSpajzJYbuQBII3LQuiXhAIJqAvAHNdy7Fs2TJavHixip/124Y8KXuYaRBlbpW/s3Nh9erViaRuS6rNUepJMntGlOuW67kQvA6Wz4vg5bhLnphs00c5ICnLouUmeKW/UdOwQfCW5e2BTmeAgB577yp4zTj+NF/wwwRvjx49VK5iPkaMGEENGzbMAG37JkDw2rNK4kwIXgeKELwO8EqoaLkJXl5MwlsEczwvp76zDeGA4C2hQY+u5IpAXgWvGbpgenjZicMeZ/by8sI11/RthTaq3r+8hmUUmpnL9SB4HehB8DrAK6Gi5SZ4xXRRU+pA8JbQoEdXckUg74K3QYMGKpbXS/CGbYeeZUPpgtc2NCzL/cl62yB4HSwEwesAr4SKQvA2pd69e4daFII3FBFOAIFUCORV8Pq9XHO6Mv7SxKm8+P9lk6Xu3bunwi+tSnXBy5tE5a39aXFJq14IXgeyELwO8EqoKAQvBG8JDWd0pQQJlJrg1V+eo35pypJ5dcGbZix0lvpczLZA8DrQh+B1gFdCRSF4wwVvnh9KJTRU0ZUyJZCm4C3Ep3hz/tAFr2xQwXl4ORNRng5d8Oax/XlizW2F4HWwGASvA7wSKgrBC8FbQsMZXSlBAqUseNlceQ2XMhfluWbQKMGhm2iXIHgdcELwOsAroaIieHmVLeenzNs2kVHz8Irponhto5xbQkMDXQGBTBCQ+48Xf1VVVTm1yUxLVggPr+zwKF5QU+DKBhV5S002d+5clWFCjry132kgFaEwBK8DdAheB3glVFQXjLNmzVI9y8KbOn/q45XNTZo0CcxPKQ+PqJOt+RAKMikEbwkNeHQldwQ4jSDPB5ynll/IXY5iCF7di8sL1XjRGs9r/fr1U12J+9LuwiGJsmaeYeTKT4Kqfx0QvA58IXgd4JVQ0awKXtuHgMvnQNuyELwlNODRlbImUGzBy6KQHQv6Ii/buS5rhoPgLaxFIHgdeGdZ8C5YsIBWrlypknHztpLYac3B0CFFddFnKwDTa81XNVdXV6sNIsK8Bi5tti0LwVsIi+MaIJA+gSwKXomFLUR4RZKEIXiTpBleFwRvOCPfM7IsePU3Xp4MIHgdDJ1TwStiFII3PdujZhAoNwKSFUH6XSiRqc9npoc374JXtmnHbmvp3k0QvA58IXgd4JVQUd3LmaVPaxC8JTTI0BUQyBABmVu4SYUWvD169KB58+bV29J81apV6m95y2UrzwtuNzulCsUyQ0OpoE2B4HXAnTXBy2/enCWAj23bttV5dTmkQcIb8rbXuIN5ClYUgjd8kR5CGgo2HHEhEEidQDEFL4vCJUuW1BOHeZ1fIHhTH6r1LgDB68A7a4JXj62SN0b+nJ3nvcYdzFOwohC8ELwFG2y4EAhkgEDWBC+vVeA1C5yFgjef4P/PwwHBW1grQfA68IbgdYBXQkWzKHj1lx9euBjk2bddeOZlsqlTp1JtbS15pTTjtGX8ZYFzf3IqJDPuroSGALoCAmVFIGuCl+GLeGzXrh1VVFTkwh56m5cvX567kIxcQNYaCcHrYLGsCV5zX25ZqAYPr4ORLYrqgnHRokXEE1exFx/ogjcsLsxF8AbFLEtcHSeLZ9ENwWsxmHAKCOSAQDEEb5g3VF9MF7ZQNyuI875LZ1Y42rYDgteWlMd5ELwO8EqoqC4Ys7JaOAuCV38Bk4UmeVtUUkLDFF0BgcQIFFPw8hcj3lDH68uVOBzy4uWF4E1sSFpVBMFrhcn7pKwJXrnZubWI4XUwbMSiELxrPXP96oJXFppA8EYcXDgdBDJIQBe8hfqaZZOzVl70ZQviDKKr1yQz1VretqXPOl+zfRC8DhbLmuDVJwQIXgfDRiyaRcHLmTkWLlyoelKskAavEJsWLVpQZWVlRMI4HQRAIEsEJHa/devW1L1794I0zUbwckMmTJigPMC8eI2Fb5aPrG5alGVmLm2D4HWgB8HrAK+EiuqTlgjNQj4IvFCa3tWOHTv6Ek8rhldvg1w8THyX0LBAV0CgpAlwzCx7JAt12AreuXPn0urVqz1DHgrVVtvrhAlel7nZtg3ldB4Er4O1syZ4a2pqiFfG8wEPr4NhIxbVJ6Ws5IPMquANyxgRET1OBwEQKBMCtoJXnA55+JoUJHgl1RqbNy+L8LI+FCF4HSyUNcGrx1VB8DoYNmLRcha84k3xmpBND28eHkARTY/TQQAECkTAFLxjx471vDI7fdj5w7l4OV1ilg/92SFhIhKKoS88huBNxooQvA4csyx4ZW9ubDzhYGDLouUseIOyUshvHN7Bnz45F3BeEsJbmh6ngQAIFIiAreDl5uQlFCAohzsEb/IDC4LXgWmWBK9+c+hdguB1MLBlUX3SklyQxV4lLJ5X7kJYih6Xh0OQ4JWsIQhjsBxIOA0EQMCXwIIFC9RGNnywQ2fw4MG+57rMaYU0QZDgXbZsGS1evFg1B2sfkrEKBK8DRwheB3glVNScXLMw2ZoZO3r37u1JXI8T8/tEGGSqIMEbtClFCZkfXQEBECgAAT1EKiw8KgtzsA2SIMEbZR2GzbVwDhEEr8MoyJLglV2tzO5wPBAH8S9ZsgRviQ629isqHl3d45CFydZW8LousoPgTWFQoUoQAIEdCEQRgFmYg21MGLRLp+7RDvtKZ3MtnAPB6zQGsiR4vVJAcefYayefRnDTOJnbs7CXYMzCZAvBm7ytUSMIgEDxCOjPuLBFXFmYg21IBeVwt53Dba6Dc7YTgIfXYSTkRfC6evEcEJV8UfGs6zuISeJzXiFcrEVatpOl69iAh7fkhzg6CAKZIKAL3rDwqzwIXskmIV8HzbnUdg7PhHFy0ggIXgdDQfA6wCuRojNmzCAOa9C951mIXTVT1PnF8ELwlshARDdAoMQJiCBs0qQJ9evXL7C3eRC85txrCl59DseWw8kMbgheB45ZErz6qny9S/wm7CpqHBCVdFHJQsBv6DwBizc3i4K3trZWbUpifgp0HRtB5bPAoaQHIDoHAmVEgNeisChs1qxZ6HbGeRS85lyqC142c5hXu4yGQuyuQvDGRkeUJcFr5iiUbkHwOhg4pKgw79GjB7Vs2bLu7CwIPa9NSLiBELzpjQfUDAIgkA0CeRe83bt3V5tnSD59CN5kxhUErwNHCF4HeCVQ1E/YxhG87H1lLyxPcJzD1/XIguCVcA/ZOci1TygPAiAAAjYEzF3LbMoU+hzTo6v/d8eOHWnWrFnEa0M4ZG7r1q1q17hirQkpNJu0rgfB60AWgtcBXgkU9RO2QQu5/LotISlJJBjXc+vy9WSb6UJ7ePPgZSmBYYgugAAIGATiOB0KDdEUvPqmRbxZjwhebtfatWt3+DpX6PaWwvUgeB2smEXBq4sb7hpCGhwMHFI0ScFbXV1NLFSTSB0nEykv7li/fn29XhQypAGCN72xh5pBAAT8CRRC8LIIFYdCHFsEpbRkx4fkzufzIHjjEN6xDASvA0dd8PLg5IMHajEOucEheAtHPynBq28Lrac34zd8Xp3bpUuXSJ3SJ1KZlKUCCN5IKHEyCIBADgkUQvC6vtDbCl4Od+MtlbFFu/tAhOB1YKgLXtfB79AMVRSC15Vg9PJJCV7J9iDeAk4hJjka+W9RV+dC8Ea3JUqAAAiUDoG0Ba8+P8eNrQ0SvPylb/ny5XUONOyUmszYhOB14CiC10WcOFy+XlEI3qRI2tfjN6l6bUYRVKss7tIFr+71jSp4OX3PwoULqXXr1sozoB9mjLBrWjKJF+bFFDzx60exXwLtLYkzQQAESolA2oJXn5/Ddn3z4+o198piuwYNGqiFalw3nwfBm8zohOB14CiC10WcOFy+XlFOYcLC2xQ5iOFNivCO9fhNqlFFpNcmES5jSl80J6E20vqkBS/X6ydsIXjTG3uoGQRAwJ9AXgWvmV4UgjfZUQ7B68AzS4JXxIUEu0u3IHgdDBxS1FbwsheUF4/xIjIzrYx4SOWNXnbUWbZsGS1evFi1IKqHV0IkOPZX6oDgTW8coGYQAIFsESik4I0bW+uVzccUvH379iX+YggPbzLjC4LXgaOX4I37ecOhGaooBK8rwejlbQWviFd9QZpczSvelgVulH3jzZbr7eKFb/pRKA+vCHnZJz46XZQAARAAgXgECil446aStBG8+rMg7nXiESzNUhC8DnaF4HWAVwJFJZWYubGCxHRLXKtMbF6CV4/3lYwKpSB4o4Z1lMBwQBdAAAQyQiBtwas7JOIKUS/Bqy9gZpQQvMkOKAheB55ZF7y6dw3xlA6G9ikaxFT/TV9QyBkY9MMr3pYnOdmIQia9KK3Xr23ux14oDy8EbxSL4VwQAIEkCRRS8LZo0YIqKysjN99L8OpCmsPcqqqq6r72xRXWkRtWwgUgeB2Mm3XBq3sUIXgdDO0oeCULg5eH10/w6rFcUWN4dVvLql/pgjlpSrgFL3bk/dvjHF5jC4I3DkmUAQEQSIKAvo6hffv2SVTp6ajgP3rN6zYXDBO8Uq98BYwrrG3aUi7nQPA6WFoEr6SB4qqKHcPLAfScksq8ESF4HQztKHiFvdfEqHsi2Ksre6bzv/UQB9vWy/aUvECuX79+dfmZpbwpbONsg2y2xWvfegheW4vhPBAAgaQJJDGvBbVJ98QmKXj1xcpSL+bS5EYHBK8DSxG8+uAvtuDl68tCJXh4HYxrUTToJUJEYI8ePWjevHmqNsnAoFetC14eR7KFpL7YLEpic3NyNFf9mpNzEg8Gr8+HmKQtBhBOAQEQSIVAEvNaUMMWLFhQL8d51K9wXLdXG/V0lOLRxVya3BCB4HVg6SV446YocWgG6Svi+bN0qQvetCczW1sECV4RgV5p4vT6ZeEbi1rx6uovLXxulJcofdMJHgsQvLbWxHkgAAKlQiDtZ4Q5r6YheCX8DII3uVEJwevA0kvwFiOwXL8hOnbsCMHrYNMoRW0EL3tUJTSB6zYnRq/FbbpXOKrgNSd6CN4oFsW5IAACpUAAgrcUrJh8HyB4HZhC8DrAcygqn5MK8XIhYpWFq3nYCF6OpeVNJ+TQBa/E20o2DT+vcBQPryzWEDbFEryy0MJlMZzDEEFREACBMiZQaMEbZY4Ws3i1UVJa8jnytRge3uQGMgSvA0sIXgd4DkV1Ycge7bQOCRXx8szy34IEr55WTG+fLnjNiUzKtGvXjpYvX15XLMpkasbTFkvwpv3AScvmqBcEQCD/BNKef8x5lZ9DPG+bO2kGkRTHDe+IqWeSkOeKzPsQvMmNRwheB5YQvA7wHIoWSvDqCwi8YrSCBK++kNFP8JrxtlLG9ApHEbzmZhgQvA4DDUVBAARySSBtwSvzrB6yJhsN2QLzyxUMwWtLMPp5ELzRmdWVEMGrr9gsxGd2s8nlFsMrE0Xan8uTErwcslBbW6vMpgtnc1L2E8m2gtcMkeDrFULwyvjn2OOWLVuqfqb9wHG4bVEUBECgxAkEzT88T3L6R3YsRPHI6shElHLYAYeRcX3m/B6GOEzwyg6e8PCGkbT/HYLXntUOZ4rg1UUFBK8DUMuiQTuXWVZhdZqeX9n08OqxVl7eX1286iEK+rlmcnRXweu1iYQZWmF6IZIQpkEJ1ItxP1gZFyeBAAiULIGgeS2JXdj0r3sc+sZpKCWHuq2Itm0HBG9yw7QsBO+WDz+mcZNn0jMrXqXddtuVKrsfTf17dVMUV/3jTbpq8ix6b9MH1LDB7nRpv9OpQ7tW6rdFT66gX9/5IH32+Re0z97fonHDzqWm+zepo+8leOMmoXYxabl6eNNmrQtQU9SGTUJ6WfZ88n/z4jV5a9e9r+LBdRW8Im711HhedQZ5meOMQwjeONRQBgRAIC0C8vLPzoaKiop6lxGh6ZJC1AxnsxWvekNsy4Q9a9JiWIr1loXgHV8zWwna8VXn03vvb6YzBlxNk8YOoMNat6CuvUdTv55d6fjO7ZT47XXJtfTU/Km05cNPqGvvUXTfrVfSAfvuTXPmP06PL32eZtWMzKzg5UTVHPxe6nl4C+XhdRG8ejiEmWO3WbNmagzJlsMiePVddvTJxjakQSZhfaMKCN5SnLbRJxAAgSACQSLRdQ2ILGZu0KABVVVVqWbYilcI3uKO27IQvH985kVq0fwA2m+f7yjaA0fVUKeftaWTj+9ArTr3oaUP3kjfbLSX+u3IrhfR7Gmj6f1NH9BVU+6ih2aOV39f88+3qc+Qanp6wbTMCV7Tw+a1lW0pbS1cKMGrhwOYojPsrZvjxLg8f97St/jV6zFtootkHmSyIELKSHu8wgSkrGwpLIMUgre4EyyuDgIgUHgCaQper7plHUMUr7GtSA571hSebn6vWBaCVzcPhzec2Gsk3TH5MmredF86b+j1dOxRh1Flt6PphVdeo6rxt9Ijc6pp69ZP6YSeVTTj+kup5fcPpBmzf0ev/+vfdP2YfrkUvLY3Vx6GcqEErx6bHVXwmhy9+AcJXvYe8FbEstUwe4WDPBMibM1PeMUSvBKfHOUBkIexhzaCAAhkn0BagnfJkiXE6zd4fYceUhdnLYTtMxmCN7nxVlaC95Otn9LgK6ZRq4Ob04De3RXF1a+/RecOmUAcj/vRx1tp4pj+1LnDoeq3hx57hsZU/4b23LOhiu+dObWKDtyvfgwvr76/99576d///rcqs99++9Hpp5+enIUsanr22WeJP4dzOMMRRxxBU6ZM2aEt0sbTTjuN9t9/f/X722+/Tffdd19R2mzRLd9TpC9ps9btqnOLw87k78Ve/ibjiP+fx5VcW+oQO+uA5syZQxs2bKCuXbtS8+bN636SsaGfO2TIkB1+96rT1kb6+Dv00EPpv//9Lz355JOqPSY32zpxHgiAAAjEJRD0bJN5tE2bNtSpUyfrS2zevJnuuOOOuvP154/Mgfw30QL6POt1Ea9nstd5vBju5ptvJj2EwrrROLEegbIRvJu3fEQXjZxCh7c9mC7uc7KCsPXTz5S3d+zQc+jnhx9Cb7z5Dp07eALNnjZKid+LL79RiVwOhVj81Aqaett8eviu62iXXXZW5Vkkf/755zRx4kTiuB4+DjzwQOrVq1dBhxm/dT799NPUoUMH4s/d11xzjbp+q1atlADi46677qI333yTevbsqd5M+ZByxWizCyDpC9dx+eWXu1QVWFY48kk6N/5v9rzOnj3b2t4mf6/y8jexHXsSdJtJHWJnaTyPPR6DfAwbNqxeqh2xsd5RnZk5duLAXLFiBT322GNq/LEnmrnIYXKLUz/KgAAIgEAUAkHzs8yjUZ97q1evVg4iOfTyL7/8Mj300ENq7hUtYM7FZvtFN4Sdx+XkWeSVESgKl3I/tywELwtbDl2o6HQ4nX3KsXU2//uatdRvxGRa8kBN3d/OH3YDdT3uSHq/dgu9uuoNumFM/7rf2hxzHv3+tzfQd/f+lvqbZGmQT9P8t8aNG6uV+IU8/GJ49VhPr88nQYuyCtn+qNfSQw3SnAB0uyYd0uC1iljy6DIPth1/yrIJaZBtfL2yVoiN+TfJP+m1qM0lfZj+yY3z8C5evLjOpLYL7qKOAZwPAiAAAn4EgsIAvNa42JA0w8P08DFz/QXXFzb3RVlXE+Vcm76U6zllIXhvnrmANrxXqzy5+sFe386nDaE7Jl1GrX7YnDZs3EQn9RlDt00cprI6jJ14p8rSwAvann3+r3TpVTerBW677rKLr+DlH9IUYV4DtdwEr+xykyZrfVthr8kralyV+cLhF/MlExunMmNRbCN4JV7WS7Tqgpf7odfH/x0n9swcgzoL9vCy11iOsEm/XCde9BsEQCA9AmkIXjOnuT7fegnesPULUURslHPTo5r/mstC8B5z+lDasLGWdtp5pzqL8SK1qoFn0pJnV1LN7ferEAYOVeh56nFqARsft939MD3w6FLato1or69/jUZcdIZKZSaHl4c3TRHmN9ySELx9+/ZVi6TycOie17ReLswJzNXDa9rIK2cus9e3lWTBy5/RZAczv0VrQYsfwgSvlNV3SYs6BiB4oxLD+SAAAmkSsBG8/Lzj557tIWkkZet3XfCaDhKuM+yrWRQRG+Vc2/6U43llIXjTMmzeBW9QFoK0mCVRbykIXj+Ryn/no3fv3jt4X+MIXj3cges1Pby2K4WD7AbBm8SoRh0gAAJJEbARvFGdU155zvX26s8l/jvnxa+srPTsktc28EF9h+BNZmRA8DpwzIrgFW+heCHl5giL4dUFr4uHzwFhrKKFELz6tsLcyKQ9vDU1NSq9jb7zmgnD9ArHEbw8sXLIA3sz+N+FErytW7cmXgUtm2zEMjQKgQAIgEAMAkkLXr8850GCN2gn0KghcRC8MQaBRxEIXgeOWRG8ppcujuAN+/zigCnxorrg1RdgJXkhc4GCKXj98t76tcEv7CQoJMNP8Jq5dm29tF7n2ZYNYsvCnQU8