oat/example_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to work with istSOS\n",
    "## An example of basic data consistency checks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next cell we are going to:\n",
    "\n",
    "* load the autoreload extension useful to keep update the external code library\n",
    "* activated the autoreload\n",
    "* activate the plot of graph directly inside jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting oatlib==0.0.7rc1\n",
      "  Using cached oatlib-0.0.7rc1-py3-none-any.whl (45 kB)\n",
      "Requirement already satisfied: scipy in /home/maxi/GIT/istSOS_2.4.0_workshop_mexico_2020/venv/lib/python3.8/site-packages (from oatlib==0.0.7rc1) (1.4.1)\n",
      "Requirement already satisfied: pandas in /home/maxi/GIT/istSOS_2.4.0_workshop_mexico_2020/venv/lib/python3.8/site-packages (from oatlib==0.0.7rc1) (1.0.1)\n",
      "Requirement already satisfied: dateutils in /home/maxi/GIT/istSOS_2.4.0_workshop_mexico_2020/venv/lib/python3.8/site-packages (from oatlib==0.0.7rc1) (0.6.7)\n",
      "Requirement already satisfied: numpy in /home/maxi/GIT/istSOS_2.4.0_workshop_mexico_2020/venv/lib/python3.8/site-packages (from oatlib==0.0.7rc1) (1.18.1)\n",
      "Requirement already satisfied: spatialite in /home/maxi/GIT/istSOS_2.4.0_workshop_mexico_2020/venv/lib/python3.8/site-packages (from oatlib==0.0.7rc1) (0.0.3)\n",
      "Requirement already satisfied: requests in /home/maxi/GIT/istSOS_2.4.0_workshop_mexico_2020/venv/lib/python3.8/site-packages (from oatlib==0.0.7rc1) (2.22.0)\n",
      "Requirement already satisfied: isodate in /home/maxi/GIT/istSOS_2.4.0_workshop_mexico_2020/venv/lib/python3.8/site-packages (from oatlib==0.0.7rc1) (0.6.0)\n",
      "Requirement already satisfied: datetime in /home/maxi/GIT/istSOS_2.4.0_workshop_mexico_2020/venv/lib/python3.8/site-packages (from oatlib==0.0.7rc1) (4.3)\n",
      "Requirement already satisfied: pytz>=2017.2 in /home/maxi/GIT/istSOS_2.4.0_workshop_mexico_2020/venv/lib/python3.8/site-packages (from pandas->oatlib==0.0.7rc1) (2019.3)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in /home/maxi/GIT/istSOS_2.4.0_workshop_mexico_2020/venv/lib/python3.8/site-packages (from pandas->oatlib==0.0.7rc1) (2.8.1)\n",
      "Requirement already satisfied: argparse in /home/maxi/GIT/istSOS_2.4.0_workshop_mexico_2020/venv/lib/python3.8/site-packages (from dateutils->oatlib==0.0.7rc1) (1.4.0)\n",
      "Requirement already satisfied: six in /home/maxi/GIT/istSOS_2.4.0_workshop_mexico_2020/venv/lib/python3.8/site-packages (from isodate->oatlib==0.0.7rc1) (1.14.0)\n",
      "Requirement already satisfied: zope.interface in /home/maxi/GIT/istSOS_2.4.0_workshop_mexico_2020/venv/lib/python3.8/site-packages (from datetime->oatlib==0.0.7rc1) (4.7.1)\n",
      "Requirement already satisfied: setuptools in /home/maxi/GIT/istSOS_2.4.0_workshop_mexico_2020/venv/lib/python3.8/site-packages (from zope.interface->datetime->oatlib==0.0.7rc1) (44.0.0)\n",
      "Installing collected packages: oatlib\n",
      "  Attempting uninstall: oatlib\n",
      "    Found existing installation: oatlib 0.0.6rc2\n",
      "    Uninstalling oatlib-0.0.6rc2:\n",
      "      Successfully uninstalled oatlib-0.0.6rc2\n",
      "Successfully installed oatlib-0.0.7rc1\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "\n",
    "!pip install oatlib==0.0.7rc1\n",
    "\n",
    "try:\n",
    "    from oatlib import sensor, oat_utils\n",
    "except ImportError:\n",
    "    !pip install oatlib\n",
    "    from oatlib import sensor, oat_utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⚒ Now we load some other libraries needed by this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from dateutil import parser\n",
    "import matplotlib.pyplot as plt\n",
    "from statistics import stdev\n",
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⚒ Set istSOS variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "server = 'http://istsos.org/istsos'\n",
    "service = 'hydro'\n",
    "user = None\n",
    "pwd = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⚒ List all the procedures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "procedures = oat_utils.get_istSOS_Procedures(server, service, user, pwd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variable procedures is now a **LIST** of **DICTIONARIES**.   \n",
    "Each object contains the information of a procedure such as:\n",
    "*sensortype, name, assignedid, offerings, id,\n",
    "samplingTime, observedproperties, description.*\n",
    "\n",
    "### ⚒ Let's print some information on procedures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of the procedures fetched: 3\n",
      "\n",
      "Keys for each procedure: dict_keys(['id', 'name', 'description', 'assignedid', 'sensortype', 'samplingTime', 'observedproperties', 'offerings'])\n",
      "\n",
      "Names of the procedures fetched:\n",
      "\tP_CAV\n",
      "\tQ_MAG_LOD\n",
      "\tT_CAV\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of the procedures fetched: {len(procedures)}\")\n",
    "print(f\"\\nKeys for each procedure: {procedures[0].keys()}\")\n",
    "print(f'\\nNames of the procedures fetched:')\n",
    "for procedure in procedures:\n",
    "    print('\\t' + procedure['name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⚒ Now we are going to choose one procedure from the list above\n",
    "to retrieve the ***time_interval*** of the data and the observed properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 1,\n",
       " 'name': 'T_CAV',\n",
       " 'description': None,\n",
       " 'assignedid': '28e70565c24f11ec91980242c0a8f003',\n",
       " 'sensortype': 'insitu-fixed-point',\n",
       " 'samplingTime': {'beginposition': '2019-01-01T01:10:00+0100',\n",
       "  'endposition': '2020-01-01T00:00:00+0100'},\n",
       " 'observedproperties': [{'name': 'air-temperature', 'uom': '°C'}],\n",
       " 'offerings': ['temporary']}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "procedure = oat_utils.filter_procedures(procedures,'name','=','T_CAV')[0]\n",
    "procedure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⚒ Take a look at the specific procedure properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time interval: 2019-01-01T01:10:00+0100/2020-01-01T00:00:00+0100\n",
      "\n",
      "Number of observed properties for the procedure T_CAV: 1\n",
      "\n",
      "List of the observed properties: [{'name': 'air-temperature', 'uom': '°C'}]\n"
     ]
    }
   ],
   "source": [
    "time_interval = '/'.join([\n",
    "                         procedure['samplingTime']['beginposition'],\n",
    "                         procedure['samplingTime']['endposition']\n",
    "                        ])\n",
    "\n",
    "print(f'Time interval: {time_interval}')\n",
    "\n",
    "# the variable observed_properties is a list of dict\n",
