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   "source": [
    "## Example for CESM2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd3f8e4a-6a62-4434-a7aa-8cf913527976",
   "metadata": {},
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    "**NOTE**: Compared to the CESM1 demo, here \"Q\" (QBOT), \"U\" (UBOT) and \"V\" (VBOT) are not included. When the bottom \"lev\" of \"Q\", \"U\", and \"V\" are merged, there is an issue.   \n",
    "\n",
    "Reference:     \n",
    "- GitHub: https://github.com/NCAR/cesm2-le-aws    \n",
    "- Data/Variables Information: https://ncar.github.io/cesm2-le-aws/model_documentation.html#data-catalog       \n",
    "- Reproduce CESM-LENS: https://github.com/NCAR/cesm2-le-aws/blob/main/notebooks/kay_et_al_lens2.ipynb   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "insured-finnish",
   "metadata": {},
   "source": [
    "**Step 0: load necessary packages and define parameters (no need to change)**"
   ]
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   "execution_count": 1,
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     "text": [
      "/glade/work/zhonghua/miniconda3/envs/aws_urban/lib/python3.8/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    },
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     "name": "stdout",
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     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'component.experiment.frequency.forcing_variant'\n"
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      "different lat between CAM and CLM subgrid info, adjust subgrid info's lat\n",
      "CPU times: user 56.9 s, sys: 34 s, total: 1min 30s\n",
      "Wall time: 58.4 s\n"
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   "source": [
    "%%time\n",
    "# Display output of plots directly in Notebook\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import json\n",
    "from flaml import AutoML\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import util\n",
    "\n",
    "with open(\"./config_cesm2.json\",'r') as load_f:\n",
    "#     param = json.loads(json.load(load_f))\n",
    "    param = json.load(load_f)\n",
    "    \n",
    "    model = param[\"model\"] # cesm2\n",
    "    urban_type = param[\"urban_type\"] # md\n",
    "    city_loc = param[\"city_loc\"] # {\"lat\": 40.0150, \"lon\": -105.2705}\n",
    "    l_component = param[\"l_component\"]\n",
    "    a_component = param[\"a_component\"]\n",
    "    experiment = param[\"experiment\"]\n",
    "    frequency = param[\"frequency\"]\n",
    "    cam_ls = param[\"cam_ls\"]\n",
    "    clm_ls = param[\"clm_ls\"]\n",
    "    forcing_variant = param[\"forcing_variant\"]\n",
    "    time = slice(param[\"time_start\"],param[\"time_end\"])\n",
    "    member_id = param[\"member_id\"]\n",
    "    estimator_list = param[\"estimator_list\"]\n",
    "    time_budget = param[\"time_budget\"]\n",
    "    features = param[\"features\"]\n",
    "    label = param[\"label\"]\n",
    "    clm_var_mask = param[\"label\"][0]\n",
    "    \n",
    "# get a dataset\n",
    "ds = util.get_data(model, city_loc, experiment, frequency, member_id, time, cam_ls, clm_ls,\n",
    "                   forcing_variant=forcing_variant, urban_type=urban_type)\n",
    "\n",
    "# create a dataframe\n",
    "ds['time'] = ds.indexes['time'].to_datetimeindex()\n",
    "df = ds.to_dataframe().reset_index().dropna()\n",
    "\n",
    "if \"PRSN\" in features:\n",
    "    df[\"PRSN\"] = df[\"PRECSC\"] + df[\"PRECSL\"]\n",
    "if \"PRECT\" in features:\n",
    "    df[\"PRECT\"] = df[\"PRECC\"] + df[\"PRECL\"]\n",
    "    \n",
    "# setup for automl\n",
    "automl = AutoML()\n",
    "automl_settings = {\n",
    "    \"time_budget\": time_budget,  # in seconds\n",
    "    \"metric\": 'r2',\n",
    "    \"task\": 'regression',\n",
    "    \"estimator_list\":estimator_list,\n",
    "}"
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   "id": "productive-negative",
   "metadata": {},
   "source": [
    "### Step 1: data analysis"
   ]
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   "cell_type": "markdown",
   "id": "mechanical-metabolism",
   "metadata": {},
   "source": [
    "**xarray.Dataset**"
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  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f9c4e90a",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (member_id: 1, time: 7299)\n",
       "Coordinates:\n",
       "    lat        float64 40.05\n",
       "    lon        float64 255.0\n",
       "  * member_id  (member_id) &lt;U12 &#x27;r1i1231p1f1&#x27;\n",
       "  * time       (time) datetime64[ns] 2081-01-02T12:00:00 ... 2100-12-31T12:00:00\n",
       "Data variables:\n",
       "    TREFHT     (member_id, time) float32 273.2 273.4 275.7 ... 276.7 277.0 277.4\n",
       "    TREFHTMX   (member_id, time) float32 276.2 278.8 282.8 ... 283.9 284.8 283.7\n",
       "    FLNS       (member_id, time) float32 93.1 82.03 82.87 ... 88.49 87.6 67.41\n",
       "    FSNS       (member_id, time) float32 90.73 84.27 91.37 ... 91.55 91.45 77.9\n",
       "    PRECSC     (member_id, time) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    PRECSL     (member_id, time) float32 4.184e-10 4.287e-10 ... 9.825e-21\n",
       "    PRECC      (member_id, time) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    PRECL      (member_id, time) float32 4.251e-10 4.42e-10 ... 1.181e-08\n",
       "    TREFMXAV   (member_id, time) float64 277.0 279.3 284.4 ... 284.3 285.7 284.4\n",
       "Attributes:\n",
       "    host:                    mom1\n",
       "    topography_file:         /mnt/lustre/share/CESM/cesm_input/atm/cam/topo/f...\n",
       "    logname:                 sunseon\n",
       "    time_period_freq:        day_1\n",
       "    intake_esm_varname:      FLNS\\nFSNS\\nPRECC\\nPRECL\\nPRECSC\\nPRECSL\\nTREFHT...\n",
       "    Conventions:             CF-1.0\n",
       "    model_doi_url:           https://doi.org/10.5065/D67H1H0V\n",
       "    source:                  CAM\n",
       "    intake_esm_dataset_key:  atm.ssp370.daily.cmip6</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-b2d3ce5c-eb3e-4ee0-bfde-053de333bcb4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b2d3ce5c-eb3e-4ee0-bfde-053de333bcb4' 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'>member_id</span>: 1</li><li><span class='xr-has-index'>time</span>: 7299</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1668cbaa-7432-48cd-bf2d-8a07c0fcb7c2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1668cbaa-7432-48cd-bf2d-8a07c0fcb7c2' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>40.05</div><input id='attrs-a6ea5a7d-938d-4a90-b5cb-32d526b11413' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6ea5a7d-938d-4a90-b5cb-32d526b11413' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d588694-6cf2-45e8-8135-d3cec58755f1' class='xr-var-data-in' type='checkbox'><label for='data-2d588694-6cf2-45e8-8135-d3cec58755f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array(40.05235602)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>255.0</div><input id='attrs-16e586ee-e916-456f-a6a8-510d5b5d699b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16e586ee-e916-456f-a6a8-510d5b5d699b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88181903-a411-4a59-8431-9c0d5fbef6f0' class='xr-var-data-in' type='checkbox'><label for='data-88181903-a411-4a59-8431-9c0d5fbef6f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array(255.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;r1i1231p1f1&#x27;</div><input id='attrs-d3546b86-3446-4790-974e-168477d09c92' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d3546b86-3446-4790-974e-168477d09c92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb632cbf-130c-44a3-8cb0-5153fb6c07af' class='xr-var-data-in' type='checkbox'><label for='data-cb632cbf-130c-44a3-8cb0-5153fb6c07af' 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;r1i1231p1f1&#x27;], dtype=&#x27;&lt;U12&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2081-01-02T12:00:00 ... 2100-12-...</div><input id='attrs-df6fcec6-a12c-4481-87f4-e33ff04af618' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df6fcec6-a12c-4481-87f4-e33ff04af618' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e316fa78-9a98-4618-8e1a-6d9b9cda30e3' class='xr-var-data-in' type='checkbox'><label for='data-e316fa78-9a98-4618-8e1a-6d9b9cda30e3' 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;2081-01-02T12:00:00.000000000&#x27;, &#x27;2081-01-03T12:00:00.000000000&#x27;,\n",
       "       &#x27;2081-01-04T12:00:00.000000000&#x27;, ..., &#x27;2100-12-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2100-12-30T12:00:00.000000000&#x27;, &#x27;2100-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bc73cd52-772f-4906-8525-2a238b3c7e0a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bc73cd52-772f-4906-8525-2a238b3c7e0a' class='xr-section-summary' >Data variables: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>TREFHT</span></div><div class='xr-var-dims'>(member_id, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>273.2 273.4 275.7 ... 277.0 277.4</div><input id='attrs-0814c902-3f5d-4594-a654-8c5f450444d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0814c902-3f5d-4594-a654-8c5f450444d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2db3c2c6-7eb5-4a63-b82d-5320e20bac93' class='xr-var-data-in' type='checkbox'><label for='data-2db3c2c6-7eb5-4a63-b82d-5320e20bac93' 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>cell_methods :</span></dt><dd>time: mean</dd><dt><span>long_name :</span></dt><dd>Reference height temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><pre>array([[273.18002, 273.39667, 275.67584, ..., 276.73038, 277.025  ,\n",
       "        277.40417]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TREFHTMX</span></div><div class='xr-var-dims'>(member_id, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>276.2 278.8 282.8 ... 284.8 283.7</div><input id='attrs-074c5d85-5097-40d7-8ff2-e64079a0dbaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-074c5d85-5097-40d7-8ff2-e64079a0dbaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a92b6431-264a-4996-8fcb-f394688eeaff' class='xr-var-data-in' type='checkbox'><label for='data-a92b6431-264a-4996-8fcb-f394688eeaff' 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>cell_methods :</span></dt><dd>time: maximum</dd><dt><span>long_name :</span></dt><dd>Maximum reference height temperature over output period</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><pre>array([[276.1923 , 278.82355, 282.8261 , ..., 283.89755, 284.8469 ,\n",
       "        283.73743]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FLNS</span></div><div class='xr-var-dims'>(member_id, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>93.1 82.03 82.87 ... 87.6 67.41</div><input id='attrs-e99093db-8605-4db8-8fcc-0162c99a8e33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e99093db-8605-4db8-8fcc-0162c99a8e33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12f68528-f263-4a33-bcf8-fc16ff9bd397' class='xr-var-data-in' type='checkbox'><label for='data-12f68528-f263-4a33-bcf8-fc16ff9bd397' 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>Sampling_Sequence :</span></dt><dd>rad_lwsw</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>long_name :</span></dt><dd>Net longwave flux at surface</dd><dt><span>units :</span></dt><dd>W/m2</dd></dl></div><div class='xr-var-data'><pre>array([[93.097984, 82.03214 , 82.87059 , ..., 88.493256, 87.5951  ,\n",
       "        67.411705]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FSNS</span></div><div class='xr-var-dims'>(member_id, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.73 84.27 91.37 ... 91.45 77.9</div><input id='attrs-bd3ff730-ed35-46ad-8bd7-3258110de20d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd3ff730-ed35-46ad-8bd7-3258110de20d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce1e07f4-6bce-4476-8f86-7844d456a5f1' class='xr-var-data-in' type='checkbox'><label for='data-ce1e07f4-6bce-4476-8f86-7844d456a5f1' 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>Sampling_Sequence :</span></dt><dd>rad_lwsw</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>long_name :</span></dt><dd>Net solar flux at surface</dd><dt><span>units :</span></dt><dd>W/m2</dd></dl></div><div class='xr-var-data'><pre>array([[90.73479 , 84.271416, 91.365944, ..., 91.54785 , 91.45452 ,\n",
       "        77.90161 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRECSC</span></div><div class='xr-var-dims'>(member_id, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-09dc9c9d-124c-4fa1-ae5f-700423e89fe8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09dc9c9d-124c-4fa1-ae5f-700423e89fe8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8ce0736-2d52-46d3-a6e6-dd11a6debbef' class='xr-var-data-in' type='checkbox'><label for='data-e8ce0736-2d52-46d3-a6e6-dd11a6debbef' 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>cell_methods :</span></dt><dd>time: mean</dd><dt><span>long_name :</span></dt><dd>Convective snow rate (water equivalent)</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRECSL</span></div><div class='xr-var-dims'>(member_id, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.184e-10 4.287e-10 ... 9.825e-21</div><input id='attrs-287a1d08-fe2b-4709-b825-0783c716aa4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-287a1d08-fe2b-4709-b825-0783c716aa4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b30d63f5-fa9d-42bf-b10f-b9ec83a255b8' class='xr-var-data-in' type='checkbox'><label for='data-b30d63f5-fa9d-42bf-b10f-b9ec83a255b8' 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>cell_methods :</span></dt><dd>time: mean</dd><dt><span>long_name :</span></dt><dd>Large-scale (stable) snow rate (water equivalent)</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>array([[4.1836259e-10, 4.2873260e-10, 1.4922830e-15, ..., 2.0208434e-14,\n",
