CMIP6_exploring.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ef7d2297-28f9-47bf-a90a-b72472e74354",
   "metadata": {},
   "source": [
    "# Explore CMIP6 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8a7ed91-5a0a-4047-bdeb-2b7800e60298",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import matplotlib.pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f3a76e7-4c76-4887-ae94-cb4ae6295ab2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 522217 entries, 0 to 522216\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   activity_id     522217 non-null  object \n",
      " 1   institution_id  522217 non-null  object \n",
      " 2   source_id       522217 non-null  object \n",
      " 3   experiment_id   522217 non-null  object \n",
      " 4   member_id       522217 non-null  object \n",
      " 5   table_id        522217 non-null  object \n",
      " 6   variable_id     522217 non-null  object \n",
      " 7   grid_label      522217 non-null  object \n",
      " 8   zstore          522217 non-null  object \n",
      " 9   dcpp_init_year  141466 non-null  float64\n",
      " 10  version         522217 non-null  int64  \n",
      "dtypes: float64(1), int64(1), object(9)\n",
      "memory usage: 43.8+ MB\n"
     ]
    }
   ],
   "source": [
    "cat = pd.read_csv(\"https://cmip6-pds.s3-us-west-2.amazonaws.com/pangeo-cmip6.csv\")\n",
    "cat.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac1c9320-e07f-430c-a377-4240c5196637",
   "metadata": {
    "tags": []
   },
   "source": [
    "# GDFL-CM4 daily"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d2ec05-fb36-45ad-809a-048126ab21d0",
   "metadata": {
    "tags": []
   },
   "source": [
    "## historical"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61cf6bf4-e82a-4ee5-b732-ab5916905c32",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Querying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2c9f2a33-a7c4-4b61-b4b9-cb689e7aa0f4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gdfl_cm4_hist_result = cat.query(\"experiment_id=='historical' & source_id=='GFDL-CM4'& grid_label=='gr1' & table_id=='day'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7e986c7d-d37f-48aa-a203-c5643de359e5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['sfcWindmax', 'snc', 'snw', 'sfcWind', 'rsus', 'pr', 'huss',\n",
       "       'mrro', 'mrso', 'mrsos', 'prc', 'tasmax', 'tas', 'rsds', 'prsn',\n",
       "       'psl', 'rlds', 'rlus', 'rlut', 'tasmin', 'tslsi', 'uas', 'vas',\n",
       "       'hursmin', 'hursmax', 'clt', 'hfls', 'hfss', 'hurs'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfl_cm4_hist_result['variable_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "763930a3-12b5-4c95-9ed7-4e354e54787a",
   "metadata": {
    "tags": []
   },
   "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>activity_id</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>table_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>zstore</th>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>AERmon</td>\n",
       "      <td>ua</td>\n",
       "      <td>gr1</td>\n",
       "      <td>s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>AERmon</td>\n",
       "      <td>toz</td>\n",
       "      <td>gr1</td>\n",
       "      <td>s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>AERmon</td>\n",
       "      <td>so2</td>\n",
       "      <td>gr1</td>\n",
       "      <td>s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>AERmon</td>\n",
       "      <td>rsutcsaf</td>\n",
       "      <td>gr1</td>\n",
       "      <td>s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>AERmon</td>\n",
       "      <td>rsutaf</td>\n",
       "      <td>gr1</td>\n",
       "      <td>s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6724</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>EmonZ</td>\n",
       "      <td>epfz</td>\n",
       "      <td>gr2z</td>\n",
       "      <td>s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6725</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>EmonZ</td>\n",
       "      <td>tntc</td>\n",
       "      <td>gr1z</td>\n",
       "      <td>s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6726</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>EmonZ</td>\n",
       "      <td>tntmp</td>\n",
       "      <td>gr1z</td>\n",
       "      <td>s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6727</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Emon</td>\n",
       "      <td>utendnogw</td>\n",
       "      <td>gr1</td>\n",
       "      <td>s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294132</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>AERmonZ</td>\n",
       "      <td>ua</td>\n",
       "      <td>gr1z</td>\n",
       "      <td>s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>420 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       activity_id institution_id source_id experiment_id member_id table_id   \n",
       "520           CMIP      NOAA-GFDL  GFDL-CM4    historical  r1i1p1f1   AERmon  \\\n",
       "521           CMIP      NOAA-GFDL  GFDL-CM4    historical  r1i1p1f1   AERmon   \n",
       "522           CMIP      NOAA-GFDL  GFDL-CM4    historical  r1i1p1f1   AERmon   \n",
       "523           CMIP      NOAA-GFDL  GFDL-CM4    historical  r1i1p1f1   AERmon   \n",
       "524           CMIP      NOAA-GFDL  GFDL-CM4    historical  r1i1p1f1   AERmon   \n",
       "...            ...            ...       ...           ...       ...      ...   \n",
       "6724          CMIP      NOAA-GFDL  GFDL-CM4    historical  r1i1p1f1    EmonZ   \n",
       "6725          CMIP      NOAA-GFDL  GFDL-CM4    historical  r1i1p1f1    EmonZ   \n",
       "6726          CMIP      NOAA-GFDL  GFDL-CM4    historical  r1i1p1f1    EmonZ   \n",
       "6727          CMIP      NOAA-GFDL  GFDL-CM4    historical  r1i1p1f1     Emon   \n",
       "294132        CMIP      NOAA-GFDL  GFDL-CM4    historical  r1i1p1f1  AERmonZ   \n",
       "\n",
       "       variable_id grid_label   \n",
       "520             ua        gr1  \\\n",
       "521            toz        gr1   \n",
       "522            so2        gr1   \n",
       "523       rsutcsaf        gr1   \n",
       "524         rsutaf        gr1   \n",
       "...            ...        ...   \n",
       "6724          epfz       gr2z   \n",
       "6725          tntc       gr1z   \n",
       "6726         tntmp       gr1z   \n",
       "6727     utendnogw        gr1   \n",
       "294132          ua       gr1z   \n",
       "\n",
       "                                                   zstore  dcpp_init_year   \n",
       "520     s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...             NaN  \\\n",
       "521     s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...             NaN   \n",
       "522     s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...             NaN   \n",
       "523     s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...             NaN   \n",
       "524     s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...             NaN   \n",
       "...                                                   ...             ...   \n",
       "6724    s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...             NaN   \n",
       "6725    s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...             NaN   \n",
       "6726    s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...             NaN   \n",
       "6727    s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...             NaN   \n",
       "294132  s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...             NaN   \n",
       "\n",
       "         version  \n",
       "520     20180301  \n",
       "521     20180301  \n",
       "522     20180301  \n",
       "523     20180301  \n",
       "524     20180301  \n",
       "...          ...  \n",
       "6724    20180701  \n",
       "6725    20180701  \n",
       "6726    20180701  \n",
       "6727    20180701  \n",
       "294132  20190920  \n",
       "\n",
       "[420 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfl_cm4_hist_result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5657f2d2-8484-494d-9dc7-5aa7f5dafe8c",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Exporting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4f5aff75-42f0-4ece-ab47-38f703acae06",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import s3fs\n",
    "import time\n",
    "fs = s3fs.S3FileSystem(anon=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e79ec145-4dd3-4b57-a745-bc13f16a6bd5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# precipitation\n",
    "gdfl_cm4_hist_pr_url = gdfl_cm4_hist_result.query(\"variable_id=='pr'\")['zstore']\n",
    "hist_pr_daily_ds = xr.open_zarr(fs.get_mapper(gdfl_cm4_hist_pr_url.values[-1]), consolidated=True)\n",
    "hist_pr_daily_ds = hist_pr_daily_ds['pr'].sel(lat=slice(-12, 32), lon=slice(42, 102))\n",
    "hist_pr_daily_ds.to_netcdf('shared/data/gdfl_cm4_daily/pr.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1d4163db-3c9c-46ed-bb28-cb9248c4dc6f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finish exporting variable tas\n",
      "finish exporting variable psl\n",
      "finish exporting variable uas\n",
      "finish exporting variable vas\n",
      "finish exporting variable sfcWind\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index -1 is out of bounds for axis 0 with size 0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[40], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m list_of_vars:\n\u001b[1;32m      4\u001b[0m     gdfl_cm4_hist_url \u001b[38;5;241m=\u001b[39m gdfl_cm4_hist_result\u001b[38;5;241m.\u001b[39mquery(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvariable_id==\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvar\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzstore\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m----> 5\u001b[0m     hist_daily_ds \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mopen_zarr(fs\u001b[38;5;241m.\u001b[39mget_mapper(\u001b[43mgdfl_cm4_hist_url\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m), consolidated\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      6\u001b[0m     hist_daily_ds \u001b[38;5;241m=\u001b[39m hist_daily_ds[var]\u001b[38;5;241m.\u001b[39msel(lat\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mslice\u001b[39m(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m32\u001b[39m), lon\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mslice\u001b[39m(\u001b[38;5;241m42\u001b[39m, \u001b[38;5;241m102\u001b[39m))\n\u001b[1;32m      7\u001b[0m     hist_daily_ds\u001b[38;5;241m.\u001b[39mto_netcdf(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mshared/data/gdfl_cm4_daily/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvar\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.nc\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mIndexError\u001b[0m: index -1 is out of bounds for axis 0 with size 0"
     ]
    }
   ],
   "source": [
    "list_of_vars = ['tos']\n",
    "\n",
    "for var in list_of_vars:\n",
    "    gdfl_cm4_hist_url = gdfl_cm4_hist_result.query(f\"variable_id=='{var}'\")['zstore']\n",
    "    if len(gdfl_cm4_hist_url) == 0:\n",
    "        print(f'no variable {var} in this database')\n",
    "        continue\n",
    "    hist_daily_ds = xr.open_zarr(fs.get_mapper(gdfl_cm4_hist_url.values[-1]), consolidated=True)\n",
    "    hist_daily_ds = hist_daily_ds[var].sel(lat=slice(-12, 32), lon=slice(42, 102))\n",
    "    hist_daily_ds.to_netcdf(f'shared/data/gdfl_cm4_daily/{var}.nc')\n",
    "    print(f'finish exporting variable {var}')\n",
    "    time.sleep(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a81469cb-6f40-492d-9102-80fb5b757820",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3566e95f70>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xr.open_dataset('shared/data/gdfl_cm4_daily/tas.nc')['tas'].isel(time=0).plot.imshow()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f012be65-aad6-439b-b20e-7c23e4ea4669",
   "metadata": {
    "tags": []
   },
   "source": [
    "### SST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b4430a5e-5bb1-4354-8e49-eed286c7752b",
   "metadata": {
    "tags": []
   },
   "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>activity_id</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>table_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>zstore</th>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6685</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Oday</td>\n",
       "      <td>tos</td>\n",
       "      <td>gn</td>\n",
       "      <td>s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6686</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Oday</td>\n",
       "      <td>tos</td>\n",
       "      <td>gr</td>\n",
       "      <td>s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     activity_id institution_id source_id experiment_id member_id table_id   \n",
       "6685        CMIP      NOAA-GFDL  GFDL-CM4    historical  r1i1p1f1     Oday  \\\n",
       "6686        CMIP      NOAA-GFDL  GFDL-CM4    historical  r1i1p1f1     Oday   \n",
       "\n",
       "     variable_id grid_label   \n",
       "6685         tos         gn  \\\n",
       "6686         tos         gr   \n",
       "\n",
       "                                                 zstore  dcpp_init_year   \n",
       "6685  s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...             NaN  \\\n",
       "6686  s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...             NaN   \n",
       "\n",
       "       version  \n",
       "6685  20180701  \n",
       "6686  20180701  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfl_cm4_hist_result = cat.query(\"experiment_id=='historical' & source_id=='GFDL-CM4' & table_id=='Oday'\")\n",
    "gdfl_cm4_hist_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "0b0506e4-4ea7-4109-8033-96c77e785a1b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<defs>\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (bnds: 2, y: 1080, x: 1440, vertex: 4, time: 60225)\n",
       "Coordinates:\n",
       "  * bnds       (bnds) float64 1.0 2.0\n",
       "    lat        (y, x) float32 dask.array&lt;chunksize=(1080, 1440), meta=np.ndarray&gt;\n",
       "    lat_bnds   (y, x, vertex) float32 dask.array&lt;chunksize=(1080, 1440, 4), meta=np.ndarray&gt;\n",
       "    lon        (y, x) float32 dask.array&lt;chunksize=(1080, 1440), meta=np.ndarray&gt;\n",
       "    lon_bnds   (y, x, vertex) float32 dask.array&lt;chunksize=(1080, 1440, 4), meta=np.ndarray&gt;\n",
       "  * time       (time) object 1850-01-01 12:00:00 ... 2014-12-31 12:00:00\n",
       "    time_bnds  (time, bnds) object dask.array&lt;chunksize=(30113, 1), meta=np.ndarray&gt;\n",
       "  * x          (x) float64 -299.7 -299.5 -299.2 -299.0 ... 59.53 59.78 60.03\n",
       "  * y          (y) float64 -80.39 -80.31 -80.23 -80.15 ... 89.73 89.84 89.95\n",
       "Dimensions without coordinates: vertex\n",
       "Data variables:\n",
       "    tos        (time, y, x) float32 dask.array&lt;chunksize=(18, 1080, 1440), meta=np.ndarray&gt;\n",
       "Attributes: (12/49)\n",
       "    Conventions:            CF-1.7 CMIP-6.0 UGRID-1.0\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          standard\n",
       "    branch_time_in_child:   0.0\n",
       "    branch_time_in_parent:  36500.0\n",
       "    comment:                &lt;null ref&gt;\n",
       "    ...                     ...\n",
       "    variable_id:            tos\n",
       "    variant_info:           N/A\n",
       "    variant_label:          r1i1p1f1\n",
       "    status:                 2019-11-16;created;by nhn2@columbia.edu\n",
       "    netcdf_tracking_ids:    hdl:21.14100/e3b86300-29b2-4220-97ce-40fa504858e5...\n",
       "    version_id:             v20180701</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-9adf48de-1479-43f9-93de-4c8c4fca2ec0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9adf48de-1479-43f9-93de-4c8c4fca2ec0' 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'>bnds</span>: 2</li><li><span class='xr-has-index'>y</span>: 1080</li><li><span class='xr-has-index'>x</span>: 1440</li><li><span>vertex</span>: 4</li><li><span class='xr-has-index'>time</span>: 60225</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-98e7e167-9d4a-4dbd-8954-07e87353903b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-98e7e167-9d4a-4dbd-8954-07e87353903b' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bnds</span></div><div class='xr-var-dims'>(bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0</div><input id='attrs-7d2b4ec7-85d0-4a3f-b0fb-8b1ec7f9f971' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d2b4ec7-85d0-4a3f-b0fb-8b1ec7f9f971' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44a2fdd6-58e6-4eff-aecf-81ca6c4231f4' class='xr-var-data-in' type='checkbox'><label for='data-44a2fdd6-58e6-4eff-aecf-81ca6c4231f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertex number</dd></dl></div><div class='xr-var-data'><pre>array([1., 2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-186638d1-0757-4f64-b8f7-56f0f7d8765d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-186638d1-0757-4f64-b8f7-56f0f7d8765d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9db40dbc-b80f-400d-bf3a-c6e8ce736bb0' class='xr-var-data-in' type='checkbox'><label for='data-9db40dbc-b80f-400d-bf3a-c6e8ce736bb0' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>cell_methods :</span></dt><dd>y: point x: point</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 5.93 MiB </td>\n",
       "                        <td> 5.93 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1080, 1440) </td>\n",
       "                        <td> (1080, 1440) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(y, x, vertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1080, 1440, 4), meta=np.ndarray&gt;</div><input id='attrs-41497ca4-18c1-45ed-bda6-6f5cfc793d65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41497ca4-18c1-45ed-bda6-6f5cfc793d65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8041106a-9f1f-4e3b-a718-f164e4a071ef' class='xr-var-data-in' type='checkbox'><label for='data-8041106a-9f1f-4e3b-a718-f164e4a071ef' 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>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude bounds</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 23.73 MiB </td>\n",
       "                        <td> 23.73 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (1080, 1440, 4) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                        <td> 5.93 MiB </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(y, x, vertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1080, 1440, 4), meta=np.ndarray&gt;</div><input id='attrs-6331f89f-b751-4183-8f52-3aa720fcf205' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6331f89f-b751-4183-8f52-3aa720fcf205' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dedd2064-b0c8-4f62-a6d9-9a6d95b5418a' class='xr-var-data-in' type='checkbox'><label for='data-dedd2064-b0c8-4f62-a6d9-9a6d95b5418a' 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>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude bounds</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 23.73 MiB </td>\n",
       "                        <td> 23.73 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (1080, 1440, 4) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-01 12:00:00 ... 2014-12-...</div><input id='attrs-c7a367f0-ed96-4ffa-979a-0e1dcceba763' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7a367f0-ed96-4ffa-979a-0e1dcceba763' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d52a36a-cfa8-4649-a9f8-62e696921b41' class='xr-var-data-in' type='checkbox'><label for='data-7d52a36a-cfa8-4649-a9f8-62e696921b41' 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>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>calendar_type :</span></dt><dd>noleap</dd><dt><span>description :</span></dt><dd>Temporal mean</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30113, 1), meta=np.ndarray&gt;</div><input id='attrs-6f6712b6-e732-43af-8898-e86d24b64e8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f6712b6-e732-43af-8898-e86d24b64e8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb6e5f22-6171-4bff-be4d-b99af401d46b' class='xr-var-data-in' type='checkbox'><label for='data-fb6e5f22-6171-4bff-be4d-b99af401d46b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time axis boundaries</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <tr>\n",
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       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 0.92 MiB </td>\n",
       "                        <td> 235.26 kiB </td>\n",
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       "                        <td> (30113, 1) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4 chunks in 2 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-299.7 -299.5 ... 59.78 60.03</div><input id='attrs-f4f1910f-75f6-40e6-8420-b0ad6f4aa290' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4f1910f-75f6-40e6-8420-b0ad6f4aa290' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8f26770-5e90-4fd2-83af-4770e8edb22c' class='xr-var-data-in' type='checkbox'><label for='data-f8f26770-5e90-4fd2-83af-4770e8edb22c' 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>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>array([-299.724244, -299.476198, -299.22815 , ...,   59.531631,   59.77967 ,\n",
       "         60.027712])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-80.39 -80.31 ... 89.84 89.95</div><input id='attrs-6594fa4b-76be-4ab8-a934-2bee62e9107e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6594fa4b-76be-4ab8-a934-2bee62e9107e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41fb7733-2a15-4b13-abc7-6a6620c27017' class='xr-var-data-in' type='checkbox'><label for='data-41fb7733-2a15-4b13-abc7-6a6620c27017' 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>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>array([-80.389238, -80.308075, -80.226911, ...,  89.729781,  89.837868,\n",
       "        89.945956])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ae22f882-fc11-4dd3-b73b-e987b8c8ecfb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ae22f882-fc11-4dd3-b73b-e987b8c8ecfb' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tos</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(18, 1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-bb2013e8-13fa-451f-a7e5-3fe1f6cb04dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb2013e8-13fa-451f-a7e5-3fe1f6cb04dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b389acc-95ae-4a8b-9d11-b040ab99f1b2' class='xr-var-data-in' type='checkbox'><label for='data-7b389acc-95ae-4a8b-9d11-b040ab99f1b2' 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_measures :</span></dt><dd>area: areacello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>long_name :</span></dt><dd>Sea Surface Temperature</dd><dt><span>original_name :</span></dt><dd>tos</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>units :</span></dt><dd>degC</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 348.92 GiB </td>\n",
       "                        <td> 106.79 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (60225, 1080, 1440) </td>\n",
       "                        <td> (18, 1080, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 3346 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "  <text x=\"35.294118\" y=\"83.717239\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,83.717239)\">60225</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-2306573c-f9de-41f2-b09c-e3700db10dc2' class='xr-section-summary-in' type='checkbox'  ><label for='section-2306573c-f9de-41f2-b09c-e3700db10dc2' class='xr-section-summary' >Indexes: <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-index-name'><div>bnds</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-37764f82-d312-4b68-9367-d44222867ca3' class='xr-index-data-in' type='checkbox'/><label for='index-37764f82-d312-4b68-9367-d44222867ca3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1.0, 2.0], dtype=&#x27;float64&#x27;, name=&#x27;bnds&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c17c6718-da00-4261-9af9-cd905ff47471' class='xr-index-data-in' type='checkbox'/><label for='index-c17c6718-da00-4261-9af9-cd905ff47471' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-01 12:00:00, 1850-01-02 12:00:00, 1850-01-03 12:00:00,\n",
       "             1850-01-04 12:00:00, 1850-01-05 12:00:00, 1850-01-06 12:00:00,\n",
       "             1850-01-07 12:00:00, 1850-01-08 12:00:00, 1850-01-09 12:00:00,\n",
       "             1850-01-10 12:00:00,\n",
       "             ...\n",
       "             2014-12-22 12:00:00, 2014-12-23 12:00:00, 2014-12-24 12:00:00,\n",
       "             2014-12-25 12:00:00, 2014-12-26 12:00:00, 2014-12-27 12:00:00,\n",
       "             2014-12-28 12:00:00, 2014-12-29 12:00:00, 2014-12-30 12:00:00,\n",
       "             2014-12-31 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=60225, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ca94eb97-74a9-4677-8eca-df278722b970' class='xr-index-data-in' type='checkbox'/><label for='index-ca94eb97-74a9-4677-8eca-df278722b970' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-299.72424385142534, -299.47619795417893, -299.22815003129284,\n",
       "        -298.9801000979502, -298.73204816972844, -298.48399426259493,\n",
       "        -298.2359383929031, -297.98788057738807,  -297.7398208331622,\n",
       "       -297.49175917771095,\n",
       "       ...\n",
       "          57.7954116364209,    58.0434365318752,   58.29146358221654,\n",
       "         58.53949277642749,   58.78752410305645,    59.0355575502213,\n",
       "        59.283593105613505,   59.53163075650182,   59.77967048973631,\n",
       "         60.02771229175239],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0a7f8526-89eb-4a7c-bc77-079009def81d' class='xr-index-data-in' type='checkbox'/><label for='index-0a7f8526-89eb-4a7c-bc77-079009def81d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-80.38923793857394, -80.30807479101273, -80.22691082925455,\n",
       "       -80.14574767486899, -80.06458691047965,  -79.9834300812582,\n",
       "       -79.90227869634805, -79.82113423022128, -79.73999812397257,\n",
       "       -79.65887178655316,\n",
       "       ...\n",
       "         88.9731671559671,  89.08125482376003,  89.18934249155296,\n",
       "        89.29743015934592,  89.40551782713882,  89.51360549493178,\n",
       "        89.62169316272471,  89.72978083051767,  89.83786849831056,\n",
       "         89.9459561661037],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=1080))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f1fcbd0e-2c38-4ed2-a8ed-8787b0628adc' class='xr-section-summary-in' type='checkbox'  ><label for='section-f1fcbd0e-2c38-4ed2-a8ed-8787b0628adc' class='xr-section-summary' >Attributes: <span>(49)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.0 UGRID-1.0</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>36500.0</dd><dt><span>comment :</span></dt><dd>&lt;null ref&gt;</dd><dt><span>contact :</span></dt><dd>gfdl.climate.model.info@noaa.gov</dd><dt><span>creation_date :</span></dt><dd>2019-02-23T02:54:09Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.27</dd><dt><span>experiment :</span></dt><dd>historical</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacello</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NOAA-GFDL.GFDL-CM4.historical.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>ocean data on native grid tripolar - nominal 0.25 deg latitude/longitude</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>File was processed by fremetar (GFDL analog of CMOR). TripleID: [exper_id_NIX4LXCc18,realiz_id_p1BNnppz5X,run_id_F8Xk5tsZcx]</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA</dd><dt><span>institution_id :</span></dt><dd>NOAA-GFDL</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by NOAA-GFDL is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>25 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>GFDL-CM4</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-1-1</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>ocean</dd><dt><span>references :</span></dt><dd>see further_info_url attribute</dd><dt><span>source :</span></dt><dd>GFDL-CM4 (2018): \n",
       "aerosol: interactive\n",
       "atmos: GFDL-AM4.0.1 (Cubed-sphere (c96) - 1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 33 levels; top level 1 hPa)\n",
