iosos_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "foreign-signal",
   "metadata": {},
   "source": [
    "# Seasonal Impacts on a Coastal Environment\n",
    "\n",
    "This tutorial uses multiple satellite data products to analyze the relationships between river height, land water equivalent thickness, sea surface salinity, and sea surface temperature in the Amazon river basin’s estuary and coastal region. \n",
    "The contents are useful for both the ocean and terrestrial hydrosphere disciplines, showcasing how to use on-premises and cloud datasets, existing cloud services and functionalities, and Earthdata User Interface (UI) and Application Programming Interfaces (API). \n",
    "\n",
    "## Datasets\n",
    "The tutorial itself will use fives missions, and a total of five different datasets, a combination of cloud and on premises datasets: \n",
    "\n",
    "   1. **TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2** \n",
    "    \n",
    "    DOI: [https://doi.org/10.5067/TEMSC-3JC62](https://doi.org/10.5067/TEMSC-3JC62) \n",
    "   \n",
    "    The Gravity Recovery And Climate Experiment Follow-On (GRACE-FO) satellite land water equivalent (LWE) thicknesses will be used to observe seasonal changes in water storage around the river. When discharge is high, the change in water storage will increase, thus highlighting a wet season. \n",
    "\n",
    "\n",
    "   2. **PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2** \n",
    "\n",
    "    DOI: [https://doi.org/10.5067/PSGRA-DA2V2](https://doi.org/10.5067/PSGRA-DA2V2)\n",
    "   \n",
    "    The NASA Pre-SWOT Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program virtual gauges will be used as a proxy for Surface Water and Ocean Topography (SWOT) discharge until SWOT products are available. MEaSUREs contains river height products, not discharge, but river height is directly related to discharge and thus will act as a good substitute.\n",
    "\n",
    "\n",
    "   3. **SMAP_JPL_L3_SSS_CAP_MONTHLY_V5** \n",
    "    \n",
    "    DOI: [https://doi.org/10.5067/SMP50-3TMCS](https://doi.org/10.5067/SMP50-3TMCS) \n",
    "   \n",
    "    Sea surface salinity is obtained from the Soil Moisture Active Passive (SMAP) satellite (2015-2019).\n",
    "\n",
    "   \n",
    "   4. **AQUARIUS_L3_SSS-RainFlagged_SMI_DAILY_V5**\n",
    "    \n",
    "    DOI: [https://doi.org/10.5067/AQR50-3Y1CE](https://doi.org/10.5067/AQR50-3Y1CE)\n",
    "    \n",
    "    Sea surface salinity is obtained from the Aquarius/SAC-D satellite (2011-2015).\n",
    "\n",
    "   \n",
    "   5. **MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0**\n",
    "   \n",
    "    DOI: [https://doi.org/10.5067/MODAM-MO9N9](https://doi.org/10.5067/MODAM-MO9N9)\n",
    "    \n",
    "    Sea surface temperature is obtained from the Moderate Resolution Imaging Spectrometer (MODIS) instrument on board the Aqua satellite. \n",
    "\n",
    "The tutorial will show exactly how all the datasets are accessed and subset for our specific location, the Amazon River basin estuary. Graphs and images of river height (river discharge when SWOT data is available), LWE thickness, salinity, and sea surface temperature will be formulated and shown side by side to see relationships between the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "employed-solid",
   "metadata": {},
   "source": [
    "## Needed Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "minimal-garage",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy\n",
    "import zarr\n",
    "import s3fs\n",
    "from json import dumps, loads\n",
    "import json\n",
    "from IPython.display import HTML\n",
    "from os.path import isfile, basename, abspath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "satellite-immune",
   "metadata": {},
   "source": [
    "## Set the CMR, URS, and Harmony endpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "applicable-pixel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('cmr.earthdata.nasa.gov',\n",
       " 'urs.earthdata.nasa.gov',\n",
       " 'harmony.earthdata.nasa.gov')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmr = \"cmr.earthdata.nasa.gov\"\n",
    "urs = \"urs.earthdata.nasa.gov\"\n",
    "harmony = \"harmony.earthdata.nasa.gov\"\n",
    "\n",
    "cmr, urs, harmony"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "posted-friday",
   "metadata": {},
   "source": [
    "## NASA Earthdata Login Setup\n",
    "\n",
    "An Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n",
    "\n",
    "The setup_earthdata_login_auth function will allow Python scripts to log into any Earthdata Login application programmatically. To avoid being prompted for credentials every time you run and also allow clients such as curl to log in, you can add the following to a .netrc (_netrc on Windows) file in your home directory:\n",
    "\n",
    "```shell \n",
    "    machine urs.earthdata.nasa.gov\n",
    "    login <your username>\n",
    "    password <your password>\n",
    "```\n",
    "\n",
    "Make sure that this file is only readable by the current user or you will receive an error stating \"netrc access too permissive.\"\n",
    "\n",
    "```shell\n",
    "$ chmod 0600 ~/.netrc\n",
    "```\n",
    "\n",
    "*You will be prompted for your username and password if you dont have a netrc file. Note: these imports are all in the Python 3.6+ standard library.*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "classical-matrix",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Your URS credentials were securely retrieved from your .netrc file.\n"
     ]
    }
   ],
   "source": [
    "from platform import system\n",
    "from netrc import netrc\n",
    "from getpass import getpass\n",
    "from urllib import request\n",
    "from http.cookiejar import CookieJar\n",
    "from os.path import join, expanduser\n",
    "\n",
    "TOKEN_DATA = (\"<token>\"\n",
    "              \"<username>%s</username>\"\n",
    "              \"<password>%s</password>\"\n",
    "              \"<client_id>PODAAC CMR Client</client_id>\"\n",
    "              \"<user_ip_address>%s</user_ip_address>\"\n",
    "              \"</token>\")\n",
    "\n",
    "\n",
    "def setup_earthdata_login_auth(urs: str='urs.earthdata.nasa.gov', cmr: str='cmr.earthdata.nasa.gov'):\n",
    "\n",
    "    # GET URS LOGIN INFO FROM NETRC OR USER PROMPTS:\n",
    "    netrc_name = \"_netrc\" if system()==\"Windows\" else \".netrc\"\n",
    "    try:\n",
    "        username, _, password = netrc(file=join(expanduser('~'), netrc_name)).authenticators(urs)\n",
    "        print(\"# Your URS credentials were securely retrieved from your .netrc file.\")\n",
    "    except (FileNotFoundError, TypeError):\n",
    "        print('# Please provide your Earthdata Login credentials for access.')\n",
    "        print('# Your info will only be passed to %s and will not be exposed in Jupyter.' % (urs))\n",
    "        username = input('Username: ')\n",
    "        password = getpass('Password: ')\n",
    "\n",
    "    # SET UP URS AUTHENTICATION FOR HTTP DOWNLOADS:\n",
    "    manager = request.HTTPPasswordMgrWithDefaultRealm()\n",
    "    manager.add_password(None, urs, username, password)\n",
    "    auth = request.HTTPBasicAuthHandler(manager)\n",
    "    jar = CookieJar()\n",
    "    processor = request.HTTPCookieProcessor(jar)\n",
    "    opener = request.build_opener(auth, processor)\n",
    "    request.install_opener(opener)\n",
    "\n",
    "    # GET TOKEN TO ACCESS RESTRICTED CMR METADATA:\n",
    "    ip = requests.get(\"https://ipinfo.io/ip\").text.strip()\n",
    "    r = requests.post(\n",
    "        url=\"https://%s/legacy-services/rest/tokens\" % cmr,\n",
    "        data=TOKEN_DATA % (str(username), str(password), ip),\n",
    "        headers={'Content-Type': 'application/xml', 'Accept': 'application/json'}\n",
    "    )\n",
    "    return r.json()['token']['id']\n",
    "\n",
    "\n",
    "# Provide URS credentials for HTTP download auth & CMR token retrieval:\n",
    "_token = setup_earthdata_login_auth(urs=urs, cmr=cmr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "horizontal-blame",
   "metadata": {},
   "source": [
    "### Cloud data: GRACE Liquid Water Equivalent (LWE)\n",
    "#### Search for GRACE LWE Thickness data\n",
    "GRACE/GRACE-FO data can be obtained from the cloud.\n",
    "\n",
    "Suppose we are interested in LWE data from the dataset (DOI:10.5067/TEMSC-3JC62) described on this PO.DAAC dataset landing page: \n",
    "https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2\n",
    "\n",
    "From the landing page, we see the dataset Short Name under the Information tab. Copy that to paste and search later.\n",
    "\n",
    "Log in to your Earthdata account at (This login step is only to search for the dataset concept-id below. The other login step which is required to run this notebook, is described in NASA Earthdata Login Setup, above line 3):\n",
    "https://search.earthdata.nasa.gov\n",
    "\n",
    "Enter the Short Name and search. Clicking on one of the search results brings us to a list of granules. Within that URL, we can grab the concept-id, a string starting with \"C\" and ending with \"-POCLOUD\". For this dataset, it is \"C1938032626-POCLOUD\"."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "innovative-colors",
   "metadata": {},
   "source": [
    "#### Collection search in CMR\n",
    "Here we use the requests library to search in *collections* by either short name or concept-id, which returns exactly one dataset, or one \"hit\", in a JSON format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "chicken-yukon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grace_ShortName = \"TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2\"\n",
    "r = requests.get(url=f\"https://{cmr}/search/collections.umm_json\",\n",
    "                params = \n",
    "                 {\n",
    "                     'provider': \"POCLOUD\",\n",
    "                     'token': _token,\n",
    "                     'concept-id': \"C1938032626-POCLOUD\"\n",
    "                     #'ShortName': grace_ShortName,\n",
    "                 }\n",
    "                \n",
    "                )\n",
    "grace_coll = r.json()\n",
    "grace_coll['hits']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "perfect-gender",
   "metadata": {},
   "source": [
    "#### See collection metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "pending-pastor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'revision-id': 4,\n",
       " 'deleted': False,\n",
       " 'format': 'application/vnd.nasa.cmr.umm+json',\n",
       " 'provider-id': 'POCLOUD',\n",
       " 'user-id': 'chen5510',\n",
       " 'has-formats': False,\n",
       " 'has-spatial-subsetting': False,\n",
       " 'native-id': 'JPL+GRACE+and+GRACE-FO+Mascon+Ocean,+Ice,+and+Hydrology+Equivalent+Water+Height+Coastal+Resolution+Improvement+(CRI)+Filtered+Release+06+Version+02',\n",
       " 'has-transforms': False,\n",
       " 'has-variables': False,\n",
       " 'concept-id': 'C1938032626-POCLOUD',\n",
       " 'revision-date': '2021-05-21T15:29:56.854Z',\n",
       " 'granule-count': 0,\n",
       " 'has-temporal-subsetting': False,\n",
       " 'concept-type': 'collection'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grace_coll_meta = grace_coll['items'][0]['meta']\n",
    "grace_coll_meta"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pleased-temperature",
   "metadata": {},
   "source": [
    "#### Granule search\n",
    "Here we use the requests library to search for *granules* in the collection. It returns 7 \"hits\", or 7 granules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "smart-poverty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = requests.get(url=f\"https://{cmr}/search/granules.umm_json\", \n",
    "                 params={'provider': \"POCLOUD\", \n",
    "                         'ShortName': grace_ShortName, \n",
    "                         'token': _token})\n",
    "\n",
    "grace_gran = r.json()\n",
    "grace_gran['hits']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "imposed-chase",
   "metadata": {},
   "source": [
    "#### For GRACE, when there are multiple granules, take the latest monthly granule (automate finding of most recent month?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "civic-suggestion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'concept-type': 'granule',\n",
       " 'concept-id': 'G2050191643-POCLOUD',\n",
       " 'revision-id': 1,\n",
       " 'native-id': 'GRCTellus.JPL.200204_202103.GLO.RL06M.MSCNv02CRI',\n",
       " 'provider-id': 'POCLOUD',\n",
       " 'format': 'application/vnd.nasa.cmr.umm+json',\n",
       " 'revision-date': '2021-05-11T03:30:34.686Z'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_granule = grace_gran['hits']-1 # If not true, then sort grace_gran by 'native-id'\n",
    "grace_gran['items'][latest_granule]['meta']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "comprehensive-singles",
   "metadata": {},
   "source": [
    "#### Get download link\n",
    "The download link to the .nc file is one of the the RelatedURLs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "swiss-charger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202103.GLO.RL06M.MSCNv02CRI.nc.md5',\n",
       "  'Type': 'EXTENDED METADATA',\n",
       "  'Description': 'File to download'},\n",
       " {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202103.GLO.RL06M.MSCNv02CRI.cmr.json',\n",
       "  'Type': 'EXTENDED METADATA',\n",
       "  'Description': 'File to download'},\n",
       " {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202103.GLO.RL06M.MSCNv02CRI.nc',\n",
       "  'Type': 'GET DATA',\n",
       "  'Description': 'File to download'}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grace_gran['items'][latest_granule]['umm']['RelatedUrls']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "secret-value",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202103.GLO.RL06M.MSCNv02CRI.nc'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From above, select the link to the .nc file (links are not always listed in the same order)\n",
    "link_num = 2\n",
    "grace_url = grace_gran['items'][latest_granule]['umm']['RelatedUrls'][link_num]['URL']\n",
    "grace_url"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "capable-contribution",
   "metadata": {},
   "source": [
    "#### Download the .nc file from the cloud\n",
    "And display overview of contents using ncdump."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "floppy-large",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "netcdf iosos_demo_GRACEFO {\n",
      "dimensions:\n",
      "\tlon = 720 ;\n",
      "\tlat = 360 ;\n",
      "\ttime = 195 ;\n",
      "\tbounds = 2 ;\n",
      "variables:\n",
      "\tdouble lon(lon) ;\n",
      "\t\tlon:units = \"degrees_east\" ;\n",
      "\t\tlon:long_name = \"longitude\" ;\n",
      "\t\tlon:standard_name = \"longitude\" ;\n",
      "\t\tlon:axis = \"X\" ;\n",
      "\t\tlon:valid_min = 0.25 ;\n",
      "\t\tlon:valid_max = 359.75 ;\n",
      "\t\tlon:bounds = \"lon_bounds\" ;\n",
      "\tdouble lat(lat) ;\n",
      "\t\tlat:units = \"degrees_north\" ;\n",
      "\t\tlat:long_name = \"latitude\" ;\n",
      "\t\tlat:standard_name = \"latitude\" ;\n",
      "\t\tlat:axis = \"Y\" ;\n",
      "\t\tlat:valid_min = -89.75 ;\n",
      "\t\tlat:valid_max = 89.75 ;\n",
      "\t\tlat:bounds = \"lat_bounds\" ;\n",
      "\tdouble time(time) ;\n",
      "\t\ttime:units = \"days since 2002-01-01T00:00:00Z\" ;\n",
      "\t\ttime:long_name = \"time\" ;\n",
      "\t\ttime:standard_name = \"time\" ;\n",
      "\t\ttime:axis = \"T\" ;\n",
      "\t\ttime:calendar = \"gregorian\" ;\n",
      "\t\ttime:bounds = \"time_bounds\" ;\n",
      "\tdouble lwe_thickness(time, lat, lon) ;\n",
      "\t\tlwe_thickness:units = \"cm\" ;\n",
      "\t\tlwe_thickness:long_name = \"Liquid_Water_Equivalent_Thickness\" ;\n",
      "\t\tlwe_thickness:standard_name = \"Liquid_Water_Equivalent_Thickness\" ;\n",
      "\t\tlwe_thickness:coordinates = \"time lat lon\" ;\n",
      "\t\tlwe_thickness:grid_mapping = \"WGS84\" ;\n",
      "\t\tlwe_thickness:_FillValue = -99999. ;\n",
      "\t\tlwe_thickness:valid_min = -1772.14897730884 ;\n",
      "\t\tlwe_thickness:valid_max = 767.736827711678 ;\n",
      "\t\tlwe_thickness:comment = \"Coastline Resolution Improvement (CRI) filter is applied\" ;\n",
      "\tdouble uncertainty(time, lat, lon) ;\n",
      "\t\tuncertainty:units = \"cm\" ;\n",
      "\t\tuncertainty:long_name = \"uncertainty\" ;\n",
      "\t\tuncertainty:standard_name = \"uncertainty\" ;\n",
      "\t\tuncertainty:coordinates = \"time lat lon\" ;\n",
      "\t\tuncertainty:grid_mapping = \"WGS84\" ;\n",
      "\t\tuncertainty:_FillValue = -99999. ;\n",
      "\t\tuncertainty:valid_min = 0.158623647924877 ;\n",
      "\t\tuncertainty:valid_max = 53.3446959856009 ;\n",
      "\t\tuncertainty:comment = \"1-sigma uncertainty: not for each 0.5 degree grid cell, but for each 3-degree mascon estimate\" ;\n",
      "\tdouble lat_bounds(lat, bounds) ;\n",
      "\t\tlat_bounds:long_name = \"latitude boundaries\" ;\n",
      "\t\tlat_bounds:units = \"degrees_north\" ;\n",
      "\t\tlat_bounds:comment = \"latitude values at the north and south bounds of each pixel\" ;\n",
      "\tdouble lon_bounds(lon, bounds) ;\n",
      "\t\tlon_bounds:long_name = \"longitude boundaries\" ;\n",
      "\t\tlon_bounds:units = \"degrees_east\" ;\n",
      "\t\tlon_bounds:comment = \"longitude values at the west and east bounds of each pixel\" ;\n",
      "\tdouble time_bounds(time, bounds) ;\n",
      "\t\ttime_bounds:long_name = \"time boundaries\" ;\n",
      "\t\ttime_bounds:units = \"days since 2002-01-01T00:00:00Z\" ;\n",
      "\t\ttime_bounds:comment = \"time bounds for each time value, i.e. the first day and last day included in the monthly solution\" ;\n",
      "\n",
      "// global attributes:\n",
      "\t\t:Conventions = \"CF-1.6, ACDD-1.3, ISO 8601\" ;\n",
      "\t\t:Metadata_Conventions = \"Unidata Dataset Discovery v1.0\" ;\n",
      "\t\t:standard_name_vocabulary = \"NetCDF Climate and Forecast (CF) Metadata Convention-1.6\" ;\n",
      "\t\t:title = \"JPL GRACE and GRACE-FO MASCON RL06Mv2 CRI\" ;\n",
      "\t\t:summary = \"Monthly gravity solutions from GRACE and GRACE-FO as determined from the JPL RL06Mv2 mascon solution - with CRI filter applied\" ;\n",
      "\t\t:keywords = \"Solid Earth, Geodetics/Gravity, Gravity, liquid_water_equivalent_thickness\" ;\n",
      "\t\t:keywords_vocabulary = \"NASA Global Change Master Directory (GCMD) Science Keywords\" ;\n",
      "\t\t:platform = \"GRACE and GRACE-FO\" ;\n",
      "\t\t:institution = \"NASA/JPL\" ;\n",
      "\t\t:creator_name = \"David Wiese\" ;\n",
      "\t\t:creator_email = \"grace@podaac.jpl.nasa.gov\" ;\n",
      "\t\t:creator_url = \"https://grace.jpl.nasa.gov\" ;\n",
      "\t\t:creator_type = \"group\" ;\n",
      "\t\t:creator_institution = \"NASA/JPL\" ;\n",
      "\t\t:publisher_name = \"Physical Oceanography Distributed Active Archive Center\" ;\n",
      "\t\t:publisher_email = \"podaac@jpl.nasa.gov\" ;\n",
      "\t\t:publisher_url = \"https://podaac.jpl.nasa.gov\" ;\n",
      "\t\t:publisher_type = \"group\" ;\n",
      "\t\t:publisher_institution = \"NASA/JPL\" ;\n",
      "\t\t:project = \"NASA Gravity Recovery and Climate Experiment (GRACE) and NASA Gravity Recovery and Climate Experiment Follow-On (GRACE-FO)\" ;\n",
      "\t\t:program = \"NASA Earth Science System Pathfinder and NASA Earth Systematic Missions Program\" ;\n",
      "\t\t:id = \"10.5067/TEMSC-3JC62\" ;\n",
      "\t\t:naming_authority = \"org.doi.dx\" ;\n",
      "\t\t:source = \"GRACE and GRACE-FO JPL RL06Mv2-CRI\" ;\n",
      "\t\t:processing_level = \"2 and 3\" ;\n",
      "\t\t:acknowledgement = \"GRACE is a joint mission of NASA (USA) and DLR (Germany).  GRACE-FO is a joint mission of NASA (USA) and the German Research Center for Geosciences (GFZ).  Use the digital object identifier provided in the id attribute when citing this data.  See https://podaac.jpl.nasa.gov/CitingPODAAC\" ;\n",
      "\t\t:license = \"https://science.nasa.gov/earth-science/earth-science-data/data-information-policy\" ;\n",
      "\t\t:product_version = \"v2.0\" ;\n",
      "\t\t:time_epoch = \"2002-01-01T00:00:00Z\" ;\n",
      "\t\t:time_coverage_start = \"2002-04-16T00:00:00Z\" ;\n",
      "\t\t:time_coverage_end = \"2021-03-16T23:59:59Z\" ;\n",
      "\t\t:geospatial_lat_min = -89.75 ;\n",
      "\t\t:geospatial_lat_max = 89.75 ;\n",
      "\t\t:geospatial_lat_units = \"degrees_north\" ;\n",
      "\t\t:geospatial_lat_resolution = \"0.5 degree grid; however the native resolution of the data is 3-degree equal-area mascons\" ;\n",
      "\t\t:geospatial_lon_min = 0.25 ;\n",
      "\t\t:geospatial_lon_max = 359.75 ;\n",
      "\t\t:geospatial_lon_units = \"degrees_east\" ;\n",
      "\t\t:geospatial_lon_resolution = \"0.5 degree grid; however the native resolution of the data is 3-degree equal-area mascons\" ;\n",
      "\t\t:time_mean_removed = \"2004.000 to 2009.999\" ;\n",
      "\t\t:months_missing = \"2002-06;2002-07;2003-06;2011-01;2011-06;2012-05;2012-10;2013-03;2013-08;2013-09;2014-02;2014-07;2014-12;2015-06;2015-10;2015-11;2016-04;2016-09;2016-10;2017-02;2017-07;2017-08;2017-09;2017-10;2017-11;2017-12;2018-01;2018-02;2018-03;2018-04;2018-05;2018-08-2018-09\" ;\n",
      "\t\t:postprocess_1 = \" OCEAN_ATMOSPHERE_DEALIAS_MODEL (GAD), MONTHLY_AVE, ADDED BACK TO OCEAN PIXELS ONLY\" ;\n",
      "\t\t:postprocess_2 = \"Water density used to convert to equivalent water height: 1000 kg/m^3\" ;\n",
      "\t\t:postprocess_3 = \"Coastline Resolution Improvement (CRI) filter has been applied to separate land/ocean mass within mascons that span coastlines\" ;\n",
      "\t\t:GIA_removed = \"ICE6G-D; Peltier, W. R., D. F. Argus, and R. Drummond (2018) Comment on the paper by Purcell et al. 2016 entitled An assessment of ICE-6G_C (VM5a) glacial isostatic adjustment model, J. Geophys. Res. Solid Earth, 122.\" ;\n",
      "\t\t:geocenter_correction = \"We use a version of TN-13 based on the JPL mascons\" ;\n",
      "\t\t:C_20_substitution = \"TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929\" ;\n",
      "\t\t:C_30_substitution = \"TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929.  This substitution is made for all months after August 2016.\" ;\n",
      "\t\t:user_note_1 = \"The accelerometer on the GRACE-B spacecraft was turned off after August 2016.  After this date, the accelerometer on GRACE-A was used to derive the non-gravitational accelerations acting on GRACE-B using a transplant procedure.  This has led to a subsequent degradation in the quality of the gravity fields derived.  The uncertainties in this file have been scaled to accomodate this degradation.\" ;\n",
      "\t\t:user_note_2 = \"The accelerometer on the GRACE-D spacecraft began performing sub-optimally after June 21, 2018.  After this date, the accelerometer on GRACE-C is used to derive the non-gravitational accelerations acting on GRACE-D using a transplant procedure.  The uncertainties in the file have been scaled to accomodate this degradation using the current best state of knowledge.\" ;\n",
      "\t\t:journal_reference = \"Watkins, M. M., D. N. Wiese, D.-N. Yuan, C. Boening, and F. W. Landerer (2015) Improved methods for observing Earth\\'s time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res., 120, doi:10.1002/2014JB011547. \" ;\n",
      "\t\t:CRI_filter_journal_reference = \"Wiese, D. N., F. W. Landerer, and M. M. Watkins (2016) Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution, Water Resour. Res., 52, doi:10.1002/2016WR019344. \" ;\n",
      "\t\t:date_created = \"2021-04-27T21:04:06Z\" ;\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "r = requests.get(grace_url)\n",
    "with open('iosos_demo_GRACEFO.nc', 'wb') as f:\n",
    "    f.write(r.content)\n",
    "\n",
    "!ncdump -h iosos_demo_GRACEFO.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "remarkable-literature",
   "metadata": {},
   "source": [
    "#### Open file using xarray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "unlimited-beatles",
   "metadata": {},
   "outputs": [
    {
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       "Dimensions:        (bounds: 2, lat: 360, lon: 720, time: 195)\n",
       "Coordinates:\n",
       "  * lon            (lon) float64 0.25 0.75 1.25 1.75 ... 358.2 358.8 359.2 359.8\n",
       "  * lat            (lat) float64 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n",
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       "Dimensions without coordinates: bounds\n",
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       "    uncertainty    (time, lat, lon) float64 ...\n",
       "    lat_bounds     (lat, bounds) float64 -90.0 -89.5 -89.5 ... 89.5 89.5 90.0\n",
       "    lon_bounds     (lon, bounds) float64 0.0 0.5 0.5 1.0 ... 359.5 359.5 360.0\n",
       "    time_bounds    (time, bounds) datetime64[ns] 2002-04-04 ... 2021-03-31T23...\n",
       "Attributes: (12/53)\n",
       "    Conventions:                   CF-1.6, ACDD-1.3, ISO 8601\n",
       "    Metadata_Conventions:          Unidata Dataset Discovery v1.0\n",
       "    standard_name_vocabulary:      NetCDF Climate and Forecast (CF) Metadata ...\n",
       "    title:                         JPL GRACE and GRACE-FO MASCON RL06Mv2 CRI\n",
       "    summary:                       Monthly gravity solutions from GRACE and G...\n",
       "    keywords:                      Solid Earth, Geodetics/Gravity, Gravity, l...\n",
       "    ...                            ...\n",
       "    C_30_substitution:             TN-14; Loomis et al., 2019, Geophys. Res. ...\n",
       "    user_note_1:                   The accelerometer on the GRACE-B spacecraf...\n",
       "    user_note_2:                   The accelerometer on the GRACE-D spacecraf...\n",
       "    journal_reference:             Watkins, M. M., D. N. Wiese, D.-N. Yuan, C...\n",
       "    CRI_filter_journal_reference:  Wiese, D. N., F. W. Landerer, and M. M. Wa...\n",
       "    date_created:                  2021-04-27T21:04:06Z</pre><div class='xr-wrap' hidden><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-acda994a-3c4d-45cf-b514-fa0b50eb8bbd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-acda994a-3c4d-45cf-b514-fa0b50eb8bbd' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bounds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 360</li><li><span class='xr-has-index'>lon</span>: 720</li><li><span class='xr-has-index'>time</span>: 195</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-529884b2-08be-403a-ab24-1a464e886532' class='xr-section-summary-in' type='checkbox'  checked><label for='section-529884b2-08be-403a-ab24-1a464e886532' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.25 0.75 1.25 ... 359.2 359.8</div><input id='attrs-7f90a7f5-4c25-479e-bea2-e0957f691036' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f90a7f5-4c25-479e-bea2-e0957f691036' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90365eaf-5271-4288-9b77-3274f27c49e1' class='xr-var-data-in' type='checkbox'><label for='data-90365eaf-5271-4288-9b77-3274f27c49e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>valid_min :</span></dt><dd>0.25</dd><dt><span>valid_max :</span></dt><dd>359.75</dd><dt><span>bounds :</span></dt><dd>lon_bounds</dd></dl></div><div class='xr-var-data'><pre>array([2.5000e-01, 7.5000e-01, 1.2500e+00, ..., 3.5875e+02, 3.5925e+02,\n",
       "       3.5975e+02])</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'>-89.75 -89.25 ... 89.25 89.75</div><input id='attrs-87543b07-7529-4cb1-8749-ee51a1a6f499' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87543b07-7529-4cb1-8749-ee51a1a6f499' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b31c56e8-9c90-422d-bdaf-604c55394fe5' class='xr-var-data-in' type='checkbox'><label for='data-b31c56e8-9c90-422d-bdaf-604c55394fe5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>valid_min :</span></dt><dd>-89.75</dd><dt><span>valid_max :</span></dt><dd>89.75</dd><dt><span>bounds :</span></dt><dd>lat_bounds</dd></dl></div><div class='xr-var-data'><pre>array([-89.75, -89.25, -88.75, ...,  88.75,  89.25,  89.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2002-04-17T12:00:00 ... 2021-03-...</div><input id='attrs-e518d5b0-907b-4264-a9e3-873dba49a487' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e518d5b0-907b-4264-a9e3-873dba49a487' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a9c3615-6651-4efb-b2ad-1ca7ce8951cd' class='xr-var-data-in' type='checkbox'><label for='data-2a9c3615-6651-4efb-b2ad-1ca7ce8951cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2002-04-17T12:00:00.000000000&#x27;, &#x27;2002-05-10T12:00:00.000000000&#x27;,\n",
       "       &#x27;2002-08-16T12:00:00.000000000&#x27;, &#x27;2002-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-10-16T12:00:00.000000000&#x27;, &#x27;2002-11-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-12-16T12:00:00.000000000&#x27;, &#x27;2003-01-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2003-02-15T00:00:00.000000000&#x27;, &#x27;2003-03-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2003-04-16T00:00:00.000000000&#x27;, &#x27;2003-05-11T12:00:00.000000000&#x27;,\n",
       "       &#x27;2003-07-16T12:00:00.000000000&#x27;, &#x27;2003-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2003-09-16T00:00:00.000000000&#x27;, &#x27;2003-10-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-11-16T00:00:00.000000000&#x27;, &#x27;2003-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2004-01-07T12:00:00.000000000&#x27;, &#x27;2004-02-17T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-03-16T12:00:00.000000000&#x27;, &#x27;2004-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-05-16T12:00:00.000000000&#x27;, &#x27;2004-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-07-16T12:00:00.000000000&#x27;, &#x27;2004-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2004-09-16T00:00:00.000000000&#x27;, &#x27;2004-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2004-11-16T00:00:00.000000000&#x27;, &#x27;2004-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-01-16T12:00:00.000000000&#x27;, &#x27;2005-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-03-16T12:00:00.000000000&#x27;, &#x27;2005-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-05-16T12:00:00.000000000&#x27;, &#x27;2005-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-16T12:00:00.000000000&#x27;, &#x27;2005-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-16T00:00:00.000000000&#x27;, &#x27;2005-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-11-16T00:00:00.000000000&#x27;, &#x27;2005-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2006-01-16T12:00:00.000000000&#x27;, &#x27;2006-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-03-16T12:00:00.000000000&#x27;, &#x27;2006-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-05-16T12:00:00.000000000&#x27;, &#x27;2006-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-07-16T12:00:00.000000000&#x27;, &#x27;2006-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2006-09-16T00:00:00.000000000&#x27;, &#x27;2006-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2006-11-16T00:00:00.000000000&#x27;, &#x27;2006-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2007-01-16T12:00:00.000000000&#x27;, &#x27;2007-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-03-16T12:00:00.000000000&#x27;, &#x27;2007-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-05-16T12:00:00.000000000&#x27;, &#x27;2007-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-07-16T12:00:00.000000000&#x27;, &#x27;2007-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2007-09-16T00:00:00.000000000&#x27;, &#x27;2007-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2007-11-16T00:00:00.000000000&#x27;, &#x27;2007-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2008-01-16T12:00:00.000000000&#x27;, &#x27;2008-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2008-03-16T12:00:00.000000000&#x27;, &#x27;2008-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-05-16T12:00:00.000000000&#x27;, &#x27;2008-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-07-16T12:00:00.000000000&#x27;, &#x27;2008-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2008-09-16T00:00:00.000000000&#x27;, &#x27;2008-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2008-11-16T00:00:00.000000000&#x27;, &#x27;2008-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2009-01-16T12:00:00.000000000&#x27;, &#x27;2009-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-03-16T12:00:00.000000000&#x27;, &#x27;2009-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-05-16T12:00:00.000000000&#x27;, &#x27;2009-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-07-16T12:00:00.000000000&#x27;, &#x27;2009-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2009-09-16T00:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2009-11-16T00:00:00.000000000&#x27;, &#x27;2009-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2010-01-16T12:00:00.000000000&#x27;, &#x27;2010-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-03-16T12:00:00.000000000&#x27;, &#x27;2010-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-05-16T12:00:00.000000000&#x27;, &#x27;2010-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-07-16T12:00:00.000000000&#x27;, &#x27;2010-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2010-09-16T00:00:00.000000000&#x27;, &#x27;2010-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2010-11-16T00:00:00.000000000&#x27;, &#x27;2010-12-14T12:00:00.000000000&#x27;,\n",