       "        2.9697208e-21, 9.8254048e-21]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRECC</span></div><div class='xr-var-dims'>(member_id, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-f28100ed-f9ff-42d9-90b9-70e3eebeaf32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f28100ed-f9ff-42d9-90b9-70e3eebeaf32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e53033f-4613-407f-a5ff-782eea0f4eab' class='xr-var-data-in' type='checkbox'><label for='data-8e53033f-4613-407f-a5ff-782eea0f4eab' 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>cell_methods :</span></dt><dd>time: mean</dd><dt><span>long_name :</span></dt><dd>Convective precipitation rate (liq + ice)</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRECL</span></div><div class='xr-var-dims'>(member_id, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.251e-10 4.42e-10 ... 1.181e-08</div><input id='attrs-c7525779-2d07-4686-9937-14b8ad5ae212' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7525779-2d07-4686-9937-14b8ad5ae212' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3ec48b4-f7fb-4c23-86bd-8fe796f8b6d1' class='xr-var-data-in' type='checkbox'><label for='data-f3ec48b4-f7fb-4c23-86bd-8fe796f8b6d1' 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>cell_methods :</span></dt><dd>time: mean</dd><dt><span>long_name :</span></dt><dd>Large-scale (stable) precipitation rate (liq + ice)</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>array([[4.2511417e-10, 4.4198506e-10, 7.8431998e-14, ..., 2.8733018e-09,\n",
       "        1.5563001e-10, 1.1812628e-08]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TREFMXAV</span></div><div class='xr-var-dims'>(member_id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>277.0 279.3 284.4 ... 285.7 284.4</div><input id='attrs-69f276b0-cce1-4afe-9ba4-167d01099102' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69f276b0-cce1-4afe-9ba4-167d01099102' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-617f7c6a-b470-4c27-ad32-c0a4b9f51ca2' class='xr-var-data-in' type='checkbox'><label for='data-617f7c6a-b470-4c27-ad32-c0a4b9f51ca2' 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([[276.99923706, 279.33520508, 284.41812134, ..., 284.34127808,\n",
       "        285.65963745, 284.35968018]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-293fa314-d8d8-4ad2-8e7a-912448df6ff6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-293fa314-d8d8-4ad2-8e7a-912448df6ff6' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>host :</span></dt><dd>mom1</dd><dt><span>topography_file :</span></dt><dd>/mnt/lustre/share/CESM/cesm_input/atm/cam/topo/fv_0.9x1.25_nc3000_Nsw042_Nrs008_Co060_Fi001_ZR_sgh30_24km_GRNL_c170103.nc</dd><dt><span>logname :</span></dt><dd>sunseon</dd><dt><span>time_period_freq :</span></dt><dd>day_1</dd><dt><span>intake_esm_varname :</span></dt><dd>FLNS\n",
       "FSNS\n",
       "PRECC\n",
       "PRECL\n",
       "PRECSC\n",
       "PRECSL\n",
       "TREFHT\n",
       "TREFHTMX</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>source :</span></dt><dd>CAM</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>atm.ssp370.daily.cmip6</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (member_id: 1, time: 7299)\n",
       "Coordinates:\n",
       "    lat        float64 40.05\n",
       "    lon        float64 255.0\n",
       "  * member_id  (member_id) <U12 'r1i1231p1f1'\n",
       "  * time       (time) datetime64[ns] 2081-01-02T12:00:00 ... 2100-12-31T12:00:00\n",
       "Data variables:\n",
       "    TREFHT     (member_id, time) float32 273.2 273.4 275.7 ... 276.7 277.0 277.4\n",
       "    TREFHTMX   (member_id, time) float32 276.2 278.8 282.8 ... 283.9 284.8 283.7\n",
       "    FLNS       (member_id, time) float32 93.1 82.03 82.87 ... 88.49 87.6 67.41\n",
       "    FSNS       (member_id, time) float32 90.73 84.27 91.37 ... 91.55 91.45 77.9\n",
       "    PRECSC     (member_id, time) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    PRECSL     (member_id, time) float32 4.184e-10 4.287e-10 ... 9.825e-21\n",
       "    PRECC      (member_id, time) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    PRECL      (member_id, time) float32 4.251e-10 4.42e-10 ... 1.181e-08\n",
       "    TREFMXAV   (member_id, time) float64 277.0 279.3 284.4 ... 284.3 285.7 284.4\n",
       "Attributes:\n",
       "    host:                    mom1\n",
       "    topography_file:         /mnt/lustre/share/CESM/cesm_input/atm/cam/topo/f...\n",
       "    logname:                 sunseon\n",
       "    time_period_freq:        day_1\n",
       "    intake_esm_varname:      FLNS\\nFSNS\\nPRECC\\nPRECL\\nPRECSC\\nPRECSL\\nTREFHT...\n",
       "    Conventions:             CF-1.0\n",
       "    model_doi_url:           https://doi.org/10.5065/D67H1H0V\n",
       "    source:                  CAM\n",
       "    intake_esm_dataset_key:  atm.ssp370.daily.cmip6"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "breeding-junior",
   "metadata": {},
   "source": [
    "**pandas dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8e42fc00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>member_id</th>\n",
       "      <th>time</th>\n",
       "      <th>TREFHT</th>\n",
       "      <th>TREFHTMX</th>\n",
       "      <th>FLNS</th>\n",
       "      <th>FSNS</th>\n",
       "      <th>PRECSC</th>\n",
       "      <th>PRECSL</th>\n",
       "      <th>PRECC</th>\n",
       "      <th>PRECL</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>TREFMXAV</th>\n",
       "      <th>PRSN</th>\n",
       "      <th>PRECT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>r1i1231p1f1</td>\n",
       "      <td>2081-01-02 12:00:00</td>\n",
       "      <td>273.180023</td>\n",
       "      <td>276.192291</td>\n",
       "      <td>93.097984</td>\n",
       "      <td>90.734787</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.183626e-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.251142e-10</td>\n",
       "      <td>40.052356</td>\n",
       "      <td>255.0</td>\n",
       "      <td>276.999237</td>\n",
       "      <td>4.183626e-10</td>\n",
       "      <td>4.251142e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>r1i1231p1f1</td>\n",
       "      <td>2081-01-03 12:00:00</td>\n",
       "      <td>273.396667</td>\n",
       "      <td>278.823547</td>\n",
       "      <td>82.032143</td>\n",
       "      <td>84.271416</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.287326e-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.419851e-10</td>\n",
       "      <td>40.052356</td>\n",
       "      <td>255.0</td>\n",
       "      <td>279.335205</td>\n",
       "      <td>4.287326e-10</td>\n",
       "      <td>4.419851e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>r1i1231p1f1</td>\n",
       "      <td>2081-01-04 12:00:00</td>\n",
       "      <td>275.675842</td>\n",
       "      <td>282.826111</td>\n",
       "      <td>82.870590</td>\n",
       "      <td>91.365944</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.492283e-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.843200e-14</td>\n",
       "      <td>40.052356</td>\n",
       "      <td>255.0</td>\n",
       "      <td>284.418121</td>\n",
       "      <td>1.492283e-15</td>\n",
       "      <td>7.843200e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>r1i1231p1f1</td>\n",
       "      <td>2081-01-05 12:00:00</td>\n",
       "      <td>275.782043</td>\n",
       "      <td>282.312042</td>\n",
       "      <td>90.888451</td>\n",
       "      <td>92.246887</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.730888e-17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.108414e-15</td>\n",
       "      <td>40.052356</td>\n",
       "      <td>255.0</td>\n",
       "      <td>283.729095</td>\n",
       "      <td>3.730888e-17</td>\n",
       "      <td>3.108414e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>r1i1231p1f1</td>\n",
       "      <td>2081-01-06 12:00:00</td>\n",
       "      <td>272.146301</td>\n",
       "      <td>275.967560</td>\n",
       "      <td>56.035732</td>\n",
       "      <td>55.395706</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.766720e-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.267757e-11</td>\n",
       "      <td>40.052356</td>\n",
       "      <td>255.0</td>\n",
       "      <td>278.923859</td>\n",
       "      <td>5.766720e-11</td>\n",
       "      <td>9.267757e-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     member_id                time      TREFHT    TREFHTMX       FLNS  \\\n",
       "0  r1i1231p1f1 2081-01-02 12:00:00  273.180023  276.192291  93.097984   \n",
       "1  r1i1231p1f1 2081-01-03 12:00:00  273.396667  278.823547  82.032143   \n",
       "2  r1i1231p1f1 2081-01-04 12:00:00  275.675842  282.826111  82.870590   \n",
       "3  r1i1231p1f1 2081-01-05 12:00:00  275.782043  282.312042  90.888451   \n",
       "4  r1i1231p1f1 2081-01-06 12:00:00  272.146301  275.967560  56.035732   \n",
       "\n",
       "        FSNS  PRECSC        PRECSL  PRECC         PRECL        lat    lon  \\\n",
       "0  90.734787     0.0  4.183626e-10    0.0  4.251142e-10  40.052356  255.0   \n",
       "1  84.271416     0.0  4.287326e-10    0.0  4.419851e-10  40.052356  255.0   \n",
       "2  91.365944     0.0  1.492283e-15    0.0  7.843200e-14  40.052356  255.0   \n",
       "3  92.246887     0.0  3.730888e-17    0.0  3.108414e-15  40.052356  255.0   \n",
       "4  55.395706     0.0  5.766720e-11    0.0  9.267757e-11  40.052356  255.0   \n",
       "\n",
       "     TREFMXAV          PRSN         PRECT  \n",
       "0  276.999237  4.183626e-10  4.251142e-10  \n",
       "1  279.335205  4.287326e-10  4.419851e-10  \n",
       "2  284.418121  1.492283e-15  7.843200e-14  \n",
       "3  283.729095  3.730888e-17  3.108414e-15  \n",
       "4  278.923859  5.766720e-11  9.267757e-11  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "designing-asbestos",
   "metadata": {},
   "source": [
    "**data visualization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "35c2c5d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b747263b2b0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qjxQyriJQZIWovYFf/+bUwGnLYJ8pyGw0LKyc3dw/mym35yFf6lSUyMzMwhzwxIkWCg7YX14QlYZVHSNDxpLlkS/mBbo+g2r1k7GcUNlQZU+GFQbcj34Z/JnJvuLdtpnB+1PiUWkEMRfOcEyHEqW0w+jUtu3aG+g+z357YI9AksJJZdBi10CkFS0UHLDrx2Xf5Wvjf0Gv4aMTdUynag4nU/kvViinPZ8qjW5USLt9RbAv2soQxHQ2DDo1KVK6XlUo7NvP6HzXZ9IWVwBw30dymyKjQqVd6ZlCHnPN61Oyvsu8zBnLN+HmdwxdbZydlJ3Nirts46rASS40T1m6sUzgS1viKKQ7U8FL6Y8L1we+tpKiGlR1pLvKNIFVjUYWVw1xcvyn8gzU1rziaxeRCQUiqkZE44loKhHNJKJ7zN/PMr/vJ6KetmtuIaL5RDSXiAZFlTdZZDqqpCwGNtgW314d/4vS/b6dF9zTqoz9e476SLLuqwje0x7/ruzzI1/Mk2p4spY7VlQE35MKfqIqK6ieAHUTyzcmLFW6PkNcHeScVbk71VUGSzL+pqYvyx44xKkyi3KmsAvAcczcFUA3AIOJqC+AGQB+C+Ab68lE1AnAUACdAQwG8AQRVY4wf8LIVIT1MVpGWBnxSbieRn9YEHxE2v/v4t4o7R2k7IJvWCq6p75egDUS60D2RVsZLnt5cuBrVXCaKch0sPsURrpLf92uNMtRYfyiYC5Q9jl04nFZx734w+Ks73EqniITCmyQ2VVUaP4xM89m5rkOlwwB8Boz72LmRQDmA+gdVf5kyIediaqjODvvTVke+FoZJ3c5nVKCj3rpr+JmwE3qVI8wJ9Hg5L5EptqoqE4G/t83GBewc7Yjk+f5a7bid0//ECid+jWr5vymIhSk9pTY3lWcfVCkawpEVJmIpgBYA2AUM4/zOL0ZAOv8cpn5m/2elxLRRCKauHZtPDv+4vKevWVn8DgGKrbbTqPFejUKA99PBrt5f5Lid6OEM798sTm34pRnmVKodIg7bPr5rRIuSdbbXFbLzCZVYoM4LcGoCEaZK+3ye/mG+IwTIhUKzLyPmbsBaA6gNxEd4nG6k8Iz5zky8zPM3JOZe5aUqJnYiRLE93wQgu4KBtQarIzaJGzsI6BrbYv8cdK4TjXhc/NPJDjnWUa4hRlbRMb78ITF2TMMGXls79hVhbmTSkkUlbTjDHwVi/URM28E8BWMtQI3lgFoYfneHEAy+9ltPDEm+A7fd38S34RWtSD4EkqVguCv0mlzXqMi8Q5SBXtD+V5hLUMVmTYbhgfMDP/83EmbKkbrkprC56rOFMJUYcgMYuxrMKs3iw9i7CqzVyQMMJyyqDJTGCPh9sI+U/h2Xnx+kKK0Piohorrm5+oABgDwWg0dCWAoEVUlolYA2gFIxpTHxpSlGwNfe+3r4jtdCxQsWlSsYZyuPK17juYuEtJkui2zLhNmtlXcitSqWiB8rtOzllpTCDEg0rnP/ohvAjp8q1YYvNuyW/V44SREVZ6BXFyE7Fa5fKN8NMSgRDlTaAJgDBFNAzABxprCh0R0OhEtA3A4gI+I6DMAYOaZAN4AMAvApwCuYOZURAjPh52IKoM4x/CKMRR58849uEvCy2fUyLkwiL9OOJnfTlu2STj0qlOWZfTzYe5d+Xn1VlwQ0HxbRnjbR9gyVrlOxf1ZMKCSSnCuD6auyDEpr6qgCZAlSuujaczcnZkPZeZDmPle8/d3mbk5M1dl5kbMPMhyzXBmbsPMHZj5k6jyJovMAqQKs1YkE9HqHQc/SzKdhX3kdmS7BkLXvf9TcAsnAFi4NtdltpJ1iMS1U5dmjzirF0ZvPe0WOOmfn/8sdL2TIHtzorh60zo4SnKhvUuzOsLnPv11dghNOe/BuWW87g2xmf/TXwffT/KcgzdW0TYVBnpHsyCijUAljsGH05JZQnGaCcm0+TrVsy2VurcUewYFCrb+APDB1NzYDSojeJkZ4d8/zdaExuE00S2F7YIdnVMd/kDCDbVV4CY5ea4poTKzP7T3pqwQ9kumUkYV4w2nZON0kaGFgiB2kzo3esYc3CYMnKxK4jBVdOvkVm0S0586qQIeVzAKiMMG/X2F/R9uqo8qgutJTjmUCb2aLRQSlAoSSTuVTrR+qRRRZYzgJLzXbo3PQlALBUGEOwyFyvC5gssGpy35ojjpqlU2NYnOqtz6I1EdudPlmQhwQVDpBEQ6yZ179uHq16YETsMtCdHd1U7qMRkde1qEgoxq08mrrKhprUpMahU/U06P9rZ3ZRap1dBCwYabG+i0LzWr7HFw2oEstQBpu756FTX9uujoVdGVTw7/+EzMVcikJRtyfotjK8uPi9TMdZ3eqMwztAr/mSs2Ky2mxoVT+URn/SrvVGYGZkdFGIWBFgo23NxAq/j5D8qNb0519NQoylyF2YPMQHCLbXeq6iBS1DRXJbbAmi25KoSpguaKZzz5fc5vSY6ci6qL7T53spO3R3Lzwrpx67dPfI+7RsY3erUi+qhvfXe6kpGISuesssY0Y3kyBicZtFAQRLTR260dVHhz0jJ8NC13MVWUxeuDzx5UujhRKx63Dkl0lKUyGhv6zI+Br3VCpH4oC8tKas3VaV+AzKZHu5pw4uLcGVMciD7HV8a5bFQTvF7lfUVheHDLO9NCv6cTWigIko8B5cPcbCSD6PKL2zTe7iHSDRX10WIFdZsTImVWVQu49TMq3Y9o57V77/6c9Zqk5kZxpavS5JcoDMjceHV8OK7H/dBCwYN+bYvLPidlgrdHMiB8h0a1w0lYJVay4LVuG9fmOG2mcyC+yNnhoDquUHHX7YZojIUv5zgZI6R9pU0Nt4HgnFXe6p2N23fj4+nOe0ryAS0UPLjw8NLY0nLbVCO7m9raxlVG0irNXbWzEJ2VuZVPZB3G7bHulRTCMqh2oW5CQeS+qiFA/29UrlVXUiIhrv08buW7+EXvGB4yHmDTiBYKgkRtEeC08AnId7DW01VG0qLJOnWi//pyvlInJJq225rC7JXBF+r+OUpsd3AQVIWlm/7f77Zbdu5BvxHOgY8+ddklbWf7HoeOLiGp8L5CfGxAPNvlfSbkhhYKgoiqNJxYIdBBui26qmyoUpkpiHasv7gEppFxPBYUFesjNxasyXWdERaqXYyb+t9vwLJFMV43ACz91aEOx6C/27U3Ofdnbu5D/KpdFPUyTrRQEGSeQmcxUsKVgB1ZoZBdH4NXztcE4+m+4eI7J47G7Fa6pMZ3fnb7UQ08/apIPvdR23c516PNCgGpRN/D2PnOccr9zHjz+HED0EIhC/t0sbhWVddjMojoqd