       "atmosChem: fast chemistry, aerosol only\n",
       "land: GFDL-LM4.0.1 (1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 20 levels; bot level 10m); land:Veg:unnamed (dynamic vegetation, dynamic land use); land:Hydro:unnamed (soil water and ice, multi-layer snow, rivers and lakes)\n",
       "landIce: GFDL-LM4.0.1\n",
       "ocean: GFDL-OM4p25 (GFDL-MOM6, tripolar - nominal 0.25 deg; 1440 x 1080 longitude/latitude; 75 levels; top grid cell 0-2 m)\n",
       "ocnBgchem: GFDL-BLINGv2\n",
       "seaIce: GFDL-SIM4p25 (GFDL-SIS2.0, tripolar - nominal 0.25 deg; 1440 x 1080 longitude/latitude; 5 layers; 5 thickness categories)\n",
       "(GFDL ID: 2019_0065)</dd><dt><span>source_id :</span></dt><dd>GFDL-CM4</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Oday</dd><dt><span>title :</span></dt><dd>NOAA GFDL GFDL-CM4 model output prepared for CMIP6 historical</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/e3b86300-29b2-4220-97ce-40fa504858e5\n",
       "hdl:21.14100/80f75fcf-ccc1-4b09-8ed1-d38b2f9e066d\n",
       "hdl:21.14100/9e541d7e-4ead-417a-ac4b-b61c79db1c03\n",
       "hdl:21.14100/6e50030e-425a-4315-b163-d0a82e8881df\n",
       "hdl:21.14100/a4f0ee9e-a642-4823-8cf1-cb64aff44ca5\n",
       "hdl:21.14100/ba47f0ed-30d5-4752-a1ac-ccce32d3201f\n",
       "hdl:21.14100/725dfce4-df03-4028-98cd-642e62e7fed0\n",
       "hdl:21.14100/5c6719b7-7732-4cae-8f85-50fdba04be1c\n",
       "hdl:21.14100/e4247811-fb48-4093-b6c5-7a4934339ab3\n",
       "hdl:21.14100/545a1d6a-619c-4b93-b587-06bf010b80f2\n",
       "hdl:21.14100/1104c48e-717b-4ccf-a845-e1030f729693\n",
       "hdl:21.14100/b4d6fa1c-5d1a-4eb9-ba99-a0fbc297ca2b\n",
       "hdl:21.14100/3be9d2d2-0991-4110-a550-64db8365420e\n",
       "hdl:21.14100/c8d3f21a-1632-4a96-95e5-3f07ffd493a4\n",
       "hdl:21.14100/73132218-563c-4875-a678-797e77e4e1c7\n",
       "hdl:21.14100/b525668d-c8c8-4eec-ba0d-b26ff9f43b08\n",
       "hdl:21.14100/11ddcf0d-560f-4237-bbef-c7af1cf5ba20</dd><dt><span>variable_id :</span></dt><dd>tos</dd><dt><span>variant_info :</span></dt><dd>N/A</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>status :</span></dt><dd>2019-11-16;created;by nhn2@columbia.edu</dd><dt><span>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/e3b86300-29b2-4220-97ce-40fa504858e5\n",
       "hdl:21.14100/80f75fcf-ccc1-4b09-8ed1-d38b2f9e066d\n",
       "hdl:21.14100/9e541d7e-4ead-417a-ac4b-b61c79db1c03\n",
       "hdl:21.14100/6e50030e-425a-4315-b163-d0a82e8881df\n",
       "hdl:21.14100/a4f0ee9e-a642-4823-8cf1-cb64aff44ca5\n",
       "hdl:21.14100/ba47f0ed-30d5-4752-a1ac-ccce32d3201f\n",
       "hdl:21.14100/725dfce4-df03-4028-98cd-642e62e7fed0\n",
       "hdl:21.14100/5c6719b7-7732-4cae-8f85-50fdba04be1c\n",
       "hdl:21.14100/e4247811-fb48-4093-b6c5-7a4934339ab3\n",
       "hdl:21.14100/545a1d6a-619c-4b93-b587-06bf010b80f2\n",
       "hdl:21.14100/1104c48e-717b-4ccf-a845-e1030f729693\n",
       "hdl:21.14100/b4d6fa1c-5d1a-4eb9-ba99-a0fbc297ca2b\n",
       "hdl:21.14100/3be9d2d2-0991-4110-a550-64db8365420e\n",
       "hdl:21.14100/c8d3f21a-1632-4a96-95e5-3f07ffd493a4\n",
       "hdl:21.14100/73132218-563c-4875-a678-797e77e4e1c7\n",
       "hdl:21.14100/b525668d-c8c8-4eec-ba0d-b26ff9f43b08\n",
       "hdl:21.14100/11ddcf0d-560f-4237-bbef-c7af1cf5ba20</dd><dt><span>version_id :</span></dt><dd>v20180701</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (bnds: 2, y: 1080, x: 1440, vertex: 4, time: 60225)\n",
       "Coordinates:\n",
       "  * bnds       (bnds) float64 1.0 2.0\n",
       "    lat        (y, x) float32 dask.array<chunksize=(1080, 1440), meta=np.ndarray>\n",
       "    lat_bnds   (y, x, vertex) float32 dask.array<chunksize=(1080, 1440, 4), meta=np.ndarray>\n",
       "    lon        (y, x) float32 dask.array<chunksize=(1080, 1440), meta=np.ndarray>\n",
       "    lon_bnds   (y, x, vertex) float32 dask.array<chunksize=(1080, 1440, 4), meta=np.ndarray>\n",
       "  * time       (time) object 1850-01-01 12:00:00 ... 2014-12-31 12:00:00\n",
       "    time_bnds  (time, bnds) object dask.array<chunksize=(30113, 1), meta=np.ndarray>\n",
       "  * x          (x) float64 -299.7 -299.5 -299.2 -299.0 ... 59.53 59.78 60.03\n",
       "  * y          (y) float64 -80.39 -80.31 -80.23 -80.15 ... 89.73 89.84 89.95\n",
       "Dimensions without coordinates: vertex\n",
       "Data variables:\n",
       "    tos        (time, y, x) float32 dask.array<chunksize=(18, 1080, 1440), meta=np.ndarray>\n",
       "Attributes: (12/49)\n",
       "    Conventions:            CF-1.7 CMIP-6.0 UGRID-1.0\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          standard\n",
       "    branch_time_in_child:   0.0\n",
       "    branch_time_in_parent:  36500.0\n",
       "    comment:                <null ref>\n",
       "    ...                     ...\n",
       "    variable_id:            tos\n",
       "    variant_info:           N/A\n",
       "    variant_label:          r1i1p1f1\n",
       "    status:                 2019-11-16;created;by nhn2@columbia.edu\n",
       "    netcdf_tracking_ids:    hdl:21.14100/e3b86300-29b2-4220-97ce-40fa504858e5...\n",
       "    version_id:             v20180701"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# query only returns tos variable\n",
    "# sea surface temperature\n",
    "grid1_sst_hist_daily = xr.open_zarr(fs.get_mapper(gdfl_cm4_hist_result.zstore.values[0]),\n",
    "                                    consolidated=True)\n",
    "grid1_sst_hist_daily"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbeff4dd-5ec6-4004-b40d-eab885e55622",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Shifting grids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "58dd1e63-eaf5-49fe-b790-3ddefa453e55",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f35331fcac0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid1_sst_hist_daily['tos'].isel(time=0).plot.imshow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "1cca42dc-94db-4cc3-b74b-8b13d45d1ae6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "grid1_sst_hist_daily = grid1_sst_hist_daily.reindex(x=sorted(grid1_sst_hist_daily['x']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ed47a9e9-9899-4634-906f-e759e37eb049",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3542c9feb0>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Hdscdd7Dp06ezRCLBxo0bx+bPn886Ojoij7+39qWhMZRAGKuigI2GhoaGhoaGxhCBFtvU0NDQ0NDQ0KgAmkRpaGhoaGhoaFQATaI0NDQ0NDQ0NCqAJlEaGhoaGhoaGhVAkygNDQ0NDQ0NjQqgSZSGhoaGhoaGRgXQYptVhuM4+Pjjj9HY2KgLc2poaGhoFAVjDB0dHRg/fnyX9TorRTabhWmaVekrkUigpqamKn0NBmgSVWV8/PHHsoSChoaGhoZGKdi0aVOXJZsqQTabxcjaBqRhV6W/cePGYePGjZpIudAkqsoQ5Uk2bdqEpqamsrZt60yjuYFXpN/eka7KeEY0Bircd6QRpa8aZTVjjIEQ4msv2o1orMO29pRvmWhfCqL6LjQm0UZb9oqDMYaRTfUAou+f4L1QCMXuvVL76AmU85uIGmehc6IuV7er1m9wIKPaWszqb7gv76X+hPb2dkyYMKFHSlsBgGmaSMPGadgZiW5G8Jhw8D8tH8E0TU2iXGgSVWWIh0RTU1PZJIrRGJpcEmWR6lwaC/6HlQmvYC0lBI77kBTfaQGiorYDgKbGOjQ1NXV7onGKPKSD49PoGk2Nhe+fphInrULXta8nvWK/CTE2Me6oY43a3gLk7zR4fNX6DQ50FPuNCqi/1a7aCZR6Pw4V9PRLYi0oEqR7JMrQ9U1C0E+JfgRhhao2tnekC06AhR5+KqES30U7hzFs70gXJV2loJSHrhijRmmohvVkIFtgVDLVFekLWp3UbQbyOegLlPpb1ug7GITA6Oaz1ADhlSA1JHR23hDDqKZ6UEIkKVIffoUsP+J7ob89AbVv/YDuXfS1xakcFBprOQRK/V+QqXL6GswIPh80Bi4oAYxufqi+BULQJGqIoFDcRxA99aAMErauiFGQzAW31+hd9DWRqMS9qG4T3L6S/vr6HPQ19O9PQyMM7c4bohABtb31YAySs1JiKPRbcHVRKpEW6we6S6tc0tMf48B6G5ooDV5UzZ2n4YO2RA0hFHNT9Kalp5J96Yd7dVGMIEW5tPoTCrniolAt95zYZrCSqt74/esXob5Fd1154qPhh7ZEDTEM5ODZqHit7ga3D2WI6z+Q0vrLJTGFLGrl9NPfz0lPoqts3XKgM201BiM0iRqCCGbWVfpQ662HYpA0VWPsGh4Ese7KOjWQrDCtnWkMbyjdJVmo3VAhUPp3NPih3Xk9A+3OG6LoSheqK4htKSG9NrkGxysIlXb1dR+lkIX+RCi6uueGlyEXEowVE59qjGOwQpOugQftzusZaEvUEEa5D8JCpKunJ9cgSQoGpesHes+ivxCFKPX9SsYW3C6q367GMdAscz2BUgU2g9AueI3BBG2J0igZUdpRlTxEuyvQqboR9cN4aKDahKUc913UvguNx2FsyJMrgWK/T2097n0Id153Pxp+aBI1wEFI9KcnEOU+ixLFLORmq7b7Tbvzegf9xY1X7XFUGi9VTH+KEhKZDai6CMtxFw5kFPtt6pef3gcBn/C789FXLQxNovohCr0Nj2isi3woRcV/VPsZVYr7LMrNFvw/6qOh0RPoCZdbKf0VIkvFyNVgIlX6d60xlKBJ1ACAmjk1qqne9+AVBGp4A//e0xe0WDkW9f9RTfUl9dcdK5J+WPccgqe1v1ijykEpxCTKqlSM1AQlQgppVlVSjiZIqgYisQq6/KN+o/o32zfoC3fezTffjOnTp6OpqQlNTU2YOXMmHnroIbmeMYZFixZh/PjxqK2txezZs/Haa69V+9B7FJpEDQCU8kbd1hme5Kr5rAo+DKMy5VRsbU+VpUheCYJyBxo9h/44oVdrTOWKiwaLFfck+juZUp8LxX6HQUKls7x6H32RnbfLLrvgqquuwnPPPYfnnnsOn//853HSSSdJorR8+XJce+21uPHGG7Fu3TqMGzcOxxxzDDo6OnrgDPQMhgyJsiwLl112GSZPnoza2lpMmTIFP/3pT+E4jmzT31hxOSrJzQ11kkipF7Un3HrFYp9GNdWHPqWSpVLJUBSh06geCAEYKz/dvydRqSUsSlA0CoUy/8o59p48T/3hGhRDOS8y+p2nb8BJUHctUeXt88QTT8Txxx+P3XffHbvvvjuWLFmChoYG/OMf/wBjDNdffz0WLlyIU045BXvvvTdWrlyJdDqNO+64o2dOQg9gyJCoq6++GrfccgtuvPFGvPHGG1i+fDmuueYa/OpXv5Jt+iMrrvTh2VMXttDDspD7bmt7qqy+unoYdycrUKNriNPfXybt1ggLa7koJmcgECRbpWyjbtcVquEO7S/XpLtIUM2iBjra29t9n1wu1+U2tm3jzjvvRCqVwsyZM7Fx40a0tLRg7ty5sk0ymcSsWbOwdu3anhx+VTFkSNSzzz6Lk046CSeccAImTZqEL33pS5g7dy6ee+45AOhXrJiQyh+YJIKEVPvNr1CsQ5BIFSNQQGG9mGKB62K7UtprVI7+GANVSUHhUsmQuk2pYym1oLNq1epuXcKBTqQIASgBEtqf1+uopjtvwoQJaG5ulp9ly5YV3O8rr7yChoYGJJNJfO9738OqVauw1157oaWlBQAwduxYX/uxY8fKdQMBQ4ZEHX744Xj88cfx1ltvAQBefvllPP300zj++OMBoN+x4nLfwNs605JAib8iLZUv6xkzeiEC0xWBElAJUSHJAkHYCq3T6Bn0RyJVDno6M69SVfOuSuz0BKKC1vvKVZu1GTKWtiT3NqoZWL5p0ya0tbXJz6WXXlpwv3vssQdeeukl/OMf/8D3v/99nHHGGXj99dfl+uCLP2Ms0hjQXzFkFMsvueQStLW1Yc8994RhGLBtG0uWLMHXvvY1ACjKit9///2C/eZyOZ8ps729vQdGXxztqYy86Zrqa9GZzqChvg7tqQwAgDIGEfkl4l16GqOa6iWRiiI6wgpViluuEHHSgeU9h966T0pBa2e67BeASgO/u1IvL2RNitqfWpMw6CbsKfJS6TH3FqljTMdEDQaIbLtSkEgksNtuuwEADjroIKxbtw6//OUvcckllwDgc+9OO+0k22/evDk0D/dnDBlL1F133YXbb78dd9xxB1544QWsXLkSP//5z7Fy5Upfu3JZ8bJly3xmzQkTJnR7rKoFqRBS6Yz8qCEGnekMiLu+v4ceVBrXFFQt19AIIorQVJKFV6i/qOXB/qtJmNS+oyxJquxCJegtq1R/IudDDbQKrrxqzCmMMeRyOUyePBnjxo3Do48+KteZpok1a9bg0EMP7f6OeglDxhL1ox/9CD/+8Y/x1a9+FQCwzz774P3338eyZctwxhlnYNy4cQDKZ8WXXnopfvCDH8j/29vbq0KkmrsooCpu5traWkmYGuq87w7jH9ke8FmjAP9bYaEHWyVvqds70r1CcrpbRFmjOAbLaS2XOKnWI7FtMU2onkKUFSwKKpHqzy5Y9X4a6LFdAxHVKNtilKlZ/v/+3//DcccdhwkTJqCjowN33nknnnzySTz88MMghGDBggVYunQppk6diqlTp2Lp0qWoq6vDqaee2q1x9iaGDIlKp9Og1G/fMQxDShyorHj//fcH4LHiq6++umC/yWQSyWSy5wZeBLW1tQCA+jr+N5PxrE9RnEg9egf+h1ohMlXKQ1mUugiKgvbUAz2qhp9GdRGlgj/YUIh0qMSJEH4ughamQlan7t7zpQh+FkOl5KS3yFeCElgO0wWchwg+/fRTfOMb38Ann3yC5uZmTJ8+HQ8//DCOOeYYAMDFF1+MTCaDc845B62trTj44IPxyCOPoLGxsY9HXjqGDIk68cQTsWTJEuy6666YNm0aXnzxRVx77bU466yzAKBfseJKguoymYz87pRoLhekylGWCXN7JWb3oJozH0u0RaqrEjGR49WEqUcxGE9voZimQv+rywv1EWxTzFoVha3tKVBCMKKRa7s1R5C0vkBQiT3q91wqop4fcUrkC59G76MSscxQH2W2/93vfld0PSEEixYtwqJFiyoeU19jyJCoX/3qV7j88stxzjnnYPPmzRg/fjzOPvts/OQnP5FtBgorVgmT+L+2ttb3F/DIlCrEKUAD34NESv2ropjbL/h/uQ/eYkRKXafdeEMHwxvqZKZqa2e6ahayKOmDYlapUrYX7bsiQeK+VclXcJ+9TaSijqc7Y4j6GZu2DobqS/QFiRoKIIzpML9qor29Hc3NzWhrays5e8G3fSqDpvrCb2uCIAlXnlgWJFG1tW6WnvLmpxIpQazEX5VElYJSyJRqjSpEeKKIUSkWKU2iqg/1dPYnd54q91HOuArFFBXKpCu3bU+6zvqTq0tYzroLMYE76F/3V1+ju3NGqf3/athuqCXdo0EZZuP8Hf/usbEORAwZS9RAQSkEqisIIlXssdeeysjg9agg9iidKtUqoJrr1XgqsV7lQV3V1NLoewyFy1ColEs5Fqio/srZfyGoltv+RKCA7v9GxeYxSkCJ/wVQo/fQF4HlQwGaRA0wFHsABdcF4w+E1amUmCv1TVF1p6gIdtPbE7G2Rg0NCPJOUbpLr9JyLOUokavbR7UrxRVYyvLBApvxhJdsqgOMxjSZ6mUYqII7T/utQtAkaoCgVCtUV+hKOiEKUYQqap26vpLA9GDWna6Pp6GiXJdzEFEkJRhArS4DCksb9Ic4pt5AtbL2ZBILA0AoaLYd0CSqV0GrYInSL61hDBmxzcGAQm9uvfVGJwiSIE5RFoHuxjqo1qWu4qg0qo+B8ECoRlFigWqKU/ZmMeKBAL+ECoHNGBhjsEGBWKLvBqahUUUMhGfmkEe1rFDdxfCGuqIECuAThFhXar2+oNRBcLla8Dj40ag++mPQbyGLZyFCVY5VqJCFSqCrWKYo1fJirryBYrGqJtmjxMsCNpiFvFFTtb41SkM1CxBreNAkSqOqiHJ1dEWmCtXWK7ROo2dAUZm7t6/QXYtUpSVgiqErgiTGXEkZmoEG1WCcd7z6nYzGdHhyH6CaBYg1PGgSNUAw0IIwGSsvJkqQpqD1SaPnoZ5mUbS6P0O1iJaLUkuplJuxV64oZbD23WCGeBaIT1brRWkMIujA8gEAAk8LqlT0JelSJ5RSq7Z3FQel0TMIxq0Uk9joD1Df+lTJjVJQSNxSLCtFILa7yuIDJZxPlLzpqfGaDkN9z3StUQBVEdvUj+cQNIkaABggz90Q+qogqq4UXzmCAq39HYJIVWKZiirbErVeoNi9XOp9PlDeEaJqBlYLpb5YaVQXVdGJ0hcuBE2iBhDKtUb1F4iHpvr7qybJUev9aZSO4PmiBP06VmW4q3MmlPbV5VEoRKbLqYtXDJUQjIFC7ns6uWCgnAcNja6gY6L6OaLKvAxEBB+a1SI8her8aUI1+CDKFqm1IFs70z6XXvD/IEohR1EWqGpaZFo7vf6qKdegoVEMxbKcy/lo+KEtUf0YQQKl1sUbytC/4+pAtd4NhLcp1frUrEhtBGvrdUWkiqHUOnndIVXivPdEYeVqo69c8hrVBzEICO1uCR/98A1iIDw7NQYogg/fapjwy/kNR0kr6GeAH4xx4iEejsFSQQMRxdx7xVBOOZbuZtYFM9YGu9yBhsZghbZEaVQdxSaDSmOXKtlGWCUKESkdl+ERhP6elVcIXYluqte+XGtPKaVcqm2p6U/lY/rTWDS6D2oQ0G5aorQ7LwxNovop+otKeTloK7FuXrnkpdLfrY43CWOwTYqqOy9K8qCYDEK1yE+1iFR/m5+qTRB1wfA+hkFBaDedT0S/eQahSZRGtyEEGlWdoWq7JkTJCMCzKIg35UrJkpZCGPiIuvbl6kf1FYba/acJVN+CUALSTaEn0q/zd/sGOiaqHyJbxArVn4LKM5kMUukMmupr0VRfC0q4zlA1JzBCvJs06I4RVpXuKFiLuCn1M1gx2KxQgP+eiLJCBduo6CpYvKfPV5BADQVCpYuHaww2aBLVTyDcd6obr7a2tl+RpiioLnZVpDFISLoiKeq6Qm27ymLqboZTuaVqBgqGSnkRgULWqWIQ50glUqVaU7trde3PxL3a9422RvUdqEGq8tHwQ7vz+iEKzePZTAY1/YhURRE8lUhFBXUDxSeNkABkYH1QaDEKwQmztTPtcwcOBQwl0lQN951wDavnrdRz2N3Yof5M3HsqY1BbpHofhHY/Joro6xaCJlH9BEIDqhj6E4EqBYLMtHWmQwRG/SkXIjdRP3fRtq3IpFnIYlWMSA20Z0NQdVstYTKUCFSpEHF7PRWzVwpEYHWhAOuhZKTRFimNwQJNovop+rsbrxwIhWlBYCg8scS2LqxEQr+IMSZJVTECFdWGBtZH7aunStL0BiqxngxF9KWMQykEirH+V+BVi20OHlTDHUd1YHkIOiaqH0HEQA0mAiXQ3FAHCn7Dqaq3zQ11ZbvZCCEghHTp1otCKfsa7AHmQwFBS2RQHieKbPYkWRDEqZAFRhD3/uZyLnZO9G9kYIEYpCofDT80idLoNTS7ythBi0BUdl2QbBXrs6fQX8mUtjaFod5DwXupM1265pq2umhoaJQDTaI0ehVduVSEtaqcfnqSSPU3aAJVHMEg83IIlEAlRKq7GZD9kax3hYHm9h7q4JYk2s3PALxRexg6Jkqj32B4Qx060xk4EQ9nQgiY8tQOBgqrsVOF0N9cJRo9g+5KXXSHDFUaQyRu2/7yVlvKMQw1sdCBDh0T1TPoL79ZDQ0JSsIxLIAXCyXQVF+L9lRGEirRRv0rMBgIlLZCVY6GMgsrV+rWK7RdqSn9/eU+LeVe0wRKQ0NbojT6EVIlul4o8SZFYYlSs/FKiaUaaNAEqjIQFNZdU2UhgstUi1I55z6qTxXF6scRAhiD8N7V6B8ghIB0swAxcfT9GYQmURoDDlFWBREXpVql+hpBLSeN3kd9AQtUOXpaW9tTGNVUXzKp6s617ksZBqD3dMY0V+x9UIOCGt1zPlGmnVdB6DOi0S/RUFfrc+uJ76I+XyGIOn5N9bUyGxCo/Eav1GWhLUcDH+Iajmqqx9b2VNnbRSFohepvNRt7QxdKrYep0XvQEgc9A30va/QrRP1EgxbocuNbZD9ltq9WzIe2QvUNuqoAUCq2d6RBCekRYizuMcb4/dnamUZrZ7pP75meIlK61IvGYIR252n0OwjOVClZUtFUX4vOdAa2m/IXJFLFAnnLyT5S2xLCJ16dvdR/EUWIqummCxKRYmVOxD1iK/ePuIcGokWzEAmjhPQ7RfahhGpYkgjTFzAIbYnS6DcQ8SuF4lgqRUNdbSizDygvE0p1uXTletHEqX+gFOX/UolRVMB5tSHuMSEcKohUb2F7R9oX91UpgYsac9AKNRiTP/o7RExUdz8afmhLlEa/QrUJVBCFhDnbOtNyXWtn2keE1Od9ISIVRZyqRaa0G6RnUQoxEutLJTXlkp+oOKHu6l2Vi54kiMIS50BnIGoMLgwpWvnRRx/h61//OkaOHIm6ujrst99+eP755+V6xhgWLVqE8ePHo7a2FrNnz8Zrr73WhyPWqBZEsHkhiCLJoiByuQG/avtqzhEjGuswqqm+eh1qRCLK6qIGkwfXtxYpgl2ov0IQ90vSID4i39voSauX+rsopAOn0cOoRlC59seGMGQsUa2trTjssMNw1FFH4aGHHsKYMWPwzjvvYNiwYbLN8uXLce2112LFihXYfffdsXjxYhxzzDHYsGEDGhsb+27wGr0CgxI4jJNpiuLuvmJWgtbOtJwwumNEGojxMAMRhWKPpPWEsRDB6MpKVC4hYQwA8+44Ub6Gsd65D9RzUG0yJX4LwgJFoElUX4ASAtrNE18stm+oYsiQqKuvvhoTJkzAbbfdJpdNmjRJfmeM4frrr8fChQtxyimnAABWrlyJsWPH4o477sDZZ5/d20PW6GcQE2dbF1YC0U5n5Q0ORE0cPRH0nXEIkOpATb33wtZbRFrdT7Xdeox5BgxhhSKsv2iza2h0D0PGnfeXv/wFBx10EL785S9jzJgx2H///fHf//3fcv3GjRvR0tKCuXPnymXJZBKzZs3C2rVrC/aby+XQ3t7u+2gMTDTUefpSzW5wr/oBPALVFZECul+UVpOw3kG5iuTVtNiIWCihVk4ss88lDqoN7srjmXkxAhDb7OshDUl0v/gw/2j4MWTOyLvvvoubb74ZU6dOxd/+9jd873vfw/z58/GHP/wBANDS0gIAGDt2rG+7sWPHynVRWLZsGZqbm+VnwoQJPXcQGn0OEbNSTnaRJlL9F5WUdBHfi21b7jWn4BYaFkug1iCIUYJa5Mvqo1qo9j0njg0AiJUFsUyQXGdV96HRNUQB4u5+NPwYMiTKcRwccMABWLp0Kfbff3+cffbZ+M53voObb77Z1y44OTLGik6Yl156Kdra2uRn06ZNPTJ+jf6D5oa6sstzaCI1uKDKARRCsWuuPlIccI0o02bIMQM5m6GpvhYkn63SaEtDKcdULoQVigAw4IARCoiPhsYgwJC5k3faaSfstddevmWf/exn8cEHHwAAxo0bBwAhq9PmzZtD1ikVyWQSTU1Nvo+GRhR0oPjgQLmim8WgCrTGKIHNPGtncnjh5061UcoxVRpT7MvGozEwY8iE4vYr6LIvPYMhQ6IOO+wwbNiwwbfsrbfewsSJEwEAkydPxrhx4/Doo4/K9aZpYs2aNTj00EN7dawagxeVxklpa1TvIup8R1lqSrkuXVmkKHg8VJz2TQ29YscQ1EWrZHxy3hXWJ22J6hPomKiewZB5Jbjwwgtx6KGHYunSpfjKV76Cf/3rX7j11ltx6623AuAm5wULFmDp0qWYOnUqpk6diqVLl6Kurg6nnnpqH49eQ4NLJ/S2AONQQDDTrrcIq0G4K88BUGelkaK1vUaiujrGKIFZIfvRVTkjdT0F4LCwpIG2RvU+qIFuxzRRnVQZwpChlTNmzMCqVavwpz/9CXvvvTd+9rOf4frrr8dpp50m21x88cVYsGABzjnnHBx00EH46KOP8Mgjj2iNKI2qoxJrFGN9I8I4lFAugSr1OgbbCZIRpwQJSkDsPAzS82repcQ9CeuYmBzU7+L/YqKyYj3AXZQCDPCsUNoSNSSwbNkyzJgxA42NjRgzZgxOPvnkkEdooItcE8Z0TYlqor29Hc3NzWhra9PxURpFEbQsFZvchHaPqK2mUT2I8+4wVpaYYDXcsjFKkKAAyXUin2hAzDFR09Bcdr/l7ruYNUk9BVFUx6A8UNxmgO12wpiI6WJyGwecJKrimgTgoqKO1WPHOdDQ03OG6P/ZLx+Dhni8W3115vOYefejJY/12GOPxVe/+lXMmDEDlmVh4cKFeOWVV/D666+jvp5XYrj66quxZMkSn8j13//+9wEjcq1tqhoafYQgGSokcigmay0WXB2k0plQjcaeUusOIniNGWOwGQFNNsC2GWK05x/JwtJkF1jPLUlEjk/AoFzriQAg2XYYyQYwyilT3nElGpjXFg7/R6uT9w9Q2v0CwtQub/uHH37Y9/9tt92GMWPG4Pnnn8eRRx45KESuNYnS0OjHUK0d2gJVHdTX1UoiVanyeKWZlkFrECEEDJyYJCnjEgC9BINwa1JwmUEJYsyC6U4PDkTwu1uypXMLHz9jYEYcoBQJGuOtKIXtMDjMs1gJyO9arXzIoq2tDQAwYsQIAF2LXFeDRFUigF2ORVCTKA2NfgotidBzCHqyyrVAdRXkH0XOovbBGEOMMJgORdIxASNR1jhKhYilE1YoIFwbUlg6HQbU1DeCZTIwbR6nxRiDaTNujWoYDZptB2IJwLYAUK5pZcQQMxJwAkQwZIjS8VB9gmpIFIjtg8QkmUwimUwW3ZYxhh/84Ac4/PDDsffeewMoLnL9/vvvd2usAsOGDStLHJkQgrfeegtTpkwpqb0mURoavYByLR6aQPUsGiLcedV25QWveVQckgOAEYoEswDHAc13AHX1VR+HCumqc91twvpkOwxJ5CFoVm1tLex0BjHHBGgMNXX13jHV1iLX0SoJEbHzYITCNigUT17YlaetUH2GakgUiO2DlTmuuOIKLFq0qOi25513HtavX4+nn3463G+ZItfl4p577pHWr2JgjOH4448vq29NojQ0+gGi9Ic0kepdBImUSnocxjCqqb6s6xKcA1QC5VMsZwClMcDgAdfVhEhGEBDZf3HKv1sO47IFrvsNjPoKIAPwBYCrx55sHM6JlO2O2bG4JhRzwCgNuQrhWJxEaUvUgMemTZt8Lq+urFDnn38+/vKXv+Dvf/87dtllF7lcFbneaaed5PKuRK7LwcSJE3HkkUdi5MiRJbWfMmUK4mUE4GsSpaHRCyDEnwU2qqlra4MmUv0H4nqVcz1U0lTIyuWjE4QCsQSy6RRqKrRGBfcjY5xc5x0xU4ARA8nlQGqbYRMCA0ACFohtgeRzgEKizBAT8iPZOBzm5vekG5KYaTAjBkIoCIl52XjuX8KckCtVo3dAKAWh3bREuduXWp2DMYbzzz8fq1atwpNPPonJkyf71qsi1/vvvz8AT+T66quv7tZYBTZu3FhW+1dffbWs9ppEaWj0MILWgHLS6DV6DoUyIcXy7oi/iH66IsIJg4CaaZB8BixeCziczHTHpUcIUGMQEObABkXM7ATJZ0AzbSD5NEAo7OadQTu3oKHtY5jj9wFNt4FmO8BicZhbP4RZNxINdbWlW93yaSDRAOYSJcdIeIFnzJEECoD8q9G7oEYVsvPK3P7cc8/FHXfcgfvvvx+NjY0yBqq5uRm1tbWDQuRa21U1NHoJKnfqTvxNeypThdFoFEK1rH+lyCbQbDuPJ4rXArR7QpRiPwlKYGTaQNOtSHR8AqNzK4zOLSCWW9CYOTB2bILR/gmYbSO+6SUYHZ9ysmNbSIzaJRQz1iVsyyWAPDaKANK1J45HZh46mkQNFdx8881oa2vD7NmzsdNOO8nPXXfdJdv0hsj1E088gb322isyU6+trQ3Tpk3DU089VVHf2hKlodFDaE9lYDmeOSNo2SjFXScmRkHAtMxB76LHjYaEAszmBMpxACPWLZkDOV6XMJF8TlqC5P6AcIC345Ig5sDc/B6cuuEliWHmWj8FZQ6IlQNLNoBRAySfBTMSxYPI7erGfmmUgGrUvitz+1K0vAkhWLRoUZeB6d3B9ddfj+985zuRLsjm5macffbZuPbaa3HEEUeU3be2RGlo9BCa6r23efEsCU7KpZbhEGjTZV/6BK2daWxtT5V0vVR0adUyYmDUACNU1pMjltmdoQIAWLyWxx9RA8R2+ytScoUwBuJYII4FI90KI7WtpOMkVg7EzIAwBzTT5q1QA+SDbjztzusTEFqFAsTdjKnqK7z88ss49thjC66fO3cunn/++Yr61pYoDY0ehMjwEsVmKQCniwKuhTAwH1/9C1vbUwCi49K2d6Rl6ZcRjXW+OoWUb+RrC3RNkqKIiFrQl2TaACMBFksAhErhzWxnW9llUQzCBTtJrgOEMcC2QLNtJbkImW3zCrUAmKuaXuzYcq2fgmbaYJgpsNQOrhlVl5D7Io7F+ym0X02keh3VDCwfaPj000+LZtzFYjFs2bKlor4H5hnR0BggYMw/aYplQRSbsBjztq2mdspQgrDgUUJCBEoQK7HeCVwgCk6ADUKU68D/dmWt6SqonFgmSD4Nmm4Fca03LF4Dk5QvumkzwGQU1EyBZHaA5jqAQkRGLIvFQAxOnlS3H8mlYL/3IqwP3/BtZn34BqwPXkGsZQNoxxawju2+GCfi2G5tPIUkqft3RJC5ztHT6D3svPPOeOWVVwquX79+vU9ioRxoS5SGRg9CTLYGJXCYEJHzkyt1PikUjCynJMZ0XFQFaA6cM1UDalRTPba2p0AJASGcLLV2puU1EhZEXufOLX9CCGywSEJcSqxbjUEQa93ESYdjA8QCS9SDWjkwI45EorxrLDJAa9JbQNOt7kEWeUcmVBImJm5Ey+Kkyo2PAgDiWLDfexHIpUEMA4QaYJlOMMcBHDdQPea94ZN8mhM3FyxeI/fHrWwugdKWqF4Hd8kZ3eyjUMXF/o3jjz8eP/nJT3DcccehpqbGty6TyeCKK67Af/zHf1TUtyZRGho9iOEN3C0kqy0QLufsRMRGqX+j+tHoGWzvSEsCpbpeBUTZEwce8VUTBoJQDV2tnemQUjljQGz7+yFrDE23gsVrQBwbJJdCLp9Fctjoko+DAjA6t4SDx4MClzJGiXkESsCyQAzG3XuOrWxig5luf05gInUcMMsE2jfz4sTN40CsLGwaAzFTcGq5W5LkvezAaouKanSNaiqWDzRcdtlluPfee7H77rvjvPPOwx577AFCCN544w3cdNNNsG0bCxcurKhvTaI0NHoYFFyVmgSWAeH6ZRo9g2JuN0FyGOOWwGDwviBQ/DuPaVOL97Z2pmE7noDq8AbehwO/xVHdn88aQ6j7vw2YabBkA19cyc1BKBh1rUlqRp4gUmqmHiCJnKBzxDDA8l5gOwsRJhvMsUFowKIh3HeUAm0tADUQs3K8j45P4dRztWgRJyUJlYZGL2Ds2LFYu3Ytvv/97+PSSy+VWYOEEHzhC1/Ar3/964oV0jWJ0tAogFQ6g/py9XLgn7BHNNbJCVhMvIQQWV8sToC8w2TcjZh0dchI9aC66oKkRhTkdYhn7bPdNqIdIQTD3UxLcW2bXQujIF+UELR1pqXbUKwHwpmZUe/yjBBugWIAzDTP2ovVIJvqCJVhCWJ7B7d01uQ7ohuoFqlC8UgODyxneTNMnMqBlQeoAZKsBTPiMCYfCPu9F0GyHTxj0LbAYgmQvNY6621QSkG7GRje3e37EhMnTsSDDz6I1tZW/Pvf/wZjDFOnTsXw4cO71a8mURoaBVAJj9nekUaMEhiEJ3NlMhmuHu2up8TrlxK4ooREEiwGFqo5pku/VI4ggQK8v8LFKmKehPVI1eRq7UzDVkiHei2GK0SKuNdPEBpBpqKIW5L6rVASEW44brHpWnSQEIJY28dyO0YoSJQmlNClgpuRp8K1MkWKYSrEilAj2hrlglkmgHqAUOQ/fgvUsUAIBaOUuyw7WsHMLDBx3y6PS6N6GMruPBXDhw/HjBkzqtafJlEaQxpBN486SZaj2iz6qYsRwDZB8qZMWyeOJcUHCaEwCOVFZ92JVQQrUwKYAzNus9+iUIkdETAuIKyAYh3A3XTCQlgIKpHy+iYy0Ht4Ay//ovZNTPeek0rehLvhhMI3AGYkwIw4wByfhasQmHAPKhIDINQr+lsMwgplmX4CFbBIBS1UkkipLj73f7ujFbSm3kfkyLZNYFYezMyGCZyGRi9g3rx5kRnOhBDU1NRgt912w6mnnoo99tij5D4HPq3U0OgG1N+TIFCtnWm0dqbLFrZMGgQk18mtB8wBNTPSKkCzHTIOhMC1QjHHK4/hLtcSBtVHoVPKM+7492Dgt0ApYUlB75jlMF7413XxqeOggJc9FwXDe68ldh6gMSSR73IM9Tved4kY9eKhRPC2Yu0ijAeNB0kMs0zuihPZgi5hYoG/cPzyBUxYr8R3VyiUUAPMzIJkO/lvINUOlk1LAtXdLDGN8tFtoc1qKJ73MZqbm/HEE0/ghRdekM/aF198EU888QQsy8Jdd92FfffdF88880zJfWpLlMaQhbAWqJo/6oTb1du/QGc6g9oYLyTrizlhDmgu5U1iRszLjFLe0KkyyRkEYMRvGREuIkKITwW9FLSnMr5thMtqKGX7FYovcyLWV8JhVVkK4b6zGUBcx23CIDBtLocQM3jsk88KZcQ4MaFuULgaAM4cENtCbscWdFC+HxHAroLk01Ik01uokKeIAHEoViQ4js/SpFqVmJUHicW7jpWyXLIXi3uWLcsES3v1ygSBIomaAp1o9BQIqYLYZjdKEvUHjBs3DqeeeipuvPFGGd/lOA4uuOACNDY24s4778T3vvc9XHLJJXj66adL6nNgnxENjW5ATHhRP4JSSUYmk0HMMUGz7fLNnxHiz4IS3+1AKQx3vcO8/w0CxCgJjalUQlcKKOBT4x7KqKbhT1iaVGJOAdhq/UT5xb03DFcM04hxAuVaLn33DrjeUoPhoMHowjYWsW1BvSjFuiQsSMLSxKRFyt1/IQIVjJ+irpaUsGxZnhVNZv3F4ojtO7f4cWho9AB+97vfYcGCBb4AeUopzj//fNx6660ghOC8887Dq6++WnKfmkRpDDlsbU9JEpE0CM+Wg2sFYoUJVCqdQWc6I100uY5WUDPtpWu7kxVhLKzLwxxFzdnySJRwuSj/G3CkqVmN0Wqqr0UqXV5WU7AAaHNDHZob6lCLPNpTgztDqtQadyqR6m5WZHNDHXfZuZ/mBi87kxA/oeILhUWKerXl4Lp63cK+JJ8Bce8zYqaR6/DcgcJK6etP3HvKPchiSTBCIvWfAITcdHBjl1Bovfg/GCeVN+Gk2sEyKZ7pZ2bBclmwDFeFJ8ka0Jr6kBK6Rs9Du/MAy7Lw5ptvhpa/+eabsF0Xd01NTVlhFdqdpzHkYLiWnqRBAMcCIzGAuBl0yiQnyoHEKUESecRsTn7ihCKbssOFYrsosMoIAXH7gBHjFgcAMTvLvytuHEr9ZT9aO9MY3lBXVOQxiGBAcnsqg6STBaMx0EwbamgMqK+Fub0FAGDXNqO2tnxJh4EIVUpCWI6qISvRnsrIbL/gvijcDExqgNgOt1i6Kf+h8QlC7sbXEVfygBlxwHF8tfVqWI678mgsfO+5xFy6mA0Djpnlek4uCZJESrydu9YjETclgscRkY3HbBtQ46siSBXv2/AUzx0bxOwsfiI1qg6dnQd84xvfwLe+9S38v//3/zBjxgwQQvCvf/0LS5cuxemnnw4AWLNmDaZNm1Zyn5pEaQxaBPWaBLihiGfRAUCMcjdL3vG7zURqfBJ5SX54bIldUHFZWpsiir4SOw8wBkYNwHH4ajcWhijuPUYoYnAAg8pjYCzC6tAFkgaRJUhS6Ywbb0W5qrXrNjK3fczPQ6wGNNeJXLYdILQspeyBhigF8WqAuRIV3KVHpBVwRCPP0BP0wm7eCbHWD/k/RsxngQIg/2eEcmJjOyD5HL9XYgmwRD2YkUBnOoO6GAFt3RIW0gwIavpKu0jrUkDSQA0YL0KMfGM1DK+t2k4R3yTxBCdRiRqPqOmyL70OalDQbpKg7m7f17juuuswduxYLF++HJ9++ikALsR54YUX4pJLLgEAzJ07F8cee2zJfWoSpTGgoeoyMXiGJMaYJBxB3aUo2AyRQdvDG+qQa9vmn6ACEJOUT8Sw0CTBHB45zhwAruXAgWcZgJgPLf7jNBLIuwfFWOkK522dadTmdmDEyPHIdrbBJgnUZFvBqMHlFrwdAUYCcCzQfBosVgPARq5tG5LNI0vc28CFao1SM/UqiZUSWlGEAJSxgnFsacdAE+DXhYqIWyLKcuZagaiZAbNMECsHg8ZgtH/C1wsJjShLFOCzRPl+DlEuuSApKpZJRw1uiSpEtCR5MgBKEZt2FOwNz+gCxBp9AsMwsHDhQixcuBDt7Tzhoampyddm1113LavPgU0rNYY02jrTSBgElsOQsxlMm8FymHR5JQyCpEEQowTtqQzaUxmk0hnUGQwJZnLrEqGwGWAGmJaw4OQ6WqUFSn3wE8ZC/8vJULhWgHBslNjWsUHMlJt9lQcsU4mLEvEwJuJ2FrUGQY1BUBfjf9UxFkJzQx2cmkbkdmzhafJOlusHWTlvjIFxATzLi+TToJlWTh4HMEY01hUVKhVq410RplKTDNR2QQIVHAdzSUUogDxAphihsoivzLBjDmhqG2jHp25fMV97CI0owJU98A7QyWVk0DgTQd8FRDNln3mzsK6TIofgxUmFrVCgFIQasDeUnjquUV0QSkAo7eZn4EuwWJaFxx57DH/6059k7NPHH3+Mzs7KXMwVWaIef/xxPP7449i8eTOcQHbG73//+4oGoqFRDkQ6ueMwOQnGXYtUxuKq3zbjlqmEmJcCb+kslgBoDDmL+Sa5re0pGJQgm05x7SbH9k1EIfIk//FPWERMbqoVK+AGJHaex7n4BuZapwi3ThEzzeO1KAViNWhPZWAzftwiQD440fMYqkaYm7eBWDmwWNI/uUL07y1jhPL9KGMdbBYpr5RLcQKlrhPxaKVAuO66QkfjLmhMt3i6TgAn1MHximw9IwamZne67mKHGq64puJGFn0J/Sbb9qxPIt7JKqA9RQ05Bqa0R6G4KPF/IIuPuDIH8QNPgPXK44hNO0puYm98HkwXIO516Jgo4P3338exxx6LDz74ALlcDscccwwaGxuxfPlyZLNZ3HLLLWX3WfYZufLKKzF37lw8/vjj2Lp1K1pbW30fDY3eQMK1yAgOQ8Fr0GUshoTByVTeYZxABUUHHYu7PWgMOZuFrASUED6J2lzBOVTpPgos3I4RItWoi0FYueRE6PYn/zIHzIgBtgViZZFgJupYDrUGQS2xkQi8HQql7WxnG3fVAdzqJc5BcDxiX5QHuzMjARav45PzIKhxNqKxThKiUjShVIkCgXIkIUot09NRNw75hrHINe0srwtzPwKMUJ7A4HCCJAsGG3FuzRIEOBaHVCov4npGzCXslHrxSWK50IVyxTiJYXhimkoclc/6RKkkUpJAuRpTJBnWgrLfe1HHQ2n0GS644AIcdNBBaG1t9SXRzJs3D48//nhFfZZtibrllluwYsUKfOMb36hohxoa3UVrZxoJSmAQAqZEeFAAcNPICSGIuzEqlMZCb/iMUFgsWn9JTIK5tgIEIkJ/R05oUROESlyiYlbU7YjhXye6EFYIy+QWLuaA5tvAjDhMw4CQXxRuSAEz2+Htn1AZWwMoVjR3/8SxfBpFgkDlWj9FcvhYmJvfA4vXIjm8smrnfY2+FoOPslLlbG5RTIpzDkTeXwDcuDnbCxQX66kSN+VQz5JEjdD9JghOSPdJiY0SweL+4PKwHhQAkFiCyyE44bgoWtcE6+VHfJpQxqT9AQD2O/8Mnh6NHoa2RAFPP/00nnnmGSQS/ozYiRMn4qOPPqqoz7JJlGmaOPTQQyvamYZGd7G9g8dB2YwHj8tAXvCMKJsxxCmBw3xqBQCU8iq2BVAaioMK7qceCAfmRkBOaK5auQgyF5YpRojftRd0ZQiXWldkS4zDzkvyxWIJDK/3SFMd8dw05vYWz2VHY5JA+bK13L6Jm6kox+COkTgW4MTc7RxggFqmhGZTVxalniZahaQUGI25khmOz0JEXDkM2U5Ykhx/O2Gp8hEvIS+glm4JkSFF1kC65iKy7aKUrl1Sx78bgFuehjk2COJg2VTBeCsdWN770IrlXJ3cjojv+/DDD9HY2HWh7yiUfUa+/e1v44477qhoZxoa3YVBIN+0VQ5kuPFQhjsLGgSIU6/ILwDPZkUpWKymqNslqWoJuASEUSPSZce/eOVegn997aLcae72IshcuvcKBbILFx8hqKnnP/zcji3IplOgZgbZdIpLFyhSC5JACWsDYyEyJxSzhUtIEivHQq6VBzEnxkwqeM76M4TFcXhDXcH4pt6wVEW5ExkDWDxCn8tWXHNBF12QQMlOKZcxMHPS7SatTqI2HrhFSv0UgrouGFwu6uOxXNYvzCnam9nIPu131xXcn4ZGT+KYY47B9ddfL/8nhKCzsxNXXHEFjj/++Ir6LNsSlc1mceutt+Kxxx7D9OnTEY/7g2KvvfbaigaiodEVRDB5LmBBYq7VKe7OK7byPWpeZIQibTEUq95lEERq7kSh2Ft1pPwBjXmq5b6BBdw4jgVCYyGrEY+Riku3mrn1Q36crjWMZtr87WnMyx5U+pBjFFangGK2V/OPB+AzI4FsOoWaunDttoGG4SVYpUS7znRGykx0Fw5jMBRmLyxT+WQTEvmMa/nzstuKd+aE24RcgIafSKnLVQgLl5X3yJKrZyb3Bf4iIooHM8fmmXuq6KxqxYonoqUPCAUjOi6qt0EMA7SbhZ8HeuHo6667DkcddRT22msvZLNZnHrqqXj77bcxatQo/OlPf6qoz7ItUevXr8d+++0HSileffVVvPjii/Lz0ksvVTSIvsCyZctACMGCBQvkMsYYFi1ahPHjx6O2thazZ8/Ga6+91neD1JCIyngihJOdpBtILqYmShDpzhP/h7R0AshmMmEiAkRakXzkRG0rvisWKh+EDEKUVco2pRCo7EoEqTMHiVG7IBvnFiihNi6lEljAKhEVRB4VfKy6EW2LK2krRXJJPgOWrEfOGfgpzgJdZdwZhKC1Mx0u1dINjGqql9IK4gNwiY1U3RiYTTtz910lbhfmgFhZb6Jza9gRlQwF4dj+unmBdQCkyjjgTqKCgFn5wll+YkgR643JB2p3Xh9Al30Bxo8fj5deegk//OEPcfbZZ2P//ffHVVddhRdffBFjxoypqM+yLVGrV6+uaEf9CevWrcOtt96K6dOn+5YvX74c1157LVasWIHdd98dixcvxjHHHIMNGzZU7C/VqC4ogOC7rRDaDD6WGTixEqQKgCQLRTOobNNv/SnkgotCOTEDao2zKKuXGgcldIRct09zQx132akoINoY6rfYcscjV8QyvXgcQsFoDEnCT2Rux5ZBoWpezCJlKxN9T0wdUcrppsNgNIyG4WpA+axSquUpQmWc/295yQNqLBRQWA8qyqIVHKtLxJhtc1KVN33FhUPt4wmQWFy69KzXVvtkDgrqTmlo9DBqa2tx1lln4ayzzqpKf91SLP/www9BCMHOO+9clcH0Bjo7O3Haaafhv//7v7F48WK5nDGG66+/HgsXLsQpp5wCAFi5ciXGjh2LO+64A2effXZfDXnII2iFEpMPY4BDohXJqbLcYV5QObfyFNeoIUEyonwnjh1t3XE1ooK6Uer66J0ViI9SvwsCZcSRbB4ps+USI8d7sU/F+i1QDkSuloWTHVlmRN2W5jrAjARoahtYshFAvSuZMPBJFMCJlLjHBHem8NThHZQuuFkKRJZesH5fLbGRYwZyzEBtooHXl1OU7PlgIq6128Yv+Gr43WwR7fl3A6CF1cZ9+3QDyVkm5RGzCHcdSdSA1NQjts/RAID8ur8gPuOLXZ0WjR7GUM3O+8tf/lJy2y9+sfz7tGwS5TgOFi9ejF/84hdS4bOxsREXXXQRFi5cCNrN6P+exrnnnosTTjgBc+bM8ZGojRs3oqWlBXPneum4yWQSs2bNwtq1azWJ6iOIyU3WH3MJkaMQKcthiLlxJip5ihHPOiWsUmq/haxRycbhXOlbgDlIDhtdXMGbOSAIEBegNCtWlAtOBH27mXuJUbsAAMxtH4PYJswtH3gWrGBWX9Cy1WVMl+Mbry97T9T2cywwIwFi5ZDtbINZPxapMkQo+zvUe2F7R1reLD11fGJ/Is4PADLMkIKxLJ4EybaX1pkTuMZ5M0xuhFCmeD4HLVrqMrWMS9AV6Dh+AiWsWKpKea1HoIBoq9NAj60ZiBCq493tY6Dh5JNP9v2v1rVUlwGIzNzrCmWTqIULF+J3v/sdrrrqKhx22GFgjOGZZ57BokWLkM1msWTJkrIH0Vu488478cILL2DdunB2SEsLjy0ZO9avgTN27Fi8//77BfvM5XLI5XLyf1GPR6M6iCI6QtZAzvPwyJLDOHkSkF9lfBL/WxuLju3JdraB5LMhqYHcDlG0l/itUSpZidKJClqhAqrSRaEUlTU3v+d3/0mNIPcnHAxUL6BZ5YsHY054EpWFlnnwulBNBxwuDWHEQIw4EnGKHAbnREiIW86wD/addxgMNTNUXi/qKecT6lPR94m00hiIwfyB5I7jK70SCjJXQSlIsgYsF86s88U3KWVj1F8SiSVAa/yJB4lD5iH//AMgo3ZBbOK+fDe7HVL0PGhUH0PVEqVWVXnsscdwySWXYOnSpZg5cyYIIVi7di0uu+wyLF26tKL+yyZRK1euxG9/+1uf2WvffffFzjvvjHPOOaffkqhNmzbhggsuwCOPPIKamsJ5WYT4J1fGWGiZimXLluHKK6+s2jg1CkMlVG1uHIvQ/hExT3EasDpFBX2rpTYU5Nq2gdr5UDwUcWwwBPSioixApSoxd9UuyvVWwL0oAsUFOZKuueDxWWakG1IqYMeToXGJOnvMiPNzQA3AMpEc67rxOtswmOGA32eFCglXAyMavZgscXVsBk9XDPCILHPAQ9JsELMTBABLNnmaXlaWSxvEXSHBQKyTJFA+y1IEoZLxVmFJAwAhEsYcW64jtfUwPnukb7259h6Q2noQKwtr02uITZhW/KRoaPQQFixYgFtuuQWHH364XPaFL3wBdXV1+O53v4s33nij7D7LppXbt2/HnnvuGVq+5557Yvv27WUPoLfw/PPPY/PmzTjwwAMRi8UQi8WwZs0a3HDDDYjFYtICJSxSAps3bw5Zp1RceumlaGtrk59Nmzb16HFocBiUyMktUrzQ/RSy+pCIeBFiu2/aigUo3HF0vJJvXbEYpCIaUWrmXGL0rkiM3jXcVm3vWCC2WTjbUMm0C+0nmDVoWyG3EHG1ooiVc61TgSLMXcSWDVTQwN+ehnAZEsKLZUcZSUXmJb8e3Erk1xFTCHGU0jilSoC5J5IpSZVaOBiIFNqUJWDUcVGDB5CL/nJhMVYS56rmIBRO/eCpwzjQQCjpfnbeAC9A/M4776C5uTm0vLm5Ge+9915FfZb9nNh3331x4403hpbfeOON2HfffSsaRG/g6KOPxiuvvIKXXnpJfg466CCcdtppeOmllzBlyhSMGzcOjz76qNzGNE2sWbOmqEJ7MplEU1OT76PR82io84sTMsb8WXhiufziuvIKpHrnWj/l7rpgBpPIVHNsKVQZJE+JkeP9pEm41tS4pigESI3ar7nlA+7CE4THsfjHNr3vxdx1EccqBDulcGdQ7kBMxFHWNaVt/qM3eX92no9xkKG5iCBnT4Ixt4iRuF+o/zoQ2+SWRqWGnrRCMkcWsmaW6bnegjXyAFn6JdIKBU6EmJAvEPpQlsmFNU0hrOneF7G4284umOFHG4YBAIwpM5AYMa7s86JRHYiYqO5+BjJmzJiBBQsW4JNPPpHLWlpacNFFF+Fzn/tcRX2W7c5bvnw5TjjhBDz22GM+n+KmTZvw4IMPVjSI3kBjYyP23ntv37L6+nqMHDlSLl+wYAGWLl2KqVOnYurUqVi6dCnq6upw6qmn9sWQNSpAwRelYLC14b/1Q+RBhRrHpJZHUQhPoYw9b0JUyr6Iv1FWrMB3GfQtrERGgZ+s2Ma2eD2ziHW+7EFBnqT7z/Zb4ALjI8zhE7hjAdRA/uO3gGQDQGPIpjqkcvpggXAXF3PlVxPM9UHHCADG3bFOTTNouhXEynnWJ/e6iQLBJO9mrgrXMjW4my9KAsGx/QWIxTLxV8gYWHlPviBhKITKI13y5USStcLxcdaWj0BrB75Aq8bAx+9//3vMmzcPEydOxK67ckv/Bx98gN133x333XdfRX2WTaJmzZqFt956CzfddBPefPNNMMZwyimn4JxzzsH48eMrGkR/wcUXX4xMJoNzzjkHra2tOPjgg/HII49ojagBAp+UgYoo95rizstkMl6ItLA8ibIoatyTSjyiiJb6N/jdyoaIlnDXBa05RLjhSg1CZw6IlZdFkEk+F6rn1+U45XFzsiQtWsp3GWdlU7BYErBNEMsEJRQYZCQK6D0CFdqvq1ouS/SI+1CxijLHlurhQoQVhPKsN8NQrEOuYrmZBUnwWFCfWw+Ak+LJMLS+ibvjVLedKrhp5f3kS8RBKcHqiUO/5DsW+42/8zZ1g+/+GGjoqsRPqX0MZOy2225Yv349Hn30Uclf9tprL8yZM6fi3zthwVw/jW6hvb0dzc3NaGtr0669HkSrkl6/vSMNgwAJg/CA8iKWFGKZMkhaiEWa21tCNfF4oV+XaBkJTqAEiSii7SQtNkEEg8GB6Fgm1+qkZmLJ5STsmhGWBl88l+hKzd5SSrswGvMIkUKWfMenuPd841D2wYwEJ1NGDPGxU8LHMsDRmc7AYUBTfURduypDSHkYBKixM/I6044t3JVsZT2LYucOwLFBEjVeUHciya2dzAHyplfLTiFRAEBq6j2LlJWHk2r3r7Py3GVnmfw+TNTwto4Nlk37A9UViydz6/TRhmFIHPol5Nf9RWb4McdG8siv9fg5HKjo6TlD9P/JiivRVFes2FUJfaWz2OnMK/T8pqAkS9T69eux9957g1KK9evXF20bVAHX0OgptEboFAX1oNSsNRFMLshFNp0CMdMggE8byUegAD+BAopahSIJlLsNCVqBgvFZanBwsF5eEI4T2sZnuXIsXnFdFdIU7Z2IQHMXhDEw6lo78qZXuFhYPKRLLyaDzZkRg7m9ZdDFuwTj7qoNVQNNQF4JN7OOxRLcairPtyUJDnNskFgCjpkFMbMgNfX8OqluN4sri7O8u03e5IHeUuPJK+MSVTCYmVnuHoxIxPBl5bmWKqdzB3JP3i4JHgDAymvBTY0+ww033IDvfve7RbPyVdxyyy047bTTSvZAlUSi9ttvP7S0tGDMmDHYb7/9IsWqAG7+rkSsSkOjEgQJlHTnCfLAHDDmcOsTFDecIBb5rLT48MBxxysOXCZCEgNRbjM50EDAt6LzUzCwnTGA2TIWRY5bDRAPuhmj4q4ig9E94sZsm0+a+QJq10HrnmOB5CzEx04ZlLFRPYVMJoO6GHEFNetguCKxtZSBGXUguU7OqIT1kca4NSqbCndm5cGoA9a5AyQW91mf1OsorEVwHFeQ0/ERHQAyi4/lFXegZfL/obygKBl7Pu0px/ECz11rV1FdKo3eQ0SSQUV9DDBceOGF+NrXvlYyibr44osxd+7c6pKojRs3YvTo0fK7hkZ/hM0AAwpJUK1CUo2Zr08OG80z8lREWGZUFHTVBdr4F0TET6kPIkVWIESgohCwGgWXyf+LxVBFEC4RqKy+GqmWBt+xWBYX5AKP12GEwvrgFdTsuk/X49cA4BJ+x0RyOJdPEVci5xAQAiQBSZ5ELUdim561SFgKXZeZIDfMoa7lKM+lDkR2XVDbKZuW1iUSS4StSr7B+u8TdX+IEO8UsVeiuDGpqZdWqPzzD/Dlto3EIfPKOGMa3QUxjG4rxQ9EpXnGGI4++mjEYqWFgGcyYZmOYiip14kTJ8rv77//Pg499NDQgCzLwtq1a31tNTR6CqohVAgWUsb8vjwAXCwy5sVEuYtzrZ9KF57PfdeF2y5ocZLLhGaSEevS/eapjStEJkpM03WXyW1c4c+QjlUwAD0qu0915zHGJzf3gcjcMh9S6yc8ev+2cC1hQkDd3b/1wSuIaSIFAGhPZWAQXpKouaEO2XTKR15jtgWSzwANzTC3fYzhI8ejtTPNrfmMgcUSIPksFzq1wsSd2Ta3HlGekSfJjQwA9yY7YYESZImZWTiZgEWLGtxyZdsgyRp/Jp5LsOV+RVC7u53XBwXL57navbq9mUX2sdvk+CTJ0tDoBVxxxRVltT/ppJMwYsSIktuXnZ131FFH4ZNPPsGYMWN8y9va2nDUUUdpd55GryAyCw/wEwrb9Ln2SIBsyBIuLoHyWZrUQHGAkzF4pEQKTVK1f8YnH0FaIoLJvQDxmNtfoIaTcNsJcicmXrFtiBC5pM4do4x5UsmZEiQOeK4630Tny8IShNI9B+4EShiTk6hjmaDJWk6kRGZYgUsyFGEzJok+j92rl0RKJDcIJEbyrGbhnm7rTPP7K0isLS4vQWJxEGrAyaTAXPceSdRw65Nw90WQFWaZnuVIrY0H8GsqiJVjg8QTcHIZTnoCBEr2hQTXDhLkLJf1kzFhtVAtWTGA1jeCjNyl4nOrUSGU+Ldu9THAUC6JKhdlk6hCZVC2bduG+nqtBaLR8xDZeAKtqqZPkGDYCqFQrEQ+vSRf+rg/Hkqm+6vWJVXZ23akdpMkZVExVXIMSqafGK/ctyorELEtPPeZb6zBMUa48yQBkl0qpEkpJBsqCwLPhSMCmkWsDLNtwOYuG5qsBSMU9sbnYUw+MHz8QwTtKe4KUOP1WjvTyLVt40KFinSBKCodhaxRC4MSxBwTzHSFDg0DSNTAaR4LkkuBWB9y0uTGITEz68kOxODFOQGS3BDqcIVxlywTVxvKSbV7AeaKJYtf8+hyLzy+KuDOcwk2iSVChYdpbT0Qi4M2j4KhLZa9D9dq2e0+NHwomUSdcsopAPhEdeaZZyKZTMp1tm1j/fr1RZW9NTSqCUHk21MZUChZTYSCmGm/uGWURQgIESgmauqpFelFW9vyrE7qvhwbTLUaCPYT2CdhDpiR8NYJy5BK+hQhT6FMzbv0W6BCrzBBd16Ea09OaMJtJ2JlAO94I8p+8InZ7U8NEhbxLmKypQYnhTQGe+PzgGPB+MzBwZEOanSmM5FyCHFKwBADMxKe5padD7UTMCiR19imCZDaZiDbAatpnJf4UNMIUlMHdLTyeCaxsSt9ACiWp2yKyxGIosGUgsTjXIU8m+IlkkRMlAgYD2Tjye2iChiLAHQ3oN1nzQQn6rRxGJKzv17yudSoPqqhOD7QFct7AiWTKFFvhjGGxsZG1NZ6D4tEIoFDDjkE3/nOd6o/Qg2NAAwCJM12wJ2wbDcrL66yKSUNX0I1sBQLvJbtHV4YyXF4/TgaAxOKz4SC2HlOfIgrPgnXGhXMAhRkJjimqPgr4Rpz3YghC5PSJpIgqn1ayvdgrTSxrFDpD4UsBbOvJAJvtoQxMHeMgkBlO9tQ0xCuVTUYEZREEBbSOCXIIc6DxRm3+CWaC8tBxJjlSUkQCkZjQKKeuwIdG9TMcLmJxlHAlo888iOIsZmVGk48Q88lbI7jZV9GWCUFgoKKTLFoqVZLAD7xTRKPc8uUHSZSah8aGoMJJZOo227jgYGTJk3Cj370I9TV9X5tKQ0N4cojee42sdxieYS4b+805lmIghur1p9iJEoEUFODl1ERrj/b5BOQS6SIaSpuw4CVCgHXIHNArCxYPOJ3ExyLO3ny4sLuOAqNN+DC8xWhDcY8RVmagpOicNcBrnWBhidAVfFauPZE1liCk0nh1hsqBCoKwxvq0J7KwHYYalgOFquB4ZIiUSqIGXEuX2Dnpc5WTX0jsp1tPC5KWEuNGMAo4LjFoBlkrTxAsRg6jitP4PgTBsRfKw/HDTL3JAwEyfLXv/PdMwpJ80G9d9x2gqTJfQBgmRQyD9wMY+Q4nZXXVyBViIkiAy8mqqdRtm3u9NNPx0cffRRa/vbbb1dcBVlDo1QYBKg12+X/QqgwAY+wsESd6x5zJ4egJUeFEkAuYqgE0SKOHQr8hm3yNlbeb21y45yIbbrkJzo+ieTTvN6ZGwBO8unI9sTOS0uSGEeoeLBYJz5u3JMvAFi4cALWJ6a69dzJFW6pEJZz9YUC38X2ch+ue4+ZWa8/gI87QrhxKIIxxt1z2Q7E09tAsx086y5R7xLrHEg+g8SIcch1tCKbTnERWMvk9xNjPIbKterQnJdVRwQZcrWhpEK4ZXL5A7dosFQhV4oHi2sWRbRDVkdxfxSC4/g/In4L8AczOzbsbS2yFIxGL0Nci+5+BgFM08SGDRtgWRHxq2WibBJ15plnYu3ataHl//znP3HmmWd2e0AaGoWwvSONmFthWKiPZ9MpHoqT7QDJdXJBQmEBsrI+IqWKWoplMptNCUCXauHM4aRJlRgQBEcsVz5ELBPbqkrnzAEsl6CZOZB0G59AVcuY2NayPCuC7eoB5U1JXphty8lVXSbJUxHXnZ88Of5JVOzLMnmmlyBPyjgkHI9IifR5Qg0+djFZa6C5oQ4NdbVIjN4VxDa5tUkR0AQAEIrcji1ewoIg8OI+s5VsPuV+Ip3b4HTugL2tBU7bNp90AVPuAUmkQxZFx2dlVN14vtg3xZoFQMZGFZxQoyxWok+XSGkMDfz973/HiSeeiPHjx4MQEiryyxjDokWLMH78eNTW1mL27Nl47bXXemw86XQa3/rWt1BXV4dp06bhgw8+AADMnz8fV111VUV9lk2iXnzxRRx22GGh5YcccgheeumligahoVEKZFKoW0cs//FbPkkCks+AZNpAsx18GY1xlweh/mw8wC97IOQKlDp0aq0yYmW9oG+xLzXOSf0bXA4AZlaKG0YqgQvLTS4DmDl/rJJq4XH/95EaxyNOkvyIAHCXJDHLjCRPLJPyiJMgbe6kKrK25EdYqUQsjQgot72JVQQWO6n2yDIhQxm5jlY4dcMBAMnmkf6sUUHwHQskn+X3r2169xChYNQATbeC2CacTW/AfP4xZF99lmfVBe+PIlYjX8accMeKdVZelomB+z0URB4ILPYR+Ij//fcyJ2122zZYrzxextnTqAZEYHl3P+UglUph3333xY033hi5fvny5bj22mtx4403Yt26dRg3bhyOOeYYdHR0VOOQQ7j00kvx8ssv48knn/QpmM+ZMwd33XVXRX2WLXFACIk8wLa2Nq0RpdFjSKUzSFCCROv7PMibUMR23QfOji2oSTb4AqoF4WFqhp5rVSKMl4IRsVNwLE/6wOExRSTVyvupdbPkmAOYWZBYDDASMpjcp0El3vwtU8aaSH0dwAv6hffGHxS5VBHMgvKV7BCTX0SMkw9q34rVSd0vcbzAYNlGuHREzIwyZnUZ4KWtw8p7x2nlgQGobNyTSDZyApXbsUW68aJA7LwkUwB47Twa41bHd1+C2boZ1vYtAACaiMOxeTta65eXEcHlnvimR5hY3hVaDZIvlzgzNTs1KhtP3Ubdp+zDu+98EhruNswyEdvn6BLOmkZV0Qc6UccddxyOO+64yHWMMVx//fVYuHChzP5fuXIlxo4dizvuuANnn31298Yagfvuuw933XUXDjnkEJ9U01577YV33nmnoj7LtkQdccQRWLZsmY8w2baNZcuW4fDDD69oEBoaxZDraAUlQDy9jcf9xJI8rsiF/Cm4VilGeTq5cJcQx+axR8wJ6T4xGgMsEzTdCtqxBWj9mPdFDW+iY24MSTbttzSBT3os0+lZc+C+0bt1y5xcxleqg3cnXCxmIF7JI0V+q5H6ES40xxfnIlw2TAkSVvtQXYII7tO1EPhiYkQb1T2obicgxBVF0HKKx6sFNYKGGuwNz8D68A3fMnPbx66VM8fj4PJZL5Yun4bRuRU008aXWznQXIcXA/X2P5H/ZCPstm1gjgNiUDDbhpO3wGzHF5fGlPtDwi0Dw2zH/dgBq1RgclQsDpJAq+4+xeKpxsNJa2g2DZZNw0l38LG58VnMsfnvSGPIY+PGjWhpacHcuXPlsmQyiVmzZkWGDFUDW7ZsCQmFA9xiFqV/WQrKtkQtX74cRx55JPbYYw8cccQRAICnnnoK7e3teOKJJyoahIZGMVhGDWK5Tq7/BHBy4wYEJoeNRq5tmy++RHWBSPFLxwEMl2SJYHDbBDEzIKlWOOkO/uadqIHTuQO0YRi3GsWUmCLAy9xzJyDHzHLCgrysXO9zZUVaiAIWJOk6K0I8gm/9rsXLrwwtJjJ42VSKW5BbsRThRMfLYORCjBFvmT4RTtd6JbITA9YMZuV5/b0hns5uv7sOSNQgtstnAQDm1g/lPUksQag9Iu9zKTMHxA64hW0TjoiZUsDcOpB2XhB671oQw/Bi5FxhVNFebEsMxQoZKN/i2496DwkEtwGUYHXHf2/KWCoj3I9G76GKYpvt7e2+xclk0qcdWQpaWnhs3NixY33Lx44di/fff78bgyyMGTNm4IEHHsD5558PwNMb/O///m/MnDmzoj7LJlF77bUX1q9fjxtvvBEvv/wyamtrcfrpp+O8884rq96MhkYpyHW0gsa475o4NkiunbvUFHdRsnmk/G5u+QAwEr6SLsRyA74tLlEgXG8kvcMTG8ybIMka/gZt87d5kjD4JCYyOBwbyJtwsimfu0JAvmUrgoUhYqK6NSSxUbSYHDuSzPjcciKVXaljBtWqIIoJ501l4uIWJmIFXINKNpZahFZ853+pX2jTzHpuvOBxDZLsnUpgffiGzLxk7j2b//RdgFBQMyNjnISmGN/I9JXmkTIZsiyQS74SNbzcSywOZnfAyVugcffxbTuwsm7pIoPCiMfguMQKeQuEUtAEv1YqkeL/K0RHLUCs3BM+BMlT0HqqWLyEtUxAlwXqW1SzAPGECRN8y6+44gosWrSosj4DFqBCVVGqgWXLluHYY4/F66+/Dsuy8Mtf/hKvvfYann32WaxZs6aiPssmUQAwfvx4LF26tKIdamiUA0ZjMNKtXtadZUkrUxDZdApUuPDyGRlXImrQUTPFg56VN2MnkwKJu8KEmRSIG1vipNphxOJALgMnm5LZSE7aewPzkR3VmiSyl+CfhOQy11IlUtKJYYAhL/sJ6VupxEsQJFUHKuLNXrhaSCy4nLtbpBiiqgPkE9VUdLYCljNmZuFYruUtFucPViH4qGoODSFYm17jBInGQMwsiGEh/9GbgBEDsS2ZqMCMBKjpyRQwI8Hd04zBfvdlxHfZDSyWhJOol/FFAECHj4bd0Qp7x3bk01kQSuHYDox4+BEuLFPU8CxKjpmXlifmOLKMTMjtGiz9EwwkVmKefAWIVYuk7aDxjCsLnqvU3VcXO5UaAwCbNm1CU1OT/L9cKxQAjBvHddFaWlqw0047yeWbN28OWaeqhUMPPRRr167FNddcg8985jN45JFHcMABB+DZZ5/FPvvsU1GfFZGop556Cr/5zW/w7rvv4u6778bOO++MP/7xj5g8ebKOi9LoFto602huqEOuoxVwHNB8xo0fceOTHBvGlBmh7cztLTzAjzkgWU50hLwBsbJwtn0Cs3UzDx5P1IDU1HEiZuWBeEIhJA5IPAHmkifpzrLyAHiGmrDAMCsvXVsis43lTSCXBRPutFgcJFnjESg3Vkq+EYo4JqkAHX5bDE50npSB47Me+doIoibi7QOxKCwGOVFyhWlakPiI8iESVh6M2p6wosML1koLnJhghxBk9qZIcLBNEEO5r3JpIFnH22VTPFYok4IxemegfgTYlveQ27gBzMrDGD4GZPSuYEYcTpKTentbC3ItLbAVixPynCgJ65KIlfKRJ9uBIa5LwAoFavgTBoBwzJtCpoLXNWQ5FdbRLlD/5Uu6bKPRA6A0TIor6QNAU1OTj0RVgsmTJ2PcuHF49NFHsf/++wPg+k1r1qzB1VdXn2jn83l897vfxeWXX46VK1dWrd+ySdSf//xnfOMb38Bpp52GF154Abkcn9w6OjqwdOlSPPjgg1UbnMbQQq5tG2ocC7nWDh58a6Z5/BEh3D3HOGnIplOgnVsAGkNi1C4wt7nB4LYp3SbUTHG3SS7txSgJy5Ntg6rV59u2uespmJkFbR7JJ4Rc1nOLAT7iI9P5M9wNCJdAOekOz1UGcBKVNmS8lXSfJbz0WkFShKyAL6Yo6qEnyE8hl4vSxttH3nPduBYjJ5eRFjaWz/vJm1ovz7ZBjPDkKsepuoHsImMa7HAV5mFwayLb/B5owzAgFoO95SOQZA1owzCY73IdHJZNAZTCSNQg/dJaEEphtXwAWHkYlILWuFbRzh3If/QeDyJ3HBg1CUmIhBtPheMGjwsww/GlpvvS1FUlcvWaFcjGDLmVxTIrDzub07XV+jP6IDuvs7MT//73v+X/GzduxEsvvYQRI0Zg1113xYIFC7B06VJMnToVU6dOxdKlS1FXV4dTTz21e+OMQDwex6pVq3D55ZdXtd+ySdTixYtxyy234PTTT8edd94plx966KH46U9/WtXBaQwdmNs+Bs11eoHbhHC3HURArpvRlKxFTV09rK3vwqkb7iNQRroVTmsLSCwBBwBpiHMLTLqDkydpQXJdWokaQGQNKQHajiA0Ig5IuDAkaaDcWpU3PRFDl4DxDhyPeAHcagM3eFsQKXHgCuEKufHc9Wq8VEEopE241HwWJGVbJ5PyW6+CZIwablyVm9Xn2ODl3PxZWj53DvVIYXB/QwHGpP1hb3gGjmWCJt0aepTi4/+5DSP3n8ZjmVxrYOemFtSNHobOj7aiORaHMXwMEjtPhLX5I05+YnFYH74jszzh2NJFB3gWJeGeI9Rz06lWKbHOsR3E4jG/xUq1PPpccW6sX/AApatauZ+U7exszrdfjf4Holoeu9FHOXjuuedw1FFHyf9/8IMfAADOOOMMrFixAhdffDEymQzOOecctLa24uCDD8YjjzyCxsbGbo2zEObNm4f77rtPjqMaKJtEbdiwAUceeWRoeVNTE3bs2FGNMWkMQfDyJ3npDhEPcRZLumVNLCCXgbWtBYndDkF+9G5IfvQyrGG78PbMQe7lv3NLUk09Yrt8BmzrR3A6dsB2LU2CwMBxy5m45MTJpNw3cvdNLd0B0jDMlyIeEo8UZTaEKyvmEra8Z0Vijs3JiOKqUwO8ZRtxvFF1ypQYlaJ6PeLhZtr+eCdV88cwFEVrL+supOVDbemK9BefdXwuPH6+eCzVUHPfAYD1wStwPtwAUAO0vokHfrvXwdn+Kdr/+RSsVBafPv08AMCodWPvbAd2lstNpN59D7mWFi9eyXZgv/dvWKksjJoEb+c4iDfWSRee4waLOzJo3HuMCxLjy76D35UnSZftuec8sUw/CRJB59JNF3ItO7DzlhyLuk8NjdmzZ4MFS2cpIIRg0aJFFQell4vddtsNP/vZz7B27VoceOCBqK/366vNnz+/7D7LJlE77bQT/v3vf2PSpEm+5U8//TSmTJlS9gA0BjfaUxlQEq5wryL/6btuPTpepoVRg0sWuIV9icXVvNUJvba2FlaqHWiyQD99B+a7r3IXVTrNSUGqA3brZh7XBCjEwrUomVnYrjqziEkiwn2Sy3A3XKIGLJuC07HDP2ARRK241YhreQrVHRPfBclSCE+wtIavf3d5lwhoTDGbB4SHgoZluntA+gDwi4FKa5kNZkbsXy1ILILp3e3EmKXA4yCH88EbcNLtXoJAskYS67Z/PoP05lYwx/G512giBjtrwjEt0EQMju2Atqf4X4P6XKoklYWTFwryVLZntgPD/Q7b8Vl/gu409X/VmkUDZMfn/nMchdSpmXZhK5OwfokP8vCyBjX6F0S5oe72MYDx29/+FsOGDcPzzz+P559/3reOENI7JOrss8/GBRdcgN///vcghODjjz/Gs88+ix/+8If4yU9+UvYANAYvcju2oDbbhvi43Qq2yX/8FvDJ2/zHXVMPlnCzPHJpPqHXNcD+dFOYeADcAmBmYH3K40jM1lY+wQyL+1xrUteIGnCstBtkrWRIiYw3Kw9Qnn1mCzIBhGKUpNWlABHx6eK4WW6isj0AkDgi3XkSaryJsGwFtXYC+5fjEK45MV+K+K3I2Cq/7o/c1sz627ukS1ry5LZxf+wUPLI62GOizLX3wPr0Ay+7c1sLv2/adyDVsh25HR1wTH4RmEt07LwFYnD3GhXLDQeWu9x2HEBoPlEKIxGX5Mp246FEn7ZpSXLjwCNFPkKlWreUAt2AF3DOIkiYYzsg1O8WVNs4ghAGiJiwRrFg/JxGv0BfuPP6GzZu3Fj1PssmURdffDHa2tpw1FFHIZvN4sgjj0QymcQPf/hDnHfeeVUfoMbARGc6g2S6FSSfAQBkUx2gmTZka0eiqZ5bpbKdbUiktyPf0QraOJzHgKRF3TsKWtcEmDmeVRaLe+tckFgczidvA1YeVnsb8h1pOLaD5DjufnM6d3ikIxbn+k55U04c4i1dvKHHbBu0vom7+mwHVAkgDwXXBrPigmRF1V6Cn/yxPCAz/QSCDyfFsqWW7iBqWQ1FMypkYQqmoitjChFSJdbL384vxsgcm9cBtG0ZTC+y8vh+4lKderDrRYl4JbMjjXxqO8z2d5Dd1h7Z1nFJkvgOcCJC44BjWj4XmLgnqUFhuXFGcK1AlmnJ+1YQKNmvsGSpAeQR7jzfsgAxUl2AwoIWta34/TDbOyYnb/nIoYbGUEFFdtclS5Zg4cKFeP311+E4Dvbaay80NDRUe2waAxCZTAbUTCNhpkAcG7GJ+8J+dx1iHa1gjo3a8XsA9dztW9PQDNvmlh+Wy/DJWKnrZX2y0S1hwSdvUCpjegDw4G7bhpPLwMqaMNv5ulxLCxIjhiG3g5OuWE1CxmswV5jQSMTkJCImNgtA3Eh5bRwHNB7zZ+iJVH5F/dtTHQ+TIm7VMVx3nphc8j5LD1G2DYsbOn69JpVAKcG9oZgtxa0oLWHS6qXsXw0YdjPxvDEFlNUd23PvuJYnJ8NjzWh9jc+6Nxhhb3wexuQDkXvydlhbPoLZnkJ2Wzuy29qQT7m17gIEXRAdQXxshZAIwUw1QFzck5bienOoAyeVgSEEM937Mgg1gLyU2CQnguwIa5KtrBOlZcTvR+3bcYmddFkaFCPPu6bLfWv0AaqoWD5QcdZZZxVd//vf/77sPit2XtfV1eGggw6qdHONQYra2lpkwcUuWSwO+911YJlOOJkUWDaF5P7+uDljj8NgvvUiUOfAadvGLUFCiVtkvgEy+Ds2emcAPKiXAki9/C+kW7aBGAZ3l1AKK50FMdqRa+0MTSbUVXNWJzoAcpJzzDysrCmJlSdK6E4iriVLLZlBEjVKELqnHSUVnBWrU7QaeUSKuQrVimUqZEsNRBcZeYHAXxkYjDwcM68sD8TOiP7l5OlpWQVLhsj92J7Fimtk8f5pbT2YmUXmgZtRe8L3o49pIMK9xszMIt/WDiudhW3mJUEXhEIQnGIWGWY73A0XYY0KQliymG2Dxj3l8ShSE9ynagXrCiK+SQStB9dJEU/HI4XMcWArbksrY+LTn1+AsT/8ZUn71OhFVFEnaqCitbXV938+n8err76KHTt24POf/3xFfZZEok455RSsWLECTU1NstpyITQ0NGDatGn43ve+h+bm5ooGpTGwUVNbi5xVj8Qonjlnvf8y0LYNJJZAft1f4Ox2MJLDPUVaZmZht24GbRwGu20baF0jnM4dPKCbGpKkkFgciUO/xDfq2IqP7vofKT4oIIJvM5t3wMnnQRFHPpWF4b7xM8qtTNSgMFwLlQqZaeSbmAIZSY4bRyImlaDAYJGUcRYLt/HFGZX6puj46+bJ4rKKG0ZVsw6uExOh7C5vwTYt2FkTNBFDrCYBuC4asS2Nx3xxMPIcuWRXWOyYZcqSM7kn/oDk508v7Zj6OYw9DgMA7HjxZaRbtiG3oxO2S2qJQUIxQqq7zVMJ99pQg/qy2rokXZS7ANVlwf1FSQyECXCEey7oylNeIqTFVpAl97ioQWVsluxbidvS0OhvWLVqVWiZ4zg455xzKk6MK4lENTc3y1o2XRGjXC6HW265Bc888wz+8pe/VDQojUEA5WEem7gvcu+/Lq0lRsdmmIwhMYLL/if2PBD5d1/j27jZcnbbNn+ZFEpR958/lH3m33uTZzkF0rwFMRDxJGLSEROBbZvS7SDeutX1UW/ggH/CCbtBLF8/RJ2QlBpixDC82Ckg2g1YYkC2ah2SBE1xqxSaUMWYLNeV5OQtb3IMaAupQcfccmKB2RRGwrO0ECsPRh0u3BlLSOkHGQumyEQMBmQfuw3plm3IbGuH2ZkHNfjZJwYFuIKBj+gI+CQGFFVxEUcUio0qYD1yAqKoqtuwWMadStKky9EOB6MH9y3uKep7qXDvDYP6yJa6TVfYfvMlGPF9Xf6lN1HN2nmDCZRSXHjhhZg9ezYuvvjisrcviUTddtttkd8L4fXXX8eMGeHSHBpDB2pRYOv9lzk5ElXeP/43Eod4dYpi046C+dZL3OVnZoGMW24lxuOPjMbhIWvG1n8+L9WaDfdN3nDjl8z2lBeAqxADO88tLYRS6YIAIF0x4jsAz60h0siz0RODmm0lrTxQBA0RQaTU2CIjrLtTaD9qG1/WVMBiJFLNUZMAVY5dbu+2sW3TCwgOxOUELXwq1OOVFha3gLPIaJSxXHkT2Ud+i5q53y7Y34CClZcEChDkhcCxGWzTgZHwu9hCViBXPiAqHkmAJsLuQBIgXcQwpAVLEChxHUUbFaorDuDZfUGrmGpxUscnYrlEHJe0zOajA9ptM48PLjsLRjyGWH2tdO1t/eVFqBnZxN3l2lrV++gDxfKBgnfeeQeWVdk92S1Bjw8//BCEEOy8886+5XvssQfWrl3bna41BgnsDc8A2RTsjlbu8knUwIlKzc+mYO3Y7k0M8QS3QFl5JE+6wNc298QffMGuYjKg8Th3O7lp36oVxQa3uKiuOT5RWCA29U0yMqYlkN5dLGDXjlgX5cIR5Ck4qfnHxEEDFgFBcGTauzsRiXMhJl+fNa0mwUmg7UhXndyXGrws9quOTZ1kA65BEXjsuNtQhwL5Thh1DkhNHW9o5bn7Mjd4rFHMtmFlLDCHuXpNBnflhdqFCZC4fsF1KgGhiZhsp8Y8EUrDOlKBGnnibzCAXd2fo4zNZ5kSYzQonIzIDHVfClS1c9v2kWexT8fOeVYpkcBhm/Je+mTpuSAGRaplO0/00KKcvQ9NokJK5YwxfPLJJ3jggQdwxhlnVNRn2STKcRwsXrwYv/jFL9DZ2QkAaGxsxEUXXYSFCxeCUgrDMLDvvvtWNCCNwQPrlcfhpNq51IDjwMmk4LS3IbHThFBbZts8VikeQ91n9kXi8K8U7Df5+dNh/vn/AMCdWKgbDOsGoSuTg+0GU9upfMjdIdoyRXMHysTFA80D7j2FLAmXWdDi5GuvuDZUd5uIMVLHqlrNAPgypIJ9soArRsSiEJdcGQFCZWdNrv9j+AOCi6XBB2OmAIDCm1BFjJRq8WN5U4p18g2d6HI2AxTMMuHYDIQSMBuwshaMRMLXxs5b8rz4iFDE/UeVeylK6kCVPAgXpnakFbVQcHlIB8qOdrvJe8+m0mUoVcyp3zUs7oUoougjbKYlVdp9cgx5C6MvvC50LjQ0ehovvvii739KKUaPHo1f/OIXXWbuFULZJGrhwoX43e9+h6uuugqHHXYYGGN45plnsGjRImSzWSxZsqSigWgMLuRb/g0ai3NJACvPyZTrOpKWChdbf3kR7LwFIx4rGifx8eLvg9k28qms7y0ZcLOHAm/5gKdhw9vwic2QwdhOKKYj6AqJUoNW3+DVN32jJiH7APwCiII8+aw/UMQYC1gOCiE4UYpxC0FHaaXKemrSNBGTqfhGgsdDCVIkJjk1yFhIQajnhlDej2ohEedGbm+Zgdp8DtKrrkPdvAu7PK7+jPSff46N9z+JfCoPYhAwm4EYDGZnHvFa4YJjIIZXBEcl3EGo58+XbReRARXlogOirKV2BNkKuoyD1lgObt3M++K1+N+cdBOKcTtKQLn47Yp1US8T4y75VcToNXoThNLIe6vcPgYyVq9eXfU+yyZRK1euxG9/+1t88YtflMv23Xdf7LzzzjjnnHM0idIAAMTH7QbLysOIJ+B0tILEE7BTWU5q3DIq6ft/ifR7H/AaYXkLoy74RWRfm37ybWS27AiRCyPgFghmGQWhuiREijYQfjCoFho1VsjXlyJ66Nu/+navvPVLkUUlBonYnntO7CsqKDcYtKu291ndAm4aK2P6SJljWjxjMR6XZMojmI7PyiQnyECsmJU1EYOnc2QEXJ80wfWwmFCKF4KfsQQyf70RtScOTEHe1F3LsOmRf6Lt/TYlmJuAgoI5DI7NYCSo75oIqGRJlmsJrFeJdCHFbztA6MVdEewvePf71gfuUdEXcxypTUWNBGwz72vn061SrLO260pmhntfyopAfBS7Li5fd0ejh0Cq4M4jA9ud9/nPfx733nsvhg0b5lve3t6Ok08+GU888UTZfZZNK7dv344999wztHzPPffE9u3byx5Ab2HZsmWYMWMGGhsbMWbMGJx88snYsGGDrw1jDIsWLcL48eNRW1uL2bNn47XXXuujEQ8CMAdOvBbG8DFcT8mgMNvT2P7Ceny8+Ptof/PfyLvESgSfbr/5Ern51l9eBABIfbTV9+YLeNlI4oHumP4YKVXEULQXsPMWrGwOTj4f8ZbuJxFRAcBBN4jM/HMDZuXHDWTnxWYdKSMg+rWyJmzTkmO3TQtWxl3m/m+72kPiY5sWrFQGVioDsz0Fsz0l++dxTzl5bPwvJ07yf9vxHTcngjzQXbWSBdPfg5YHEYelBqzbecuXeWh3dnB9L8fhSQVmFqm7r0b6zz+PvF36KzpWXoFP//EKtm7YCgBINMSRaPBS8KR7U4ldAjzyFCTg1L13VdLscxEr59xR7g/1OgjdJhGfJGQJBElXPwD8bUy/Oy5Y/85S71nTgp0x/ft3vP0X+p0YNQnU7zy6ehdBQ6MKePLJJ2Ga4aSZbDaLp556qqI+y7ZE7bvvvrjxxhtxww03+JbfeOON/ToOas2aNTj33HMxY8YMWJaFhQsXYu7cuXj99ddlJefly5fj2muvxYoVK7D77rtj8eLFOOaYY7BhwwY0Njb28REMPMQmTIO98Xk4jg1j5DiAbkFuRyesbA4sbyEGIJ/KwMrmIHL5Up9swwgAb37rZBCDYuu3TvYFaPssK7aXHh5E0EoUBHO4zIH4Xqy9CJ5WUchqFBVnIsaqTlQAvOKyhuFOZJ4rxnYlGoyaZCh2SfbnODDiMTDblrFfYr+2PCYeCGxnTcW6xi1/xHakCjYASaYAgMJTxwa88xlMpRdB+/J7Og2jJglHsWQQw+uX5U0wcMtO/X9dGnm++xPab7scHz/9Crb/exuYzQBF0sBIGPI7VVx4gF96oCv3rOrKFfFNoaw+NZ7NdkIZfL4+FJ0zO2NG/n5CH9fCacT91shgAoR08amEm/pdeLGaBCYuW9H1ydXoXRDS/QLChHTdph9i/fr18vvrr7+OlpYW+b9t23j44YdDCXKlomwStXz5cpxwwgl47LHHMHPmTBBCsHbtWmzatAkPPvhgRYPoDTz88MO+/2+77TaMGTMGzz//PI488kgwxnD99ddj4cKFUlB05cqVGDt2LO644w6cffbZfTHsAQ9j8oFgrzwOgE+gyWG8PFBmcytM05IyBQLplu1445ueqzgY8+HTa4IFIOYjOIZ7SzuIIFYRgbsqgoHdauCu2kcwRV0QquAyJzBJyf24xEZuq/4Pf5A9FVaHVF4SHse1avlcQAErXSHXpoh1YrbD3agK+fKNXaiPuxmPwg0IqO5OS/YDQCrBqy5AcTx8PFl5nkkXxKI/YMdvF+Ld/1uHbGsG+YyFZFNSEkghskkNAiMesDTFY1KDS0Bck6gsTtiOL34uKjtSrJMyBgE3q9BLExBkLJ/O8vGIPtzzbranAXDCo+4vqAsl7yv3fg7uR4zLHwA/sONmBi0IrQKJGpjXdr/99gMhBIQQRCmT19bW4le/qixur2wSNWvWLLz11lu46aab8Oabb4IxhlNOOQXnnHMOxo8fX9Eg+gJtbW0AgBEjRgDg1Z1bWlowd+5c2SaZTGLWrFlYu3ZtQRKVy+WQy+Xk/+3t7T046gEKx+bV7pM1MNy3XcBzFamaRHyC5/lcRkJsrhIM702YIgZG/VltNiwgD0+hXHlDVwU5VUuUIBLCshMavkIoxLjFX2JQmTbusx64wdfMdZ/5dHcC2lWOacnUdjVIV5wX4YKxUlnE6mu8fgL7lDo/WU8vKGbwuDE7a8JO8aw94eoULhua4P8b8Rgfj+/o1YzHPIhjyJR3EYMlstEAnt3ly8TiV9V3/EFB0/6KT//1Bjo+7gCzGZJNScRrPRexYfDzT+OBIG6hA5X3W5RYF8cv4ud8llbfeu8/QcQEYRGkmBjUJ5kh3XTULTHjLlezKfNpTmzVTFE+vpivH8vVV4uSaZDjywMsHsOU6+4odlo1NHodGzduBGMMU6ZMwb/+9S+MHu25mhOJBMaMGSMTjspFWSQqn89j7ty5+M1vfjOgA8gZY/jBD36Aww8/HHvvvTcASPPe2LFjfW3Hjh2L999/v2Bfy5Ytw5VXXtlzgx0EYI4Nlu5w43VMGdRsdqRlDMfb537FndS9hHjbtH2ZTkD4jbuQ+8yyTS/N33ZCekxA2BUivnuSCd5kx7MA8zJwVoA4hlymWpQMJVtKjR2R27kTbdB9o8arOHlLZsIJoiK0nhzTkoRL9MXPmftXWL9qEjLeSljQjBq/JUPGOrmWJV+pDyiuy8B58VkkVNdoxHf1ugny137b5Wj65s/QsfIK5NPZfqVg3XL1+Wj/sA1O3kGsJoZEQ9y9L/j9yBwGGjekS09AdW0VcuNFLZeEXLVeBUrzBN3HaiKBWCeU6KP2IyxM+XQ2ZLlU+/GsvNHjVcl7SNogQq1do3+AEQrWTUtSd7fvK0ycOBEAl2iqNsoiUfF4HK+++qosATNQcd5552H9+vV4+umnQ+uCx8YYK3q8l156qU/Aq729HRMmhHWQhiry6/7C/6YyXgC0EsjKXV4MZkdasUDxiUms86WM0+hYJCCcnSf0kvikACDg4guWvYhSXxbrme3ACaSrE0qlK8ZRjou6cUoiFinoVhPWHxGkC3BCCQDxuhpvgqIUudZO6S6xXTIUsxOyH8v2hBGDNdiY7cDsSCNeV8Nr4bntzPa0tGSobsh4fU0oQJ85/NxJmYNAHJcKx+aBxzHl3AZJoir2Kch04xlXovU3/Ss+qvXN95H6NIVYTcx3/zl5GzRugFACapBQLJQaXxe8FkGyAnj3c1cxfLL/QBvhDlfjmwTR9REzl+jbgbGo/ToK2VeDzuVvLvgCETHmYkr3Gn2MIezOC+L111/HBx98EAoyV1UHSkXZ7rzTTz9d6kQNRJx//vn4y1/+gr///e/YZZdd5PJx43gdt5aWFuy0005y+ebNm0PWKRXJZBLJZLLnBjzAEZ/Bb0pr7RovHVpYZxxOkhybuWVEeJq4IDNcebzroNzgW3+QSAmIGCXAJUIqARLWGNvxW7Dc9YSGLUmS3ERZxwy/2yPodhPil1RxxwCezlPwGG03Roq545NZe8rxqHFjqkVDHadsa/MxqvFLckwOBUXMZ0XiiMFIxKEGoAsyJeQUAE9aQR5HXrHwBchkX2DbjT+C2Z5G+3ufIJ/KYfjuO2PnK24NtWMOJ/BGwGUniJSKoMUUCF9z9bvqorZNW7quAfhikuQ1UJILooLLg/eBsPCKGEE11skn72H4tap84rGBeMSQZVgdJ/WPV0OjP+Ldd9/FvHnz8Morr4AQAsb4i7swlNjBYvIloGwSZZomfvvb3+LRRx/FQQcdJDPbBK699tqyB9EbYIzh/PPPx6pVq/Dkk09i8uTJvvWTJ0/GuHHj8Oijj2L//fcHwI91zZo1uPrq/uNmGIjIPnwrr5clpAXc9H+z03sLEFKCsvaXjE3yx9Oo5CUqM0lAtUox2wFx/JlN3qTmL4MihCqldSrvvnEbYZIWRd7UDxB4k1cgrFSoSfjIkJXKIrejE8xmiNcnfYH3tslj7+x8e2jyUo9VrfPHbO6YUWNZVM0n0Yca4+SAF9G1M+EyMUHiBpdAiqB3K8uFGVWrhRR/dJepcWetv7kUw89eBoC70Kopyrj95ks8t2g8hpHnXYNPf34B7KyJLS9vhJ13YMS57MbGi76O5PBGjL/sZgDAZ2/7C1r2P9g7dpsBtg1qEPceZHAAqFSKy1I4iNeGtaCYzSRREmRE3OuOzXyB9sxm/OVBsVgJQiRJkkJ4QgRaqX0n2viCxQMZlw6sgoRPBNB754ApRZeJbxtCKWpGNpV+gTR6F4R0P7tugHuhLrjgAkyePBmPPfaYjI/atm0bLrroIvz855VJr5RNol599VUccMABAIC33nrLt66Y20tkvJWDW265BWPGjCl7uyice+65uOOOO3D//fejsbFRxkA1NzejtrYWhBAsWLAAS5cuxdSpUzF16lQsXboUdXV1OPXUU6syhqEKa+snkkCJlOvM1k44NpN6O6rbTs3wITaV2XcyTVtxw4kJSU3NFuuCREtYYoJZeGJ9FCGS8VG2E8qiUtW+RXC4jG2KKgoMT2Fdavq4JEROjK5lDgBy7eGac9RVyrZt1yqgjDmYhq6eC4Eoi0HQtQRwgkUj4noKxfP45ByCDWxPSkG4C4USutmRxpbrLsToC6/Dx4u/j4+u/G6kVahcbLnuQpjtKeR2dEq3bvtFX0d2ezts04aV5feCZTtofWczEvVx2HkL7118OiYt/wMAIFYTg5WxYMQNWBmLn++EAbhvq7GaGLeiGgRWxpLXjR+fIQmIkTD4OoWQ2KYDGyrR5/e/apkF+JuxkTB82XKEUjDThuO+eqi/A/HSIWMLDfdeNbyM0qCSPnX86/KZ6FJHXsA7dV2ZXp3JRGMdJv/i9m5fN40eBKX8090+BjCeffZZPPHEExg9ejQopaCU4vDDD8eyZcswf/78UFmYUlA2iapUNv2+++7DV77yFdTW1pbU/o477kBnZ2fVSNTNN/M3zNmzZ/uW33bbbTjzzDMBABdffDEymQzOOecctLa24uCDD8YjjzyiNaK6icyWHdICBfAJ2lECyFW3HXdtGJ5Fxwk8+JVJ3FPrZiGVZ+EiUScUOckZrgnX8PrPZyyuOO3QUOyH2rcauCtirMRkpJZJEengatyTOHYxZj4GjwiJCbSYm0tYm4V8gH+dLY+TW0yI7zzJch5uZqPoRx6nS/ZE/FShIPJgMHHI/RggqYVU3/OpLMyOFGpGNrvnJodca2fBYy8Vm37ybWS3tYM5Djo/aYelkAKvXItrvjdtRUqgAzUjvd+6kaAwUybsvLBAGVATHwB+DvKme3/lBUkyANOGnXezT12LlwPqI1YqwXdAAfXaG4a8Xx1l/EaCetmbLhGjBpH9qtfCSASkF1wiFXTL2aYdtjjJbYgco4q9/+cBKUVSM7JJ60INAAzlwHIB27bR0MBldkaNGoWPP/4Ye+yxByZOnBgS3y4VZZMoFZs2bQIhxBdbVAw33HBDyaTonnvu6c7QQhC+z2IghGDRokVYtGhRVfc9lNGx8gpktrVJt4rZnoaZ8lxUXMBQ1O0SJIDHIAUJFG/vCQMKiLd8MUHapg1HvNEbJERMbNs/UagQk5Jwt4h9qgHSInjcgRevBLip4Gkg3lgHJ2/xGn/KfiL3qYxR7ktpxxwmM8JUUDg+QiWsBdxSxdcZCUOeEwDSRUSVmCkhSaBCaFRBIV/qPtR9ijGLZSokeTT8BEy4TAFumUp/sg0ALxHy9rlfwWun/wem/eH/wueqBHxw2Vn49IX3uQUpYYQV522/1dPKWojVxKRrOV6vuJht5t2XCYNv7IKfQxuOQrj5fSYIlXK9hdBo1pIZfuI6y2tve7pT3v0cXYeOZz0SSbrUMI7gtVCP3za9FxWxLzFur2//PRqk89wqRvDqaSeAGASEOppAaQwY7L333li/fj2mTJmCgw8+GMuXL0cikcCtt96KKVOmVNRn2bTSsixcfvnlaG5uxqRJkzBx4kQ0NzfjsssuQz4fFu4TWL16tdRkKgUPPfRQxQqiGv0DO367EOktO2DEeeFbK2vCyuaRT5k8RTzwNuxISwwLqX0DrqVEcXtZGQu2acsJyTYd5DMWrKwFJ89dNnm3jZ13YGUt2HneljkMTt4Gc5j8qG/jYr9mykSuPQsrm+flMBQ3nJO3YKWzsPNcbkC80ec70sinstxS4C4T42S2U/B/cQ7E2Jy820YZIz8P/DyJY5HbuX1JC4Z7LsV5FZNo8JwGhTsB+DK1xP9qILOqgK4GP0e5RHlCgS33ZSuB9EZtAlbWxMaLvg4AmHrT/4IaFOu/enz5Nxx4HJeVsVxto7xyHvk54feAjXwqj3wqz+8jcb9kLKQ2e5aww93s3WETm11yzu8h/sm719dx7zFHEvh8RrnvTNtH7PnLhHc/mqm83L9Yzhzmu1/V88ndhoq7zr3OwY9t2jA788i1m8i1mzA7+X5s07vn7bwj9yc/gZg+fo8UfgFVdcs0+jlEdl53PwMYl112mZQ5WLx4Md5//30cccQRePDBB0NVWEpF2Zao8847D6tWrcLy5csxc+ZMANzPuGjRImzduhW33HJL5HazZs0qaz+HH354uUPT6GfI7ehE6qMtAID2D7ZLsmMkDBhx6ntbVq1FDqh0YcjJVtHjEZOEeOgTg8Jwlzt5G7apZCIpaeg81sQBs/1WAo/McauD7e5PfWN3oBR8hQPHNgPWM28sTsaSxxq0JKkTkh0xOfndlUxaljyIeBbm+0sokfvjY+THxlR5CDf2RsRTBYONQ0HqilYUPx4vu4tQ6ssYCx6DL1vMduBQJ/TGRihFrCYJ1uQgu60dr552Avb+nwfkOa8Ek39xOz6ZdUTI6kcVuUmm3D+AG9sEL+D7hZPn4oD7HsHj02eA2Qw73m/zOnJ5NjMogLwSsC+uhwPm+F2X3ALobUcMAqbUUKQGhS2sY4pFSpy7fMoMWKUsBK1HXvwekf/7QX1ti8Hv4mVSzsHfhmDEnhOx0/+7qWhfGv0IWuIAX/jCF+T3KVOm4PXXX8f27dsxfPjwiqWbyiZRf/rTn3DnnXfiuOOOk8umT5+OXXfdFV/96lcLkigVL7zwAuLxOPbZZx8AwP3334/bbrsNe+21FxYtWoREItFFDwMLH1x2FnZd/Hv5d6ggs7kVZntaWmmoQRCrifkmCB85cScGirBLS7yRi0kCgHzLBxzYpuK2ct/2eSMx4blFfxUtKhnIC0iywmxRGkWRRnDHJONU3D5sxdkhJmVie+TGc3f53V9R8Nx5nqtJjE0FhV9vCBAZi547UP1fbO8jkkoAfxC+jEYh06AQLFGvUNWkipJkCH53YIE5/gK93FqUA7MdxOtrkE/x7EOzk8fMrZ11BA5dU15R0FdPO8F3LiUM4st0s/Oe1dHLluMB4lQ5/8QgqB1ei0xrxrfMsR0Q2yM9gowL6x9x4+54X1S51xRCpbpZAahRbrbSBwAw05IkK3gfBd2GUaDwk3MV6j1nJAwf6RfuOwDSxV07ehhoIubL3NTQ6O+wLAs1NTV46aWXpMg2gLI8ZFEo+1dQU1ODSZMmhZZPmjSpZPJz9tln48c//jH22WcfvPvuu/jqV7+KefPm4e6770Y6ncb1119f7rD6HZ6b9x9oiHmn9+M5swEArV8+Fvve/XCBrQYP3j73K+j8pA373/s3/GPObMRrY1JbR00XF0TKNm1JfESciY9I2X7RTUAlUX5wlx8DYHOLCUTGnDpxeinaFBR5aXWiIAaLIB2GzK4SFi8aN+SbuujbZ+XyDdabZNVjArxg5+ByTlj81jSvjeOzVHVF0sQ2ACRxlZO3kmUVbF9I90eokYvMxOA+BNQ4MoC772zHAQLuQu7+5Odw37sfxgsn8/JLaz43E7P+9WzR41KRaXXr80VcA+kqjlqXd5AHr49nmw6emnmYL0tO7VO6Bt2MPak1pmTAGW4QuiTgEJa9cFakWBbUqAne71S2i3avBe8jtY+o+06Fj7wr1k0vHgxI1idB4zEYNQnUDG/E6AuvixyHRj/FELdExWIxTJw4sSItqGIo+4yce+65+NnPfuarF5fL5bBkyRKcd955JfXx1ltvYb/99gMA3H333TjyyCNxxx13YMWKFfjzn/9c7pD6JQglMBIUsdoYjARFvJZPUrn2wa/o+97Fp6P9g+3Y/96/ARAPaOrFp7jxFyKuRxAoEVsiPsLtImKfLCXORGxju7FPtvvdI1Cu289d7+S9mCvHjbkSE6qIMRH7E/ErYgwyTsmNn+GxJW5sizsO0V5sI11GysStrhP7FdlSfLyepUT9rsY1CYStCd5x+/SqHO9cRJE7Ix4LSUEEP4AXO6Var3wK10rhXPG/bONas5jjwEplYWdM2BmT6xm5mmH5VA5W1sK6448GABxw3yOI1XBZhDWfm1nyvZdrz8lzq0LEpwWLRFPX6phtyyG9NYP2Dzuw4/02pLdmlHOtuuuY/yPindwYJvXai+1sk1u+xP0oPuq9qELG/uW9+0eMQ70v1GX+LFS/5lTwo0JsK4h6MBZKxAqKxBAhyqoJ1MADI0Rm6FX+Gdg6UZdddhkuvfRSbN++vWp9lm2JevHFF/H4449jl112wb777gsAePnll2GaJo4++mifHtS9994b2QdjTAZ3PfbYY/iP//gPAMCECROwdevWsg+iP0K4rACAxg04eVvG2aixH4MN7118OnI7wmnqatA2M7mcgG36SQwAWKKsiKKaLN18rmvEU1BWsvRsIZUQsOQY/rd39c2cQmhJeROZFxfkTcSGYfjS5OUElvHiWYLHKI9beeshATeRLzg44LhjgfF6+2bSNcPjm/zWLBFbExVTFFwma+LBL2cgxxtQtgbgFaI1vLIxheq7STFP8TswokvqqKrpsG08e9QszFy9hut/Jfznviv4A66d0DIBI254ZFghm8KNSyiRBUmpQXntPMolD0QbqrhNveNhbvZd2FWn3o8qbJu5chzhcXpuOBHETzxiDcd3n/gzVv37CpLHKDFQb09KFibl18BIUBg1CVipbGSRbg2NgYAbbrgB//73vzF+/HhMnDgxJBb+wgsvlN1n2b+GYcOG4T//8z99y8qtFXfQQQdh8eLFmDNnDtasWSM1nDZu3Fi0xMpAghpozMtHUDCTv6l2fprCa6f/B5jNepRMvXjKF6Q1qKfx1ve/BACI1STQ/mEb8q6MwdOHHw4jbkg9HvnwNkUckQgq979Fi0ByX0FiN05EkBXmWohoXIklUdxyan9imZ+4+GNFBIITVFBnSLjuVFehIGJ+whfo17eroIq5ewyG2t51/xQpLq6SSK8nKuO8/G39bkqikAxi+AkHMUg4EFzRmTKMhNy3aMfT7VVLhmvJyotSNIh0GwL8ehvutbMyFtbOOgKxmjioS6Qenz4DR69fV/hEBM6HajkCION8fPFmgsC7xOe4d18C4HchPjbtILR/2I76MfXSqhncVxAUSqFiQbCFhhe8epBSnd+gsE3Hp2HlxV75452oQ5Q6ht59qN7XhfTB1Hgu9X8BOS7X7c4chngtrx1oxL1aj7q0ywDFEHfnAcDJJ59c9T7LJlG33XZbt3d6/fXX47TTTsN9992HhQsXYrfddgPAtaEOPfTQbvffX6BaDQRE9k62NYsD//pYj+4/WGG+p7D+q8fLwPH01gx3q9kMTx9+ODwhTOojJg4cH4GSxMid0IKki9lMWvKcvN+lQSjxvaHL/gOTRHDSo4GrQ5XgYxY9P0JQBu/a+i1m3v6LQw2eF/uVyugKeL+eVSQYbK5mCALRNdyCUFWyvWXhCdVH6BR3n4xzEgWX62u866SUuQmmykcV4AUgy5SE9LxM229trBAytiygt8WtoZ74pXAbUoPisWkHYc5rz2HOa88BAB6fPiPSilQIKnnzfY+wlKljBPyxV0HrZVBTyr9PJ2TpE30H47lUeHX6iLKMyOxR7zfMCZtQdNcYYNBlX3DFFVdUvc8+sctOnz4dr7zySmj5NddcIyeLwYJQqrXr2su157D+q8dj+p0PVm1fb3zzi2jb1I5kUxJW1kLt8OppuHy8+PuyrhgAbLzo60hv2QGzk+vtcC0mv/KxcLHlXeFD6ihBrqKqvNDRcYkXz0DiJUycvH8CtfOeBUqFDVuWPAlCWKuiBCvVTKmgKzBIRuRbukJ+1AlNPRZ5DIaXlRXeN3yESxApkR6vWrXEIKOsc47tYObqNVg76wi+zgln3anHLyQmpEq2AjVLkioWC5GNp5If2/bS7s32tNQLEsKkAC+mHCyTY9ueRlSw1I2Qp/BKzyi6RjYryRqVqE8g157zXz+fGCW/H4568Z9YrdTGy7t6TYBntXpoyn6oaU7iqBf/iaPXr8Pj02f4+gmRToTdauI+CCKqfSRZFG5qW81jhSRUKvEP9i3/j3C5qm5rGME6eAxwM2lFUXBvvwPbEqGhsWPHDtxzzz1455138KMf/QgjRozACy+8gLFjx1akTUlYKVLePYBqH0h/QXt7O5qbm/HEnCPREHBdyGBjhwcgGwmK2pF1SDTEYWWsbmftPTXzMCSbkgB4gG28Po5EQwIH3PdIt/r99/yvwkplkRzWgMm/uB1vfutkdHzSIY9HJTdBq4ggSEbciLSSCKIkJlI1nVosUxFFctQSJ96yrh/2qotHdf+p7haxrFifvvIzCmGJImJy377x+y0BwZgu7k4Jnz+fJclxcOiap7B21hFcf6nWk5IIHq+KYBuxLGqc/jb+bQoVWRbjVK1ujs1kkV6zk7NpkVggiBQA371lZWx84e3S4hWePPAQ37kUBD1eHw9l+j00Zb9QXFPYYmVg7pvPA+DuPX5s3jbFNK3UepBR+yiWURckat7+wpYjdRxR1yK4PGixIgaRyS8A184SCvhqGaFYTQJ73Bod66pRGcSc0dbWhqamph7rf+s7r6KpmyXM2js6MOoze/fYWHsa69evx5w5c9Dc3Iz33nsPGzZswJQpU3D55Zfj/fffxx/+UL6VtU9eK9avX4+pU6fi6quvxs9//nPs2LEDALBq1SpceumlfTGkqkO8PQezsQAvUyifsZDanELHx50yNbtSPHngIbJ/oWmTa8+h9d1WmfFUCTZ89xTkO9JIbe5Edns73vjmF5Ha3Il8ykSuPcdri8mMNseXfSTe7s3OvKLM7Kk+qwRKQNX4CaZlqxltUZlGQQtNoWy3IES/QckEdZm678jsubwjLXF23o68/gBczSvukgxmcgUzENUxiHOmumVEjI6woDx9+OE4dM1TvglUZFaJfctjVjIkg9l4quq1FZExqY5ZZhjmLf//ritO3OtinOI+yLRmkW3NSmV5T1FbyTaT2W9+XaeuUIpLEwAenLRvSe3yKa8Sw5zXnpNq5OJ68DEr1skIBO8bprjKxP/qMvU6B+8hH2n3Lfey/aLGESRWxCVGwsIptiEGlfdFqK5e3sKrp51Q0nnT6F/ofmZe92vv9TV+8IMf4Mwzz8Tbb7+NmhrPU3Pcccfh73//e0V99ok77wc/+AG++c1vYvny5b7ivscddxxOPfXUvhhS1cFsBw5xQhYO6cJwvDdkWSG+Qjw+fQYS9QnE6+MwU6bn9jEZjLiBNlVxuQxs+O4pYLaD9NY0aNzAZ2/7CwDvTb9QfIVX4sINknW8dH8jwZR1oniqGjfjuZ2C/QvLTCEipH6XLil424TjjfyxKlHlLQqVvCAG8aleq9Y02AzMiLYiqDFbIk5HTKBq/EwIBt/WskVmo8iaI3IiD547cbTc6in2749Nigytt734NAAgioVQrdeHiOsPcItI3hWtVGUXVPjuD9vLjvQFPQeCsx+YMB0nbFoffX4UzPrXs3hq5mGSXHruwvJiq8Q1IQbBAxOmu8ctrD183F94z7OOPTbtIJ9VStwLoQB/954sdK3V5eKeUM+F6gIMBo3L/XZ5bPzaqyKb4p7hWauGLKAMALZLtmzbRnJYfeGONfovCAUiYubK7mMAY926dfjNb34TWr7zzjujpaWloj5LOiMjRoyQ0gNnnXUWOjo6KtqZwLp163D22WeHlnfnQPobVOuFtGYob4iEElgZG7l2XkdOzfopB08eeAhiNTHE6+Oee821IAD8gWwkDDw187Cy+97j1nt98SkA8K8vfJ5bMPKOos3EFMuG4z2QbU8DKWiJkFYbqaHjd+sVs/wEz3PQYhWcRIoFJkuLR0SqerGaYYJ0qVYXVcxR1ZnyrDdhC4HoQ3wH3ED5AKHyW3n8BEolHk8r5ZKirBSq5Umt5aZqeHlaWKoel+Or58bkfe1ZsGTtwIxXu1D2bXs6WvmMFdL+AryYMifvvWT4r6uDv473lIaL4Yhnn4Fj830Jq9pRL/6zpG3VayGvScDVGgVhpRJ6ZwDCpEo5lihradR9KP6q2lTdha8sjeLCFveplbXcOn1udqFM5mDo/KQNG757SmS/Ghr9GTU1NWhvbw8t37BhA0aPHl1RnyVZokzTRHt7O0aNGoWVK1fi6quv9lmQykVPHEh/g8zyssOTAQ9i9h6eZme+IkvU6v0PdmMXiBeUq7gHxMPRSBjItee66C0a+YwFGjeQ3tqJZ4+ahfS2tDwOATXNH+Dp3GJCOP69lwEAfx2/twyOjcqYk8rfNg/2Dqamq1Cte973Am/1tn8SVPdZ6P+ofYkx+sasEL5gXyIIWASNq5Yiua2vFIcT2pewggShtjUSFMT2rILUIDLLT02hFxO3CKAH3JijwDESIzxJqyVIpEXKnVTV+n9q5p4gEUHrYfBlQj0fKjH1W1aU8+QwSaSMhCHvryiUIokAcM2oYvcAs5m0gAmLFJctibZQAcAX3n4Bj+x5YNE+BYL3QRSKxeOJuLnwusJkS9VIAyDrKYqxqTGBoiafKOTNbEfHRQ1EaIkDnHTSSfjpT3+K//3f/wUAEELwwQcf4Mc//nFIuqlUlESiZs6ciZNPPhkHHnggGGOYP38+amtrI9v+/ve/77K/njiQ/gZViTgqQFR9iFpZ7s5bvf/BJb8tPz59BmI13uVTi/IGiUc+Y1X89jr9zgfx9OGHc7KX8qute2VH/PEghVxjQfiCsAsU6VXbRE0kwawl3zp4k35U8G5UoHdwWTHLVzBGRR27nBhtyLIZQRQKNAc82QaVBEa5MsVYjIRfyFIlaSqBsW07lOUnNIx4Ydzo7ES15Iha0030I9vbtiRskjCJcj4BSwzgXVNx74bcou5LB3OYV6zGYaCiJEk3IKx9avkbAQeOz/2nkqXgvaTqlAk8NGU/GAnDR2Tmvvk8HtnzQJ9WFxDOioy6N4MkO4hg+2L3Vil9iO2ClrTuhh5o9CE0icLPf/5zHH/88RgzZgwymQxmzZqFlpYWzJw5E0uWLKmoz5JI1O23347rrrsO77zzDgghaGtrQzZbeSB0TxxIf0TYzeQPok40xF3XB3eFZNtKsxaJ6vJiMhTaSTJwPcP3k2iIeyJ/yuRbbj0yIZYpJjSVlKl1vFSrkm3aOPHjV339FMpQEtsUiwkqFAsVhWIWqiCpiiKCapugeGaUMGgUgnEspULVNBJklNAwgVIFGb19KusVJXZV/Z3vwx+H5G3vV2z3bZO3IzO6ihHRoCSFj2wqBM7n0gyQVLFcnEvDMECV5cIqlahPlJy9FwRzeOxgMQg3tXC1CoIVRaAKSVv8beoBvK9Q3/7EimCPahyUIJTq/aVakYKCsqFjFdcC/mg4cW3VfmRmnhsjxd28eZkBrKEx0NDU1ISnn34aTzzxBF544QU4joMDDjgAc+bMqbjPsiUOJk+ejOeeew4jR46seKcC1TyQ/gKRTvrQQZ9DQzLum0BUC42aqZdP5eVDuph74skDD4Gdt93yGTzQ00yZ8iGrBmgD3DohAr2JQXD0+nU+QcEjnn2m5ONa87mZvEhrxvJZvGTx1QD5UUnUAxOm+ywPxPDH/ARdblGWuyDBinLzqQgRtoAkAlBaAG4Q6sQeWhdBAMU5KjSG4PiCrhTRh5dB5fgmaW/SK5JmH0h5L5QCz/svHCxeCCrxVPsInqvg5K6SJ/XeDbpHQ3FCgX7UODwapzju3ZfcAsJOwZeFByft6/utPTRlP/c4vfMu4gDFGFXxWpV0iTF2aTH1uU3DJFig0L3hrS8sW1FIUiGI4H0TJMgxV+7AkxyhSG9NwzZtNI5v7LZsioaH3pI42PLBu2hq6qbEQXsHRu86ZcBKHPQEys7O27hxY9V2/vnPfx6HHnookskkyABXQg3CcaILgor/xRu3lXVTwx0Gp4vgcuYwJOoT8gEsCBTgTSyqO4DZDDD4pJaoicmgY2YzmNnyCiHP+tezMsaDGhS2Y0tXSCmuu2BcjDymAt+DUF0rknAUENGU2yjETSVqQXecilICiOV4I1SpC40jOIZQX13sLyobS/2r3mFqVlykGGNETJLYJuhqIwFyFEV4AYRq+Ikx+65vIFA+eOxR5AhAJOEW+yKUgMY9YVZiEDw27SCM3GMkqPsdgFQeFwi+rIiSLw9N2c/v1gvcQ+K4hfZZOdILKqKOWc2uU/WZivYT+A0EiZl6noPEynefFKjdKOPnMqZMVtEEamBCFCDubh8DHY8//jiuu+46vPHGGyCEYM8998SCBQsqNuJUdEbXrFmDE088EbvtthumTp2KL37xi3jqqadK3t5xHPzsZz/DzjvvjIaGBknMLr/8cvzud7+rZEj9DjzLxQ4Rh+DEIiwVlBLYrLh2DbdIUJ9GDe/TL1Sp9q+qaIvthLRCuRClXcQ+grFPcpnb9/3jpgHgLg8ap26MSGHLBOBp10Stj4o9KpTN5AtGDuxTHaPaRloLA5aQqCzBYNxWV1CzsASBDn7UMarti10vX8C2JEF+95D6PbivYDthBQu6n53AOZGZdIFzo+pHiftDzdwMnmcx3q7uR+FGkxaUOIURN9ziuAZiNTH5d8TUEahpTiK9NQORFVhNEEoig9GL3QdBK1QUWMR1UzM+vfMV3m/wd6jeq4X2pZ4X9d6Qtfnc6+RlaNoVk0YNjf6AG2+8EcceeywaGxtxwQUXYP78+WhqasLxxx+PG2+8saI+yyZRt99+O+bMmYO6ujrMnz8f5513Hmpra3H00UfjjjvuKKmPxYsXY8WKFVi+fDkSiYRcvs8+++C3v/1tuUPq12C2l7IdfMhSg8gCnwL5ItYo5jAZj8Qz2ZjvIeuJJHriiHn37THXbiLbmoOVqYxAAXBr1wXIRICUyInS8YhUJpXHce++5Du2YHt1udpfcOKIijUppO+knvMoMhRFjKKWB/dRUN8nQAZkTJRCIqLal0KQBJEKjk8l61EEhfcTrfwetV9e8Je6GZ/R/anXxDe55x2fiKh6HFHkWb1/o6ASDx6jw93YgjCpvxthlaodXiPjC3PtOTnOh6bsh4em7CfjkgpBvbY0Tr39uuReEDdBNlW3nmoRikpKEMvVv13F+UUWso4gScKlqMogRJGvKNIbbKeWb1KJtCBe/5gzu+iYNfopRGB5dz8DGMuWLcN1112HP/3pT5g/fz7mz5+PO+64A9dddx2WLl1aUZ9ln5ElS5Zg+fLluOuuuzB//nxccMEFuOuuu3DVVVfhZz/7WUl9/OEPf8Ctt96K0047zVcrb/r06XjzzTfLHVK/BHMQegNX4dftYV6WUhF+U8h1JR7wwYel0HASD0tPj8k/qYp4kK5gJPzxT1FkwwkQjJNaXgMA3Dl6L/ldtbJ45yM8KUSSmcBErlrE5ORRQA26kBaUTy06SpcnsCwKUUQgZCEowWJVrL8uj7uQpSJg5SpEntQJWxAWQaSKXovAfV7KeYpyqRYiFIXi24Jj5lYpbkXLteekDIEajG3nbTw0Zb+i8gNiLCp5AngplERDHPHaGBL1cSSbEkg2JSSRClpBo0ReRfmYYsfrH4f3W1W/R72EBEl/V7FkUeNQnxWcGNvS/SueVYc89mSX49bohxAFiLv7GcBob2/HscceG1o+d+7cSNmlUlA2iXr33Xdx4oknhpZ/8YtfLDle6qOPPsJuu+0WWu44DvL5fMQWAw9CdM/szEv9JiHKmM9YUtBQBJU7tgODADZjuHfstMg+gxOyOoH43IWOn0gIS5jM5nHdeiq6mlT4dl4pjiAc24GVt0EpgeMwHxm0GYNBgP8Z9Vmc+PGrPP6rwAQSRcrER6wPtg264Qp9BwoTKhVR8TfFUEo70aaUmn5R2wkLgRhfML5IlIAJXp+gqGUhS0TQiqmeZ1miJ++l/UdZ+uQ4TU8nLOpYisWkBREMyhdZY0aCKhYh/kk2cau2bdryd0UVC5JqxWI2i7RKSa0yF7GaGCdP9XFZW06cb4AHYCca4j4yJY5NJHao1/xvUw+ItNQFz1EQUS7aIKEWfUWRKUl0FWuh2ofvXsg7sNxnlGNz67daZFxDY6Dii1/8IlatWhVafv/990fymlJQdmD5hAkT8Pjjj4dI0OOPP44JEyaU1Me0adPw1FNPYeLEib7ld999N/bff/9yh9QvwRwGBmUCtP2TjSznYBBZZZ5SAoNx0tFV397brpeVF+y74AM574TiMmzTxl/H7x2SJQi28fXjTpJUsZAFLVH3jp2GL29+HXeO3gtGGW8xwX78y8KWL2qELQACXaV9FwOzPSXnUgLCHTssKKq2dajjy/LybxsmxkGBTsvm5Fe9b3zkxOQ6UFbWCgW8F8puFDIYwTbieADv2hODFNTlijoXhdaJ/30Zi4gurBvVVhyDQQ2QGte1lnBrvpmWb7yGa40Sx2xlLZ80h8ADE6ZLyQKpH2VD6j3lXQkSI2FwclZDcPjTT+OBCdOllco7X9x6Uz+2ThLLqPqY4uVGWL66grzWgeU0cI+q/UW9sIhzHbTAirYG5TFfzHQteKZXD/KhKfvByTslleDR6EfoQ52oX//617jmmmvwySefYNq0abj++utxxBFHdG8sFeCzn/0slixZgieffBIzZ/JM9X/84x945plncNFFF+GGG26QbefPn19Sn2WTqIsuugjz58/HSy+9hEMPPRSEEDz99NNYsWIFfvnLX5bUxxVXXIFvfOMb+Oijj+A4Du69915s2LABf/jDH/B///d/5Q5pQECNu1EhXHkijiiuTI7FIFTP1Um+kNyA2I94AFt5G9QOi1sWe4irlioxkTqOGzcR2J/NgFM+fU0GlgPAV7e8jrvH7MX341qromAzQJ0rg3OdQaL3B9cKBkRnQ8rvDvORvmKIyoQrJXsvikjJEi15/2QpiV8R4iW2V+PBgsHScrk6RtOvc+UL7I+4x6LGFYRtOkDC315YAlVrCVWOmbfxuxSjEE7l53F/QdHH4HlVxyatkg6LvJ+F9SpWwzNjH5t2kC9zL0g6Q1ZM93c2R3HLBcnEms/NBLN5FYJcew5Hr1+HZ4+ahVy7GXoOqGRWEJ+u7i0APiKrZpxS37LC7kLHdkIkXO1PtVQLIV+xXawmBjM9ODwGQwnVKCBcyfZ33XUXFixYgF//+tc47LDD8Jvf/AbHHXccXn/9dey6667dGk+5+N3vfofhw4fj9ddfx+uvvy6XDxs2zJfURggpmUSVrRMFAKtWrcIvfvELvPHGGwA4u/vRj36Ek046qeQ+/va3v2Hp0qV4/vnnpU7UT37yE8ydO7fc4fQrCE2OP0+ZjnoayEZzos34KokCPJIhYogAXuJFPMBEnIqV8R5ssg93XTFxyrw7hlM+fU0qMQtYeVuSoD+M/CwAYGRdnO9XIWgy9iqCDAVdeQDw5c2v4w8jP4vTt/F75v5x0yLbBa1VhZZHre8iG7wsFCJaXREN9bwLwhYlvhgsEyLOqxE3Qtan4DYyqNsJpLc7nnZXFKIUqP1j94Keo122fjLZld6WvwBx4cdMkJh2pX0VpYVUSPFeKuIb3nUw4gaYw8stmam8L07pgQnTI8+/yG4FUNRaWwz3j5uGk1peC8UgBi1GhXSmorYRiCKgXcFHeg1/aaLgc0o8u6y8jVjckLIS2hrVffSWTtSnLS3d7r+9vR1jx40ra6wHH3wwDjjgANx8881y2Wc/+1mcfPLJWLZsWbfG0x9QtiUKAObNm4d58+ZVtEPLsrBkyRKcddZZWLNmTUV9DAQw24HDoh/s6iTL/3ZtenJsB0evX4fV+x8MALJwr4Dt2D53W1Swt2oBEuQjZ9pIKhYKSgngMNw9Zi8YBGiIUVleRuhDqQhalQQBu3fstJBbsiFG5UQintE2Yz7rk0qK1O1Nh3VJktQ+g6TL6z96fQgR54wq8TKR1jAAhnJ+bMYtQ4bjt/zxldG7LdftGEl2ELZ6EsotnMHdqpOxHUHYiu03yiWmbusE+isWLB/8v9D+VUVvteZclIsxikAJixzAXapmKo+/jt9bCt2esGm9fLFwbAcnfvwqV0W3EXqxAYDfDt8TzXGKL29+HV1BbBtFkqR1NxDzJ9qq5Dxq+yABjSK2KqlVtwH4tRcW1KA1W423AviLlrD8dhUCoDE4EQzCTiaTSCbDSvamaeL555/Hj3/8Y9/yuXPnYu3atT06xt5CRSSqWzuMxXDNNdfgjDPO6O1d9wmi9FsAhIKvAT7RCwtITAlSfWjKfqGYhWBguG3aBV1kAEIB3+LvKZ++hr+O3xvUoDIwXMRmDQvIL0QFlaruMcdhBQnUnaP3QoIS3yQkCJQYj9/qFD4/frIVHImfyKn/q+0FeYoiZWIdH4e7UpJOlxBF7N9/rCRiOSlIOIJjUAl1lNtTtZAVsjgF7zliFFZ7L2ThKBSHJJZFuYDU7aKsVMHtgqRAJQ3MZr7ah2pfUSKUwZgilUD5xqloIEmxW4dJKRDA7557aMp+OPHjVyX5D+LbreVnE1tZC/H6OAAv1ixIAtWMu2LFhwWC101AjecLxk4GwRwmX5LUMIFge/E7jHqGafRfcLHN7pnrxfbB+OcrrrgCixYtCrXfunUrbNvG2LFjfcvHjh2LlpaWbo2lEjDGcM8992D16tXYvHkznIAQ8b333lt2n71OogBgzpw5ePLJJ3HmmWf2xe77BDKmQJ2UlcnXICTkQhKBriphOnr9ulAmnUqgxAMuGFvktxbx7/8z6rM4besbnAgZXjtKCZIJQ+rkAIgUfOSZR+6bqYsggRLHedrWN8LnRLEK+QkVQ62hEhoVfpIS3NYjQqHdhfpT24jMyGA7fh69sUT16+07OikgeJ391rZwf+L6qaQNKOxiVFGI3MhEhog+gsrk5UBYd/yuxmgriDcpe7FFwWyy4LEUiuoRFqlCQf/qsapWKEAUO+YZaPmsJa/B/eOmgboZfMe9+xIenLSv7COKQFUKQdIenLQvYjUxnyiviihyKf4PotD2ajxfMLQgiELB/0FU022u0XtgjH+62wcAbNq0yefOi7JCqQhWJGGM9UmVkgsuuAC33norjjrqKIwdO7YqY+gTEnXcccfh0ksvxf9n79/jLSmqs3H8qaruPvtc5swMDLdB7igX0QhoFI0iShThVRP9kSiKDFF8RQ0GNMaJvooo8lO5RDFRicZLNPGGt0hUjNcXRV8DIshNQcgQYCIOMDPMnHP23l31/aNqVa2qrt7nMnPmAnt9Pmdm797d1dW1e3c99axnrfWrX/0KRx99NMbHx6PPX/CCF2yPbm1V0z0NzYTOC1218XInZPzBSBYzI81tQP7hR0CKi8Clko3Cqiml39BxuGvt9nXDFfey39/s9VWkiZrKPPRTcJEDPJQuIT0mXGveHRjGoE1bld0cnXNrrLpni7zkfeHMJBkP+c/me3LAl1wyUY036MbEzPVHxPDMBojIKM0AiblDH0X0mtpTpQLKODpuUAQZ9YGuNx6HmPmygCtoAWNGy4qo+7SvDulG6D6rEk0VASijDa7Y5/GLov2Znupj3CXvzGmvcpbTwZGl+rpa1w3QPBfwNCiyj9hRf1/qkJLlRf+z9YDm0HZsm5ycnJMmasWKFVBKNVin3/3udw12alvYZz7zGXz5y1/GiSeeuNXa3C4g6swzzwQAXHzxxY3PhBBRyPXObFx4nWccmhO61sa78ojhSSfSdNLKi7tjxov+X5pUq6dmXrj2Rnxx98P9ZMJDr4H8pM2vj+tFbDqDdhBn2ap426AxyV3TbJqmuWio5mNzAVAp+9dmvO+5/ctO0QCtQGBU0jxDlK0bCEVyeRJbfzxzGVFKADpeKgmFwJjmQBEXiVNW89mMi8C9eN6lDGjrW1sb3HjUYbxdR+kM+LXYTNyuNE1PY6Zb+3uKfnO+Hp8DEf3e4HqWW2LkSqdyNXQ9ZgBDN6hOY1vm/uy+OS1doseaixWlgnT7trk8h7ZjmDYGegupqPkeX1UVjj76aHznO9+JdNTf+c535hWItrVs6dKlOPDAA7dqmwuKzgOsYOyOO+7AQQcdhKLYLlhshzSKhPiX3Q7BmPORzYfBKN2EpmuNnnPn8Ac8RW+Z2mB64wykFD7aLrXaGHQZA0bAYtRnR7efn35/0HV8brfDscRF4tGEXHdr9GqDkUpFwI6Dt642kbj2sysOgxLCnzMFGF127KiSqI3BlLvetgg9bsQ05cBU7nzzyVG1EKNzcddf2zlz2ENK4cdbuNc02XP2j6Ki/HGOeSpG40SPALzuBoBLPhlrpYhJorqGgEvO6b7juqs90OJuuGLUTvp0n/I6jrkoRAI1BMyJCeLaplxZGt5Gm6WpDzxT584TJwHVvqRJrzYR89kZLRruP3+cc2Vvq0i0uVYQIJtrJnyuc0otBetzBVGpmzEnvh/aYNtW0Xn/fe/Wic571F7zi877/Oc/j1NPPRUf+chHcMwxx+Cyyy7DP/7jP+LGG29s5IpcbPvUpz6Fb33rW/inf/onjI6ObpU2541+Nm/ejL/8y7/Epz71KQDAr3/9axx44IE466yzsHLlyoYK/5FuOQFyjomg/XpWDBO1wXUtvPZWbUJemDSqrTZwAIoxVgBelQAmwOCy5Yfi1U4g+5L7bvLaEC96VRJ1vx9pnwi4dbXBS+7LRyZxNqhNT9TcDwCC1im1eOzyAIqMgNTWBlC5Ob2ZYqGdbcppm1SlPANSTVjww/NKRUkpSxmly+DgoRwvQwoENuBFp/AZqOkYoQRkKVGOFn47FfS1x/d8G6pSNpHnaOHP4fteKp8ENAegUuBHE3Q5WthrqU3UNzp2LsYZKVMbCrpjtQZZKaSe9ik8gHBfjLDkp8TQEdioa+sS1XJuoGJr2PN+e10j8WdbyadcUt3ZIiFzn6fi9bkCqUZAgRQNRopHOwI2RcS3H30Unvuba2dtf2g7v/35n/851q1bh/POOw/33nsvjjjiCPz7v//7NgdQAHDyySfjX//1X7H77rtj//33R1mW0efXXjv/e3LeIGr16tX45S9/iR/84AdRDZrjjz8e73jHO+YEonhWUG5CCHQ6HRx88MF4xjOekXVJbAvbGtlV20TA9OBu09ikwKFsSb6XCtX58cGFN1iHRedKHSkEztIM4byt2dxlOY0Wf50em+4Ti7GbfR6kbQqsUHv/5mopyE3ZLWKcaDsxdHy/olTZlT53zxWdIgIVacHidBLl71Ulw3EDvnAbRh9MKolqvILR7ZMlB/CqUijHS6ufQgyYiDnjx+T6zV16BBJ8fboWXRXQnosqtbpbR2H+KYBKLRLuOwBVdAqfsdyDhN5WEMTNw06663p8bc/HohorW/dpq0owKJnpfFNoDDLOMJKRHIFSH5C7kgIJACuoV+X2ebY/kk0bH2y8RW0sxF772tfita997ZadfCvYqlWrcM011+DlL3/59hOWf/WrX8XnP/95POUpT4k6cPjhh+P222+fUxuXXHIJ7rvvPmzevBnLly+HMQYPPvggxsbGMDExgd/97nc48MAD8f3vf3/OpWS2lm3t7KrtYCmdkJtAYpTtQDlc6CHY3dzzwmMS6abgwx+LAJQ+tPQQVFLg1Q/c4gXf08kkmgIkCwoC+OMuuhwLRSJyDmZSsXiOoZvN4ki82F02W8Qb7TMXVoqzV9w1mAK4lOWSXkvD8vswVxlPkkkLBFUqVMvLSMhPk1I5WqBW7TUG/XmV9HXd0kzm3KSSXm9I2h8LftBgP627TzXYT5vslYGlOoAm0vUQALRuxgL9qX7kXqvGq+gaAtsVciLR5949x/ZtSxLK+569fgZwa2Nd53ws6DooGpaL7unatqW9cO2NkcCdC8cXCob8tTLw0yreR/geCAilrmUgAGWlFBRCFQaKPuTnoP3pux7atjNjDBao3ona2JntiiuuwLe//W380R/90VZrc9538n333Yfdd9+9sX3Tpk1zRnXvec978KQnPQm/+c1vsG7dOtx///349a9/jSc/+cn4wAc+gDVr1mDPPffE2WefPd/ubbFdfPHFeOUrX4lXvepVOOyww/B3f/d32GeffaJsqwuxAB4yrJJpAih6nmk2IQH2Ad/d1PX7krYjBShpm3xq5XqkV6y7uQE6pmqNrrYapanaNNyCtQGm6nY3Xtt1pv2YzbT7o2vh+q6YbWvThMX7DeoXt7ZEnZZxEg48hc+lFChclJVQwrMspStam07yNIlV4yU6y0diAOUu0Ncwc64lDmRIy6RKhWqijNie2dxg1D9VKoytGAu6KNa+Pa/yxXotQ2P3t+xDMydRM3pOetaKf6ZKFU2gIa+UtJ+5QsG078hkZYFoqULG8Uq64r6i0W+yFGjRdRelsvoz+k4J4Dpg0psKbkVTG+/q5FnNt6Vt2NTzdRAXYnTvzHZfyDKI8YnRpHswBZBSSa+JA5qaLH9OntOslJH7mOv1hja0bWX77LPPVtedzRtEPelJT8IVV1zh3xNw+sd//Edf0G82e9vb3oZLLrkEBx10kN928MEH48ILL8Tq1avxqEc9Cu973/vw4x//eL7d2yKj7Kpp6ZlB2VVnZmawYcOG6A8Iiehy7FBb6H66H4EGrW25BfojPVIb88TPoRGDDXqWfmjpIX7f16+/FR+YfAw+MPkYAMDp99/SAHUWVGn/P6UqyFnK0KTjQEBKY3CyQsn2S/94u/Sarns2oDRon9xcw8eC2L+itBNy6YAGX+ErB6jSCDavEXKgJJ1IPCPASsUQaJAMPKhKoRy1BW8p0KDolJAOhHiQVanG5KlcyY5qokQxWkCWCsVogXK88i4WPvHS+UeXdzwgSnND8b4DdsIcmawwtmIM5XiF0iVtpfMK2YzsIyaI/opO4ftXjhZBAO/AFI0hv0YvzHf9k77/Kro2qaQFUxlXuW2b6Q679XYDUID93W3q6ShXHGfN2qwNONE4pMCHR0/6fTP3KL8v/fs5ADxVKVTjJaqJcgigtpORO29L/3Zmu+iii/DmN78Zd95551Zrc94c9QUXXIATTjgBN910E/r9Pj7wgQ/gxhtvxNVXXz3nMi733nsv+v1m6HC/3/f5JFauXImNGzfOt3tbZAvJrnrBBRfgne98Z2N7bQAwF1CbFsh+lgNVzc/4sdxrM4j1yYuzbfsfXnYIznzwVgDAGzb8GoAFV69ff2vk+iKmaxBw4vay39+MT+xyaFYL9eoHbsGHlx0CCOtSJNffbAxVmrAy2j8ZM9qfuxHpuuP9mu655nnj7UVS2y5ysTiXC03igVVqrtTpc5r4eWFaAmMSsqEz4m4UYgRUZYGQqTV6U32YZDA5i0XHFC4aTSoB4Sa4erxGd1PPJqN0AmQCKmQSGsZHwDUzhwsp0XGAC7Dlj8rxyrNX1NfuQ90otxMBJw+MpEBn6Qi6D3VR9zRm1s+4/FIh9xH1z46FgCwVeo65gbJjWlKUI4BCkcuzjgoAc20aifp1rQe6RreVvWLdzfjsisMw3deYYNsHaeSAfMSeB1/Mc0vfs1ACyqnytdRRCgzjyj3RQoFcx3506jj1Ao9y5OeBCgEQg4qdD23xbCfHQFtsL3/5y7F582YcdNBBGBsbawjL77///nm3OW8Q9dSnPhU//vGPceGFF+Kggw7ClVdeiaOOOgpXX301Hve4x82pjeOOOw7/+3//b3zsYx/DkUceCQD4xS9+gTPPPBPPetazAAA33HADDjjggPl2b6vYfLKrrl69Guecc45/v2HDBuyzzz6twu54AqfJm7fdLgbnwIpHr+X0RYNYqmZfgr1+/a340NJDMFlI0E9uLm1xI2DEUyeQfXjZIVE/p30erbzGCQhuNNqeapP4VNcUyfN+xxF0XOtEYIve8+SLQevUdBvlJqsUWPCaZTw3U92rGQiQfuJPBdu+9x6gBdARAICGUBLVRIX+VN+3Y7SOQJxyzBO58ARjXmg7lUDJl4qRUBWgIKGZIB4gIbc9H6UvKDoFahaZR4xE0SmAFSEZJ3cTCp8oU2NksoPupi6EFBZEOYBHYIyDWAJEqpKN0jDdh3p+v1xxXkofQrqtumvHb3tHkF22/FCMM60WAcw2o6CFdB8OWngQAAAPpuHuU6VilytPn8EjF9vOmUsIG8T+sQ5uaNvOtqewfEexv/u7v9vqbS5ILfm4xz3OpzhYiH384x/HqaeeiqOPPtojwX6/j2c/+9n4+Mc/DgCYmJjARRddtOBzLMQWkl21rfAi0GSTaJKei7i5LQotp+0hZikFOm1pAvjxxDzlPl9IaoCPLT/UA5APLzuk4QoL7QdAxfuqhI0g65omMzQXIEciehqT2NqvKU3kGQHT2qB0UI0nhaSVecpqpBN4W2SZVJZp4i6psiqiqLI0jYF3pcl4la9tRyNWKm2HM0tx/byYyrMiYh2DUy8iEkAZT8jUhql1BJhqbaCc1sYCPqdL6pQO3CX17pS053UJLq3bT2JksoPephnbh/ESwkfRVTC19m0ZN+6d5R0PKqlPujboT/W99idlTWh8LYCyAGN7AyjAMrcfW34oAKDuaVS1wfikfd7oAfxtyvTkNGsAABUzlarFTVhNlP770rX2kZ5t93YKtExt0OvWDuTu5DPx0HZaW4yavfPmVJVS+N3vftfYvm7dujmnJNhzzz3xne98BzfddBO++MUv4gtf+AJuuukmXHnllR6sHHfccQ1t0mIbz67K7Tvf+Q6e+tSnzqstAjVcDB0+S9MQtAukBwGHIHS2DEslA2MT5rxmtFpwb+UfmFO1Rs/rmOaXKoCfd1xJTBYSSsC5CLlA3ODMB2/1/SO91asfuAWveuAWVNJuJ3djzmYT66d9onPHx+aF+NRHz5j1NTa5LNeUTZryOJH7DQDqXu0YE+taI0aD/mzGbB1F6hG7AoRJiQMeYohIG8WF3TTpUxv9KYosk5EmqBgtvMjdApUadS8c2/jr6ZiJUjL6o23UZ39MHbepHWuU6peElI2JXvf6qHt91oc+jBc3lxiZ7KCaqDAy2UE53gnfdTfkhCLgBMDnnjK1xtiKMTzzmp/6sZWljFIs+FI5OyBD8qoHbsGGvsZL7rsJXW3wwIPTjQLkZLxwMa82AATBOLdU18b34e+pLV8upxuDdCC4tOmepD7434u2iUu5eH9o284oOm9L/3Z2u/322/G2t70NL33pSz2W+da3voUbb1xYkth5M1FtgzgzM4OqqubV1oEHHgghxA6V9fycc87Bqaeeiic+8Yk+u+qaNWvwmte8Zt5tWZBiIjCSuvOCbqfJInGbjRkKn9l2a2NQuvNZQJJqq0Jbn9jl0Mj19tcP/QYfWnoIJOBZJSUEPrb80IFC8A8vOwTjbmJs00/Ruak/BGqUEBEj9uoHbsGHlh6CDy87BDzzuR2LAIxCuoUmc0esVO55nRYu5v3jLj56T9bVFhhXUqDu99Ep4uzyABqJSnmupdCQ/U8onc26TRN6MRp+F8RwUV4nISmnlL1SYib6U/1sCDmPOrPnMpG7kBsBEcP0M5FrkVie2rFQDgzG7Yfs5CW7Dup/mswxJMd0wGm0gNGWaZKlZdfsNVu2KrjdWD4tJT0bFr4DgekHpvCTY58esXk6Ayr4OO9IRosJioj93G6HY7QnBuaRAkJqFG65lAYc1HA9X3pM25gBQReVJgflrFd63qFtO+NBOVvSxs5sP/zhD/G85z0PT3va0/CjH/0I559/PnbffXdcf/31+NjHPoYvfelL825zzsiFEmQKIfCxj30MExNB5ljXNX70ox/h0EMPnVNbO3LW88XIrlpF+oQUzDQ1TnlXVM5dF0BD+EyA660AY2uiRbqpuN3pjKObAA13z1UDtBhpv7hdtvxQL/Smz2sTIgSp7x+YfIwDRQLnbPy17+uZD8bAzUYRMr2L5gDKoEYYF54jS8P69Gtjou2p8VxUtYnzddHnpDne1NOopIjSRigdAJjWNdALuirKueNdMTUAKq/Tq1Fv0jaazU1IvU29bB+JRfGaKnZFdV03Jqo0xw9Nrlwbk7ogA6sloNxNQzoqvp9ND8DzOAXXp90uXRJMCdQGdcujOAVDNmO4vRZdd6PJn8ATdx8az8I03ZhFp4xza5UKRScAXCp3Y92rO77o+SX33YTPrjgMo5t6vmRNLst4LucWMDuQaWOK+PcbbWf3P7FX5O7m9x3PvTW0oW1re8tb3oJ3v/vdOOecc7BkyRK//bjjjsMHPvCBBbU5ZxB1ySWXALBM1Ec+8pHIdVdVFfbff3985CMfmVNbWyPr+WLa1siuyt1auff8dc6Vlz5jUrdcqq0KmiKAgFTQ9zRBWjTpCxGVfuFGzNMndjl0TmwUtyv2eTxOuuv6EJHHjNx1BNY+tPQQf005jRY3iiS8zGlF5hJEpRy2zK3GOMPFWS4a76laQwkRMXo84WgqgicAxoGe1Y+4gs3dOmJ16m6NugoZx3WtYbqMzalNgykA7Mrfg6BKQcIyNSQcTuvZkciczjEyORIBKe5usxFsdn+pQ9HjYrSAcEyNro2ryReDM4CisRyrA40agOAamaxovQnkAMB040SgKXDz7Th2TCqbgFYqy84RGOMMHo0LXTeNWX+qD6lkq7tsR7KX/b7J9v7byiOiHE5AAFApkCIjwBP2H+yy4cAMaIkEVHEh5xQ8DdmobW/G2L8tbWNnthtuuAH/8i//0ti+2267Yd26dQtqc84g6o477gBgEduXv/xlLF++fEEnBLZO1vMd3Wo2h3IAlbJM6QScbuOTesoEpULtFEhxF1evkasqPRZZIPWx5YdG51EibKNKGK9+4BZcvOQxDgRZN+Kndz0scunFIE54YEUZ1Amg8GucDUzxvvK8V/maenbsp7WJGKgY3A5+QqRJR+kLpvGL3bLhdcWKNdfGAqo0qSZ3gfSn2CTD3CderKtEVMDXA7JKgTKPc8AUHYvg5upP912JE+sWm9kwA6EERpxwWUjp26t7tRdw88LUwZ0YR1zZbQq+/lxlXYOknwJCmRVi0Eyt0Z92mdJbAFdqsXsp7yYM/QmgDi77P+WCUpXCyOQIRiZHMLNhBtMPTLeec0e259/zK3xx98MxudRqxrhGai4Ailtbceh4nxBVShbn5IrbHoKn7WfD6Dxg2bJluPfeexuR/7/4xS+w9957L6jNefPW3//+97cIQAFbJ+v5jm6kpUkBFP+MtvP/m68Hnyet0dcUmrv9kAcXQND5pPaBycckQC0GdzT9fXjZIRhVTabmQ0sP8ZFFqX1oaYjc4+kgJCxIS5mr2SzXT35zv2LdzXjFupv9tlTrlMvVxd/TGBFbxbfRPoMSeIZjjc9C7yc4EpcnLpEgzg5Cdt8nL04Pbis6prupa/NFMaAWtaMDmKJ9+tN9KwruWTDF+xHYq3COvmufwJrmYMqBMt5/6jO5CLmZmgTp2rfvS5I4xsn2NQZHOjNGQfBfN4TU2o+HAY/oM3V8jtHlHTz96m2b6Hdr2sm/uyliB+mPW05k3gZ4Us1eSMKqotd0Hp6QNd9esz9DG9pi2o9+9CP0ej2ccsop+Ju/+RusXbsWQghorfHjH/8Yb3rTm/CKV7xiQW0vSM393//93/j617+ONWvWoNvtRp9dfPHFsx5PWc//8i//EsDCsp7vTMYBFFlzoo6F0Xk2JX5PtcCICeG6G5tQzzCWJKwi6NzEinFtVpqA87Llh6JsMF7p/7HbqjYh0WfPWDA2riTAmLZU7E7tca0SaamAwVF6/HM6htooHUP05T0e2wBOYVzDRt6nNt0UbdfsmDSpJ7n7lBCYIgaI5/Vi4Ms2aiBry0bVvbpR2NU27M7XDQBMKcv2wOmLTd0sXkwJOukzAOx/7dtLJ7agEbIFlJVSNrcVVHQsAKdbMlDS9k9wAbxSHlRZobhjucjdp0QE+ribjcCVrrXPEUkTdRTV2Gu6/HL1APl3KGTQXKELH0kolMQP//AYHPv/rsbOas/77XX43G6HY3I8ZLUfVKaHjNy0jTJA7v+cqHwu7NIQNG1/eyTXzjvuuONw77334vzzz8eqVauw9957wxiDww8/HHVd45RTTsHb3va2BbU9bxD13e9+Fy94wQtwwAEH4NZbb8URRxyBO++8E8YYHHXUUXNqY2tkPd8ZLE3YyCO4gHxeJyqKqjPMUMo6UfkXDqBk6TJS1xrdzVaUTGLt2hierDjjVhTeXbep1v5zLqxWQngwxrtIoCJExBkWVRfciQF8tSUiDX3kEYlt+axSG1cClZQ4+XdxXb/P7Xa4B32vcpnSB6VI4EBJJttTo36TzouuOb2GsH/Qq3GwRfl3AEAzwbQXozNtCxm594hJAuBdfd5V44Jmc0kauXuN9pFK+szfXBhMgIrAEc/oTecyOjdCLomoTpkkC8y0Y47s+aVno6JjIwBEiwQ+gcuGC9NfU6T3CYJ+6jNpvog96U/3MbZiLHsdO5NRJN+/7/8HUIiBVC5Cj0wmgInvk0ZzzlbyJad9G7r0to89kqPzCPyVZYnPfvazOO+88/CLX/wCWmsceeSRePSjH73gtucNolavXo03vvGNOO+887BkyRJcfvnl2H333fGyl70sEokPsq2R9XxHtxRAkbW5fQhAUSJE3dNBiOyMXkumseHtU22yuqthegbVWAk53Qf6GlMOwGkfvRcLqAnYgH1OEXIA8P6J+CYjoXV6vRyocZCSarDStsg4ALGpBAKT9oHJx3hReZuR2PazKw5rpHeojQWEn9vtcLxi3c0NvVfat/SBoVte03HUdz4u7ck9eUCBCOwU0wvltD1APDFpaKDrJiZ3LDFBth3StdhovJRx4pOqhoaSaqCouu7WMI4JSvvHg00oAagqVUN3I6QFPIJYtNw1MjA0aMKP0yk4JoX11b7IlCBBiGKsEUroUGbzh5OZ2thSLgmvOhcwM2if2QBUcOs1owJ3xHxcQ3t4G5cKHXTQQVHt3i1q18yTn1uyZAmuu+46HHTQQVi+fDmuuuoqPPaxj8Uvf/lLvPCFL9yqhf12RtuwYQOWLl2Kf9ntEEwUBbQ2noXK5TrikywPVSZXCnfX0XGpKWFruhUdW+iVJkFVKkxvnMFUzbU7tr1NLuJsUP6prjYRkErtA5OP8UAlvZa0/AzXP/EcTaTd4uAyCM0DCLKFjw3esuk3s34H3370UXjgwWlM1ZqBO0TnTMdyrq47/hpsGxkfh0Guw2ZZmwCkOVj25yCtCeXiYeCB138DGCtUU5by5pVR4Vhe5sQm84yTTnp3TuISyqVQCNoYEbkIVSUhHZjibBRFEgKImCf/uYpzZvFzphFi1Afefsqy8WNS1xZ9RmL7crzEyOQIjvzytxv92hntmwc+YWCKg1T3lLPo+AEAKvc95Wxn1p1tTaM5Y/369ZicnFy09m+4424sWbJl7W/cuAGPO2DvRevrYpmUEq9+9asxNjaYYZ6LHCm1eTNR4+PjmJmxwtOVK1fi9ttvx2Mf+1gAtoBvm23YsGHO59iZvpw2K0oFmDARhsiu/ASrRHCX1LpurNQ4CAnHxI30p/shC7PT1lDbBEaonXEl0TMGFSsfkhYdrqRwOZnC+bhLjVghSogJBEBRSuGi94zXBNF1dLVmYCa4/P76od94xqs2QEdaVoFA1yAARazS6fffguf+5lp8eY/HojaCATZ77T1jHBtnr7EjBTbVBuNK+GhDrgFTsFGIfCrIASluaS2+9DM+FtYYA+jmfH6c1gZa15B10Apxt5RUEkYb77JJI/EoczkJt1Vl9Uh1L77PiI1Kt0UZrwmM92oPxPi+5F6kQre61tBTmiRbkd7GaqraJ22eJ6ptYk4ZO14Oh3ZrCqhD6gNe041cqN1NXQ8IH06W073Nlq8pB4IaxY8bgKw9ncLQtp9pY6C3UNO0pcdvT7vhhhsGJgRfaFDbvEHUU57yFPz4xz/G4YcfjpNOOglvfOMbccMNN+DLX/4ynvKUp7Qet2zZsjl3sq6bq9KdzXStIUub4G+670TALRF4/hjmziEGi1t7HTw7WZbsYUYs1iDryKQkTEtqADp3bYwHVWkB5InC1tKiHFI8XcL7Jx4dia7fsuk2AMA7OgehkoGVe//Eoz0LpYSI2KGJQvpafFO1xl8/FADVh5Yegg4reQMAL/qfG/HZFbYQ8qbaNIAP9X1Tba9rE6VbSlyvSlhQ1TPh/xCUH8y3nQCnPJBqDHPkSvUidZ0ALvdeN9xtzlUndRCag3RIBt1NPVvwl9pyJTs4Q2UyHaVoNgl44ToHLGk2bJ63ikAI5aTqufxL0VjBJgn1QvOM5QBWOkG3uT5J75R+YpyAPI7cYwV0IdGf6qMar/DLk0/AH3zxW9m+7Sz2zQOfMKf9WuvrIQZOuf040FaV9Bn5ZzvH0LadGcyWxGVubeys9pWvfCWbFWBLbd4g6uKLL8ZDDz0EADj33HPx0EMP4fOf/zwOPvhgn5AzZ9///vf96zvvvBNvectbsGrVKh+Nd/XVV+NTn/oULrjggvl2aYc0qST6vbrV/cb/B4LOiecT6jLwNagsTMRasLB5vi8XMxNwoZxS+T7aCT2An7wbCgC4t+iy5Yeiqw1ev/5WnD92sN9O7bx96jacN3pwlKySNE+2LQuGeCqC2gGY0p2vI5U/T20MJgvp+8bzU1UyRMZRtGCb1cbgDRt+jQ8tPcRq0xKXI11DG3jiWdFh/D8RIxWX5uHsYupi5Iwg72MYk6gPnqmK690RgMkljxS18J/zbXQdXDSuYVnOHDMTRQFCNoBUmvTR79fGPmndKK7MQ+NzZovhhrI3zf2SRRmxT+wcUknUdUiC6msS9gZxjjuXtSW+5Nv4fv6zBEANcgsSMKf/Sf8G7JjldIb2yLDFTJ00b03U1rBnP/vZeNWrXoWXvvSl0fZ/+Zd/wWWXXYYf/OAH27pLW83I//yFlYdD9WM9EAcKqXHWiXRQOWumQmjuQ21FgMwBDgIpvDhyV5uEHQjtTid9CSVWwjbeF649Omfjr3H+2MF+2zunb8d5owdHbkNyM5Igu5ICHRkmOXpc84Sc1N7p99/i6/WNKhHlbZooQoTex5Yf2kg2mgOFtJ3SIrTX1os/S4EU34+/p/bb2qXP+eu5WArGyYjNbEyKLROZkE3QwydecgsCebF3w1VUtrtz2kqA5BipFERxvRcdnyaE5IkioyK53FXJJnf6jCcT7U/ZKL3xPcbw2E9/o/VadmT79qOPai35kssePkj3xL+HQeVb0ozw3Pj4H/P9H87vYh6mtq00Udfe/t9bRRN11EGP2ik1UWvXrt0xmCiybreL3/3ud9BJ2PK+++4767FXX311tkTME5/4RLzqVa9aaJd2OMtN0Eo0UxVwBoo+y7FUxCSF9mIWI22XR/HRpB4AHE3WIWdTqv1RQmBcCWzo6wZjFZeNQfSagNn/f/zR7HOBd3QO8n0YVRJKhIg2ih58qG9wvzbYs1NEbNREQftT+zYreimC+48YHhKlf263wwHYiLxev11LRgxXsxxMcK8BQetE1+PZJsRjk9O75dod5Naz4xiL/nNFlpGcu66bIDe1VnG4NqGmnzOagCnaLoU4qa4ojYKbixi9jaHIMVC0LU3mGAAB1euzryU0oAQUZDOdg4smFNIK0qlUjC+krA36072IlduZbL4Ayu8zADylbfhIS5l+1gTDPIXH0LaDma1QtmUn9ed94hOfwNKlSxel7Xkr/37961/j6U9/OkZHR7HffvvhgAMOwAEHHID999+/kUq9zfbZZ58siProRz+KffbZZ75d2iGt36ujyS5lnyJNR0ukC2eUAsPTFCa3sVapVVJE5yJg5UEY2zftO2fTiBWic6eRf3Gx4TiDOm2jNkaVRCUF3j51G/76od9g7XQfb5+6DWs29/D7bo0NfRthN1IplJ2CaaZsFvJXPXBLo11+fWSkmeKuwjSLu0QsKg/9j6+bR9OljKAGGmDXjgm9boLP3Lwy1+90LsazeTei2ZITGW38X2ppDT+gfRKmiTgXyj6b4JiHxvOs2CIBTULJ6E+WCqoM71Vpt/n3lUI5WviM2VQzz6YVsSkOKEKRrqPuavQ2dfHrM/9/A/u8o9m3H31Uq+stBVBtWc35mLcBKABujMN3xrOWpxnO+bgPbWjbyk477TSMjIwsStvzZqJOP/10FEWBb3zjG9hrr70W5Gu85JJL8OIXvxjf/va3vRj9pz/9KW6//XZcfvnl825vR7ccgEpzQAEs31OSYyqwSXE7KVuVCmxTXVR0Hh2EzEDQSfE0DICd9FNwFRiwwJhw1oRrpbpJ35UQmChk5BbsauCNxQG4qH8H/q6+AwDw7pm4huK/rTwCSilIKbxWLLXAEHGNEWfw7GtiVJQAVHR8+/i2ZZBPMYL029tTRwzSZg2y1G06yPznmXxivCQIMLh+2lxtkF6pbb/0MyFlNPEHF6JkqR00BAM5sa7HbXfMllASlLqK2Cktg+uPjq3Z9RM7ha7NwUWi+O6GqfkNyHa2NgYwB6D8MZlkmG0gbBDzNMjVN5AiHdqimoaB3kIqaUuPfzjavEHUddddh2uuuQaHHpqviTYXO/HEE/Gb3/wGH/7wh3HzzTfDGIMXvvCFeM1rXvOwYaIGGQGoNgYqNQI9qcsozSMUnWMOGc+JTSGmxCa3jJkcOmclRaO+Hj0PR5XEVG2ywCGXuZv0WOH8Bhf172i9/suWH4rlpcS4W8lWmbGw7XJdVjtYaQOFaXvpPnSOJpvECz8HGwSkBp0rHA+QOzF13fL+zMfmet/l8gA1QM8srqDZJmO73U3cssmC8DYpiaaQKjqWmCogsF5yNDzWVKVspCpsvqrUNWe0gVDGRzXaKD0JoQykO0YqGdUT3NEt58bjFmmbMt8X1zyRpbq13HebF6wP0xzsKGa2gjtvJ85wsGg2bxB1+OGHD8wH1WbXX389jjjiCEj3sHzUox6F888/v3X/G2+8EYcccgiKYsGyrR3CcuxBmoV8rmCKWy5SLLfP3PoIkA6HWBRuOd6HRwwS+OpqYpbi6DN+Hgu4NKZqjVElARg81MIskZFuSpUKWmqUSqBmhWyLUnn36aBSLtQHHnGYc6nxdniUXDo2ORcdb6PNUp3Ty35/s0/HkPZ10Pv0emaz2VJekIkMOJ8vUzVokrXbmuApC8qU9PumkzMXulPup+a1SAilbVoKJX0JHMCyUxpBmC6U8GBKKOFL0dRdm6vq+peciMd/7t/nNQ7bw9J0DzmmMQegBoKh5HuaC/PEGcOhDe3havNGKO9973vx5je/Ge95z3vwuMc9DmVZRp+3KfaPPPJIrF27FrvtttucznPMMcfguuuuw4EHHjjfLu5wNkj7MhuA8onrMmwU34fo+5D/Rjf2z5WKAUINvlAiJTBG6XWQpih23yFKWWCPjY+ppMDyUuL33Toq3fK2kYMarrvUHupr/L5bo14/jUoKlJ0iyom1eaaPTiGj600F+LycTFr2JifeTvNFhbY4s0Zb825ADrLakm8qITyAykVcNtywCKCk7yZ5nuV8PswUZyuipJraNCa+WEBu3WVpPTtK+jmIxcpNzCng8uHxjYle+jZyIE2V8eOMou9UWcAo5sKs6JqEd/3pmiUS7daQkKHsjNYub1T8rNuRLcdCzUW8zzPUp59zN3DeRZhPzDlMa7BjmDZxvdOFtrGz24MPPogvfelLuP322/HXf/3X2GWXXXDttddijz32wN577z3v9uYNoo4//ngANk0BN2MMhBCtiTKNMfg//+f/zJp2nazbbcnW9jCwQcAp//ALQIpW1P1e3WCxZOkmMSVQlAV0T0M61yG5uOh5xs+jNP/OBGNcRMS+lGlyTvAIwWYuKwIio8oKsH/frbFmc1yb7N0zt+M1Yn98xNyZHY83lwdiqQuVf6jv2KvpftQPSmXwtT0f67VPqY2qXHFgAnm8zyHnlL0+214zR1MOTKExziGdQezOpLYJZM7F0qSGqXYOQDNB5yzGJ0YeTWdqA1nKxucAfERd6Jfw2c4JSNF+zfPFkXhtk/hczTIqAXRxzVQ4RxxBFnJ6WYYKStkktypECmqA5Yyy19ef7uPWV78Ih1z25Xn1cXvaXFywuZQFOWZKKdUKmFLgTPvl3K1pcMLQto0N3XnWI3b88cdj6dKluPPOO3HGGWdgl112wVe+8hX813/9Fz796U/Pu815gyieNHM+9oxnPAO33nrr7Ds6O+aYYzA6Orqgc+0oxp9XjfQDCR3eSHyoRDSp8VIKQgkUUNF7LwplbIXXg/Q0CiC7khTS1mvziRlBjJUFDynIoNekkUoZJ/s+BlTWvWewvqez2qc2AAUA7+v9FqurA1GbvgdTtRFRKRlu5Jps+yyXLLQ2yET1hff8WnNMkm0jsFtcY0bb0uCCNJkmRRzmLis9J33HjYK/aUHg5Li0cDU3ngjTb+tRqgARucqiLOUMeLQBKZ7ZfBBIas1fxSNZB+zj3UfRb8sCK+lYKlPHYIquy7NcFj5BloDu1XbCry0b1ZvqY/PvN+G2s16Cgz/4udbr2N42W2mX1JXH9+Hifi7sTy0n6jfR80t6zZo391K0FNYe2tAW28455xysWrUK73vf+7BkyRK//XnPex5OOeWUBbU5bxB17LHHLuhEO3MCzYWYTNIJpJZbIaaZnP3q0DFM9DmFkPNkhumDU1V8ddnMGh2VvVDwZT3qXg0FoKgNZrp1FkR1tYkScsYaIt97KGFzMD3Qq3HfTI339X7bOh6D7IKuPe78sYN9VvGcDiloo9y1JGJxAkuxQJszSiyPVwI20iLJ7WAK4MzVqx64JdI75Vx2tG9RKhQIbjpu6b0U1azTQc+DTCHf1NrcujnjLj4672x10XxuqRRI8XxTGTA0W94o23b4jFgomuRNMjkPEjUH0OW0dRXVDnS5z2tjP69tjUDdq6F7NXqbelh/x+9x4yv+1w6bgFNVsjUVBQGoHNOUA1D+eL84azJRPrEpA9e5otcEXlVLiZ+hLa4No/OAn//85/joRz/a2L733ntj7dq1C2pzTiCKi8Kvv/76gfs+/vGPX1BHHm6mKgVpZsmHk5kYc5oSLprlWaVTXQKfYPxDMqm3WHfDw5BPZimo01KjrDVGKhWVr7EicmJZ2DGtGcCxRQCK21s325IxStjXqZ101/X48h6PjTRozYi2vJic2LOSGIlMQlR7THNyT6+Zz1+f2OVQJ6CPAVjcjo1s7PdqlJ3Cs4ypEaOTCr85S5Tm32nmaQplgdLoztw5uRZqLgLhuaZL0LVxrI90vQogix9vtGFFlFMXUi67+dy28TaM1n4cLTtlgVStg2sPsKVwelNTKEcL9Kb6+PmJz8aSvZbg0I9/ddbr3ZbW5i6bD4Ai4zq03Puwn4gCXZqlY2Tkbk0B79AW34buPKDT6WDDhg2N7bfeeuuc9dqpzQlEPeEJT/Ap05/whCdACIFctZhBmqhHmpnaQCSZG7mmJBcBxYug0na/gnS1wXKrxLB/0CEADnAlk64qFepe7QuFkoDUaBNlFJawyQeNNigQGBICIpRXCkAj9QEQAEpvK//o3j7VBE/cutpgVAnvuqJ+5N4DYexlraF0nrXzgIO59FI2i7NUo0p4nRZlTQfiZKPcAstlCUFZhkCB1No0Ld5U3PfGvmCBCpnjB7FMbaLxXP9yovXZGCx+vH8/QJhMOqesDsoBLBKX+8zajr0yWvvjvMsPsU5K1wZCKui6D1kq79qruxp1V8PUBg/89gH8/MRn40n//t2B17Wt7Gt7Pha7HLAMUw9Mty6QyAblc+LbVBm7Sds0bWl05CCQO0x9sO1NGwO9hShoS4/f3vbCF74Q5513Hr7whS8AsJhlzZo1eMtb3oIXv/jFC2pzTiDqjjvu8Cjtjjuampah5S1dVc82AaaV5cM+djVcVoVnm1QV2IpcwVDBHqAceFEot2HRSBw4GcVETl0AyrJSRhvIWqACvD7IFvglLU1IhVAKgVElsKm26Qu2Bgs1V5uqNSqpskkmG68jl4SEVLEWjXQ8UllQUJQKcK42cvulZXXIvrh7AE/0OZCvm0if18bdMzK4bNPM4YNADL+/lLJ6uFyUXXrtufFotE3JLh2Q4u45AJ7VzDFJvA+61pGWq9mHmPEwrK6jRABLXn8FHuXX7kLKXhNzAwZNVNBJkabQC8x7NWqElAczG2Z2uMzby/adzAJoycYzZbC9RixZoMlSNQBWCqDS3xAwO3gaslBD21524YUX4sQTT8Tuu++OqakpHHvssVi7di2OOeaYgSmXBtmcQNR+++2XfT202W2u7g0OeoDMwyrdX4ZJnt5z9qJtQpRKwLhVI19F1rWdcAmcEVMFwIMpK3AHwNx7UTScAcYLidoAv+/W6OrZc0BtbTv9/lu8C43E2mQp++S3lzLS/OSMJphSFehN91lh35hdoUSqsxWIzumSpDuea91yrt1BFtWTGw2gmwrxSiU9E+nP0cJ60WcABo4Pv7+tm869RlOszkEqufQsm2QAZVkee7/HJbGpnIs/R1ZTFUTinIHybiTGThEbxY81SR1Q+1sRgBL+eBsZ2/fjoWuN7qYdJ5I4BKCE3zIvY0M22+IuNZ6va2BwwIBISQ9Uy50799/OarW2f1vaxs5sk5OTuOqqq/C9730P1157LbTWOOqoo3zWgYXYnO7mr3/963Nu8AUveMGCO/NwMl1raBNWam0J71Jre1B5Fx2PgJmFleCV7PmKU1XwBVnJitEimkjrbm1F5qWCqoxbfZf+OuqpfpQbalRJPNTXeKCncd9MH++cHpz7aTHt9PtvAWBdaZVzw8mEDQGaUYpk/DUxEd4VKwU6S0Y8EMm1IRV8agnfjgnsFYEvIGiteK6vNl2c79+gSSz5rHT3jXLXomsDUYtG+znWK2e8b5yN4mkPeLRemnuKGCTU5AKCB1I8QtCzTw7AaBkDK4r3IzbK6CQCTwXA1BgjGRgTD7aYW0+57QQCdW0gS4VqIowvsVFbo2TO1rKr/uiP/Bg2Ukewki7ehR0xUpzhc+Ou4yCAHHj1EcIJKLXnkhi67XYcG7rzgj3rWc/Cs571rK3S1pxA1J/8yZ9E71NNFK+fN9REBeMPJvo/98Cd7SHMH4RKqmh/rqeSSgLMJUWPtDRxHhDSHwBsZa7YJFVrKITCooAFcP2p8P0GbZDAXVM9/PVDv5nLsGwzC3UABeq6qYfiRuMRsUrQeP49v8K/rTwCRcf+VLKregZAeEoKnutdoqlxo7NwDRt9V0AT7PH7p1UnRK5IDrpIi1WpCIRHyTITPV7UZgu44udI+8YnXB8R6kq2ROCK7tO6htQW6DdAlrLZw40OY08ycO5+A/oNAfMgd55tK3GFc3dTrd2YGbdNURBrYO92LG9edB/PNVM4v47AiEsGlk3DlZqCNJ6HazYh/2z7DG1oi2VnnXUWDj74YJx11lnR9g996EO47bbb8Hd/93fzbnNOd7LW2v9deeWVeMITnoBvfvObePDBB7F+/Xr8+7//O4466ih861vfmncHHok2V3Gu1enIaBVJEyT98f054Ck6BZTTNKTHpfvL0lZVH5kcgXSTOFVal0qiHC2gayum9a4+QbmNbA6lHQ1AAcAr1t0MINTqI8ygmSuGA9xmmgiFbz/6KDz/nl9ZTZgbO3JzVuMlqvHSf0eclRHOncL/0vQUspQenKXFX/nxIV9PDKDS75F/z/w8/rt05ytHCxtU4NJg0Ln4fRVp9UqVvWdz5UXsdhNtC5m/gw6P8lrZ+6p2741n+Ohz2m6P4SDfAhtdGy/y1rVG3etH+xE7YrT2f2ShLImM/ugzVRZu7CSqiQq+qHGl7O/LjV+5g2UyD/0Liyf+HXOWmqLy+HdJn6XgmcY77MeB+uxgle8z1EVte9OuUPyW/O3sTNTll1+Opz3taY3tT33qU/GlL31pQW3O2zn9V3/1V/jIRz6CP/qjP/Lbnvvc52JsbAyvfvWrcfPNNy+oIw83y7nq0qi85jGDtAYxE5GKQ00k8OQsifTlYOg4qg+WZzionTqqK8a1VhSx1+/VqKTAqx64ZfBgbEebqg2Wj6hGkkke0m/dFvnIMVVJfPvRR+HEO3+Jbz/6KK81oUK2fNwi1i7zPfNz5iIyOSjOuVByUVGDXMRpyDoXdMtS2mSaPK8U+65JWM/7q6EbE+tCIu4aYnkVEnIKJVjUnfFaPO5WCuJ2A+WjEYMWizOqXB8FzD7Z0zH+2FpDSB0i+/jvoDZQMpdGYvsZue2NsoAnzRieY71z29syv8/HhmzTjmW27MuWuvO2Ume2k61btw5Lly5tbJ+cnFxQTWBgjkwUt9tvvz3bCUqjPjRrKTuQCnS5hZV3WKUPsnTVKGSoN0bvc+dOzbIRCkVU9Z5W2qVjIOKJvZooUXQKy2qUCi/6nxvnMSrb3l6x7mZIJTG6rBME3C66rOgUre5VnixQKIHvPv5JKEYdMyTtOOT2J9aGMsaXo0UjgomDJc4A0Rhz9pCzRel3yYFt1K5sfueStc0ZTs+QsfP5a3H3QspwpaYTFyH1i4BmYKh09MdZKc481V1tGSltWaqIqerWTgRufA4pYjW020c7kbip7Z/utrBTdZOV4m4pOt6OacjATSwU/W5yuqttbdc8//jG4snqGaX/3uk1kM8lpdk4ktGxxPy12Y4wBkMb2mx28MEHZz1m3/zmNxdcp3feTNSTnvQk/NVf/RU+85nPYK+99gIArF27Fm984xvxh3/4hwvqxMPRZKmgZJyGIAY/sSuOLNWS5JiFnAlp5biSaWrSlAlWPO764Fen2rsofN+gAT9xCsxssIAjTNox87Wj27rNPaxw11ubsHIw2iDH7nHmxzIyYbWei3Cyk5P0eiNTB9E+pZTIlT2h9glscDcdz9+VmoZuaJ94dGa0Pbm/LOAO76nYbnSvqRDAoJTwAIZfh722AIR4DjN/LY1xbSb05EJ1UxsbJaqELbWiJIQiV2AANxbYsXtRc4ZIoO4CRhmoqgmKUstFj9E4pYkhuaZMKoG6Z0Fef3r7AIirjzuW1ffT0YInWCywp+cEt3Af27ZyiU1zLHkc+TdY68S/h0H7DW3xbBidZ8u+vP71r8d9993nheXf/e53cdFFFy1IDwUsAER9/OMfx4te9CLst99+2HfffQEAa9aswWMe8xh89atfXVAnHo5WdBQUJETNJpUui2ZR+bDybOi2m2hyAIqHiVMIdg58AeRiioXRaSSYzSNDjIN19chSQXa1F63bNmuf/XxHt1esuxlX7GMz6ZfRaj1M3gRauCiawIjPG1TGKuJUvE3C6Ro1FHOheADi2lZVnJk7Ohe7P3RPZ6PmqE+pXo7+T3MX8chM29cQpWlBXN24H7grj4CkrmNBeJtx/RYfJyFtYWyukUqjxgYn+7T9qbtsfyDJfA6grm1qAgRgkLqneKJOe/5m/iKhJHS3H51bKglUwMyGPttv2/4OfnLs0x0Ybma2TwF0qKmY1y/lmE2f0kFJ9lvYMrH4XNyoQ1tcG0bnAX/xF3+BmZkZnH/++XjXu94FANh///3x4Q9/GK94xSsW1Oa8QdSjH/1o/PKXv8R//Md/4JZbboExBocffjiOP/74KErvkW5SSV/LLJrgWCX0XA2xtsknt09OI8MnYSCuYZXL9ZNmhNZuP0vjF+hP951rSqLuavSnwuSRq4+1o9pJd12Pf9//D3zkErlkyEUEUJRScM3RfjXTDVH0Uzr5193aj2+kd3IAxOfsoc8ZzuGAiKIvZ9MZKakisJRz3/nPkvxI1BehbKFdVSr0p/u+z74NzjpBe2BIuZxQO/elA2Kp5dyLpM/j2fH5vvzeTa8lnYh1rRuMVdBNufD6bm3BYsJGpYCKb0vdfCm4sqBb+jEbpGXcmvbtRx+FsRWjXsie3pd8kZPaXIBeVi9FaQ5YFGlznxiQ2rZihio+z87z3Bjaw8/OPPNMnHnmmbjvvvswOjqKiYmJLWpvXiCq3++j0+nguuuuw3Oe8xw85znP2aKTP5xNSCveTevhRQwEmivBnJsGbL+ce4Zb+qDj7kFbzT4wLr4NHoml4PcNbUp0N8UrkKJTQPc0/m3lEXj+Pb+abTh2CDvxzl/iin0ez7Q/Cgr2+vpTFuxasXKs/+FjHpXGyQjzuRGb49MjICQ1JSMXYc4FR0LwHOvjAVoClrjbJmde+8Ta1nDuWq19/jCuUQIsaPMaJhUza1IJoGreV43xYOBxtrELx+QBFC+hxN/X3u3oov4cwKsmKj/ZzyVrdltRY4DciaQnk5hZPzOwra1h3zzwCZjYYxzleMmYvNR9T+A9TqTKwXDOUvYv5Ply7kxtfNBBLrdUajnGb2jb3yjCbkvbeLjYQmvlpTYvEFUUBfbbb7+dLhfUnXfeiXe961343ve+h7Vr12LlypV4+ctfjre+9a2oqiAQWbNmDV73utfhe9/7HkZHR3HKKafgwgsvjPaZq9n0AnbSoNV9ocJwm9r4EiseYCn28GvJLkzGBeRhm/QTG+1jj2f5dZJtuXYBm8wwKofhNC6cdShdeP/OZBy0AMENR64kLoIm91w1bifg3hQxD2mRVjdRKUTjQ2kRKJFpWyJEzkoROOH6Ft1r/t4U4pV+ANmDEhdJf494IOHczLbvhWPZDIzSELUIOYfcdXHQQxohwybX9NpIvJ9mSJ/NckxaznzyTnbOkCOthu7Ze7031bfaNd+HfM09Ak3kKhS6eW4qWAwFCMbMLqZ1lo54MB5nWg/fB4GcBpBlz4QUfAIBrDeLmIdFoC8Y7fcN9182bcRO9lx4JJjGlkfX7eSSKADAl770JXzhC1/AmjVr0O3G1Qauvfbaebc3b3fe2972NqxevRqf+cxnsMsuu8z7hNvDbrnlFmit8dGPfhQHH3wwfvWrX+GMM87Apk2bcOGFFwKwSUJPOukk7Lbbbrjqqquwbt06nHbaaTDG4NJLL533OYtOAWGajAJfOfNJm0de5VMOhAkWQBZAAWgAKH9sazmT9u11bY/TvRrluAWSqlLoT/Uxs2EGUgmcdNf1cxqPHcUoIk0qm6eJwBOBK6PC92LFyRQdpzDaKdGf7kXusaJTOsajyQDlhNSytHm3AOYWgwXdXnfD2AUODDgYSdkwDp5zRukCYvDn8kkxZoZKxZjaoD/di0rHGCUhqqS8EAK75JNqlk0gN1c3UxvzBKQJHcNvJR+ibxqsFACgAlBrSC0hyyICUkFAHh6LsiygEQMlXjZGVSqK1Fwso1JAOUDt+1WHBLu5mnc6M07pflxDBqS6Nek1ff6cbCwGuUxz74e2ba3WBvUWoqgtPX572wc/+EG89a1vxWmnnYavfe1rOP3003H77bfj5z//OV73utctqM15g6gPfvCDuO2227By5Urst99+GB8fjz5fCJJbbDvhhBNwwgkn+PcHHnggbr31Vnz4wx/2IOrKK6/ETTfdhLvuugsrV64EAFx00UVYtWoVzj//fExOTs7rnEIJKKHihwubaGmlz1foOToeiN056SRt99cRJd/GNs1WLDV1rxSjBequ9u3VPRL21hZcSImfHPt0PPWH/3fW8dgRzSfPdHmzqJ6gkAI1apesMiQoNLVB0Sn965otYmSpgitWC/++7tWWHegUtoBtN4SR0/cdALFECp656w0I4Js+920oiaIMrmMbecjLpMQTJoEHqQSUA0qk6yIpPSVhJQaKovqMY2Hqbu2BaN2tvWicuyYp/9Mg/VyDOc0AJaujCr8XntQ0tJMAVkW5psJ41F1iVWfPheSF7mUBI0OiTlGH781qBRc/bbmq7LOkrW4gt6bO0t0rKgCctJ3ZAA659/gzJJTpscaTmRqtIVFY4NXrRwxVbtyHNrTzzz8fV1xxBa677jpUVYUHH3ywsc+Weov+4R/+AZdddhle+tKX4lOf+hTe/OY348ADD8Tb3/523H///Qvq97xBVFoCZme19evXR0za1VdfjSOOOMIDKMAmEZ2ZmcE111yD4447bl7tCyWhCvvg5+4QsmK0srXM3MqSJhIhJej5Qg/N3Co+ouN5eoJZ3HWDLLei59nKFYDuph6M1hiZHEH3oV7EmuwMZlmozGpcCR9JmTKGohJ+Hz/5KOGF6ZyJ6E31fZRkXddMj2ZQjlcoOpbhoZQREeBw4t2UbbLbQmqKtO4hMUo8rYAGIJiOiXv5SBtHeY+sSNwVKrYjYoXEE5UfHwv+4mgwVUnv4vT3YFI+hAdSNFw/FCUok1QEfh/KEB9cSJTeIVxL0z2Vmq4NQAW2lYsyrG2NPG6CAT86X/hu4PvGgUC9SePIL3+79dxby6Ko0Yw+jIITBv3+bRZ75YEy36/JVMc5wShxJz3DIjCVcfIEBtWBtm4fqlM1Ph/atjOzFaLzzCJqorrdLk4++WQcc8wx+PjHP974fGt4i9asWYOnPvWpAIDR0VFs3LgRAHDqqafiKU95Cj70oQ/Nu9/zBlHveMc75n2SHc1uv/12XHrppbjooov8trVr12KPPfaI9lu+fDmqqsLatWtb25qZmcHMTBCWbtiwAQBs1XkZR+OpiapBdftaV2VYcZPVXfj0BfaYEAaeWps7Jw2Rb1vF5tyH4Rgb1l13gf5UqE82lyiyHdXiCEXH4FHBWaDB/sCPj4gmHpl4clRtfASjBQClZ47IFVp0Snce2u7cJHSvZCbCdLKj87aFn/tcQIleBgBQhntPOIBkz+uiSSt+7bb4r5/A6/Cd193auybt+zTiLscstYiR2T4iI6jn93wqJuf3bpoRPgrccGyUcAwVz2xuasuc2GMykWWMPeF1JnNRiYthqVgcaF6/UE3xNze6V2yf8zpJKu2iyvbfNU99wPvB+wLAMlC81Ms8M8cPbetabYDMFDHvNhbL3vnOdwIAPvnJT2Y/3xreoj333BPr1q3Dfvvth/322w8//elP8Qd/8Ae44447FgwQF3wnX3PNNfjMZz6Dz372s/jFL36x0Ga2yM4991wIIQb+/ed//md0zD333IMTTjgBJ598Ml71qldFn+VSNBhjBqZuuOCCC7B06VL/t88++/jPQs0y5RkboaR/XY2XGJkcQTVRoRwtUE2UCJFjLHM4q2NHK3NeZkQmE7w/l3tA0gPRkPYnovHt+6JTQZVUxy2uI0ZMVG9T10+8xgEDXWv832OatYh2VJPK1jrj40Cv6ZpVqaBK5UXRETBwGclpbEM26MDsVBNlSMCpBKqJCiOTlRe003ccvkPpa53R90fnITBOr+kclA29GLW18OiPslJTAlW+L22zALiZSoD2pXHy95MSLku9HZNi1I5LMVqgHK8cw2avuegU7p5T0T1E5sc6GX/6P52QuSA/Bey8pEn4fps1JbnxzNs8s3laHia0If176cYN2LYg4CfHPt2504zXq9G9U7iM+IMWM3T/APD3T9EpXN3HcK8AiO6R1P2fRiryhLKa9Y1/Zzx9hKlD+ZxhhvOd2zZs2BD9cSJhsWw2b9Fc7FnPehb+7d/+DQDwyle+EmeffTb++I//GH/+53+OP/3TP11Qv+bNRP3ud7/DS17yEvzgBz/AsmXLYIzB+vXrcdxxx+Fzn/vcVgsbnIu9/vWvx0te8pKB++y///7+9T333IPjjjsOxxxzDC677LJovz333BM/+9nPom0PPPAAer1eg6Hitnr1apxzzjn+/YYNG7DPPvvEbAVzsXDGiB5S1bgVJ1vXHddIwT+YyFoZJ2K2SICabmfPQ3I1ee1Tp/L7pQ83WRYA+i6s3Wa7VrATfd2N3U87i/FIPEp8SXl2clnlo2g2/1lTTxJqq9F7cr+4iUrZ42Y2dCN3YOqK40bHxf0PoMOmYggTqPJ6u2amfMo+Ttfsj2HarKIjnRs56F00pNfTEJORTrD9qb7Xj5HNnr5A+kjfFFCF6481T2kiSWon/Tw9/1z0P56RjNqIAwaUlKgRyshsixxRzQi4kKai4WZGSMALhCCF6DM21jkpwKAABSDWkvHi0ClDxb8Pnv3dSA20fAdDWzzbmsk2OVkAWA/Vueeeu0Vtz2YL9RZxu+yyy6Ddb+k1r3kNdtllF1x11VV4/vOfj9e85jUL6te8QdRf/uVfYsOGDbjxxhtx2GGHAQBuuukmnHbaaTjrrLPwr//6rwvqyEJsxYoVWLFixZz2vfvuu3Hcccfh6KOPxic+8QnIZCV5zDHH4Pzzz8e9997ry9lceeWVGBkZwdFHH93a7sjICEZGRhrbhRQ+JBlwK0D32v9fqRDZwtwq9GAiNx9go3I4ICI3Ak3wcUoEq2nxtb3cZKLKgq0MhWeeJNtejHXsJNoNYlDp9hth7sTuQz0/kfHaezu6WZdHnGogYvS6iD6LyuZ0ioYWCQggxNQGYPolArRCBWF13Y3L7xQdyyDZEPymXivtO1lbuQ2aqLib0RfQdQCAt8XbicCgO0ZDh7QALmqN36Pk/qG2VBWy9Kfj16bta883FJgoDgZJrM77n77m4Il/Z1RvD5XyyUP59aegVCSASiiJQlm3fN3rzxqssaVGpV3606HWI7GKvF82zxdFTAbxOC3Kcol923JtpSCLC//TGnrR9+/d4dLnwKMEsx6glhSZum1SQwwt2NaMzrvrrrsi91luDgSst4jcdG3285//HE984hPndP6FeIte9KIX4ZOf/CQmJyfxmc98Bn/+53+OorD34Z/92Z/hz/7sz+Z07jab9+z3rW99C//xH//hARQAHH744fj7v//7HTb55j333INnPvOZ2HfffXHhhRfivvvu85/tueeeAIDnPOc5OPzww3Hqqafi/e9/P+6//3686U1vwhlnnDHvyDwAKEdVtOJPH7ZBV+Qmrm4fkqL3Mqtc7SZneqilmoUonxPApr6QLZvOS3luoolX2ygkO+EXQKfyDzqKtJGlAwSw+heKBBsUdr0jmnFicL7Ct26+CkL1I6aFRNjezYW4nprkYMTXnIuZHgABwFaAVCV6U32fGZ4AnPSTlvbHAEDdC/mpALRO8tTfXMSZ768OICGXCJGH+9sgBwmUTHTd60fZy00tIGqDcrSI2FQat1BkuOkKoiSdudxmfkEBlscIQcuVY7licEfpKWQUlcbHydTCpX1oArJ0bPlvlUrBqLJYVBB15aFHY/mBy9xiSrkgD16WyY1XKf0zwTKbdtxpsZUD5zlAnmVi3T3dxjbTgo3ceRTFx4EUt2HyzYeHTU5OzmlenK+3aJAt1Fv0jW98A5s2bcLk5CROP/10nHDCCdh9993ndM652LxBlNYaZdnMi1KWpafJdjS78sorcdttt+G2227Dox71qOgzEpMppXDFFVfgta99LZ72tKdF4ZMLsTTZJl/pk1n9SxGFAMuqgKBVWhn2Iz0BYJksAgDESIV8Q4VdIbMVvp8MESYDAkw+TxJbiftrqArIskBv03Tos5Tobtwcaa10b0FD5O3/PfdZKMdL6F6No//tP7assQH2wz88BlJJjK0Yg1DCi+Q5oKRr8tfGtE8UoQS41XailwKYaymJWPIlV1pEyaRRsgViY0vZokGRl6nrh7b5tthPPgJMHDBIae/DpC0CVpxZINchMZL9qT40rIuYzCbwjAFVCkpToFM4UJYK+X3qhoRhovaoHT4CvGakKvPjZ39LfQcKmgwfvaaxWmxt1A//8BisOHRX6yZ1DJ8sQ+Flq18LDKhwDJEHS8QmyVjbBlgAOzI+Au3AOc/z1Lj3nDsfjP3OPscY+0VAqq7dvhUcoApAnBipoW072x618+bjLZrNFuotOvTQQ7F69Wocd9xxMMbgC1/4QisAXEj9vHnfyc961rPwhje8Af/6r//qBV533303zj77bDz72c+edwe2ha1atQqrVq2adb99990X3/jGN7bKOWVZ+GR+ReI2824BN9EYl3dG+fcsUzh72Ohe0GKEz3W0jWubUuDWlp/FF3JVoR9x2ZciuFJ69qEuSumZB+62nKv98uQTUPc0VCkxtmLUgZESv3rZSehu6uGor1457zZnM66dQR0m6uDO0F5U649xbtGiU6G3aSbS4qTGJ17uQuO5cjzDUyFygxllIjCXRjLFrrZ4YieWy+q8mqVNVFV4VjFNjAg4IJSAAbrvUkEwbef3sZDas1UFu4eJQbPXyFxBnEUtpc8/FoEUlxSVu6l5CgYAKEr3vbHcWLbvtiZgxL54F1XT5ceBUerOS41/DzQOi2HLD1yOyX1X4He//G90lndCP1kAA32XxAxKFdKREMAnMToZlTryzyd3L9CzRSSLLS7CB5ouwZDyQ3nQG30Gy/j1un2oWkO5xdtQWL7tbUePzluzZg3uv/9+rFmzBnVd47rrrgMAHHzwwZiYmFiwt+gjH/kIzjnnHFxxxRUQQuBtb3tb1v0nhNg2IOpDH/oQXvjCF2L//fe3AmohsGbNGjzucY/DZz7zmXl34OFqfsJhbBL9r2FXYvyBpdzKn7ND/oHjJjGpqoZ4libO9LyaTWB+UuA6mWTCIKZKVrHeAgDQiwXKURuYX+K8X558AorRAp3lYxG75k0Bo5XCL08+AX/wxW/Nud3Z7OcnPhs8sWacgymtNWaNM09CSZRu9c7zPxUOcPIoJPojXZkdpyICCXWvHzFZxELxNrhxUJ3eH4CdzFVZQHSYAL603+egOnG61v5eABjDxL6X9GjK6F0njGlkzAWoSse89fogVycXIFNS1/40idPdmCSi51B6JA7JJ1bKs4de65MAwwxr6Nk9x0h6kJiyc+y3si3cUUsP2B2kpaNITl9cXMZgmy9+uCyAFnERmGUgOL2W3MINIPaybxdOrC1ymVKyVc/S+nQYIXqP22x1C4f2yLS3v/3t+NSnPuXfH3nkkQCA73//+3jmM5+5YG/RU5/6VPz0pz8FAEgp8etf/3r7uvP22WcfXHvttfjOd76DW265BcYYHH744Tj++OO3WqceDmbZhpAyIAYLRexCchMsTQr+YViFiVd3+36iBJiAGC6vEXs42jD9IBanCZd/nq6oc+4Jr6PxTBqg3UPcuwLU4GzU3H71spMwvteyOFKH+siEpkbrhlj9u49/Ep59/c/ndJ42owSXAIWs87w5gUkhxqnqlNGkwZMFAogYH17yQvDJ2U3MRiYMorKJI+l7IkDEV/+c+SGjSbRgAEoo637joI2fK203voYmMOb/83vMMnVFBDz4PZaCe6UCK8WTVPLoQbLSie0HaYx8LitY0EnRoQSuyGUlspU7eQAAjsJJREFU2KQdR+U186xx0NvmwrPjRG5fnkOrbtwTW8N+9bKTsOLxB2LDnfeiHK9gS9g4bRPdJ0yoLZREPd21v3vXHy4RUMljPnetEkX0bCBZAF27fzYhPFfSmo9kEqGuYs5mA/ZDWxzbHu68+dgnP/nJ1hxRZFviLer3+3jFK16x1dMxLNgx/cd//Mf44z/+463Zl4eXJStGPimpVFSeTHZAYISETAoBV82vjE+oBEb4g0qW4Tgvik0e/qoqXf9q338K5aa2/DlqDb25Pyt4+s3r/sy7sgBgZNm4v870IZpuk0ri+peciMd/7t/x8xOfvUUA6to/eY4X1/acxkQo4cO/KbcRTbjl+IjvU/rAJ1YJsBNY0amAMWDmwYf853zf1MiFQkJ92i/HeuTcS9n7xk3wqiwigJBjG1Jr+y5SSwXmvB8WROrITUgtCHccdzcarSFrHWlygNnLEkXRrFJCKttm6cT4PgpVUSqHTA0/pg/k28hS1zhPeyCUisCHBvCYD39pYJ8XYksP2gvleAdTv3+IMaHCLq4SF70Hri3ZwHliUJ6ugX/vVruZ+c7Lwh+ra5eaoETEcvsoU8d6Ga09uFIJ68UB4FBcvu1NawO9hdF5W3r89rSiKHD55Zdv9VQMCwJRP/zhD3HhhRfi5ptvhhAChx12GP76r/8aT3/607dq53ZmkxkANei135a4DKSSMDoAKJooObOVWw1zFiMwHQp1MinQuTxLUFMZmkRQ7CZpAMC0zQOgGEtyyyv/BId+/Kv+/W1nvQTluNVygLm8hIxdAry/6YROIG1iDwu+rj7uWBzz/R82jp3NhBQ+eoyfL41a8mHxbNx43TTLXgWXat3to+71oXu2pAXphOJzN+8Bvp0+o+zOfD/uzuX/+/HKTEQEhkPgQc+3pWWcAJGfn7OLvL+pkDr9nF6nYe+5/YjREkp6Zo6DlqJTtjJbfMw4O0eTtwRiZoQSzCb6rwZg5WA1AagETu05y+hzVRboZu7j+doPjn4KitECI5MjWLLXEsiqwIrHH4R1v7oDQAzoc8EfdA2KsYI231flgav/LjOsIwfp2eeRB+rau21rFbPIZLRQS3WbvJ9t5xna4pveCpqonRhDAQCe/exn4wc/+MGcNNJztXmDqM985jM4/fTT8aIXvQhnnXUWjDH4yU9+gmc/+9n45Cc/iVNOOWWrdW5ntrbQ8dznvBI6WbqajCKs3CqYrzLD/kUEmgDLLsmyhKyC2D03MdrtyrkFJOrpGWipoaoS/ekZqM4IVGcE/U3TXpiqqgJ950q47ayXeJdPCgACSNO+/+nY8Iev7lrG6ObTX4DOLpO4/iUnLghAAUA1UfroMV0bjC4fRXdTNypZQuNM11R3Q+SeZCCVM4k8bcSj//4L+M3r/syPLxfj51xIvi3PLNr/uc6J9s2xP9SuUAqmrv335id5dv4weRFwIfelCiCrKrO6NwKHbeaZoQGgipvfi8aAi9qJser2Ubj7C0DkOqZx4+9z93Kjn8nviW/PpTbg7js7vtKP6ciyCe8+21J75jU/xc9PfDYm9hjHyLIJjCxfgmrJGKbWPYRqomoI/FN2ZzYdneDAmX2/gxjOFKzzbVxvV/f6MLIJ8DlYy7FmQxZqaNvLnve852H16tX41a9+haOPPhrj4+PR5y94wQvm3ea8QdT555+P973vfTj77LP9tje84Q24+OKL8a53vWsIopzJSjVdQbL5QAGCcJczOwA8CFJssqMVOk9NwB/2RM0LpcKKna/c3aTY9iDj2qRifNQ/HMvxjm9PuRxS1eSYBR2dCr1N0y5qr4gmVcl1YUp7IBWSObLxYGNSO0BQT3cx8+BDKEcL3PLKP8HMhul5C84pM7YqC3SW2nZHJjtReoloQnF6Nh/R5li9errbYIqM1qjdd/zov/8Cfnv2KY3vk4t/+TkIAOdcTHTeQeCb3qdu45BpXQKdyt8rYexDG0VnJJocFWMn4/QGTYaS34ONvjO3W7Q4iNzMwQXEky8ql0FfsXspcvWy+zliwNKowxTcpTogNqnz/aIoteS+KMiFCwAbNjeueyFWjZcol4xBVgXK8Q7ESMelNODJeu3Cg9jR1MWempcCJJrDtNZjjhlPWTgA0NItfmQ4pxqtUE+57LSsXQ6M+XeXY+aHtu1sR9dEbQs788wzAQAXX3xx4zMhhK+gMB+bN4j67W9/i+c///mN7S94wQvwt3/7t/PuwMPVuO+fVtJtmZlDKYvAHHFXG2CZAlPXVo/EgBQ/Tib5u0gEDMQ1rNpod1kV0GW/8bBL96OINMvalCg6I5Y5cjou3e17gTx3G5q6ti6lkoXIs+MAeJaLJgC1ZAz96W40eczHfnv2KdFkxEXRqioi8X8T+HU9gPJjl0Y+OiBB7swDL/kX/PbsUzLBBHDfkT1+tqSDgk3wUVbusmy0y8FWutr3UZcIrrRB5+OWMoNG66gwM7Xbagzc8++dn6ue6nr3EzGaVIbIj0G375kPukY/vnyiz4jbfT8HsCAp28f3bwAzx9aqssBef/v3g65+zmZr2VnWaWzPXf35Ta2hxosAVBN2L/0tpL91vs2nT9HNaN50oZfqv4AAvoQKLn/an6deoTZTZstvl03mb2jbxmpjUG8hCNrS47e3LUYuywVF5333u9/FwQcfHG3/7ne/26in80g2/rAgTZMPUU4mIgJN3n0BNFZygXGIH2L8fLR/1LYDH/QZAYFUZEpWJPqqNL+Q7vbtdXggVmNk2QR6m6f8NRedkYiJIJNlAcEqu+teD9oBMQKIxEJQ/0ytvbZqIRE9niVjY1COdRosj2HXZAFW6fVnRWeEZXlXgdlxzJoqC5hk3Pi11y63FtkgVy9F2fH+2H2CW8m7RnUYo0HgiO4f2iNiKMi9WofweW9luBaTAd8EpPl5aDt3N1k3abg2arPu9iJGpXDb/ffAGI9WXRhnOhLXkixZZOAc9Dieaa3CYoT/Pubaznyts+sSlOMdLD1ob4iqA6PD77sY7/j7gde4pL4BzUVY3e3Z78wL8GM3LS/zxK+b2koF9GT8OeT7oFg6DRWec/zcIc1LvDAc2tAeDjZvEPXGN74RZ511Fq677jo89alPhRACV111FT75yU/iAx/4wGL0cac0Kyx37jjEACp1ORCD5FfBKBqTRTOCKgCx1J2TcyHm9BL0f+rW8JNhxh0iqwB6uA6n6YpkQteISYlzGxWOWRFKAXxSbriT3MSo5/cAjs5XBzaL8i0ROKkduDPaasD45O/HhsZHSgC94IqsLDi89dUvwiGXfdlnvj7YsVLeLdUCnhruKgcsaJuGdbMJpRLQ0O6eisaA0h8k94lm4E4mfUnPQ6wmuWBTkMf7ATQZHNqXAzFT136BIZT0YInet7FGbe9nA1m549NtHEgOyvwuyq2T2uDWV78II8smrGZxyTKYzRvR22QXJJ1dJ+29Q25Ydj1CWaBfd3sROKH7l9z/ABo6t/Ra4pqDat4gp+iEumlCSRd52fPtpWk6hu687WOP9Og8ADjvvPMGfv72t7993m3OG0SdeeaZ2HPPPXHRRRfhC1/4AgDgsMMOw+c//3m88IUvnHcHHq5WjHciIECr29wDxa+4SUOjdOSaoEk5CGxVshqM3Tl+cmdZifmEGGplyUafhJusvU3P+P1UZwSmrlF0Kv9gpgd6Z9elAGBdbwR43O1VdCrviinHO1FoNS8rQwJ4298awk0G3pWnJPrTXdzxxpdDVgX2u+CT2bH/r9WrMLJsiY2aq0rPrpF+i85N4wEAcPvSfjSeuhuLmgPQLaHrGQsw3ETXd9fx6L//QtQfipLiU0fDdZiCFmIlExcVF63HwvRgqe4oOh+7H9q0RTm9TQQ0gGxIPPWXriEFZGl7qjMS3Wvk9mtEfSlpRdFJGgXfFmNbGuH8CeOYHhedA3T/NxceURtSod68dfRQ5XgHqlNhfK9dIKSCAXD/jXdgdPdlGFm2BPX0THQ/2oSqTv/I3N5cw8VL9tBih6cuyY7FgGcK38bH1dQaxWgVwDXp6qoCdTJ2s0UBDm3xrcZWyFi+VXqy/ewrX/lK9L7X6+GOO+5AURQ46KCDtg2IAoA//dM/xZ/+6Z8u5NBHjBFACILO8CCMyo8ggCf+cOFCYz5R+smRTZSp9ilK3JiwQVxrQ+/9RCYVkExQqsM0WlIBde31GbzEBwDU0904a7bTwBBw8ZoICktn/fQui1G7b38q6JH4BFz3+h543PnmV3gA1980hZkHH0Ix3sHY7sshpEQfNrJrdLfl2ZxJHuB2KqhOFWcYp++IjYXf7ibs0k0kutdDf7obpXig/vlrbYz54LQBgNU/yTJmdQy/Z8r28jAN8MSSJFIm/UiUrZrlYoLbtV0n54+n+xKIosByAMoL0pWME3I6dzWduxmBluhxEre4Iv1VrbPHA/FY5qIbc32miFIvlO91s+zOfIy0ep1dl0J1KhTLdvGf1d0+yiVjTlc44l3g9FsiVpF+f3Wv7+9dehZQskwOXmqnXUrF+DRGKWj0zzCeykE1IzjpeH738gCWtmfP0Ia2re0Xv/hFY9uGDRuwatWqBWOaeYOon//859Ba48lPfnK0/Wc/+xmUUnjiE5+4oI483EyOVKjGR/2qP+dSEDJeLeeiWCL3hJTxg5Ela0wZJ27kjom2EXiSBKC4207B6BqiqGAAiKIEtIYoSoiqA+gasjttj9U1xEgHcCtzeuCqTuUFp/RA5XorjTi6S1YFCm1XtdXkGIqxfmPsbBsjIVIRfe9KVMuWALARhbrWqMY66E93G2CiAVRU0APJ0SrK7i6URMkmIG6qLHwyUqEUDv7g56LPiS3zADGTBoB/P6lZfdhgZgAIEW7RfsmEmEumKKSMEinS/cFf+/eqaoiBORMJIGKATK2jRQB9xo3X+6P+1QwIABZMpIySmWUCFgyI5bSD8PcBy5GWgNuIzXWv+X2he33ULlfafOya5x+PJY9ahnKsg2K8g6IzYtnRsoAcXwK9aSMgFcrxjv0duchJ4WprVi6Cb6b7EIrRyqefEEpGyTZTd6YdCw2pw3VFrlf2GyOWk54xPCM6GQfBQBOIpTVC+Wd0/NC2vQ2j8/I2OTmJ8847D//rf/0vnHrqqfM+ft4g6nWvex3e/OY3N0DU3Xffjfe+97342c9+Nu9OPCyNiWtp5c9XdJIxCUFrE6/euSicHpa5yDqv73EgK11pqqpA6rIjAGQ7IwNwkvYz4VgpUbmEmVrb90UF0+9CAFYA6/ZRVQdKa5h+1+6ra9TTIb1+5VbWkvRUTgSsOvZ8ptcFloxBKAXd7TVYNC+OLkOuK+PamnlwI8b33g3djZv9Cnzqvgcbrjsg0bW4dnmiQp6niU9C/Duh9+gF7RexYm0uxlTcz11QzTxFTWEvd4k0GIOMi7gtUWW2b2yik6yvtt+xcJ2SghJIIoBEAAhMzB0d1+1H/fZjkRRqbnP70PjLDCubAoLcNefyKvG2+VikrAqPBqR70CeSnYeV45XTPxXRd1lNjkEUlf09su+3Pz2Dkt9/7nlAAGoQs+OfF+5cBEiVlJ4p9swakw9Yl3cMmuiaaXxlVUDWGv1kn2K8E12X7vXjNCGZ+3do286G0Xnt9uCDD2L9+vULOnbeIOqmm27CUUcd1dh+5JFH4qabblpQJx7ORqVB7KRf+AcPPVR4fhxuabJGv70zgt6mqQhw0cMPcHWyWJmNeLJQ3mUXHtrOhac1hD1Zg5WC3+5SKYyMwhQVhK5h+j3IkVEAzu1XWHpMT2+CkD3IqrSgqKwgRiTQt0JY3hcAkKPjPipJFiWk1qg3b7YPfzch1NPdyHXZn+46F5DE9LoNEFJ6fVVv05Sf6HLAg7ZzlxjPkSXYZO5TVLRohcx0jaUH7Y31t98NIE6rkBMEp1qcNEjAn18F92F6HTm3bGq8DzlLXV7BzcgYSgCCgtV07V1m0fUztjFluHLnapzb9Z3qslE79Lsh9pWzQ0DMZra5X1OgxYFYIxqRHUcWuf9I+zbdRTk+mh3TQTYy2UHRqZy2MBR+rvbeD3rTBnvuIqnPqKTXTfnnyFjHR4umY8Bdd0CQCqgqlhOQpi6wTBYcV0vG7DgmrCBnJ5UTks88uLHByOlao2DBIZzZaizkhja0bWwf/OAHo/fGGNx7773453/+Z5xwwgkLanPeIGpkZAT/8z//gwMPPDDafu+996IoFlyK7+FnyuW7kdKDhnp6JtJj+JQGjEqP3rNQZP+wlwrl+Ci0E1ynEUOiBKBrN9HxAqLBbUcsU6R/ovf+fCpqN9rP96e05/RsVQ30ravNt9zvQU0uC+eSCsbt4/tSlPY4x3bRZ2piAsqBKQ8+GZAoxzteyL7n31waDf/vP/BGuz9jflL3aCP0uyUHVZpnCoCfPOrpLvZ+x2UAgOn3vA5AswwPPz64CoMWh7fL+9w2ybdFO6WMm287AT65Y2k/11BDGxdOLqN7A7puTIg+IzoTiafghvfTG8sfxvctnG4qLZZMR/JxUoxZAWL3lGbAhYPG0O+8uD4FobrXx/hL35Yfn8RuO+slGN9zV0zd9wBWPP4gf7x2wHPsgAMhqg7qB+4DpES9Yb3XIRWdEQeyXVkgB9x4Ili6nyTi30ahquh7t6wUjamCZPeeVHbxYZluu+ghcbrqjCSstYLoWOZspCjRvf/BKAmt1Qj2PdCSVQnhnlXhO41B+tC2jWltUD/Co/MuueSS6L2UErvtthtOO+00rF69ekFtzhv1/PEf/zFWr16Nr33ta1i61EZkPfjgg/jbv/3bYUFiZrIsoMbGAjDR4WFGnwtlK52nLhQhNWSn0zjWW1FCMkZJFCVMd5qBEwVRlFCqFx6AtD9310Udjt9HEy8DQLZ/Lnkm7xdpS7xbUEKNjttzayYILirrCuxOhzFx2ivaNz6nhBobg+qMoJ6eiVwIVBoGSuK+S87GbmeHH8jIsgl0N25uCvoTxsZngq5bMm9nGB6feqDbx8q3fdhvL0YrrH3vX6JaMo7e5im/vRwfda6NEI6eghru0srpSCImSw1wBcoWNolPhG02iIWke4jeU7/7aAAuAQpGYIwb0IwMYyCWu8x4oeJU3J7T2+S0hBxwKQ6wknGOAGnZdAfyXFyhbYn7P/w3mF63Ifr+cza666TN7O+ykfc2TXt3aDnegRxfgnr9OgBAb/0Gm2xWSVSTSzGybAJ9t39bAAF915qlNLDb2e+cf3f+OWQXL0IqKMlKBkkFoOe1WnDMMl/wiKKCHB2HGOmgcv2up7uonQax7vbt784vvnqMMXXPkCLk4hratrF6K4CoLT1+e9sdd9yx1ducN4i66KKL8IxnPAP77bcfjjzySADAddddhz322AP//M//vNU7uLNalDeJuQN0zehvqSBUmNjIpSVIfyQVRGfcAiR6+BGAgaP+pbQPK6mse83pmky/Bzj9UsT4cCNQxFgqD2KKErLqWLCTA13Ul5bt/qdG52Cfm9ppPwrKj6UglHURek0VEB7+uobp1r52n+72fEQSRSulVrOIMp8LiYmweUkSHnGXsxSo1MmkDQD3XXK2F2XrWmN0dGnDVeuTrzIARWkdqD2hZCNa0zNpMr6eXG6uVjZJ6/zq3zOLTYCUdL5xjA02SD7Tta2nRkEHLElozuLgCgcGEk0f1+QA8EWh/T4soo+OSRNKArFrNb40xvgxVzj1y0ibOoFnm9/lzPfi7ne+Oj9WzIilfOCjYZWrOhU6K/eGGB2H6fdgNm+0Q+ciT5fsu0dgPJUM7BxFxyqbL01NTHj21rNyE8vss6Lf89+L6XchO7ZGmOlO2990UUK6ZwukjBZtQkmU1agHWY0FWFnaz/qAWrqr/b13p9HdsCke65HE9RwFsgyZqG1tQxDVtP/6r//Cpk2bcOihh0IO0I0OsnmDqL333hvXX389PvvZz+KXv/wlRkdHcfrpp+OlL30pynK4uogsYWr8S3qIM1cfaY5yDJGoOs7dVVsgpDXU+CT0TGA7hFIAz/VC4MVF0/n3I7YtPTMVABnlkun17AOSmeiMxW42oPmenc9H7LGPDP8czNVEoM0xWEJKe/7G5Ff7B7/9rGfdoc7l0Nl1EjMPPoTU/GqazltWMPW07wP/H7BMhdfftAiUaxeZVff6kQux7va9O9aDHOZKStMGpK99H1jahzBWiYaqRQeV6sy87s0eGDOHSEBwjoFCzDbm2cvS72eDE6QHUEa7STllwRgrkroChZJQcJGPuj2XVU6kTkbjnrri0nHLRsyyBLmmrq0ejrWte6Edyo02m939zldjcv+90NtsXWZWP1ZDVh30770Tpq6jSFLA3mflkgmUmjRYHf8dmVqjmFwaM4JaQy3d1V4DAIyM2t9Nvxt0Vm5RJqpO9HslLaRfqI10Ym0WB1BKWfZbKhhpnytyyTJAa4x0NkFv2sDKGrlzOEaKFk3pfTi0oS22fepTn8IDDzyAv/qrv/LbXv3qV+PjH/84AOCQQw7Bt7/97QVVXVmQiGl8fByvfvXsq7ChxSbKyg+4UKoBFrjexE74bpIpS4iRDszMtGOdHCMxPgkzMx252ywL1Y3cRtQW4EBV1bG11Gam/MMyuO7iqD37Xkdgx7MalAYhfSjS5Ozcc16DRa4Rfh5qk7bx/wEPyOTEMujpTVbcPLkMgL156w0Punw6Fe7/8N9glzPfiwc/9tZQ36+KQaHsdGB6Xe9qML1u5BoicXPKPnltT6+PutfHHm8K2fkf+OjqkNCzUXRaR6VMOBtC4t2QgTquDZgLC2+K5OMx826SjK4pC2ZSjZNUEcAl15xvoyxhWDZqb3Sv1rUHUEKH8xmd6QvKcO+QFs+1K1lWfNL/pcJyGo+UieLuPJ5egTNVdByxkaZmLnTavyihSs00hkApJXqbp/Hgx96KXV//fsxm/3PhG7Bk3z18XUBVFVATS6CW7w7j7mfSEaVat3rzZuvKdoyTBy/9ni0P02cuvFTfyLSNxB2Isox+l6bXBddGCoQFjCjCdxODcbd/UbptoQ+i6thKDdOb44Vbp4QctUyYntoE0UfQUQ5tm1mtt5xJaiHrd3j7yEc+EmGWb33rW/jEJz6BT3/60zjssMPw+te/Hu985zvxsY99bN5tD5Xgi2RCMS6GHlKdsXgbgRGnbZJu9UgPu5SpEKQxcpon0+9ZZom2sbaI0o/EpWUZgE1nDKrqwPS70aQoyIWna4hiPL9q5ExHUUJo6R/oDRbDP2zDNQc9RMJcpgwU00iJsgKmN3mXpCjsGBUr9rTAyl3vAx9djWrZZJi8eaJQ1wc7ZtozgfH3FibaqGtOp1NPdyMABQDL//cFePBjbwVFW1JGd1NbN5Du9i1oZS4in3OIJ/gkkXCSYDQFUClwyrKCTFMWf5a475AAdtpHKn8fRfehVC54IWYjSQ8nAMuIwgJUAkpCx5qcVF8H54r2+7h73HbHTvyypu80ZpA44PJ9VOH7p2No7LjYGoA/njMukNK7xIhdM72uZ5ImT38X5mIT++zpk3N6t/L4JETVgV6/DqRB4n2k+0RTSgKpgKrjgafojDuGqWRpSVpcEe478S5/zjiVFQxns/21qwgQ+3bK0mkwrWgdjuU2fTreja8Meeag6ygwRUgFU6Dxuxva4tsj2Z3361//Osph+bWvfQ0veMEL8LKXvQwA8J73vAenn376gtoegqhFskhLQNQ4BxDEGDGtkl/xe3ClwoqNicY9kJB2IjP9xNVCeiMCWtSnoopZnsLViONsEl99svb8ud3/6YO76epJGCYvbk/2a3F5RgJzJkyXY5P2fC0C7WqXZR4IcpdibnVu/+9FD/QQWcYyVOsgdObidQBY+96/RGfXSVcc1rZjc+3YydrUGj1MW90Wiw7jlrqmoigxBqAalgFEjQCBrAuOzsuAZY61krLp6iFmI2EL0z4BDJx5917Ld83PySbl6HyU1LXfy+aHakzKKUhDNxZb5/qe9o3aoNeOkRqUcyvXligryF5au1B5VjmNEGzox7g2ybtAqwB03HWmQFhIaRdIKchiAFiOjDqwy65XqXBPaPY7o3Yitq4KjJj/zRJwK2G43l0qiNFxiH6vqc8c2tAW0aampjA5Oenf/+QnP8Ff/MVf+PcHHngg1q5du6C2hyBqkUxO7gI5NhpWviT6rjpOf9SNJkGjrX6BaG/aTqtW0++ylSudhBihWEcUaSCK4P6D1hZYTW9quM6EtEkzzfRmK2ivOmxyLcODV+s8O0UPxRYNjUm2Gb7CTbdR0kF3btPvwTgNF42Pn9SBoAUjFwWQuDpUmIR4PzjrwNxJqXk3UIbLnth7RehTvwc1MeFX4GbKCm2LTgVdFpBODB+17dx5uYzyORdexLbwa+LXwCcoPjH6bQkopnP675Dp1IiN0uEY0WftaB19t9E6lbOpOmE26PzEGPK+8SCK9Do7FaScboIfYmeJmUmMtIH+c/rNUK6zxL0UubDI7egWJJSLbDa7/8N/g86ee9rftYvG9YugovTMlxifRMESU5oWsO3HDOH7jLSFuf1G0ns+fhaIwqYosaxvHbvtwZ4l2uaAE0VlwV9RxgDO/WZNrxe78kjjyBeEVPlgaNvUHslM1H777YdrrrkG++23H37/+9/jxhtvxB/90R/5z9euXeuzDczXhiBqkUx2xqGWLofadU/ozRsBKaHXr4NcshyiMwazeSPqdWutJseLcusg7mZpCcz0Jr8S9A8sosultO43oJ3S951yq88ly31yP4C55ZSL6uvH4dL0wAUAMTIKOHBAtH/rz4o9sCNdFDEcyX4erPQR9Fb9LkAJPSl6sSj98aZAY0L1InS6Pg4YkxU5uQVNn2VfRhyiL5QCprsNDczmyy8MoFcqiI6dMEx3Gnp6OuioyLWXgDBVFlFOo0bRYL7iT0Tj6fVEoJBZI/KSwFGuLTrGg3DSysSfk6DY78/76dsL7mrj3c2W2QjuNAVQ/2oGoNn5AyAqvXuXAyIAzu0mnWs5YWR5P8txH4wRgFnpQQR3dQHMfa617V/Ra2i72mzTv74bnd13i8fTj5cdfzm5K/TGBwFdo5hcah/GtKCY3hT6yY/lYDh1lc/FGOiOQVXduHd8u1JZvRSdk5ixkY4VjUsJdMlVX/nfUnQfOZAbwFXVPNfQFtUeyXmiXvGKV+B1r3sdbrzxRnzve9/DoYceiqOPPtp//pOf/ARHHHHEgtqeN4hatWoV/uIv/gLPeMYzFnTCR5qJsoIcWwKjaxR7HQCMWF2UGJu0YumHHvQrs3rdWvtQBfzkI6SCnrEgjHRQAJJyLMmqkZtzJwAunH7cUpqqKC2QIlDjInJU1bF9GMBWGL7yZSvSdH/PwmXYEO9CyI1ZUQadRVEBHViGzIG9aF92Tj21yQMJn57BjZWh6EY2sVJdQK7fMJl2Ta+L5f/7gkY/ja4hO+P2O2TXT9oZu0+s3yHmyTDdi0z29UkeeXQauddyLEtuos1NqomeLHdsuA6Zf434Pms8Uqk9pofjbFRDY+WvgTFWQMwo8Tb9GNgErabfjQFzen+U8USegxrkchSKItUYo0JGIEPX2XuB2+bLL7Rt+jGRDvhpz/B63ZHfJ/kOKYIuZdY4GE7HJmmrwfAS+BlbEukovayAMb+8Lc90s+eEXQi5+6kzZn9rlFaBhPiO5RNF6TVZ0fUNbWjbyP7mb/4Gmzdvxpe//GXsueee+OIXvxh9/uMf/xgvfelLF9T2vEHUxo0b8ZznPAf77LMPTj/9dJx22mnYe++9F3Tyh7UVhWWW6hqoRiD6fZjOBABAGDexdsZ9yRQAKFcegN5/3xaYJcDmc1myzOZ3maHwfJW470p4kTbfPtKx53cPz3TlKccnfaQPB2hyyTILSBDYgjhCq3JAwbVZBteHSQCVoGzk7n3keuCTQzIJ2Ki2oM8Skx1/nGlbNfvjJcTouL0+x9r53Di8RA1NZN7N5Fb2/ZhxEErhwY+9FabWfgLd9MX3+tw7kVuq1/XHmNqyTL5YMWmsWK6qtBwGtyjrOI+6c9do90nclASg0kmZH9cGoBoi9DaAFa435/7kxzbce1p6MNV6XB0DB59ryoG3SFifuIWjSFSXUNJfq5Rxf5iAPQIcmmmLnB6PGFDOWLaadG5n1q7fPr7EAyn6bfrfB3eDkYstZSOZcXa3kYrCs7pu0VWUIS8bjR3tT6CY7jFKhNvGHnEBvusXicm9S5kAlKtoQGJ405227wf9foe2KFabreDO20lr50kp8a53vQvvete7sp+noGo+Nm8Qdfnll2PdunX4zGc+g09+8pN4xzvegeOPPx6vfOUr8cIXvnCYK8pZsfJAqALAyBiMkBBKA7IAdB9GFoDRFkwJJio1GnLprtAP3GdZjollQbhZlMDMHLQYRPGnqz7muvIPPXqgOfdIpBsZCceQUJvrmCLXEelY0gSdtOqmPFd0LAGbjFvHbxvpAIngFUDDNegF4rqGHF8CvWmjnUyZTsymgYhBJM+nRboqL4L2fdSe6ZBqBnWtsflrH4DZvBFyYpltm8CEY7t4SgDKeQSExJhRpu0kGzYd40uTqIRtaLhiEl1MYg0Gg643t50+my1qKmE+GuVkEnAU5QvjIKHN/aS1dRNRW4Pcv0XpNTc+qhUBcEaLBs/W8nMl40BuKwLdbowF3d9SQXbGMfVvH8Lo81+f7z+AsT89G5u++F5guge5dFcPpnzOJgb4vJvaMV+eMUUTgDWu32kluUVsrV+EBA0SLYBy5oX3SUkizia3mlsoAgCKAhASgj3boPtAd9pH9w1t29sjWRO1mLYgTdSuu+6KN7zhDXjDG96AX/ziF/inf/onnHrqqZiYmMDLX/5yvPa1r8WjH/3ord3XncqMqoCJCfu/0Rm3RwHjHjJGCIi6B2E05NIVkEtXAMRW0YOp37MZjjdtaBfPjozaB2cZSsGIkY4DGXa16oXYZRWv2snlVxSQxkADYWUvFWRZWT2J1oCeDg9Y6kulIkDR0O8wIa/fh9XQ89fAwV9Us8+5CfgE4MXn8HobYu28pefg+i6potW+YKt6GjMP0MrC/lHxZaXCeNDxnTHIkVFfxoNyHkEq6OnpSANlZIj44267BisVAYGYfUpTEnBr0zwN1ELlapq1gR3eF24MHPv2U9ZKqgZzEgUaEAvIQI4XPifm2alcn/m1MXeVZ27Sz+lcrF0A/t4lMBHdXy02fvLfYMMn/g/UrnvazOD023PaQ8DeL2q3vQFdo/+7u931BFc2XYMVplOkIwHIKtZE0fVyAEWLoKJqCs/5+PCxSL9XHbLONwA4DwooCg+eIKRdKMKx7oadW0q739CG9jCxLbqb7733Xlx55ZW48soroZTCiSeeiBtvvBGHH3443ve+9+Hss8/eWv3c+UxIQFXxe/qf/piZYsRucw8dofv2dVEw5kdC7bpnPOn0ezaBHS8EDHhmyWjmimAPXCPctEMPNEbTGqmC20uVgJDQqvIJOlGUUEuW24zOmzd4bVDklgCbHAk8cZYHiCfoQQ95JlJuuBZSRsRdNxfgy7ElMJs3WrdMrxtlTiZAEk3qNGEVAFBa98ZIB8Wue9kdusGtaoCQdsEBVjmxzLoupjeHSYmSakpbokOURVRwWvf6PkpvIEvD9TRturUcA5NMkjmdWgP4Ru2pLIhpLg5CxvLWfXg265QZZBo/D5yYG9Bvp36lUaFtY8EYlgio+XPG0YcmeTL6zP+6xuhJZzbGIWfFLisCA+QWGlGeqqKCGFOe+TUz0z7/E/WF2CvRGQOmN4f+dMaiAJAGA8Vc5nzRZXpdJ5jXMQiia5dphGUAVZ5ZTswHl/BnG3uWEYjykcmAZeWHtk1tyEQtjs37Tu71evj617+OT3ziE7jyyivx+Mc/HmeffTZe9rKXYcmSJQCAz33uczjzzDMf2SBKFpZhMsatzNyklbjwoPtAMWIfNELa9/whJGSof1d1bAV1YwAqhcLMdKd9fijBHqiUHI9cTR5AAb4vJhG3RPsY64o0qoQoAmgRSkEtWe71U3Q+sImLJp7IGCuVskGRkSvHsUJAhi3hIIGJfwHHTjhAJcj9RuAGAGlkhMuXZUWwLtpoenMQmVedkMaAjbkoq8gtaIfKuRU3PgiTiNht/2UEqEgXpXuWhWqkM8iAoYGC5LaxGeS2S92EqaXuRCACyqmlj1khKaUHa6cF9HB2xEfz6TgzvtBMV0dAos2FRyAsLcgbFdhO2DiWf4naN7J290hzeHK2+SuXQC3fLUobQSypUAp6ehNkZ9zeQwDKfR4D051Gfd/dqDc+wMZAQe26p62v565LTu7qdHuJe90d49MXEICifHH8fmCRr8JpGv2zox8vMKL7wgG7tJZnw4S0QKnuQhBYK4oYbA1tm1pfG6gtBEH9IYhq2LxB1F577QWtNV760pfi//2//4cnPOEJjX2e+9znYtmyZVuhezu5CQkDB46ITREygCsCTFICtbbbCTw5JkoYYyfxkVHLCgEw2j7JSVAumBDUPyxTBgIOGHEqnbNjfifdXEnS50bbSYYl4PNZzn0GZTbBsQd6I8N1JgVBpO8gAMXdfmQ5BotWzECYILW2k00Rzik648E1mLTrV9sAxEQF/dCDwYUzMw2Q3o+J40O2d5Zfh8aeAJm248Y1JlR+hordUkkQ2xabvMhdRUxiy6SVnczS/Tn7FG1vc9llmKlcUEByzjZ2LwLTifYqShSJGIg16hymwnQCAEm/ohQKDSDJs5AnY8TbTjSGneNfnr/uxIgx9n0ZXxK+P9cPPb0JEg6wjE5YMLXvYyA3PuCjdOWSZRaQu9+GnNzVXu/ouF0UzUx5Nz5p/2wRbxtpKkfHbRTw5o2BpXXudnL5h6hN59JngMuOe2B/jRBRofPoPqDnhWOZ7POmA9Td6DliZBGCa4a2zWzIRC2OzRtEXXLJJTj55JPR6bSHqC5fvhx33HHHFnVspzcOQJimxdA2U6PY93Go7/wFTDlic+M47ZSoe0FbYDTE+DILnBzoMuUYUGiI/jQgJOTokgC8ujPBhcdWpwaOds8wTB4kMRbM8NWikBB1F0ZVEHrKPjirDtCdbkbjZRghuvbIpAKVu/BpBpwAPmLepM0qHh1HtcOIwdI6gDRaIStltWHEILnJUk9t8uxcBAicMJ4DJNEZg6D++D6z4WuIqkN9M9KvNd0jLBpM1xC1tm5SWJDga/0xt5if9AhsMhG1/XrybriGhgUJgMpp0WYDVzy6chaLtU4xgImANvWLnd/fpbnM9DIIwEMdx0wZn+RaotQWjt1sK6ET1QyUNloUUmLmR/+KkWcMDoWe/o9P2Ht3dDxo+Nw9mwOfoqhsEIUTfcvJXSAnd4EwBv3f3+vvAbXb3hBSoX7gdyGitjNmr6Xfgxhb4sec3NdyYpntP4GnsgrPJePcqkVhtZe0wKL7iX/PHFSVrr/uWM8u5YCRLHwgjf9cFvY5OLShbQf77//+b3z961/HmjVr0O3GQRYXX3zxvNubN4g69dRT532SR6QJCWGMZaI0wiSi3CrNaPTX3GBXagRaZAEUHfvQ0X2Iugv0uxD9GQh6EJHJwoIpB3C8BmtEQvAwe8d62RIkSeQU6ROMtqtD9rATsCtGzkJBSOhqPGi2ysp/bkWz7mE8Mmq3d6chihBmzXPc2AjBIOS2Q6bCCpmvdHmouK5hunUjoSYA5/LRTfDGJksCNQTUoskhLUVBkzPgdE692J2XAxTSgmY5scxqsfo96PXrYFgou7/OqgN0e7bILLFuQHA3AjbDvK4hWeFpDhqz4CcVCufcZ23C8C21BDyngu2wH9O5Zb4vH/rPGdVURJ2I2GetKeg+E8n7CGD56DhE1Qa8rkrOY/LXdZRhny8Umgwfu7Z+30fQqt0fZfuia+iZKcdajft7xQvOx0Y8AyTrnv0NAoH1Lkr7e02NMdOmHIPQ/aD/Ylnb0wUYBZy4i3Mbm7pPI2zfuQvP9mnuwzi0rWOP5GSbZN/97nfxghe8AAcccABuvfVWHHHEEbjzzjthjMFRRx21oDaHjulFMiNEzPqkk60qYIoRy0LljnWuOwhp9ys69o+ADbFI9ABjLkAUhV3VShXpsbLGUywIaaMJOXCidv1OTPTu+mJUZZkp2Iet0LWPzoG0OXO86J3cKEJEDErQX6j4PeBLU1C+mSjRaOLOi4CRBx0JePRgzbFGqVvCAaFG4sZB9b6k9GU0vBGjNToehL+8n0Vp9U+cNeR9pGtPXXLUTgqcfD+r6DrpuCiaj50DydjnLNLTtLn/eB/5/3T+5C/SLkV9zQvmG31k1xRcoMp/f7zvHIQP7Hcm0nFOrk+++9iSRukd3gdKv+HfM/aMu3yFrv3v26ffAHwgQ3oMtwZLmvtuZYFI4O0WU6g6QDVinyPViHfjBWAk/LMl+qPnAT1z3PdgirKhwxz4TBraolhtzFb525lt9erVeOMb34hf/epX6HQ6uPzyy3HXXXfh2GOPxcknn7ygNh9xIGpmZgZPeMITIITAddddF322Zs0aPP/5z8f4+DhWrFiBs846q0H3zdlaovD8g0gWMEVlHzhCRqHAtHIzuQiWZJsgNx8BG1X5fUzRgSnH7N/YMpixZTZj+sgYzOgkdDkaryKFhFEljKoCBc9cfKI/7V2Ippqw18Bdgel1jowFMFcU1j1GtbcAJ8xOJlwdMwu5Pzk+CTm5q8t7JUMeHM7MZBIdcj2K4JOtd68NYBm0ht68MQZdvA2pfH88S+JE9YIBSQKEFP3oMznTRFtUIc9R1YEoyyjVAz9/dA3scw8AfWSgYueR/jwN4MLB4wC3ne8jA4Th2sOYhv8zYC3tP507BVkROGZts+81yobNvt8IPKXn523677hm51W+bRq/WXNoAej9/Ov2/nQ1L32QB41DA/Qzhoc9K3yuJmPs86CsgJFR+xti+joAAeC4NkwxEi+iVGl/i7TgIbAjCxjHhKMo/OIoem4UHehy1G6jY92fUVXzr+jY86v4HGZkIiwYpfSM/NCGti3t5ptvxmmnnQYAKIoCU1NTmJiYwHnnnYf3vve9C2rzEXcnv/nNb8bKlSvxy1/+Mtpe1zVOOukk7Lbbbrjqqquwbt06nHbaaTDG4NJLL53/iaSIJyLJ6ewmds1uK0dhihHIqfVOoK6C+6zuWTYHCA9H3kbLa6Mq6yok0EMrR1WyfUqbC4n2oWtwKRuMoknWsk4NAMWNIg4p35WuYZI8MRQ9aIQAXN08yCThH7klADc5CF/CBggrb0N11LixidRnWx8ZbTAypjsdMo6zTPJmcw09zSIQwfQ+DjBFwIq57Yw7vwAgWV4fm1zRueQ8KFEWNKnJJkuDAJAA+JxDNKYBxLVM9Dn3XYZV4QCNyoFks4u7a5rTujT9HZClqTBY2wCivFIAixhz21qznifHNQIRWtyNfFvW9Tjb+eickhXbhWOKXBCBBdfSXw90HYFAI4TXMKIoYOh3Q0BJFoD77Xs9Uui4f+nBETFZqnQMsQJMYJaErkFRdNB9CCGbbbJAExNpPePfsQeDJFnwbDQJzS2oEoOeF0NbNBsKy4Hx8XHMzMwAAFauXInbb78dj33sYwEAv//97xfU5iMKRH3zm9/ElVdeicsvvxzf/OY3o8+uvPJK3HTTTbjrrruwcuVKAMBFF12EVatW4fzzz8fk5GSuyXbzFHd47xknvg8QR8Cl22QBPb4r5NQDMTPAAVkiAucPT2rLp1gAMU6VfXDKAqYahejNBAo+7aeUMCgd01UENxqLxhE8z5R7mBoh7QOTWKkkb4xtm7lkjIEh0Sqz0J6IJwoGrAAEMBF9DWxCZDmI+MRKIfQUuefz4fDkoBnwEfI5hfB6X8TZtcuPoXIbdnvPC9wpf04jui89ZzLhp0Jpf1yqDeLXkERKzpY9eiBQIaaH2qe2iIXj15CAHgoYiFi19Fy0LTce9H5WIBWzdIa2kTstd05YABflVyLXbFmh94tvojzyefnz8XQdBJCoXWlBU/HY4/zu9c0/si+qDigal7u6SBvlzWi7iKHfFWeSKSVKbkElJExlmW/4QAnrSjYiZBePdJBMw2THhLXpUrh4Y8ySB0/JbxVC+eeKKYa187a1DUEU8JSnPAU//vGPcfjhh+Okk07CG9/4Rtxwww348pe/jKc85SkLavMRA6L+53/+B2eccQa++tWvYmxsrPH51VdfjSOOOMIDKMCmapiZmcE111yD4447rnEMYN2DhGwBYMOGDfEOnKI3OmaceDoBADxSzq4a3fHSRuQZqSBnNtpDpQTqPkw5ipRRIEAk+ARBK0khAJdAE3I0Pq7uwRjlIwUbAlhy6+ke00g4dogE7FLZh7i7XsCyX4K7BmczBqTsytlNYMbY6+bjB7QXM6YJ0ZfdcAVriS1KUy9I6cXggwBUCjx4gVfaJ0ooSftQP4jVcuwRFx8PirSL+iKVr40o+q5fioEkHhVHbk7mCvRtZNxC/r7xBYPzIKZhOe3ZgPfh2mQQk6cuxCQVhkkz0OfGKk17wUEJA54NUJ07jgAhO08rgKL+uohOfy4CIUUFFAGE9W/4LsTYROM3wUXcEVCxncr+HwEXv03BJ4CTMugYZTjWFKVluVxlhUFmhABkyRZ49hnUWLjxhWAKthzIM0VTCzq0oS22XXzxxXjooYcAAOeeey4eeughfP7zn8fBBx+MSy65ZEFtPiJAlDEGq1atwmte8xo88YlPxJ133tnYZ+3atdhjjz2ibcuXL0dVVVi7dm1r2xdccAHe+c53Ns+ZYZ4iAJXRSpGGgSYxYYylzzWgR+xEq2kCr/sWUNX9WGwOABUBJhk/8PgzkiYNZXVNZnQpRH86RAnqPkQ9HUcW0nVIZQGXhHflmaLTZL9ohetcBMK166/fZFgEGpckFNuOnwgPZpdrxqSuAbe6JkaJMj7zHFRehEz1BAG7n6t/B8DVTpPQmzaEiZzcdCOdCLhRlF5DM8Oj6RDcVlSU2UjpXDmxi867t1JQwhI3+tw+LsKSAJS/VgYKGsJ5wAOonB4nAq4McGXLrlCEZxL91yhMzM+fRgrysWpxMaapEgYBOp/YNGq/yco1koKyqNDgegssGwGj/i+vRPEHz2mcd+ZH/xq5mAEGTJlbWo5Nor71xzGA4oscDkLigWi+F0k6En5eIDDHCCyT/+3Q80lQFB08U2ykille37D7Taom25QFeA2wB0AOAdT2sCETBRx44IH+9djYGP7hH/5hi9ucAy2w49q5554LIcTAv//8z//EpZdeig0bNmD16tUD2xPpqg8WgOW2k61evRrr16/3f3fddZdrLDzg6C+cKHnIMJ2TqHsNMAKjIep+AGT0UCw6XsAZ2Bq2jyosq5QRCfuVa913aRWUTV9AWgoCZtIJ3FURAFc1Bj0y7h+yQtu+kdBcsIR7EQgiN4Nr36gyKoMRuS245iKNIOJjqyqYsaVAZxxmbKlNWuhy6AjFIqIkCa6r8DkrzBqNa78LMzMFs8myfj6vk3Op2VxTGcaEA45I0yS9OyuIuiXkyKgXm0eh9iSWZ+JvzySVNsqP/vfi5864jdqia5QsH5XvQ+hT5CZqYQe56N1fC7fZIt14G5nPIjE5j5Sk83tXWtCcZcFThvFqRB5SfzkLJ9P7Q0XfBVi/PAPp9u/f8N1GN0ae8VKUR58U9d29CRFt0kbOogwC74jB4fd67ntxIIgWEkZVIZiEtnv9VAZ0AfB1O31bFhRF0bzuvQ0KqcJzJm2XPxc4oPO/0Vj07p9HQ9vmVhuNWm/h38Mgv9eDDz6Ij33sY1i9ejXuv/9+AMC1116Lu+++e0Ht7dRM1Otf/3q85CUvGbjP/vvvj3e/+9346U9/ipGReAX0xCc+ES972cvwqU99CnvuuSd+9rOfRZ8/8MAD6PV6DYaK28jISKPdgdbyYATQpMP55+mDx68YQ6HPiP0a8KAyRRXadA/NyPVHbRPrxUEgRe0JCV2NQvSmwyqTxOl1z74uR4DeTHDj0UOfXSMVaI5SNXAGDQiMFnd/JqCUWDejKohR18easokrm7Sx12X6qJDE0gu/R8dtbUAy7rrLaJL86VuAgmcfAFeHj308Nhm576jECYidAlyYOwM+I52IPRNSWS1Nvxdcgn1iVKhcTjOzNK8hF7limA7Ps5jKgXjKycTboShL1S4+j86Xjk+yX6pH8ufJuQUHgSldD/48YdcEgVMgqmtnM39rD7h4DrIou35qugZkovnJsDK2oXDfe9F1CqwyzweTbk9cZ8IxmamO0LfpfseBiXIAhzPFKvz+ICSEYL9R6n6GBQt9zLFq7hmFnX8yHtrOZ9dffz2OP/54LF26FHfeeSfOOOMM7LLLLvjKV76C//qv/8KnP/3pebe5U4OoFStWYMWKFbPu98EPfhDvfve7/ft77rkHz33uc/H5z38eT37ykwEAxxxzDM4//3zce++92GsvW2j2yiuvxMjICI4++uj5dy5HaaciUL4f4B+CPgotZcD63Wg1CcZaEcjxOqS20gqcuWLgiLt1RORyI7Fqad2LQkC6hJNGSJiyEx68WkcPWDoOLjKoMR6ufX8+di1R9Xee9TgdMza26fUaIYBqJAA1F80YieDdpCnHJy2YLCrnanNsTa8bIvoiBoWBCcqeTu+BhvuoASikigESdwXyPEJMb5UFD1pbQTwQ+ihVSMJI10FiZND3NgrZm3I6F3dPUIg7dxP5Me+7PjeTtvLr45a6N6Piu23Ai8aDjmGMVlrrLgJnOddduk+b5QBXUdjfW0tkIfWv/1+/hDAaav8j/Wd6ahOU+04aoD+9hxMA4oFUer+zRQWxvlF7gI+u44BYyMLKotrOl4AgnlYl1XBGzxhivnkbbJHD80rlzjtko7a9DZNtAueccw5WrVqF973vfb7WLwA873nPwymnnLKgNndqEDVX23fffaP3ExMTAICDDjoIj3qUzQj8nOc8B4cffjhOPfVUvP/978f999+PN73pTTjjjDPmH5lH1uIm8Z812JSM2zAHGABQlI4AbMZyJJorV1rBr2ylO4eK2aUoaR4A40CWdytyFimJrGuEKksJ1LGOCcpORlEkXQIijSz8NeSus/U9d32kpSSMtuHafNVNbBW5SI3xmdP92BdFcJux8HOaxNNJ2Re2pdPSOJBpVhOOu47cZz6NQyNbOjFJMhyTAjcSqLtQd6/fIeAo+57hMLIIfXRuH12N27xfbtI2qgrfQzJpA3C5gAAhWVHZ2QAKHyf2vxEiBkY5k/ExXn/V0vZs504tZdb4/gaw9SqpvIlkKR0cO9W/4bsoHvfsxvHVU/7Ui8aFi34D0GSaWhZTaXRbo2g57G/PCGldeXzRw/4v9n0c+v99MzuohQXnAI/e59IQJKxlI48dX5ClSWcTSUEupcvQFtdqbSAf4Zqon//85/joRz/a2L733nsP1D4PskcEiJqLKaVwxRVX4LWvfS2e9rSnYXR0FKeccgouvPDChTWYAz/8oZnuQ5sYS2KZHzh2oAirTCB+4CHzUPKgiLnLMitG3xYPUVZOWK6bD286V2sBUaePQMbdYR+uIw7AhG1CWhAgNBPJ04M8XcmK2P0YGDQFwIlh6268WvfHFRY0eLbOCqgFuTdpdV2USTHkpL4bAsMhOuMx2+TSFgAIkYA5i/RArMwNEF4XpasRF7tnfUZyEsK7iZQSeHrQlEzQBJ6ie5DXNWP3hDA6ronGJ3TODLox9HnABli6SKBcR1SWKB6fFtdpDsQOOIftc8zsei0ffYcc2OXKmxB7VlZAXTM3n0T/xu9DSAV12DOic3JwVd9xTRB1U6Rq1MEWQMHuSf6b4651KwDX2WP6d90Y/8bdd2aPSVyBvB+cicwwXuG7TyomuGNtuStmbdqsoW1T62tAbCEI6u/kXthOp9OMoAdw6623YrfddltQm49IELX//vvDZCJP9t13X3zjG9/YeidKQRNtI2vTQNHHFI1G71UBkYITISM6PQtwcv3wdLx09f0SHVVROWBTN0o0mMoCB9HbHLsF6TyO9fB9IYCWyXPF2SnPkhEAapSkUNGknoZO2zEDhClgKIM7XStP8pcAXKEUKwwdavlFlrrnAKtR4p+3pShgKQaiNth7njaB3IEhoSfTZumYDRMTy5zGBDZJaibPD79WU47YFBiUfJHpYgBEkVvcGqk5knY5GJpLZu/UGmCorhttEpvZSMsAWnBk0gEYnWd4M+f1bSTHNdySUjUiAOtbfwx1yNP8PvXtP4vHn93LfiyNDrow9poS4pKLOv19U3ukQfTfpdcgZTSO9FNw6U0MLVDUCFD3LfNM+/L+piwV/Z/8NgHAFCP292x0+K27378Rcoidhrbd7YUvfCHOO+88fOELXwAACCGwZs0avOUtb8GLX/ziBbX5iARR29Rme3Kwz325F5osaAXtHpYekDAgZbVK7sFWd8ODllx17hyiDp/7B/Is0VmmZGkLalYvD4BREoDLt5XoMgC2WqXXAIRwIE/3fbqAaBz4qjhl2lIglwnNBwBKDBppquh4LX2G9/BBc5IVLiLJFxsmt1JRArITJiifZ0ohG/7vRN/Z6DQZhN+NjNqZCDVfKLYfGBwQJjQ6AFDGHHgmgefqIXZRFZZxlApiZmNW+9LKcuZMODBeFABjfrJh8nxiRsweRUDMgZpZQZmQnrHNnitxnUX3jnPr+u0Z1thm0u87t2sBwLHCM1P+uzf9LvRtP4URAuogq7PkQRjQfZ/xn+7z6B5tAalBzxeXgeIVBuw9j8AC0/n6Xf8cMQKOobU1O62InMBP5Y8ROu6LB5LpuKRsNvsehIavqMBZy1YR+tC2iQ3decCFF16IE088EbvvvjumpqZw7LHHYu3atV4TvRAbgqhFs7lNPAAaIESwh68Rwj7k+OoueZAZLtwmkENuFmcebBEQSleTlH8nw0Igdesl2iPedw6CsqtoIeOSFHU/nmj5JMAe1FmmITUCI8rpwXi6CFVYoMNdIkJaDRB/uNdWSI5qxOeZst1qMklR7iKXUyhilKSCkTrsw8eT9uGuP99WAgZSoTSNU0HumZacPQSeEibQJ3IkF1PNggPo2BYXUasr14ERXng7BVBpNu6FWtR+es2DmN+WfbOAnLNFqXuKdiFXH+mlXDv6tp8CjhWMtH6uvcHXJvPjz8EgN5ZfqwFSCNjSfvR5mpNLSFvmqQ8PXKNzs2eRZ5pmG/e0f7Rf7vXQtokNQRQwOTmJq666Ct/73vdw7bXXQmuNo446Cscff/yC2xzeydvLUuYl3U5MErm1VBUxQ7TCFP0ZCxa4q0rIaB8yT+WziY7nD0oj/ETdtwwU7UfshZAQ/Zl44k0i+iIwxletNHFRwdJqrJkfKh2L3NgNMCMLW/C0GLFAohix28oRVih1xK+S/bYoMskARQE5Mmq1RuSua0uoCQJa3IWjQk4qyXMXJe49wNXPo7+S1dJzhW8JgPKoJ5YbKHXhGVUGAEWfqZATyH8vqrKFYduA2DyMM6g5BmrWiC0RXLRc99RgCnPRq2m/03vPN6bjfXIMS67Nomj0g1gyH1zgWR/hc6Kl2fsbxtg+KvDbyhDTZ7QoIQYpvR6XosIU7plRxPe2P5+IgZcpqsBwJWNDzyKjSlvTk+eUcn2Lft+0f/pdD9mooWXszjvvxCtf+UoccMABGB0dxUEHHYR3vOMd6HbjRciaNWvw/Oc/H+Pj41ixYgXOOuusxj5zsWc961l405vehDe/+c1bBKCAIRO1qOaz/rY9mAesnP1DipgK+qyoIOo+jITVQajSAhrGSgHICK8dCOJiUAaeRN0HFMszxVkvtsr1ld4p5QGJsznFz/tLk326IqfL1X1AFT4Tu9AWuBEbF2VO5qtoPmwpe0Nj0ZkMkXg1KyPj+6ghVNU4ThcuSs3llRJKNRI12j6wgrgVZRkP2jKeJ4rem5kpiITR8skvnQ4IKMJkzPuWRkMB8eTIWUgaL61twWcPpFigASU/pdIdXETccm828hixz2hMG5ZjUTjISfd1rkt+Dxkhgbrb7salc3BASdeZ6nl4/9NJnbuTyRWay91Eu9NCR+lQGJgAlLTBDvRbiY6VBYwLgPBjz9ke/lvmhbcBwNRNxikDuHwfU9eb1vZyMs8io1gUpwPbglz5JCcAAM0Yzci1WPgIVZPeT+l3NbRtajsyE3XLLbdAa42PfvSjOPjgg/GrX/0KZ5xxBjZt2uSDu+q6xkknnYTddtsNV111FdatW4fTTjsNxhhceumlA9v/2c9+hvvvvx/Pe14o2fTpT38a73jHO7Bp0yb8yZ/8CS699NL55Xx0NgRRi2RZPUi62s09vBEeloYxBiTUpgzForsZRui4CjsQRceZ0gqfRc9FpGmE89OE6x/qMkS0JQ+4yE3AamWZ9OHIrskUI5YhowcpFTyOGs6fLxoHIZ3+ilkmb096/qDl6vpJRrjx4fmsou+JaYuMquz+vW4IhXdpDwACVnFuIh9dx+qm0b5GWvG6KEIb4XpqC3SKwAZRybPGhNPGuNCY8XIfgE1j0JuCKUdDkABp1HohsShN+KLuDT4nELupcoCEv28DKxz0+IaT30X6OQ/nz/Qrapf3pY354udNIjmj17nj+bWxBQy5qv195QCYUWUQbwO2Zh3T0kVZxnsz4byqsPcTASkK+GgDuunYJwyQ1SllriO5Rs84k8axGrPPkajWZOEXMB5weSYqM545ED20bWY7cp6oE044ASeccIJ/f+CBB+LWW2/Fhz/8YQ+irrzyStx000246667fI3biy66CKtWrcL5558/MBXRueeei2c+85keRN1www145StfiVWrVuGwww7D+9//fqxcuRLnnnvuvPs+vJMXy7iYN30Qt1HanLkRLOGk7kPUXT9x1cY+1CxN70ozUOkH146pxsIDsewEV45rP3ro0X7sf6MK+8Bm4f+CJ/tMH+KMyfArUy5ylsq60FQ1+0NVsdIVqaV6IT/eyb6GCcbZgz2rH/LnDaU4BInwyZ3G2Jwo71Muz5BkGbCp3Aeds6yAkVGruSor/xeNQ3qPpJGKQvoSO3OaoAgYVqP2nlEFRHeTvT6eF0omecMyLI3x33EVjyVN0ty9mAMwKchN76f0PZ16ECAaBJLa9s+NXe53mdMm0XUnrJcdE9U4xqiyte9GCOtOpdIqdAy5hnlbMklzIIJLLhKpJ6xw+H2LdhcbB6dcKyVkkBFw1381Zp8R7llB42F4+3z8hqDpYWUbNmyI/mZmZrb6OdavX49ddtnFv7/66qtxxBFHeAAFAM997nMxMzODa665ZmBb1113HZ797JB25HOf+xye/OQn4x//8R9xzjnn4IMf/KCP2JuvDe/sxTQWhgwgpCygB2AxEv7K0aBjIXOsD9dTCABSWFeHD2dXTu8jVRziDkQAyRB4SjRQfnJkx/mHMnsIhodlMhHSedxfpIlpc+PkLJ3YXF2urEk5twczF9HTypoDSmYNzQ4QMnRnVv0eXDl3nAdX1UjQneXAMwHZnFC+RZdD358HcsR+ZPocTb7s+o20LBylyRC630yY6iZw0s81xjgBObxPkQYux1Ky4xptJmOTy0/U6ANvdtA9NggkcUCaO1cCAiLwmt4/xYiPsCUWKnJ1+fPZ36lxwClye2kda9moSDhzQ0fXRC5Hl5oiq7NEYHU5EMsBSbofRN33SVj5OPFxoZxhgoM3+u74PZB7HjVHemiLbFSAeEv/AGCfffbB0qVL/d8FF1ywVft6++2349JLL8VrXvMav23t2rWNEmzLly9HVVWzJsp84IEHomN/+MMfRszXk570pFD3dp42BFGLaARYPFtED1DOTpDRpM4z/QJxXqj0oS0L6GosPNhVu2ssYgvYgzNiM/iEm9S4C5Nbcowit6AI1+keoF4zwcBj9tpzJmUEHDhgjCae3KVy16gDqf7a6CGuqqA3Y4yh7z/tR8kVeU299HwsQWMkPE9X5BmAFAGpnGtrkPnvUzTG1d4b4+GcpNPqbrIaOm45sJoCZX4fzNY3IUOxaepPItzn92IUkdY2+QKtCwQAEaPa6AsHopl90qSkuTbSz7PJbRlz7KNL2dj770eFgABTuFxjvP/8u+ARpwlojZgnoPmbEM7FyAFYqh2k//1vlp4FGbaKBWFASOii4wTsnWZwQxLwwMd+CKC2jxljYPQW/rnF8V133YX169f7v9WrV2fPee6550IIMfDvP//zP6Nj7rnnHpxwwgk4+eST8apXvSr6TGQWnsaY7HZue+yxB+644w4AQLfbxbXXXotjjjnGf75x40aUZdl2+EAbaqK2hzEtgX0fP5yFhF+BhrB0GU8wQEjUxyc2FYOwVpdKuhrNTBQNIbNhaQrYg97nXvIAJbgvjHTt8PQHqdFD15Wy8NostvqPHvCzWOt+NAlxkFV2IHrTwX2iXakcWpGT9smBK9K6eBG4CDmKGq6ctomdjK6LTYL8O43SCbjvrFFDkJXaMWUnMEuysIC8dmxIvwtUjCVg5/FWZ/RG/F7h+jYgqsUXAVc2caaPtqj0CTuPAaLEk42+seM9C8LGCYjBM9+X9ykXJTdIl5f9XbS04/WJBKTcNsrhROPrASYDMZ5JYgwp5fkSPN1Hek/lWD9/fCLC5yAwBT2uba+BYmxgyvJpY/+XgC+FE1VTSMy4Y4hB12YIpHZ2m5ycnFM5tNe//vV4yUteMnCf/fff37++5557cNxxx+GYY47BZZddFu2355574mc/+1m07YEHHkCv12swVKmdcMIJeMtb3oL3vve9+OpXv4qxsTE8/elP959ff/31OOigg2a9npwNQdRiWvpgoYddFAafmSgEy+4rk9WzcTXhMqdrgIfcZDQXJiF3DelEys9B18Qj+kRyjUbGE0GuHQJbBKqEDJNRCqJoDESiB2Gf+ySDQgKmdiHpTOBO/ST2z+go0tGzadWIa8sBSKMtqMqxESnYTQEr3xcIkyddE9fbADC6H7mJGmMnZHD9SJsfy4wsCewHD4OnPlFUWPodsoneHltCuGLMEDISRgMI+cl0cDtnAUnLOBjn7hYuf1eaI2lQWoBsZFkytun3kQVQOZA7G1hnv78IJNLCoRwBMAJTdCCnN9rP6r5Nu5H2j5UoooVSIz0InYsAYXLdjd8bf8/uF6OKODFr2g4fD3aPRXUhASh+0UY3gHI6RmRckzxLxrehLYJpbbZYGD7f41esWIEVK1bMad+7774bxx13HI4++mh84hOfgEzmRkqIee+992KvvfYCYMXmIyMjOProowe2/e53vxsvetGLcOyxx2JiYgKf+tSnUFXhd/BP//RPeM5znjOvayMbgqhFMtPm1gHsRMUp+7rf2CeX1NA/XOlhDYTyKvSgxBwfUG0AL90n6lSG1QI80BOmH7Fnfj9i1eg6OehJ+8HZIjYZDHLfRV1MJxS/vb24sylCskkhZqLyMz6lgwj751INpG3SdUSpH5Jr9YwNy0odTX7uXKbsQExvdK9HIPo9D/AoPYUpYpbI1gnsQsDmE/IRmsm526K8fJZ5lllej4z7Y0RvhuUNGwV6MxD96cBksPs3uj40v6NG/bdB1nYPtrAxc3E95t77/qf3XzpOKbAXwqWVcC6vatSn7cj+nmRSOJudO0pUS9/HAECejml0PQSg6LfFwW76W+SZ1dnzRqTXn4LZoe3QZozJljubbxuLYffccw+e+cxnYt9998WFF16I++67z3+25557AgCe85zn4PDDD8epp56K97///bj//vvxpje9CWecccasrNhuu+2G//t//y/Wr1+PiYkJqCTf3xe/+EVMTEwsqO9DELUtTDLXTKaieeSiy63m+SRHq2pVRSu9eT3M0hUsb3vQyrzlHNGEk3MJcX3FoElt0ASRjgmxFW0h7xmmwgKbTJZyIX34vB4Zd+48m/HcMw5spe4nOCagjRbnPDqNjXPDFecnOJcPiNILpGJvBtqMLIAyTKymHLWgxmj4eoEFy3VCWiievZ3utYyo3Ksk3Tn1yDhE3Q2AkFxS5QiohI8REmZkHKhsX9DvBjcWv9cy30c6FnyCbyRrpdeD7pMBxl2BDaaHFiUJ49nQYSXH+Pa45k6F79+Uo0Ddg9Cb42tPx4HfKymoIpBvQm3MVtbN98lEWsR0AdZwA6bfyaBnwgKAkxQxEzW0bW+ka9rSNhbDrrzyStx222247bbb8KhHPSo+pwNuSilcccUVeO1rX4unPe1pGB0dxSmnnOJTIMzFli5dmt3OowDna0MQtZhGEy+fFBRzPxEb5bQrHoRwLcKWrPQGPfznOQFlV/Yps5Hmc6JDPasig35HggEjl7CTzx1tgCuZQH2x1hyLw410aEDDPRbVBxMyyiPlkyCyHFcRgOJMQWZypfd+AuNpAdBkyIwQEKwtU43a43m0HJ3X9U13lsZZ67krLx0+ViKIGBAPAgDLbCZuVf+et8+vBbBMF72m8jopYODfDZ+8pQRq5FkmOo9Ici+liwzeNlhbGQAQudTS8eEAquX3R4AkBfA+vxsHKap0OdNYKokckErGxLdBzGI5al3NQvoEstH1R8DP1XpsK8qcOz9vs2VsfcJdOo/7n+ZVAkq5/4c2tEG2atUqrFq1atb99t13X3zjG99Y/A7Nw4YgarFswMOXatFxVoMKfFKCzGhi8BMua7ONNVpsawM39Bm3dD+dTJIJqLNMQFJHL7c6Ts/X5l4jgAYkOrQCIGYmN6lIaROTCuNBliAgkfaLmIj5jg0QROrQNn8TsWUqjFGrfiWdOFUJqNKL8xvnosz29Lru+QSoJCIW/emm2zSdYNPPkvFLM/RHIf5GQ6QLWWJlc+xS7h7P7TcX4+64tu8pt73ld5Zm6A/3TX6cPFOl+9Z9CXZPt4FNv5hi0a3FiHXN9abjkk5pvULh6ua1jV+OHUzHgt/vvInmFQKImaYhcNrxbHtooh4JNgRRi2UMGMSTjGMYisrrIUTd9RmCB7bH2REgciHRim/gsytH48/X0lXqLKAhWq0PmGQsK2fBTSgl01JgNtWnRCt4EQmgbWmcDKggNsq145kp+lN2xU+TnXFFZjnrZYSA0MiCFs8m+Gt3bKRUtk8uv5NRbEyKKko/AQTAw0XyphyLx4OPQVE1xwzwBZgtyKnjz4V0JVVk/ljO9PFLZROtqcYgupvDZF+N2n0oQWvEyrXr07JsFLcMkLZgNBmP7D4JQM+1nYI4vj33Gb31JVk0ZHcKpihZ3bwALFNQGfUnOY/PAeZ+C/S7kN0pfzyvpef7wX/jDtwNKgvTNt5p0MNCjJ5J6dQ7nIq3vbWthebbxtBiG4KoxbQMADBCugSNRQBYPDld7jigfeVobLTenFd+szwMZxWmpxNVjiViD/FGaLmvfZdcq0/lwHI2gbEbvI+5CbGN/ciZ618owlxEGhNTjftwdWO010hBmPh6ZWlBEK/vF4HGzM+rqGzeKeqKLCCE9q/99SQsnBHCZpBnZVvsOQIII/FwY9LXfaelghWE03VKZRlQnlSRDxNdF4FGCrcHQqSiLHw9PjMyAXQ3JwWObaCBrwXIARRP4aFK59brB3DLwUTOBMvrxbalr6N8ZUKF9jPttS40jKsXmZlEdDka52gzOuR/EjIGray98Lm2mjKtW9kf30XSzdF7AtepO5nGNmVgMyCV/+YbYMftMxcCgp5BBJpqE7vzam1/0Qtcvg1taDukDUHUIprXEBCg4FoXvmMyYWaBCdksCRmzodeD+jhg/1ZA1Qak6C2fTNhnDUDVwuB48+DSpScY8PiNEngOclMAjVB+fy7X5+hYxyYKoV2Zi9E4ASoYyMu5ZJLvkrtu4/6zNAVJyRtTjtqEofwcRgfNk2L3WNqwcMWlAefqE2FclQOJkXBdBYaJ3J6c6SNwUHdtOSKjrV6MIvAAG85PdRt1HwDLws2ZSMrGLZi+SBVNt25yPSnA8UDPA/fEteVep4lUB7KqbYsXIAqkaCS5FRJ6dCJ8j0BwB1OfOOAXApBlADyGASAGyDmQ9N8h0AxC4H1Pa+Tl/qc22WsOmAx7n2qcOPtN+9X0UzChFLVfXyD+f2jb1nbk6Lyd2YYgarEsWsFJKMFABAKQaORloQksw0Y0AAGbODllvhA5gml5zd9nkyGmwIFrcpLJKgpj5w/4OrP65i4Kcn3ZCsKeRWroa3JjRsbYGgAQPOUCK4PjI9AEm5iLDgx347FoKaH7MGl2Zxed5q+dGBPqnyqCmwuI6s1lWTTSaKWX65M3srIlmXvFlB3ranPn5m47ga7tG20nUzZFghECoGLS/PssOhZIcZeXO84L7+uuLeRsNIysXDJJxONe95v3K33XbfeZHzdKkFqH9ykYApBGqmWZIRKryyJOD0BuZWNziFk3MEtfoVhQiCpgSlZrUusAfjlId/2odl2JmQfvi3N5uXtL6KRSgQj3aGTpoip3P2fGN3qLGCz55tg2Y4wHSBrCgyQOkJQQqDOT7BA07Rg21EQtjg1B1CJZ7VZlnCbnmXpVAhh8eDUQmIpUkzHA2kBQG8M0V8sCs1xfcg/raMXd8kBPNVKZSbBxPnKBOHdTWmi4YW0JTVmurijLNgEB3QcEleoo4/7Re1N5MOFdaUUFLQuI7mbbv6KMgKI/N4ENniqh7rlkjW3XwFykdT9OH0FjkGEdzAjLgULJMXvTXodF341Pw+HPyyL1XPs+UlAqe38YHSL+UkZPFj5Dtwei7JqFYJF2afqPDEg3SoaxZCWHcu5df1+kOdnI2u4XOi+5IylXFgFgp7njgEbo2rJ6KpN+gJ/LMXzVrisx88D/QHcmo+eCMDYUwiiWvZ67wHnf3DlMUYXi4HR/Gd0KoNp+/5xhMoCvk8ZNAwE5IQZIOQA1tKE93G0IohbJTPI/6QO4qQzC8fmWuDaCrZIbk+QAgJUTm8/nMcep+4HArIVBCidNXDhAmBhaWJZWa5v4+Oc5dwY3P8k6MJLqU4TLGG8ybh/aj/4nEbvTVflQdlXBjLC6cFGKgCJOiUCMW1rjjOUXy2bobgtGYMxF6/hwJpSDeCFtgk7HRAGwQMtplTyAItDHGTd3bXZcrIvPXtNIHCTAAYCqIPouPQKBooISfXbz15G6BSmysWblUXK/DQ+UCVyVIQiBfl9AAIRC2O+pgNWSCRlK8zjW0BRVyJWV6pLcWPiUDw5EVruuRPf+tTCjS9Gtg9tLAlBS+HM0GF2pPVhPGWxfhomuvY51d4PuifS5VBtk3TZDRmnnth05T9TObEMQtUimGWgalGguF7niH+jkLuETcQqkWs4NYKCeId2XbC4C9Vb33mwAx58kdUnIeFIcBMqS9wMjvXifU1aAA4lIqO2i6ADLFvHwe6BRqNegBChVAYvQ4t+R1XNlrovaoONYXieTsmQcFORAAumTkuSQje+KT7CsThsxGPbec/2uxmJQ0O/Grkhhowt9ZCMxTca5scitxcPuhQDAhPgEoDir40CCKTsWlBJDFX2vAQT6hUdaRBkJq+XbZ+wl1xYJGd9PQlpXrmPnSIOmR8aj8imGSgnR2NM10X1Af47V6/7uTtRL9kBPBzdYXxtoAf8jFQBkIvb34C25nsbzgGupuN6NPk40T0i2E4BaDNBEvZytYOzQFsG2AogaZkxt2hBELaKFh5ForuxIdIpwX6qUIaFEkmmphpzrBjF48n2AffgSRZ/TPeRSJfD9UvCljWPRuFsh7Vd0rTHDk00JoBIXlc60O9t56FxABNTaNFpZ1izpM6QEZNUAUx7YSABw35PWQdPEGQjHSqSCZ1+oOZPaIHu9KZuQAgDe78RtE33PjH3KjWcj4zoZgUo2bv5apPJgEkKG/FdCwgcGMAG4TQ9RRykwcrUfefQi76MP22fX3SjUS/eQ1vZ7ov3dvZumHkhdgt4FSq/ZeET5u4S02igHwgTpxdLvio21qLsY6Xcxsmw3rH/IZjM3xuYcFex3TN+ZTDSV/rPE3RmNZc61S5+z17qFedraJmHBkxLDPFJDe/jYEEQtkvGHUpYaNxlmyGjUkHEMGq3w2T7Rg3MAI0Xn4aDIGONXgXx7W7Zh+jwAvQRAMS3NXB7DDbcUvy4gdvNxJiFnfILIgKHWc6UgNGV53GcRsBFBY5Jr14b/c4bGTZo1letgqRukDECR3EC5a0v7lLsW/p5N1Lnvwk+8g8oK5fIHsesWEpaiIPDnyuPQsUb3WyPrOMsjjLFuvLZryWl6uBswHZcU4MrC6q1IfC+de5LYqn63cc94oToQMuGnYIl/L9wNa5LoOV56x+0vKP1D3Udnt30BAPdv3NwYp9oYlDIssmgRRMYj4iL9ZHq/cJCJ/CKLAygNC3QWi4ESQkCKpsRgaNvGtDHtUa/zaGNosQ1B1CKZBiKtQ+t+DJwYIWGYGD1yA6SvgeAucQ/KHIPEo2qoXyQM5dvpde37I3y/APbg48xTMgmJuh9yFSF2JbSyR8lK2rDJaU5FadMVd5sOqmXFnm7zQCMFSpy5Sdulz7n7C4DRfaAag5h5yPWtiCKnUBR5INDGSrUwkBHQTgBUq7tWyGawwFyYuhTUGsa+EYCiY9RIA0B6BsjpiBrAlPpByT+Bxv3ECwN7gKIDWCLhuk3fIEObLhrQFJUr8eNYNIr+LKoQHEBg1LOH8WV79o/clnzc+LASwKy7/vzlyscAyAMowP48Uz1iCjpy2yIW0vVprq47vz3bo61ntbuwYoiitrkZsxU0UUMQ1bAhiNoGNhcwVSf3ZjqZG5r0BgCLHIBK957rb6CGgQSCnof3yU0yUSSXDBNexBKkE3HufdTBBEDlspznwEUbeOLnHMToMPDhxzrX37ayKCx5qh8jSlswMgHRm7aamtnA3CAXZdt7DtzY5tzzkjOPxGQ0JuQMgLQH9z0QaCT8rBkDJWRInqkRQB5N8FLa6EajWf2+AGiivE+SRUrycfZ5parQN6ObjKhz5fmEqcomBzUARHcq5ORShdU/UVt03fw7oe+UANTIeDgHfcaHkdJCMBBY7bl/80vJmP3tigaQ4kyx17ulDJ2zOnke5M7hP59TrxZm/G4V7HkytG1rQ2H54thgX9DQFs1yiN6w/7VBks8H8YO9rV32uu3BOB9NZ1ZL5dwlxBr5pI85wMMnIH78HKytnErDZgNQuf/556xvWTAxaNyT7TReJpnQomLAbexTxhXjQTT7iywpgsv74U+ROZZctfN6JKZjSKDEbfPJJXO5rjggIXcmYMX0RQVTjvjIv4i58ixSEWvAcv2hU3EgTv1zWjtTdGKWyQEo494bIUI/WL6oqJwKYGvYpWCTrss4F6fbv1rxqEbKgQceCixU7vfY9tvl7nXvbmdMKd8e7eNssZmEQc0TIBzqoYb2cLIhE7UdLI1MyUXMRboHrsHgYIRNwCnYyYI0Yx/YEgBEeFBHelouIHcfeoJBCEgh0fNP5RKyKAEDdFhyQOFcJq0AIecSownSrfSjvE3IZGVmJS3SHFQm1fyk50rfcxbK/V8n38FAmw0UpowGP4aBJADZbOZA3I+IOaIJlLFnbS6c6DzJvo10Gy2MmBWd62YAAOBTIQBoZRA5mLQlZ9z3mGSBb7CH9JpFAMZut3D9/t4pxyB6m11pmoRRI/bJsUgCiLKzR5GWgGMVXX6ocjT/nQJBeI/aR0H2/ue3KPc40O9y/8bN0e+sDXjYvEsi0hCl35/dL/6fW8o2ycy2rWVt10HnEKY9Snhoi29aA2KLk21upc48jGwIohbR2qZWLu4Gmg8VPkl6cJAr75BzUSE8JNNPtACWT9jitRs2TQHGRIBKAlg6Mea1Gp4QcC/qRF8FhAfkTDGGUgKyuxmQ0ufO8f3k0UnpxOjcKzQWEOG2VHB1yOpkklUymmhNG0jh79s+dxMwvzT+fcwGpAY9l6JJbza33Bwtcq+SOy5zLwzqczoZZ116HLynbZP2LWUBeWoGt09DBJ60AwDS6aRax4TfP7n+lKMBBLHvWlfjEP2ZLMvnS9gADTcrRRdS6gpTVP43KHQdUojJAqK/2TJqdd9meu9ORfd8ufv+/jT025qNEKIFT20MpBCoTQC6tTZepJ1jmYTIRAMj8zwY3IVGf1ITAmyBle9/avSs62lgdB7nH9qW27Dsy+LYEEQtktEDi4OlXGRcuiIj3UMDNOUAACXaa+mDco37NplNjo9iw6YpSAFMjNnXtTF4aPNUfFpj9VGlFFbwmnxGVsOgrgGpRtGpp6zIGuw6+AQoC3SNhJK2XzwRKS9iqjh6odpsfKLlE+YgrRE/P73mgnDEzMyWRg+lkVQAAymJdqYBXuZgJnktXBqMhT7elAjuvlZ3ZmY71+RkXa+AB1Bp4WJef1D0M+Vjcsb7477DyHWY1AAkbZWpxoPAG4jr/KXsVALQfM0/wOWt6sOQC5vtSzURRXcqAmx6dGn79czRurXxgMWPTrKg4aOf00KmtjD4Hpsxs7/mVrtFWzbly9CGtpPaEEQtouUSyhGQEgBKmbjqyOp+LNomtoVW+UbHK3whmTtGAy7ST/BcNQY2xHo0rP8mx0c9aKLXE2OjUdSQYKCmkAJdhxCMAXZZMoYHHtoMY8LKUwOYVqNQhqq2K3SYr0hDotb2gWqMbTNNqeCrvhubO8tP7qSNaROJp+5OMp6Aso29QzOdw1xdDrxcRmo5ITd3Ic52ikFTTQ6U8T7MxRXJQWvb+VPmK3JxcQDFGUeg9TvxAMroUByZ2qNzUQLT5Dfko+ToXkB4b6S0ZXO4fklk9Flg7joe8YcAvnw9wTSQQEoYkWxzUX1GFcE16cahMxrzLZy9mc3oN+UBVMYFSMMzXy/LlrJQ8zkuJxEY2ra3tsfjfNsYWmxDELVIlqPbOaiSAqFECBAK3wLx5MMmfMEK2xpVQsAyM4IJbmkCEEJaN4buQ+g+RG/GTgzL94j6OTE22ni9yxLr0uOUfN+5EEaViCaG5cz9R6YBaG388dO1gWIuBq/hcPvZ9u22Qgr0tIncjj43FR+b1PVjWvI4uc9mc6W1AafU3TXo8T8oM3167pRNyr2ekyWsELfZ2hoE4Oh6RfI6PW+jLzkdU/LkNaqw4Ek4IXYd6iACjv1Jo/A8eA5X1Uid4JKbCt08N6+v5wEaJTuF+z3xQsZAnP09BYIunQfpxER3s/0dSnIVVuiML2kMUe73MshmY3jmikfmmyB8a+KcNpff0LataW22giZqCIBTG4KoRbK2SBTlJloBhNpgObFz6t4wrHyG0RDdTVYHAmBk6a6YmpqyoKw3DTmz0TIE5Rj0+K4o7l9j3Qrz0ODssmTMv6ZIos7MeoipB4G9D23s+wDLupxbbdfGoHJMFrVNQK1SIgJz3CbHw/bpqalw/YzB8JOZe+9rwKUsCXPftRlnwvhXt6XPfx+WPg/d0myW6poGCcpna0cMeJ9+BsGE/G1gipsrYcS1UVRzzr/ud+GdULIIQIteG23BFmeoSBxeh/xUvp4dMbk+Ui+wWv51slCJClnzwtDsOkzmPoPuW01Uv2uJYGNa3ZvA/NiorWVtwSPbqw9DG9rDxbaGa3ynsSuuuAJPfvKTMTo6ihUrVuBFL3pR9PmaNWvw/Oc/H+Pj41ixYgXOOussdLvdltYGG63cCwEU0ChMH4XpQ9RdKNMP2ZpzEXdpVBu12bVJG6tdV0JOb4TobsKIY5ZGR0fRmViKzeUSGFnAlGOodl2Jztg4ikcdhmrXlah22XNB17J8YgwjooboPoQyAVB8H4C5/0T82ZiZwfjYqN+PW2EyovmMdUZH7d+E1ZkYZSP5RG/aTshCoits9FcU5g5kuexcCDhtB5pAxCT7DAolz1nbuQa10fae9iWmaL4LxEHzaAM0ZXdq0Uul+zDNkHerEpgVtgAxZGE1RrKwEX6kQVJFnBqCWybLu1Fl0C/lFid8v6KK+uL7S7m9mOs8+oNLDaEqaEqXAAS3n5Aw5Qg6Y+Pt47Kdjdzvg/6G9vAzyhO1pX9Di+0Rw0RdfvnlOOOMM/Ce97wHz3rWs2CMwQ033OA/r+saJ510EnbbbTdcddVVWLduHU477TQYY3DppZfO+3xUH0r0p6MCsj4jclobThW2Ujy4HgQwZQdyar0DCzO+fVF3IbvNCWz5xBgwse+8+zubdcaXABn3ROPcLTayZHljG7kNc26P2Wxk6a7ulQVUMxsfQGd8CaYeyrhKkqiunFsul+2dtnObyzMkp3Xi2+bSxiBdlhe/G4qpzNtcNFG0X2o5F2NjvxxQSbclQRARY0gCbwIipQUlUWZycucxV7eB9ok8jZAQqoBOwZYI6RRIr2RcfTt7Mg2hk0LBvO+p+Ny5F+OxRyiQ7K5xZOmuWP/QZrCiLw0bgpShbQ8bJttcHHtEgKh+v483vOENeP/7349XvvKVfvshhxziX1955ZW46aabcNddd2HlypUAgIsuugirVq3C+eefj8nJyfmdVPch+9PW1QBYkDSzyeZAcmHXRpUhq3HdA9XqEsYAdRey1wc2rbNRQclkovY/ckuGZIcx7jbcEiOQ1lEC6IbQ+0Y5Eea+ochAJdqBDXfvZbM+I9ZN5cxkPk+1VqkNAm++duEAm4/HJtevObWXRrKBxotlbWdApE5YjkLAM1K1Ce/JakgYWVmm0iXEJDedzQvFElimiThpm5AhTxHYeJMei+un6DqqsShthBGywU5yd7GQiFyHSwcsJoY2tKE9vOwR4c679tprcffdd0NKiSOPPBJ77bUXnve85+HGG2/0+1x99dU44ogjPIACgOc+97mYmZnBNddc09r2zMwMNmzYEP0BlrkZWbIcI8t2w8iy3SCmN/pj5MxGyyRNrYeYecj+9TbbVW3d9WJwABC6tvv1XYj2PHRNj0RrMH3ceO4qBJdYT8MXRs1ZCp4M+0Pyum2fqI8I55sL2OGsVCSwb2mf92HQZ23Xmzsux0YR+KkN0DchYCDKhO7Aa1qCRCf7a9cGf0+gbMYo6zqTlk2yUXCFZa6AIAAX0jJI5JITIYt3xMrpvtdomaKCGZmwf9UYzMgEarCyP0b7dBuG9Y0+swugPkTdw8iy3QaM+NCGtn1NG7NV/oYW2yOCifrtb38LADj33HNx8cUXY//998dFF12EY489Fr/+9a+xyy67YO3atdhjjzhybfny5aiqCmvXrm1t+4ILLsA73/nOWftQ7ZZ3sfXuvgUALEiqu0AZC8qNVJD9aRhZoNjnsbOe55FunbFxTOt+EJ6zaD5hNGpR+EhDDiJqEzMVxFLxzwcZT9PAhek5lodPyKm1Za9PzdC1zdN4n+iaU6atzYUI2L4Ty6QRXHO5a1EJ0MsBzdoEQKeNjdikz3ibPQ2UkoEbndTvq7shaas7b2vKCVFAuaGrGdKVCeMkeOFvISP2UBt3fQ6IiemN6K67x/aj6AzUH24PYfnQhjZ05y2O7dS0xrnnnmtzLg34+8///E9ox0q89a1vxYtf/GIcffTR+MQnPgEhBL74xS/69gbldWqz1atXY/369f7vrrvumv+FuAe1MKHmlqh7ELp2KQuGAGo+1plY6tiGvo36UhVmUGKzGMFMbRx7YmxFeWecPakdO5WKuPnrnBicAykgADKeHoAXjk2Ng5e2Z5UBwr0y4Hgk501f8/95fi6TbKPXEatmtHW1tfTVuESQBJLaCuHSa4F4zPj1UFoMxUGt0yhZd1+IoONMEZ3ft4PQPmfOBhbpzUTJGmP8eNSQgT0b3xVGldBjy9HtLMXQhrajmTFbQVg+RP8N26mZqNe//vX/X3vnHhtHdfb/75nZm+NbHZzYTpM40HKz3ITKoa0JKiVpTasEElWF0lZp+FFFohAuhaACUoH+kSZqANEWlbZCRSqqFL0qUCpCqUMTQtNwSUIIKfwErUiJgfgNSUzs+LbemfP+MXNmz8zO7tprr+1dfz/Syt6ZM7Pn7LH3fPd5nvM8uPbaa3O2WbRoEfr6HFdaS0uLdzwej+Occ87B0aNHAQCNjY149dVXfdf29PRgZGQkw0KlE4/HEY/nCiPNjZ5JWRVHFbbllE1x3RKR5iUF33+mEtfyYZ12g81V4LtKrWALt9wI0tndg4Qt+rnIFTcVLCWjW4ByWX8y4qFyvOZoc1X54oNy3EcJBtXeqSloeJYUT/wEPlwdIZW+e7YuBS1OYX0XbtkT1fGUiMCypWNNctMnKJehYyHyX6+L27D3NPg+ZHYynRw12HfLloARg2VJRGJVue6Svo7rECFlQ0mLqPr6etTX1+dt19bWhng8jnfeeQeXXnopAGBkZAT//e9/0dzcDABob2/Hpk2bcOzYMTQ1NQFwgs3j8Tja2tqKNobIws95v1tH3NgrlShTJCGjDFIdL8FAXz2ZqMoCLfIsbEGREFZE2tu5Fbg2W+C6EiZhBWZ9rw2/G825OG1E1oVTttcJI2wHoYofCvZFBn7aMm2lzVpmxM5uxQ11UeZRF1JKJG3hva7lvuER141nuUpL5XsKbgpQQizMuhwqpNy4KnX9iC09070av/rdhpOQFsCEFvUlZKKQthx3sky68zIpaRE1WmpqanDDDTfgvvvuw4IFC9Dc3IytW7cCAK6++moAQEdHB1paWrB27Vps3boVp06dwsaNG7F+/fqx78wrEPPsNqQ++P8QyTNu0sAE+urORmZyADJe9AShgOPeU4zGxx22EOvCQF+89cBkPWAZgFNGJUDQIhS8JiyoPJjcdSyfdT6xpKWp12OklICIGMJzhwJpS14YeokPNcowUZWRXgLZ50AJF13AJF3rkwj0Ox/ePdx3OOhO1es6WjItkPQx6wLdS0obZi50GUu2ckImEhYgLg4zQkQBwNatWxGJRLB27VoMDg7ii1/8Inbu3Im6OkeimKaJ7du348Ybb8SyZctQUVGB7373u3jggQcmtZ+p2QsROfEe7EQ1YnMXUUAVET2v1ekzA54FwUba5aLWfF0EBD9IctXY87nwglakHDvsRlPTbrwE76FixJKuYFJjHVFWLveaET2WLHjPwPuW8Zpa6R/TrZsYJExAhX1464JMWZmCQigY6xV2v2yWKSWgwl7bBmAGrokawqs9GZ5/nxBSbghJaTmh9Pb2ora2FqdPny7IgqVqxVVmKYNCisepvgFv8Y8aAhHYXo4gZZWwbOlZY4C0pUi3MgVdehIBAaWXGnHbZIuL8h0KqQEoQ9oF44yytdFfV4kFXRR5rqvAsTB3VbZPEV1nFLqLJUy45moLZFq3fDsxNbdb0EIWvE5qFijAP/6oKwJVaaJTfQN5857REpVmonLElTrjXTNGe/9F/+8PMGLje8/t5AD++/j3i9bXUmTGWKJKBb1WHJlcVKyUcN1XtjAQE+lA5YgAhLsaB91zoQJKuey015BAeru8tD13nu4ByhA9gWLL6nXzBUTrsiObgAL8okInTCyNRUCNFyVyslmhcgkrPeBdCJFVpIYRvK8asymE9//Zc2bAJ6CcPuW/90xObxAsBUUmF1vfWTGeexAfFFGEBDCFcAsmw7/NXRgw3VxTOll3xGUp0usFoOvlRgJtBAAv+3bA/ecTbiFxUsG+hAko73m2oHD4F3uvNq/0L4a5BMFEFLrNXdgm+HrCZ1VT6EIqH5ad/f2wpPQsxWEiYDTCoBwFlBKGukDUj6n5ixtOYeaKCn5RJOUDRRQhGmEuhlN9A5hlSk9QmTIJy4ilxYydgqHXewvWjgupMWdLwDAimeVfVP6nsJp0imyFdZGZoyoXwYSiudAX/9EKAV1wFeLKy3WNvjNQPxY2nmAwuDqm/zQCbrtsWFKWpRAqFL3guAEg4ppULQnEkESiqhaDg4MUTtMAaVuQtpW/YZ57ED8UUYTkQRdWwz3/6+TuMkOW21wZxJUVSdq+zNg+l1+YOAoTYSHPg+JhNGkNJIrrntMtUWGxVup48F3L1jaILqDyiR/9dUbrpgyiu/MKRe0KLXUhFrQyGgDiwoKwUlqxcee9ooCaHlBEFQeKKELGQiQGVZdPCgPCSjm13ICs7jvfcTsFYUSckiFh1qYwIRU8FhRUweYIzwEF7biVZdfZZC3uStSEiaRswev6tUHy9VuIcHE22pxOBhwrlJ4WY6bG9eguOiGE4/a2U0hUVk9ltwiZEiiiCBkD6lv2cF8PvLppemFjaTtZtPVgcBXbBMcSJdVzrWSJL+5JP+9dGG6TyYhxCjxXYkntPgvuNvNdO8ECKhg/pdBdadksTfmElP4awOgDtgvdIRgUYMEErmOl1ALM9Zg457lzICZsGP09iNXPn6KekdEibXsCLFFMJRuEIoqQAohX1+HMwCAi0q3RZ9uA4QZ3W0kATqwU4FqspA1pRByxpYSWIpD2wCeucqCvwXp8U5i1KZd4cs7nfbkxM5rA8lyjDBM8entdpOXrv2o7EUvAeAVUKRGcQyHc9B/SqUspRgZ9XxLI9EVaFqQ1ThE1zuvLEYooQgrElgCk7VijlOix7VCjkRRusVwAwhVZCO7OC16Yzb2XBVWkVy9v4vU1x3VTJaDGS648Q2H5mMJ2G04l090ale09MgUghgcgbCv875ZMS6ScgJgoSREVhCKKkALo7R9E1ACAGERqCHYkAQFApIYgjYhnhdKxpJtrSnfpAT7LlBfPFLROIdNVB2TWhwtu759O4mm0LjqFnuAyeI98fVc5v/R+BXcYhiUCDfYvrM89gYLW5YiKe1KFuVVhZ9NOQqScv9dYXfbC7ITMFCiiCCmAmsqKtDsPrngBEIkkIJIDvhxPQhhIRWd5O/CUew+AP5bKTcLpBYGHlIUJiib9eFBXFENA5bPg6OViwvowViEVvIcBIGYImKO4i26p0rOJ65npg+iCTY1DiSU9qFw9L0RI1VVN7x16QjjvMZD+W4sIAFYSMGOIc7ddScLdecWBIoqQAujtH3QSZqaSgJ2CGUkgaQNSAMIwAGkAZsQTTKZbQgaWZoGybcAML+Oi/x7Mtq3nggLSNd68entjKJNSCLmEkJfw0o3MMpGZ0FNdX4igAhyLSKKickzX6IJK/a6LIiEEYsL2XKzCSmHIrPDFP9WFFBsuFAOAPc3ceUpYxgzhfCEQ8N4PZ+cdxVMpQxFVHCiiCBkjvf2DkFIimhr0jqk4JEsCtpmAadoQVhLCSsF261UJwEmJEIk5gkr4UxVkW091AZU1OzpGV9FhoixQ2erqKVRZHJ+1TGsXJqCCfctq9RpDnFgudFHU2z+IpDQgpQBgImHYqK2ahd5+Z44NAVRp9SzLzZUnRLqgsmfls+1QtzQhJA1FFCFjxJISUUMAKS0wHM5CG7WGgFQSMCJIRWfBiDjCSgCIWEOQZgQilXR+KjEgbcf9F0k4T7XX0gsE62IqKFDGknhyNGSLF1LuOu+c9rrKaqOERzZGm14hTFQ5ry0xNNAPCAOJCXItpWzpy66uUlkUo5bl0GDu92cy0MuyAG78kyt8LRhO8Li7ASKdPJOUMrREFQeKKEJGge6+MYVAxE46u5OkDQgJCSA64sRCCSlhx2bBsh0BZQh3gTJjgK0l5wQcl56bGkGkhiDdAHWFEk66BUoJqFx174KM1gKVzeIkhICp9Uf1RRdUp88MoLZqlic81HOFiksKc4WNdqeaKtScmDU2d14+gvFTxSQl00JyKnfoBUvypGyJuCkA14IKgOkLygjmiSoO3JtKSB56zgwgZggkTIEKUyAmk84OOyG8XXTR5BkIawRCSsho3BNPSnSklOiQtvfwsFJeFnQv7YFWDkbAES2WdBY6y5Y5Y56Cp8aySIe1VULJEOn+mCJgGZMyI39S8LkSKnVVs7yH/nw0KLfp0EC/k/C0CORKnTARjNjp+ntTHROli+SIO6GJqlqkorMAw0CcO/DIJHHVVVdh4cKFSCQSaGpqwtq1a/HRRx/52hw9ehRXXnklKisrUV9fj1tuuQXJZHKKeuxASxQheTCFgClTGdvfhJTON3VpQwjDEVUAUmbCsxYpC5KyoEgtZYFIJf25oCw7bYVyLVTSjLl9cK5TYiyYdDJIIYtz0DKhiyedSjc2SLntwgTUaNHFUzZrkEpXICWQkhKG6biblPuz1FButKm2QKWfO5sBknZ6J6IzxwwkLyds2wLGaYmyi+jOu/zyy3HPPfegqakJH374ITZu3Ihvfetb2Lt3LwDAsiysXLkSc+bMwZ49e3Dy5EmsW7cOUkr86le/Klq/8kERRUgOzgwMIp5yFnRfHFMq6YuHUgJKJmq8xVFZbjwRolmfhNoFpj6UpAVpmBAjw959pS0AV0TBTsE0IjBM4Vhj7HQKgdEuyMHFM6wsixkSza0HVOsUI15Iodx+YRaqpGuOihgCpSajlCtzMgTUaNNRGAKIyBQiBh0T5cx0j4n60Y9+5P3e3NyMu+66C2vWrMHIyAii0Sg6Ozvx9ttvo6urC/PmzQMAPPjgg7juuuuwadMm1NTUFK1vueB/DSFZ6HfzQInUMABX+FhumRcpfW43AJDRBCwJxAZ7EBs4idjASUQHTiI2dBrR/o9hDPWl7xN039nu66SG0tYt2/JiUqQR8bKRj2YXXhhhgdp6EWDvp0jHcRVTKOVDyrTomF09y2epEsJxbZYaxd7V5wTf+62KwYc6bhrCSaDppjIQw2eQPNVd1P6R8qC3t9f3GB4entD7nzp1Cn/84x9xySWXIBqNAgBefvlltLa2egIKAK644goMDw/jwIEDE/r6Y4EiipAQes4MON47TeQg5QSTC2vESTxopzzBAzjuuejASb+4krbTFnCSaaaSjrXJTvnvo65RNfWspJevyAlWt72t52HxULrVIbiQ6uiXRg2BmOnEwphCoLZqFqSUqJpVgapZFZ7bbqqYXZ0ZK6XEVF3VrKLHLhWL8fRbn9vgHAd3VJpCQAQe6lzEFVCG0OP0JGKzGwvuG5neKEvUeB8AsGDBAtTW1nqPzZs3T0gff/zjH6OyshJnnXUWjh49imeeecY7193djYYGf4xeXV0dYrEYurunTvxTRBESQl3VLERgQ4wMQaSGIKRM78ZTAkcLEPfOB9Hrilnpgq2eJQvInvfIiDhtR4a8e5kiXYoDCGbY9l+ey2VkwHHTVVQ4YklZnGZScd2pRAkpNWf5XG/ZRHHwnLI2+dJQaJsA1HFTbRKQTj6zeHUdBVS54xYgHs8DbgHirq4unD592nvcfffdoS95//33Zwj54GP//v1e+zvvvBMHDx5EZ2cnTNPE97//fd+XRhHyTyClDD0+WTAmipAQhs6chjF42ks2KIZ6nRN6PTst8Fu3SCFQysVDOskLpRnLcAVmtFUB52bcS4EAACN2uCXKuyzPZ0mpWm/KET1zerYpzVfzDwgXTRn30Y7bcNIYqL8xkRxE8uM+xOYsLHwwZNoj5fgDy1UB4pqamlHFIG3YsAHXXnttzjaLFi3yfq+vr0d9fT3OO+88XHjhhViwYAFeeeUVtLe3o7GxEa+++qrv2p6eHoyMjGRYqCYTiihCQhhGFBVuDJSMxCCQjoWSpgGRGoGUNoRtA27eJ2lE/EJKT1VgpyCFkW6j599x80epnzISB4QBO17lXQs7BRgRGMLwtvmr5JejYQq/qJE86LX58gWc66LK574LScgaJCLgxt4lvaSvMAzY8UoYw/3jGwQhIShRVAjqy6KKt2pvb8emTZtw7NgxNDU1AQA6OzsRj8fR1tY2MR0uAIooQkKorZqFYVkL43S3sxNPIW2I1IgjhCJxwLaziyf1Td9z+dmQZszJBK0VIpaWk+FcxU45bkEL5pmPvXtKMwoZmwU7IJtypb4LCqdyK1VSbuiFidXc6fmkwlx/uhUqKKBC9ZRMJ3eVkZhbEw9AVe2EjIFMX6Rtj98SVaRkm6+99hpee+01XHrppairq8N7772He++9F5/5zGfQ3t4OAOjo6EBLSwvWrl2LrVu34tSpU9i4cSPWr18/ZTvzAMZEEZKdVBJWVT1kvMqxNpkRSDPqCCAz6jUTVhLCTqUfKl7KSnqpDAC4geKOUJJeagT3X1B3B+oizD0vrBEIO+Xlm1Lou62C/8xqAR5LMksytdRVzfKsTUGLkzqmW6uCS5qvFFDgp3MzAzKSwCCiaQFFZgQTGVg+0VRUVOCpp57CihUrcP755+P6669Ha2srdu/ejXg8DgAwTRPbt29HIpHAsmXLcM0112DNmjV44IEHitKn0UJLFCHZiMQghvo84QNhQNgjjrAxI44AMlx3mwhIGJUOwYxAGhHXJWcDhuv2g2uZ0oSS71p1TP20koCVghVoqrv04oazXA5Ywlt4KZ5KDzVnesLRbO5Y38aCHL8bwkmPYRoRJCoqSi6/FilvPve5z2Hnzp152y1cuBDPPvvsJPRo9FBEEZKFARFHpeHspos2nIPkx0chKxKOoFEuPOm483wWJyWMdIHkCi7AjY8yIp6Y8gWih+GmPhDWCOAawPSWwR11XCDLAz1Te5iIUqkKgExXnkRaSKlzU52ygkwtjjtvfO441s7LhCKKkCxICdgVtYCoQxQY9e6l5IkPIKwkZCSm3cz98FEfQl5tFU1kBS1QbikZYcMTbDFhY1AySnymoAspU6Qtj6YhEHFLEVkiLeJNI+KVHALSAecUUEROQNmXYmYsL1UoogjJQqHpAGL18zOOJT8+6tbY03brKUuUsmop4WSYTh4pIJ21XNqwqhswYgOGG+USMwUqKrg4ljthf4f9A4Pejk9TupZRFT8HR3CpWCgKKEKKB0UUITk41TcwIbmVdCtW8uOjzi/K+qRiqtQiqBJxCiMdcG7GnN16VXMgIJCgeJrRZCsQPDToFoV2n1NkEwUtUcWBIoqQHBQjOeVYkxr29g8inuxFqqIOlVwUSQ4orkk2bNsKr6owBiiiMqGIImSaU1NZAVRWID7VHSGEEOKDIooQQggpc6RlA2KcliiLu/OCUEQRQgghZc5E1s4jaWZMxvJ3330Xq1evRn19PWpqarBs2TLs2rXL1+bo0aO48sorUVlZifr6etxyyy1IJpNZ7kgIIYSUBtM5Y3kpM2NE1MqVK5FKpbBz504cOHAAF110EVatWoXu7m4AgGVZWLlyJfr7+7Fnzx5s27YNTz75JO64444p7jkhhBBCpiMzwp134sQJ/Oc//8Hvf/97LF68GACwZcsW/PrXv8Zbb72FxsZGdHZ24u2330ZXVxfmzZsHAHjwwQdx3XXXYdOmTVNa4JAQQggZD9K2xh8TRUtUBjPCEnXWWWfhwgsvxB/+8Af09/cjlUrht7/9LRoaGtDW1gYAePnll9Ha2uoJKAC44oorMDw8jAMHDmS99/DwMHp7e30PQgghZDpBd15xmBGWKCEEduzYgdWrV6O6uhqGYaChoQHPP/88PvWpTwEAuru70dDQ4Luurq4OsVjMc/mFsXnzZvz0pz/NOE4xRQghJB9qrZBS5mk5TqwRjPsVrJGJ6ElZUdIi6v777w8VMDr79u1DW1sbbrzxRsydOxf/+Mc/UFFRgcceewyrVq3Cvn370NTUBMARW0GklKHHFXfffTduv/127/mHH36IlpYWLFiwoMBREUIImWn09fWhtrZ2wu8bi8XQ2NiI7rf/Z0Lu19jYiFgslr/hDEHIosvf4nHixAmcOHEiZ5tFixbhn//8Jzo6OtDT0+OLbTr33HPxgx/8AHfddRfuvfdePPPMMzh06JB3vqenB7Nnz8bOnTtx+eWXj6pPtm3jo48+QnV1dU7xNV3o7e3FggUL0NXVVXZxXxxbacKxlSYcW2FIKdHX14d58+bBMIoTYTM0NDRhO81jsRgSicSE3KscKGlLVH19Perr6/O2GxgYAICMP1DDMGDbTvKw9vZ2bNq0CceOHfMsU52dnYjH417c1GgwDAPz52cWoJ3u1NTUlN0Hn4JjK004ttKEYxs7xbBA6SQSCQqfIjEjAsvb29tRV1eHdevW4dChQ3j33Xdx55134siRI1i5ciUAoKOjAy0tLVi7di0OHjyIv//979i4cSPWr19fth8IhBBCCCmcGSGi6uvr8fzzz+PMmTNYvnw5li5dij179uCZZ57BkiVLAACmaWL79u1IJBJYtmwZrrnmGqxZswYPPPDAFPeeEEIIIdORknbnjYWlS5fib3/7W842CxcuxLPPPjtJPZoexONx3HfffYjHy6+8LcdWmnBspQnHRmYiJR1YTgghhBAyVcwIdx4hhBBCyERDEUUIIYQQUgAUUYQQQgghBUARRQghhBBSABRRZchLL72EK6+8EvPmzYMQAn/+859956WUuP/++zFv3jxUVFTgK1/5Ct566y1fm+HhYdx8882or69HZWUlrrrqKnzwwQeTOIpw8o3tuuuugxDC9/jSl77kazNdx7Z582ZcfPHFqK6uxty5c7FmzRq88847vjalOnejGVupzt2jjz6KxYsXe4kY29vb8de//tU7X6pzBuQfW6nOWRibN2+GEAK33Xabd6yU545MDhRRZUh/fz+WLFmCRx55JPT8z3/+czz00EN45JFHsG/fPjQ2NuJrX/sa+vr6vDa33XYbnn76aWzbtg179uzBmTNnsGrVKljW1Fbxzjc2APj617+OY8eOeY/nnnvOd366jm337t246aab8Morr2DHjh1IpVLo6OhAf3+/16ZU5240YwNKc+7mz5+PLVu2YP/+/di/fz+WL1+O1atXe4ttqc4ZkH9sQGnOWZB9+/bhd7/7HRYvXuw7XspzRyYJScoaAPLpp5/2ntu2LRsbG+WWLVu8Y0NDQ7K2tlb+5je/kVJK+cknn8hoNCq3bdvmtfnwww+lYRjy+eefn7S+5yM4NimlXLdunVy9enXWa0plbFJKefz4cQlA7t69W0pZXnMXHJuU5TV3dXV18rHHHiurOVOosUlZHnPW19cnzz33XLljxw552WWXyVtvvVVKWV7/b6R40BI1wzhy5Ai6u7vR0dHhHYvH47jsssuwd+9eAMCBAwcwMjLiazNv3jy0trZ6baYzL774IubOnYvzzjsP69evx/Hjx71zpTS206dPAwBmz54NoLzmLjg2RanPnWVZ2LZtG/r7+9He3l5WcxYcm6LU5+ymm27CypUr8dWvftV3vJzmjhSPGZOxnDh0d3cDABoaGnzHGxoa8P7773ttYrEY6urqMtqo66cr3/jGN3D11VejubkZR44cwU9+8hMsX74cBw4cQDweL5mxSSlx++2349JLL0VrayuA8pm7sLEBpT13hw8fRnt7O4aGhlBVVYWnn34aLS0t3kJaynOWbWxAac8ZAGzbtg2vv/469u3bl3GuXP7fSHGhiJqhCCF8z6WUGceCjKbNVPPtb3/b+721tRVLly5Fc3Mztm/fjm9+85tZr5tuY9uwYQPefPNN7NmzJ+Ncqc9dtrGV8tydf/75eOONN/DJJ5/gySefxLp167B7927vfCnPWbaxtbS0lPScdXV14dZbb0VnZycSiUTWdqU8d6T40J03w2hsbASAjG9Jx48f975xNTY2IplMoqenJ2ubUqGpqQnNzc3497//DaA0xnbzzTfjL3/5C3bt2oX58+d7x8th7rKNLYxSmrtYLIbPfvazWLp0KTZv3owlS5bgF7/4RVnMWbaxhVFKc3bgwAEcP34cbW1tiEQiiEQi2L17N375y18iEol4/SvluSPFhyJqhnH22WejsbERO3bs8I4lk0ns3r0bl1xyCQCgra0N0WjU1+bYsWP417/+5bUpFU6ePImuri40NTUBmN5jk1Jiw4YNeOqpp7Bz506cffbZvvOlPHf5xhZGKc1dECklhoeHS3rOsqHGFkYpzdmKFStw+PBhvPHGG95j6dKl+N73voc33ngD55xzTtnNHSkCkx7KTopOX1+fPHjwoDx48KAEIB966CF58OBB+f7770sppdyyZYusra2VTz31lDx8+LD8zne+I5uammRvb693jxtuuEHOnz9fvvDCC/L111+Xy5cvl0uWLJGpVGqqhiWlzD22vr4+eccdd8i9e/fKI0eOyF27dsn29nb56U9/uiTG9sMf/lDW1tbKF198UR47dsx7DAwMeG1Kde7yja2U5+7uu++WL730kjxy5Ih888035T333CMNw5CdnZ1SytKdMylzj62U5ywb+u48KUt77sjkQBFVhuzatUsCyHisW7dOSuls3b3vvvtkY2OjjMfj8stf/rI8fPiw7x6Dg4Nyw4YNcvbs2bKiokKuWrVKHj16dApG4yfX2AYGBmRHR4ecM2eOjEajcuHChXLdunUZ/Z6uYwsbFwD5+OOPe21Kde7yja2U5+7666+Xzc3NMhaLyTlz5sgVK1Z4AkrK0p0zKXOPrZTnLBtBEVXKc0cmByGllJNn9yKEEEIIKQ8YE0UIIYQQUgAUUYQQQgghBUARRQghhBBSABRRhBBCCCEFQBFFCCGEEFIAFFGEEEIIIQVAEUUIIYQQUgAUUYQQQgghBUARRQghhBBSABRRhBBCCCEFQBFFCCkaH3/8MRobG/Gzn/3MO/bqq68iFouhs7NzCntGCCHjh7XzCCFF5bnnnsOaNWuwd+9eXHDBBfj85z+PlStX4uGHH57qrhFCyLigiCKEFJ2bbroJL7zwAi6++GIcOnQI+/btQyKRmOpuEULIuKCIIoQUncHBQbS2tqKrqwv79+/H4sWLp7pLhBAybhgTRQgpOu+99x4++ugj2LaN999/f6q7QwghEwItUYSQopJMJvGFL3wBF110ES644AI89NBDOHz4MBoaGqa6a4QQMi4oogghReXOO+/En/70Jxw6dAhVVVW4/PLLUV1djWeffXaqu0YIIeOC7jxCSNF48cUX8fDDD+OJJ55ATU0NDMPAE088gT179uDRRx+d6u4RQsi4oCWKEEIIIaQAaIkihBBCCCkAiihCCCGEkAKgiCKEEEIIKQCKKEIIIYSQAqCIIoQQQggpAIooQgghhJACoIgihBBCCCkAiihCCCGEkAKgiCKEEEIIKQCKKEIIIYSQAqCIIoQQQggpAIooQgghhJAC+D8mK3D5fqHTeAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_neg.isel(time=0).plot.imshow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a53fc986-a88a-4023-a1f1-f57e4ec67350",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mask = (grid1_sst_hist_daily['x'] >=0 ) & (grid1_sst_hist_daily['x'] <65)\n",
    "ds_neg = grid1_sst_hist_daily.where(~mask)\n",
    "ds_neg['x'] = ds_neg['x'] + 360\n",
    "ds_pos = grid1_sst_hist_daily.where(mask)\n",
    "\n",
    "grid1_sst_hist_daily_reordered = xr.concat([ds_pos.sel(x=slice(0, 65)), ds_neg.sel(x=slice(0, 360))], dim='x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ba3a9fac-4d12-4761-a9f4-7b21bc68d2f6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(60.02771229)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid1_sst_hist_daily['x'].max().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "d36a56d5-4891-4ee5-9a01-a9caa2573956",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f35321206a0>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid1_sst_hist_daily_reordered.isel(time=0).plot.imshow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f168ec7c-db9f-41e9-92a1-27cfca1b17d4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# implement for dataarray\n",
    "# shift back to coord 0 -> 360\n",
    "# shift right only\n",
    "def shift(da, coord_to_shift, shift_position='right'):\n",
    "    # make a mask (can also manual split)\n",
    "    max_lon = da[coord_to_shift].max().values+0.1\n",
    "    mask = (da[coord_to_shift] >=0) & (da[coord_to_shift] < max_lon)\n",
    "    \n",
    "    # partition\n",
    "    # split data to negative and positive coords, then reorder back to 0\n",
    "    da_neg = da.where(~mask)\n",
    "    da_pos = da.where(mask)\n",
    "    da_neg[coord_to_shift] = da_neg[coord_to_shift] +360\n",
    "    return xr.concat([da_pos.sel({coord_to_shift :slice(0, max_lon)}), da_neg.sel({coord_to_shift:slice(0, 360)})], dim=coord_to_shift)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3256c81-ce6c-4240-ae58-74ba0fb44e30",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Back to processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "84d946ed-ddf5-43fc-8c89-1707e2c691f0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "grid1_sst_hist_daily_da = shift(grid1_sst_hist_daily, 'x', 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "b9dca5d5-465e-4e98-8d08-775624f099e7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "grid1_sst_hist_daily_da = grid1_sst_hist_daily_da.sel(x=slice(42, 102), y=slice(-12, 32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "9247c530-e1c3-422f-8d76-319fd4a3ff2f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f352a76ee80>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid1_sst_hist_daily_da.isel(time=0).plot.imshow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "e4c07d6b-8355-479c-88a4-4c97dc6f7e5d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "grid1_sst_hist_daily_da.to_netcdf('shared/data/gdfl_cm4_daily/hq_tos.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e1efff9b-49c2-4eae-9578-80f0f041e776",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "grid2_sst_hist_daily = xr.open_zarr(fs.get_mapper(gdfl_cm4_hist_result.zstore.values[1]),\n",
    "                                    consolidated=True)\n",
    "grid2_sst_hist_daily['tos'].sel(lat=slice(-12, 32), lon=slice(42, 102)).to_netcdf('shared/data/gdfl_cm4_daily/tos.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "bfa14e1b-6157-480c-9094-9e27f813e53e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hist_pr_daily_ds = xr.open_zarr(fs.get_mapper(gdfl_cm4_hist_pr_url.values[-1]), consolidated=True)\n",
    "\n",
    "# add attr template\n",
    "with open('shared/data/gdfl/historical_daily/attr_template.json', 'w') as file:\n",
    "    json.dump(hist_pr_daily_ds.attrs, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6314ebae-e1cd-4d14-b0f2-83160ba80b48",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## ssp245"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7bfe8d-19c9-44a3-b23e-c26b97ed269d",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Querying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "ff30a249-90d7-471a-a529-97b466ae2e2b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gdfl_daily_245_result = cat.query(\"experiment_id=='ssp245' & source_id=='GFDL-CM4'& grid_label=='gr1' & table_id=='day'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "4b6f5273-6e4a-4835-8c2f-5df9846bdd01",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['vas', 'uas', 'snw', 'tslsi', 'tasmin', 'tasmax', 'tas', 'sfcWind',\n",
       "       'rsus', 'rlut', 'rlus', 'rlds', 'rsds', 'sfcWindmax', 'snc', 'clt',\n",
       "       'mrso', 'mrsos', 'psl', 'prsn', 'prc', 'pr', 'huss', 'hursmin',\n",
       "       'hursmax', 'hurs', 'hfss', 'hfls', 'mrro'], dtype=object)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfl_daily_245_result['variable_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "be1dad4f-ccc8-4510-87b3-a97261402dd4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finish exporting variable vas\n",
      "finish exporting variable uas\n",
      "finish exporting variable pr\n",
      "finish exporting variable psl\n",
      "finish exporting variable sfcWind\n",
      "finish exporting variable tas\n"
     ]
    }
   ],
   "source": [
    "list_of_vars = ['vas', 'uas', 'pr', 'psl', 'sfcWind', 'tas']\n",
    "\n",
    "for var in list_of_vars:\n",
    "    gdfl_cm4_hist_url = gdfl_daily_245_result.query(f\"variable_id=='{var}'\")['zstore']\n",
    "    if len(gdfl_cm4_hist_url) == 0:\n",
    "        print(f'no variable {var} in this database')\n",
    "        continue\n",
    "    hist_daily_ds = xr.open_zarr(fs.get_mapper(gdfl_cm4_hist_url.values[-1]), consolidated=True)\n",
    "    hist_daily_ds = hist_daily_ds[var].sel(lat=slice(-12, 32), lon=slice(42, 102))\n",
    "    hist_daily_ds.to_netcdf(f'shared/data/gdfl/ssp245_daily/{var}.nc')\n",
    "    print(f'finish exporting variable {var}')\n",
    "    time.sleep(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788dce66-0c10-4ab9-8b5d-495316bb0fb4",
   "metadata": {},
   "source": [
    "### SST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
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       "      <th>activity_id</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
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       "      <th>table_id</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17861</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>ssp245</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Oday</td>\n",
       "      <td>tos</td>\n",
       "      <td>gr</td>\n",
       "      <td>s3://cmip6-pds/CMIP6/ScenarioMIP/NOAA-GFDL/GFD...</td>\n",
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       "      <td>ScenarioMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>ssp245</td>\n",
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       "      <td>Oday</td>\n",
       "      <td>tos</td>\n",