       "       &#x27;2011-02-18T12:00:00.000000000&#x27;, &#x27;2011-03-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2011-04-16T00:00:00.000000000&#x27;, &#x27;2011-05-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2011-07-18T12:00:00.000000000&#x27;, &#x27;2011-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2011-09-16T00:00:00.000000000&#x27;, &#x27;2011-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2011-11-01T12:00:00.000000000&#x27;, &#x27;2012-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-01-16T12:00:00.000000000&#x27;, &#x27;2012-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2012-03-16T12:00:00.000000000&#x27;, &#x27;2012-04-04T12:00:00.000000000&#x27;,\n",
       "       &#x27;2012-06-16T00:00:00.000000000&#x27;, &#x27;2012-07-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2012-08-16T12:00:00.000000000&#x27;, &#x27;2012-09-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-11-18T12:00:00.000000000&#x27;, &#x27;2012-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-16T12:00:00.000000000&#x27;, &#x27;2013-02-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-21T00:00:00.000000000&#x27;, &#x27;2013-05-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2013-06-16T00:00:00.000000000&#x27;, &#x27;2013-07-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2013-10-16T12:00:00.000000000&#x27;, &#x27;2013-11-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-12-16T12:00:00.000000000&#x27;, &#x27;2014-01-09T12:00:00.000000000&#x27;,\n",
       "       &#x27;2014-03-16T12:00:00.000000000&#x27;, &#x27;2014-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-05-16T12:00:00.000000000&#x27;, &#x27;2014-06-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-08-16T12:00:00.000000000&#x27;, &#x27;2014-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-10-16T12:00:00.000000000&#x27;, &#x27;2014-11-17T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-01-22T12:00:00.000000000&#x27;, &#x27;2015-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-03-16T12:00:00.000000000&#x27;, &#x27;2015-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-04-27T00:00:00.000000000&#x27;, &#x27;2015-07-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2015-08-16T12:00:00.000000000&#x27;, &#x27;2015-09-14T12:00:00.000000000&#x27;,\n",
       "       &#x27;2015-12-23T12:00:00.000000000&#x27;, &#x27;2016-01-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-02-14T00:00:00.000000000&#x27;, &#x27;2016-03-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-20T00:00:00.000000000&#x27;, &#x27;2016-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-07-15T12:00:00.000000000&#x27;, &#x27;2016-08-21T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-11-27T12:00:00.000000000&#x27;, &#x27;2016-12-24T12:00:00.000000000&#x27;,\n",
       "       &#x27;2017-01-21T00:00:00.000000000&#x27;, &#x27;2017-03-31T12:00:00.000000000&#x27;,\n",
       "       &#x27;2017-04-24T12:00:00.000000000&#x27;, &#x27;2017-05-12T12:00:00.000000000&#x27;,\n",
       "       &#x27;2017-06-11T00:00:00.000000000&#x27;, &#x27;2018-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-07-10T00:00:00.000000000&#x27;, &#x27;2018-10-31T12:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-16T00:00:00.000000000&#x27;, &#x27;2018-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-16T12:00:00.000000000&#x27;, &#x27;2019-02-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-03-16T12:00:00.000000000&#x27;, &#x27;2019-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-05-16T12:00:00.000000000&#x27;, &#x27;2019-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-07-16T12:00:00.000000000&#x27;, &#x27;2019-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2019-09-16T00:00:00.000000000&#x27;, &#x27;2019-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2019-11-16T00:00:00.000000000&#x27;, &#x27;2019-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-16T12:00:00.000000000&#x27;, &#x27;2020-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-03-16T12:00:00.000000000&#x27;, &#x27;2020-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-05-16T12:00:00.000000000&#x27;, &#x27;2020-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-07-16T12:00:00.000000000&#x27;, &#x27;2020-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-09-16T00:00:00.000000000&#x27;, &#x27;2020-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-11-16T00:00:00.000000000&#x27;, &#x27;2020-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-16T12:00:00.000000000&#x27;, &#x27;2021-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-03-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-265f443e-82d5-4328-a646-2f2608aee747' class='xr-section-summary-in' type='checkbox'  checked><label for='section-265f443e-82d5-4328-a646-2f2608aee747' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lwe_thickness</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c495c2d0-5058-4da3-ab67-c388031acabe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c495c2d0-5058-4da3-ab67-c388031acabe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58010c51-2a8e-43c3-9db0-0da6139dd1d8' class='xr-var-data-in' type='checkbox'><label for='data-58010c51-2a8e-43c3-9db0-0da6139dd1d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>cm</dd><dt><span>long_name :</span></dt><dd>Liquid_Water_Equivalent_Thickness</dd><dt><span>standard_name :</span></dt><dd>Liquid_Water_Equivalent_Thickness</dd><dt><span>grid_mapping :</span></dt><dd>WGS84</dd><dt><span>valid_min :</span></dt><dd>-1772.1489773088445</dd><dt><span>valid_max :</span></dt><dd>767.7368277116782</dd><dt><span>comment :</span></dt><dd>Coastline Resolution Improvement (CRI) filter is applied</dd></dl></div><div class='xr-var-data'><pre>[50544000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uncertainty</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-10250961-ee00-4095-932b-e57d0ca8ae37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10250961-ee00-4095-932b-e57d0ca8ae37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18e53ef0-a729-4e2b-9459-2a909567670c' class='xr-var-data-in' type='checkbox'><label for='data-18e53ef0-a729-4e2b-9459-2a909567670c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>cm</dd><dt><span>long_name :</span></dt><dd>uncertainty</dd><dt><span>standard_name :</span></dt><dd>uncertainty</dd><dt><span>grid_mapping :</span></dt><dd>WGS84</dd><dt><span>valid_min :</span></dt><dd>0.1586236479248768</dd><dt><span>valid_max :</span></dt><dd>53.34469598560085</dd><dt><span>comment :</span></dt><dd>1-sigma uncertainty: not for each 0.5 degree grid cell, but for each 3-degree mascon estimate</dd></dl></div><div class='xr-var-data'><pre>[50544000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bounds</span></div><div class='xr-var-dims'>(lat, bounds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e5be15d2-a126-4b19-b12b-f317a6245e1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5be15d2-a126-4b19-b12b-f317a6245e1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e957cad4-431d-412a-ac65-e54219ce4d14' class='xr-var-data-in' type='checkbox'><label for='data-e957cad4-431d-412a-ac65-e54219ce4d14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude boundaries</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>comment :</span></dt><dd>latitude values at the north and south bounds of each pixel</dd></dl></div><div class='xr-var-data'><pre>array([[-90. , -89.5],\n",
       "       [-89.5, -89. ],\n",
       "       [-89. , -88.5],\n",
       "       ...,\n",
       "       [ 88.5,  89. ],\n",
       "       [ 89. ,  89.5],\n",
       "       [ 89.5,  90. ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bounds</span></div><div class='xr-var-dims'>(lon, bounds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dc1dc6f3-3325-47df-b07f-e09c9359a0c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc1dc6f3-3325-47df-b07f-e09c9359a0c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d859dfa-5c4e-4556-ac29-a3aa0e8a60d0' class='xr-var-data-in' type='checkbox'><label for='data-0d859dfa-5c4e-4556-ac29-a3aa0e8a60d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude boundaries</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>comment :</span></dt><dd>longitude values at the west and east bounds of each pixel</dd></dl></div><div class='xr-var-data'><pre>array([[  0. ,   0.5],\n",
       "       [  0.5,   1. ],\n",
       "       [  1. ,   1.5],\n",
       "       ...,\n",
       "       [358.5, 359. ],\n",
       "       [359. , 359.5],\n",
       "       [359.5, 360. ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, bounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d1790b44-20ab-44d0-b98c-a7d180d063f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1790b44-20ab-44d0-b98c-a7d180d063f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-707e15bf-2a01-4132-9b00-d9eaa5fa4bb4' class='xr-var-data-in' type='checkbox'><label for='data-707e15bf-2a01-4132-9b00-d9eaa5fa4bb4' 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 boundaries</dd><dt><span>comment :</span></dt><dd>time bounds for each time value, i.e. the first day and last day included in the monthly solution</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;2002-04-04T00:00:00.000000000&#x27;, &#x27;2002-04-30T23:59:59.913600000&#x27;],\n",
       "       [&#x27;2002-05-02T00:00:00.000000000&#x27;, &#x27;2002-05-18T23:59:59.913600000&#x27;],\n",
       "       [&#x27;2002-08-01T00:00:00.000000000&#x27;, &#x27;2002-08-31T23:59:59.913600000&#x27;],\n",
       "       ...,\n",
       "       [&#x27;2021-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-31T23:59:59.913600000&#x27;],\n",
       "       [&#x27;2021-02-01T00:00:00.000000000&#x27;, &#x27;2021-02-28T23:59:59.913600000&#x27;],\n",
       "       [&#x27;2021-03-01T00:00:00.000000000&#x27;, &#x27;2021-03-31T23:59:59.913600000&#x27;]],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ef88e1cf-fda2-47fc-b34a-028d19271afb' class='xr-section-summary-in' type='checkbox'  ><label for='section-ef88e1cf-fda2-47fc-b34a-028d19271afb' class='xr-section-summary' >Attributes: <span>(53)</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-1.6, ACDD-1.3, ISO 8601</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention-1.6</dd><dt><span>title :</span></dt><dd>JPL GRACE and GRACE-FO MASCON RL06Mv2 CRI</dd><dt><span>summary :</span></dt><dd>Monthly gravity solutions from GRACE and GRACE-FO as determined from the JPL RL06Mv2 mascon solution - with CRI filter applied</dd><dt><span>keywords :</span></dt><dd>Solid Earth, Geodetics/Gravity, Gravity, liquid_water_equivalent_thickness</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>platform :</span></dt><dd>GRACE and GRACE-FO</dd><dt><span>institution :</span></dt><dd>NASA/JPL</dd><dt><span>creator_name :</span></dt><dd>David Wiese</dd><dt><span>creator_email :</span></dt><dd>grace@podaac.jpl.nasa.gov</dd><dt><span>creator_url :</span></dt><dd>https://grace.jpl.nasa.gov</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_institution :</span></dt><dd>NASA/JPL</dd><dt><span>publisher_name :</span></dt><dd>Physical Oceanography Distributed Active Archive Center</dd><dt><span>publisher_email :</span></dt><dd>podaac@jpl.nasa.gov</dd><dt><span>publisher_url :</span></dt><dd>https://podaac.jpl.nasa.gov</dd><dt><span>publisher_type :</span></dt><dd>group</dd><dt><span>publisher_institution :</span></dt><dd>NASA/JPL</dd><dt><span>project :</span></dt><dd>NASA Gravity Recovery and Climate Experiment (GRACE) and NASA Gravity Recovery and Climate Experiment Follow-On (GRACE-FO)</dd><dt><span>program :</span></dt><dd>NASA Earth Science System Pathfinder and NASA Earth Systematic Missions Program</dd><dt><span>id :</span></dt><dd>10.5067/TEMSC-3JC62</dd><dt><span>naming_authority :</span></dt><dd>org.doi.dx</dd><dt><span>source :</span></dt><dd>GRACE and GRACE-FO JPL RL06Mv2-CRI</dd><dt><span>processing_level :</span></dt><dd>2 and 3</dd><dt><span>acknowledgement :</span></dt><dd>GRACE is a joint mission of NASA (USA) and DLR (Germany).  GRACE-FO is a joint mission of NASA (USA) and the German Research Center for Geosciences (GFZ).  Use the digital object identifier provided in the id attribute when citing this data.  See https://podaac.jpl.nasa.gov/CitingPODAAC</dd><dt><span>license :</span></dt><dd>https://science.nasa.gov/earth-science/earth-science-data/data-information-policy</dd><dt><span>product_version :</span></dt><dd>v2.0</dd><dt><span>time_epoch :</span></dt><dd>2002-01-01T00:00:00Z</dd><dt><span>time_coverage_start :</span></dt><dd>2002-04-16T00:00:00Z</dd><dt><span>time_coverage_end :</span></dt><dd>2021-03-16T23:59:59Z</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.75</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.75</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.5 degree grid; however the native resolution of the data is 3-degree equal-area mascons</dd><dt><span>geospatial_lon_min :</span></dt><dd>0.25</dd><dt><span>geospatial_lon_max :</span></dt><dd>359.75</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.5 degree grid; however the native resolution of the data is 3-degree equal-area mascons</dd><dt><span>time_mean_removed :</span></dt><dd>2004.000 to 2009.999</dd><dt><span>months_missing :</span></dt><dd>2002-06;2002-07;2003-06;2011-01;2011-06;2012-05;2012-10;2013-03;2013-08;2013-09;2014-02;2014-07;2014-12;2015-06;2015-10;2015-11;2016-04;2016-09;2016-10;2017-02;2017-07;2017-08;2017-09;2017-10;2017-11;2017-12;2018-01;2018-02;2018-03;2018-04;2018-05;2018-08-2018-09</dd><dt><span>postprocess_1 :</span></dt><dd> OCEAN_ATMOSPHERE_DEALIAS_MODEL (GAD), MONTHLY_AVE, ADDED BACK TO OCEAN PIXELS ONLY</dd><dt><span>postprocess_2 :</span></dt><dd>Water density used to convert to equivalent water height: 1000 kg/m^3</dd><dt><span>postprocess_3 :</span></dt><dd>Coastline Resolution Improvement (CRI) filter has been applied to separate land/ocean mass within mascons that span coastlines</dd><dt><span>GIA_removed :</span></dt><dd>ICE6G-D; Peltier, W. R., D. F. Argus, and R. Drummond (2018) Comment on the paper by Purcell et al. 2016 entitled An assessment of ICE-6G_C (VM5a) glacial isostatic adjustment model, J. Geophys. Res. Solid Earth, 122.</dd><dt><span>geocenter_correction :</span></dt><dd>We use a version of TN-13 based on the JPL mascons</dd><dt><span>C_20_substitution :</span></dt><dd>TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929</dd><dt><span>C_30_substitution :</span></dt><dd>TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929.  This substitution is made for all months after August 2016.</dd><dt><span>user_note_1 :</span></dt><dd>The accelerometer on the GRACE-B spacecraft was turned off after August 2016.  After this date, the accelerometer on GRACE-A was used to derive the non-gravitational accelerations acting on GRACE-B using a transplant procedure.  This has led to a subsequent degradation in the quality of the gravity fields derived.  The uncertainties in this file have been scaled to accomodate this degradation.</dd><dt><span>user_note_2 :</span></dt><dd>The accelerometer on the GRACE-D spacecraft began performing sub-optimally after June 21, 2018.  After this date, the accelerometer on GRACE-C is used to derive the non-gravitational accelerations acting on GRACE-D using a transplant procedure.  The uncertainties in the file have been scaled to accomodate this degradation using the current best state of knowledge.</dd><dt><span>journal_reference :</span></dt><dd>Watkins, M. M., D. N. Wiese, D.-N. Yuan, C. Boening, and F. W. Landerer (2015) Improved methods for observing Earth&#x27;s time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res., 120, doi:10.1002/2014JB011547. </dd><dt><span>CRI_filter_journal_reference :</span></dt><dd>Wiese, D. N., F. W. Landerer, and M. M. Watkins (2016) Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution, Water Resour. Res., 52, doi:10.1002/2016WR019344. </dd><dt><span>date_created :</span></dt><dd>2021-04-27T21:04:06Z</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:        (bounds: 2, lat: 360, lon: 720, time: 195)\n",
       "Coordinates:\n",
       "  * lon            (lon) float64 0.25 0.75 1.25 1.75 ... 358.2 358.8 359.2 359.8\n",
       "  * lat            (lat) float64 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n",
       "  * time           (time) datetime64[ns] 2002-04-17T12:00:00 ... 2021-03-16T1...\n",
       "Dimensions without coordinates: bounds\n",
       "Data variables:\n",
       "    lwe_thickness  (time, lat, lon) float64 ...\n",
       "    uncertainty    (time, lat, lon) float64 ...\n",
       "    lat_bounds     (lat, bounds) float64 ...\n",
       "    lon_bounds     (lon, bounds) float64 ...\n",
       "    time_bounds    (time, bounds) datetime64[ns] ...\n",
       "Attributes: (12/53)\n",
       "    Conventions:                   CF-1.6, ACDD-1.3, ISO 8601\n",
       "    Metadata_Conventions:          Unidata Dataset Discovery v1.0\n",
       "    standard_name_vocabulary:      NetCDF Climate and Forecast (CF) Metadata ...\n",
       "    title:                         JPL GRACE and GRACE-FO MASCON RL06Mv2 CRI\n",
       "    summary:                       Monthly gravity solutions from GRACE and G...\n",
       "    keywords:                      Solid Earth, Geodetics/Gravity, Gravity, l...\n",
       "    ...                            ...\n",
       "    C_30_substitution:             TN-14; Loomis et al., 2019, Geophys. Res. ...\n",
       "    user_note_1:                   The accelerometer on the GRACE-B spacecraf...\n",
       "    user_note_2:                   The accelerometer on the GRACE-D spacecraf...\n",
       "    journal_reference:             Watkins, M. M., D. N. Wiese, D.-N. Yuan, C...\n",
       "    CRI_filter_journal_reference:  Wiese, D. N., F. W. Landerer, and M. M. Wa...\n",
       "    date_created:                  2021-04-27T21:04:06Z"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_GRACE = xr.open_dataset('iosos_demo_GRACEFO.nc')\n",
    "ds_GRACE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fleet-search",
   "metadata": {},
   "source": [
    "#### Plot a subset of the data\n",
    "Use the function xarray.DataSet.sel to select a subset of the data to plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "operating-evolution",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lat_bnds, lon_bnds = [-18, 10], [275, 330] #degrees east for longitude\n",
    "ds_GRACE_subset = ds_GRACE.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))\n",
    "ds_GRACE_subset\n",
    "\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.coastlines()\n",
    "ax.add_feature(cartopy.feature.RIVERS)\n",
    "ds_GRACE_subset.lwe_thickness[193,:,:].plot(cmap = 'bwr_r') #106: July 2011\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "literary-shower",
   "metadata": {},
   "source": [
    "### On-premise data via OPeNDAP: River heights\n",
    "Use xarray and OPeNDAP link to see an overview of the dataset\n",
    "\n",
    "Currently, Pre-SWOT MEaSUREs data do not have the tools required to access them on the cloud, but methods are in the works! We will obtain this dataset using OPeNDAP. OPeNDAP provides an API on the host server to access data without downloading it. OPeNDAP will also have a cloud component in the future though so this method of access can be both on-prem and cloud-based moving forward. \n",
    "\n",
    "To find the OPeNDAP links needed to open the data using the python package xarray, go to the specific satellite's page on PO.DAAC (ex. Pre-SWOT MEaSUREs's site: https://podaac.jpl.nasa.gov/MEaSUREs-Pre-SWOT?sections=about%2Bdata) \n",
    "\n",
    "Click on the dataset you want (e.g. https://podaac.jpl.nasa.gov/dataset/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2) and click the \"Data Access\" tab. This will give you a link to where you can find the data in OPeNDAP (ex. https://podaac-opendap.jpl.nasa.gov/opendap/allData/preswot_hydrology/L2/rivers/daily/). \n",
    "\n",
    "From there, navigate to the desired NetCDF file and copy it's link (ex. for MEaSUREs, we want the Amazon estuary, so we choose the South America Amazon file: https://opendap.jpl.nasa.gov/opendap/allData/preswot_hydrology/L2/rivers/daily/South_America_Amazon1kmdaily.nc).\n",
    "\n",
    "The guidebook explains the details of the Pre-SWOT MEaSUREs data:\n",
    "https://podaac-tools.jpl.nasa.gov/drive/files/allData/preswot_hydrology/L2/rivers/docs/GRRATS_user_handbookV2.pdf\n",
    "\n",
    "Our desired variable is height (meters above EGM2008 geoid) for this exercise, which can be subset by distance and time. Distance represents the distance from the river mouth, in this example, the Amazon estuary. Time is between April 8, 1993 and April 20, 2019.\n",
    "\n",
    "Let's look at this example file to see how the data is organized:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "controlled-preserve",
   "metadata": {},
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:              (X: 3311, Y: 3311, charlength: 26, distance: 3311, time: 9469)\n",
       "Coordinates:\n",
       "  * time                 (time) datetime64[ns] 1993-04-08T15:20:40.665117184 ...\n",
       "Dimensions without coordinates: X, Y, charlength, distance\n",
       "Data variables:\n",
       "    lon                  (X) float64 -51.04 -51.05 -51.06 ... -73.35 -73.35\n",
       "    lat                  (Y) float64 -0.6559 -0.6553 -0.6551 ... -4.179 -4.187\n",
       "    FD                   (distance) float64 104.9 1.105e+03 ... 3.31e+06\n",
       "    height               (distance, time) float64 ...\n",
       "    sat                  (charlength) |S64 b&#x27;--------------------------------...\n",
       "    storage              (distance, time) float64 ...\n",
       "    LakeFlag             (distance) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    Storage_uncertainty  (distance, time) float64 ...\n",
       "    IceFlag              (time) float64 nan nan nan nan nan ... nan nan nan nan\n",
       "Attributes: (12/40)\n",
       "    title:                         GRRATS (Global River Radar Altimetry Time ...\n",
       "    Conventions:                   CF-1.6, ACDD-1.3\n",
       "    institution:                   Ohio State University, School of Earth Sci...\n",
       "    source:                        MEaSUREs OSU Storage toolbox 2018\n",
       "    keywords:                      EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURF...\n",
       "    keywords_vocabulary:           Global Change Master Directory (GCMD)\n",
       "    ...                            ...\n",
       "    geospatial_lat_max:            -0.6550700975069503\n",
       "    geospatial_lat_units:          degree_north\n",
       "    geospatial_vertical_max:       92.7681246287056\n",
       "    geospatial_vertical_min:       -3.563409518163376\n",
       "    geospatial_vertical_units:     m\n",
       "    geospatial_vertical_positive:  up</pre><div class='xr-wrap' hidden><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-4dac0e97-a2f5-4bd0-b84b-189bdaa96454' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4dac0e97-a2f5-4bd0-b84b-189bdaa96454' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>X</span>: 3311</li><li><span>Y</span>: 3311</li><li><span>charlength</span>: 26</li><li><span>distance</span>: 3311</li><li><span class='xr-has-index'>time</span>: 9469</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d2e747b9-183b-4778-a411-dc291491d225' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d2e747b9-183b-4778-a411-dc291491d225' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1993-04-08T15:20:40.665117184 .....</div><input id='attrs-0b3f5637-6205-4ef6-9724-aa82f85d3f01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b3f5637-6205-4ef6-9724-aa82f85d3f01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1687e711-2240-4a5f-b924-b62f3c72df10' class='xr-var-data-in' type='checkbox'><label for='data-1687e711-2240-4a5f-b924-b62f3c72df10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1993-04-08T15:20:40.665117184&#x27;, &#x27;1993-04-09T15:20:40.665117184&#x27;,\n",
       "       &#x27;1993-04-10T15:20:40.665117184&#x27;, ..., &#x27;2019-04-18T03:39:13.243964928&#x27;,\n",
       "       &#x27;2019-04-19T03:39:13.243964928&#x27;, &#x27;2019-04-20T03:39:13.243964928&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0f25b755-7084-417a-b1a9-b45f570b8eec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0f25b755-7084-417a-b1a9-b45f570b8eec' class='xr-section-summary' >Data variables: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(X)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-12dd9686-a70c-4c07-8ad0-1bd06a1711a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12dd9686-a70c-4c07-8ad0-1bd06a1711a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-238495f6-3bee-4b06-a2f5-21852a2a7697' class='xr-var-data-in' type='checkbox'><label for='data-238495f6-3bee-4b06-a2f5-21852a2a7697' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-51.042645, -51.051273, -51.06017 , ..., -73.354331, -73.351882,\n",
       "       -73.348082])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(Y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d036d44a-ebca-47f8-8ba7-5d2fa197961b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d036d44a-ebca-47f8-8ba7-5d2fa197961b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38ae92c4-4383-4b63-a52e-bd7a6f97a21e' class='xr-var-data-in' type='checkbox'><label for='data-38ae92c4-4383-4b63-a52e-bd7a6f97a21e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-0.655885, -0.655342, -0.65507 , ..., -4.170956, -4.179361, -4.187492])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FD</span></div><div class='xr-var-dims'>(distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dfd46485-2288-4d8d-99b8-356700120e7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfd46485-2288-4d8d-99b8-356700120e7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7aacc912-8d7d-42f6-a377-b53cb31036ad' class='xr-var-data-in' type='checkbox'><label for='data-7aacc912-8d7d-42f6-a377-b53cb31036ad' 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>distance_from_river_mouth</dd><dt><span>units :</span></dt><dd>km</dd><dt><span>comment :</span></dt><dd>This is the distance along the river centerline from the river mouth to this VS.</dd></dl></div><div class='xr-var-data'><pre>array([1.049362e+02, 1.104936e+03, 2.104936e+03, ..., 3.308105e+06,\n",
       "       3.309105e+06, 3.310105e+06])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>(distance, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5a8649fa-e985-41a2-947e-29d50c5bcc99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a8649fa-e985-41a2-947e-29d50c5bcc99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a162dc3-6b3f-4f14-96e3-3747da6eb992' class='xr-var-data-in' type='checkbox'><label for='data-4a162dc3-6b3f-4f14-96e3-3747da6eb992' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>interpolated_heights</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>valid_min :</span></dt><dd>-3.563409518163376</dd><dt><span>valid_max :</span></dt><dd>92.7681246287056</dd><dt><span>comment :</span></dt><dd>A time flow distance grid of river water heights with respect to the EGM08 Geoid).-9999 fill values are for missing data, while -9995 fill values are for lakes and reservoirs.</dd></dl></div><div class='xr-var-data'><pre>[31351859 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sat</span></div><div class='xr-var-dims'>(charlength)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5346df54-4a3c-49bf-983f-b09fe13b255c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5346df54-4a3c-49bf-983f-b09fe13b255c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78c3a395-ee77-4c4e-b727-bc40d3351968' class='xr-var-data-in' type='checkbox'><label for='data-78c3a395-ee77-4c4e-b727-bc40d3351968' 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>satellite</dd><dt><span>comment :</span></dt><dd>The satellite the measurement is derived from.</dd><dt><span>string_length :</span></dt><dd>9469</dd></dl></div><div class='xr-var-data'><pre>array([b&#x27;----------------------------------------------------------------&#x27;,\n",
       "       b&#x27;EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE&#x27;,\n",
       "       b&#x27;RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR&#x27;,\n",
       "       b&#x27;SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS&#x27;,\n",
       "       b&#x27;1111111111111111111111111111111111111111111111111111111111111111&#x27;,\n",
       "       b&#x27;cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc&#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;,\n",
       "       b&#x27;                                                                &#x27;],\n",