0arorVUxydwcs/LnH8PQ5rLddnlpBVwP2fzPY+QTFbbo/07UneTu28OrGze7ZwPebHwrXbMG3ZxsDXi+D2jlVmokluDtsUk3NNFbRQsGAPaVm/ZpWyzyp9nMp0Unbd05rPOFSibotqKrtBRRut21ON03mYFT+3ylF1Rn7rN17Vb+z8dUpC9DePfRf4WhHcZugq452o64fX8+567+eRph0GWihY8DKlVPKDoiAUZEfc1nwmuctW1dWFCG7+ZZJah/EThF6vQ6RjjuJtLt+4Q3hfSBKc9dQPzgeUHM4Fv1YEmV3iaUQLBQtelUVtphD8WpENaFb9qrVDVB0RbVHQ234wdUXkahw3F8gqzshUUBHCMoFjHNP2eNZ+T2OBQ1yKtOMYGEoQ1XaxcpOaeW/a0ULBgr1Ry5uDHjjfuvFNpI9yExwigcIXrj2we9Ka5Tven+EbStSrjD+vDt5ZfDJjFcbMXRP4ehHcwljWrubtb39bRHbk4xb96nncqzaJdFRe78p7luvNnr35Z3r51w9nBb426hl0om7FQ0ALBQte7VJkcPGTpQM+oWOjss+dmtQJnCeRzWvWXanWCrlx+x4MfcZl+g3g+wXr0OqWjz3urFa5d0XoRdOrg/Rrk9sEXXOHzbsekeaEhILHsc07gs/q9kTs4nWTQt6iQNTNuRt+l+e3SNBCIQuvKbjImsJWBXtwt4p2cJMi32utswz7iNGrAr872Tvgi+qAJ8oRk6d+PsZWe2rXpmWf+7f1Dpk4c4W7xUznuz5TyscL3y92PZZoPBxmnP20+8DEi6jUjyK3dQt9Cvirg/N905sWChZy1EfWzwLvWcWs1O2UYosFlBvWhWz7oM8rZZUFcBGCtunv5q/3VPEwMx7ziLDml+5fP/IxHZXg0aHdUMNcVPcLrq66AOlVjZxiYpRd5ycFI+zDnv12UeAAUH4zGD/VqBsiszKvYEh+7SbPZYIWClZUFqCYGcOen+B4TEgouLTM1wU3kVnz8eBZXYXO9fPE/NH04G67AbWRntcu8IXrtuEhD3cUfu/xhwXOm5JEsI8CiajMJ5Ffaf1ksN8aTOb+R7cvyTmm4rMpyhndT0s3BL7WS9AB3jNzr82TSzc478IXxU8oHPnAGKX7W7n71E6h3UsULRQs2PuSbJt/7wq6aceerM5Idpbh1o+NnLrC1yujtY7uZ+DMHs0tiXtd5125n/9usedxO42KquLcPi0teXFP3C/kptcj89UOqWz48xnNfzE7t+PO6My/nKO2sL7UxWVIhqkeI2OvtSe/x/Geoi8hL1T26OxRWJNaudE9DvMDn84NfF9APHJbGAw+pElsaWXQQsGCV2fiN+j1XqRWsyzZuce7cWQLhez77PYYQUahPLL65/casW9zcV8ggp/bEP8JSvCS/7p9t/9JLvhVg10+79kLr7jDSVrD+D3pDdvcn6ffAvWExe7WXlGrRqNCND55lGihYMGrM/FrV14NT0SL8up4lyhR8FeHWFVMMhqbKLoKa2P0yrefma7X8370i1w3zlb8HRC63/zTmatcLU+YObAeO3O9d67cj1vjajj1d2k1K/WbKXiZ0vqp07wGB4pB6hLjcdtaWRIuOfL00UWDVyfm93Jy9zgc+HyJS9znDFt37cW/v13kn0EHrnt9Spaap2X96gCApnWqBbqfKtaZwh6vDVV+i3UKjeGa16cEvhYAVm1yVj18NH2le4hHAVRG7O1u+6Tss9OT83peSS58+o3XvfLWrUVdz2sLPWJ5f/Oz+7pRo6KqrseSZtWmAxsy69UQi70dNlooWFi31b5D1rJG4KvHDp7uuIXrPY979Z/v2GzfnzyvBwBg5WZ3nWpYOI18C6zqI4XFzyg7Mr97uz3vJevVFiij7JvdvNUC6uojlZ3t67c57zrP4LnfxOfedaq7d5pO8agzJBWmVoS3Jx94j+0a1U5EoEcmFIioGhGNJ6KpRDSTiO4xf69PRKOIaJ75v57lmluIaD4RzSWiQVHlzY0L/jPe9Zjfu7E3PBndYIFPmEUZ7WijImOGEEdlcnJWZnUx4bX4aX9eY244Juv7jz6CUgW/R+Omj1YNxu6n2hONeyC7eKuysLp/P6PL3cGduP26zVugqBgUHHtwQ9djXu9q/bbdmOixHmHn2Qt6Cp8bJgSgQa34ZzVRzhR2ATiOmbsC6AZgMBH1BXAzgC+YuR2AL8zvIKJOAIYC6AxgMIAniCh6r2oeNK9Xo+yznx77zCezN+jYLXe8dNH5uSSWO+Lq0qwO2pbUKvvuZVJoFwqtGtRE79L6Zd/v+UDOjcHQXuIuoP10+259rup7spbZaU+D3UuvG7L5UDEtVp1lqKiP/N5Traru7kw2+BgEnOnmaA+5u5n7t2uAP/Zv5Xm/qKhSUAkX9Ys37ciEAhtkhpKF5h8DGALgRfP3FwGcZn4eAuA1Zt7FzIsAzAfQO6r82XFyclWtMFsmeTVau/tiu4uHzwXDHjqR1khO1mx1a1EXjwztjuM7Hhi9efltCnsmI/OI/IPSON9M1aJlyi8bPY8n5PHbE9U8OT2yNiU1PY9nUEn6+wXBZ5r2GS4RUFI7/hF75tnEvdgc6ZoCEVUmoikA1gAYxczjADRi5pUAYP7P9CLNAFh3ai0zf4uFG9+c5nuOjP273RR08fptLmemGy81mF0o1KxakKU+ql/TvSEl7XrBin3U7tZPqcpm68ChWd3qvvlyI84xQhTmrFah63X7OT4hYeOqQ/m+Q1mWSIUCM+9j5m4AmgPoTUSHeJzubFRhP4noUiKaSEQT1651X0ySxcvOO4PMSPHq49tlfV+0LvgiZZLzhE53uvvkcdroZX1GzevldnwZHAPSKBVU/GJ7pbLrn936gDBt3586v0fOb3EFB7rzlNxdsntcjAJUO0SVR3bH+zM9j3/nEi4z330P2Ym7OLFYHzHzRgBfwVgrWE1ETQDA/J8xRl4GwKoYbg4gxxCZmZ9h5p7M3LOkJHe7f1BE/NLIVPCDimtkfZ/tMepRdY+QFAUOJoFWj61e5Xp2bK4JbhzF3LNvf86CboFdKLi0wjDDNNSrkevTSjymgqIPJYffhrv4gxKZKeRa7RksWrcNM5bn1ntr7lVUI5/PWu34u5tLdVFmLPcP9Skag7xDo9qB8+HUJ+3YHf1u6iitj0qIqK75uTqAAQDmABgJ4ELztAsBvG9+HglgKBFVJaJWANoBcDcHChl75fzoqv6550jUX78g7uUBq0/7TEdq3TTkNWJb5bCGE4fw+9hh0fU/w3plfXfLtsjr3yi449mprG7p2oMJRfGc3Ky97Fk6+dBctwv3ucQ2OO1x51CdVoEYxeToJZe44XbcNikOfebHrO9ORgEdbv9UKI0RZ3TJ+U1lRtjxTrF0VYhyptAEwBgimgZgAow1hQ8BjABwAhHNA3CC+R3MPBPAGwBmAfgUwBXMHJuTEXuDzFTcq45rW/abjH61bkIbT+Lkq7m56jvr6MbraakE8FHBbhFVuRKhtcViyguR1+/ngsP7/s4J+O/Qjg5rnR/QsRHqOuwNcHssbm4q/nz8gTaVpKrHywWMFRVDj6oFuQaUl788STBd43/cz8g7RJUCzDwNQHeH39cDON7lmuEAhkeVJy/sFTjzQqpYRgkyAv7YDg0x/PRDcNu7M5TzlsaYr/aKmvmWpYoRfF6qaplDmhUFHj1XJspJX3Sm0NpiRZNBdIbolF03C1572a4Z0A53nNwJv27f7Toal8VtwMNZxWHH5yy71lJo2ZeTrMFBMum6qb3ciNsqTe9oNrHrRTMdcbalhPjbIaKs6GtubNu1Fxd6bJqT4YaB7YXP3befc1w21KtRiKfOO0zo+jcnOe+gtVofic6sLji8VOg8N165pK/wubmur3MXmkfPdm609mu/vP6YnHNUrHV6ldbzPwnAQcU10bK4hq8bCBncOh5reUY7eIgF5NVZVgMEWU+8IghnR8ZPWJLCqzyZpOYzmX6ikuDCqRMi0865TlY4Np4du1AovbN7tfQ/ycRpQ9NPdw4UdtUr4hhOtCHdZfqMDzoj8gtuY2XyLxuyvlciyhEK9yrE/xX1/upUN+z7YlyvlcqRGG73dLNKsiIzU5h618CsTaHvTfGO/hclUZjbui26ixDH2pEIWii4Yb6Q4lrWRTG5SiSiFhF57+8L+ru3W9F44aXmuPDwg3yvryxQYxeuE1s3UN2cRw7ixL6ZMMOr47ODFlWulCsUVHjEZ+d7GKh2Fk4zXrea/cRXC/zzI5gGkOuvKC4zXCdk2rPoM+953+iAuQF6DXe+tlyapOYjmdHPKRZrC9n6KzKCCtP2XeZeXmfeenJHgbSyvztV3L99PEc4Pyo4FbvfiC+Frj2zR3MUCPpZDrNxOj5/tzUF29lxrjEtE4hS5pTthevENmvGtYi6eMTJuWlLXB80myqqn8x7jltsaqFgYh/BZJpddvxjudcj0keHOkWUuJdXuk4WE3YqeYyuR17ZTzwjFpKYLv+uZwvhhe4VDma0Krx/RfZzcutA7M9F9Dnt2885M6ZLj2rteO58B+eGgGjUQIeZh2BTibPDs2/aUwkXW57RQsHEPsp2UmmI6FcBoG/r+v4nmSz9NbyORqZT3aa4CcauPkoiGEgGQnCB0qlpEYgIz13o7Qnzq7lrQl0UJQK6tqiLw1sXl/0W9qD5kdE/58yYbj3JfxZoxStOyIEfc38SfR9xqkYusjm1W7MluP4/TrT6KAGW/ro9xw200+hR1HNnxnujyDT/ilcmC91TBJl+8Y73vE1l/RyApWmMFYbDwGM6uLthBoCZK7z98KSRb13cQMggMph2OsWprRzavE7utQma9ajsKYmDA9VaWx/FjjWwhRdfuwTu2LrL7jSOsv7FRZjrE34Lyc/Z3FSE0bbT6s4DUI+lYOeAyfOB30QfoeqzHiBgKp0haHxxpyA3BxXn7utQrTYqQiUO9VEY7ULPFFKCzIt4zRZfOanOLcx0w+oDRVVuKhDUF1/9rlZ5HmPnrXO1hsoSCi517i3bnpDqVdTCjJQ2qOm48OqEPU+C2iNhtiuqMScs3pDz22bBSHFOMmH8IvHgO3GhhUJKULFMyLTzIJ303ad2wiuX9BE6197hhmmV4rWQ7ITb89rh4l8mTIiiX9NQmYWd99w4DHzo6+wfHW53/ye5Tul27tmHF75fLJTOIpvFz08+8RtEEJkpiA64pVzPC+4M3+zgSmPnHrFrncr2hcvGxeKauQ4M48Jet90GGGHhKhSIKJkYdClBxYY504EE6UZ6taqPI9o0wFe28JROPP9dtgrHrd8KsqEmLHWJyH4GGb6am7urNow1Bb9bqKrm3Bb2rYJcNBynG8c++JXS9U4EVR85nieR7rVvTJE4OxhOZXMbDJ3ZozkePrub5/1EZygitG5QE3873XCmN7BT46xjuyIeaHnNFP5txlG+1wyVWW5xqtP1HVwbi9KivnscAdG81K7m75Zqq2AnMux5eTcasp1g0CmuyEY5K0s3OI+SrjquHY7pEJ4rdTtzV/nvPJchzesnGVZt2okfF/qrU0Rf/RFtiv1PMvlsRvBIhaI41Vm3sVClSoTTunvH/LrylZ+E0hDh4aHd0KK+sfN7QCfxNaAwcBUKzNwdwCkA9gF4i4imENFfiEiuFecpmRGDSOOdZbNMufyYtua10bb8qoJuEYKYvYa1puDXJk4/rHnZZ7v6y2mx0i1bDYuq4YU/RBe99fWJS/1PMpFRk/y+j7hrkrg56dFvc34TNUl14ve9xcsqOlMVCY7lhpOL7cqCGxmdWOCy1yMIXqrgqJcYPJ8AM89l5nuYuROM2Ad1AXxJROG4ZiwnvPNTtv8WFdVLRo6ICBS7W4sqlZ1fZ6FDMBw/rDOFacs2Sl+fwa+D9MrZaxN+yfktqBpn7qotKL35I/d8SN73tx6jRhEdeya1wZ0be57nJ1+qSPh9ksUrJrkV0fWcMAZJp9ue++Ydaio3OyrqTscYGRF04VEH2hGqUURUCUYs5UYAagIILw5mOSTTVwepXplOQORaq/AZ0LGhqz40iJCydr5eUeP8kGkS1Qqzq+P6rbmdUlB5O+jhb4Jd6ELvVu4bFMN0tObXqfx834mhpSXCUe0a5PwWhXXMLoeF5sUjTsb/2fT6YU/GVWbIYZqEe93qzvfV3fF74SkUiOhIInoCRqjMGwGMBdCBmU+LNFd5TmZE5NVWmNnRGVg9CSsHa2fvNQoLYpVkFTBi1x8oS1agHYnZ/d2/6Zz13e41Egi34UWFiFDIvC97cey282kLN3xilyaYfvdANC6qVvZb7j6daLnAsg5V5BD0Jy6+nJNtqWQX4H1a1Y/k/a3eHO1ObC/ro6UwoqLNBtCdmQcy83+YeRMRlauwYvPXhhsFLNPOvTqH579bjDa3fpzze7O6xiK1SN+XJRSkcuhPPWvkOIGbn+CyGOY30rWW0x632Clm8QbBcJdJItMR2IX557PCWWANuqlr8i8bcMJDX2P7bveOvna1wqwR9bfz1HdOy2B11SHjGVgEu2t1Ly56YWLWd/vyxs0nHoyW9bNjtYdBFC6/rXjNFPozcz9m/hczryaD44joWRgzh3KD3Sa6vmW07jdKto8WvJhniZ1g35BkR2R0bh01hz2ArllVLijfcQe7CAWF+us0k7r/k3g8r6ogNFNw+d2uNgn6+II+9+Efzca8NVt93Xr4rQ9EOaGzxp24/OXw3MQAwBiHELOiOO0fqFezChaPOFnaItHr/UU9e/SyPloCAETUh4geAbAEwEgA3wI4ONpsxcte2yawf5x5qPC1qzblTuUOxFbN/n2WRTfva+Qg0KgmLj5gLhi2O2Vy+SyLTP31U6XkC05rIaIss5ncBh3xR/3k4tbiOYU+BcTjLGdQMRdXwfoaCyqR707/qGcDXnipj4YT0TwAfwMwHUa85bXM/CIzi8+x8gB7bNzjJXzDeDWOWlUL0NXBCRgg34lPtUU6W7RuG96zBN/xykcQCwjr/WStRrJdN/hZH7nfe0+eCoUjHxjje47bI/3HZ3Ozvgd9Aqqdit8bD9sXlB/PD+sVyn2iikWxevNOz+M3DOxQ9nnvfsZVr+buabDiuR4Z9e59j2OXAlgN4EkALzPzeqTLOWZoqDjVcqpimYpXuRLh/Sv7l/0uo+6xHx9iC9C+yWF7vxtBiufWeJZt2J6lBvNNWyFNp4Xmikbg4C6KLdXv8riX++MMLBSEvQ4DGOtg6rTuzXBw49pl3z/x2Zzn1SdFPVbyUhw3BjAQwDkAHiaiMQCqE1EBM8drbhAxx3Zo6LpY5tV5/+7pHxwdaLldIzPg9jtVpomI1KHaHmsI1rT6/91/FJyVdrkcRmRTWJmwxz7dNHFr3MKdXFChEPDCTK7OeuqHnGNeeypWbtqBJnUOqGbCfu9ebWf+mi1o27C2+wmC95m5YpNkrgxWb97pGOkvKjEWtusYO15rCvuY+RNmvgBAWwDvA/gewHIieiXSXMVMQYDNXYC8R0VrR6C6kUfGNHP/fsZ3Pr71ux9UL+t7tvpIKmtZ+KqPBNPZv59x01tTg2ckQj6+6sissK1W1EfsAdcUJC9rVOQdPwPwXgc7/H6x8KdOBHHwZs2viommVe1z8qNjA93jncnL/U+SxOv1rdq8E9siNAMW2rzGzDuZ+S1mPgNAOwCfRpajBIhrNGtVw/rOBCStOwpcdjMDwPptu3Hus+Nc40GI3F8G67VOXlL/9nGuN1A/1m/bjTcmpsfozVpl2jWqjXP7OHt/cataUUcmk70u48LaK1/Z6k/nE/ftZ1zzmre+3M7oWf4WfPbkrrfo6JUMIUJo+1EM3O2DKfvO9Ue/nBd+oiZeC83/IiKnOVlTABdElqMEiCv6k3VU4rumIHnvxgIjvUz6suVVqfTXvD6l7DMz419fzMMz3ywMfkMJLn5xIu73EEBjBDzRAsAWBe+XIs/6kiNbuV8fNF3JK0U8tFqFQv+2uTubAWP2bDWACAu7EHKbKfs97yiaumjzkNEO+OVT1LV4ELxmCqsATCGi3wMAEdUgogdgmKU+FlmOEsBr4SZIf+j27u/+YBYmLQluuOXl80Smwr0+wd+5W5aqS2EsZvXp//PqrfjnqJ8D3SeIYBo9ezWe9hBArRo4mzna6XL35/KJm4gsCl4zoH3g+7sh0vmd2aO5/0kWzrfsJL7z1E6OLiGCqLtEBKc9razvls9+dXvEGV0kcibGhMXhB+bJeSK2H6Icx3qtKQwHMADAuUT0DYBpAPYC6MbM70WXpfgJ+/l6daIZ53L+6qPc3zreqaa122juBh7tEkhEJi9BcNqMlu2qwyMP4WQhdkQ6Sa/1oSj3KTx4VtecCGxedbe9ZTG3sHKlnN26b05cmrOrNwhzVuVunLPny1pvZq88YA0318cyrmmdcPcprNu6C6Nn58b4cEJtv0/2GxUNvBQEvzWFTE4KzHNnM/N2kRsTUQsiGkNEs4loJhFdbf7elYh+IKLpRPQBERVZrrmFiOYT0VwiGhSkQEFwit7kxcbtuwOPDsoC8PitGUhWIfvZTpGi/vbxHKG0HW8ogVvDc0q2srSPpXTiVLade/Y5Onazn++9xyQYQfcpeAkxsvUW9jNvfGsa/jduSYA0s9m4Pbc9ugWyAoC/fjjL8fcwGHZEqedxr9l7HQW/TEla7bnaIRLR7QCGAbiNmV8nomYAHiGiiwFczsyz3K412QvgemaebK5NTCKiUQCeBXADM39NRBfBcLR3hxnIZyiAzjDWLUYTUXtmjjye4yNfyC3anP/ceExf7m6+5jniNY+F3f11alqU9X3sX45znVmImLSp5E/GqV99wXOdBNnVx7fz9FaaNB3v/FTd+iihfQpO2N+AUxqi7rat2AXn0Gd+9E3bbfOcbL31m8mdcZi3is2tKf3v4j4oFVRPArkzwrjWOZ3wmimUwHCE9zoAMPNyZj4ThpO8t/1uzMwrmXmy+XkLDMd6zQB0AJDxYzwKwBnm5yEAXmPmXcy8CMB8ANFFTRHEqTPyEgi+9xNOV+6+Q7pl+5n3Cu4uNFGwnBRW/XRKt0Gtqp7Hvbj2hPbo57LgGTdOWfd6btnmye7nBd69GuCyF75b5Dlbs4/Cw9pZOyKAPyu3GYFfHZLNcWfbYMuO2/6U7i3r5vzmaXJtu41sjPQw8VpTuNrszO2/fwKgm0wiRFQKw03GOAAzAPzGPHQWgBbm52YArKtEy8zf8g6R15mUC4fZKzf77qZMI0mOnJwIMzteKg9ZP0o7TRPgIB323R94T/5FVDNxvSXXmYKvWlacXqX1fDtnN/VRoYOJuKfwt1Uoe7JxVn8vk9RH3f4A/EM0ASKqBWNmcQ0zbwZwEYAriGgSgNoAMrXecbDlcL9LiWgiEU1cuzb+WD8inZO9YlpNDjNX230Z5d7DLx++2XDktnenC51nTV51RLhR0N21V5HjFglrfHzZ2LG/81/Wey+9Za0peJx34iO5ITG9yDhdDDrm2LXXy8It+7tKaE5V3Ppqv07/oOLsxfHv5693PVdkjcvteTkKBYkQm4c2r+ubdlR4qY8uA9AfwAoAEwFMsv35YsZdeBvA/5j5HQBg5jlmbIYeAF4FsMA8fRkOzBoAoLmZdhbM/Awz92TmniUl0QVqd+Pdn+R3L3ZtUTf8jARkssVE1AtrB6AStxYAut07Cj/9siHQQnJGeDpZLkWJ6uag3zwuvjs2iljeQWdWU5eJr5XFOnmzpe32yBau2+Z9G6IsT6nfL3AXCnHaPXxhs2CyC5U4H7VXa28C4BkAgwCcD6AQwEjTS+qLfjcmo6Y/B8Ni6SHL7w3N/5UA3A7gKfPQSABDiagqEbWCsXN6vHyRwsVeLxau9a50ToTpkC4JqgR0A2JllkBIT6fOMbMj+smvFuQci5JVm+Q2+tmz7mRBk3W+y2dVMtmNohNJU9Q7e/uYsnQjVm3aiVEiu6Mt13pph8JW63s9vlfGH4hHftPgDjnH41SfulofmV5RnwLwlGl5dA6AmUT0F2Z+SeDe/WAIk+lENMX87VYA7YjoCvP7OwCeN9ObSURvAJgFw3Lpiqgtj5gZj34xX+4agebmqQYRfLd+7W/lJnl/MZ7peXxPgzo/iH8cFTJlfnW8/0Y/VdzetZ/PfWeMjEfxzkSEwp4wNiqIYMvKaY9/l+MKQj2J+NZQrCld1K+V63lx4Btei4gOgyEQTgDwCQRVR8w8Fu794yMu1wwHMFzk/mGwcN02/N/o7B22bvEPMng1tppVKmPmvYNzr8m5h/NN/NK28uDnc/1PksCrcoeluQky0Pxq7lr0bV0cTgYC8OzYaFxyWGdFbuqjNQFchzMbG6qiGFnmLn7mpvGToHpSFacnFsT1g6c3A4H6GkUgKGtkuSTw2qdwD4BTYJiSvgbglvLmMtv+zutUL8R7V/TzvMarCng5pcswffmmnOhaGV69tK9r3jLs2rsPBYo6fhGyTFJDGg/59VOVCDi3T0v8b9yBqfRXc9fg5hPDDfT35fVH+8cLoEyenN/EoM7ZgZiiUKwEUV9c9MIEbN65F/cO6ZxzTNTXkxsqax+fXH2kUtp2RFVZJx/aBGd5uPPwqtsiacisddnvtnXXXtQyXdb/4LW2ETNevcsdAOoA6ArgfgCTiWiauRN5Wiy5ixi7WRtRbsWXWVxzsjgwrjlwkVds5hpVDshotwbY4fZPccF/xrlnIiBe6qO41niJCMNPz/ZNk2mYogPfKgKCuXVJLbQpqZXzu1X4ZNJz6pirF1ZGcS1/B4ReiHRpS3wsmJzYbDq2G2dz6965aZGwrycnjmiTO1uTqRYdm3jb+8siKp8e//1hOKZDw6zf2jc64K5D1e21TNsYaItHMX/N1rLPfmFF07LQ3ArA8TBmC6cAONX8y3zOe3KEguL9CgUXZFXX676bvz76hWjL7ZPcI3DgWYnloYctLoQM1mJ+McewBnEaLTq9v7DXYHfv3e+4s1eUsNUaL16Uu480zmphr+8q9f+yo1uLpSmiPpJ4CH86pk3Wd5mZYCr2KQBowsxL3P5iy2GEBIkz6zXddJsp5NxD4AX75czP8ZcMQ7o1xX2nHeJ63C+/7RvljrrDItMwk5JLTkIhDCscv1uoxli2qzVUn59TmcOMFfzRtJWex+2LyGrxPnIv/nSGd/puSKmP7HtZfpWfCcaBVy/2ROYDEeXG5isH2Cu6iAMrr8Z1sYtffPs1QkIhJuu/v53eBY8M7Y4WNo+X1pGYVwc19c6BGGmJQ+3Gjt37sEpyQxgAzFjub8rqR8YzbRBEl2+KqgV3fuaE6vsPW+UXdXW84pXJrsfeuuzwnLYZdn4ue9k9fS/2KUjbK1/5CYBa/YwCL+sj63OvFnVGksD+Pl++uE/OOXbp7jUyuODwUud0bCOqMEdYqriFIqUs9ZH79XVqiHWG930kH23NSkad44fTs/3NY98FTtdRfeRwXrtGtTGgY0NxN8oRS327IFetcU7ZjWP21qFRbfQsdXB6GMOgSUSTEIaabprHhkEvmDmSeuQ1DqpERPWIqNjyuX7mL/ScpIDm9Wr4nhNkZ6298YjcIoyXfXr3cFxHubl/Lu8cevdnjhHJ3F7N0bYFTRVUO9xc9ZHaDZ3qYxxDmyuPa+v4u8qagqhL6wIRoaD4ELbt2puiIaKBl1CoA2NPwkQARQAm44CLi4nRZy16gozYwxAKaXLu1ttpFIbsgdit70739IkTNke28/d8Wrua7xYbZTbv3ItFDm4Toh7lA8CVHuoUEVTXJNKC26NWeQVtG4a3BqbqfqXzXZ/hg6nBwpdG9Yq9djSXRpNkegjyUPcGEQo+35Niwm0DUFLb2bSyamH2eGHnnv2oWhDPpppnL+yJDrf7RJmL4CGKDhLiWO8RVUO5kaM+iuJ5JViR0+JwIwzhO35RsIBdUT1+Ly+p51k+97MduzKi/MSKaFyEyy2mZEF0iGkKoGHFq3O7afDBGNKtadn3ffsZpTd/FEOuICR8TuzS2PecqHD15R/S/bvd6xwT+o5TOuHL648Wuofd24SXwDs88I7x8Ouxn/fgDGE6mXz22+C71rc6qBfjYtYKdSMMJ7zUR9dZPv/LduyiCPISO//vJSGPHfjL4INR11xQDWWmkA6Z4NmJFVUrxK0ndSz7vm1Xujaz3zvE3YQ2atx9+Ydzfzdnej0OqofWDpvunPhhYfYOWa8616FxbfeDMTPk8WyjALd8h+kKws0IQqSd3vR2cvt4T31M3BOvDF5CwcuRY1pmb7GRWXR6e7L7jmRX7GsKgpedcmgT+bQk8A1IYjl8yzticRjiIsgeEz/SIqyjIF+L1i1FbucrCl5CgV0+O30v94TpNli08znxkGiFgh9WC4+ges8wEFUpJE3Uu8yjunvQuh2HELXvn9FEj5cJx8GmjyMC0Mbi74gAiO0TTzFbJdUhYS4uii5oRr2fwa9I1sG4yiYdVewqhbQS9QK0yhvwWseKOt/f3HhstAloQsVLKHT0OJbXLN+4A/1GfCl1jcoo0N65x+Vy3g+/zsCqXkqbiWOS+ku3tNOsU03y7bUszs/R/p+Pb5d0FhLByyTV0b8REVUGMBRA3vo/Wpawz5G07Gj2E3TWmULKZEIqaVQU7cb/qIROmoVZkqR9PUPEI3AQvExSi4joFiJ6jIgGksGfASwE8LtIchMTbq4dvAhVfSTYwUbeEUvMFDT+HHtweDuanVCqDorBZCRvqYmB07o39T8pAF6i5iUAHQBMB3AxgM8BnAlgCDMPiSQ3MZF0ZycsFKLNhi8qj2lAx0a+5xwqEWlOo4ZXXRJ1+xCE54f1cvz9iXMPw+/7tIws3YpAVIYNXmsKrZm5CwAQ0bMA1gFoyczh+WyuoIStnz8ooM7Wr9NXsbga0q0pRs/2D6IeFDfBLuq+PEmCxnxQ6QK86lz1KtG5DHGbPZ3UpQkGdW6MVyxR9tLEJS4ej1WpUlApUNjQOPFqQWU7aJh5H4BFFVkghCmTw54B/M/Bu6sIfmWKej4VhXrsH2d2Df+mNlT2SBzboQRvX35EiLlBWUjHoCS1wz7Nysm/DA43BGwURKXw8BIK3Yhos/m3BcChmc9EFM3+6hSjZg6Y/X3j9t2C14mlGnR0LLN5LV9oXKcamtWtHuha0eetIhRUVJduufvwz/7xLPLBUGDNlux4G1FsUBRFJN56FJzWTXydIKr26TXEmMrM3aNJtmIz7PkJsaTj18n5zxSibZRRWWFFLcxUYh1HQalAfqJ41mHPMHoP/yLre1TWNWkmyvUdUUR3NGvyED8/Tf77FELMTMgklbWnz++Bx845TPq640zdukq+Va5N40whzfXLjdtPTs/2rbcmBXC5I4DXTKEhEV3ndpCZH4ogP7EQd11Mqj3u2ZfuBa20MaBTIzz4+c+e5wzqHMw7a+9W9fHlnDVqakiFa70I6nHULz/XndA+0H3TzMVHtkZRtcLIHOG5PdN3/nQEPpuxCk9/c8Cj65590dQIr5lCZQC1ANR2+atQpHlQ4zYK9POo6aceitqSJy07uzMc3Lio3Lpk8Jop9HIJtKTKVYo7gtM6k6hWJbq4Im7v6bCW9XDLSfHMUrxmCiuZ+d5YcpEHpHD27ct1J7THk18tcD3u1+hUFvqSfF5p7EzamAJaZP+GGykslhK+hg4x5UOWk7s0wSOjf8aCtblR+fyQ8TeWFKKusys8KjrZpPzV54PNflDS2PF70apBDcy4ZxDO6d0i8D28qmANn9FrWgI7yZD0JlM3Klci/L+j2vifGIDzDz8okvvK4NVrHK9yYyJqQURjiGg2Ec0koqvN37sR0Y9ENIWIJhJRb8s1txDRfCKaS0SDVNIPG5X6eVjLYJuVxHc+B2vwKW1ziRLdMyHUqlqQWEcXhUg4u1dwAadxJszgQUFxFQrMrOpAfy+A65m5I4C+AK4gok4AHgBwDzN3A3Cn+R3msaEAOgMYDOAJ0/lehSUtjvOiIu7SNahVBVccG80ILw5UxMmIMw4NLR8Zbh58MObeN1jpHm1K3M1p0zxmiaptpmFCF5l+gZlXMvNk8/MWALMBNIPRFxSZp9UBsML8PATAa8y8i5kXAZgPoDdSQvUUSPCwiXofgh+FARwTqnBWzxa4cVDwnarHRezwLkqObl8S+j2JSCiethetGngYQ6RZKjhQv2YV33PyYXYei9KZiEoBdAcwDsA1AP5BREsBPAjgFvO0ZgCWWi5bZv5mv9elptpp4tq1a6PMdhZVUywUgo4uktwxCgDdFVwTe6lh3ISdamm9RrVx0KSOu2vu4xUWsNNKmvtPpzqW5vzKELlQIKJaAN4GcA0zbwZwOYBrmbkFgGsBPJc51eHynO6OmZ9h5p7M3LOkJPzRT5qI2ptqGiwd4kR1lKbiIFA17Zn3DEJDj3gND54VvnooadK60Bwl5Vp9BABEVAhDIPyPmd8xf74QQObzmzigIloGwLpy1RwHVEvllml3D4z0/o8M7eZ6rKI1OlV1WZIzq5o+Tu9U1TjJ4d4L9m/XIMZ8pIOqhclbDEaWAzJ6nOcAzLbtfl4B4Gjz83EA5pmfRwIYSkRViagVgHYAxkeUtyhuG4iiauq+TtJobpjGPIn06V5VQ6TavHnZ4eIZEmRwwF3U+YCXJ5Z/nhWdx1vl9ayIupCoo/eJEKVY6gfgfADHmeanU4joJACXAPgnEU0F8DcAlwIAM88E8AaAWQA+BXCF6bK7wpLCfjUvcO28FQcDIu+jV2n9wF5a3aiU/OAxMvZ5SIUozTO/Vt25HrBtJm3cIUJk0TWYeSzc5WkPl2uGAxgeVZ5UUH2VtaoWYOuuvaHkxU5Bee41QkT1HYrKFKfzlBzhpWhmGzZhB5wSxU8dF4Ty8pp0bxITH13l7/M+CH86pg0ae1ilaA4g0mjD6IBVFqTjuF8cPHtBT6HzojT17tikyPWY1yM9oVN0llwPe6zxpYXo4vBVIO48pROKa3nbKB9U7G3O2NrB3FFkDJXPu0qDeucMSlydq9PahcpsLh+txAYIdqxRmXq3blATn1x9pOtxr0daVySmQcB3MqhzYwzt1QKvTVjqf3JCaKEQAn1bF6NTU/dRiR+vX9oXbRvmbuIRWaxN67qDnzXMtzcdixb1g8WW9sOt84+rb3VKX0XDVznlM4WqBZWwK2Dc4cRCgXo806aB14TE3lNa22wGrT5KAX1aF6O4VtVA16a1vxjoM1L0EwifX3tU4LTdTEfjelZObb521eBWZmlfUxAJB+pGUv2j2+yrd2l9XHlcW9/r0/1G1NBCIQ9o38g7LkIaqaSo82jfKLhn2d90dY5zK9K5htHY7aPfQZ0boU4NFaGgmqNoaafwrtykQjuHmXOYuFkBDTqksZB34VNd6pgIafdpViGFQs0Ig2SESabqdGlW1/WcfDBxi5s/C4z0osTe5JvUUTNRrVJQ8Zpp1N2mm6AVVWc5mcumXXiLUvFqGxRHNgmgUtnSrr+MAiLCVzcc4/C72n1FNxbZn7mq3rw8B7B38yeVlAPKvq2LI08j7W2y/Na2kEnre/Tr6NI+VY2K0ga5nY2I9ZHbKVcd3w7n9RELgGJ/5km9gdoR2OKHjVvIzqfOd9zKFBr29/zKJX2w6P6TcEizOpGmCwAltYOtHwLAq5f0Lfvc2qGOh4EWCiEQ2bQxhN4k7aOSOFF5TYM7NxZeJ7E/86Q2aA3rV5pIujIUOMyCTu3aNPRd4Xbsatcj2jSIbUH/mgHtA197eJvisn0UNw7qEFaWstBCIQSiqkuZEafK7dMoFK5KSOev8p6c9pG4kas+8r/m1Uv64og24aourjsheOeTJHHsy0hS/29dI3ry3MMC3ycqIaaFQh6gtKagmPYf+7dSvEMuFx5RGvo9RRBZlHc7R8UPj8g7OLxNMXoe5By2VfT9335yR9t16p3GNzcei3f+dITyfbyYN/zErO9xDGSi2MgY5I5Wd+gqpr1hooVCiulxUH0AwEldmgS+R59W9ZXy4DVqaxnR5rOoiG2fgq1XE15oVsxgFCPHlsU1AscYF8W+OS+oTKhdtUB4gTqNhkIt6om1p6jzroVCCNSt7h+GLwhtG9bC4hEn45gOuWEgj+1QgtrVCnwtYlrUr4HFI04u+/7eFf2k8uAV0ctvBBl3uM2o+Otph0idf1bPbNcj+4Nt9pUmX5+2XZYFtda6+MjW+FDQx1gY8jN0l+bSeYpmSqWFgiL1a1ZJxCHd83/ojel3D5K2Ye8m6W/Ia6bRwGcXtuoeijoiPmhCxqmzOL+vmNVRhmsGZFvU9BMMFrPfK7iAAGF3Eef0jsevln2GE1R9JGNpF8asqkvzbEsl9ch+YudFPePVQkGRqMzCwubL64/G5DtOkL5OqfG4XCp6z6fOi9YsMSqs5Zt610DXHdZ2fntYMxRVyzUjFRWuKvshvr0pN77ApUe1CXy/JIjbqOKCw+UGC37ItrWoyquFgiBROO5S8e8jS+uSWqhfMxo1lysps3xKwoeQzGyndUktTLt7UNn3/3d0awDxrIU4+aJKSh0VVO0Yd3WrHULURCuypY6qXmihoIhKTFUhF715zO59MSnTXTi0ufxGpDTp5ZN2YZKU2WZg77nMKC2uiTN7NMfjCqaeQZF5XzUcXO2IPu+oZ0RaKCjy0O+6CZ9rX5hKu/dLVW4YqGYnr7ob+xXL7s98JFM9RGuJX0wP6fQTEkpB2wXD8JD74FldPQPspIEfbz0+R50rbyar9ymkEplA2/ZOLqngKbef3BFvXx6t7TkAXHlcO4xyUJHFVexaeeDmwQvZ53Rat2bhpi+RgZf/2CfUtIOQ1P6XIBRVK4xfnSuIFgoJIjMiuj7E3akXH9kaPVw2SoVNmpwPCj3tFE3eZEeOTvXp2gHt8a7g5jMVX0n9BS2sRDivb0vpa+pUL/S1hosa1Ym/sPpILRlf8nsoFSIyi1vn9z0IL/24RDoN+3RcZqbQozSeTjztvH5pX6zbujvQtfmmrQtjJnn1AGeHcyIk9bwa1q6YMcdl1XVRvR8tFEw+v/Zo4XMbFYUzIpGpBEkvOqaFPjG4Nk4NZqtPqnMu72teaSMtsbi1+sikVQL7DUg/fU86JKB6SpPwTTonSadfEbCauqdFCOuZQgDCMglLRxUIRlWFaGCidT9o3GoV0hR/IjNQaVMSUzhW23tJSR+VNxAMr6ci4TwzWGub6OOO2iRVC4UAhPVOovDUmGbqVC/Eph17pK7p3rIufvplIwDg46uOVEpfyMVHSC93xG+7KC+yDz6kMd4pPgLdJV2ThEXQWdMnV6u9p3zmRAXnlbLdQVS9hxYKMWIfhVYwmRBoV7j1kk5N1WzPuwt4+wxL4A/tLW9BY4cZkXso9SJo/Uxij0AUHgfiwJptUfVRSW3DlLVGlWi678i02kTUgojGENFsIppJRFebv79ORFPMv8VENMVyzS1ENJ+I5hLRINebJ4D15QWtf6d3z7Yjl5kpWBehRvy2S7AMpIQ06e3tJBUlzQkVFV0Q7G8lvW8pHMKI7nbFsW3wwBmHBr4+iDC745ROuP+3XdCvbTRGF1HOFPYCuJ6ZJxNRbQCTiGgUM5+dOYGI/glgk/m5E4ChADoDaApgNBG1Z+Z9EeYxEEH1zk4usEUpsJjMntY93E1Kfgzp1hTvT1kRa5pJkWmjDWpVxbqtuxLJw4NndUW9GoXC4T/D4oLDS/HYmPkHfpBMvrS4Bhav3x5upmLgpsHBw1reOOhgLNsQvMxBepIaVQpwTggzUTciG4ow80pmnmx+3gJgNoCy3oyMudLvALxq/jQEwGvMvIuZFwGYD6B3VPlLAvvEQGam0DTimLVeXHpU61Du84d+RhS3alWCLcTFQSa9KgnGgjizR3PPOBZRcf3A9lj4t5PKvsvO6Eb+ub+jt9U4CGK5kxmli3qxjYIUTUzLiGVNgYhKAXQHMM7y85EAVjPzPPN7MwA/Wo4vg0WIpImgL9LeyGTqcZM6B4RC3BWpXo1wtuNfe0J7XCu7MzvmwmZiGqTFPDBOiCirTso+gqJqhSgK2XOoKCprCmG960CCKUXWbhkiV1oSUS0AbwO4hpk3Ww6dgwOzBMB5sprzxIjoUiKaSEQT165dG25mPVBd5ATUZgqAYdVx1XFtUd3Bw2KUNK1bPcfNd1xVOfaZgplgBZQJOZT3R5C+7jgdRCoUiKgQhkD4HzO/Y/m9AMBvAbxuOX0ZAGuop+YAchTZzPwMM/dk5p4lJSXRZNyB+3/bBf3bGv5dglYm1YW8jk2KcN3A4PpPFdon5MNIdaLw2qVynlIzI7eKZi7sREWZLSVayhRKpsjUR+aawXMAZjPzQ7bDAwDMYeZllt9GAniFiB6CsdDcDsD4qPInS7XCyuhZWg9j568L3FPZG1kFaXOJ0rd1MT6+6kjhxcBMRMxKZESr27Bdbl9FeSKfqqc9NKYIYWkmVe6TQpkQ6ZpCPwDnA5huMTu9lZk/hmFlZFUdgZlnEtEbAGbBsFy6Io2WRyrkzBS0VIiFTk2LhNV/GYOf2tUK0TquncQpJW7rp6C8ffkRaN9I/l1l1LBJzgor1EIzM4+Fy2CDmYe5/D4cwPCo8pQ0WgbIU1TdqKJHt49HVdiyfg3cetLBODVBi5S0kCcyIbAb+Bf+0AsfTF0RmoPLIFTIhebyRMZ6iIFAnYaeGchTUMmoosNiCqBCRLj0qDZZ1l4VlfK+rnJQcU1ceVy7RNtlGmcKWihIMKxfKU4+tAn+2L8V/nVOdywecXLSWSr3ZEaBLYsDxu3VBCZOoaBnZumhwvo+qlW1AFt37QUAPD+sl9A1daoX4vHfxx8QPJXENMK54ti2GNKtKQ4qjt+1eUUnzgF0vvouUiWNpa6wM4X3r+xX9vnYg4O7n9BES+VKpAVCAL65UX1ncZwzhTR2jnGQRmFYYYVCbD7qNZoECEPdFudC81EhxnjOJ2opxMWOigorFDQajTdxzhR+17OF/0nlkJ6l9ZPOQg5aKGgC8btezZPOgiZi4lxTKA+WeeWgCAAq8EKzJjiz7x0cu69/TfyUh45aI48WChpp4nbIp9GkmczO7zSuDwShfJRCo9FoEqJZ3eq4/eSOgeMz16tRmCofW1ooaDQajSIXHxk8ENXo647Ghu27Q8yNGlooaDQaTYIU16qK4lrJ+V+yo1cLNRqNRlNGhRcK3VvWTToL5R5txJJfnNsnuqDwYVJTGzxEQoVWH31707EorhVO/GFR7jylE+79cFasaSbNuFuOx6Yd6VlI03gz/PQuGH56l6Sz4cv42wZgXwrdROQ7FXqm0KJ+DdSoEq9cvKh/q1jTSwMNi6qhXULhPDXll5pVC1BUrTDpbJQ7KrRQ0Gg06aJxUbWks1DhqdDqI41Gkx7G33q83hiZArRQ0Gg0qaChniWkAq0+0mg0Gk0ZWihoNBqNpgwtFDQajUZThhYKGo1GoylDCwWNRqPRlKGtjzTCPHDmoSiIM3CvRqOJHS0UNMJU1Di6+URJ7apYu2VX0tnQ5DFaKGg05YhvbjxW+wPSKBHZmgIRtSCiMUQ0m4hmEtHVlmN/JqK55u8PWH6/hYjmm8cGRZU3jaa8Ur1K5XITFlKTDFHWnr0ArmfmyURUG8AkIhoFoBGAIQAOZeZdRNQQAIioE4ChADoDaApgNBG1Z+Z9EeYxEabeORAMPZrTaDTpIzKhwMwrAaw0P28hotkAmgG4BMAIZt5lHltjXjIEwGvm74uIaD6A3gB+iCqPSVGnhvbsqNFo0kksJqlEVAqgO4BxANoDOJKIxhHR10TUyzytGYCllsuWmb9pNBqNJiYiVz4SUS0AbwO4hpk3E1EBgHoA+gLoBeANImoNwMnWMUfHQkSXArgUAFq2zI8IURqNRpMvRDpTIKJCGALhf8z8jvnzMgDvsMF4APsBNDB/t9o8Ngewwn5PZn6GmXsyc8+SkpIos6/RaDQVjiitjwjAcwBmM/NDlkPvATjOPKc9gCoA1gEYCWAoEVUlolYA2gEYH1X+NBqNRpNLlOqjfgDOBzCdiKaYv90K4D8A/kNEMwDsBnAhMzOAmUT0BoBZMCyXriiPlkcajUaTZqK0PhoL53UCADjP5ZrhAIZHlSeNRqPReKMd4mk0Go2mDOI83hJPRGsBLEk6HzHTAMYaTEVCl7lioMscHwcxs6OlTl4LhYoIEU1k5p5J5yNOdJkrBrrM6UCrjzQajUZThhYKGo1GoylDC4X845mkM5AAuswVA13mFKDXFDQajUZThp4paDQajaYMLRQ0moQwXcFoyjH5+I61UEghRNTA/F856bzEBRHVsXzOu4YUkArV/oioZyaoVgUi74KnVKhKmWbIoAYRvQrgfQCoCL6fiKgPEb0P4FkiuoiIqnI5X+giot5E9DKA+4moCxGV63ZIRJ2J6HsAdwGom3B2YoGIDieiNwE8SESd8mmAV64rYz5huhLfbn5tQESXA0B57jCI6FAAjwN4C8CbMLzntk00UxFCRJWI6C4AzwL4BIbvsSsAdE00Y9FzNYB3mflUZv4ZKN+zQXM29BiAj2HsVr4awEXmsdSXu9x2OPmG2WE0AbAawB8BXE5EdZl5fzkWDD0BzGfmlwCMAlANwC+Zg/nQgGRg5v0w4oYMY+b/wXD+eBCAvBlFymKqQhlGJwkiOp2ImgOobn4vV+/YpCuAn5n5eQD/BPAOgCFmzHlOe5m1SWpCENFZAE4CMBXAvzKqIiJ6D8CVAP4CYBuAfzPzgqTyGSaWMk9n5ofMEdVSAA8CuBBGhzkbwBxm/ntyOQ0Ps8wdAExg5s+IqDqAXQAKmXmX6S7+JWb+INGMhoSlvOOZ+XOzvJMA3AjgHBi+flYB2M3MlyaX0/AgonMAHAxgEjOPNAXhDwAGM/MCIqoP4CoAtZn5+iTzKkJ5HYGmFiIqIKIHYFSS92GoDy4wjx0MYCEzL4Mxcv4TgDfNwEN5t2CVwaHMlxPRpcy8Bkbs7gIAtzJzXwAvAOhPRIcnluEQMMs8AkaZfwZwDxGdCxgzBlMgFMKIMDg3wayGgkN57yWiYcy8A8C/ATwJ4DNmHgzgNgCHENGJyeVYHXMd8DIANwFYDOAfRHQxgK0A/gtDbQQAGwGMBlDD1AakmshjNGuyYea9RFQCoxP81pxK1zQP/wKgHRF9AKA9gK8B7GXmXQllNxRcylzFPDbLFIafmadPArAGxmg6bzHL3ALAdcw8gYg2AXgYRtlGmad1BLCamX8motoAOpohavMOl/I+SkRLYURgvBFAQ/Pc5UQ0FkYo3rzFVAX1BTCCmV83yzoMwEoY6wl/JaIBzDyaiNYDaAZgU3I5FkPPFGKAiC4goqOJqIiIqgLYDOAyIroDwEMAjiCim2CMGhcDWASgBzOfCqAFEfVIKu9BESzz7URUDUYo1rtNXetQAJ0BrE8s8wGxlbkIwA4ATQCAmT8DsB3AUZbRYjGA7UQ0DMD3ALqkXd9sRaC8W2EYD2yFMWq+gIi6mUYUA2DU9byCjBDCVmYAaEZEBcw82vzeF8BaAK8AeJiI2gI4HkbQsSpx5jcIek0hIszG3RhGxdgPYAGMGcGfYFSMcwGcCSMKXTUA98BYkPqUmTdZ7lPH+j3NBCzzuzCEwtMASmDMXq9i5llx5z8IHmW+FIaAOxHAQhizgkUAOgG4jJnnE9HfYYygXwDwMDNPi70AkkiWdzEOlHceEf0ewCEwhP6tzDwz9gIEhIi6wYgvvxvAIGZeZP7+JwBNAbzMzHOIqBTA/wG4l5l/IqJbALSBseZwCTPPTiD7cjCz/gv5D0Bl8397GJUFMDq7xwG8Y34/BMDTlmvuBPCM+bkSgEpJlyOGMt8FYyEdMIRG46TLEVKZn4CxeAwYlih/AXCG+f15GJ0kABwB4OykyxFDeS+33IOSLkfAMp8H4DIALwG4BkB18/fWMNZMzgdQx/ztBQDDLfeoknQ5ZP70mkKIEFEBgHsBVCaijwEUAdgHlOlcrwSwkoj6wVCn7COi45j5SxhWGQvMc/NG16pY5mIA881zd8OwSkk9gmVeQUTHMvMYIprOhmlxNRgqhM/Mc79PqAhShFDeTzP3YrOXTDuWMhcQ0UcARjHzaiKaAuBvAL4ioqnMvJCIvgLQC4b6934YM6iyd2vW7bxBrymEBBEdDWORtB6Mju6vAPYAOJaIegNlDeJeALcz83QAKwBcR0Q/AagPw0Ijb9Bldi3zfgB3w5gJAUbHcgkMffM2GB1oXqwdVLTyAjll/hnAAzDMbMHMPwKYAmNmUN+85E0A/4FhNTfOvO6rWDMdInpNISSI6EgApWxsxAIRPQFgOozFtz8zcw8yNqE1hKFSuYKZVxFROxhT1DlJ5T0ousy+ZX4UwI3MvISITgOwJl9mBxkqWnkBxzI/AmA7M99ifm8O4GUAtzHzd2RsMt1IRMUAqjHz8sQyHwJ6phAekwC8QQd8nHwHoCUzvwBj2v1nc0TVHMAeZl4FAMw8Lx87RxNdZu8y72PmJQDAzO/lYweJildeILfMP8LcdW5aGS2D4arkL0T0IQxTYzDz+nwXCIAWCqHBzNuZeRcfcGJ3AgyzNAD4A4COZgV6FUaly3t0mQF4l3kykN+uHCpaeQHHMg+CsfMezLzX/K0zgMEApjLzsPhzGR16oTlkzNEFA2gEw9QSALYAuBWG9c2i8jCasKLL7F3mfFlc9aKilRfIKfPH5m8dYfht2gqgPTMvTiyDEaFnCuGzH4YP9XUADjVHUXcA2M/MY8tb52iiy1z+y1zRygtkl7mraYV0PQwBOLw8CgRALzRHAhlb3783/55n5ucSzlLk6DKX/zJXtPICFbTMWiiEj2mdcD6AhzjP/RaJostc/stc0coLVNAya6Gg0Wg0mgx6TUGj0Wg0ZWihoNFoNJoytFDQaDQaTRlaKGg0Go2mDC0UNBqNRlOGFgoajQREVNcMrAIiakpEbyWdJ40mTLRJqkYjgRlZ60NmPiTpvGg0UaB9H2k0cowA0MYMtjIPQEdmPoSMOMunwfCmeQiAf8KIJnc+gF0ATmLmX4moDQw34iUwYjZfksceYzXlEK0+0mjkuBnAAmbuBiO+spVDAPweQG8Aw2H44O8O4AcAF5jnPAMzDgGAG2CEstRoUoOeKWg04TGGmbcA2EJEmwB8YP4+HYYTuVow4jK/afEuXTX+bGo07mihoNGEh9U3zn7L9/0w2lolABvNWYZGk0q0+kijkWMLgNpBLmTmzQAWEdFZgBGMhoi6hpk5jUYVLRQ0GgmYeT2A74hoBoB/BLjFuQD+SERTAcwEMCTM/Gk0qmiTVI1Go9GUoWcKGo1GoylDCwWNRqPRlKGFgkaj0WjK0EJBo9FoNGVooaDRaDSaMrRQ0Gg0Gk0ZWihoNBqNpgwtFDQajUZTxv8HfXxrBQno0wsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds[\"TREFMXAV\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confused-affect",
   "metadata": {},
   "source": [
    "### Step 2: automated machine learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "defensive-footage",
   "metadata": {},
   "source": [
    "**train a model (emulator)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "023c3399",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBMRegressor(colsample_bytree=0.7463308378914483,\n",
      "              learning_rate=0.1530612501227463, max_bin=1023,\n",
      "              min_child_samples=2, n_estimators=60, num_leaves=49,\n",
      "              reg_alpha=0.0009765625, reg_lambda=0.012698515198279536,\n",
      "              verbose=-1)\n",
      "CPU times: user 3min 20s, sys: 2.53 s, total: 3min 22s\n",
      "Wall time: 15.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# assume that we want to split the data into training data and testing data\n",
    "# let's use first 95% for training, and the remaining for testing \n",
    "idx = df.shape[0]\n",
    "train = df.iloc[:int(0.95*idx),:]\n",
    "test = df.iloc[int(0.95*idx):,:]\n",
    "(X_train, y_train) = (train[features], train[label].values)\n",
    "(X_test, y_test) = (test[features], test[label].values)\n",
    "\n",
    "# train the model\n",
    "automl.fit(X_train=X_train, y_train=y_train,\n",
    "           **automl_settings, verbose=-1)\n",
    "print(automl.model.estimator)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "secure-worst",
   "metadata": {},
   "source": [
    "**apply and test the machine learning model**      \n",
    "use `automl.predict(X)` to apply the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2bbf7bfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model performance using training data:\n",
      "root mean square error: 1.0953179201882188\n",
      "r2: 0.9908044117105271 \n",
      "\n",
      "model performance using testing data:\n",
      "root mean square error: 1.6714483065300212\n",
      "r2: 0.9799441548983633\n"
     ]
    }
   ],
   "source": [
    "# training data\n",
    "print(\"model performance using training data:\")\n",
    "y_pred = automl.predict(X_train)\n",
    "print(\"root mean square error:\", \n",
    "      mean_squared_error(y_true=y_train, y_pred=y_pred, squared=False))\n",
    "print(\"r2:\", r2_score(y_true=y_train, y_pred=y_pred),\"\\n\")\n",
    "import pandas as pd\n",
    "d_train = {\"time\":train[\"time\"],\"y_train\":y_train.reshape(-1),\"y_pred\":y_pred.reshape(-1)}\n",
    "df_train = pd.DataFrame(d_train).set_index(\"time\")\n",
    "\n",
    "# testing data\n",
    "print(\"model performance using testing data:\")\n",
    "y_pred = automl.predict(X_test)\n",
    "print(\"root mean square error:\", \n",
    "      mean_squared_error(y_true=y_test, y_pred=y_pred, squared=False))\n",
    "print(\"r2:\", r2_score(y_true=y_test, y_pred=y_pred))\n",
    "d_test = {\"time\":test[\"time\"],\"y_test\":y_test.reshape(-1),\"y_pred\":y_pred.reshape(-1)}\n",
    "df_test = pd.DataFrame(d_test).set_index(\"time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "technical-summary",
   "metadata": {},
   "source": [