    "observed_properties = procedure['observedproperties']\n",
    "\n",
    "print(\n",
    "    '\\nNumber of observed properties for the procedure {}: {}'.format(\n",
    "        procedure['name'],\n",
    "        len(observed_properties)\n",
    "    )\n",
    ")\n",
    "\n",
    "print(\n",
    "    '\\nList of the observed properties: {}'.format(observed_properties)\n",
    ")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⚒ Load data from *istSOS* using *OAT*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "event_time = '2019-05-01T00:10:00+0100/2019-06-01T02:00:00+0200'\n",
    "\n",
    "observed_property_name = observed_properties[0]['name']\n",
    "observed_property_uom = observed_properties[0]['uom']\n",
    "\n",
    "istsos_url = '{}/{}'.format(server, service)\n",
    "\n",
    "#basic_auth = (user, pwd)\n",
    "basic_auth = None\n",
    "\n",
    "SENSOR = sensor.Sensor.from_istsos(\n",
    "    service=istsos_url,\n",
    "    observed_property='temperature',\n",
    "    procedure=procedure['name'],\n",
    "    verifySSL=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⚒ See what SENSOR looks like --> it has the metadata automatically filled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name: T_CAV\n",
       "desc: Cavergno\n",
       "lat: 133073.0\n",
       "lon: 690081.0\n",
       "alt: 455.0\n",
       "tz: 0\n",
       "unit: b'\\xc2\\xb0C'\n",
       "prop: temperature\n",
       "freq: None\n",
       "statflag: None\n",
       "use: True\n",
       "data_availability: ['2019-01-01T00:10:00.000000Z', '2019-12-31T23:00:00.000000Z']\n",
       "ts: None"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SENSOR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⚒ Now we gather the actual observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name: T_CAV\n",
       "desc: Cavergno\n",
       "lat: 133073.0\n",
       "lon: 690081.0\n",
       "alt: 455.0\n",
       "tz: +01:00\n",
       "unit: b'\\xc2\\xb0C'\n",
       "prop: temperature\n",
       "freq: None\n",
       "statflag: None\n",
       "use: True\n",
       "data_availability: ['2019-04-30T23:20:00.000000000', '2019-06-01T00:00:00.000000000']\n",
       "ts:                             data  quality\n",
       "time                                     \n",
       "2019-05-01 00:20:00+01:00  10.54      210\n",
       "2019-05-01 00:30:00+01:00  10.37      210\n",
       "...                          ...      ...\n",
       "2019-06-01 00:50:00+01:00  13.83      210\n",
       "2019-06-01 01:00:00+01:00  13.39      210\n",
       "\n",
       "[4469 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SENSOR.ts_from_istsos(\n",
    "    service=istsos_url,\n",
    "    observed_property=':{}'.format(observed_property_name.replace('-',':')),\n",
    "    procedure=procedure['name'],\n",
    "    basic_auth=None,\n",
    "    # aggregate_function='AVG',\n",
    "    # aggregate_interval='PT10M',\n",
    "    event_time=event_time\n",
    ")\n",
    "SENSOR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⚒ Plotting the TimeSeries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5fXVD9ud5HKd85wGjnPRkgHz/f/L0hQCgLa6S32rNCs6KbtKiIricYyVWNJc9Dsc3LoWsPanZRys37gEAtOcctOacQHFWRWAUsuQppDmFpoEuivAUUqMwGihW3Kbc1DJmdeYBiD64jUDvUAUbdg4Y5aQnA1zfVfj+u5bg/a+fB0DvKXAg8BRacw5asza298aF7IpSToEqmnW5h3LFCzyJQmZyewr7dRUAAJbF0ObnCjzNvEOGMZ+x0Z5LcwpNxUC5gqxtIedYqacwSrjwJ4/iiMv/2NRj7tORAwAcNaerypa1YTLHuQmUU7AYC1buOvIF5xwyKamjkNFSJuXwURJ1teLxMHyUmdieQrHiYu1Wc1vf+dNbccz+4p5szwkvSycdTsn2fMZGS84cZmokUqPgoxRos1ippzBKeGrT3qYfk8IZewYb02J1MnPnCTRfW4yFbCFNns3jHIyFVqHNsJIlSmrWEYsuwOApuF5ghDK2BY/rPZSJgIP/7R6c/Z9/wW5DVXhfsYJW3xh0+gykvRoqsOwpZBN6UTQSqVHwUax4yGUs2FZydWGKiQUyCreueBVb9urbrT75ym584X+fqqlH8GQOaRDo/rctQTVlTBSqqeAcEU/BRJmMFq8lG4WA4pqQe5hIUJVfCf3FCtp8o9CRYBRIiLCrkEHWMbcybSRSo+Cj5MczHYul7KNRRjMbtPdIOYwv3qKXYn77jx7Bb5ZtwiPrd1bdH4WPdMVckwWef30YYyLmnXO08goeR9RTyGe021H4KCsJ4uk0ksouD1hMSdtNJAwaPEutp6BRoiWj0ppz4FjW+DYKjLGljLFPM8a+wxj7GmPsQsbYlEYOrpkoVTxkHd9TmOCrk/GOZq7+5JXrKYtmJG773p8/XnV/5CnQ5DUZQUbB9if8jryeCsk5h/wttOcc9GmIBCVNRXNJcw9UNJ5C0TWwckount9WG3ngkfXdmHfpXXh5Z39N2zcSJs+ybyj0FChk9p0/Phfbbkhmbo3X8BFj7J8YYysAfAlAAcBzALYDOAnAvYyxGxhj+zd2mKOPYkU0AanWGi9F/bjx0Q1NO1bvUAVTWsRKLJfR3+4n+8Zits9USgKt/IglMxlBtz95Q+0GKqQIH1XPKVBSuZqnUHI5HL8aLpegugoA/3LTcpz1vYdqyjn877JNAFCTJ9gIyAnyPzyzNfb5YMlFb7GCGe2CBLGvz0RS+0kAIZ03nxEtT009sBuJkdzZLQBO5JxfwDn/Juf855zzazjnn+CcHwvgewAWmv6ZMTaXMfYAY2wNY+wZxtgn/fenMsb+xBhb5/9uqtcR8RRSozCq+PpdzzbtWL1DZRznK0xSbFvFgD+R7T+1per+aOWXdeKyD5MFdP/TfN9uqKT1OA8qmgEIaqW2TsE3CnZY0aybzCuuh6xTW/jowedEy9xVr1YnL9z65KuR8xptyN7BDx9YH/ucQppd/mJl3848GAPOWjwrtu1QmSqaLT/RPA6NAuf8h5xzfcZOfL6Sc54ksVgB8FnO+WEATgDwUcbYYQAuBXAf53whgPv8101DyRVGIWUfNQfNyCtQIxNakZmatgz54Y2hGuLX9MATi2YyIggf+Z5CPmMHDCJ1O4aop9BfcmOTb8n1wJgIuVVLNJOnYKKuPvHSLpx21YPB64uve6Lm8ypoNJ1GA7rvSgbRSqngjzGGnGNp70/aV97vRcH56Bu3kYSPvsMY+5Dm/Q8xxq6s9v+c8y2c8xX+370AngWwH4C3AbjB3+wGAOcPd2z1oFgWlNTUUxgdqEagGbrwPUMVDJW9wAMw9w/2/N/VmUWDpZBeOVlBRVQ2C+mhWu0jhN4EIDwKIM63JxIHY0xqx6nXV1JzCmp9xDfufhYvdoe5gWq1DPI1zzTpmn3qf1YGf5+/JN5SlBYWBUlkMG+oyxgqe7AY4FjJBrWRGMm3dBqAn2ne/y8Abx3Ojhhj8wAcDeBxALM456QfuxVA3JcS//NBxtgyxtiyHTt2DOdwiQg9hZR9NBpQva9m8M9vXSFiyft2FZCxmXEFR55CLYVpQfhoEucUXIl9BIhz1RlUNafQ4q981QKroh+apX1mbUt7/QUlNVlLyVFYX9USr/c9uy0cRxOK4Squh8df2pW4Dd1nsvJs3jEZBRd5X048SV+qkRjJnZ3jGt+fc+4BqJmSwRhrA3ALgE9xziOlf/7+tVebc/4zzvlSzvnSGTOS2STDQeopjC7U1U0z+u9e8bs1AMTDl3Nsc/jI9xRqqaClB5rrb89JAVoTUfgo41jaiUgUr4WvTdXPJdeLhNsytp5vr5XiVq6ZzYbH+pJX47WEB+vFjr5otzrdPUcLC9kobO0Zwm/8hLiMoYowCoBUuzHK5zESozDIGIslkv33jLkGZdsMhEG4iXN+q//2NsbYbP/z2RCMpqZBeAq27ylM3gd+rKCu6JohYTDTzyUcNbcLOccyHpNWkNt6ilXjwcQ+msz3SJhTEK9r9RRoJRvLKUiaRgDgGMJRFTcUxDMJ51nKjHX8/KmJ5yIXhDXDU9i4K5wCD5vdof3eiNhAnlUShsoe8k40zzLatNSRGIWvAPg9Y+z9jLEj/J9/AnCX/1kimPBJrwXwLOf8u9JHdwC42P/7YgC3j2BsIwbduLYlkjn1hjeuuX8d5l16l1bk6rUI9ftshqdw1NwuHLJPO6a35QTV2PAwySvIi376WOI+aZU3mb3JWPjIQIVUPQXbn7HV8GtJCh8BYnLT7a/khsbDFD93FKtw9P7JmlY7+0KZiWbQOS/86aMAgLccORu5jP48iX1EOZgkCKWFaMvTcZdT4Jz/HiIJfCqA6/2fUwBcwDm/u4ZdnAjgHwGcxhhb6f+cA+BKAGcyxtYBOMN/3TQUK65QcfRXKg+/0F3X/oiK9p/3Pl/32CYDaHVz5mEiVdQMT2GgFFaNmlhlnsdRqng49gDBgCZJY+M+/XFP5u58aqI5yVOQ2UcZP9ykfs+qUcjaTE9JlQTxgmY8ynZqJXm16yDniZp5zY47YAqytp5RNBiEj2rxFNwg9NasnEL1UWnAOV+NcFU/3P99GObcw+kj2WcjUPQ9hSPniAbZa7b04JSDZ454f3QzXn3/C/jMWQc3ZIwTGfRwkyLkkKFmoJEYKLmRqlFX1+3Lf++0Q2Zi+cu7cfHfHZC4z8HXgKegFq9lDEbBU1RSaXt18pWppoAIDakhEM/jcKV+CllDqCSeaE6+j/qLlSAk3AyOP6HicWQdS1vfQadUS36EEs3AOPYUGGP7MMb+w/+J860mKEgQ78AZbQCA6a252DY7eouYd+lduH3lq80e3oQHrR5p5V4tdt8IDBTdIJlnG3JFRJlgTBgsq4qm0cBrIKegFq9lHUvLyIvnFCh8pBgFj0fooI4VD0eRcaZ9OIYJUL0+1eLrAyXR97kZCqMy/6YjnzHmscgTkyNh7zMsRoplD/mMElLTtDxtJEaSU/hv/+eX/s+EB+cihJCzpSYgmodg/Y4+AGErvSRMZsriSEAPN0lONKOtYH+pEhQIOZY+p0DPMfUOqBqOCDyFyUtb5krxGtUpqPmxeE5BX61ccb0gtBTsT/E8aMJWBfHUxYO6uq7FU2jNOv61Hd1rJoeKLjh2Dma059CtsJGAMGcjh8I68hno1iMkvwNIIbVRvvdGMnP1A9jf/4mf8QQE3ZBUpwDowwPBNavBUOcNOjuvVVBuhaqLr/vrS6N+zMGSi5aceKCMOQVKqkKsTqvVqFCieTLnFGjSslh0glYnI1UlNaCkxmpSwrAQ7U+dzGnCptVwl68cukdRDrUVIcJqYbyd/SVMa8s2hVVIXsGX33oYbIsZhQRpzKqX5WmqlYckT4EWmqOtHDuSmes9EAZhPoB3N3Y4Y4OipOLoJNC+6AHwapBoqBaGeK3h7qeFMBgl1558JTmh2wj0lyrB8Uw5BbqSFmPY0VvEr5/YmLj6JKPQDCbLWEGdtEzxfSCaU6C8QSyn4IX1B4CYAGPJaP/7pOdP1JZY2Kk0qRluTqG7r4jpbTnf2xnda7Z5j2hFSpN4W85BseLFJvEgkW9FDSUQPx9dncK4Mwqc837O+U855z/mnDe/ldYogL5k2VPQsyPEe7WsN1L5bT0sC1i8bwfOOHTkSfxa4HocQ2Wvak4h7B0QvnfHys3G/RKBoJlJy2aD1jxh+MgPW6iTm5JTCMJHnuoF8Mhk7lgsNrGRIcn6x2KMoaOQQY/SeEYOH9XSOre7t1iVktwoXHab6NexbMNuACHlVFWOpfvQ1tR4qIuNobKLvB8+ojDSuDMKjLF/S1IwZYydxhgbltzFWIMuRM6xjC4wEGrk1OIpTGvLBn9P1JaCowGLMRQy9qj3Oqb9V80p+JdGDoNMla6dCko061z9yYLQUxCvM4ZVrJpTyBgqmkWbTYmSqgkf0WuZpZTPxBO1sgfelnMSk8ecc3T3lTC9PdsUT4EMGB2nLS9CYKpyLH1vls5TqKhGwQsk38M8y/ijpD4N4E7G2BCAFQB2AMhDyGUvAXAvgG82bIRNQCDt61jIGFxgIIwZ1jIXEMsGEL2JiQf/WsU+HXls7RnCYbM7UMjao96APKgalXIKukmc5CrkqIS6OiXs7CtiqCw496WKh7LrwbYmn4S2mlOg0I86Ganso9BTUHIKXthRjfanTuZBolliKeU10iSyx1HI2okJ/539JZRcD61ZRxxzlI34obM78GJ3P1534DQAoafQW4zeT67HYwlzU7VyUQof5QzJ90ZjJOGj2znnJwL4MIBnANgAeiAYScdzzj/NOW+cUl0TIDcBsSwGxuIuMCA9FDV4CrKLV00met6ld+Frvk5Ps3DP6i1Y8cruph3vTYtnIWMzLJzVjnzGxuAo1ylQ7L9VyinorinNE/IjamJGUWc2eqCr5RVWvLIbm3YPDGfY4wIq+yhroIfGPQWxnWp8K2qdgkb7iF7LLCWdcqgch2/NOonJ43vXCDG8Zzbv9T3F0b3nls4TC79zj5wNIDynP63ZFtnO5TyWc8xoksicc0GV940BeQzjLnxE4Jyv45xfzzn/D875f3LO/5DUZ2E8Q24CAgAZS89p/sUjGwDU5inIN33SjXuDv89msHFkfPi/V+AdP3qkYX0NBksublm+ybg/l3O0++50IWMbe9c2CkMBeUBcU3OdAnHGw4fUFGZYu1W0f6Q8RTVhsnf86BGc9K0Hhjny5uCe1VuwW0niEuj0Y+wj5ZlQK5pNOYWywj5yNKGcssI+Aih8ZK5obsnZiXmCOVOEZPr7Xz/fqLdE+M4f1uLup7cYP68F6jl0FUQYUiVVeFpPIb7QqHgcnIfzEuUURjt8lPImEVLt6OY3MVWe8iUQaqGbytY8SdLhB/e/MKyxNho7ehvDKv76XWvw2d8+hScMssGuF04yzcgp0OWjyd6UU5A9hfe/fh6A6klkCkk1s9d0I9HdV8SH/3sFPvjLZdrPg/CRf5sTA2jNliivhCsVzabQa9mNCuLpCsnotWw8dM195HBV3rETKcQllxrUiI5vpm055/jhA+vxkZtWGPcFCI/+X/1ksg7qObzuQCHWp4aOXS8u15HT5G0CI+OoOYVxFj6ajCB3l1xc22LaB54kEGpJMMoW32QUHl2/U1vcMtqQb7weTe/dkeCZzUL93PTVeB4PVDcLWTsoAhstyPUHQPWcAmMMXzrnEADVJ3vq4DVRGUhEiXypWx/aCsJH/gT86HqhA3bz3zZG98OjHpZt6z0FWdMISA4fycZDVAQrFc1KXUSSFy6zCpMSzdTasxYkFa6SMcxI3ePacg52D0Q9MlUehLYFFKNQ4ZHPgpzCKIdeU6OA8GLKFZy6VQXdgAM1TGiliofF+3Ykbv/Nu5vXq1jG5j1hlG9Fg+oFKHFs6jPg8tBlFrHi5kyoNInYhpWiLHNBD3O1yb7DD4Ml5RSa0W50pHjgOaFKb1oQqHUKHzr5IADAu47bP7KdaMcZwiSIV654EQ9AN0FXglW2ZBQydhAGJMj3l8n7I1CYJedYviHSb7tpT/Wot3o9t/cOxRZ7ZVd0SZMNZVdLBnsHNIlmQ05B/l7kXCcgztdi47PJDgCAMbaIMXYfY2y1//pIxti/NW5ozUPgKdhhqEG3qqQbUOUd61ByPSyYKYZnQbgAACAASURBVHSUTKGSp2toOj4akGOSjdKYp4nA5EV5XphcK2RslFxvVBN/nhICMVW0yjIXlsV8LzF5XBccOwdAsvFohjT4SHHl79cCMCcsA6PgX6+2PPUSjm7HEaXyGgXxvGj4SCewRwY7mki2MVCM3p/k5fzfR0/0K9Br8BRsO9FTeNdxc8V55sxkTPk4f32hG8d/4z4c8uV7ItuoRXoAMKUlG/MUXG42CvJ9E3pPYe1G1tDLuZGox1P4LwBfAlAGAM75KgDvasSgmg31hnQM4SO6SLXQKUsVD51+qf5oh0qGC9n9dOzGOIv03ZhWbvKDUMiKY45mJ6wwVxAaIp3HFhgP4uQnFDm1+gnmGW1CqiNJmKw4DPZZs1GtiCs0lOK3SV5BzSkEFc2qp6AkmjOasA9dB5ly2pbLoFfxZjwOTG3NYsncrqqMIlm+JptgFDK2hX8+aX5i/ZF87ite1rP2Ki6PGYWulgz2KBRnz+ORMJgYI4uMWfwdT77nHHvUmwXVMyO0cM6fUN4bfZWzUUDgupJRMPWQ9W/k/mLyRXE9Do+HK4/xJokgu+SPrt9Z9/7KrodNuweDv3WQudk0eYxm1bdaqdypqY6NbOcbj4ylb4wCCB766w+aFiT+kq6r/OBu2Ts0/BMYRVQTVFMF20wFnUL7KHwdtuMM90+S2KrMhSl8JK+g2/MO+ktuxPt0JUPk2MkVzSX/Pg9yCglGvCUryA8mAy4bhUX7tGu3EUV60cm+s5CJ6Tclho80nkJGybOM2/ARgG7G2EHwVR8YY+8EUB+na4wQlJ1LZf26Qhe62Uuulxg6oBuI3O5aE0PNaDyjHueOp8ySDrWCaLWAmX7rSdxsx5CQbCTksBAAdBQyKFa82Hcs5xQA0vrXj6u/5KI15xgbwMjYLBkCdVIYa5AooQmh9xQtXlMXSsJTSC5eUyWx6e+yyyMTsHpMQJKJkOpG5GNmLGY0cO/88SO43K/9yfpKBUnXq5C1wbm5z4c8Ecue/0vd/eG5ujzWGW7j7kG81N0fue9cHvcUiG4qF7qVlEQzAGzvLeKRBizkklCPUfgogJ8COIQx9iqAT0EUtE040Goj7PoUl/aVtwOSJ/AgQWRbxu5LOjSLiSQbqXf68fF68PW7woS56cGT9W9o8hhNmQiuTDIdfihPTa6GRiFcEJjCK0KG2TYWc8n46u2rg7/3DOjrAcYKp1VpHhXo/bPQewKAktoYh0dzCqHxkEMg9GzpxN8kD8D/KlVPAYhOlK4UerEtZvQ2l0khHnoOk1bYLT6jTI3/E+Q8omz0Tr3qwfB914ucJxDS2Ndt6wve8zSeAknK90lRiCCn4ES3fXnn6BZEjsgoMMYsAEs552cAmAHgEM75SZzzlxs6uiYhllMwUN3kmGNSnqAksR5yjmXkFas3RrO0dOTwUb1Jq71KSMY0oXrS6sjRrCgbDU/xACi/o4aQVJkLJyF8NFCqoCXn1NQB643SxKvGlMcarnQf60gT6gStCwvxIOwWgr5DucaH/kdeQQc9SyKFWvFEc1su3nvD4+E2uYxVU14qY7Oq2kekpvv6K+/XbrdqU8jSM/VdLyptRwHgexcdBUCwlQiVhPCRq6tTkDyFQ/Zpx6GzO4zn0QiMyChwzj0AX/D/7uec9zZ0VE1GWKcQusu6G0OetJOKr2QqWS5j9hTmTWuJvG5WMRR5Cu15J9AIGilisWGDOy/HUanBeyOM4N1PbwlE6mRwJafQ4a86VSOmGo+splUkoa9YQZtkFJLkBuQCx/EWPpLH/clfPxn7PJ58jxtBNTwHCK9BTSLT3yolFYguIDwljwEArX6RoEwQ8LxQWmNqSxZ7BkqJ91HWscS4HDMlFRDhI8KvNLUIG3eFtFVXyTv84RkhC9/v3x8yBkviO/uE9D17GvaRrcnblDRGYWZHPmZ4Go0R9Wj2cS9j7HMAboZovAMA4JzrS1rHMWI5BctgFKR7IckoUOgpY1s+W0A/ecSakDepmxd5CtNas+ivU25CfSBND57Lo9XFQP2ewprNPfjITSvw9qP3w/cuWhL5zFMmrdBTiJ6vGmbKGDp0yVLc5M6r4RQZsidpCkmMFeRrRLU0MkjTSA6pqf+nGg6CKieihucAaBP1gXcibUdFgvJ3KXuc09py8LgIz01r0+dJclVCwstf3oVDZ3dEWE86Y//dP4kmUZ2FTHDP79uZx+a9Q/jQL5cH26m0XdJD6i+52N4zhJkdea0gnu6ZCENvSjX4OKakXgSRV3gIwHL/R183P86hVjSbtNdlt5FihTrInoJOJphwwLTWyOtmdfOiZNrU1mxNhXhJUB8g0+q5WHYjOkRA/S0t6XvWCfup4Q3KKZg8BYJjWBCQN0KKm4BZ++jOVZuxcuMetOUc5DNW7JhjjbLr4cDprbCYvgJdnbQYY/5kL7GKFA+LkLGsyH3MNcYjq0nUB41nJI+CQjryAkwOH5E8fXef2eh2SB3ceouVSE5psOTigh8/ikuu/xtapBX+qwnFbBXXCwziFW87PPb5Ar/HO2HRrJCpdPw37wPgS75YqlGIe8/loM4inKYffG471mzpGVWacz2CePM1Pwc2cnDNAq0MIz1pNU+LfMG+eItZA6UkeQq6kn75uG05B19562HidZNyCpTjmNqaq1vCWl7t5RwLW3v09EtZ7bFRngI9LLrEW+ApWIqnEEs0K56CY2k9AJqY8lm7ak7hY796En9Z141C1kZXIWsUnhsrlF0R+27NOdqcgipfAZA0hTTZS/IgMtTKcdVjA8IJUCcaGfEUshQ+CsfoSsqs01qFd/BSd5jEVTGlVVz32558FQBwy/JNwWd073bkMzjxoGn413MOxYz2XCT+r6LsU2wB4Lh5cTn8i/xCOBlqtzgRPtJvo9M+kkNv9D2NJi21norm9+l+Gjm4ZiGIe8qUVAP7qDVbXT9f9hRMK0867sJZbVg4S6wumtWMJ/QUMjVVZydBPrfOQrzYiFCseIEuPE049XpGScVGagK5syAao29VagZoD2HrSX34iEKAOZ/zDlSXw2jN2tripbFG2S+yajcaBY02j3If63IKQLwXtqpBBeib9qgifEBoFOTwEZfkUqb7nsKH/3uFNq8EiIpiIKwZkimntCD4u4OmwbEtfODkA7FgRhu298RZgHOmFMT5SEbBshg+d9aiyHatmqroA5TcoS58RM/Ef967LnhPlukgnHXYrNh5NBr1hI+Ok37eAOByAOc1YExNR1zmQq995HKuvegqAvaRbRlXnoB4KDK2Ja2cmucp5BzLL+iq01Pwz/WK8xajNecYC/vomEBofOtNNP/1hW7jZ2F4I/T+ZrTnsLMvLk4mtoM/Nr0Rl7vzBRW+1YTzso5W+2as0d1XhOsJKXNdaEvbBMaxtIlhNXykVhmrRhcIw0clqZjM03gKRBOVw0cyJVXOI8iJYBl/WSfukV994HUAwtoh2pc6tpkdOWzTeAp0n7uch0lxxvCx0xbi2xccGY5Zs2hUDadcs5MEor3TYgoATl40A0Dj5Gl0qCd89HHp5wMAjgHQVu3/xiNCT8FPSikPAMH1eKI+CkGWvDWtPMV23KfLNa6Yq1Tx8NnfPBUpqlFRLItVe2chg8GyW1fTDjrX+dNb0ZK1jSu2YtkLCnRMHbqGi//wNXx00K1QuwpZ7BmMGgVV0sFkxEtSfNfUZF3sL/zflqyt1b4Za6zatBdrtvRgamtWW0Ohm7QcRRNK/d4IaqKZJnt5XsxlqC9AdLIXxwmnpDB8FM0p0Ni6/JAgEM8VEajWYeFMEduXPVk1xAgAM9tz2N5TjMXs6dw5jxNTLpRCRts04VOZUPK/yzdpja4OOqNAf49XT0FFP4D5Ddxf0+AqFzljMW3MzuMc7dKNaII8gSTxo6kjlaOh6I0UDz2/A7es2ITP/malcRtatZsKuoaDopQ/ac1W8RQy5Ck0jpJqgporAIDOlrjkQBiCSg4fBd6fr80P6BPNqqEbb+Gj/3lC0C1PXjTDzylo9KA02jxqC01dBbLYjimJZsS2Kxg8ACAaPso5FhhT2EdeGNqSJ3PVuE1tzWLBzDas+upZwb6AaOGmjkE1qyOPYsWLeNDFiovd0n1D56eeOwBs04SernnPMcHfn/vtU9g9UE70FOje1YWPiOqsqsc2EvXkFH7HGLvD/7kTwHMA/q9xQ2se4tpHevaR63FMazU3dSdEE81J4SPhKdBFr5cJBAD/342CAJYkiT0keQqAeZVVC+iBbcnaKFTxFPKKp9Aoo3CYpphHNxl1aXRo1BWvKXxUDAy9Ddtv2arbTr5vsraFLp9LP15E8S69VRAkFs1sQ2tOf71khg9BJUyYLl1rzonsUxdmKgSr3aj0AxA9LmMMLUpDJpXj/9DnTwUQv4ddj+PEg6YF4UPLYr5SbtwoyKt2kgCRk81qO036HuRx/OL9xwEALjvnUKhYMLMNXzz7kPC4VTwFei7KUm6SoKPpNhr11ClcJf1dAfAy53yTaePxDNfzwJjEozf0U6ACrIv/7gDcnqAZJCeaTfxoAFizpQdl18PcqSIRtWGnOeTTSAz59FDqC2BqVF8L+qkXcs5Ga87G5j36m3VI9hTsxhgFki7QTc46ymSHJhEek7kwFK/JDVtEkZbe2MtaPJ8/+2D87aVdKLscA75u0ngBsY907DNZdI4QeyYMiebnt/UGDZekzaId04KJTa5TiBsFQORl5MWSy6OMp5BVFj0PISURXfM6CoNKm1NozwMQGkMLfTqpas/p+ZaHeuohM7HhyrfABJmBxBEvXpPhcg4HekYW/fk/f9uIo+Z2GfdRD+oJH53DOf+z//NXzvkmxti3avlHxth1jLHt1IvBf+9yxtirjLGV/s85dYxtWKh4PHLRshq9d8BfpdCEkBCHl2Uusk6yENe67X1oyznoyDvYbqBzDgfETnijn5DSgZhAJu7+cEAV0a05By3KA0xwPY6yy2N1CvXkUDyPBw+nrmJct0JtzzsRyQQgzlIyUYiptSOdg0mKmbR4OvIOlszpCtgv4y2vMHdqC1qztp59pAkfqXLypuI12oa8hSRPQRc+UlfQhawV8SgE+yj8nBLH6sLG1dA+1ec28BLlnEKH8BTk3ECs53RFGE2VjpsE2QhU3OREM43L9UNl8rb0fP36CXMHuHpRj1E4U/Pem2v83+sBnK15/3uc8yX+z90jHtkwoWqRmBqykKeQpKQJSJz2jJ1ISZUxvS0X9MKtBzTpJRmtobKLfMZqSPiIJpWWrIPWrK2tkKax0AqxXvbRtp4hXPyLULVdpy2lyyl05DPoLVYix1V59KZqdtlTAMzGgzyFL5x9CCyLobMlLJ4aDzhwhiiYvGjpXLTmHAyVvdh10MkwqEWY4WQf3e7o/cXqlRKhuuuQp34aOqOgHLclEw1HuV5cmbU958TyYq4Xn3hVQx56CuE2szqEp0BS8EC0ohgQYZ2klb4O8jf8Ynd/xLCpoHGJxWr02G9avA8A4JITRy99O2yjwBj7F8bY0wAOZoytkn5eArCqln1wzh8CMG7kMCouD5QgAeqnEJ+wiPlAjcdNcWJatXQWMrEEXbAv5UGc3pbD9t76VVIffVHI6iZNuKKQzEZHwV9lDY2clkorl9asjZacE+uUBYQPf9xTGJlR+P596wKqIaD3FHQ5hUB1M8JAofiR+GXSyCnGjILeeAR9eu0oQ2a8VDVnbQtvWjwLlsUCJp1qyF1PV3+gJJD93+rceMExQnWXkvW6MJ4up6BKawTbZm2FfRSf7Ds01GpX8f4Bv81uFb2ltpyDWR25SD9qMhRvWDhdnJvnaZPMSfj3O9dEXicZFRqXqe9CPmPF+jY0EiPxFH4F4FwAd/i/6edYzvk/1Dmej/kG5jrGWLxcEABj7IOMsWWMsWU7dtTecDsJrudFyuuztmAfqZO+oJLppX9l7B0so5CxfZkL/YpSFdXatysf6Z08EsiGJqmRCnkKDckpFCvIOYJB1Zq1tTF+YkoQJdXUyatWyGX/LVnbEOoTv+Vnd3anKEC679ntwXuq8TDlgFQmiAhFxK9/SFoQ25GnMF6MguwVU2hrl6Z2w9KEXmrxFAJmln8BdMY5r0mW6iZxQBiQiPHw4oZoSmsGO/ujCyq5J3gwNkdNluvPYVtPEa/uGQyuGS2wqAahVEnOCdSCRKPgD1GWm5chWH6j189s2EaBc76Xc76Bc/5uXyp7EGLh0MYY27+OsfwYwEEAlkA06/l/huP/jHO+lHO+dMYMc9x8OFBzCkQRVVfb5JJWa7KytaeIqT5LybSipBvyH04QX9n0tnhh1XAhx8tr8RTyGRs5pz5tnt5iJUigklaNmleg86Jwla5SdTiQNevb805iTkF+pkj87ZVdoSyGqpFUyOg7cKnho6yhAxYZY1rJ6VbFowHOeU3UYldKwM7uFCvgzXujixFPM6Gq56s2JyIELTkDTyF+HUj+xVSUJqNF4ymoE+o+HYVIpTrnHJwjLtWhyKLTI6Ke6+XnCtmZGx/ZEIwNALL+omYk4SMVuv//7JmL/HGRpxBdrBJacvrWso1CPZTUcxlj6wC8BODPADYA+P1I98c538Y5d31Z7v8CcPxI9zVcqG6auf1gmGgGzEahVHED19wcPhK/9+sSzKNGFJLJycyk6uihcsgEMrWprPmY/aWApqvTqqHj0bEAfaJxOJApel2FLFyPx2oLdKvAffxJUP5/NZbdmnNQ8XjM0IRV6mLsJimUMHwkjpGv81wBUYH88V8/mTjpf/CXy3Hk5X/Ejx58AS9sNyvZVzwvWAB1+Z5CLEmrmaBzjh0pmDLWKShetGk1nleoprpQCSDuqUGFuqozHmu39ga5JVPSWl2g6XIKAPDu14mF2s8eejGynbwYrNco6M6hy/cqKYqgLlYJ485TkPB1ACcAeJ5zPh/A6QAeG+nOGGOzpZdvB7DatG2joSZ0QhmD6EMfJJqrhD9cL2xUnjF0fAp52eI1MYFM2kG1gIqkso6+xzRhSKou7izopQ5qRaniBQYm6EimhFVoMqHtTB5FrZATf7P8id7Us0J+pKgYqqjhx9NDGrSAVB46NadQyOiT6oGImf8wN6IC9dqHX8LvntqMmx4zM06IS//te57D2675q3E7zwsNIC1c1AI2Xdy+JWtjUDpfU0VzRmGWKSmbALGwEI9P4rRdd28xmJg9Hjcwpx0iGhrd74cFAyqnrRqsaF5E1jCSEbbGrES2y0kGr5aKZBlnHDor8lpnVOi8knIKANWCjENPAUCZc74TgMUYszjnDwBYWss/MsZ+DeBRiGT1JsbYPwP4NmPsacbYKgCnAvh0HWMbFmKegkGwjR4WmhhMSoWyhc8aWCrqSqsRSV/yFGa05aqEj9ygMrKjXqPg6zcBMH4vxUo00UzH/tY9a43G64XtfXhuq37FK+dOZvnFRqpR0K1kGWPIO0oxlP9vsYlSuQ5q+Ghqaxa7NGwx2jd5TUEFah2eAp1BrVLj/QkThuwpUBMbddXpeRpqqKaITIxNSUgr1fkm4bx8Ju556ObZfMZGz1AFX/Hbm3oejzF3Llw6BznHwt827I6MTecp6EJgugn+LUfODgT3VBmcsuvVpF0k4/D9ogWWumPSPUiX2dWwjwBhoOsVskxCPUZhD2OsDaKfwk2Mse9DaraTBD8fMZtznuGcz+GcX8s5/0fO+RGc8yM55+dxzrfUMbZhoex62pyCOmHRZB/26NVPvBU3NDIZ2wLn8Ri/2ge3EUlfKvWf3p5LTDST9hGgr/IdDkoVL/g+TB5UmKQVx5RXersM/P0zvvtnvOk/H9J+dvX9LwR/EzNEnXTp9OOTkRWZjFSPrTWn9xRKrgvHYsF1bc/rFWZlii4gvCeL1WkU/FNIKoqudeEqL4DyGg0iICpPTVBZQMacAondqTkFZaZpzTmRe920KiaP47fLNgX7izGjbAv7dRVw3V9fgiupmMaYOwrdWKfMStinIx/kvCgcSl5k2fWG7SmoSgg6o0JvuVU8hb+s68bKhH4u9aIeo/A2AAMQK/p7AKyHYCFNOMjhHiA5fGTVkFOoeF5gWBzDtuqN2wgdot394n9ntOWMDc2p6ItW7V0t2bo8BdLmB8IG4+q5qp6CjHrL9Q+aKXj36jnQ2avPbj7GZlHCRyajoPTfbTUk+2iFTOfKGBPx8zrOk1bjSQTeWqco2YulMaqhLV1j+ULWVorIxO9YTsGKegomj6Kz4AThGdpONwHSgqLketg7WNb2egCAc44Q0eeXuvtjob5gbI6pAC++v/a8g/6SC9fjwb1F5JGR5BTOP3q/yGudUaFx0D1pyikQRks6ZURGgTFmA7iTc+5xziuc8xs451f74aQJh4qSWAsblUe/dM7hGwV/NWSIY8tUsoyyciKoCo2hpzByt3DPQAmMiT4JuiZBQPiQBZ5CS6auattI+MhPwqrnSpOOrPZIqCc2+qdPn4w5U0SiXlWnNEk755xoz2zVOFOFrBo+kpsEAUAho+9vrWu2roZehotaPAX1I9OCxXVD9pFjW3AsFvNidMlcm4kJ9cZHNwAwewDhs2OuUwBEslTVSNJVCN/5VBgw+PUTr0QE8WS8+QhR1PXslp6gVqY1G5UVEXUKUshKI3NBIDJEX7ESdGIjCm+p4g27TkANA+kMWxA+quIpEP7x2ieMn9WDERkFzrkLwGOMdTZ4PGMCzlWj4K92vPjq3rb0TUJkyFzwoKbBGPMWr8k1rcdT2DNYRkc+g6xjGXMK6qp9SksGAyVXWxVcC8oVLoWPfE9BDR+V457CtReL9FM1o/Bygh7UwlntgVuuxvd1lbSAeBjlGpGwCCtkHwHxgi7VU2jJ2hjQUFd13bLU+PlI8b17n8fvDJpbqsEwsVMqilecz9ixfIxuMiLP6et3PgvA7AGodQqA/jqohY4mOenPv+ng4O+sbRk9iv26RA3Kjt5iMFZVa0rVqwooqZr93blKGKOjrvgjfvpnwUIiokRfsRKIO9YK1XiqEhyA5Cn446p4yR7Jwwn9ROpBPeGjPgBPM8auZYxdTT+NGlgzITjN4WtdExAgTDTnamAfZZQ4u5p/UGlzQXy3jhXl7oEyprRkEqU11FV7IHUxwrxCyfUCI0m/i7HwUZR9BMidsJLP943feTDymlZ6nz5DcLqp0YpqFFT5CoJjsUiiWvUoKHykaiSV3WjSryVng/N4gpuus1xgl89YGCyP3AOUV9Af//WTNf2PKRGpTvg5x4rnY3g8bEHGl7xAU3guXqcg3levgyqJolNmBYAPnHxg8Hcha/v5jvh2HXnRWW/3QCnYLyXSCao0CT2DuvQAyYHIIEOwd7ASuZdrgWrwZNFAAnkPNC41rJ00tkaiHqNwK4AvQySal0s/Ew5q8iq4sRVPIQgfValolmOOpvxD6H5HPYrLfxcthx8OHli7HZ0tWWRsZvQU7ly1OXL8sOHJyFaycqI5pKTqw0fRSuSR0VJlVVZACM9lbBZr3m6iTFosqmvFlRUvhY/UlTZXVqjUFUwdvy581GboW1ArhheoEDD1tZDZR4Dei9EJtr3j2DmR1yZPzAm4/H5OwTDxtmSjnoLn6dlHMsquB26grloWg8eFemh/gqcQbRSkZykBwHfeeVTw9+H7deC0Q2YG+9vZXxy2p2BbDEfOCQMrT2qk7ekrp3FVNEqvAPBPo6h7BNTXee0GAL8B8JifU7jBf2/CQeU+qzc2gSSFqyWahacQzSnEjILCjskMM0apg2MzWAywLb12EwB8427h/lMFaNB8ZKRGwfWCBLNJ/qNYEcwdR46zGwrdgORqbFoBk6fBGMO01hx29kVlDlRNI4JjRz2FUCnTH1fGhmOxWGMctW9xaNRUj4KMQrixSaJ6NGFSP1VX5CLHEvcU1ImSwjPhNuK3Op9mlWfDmFPICU+BJkDXEBYCgMe+dDoAYehIOdSEHb3FwCDqcgrygsVVFmaR83AsfOzUBbAYMFB0UcjYQYiXcwzbU2CM4Y6PnZS4DX3nEfZR/dPCsFFXRTOAlRDMIzDGljDG7mjUwJoJj/PI3EGTmxrKCSua9cljgiwlYKKvqlRIeRVdi6qqDqWKh2P3nyK6XxkoqWrsuV4dorKUaA4oqW70eyOpbhlUl7FDIwL44wdfiL1HoMlV7rU7rS2L3y7fhHmX3oXLbnsaDz2/A8tfFpz1WMI01lgeke0YY5jWltUYGaV7mEGqI2iw5EQ9hXqMwjDZjwD0RZB0z8meQk7jKZjCFiQT4nnczD5SjAI35BRacw48HtZ1qN+vjFkdOTAmDLBO/ZTwhbNF/uGZzXv9Y0TvuZasWmuhPwdCR0GMcUdfEYWsHfE8csP0FFTM6sjF3qPzimofxafoA6eL8NHnzlpU1xhMqCd8dDmEFMUeAOCcrwRwYNI/jGfI9xmxDGSaI+mpMMZiqyEVZclFN1FSVTqc7KnoQir9xQo++T9PYu3WeCySxjdYdlHIis5gHo8rsQJhzJxixLTiMRm4apBvXF3LQyBs6iODchm/WxUvR3l+W1/kNXkO/cUKzvqeqF2QH9CpEgf8V4+/gvdd9wRue/JVABqjwKKeiK6Xs/A8ouEolbsfFH4p14oMjqy625pztL17a0W1RYKOmqgrgiTvUQ5J5DNxT8GkQ0Rx8Duf3mLsp6CSMEzGI1Ss9fsuJHgAjDG0Zh30FSsYLLuBMJ0KkpX+3+WipkH1FEQYL/ROQvaR/rjteVIZqKAla0eMzMqNu/X/VCO+8KZDYu+FieZk9tGJC6bj9598Az566oK6xmBCvRXNe5X36qdYjAHUnALddKZVRVLjdkDQ/qpSUhPocDpO+/PbenH7ys34kt9OUUWxIuKthawdrNZ00tT/dJKIR77reKHvEtBIR+gpCDaI+LtgiLMPltyYp5BzhDuu0w9SDQh9z39cszV4jww3kNzsRP3EsaLMrDBhGm45vT2Hbg2bKdpn2BA+qsTDR1v3DmH3o1LFhwAAIABJREFUQHnE/SNUI6sae13oT+cp6OpFco4V27+rSTSr+1Z7WxPCGh+lTkHZHd3jy/wq5O29Q4lVutQgqW+ogracvk/6QTPaMLU1G0jQqzmFtryDshvqWumks9VjEgoZG9Naw9W9msMaLnTHpOco7Kdgpr4eOrtjWE1+hoN6jMIzjLH3ALAZYwsZYz8A8EiDxtVUCDle+YHXGYUw3FNN+6gsud+m8EwSHU6nqUMuvqkIiibiQsZO7GxGccsgOezUFz6SV5UFjTEFRHy7TXlAAeAtR8zWrqBVA1J2PZQqHj5981PBe4fObg/+ppXf188/PLavePgomoTnmhXv9NYsupWwlnqPtBjCR2VXtHaVr+uxBwgV+O6+kfXLUBcUL+yIelIUgjtpwXR84A3C6OvqXXQssHzGjjWBr9YZzLGYMZGvkg1M2xHB4eEXhPz93zbs1ja9J3QWMtjRW0TJ9SKTtYoFM9oAiPobtXhNLUzUteNUj0kgD5xQtyCe5v9lGQ0aX73HGQnqMQofB7AYQBHArwH0APhUIwbVbKi6K3nNAy+rPWaU1ZAK+WKSC6oWQ5kUGtXjEh5dLzjJJtf5pW4xUezbVQhWeTp2lNqPOtQrGhk7RhZPyzlC0kFdPYv+xPFxz5/eip39pVg1suop3LN6a0Tu+qmvnhWJ6Z7ptyCd2prFR045KPK/LMYPj9Yp6FQ8p7VlsbO/GAnLqPcInY/qFZVcQUeW93f4foJ1MlKjoK7kN0rfBRAa4XcdPxeXnXMosralrXch6qnMnClk4jo6ukQzADzwuVMAiEnUmFNQqtpNKqnv8Ct8502rjV7ZUcgERWRJRuGgmcIoTG+Lx+zVwsRqOYWDZ4ULD1ooUl7l6cvPqmncJui+X7UTYrWK5tHCiDuJc84HAPyr35eZc87Ner3jHGqdQuApyEZBYguZqJeEiusFVp/kcFUefVhJG7fLuqQk6f3MaI/f7ICQDgbETUvH6i9WIqsdQN+PGqjPU6AbnDGm7dPcV6xoH+T5fsJsQ3d/pAm56il8/n+jDf06lH2993UHYO7UFpyyaAbOOWI22vMZfOuetQD0nkKUkgp/u3Cb6W05DJU935iJY6mJUF2jGADYO1iKjW+Kfw+QDImK1a/uxYKZbdqKbyDuKahaVfR9t2RtMMbQUXC0Glo6T2FKaxYv7ujHms09OMyf8FyPa3X8OyS6rikspHrRJuPRkhVNqEj7au7UAo47YKru9AGICXPVJkHj1HmdBPIgdUWPFHYiI2iqyiYUpAUYLcbu+sQbjMceDnTFa21KnmXCeQqMseP8tpyrIIrYnmKMHdu4oTUPHNF4ccYv/zeGjwwaPwR54p3iJ0H3DEaNAklL6G4OXaJ5iT9pmiZvupGmteYSey+rScRsHZRUznlMi0bHe+83hI+oCOel7ugDnK9C91NXnbbFcOrBM4P3ZUMYzymwiNqobrVIBXFyslmtUyBPRS3U6yu6gWQJgRLhOvG/LXsH8dYfPIwrfvdM/ER9qIlgVZaEDBMZlfZ8Rpto1nkKZKzPufovwXum6mIykDv6irFKcELopSqegrIvxhimtGSw21/AlCs8UtuhorOQCe6rZKPQ4R8/7iW35eKTLqBftQMh7RgACtkRr5+10OUk1N7luh7NzUA9R7wWwEc45/M45/MAfBTALxoyqiZDR4dTm3vIbKFq7CMhJSC2ac36vHdldUcr2T+s3hb7f13CjVYqJllkuQ9yklFQPYVcHTmFIC8iGxmbxdRlB0pu5AEjzJ0qdIt+cP+6yPtJdL8fvueYquOSw09amQtpeB6PTx7TfMnkHVK4R61ToNW2SlvWJdXJKOjotxRHf2qjytkIIV8bi8U9BaqWpu+4I+9orz0ZD3kFTCyY1x80LXjPVDNA3+sPH1hv9BSYT8Sg0KrJUwAEWWC3fy5l1wsWWzrIhr4tIXy09IAp+MRpC/D7T8ZX9GqvDFNoiyB/BwWDFzdSPLB2e+w9Oh4RKiacpwDA5ZwHywvO+cMAmluh0yDotNx1vWEBcQPZFgNjyTkFmngZY77oXPQhPfVg0RjkjQfHW4o++Fy89zRNDLpiL0AkorO2BctiVT0F+Uar1hsiCbL3RMg4cYkNuX+DDJr81+/ojyR/k0gVavJQBzk8EpdhiHoKtOSVvZ0ZgacgG4Xo5EGrbdXDov7XMqa2ZpHPWNoe3HSMNVv0VGP1GFNbs7HkfChdIo67raeIh57fEaOqDlWi2wFiIjpm/67IpO26+slIPn+eMKFmpcphk/EAhFH405pt2N47hFLFq+opENoN7CMaz2fOOjjwGGSQB/fTP6/3C/mS2UcyRqoNZh5n/D0axx+eEQtFtfq8WajHKPyZMfZTxtgpjLE3MsZ+BOBBxtgxjLHqy7lxBPWBB3xPQZNotlmYbNatrjnnsYlXyFNH3cVF+4jY5yIpmUXQTeY0aQ8Y5AvUFpum/biSFwPUl1MItWPCc3UsFq9oljq9qXjXcXMBCMqtul8daqn8lh8k9eGzFPaRtk7B9xR2Snkg1VPI2CzWxQ2gCu/oY8UYw1DZw7UPvxQb69Ya6hdko7BvVyGgXIafk5doR/a5bnuUpRR6k3FPRj5XXdN7FYaCcQDiuykFtE/xns5ToIT/h3+5XHxvNRqFKa1mo5CEuVNFVfayl3fj7tVbwgZLNVA7dfTueqBTvFWNk8k4jzbqMQpHAVgE4KsQhWyHAjgawP8DcFXdI2siOI8Xzpg6TQVMG4NRCIqXpMlL18hGR4X8yxdORSFjayc+OpaOrgpEq4ZnduRgWwyv7ByIbVcxeQojCh/FV1q69qNFqWWnijcsFJ7Sm7//F7znvx7DW67+SxAi+xeFSQQAR83pir2nQp6AdDFvXZ2CvB2Fe2RaKuleERhjMRlusZ2+8MsEXUhJhXyMrpZs0EyJoHaFu+K8xQDiuRoyCoVs3Cjs6g/H4XrxVpaE2X77UzL8utV9VpLO4AmeAq3ct+wdivTl0IEIGwACufThQi0QdRPGpuIdSj+EkYIM33lL9o19poYdVUXbZqEe7aNTE35Oa+QgRxvqAw+ICyQnfF3FXRadqGqrGtWFj3QNPuZObcFBM1u1yqFB+MjgKRSlquGcY2NKS3T1F5yH4pLWYxR0ibqsEj6ipj6mVaBcjfzI+p14ZnMPrr5P5Bg+c2a0jH/DlW8JEvdJkB8kbZ2ChpIayRc4NjryjsZTiO4r58Rlp01yDZ84bQEYi3fzkxcLphAFXZtvX3CkSM4q91JgFPzv+O3HiAls3bYoITBINMdyHjns6i+FOkSeubPYRb5nR2PNavIAclc6U0IaCBdOxYoHj+sNDKGjMDLvwIScY4ULs4TV+KVvPkQoDyeMbTh4z+tE0ejrD5oe+6wjnwFjwAkHChbWWOUURpxSZ4x1AXgfgHnyfjjnn6h/WM2FGhoA4jkFmkfoYWnLOdqkLxWMOUr4aI0ilUtzQ8wYOXr2zov+qm/AIDU9VIkmODsKjparrnoKjiUSg+pEUwsCmq7iKchGgbwGk6dAoRodhtvykBD1FOL7dDWa+mocRFTGhqEdV6PiaRKT0w17ZkcenItVMSXYAeAWX5IBEGynfRXhOUBM5m89cjYuPG4untm8N+YpqJ3GOvIZdLVkYqGpIPfgqDmPDMou96nDmcTJiI5BonO6ibwj7wTFcyY5DCD0MIlCnWQUuhpsFAZLriRJYr7PPvzGg/DhN8Y91pHisnMOxSUnzjdSyzkHHntxF4CJyT66G8IgPI0JL51tyClIE7BabNaSs7X1BLr+sF2FjFZ1E4hzpAvZeIXp5XeEdEW5B60MEbcPd9aRz2i56nISHBDnPaergG29w9fmcaU8CyFjM5SlPhRUeGXKKchyFQBwwLRwwhxuc3SC/CCp19Wxo3UK0HhsALBh5wDufnprZLOYp6D0ewbMngL1kv7evc9L++SRlpRqroDQM1gOYupdLVn0DFUiHkdgeKXrP7M9h+1KhfCgMXwU7Ulh6m0gjkEdycS9pTUKhUywIAlVUvUJaRlJ+SJVsmKkuO0jrwcgvgv6DpOMUaORdSzsP6228NdEZB/lOeef4Zz/YqJLZ3NFJRUwJ5qDDl1ZvfIlTThyCKNL093MpEef03gKD62LspF0VNiyF+V5iwdTPz71RpvSmsV6JSlZC9Q8CxDPKST1ZwaA6W1ZfOiNB+LIOZ249zMn4x9POGDY41CRtLhyqqikEvbxJ/GKxKJR9yu8uqgB1+WnAODkRSJcIOsW9Sr3z3ZN0plzjp6hchA+oUI4mUSgho8AYGZ7PmZkdHUKQCiOSOGypI5fdB2J66/3FDLB+JJyCp84fSEAYM4U4R0l5RT2972rt2li8cMBsZKWv7wbe31vZizi9rVgIrKPfskY+wBjbDZjbCr9NGxkTYRuFVhQirBC3X3fKOQcbdJXJ3TXRaqrUojGNBm1aHIVqiaMjj4qqqjDfXXkHfTqPAWNHC/nHGu39g67EbjuXLO2FdFcCqpoDQ88YwxfevOhuONjJ2HBzPaGdJVKcrnV79dUXPUZX5aYpBV0OYV8Jm4UdNsB4Qr7/1aG7TQpyXzpm0WtgM5TGCp7KLs84NjTvbRHMQoZm0WMc2chExPFk2nLMoLiOr+gStV5ip5HNHykyxV1FmSjIN7T7W+eX9G+abf4jpNW7PmMjQ1XvgXff9fRxm1qAY3/zlVbAq+tmZ5CNbz1yNnB3xPRUygB+A6ARxGGjpY1YlDNhm4VqLKPYuGjrK1N+uo064k5IT/IJu0jUoOUQSJnl/gdl0ysJ/kGkl14dTt1Uli8r9DmGW5Vc9gTQskpyOEjJd5dDarc8UiQ9IwXssLY0/dvmrRoZbpxFxkFTYhRuUcAMaHWql5J7KZD9mkHY3qjQBM7MXWCe0nKKwyW3djqn85Thq6GAohWXHMuiAEmI05CdkH4SJNo7iyI0KVcC1DL3JZESW0UdNdmPBmFBb52k+fxMdM+qufb+CyABX5F83z/58BGDayZEMnB6Jefsa0II0OlX5I2uwpiH1mRnIK/upM8BVPxT3s+ExPPc2wLGZth0Sxxw+jCR64aPspn0DNYia3+VfYRgGC/OiOSBB37SC1eUzn01TC1BnZRNej0pAiqLHrgKSj/QpII5A3qwkJ5zcSrIy0Q3v/6eQDC740qpvfpzGNaaxY7NHkduiYUPqIE5da9oQEZKnuBiCOhkIl7nMIoxK8Dicft6C1q9ZFk0MRN974+pyCa0/SVKlVF5y44JmzzOSb0S1a/4mkjQd/nM5t7ROvRCZZofgFAnAg/AaELH133V1FodPPfNgKIJ8x0wm9iO7OnIHPBTdLZ7XkHJdeLhCWoD3LQ1aoSD/PEPQWxH3X172qSiCYl12rQs4+YklNInmRU6NQth4sk1hI9ZBS/NxVhqdLYugRyXtP0XncvEQ7yQ2NUxUzhoxltOcxoz8cSwwCCuDeJ0ZGI4IuSfHZR4wGIMFl0bCajUMjamNmew0vd/VK4T2/E6Tom5RQoKd4zKPdd0EOV8W42GkU1bRRovjn3mocBIFp93yTU8430A1jpVzVfTT+NGtho4oXtffj2PWsDuQC1HaeMrdI2QOgGtyk9ZgmUxJQn3gNntCLnWHjipbBbk8mtVvVZANKFsRIlKdScAj2YqniazlNQhcJqhe4csiollSaZGh8+VdWVvsfvvPPImseVtLjar0skkO/yO74ltYsEQk9BS1vOGnIKhuNT0dXG3WIttaO3CNtimNKSFWwhTfhI9RRasg726ypEeirowkf5jKihkBPbQ2XPqOFz0Iw2rN/RV5UYQO/T/akL+dB3uXZLr1ZGRIbMojIdczTRjJDVcKAa0Z8+9GLTx1DPN/J/AL4B0VhnQlFSN+0ewI8eXB8kuDjik/MNlxwPIFwJhTIXvqeQc8B5vKGMpzEKLVkH86e3RnoCmKh6aptCIOyDTGPRFTmpfXVJv6e7N2oUKprSed0xa4GpolmWuRiup6BOHvTytENm1jyupETzmYeJlo1kvE3hDbUHc805BU0okkBUxJf9SvNtPUXMbM/Bshj26chjy15N+GgwmlMARAjpdilhrfMAdN0DBw05BUAsXF7Y3idRiE1GwRdmDMJH8XPdx6967pE6tJkiNLJker19j2vFnz59ctDnuBbZlGbi8nMXR16f4fcKaSbqqWi+AcBvADw2XEoqY+w6xth2xthq6b2pjLE/McbW+b+njHRs1RCES4ryKjB6c7xx0QzkM1aQ0JMF8QChfirvg1DRxNkB8SA/+FyojGjqDxtovksTdNEPHwVxbk2CWy10odjzjr7oRKNryk6qk0/5evW1QmfYRKJZfFkv7ujDxdc9AWDkDzzzfbhaE9VAcqLZtlgk3m4SbGtRWotqcwoamfCk8BFRLz/zG9FB7tU9A5jpU1/nTCmgu68Y8zyIVtxRCBPwW/aKxQzJTu8eKMc8LF0XPKGPpb8OC2e2oXeoEvTlMG1HxuJvG3bHOswRqHGO6/GqOYX3+G1hxVibo6e5cFZ70A1vOPdVM9CiNKNaNDOujTbaGPE3whg7F8BKAPf4r5cwxu6o8d+vB3C28t6lAO7jnC8EcJ//elTQrnZgMjBGBItCX5kpN/WWoSteA0S1asXjEYqjbrtw1R4mfcsuR9axtJ8RKm6UWx4YBSUkIXIP0ctOq9Dv/OG52H6ToNNvyjhhTuGJl3YF7w/n4fvsmYvwbQoX+fseTmVnteRca84OqtGDnIJy+R3bQta2AqNQ0TSzzxs8BdOqWDaM/cUKHntxF57aKAzxHF+sjbxXgs5T+NrbRNvRZ/wq+d4hjVHQNAEaqpjDRzPahXG6+W+v+GNNDh8BiHWYI4SLF6kZj3Zv0edOJw45WiDPVe19MdZQx3OKRkV5tFGPmbwcwPEA9gAA53wlgJrYR5zzhwDsUt5+GwDyNG4AcH4dY0tEGEMPC2xMgl1hZWZ0EjeFXExG4aOnLgAgimbE/sT76kRDY5MLz8rkKSSEeVT6GhkFNXmpVjQD0WpRne6SCbpzyPt6QOp+hhMv/vjpC3HhUqGxc93Fx+GMQ2dVbbwjoxqNTy5MNBURhtuJ73qw5MYqgQsZG6WKp6iumlfFAPCv5xwKAPis7y0c4qvlzvXzDZt2R7kbvUMVZB0rEh46fp4oB3pm895gbGqbVuqtEPEUSubwEU0+NElXCx8B5jxRq5SjClREEr4TImI0gmRQK+g8VGM61qDWrQQ5vNYs1GMUypxztTNIPanyWZzzLf7fWwFog2mMsQ8yxpYxxpbt2BHvO1AL6AGjeLd4kOPbdUhFOOoESIk/VYeGuPtqbPykBaKilVbuprBFhxLaAkSyM5+1MdUvXNL1+lXDQjnHRnvOiYni6SqauwqZwBi9sqt2QpkuXkzG6JnNe3HprU8H74/UTT