{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fcd37679-9b6c-4750-bae9-f6b34c73ab69",
   "metadata": {},
   "source": [
    "# Pyaro and PyAerocom "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ecb93e04-d5d9-466a-8911-ad9e417673e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking access to: /lustre/storeB/project\n",
      "Checking access to: /lustre/storeB/project/aerocom\n",
      "Checking access to: /lustre/storeB/project/aerocom/aerocom1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/danielh/miniconda3/envs/pya/lib/python3.11/site-packages/geonum/__init__.py:26: UserWarning: Plotting of maps etc. is deactivated, please install Basemap\n",
      "  warn('Plotting of maps etc. is deactivated, please install Basemap')\n"
     ]
    }
   ],
   "source": [
    "import pyaerocom as pya\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dea14546-5ee7-45ca-966e-61970e7cdab6",
   "metadata": {},
   "source": [
    "## PyaroConfig\n",
    "\n",
    "Pyaro can be used with PyAerocom through the class called **PyaroConfig**. This can be thought of as a dictionary that enforeces you to give the correct inputs. We start by finding the available engines with pyaro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f114dcb-6b6e-4109-8f57-139e93905246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'aeronetsdareader': <pyaro_readers.aeronetsdareader.AeronetSdaTimeseriesReader.AeronetSdaTimeseriesEngine at 0x7f60204ec450>,\n",
       " 'aeronetsunreader': <pyaro_readers.aeronetsunreader.AeronetSunTimeseriesReader.AeronetSunTimeseriesEngine at 0x7f60209d3ad0>,\n",
       " 'csv_timeseries': <pyaro.csvreader.CSVTimeseriesReader.CSVTimeseriesEngine at 0x7f6020705dd0>}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyaro\n",
    "\n",
    "pyaro.list_timeseries_engines()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f39031-7943-40ac-8e79-da79ce688259",
   "metadata": {},
   "source": [
    "We want to read aeronetsun data. We can now make the config with PyaroConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "10692a2e-0669-40b4-8a56-29494584348a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyaerocom.io.pyaro.pyaro_config import PyaroConfig\n",
    "\n",
    "data_id = \"aeronetsunreader\"\n",
    "url = \"https://pyaerocom.met.no/pyaro-suppl/testdata/aeronetsun_testdata.csv\"\n",
    "filters = {\"variables\": {\"include\": [\"AOD_550nm\"]}}\n",
    "name_map = {\"AOD_550nm\": \"od550aer\"}\n",
    "\n",
    "\n",
    "config = PyaroConfig(\n",
    "    data_id=data_id,\n",
    "    filename_or_obj_or_url=url,\n",
    "    filters=filters,\n",
    "    name_map=name_map,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9093ae7-f51a-4713-9750-6e291e63a7a3",
   "metadata": {},
   "source": [
    "The config contains four fields:\n",
    "\n",
    "1. data_id: The name of the reader/data. This name must be one of the names allowed by Pyaro\n",
    "2. filename_or_obj_or_url: The path to the dataset, that being a path or an url\n",
    "3. filters: Dictionary of filters\n",
    "4. name_map: A dictionary which gives new names to variables found in the data set. On the form ```{old_name: new_name}```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60a56266-3065-4b28-93d5-595a6927b316",
   "metadata": {},
   "source": [
    "## Filters\n",
    "\n",
    "Filters are objects that can be used to read a subset of data. In the above example we've used a filter to filter out all variables that are not AOD 550nm. Different engines have different filters, so to find out which filters are available use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dbcdf95c-c6c6-4b41-b309-8f0c340cf2b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[VariableNameFilter(**{'reader_to_new': {}, 'include': [], 'exclude': []}),\n",
       " StationFilter(**{'include': [], 'exclude': []}),\n",
       " CountryFilter(**{'include': [], 'exclude': []}),\n",
       " BoundingBoxFilter(**{'include': [], 'exclude': []}),\n",
       " TimeBoundsFilter(**{'start_include': [], 'start_exclude': [], 'startend_include': [], 'startend_exclude': [], 'end_include': [], 'end_exclude': []}),\n",
       " FlagFilter(**{'include': [], 'exclude': []})]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine = pyaro.list_timeseries_engines()[data_id] # Finds the engine associated with aeronetsunreader\n",
    "engine.supported_filters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbab2a1d-efcb-49af-83f9-580ca26b74c2",
   "metadata": {},
   "source": [
    "Here is a list of the available filters. The name of the filter, is given as the first part of the names in the list above. The filters are written as dictionaries, with the name as the key and another dictionary as the value. There we define how the filter is used. In our case ```\"variables```and ```{include: [\"AOD_550nm\"]}```."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd7a24a2-6dcd-41e9-ad4a-0858469712f8",
   "metadata": {},
   "source": [
    "## Passing the config to PyAerocom\n",
    "\n",
    "We can now pass this config into ```ReadUngridded```to read the data. For this we use the new argument ```config```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "98be1b65-2350-45f3-a067-69111c6433f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████| 9993/9993 [00:00<00:00, 34798.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pyaerocom.io.read_aeronet_invv3.ReadAeronetInvV3'> ['AeronetInvV3Lev2.daily', 'AeronetInvV3Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_invv2.ReadAeronetInvV2'> ['AeronetInvV2Lev2.daily', 'AeronetInvV2Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav2.ReadAeronetSdaV2'> ['AeronetSDAV2Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav3.ReadAeronetSdaV3'> ['AeronetSDAV3Lev1.5.daily', 'AeronetSDAV3Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv2.ReadAeronetSunV2'> ['AeronetSunV2Lev2.daily', 'AeronetSunV2Lev2.AP']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv3.ReadAeronetSunV3'> ['AeronetSunV3Lev1.5.daily', 'AeronetSunV3Lev1.5.AP', 'AeronetSunV3Lev2.daily', 'AeronetSunV3Lev2.AP']\n",
      "<class 'pyaerocom.io.read_earlinet.ReadEarlinet'> ['EARLINET']\n",
      "<class 'pyaerocom.io.read_ebas.ReadEbas'> ['EBASMC']\n",
      "<class 'pyaerocom.io.read_aasetal.ReadAasEtal'> ['GAWTADsubsetAasEtAl']\n",
      "<class 'pyaerocom.io.read_airnow.ReadAirNow'> ['AirNow']\n",
      "<class 'pyaerocom.io.read_eea_aqerep.ReadEEAAQEREP'> ['EEAAQeRep.NRT']\n",
      "<class 'pyaerocom.io.read_eea_aqerep_v2.ReadEEAAQEREP_V2'> ['EEAAQeRep.v2']\n",
      "<class 'pyaerocom.io.pyaro.read_pyaro.ReadPyaro'> ['aeronetsdareader', 'aeronetsunreader', 'csv_timeseries']\n",
      "<class 'pyaerocom.plugins.gaw.reader.ReadGAW'> ['DMS_AMS_CVO']\n",
      "<class 'pyaerocom.plugins.ghost.reader.ReadGhost'> ['GHOST.EEA.monthly', 'GHOST.EEA.hourly', 'GHOST.EEA.daily', 'GHOST.EBAS.monthly', 'GHOST.EBAS.hourly', 'GHOST.EBAS.daily']\n",
      "<class 'pyaerocom.plugins.mep.reader.ReadMEP'> ['MEP']\n",
      "Successfully loaded cache file /home/danielh/MyPyaerocom/_cache/danielh/aeronetsunreader_od550aer.pkl\n",
      "Successfully imported aeronetsunreader data\n"
     ]
    }
   ],
   "source": [
    "rp = pya.io.ReadUngridded(config=config)\n",
    "\n",
    "data = rp.read(vars_to_retrieve=[\"od550aer\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be29f612-7e1d-45a5-9c82-daf658d9ab61",
   "metadata": {},
   "source": [
    "We can now look at the data for a station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "176c663a-100e-49a1-8c97-8ca568c9dd2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input time frequency daily equals current frequency of data. Resampling will be applied anyways which will introduce NaN values at missing time stamps\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Alta_Floresta'}, ylabel='od550aer'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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SX49ddtlF48eP19/+9res87bZZhtdccUVmj59unbZZRd97WtfUyQS0X333aedd97Zq6Is5XH2xXe+8x3dd999WrFihS655BJFIvn/6XryySc1cuTIjPcUAAAAAAAYZFz0WVtbmyvJbWtr6/FyHR0d7ttvv+12dHRU6Mhq0+zZs9299trLHTJkiDtkyBB3r732cmfPnp3zsrFYzL3mmmvc8ePHu/X19e748ePdn/3sZ+57773nSnLPOOMM77JtbW3uOeec4+6xxx7uqFGj3Egk4ra0tLgHHHCAe+ONN7qxWCzr9m+66Sb3iCOOcDfffHO3sbHRbWxsdLfffnv3Bz/4gbt06dK8j2HBggXuCSec4G644YZuQ0ODO2HCBPdXv/qVG4/Hsy67YsUK96yzznI33XRTt7Gx0d15553d//f//p/7/vvvZz0G4/PPP3cvu+wyd+LEiW5TU5M7bNgwd9ttt3VPPfVU9/7778+47JZbbuluueWWeY+1J9dcc40ryf3Xv/6V8/ybbrrJ3Wmnndz6+np38803dy+66CK3vb3dleROnjy5pMc5efJkN/hPz9NPP+1KcqdPn57zeBKJhLvZZpu5juO4CxcuzPu4Fi9e7DqO415wwQW9PgcGn08AAAAAAKqn0HytWI7rum7VElfLrVmzRi0tLWpra8tYxBPU2dmpDz74QFtttZUaGxsreIRAbq2trdp666116qmn6oYbbqj24fRq+fLl2nLLLXXggQdq7ty5eS93xRVX6Oc//7neeecdbb311gXdNp9PAAAAAACqp9B8rVjM9AQGoVGjRumSSy7R7NmztXTp0mofTq+uvfZaxWIxffe73817mdWrV+tXv/qVvve97xUceAIAAACw3/qumC64+1U9/tYn1T4UADWEmZ7AIDVt2jTFYjEtWbJEY8eOrfbhZGlra9MNN9ygDz/8UDfddJMmTJig448/Pu/lFy9erAsuuEDnnXdeBY8SAAAAQLX984PP9OBry7V0VYcOn7BxtQ8HQI0g9AQGqaamJl1xxRXVPoy8Vq1apUsvvVRNTU068MADdeONNyocDue9/G677abddtutcgcIAAAAoCZ0xxKSpFg8UeUjAVBLCD0B1KRx48aJkcMAAAAAemOyTn56AODHTE8AAAAAAGCteKpYgpoJAH6EngAAAAAAwFqJRDLtTJB6AvAh9AQAAAAAANaKJ6j0BJCN0LOCmE8I1B4+lwAAAIDdTHs7lZ4A/Ag9K8BsnI5Go1U+EgBB5nPZ02Z4AAAAALXLtLcDgB+hZwXU1dWpoaFBbW1tVJUBNcR1XbW1tamhoUF1dXXVPhwAAAAAfUClJ4BcItU+gMFi1KhRWrZsmT766CO1tLSorq5OjuNU+7CAQcl1XUWjUbW1tWndunXabLPNqn1IAAAAAPoowUxPADkQelZIc3OzJKm1tVXLli2r8tEAkKSGhgZtttlm3ucTAAAAgH3ibG8HkAOhZwU1NzerublZ0WhU8Xi82ocDDGrhcJiWdgAAAGAAiKeyTiJPAH6EnlVQV1dH2AIAAAAAQBnQ3g4gFxYZAQAAAAAAa5lFRiwOBuBH6AkAAAAAAKyVnulZ5QMBUFMIPQEAAAAAgLW89namegLwIfQEAAAAAADWSre3V/lAANQUQk8AAAAAAGAtFhkByIXQEwAAAAAAWItFRgByIfQEAAAAAADWiieSv7PICIAfoScAAAAAALBWwmWREYBshJ4AAAAAAMBa8VSJJ5WeAPwIPQEAAAAAgLXiLDICkAOhJwAAAAAAsFaCRUYAciD0BAAAAAAA1vIqPat8HABqC6EnAAAAAACwVnqmJ7EngDRCTwAAAAAAYC1megLIhdATAAAAAABYK+5S6Qkgm1Wh50svvaSjjjpKI0eO1NChQzVp0iTNmTOn6NtZu3atpk+frokTJ2rIkCEaMWKEvvCFL2jmzJn9cNQAAAAAAKC/JFKVngz1BOAXqfYBFGrevHk6/PDDVV9fr5NPPlktLS26//77ddppp2nx4sX6yU9+UtDtLFmyRAcffLDef/99HXrooTr66KPV1dWl9957T/fdd5+mT5/ez48EAAAAAACUS5zME0AOVoSesVhMU6dOleM4mj9/vnbffXdJ0vTp07Xvvvtq+vTpOvHEE7Xtttv2eDvxeFwnnHCCli9frqeeekpTpkzJuh8AAAAAAGCPBIuMAORgRXv73LlztWjRIp166qle4ClJw4cP1+WXX65YLKZbb72119u599579dJLL+miiy7KCjwlKRKxIgMGAAAAAAApLDICkIsVKd+8efMkSYcddljWeea0Z555ptfb+eMf/yhJOvHEE7V06VI9/PDDWr16tbbeemsdeeSRGjZsWPkOGgAAAAAA9DsWGQHIxYrQc+HChZKUs3195MiRGjVqlHeZnrz88suSpGeffVbTpk1TV1eXd97o0aN1zz336KCDDirPQQMAAAAAgH5n2tuJPAH4WdHe3tbWJklqaWnJeX5zc7N3mZ6sWLFCknTeeefpggsu0NKlS7Vy5Ur96le/Ultbm4477jh9/PHHea/f1dWlNWvWZPwCAAAAAADVYyo9XSo9AfhYEXqWSyKRkCQdc8wx+vnPf67NN99co0aN0nnnnadp06apra1Nt9xyS97rX3PNNWppafF+jR07tlKHDgAAAAAAcmCmJ4BcrAg9TYVnvmrONWvW5K0CzXU7X/7yl7POO/bYYyWlW+BzufTSS9XW1ub9Wrp0aa/3CQAAAAAA+k+CmZ4AcrAi9DSzPHPN7Vy1apVaW1tzzvsM2n777SVJI0aMyDrPnNbR0ZH3+g0NDWpubs74BQAAAAAAqifOTE8AOVgRek6ePFmS9MQTT2SdZ04zl+nJwQcfLEl6++23s84zp40bN66vhwkAAAAAACosNcmO9nYAGawIPQ855BCNHz9ec+bM0WuvveadvnbtWl111VWKRCI688wzvdNbW1v17rvvqrW1NeN2vvnNb6qhoUG//vWvtWzZsozb+dnPfiZJ+trXvtavjwUAAAAAAJRP3Jd2sswIgGFF6BmJRHTzzTcrkUjowAMP1Le//W1ddNFF2nXXXfXWW29pxowZ2m677bzLX3/