    "**visualization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1d051ff8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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77MCMGTNc2zQ2NnLe0qUsBkpWrEApRfXChRyjty/55BNefvll1qxZk+beG9oSfgzq7e0vRCQT2Ck93Wl9VnzxBQD99DROW0YpxdvAXoA67bSE2++zzz4ceeSRgc+dKJdddhlPPfUUQLsJqXnqxBP59KGHWuRcZWVl7LfffoHazpsxgzeBV2++OeG2N910E2PHjuXHH38MdO7WYKuttmL8+PGB2i5YsCDh63eIbfnlBx4IdF5D+0ApRWNjo+u2SZMm8c477/Dss8/6OlZDQ0Mqu2ZIAyu//z66HDGo+z38MCdPn84999zTbH+lFA888ADXKMVWwDk1NUyfPp3fvvsuus+ir7/m1eOO4w8Bn6+G9omnQS0i14pIOTBSRMr0XzmwGni9xXrYwvwQMSpa2HMbhMfvvpsD9HJpSUnC7T/77DPeeuuthNt9//337BoK8XGCXuan776bTno5FAqSD9vynPbCC+z1hz+0yLnmzp3L/gHbFs6dy6HANQlMWUaY+e23nAes0lOX7QX7VK0f6uvrue+++9hmm2246667Ap93148/DtzW0DZZtGgRQ4cO5Y9//CM77rgjf/nLXxg7diwjR47kxhtvBOAPf/gDv/32G4cffjh33303lZWVnHXWWYwdO5YddtiB11+3HotPPPEExx13HIcddhj7779/zP2OPvpoDjzwQLbddluuuuqqaH/ee+89dtxxR0aNGsU+++wD4HkcQ3JU27zQVXPmUFpaylV1dVwBLHr0Uf70pz81GYB/8cUXXHDBBUQipXcEdtxxR56+b1NqWdWUKfwX+FM7m/kzJIenbJ5S6nbgdhG5XSl1bQv2qVXpqP9PXLCgVfvhh1ydXQzQubLSe0cPrgHWBTjvsgce4Bvg1dtug3339d0ucgNaCTz3448ceOCBCZ339ddfJz8/P2EvbkVFBcuWLWPIkCHxd7axYMECBunl6upq8hKQVKqpqWHKlCnssccevtvkNDbyfvzd3NGesOLKSlAKEkhIOm/mTPYDJj3+OBx8cNAetCg/A88l2Obaa6/lzjstFdDJkydz+eWXBzr3wqVL2RzLx4pILvA5kIP1bHhZKXWjiBwH3AQMBXZWSk2ztbkW+D0QBi5SSgW+hCNccsklTJ8+PdnDNGH06NGu3kY7v/76K48//jhHHnkkL7/8Mt999x1KKQ4//HA+//xzHnzwQd577z0+/fRTunbtynXXXcfee+/NY489xoYNG9h5553ZV98Pv/nmG37++Wc6d+4cc7/p06fz448/kpOTw+DBg7nwwgvJzc3l7LPP5vPPP2fgwIGsX78esFQl3I7ToUOHlH5WmwONjY2+nDZHH344/X7+GYB6QC1axJwffiAy93UkcOFtt3HppZfSpUsXAH6cMoV7gW56n2L9v7PtuP10OFofj5kOw+ZJ3CtOKXWtiHQSkZ1FZGLkryU61xr80bYcfvPNVuuHH3Lr6pJqfzvwSIB2BaWlABw9eXKg8/YCBvz97wm3O/LII9l//8R9uIcffjhDhw5NuN20f23Kvb0lwam7vLw89txzT+bMmeO7Tb7OEo+wcL7/SOo/ai96f6C6qMh3O4AiLa014eWXE2qXLJ988gkiEsh4GgH8NcE23T/8EAWcA2QlMRW/7fbbx9+pfVIL7K2UGgWMBg4UkXHADOBoLGM7iogMA07ECgs8EJgkIhkt2uMU0r9/f8aNG8cHH3zABx98wA477MCOO+7InDlzmDdvXrP9P/jgA+644w5Gjx7NnnvuSU1NDUuWLAFgv/32o3PnznH322effSgqKiI3N5dhw4axePFivv32WyZOnBjVj/ZzHMMmJk2aREZGBgceeGBML35FRQX/ePNN7gXKCguZlZ+PrF7N/449NrpPJDV84cKF0XXh115rovZTDBwLPKpf12ZnM0IvN9odGy++CIMGQTubCTT4x0/p8f8DLgb6AtOBccA3wN5p7VkrsY1t+Yd772XsYYe1Wl/isTSZ5KgkkiXefPfdpL/8/i0YQ/3pp58CVuxbIlJSv73zTnT5yARjbvsDlwKl6/zPAYQcFc2+GjuWgXrwEo8TbMt5CSbr7azPMTKhVsnz+gsvcAlw9z/+wZM+Y1KTYS+dlf8Q8P4vvwQ+TuNmWnlOWfPaEd2wLP2nlFKzwVWG7QjgeaVULbBQROYDO2M9HwITz5OcLiKeXqUU1157Leeee27M/ZVSvPLKKwwePLjJ+ilTpjTxGsfaLycnJ/o6IyODhoYGz/uU13EMTXnssccAeP/995n/888cccQRrvtN+/Zb9tTL+SNGUDt3LjuvXcuOep0aM4a9vv+eA5Vi0aJFjBkzBoD+tnuHGjGCbVet4iWbVF79dtvRW4eR1NlDR59+Gn77DS64AF55JSXv1dC28BPIejEwFlislNoL2IFNs/ebNRUBwiiCsHz5cq688sqEFTc62m7GiVBaWsoBEyZEX7+pbxR+2VhdHei8dvICTIXdi6XhGJREE9Hm2rxSu3z4oe9277//Ps9j/XA6zprlu53zIXrShg2+2zof/fVJzl60BAf98AN3A+q//22R89k/3QOWLw+sEtLQTuL/gyAiGSIyHVgDfKiUmhJj9z7AUtvrZXpdu+aAAw7gscceo0JrEi9fvtxVreGAAw7gX//6V/S+4pXU63e/COPHj+ezzz6LekUjIR+JHmdLZUTfvuyOdU+cv3IlTHG/hOe8+250OXPffQkXFjbZLkccgSjFu8A6PYumlGLXigp+69sXbr0V2WUXujiK53TYYYfocp1SHHrooVxzzTUQyXN6662E9K4N7Qc/T4YapVQNgIjkKKXmAFvEEDkjI8HZy5UrrVFoguy1114s+ec/efnRR+PvbKNfr14Jnwvg3XffZb7NWDzMluUcj/r6elKRt16cYFjCu+++y0VAMkE4iRrUQZWyc//1L8bp5XACoQVOZYFkzLZPjj8+idYJ8o9/gC3D3S91Wkknth8wfZT4LMDw6aef8rXtdffNWApLKRVWSo3GmpHcWUSGx9jdbbrH9WcjIueIyDQRmba2jRe+2H///TnppJMYP348I0aM4Nhjj3UdfN1www3U19czcuRIhg8fzg033OB6PL/7RejWrRsPP/wwRx99NKNGjeKEE04IdJwtlfM/+4zPgQf1a/XBB3DQQfD887BiBWgnx0o9c8njj8P110OnTtFjqIcegh13jL4u+vJLysrKePu11+gFLBsyZFMbx7Uh/ftHlxuAnd5+m37/+heNM2dS1qcP1NWBkY3dPFFKxfwDXsMKE7oJK4budeCdeO1a4m+nnXZSKcdK6VIK1Je77+67WWN9/aa2JSUJnXKobveqNb3qmw/692/SX7+8fs89Tdsl0PaR7bdv2q6x0X+Hbe1W9+3rv51S6vTTT4+2/eq22xJqezGoBlB1dXUJtbs/4GdkbzP1nnt8N5v34YeBvxdnuy9HjQrWNggB2s6bN0/dncx5A7SbmpfX5L0u+d//fLV7aNKk4N9LHIBpqg3cS93+gBuBK2yvJwNjbK+vBa61vX4fGB/vuG737VmzZiX5SRrc2BI/11qR5r9XUOqAA5TabjulQFWXlKhrMjObPK+/O+ggpfSzQjU2KhUOK/XWW2pFVpb6tnt3dcQRR6h++liTTznFOtlttzU/z1NPRZeXdejQZNsloOqLiqy+GNolse7ZfpISj1JKbVBK3QTcgBV7f2S8diKSKyLfichPIjJTRG7W64/TrxtFZIyjzbUiMl9EfhWRA9yPnD6cCQxZCcTbVtjCL+oSLLJyq/5/VEKtICOgtF83lwQbv/yfU6osgZAGO2v7JDYzvJdN2qjuq698t/vss8+4B8gAVIJhJucntLc72dnZvvf9LoH3FY9wZtz0iCiB89ArKqgKkFwKsGTxYs4LeNrZttj2RBjjCFVSPkOXBkydGuh87Q0R6SYixXo5D9gXiJVV+wZwoojkiMhAYFsg8akKwxbPTz/9lLLY+TrlPrdYNXcuzJ0LwMw//YlBDQ3UFheDTvrM7tkTsNQ+EIFQCA45hHndutFrwwbmzJlDZE441Lu3tVBc3PxE220XXezjCBv9Enhhm23g/ffhm6RSDQxtkJgGtYiERCRqySilPlNKvaGU8hOg2e4yxpc6MqazEoiVLLAZa+q22xI6r3/huabsvWxZ0xU+DcZEEvPisfSSSwK1W7XVVgntf7o9LCWB7+WhPfeMLufl5kY8aWnDefyiBEJbut90U+r6kcBnFDS0JHzeeeRffXWgtp0+/5xAGQAVFeQEKF7jhu/haDvQpE8RvYBPReRnYCpWDPVbInKUiCwDxgNvi8j7AEqpmcCLwCzgPeB8pdQW82EZUsPatWsZPXo0l156KVVVVUkdq6akJCp96yTfptSx9oEHGCpC5rBh0XUdtJPHeQFXdulCj7o6Cjp0iBrUWZHnV8Sgtt9vt9222bnPHTKES045hfEXXsgNungMs2f7e1OGdkNMN5ZSqlF7mLdSSiWkz6Nd462eMZ4IQx1xoJkBDc/V8+aRiLlYGH8Xf9TWgg+t5FQalZUeCR9x+5CMUZ9AbLs93e0AoLy8nMLClH3izXjhhRc40fa6rrbWd9ugAys3VKLx/wFoeO457GdRdXWIT4/8DrfcEuics488stnvNCh+k4C38ig/vLmhlPoZK+ncuf41rNA/tza3AYl5EAwGGy+99FJ0uaSkhPz8/Gb7PPzwwwwePDiurn/pN99EjV61++6UffEFTpfGqlCI3RsbUaEQGTYJzAJtJDufjvW9e5Pzyy/0yciIHjtv662thYhBXVwMZ5wBQ4dGPd52HpoyBQoLWbduHU//5z9QXW3Fcxs2K/w4p3oBM0XkYxF5I/Ln5+DpyBhPZ3JLrmN6RgIanluVlVkRUz6Yn4DWcFx8JsB9GVA/2g2/ZrHTiN/3xReDnzSgysI7QE2aNUBLHQlrnz34oMeeaSaBzyisBzeJ5p3nOAzSDe+9l+AREmfm11/H38knjT5/o0MiHiWDwZBy7NV613nIjF547rks3XNPcAtXtP2Oy3UI4hf33IN8/jlVWuJytm2mMOPSS+kAdAyHwVbsq5M2krNyc5seXhvaXSoq6IkVItcxYlBHjltUBHfeCf/3f9Zre9GyXr1AO3G6du3KLhMnUpqRETWof/31V8455xxTpn4zwM9T92YstbJbgDttf3FRacgYV0o9rJQao5Qa061bN5cmwWl0GAgvJGF4lvssbbxu6dL4O/nF5w/yygRLNsei0aen2c0bWB+gTDaAJOF9bUhAFzoIezgM9v/77jtK/ve/tJ7TjaIEZJnm9+gBwOokz1nv0xv/008/BT5HtSPuuT4J+amGgFroQYohGQyG5jQ0NPDTxx/zZufO9MbboN4XOAWou+CCpht++gn694fXXoP776fnAw8A0HEbq6JEg54xe2njRv4CvLbrrnSzq6PYDOpIDHWOY5Y3Sx9rq9JSLsaqLtwpYntEnkXOWOp334VH9J3CIUu7xx57sDQcplaHoJxx+OFUP/IIM2bMsAYHRlKv3eInKfEzYBGQpZenAgm5bJRSG7AyxGPVml4G9LO97gu07JyIIwb5aggcP/mT/mHHIz8Fms4RVCuMcP0a1NNcQkOWT5vmsmd8KpKIs2vwqy0ecHYiz6Xox+w34k/opNo7sYMup+uHwatWAUTLrAflheOP52YfZeEXL14c+BxHO17XJzEgrV4dbAhhHncGQ2pYsWIFx9fVcej69TwHTJs2jZycHL799tsm+0X8y2vLyjj44IO56KKLYM4c1L77wtKlVnL8hRdSuGgRjUDhIH030/fxtcCfgaUnnGB5ky+/3No+YsSmk0SMYofDJl9X2f3D6tUUA3Ox5cYMH24lId57b/M3Fynuc1RTuYE99tiDFUCZrqL7n7lzeRoomToVnnwSCgrcPfHxeOghT81tQ8sQ16AWkbOBl7EKjIEVhvE/H+3aXcZ4yOGx6gjUB/Qu+o1TzkthSEAimsdBcdaXa7Rpd8ZiT1shmQhhnx7Nx++/v8nrQwKqPIB/L2pDRKPUhvIxuHINE/LR7mWXst/vAP9toaInqeBC4EYf+qqhJAqjdHC8zrclFcXC7fc46LrrAvfDYPBi8uTJHHqoVYLqjTfe4I477vDcd8OGDUyaNCn6esWKFRxrK329ubNkyRL218sTgReffpq6ujpusyX219fXozU12BAOM/rddznkX/8ivMsurC8psVSKbMVV1gFddY2G7l26AHDuVVex7777cvDBB1s7/eMfliHez+bD0/ty441N+thlwAA2AD30PWQ/bDUqOnaEX3+F3Xdv/uZOOAE++cSKrbYxfvx4ajp3Jrx0KZW//UYkinvDTz9B5NmWqLJQfT384Q8wblyT1ffeey8777xz2pPxDRZ+nmznA7sBZQBKqXlAdx/t2l3G+G7vv99s3cyA1aj8em7XBJXOcfG0OguDpIOODjm26r39FSEf5bJuvfaMxmPQhRc2W1cXsBKgX4P6R4eHBKDRR1vXUuM+DOoGF6/7wcBrj/gIMEhlcksCnu2gJGNQB8XtgVIc8CFzabKdMbRLEq1kC3D44YdbVfI8cBrUvXv3dh1cby7MmjWLN2wzdssWLWIvNiWpL9XKFwsWLLB2eO89Mvr2jRqd61at4hqsBPOMsjLuUopqoNZWVbYaoonnOfp5NXzCBD788EO20eEbiEDfvk07l5trebQdYSU9evQgkhmzFGhaFzEGoRDstZd1LhsiQq8xY+gWDjPzhRc29Xvu3E0JjZGqin7xyMW65JJLmDp1alJhdgb/+Hmy1dpl8kQkEx9F5JRSPyuldlBKjVRKDVdK3aLXv6aU6quUylFK9VBKHWBrc5tSapBSarBS6l3vo7cctQHjLP0+qr1ixuIRfuih5itbIFZ3oMML7sdrC+A2EbUqhufGzkSXdTddcYWvtk4KP/kkUDuAhprYt9LJkyfzkMuMgx/vQB+PuPbKL7+M37FkkuZ+ccw5tEA54+Jff037OZy0xGDT0H5ZtGgRQ4YM4fTTT2fkyJEce+yxVFVVMWDAAG655RYmTJjASy+9xAcffMD48ePZcccdOe6446Llyd977z2GDBnChAkTePXVV6PHfeKJJ7hAG2irV6/mqKOOYtSoUYwaNYqvv/6aa665hgULFjB69GiuvPJKFi1axPDhVqpRTU0NZ555JiNGjGCHHXbgUz1r9sQTT3D00Udz4IEHsu2223LVVVe18KcVjJUrV7L99ttzxBFHsEI7AUpnzCAXaNSe1Yj+0uzZs63P9oorCK1ZQ8Rt02XFCgqBd7faihmjR7PTI49QAyyx3VPyRTapiEV+92560T7p0qVL1KBeB+y6666BjxWle3cygGpbknXd/Pk8pQdTj996a7T0vC9s9SDqp0yhWif9Z+kQxBeTEQEw+MZP9YfPROQ6IE9E9gP+SHIVoNskjz32GGe5bfDjCXUJD/DroXY+5n/+7jtG7rxz3HbffvstuznWhf7yFzg/FeVI/OPXoHZjWseOHBaw7Z6vvQb33Rdzn8bGxmYjxt4PPAA2j5Ab6xYsYOz11zdbr+IMrhZ99hmuATw+PqN6j+ss008Yz2HNP8UfgB2b79kchzLHh//+N/uedlpKtcqd5KxfH6jdlClT2CXgOZ0Jx4Y2zCWXwPTpqT3m6NEQp3DIr7/+yqOPPspuu+3GWWedFfUc5+bm8uWXX7Ju3TqOPvpoPvroIzp06MDf/vY37rrrLq666irOPvtsPvnkE7bZZptoqXAnF110EXvssQevvfYa4XCYiooK7rjjDmbMmMF0/X4X2RK1//3vfwPwyy+/MGfOHPbff3/m6sIk06dP58cffyQnJ4fBgwdz4YUX0s8evtAG+fjjj6PLTz/9NFdffTU12hDOGD0avvmmiYRsRUUFHbVB3F+vG65/xwc+/jiy994MDYdZefbZlNkUv3Lt966IQe0zNNGNjIwMNmZnQ10dWT178lUqCnB17QpAtvbIV2RkEF64MGoP7LBmDc+ddRbXffgh+CnSZTOos8aNIwtYv+++dK2v5y1gziuvwF//mny/DTHx46G+Bium/xfgXKzQzj+ls1Otwf9+/3vX9Y0+DOqwS2KUX8k9p0E9dZddKC8vj9uuzsW4W2ub9koXzkCTZBIhV/tNEHSh2Mf3clPAQimLn3/edX28a2HgHI8UAR/e0ZDH9RJfVdydoObwflOnNnmop4WAoRbjHPGBidCY4vwCE5O4+dGvXz92281yU5xyyil8qWeHIgbyt99+y6xZs9htt90YPXo0Tz75JIsXL2bOnDkMHDiQbbfdFhHhlFNOcT3+J598wnnnWfVBMzIy4hZ9+vLLLzn11FMBGDJkCP37948a1Pvssw9FRUXk5uYybNiwpBJ9W4rS0lJ2Bx4uKOA7PZuqIvcanRy4HbAiL4/hYBV58bh3itaPzsjIoC4jgxWREBEcXsLIQLqgIKm+V+oEw+oOziyOYGRqlZDuK1eyFljXpQtbAcV6+2jgusmT4Ykn4h6rrq6Oki++aLZ+yjffcA+WY2WcswicIS3EHfro4i5PYs3aK+BXtRk+TV7xWO9HWuvLjz7CKTe/l+0HHgunxscorKm+gjg3ADcN3XofhpuaNSuwsQWQ63jtx0OtlHI95z4p1hF38sILLxCkfEh2rvNdWsTzUDtj5aLtfHwv4vE5BjWo/QoLfv/DD+zk7EsLDMxamkaP7662tpacHO+ajdOnT2e02/HCYTISKO9uSIAUlaBOFOesTOR1B21EKaXYb7/9eO6555rsN3369LTM6MR6zNqv2YyMjHahYVxaWsqfgP3Ly5kwdSrU1ZEdyaPRBvINQ4fSa/ZsbsTboN6YmUlR901pXA1ZWSibE+ovu+5K1Bd75plw222bEg4DUlNYCKWl1KXKoNZSpYOqqpiRk0NG165stWYNy507Oqo3N6OxkWuvuYZzP/yQuvx8etlycS4+9NCoiECNCXlrEfyofBwCLADuA+4H5ovIQenuWEvjdevyo5yRkWgCQeTY4TBOX4bCn/cr+93mIeZ+bqk/eCkblJXFb9zQ0MxQCzkKmbixwiNp7rj4Z/Skq4/z9vYwouJ9vuKRNBfXoPZKtvOjDuJxw4tnUFd5yCv5FWP80cUbn9EKHup6HzJRyZgsXh7qC10SXu38bYdmhQNjHs/QflmyZAnf6CTx5557jgkOZaJx48bx1VdfRYtxVVVVMXfuXIYMGcLChQujiXROgzvCPvvswwNaTjUcDlNWVkZBQYHnjOTEiRN59tlnAZg7dy5Llixh8ODByb/RVmJjSQnjgPLiYoaGw5RNmkRBaSlleXmgDcwR/a3gjr5YJclXLN9kYkbkKmd17drEeaGys4kEdFwJfGW/D99yC5SXW2ocSaB0yEimiyxqEHJ6944ubygooL5PH/oDnYCvi4u59rLLWAeEY0l7NjaicnMZce+9bAfcX1XF4+PHEwlIORPIAZYXF1MUMBfMkBh+Qj7uBPZSSu2plNoD2Au4O73dajvkxfBeRfATFuLGKheVi7H4M6h3dzmnn0f8Bi/ReFv5V09cbibjPvuMmjjJesmqOri9r619tPu3hyEfL0HDq4JePIPa81vz46H22MfdV76JWg/ZOL+3/f9zO2dQ5RmfrHa57n8+55y47ZLxsZR4DMC+ifNeH/VYn0zugKFtMnToUJ588klGjhzJ+vXro+EZEbp168YTTzzB7373O0aOHMm4ceOYM2cOubm5PPzwwxxyyCFMmDCB/v37ux7/3nvv5dNPP2XEiBHstNNOzJw5ky5durDbbrsxfPhwrrzyyib7//GPfyQcDjNixAhOOOEEnnjiiZizKW2dwgULKAQWnXceYWDVN9/Qq7aWii5dopUEI2oVfbA8/zU2j2vEJF7cp2kBZZWXR6TYdxWw1B6CGQolbUwDdNDhOVkpmpXKtRnUVV27Iv37k49VC2DcQQcxeMQI1gLVMXT2l33yCVJfzxn62bGiVy/O+uYb/q4TMA8GVG4u87femk7mftUi+Lk61iil7JosvwHx3YPtDC9jKF6cGwSvuOY1zR80osZPP8RjhL1y1Sp6BTor1JSVkesRJgEQSnK6KWh80TAPmbulcYqBePW2ZPVqCoZ7F/v0mvat/vzzmOcDPL3Ye8VJUO3k4SlNxo+S4xULniIqH3us2boVK1c2Cz1JJZN22YW/uawfGEcKMd9jvfFQb36EQiEedKj0OPMJ9t57b6a6aAQfeOCBzHH53ZxxxhmcoXWIe/Toweuvv95sH6fW/IwZMwArGfIJlxha+zGhaenutkyxdnD0OOQQ1t9+O+t/+ondgBWDBzczqHsDZWvWNPH4PVhYyDFlZczesWm6teTlEQnoGLP77pxo07BOFfnaSA2laEDTodemp21t377kaDm/YoBOnejXrx9rgV4xJFGfv+QS7FpXh99yC0+cfTYHnHYa3HefJVU7YgQNxcXkA/UbN5Llw54xBMeP63CmiLwjImeIyOlYCh9TReRoEXEWLtvsqPVR3nxGvDgnLzweyhKwEqCfEhchj7LdKxwlsxNB4nmoA1aki5Dq6K/+cWTbvDzUB+67b+wDexjUB/iISfbyUE/7LlhtoxEALlrafvhmyhR+++23QG394Bbqc1iapfT+5jEz80DA8JZ//+tfSfTGYNiyuPbaa1mmJTm7jRhBiQjjfv2VDkD1EUdAfn6TkLkMoHrVKuzm6w89etAdyHDoR9dnZkY91Gf+8Y/s7lZkJUlW7bsvDwMzHFUPg1JQuEnPpGHAADraZxqLi9lqq61YC6iIrK7L86HfsmWEgbITToDJkzny97/n9ddf5yx7WOeoUaBtmDKfeV2G4PgxqHOB1cAewJ5Yih+dgcOAQ9PWsxbGa9xZ62GA2lkQ0KPsFRPqJ4ktMF5TVkkk1aiAIS9+SXW6T5c4BWXCHp//nXGO61fZxbWtxzmTGrGOHx+o2QFr1vCn449P5syevP7002k5blAyghZ3ueEGVgYdSBvaHAMGDIh6hg2pR91xB+cDtaEQUlBAhfb0lgFFhxxiPX9sRiZA9bp12P2p1XoWtJNDAq8uFCI78iIvaBp3bLr068e5QIaO9U4We+hOaLvt6DR69KaNxcUMGDCAmo4daVy1irqZMyE7G3Q8fYR+NTUs6NGDwuefhz32QEQ4/PDDybUlbLLDDtEEyIp058YY4hvUSqkzY/y5SjdvTrystUA9UYp7A16oXh9+MgZ1PMm9kIdB/e842syxuOuYY2JuD2xmKgVffeVr1JfQYePEdDurMEbUUw+Jc9ykDH+P73x0MsdMgj/F8xgHvEazLvWuMxi4+EoSv5eMgLGFIYB2IFXWntgMxaNalbbyeZaVlXEHMAAoz8sDEWq0WsZCoHdEP9thUIfXrGkSclWtjWWnQT3Enjic7xWklRx9tVfcee6g2MMD84cNI8/ude/Rg6ysLMYedBBdwmEad9nFCgm0hfYopSisq6PW8Znpg8OAAdbg4tRTydLhJTVGOi/t+FH5GCgid4nIqyLyRuSvJTrXFjgn3jRJItWMHHhqOCdxI9x+++09t3399de841JeHWCvJIyS2p9/jnnzDjxAePppmDDBV6B/IqyNo8rifC8P+zzuzBghFnGVRWJ8Rq3xYMyO850pP0msLmTG+OwDl8dNIp65QxKfrVdokCFxcnNzKSkpaTNGYHtHKUVJSUnM3JaWYolNwadM96dBxySXdugQreYXMagbe/YEINdRRbhGG9SFDiMy3y6JlyYP9Y477sjbb7/Nfvvtl/Jjd+vTB0T4aOBAZu6yCxxnBcVtG6nIqOs1lK9Ywf333w9YCjNdlSLsZeBPmwalpVBQQK4esNQubybKZ0gxfmyV/2Elu79J6sNZ2yRfDhvGBF15qCjeDT5gCWzwTmxKRkEgVsLdbrvtxh88tp0KliEfIPTjb0BDOEymh/e7oqICr4kypZS3hqsuYhCEqqoqz4Sy8jgx6s4BgN9H/B/mz/fc9ve//pWrXaovRolhwE6ePJm99trLZy9SQzjGdaCUYu5ZZxFEwCtWSsyCBQvYwUOmjhhx+qq+HsnO9ty+YMECBnlsi/lLi1PR0Rh/qaNv374sW7aMtWnWpt+SyM3NjXpWW4KqqioaGhrYsGEDTz31FNdeey0ZGRmsnj6dSCp3iQhbA9KlCyxYQL0tOQ+txCPbbw+rVtHRkXtSr3/jWc7EersRnSYPtYhw8MEHp/SYo7CSEJ/TIRr7OvJWIiGE/wDO2Gkn+n3+OSs+/5yqs85i3dq19AFWe+V42QYZ+VttBUA4TqijIXn8GNQ1SqnYNZ43M4accALceCMQ34Xf+NtvwUMSPLx1sydPpreHHFoyCHF0jadNg7FjAx27fuNGMj3E8++6+mq8AmceeeQRzvGQTFv54YeBlUduvvlmV1UHgJw4N931ju/lol13ha+/DtgTfc4//QliGNSxPNSZP/4ILW1Qx9g2efJk9gqYOBurdPgJxx1H2MNIDT/3nGexmnBtLZkxCi6cfPLJeM0dxBo4VF98cczfyy8zZtB3D2dJJ0MQsrKyGDhwYGt3w5AEgwcPZtmyZZx77rk89NBDDBw4kJNPPpmNtrLY+Tp2WOn/IbtBePbZ8PPPyBVXwMcf09FRSTeSyJfn9ELbvfBp8lCng5/1/666DHkzTj+dyqVL+evdd9Ph+++5DPgrMHvBAmpLSuiPv5juov79CWOF0BjSix9b8F4RuVFExovIjpG/tPesFck8elMqWLwPaKlN1mZ+L4f5F8fDlfPRR67r9zn//DhnbYrq3Dm6HCs84Z/AXTG2L0siJjSWZN+/YyhV5L79tue2XgEVLgA2btzouW3QttvGbPvf+zaNHxu23ZaMFEikHRBn+2svv+y5LcNLOzwZlIL7vMfJXoYtEFd3PBZfxdj21xjbvvv+e89tU+NoScfSQW+I8T6/j6OS8k1AFRWDoT0yZ84c6mJ4OZctW8bzwAT9HHn88ccBqLbN3A3R3tIi7W3uql8DcOut8MYboHWmCxySlg888AC33HILEydObHriFvBQp5Nsr9m1zp3pcNddTNx/f+ziect//ZVy7c3O9jED0aNXL9aLUJ5EeKrBH34M6hHA2cAdWEIHd2LZZpstIRHu1cvxAiD626Yoa666qunGI4+M2XZNElJ1dqSkhAf08tkx9rssznHqYulYOzyopQ4jpT6g0ke/GDqbybBLjMp7X37lbdZtXLyYF2yvy199Nfbn4oHTOz401s6//z13xTDs/FTrTJgvvoCLL/bcHCu2Ky9gZVCA3WJsOzXGtlgG/vo4npeutu9vsmNbzG82zjWdVjWeVkBEckXkOxH5SURmisjNen1nEflQRObp/51sba4Vkfki8quIxBs3GtopGzZs4IKhQ8nu1QvefNN1n47ACcAp770HWNUdAcI2NZwMbTyOOvBAAEa6PSP1bFPEd70vUIhVWOeGG25oPkC2G9TtyEPtl7feeovONhWQVXPnUqU/0/xIQmcMQqEQNR07Ur1kiQlTSzN+DOqjgK2VUnsopfbSf3unu2OtSSgjI2pQJBTO4bxYv/jCc9fq6mrKHnkk0a4x9+23XeOc+6QgVi7mT81hVB7WsyfL7P3waVw4gx4SKfoyy/H66xhhGINieFJ+jWFsdxwwoMlryctDBYgrTyj626XQiZ3SdMSVxhkkNMYI6SiM5+kIaGj2jrFt8mefeW7byaV8up0bbd+3c04oVkhRdrxB4mZmUAO1wN5KqVFYAjMHisg44BrgY6XUtliiN9cAiMgw4ERge+BAYJKIxNcZNbQ7Fi5cuEnl6PLLXfcZYls+GzhfD3QzV6+mXgQuvRQetWqPhi65BF5+GTnhhOYH0pUNI4EQux16KFvFSLZvrx7qX375hS9i2AgRsrKymDN8eNRJs3bBgmiCYYHPMKlQjx50qK5msVEmSit+7MWf0AV8EqE9eTtKzjyzyWu7QV0I3vJYDgM6EcNryfTpMeNJvVjgIW+XUV0d4GhNiSVbFrYZYN8CSzMzuc8Wu+a3clypo5xvPO+iHWe9wXXvvuu5b2OM7yKW+eq0BiQUojZGwltLcLOjelsqcEoDOon1jj2TSDWNadAl/3nmTM9tPT/4gPqbb/bcvpMt/CeRdN94v+bNrfy4sojEF2XpPwUcATyp1z8JHKmXjwCeV0rVKqUWAvOB2KU9De2SxYsXE82umTev2YBcKdVkFu5h4OraWpRSZG3YwMYOHeCuu2CQTg/OzIRjjnFPgtdGccRDffPf/hZbI7ydxlAPHz6cCRMm+Nq3R8+efKKXSxcvplp7qP0a1NKtG12B9XHCUA3J4ceg7gHMEZH3E5TNazfeji6O8q6hUKiJtzbsEb9Z5/DiJTKZkhFHC9mLZrcfPb1WECNm2PexYxjlDbZ4tgZg6NChdLJ5xf0aF5MmTeK322+Pvl6ZgDam8/PtFqNEdspMnVCIcIDv6oD9909VD0gm4MNLmvGn6dNjtpudxDnDAUJk4hGvPlnGTTf5Os7e++zDv205B7GIV6hna8dsxuaAiGSIyHRgDfChUmoK0EMptRJA/49UjugD2GWFlul1hs2IF198kZOPOoqxQDRN0GGYVVdXNwtra8AKFcmprqZee519EQpRFQpFDeq4RrJ9u49CbO2R66+/noO1N79y1Spqde