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      "text/plain": [
       "       activity_id institution_id source_id experiment_id member_id table_id   \n",
       "17861  ScenarioMIP      NOAA-GFDL  GFDL-CM4        ssp245  r1i1p1f1     Oday  \\\n",
       "17862  ScenarioMIP      NOAA-GFDL  GFDL-CM4        ssp245  r1i1p1f1     Oday   \n",
       "17990  ScenarioMIP      NOAA-GFDL  GFDL-CM4        ssp245  r1i1p1f1     Omon   \n",
       "17991  ScenarioMIP      NOAA-GFDL  GFDL-CM4        ssp245  r1i1p1f1     Omon   \n",
       "\n",
       "      variable_id grid_label   \n",
       "17861         tos         gr  \\\n",
       "17862         tos         gn   \n",
       "17990         tos         gr   \n",
       "17991         tos         gn   \n",
       "\n",
       "                                                  zstore  dcpp_init_year   \n",
       "17861  s3://cmip6-pds/CMIP6/ScenarioMIP/NOAA-GFDL/GFD...             NaN  \\\n",
       "17862  s3://cmip6-pds/CMIP6/ScenarioMIP/NOAA-GFDL/GFD...             NaN   \n",
       "17990  s3://cmip6-pds/CMIP6/ScenarioMIP/NOAA-GFDL/GFD...             NaN   \n",
       "17991  s3://cmip6-pds/CMIP6/ScenarioMIP/NOAA-GFDL/GFD...             NaN   \n",
       "\n",
       "        version  \n",
       "17861  20180701  \n",
       "17862  20180701  \n",
       "17990  20180701  \n",
       "17991  20180701  "
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   "source": [
    "gdfl_daily_245_result = cat.query(\"experiment_id=='ssp245' & source_id=='GFDL-CM4'& variable_id=='tos'\")\n",
    "gdfl_daily_245_result"
   ]
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  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "84b4e2ca-51fb-477f-844f-eab2bb13e349",
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   "source": [
    "# we want to process the first two rows\n",
    "sst_245_daily_ds = xr.open_zarr(fs.get_mapper(gdfl_daily_245_result.zstore.values[0]),\n",
    "                                    consolidated=True)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "c3654e65-acfc-4fef-95b1-f96d8d1fc624",
   "metadata": {
    "tags": []
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   "outputs": [],
   "source": [
    "sst_245_daily_ds['tos'].sel(lat=slice(-12, 32), lon=slice(42, 102)).to_netcdf('shared/data/gdfl/ssp245_daily/tos.nc')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "c3f10940-2d9d-41cf-828d-6a3384c32f55",
   "metadata": {
    "tags": []
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   "source": [
    "sst_hq_245_daily = xr.open_zarr(fs.get_mapper(gdfl_daily_245_result.zstore.values[1]),\n",
    "                                    consolidated=True)['tos']"
   ]
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  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "50981a80-35ae-40f4-80ea-76f6ac99d40b",
   "metadata": {
    "tags": []
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   "outputs": [],
   "source": [
    "sst_hq_245_daily_shifted = shift(sst_hq_245_daily, 'x', 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "b4bcc945-65f7-418f-bcea-9f2e2ab9a8ae",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sst_hq_245_daily_shifted.sel(y=slice(-12, 32), x=slice(42, 102)).to_netcdf('shared/data/gdfl/ssp245_daily/hq_tos.nc')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "6c85378e-a197-49a3-bb10-88e3a2a11a1d",
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       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; (time: 31390, lat: 44, lon: 48)&gt;\n",
       "dask.array&lt;getitem, shape=(31390, 44, 48), dtype=float32, chunksize=(420, 44, 48), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    height   float64 ...\n",
       "  * lat      (lat) float64 -11.5 -10.5 -9.5 -8.5 -7.5 ... 28.5 29.5 30.5 31.5\n",
       "  * lon      (lon) float64 43.12 44.38 45.62 46.88 ... 98.12 99.38 100.6 101.9\n",
       "  * time     (time) object 2015-01-01 12:00:00 ... 2100-12-31 12:00:00\n",
       "Attributes:\n",
       "    cell_measures:  area: areacella\n",
       "    cell_methods:   area: time: mean\n",
       "    interp_method:  conserve_order2\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    original_name:  tas\n",
       "    standard_name:  air_temperature\n",
       "    units:          K</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 31390</li><li><span class='xr-has-index'>lat</span>: 44</li><li><span class='xr-has-index'>lon</span>: 48</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-76223f82-449a-4127-bfd1-eb62b681fab6' class='xr-array-in' type='checkbox' checked><label for='section-76223f82-449a-4127-bfd1-eb62b681fab6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(420, 44, 48), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 252.90 MiB </td>\n",
       "                        <td> 3.38 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (31390, 44, 48) </td>\n",
       "                        <td> (420, 44, 48) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 75 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
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       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">31390</text>\n",
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       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-b74ba11a-78d5-461b-bc3d-cc54a8ec3034' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b74ba11a-78d5-461b-bc3d-cc54a8ec3034' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-80e5226c-02d4-4e48-b6c9-78b76e2c3201' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80e5226c-02d4-4e48-b6c9-78b76e2c3201' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a44270d8-6af3-4706-932f-4088627c88e7' class='xr-var-data-in' type='checkbox'><label for='data-a44270d8-6af3-4706-932f-4088627c88e7' 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>axis :</span></dt><dd>Z</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>description :</span></dt><dd>~2 m standard surface air temperature and surface humidity  height</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-11.5 -10.5 -9.5 ... 29.5 30.5 31.5</div><input id='attrs-d931da36-47c1-44c1-a671-f326c71e47b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d931da36-47c1-44c1-a671-f326c71e47b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff3f56bb-2bb1-41a9-af29-f6a3fa21bf85' class='xr-var-data-in' type='checkbox'><label for='data-ff3f56bb-2bb1-41a9-af29-f6a3fa21bf85' 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>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-11.5, -10.5,  -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,\n",
       "        -1.5,  -0.5,   0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,\n",
       "         8.5,   9.5,  10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,\n",
       "        18.5,  19.5,  20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,\n",
       "        28.5,  29.5,  30.5,  31.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>43.12 44.38 45.62 ... 100.6 101.9</div><input id='attrs-745871d4-0323-4808-97ca-832009b4533e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-745871d4-0323-4808-97ca-832009b4533e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-442760d3-3c38-43aa-8482-25f793570a94' class='xr-var-data-in' type='checkbox'><label for='data-442760d3-3c38-43aa-8482-25f793570a94' 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>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([ 43.125,  44.375,  45.625,  46.875,  48.125,  49.375,  50.625,  51.875,\n",
       "        53.125,  54.375,  55.625,  56.875,  58.125,  59.375,  60.625,  61.875,\n",
       "        63.125,  64.375,  65.625,  66.875,  68.125,  69.375,  70.625,  71.875,\n",
       "        73.125,  74.375,  75.625,  76.875,  78.125,  79.375,  80.625,  81.875,\n",
       "        83.125,  84.375,  85.625,  86.875,  88.125,  89.375,  90.625,  91.875,\n",
       "        93.125,  94.375,  95.625,  96.875,  98.125,  99.375, 100.625, 101.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2015-01-01 12:00:00 ... 2100-12-...</div><input id='attrs-7896889a-565b-47d3-801c-b2476a178c7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7896889a-565b-47d3-801c-b2476a178c7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14df5a01-7c49-41bf-b0ca-c2a00c8905bb' class='xr-var-data-in' type='checkbox'><label for='data-14df5a01-7c49-41bf-b0ca-c2a00c8905bb' 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>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>calendar_type :</span></dt><dd>noleap</dd><dt><span>description :</span></dt><dd>Temporal mean</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f41eda0c-3e1a-4fb3-b6f3-9e326a45902c' class='xr-section-summary-in' type='checkbox'  ><label for='section-f41eda0c-3e1a-4fb3-b6f3-9e326a45902c' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8565e096-dcfb-4967-bacb-09fea1f3bc64' class='xr-index-data-in' type='checkbox'/><label for='index-8565e096-dcfb-4967-bacb-09fea1f3bc64' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([              -11.5,               -10.5,                -9.5,\n",
       "                      -8.5,  -7.499999999999999,                -6.5,\n",
       "                      -5.5,                -4.5,                -3.5,\n",
       "                      -2.5, -1.5000000000000002,                -0.5,\n",
       "                       0.5,  1.5000000000000002,                 2.5,\n",
       "                       3.5,                 4.5,                 5.5,\n",
       "                       6.5,   7.499999999999999,                 8.5,\n",
       "                       9.5,                10.5,                11.5,\n",
       "                      12.5,                13.5,  14.500000000000002,\n",
       "                      15.5,                16.5,                17.5,\n",
       "                      18.5,                19.5,                20.5,\n",
       "                      21.5,                22.5,                23.5,\n",
       "                      24.5,                25.5,                26.5,\n",
       "                      27.5,  28.500000000000004,  29.500000000000004,\n",
       "                      30.5,                31.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-710a1ab4-c271-42d7-91d1-4675af67496f' class='xr-index-data-in' type='checkbox'/><label for='index-710a1ab4-c271-42d7-91d1-4675af67496f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([           43.125,            44.375,            45.625,\n",
       "                  46.875,            48.125,            49.375,\n",
       "                  50.625,            51.875,            53.125,\n",
       "                  54.375,            55.625,            56.875,\n",
       "                  58.125, 59.37499999999999, 60.62499999999999,\n",
       "       61.87500000000001, 63.12500000000001,            64.375,\n",
       "                  65.625,            66.875,            68.125,\n",
       "                  69.375,            70.625, 71.87500000000001,\n",
       "                  73.125,            74.375,            75.625,\n",
       "                  76.875,            78.125,            79.375,\n",
       "                  80.625,            81.875, 83.12500000000001,\n",
       "                  84.375,            85.625,            86.875,\n",
       "                  88.125,            89.375,            90.625,\n",
       "                  91.875,            93.125, 94.37500000000001,\n",
       "                  95.625,            96.875,            98.125,\n",
       "                  99.375,           100.625,           101.875],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3d992e77-fa12-4e3d-a47c-a7ce0d61a555' class='xr-index-data-in' type='checkbox'/><label for='index-3d992e77-fa12-4e3d-a47c-a7ce0d61a555' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2015-01-01 12:00:00, 2015-01-02 12:00:00, 2015-01-03 12:00:00,\n",
       "             2015-01-04 12:00:00, 2015-01-05 12:00:00, 2015-01-06 12:00:00,\n",
       "             2015-01-07 12:00:00, 2015-01-08 12:00:00, 2015-01-09 12:00:00,\n",
       "             2015-01-10 12:00:00,\n",
       "             ...\n",
       "             2100-12-22 12:00:00, 2100-12-23 12:00:00, 2100-12-24 12:00:00,\n",
       "             2100-12-25 12:00:00, 2100-12-26 12:00:00, 2100-12-27 12:00:00,\n",
       "             2100-12-28 12:00:00, 2100-12-29 12:00:00, 2100-12-30 12:00:00,\n",
       "             2100-12-31 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=31390, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-87e0909b-19e7-4bf6-8916-d9fbac22af52' class='xr-section-summary-in' type='checkbox'  checked><label for='section-87e0909b-19e7-4bf6-8916-d9fbac22af52' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>interp_method :</span></dt><dd>conserve_order2</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>original_name :</span></dt><dd>tas</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' (time: 31390, lat: 44, lon: 48)>\n",
       "dask.array<getitem, shape=(31390, 44, 48), dtype=float32, chunksize=(420, 44, 48), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    height   float64 ...\n",
       "  * lat      (lat) float64 -11.5 -10.5 -9.5 -8.5 -7.5 ... 28.5 29.5 30.5 31.5\n",
       "  * lon      (lon) float64 43.12 44.38 45.62 46.88 ... 98.12 99.38 100.6 101.9\n",
       "  * time     (time) object 2015-01-01 12:00:00 ... 2100-12-31 12:00:00\n",
       "Attributes:\n",
       "    cell_measures:  area: areacella\n",
       "    cell_methods:   area: time: mean\n",
       "    interp_method:  conserve_order2\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    original_name:  tas\n",
       "    standard_name:  air_temperature\n",
       "    units:          K"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_daily_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "04a615fd-d034-43a3-bb9b-82ed261066b3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open('shared/data/gdfl/ssp245_daily/attr_template.json','w') as file:\n",
    "    json.dump(sst_245_daily_ds.attrs, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca4b8161-9475-459f-8f5a-a2e59e31d639",
   "metadata": {
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    "tags": []
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   "source": [
    "## SSP-585"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "a207dad4-b425-4842-beab-eb0cc5096f05",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['tas', 'tasmax', 'tasmin', 'rsus', 'rsds', 'mrso', 'prsn', 'mrsos',\n",
       "       'pr', 'prc', 'rlut', 'rlus', 'rlds', 'psl', 'snc', 'mrro', 'huss',\n",
       "       'sfcWindmax', 'sfcWind', 'snw', 'hursmin', 'hursmax', 'hurs',\n",
       "       'vas', 'uas', 'tslsi'], dtype=object)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfl_daily_585_result = cat.query(\"experiment_id=='ssp585' & source_id=='GFDL-CM4' & grid_label=='gr1' & table_id=='day'\")\n",
    "gdfl_daily_585_result['variable_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "550e688a-2679-44c1-88ac-c9a9aa7a4dcc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finish exporting variable vas\n",
      "finish exporting variable uas\n",
      "finish exporting variable pr\n",
      "finish exporting variable psl\n",
      "finish exporting variable sfcWind\n",
      "finish exporting variable tas\n"
     ]
    }
   ],
   "source": [
    "list_of_vars = ['vas', 'uas', 'pr', 'psl', 'sfcWind', 'tas']\n",
    "\n",
    "for var in list_of_vars:\n",
    "    gdfl_cm4_hist_url = gdfl_daily_585_result.query(f\"variable_id=='{var}'\")['zstore']\n",
    "    if len(gdfl_cm4_hist_url) == 0:\n",
    "        print(f'no variable {var} in this database')\n",
    "        continue\n",
    "    hist_daily_ds = xr.open_zarr(fs.get_mapper(gdfl_cm4_hist_url.values[-1]), consolidated=True)\n",
    "    hist_daily_ds = hist_daily_ds[var].sel(lat=slice(-12, 32), lon=slice(42, 102))\n",
    "    hist_daily_ds.to_netcdf(f'shared/data/gdfl/ssp585_daily/{var}.nc')\n",
    "    print(f'finish exporting variable {var}')\n",
    "    time.sleep(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "a35a68e4-8571-4359-b3bd-8efd0fd74f62",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pr&#x27; (time: 31390, lat: 44, lon: 48)&gt;\n",
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       "    interp_method:  conserve_order1\n",
       "    long_name:      Precipitation\n",
       "    original_name:  pr\n",
       "    standard_name:  precipitation_flux\n",
       "    units:          kg m-2 s-1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'pr'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 31390</li><li><span class='xr-has-index'>lat</span>: 44</li><li><span class='xr-has-index'>lon</span>: 48</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-887d4b7a-1d8b-49a9-a07f-8b6e4db6f225' class='xr-array-in' type='checkbox' checked><label for='section-887d4b7a-1d8b-49a9-a07f-8b6e4db6f225' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[66295680 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-a2b4f056-3560-4ff3-afd8-db5800d90779' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a2b4f056-3560-4ff3-afd8-db5800d90779' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-11.5 -10.5 -9.5 ... 29.5 30.5 31.5</div><input id='attrs-d8717846-0687-414b-816b-aa92bac9af08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8717846-0687-414b-816b-aa92bac9af08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3d6af0d-7365-4194-975d-1a76fe0e4546' class='xr-var-data-in' type='checkbox'><label for='data-f3d6af0d-7365-4194-975d-1a76fe0e4546' 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>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-11.5, -10.5,  -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,\n",
       "        -1.5,  -0.5,   0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,\n",
       "         8.5,   9.5,  10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,\n",
       "        18.5,  19.5,  20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,\n",
       "        28.5,  29.5,  30.5,  31.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>43.12 44.38 45.62 ... 100.6 101.9</div><input id='attrs-486b5d4b-a649-48b9-a8d7-4a601c5156c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-486b5d4b-a649-48b9-a8d7-4a601c5156c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-809d749f-e478-450f-9e84-8e92fd941a76' class='xr-var-data-in' type='checkbox'><label for='data-809d749f-e478-450f-9e84-8e92fd941a76' 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>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([ 43.125,  44.375,  45.625,  46.875,  48.125,  49.375,  50.625,  51.875,\n",
       "        53.125,  54.375,  55.625,  56.875,  58.125,  59.375,  60.625,  61.875,\n",
       "        63.125,  64.375,  65.625,  66.875,  68.125,  69.375,  70.625,  71.875,\n",
       "        73.125,  74.375,  75.625,  76.875,  78.125,  79.375,  80.625,  81.875,\n",
       "        83.125,  84.375,  85.625,  86.875,  88.125,  89.375,  90.625,  91.875,\n",
       "        93.125,  94.375,  95.625,  96.875,  98.125,  99.375, 100.625, 101.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2015-01-01 12:00:00 ... 2100-12-...</div><input id='attrs-b34e2636-7d3b-4d0f-83a0-5cfbdcd99ebb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b34e2636-7d3b-4d0f-83a0-5cfbdcd99ebb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1909b01a-54ef-4e45-94d2-bdbd1045999e' class='xr-var-data-in' type='checkbox'><label for='data-1909b01a-54ef-4e45-94d2-bdbd1045999e' 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>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>calendar_type :</span></dt><dd>noleap</dd><dt><span>description :</span></dt><dd>Temporal mean</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2171f9de-b748-46a4-8677-579cb181dab3' class='xr-section-summary-in' type='checkbox'  ><label for='section-2171f9de-b748-46a4-8677-579cb181dab3' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-868c8795-a38c-43d7-ade4-51e5e223fef1' class='xr-index-data-in' type='checkbox'/><label for='index-868c8795-a38c-43d7-ade4-51e5e223fef1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([              -11.5,               -10.5,                -9.5,\n",
       "                      -8.5,  -7.499999999999999,                -6.5,\n",
       "                      -5.5,                -4.5,                -3.5,\n",
       "                      -2.5, -1.5000000000000002,                -0.5,\n",
       "                       0.5,  1.5000000000000002,                 2.5,\n",
       "                       3.5,                 4.5,                 5.5,\n",
       "                       6.5,   7.499999999999999,                 8.5,\n",
       "                       9.5,                10.5,                11.5,\n",
       "                      12.5,                13.5,  14.500000000000002,\n",
       "                      15.5,                16.5,                17.5,\n",
       "                      18.5,                19.5,                20.5,\n",
       "                      21.5,                22.5,                23.5,\n",
       "                      24.5,                25.5,                26.5,\n",
       "                      27.5,  28.500000000000004,  29.500000000000004,\n",
       "                      30.5,                31.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9825b427-0c28-4d45-9a5e-8bc116f9732e' class='xr-index-data-in' type='checkbox'/><label for='index-9825b427-0c28-4d45-9a5e-8bc116f9732e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([           43.125,            44.375,            45.625,\n",
       "                  46.875,            48.125,            49.375,\n",
       "                  50.625,            51.875,            53.125,\n",
       "                  54.375,            55.625,            56.875,\n",
       "                  58.125, 59.37499999999999, 60.62499999999999,\n",
       "       61.87500000000001, 63.12500000000001,            64.375,\n",
       "                  65.625,            66.875,            68.125,\n",
       "                  69.375,            70.625, 71.87500000000001,\n",
       "                  73.125,            74.375,            75.625,\n",
       "                  76.875,            78.125,            79.375,\n",
       "                  80.625,            81.875, 83.12500000000001,\n",
       "                  84.375,            85.625,            86.875,\n",
       "                  88.125,            89.375,            90.625,\n",
       "                  91.875,            93.125, 94.37500000000001,\n",
       "                  95.625,            96.875,            98.125,\n",
       "                  99.375,           100.625,           101.875],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-70330dfb-9f40-4323-846a-8b6f7554afe3' class='xr-index-data-in' type='checkbox'/><label for='index-70330dfb-9f40-4323-846a-8b6f7554afe3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2015-01-01 12:00:00, 2015-01-02 12:00:00, 2015-01-03 12:00:00,\n",
       "             2015-01-04 12:00:00, 2015-01-05 12:00:00, 2015-01-06 12:00:00,\n",
       "             2015-01-07 12:00:00, 2015-01-08 12:00:00, 2015-01-09 12:00:00,\n",
       "             2015-01-10 12:00:00,\n",
       "             ...\n",
       "             2100-12-22 12:00:00, 2100-12-23 12:00:00, 2100-12-24 12:00:00,\n",
       "             2100-12-25 12:00:00, 2100-12-26 12:00:00, 2100-12-27 12:00:00,\n",
       "             2100-12-28 12:00:00, 2100-12-29 12:00:00, 2100-12-30 12:00:00,\n",
       "             2100-12-31 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=31390, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-889db4a8-f3f5-4112-b1f9-7d8b88b5cba3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-889db4a8-f3f5-4112-b1f9-7d8b88b5cba3' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>interp_method :</span></dt><dd>conserve_order1</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>original_name :</span></dt><dd>pr</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'pr' (time: 31390, lat: 44, lon: 48)>\n",
       "[66295680 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -11.5 -10.5 -9.5 -8.5 -7.5 ... 28.5 29.5 30.5 31.5\n",
       "  * lon      (lon) float64 43.12 44.38 45.62 46.88 ... 98.12 99.38 100.6 101.9\n",
       "  * time     (time) object 2015-01-01 12:00:00 ... 2100-12-31 12:00:00\n",
       "Attributes:\n",
       "    cell_measures:  area: areacella\n",
       "    cell_methods:   area: time: mean\n",
       "    interp_method:  conserve_order1\n",
       "    long_name:      Precipitation\n",
       "    original_name:  pr\n",
       "    standard_name:  precipitation_flux\n",
       "    units:          kg m-2 s-1"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.open_dataarray('shared/data/gdfl/ssp585_daily/pr.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "cc18628e-1d80-4809-9596-5101670d7539",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gdfl_daily_585_result = cat.query(\"experiment_id=='ssp585' & source_id=='GFDL-CM4'& variable_id=='tos'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "08cec26b-ff19-4a79-975b-d7b05ddaa67b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# we want to process the first two rows\n",
    "sst_585_daily_ds = xr.open_zarr(fs.get_mapper(gdfl_daily_585_result.zstore.values[0]),\n",
    "                                    consolidated=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "34fb4499-b50a-4ad4-9284-13edb5663572",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open('shared/data/gdfl/ssp585_daily/attr_template.json', 'w') as file:\n",
    "    json.dump(sst_585_daily_ds.attrs, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "e7d9f746-ad3a-4668-aa36-428000d6fd01",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sst_585_daily_ds_shifted = shift(sst_585_daily_ds['tos'], 'x', 60)\n",
    "sst_585_daily_ds_shifted.sel(x=slice(42, 102), y=slice(-12, 32)).to_netcdf('shared/data/gdfl/ssp585_daily/hq_tos.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "565b45f6-6d52-4699-90e1-8c2413e80a8e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "xr.open_zarr(fs.get_mapper(gdfl_daily_585_result.zstore.values[1]),\n",
    "                                    consolidated=True).sel(\n",
    "    lat=slice(-12, 32),\n",
    "    lon=slice(42, 102)\n",
    ").to_netcdf('shared/data/gdfl/ssp585_daily/tos.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e9162ca-6dcd-44d2-8e5a-408de8b8f4f3",
   "metadata": {
    "tags": []
   },
   "source": [
    "# GDFL-CM4 Monthly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1411dd3-1fe6-4c74-a967-5fc16e1099ba",
   "metadata": {},
   "source": [
    "## Exploring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "a72e14ef-ef20-4469-89c2-e6027853771e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AERmon', 'Amon', 'day', 'Oyr', 'SImon', 'SIday', 'fx', 'EdayZ',\n",
       "       '3hr', 'Eday', 'CFday', 'CFmon', '6hrLev', '6hrPlevPt', 'AERmonZ',\n",
       "       'Omon', 'LImon', 'Lmon', 'Eyr', 'Oday', 'Ofx', 'EmonZ', 'Emon'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_hist_result = cat.query(\"experiment_id=='historical' & source_id=='GFDL-CM4'\")\n",
    "monthly_hist_result['table_id'].unique() # Amon, CFmon, Emon, Omon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "13dd6920-cda8-4292-a3a8-0c3b18c4e923",
   "metadata": {
    "tags": []
   },
   "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>activity_id</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>table_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>zstore</th>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6523</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>vas</td>\n",
       "      <td>gr1</td>\n",
       "      <td>s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     activity_id institution_id source_id experiment_id member_id table_id   \n",
       "6523        CMIP      NOAA-GFDL  GFDL-CM4    historical  r1i1p1f1     Amon  \\\n",
       "\n",
       "     variable_id grid_label   \n",
       "6523         vas        gr1  \\\n",
       "\n",
       "                                                 zstore  dcpp_init_year   \n",
       "6523  s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...             NaN  \\\n",
       "\n",
       "       version  \n",
       "6523  20180701  "
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_hist_result.query(\"table_id=='Amon' & variable_id=='vas'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "a2462730-e5e2-43bb-b852-8f1ba73eb0dc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['expc', 'fgco2', 'hfds', 'masso', 'mfo', 'o2', 'ph', 'phyc',\n",