       "      dtype=&#x27;|S64&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>storage</span></div><div class='xr-var-dims'>(distance, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a384e6d2-12b9-44a8-9560-a760faf70abf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a384e6d2-12b9-44a8-9560-a760faf70abf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53b64371-5888-42b3-9e8a-1535bb458b4a' class='xr-var-data-in' type='checkbox'><label for='data-53b64371-5888-42b3-9e8a-1535bb458b4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>km3</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>river_channel_storage</dd><dt><span>comment :</span></dt><dd>A time flow distance grid of river channel storage values.</dd></dl></div><div class='xr-var-data'><pre>[31351859 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LakeFlag</span></div><div class='xr-var-dims'>(distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0fb03000-a9ad-4352-94ab-d8c7bdd43cea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0fb03000-a9ad-4352-94ab-d8c7bdd43cea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fe55d01-eef8-4cd9-8c84-df516e7e838d' class='xr-var-data-in' type='checkbox'><label for='data-6fe55d01-eef8-4cd9-8c84-df516e7e838d' 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>Lake_Flag</dd><dt><span>valid_range :</span></dt><dd>0, 1</dd><dt><span>flag_masks :</span></dt><dd>1</dd><dt><span>flag_meaning :</span></dt><dd>River sections that are lakes or reservoirs</dd><dt><span>comment :</span></dt><dd> This is a flag for masking out setions that are lakes or reservoirs.</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Storage_uncertainty</span></div><div class='xr-var-dims'>(distance, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-78811f0e-a070-402a-aa53-d38d2060c7ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78811f0e-a070-402a-aa53-d38d2060c7ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c057974-8173-4b71-9a57-3a2b9eddfdd9' class='xr-var-data-in' type='checkbox'><label for='data-8c057974-8173-4b71-9a57-3a2b9eddfdd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>km3</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>river_channel_storage_uncertainty</dd><dt><span>comment :</span></dt><dd>A time flow distance grid of river channel storage uncertainty values.</dd></dl></div><div class='xr-var-data'><pre>[31351859 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>IceFlag</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1ca0af29-6300-4ace-a566-cdf1cf97f580' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ca0af29-6300-4ace-a566-cdf1cf97f580' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cfa65d1-8afa-45a5-814a-12cdbaada196' class='xr-var-data-in' type='checkbox'><label for='data-9cfa65d1-8afa-45a5-814a-12cdbaada196' 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>Ice_Flag</dd><dt><span>valid_range :</span></dt><dd>0, 1</dd><dt><span>flag_masks :</span></dt><dd>1</dd><dt><span>flag_meaning :</span></dt><dd>Time of ice cover</dd><dt><span>comment :</span></dt><dd> This is a flag for masking out times of Ice Cover.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fbe730f2-97de-4115-8ca4-c98acdadfed9' class='xr-section-summary-in' type='checkbox'  ><label for='section-fbe730f2-97de-4115-8ca4-c98acdadfed9' class='xr-section-summary' >Attributes: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>GRRATS (Global River Radar Altimetry Time Series)1km daily interpolation for the Amazon River</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>institution :</span></dt><dd>Ohio State University, School of Earth Sciences</dd><dt><span>source :</span></dt><dd>MEaSUREs OSU Storage toolbox 2018</dd><dt><span>keywords :</span></dt><dd>EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURFACE WATER,SURFACE WATER PROCESSES/MEASUREMENTS,STAGE HEIGHT</dd><dt><span>keywords_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD)</dd><dt><span>cdm_data_type :</span></dt><dd>station</dd><dt><span>creator_name :</span></dt><dd>Coss,Steve</dd><dt><span>creator_email :</span></dt><dd>Coss.31@osu.edu</dd><dt><span>project :</span></dt><dd>MEaSUREs OSU</dd><dt><span>program :</span></dt><dd>NASA Earth Science Data Systems (ESDS)</dd><dt><span>publisher_name :</span></dt><dd>PO.DAAC (Physical Oceanography Distributed Active Archive Center)</dd><dt><span>publisher_email :</span></dt><dd>podaac@podaac.jpl.nasa.gov</dd><dt><span>publisher_url :</span></dt><dd>podaac.jpl.nasa.gov</dd><dt><span>publisher_type :</span></dt><dd>Institution</dd><dt><span>publisher_institution :</span></dt><dd>PO.DAAC</dd><dt><span>processing_level :</span></dt><dd>L2</dd><dt><span>doi :</span></dt><dd>10.5067/PSGRA-DA2V2</dd><dt><span>history :</span></dt><dd>This GRRATS product adds data river surface height data from ERS1, ERS2, TOPEX/Poseidon and Jason-3 to expand the temporal coverage of the product. GRRATS1kd includes interpolated daily 1km resolution height measurements as well as river channel storage measurements. </dd><dt><span>platform :</span></dt><dd>ERS-1(L2),ERS-2(L2),TOPEX/POSEIDON(L2), Jason-1(L2),OSTM/Jason-2(L2),Jason-3(L2),Envisat(L2)</dd><dt><span>platform_vocabulary :</span></dt><dd>NASA/GCMD Platform Keywords. Version 8.6</dd><dt><span>instrument :</span></dt><dd>RA(L2),RA-2(L2),ALT(TOPEX)(L2),POSEIDON-2(L2),POSEIDON-3(L2),POSEIDON-3b(L2)</dd><dt><span>instrument_vocabulary :</span></dt><dd>NASA/GCMD Platform Keywords. Version 8.6</dd><dt><span>references :</span></dt><dd>in review :doi.org/10.5194/essd-2019-84</dd><dt><span>id :</span></dt><dd>GRRATS(Global River Radar Altimeter Time Series) 1km/daily</dd><dt><span>summary :</span></dt><dd>The Global River Radar Altimeter Time Series (GRRATS) 1km/daily interpolations are river heights from ERS1, ERS2, TOPEX/Poseidon OSTM/Jason-2 Envisat and Jason-3 that are interpolated and processed to create a continuous heights for the study over the temporal range of the altimeters used. The purpose of these heights are to provide satellite altimetric river height data in a form that is more recognizable to the observational community and as a way to get users use to using satellite data for river hydrology.</dd><dt><span>time_coverage_resolution :</span></dt><dd>1 day</dd><dt><span>date_created :</span></dt><dd>2021-05-17T21:26:51</dd><dt><span>time_coverage_start :</span></dt><dd>1992-04-08T15:20:40</dd><dt><span>time_coverage_end :</span></dt><dd>2018-04-20T03:39:13</dd><dt><span>geospatial_lon_min :</span></dt><dd>-73.35433106652545</dd><dt><span>geospatial_lon_max :</span></dt><dd>-51.0426448887506</dd><dt><span>geospatial_lon_units :</span></dt><dd>degree_east</dd><dt><span>geospatial_lat_min :</span></dt><dd>-4.380427586763687</dd><dt><span>geospatial_lat_max :</span></dt><dd>-0.6550700975069503</dd><dt><span>geospatial_lat_units :</span></dt><dd>degree_north</dd><dt><span>geospatial_vertical_max :</span></dt><dd>92.7681246287056</dd><dt><span>geospatial_vertical_min :</span></dt><dd>-3.563409518163376</dd><dt><span>geospatial_vertical_units :</span></dt><dd>m</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>up</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:              (X: 3311, Y: 3311, charlength: 26, distance: 3311, time: 9469)\n",
       "Coordinates:\n",
       "  * time                 (time) datetime64[ns] 1993-04-08T15:20:40.665117184 ...\n",
       "Dimensions without coordinates: X, Y, charlength, distance\n",
       "Data variables:\n",
       "    lon                  (X) float64 ...\n",
       "    lat                  (Y) float64 ...\n",
       "    FD                   (distance) float64 ...\n",
       "    height               (distance, time) float64 ...\n",
       "    sat                  (charlength) |S64 ...\n",
       "    storage              (distance, time) float64 ...\n",
       "    LakeFlag             (distance) float64 ...\n",
       "    Storage_uncertainty  (distance, time) float64 ...\n",
       "    IceFlag              (time) float64 ...\n",
       "Attributes: (12/40)\n",
       "    title:                         GRRATS (Global River Radar Altimetry Time ...\n",
       "    Conventions:                   CF-1.6, ACDD-1.3\n",
       "    institution:                   Ohio State University, School of Earth Sci...\n",
       "    source:                        MEaSUREs OSU Storage toolbox 2018\n",
       "    keywords:                      EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURF...\n",
       "    keywords_vocabulary:           Global Change Master Directory (GCMD)\n",
       "    ...                            ...\n",
       "    geospatial_lat_max:            -0.6550700975069503\n",
       "    geospatial_lat_units:          degree_north\n",
       "    geospatial_vertical_max:       92.7681246287056\n",
       "    geospatial_vertical_min:       -3.563409518163376\n",
       "    geospatial_vertical_units:     m\n",
       "    geospatial_vertical_positive:  up"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_MEaSUREs = xr.open_dataset('https://opendap.jpl.nasa.gov/opendap/allData/preswot_hydrology/L2/rivers/daily/South_America_Amazon1kmdaily.nc')\n",
    "ds_MEaSUREs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thick-grade",
   "metadata": {},
   "source": [
    "### On-premise data via OPeNDAP: sea surface salinity (SMAP and Aquarius) and temperature (MODIS)\n",
    "\n",
    "Pre-SWOT MEaSUREs data resides in one NetCDF file for the whole time period, but the same cannot be said for SMAP, Aquarius and MODIS data. They have one file per month for their monthly datasets. SMAP has data from April 2015 - present, Aquarius has data from August 2011 - June 2015, and MODIS has the entire 2011-2019 time period.\n",
    "\n",
    "First, we create strings of OPeNDAP links in lists for each satellite product so we can obtain them and merge them into one file. The links change depending on the date, so the pattern of how the links change needs to be observed and then looped over and appended to the links file list. For example, SMAP has month and year numbers in its links, while Aquarius has start day and end day of the year in its links, so writing their strings takes different logic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "toxic-majority",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2011/AQUA_MODIS.20110901_20110930.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2011/AQUA_MODIS.20111001_20111031.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2011/AQUA_MODIS.20111101_20111130.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2011/AQUA_MODIS.20111201_20111231.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120101_20120131.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120201_20120229.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120301_20120331.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120401_20120430.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120501_20120531.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120601_20120630.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120701_20120731.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120801_20120831.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120901_20120930.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20121001_20121031.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20121101_20121130.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20121201_20121231.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2013/AQUA_MODIS.20130101_20130131.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2013/AQUA_MODIS.20130201_20130228.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2013/AQUA_MODIS.20130301_20130331.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2013/AQUA_MODIS.20130401_20130430.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2013/AQUA_MODIS.20130501_20130531.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2013/AQUA_MODIS.20130601_20130630.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2013/AQUA_MODIS.20130701_20130731.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2013/AQUA_MODIS.20130801_20130831.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2013/AQUA_MODIS.20130901_20130930.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2013/AQUA_MODIS.20131001_20131031.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2013/AQUA_MODIS.20131101_20131130.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2013/AQUA_MODIS.20131201_20131231.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2014/AQUA_MODIS.20140101_20140131.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2014/AQUA_MODIS.20140201_20140228.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2014/AQUA_MODIS.20140301_20140331.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2014/AQUA_MODIS.20140401_20140430.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2014/AQUA_MODIS.20140501_20140531.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2014/AQUA_MODIS.20140601_20140630.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2014/AQUA_MODIS.20140701_20140731.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2014/AQUA_MODIS.20140801_20140831.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2014/AQUA_MODIS.20140901_20140930.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2014/AQUA_MODIS.20141001_20141031.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2014/AQUA_MODIS.20141101_20141130.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2014/AQUA_MODIS.20141201_20141231.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2015/AQUA_MODIS.20150101_20150131.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2015/AQUA_MODIS.20150201_20150228.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2015/AQUA_MODIS.20150301_20150331.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2015/AQUA_MODIS.20150401_20150430.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2015/AQUA_MODIS.20150501_20150531.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2015/AQUA_MODIS.20150601_20150630.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2015/AQUA_MODIS.20150701_20150731.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2015/AQUA_MODIS.20150801_20150831.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2015/AQUA_MODIS.20150901_20150930.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2015/AQUA_MODIS.20151001_20151031.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2015/AQUA_MODIS.20151101_20151130.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2015/AQUA_MODIS.20151201_20151231.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2016/AQUA_MODIS.20160101_20160131.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2016/AQUA_MODIS.20160201_20160229.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2016/AQUA_MODIS.20160301_20160331.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2016/AQUA_MODIS.20160401_20160430.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2016/AQUA_MODIS.20160501_20160531.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2016/AQUA_MODIS.20160601_20160630.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2016/AQUA_MODIS.20160701_20160731.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2016/AQUA_MODIS.20160801_20160831.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2016/AQUA_MODIS.20160901_20160930.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2016/AQUA_MODIS.20161001_20161031.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2016/AQUA_MODIS.20161101_20161130.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2016/AQUA_MODIS.20161201_20161231.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2017/AQUA_MODIS.20170101_20170131.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2017/AQUA_MODIS.20170201_20170228.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2017/AQUA_MODIS.20170301_20170331.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2017/AQUA_MODIS.20170401_20170430.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2017/AQUA_MODIS.20170501_20170531.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2017/AQUA_MODIS.20170601_20170630.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2017/AQUA_MODIS.20170701_20170731.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2017/AQUA_MODIS.20170801_20170831.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2017/AQUA_MODIS.20170901_20170930.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2017/AQUA_MODIS.20171001_20171031.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2017/AQUA_MODIS.20171101_20171130.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2017/AQUA_MODIS.20171201_20171231.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2018/AQUA_MODIS.20180101_20180131.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2018/AQUA_MODIS.20180201_20180228.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2018/AQUA_MODIS.20180301_20180331.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2018/AQUA_MODIS.20180401_20180430.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2018/AQUA_MODIS.20180501_20180531.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2018/AQUA_MODIS.20180601_20180630.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2018/AQUA_MODIS.20180701_20180731.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2018/AQUA_MODIS.20180801_20180831.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2018/AQUA_MODIS.20180901_20180930.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2018/AQUA_MODIS.20181001_20181031.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2018/AQUA_MODIS.20181101_20181130.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2018/AQUA_MODIS.20181201_20181231.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2019/AQUA_MODIS.20190101_20190131.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2019/AQUA_MODIS.20190201_20190228.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2019/AQUA_MODIS.20190301_20190331.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2019/AQUA_MODIS.20190401_20190430.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2019/AQUA_MODIS.20190501_20190531.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2019/AQUA_MODIS.20190601_20190630.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2019/AQUA_MODIS.20190701_20190731.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2019/AQUA_MODIS.20190801_20190831.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2019/AQUA_MODIS.20190901_20190930.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2019/AQUA_MODIS.20191001_20191031.L3m.MO.SST4.sst4.9km.nc',\n",
       " 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2019/AQUA_MODIS.20191101_20191130.L3m.MO.SST4.sst4.9km.nc']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize SMAP file list\n",
    "file_list_SMAP = []\n",
    "#create an array of the months of the year and an array for the years\n",
    "counter_month = np.arange(1,13)\n",
    "counter_year = np.arange(2015, 2020)\n",
    "\n",
    "# Make list of file paths\n",
    "for j in counter_year:\n",
    "    for i in counter_month:\n",
    "        if j == 2015: #data starts in april for 2015\n",
    "            if i > 3:\n",
    "                if i < 10:        #for single digit months, only one number needs to be changed\n",
    "                    file_list_SMAP.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/smap/L3/JPL/V5.0/monthly/%d/SMAP_L3_SSS_%d0%d_MONTHLY_V5.0.nc' % (j, j, i))\n",
    "                else:             #for double digit months, 2 numbers in the string need to change\n",
    "                    file_list_SMAP.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/smap/L3/JPL/V5.0/monthly/%d/SMAP_L3_SSS_%d%d_MONTHLY_V5.0.nc' % (j, j, i))\n",
    "        else:\n",
    "            if i < 10:        #for single digit months, only one number needs to be changed\n",
    "                file_list_SMAP.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/smap/L3/JPL/V5.0/monthly/%d/SMAP_L3_SSS_%d0%d_MONTHLY_V5.0.nc' % (j, j, i))\n",
    "            else:             #for double digit months, 2 numbers in the string need to change\n",
    "                file_list_SMAP.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/smap/L3/JPL/V5.0/monthly/%d/SMAP_L3_SSS_%d%d_MONTHLY_V5.0.nc' % (j, j, i))\n",
    "\n",
    "# Initialize Aquarius file list\n",
    "file_list_Aq = []\n",
    "# Create an array of the months of the year and an array for the years\n",
    "counter_month = np.arange(0,12)\n",
    "counter_year = np.arange(2011, 2016)\n",
    "\n",
    "# Make list of file paths\n",
    "for j in counter_year:\n",
    "    for i in counter_month:\n",
    "        if j == 2012: #leap-year\n",
    "            d1 = ['001', '032', '061', '092', '122', '153', '183', '214', '245', '275', '306', '336']\n",
    "            d2 = ['031', '060', '091', '121', '152', '182', '213', '244', '274', '305', '335', '366']\n",
    "        else: #all other years\n",
    "            d1 = ['001', '032', '060', '091', '121', '152', '182', '213', '244', '274', '305', '335']\n",
    "            d2 = ['031', '059', '090', '120', '151', '181', '212', '243', '273', '304', '334', '365']\n",
    "        \n",
    "        if j == 2011: #data starts in the 8th month (index 7)\n",
    "            if i > 6:\n",
    "                file_list_Aq.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/aquarius/L3/mapped/V5/monthly/SCI/%d/Q%d%s%d%s.L3m_MO_SCI_V5.0_SSS_1deg.bz2' % (j, j, d1[i], j, d2[i]))\n",
    "        elif j == 2015: #data ends in the 6th month (index 5)\n",
    "            if i < 6:\n",
    "                file_list_Aq.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/aquarius/L3/mapped/V5/monthly/SCI/%d/Q%d%s%d%s.L3m_MO_SCI_V5.0_SSS_1deg.bz2' % (j, j, d1[i], j, d2[i]))\n",
    "        else:\n",
    "            file_list_Aq.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/aquarius/L3/mapped/V5/monthly/SCI/%d/Q%d%s%d%s.L3m_MO_SCI_V5.0_SSS_1deg.bz2' % (j, j, d1[i], j, d2[i]))\n",
    "\n",
    "        \n",
    "# Initialize MODIS file list for sea surface temperature\n",
    "file_list_MODIS = []\n",
    "# Create an array of the months of the year and an array for the years\n",
    "counter_month = np.arange(0,12)\n",
    "counter_year = np.arange(2011, 2020)\n",
    "\n",
    "# Make list of file paths\n",
    "for j in counter_year:\n",
    "    for i in counter_month:\n",
    "        if j == 2012 or j == 2016: #leap-year\n",
    "            d1 = ['0101', '0201', '0301', '0401', '0501', '0601', '0701', '0801', '0901', '1001', '1101', '1201']\n",
    "            d2 = ['0131', '0229', '0331', '0430', '0531', '0630', '0731', '0831', '0930', '1031', '1130', '1231']\n",
    "        else: #all other years\n",
    "            d1 = ['0101', '0201', '0301', '0401', '0501', '0601', '0701', '0801', '0901', '1001', '1101', '1201']\n",
    "            d2 = ['0131', '0228', '0331', '0430', '0531', '0630', '0731', '0831', '0930', '1031', '1130', '1231']\n",
    "        \n",
    "        if j == 2011: #data starts in the 8th month (index 7)\n",
    "            if i > 6:\n",
    "                file_list_MODIS.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/%d/AQUA_MODIS.%d%s_%d%s.L3m.MO.SST4.sst4.9km.nc' % (j, j, d1[i], j, d2[i]))\n",
    "        elif j == 2019: #data ends in the 11th month (index 10)\n",
    "            if i < 11:\n",
    "                file_list_MODIS.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/%d/AQUA_MODIS.%d%s_%d%s.L3m.MO.SST4.sst4.9km.nc' % (j, j, d1[i], j, d2[i]))\n",
    "        else:\n",
    "            file_list_MODIS.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/%d/AQUA_MODIS.%d%s_%d%s.L3m.MO.SST4.sst4.9km.nc' % (j, j, d1[i], j, d2[i]))\n",
    "\n",
    "# Un comment to see links lists\n",
    "# file_list_SMAP\n",
    "# file_list_Aq\n",
    "file_list_MODIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "hollow-spring",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:               (latitude: 720, longitude: 1440, time: 57)\n",
       "Coordinates:\n",
       "  * latitude              (latitude) float32 89.88 89.62 89.38 ... -89.62 -89.88\n",
       "  * longitude             (longitude) float32 -179.9 -179.6 ... 179.6 179.9\n",
       "  * time                  (time) datetime64[ns] 2015-04-16 ... 2019-12-16T12:...\n",
       "Data variables:\n",
       "    smap_sss              (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    anc_sss               (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    anc_sst               (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    smap_spd              (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    smap_high_spd         (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    weight                (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    land_fraction         (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    ice_fraction          (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    smap_sss_uncertainty  (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "Attributes: (12/38)\n",
       "    title:                       SMAP 0.25x0.25 deg grid averaged monthly SSS...\n",
       "    institution:                 Jet Propulsion Laboratory\n",
       "    source:                      SMAP L2B SSS\n",
       "    history:                     DATA_SOURCE_VERSION V5.0 L2B SMAP SSS/WSPD\n",
       "    comment:                     Gaussian-weighted map gridding of SMAP L2B S...\n",
       "    Gaussian_window_radius:      45.0\n",
       "    ...                          ...\n",
       "    creator_name:                JPL\n",
       "    creator_email:               fore@jpl.nasa.gov\n",
       "    publisher_name:              Alexander G. Fore\n",
       "    publisher_email:             fore@jpl.nasa.gov\n",
       "    contributor_name:            Alexander Fore, Simon Yueh, Wenqing Tang, Ak...\n",
       "    references:                  10.1109/TGRS.2016.2601486, 10.1109/TGRS.2016...</pre><div class='xr-wrap' hidden><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-70f963bb-8021-4ae4-9f79-79417685a503' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-70f963bb-8021-4ae4-9f79-79417685a503' 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'>latitude</span>: 720</li><li><span class='xr-has-index'>longitude</span>: 1440</li><li><span class='xr-has-index'>time</span>: 57</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2e7978bb-103b-40d8-a999-e045625bdf12' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2e7978bb-103b-40d8-a999-e045625bdf12' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>89.88 89.62 89.38 ... -89.62 -89.88</div><input id='attrs-252d2e28-2c6e-4e67-b6bd-4484c29af047' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-252d2e28-2c6e-4e67-b6bd-4484c29af047' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b8042e0-0189-45c5-bd5a-2d090b760aa2' class='xr-var-data-in' type='checkbox'><label for='data-8b8042e0-0189-45c5-bd5a-2d090b760aa2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude of grid cell</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 89.875,  89.625,  89.375, ..., -89.375, -89.625, -89.875],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-9ea43a8e-3d90-42b5-87ff-665fe815de3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ea43a8e-3d90-42b5-87ff-665fe815de3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b6c4fd5-ac99-47e1-ab70-e156d9472a37' class='xr-var-data-in' type='checkbox'><label for='data-1b6c4fd5-ac99-47e1-ab70-e156d9472a37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude of grid cell</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-04-16 ... 2019-12-16T12:00:00</div><input id='attrs-756bd871-8098-4568-ac1b-7ddd45a7fbab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-756bd871-8098-4568-ac1b-7ddd45a7fbab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3004e1c4-0231-4fd6-9398-72aa74e650b5' class='xr-var-data-in' type='checkbox'><label for='data-3004e1c4-0231-4fd6-9398-72aa74e650b5' 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>Reference time of sss field</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>comment :</span></dt><dd>Midpoint of time interval of analyzed fields</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-04-16T00:00:00.000000000&#x27;, &#x27;2015-05-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2015-06-16T00:00:00.000000000&#x27;, &#x27;2015-07-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2015-08-16T12:00:00.000000000&#x27;, &#x27;2015-09-16T00:00:00.000000000&#x27;,\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smap_sss_uncertainty</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-48b1e08b-1bcf-4516-a6c9-3016e3f09356' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48b1e08b-1bcf-4516-a6c9-3016e3f09356' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5f8d680-ffdd-4e6f-b1de-048cc048c5bf' class='xr-var-data-in' type='checkbox'><label for='data-c5f8d680-ffdd-4e6f-b1de-048cc048c5bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1e-3</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>50</dd><dt><span>long_name :</span></dt><dd>SMAP SSS uncertainty</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr><th> Shape </th><td> (57, 720, 1440) </td> <td> (1, 720, 1440) </td></tr>\n",
       "    <tr><th> Count </th><td> 228 Tasks </td><td> 57 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f4f86c53-16a9-4042-a27c-e7376448b3c8' class='xr-section-summary-in' type='checkbox'  ><label for='section-f4f86c53-16a9-4042-a27c-e7376448b3c8' class='xr-section-summary' >Attributes: <span>(38)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>SMAP 0.25x0.25 deg grid averaged monthly SSS/WSPD</dd><dt><span>institution :</span></dt><dd>Jet Propulsion Laboratory</dd><dt><span>source :</span></dt><dd>SMAP L2B SSS</dd><dt><span>history :</span></dt><dd>DATA_SOURCE_VERSION V5.0 L2B SMAP SSS/WSPD</dd><dt><span>comment :</span></dt><dd>Gaussian-weighted map gridding of SMAP L2B SSS Product</dd><dt><span>Gaussian_window_radius :</span></dt><dd>45.0</dd><dt><span>Gaussian_window_half_power :</span></dt><dd>30.0</dd><dt><span>revs_used :</span></dt><dd>[ 870  871  872  873  874  875  876  877  878  879  880  883  884  885\n",
       "  886  887  888  889  890  891  892  893  894  895  896  897  898  899\n",
       "  900  901  902  903  904  905  906  907  912  913  914  915  916  917\n",
       "  918  919  920  921  922  923  924  925  926  927  928  929  930  931\n",
       "  932  935  936  937  938  939  940  941  942  943  944  945  946  947\n",
       "  948  949  950  951  952  953  954  955  956  959  960  961  962  963\n",
       "  964  965  966  967  968  969  970  971  972  973  974  975  976  977\n",
       "  978  979  980  981  982  983  984  985  986  987  988  989  990  991\n",
       "  992  993  994  995  996  997  998  999 1000 1001 1002 1003 1004 1005\n",
       " 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019\n",
       " 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1032 1033 1034\n",
       " 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048\n",
       " 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062\n",
       " 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076\n",
       " 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1090 1091\n",
       " 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105\n",
       " 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119\n",
       " 1120 1121 1122 1123 1124 1125 1127 1128 1129 1130 1131 1132 1133 1134\n",
       " 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148\n",
       " 1149 1150 1151 1152 1153 1154 1155 1157 1158 1159 1160 1161 1162 1163\n",
       " 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1178 1179\n",
       " 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193\n",
       " 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207\n",
       " 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221\n",
       " 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235\n",
       " 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249\n",
       " 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263\n",
       " 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277\n",
       " 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291\n",
       " 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305\n",
       " 1306 1307 1308]</dd><dt><span>revs_missing :</span></dt><dd>[ 881  882  908  909  910  911  933  934  957  958 1031 1089 1126 1156\n",
       " 1176 1177]</dd><dt><span>l2b_files :</span></dt><dd>SMAP_L2B_SSS_00870_20150401T004402_R17000_V5.0.h5\n",
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       "SMAP_L2B_SSS_00887_20150402T043749_R17000_V5.0.h5\n",
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       "SMAP_L2B_SSS_00889_20150402T075444_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_00890_20150402T093311_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_00891_20150402T111139_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_00892_20150402T125006_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_00893_20150402T142834_R17000_V5.0.h5\n",
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       "SMAP_L2B_SSS_00907_20150403T132658_R17000_V5.0.h5\n",
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       "SMAP_L2B_SSS_01130_20150418T192309_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01131_20150418T210137_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01132_20150418T224005_R17000_V5.0.h5\n",
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       "SMAP_L2B_SSS_01136_20150419T051357_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01137_20150419T065224_R17000_V5.0.h5\n",
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       "SMAP_L2B_SSS_01140_20150419T114748_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01141_20150419T132616_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01142_20150419T150444_R17000_V5.0.h5\n",
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       "SMAP_L2B_SSS_01150_20150420T041227_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01151_20150420T055055_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01152_20150420T072923_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01153_20150420T090750_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01154_20150420T104618_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01155_20150420T122446_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01157_20150420T154142_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01158_20150420T172010_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01159_20150420T185838_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01160_20150420T203706_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01161_20150420T221533_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01162_20150420T235401_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01163_20150421T013229_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01164_20150421T031057_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01165_20150421T044925_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01166_20150421T062753_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01167_20150421T080621_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01168_20150421T094449_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01169_20150421T112316_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01170_20150421T130144_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01171_20150421T144012_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01172_20150421T161840_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01173_20150421T175708_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01174_20150421T193536_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01175_20150421T211404_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01178_20150422T020927_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01179_20150422T034755_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01180_20150422T052623_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01181_20150422T070451_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01182_20150422T084319_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01183_20150422T102146_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01184_20150422T120014_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01185_20150422T133842_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01186_20150422T151710_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01187_20150422T165538_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01188_20150422T183406_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01189_20150422T201234_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01190_20150422T215101_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01191_20150422T232929_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01192_20150423T010757_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01193_20150423T024625_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01194_20150423T042453_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01195_20150423T060321_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01196_20150423T074149_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01197_20150423T092016_R17000_V5.0.h5\n",