2LkL2ISwzqi4roCjSkI3zQEMWPpXAj1vPsr2yKoY5XNK5dezskFGKMLWbphx9+cN1vo7OkdALxSUFjmZoZ1AdYjnyvSo+JsM3ZHhHY4TA5tkzkjVlZvPDCCxxnm67z66EGqLHFff0xgf4VFBQ0eb0+RihEqmLFQqFQc5kmH/RyJqgmwbgk2jZ6GLdBB3T88AM73HBD7HOm4ab9uxQdJzs7m1GHxCvRYxHvCtpum22S71AbQykVVkqNBvoCO4vI8Bi7uznxXT82ETlHRKaJyDQjjdfCKJVUeNIJJ5zA77CeMdHZPYdG9Pr163EGZZQCnTt3tqa3te60X2ozM6MhH3EN6sj9JmBl2PZAcXExF+v7rpSVsd2SJSwpKoqGx8SjobiYLkCDY/Zw0aJFTZLMTYx1cviplPgZsAjI0stTAXcL00E6vB0tcWMO9enTdFrbY4o7M6hRQvCqel7T7akKSvj8c2dgBeBY1617d4qKiii3yxolcMMO+pMN7bRTk9dZCXhCXd6VLyQUIsOmr618htaMGjUq4Bmb81ASbRd4TJXGUr4A6O6lb6pjIGPhZVAH/b3Gq/6ZCEu33db/tWrb79A42zc3lFIbsHI4DwRWi0gvAP0/Eqe1DLBnRfUFXLOMlVIPK6XGKKXGdPO6tgzp4Q9/SMpzO2TIEM4GZgCvR1Y6DeqSEnYBngEuB97u2jXq5CkGT0lVL2rtNQ3ixUXvsYflWHrhhdj7tXe0bGDR+vWMralh0ciRvpuGO3UiExDbTLZSioEDB3LEEUcAsGTJEgoLC5k0aZLxZAfET6XEs4GX2fRc74NV7CUu6fB2pPvGvBGaGFAAGV6jNud6vzHUSjEoUgUpxj5ueJ1hmxT9AN6ylTeN15cKm0GdiFcy3lS6F1NGjGi6wsMorKmqYt9ZTVMYL9LVtxJFHOco/fvffbUrPPbYQOdLxkRrcCmsE9rZfZInnod6oofxu9pHDJ6XV7xq9eq4bd3wU1DB6908/lDT4Uj+oOYlXp577jnXtmNtn4GrHs9mZlCLSDcRKdbLeVgCC3OAN4DT9W6ns8muegM4UURyRGQgsC0QXOvSkB4e1mKqAWP+x3Ttyi7AUxCtuuc0qKtmzqQHlizmXUBm375NDOr83rFSjptTn2Obc43noe7Xzwp99KF40a7RBvWEykoygLq9/WtDRCoqiu17i3imP/jgAwCmTp1KRUUF559/floqQm4J+HGxno+ldlUGoJSaxyavsi9S7e1IKyKEQiGGDd0UEaZy3AMqnHHDvqdLGhvjG5UeyYdeBnXyKYkx8BgoVNnit7xiqCufeqr5yoAGdcjhZVnkkahW6VIt8t+6ZGuiZDkSEjv7jNUNenM/M/4unny1zz44b7FeQQl2ybc3EwhdWOChkPLT1ltHlzOefNJ1n1DA7/3LL78M1A4g5w9Na4PusfvuzfY56aST4h7nCUeeBcAFCerFtwN6AZ+KyM9YM5EfKqXewpJM3U9E5gH76dcopWYCL2IJ8LwHnK+U2rwyNTcnAurZj9H303Pff5/ukYJjDoM6Y+pUAC589ll++eUXthsxImpQ98rLo0OfxELrw9qIbgAIEHK3WaJDHiOuuEKfCY0AKhIaYnOCVTlywH6bPZsbgIsyM+n95ZcxRQoM7vgxqGuVUtHAGxHJxMesfbvxdjgf8vp1js2I7ughwdbMoAaW3BWrdIrmjfg5neo//3Fd7/WFxXuK+Z3CERfD2EsRYsiJJ0aXvQzqrAceaL4yoGF1tK3gDsA5paWu+7kdvaM9E5z4KhcRsjwGU+niyJtuYsPBB9MQ4LxKhE/97mv7Djp5VepyIcPF+yxAoX1w5VGsJ2iAVCxt9Xgc7VyRldVs4Outxr2JkMug8rDAvWqbKKV+VkrtoJQaqZQarpS6Ra8vUUrto5TaVv9fb2tzm1JqkFJqsFLKW3bH0DrYw6UChk712biR0txcBu2/P4P0jFe1w2mRsWABjUDHXXZh+PDhZGvjLw/Iqq5OOIZaaS3q2iTCKjc7QiFqc3LIAZYAXW1OjLhN9fOk0SYuYDeoGxsbyZ48mVuAexsaeLahgWUJCAYYLPxcrZ+JyHVAnojsB7wEuCu7N6VdeDvcjGKgSThBlkfcrJsRudWllzZd4WbIHt3sMd8M8VBh8PJQx5Ps+/Of/9xs3afXXgvHH99k3e4uiiZfOfSeI59RJ5tagqfKRwpviLu7eBfdWO/wngBkOT6fW2+91dexnCEf6Wb74cMpfvttlMdgoQlJJJCUl5VFl+sSCNepcqmSePrpp9PHJu3n5cX2Ferjso9X2Xo/1Dpei8vU8z1+DuTSr78E6ZDB0IJ8p6sVAmD7zSdCXm0tVdohsecBB1AGLPvppyb7yJo1rAM661LYEYM6GpSZoEEd8camdea1HdKg48lnAN19KnzAJoNa2RxJEYP6AGDp4sV01apZtTrefV4cJShDc/xYC9cAa4FfgHOBd5RSzvoczWgv3g6veM/QnntGl6sj01wO/EjFrYxTFjlhPIpqxIvenjJlSrN1e/31r/DCC9jT1jrYEw01zphyN15+8UXX9ctcwi+SMQSd1LgMdj7SUoIRhgIh7fGIsMJvhcbMzJbNfNYGvC9DPol+ffjXTYW+GxMYNHSb27xkzRNPPEG2LQkz7PG78FVZ0KWt27X9P59edecZMzZTWS2DwY219udPQA91h7o6anUIxs4770yJCOt//bXJPrJmDetCITrq0IKcoiLA0twFEjeo9e/bGNRNydTOjxlAB8czLRaiQxdVXR01NTXce++9lJWV8X9Y3suNDzzANqtXM797d6q0rO2aGIXTDO74eZJeqJR6RCl1nFLqWKXUIyLiZ5a0XeBMpsvUD9yOp5wSXefpWXMkaDW4KDu8/Y9/JNnDpixzJNtFiPdFzp7trSz8itOr7jy2h0FdaJPseeLee133KXPziqTQQJ339NPN1g1wfEZn33knWX37coVtnZuh72pkZ2b6TzZ14B74EJv1euATz6BeuHAhv+rs7Aj9Eojbvtn2HXSMURXRiTOreIPvlvhL4nPxlrt9+nt6lD+O1zYrK4uNCYS4GAztmSb1CQIY1EopOobDNGjjLSsri5qOHVGOpOXc0lLKbGocEYM6mgqeoEGdoT3d1UbGrQk5+jt8jPgFtuzYPdT/+Pvfef6SS3j4oYeiOTc/vvwyOzY0oCZOpHjMGACqHLMQhvj4MahPd1l3Ror70WrMs0+JgavxtGHyZNe2xb//fZPXO+21V7N9RqT4ovT6CcX7afVc6apTAMDFdoPa5QbW0/EeonvYkkWme/XLdryb9CDFl6fSgw2O18qpBQ4UOAY6l1xyCYMGDSJ34sTouuE6s9lOH0fiTEXnzhAKBfZQH1Bfzy8JtlkSUa6Jc7Os33prBjsUWQYlkPVtZ6guEhCPBpfZgFfcdvT6vHx878ojRttJcaIeL01+fj7Lhw1jdKINzYPd0A7Jst336+OEkc2YMYPJjmddZWUlxUCDrQZAZVERnRwzmR0rK6m2/SaztbMlqIc6W+v4Z5nfXRMW3HYbpwG/xt2zKXaDuvMnn/ANMOjrr4loHu2/cCFZwDY334xsuy0AOT6fC4ZNeBrUIvI7EXkTGGivkCginwIlLdfF9DLs4vjO9l3fecd1fYZj+tttxBirBHaEY/BfXuwUj/XxzuKitREl167z6XID6/ipI9Utsk9uLqsSKMtdom+qDVr+Jwi9aTqaa3AL2XEYbhHN5eNs8eIn0JytHK9/9CgA4pb9XOciJ5eZmUmiRbjr9WxAPA/1do7Xi88+G05we1fxUT5DPr7905+arfuDy34dPcKo8l99Ne45ylw+x2Si2Itd1imliDfMbTaIMg92Qzskv6SEyBzheo9wwQgjRoxgL4dTqKysjGJA2e7ZNV270rO+PvobUY2NdKmvp9EmYys65jqoQZ2nq/DGUaDe4mg87jiaz8nGx25Ql+tkwxHz5kWrW/YCfisuRoYNg44dWZ+TQ4c1a9wPZvAk1rPqa6yKiHNoWiHxciz5u82SRQmUSPWjnOHHF1uCpQ+4wPeZmxNPkiyWALg9pKPR5TjrXbzAEZbF0wh1OV7tVk7T1e3A7hnG1djK3+IuVSgenlD7gGesy/bFjtfrPEpLf2fTNlZKceSRR3LtH9xMy6bUuCT0Pel4gO2zzz7N+goOI94lzrhy3DgQYalbzHpQHJ+tm9Z4R5cH5SCPqeXM++6Le8oXfWhOA4HDcMCfvKV6t2kKh6kgZmiPFG7cSERcdP1i5x2uKUcCDwBlP/8cXVe2caM1KNU6xgDhPn0oAjbqe82GpUvJAzLtM3zagLs2N9fKC0mwcmyH/v2t/wm12vwJOjNnV/lYs2QJAGMbGrDXHi611Wqo6tiR3DghQg8++CDPPPNMoP5srnga1EqpxUqpyUqp8Uqpz2x/PyilNtsyOg8loMnrx3Pm57F/+TXXsHDhQv6ZRGyn/TwrfMjy2WkSI+1ijA5zSMzZTYt46iJ2IkaiH+NkaYzCKPYzuoWPLNLejWRZ5xGTnGmTUayvr+f111/n+Zdect3X/k7POeecZttPd0yxZuoBndOgvsmmf73OxWiO7N83wHv3yhFwJt2KS0JfIoZmgUvCq5OzPXIEmrHddoSvvNL3ue3sv//+cfcJ+SxPbjC0ZbpUVrJce5fLYiRil5aWch7WjFO1TRGqYvVqMoGQTdEpc+BAAEq0CsQqHRKYa3dAaAOuQ00N3HILJKBIAZCpY6iNh7opRTo2vbPt+/BDhv4+aioqojOIzm+k3va8q+/UieL6eqqqqmhsbOSqq65isWNAdt5553Hqqacm+A42b4zIo4P/u+ACfzs2NBBf+8IfBfvvz4ABAzjpFK+ADncusi3bza/ejmS1eMQzqGMRz5yyG2vXX+8uDlPqEqP+22+/ue573XXXkRtLo3nRIhYHkPvZ4OKFH6bVXZzv0c0DfmKzNc2ZqosfxEIKClzXv2hTUZl2wAHNd0hAqeOVV5pGPjeGw0wAnFd+2DGQcjOozzjjDN/n9cOPfncUofHmm5uu8ykLNmjQoJR5nI3n2tBmqaykMBymtn9/6oGqGHk0M2bMIBLUUWnbr1ov20uH521nBZyV68FvtY61zbU7IOz3aJdk/bho55IxUJqSnZ3Ngw8+yDcJqodl6BCcuspKvEzxDLszsVs3ugFLly5l/vz5/OMf/+AAt+eOoQnmenUw6kQ/phHwq3dawFm2Za/wAzuRR3Ii3l4AbDFrsb7IeR66wBHshtIvvySWRhcvRtxuUPfo0cN1nwKXZE6vDObbbruN/3Mkg0YoLS2FgQO5XutpuhzUs5+vX3hhs3W77bab6752r3hjYyMKKxYqHhMTrFRm92dvsHmXOrskFyWS8X2sw/tfV1PDVzTXew7XNlVxdovrvstPISMP7gGc2jDu0dfuOPuz5BXXFMm04iURaDC0NCtWrGDPPffknnvusVbofATp2pXKUIhaF31+AL78kkXffBPVjK4p2ZQiVaNn47Jsz5rC7be3ts2fD0Dd6tUA5Ng13u0GdZCcGZsBb2jKueeey3bbObNoYhMJ+aivqvI0qPP19wqQ1acP3YElS5ZQXV3NUGCuzeYJh8MUAR2BjXYlmS0cY1A7SUFlPLtuSCgBj6+XPJ0X/3z++ehyLHPKLXa3zFa21G5Q+zHLmvjk4njo3I7X0RGn7vauYxqIts/UbtzuscceMfsSi24OGSg7zTzUtvfcWOssHdKUfFuM+VX6weOXy2zVMv9t+w5dB2lJFKARNsVu23HGEbsZ1KEkzjvyssvY+euvsZcS8vahNcd5jVT4CCnxojbO94hS3Az80Gy18VAb2gY33ngjn332GQ/pHI96bUBndu1KdVYWIa/fx+67c+rVVxMJOFQ6dnZGt270vuoqAAps3uduo0YRBpQOAQjre2euPU7a/hz1mHWLiQ5tMKSGiIe6oabG06DutMMO0eXcfv0oBNYsWULtkiXMAp7AUn0BKCkpYQNW3tGCBZuyv7788kuOP/74LbZsedynoYgcKiI/ish6ESkTkXIRCVZyaTNiqU85vF2WL48ulyxZwvu25I4IO+gLuVnRiTix0Nk2mbREzZpVF9kCRmyGSfzUOsizGcTxDHC3+NztbSPhZlRVwS67sHss49PDiFkYx7sey0hPaG7AdpzGBDyUrookDgpt3pwhNk/8Mba2rjHPcTzU69at48orr6ShoYGXnU2V4r333qPaIYu38csvm+6Y4qqRjWPGMH78eEK2okM/x9i/Gc737Hi9OoEBzGMusyR2hh10EDcBOwH/sa3fUh8chrbHvHnz2A/orWewynWuRU6PHtRkZpJjC+FqbGzkxRdfpN52X4mYsBl68D583Tq214nIRTpJEKCwc2dWARk6HKRRe8I7eIV8BPFQi8CYMeBR38CQGJnasROurqYzm9SkAH44/nh+yMig+447Rtdl6wTT8KpV1Gkb5jRg7kwrxXWtHkR1pqlBfeSRR/LSSy8ldO/dnPDzhLwHS4u6i1KqUClVoJQKrnu2ObBgAd3jxDtPc1k389prOcAlVjeSudvM4EsgFjrWFxlyK5bhYYD58c9vP3Kkv04FYcoU+O67mLvYDUq7h/p9l33tah7DjjvO+5h++0dT5Za41TJtn/NgH8fOsml7e+KmbBLHoL7kkkv45z//yeuvv84xjm0hLIm/XO3FiDBn5swmr1Ndhr1WP2h32HnnaEW0ROaHMpyfleMz6GvLWo/HeXFiEkO9ezN//nyeeeYZ7HntxqA2tBWqFyzgA+C/q1dTXV0dNajzevWiJiuLHJvx/N5773HCCSdw43XXNTtOZl1dM0dB5tZbR5dFhNU5OeRrD7isX08jUGBPiLbfS4LKpE6dCnbHjyEwkZCPhpoaugBlthCeHe+8kx0bGsi0fWc5enDUuHp1dKYDoOxHK8tlrS3Ofr4O/QHokpPDTiTmzNic8POEXArMUGZucxPbbENOjI9j1KhRNA+yAOKVPY5jFF0U4+YSq2V2HP3RRMm2VX+MZ4h2r0tQidlHLLD9UrR/C7u67Gsf2GTEiMuLddYVjtjvW+1lu+NIJzqvkvIvvoi5vy9cjDi3hEE7Q5csQQG5thmTaFuPa7mDIzYulOKy3fvuu290uU5/78kEXDnfhb9ais35z3/+47p+0KBBTJgwocl5Yv0mDYaWIhwOc7T+bffASuqu0nkXBf36UZ+VRU7kXlVdzZrFi7kd+Oy//21ynJrMTLLr69lgky2tB3DcAzd07EihTgIObdzIBiDHLqGabMiHIaU4PdR19tkEl/CaiIeaNWtosMXUl+prrNxmRNurMD+0YQPTgDUptjnaC34M6quAd0TkWhG5LPKX7o61Zz777DMuc9FnTiRxzI2Kf/3Lc1usLzLPLYHSR19+++03/mozHqPY5JFi6l/Pn083H1rdieIn0TNhYryPeQMHNin68rptOZ5B7fyU13qolySCmwFsX/OAS5u99Q3uEJdCRtkexXnGOMOaUuyhzrE9dCPDrv2A8jgV3bxweouDiOVXVVVxzdlne27v378/5513XvT1tx7Gt8HQkqxcuZJhtvvC4u+/jyYUFm61FfU5OeSEw/Cf/0B+PidceinXAH9zSOltKC4mLxym1HafmuaifVzZuTPdampAKbLKy9kQCjV9vtkN6hTkJRmSI2JQq9paOgONWvoQAJfaG6IHUBnr1xO23Y83rFwJjY0UffJJdN0sm9TpzjpcqGQLrbLo5wl5G1AF5AIFtr/2j8MwWTHNLVAjcYqKiigcMqT5hiQNksdibHMe+WebOH9fl2k9P5yxzz40eEjdRQh7GebTpoGL7nI8Ep0IySktBVtyZjoYN24cXuVSVIIDhnAKBhhuBrX9YfbHBI+3PkaFxekRCcLPPmOXG2/0fUyVoFF8h06SHQZMtnmu3fAKsrn40kujy99++21C54/w2siReGghRBk1enR0OdHS8gZDOliyZAm9gbCeRaqbOpV6HefaacAAGrKzyQ2HUd9bKcB5Ogl3guM4lV270gFYo5WSXgP+PGgQTup79iRPKVi/nuzKSiqcCfV2IzpJR5IheSI61NnV1WQAjfYZB7fvR4eEZJWWomxhquWrV8NTTzHRVnfh11mzos6MiB1SqovHbGn4sfA6K6WOVkrdqJS6OfKX9p61BD6SxILiOj0e58aSTEyN88ijRo3irbfeSuKI8OTSpfw5zj7/tYvB2z/PsWPBWbLcB7P8FPawGZSj778ffvc7iKGx6odY38xhhx3GGo8yrPFiqJc4ptNSIrPmZlDHCseIE3ZTOm6c57aIYgB77umnZ1Hmvd80on2Ol5Sh5uZ33okud4mhuALW6D7CDNtynW2wkqhOa4STbQk2XqQ6ltxgSJaIQV2xqxX4lr9wIeGSEiqBrr16Ec7JIa+xkZkffujaPjJBX9unD/nAyhnWL+tewHVorO/71XPmkFtdTYVzlst4pdsUEgrRAORrD3IonopKYSF1IuSWlTXR969Yu5awI2wwo7p6U9EX/Wwqdwkt3BLw82T4SETilxZrj9higYG4EnCJ0GAzcD74t6XwG+RB7DfpySk993dghU09wYmfdzrQh/FXY3ufb11zjY+jxqbOY5DTRDnZ5Xva4GHwLvIx9VRVVcUHH3wQc59utiQOO/FCPnb4+mvspmQqktjiqXyMGDGiyaaSb7+NqXFeECNpaKhHCfh4OD+Xd+OUZu/QwVZk2NbXGTNmNNvX/ivyuo6DyPkpF3lJj4MnfGyDIZ2sW7WKHkDW2LHWdPKaNaiNG9koQnZ2NuHcXHKVotpjwPj4yJFULVgAW1nBbeU6LrYUdydAeIcdCAM1Tz1Ffk0N1Y6EZjzCyAytRz3QQTtXMuIZ1CJsyMwkr6KCkK0E+YaVK7nTVrUX4BCg7Cyr+kbk2VRlqyacKDfffDPXBZxVb238PBnOB94TkepEZPNEpJ+IfCois0VkpohcrNePEpFvROQXEXlTRAptba4Vkfki8quIpL8sj/NLT6FB3SkS1A/sH6m+GK8Iisv2b3S1Picnxzn/lcDBkybF2Su1dIpR7CbZkhuv2ZbdPsVK24/eTn+b3JMXU6ZMSUw2z0Y8g7rHgAGssX2HKQn5iGOUT548mZXHHx99He+MW9vj6V5uKqpX+MEHvPfee4l2kc6vv97kdb/PPou5v/3aX2ybLrzvvvua7dvEoLa1s/96gxjUP3qUu29mTpgpbEMbo375ckJAztZbszwjg44lJWSUlUVDMRrz8ugA9AbXkKbDTzyR/K23Jk9XKGzQORd9hg/ngQeaZ2UUjRzJy0DHZ56hS20tNfmOIuFm0NnmqIdo2fFMl7h4Jxtzc+lYVUWGrSDZ2iVL6KqfYWriRACeBUZNngwNDdHnaE2MWcaffvqp6Wy2g5tuuonbb789bv/aInGvei2TF1JK5SUom9cAXK6UGgqMA84XkWFYMq7XKKVGYNlJVwLobScC22PlE00SkdTKCjgodxhh8ZIG//GPf7C1TT4oFv9ySSAM4qHezcNIdUaI/mjTpI7QV+uDupFsgmSEI488ctMxYwxI/Pplvfq10047RZdTLTiT1dDAP+Lv1owHH3yQ820Jap7YvPipCPlwjaG2naNz5870emaTuFvc627nnTctH9NUVG+3ujoOOuighPtY/Pnn0eXa2lrcTVV37O/O7XqYalv3lk1BwF6Gxq3drTHO+QtQ7pGZfoyzYqYxFgxtDZ1cmNGvH6uysyncuJGsykpqdOiFys0lG+hD82cHQJ5WQOrQvTsAmTqE7p1vvmGcS0jYrrvuylO5uWRVVZHf2EiFDwPN0Lo0AIX6+ZPVqZNV7TmGE6wiL4+C2loyq6up1vfTXKXoBZQMHIg48qvKV6+OGpQhFwfX8uXLefXVVxk9ejTXpGA2m/x8sDmO2gJ+CrtMdPuL104ptVIp9YNeLgdmY/2eBwORp+2HEJXFPQJ4XilVq5RaCMwHdiaNfP/9901eq1g3hSee4KqrrmKRTzkYZzVAIO6DOBEj98833NDk9Q4ff+y6n6cBlyKDup9Ne3So1qh0o9Bv5SuPfn0dMCbWD7mR+K8EOe+88/jKWfzEDdv3rhzFU1KG83OzaTR/esYZscvax9C+3jZgdyptXo1HHnkkobb2wZebp9k+TJxk+53FG+Xv/dVXnttGgOe19z/Hd5yqwWhbo13MKhpcyYjo/vbuzbr8fDpXVJBTU0NdRN3B5kF2M6jztUHdScdGd62ttZR37KFYNjp27Eh3m67/tFGjkn4PhvRSL0KxXs4sLobttrP+PKjq2JFOdXVkV1dTqp8ReUAvoK5r12ZyewtnzowalBkuz7lBgwZxjHbYfBXjXhzBWWjMZQewJUe2Bfy4Wq60/d2AJe96UyInEZEBwA7AFKw8osP1puOASFZbH2giprBMr3Me6xwRmSYi09bGSV6K2y+bp08A5SJ1F+Wll3gS/57WzATLiAOxjR4He8ap7BbhdyeemHA/gtI9RmJgsU8PhutU/cEHx/08Y3nH45GoUgdA2cMPczoQu7yPxvaeBj71VHR56tSpCZ8X4qt8ODlhwYLAIS0AzXP8LR6M0UaqNqUO9rYpzvjB/pm6va8im1d694nuY/t+Nm3UCLvu6qZUvgnl1/OcYj3uNkSbn1U0uJMd0Qru3Zv1BQV0rq2lsLaWcMQgthnUkV9j48EHR9d1jKg66Pt0P7ASDWPcVw485BAOwvKEOXWqo+y+e8LvxZAeGkSI1GnO7uxVgHwTNQUFdAqHya2pYYOOkY8Y1OFu3ZoZ1ItsBnW2Sz5KrVaWgdghed313wqHpGPTN5N6Od5U4Cfk4zDb337AcMB3GRwR6YgVQnuJUqoMOAvrRv09lvxeRILA7ZfbzHJQSj2slBqjlBrjlSjml+EJSLssW76c02JsP8rxOt8ZUwbkuVRJtJOI0ePXS9bTERObbrz6FW+w8OabVhmOcrcfyttvp0UhZUXE8xjgx1l47rk8Ab5CReyfSaZtKuyqq65K+Lzgof0dxxhclISM0XyP9X+P0abYttwpiapZK7/+utm6DTrOE+Cxx9zFJIuCDLYDhnJU2Lzx7Zn2MKtocCe/tNSK9e/enfKIURwOI5EZHJuneRXQu1cvQray3gU61COy31ZAnYd3OsL48eN5D3gDGDNmTPMdKivBY+bU0PI0iET1jv3EUNd36kRHoLi2lsq8PMKhEIVAN0D17NnMoF5hq6ybEyPBuwNwxJo1rjlrSikWYhmYy2MphdiKzbQlgjxBlmEZ1XERkSwsY/pZpdSrAEqpOUqp/ZVSOwHPAZG042Vs8lYD9AViDFGSo/yee+jikcjmRkmMB/T9wP98HGPHFOol+43HvsFrQ5qmrT0DM+Kcb+7cuQDc/re/pbZDMdioQz1eee21Ztu8heQC4JE4l0oPddiHxyHVpEAA0JNTTjmFa6+9ltsdxWX2A54eOjT6Os9rVinIjIXP35Tzt1dZWZn4udo4qZxVNKSfgooKNuTkQGYmjTZDJ6QVfEK20KiH//c/S/3Iti6nk/ZdaiO6GFCRdR70s0mmHmzzdkfJz48ZTmZoWRpszyHxUb2yXjsst66tpS4vj3B2NgPQRmPv3uAwyjfapFE7NDZS55BrvQkr3GgScPWCBeBSK6CyspKIK7LEZqA7qW2jsnx+Yqj/JSL36b/7gS+An3y0E+BRYLZS6i7b+u76fwj4E5tmjt8AThSRHBEZiBW++V2ib8gvBbYiEBE6xBmRe1E9fDhfu3jS0olfD7WXD78x4HuNh6ch6uiv09D/WYdCdPcxyHEzldwSFeMNXyKJc6/Yqj5F8BYcTBy7AWbvZ6KG2MaNG5kyZQqFDpm7wUAozmzNSR7rY4VtxMO3AGAA43bhs8/y8R134CyP9BFWLGA8XgowM+M75MqxX6qTZFubVM8q6mOmLFRvS6a2tpYFLtJ3xVVVlOl7eshm6EQ8kXYDqtPgwVZ1VNs6icyo2ozsDNtMkBsiwi233MI111xDgSkv3uaxG9Ru1RGdrB++yW+6YdAgGnNyiEgyZPTtC3l50LNndJ86W35ZIU2fb0opbgR2AcZGVrqEcZbZNK9zJ0/27FtlwLyndOPHJTMN+F7/fQNcrZTyEzq6G3AqsLeITNd/BwO/E5G5wBwsD/TjAEqpmcCLwCzgPeB8pVQ6nWBN+PXXX2PG+cZ6aPY98kjGjx+fdB+8vM6XXXYZNc4plCQ9zPW9eiXVPopfY8LR374PNjXlntRxtvdUVRGPHi7Go1svtrvrLpe1m+j18MMAuA2FnrGpZCSN7Xut1p7kxsbGhEeLxcXFjBs3jhzHjejQ005jm222CdQ1HxolnriWpfdJvDI8X+E+mj722GNdpfQiRH4nQYaLfmOonUWbnrVNnbd30jWrmMpQvS2ZK6+8km222QbnoKRrbS0V2jNtn86PeJ4zbAZUUcQIsoclRmZ6bEZ04YABcftzww03tFuJsy2NSFXjevClE97JVhG29oADaMzJiebTZGu9cmyiBBm20L5CmobC2RXVovOLNg92fX099fX1lNmUyfJiGM3VS71qF7cufmKon7T9PauUip+eabX7UiklSqmRSqnR+u8dpdS9Sqnt9N81ymapKqVuU0oNUkoNVkq9G+v4qWa7GNmuEDtGt8rHjceTOIlSAHfdfTcbbNPc8frjhyBKBbui1RCCnM/x+txzz3Xdz8/7Gjx4cLN1IRch+R1dEjcXu4yK3R7vJ58cT+nbP/bPeqH+vseOHLlppO6T6CPRoUN9zbXXBu5b0BLdAPvEKREewTkY3QPYMeA5X3rpJQbadbMB+5V0vZZyihXf7YlPg3rQtk21T6b4yFhvD7T1WUXDpjCxb7/9Fp59FkRoLC+nR2MjNdp4tiec5WoDOcM2qxWd4bI/AyL3xeLiqLGVmyqni6FNENb3t6pQyJdDbsyYMdwGfAn0mTgRlZcXTWrMidg8tnobnW2KYgU0NaJdZ6VsKh5bbbUV22+/fRNDORRD5aNOh3xUQUxN65bGT8jHoSLyo4isT6Swy+bGwBiazkFDRQCIMa1hp6ej4l+y0l1Dhjgn0+Mzo6CADrvsktR57cxz+dycYQR+R1U9zz67yeuVF14IthF2KrgyYDv7dxXWxvAzMeLDvBiLzqh3GKjJlMLeJYnvMyOO2sUVV1zhuv4LYIcA2tZe2IvVL1++nNdcYuL94Dfkw/l5D/nii0Dna4O0q1nFLZFR/fqxD5bsWKPW8q2cM4fubIp5zbF5mfP0ukybQR2z6JEIaAk9WiEvw5A+Ih7qKp/Pi969e/MnYHe0E0vPYjQC+RGnxo6bXCORq66mc2e2oqmHeu3atTiDOettBrdatYr18+ZRZ6vM6ya9F6FBO9AqgdLVq1m2yy78ds45vt5XOvHzyd4DnA50SbCwy2ZFsU3yxcmxHhXWfBE0aSNJgzqeMeRGWVmZL4+mq7Hvlkjn0rbRsd/dPvuWUdZ0jFe+zz6+2tVu3OjzDDDfFi+WELbPo1GP4od67RuDT7CSX7uva1rrLBRAojEVZMQ575133um6fv369fzvf/9LWT+cg7BfAnrs3QxqV40CR8jCLYHO1vZob7OKWyInT5nCR8CSTz6hUhsVK/Q9uVFL1+Xars+Oel1WIoVXImXE4yQlGtoXDdqQrgnw7C8uLo7G2a8DMiLXyDXXgA7908Mw1k+YwLZAo026dO2aNTiK01Nhe44t18fdOG9edF1mDJtLaY93CJC77qLvd98xIMF6B+nAj0G9FJihnPO2mxGLfRi1JbrilBu+NKfbkRJATLmagLiZ/69FpJpsdHG8