{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Climate indices\n",
    "\n",
    "The current production version of PAVICS provides ICCLIM indices through the FlyingPigeon server, which includes over fourty different indices. In the example below, we're calling on one of these indices on a small test file available on a public URL, but it is also possible to send local files to the server. \n",
    "\n",
    "Another implementation of climate indices is in the works. It relies on the xarray+dask backend to parallelize computations in the background. The library of climate indices is [xclim](https://github.com/Ouranosinc/xclim) and the WPS server hosting it is [finch](https://github.com/bird-house/finch), but is not yet in production."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from birdy import WPSClient\n",
    "url = 'https://pavics.ouranos.ca/twitcher/ows/proxy/flyingpigeon/wps'\n",
    "fp = WPSClient(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method icclim_tx in module birdy.client.base:\n",
      "\n",
      "icclim_tx(resource=None, grouping='yr') method of birdy.client.base.WPSClient instance\n",
      "    Calculates the TX indice: mean of daily maximum temperature.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    resource : ComplexData:mimetype:`application/x-netcdf`, :mimetype:`application/x-tar`, :mimetype:`application/zip`\n",
      "        NetCDF Files or archive (tar/zip) containing netCDF files.\n",
      "    grouping : {'day', 'mon', 'sem', 'yr', 'ONDJFM', 'AMJJAS', 'DJF', 'MAM', 'JJA', 'SON', ...}string\n",
      "        Temporal group over which the index is computed.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    output_netcdf : ComplexData:mimetype:`application/x-netcdf`\n",
      "        The indicator values computed on the original input grid.\n",
      "    output_log : ComplexData:mimetype:`text/plain`\n",
      "        Collected logs during process run.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(fp.icclim_tx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Dataset'>\n",
       "root group (NETCDF4 data model, file format HDF5):\n",
       "    source_data_global_attributes: {\"title\": \"NASA/GISS  model output prepared for IPCC Fourth Assessment AMIP experiment\", \"cmor_version\": 0.9599999785423279, \"institution\": \"NASA/GISS (Goddard Institute for Space Studies)New York, NY\", \"source\": \"E3OCNf8aM20A\", \"contact\": \"Kenneth Lo (cdkkl@giss.nasa.gov)\", \"references\": \"www.giss.nasa.gov/research/modeling\", \"experiment_id\": \"sresb1\", \"realization\": 1, \"directory\": \"/ipcc/sresb1/atm/da/\", \"table_id\": \"Table A2 (20 September 2004)\", \"calendar\": \"noleap\", \"project_id\": \"IPCC Fourth Assessment\", \"Conventions\": \"CF-1.0\", \"id\": \"pcmdi.ipcc4.giss_model_e_r.sresb1.run1.atm.da\", \"history\": \"Tue Nov 22 09:25:17 2011: ncks -D 0 -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,2040-01-01 00:00:00,2070-01-01 00:00:00 -v tas http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresb1/giss_model_e_r/pcmdi.ipcc4.giss_model_e_r.sresb1.run1.atm.da.xml /home/david/projects/ingenieurs/data/CMIP3/tas.sresb1.giss_model_e_r.run1.atm.da.tas.nc\\n  At 10:47:22 on 11/03/2004, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements [2006-4-6 11:6:16] cdscan -x /var/www/html/ipcc4/sresb1/giss_model_e_r/pcmdi.ipcc4.giss_model_e_r.sresb1.run1.atm.da.xml -d pcmdi.ipcc4.giss_model_e_r.sresb1.run1.atm.da --exclude=plev --ignore-open-error --var-locate ps,ps_.* -f /tmp/scan/list.pcmdi.ipcc4.giss_model_e_r.sre ...\", \"NCO\": \"4.0.8\"}\n",
       "    history: Tue Nov 22 09:25:17 2011: ncks -D 0 -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,2040-01-01 00:00:00,2070-01-01 00:00:00 -v tas http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresb1/giss_model_e_r/pcmdi.ipcc4.giss_model_e_r.sresb1.run1.atm.da.xml /home/david/projects/ingenieurs/data/CMIP3/tas.sresb1.giss_model_e_r.run1.atm.da.tas.nc\n",