9p4XT8/OKlNXP/gbiMgwq5Gj0IgWm3Cxk8g+X4xBv2aY4WwyUNlSpq73lGSGiQommQhFa+/427B4JQJWFKaxaOxXCN36K1X2MUClnLH1toFPqKFWMdSGvOQVvOwau+p1KNfQQI+rEOdK0fWb9T602q+Obbj8A5R+zT1EQzHavRQnuTAfVchWcYY+8BYDPGFjLGfgCRdK4bXNxJWi4b5/xnnPOlnPOlM2aMzLWiSZHi/2o7TkJHPhSVI5YE/S/Fh19Q5CFo1aiuVjsKDmyLBbRUXTUwoA8fvbijH/tPbUFXSwatWTsWYqBzUcMmGceK6STp2EeWxfCrD7wOQFxuOQm6zlU0/nf+5NHIts1KIgLVC+BkumZSxW1B2q5fM6GS8ZEn3qScAgAcvl+0+cvrDhQdz2a255CxGa5/ZEPk87tWbdGKFTImZDJ6h8rCU1C0gQoZxx8bhUg5tvcOYZafBNZhRnsuYEaZiAHR8FGyoX70xZ3BfZ5k1M85YjZ+9N5jh2X46wXdj0mtPccKV7+7vqrtelGPUfg4gMUAigB+BWAvgE/Wsb9tjLHZAOD/jveraxDoZiaut45ZAoi8AYVqyKsgL2Ofjjxsi2kon/pEs+hJm42Ijum2a1PyHRXXw6t7BnHg9FYwxjB3akssxEDbqZO9aFIfdd50Fc1AKDkwHKOg4/jTQ6bS05uZ0KsWapIlSpI6g4lalApcj6NY8WItRclIDCgaWUmr4q6WLJ6+/Cw8+LlTsOLLZwbvWxZD3rGxfkd/rBeBDt+6QORclr+8GyXXCxLjBDJYFMbb2V9C2eWBppMOM9pywf2uUlwJ8nVkxkxBCJ0W2HgAUZEbJbTXSFB4cKxQz5P6Fs75v3LOj/N//g3AeXXs7w4AF/t/Xwzg9jr2lQha7d/0uEiqmUTM2vNOMDmr/G3GWEQimKCjpBKmtWaDZvCmyShjW8hnrEAsjaQxqNJ3zpSWQHpBhi4slLFYUGFN0OUUgDBsdeXv18Y+M0G3ytb1YgaaywevtuKUJUqSYt6FrI3ntvbiF34hYzxEE6VnAuL7rdZkpz2fwbzprbHq7ZMWihDjQ893AwgXIv980vzYPk4/RERXP3ijYIGrnkKL4sVQTc6sBKMgy5jP6tDH92WPr5aucVTFP1IZ9NHC2i2CZaV6buMB9BwPJ4/WSNRz1C/V+F4MjLFfQ2gmHcwY28QY+2cAVwI4kzG2DsAZ/utRgcpkEZ5CfLs2SYeIqIeyW92pkcTWFa8ROgrh/ii5bfZQxH4pZzElMAoFbNo9oJGv4DF33raZJnxUXWRrve9BVRPI08l/t+aqrzCbgc+cuSioNVHRmnUS5StkbN47hK/f9SyA+D3SqknmmrzOWvCBkwVPgyqeKa90gEZqudNPzhLTSzVY1H+AaKtb/RqIfRLCR2Skco5llBuxLYaZhl4AMi7yiQLXPPBC8H/jCZ84fSFOXjQDb1vSmI5qjURXi8jxffPtR4zJ8YftOzHG3gzgHAD7KRXMHQBqIhpzzt9t+Oj04Y5nJFBvUFMLxY58BiXXw+Y9g1oNH8e2cNeqzfiBFAPUicQR2nIOun1PwU1o29ieC40HxZNJMnnu1Bb0l1zsHihHHlxdTkGViQbMngIgkp5fveMZ7Bkoo1TxcO4PHsZQxUV73sEt//L6WF5Al1MweQrNBsky6yCzj5KKzWRKLQC8Q9Lpof0AUU+Bc55YJ5GEI3zmydfuXIN3Hb9/4KWa4t7Hz58ajFE1CtPbcsg6Fjb6+act/qp+doJRoNBSV0sm0bCdd9S++PnDL+Hvj51j3GbJ/l24ednG4PV4MwqH7duBGw2LhrFGxraw+oo3jdnxR3L7boZQQx1CtJL5DgBjdyZ1wEQjpIfx9Vfej5e6/QSctOJ9YXsfPB4WEwEIunrpHoLWXJT1YnpOOlsyQe6BflMzmrl+gltlCQ0np2Di8R/nxzK39wzhqCv+iOe29eLlnQNY/WoPPnPzU7HtdZRUdXIaj5BzCjzhOsiY2pqNrZ5D9pGaUxjZBJixLRw0oxUDJRd7BkrY5XuJJi69HHtWjbFlMcyZUgj6dG/bOwTbYom0z4N8hc69Gsl1GZRXo/yXDmq4cCzUPlOMDMM2Cpzzp/zK5QVSFfPTnPNbOee7q/3/eISJRkg8cABBExrZKBw3T1RF/vWFncF7SZ5Ce94JcgVJk8e+XYXA3b//WZFvp4eZqIs3SiwVz68cVY/pDCOnAIRx5C17h2JFWX9asy22vY5uqDJ0vn7+4Vj772qd4tiiRZLPrjXco/ZWBoAWn+EjM9Cq1SlUw1d9mYMHn9uBbf49oGvRCUS9IZ0xPnhWO57cuBucc2ztGcKMtlziin0//zhqlbYKklsxFcIBcfZSM5lFKepDPTIX8nLi5w0YS1ORsVnwEJhohPLqi1aWcvk/Ucfk/gam4jUgyo9Poi7OaMvhxe5+eB5H2V/pUyz40Nmi2OnWJ18NlD7JEKk5BUeTU1Arn2VMackiYzOsflUYw3lSLPvo/eNFNDpPQa4R+NxZi/APJxwwJuqXSQhZQxVt1z3C/3zwhODvb74jHt+d1SmMaLR4LblOoRqOny9W/39Z1x3UmJj6WMt5mnbNqv3o/buwraeInqEKlm3YlZhPAPD/t3fmcXKVZb7/Pt1VvaTT6eyBLBACJIR9SSTIHoQgiTOIVxxRULnCiKLeUUQGRfFe7pCZO+roMA5mnLku9wrXK+ro6BUXZlSiIIthUxYh4ZrAANmXprfqZ/543/f06eqq6qWWc6r6+X4+9elzqk91/epUn/c57/M+C8cv7KI108Ttbz+l5HFD/2/Fh49x9msyUkSlVv/q7jbgjCNmR+6AYlVSr1uzLNoO0SrxmUIIZ4snLxXLUwA33e7uy7k71MHiC5zh4t284wB9A4NR8S4Yfsf1f33T9yFDNPzrbB7nmkJTkzC3s41fb3F+6tCw/KBpbdy/eeewiptQOIIqPlBdftriwh8wYabE3D6uGGLh87FqySxWLXGDdKHch9ZMM13t2WFhyaPlKYxGW7aZ1kwTdz28NUp0zO9jXYhCbqEF00MBvgO8uKenoOGIIyI8dcvrufDYg0oeFxLcCvX1CDz3ylBo89Vn1WWblUnLhIyCOBbFnvpUhfTUjEysxLNq4dK+8QW+nd19NDfJsLuj/KJpEO+oVqImTN9AyYXmkw9xbqmtu15lV3dfFHkU+MLbTgaGsqODDzjfx5sttqZQImZ87rTWKDnutMNnsWX9Wl53tKtQmp+Tkd+3OvDVK1/DxhtWp66EQCAM8Ad6B0oaZxi64y0WPTWzYyj3BEbPUxgLwQh87qfP0NmWKRm59fBN5/OlK1YUdDGFm4s7H/gDvQOD/MnKQ0YcMxHevMItMIdSJIW4/LRDo+0bfWkPoz6YkFHwGcc/iO1/p2KKakSmaci1MpYp/84DfSPS8POLpsEo7qNYobBihghggV9M3rbrVXbs7xtxp3jRcQczv6uNL/9yC6pDRfbm57kHmse5pgAwr3Pob4SBZpXPug2RU4Fi2cBnLZ0TueaWqES5AAAaRklEQVTSSDwTebQ1hfAZi7ncpk/JDuuoV85Cc+Azbzkh2p4zSj2gmR0tRcsrh9d+88GttGaaIuNeLquWuJuFxbOL99Oe2dHC7W8/hY/EZttGfVCO++hhEVlZMSU1JtM85FoZy5S/kFGA4UXToLRRCDOF/T0DJe8o53W2kmkStu3u5qW9PcwtkEj0gl+E/NWzO3hxd+EFyfw1BVXX97VUIlF4r+lTstGsaK43FC/lJSuVygZOM/FMZKX0DUHIfC12yKyOlmG1qMrJUwi89vDZLPFd2koNvKMxu9PdTPTlBjn9iNk1LTUCcOGxB0WFII36oRyjcCrwKxF5VkQeFZHHROTRSgmrNtkmidxHpTqvvTuWTVpoGp/f9nC0PAVwSUml7igzzU0c1NXGky/uY1d3P4fOHJm89P7V7mK77Ev388+btgEwvyvPKOStKQy1AC3+tQdDEPcDh9j2/AzWoYXmon8ulYQ1Bdc5r/QNQfQZi3zIudPahnVMGy0ZbrzMn156cbgU8UCJsIBtGKNRjlFYAxwOrAbeAKzzP+uC5jz3UbGB4WNrh/yhhRKz2lua6Y6FbwZDU6j+f/D5H+jNkSvSwyGwYHo7P33ShaMuKmAUrl09dAf2o9++xNTWzLBGLE7D8DWFaBG8xJrCn561hI9eeNSw0gph9nD9Nx+NXFVA1EynVBRKGolmCr25UV2HIdSzWAXPWR0t7Oruixbhyw1JDXz20hMBuOrMyizSmlEwxsq4r2YRCcVC9hV51AWZWNtIF4FS+Lj4wF2ohMOUWHYsDHVFK5SFGgaj/b395AYHS1aZDOsKMFSyIE5rpnnYLGZ/78AII5O/plDKtRWYO62Na845fHjd/Ewzl5zkygFsfGZ79HwwgPVmFMJAP5aZwqcvPYGPrFkWZRvn09WeRdVVLP3Z06/42kflazxh0XS2rF/LobMm7j4CFxa8dN5Uji+i3zDymcjV/HX/8yFcZnM8q/nBCumqOtlmdxetqugY/cCFwhIf37aXe558ObpT7O4bQKRwMasQEvi9R17kGw9uLdiTNzo2ZlRe63sx5PPxdUeX1JuNRVgB0axhItml631Vztt/9mz0XKi7k7aWhqMRFvxf7cuNGi00t7ON9517RNH/j2mxGkOh9WiaErWuXX0kP/qzs8nUmeE2kmMiGc3r/M/DVHWJ/xkedROQ3Nwk5HJasp5+4ETf/ej+zfnN4obY7xebD/Tm6GjJFBwYwmD0/cdeHPG7Ee9ZIFmsEI/dfAEXHnMQ9984smxUe94sZiwzhWK0ZJpYOm8qz20/EGUyh5lWLSugVoL4TMGdkokP4mEWFy85Xgn3kWEkRVnVy0RkBnAkEK2G+VabqaejJUN3fy7KGC51HednBcd540kL+PZvtrH7QD/T2rKuGUuRSqHFni/ExScu4ORDZkSRP8XobMty++WFM1CntmaGdXCLattP0L+x9rj5PP3S02zefoAlc6bWrfuoNdNEk4SExPLcPSEXI17qot4W3g0jzoSvZhF5N/Bz4G5c8trduL7NdUFXe5bcoEaZyqUu5LAwu7hACeO1xx0MwO5XXQz/gb7iLQ/zQwLPLdGUW0Q4dFbHqK0lS5FtFvb2DER39sUyn8fKuUc5vU+/5AbAUN6j3txHIhKVzx4cLC+kNpSwHmYUzCoYdUw5V/MHgZXA86p6LnASsLsiqmpA8PmHO+lSF3JwB9x6yfEjfheajocEpu6+XBTyOBrF7vArxT0+eik05Sl3phAS0kJV2KGZQv0NglNaXaOdYq1Yx0qYKcTLRFtFUKOeKcco9KhqD4CItKrqk0DdpC+GwnahB3OpxJ5SyV6RUfClJvb3Doy5p0C1k4lC7aJ9vU5bqbLeY2FmRwtt2aao9k1fnbqPYKjRTrkhpNPbR9YlSlvvAMMYD+VczVtFZDrwHeDHIvLPwPOVkVV9QuXOUDeolAskFAhbOGNk6YYuPyjs8XWBuvsGSjYDP9kvIJdqUFIpQpnt8BlD9NFEBy0R4dCZHdEMpH+gPheaYWgRPmRlT5RCUWa9A6VLTxtGmpnwQrOqvtFv3iwi/wp0AT+siKoaEJKRbvzWYwC0lTAK7zp9MZecvIDpBapVBvfBzgPefdSbY8qs4jOAb733dHr6czW5uw7JbKGLWznRR4FFM9v5ye9e5tLbf8XKw2aQaZK69KGHmUJXe5YJLrEAhcNPe/N6URhGPVGRkUlVf6aq31XVvtGPTgdhkXSL70xVqua/iBQ0COBmGDM7Wnhpn8s5KLXQHGjLNtfExRAMX/is5a4pAFx/4VEA/HrLTvpzWpeuI/BrCn25ktVqx8odV60atn+qLyBoGPVIfV7RFSC/s1ipLlKjMWNKlj1+oflAby7KR0ialmb3mYJRqMRMYem8Tq4553CyzULfQOms7DTzb0+9wqNb9zCQK101dizMiTWy3/SJ8zm9SLKhYdQDk9YonLRoeHJYOd3BprVn2dvTj6q6mcI48hGqSTbjBruwIDyW2kdjoas9S39O2ftqPy01rrxZaR7YsnNEI6LxEi9aV2xGaRj1wqQ1CkfO6+SEhV1RfkKhBcOx0tmWZW/PAD39g6gWLpyXBGEBOISODpSZpxAIjeRf2d9LS53OFD50/lIAXt7Xy/PehThRprRkuO2yk/jetWdUQpphJEo6Rq+EmNqWiUoj53ctGw/T2jJs3dkd5TykZ6YwfE2hVKvQ8RAW17fuejWq/VNvFOpUVg7rjp9f0b9nGEkxaWcKABt/vyPaHq2cRCncTKE/6tU82kJzrQgzhd5oobm8kNRAiGr6/zu769YoxFuFvuEEG9ANIzCpjUKcQuWpx8q09gx7ewaistlpmSnku48qPVPIDWpq+zCPxu5Yv+mzjrSFYcMITGqjcH6st205sfadrRn6Bga56PO/ANKzptDUJGSaZERIarkzhbghCOsL9cZ5y4e++1lTbXHYMAKT2iisOcZlKpcTjgrw6NY9w/bTMlMAl0cRrSmUWeYiEDcE9TpTmDFlSHdbnUdQGUYlmdRGISR3lbPIDHCBNy6BNN09Z5ubopDUSs0U4usICwqU/qgH4pnIxy20rmSGEUidURCRLSLymIhsEpGqdnILRqGzzGSzN528YNh+mhZfO9syI8pcFOofPR7iRuXQAv2j641StaoMY7KROqPgOVdVT1TVFdV8k1AEr2tKeYO4iPD4p9ZE+2maKcye2sr2/b0A5LQyM4U4i2fXv1FIU/tMw0iaSX2LFO6cy4k8CkxtzfAPV6zg7if+vaxEuEozY0qW7ftdpE2ujB7NxQiVWOuRP1m5KCqdbhiGI41GQYEfiYgCX1TVDfFfisjVwNUAhxxySFlv1NPvBslK9TU4/+h5wyKa0kBbtpneARcqO1ChhWaA1x4+i/ue21FWeZCkWf+mkU2TDGOyk0ajcIaqbhORubg+DU/G+z57I7EBYMWKFWUVrQmhiMcumFbOn0k1rfHoowrVPgL4el5lUMMwGoPUGQVV3eZ/viwi3wZeg+sFXXFWLZnFnVevYuXimdX486mgNdMcy2iu/JqCYRiNRXqc34CIdIhIZ9gGLgAer+Z7rloyq6EHydZsU2QUKhV9ZBhG45K2mcI84Ns+GiQDfF1V66abWxppzTRFncCimYJF2xiGUYRUGQVVfQ44IWkdjURLZmimEBacW1MUHWUYRrqw0aHBac00MzCoDOQGY9FW9rUbhlEYGx0anKhPc26Q3v4crZkmS9YyDKMoZhQanCm+hMP+ngF6vFEwDMMoho0QDc5M3zN4V3c/Pf2DdZ1sZhhG9TGj0ODM6HAlPHYe6KNnIGdGwTCMkphRaHBmdriZws4DffT058ruHWEYRmNjRqHBCb2nt+7qdu6jFjMKhmEUx4xCgzOzo4XWTBM7DvTR3TdAu+UoGIZRAhshJgG9A4Ns+PlzvLyvl84U9XowDCN9mFGYRDy/o3tYb2LDMIx8zChMMiz6yDCMUphRmAScethQaXAzCoZhlMKMwiTglouPjbbbLKPZMIwS2AgxCZgW60HdajMFwzBKYEZhEjAllpvwvUdeSFCJYRhpx4zCJCCexXze8rkJKjEMI+2YUZgEZJqHvubXLZ+XoBLDMNJOqjqvGdVjwfR2jp4/jZMOmZG0FMMwUowZhUnCxhtWJy3BMIw6wNxHhmEYRoQZBcMwDCPCjIJhGIYRYUbBMAzDiDCjYBiGYUSYUTAMwzAizCgYhmEYEaKqSWuYMCLyCvD8BF8+G9heQTmVJs36TNvESLM2SLc+01bZ9zxUVecU+kVdG4VyEJEHVXVF0jqKkWZ9pm1ipFkbpFufaavde5r7yDAMw4gwo2AYhmFETGajsCFpAaOQZn2mbWKkWRukW59pq9F7Tto1BcMwDGMkk3mmYBiGYeRhRsEwDMOIMKNgjEBEJGkNhmEkgxmFhBCR1SLSkbSOImTDRhoNhIjMFpFmv50qfSLylyJypt9OlbY0IyKXiEhq2wKKSGdsu6G/14Y0CiJylYh8QUQOT1pLPiLyNhF5CDgX6E9aTxwReavX9t9F5IMAmqJIBH/uNgF/DXwJ0qPPa7sHuMo/0qTt3SLyjWCs0oSIvF1E7gPOAHqS1pOPiFwqIk8A60Xkr6D632vi45eqNsQDEKAZeAvwDPBz4DKgLSXassB1wC5gVdKaCmhcAfwSWIVLoX8EuDLoT1hbBrgW+Blu8GjGlTc5LQXnbQpwK/AT4DTgbOB/AFOT1ub1rQF+C9wFXA/MSPo79ddDE/AuIAecmvR5KqJzGfAL4DV+/17gg1U8J6kYvxpipiAiberIAQ8DpwJ/D5wFLE+Jtn7gaeB/A8+LSIuIvElE5iepLba7HPipqt6nqttxOv9CRLrU/9cmhaoOAN9X1bNV9V5gEfAg8EqSugBUtRv4qqq+TlV/BcwAzlbV/UlpEpHW2O5DwHnAbcBCnNEiqe9URFr99TAI/Br4P0CviDSJyDtEJOnrNX7uFuJujh73+/8A3CQiJ1X4PVM1ftW9URCRm4Afisj7ReQYVX1GVXcC38RZ3zOT8lXGtH1ARJYC/w/4g//5MPBG4Csi8jF/fM2+jzxti4CngNeLyNH+kEFgL/Bntdbm3+9TIrI27KvqZv/8SuBbQCvOaH2i1voKaPtdbPs7QHtSrhoR+XPgW/57PUZVt6vqi7hZ1jZghYgs9sfW1Deep225qj4B/Aj4F9zgexrwTyJyqz++1v9zcX2LgReAxcD5/lx1Ac/irtuK6Evj+FXXRkFErsTdBX0UmIPzhS8G8HfmdwGnACfnva7qF0Oettk4l8Js4Lu4C+FCVX07btC9TkRm+bunqlNA223A73CD7fV+XWEubvq6TkQ6aqhtpohsAD6AG/QzeYdsBc5T1XU4d8j7RWR+LfSNQRsiMg24B5hXbT1573uYX9M4Brfmsgx4Z1gg9efnJ0An8Dr/XE1mC0W0XSkiU4C7gS8CF6vqe4DLve6afKcl9F3rjf33gItwrtWlwNXApSIyvVx9qR2/au2vqtQDZ0U/CVzq96cCtwB35B33KeC/AGuB9yWo7S9wbgbI8xMCdwDLEtS2HvhHv98FnOi3FwOfx62H1MQHjZsBXOS3fwB81G83FTn+LuD0hLVJ3nF3Ah/w28010tYFfCi2vwbnAmyL6wPeAXwCeCdwQ4Lavg604Pzo2QLfac3Wi4rou5OhtY8scFT4PwRuB2aWc02kefyq25mC+jMGXOH39wOfAw4XkXNih/4QuBHnD2xJUNtngeUico6q9gCISFZE/haYxsT7QlRC26eB40VktaruUdVNItIC3ATkVLU/9rpq6+vFLbKBu2iuEpGDVXUwfockIm0i8lmcD/+JhLWpOJr9774PvNW/JldtXSIiqroH9z8eeAK39pIN+vzzv8EZhvXV1jWKtoVAq6rm1N0VIyLtIvI3uAH3twnrm48LFhj0//9PishU3KxmiqruLOeaSPP4VRdGQUSWxf13sX/w9cASETnL728H/hdwgT9uDvBXuCngEar62YS1fS2m7WLclDQHvDkYioS1neePOxnnAgH480rrKqYvoKr7/cX6AM4X/t/882Hw/SP/PMA6Vd2dBm0xA/DvwN9Vyyeery0MMKq6L3bYqcAfwnP+3LUAf4OLqFmiqhU3DBPR5l93LvBTv7vWD9QVpwx9h+HGEYA/Hed7rhaRg2L7qRm/ClKL6chEH8D5wP04f3wm9ryEfeB9wP2x370P+LDfbgVmpUzbdX77KFz3ozRqmwXMS+h7bfLbQecc4EngSNxC5HG4O8lFKdO2Cu/yoEouo3Fq+xBwvd8+DTjGb3elTNtrgcNwM74FKfmfy9e3zG+Payzxr30C+CpuUB/rdVj18auk7lq/4RhOZIjp/6+4eN1L8n7fHNs+2P+8B2d1z8AtXH0kxdquT7G2qpy3iejDTdHD/udw0VCbgJWmraS2qX778zgf9QZcYMPRKdX242poS/rc4dZK/idwWd7zTfH39D9rdh2OSXuSbz7KSb0ZuDW2f6b/goNV/zTO/bIYWAK8Bzed/5hpS6e2cej7ObDSX9TrgM1UyZg2oLZTcBFGL+DCjKuSbFUv2pLSh5v5fBmYjlsLuBw4Ah9kAnwmyeuw1CM1/RRE5AM418ADqrrB++CCz/MUYAsuG/jHuMiPTwA3q+qu2N9oUdU+05YObZXQJyJHAi9rFXzMDa7tGuAbqrpjMmlLSl/sPe9T1X8UkVnAt3HBGh8CegEFdvv9W4FP1uo6HBdJWyVvlN4J3AdciLOWH8dZ2ItxYXVH4e7M/hj3Jc6PvbaqIX+mLTF9GdM2IW0tk1VbUvqKvGc7zhD9nqGw005gB3BC7LU1CVke1+dJWoA/MV8D3ui3V+AiOm7w+x2x45b4L3a+36967Lxpa0x9pq3xtCWlr8B73gJ8GOc2egn4z7Fj/xa4oJbnZLyPRENSY6Fhv8H5Z1HVB4GNwGEicrqqHoi95AqcBd7pj62a78u0NaY+09Z42pLSV+I978VlRx8MfARYIyJvEJGPA6fjczCqfU4mSq1riwyrga9DaeIbgaZYvO7jwIu4BBLEFY57BGfdr9HqxPSbtgbUZ9oaT1tS+sb5nn8ATlHVr+IyoM8ADsHl1Wwd/yeuHSNqt1QDETkNuBJ4SkS+qD4pREQy6ipgPoOL532LiGxU1a0iMg/o9n/iaeA96qpQmrYUaEu7PtPWeNqS0jfB95yLy19BVe8RkX/TGtVyKpeqzxRE5GxcwbV7cNb6RhG5AKKSyAD7cFmWrcBfi0gWF9K13R/3WJUuANPWgPpMW+NpS0pfme8ZlXavF4MAtXEfnQJsVNU7cAsw84C3euuNiNyCK461Bxe+NQN3gvcAXzFtqdSWdn2mrfG0JaUv7eek4lTcfSQiq4Cdqvq0f+op4ERxpXBfEJH9uDIKF4vIv+J8ezeo6rP+9VfiogT2Ffr7pq322tKuz7Q1nrak9KX9nNSCis0URGS6iHwflxByqbiKguB8eHuBL4vIXbjKjZuATlV9WlUvU9Vnxa/kq6tKWNETatoaU59pazxtSelL+zmpJRXLaBaRBcCbcCdwGfALVf2B/10LLhRrnqreKSIXAe9V1ygFEWmqps/NtDWmPtPWeNqS0pf2c1JLyjIKInIFrg/Ab1R1r7iev0242FwBNqjqCwVe93Fgt6reNuE3N21VI836TFvjaUtKX9rPSVKM230kjoO9P+0dwNuAvxeR2arao66R+U9wCy6r8157hrhWj2fi+rJWFNPWmPpMW+NpS0pf2s9JGhiXURCRZnVTi05gm6qeB1yDywrcEI5T1Y24olNHiUiXiHT4Xz0H3KSqa1R1SwX0m7YG12faGk9bUvrSfk5Sg46ttkczrsfwXwJnA28AvhL7fROu29TZseem4ro8/RpX/2PhWN5rvA/T1pj6TFvjaUtKX9rPSdoeo84UxCVvPISbTv0eV2CqHzhXRF4DUWLGzf4RWAu8F3gEOE6rkNpt2hpTn2lrPG1J6Uv7OUklY7CyZwKXx/a/gJtyvRN4KGZpDwK+ASz2z/0xcFY1LZppa0x9pq3xtCWlL+3nJI2PsZzUKbj07Wa//zZ8FyNcvO77/fYK4I6aijdtDanPtDWetqT0pf2cpPExqvtIVbtVtVdVc/6p8xmq6fEuYLmI/AtwB/AwuBX+0f5uJTBtjanPtDWetqT0pf2cpJExl7kQVzZWcbU/vuuf3gfcCBwLbFbVbVD7OuGmrTH1mbbG05aUvrSfkzQxnpDUQVyz6+3A8d663gQMquq94YQmhGmbOGnWZ9oaTxskoy/t5yQ9jMfXBKzCndx7ibWYS8PDtDWmPtPWeNqS0pf2c5KWx7jKXIjIQuBy4DOq2jse41NtTNvESbM+0zYx0qwNktGX9nOSFipWEM8wDMOof2rao9kwDMNIN2YUDMMwjAgzCoZhGEaEGQXDMAwjwoyCYRiGEWFGwTDGgYjkRGSTiDwhIo+IyIfF9+ct8ZrFInJZrTQaRjmYUTCM8fGqqp6oqsfg6ui8HvjkKK9ZDJhRMOoCy1MwjHEgIvtVdWpsfwnwADAbOBT4GhA6dV2rqr8UkfuA5cBm4CvA54H1wDm4Cp5/p6pfrNmHMIwSmFEwjHGQbxT8c7uBZbgCa4Oq2iMiR+JKMa8QkXOA61R1nT/+amCuqt4iIq3ARuDNqrq5ph/GMAow5iqphmGMSha4TUROBHLA0iLHXYAryvaf/H4XcCRuJmEYiWJGwTDKwLuPcsDLuLWFl4ATcOt1PcVehmvucndNRBrGOLCFZsOYICIyB7gduE2dH7YLeFFdz9/LcQ3jwbmVOmMvvRu4RkSy/u8sFZEODCMF2EzBMMZHu4hswrmKBnALy5/xv/sCcJeIXAH8EDjgn38UyInII8CXgc/hIpIe9l2+XgEurtUHMIxS2EKzYRiGEWHuI8MwDCPCjIJhGIYRYUbBMAzDiDCjYBiGYUSYUTAMwzAizCgYhmEYEWYUDMMwjIj/AEtoMV1HO371AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SENSOR.ts['data'].plot(legend=True, label=observed_property_name)\n",
    "plt.ylabel('{} ({})'.format(observed_property_name, observed_property_uom))\n",
    "plt.xlabel('Date')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality check functions\n",
    "\n",
    "[Click me](./qc_aws.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⚒ define the functions for quality checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "range_values = [-80, 60]\n",
    "\n",
    "# verify that data are within a plausible range for the observed variable\n",
    "# ranege_values: a list of 2 values that defines the interval\n",
    "def plausible_value_check( x, range_values):\n",
    "    if x >= range_values[0] and x <= range_values[1]:\n",
    "        return 300\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "# verify that data changes in consecutive steps are within 4 sigma\n",
    "# x: a list of 3 consecutive values and the central value is the one \n",
    "# to be evaluated\n",
    "def time_consistency_check(x):\n",
    "    if len(x) == 3:\n",
    "        sum_abs_val = abs(x[1] - x[0]) + abs(x[1] - x[2])\n",
    "        four_std = 4*stdev(x)\n",
    "        if sum_abs_val <= four_std:\n",