/jzG5x7zfD6NTL/62AAPPfRQoL7YDequ8+axIsWfbzIe6qC8hpZO8oHzxlHcqZOVFJUi7Eof3cvLOSSgTJ+bQd3gNjjs1YvvUjhTYzDw1lvgkF9USnHdddcxffr0TSu1127I8uXkarnPslnWHE22/j1OOGBTfZ38iL6038JasCm+1nioNysiIR81CThg5s6dyxd6Bi6kZ5Sb5L9kZ8NFFwGbPNQVhxwCQOFHH0V327h0KVnAxqOOolo/c8vXrAGgrq6OiIlfqYUCKoEMh0qInUh8dS5Qo5Wg6gFaOfzDz5P4KuAdXRHrsshfujvWkoR9eHvXBTTsIpTcEt+PlUgYh5sR9dkZZ8Rv+NtvoC9kL444/HD/ChQu4yw3r165SzzekW+/HffwZztCOfziZVA7P+HfJ3LMQD2hiUE99IsvuNWWyGGnd9Djt4JBfRzxDfnIu+6eZomjv95xR3T57GnTEiqQFI/OHkoHZYVb3CRd+6SqCk46CXxWuE07X36J6t+fRvu9etYsOOwwuOCCJrvW1NRw++23s8MOO0TXLdPX9rgNG4i4gWp++w2AfH2P7RNJGANCWn0jW3ubY9/5Ncag3iyJGtQJzIpvu+22TJgwAdiU2NosoVw7NCJ3ysxhw/gEGPLss/DKK9a5tc0R2msvfnr0UQAq9LoSm6d6rK62vCwriywvg7q8nF66ZHoeUKZrLORAzFLqLYGfJ/FtWLHfuVix5pG/dkuVQ0nC1wO40bdImCuzHHq6riRpUKt4Uzl33w0DBzabsnZSW1rKGUncTPd2Wbfbe+81Wzd0aPzAh4PcYm1diuY48QqDcH7C98c9kndb/w2btnSLXq8DHggYBuFl2H6WRLJiPMLEHwA+qv+PiFGSPhVk526aTFy9Zg2lpaWBjrNfIuXtW2EQYwjAK6/Ac8/BjTe2dk8A2Pjyy8iSJZT997+bVuoZlUabji/QTMcXNsWV9rI9wxr1gLXALYlQG0E5xcVcCkxwbnercBhxHplB42ZFo75nVQecHYx4jH9wbgiFaMjOjs4u53fpwp/RBciOPRaWL48WYsnu1Ysu2qFUqfPSSmxFx/rW1VENrM/PJ9vL2/zxx2SGw7yPZcDWrFhB9I7vx85KI36eCp2VUkcrpW5USt0c+Ut7z9LIjBkzmrxu9GHIDk6yGpr4MMgH7LlnUueIyyWX+Nrt8qVLeSnGdIsdv5FAuT50L91wLRhw/PFx24nH4KIuCe+ln+/QDxd5rD/iiCMCHc8p4xahz9FHBzpeqjizhc5j/65zgebaLqmvEOW8ipyDdEMbIaL+08UZTNY6LNPVWbNsxsI8Pc29xDGTYzeoI9dXJK7Uns6dpeNJi/rZFQw1epo+NzeXe4B5zu0LF4JT+z8SytVKuRmG9BDW98mKgM/ikK56+ZLLtsbcXCJmeodu3fgKePskXf1gxgxEG89ZPXvSXc+g1Oh1ZQ5d9VkZGYRzc8nxMqh14uJM7WkvqKriF72pNomKwKnAj0H9kYjsn/aetCDOmOdUThF74cfs7DZxIst8eF8BlNsoM0Vh7gckUIo7nMICMTf69SL5SKrwMqirkviuQwE9n35mHhIvfm47vMd7jZc0mCyeVQptbHQkfX7ksV8y2N//BMDtXfsq7ZoAznvGpf/3fyk+gyEVNOgHbFkL3OP9kKunuTsAZdoQ/vmddwAocwzKIgb1cODnn3+msbGRHL3OHh6WrxOyC3q7BI3ZDGpwmVXKy2te5OPRR+HWW2FnU0V+cyJi7FUmkqBq5957mQdMd9umr7NGoIMOLzpdz8LUT59O5oYNVh+6daNQ507V6OdppQ7HWqqfV8u7dqUhO5ucsIdgkB4ARqoDb5WTQ2cte1zZyiXJ/RjU5wPviUj15iCb9+STT3L4Tjs1Xel1s50yhUUBR3NBWe3DSAFQcapYJYwtnjmRaPHqrbeOv5NPbvERZx4hXmlwrzCIZIYc9bZkn1TzdhIhBF7v1ctznSznAHPmzCHfx+Dvueeea/J6mouGeEvQJWDhG7/s8+GHaT2+ITbvvvsuq1y06Jdrj/B3uhpqbW1tq1a2LLQNylfqZMLyiBqCYxa0vr6eXYBfgLLJk6moqIiWZbYrJnTSxYwy3FQ59PMrYlCf4SfPpnt3uP76pJWkDG2LYu0oqw2q3nLRRXhV64jEV9dnZhLKyKCoqIgSYDVQMWUKWREVrq5dkVCIGhHqtLMlIpW3Uc8iZfTqRUNuLrleBnVFBWGgo56Rya2tpUDL+FVrw7218FPYpUApFVJK5W0Osnk3nXEGVznWNXoZMzvvzGqfHuN4dPBRUhtAkrnZJ9PWFlrRHlJR4hUeTYdBne8zpvDJAOfslET1OK/3mpmmON81uBfXcaPqu6Z1Pua1Ulzm999/n9oDOoyN3M1XBKnNU19fz8EHH8zuu+/ebFuu9lAXAKWlpXTp0oV3tEe4xQmH6VRZGS0zuVYnUHXWnukudXWssSWM19XVESmbUT1/PqWlpbi5d6KR027OH+05zMrKYu3atTysq8MatjyKdAhFfZKVSrd2caKF9H1d6fj7r7/+mscee4wZQOiXX8gqL7fkTbV3vC4Uol4PIBtWWmmOv+gZkSWjRxPOzSXfI8Syfv16yoEetoJ6WV27UgtU+5DBnTt3LldccQVhL4M9CTyfuCIyRP/f0e0v5T1pIT6leQxrOIYnL1XhIDv6rEiXzGO5NfwJzqp1LUW8aGYvI9PPZ+QV+9vRx2zF2cAZzU4a/6x5uU6VTv94JWCG0qQjm8htqNHhdTv00ENT2xn8/WYKU2zIO7/RWPcQQ3qJJKHOnz+/6Ya6OrrobRmVlSxbtozKykpmzZrlPETLsGIFmUpFC1qVLlhATU0NvfSDvQewcvly6urqePnll6mtrSUidle5cqWnQZ2NLtbhdg3a7itdu3b1J/Fq2Cwp0vH3jUHrKWDVonDmoAFRjepcHYYxbNgw9t57b94HiubPZ5uVKynLyIheo/WZmYT1s0F06JMceSSZQLdDDkHl5ZEHrmIQG5cvpxzobTPs87p3pxaoKYsfPPHYY49x55138ssvv8TdN1FiubAi0nh3uvz9M+U9aSHcvK9dY1xgyRi4Lul0cfFrFBe56Yq2gpesZ8+eTEnRsf6YwL7x3qmXQR2voM2PwBMJ9MON/fdv2ZSDlg75OPbRR+PvpKnV09ER/Ci7tAscg6TObaj87ZbG+vXrGY4loWVH/formfqeGCovZ71OglrnKIzUYmjt3PX9+wNQvngxJSUlUS90JlC7YgWPP/44xx13HA888ACRYWDtmjWUlZXhlbHiKYVmwjYMmiId8iFusfY+KSgocM+diThvbNt69+7N40BDRgbD165lo20wF87OtiQtgayNG6kU4YQzz+TrKVM45phjaIxEBrgke5csWkQF0G+7TQEoeT17UkfT6oteTJ06tcn/VOJpUCulztH/93L5c1NGaxe43V6KYknEJXFDClTKxadRXFDQdpQLU+XF/3cC+0564IGY272MzHfixJ4naxadctJJvP/++03W+flGk/kEvZIS00XM34uDRVojN0IiWuu+iXPMT89Mv97IHkETVg1Js37dOn7BUrBotHm0KqZYQ/3VQGZVVdSgLnEkLl191VUctP/+0YJWxx1zDG/70MhPGG1Qb9CzejUrV7JuzRq6AWv1VHjD0qXR0uCvvvpq1KCuLymhoqKCjjSVPYu82zrnDNf778Of/5z692Bot9To+2Smm7xiskSeqzaDOisri+zevVmpZwcrbNdtOCuLTG3g55WVsSEzExFh5513tp4R2qAOOzzOFRUVLJ09GyksbOKhzunWjVqgIY7aUmNjI4XffsvXwPSvvw76bj2JG2QpIn8RkQzb60IReTzlPWlNYkyDJWXoJNE2CGtsMUVBaCbY7pMOKVT68Mve++4bc7uX4fZCt25cGaNdULWN5ulQTToT8KjQ6KOsfao90fFmHDyTuo47rtmq2TNnJt+hJMnxGHzugUdpcT8Yz1+boWyFJYrYB1j78aZvtOLbb2kAvg+FyKmtpXzZMuqBQY6p3pPvuos7PvyQBx98kIqyMl569VVmpiE0KazVDKp1/kHGxo1sXLSILGCDvnerlZvuwqtXr44a1I2lpVRWVloGtS18aZU2vpUzHG3//eHmdq1ua0gxR/XpwxlAYVCVj1hEZCkd3ut+/foxWxeJq7QN+sLZ2WTrUKcOlZVsdBbO09dzRFovwoIFC8htaKBzv37RMBMAKSqiIRQiHMeg/uXnn3mtqorxQOizz3y/Pb/4yVrKBL4TkZFaPm8qkOIMn5bD7THYsJ1X7mpy3teWfuSuTlJrNfUh+unDK24Y4C5AuZQ1B9hu8OCYRnNmfn6gqaCFPtVZvPDyYod23TVuW8+kxIDxkuNwKQBhY/vtt3ff8OKLzHbMHDRLf2lDhuhfPvuMfYEvXLa9HKdtWjzthkCUr9ikMl5iy1VpnDGDeYB060Z+fT1qwQIygdN+/rlJ+5HhMKOA1fPns04XV7kkDf2smzuXNUBO//40AtkVFVQtWgRA/bBh1k6rVjXRNI8E9kl5OZWlpWQD9bbciJXaI++q8GEw2PgpHOZJ0jS77WF7bL/99szTUo9VtmdkODubHKVobGykoKaGCof4Q0gb1LUOgzqSRyBFRU3yAygsJJyZSaMjxNDJk9dcE10+f9CguG8rUfyofFwLXI3luHoCOEQplUiBuTaFW1JH5bnnpuVcLf3I7Z1EbFR7I1aYw+V4G5kPP/ww28b4Ic3q0YMxY8Yk3J+8GF76pCLbL7445uaD8TbuuiQxwJobY9t2MQagDY4kktcC9yB1eH3+EydO5MYbb3QdYMUt4WMqJbY4n376KT87jGGAKl1pEKDGVhwlf8ECZgIFfftSoBQbtOJHZ52cBVBui7ncZto0NuqExfhaAYkT/u03FgNdu3enIjOT3MrKaCGKnLFjAchYs4auM2aggG0g6qHuEA6zUQ8cGm0qDZEJ8dwklRsMmz+RgVqqE7SBTSEfDg/xAw88wFpt+CqbF7oxO5s8LBnL4ro6qhwzLCFt9LsZ1AVAhtOgLioinJmJimNQL5w8OdrfIb/9lvK8Mz8hHxOBe4FbgMnA/SIS13ITkX4i8qmIzBaRmSJysV4/WkS+FZHpIjJNRHa2tblWROaLyK8ikj7RXyexHo4t7IlKRjbvqKOOSurc7clD7WUwR7d7fG8dO3aMmRj3TMCwmX46LnL0qFFunYnbvsTLII/zPhXp8ZYGvQo3eHmvNfG+t0Ak8f6vu+66YAa1ocXZe++9GeXy+6qxSc2FI8vV1RSVlDAvO5uOffpQCGzQKiB5tnvsKluIxaXz54PWrd6Q+u4TWrrUMqi7dmV1x470Ki+nQRvJXcaOpRzILClhmI79nsAmg7oIWK+N75DNcRLJ08lPQrnBsGVQrcvWp9VD7TCos7OzKdA1AOw+6MacHHKBmupqOofD1DqM/Az9ut6hKx3xUGd16tQ0vKRPHxqzssA2WI7yxRfw3XfU19dvGkw//DC8lnp3j5+n2z+B45RStyulTgIeBj7x0a4BuFwpNRRrFvl8ERkG/B24WSk1Gvizfo3ediKwPXAgMMkeu51O0vKQB47zEf+aSpI1rNqTkm6skA+I/VnE/JQCXgsdteqKq/pKHG4HXh4xItB5IT0GdY8ePQK16xLHUxZrZqG8FcIosrOz6egSUxh3cGlCPloU17j9l1+G1aups6l2qEjC4Zw5hJRife/e5HbrRgho1OEVAI36Qb1Glz3+uHNnsoBCbcxuoGmCYwreANmrVkUN6lVdujCwpgbRA4CCQYNYBeSuX4/SU+QNbAr5KARKly4FINNmUEeiwTMnxArSMhg2Vd5Mi4faw6AGyNXea7uutMrNJQ+oXr2abKDBEbKU6WJQz549m7fffpsCILtzZ+jXD265BT7+GHr3huxsRL/HKOEwTJwIu+xC5UcfbaowevDBMHJkyu/jfqyH8UqpqHCnUupVYLd4jZRSK5VSP+jlcmA2Vt6IounAOxIAdwTwvFKqVim1EJgPtEjt05gqCUl4jHfW03jthaDvNKliNAGJZVDfdNNNMQ3CWJXSTj/99GAdiqVlHqfpdQTXMR4yeHDKDeq1a9fyaWRqzMH342LXqBwWiQX1INbgtTUMaoBGl/PGlWVMT1cMHmxwVkArKbGSYMeMod42LRzSait1P/4IQOeJE8nXBmhXW3hHuQ7tKNUGdYP2ohVpVZpKrN9Byli7lsy6uqhBXdKjBz3CYQpXriQMZHTvzmoR8jZuRGkJRueDskx70zP79Ike9rh585j67bfwx0RERw1bMmnxUHuEfACUjBrFfOCLPfbYtFIb1Hvo50WjQ30rK6J6YyvUMmzYMP736qvkA9lduljG8A03wN5adC4nh5BTvtR236hZuJDeQE3HjuBMgkwRfmKowyJyiIhcJSJ/FpE/Y5Uj942IDAB2wIrDvgT4h4gsxfJ+X6t36wMstTVbptc5j3WODhWZlqobXro81K2hC31JEm3b0zR3LIP6xhtvDOyhPvXUUwP1pyFyswgY8rHzzsHGjqeedlqgdrHo2rUrHTyK2JQmmfzUGMM70lpGatjl979VRHXBq5HxULcoa376iSbVxObqKP9lyyjWMdQVItESx8s+/pgGYPQxx1CoDVB7CapIPHKZlrIr1HkTkaqFuRCV0XOjoqKCWrfpZS/0eSIGdVnfvgAMXrGC0owMCIUoycqiQ0UFaC9bRzYZ1IVAWJdWz7TlgGyzzTaM3WUX//0wbPHkJlFEzJNYHurevdkW+M327FB5eeRjS1p3iAhk630bHJUPI08lcRkUSG4uoXC4aQVE2+xVTUkJvYDaJMUbYuEnhvpB4ATgQqxn3nFAf78nEJGOwCvAJUqpMuA84FKlVD/gUiBSJcLtCdXseaaUelgpNUYpNaZbqhIxYj0cY2z7P2B8jMN6ljTXuAm8JOvt7X3MMYHbqlaoolUV0DBJRns5loc6KPXHHw+rV4OLBzfe2a6//nouushZv3MT3113nXfjNBl2npUmk/jsDgcaPdRXILhBnY4BceRdevWpZAtKAG4L9D3lFL4HsoDKyspNBjWwjQ6bWJGVRbaW6Cr74QcWAxP32YcCbVDbCyaXa29v5bJlAHTfaSeqbds7YsnWeVFQUMAuiRiy2qBem5dHbm4uNVttBUC/8nLKdVGW9Tk5FFVUoLQ+byGbQj6KgGwdzpIdJ0/BYHBj0qRJ7LTTTulRKIrUJnCpnBwJMdloM45VQQFFbPKYhhza2PnatnPGUEfdPC4GdSgvjxx9nnXr1nHssceyyibbWltSQm8gHOMZlCx+nkS7KqVOA0qVUjdj2ZD9/BxcRLKwjOlndagIwOlAZPklNoV1LHMcty+bwkHSS8AL7AkgVkHxkuHD3TecfDK70NRjkioOO+ywwG2XpcmgjlVXb11AwzhdVQCDIpmZzUbZfrnlllti3uTWpUHeJx4hj/5kJ3GNzAEGaw1eN4Ia68VxdFVVvMROt5CPOPeE5dttx5HxOmZIGR20t3gclnLAf2zyV721Eb0mP598nXiVtXgx6zt1okOHDmToh73doK7U3t5IzHXnQYNYY9vekaYGgBs//fST/zegDepKPbWdZXMGVWuDel1+PnkNDRRqL18xEDEbirAeiABZW9vficHgj/POO49p06al5+CZmfDmm1Y8s4NIXlGZrUiLKi4mG9gFna/ieC4U6CTb0IoVlqMKy5u9Q2QHl3t+pjao169fz6RJk3jllVd49t57Nx3zm2/YGVDpKGyj8WNQRwbuVVrdox4ftqBYFsKjwGyl1F22TSuwaioA7I1V4ArgDeBEEckRkYHAtsB3PvqXNEE9XPEe/2GvpJZnnuE7aHIDbwtUJFABzy9hLE++F0HHyvGSEtOGhyEf02MexzgLxVMsaYX36mXcjh8fa04mNnk5OWnxjmzVP/aEWUMS17XXb1xEWBj4qG2TNqvMZJvC3R948sknKVy1itX6dzOwoYGGUIiyggI61taydu1aelVXE4rIO2oPWS6b7nE1OlxQtAesaOBANj3uoQPxDWrf1NVR9txzVALZetCdaxt81+gKcqU6zGprHevdHesB3dC3L5nAWKA0Kwuys7kPuDaJRGaDIeUceij0aRalG31mnHDCCdF1okM69gR+RZcit1HUoweNwMj334eePakpL2cu8FZkB5d7fkZ+PtlY+RaROPEKPZAF6P2dZU7WH3xwgDfnDz+W5FsiUgz8A/gBWAQ876PdbsCpwN76RjxdRA4GzgbuFJGfgL8CkRLnM4EXgVnAe8D5SqkWUXJLl0EdxPjw46WrTlNlwnyHuHpbJhkPdVIhH44ffoSQx3pIgXpKK3jjQx6fUWYy4RVxPvdYpvZKYBuvZJo4iZC72byZbsRUc/AYAIwfP35zTExsm8pMtvL1OwK/zZjBBODbxkYacnPJwCq93VBYSGFDAwunTaMzkD9ypNXIFrcfiT+uLSmB2bM5WxdxCnXpQpG+tjd27OjLQw3Ny5g7KS8v59PRoyn8/nsWAMXakOjYpQsRxdw6nSC1UXvyMvXvJGpya8N5T2C9jn89cfVqbvyuRfxNBkNSDBgwAKUURx99dHRdxKAeA0yn+cxlx4KCqCQkQOUjj1Dc9KDNziPaQ11TU0NRURGFQJ1NFhMswzIrjeprfpIS/6KU2qCUegUrdnqIUuoGH+2+VEqJUmqkUmq0/ntHr99JKTVKKbWLUup7W5vblFKDlFKDlVLvJvfW/BPUoH700Uf5UuuWutE5jmdsp512CnTevNmzA7WLxwcxCnbE4rs4UnEDXeKqkkVCIW5L+VF94GVoxkj0KAsgpdeEGAZ1vGvXO60qNuL1fnwMRrwqUWYmMTBYk53Nj15JYhkZzNbT5m5kxdgGkONW5TKOJ33s2LE88/TTMfdpb7RVZaayb74BYE1ODv2BPwK9gbszMlinZ2/C+fk0FhWRBTTq0uKhSGiE7feXozXo60tLLX3aCPn5FOmBVdmgQeQB5VoxJBa//vprzO0v3X47e8yezTTgDGCWVhcpLCyMesTr9W+t0vG8iASFhPTAoCOwUTtTunfvnp7kMoOhBQjZEgP3uOCCZgZ1KBRqkl8V/vTTpgdwCa8M5eaSg1UspvO8eWwETq2row5Yp4+1kTTJBkb6EG8HEckQkcNF5CIsdY/fi8hlaetRmlnssi6oQX3mWWex225xFQRdKSsr46uvvgrUln6+QtgTZnnAqfHie+7x3KaA32wepmZt48S/xuLuoA2T8VD/97+uqzNjyPD8ssMO/D74GekXq9hMHMMvqHJLqHNnHnbb4OOzc/PZLQNOuPZaly2biPVOMjMzY8o9hZ1ySQkwO2DsezxDvT2TSmWmZPlwzhweADIOOYT+WF6tVR07kr333vym46dVx46EtNcrvNAKxsmNxEraFGtEe6gbNmywpPeAR4YPB5FoknijVuqpXrfOut5feCGqvAHQ0LBpyDhfF4vxIn/pUkLABcCPwO677w44DGo9oFvW0HQoGjGoZZttqNG/8/J0SJ4ZDC1Mpi2HINPDkVdjc8BUz5rVdKPLcy9iUNfU1JCjPdNDgHVAlbbxKjMz03rf9mNJvok1uO6ClSMR+WuXuClZpE02LwYFBQXkxDDCPgloNLeGJvTRxx0XuG1BwMqEYBluseKz08JRR/GjQzMTIDNGyAciPJbEKQdqjVw7fmOhghrUIsIQN03uc86J23ZPl9/Y9sARxx8fsDfxcTPGJ0ATNQgvDn7rrbj7uBHeTD2EqVZm0scMLHd6zF//yh4zZ9Jl993piI4l7tyZo446ipX6fidFRWRqr1edNqjzI1r09pmRXr2oCoVQZWWwahXlInyxg5XqtB9wGVCgvdh169fDp5/CiSeCbTAYkcvLBNbHCfkI64SqtcBll13GY49ZdwK7Qd2gQ+1m2bzdZZmZmwzqvLyoAbHaVEQ0bAZk2p6hXkm2tbbnSNfFbq7QpoTy8sjGMqgbbCXIewMV+j5Rkyb96WgffOzTVyl1tFLqRqXUzZG/tPYqjcz9y1+ar2wFg9qLiEHc5aqrArVPhyRcMtwbLwnt9dcDH3v16tXMjL9bczw+oyN8Ns8Ou5izadQlznAx1qPvIF7CYzLx147Y4p9uv32TgH8M6l0SRuqJX3ku1mAw3qeb5fI+F/TsCdtuG6cl9AoogVerpc82J9KlzJSs3OmwYcOiiUhbA5U9enDkkUdGK5/V77cfWfraVLqiYEe3bP6ePanJyEAqK2HlSlaJRKeAdz7zTO4GirVGdP2GDZtKGdumnGtra+mJdU1v7VEAKbqvDlNaC4wbNy6ap1JcXExkTqVRh3E899xznJ2Tw5HAj926RSXzyM3luf335xLgmx2iOgcGQ7slyzYrmOviMIKm9QE62J+5HkmFGfn5loe6uhpsRZxeAOr0s6cuzXlifizJd0Vk/7T2ogUZ6+Ilaw0PdTy8+nRrnHbJlMuNpcAwz3NLbK6O99kmoefbvXt3LnvxxcDt7TwKnOPTU/lCC8vYuRnUfs33VGaI+f2dTHYxMv540UUMGTIk9vGDdCrauHnrmTMDDbd898VuGIZ9DDTaOm1emck2UKvv25devXrx3jbbsADIvuIK8rT3Nlt7wHPdQnl69qQuO5sDV6+Gl15iRWNj1KB+6KGHKCkpIaRfd1+1CnQp8sqff6ZGe71qa2uJlG8a9sMPMbusVq+mFigH+vbtG11vD3Vr1GEce+yxBwc88wyvA+vtBkRODqe99RbDHnqIC2Lo1RsM7YUcu0HtkWMV0r+BGfaV778Pr73mun+GDp2qr6y0BsxYCR+/w5pNAginOWTKzxPyW+A1EakWkTIRKReRsrit2ihuEmRtyaB+VsvOVHmEQsRNxEvCQ73XXnt5bhsa8JhXX3113H2CK2cHxOUz+j/gkEMO8dX8O0elpfkuUkF2Dj/8cN9dcyPDJearPHIdx/FQX5JEZUMJODizGw4R/nHXXXEl85KZW3HTjY6XFJws9vdZHkdJpJ3QtpWZbFPDjfohPPjmm/m/PfekQ/fuUYO6SOvdilvyUc+eNOTmkq3vASvYlKSUlZVlXTPaY3zzrFlwszUZ2yEc5udnnwWsKeXI3ER5nGdH1oYNRAJc+tjuExn2GRXbQ76fDvUrsVdhzMkhIyODc845h0GtoElvMKSaXNs176WQlaGfP7/awzS2285TaStDe5/rKysJaS33a/7yFx75z3+iBnVmGqskgj+D+k6sYi75SqlCpVSBUip9aZJpxk2/uC0Z1N/17IkAjR7Jel4KChGS0fk944wzPLcFfUredlt8LY6g3u/AuBjUl19+eQLNm7b/Io4e7IgRI5IKxQllZDRL9PvNozS4k6+TiBlTScx2fOZI1vVT2TLWlVsXrziLc8Whh8Y9X9C+uO7vphTSzmjzykzFxXygjepSbTyfdNJJfPrpp4hItCJit9pa637lNr3bvTv5tu9K4ZL17/LQ3Qj0/vOfActDHdEkqHEL/9LU1tbSKRymIjeX/v3709s5G6fv1XbDf8SIERQXF7Prvvtu2i/NcZ8GQ0uTHSvnSJOhn5mrhw1jETB5m21c9acjZOqBcH1FBRnV1VSJcO2f/kS3bt2iBnV2Gqskgj+Deh4wQ7W14NyAtLRB/X38XZrw9NNPc+WVVzLOpYR1P2BEnBi6oUOb+pKfiaPRayeWMX6Oj2S01iDIAMJNY3nixIm+29slfroDP3jEgDnZi00Bps3rScWm0eN9xvX6urzXAwA2brT+Yjf22bvmZO+zT8JtvN7J7cCf4lSHs38OHx13nFW1KwFOc7yOVykxwkt6oKB23TWh8xmC0ef11zlo0CDGnXlms22F2rvbE6gOhdxnb3JyKKyoiL7MYFMltyiOa60GeC87mwydgGg3qDtUVeFFWVkZ3YD8AQNYtGgRmY5nT+SaDdmcJ/n5+ZSWljLULqlqDGrDZoZoVZ1YlaYjeuzF48YxEPj81FNjzshm6gF0Q2UlGTU1VGq7rlOnTkTmePPTWCUR/BnUK4HJuiLWZZG/tPYqjbS0Qe0UjIuX3963b1/+/ve/N6ue1whMXbkypu41gOy0E9i8Gwf96U/+OxuDhx56KOb24KmFMCGg9GBQ3EIZEgnL+Mc//hFdXgscdNBBvtpNxqrABpCoufmix4g+nskbkemy02f8eKvYRTw9TodBnZB5HWCg49XiOmC1D49GhMYAA4FVCbewOCM7m1xsmseGtLL98OG8O38+XV1i1ov79o3OpFU5w6Tuvx9OPRWATF0d8R4sPcBmHmrH6xDQa9AgQnV1KKWoqakhMoQurK7Gi/LycroBYY/ZxshzJ9vFky724l3GoDZshhRgxZh50UPPFG2vnRXbxkkwj3iow1VVZNXUUK2dHQMHDox6qDu6hCOmEj+W5EIsh1o2m4FsXjIG9ZwA5zs7QBs3QkDPnj3jVzPMyYEPP4y+THSwEFTb+UhgQMC218XRJ45HohrPQWODI9jlDuvq6jjYZynT119/PZrZnyj3OiTaIooY8TzUjz/+eLN1N9x4o69zzh0zpumKBBRDggQepUwnJYBBfb3jGvQrPxlubKQWmnkfDS1Pbl5eVIqu1jkAO/98eOqpJquuwhpIdXCpPGufocgGCrp1I1cp1q1bZxWO0Ns62/SpnUQ81GGPuM1IGJNrYSH7OmNQGzZDGokt6xr5BY/cfXeeeuqpJpUW3YgUIwtXV5NdW0uNvif36dMnalAXpVmZyU+lxJvd/tLaqzQSSlDU2/5YDaIn8eoHHzR5XZhAlml85cUYbL89XHop9Xp0d4XP865J4pRBzdRkoom23XbbZrMAPk4Y+HxOEhGJP/zwwwmqBdHg1ec4BnWey8Pa7yCrNi+PU22v/YZBpJrBgwdz5513+t4/yLdb6IxJ9/leTzzxRABTta6NUKEHfXWxjNA334Srr+bmv/4VcAn5AMTxuy7s0YM8YO7cudTU1EQ9SoWNjVBTA4sWgaMwS0VpKYWAeMgEXt2jB3cBdW5hfHbHiTGoDVsiWphBioo49dRTY9btAKK/k3BVFdl1dVEdaxFhmt6lSxqqNttpO9l4LYSf5KgmeBgy4WnTXNc72TOGckZamTED7rqLjp07I0D49/78uEE9bddcc01glYb6JIoVjBo1Kv5ODlrHLEyOK6680n1DkNAKnwb12LFjm36nCcx2BAmjcr6TJ885B266iTlz5sSNcd9o+10Hug4dv/ON2kBeEif585FHHrGk1tpQYvOWTCRusiHWTN6hh8Idd3DNNdfw448/spM9XjnCnns2edmpd2+ygbmzZ1NXVUUesFI/wFeNHg0DBzbzgFevsDImMjwkFUs6dOByIN/N2WEMasOWzhtvwEsvgd9qyjrhML+0lJyGhiazVL8DdgW6plklZ4t7CrgWuohxwxKP5Qy3m7Bbe+eDNgEDKBV+1A4dOrBx40b++c9/+tp/n733DnSe22+/nQ/eDZjk72K0fMqmcmzxODfRhMkkQz5agzMdA6JBOmbXniDpF7+Dyp133pnTTtuUrrdx+PAETrLpOg/6aZ/6wAPgMzzlMpskWXddTCMRxFG6fGHXruwOPBNHOzsqtWZoE6zV32MXHxVYRYTRo0e7b3zxRXjvvejLIp3M9NvMmYR1Mu9C7XnuqSsc1v30U5ND1OkqiV7KAhGPm6sTw4R8GDZznnnmGT60hac2o0cPOPZY/wfUz6ceq1eT19DQZJbq3sceY2ZhIT3TXGk0rkEtIpvV08LpSToIyPJ5w9rVRXkjLklMk2emyOtVWFjYVPc0BjGnSuOQqcXUE0Up1UwNJf/bbxnvszDHdgkalanyUAdJZPvZ8T0s8dnOOTAr1BJcefESC30cKxaRkJH/AiqB2Qt7bPcPPvvo/F4S8fqut03R99KV8hJBXOTPvqRptS5D26e7jvvvGik7HpSiIjjgAGv58MPJ0HHW5WvWENZJjUu23poaW5PFX3/d5BD1a6wAumyPvkRit+vrXTIrjIfasJlz8skns69dHjJZiopYnplJ75IS8hoaaLD9bs4880w2btyYUIhmEPw8LaaIyEsicrAkI3LcRnC+hZtvusl3mENugBub83xrA4QotCQ/7Lxz/J08EEcikN8EvF69ekWTiSLssssuVrnhBHnfz04piKHuilUWLlHsUZYjgR19tmvmVX78cbjtNvAh1/aLw6BNxKDeoHU/3yUxiUL7vl6Sf83a+D567PMFivV2GNQ5OuQjSJlsQ+sxODITN2NG7B39sm6d5a3WA0tVVUWjLhyT2b17NDZzNqCWLWvSNLxuHQB5HtVgn3nmGc477zz3kBNjUBsMCbMgL4++GzaQ19hIQyvktfh5sm4HPIxVQWu+iPxVRLZLb7dajnhGQpNs/xQYYosSKDjRGsLfKtEYcxt2o28U4C3B3pRiR4zUq0n8EJ6Kv0tSBUsilABdfUwrO1lqu96WFhdzjk+Fk2ZGcLducN11vmZAGhxtE8kj2NivH4XAM8B22/n/2duv3YwW8PI2MagDtF+9665MwvJKg1Vg4+GHH+a+++5LRfcMLcXEiXDhhZCq761LF8ugtRnUlJcD0HObbXga+B/wc2YmHUpLmzRV69cD3gb1wIEDmTRpkrtDx25Qp9mrZjBsLizp0IHe5eV0VIpwPEW0NOBH5UMppT5USv0Oq0Lz6cB3IvKZiIz3aici/UTkUxGZLSIzReRivf4FW1nbRSIy3dbmWhGZLyK/isgByb+95GlirqTAEAuqef1qK1wciWI3an7GEjD3S5HNqD46hrarK7aBzu+06oLf/YOyceNGZs2alXC7P9gejqWlpfxVKw3EI+Fk2hi45hHEoByrIl2vBETx7deCX892Mh7qyy7bJI3v1yNuJ6tDB86nqU782WefTUefFSkNbQQRy5hO5VQybDKoa2pQ2kM9YtddeRg4CugweDBda2tptCl9iA4NCQUpd2yPoW7/E8MGQ4uwIT+fbKXIAsIucpjpxk8MdRcRuVhEpgFXABdizXhfjhVa6UUDcLlSaigwDjhfRIYppU6IlLUFXgFe1ecZBpwIbA8cCEwSkdRZEQGpsxsfKTDEEjFmru/UKbo8NlVTmHHYKgmdxqQ8vymSsuvkJ0FM9/OqJM5TWFjoKkkXj9MuvDDQ+ZIxqLOdU8YJDOoiJWL9lIq1EyQ6LBmz4ayzzoou5wcwgvfYYw9uvfVWumrjZzOIbjOkEv1bl+pqROeK5HbrFp216TJyJDnAkh9+iDbJ0IY3tvu4b9qBA8VgaGuU2343tUF+d0ni58n6DVAIHKmUOkQp9apSqkEpNQ140KuRUmqlUuoHvVyOFWYWTcXX8djHA8/pVUcAzyulapVSC4H5QPCA3hTx8IgR1OrlIBXYnCTyoM49+mgOBHYC+qVZPzHCSSedlLJj7bij3wjh5MJbqmxV6ip8ZPFGdK/3339//gckkEecNLfddpu1cMMNCbVLpprnYEeoRiLHOvroo7n66qsT0oGG1glXitAlwc8WrATI66+/ftPAIYVa5YbNgMjguboa0aXLszt3ZurUqfz2228U6nyP1TY51czycqpEIMHBKGAMaoMhAJU2CcrGNJcZd8NPNt5g5VF5Qyn1Nz8nEZEBwA7AFNvq3YHVSql5+nUfmpZ2X4bNAE8X8QzcsuxsOgF3AiW77srujkzuREnEQ33//feT88gjLVo0IinPnO0y2bhxY3wh9hRRX1yMYCUJ3rzNNvF2j3qoQ5mZHJXWnjUnMzMzkLGWjIc60xGDmcg1mJmZyR133JHwOYMkCabMJ5yswoPB4CTioa6pIVRVZS0XFlKo/5aOHAlAlS0MLLuykvLMTAKZxgFmvwyGLZ1KWwJ+jh9bIMV4GtQi8iba0eRmZCmlDvdzAhHpiBXacYlSyi7m8Ds2eafB/XnazPIQkXOAcyC58IToCeIYN5MmTaLvG2/wR+C67Gx2BDph1WIPQiKGUXZ2NsuXL2+XVdgKE5RzS8YfGFEDmYd7GWEn9dpD26gNzR7twABLxkPd7FgpjMf2orEVDIK3gUMAxnumdvjHhHwY7OjrOVRbS6Y2qLF5w7ppdaTw/PnRdTnV1VQG8U4DmFL2BkPC1NqqnnZMIIk+VcT61fqrBBIDEcnCMqafVUq9alufCRyNFc0QYRnQz/a6L7DCeUyl1MNYqiOMGTMm6XnZ+jiGX58+fbjtttu4/vrraWxs5Mckz5doRbXeHhnibZH6JKZY3ujRgzG6YEKi9OjRg8rKSh5//HEOO+ywuPt/OHYs3333HXnjx/P+H//I9ttvH+i8LUlSRvB998FFF8Enn1jHagHVjY1DhybcJlkTNqKfo1JgzJsYakMTIgZ1XR2h6moagZAtLCO3Z082iJBlk87Lr6mhxoRuGAwtRthmz6W7zLgbnga1UuqzZA6sY6QfBWYrpe5ybN4XmKOUsgt3vgH8V0TuAnpjzeB/l0wf4vEXYIyPoiARI7ixFVU+WpShQ2H27ISbqSQeHnN69oS5c1kbChFE+Tc/P5/zzz/f176n/OEPjJw0iQXHH8/AVvjRBSIZA2/77eHjj6kTIRvIaIFQnFSUHk+U/v37N0lONBhShjaoO9XUsMeMGdSKkOf4Ta7MzaWDLuYCUFRXR5lHlUSDwZB6sm2z+d1b4bcXK+TjRaXU8SLyCy4z8kqpkXGOvRuWdvUvNmm865RS72CpedjDPVBKzRSRF4FZWAoh5yulmpcvSwFf6c69D4zxsf/WOult4MCBXHzxxfyqS80GoSWm25Pmm2/AoQ3th0SVIOxI164sBNZccUUggzoRhg8fnpLBUYuSAo9p5NtxFuBJB2PHjk37OZwsWrSoxc9p2ELQBvVxFRV0dKmqCbC+qIheNi3qLg0NrLVNQRsMhvRSZPu9dfaj+JViYoV8XKz/+69EYkMp9SUeTiel1Bke628DbgtyvkSIdEoBI0fGGxfAcccdR9euXdlrr72SngpONOSjVSgqogxL2uU2YAHwmI9mgwYNCnzKO++9l7/17s1dt94a+BgGf2S0QHxzUVERJxFbV7MtYrQ9DK7o30xPbUx/ddhh7ObYpap7d3qtWgVKUVdVRRegoRUe6gbDlsrYsWPZBqtK8+JWcF7GCvlYqf8vbrnutAwRk/iBBx+kT5/4QiIiwt57752ac7cHg5pNn9HfsAp7+DGok6Fv377861//SvNZDNAyBjVY1SQDE6DsvMGQNvRUchFQCfS4yxnFCLW9e5P388+EV6ygfP16ugDKhHwYDC3G+PHjWdCK5/dT2GWciEwVkQoRqRORsIiUxWvXlimIFH5ohcSjRKvUtRZ2L75h8yAyGZ3RQgoCp5xyirXgUyYwcs3VLVgAU6emp1Nx+O/QofwIrBkxolXO35psLtVt04II9fp3sx5cq4Y29O0LQOWMGVT89hsAIR+6+J4sWwarVgVvbzBsYbRG3LQdP0/W+7Finl/CCjk+DWh5gb8UIvoB3xre4vbioY7Qp3dvBo0enVCbh4Bz09IbAwABq2bupP+ea6FB3bAEvczR4W23bq1W2GJpYSE7Aq+2QtnaNkCkuu0PIlIAfC8iHyqlTojsICJ3Ahv1sr26bW/gIxHZLl25L61NODOTrIYG1gN9Xa5P0bk21bNmUaON70wfM6CeJNPWYNhC+fHHH5PK50oGX64qpdR8EcnQN8rHRSS56iZthVbwUIujyEZb5/vvv6dDAl6WyCdqDOo0ElDmb6H+e6GFB3WJ/spac9CZpX+f7SLXIcXoML9IqF+5iESq286CJtVtI/Fv0eq2wEIRiVS3/aal+94S1HToQG5NDaW4yypmbbstAPVz50blWHP69Wu2n8FgSB+jE3QAphI/BnWViGQD00Xk71g33HbtvqmJPCxbQzy/vQn2Gz3ezY62aiy2hSvt3//+N/369eOQQw5p7a60Km25um1rUdm1K8UlJWz0mOEp7t2bVQALFxLWU8/57UWW02AwJI2fJ+upQAZwAVY+Rj/gmHR2Kt3csPXW3AhU+dCgTgX2W6q0E4M6YtyYAheGFqcVr7lu3bpx5513WiXit1BSXd1WH/McEZkmItPWrl2bus62IFXaSC7zuDY6derEb0DWkiVkL1pEKVBowjYMhi2GuAa1UmqxUqpaKVWmlLpZKXWZUmp+vHZtmbVZWdxCy00tL8Kaagf4dX47++iMQW0ISKIJrftkZFglULdgY7a18VHd9gXb7r6q24JV4VYpNUYpNaZbt3QrzaeHGh36VudxfXbu3JlZQMHSpXSdN49vgOJOnVqugwaDoVXxtChF5BcR+dnrryU7mWomTZrErrvu6kuDOhXstddeRCYJaxsaWuScqSJRD/VOO+3Eww8/nKbeGJJh2rRp/P3vf2+x81VutRUAH/ssSX/rJ5/w9emnk9nO8gw2FwJWtz1RRHJEZCAtUN22NWno2hWAQo97YqdOnZgF5FdU0G3VKr4Lhcg3pccNhi2GWK6gSEGXSD3np/X/k4GqtPWoBRg7dixfffVVi53v448/ZoX2hqs2Gr/qJGjIx7Rp01LfGUNK2Gmnndhpp51a7Hy13bohwL69enGlj/0nTpzIxIkT090tgzdttrptW0B0FbZij3tidnY2v+XmQk0NADOLi03InMGwBRGrsMtiABHZTSllLwp1jYh8BdyS7s5tLohI1EO91YABrdmVxDEPBENAdt11V0aPHs3f/va31u6KwQdtubptW6By3DhWAE/16sV+Hvus6twZVlhRL/NN/LTBsEXhJ1ixg4hM0DdbRGRX2rnKR2uQIQJKse2QIa3dFUM75ffAj8APrd0Rn3Ts2JEff/yxtbthMKSExs6d6QOM79LFc5/abt14OTeXt/Lz6dqjR8t1zmAwtDp+DOrfA4+JSBFWntFG4Ky09mozJBLoEWonCVfRSok+q9wZ0k+6y78bDAZvamtrAcjLy/PcZ8jQoVz+9ddkVVezcztNvjQYDMHwo/LxvVJqFDASGK2UGq2Uai9OsjZDJOSjvRjUH7amVrfBYDC0MTp27AjErgA6ZswYlixZwoIFC2ivaiYGgyEYvq0lhx6pIUEi5c7bi4F6WmYmXevq+KWVSngaDAZDW2LcuHG89tprHHTQQZ77jBkzJrpsDGqDYcuifUhObAZEQz48qmy1NR588klkm23Iyclp7a4YDAZDm+DII4+MeU/ccccdo8vGoDYYtizSZlCLSD8R+VREZovITBG52LbtQhH5Va//u239tSIyX287IF19aw0OzMvjTiBUWNjaXfHFiSeeyLx589psmeotkb333ptJkya1djcMBoMHBQUF9O3bF7AKvRgMhi0HX/EHWtljgH1/pdRTcZo1AJcrpX4QkQLgexH5EOgBHAGMVErVikh3fY5hWFqn2wO9gY9EZLvNRde0z6GHcsVLL3GB8fgaAvLxxx+3dhcMBkMcrr76ai688EJ6+SxoZDAYNg/iuh9F5Gngn8AEYKz+GxOzEaCUWhlJXlRKlQOzgT7AecAdSqlavW2NbnIE8LxSqlYptRCYD+yc8Dtqozz99NMsXLjQhFAYDAbDZswFF1zAzJkzmTBhQmt3xWAwtCB+PNRjgGEqCf00ERkA7ABMAf4B7C4itwE1wBVKqalYxva3tmbL9LrNgpycHAa0t6IuBoPBYEiYWEogBoNh88RPgOwMoGfQE4hIR+AV4BKtFJIJdALGAVcCL4pVn9WtQlczI15EzhGRaSIybe3atUG7ZTAYDAaDwWAwpAQ/HuquwCwR+Q6ojaxUSh0er6GIZGEZ088qpV7Vq5cBr2qP93ci0qjPsQzoZ2veF1jhPKZS6mHgYYAxY8aYqiMGg8FgMBgMhlbFj0F9U5ADa6/zo8BspdRdtk3/A/YGJovIdkA2sA54A/iviNyFlZS4LfBdkHMbDAaDwWAwGAwtRVyDWin1WcBj7wacCvwiItP1uuuwKig/JiIzgDrgdO2tnikiLwKzsBRCzt9cFD4MBoPBYDAYDJsvEi/XUETGAf8ChmJ5kzOASqVUqwsqi8haYHELn7YrlkfdnNOc05yzbZ63PZ2zv1Jqi6oAEvC+3VrXbzK0xz5D++03mL63Bltavz3v2X5CPu7H0od+CUvx4zSscIxWpzUeRCIyTSkVVzbQnNOcc0s/Z2udd0s5Z3slyH27PX6+7bHP0H77DabvrYHp9yZ8FXZRSs0XkQwdgvG4iHydyk4YDAaDwWAwGAztFT8GdZWIZAPTdZnwlUCH9HbLYDAYDAaDwWBoH/jRoT5V73cBUIklbXdMOjvVxnnYnNOc05yzTZ93SznnlkR7/HzbY5+h/fYbTN9bA9NvTdykRADtoR6CVWjlV6VUXao7YjAYDAaDwWAwtEf8qHwcAjwILMCqZjgQOFcp9W76u2cwGAwGg8FgMLRt/BjUc4BDlVLz9etBwNtKqSEt0L8WR0RE+XHbG9oNrfGdmuto88N8pwaDwWDwwk8M9ZqIMa35DViTpv60Bfx8JilFRLrq/xktfN4xItK9hc9ZZFuWFjptVgudx84WcR1tQdcQtM51ZDAYNgNa+F5laAU8H/oicrSIHI1VwfAdETlDRE4H3gSmtlgPWwgR2VlEngFuF5ERIpJWg0gs8kXkOeB1gJaqDCki22vpwxuB4hY65y4i8jrwHxE5S0Ry0u3tE5HxIvIS8E8RGdYShuaWch1tKdeQPm+LX0dbIu3N4BCRzrbl9tb3PUWk3RUUEpHLRWR/vdyuPnOgILLQ3vre3voboaX7Hethf5j+ywVWA3sAewJrgU5p71kLoI2RkIjcCPwHeBdLSvB8YFQ6z60sqvTLriJynu5TS3g2LwZeU0odppSaq8+btgtPREYC/wZexioQtDewTbrOp8/ZHaso0TtY1ZAuBs7S21L+XrfA62izv4b0eVv0OtoSEZEjRORJ0vxbSRUicqCIfA7cIyJ3gvU7bOVu+cLW95OB2tbuj19EZH8ReR+4Gqu4XHv6zPcTkS+xBuRXQbvqe7v6bUZorX576lArpc7UnpiLlFJ3t2CfWgx9USsRWQqcoZT6QUTeA57BKrGeNrTB0wNrsPJ7YJKIPKeU2iAiIaVUY5rO2wVLreV+/foorBmH9Via4+mIEx0DzFdKPS0inYDjgSW2PqXjnKOAuUqpx0WkAzABuFBEPlNKzU31OZVSjfL/7Z15tB1VlYe/XyYgRKChGRNaQgAZ0gyNMgSiKyhieq0W1AaBIIRGRpEhNhJNGMJgGFQkNEFAhmYWFGW0WYQG2oDdKB1CgGZoSAgECG0kGAggIbv/2OfmFS/v5Q333hre3d9atW5V3aq7f3XurlOnztnnHOlV8vej/sAG5OhH8tCSvH1oF/L3IcjZj1qFWrpJGgOcDXwI7C7pZTN7q2B5K5Fenvrh99g/AVOBWcB1ksaWuZN+0i7g68DlwBFmdluxqrom6R4InI5X6E0FBgGfkTQQWFb2e0/SMOBM4DzgIeAWSeuZ2allzzuqcm/WKEOessparNR0/OU8hOSJpP0lXSNpQrppbwJmpybkRcASYOMm2TxJPuvkcjN7HdgMmAc8DEyUNKKRhaCM3ZPTrneBzwJ7yUMTjgbOAX4CjXlzzqZv2nU38BVJ5wJzgGHANEmnNtjmZEn7pF2zgE+n9HwXL/D9Ab/eZtm8CZ8Aqdl+NFnS3uD3aPKj4TTJjzI2v5h2vQuMpvk+lE3bu2iyDyW7B0maIqmW7zXdj1qNdgWJucA+wCnArsD2hQnrhJre9DycCexpZncA7+P9iZ5OFSSla7XIaF8OvAZcB9QGGNhf0rBUOC2V9ozuvwB3mNloM7sXeAs40Mw+LOu91y4dtwbmmNldZrYEb2U7WdKWqfBXmjTvgLnAFynxvVmjLHlKd5qFH5X0L5JGS/q72tJ0ZU1A0gD5bI8n4PGmx+I1iu+ngskHKXMZBjzXJJvfIjVZSdoaeMnMXgXuB44DbpO0Wi2Ta6Dd4yQdZWbvA/+K39j3mdmXgEnASEljG2zz2GTzTWAnvEXk+2a2G3AtsKek3Rtg87xk83lgiqRDgcV4DfGJ6dDFwAxgsKS6Crmd2BwHXlPdRD/K2jxb0jhJq0saDsxttB91YPMsSePN7D3gSuAymuND7dN2vJktBLbFa6wa6kPJriQdA3wXfzG5UNI3gXfwQkjD/agVkXQ8cLukkyVtZGbzzOx1M/t3UmihpKEFy1xBO70bm9kzZrYsPQN/jVeInAr8uHZKMUpXJqN9grxVaSbwJHCZfPSuA4BLgOm1U4pR+nE6SPPfp/0Dzexh4KV685lm0U77Wngels2jNgCeBiYXpbEzJB0n6WtpXcArZvZGWe/NGmXKU7oz9fio9HlWZp/hMYyVImWE6+MP5N/Km2MGtztsW2Bhas79BLCNmT3WYJu1qdvnA1tKugvYCq9dXGZmdce2dWJ3tfT1JcBJ+M2NmS2Qx3jVVaPZic1B6btn0gvEfenwx/HanbquNdncFJhgZr+X9DZeU/oGXqt5jqQvmNkMSYuAocDbTbL5Jl6gBdiGxvtRe5sX49f5GDC80X7Uic1p8hCpq/AagGb40ErXKWmBmd0vaQvg39LhDfGhZNck7QacZ2Y/T9c4Hngdj58+u9F+1GrIQ4MOw19aDgVGSPqZmT2RDrkxfbcrcHvmvEKaxrvQW6spnS8PBVog6QYz+0PeOjuiA+1TgGl4a+Fw4FYzmyXvWPmcpJ+a2eOFCU50kuZXmtlsYFnS+zKQSwf+ntCB9vPxcI+LgKMl/RDX/lXg15I2M7N5xahtIz2fLgT2A4ZIuiPlw5a590p1b2bslypP6bKG2szGdLBUpjAt6VBJn5O0lqTVgD8Dx0g6Da9V2EPSRLX12F4PjwMdDzwK/G16W2ukzVHyzgnD8NqwucDOZvYPwKaSdm7Ste4uaVJquj4aOEzSjvKObF9IWhptc5S86X514E7gzJSeBwLbAYvqtLkW8B4ptMLM7kvbo/AmzlvwzkNbAJ/Ha2EGNcHmUuCzaqu1bLQfdWSzFr4zHC9Yv0SdftQNm+/gL9Pv4LW2hzbYhzq7zjGS1sFfWM6o14eS3a3a7XoKGCppgJnNSNu74R2xb6IBftTi7ApcZmYP4gWNuXhLBABm9iQeUjNS0l5qcDhPL+hI74lJ01wzm5/W3wVuBdYqSGdHtNc+DzjFzF4DppjZLAAz+xNe0z6kGJkrsao0t6R3DWAM5NaJv7t0pH2KmV0FHAmcbGYH4xVpj+HPysJJoSgPm9lG+AvXpemrFYXOEt6bNcqVp5jZKhe8Q8BKS1fnFbngD7uNgQeBB/A5228E1gbWx2tnZ+LNdVvjmeFB6dypeC3b1cD2TbT5c7yTyNrtfmftHK51XDr3QOAHeHjGdk2+1gPx1oDr8RrGGcC2DbC5Jt5R6BfABXit9CXp97dM507ER9+YidcUN8PmtGRzi3Tu+Q32o86u835gc2BQb/2oFzYfyKTtwQ32oVX9nyPSuTf2xocytnfECxjPA8Mz+4/DY8G3TtubAb8Cdkrb3+uNH7Xagj+IV9oGjgJmZPbX8oYvZ/ZtgBc4/g+4sKPfK5PetH8y8AiwUZXSOu0/DX/h37AquoGxKc9YPe/07qX2r7Q7/gf4C/rAEmlfM31uhBf0a/n7gMwxud+bXV1H2fKU7gj/TmaZBPwOuLqIROxmQvdPn1sBN2Sc4lLg9rQ9Erg8c87pwM/S+p7A13OyeUVa7wf0y/tae+NYvbR5BnBlWh9EDx88q7A5Hbg+be+AxzJ+LW1fAxyb+Y1BOdk8Jq2PaqAfrcrmtcBRtf+yp37UoLRtlA91dZ21tB3YUx9qZ/cQ4Bj85e4kYI20f3M8NvwbpBeSZPfc3vpRKy6dpRGwLt5KtW/aXhvvxzIx+e4Q4GbgHmDjMutN22Pxl6vr8tTbIO2j8ZfZ64vQ3lsfSfu+itf45l4grTPNt8QrHu4k5xeYrrSn7/qlz/PwGuvsd2virb253pvtNPTvqb/knad0GUNtZj/Kbqc4oDu7Oi9vJA3A47z7S7oXb377CFbEZR4PvC5pD/wN7CNJe5kHrv818EI6dmaONl9Mx/Yo7rRR15qOtxxsrkfqWW7ea/uNBtp8TdIYM3tQ0hzz4etWx2+mWqxtzW4eNu9Lxz7aHXsNsAkpbjv9l438P7ubtnnYhLa0/ZBu+lA7uwMk3QPcb2YLJT2B1xY9JGm2mb0k6SHgM3g4Vq21asV/2V0/akXkHa8m4P/h5cBzZvZRCqFZhscd/wrvrHynmb0taQhe02iS3gdONO/EXGa9a6Sf+B/8Je+pPPQ2WPs84Ftm9kxFdGcncbrDcpoErUHaa/nXG3iav1oi7f3xLHxFGcTMJkqan855Cfgb8/4sJ+R1b7bTPdbMTs/+59KKGOhS5Sm9iT8ajNfklAZJn8M7KP0VXnCrjUE4RtIusOKhfxYw2czm4PG1EyTNwt9yLiu7zVa61m7aXI7HTZ2RThsg6Ug89vVdPPPodtxy2OxbNjuw+zweRvKpZO8/gSfwGulaH4rb8DCdPSX9VzrvoZ7YbEX08QlwFpGZACcVNsALc/fhHT2vkLQJPvrPh7XjcixM16P3L+m4eQUVphuh/ZUCCtP16K59T0GF6Ub495KCCtOr0v5RqrQYgtfu1jgfD2P6LWnghgIK04fhI5JNlnRA2jcgaam9XJUmTyEZ7KqafQ4+1M6T+HAvbwLHd3VengvefPWNzPZ00pB4wONpXz88PuiXpOZivAlm66rYbKVr7aHNW4FPpn37AaPCZtjsxO7FwNTM9jC8wLxH2l4nfa4HDO2t3VZbgL2Bm9P6mvg4sHfTFo9+Dv7g2wl/eTknpft0OmjKDb19T3tVdbeA9rPxFsfRaXss8CzwQwoKq8noHoaPhT0/s78Wundm2dK8Oxf1ycwyFBhQpHN0onEwPiRcLaHHkR6aeA3Ut9P6p4Fbqmqzla61hzZvDpths5t2DwIuSOsD0ucheBjb3cC1jbLdl5eUjlNInX9oCyWrdcpdF29pOD/9BzeROpVm/5vQ23e1V1V3q2vHhw7etEDd+6btfpk8eiZwdubYDcqU5rWlO8PmvZxZFlhb80ZpMLOlZvaBtTUF7Y336AQ4HNhG0t14cHpDxtoswmZRditg87+h/pm+wmbfstmJ3X2AV9J3tbxsO+BLwGwzG1+vzb6MnFVNgFMbsmoxPhLDung848Fm9qIyw5yZ2dLQ2/e0V1V3aFf/pPsZM3ulQN0XSDocH3mklkcfDZwgacOk8c0ypHl7ujOxS2VIDmHAhrR1nFwCfB8feWKumS2ous2i7JbdpqVX1LAZNruwe2/atw0eg/cOsJWVYJKFsmPWrQlw9jafhGcRnt4fgI8ZbD3sgN1qevuC9qrqDu35x6avQvfhwEJJvzHnaUm34SOQHC5prJn9Jp1fWJq3p0yDojeC5fiQWn8Etk81XqcBy81sZjMKtgXZLMpu2AybVbTZ3u4O8tE+voMX4M+NwnTnqOcT4Fykj0+AMxDye/BVTW+Wqmqvqm4I7VBKf3kSH8J4WO0AM/smPhndW3geXr7yqxUYb9KMBXee5XjMzRF91WYrXWvYDJtVtlvVhYpNgFM1vX1Be1V1h/bK+MuOaXtzfOrwh4GRRaR5d5baQOl9BknD8GGwfmxmH/RVm0XZDZths4o2i7RbNST1Nx+j9hB8YoQ98L4Rl5vZe5I2xx/M/wHUxn69FlhgZpPSbwyynMbsrprevqC9qrpDe+X8Zb6ZnS5pbeBTZvZYnrp7Sp8rUAdBEAQ9R5kJcPCZxZ41nwBnN3wCnAl4x02TNA6fAGehmU2VdDXwSzO7J/T2Xe1V1R3aw1/yoHwxKEEQBEGuqGIT4FRNb5aqaq+qbgjthL/kQtRQB0EQtDiSRgObmdn1aftiYKmZfS9tDwNuACaZ2SOS1jGzxZLWw4fealZn0j6hty9or6ru0B7+khdRoA6CIGhxJA0GPgKWpVjHg/COTN+V97xfluIfD8BbNv9oBY7ZXTW9Waqqvaq6IbQXQVV110OEfARBELQ4VrEJcKqmN0tVtVdVN4T2Iqiq7nroUxO7BEEQBL1HFZsAp2p6s1RVe1V1Q2gvgqrq7g1RQx20FJLWkXRcWt9E0i+K1hQEJaJqE+BUTW+Wqmqvqm4I7UVQVd09JmKog5ZC0mbA3WY2smgtQVBG0pBWj6blGjO7qmBJq6RqerNUVXtVdUNoL4Kq6u4pUaAOWgpJtwD7As8BL+CzRY2UNB7YD+gPjAR+BAzCh/X5APh7M/uTpBHApcD6wFLgSDN7Nu/rCIJmoYpNgFM1vVmqqr2quiG0F0FVdfeUKFAHLUW2hrrd+nhgMrATsDrwv8CpZvZTSRcBL5vZTyQ9ABxjZi9I2hWYamZ7FXIxQRAEQRCUguiUGARtPGhmS4Alkt4G7kr75wDbSxoCjAJuk1Q7Z7X8ZQZBEARBUCaiQB0EbWSbopZntpfj90o/YLGZ7ZizriAIgiAISkyM8hG0GkuAT/TmRDP7MzBX0v4AcnZopLggCIIgCKpHFKiDlsLMFgGPSHoKuLAXPzEOOELSbOBpvINjEARBEAQtTHRKDIIgCIKgtEhaBzjYzKZL2gSYZmb/WLCsIPgYUaAOgiAIgqC0xPwBQRWITolBEARBEJSZ84ARkp4g5g8ISkrEUAdBEARBUGYmAi+mEZZOaffdSOBgYBfgXGCpme0E/A44NB1zBfBtM9sZ+Gdgeh6ig9YiaqiDIAiCIKgqMX9AUAqiQB0EQRAEQVWJ+QOCUhAhH0EQBEEQlJmYPyAoPVGgDoIgCIKgtMT8AUEViGHzgiAIgiAIgqAOooY6CIIgCIIgCOogCtRBEARBEARBUAdRoA6CIAiCIAiCOogCdRAEQRAEQRDUQRSogyAIgiAIgqAOokAdBEEQBEEQBHUQBeogCIIgCIIgqIMoUAdBEARBEARBHfw/s2oovfsVpsUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1,ax2) = plt.subplots(1,2,figsize=(12,3))\n",
    "fig.suptitle('emulator evaluation')\n",
    "df_train[\"y_train\"].plot(label=\"reference\",c=\"k\",ax=ax1)\n",
    "df_train[\"y_pred\"].plot(label=\"prediction\",c=\"r\",ax=ax1)\n",
    "ax1.set_title(\"training data\")\n",
    "ax1.set_ylabel(\"urban daily maximum temperature, K\")\n",
    "\n",
    "df_test[\"y_test\"].plot(label=\"reference\",c=\"k\",ax=ax2)\n",
    "df_test[\"y_pred\"].plot(label=\"prediction\",c=\"r\",ax=ax2)\n",
    "ax2.set_title(\"testing data\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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