99dpxxx11/fXXZ9zOVlttpV/+8pdasWKFdt11V5199tk699xztcsuu+i1117Tt7/9bR1yyCGVfngAAAAAAKCPTHu7JCXIPAGkRKp9AIWaMmWKnn32WU2fPl333HOPuru7NWHCBF111VU67bTTCr6d8847T+PGjdMvf/lL3X333YrFYpowYYJ+8pOf6Oyzz+7HRwAAAAAAAMotkVXp6VTvYADUDMel9rvPzNb4trY2lhoBAAAAAFAFR//q73pr+RpJ0sL/PFJ1YSuaWgGk9Fe+xr8EAAAAAADAWpnt7dR1AUgi9AQAAAAAANbKbG+v4oEAqCmEngAAAAAAwFr+Sk9CTwAGoScAAAAAALCWf2O7K1JPAEmEngAAAAAAwFqZMz2reCAAagqhJwAAAAAAsFZmezupJ4AkQk8AAAAAAGAt/yIjKj0BGISeAAAAAADAWvHMoZ4AIInQEwAAAAAAWCyz0pPUE0ASoScAAAAAALBWxkzPKh4HgNpC6AkAAAAAAKyVub2d2BNAEqEnAAAAAACwVsZITzJPACmEngAAAAAAwFqZ7e2kngCSCD0BAAAAAIC1MkJPMk8AKYSeAAAAAADAWnGX0BNANkJPAAAAAABgLRYZAciF0BMAAAAAAFgpkcgMOYk8ARiEngAAAAAAwErxQGVnMAQFMHgRegIAAAAAACvFCTkB5EHoCQAAAAAArBSc4clMTwAGoScAAAAAALBSsNKTzBOAQegJAAAAAACslEgE/k7qCSCF0BMAAAAAAFgpuMiIyBOAQegJAAAAAACslN3eTuwJIInQEwAAAAAAWCnYzk7mCcAg9AQAAAAAAFbKqvSs0nEAqD2EngAAAAAAwErB0JNFRgAMQk8AAAAAAGAl2tsB5EPoCQAAAAAArESlJ4B8CD0BAAAAAICVqPQEkA+hJwAAAAAAsFI8kfl3Qk8ABqEnAAAAAACwUvb2dlJPAEmEngAAAAAAwErB9vYEmSeAFEJPAAAAAABgpaxKT/rbAaQQegIAAAAAACvFqfQEkAehJwAAAAAAsFIiK+Uk9QSQROgJAAAAAACslN3eXqUDAVBzCD0BAAAAAICVaG8HkA+hJwAAAAAAsFIikfl3FhkBMAg9AQAAAACAlaj0BJAPoScAAAAAALBScJGRyyIjACmEngAAAAAAwEosMgKQD6EnAAAAAACwUrC9ndATgEHoCQAAAAAArBRsb0+QegJIIfQEAAAAgAqb9sfXdPG9r1f7MADrxbJmegJAEqEnAAAAAFTQ2s6oHnh1me55+SN1RuPVPhzAasHKTio9ARiEngAAAABQQYlE+s/d8UT+CwLoVXCREaWeAAxCTwAAAACoIH8lWjRG6AmUIhh6UukJwCD0BAAAAIAK8kcy0TgBDVCKYMhJ5gnAIPQEAAAAgArKqPSkvR0oSfAjROYJwCD0BAAAAIAK8oeeXbS3AyWJs8gIQB6EngAAAABQSb5MhkpPoDSJBO3tAHIj9AQAAACACkoQegJlE1xk5JJ6Akgh9AQAAACACmKmJ1A+WYuMqnQcAGoPoScAAAAAVJA/lOmOEdEApQhWejLTE4BB6AkAAAAAFeSfQUilJ1Ca4CIjMk8ABqEnAAAAAFSQy0xPoGyCi4yo9ARgEHoCAAAAQAW5otITKBc+QgDyIfQEAAAAgAryF6Z1xUhsgFIE29up9ARgEHoCAAAAQAVlbm8noAFKEWxvJ/MEYBB6AgAAAEAFuS7t7UC5ZFd6VulAANQcQk8AAAAAqCAWGQHlk13pSeoJIInQEwAAAAAqyJ/RdDPTEyhJPBh6Vuk4ANQeQk8AAAAAqCBmegLlE2xvp9ITgEHoCQAAAAAVRHs7UD4sMgKQD6EnAAAAAFSQv9KT9nagNCwyApAPoScAAAAAVBCVnkD5BD9CLlM9AaQQegIAAABABflDmW5CT6AkwfZ2Kj0BGISeAAAAAFBBCSo9gbIJtrcz1BOAQegJAAAAABWUsb09RkADlIJKTwD5EHoCAAAAQAUx0xMon2Clp0ulJ4AUQk8AAAAAqCB/KMNMT6A0cSo9AeRB6AkAAAAAFcRMT6B8EsFKzyodB4DaQ+gJAAAAABWUUekZI/QEShGs9KS9HYBB6AkAAAAAFZRZ6UlAA5QiWCxN5gnAIPQEAAAAgApipidQPvFE5mfIpcEdQAqhJwAAAABUEDM9gfIJFkuzyAiAQegJAAAAABXkr0Qj9ARKk0ilnHVhRxLt7QDSCD0BAAAAoIIyKj1jJDRAKcwio0goGW8Et7kDGLwIPQEAAACggvyhDJWeQGniqc9TJFXpCQAGoScAAAAAVJKvEI1FRkBpEl6lp5PxdwAg9AQAAACACvJXenbHCD2BUqQrPZPxBpEnAIPQEwAAAAAqiO3tQPlkVXoy0xNACqEnAAAAAFSQmzHTk4AGKEVwpieZJwCD0BMAAAAAKijBTE+gbMxHyGxvd0k9AaQQegIAAABABbmB7e2ENEDfBdvb+TQBMAg9AQAAAKCC/KGM60pxtk0DfWba28PM9AQQQOgJAAAAABUUDGWY6wn0nan0rDPb2/k4AUgh9AQAAACACgoWdnbHmOsJ9FWw0pPME4BB6AkAAAAAFRSc4ckyI6Dv4oGZnrS3AzAIPQEAAACggoKZTJTQE+gzb5FROBl6UuoJwCD0BAAAAIAKyp7pSegJ9JVpb4+EkvEGlZ4ADEJPAAAAAKggKj2B8jEfH1PpSeYJwCD0BAAAAIAKClaidcdIaYC+SrjBmZ7VPBoAtYTQEwAAAAAqiEpPoHzSi4yS8YbLUE8AKYSeAAAAAFBBzPQEyscsMgrT3g4ggNATAAAAACoomMl0xwg9gb4yi4zqQib0JPUEkEToCQAAAAAVlDXTk0pPoM9Me3vY295ezaMBUEusCj1feuklHXXUURo5cqSGDh2qSZMmac6cOQVff968eXIcJ++vF154oR+PHgAAAACyQ5lonJQG6CvzJUKdaW9npieAlEi1D6BQ8+bN0+GHH676+nqdfPLJamlp0f3336/TTjtNixcv1k9+8pOCb2vy5Mk66KCDsk7ffPPNy3jEAAAAAJADMz2BsklXerK9HUAmK0LPWCymqVOnynEczZ8/X7vvvrskafr06dp33301ffp0nXjiidp2220Lur2DDjpIM2bM6McjBgAAAIDcsis9CT2BvnBd1/s81YVT29sJPQGkWNHePnfuXC1atEinnnqqF3hK0vDhw3X55ZcrFovp1ltvreIRAgAAAEBhsmZ6ssgI6BP/Fwim0jN7VRiAwcqKSs958+ZJkg477LCs88xpzzzzTMG3t3DhQv3qV79Se3u7ttxyS33pS1/SqFGjynKsAAAAANCTYCUaMz2Bvon7Us+IaW/nOwQAKVaEngsXLpSknO3rI0eO1KhRo7zLFGLOnDkZC5Campo0c+ZM/ehHPyr9YAEAAACgB8FKT9rbgb7xf5YiLDICEGBFe3tbW5skqaWlJef5zc3N3mV6Mnr0aP3yl7/UO++8o/Xr12vZsmX6/e9/rw022EAXX3yxfvvb3/Z4/a6uLq1ZsybjFwAAAAAUI1jpSXs70Df+Ss9wKBlvsMgIgGFF6FkuEyZM0EUXXaQddthBQ4YM0aabbqrTTjtNjz32mOrr6zV9+nQleqiFv+aaa9TS0uL9Gjt2bAWPHgAAAMBAEKxE66bSE+iTuO8bhLpUezuLjAAYVoSepsIzXzXnmjVr8laBFmLixInae++99emnn+q9997Le7lLL71UbW1t3q+lS5f2+T4BAAAADE5sbwfKI+6bhxs27e2kngBSrAg9zSzPXHM7V61apdbW1pzzPothFhm1t7fnvUxDQ4Oam5szfgEAAABAMZjpCZSHv9LTLDIi8gRgWBF6Tp48WZL0xBNPZJ1nTjOX6YtYLKZXXnlFjuNoiy226PPtAAAAAEBv2N4OlEciVTbtOFLISW1vp9ITQIoVoechhxyi8ePHa86cOXrttde809euXaurrrpKkUhEZ555pnd6a2ur3n33XbW2tmbczvPPP59V6h6LxfSjH/1IH374oQ4//HBtsMEG/flQAAAAAAxywZ9JWGQE9I2p9IyEHDkOMz0BZIpU+wAKEYlEdPPNN+vwww/XgQceqFNOOUXNzc26//779cEHH+jqq6/Wdttt513++uuv18yZMzV9+nTNmDHDO/2UU06R4zjab7/9tNlmm2n16tWaP3++FixYoC222EI33nhjFR4dAAAAgMGEmZ5AeZjt7SHHUaq7nUpPAB4rQk9JmjJlip599llNnz5d99xzj7q7uzVhwgRdddVVOu200wq6je9973t67LHHNG/ePLW2tioSiWibbbbRT3/6U1144YUaOXJkPz8KAAAAAINdMJOh0hPom0TqoxMOOUplnsz0BOCxJvSUpEmTJunRRx/t9XIzZszIqPA0LrnkEl1yySX9cGQAAAAAUBgWGQHlYdrbw46jUIjt7QAyWTHTEwAAAAAGimAowyIjoG+89nZ/pScfJwAphJ4AAAAAUEFmpmd9JPnjWDeVnkCfmKrpcMhJrnAXoSeANEJPAAAAAKggNzV1sCEVetLeDvQNi4wA9ITQEwAAAAAqyFR6NtaFJRF6An1lQs9wSDIN7kSeAAxCTwAAAACoIFOJVh9OVXrGiGmAvkj4FxmlKj1ZZATAIPQEAAAAgEpKZTINdcz0BEqRscjICz2reEAAagqhJwAAAABUkKlOa4gk29u7Y4SeQF/4Fxk5qdSTmZ4ADEJPAAAAAKig4PZ2ZnoCfWM+OmHHTPRkpieANEJPAAAAAKggU4jG9nagNP729pBX6VnNIwJQSwg9AQAAAKCC0u3tJvQkpQH6wr/IyGGREYAAQk8AAAAAqCA3ONOTSk+gT3JVepJ5AjAIPQEAAACggkwmY7a3D5T2dtd1FYsnqLRDxcS9RUZKV3oy1RNACqEnAAAAAFRQVnv7ANnevqYjpm1++qi2uvQRNtKjIhKJdHu7QeYOwCD0BAAAAIAKSgQWGQ2U9vZoIv046sJOD5cEyiP3IiNSTwBJhJ4AAAAAUEHp7e3JmZ7RuDsgWsJNABUOOXIcQk/0v9yLjKp4QABqCqEnAAAAAFSQG2hvlwbGBnczmzQcIvBEZZgiaRYZAciF0BMAAAAAKig401MaGMuMYqngto7QExUS91d6pk5jkREAg9ATAAAAACrIm+lZF/ZOGxChZ+qBRcL8mInKSOQYqZAg8wSQwv9GAAAAAFBBpv02EnJkiiIHwjKjWGqRUYRKT1SIf5FReqYnqSeAJEJPAAAAAKggE8qEHEd1qarIgTDT07S3R9jcjgpJt7fLt729mkcEoJYQegIAAABABZmZno4j1ZvQMzYQKj1ToWeIHzNRGRnt7anTyDwBGPxvBAAAAAAVZEIZx3FUl1pmNCDa21OPgUpPVErcVzVtsnba2wEYhJ4AAAAAUEGm/TbkSHWpgLB7QFV6EnqiMjIrPZPvOzJPAAahJwAAAABUkNfeLvlmeg6A0DNOezsqK9ciowSpJ4AU/jcCAAAAgEoylZ4hJz3TcwAsMoomaG9HZZnq4rDjyHGo9ASQidATAAAAACoovcjIUX1k4FR6xr3t7fyYicownyUWGQHIhf+NAAAAAKCCcrW3D4hFRqbSk5meqBDzsQk5jkJepSexJ4AkQk8AAAAAqCDXW2TkeIuMogNgkVE0ziIjVFa60lPeTE8yTwAGoScAAAAAVFDm9vaBM9PTLJWpo70dFRL3treHWGQEIAv/GwEAAABABbneTE95Mz274/FqHlJZmLmkYSo9USHp0FMyUz2JPAEYhJ4AAAAAUEEmlHEcJ13pGbM/qklXehJ6ojK89nbHUYhKTwABhJ4AAAAAUEEmlPHP9BwIi4yiifQmbaASTNAeCjlyvKGeVTwgADWF0BMAAAAAKsjM9PRvb48OgNAzlnoMEWZ6okLiVHoC6AH/GwEAAABABZmZnqFQeqbnQAg9vfZ2Kj1RIQlfdTGFngCCCD0BAAAAoIJcb3u7o/oBtL3dPIZwiB8zURnmuwJ/ezuVngAM/jcCAAAAgApKeNvb04uMumP2V3qa9nYWGaFS/IuMzLuOzBOAQegJAAAAABXkhZ5Kz/QcCIuMYqlW4wihJyrEv8golKr0JPQEYBB6AgAAAEAF+dvb6yLJoCY6ECo9E6lFRrS3o0L8i4y8mZ6kngBS+N8IAAAAACooHXrKN9NzIISeqUpPFhmhQtKLjCTT4E7kCcAg9AQAAACACkrP9PS3t9sf1cTMIiPa21Eh/vZ2U+nJIiMABqEnAAAAAFSQiWQcx1F9ZABVeppFRrS3o0Jyt7dX8YAA1BT+NwIAAACACjKVaCHf9vYBEXqyyAgVlm5vTy8yShB6Akgh9AQAAACACjKhjCOpPhUQDojQM85MT1RW3LcUzPHedqSeAJIIPQEAAACgkkylZ8g30zNmf1ATNdvbw/yYicqg0hNATyLVPgAAAAAAGEy8Sk9fe3v3AKj0jLO9HRWWscgodZrLUM+qeuzNT1QXdrTHliM1Ykh9tQ8HgxxfwQEAAABABXnb2yXVmUVGMftDT9rbUWmZi4yo9KwFv3jsXX3r9pf11vI11T4UgNATAAAAACrJ9c0hHFAzPWlvR4Wl29vl295O6lkt3bGEPvy8XZK09ehhVT4agNATAAAAACrKv729PjKAtrdT6YkKi/s+S2amJ5ln9Sz5fL3iCVdD68Ma09xQ7cMBCD0BAAAAoJJcb6anb5FR3P6kJmpmelLpiQqJ+xYZeTM9q3c4g957K9ZLkrbeaJg3bgCoJhYZAQAAAEAFualYxnGkutDAqfSMp9rb68KEHagMUzWdub2d2LNaFq1cJ4nWdtQOvoIDAAAAgApK+GZ6mkrPgRB6RuPpAAqoBG97u+P4ZnpW8YAGuXToObTKRwIkEXoCAAAAQAVlzPQMD6Tt7alFRiF+zERlpIqLM4J2lwb3qlm0Ihl6brMRlZ6oDfxvBAAAAAAVlDHTM5IMa7oHQKWnqbqjvR2VkrHIKGTa26t5RIOX67patDI105P2dtQIQk8AAAAAqCDXC2p8i4wGQKUn7e2otFyLjCj0rI4Va7u0riumcMjRFhsOqfbhAJIIPQEAAACgohJepaevvX0AbG9PV3ryYyYqI73ISCwyqjLT2r7FBkPUEAlX+WiAJP43AgAAAIAKMqGMI6k+MoAWGSXMTE8qPVEZsXiORUZVPJ7BjCVGqEWEngAAAABQQW6O7e2xhKuE5cMITQAVYaYnKiRd6ZkOPan0rA7meaIWEXoCAAAAQAW5vuUr/qU/plLSVqa9ne3tqBRvpqfjyEz1JPOsjnSlJ6Enagf/GwEAAABABSX829t98y9tn+tpWvRZZIRK8ba3hxz533YuyWfFmZmeW29EeztqB6EnAAAAAFSQm5o6GAw9bd/gHmORESos4VUXO3KcdOpJ5llZ67tiWt7WKYlKT9QW/jcCAAAAgApK+GZ6hkOOVxlp+zKjWOr4memJSslX6clcz8p6PzXPc9Sweo0YUl/lowHSCD0BAAAAoIJM660pTDNzPQdKpSfb21EpZgyuf6anxAb3SjPzPMdT5YkaQ+gJAAAAABXk394uSfWpdnDrKz1N6El7OyrEW2QUcuT43nZUelYWS4xQq/jfCAAAAAAqKOFtb0/+vT5iQk+7gxrT3l5HpScqxGtvd/x1nsz0rLR06MkSI9QWQk8AAAAAqKCEF8gkYxqz+Mfm9vZEwvUeF9vbUSkJf6Wnw/uuWhatSM703HojKj1RWwg9AQAAAKCC3EClp2lzj1tcnhZLJ7m0t6NizGcmHBKLjKoknnD1QWsy9NyG9nbUmEi1DwAAAAAABpPgTE9TGRlP2BvUxBLpKlUWGaFSzGcmFFxkZO9HyTor13apoS4kx5E2HdFU7cMBMhB6AgAAAEAFJQLb203oaXN1mn8eaSRM6InKyGxv951u8WfJNhu3NOqN6YdpVXuU0RaoOfQdAAAAAEAFmTjGVHqanMDmSk//sdeF+DETlZGxyMiXt9n7SbKT4zjaYGh9tQ8DyML/RgAAAABQQXkrPS0OPc3mdseRQhZVey35rF3X/u3fWt3eXe1DQR+YqQrhkON9iSBJrr07wQCUEaEnAAAAAFRQIjDT0/xucebpLTKyrcrzd39fpGv/tlAPvLqs2oeCPkgvMvJP9JRcaj0BiNATAAAAACrKDVR6Dojt7amZnrbN81zbGZMktXfHq3wk6Av/IiN/pafNXyAAKB9CTwAAAACooHzb221ub4+m+oxtW2RiwtpY3N7nfrDyf16Ci4xci79AAFA+hJ4AAAAAUEHBmZ5mBuZAWGRUF7brR8xoahapzVW2g5X/NQs7jhwqPQEE2PU/EgAAAABYzgQyZgqh6Qi3OXgz4aF1lZ6pFyOeYPONbfxfEphRsib3ZKYnAInQEwAAAAAqxt92GxpQ29vNIiO7Qk+v0pPM0zoJN7O9XUqPjLD4+wMAZUToCQAAAAAV4g9jgtvbba70NBWTEVvb26n0tE5GpWfqM2Qid4s/SgDKyK7/kQAAAADAYv7qNCdQ6WnzTM9YKjyMWFbpaSpUqfS0jz+nNp8h2tsB+BF6AgAAAECF+KMYxxk4Lblxr9LTrtDTVHombH7yB6ngIiMp/Zmy+PsDAGVE6AkAAAAAFZLIMdNzIGxvj5rQM2TXj5jRVKVnjPZ262QuMgq2t9v7WQJQPnb9jwQAAAAAFvNnMaYqbSBsb/fa2y2r9DRhJ+3t9jFfIIR9IxUGQtU0gPIh9AQAAACACslcZJT8fUBsb/cqPe0KPaPeTE9ST9uYSk/T2i75Znra+1ECUEaEngAAAABQIZnt7QNoe3vc9u3tVT4QFM2Env6JCiFvpqe9nyUA5WPX/0gAAAAAYLFcYczAqPS0fXs7qadtvPZ2f6Vn6nd7P0kAyonQEwAAAAAqxB/GeJWeA2CRkfWVnvY+9YNWutIzu72dSk8AEqEnAAAAAFSM6yso9La3ey25VTigMrG10tOEnjZX2Q5WuRYZOSwyAuBD6AkAAAAAFZJrpmd4AFSn2brIyBx3jPZ268RyLDIKeYuM7P0sASgfQk8AAAAAqBB/FGOymoHU3l5na3s7mad1cre3pyo9q3JEAGqNXf8jAQAAAIDF/NWcjlfpaf/2dhMehi2q9HRdV1EWGVnLvGS5FhnZXDUNoHwIPQEAAACgQkwY488GB8L2dlN1FwnbE3r6K2tZZGSfODM9AfSC0BMAAAAAKsSEMY5/DqHX3l6NIyoPM1+xLmTPj5gxf+hJpad1TGgdzrG9ndATgEToCQAAAAAVY8KYjErPgdTeblGlZ7cvZbZ5nupglWt7e2gALAWrtucXfaafPvB/WtcVq/ahACUj9AQAAACACjFhjDPANk7HvUpPe0LPmK+nnUJP+3iLjHxvOUf2vP9q1W/mvac//HOJ5v97ZbUPBSgZoScAAAAAVIgXevpOGwjb281CoLBN7e2+Ss8Yqad1Ejna26n0LF1nNC5J6uiOV/lIgNLZ8z8SAAAAAFgu3d6eDmoGQnu7mYlZZ2t7u71P/aAV95aCscionMyXL1GbhwwDKYSeAAAAAFAhOWd6DoDt7abS06bt7f72dhYZ2aenRUZUevYdoScGkki1DwAAAAAABoucMz0HxPb21CIjm9rbE/5FRlU8EPRJrkVG3vb2ahzQAGEqaKOB8ueP2zq08NN1GjWsQTtt2lyNQwOKZs//SAAAAABgORMjODm2t9tcnWbjIqPuGJWeNjNBdShjKZhpb7f3s1RtpgI6WOk5/98rdfrsF/U/TyyoxmEBfWJV6PnSSy/pqKOO0siRIzV06FBNmjRJc+bM6fPtRaNR7bbbbnIcRzvssEMZjxQAAAAAsiVyzCEcSIuMImF7fsTMrPS097kfrHK2t6d+J/PsO/NvVCzwmehOfcbrLPqMA9a0t8+bN0+HH3646uvrdfLJJ6ulpUX333+/TjvtNC1evFg/+clPir7Nq666Su+9914/HC0AAAAAZHO99vb0aSazsXmRkdmEHrGo0tPfvkvmaR+vvT1HpSevZ9+ZMLk7llnpGU393aa5vYAVEX0sFtPUqVPlOI7mz5+vm266Sf/93/+t119/XRMmTND06dO1cOHCom7zlVde0TXXXKNrrrmmn44aAAAAADL1tL3d5pZcUxVmUyDib9+N0d5uHRPOZYyRNTM9Lf4sVVu+RUbm7/VUesIiVrxb586dq0WLFunUU0/V7rvv7p0+fPhwXX755YrFYrr11lsLvr3u7m6deeaZ2meffXTuuef2xyEDAAAAQJaEF3qmTxsI7e1mDqBNlZ7+7e1knvbJucgo9bu9n6Tqi+dpbzehJ+3tsIkV7e3z5s2TJB122GFZ55nTnnnmmYJvb8aMGVq4cKFef/31jK2JAAAAANCf0suKfJWeA2J7u30zPaMJKj1t5lV65mxvJ/bsq3g8T3u7mekZIUOBPawIPU3r+rbbbpt13siRIzVq1KiC29tfeukl/eIXv9DPfvYzbbfddkUdR1dXl7q6ury/r1mzpqjrAwAAABjc3ByVngNhe7sJDW2t9LQ5cB6sci4yotSzZKbSM197O5WesIkV79a2tjZJUktLS87zm5ubvcv0pKurS2eeeaZ23313XXjhhUUfxzXXXKOWlhbv19ixY4u+DQAAAACD10Dd3u61t1s60zNOpad1WGTUP8y/Q/4vBSRmesJOg+rdevnll2vhwoWaPXu2wuFw0de/9NJL1dbW5v1aunRpPxwlAAAAgIEqd6Vn8nert7d7lZ72/IiZGXra+9wPVublC+WoLnYp9eyz/IuMUu3thJ6wiBXt7abCM18155o1a/JWgRqvvPKKZs2apcsvv1w777xzn46joaFBDQ0NfbouAAAAAJjqNP9uAdOem7A4eIt5gYg9lZ4Zi4zsfeoHrTiVnv3CCz0DT2J3KgS1qZobsCKiN7M8c83tXLVqlVpbW3PO+/R74403FI/HNWPGDDmOk/FLkhYsWCDHcTRixIiyHz8AAAAASOlRg/59qs4AmOkZ9eYrWvEjpqTMSjYWGdkn0cNMT9fiz1K1eaFncJFRjJmesI8VlZ6TJ0/WNddcoyeeeEInn3xyxnlPPPGEd5mebLfddvrWt76V87xbbrlFLS0tOuGEEzRkyJDyHDQAAAAABOSa6TkQtrebmZg2VYH5K9nIPO3jbW8PZVd6knn2Xb5FRrHU881MT9jEitDzkEMO0fjx4zVnzhydf/752m233SRJa9eu1VVXXaVIJKIzzzzTu3xra6taW1s1atQojRo1SpK03377ab/99st5+7fccos23nhj3Xzzzf39UAAAAAAMYq7X3p4+bUBsbzft7RZVesao9LRaepFR+jSv0pOZnn3WW3u7TSMsACv+R4pEIrr55puVSCR04IEH6tvf/rYuuugi7brrrnrrrbc0Y8YMbbfddt7lr7/+eu244466/vrrq3jUAAAAAJApvchogG1vz9FqXOv8lWwJl5Zo2+Sq9PRGRZBh91mv7e0RK2IkQJIllZ6SNGXKFD377LOaPn267rnnHnV3d2vChAm66qqrdNppp1X78AAAAACgVybXzKj0DJnz7A3dYhZWgUXjmc93POFa1Z4/2OVaZGT+ZO8nqbpc1/X+jcre3s5MT9jHmtBTkiZNmqRHH32018vNmDFDM2bMKPh2+UYPAAAAQCV429t9p5mqT5srPU2AaFOlZywYerquXT8gD3Lx1OvnD6rN28/mLxCqyf9vULC93XzGbfpiAyCiBwAAAIAKydXeHh4A7e3m2G2qAgtWstESbZfOWFyS1BAJe6c5LDIqSdz3xAXb27up9ISFeLcCAAAAQIW4Oba3D4SN0zErt7cHt1OTetqkozv5ejXV+0JP708Wf5iqyP/FS/DzECP0hIV4twIAAABAheSa6em1t1uceppFRhGL29vJPO1iKj0bfZWe5rNkcdF0VWW0t8dzt7fXE3rCIrxbAQAAAKBC3FQFmjPA2ttNgBgJ2fMjZixOpafNOruToWdTve89l/pYWfz9QVX5/w3qDm5vp9ITFuLdCgAAAAAVkvBmeqZPGwjb200gYlN7e3eORUawh1fpWeev9Ez+bvNnqZp6am9Pz/S05zMOEHoCAAAAQIV429tztbdbXOkZT9hf6Wnz8z8YdUaTr5+/vd1JlXrySvZNxiKjrPZ288WGPZ9xgHcrAAAAAFTKANze7rpueqanRVVgscDzbevzP1h1pNrbG32LjEzm7lLp2SeZMz0D7e0xZnrCPrxbAQAAAKBC0pWevtDTW75iZ1DjDw9tWmTUHQh1GOlpl/Qio3Ss4VV62vlRqroeQ0/T3h6x5zMOEHoCAAAAQIXkmunpWL5x2h+U2NT6yiIju3V4i4x87e3M9CxJz9vbWWQE+/BuBQAAAIAKMW23/lop096esDT19FeE2VTpGQuEOgRldulKbRf3LzIyXyDwUvaNP/SMJ9yMf5NMCEp7O2zCuxUAAAAAKiSRc6Zn8ndbt4fHB0h7e3DGJ2qbN9Mzwvb2cgnOtY36qp+p9ISNeLcCAAAAQIWYSk9/6Gn79nZ/G2zYotAzWOlp6/M/WJmZnk31/pmeSbySfRP84sV8thOJ9LKyOouWlQGEngAAAABQIV6u5ssNbG9vN7Mw68JOxoKmWhec4clIT7t0RpOhZ0NGpadpb7fzs1RtweDfzL31V3zaNLcX4N0KAAAAABXiylR6pk/zKj0tDWpMxaRNVZ6S1B2o9GSRkT1c11VnNPl65VpkZOlHqeqCoacZAeGv5mamJ2zCuxUAAAAAKiT3TE/T3l6NIyqd1/YasuvHy+D2duZA2sMsMZIyFxmZEmpeyb7JrvR0U7+nn2/a22ETu/5XAgAAAACLedvbc7W3Wxq6mUAkbFkYEpzpGfw7apdZYiRJjZF0rMEio9JkLTJKfbZNxafj2FfRjcGN0BMAAAAAKsTNUelpe1BjKj0jllV6RgPt7LaOFxiMzBKjurCTMWOS9vbS5As9TXt7XThk1dxewK7/lQAAAADAYgmv0nPgbG+PeYGIXWFINNDebuvzPxiZeZ6NkXDG6SwyKk126Jn8ezQ1ToB5nrAN71gAAAAAqBCTKfjjwYGyvd22ttdgO7sNoafruvrZI+/oTy8vrfahVJVpb2+szww9vUrPSh/QABGsdk5XeiZ/j1j2xQYQqfYBAAAAAMBgYSrQBtT29kS69dUmpoqtPhJSdyxhxXiBD1rX63fz39eGQ+t14p5jq304VWPa2xvrMt9zpoLa1i8Qqq23mZ62fcYB3rEAAAAAUCG5ZnqmKz2rcUSl86rALKv0NMdtFuHYsMioI5oM+9Z2xqp8JNXVmar0bKoLVHqmfq/9V7I25WtvN58N2tthG96xAAAAAFAhiR62t9ta6WmCEvva21OhZyo4s6HS04RQ3fGEd/yDUbrSM/dMTwo9+yb/IiNT6WnXZxwg9AQAAACACjGRwsBcZGTXj5fR1PPdkGqRjlnw/PuDzvZU1edg1NGde5FRent77b+WtSj4b1DMF7JL9n3GAd6xAAAAAFAhiZwzPdN/tjGssXXJSbq9PRmc2RA6d/tCT9PiPRh1RnMvMkpvb6/4IQ0IwWrzbq/S084vNoCS3rEHH3ywDjnkEC1atKhcxwMAAAAAA1Z6e3v2TE/JjuAtyByzTTM94wnXC8ZMi7QNz71/7mj7YA49TXt7JLDIKPW7y1TPPsnb3h5LVXpGCD1hl5K2tz/77LOqq6vT1ltvXa7jAQAAAICBy1R6+rKDkD/0dN3SfkirgqgXetoTiER9FZNmA7gNoaf/uAdz6NlhFhnVB9vbmelZinzt7d5MT4u+2ACkEis9x4wZo/r6+nIdCwAAAAAMaF6lp397u+/PNm5wjyfsa2/3h4cNEfsWGUlSR3TwbnDvivU207PSRzQwBENP097OTE/YqqR37Be/+EWtWbNGCxcuLNfxAAAAAMCAlZ7pmae93cK0xgRxNrW3+9vEGy1aZESlZ1LeSs/U77S3902+9nZvWRnt7bBMSe/Yiy66SJFIRBdeeKGVA7cBAAAAoJJcb6Znmj8AtaHFOsgEIhGLqsCiqepUx0lXryUseO5jvlLgjkEceppFRg11me85FhmVJvilS7C9vd6iam5AKjH03H333XXXXXdp3rx52n///fXAAw/o008/JQAFAAAAgBxybW/3V3raELwFee3tFlZ61oVC3vNvRaVnzN/ePohDT2+RUb729tp/LWtR3kVGtLfDUiXNyA6H0//A/POf/9QJJ5zQ63Ucx1EsNnhnjwAAAAAYvEwW46/u9GeFNsyVDIraWOnphTiOF3raUGUbTdDeLkkd3cnngUVG5ZUdeib/3m2+JLDoMw5IJYaefHsCAAAAAIVL5OhvdxxHjpMMRG2c6Wlarm3a7OwPaisdej746jK5cvXV3Tcv+rr+WaSDOfRMV3pmhnAsMipNb5WeNi0rA6QSQ8+nn366XMcBAAAAAAOeiRT8lZ5ScoN7zHWt3N5u2sLDFoWeXlAbdhROvRaVCJw7o3Fd9KfXJUlHTtxEjXXhXq6Ryb/IqKN78HZQduZZZGTegjZWTNeCvKFnzMz0pNITdikp9Jw8eXK5jgMAAAAABrxcMz0lKRRypIRrZ6Wnje3tsXS7rqleq8Q81a5YwguJu2KJPoSezPSUfJWedcHt7alFRhU/ooEh+O+Peb9FE7S3w068YwEAAACgQtLd7dmVnpKdi4xMiGfTIiMzGzMSdryq20osMor5KjX9fy6Uv9JzMLe3m831DZHclZ6M4uubeLzn9nZCT9imrO9Y13XV2tqqJUuWlPNmAQAAAGBAMGFMKPCTmE3LdIJiFs77y7W9vRKBs79Ssy+vdSyjvX3whp6d0Z4XGZF59k2w0jMWaG+vi9jzGQekMoWer7zyiv7jP/5DLS0tGjNmjMaPH59x/qpVq/Sd73xH3/3ud9Xd3V2OuwQAAAAA65icywnM9DQVala2t1vY+upfzGJCz0pUevorNaN9uL9uFhlJ6n2RETM9+yYYxJv3m3nfMtMTtin5HXvnnXdq33331YMPPqh169bJdd2sUvKRI0fqgw8+0E033aQnn3yy1LsEAAAAACuZMCZYLxWqYLVhuZmqSZsWGfnbdSu5yMgfrAZbiQu6vr/SczDP9MyzyIiZnqXJt8jIhJ82fbEBSCWGnu+8847OPvtsRaNRnX/++Xr55Zc1atSonJc9/fTT5bqu/vznP5dylwAAAABgLZOr5dreLqUrQW3ibUK3KPT0L18Kp9ry+xJCFn+/vpmeidJmeg7q9vZUu3VwkRHb20tjQk/zz1MsMNPTphEWgFTi9vZZs2apu7tb55xzjq699lpJUjice/vcwQcfLEl6/vnnS7lLAAAAALCW29P2dtk50zNq4/b2eDqorWSlp3+mZ1/a6f0t8e3dsbIck41M4NsU3N5uPlf2fYxqgvn3pzESVkc07r1fY7S3w1IlvWPnzp0rx3F0ySWX9HrZTTfdVEOGDGHJEQAAAIBBK99Mz3Slp31pTTxVsWhVe7tvDmmkgqMF/NWdsT5UlpqFMpL9Mz3XdcU0869v6V8ffl7U9VzX9WZ6NtQFZ3rS3l4KE/w3pp7Xbq/Sk/Z22Kmkd+zy5cs1dOhQbb755gVdvqmpSR0dHaXcJQAAAABYy1Vm+6hh9/Z2E4jYE3r6N86HKrrIqMTt7b7r2D7Tc96CFbr1H4v167nvFXW9rljCGxMRbG/3FhlZ+DmqBV6lZ+p5jXkzPdMzcAGblPSObWhoUHd3d9biolw6Ojq0evVqtbS0lHKXAAAAAGAtk8UEZ3qGUj+Z2by9PRyyJxDJtcioElW2sYzt7cXP9OweQDM927uSx19sxWpXNP0cZLW3s8ioJMHQMxrY3m7TFxuAVGLoOW7cOEWjUS1cuLDXyz7yyCOKx+PaaaedSrlLAAAAALBWIs9MTy94s7BCzVtkZFEgEvVVp5pFRn1pNy9Wxvb2vlR6DqDQ04S+/uVMhTCt7eGQk1V5yCKj0pj3ZEMk+bxGA4uM6iP2fLEBSCWGnkcccYRc19V1113X4+U+++wzXXzxxXIcR0cffXQpdwkAAAAA9soz03NALDKyqNIz3d4eqvAioxJnevqu0x6NF9R1WavMfNJiQ898S4ykdHu7xU9LVWVXeqZeo5h9n3FAKjH0nDZtmoYNG6Ybb7xRM2fO1Nq1azPO7+jo0Jw5c7Tnnnvqgw8+0IYbbqjvfve7JR0wAAAAANjKVKAFZ3qGvBbrSh9R6UxQErFokZGpuKwLORWdpxrL2N5efHu7PyCMJ9yMdnfbmNfABGqFMpWejXXZcYb5HNkcBldTOvQ0lZ7Jv3fT3g5LlRR6jhkzRnPmzFFdXZ2uvPJKjR49Wp999pkkacKECdpggw30jW98Qx9++KEaGhp01113qbm5uSwHDgAAAAC2yTfT0+bt7VHfUiBbdPsrPSsZevq3t/epvT3zOja3uHvzIosMf81jDi4xkiTzDrTvU1Qb0tvbMys9vREWtLfDMiW/Y4855hjNnz9fe+yxh7q7uxWLxeS6rt555x11dXXJdV3tvvvumj9/vg455JByHDMAAAAAWMmr9AycbnN7uwniIhZtdk5vnA95FaqVCJz97el9a2/PDAht3uAenBdZqM7UIqOcoafFXx7UAq/SM5K7vb3eos84IEmRctzIpEmT9OKLL+qNN97Qs88+q+XLlysej2vjjTfW/vvvrz333LMcdwMAAAAAVnPzVXpavL3dyvZ2X7uuCZwrs8jI357eh/b2QChe7ObzWhILBGqF6qm9nZmepcnX3p7e3k7oCbuUJfQ0dtllF+2yyy7lvEkAAAAAGDDcAbi93bQn2xR6dvuWL1VytEBGpWcfXmuz/Mewur09kRmoFaqzh0VGNs/GrQXBRUYmmGamJ2xFTA8AAAAAFeKFMQNoe7s5ZpuqwLxKz0h6kVFfQsji77e09vbg8iObKz1NgFvsMqZ0pWf+mZ5M9eybYOjZTaUnLMc7FgAAAAAqxFUvlZ4W9uWa6sWwRZWe6e3tdi0yisaD7e2xko+pWszjLzb87ejOP9PTfHnQh8kBUPYiI/N+jcbt+2IDkMrU3t7V1aX77rtPzz77rD766COtX7/ea9sIchxHTz31VDnuFgAAAACskm97u81tuTGrt7c7FQ09/aFln2Z6xtOjBGIJV52DcpFR/kpPw6XSs0+yZnrGMl8j2tthm5JDz+eee04nnXSSli9fLtd1vW1pJvR0fP+Z+88HAAAAgMHGzbu9Pfm7ze3tkZA9VWAxX7tuRSs9fQFfsGqzECZ8ammq02fru+1ub089lljCVSLhelWavTEb6xsjLDIqt2B7O4uMYLuSQs+lS5fq6KOPVltbm3bZZRcdccQR+sUvfqFhw4bpggsu0CeffKK5c+fq/fff16hRo/Td735X4XD+b2MAAAAAYCDztreHgtvbLW5vT9hX6Rnz2nUdbwFTJZ57f0t7X0JWc9zNAyD09Le1RxMJNYQKywq6UqFnUz2LjMrNCz1TgXI0kZDrul74WZ8jaAZqWUmh56xZs9TW1qYjjzxSDz30kBzH8ULPK6+80rvcDTfcoPPPP1+vv/66/vznP5d80AAAAABgo4TXEZd5uglrbKz09AJEiyo9vfb2UMh77iuxyCiaUelZfHu7Oe7mxuSP8gNhe7uUrChsKDCd6Izln+lpPla0t/dNsNLTdZOvjY3LygCpxEVGTzzxhBzH0cyZM3tsW//e976nmTNn6qGHHtJNN91Uyl0CAAAAgLVMzuMod6WnlaFnwsJFRr5KT6/KtsLb20ut9JTSrd42MvMipcy2/96YoDfnIiNv3F6JBzdIBRcZSZnBOjM9YZuSQs8lS5YoFArpC1/4Qsbp3d3dWZc955xz5DiObrvttlLuEgAAAACs5bW3D6Dt7TELl5yYrdT+mZ4VqfQseXu7qfRMhp5Wt7f7novuIkLP9CKjnmZ62vc5qgXBRUaStL475v2ZSk/YpqR3rOu6Gj58uEK+NoahQ4dqzZo1Wf/ItLS0qKWlRe+++24pdwkAAAAA1jI/J2Vtb/cqPSt+SCUz4V3EokDEzCiMVHyRkZvzz4VwXdd7rpubTHt7rKer1DT/IqdiljqZ6tamXO3tzPQsifkMNETSz217RqWnPZ9xQCox9Nxss83U1tamrq4u77TNN99c8Xhc77zzTsZl29vbtXr1arW3t5dylwAAAABgrfwzPZO/xy2sUDPhXcSi9vaorzq1Wtvb44niEm5/Zahpbx8olZ7+VvfedEYLmemJvoh7X2CkF3yZ9vaQY9cIC0AqMfTcdtttJUmLFy/2Ttt7770lSTfeeGPGZWfNmiXXdTVu3LhS7hIAAAAArGXCmOBOBBMm2NiWGythe/vn67t1wd2vav6/V5b7sHqUnukZ8kYLVCJw9i/vKba93b/4yGtvt3qmp7/Sszzt7SaTs3FMRC2I++bzms+zaW+nyhM2Kulde+SRR8p1Xf31r3/1Tps6dapc19X/+3//T0cddZR++tOf6phjjtH06dPlOI5OOeWUkg8aAAAAAGxkcq5gwZTV29v7sMjIdV39+bVlOnTWM3rwteW6/M9vFrXMplRmtmbEF+5UZpFR32d6+lvAW1KVnp0WV3r655sW097eWUB7O6WefWOC/7DjeCFneyr0rCf0hIUipVz5y1/+su69914tWbLEO+2AAw7Qj370I/3yl7/UY489pscff9z7tvKLX/yifvzjH5d2xAAAAABgqUSemZ62bm+PJ1xvOVNdqLBQZHV7t354z+ua++4KSdIOGw/Xz4/fpaIzQdPt7SHvtajEIqNSZnr6qyGHNyZ/lLe6vT3ex0rPWPIxN+Tc3p78nUrPvvFXeqZDz+TzXRch9IR9Sgo9x44dq6effjrr9P/6r//Sl770Jd19991aunSpWlpadMQRR+j0009XJFLSXQIAAACAvVJZTHCmp63b2/1hVaHt7df+baHmvrtC9eGQzjt4G31n8taqr3CgktHeHqpcpWdme3uRMz19s1OH1KdCT5vb2+OJnH/ujZkxmavS03ywLPsY1YzM0DP5XLZ3JZ9vm2b2Aka/JZCHHnqoDj300P66eQAAAACwTnqR0cDY3u6vTI0UUOnpuq6eeOsTSdJ1J++mI3fepN+OrScmZIv4FhlVptKzlPb2dHXqkPpk4Gf39vb0c9Fd1EzP3hcZ2fblQa3IXenJTE/Yi3ctAAAAAFRIur0983RbKz39LcqFVHq++8laLW/rVGNdSFN22Kg/D61HUa/SMx16VuK598+ujPexvT0SdtSUCj2tbm9P+Nvbi5/pmXuRUarSs8RjG6xyhp6p57vS1dhAOZSt0rO9vV1r166VJA0fPlxDhgwp100DAAAAwIBgch5HwUrP5O+2zfT0t2gX0v761DufSpL233pUzkq9SjEVl5FQyDvuilR6+pf3FNneboLBel+lZ6fN7e0xX9VrH7a3515klPzdsu8OaoYJ/nO1t9cVOL4CqCV9Dj3fe+89/eEPf9ATTzyht99+W2vWrMk4v7m5WTvttJMOO+wwnXbaadpmm21KPlgAAAAAsJkJY/Jtb7eu0tNXGRZs2c/lqdTyokN2HNOvx9UbM1vTv8ioEoGzvzK22PvLqPSss7/SM5pR6VlY6Om6rjpj+dvbQ076coNZPOF6FczFMJ/nSK5FRrS3w0JFv2uj0ajOPfdc7bTTTrryyiv1/PPPq62tTa7rZvxqa2vT888/ryuvvFI77bSTzjvvPEWj0f54DAAAAABgBbeX7e2VWKZTTl4QV0DA0rquS68tXS1JOriKre2Sfz6m480ircgiozLN9DTt7R3RuLUBXyxjpmdhjyEad72wOPdMT9rb5/xziSZMf0zPvdda9HXNcxtyHEWY6YkBoOhKz2OPPVZPPvmkXNfVsGHDdOCBB2rixInabLPNvJb29vZ2LVu2TG+++ab+/ve/a926dfrNb36jRYsW6ZFHHin7gwAAAAAAG3iVnHkqPeOWBVhxX2VYb55+d4VcV5q4WbM2bmns70PrkbcJPRzyRgtUpr3dt729yK1VMV91qtne7rrJxT4mBLWJf46nv9W9J52xdGVrrpme5rsE2yqmy+nxtz5RZzShV5as0n7bjCrquv6ZnvWpdvb1qUrPekJPWKio0POWW27RE088obq6Os2cOVPnnXeehg4d2uN12tvbdf311+uKK67Q448/rltvvVXf/OY3SzpoAAAAALCRiWLyVXratr096gsPezM31dp+8A7VbW2XMis9K7vIKP0CF93eHktX1frnWXZE45aGnomcf+5JZyqACzm5QzgzYmEQZ55atHKdpMKrZ/38oaepgO5IVXoWsqgMqDVFRfW33367HMfRtddeqx//+Me9Bp6SNGTIEF188cX63//9X7muq9tuu62vxwoAAAAAVjM5V9b29goGb+VkFvP0tuSkKxbX/H+vlCQdUuXWdimzajJcwUVG/urGotvbA8dstmmb9mPbZGxvL/C56Iym53nmmiEbGuSVnp3RuJat7pAkdRdYPeuXsb09wkxP2K+od+1bb72lSCSis88+u+g7mjp1qurq6vTmm28WfV0AAAAAGAjM/MVgXlPJZTrlZNrEe1ua8uIHn2t9d1yjhzdo581aKnFoeSUSbkZbfthXHdjfcz39Le2xIivxTKWnCZjNBvcOC5cZ+V8DqfD29o7U5vZc8zyl7M/VYPNB63qvyrVPoaeb3d5O6AmbFfWu7ejoUGNjoyKR4pe+19XVqbGxUZ2dnUVfFwAAAAAGgvT29mB7e/J360JPLzzs+UfLp95JtbZvv5FCfdgqXU7RRDoMqouEMo69v2eqZsz0TOQPpT78bL1+eM9r+vena7Mub8KnIRZvcI8GHnvB7e2p0LMpT+hpPleDtdLTtLZLhT+nRiLhev8+hZ10e7upJK6PDPJEGVYqKvTcYosttG7dOr366qtF39Err7yitWvXauzYsUVfFwAAAAAGgoRX6ZkZIIS8akO7wpp4ge3tf1+Yam3fsQZa230VlnWh9CIjqf9D52iBlZ73/usj3f/KMt314hLfdc381ORzbeZ42hh6Bh97saFnQ44lRn6WfYzKZtGK9d6fi6309Af+kVDIa29f30WlJ+xV1Lv26KOPluu6+sY3vqElS5b0foWUJUuW6PTTT5fjODrmmGOKPkgAAAAAGAi80DNwuq3b26MFtrf/+dwDdOPXv6ADti1um3R/8AdskbCTUenZ3xWCsQJneq7tTFbXtXelA8308qVUpWdqg7sJAm0SDDkLXbrTQaVnj95vTVd6dhdZ6ekP/EOh9BcZ5jkn9ISNinrXXnLJJdpggw30zjvvaKeddtLZZ5+t++67TwsWLNC6deuUSCSUSCS0bt06LViwQPfdd5/OPvtsTZgwQW+//bY23HBDXXLJJf31WAAAAACgpuVvb7dze3vct1ynJ8MaIjpi4iZeUFdN/mVCkZCTUenZ38uM/C3tPVWVmpbizlg60DSBqXmum2xubw+EnLGCKz3Ti4xyMR+rQZp5ZrS3F13p6Xs/hkOO6kKm0jP5XuytmhuoRUX9j7PRRhvp8ccf1zHHHKNPP/1Us2fP1uzZs3u9nuu62njjjfXQQw9p9OjRfT5YAAAAALBZOvTMPN3b3m7pTM/eKj1riX/jvOOkFxlJ/f/8+8O+nlq6TZDpr+LsjmeOEki3t9u3vT04z7RcMz2dVA21XZ+i8kgk3Mz29mIrPd1A6Jma4dkVy6wwBmxS9Lt2jz320IIFC/TTn/5UY8eOleu6Pf4aO3asLrvsMr377rv6whe+0B+PAQAAAACs0NtMT9va281Mz4hFoWc0lrl8yR/Y9nulZ7zQSs9kuNcV888ATT3XXnt7anu7je3tseBMz8Ke905ve3vuKCPkVXra9Tkqh0/WdGa8F4qu9PS9Bv5FRgahJ2zUp96C5uZmXXXVVbrqqqv073//W2+++aaWL1+utWuTm+WGDx+uTTfdVBMnTtR2221X1gMGAAAAAFuZWMHJqvRM/m5bpWehMz1ridkcbhYCOY6jkCMl3ApUevpuv6D29qh/pmfy8vWmvd3iRUbB7e2FViV2eIuMaG8P8re2S6UtMgqHHNVHCD1hv5IHqmy33XYEmwAAAABQAFPpGZzpaW+lZ2bVpA1igfBQSh5/dzzR78+/v9IzGPz5dXjt7dmXN1W1psW7w8LQM2t7e4EBnXk+8ra39/Mioztf+FD/99FqXfmViXnnilbL+yuTre2RkKNYwi14ZIBhAv+Qk3wegzM865npCQvZ8z8TAAAAAFjOFPcFCyO90NOySk/TDh6xKBCJxjMrPSV5y4yCYVw5JRKu/C9vvIf7ytXeblrC6yIDoL09EMgVOlagt/Z284r216t4/dyFuuflj/TAq8v66R76zlR6bj16mKTiZ3rGAl9g0N6OgYB3LQAAAABUiOvN9Mw83bSHW1bo6c30tKq93YSeocxKT6n/KgSl7MrOaCEzPX2BpreAyVR61kdSl7VvkVEw9Cw0oOttkVHIq/Qs4eB6YEYM3PaPxTU3N9SEnjtsMlxS37e3m49FVnt7hPgI9im5vT1o0aJF+utf/6r3339fkrTVVlvpmGOO0bbbblvuuwIAAAAAq5icJGuRUcjSSs+4qQ6zJ/Q0FW3+UMccfiEVh8tXd+jz9d2auFlLcfcbqOwsdqZnent7ZqWnjTM9g89z4e3tptKz55me/fXtgRlPsODTtXr+/c+039aj+uV++sJsbt9xk2b9+bXlxW9vT70m4dSTGPxM2/QZB4yiQs+5c+eqoaFB+++/f9Z5iURC06ZN029+8xslAt9gXXzxxfrud7+r6667TiGLZr0AAAAAQDnlm+kZtnymZ9iin/PSlZ7p18BUqhayyOibt76k91au0wuXHqLRwxsKvt9g6BnrIZTKvb3djBIItLdbGHoGQ85C50929BJ69nelpz+ovu0fi2sm9FzXFdMnazolSTts3MdKTzdzKVmwnT1Y+QnYoKjQ89BDD9Umm2yiZcuy51ecffbZuu2227wS71Gjkh/+1tZWxeNx/eY3v1FnZ6duuummMhw2AAAAANjHZCbBmilrt7cn7Kv0jAbCQykd2hZS6bl0VbviCVcr13YVFXoG29vz3Vc84XphZ+b2dlPpmXyuTfBn5UzPYKVngbNUzSKjhnwBnNne3k9TPf2v2ZPvfKqln7dr7AZD+uW+ivFBaonRqGH13nuyr+3tXujJ9nYMAEW/a3PNrXjmmWd06623SpK+8pWvaOHChVqxYoVWrFih9957T8cdd5xc19Xs2bP1/PPPl37UAAAAAGAhd6Btb08FcWGLFhmZCkv/NmqT5/Q2XsB1XS9kLHYUQValZ57r+2d0dvoXGaWuPyDa2+N9q/Q0l2vordKzuLyvYKZSe7sxw+S60h3PL+6fOyqSmec5fvQw1afeH31ub099AVAX+CKD0BM2Ksu79uabb5aUrAR94IEHtPXWW3vnjR8/Xvfff78OO+wwSdLs2bPLcZcAAAAAYB2TaQYLI8O2zvRMHW8wIKlluSo9C11k1BVLeK9hsHKz9/vNvHy+19rfrh5PuF5AGNw6b3V7e4mhZ773W39vbzfv96kHjJck3f3SUq3vqv4iqfTm9qFeG3qhc1KNdOiZ/Hsw5Kyz6IsNwChL6Pncc8/JcRzNnDkz72VmzJgh13X13HPPleMuAQAAAMA6XqgWyA/SswjtCj0HykxPc/i9tbf7A8Zg5WZvgrcdT7g5OymDlZum2jNdoZo82KY6m7e39629vTtQ7RpkPkf9sVk9kXC9wPvgHTfSuA2HaG1nTHPfXdGn21vcul4ftK7PGGHQV++n2tu3Hj3MCz37WulpvgAItrfXU+kJC5XlXfvxxx+rvr5ee++9d97L7L333mpoaNBHH31UjrsEAAAAAOuYKCarvT3Uv225/SVm4UzPWCJzC7rkq/TsLfSM+kPP4l4sc3n/U5UrZF0fCDFNKBacn9qUqvQ0cy5tYl4D8zEotNLTPIfBQM4wt9cf3x34X6u6cEh7jdtAUjK87Itv3f6Spvz3PL22dHXJx/bRqnZJ0hYbDPHe19G4W9SMYDNaw3wBEKymjRB6wkJledeGw2ENHTpUjpP/PzrHcTR8+HB1dnaW4y4BAAAAwDoDdnu7Ra2v6dmYvkrP1B97rfT0h55FjiIw9+vfPJ6rxT3Yrm6WGpl2ZRP4pWd62lvpOST1XBRalRjNMY/VL93e3g+Vnr7PZiTkeAuMlqYCx2KZ56AcW9HNe6SpPpxxe8WMYPA+y07u7e20t8NGZQk9t99+e61atUrRaLTHy61du1YjR44sx10CAAAAgHW87vasmZ7J323b3h7L0Spe69KzMf3b202lbRHt7UWW5ZrLN/lCz1wVjlnt7amgNT0/1bS327vIyDzupvpki35wVMCt//hAv35qYdb1THt7JM84BccbE1G2Q/X4Q+5wyNHYDZokSUs+72vomTmuoBzHFgmFMm6vmA3uwe3tkUDISXs7bBQp9gorV67U+PHjs06TpAULFmjixIk5r/fBBx+oq6tLo0eP7sNhAgAAAID90tvbM0+3dXt7LBCU2CCWo9LTzCQtptKz0DmUwcv3VumZL/T0lvhEMhcZdcUSiidcK18D8xj84W8sntDVD7+jeMLVN/bdUiOG1HvnBatdg9Lt7eX/HMXjgdBzZKrS8/OOPt2eCSTLsRU95ltyVa7QMxhy5nvOgVpWdOgZj8e1ePHinOfdf//9eUPPJ598UpK02267FXuXAAAAADAgJLxKz0B7e4GVhrXGBCXlCG4qxQsPMyo9k7/3Fjq3BzarF8MEUw11vvbjHMFpsF3da2/3qmpNe3v6x/mOaFzDGor+8b5qzGPJFXp2pkJcKXteaXoea+6AN73IqLzH679vKdkCbtrbP27rUDSeKPoz0B3v+bEUd2zpea+hkKNIyFEs4Ra1zCi4lCwYctr0GQeMov5VvPXWW3s8f9NNN8173i233CJJ2nfffYu5SwAAAAAYMMxcwGDMYRYZUenZ/6I5WqTDhS4y6vZXehbb3p5uTzehVHGVnpkBc0MkJMdJBnzt3THLQs/MSk9/RWJnNP9z7M3BzBPAeZWeZTvStLivSjsUcjR6WIPqIyF1xxL6eHWntthwSFG357W3l6GCMhZ4b9RHQop1xxWNFb/IyDy1wZEVzPSEjYr6V/GMM87o8x3dc889kqQxY8b0+TYAAAAAwGYm08za3m7a2y1bxB33VZjZIpajws78sbf29s6M7e3FtrenW5DDqdAz11zQrEVGqWrH4HGHQo6GN0S0pjOmNR1RbTS8sajjqaaYV+mZjCT8Fa/+5zhYqWjC0XybxM3bMNEf7e2JzLA8FHI0dmSTFq1cr6Wr2osOPc1jKc9Mz/R7S0qGnu3dcXXHC5/3atr3vUrPrEVGVHrCPhX7KmjLLbes1F0BAAAAQE1ye9ne3h+zCPuTCVusqvTM0ZIfKbDSs7T29lRoFk5WenYpd3AarPTsiiX/3h3PPu6RQ+u1pjOmVe09LxWuNeY1aEpVevrDX39Le3alZ28t4f3Y3p56/v07lMZuMCQZeha5zCiecL1RF+UIE9PVy5mb17uKmelpKj2d3MdF6Akb8a4FAAAAgApJz/TMPN0EKcUGadUWi9tb6enfTm2e/6IWGfVxe3tdyPEqFXPdX3CmZ2eg0tN/3GbJz6r13UUdS7Vlz/R0vcA/o7090J5tnq981ZGVrPSU5C0zKnaDuz/MLUd7e/DYzPNTzLKt4CKjYLBMeztsZFXo+dJLL+moo47SyJEjNXToUE2aNElz5swp+Prz5s3Tqaeeqh133FEjRozQkCFDtP322+uss87SggUL+vHIAQAAAEByU9MGg6Fn2Prt7fb8aJl7kVFqkVQvz39p7e2m0tPxQuK+bG/3B34jh9RJklZbVukZC4SeUvr56am9PdpLe7vTj4uM0jMv0x/esRs0SZKWripug7u/ArM8lZ6ZgXhDKkgtZXt78LjK0YYPVFrZ29vnzZunBx98UO+//74kaautttJXvvIVHXzwwSXf7uGHH676+nqdfPLJamlp0f3336/TTjtNixcv1k9+8pNeb+Nvf/ubnn32We29997ebb3zzju64447NGfOHD366KOaMmVKSccJAAAAAPmYjCurvd367e32VIEFW4GldGjbW5Dpr8IsfpFROmw1r3eu28hubzfb29Pt8caIplTo2WFbpadZZBTxnZZQfSTUY3t7bxvPzUvaH2Mics2vNZWexba3+x9XObe3m6DS/F7O0JP2dtioqNBz9uzZGjJkiE4++eSs89rb23XKKafooYceyjrv+uuv11FHHaW7775bQ4cOLfogY7GYpk6dKsdxNH/+fO2+++6SpOnTp2vffffV9OnTdeKJJ2rbbbft8XYuu+wyXX311VmnP/XUUzr00EN18cUX66WXXir6+AAAAACgEPlmerK9vXL84aNhcqfenv+O7nSIVOwoAn/Yau47d6VnsL09s9LTH7p57e2WVXoG29v9p/W8vb3n5T+OmelZvkP1pGd6+is9k6HnR6v6FnrWh0NedWpfua6bFVialvligvl4oGo7q729DG34QKUV9a6dOnWqLrzwwpznnXjiiXrooYfkuq4aGhq0xx57aI899lBDQ4Nc19UjjzyiU045pU8HOXfuXC1atEinnnqqF3hK0vDhw3X55ZcrFovp1ltv7fV2Ghtzb7M75JBDNHLkSL333nt9Oj4AAAAAKES+mZ6mvb3IMZFVF09kB3G1zsyJzNjeXuAio45oOpDsbf5nkH+RkQmncm1vN5WeJrjyZnqaeZYRf3t7MvRc3W5Xpad5LhrrcrS3x3KHnoUs/zGfq/6d6Zkderau684Kq3uS6z3YV/73YZ2Z6RkpwyKjSLDS057POGAUHdXnKhP/y1/+okcffVSSdN5552nlypV68cUX9eKLL2rlypU6//zz5bquHn74YT3++ONFH+S8efMkSYcddljWeea0Z555pujbNZ5//nmtWrVKEydO7PNtAAAAAEBv0pWemaeHe5jxWMtMeGXVTM9E9lxI88deFxl1569C7E26wtTxZi/maqc397FBKtA029tzVXqOHJpsb1+13rJKT19Ynl66Yyo9089rt2+Rkf/5juQJ4Byvvb2shysp90zPlqY6NTcmG2iXfl74XM/uePI1LUf1pP89ZJ4X85wGZ6L2JKvSM/CZDv4dsEFZZnrecccdkqSTTjpJ1113XcZ5Q4cO1bXXXqvW1lbNmTNHd955pw4//PCibn/hwoWSlLN9feTIkRo1apR3mULMmzdP8+bNU1dXlxYuXKiHHnpIo0aN0v/+7//2eL2uri51dXV5f1+zZk3B9wkAAAAA6UrPzNCmPyvU+lMsR/VbrTMhkb9a0Gy97u35929v73t7e6jnRUapatKRQ+v1yZpOLwTMtYAp3d5uV6Vn1Pca1IUddcd7b2/PnINZ+fb2fFXNYzcYoreWr9HSz9u1/cbDC7qt7lj2e7CvoonsMLiupEVGSh2bvxLayWjrB2xRlqj+xRdflOM4uvTSS/Ne5sc//rF32WK1tbVJklpaWnKe39zc7F2mEPPmzdPMmTP185//XPfdd5/Gjh2rxx57THvuuWeP17vmmmvU0tLi/Ro7dmzhDwIAAADAoGdCtWB8UOj28Fpj40zPaI5lOCbQ6X2RkT+QK7a9Pb1h21TTRXOFnl2pSs9UFadp984V1ppFRm0ddlV6xnyvQV0kWOmZL/T0tXHnCQtNMWJ/LDLKNdNT8i0zKmKuZ2+zSftyXJKvvT1QPVuIdPt+8rr+zzSt7bBVWULPlStXqrGxUTvvvHPey0ycOFFNTU36+OOPy3GXJZkxY4Zc19W6dev04osvaocddtD++++vOXPm9Hi9Sy+9VG1tbd6vpUuXVuiIAQAAAAwI+ba3O3a2t3vVbzUeinzS1qkPWtdLyqy4NMIFVtr6A7lYke3tXtgaCnkhUryHmZ5mXmdXNCHXdX0bun3t7ZZXekbCIe91MNWP/jmU/tDOPN/hkJM3ZPcqPfujvT1PVfPYDZokFdvengo9y9HennoPOU46kG0oodLT3IbjpEcPsLkdtirLO7ehoUENDQ29Xm7IkCGKRov/BspUeOar5lyzZk3eKtCeDB06VHvttZceeOAB7bDDDvr2t7+tlStX5r18Q0ODmpubM34BAAAAQKESvW1vtyz0jOUIEGvRib99Tl+a9Yz+8V5rxmxNw1Re9jrT0x969rW9PZwO7XJVlpqFOBsMTQaanbF4RpVjJKO9PTXTsz3aL9WN/cXfql9v5pumXhf/3NRu3+PuzjHTNChUYHjdF+mZnpnvdbPMqKhKz1j2e7CvvApg33GZ2+1Te7vvkOoCM0IB25TlnTthwgS1tbWps7Mz72Vc19WaNWu0wQYbFH37ZpZnrrmdq1atUmtra855n4WKRCKaMmWK1q9fr5dffrnPtwMAAAAAPel1e7s9uZUk/xzA2q30XN8V09LPOxRLuPr+H17Rks+T4VRdjkVGvYXO/vb2XJvXe5IOW9MzPXMFpyZYTVd6xjMqHv0B1MhUMNodS2QEsrXOHzz32N4ey25v7ymA8xYZlfVok/LNr/Xa2z8vPPQsa6WnL0w3zO0WtcgoR6gbodITliv6nfvpp58qHA5n/HrhhRckSW+++Wbe6y1YsEDRaFRjxowp+iAnT54sSXriiSeyzjOnmcv01fLlyyUlA1AAAAAA6A9uKo7JCj1trfS0YJHRyrXpZbRtHVG9vzLZ5h7JUemZ6OX57/SHnkXP9Ew/V5E8laXdsYQX7plKz65YIueGbkkaWh/2qvFWtdsz19M/YsAEaqa93cwwTV4uu729p43nZkFYb69jX8TzzfTcIB16FlptG80xn7WvojkWLNWXYZGR//hqfXwFkE/RnzDXdfP++tOf/pT3eo899pgkaY899ij6IA855BCNHz9ec+bM0WuvveadvnbtWl111VWKRCI688wzvdNbW1v17rvvqrW1NeN25s+fn/MfoSeeeEIPPPCAWlpatN9++xV9fAAAAABQCJPF5G1vt6hFWUpX7IVrOBRZuS4Zeo4aVq9NWhq90zNmeqb+WEx7e7GLjDLmWJqW7kAlnr+121RxdkbjGRV7/nDLcRy1NCUvt9qiuZ7+ZVJ14WClZ+6Znt05FlAFmXMqWem5+cjkTM/13fGCg+fuWLrqt+Tj8r2vjPpwOHk/fVhkFM7RJk97O2xVVFnj008/3eP5I0aMyHveb3/7W0nSvvvuW8xdSkpWX9588806/PDDdeCBB+qUU05Rc3Oz7r//fn3wwQe6+uqrtd1223mXv/766zVz5kxNnz5dM2bM8E7/8pe/rFGjRmmvvfbS2LFj1dHRoTfeeEPz589XXV2dbr75Zg0dOrTo4wMAAACAQpgijGClpzeL0Be6tXfHNG/BSn1xu9Ea1lCbHWn+6sVa1Zqq9Nxyw6Ga+eUJOuHG59QZTai5Mf2cmgC0t1mQ/vb2XEuIeuJv6Q7naW9vj8ZSx+NoeOo174wmMq7rBN48I4fUqXVdl1ZbVOnp30RfF5jp6W9v98/0zLWAKsh8mdAf3x0kvPbvzOe/sS6sjYY3aMXaLi39vN2r0O1JWbe356j0rIsk/xwtU6Un7e2wVVH/c5bSQv7OO+/0+bqSNGXKFD377LOaPn267rnnHnV3d2vChAm66qqrdNpppxV0GzNnztRjjz2mZ599VitXrpTjOBo7dqymTp2qCy64QBMmTCjpGAEAAACgJ26eSs/0TM90WnPn8x/qmkff1UWHbadzD+77DoP+lN5oXbuhiKn0HD2sQRM3a9Efpu6jF97/THuP39C7jHk9eqr0TCTczM3ifV1kFEpvLA+OM1jflQz8htSHvQ3cXbG4orH87dA2bnA3oV/EV+nptbfnqfSMFjAH05vp2Q+pZ0+jHMZuMCQZeq5q165jR/R6W/0x09P/3mgwz2kfKj0jOSo9TYgK2KY2vy7MY9KkSXr00Ud7vdyMGTMyKjyNH/zgB/rBD37QD0cGAAAAAL1L5Kv0DKUXGbmuK8dxtCJVodi6rnbDLC8oqeX29tTzOHp4gyRpjy1Hao8tR2ZcxuRFPc2CDC4KCram9ybmC/q8RUZ52tuH1EfUUJdsUe6MJnLObTT8G9xt4d/eboI1c1pXLN8io97b20P9uBDMVPbmWto1dmST/vXhKi39vKOg2+ou5/b2RPp9ZfRppmfq3yb/FzJUesJ2vHMBAAAAoELyzfQM+/5uLmNCnmK3hFdSzILt7Sb0HDWsIe9lwnkWC/kFQ89il06Z264LO+mZnsH29u5ke/uQ+rAa65LH1Onb3p6rMtBUeq5eX7vheJD/ucie6Zl7kVEh7e2G2w9TPU1FZa73+kbNyVmxn63ryjovF3/oW6pojuNKV8/S3o7BjXcuAAAAAFSAv+U2GJv4N0Kb8MEEFrW80d2rXqzh0LN1XWalZy4m0+npufYvGZL6ssjIPFch7/kK3l97KvAb0hBWQyRZ6enf3p4r8LOx0tP/eOp7WGSUMdPTVEf20BLur5guN1Olneu93hgprp28kFb9Qpn3UJ3vvVFf5PH4b4dFRhhICm5vHz9+fFnu0HEcLVq0qCy3BQAAAAC28I8ZzKr0DPkrPVOhp6n0LDJcqySbKj17Dj17X2SU1d5e7CKjeLq60dxfMDhtNzM96yIZlZ7d8ewWZmOEqfTssKfSs9tX6Rjx2tuTz0VH3kpPs/yn9+3t/bG+vaf3uhlF0BUt7D1hvtAoR5gYzfHe6FN7e45KT7MRvpbHVwA9KTj0XLx4cY/nO46Td1iw/7zgpjkAAAAAGAz8gVpP7e3BSs+eWq6rzYpFRl57e/6t2uECFhmVWumZnr2YnmMZ3ABv2tub6sNqrEtXekZ7CMlGpio97drenp5pWXB7u9cSX8Aio35IPeM9hZ6+pVOF6M6xfKivvKrZsL9CszyVnvW0t8NyBYeet956a87TV61apSuvvFKrV6/Wvvvuq4MPPlibb765JGnZsmWaO3eunnvuOY0cOVJXXHGFRowYUZYDBwAAAACbZMQwgdzEn4GahSLpmZ61G3rGanyRkeu63iKonio9zfEXs8io2LED6ZmUjhecBV9bcx/+7e2S1J4KXHuq9LRpe7s/qOupvT0j9IylQ+N8+nORUXqmZ/b9F9tOXs729liOJVfmvRMtJvRM/bvj/wImQns7LFdw6HnGGWdknbZ+/XrttddechxHjz32mA477LCsy1x55ZX629/+ppNOOkk33XST/vnPf5Z2xAAAAABgocxKz8zz/NVjbiqnSM/0rN1FRulKz9oMPdd0xLwgqqdFRqE+VHoWvb094Wvp9ra3BxcZpbe3m0pPSVrbFfOuG2RjpWd3zkrP5HPR5QuXu2O+mZ5FtLfn60ItRU8zPb1KzwLb2735pOWo9MzxGazvyyKjePYXGOlFRrX5+QZ6U9In7JprrtGCBQt0ww035Aw8jUMPPVQ33HCD3n77bf385z8v5S4BAAAAwEo9zvT0t7cHZnoW20ZdSSbIq9WZnivXdUqSmhszQ8Qgkz0VU+lZ/CKjdKhkqhWztrd3pbe314VD3vO6tjOaum6uRUYWVnr6WtXrIsnHaAK6zljPMz17bm/vx0rP1I0GP7uSMpZOFaK7gAC34OPK0Srfp5mebvbjo70dtivpnXvvvfeqvr5exx9/fK+XPf7449XQ0KB77723lLsEAAAAACv5Kz2DuUmu7e3RVJVbbW9vr+2ZnivXJoPAUT20tkvpluWeKj1NFWZ6HmeRMz1zbG8PVoumKz2TIZqpIFzXmQxDc4VkptKzrSPaY2hbKxIJN6NCOOI99wnFE25GmJwZehY+01Mqf7VnT1XNxc70LCTALVSuRUbmdgsNYaV8i4ySt1lXhjZ8oBpKeucuWbJETU1NCofzf2NmhMNhNTY2asmSJaXcJQAAAABYqadKTyldLWnC0a4an+npum7Nb29fuS61ub2H1nZJMnlRvIDt7cMbkyFjtNjt7V51o+OFSVmVntF0e7skrzp1XaoCNFe4bCo9XVda01n7Le7+560uEvKqEqNxN2OJUfK07ErPnubH+j9X5e5w92Z65rj/hrriQkbTtl+emZ7ZXzzU92WmZ45FRl57e41+voHelPQJGzp0qNra2rRw4cJeL/vvf/9bbW1tGjJkSCl3CQAAAABW6qnSU0q3uKcrPVOhZ5GzIyvFn9fV6kxPs7m9pyVGkhROhTs9VUp2dpvQMxlIBudx9sZfqWiCpWC1aEeg0rMxFV6tTVV65qq4q4+ENDR1+VUWzPX0P291ofQm++5YIiv07M5R9dnTUh3/u7DcXxXEe5zpmWpvL3CmZ3cZKz1zzvQscrGS5F9klD6tjvZ2WK6kd+7+++8v13X1ve99T11dXXkv193dre9///tyHEf7779/KXcJAAAAAFby51tOcH27JFNgZYKw7hqv9Iz5KvZqdXu7CT17WmIkpQPnQtrbTejZ5/b2sJNubw9Ui65PVXQ2mfb2QKVnvoo7m+Z6+kPPSMYio4Q6A5WS0Vhx7e3+Ss9E2dvbE1n3YRTd3m4WGZWj0jNHBWwpi4zC/tmgtLfDciW9c3/84x8rFArp6aef1m677aZbb71VixcvVjQaVTQa1eLFi3Xrrbdq991319y5c+U4ji699NJyHTsAAAAA2COjvT377JCT2d5uKttqdaan/7hqYaZn67ounXDDc7rrxSUZp0m9V3qaELKQRUbDG1Lt7UVvb0+Hdl57e6BatCPa80zPfIHfyKFmg3vth57+9vZIKDP0NJWu3mWLXGTk/y6h7O3tPc70LG6RkXksDeWo9OxhkVExy7bSlZ7px3fExE00YdNmHbbTmJKPE6iGSClX3mefffS73/1O3/3ud7VgwQJNnTo15+Vc11U4HNZvfvMb7b333qXcJQAAAABYyV95lnOmZ2DzdHeNt7f7qyJrYabnvAUr9fKHq9S6rkunTNpCUuHt7aFQ75WendFAe3uRYbQ3kzKUrvQMBtrpRUaZMz3Xdpnt7XkqPZuSlZ6ra7C9/a4Xl+i3zyzSrd+cpK1GDfWFl44cx/GqEmMFzvSs63GmZ/rPZa/0LOdMT/NYIqV/bkyInNHe3pdKzxyh7r5bb6iHzz+w5GMEqqXkrxXOOussvfDCCzriiCPkOI5c18345TiOjjjiCL3wwgs6++yzy3HMAAAAAGCd3mZ6hgJBWLTW29v9bco1EHou+bxdkrT4s3ZvoU/BMz1TPxn3FJS1dyerLb1FRsVWeuaY6RkNvLamvT3/9vbcP8KPSG1wr8WZno/838da/Fm7nlvUKin9PJjqYBPkdscTWe3hmZvcC9ne3n/vw55neqZCz2hh7e0mjCzHrEwTxma0t0f6HnqGauCzDJRLSZWexhe+8AU98sgjamtr0yuvvKIVK1ZIkjbaaCN94QtfUEtLSznuBgAAAACs5Y+3coUzWdvbY7Xd3m7mUTpObQQlH6VCT0l6c1mb9tt6VLq9vbeZnqF0tWE+HaklNX2e6ZlIz1401YrxwEzPYHt7utIz5l03l5FDTKVn7bW3m5Z183uwYjNjpmdgEVB3ke3t/Vrpmchu/zaKXRxUUKt+wIefrdfXb/mnhtRF9Pi0L6ZvK8f2dnO73fGEV4zWm/T29oIPCah5ZQk9jZaWFk2ZMqWcNwkAAAAAA4IJYfLlg6HA9nZTpVVsRWGl5GqHraYlvtDz/z5q095bbajP1idDwF4rPc1z30NQ1pGq9GwucXt7JBTyAu7gzMXs9vYCZ3p6lZ41GHqmgtz1XSb0zKzYrA+n50+a9vah9WGt747nbG/vaWmWf0FYf830DOeYX2tmekbjruIJt9dxD+Y5qC9yQdDSzzs0rCEzxsm5yMh3u9G4q/oC2ujjPTw+wFZlCz1feeUV3XXXXXr55Ze1YsUKOY6j0aNHa6+99tIpp5yi3XffvVx3BQAAAADWMSFMrnmeUrrCKtjeXrOVnvHsCrNqWrrKF3oua9Oq9m7FE64cR9pgaH2P1w0Xs8jItLcnim1vT1c45pvp2dEdXGQU2N6et73dbG+vvfZ285jMeIBgeGnmWvorPZub6pKhZ47t7fla/KXMsRHl/tTkaiM3GnwhY3csoabU65eP+UKjp8cS5K/e9PMvyMp5PPFEQeFqrkVGgO1KDj3Xr1+vs88+W3/84x8lJZcW+c2fP1//8z//o5NPPlm/+93vNHTo0FLvEgAAAACsY35UypcphH3b22PxhLfQqFZnetZSpWdnNK5P13R5f39zWZvX2j5ySH2vbcThAhYZmfCuuSn5Y7TrJkPSQlv7vTbkcMgLiv3357qu1ndnzvQ0lZ6mAjTfc21merbVYuhpKj1Tjy0Y0mW2t6eXRX3cllkJ213AIiOnP9vbTSjYw0xPSeqKxXsNPfvS3l7nW07kb1k3Xz74j8t/u92xhNRzobMkf6Vn9T/PQLmUFHomEgl95Stf0dNPPy3XdbXJJpvo4IMP1uabby5J+uijj/T0009r+fLluvvuu7VixQo98cQT/TpcGAAAAABqkQlh8v085F9k5A97arbS04QkPYRQlfLRqg5Jybbe7lhCiz9r16IV6yX1Ps9TSoeJPS8yyqz0lJLVng2hngMuw6v0DDletWDMV7XXFUt4wXhTYKanUZenYm+kV+lZu+3t7V3BmZ6pRUYhX3t7LPM59s+k9J6/HqoW+7O9vaeZnpFwcmRBPOEWtMHdBLjFtLf7q0JjCdcLf82s2DpfWBkOOd7xFDoeg9ATA1FJoecdd9yhuXPnqq6uTv/zP/+j73//+woFWhsSiYRuvPFGTZs2TXPnztWdd96p008/vaSDBgAAAADbFDrTM+G6GVuXg3Mfa4W3mKcGQpKlqXme40cN1frumJZ+3qGnFyQX7PY2z1NKB849zek0VYj+mYqxuKuGAn6qTiRcr3I3X6WnCVWl9EzPhkAoVtdLpefqGqz0NI9rfbC9PfVY6n3t7R3d6UpPwwR83izQHsYp+J+eYBdqqWK9hIINkZDau+PqihYQesZ6r1oNqvPN5YzGE74K2XQFsV99OKSORLzgDe61VLkNlEtJw1d+//vfy3Ec/fKXv9S5556bFXhKUigU0ve//33993//t1zX1R133FHKXQIAAACAlbz2duUOFdLb2zPn9gU3fNeKXG211WLmeW6xwRDtvFmLJGnegpWSpFHDep7nKWWOFsjHVCw2N6UrPQsdPeCf/xkJO95zFs8IPZOhYEMkvegoq9Iz7yKj2qz0jCfSAb4JP2Px3O3t3bGEVyWZUU0bz1zoVdfDUh5/FXX5Kz17XqRkAuquWDzn+X7msRQz09N/2Wgs/eByLTKS0lWkhVSeSun3YqHjGgAblBR6vv766wqHwzr77LN7vezUqVMViUT02muvlXKXAAAAAGCl9CKj3Oeb0+MJNyP0rP2ZntVfZLTks2ToOXaDIZqYCj3NTM9CKj0LmenZnqsKscDWYX8FaV0o5FXT+a/fHlhiJGWHnsFqPsOEnu3d8YJCt0oxQbEkre/KrPQ0VY75ZnoaJuBLV0cWVulZ7pmevYX8ZulUISFjX7a3h0OON7M080uR3BWa/ue1ED217wO2Kul/p7Vr12r48OFqamrq9bJNTU0aPny41q1bV8pdAgAAAICV0u3tvVR6JtyMrdU9tVxXU8xbzFP9kMRf6bnLZiMyzism9Oxpe7sJ5IbUhwsKSf38r2Ek7JvpmaO93bS2Szna2/M818MbI17gV0st7h2+ln3z+ILt2CaciyXcjBECwYAv15byoIxKz3I8AJ9EL9vNG+oKr/Ts7sMiI8dxcgaZ0TxfPpj3TsHt7T0sagJsVVLoOWrUKLW1tWnFihW9XnbFihVavXq1Ntxww1LuEgAAAACslEj3t+dkwtC46+as5Ko1tbT4ZMnnyUVGYzdo0sTNmjPOKyb0jOepDozGE15Y11SXDj0LraLLaG8POV5Alau93V/p2VBge3so5Kilqfbmenb6Kz297e3BSs/UcxlLqDM1D7MxEsoK+IIVovmYTLLslZ4FzPSUeq/0dH0ze4sJPaV0i3t3xpciuZ8XU0XaXeB7NFFDn2egXEoKPffdd1+5rqsZM2b0etnp06fLdV3tv//+pdwlAAAAAFjJRDC9VXr65yBK6ZCo1tTKIiPXdfXR5+lKzxFD6rXFBkO880cVsL3dq9zMU1Xrb9Nuqg97C4UKDaTN7UZCjhzHyRmamu3mGe3tWZWe+X+ENy3un63vKuiYKsG/nCnf9nZvpmc8XenZWB/2Aj5z+UKDQu/d2E/b2/NVNhc6Q9Nf3VvMTE/JFxD7Kz3zLDIyly200rO3UBewUUmh5znnnCPXdfXb3/5W3/jGN/Tee+9lXea9997T17/+df32t7+V4zg655xzSrlLAAAAALCS28v29vQio8xKz4Tbc9t1taRnHFZ3pufq9qjWpuZFbj4yGXaaZUZSgZWevSwy6kyFdyEnGVRFApuzexMNLJsxgVRGpWcq8GvqodKzp1EC40YNlSS98/Hago6pEjpyVHpGfQGwFJjpGTOVnuGsgC8YluYT8l7LsjwET2/vd2+mZy/b2/2BZTEzPSV/QJy96Cz45UOxlZ61VLkNlEtJ/zsddNBBuuCCC+S6rubMmaPtt99e48aN0/77768DDjhAW265pbbffnvdddddkqRp06Zp8uTJZTlwAAAAALCJCWGcPJWe5vR4Irs6qxaXGeVboFJpZp7nRsMbvMU/E/2hZzGVnnmeZ1Ox2FQXluM46UVEBVbhevMoU4FZutIzfX8dqVBwqG+mZ7DSs6fKwD22HClJ+teHnxd0TJVgWvYlqTOaUDzhpqteU4+lPscio8a6sG+re/Lyhcz0lNLt7W6ZSz3jvc30LHB7u/+z3VurflBdjrA932zdXK3wPamVzzNQTpHeL9KzWbNmafz48ZoxY4Y+//xzLVmyREuWLMm4zIYbbqgZM2ZQ5QkAAABg0Opte7vJLBKumzUrshbnetZKO+xSb55nuqV9l82ToWc45Hht3z3pbZFRh1eFmfwR2ltE1OdKz1wzPbMrPbO3t+d/rk3o+fLiVXJdN2+4Xkn+mZ5SMgQ1z4UJ5eoi6ecyHXrmmOkZyz27MsiRI8kte6Vnb5WQhc70NJWXjlP8Z8fcR2Z7u6n0zD0KodhFRvnGbwA2Kjn0lKRzzz1XU6dO1ZNPPqmXX37ZW2y00UYbac8999SXvvQlNTY2luOuAAAAAMBKpnU6XxjlD96CQUU0kVCTwrmuVjXxRGEhVH9b4pvnaXxhi5HaevRQbbvRcIUKCJZ6q/RMh57JIMkETIVW4KZDz8xKz9zb233t7UXM9Nx18xGKhBytWNulj1Z1ZITA1dLRnfk+bu+OZwXA5rnsDlR61gcCvu54kZWe/bTIKF8lpNfe3kvIaKo068OhooNpLwjOWGRknpfc7e2FLttikREGorKEnpLU2NioY489Vscee2y5bhIAAAAABgwv9Mxzvn97e1alZ4EVhZVUM5Weqfb2sSObvNOa6sP62w8LH63Wa6WnCSTrgpWeBba3m2AqZIK+7Pb49PZ2X3t71vb2/M91U31YEzZr0etLV+vlDz+vidDT394uSeu7Yr7wMLO9XZLWdiYvn6z0TC3iST3H6a3vhYaeJR58gAn5w3leg4a6VKVntLD29mKXGEnpqtguf6Vn4Pn0jidSXKVnrXyegXKq7sRpAAAAABgk0u3tvW9vD1aL1fZMz+r+WLk0VekZDPkcxym4ks6EkPE8SZkJPRtTVZiRXipDg7xN96mgy/zuD7NzVXpmtbf38lzv6WtxrwXZ7e1xr0qxPhXgmSBP8oWekXDW/MpC29vN56v8oWfy995neha2yKiuyCVGUu5Kz3xhbLGLjKj0xEBE6AkAAAAAFdDrTE/f9vbgVvBanOkZjddGSJIv9CxGqLdFRqa9PVXNZ8Kn4EzPfFV13sbycLDS07/IqLT2dikdev7rw9oIPU2Qa6zvimVVJvof07quZOjZ4FtkZAK+aKHt7anfy77IKM+WdKPQ9vbuAsPbXHIuMvKqiEub6dlb+z5gI0JPAAAAAKiA3mZ6hnrY3l7oXL5K6i0EqswxuFq2OrnIaIsSQk9TvZevvb3TCyQz29ujvvb0B19dponTH9djb36Sdf1gMBXO0d6+vjtzWZJUXHu7JO0xLhl6Lvh0rdo6oj1ethI6clR6xrJmevorPZPH3FQXztjq7rquV7HYW+hpPkfl/p6gt/bvQtvJvUVOfaj0rA8sd/L/Oe/29gL/7fAWGRF6YgAh9AQAAACACjAZTL6Oa5M1JBJsby/UJ2s6FY27qgs7GtPc9+W5BS8ySoWQ4VB2pee/Plyl7nhCLy/+POv60UQg6AvnqvRMVjkOzWhvL67Sc6PhjdpigyFyXenVJdWv9gyGnutzbG93HMcLc83T0VgX8treu+OJjPd/r7Mw+2mRkTfOIU/wXO+1txc207O317Kn+/AHmeY9lG+RUcHb26n0xABE6AkAAAAAFWAqPXub6Zlws7e31/JMz76EN+Wy5LNka/vmI4eUFL76r5ur2tO0aZvKyzpv/mr6dTKv2fru7NAr5rW3Z25/d93089juVXr629sDMz0LaInec1zttLh3BJ6L9q54Vqu/lP0eaqzLnOnpb+fu7Tnor0pP8zrl+/z2ZXt7sUyw6f9SJOaNmSitvb23xwfYiNATAAAAACrA9ULP3Of7t7cHW1L9bdC1IlYDMz3N5vbNfZvb+8L/GHIFzKZi0czb9NrbfWGcqfBb3xVTkGnp9ra3h/33lxmW+re3B2d6FhKU7bnlBpJqY5lRMPRc3x1LL3XyhXQ9h56JjM9DodvbVfaZnj0v7kpvb++/9vZci4xiecZMmPdOIaMx/EF/tWf0AuVE6AkAAAAAFWC6bfPN9PQqPRM5Kj3jtVvpWc122I/KsMRIClR65miLNlvIm7zt7an2dn+lZypcyhV6RgOt0f7nzDyPpr3dv8goFHIywrFIIaFnqtLztaWrqz4LNtdMz2gsVekY6Sn0DGXMr4xmhJ7VqfQsdKZnb+3tXaW0t+eY0xnLs+CpmPb2GKEnBihCTwAAAACogIQXeuY+P+S1TGdXetbiTE8zp7KaIcnytk5J0mYjSqv0zBVC+rWnAkkz09ObyekLo024tK6nSs9w5iIjKV0t2p5je7uUWe1ZyMbvbUYPU3NjRB3RuN75eE2vl+9PwY30ye3t2ZWJ9YHH1RgJe4+1O5bwBXtO3i8NDG97ez+1t+drry+8vb2829vLscjIH/QTemIgIfQEAAAAgArodaan196e2b4q1WZ7ezzHbMZK+7gtubl9k5a+LzGSMl+TnO3t3cnnP7vS09/enrxMe08zPVOBUp2vRTpd6Znd3i5lbnAvpDowFHK0x5bJas9qt7ibSs9Rwxokme3tmfNNg3+uD4cUCjmBmZ7ZLfH5OF6lZ/+0t+ef6WkqPQttbw/3eLlcvOVOvvvIV3Fd51V69v48+N/HLDLCQBLp/SIAAAAAgFJ57e15zs9obw/O9KzB9vZa2N7+carSc+MSQ8/eFhl1Bra3m2Ao5nudTNiVu73dVOMlg6hQyJHjJN8T6Zme2e3tUuYG90Jbog/cdrSicbekjfblYALgDYfVa8nn7clKT297u3+RUfrPZjZmnW8mZXcR1ZEmk+y3Ss987e3eTM+e29tzPf5C+eecereXyA6Rk7dfeKWnv7qZRUYYSAg9AQAAAKAC3F4qPc3JcdfNmsVYi+3tvS126W+u6+rj1cnQc9OW0trb/TlWrkpPr709sMjIf9me29vT7dlGJOQoGncVi7tKJFx1phbgZLe3p/9eaFXtWQdspbMO2Kqgy/YnExZvODRd6RnNUenpD3NNsJxrpmchy3/MM1TuSs9YL+McCm1v7y5lpmeO5UTBJVnBy3b3MmNUyvz3hUpPDCS0twMAAABABfQ209Nrb0+4WcFJtAZDz1iVFxm1dUS99ulSKz0dx0lX2uYIyzoClZ4msMo10zNnpWeO9mzz53jCzVj4k93entn6bRPzuEYPr5cU3N7um+npCzMbvec41codT+Rd1pNLf1Uq9j7Ts7DFQd1FPJag+sBMz0TC9f5dyVvpWcAio4xKT0JPDCB2/YsJAAAAAJYyYVpv29td181YVCJJ8Vqc6Wkq36o003N5qspzw6H1GXMv+yrsWyQVZOZtBtvbowl/e3vyMu3ReFaLfCxHYObdRjzhtYE7TmbIKSWX+gSvYwuvvd1UenbFc1Zt+gNA8/jrfKFdd55lPbn0d3t7uNeZngW2txdQtRpknhPzpUhPW9fTVaG9PxHm3ybb3l9Abwg9AQAAAKACTPSQL1dIb2/PbkmtxZme0Xh1gxKzxKjUKk/DX2kbZCoWhwTb2/2Vnqkwy3WVUbmZvJxpQU7/CG7C4njCzdgOHwzFzaxIx7Fvs3anb6anlKz0TL9v/FWv6cfVGKimjcYT3mKvYio9y9ne7q+ozPca1Be4yKiU9vbgTE//grPgvNP6AitPk7eTGr1h2fsL6A2hJwAAAABUQOHb23NVetZe6OlVvlVppufy1BKjTUqc52lEeqr0TIWYjT1sb/eHS8EW92iOTff+22jPs7ldSld61oVCeauEa5Hruv+/vTuPc6uu9z/+zj77dN9LaWlLaSk7KLKJCEXcBXHhKsgF9V6vekXFneV670W9mz/F64bCvXpR8bpdF1ahQEH2fWtLaUv3dtrOPpP1/P7I+Z6cZJKZZJJJTjKv5+PBgzaTSU4yOdOZdz6LBvNubx9ZtZnV3m4/XqdSMWE5z3Mp7f2VPGOSrgC10AzbYmd6lrfIKFMdnP5/4eNyqkKLWGSUGqOKFahXhJ4AAAAAUAWW096e/+NZ29uZ6Tmm3Xal57wplan0NFVu+RYZjWhvdyo9M18n99csd5mRqchzV/dlNsC7Q8+RbfpNOfdZL+JJywmQnUrPaCJvgOl+Xpzt7a6Ar5T2dpP9VbLS0x2EFxrnUOz29lgZ7e25i4zcr79ClZ7xEio9aW9HoyH0BAAAAIAqMBlMoWo9v6vSMzZie7t3Z3rWKowzm9srVek56iKjnFAymCcgdVf4mRDTSOQZBZDZAJ9y2tvzhZ5mVuR42qFryd3iP9NV6WnCYffrxh3Y5ba3x0psb/fJzMYt5+izZYWeY870nMDt7c6cUyvruAJ+34jvK2HX8yelX8Mv7upV92BsxO0maW9Hg6qv75oAAAAAUKdMblIoVzAZSL5KTy/O9Kx1ddjOCld6BlyVl26WZY3Y3h4cZXu7NLLSM9PePrLSM5kavdIzkrPNvF6YoDjo96mzOSTJzPQcuck+e5FRnpmeJW1vT//fqmDqOdrCIMPd3j7afZvHX4mZnnFX6Jkrd6bn8zt79Kb/d7/edv0DI66bpNITDYrQEwAAAACqwNneXuDjftciHRNUmOAiX8t1rdV6pucue6bnnI7KLjLKrfSMJlJOYN0cNvM1M1WaUjqodn+Ncmd6muu55zgGnO3tlquSdORMz3qv9GwOBdQSST8uy8oEwuGgu9Iz89ia7TbxsBPwWa7nr4hKT/vrWNGZnq6vbaFg0LS3SxpRqe0Wt6s0x7W9PSfIzCzIyhN65gSkzvmSZ/EXlZ5oVPX1XRMAAAAA6pTlVHoWaG932qszQYWp/PNi6JmvZbtaLMtyQpx5Uyrb3p67yGjY1aZtqhBN0GsqEHNDrmIqPU3Q5670bG6gmZ5DrsdkKmQlqXswLim30jNPe3vQNdMzT0t8Ieb0SlXwnDGhq89XOBiMuELM0VrcM4uMxtPenn+RUTDPbeVWeu4e5U0CE/RT6YlGQ+gJAAAAAFVgFbm9PeWa6dliB0DJIjYwV5sJggq1+06k/QMxxRIp+XzS7EpVehZYZGQCyVDA5wSVJnwzc02j8eyvz0A0d6bnyNAu4KoWHW2mZ1OoXis904+pORxQwO9zgs9o3pmehdvbY4nS2tvNrVbybQIzUne07ebuEDP39eAWddrbx7O9Pbt60wT0+W4rN/Q0bxLMzVPpaV7zhb43AfVqZO08AAAAAKDiTJZWKFfwuyoNzeKWZg9XetZyDqCpWpvRFhlXm3A+wQKLjGa1R/Tg59+QVfEZcra3p68bTWaHnCbENMzXL+Sqbsy/vT1fe3tgxOfWg6GY/Rq2Q8zWSCBruVGh7e1NOe386ZmexVdH+guMKShHMQG/z+dTJOhXNJEao73dDj3H097uLCdKPzbzvOQ7LvMaNSHrnt6x29tr8QYGMJEIPQEAAACgCiy79qxQ6BnIs73dy6Gns8ioBhWIO7vTS4zyVa2Nl7/AIqNgwD+ihd60ZptFMrmLp0a2t4+sbnSWIY2xyMip9AzWVyBlgl/zGk4HupnN4e7XjTu4Noub3DM94yVUR/omoNSz2IA/bIee0Xiy4HXKam8PZld6ZpaJFW5vjydT9jiI9DmTr72dRUZoVPX1VhEAAAAA1KnM9vYC7e15tre3hNJ1KrlzJr2glkHJaK2641VokVE+QafS025vT+S2t+dUeuaZvehubx8atb3dnulZb5WeORvvcx+b+3Xj/vNo29uLCdh9yszGrZREkZWQ7g3uhZg3NMa1yCinvT0xShgcCaSPxbLSx7+bRUaYhOrruyYAAAAA1KmxZnq629tzKz3jHpzpOVpr7UTb2WMqPSuzxEgqPNMzHxNAJgpWeubM9EyN3LIdCmS+3gNFbG8fT2VgLZlxACbsbI1kPzZ36Jc909O0t6efn5irvb2omZ72U2xVsNQzVXTomT6+URcZJYqfT5or7JpzKo1ebe1+fofjSe3ti0rKf85Q6YlGVV/fNQEAAACgTpkCwoIzPV3t7aayzQRGVHpm29VtNrdXsNLTzPQsJvTMqfTMDT1zZ3rmq1Q0G+ATScvZdJ6v0nPFnA4F/D4dMbe9qMfhFaZlv6mISk93QNdkZpgG3ZWepiW8mPb29HUqONLTVek5eoQSsQPb0drbY2W0t7s32kuuCuJRZnpK6croRMpSwO/TzPbIiOsmx3hDBqhXzPQEAAAAgCowbdO+Mba3J5KWEyjWw0zPWlR67nba2ytf6VlMwBzMqQrNXVwzor09NbINOehqb8+df+m2cl6Hnvjy2eporq9f33Pb21tzqliDWYuMMs+LeQ6cmZ4Jq6T2dvNyrOQio2ID/mLa252q1XIWGdm3H0+NnBVrBAN++X3pNv+t+wclSTPbInnP15RTMUroicZCpScAAAAAVEFmpmf+j5v2dveWcFMdl/Bge3uyhkFJpr298pWeRbW3BzJVmpIUjY+1yGjkwhl3cDowSqWnJHW2hAqG5V6VW73aEsl+bAW3tzvt7SMrPUtrb6+c4md6jt3ebgLLYpYy5XIvd5LclZ75nxdTQfvqgXTomW+ep5R5fFR6otEQegIAAABAFZiZnoViBZOBDGWFnunqOE9Weo4RuEyUVMrSnl670nNK5Ss9i6kQDLmqNCUplsxuZx7InemZd3t7prLUBIS51ZD1zDympnChSs+Rm+wlV3t7npmexbS3+5329spXehYfek7M9vZSFhm5r//q/gFJhd8kKPbxAfWG0BMAAAAAqsByKj0LbW9PX+6uGmyyQxRmemZ09UcVT1ry+6TZeeYTjpczXqCESk9TcZc703MgltvebpbXZJ4rMx8ynrRGbW+vVya8bwmlw87cSs+smZ6u5yWSd3t7CZWe9v8rOdOz6PZ2+9hzK3/dzGtmPNvbzeckUpZSKWvMCtRITqXn7A5CT0wuhJ4AAAAAUAVjzfQ07e0mLAoH/Jk2ag+GnmaeYLWDkp32PM9Z7U1FzXgsVkmLjHLmf5p25iktIUkjZ3rma28PObeRGnWRUb0yj6k5nH7M7krPoN+XdR7ka28PBzOhcCkzPc3tVvKUSRT5WjchY+6MV7doovgAN5c7NI8lU65ZsQXa2+3Lt9qhZ8FKT/t7U4D2djQYQk8AAAAAqAITwhTKFUzgYGZ6hgK+EVvCvaRWMz13dafneRaaTzhepc30zN6ibYKsqS1hScW1t5v7iyfdMz0bqL09Z5GRO9DNDemyQ8/sRUbJlJV1TozFmelZy/b2Uba3l1K1msv9OekK2NErUE1wvP3A6OdMcpSFSEA9I/QEAAAAgCqwZJaF5P+4aXt3Kj2D/hFbwr3EzPQMVHmm5y670nPelIkJPYur9MyuwI05oadd6RlLZIVu+YKufDM9G6nSc9Cp9EwHua0RV6VnTriWL/R0bzc3z08xLeH+Caj0LDb0DBexyMi8FiJlbG9P346VmatbIEB1tr3b9zmnYHt7+v8sMkKjIfQEAAAAgCrIbG8fvb19OJ4JyEygyEzPjF3O5vbKLTGSMoFWsogKwdwKXBN6TmtNV3paVvZCqnwVeSY4jSaSTijVSKFnaZWemefFzLF1X2YC1FJmelZyf3vRMz3tJUzFbW8vPY4J+H2uCuGUU6FZqAI2NyQudM6kanQuAxON0BMAAAAAqsDZ3l6ovd3+7SzqqvQMOeGa90LPRI3a23f3RiUVnk84XiUtMsqpwDUhV2dz2Pn69rvmeuabvWjCq96hzPUaaZGRaUnPN9MzN6RzPy/mOQi5KojNoqdiQrmJqPQca2GQMdb2dvfyoWJa9fNxttonMu3thaqtc0PPWR35F3+ZY/ITeqLBEHoCAAAAQBWYAsKCi4zMTM9EJvTMzJn04kxPew5glYMSsySovamy8y8DgeLb201Il8jZ3h4J+Z1wzz3XM9OG7F7eY4eew/H0x/w+Z45lI3Da2/Nsbw/6cys9Xe3tdrWk3+9zXlsDJbS3m1LPidnePvr9R0Jmpmf+8zXuOo9D42hvlzKzTuPuRUYFzkH38zq9NeyMDsjFIiM0qsb5jgoAAAAAHma2txdqb3cvtpHs7e05W8K9pFYzPU0FYaEAZ7xKqfTMDaNjSTuUC/jVaod77g3uZo6jOzQzz1vfcPp6zeFAwUC8HmW2t6efj9EqPcPB9N/DAX9WtaEJ7Qbt57KU9nZrAtrbx3qpj9XeHndVbI834A67NsTH84Tp2ceTuY/ZBeZ5SlLSfn0GWGSEBkPoCQAAAABV4GxvL/Dx3CqrdKWnqeryYOhZozmAJvQ0AVOllLLIyBk7kBqt0tPd3j6ypTnotLenKz0baZ6nlPk6mcfVGhl7e7uplMxcnn6OBuPFz/ScyEVGY1Z6jtHeHnOFoeOZ6en+vHjCyhxXgdtyB6ujjYMw316o9ESjIfQEAAAAgCqwnErP/B/PnacXCmRmenqx0rPYjdaVNmS3DjeFKvvrbMBffKWns709mT3TMxLwO1vKB2Ku0DPPlu1gTnt7S7iy7fq1lmlvN4uM3Nvbs792U1vSC6BmtGXPnDRVjYP2qIBi5sf6nPb2yp0zpc/0LFTpmXJuZ7znjXsjeyI5+ogJd7A6Z7TQs0ajKoCJ1ljfVQEAAADAo0wGU3B7e26lZ8DbMz0TNQpKojlbwSsl4FQIFr+9PZ6zvT0czLS397tmesbzzF40z5tpb2+kSk/Lspzt7WYMwWjt7Quntehb7ztWi6a1ZF3uDvik4lrCzXlU2Zmexb3WI/ZjjRUIPZ3XSRmzW0Ou1158jApU9wzUOaO1t9uHyyIjNBpCTwAAAACogpSzvb3QTM/sv4eCmZmeXtvenkpZTvtwodbaiTJhMz1LqKp1Kj1z29uDAbVFstvbkynLCeCCWdvb039uxPb2YdciH/O43Jvp87V2v+3oeSMuK9QGPxqn0rOiMz3T/x8rFByr0tOEt+Pd3J7+XNciozFuLyv0HKXS03xvotITjYb2dgAAAACoAhPBFBqbl6/SMzdc84qkq4yu2u3twwnT3l67RUZBV0BqWZYTcoWDfqeN24SephrU/XlSJqgacBb+NE5NkqnylDJfp3DQ71Q4Fhuu5YZ5xbW3m9msRd1FUYqu9Bxjpqd5LRS1hX6M+0hvbx99kZH7fuZ2Nhe8TfOmCpWeaDSEngAAAABQBakxZnrmhofhoK+k6sNqclee1mqR0UTN9CxqkZGrnTiRyg49nZmednu7O0QNZW1vz37eWhuo0nPIWTblz3qcLXbrf7FLfHKvV0xbeGZ7e+WUPNMzXmCmZyJ9O+W1t9st/4mUcx4GCrW3FzvT0/7exCIjNBpCTwAAAACogrFmeo7Y3h5wtbd7LfR0ldFVs9LTsqyJa28v4bkOuCrrEkkra+Zkmx3smUVGiQKVnrljAZobKfS0H3vuYzJzPYtt786tiCxue3v6/8XMZi1WZnv7WKFn+vGO2d5eRqVnZs6p5ZyHoQLHVWx7u6lkrXbVNjDRCD0BAAAAoAosZ6Zn/o/ntpaGg6729qS3Fhm5K0+rWekZS6acWaJNwYkJPYtaZOR6zIlUSjG7nTkSylR69jvt7fmfq9znrZFmeg7F0q/X3GVT5jEWOwd25EzP4tvbK1nqaV7vxc/0zN/ebma/Flvpmo8JTOOJlPPaKvR8mkrP9kjQmTWbj/n2QuiJRkPoCQAAAABVYMK6wouMsi8PBfxZsyO9xF0NWc2gxL0gJzJB7e3FLI1yh1aJpKu9PeAfscjIveXe/bXPfd5aGmim52CBSs+WSGmVnrnXq1WlZ6LYSs/Q6IuM4snyQ8+wa3u7+b5Q6Pk09zNaladU/MxSoN4QegIAAABAFYw103PEIiPXPESvtbe7230LhbgTIWq3tvt8maq6SjHjBYoJy9xfw3gq5VTwZS8ysmd6OtV4uQFeA1d62l+n3EpPM7d0vDM9i/u89PNayTMmmRp9dqbhtLcXmulZgUVG7u3t5vYKvfFg7mfs0DP9fxYZodEQegIAAABAFTiVnsofLOTmDeGA31l847X29mIXu1SaqfRsCgYqHraax1JMVa3P53NCy2TKygo9W81Mz5zt7aGcwCw3QGuk0HO4QOhpAuHgGOGhkbvwp5gK0ZpWehbZ3h4ustI1n6yZnmO0ty+e0SJJWj2/c9TbNM8VlZ5oNI1TPw8AAAAAXlby9nbvVnqaELbqm9sTE7O5XSptkZGUDu7iyWTWIqNIMJBpbzeLjFL5Kz1zn7vmhmpvt0PP3EVGzvb2Ytvbc0LPIioknZGeFTxlUkVvby9ykVE57e1B1/b2MRYZrVk1R3ddcYYOnd4y6m2a2ym0ZA2oV43zXRUAAAAAPGysmZ65gUM9zPSsfqXnxGxul1yLjIoNPQM+KZ6u5DTtzJFgvkVGdkCcE3Tlhp6tDVTpWai9vcXZ3l5ke3vu9vYiKkR9E9DeXuzr3cz0jBWc6Zm+nUq1t2cC9fy35/P5tHRW25i3ySIjNCra2wEAAACgClJjbG8frdIz7rH29uQYYctEcdrbJzD0LL7SM3P9mGtWY6sd7A3mzPTMrcbLrfxspPb2IbvSM/cxtTrb28e7yKiI9nb7JWlVsNSz2EU/pr09kbLyjqSoxPZ29yIj57VVRru8lHl8hJ5oNISeAAAAAFAFJoIp1EKab3u7qWzzXKVnsjaVnqaCsNJLjKTSFhlJmcA3kczM9Izkmem57eCgJGlmeyT7/nKqFhupvX2oQHv72Stna+msNp29cnZRt+Oe6enzFfd6cyo9K3jKFFvp6a7gjOUJPZ1FRmWEnpmZnmMvMioWlZ5oVI3zXRUAAAAAPGxc29sD3pzpaULYQrMEJ4qzIGcCqiI7m0OaP6VZU1pCRV0/5FR6ppzFNeGg3wlPB2IJWZalZ7f3SJJWL+jM+/lGQ1V6FhhD8Jol03XXFWcUfTvuishQwF/U8ipzlUouMip2pqc7zIzGU2oJZ3/cWWpVziIjO1iNJyzXgqXy3gQotpIVqDeEngAAAABQBdYYMz1zA5VIwJ/VQu0lZvFJoMy22lI5Mz2DlQ8ILzxxoS48cWHR1w+42oydWY0BvxP0pax0+PeMHXoeNX9K9uc3cOg5WKC9vVRZoWeRgZw5v2pR6Rm0z9lEysq7zMg9BmG88s30LLu93X6uWGSERkN7OwAAAABUgTXGTM/cPCUU9DmhZzJlVXRGYbkqVWFWqqgz07P2v8qa0QMm4JPSYVZzKOB8jfuHE3puhx16Lsyu9Mydh9rSQO3twwUWGZUqFPS5/lzc19w/AZWezgzbIoJXM3rBVP+6VWKmZ8S9vb3AkqxSpZwZvYSeaCy1/5cCAAAAACaB1BjVVCPa2wOBrFDRS3M9azXTczgxcdvbS2UCooFoJtyKBAPy+31qsY/vuZ096osm1BTya+nM7C3auQFaI1V6FmpvL1U4p729GBPxisxUeo59DBH7Meer9My0t5dT6TlykVG5bemmcptKTzQaQk8AAAAAqIKxZnqOXGTky2of91KLeymVb5U0XKEwrRICTqVnwrnMBFKtkXTV5l827ZckrZrXOaIab3K0t5dXvTqe9nZ/iQupipGZ6Tn2dZ1Kz3i+0NPKus54ZC0yqtAsThYZoVERegIAAABAFTgzPQvUouUGDuGgPyvM8FLo6cz0rHro6aH2dlPpGcssMTLzJNtM6PlKOvRcPb8zz+dPgvb2cHlfp6zQs9ig0H5JTsxMzyIqPSe4vT1rpqep9CyzvT1Zo/MZmGi1/5cCAAAAACYBq9Tt7YHs0DOZ9E7o6VR6lhm2lMq0TUcmYJFRqczXZsiu9Iy4ngtT6fn8zl5J0lELRoae7oApHPQ3VOBkKj2bQ+VWerpmehb5WstUepZ111lKm+mZfm3GRllkVE7oGXZCT8s5rkotMgrQ3o4GQ+gJAAAAAFVgQphit7fnBmGmutILEjVub2/2QCu4CXz77ZmekZA79Ewfn6k2zBd6up+7Rmptl6ShWGW+Tu4t56XO9LRUudSzlMpm8zrIO9MzUYHt7cHMTE8zI7TcwNxUerLICI2G0BMAAAAAqsCEMIWKqXKrrEKBdLu0CTQ81d5eq0VGpr3dQ5Weg9F0pad76U6rq1W9NRzQ4hnZS4yk7ICptYFa26UKbm/PWmRU2kzPSra3lzLzcrT29swio/GfN+Y5iSZSzveEcipHpUwlK4uM0GgIPQEAAACgCsba3u7L+e3MVIMFvRh6VmiBSqmiziKj2v8qayo93TM9DdPeLkmr5nfmDcuCrvmQXqhcraShCQk9i6z0dGZ6Vu58KWXmZThYuNLTtLeXU+kZds30NCFqueehKSKv9vkMTLTa/0sBAAAAAJPAWDM9cys9c0NPL870rHqlZ8I729vNNnGzvb1Q6HlUniVGUnalp9fa29eu36vP/vJp9UcTY185j1gF2ril3Jmexb3WMqFnWXedpZRxDmamZ77t7bFE+ZWZZqFTPJlyzfQs73k2b2L4CT3RYAg9AQAAAKAKrFJnetpBhrk8zkxPT21vN1+XgejI5UptkcyfV+eZ5yl5e6bnd9du0i8f364HXu4a1+fHKtDGLWWPDCi+0nPiFhlVqr09XIlFRgmrYmMmWGSERlX7fykAAAAAYBJIWaPP9Byxvd0OT0zYk/RQe3vNtrfHPFTpaT/2fJWeLa4ZnUctmJL38wNZoae3ZnqamZzm/6WqXKWnNxYZjS/0zFfpaYfB5Swycre3V2gBkdO+zyIjNBhCTwAAAACogrFmeuYGKqGcSs+Eh9rba1bp6aH2dhM0DZqZnq5Qrs1ub29vCmrRtJa8n+8O8bw209MEdvFxvuYqUdEoZYeDpS4ymohKz5La2/Ntb3eel3IWGaU/dziedKrHQ/7ynmdnUROVnmgwhJ4AAAAAUAVOpWeBj+fmKSMXGXmovT1Z/GKXSsq0t9c+JMy0t6crPSOulvvOlpAk6egFUwrOScyq9PTA43Ez4Zz5OpcikUw5gWNlZ3qWtsiokkM9E06l59jHYF4H0TxVsvEKLDJyKoxdt19upWeqRm9iABPNWzX0AAAAANCoxtre7vPJ78tUqJnAx7SQe2l7eymVb5XkbG8vM0yrBFNdl6/Sc82qOVq/u09vP2Zewc93P3fuxUdeYCo84+N4zbmrQ8tdsON+ToutGq19pacdeuYJjE31ZznPi7l987or9/YkFhmhcdX+X4oSPProozrvvPM0depUtba26qSTTtLNN99c9OevW7dOn/70p3X88cdr+vTpampq0ooVK/S5z31O3d3dE3fgAAAAACa9sWZ6StnVfybkcba3eyj0LKXyrZLMjEkvVHpm2ttHzvTsbA7pK29ZWXCep5QOuc3X22vt7aYiMZ6nRXssMdfnVHKmZ6nVjBMx07OYUHC07e3xZPmhp/lc9/NcbsW1+dZCpScajbfeThrF2rVrtWbNGoXDYb33ve9VZ2enfv3rX+uiiy7Sli1b9MUvfnHM27jgggvU1dWlU089VR/84Afl8/m0du1afeMb39CvfvUrPfjgg5o1a1YVHg0AAACAycYEC4W2t2c+ZikU8DnXc7a3j6PVeKLUqtJzOOGd9nYTPpnt7eMJ+AJ+n5Ipy3Pt7SZQG89IhZjrdVru62M8i4w8U+mZd6Zn+nbKCj3zvM7KfZ6p9ESjqovQM5FI6LLLLpPP59N9992nY489VpJ09dVX6+STT9bVV1+td7/73Vq2bNmot/OpT31KH/zgBzV37lznMsuy9LGPfUzf/e53de211+o73/nOhD4WAAAAAJOTyWBGyxXMIpFwVoWb97a3m6VK1d72nKn0rH3TogmjTchnKvxKEfL7FJPU4rH2dvOYxrPIKOaaWzlawF+McHD8Mz3NSM/fPrlDu3qG9TevP2zcx2FCwWIqKsNO6Fl4pmekrJme2ccQ9PvKfp5Ntk2lJxpN7f+lKMLdd9+tTZs26f3vf78TeEpSe3u7vvKVryiRSOjGG28c83Y+97nPZQWeUvqd1K985SuSpHvvvbeyBw4AAAAANtPeXmimp5QJVdxVg5lFRt4JPZN2ShKqYkhiWZaGPNjebownyDJf7xavtrePo7rYtMSXu7ldyq30LHZ7e/r/ln2+feV3z+nrt72k9bv7xn0clar0jFVgpmfu81ruEiPJVenJ9nY0mLoIPdeuXStJOuecc0Z8zFxWTmAZCqU36wWD3np3DQAAAEDjsIqY6WkyFXcoYoKx5Diq7iZKvAYzPWPJlFO954XQM5Tz2MfT3m6qeL0Xeqaf6ESZlZ7lGk97u6l6tJTeSt43nJ65+vT27nEfR9IqYaZnqPBMz5gz03P84WLu8xCswDlosu1yZ4MCXlMXoefGjRslKW/7+tSpUzVjxgznOuPx4x//WFL+UNUtGo2qt7c36z8AAAAAKIZVxEzP0Ss9PTjTs4rt7cOuEMlL7e3GeCobzde22QMhrpFMWc7XNz6emZ6J8oM9Y3yhZ/r/qVSmMliSntvRM+7jMG84lFbpWbi9vZxAOPdzK3EOmip02tvRaOqitLGnJ/3NqbOzM+/HOzo6tH379nHd9lNPPaVrr71Ws2bN0pVXXjnqda+77jpde+2147ofAAAAAJNbpr298HWc0DPP1movtbc7Mz2rGJJE7QDL56tM63S5ckO98bS3m5Cp1UMzPd0t7bWu9HR/nYu9PZ8ylZ6DsUzw+Mz28YeeiVTxr/c3rpyttZ95vdqaRn5NYxVo/c8NJitR6fmxM5dqMJrQ9LZI2bcFeIl3vrPWwObNm/WWt7xFyWRSP//5zzVjxoxRr/+FL3xBV1xxhfP33t5eLVy4cKIPEwAAAEADMJnlaHPzzMeyKz29t8jIzPSsZmWYqfRsCgbKXtxSCcFA+e3tnS1h7ewZ1gwPhU3u7evlzPQsZ26lEXItMir2teZeZDTkCj1f2NWreDI1ruPKzPQc+3M7mkLqaArlvQ1zCpfz3Ph8PoUD/oq0yht/ferism8D8KK6CD1Nhaep+MzV29tbsAq0kK1bt+rMM8/Uvn379Ktf/UpnnnnmmJ8TiUQUiXjnHyMAAAAA9cNpbx/lOib0zDfTczybtCdKKZVvlTKc8M7mdmlkCDee0PNfLjhKG/b06fA57ZU6rLLFE+7Qs4xKz4ovMiru9syXJWVZGownMseVSGnjnn6tnNdR8nGYmZ7lPCR3gFxuFWwo4JPJc5nDCRTmjX8txmBmeeab23nw4EF1dXXlnfdZyJYtW/T6179eO3fu1C233KK3vOUtFTtWAAAAAMjHGuf2dlPJlfTQTE/T9lyJar5iDXtoc7s0MvSMBEs/riPnd+pdxy2o1CFVRCyrvX0clZ4VbG93P8ehEtvbpez2dkl6dkf3uI4jmSx/cVck6NfTV52jR7/0xrIXV4WCpYfBwGRUF2fHGWecIUm64447RnzMXGauMxYTeO7YsUO/+MUv9Pa3v71yBwoAAAAABaSK2d5u/4bmblkNOIuMJnelp2lV9srSn0q0t3tRPJF5nY2nvb0ScysN08qdvr3iXmvuSs+hEaHn+OZ6JlLlL/rx+XzqbAlpZnuk7PEM7qCT5UNAYXXxXfmss87SkiVLdPPNN+upp55yLu/r69NXv/pVBYNBXXLJJc7lXV1deumll9TV1ZV1O+7A8+c//7ne+c53VukRAAAAAJjsTJQ06vZ2Z6ZnJtgzcwTHs1RmotRkpqcdpkU8EnrmzlJslNAza6bnOIL2WIWrgM3zXPTCHvscsqw8lZ7jXGaUrEHIP5rsRWeN8boDJkJdzPQMBoO64YYbtGbNGp122ml63/vep46ODv3617/W5s2b9Y//+I9avny5c/3rr79e1157ra6++mpdc801zuWvf/3rtXXrVr32ta/VM888o2eeeWbEfbmvDwAAAACVkllkVPg6fmd7u2uBixe3t9dipmfcWzM9c1udvbBRvhLiZba3O5WeFQqBQ0G/FEsW3d6eNdMzlp7pOX9Ks3Z0D+nF3X2KJVIlH1tmpqdHQs8glZ5AMeoi9JSkM888U+vWrdPVV1+tW265RbFYTKtWrdJXv/pVXXTRRUXdxtatWyVJDz30kB566KG81yH0BAAAADARiprpmWd7uwlavDTTM1mBdt9SOaHnOGZnToTcSs+IR8LYcsXKXGQUT1Zue7v7doptbzczPS1lRiKsmNOu3uG4+oYT2rCnT0fOL34RsmVZnqv0DOV5UwTASHUTekrSSSedpFtvvXXM611zzTV5w0vzQwYAAAAAVFsxMz2dRUZ5ZvZ5c3t79YK+aDwdpnml0jO33TrSgJWe5cz0jFSo0tOcC6Vub7csy2lvb4kEtXp+px7ctF/P7egpKfRMuiqsvVJVmbXVvornIFBvODsAAAAAoApMDcZoMz3Nx0J5ZvYlvdTebodh1awyG054a3t7btVfI870HM8c2Upub5dcMz2LDD19TugpDdnVwS2hgFYvSAedz5S4zMg9VsLvwdCTSk+gsMb4rgwAAAAAHpdy2tsLX8dkGflm9nlxpmdN2ts9Eno26iIjd0XxeCo9ownT3l6Z10ZHc0iS1N5UXKOqeePAPdOzORzQaru687kSQ8+U5b1KTxYZAcWpq/Z2AAAAAKhXJrM0MwfzCeSp9PTyTM9qzjgcipn2dm+EnrlhU8Qjs0bLlTXTcxxBe6UrPa952yo9sfWgjlkwpajruys9nfb2cEBHzU9//ku7Sltm5H6zwTMzPYOumZ4eOSbAi3hLAAAAAACqoYTt7ZF8lZ4enOmZO9dyImXa273xa2yoztvbLcvSFbc8pX+5/aWsyyu1vb1Si4yOO2SqLjttSdGt5eZNhZSVWWTUEg5o4bRmdTaHFEumtGFPX9H3n0y6Kz298TXON/MXwEjeOGMBAAAAoMFlFhmVVulpKgq91N5ei0pPr7W31/tMz509w/r1Ezv0g/teybq8UouMavV8OIuMlFlk1BwOyufzOS3uz2wvvsU9a6anR/LFrEVGtLcDBdHeDgAAAABVUMz2dlPNlm+mp6cWGdVkpqfd3u6RNvLc9vZwnYVPQ/a8y3jSUiKZch6Pu729rEVGNXo+nPMrp71dkj7/phWKBP1aMrOt6Nsz523A7xv1DYtqCgVZZAQUg9ATAAAAAKrAxEf+Eis9TUXheKruJkottrdH4x5rb8957BGPHFexTIgspTe2O6Gnu9JzHHNkYzUOPf2uRUZD8XSwa0LPI+1Kz1IkalDVPJZwnu8PAEaqr+/KAAAAAFCnUkXN9Ez/313paQJQL1V6Jms609MblZ4j2tvrrNLTjAuQcpYXZf259NdczP6cUI3b/S1lKj2by3jNmJmeXpqd6Q7cQx6ZMwp4EWcHAAAAAFSBZbfJjlbpaT7mDj1NuOalmZ61qH4bqkCAVUm5sxTrL/TMhJtRd9DpamlP1HWlp3uR0fibXM1zEPBIa7uUO/PXO8cFeE19fVcGAAAAgDplmSxplIzChBmRPNuZx7NJe6I4lZ5VDFxMSOeVNnJ35V844C96u7hXuCs9ozmt7kZ8PDM9zfb2GlV6mmzSstyLjMYflDszPT0ULuarBAcwEjM9AQAAAKAKUkVUer7vpEMUT6Z0xuEzncuCnqz0tKvfqrnIyGPt7e7W/nrb3C5lnk9JiiULtLqPZ3u7/TmRGld6WnkWGY1HLZZ2jYWZnkBxCD0BAAAAoAqKmel59srZOnvl7KzLAl6c6VmDOYfO9navhJ6uyr96DD3d1Z3uVnd30FnO9vZQsDZhnFPpKcvZUF9W6Jn03iIj2tuB4tTfd2YAAAAAqENmpqdvtP72PILO9nbvhJ7xGiwycra3eyRgzAo967DF2F3pGS1Q3Tme7e3mtsKB2oTTPjPTMyUNxstvbzdvNnh1pieLjIDCODsAAAAAoAqsIio98zGhZ3IcAdREqc1MT2+1t7vDJq/MGS2Fu7ozVmCRkWWVXmHsVHrWqALR3OtQPOmcc+UsMkp6cKanu4qWSk+gsPr7zgwAAAAAdcjM9PSVWDFmQg1PzfRM1mKmp7fa2wP1XunpXmRUoOpTKn2upwlQa9Xyb2Z6Dtqt7ZLUXMZrJlmDquaxhPMsOgMwknfOWgAAAABoYM7y9hIzioDfgzM9a7DcZchs4vZI6Bmq80VG0fjY7e35/j4Wc/1aBcHm/OqPph9fJOgvK5z3/kzP+nvtAdXC2QEAAAAAVVDM9vZ8nO3tHprpaapOqxUEWZbl2t7ujV9j632R0XCiUHt7dshZ6uuu1pWe5qsyWIElRpI3Z3q6n1sqPYHC6u87MwAAAADUoXJneia8ONOzSi2/sWTKef4iHqn0dIdNkXoMPSes0jP9hQrVqALRvKkwYFd6ljPPU3LN9PRQuJi1yIhKT6Agzg4AAAAAqILMTM/SPs9UFHqlvd2yLKfSs1pLVNxLd7xS6enz+ZwgLBz0RhBbikIzPWO5Mz1LfN1Fa1zpaUo9B6LpSs9yNrdLmQViXloY5F4S5aUwFvAab/xrAQAAAAANzlQqlrrIyMz0jHukvd0dvlartdbMn/T5vLU0yDx+Lx1TsQptb4/lvM4S45zpWetKz6G4qfQsL/T04kzPcFalp3eOC/Ca+vvODAAAAAB1aLwzPUN+b1V6urfIVysIMgFdUzBQcmg8kUzoGfFI9WkpCra3V2h7e61a/nNfHeUuvvLiTM+sRUYe2ioPeA1nBwAAAABUgVPpWeLnBTw20zO70rM6v1KaJUbltipXmtmcHanHSk9XuBmNjzbTs7SwveaVnjl3W/YiIy/O9HQvMqLSEyio/r4zAwAAAEAdssa7vT1gQs/JW+k5FLM3t3tsYZBpLa7L7e2uSs9Y0v3n8W9vT6Uy815rt709+zVZ9iKjKs+vLUaYRUZAUTg7AAAAAKAKUs5Mz9I+z1RTlhI+TaRazPQ0AV2TRza3G+ZrU4+hZ9Td3l5gvqc0MgQdjfu6tZo1mXt+lVsdnJnp6Z2vcTjIIiOgGN45awEAAACggY13e3vAazM97WDL75P81Qo9zZxIj4We5mtTq/mV5XDP8cya6Tmi0nN8oWfNKj19uZWelZrpWdbNVFSIRUZAUervOzMAAAAA1CETWY6/vd0bMz1N+3I1F6hkKj299Stso7S3RxOjtLeXELa7lyCFalQZmZvDl1vpmZnp6Z2vMYuMgOJwdgAAAABAFYx7pqdpb/dIpadT+VbFtlon9Ax6q9LTLDIKB7x1XMUYLtDSHk9kVySPp709FPBVrQo414iZnqHyZnpmQn7vVFRmhZ5UegIFEXoCAAAAQBWYzLLU7MSELUmPzPSsRQhkZk56rdLTPAd1WemZcFd6jmxvb7FHCZQyS9YEprVcrpP7siy7vd1+PgIeChfDVHoCReHsAAAAAIAqsMqc6Rn3SHt7MlX9EGjIrvQst1W50kyVXT3O9Mxub3dVfdohX7O99by0mZ7p26xlCFzxRUbOTE/vhJ4h1yIjKj2BwurvOzMAAAAA1KHM9vbxzfT0zCKjGlR6era9vU63t1uWVbC93fy5NZJ+rktqb7crPcM1rPRUbnt7maGnWUDmpfZ29/Nbq9mpQD3g7AAAAACAKkg1yExP0+5c3UVG3tzeXq/t7e7KzvTfM1Wfpr29eRzt7ZmZno3T3p6owQzbsYSCzPQEilFf35kBAAAAoF6ZSs8SP80Ea5YlpTwQfNYiBDLzJ70209O0TpcbrFVbNJ4beqb/nkxZTkVya8Ruby9hrIIJTGvZ7p9bSW3a9MfLzNL1UriYPdPTO8cFeE15Zz8AAAAAoCjjrfR0z86Mp1KK+GsbsJmZntUMgZz2do9Ven7k9MM0p6NJZyyfWetDKYl7iZGUaWmPu1rZTZAbK6XSM9G4lZ6lnrcTKXt7u7feCAC8hNATAAAAAKogM9OztM9zz+zzwlxP0+5c1UpPs73dYzM9T102Q6cum1HrwyiZe4mRlKn0dLe9m7CwtEVG6et6apFRmUG5F2d6Bvw++X3p7yleOi7Aa3hLAAAAAACqwFJ529slb8z1TNZgkVHU2d7Or7CVMJzb3m4/v+5Kz3HN9HQqPWsXxOW2t1dupqe3Xnsr5nRoWmtYM9sjtT4UwLOo9AQAAACAKjB5ZemLjFyhZwkB1ESpRQg05NH29nqVW+lpKjTjyUxoaao14yXM9DShZ00rPXP+3lLuTM+U92Z6StKv//Z1iiVTnBPAKAg9AQAAAKAKrHHO9PS7WllLWSozUcwxVLOaz5np6bH29nplnk/zujKLjeKJ9Gs0HPA7syLNZcWIe2J7e+4iozIrPZPem+kppd8AIPAERuet+mwAAAAAaFDjnekpSUG7qnKyz/SMeGx7e70yszvbm0JZf48l02FoKOhXyP76lhK0m0rP2m5vz/57ue3tXpzpCaA4/IsBAAAAAFVgKj3HE3qagNEL7e21mOlpto1T2VYZptKzszkdesaSKVmWpZhd1RkK+J1qzXgJrzmvVXqGAr6yj8WEvtUM+QFUBqEnAAAAAFTBeGd6Spl5gl5YZJSZ6VmD7e2EnhUxbFdkmtBTSld7mtAyq729hO3tUY/N9Cx3c7tUm5AfQGUQegIAAADABDNVntLIRSvFMIFL0gMzPTMhUBUXGcUSkioTYiFT6dnRnFnzkRV6Bv3OzNZECaGnqQqtZaWne3t7uUuMJNdMT0JPoO4QegIAAADABHNlnuOq9DSb0ktpNZ4oJhirZqVn91BckjSlJTTGNVGMqB16tkdCzriFWCLlzOR0t4XHS6gu9sT2dtfLstx5npKUZKYnULcIPQEAAABggqVcqee42tudSs/ah57mGKq1vT2ZstRD6FlRmXEBfoXtcDOaSCrmmslpRirEE6VUemba42slq729EqFnDcY5AKgMQk8AAAAAmGDurNI3jt/CvDTTc8iuEqxWCNQ7FHcqZac0h6tyn43OtLc3hQLOpvV0e3v6iQ4H/QrZ1cWlvOZiydpXerrb0CtR6ZlgpidQt8ofcAEAAAAAGJWl+p7p+dS2bl37++e1aW+/eocT9jFVJ9g6OBiTJLVFgjUN0xrJcMIVeoYC0nBCMddMz/T2drvSs4SZnu72+FrJrvQsP/JIJqn0BOoVoScAAAAATLDyZ3qaAKo2lZ6/ePRVPflqt/P3uZ1Neuex86ty3wcHaW2vNNPeHslqb8/M9Bzv9nan0jNQu4VTWYuMKrG93TKhJ4E7UG8IPQEAAACgQvb1RfX7p3fq/OMXqLM5E9KVO9PTLJWp1UzPgWi6MvDvzlyqj525tCKzEovVbVd6Tm2htb1SnPb2YECRkB16xt0zPX2u7e3Fv+bM/M9QsIaVnpVeZER7O1C3eKsCAAAAACrk+/du0j/84QXd+MDmrMvdlZ7jyDydSs9azfQcjKVDsvlTm6saeEpUek6EzCKjgCLB9Nczlsy0t4eD/vFtb/fAIiP3mwqVeK0mWGQE1C1CTxRlKJbU+t192tE9VOtDAQAAADxrZ0/65+Vnt/dkXe6u9BxP6GmqzBIltBpXkqkMbK5Au3CpqPSsvMxMT78zJzUaT2UqNd3t7ePZ3l7D2avu06sylZ7px0ToCdQfQk8U5aXdvVrzzft04ff+UutDAQAAADxrf386oHtxV2/W5akyZ3qaAKpWlZ5Drm3f1XZgIP2cTmsl9KyUaIHt7e5KzZBTXVz6IiPvVHpWYJERlZ5A3SL0RFGG7HaWSrxTBgAAADQqE9Dt7Bl2KhQlSe729nHcbsDZ3l6j0NP+faDare0S7e0TIWqHk5Gg3xV6Jp1FWVmVniXM9Iy5Pr9WmOkJwCD0RFHMO7uEngAAAEBhB11B5wuuas9yFxkFne3ttLejfMN5Kj1j7u3tQb+zyMj9mtt2YFB3PL9bz+3oUT4xu22+pu3tFQ49mekJ1C9CTxRlsIbv7AIAAAD1IJWynKpESXpxV1/mY+XO9Kzx9vahGoaeJkim0rNyMouM/M4io2gis8goFMgsMnJvb79/Y5c+/JPH9f/+vDHv7ca9UOnpqqWuxOuV9nagfhF6oihOO0sNfsgBAAAA6kHPUDwrlHwxq9Izcz1fGZWetZ7p2Ryu/q+Q3XaQTKVn5TiVnsFAVnu7qfQMBX2Z6mLXTM+xwu+Yq22+Vvyuu25hpicwqRF6oiiDsYSkyvyjAQAAADSi/QOxrL+/sDMTelr2UM/x5iYBZ3t7rWd6Vv/3gYO0t1ec2d4eCQWcVvSYq9IzEvArFDQzPTOh51hjDtyVorXirvSs7ExP4hOg3nDWoiiDcdrbAQAAgNGYJUZNofSvWS/v7XdCINPdPp55nlKm0jNZwibtSkmmLGfxTbU7vyzLYpHRBMhub3dvb8+0p4f8I9vbx1po5Z4JWivuU6wSv78y0xOoX4SeKArb2wEAADCZWZalK37xlL5x20sFr2NCzxVzOtQeCSqWTGnTvn5JmZme48w8nZmetWhvN9V9UvVDz6F4puV6aiuVnpWStcgolGemZ9CvoLPIKPOaG2vXQ8yp9KxdQOifoO3thJ5A/SH0RFFYZAQAAIDJbPvBIf36yR363r2bZFn5g0cTes5oC2vF3HZJmbme5lPGM89Tcld6Vj/0HHKFntWe1WiqPEMBn1r5XaRiok6lZ0DhwMjt7VmLjMYx07OWlZ6aoPZ2Qk+g/hB6oigm9GwJMdMTAAAAk0/PUDp8S1mZ1uBcBwaikqRprWEdMbdDUmaDu6n0LHemZ7wGMz1N11dTyC9/lYOfgwNmc3t43IExsiVTllOR2RT0Zy0yMpWe4aDfqdaMJ0qf6Rmu4UxPf1Z7e+UWGQUJPYG6Q+iJogw5i4x4dxUAAACTjwk9pcySz1xmkdHU1rBW2qGnWWbkVHpqfMGJCaBqMdNzrKBrImU2tzPPs1KiiUzlbrq93Q494ylXaOlzRirEUyNnejZ5eqanq9KzAq9ZU+lKpSdQfwg9URTa2wEAADCZ9WaFnsm81zHt7dOzKj17ZVmWa5HR+O7f2d5ew/b2WoSeZnP7FDa3V4y7Utnd3h5N5iwysoP2RLL49nZTiVzL0NNf4UVGtLcD9YvQE0Ux/7hR6QkAAIDJyF3pOVCg0tOEntNaIzp8Trv8vnT1576+qKu9fbwzPUdu0q6Wsar7JpIJPacRelaMqdwNBXwK+H2ZRUbxlGIJ87HM9vaUlQn+Rgs9LctyLTKqYaWnXU3t91VmBi3t7UD9IvREUQbZ3g4AAIBJrHfYFXpGx670bAoFtHhGqyTphV295W9vn6yVngN2e3sr7e2V4mxuD6a/ntkzPTOVmkHXBnbT9u6MOgiPjBLc82ZrWek5vS2stkhQy2a1V2QObIJKT6BusZUGRcm0t/OSAQAAwORTzEzPTKVnuirxiLkd2rRvQC/u6tPCaS2Sxr+9PeCBmZ61KICgvb3yTHu7qfA0AWUskcpaROSu1jTBX2ap1cjXQszVBl/LRUatkaDu+czrKzaaLVPpSc0YUG84a1EUFhkBAABgMusdygSd+So9LctyFhmZ0HPlvPRcz2d3dMsqc3t7sIbb2wdHCbomWrcderLIqHLMIqMme4FRxK74jCZSziKiUE7oaTa4ZzoARxbDuLe817K9XZJmtkfUFqlMwY4JfMk8gfrDaYuiOJWeNfhBBwAAAKi1sSo9B2NJJzAyoedJh06TJK3b2KVYojIzPZOTrb3d3t5OpWflOJWeQX/W/6OJlGsmZ3rep3m5xlM57e2jVHoG/L6GagVPUekJ1C3OWhRliJmeAAAAmMTcMz3zbW83re2RoN/5mfnYQ6ZqSktIvcMJPb71gKQ6nenpjLqqZaUnoWelDCeyK3cz7e3JTHu7fZmp2DQLtEYLwE3oX8vW9kqzLIuZnkAda5zvRpgwlmVpMF64jQEAAABodGNVeu53LTEyczsDfp/OPHyWJOnOF/dKKn+mZyJZu5metaz0pL29cqLx7NDTXekZtyuSTdgZcsYqpGRZlhN6NuVZZOSuEm0U7vcY2N4O1B9CT4wpnrScNppavLsLAAAA1Frv0Ojb2w8MRCVJ09qyKxLPOiIdev5lU5ek8md61rK9vRYzPVlkVHmmvT3vTM+cSs+gHX7Gk5aiiZTs0bSjV3oGG+d3xoRrcZif0BOoO5TtYUxDrvYdZnoCAABgMupxLTLKW+nZn78N+7RlMxX0+5wFRD6VN9OzNu3t6eCn2gUQiWRKfcPp55pKz8oxlbtNwZHb2838SqfS07S3p1LO50n5A/DM5vfGCQddmSeVnkAdotITYxqMp3/QCPp9zj+IAAAAwGTinuk5kGemp6lInN6aHXp2Nod0or3QSCqj0tO0t6eq395eq0VG3a7q2s5mQs9KGS7Y3p4cUelpWtXjiUxreyjgy7udPVPp2Ti/M7rPN2Z6AvWncb4bYcIM1nBwOQAAAFBrw/HMZnZJGowWnuk5rTUy4mOmxV0qY6anWWSUrH6lZ61mepolRh1NQafNGuUbtl/LEdPeHsq/vV3KhO3xVMr5vbDQmIPM5zbO18o9ToJKT6D+NM53I0wYNrcDAABgMnPP85TyV3oesNvbp7eNnD35hhXu0HN8xxCy29trMdPTtPNXuwjiwED6eZ/WyjzPShpR6RlI/9+y5MzsDDuLjDLb283vhYXC78as9Mycb1R6AvWHmZ4Y02CMze0AAACYvHpyQs+hfKGnU+k5MqBbMrNNS2a06pWuAfnLrfSsySIje6ZnlSs9WWI0MZxFRvZMT1Pp6ZZpbzeLjFIyEx4KFcOYubWNVOk5vTWsDf/4JiVT1rirtAHUTuN8N8KEcd7ZZYkRAAAAJiH3PE9JGsizyOjAYP5FRoap9qzHmZ7DNRp31e08p8zzrKRMpWc6DgjnCSlNcOm0tydTzkzPgu3tDVjp6fOl91ow6g2oT5TuYUy0twMAAGAy6x3KDjkHo4UrPfO1t0vSmiPn6IZ1m9UxzoU8ZyyfqT98/FR1NFU/AKzVIqODg+mwuVCQjPGJJrLDS7/fp1DA51RqSpn5lWaWaiJpOaFmoQAws729cUJPAPWN0BNjYpERAAAAJjPT3t4SDmgwlsxf6dlfuL1dkk48dJq+91fH67CZreM6hikt4Zq1eY9V4TdRaG+fGE57u6utPRIMKJ5Mv67DAb/Tyh12VXqaRUWTaaYngPrGdyOMaTBOpScAAAAmL9PePrezSVKmKMCIJpLqsze6Tx9l6c65R87RstntE3SUE2eoVu3tA6bSk/b2Ssqt9JSkiCuodIeWQXuRUTxlOW3xBUPPnM3vAFBrhJ4Y05D9TjaLjAAAADAZ9dht1vOmNEuSBqLZlZ4H7XAu4PfVpP18oo0Vdk0Up9KT7e0VlbvISMoOOt2hpTNLNplywu+mAuF3ptKTYhkA3kDoiTHR3g4AAIDJzFR6zulIV3pGEyklXVvUzTzPqS0h+ce7qcjDajXTs3uQSs+JYELsSFZ7uzv0dFV9umZ6Do7xOohT6QnAYwg9MSZnkRHb2wEAADAJmZmepr1dkgZdcz1N6Flonmc9sywrM9MzXN1fHw8629sb73mtJSf0DLrb2zN/doeeptIzlkxpODZGe7td6RlhpicAj+C7EcY0yPZ2AAAATGJme/vM9ogCdiWne67n/oGoJO+Engm74q4SoomULLuotdrjrsz29ilUelZUvkVG7vZ2d2iZ2d6ecsLvQr8XZio9iRkAeAPfjTCmTHs7Mz0BAAAw+ZhKz47mkBP4uOd6mkrP6a2R6h9cji/+5lmd8E93aU/vcEVub8gV7jZVsYLPsix1U+k5IYbHWGSUt7095ar4LVDpGbVDzzChJwCP4LsRxjQUN4uMKl/pGU+m9Pc/f1KX//djWT9QAQAAAF5hZnp2NIfUahcCuCs9D5qZnq21r0i8b8M+dQ/G9fjWgxW5PRN0hQN+p+qvGvqjCSXsuamEnpUVdSo9XaGnq+ozFHQtMvJn2tuHYunPK7TrIZ6w7M8nZgDgDZTuYUxDE7jIKOj36XdP75RlpX+wYVkSAACAt9zy2Db9/umd+s5FxzXkZvJimErPzuaQWiIjKz33OzM9a1/paZb/bD84WJHby1T3VTfIag0H9ciXztLBgTi/I1TYcJ6vqbs6M3umZ2aR0fAYi4xiyUxADgBewHcjjGlwjIHV5fD5fK53yxNjXBsAAADVdtMDW3T/xi49tGl/rQ+lZnpNe3tT/krPTHt7bSsS48mU+u0wdsfBoYrc5kQWQIzG7/dpVnuTDp/TXtX7nQyc0LPAIqNwVnt7utLTPdOz4PZ2u9IzTKUnAI/guxHGNNbA6nJl5iLR3g4AAOA1fdF04NcfnZxvUKdSlvrsx97pnukZy1fpWdvQ01R5StL2CoWeY1X3of4MJ0Zvbw/nWWQUS1pOAN5U4PfCGDM9AXgM340wpsEJfnfX/OBIpScAAID39A+nf0abrKFnXzThbC/vaA66fnb1XqWnWfwjSTu6K1TpOcbyGtSXeDKlpD0rtbj29kyl5+CY7e1me7sv78cBoNoIPTEm845eywRtbze3O8AiIwAAAE+xLEt9duhp/j/ZmNb2ppBfkWBALRG7vd0VAmcWGdU49BzKrvS0TFpbhlq1t2NimMpdaZRFRq7Q0r29fXiMsWcxu4I0HOS1AsAbCD0xJlOBOVHt7a32MPjBSVo9AAAA4FXRRMrZoD1ZKz17XPM8JanVaW9PB0CplKWDg96o9DThq5T+evUOlf81G2uOI+rLsL25XZIirjb2rJmerj8H/aa93TXTM5w/Roib9nZmegLwCL4bYUwTuchIotITAADAq9zVnf2TvNKzszkderbkLOHsHorLzoVrX+npmukpSdsqsME90/VF6NkIoon01zMS9Mvnc1V0BvNXegbzLjLK3wFoKj1pbwfgFYSeGFUyZSlq/+M10ZWeQ8z0BAAA8JS+4UyINlkrPXvt56DDDj3Nz65mCeeBgWj6403BrFmItdA9FMv6eyXmejLTs7GYSs/cr6e76jN7e7vd3p50tbcX+L3QVHpGqPQE4BF8N8KohlwzX5jpCQAAMLm4g87JOtOzZ4xKz/39dmt7W6QGR5ftYE6lZyU2uNPe3liGnRA7OwrIbm8fucgoq719jJmetQ7/AcDguxFGZX6Y8/lG/sNYKWYuEjM9AQAAvMXd0t4fjY9yzcZl5mJ2NKXDztyZnmae59SWUA2OLpvZ3m66lndUIPQcq7oP9cW0t+dWema3t7tDz/Sfh+NJZ75v4e3t1ojbAoBa4rsRRjXkmufpnvlSSWYDJpWeAAAA3tKbFXpOzjeoC1V6mp+T99vLg6a11r7S08z0PGxmmyRpeyVmelLp2VCc9vZg4fb2UFZ7e/p3QPf3gqYCi4xidqBKpScAr+C7EUY1WIXB5U6lJzM9AQAAPMUddE7aRUbDOaGnM9Mz/Xwc6PfG5nYpU3V65LwOScz0xEimvT0yor19ZNApZba3m4Vefl/2zE+3GNvbAXgM340wqsEqtLM4Mz2j2ZWe927Yp/f+4C/a0jUwYfcNAACAwvqzFhlNzq4cU+npLDJyZnrmVHq21T70NJWeR87vlFShmZ6xdJBFe3tjOGrBFP3o4hP0+TetyLrcHVTmm+lpZvqO1gEYT9jt7VR6AvAIvhthVKZtpyU0MUuMpMwGzNxKz1se3aaHXjmgO17YPWH3DQAAgML6Gnimp2VZTtXbaHpzQs8WZ6Zn+rkx1ZXTWmofeppjWTUvHXr2DMXVN1ze120ongm7UP9mtkd01hGz9brDZmRd7l5k5G5PN382Fc+jhd9UegLwmrr6bvToo4/qvPPO09SpU9Xa2qqTTjpJN998c9Gfv3fvXl133XW64IILtHjxYvl8vgmbU9koTBBZi0pP8w/rZG2lAgAAqDV3e/twPKW4HWo0gi/+5lkd/9U7x2wBdyo9m+xKT3se/aD9s+sBZ6Zn7UNPU+m5YGqzptiLlcptcXfP+Efjcre75ws9zfeC0X4vjLO9HYDHTFz5XoWtXbtWa9asUTgc1nvf+151dnbq17/+tS666CJt2bJFX/ziF8e8jRdeeEFf/OIX5fP5tGzZMrW0tGhwsPzh3o3MzPCZyJmeLQVmepph2ZO1lQoAAKDW+nKWFw1EE5rigYrGSlj3cpcGYkk9u71H86c0F7ye+Zm0s0Cl5/5+b7S3D8WSitqh05SWkOZPaVb3YFw7Dg5pxZyO8d9unO3tk0HEHXTmaW+30p3ro4bfUSo9AXhMXXw3SiQSuuyyy+Tz+XTffffphz/8of71X/9VTz/9tFatWqWrr75aGzduHPN2jjjiCN17773q6enR+vXrtXDhwiocfX0bqsIiI6fSM2d7u5kh1WitVAAAAPUit+Omr0E6cCzL0r6+qKRMS3ghmfb29M+sTqVnLCnLspxKz1ovMjKPI+j3qS0S1IKp6SC33LmeQ/a2byo9G5u70jMrAPVnRwaFXgeWZTmV4KEA3ZQAvKEuQs+7775bmzZt0vvf/34de+yxzuXt7e36yle+okQioRtvvHHM25k9e7ZOP/10tbe3T+ThNhQzoH0itzU6Mz2j+X+ozm17BwAAQHXkzoPsjzZG6NkfTWjYDvNMaFmIaW83lZ6m4jGZshRNpDzT3m5a26e0hOXz+TR/Souk8tvbh6uw2BS1lzXTM5gJLXMDzEK/FyZSllMNGgnwWgHgDXUReq5du1aSdM4554z4mLns3nvvreYhTRrVaW/PX+lpQs/ctioAAABUR27I2Sihp6nylEYPPYfjmZZxZ5GRK/TZ1xd1lrfUPvRMPw4zy3O+U+lZ3jgv8/vARBZBoPbcLenumZzBnPmchcJv97xfd2gKALVUFzM9Tev6smXLRnxs6tSpmjFjRlHt7eWKRqOKRjM/IPX29k74fdaambNpgsmJkG97eyKZcn7AGmiQH64BAADqTW47e6MsmHSHngdHCT3NYk2/T2qzfx4OBvyKBP2KJlLaZgeKTSH/hP68XIyDdqXnVDv0NO3tO8pub2eR0WQQKRB65lZ6FnodxBKZ0DPMIiMAHlEX3416enokSZ2dnXk/3tHR4VxnIl133XXq7Ox0/psMM0EHq9DOYn5AjCct5x9LdxVBo/xwDQAAUG9M6Nlkz/trlA6cff2uSs9RZnr2DqUfb3tTSH5/Jvwxcz23H0gHitNbIxNxmCXpHjKVnumKU7OcqdyZnrS3Tw7u9vZCVZ9S4dCzvSmkuz99hm77+9MU8FPpCcAb6iL09IovfOEL6unpcf7btm1brQ9pwjmLjCbwnV1367yp9nRXFTRKGxUAAEC9MT+HzetMB2iN8mZ0sZWeufM8DfPzq6n0rHVru+Sa6Wkf68Kp6Zme+wdizs/0pbIsS4NUek4K7qAzPEqlZ1OB8Dvg92nJzDatmNMhn4/QE4A31EXoaSo8C1Vz9vb2FqwCraRIJKKOjo6s/xpdNSo9QwG/84+suT936DkQa4wfrgEAAOqJZVlO6Dmns0mS1B+Nj/YpdSNrpmeBSk/LsvS/j6eLHGa0ZYearXankqmi9ELoacLbqfaxdDQH1WZXpO7oHt9cz3jSUjKV3k5D6NnYCrW3B4vc3g4AXlQXoaeZ5ZlvbufBgwfV1dWVd94nymdCyImeUdQazp7r6d4U2j+ckGVWAQIAAKAqhuJJJ/DKhJ7jqxj0mq5+d6Vn/iD3R+s262ePbJPfJ338Ddm/a7TYM+m3e6nSc8hsb09Xevp8Pmeu53hb3M08T0lqCtfFr44Yp+zQ07W9PZj9dZ/IBbcAUGl18S/XGWecIUm64447RnzMXGaug8oaiptFRhP7j5uzwT06stIzkbKcrZkAAACoDtPK7vNJs9qbsi6rd+5Kz/5oQtFEdph71wt79E9/elGS9KU3r9SZK2Zlfdxpbz/gnUpPZ3t7c+ZYTOj57Pbx7T8YtkPPgN/HcpoGFwz4ZUZxZs30zJnP2USlJ4A6Uhf/cp111llasmSJbr75Zj311FPO5X19ffrqV7+qYDCoSy65xLm8q6tLL730krq6uqp