    "            return 400\n",
    "        else:\n",
    "            return False\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⚒ apply the functions on a moving windows along the series and then update the series with the new indexes\n",
    "\n",
    "- rolling(T) applies a moving windows of T width\n",
    "- apply(X) applies to the moving window the function X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data checked.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    ts_tmp = SENSOR.ts['data'].rolling(1).apply(lambda x: plausible_value_check(x, range_values), raw=True)\n",
    "    SENSOR.ts['quality'].update(ts_tmp.where(lambda x : x>0))\n",
    "    ts_tmp2 = SENSOR.ts['data'].rolling('1440s').apply(lambda x: time_consistency_check(x), raw=True)\n",
    "    SENSOR.ts['quality'].update(ts_tmp2.where(lambda x : x>0))\n",
    "    print(\"Data checked.\")\n",
    "except:\n",
    "    print(\"Can't check data.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⚒ Filter values with QI == 400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:40:00+01:00</th>\n",
       "      <td>10.20</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:50:00+01:00</th>\n",
       "      <td>10.04</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-01 00:50:00+01:00</th>\n",
       "      <td>13.83</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-01 01:00:00+01:00</th>\n",
       "      <td>13.39</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4442 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            data  quality\n",
       "time                                     \n",
       "2019-05-01 00:40:00+01:00  10.20      400\n",
       "2019-05-01 00:50:00+01:00  10.04      400\n",
       "...                          ...      ...\n",
       "2019-06-01 00:50:00+01:00  13.83      400\n",
       "2019-06-01 01:00:00+01:00  13.39      400\n",
       "\n",
       "[4442 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SENSOR_400 = SENSOR.copy()\n",
    "SENSOR_400.ts = SENSOR.ts[SENSOR.ts['quality'] == 400]\n",
    "\n",
    "SENSOR_400.ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⚒ Calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/maxi/GIT/istSOS_2.4.0_workshop_mexico_2020/venv/lib/python3.8/site-packages/pandas/core/indexing.py:844: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/home/maxi/GIT/istSOS_2.4.0_workshop_mexico_2020/venv/lib/python3.8/site-packages/pandas/core/indexing.py:965: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "MAX_COL = 'T_MAX'\n",
    "TIME_MAX_COL = 'TIME_' + MAX_COL\n",
    "MIN_COL = 'T_MIN'\n",
    "TIME_MIN_COL = 'TIME_' + MIN_COL\n",
    "MEAN_COL = 'T_MEAN'\n",
    "COUNT = '{}_COUNT'.format(MEAN_COL)\n",
    "\n",
    "daily_max = oat_utils.sensorStats(\n",
    "    SENSOR_400, \n",
    "    frequency= 'D',\n",
    "    stat='max',\n",
    "    column_name=MAX_COL\n",
    ")\n",
    "daily_min = oat_utils.sensorStats(\n",
    "    SENSOR_400, \n",
    "    frequency= 'D',\n",
    "    stat='min',\n",
    "    column_name=MIN_COL\n",
    ")\n",
    "daily_mean = oat_utils.sensorStats(\n",
    "    SENSOR_400,\n",
    "    frequency= 'D',\n",
    "    stat='mean',\n",
    "    column_name=MEAN_COL\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name: T_CAV\n",
       "desc: Cavergno\n",
       "lat: 133073.0\n",
       "lon: 690081.0\n",
       "alt: 455.0\n",
       "tz: +01:00\n",
       "unit: b'\\xc2\\xb0C'\n",
       "prop: temperature\n",
       "freq: D\n",
       "statflag: None\n",
       "use: True\n",
       "data_availability: ['2019-04-30T23:20:00.000000000', '2019-06-01T00:00:00.000000000']\n",
       "ts:                            T_MAX quality                 TIME_T_MAX\n",
       "2019-05-01 00:00:00+01:00  23.78     400  2019-05-01 14:50:00+01:00\n",
       "2019-05-02 00:00:00+01:00  19.09     400  2019-05-02 11:00:00+01:00\n",
       "...                          ...     ...                        ...\n",
       "2019-05-31 00:00:00+01:00  23.18     400  2019-05-31 16:20:00+01:00\n",
       "2019-06-01 00:00:00+01:00  13.92     400  2019-06-01 00:10:00+01:00\n",
       "\n",
       "[32 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name: T_CAV\n",
       "desc: Cavergno\n",
       "lat: 133073.0\n",
       "lon: 690081.0\n",
       "alt: 455.0\n",
       "tz: +01:00\n",
       "unit: b'\\xc2\\xb0C'\n",
       "prop: temperature\n",
       "freq: D\n",
       "statflag: None\n",
       "use: True\n",
       "data_availability: ['2019-04-30T23:20:00.000000000', '2019-06-01T00:00:00.000000000']\n",
       "ts:                             T_MIN quality                 TIME_T_MIN\n",
       "2019-05-01 00:00:00+01:00   4.744     400  2019-05-01 05:30:00+01:00\n",
       "2019-05-02 00:00:00+01:00   6.632     400  2019-05-02 23:50:00+01:00\n",
       "...                           ...     ...                        ...\n",
       "2019-05-31 00:00:00+01:00   9.450     400  2019-05-31 05:10:00+01:00\n",
       "2019-06-01 00:00:00+01:00  13.230     400  2019-06-01 00:20:00+01:00\n",
       "\n",
       "[32 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_min"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⚒ calculate completness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name: T_CAV\n",
       "desc: Cavergno\n",
       "lat: 133073.0\n",
       "lon: 690081.0\n",
       "alt: 455.0\n",
       "tz: +01:00\n",
       "unit: b'\\xc2\\xb0C'\n",
       "prop: temperature\n",
       "freq: D\n",
       "statflag: None\n",
       "use: True\n",
       "data_availability: ['2019-04-30T23:20:00.000000000', '2019-06-01T00:00:00.000000000']\n",
       "ts:                               T_MEAN  T_MEAN_COUNT  Completeness (%)\n",
       "time                                                                \n",
       "2019-05-01 00:00:00+01:00  13.659429           140         97.222222\n",
       "2019-05-02 00:00:00+01:00  11.044090           144        100.000000\n",
       "...                              ...           ...               ...\n",
       "2019-05-31 00:00:00+01:00  16.288194           144        100.000000\n",
       "2019-06-01 00:00:00+01:00  13.677143             7          4.861111\n",
       "\n",
       "[32 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_mean.ts['Completeness (%)'] = daily_mean.ts['T_MEAN_COUNT'] / (6*24)*100\n",
    "daily_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "⚒  plot the series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f13bbf20d60>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_mean.ts['T_MEAN'].plot(legend=True, label='T_MEAN (celsius)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f13bc5a6c40>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_mean.ts['Completeness (%)'].plot(legend=True, label='Completeness (%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}