L9hs1KjRDqI6SbugLhAAARAII6DPSeYi8jgOE3GA8It89+7bU5qax/Tp09X28RLywBkb+PnkdUDwhlkwnd8heB24suB955131BaxMsi5urTiSv2aaoqWCRMmEOfuC/PwSkwS11sIwcuT0MqVK5UoYu9f3CMoe0LcOs1ypuA1+URd7JWk4DU9BzbeYu5fWoKX69ZXPpte5KRsgnpAAARAwJaAn1c0juC1me9lfmVRzM+5IC0AwWtrxWTPg+B14MmClz8X8+IiyW1bjIe9KWS8Pn0H7fTFCILijRwQ1SsqqbiiLhYwr1+ugleErSl4bT93QfAmNZJRDwiAQNYJ2AhevywyUlYWc9ss7tWfu2ZKSWbFC9sWLlyocvWy04ez2QR5jHW+tnN81m1S7PZB8DpYgAXvlVdeqWrIu+ANijdyQFSvaFJ5f8tV8Prlj7SdDL32e08ipAEe3qTuENQDAiCQFIEkBK8IYvkaGpTNiL9g8v84tIsdYXqGHe4Ti10OjRC9wM4x2y+rtnN8UuxKtR4IXgfLlpLgdfW62mDU8xgGLdYKqyuPgldiwCT+1mYC8wuDiOvh9fosl5TgnTp1KvE22zzZ86SP/Lthoxi/gwAIpEkgCcEri7lt5mu9L+Zc65Wnl8+H4E1zBOxYNwSvA+88C17JG8j7c3O8Lx9pxx4nlQYtj4LXjNmymUDzJHh12xZiLDnctigKAiBQBgTiCl5Z8CaClNebcDgZhyEMHjzYipy5kyYErxW21E+C4HVAnCfBK7txdenShdq3b6+2rZ01a5YKxeBPK4UQKbpQlXbEwQ/B21Tl6ZXDRjzzuWl6eCF444xklAEBEEiLgI3g9fKw6uKUf+fUivKs1OfdoHabDg4/wWv7HLSd49NiWSr1QvA6WDIrglcWM0mKLq9Fa36fWFjwsreX06mkvduaLlRtP+V4mScp4Rxk+qSzNOgTICcj5+2Rw14y4OF1uDlRFARAoKwJJCF4eTE3C97FixeTmQ4yCK5sLMEhXhy+Jx5fPZsTl7cN/ZKUZ2l/hS31AQPB62BhXfDyzcDCkdORhO2h7XBJz6LmjR1V8HL87vLlyyPd0HpDWJixl1g2G/BqpPmGG3eRnFmPi3AOsoMwFA+4a1oyXfB27NjRymNgI3ijpLdJ08MrC+KEKSbmpO9y1AcCIBCFQBKCl+d/FrycUSHqs0a/vr7Fu3xRjSJ45XmEeTXKCNjxXAheB3664OWbgY84N4ZDE1TROIJXtpzlN1iOUZo3b57ajSvK3uLSbr8dwPR+yfUkZhiCd3s4SdAnsjwJXt0ju7cIEgAAIABJREFUzjauqqpyHdYoDwIgAAKxCXgJXhuHiXhj+cL8TOT5LE66UVnIyx5eXrTM2oAdY+xcksPWwwvBG3sY1CsIwevAkQXvLbfcosIBvAQvf9bgTyH8GaOioiL0SiwK+WYI8pR6VeInePXtgk3xZP63bFYh7WQxxje7zWEjePXr8Y3PR5y3VZsJy6bNYedk2cOrv5hkxcOrC94wIR/GHr+DAAiAgCsBG8HrlX/eDGeTzDNRQ/70DDg8T4szTJ5/3D8IXlcrRysPwRuNV/23hZ12onvuuUfl2/MSvLo4sxF3XhsLyOcPFhEsoOWtU2+IeWPz2ySvNGXPLd+sfIQJXvOTNJex/YRjI3ilfg7S57ZwZog42wIXWvDKG7k5MUqKNdvwlSRDGvSXhSiC1+vcpNKSQfA6TCQoCgIgkDgBr7nNJrTOFLzSMJtnuN4JPe85P49F8LIHOWpmJHh4kxkeELwOHNnD+89//lNlPOA4H/0tjuM0owheydPKzdE9ZPqOL5z8Wg59dafNCs4wwSvX58833BcW8ba7r9kIXn3ykZhf27db3USFFrws+nmiMr2WUYWipIHjXXbY826z6tcvpCGLgtdv/DrcXigKAiAAArEJJCl4bZ+FemP1+ZudVbIRBQveqJmRIHhjD4P6Tspt27ZtS6aq8quFBa+OLyjZdNjboZ7WScSVrPRksixwOTxCDt37GkfwmrtusSDjG5FTlvF1RZCxl5ivyzd89+7dPY3st+WtfrKcw/FMfO04MVFcX14FL7dd7MRC30bw6rkc+QVKMjvEFbwynvSQiKjC3e8u1+2CkIbymwvRYxDIGoG4gtdMsSjPX342RjnECcDPTn6+yjNPHD76PB5WLwRvGCG73+HhtePkeVaSgpfFDN8UfIhgMEWEvrpTBK8kyQ5bKGSK8SCho3/6lhWq7Jlkscr/bx5+W97q5+mi3EVk2XyScjBpXVHda52EhzeO4PUKg5AGyguUTKpx92SXLTPjeNt1zrpdbNuShJ1QBwiAAAh4EQgSvJIejJ9nHFqnH1KON5rg3SP5iBq/y2X0+Zv/WwSvrNWB4C38uIXgdWCepODVc8uK4PWKJZIsB5IT0DaGM4rgFRHNkwFPDCK0/eJVwwSv1Cc71UDw2nl4bQSvV6qxoCFtfg2w+Tpgc4uYydrZI40DBEAABIpFIEjwBm24ZC5YDnMm+fVPz8XLzz4RvBL6GOVLGDy8yYwiCF4HjmGCV1Jx2bzJeQle2R1NbyJ/HuFYINMLHHbzRBG8ujdSnxj84pjCBK8pystd8PKLw8KFC1U2Dr8wEdNDILl7TQ9vVMEr2ThkwWBSgtfcjhOC12FiQVEQAAFnAnEFr3z1YqcSi1YOAbPJsuTVYP3ZKIJXsjeFPbO9vM5hoZHO0Eq8AgheBwObgtfcP1v30IZlJNAFr8RYesUSmYuo4np49ZhayeSgo5D2cFskO4SfcA8TvMJFBF65C16xYVgWjDQ8vCb7pASv/pIU1i+HWw5FQQAEQMCKQFzBm8acyF9LOWSRdQA7B9iZFUVIw8NrZfLQkyB4QxH5n2AKXlN86oI3KEZSyunbDvKbnIhSCWPglvAKf94kQt4OTZHt11q/Ff9+b4yylaFZn3m+7tnzE8RRvctBJimFGF4IXoebDkVBAARAwIJAmODlZxfH6PLaFN3pk4bglebG9dBC8FoY3OIUCF4LSH6nJC14zbgi83MIxwHxJ3B9hb/tJ+2ogtfLu+wlaE0B6nVDS85a2QjDxcOb1I5tYWZPY9GahBMUU/Ca2TnSmNxtcxOH2QC/gwAIgEBcAmGCl+v1yhaU5JxoPkcheONaM5lyELwOHNMUvPzWyR5eFrktW7ZUO7CxIJZYTnNhW9hnZHM1f9hNrd+ouofZvGFtBK858bgIXhHuHB6xcuVK9WbOrJI+0hC85mKIMJtFCWnQ8zIHsYj64hOFqwh614wPUa6Jc0EABEDAi0AcwWsusHYlC8HrSjDZ8hC8DjyTErzitZQFadwkPVerbGLBKcJY4LEQFqFn6+HVxRMH4HNgvmRN8EKgL5gLWtFqI3hFXEsccxKCV7ykXl5nB5PWFZVwEk5Hw6zM9DVx+pCk4BVRGbUd+njhFynuG4fS9OvXzxlb1LY4XxAVgAAIgIAPAXmG6Y4Fv1Rh/Gzlw3ZNjC30pASv9CWuh9i2vaV+HgSvg4WTEry6CJF9tmWjCUk/pjczTk5bL29h0CpRc6tYv51hTMHrtTjP9CZL3bZeSb3vXqzSmAT0Nnt5w+Pkr82C4NU9/TzJc7aIOLsIed02vLiRF2bwYgyvfM0OtxqKggAIgEAkAl7OIP05yE4j/kqoh2AlLXj156iLY0HqSeNZFwlqzk+G4HUwYJqCNyjOs9CCl8MHeCLgAH8zAbe+pax4puVtWX9j1m92W6+0l2myInjDQkK82p4FwatP6CxMOVQmLLTC4RZBURAAARAoCgF5VuhOI/35wY1iB5OfB7h3797O7TYdR3HrhOB1NoWqAILXgWOY4JXFWnyJoIU8XiKu2IJXzyHMbWGh5BXgb26OYcZver0xQ/A2VSzDhKbOjre15OwccsQNaQj7pOdwO6AoCIAACGSGQNizx0vw2mY9su0kBK8tqcKcB8HrwDlM8OrxO0HiRl85z5+Y+bAVvGH5dKV7+s3PsZuLFy8mr3AJ83xpCwQvkf45KY6HV+KwJCejZK3wG4K6zWSLZ1fByyEHvI21tIHrC8sR7XCLoCgIgAAIFIVAHMHr4ozx6qTuOIqy0YRZFzy8yQwhCF4HjkkJXn2xDw9s9v7JAjYvoRwWX+rVJS/xFCTC9dhcFmb85hvHw6svyKusrFRNc5lU8hzSEOYNN+2WhuDla+ibnAQtXHS4NVAUBEAABIpKQN/aVzL56M8Pfuk3HT8uz6ag5y7/BsFb1OGgLg7B62ADU/BKShOvndKCxKWX4JXMCF7lZFMIyeTAXQgLZo8qeMUTyHXzdUSImyELpogzvZZeE4jLpKKXZTG9fv36HeKKHUxaVzTspSKOhzcNwcveWtnBx3ahmC54k1qwlgRz1AECIAACSRLwWzDNz1X+aqbntHd1xoQJ3qAvqmF9hoc3jJDd7xC8dpw8zzIFr+49YwFqG9LgJXjlgl6fvfXz+YZNQ/DqfQkSvBKOwZ5CXtRmCvQ0Ba9fmIWDSXMneOMIb7YZvyxs3bo1NI44CZaoAwRAAASKQSCq4E06taK+E2nYmo0gPhC8yYweCF4HjkkJXj1PLS90kxRg4l3Vsx7w3+SmlNRlNulOdA8vewJXr16ttinmeF6/g28yPjgPsN9EEJZ5AIL3K7ppeHjjCF6HIY+iIAACIJAbAlOnTq23fbD+PPLy8CYteHXHEQRv8YcNBK+DDZIWvKZXOEzwysI2m9gg19X5ELzui9b0BQx+tjWHowhafaMNvSwEr8MNjKIgAAIlTcB8bumCVzbekRBE3ZmU5G6R+hzOzqM4Bzy8cajtWAaC14FjmOCVDAp8iaBYSa+8utIsrxsvbNtbry4VS/BKZgJ9kwmXGF49owXnAPZaSOdg0rqiuk1ML4H+1h4WO623xdykw2ZS9RO8whOCNwlrow4QAIFSJBAkeFl8mvOnbChk5pt3YSPPD3h4XSgmUxaC14FjmODVFwf5eWH1xWG2Hl7J78sbQvBOMVnw8EpWCW5T9+7d66h6eYZdBK9XvLONcIxq5rDNPeIIzSQFr0yecdoRlQXOBwEQAIE8EjAdLuazx5w/05hPkwiTgIc3mdEHwevAMQnBa+YKNPfe9hJzZiyozZujfh2JEY6Sf9XvphUvNm+swTmETfGdpuDlVGk2schxTJwHwcubUXCWhgYNGlBVVVWcbqIMCIAACJQsAVPgFkPwsujmFGkVFRVq2/U4BwRvHGo7loHgdeCYhuCVN1JpFucP5D2/9SOO4JXVolwX/5uPKJ/j/QSvCENJkVZIwcsi3twa0sGc9YoGCV55ebBZLKhXanp4bfj7hTT4pdVJqv+oBwRAAATyTiALgjcJhhC8SVBEHl4nimkIXlPMeomiOIKXO6qHWBRT8HLsLXuD4+Ql1IV3WoLXz+su3navHXxsBpKeosaWf5DgZW8BbzdsE9Ji0z6cAwIgAAKlRMCcq00BrD9P2BnEXyyzuBmPLHi2cZKUkv2S7gs8vA5EsyJ4bWNYXQSvvlisTZs2ddTCPLxeiwDiCka+aJ4Fr/nSYTN5CV/ZiIS9yrzZBnt4Of57+fLlyKXrcA+jKAiAQOkSiCJ4mYK5EUVWyEg/bJ4ZWWlzFtsBwetgFS/BKysyOT6W4yvl8PPC+X1ykXI2Hl7bFaU2i+j8cPgtNJM6OfSC3445xy/3XQ6vRQAQvNvp2ExepuDVd+Dj+GWODbN94XEY6igKAiAAArkjINsLm7ufypypO1AgeHNn3sgNhuCNjOyrAl6C19wFjRcU8Y5WfgInjuCVkIAgUezVraQFr2SYkEVTXuI2TPAymyiirdw9vCJ4JUMHFqw53MAoCgIgUPIEgtZj6F8uOaQBHt7SHg4QvA72DRK8+qYQkhUhyFsrmRZsYnj1xU9RBE/Sgtf01E6fPl19btc9zmGCV9jYeikheJuq3MOy+DAov7PD0EZREAABECgJAkGCV3c48dfJxYsXx1pbkjYohDQkQxiC14Fj2oLXT8zqgjfKgiVd8Jr5csMweIU0hC3u4jrTFLy8CIwXv0XtS1hfw/rlEpIhIS9+Hn+zbX4hDXKevqFHWL/wOwiAAAiUGwF94yD26OqbFenPNeaSVtYfV+YQvK4Et5eH4HXgmLbg9ROzuuCN4uHTBa9N7l4djY3glZRqUrcZ8iD1eeUEjuPh5frS+ARlClrZ6KNHjx7E21G6CF49z3KcGF7ObMEL1eSw5eYwzFEUBEAABHJLIGizIs7lLl5d9vBC8ObWzFYNh+C1wuR9UhqCV25AvqKN4I0iXNMWvKYo9hOGeRO8tv2yGUoy+dqGopgeXgmVkWtF2TzEpn04BwRAAARKiUCQ4NWfRc2aNYPgLSXDe/QFgtfBwFEFL6fzYhGrp/UyN3SwCVfQz+EdzvT6grqjC17xVtp238bDK7kCRainIXg58wV7jlno8WK3Qnh40xC8tqEoYYLXxktsa2OcBwIgAAKlRkDfXpifUXpIg/6M4n7rv2WJA0IakrEGBK8DR1vBy8JMMjVIehS5bBzBK6ECXEeUT9q64I1Sjq9jI3hNgZuG4NVjgl1CC4LMHpa70eW6Yu8kBK9tHQ5DHEVBAARAINcE9GcXz91egpefy/zVDYI316YObTwEbygi/xNsBa+8OUpNulcujuDlevT8t+bWw34tTlrwSno0PY7YRpC6hDTY1O9gUlU0TPBKv+MslpM0OLZiNcjDGyV+25UJyoMACIBAHgkECV79WSopH6M6gwrBBB7eZChD8DpwDBK8LEZ4YwBeZMQeXkm/xZdLQvDyTcw3Qe/eva17oAveqLGfXh5er7/JNbh+bh9vfWsKs7wLXr9NOGwMIWVtBa++yI3r12N4o8Rv27QN54AACIBAqREIC1uQZ5akeuRNlGydSIViBcGbDGkIXgeOXoJXPHi8H3dtba0SKPIZJaqHN2lBowveqLGftoLXJk9uUoJXfzuP2p8gs4d5eIspeDlmm1Ox8RElftthmKMoCIAACOSWgK3g9Xo+Z6XTELzJWAKC14Gjl+A1N47wErz6xgxBIQ1JC94JEyYE7voWhCINwatfz/YzkpnX1yvPr4NJVVFZfCeeab/d8OLYx9XDy5x4oR4ftsxceaA8CIAACOSVgL69sFecrmyYxP1r0qQJ9evXL3NdheBNxiQQvA4c4wpeXaiYgk3PwBBHUAV1J+qCKb0uPV9hRUWF+slLBOu5ePkcr7yG+qI7uYateCuE4A0TuMX08OqCN2pYisNQR1EQAAEQyC0Bcy2E/rzRw8ayui5CnplJfsnMrTEdGg7B6wJvp51o27Zt9Wrw8vDKZgUsGjnMIY+C1yszQZjX10/w8t/18Ar+b9vP83kXvPLiEDeGVxe8mPwcbl4UBQEQKBsCtoI3aSdTkoC5D5jz3YhC8Drws/XwduzYUV3FDF/QhZ8+kOXmTPrmc/HwxhG8/FbKu4J5bX9rCl7bvhZD8JpZGVw8vFGHm7loTfIP865AnEoHBwiAAAiAQDABfUt3PlN/3sq6G/571Pz0heQOwetOG4LXgWFUwRuU1aCQgtdWXOpovASvTBS6dzYsBYzUmSfBG7aIzWEIhRY1BS/e8EOR4QQQAAEQqEcgaB7Vv8rq62uyhhCC190iELwODG0Er58YFK+v16Ir+VvSb5ty0ycleL081vqCL/bw+iXyjiN4JY5J35ZXFuIlGc8atrNaMT28ELwONyyKggAIlCUBW8Gb5fkVgtd96ELwOjC0Ebx6vK4IJQ6M553XOHXZypUr1Q4vVVVVdS0RMWi7kMu2C4UQvGEpYFw8vF5eZi/RbcvD7zwIXleCKA8CIAAC2SEgi6mlRbqwlZC1rGZo0J+ZWRbk2bG2f0sgeB2sFFXwyoIljr9kb6Uc5gKmtAVvHCFtKzb183iB3qZNm8grkXccD69tG+KYVE/1BcEbhyDKgAAIgEA2CZiLyXXhyM8oFr282USbNm2y2YH/LfSG4HUzDwSvAz8bwasPUD3lmH7ZQglezkfIQpsXO7HojnLYik2vTSW8btKsCV59q2ae/PR0ambf9dRrEpoShWWUcxHDG4UWzgUBEACBHQkECV7wKh8CELwOtvYSvBLD6vXppNiC16Grarc49oLq4twrnEDibHUvdjEEL4t7Dhvhz1Q24l73qnNfgwRvGmEUfrbRBa9tKjMXO6MsCIAACJQaAfPZC09pqVnYrj8QvHacPM/yErz6jcUxuoMHD64rWw6Clzure2/N+GSBkbaHN6oozYPgbd26NXXv3t1hxKIoCIAACJQfAQje8rO5V48heB3GQZjgNT1yXjuM8eXN8/jm5CNO6IFDdwKLenl4q6urVYiEGaOri1k/ryQEr52ldA9vnOwadlfBWSAAAiBQugRke2HpITy8pWvroJ5B8DrYPargNb2fcumsbmeoo/ESvF4p1cw+pi14