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       "SMAP_L2B_SSS_01215_20150424T145238_R17000_V5.0.h5\n",
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       "SMAP_L2B_SSS_01218_20150424T194801_R17000_V5.0.h5\n",
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       "SMAP_L2B_SSS_01226_20150425T085544_R17000_V5.0.h5\n",
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       "SMAP_L2B_SSS_01238_20150426T043718_R17000_V5.0.h5\n",
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       "SMAP_L2B_SSS_01240_20150426T075413_R17000_V5.0.h5\n",
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       "SMAP_L2B_SSS_01242_20150426T111109_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01243_20150426T124937_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01244_20150426T142805_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01245_20150426T160633_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01246_20150426T174500_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01247_20150426T192329_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01248_20150426T210156_R17000_V5.0.h5\n",
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       "SMAP_L2B_SSS_01255_20150427T083111_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01256_20150427T100938_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01257_20150427T114806_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01258_20150427T132634_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01259_20150427T150502_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01260_20150427T164330_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01261_20150427T182158_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01262_20150427T200025_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01263_20150427T213853_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01264_20150427T231721_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01265_20150428T005549_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01266_20150428T023417_R17000_V5.0.h5\n",
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       "SMAP_L2B_SSS_01268_20150428T055112_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01269_20150428T072940_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01270_20150428T090808_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01271_20150428T104636_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01272_20150428T122503_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01273_20150428T140331_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01274_20150428T154159_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01275_20150428T172027_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01276_20150428T185855_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01277_20150428T203722_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01278_20150428T221550_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01279_20150428T235418_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01280_20150429T013246_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01281_20150429T031114_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01282_20150429T044942_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01283_20150429T062810_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01284_20150429T080637_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01285_20150429T094505_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01286_20150429T112333_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01287_20150429T130201_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01288_20150429T144029_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01289_20150429T161856_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01290_20150429T175724_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01291_20150429T193552_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01292_20150429T211420_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01293_20150429T225248_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01294_20150430T003115_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01295_20150430T020943_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01296_20150430T034811_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01297_20150430T052639_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01298_20150430T070507_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01299_20150430T084334_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01300_20150430T102202_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01301_20150430T120030_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01302_20150430T133858_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01303_20150430T151726_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01304_20150430T165555_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01305_20150430T183423_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01306_20150430T201251_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01307_20150430T215118_R17000_V5.0.h5\n",
       "SMAP_L2B_SSS_01308_20150430T232945_R17000_V5.0.h5</dd><dt><span>TB_CRID :</span></dt><dd>R17000</dd><dt><span>Year :</span></dt><dd>2015</dd><dt><span>Month :</span></dt><dd>4</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>processing_level :</span></dt><dd>3</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>date_issued :</span></dt><dd>2020-293T18:28:59.223</dd><dt><span>date_created :</span></dt><dd>2020-293T18:28:59.223</dd><dt><span>time_coverage_start :</span></dt><dd>2015-091T00:00:00.000</dd><dt><span>time_coverage_end :</span></dt><dd>2015-121T00:00:00.000</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>platform :</span></dt><dd>SMAP</dd><dt><span>sensor :</span></dt><dd>SMAP</dd><dt><span>project :</span></dt><dd>SMAP</dd><dt><span>product_version :</span></dt><dd>V5.0</dd><dt><span>keywords_vocabulary :</span></dt><dd>http://gcmd.gsfc.nasa.gov/Resources/valids/gcmd_parameters.html</dd><dt><span>keywords :</span></dt><dd>SEA SURFACE SALINITY, SALINITY, SMAP, Jet Propulsion Laboratory, NASA, https://smap.jpl.nasa.gov/, SMAP Radiometer</dd><dt><span>creator_name :</span></dt><dd>JPL</dd><dt><span>creator_email :</span></dt><dd>fore@jpl.nasa.gov</dd><dt><span>publisher_name :</span></dt><dd>Alexander G. Fore</dd><dt><span>publisher_email :</span></dt><dd>fore@jpl.nasa.gov</dd><dt><span>contributor_name :</span></dt><dd>Alexander Fore, Simon Yueh, Wenqing Tang, Akiko Hayashi, Bryan Stiles</dd><dt><span>references :</span></dt><dd>10.1109/TGRS.2016.2601486, 10.1109/TGRS.2016.2600239, 10.1109/TGRS.2013.2266915, 10.1016/j.rse.2017.08.021</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:               (latitude: 720, longitude: 1440, time: 57)\n",
       "Coordinates:\n",
       "  * latitude              (latitude) float32 89.88 89.62 89.38 ... -89.62 -89.88\n",
       "  * longitude             (longitude) float32 -179.9 -179.6 ... 179.6 179.9\n",
       "  * time                  (time) datetime64[ns] 2015-04-16 ... 2019-12-16T12:...\n",
       "Data variables:\n",
       "    smap_sss              (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    anc_sss               (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    anc_sst               (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    smap_spd              (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    smap_high_spd         (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    weight                (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    land_fraction         (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    ice_fraction          (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    smap_sss_uncertainty  (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "Attributes: (12/38)\n",
       "    title:                       SMAP 0.25x0.25 deg grid averaged monthly SSS...\n",
       "    institution:                 Jet Propulsion Laboratory\n",
       "    source:                      SMAP L2B SSS\n",
       "    history:                     DATA_SOURCE_VERSION V5.0 L2B SMAP SSS/WSPD\n",
       "    comment:                     Gaussian-weighted map gridding of SMAP L2B S...\n",
       "    Gaussian_window_radius:      45.0\n",
       "    ...                          ...\n",
       "    creator_name:                JPL\n",
       "    creator_email:               fore@jpl.nasa.gov\n",
       "    publisher_name:              Alexander G. Fore\n",
       "    publisher_email:             fore@jpl.nasa.gov\n",
       "    contributor_name:            Alexander Fore, Simon Yueh, Wenqing Tang, Ak...\n",
       "    references:                  10.1109/TGRS.2016.2601486, 10.1109/TGRS.2016..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For OPeNDAP, we need to set the number of files that can be open at once to 10, \n",
    "# So that xa.open_mfdataset() actually reads all links (see https://github.com/pydata/xarray/issues/4082)\n",
    "xr.set_options(file_cache_maxsize=10)\n",
    "\n",
    "# To use xa.open_mfdataset which combines netCDF files\n",
    "ds_SMAP = xr.open_mfdataset(file_list_SMAP, combine='by_coords')\n",
    "ds_SMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "federal-converter",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (eightbitcolor: 256, lat: 2160, lon: 4320, rgb: 3, time: 100)\n",
       "Coordinates:\n",
       "  * lat        (lat) float32 89.96 89.88 89.79 89.71 ... -89.79 -89.88 -89.96\n",
       "  * lon        (lon) float32 -180.0 -179.9 -179.8 -179.7 ... 179.8 179.9 180.0\n",
       "Dimensions without coordinates: eightbitcolor, rgb, time\n",
       "Data variables:\n",
       "    palette    (time, rgb, eightbitcolor) int8 dask.array&lt;chunksize=(1, 3, 256), meta=np.ndarray&gt;\n",
       "    sst4       (time, lat, lon) float32 dask.array&lt;chunksize=(1, 2160, 4320), meta=np.ndarray&gt;\n",
       "    qual_sst4  (time, lat, lon) float32 dask.array&lt;chunksize=(1, 2160, 4320), meta=np.ndarray&gt;\n",
       "Attributes: (12/59)\n",
       "    product_name:                     AQUA_MODIS.20110801_20110831.L3m.MO.SST...\n",
       "    instrument:                       MODIS\n",
       "    title:                            MODISA Level-3 Standard Mapped Image\n",
       "    project:                          Ocean Biology Processing Group (NASA/GS...\n",
       "    platform:                         Aqua\n",
       "    temporal_range:                   month\n",
       "    ...                               ...\n",
       "    publisher_url:                    https://oceandata.sci.gsfc.nasa.gov\n",
       "    processing_level:                 L3 Mapped\n",
       "    cdm_data_type:                    grid\n",
       "    data_bins:                        Attribute edlided: Unsupported attribut...\n",
       "    data_minimum:                     -1.665\n",
       "    data_maximum:                     35.065</pre><div class='xr-wrap' hidden><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-4004b229-56e1-49dd-b9cd-5dd15952e415' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4004b229-56e1-49dd-b9cd-5dd15952e415' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>eightbitcolor</span>: 256</li><li><span class='xr-has-index'>lat</span>: 2160</li><li><span class='xr-has-index'>lon</span>: 4320</li><li><span>rgb</span>: 3</li><li><span>time</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-295a2b9a-9694-4dc2-80ce-05f068a8327e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-295a2b9a-9694-4dc2-80ce-05f068a8327e' class='xr-section-summary' >Coordinates: <span>(2)</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'>float32</div><div class='xr-var-preview xr-preview'>89.96 89.88 89.79 ... -89.88 -89.96</div><input id='attrs-1a2ce890-37c6-44e8-b919-2e69a714fa0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a2ce890-37c6-44e8-b919-2e69a714fa0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9cc1268-30b7-4a96-a33d-96d2f4c9388b' class='xr-var-data-in' type='checkbox'><label for='data-f9cc1268-30b7-4a96-a33d-96d2f4c9388b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><pre>array([ 89.958336,  89.875   ,  89.79167 , ..., -89.791664, -89.87501 ,\n",
       "       -89.958336], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-180.0 -179.9 ... 179.9 180.0</div><input id='attrs-e910d39b-05ef-4489-9c12-0646739213cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e910d39b-05ef-4489-9c12-0646739213cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efe6e11c-fc76-45b7-bcb7-0b397f8ee439' class='xr-var-data-in' type='checkbox'><label for='data-efe6e11c-fc76-45b7-bcb7-0b397f8ee439' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><pre>array([-179.95833, -179.875  , -179.79166, ...,  179.79167,  179.87502,\n",
       "        179.95836], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2825f590-93be-42a7-a68f-c7299a71524f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2825f590-93be-42a7-a68f-c7299a71524f' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>palette</span></div><div class='xr-var-dims'>(time, rgb, eightbitcolor)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 3, 256), meta=np.ndarray&gt;</div><input id='attrs-66e14476-c464-4a15-ae7e-f6e0a3274423' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66e14476-c464-4a15-ae7e-f6e0a3274423' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c6fc873-93bb-4011-93bf-979a44da9f38' class='xr-var-data-in' type='checkbox'><label for='data-0c6fc873-93bb-4011-93bf-979a44da9f38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
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       "    <tr><th> Shape </th><td> (100, 3, 256) </td> <td> (1, 3, 256) </td></tr>\n",
       "    <tr><th> Count </th><td> 400 Tasks </td><td> 100 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> int8 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst4</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=(1, 2160, 4320), meta=np.ndarray&gt;</div><input id='attrs-fb03ebeb-02e6-47ed-8227-36dc8f9e0d05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb03ebeb-02e6-47ed-8227-36dc8f9e0d05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da61e8a7-e815-45e1-be24-6debb6ae0a01' class='xr-var-data-in' type='checkbox'><label for='data-da61e8a7-e815-45e1-be24-6debb6ae0a01' 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>4um Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>valid_min :</span></dt><dd>-1000</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>display_scale :</span></dt><dd>linear</dd><dt><span>display_min :</span></dt><dd>-2.0</dd><dt><span>display_max :</span></dt><dd>45.0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qual_sst4</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=(1, 2160, 4320), meta=np.ndarray&gt;</div><input id='attrs-88939000-6216-45b0-a6f9-391a5c7ef489' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88939000-6216-45b0-a6f9-391a5c7ef489' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9e6812e-8c03-47a3-a08c-c16df09492d8' class='xr-var-data-in' type='checkbox'><label for='data-d9e6812e-8c03-47a3-a08c-c16df09492d8' 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>Quality Levels, Sea Surface Temperature</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>5</dd></dl></div><div class='xr-var-data'><table>\n",
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      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (eightbitcolor: 256, lat: 2160, lon: 4320, rgb: 3, time: 100)\n",
       "Coordinates:\n",
       "  * lat        (lat) float32 89.96 89.88 89.79 89.71 ... -89.79 -89.88 -89.96\n",
       "  * lon        (lon) float32 -180.0 -179.9 -179.8 -179.7 ... 179.8 179.9 180.0\n",
       "Dimensions without coordinates: eightbitcolor, rgb, time\n",
       "Data variables:\n",
       "    palette    (time, rgb, eightbitcolor) int8 dask.array<chunksize=(1, 3, 256), meta=np.ndarray>\n",
       "    sst4       (time, lat, lon) float32 dask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>\n",
       "    qual_sst4  (time, lat, lon) float32 dask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>\n",
       "Attributes: (12/59)\n",
       "    product_name:                     AQUA_MODIS.20110801_20110831.L3m.MO.SST...\n",
       "    instrument:                       MODIS\n",
       "    title:                            MODISA Level-3 Standard Mapped Image\n",
       "    project:                          Ocean Biology Processing Group (NASA/GS...\n",
       "    platform:                         Aqua\n",
       "    temporal_range:                   month\n",
       "    ...                               ...\n",
       "    publisher_url:                    https://oceandata.sci.gsfc.nasa.gov\n",
       "    processing_level:                 L3 Mapped\n",
       "    cdm_data_type:                    grid\n",
       "    data_bins:                        Attribute edlided: Unsupported attribut...\n",
       "    data_minimum:                     -1.665\n",
       "    data_maximum:                     35.065"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #for OPeNDAP, we need to set the number of files that can be open at once to 10, \n",
    "# #so that xr.open_mfdataset() actually reads all links (see https://github.com/pydata/xarray/issues/4082)\n",
    "# xr.set_options(file_cache_maxsize=10)\n",
    "\n",
    "# #to use xr.open_mfdataset which combines netCDF files\n",
    "ds_MODIS = xr.open_mfdataset(file_list_MODIS, combine='nested', concat_dim='time')\n",
    "ds_MODIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "operating-interface",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (SSS_0: 180, SSS_1: 360, SSS_ran_unc_0: 180, SSS_ran_unc_1: 360, SSS_sys_unc_0: 180, SSS_sys_unc_1: 360, palette_0: 3, palette_1: 256, time: 47)\n",
       "Dimensions without coordinates: SSS_0, SSS_1, SSS_ran_unc_0, SSS_ran_unc_1, SSS_sys_unc_0, SSS_sys_unc_1, palette_0, palette_1, time\n",
       "Data variables:\n",
       "    SSS          (time, SSS_0, SSS_1) float32 dask.array&lt;chunksize=(1, 180, 360), meta=np.ndarray&gt;\n",
       "    SSS_ran_unc  (time, SSS_ran_unc_0, SSS_ran_unc_1) float32 dask.array&lt;chunksize=(1, 180, 360), meta=np.ndarray&gt;\n",
       "    SSS_sys_unc  (time, SSS_sys_unc_0, SSS_sys_unc_1) float32 dask.array&lt;chunksize=(1, 180, 360), meta=np.ndarray&gt;\n",
       "    palette      (time, palette_0, palette_1) int8 dask.array&lt;chunksize=(1, 3, 256), meta=np.ndarray&gt;\n",
       "Attributes: (12/68)\n",
       "    product_name:                      Q20112132011243.L3m_MO_SCI_V5.0_SSS_1deg\n",
       "    instrument:                        Aquarius\n",
       "    title:                             Aquarius Level-3 Standard Mapped Image\n",
       "    project:                           Ocean Biology Processing Group (NASA/G...\n",
       "    platform:                          SAC-D Aquarius\n",
       "    temporal_range:                    MO\n",
       "    ...                                ...\n",
       "    identifier_product_doi:            http://dx.doi.org\n",
       "    keywords:                          SURFACE SALINITY, SALINITY, AQUARIUS, ...\n",
       "    keywords_vocabulary:               NASA Global Change Master Directory (G...\n",
       "    software_name:                     smigen\n",
       "    software_version:                  5.20\n",
       "    source:                            Q20112132011243.L3b_MO_SCI_V5.0.main</pre><div class='xr-wrap' hidden><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-f99a5ee8-c527-44df-8e0f-e911cd24af77' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f99a5ee8-c527-44df-8e0f-e911cd24af77' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>SSS_0</span>: 180</li><li><span>SSS_1</span>: 360</li><li><span>SSS_ran_unc_0</span>: 180</li><li><span>SSS_ran_unc_1</span>: 360</li><li><span>SSS_sys_unc_0</span>: 180</li><li><span>SSS_sys_unc_1</span>: 360</li><li><span>palette_0</span>: 3</li><li><span>palette_1</span>: 256</li><li><span>time</span>: 47</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b0b7e715-16a9-4c89-9da9-289fd184ae8e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b0b7e715-16a9-4c89-9da9-289fd184ae8e' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-f838b284-f348-4557-a2b1-db9295da3bf7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f838b284-f348-4557-a2b1-db9295da3bf7' class='xr-section-summary' >Data variables: <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>SSS</span></div><div class='xr-var-dims'>(time, SSS_0, SSS_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-3754d91f-241d-4dac-a157-f4e92260fdaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3754d91f-241d-4dac-a157-f4e92260fdaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4271809-56e6-41ac-a13c-0a4c896ec540' class='xr-var-data-in' type='checkbox'><label for='data-b4271809-56e6-41ac-a13c-0a4c896ec540' 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>Sea Surface Salinity</dd><dt><span>origname :</span></dt><dd>SSS</dd><dt><span>fullnamepath :</span></dt><dd>/SSS</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
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       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 12.18 MB </td> <td> 259.20 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (47, 180, 360) </td> <td> (1, 180, 360) </td></tr>\n",
       "    <tr><th> Count </th><td> 188 Tasks </td><td> 47 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSS_ran_unc</span></div><div class='xr-var-dims'>(time, SSS_ran_unc_0, SSS_ran_unc_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-af23f542-8fda-4138-811f-2623851543cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af23f542-8fda-4138-811f-2623851543cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61b580b5-3de9-49d7-9f76-f57b6ef90dc6' class='xr-var-data-in' type='checkbox'><label for='data-61b580b5-3de9-49d7-9f76-f57b6ef90dc6' 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>Sea Surface Salinity Uncertainty (random)</dd><dt><span>origname :</span></dt><dd>SSS_ran_unc</dd><dt><span>fullnamepath :</span></dt><dd>/SSS_ran_unc</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSS_sys_unc</span></div><div class='xr-var-dims'>(time, SSS_sys_unc_0, SSS_sys_unc_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-4e4ac1be-63e7-4c18-bee8-61961ae6760c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e4ac1be-63e7-4c18-bee8-61961ae6760c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-411c236a-9977-4c5a-8d15-0c7ba053373b' class='xr-var-data-in' type='checkbox'><label for='data-411c236a-9977-4c5a-8d15-0c7ba053373b' 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>Sea Surface Salinity Uncertainty (systematic)</dd><dt><span>origname :</span></dt><dd>SSS_sys_unc</dd><dt><span>fullnamepath :</span></dt><dd>/SSS_sys_unc</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-cfe4875e-5ba7-44fb-8edb-fb7a8f4e4c26' class='xr-section-summary-in' type='checkbox'  ><label for='section-cfe4875e-5ba7-44fb-8edb-fb7a8f4e4c26' class='xr-section-summary' >Attributes: <span>(68)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>product_name :</span></dt><dd>Q20112132011243.L3m_MO_SCI_V5.0_SSS_1deg</dd><dt><span>instrument :</span></dt><dd>Aquarius</dd><dt><span>title :</span></dt><dd>Aquarius Level-3 Standard Mapped Image</dd><dt><span>project :</span></dt><dd>Ocean Biology Processing Group (NASA/GSFC/OBPG)</dd><dt><span>platform :</span></dt><dd>SAC-D Aquarius</dd><dt><span>temporal_range :</span></dt><dd>MO</dd><dt><span>processing_version :</span></dt><dd>V5.0</dd><dt><span>date_created :</span></dt><dd>2017-10-21T21:22:21.000Z</dd><dt><span>history :</span></dt><dd>smigen par=Q20112132011243.L3m_MO_SCI_V5.0_SSS_1deg.param</dd><dt><span>l2_flag_names :</span></dt><dd>POINTING,NAV,LANDRED,ICERED,REFL_1STOKESMOONRED,REFL_1STOKESGAL,TFTADIFFRED,RFI_REGION,SAOVERFLOW,COLDWATERRED,WINDRED,TBCONS</dd><dt><span>time_coverage_start :</span></dt><dd>2011-08-25T01:45:23.698Z</dd><dt><span>time_coverage_end :</span></dt><dd>2011-09-01T01:30:15.207Z</dd><dt><span>start_orbit_number :</span></dt><dd>1111</dd><dt><span>end_orbit_number :</span></dt><dd>1213</dd><dt><span>map_projection :</span></dt><dd>Equidistant Cylindrical</dd><dt><span>latitude_units :</span></dt><dd>degrees_north</dd><dt><span>longitude_units :</span></dt><dd>degrees_east</dd><dt><span>northernmost_latitude :</span></dt><dd>90.0</dd><dt><span>southernmost_latitude :</span></dt><dd>-90.0</dd><dt><span>westernmost_longitude :</span></dt><dd>-180.0</dd><dt><span>easternmost_longitude :</span></dt><dd>180.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>latitude_step :</span></dt><dd>1.0</dd><dt><span>longitude_step :</span></dt><dd>1.0</dd><dt><span>sw_point_latitude :</span></dt><dd>-89.5</dd><dt><span>sw_point_longitude :</span></dt><dd>-179.5</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>1.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>1.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>deg</dd><dt><span>geospatial_lon_units :</span></dt><dd>deg</dd><dt><span>spatialResolution :</span></dt><dd>1.00 deg</dd><dt><span>data_bins :</span></dt><dd>32001</dd><dt><span>number_of_lines :</span></dt><dd>180</dd><dt><span>number_of_columns :</span></dt><dd>360</dd><dt><span>measure :</span></dt><dd>Mean</dd><dt><span>data_minimum :</span></dt><dd>3.82171</dd><dt><span>data_maximum :</span></dt><dd>38.76831</dd><dt><span>suggested_image_scaling_minimum :</span></dt><dd>0.0</dd><dt><span>suggested_image_scaling_maximum :</span></dt><dd>70.0</dd><dt><span>suggested_image_scaling_type :</span></dt><dd>ATAN</dd><dt><span>suggested_image_scaling_applied :</span></dt><dd>No</dd><dt><span>_lastModified :</span></dt><dd>2017-10-21T21:22:21.000Z</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>institution :</span></dt><dd>NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.gsfc.sci.oceandata</dd><dt><span>id :</span></dt><dd>Q20112132011243.L3b_MO_SCI_V5.0.main/L3/Q20112132011243.L3b_MO_SCI_V5.0.main</dd><dt><span>license :</span></dt><dd>http://science.nasa.gov/earth-science/earth-science-data/data-information-policy/</dd><dt><span>creator_name :</span></dt><dd>NASA/GSFC/OBPG</dd><dt><span>publisher_name :</span></dt><dd>NASA/GSFC/OBPG</dd><dt><span>creator_email :</span></dt><dd>data@oceancolor.gsfc.nasa.gov</dd><dt><span>publisher_email :</span></dt><dd>data@oceancolor.gsfc.nasa.gov</dd><dt><span>creator_url :</span></dt><dd>http://oceandata.sci.gsfc.nasa.gov</dd><dt><span>publisher_url :</span></dt><dd>http://oceandata.sci.gsfc.nasa.gov</dd><dt><span>processing_level :</span></dt><dd>L3 Mapped</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>identifier_product_doi_authority :</span></dt><dd>http://dx.doi.org</dd><dt><span>identifier_product_doi :</span></dt><dd>http://dx.doi.org</dd><dt><span>keywords :</span></dt><dd>SURFACE SALINITY, SALINITY, AQUARIUS, Jet Propulsion Laboratory, NASA, http://aquarius.nasa.gov/, AQUARIUS SAC-D, Aquarius Scatterometer, Aquarius Radiometer</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>software_name :</span></dt><dd>smigen</dd><dt><span>software_version :</span></dt><dd>5.20</dd><dt><span>source :</span></dt><dd>Q20112132011243.L3b_MO_SCI_V5.0.main</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (SSS_0: 180, SSS_1: 360, SSS_ran_unc_0: 180, SSS_ran_unc_1: 360, SSS_sys_unc_0: 180, SSS_sys_unc_1: 360, palette_0: 3, palette_1: 256, time: 47)\n",
       "Dimensions without coordinates: SSS_0, SSS_1, SSS_ran_unc_0, SSS_ran_unc_1, SSS_sys_unc_0, SSS_sys_unc_1, palette_0, palette_1, time\n",
       "Data variables:\n",
       "    SSS          (time, SSS_0, SSS_1) float32 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>\n",
       "    SSS_ran_unc  (time, SSS_ran_unc_0, SSS_ran_unc_1) float32 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>\n",
       "    SSS_sys_unc  (time, SSS_sys_unc_0, SSS_sys_unc_1) float32 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>\n",
       "    palette      (time, palette_0, palette_1) int8 dask.array<chunksize=(1, 3, 256), meta=np.ndarray>\n",
       "Attributes: (12/68)\n",
       "    product_name:                      Q20112132011243.L3m_MO_SCI_V5.0_SSS_1deg\n",
       "    instrument:                        Aquarius\n",
       "    title:                             Aquarius Level-3 Standard Mapped Image\n",
       "    project:                           Ocean Biology Processing Group (NASA/G...\n",
       "    platform:                          SAC-D Aquarius\n",
       "    temporal_range:                    MO\n",
       "    ...                                ...\n",
       "    identifier_product_doi:            http://dx.doi.org\n",
       "    keywords:                          SURFACE SALINITY, SALINITY, AQUARIUS, ...\n",
       "    keywords_vocabulary:               NASA Global Change Master Directory (G...\n",
       "    software_name:                     smigen\n",
       "    software_version:                  5.20\n",
       "    source:                            Q20112132011243.L3b_MO_SCI_V5.0.main"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#open and combine Aquarius files into 1 .nc file\n",
    "xr.set_options(file_cache_maxsize=10)\n",
    "#to use xa.open_mfdataset which combines netCDF files\n",
    "ds_Aq = xr.open_mfdataset(file_list_Aq, combine='nested', concat_dim='time')\n",