       "  At 10:47:22 on 11/03/2004, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements [2006-4-6 11:6:16] cdscan -x /var/www/html/ipcc4/sresb1/giss_model_e_r/pcmdi.ipcc4.giss_model_e_r.sresb1.run1.atm.da.xml -d pcmdi.ipcc4.giss_model_e_r.sresb1.run1.atm.da --exclude=plev --ignore-open-error --var-locate ps,ps_.* -f /tmp/scan/list.pcmdi.ipcc4.giss_model_e_r.sre ... \n",
       "2019-01-18 17:30:14 Calculation of TX indice (annual) from 2046-1-1 to 2065-12-31.\n",
       "2019-01-18 17:30:14.427279 UTC ocgis-2.2.0dev0: OcgOperations(calc_sample_size=False, optimizations=None, geom_select_sql_where=None, output_format=\"nc\", spatial_wrapping=\"None\", format_time=True, select_nearest=False, output_crs=None, time_range=None, calc_grouping=('year',), prefix=\"b6959f22-1b46-11e9-871f-0242ac12000b\", abstraction=\"auto\", regrid_destination=None, output_format_options=None, allow_empty=False, vector_wrap=False, aggregate=False, interpolate_spatial_bounds=False, dataset=<generator object __iter__ at 0x7f952019f320>, geom_uid=None, dir_output=\"/opt/birdhouse/var/lib/pywps/tmp/flyingpigeon/pywps_process_M9RLpn\", backend=\"ocg\", search_radius_mult=None, add_auxiliary_files=False, slice=None, regrid_options={'regrid_method': 'auto', 'value_mask': None, 'split': True}, callback=None, calc_raw=False, level_range=None, geom_select_uid=None, snippet=False, time_region=None, melted=False, geom=None, time_subset_func=None, conform_units_to=None, spatial_operation=\"intersects\", calc=[{'meta_attrs': None, 'name': 'icclim_TX', 'func': 'icclim_TX', 'kwds': OrderedDict()}], optimized_bbox_subset=False, file_only=False, spatial_reorder=False, )\n",
       "    title: ECA temperature indice TX\n",
       "    references: ATBD of the ECA indices calculation (http://eca.knmi.nl/documents/atbd.pdf)\n",
       "    institution: Climate impact portal (http://climate4impact.eu)\n",
       "    comment:  \n",
       "    dimensions(sizes): lat(6), bnds(2), lon(5), time(20)\n",
       "    variables(dimensions): float64 \u001b[4mheight\u001b[0m(), float64 \u001b[4mlat\u001b[0m(lat), float64 \u001b[4mlat_bnds\u001b[0m(lat,bnds), float64 \u001b[4mlon\u001b[0m(lon), float64 \u001b[4mlon_bnds\u001b[0m(lon,bnds), |S1 \u001b[4mlatitude_longitude\u001b[0m(), float64 \u001b[4mtime\u001b[0m(time), float64 \u001b[4mclimatology_bounds\u001b[0m(time,bnds), float32 \u001b[4micclim_TX\u001b[0m(time,lat,lon)\n",
       "    groups: "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thredds = 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/fileServer/birdhouse/testdata/flyingpigeon/'\n",
    "resp = fp.icclim_tx(resource=thredds+'cmip3/tas.sresb1.giss_model_e_r.run1.atm.da.nc', grouping='yr')\n",
    "tx, log = resp.get(asobj=True)\n",
    "tx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now check that we indeed computed an annual maximum temperature. Since the file object is already opened using `netCDF`, we need to tell `xarray` to create a dataset from a `NetCDF4DataStore`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f720f4fb588>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AXhHZbZ/7B6zV1tdFJAMYBm4FEJEKoBFLeBAUkduBlarqFZGHgQ+pajtwr4iUAQLsBv4qwV+HIQl0+4bJyXRRlBPLj/BZVlcX8fvmbrOB7SCOnBygb2iUjXUlqMK2A+b7M9eJ5bd2AXBARHYC45vy8ZZTq+ozWM5hMjZMMr4TqIkw1w1hr7fGxUCDowh1Hp3uH6eGag/3N7bS3j9MdXFugqwzTIedLX0AbKor4czIGA/saqXL66fCE/tq1pBexOJ4PpdwKwyGadLlHY65akE4DaEKBq39xvE4hJ1He5lfkEVdaR691VbmRFNbv3E8c5gpHY+qPp0MQwyG6dDt87OyqmjqgRNYWVmE2yXsa+/nuoaKBFhmmC47j/WycVEJIsKKyiJELHn1VSsXpNo0Q4KYUtUmIj4R8drHsIiMiYjZ/TOklJmueHIy3SwpLzDKNofQ2T/Mid4zbKovASAvK4MLygqMwGCOE8uKpzD8vYjcyDQlzQZDPBnwBxgaGZuWlDqchmoPT71iNrCdQOMxK39nU9288XMNVUVsP9ob6RLDHGDaRUJV9VdYsmaDISXMJHk0nIaqIk4OjNBpz2NIHY0tfeRluVlZeTZs2lDtoaN/mJMDpsDIXCWWIqFvC3vrAjZi1U8zGFJCyPGUz3DFs7rmrMCg0mMEBqlkx9Fe1i8sJsN99hl4VVWowoSXy5eaZO65SCwrnjeHHdcCPuBPE2mUwRCNUPJo+Qz2eABWVnpwCTS1m32EVOIdHuVAp5eNi0pecz4kGjGlc+YusezxfCAZhhgMsdLtm365nHBys9xcWF5g/rClmJeOnyaosLn+tY7Hk5vJotI8U1NvDuOkfjwGQ0x0ef3kZbkpyJ5e1YJwGqo9/PHgyThaZZgujS29uF3Cutricz5rqPIY5eEcxjH9eAyGWOnyDs+oakE4q6s9/PLFtvG5DMlnZ0svq6qKyJ/kAWJVdRG/3dtB/9AonrzMFFhnSCSO7MdjMESj2+unvHBmYbYQ4RUMFqw0jifZjASCvHT8NO/ZsmjSzxtCAoOOfi65YH4yTTMkAcf14zEYpqLLN0z5LFcpK+0MeRPOSQ2hxm/h+TvhrLIFBvtMIumcJJYVz2qsqtFbORtqUybk8ojIJ2KYa1BVvzMtCw2GMFSVbq+fBbNc8eRnWxnyZgM7NYQav22sK5n089KCbKo8OaY3zxwlnv14/gb4NpErTIPVksA4HsOM8fkDnBkdi8u+zOpqD88dNgKDVLCzpY/6+fmURXmAWFXtMcrDOUo8+/H8SFX/KdoAEcmP1TCDYTK6Z5k8Gk5DtYf/famNbt/wjHOCDNNHVWls6eWqFdGLgDZUefh9cxeD/sCkAgRD+hLLHk+oH89jIvJQ6Jhk3L9EmkBENgGo6t/O0E6DAbCk1DDz5NFwGkyiYko43GM1ftsUIcwWoqG6CFVo7jD7PHONePbjeUJErlbVvvCTInINlhy7drrGGQwTOVunbfYrnlXVHktg0Opl63JTgj9ZhBq/bYwgLAgRUh42tfVH3AsypCfx7MfzHeBJ2/n0AIjIn2OthN44cxMNhrN0++wVTxz2eAqyM6ifn2+UbUlmZ4vV+K1+fvTIe3lhNvMLsk1pozlIRMcjIs+o6mUi4uO1RUEFUFV9TRcuVf0fERkGttmrnHdhiQmuUNWW+JtuOB/p8g5TkJ0xq6oF4ayu9rDDlOBPKo0tfeON36IhIjRUF5lQ6Bwk4h6Pql5m/1uoqkVhR+FEpxN2zY+AfwJeAv4cuNQ4HUM86fb64yIsCLHalOBPKl3eYY73Dk0ZZgvRUOXhYPcAw6NjCbbMkExi6UB61STnbpnk3F4R2QN8FsgDSrFCb6HzBsOs6fIOz7pqQTjjFQzMU3VS2NkSavwW255NQ3URY0HllU5fIs0yJJlY4hWfFZG3A58CCoDvAX7gngnj3hRn2wyGc+jyDXPRwtielmNhvAR/az9XLCuP27yGyWls6SM30z3+/z4Vod48Te39rJ2kmKghPYnF8VwOfBLYbb//rKr+dOIgVT0WT8MMhomMVy2IY1HPopxMIzBIIjtberloUTGZ7tiaH9fMy8WTm0mTKZ0zp4jluz8P2AIcxlrpLJJJdgVF5DdTTRTLGIMhEt4zAfyBYFxDbWCF2/YZ5VTC8Q2P0txxbuO3aIQEBqa00dwilhXPC8C/qer3RSQX+BLwLHDJhHGXRUgsDSHAypmZaTBYYTaIj5Q6nNXVRfz65XZ6B0coyc+K69yGs7xoN36LdX8nREOVh7ufbWF0LBjzSsngbGJxPFep6nEAVT0DfFxEXj/JuFjaYU9V781giMh48mgCVjxgCQwuX1oW17kNZxlv/LZwens1q6o9jIwFOdg1EPPekMHZxJJAelxE5gFLgIiPmtNINJ0UEakFfghUYFXB/q6qfl1E1gH/bd87AHxUVXeIyHLgbuAi4DOq+pUI89YDPwNKgBeBm2MoeJqWDPgDZGe45uxTYbddLifejdvGN7CN40koO1t6WVlZNO0crPHSRu39xvHMEWKRU38I+APwGHCn/e/nE2BLAPikqq4ALgY+JiIrgS8Dd6rqOiyp9pft8b3Ax4FJHU4YXwL+Q1WXAH3AXyTA9pTz3OGTbPmX3/PlRw+k2pSEcTbUFt8Vjyc3k0WleextNfsIiWIkEGT3idPTDrMB1JXmk5/lZp8RgMwZYnk0vg3YBBxT1SuA9UBPvA1R1Q5VfdF+7QOagWqsqgmhxxwP0G6P6VbVncBopDltEcRW4Of2qXuAG+Nte6r5/f4u3n/3TgZHxvjNng5UdeqL0pBur5/CnAzysuJfqbih2mOUbQlkX3s/w6ORG79Fw+USVlV5TOmcOUQsjmdYVYcBRCRbVQ8Ay6JdICK5IhJ1zBTX12E5uO3A7cC/i8gJrNXNp6cxVSlwWlUD9vtWLGc2Z3jo5Xb+6se7WF5RyN9fv5yO/mEOzNFku3gnj4azutpD2+kz9A3OyShsygkljm6YgeMBWFVdxP52L2PBuflQdb4Ri+NpFZFi4FfA70TkQexVx2SIyJuxcn4etd+vm0LtNvH6AuAXwO2q6gU+AtyhqrXAHViVrmOebpJzk/7kisitItIoIo09PXFf0CWEn2w/zm0/e4mLFs3j3g9t4W3rLZ+67cBUrZPSky7vcNz3d0Ksrj6bqGiIPztb+qgrzZtxO4uGKg9nRsc4enIgzpYZUsGUjkdV36qqp1X188A/Yv3hjxau+jywGThtX78bqIvFGBHJxHI696rqL+3TtwCh1w/Yc8fKSaBYREKxmRoiOE1V/a6qblTVjWVlzt9g/p8/HOEf/ncvly8t454PbKYwJ5PyohxWV3vmrOPp9sU3eTSchipTOidRhBq/zaa1wdkWCSbcNheISf4kIvNEZA3gwwpXNUQZHlDVaf/22vsxdwHNqvq1sI/asaongLVfczDWOdXa7HgSeId96hbgwena5iRUla/97lX+5eFm3ri6ku/evJHcLPf451uXl/Pi8T5651jIKFS1IN7CghCevExq5uWaRNIEcLhnkL6hUTbPwvFcUJZPdobLVKqeI0y5SysiXwDeDxzBkjmDFa7aGuGSJrsPj1tElmApz56LwZZLgZuBvSISKs/zD8CHga/bq5Zh4FbbrgqgEUt4EBSR24GVquoVkYeBD6lqO/B3wM9E5J+xqmZPJ1TnKFSVL/ymme8/e5SbNtbwxbetwe16bTRx6/Jyvv7EQZ5+tZu3rq9JkaXx5/TQKCNjQRYksEX1BWUFHDs1mLD5z1ca7f2dWCtST0aG28WKyiITCp0jxCIPugm4YBq5L38NfAarvM5PsOTX/zzVRar6DJPvyQBsmGR8J1bobLK5bgh7fYTpheccyVhQ+fQv93B/YysfvLSe//vGFbhc5/53ra72ML8gmyea55bjSZSUOpxFpXm8eKwPVZ2yV4whdna09FKaP3Xjt6loqC7iwZfaCQZ10p99Q/oQS6itCYgp1VhE3Fg5N59R1U328X9DqjjDzBgJBPn4T1/i/sZWbrtyCf/4psmdDljS0yuWlfGHV3sIjAUnHZOOdCUoeTScRaX5+PwB+oYiKvQNM6CxpY+NdfNm7cwbqjz4/AFO9A3FyTJDqojF8XwReElEHhORh0LHZANVdYxJVieGmTM8OsatP2rkt3s7+MwNK7jj6qVT/gJvXV6OdzjArmN9SbIy8XSPl8tJnOOpK80DoMWE2+JGqPHbTBJHJ+J0gcHzh0/xyN6OVJuRFsQSarsHK/t/L2f3eKLxku2YHgDGf4PDVGqGGPENj/KhexrZ0dLLF9+2mndvXhjTdZctmU+mW9h2oJsti0sTbGVy6PZZK57EhtqsUNCxU4Nx7flzPtPYYj38xMPxLFlQQKZbaGrv541rKmc9X7z56uOv0HJqiOsaKkyodgpicTwnVfUb05izBDjFa8UHyllJtCEG+gZHuOXuHexv9/L1P1vPW9ZWxXxtYU4mm+tL2Hagm0/fsCKBViaPLu8wRTkZ5GS6px48Q2pLchGBlpMmlBMvdrb0TqvxWzSyM9wsXVDoSGVbMKg0d3gZHBmjvX+Y6uLcVJvkaGJxPLtE5IvAQ1iCAQBC5W0moqofiJNt5y3d3mHee9d2Wk4N8Z2bN3DligXTnmPr8gV84Tf7OdE7RG1JXgKsTC6JTB4NkZ3hpsqTy/Fe43jixc6WXtYvjL3x21Q0VHn4XXOX4wQgJ/qGGBwZA2DPidPG8UxBLD8N67GKdv4r8FX7iFiYU0TuFpHvTzziY+7c50TvEO/8zvO09Z3hBx/YNCOnA9Y+D8ydKgZdce48GolFpXlmjydOjDd+i0OYLURDdRG9gyN09DtLr9TccXbf6WVTbHZKYmmLcMU05wzvMpoDvJUoJXYMZznUPcB7v7edM6Nj/PhDW1g/i32G+vn5LJ6fzxMHurnlkrr4GZkienx+FpfNTo4bC4tK83lsX2fC73M+8NJ447f47Zetqj7bwqLKQauK/e1eXGLlgr184nSqzXE8cW/coqq/CDvuxcoDilbpwACMjgV57/e2EwgqP7v14lk5nRBXLC/nhcOnGPQHph7sYIJBpduX+FAbWMq23sERvMNGUj1bQo3f4vGzHGJFRREuwXGVqvd3+FhcVsCWxSXsbesnaIqZRiUZHcOWALHJsc5jth/ppdM7zD/f2MCKyvg0u7pyeTkjY0GePXQyLvOlir6hEUbHNGGVqcNZZEuqj58y+zyzZccMG79FIzfLzYXlBY7rzdPc4WVFZRFraooZ8Ac4YoqZRiXujkdEfCLiDR3Ar7HK1hii8Oi+DnIz3bxhWfwKlG6sK6EgO4MnX0nvfZ5kJI+GCEmqzT7P7Ag1fptNmZxINFR5HFU6p39olLbTZ1hZWcS6WivX/uUTzrHPicTSgbRRRD5mt7+eElUtVNWisGOpqv5i9qbOXYJB5fF9XbxhWVlc5cJZGS5ev3Q+2w50p3VzuG67XM6CBObwhAiteI6ZFc+sONv4LX7CghCrqj10ef3jPxepprnTCvutqCzkgrIC8rLc7Gk1+zzRiGXF82dAFbBTRH4mItdKFB2jiDwRyznDWV46cZpun5/rGiriPvcVy8rp8vrTuupyt73imWkvl+mQl5VBeWE2LSfNimc2hBJHE7PisULRTvmZDinaVlYW4XYJq6s97DbKtqjE0o/nkKp+BliKVfTz+8BxEblTRMYfZ0Qkx34/326jUGIfdViOyxCBx/d1kukWrrAl0PHkDcvKEUlvWXWXXS6nLAl7PGCteo6ZXJ5ZsbOld1aN36IRSkZ1yj7P/nYv8wuyxn8+19YW09zuZSQwd2olxptY+/Gswcrf+XesRm3vALzAtrBhfwnsApbb/4aOB4H/jJ/JcwtV5dF9nVxywXyKcjLjPn9ZYTZraorT2/H4hinOy0xo1YJwFpXmm/YIs0BVaTzWF9f8nXAKczKpn5/vmJptzZ2WsCAUCFpT42FkLMiBTmfY50Ri2ePZBfwHsBNYo6ofV9XtqvpVrB49AKjq11W1HviUqi5W1Xr7WKuq30rYV5DmvNLl49ipIa5dFf8wW4grl5fzcutpTg74px7sQLq8/oQWB51IXWkeXV4/QyPpLUNPFYd7BukdHIlr/s5EVlVAUuHHAAAgAElEQVQ5ozfP6FiQV7sGXqNEXVtjCwxMuC0isax43qmqV6rqT1T1NX+5VPVtEwer6jdFpEFEbhKR94WOuFk8x3i0qRMRuHrlzCoUxMLW5eWowlOv9CTsHomk25e4zqOTEVK2mdI5M+Ns47fErHjAqlTd2neG00Op7bR7pGeQkUCQlWGOp2ZeLiX5WSaRNAoRBfYi8omw1+d8PqE9dfh1nwPeAKwEHgauB54Bfjg7U+cmj+3rYuOieQndv1hVVcSComy2HejiHRvSrzlct3eYJeXzk3a/cGXb8or45FSdT+xs6aM0P4vFs2z8Fo2GKquCwb52L5demLyfjYmEhAXhKx4RYW2NxyjbohBtxVM4xRGJdwBXAp12wdC1QPIeV9OI46eGaO7wJjTMBtYvwhXLyvnjqycZTbPmcFbVAn9SkkdDLCo52x7BMH0aj/XGpfFbNFbZAoNUV6pu7vCS5XadU85pTU0xB7sHGEjzqiGJIuKKR1XvnOGcZ1Q1KCIBESkCuoHFM5xrThOqCZZoxwNWuO1nO0+ws6WXSy5I3RPidDk1OMJYUJOSPBrCk5fJvLxMWkwuz7Tp9g5z7NQQN1+8KKH3mZefRXVxbspL5+zv8LK0ouCc6tvraotRtRzjxXOkJ1Y8iRZq+1tV/bKIfBOrn85rUNWPR7i0UUSKgf/BUrUNADviYexc49F9nayqKkpK24JLL5xPltvFtubutHI8ISl1MpJHwzHKtpmxczx/J3H7OyEaqotSLqlu7vByxbJz0yDW1FihwD2tp43jmYRoobZm+99GXiuPDh3nYCeWflFVT6vqfwNXA7eYHj3n0u0d5sXjfUlZ7QDkZ2dw8QWlbEuz8jk9451Hk7fiATuXx6x4ps3Oll5yMl3jobBE0lDl4cjJQXwpKuja7Rvm5MDIpLUVSwuyqS7ONaVzIhAt1PZr+997Yp1MVVVEfgVssN+3zNbAucrj+7tQJSHVCiKxdVkZn//1flpODlKXwI3feHJ2xZNsx5PPr19uxx8YIzsjOflDc4HGY72sr50Xt8Zv0WiwWyQ0d/jYXJ/4FdZE9tthvkjdVdfVFvOyERhMSix5PGUi8hUReVhEtoWOKJe8ICKb4mjjnOSxfZ3Uz89nSXlB0u65dbkl2U6nZNJQgdCyguSG2upK8wgqtPadSep90xnf8Cj7271sSpITWFWdWoFBc4cPsFo1TMaaGkvyfSpN8+cSSSyPJfdihd3qgTuBFqxk0khcgeV8DovIHhHZKyJ7Zm3pHKJ/aJTnD5/i2lUVSW3fu7A0jwvLC9LL8fiGKcnPIisjGR08zhLK5TH7PLGTiMZv0SgvzKG8MDtliaTNHV6qi3Px5E1ecWStXal6j0kkPYdYGmWUqupdInKbqj4NPC0iT0cZf32cbJuzbHuli0BQuXZV4pJGI7F1eTl3P3uUAX8grn1SEkW3N7lS6hCmSvX0aWzpxSXEtfHbVDRUe9iXotI5++0ePJFoqPYgAi+3nk5IHcZ0JpbHyNDOXYeIvFFE1gMRsxBV9RhQC2y1Xw/FeJ/zhkebOqkoyhkvrZFMti4vZ3RMeeZgelQxSFbn0YmU5mdRkJ1hHM80eO7wKVZWxbfx21Q0VBVxsNvHmZGxpN0TYHh0jCM9A6ysjJzSWJCdwZJy0wp7MmJxCP8sIh7gk8CngO8Bd0QabFcu+Dvg0/apTODHU91ERGpF5EkRaRaRfSJym31+nYi8ICK77d5Am+3zIiLfEJFDdkjvogjzPiUir9jX7xaRlD56nBkZ4+lXe7hm1QJcruSF2UJsWDSPwpyMtAm3dXmHU7LiEREWleaZhnAxcvTkII3H+ri+oTKp911V7SGoJL0g5yudPoLKlN2C19QUs6e1P637YSWCWNoi/EZV+1W1SVWvUNUNqvpQlEveCrwFGLSvbyd6pYMQAeCTqroCuBj4mIisBL4M3Kmq64DP2u/BCuktsY9bgW9Hmfs9qrrOPlL6F/fpV3sYHg1yXZJk1BPJdLu4fGkZ2w70OL4v/FhQ6fH5U7LiAagrzTcrnhi5v/EELiHpJZlCyrZkJ5KO9+CZQja+tsbDqcERI1KZQLQE0kkTR0NESSAdsWXVas8Tk25XVTuADvu1T0SagWrbhtB31wO026//FPihWo8SL4hIsYhU2vM4lsf3dVKcl5kS+WeIrcvL+c2eDpra+1mTgnBfrJwa8BPU5CePhlhYmsfj+zsJjAXJSII8OF0JjAX5+a5Wti4vT/pDQpUnh3l5mUlPJG3u8JKf5aZ2XvTk73CBQTISxdOFaL9NocTRHOAi4KB9rAOiBVTvF5HvAMUi8mHg91hVDGLGbh63HtgO3A78u4icAL7C2RBeNXAi7LJW+9xk3G2H2f4xWvfURDM6FuT3zV1ctWJBSv+QhZrDPdHs7HBbd4qSR0PUleYxOqZ09DujxbJTefKVHnp8fm7aWJv0e4sIDdWepCvb9nd4WV5ZNGW4fHlFEVlulykYOoGIf/1U9R47eXQJcIWqflNVv4lVAHRdlOu+Avwcq2HcUuCz9nUxISIF9rW3q6oX+Ahwh6rWYu0t3RUaOtntJzn3HlVdDfyJfdwc4b632ntIjT09idl4f+HIKbzDgaRVK4hESX4W62uLedLhVQxSlTwaIiSpTtY+z1/9aBd/8uVtfO7BJp56pZvh0eRumM+U+3aeYH5BdsqUW6uqPLzS6Utax09V5UCH7zWtECKRleFiRVURu43A4DXE8thdxWv3aAqYupX1XuCPwB/s1zEhIplYTudeVf2lffoWIPT6AWCz/boVSz0XooazYbhxVLXN/teH1bp788Qx9uffVdWNqrqxrKwsVpOnxaNNneRlufmTJamvlXbligXsae2n2+vcp/lQ8mgqxAVg7fEASSkWOhIIsu1AN8Eg3Nd4gvffvZP1//Q7PvzDRn664/i4E3Ya3d5hnnylm7dvqE5KtYLJaKguYnRMebXLl5T7tfadwecPTCksCLG2xkNTWz9jDt9TTSax/KT8G/CSiPxARH4AvAj8a6TBIvIhrKKgb8NqkfCCiHxwqpvYIbC7gOYJvX7agcvt11uxwn0ADwHvs9VtFwP9E/d3RCRDRObbrzOBNwFNU9mSCIJB5fH9XbxhWVnSWjhHI1TY0MnN4UJ/bBPZqyga5YXZZGe4OJ6EFc/Bbh8jY0H+/vrl7P7sNdz9gU28Y0MN+9u9fPqXe9nyr0/wpm/+ka89/gq7T5x2jDDk5y+2MhZU3pWCMFuIs715khNu29ce6sETi2bK6kg6ODLG4Z6BRJqVVkwpuFfVu0XkEWCLfervVbUzyiV/A6xX1VMAIlIKPAd8f4pbXYoVBtsrIrvtc/8AfBj4uohkAMNYCjawmszdABzCyhUaL0QqIrttFVw28JjtdNzMYL8pXrx0oo8enz/lYbYQKyoLqfTk8MSBLm7alLo/GtHo9g0zvyArZU/SLldIUp34FU+o7EtDtYecTDdXLCvnimXl/JMqr3YN8MSBLrY1d/OtJw/xjW2HmF+QxRXLytm6vJzLlsynMGfy7PlEoqo80NjK5roSFpclr/TTRBaW5FGYnUFTm5d3JaFYV3OHF5cQc5PAtbWWY3z5xGmWLojNWc11Ysr0sh3NgzHO2QqEr3l9vFYEEOkezzD5vg3YRUcnjFfgYxHmWmf/OzjZtangsX1dZLrFMRnMIpYtD77U5thCmFbVgtTs74RIVnuEpjYvhdkZLJqgfBIRllUUsqyikI++4UL6Bkd4+tUenjjQzWP7OnlgVyuZbmFLfSlXLC/nxnVVlCaprt2Oo70cPTnIx664MCn3i4TLJaysKkqawKC5w0vd/Hxys2L7nVk8v4CC7Az2tPbzzhSuDJ1EIh4l24DtIvJ5O5n0BeCQiHwivJ32+YSq8mhTJ5deOJ+iFDyZRuLK5eUMjoyx42hvqk2ZlC7fMOUpklKHqLPbIyQ6tNXU3s/KqqlVUvPys7hxfTXffPd6XvzHq7nv1ov54KX1dHqH+cJv9vPeu3YkLQx3384TFGZncMPq1K/iG6o9NHd4CSShw+5UpXIm4nIJq6s9plJ1GBEdj4jUz3DOw8CvOKswexArP2eqltlzlgOdPo73DjkmzBbikgvmk53hcmwVgy6vnwUpXvEsLM3HHwjS5Uvc5n5gLEhzh3c8GTJWMtwutiwu5dM3rOD3n7icf3/HGpo7vDzSFC0SHh+8w6M83NTBm9dVkZeV+pp/DdVFDI8GOXIysatT7/AorX1nYlK0hbOm1nKM/kB6KBUTTbSfmJ8DG0TkCVW9MtYJZ9Eye87yaFMnInD1yuQXBY1GbpabSy4oZduBbj77ppVJrZQ9FYGxICcH/ClLHg1RF1YstNKTm5B7HO4ZZHg0yOppOp6JvO2iGr7zhyP8v9+/ynUNFbgTWJLpod3tDI8GUyoqCCckMGhq60/oPsoBuxXCdB3PuppiRseU5g4f62qdm7SdLKKF2lx2qGxpKEwWfkS6SEQ2isj/isiLdg21Ped7W4TH9nWyaVEJ85PcUyYWti4v59ipoYQ/KU6XkwMjqKYueTREXRLaI5wVFsyua6fbJdx+1RIOdg/wmz3nZBbElfsbT7C8onC8xXOqWVxWQE6mi6YEV6reb+8jTSfUBrBmvIKBCbdBdMfzZ1gqsgzOhsnCj0jcC9wNvB14c9hxXnLs1CAHOn1cm8ROo9MhJHZ40mHhtm5fapNHQ1R6csh0S0KVbXvb+snLclM/f/bKsBsaKlleUcjXf38wYfsd+9u97Gnt512bah2zSna7hJWVRQnfR2nu8FGSnzXtlXiVJ4f5BdkmkdQmWuvrV4AvicgeVX1kGnP2TFFE9LzisX1WvP0ah4XZQtTMy2PZgkKeaO7mQ3+yONXmjJPq5NEQGW4XtfPyErri2dfez8rKoriExlwu4farlvJXP97Fg7vbeXsCinbe33iCLLeLG9dFqlCVGi69cD7/+eQhTg74ExZdaO70sqKycNoOV0RYW+MxTeFsYlG1PSciXwuVkxGRr9ptEiLxORH5noi8W0TeFjriZXC68WhTJw3VRY4uELh1RTk7W3rxDo9OPThJpLpcTjgLbWVbIggGlX3t0xcWROPaVQtYVVXEN7YdZDTOq57h0TH+96U2rlm1gHn5WXGde7Zc31BJUOHxfV0JmT8wFuRApy9iq+upWFNTzOGeAXwJ+j073DPA84dPJWTueBOL4/k+Vi7OTfbhxQqlReIDWLXcruNsmO1NszMzPen2DvPi8dNcu9KZYbYQW5eXEwgqf3z1ZKpNGafbO4wIzC9I/R+3UHuERPRUOXJykKGRMVZNUV5/OogId1y1lGOnhvjli61xmxesFXz/mVH+bNPCuM4bD1ZUFlJXmscjTYkpUH/05CAjgeCUrRAisbbWg6oVWo03qsod9+3m1h81pkVpnlgczwWq+jlVPWIfdwLRYjJr7Zpnt6jqB+xjypI5c5HH9ltPXtc5dH8nxPraYorzMh0lq+72WeESJ7QjWFSax4A/wKnBkbjPHSrzsjrOm/RXrihnbY2HbzxxKK7FM+9vPEHNvFwuuaA0bnPGCxHh+tWVPHf4FH0J+F7t7wiVypn5igdISLit8Vgfe1r78Q0HxnsFOZlYfqvPiMhloTcicikQravRC3YDt/Oex5o6WTw/nwvLU1dOJBYy7OZwT73S7ZgaYKnqPDoZiVS2NbX1k53h4sI4l5wREe64eiltp8/wwK4pC4fExIneIZ49dIp3bqhNSffcWLi+oYKxoPK75viH2/Z3eMl0CxfM8HtVkp/FwpK8hLTC/v4zR8m3Kym8cMT54bZYHM9fAf8pIi0i0gJ8C/jLKOMvA3bb7ab3iMje81FOfXpohBeOnOLahgrHKH+isXV5OacGRxyTXd3lTV3n0YksDMvliTd72/pZXlmUkJXd5UvLuGhhMd/adiguiYv3N55ABN65MbldRqfD6moP1cW5PLI3/uG25g4fS8oLycqY+fdqTQIEBid6h3hsXyfvu6SOhSV5bHdoJZJwYml9/bKqrgXWAGtUdb2qRnMk12H18LmGs/s7552c+onmbgJBdVy1gkhcvrQMlzhHVt3tG0558miImnm5uCT+7RGCQWVfm5fVs8zfiYSI8Imrl9HRP8zPdsxu1TMWVH6+q5XXLymjqjgxibTxQES4YXUFzxw6Sf+Z+G7iN0+zVM5krKstpu30GXrsJofx4J7nWnCJ8L7XLWJLfQk7W3odE7mIRMyuW1W9dmO2qcYdw+qTs9V+PTSd+8wVHtvXSaUnhzVxVCslkuK8LFbXFPPCkdQ/LY2OBTk5MJLyAqEhsjPcVBXnxj3UdqJvCJ8/MJ51nwguvbCUzfUl/OeTh2bVWO4PB3vo6B/mXQ6tZB7O9asrGR1Tth2IX7itx+enx+ePuRVCJM7u88QnsjDgD3DfzhPcsLqSSk8uWxaXcnpolFe7k9ObaKbE3SHY1Q7+jrMtqjOBH8f7Pk5maCTA06/2cM3KBY6NhU/GpkXz2N16OuX1pE4OWE+DTgm1gbXPE+8Vz96wVgiJwlr1LKXb5+fe7cdnPM/9O09Qkp/FVSucmY8WzrqaYio9OTy8N34160Ib9jNVtIVoqC7CJfBynMJtDzSewOcP8MHLrNKaW+pLANjugAfIaCRiJfJW4C3AIICqtnOeFQf9w6s9+ANBx1YriMSm+hJGAkH2pjjJzSnJo+EsLM2Le0O4pjZrszrRPVouXlzKJReU8u2nDjE0Epj29ScH/PxufxdvW189q/2NZOFyCdeuquDpV3sY8E//652Mccczy1BbXlYGSxcUxkVgMBZUfvBcCxctLB6v/1YzL5cqTw7bjzpbYBDTT5GIXCIify4i7wsdUYaP2L1y1L42Px6GphOPNnUyLy+TzXUlqTZlWmxcNA+AnS19KbXDScmjIepK8+gbGqV/KH77Bvva+1lWMbvN6lj5xNVLOTkwwo+ePzbta//3xTYCQU2LMFuIG1ZXjrcTjwf7O7xUenIozpt9XpklMDg967ywbQe6OXZqiL+47Gx2i4iwZXEpO472JiTvLF5M+RMvIj8CvoKlVttkHxujXHK/iHwHKBaRD2N1/fxeHGxNC0YCQZ440M2VKxY4IgdlOpQWZHNBWT47W1K7TO8edzzOWfEsCkmqe+Oz6lFV9rb1J3R/J5yNdSW8fmkZ//304WmtAlSV+xpPsH5hMUvSqHvmhkXzKCvMjpu6rbnDO+vVToi1tcX0DY1yojdaVsrU3PXMEao8OVy76rXhzy31JZwcGOFwj7MK/4YTy1/GjcClqvpRVf1r+/h4pMGq+hWslgq/AJYBn1XVb8THXOfz/JFT+IYDXJcmaraJbKoroTHFqpgurx+XkLROmrEQyuWJ1z5P2+kznB4aTej+zkQ+cfVS+oZGuee5lpivefF4H4e6B/izNFrtgFU09LpVFTz1Ss+MwovhDI+OcbhncNaKthBrbYHBbFIX9rX388KRXm65pO6cB9wti63kXieH22JxPE1AzH9FReRLqvo7Vf0bVf2Uqv5ORL40cxPTi8f2dZKX5eayJfNTbcqM2FRXgnc4kFJVTLdvmLLC7IT2k5kuC+1ae/Ha5wmV70+m41lXW8yVy8v57h+OxFyX776dJ8jLcvPGNVUJti7+XL+6gjOjYzz9Ss+s5jnYNcBYUOPmeELh1dko2+5+toW8LPekpYvqSvMoL8x2tMAgFsczH9gvIo+JyEOhI8r4qyc5d/3MzEsvxoLK4/u6uGJZOTmZsfVjdxqb7H2pVO7zdHn9jpFSh8jNcrOgKDtuK56mtn7cLmF5RXLDV3dcvZT+M6Pc/UzLlGMH/AF+s6eDN62ppCA79V1Gp8vmuhJK8rN4eJYdWeOlaAuR6XaxqqqIl0/MTMTT7Rvmod3tvGNDDZ68zHM+FxE215ew/egpx+7zxOJ4Pg/cCPwr8NWw4zWIyEdEZC+wLLwBnIgcBc6LygUvHe/j5ICfa1Y5X3IaidqSXBYUZbMzhdnPXV7nJI+Gs6g0P265PE3t/SwpL0j6A0pDtYdrVi7ge88cmVIo8ds97QyNjPEuBxYEjYUMt4trVy1gW3PXrHKY9nd4yctysyiOFebX1hSzt61/Rj2T7n3hOCNjQT5waX3EMVsWl9Ll9SesqvpsiaVywdOTHZMM/QlWhYKHeG0DuA2q+t64Wu1QHm3qJMvtYqvdXC0dERE22vs8qaLb509559HJqCvNi8uKR1VpautPapgtnDuuXopvOMD3njkSddzPdp7gwvICLlqYvq2ar2+oZHBkjD8enHnl9f0dXpZVFMY1J29trYczo2Mc6hmY1nXDo2Pcu/0YVy4vp35+ZMHwxXY+zw6Hls+JRdV2sYjsFJEBERkRkTEROaeCgar2q2qLqr5bVY+FHc78yuOMqvLY/k4uubCUwpxzl7/pxOa6Etr7h2ntS/7T0kggSO/gCAscFmoDa8XT4/PPerO6y+vn5MAIDXFshTAdVlQW8cbVlXz/maMRqzgf7PLx0vHTvGujc7qMzoTXXVCKJzdzxuo2VY2roi3EeAWDaYbbHnq5nZMDI+MJo5G4sLyA0vwsXnCowCCWUNu3gHcDB4Fc4EP2OUMYA/4AyxYU8eY03ISdyMY6K5+nMQX7PD121YJyR4ba4lMstKktMa0QpsNtVy1haHSM7/5x8lXPfTtPkOkW3nqRs7qMTpdMt4urVy7gd81dM6rI0Xb6DL7hQNyEBSHqS/MpzMmYlrJNVfn+M0dZXlE4ZVuK8X0ehwoMYko0UdVDgFtVx1T1buANCbUqDSnMyeR7t2xMSKvhZLO8oojC7Ax2pCDc1uXAHJ4Q8WqP0NTej8jM+7rEg6ULCnnL2ip+8GzLeImiECOBIL98qY2rVixIWAvpZHLD6gp8wwGeOzT9p//97bPrwRMJl0tYU+OZluN5/vApDnT6+OCl9TGtQjfXl9B2+kxKIhdTEYvjGRKRLKxWB18WkTuA864awfmE2yVctGheSvZ5QsmjTlO1wdn2CLPd52lq6+eCsgLyslKrFPv4lUvwB8b4ztOHX3P+981d9A6OcFOa5e5E4tIL51OYncHDMwi3NXf4ECEh6sO1NcUc6PDFLHz4/rNHKc3P4i3rYouqbKm3VkVO3OeJxfHcbI/7P1j112qBt8fbEBGpFZEnRaRZRPaJyG32+XUi8oKI7BaRRhHZbJ8XEfmGiByy1XMXRZh3g90T6JA9Pn0D1klkc30Jr3YNJKSTYzRCddqcVC4nRFFOJiX5WXEItXlZ7YCq5ReUFXDj+mp++PyxcYcPVpit0pPD65eUpdC6+JGd4eYqO9w2Ok0VWXOHl7rSfPITICdfU1NMIKjjnU2jcfTkIE8c6OY9Fy+KWQm5vKIQT26mI8NtsajajgECVKrqnar6CTv0Fm8CwCdVdQVwMfAxu5Ppl4E7VXUd8Fn7PVi5QUvs41bg2xHm/bb9eWjsdQmwfc4Rqtu261hy93m6fcO4XUJp/uxrYiWCRaV5swq19fj8dHqHWZUiYcFEbrtyCYGg8l9PWauettNn+MPBHt65ocZRCbyz5fqGCk4PjU67O+f+Du+sWyFEYm2t9fCxJ4aCoT949igZLuG9F8cubXe5hE11JY6sYBCLqu3NwG7gUfv9uikSSGeEqnao6ov2ax/QDFRjFRsN/ZZ6gHb79Z8CP1SLF7Bqw1VOsL0SKFLV5+3CpT/EykkyTMHa2mKy3K6k123r8vopK8h2bDuJutL8Wa14mtoT3wphOiwqzecdF9Xwkx3H6eg/w88bW1GFd26cG2G2EK9fWkZelntarRJ8w6Mc7x2Ku6ItREVRDuWF2VO2SOg/M8oDu1p589qqaYegL15cQsupofG9U6cQawLpZuA0gKruBuoSZxKISB2wHtgO3A78u4icwCpWGurzUw2Et1Vstc+FU22fjzYmdM9b7VBeY0/P7EpszAVyMt2sqfGkwPE4M3k0xKLSPNr7z8y4Z9E+W9HmlBUPwP/ZeiGqyre2HeKBXSe49MJSauOYLOkEcjLdbF1ezuP7OmNO2jzQaZWNSpQIRERYU1M8pcDg/p0nGBoZ44NREkYjsdnO55nuSi/RxOJ4AqqatAYtIlKAVWD0drvj6UeAO1S1FrgDuCs0dJLLJ9aHiGWMdVL1u6q6UVU3lpXNjdj2bNlYV8Letv5ZZX1Pl26vM5NHQywqzUOVGVcWbmrzUj8/31G5XrUledy0sZZ7tx+nte9M2lYqmIobVldyanAkZrVmqFROItWH62o9HOkZjFg7LzAW5AfPtbClvmRGq+SVlUUUZGc4TmAQU5FQEflzwC0iS0Tkm8BziTBGRDKxnM69qvpL+/QtQOj1A1irL7BWL+HxgBrOhuEIG1MzxRhDBDbXz2N0TNkdh6ZVsdLlc/qKZ3aS6r1t/Y5a7YT42BUXkuV24cnN5JqV6VvyKRpvWFZGTqaLR2Os3dbc4aU4L5NKT+IehEKJpJGaLz6+v4u202f4iykSRiOR4XaxsW4e29PQ8fw1sArwAz8FvFjhr7hiq83uAppV9WthH7UDl9uvt2IlsoJVmud9trrtYqBfVV+jl7Tf++zqCwK8D3gw3rbPVTYsLEGEpNVt8wfGOD006kgpdYjZtEfoGxyh7fQZRyjaJlJVnMs/39jAnW9ZlbYFbqciLyuDK5aV80hTZ0xtP/a3e1lRUZTQyg1r7CTiSOG27z9zlIUleVw5i5bjW+pLOdQ9cE6+ViqJRdU2pKqfUdVNdijqM6qaiJ2qS7Gk21tt6fRuEbkB+DDwVRF5GatQ6a32+IeBI8Ah4H+Aj4YmEpHdYfN+BKsR3SHgMPBIAmyfk3jyMlm2oJCdSVK2dY9LqZ274pmXl0lhTsaMVjz72pPfCmE63LSplhvXp3elgqm4rqGCHp+fXcej/0yPBZVXunxxq0gdieK8LOpK8yZthf3yidM0Huvj/ZfUzUphuGWx8+q2RcdvguAAABl7SURBVBSnT6VcU9W3xNMQVX2GyfdkADZMMl6Bj0WYa13Y60agIR42no9srJvHr15qZyyoCZfXdvvs5FEH7/GIiC2pnv6KZ68DhQXnG1uXl5OV4eLhvR3jLUAm4+jJQYZHg0mpLrGmpnhSEc/3nz1KYXbGrBN5V1d7yM10s/3IKW5YXTn1BUkg2orndVh7In/EUpN9lShtEQxzk011JQz4A+MbrYlkPHnUwaE2mHl7hKb2fmpLcinOc2aO0vlAYU4mr19SxqNThNv2jwsLEt8vaW1tMR39w69J4u3sH+a3ezq4aVPtrHshZbpdbFjkrH2eaI6nAvgHrNXC17EavJ2M0hbBMAc52xgu8T+0Tq7TFk5daR6tfWemnQW/r62fhipnhtnOJ25YXUFH/3BUGXNzh5dMt7CkPAmOZ3yf56zA4IfPtxBU5f2X1MXlHlvqS3ily8fpoeRWIolERMdjFwR9VFVvwaokcAh4SkT+OmnWGVJOVXEu1cW5SalU3e3zk+ES5jl8RbCoNJ9AUGk/Hbuk2js8SsupIcfu75xPXLliAZlu4ZEo6rbmDi8XlBWQlRFTHeVZsarKg9sl462wz4yM8ZMdx7lmZUXc8qm2LC5F1Tn7PFH/V0UkW0TeBvwYaz/lG5yVNhvOEzbVzWNHS2/C2+h2eYcpL3Ru1YIQoU6U09nn2dfmbGHB+YQnN5NLL5zPw3s7Iv5M72+Pfw+eSORmuVm6oHA8beGXL7Vyemh0yp4702FtrYesDJdjwm0RHY+I3IOVr3MRVq20Tar6BVVtS5p1Bkewqb6EHp+f472JLa/u9OTREHXzp5/Ls6/dCAucxA0NlbT2naGp7dy9y1MDfrp9/oQr2sJZV+thT2s/waDVc2d1tYdNdl+seJCd4WZ9bbFj6rZFW/HcDCwFbgOeExGvffgm60BqmLuE9nkSvUx3ermcEOWF2eRkuqaVy7O3rZ9KT86c6G8zF7h65QLcLuGRpnNbJTR3JLZUzmSsqSmm/8woP95+jMM9g3zwsrq45w9tWVzK/nZvxCoJySTaHo9LVQvtoyjsKFRV89h2HnFhWQHFeZkJFRj0+Py0nBqkfn5Bwu4RL0TELhYa+4qnqa3fhNkcxLz8LC65oHTScNv+Dmt1mlzHY/1sfOmRA5QXZvPG1fHvZHxxfQlBhV0p6Cw8kcTvnBnSHpdL2LhoXkIFBj/bcZzRMeWdG9Ojg+t0cnkG/QGOnBw0ijaHcV1DBS2nhsaLgYZo7vBRUZRDSRJbcyxdUEhOpovBkTHe97pFCRE1rF84j0y38IIDwm3G8RhiYlNdCUdODtLji3/ZjcBYkJ/sOM5lF87ngjLnr3jAzuXpHYqt9EqHF1VoqDaBAidxzcoKXAKPTOhM2pzAHjyRyHS7WFXlITvDxbs3J6ZIa26Wm7U1xY5oDGccjyEmNtr7PLuOxf+H9vfNXXT0D3Pz6xbFfe5Esag0j5FAkM4Y+pw02RULnFij7XymrDCbzfUlr5FV+wNjHOoeSGqYLcTfXLuMr960ltIE7gNurrcqzg/6Awm7RywYx2OIidXV1tPYjqPxD7f98PljVHlyuHJ5edznThRni4VOvc/T1OalrDA7LRR75xs3rK7kYPcAB7uscNvBrgECQU2qoi3ExYtLedOa+O/thLNlcSljQeXFKWrVJRrjeAwxkZXhYl1tMY1xXvEc6vbx3OFTvOfiRWS40+fHcVGplctzPIZ9nqa2fhqMjNqRXLuqAhHGVz3J6MGTSjYsmofbJSkPt6XPb7oh5WyuL2Ffuzeuy/QfPX+MLLeLd82yEGKyqfTkkumWKSXVZ0bGONjtM2E2h7KgKIcNC+fxsL3Ps7/DS06ma3xFO9coyM6godqT8nwe43gMMbOxroSxoPLS8fg0hhvwB/jFi23csLoi7fJb3C6htiRvSkn1gU4vQYVVxvE4lutXV3Kg08fRk4M0d3hZXlGU8ErsqeTi+hJePpHczsITMY7HEDMXLSzGJcTcOngq/velNgb8AW5+XV1c5ks2daX5U654QsICk8PjXK5rqADg4b0dNHf45myYLcTm+hJGxoIp3ecxjscQM4U5mayoLKIxDo5HVfnR8y2sqiriooXFszcuBSwqzeP4qcGoNeya2ryU5GdRlcD2yYbZUV2cy7raYn78wjH6z4yyMslS6mSzsc7qLJzKgqHG8Rimxaa6El46fnraLQEmsv1oL692DfC+1y1KaGvhRLKoJI/BkTFODkQuNd/U3s+qqsS2TzbMnusbrFYJQEoUbcnEk5vJysqilAoMjOMxTIvN9SWcGR0bb+M8U370/DE8uZm8ZW36tlpeNEWxUH9gjFe7fCbMlgZc33C2M+eyirnteAC21Jfy4vE+/IHU7PMYx2OYFhvtirk7Z7FM7/IO89i+Tt65oYbcLHe8TEs6Z3N5Jt/nebVzgNExNYq2NGBhaR4N1UXUz8+fdcfPdGDL4hL8gSB7wprPJRPjeAzTorwwh7rSvFkVDP3J9uMEgsp7L06fSgWTUV2ci9slHI+w4vn/7Z15lF1VlYe/X42hhqSGJAQyVCUBQiCEAJWqCKKgYi9HGrojKoPg1MtGBAWH1SIqDrC01Wg30ILaLYgQkUFB7damcdlgGBIFkhCGxlQFEoIhhMpEkqrU7j/OqeSlqKqkqt69b6j9rfVW3Xferfc757793r5nn33OWRG3QvA12gqDRWfN47vvnZfraqRC74rzD/0lN2nV7nicIdPS3MDSjk3D2hiua3cPtzy8hjceMWHPvjaFSkVZCYfWjRmwx7N8bSdjx5QxteGglGvmDIfDJtYyd0phJroMlYbqCmYdXJuzjeHc8ThDprW5gZe37eLZDQe+LUAv/7VyPX/dspPzCmhdtsEYbHuElXErBE8scPKRthkNLOvYNOJEoeHgjscZMnvGeYYRbrtxSQdT6g/ilFmFsy7bYDQ1VvXb4+na3cOq9Z5Y4OQvbdMb2b5r9565ZmnijscZMtPHVzO+pmLIjufJ9Zt5ePXLnLOgqWhmhjc3VtP5ahevbN83pfqZF7eyq7vHt7p28pbW6XGcJwfhNnc8zpCRREtTw5Adz01LOqgoK+E9LYW1LttgTGsIi4X23RSuN7HAM9qcfGVCbSUzJlTnJMEgbxyPpKmS7pO0StJKSRfH8sWSHo2PdkmPxvIKSf8uabmkxySdMsD7fknS2oz3eHuKzSpa5k9v4LmXX2V95/73owHYvKOLO/+8lnfNPTTVnR2TpjdBou/2CCvXdlJdUVq0i006xUHb9EaWtm9i9wFsaJhN8sbxAN3ApWY2G1gAXCjpKDM7y8zmmdk84Hbgjnj+RwDM7BjgNOBbkgZqz3d638PMfp1wO0YF84c4znPHsufZHrf1LSYG6vEsX9vJ0YeOo6RIQopOcbJgRgNbdnbv2Q4iLfLG8ZjZC2b2p3i8BVgF7JnWrpAa9B7gllh0FHBvPP+vwCtAS5p1Hs0cdchYqipKD2jdNjPjpgc7OHbKOI6dWlzpqmPKSzlk3Jh9HM/uHuOJFzZ7YoGT97RNbwTgwZTDbXnjeDKR1AwcBzyUUXwy8KKZPROfPwacLqlM0nTgBGCgwYOPS3pc0o8k1SdU7VFFWWkJx0+r5+H2/a9w+8dnN/Lshm0Fuwr1/pjWZ3uEv2zYyo6uHuZM9sQCJ7+ZNG4MTY1VqScY5J3jkVRDCKldYmaZ/b/3sbe3A/Aj4HlgKbAI+CMhXNeX64CZwDzgBeBbA+h+VNJSSUs3bNgw4naMBuY3N/Dk+s1s3tE16Hk3Lmmnvqqcd849ZNDzCpW+2yMs960QnAKitTkkCvWkOM6TV45HUjnB6dxsZndklJcBZwKLe8vMrNvMPhnHbU4H6oBn+r6nmb1oZrvNrAe4AWjtT9vMrjezFjNrmTBhQnYbVqTMb67HDJZ1DNzrWffKq/zuiRd5z/ypjCkv3HXZBqNpfBUvbd3J1rgz64q1YRfLmRNqclwzx9k/bTMaeWV7F0//dUtqmnnjeOIYzg+BVWb27T4vvwV40syezzi/SlJ1PD4N6DazJ/p538zb7DOAFVmv/Chl3rQ6yko06DjPTx9agwHntBVXUkEmvZlra2KvZ8W6To46pLh3sXSKh7be+TwpbpOQN44HOAk4F3hTP6nP72XfMBvAROBPklYBn43/C4CkH0jqTTT4Rky5fhw4Ffhkoq0YRVRVlHH05HE8srr/Hs/O7t3c+sga3jRrIlNj9lcxsjezbRs9PcYT6zyxwCkcpjZUMbnuIB5anV6CQd6s/21m9wP93iKa2fn9lLUDswY4/8MZx+f2d46THVqb6/nxkg52du+msmzfUNp/rljPS1t3cW6RpVD3pakxOJ72jdtp37iNrTu73fE4BUXb9Ab+8MwGzCyVtQXzqcfjFCAtzQ3s6u5heT/7ety4pIPmxirecHhxj5nVjilnfE0FHRu3sSJukOdbITiFROv0Bl7aOryFf4eDOx5nRLQ09U4k3TfctnJdJ8s6NnHOgqZRMYmyqbGa9o3bWLG2k4qyEg4/2BMLnMKhbUaYz5NWuM0djzMiGmsqmTmh+jUrGNy0pIMx5SUsPKF41mUbjKaGKtZs3M6KtZ3MnlRLeal/tZzCobmxiom1laklGPi3wxkxrdMbWJoxD6Bzexd3PbqW04+dzLiq8hzXLh2aGqtZ17mD5c93crSP7zgFhiTaZjTy0OqNw9rgcai443FGTEtTA5t3dO+ZB3DbsufY0dVT9EkFmTSPDwkGW3Z2+/iOU5D8wxtmcN05J6SilTdZbU7h0ruvxyPtmzhiYi0/ebCD