JRewublFFA+vPhnygr6shjQknabOYeijKAiAAAjkioDuZIHgzZXpEmssBK8DyjDB6+WRMz2bfPk8eO6iCF59V5skBW/YVsb6AjLxPnvt8maaXP/c5SV49bhkzvcbRUw7DC9VVPfwZjkpums/UR4EQAAE0iQAwZsm3XzUDcHrYKdyErziBeUwCxZeuifXfFvWRVq7du2ooqJiB8pxQhqiCN4o2zObgpfTx61evbreNpO6N7uQgldfXQyvhMPNiqIgAAJlTUA2KWIImEvLcyhA8DrYPUzw6rusyWVELPFiLs4ikBcPr5fA9QtpsIk7NdNt2Xi5CyF42SPMmTbMHeKKLXiznhTd4TZCURAAARBInYD+zIHgTR13Ji8AwetgljDB67XBg9x0vOKed1mD4N1uAJsMBF6CV9/KuLKyss6acT28LLzZ45s1wZuHOG+HWwlFQQAEQCBVAhC8qeLNReUQvA5miiN4ZfMHjgudN29eSQpeWUjGnfPycvPfTQ+vTY5ZL8HrFVuse6NtPMd6SENWBa9NPxyGMoqCAAiAQEkTkOeSzbOmpEGUcecgeB2MHyZ4gz6b8HaGNTU16up+otChaakU1T/rS0yvmWuYL6zHnfptY5wlwSt7qYu3PUse3lQMiUpBAARAoMwIyHMJgrfMDK91F4LXwfZeglfPtRsWJ6RvdtCsWTOHlhSmqC54/TyrWRC8+suEjWdUF+hZ8/AWxrK4CgiAAAiUNgEI3tK2r03vIHhtKPmc4yV4+VTZOCJMxJaD4PVLpRXHwzt37twdsieYKcOEP2+DLB5bPV2ZlymjCl6xG6cos9m22GGIoSgIgAAIgEACBGS9Bzy8CcDMaRUQvA6G8xO8tlUWMr2VbZuCzrP18HJar8WLF6uq/LzccQSvHy8zW4QekxtlMZwI5LCQBr/sFEkwRh0gAAIgAALJEwj6Kpn81VBjFglA8DpYxVXwygK2LG0hnITg1QVnsQWvzdu87uHl8/kIytIAwetw06AoCIAACIAACBSBAASvA3RXwetw6aIUtfXwlqLg1b3LEi4RFqNdFCPhoiAAAiAAAiAAAjsQgOB1GBQQvLPIy4PKnutFixYRe669dllj5EmGNEyfPp3Wr1+vdoDja+qC28bDy21dvny5Ggl+Hl4IXocbBUVBAARAAARAoMgEIHgdDFDOglfidP22Dg7DmqTgNWN79TRjNoJXbwsEb5jl8DsIgAAIgAAI5I8ABK+DzcpZ8HptAhEFZZqC14zJ7d27d2DTIHijWA7nggAIgAAIgED+CEDwOtgMgncJ2eS59UIcR/DyRh2cY3fQoEHUuHHjumpND6+r4N26dStxWIaeUg0hDQ43CoqCAAiAAAiAQJEJlIXg3fLhxzRu8kx6ZsWrtNtuu1Jl96Opf69uCv2qf7xJV02eRe9t+oAaNtidLu13OnVo10r99s76jVR17a3019X/pAP23ZuuGXE+/ajFVxtElJvg1UUfx8kuWRJf8OrbDzNrm9ADv+wIEoPbpUsXat++fb2d3mzq1cU3C2kW1Xzoi9IgeIs8U+HyIAACIAACIOBAoCwE7/ia2UrQjq86n957fzOdMeBqmjR2AB3WugV17T2a+vXsSsd3bqfEb69LrqWn5k+lr+3RUP2705Ft6exTj6P5jyyhF19dQ9Wj+9bhhuCNL3h1L6yr4DXDK/S6eREbe2qDDtPbLOdC8DrMLCgKAiAAAiAAAhkiUBaC94/PvEgtmh9A++3zHYV+4Kga6vSztnTy8R2oVec+tPTBG+mbjfZSvx3Z9SKaPW007b7brkrw/uHeKbTzzjt5mgyCNznByzuW8c5lQYefhzdI8JqeWq/6IXgzNCOhKSAAAiAAAiCQAoGyELw6Nw5vOLHXSLpj8mXUvOm+dN7Q6+nYow6jym5H0wuvvEZV42+lR+ZU09JnV9KcBx5ToQx/fu5V2n/fvWnMkF6qjBwQvMkJXhthait4zXCJsHy5Uq95f3l5eLt160YLFy6kRo0a0eDBg1O4JVElCIAACIAACIBA0gTKSvB+svVTGnzFNGp1cHMa0Lu7Yrn69bfo3CETiMXrRx9vpYlj+lPnDofSg79/mq6achdNrx5Kh7dpSXPmP04PPLqUHvzNNfUELy9wKpdjzpw59NZbb9FZZ51FS5cuVf8+5ZRT6KCDDoqM4Omnn6Y//elP9cqNHDkysJ7rrrtO/W6e99prr9H8+fPpBz/4AZ166qkk7ZTKbOs1L66Xu//++2nNmjV02GGH0fPPP08HHHAAnX322ZH7jQIgAAIgAAIgEIfA7rvvHqcYyvyPQNkI3s1bPqKLRk6hw9seTBf3OVl1f+unnylv79ih59DPDz+E3njzHTp38ASaPW0UrXnj33TzrAU0//ar1LlffrmNDj3ufHpy/tS68AcWyVu2bCmbwTR37lx6++23qUePHvTMM8/U/ZvFX9SDyz/77LP1ig0bNiywmokTJ6rfzfNYeM+bN4/2339/qqysJGmnVGZbr3lxvZy0l6/BDORaUfuN80EABEAABEAgDoE999wzTjGUKSfBy8KWQxcqOh1OZ59ybJ3x/75mLfUbMZmWPFBT97fzh91AXY87klo0P1AJ5Cfunax+++KLL+nQ4y6gPy2cRnt9/Wvqb+Uc0sBxs2vXrqVzzjmHmjX7KnOF7Z1lLlrjckGhB5988glVV1dTgwYNqKqqqt5lZGc1ychgxuTGCWkwQxakvXwN7rdN9gdbFjgPBEAABEAABEAgXQJl4eG9eeYC2vBerfLk6gd7fTufNoTumHQZtfphc9qwcROd1GcM3TZxGB38g6Z0Up/Lqeepx9FJv+xAs+9/jH73+LN0361X1lUBwVs4wWuKWt2OSQleFrm1tbWqalPQQvCmOxGhdhAAARAAARBIk0BZCN5jTh9KGzbW0k5atgVepFY18Exa8uxKqrn9fhW/u8suOyuBy7/x8dobb9Oo626jt9/ZoBarXXVZHyxa+59XNykPL3tsJQ46yBMbJHjF+yuZHuJ6eMV7C8Gb5pSDukEABEAABECg8ATKQvCmhRUeXncPry4y4wpetq+ewUF2ZBO729TbpEkTYsHM4Qp8mDvIwcOb1l2EekEABEAABEAgfQIQvA6MIXizKXhF/LKIXb9+fb0tgk1z655j/g2C1+GGQFEQAAEQAAEQyCgBCF4Hw5Sz4J01a5YiF7YgzA+v6TENqysopMH08IrgFe9x0MI6P8HLmShatmxZ13x4eB1uFBQFARAAARAAgSITgOB1MAAEb3YE79SpU9WCs0GDBhGHNPDhInhNkQzB63CjoCgIgAAIgAAIFJkABK+DAcpN8C5atIiWL1+u4luXLFmSKQ+vLFRjoSreZxfBy8K5cePG8PA63B8oCgIgAAIgAAJZIQDB62CJchO84uVMQvCuWrVKbRZhu2hNv3bHjh13sFoSgpfzCfsJ+ZdeekltKbzPPvvQunXrkIfX4b5BURAAARAAARAoNAEIXgfiELzxQxr02FlZKBYUD2wreLt166aEKefUZQ9t2OYYy5Yto8WLF1Pr1q3V+X6CV9orw8XM4uAwjFAUBEAABEAABEAgZQIQvA6AIXjjC95NmzYRe01ZZPL/hwnTMMHL2wmvXr2a2rVrp8Iu2HPMR5R6+XwWvF67qEHwOtwoKAoCIAACIAACRSYAwetggHIVvCIqGV3cLA06dj0cwW+b4jDB67WoDILXYXCjKAiAAAiAAAiUEAEIXgdjlqvglbhbL09oHJxpCV72Hq9cuZI4zKFNmzaeTdOFtHh4WdBXVFTUOx8e3jiWRRkQAAEQAAEQyAYBCF4HO0DwNqXevXs7ENxeNEnBy7ul8VbDLFqAsrhXAAAgAElEQVT53xyiEBRvG+Y5ls5B8DqbGRWAAAiAAAiAQNEIQPA6oIfgzZ7gFXOyyBWPLQSvwyBHURAAARAAARAoAQIQvA5GLDfBK6nEBFkhQxpkUZq5A5qfB7ZLly7K0wsPr8MAR1EQAAEQAAEQKBECELwOhiw3wWt+1i+k4A0LezDbxhtQ8N8geB0GOIqCAAiAAAiAQIkQgOB1MCQEb+FCGiB4HQYqioIACIAACIBAmROA4HUYAOUueFu0aEGVlZUOBLcXDROzNueYHl7eGpjz+4Z5eBcsWKAyOXAIRPv27X37wnmDa2pq6n7HxhPOZkcFIAACIAACIFAwAhC8DqjLTfByTGx1dXXioi8JwWu2jfMD22RgsLm2dHjcuHHqnw0aNFDZKXibYRwgAAIgAAIgAALZJwDB62CjchO8jEpEH/87KS+njei0OUdvWxqCl73B7ElmrzbErsONg6IgAAIgAAIgUGACELwOwCF4j6KOHTs6ENxe1EbM2pwjglcW00mYQ9DiOpt6nTuICkAABEAABEAABIpKAILXAT8Eb+EEL8fPchwtx+byDmpeBwSvw2BGURAAARAAARAoYQIQvA7GLUfBO336dFq/fr2illRIQ1iOXb6WiFkOVfA7pk6dSrW1tSSL6cTDy+EHvFVwo0aNdhDL8PA63AAoCgIgAAIgAAI5IQDB62CochS8IhCTFLw2i8tsBK+0TYS4V27eZs2a1bM4BK/DDYCiIAACIAACIJATAhC8DoYqd8EblsrLFi0Ery0pnAcCIAACIAACIBCHAARvHGr/K1Pugpd3MzM9pnFwJi14u3XrRm3atKlrSpAXFx7eOBZDGRAAARAAARDIFwEIXgd7laPgXbRoES1fvlxRy5rg9TMlBK/DIEdREAABEAABECgBAhC8DkYsR8Er3thSEbwSGzxixAhq2LChw2hAURAAARAAARAAgawSgOB1sAwEb2FCGmQXNd7hrKqqKrLFgkImbBbDRb4gCoAACIAACIAACGSKAASvgzkgeAsjeG02kAgyIwSvwyBHURAAARAAARAoAQIQvA5GLEfB+9JLL9HChQsVtULF8ELwOgxSFAUBEAABEAABECAIXodBUI6CV89t27dvX+JNHVyPsCwNELyuhFEeBEAABEAABMqbAASvg/3LXfAG7XoWBSsEbxRaOBcEQAAEQAAEQCAqAQjeqMS08yF4/bf5jYIVgjcKLZwLAiAAAiAAAiAQlQAEb1RiZS54N23aRDU1NYoCPLwOgwdFQQAEQAAEQAAECkYAgtcBdTl6eBlX0qm80vbwSgxwixYtqLKysp7Fk+6Lw3BCURAAARAAARAAgZQIQPA6gC1XweuAzLPoqlWraN68edS0aVPq3bv3DueECeKw9piL3uR6jRo1otraWoqb3zfsuvgdBEAABEAABEAgGwQgeB3sAMHrAE8rum7dOpoxYwY1btyYBg0alLrg1XeL44v5Ce1keodaQAAEQAAEQAAEik0AgtfBAhC8DvCMokGhBUl7eCF4k7MbagIBEAABEACBPBCA4HWwEgSvAzyj6PTp02n9+vWem1lA8CbHGTWBAAiAAAiAQDkSgOB1sDoErwM8o+jcuXNp9erV1K1bN2rTpk29XyF4k+OMmkAABEAABECgHAlA8DpYHYLXAZ5RNEjUQvAmxxk1gQAIgAAIgEA5EoDgdbA6BK8DPKNoUKYGV8H7ySefUHV1NTVs2JBGjBhBiOFNzm6oCQRAAARAAATyQACC18FKELwO8IyiXpkaOJ3Y2rVricUw/96jRw9q2bJlrIvqi+Jmzpyp6pUDWRpiIUUhEAABEAABEMgNAQheB1NB8DrA8yhqZmowPbHnnHMONWvWLNZFIXhjYUMhEAABEAABECgJAhC8DmaE4HWA51FUMjX07duX9tlnHzI9sRC8yfJGbSAAAiAAAiBQLgQgeB0sDcHrAM+jqAhcEbaFErzt2rWjioqKZDuD2kAABEAABEAABDJDAILXwRQQvA7wPIqaqcl49zWO3ZUjLQ/vUUcdRR07dky2M6gNBEAABEAABEAgMwQgeB1MAcHrAM+jqJmNQeJuIXiT5YzaQAAEQAAEQKDcCEDwOlgcgtcBXoEF79SpU6m2tpYGDRpENTU19a4OD2+ydkRtIAACIAACIJA1AhC8DhaB4HWA51GU05DNmjWLOE0Yx9RySIN+sFht3LhxrIvq8cF8Df2A4I2FFIVAAARAAARAIDcEIHgdTAXB6wAvRPByTK0pTMeOHRv7ghC8sdGhIAiAAAiAAAjkngAEr4MJIXgd4HkU1XdE69atG82bN6/eWRC8yfJGbSAAAiAAAiBQLgQgeB0sDcHrAM+nqCxU4zCDJUuWUOvWrWnlypXqbAje5HmjRhAAARAAARAoBwIQvA5WhuB1gOdTdMKECbR169Y6ocvClzehaNiwYexd1vhSCGlI3laoEQRAAARAAATyQgCC18FSELwO8HyKijBt1KiRyqrAoQ1t2rRxvpDU26VLF1q8eHG9+vhv7du3d74GKgABEAABEAABEMgmAQheB7tA8DrACxG88rPLZhP6JWRTC95Vbfny5fWuntQ1kqeBGkEABEAABEAABJIgAMHrQBGC1wGeT1HZfCJpwSv1csqztWvXQvAmbzrUCAIgAAIgAAKZJQDB62AaCF4HeJaC12Whmn4JCN7kbYUaQQAEQAAEQCAvBCB4HSwFwesAz6cobz7B4pQPTlPWr1+/RC4CwZsIRlQCAiAAAiAAArkkAMHrYDYIXgd4BS5qCt4mTZrQ+vXrVSsQw1tgY+ByIAACIAACIFBgAhC8DsAheB3gFbioCF7J/qDn94XgLbAxcDkQAAEQAAEQKDABCF4H4BC8DvAKXNRcDCcbW8DDW2BD4HIgAAIgAAIgUAQCELwO0CF4HeAVuOiyZcvq5d+F4C2wAXA5EAABEAABECgiAQheB/gQvA7wClyUF8PNmjWr7qoQvAU2AC4HAiAAAiAAAkUkAMHrAB+C1wFegYt6Cd6WLVuqTBCydXGBm4TLgQAIgAAIgAAIFIgABK8DaAheB3gFLrpu3TqaMWNGPQ9vx44dC9wKXA4EQAAEQAAEQKAYBCB4HahD8DrAK0LRcePG1V21W7du1KZNmyK0ApcEARAAARAAARAoNAEIXgfiELwO8IpQdObMmXXbCiMVWREMgEuCAAiAAAiAQJEIQPA6gIfgdYBXhKJ6ajII3iIYAJcEARAAARAAgSIRgOB1AA/B6wCvCEVXrVpF8+bNU1eG4C2CAXBJEAABEAABECgSAQheB/AQvA7wilCUMzJUV1dD8BaBPS4JAiAAAiAAAsUkAMHrQB+C1wFekYrOnTuXGjduTO3bt1f/jwMEQAAEQAAEQKD0CUDwOtgYgtcBHoqCAAiAAAiAAAiAQIEIQPA6gIbgdYCHoiAAAiAAAiAAAiBQIAIQvA6gIXgd4KEoCIAACIAACIAACBSIAASvA2gIXgd4KAoCIAACIAACIAACBSIAwesAGoLXAR6KggAIgAAIgAAIgECBCEDwOoCG4HWAh6IgAAIgAAIgAAIgUCACELwOoCF4HeChKAiAAAiAAAiAAAgUiAAErwNoCF4HeCgKAiAAAiAAAiAAAgUiUBaCd8uHH9O4yTPpmRWv0m677UqV3Y+m/r26KcSr/vEmXTV5Fr236QNq2GB3urTf6dShXat6+Fe8uIrOHTKBfnfXdfS9A79b9xsEb4FGKS4DAiAAAiAAAiAAAg4EykLwjq+ZrQTt+Krz6b33N9MZA66mSWMH0GGtW1DX3qOpX8+udHzndkr89rrkWnpq/lT62h4NFdZPP/1Mnb9h4yaaWTMSgtdhsKEoCIAACIAACIAACBSDQFkI3j8+8yK1aH4A7bfPdxTjgaNqqNPP2tLJx3egVp370NIHb6RvNtpL/XZk14to9rTR1Lzpvuq/f33ng7RtG9FjS5+nqVcNhOAtxijFNUEABEAABEAABEDAgUBZCF6dD4c3nNhrJN0x+TIlas8bej0de9RhVNntaHrhldeoavyt9Micatpt113oX2+to8FX3ET3zhhLp154JQSvw0BDURAAARAAARAAARAoFoGyEryfbP2UBl8xjVod3JwG9O6umK9+/S0Vn8vxuB99vJUmjulPnTscqn7rM6Sa+vbqSu3aHqxCH7w8vO+//36xbIfrggAIgAAIgAAIlAmBxo0bl0lP0+lm2QjezVs+ootGTqHD2x5MF/c5WdHc+ulnyts7dug59PPDD6E33nyHzh08gWZPG0UvvLKGnl+5mq4ZcZ4610/wfvHFF+lYBrWCAAiAAAiAAAiAwP8I7LzzzmDhQKAsBC8LWw5dqOh0OJ19yrF1uP6+Zi31GzGZljxQU/e384fdQF2PO5IeX/oXFeKwy/8G2KbNW2ivr3+Nxo84nzoe2UadjywNDiMPRUEABEAABEAABECgQATKQvDePHMBbXivVnly9YO9vp1PG0J3TLqMWv2wucrEcFKfMXTbxGF08A+a1jvXz8O7jVe04QABEAABEAABEAABEMgsgbIQvMecPpQ2bKylnXbeqc4QvEitauCZtOTZlVRz+/0qfneXXXamnqcepxawmQcEb2bHMBoGAiAAAiAAAiAAAoEEykLwpjUGENKQFlnUCwIgAAIgAAIgAALJEYDgdWAJwesAD0VBAARAAARAAARAoEAEIHgdQEPwOsBDURAAARAAARAAARAoEAEIXgfQELwO8FAUBEAABEAABEAABApEAILXATQErwM8FAUBEAABEAABEACBAhGA4HUADcHrAA9FQQAEQAAEQAAEQKBABCB4HUBD8DrAQ1EQAAEQAAEQAAEQKBABCF4H0BC8DvBQFARAAARAAARAAAQKRACC1wE0BK8DPBQFARAAARAAARAAgQIRgOB1AA3B6wAPRUEABEAABEAABECgQAQgeB1AQ/A6wENREAABEAABEAABECgQAQjeAoHGZUAABEAABEAABEAABIpDAIK3ONxxVRAAARAAARAAARAAgQIRgOAtEGhcBgRAAARAAARAAARAoDgEIHiLwx1XBQEQAAEQAAEQAAEQKBCBshC8i55cQcOvvoW+/HIbTRh1If3quCMTwfvHP71Ak2bcSxs2bqIWzQ+gccP70PcO/K6q+7a7H6ZZ9y6mz7/4go7v3J5GX3I27bLLzvT6v/5NV06aRatff5O+861GNKx/JR39s7aqzNPLX6Zrb5yj6mv9o+9T9ei+6pwsH2cPHE/r//s+PXbPDcSL+OR45A/L6LKrp9PtE4fTEYf9KHYXgnj5Md7y4cc0bvJMembFq7TbbrtSZfejqX+vbqoNb/77XRp13W309zVrab99vkNXXdaH2vzo+7HbV4iCaTOWPoD1jtbs2ns0vbH2P7T/d/8fLfrt9YHmvnh0DR131E93mF/ijsdb7lpIcxf8kT777HM68qc/pquGn0tf26MhfbL1Uxp7w5305J9fpD0aNqCBfU6i007sWIihmOg1Nm/5iKpv+i39+flXaeunn1GjvfakM086hnqeelxi18EcTcTzx8efbKX7bh1HO++8fY5m9sf2uJSWP3JLIqzB+SuMedEbrE2m3nY/3Tn39/SnhdPom432SmQsZLmSshC8YoBzBl1Hp55wVCKCd/2G96lr71E04/pLqdXBzWnabx6gl/66hu6cUkXL/vI3uvz6O2hWzUg1ifevmkLHd25HZ3TvTN3OHa3acNbJx9Izz71KQ6+8iZY+OI0++/xzqjhjOE0c259+2uZgmnrrffTOuxtp8pUXZXn8qMn0nfUb6YYr+tOhh/ygrq0DR9XQq6v/SdeNvMBa8H7xxZfqpUA//Hit/Os/fBmPr5lN7236gMZXnU/vvb+ZzhhwNU0aO4AOa92Cel48nn7200PovDNPoCXPvqReMBbfM5F223WXzHJOknFQJ8Ham86KF1fRFTf8JrbgjTMen3zmRbrxjvn0m8kj6Ot7NqSLL7+RftKqBQ04p5v6+9/XvEmTxvYnnod4Xrtj8mX0g//bP7Nj2KthYyfeSXzPjx7Uk/ZouDut+efbdN7Q62l81QXUod0hzn3BHL0dIc8f697dSBf27Eqn/2r7i1GSghecvYdqlvUG32/8gt7y+wfS9NkP0dIHb4TgdZ5xMlaBPgBfWfVPumryLNr8wYfUYPfdaPTgntSu7cG06h9vKg/gUUe0ob+8vJo2bKyl0YPOpp8fXn8C5pv85b+/Tsf+4jDVS/YYXjRqKv3xvil01ZS76Lt7f4suOOtE9Rt7Ytjbe/uk4fTg75+mk37ZgXbdZbvAandCf/Xm/bfX/kUPPLqUbr1hmPr7B1s+oqNOHkTLHr6Zdt99t4yR/Ko5PJn+34Hfpd1325XGDOlVN5mefN4YOnDfvRUD9vD68Z557yJa88bbit8v2remwRecWlc5v4H68eJyXoxnTq2iPz7zovK4sweXDxbfnX7Wljoe2YYqzhxOzz58cx3/Uy8YSyMuOoN+2qZlSTLmCa3TKYPpsbkT674WXP/re+jLbduoauCZYG1hdV3w8leF/6z7L429tLcqqf+3n4c3znjcY48GyrPb9sfbXyJn3bdYzRH81edXvUbSNVXnU+sfNle/sT2/vuceNKB3d4veZOcUfvms7N6ZTujcvq5R/PLcuNFeSgD/6611xKL4v+/VKs/2qEvOUjz+uvpfNPLaW9W8wv9m8Xb5oJ50eNv69zDm6O1YeY4++fgOVHP7fHr4rutor69/bQfBu/ipFfTrmQvp888/V/PElcPOVf/Pz6A/3DuZvtV4u/fv2hvvpoYNdqOhfU+vsxk4e99TWdYbB+63t9I6/Hw45OhzIXizMy0m1xJ9ALLQ4U9n3br8jPjz+80zF9AjsycoLwOLtZuvG6q8DI/+YTnd/cDjdPevLw9syB33PEqr//EmXT+mH5136fVU2e3oOjH8zzffoXOHVNNT86fWq+OVv79Bg66YRk/Mm6wenBvfr6VRl5xddw5PNnfdOIqa7t8kOQgJ18STaZ/KX9K4ybPoD/dNVkKShTvfTKtff4v69eyqHkx+vJntLbMeontuGUMH7Lt3YOt0XhcMv8GKMX9OPrHXSOUBq938oXrJWXDnNXXXGXbVLdTu0IMz/UnYlTG/iB3xkx/R2accq/p9XOUw5ZEXweQFvVxZe7FwFbx6nXHHY78Rk6lzh0PVOG3d+Tz1gGr0jT1V1fc+9CQ9v3K1mnvydNx132LieZNfijlkQ8LBpA+nnH+FCkfiPvMLM79QPD53Ir2+9j/Ev902cRgdediPiT+n33DLPPr93dWYoz0I8PzBAvXRPyxTIV78gq97ePkl46TzxtC9M8bSgfs1oXt/9xQ98MgSmjt9LPW9bBJVdDpcOWn4OKbHpXTj1RfTDw9q5su6XJ+FJpAs6w0JbeE2Q/DmadaM0FZ9AHIc3G677qo+oXPM7DGnX0or/3CHErxnXXQNrXh0uqqZRdtFI6fQE/dO9r3Sn1a8QldPuYtmTxtNe3+nsSrft+evlMeSD/YIde9zeV2d/Le339lAFw6fSGMG91KCkGNp2KM5rF+PuuscWzlMTS4H/6BphF4W9tTtk+lpdOuch+nMkzqrPrPgv7jPyTTl1vvqBK8f77sfeEKFFohn26/1Ji8bxnzNwVdMUyEn7P3iWMGa2+bTvBlj6y4zesLtdFDzA+ic07oUFlyEq7ky5pe22fMfo3tuHqNeRAaOmkqPz5tUL+Zab045s05T8MYdj/wy/peXX6PbJg6nL778ktoccx49v+hW5QXlY8GiP9ETS/9CN107KMKoysap/PXrgUefphUv/p2+tkcDOr1rJ7rwrF/Ru/99n351zkha8eiMurjT0/teScP7VyoPJc/lEn/K8ya/BATFIWKOPo2aN91PMZ1ZM1J5byWGd/4jS+mJp/9Ct0wYogYFx1MfetwF6uviY0uep6eefYmmXXOJ+sIw9MqbA0N7yplzkODNmt7Q2wrBm425MPFW6IKXRcA9C56gzz7/QsWRrfrHWnrlj3cqwctC9Mn7t3tjzf82G/XwE8/SLbMWqsmC3475OH/YDXTyL3+h4nZFNPObsnh4WUQPGjNNfVLmz+x83DrndyoWVj6V8t+OOHGAesvOuoeXBe9/1m2kpctW0oiBZ9LZA6+h3999vXooiYfXjzcLXo7HDfJOefEKY8weDH5RObztwUp88/Hiq2vo8uo7lCdfjkvG3Egd2rXKvIfXhfHHn3xKvzjpEnrgjqto4aJn6JNPP633YqWP6XJnnZbgjTMet23bRtdNu5vWvr2epowbqAQhHyzu+GuKLGidM/9xevlvr+fOw6uz5gXFfH+Ouf4OOvn4X1D7n/yQzuh/FTX5f9+qO40XXl0x5Bw1Hw4cXUNPzJtU99tPulxID9xxtedciTl6u1Pi0EMOUl8rly57WX3hEcHLXxffWPsOXTfqgno8H/zN1fSNr+9JXc4crr4o8DOKn5V62Jluw3LnHCR4s6Y3IHgTl5fFr5Djuzj+66gjtntaK/tfReefeQId0vJ7VHHWZXT/beOoedN9ad2G99TNH1Xw8qe0G+94QMXm6tkUeJFK4298nS469yR1XR7s8x9Zoj6rv/Wfd+mCYRPp2pEX1FvkxW/SPBnxQjc+2OtcceZl6i2bP0Nl9RDvY8vvN1XxsRee/SvFnCdFEbzfa7qvL28WvPywrr68r2cX/XgFMWYPBS9+4U9x8hmfK3+/9gPlyX/moZuoYYPt3rETelbR1Zf1UQ+DrB6ujLlfI66ZQd//v/3okSeWqcV8P2qx4ydJsN4+Alhosohk4bXn1xrS08tfockz5tGDv7mGfjP3UXrz7XfpymHbY3j5y0zt5i3qRdUvhjfueOTYXM6AMmF033qLKnlx4ehLvopZHTdpphKG/Xp1zeoQ3qFd/BL2h6f/Qicc077elwYWX/zSNax/D+rWe7RnFgH+StHz4mtpxaO3qLKffvoZtT3uAvrzQ7+uC/OQC2KO3h7DK4KXveEn9xlD/c/pRldOmqn4sof38aXP0fTqSxU2ts1hFReqL5I8/vmLXa9Tu9DkW+9VMeQc92ke4EwqnjwvegOCNzdTpX1Dlzy7UqWnuvumy6n2gw/VCv2HZl5LWz76mHoPnkBP3jeFdtllF5py230qNcdfFt+qBKmNh5frO6nP5SrGllMW6ccLr7ymUnLxb3vuuQddOGyi+lR3ygm/UNft0bUT/fLo7d5fOT786BMlGFn4/bR1S5pw029VO3mCyfKhT6YcD8sL/WZcP4wO+t7+dYL3O99u5MubJ9sgwevHK4gxfwLe8F4tjR16zg7oePLm1e4cN8gLNXghB8f+ySLCLLJ2Zczinu+Fq6fMol133dX3kyRYf2V9/nze8ci22+PTJ81SC1tZ5LIXi1OFzblptEoPduaAq1UsdJDgjTMeX3xlDY2vmUP33z5uh7E5Y/bv6MVXX6PJVw5UoVHnDplAc6aNVotH83Kwp/CXZ12m1lD0OeMEFZ7BfeEvX6eccJQKj+K4/3Mrf6kWtXHWleumzaFxw85VqQVPu3AsTbxiAHXp+FP63WN/ptt++wg9NHN8ve5jjt6OQ58/+L85OxCH4LEDgAUvO3z45eK+W69UXynZ8bLoyedo9rRRqjzHiD+94hWVUvPROTvGSYPzds550hsQvHmZKSO2c9L0e+n+h5+iBg12p0vOO1l5bfgYee1t9NzKVSpt2PABlfTrOxfQl19+qR5qNoKXswfw53HT+/rU/VOpcaOvq1XVt9/9sAqZ6F7xc7VQ4N/r/ktdzhi+Q5mJV/SnYzr8hJa98Df1cN2w8X06jEXvqAtVXVk+9MmU4/Gm3Hp/3YNHD2nw4805iv0ELz8Ag3h5MWaPzzGnD1XZNXb6X85J5seLCDmEhMNGRoyfod7GeZGcn7czS8xdGfOCS/bs8CJIXgDk9UkSrOtbnL2MnK1l7dvrlPf/ulEX0re/+Q3l/Ro4eiptqt1C393729S82b5KOFw1vI+vhzfOeOT75eEn/qxeyOX4frP91Fcpzt7A3rnHlz6vshcMufA0JRzzdrBwnTh9Lr3w8ho1PjkPKGcT4K9wfB9zlgbu57p331P/3fv0LtSj29EqDn3I2F9TpyPbqPhS/u3qy86r98WMWWCO9ha8/FdeyMoLHSUOevFTz9GvZy5QY2vfJt9Wz0FZRMwvGx1PGaTscsl5p+wwzMD5KyR50RuHtWpBHU8drBrONhcdw2FCWc/97zLPlVUeXhdQKAsCeSfA2SqmjhuoQhtwgEBeCWwXvDepdQI4QAAEQMCWAASvLSmcBwI5JsBeGP70+5spI3LcCzQdBEh5eAdfcVPoRiBgBQIgAAI6AQhejAcQKHECHBLBC6t+fd3gukwiJd5ldK+ECUDwlrBx0TUQSJEABG+KcFE1CIAACIAACIAACIBA8QlA8BbfBmgBCIAACIAACIAACIBAigQgeFOEi6pBAARAAARAAARAAASKTwCCt/g2QAtAAARAAARAAARAAARSJADBmyJcVA0CIAACIAACIAACIFB8AhC8xbcBWgACIAACIAACIAACIJAiAQjeFOGiahAAARAAARAAARAAgeITgOAtvg3QAhAAARAAARAAARAAgRQJQPCmCBdVgwAIgAAIgAAIgAAIFJ8ABG/xbYAWgAAIgAAIgAAIgAAIpEgAgjdFuKgaBEAABEAABEAABECg+AQgeItvA7QABEAABEAABEAABEAgRQIQvCnCRdUgAAIgAAIgAAIgAALFJwDBW3wboAUgAAIgAAIgAAIgAAIpEoDgTREuqgYBEAABEAABEAABECg+AQje4tsALQABEAABEAABEAABEEiRAARvinBRNQiAAAiAAAiAAAiAQPEJQPAW3wZoAQiAAAiAAAiAAAiAQIoEIHhThIuqQQAEQAAEQAAEQAAEik8Agrf4NkALQAAEQAAEQIp+eCAAAAOcSURBVAAEQAAEUiQAwZsiXFQNAiAAAiAAAiAAAiBQfAIQvMW3AVoAAiAAAiAAAiAAAiCQIgEI3hThomoQAAEQAAEQAAEQAIHiE4DgLb4N0AIQAAEQAAEQAAEQAIEUCUDwpggXVYMACIAACIAACIAACBSfAARv8W2AFoAACIAACIAACIAACKRIAII3RbioGgRAAARAAARAAARAoPgEIHiLbwO0AARAAARAAARAAARAIEUCELwpwkXVIAACIAACIAACIAACxScAwVt8G6AFIAACIAACIAACIAACKRKA4E0RLqoGARAAARAAARAAARAoPgEI3uLbAC0AARAAARAAARAAARBIkQAEb4pwUTUIgAAIgAAIgAAIgEDxCUDwFt8GaAEIgAAIgAAIgAAIgECKBCB4U4SLqkEABEAABEAABEAABIpPAIK3+DZAC0AABEAABEAABEAABFIkAMGbIlxUDQIgAAIgAAIgAAIgUHwCELzFtwFaAAIgAAIgAAIgAAIgkCIBCN4U4aJqEAABEAABEAABEACB4hOA4C2+DdACEAABEAABEAABEACBFAlA8KYIF1WDAAiAAAiAAAiAAAgUnwAEb/FtgBaAAAiAAAiAAAiAAAikSACCN0W4qBoEQAAEQAAEQAAEQKD4BCB4i28DtAAEQAAEQAAEQAAEQCBFAhC8KcJF1SAAAiAAAiAAAiAAAsUnAMFbfBugBSAAAiAAAiAAAiAAAikSgOBNES6qBgEQAAEQAAEQAAEQKD4BCN7i2wAtAAEQAAEQAAEQAAEQSJEABG+KcFE1CIAACIAACIAACIBA8QlA8BbfBmgBCIAACIAACIAACIBAigQgeFOEi6pBAARAAARAAARAAASKTwCCt/g2QAtAAARAAARAAARAAARSJADBmyJcVA0CIAACIAACIAACIFB8AhC8xbcBWgACIAACIAACIAACIJAiAQjeFOGiahAAARAAARAAARAAgeITgOAtvg3QAhAAARAAARAAARAAgRQJQPCmCBdVgwAIgAAIgAAIgAAIFJ8ABG/xbYAWgAAIgAAIgAAIgAAIpEgAgjdFuKgaBEAABEAABEAABECg+AQgeItvA7QABEAABEAABEAABEAgRQIQvCnCRdUgAAIgAAIgAAIgAALFJwDBW3wboAUgAAIgAAIgAAIgAAIpEoDgTREuqgYBEAABEAABEAABECg+AQje4tsALQABEAABEAABEAABEEiRwP8Hu1kPwgfYCPcAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from earthkit.plots.interactive import Chart\n",
    "\n",
    "TIME_FREQUENCY = \"1d\"\n",
    "QUANTILES = [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1]\n",
    "\n",
    "chart = Chart()\n",
    "chart.title(f\"ECMWF ensemble meteogram at {location_to_string(LOCATION)}\")\n",
    "#chart.box(ds, time_frequency=TIME_FREQUENCY, quantiles=QUANTILES)\n",
    "chart.line(ds, line_color='grey', time_frequency=TIME_FREQUENCY)\n",
    "chart.show(\"png\")  # Replace with chart.show() in an interactive session!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae75ea72",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "polytope_venv",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}