    "ds_Aq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "sharp-destiny",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w7cx9D0x+KPfctQeyhx1Pdf6v30BBBt9m5Q0dbmfG4VY0O+y4aOhvKxmHe3FYcd7Pr1OZinuhmUvSwcBngemSHq1tBlYCCzNPbEH3n3oM0ja++2xfmXWM7YW259ueP2fOnA6WLoQw0dXWgC/zams57K/Zngl8y/Za6Wum7WfYPrgd9+y3mslOwF6SXgtMA9aS9Avb7+xyuUIIAQNDvVEz2cr2zcDJknYYvd/2VVXfs6+CSRpRDwaQtDPwyQgkIYRe0gvNXMDHSZr7v91gn4Fdq75hXwWTEELoaR1owipVDHtB+u8unbpn3wYT2+cD53e5GCGE8KTa4li9RNLLgE2p+3tv+2dV36dvg0kIIfSiXqiZ1KRzSrYAruGpsaIGIpj0hdxRnM3/og2vyP5xPVbwC+yc/ZMH8oedzszJKjxn8tLcc2cN3JW5b5ryhyTnDbXt1DDPkK2VYcWtDMHu5XtWuThWReYDz2tXpuB6EUxCCKEiRgyN9NQHnetJ0tDf3e4bZQYTSf9Lzgw723u1pUQhhNDHeqzPZDZwo6TLgBW1je34+51XMzm86puFEMK45p5r5jq0UzfKDCa2L+hUIUIIYTzotT6TTv4dz2vmuo7GzVwiWW9lm7aVKoQQ+lRVwUTSxiSjrtYnGdaz0PaRkg4FPgjcnx76WdtnjDr3Itsvl7SU1f+O1/5+r1VJIevkNXPFOiHNyvld8nDBL1rOmAutyj53KGcfwNKcdemHR/LPnTKQnX1yncnLc8+dM/nRzH2zBvJHgk3PSW7YriSQ/aiVdelbWT++33RiBKARw9V1wA8Bn7B9laSZwJWSzkn3HWE7syvC9svTfxsmxG2HvGauJ3PgS9oEeLbtcyVNzzsvhBAmsqo64G3fTToKy/ZSSTcBG5Y5V9K6Bdd+sPUSrq4wKEj6IEmOl3VJJr9sBPwIeFXVhQkhhH7msXXAz5Z0Rd37hbYbpoeXtCmwPXApScLbAyW9m2TF2U/YHr0WxJUk7RyNCmNg87KFLKtMDePfgB1JHgLbf5P0zKoLEkII40HeBOFRltieX3SQpBnAr4H/sP2opKOAL5MEhS+TJHN83+pl8GZjKnQFygSTFbZXSsk3SNJkclr2i6pXqRHbD5cqYQgh9I1qEz1KGiQJJMfb/g2A7Xvr9h8NnF5wjXWAZ5Ms20F6jQsrK2SqTDC5QFJtxa5XAx8B/jfn+LvSV953dBLQnWUQQwihjcZQM8ml5BP8T4CbbH+nbvvctD8FYB+SWe5Z1/gA8DGS7olrgJcAf6ZLKegPAt4PXAd8CDiD/PXXb7K9fd4FJV1duoQhhNAn7OLRkWOwE/Au4DpJ16TbPgu8XdJ2JC1Et5L8Xc7yMeBFwCW2d5G0FXBYVQWsVyaYTAeOsX00PLkg/XQga0zoS0tcs8wx/Svvl6ngF015IzlzztVQwS9wztDg5Z6ee+rtA+tk7ps19fHcczeYkr1G/JxJj+WeO0NDmft6MQlk0X27sb580Xl5Q4fbu378+B3aXeForoto3MJzRoNtWZ6w/YQkJE21fbOk51RSwFHKBJPzgN2AZen76cDZwMsaHWz7yRSzaeBZj9Xz6N9ef0wIIYwXprpmroosljQL+B/gHEkPkXRDVK5MMJlmuxZIsL1M0hpFJ0n6d+AQ4F6eSspuIGbOhxDGqd5YabHG9j7pl4dK+gOwNnBmO+5VJpg8JmmH2gL0kl4I5LdtJD4GPMf2A60UMIQQ+kn7Vw4pT9IWwGLbK0iazDYF1gBWVn2vMsHkP4CTJdWqRnOBt5Y47w7gkSbL1ZCkacCFwFSSsp9i+5Aq7xFCCM2yYaS31jP5NTBf0rNIRoadBpwAvLbqGxUGE9uXpyMAnkMS2W62nblEnqSPp1/eApwv6Xesnkf/Ow1PLGcFsGva1DYIXCTp97YvaeGaIYRQmV5q5iKZ0zckaR/gv2x/r12jaUvl2EqDR+ZY5lFqicVuT19T0hfkpjEsVQ7z1ECAwfTVQ5XKEMJE10vNXMAqSW8H9gfekG4bbMeNKk/YaPswAEn72j65fp+kfVu9fjpC7ErgWcAPbF86av8CklxizJvXnXmReZmBVZQ1uElFQ4Nz968syDg8kj3e4m9T5uSeu/H07KHBmwwuyT33GQPZraSDnpS9r+Bb3Mqw1Had28qw4aLMwO1SNHQ4z3jLSFyvx0ZzvRc4APiq7X9K2gz4RTtu1M7GvYNLbhsT28O2tyOZ0bmjpK1H7V9oe77t+XPm5P+hCyGEKhlhl3t1pDz2jbY/avvE9P0/bX+9HffKWxxrh7wTa6O7Gpy3J0nnzoaSvlu3ay2S/PyVsP2wpPOBPSjfBBdCCG01futc+fKaub6d/jsNmA9cS9IBvw1JBuGXZ5x3F0la5L1ImqNqlgL/r5XCSpoDrEoDyXSSyZTfaOWaIYRQGYOrS6fSV/IWx9oFQNIvgQW2r0vfbw18Mue8ayVdD+xu+7iKyzsXOC7tNxkATrKdmzEzhBA6qcf6TACQtKbt/PxFLSrTAb9VLZAA2L4+TTKWyfawpGdImmK7sskxtheRLBATQgg9qZdGc0l6GUli3hnAPEnbAh+y/ZGq71UmmNwk6cckIwAMvBO4qcR5twEXSzoNeDIitjjPJIQQelYP5uY6AngNyWTFWsvRK9txozLB5L3Ah0nSo0AyA/2oEufV1jUZ4Km5J6HoU0uTv4cquO6kJ7IvPFAwrHhoKHtY+pKpM3LP/efaszP33TU9OxsxwPqTl2bum6HhzH1FQ1YHupRxuJXhv6FPGOitYILtO2qLG6ay//O0oMwM+CdIotsRY7lw3XyTmcnbp5JFjne3feBTmfs2/dHhHSxJCKHTeqmZC7gjbeqypCnARynXsjRmeUODT7K9n6TraPB52nZu9t+0o/7nwLrp+yXAu23f0FqRQwihV6nXRnMdABwJbAgsJlk+5N/acaO8mkmtWev1TV57IfBx238AkLQzcDQZ66CEEMK40EM1E9tLgHd04l6ZDcS2706H4P7E9m2jXyWuvWYtkKTXOx9Ys/UihxBCjzI9NQNe0nHp4li19+tIOqYd98rtbbQ9DCyXtHYT175F0hckbZq+Pg/8s6lShhBCv3DJV2dsY/vhJ4tmP0SbpleUGc31BMmC9uew+hDfjxac9z6Shet/QzJG6UKSkWGhWTlJIkcmweTl2fsnFSyUPHl59r5JK7Kvu3z6lMx9AIuXZX8OuXft/M8oDw9Oy9w3ZyC7wMs9xNoDU3OvHcrJW0O+Wwkme19P9ZkMSFonDSJIWpc2JPil5EV/l77GJC18UcCZcG49IDN5AACb/ndzo73yAknxuU2f2pMikFQjL5CEHBXFWEkbAz8D1k+vutD2kWlA+BXJqom3AvvVgkUD3wb+JOmU9P2+wFerKeHqygwNbioliqQtSdKubFp/H9u7NnO9EELoedXOMxkCPmH7qnSKxZVpC9F7gPNsf13SQcBBwGcaFsf+maQrgV1Iqkxvsn1jo2NHJebN8qjtzzfaURhMMoYGP0KSzPErOWu8nwz8iGQqf1smyYQQQq+pap6J7buBu9Ovl0q6iWSI797AzulhxwHnkxFM0nNvkHQ/SdJeJM2zfXuDQ/cGvlhQrIOA5oIJ8HuSYHBC+v5tJBHuEeCnPLV612hDtsvMlA8hhPGjDZ3rkjYl6Ti/FFgvDTS1UbfPzDlvL5Kmrg2A+4BNSCYtPr/B4UcUtURJykxbUSaY7GR7p7r310m62PZOkt6Zc97/SvoIcCqrrwH/YIl7hhBCfyrfzDVb0hV17xfaXjj6IEkzgF8D/2H70VGpUYp8GXgJcK7t7SXtAry9YbHt/yq6WN4xZYLJDEkvri2PK2lHkgyUkL/Y1f7pv/W5RQxsXuKeIYTQl4ry5NVZYnt+7rWkQZJAcrzt36Sb75U0N62VzCWpcWRZZfsBSQOSBmz/QVLDNaCURKl9Sf5OnwLsStL0dTPwIzt/+F6ZYPIB4Jg0OkKyyNX7Ja0JfC3rJNub5V1U0qttn1Pi/hPK4LrZY3iH78xeiz0vkSPAYM5KBoMFWdNGcoYVr1ozey12gPsfzU4Eeccz1s0997nTsp/3iZylGWYWDKfJS7jYzkSPeVq6b86Pvmj4brdGbLWyfnyerq8tb0FF6VTSP+4/AW4alW39NJIP619P//1tzmUeTv92XwgcL+k+sisBPwCeCUwhCSJTgf8lWTn3OTyVFaWhMqO5LgdekE5cVP0EGOCkovNzfAOIYBJCGF+qi2c7Ae8i6Vq4Jt32WZIgcpKk9wO3k9QmViNpqu0VJEHhCZJVbt8BrA18KeN+r7D9grQ2dA8w1/ZKSScAVxcVtvTkFduPlD22pJ6a2RNCCJWobjTXRWT/nXxVwel/BnYgaZ56V7qtaJrHUHrfVZIury1saHtIylnzIdWWmZAl9VA6tBBCqEhv/GWbIml/4GWS3jR6Z13/S717JM2wvcz2HrWNktYHClfMzUtBv6/tkyVtZrsncmplzQjtbqlCCCHVO4tjHUDSrDWLp0/fMEmaq9U32ntmXGspJbLH59VMDiaZePhrkupS1W5t4pyGM0KzZnSGEEKnjWE0V9vYvkjSn4DFtptOnyLpUNuHUpeXMUveUI4HJP0B2EzSaaNfJQqxb/oHH0mfl/QbSU8GJdtPq3oVsX237avSr5eSTL7ZcKzXCSGEtumRrMHpUN5m16Oq2avsgXk1k9eR1Eh+TjKDcqy+kDaTvZxkQfvDSdaOf3ET13qaUTNCJwTljPKcUjA8YtoD2b+9g8vzf7OHpmVX21fNyK/SL3s4O+niLcuekXvu/TPWyty3YnBJdpkKhsNOzhsa3Mahst0YdtzOob+9mFE4b8hxp4YN90LNpM7Zkt4M/MZuKtFL6Ta7zGCS9uRfIulltu9vYi33Wu//64CjbP9W0qFlC5Zn9IzQUfsWAAsA5s2bV8XtOupv+zZMewPAFt/5Tua+EEKP6I0+k5qPkyxKOCTpCZLgYNvZn9RW98KyNyrzsWU9SVcD1wM3SroyXd+9yJ2S/hvYDzhD0tSS98uVMSP0SbYX2p5ve/6cOXNavV0IIZRXtomrQ7UX2zNtD9ieYnut9H1uIJG0paTzJF1ve0TSNunihrnK/HGvreW+ie15wCfSbUX2A84C9kgnOq7L6qlVxixnRmgIIfSGHgomkl7Z6FVw2tEkA7BWAdheRJLgN1eZeSZPW8s9TaWSy/bydOr+y4G/kYzE+luJ++VpOCPU9hktXjeEECqR17fZBfUf4KcBOwJXkuTdyrKG7ctGJZTMy8MIlAsmt0j6AklHPMA7KbGWu6RDgPkkOV2OBQaBX5AEhKYUzAgNIYTu66EOeNurzTFJ5+p9s+C0JZK2IH0SSW8hXVclT5lgUr+WO5Rfy30fktFWtaG8d9WGCocQwngk99xortEWA0V93v9G0pWxlaQ7SSoPecuNAOUSPTa7lvtK25ZUi26FTWOhfda8p7CWmmlgKLtrbeXS/KzBgw9m/4rd/nDmOjsA/GNW5po/PH/qnZn71p2Un/lhes6+vIzC0L2swnm6Vaa871XRkORuDR3uiB4azSXpezxVVxoAtgOuzTvH9i3Abunf7IF0Tl+hdubmOikdzTVL0gdJajhHt/F+494/Pv7xps/dec+imm0IoRK9VTOpX3xrCDjR9sVlTrSfWudB0nttH5t3fNuCie3DJb0aeJSk3+SLsX5JCGG866VmrvpleNMldzdu8lKHkfR9Z2p31uC/kkyQOVfSGpJmlq0yhRBC33FvjeaSdD5JSpTJwDXA/ZIusP20Zg5Ji7IuA6xXdK/CYCJpS5I0KOvZ3lrSNsBetr9ScN4HSWairwtsQZJD60cU5+EPIYT+1UM1E2DtdN34DwDH2j4kJ2isR5L66qFR2wX8qehGZXrumprAQjIiYCeSZi5s/41kScgQQhi/emjSIjA5XSd+P+D0gmNPB2bYvm3U61bg/MIblShMUxNYgBXpko8ASJpMr8XsEEKoWC/1mZAs0XsWcJHtyyVtTsbkcdvvz7qI7X8tulGZYNLUBBbgAkmfBaanHfEfIVmcPnTBlAeeyNznKfnDe5P5phnXXZpfuZ36YPYwyaUP5I8Wv3W97KzC983InrK0weT7c687I+d/e9F3IlSjGxmH8zIKj1e2TyZZl6r2/hbgzY2OlXSV7dy1q/KOKRNMmprAAnwG+ABwHfAh4AzgxyXOCyGE/tVbNZOxeG5OfwokfSdrZ+0sM2lxzBNYJA0Ai2xvTcwtCSFMFD02mmuMtipxzHDWjrw14BvOkKv1geRl7U3TFl8raZ7t20sUMIQQxoeKaiaSjiFZKfG+9IM56ZpQHwRqbbmVJbq1fVsr5+fVTGqN0s8BXgTUlup9A0l+riJzgRskXUbd+sG2Sy8DGUII/URU2gH/U+D7wM9GbT/C9uGlyyS9Dng+SdZgAGx/qYoC1stbafGwtCBnAzvUmrfSyHhy1nl1DquigCGE0FcqCia2L0yXJ2+apB8BawC7kPRZvwW4rPXSPV2ZDvh5QH3mvJXApkUn2b6gyTKFNpi0LG80V/OJEKZOzR8hMzQ9+9orluTf945lszL33TUrO0nkXavWYec1spfOGRnIa9SO8Vzd1q516zuSXLIzWYMPlPRukrxbn0iT8WZ5me1tJC2yfZikb/NUBvhKlfmp/Ry4TNKh6Roll/L0atfTSFoq6dFRrzsknZqOdQ6hLfICSQhtN1LyBbMlXVH3WlDi6keRZBTZjmSKxrcLjn88/Xe5pA1IJp9vNpbHKavMaK6vSvo98Ip003ttX13i2t8B7gJOIGlKfBuwPvAX4Bhg52YKHEIIvWwMNZMltueP5dq2733yPtLRlJjVLmkW8C2StaVMm6ZolMnNNQ9YApxav63EKK09bL+47v1CSZfY/lI6mTGEEMafNjZzSZpruzZpfB/g+tyi2F9Ov/y1pNOBabYfaUfZyjSW/46nvj3TSapIfyEZHZBnRNJ+wCnp+7fU7Wvq291oqFwIIfSMCvNuSTqRpAVntqTFwCHAzpK2S+9yK8mE8LxrrAF8Aphn+4OS5kl6he2iGs2YlWnmesGowu1AwQOk3gEcCfyQ5MEvAd4paTpw4NiLCmQPlQshhJ5QVQe87bc32PyTMV7mWOBK4KXp+8Uko3E7H0xGs32VpBeVOO4WkjkpjVw01vum12x5qFwIIbRVb6VT2cL2WyW9HcD24xqVtbcqZfpM6mfCDwA78NTsy7zzmloHJTRnzzkH5B8wY43MXRrKTuQIMGkke0jl1ILxgCOD2Qc8Pif/5AeWZSeCvHdVZoogHnP+r/Wwc/63F/w3K1ojvl16ce35ftOuIcej9Vg6lZVpa1AtUe8WwIp23KjMd3dm3WsqSR/K3iXOa3YdlJZIWlAbanf//YUxL4QQqlN2LZPO1V4OAc4ENpZ0PHAe8Ol23KhMM9eNaRrjJ0nal+JZ8M2ug9IS2wtJshwzf/783qpwhhDGNVFYue0o2+dIugp4CUnRPmZ7STvuVaZmcnDJbaM1uw5KCCH0rx6qmUjaCXjC9u+AWcBnJW3SjnvlZQ3eE3gtsKGk79btWotyNYxm10HJ1GionO2xjm4IIYS26bGVFo8CtpW0LfApkgnjPwP+peob5TVz3UWS+2UvkqFlNUuB/1d04WbWQSlxzUZD5UIIoXf0Vgf8kG1L2hv4ru2fSNq/HTfKyxp8LXCtpONtl+7raGUdlBBC6GudSfQ4FkslHUzSKvRKSZPIW4e7BXnNXCfZ3g+4Wnr6t8f2NhmntroOSmiHVc2PfcjrUJz8aH6325ScrMGTH5uSe+7yJ7J/5x9YlT1s+ImCocEjyQDDhooyy3ZqeGk/6MXhyt0aur2a3gombwX+FXi/7XvS9FjfaseN8v7XfSz99/VjuWAF66CEJvz+/h/l7t9zo492qCQhTGy9VDOxfQ9J0t3a+9tpUwaRzI8WdcnEPmL7tvoX8JES125qHZQQQuhrPTSaq5PK1FNf3WDbniXOa7QOynFjKVwIIfQbudxrvMnrM/kwSQ1kc0mL6nbNBC4uunC6DsqZwMvTTWXXQQkhhP5kemY0l6TtSRbSusH2Te2+X16fyQnA74GvAQfVbV9q+8GskyRdZXsHANtXsvqw4qcdE0II44XojVqHpC+SjOC6EvimpK/ZPrqd98wbGvwI8Ajw9rRwzwSmATMkzchZHOu5o2oyownIztIXQgj9rAeCCckoru1sL5f0DJL8XN0JJjWS3kAyGmAD4D5gE+AmshfH2qrEfYfLFjBUY+iu7Ew2kzfZuODk7B+XVuYPOR5YmV3nHyj4LRhekf3ruWTljMx9j41Mzb3uqpykqVOL/hLkDB1u57DhvCGvvThEt1vyvhedGjasvKzUnfOE7eUAth+Q2j+mvUyix6+QJAk71/b2knYhra00ko72CiGEiad3RmptIak2x0+j3mN7r6pvWCaYrKpFNkkDtv8g6RtVFySEEMaDXugz4enLhBze7huWCSYPS5pBMnv9eEn30YFU8iGE0I96YXEs2xc02i5pY5J1pRrub0WZdrS9gcdJkjueCfyD7OV4QwhhYuuxSYuSZkv6sKQLgfOB9dpxn8Kaie3H6t7GpMMQQsjSIxMSJc0E9iHJy7UlcCqwue2N2nXPvEmLS2kcPwXY9lrtKlQIIfStioKJpGNIciPeZ3vrdNu6wK9IUlPdCuxn+6EGp98HXAZ8HrgoTUO/TzUlayxvnsnMrH1hglm+vOlTNZzdgDycnzQYDWSfu3I4u1K9dGRa7nVX+ZHMfSMFDd4DTMrd36xWshV3K1Nuvw1J7kR5K560+FPg+6yemPEg4DzbX5d0UPr+Mw3O/SxJ38hRwAmSflVZqTKU6YAP48A5I80nbN5z/TJ5PUMIABqpJprYvlDSpqM2702y2iwk3Q7n0yCY2D4COELS5iRTOf4H2EDSZ4BTbf+1kkLW6a+PFiGE0MvKdr4n8Wa2pCvqXgtK3GG9Wkb39N9n5hbHvsX2V22/gGR9qVkkabIq13c1E0l7AEcCk4Af2/56l4sUQghPGsPQ4CW257exKKuxfR1wcPqqXF8Fk3TJyR+QpMVfDFwu6TTbN3a3ZCGEkGrvaK57Jc21fbekuSQd7U/TYACV0vdtG0DVb81cOwJ/T6tuK4Ff8vSZniGE0DVtXs/kNGD/9Ov9gd9mHHcecCNJOqytbc+0vVbt36bvnqOvaibAhsAdde8XAy/uUlkCMLJudsJFgMHbl2TuG3rVvPxzp2cnWhjJWZl+VcEa8MM5Hx2Hi5L0KTs75UjBuQM5ZS6SN9qrW+vSR/LJBgxUlOhR0okkne2zJS0GDgG+Dpwk6f3A7cC+DYthv1HS2sCbgKMlTSMZUvzLvCVEWtFvwaTR/8bVfnJpJ9YCgHnz8v9YhXJ+f88PM/e9Zv6hnStICH2gqnQqtrMS6r6q5PmPAMdKOo4kJf33SJYR+U7uiU3qt48Pi4H6fOkbAXfVH2B7oe35tufPmTOno4ULIUxstXkmvbBsr6SXSfoecBWwE7CP7bYEEui/msnlwLMlbQbcSTIp51+7W6QQQkjZlTVztULSrcDDJP3KC0iT80qqrYJ7VdX37KtgYntI0oHAWSRDg4+xfUOXixVCCE/qhdxcJKlWDLwG2J3VuwgM7Fr1DfsqmADYPgM4o9vlCCGEhnogmNjeudP37Lc+kxBC6Gm90Gci6UWS1q97/25Jv5X03TRZZOX6rmYSJo4NZmUnZBwayf4c9PDwGrnXfcLZQ3TXKBqK45zPX4XDeLLPbWXYcOghBoZ7oGoC/w3sBiDplSRDiv8d2A5YCLyl6htGMAktOeuKQ9t27Z3P+2Tbrh1Cu/RIn8mkuvkkbwUW2v418GtJ17TjhtHMFUIIVaqN6Cp6tdckSbXKwquA/6vb15ZKRNRMQgihQj1SMzkRuEDSEpJl1/8IIOlZQHb7cQsimIQQQlU6vL57FttflXQeMBc4236yKjRA0ndSuQgmIYRQEQHqjQ54bF/SYFvli2LVRDAJIYQKqQdmwHdDBJMQ6jwykp0VGPKHDk8rWB9+MHf0b/5YmG4MHW5lXfoJq0eaubohgknoWee/6vCO3/OWxXM7fs8wnvRGbq5uiGASQggV6pHRXB0XwSSEEKoUNZMQQggtce+M5uq0CCYhhFCliRlLIpiEEEKVYmhwCKFrlntV7v7BnKHDU3vwv/EI+cOKB9qUFjDvvu2659NEMAkhbL7R3V257xN3bdSV+4aKGQri6Jiky+8uBYaBIdvzq7t6tSKYhBBCRYTb0cy1i+0lVV+0an0zhVXSvpJukDQiqWejcwhhghsZKfcaZ/ommADXA28CLux2QUIIoaFaM1eZV/krni3pSkkLqi5ulfqmmcv2TQBSLG8aQuhdY2jmmi3pirr3C20vHHXMTrbvkvRM4BxJN9vuyQ/UfRNMQgihL5QPJkuKOtRt35X+e5+kU4Ed6dHWmZ4KJpLOBdZvsOtztn9b8hoLgAUA8+bNq7B0IfSfkXbOoMvJKlyUUbgbQ3iLhitXo7pEj5LWBAZsL02/3h34UiUXb4OeCia2d6vgGguBhQDz58+fmAO+Q995xgaL23Ldx+/erC3XDRkMVJdOZT3g1LRpfzJwgu0zq7p41XoqmIQQQr+ramiw7VuAbSu5WAf0zWguSftIWgy8FPidpLO6XaYQQngau9xrnOmbmontU4FTu12OEELIZGBk/AWKMvommIQQQu8bn7WOMiKYhBCakjdSbFLBuR1LutgNEUxCCOPN9Ln/bNu1Y6RYAwaGx1+qlDIimIQQQmWcO/9mPItgEkIIVYpmrhBCCC2J0VwhhBAqETWTEEIILYtgEkII5a3ycOa+QeUPDu5M0sUusGE4+/synkUwCSGEKkXNJIQQQssimIQQQmiNYzRXCCGEFhkckxZDCCG0LNKphBBCaIkNIxFMQgihEkVZgfOGBg+3sLZ8T4gO+BBCKG+tDW7P3Ddyz5YdLElvcdRMQgghtCYWxwohhNCqCZzosQ8aIBOSviXpZkmLJJ0qaVa3yxRCCPUMeHi41KsMSXtI+oukv0s6qL2lb03fBBPgHGBr29sAfwUO7nJ5QghhdU4XxyrzKiBpEvADYE/gecDbJT2vzU/QtL4JJrbPtj2Uvr0E2Kib5QkhhEY84lKvEnYE/m77FtsrgV8Ce7e18C3o1z6T9wG/arRD0gJgQfp2haTrO1aq7psNLOl2ITpkIj0rxPN2wiatXmApD5117shJs0sePk3SFXXvF9peWPd+Q+COuveLgRe3WsZ26algIulcYP0Guz5n+7fpMZ8DhoDjG10j/WEsTI+9wvb8NhW350yk551IzwrxvP3C9h4VXk6NblHh9SvVU8HE9m55+yXtD7weeJU9QcffhRAmisXAxnXvNwLu6lJZCvVNn4mkPYDPAHvZXt7t8oQQQptdDjxb0maSpgBvA07rcpky9VTNpMD3ganAOZIALrF9QME5Cwv2jzcT6Xkn0rNCPO+EY3tI0oHAWcAk4BjbN3S5WJkUrUUhhBBa1TfNXCGEEHpXBJMQQggtG5fBRNKhku6UdE36em26/dWSrpR0Xfrvrt0uaxWynjfdd3CaiuEvkl7TzXJWSdInJVnS7PT9oKTj0p/tTZLGVYaE0c+bbttG0p8l3ZA+97RulrEqjZ413T5P0jJJn+xW2UK2fuqAH6sjbB8+atsS4A2275K0NUnH1oadL1pbPO1509QLbwOeD2wAnCtpS9vlEgP1KEkbA68G6nOg7wtMtf0CSWsAN0o60fat3ShjlRo9r6TJwC+Ad9m+VtIzgFVdKmJlMn62NUcAv+9siUJZ47JmksX21bZr47RvIJmBOrWbZWqzvYFf2l5h+5/A30lSNPS7I4BPs/oELgNrpn9kpwMrgUe7ULZ2aPS8uwOLbF8LYPuBfv+QkGr0rEh6I3ALyf/b0IPGczA5MM0wfIykdRrsfzNwte0VnS5YmzR63kbpGPq6JiZpL+DO2h/ROqcAjwF3k3yqPdz2g50uX9VynndLwJLOknSVpE93oXiVynpWSWuSzDE7rCsFC6X0bTNXXuoV4CjgyySfbr4MfJskn1ft3OcD3yD5dNcXmnzevkrHUFPwrJ+l8c9tR2CYpDlvHeCPks61fUvbClqRJp93MvBy4EXAcuA8SVfaPq9tBa1Ak896GEkz7rJ0jlnoQX0bTIpSr9RIOho4ve79RsCpwLtt/6NNxatck8/bV+kYarKeVdILgM2Aa9M/KhsBV0naEfhX4Ezbq4D7JF0MzCdpGulpTT7vYuAC20vSY88AdgB6Opg0+awvBt4i6ZvALGBE0hO2v9+ZUocyxmUzl6S5dW/3Aa5Pt88CfgccbPviLhStLbKelyT1wtskTZW0GfBs4LJOl68qtq+z/Uzbm9relOQP6g627yFp2tpViTWBlwA3d7G4LSt43rOAbSStkfYT/QtwYxeL25K8Z7X9irrt/wX8ZwSS3tO3NZMC35S0HUmTzq3Ah9LtBwLPAr4g6Qvptt1t39fxElar4fPavkHSSSR/ZIaAfxsnnbSN/AA4liSQCjjW9qLuFql9bD8k6Tsk+ZsMnGH7d10uVpjAIp1KCCGElo3LZq4QQgidFcEkhBBCyyKYhBBCaFkEkxBCCC2LYBJCCKFlEUxCaZKWteGae0k6KP36jWlyyrFe43xJ8ysoy1Zp1uWrJW3R5DWefJ4G+1r+/kl6j6Tvp18fGhl0Q6+IYBK6yvZptr+evn0jMOZgUqE3Ar+1vX0z2REkTR71PCFMGBFMwpils8y/Jen6dB2Nt6bbd05rCadIulnS8UpzY0h6bbrtIknflXR6uv09kr4v6WXAXsC30trBFvU1DkmzJd2afj1d0i/TxJa/IskSXCvb7krW+LhK0smSZjQo/3aSLknPP1XSOkrWgPkP4AOS/tDgnPdL+mtapqPragc/lfSd9JxvjKo5bJaW5XJJX875fr47Lcu1kn6ebpsj6dfpuZdL2mnsP6kQOieCSWjGm4DtgG2B3UgCQC2ly/Ykf5SfB2wO7KRk0ab/Bva0/XJgzugL2v4TSfqXT9nerqBm8GFgue1tgK8CL4Qk4ACfB3azvQNwBfDxBuf/DPhMev51wCG2zwB+RJJQcJf6gyVtAHyBJEXLq4GtRl1vy/Senxi1/UjgKNsvAu5p9CBKko5+DtjV9rbAx+rOPSI9983Aj3O+HyF03XhNpxLa6+XAiWlqlnslXUCSvfZR4DLbiwEkXQNsCiwDbknXVAE4EVjQwv1fCXwXwPYiSbW0KS8hCWIXpxWiKcCf60+UtDYwy/YF6abjgJML7rcjSVLFB9NrnEwSQGpOzkhTsxNJIAD4OUmm6tF2BU6pJWysS5u/G/A8PZUldy1JMwvKGULXRDAJzcjLA16/Pswwye9Ys3nDh3iq9jx6SdpGeYAEnGP77U3eL0tR+R/L2VeUr0gZxwwAL7X9+GoHRwr20KOimSs040LgrZImSZpDUlPIy0Z8M7C5pE3T92/NOG4pUP/p+1bSJizgLaPu/w4AJcsvb5Nuv4SkWe1Z6b41JNXXILD9CPCQpFekm94FXEC+y4B/SftWJvNUbaPIxSTLJlMrbwPnAfspWXYXSeum288mSUxKun27kvcMoSsimIRmnAosAq4F/g/4dJoWvaH00/VHgDMlXQTcCzzS4NBfAp+qG5p7OPBhSX8CZtcddxQwI23e+jRpILN9P/Ae4MR03yU8vX8DYH+Sfp5FJH0/X8p7WNt3Av8JXAqcS5KFuVH5R/sY8G+SLgfWzrj2DST9PhdIuhb4Trrro8D8tGP+RuCAEvcLoWsia3DoCEkz0pXyRJIu/m+2j+h2ucqqK/9kkmB6jO1Tu12uEHpF1ExCp3ww7ZC/geRT+n93tzhjdmha/uuBfwL/09XShNBjomYSQgihZVEzCSGE0LIIJqHtJG0q6fG0mWj0vgmTXyqdef/auvdvlfT3WjaAEPpZBJPQKf+wvV27Li5pUruuXaHtgCeDie1fAR/oWmlCqFAEk9Bxkj4n6S+SzgWeU7d9C0lnSrpS0h8lbVW3/ZI0R9WXlGbfVZIL7A+STgCuS+e9fCs9bpGkD9Vd+1N12w9Lt60p6XdpTqzrleYYyyjzCyVdkJbtrFr6GEkfTK97bZpLa410+77pNa+VdKGkKSRDkN+qJPdY5r1C6EcxAz50lKQXkkzk257k9+8q4Mp090LgANt/k/Ri4Ick6UaOBI60faKk0fMtdgS2tv1PSQuAR2y/SNJUkrQqZwPPTl87ksw4P03SK0lyhN1l+3Vp2RrOBZE0CHwP2Nv2/Wkg+CrwPuA3to9Oj/sK8P702C8Cr7F9p6RZtldK+iIw3/aBje4TQj+LYBI67RXAqbaXA0g6Lf13BvAy4OS6lCFT039fSpIeHuAEksmMNZfV5fzaHdhGUm22/NokQWT39HV1un1Guv2PwOGSvgGcbvuPGWV+DrA1cE5atknA3em+rdMgMiu97lnp9ouBn0o6CfhN7nckhHEggknohqxcVA830a9SnxdLwL/bPqv+AEmvAb5m+2lzW9Ka0muBr0k623aj2fACbrD90gb7fgq80fa1kt4D7Axg+4C0dvU64JpIhxLGu+gzCZ12IbCPkjVJZgJvALD9KPBPSfvCk2umbJuecwlP5cN62+gL1jmLJP3KYHqNLSWtmW5/X1r7QdKGkp6pJLX8ctu/IKnt7JBx3b8AcyS9ND1/UEnqeEhyid2d3vPJ/FuStrB9qe0vAkuAjXl67rEQxo2omYSOsn2VkgWtrgFuI2lqqnkHcJSkzwODJLm6riVZH+UXkj4B/I7svFg/Jkl5f1WatuV+klrD2ZKeC/w5baZaBrwTeBZJjq4RYBXJOimNyrwybTr7btqvMhn4L5LZ/F8gydl1G8naKLVg8S1Jzyap1ZyXPsftwEHpEOmvpaO5QhgXYgZ8aDsl2YJPt711k+evATxu25LeBrzd9t5VlrFbJO0MfNL267tclBBaEs1coROGgbUbTVos6YUk/Q6LSLIPj17RsC+lo8J+CDzU7bKE0KqomYRQR9KpwGajNn9mdKd+CGF1EUxCCCG0LJq5QgghtCyCSQghhJZFMAkhhNCyCCYhhBBaFsEkhBBCy/4/aHdt9MFHytoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SMAP\n",
    "lat_bnds, lon_bnds = [6, -2], [-52, -43] #switched lat directions from GRACE, and longitude has positives and negatives\n",
    "ds_SMAP_subset = ds_SMAP.sel(latitude=slice(*lat_bnds), longitude=slice(*lon_bnds))\n",
    "#ds_SMAP_subset\n",
    "\n",
    "#plot SMAP subset\n",
    "ds_SMAP_subset.smap_sss[2,:,:].plot() #at time '2', indicating June 2015\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "flying-factory",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Aquarius\n",
    "lat_bnds, lon_bnds = [84, 92], [128, 137] #See how commented out plot is distorted, only positive numbers\n",
    "ds_Aq_subset = ds_Aq.sel(SSS_0=slice(*lat_bnds), SSS_1=slice(*lon_bnds))\n",
    "#ds_Aq_subset\n",
    "\n",
    "#plot Aquarius subset\n",
    "#this map is inverted compared to SMAP, but still capturing the same area\n",
    "ds_Aq_subset.SSS[10,:,:].plot() #at time '10' indicating June 2012\n",
    "#ds_Aq.SSS[10,:,:].plot() #whole world map view to see the inversion of the data\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "documented-pennsylvania",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#MODIS SST\n",
    "lat_bnds, lon_bnds = [6, -2], [-52, -43] #like SMAP\n",
    "ds_MODIS_subset = ds_MODIS.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))\n",
    "#ds_MODIS_subset\n",
    "\n",
    "#plot MODIS subset\n",
    "ds_MODIS_subset.sst4[2,:,:].plot() #at time '2', indicating Oct 2011\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "amended-wiring",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the river distances and associated heights on the map at time t=9069 (March 16, 2018)\n",
    "#Change the extent to capture the data of the netCDF file\n",
    "extent = [-85, -30, -20, 20]\n",
    "\n",
    "#Add basemap\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.coastlines()\n",
    "ax.set_extent(extent)\n",
    "ax.add_feature(cartopy.feature.RIVERS)\n",
    "\n",
    "plt.scatter(ds_MEaSUREs.lon, ds_MEaSUREs.lat, lw=1, c=ds_MEaSUREs.height[:,9069])\n",
    "plt.colorbar(label='Interpolated River Heights (m)')\n",
    "plt.clim(-10,100)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "placed-issue",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#GRACE-FO (different bounds than others because GRACE is over land)\n",
    "lat_bnds, lon_bnds = [-18, 10], [275, 330] #degrees east for longitude\n",
    "ds_GRACE_subset = ds_GRACE.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))\n",
    "ds_GRACE_subset\n",
    "\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.coastlines()\n",
    "ax.add_feature(cartopy.feature.RIVERS)\n",
    "ds_GRACE_subset.lwe_thickness[171,:,:].plot(cmap = 'bwr_r') # 171 for 2019-04\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "embedded-timing",
   "metadata": {},
   "source": [
    "## Time Series Comparison\n",
    "\n",
    "Plot each dataset for the time period 2011-2019.\n",
    "\n",
    "First, we need to average all pixels in the subset lat/lon per time for sea surface salinity across both satellites, and sea surface temperature to set up for the graphs. This could take a few minutes each. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "unique-jesus",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SMAP\n",
    "time_smap = np.arange('2015-04', '2020-01', dtype='datetime64[M]')\n",
    "sss_smap_mean = []\n",
    "for t in np.arange(len(ds_SMAP_subset.time)):\n",
    "    sss_smap_mean.append(np.nanmean(ds_SMAP_subset.smap_sss[t,:,:].values))\n",
    "    \n",
    "#sss_smap_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "operating-mobile",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Aquarius\n",
    "time_Aq = np.arange('2011-08', '2015-07', dtype='datetime64[M]')\n",
    "sss_Aq_mean =[]\n",
    "for t in np.arange(len(ds_Aq_subset.time)):\n",
    "    sss_Aq_mean.append(np.nanmean(ds_Aq_subset.SSS[t,:,:].values))\n",
    "\n",
    "#sss_Aq_mean    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "sufficient-arnold",
   "metadata": {},
   "outputs": [],
   "source": [
    "#MODIS\n",
    "time_MODIS = np.arange('2011-08', '2019-12', dtype='datetime64[M]')\n",
    "sst_MODIS_mean = []\n",
    "for t in np.arange(len(ds_MODIS_subset.time)):\n",
    "    sst_MODIS_mean.append(np.nanmean(ds_MODIS_subset.sst4[t,:,:].values))\n",
    "    \n",
    "#sst_MODIS_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "convinced-clinic",
   "metadata": {},
   "source": [
    "### A close-up view of salinity and LWE thickness in 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "early-recovery",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot SSS and LWE thickness\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=[10,6])\n",
    "#plot LWE thickness\n",
    "ax1.plot(ds_GRACE_subset.time[167:179], ds_GRACE_subset.lwe_thickness[167:179,34,69], color = 'darkorange')\n",
    "\n",
    "#plot SSS on secondary axis\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(time_smap[45:], sss_smap_mean[45:], 'g-') # 45:\n",
    "\n",
    "ax1.set_xlabel('Date')\n",
    "ax2.set_ylabel('Sea Surface Salinity (psu)', color='g')\n",
    "ax1.set_ylabel('Land Water Equivalent Thickness (cm)', color='darkorange')\n",
    "ax2.legend(['SMAP'], loc='upper left')\n",
    "ax1.legend(['GRACE-FO'], loc='lower left')\n",
    "plt.title('Measurements Near the Amazon Estuary for 2019')\n",
    "ax1.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "governmental-artist",
   "metadata": {},
   "source": [
    "### Combined timeseries plots of salinity and LWE thickness, followed by temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "loose-moscow",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2285f8fd730>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V3bBo2CLe3Psmm89sZu6guS5epeOob67n0/RPMbQauPHjG3lg0gO8eNmL551XcUfuDq5acRVjo8fy3R3ftb3vOjo65wYmj59dOol1xVB1Wv0cGAPB8b0eXlpayvr16zl06BBCCAwGA0IIJkyY0KO8jPl2804xv/rVr3jkkUe4+uqrSUtLY+nSpW37AgO71bXGy8uL1tZ2w9Y03rBhw9i9ezfffPMNTz31FAsWLODpp5/u9VrOJvRPbxfQbGhm0+lN/G7973ho9UM0GZpcvSSHc7z0OP/a9S9+NulnLJ2ztIOhMH/wfHw9fVl1fJULV+h41mWuo665jo9v+pjHL36c13a/xtTXp3Ko6JCrl+Y0vjr2FXPenkOToYm1GWtZvnt53yfp6OicfxgaQAjw8le5iX3wySefcNddd3H69GmysrLIzs5m0KBBTJw4kffffx+AQ4cOceDAgbZzoqOjSU9Pp7W1lc8//7xte2VlJXFxcQC88847Fi134MCB7Nu3D4A9e/aQmZkJQF5eHgEBAdxxxx08+uij7Nmzx6LxzhZ0T6KTaGxp5K29b/Hdqe9Yn7me6qZqPIUnBmnAQ3j0mYtxtvH4msfx9/Jn2dxlXfYF+QQxd9BcVh5fScplKS5YnTZ8efRLgn2CWTBkAVddcBXzBs/jrs/vYsrrU3j9qtfPyRxMc/6545889O1DTBowiZW3reTOz+/ksTWPsTB5IUlhSa5eno6OjjvRUq8MRC9/aKrq8/AVK1bw5JNPdth2ww03sHfvXurr6xk7dizjx49n6tSpbfufe+45Fi1aREJCAqNHj25ribd06VJuuukm4uLimDZtWpvB1xs33HADb731FuPHj2fKlCkMGzYMgIMHD/LYY4/h4eGBt7c3r776qjWvgvsjpTxnHgEBAdJdeXr905KlyIF/GygfWPmA/PTIp7K8vlz+evWvJUuRHx76sO3Y1NRU1y3UAaRmpkqWIp/d+GyPx/xj+z8kS5HHSo6pc87yaza0GmT0X6PlTR/d1GF7QXWBnPbGNBn2XJhsNjR32He2X7MJQ6tBPvrdo5KlyKtXXC1rGmuklFJmlWfJoGeD5KXvXipbW1ullOfONVuDfs3nB+f6NR85cqTLtqqqKtsHLNovZcUpKWvypczfKWWnz0dbmT17tty5c6dDxuqMXdcru38NgVrpBvZTTw893Owkvjv1HRfFX0TGQxn8e9G/uX7E9YT5hfGXS//CRfEXce9X954TItOtspXF3y8mMTSRh6c93ONxi4YpWYFzJeS8PWc7hbWFXDv82g7bo4OiWXzRYioaKtiWs801i9OYn6/6OS9sfYFfTPkFn938GYE+KqcnKSyJ5+c/z5qMNfxn339cvEodHR23odUAhibwNHoSQXkWddwO3Uh0ApUNlezM28m8QfO6JNj6ePrw0U0f4eflxw0f3UCNSRLgLOW9A++xJ38Pz17yLP7ePVeIJYUlMab/mHPGSPzy2Jd4eXhxxdAruuybP3g+nsKT1SdWu2Bl2pJVkcXre17nl1N+yd8X/h1Pj45yGD+b/DNmJ83mke8eIbcq10Wr1NHRcStMBqGXmZHY3HdeoiWkpaUxefJkh4yloxuJTmHTmU20ylYuGXRJt/vjQ+L54IYPOFpylPtX3o/URu5Ic+qa6/jNut8wOXYyt425rc/jFw1bxKYzm6hoqNB+cRrz5bEvmZ00mzC/sC77wvzCmJ44nW9OfuP8hWnMG3veQAjBY9Mf67bC0EN48MbVb9BkaOJnX//srP3b1tHRcSAtxkpjLz/w8AYPL92T6KboRqITSM1MxdfTl4sSLurxmHmD5/GHOX9gxaEVfJH3hfMW50Be3PoiudW5vLjgRYtkTxYNW0RLawvfnfzOCavTjmMlxzhacpRrLrimx2MWJi9kX8E+8qvznbgybWk2NPPm3jdZmLyQxNDEHo9Ljkjm2XnPsur4KtYWrXXiCnV0dNwSQ72qbPb0Natw1o1Ed0Q3Ep3A+qz1XJxwMX5efr0e99TMp7hy6JX869S/yK7MdtLqHENBTQHPbX6O64Zfx8wky8REL4y7kH7+/Vh14uwOOX957EsArr7g6h6PWZi8EOCcake48vhKCmoKeGDSA30e+6upv2Jq3FTeznpb+4Xp6Oi4Ny0N4OmnDERoNxL1SIPboRuJGlNaV8q+gn09hprN8RAe/HHuH2mRLWzN2eqE1TmO5zc/T6OhkefnP2/xOZ4enlwx9Aq+OfENBgsV992RL499yYSYCb3KvIyNHktscOw5FXJ+bfdrxIfEs3Dowj6P9fTw5NZRt5LXkKfnJuronO+Y5G9MeAWoDiyGRtetSadbdCNRY9Ky0gAsMhIBRkaNxFN4sr9gv4arcjybzmxizsA5DO031Krzrhp2FWX1ZRypOqLRyrSlsKaQrdlbew01g1L+X5i8kDWn1tDS2uKk1WlHRnkG35/6np9O+CleHpbJrZo8zJvObNJyaTo6Ou6MqbLZ3Ej0trzC+ZlnnmHUqFFtuojbt29nzpw5JCYmdsh5vvbaawkKCupw7ksvvYSfn1+H/sppaWmEhoYyYcIERowYwbJlXbV9z2d0I1FjUrNSCfQOZErsFIuO9/XyJTEgkf2FZ4+R2NLawqGiQ4yLHmf1uQuGLMDLw4utpWeX59TEquOrkEiuGd67kQhwxdArqGysZGv22Xmt5izfvRxP4clPJ/7U4nPGx4zH39OfTad1I1FH57zFYFa0YsLT+HMfRuLWrVtZtWoVe/bs4cCBA6xdu5aEhAQAwsLC2LJlCwAVFRXk53fN/16xYgVTpkzp0H0FYObMmezdu5ddu3bx3nvvsXv3bhsv7txDNxI1Zn3memYmzcTb09vic5IDk9lXsE+7RTmY46XHaTQ02mQkhvqFMitp1llrJH557EuSQpMsuvb5g+fj5eHFNyfO7pBzk6GJ/+z7D4uGLSIuJM7i87w8vBgVMkr3JOronM+Yy9+Y8PBURmMf7fny8/OJjIzE19cXgMjISGJjYwG49dZb+eCDDwD47LPPuP766zuce+rUKWpqavjTn/7EihUruh0/MDCQSZMmcerUKVuu7JxENxI1JL86n/SSdC4ZaFmo2cSQoCHkVudSWleq0cociyk0Pi7GeiMRYNHQRWTVZZFVkeXAVWlPbVMtazLWcPUFV/fYYN6cEN8QZiTOYPXJs1sv8YujX1BUW2RRwUpnxoaO5VDRIcrryzVYmY6OjrOY8/YcrvjoCua8Paft8a+d/wKUHJr5dtPj7X1vQ0sDJQ2VzHnv8g778PKH5t49iQsWLCA7O5thw4bx4IMPsmHDhrZ98+bNY+PGjRgMBj744ANuueWWDueuWLGC2267jZkzZ3Ls2DGKioq6jF9aWsq2bdsYNWqU3a/PuYJuJGpIalYqYHk+ookhgUMAzpqQ8/7C/Xh7eDM8crhN51+WfBkAazPOLnmUNRlraGhp6DMf0ZyFyQvZX7j/rC7eeG33aySFJrFgyAKrzx0TOgaJZEv2Fg1WpqOj4/a01Cvpm854+avCldaeixiDgoLYvXs3y5cvJyoqiltuuYW3334bAE9PT2bMmMGHH35IfX09AwcO7HDuBx98wK233oqHhwfXX389H3/8cdu+TZs2MWHCBBYsWMCTTz6pG4lmWJZxrmMTqZmphPmFMT5mvFXnDQkyGokF+602MF3B/sL9jIwaiY+nj03nj4gcQYRPBOsz11uV4+Zqvjz2JWF+YcxKmmXxOQuTF/LE2if49uS3DGGIhqvThhOlJ1ifuZ4/zf1Tl+4qljAieATeHt5sOr2prTWjjo7O2UfaPWlUV1cTHBzcZV+AdwBp96R1f2LxASKDY7vubzBGF1oawNjaszs8PT2ZM2cOc+bMYcyYMbzzzjtt+2699Vauu+46li5d2uGcAwcOcOLECS699FIAmpqaGDx4ML/4xS8AlZO4atXZLcWmFS73JAoh/IQQO4QQ+4UQh4UQy4zbI4QQa4QQJ4zP4a5eq7Wsz1rP7KTZVn+ZhvuEMyBoAPsK92mzMAezv2C/zaFmUJW/E8ImsD5z/VnTkaOioYJPj3zKNRdcY1W+6ej+o4kPiT9rQ87Ldy/Hy8OLn0z4iU3n+3r6MiVuip6XqNM9LQ1KCkXn3KStsrkbzeC2Hs495yUeO3aMEydOtP2+b98+kpLapcdmzpzJU089xW23dez4tWLFCpYuXUpWVhZZWVnk5eWRm5vL6dOn7bue8wCXG4lAI3CJlHIcMB64XAgxDXgSWCelHAqsM/5+1pBVkUVGeYbNnsBxMePOChmc4tpi8mvybSpaMWdi2EQKaws5Unx2SOG8uvNVqpuqeXjaw1ad1yaFk3H2SeEYWg28vf9trrngGgYED7B5nJmJM9mVt4s6B/Vq1TmHeHcc/HcilBx29Up0tKCtstm/6z5PXxAevVY419TUcPfddzNy5EjGjh3LkSNHOngNhRA8+uijREZGdjjvgw8+4Lrrruuw7brrrmsrdNHpGZeHm6VyHdUYf/U2PiRwDTDHuP0dIA14wsnLs5nUTJWPOHfgXJvOHxc9jnUZ62gyNNkcxnUGprxJu43E8IkArMtcx6j+7p0P0tDSwMvbX+ayIZdZnUoASgrn9T2vc6jqEPOZ7/gFasSR4iOU1JVYlYPZHTMTZ/L8lufZnrOduYNs+//QOQdpaYDy4+rn9ybBzOdg4kPKcNA5N2irbO7Gk2hBe75Jkybxww8/dNmelpbW7fE1Ncq0yMzM7LLvxRdfbPt5zpw5Pa/5PMflRiKAEMIT2A0kA/+UUm4XQkRLKfMBpJT5Qoj+PZx7P3A/gJeXV49/LM5mxdEVhHmHUXykmLT0NKvOrampwafOh+bWZt5d/S7JQcnaLNIBfJb9GQA1p2pIO5Nm8zhBLUHE+sXy0c6PGFs/1kGr04aVeSsprC3kssDLbPp782nxwUt4sblgM+PTxjt8fVrxdf7XAIhcQVp5mk1j1NTUwBkQCN7d8C7idN9V4Wc7NTU1bvO55CxsuWa/xgKmARlx9xFSc5jItP+jfPd/SR/0W5q8IzRZpyM519/n0NBQqqurO2wzGAxdtvWGT1MVPghq6ppBdI2k+EovvFpqqK2qam/Z50ZYe72daWhoOPv+RqSUbvMAwoBUYDRQ0WlfeV/nBwQESHegtbVVxqXEyZs/vtmm81NTU+WRoiOSpch39r3j4NU5ljs/u1PGpsTaPU5qaqr86Zc/laF/DpXNhmYHrEwbWgwtMvmVZDl5+WTZ2tpq8ziXvHOJTPpLkjS0Ghy4Om2598t7ZcTzEXZdd2pqqpRSynGvjpPz353voJW5N6ZrPp+w6Zpzt0r5AlKe+lrK1lYp9y+X8m9+Un79I4evTwvO9ff5yJEjXbZVVVVZN0jZcSmLD/W8v7ZQyvydUrY0Wrk652D19Xaiu9cQqJVuYH/19HArP76UsgIVVr4cKBRCDAAwPncVNXJTTpSdILc612p9RHOG9huKn5ef2+cl7i/cb3eo2cS8wfOobKxkb/5eh4ynBZ8f/ZyTZSd5YvoTFmkj9sRto2/jdN1prvngGqoaq2wbRErY8jQU7LJ5HdawPXc7U+Om2nXdJmYmzmRr9tazLi9TR0NqC9RzYIzyIo29D0bcCSe/AD1/1S2Q9hYWtjR0H2o24WV5e76zDbtfOxfhciNRCBElhAgz/uwPzAeOAl8BdxsPuxv40iULtIH1mesB7Mq38vLwYnT/0W5d4dxkaCK9ON1hRqIpf3Nd5jqHjOdopJQ8v+V5hkYM5brh1/V9Qi/cO+Fefp38a1afWM20N6Zxsuyk9YNUnIJtf4Qdf7ZrLZZQ3VjN4aLDXBh3oUPGm5k0k9rmWre+IdBxMrXGNmqBZkVRw2+F5lrI+No1a9Jpw8/Pj9LSUtuNnVaD0kHsrmjFxDlqJEopKS0txc+vFwPZTXGHnMQBwDvGvEQP4CMp5SohxFbgIyHEvcAZ4CZXLhJUxXJscGyvhSRHio/wly1/ITE0kaERQ+2ab3z0eD4/+jlSSod4bxxNenE6za3NdsnfmBMdFM3o/qNZn7meJ2e4XzF7alYqu/J28dqi12zSCDRHCMG1cddy9cVXc9PHNzH19al8dNNHzB9sRSHLGaMxnblaeVq8A+xaU2/sytuFRDItfppDxpuZOBOATWc2MSXOsr7mOuc4tQWAgICo9m3xs5Vn8dgHcIHLvwLOa+Lj48nJyaG4uLhtW0NDg+WGj6EJakvAX4B3Zc/HVZeDVz34u19XJquutxN+fn7Ex8c7eEXa43IjUUp5AJjQzfZSYJ7zV9Q9hlYDV6+4moaWBl667CWuHHZll2O+Pv41t316GwHeAXxx6xd2G3bjYsbxxt43yKvOs6pHrrMwVTaPjXZcocklAy/h9T2v09jSiK9XN6r8LuT5Lc8THRjNXePuctiYlwy6hJ337eSaD67hsvcu48UFL/Lrab+27OQza0F4qrvurO9gqH3ezd7YlrMNgKlxUx0y3oDgAQwJH8KmM5t45KJHHDKmzllObT4E9AcPs68lD08YdjMceA0aq8A3xHXrO8/x9vZm0KBBHbalpaUxYUKXr+/uOfIebL4T7jkC/Ub0fNxni6H8BNx9CNzsO8Cq6z1HcHm4+WzB08OT5+c/jxCCRSsWccX7V3C05CigXMl/3fJXrlpxFckRyey8b6dDPC6mMO6+gn12j6UF+wv24+vpy7B+wxw25rzB86hvqW8zStyFfQX7+P7U9/z6wl/j11tOjQ0MDh/MDz/5gUXDFvHwdw9zuMgCjTjZCmdSYfht4BcBJz5z6Jo6sz13O0MjhhLh77gq05lJM9l0ehOtuniyDihPYmBM1+3Db1VhypNfOH1JOg6k9LC6AQjrQ61j1I+h4iSsvqvXFn06zkE3Eq1g4dCFHPz5QVIWpLAlewtjXh3D4u8Wc8+X9/D42se5ceSNbPrxJhJCExwyn8lD5649nPcX7md0/9F4eTjOIT0raRYewsOt8hKbDc38PvX3BPkE8fMpP9dkjmDfYP4y/y8A7Mzb2fcJRfuhoRQGXgZDroaMVSqcowFSSrbnbufCeMfkI5qYmTiT0vrStpstnfOcuh6MxAHTICRJhZx1zl5Kj0D4MOirQ9UFN8Gsv8Dxj2D9r1SBno7L0I1EK/Hx9OGRix7h+C+Pc8+4e3hp20u8u/9d/jDnD3x444cE9tJz0lpC/UIZFDbILY1EKaVDK5tNhPmFMTl2clvxj6spri1mwXsLWHV8FUtmLyHML0yzuZIjkgn0DrSsmOPMWvWceAkMvR4aKyA7TZN1ZVdlU1BTwLQ4x+QjmmjLSzytt+jTAWryOxatmBACLrgFTq+BuhLnr0vHMZQfh4jhlh075TGY8jjsfxV+WKrpstyZXtoWLxVC5Aoh9hkfV2i1Bt1ItJHooGhev/p19j6wlw33bOD3s3+vSXHJuJhxbhluzq/Jp6SuxGFFK+ZcMvAStudup6appu+DNWRv/l4mvz6ZbTnb+O91/+XRix/VdD5PD0/GRo+1rKL9zDroNxKCYiHpUvAO1CzkbAr9O9qTmByRTHRgtN7HWUelT9QVdu9JBJVW0doCJz517rp0HINshcpMCB1s+Tkzn4PRP4Ftf4A9f9dube5NT22LAV6SUo43Pr7RagG6kWgn42LGMStplmbjj48ez4nSE9Q21Wo2hy2Y9Bsd7UkElZfY0triUg/TioMrmP7WdFplK5t/vJk7xt7hlHknxExgX8G+3vP0DE2QswkSjXVdXn4w6AqVs6VBDs/2nO34evo6tEAJVIX3zKSZbDy98azVENNxEA3l0Nrcs5EYNQ6CEyBng3PXpeMYagtUXmnooL6PNSEEXPoaJF8LqQ9B+grNlueuGPW2u2tb7DR0I9HNGRczDonkUNEhVy+lA1pUNpu4OOFifDx9XJaXuCR1Cbd/djuTYiex675dTIqd5LS5x8eMp6qxiqyKrJ4Pyt8GLXXtRiKokHNdodrnYLbnbmfigIma9BC/etjVZFdl88XRLxw+ts5ZRHcaieYIAVHjodj9Um90LKAiQz1b40kEVehy5QolhfTdj6Eq2/Frcz1eQohdZo/7zXcKITyFEPtQDUXWSCm3G3f9UghxQAjxlhAiXKvF6Uaim+OuFc77C/eTGJpIuL/j/zYDvAO4OOFil+QlNrQ08IeNf+D6Edez7q51RAdFO3X+CQOUvEKveYmn14LwgIQ57dsGXQGePg4POTcbmtmdv9th+oiduW3MbQyPHM7vUn+HQa9kPH8x77bSE/3HQdkx1bVD5+yiKlM9W+NJNOHlBwvfAaQKPZ97tEgpJ5s9lpvvlFIapJTjgXhgqhBiNPAqMAQVgs4HUrRanG4kujkDwwYS4hvidsUr+wscX7RiziUDL2FfwT5K60o1m6M78qrzAFg0dJEmnrO+GN1/NJ7Cs/ebgjPrIGYK+Ia2b/MNUbmJJz7rWg1YnQO7XwJDs9XrOVB4gIaWBod1WumMl4cXf5z7R44UH+G9A+9ZfmJVNnx3r96u7VyhL08iqJCzNCgpFZ2zi4oMQKgqdVsISYJxP4dDb6kbhfMQ87bFUspCo/HYCrwOOEbAtht0I9HNEUIwLnqcWxmJ9c31HCs9pqmROH/wfCSSR9c8Sn2z81o05VblArhMvNzPy48RUSPYW9CDJ7GxCvK3dww1m0i+HqqyoGhf+7baAvj4Ekh7RHVmsZLtuSqy4eiiFXNuGHEDkwZMYknaEhpbGi07KfNr9YWRs1Gzdek4EUs8iVHGz5si9/ks1LGQqkxVZGePxuyFv1Ft+7b83nHrcnN6alsshDC/m7oO0CwfTTcSzwLGRY/jQOEBtxEdPlx8mFbZqklls4lp8dN4cvqTvL3vbaa9OY1jJc65e8ytNhqJwa7rcDM+ZnzPnsScjcqbkthN+74hV6kw9EljyLm+FD65FGrywCsAMlZavZbtudvpH9ifpFAbPQAWIITg2XnPcrryNMt3L+/7BICq0+q5YIdm69JxIrUF6m/UO6jnY8KGqCp+PS/x7KMiw/p8xM4E9IdJj8Dxj6Fwt2PW5f4MAFKFEAeAnaicxFXAX4QQB43b5wL/p9UCdCPxLGBczDhqmmrIKM9w9VIAbSubTQgh+PP8P/P17V+TW5XLpOWTeP/A+5rNZ8LVnkRQFc651bkU1xZ33Xlmnbobj72o676AKIifBSc+Vx7HTy9X7a2u/QoGX6EEt6280diWs41p8dM07x1+6eBLmTNwDn/a9CfLpI+qzqhn3Ug8N6jNh6ABqkClJ4QHRI7RjcSzkcpM2/IROzN5Mfj1g02/sX+sswAp5QEp5QQp5Vgp5Wgp5R+M2++UUo4xbr9aSpmv1Rp0I/EsYHzMeKCPYgaNqGmqYXvOdj5L/4xXtr/C42se5+87/k6gdyBDIoZoPv8VQ69g38/2MT5mPHd8fgcPf/uwpvPlVufi7+VPqHm+n5Mxvd/dehPPrIPYGT2HbZKvVzlbH86E4n1w1SdKcHvwVcpbU7jH4nWU15dzvPS4ZvmI5ggh+PO8P1NUW8TL217u+wSTJzF/h96R4VygtgACegk1m4gap4xE/T0/e2hphJpc+z2JoPKwL3wKTn+vWfMAnY7oRuJZwNjosfh5+bEle4vT517w3wVMe3MaN3x0A7/+9te8sv0Vqpuq+eXUX+IhnPPnEx8ST9o9adwx9g5e3v4y1Y3Vms2VV51HXEic5p6z3mi7Keicl1hbCCUHu89HNJF8rXouOQRXvA9DFqnfB12hPDGnug85bz6zmarGqg7bduQqL50zjERQKQZXX3A1f/3hr5TVl/V+cNVpVc1dX6zyMHXObnrq29yZqHGqu1D1OSmFYhnVuSov+Wyh6jQgHeNJBBj3IATFwaan9JsFJ6AbiWcBPp4+XBR/kdM7U9Q21bIjdwf3jL+HvQ/spfixYup/W8+ph07x3PznnLoWLw8vrkhWnYeyNdTKyq3OdWk+IkCEfwSJoYldPYlnjJJASd3kI5oISYAZz8Cij+CCm9u3B0TCgIu6zUvcmbuTmf+ZycC/DeQPG/5ARUMFoPIRBYIpcVPsuyAreOaSZ6hqrCLlh14UHQzNUJsHSQvU7/ntIecduTt4Y88bNNtQya3jQmp7aMnXGVPxyvkUcjY0wZlU2PA4vDMWlsfD/6ZBxSlXr8wyKm3USOwJb3+4aKnShD31lWPG1OkR3Ug8S5iZOJN9BfuobKh02px7C/ZikAauH34942PGExkQ6VIPW0JoAgDZlRoaiVW5Ls1HNDEhZkJXT+KZdeAbBv0n9H7yhb+BYTd03T7kKijaqyRxzFiTsQaAixIuYknaEpL+lsTTqU+zLnMdI6NGEuIbYseVWMfo/qOZN3geK4/3UmRTk6NyKwcvUmF3s7zEz9I/48GvH8TTw9MJq9VxCC0NyjtokSdxjHo+143EyizY/2/44lr4Zz+lULDnb+AfCeN+po4pO+rCBVpBpR0aiT0x+h4IHwabf6t7EzVGNxLPEmYlzaJVtvJD9g9Om3Nn7k4Ap3qSeiMhRBmJOVU5fRxpG1JKFW52sScRVMj5WMmx9naMUsKZtZAwF2w1gIZcpZ4zVnXYnJqVyuj+o/n69q/Z+8BeLh18KX/c+Ec2nt7otFCzOXOS5nCw6GDPIWdTPmLYEOg/sYORmF2VTUJogtNSIXQcQJv8jQWeRJ9g9b6fa0ZiSwNkfQ+p/wf/GQFvDIK1P1d5xSPvgGu+gF+Uws3rlRcNzi5PoqevKkxyFB5eMPHXKv/a9Hmgown6J+lZwrT4aXh5eDk15LwjbweJoYnEBFlwh+8EYoNjEQjNws2l9aU0GhrdxkiUSA4WHVQb6orUh2G8HX3CI0aokI9ZXmJjSyNbzmxh7sC5bfN+cvMnHPjZAX455Zf8Yuov7LkMm5g9cDZAz727TZXNIUkQM1XJYRjDy2cqz7TdTOicJViikWiOqXjlbKf8BOz5O3x2JfwzAj69DPa/CsGJMOcluCcdfpoJ81+F5GuUgQxKCsY7CCpOunb9llKZCSEDVU60I4k0toQtPeLYcXU64OXqBehYRqBPIJMGTGLjaeeJB+/I3cHUOM2E3K3G29ObmKAYzcLNJvmb2OBYTca3hgkxKqS8r2CfaolXV6h2BMfbPqgQypu4/9/QXAvegezI3UF9S32bkWhiTPQY/n7F322fyw6mxE7B19OXjac3cs3wa7oeYPIcBCcoI3HP35RHof94siuzmZk006nr1bETW4zEE5+3/Q2fNTTXqorczNWQ9W27JzB8KIz5KQy8XLXa9A7ofRwhlDf1bPIkhjkoH9GcfiPUc+kRJfGlowm6kXgWMStpFn/b9jfqm+vx9/bXdK6SuhIyyjN4YNIDms5jLQmhCZp5Ek0t+dwhJzExNJFwv/D24pU6o2aif5R9Aw++Cva8rPo/J19DalYqAtHmvXMHfL18mRY/jY1nerghqjqtDAovPxhgDIcX7MAQOYbc6lzdk3i2YUlLPnOixgESig9CrDY9xR2ClFCWDpnfKsMwd6MqQvEKgMS5MPH/YNDlyuCzlrAhUHKWtCeszFRFc47Gvx8ERKvXWEczXB5uFkIkCCFShRDpQojDQohfG7dHCCHWCCFOGJ/DXb1WVzMraRbNrc1t0iRaYspHdCdPIqi8RK2MRHfotmJCCMH4mPHtxSt1Reo5oL99A8fPBJ+QtpBzalYq42LGEeEfYd+4DmZ20mz25O/pIssDKCMxOFH9HDpIievm76CgpoCW1hYSQxOdu9jzhIqGCm26PtUWAEKJwVuCO1c4N1YpL+eaB+D1gfD2KNiwWFXjj/8l3LhG5RZetwom/MI2AxEgdIhqdddqcOjyHU5DuSpK0sKTCNBvpB5u1hiXG4lAC7BYSjkCmAb8QggxEngSWCelHAqsM/5+XjM9YToC4ZSQ8868nQgEkwZM0nwua0gISSC7MhupQUWbKdw8INiBCdZ2MD5mPAcKD9DS2qL0AMF+T6KnjwprZayiobmOrdlbuWTgJfYv1sH0WqhVfVrlI4IKvQ2YCgXb224edE+i46lsqCT8+XCeWPOE4wevzVcGooeFga2QJCWq7A5GopSql/T25xh/7GH4Vz/46no4ugKiJ8Glr8F9p+GewzAnRclX2dO/2ER4svJK1mhTxOcwtKhsNsdkJOoVzprh8nCzsZ1MvvHnaiFEOhAHXAPMMR72DpAGaPAJdfYQ7h/OmOgxPYfhHMiO3B2MjBpJsG+w5nNZQ3xIPLXNtVQ2VhLmF+bQsXOrc+kf2B8fTx+HjmsrE2Im0NDSwPHS44ysL1aJ347w+A25Co5/xNYDb9NoaGTuoLl9n+NkTIVaG09v5PLky9t3yFZVuDLELFcxZipkfkt26XGgXSpJx3GYJIk0UTqoLbA81AzqxiByrOuMxIZyOL1GhZGzvm0Ll3v5D4HJj6qbsNiL1A2ZVoQaPZAVp9pvmNwRR2skdiZiBDRVqf70bhABOhdxuZFojhBiIDAB2A5Em/oRSinzhRDdxtmEEPcD9wN4eXmRlpbmnMVqSE1NTY/XMcRzCKuzVrN2/Vq8LL3zthIpJVuytjAtYprTXs/ertmcqiIVfvxs7WcMDnLsB8+BrAOEilC3uebmGlWx+7/1/+PBhv1Eeobywwb7bxC8WoKZjgefb/k3HnjAGUjL63kdjsTS9xlgWOAwVh5YyQLPBW3bvJvLmG5o5ERhE7nGcSIq/RmLZOvOTwE4c/AMZel9dGxxItZcs7vy2qHXiPSJJLIokrTitD6Pt+aaJxacoMUrmANWvEbJTZHElH7L5tT1jq+a7YxsJbjuBBFV24mo3EFIbTqCVpo9gygPmUxZ5J2UhUyhrMmPIEMQnJJwSlupMr/GYqYBx7Z/Q36G6wKCfb3PCQVrGQJsOpiDwbPC4fOHVTcxHtifuoLykMkOH78z58L/stVIKd3iAQQBu4Hrjb9XdNpf3tcYAQEB8lwgNTW1x30fHvpQshS5PWe7ZvNnlmdKliL/teNfms3Rmd6u2ZwfzvwgWYr8+vjXDl/D+H+Pl1e+f6XDx+2Jvq65qaVJ+v7RVz763aNSfnGdlP8Z5bjJV8yUM/4cKKcsn+K4MS3A0vdZSimfWPOE9P6Dt6xtqm3fWLBbyheQ8vjn7dtqi6V8Afnrt2fIoGeDZGtrq8PW6wisuWZ3pKqhSvr+0Vc+9M1D8vP0z+Ur217p8xyrrvnf8VKuvtu6Re1/Xf0dlJ+07jxLqS2S8sh7Un59h5T/jFJzvYCU/50s5ebfS5mzRUpDc4dTnPo+G1qkfNFbyg1POG/Obujzmtf8TMp/RGi3gJoC9b7sflm7OczQ4j0GaqUb2GA9PdwhJxEhhDfwKfC+lPIz4+ZCIcQA4/4BQJGr1udOzExU8h5a5iW6a9EKaNt1JbfK9S35zPH29GZ0/9HsK9ynchLtLVoxoy5pAdsba5kb6145p+aYCrW255j1qa0vUc/+ke3bAiIhdDDZFZkkhCS4tCuQUyk/AYfe1nya7059R6OhkRtG3sCXx77kt+t/S11znWMGl61K3smacDNAfwcXr7QaIG8rbHka3p8Kr0bDN3eocPLABbDwv/DzQrhjJ0z/A8RdbHkOpRZ4eKo8v0o3l8GpzNQuHxHUZ6JfhF68oiEuNxKF+kR/E0iXUr5otusr4G7jz3cDXzp7be7IgOABDI0Yqqmo9o7cHfh6+jImeoxmc9hKTFAMHsLD4V1XGlsaKa4rdgv5G3NG9R/FsZJjqrrZ3qIVM7b4DaAZmOvl8o+AHpmeMB0P4dHxhqi+VD379+t4cMxUztQWnV+Vzdv/DN/9GJrrNZ3m+hHXs+nHm5ieMJ27x91NdVM1n6d/7pjBG8qhtdlyjUQT/UarMHORHUZiTb4yslfeAq9GwYqLYfsz4OENFy+DH+1QhuEV76muJw68SXMIYUOg3M0FtSsztMtHBJWfqlc4a4o7fENMB+4ELhFC7DM+rgCeAy4VQpwALjX+roPyJm46vUkbOQpUp5XxMePdpoDDHC8PL2KDYx0ug5Nfo5LP3cmTCKpSN686D0NdkeUSIRaQWnoSL2BG5XGHjeloQv1CGR8zng2nN7RvbDAZiZEdDx5wIdktzSQEdDIeNSK3KpeKhgqnzNXzIozGs8YVrh7CgxmJM/D08GRW0iySQpN4Z/87jhncWo1EE97+qnevNZ5EQzNkb4BNT8G7E+C1WGVk526kduAVvDniAfLvPAi3bSF31E9YV1fFnoJ9migpOITQIcqT6K7razUouSotPYmgildKD7vv63CW43IjUUq5WUoppJRjpZTjjY9vpJSlUsp5Usqhxmf3yUR3MbOSZlHeUM7hIseLqRpaDezO2+2WoWYTVmklthqgpbHPw0zyN+7mSYwPiccgDRTWVzjUk5F6Oo0pIQMIytsITTUOG9fRzEqcxdacrTQZmtQGkyfRr6NsamPUeAolJAjtvyhqm2oZ/s/hRDwfwZhXx/DzVT+n2dgW0GlU57Z33KjWRjcU4LuT3/HQ6oeobKgElMF417i7WJux1jHefGu7rZhjSXu+qmw48Dp8eb2Sp/loDux6QUnozPgz3LkXHsjjj77x/HTna5w26nJ+d+o75v93PpOWT+LLY24axAobAk3V7SkY7kZNnpLp0dKTCMqT2FDWLhOm41BcbiTqWM+sJNW/V4uQc3pJOrXNte5tJIYmWJaT2NIA/5sGX1zV56HuJKRtTnyIasOXLXFYuLm6sZqduTtVKz5Dk+q+4qbMSppFQ0sDu/J2qQ31JeAb1iUfLMdPvTaJzdWar2lrzlZqmmq4a9xdxAXHsTVnK96e3gA0tDRoPj8AOWYheFMvaw14Z/87/O/g/wj0aW9/d9e4uxgeOdxBRqLRkxhgo5FYlQWNle3bWhrV33Pao0rI+vVEWHM/FO6CC26Fqz+DB0vgljS48EnoP57iuhL+seMfXDn0SiYOmAjAlUOvZMM9GwjzC+OrY1/ZfZmaYBLidtf2fFUaaySa6DdSPeshZ01wKwkcHcsYGDaQuOA4Np7eyINTHnTo2KZuLu5sJMYHx7Py2EqklL0XKWx8Qn05AJQcgsjRPR5qasnnDn2bzTEJQ+dIuNBBnsTNZzZjkAbmjr4Tcr+GjJUw9FqHjO1oTH2YN2Rt4OKEi1W4uXM+IpBtbFuYUJun+Zrqm+sZ038Mryx8hRDfkLZw5PLdy1matpSjvzxKiG+ItovI3QjeQaofcLU2RmJDSwMrj6/k1lG3dpDbSo5I5vCDhx1TIGTyJAbZIGBv6rxyaqXSystcDWfWQ0ud0iiMmwWjfwKDFqqQZA/rfeGHF6hvqeeFBS+0pdhEB0UTHRTNZUMuY/XJ1bTKVjy0ltqxlrBk9Vxx0j3bE1ZorJFoos1ITFe9r3Ucipv91etYghCCWUmz2Hh6o8PzZXbk7iDUN5TkiGSHjutIEkITqG+pp6y+lwyEU6tg7ysw8i71hXHg9V7HzK3KxdfT1+3a07V5EltxWE5ialYq3h7eXDxwlrH7yteqytQNiQyIZFTUqHYB+frSrvmItFe7J1Qd1/xarrrgKg78/ECbIWgylibETCC/Jp+/b/+7pvMDKrcufqYK02rkSfz+1PfUNNVw48gbu+wTQlDfXE9JnZ2hztoC1cvYO8j6c01G4uo7Yd0vlCdp9I/h2pXwYCnctAYmL1ZGRA8GYnFtMf/Y+Q9uG30bwyOHd9m/MHkhZfVlZJRnWL8+rQkdBAj39SRWZgICQjQuJguKA59g3ZOoEbqReJYyK2kW+TX5nCp37AfEjtwdTImb4n53zWaYvGs95iXW5KuE9KhxcOlySL4e0v/baxVobnUucSFxbiefEuEfgZ+nNzkODDenZqUyLX4aAd4BqvtKXSEU7HTI2FowK2kWW85sMbYnLFG9mjtxplIZSgnNNUoaRiNaZSuGHvrlTombwqJhi0jZmtJ9z2lHUVcEZenKUxacoJkn8dP0Twn3C+eSQV3bNjYZmhj08iCWpi21b5LafGXo2vJ/FxQLs/4Kc1+GnxyHe0/CvH/AkEXgY5nR2dDSwJVDr+R3s37X7f6bRt1E6eOl7nnT7OWnDCR3lcGpzFB/n1oXQAqhPMVlupGoBe5rCej0yuyk2QCkZaVZfW6rbOXmj2/mhR9e6OCJrG+u50DhAabGum+oGfrQSpStyrPQXAtXrgAvXxh7n5LaOPFpj2PmVruXRqIJIQQJfmHKSHRAuLmyoZI9+Xvav/gHLgThqUJ2bsqspFlUN1Wzv2B/z+Hmqmwi/cLxF0DBDs3WsjV7K5F/jWRr9tZu9y+ZvYTyhnJe2f6KZmsgd7N6jp+lvDQaFa708+/H3ePubsu3NMfH04c5A+ew4tAKGi0oDOsRa1vymSMETHkUJj4E4UNtMjQTQhP46KaPuvUiAgR4BxBkocHpEsKGONSTeKDwgOM0MLXWSDRHl8HRDN1IPEsZHjmc6MBom4zEbTnb+PjIxzy25jHuW3lfW2XmvoJ9GKTBrfMRoQ9P4s6/wpl1MPcV6DfCeMIclb9zsOeQc25VrttVNpuI9wlQ4eZOFb22sPnMZlplK3MGzlEb/CMgbrrKS3RTTIVaG05v6DncXJVNQthAFbbM395lv6PYdGYTFQ0VDO03tNv9k2Mnc9Wwq3h5+8v2GU+9kb0BvPwhZjIEJ6pwswbyHy9e9iIvXf5Sj/vvHnc3ZfVlfH3ia9snqS2wrbLZAaw4uIIjxX0bFmlZaUx/a3rv6S2uIixZ5SQ6gFbZysL3FxL4bCC/Xfdb+wfUWiPRnH4j1d9SQ7lz5juP0I3EsxQhBHMGziE1K9XqvMRPj3yKj6cPiy9azJt73+TK/11JVWNVW9HKlLgpWizZYfQP7I+Xh1fX6kpDE/ywBJKvhTH3tm8XHjDmp6oitOxYl/GklORV57mlJxEg3suHHDwc0qP2YNFBgLYqTgAGXwXFB5SmmRsSGxxLckQyGzJTobmmx3BzQmiCMpw09CRuPL2RkVEjiQzoaqiaSFmQwtZ7t+Lr5avNInI2QuxFKowXkqgKNRoca8DkVuX2+bly6ZBLCfcL55sT39g+UW2+7Z5EOyisKeTer+7luc19y+/6efnxQ/YPfH/qeyeszErChqj0gyb7q/p35e1qK+BbdWKVfYM116v31pmeRFDFKzoORTcSz2LmDpxLXnUeJ8osz8GSUvJJ+icsGLKAFxa8wJtXv0lqVioz3prByuMriQuOc7sK3854engSFxzX1ZNYfgIMjTDsxq6hp1H3KNmUbgpYKhoqqG+pd1sjMcFDkNfacy6cNaSXpBMbHNux+naIUSLolJ1fDBoyI3EGW3O2KodZd+HmymwSQxIh5kIo2meRNqa1GFoNbMnewqzEWb0eN7Tf0LYcNocLMTdUKG3AeJVuQrCxKMCBxSvNhmbG/nss//fd//V6nJeHFzOTZtreIrSlARorXOJJ/MuWv9BoaOT3s37f57FTYqfQz7+ffcawVrTJ4NhfWPPl0S/xFJ48Mu0RDhQesK8oqSrLuD4nehJBDzlrgOVGYoqYTIr4P1LEX0kRfyBF3EyKcK9S0POMuYPmAtblJe7K28WZyjPcOEJVLP5kwk9Y/aPVnK48zbrMdW4fajbRrVai6QMiYmTXEwKjYcg1cOSdLgaESSPRXY3jeNlCC1BYW2j3WOnF6YyIHNFxY8QFKqfLjUPOU2KnUFxfqvQiO3kSqxqrqGysVJ7EAVNVmzdH9fQ140DhAaoaq9rC373RZGjimg+u4U8b/+TYReRuBqTKR4T2ylEHFq/szNtJWX0ZMxJn9Hns72b+jneve9e2iewR0raDgpoCXt31KneMvaPHtAFzPD08uSz5Mr49+a1mXa5sxmQkOqB45ctjXzIzaSY3jLwBwHbjH4yVzUCIkzyJIUkqBUMvXnE4fRuJKeIeUsQe4CnAHzgGFAEzgDWkiHdIEedRw1T3YWjEUAYEDSA1K7V9Y2NltyFVE58c+QQvDy+uvuDqtm3zB89ny0+2MHHARG4ZdYuWS3YY3XZdKUsHhDJ6umPsfao69uQXHTa7a7cVEwmtSqDZXvFiKSVHS452NRJBhZyzUx0SttKCKbEqBWJnK11yEtvkb0ISIMZ4k6NBXmKYXxhPzXiK2QNn93msj6cPAkHK1hRqWhzY0SZnowozx1yofg9W+bmO9CSahMsvTri4z2OnxE1hWryNGn1tRqJzw80rj62kvqWexy9+3OJzrki+guK6Ynbn7dZwZTYQ6hhB7VNlpzhcfJhrL7iWKbFTCPAOIDUzte8Te6LS6Nl0lidReEDEcN2TqAGWiGkHAtNZLLvXD0kR44GhgHay/zrdIoRg7qC5rMtY1y4s/fXtkLMB7jujihLMkFLyafqnzBs0j3D/jkUQo/uPZvf9bvYB2AvxIfF8mv5pR5Hb0nQIHQjeAV2OL6otYtyn9/ChbzSzDr4Ow9uNYXfttmIivlnJqeRU5djl6c2rzqO6qZoRUd0YiUOugt0vwuk1MPR6m+fQirHRY/H28GSnwcANncLNppuFxNBEJQkSOECTvMRB4YN4dt6zFh+/ZPYSvjz2JZ/lfsYiFjlmEbkblSHs7a9+948CT1+HVjjvzt9NTFCMxZ71b09+S3VjNTeNusm6iVzkSfQQHoyKGtX9/0EPXJZ8GQuTFyJxs/7AfmHKs25n8crg8MHsvn838SHxeHt684spv2BwuB0GXmWm8uwFRNu1LqvoN7JjJyIdh9C3J3Gx/GePBqLav4/Fcp0jF6VjOXMHzqWwtpBjpccg6zvI/EbJv3RTybu/cD+nyk91K457tpEQkkCToalj3kzZEaWX1Q178/dSUFPAf/3iVfWz2Z23yZPoluFmQzPxRk+URa0IeyG9RCV1dyv3ETtdtbtzUykcXy9fxobEKU9ip3BzmycxNEHlosZMdbiRKKVkQ9YG6nvR2uzMhAETmJ00mw3FGxyziKYaKNjVHmoGdb0hiQ4NN+/K28Xk2MkWH//K9ld4Ou1p6ycyteRzspF478R7OfTgIau0YCMDIvnmR9+4ZzqOA2RwhBBMHDCR/oFKZusvl/6Fn03+me0DVmaoohVn6s72G6lultw0GmILQgg/IcQOIcR+IcRhIcSyTvsfFUJIIUTPlXR2Yk1O4iBSxIukiM9IEV+1PXRciknKJDVjLaQ9oj4w4mfB3n+AUdrGxCdHPsFTeHLt8Gudv1AH00UrsdWgwuz9uslHBE6WqTvtrytyaEXAwTfa9uVV5xEZEKldNao91JfQD/Dz8LY73JxerIzEbsPNnt6qfVnG1+q1dEOmBMew2wCtnaSAsquy8RAe7Ub+gAuh/LhD5TCOlhxlzjtz+N/B/1l13oIhC8iozaCotsj+ReRtBWnoaCRCuwyOg/jj3D/y6wt/bfHxs5Nmc7TkKIU1VubM1hYAwiH6n86isKaQmiYHpg84AjuNxOLaYu776j6OlhztsL22qdb2v1tnaiSaMOWilx3t/TgLcKPc00bgEinlOGA8cLkQYhqAECIBuBSNo7jWVDd/AWQBfwdSzB46LmRI+BDiQ+JJ2/8flY8x668w+TGoyekgHi2l5JMjnzB74Oxe5TvOFrpoJVZmqsrmHjyJJiMxv7aQvTHT4ch/27Tl3FVIG4C6IoSA+IB+5FTbZyQeLTlKqG8oMUE9eG4GXwX1xZpKyNjDFP8wKoGTnYzlM5VniA2Obe8vbMpLdGAXmU1nNgFYVLRiznXDr+PBIQ926H1sMzkblPB5bKdcwWDHehKvH3E98wfPt/h4U46m1YUOdQWq1aQjXhsLMbQaGP6P4by+u/c2nd1xuOgwMSkxfHH0C8cvzB7CktX7b2iy6fRVx1fxxt43Oohot8pWEv+WaFtHHSmdq5FowkEVzqV1pVz4xoV8dcz1PjCpMN2VeBsfppyHl4DHzX7XBGuMxAYWy1dYLFNZLDe0PXRcihCCuQnTScvfi4ybpTQCB1+hPjj2/K3tuCPFRzhWeqytqvlsp4snscyoj9WTJ7H8JPEh8QgEq3wioSYXivYAykh0y1AzKKMNiA+OcUi4eUTUiJ5bDw663K27r0z2UZ7enbkdjb/squy2mwZAaSWCQ43djac3EhMUY3V7thFRI7gp/ibH9ATP2QjRE1WfWnOCE6Amr0vkwBb2Fezjh+wfrJLumTRgEoHegUrs3BpqnK+RmF2VrVJzbGBE1Aj6B/Zn9cnVDl6VnYQlq05TNuYlfnnsSxJCEpgQM6Ftm4fw4MK4CzsWRVpKfYkK+Zoqr51F2GBV1GWHkVjRUMGC9xZwsPAggd6BDlxcr3gJIXaZPe433ymE8BRC7EMVDK+RUm4XQlwN5EopHS/j0AlrjMSXSRFLSBEXkSImtj10XM4cQxVFUnJk3K9UDojwgIm/VhWeedsAFWoWCK4bcZ2LV+sYIgMi8fH0aQ/Bmj4Y+nXvSTxVdoopsaoSc2VppnqNTqo7xdwq9/YkAiSEJNofbi5J77H9GKA6usTNgAz31EscKVrwF4KdeZ2MxMrstpsGAHxDVaVjvmOMRCklG05vYFbSLJt6e1c2V9rvfWppgILtql9zZ0ISAQm1efbNAaRsTeGmj2+y6jq9Pb25OOHiNqF2i6lzfreVE6VKU9YS6ZvOeAgPLk++nG9PfusQzVKH0d9o3BXusfrUuuY6vj/1PddccE2X93zuwLkcLTlKfnW+dYOaKpud7Un08ILwYTYbidWN1Sx8fyEHCw/y2S2fMW/wPAcvsEdapJSTzR7LzXdKKQ1SyvFAPDBVCDEW+C1gQyKw9VhjJI4B7gOeoz3U/IIWi9Kxjrn1qvAirdosJ2jUPerLcrdqq/VJ+ifMTJrZc6jxLMNDeBAfEt8ebi5LV14J39AuxxpaDZwqP0VyRDKLhi1id+F+8vpPglNf0Wxopqi2yG3lb9o8ieGDya3OtfnLqaKhgoKagu7zEc1JmAslh1Txk5vh1VDORP/QNokWUAZcdpVRSNucARcqT6IDxKxPV54mpyqHmYkzbTp/fdF6rvvwOjLLM21fRP52FU5M6EZ+x4GC2tYWrZj48MYPSbs7zbqTavKdbyQaGw8MjbDeSAQlhVNWX8aW7C2OXJZ99BsBXgE2pVesObWG+pZ6rhl+TZd9Jh1eqz3EFS4yEkHlJdpoJD6w6gF25u7kwxs/5IqhVzh4YfYjpawA0oBrgEHAfiFEFsp43COE0OSfyRoj8TpgMIvlbBbLucbHJVosSsc6BjWWkuQT1DE04BMEY+6DE59yLCuVQ0WHuGHEDa5bpAZ00EosPdJjqDm3OpcmQ1ObkQjwTUAiFO+joGA3EunGnsRiEJ7EhyfT0tpicyK5KSm9TyMxahwgoeSwTfNoSn0Jk4P6syd/Dy2tLQCU1JXQ0NLQ0ZMIKi+xrtAhuXpxwXFsvXerzf8/E8NUwGVdph0iEDkbAaE8vZ1xkKB2dWM1x0qOMWnAJKvPDfcPt87LKlvV++PkcPOJ0hMEeAfYnF5yxdAr6Offjxe3vujgldmBh5fyJhbu6vvYTlQ1VjEyaiSzk7refIyPGU+Ib4j1eokmYW9nF66A+g6ozFRtAa3kT5f8iY9v+titom1CiCghRJjxZ39gPrBXStlfSjlQSjkQyAEmSikLtFiDNUbifiBMi0Xo2EGrAWoLmNNvMGlZaR2rsib8CoBPNy4BVEK6RchWt+3ja05b1xUpVUVbH0UryRHJjOk/hsTQRFbVVACQe/RjwH2FtKkrAv9IEkKVIWBryLmtsrkvbbiosepZg44ldtNQypSwROpb6jlSrLwFppuEDjmJYCaqbX/I2dvTm2nx0xgQbJtBkxiQSGxwLGsz1tq+iJyNEDVGpQR0xkGC2nsL9iKRNnkSAX75zS8t6oUMqMrz1manexKTI5K5ffTtNqUNAAT7BvPpzZ/yxtVv9H2wM4mZAkV7wXjzZCl3jruTww8extvTu8s+Lw8v/nPNf/jVhb+ybi0VGcr470avVnP6jQQk5Fnm6a1rruPv2/9Oq2xlcPhgtzIQjQwAUoUQB4CdqJxEp+YDWWMkRgNHSRHfOVICRwjxlhCiSAhxyGxbhBBijRDihPG5m09GHUAZEdLA3JjxlNaXcrjIzAMUkghDr+eTMz9wUdxU4kPiLRvz4Bvw5lCwp3enE4gPjlch2KozKlG6D/mb5IhkhBAsGrqINTlbaQgbSu5p9cXttp7E+mIIiGp777p0mbGQ9JJ0fDx9GBg2sPcDQweCdxAUH7BpHs1oNUBDBVP6qW46ppDzmUplGCWGdgo3R41VItMOKF55ZuMzbMvZZvP5QgjmDZrH+sz1tklrGJoh74f2fs2d8Q5Q2pF2ehJN3URs8SSC+hv78PCHlh1s0kgMcK6R+Iupv+D1q62vbDbHpBDRKlvdRyolZgq01FsVaq1tqu1z/dePuJ7R/UdbtxZXVDabGLRQpV+k/rrP/u2mHMSHv3uYHbnuqeggpTwgpZwgpRwrpRwtpfxDN8cMlFJq9mVtjZG4BBVyfhbHSuC8DVzeaduTwDop5VBgnfF3ne6oUfmIc4x9Vs1Dzq2ylb94RrHXYOCGMCuMoFNfqbv8KjtyqJxAQmiCCsHm/qA29FC0crLsJL6evm2G1qJhi6hrriMtfBy5RerexG2rm+uKIaB/WzjVVk/i0ZKjDOs3rG8pFuGhDKwSNzMSG8oBSXK/oYT6hrZVOHcQ0jbH00eF4Ow0EgtqCvhd6u/YdHqTXePMHzyf4rrijjdxllK4G1rquuojmhOSaHfXlfsm3ccPP/mB6CDbumTMTprN/oL9lNdboE9p6rYS5Lxws5TSYQUnRbVFTHtjGu8deM8h49lNtKmi3/K8xCVpSxjyypC21I3uaDY088mRT6wzoipOOa8dX2d8gmD+q8pY3vl8j4eV15cz/7/z+SH7B/53/f9sby15HmCNkXgG2G4mfbMDsDsmKaXcCJR12nwN8I7x53eAa+2d55ylRlU0JsWMZ1DYoDYjMacqh/nvzueJnf/ihsBwflZxUIWR+6KlEc4YDU1juzp3pU0rMd/4wdiDJ/FU+SkGhw9u67Awd9BcArwDWNXSSm5rKz4eXu6rHVlfBP5R9PPvh6+nr+3h5pL0vvMRTUSNVZ5EBxR9OIx6daPsERDFpNhJbRXO2VXZ+Hr6EhUQ1fWcmKmqQ4mVIThz1pxaA8Alg+xLv77mgms4+auT1ntloL3VWFwvhTMOENQO8gniooSLbD5/VtIsJJLNZzb3fbDJSHSiJ/Fk2UkCng3gs/TP7B7L5En83frf0dDS4IDV2Ul4siraszAvUUrJ50c/Z0TkiF5vHD09PLl/5f0s3728x2M60NKgHBehTpa/MWfwFXDBLbD9GSjtKqxdVFvE3Hfmsq9gH5/e/Cm3jL6lm0F0TFijYvoxYK7iajBum+LQFSmipZT5AFLKfCFEj5L8Rk2h+wG8vLxIS0vTYDnOpaamxuLriC1OZRjww4HTDPcbzrqT61jy0RJeOvESza3NPDbsMe728SEw6xkOfPU8ZaG9fwmEVe9lfIsSVT2+N5W83DD7LsZCrLlmE6YOD/uPbWSCZwhbth8G0TXcsu/MPqJ9ozuMPz5kPJ9kbOYSvIkWnmzY4HzJT0uueXpVPoVeTZzcsIF+3v3YfWI3ad69n9OZptYmMsoyuDj4Yote49hyP4Y1VrB1zUc0+ji296ot7zNASM1BJgL7j+cQ3RLNxoKNfL/+e3ad2EWkT2S371//8iBGttSx89t3qA2w7Uvr7SNvE+ETQeWxStKOW79uUNe8d9teALKx3ts35sRn+PklsnNnOpDe7THJVR7ElGew2cbPv9qWWlZkr2BB9AISAxL7PqEbGg2NeAtv3tv8HndG39nr+5xQsJkhwKa9JzF42i/dYwnbSrfRZGgi93guaYU9r81SfhT1Ix7Jf4SHVzzMrQm32vy37SjG+QzG68R6dnv1vYaTNSfJKM/guqjr+lzzyMCRrE5fTVpI1+M6X7N/wxkuRJKe30ihC18Lb9+bmco31H5yC/uGvaQiJEb2lu8lozSDZ0Y+Q0h+CGn5lq/T1e+xS5BSWvZ4gX3dbNtv8fm9PICBwCGz3ys67S+3ZJyAgAB5LpCammr5wZt/J2WKh5SGZvnf/f+VLEWyFDll+RR5vOS4OqalScp/x0n50fy+x9vwhJQvekmZ4inlxqdsWr8tWHXNRopriyVLkS/9c7CU/5ve7TGtra0y4JkA+fDqhztsX75ruWQpMuYZfzn9T57qNXIyfV5zS6OULyDlD3+QUko55+05csZbM6ye52DhQclS5P8O/M+yE3I2q3lPrrR6rr6w5X2WUkp54ku1poJd8pPDn0iWInfk7JAXv3mxnPv23O7PKTuuztn/uk1TNhuaZfhz4fLHX/zYtjUbMV3zD2d+kHd9fpdsbGm0/GRDi5SvhEj5/f29H7fjr+paGypsW2NmqmQpcvWJ1Tadb+LOz+6Uz258tu/3OfURKf8WIGVrq13zWcPftv5NshRZWFPosDEXvrdQhj0XJkvrSm3/23YUG56Q8kVvKZsb+jx0SeoSKZYKWVBd0OexptctpzKny74u13zqa/V3mLPZ0lVrx4E3jf//r3XZVVFv4/+JBu8xUCsdYEdp9bAm3FxMiri67bcUcQ2gVbJkoRBiAIDx2QGNT89RavJUhaCHFwuGLGBYv2H8ZsZv2PKTLe2CsZ7eMP6XcGat0sDrjdPfq7ZfQbFt+Y7uSj//fvh5+ZFTk9djqLmgpoC65rounTKuHHal2t9cTxwGi6vhnIoxxIoxlBofEm9TuNniymYTkWPUszvlJZpeC79+bdW3O/N2dhXSNicsWVUD25iXmFWRhbent8M00wpqCnh3/7vW5XcVH4Cmqp6LVkyYKpxtzEs0FQLZWrRi4t3r3uWpmU/1fWCtUSPRxipjWzhRdoIQ35DuUxNs5Ln5z1HZUMk/d/zTYWPaTMxklUtuwf/t50c/Z3ridIvyT00pEiaNyV4xCWk7u9tKd4z+MSTMgY2PQ00+h4sO848d/6CuuY5Qv656ujrdY42R+DPgN6SIM6SIM8ATGMO8GvAVcLfx57uBLzWa5+ynJhcCVdFF/8D+HPvlMZ6Z90xXSYOx94GXP+z+W89j1RYqGYWBl0FQnNsbiUIIEoJjyW5u6LVoBehiJMYGx7Z9IcZ5eKhiHXejTglpE6CyLeKD48mtyrW6ojK9JB2B4AJjZXCf+IYojTN3qnBuKFXP/v1IDE0kKiCKbTnbyK3O7Sp/Y0IIY17idpumTI5IJn9xPtcOv9a2NXdizsA5eAgP66Rwcoxh9N7yEaFdK9HGvMTd+bvV6xpovwElpaTR0HtlKbUFztdILDvB0IihNsvfdMfY6LGs/tFqHp/+uMPGtJkYY+ZXH8UrUkqem/ccT8+yrGHH4HBVhJJRntH3wZUZ6nsmwLFpKjYhBFy6XOVJrv8VL259kSfWPkFjH1XPOh2x3EhcLE+xWE4DRgKjWCwvZrE8Ze8ChBArgK3ABUKIHCHEvaiuLpcKIU4Alxp/1+mOmjxl0PWFfz8YeRekv9dufHTmjPHLK2nBWWEkAiT4hZHdikXyN50xCWvHRQxTRqI7FWpAW0s+/NUXd0JoAs2tzVYLaqeXpDMwbCD+3v6WnxQ51r20EutLwcMbvIMQQjAlbgpfn/iaVtnaVf7GnJipNneQkVLiITz6rgi3kHD/cCYNmGSdqHbORmWw92QImzB1XbHDk2irPqI5La0txL8Uzzun3+n9wFrnt+S79oJruXfCvQ4f97Lky/D18nX4uFYTnAj+kapYqxeEECwcupBLh1xq0bCJoYnkPpLLPePv6fvgilNK/saJHuJeCR8KFz1N8fFPef/ge9w19i7C/XVFPWvo20hMEXeQYpb1uVjWsFhWm+0fQoropg2AZUgpb5NSDpBSeksp46WUb0opS6WU86SUQ43PnaufdUzU5KrQsCVM/DUYGuHAa93vz/pO6a1FTzx7jEQvH7IlPQppnyo/hZeHF0lhSV32mTxEyUlz1IebHY3hNcHYks9kJJokfKwNOR8tOdp7z+buiBoL5cdt6lygCQ2l6gvQ+OUzecBkyurVx0KPnkRQRqJstbqvbW5VLoNfGcz3p763ecndMW/QPLblbKOmqabvg6VURmJfoWZoSzmxRSuxrrmO4tpiu0PNoASY+wf2J6O2D69TrfNb8v18ys/5+ZSfazL2o98/yue5n2sytsUIobyJhb17Ev+9699WSTF5engSGxzbpg7RK5UZ7hFqNmfUPbzeDI2GJuuFwXUsqm7uB+wlRewGdgPFgB+QDMxG5SXqOoauoKUBGsosNxL7jYCBl8O+f8Lkx8D87ldKyPoeki5VlWBBcUqguqkafIK1Wb8DiMdAnoSWwAHd/jGfLDtJUmhSt96g8THjSf9FOsN8AuHwv5U3MXKU9ou2lPqO4eY2yZ/KbIu9Pq2ylWMlx7hkoJUSLlHjlHFVdgSi7Tce7Ka+RHnDjUyJaxdV6DEnEWCAsfNKwQ6It7z38uqTq8mqyHK4fub8wfP56vhXnKk8w8io7r3fbZQeUcZxb/qIJjw8ISjepnBzgHcAZU+U0WRosvrc7kgKTWJ/di9e6JYGaKxwari5rrmOqsYqogOjHRpuNrEmYw1BLUEOH9dqoierm/3mWvAO7LK7oKaAB79+kGVzljGqv+WfdR8c+oATpSf4/ezf93yQlKrbSuJ8W1auGc1+kfyrGS4NT+z7f06nC33fGiyWLwMTgRVAFDDP+HsucCeL5Q0slhZktOo4HKNGokXhZhOT/k+Feo5/1HF78QHVS3XgZep3UwcSd9dKbKmmFciv6b5t5cmyk92Gmk0MjxyOR0iC+nA95Wapr3VFIDzBLwywzZN4uuI09S31lhetmGhrz+cmeYn1pcrLbWRKbLuR2Gu4OaA/hAyEfOvyEr858Q0JIQmMinLsTcO8wfM4/OBhy76sTPqIlhiJYBTUti0n0UN44OflZ9O5nUkKTaKwsdCkTNEVk0aiEz2JazPWMiBlgGadNWKDYylpdIMOVTFTjJ7zvd3u/vLol0ik1e3n0rLSeHn7y70fVFeoRN9d1W2lBwrrihjsE8BDoc71XJ8rWJZss1gagDXGh4670GYkWuHtSLpUhWZ3vwQj7mjPHTn9fft+aDc8a3Khn5WhSicy0vjBvCtvVxePkpSSk2UnLVPTH3QFbP8TNFYqUVp3oK7YGGJV93KRAZH4evpa1ZrvaIkSk7VYSNtE6GDwCnAfI7GhFCLa/w6jg6JJCEmgsrGSEN+Q3s+1snilydDE2oy13D7G9h6/fSGl7HvsnA3q/9DSL93gBMi1vkr/qbVP4SE8eGbeM1af2x1JYUnUG+opbygnwj+i6wFtRqLzPIknSpUfo03xwcHEBcex84zl3U40w9R5pXAXxHfNAvvs6GckRyRbffMzOHwwpfWlVDZU9lwZXOFGlc1mxIfEs3HEAmQ3wto6fWNNdbOOu1FrgydRCJj0sKpizjVrNZb1HUSObvcgmhuJ7kpjJdMaSwjx8mX1ydVddpfWl1LZWNmrJ7GNhNnqDtyGL1nNqCtqCzWDSji3VgYnvUTJ31idk+jhqf4e3KV4pd6Yk2jG3EFzGdN/TN/nDpgKVadV9b4FbDmzheqmaodJ33TmjT1vEJMS03unjrZ8xFmWFwEEJ6r/Vytbz3105COOlx236pzemJE4g9sTbncrT+KJshP08+/XvdHqAGKDYylvKu+1xZ1TCBqgPru7qXCuaKhgfeZ6rht+ndU3P6YK58yKXlq1muRv3MiTmF2ZrZouhCUjqjIt6zqm0wHdSDybMRlwgVbmTY24U4XuTHI4zXXKYExa0H7M2WAklh3FW8D8ARNYfXJ1ly+l3iqbuzBgmkr8N4X43IH64jaNRBNWG4nF6UQFRNEvoF/fB3cmapx7tOeTUnkS/Tpew7+v/Derf9T15qALMaa8RMs8PRH+Edw74V7mDZpn7UotIswvjKLaIg4V9aJZWnFKFXdYGmoGFW5ubVZhPwsprSslozzDIUUrJqbGTeW+wff1/DdXm6+enWgkHi89rpkXEVSIvb9ffyoaKjSbw2KiJ3fbnm9/wX68Pby5fsT1Vg9pkQxOxSlAQOhAq8fXit+u/y2j/jWKppCBqmjTzdOn3BHdSDybqckDLz8lGGwN3v4w7gE4+YUKEeRsAENTez4igHcA+Ia5t5FYqrxkC4cuIqcqh8PFHSv2TpUphSaLjETvAIie4n5Gon/HjpTxIfFWhZvTS9Ktz0c0ETVWGWemL3VX0VSl+i/7dzQ6/L39Cfa1oKgqeqLK7bQw5DwuZhxvXP0GgT5dE/8dwcQBEwHYm9993hjQro9oSWWzCZMMjql4pSYftv8Z/ncR5Hefi/f8lucBVVDjSGpaaiitK+1+Z20BIDp4ybXGpJGoFfdOvJcVF65wjx7wMVOUMkEng3X2wNmUPF7C1LipVg85OHwwXh5elNT1kndZmaGcCw7KbbWXgpoCPjj0AbeNvg0fU6pKxUnXLuosxHIBsBTxa+A/QDXwBjABeJLF0rEaETqWYxLStiVvatyDsPMvsPfv6ncvv66Cve4ug1N6BDx9WDjmDlj/O7458U1bdwBQnkSBYFDYIMvGi58Fu1OUZ9U7QKNFW0FdURdPYkJIQpugtiWSFEdLjnLjyBttm9+8eMWavFdHU28S0rbxC9g7UIXOezCUzCmpKyGvOo8x/cdolo84KGwQob6h7MnvRZYnZ6O63ggr0gRMgtpHV8CO5yBjFUhj6Dnzm/ZKbyN78veQsjWFn074qUM0Ek1IKblx6408aHiQFy97sesBdQXq79pB+pOW8Mwlz/QulXQuEWN8L4v2QKJSNTDlwAbY+LkW5hdGw28b8PTw7PmgygwIc59Q8/Ldy2lubVayN95GJY+Kk5A417ULcyJimfADFgEzgVigHjgEfC2XSIt0kKzxJP6ExbIKWICqcv4xusi1a7FUSLs7guPgglvg0JtK+iVulvIwmuPuRmJZOoRfQFxYkup80Ckv8WT5SRJCEywXuk2YrTxWeVs1WKyVGJpUEY1/13Bzc2szxbU9CKKbUVxbTGl9qfX5iCZM7flcnZdo1pLPZmKmKhmcPkLnHx3+iHH/HmdZCzIbEUIwYcAE9hT0YSRak48I7Z7Eva9A/laY/Cj85LgS4y7vmnOYFJrEL6b8gr8u+KuVV9A7Qgj6+/bndOXp7g+ocb5G4l3j7mLuIO2Mg+rGap448AQfHf6o74O1xlS8YpZesfL4Sia8NoGsiiybh+3VQIR2IW034eMjHzN34FyG9Rum5KE8fc4rT6JYJpYCW4CLgO3Aa8BHQAvwnFgm1ohlYmxf41hzK2f6tLoC+A+L5X5S3EVW/TylJtc+DbuJD0P6+0oLcfyDXfcHxUFpH72eXUnpkbYPxIXJC0nZmkJVY1VbtWtf8jddiL1YVRLnbIQkbfLRLKatb3PXcDNAdlV2n31XTUUrVlc2m/ALV4aHqyuczVry2UzMVDj4uvqSCO857PjNiW8YEj5E09AkwO2jbye3p/yoqjNQlaXkqqzBNwQWvKHSRIZcpb4UAcIv6NZI7BfQj1cWvmLdHBYS7RfNmcoe5HjqnNuSL7sym4KaAsbHjO/artRBBHgHsKt8FwcKD3DzqJs1mcNi/PupGwOzvMSvj39NRnkGccE2OhVQBVdbsrfwn2v+03Vnc51KS3GTyuaSuhIOFR3iz/P+rDZ4eCoD9jwyEoGdcolc2sO+F8Uy0R/oRT9MYY0ncTcp4nuUkfgdKSIY0EuFXIWURk+iHWHAmMkQO139bJ6PaCI4TuUPubpirzua66Eys61n88LkhbS0trAuo73l2cmykySHW2Ek+oZC1HjIdYO8RFNLvs7hZqPMjyXFK6Ykc6sM5c5EjYUSFxuJpnCzPZ5Ec1FtVEeVpWlLVeUjsDV7K79b/zvWZ67niqFXaBZqNnHfpPtYOmdp9zut1Uc0Z8y9MOyGdgMRIHwYlB1r86KeKD3BrP/M4nip4yqaOxPtF83pCvfwJL5/8H2mvjG192pyO/H08CTCJ4K86jzN5rCK6CkdPIkbTm9gVtIsu4zkU2WneO/Aexi6q56vylLPbuJJjAyIJOf/cjq2YQxLPq+MRLlEft3H/iK5RPbewxHrPIn3AuOBDBbLOlJEBCrkrOMKmqqUcKmt4WYTc1Lg6AfQrxvdrKA4JRlQW9gujeMulB8DZFvP5osTLibEN4TVJ1dz3YjrqGyopKSuxHoDKX4WHPg3tDR27EjjbEz9tbsJN4NlRmJulfJUxYXY8d5FjYWsb137ejTYmZMI6u/bOxDyt/ND0CBu+OgGCmoKuHnUzUQHRbMzbyd/3vxnfDx9uHX0rY5Zdx/UNddR31zftQo4Z6O6YYm0QN7HEsKHQXMN1BbQGhjNfSvv40DhAYJ8tOsQEuMbQ3FdMXXNdR3z4GSrqr52skZidGC0ZUVOdhDpG+k+RmLMZNUwoa6YfEMLx0qP8dOJP7VryMHhg2lpbSGnKqdrm9MKVSToLkYidPO5F5YMZ9arm6XzKAgqlolUoEuejVwiLWrDZY0n8SLgGItlBSniDuB3QKUV5587VOfC2p93qR5zKrbK33RmwIUw96Xu/2ncWQbHWNls6tns7enN/MHz+ebEN0gpOVVuRWWzOfGzVduwbiQknEq9yZPYMdwcFRCFj6cP2ZV9VzjnVufSz7+ffZ00IscqT3JZuu1j2Et9iUoDMHaesQkPT4iexJvHVjHn7TkEegdy6OeH2jqfPHThQxieNlD/23ouTrjYMevuhVbZSswLMfxx4x+77szZoIrI+soBs5TwYeq5/Dhv7nmTDac38MKCFxzectCcCyMu5J9X/LOrVmJDuZLpcbJGopbyNyb6+fTrOYXA2cQYOxIV7mLjaeWZnp1kRaV8N/Qqg2PSSHSTcPMDKx9g9YlO8lhhycqxYtLpPH94FHjM+Pg9sA+w+AvOGiPxVaCOFDEOeBw4DbxrxfnnDnVFsP812P6s69Zg6raipYfPnY3EsnRlOJi+AFEh59zqXA4VHbJOI9GcOGOXApMEiavowZPYJqhd3bcnMa86z35DIGqcenZlXmJ9KfiGt3WesZU/N0p+WpTJnKRZ7Lhvh1W9ax2Nh/BgdP/R7C3oJINTW6DyB20JNfdEhPofyc/fwWNrHmPOwDkdw3AaMDR4KA9OebCrjJBJTinAyUaixjmmAMlBySSFJvV9oDPoPxEQULCL6KBobh9zOxMGTLBryF6NxIoM8A6yz9vvIDLKM1i+Z3mbo6ANU+rReRRyBpBL5G6zxxa5RD4CXGjp+dZ86rawWErgGuBlY09nbf337kr0BBh1D+x9ub0VkbNxlCexN9zZSCw9ou5azUKglydfDsDqk6vbjETTB5vFBESq0KSr9RLri5VESDfeM0sFtXOrc+0LNYP6YPXyc72R6IAvn2uHX8dT3vDN/D9p1nnDGiYOmMje/L20mneBOPahek50oG5hcAJ4+rI8/QuqGqt4bdFrmudctspWDhUd6lq8YvLiBDkn3FzdWE1BTYFTjMR7Bt7DqttXaT6PRfiGQMQFULCTOQPn8P717+Nlp+RQQmgCg8MHd/x7NVF5SsnfuEEY15SXfsmgTtHUUKOXs6KT8XiOI5aJCLNHpFgmLgMsvkuzxkisJkU8BdwJfE2K8AS0KRU7G5jxJxBesOlJ18xvS99mawmIAg9v9zQSy9IhYmSHTfEh8YzpP6bNSBwQNMA2QeT4Wao9nysLduqKOvRtNichJMGycHNVrl3VjIAyVPuNcm3xSkOJXZXNpXWlNBmaGDH8Rp71Ba/Mbxy4ONuZEDOB6qbqNtF3DM2wK0X9/UXb5/XpgPCA8KE8FhzG93d+ryRBNEYimfDaBF7b9VrHHSYj0UmeRF8vX9beudb1FceuIGYKNfnbya7oocrcSrw8vDj10Cnum3Rf150VGe1GmItZl7mOAUEDuqo6hCQpUf3zzJMI7EaFl3cDW4HFqBoTi7DGSLwFaETpJRYAcYBjBbbOJoJiYeoTcPxj1/T7rclVUhdaij4LD5Vg7m5GoqFZheT6dZV2WZi8kM1nNrMnf4/tVb3xs1Wif9E++9ZpD3XFXULNJkyexG7v6I20tLZQWFtov5EIKi/RlVqJ9V1b8lnDfSvvY+JrEyEkAYbeALtfdIu8JFPnlTZR7aMroDobpjzh+MnChxFQmeHwzio94Sk8iQ+J76qV6OSWfD6ePswbPI8hEdobMMeqjzHm1TFsz7Gss4/mRE/m2+oiEl9OYnfebu3mka1QlekWRStSStZnrueSQZd09ZZ7equWgeeZkSiXyEFyiRxsfB4ql8gFconcbOn5lhuJyjD8FDDF90qAz61a7bnG5MUqJJv2iPMbh9srf2Mp7iioXXFKefkiujEShyopnP2F++0wEo2dZ1yZl1hf3GPbsoSQBJpbmymqLerx9MKaQlplq2OKEyJHKc9mfZn9Y9lCfaldnsRjpcfa/xZm/ln1cP1hqWPWZgej+o/ipcteUt1OZCvsfF5VNA9a6PC5nqks4R9Fx53qHU8KTerGSCwArwDwcU6m0tbsrXxx9AunzOXj4cOhokM9i4g7m5gppBkg0MuPsdF9aiZbxD93/JOL3+xU2FWTr4r93KDbSkldCYmhiVw6+NLuDzjPZHAAxDJxk1gmgo0//04sE5+JZWKipedbbiSmiPuAT1Cq3aA8iV9YvtRzEO9AmPGs0l47+oFz5661o9uKNQTHuV9T9LIj6rnfyC67pidMJ9j4BWSzkRgUqz5MXJmXWFfUoycxMVTpn/YoVgxtVZZ25yRCe3FQhXZdSHqlwfacREOrgZNlJ9tDrOFDYdzP4eAb7RXyLsLH04eHpz2svFynVqk826lPOjyvq765nr+c2cmullaocp4BkxSW1FUrsdaokeik3LXX97zOg1930yhAA/r5qBsZk/SUy4kaxwYDTA+JdZiIeF1zHVtztlLZYCZs4kaVzVGBUey6fxd3j7+7+wNMRmIfnZfOMX4vl8hqsUzMAC4D3kEVIluENeHmXwDTgSoAFssTgPM6tLsrI+9QlWSbnlQCz86iOtdJnsR49/MktsnfdG035+3pzaVD1F2kXSLS8bMgd5PzPcQm6ou7CGmbMBmJveUltmkkOiLcHGZM+u+ma4fmNNdBS73N4ebTladpMjRxQb8L2jdOe1rd4G3UIKxrJYU1hXx59Avk9j9DyEC4wPG5c18d+4qqlnru8sKp72FSaBK51bk0G5rbN9Y6t9tKRnkGg8It7N1uJ8Fewfh5+bmNVmJJcx2HWmGOl32qAOaYCgEzKzLbN5qMRDcIN3cr9G1OWLJqd2oS6D8/ML0oVwKvyiXyS8Cnl+M7YM1fTyOLZVPbbynCi24EGh2JEOJyIcQxIcRJIYSLKkT6QHjAnBdVLtGel5wzp2xVd+TO8CQGxan8vMYq7eeylLJ01S6uBzHgRUMXAXa0owOVl9hQDiUW9UB3LKa+zT2Em63xJDok3Bw2WP2dl7vAk2j6MLcx3GzqKtKhWCMgEqY+BRkrIdu1Ukefpn/KtR9eR07eNtVr2c4K1O5498C7JATHMccTpxqJt46+lS9v/bLjxtoCp2okZlZkWq9wYCNCCGKDY8mrcQ8jsU0fsbHQYZ6zbmVwKk6pz4cQ18r/tLS2EP9SPC9ve7nng8LOLhkcIYSfEGKHEGK/EOKwEGKZcfsfhRAHhBD7hBDfCyF6+6DPFcvEa8DNwDdimfDFCtvPGiNxAyniN4A/KeJS4GNgpRXnW4UQwhP4J7AQGAncJoToGl90BxJmQ/J1sP3PzkmIrysCadBW/saEO8rglB7ptmjFxF3j7iLt7jTGRNvRscKkU+eKvMQeNBJNhPmFEeQT1KuRmFedh7eHN1GB3Y9hFZ4+6gvAFUainX2bh0YM5S/z/9JVE3Hir5U0zIZHXectxqx4xSsYRju+gVVBTQHfnfyOH429Aw+/MChznpE4Mmoki4Yt6hjqNIWbnUCzoZmcqhwGhTnHkwhwRfIV9t2cOpAZiTN4d+I9TG6pVi1MHUC3RmLeD8qL6Gmxc0oTduXtoqCmoPcb47PMSEQVC18ipRyH6nh3uRBiGvBXKeVYKeV4YBXwdC9j3Ax8B1wul8gKIAIlrG0R1hiJTwLFwEHgAeAbVNcVrZgKnJRSZkgpm4APUBqN7snFy5TH7ZQTdLLa5G+clJMI7mMkylYoO9ptPqIJTw9PZg+0r7sAIUnKiHBFXmK90UjswZMohCAxNJEzVb17EgcED8DDTgHqNsKHuSYnsb5EPduYkzgkYgiPTX+sqy6itz9M/5PqrHP0QzsXaTtjPQQewJ6oSZooFZTXlzN/8HzuHHeX0s1zoiexydDENye+4VjJMbWhpQEaK5wWbj5TeYZW2epUI/HvV/yd383S8mvRcvoH9ufOqb/ER+CwDlKhfqEsTF5IlDEVxr8hG86sg5F3OWR8ezDpI84dNLfng0IHAeKsMRKlosb4q7fxIaWU5qG9QHqJ6solsg7IAhaKZeJXwAC5RH5v6Rosj20slq3A68aHM4gDzJOucuhGJVwIcT9wP4CXlxdpaWlOWVyXdUgDs/DgzMENZJbZkQsH1NTU9Hod/Sp+YAyw+3g+1bk9H+cI/BpzmQak71pLYabjQ2Em+rrm9vXkM62lnmPFHuRr/F6P8L6A8Iy1/JCaqkmifU/XHF61k3HA3mM5VPbw/ga1BHE453CPr9mh04cIlsEO+39Irg0gpjSdzXa+Fpa+zyaiyjYyCthx8BR1J633+B2vPk6ETwSRvt0YmTKeSf7JeK19hB35/ZAe2nhCervmEZnPMNxDsLaslrka/T0/Gf8kRYeLiGgMIaz0ANuc8BlZU1ND2oY0rtx0JT8e+GPuSroL38YCLgKO5lRQ4IQ1GKSBd6e8S2hJqFO+F6z929aS6uZq1hWtY1a/i7hWeJOz8zMy8h1TQvB43ONQAWlpaSTmfUKr8GJbzSiaXHztn+7/lOSgZA7tONTrcdN8+lNx7AeONqVZPYdG77GXEMLcil8upVxu+sUYVd0NJAP/lFJuN25/BrgL1R65R8tYLBNPAzcBnxk3/UcsEx/LJfJPFq1OSmnZ4wWmyxdYI1/guHyBDPkCmfIFMiw+38qH8aLeMPv9TuDvvZ0TEBAgXcrygVJ+/SO7h0lNTe39gH3/lvIFpKzKtnuuPmmqU3Nte0bTafq8ZhOnvlbryd6k6XqklFLuX67mKj2myfA9XvOR94zzHu3x3Pu/ul/2/2v/HvcP/8dwecOHN9i5QjN2v6zWVFNg1zAWv88m9v7TOG++TfMlvJgg7/zszp4PyFqjxt/5gk3jW0KP11yRKWWKp7zjXyNkXEqcw+fNq8qTOZU57Ru2/lFda1Odw+fqjOmao/8aLe/98l61MXermv/UKs3ndwWpqanyzT1vytA/h8qK+gqXruWL9C8kS5EbszZK+d4UKT+c49DxW1tbpWyqkc0vBUq58laHjm0LdU110vePvnLxd4v7PvijeVK+P82meaz+/LIAoFZaZhOFAanA6E7bnwKW9XjeUtJZip/Z7/4sJd2SOaWUVoWb3wReBGYAU4DJxmetyAESzH6PB9wjI7gnQgc5LPejV2ryAOGc3B5vf/CL6F4GJ387nPxK+zWYU2qSv3FC3o+r8hLreg83g2qRVVRbRH0PFfV51XmOqWw2EW6qcHZyyNlUuGJDdXNtUy3ZVdm9dxhJmg+x0yH9fzYu0A7S3wPZyu+v/Depd6c6fPiXtr3EkFeGUGUqOnOBlFFSmJlWoilf20nh5lXHV/Gvnf9yylwm/L38qWysdHmF84bTG/Dz8mNq3FSIngKFux2We/vytpfp95d+tBz+L16GWhj/C4eMaw+NhkaemvEUN468se+Dz1KtRCllBZAGXN5p1/+AG3o5NQvwM/vdF7C4N6E1RmIli+VqFssiFsvStod27ASGCiEGCSF8gFsBJ1skVhIyEKqytJ+nJhcCozWphOyWngS11z8Eax9wzhpMlKUr48kOcWWLCR+m5nJ2XmJdkXpvfcN6PMRU4dxdD+eaphqqGqsco5FowmRgOFsGp74EfEJUtwQrMfXv7iB/0x0Dpqq/q77kMxxNfSn4BDEscRZD+zm2t7Ch1cB7B97jsuTLCPENURtd8B4mhZppJTq528p7B97jpW1OUpwwYiqacLWRmJaVxkXxF+Hr5Qsxk6Gp2mFFS4E+gZQ3lJOz+xVq/AdD3HSHjGsPYX5hLJmzhGnx0yw4OFl9rjRUaL4uexFCRAkhwow/+wPzgaNCCPMPjKuBo70M0wgcFsvE22KZ+A9wCKgRy8QrYpl4pa81WGNlpJIi/oqKaze2bV0s91gxhsVIKVuEEL9EVeV4Am9JKV2gR2IFoQOVl6+lEbx8+zzcZpwlpG2iOyOxtkCJiIOSxzF9EWlNaXqvRSsORQjlTXS2kVhfbOzb3HPuX5tWYlV2FwPDoRqJJkKSlOHq7OKVBtu7rRwrVQUTffYq7jdaaTFWZkK4ffnEVtFcC96BSCl5bfdrJIYmcsXQKxwy9LrMdeTX5HPXWLOCAlNlp5ONxK+OfUWrbMWjtgAQvXrIHUlGeYZTi1ag3UjMdWEDgoqGCvYV7GPJ7CVqQ4wx4Fe4E/p11Za1lrYK55J0Ggf/Hxc4SRi9Nzaf2cy46HEE+1rQycck+l15Cvwmabsw+xkAvGPMS/QAPpJSrhJCfCqEuABoBU4DP+tljM/p2B0vzZoFWGMkmopGJpttk8Al1kxoDVLKb1BV1GcHIQPVc/WZ9vCcFtTkQrATNamC4rr27s34uv3nipMQbXGXH9uRUnVbGX679nOZiJ8Nxz9RnSqcpQNW13NLPhO9aSU6VCPRhIeXkrlwRbjZRiFtk0Zin6LqkaPVc8khlxiJQghe+OEFJgyY4BAjscnQxJNrn6R/YH8WDVvUvsMnSP0vO9FIfHDKg/xo7I/UL3UFSiDeSRGQzIpMbhjRWxTO8biDJ3FfwT4A5gycozZEDFetEAt2wcg77R6/zUj08MM74lL68NM7hUX/W8QdY+/gH1f8o++D226WTkK0exuJUsoDwIRutlv8hy2XyHfsWYM1/60LWSwbOmxJEU6I+Z1FhBrvWiszNTYS8yD24r6PcxRBcVBXqPq+mj7gM1aBp6/qg1t+3DlGYm2BEpl2licR2vMSszfAKCfJPPTSks9EXHAcAtGtkWj6gnJouBlcI4NTX2Kz/M3tY25nVNQoAn0Cez/Q9PdUehiGXmvTXDZhNBIBxkSP4VBR71WZlrIzdydHio/wwY0fqHCjOeHDnKqV2KHbSY3zNBKrG6spqStxuicx0CeQn4z/CcMj7ffY2cqcgXMofbyUIFOzAQ8v9flcsNMh4yd4+uAFnIoYRbKnv0PGtIe65joqGystj5yYekyfhXmJ1iCWiZXAcuBbuUQ2d9o3GLgHyJJL5Fu9jWNNTuKnxi4rihQRA1istXNeYPIkapmX2NKovjidIaRtIjjO2OXFmHje0gBZ37d79JzlmTAVrUQ4Uaw2crTKDXRmyLm+b0+ir5cvMUEx3XsStQg3g7rxKT/hXPFpO8LNg8MHc92I6/o+0CdI/e+WOMZIs5jmWvBSRuLoqNGcKD1BQ0tDHyf1zfTE6Zx66BTXDr+2687wYU71JNY01bB893IOFB5QnkQnaiQCTmvJZ86b17zZ/WvvRML9wzuKmEdPhuK96kbfTjwP/4dfeMP4UT+yeyxHkF+tcl0HBFv4t+UdqFranuNGInAfMBM4KpaJnWKZ+EYsE+vFMpEJvAbs7stABOuMxC+AT0gRnqSIgSgD8Snr130OExSr7toqs7Sbw5T87Yy+zSY6d13JToWWOrjgJtUez1lfOmXGns3O9CQKD4ibCblONBIt8CSCCjn3FG4O9Q3t24NmLWFDVe6eM9uO2RhullLy7v5324pX+iRyNJS6wEg0ehJH9x+NQRrahadtoLqxmi+PqjZ4PXqRw4cpw9tJvWsNrQYeWPUA3538zqkt+Ub1H0Xtb2q5+oKrnTJfZxxh7NvC+sz1zH1nLlkVWR13xExRN/f2thltbYEDr/G3ofO4Zdr/2TeWgzBFTqxKrzlLK5ytQS6RBXKJfFwukUNQkoJ/BB4BRskl8lJjD+c+sdxIXCxfB9agjMWVwM9YbLlq93mBh6cymrSUwXFmtxUTnY3EUyvVl1vCXKNnwkkhyNIj4Bvq1N6vgGq7WH5Chcu0pqURmqpU7lZfywpN6NFIdGg+oglny+AYmtVrYUO4ubiumLu/uJuvj3/d98GgjMSyY2pOZ9HSbiSa2gZabNR2w8PfPsz1H13fu6HZVuHsnPcw1C+UUN9QTldmKSMxwHn/uwHeAfh5+fV9oIO576v7uOAfrsnUe33P6+wv2E9MUKfXua14xc7OK6dWQXU2jP8FFW5SHZxv/FzWjcSekUtkllwit8olcp+xA4vF9G0kpohH2h5KaycB2AdMM27TMSd0kLbhZpOh5gpPYnWuKh7JWAVJl4KXX3v4ykEN5HulLF2Fmp1dTWfKS8zdpP1cpjZ0FlSAJoYoT6Ls9NrnVec5Ph8RnC+hYkffZlPRSp+VzSYiR0Nrs3NzLs08iSMiR1D1ZBU3jLSt0OKLo1/w1r63eGL6E1wQ2YuB4goZnLAkTpedVK9vkHPCzW/tfYslqUucMldnIgMiya/Op9XJPcFL60r5LP0z7hh7R1fjOGyIusG2Ny/x0JsQFM/LJZmEPx9OTUtN3+dozEXxF/H+9e9bl38alqxuWpprtVvYOYIlnsRgs0cQqpT6pNk2HXO01kqsdYEn0T9SNW+vyVVVztXZMNhYNRkxTPVjNRk3WuJM+Rtz+k9QX+bZThDVritSzxaGm+tb6intFDrMrcp1fD4iQHC8ujFwlifRDiFtkzetV4PJnH7Kk+fUvEQzI9HTw9My+Y5uKKwp5L6V9zEhZgJL5yzt/eDQQSolxulaicboipM8iZ8f/Zwvjn3hlLk6ExscS3NrMyV1TvhMNOO9A+/RZGjipxN/2nWn8FB5ifYaieUnIPYiEsIGApBf74ToSh8khCZw+5jbrUuvMVU4V1isKX3e0nd182K5zAnrOHcIHWi8Q6lX3UocTXWuqir2i3D82D0hhCqUqclVXkSAwVeq5zBTCPK4RSFSm6kvUxXWzixaMeHhpbpyOCMvsd7YbcVCIxEguzKbyAAVkm2VreTX5GtjJAoPCB3iPG9b22thmyfRx9OHpFALZYsihqvrKzkEF9xs9Xw2YWYkAnxw6APWZqzljavfsGqYP278I1WNVbx3/Xv4ePbRf9rT2yhl5FwjcUPmOtXnwUmexMzyTIcLlFuKyYufV51H/0DnaEJKKXl9z+tMiZ3C2Oix3R8UPRl2v6hyE20Nw9cXg39UW/X20ereNJydw/ac7Xh6eDI5dnLfB5tok8E5AVE9vF7nGGKZ8AZGA7lyiSyy9DzLcxJTxBpSlPK38fdwUsR3Vqzx/MAkg1N1Wpvxa/NUqNnZIVeToPaplRAztT0v0FnhK1cUrZiTMFsZEFon/JsMI0vCzd1oJRbVFtHS2qJNTiK0Vzg7g6J96tmG9/xY6TGSI5Lx9PC07AQvP3XDU+okvX4puxiJJ8tO8ubeN6lpsi6Ed2Hchfx25m8ZGWXh6+TkCuenZz9N5hUpKiPFCZ5EKSWZFZlOl78x0SaoXeU8QW2DNHD/pPt5akYvtaQxU1TIv/iAbZO0tkBDOfhHMiJyBEPCh7CxxMmNBrrht+t/y0OrH7LupDZB7QzHL8hNEMvEv8UyMcr4cyiwH3gX2CuWidssHcea6uYoFsuKtt8Wy3LAObdJZxNay+DU5DpX/sZEUByUHFRdVoZc1b49dKAxfKWx4eDMns3dEWfKS9ys7TymcLMFXtnujETNNBJNhA9TnQqc0cIufzsExduUf/v6Va/zyU2fWHdS5CjnhZsNjUpKyMxIHN1fiXofKT5i1VB3jruTp2c/bfkJpmIzJ+XMRQVGEdFcre5rnVB0VlRbRF1zncuMxCHhQ3hi+hMkhTmv4YGXhxcPXfhQ75JPMUZPm63FKw3lgAT/SIQQ3DzqZvaU76G4tti28RxEXnWe9TfFviEqjeUcNhKBmXJJW5e6HwPH5RI5BpgEPG7pINYYiQZSRGLbbykiCdVxRcccZxiJzsxHNBEc1553aMpHBGMnjiHO8SR6+Tuv60lnYqaoMH+OxnmJdcV99m02ERkQiZ+XXwcjUTONRBNhQ8HQpLoKaU3BDtVX2QaiAqMYEWXlDUW/0arisbnepjmtwpQw342RaI2o9qdHPqWyodK6ucOHKQkrJ0kZldSV8LtDn7IHP/DRPo29qLaIfv792jqDOJuowCiem/9c2/upNTVNNby1962+PdDBiSqNxda8RNPnv1Ft4J7x9/Dk8CcdL7VlJTYZiaBEtSvOaSOxyeznS1HKNMglssCaQawxEn8LbCZF/JcU8V9gI7pOYleCBqgiDy20EqVUOYlaGQC9YTJMgxMgalzHfeFDtTcSS4+05425Ai9fGDBNe1Ftk0aiBekEQggSQhI4U2VmJGrRks8cZ8ng1BWru/yYC/s+thP51fn8YcMfyCy3UooqcrTyrpU5Ic+qGyNxUNgg/L38LTYSj5Yc5caPb2T57uXWzW1KESmzXZPRGgytBp45s50fPAOdkiYzJnoMJY+XOKwPti1UNVZRUGPVd7HNfHDoA+796l4OFh7s/UAhlDfRVk9iWyqMinIM6zeMS6MvJcA7wLbxHICp28oAW3JdQwad657ECrFMLBLLxARgOvAtgFgmvACLCyas0Un8FpgIfAh8BExisdRzEjsjPJS3SwutxMZK5QFwVbgZlBex8we9qV2bluGr0nTXFK2YEz8bivZCY5V2c9QXW1UA1FlQO686Dw/hQXRQtBarc57OXsEO9TzAeiNxX8E+lqQtsb5/rqmHszPyErsxEj09PLk44eIukkY98caeN/Dy8OKucVa2i3SyDE7/wP74CQ9Oe/j2fbADEc7O2zZj9tuzuW/lfU6Z6409bzAqahTT4qf1fXD0FHXDbYv0SydPIkBFUwUpP6S4LORs6rZisyex6rRzUmdcwwPAL4H/AA+beRDnARYKyFpS3ZwihrNYHiVFmJrzmj55E0kRiSyWeyxf83mCVjI4rpC/MWEy0Ibd2HVf+DBVMVedCyEJjp+7qUaFN11VtGIifhZsa4W8H2DQ5drMUVcM/pan+iaGJvLdqfZ7tdyqXGKCYvDy6Ptf2yYCY8A7SHsDI3+7UbZjktWnHiu1Uv7GRFgyeHg7Jy+xGyMRYO1day06vcnQxDv73+HqC662/oYgKBa8ApxmJAohSPT04rR0jtH27KZnyanK4V9X/ssp83VHbHCsUwpXDhYeZHvudl667CXLjOKYyepmvnAvxM+wbrJujMSSphIeXfMoQT5BPDD5AevGcwADggew4Z4NDI2woZI9dLAq5KnJhZDEvo8/y5BL5HGgyxeVXCK/Ayx28FnyTfIIcD+Q0t06gEssney8IWQgnLKo4411GEOJLgk39x8HD+R2X0Rg7pnQwkg0hf9cVbRiInaayhfM2aCdkVhfBKGW5+ElhiaSX51Pk6EJH08fcqs10kg0IYSxW4ETPIn9Rqm+ylZyvPQ44X7h9LNWOsfTW6U0OKM9Xw9GoqV8efRLSupKuG+iDd4qIZxe4ZwkWsloaer7QAewNmOty9rimYgLjmN33m7N53ljzxv4ePpwx9g7LDsh2lS8stMhRuKQwCEM6zeMj4987BIjMcA7gFlJs2w7OdSYs1qZcU4aiWKZuA9Ik0vkCbFMCOAt4AYgC7hbLpF7LRmn73DzYnm/8XluNw/dQOyO0IEqt8zRau5t3VZcYCRCz1WmbXlqGn3pmORvIlzsSfQOVB+yWuYl1hVbpJFoIjE0EYlsC61q1pLPHK1lcKQ0Fq1YH2oG5Um8IPIC28KNkaNd6kk8WHiQScsn8UP2D72evi1nG4mhiVw6+FLb5nemkdjSwCRayGmqp9kJbQ8zKzIZFO6aymYTscGxFNUWaX69x0qPcd3w69p0UvskaID6/iiwIS+xrlhFEcw0FoUQ3DzyZlKzUimqtVh6z2Hsyd/DioMrMNgSMg4zGonnbvHKr1EGIcBtwFhgEMrx94qlg1hXBZAiLiZF3E6KuKvtodOVEI20Ek1GoityEntD6/BV6RHlwTNpW7mS+NmqOrDZqvaXltHWt9m6cDO0y+DkVedp60kEowxOpnZ9jstPKLkNG4pWAE5XnLa8HV9nIker/9umatvOt5RmYyVqJyMx3D+cPfl72Fewr9fTUy5LYd8D+yzXgexMhOk9dIJ3r66Q3/lA3pUv4u3prelUzYZmsiuzXSZ/YyIuOA6J1Lx4ZfWPVvPWNW9Zd1LMFOVJtJb6km77qN886mZaZSufpX9m/Zh28sGhD/jxlz/Gw5aCxuAEEJ7ncvFKi1wiTR/Si4B35RJZKpfItYDFIQxrxLT/C7wAzACmGB9WSJyfR4QOVM+OrnCuyVWdVrTo5GIPwkN5l7QKQZamK8NE4y8Yi4ifpfJY8rc7fuxO1YOWYG4k1jfXU1Zfpp1GoonwoSAN2hRngV1FKwDHfnmMfyz8h21zm9rzmXQ5taIHT2JccByhvqEcLuq5eMbknQr3D7d9/vBh2r6H5tTkEyjAwwn95rOrsjFIg8uNxJlJM/n7wr9rJg/TKlsprStFCGF9dXH0ZOONWIV15/VgJI7uP5pRUaM4Veb8Fnf5NfkMCB5gW9TAw8tYZHrOGomtYpkYIJYJP1SxinnCs8VGhDXZ7ZOBkSy2sPTufEYrrURXaSRaQvgwKN6nzdhlRyDSTVonxU0HhMpLTJzr2LHrTG3oLPckJhhzQM9UnmkX0tbak2hqxVhxQnmkHE3+dmU82VioZE8f5LYK55JDNhupFtGDkSiEYHT/0Rwq7j7k3dLawvB/Dudnk37GY9Mfs31+8zziCCsLfKylVnnTXjy5ju173uXDGz/UbqqmWsbHjLe+aMnBDI8c3ta6TgtWHV/FrZ/cyqYfb2JSrJXFXTFT1HPhbkiaZ/l59SXd3sAKIdjzwJ6+W0JqgM0aiSZCB5/LRuLTwC7AE/jKJKwtlonZgMUXbY2P9hDgnO7sZzuBMSpvw9F36W5tJA5VuR2ODkEamtW4Edp94FqFbyj0H69NXmK95d1WTPh7+xMZEMmZyjPaaySa0ForsWC78nbYEEpNzUzl/pX3U1pnY/vE0EFKtF3rvMReCldG9x/NoaJD3UrhfHfyOzLKM0iOSLZvfmfK4NQqmZIKKfnkyCfWi39bwZjoMex9YC8zEq0synAwrbKVw0WHya7M1mT8l7a9RFRgFONixvV9cGdMigHW6iXWF3frSQTaDESbcgPtwG4j8RwW1JZL5CogCRghl0jzCredwC2WjtO3kZgiVpIivgIigSOkiO9IEV+1PexACHGTEOKwEKJVCDG5076nhBAnhRDHhBCX2TOP0xECgpPOP0+iFuGr6jNq3DA7vxQdSfxsyN/q+HyuNk+i5UYitGslat6Sz4R/pOoIo4WB0dKoejbb6MXbfGYzr+95HX9bUzKEh/Jgaq2VaDISvbquc87AOcwfPL/bCt1/7vwn/QP7s2jYoi77rMIvHAKiVatNraktAARzh15Jq2xl05lN2s/pBox/bTz/2ul4GZ69+XtJy0rjV1N/ZZvUlX8/5UGztvNKD+FmEw+sfIDL39dI9aEH8qrzbBPSNhEySBm/WucguwCxTEwBIuUSWW78/S6xTHwJPAdY7Pa15C/sBduWaBGHgOuB18w3CiFGArcCo4BYYK0QYpiU8uxRvQwd6NicREMz1Bba1MfWKXQIXzkwBGm6ywt1bY5RB+JnwZ6/qQrBuIsdN25bTqJ1LdETQxM5UXpC+5Z8JoTQrsK5eJ/K+bTRSDxdeZqYoBj7ukBEjoas720/3xKaa1WxVzcJ97eOvpVbR9/aZfuO3B2sPrmaZy951jEFIAOmQd5W+8fpi7oCCIjiosQZ+Hr6kpqZar+R2wO/+uZXFNYW8tFNH2kyvqV4CA9ig2M5Ve74PL2Xtr1EoHcgP534U9sHiZ6sPPaW0lyv/mZ7MRJjgmJ4Y+8bFNQUEBPknKDj3gf22hfmNlU4V2ZClJukNDmO14D5AGKZmIUyDn8FjAeWA92IHnfFknDzb1ksN/T4sAMpZbqUsrveUNcAH0gpG6WUmcBJwLYmrq4iZKBjvWp1hYB0b08iOL54xZQvYtK0cgfiZqpnR/dxritSYs6+oVadlhiS2BZuDvQOJMQ3xLHr6o4wjQqVTAVBMbb9u5+uPE1SqJ39vfuNViHS+jL7xumN5to+NRI7y6f8aeOf6Offj19O/aVj1hB7sbqpqytxzHg9UZMPgTH4eflxUcJFpGalajbVzrydlGn5vlnBgsEL+ObEN9Q2OU4Krby+nA8Pf8i9E+4lzC/M9oFipqgqflP0oi8ajOkbvUQ5bhp1E62yla+O2RVgtIrB4YOJD4m3fYBQMyPx3MNTLpGmf4ZbgOVyifxULpG/BywOzVniSbRQgMmhxAHbzH7PMW7rghDifpTYN15eXqSlpWm+OEtIKJUMaShl07pvMHha59Woqanpch3BtUeYBBzMLKO0PK2701zOdM8Qig6lcaJ6gtXndnfNAINzNhAvvNm4+zgI51fP9cQUv4E0HPiCg/UX2TxG52u+IOsAEZ4hbN1gnfHZXNJMdVM1m49tJtwrnA1Wnm8LAyu9Sao6w6b139FqRbu1nt5nEyMyvyLMO5Ktu0+i7g2t41j+MQYHDbbrcyCispWxwN6171EZbL93obtrHp6TQajBk+09rPPeXfdyQfAFPH7B423bfhT+I2b4zmD3VseINIfW+DMBOPj9ckrDHOgRp+M1Tyw4QYtXMAfS0hjnNY5jHGN96nrbZEv64HjRcS7ud7FLvgc6v8+jW0dT21zLs58/y6XRNupZdsPyCcsJ8Aiw6xpDq72YABxY8yZloX238wuqO8Fk4NCpAkrK2uc1v2YpJd7Cm9T9qQyr1qCgrRO59blsKtnEgugFRPhE2DSGV0sVM4CTu78nJ6fvm/O+Pr/cDE+xTHjJJbIFVd18v9k+i/MULDkwjBRxfY97F8texZGEEGvpvuDlt1LKntqSdFfP3m1VtZRyOcp1SmBgoJwzZ05vy3EeRwshdzkzxyW2V0xaSFpaGl2u40QZHIUxF10O0dYbYU4hbwRx3jXE2fAedHvNACv/BU0DmTPXiio8Z9CykMD095gza4aSUrCBLtf8xUvgEd/969ALxYeLeTXjVU7Un2B8zHirz7eJowXw9TvMGjPAqjBNj++ziVP3QdJMm68h6HAQFw27yL7XoGoInHyCCQneMN6OcYx0e82Vr4Do1+M6B50ZRGlTqbbvZfOFcGIxY/pVw0zHztPhmo/XQsKFzJkzhzk4dh5zaptqKd9QzvSR05nj4OuxhM7v8yw5i5eyXuJg60GemfOM09fTK02T4PgjjO3fBBfN6fv4rGZIh9FT5kD8zLbNna85Zl8MvhG+TvkM+uDQB7y24zUeuvwhRkbZ2GhBSjgaSnKkB8kWrLnPzy/3YgWwQSwTJUA9sAlALBPJgMXVY5Z8u4X+P3vnHR1Xde7tZ49Gvfdmuchy7wXbYAymhRICAUIPgZtCCtwPAk6Am9w0Qi7EJiEJIQmBFAKhdwgdm27j3mVbbrJ6L6Muzf7+OHPGY2kkTTlnmvaz1ixJp827Ne03b0VrxDiccBtRJEopz/bUGBcqANf5buM4PjM6PHDtleilSHRLMEfyeUr6VCg3OJTUeii0Qs06406D7X+C+u0+zRd2S2ed10UrcLxXYkt3i/n5iDp6e5rGvcbl8nQ1QksZzPE912rPTQb0N0wep4X8G3b4f63hGCXcPCt7Fo9ufRS7tPN55ef879r/5ZEvPcKEND9D6a5Ex0PuQm0WuVlIqRWuJOS5bJI0djV6PiXEQw63aCHDYE9b0bEICy9e+aL/legOHtnyCG+Wvck/vvwPknwYV3kCMclaxwhPi1fcjORzx3cXf5eiVBNGs7pBL9Tzq7pZiIhtgyN/Ku8RPxfvAfnA2/KnznYJFrTcRI/wRCQe5Xb5dR9s9IdXgH8LIX6DVrgyBfg8wDb4h94r0ahcB1ullq82yos0qKRPhT3/0qaR+FM44ErrYZ/z00xlnGNe6LEPjBOJXfU+CWLXN+WAicT0qVrRhZFNp/Um2sF+vIWA7HlalbVZ9HWMOJd6do4WqixvLefnH/ycbTXbyEzwcha1JxScon3ZGegzp1l9d5NWiORSgXrV81exp34PO79rbGW1QPClqV/y3atkAgvyjYv6PPj5g1gtVhJ9nPc9hLzFcPQdTciP1oy6y7POC3etuMsY2zygqr2KOGscqV7mcA8htdj8bgZBQv5Urnezzau2FJ4khfjQytwzhBCXCCEqgJOB14UQbwFIKXcDzwB7gDeBm8Kqshm0ClVrvHFtcGyVWmWzCXk8huEsXvE+l8wt3S3ah0woVTbrJBVobXmM7JfYWedVj0SdvKQ8oi3aB7zpPRJ1rHHam2uTgSKx+nNAaB9ePvDeofc4/4nzqWir8N+WnAVQvwPM6vvW1wHW4T/sZ+do0Ye/bv4rb5a9yQ9O+YH/3iN3FJwC/d3mNcJ39Eh09STOz53Prrpdhs/6nZUzi1eufoW5uaFVpfr0rqf56gtf9esazV3NbK/dziXTL/Ftuog7ck/SvLz6uNeR6GoAhNY6aQQG7APUd3hYDOMn1bZqCpIL/P9/pBZrzghpN8awCMMTxXGdWXcupXxRSjlOShkrpcyVUp7rsu8eKeVkKeU0KeUbZtlgGkJo3kRDRWIIh5rh+CQOo/rn6V7YUAw3g+ZNrPzImDeX/h6tV5cP4WaLsDgr/EzvkehK5kwt3GwUNRsga5YWCvOBnXU7ebPsTf/a3+hkz4f+TvMahvePEm7OmcX3l32fv237G1kJWXzvpO+ZY0eBo2DFrJCzY9oKicdF4pmTzgRg3ZF1ht6Vu+bjoUCNrYYndj4x4qjF0fi8UvOyLxs3epGJx+hfxmo8aKrd1aCNhB2lwf1P1/2U/PvzsQdAcPndSFsndRIM9GhV+IohjC4Sb5cmjx6IYFInGhtuDtUeiTrpBovEtjAQid1NxoRcfeyRqKPnJQYs3AyQMUN7rI2YsiOl5knM830UXnlrOYnRiaSP4u3wiBxHmLBuq//XcscoOYkpsSlcPvNyamw15nkRQXtPSZkAlWaLxOPh5kUFi0iOSWbtYePyl6WULHlkCec/cb5h1zSKq+dcTZSI4vEdj/t8jc8qPkMgWFJoYCpG9nwQUVDrQV7iKI20dXITcxmQA75PPPKCN699kxevfNH/Czl7JYZeXqIQIk4I8bkQYrtj8MjPHdtXCyFKhRA7hBAvCiHSzLIhhGOXEYBRnkQpw8OTGJOkfegYJRJDsZG2K3peohEh505H6M0HTyIcF4kBCzeD5km090GLAa2JWg9pvdjyff8QPNp6lPGp440Jx2XOhKgY88KwHvRJnJUzi/vOvo+bTrrJHBt0Ck4x0ZPo8M64eBKtFisrJqwwtF/ik7ueZFPVJq6YeYVh1zSKnMQczi05lyd2PuGzh60opYjr5l3n+0xyd0THa0WVHnkS6z1KhclNygWgtqPWX+tGJdYaa0zxU2roikSgBzhTSjkPrQn2eUKIZcA7wGwp5VxgP2BaMqgnY/mG78x7vxhvpDERR+ok6G6GHj9nlfa2ax8qoS4SQctLNCpE13pIy4Hxp2msmaRMhOQirXjFX5yeRN9E4pSMKcRZ48hP9mNElbfoFc5G5CXWOnr/5fqWjwiaJ1EXy34TFa011Q6SJxE0b+IPl/+QxBiDChWGo+AUsFVAmwlzhjvrtPzVQSkEty27jV+d9StDQsSdfZ3c8e4dLMxfyPXzr/f7emZw3dzrONZ2jA+O+PZe8Y2F3+CfX/6nwVahNdWu3aQ5IkbCC08iQK3NXJHY2dfJLW/cwvqKIXUZ3pMyARAhOcNZatgcf0Y7blJK+baUst+xfT1aBxhT8MSTuM752/3ivUH7XjLQlshDr3BuO+rfdfTE4nAQiWlTjM1JDNVQM2h5p+NOg8oPR3+THY1O/8LNty67lc++8Zl/I6q8JWO69tOIvMS6rVr1fuYsny8xLmUci/INqjQHyJmv2WV0rttAL9j7RxWJAcPMvMSuBs07Psi7e1bxWVw641JDvL6rP1lNRVsFD5z7gCkNuo3gomkX8cUpX/RpnGJXXxc9/T0mWIX2pay7afS0KE9FosOTWGOrMcK6Yalsq+T3n/+eA40GOCSiYrQv+8HzJFqFEJtcbq5NrxFCRAkhtgF1wDtSysHzFL8OmFa34W118+C25qZVPkcEzl6JfuYlhpNITJ+qvaEYMRqr9VDohpp1xp2m5V35W9HtZ7g5OTaZ+Xnz/bPBW2KSIHm8MTmZtVu00JfV8+ktg3nxyhe55ywDmxbnLNCey55Uf3pDn2NMW6iIxOy52hxp00Sie3HxcfnHzoIMX+m39/Pkrie5YtYVrJiwYvQTgkRCdAKvXfMap44/1etzn9r1FCn3pnCk5YjxhuWdpP0cqV+ilMfF/iiMSxnH3WfczZzcOQYZ6B5DeiS6klYczNF8/VLKxS63h113SikHpJTz0byFS4QQzsbLQogfAf3AE2YZ54lIlMP87u5vhSspDoHj7zeUcBOJ4P9cX2nX8jlD2ZMIUGhQXmJXvU9zm4NO5kz/RaKUULcFchYaY5NROItXthl73VATiRYr5C8NuEj81qvf4t6P7/Xr8laLlS3f3sKD5z/o13UCgZSSyrZKGryclb2+Yj0J0QnGpVK4kjVb86TVjpCX2NOqeb498CQmxSTx49N+bHobIsNFYhg01JZStqBFds8DEEJcjzbo5FppYmm/JyIxh/vFbdwvbnf5Xf/bN7fHWCE+E+Iy/Q/HhaNI9Dcv0ValheVCXSRmTNNCxBV+5iXqPRKN6oEWKDJnQnOpf/0E2ys0MZHru0j84MgHzH5oNjtrDWzQnD0XEMbnJYaaSAQt5Fy39bhtRjGCSJycPpmDzb4XPR1rPUZPfw8J0QlkJ4b+R1F7bzvjfjuOR7c86tV56yvXs7RwqTmh9KgYrcp5JE+ih9NWdKrbqzna4meK1SjoItGwHOzUYq3Iqq/TmOsZhBAiW69cFkLEA2cDpUKI84A7gIuklKYa7cmz7q9AMpDk8rv+9yPmmRYBCKH1ffO3m3t7pVbAER1vjF1mklasNfz2Ny+xNcQrm3X0vER/PYmd9T6HmoNK5kytGbM/ebd1W7SffngSDzYfZHf9bmOrP2OSIb1k7IhEOeBZpas3dDVoX5TdMDl9MgebDvpUvCKl5PJnL+fsf50dsv0RB5MSm0J+Uj6ljaUen9Pe086uul3G9kccTO5irXBsuMprL0XixU9dzHde/45BxrmntaeVxOhE/6et6OjOCKP6GhtHPrBWCLED2IiWk/ga8CCaDntHCLFNCPFnswzwZCzf77ldNptlQMSTOQtK/+3Z6KPhsFVCYoj3SNSJitEKdvwWiSHeI9GVwtNg/3OaUErxcbZuV73PRStBJWOG9rNxz/F+Y95Su0X7YuHHDOijLUcRCOP7RGYvOD4u0ChCUSTmO0RI1adQdLox1xzog56W4T2JGZPp6OugrqPOWfDgKU/uepINlRv4+8V/N24CSQCYnjWdfQ37PD5+Y9VG7NJurkjMOwm2PwRN+yBzxtD9ukj0sPNCblKuMVOPRuAXZ/yCn5z+E+Mee90Z0XLoeNeGEEBKuQMYMttRSmnMQHAP8MSTuI/7xW7uF3/lfnED94upplsVSWTO1HI6Ovzo5h4OPRJdSZ/qv0hsOaQJh5Qw6LJUtFL7eXRw8b8XdNaFqSfRRST6St0WTWz6MSmlvK2cguQCn6pHRyRngeZd6G4x7pqhKBLjM7RqdSPzErsdxWvDiMSSDO1zzpeQ89+3/Z1pmdP42ryv+WxeMJieNZ3ShlKPvZ/F6cXcd/Z9JotER9up4fISnXObPfMk5ibmmt4CB7R8VMMI4YbawcaTiSs5wCXAJ8ApwAvcL2q5X7zM/eKHJtsX/ugtPRr8CDmHpUg84F/rkNZDkDRO80yGOlmztSbiR97y/RoeNqsNOeLStWkaTX7k3dZt8SsfEbQeiRPSfPTijkTOfO2nkU21Q1EkwvGm2kaFb0cJU55SdAqffeMzn6ryd9ft5uSik0O25c1wTMucRnN3M/V6y6tRmJg2kR8u/yFpZvaKzZihVbcPl5foZbg5LymPuo46U0fz3fyfm/nb1r8Zd8H4bO31qETiEDx7hd0u93O7/Ae3yxuBi4FfArOAn5toW2SQ5RCJvuYl2vuhsxYCOW7NX9KnQp/t+EguX2g97Hv4MtAIARPOhfJ3fCvg6O/WGqaHY7gZNG+ir57EjlqtSClnSETlBKSUI1aFLshbwBeKv+CbDSNhxni+UBaJ3U3G9TkdRVykxaWxbNwyr2dtN3c1U22rZmZW6IQFPeX8Kefzr0v+Rbx19PxyKSWv73+dJiPaiY2EJUr7kjZcPmpXg/ZlPdqz0ZCBGM33z+3/NLZITQgttSkEG2oHm9H9tfeLU9A8iCcDRcAhtA7fXwW2mGlcRBCf7ahw9lEkdtRqCcVh5Ul0meGc5GP1WeshmBR6c1iHZdJ5sPvv2rfxAi9DQ7pXIRzDzQAZM2H3P3zLu9XF1yhFK3e+eycPbXqIljtaiLJEDdm/5gtrvLtfT0nM1TylY0UkguZNzJjm//U88EC9XPoydmnnkhmXeHzZ5Nhktn17mzEj2QLM1MypTM30LGPrUPMhLnzyQv5y4V+4cdGNo5/gD3knwfY/aXmkg1M2hmmIPhxnF5/N3y/+O/EmFVq297Rj67UZP10qtdj/frcRiCdB/Y/RxOBvgJe43dxy64jDWeHso6clnNrf6Djb4Oz3LQm+r0vL4Qz1ymZXxp+t5VAeedN7kegcyReunsSZmue4vQJSirw711nZPH/Ew2blzMLWa2Nvw15m58w+YZ+U0tzihZwFxvZKDFWRmDFNSx+o+hRm/5f/1/NAJD6w4QF6+nu8EolWi5V5efP8tS5obKrahJSSkwpPGvE4feScqfmIOrknQf9vNWfG4NdiZ73HoWaAGdkzmJHtpgDGIKptWn6/4XPq04rh6Nv+FZlGIJ6EmwuAXwELgTe5X3zK/eJB7hfXcr8Ik3hgkMl0tMHxJdcnHEVichFExfoettLbEIRDZbNOfAbkLdFEorf4OW0l6DhnOPuQl1i7BdJKRm0ivrRwKYDbCR0bqzaSem8qaw+v9f7+PSFnvvYlr7/bmOvpItHqe6GOKQgL5J9sXPGKUyS6b4EDUJJe4nXhyiv7XuHxHY/7Y1lQ+eYr3+Sn63466nHrK9aTGJ3IrGzfR1V6jF684i7k7OFIPp2+gT42VGwwrcLZ8EbaOqnF0N+lpXcpnHhSuFLD7fIFbperuF2ehqOZI1o+ogGDE8cAeoWzrcr7c8NRJFqitA9+Xxtqh0uPxMFMPFcLN3d5mYvj9CSGq0j0o8LZw0krUzKnkBaXxoaKwWNLtfY3bT1tZCYML0b8ImeB1kOwYZcx1+vrAGuc9joJNQpO0R7HbgO6nnU1aHls1rhhD5mcMZm6jjrae9o9vuxDGx/it+t/6799QWJ61nT2NY7eBuezis9YUrjEbXqF4ehf1GrdFK90ezaST8fWa2PZo8t4ZvczBhp4nK6+LvKS8swRiRDM8Xwhyegi8X6Ryv3iPO4Xv+B+8S5wDLgOeBW40mT7IgO9wtmXD1FbpTY2K9wERPoU3z2JevJwOHkSASaep+WPHn3Xu/M6wzzc7My79fL53d2svSF7UNlsERaWFC5hQ+VQkVjeWg5gztgyMH48X18HWEMs1Kyj5yVWr/f/Wh54oCanTwa0/DtP2VO/h5nZ4Ve0ojM9azqHmw/TPYJnurOvk+212zl53MmBMUoIram2AZ7EtLg0YqJiqLH5Ubg4AudPOZ/q26uZnjXd2AunqjY47vAk3FwG3AR0AXcD47hdLuV2+X20gdOK0fCnwtlWqSXOh1mrB9KnaknAvlT7th3WQnHhJpryTtJyurwNOXfWaXObY1LMsctshNC85d6Gmz0sWtG5Zekt3HXqXUO2l7eWkxKbYl6bkNRJ2mNjVPFKf0fo5SPq5J0EIsqYkLMnIjFDE4mehpzbeto41nYsLCubdaZnTUciOdA4fKQlzhrH9u9sN79gxZW8k6Bhx4lpFQN92pc5L0SiEELrldgRZmHb1InaT1XhfAKjF67cLkdyYd0GPGCUMRGLPxXO4dYjUSd9Ktj7tCkk3rayaTmkfTCHW/KwJQrGn6P1S/Qm+VnvkRhu63Ulcwbsf9a7ddfqRSsjt7/RuWDKBW63l7eVm+dFBMc0mHnGicS+EBaJMUnaWo0Qid2No4qL2Tmzqfh+hceVqnvrtS8i4e5JBChtKGVO7hy3x1iEJfBrzF2stVyr3wH5S7RtozREH468pDzTGmr/ZO1PaOhs4KEvPmTsha1x8K1yreetwom/7im/PtWEEKuFEKVCiB1CiBf1QdaOfXcJIcqEEPuEEOf6aWdw0SucfWmo3R7GIhGgxYe8xNZD4Rdq1pl0nlaZ3eBFD6/OeogPM6/pYDJnah4HvQjHE+q2QvJ48KKVyZbqLWyr2XbCtrMmncV1c6/z/H59IWcB1G/3zTM+mFAWiaCFnKs3aILBHzzwJMZExVCYUuhxU+wDTdr7STiLxBlZM1h3/Tq+MHn4vp5/3vRnXtz7YgCtQvMkwolNtb0cyaeTm5RrWrj5w6MfsqvOoPzgwaQUhWaucBDxVyT625r/HWC2lHIusB+4C0AIMRO4Cq1h93nAQ0KI8H7kMmdB0x7vK5w7qsJbJDZ5mZcoZXg10h7MRMf3mcNehJy76sIv53QwGY4PbW/yEuu2eOxF1Lnsmcu456N7Tth285Kb+eFyk4c/5SyA/k5j+qj12kJfJPZ1QL2fzYo9zGV7bPtj3P/p/R5d8qtzv0r9D+qdYepwJNYay+kTTyc1zn1Fv5SSX3zwC57f+3xgDUsu0qJeruP5vJy2ovOjFT/iD+f/wUDjjlPVXmV80YpiWDwpXGnnftHm5taO1h7HZ6SUb0sp9a+r6zme43gx8JSUskdKeRgtL3KJP/cVdLLmaBXO3lRI9rZrt3AUiQk5EJPsffFKV4PWcy/cKpt1kgq0x/qoFyP6OuvDL/9yMN60wenrhC2/g6Z9XovEpYVLT6hwHrAP0NbT5tU1fELvHWdEyDnUPYmFLk21fUTYe7X3Lg/ExZtlb/LHjX/0+NpZCVlhN45vMB+Xf8wjWx5xu+9A0wGqbdWcOv7UwBolhOZNPMGT6N3cZp1l45axYsIKA43T2N+4n6r2KvJ9HdKg8BpPchKTA2AHwNeBpx2/F6KJRp0Kx7YhCCFuBG4EsFqtrFu3zkQTfSe6v4BlljjqX/8BpRPvHPFYm83GunXrSOguZwmw51grdZ3rAmKnkSyy5tN3aD07otaNeqy+5uSOPSwCdpZ30Ng2+nmhSHHUTMYde55P3nuDgajhpw7oaz61vZrqxh4Ohuhz1yOk5FRLArU73uFAi/tQYNSAjdyKZ+ndfgkx/S20JM1jT8dser1Yd2Z3JsfajvH8W8+TGZvJ0Y6j3LDpBv53xv9yZs6ZBi1mKMLexwphpWLTyxyqyfPqXP1x1lnSWo+tL5k9ofp4S8nJ0Vm0bHuRva2+9ejra9MaHu871kh197oRj7W2WTnacpR3338Xq2Xkj6TfHfgd89Pmc3q2D036TWbw4zwSfyj7A2/UvMHk1slDGsE/V/EcAKn1qQH/PJvYncWExjf56P03sVviKKj/lKnAp1v30xszdDzgcGtu6Glgd9tulmQsIX6E98DReLbiWY50HOEH034AwF0776KjrwPRJILyWe/NYxwxSClNvQHvArvc3C52OeZHwIuAcPz9R+CrLvsfBS4b7b4SEhJkSPP+LVL+xipl69ERD1u7dq32y5F3pVyDlOVrzbbMHF67Wsq/TvLoUOea9z6prbl+l3l2mY3+uJW9OuJha9eulbKvSzt2/T2Bsc1Mnlgq5dNnDN3eUS/lxz+W8g+p2lqfP1/Kio99uotPyj+R/Az50t6XpJRSvnHgDcnPkB8d/cgPwz3ksQVSPnuO16c5n9s6fx4n5Rs3GGOTWbzyFSkfnujz6Z+//oj2WO97btRj/77175KfIQ80HhjxOFuPTfIz5C/W/cJnu8xkyOM8Ag99/pDkZ8iK1ooh+87917ly2h+mGWiZF+x9Snvc6nZof392t/Z3f4/bw4db89O7npb8DLmzdqfPptTZ6iQ/Q056YJLsG+iTUkq5tXqr3Fa9Tdrtdp+v6w/ePMaeAnRIk3WYPzfTffZSyrOllLPd3F4GEEJcD1wIXOv4h4HmOXSd7zUO8KETdYix6Dbt5ybP8m/CspG2K+lTofUI9Pd4fo6zkfZEMywKDIWnai18PGmF45zbHObhZtDyEl3DzbZqWLcKHpkI638J489i0/Q/w6X/gcLlPt3FgrwFWC1WZ79E03skuqKP5/NlcpIrodwCR6fgFG3ykWMEmrdE97dqv3gQptR7JR5sGrkNTmlDKRDeRSs607K02dj6mnTs0k5tRy3nlwRpbn16ifZTz73tatDaP0XFeHWZ3MRcAL+KV8qaNBv+cP4fnB7m+XnzmZc3z9wxnIoTCGpihxDiPOAO4CIpT5gJ/QpwlRAiVggxCZgCDJ3HFW6kjIcZX4Wdf4XOhtGPd4rEME3STZ8CSGj1YuxWyyFIyA39D9GRsMbC+DM9E4ldjmrgcC9cAS0vsaMGarfCu9+DRybBlgeg5BK4YTdc9Dy2xGl+3UV8dDwf3vAhdyy/A9BEYpSICkwie/Z8LUfLl8lJroR6TiK4NNX+zKfTj4vE0afgTM6YjNViHbWv3p56rShqVk4AxtSZjGsbHFcswsLWb2/l1+f8OhhmQaqjIEgXiV7ObdbJTdJEoj9tcPTemcXpYVrEGCEEO/v3QSAZeEcIsU0I8WcAKeVu4BlgD/AmcJOU0oDeEyHAST/U5kNu/f3ox9oqtVFJof6BMhy+VDiHc/sbVyaeCy0HoXmUalinJzESRKJjPN/jC2HnIzDrevj6frjgX8cLWwzg5KKTnZWhR1uPUphSOGoumyE4J6/4Ubxi74eB3tB/Tecs0OavV/pWvOKNJzE/KZ+uH3XxtXlfG/G4PfV7iLZEOz2P4Ux+Uj7JMclDxvPpwbToqOhgmAVxadpj5upJ9OELbF6SlrfrT0Ptg00HEQgmpYdpEWOEEIB31uGRUpaMsO8e4J7h9octmTOg5Muw7UE46QdaBfBwhGsjbZ20KdpPbyqcm0qPt5EJZyaco/08tvZ4CMcdzrnNERBuzl+miYtxp8HiVZBszkCmyrZKHtnyCF+b9zUum3FZ4EaX5cwDhCYSJ1/o2zX6OrSfoS4So2K0SlcfK5ydIjFudE+iEAKrGP2jqLu/m/l584MnoAxECMGem/YMqdJd+shSvjLzK+a3dBqJtJITRaIPlcSpsal+j+Y7q/gs4qPjiRth9rfCfIIqEscsS+6Espdgx19h8W3DH9dSFr6tYED7VpqQ43lD7Z5WrRF1hsEzOYNB+lSIy9CaEs/91vDHdUZQuDk+E67bYvrdtPe287MPfkZRahFfX/B10+/PSUyyJvjrt/l+jXARiaCFnLc8oI1p8/KDOrq/VYuCeCjo/rLpL2yp3sJfvvSXYY/57Xm/RfqbDxpCjEs58UtUWVMZG6s2cv2864NkkYO0Eqj4UPu9qx6y3U+FGQkhBGuvX8uE1Ak+m3Hq+FMD3wZIMYRgh5vHJvlLoegM2Hz/8EUdA72aVy1zdmBtM5q0KZ57EpscoZdIEIlCQN4SqNkw8nGd9eE9tzkITM2cSmpsKusr1rO7bjedfZ2jn2QU2fP9Czc7RWKSIeaYSsEp2vtQrffCP7q/1atctn2N+3h85+OjisBIKljYWLmR7772XWy9NgDeOPAGAOeVnBdMszSR2H5M+3LQ1QBx3uckApxSdAqFKb5HwrZUb6G1u9Xn8xXGoERisFhyp5YAv/dx9/ubD2j5Sz58iwsp0qd6IRIdSdwZM8yzJ5DkL9VGMfa2D39MZ53mbY2gDz+zsQgLSwqX8FLpS8z+02we2/5Y4O48Z4E2Eai7xbfzw8qT6Ajj+xByju5v80okTk6fTGdf57Dhyd11uzn1b6eyqWqT2/3hyLG2Y/x585/Z36i9P7558E2mZEwJ/jQZveCwcbeWP+9jlOOjox/xxI4nfDq3o7eDRQ8v8qrJusIclEgMFhPOgZyFsPHX7ufB6pNZwt2TmD5Vq3j1ZCpG017NqxbOIXZX8pcCEmpG+GDrqo+MopUAs7RwKfWOop+AtL/R0YtX6rf7dn44icSEHM2r5JNI9M6TqAsjvaJ1MDtqd/DJsU8iKj9tWubxNjjd/d2sPbw2eK1vXElz5FBXOSrbfahuBvjn9n+y6p1VPp17qFlrhRYJRUrhjhKJwUIIzZvYvB/K3Axyb9gJIgoy/GsZEnQyHBXOnsy8bSrV3qAiIDEd0MLNoOUlDkdXBIzkCwJLCo9P6fQn78lr/K1wDieRCFrIuepTr3tDei0SR+mVuKd+D1EiiikZU7yyI5QpySjBIiyUNpTS2dfJTSfdxOWzLg+2WYaJxLykPOo76rFLu9fnqvY3oYMSicFkyqWaa//ze4e+CTfs0vaF+zdnbyqcm0ojIx9RJz5Te8MdKS+xsy4yilYCzHkl53HPmVrzg6LUolGONpDEXEjMG1sisbNWC7F7QXR/q1e5bBPSJjAxbSIDw3Q6212/m5KMEmKtsV7ZEcrEWmMpTi9mX+M+MuIzWP2F1aFRqBGfAXHpUO2YjOujSMxNzGVADtDY2ej1ufqXhaCH3hVKJAYVSxQs/iHUboby907c17gLssI8HxGOfysdRSQK2a95GyNJJIIWcq7eMLwnplOFm30hOiqayrZK0uLSSIkNcNFPzoKxJRLBu5BzXydRsscrcRETFcPhWw4PW62+p35PRExaGczM7Jm09bTx2bHP6BvoC7Y5x0krOT79ysf3J72hti9tcA41HyItLo2M+Ayf7lthHEokBpuZ10FiPnz+f85NloEubfJIVpjnIwJEx0Py+FFFYlxPlVaoE2kiMW+p1tZHn57jgsXeC302FW72kUnpk4LjeclZoOXP9nd7f264icTMmVrlvTciscsxTcpHD9RgpJTMzJ7J6RNON+R6ocSLV77IQxc8xCl/O4U/bfpTsM05jv7lHvwKN4NvDbW/tehbPHrRoz7dr8JYlEgMNtZYbaZz+ftQrU0eTOwuB2RkiERwVDiP3CsxoVubweuc2hEp5A+fl5jY5ci/ioQJM0HgtpNv45WrXgn8HWfP177QNO72/txwE4mWKK1Julci0RFe9FJc/PHzP7L8b0NnegsheOHKF7hl2S1eXS8csAgLb5SFSOsbV/Q0IWHR+t36wOKCxey/eT8rxq/w+tz5efO5dMalPt1vJCGEiBNCfC6E2C6E2C2E+Llj++WOv+1CiMVm2qBEYigw79sQmwYb7wMgscvh5g/3ymYdvQ3OCMnvibpITA/zQp3BZM/Xple4EYnpbZu1X8afGVibIgSLsASnb55evFLrQ8g53EQiaCHnhp2edSgAnz2JHX0dfHrs0yG98SKpgfZgjrQc4ab/3AQQWkU5+pSouExNKPpAQnQCUzKneJ1H2m/v54W9L1DZNjT6MgbpAc6UUs4D5gPnCSGWAbuAS4EPzTZAicRQICYZFtwMB16ExlISuw5rBStpEZK0mz4FelqOf3i4IaG7HJIKIND5ZWZjjdWEopvilYy2TZrgUIUr4UVasfaa9SUvsa9D+9IQiFnTRlFwCkg71Hzu2fE+ikRnhfOgNjh3f3g3k38/mX57v1fXCweSYrSm6tOzpodWo3A93Ozne9MfNvyB/xz4j1fnHGs9xmXPXMabZW/6dd+RgNSwOf6MdtyklHKvlHLfCKcahhKJocKC/6cJw42/JrH7MGTM1EI9kUC6ow3OCHmJCd3lkZePqJO/VOuV6Poh19dBSsduGH928OxS+IawaMLfl/F8fR3h5UUER79P4XnI2VeRmOG+Dc6uul0IBNZwEtYekpWQxZOXPcn7X3s/2KaciC4S/cwrXf3pap7e/bRX5+hfEsZQZbNVCLHJ5Xaj604hRJQQYhtQB7wjpRxljJexKJEYKiRkw5xvwt5/kdKxN3LyEWF0kSilJhLTI1gk9ndq01d0Kj7EIvu1puqK8CNnvtZQ210j/JHo6wBrmInE2BTt/cgLkSgRWhsVL3D1JPbb+/nFB7/g3MfP5ZV9rzArZ5a3VocNV82+ivzk/GCbcSLxWVrBkp8iMS8pj1qbd4Ur+peEMdQjsV9Kudjl9rDrTinlgJRyPjAOWCKECKg4UCIxlFh8OwDWgY7IEompE7Xw2nDFK5212pojrWhFJ2+p9tM15Hz0HewiGgpDoC+awntyFmiCz5Mm8a6EoycRHE21P9PCzqPR1UB/VLLXkZDk2GQumHIBOYk5WC1WHt36KNXt1fzX/P/i7jPu9tFwhU8IASf/FGb9l1+XyU3K9boFzsHmg8RExVCY7Pvc50hEStkCrAMCWuEUef77cCZlAky/BvY8Fhk9EnUsVq2CdzhPonNmc4R6EtMmawng1RtgriOScPRdWpPmkB4dH1zbFL7hOnnFm6lI/WEsEnf8BRr3jP4FtquBPmsqvsxNev2a152/7795f0Q1zw47Ft/m9yVyE3O9nrd9sPkgk9ImERUp6VZ+IITIBvqklC1CiHjgbOC+QNqgPImhxik/ozbj7MjzMOkVzu5o3Kv9jFSRKITWCkevcO6ogYadNKWY2rlAYSaZM7U543XbvDsvnD2J4FnIubuBPqv/BWhKIIY/+mi+AS/SMu7/wv08cekTJloVVuQDa4UQO4CNaDmJrwkhLhFCVAAnA68LId4yywAlEkON1EnsnfQjcFS9RQxZszWPobs2Gk2lDFjiICmCwwt5SzUvTE8bHH0XgOaURUE2SuEzUTGQOcv7CudwFYlpk7XJG56IRIcnUaG4Y/kdtN3V5pVXcGLaRBYVqPdGACnlDinlAinlXCnlbCnlLxzbX5RSjpNSxkopc6WU55plgxKJisAw6QKw98ERN20NmkrpjBuvedwilfylgITaTVD+LsRlYosvGfU0RQijj+fzpo9fuIpEIRx5iUokKjwnOTaZhOgEj49v7W7lgfUPcKj5kIlWKbxBiURFYCg4WcvLK3t56L6mUjriJgTepkCSd5L2s3oDHH0Hxp/lc5NaRYiQswC66rWxi54SriIRNJHYfECbNz4cUiqRqHDS2NnIbW/dxqfHPKuM39uwl++/9X121/kwzUhhCkH9lBJC3C2E2CGE2CaEeFsIUeCy7y4hRJkQYp8QwjRXqiJAWKww+UI4/B9wHWTf1wHt5ZonMZKJz9R6j+15DGxVqvVNJOBavOIp4S4SQatyHo4+Gwz0KpGoAMBqsfLb9b/l4/KPPTpeb38zhnokhjzBdmWsdsTa5wOvAT8BEELMBK4CZqGVez8khFClTuHO5Iu1ySuVHx3f1qQVs3TGFQXHpkCSv/R4JfdEJRLDnuy52s+xIhJzF2nFOiOFnB2NtJVIVACkxqWSGZ/pcfhYb6Q9KW2SmWYpvCCoIlFK6VrFkAjoyT0XA09JKXuklIeBMmBJoO1TGMzEL0BU7Ikh5yatsjniPYlwvF9iWonW7kgR3sSmaI+lpyJR2qG/K3xFYnQ85C5UIlHhFcXpxRxuOezRsQebD1KYXEi8ag0WMgS9T6IQ4h7ga0ArcIZjcyGw3uWwCsc2d+ffCNwIYLVaWbdunWm2BgqbzRYR63DHnKQFJOx+hg3iyyAEE6veZAIWGvrSInbNOskdVhYBldEzObBuXUQ/zsMRaWueKQpJPrqeDSOsSV9z1EAXK4CD5TUc6x3++FBm8kARBTWv8PH77yAtQzshZrRuYC7Q2hcTUY+zJ0Tac9sTPFlzUl8Su5t3e/S/2XJ4C5mWzJD9P47Fx9h0kSiEeBfIc7PrR1LKl6WUPwJ+JIS4C7gZ+CngrszVbQmhY4TNwwCJiYly5cqVhtgdTNatW0ckrMMtGV+Hd25k5exMLVz36kPQVUxCSnrkrllnYDlEfU7hwlspzJkX2Y/zMETcmuPPhY8/YOXJCyDWvffMueaOWtgGk6fPZfL8lYG00jj2N8Crz3H6jFSt9+dg9hyDMohOzuPUSHqcPSDintse4Mma3x54m/3b93Pa6adhGaVYb8PyDbR2t4bemEIHY/ExNj3cLKU829HfZ/BtcJnrv4HLHL9XAK5JauOAKrNtVQSAyV8CxPGQc1MpZEToOL7BREXDeX+HnHnBtkRhFDnztZ+eNNXu69B+hmu4GUZvqq3CzYpB/PLMX1JxW8WoAhEgITohZAXiWCXY1c1TXP68CHBk9fMKcJUQIlYIMQmYAnweaPsUJpCYpxVwHHwZ7APaFJZInbSiiHy8qXDus2k/w1kkJhVo+bQjiUQRRX9UGK9RYSieiEOAoy1HWfX2KsqavJyHrjCVYFc33yuE2OUYOfMF4BYAKeVu4BlgD/AmcJOU0vO5PorQZvLFULsZqj6BgR4lEhXhS2KedqvfNvqxkeBJBEdT7U/cNxHvatTaPakeoAoHLd0tXP7s5bxc6qZHrgvba7dz/2f309TVFCDLFJ4Q7Ormyxyh57lSyi9JKStd9t0jpZwspZwmpXwjmHYqDKbkIu3nxl9rP5VIVIQz2fM99CRGkEi0VUH7saH7uhogPivwNilClqSYJF7c+yKfV44cDHT2SExXPRJDCfV1TxF4MmZorUMOve74W4lERRiTs0Cby93fM/JxkSQSwX3IWYlExSCsFisT0iZwqGXkXokHmw+SGptKRnxGgCxTeIISiYrAI4QWcgZIyAH1pqAIZ3IWgL0fGneNfJwuEq1hLhKz54I1QYlEhccUpxeP2lD7YPNBJmdMRgh3zU0UwUKJREVwKHGIROVFVIQ7zuKVbSMfFymeRItVKz5TIlHhIcVpo4vEpq4mFWoOQYLeTFsxRik4GZIKtVFfCkU4k1YMMcmj5yVGikgELeT8+b0njhmU8rhI7A+ueYrQYk7uHDZVb6Knv4dYa6zbYzZ8cwN9A30BtkwxGkokKoKDxQrXbYuMD0zF2EZYIHve2BOJcgBqNkLRSm1b1afatvgsaA+qdYoQ4+YlN3PzkptHPS46augUH0VwUeFmRfBIyNLmwSoU4U7OAqjfrs1nHo6+Du3LUVRM4Owyi/xl2s+qT6FpP7x6BTx1qiYQJ54XXNsUYcf7h9/nyueupLq9OtimKAahRKJCoVD4S84CTQQ2j9AIWA/NRkJifnyG1qVgy+/gHzPh8H9g2U/gGwchc4xMUFJ4TFdfF8seWcbDmx92u/+9Q+/xwt4XSI1Tk3pCDSUSFQqFwl+y52s/Rwo5u+bvRQITzoHuJpj3HU0cLv85xKYE2ypFCBIfHc++xn3sqN3hdv/6yvXMy51HQnRCgC1TjIYSiQqFQuEvWbPAEj22ROJp98F3auCsByExN9jWKEKc4drgDNgH2Fi5kaWFS4NglWI0lEhUKBQKf4mKgcxZI4/n6+sI/x6JrljjtBF8CoUHDCcS9zbspb23nWXjlgXBKsVoKJGoUCgURpCzQPMkuptpDNAfYZ5EhcILJqVN4nDLYeyDirtau1tZmL9QicQQRYlEhUKhMIKc+dBZBx3DVGhGWrhZofCCJYVLOL/kfGy9thO2Lx+/nM03bmZK5pQgWaYYCdUnUaFQKIzAOXllKyQVDN3f1wGJbrYrFGOAr8z8Cl+Z+ZUh2+3SjkUof1Wooh4ZhUKhMILsedrP4cbzKU+iQoF0Scdo62kj/b50/rntn0G0SDESSiQqFAqFEcSmQNrk4SuclUhUjGEG7AOM/+14frbuZ85tGys30tbTRn5yfvAMU4yIEokKhUJhFHrxijuUSFSMYaIsUViEhYPNB53b1lesB7R8RUVookSiQqFQGEXOAmg9BD2tJ26XEvo6lUhUjGkGt8FZX7meGVkzSItLC55RihFRIlGhUCiMQi9eqd9+4vb+LkAqkagY0xSnF3O45TCg5Saur1ivWt+MgBAiTgjxuRBiuxBitxDi547tGUKId4QQBxw/082yQYlEhUKhMIrhxvP1dWg/lUhUjGGK04upsdXQ2ddJ70AvN510E5fPvDzYZoUyPcCZUsp5wHzgPCHEMuBO4D0p5RTgPcffphASIlEIsUoIIYUQWS7b7hJClAkh9gkhzg2mfQqFQuERSfmQkKtEokLhhlPHn8rNJ91Md383sdZYfrbyZ5w/5fxgmxWySA29sWS04yaBiwG9JPyfwJfNsiHofRKFEEXAOUC5y7aZwFXALKAAeFcIMVVKORAcKxUKhcJDchYMbYOjRKJCwWkTTuO0CacBcKDxAHlJeSTHJgfZqqBjFUJscvn7YSnlw/ofQogoYDNQAvxRSrlBCJErpawGkFJWCyFyTDPOrAt7wW+BHwIvu2y7GHhKStkDHBZClAFLgM+CYJ9CoVB4Ts4C2PQu9PeANVbbpkSiQgFA70Av3f3dXPvCtSTGJLL2+rXBNinY9EspFw+30+Ecmy+ESANeFELMDphlBFkkCiEuAiqllNuFEK67CoH1Ln9XOLa5u8aNwI0AVquVdevWmWNsALHZbBGxDm9Qax4bjIU1ZzdFM8vez6a3/4ktYSo2m409n73LTGDr7jJay9cF20TTGQuP82DUmj3jok8u4rTs09hSs4Urx10ZVv+zYD7GUsoWIcQ64DygVgiR7/Ai5gN1Zt2v6SJRCPEukOdm14+A/wG+4O40N9ukm2043LIPAyQmJsqVK1f6ZmgIsW7dOiJhHd6g1jw2GBNrbi6Ew79gcVEUzFnJZ28/y8zyv0DWHBac/22wxgXbQtMZE4/zINSaPaO4tJjPWj5jQA5wxSlXsHK6d+cHk0A/xkKIbKDPIRDjgbOB+4BXgOuBex0/Xx7+Kv5hukiUUp7tbrsQYg4wCdC9iOOALUKIJWiewyKXw8cBVSabqlAoFP6TNhlikrW8RHs/Mw7frbXA+dKzY0IgKhQjUZxezPZarUXU0nFLg2xNyJMP/NORl2gBnpFSviaE+Ax4RgjxDbR6DtNKxIMWbpZS7gScyZZCiCPAYillgxDiFeDfQojfoBWuTAE+D4qhCoVC4Q3Cos1xrtsKn/wvabadcMHjkDEt2JYpFEGnOL0YgAmpE8hLchdkVOhIKXcAC9xsbwTOCoQNoVC4MgQp5W4hxDPAHqAfuElVNisUirAhez7s+AtUfUJV1hcpmHFtsC1SKEKCSWmTAPjp6T8NsiUKTwgZkSilnDjo73uAe4JjjUKhUPhBzgKw90H2XMrG/TcFwbZHoQgRTp94OvedfR8XTbso2KYoPCBkRKJCoVBEDJPOg0nnw8oHsO9Q6dQKhc7snNnMzgloFxeFHyiRqFAoFEaTVACX/sfxhxKJCoUiPAmJsXwKhUKhUCgUitBCiUSFQqFQKBQKxRCUSFQoFAqFQqFQDEGJRIVCoVAoFArFEJRIVCgUCoVCoVAMQYlEhUKhUCgUCsUQlEhUKBQKhUKhUAxBiUSFQqFQKBQKxRCUSFQoFAqFQqFQDEFIKYNtg2EIIexAV7DtMAAr0B9sIwKMWvPYQK15bKDWPDYYa2s2Y73xUsqQddhFlEiMFIQQm6SUi4NtRyBRax4bqDWPDdSaxwZjbc1jbb2gws0KhUKhUCgUCjcokahQKBQKhUKhGIISiaHJw8E2IAioNY8N1JrHBmrNY4Oxtuaxtl6Vk6hQKBQKhUKhGIryJCoUCoVCoVAohqBEokKhUCgUCoViCEokBgAhRJEQYq0QYq8QYrcQ4hbH9gwhxDtCiAOOn+mO7ZmO421CiAddrpMghHhdCFHquM69wVrTaBi1Zse+N4UQ2x3X+bMQIioYaxoNI9fscs1XhBC7ArkObzD4cV4nhNgnhNjmuOUEY02jYfCaY4QQDwsh9jte15cFY02jYeB7WLLL47tNCNEghHggSMsaEYMf56uFEDuFEDsc72dZwVjTaBi85isd690thPh1MNYzGj6s9xwhxGbHY7lZCHGmy7UWObaXCSF+L4QQwVqXoUgp1c3kG5APLHT8ngzsB2YCvwbudGy/E7jP8XsicCrwHeBBl+skAGc4fo8BPgLOD/b6zFyzY1+K46cAngeuCvb6zF6zY/+lwL+BXcFeW4Ae53XA4mCvKcBr/jnwS8fvFiAr2Osze82DrrsZOC3Y6zNzzWgNmOv0x9Zx/s+CvT6T15wJlAPZjr//CZwV7PUZsN4FQIHj99lApcu1PgdORvuceoMQ/Wz29qY8iQFASlktpdzi+L0d2AsUAhejvXhw/Pyy45gOKeXHQPeg63RKKdc6fu8FtgDjArEGbzFqzY59bY5frWjiOCSrrYxcsxAiCbgN+KX5lvuOkWsOFwxe89eB/3McZ5dSNphrvW+Y8TgLIaYAOWhfdkMOA9csHLdEh3cpBagyfQE+YOCai4H9Usp6x9/vAiHnJfdhvVullPpjtxuIE0LECiHy0ZwZn0lNMT6mnxPuKJEYYIQQE9G+jWwAcqWU1aA9WdHeMD29ThrwJeA94600FiPWLIR4C+3beDvwnDmWGocBa74buB/oNMtGozHouf13Rxjyf8MhXOPPmh2vYYC7hRBbhBDPCiFyTTTXEIx6DwOuBp52fKiGNP6sWUrZB3wX2IkmDmcCj5pprxH4+TiXAdOFEBOFEFY0wVRknrX+48N6LwO2Sil70IRlhcu+Cse2sEeJxADi8A49D9zq4h3z5TpW4Eng91LKQ0bZZwZGrVlKeS5aaCAWOHOUw4OKv2sWQswHSqSULxptm1kY9DhfK6WcA6xw3K4zyj4zMGDNVrRIwCdSyoXAZ8AaA000HKNezw6uQnsfC2kMeD1Ho4nEBUABsAO4y1AjDcbfNUspm9HW/DSap/gIITzj2dv1CiFmAfcB39Y3uTks5L/8eIISiQHC8UbxPPCElPIFx+Zah5sax886Dy/3MHBASvmA4YYaiMFrRkrZDbyCFgoISQxa88nAIiHEEeBjYKoQYp05FvuPUY+zlLLS8bMdLRdziTkW+49Ba25E8xTrXwaeBRaaYK4hGPl6FkLMA6xSys2mGGsQBq15PoCU8qDDa/oMcIo5FvuPga/nV6WUS6WUJwP7gANm2ewP3q5XCDEO7TX7NSnlQcfmCk5M/RpHiKYUeIsSiQHAETZ7FNgrpfyNy65XgOsdv18PvOzBtX4JpAK3GmymoRi1ZiFEksuL1QpcAJQab7H/GLVmKeWfpJQFUsqJaEnh+6WUK4232H8MfJytesWn4037QiAkq7oNfJwl8Cqw0rHpLGCPocYahJHvYQ6uJsS9iAauuRKYKYTIdvx9DlruW8hh8GdVjuNnOvA94BFjrfUfb9frSBF5HbhLSvmJfrAjJN0uhFjmuObX8Py1ENrIEKieifQb2ge9RAszbHPcLkCrAHsP7RvWe0CGyzlHgCbAhvYtZSbatxOJ9gajX+ebwV6fyWvOBTY6rrMb+AOaByLoazRrzYOuOZHQrm426nFORKt01R/n3wFRwV6f2Y8zMAH40HGt94DxwV5fIJ7bwCFgerDXFcDH+Tto79s70L4YZAZ7fQFY85NoX3r2ELodKbxaL/BjoMPl2G1AjmPfYrQvtgeBB3FMtAv3mxrLp1AoFAqFQqEYggo3KxQKhUKhUCiGoESiQqFQKBQKhWIISiQqFAqFQqFQKIagRKJCoVAoFAqFYghKJCoUCoVCoVAohqBEokKhUCgUCoViCEokKhQKhUKhUCiGoESiQqFQKBQKhWIISiQqFAqFQqFQKIagRKJCoVAoFAqFYghKJCoUCoVCoVAohqBEokKhUCgUCoViCEokKhQKhUKhUCiGoESiQqFQKBQKhWIISiQqFAqFQqFQKIagRKJCoVAoFAqFYghKJCoUCoVCoVAohqBEokKhUCgUCoViCEokKhQKhUKhUCiGoESiQqFQKBQKhWIISiQqFAqFQqFQKIagRKJCoVAoFAqFYghKJCoUCoVCoVAohqBEokKhUCgUCoViCEokKhQKhUKhUCiGoESiQqFQKBQKhWIISiQqFAqFQqFQKIagRKJCoVAoFAqFYghKJCoUCoVCoVAohqBEokKhUCgUCoViCKaJRCFEkRBirRBirxBitxDiFsf2+UKI9UKIbUKITUKIJSNcI0oIsVUI8ZpZdioUCoVCoVAohmI18dr9wO1Syi1CiGRgsxDiHeDXwM+llG8IIS5w/L1ymGvcAuwFUky0U6FQKBQKhUIxCNM8iVLKainlFsfv7WhirxCQHBd9qUCVu/OFEOOALwKPmGWjQqFQKBQKhcI9ZnoSnQghJgILgA3ArcBbQog1aCL1lGFOewD4IZA8yrVvBG4EiI+PX1RUVGSIzcHGbrdjsYytlNGxuGYYm+sei2uGsbnusbhmGJvrVmv2nv379zdIKbMNNMlQTBeJQogk4HngVillmxDil8D3pZTPCyGuAB4Fzh50zoVAnZRysxBi5UjXl1I+DDwMsHjxYrlp0yYTVhF41q1bx8qVK4NtRkAZi2uGsbnusbhmGJvrHotrhrG5brVm7xFCHDXOGuMxVfILIaLRBOITUsoXHJuvB/TfnwXcFa4sBy4SQhwBngLOFEI8bqatCoVCoVAoFIrjmFndLNC8hHullL9x2VUFnO74/UzgwOBzpZR3SSnHSSknAlcB70spv2qWrQqFQqFQKBSKEzEz3LwcuA7YKYTY5tj2P8C3gN8JIaxAN458QiFEAfCIlPICE21SKBQKhUKhUHiAaSJRSvkxIIbZvcjN8VXAEIEopVwHrDPSNoVCoVAoFObQ19dHRUUF3d3dwTbFdFJTU9m7d++ox8XFxTFu3Diio6MDYJVxBKS6WaFQKBQKxdigoqKC5ORkJk6ciJZ5Frm0t7eTnDxiExaklDQ2NlJRUcGkSZMCZJkxjK1adYVCoVAoFKbS3d1NZmZmxAtETxFCkJmZGZaeVSUSFQqFQqFQGIoSiCcSrv8PJRIVCoVCoVAoFENQIlGhUCgigJbDh3nvv/+btbfdFmxTFIqgI4Tguuuuc/7d399PdnY2F154oXPbSy+9xNy5c5k+fTpz5szhpZdecu674YYbmDRpEvPmzWPq1Kl87Wtfo7Ky0rl/4sSJNDQ0AHDPPfcwa9Ys5s6dy/z589mwYYP5CwwQqnBFoVAowpj6nTv5/L77KH3qKeTAAMJi4bR77yUqJibYpikUQSMxMZFdu3bR1dVFfHw877zzDoWFhc7927dvZ9WqVbzzzjtMmjSJw4cPc84551BcXMzcuXMBWL16NV/5yleQUvLAAw9wxhlnsGvXLmJcXlsbNmzgtddeY8uWLcTGxtLQ0EBvb2/A12sWSiQqFApFGFK/axcf3Xknh15/nejERBbdeitxGRl8/KMf0XrkCBlTpwbbRIWC92+9lbpt2wy9Zs78+Zz5wAOjHnf++efz+uuv85WvfIUnn3ySq6++mo8++giANWvW8D//8z/OauNJkyZx1113sXr1av71r3+dcB0hBN///vd58cUXeeONN7j44oud+2pra8nKyiI2NhaArKwsg1YZGqhws0KhUIQhb33jG1R+8gnL776bG8vLWblmDUWna8Osmg8MGWQVsbSVl/PYggXUbNoUbFMUIcZVV13FU089RXd3Nzt27GDp0qXOfbt372bRohNbNi9evJjdu3cPe72FCxdSWlp6wrYzzzyTY8eOMXXqVL73ve/xwQcfGLuIIKM8iQqFQhGGtB09yrQrruDkH//YuS1tyhQAWsrKgmVWwPnwjjuo27aN0qefJm/x4mCboxiEJx4/s5g7dy5HjhzhySef5IILTpzVIaUcUnHsbtvg/YNJSkpi8+bNfPTRR6xdu5Yrr7ySe++9lxtuuMGQNQQb5UlUKBSKMMM+MEBXfT0JubknbE/IziYmJWXMeBIrP/mE0qeeQlgsHFu7NtjmKEKQiy66iFWrVnH11VefsH3WrFlsGuR93rJlCzNnzhz2Wlu3bmXGjBlDtkdFRbFy5Up+/vOf8+CDD/L8888bY3wIoESiQqFQhBldDQ1Iu53EQSJRCEH6lCm0jAGRKO123r/1VpIKClh0223Ubd1Kd0tLsM1ShBhf//rX+clPfsKcOXNO2L5q1Sr+7//+jyNHjgBw5MgRfvWrX3H77bcPuYaUkt///vdUV1dz3nnnnbDvwIEDHHB5vW3bto0JEyYYv5AgocLNCoVCEWZ01tYCDPEkAqSVlFCzcWOgTQo4ex5/nNpNmzj/scdIGT+eTWvWUPHhh5RcdFGwTVOEEOPGjeOWW24Zsn3+/Pncd999fOlLX6Kvr4/o6Gh+/etfM3/+fOcxP/jBD7j77rvp7Oxk2bJlrF279oTKZgCbzcbNN99MS0sLVquVkpISHn74YbOXFTCUSFQoFIowo6OmBoDEvLwh+9KnTGH/s88y0NsbsW1wem02PrrrLvKWLGHmtdcy0NeHNS6OY2vXKpGoADTxNpiVK1eycuVK59+XXnopl156qdvz//GPf4x4fd0DuWDBAj799FNfzQx5VLhZoVAowowOhydxcLgZNJEo7XZaDx8OtFkB4/P77sNWVcWZDzyAsFiwxsZScMoplKu8RIXCUJRIVCgUijBjtHAzQHOEVji3Hj3KpjVrmH711RScfLJze9EZZ1C/fTudjikYCoXCf5RIVCgUijCjo6YGa3w8McnJQ/al621wIrR45aM77wQhOO2++07YPv6MMwCoiLA+deGKu3YxY5lw/X8okahQKBRhRmdtLQm5uW57usVnZRGbmhqRbXBqNm2i9KmnOOkHPyClqOiEfXknnUR0YqIKOYcAcXFxNDY2hq0wMhopJY2NjcTFxQXbFK9RhSsKhUIRZnTU1rrNRwStDU7alCkRKRJrt2wBYM43vzlkX1RMDIWnnqr6JYYA48aNo6Kigvr6+mCbYjrd3d0eib+4uDjGjRsXAIuMRYlEhUKhCDM6a2tJmThx2P3pJSVUb9gQOIMCRG97OwCxqalu9xedcQYf3XnniCJaYT7R0dHOmciRzrp161iwYEGwzTANFW5WKBSKMKOjpsZt+xudtClTaDt6lIHe3gBaZT59jrYm0YmJbvfreYnH1q0LlEkKRURjmkgUQhQJIdYKIfYKIXYLIW5xbJ8vhFgvhNgmhNgkhFji6bkKhUIx1rEPDNDV0OC2sllHb4PTcuhQAC0zn972dqwJCViiotzuz124kJjkZMrffz/AlikUkYmZnsR+4HYp5QxgGXCTEGIm8Gvg51LK+cBPHH97eq5CoVCMaYYbyedKuqMNTkuEtcHps9mISUoadr/FamXcaaepvESFwiBME4lSymop5RbH7+3AXqAQkECK47BUoMqLcxUKhWJMM9K0FZ00RxucSCte6W1vd9v2x5WiM86g+cAB2isrA2SVQhG5BKRwRQgxEVgAbABuBd4SQqxBE6mneHGuu/03AjcC5Obmsi5CclFsNlvErMVTxuKaYWyueyyuGYxZd6tjLvO+qiqqhrmWlJKopCR2r1uHLchJ9UY+1tVHjtAjxIjX63CIyLcfeojMc84x5H59YSw+x9WaIxAppak3IAnYDFzq+Pv3wGWO368A3vX03NFuixYtkpHC2rVrg21CwBmLa5ZybK57LK5ZSmPWveuxx+RqkE3794943L9OOkk+c845ft+fvxj5WD91xhny36eeOuIxA/398g/p6fKNr3/dsPv1hbH4HFdr9h5gkzRZh/lzM7W6WQgRDTwPPCGlfMGx+XpA//1ZYEjhygjnKhQKxZhGDzePVLgC2ni+SAs399lso4abLVFRjDv9dJWXqFAYgJnVzQJ4FNgrpfyNy64q4HTH72cCQ97FRjhXoVAoxjSdtbXDjuRzJX3KFNrLy+nv6QmQZebT295O9AiFKzrjzziD1sOHaT1yxHyjFIoIxkxP4nLgOuBMR7ubbUKIC4BvAfcLIbYDv8KRTyiEKBBC/GeUcxUKhWJMM9JIPlf0NjitEdQGxxNPImjFKwAVH31ktkkKRURjWuGKlPJjYLh3sUVujq8CLvDgXIVCoRizeDpNJM2lDU7mjBlmmxUQetvbR2yBo5MxbRoIQcvBgwGwSqGIXNTEFYVCoQgjRpu2opMeYW1wpJQetcABbY5zUn4+7eXlAbBMoYhclEhUKBSKMEIPN49GfGYmcenpESMS+7u7kXa7RzmJAMnjx9N29KjJVikUkY0SiQqFQhEmeDKSz5W0KVMiZupKb3s7gEeeRICUCRNoU55EhcIvlEhUKBSKMKGrvn7UkXyupEdQG5w+mw3AY09iyvjxtJeXI+12M81SKCIaJRIVCoUiTOiorQVGHsnnStqUKbSVl9Pf3W2mWQHBF0/iQG8vnXV1ZpqlUEQ0SiQqFApFmNDpEImehpvTp0wBKWk9fNhMswKC7kn0pLoZNE8ioPISFQo/UCJRoVAowgR92oo34WaIjApnXzyJgMpLVIzIkbff5uDrrwfbjJBFiUTFmKGvs5O/FhdT+swzwTZFofAJX8LNECEiUc9J9FAkJitPosIDPrzjDl66+GIlFIdBiUTFmKF282ZaDx/m4MsvB9sUhcIn9JF8nhZvxGdkEJeRQUskiETdk+jh2uPS0ohJSVGeRMWwSClpLitDDgzw6hVXUL1xY7BNCjmUSFSMGao3bACgav36IFuiUPiGpyP5XEmfMoXmCGiD48xJ9NCTCI42OMqTqBiGzro6+mw2lv3oRyTm5vLCF78YEa8VI1EiUTFm0EVi66FDdKiKR0UY0lFT43E+ok5ahLTB0T2JnnpR4XgbHIXCHXoP0YLly7nszTfBbuf588+ns74+yJaFDkokKsYM1evXO5PZq5U3URGGdNTWepyPqJMxfTrt5eXOnL5wpc9mwxIdjTU21uNzlCdRMRK61zC9pISMqVO55NVXsVVU8MKFF9Lb0RFk60IDJRIVYwJbVRXtFRXM+/a3sVitSiQqwhJPR/K5kj13LgANO3eaYVLA6G1v9zgfUSd5/Hi6m5udXkiFwpWWsjJEVJTTeVBw8slc+NRT1G7axGtXXYW9vz/IFgYfJRIVYwI91Fy0ciXZ8+ervERF2GHv76ezvt57kThvHgB127ebYVbA6G1v97iyWSdVtcGJKHptNjbcdx99XV2GXK+lrIyUCROIiolxbiu5+GLOevBBuhoawt77bgRKJCrGBFXr12OJjiZnwQIKli2j5vPPsQ8MBNsshcJjuhoaQEqvw80p48cTm5ZGfbiLRJvNJ08iKJEYKRx5+20+uvNOdv3974Zcr7mszNlL1JX53/0uV334IXFpaYbcTzijRKJiTFC9YQM58+djjYsjf9ky+jo6aNi1K9hmKRQe4+yR6KUnUQhB9ty5YS8S+9rbvapsBpeG2iovMSLQRyxu++MfkVL6dS0pJS0HDpDmRiQCREVH+3X9SEGJREXEY+/vp3bTJvKXLQO0vBNQxSuK8EKftuJtuBm0kHP9jh1Iu91oswJGr83mVWUzaE3HLVarqnCOELocVceNe/ZwbN06v67V3dRET2vrsCJRoaFEoiLiadi9m76ODvKXLgUgddIk4rOzVV6iIqzo9NGTCJAzbx59HR20HDpktFkBo9cHT6IlKorkoiLlSYwQOuvriU5KIi4jg60PPujXtfT2N2mTJxthWsSiRKIi4tGLVnSRKISgYNkyqj/7LJhmKRRe4e1IPlf04pVwDjn3+ZCTCFpeospJjAw66+pIys9nzje/SdlLL9F27JjP12o5eBBAeRJHwTSRKIQoEkKsFULsFULsFkLc4tg+XwixXgixTQixSQixZJjzzxNC7BNClAkh7jTLTkXkU71hA/GZmSd8Yyw4+WSa9u2jq6kpiJYpFJ7j7Ug+VzJnzUJYLGFd4exLdTOoXomRRFd9PQk5Ocz/7neRUrLjL3/x+VrNZWUgBGnFxQZaGHmY6UnsB26XUs4AlgE3CSFmAr8Gfi6lnA/8xPH3CQghooA/AucDM4GrHecqFF5TvX49eUuXnjDKTM9PrPn882CZpVB4RUdNjdcj+XSi4+NJnzYt/D2JvojE8eOxVVaqnncRQGd9PfHZ2aROnMjkL32J7Q8/TH9Pj0/XaikrI3ncOKxxcQZbGVmYJhKllNVSyi2O39uBvUAhIIEUx2GpQJWb05cAZVLKQ1LKXuAp4GKzbFVELj1tbTTu3UuBQxTq5J10EsJioUqFnBVhQqcP01ZcyZk3L2xFor2/n/7ubp/CzSkTJiDtdtorK02wTBFIOuvqSMjOBmDBTTfRVV/P/mef9elaLWVlKtTsAdZA3IkQYiKwANgA3Aq8JYRYgyZST3FzSiHgmmxQASwd5to3AjcC5Obmss7PiqdQwWazRcxaPMWMNbdt3gxSUhcXN+TacZMmsfs//6HvjDMMvU9vUY/12MGfddcdOkRsfr7P57cnJ9N29CjvvvYaVh/Elq8Y8Vj3O5oal9fW0uPltVqbmwH48OWXSXZMnwkEY/E5buaapd1OZ3099V1drFu3Dmm1EltUxAf/93/UjRvn9fXq9u4lbflyv+2N+MdZSmnqDUgCNgOXOv7+PXCZ4/crgHfdnHM58IjL39cBfxjtvhYtWiQjhbVr1wbbhIBjxpo/++Uv5WqQXc3NQ/a9/e1vy9+npkr7wIDh9+sN6rEeO/iz7gezs+VbN97o8/kHX39drgZZ/sEHPl/DF4x4rFvLy+VqkNv/+levz20sLZWrQe7+17/8tsMbxuJz3Mw1dzY0yNUgNz3wgHPb5t/9Tq4GWb1xo1fX6m5pkatBbrjvPr/t8nfNwCZpsg7z52ZqdbMQIhp4HnhCSvmCY/P1gP77s2ih5cFUAEUuf4/DfVhaoRiR6g0byJg+3W3n/Pxly+hpbaWxtDTwhikUXmDv76erocGnHok64VzhrM9e9iUnMblI+yhRFc7hTaejR6IebgaYdf31RCcmsvWPf/TqWqqy2XPMrG4WwKPAXinlb1x2VQGnO34/Ezjg5vSNwBQhxCQhRAxwFfCKWbYqIhMpJdUbNjhb3wxGNdVWhAu+juRzJamggPjMzLAUiX2OcLMvld3RCQnEZ2erCucwR2+knZCT49wWm5rKzOuuo/TJJ+lsaPD4Ws2OHonuRvIpTsRMT+JytDDxmY52N9uEEBcA3wLuF0JsB36FI59QCFEghPgPgJSyH7gZeAut4OUZKeVuE21VRCCtR47QWVfnrGQeTPqUKcSlpyuRqAh59GkrvjTS1hFCkD1vXli2wfHHkwhahbPyJIY3+ki+eBdPImhzlgd6eih96imPr6U30k5VjbRHxbTCFSnlx8BwvRoWuTm+CrjA5e//AP8xxzrFWKBmUBPtwQiLhbylS9XkFUXIozfS9ifcDFrIefuf/oR9YABLVJQRpgUE3ZPoS3UzaBXOjXv3GmmSIsB0uvEkAmTPnUvW7NmUPvkkC2++2aNrtZSVkZifT0xiouF2Rhpq4ooiYqnesAFrfDzZc+YMe0zBySfTsGsXPW1tAbRMofCOTj+mrbiSPW8e/d3dNB9wl+UTuhjhSWwvL9cLIRVhiB5ujs/KGrJvxjXXUPXpp7QeOeLRtVrKylSo2UOUSFRELFXr15O3eDEW6/AO84Jly0BKajZuDKBlCoV3dPgxt9mVnDAtXun1IycRNE9iX0cH3WrCUtjSWVdHbFoaUdHRQ/ZNu+oqAI9Dzi0HD6qiFQ9RIlERkQz09lK3dSt5w4SadfKWaMX1+nxnhSIU6aip8XkknysZM2ZgsVrDTyT66UlMHj8eUBXO4UynYySfO9ImTaLg5JPZ++9/j3qd3o4ObFVVJ4xpVQyPEomKiKR+xw4GenqGzUfUiUtL0/KVdqu6KEXo0llb6/NIPlessbFkzJgRdsUrzurmhASfzk+ZMAFAVTiHMa7TVtwx/ZpraNi5k/pdu0a8TuuhQ4Bqf+MpSiQqIpLWw4cByJg2bdRjM2bMUEntipDG35F8roTjeL7e9naik5IQFt8+slIcnsR25UkMW7occ5uHY9rllyMsFkqffHLE6+iVzUokeoYSiYqIxNkyxIMP1swZM2gqLUXa7WabpVD4REdNjd/5iDrZ8+Zhq6ykq7HRkOsFgj6bzedQM2jFDtb4eFqVJzFsGSncDFq+7oSzz6b0ySdHLFDSeySqcLNnKJGoiEg6amqwWK3EZ2aOemzmjBn0d3WpDxBFyNJZV+d3+xudcJy80tve7nP7G9B6RKZMmKA8iWGKtNu1iUMjeBIBpl99Na2HD4+YY95SVkZ8VpbbKVyKoSiRqIhIOmpqtBwuD8JTGTNmANCkQs6KEMTTD0hP0UViOOUl9vrpSQRHQ231RdArpN3OixdfzIGXXgqqHd3NzciBgRE9iQBTLrmEqNjYEQtYWsrKVKjZC5RI9BD7wADrVq1i45o1wTZF4QEdNTUe53BlOkSiyktUhCJdTU1Iu33UD0hPSczJITEvL+w8if5WdqdMmKCqm72kdssWDr7yChUffBBUO/RG2iPlJII2pq/4wgvZ98wz2Pv73R7TrHokeoUSiR5iiYqi5eBBPv3Zz7BVVQXbHMUodHohEuMzM0nIyVGeREVIMtw4Mn/IDrPilb72dr89icnjx9NZW0t/d7dBVkU+B199FTjepzJY6K8BT7zpM66+ms7aWsrXrh2yr7+7m/Zjx5Qn0QuUSPSClWvWYO/r46P/+Z9gm6IYBVt1tVfVoKrCWRGqdA0zjswfsufNo3HPHgb6+gy7ppn02myGeBIB2o8dM8KkMcHBV14BjrcgChbevAYmXXABMSkpbqucWw8fBimVSPQCJRK9IG3yZBbeeiu7//lPqtWEjpDFPjBAZ12dVyIxc8YMmvbuVWO7FCGH04tioEjMmTePgd7esOkP2muAJ1Fvg6PyEj2j7dgx6rZtA0LHk+iJNz06Pp4pl1zC/uefH+I1bjl4EECFm71AiUQvWfajH5GQm8vaW25RgiJE6Wps1JKcvRSJ3c3NzjejSKKntZX2iopgm6HwEW9CbZ5SuGIFAMfWrTPsmmbSZ7P5Vd0MLg21VV6iR+ih5sS8vKB7EjtHmNvsjhnXXENvWxvrf/lLelpbndtVj0TvUSLRS2JTUljxq19R9dlnozbtVASHTi96JOroFc6Ne/aYYlMw+eCHP+Sp008PthkKH/H2A9ITUoqKSJ8yhaPvvWfYNc1CSqmFm/30JCYVFiIsFuVJ9JCDr75K+pQp5CxYEHRPYld9PXHp6W7nNrtj/JlnMv7MM1l/zz38qaCAN7/xDao3bKD5wAFi09KIy8gw2eLIQYlEH5h9ww3kLlzIh3fcQW9HR7DNUQzCm0baOpFc4Vy7eTOthw45598qwovOujriMzOxWK2GXnf8mWdS8cEHw1aBhgp9nZ0gpd+exKjoaJKLimgqLTXIssilt72dY++/T/GXvkRMcnLwPYl1dV4VblmsVq547z2+unEjM6+9ln1PP80Ty5ax/S9/Ia2kxO/xlmMJJRJ9QFgsnPHAA7RXVLBx9epgm+MTNZs2sfb734/IKSO+iMSkwkJikpMjrsJZ2u1O4du0f3+QrVH4grcfkJ4y/qyz6G1vp2bTJsOvbSR9ji83/uYkAoxbsYLytWsj8n3PSI688w4Dvb2UXHQR0UlJwReJ9fU+pVvkLV7MFx5+mO9UVXHOn/9M3kknMeWSS0ywMHJRItFHxq1YwbQrrmDjr39NWxhWy+3829/Y/MADI3amD1d8EYlCiIiscG4rL6e/sxOA5n37gmyNwhe6RhlH5itFZ5wBQHmIh5z1UKe/1c0A488+m676eup37vT7WpHMwVdeIS49ncLly4lJSgqJcLM/r4HYlBTmffvbXPvZZyxT3Um8QolEPzjt178GKTWPXJgVsdRt2QJA6dNPB9kS4+moqSE6Kcnr8JRe4RxJuOZYNimRGJZ01tWZIhITsrLInjcv5PMSew30JE44+2wAjr77rt/XilTsAwMcev11Jl1wARar1elJDOZnnFnedMXoKJHoB6kTJnDyT37Cgeef551vfztsQhj2/n7qd+wAYP+zz4aN3Z7izbQVVzJmzMBWVXVCNVy4o4vE+KwsJRLDFDM/IMefdRZVn35KX1eXKdc3Aj3UaYRITC4sJGPGDCUSR6B6/Xq6GhqY/KUvARCTlIS9v5+Bnp6g2OMcS2nCFyXF6JgmEoUQRUKItUKIvUKI3UKIWxzbnxZCbHPcjgghtg1z/vcd5+0SQjwphIgzy1Z/WHLnnSz9n/9hx1//yhs33BDySeCgeZT6u7qYdMEF2KqqqPj442CbZCi+ikS9eCWSEtsb9+whMS+PvJNOGlPhZiklR955JyxejyNh7++nu6nJtA/ICWedxUBPD1WffmrK9Y3A6Uk0INwMmjex4sMP6Q+S6Al1yl55BYvVyqTzzgOOh/mDFXJ2jqVUnsSgYKYnsR+4XUo5A1gG3CSEmCmlvFJKOV9KOR94Hnhh8IlCiELg/wGLpZSzgSjgKhNt9RkhBCvuuYdTf/lL9vzrX7x29dUM9PYG26wRqdu6FYBTfvITrPHx7HvmmSBbZCwdXk5b0dFFYkMEtcFp3LOHzJkzSZ86lab9+8MuLcJXqjds4LkvfIGdjz4abFP8oquhATC2kbYr41aswGK1Uv7++6Zc3wicOYkGeBJBE4n9nZ1UffaZIdeLNA6++irjTj+d2NRU4LhIDFbxSpeHc5sV5mCaSJRSVksptzh+bwf2AoX6fqHVoF8BDNds0ArECyGsQAIQ0gOTl/3oR6y8/372P/ccL192WUjPB63dsgVrXBy5ixZRfOGF7H/uOewDA8E2yzB89SSmTppEVGxsxOQlSimdIjFj2jT6OzuxVVYG26yAUL1+PQClTz0VZEv8w4xG2q7EJCeTt2RJSBev9BnsSSxauRIRFUW5CjkPobmsjKa9eym56CLntpggexI7TRhLqfAcYxtvDYMQYiKwAHAtpV0B1EopDww+XkpZKYRYA5QDXcDbUsq3h7n2jcCNALm5uawL5gSBhQsZf+utHHrgAf5+xhmU/OpXPvdjstlspq1l/9q1xE6cyIcff0z/rFl0Pvssr/3ud6QsXGjK/XmKEWu29/bS09JCbVeXT9eKKSxk/8cfIwP4PDLrse6tr6e3vZ1Gq5UOR87Z2qefJmXRIsPvy1vMfH4DHHJMizj2wQe8/fzzxGRmmnZf3uDtuts2bwZgf2Ul1Sb9v+TkyVQ/8QTvvvYaVoOEmCv+Pta1jsjH59u3Yz10yBCbEqZPZ+cLL9DvKGQxA7Of42ZQ8+yzANRnZzttb3X8zzd8+CFJDs/2cJix5qYPPgBg95EjHA7B/2c4Ps5eIaU09QYkAZuBSwdt/xNaONrdOenA+0A2EA28BHx1tPtatGiRDAXW/+pXcjXIms2bfb7G2rVrjTPIBbvdLn+fmirf/va3pZRS9nZ0yAcSE+VbN95oyv15gxFrbjlyRK4GueORR3w6/5UrrpB/nTzZbzu8wazH+tCbb8rVIMvXrZNtFRVyNcgtf/yjKfflLWatWeevJSXyb7NmydUgN//+96belzd4u+49//63XA2yYc8ecwySUh5du1auBln2yiumXN/fx/rTu++Wq0H29/YaY5CU8uOf/ESusVhkV1OTYdccjNnPcTN4auVK+bdZs07Yduyjj+RqkEfeeWfU881Y89aHHpKrQbZXVRl+bSPwd83AJmmyDvPnZmp1sxAiGi3v8Akp5Qsu263ApcBw/VfOBg5LKeullH1oeYunmGmrkcz7zneIio1l19//HmxThtB6+DA9ra3kOLyG0QkJTL7oIg48/zwDfX1Bts5/9JF83sxtdiVjxgxaDx8O6XQBT9ErmzNnziSpoIDopKQxUbzS1dRES1kZM6+9lqw5c9hnYpun+l27+OCOO0zrEBCIfKyCk0/GGhcXsq1wetvbiYqN9XgkmydMOOccpN0eNrOrA0FXUxMVH310QqgZQifcbORYSoXnmFndLIBHgb1Syt8M2n02UCqlrBjm9HJgmRAiwXGds9ByGsOCuPR0plxyCXufeCLkxIZetJKzYIFz2/Qrr6SrsZFja9cGyyzD8KWRtiuZM2Yg7XaaI2A6SeOePcRnZZGQna01C3cUr0Q6tY4JInlLljD9yiup/OQTUxreD/T18fo117Dx17+m0aSK+M66OoTFQryJs2atsbEUnnpqyOYl9tlshuUj6uQvXUp0UpJqhePCjr/+FTkwwLQrrjhhe7ALVzrr6rya26wwFjM9icuB64AzXVreXODYdxWDClaEEAVCiP8ASCk3AM8BW4CdDjsfNtFWw5n9X/9Fd3MzZa+8EmxTTqB2yxZEVBTZc+Y4t00891xiUlIiorG2LhKT8vN9On+kGc5H3n6b92+9la7GRt8NDCB60YpO+tSpY8KTWLNxIwC5ixYx7corAUyp4N/0m9/Q4JjcUevIHTQavUeisJjb0nb8WWfRsGsXHbW1Q/bV79wZ1ChDb3u7YZXNOlHR0RSdfroSiQ76u7vZ/NvfMuGcc8iZP/+EfcH2JHbV16vK5iBiZnXzx1JKIaWcKx0tb6SUugi8QUr550HHV0kpL3D5+6dSyulSytlSyuuklGHV1Gr8WWeRPG5cyIWc67ZuJXPmTKxxx9tOWuPiKLn4Yg688ELIt+8ZjY6aGhDC5zeV9KlTERbLCZNKQAt5vH7NNWz53e/4+8yZHHjxRSPMNQ3pUtmskz5tGq1HjoScd9toqj//nPSpU4lLSyO9pITchQsNDzm3HDrEZz//OZMvughrfLxzgpHR+Dqz1lvGn3UWwAnRBPvAAB/88If8c+5cNv/2t6bbMBxmeBJBa4XTvH8/beXlhl873Nj1j3/QWVvL0rvuGrIvFDyJqrI5eKiJKyZhiYpi1vXXc/Ttt2kPobYjtVu2kOuminnalVfS09LCkXfeCYJVxtFRU0N8VpbPoQlrXBypxcVDPInv33ILPW1tXPj00ySPG8fLl17Ka1dfTeco1X7BoqOmhp6WlhNEYsa0aSAlzWVlQbTMfGo2biR/yRLn39OuuoqajRtpMagyVkrJu9/7HharlbMfeojsefOoNUskBugDMnfhQmJTU515ib3t7bz05S+zcfVqomJiKA9iKkpve7sh01YGM+GccwA1os/e38/G1avJW7KEopUrh+yPTkgAgpuTqBppBw8lEk1k1g03IO129jz2WLBNAcBWXU1nbe0J+Yg6E885h9i0NFOT/AOBr420XRk8w/nga69R+uSTLPvRj5h+xRVcs349y+++m/3PP88/Zs3ioKPdSijhWrSikzFtGkBEh5zbKyvpqK4m76STnNv0HCujQs6lTz3Fkbfe4tR77iG5sJDchQup27rVlOKVQIlES1QURStXUv7++7QeOcK/TzmFw2+8wVkPPsisG26g+rPPgja+s9dmc3qzjCRz5kwS8/LGvEjc9+yztB46xNK77nLbsk1YLEQnJga1mbbyJAYPJRJNJL2khHErVrDr738PiUkX7opWdKJiYphyySWUvfRSWIecfW2k7UrGjBk079+Pvb+fnrY23v3ud8maPdsZiomKjubkH/+Y6zZvJjEvj1evuCLkKsPdicT0qVMBInqGc83nnwNa0YpO6oQJ5C9bZsgXoK6mJtbeeit5S5Yw/3vfA7Tcx972dlM8tIHMxxp/1lm0HjrEvxYtor2igsveeIMFN91E4fLl9LS20rB7d0DsGIxZnkQhBBPOPpuj774bcfPrPUVKyef33kvGjBlDqppdiU5Kco5HDCT63GaVkxg8lEg0mVn/9V80HzgQErNR9ZDY4MRknaKVK+ltb6f1yJHAGWUwRojEzBkzGOjtpfXwYT68807aKys595FHiIqJOeG47DlzWPT979Pf3U17iOU1Ne7ZQ2xa2gn/i5ikJJIKCyOicns4ajZuxGK1DnmOT7/ySuq2bfNbIH94xx10NTbyhYcfxhIVBeBM3zC6eKW/p4ee1taAeVEmOBpLx2dmcu369Ux0hGMLly8HoPKTTwJix2D6TPIkAow/+2y66uupdxQgjTUOv/EG9Tt2sOSOO0YsjopJSgqKJ1HNbQ4+SiSazLTLLyc6MTEkCljqtm4lraSE2JQUt/tTJ00CoC1MRaKU0jCRCLD94YfZ/qc/seiWW8hfutTtsanFxQCG5bsZhV60Mjh8lDFtWkR7Eqs//5ysOXNOKMwCmHr55SCEXxX8FR99xM5HHmHxbbeRM2+ec3vmrFlExcQYnpeo90gM1Adk5owZXLluHddu2OBMTQDtOZ6Qm0tVkESiWZ5EOC6Mx2rI+fN77yW5qIgZV1894nHRSUlByUnsUiP5gs6oInGNEDlrhLhkjRA3rRHi62uEWLJGCCUuPSQmKYmpl19O6dNP09vREVRbhita0UmZOBEgbD2JPa2tDPT0+B9unj4dgE1r1pAycSLLf/nLYY9Nc4jE1hAUiVmzZg3ZrrfBCYX0B6ORdju1mzadULSik1xYyLgVK/wKOa//5S9JHjeOk3/60xO2R0VHkzV3ruEVzsGYWVt0+unEpaefsE0IQeHy5UH1JJolEpMLC8mYMYOjYV6w5wuVn3xCxUcfcdKqVUOiJIMJlidRn12uws3BY1ixt0aIM9YI8RbwOnA+kA/MBH4M7FwjxM/XCOHeJaU4gTlf/zp9NhsHnn8+aDZ0NzfTduSI23xEnaSCAixWa9h6Ev1tpK0Tm5pKUmEhAF94+GFiEhOHPTapoIComJiQ8iR21tfT1dBwQj6iTsa0aXQ3N9MVolXZ/tBcVkZPa+sJRSuuTLvyShr37KF+1y6vr93d0kL5++8z/Zpr3D4fchcupHbLFkPFt/4BGQpelMLly2k9fBhbdXVA73egt5eB3l5TWuDoTPzCF6j44AP6OjtNu49QZMO99xKfmcnsb3xj1GOD5UnsDLA3XTGUkTyCFwDfWiXlSaukvHGVlD9eJeWqVVJeBMwDtgLnBMTKMKfw1FNJKylh59/+FjQb9KKVkTyJlqgoksePD1tPolMk+thI25VZ11/PkjvvdOZlDYewWEidNCmkPInuilZ00h1hxEgMObsrWnGl+ItfBKDy44+9vvbh//wHe38/Uy65xO3+3EWL6GlpofXwYa+vPRyh5EXxJC9x56OPsu+55wy9X71YwixPImjPi/7ubsrff9+0+wg1mvbv59Brr7HwlltG/BKsEx1kT2IofFEaqwwrEldJ+YNVUrrNxl8lZf8qKV9aJWXwXGNhhBCC2TfcQMUHH9BeMdwkQnOpHaGy2ZXUiRPD1pPYaZAnEWDFPfdw2v/9n0fHphYX03LwoN/3aRQjicRIboNTs3Ej1oQEZ07pYJLHjcNitdLuw4i+Ay+9RGJenttQNrgUrxgYcg6lfKycBQuwxsUNm5fY29HB+7fcwpbf/c7Q+9W9V2YVrgCMO+00ohMTOfT666bdR6ihTyWactllHh0fk5wc1JzEuMzMgN+3QmOkcPNta4QY4odeI8R/rxHiVlOtikDGnX46AA0+hLqMoG7rVpIKC0d126dMnBj+nkQDRKI36CIxVPL8GvfsISY52RkydyVlwgSiYmMjcoZz9eefk7twIRar1e1+S1QUSYWFXlei93d3c/iNNyi5+OJhK0Cz5szBYrUaWuHcWVeHJTqa2NRUw67pK1ExMeQtWTKsJ7HspZfo6+hwvgaNIhCeRGtsLBPOOYdDr78eMq9hs2nauxcRFUV6SYlHxwctJ7G+Xs1tDjIjhZu/DvzLzfaHHfsUXqC/GIM17WK0ohWd1IkT6aiuDsvRbR01NUTFxBCblhbQ+00rLqa3rY3u5uaA3u9wDFfZDJpQSispiThP4kBfH/Xbtg0batZJGT+eNi89iUffe48+m42SL3952GOssbFkzZ5taPFKZ10dCdnZbh/HYFC4fDl1W7e6zd3b8/jjgNbM3kj6AuBJBJh84YW0HzvmnMUd6TTu3Uva5MmjFqzoBDPcHAqe9LHMSCJRrpJySFflVdoM5dB41wojEnJziU5MpCUIIrG3o4PmfftGDTXD8Qpno+aZ2qqrAzaW0FZdTUJeXsA/VFNDrMJ58MzmwWRMnRpxOYkNu3bR391N/jBFKzrJRUVeexLLXnqJmORkis44Y8TjchctonbzZsO8UZ0hNmmiYPly7P39VDtyP3U6ams5+vbbxKal0dfRYWjT5UB4EgEmXXABQFiFnI99+CFH3n7bp3ObSkuHTctwR0xSkrOIKJAEspm8wj0jtrJZI0SuJ9sUoyOEIK2kJCgisWHHDqTd7rEnEYzrlfjG9dfz4oUXGnKt0eg0oEeiL6RNngyERq/ErqYmOmpqRhSJ6dOm0XLwIPb+/gBaZi7OopXRROL48bRXVHg8YcM+MMDBV16h+ItfxBobO+KxOQsX0tXY6FPOoztCzYtScPLJAEPyEkufegpptzP/u98FMDTkrHuvzKxuBkjKzyd34cKwEomf/PjHvHfzzV6fZ+/vp/nAAWerL0/QPbl9AW7jFmpflMYiI4nE1cDra4Q4fY0QyY7bSuBVYE0gjIs0giUS/RleTQAAUiJJREFUPS1aAS1nDYzplSilpHbzZuq2baP16FG/rzcaRjTS9gW9CXkoeBJHKlrRyZg2DXtfn6GVuMGmZuNG4jIynF7d4UguKsLe10dHba1H161ev57OuroRQ806RhevdNbVhZQXJT4jg8yZM4fkJe7517/IXbSI8WeeCWBom5xAeRIBJn3xi1R99hldjY2m35cRtFdU0FxW5nX/3ZZDh7D39ZHhpScRCHjxip5yoQgeI1U3Pwb8L/AL4AhwGPg58NNVUv4zINZFGOklJdoLdGAgoPdbt2UL8ZmZJBcVjXqskb0SO6qr6W5qAgITxgmWSIxJSiIhJyesRCL43ganv7ub5847j6rPPvPpfDOo2biRvJNOGjXVIGX8eACPQ84HXnoJS3Q0k84/f9Rjs+fNQ0RFGVa80hWCXpTC5cup+uwzpye2ce9eajdvZuZXv0qC47VnpCcxENXNOpMvvBBpt3PkrbdMvy9/kVJiq6oCKb3Oo2zauxfAq3Cz/v8P5Pxm+8AA3Y2NIfVFaSwyYrh5lZRvrJLy9FVSZq6SMsvx+xuBMi7SSCspwd7XZ1g4ylNaDh4kY/p0j3L1LFYryUVFhngS9XmoIiqKQ6+95vf1RsLe309nfX1QRCI4KpxDRCRaExKcYsgdeq9EX2c4123dypG33jK8J56v9HZ00LBr16hFK3BcJHqScyulpOzFFxl/1lnDjrJ0JTo+nswZMwzxJPZ2dNDX0RFyIrFg+XJ6WlpocHwZ2fvEEwiLhelXXUWSoz+pkcUrgfQk5i1eTEJODgdNfq8ygq7GRgZ6egCo377dq3MbHSLRdfTiaDjDzQH0JHbrc5tD7DUw1lDj9QJImqPCOdAh546aGq8aTKcY1CtRb/cz45prKH//fVPHEnbW14OUQROJacXFIeNJzJwxY9hWLaCFDeOzsnz2JNY4PGV6g/ZgU7d1K9JuH7VoBXB60z35otawezctBw8O20DbHbmLFhlS4Rzouc2eojfVrvrkE6Tdzp4nnmDCOeeQmJdHXEYGluhoQ0Vin82GsFiwxscbds3hEBYLk84/nyNvvhny+bo2l2LAum3bvDq3qbSUpIICr1orxQRBJKppK6GBEokBJFhtcDqqq70SiakG9Ups2LmTxPx8Zl1/PQM9PZS/957f1xwOI6et+EJqcTFt5eUM9PUF5f51Wg8fdn4ZGYmMadN8Fom1LiIxFPrK6V9GPMm5jU1LIzopySNPYtlLL4EQlFx0kce25C5cSEdNjRYK9INgzG32hLTJk0nIyaHyk0+o/OQT2o4cYeZXvwpoxXmJeXnGhpvb24lOSgpYx4LiL36R7uZmqtavd7vf04Ins9FFYnRiok+eRG+KVsAl3BxAkah/UVLh5uCiRGIASSoowBoXF1BPYl9XFz2trc5QkCekGNQrsWHnTrLnzGHcihXEpKSYGnIOViNtndTiYuTAQMBTCQbT3dREvAfTCdKnTqWptNSn+6jdvBmEoKelhbYAFCSNhj6H2hNBJYQgZfx4jx6nspdeomDZMq+eUzkGFa+E6jgyIQSFy5dT+ckn7Hn8caITEylx8bQm5ucbHm4ORKhZZ+IXvoDFah2SQ23v7+ed732PPxUU0NPaGjB7hkP/EjLh7LOpd3Sv8AQpJU2lpV4VrUCQPIkh+hoYa4wqEh2TVwbfvrFGiPkjnSeEKBJCrBVC7BVC7BZC3OLY/rQQYpvjdkQIsW2Y89OEEM8JIUod1zjZlwWGEsJiIXXy5ICKRP0NO8GLD7pUA3ol2gcGaNyzh6w5c4iKiWHiuedy8LXXTPM8BVskpoVAr0Rpt9Pd3ExcRsaox2bNmUNnbS0djjdiT+nr6qJxzx4mnH024H2oywy6m5qITkryuDFwclHRqM/ttmPHqN282aOqZldy5s8HIfwuXgmluc2DKVi+nNZDh9j7xBNMueSSE2b/JublGVrd3Gezmd7+xpXY1FQKV6w44Qttb3s7L150Edv/9Cc6a2tp2L07YPYMh+5JnHjeefR1dHicD91RXU1vW5tXRSsQHE+iCjeHBp54EhcD3wEKHbcbgZXAX9cI8cMRzusHbpdSzgCWATcJIWZKKa+UUs6XUs4HngdeGOb83wFvSimnA/OAvR7YGvKkl5QENNysiydvPYngX6/EloMH6e/uJmv2bECrHOyorjYtj02f25yQG5w2nnrrlWAWr/S0tYGUHonEnHnzAO+T3uu3b0cODDD7hhsQFktI5CV2NzV5tGadlPHjR61uLnvpJQCvRWJMUhIZ06b57UkMpbnNg9HzEvs6OpjhCDXrJOXnGx9uDqAnEbSQc8OuXbSVl2OrquKp007jyNtvs+TOOwHfC76MxFZZSUJOjrMvqKevY2fRipfhZt2bGwxPoprbHFw8EYmZwMJVUt6+Ssrb0URjNnAacMNwJ0kpq6WUWxy/t6OJPOcwWaElmVwBPDn4XCFEiuP6jzrO75VStni2pNAmraSE1oMHA5bbonsSvc1JBP96JeptGbLmzAHQWogIYVrloK26mtjUVKIDkODujqSCAqJiYoLqSdTbDcWlp496bLaPIlH3kBWuWEH6tGkhIxLjvRCJyUVFdNbVjZhOceStt8iYNo2MqVO9tseI4pXOujqs8fFEu3jpQoXchQuxxsWRkJvLhLPOOmFfYn4+XfX1huXmBtqTCJpIBNi4ejVPLF1Kc1kZl7z6KqfefTcWqzUkRGJ7ZSVJhYVkzZqFiIry2KOvp5h47Ul0PA8D6UlsP3aMxPx8Nbc5yFg9OGY84DqLpw+YsErKrjVC9HhyJ0KIicACYIPL5hVArZTygJtTioF64O9CiHnAZuAWKeWQ8lghxI1o3k1yc3NZt26dJyYFjTq7nf7ubt55/nliRnCj22w2Q9ZS9+GHAGw/dIhoD2cLy4EBRFQUOz/8kGYfPiQBKl99FYRgT0MDpY51JM6cybYnn6T3tNPcnuPPmo/s2IFISQnq42/NzeXA+vXYvbTBqMe6w1GIUlZZSYMH14vJyWHnW2/RsXixx/dx+LXXsKalsbmsDAoKKF+/3ifbjVozQO3hw4joaI+v1+Cosn/3+eeJKyx0e0zFxo0kzZ3rk43tKSm0V1TwzksvET1ojrin6z68YweWlBQ++OADr+8/EGR/5SvEZGfz4ccfn7C93pGv995LLznf3/x5rBurq4nJyQno61pKSWxBAVsffJDorCym/Pa3lMfHU/7xx8Tk57P/k08Y8MAeI5/jg6nZt4+YnBw+Xr+e2HHj2Pv++/QPEuzuOPruu1gSEti4bx/CS7FriY3l4J499I6wJiPXfHTbNkhPD/nPdDMf55BASjnibTX872rYshp+6rhtWg0/WQ2Jq+GJ0c4HktBE3qWDtv8JLRzt7pzFaOHqpY6/fwfcPdp9LVq0SIY6R955R64GeXTt2hGPWzvKfk/58H/+R66JipL2gQGvznt40iT52jXX+Hy/L116qXxkypQTtn12zz1yNUhbdbXbc/xZ85MrVsgnTz/d5/ON4NnzzpOP+fAcNOqxPvzWW3I1yGMffeTR8c9feKH826xZXt3HP+bOlc+ed56UUsrPV6+Wq0F21Nd7batRa5ZSykdnzJAvf+UrHh9/9P33tdfg+++73d/d2ipXg1z/q1/5ZE/Za6/J1SArP/10yD5P1/3seefJxxYv9un+g8mBl1+Wq0FWb9zo3ObPY/3XkhL56tVXG2CZd2z8zW/kk6efLlvLy0/Y/sKXviT/PmeOR9cw8jk+mAezsuTb3/62lFLKV6++Wv55/HiPznv6rLPkv5Ys8e0+s7Pl29/97ojHGLnmv5aUyFeuvNKw65mFv2sGNslRtE0wb6OGm1dJeTfwLaAFaAW+s0rKX6ySsmOVlNeOdK4QIhot7/AJKeULLtutwKXA08OcWgFUSCl1z+NzwOiDh8OAQPdK7KipITE3d8S+ee5I8bMNTsOuXc5Qs85kxwznQ//5j8/XHY5gTVtxJdi9ErsdnmJP8/Ny5s+nqbTU4yr2vq4uGnbvJm/RIu18R8uZYIecvc1J1HslDle80ugoTNDzab3FWcTkx9jDrhCb2+wpiQY31A50dbPO4u9/n6vWrSNl0JSq9KlTaSkrC2ornP6eHroaGkhyeMGz582jvbzc+fofiaa9e70ONevEJCUFLCdR2u20l5c7x8QqgoenyiEeaFsl5QPA0TVCTBrtBEfO4aPAXinlbwbtPhsolVJWuDtXSlkDHBNC6C3hzwL2eGhrSJNcVIQlOjpwItHLHok6qX401O7r6qKlrGyISMyaM4fkoiJTWuGEgkhMLS6mu7nZozdrM/AmJxG0Dxc5MOBxtWbDjh3IgQFyQ0gkSim9F4njxgHDj+bT/x+Zs2b5ZJNe+OVPEVO4zqxNNHg0X5/NFpCRfJ6SPmUK/V1dtLs0sw40evsbXSTmzJ8PQN0o+cU9ra3Yqqq8LlrRiQ6gSOyorWWgt1eJxBDAkxY4PwXuAO5ybIoGHvfg2suB64AzXVreXODYdxWDClaEEAVCCFcX038DTwghdgDzgV95cJ8hjyUqirTi4oBVOHdUV/sknlImTsRWVUV/j0dppyfQuGcP0m4ne5BIFEJQfOGFHHn7bZ+uOxy9HR30trcHXSQa4UHyB29Fov7h4mnxij5pRReJ8RkZJI8fH9Q2OH0dHdj7+rwSiXrRxXC9Eht378aakOAs4PKW6Ph4EvPyfH4eSCnpDMG5zZ6Q6OguYEQbHGm309fRERRP4nCkO3K0g1m8ore/cYpED4vQ9Ob5/ngSA1W4ovdfHWm8qCIweOJJvAS4COgAWCVlFTDqq1ZK+bGUUkgp50pHyxsp5X8c+26QUv550PFVUsoLXP7eJqVc7Dj/y1LK4LhnTCCtpCRgnkSbH55EGN7bMhLOymY34brJF15IX0cHFQYm5HfW1gLBm7aikzZ5MqC1/wkG3c3NWBMSsMbFeXR8WnEx0UlJHou82s2bic/KcoZrAXIXLAiqJ1EXxt5UN8PIvRIbdu0ic+ZMr1M0XEn1I/Wgt72dgZ6esBSJUTExxGdmGhJu7nMUGAW6unkkQlEkJublkZCTM6pI9LX9jU50UpJzlrbZOEWi8iQGHU/eBXtXacmVEmCNEKHXkyHM0EWiNHmkmX1ggK76ep/EU4ofbXAadu3CGhfndjxc0RlnYI2PHzLRwB+C3UhbJ3WSloURrF6J3raCERYL2XPnUu+FSMxdtOiEEWnZ8+fTtG+fqXO5R8LpPfVSJI40daVh926yfAw166ROmuSzJzGUG2l7QqJBvRJ1QRJKnsSkggKsCQkhIRKTXSrzs+fNG92TWFqKJTra+WXWWwKZk6hEYujgiUh8Zo0QfwHS1gjxLeBd4K/mmhXZpJWU0NfR4fSAmUVnXR3SbveqkbZOqh8NtRt27iRz5kwsUVFD9kXHxzPu9NMpX7vW6+sOR3uFltrqyzqNJCY5mfjs7KAVr3Q3NRHrYahZJ2f+fOq2bx/1C0t/dzeNu3c7Q806uQsWgJQ07Njhtb1G4BSJXjbc1T2Jg9fd1dRER3W1z0UrOqnFxbQfO+ZTv8BQbqTtCUaN5tNDm6GUkyiEIGPqVJqCKBLbKyuxxscT69JeKXvePBp27Rrx+da0dy/pU6ZgsXrS+W4o0QEON8empRGbkhKQ+1MMjyfVzWvQqoufB6YBP1kl5R/MNiySSXd42MzOS3Q20vbBw5ZUUIDFavXJk1i/cyeZI3zIpk6YYOhUhqbSUhDCrecy0ASzwtnTkXyuZM+bR29b26iPc/2OHdj7+4eIRL14pTZIIeeuxkbAN09in81GT0vLCdsb/Sxa0UmdNMlZoektzpm14epJNGg0Xyh6EsFR4XzAXXvfwNBRVUVSQcEJHv2c+fMZ6O115h26o6m01OdQMwS2cKXt6FHlRQwRPEq6WSXlO6uk/MEqKVetkvIds42KdALVBsfmw7QVHYvVqnlbvBSJXY2NdFRXDylacSU+J4euhgbsAwNe2+WOpr17SZkwgeiEBEOu5w+pxcVhE24Gl+KVUULOtYOKVnSSi4qIy8gIWl6izzmJjoT4wSFnvbLZiHAz+FbE1BkBnsTOmhq/02lCViROmULLoUOGTZXxFptj2ooro01QGujtpbmszOeiFQh8uFmJxNBgWJG4Roj2NUK0DXcLpJGRRsqECYioKNNFoj7P2NeCDl96JTbs2gUwpP2NKwk5OSCl0wvkL41+9P4ymrTiYtqOHsXe3x/w+/Yl3Jw1e7Y2g3mUfKbazZuJz8wcUm0ohCAniMUrukj0dt0pw/RKbNi1i5jk5BOKc3whzY9Z3mGfk5iXx0Bvr9+toPpCMNwMmidRDgwErYtBuxuRmDFtGlExMcOKxOayMuTAgF+exJjkZPq7u01/b5NSKpEYQgwrEldJmbxKyhTgAeBOtLnL49Da4fwyINZFKFHR0aROnGh6uNnmR7gZfOuVWO+obB7Jk6h7SPTcK3+wDwzQvG8fGSEiElOLi5EDA7QNUxRhJt72CwSITkggfepUjzyJg4tWdHIWLKBh586geFa6mpq0GcdezuzWPYmDRWLj7t1kzprldp3ekFRYiCU62jdPYl0d0UlJQZtD7i9JBjXUDllPYhArnKWUbj2JUdHRZM6aNaxI1Gc2+/M+qYv1PpOL1HpaWuhtb1ciMUTwJNx87iopH1olZfsqKdtWSfkn4DKzDYt0AtEGp6O6mrj0dKyxsT6d70uvxIadO4lLTx/Re6mLRN1j4g9tR4/S391N1syZfl/LCFL1XokBDjn3dXXR393tddgVjhevDEd/dzcNu3YNCTXr5C5YoOVDOT6IAokvwhi0fn6W6Gi34WZ/Q82g9UNNmTDBJ5HYFaY9EnWcU1f8zDvWPYmh1AIHgisSu5uaGOjpOaGyWSd73rxh21k16e1vpk1zu98T9MfB7OIV/YubEomhgScicWCNENeuESJqjRCWNUJcCxiTTDaGCUQbnI6aGr96B/rSK1EfxzeSJ8ZIkejs/RUinsS0IInEHi9H8rmSPW8ebUeO0D2oiEOnfudOt0UrOsGcvOKrSBQWC8njxp3w3O6oq6Orvt7vymad1EmTfHoedIbpSD4d59QVPz2JXQ0NgPepBGYTn5FBfGZmUETi4B6JruTMn09nXZ1bcd5UWkpyUZFfgtvpSTRbJKr2NyGFJyLxGuAKoNZxu9yxTeEH6SUl9LS2GpaX5w5fR/LpeNsrUUrpdmbzYPSqTSNEov4NOVRyEvUwY6CLV7p8zM2D0SevDFe0opM+dSrW+PiwEomghZxdPYlGVTbr+NorMVxH8uno7zn+Vji3V1QQl55OTGLoteZNmzKF5iBUOLePIBL14hV3UQEj8rYD5klUIjGk8KQFzpFVUl68SsqsVVJmr5Lyy6ukPBIA2yKaQFQ423wcyaczUq9Edx7QtvJyetvaRsxHBM3bJSwWYzyJe/aQkJvr8Sg6s7FERZE6cWLQPIm+hpthZJEYl5Ex7Ju2JSqK7Llzg9IGx5eKbp2UQVNXnEVXRonE4mK6Ghq8nlIRriP5dGKSk7EmJPgdbm6vqCDJMWc71EifOjX0PInDVDhLu93v9jcQWE+iNT4+rL8oRRIjVTf/eI0Qw777rhHizDVCXGiOWZGP2SJRSum3J3G4Xokthw7x1+JiXrv66hMqGEcax+eKJSqK+Kwsw8LNoeJF1AlGGxxfJ4/A8bFew+UzjVS0opOzYAH127aZPkVoMH57EisqnK2YGnfvHjWf1ht8aYMj7fawz0kUQpCYl+d3uNlWWUlyiIrEjKlTaa+oCPikIadIdPMcjUtPJ7moiNrNm094HbZXVtLX0eF3Sk50AD2JKePH+108pjCGkVqv7wReXSNEN7AFqAfigCnAfLTJK78y28BIJXXSJBDCtArnntZWBnp6/JpC4q5XYldjI8+ffz7djY3sf+45Kj/+mPMfe4zxZ5xx3BPjQU5XQk6O39XNUkqa9u5l+jWhlf2QWlxMzcaNAb1Pf8LNoHkT3XkS9aKVxbffPvL5Cxaw/c9/pvXwYWdeZiDwRySmjB+PHBigo6aG5MJCGgyqbNZxFYnZc+d6dE53Swv2/v6wbX+jk2TAaL72igpyFy40yCJj0YtXWsrKnB68QGCrrCQhJ4eomBi3+/MWL2bfM89Q8dFHFJxyCgUnn+x8PvvrSdTDzX0mz29W7W9Ci2FF4iopXwZeXiPEFGA5kA+0AY8DN66SsiswJkYm1thYUsaPN82T2OFHI21XXHsl9nV18dLFF9N29CiXv/MO1vh4Xr/2Wp456yxOWrWK1sOHSR4/ntjU1FGvm5CT47cnsaOmhp7W1pDzJKYVF9Pd1ER3SwtxLqOzzMTXptI62fPmseV3v2Ogr4+o6Gjn9o/uugt7Xx8TzjprxPNdi1cCJRL1im6fPYmOXojt5eUkFRTQsGsX06+80jD7fOmVqL9ukwoKDLMjGCTk5TlzPH1hoLeXztrakA43g1bhHEiR6K5HoitnP/QQ488+m6pPP6Xq00858Pzzzn3+vk8G0pOop8Aogs+oQxxXSXkACN4MogjGzDY4/ozkcyV14kSOvPUW0m7nja99jcpPPuHCp59m3IoVAFy3ZQvrbruNjatXAzDpggs8um5CTo6zIMJXQq1oRUf/ANn+l7+w9I47AnKfPc3NiKgoYnycdeoc61Va6swp3fvvf7P5gQdY+P/+HxPOPnvE87PnzEFERVG3dStTLwtMhyynMPZybrOOs1fisWOkTJhAT0vLiOMkvSUuI4OY5GSvws22qirA/y93wSYpP5/yd9/1+Xz9/xCq4WY9XSjQeYm2ysoRG70n5uWx4HvfY8H3vgdoX6SrPvuMgb4+EnNz/brvmADkJPZ1ddFZV6c8iSGER2P5FOaQbqJI9Gcknyt6r8T3/t//Y/9zz3H6mjVMv+IK5/6YxES+8Je/8OWXXyapoICJX/iCR9eNz87225MYau1vdCadfz5TL7+cj+68k49//OOA5Ol1NTURl57uc6g0e1DxSt22bbz1zW8y7rTTOH3NmlHPt8bFkbNgAUfeftun+/eFbh/nNuvo02Pay8sNG8fnihCC1OJin0RiuHsSE/Pz6Wltpa/Lt4BTe0UFELoiMSYxkaTCwoBXONsqK932SByOxLw8plxyyQnv2b4S7agyN9OTqLekSh402UkRPJRIDCJpJSV0NTb6Pb7KHXo+kD85iXC8wnnbH//Igv/+bxbfdpvb40ouuojvVFay6JZbPLpuQk4OPa2tXjXqHkzj3r3EJCeH3AdqVHQ0Fz75JHO/9S3W33MP7950E9JuN/U+ux0i0Vcypk4lKjaWum3b6Gpq4uVLLyUuI4MvPfPMCeHnkZhx9dXUbNxI0759PtvhDV1+FOsAxKakEJOSQlt5uTOf1qj2Nzre9ko0Kk0k2Dh7JfqYl2hziMRQDTdD4Cuc+3t66GpoGDHcbCZRMTFExcSY6klU7W9Cj5Gqm28OpCFjkbTJkwFoOXjQ8Gt3VFdjjYvzOfyoo08QKfnylznjt781LKnfiNF8TXv3kjFjRkhWwVmiojjnL39hyR13sP1Pf+L1a69loLfXtPvraW72WSyBVqSUNWcOtVu28PrVV2OrrOTi55/3KkQ1/eqrERYLe/71L5/t8AZ/Krp1Uhy9Eht37yY+O5tEg6uK9V6JnnqTbVVVxKSkhGRvQG9I9HM0n9OTGCRB5AkZARaJoZCvGp2UZKonUYnE0GMkT+LXA2bFGEV/sXfU1hp+bb39jb8CqvCUU/jSM8/wxX//G0tUlEHWuUxd8UMkhmL7G1eEEJx2772suPdeSp96ipe+/GXThGKXH1W+Ojnz53Ns7VqOvP02Zz34IPlLl3p1flJ+PhPOOYc9jz9uuucUjBGJyUVFWrh51y5DQ806qcXF9Hd10enha9xWVRVynnFfSPJzNF97RQXRSUl+f8k1k/SpU+lqbDR1IIIrI/VIDBQxycmmexJFVFRIfzkYa6hwcxCJN3A83WBsfvZI1BEWC9Muv5zo+HgDrDqOv6P5ulta6KiuJjNEZjaPxNI77uCMBx7g8BtvcNSPZP6R8KcVjI4+sWHut77F3G99y6drzPzqV2k7epTKTz7xyxZPMMqT2Hb0KI179hgeagbveyV2VFdHhEj0dzRfe0UFyePGhWSUQMdZ4RygvMSRpq0EikB4EpMKC7FYR62pVQSIkUTi3DVCtLm5ta8Roi1gFkYwRs4wHoy/c5vNxt+1h2pl83DM/OpXgePFNkbjb04iaDmFp69Zw5l/+IPP1yi55BKiExMDEnLubmoiKiaG6IQEn6+RPH48XY2N9La3Gzaz2RXnLG8PRaKtqiqkX7eeEp+djbBYfBaJNodIDGXSp0wBAlfhHBKexKQk0z2JqSrUHFKM2Ex7lZQLfL2wEKIIeAzIA+zAw1LK3wkhngamOQ5LA1qklPOHuUYUsAmolFJG3HSXmMRErPHxfjeVdkdHdTXjzzzT8Osahb/zm0O1snk44jMzic/Koqm01PBr2wcG6Glt9duTGJ+ZyUmjNM0ejZjERKZcein7nnmGM3//e6xxcX5dbyR076k/3ibXdiJmhJv1+eee9EqUUtIRIeFmS1QUCTk5dNTUEOvD+bbKSsaP0nYp2KROmoSIigqYJ9FWWYk1Li6oI0ijzRaJ5eXO9mqK0MDMcHM/cLuUcgawDLhJCDFTSnmllHK+Qxg+D7wwwjVuAcxxvYQIRjSVHkxfVxc9LS1+VzabSUxKClExMX6JxKjYWGc4LxzImD6dZhMqf3taW0FKv0WiUcy87jp6Wls5+Nprpt6PEXmYKS6tNswIN0fHx5OYl+eRJ7G7uZmB3t6IEImgFa/YfPAk2vv7sVVXh7wnMSomhtRJkwLqSUwqLAxqCD7GxHCzvb+f9ooKVbQSYowkEp/158JSymop5RbH7+1oYs/pJxfaM/0K4El35wshxgFfBB7xx45QJz4726/iDXfoyeKhHLYSQvglkJv27iV96lRDi2nMJmP6dBpN8CQ6c/OC6GFwZfyZZ5KYn8/exx839X6MyMPU+7El5uf7PK1mNFKLiz1qgxMpjbR1EvPz6fShcKWjthY5MBDyIhEC2wbHNsq0lUBgpifRVlWFHBhQIjHEGCncXL9GiCmrpDywRhN0fwMuA44AN6xyCEBPEEJMBBYAG1w2rwBqpTbRxR0PAD8Ekke59o3AjQC5ubmsW7fOU7NCgu6oKNoPHhxit81m83ktNkdj4IN1dTSG8P9jID6eitJS5zq9WXPl1q0kTJ0aVo93s9VKV3097778MlaX0YX+PNYAHQ7heaCykvoQ+X8krVjBwRdeGLJWHX/XDNBQXk6sn695e18fCIGloMC051J3QgK2nTtZt27diOtudcz7PlBbS02IPI7+0C4lrUePku7lY23bsweAw01NNIf4/6EjIYGG0lLWrl07xMNnxHPclfqDB0mcPj2o73kNbW3YmpqGtcGfNbfv2AHA4ZYWmkL8cXfF6Mc51BhJJN4C/MPx+9XAXGASmtj7HZrIGxUhRBJaWPlWKaVrwcvVDO9FvBCok1JuFkKsHOn6UsqHgYcBFi9eLFeuHPHwkKNrxgzK332XwXavW7duyDZP2d/URClw8rnnkrvA57RS02mYPJnuxkbnOj1dc19XF5uqq1n4rW+xPIwe74M2GxV//jMzsrMpPOUU53Z/HmuAwz097AVOOv30E64bTOrS03nsmWfIrKxkwcUXD9nv75oB9vX2Mm7aNL+vU7NsGSVf/jJLTHouWd9/nw3vv8+K5cv56JNPhrV315EjHABWfPGLzh6q4czH777LhrfeIjE+3qvHaH9jI6XAKRdcENC5yL6wbe9e3n3uORZPmTLE82nEc1xHSsnWxkaKFy407Jq+sO6112h+771hbfBnzXsqKtgHrLjoIjKmTRv1+FDByMc5FBkp3Ny/Sso+x+8XAo+tkrJxlZTvAh51ehVCRKMJxCeklC+4bLcClwJPD3PqcuAiIcQR4CngTCGEubGrIJHgCDcbObrN2XQ1xMNWvoabm/fvBynDprJZJ2P6dADDJ5IY0QrGaLLnziVr9mxTq5yNCDcDXPPppyz54Q8NsMg9qZMmIe1258ix4YiUaSs6CXl5SLud/tZWr84L9ZF8ruiFc/pYR7PobmpioKcn6P0Do5OS6O/sxD4wYPi19UbaaiRfaDGSSLSvESJ/jRBxwFmAa4O3UZvmOXIOHwX2Sil/M2j32UCplLLC3blSyruklOOklBOBq4D3pZRfHe0+w5GEnBwGenrobW837JodNTUIi4V4RwVxqJLgmN/srUAOt/Y3OqkTJxIVE2N4hXOo5SSClnM687rrqF6/3pTqz/6eHvo6OkJKGA+Hp70SbVVVxKam+tXSJ5TQv6T2OZ6fntJeUUFUbGxYPLbZc+YA0LBzp6n3Ewrtb0ArXAHo7+w0/NptR4+SkJNjeE9ehX+MJBJ/gtZ+5gjwyiopdwOsEeJ0wJNhpMuB69C8gNsctwsc+65iUKhZCFEghPiPl/aHPWb0SuyoriYhJyfkizoScnLo7+qir6PDq/Ma9+5FWCzOZrbhgsVqJa2kxHiR6Jj9HUoiEWDGNdeAEOwxoYAlFL2nw6H3ShytDY6tqorECKlshuMe0T4vJ5LYwqCRtk58ZiaJ+fnO2d9mEQqNtEHzJAKmVDi3HT2qilZCkGFzEldJ+doaISYAyaukbHbZtRG4crQLSyk/Bty+yqWUN7jZVgVc4Gb7OmDdaPcXrujevq76etJLSgy5plHTVszGVSDr31A9oXHPHlInTTK1B59ZZEyfTqPBoanupiaik5KIiokx9Lr+kjxuHHmLF1P58ceGXzucRGJSYSGW6GjNkzjCF5tImbaio09d8cWTmBQGoWad7DlzTPckdjgq34MtEvX3aTMqnNuOHiXL4ZlVhA7DehLXCHESkKULxDVCfG2NEC8D9wKh9WkUxpjlSQwnkehtM/HGvXvDpon2YDKmT6fl4EEG+vpGP9hDjMrNM4O0yZOduUZGootEs9rWGIklKoqUCRM8CjeHeh6xN/jqSWwPg2krrmTNmUPjnj2m5OnpOD2JQX5+xCRrzUaM9iRKKWkrL1eexBBkpHDzX4BegDVCnIYmDh8DWnFUEyv8xzl5xMBeiR01NUF/M/EEXwSyvb+f5v37w2Jmszsypk/H3t9Py8GDhl2zu7k55ELNOikTJ9JWXm74B2g4eRJBy0scqVeilFL7chdBnsTo+HhiU1O98iRKu52OqqqwE4n93d20lJWZdh+2ykoScnKCHi2INsmT2FlfT39X1wnN7RWhwUgiMWqVlPqr+0rg4VVSPr9Kyv8FjImLKpzhZqM8ifaBATpra52hnlDGF5HYcugQ9r6+sCta0dFbOxiZlxjKnsTUiROx9/U5G0UbRVc4isQRPIndTU0RNW1FJzEvzyuR2NnQoP0fwkgk6sUr9SaGnG2VlSHx3IgxKSdRjzYoT2LoMaJIXKO1qgGtuvl9l30j9VdUeEF0fDwxycmGicSu+nqk3R4W4WZfBHK4VjbrOEWigW1wQlkk6rOL244cMfS6YedJLC6mq6GBgWGqQiNt2opOYn6+V+FmWxi1v9HJmDEDYbGYmpcYCtNWwDxPohKJoctIIvFJ4ANHHmIX8BHAGiFK0ELOCoOIz872Oi9vOGxh1GstOj6e6KQkr0RizaZNwPGeg+FGbGoqifn5xnsSQzTcnOoQia0miEQRFeXMkQp19DY4PcPMMtZFYih4i4zEW09iOPVI1ImOjyetpMRUkdgeYiJReRLHDsOKxFVS3gPcjjZ15dRVx5vZWYD/Nt+0sYM/M4wH45zbHAbhZvBu7cc+/JDP77uPieedR6ybUW/hQsb06YaJRCmllpMYoh41vTGuGZ7EuIyMsGiTAsfb4PQME3Z3NsCPMJGYVFhIryO64QnhKBJBy0s0K9zca7PRVV/v/MIVTMyqbm4vLycmOZnYtDRDr6vwnxHDxqukXO9mW2CmmY8hEnJyDKsADbepDQk5OR4V7TSXlfHyJZeQVlzMhf/+dwAsM4+MadPY9/TTSCn9Fjn9XV0M9PSErEiMjo8nMS/PFE9iOFQ26+ghyc5hGotHarg5raQE2ddHe2UlKUVFox5vq6jAYrU685XDhew5czjwwgv0dnQQk+jRQDKP0QueUkNgVKNZ4Wa97VG4fOkbS4wUblYECH3yiBGEy0g+HU88id3Nzbx44YUAXPLaayEbWvWUjOnT6W5uNqSiPRxawaROmmSOJzEz09BrmklMYiKZs2bRsd/9d2xbVRWxaWkRN20ifcoUAFo8nLrTXlFBYkEBwhJeH01Zc+aAlDTu2WP4tfVOCKEwzzsqJgaL1WrohDDQci7DzXs8VgivV2KEkpCToxWcGDC/2VZdTWxaWtg0mh5NJA709fHK5ZfTcugQF7/4omENx4OJc4azASFnfdpKbAgL55SJE03xJIaq93Q48hYvprO01O3rPNIaaeukOV6vzR62hwm3Hok6Zo7nCyWRKIQgOinJ8JzEcH3cxwJKJIYA8dnZ2Pv76Wlp8ftaHTU1YZOPCC4C2U3OkpSS926+mfL33uMLDz9M0WmnBcFC49FFYrMBFc5h4UmcOJF2g3sldjU2hp1IzF28mP7WVtrKy4fss1VVRVyoGSClqAgRHe3x/G5bmIqF1OJirPHxponEuIwM4kIkXy8mKcnQcLO9v1/7khSGj/tYQInEEMDIqSvh0iNRJ8EhkLvdCOTKTz5hx8MPc9IPf8jsG24IuG1mkVJUhDU+nkYjPIlh0AomZeJE7P392BxTI4wg3HISAfJOOgmAWkeFviu2qqqI9CQKi4XYggKPGk1LKcNuJJ+OJSqKzFmzTCleaTl4MCS8iDpGexI7amuRdjvJIVC9rRiKEokhgJEisauhgfisLL+vEyhGWnvFBx8AsPTOOwNqk9kIi4X0qVONCTeHgUg0ug3OQF8fve3tIb1md2TPnYuwWqnZuPGE7fq0lUgUiQBxhYUeeRK7m5vp7+oKS08imDfDORRFopGeRL03Zjh+ORgLKJEYAhg5mi9cRaK7PpGVn35K5syZYV+o4g6j2uDoOYmh/D8yuqG2c81hJhKtsbHEFxc7e33qdDU2Yu/ri8hwM0DsuHG0Hjw4ahsc3dMcriIxa84cOuvq6DCoCBG0L0RtR4+GlEiMSU42VCSGa9ujsYISiSGAUZ5EabdrYbgwqvocbu3Sbqf6s88oOOWUYJhlOhnTp9N25Aj93d1+Xae7qQmL1epsTRGK6PNYjfIkhoP3dDgSp02jdtOmE4pXOiK0kbZObGEh/d3dtI+SbhDuYsGM4pX28nLkwEBItL/RiTE43Kw/7qHQLFwxFCUSQwDd8+fv1JXulhak3R6WnsTBIrFp3z66m5sjVyROm4a02z2u+hyOcGgqbY2LI6mgwDhPYhiLxIRp0+hpbT0hR88WoY20deIcH/6jtcEJ97BjlgkiMZQqm3UMDzdXVhIVGxtWzo2xhBKJIUBUTAyxaWl+exK7Ghrg/7d359Fx1VeCx7+3qrSU9l3eZMuyvAkMdjBJwCHYQCaAIQs5zZLGhJnkMOmQIZmEPifbHJiQOemEJitz6ENDFjpAoBuS0NgMaRyLzeAmdiDg3ZZsSd4lWZslWdtv/qj3irL2kl6p3nt1P+foWKpXr/y7elWlW7/l/sBTSaLd1uGxH926FYC5fk0SHSqD03v6tKuHmm1OlsHxcpKYbe3dHTvkHC2k7dMkMcNKEif6QNTZ1IQEAp5aeBcru7yccGmpo4tX3JgkJqInMVcLabuWJoku4cTWfL0tLQCeKjIcCIXILCoaEfuRrVvJLCqicMmSJLUssey4plsGxyv1AvMrK2mvr3fksbxQ9mcsmZWVhDIzz1m8Eh1u9umcxPSyMoIZGRMuXulsaiJ71iyCaWkz1DLnlZx/Ps3vvefY47UdPBjpiXfRcyMRC1d0qNm9NEl0iazS0mkvXPFiTyKMniAf3bqVOZde6ttPl+nZ2eTOnz/9nkSPJIl5lZV0NjYyNDAw7cfyck9iIBSibNWqc8rgdB07RmZhoWcK4MdLAgHyq6omLIPjh2ShdMUKWnbunPRe1RNpO3iQvIULXbUDjV0Cx4nNHwA6dbcVV3PPMy/FOdGT2GP1JHptbsfw/Zt7Wltp3bOHOZdcksRWJV7R0qXTrpXYe/q0J5Kl/MpKzODghIsXJqOntTVSfy8/34GWzbzy1as5sWNHtLj4maNHfTvUbCtcvHhSPYleTxZKVqyg/8wZx3rN2+vqXDXUDJHhZoxhoKdn2o9ljIl8OPD4dfezhCWJIlIhIltEZLeI7BSRr1i3PyUib1tfh0Tk7cme62eOJIk+6Uk89uabAL5dtGKzy+BM5xN5b2urZ+YkgjNlcOyY3dS7Eo9Zq1fTf+ZMtBe56+hRVw0nJkLh4sUTlsHxaiHtWPYKZyfmJRpjXFcjEYhWUnBi/+ae5mYG+/o8/+HAzxL5LjsAfN0Ysxz4MHCniNQYY24yxqw0xqwEngGeney5CWxr0oVLS+ltaZnW1mU9LS0E0tJIz811sGWJl1VWRk9Mknh061YkGIzuUOFXRcuW0d/VRb/VAxyvoYEBzra3e6YnEZwpg+OVIfax2M9re/FK17Fjvu9JLKiuHrcMztmODvo6OjyfLBSfdx4wuRXOQwMDvPXAA/SdOTPq8e6TJ+k/c8Z1SWK6lSQ6MS9Ry9+4X8KSRGPMMWPMDuv7TmA3EH0mSGSy2Y3Ak/Ge60dZZWXROodT1dPcTLi42HPz+LLKyuhpacFYCfKRrVspW7mS9OzsJLcssYqtFc69o+zlOxn2VoZeSJhy588HEWd6Ej24b3OswiVLSMvJidRLHBry9W4rtsLFi4Gxy+B4vZC2LT0nh/yqqkn1JDa9+iov3303ux9/fNTjblzZDDE9iQ4kiX657n4Wmon/REQqgVXAtpibLwNOGGPGnagyxrmxx+8A7gAoLy+ntrZ2+g1OglZrTt7LGzcSrqykq6sr7lga9+xhKBz23O/gpJXstB89ypbNmznyxhuUXHON5+KIV5/Vg9i+f/+UYu21PoXXHz9Ohwd+V2nFxex98036amun9Py2nTx8mFBBgSefH11dXbzy6qtkLFrEvs2bGXruOYb6+zk2jd+H23V1dbHHSii2bdxI3SjTBDq2bwfgwKlTnPD470Fmz6Zh2zYqb7tt3Gt6atMmALY/9RSnR6ni0PLHPwKwr7mZBhf9Ttqt5HXbK6+Qa+1+ZIv3dX3yT38C4N2GBvZ2dzvWxpk0nfcyL0h4kigiOUSGlb9qjOmIOXQLo/QiTvLcKGPMw8DDAKtXrzZr1651otkzrmFoiLr77uO8ykrmr11LbW0t8cZyXISc+fPjPi/Z9jY30/CTn5De10dNYSHbe3v54I03ssxjccTLGMO7wSDS2Tmla3Zs2zbeA1atWUOVB35Xx5YuJdDby9opPr9t+/v7mVNd7bnnOfB+3FddxV8efJAVFRW8A6y8/HKWejCeyaitreXya69l1+23UxoIjHrd3jt0iH3AZddfT0FV1Yy30UmhdevY9v3vk5WePu5z9LWXXuIw0LtzJ5dffvmIEaDXa2upF+Gqm24ilJGR2EbH4Uh6OvuB85csYeGw+OJ9Xb/20ks0BoNc9elPEwgGHW3nTJnOe5kXJHTmt4ikEUnyHjfGPBtzewi4AXgq3nP9KuzA1nzdHtu32WbvujLQ1sYRq4i23xetAIgI4ZISBtrbp3R+j8dKweQ5VCvR63MSIbJ4ZfDsWQ5bvUV+H26WQICCRYvGLIMTnZvmg99DyYoVmMFBeg8fHvd+9muh+8SJUUthtR88SG5FhasSRHB4TuKRI+TMnu3ZBDEVJHJ1swCPAruNMT8advgqYI8xpmkK5/pSVmkpML2t+XpbWsjycJLY39bG0a1byZk7l9yKiiS3amZklZZOOUk8aw31eCVhyq+spLOpaVq1EocGBznb1uaZmMdiL1458NxzAGT7fHUzRBavjFUGp7OxkXBpqS9qRdornLvr6sa9X3t9PXkLFgDQOMpwpRtXNgOkWQsjHZmT6IMV7X6XyJ7ENcAG4IqYkjfXWsduZthQs4jMEZFNkzjXl8LFxSAy5Z5EYww9LS2e2m3FFu1JPH3a90W0hwuXlDBgzcmMV7Qn0QMlcCDSk2gGB6O9RlNx1vpdeXG3lVj5VVVkFBRwbFtkqrXfS+DA+GVwml59lbILL0xCq5xXUF1NIBSacEFae30986+4gtx582jYsmXEcbcmiU6vbtZFK+6WyNXNrxljxBhzgV3yxhizyTp2uzHmn4bd/6gx5tqJzvWrQChEuLh4yruunG1vxwwOenK4ObOggEAoRPeBA3QcPuzb/ZpHE55GT2Kvx5LE/IULgemVwfHybiuxRIRZq1eDMWQWFfmiB20iY5XBaTt4kNbdu6m67roktcxZwbQ0CpcsoXec53l/Tw9njh8nv6qKinXraKytPadeal9nJ90nT7oySXRydXOnD3bZ8TtvVqP1qXBp6ZR7EqOFtD3YkyiBAOHSUtrfeANIjfmItqzS0in3JPa2tpKel0cgNCNFCqYt34GC2n5JEuH9IedUGGqGscvg1G3cCMAinySJAMU1NfSM05NovwbyFy6kYt06ek6donnnzujxNmuo2o1JYigzEwkEpt2TeLajg/6uLu1JdDlNEl1keFHpeHh1txVbVmkpAx0dhDIzKVu5MtnNmTHhkhIGOjunVETdK1vy2XIrKkBkWj2JXlusM57y1asBfyzWmIyC6moATg9bvFK3cSNFy5a5MiGaqqLlyzl79CgDvb2jHrcXreQvXMj8desAaIwZcnZrjUSI9IKn5eTQdfTotB7HnnaiSaK7aZLoIsP3MI5HdN9mryaJ1rzEWRdfTDA9PcmtmTnh0lIwZkpF1L2yJZ8tmJ5O7ty5jvQkerHHfDi7JzFVksS8igqCGRnnLF7p6+yksbbWN0PNtpKaGhgaGnOhTmySmF9ZSd6CBZ5JEgEWf/rT7HrsMU7s2DHlx+jS3VY8QZNEF8lK0eFmeD9JTKWhZnh/VftUPhx4sRTMdMvg9FofhjI8lByPJXfePCrWraPCxzXWYo1WBufwSy8x2Nfnq6FmiPQkArTs2jXq8fb6ekKZmWTPmgUQmZf48svRRT3tBw8SLi4mIz9/Zhocp3U//jFZZWVsuu02Bs6endJjRMseaU+iq2mS6CJZZWX0trYy2N8f97m9PulJTLUk0b5eUyl95LXhZojMS5zOcHNnUxPBjAzPr26GyLDdTX/6E+fffnuymzJjhpfBOfj882QUFPjudV+4ZAkEAmMniXV15FVWRqs4zF+3jt7W1uh2fm0HD5Lv0l5EiCyW+y+PPELLzp1sveeeKT2GvSVfqvSke5UmiS4StnqV7IQvHj3NzQRCIdLz8pxu1ozIraiAQIA5l1yS7KbMqGh9TKsnOB7dJ0547kNBXmUlXdOoldheX09+ZSUyytZuyv1iy+CYoSHqNm5k4dVXE0xLS3bTHJUWDpMxezYtu3ePery9vj662h8iPYnw/rxEt5a/iVV1zTWs+MIXeOv++zn65ptxn9/Z1BSpjemyYuHqXPpO6yJZ09h1pae5mcziYs/WF7zgjjtY/tBD0aQpVYSnONzc391Nb2sreR4rOp6/cCFmaIj+Kc69ba+vJ89aJa28J7YMzvHt2+k+ccJ38xFtmQsWjDvcHJsk5lVUULBoEQ1btjDY309HQ4Prk0SAtQ88QM68ebzwuc/RH+fey1oj0Rs0SXSRaJI4hT+gPS0tnutVipWek0P2KJvc+509hzTe4ebOxkYAcufPd7xNiWSXwTl7/PiUzh/+x1V5S2wZnLrnn0cCARZefXWSW5UY4QULOL1v34he897Tpznb3j7ieVyxbh1NL79Mx6FDmMFBTySJGXl5XPPLX3J63z5e+/a34zq368gRTRI9QJNEF4kuYphiT6JXF62ksmB6OsHs7LiHmzusGmxe277Q7gXsm0KSeLajg97WVk0SPSy2DE7d888z59JLffu+lblgAUP9/dGVyrbYlc2xKtau5Wx7O/ueeQZw78rm4eZfcQUr77yT7T/9KZ1vvz3p83RLPm/QJNFFpjXc7PGexFQWKiiIu/fY7knM81hPYu68eUggMKWexLH+uCrvsMvgNL38Mid27PDtUDNEehJh5ArnsZ7Hdr3Edx99FPBOkgjw0R/8gIKqKvZ/61u89cADEy6+7O/poaelhVwtf+N6miS6SGZhIRIMTmmlq/YkelcoPz/ua97R0AAinqsxFkxPJ2fuXPqOHYveNtjfz5GtWznwhz+Me64mid5nl8HZ+/TTgL92WRku0/oAN3zxyljP45w5cyhcsoS2Awci5XE8tBNPenY2f/PSS+ReeCEv3303j61cSUNt7Zj3j65s1p5E19Mk0UUkECBcUhJ3T6Ixhl7tSfSsUEFB3MPNnY2NZM+a5clVofmVlXQfPMiff/xjnr3uOh4sKuLJNWv4/ac+NWbxYdAk0S8KqqsZGhggr7KS4pqaZDcnYYJZWeTOnz9qT2JGfv6ohfDt3sT8qirPLULMr6xk8fe/z6efe47+7m6eXreO52+5ZcRe3fB+kqhzEt1Pk0SXySoriztJ7OvoYGhgQJNEj0rLz5/ScLPXhppt+YsW0VNXR+3XvsbpffuoufVW1nz3uwC07ts35nnt9fWk5+Z6rjakOpe9eGXRddd5LhGKV/Hy5bSO0pM41gcduxSOl4aah1t0/fX81127uOSee9j/u9/x2KpV9A3b57lTd1vxDE0SXSartDTuhMHuhcrU4WZPsoebjTGTPqezocFzi1Zsl957Lwu/8x3uaGjg8/v28bGHHuLCL34R4JzdOIaz/7j6PbHwOztJ9PN8RFtxTQ0tu3dHd1IB63lcVTXq/e3dd+wFPl6VFg6z5t57ueH55+k5dYoDv//9Ocej+zZrkuh6miS6zFR6Er2+b3OqCxUUMNjXR/+wT9tjMcbQ0djo2SQxf8ECiq+88pwaj+GSEjLy88cdbu44dEiHmn1g6Y03cvn997PgyiuT3ZSEK16+nIGeHtoPHwbADA2N+zzOLi9n/RNP8IG77prJZibM/CuuIG/BAnb9y7+cc3vXkSNk5OeTnpubpJapydIk0WWyysriXsTg9X2bU13I2p91sj3Iva2tDHR3e3a4eTQiQkF19Zg9icYYLaTtE5mFhVx8990EQqFkNyXh7DmX9pDzmePHGejtHffDzvJbbonWE/U6CQSoufVWDr/0El0xi9W6mpp0qNkjNEl0mXBpKWfb2xnq65v0OdqT6G12kjjZDwfRQtoe7Ukcy/B9fWP1NDfTf+aM9iQqTylavhx4vwxOKi6+qtmwATM0xO4nnojepruteIcmiS5j10ocaG+f9DnRnkRNEj0pVFAAQPckVzh7dbeViRQuXkzHoUMMjvIBKRX/uCrvCxcVkVVeHi2Dk4rP46KlS5l18cXnDDl3aiFtz9Ak0WWiSWJb26TP6WluRoJBMqweKeUtaVaSONmeRHu3Fa/t2zyRgurqyJwta/5WrFT846r8obimZkRPYqpNm6jZsIFT77zDqXffZbC/nzPHj2tPokckLEkUkQoR2SIiu0Vkp4h8xbr9KRF52/o6JCJvj3H+1SKyV0QOiMg3EtVOt7G35us/fXrS5/S2tBAuLtZVnx4VHW6OoycxkJYW/UDhF4X2lm2jDDlrkqi8yk4S7Xm12bNmkRYOJ7tZM2rZzTcTCIXY9ZvfcOb4cTBG5yR6RCJnDg8AXzfG7BCRXGC7iPyHMeYm+w4i8gAwYlxVRILA/wU+BjQBb4nIc8aYXcPv6zdTGW7ubm7W8jceFgiHCWZkTHrhSqe1slkC/hoIKLBKo4y2eKW9vp5wSQnpOTkz3SylpqV4+XL6Ojo4c+zYuDUS/SyrtJTKq69m9+OPU/2JTwBaSNsrEvZXxhhzzBizw/q+E9gNRD86SKTb60bgyVFO/yBwwBhTZ4zpA34LfDJRbXWT7FmzQITeUarUj6W3pYUsnY/oWSJCVmlpXMPNflu0ApE/JOm5uZweI0lMxT+uyvvsFc4tu3al9PO4ZsMGuo4cYddvfgNokugVM1KDQEQqgVXAtpibLwNOGGNGW844F2iM+bkJ+NAYj30HcAdAeXk5tePsF+kV2UuXcvrNNycdS/Phw2TMnev52Lu6ujwfw1R0dXUxkJlJ4969k4r/1P795F5wgad/V2Nd69CsWRzcto3gsGPHd+0ia/FiT8cMqfkcT8WY4f247alDbzz7LB2NjWQHAr79fYx3rYfy8wlmZ/PXRx8F4J36ekKtrTPYusTw+/M74UmiiOQAzwBfNcZ0xBy6hdF7EQFGm1w36nYUxpiHgYcBVq9ebdZaFeu9LOOzn+X1e+7h4poasicx72x3by8Vy5bh9dhra2s9H8NU1NbWUrZwIX2dnRPGPzQ4yPbmZqovvpjLPPy7Gutad65axYkdO845ZoaG2HHyJItvvZWPejhmSM3neCrGDO/HbYxhb2Ehobo6GBrigrVrWeHT38dE17r/5pt599FHCWVmcuUnPuGLefR+f34ndFKTiKQRSRAfN8Y8G3N7CLgBeGqMU5uA2PG0ecDRRLXTbarWrwdjqH/hhQnva4yhp7lZy994XLikZFLDzWeOH8cMDvpyuBkiK5w7Dh1isL8/elvX0aMM9fen3IpQ5Q8iQnFNDY1btgCpvfiqZsMGAHLmzfNFgpgKErm6WYBHgd3GmB8NO3wVsMcY0zTG6W8Bi0VkoYikAzcDzyWqrW5TtmoVacXF1G3cOOF9+zo7Gerv191WPC6rtHRSq5vtGol+2m0lVuHixQwNDJxTBkdXNiuvK1q+PFr/My+Fn8fzLruMvAULyFuwINlNUZOUyOHmNcAG4N2YMjffMsZsIpL0nTPULCJzgEeMMdcaYwZE5MvAi0AQ+IUxZmcC2+oqIkL+hz7EoRdfZLC/n2Ba2pj31d1W/MHeaWewr49gevqY97NrJPq5JxEiK5ztkjiaJCqvsxevSDDou/qm8ZBAgBs2biQwzt805S4JSxKNMa8x+txCjDG3j3LbUeDamJ83AZsS1T63y7/kEpo3beLI668zf5z5Drpvsz/YSX5PczM5c+aMeT+/bslni9ZKPHAAOyVsr68HEe19UJ5lJ4l58+enxJ7V4yk577xkN0HFwV+F1nwk76KLCKanU/f88+Per1d7En3BLqI+0ZBzZ0MD6bm5vt1dJ6u8nLScHNpiCmq319eTM2cOoYyMJLZMqakrtvZw1t5w5TWaJLpUMBxm3uWXTzgvUfdt9oewlSROVFA7Wkjbp5O+RYTC6upzaiWmcm055Q+5FRVk5OdTaBWMV8orNEl0sar162nds4e2urox72MnibrjirdFh5snSBI7GhvJ9emiFVvB4sXn7LqiSaLyOhHhbzZv5tJ77012U5SKiyaJLla1fj3AuL2JPS0tSCBAZkHBDLVKJYI93Nw9ieFmv098L6yupr2ujqGBAQb7+uhsatIkUXnerIsuiuyopZSHaJLoYoXV1RQtXTp+ktjcTGZRke/28U01mUVFIDJuT+JAby/dJ0/6dtGKraC6OlIGp6EhslDHGK2RqJRSSaCZhcstXL+exi1b6OvqGvV4T0uLzkf0gUAwSLioaNw5iZ1NkbKifh9utudttR04oOVvlFIqiTRJdLlF69cz2NdHw+bNox7X3Vb8IzxBQe1oIe0U6EkEOL1/vyaJSimVRJokutzcj3yE9NzcMYece1tatEaiT2SVlo473BytkejznsTsWbNIy86O9iQGQiFy581LdrOUUirlaJLocsH0dCo//nHqNm3CGDPiuPYk+ke4pGTc4ebobis+T5hEhAKrDE57fT258+cTCAaT3SyllEo5miR6QNX69XQdOcKpd94553ZjTGThivYk+sJkhpvDpaWEMjNnsFXJUVBdTZs13KxDzUoplRyaJHrAwmuuAeDgsN1X+s+cYbCvT3sSfSLLShLN0NCoxzsaGsjz+VCzrXDxYtrq6mg7eFCTRKWUShJNEj0gu7ycuWvWsOuxx85JIHS3FX8Jl5RgBgfpbWsb9bi920oqKKyuZqi/n57mZk0SlVIqSTRJ9IiVd97J6f37qX/xxehtPfa+zTrc7AvhCfZv7mxsTJmeRHuFM+jKZqWUShZNEj1iyWc+Q/bs2ez46U+jt2lPor/Yu66MtsL5bHs7fR0dqdOTGLPHbb4W0lZKqaTQJNEjgunprPzSlzj04ou07NkDRMrfgPYk+oWd7I+2wrnDLn+TIkli9uzZhMJhQHsSlVIqWTRJ9JAL7riDYHo6f/n5zwHtSfSb8Yabo4W0U2S42S6DEwqHySovT3ZzlFIqJWmS6CHZZWUs++xn2fnrX9Pb1kZ3czMSCJBRUJDspikH2Mn+aMPNnXaNxBTpSQQoW7WKkvPPR0SS3RSllEpJoWQ3QMXnA3fdxc5f/Yr3fvELeltayCws1ELDPpEWDpOWnT3qcHNnYyMSDJI9e3YSWpYcVz34IANnzya7GUoplbK0J9FjyletYt5ll7Hj5z/nzIkTOtTsM2MV1O5oaCBn7tyU+kCQnptLlj6/lVIqaTRJ9KBVd91Fx6FD1L/wgu624jNZpaVj9iTmpdBQs1JKqeRLWJIoIhUiskVEdovIThH5Ssyx/yEie63bfzjG+f/TOv6eiDwpIv7fi2ySFn/qU+RWVDDQ3a09iT4TLikZfU5iYyO5KbJoRSmllDsksidxAPi6MWY58GHgThGpEZF1wCeBC4wx5wH/OPxEEZkL3AWsNsacDwSBmxPYVk8JhEKsvPNOQFc2+81ow81maCildltRSinlDglLEo0xx4wxO6zvO4HdwFzg74B/MMactY6dHOMhQkBYREJAFnA0UW31ogu+8AXScnLIW7Ag2U1RDhptuLl1714G+/r0WiullJpRYoxJ/H8iUgm8Apxv/fsH4GqgF7jbGPPWKOd8Bfg/QA/wR2PM347x2HcAdwCUl5df9Nvf/jYRIcy4rq4ucnJyxr1P36lThPLyCGRkzFCrEmsyMftRbNzHnniCI//8z6x64QWCmZEZFnX33Ufb1q2sePxx0oqKktlUx+i1Th2pGDOkZtwac/zWrVu33Riz2sEmOcsYk9AvIAfYDtxg/fwe8DNAgA8C9VjJasw5hcCfgFIgDfg9cOtE/9dFF11k/GLLli3JbsKMS8WYjTk37r8+8oi5H0z74cPGGGOO79hh7gfzyre+laTWJYZe69SRijEbk5pxa8zxA/5sEpyHTecroaubRSQNeAZ43BjzrHVzE/Cs9fv5T2AIGD6x7iqg3hhzyhjTDzwLXJrItirlBvauK/aQ82vf/jaZhYVc/Pd/n8xmKaWUSkGJXN0swKPAbmPMj2IO/R64wrrPEiAdGF4YrgH4sIhkWY9zJZE5jUr5WnTXleZmml59lfoXXuCD3/gGmbqrjlJKqRmWyJ7ENcAG4AoRedv6uhb4BVAlIu8BvwU+Z4wxIjJHRDYBGGO2Af8G7ADetdr5cALbqpQrZNn7N586xavf/CbZs2ez6stfTnKrlFJKpaKEbctnjHmNyLzD0dw6yv2PAtfG/HwPcE9iWqeUO9nDze/96lccef11rnroIdKyspLcKqWUUqlId1xRykUy8vMJhEI0bN5MwaJFrPj855PdJKWUUilKk0SlXEREovMS13z3uwTT0pLcIqWUUqkqYcPNSqmpya2oIKu8nGU36yZDSimlkkeTRKVc5vqnnyYUDiMB7ehXSimVPJokKuUy+ZWVyW6CUkoppXMSlVJKKaXUSJokKqWUUkqpETRJVEoppZRSI2iSqJRSSimlRtAkUSmllFJKjaBJolJKKaWUGkGTRKWUUkopNYImiUoppZRSagRNEpVSSiml1AhijEl2GxwjIqeAw8luh0NKgOZkN2KGpWLMkJpxp2LMkJpxp2LMkJpxa8zxW2CMKXWqMU7zVZLoJyLyZ2PM6mS3YyalYsyQmnGnYsyQmnGnYsyQmnFrzP6jw81KKaWUUmoETRKVUkoppdQImiS618PJbkASpGLMkJpxp2LMkJpxp2LMkJpxa8w+o3MSlVJKKaXUCNqTqJRSSimlRtAkUSmllFJKjaBJ4gwRkQoR2SIiu0Vkp4h8xbq9SET+Q0T2W/8WWrcXW/fvEpEHYx4nS0Q2isge63H+IVkxTcSpmK1j/09E3rEe559EJJiMmCbDybhjHvM5EXlvJuOIh8PXulZE9orI29ZXWTJimgyH404XkYdFZJ/1+v5MMmKaiIPvZbkx1/htEWkWkZ8kKawJOXytbxGRd0Xkr9Z7W0kyYpqIwzHfZMW7U0R+mIx4JmMKMX9MRLZb13O7iFwR81gXWbcfEJGfiYgkK64pM8bo1wx8AbOBD1jf5wL7gBrgh8A3rNu/AfzA+j4b+AjwReDBmMfJAtZZ36cDrwLXJDu+RMZsHcuz/hXgGeDmZMc3E3Fbx28AngDeS3ZsM3Sta4HVyY4pCXH/b+B71vcBoCTZ8SU65mGPux34aLLjS3TcQAg4aV9f6/x7kx1fgmMuBhqAUuvnXwNXJjs+h2JeBcyxvj8fOBLzWP8JXELk79YLuPRv9Xhf2pM4Q4wxx4wxO6zvO4HdwFzgk0ReMFj/fsq6zxljzGtA77DH6TbGbLG+7wN2APNmIoZ4ORWzdazD+jZEJDl27YorJ+MWkRzga8D3Et/yqXMyZi9xOO7/Bnzfut+QMcaVO1ck4lqLyGKgjMiHXldyMG6xvrKtnqU84GjCA5gCB2OuAvYZY05ZP78EuLKnfAox/8UYY1+/nUCmiGSIyGwinRtvmEjG+Jh9jpdokpgEIlJJ5NPHNqDcGHMMIk9OIm+Uk32cAuB6YLPzrXSWEzGLyItEPoF3Av+WmJY6y4G47wMeALoT1UanOfT8/qU1BPm/vDJEM524rdcywH0iskNE/lVEyhPYXEc49V4G3AI8Zf0xdb3pxG2M6Qf+DniXSHJYAzyayPY6YZrX+gCwTEQqRSREJFmqSFxrnTGFmD8D/MUYc5ZIYtkUc6zJus1TNEmcYVbP0DPAV2N6x6byOCHgSeBnxpg6p9qXCE7FbIz5OJGhgAzgignunnTTjVtEVgLVxpjfOd22RHHoWv+tMWYFcJn1tcGp9iWKA3GHiIwIvG6M+QDwBvCPDjbRcU69ri03E3k/cz0HXtdpRJLEVcAc4K/ANx1tpMOmG7Mx5jSRmJ8i0lt8CBhwso1OizdmETkP+AHw3+2bRrmbJz4ExdIkcQZZbw7PAI8bY561bj5hdUtj/Xtykg/3MLDfGPMTxxvqIIdjxhjTCzxHpOvftRyK+xLgIhE5BLwGLBGR2sS0ePqcutbGmCPWv51E5mJ+MDEtdoZDcbcQ6S22PxD8K/CBBDTXEU6+rkXkQiBkjNmekMY6yKG4VwIYYw5aPadPA5cmpsXT5+Dr+t+NMR8yxlwC7AX2J6rN0xVvzCIyj8hr9zZjzEHr5ibOnQo2D5dOKxiPJokzxBoyexTYbYz5Ucyh54DPWd9/DvjDJB7re0A+8FWHm+kop2IWkZyYF2cIuBbY43yLneFU3MaYh4wxc4wxlUQmg+8zxqx1vsXT5+C1DtkrPa036usAN6/qdupaG+DfgbXWTVcCuxxtrEOcfC+z3IIHehEdjPsIUCMipdbPHyMy7811HP67VWb9Wwh8CXjE2dY6I96YrakiG4FvGmNet+9sDUl3isiHrce8jcm/JtzDuGD1TCp8Efkjb4gMLbxtfV1LZNXXZiKfqjYDRTHnHAJagS4in0pqiHwaMUTeVOzH+UKy40twzOXAW9bj7AR+TqTnIekxJjLuYY9ZibtXNzt1rbOJrHK1r/VPgWCy45uJaw0sAF6xHmszMD/Z8c3E8xuoA5YlO64ZvtZfJPIe/lciHw6Kkx3fDMT8JJEPPrtwd3WKuGIGvgOcibnv20CZdWw1kQ+5B4EHsXa589KXbsunlFJKKaVG0OFmpZRSSik1giaJSimllFJqBE0SlVJKKaXUCJokKqWUUkqpETRJVEoppZRSI2iSqJRSSimlRtAkUSmllFJKjfD/ATgVj8qJOp+oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Combined Subplots\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "\n",
    "ax1 = fig.add_subplot(211)\n",
    "plt.title('Amazon Estuary, 2011-2019')\n",
    "ax2 = ax1.twinx()\n",
    "ax3 = plt.subplot(212)\n",
    "\n",
    "#lwe thickness\n",
    "ax1.plot(ds_GRACE_subset.time[107:179], ds_GRACE_subset.lwe_thickness[107:179,34,69], color = 'darkorange')\n",
    "ax1.set_ylabel('LWE Thickness (cm)', color='darkorange')\n",
    "ax1.grid()\n",
    "\n",
    "#sea surface salinity\n",
    "ax2.plot(time_Aq, sss_Aq_mean, 'g-')\n",
    "ax2.plot(time_smap, sss_smap_mean, 'g--')\n",
    "ax2.set_ylabel('SSS (psu)', color='g')\n",
    "ax2.legend(['Aquarius', 'SMAP'], loc='upper right')\n",
    "\n",
    "#sea surface temperature\n",
    "ax3.plot(time_MODIS, sst_MODIS_mean, 'darkred')\n",
    "ax3.set_ylabel('SST (deg C)', color='darkred')\n",
    "ax3.grid()\n",
    "ax3.legend(['MODIS'], loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "generic-portable",
   "metadata": {},
   "source": [
    "### Combined timeseries plot of river height and LWE thickness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "studied-lucas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot river height and land water equivalent thickness\n",
    "fig, ax1 = plt.subplots(figsize=[12,7])\n",
    "\n",
    "#plot river height\n",
    "ds_MEaSUREs.height[16,6689:9469].plot(color='darkblue')\n",
    "\n",
    "#plot LWE thickness on secondary axis\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(ds_GRACE_subset.time[107:179], ds_GRACE_subset.lwe_thickness[107:179,34,69], color = 'darkorange')\n",
    "\n",
    "ax1.set_xlabel('Date')\n",
    "ax2.set_ylabel('Land Water Equivalent Thickness (cm)', color='darkorange')\n",
    "ax1.set_ylabel('River Height (m)', color='darkblue')\n",
    "ax2.legend(['GRACE-FO'], loc='upper right')\n",
    "ax1.legend(['Pre-SWOT MEaSUREs'], loc='lower right')\n",
    "\n",
    "plt.title('Amazon Estuary, 2011-2019 Lat, Lon = (-0.7, -50)')\n",
    "ax1.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "surface-scheduling",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}