46fVjarMrqaMVah9KwSnEEnz++qOxxkxU+oP4uCxlTyy+mWmNYQdOS95yxG5rlaqNMeU6rISccQkTyxwnMFwx+OMGEnMj+s9bd/VTWN1BW87ZlKuq5UqdVUVjB1TxpT6qtfMZ3IcZ1/c8ThZoXV6A/c8/gIvdO7gwlNnjsof3zOOm1zUKzQ4TrZwx+NkhZamMM5TInh/Ea/LNhhfPn1OrqvgOAWBOx4nK8yaVEt9VTmt0xuYXHdQrqvjOE4e447HyQqlJeL2j51IY3VlrqviOE6e447HyRozfP8Zx3EOAJ9A6jiO46SKOx7HcRwnVdzxOI7jOKnijsdxHMdJFXc8juM4Tqq443Ecx3FSxR2P4ziOkypKY9OfQkPSBqBjgJfHAy+lWB3Xd33Xd/1C0G8yswkH8ibueIaIpKVm1uL6ru/6ru/6w8NDbY7jOE6quONxHMdxUsUdz9C53vVd3/Vd3/WHj4/xOI7jOKniPR7HcRwnVdzxOI6zD5KU6zo4xY07nn6QNDPH+uU51i+Nf3PyA5TrHz5J4+LfnHw/JB0taUwutCM53ULW7a/47c8dTwaSjpf0B+BqSWNzoL9A0q3ANyXNyYH+SZJ+DFwuqcFSHgCU1CbpBuCzkg5oIloWtUskjZV0D/A9ADPrSbkOcyXdD3wVaExTO+ovkHQ7cI2kt/Y6gBT13f5Gif2544lIqiBc8MVmttDMNsfyVO5+JC0ErgPuAcYAn0pZfwZwLXAf0AR8RdI7UtIulXQVIWPmAeB44IuSDk5DH/Z8ybcA5cBkSWfFuqX5Hbkc+LmZnWFma6N+op9/7/tLOoXw+d8BPAWcA9Qnqd1HPyf2p0DO7K+3/bmyvz72lZr9uePZy/HARjO7BkDS6yRVJn3XlfHBHgbcbWY/Ab4TXytPQb8iHrYAq8zsP4BLgUeBd0qamrS+me0GngcWRv1LgAWkEPLpE9Y8krAcyCLgbEm1ZtaT5I9/r76kw4CtZrYoPj9NUh2QdNipt/3HAo+Y2c3ATbF8a0KamVTFv/PJgf0BVdH+1pID+2Nv+yEH9kdso6TDSdH+Rq3jkXSipLskHRWL2oFZkt4l6XfAF4EbJL0vSX1gdix6CjhD0meAJcChhJDH/IT03y1pMeELBvAwMEXSVDPbRLjzewU4I0H9nwGtsehmM3s6Ovt1BEc0PgntDP3FQFtG8f8Bu4DV8fEBSdOScP79XP8XgZMlvSPaxWWEkMunAbJdh370fw8slHQF8CfgEODa2BPPOpLOlLQU+HAseoh07e9MScuAC2LRT1K2v972fzCjOE3763v915Oi/WFmo/IBfIzwQZ8P1BHu8L4E/A/w5njOBcANwJEJ61cTbgLeCtwNvDOe8zXgamBSFnUnAPcC/w28LaP84Kh1aXxeCpwLfBkYk7R+n3NqgceAQxO47gPqA68DvhuPPwpsAH4VbaMkKX32zqf7J8KP/rvj8zcAvwAWpNT+VuBHwN/F5x8CfgjMzvJn8HrCjc47+tTrG0nb30D6adnfYPrAicD3krS//vQz7O/zSdtf72PU9niABmAFIcQ0x8y6gKXAXMIYCwQnVAtsS1j/BDPrMbPfAl2E3g+ED31ulvUnE+7kvmpmv5FUBWBmLxJ6WsdKarW94YeTzGxHgvr9hTPagJVmtk5STQwDpKHfDoyNPYHPAMuAp82sy7I30Pua62/xWw78EpjB3rGVpYSe0M4safer3/uCmT1MuAFpj0VJ2f/fADea2a/i5zvezDYQejnHSGpL0P766ldLmtjn9QUkZ3/96U+K5e1AdYwEJGV/r9EnOH0I9tdM+G2CZOwPGCWhNknnSXqj9qYplhK68VcD24HjJdUQvP2VwMVxUO80wocwIsM/AP25kuolNQMrgd7wxnEj1c7QP0VSjZk9ClwDfFDSxcAvJX1X0kIz+wXwZ2BRvB5HA2syf5wS0L9b0iJJ52b8Sx3wnKQLgEeAeSnon0Vw+hMJYYfjCL3SWZJmD/jmI9fvvf5nm9kKQmjjIknjCQP8c4CNCesvkvSBePofgQvj8ZsJ2U3Zsv/eH7Q1QF3UvB/4YQwxPwU8SDL2N5D+A8D3JV0RrzkkY3+D6V8n6XPATGAsydjfYPrXS7qcEN77BFm2v37rtPdmq7iQJGAS8FOghxDWqgEuMbMNCmmTPwCei+c0Ap+Jd4FXEcZYpgIXmtmqhPVvjvqXApWEEN9kwuDux83sySzoP0sI6V1EuOG4kxBP/hwhseHrhLvLdZIWxfY3AeeZ2VOvVciq/kyCEz7JzNZKuhl4H/Bj4Dtm9ngK7f8aIdTRaWavxveoASrM7OWU238Z4c7zcOCTZvZECvpXEUKNlcC3CHfB3QT7y6b9XwicCryJ0LO7jHBz9wXgLjNbLOnbwBSya3+D6dcTxnTvjPo3AWeTXfvbX/svJ2QU/t7MXorvkU3725/+FYTrf6ukTxF63sO2v/2SRAwz1w+gNP49gjBoCFAG/Eu8uMQL/XbgNkKX/pfA5PhaCVCTsv49QGN8rRY4JgH9a4Db4vMj+/zPncCVGe2vTVn/DuDL8fgs4O9z0P4vZrR/2DH1EbT/yozn5Snr3wV8IR5XAjMS0L8WuDFe358SEhqq4+tfAG7o/f+E7G9/+t+Px+9NyP4G078C+LeM9idhf/tr//XZsL8DeZRRREgqI4TKSiX9mtBt3Q1gZt2SPgGsV5icWQn8nPCBX0xIYT5Z0u0WxnuGnEo6Qv1FwGmS7jCzLcDyBPQvAtZJOtnM/ldSiYV0zTJC6O+38dzeOQVp6m8GfhfPXTxU7Sy1/96M9qet30m8/vH8rpT1NxHm0GBmO4G/JKD/ceAFQujqGkKv4gLgXwk9/lXx3N0kY3/7038qnnvrULWzoN8APB3P3Z0D/UZgT2RlOPY3FIpmjEfSGwmDcfWEbuVXCDH7UyW1wp6UwK8AV5nZ5wlZK/9sIX3yOsI8mmFd8CzoXxv1dyWo30PIEroy/ltNjGM/HM9dNhztLOnvyrF+rts/WvSvJNj/AwSbf53CaiGTCBmkrl+A+kMmye5Umg/gZODcjOfXEgbnzgeWxbKSeIFvI4YSyFKXssD0f0YYwzmCYIhtru/6Kdt/cyyrIwtpy66fW/0h1zdtwcQaEmYAV7I3vnk2wbNDmAV9UTxuAW4Z5fq3ur7r51A/1/bv+lnWH+qjaEJtZrbdzHba3vjoaYQJWBDimLMVFuC7hZA2ndVlIApMf5nru36u7T+buH5u9YdMrj1fAp6/lNCl/A1wWCw7jNClfD0xc831Xd/1Xd/1c/Momh5PBj2EJSZeIkzMvIeQKthjZvdbXHXV9V3f9V3f9XNErj1fQl5/AeEDuB/4kOu7vuu7vuvnz6MoVy6QNIWwwOC3LcxJcH3Xd33Xd/08oSgdj+M4jpO/FOMYj+M4jpPHuONxHMdxUsUdj+M4jpMq7ngcx3GcVHHH4ziO46SKOx7HyTGS6iT9Yzw+VNLPc10nx0kST6d2nByjsOX5PWY2J8dVcZxUKKqN4BynQLkamCnpUeAZYLaZzZF0PvC3hPW35hC2pK4gTA7cCbzdzF6WNJOwsdcEYDvwERvGdumOkxYeanOc3PM54Fkzmwd8us9rc4D3A63A14DtZnYcsAQ4L55zPWHZ+xOAywh7sThO3uI9HsfJb+6zsBX6FkmdwN2xfDlhEcga4ETgtoxdDirTr6bjHDjueBwnv8lca6sn43kP4ftbArwSe0uOUxB4qM1xcs8WoHY4/2hmm4HVkhZC2NxN0rHZrJzjZBt3PI6TY8xsI/CApBXAN4fxFmcDH5L0GLASOD2b9XOcbOPp1I7jOE6qeI/HcRzHSRV3PI7jOE6quONxHMdxUsUdj+M4jpMq7ngcx3GcVHHH4ziO46SKOx7HcRwnVdzxOI7jOKny/wQcvCeHLb1SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "ds = xr.open_dataset(xr.backends.NetCDF4DataStore(tx))\n",
    "ds.icclim_TX.isel(lat=0, lon=0).plot()"
   ]
  }
 ],
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