       "       'phyp', 'po4', 'pp', 'sf6', 'so', 'sos', 'dissoc', 'talk',\n",
       "       'dissic', 'co3satcalc', 'zos', 'thetao', 'tauvo', 'chl', 'tauuo',\n",
       "       'co3', 'dfe', 'wfo', 'volo', 'cfc12', 'thetaoga', 'tos', 'uo',\n",
       "       'vo', 'volcello', 'cfc11'], dtype=object)"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_hist_result.query(\"table_id=='Omon'\")['variable_id'].unique() #tos, sos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "f0d691eb-b277-4869-b41a-246d869d7da3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ps', 'treeFracBdlDcd', 'treeFracBdlEvg', 'treeFracNdlEvg',\n",
       "       'utendogw', 'vegFrac', 'vtendnogw', 'vtendogw', 'utendnogw'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_hist_result.query(\"table_id=='Emon'\")['variable_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "f6c93494-e881-440b-9f6c-2799967f0858",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ccb', 'n2oglobal', 'o3', 'pr', 'prc', 'prsn', 'prw', 'ps', 'psl',\n",
       "       'rlds', 'rldscs', 'rlus', 'rlut', 'rlutcs', 'rsds', 'rsdscs',\n",
       "       'rsdt', 'rsus', 'mc', 'huss', 'hurs', 'hcfc22global', 'hfss',\n",
       "       'hur', 'hus', 'hfls', 'evspsbl', 'co2mass', 'clw', 'clwvi',\n",
       "       'rsuscs', 'rsutcs', 'clt', 'clivi', 'cl', 'cct', 'cfc113global',\n",
       "       'cfc11global', 'cfc12global', 'ch4global', 'ci', 'cli', 'ua',\n",
       "       'uas', 'va', 'vas', 'wap', 'zg', 'ts', 'tauv', 'tasmin', 'rtmt',\n",
       "       'sci', 'sfcWind', 'ta', 'tas', 'tasmax', 'tauu', 'rsut'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_hist_result.query(\"table_id=='Amon'\")['variable_id'].unique() #pr, uas, vas, psl, sfcWind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "d1337cda-945e-4faa-978f-8cad17b0bc48",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (bnds: 2, lat: 180, lon: 288, time: 1980)\n",
       "Coordinates:\n",
       "  * bnds       (bnds) float64 1.0 2.0\n",
       "  * lat        (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
       "    lat_bnds   (lat, bnds) float64 dask.array&lt;chunksize=(180, 2), meta=np.ndarray&gt;\n",
       "  * lon        (lon) float64 0.625 1.875 3.125 4.375 ... 355.6 356.9 358.1 359.4\n",
       "    lon_bnds   (lon, bnds) float64 dask.array&lt;chunksize=(288, 2), meta=np.ndarray&gt;\n",
       "  * time       (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "    time_bnds  (time, bnds) object dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;\n",
       "Data variables:\n",
       "    pr         (time, lat, lon) float32 dask.array&lt;chunksize=(600, 180, 288), meta=np.ndarray&gt;\n",
       "Attributes: (12/49)\n",
       "    Conventions:            CF-1.7 CMIP-6.0 UGRID-1.0\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          standard\n",
       "    branch_time_in_child:   0.0\n",
       "    branch_time_in_parent:  36500.0\n",
       "    comment:                &lt;null ref&gt;\n",
       "    ...                     ...\n",
       "    variable_id:            pr\n",
       "    variant_info:           N/A\n",
       "    variant_label:          r1i1p1f1\n",
       "    status:                 2019-08-07;created;by nhn2@columbia.edu\n",
       "    netcdf_tracking_ids:    hdl:21.14100/29b4a002-10ac-428c-8f1d-b57656ab1d02...\n",
       "    version_id:             v20180701</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-a626246d-5646-42c8-a7b7-2033f8616b0d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a626246d-5646-42c8-a7b7-2033f8616b0d' 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'>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span class='xr-has-index'>time</span>: 1980</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-143721ec-7d07-4da6-8ece-1f94de8c9734' class='xr-section-summary-in' type='checkbox'  checked><label for='section-143721ec-7d07-4da6-8ece-1f94de8c9734' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bnds</span></div><div class='xr-var-dims'>(bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0</div><input id='attrs-aea05cc4-f260-4d9f-b1d8-f02f92ce8e3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aea05cc4-f260-4d9f-b1d8-f02f92ce8e3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-650470f3-a7b8-40e7-b817-01f823203543' class='xr-var-data-in' type='checkbox'><label for='data-650470f3-a7b8-40e7-b817-01f823203543' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertex number</dd></dl></div><div class='xr-var-data'><pre>array([1., 2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-2b99c883-6216-4a88-9e76-d14990a1d778' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b99c883-6216-4a88-9e76-d14990a1d778' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-245620ef-e544-4487-aec5-f8fe1a961b67' class='xr-var-data-in' type='checkbox'><label for='data-245620ef-e544-4487-aec5-f8fe1a961b67' 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>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(180, 2), meta=np.ndarray&gt;</div><input id='attrs-a53c1f53-0e5f-43fe-af40-686807b5c27c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a53c1f53-0e5f-43fe-af40-686807b5c27c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5040712f-71d9-48e7-936c-446c14f721d5' class='xr-var-data-in' type='checkbox'><label for='data-5040712f-71d9-48e7-936c-446c14f721d5' 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>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude bounds</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.81 kiB </td>\n",
       "                        <td> 2.81 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (180, 2) </td>\n",
       "                        <td> (180, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.924160051965714,0.0 25.924160051965714,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.962080\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"45.924160\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.924160,60.000000)\">180</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.625 1.875 3.125 ... 358.1 359.4</div><input id='attrs-c16a2689-48b0-4fcb-bb01-4568bd03c31d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c16a2689-48b0-4fcb-bb01-4568bd03c31d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23774335-3046-4803-b2dc-66b236e57621' class='xr-var-data-in' type='checkbox'><label for='data-23774335-3046-4803-b2dc-66b236e57621' 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>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.625,   1.875,   3.125, ..., 356.875, 358.125, 359.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(288, 2), meta=np.ndarray&gt;</div><input id='attrs-7420e830-c4bd-4027-a3f1-adeda6494b16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7420e830-c4bd-4027-a3f1-adeda6494b16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85e178f5-0cba-4f2a-b9bc-2f458d1cbf04' class='xr-var-data-in' type='checkbox'><label for='data-85e178f5-0cba-4f2a-b9bc-2f458d1cbf04' 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>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude bounds</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.50 kiB </td>\n",
       "                        <td> 4.50 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (288, 2) </td>\n",
       "                        <td> (288, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">288</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-5980fd20-6e10-44ea-a5c0-6e0224d95e2a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5980fd20-6e10-44ea-a5c0-6e0224d95e2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78ee8dfc-433a-4bdf-8366-56140950b2c1' class='xr-var-data-in' type='checkbox'><label for='data-78ee8dfc-433a-4bdf-8366-56140950b2c1' 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>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>calendar_type :</span></dt><dd>noleap</dd><dt><span>description :</span></dt><dd>Temporal mean</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-e868ffc6-e870-4ba0-b7a0-cdaba20cfe26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e868ffc6-e870-4ba0-b7a0-cdaba20cfe26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b94d72b7-8311-456b-8d0e-2f6eb3060693' class='xr-var-data-in' type='checkbox'><label for='data-b94d72b7-8311-456b-8d0e-2f6eb3060693' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time axis boundaries</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 30.94 kiB </td>\n",
       "                        <td> 30.94 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1980, 2) </td>\n",
       "                        <td> (1980, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "\n",
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       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">1980</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-cb6c3bc1-5191-4741-8550-295a1863505d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cb6c3bc1-5191-4741-8550-295a1863505d' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(600, 180, 288), meta=np.ndarray&gt;</div><input id='attrs-31a54ac6-ad24-402e-91ba-7facaed6f083' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31a54ac6-ad24-402e-91ba-7facaed6f083' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c21acb93-1d44-48f2-a37a-351ccac4bd9b' class='xr-var-data-in' type='checkbox'><label for='data-c21acb93-1d44-48f2-a37a-351ccac4bd9b' 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_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>interp_method :</span></dt><dd>conserve_order1</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>original_name :</span></dt><dd>pr</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 391.55 MiB </td>\n",
       "                        <td> 118.65 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1980, 180, 288) </td>\n",
       "                        <td> (600, 180, 288) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
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       "  <text x=\"100.866665\" y=\"128.650413\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >288</text>\n",
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       "  <text x=\"35.294118\" y=\"93.356296\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,93.356296)\">1980</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a4b312d-2425-4625-9b90-989b2c67e0f7' class='xr-section-summary-in' type='checkbox'  ><label for='section-5a4b312d-2425-4625-9b90-989b2c67e0f7' class='xr-section-summary' >Indexes: <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-index-name'><div>bnds</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a246d2f2-ea3c-4faf-998f-c08b05a7f7a4' class='xr-index-data-in' type='checkbox'/><label for='index-a246d2f2-ea3c-4faf-998f-c08b05a7f7a4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1.0, 2.0], dtype=&#x27;float64&#x27;, name=&#x27;bnds&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2842bdf8-ef16-4ad6-a6c5-42a1490f08ae' class='xr-index-data-in' type='checkbox'/><label for='index-2842bdf8-ef16-4ad6-a6c5-42a1490f08ae' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       ...\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4928a1e3-404d-4d0a-9baf-ee75b2159206' class='xr-index-data-in' type='checkbox'/><label for='index-4928a1e3-404d-4d0a-9baf-ee75b2159206' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             0.625, 1.8749999999999998,              3.125,\n",
       "                    4.375,              5.625,              6.875,\n",
       "                    8.125,              9.375,             10.625,\n",
       "                   11.875,\n",
       "       ...\n",
       "                  348.125,            349.375,            350.625,\n",
       "                  351.875,            353.125,            354.375,\n",
       "                  355.625,            356.875,            358.125,\n",
       "                  359.375],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-05c18058-5452-414f-84f2-c46b2f2ff2c9' class='xr-index-data-in' type='checkbox'/><label for='index-05c18058-5452-414f-84f2-c46b2f2ff2c9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-16 12:00:00, 1850-02-15 00:00:00, 1850-03-16 12:00:00,\n",
       "             1850-04-16 00:00:00, 1850-05-16 12:00:00, 1850-06-16 00:00:00,\n",
       "             1850-07-16 12:00:00, 1850-08-16 12:00:00, 1850-09-16 00:00:00,\n",
       "             1850-10-16 12:00:00,\n",
       "             ...\n",
       "             2014-03-16 12:00:00, 2014-04-16 00:00:00, 2014-05-16 12:00:00,\n",
       "             2014-06-16 00:00:00, 2014-07-16 12:00:00, 2014-08-16 12:00:00,\n",
       "             2014-09-16 00:00:00, 2014-10-16 12:00:00, 2014-11-16 00:00:00,\n",
       "             2014-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=1980, calendar=&#x27;noleap&#x27;, freq=&#x27;None&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4b609d25-dd32-4339-888c-e87ba57edeaf' class='xr-section-summary-in' type='checkbox'  ><label for='section-4b609d25-dd32-4339-888c-e87ba57edeaf' class='xr-section-summary' >Attributes: <span>(49)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.0 UGRID-1.0</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>36500.0</dd><dt><span>comment :</span></dt><dd>&lt;null ref&gt;</dd><dt><span>contact :</span></dt><dd>gfdl.climate.model.info@noaa.gov</dd><dt><span>creation_date :</span></dt><dd>2019-02-27T02:00:08Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.27</dd><dt><span>experiment :</span></dt><dd>historical</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>monC</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NOAA-GFDL.GFDL-CM4.historical.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>atmos data regridded from Cubed-sphere (c96) to 180,288; interpolation method: conserve_order1</dd><dt><span>grid_label :</span></dt><dd>gr1</dd><dt><span>history :</span></dt><dd>File was processed by fremetar (GFDL analog of CMOR). TripleID: [exper_id_NIX4LXCc18,realiz_id_p1BNnppz5X,run_id_F8Xk5tsZcx]\n",
       "File was processed by fremetar (GFDL analog of CMOR). TripleID: [exper_id_NIX4LXCc18,realiz_id_p1BNnppz5X,run_id_F8Xk5tsZcx]</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA</dd><dt><span>institution_id :</span></dt><dd>NOAA-GFDL</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by NOAA-GFDL is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>GFDL-CM4</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-1-1</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>references :</span></dt><dd>see further_info_url attribute</dd><dt><span>source :</span></dt><dd>GFDL-CM4 (2018): \n",
       "aerosol: interactive\n",
       "atmos: GFDL-AM4.0.1 (Cubed-sphere (c96) - 1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 33 levels; top level 1 hPa)\n",
       "atmosChem: fast chemistry, aerosol only\n",
       "land: GFDL-LM4.0.1 (1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 20 levels; bot level 10m); land:Veg:unnamed (dynamic vegetation, dynamic land use); land:Hydro:unnamed (soil water and ice, multi-layer snow, rivers and lakes)\n",
       "landIce: GFDL-LM4.0.1\n",
       "ocean: GFDL-OM4p25 (GFDL-MOM6, tripolar - nominal 0.25 deg; 1440 x 1080 longitude/latitude; 75 levels; top grid cell 0-2 m)\n",
       "ocnBgchem: GFDL-BLINGv2\n",
       "seaIce: GFDL-SIM4p25 (GFDL-SIS2.0, tripolar - nominal 0.25 deg; 1440 x 1080 longitude/latitude; 5 layers; 5 thickness categories)\n",
       "(GFDL ID: 2019_0065)</dd><dt><span>source_id :</span></dt><dd>GFDL-CM4</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>title :</span></dt><dd>NOAA GFDL GFDL-CM4 model output prepared for CMIP6 historical</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/29b4a002-10ac-428c-8f1d-b57656ab1d02\n",
       "hdl:21.14100/dc7ab3cc-fb31-4f6c-bbca-cc4e7e38489f</dd><dt><span>variable_id :</span></dt><dd>pr</dd><dt><span>variant_info :</span></dt><dd>N/A</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>status :</span></dt><dd>2019-08-07;created;by nhn2@columbia.edu</dd><dt><span>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/29b4a002-10ac-428c-8f1d-b57656ab1d02\n",
       "hdl:21.14100/dc7ab3cc-fb31-4f6c-bbca-cc4e7e38489f</dd><dt><span>version_id :</span></dt><dd>v20180701</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (bnds: 2, lat: 180, lon: 288, time: 1980)\n",
       "Coordinates:\n",
       "  * bnds       (bnds) float64 1.0 2.0\n",
       "  * lat        (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
       "    lat_bnds   (lat, bnds) float64 dask.array<chunksize=(180, 2), meta=np.ndarray>\n",
       "  * lon        (lon) float64 0.625 1.875 3.125 4.375 ... 355.6 356.9 358.1 359.4\n",
       "    lon_bnds   (lon, bnds) float64 dask.array<chunksize=(288, 2), meta=np.ndarray>\n",
       "  * time       (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "    time_bnds  (time, bnds) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "Data variables:\n",
       "    pr         (time, lat, lon) float32 dask.array<chunksize=(600, 180, 288), meta=np.ndarray>\n",
       "Attributes: (12/49)\n",
       "    Conventions:            CF-1.7 CMIP-6.0 UGRID-1.0\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          standard\n",
       "    branch_time_in_child:   0.0\n",
       "    branch_time_in_parent:  36500.0\n",
       "    comment:                <null ref>\n",
       "    ...                     ...\n",
       "    variable_id:            pr\n",
       "    variant_info:           N/A\n",
       "    variant_label:          r1i1p1f1\n",
       "    status:                 2019-08-07;created;by nhn2@columbia.edu\n",
       "    netcdf_tracking_ids:    hdl:21.14100/29b4a002-10ac-428c-8f1d-b57656ab1d02...\n",
       "    version_id:             v20180701"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.open_zarr(fs.get_mapper(monthly_hist_result.query(\"table_id=='Amon' & variable_id=='pr'\").zstore.values[0]),\n",
    "                                    consolidated=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "5ec96ce8-47ab-49a2-930e-bc5465d77ab1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finish exporting variable pr\n",
      "finish exporting variable uas\n",
      "finish exporting variable vas\n",
      "finish exporting variable psl\n",
      "finish exporting variable sfcWind\n",
      "finish exporting variable tas\n"
     ]
    }
   ],
   "source": [
    "var_amon = ['pr', 'uas', 'vas', 'psl', 'sfcWind', 'tas']\n",
    "for var in var_amon:\n",
    "    gdfl_cm4_hist_url = monthly_hist_result.query(f\"table_id=='Amon' & variable_id=='{var}'\")['zstore']\n",
    "    if len(gdfl_cm4_hist_url) == 0:\n",
    "        print(f'no variable {var} in this database')\n",
    "        continue\n",
    "    hist_daily_ds = xr.open_zarr(fs.get_mapper(gdfl_cm4_hist_url.values[-1]), consolidated=True)\n",
    "    hist_daily_ds = hist_daily_ds[var].sel(lat=slice(-12, 32), lon=slice(42, 102))\n",
    "    hist_daily_ds.to_netcdf(f'shared/data/gdfl/historical_monthly/{var}.nc')\n",
    "    print(f'finish exporting variable {var}')\n",
    "    time.sleep(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "64c4199d-29c5-4f63-bc82-e064a1bc97a2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>activity_id</th>\n",
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       "      <th>grid_label</th>\n",
       "      <th>zstore</th>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <th>version</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6699</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Omon</td>\n",
       "      <td>tos</td>\n",
       "      <td>gn</td>\n",
       "      <td>s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6700</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Omon</td>\n",
       "      <td>tos</td>\n",
       "      <td>gr</td>\n",
       "      <td>s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     activity_id institution_id source_id experiment_id member_id table_id   \n",
       "6699        CMIP      NOAA-GFDL  GFDL-CM4    historical  r1i1p1f1     Omon  \\\n",
       "6700        CMIP      NOAA-GFDL  GFDL-CM4    historical  r1i1p1f1     Omon   \n",
       "\n",
       "     variable_id grid_label   \n",
       "6699         tos         gn  \\\n",
       "6700         tos         gr   \n",
       "\n",
       "                                                 zstore  dcpp_init_year   \n",
       "6699  s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...             NaN  \\\n",
       "6700  s3://cmip6-pds/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/h...             NaN   \n",
       "\n",
       "       version  \n",
       "6699  20180701  \n",
       "6700  20180701  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_hist_result.query(\"table_id=='Omon' & variable_id=='tos'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "95aafb9e-e4d1-45b5-84b4-178507edd7e4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finish exporting variable tos\n",
      "finish exporting variable sos\n"
     ]
    }
   ],
   "source": [
    "var_omon = ['tos', 'sos']\n",
    "for var in var_omon:\n",
    "    gdfl_cm4_hist_url = monthly_hist_result.query(f\"variable_id=='{var}' & grid_label=='gr' & table_id=='Omon'\")['zstore']\n",
    "    if len(gdfl_cm4_hist_url) == 0:\n",
    "        print(f'no variable {var} in this database')\n",
    "        continue\n",
    "    hist_daily_ds = xr.open_zarr(fs.get_mapper(gdfl_cm4_hist_url.values[-1]), consolidated=True)\n",
    "    hist_daily_ds = hist_daily_ds[var].sel(lat=slice(-12, 32), lon=slice(42, 102))\n",
    "    hist_daily_ds.to_netcdf(f'shared/data/gdfl/historical_monthly/{var}.nc')\n",
    "    print(f'finish exporting variable {var}')\n",
    "    time.sleep(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "3d388f65-6058-4576-8c67-6861d2af03e6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hq_tos_ds = xr.open_zarr(fs.get_mapper(monthly_hist_result.query(\"variable_id=='tos' \\\n",
    "                                                                 & grid_label=='gn'\")['zstore'].values[0]),\n",
    "                        consolidated=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "469c23dd-d943-4ccd-847f-317e25701335",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open('shared/data/gdfl/historical_monthly/attr_template.json', 'w') as file:\n",
    "    json.dump(hq_tos_ds.attrs, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "c10e41f7-0a3a-46f9-81d7-f32f6b0aa514",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "shift(hq_tos_ds.tos, 'x', 60).sel(x=slice(42, 102), y=slice(-12, 32)).to_netcdf('shared/data/gdfl/historical_monthly/hq_tos.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20ff5c21-8f61-4eac-aedf-e1f795a95971",
   "metadata": {},
   "source": [
    "### SSP245"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "91f11417-f2d3-43c1-84d1-415dd041150a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finish exporting variable pr\n",
      "finish exporting variable uas\n",
      "finish exporting variable vas\n",
      "finish exporting variable psl\n",
      "finish exporting variable sfcWind\n",
      "finish exporting variable tas\n",
      "finish exporting variable tos\n",
      "finish exporting variable sos\n"
     ]
    }
   ],
   "source": [
    "monthly_245_result =  cat.query(\"experiment_id=='ssp245' & source_id=='GFDL-CM4'\")\n",
    "var_amon = ['pr', 'uas', 'vas', 'psl', 'sfcWind', 'tas']\n",
    "for var in var_amon:\n",
    "    gdfl_cm4_245_url = monthly_245_result.query(f\"table_id=='Amon' & variable_id=='{var}'\")['zstore']\n",
    "    if len(gdfl_cm4_245_url) == 0:\n",
    "        print(f'no variable {var} in this database')\n",
    "        continue\n",
    "    daily_245_ds = xr.open_zarr(fs.get_mapper(gdfl_cm4_245_url.values[-1]), consolidated=True)\n",
    "    daily_245_ds = daily_245_ds[var].sel(lat=slice(-12, 32), lon=slice(42, 102))\n",
    "    daily_245_ds.to_netcdf(f'shared/data/gdfl/ssp245_monthly/{var}.nc')\n",
    "    print(f'finish exporting variable {var}')\n",
    "    time.sleep(1)\n",
    "    \n",
    "var_omon = ['tos', 'sos']\n",
    "for var in var_omon:\n",
    "    gdfl_cm4_245_url = monthly_245_result.query(f\"variable_id=='{var}' & table_id=='Omon' & grid_label=='gr'\")['zstore']\n",
    "    if len(gdfl_cm4_245_url) == 0:\n",
    "        print(f'no variable {var} in this database')\n",
    "        continue\n",
    "    daily_245_ds = xr.open_zarr(fs.get_mapper(gdfl_cm4_245_url.values[-1]), consolidated=True)\n",
    "    daily_245_ds = daily_245_ds[var].sel(lat=slice(-12, 32), lon=slice(42, 102))\n",
    "    daily_245_ds.to_netcdf(f'shared/data/gdfl/ssp245_monthly/{var}.nc')\n",
    "    print(f'finish exporting variable {var}')\n",
    "    time.sleep(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "3825f557-96d9-4889-a436-f419bfd76f07",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hq_tos_ds = xr.open_zarr(fs.get_mapper(monthly_245_result.query(\"variable_id=='tos' \\\n",
    "                                                                 & grid_label=='gn'\")['zstore'].values[0]),\n",
    "                        consolidated=True)\n",
    "\n",
    "shift(hq_tos_ds.tos, 'x', 60).sel(x=slice(42, 102), y=slice(-12, 32)).to_netcdf('shared/data/gdfl/ssp245_monthly/hq_tos.nc')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "23038302-ce59-447f-bb0a-91e7092f28cd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open('shared/data/gdfl/ssp245_monthly/attr_template.json', 'w') as file:\n",
    "    json.dump(hq_tos_ds.attrs, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a8fb46c-648f-47d6-9822-a46c336c20d3",
   "metadata": {},
   "source": [
    "## SSP585"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "25e568f8-acaa-473e-b036-d469c7e90007",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finish exporting variable pr\n",
      "finish exporting variable uas\n",
      "finish exporting variable vas\n",
      "finish exporting variable psl\n",
      "finish exporting variable sfcWind\n",
      "finish exporting variable tas\n",
      "finish exporting variable tos\n",
      "finish exporting variable sos\n"
     ]
    }
   ],
   "source": [
    "monthly_585_result =  cat.query(\"experiment_id=='ssp585' & source_id=='GFDL-CM4'\")\n",
    "var_amon = ['pr', 'uas', 'vas', 'psl', 'sfcWind', 'tas']\n",
    "for var in var_amon:\n",
    "    gdfl_cm4_585_url = monthly_585_result.query(f\"table_id=='Amon' & variable_id=='{var}'\")['zstore']\n",
    "    if len(gdfl_cm4_585_url) == 0:\n",
    "        print(f'no variable {var} in this database')\n",
    "        continue\n",
    "    daily_585_ds = xr.open_zarr(fs.get_mapper(gdfl_cm4_585_url.values[-1]), consolidated=True)\n",
    "    daily_585_ds = daily_585_ds[var].sel(lat=slice(-12, 32), lon=slice(42, 102))\n",
    "    daily_585_ds.to_netcdf(f'shared/data/gdfl/ssp585_monthly/{var}.nc')\n",
    "    print(f'finish exporting variable {var}')\n",
    "    time.sleep(1)\n",
    "    \n",
    "var_omon = ['tos', 'sos']\n",
    "for var in var_omon:\n",
    "    gdfl_cm4_585_url = monthly_585_result.query(f\"variable_id=='{var}' & table_id=='Omon' & grid_label=='gr'\")['zstore']\n",
    "    if len(gdfl_cm4_585_url) == 0:\n",
    "        print(f'no variable {var} in this database')\n",
    "        continue\n",
    "    daily_585_ds = xr.open_zarr(fs.get_mapper(gdfl_cm4_585_url.values[-1]), consolidated=True)\n",
    "    daily_585_ds = daily_585_ds[var].sel(lat=slice(-12, 32), lon=slice(42, 102))\n",
    "    daily_585_ds.to_netcdf(f'shared/data/gdfl/ssp585_monthly/{var}.nc')\n",
    "    print(f'finish exporting variable {var}')\n",
    "    time.sleep(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "651d6f19-4351-40be-9f72-c62d1f284be7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hq_tos_ds = xr.open_zarr(fs.get_mapper(monthly_585_result.query(\"variable_id=='tos' \\\n",
    "                                                                 & grid_label=='gn'\")['zstore'].values[0]),\n",
    "                        consolidated=True)\n",
    "\n",
    "shift(hq_tos_ds.tos, 'x', 60).sel(x=slice(42, 102), y=slice(-12, 32)).to_netcdf('shared/data/gdfl/ssp585_monthly/hq_tos.nc')\n",
    "\n",
    "with open('shared/data/gdfl/ssp585_monthly/attr_template.json', 'w') as file:\n",
    "    json.dump(hq_tos_ds.attrs, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "944586d2-6efb-4bd3-a219-1586dcc0d9e2",
   "metadata": {},
   "source": [
    "## PLOTTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d984437c-f120-4723-8bf0-13c9d29cf142",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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