/sA2mGu3tUmaDu2llz53jyVxPAACA6jJLi9oiQbU3pd+gbpj2dlelp5SZhylJr+zr1yd//qQsS3rfSYfo0lMOHfH55g37PX3DkrwReuZub5eks1fOliR9/75XtKd3uOTbNLNAm0MB5jROAmaZkTvgDha5yAgAvKguQs9gMKgbbrhBqVRKp512mj784Q/rM5/5jI4++mg9//zzuuaaa7R8+XLn+tdff72OOOIIXX/99SNu65JLLnH+27Vr14jLCEqzOYuMqlTpOehUemb/QD1A6AkAAFBVpvOmoynkCj0b42cyd6WnJB1wLTP6wzO7NBBL6vhFU/UPb1+VN+wzo5nMBKbpHgg9nUrPlsyxvPv4hTpm4RT1RxP66h9eKPk2TXs71X2Tw/LZbWoNB5xxFtLIRUYTXQwDAJU0sYMaK+jMM8/UunXrdPXVV+uWW25RLBbTqlWr9NWvflUXXXRR0bfzX//1X6Neds0112jGjBkVOeZGMFi10DO70rM3p3Wqr0FaqQAAAOqFaWVvi2QW4jTCz2SplKWu/nRA2N4UVN9wImuD+66edMv6qUtnZC10cWuJZP8aNdUToWf2TE9J8vt9+sd3HKm3Xb9Of3hml95z4j6dtmxm0bdpQs9m5nlOCr/4yMkajCXV3pR5DeWeA1R6AqgndRN6StJJJ52kW2+9dczrXXPNNbrmmmvyfoyFOKVx2ttDE/tScSo97fvLrSKg0hMAAKC6TCt7W1Mm9GyESs+DgzFnQdOyWW164tXurA3uu3vSbeBzXdVuuVpzCgJqXelpWZazyGhqS/axHDm/Ux88+VDd9OAWfeW3z+kf37FaL+3u1eauAb39mPk6afG0grc77GpvR+NrCgVGVPUGmekJoI7VVeiJ6huKVWeRkTPTMzpye7uUqQAFAKDRxBIpve5rf9a8Kc36n8tek1VhA9RSn7vS07S3N0Clp5nnOa017Cxoyq70TIeec0YJPc0b9katZ3r2RRNOkOuu9DSuOGe5/vjsLm3ZP6i/+tHDzuUv7OrVb/72lIK361R6EnRNWgG/Tz5fZpQD7e0A6gl9CijIsiwNxqs80zM2cnt7vr8DANAodnYPqas/pg17+pxqOsALzM9f7U1BtUfSQVojVHqaeZ4z2yJOW/qBgcwb7rt7TaVnc8HbMG/YG9NbI5U+zJJ028ffFPLnrcTraArpn9+5Wk0hvxZOa3aqO/f2Rkdc1838bE513+Tl8/kU8mdiAwJwAPWEn6xRUDSRqto7eq05Mz1zqwgG7AVHAAA0mu0H0/MDF0xtYTsyPMUEnO1NDVbpaULP9oimtabD3IN2e/twPOnMxpzTUbjSs9lV6Rnw+9TRXNtfq8zx57a2u529crZe/Idz5fP5tKVrQK//17XO5xWSmelJ0DWZBQM+2fn3hBfDAEAlUemJgszmdmni39Ezw+Az29vTP1CHg+mXqJkpBQBAo9l2cFCStHBq4aoyoBZM6OleZNQfS9T9jPyu/kzoaUJCs73dzPNsDgVGDTLdMz2ntoRr/oaFmec5ZZTQU5JznKbCdTCW1HC8cHHBMO3tUPYyI6p+AdQTQk8UZFrbwwG/ggU2V1ZKbqVnrz3T0wyQ76fSEwDQoLYdsEPPaS01PhIgm5mx3t4UUrtd6WlZmZbnepVd6ZkO/0zF4y7XEqPRgkz3TM9aLzGSpG77+Kc0FzcTuKMp6CyoGa3a0xRBUOk5uYUCmXOB1wKAekLoiYLMEqNq/MOWW+lpKgtM6Mn2dgBAvdvbO6yP/OQxPbblQNbl25z2dio94S3uRUaRoF8BOySr97me+Wd62pWevenzcbQlRlL2TM9aLzGSMouYprYWF3r6fL4Rjz0fFhlBkoLM9ARQpwg9UZB5F78ac1tyKz3ND9lmgHwjzI8CAExuP31oq25/fo/+c+2mrMudSs+p1av0/N69m3TydX/Wv9+5oWr3ifrjtLc3BeXz+ZwW93pfMGm2t89oD2ua3Q5+0GlvT39stHmeUnal57S22oeexba3u2Uee+ExUoSekNIzPQ1eCwDqCaEnChqsYjuL+cFxKJaUZVnOD9nmXfb+WH3/cA0AwHM7eyVJL+/tz7p8+8Hqt7dH4ynt6hl2Kt6AfEy42WG3tjtzPRum0rPJqdI8MGhCz9IrPb3R3m6HnkW2t0uZqtADo7S3D9PeDqXHnRmRIBECgPrBdywUNFTNSs+IqfRMaiieVDKVHpDvzPSs84oCAACe29EjKb24yCwHGYwl1NWfDhyqWelpFrSYGdpAPv1Oe3s6HGtvkA3u7pmepsV7OJ7SUCyZNdNzNK2uSs/RNqZXSzHb23M5gW9/4Tc/TKUny2smN1Pp2RTyy++v7dIuACgFoScKctrbQ4U3V1aKqfQcjCWcqoKA36eZbRFJzPQEANQHy7K0pWtAP31oa9ZG5L19w9prBy2WJb2yb0CStN2e59neFFRnS/EVWuXqaErfV+8QoScKc7e3S+5Kz/p93cQSKR20qyJntkfUGg44VWwHBmPa05sOPed0jj5j110UMN0L7e2m0rOE7yPO5vrBwl9Pp/OL0HNSMzM9eR0AqDeEnihosJqLjMxMz2jS2RTaFgk6P2TXexsVAGDyeO8PHtKXf/ucHtty0Lnsebu13di4t09SbeZ5SlJHM6Enxtbr+plMyoSf9TzTc/9A+s2HoN+nKc0he6FP+nw4OBBzKj1LmunpifZ2e3v7OCo9D46yyMi8eUN7++QWChJ6AqhPhJ4oyLSzVGeRUabSs9f+Qbq9KajWBpkdBQCYHHw+n05ZOkOStO7lLufy5+3WdmOTPdfTCT2nVXdze6cJPes4vMLEcs9Yb6SZnqa1fUZbxGnTNRWPe/uGnSVHY830bAr55bO7fL0Qeprq1aklVHrmzjPNh0VGkKSQfa4QfgOoNxPft4y6dc7KOVo8o1VTmif+B7kWe6bnYCzpVJ20N4WcH65pbwcA1ItTl03Xr57Yrgfcoadd6Tm3s0m7eob18j479LTb26tf6WnP9KTSEwUMxpKy0iPWnQrPRpjp2dWfmedpmPBv/e5+WZYUCvjGXE5kttn3DSc0oy0y6nWrodKVnk+8elDfW7tJD27aL0lOIQImJzPTk9ATQL3hXy8UNKezacx3uSvFPQzezDxrjwQboqIAAOpNLJHSy3v71TMU18mHTa/14dQdU+n53M4eHRyIaWprWM/tTFd6vu3oefr+fa84G9wzlZ5VDj3NTM/huCzLks/HYgpkMz97Bfw+p8ov389l8WRKoUD9NI+5lxgZZpnRC7vSb07Mam8qalnLFWcv16Z9/Vo2q20CjrR4iWTKqdoe10zPnNDzilue0q+f2OH8/dxVc/Q6/i2Y1Mw5TsUvgHpD6AlPMC1CliXtsWcpudvb40lL0URSkSD/0AJetrtnWHt6hxUK+BUK+BQK+NUSDmjWGLPR4C2Pbz2o9/3wIS2c1qz7r3xDrQ+n7sxqb9Lhs9u1fk+fHty0X6cunaFtB9IVnW87Jh16bu4aUCKZcio9F0ytbnu7mekZT1oaiiez5hMCkrJmrJtQ3Gxx77NDz4de2a/3//Ahffqcw/WxM5fW5kBL5ISerurMaXb494L95sRYm9uND52yuMJHNz5Jy9InzlqmnsGYpjSX3t5+0NXeHk+m9Nsn04Hn+cct0N+8fomWzmqv7AGj7pjQs4nQE0Cd4SdceILP51NrOKj+aEJ7+jKhZ5urlaZ/OKFIG//QAl72y8e26d/u3JB12bGHTNFv/vaUGh0RxmPFnPQvuNsODKk/msj6XozinLJ0htbv6dO6l/c5S1IWTG3WEXM61BwKaCie1NYDg9p+sDaVnq3hgAJ+n5IpS71DCUJPjGCWFbnPf9PmbsYO3frsLqUs6dt3b9S7T1igWe3ef4NrtErPzV0Dksae5+k1kWBAV5y9vOTPM4/7wEDMqfje3TOslCWFA379ywVHFVXxisYXNDM9CT0B1Jn66UVBwzMLk3b3pH8YbWsKZrVUDUSTNTs2AMVpjQQ1f0qzZrVHNLUlPZe3lTCl7kxtDWt2RzoQWL+7r8ZHU59OW5ZZZvT8jnTL7JHzOuX3+7TUboV9fOtBJ1iqdqWnz+dzltOYDd2ovT29w3ouZ+lVrZgWdjPHU5La7BnsZqbnM/axDsdT+t7aV6p8hONjFhXNaMvMvpxmt4Sn7BmmxVZ61jtT4RpPZpZW7exOV5/PnVJciz8mB2d7OzM9AdQZfhOFZ7RGglJfVHt6TaVn+gfQtqaghuJJ9UX5pQzwuktPXaxLT/VGux/Kc/icDu3p3af1u/t0/KKptT6cunPS4mkK+n3admBIf3pulyTpyPkdkqSls9r07I4erV2/V1I6fKlFpWVHc0gHB+MsM/KQi3/8iDbs6dP9n3uD5k+pbhCeywTy2aFnpr09nkzpBXtBlyT99OGt+sgZSzTb4+NMMpWemeOcmrO0yOuPoVKawwGn8vzgQFztTSHtsEPPWr/+4C0hKj0B1CkqPeEZptIzE3qmf8jObHCfvJWe7/7egzr/uw86774DwEQzLe7rd/eOcU3k0xoJ6rhD0mHxk692S5JWzeuUJKfS8/4N6e3uC6q8ud0wy4x6CD09IZpIav2ePqUsaZO96KqW+kdpb+8fTmjjnn5FEym1NwV1/KKpiiVS+u7aTTU51lLka2+flhN6zu2cPIGfeewH7LmeOwk9kUeQmZ4A6hShJzzDtMB29We2t0vu0HPybnB/alu3Ht96UCzXBVAth89Oh54v0t4+bqfaLe7GKlelp5RZBlPteZ5GZ3Nmgztqb9uBIVl2e7V5A7iWzOuzrSmzGMe9vf3ZHd2SpNXzO515kjc//Kp29Xj7Ddq8Mz1bskPPepvpWQ4zc/igvcHdVHrOI/SEi1lk1EJ7O4A6Q+gJz2ix50SZeUqmvb3Vvrxvkoae0URS8WT6SWHRBIBqWTHXVHr2yTJJDEpyytJM6DmrPeIseTGhp7GwyvM8jY5me6bnUHn/vnb1R/X41gN6eW/tA/LHthzQdbe+qEe3HKj1oZRs6/4B58977WCulsz2dnd7u/lzfzShZ7an53muXtCp1x02XSctnqZYMqX/vMe71Z49g3ENxNKdQ3NcLewjKz0nUejZkllmJEnbD9qVnjX6vgRvMt0fK+Z21PhIAKA0hJ7wjNxlJ21OpWc6/JyslZ6Drrb+Vt5dBVAlS2e1KeD3qWcorj29tQ9g6tHRCzqdroVV8zK/KC6a1qJQIFO6X6tKT9PeXu5Mz/956FWd/92/6EfrNlfisMpy23O79f17X9Gvn9he60Mp2db9g86f93qg0tO0t7e729sjmfb2Z+0lRkfNnyKfz6dPnrVMkvTbp3YolkhV+WiLs+1g+jme0RbJWsjirvT0+bKrQBudCXwP0t6OUVz8ukP12JffqLcdPa/WhwIAJSH0hGfkbgPMzPTM3hQ62ZhtmpGg35mnAwATLRIMaPGMVknSi8z1HJdgwK+TD5suKd0C7L7cPLeStLBWMz2bKzPTc0Z7OjTp6o+VfUzlemDTfknSyYfNGPV6Xf1RT7SQu7krPb3wRoP5+SPfTM+Ya4nRUQvSr+2Tl0zXjLaI+oYTeuiV/QVv938f3673fP8vNQl2tx1Ih54Lp2UHemahjyTNbIs4rbyTgbvS07IsFhmhoBltk+fNAACNY/L8iw7Py61izLS3Z1qpJqNBuw2rNUJrO4DqOnxOpsUd4/OF847QJa87VJeeujjrcneLe24AUy2Vmuk5vTX9i/D+/toGdfv7o3pxVzqIe50dNueKJ9PLdk752t06+9/vdVq4vWDrgUyl556+2geyZnt7m6u93d2Vk0hZmtIS0gK7Ddrv9+nslbMlSbc/vzvvbcaTKV33pxf18OYDuvmRVyfq0AsylZ753mgwFY+TqbVdci0yGojp4GBcw/F0le7cKZPreQAANCZCT3hGS06o51R6Nk3u0NM8bjPbFACq5QhCz7ItntGqa962SlNyFqUsnZkOPX2+2m2K7miqzEzPGW3px7Z/oLaVng+9kp7juWJOe96KpKe2deut316nr9/2kqKJlHqHE3psy8FqH2ZB2e3tta/0NLPU212LjAJ+X9ab1Kvnd8rn2rJ4zqp06HnnC3uUSo2cBXzv+n3O6+TOF/Zkfax7MKYv/PpZfXftpglbhvRqgUpPKbPQZzItMZKyQ88d9jzPme0RRYL83AkAqH+EnvCMkZWedugZntzb2wdjdujJEiMAVXb4nPQcypcIPSvuMLvSc25Hk8LB2vw41lGpSs82U+lZ29DzgU1dkuSMFHC77bndeud/PqCXdvdpSktIR85Pv7Yf2ly4DbuakilL2w+6Qs++4ZovEOu3XxdtOW9Kuys/TWu78brDpqstEtTevqie2t494jZ/5Zq1+vzO3qzH/N17N+lnj7yqr9/2kl73tbv1Vzc8rKe3jbyNcmw7kA718lV6mjZv94KjycA905PWdgBAoyH0hGfkbiZ3FhlN8krPgSjt7QBqw2xrfXlvn+JJby4mqZS7Xtijf/j9C1WbM3j6spk6fHa73nPiIVW5v3ycRUZlh57p0KQ/mtBwPDnGtSfOgy+nQ89T8szzPG3ZDM3rbNa7jp2vP19xhi49JT1uwFSH1trO7iHFk5az4CqetHRwsLat96a93b29XcoOQd2zaqX0LODXHz5TknTH8yMrOf/84l5JmVDtLrvaM5ZI6VePpwPRZbPaZFnSupe7dNl/P6ZknorR8XLa2/MsD5tth50LajRjt1bcMz0JPQEAjYbQE57hbt9uDgWcpT2TfaanqXBtYXM7gCqbP6VZreGA4klLm7sGxv6EOnbN75/Xjx/YrDf++7365WPbJrzKbmprWLd/6nR98o3LJvR+RpNvkZFlWSU/9vZIUGH73+yuGs313NE9pC37BxXw+/SaJdNGfLw1EtSfPnGa/v09x2h6W0SvWZKuBn1uR48nfr4wre2HTGtxKu8quWipdziu+zfuKylA7I8WEXoumDLi89asmiNJuuP53Vmvpd8/s0uxZEpHzO3Qh045NH0dO/S8+6U96uqPaUZbRH/65Gm6/8oz1dkc0r6+qB7fWpkRBKmUpe0HC1d6/u3rD9Pfvv4wvfuEBRW5v3qRqfSMO+3t86cSegIAGgOhJzzDXenp/gG7PUJ7uzSyvQwAJprf79Nyu9qzkVvc+6MJJwzpHU7os//7jC6+8dGs1ttG1Nk8cqbnvr6oVl51u87+93vzzmTMx+fzZeZ61qjF3VR5rp7fmTWD0q2zJXP5/CnNOmRai5IpS49tqVy1Z/dgTM/kaesey9YD6TcVDp3eqlnt6XEBe/vGDpCH40l96hdP6cYHNo96vev+9KI+8KNH9MdndxV1PJZlZRYZFWhvn94a1rw88y9ff/hMhQN+vdI1oJf39juXm0rO84+b7yw8enjzAfUMxvWzR7ZJkt59wgKFAn4tnNais1bMklR4KVKp9vVHFUuk5PflX9KzZGabrjx3xYj5u43OzDLtHow5lbD5vq4AANQjQk94hrvS0x16mkpP88P3ZNNvt7fntv8DQDWssOd6rt/dW+MjmTgmmJnRFtbnzl2hcNCv+zbs04/WjR4k1TvT3t43HHcCzp09wxqKJ9U3nJDf7xvt07M4cz0HalPp+eCm9GzOU5bm39qez2vtitBKtrh//GdP6m3XP6AH7BC2WE6l5/QWzbLbrIup9LzlsW36zZM79PXbXhp1BMWTr3ZLkjYU+ebF09vTFbBNIf+IxT4mBF29IHuJkdHeFNLr7K+DqeTctK9fT23rVsDv09uPma9F01t1+Ox2JVOWfvrwVt23cZ8k6b0nLnRu5xy7xrqx9AAAN2lJREFUYvT2nIrR8dpmLzGa29msUIBfgQzT3p6ypBd3pb/Pz59kLf4AgMbFv/jwDHeo1+aq0jAVBQOxyRl6OouM2N4OoAbMXM+XdjVupefGPenHtnx2u/7m9Yfp1k+epjcfNVefPufwGh/ZxDLt7Skr82/sLnumX75KuNGYuZ5dE1jpGU+m8gZ7lmXpwU2F53kW8prF6WDuoVcqs8xoX19U6+yw87dP7ijpc7fuT1d6LprWotmm0nOM0DOVsnTjA1skScPxlBNY5UokU3plX/r2dxa5Ff039sKhc1bOGfGmq2mHPipPa7thWtx/+tBWfe3Wl/SN216SJJ2xfKZm2o/PbHr/5l0bZFnpJUiLprc6t3HG8plqCvm1/eCQXijw2NzufmmPjv2HO/SHZ3bm/bipYjwkzzzPySwU8KvD/lnbVLwz0xMA0CgIPeEZ7u3kHa5Kzzanvb12yxFqyczUYpERgFo4fBK0t5tKz2X2RvXDZrbpO+8/ruHHijSFAs7meDPXc2dPOmib11la6DG9dWI3uMeTKb3p/92vt357nRI5weemfQPa0xtVOOjXcYumFn2bZvbnszt6KjJC584X9sgUJN714p4RxzkaU+m5aEars1BnT+/oVbP3rN+bNWv3iQKzL7fsH1TMPpZd3WNXj8YSKf3f0+ng8F3HzR/x8b8+dYn++tTF+uDJiwrexhuPmK1w0K9dPcP63r2bdLu91Mh9e6bFPZ5MP2nvcVV5SlJzOKDTl6WXIt2esxQpVzJl6at/eFEHB+P69zs25K0MdTa3TyPQy2WCbIPQEwDQKAg94RktY7S390/S9vZBs72dRUYAasBUeu7oHtI379rQkFvcN9qh59LZ7TU+kupzNrgP5VR6ljjTLzPTc2La21/Zl54P+dLuPm3Znz1r1VR5nrBoqppCxf9buWBqixZOa07P9azAspzbXLMnDw7G9eiW4m7Tsiy9ardeL5rWolkdZqbn6AGlGb/QaVfsPm63sOd6eW/mDYtdRVR63rthnw4OxjWzPaJTl46snF06q01fectKzbBHGuQzsz2i3/7tKbr6rSt18cmLdPrymXrXcfN1zso5znVWz+/UHDvgndIScqpD3dxLkUbzh2d2OgHwK10DzrgDN9Penm+J0WQ31RV6tkWC6mhu7Dd8AACTB6EnPMNd6emurjF/jiVTiiYmX7XnQIxKTwC1M6UlrA+8Nl3R9c27Nur87z6YFaI0gg2mvd2u9JxMTLjRO5yu9NxlV3rOLbHSy7S37x+YmEpP8zWSpPU5Vcd/sQOu1x1W/DxPo1It7j1DcWeZ0gl2tWmxC3j29Uc1GEvK70sHsbPax670fGFnrx7ctF8Bv09ffvMRkgpXem7Yk1kmtKtneMz5mL+2W9vffvQ8BcuYfblyXoc+dMpiXfv2I/Xfl56kf7/wGKeyWEovwDpv9VxJ0ruPX5A3sD7riFkK+H16aXefMwIgVzJl6dt3vywpU7H4k79sHXE9096+kPb2Eaa5ljfNm9KUd1YrAAD1iNATntEcdld6ZmZ6uiscJ2OLu2m5a2WREYAa+Ye3r9L/e+8x6mgK6pntPTrvW+sKzs2rN4OxzOb2ZZO60tOEnunnotTtzabqr2uCKj03ukLPl3KWaj2zvUeSdPyiaSXf7muXpEPPh8sMPe95aa8SKUvLZrXpw6cvkWTa3cdewGNa2+dNaVY46NfsjrFnev7Y3tZ+7pFzdN7qufL70tXYu3tGfo47MI4mUjowSjDdMxjXn1/cK0l613ELxjz2cn1mzXJ9633H6jNr8s/PndISdhZOFQqRb31ul17e26+OpqB++MHjJUl3vrhnxHNBe3th7kpPWtsBAI2E0BOe0RLO394eDPjVbL/7X4mZW/VmIGa3t1PpCaBGfL70xuU7PnWGzlg+UwGfT0fO66z1YVXEpr3p6rHpreERc+0mA9Ma3WuPkBl/pacJPSeq0jNTrfiia6nWgYGYdtgt+avmd5R8u69ZnA7Untneo8/+8ml98MeP6P0/fEhbuvJXFRZy23PpQO7cI+fo9OUz1RwKaEf3kJ7bMfYCHhN6Hmov8TEzPff2RZVKjQxN9/VF9X9Ppd90+OtTF6s1EtSKOenH/sSrI6s9N7qeOynzNc7nD8/uVCyZ0oo57Vo5r/Tns1Qt4aDedvQ8RYKFxxKscba4j5zrmUpZ+vaf01Wel566WMcvmqaTDp2mZMrSzx551blePJlyAn3a20dyf++bR+gJAGgghJ7wjFDA77Q95S6PMIFf33BCe3qH9fbvPKDv3bup6sdYCybobWF7O4Aam9PZpJs+dKL+7+9O0aEzWsf+hDqw0W7VXzoJW9ulzAb3nqG4EsmU9vSaRUYlbm9vndiZnhv25q/0fHZHuspzyYxWp2q1FAunteiQaS1KpCz98vHtum/DPj24ab/+6U8vFn0bQ7Gk7t2wT1I6oGsKBXTGcrOAZ+wWd9O2fcj0dBhnqmYTKUsHB0eGyD9+YLNiyZSOPWSKjjsk3Up/vN1S/3hOi3simdIrXf327aa/Rju788/1jCVS+t/H063t+RYY1YqZA/rEqwfVMxjP+tgdL+zW+j19ao8E9aHXLZYkXfTaQyRJP3/0VWcG8c7uIaUsKRL0O9vjkTHV1d4+fyqhJwCgcRB6wlNMK3vuLy6m8nMgltCP1m3W09u69W93rM/bxtVoBk2lJ+3tADzA5/M1VBu4qSBc3kCPqRQd9r+vvUPxdGWhJYUCvlGX1ORjrn9gIJa3OrEc0UTSqYaUpO0Hh5wZpM9u75YkHTl//JXH/3bh0br0lMX67JrDdc1bV8rvS7emP2u3zY/lvo37NBRPav6UZq2yqyPXHJneTF5c6JlZYiRJ4aDfCZFz53oeHIjpvx/cIkn629cvdS43oWdupeeW/YOKJy21hANOQOqu9EylLP3v49t1+X8/pmP/4Q49+Wq3/D7p7cd4J/Sc09mkQ6a1yLIyIbfxi0e3SZI++LpF6mxJ/+x47pFzNKMtrD29Ud31Qro61LS2L5jazLzKPKbT3g4AaFCEnvCUFjvYc7e3S1KrXeXY1Rd1fsCNJy398P5XqnuANdBvZnpS6QkAFWeWMi2bPbkrPXuH40777+yOJvn9pQVDpj02kbKcQLJSNncNKJmy1N4UdLZ9b7CXGZkQbHUZoeeJh07TVW9dqY+duVSXnLJY77ADv/+4a0NRn3+73dq+ZtUcJ1B7w4rZCvp92ri3X6/s6x/t051Kz0XTM9XTs+zHuSdng/uP1m3WQCyplXM79MYjZjmXm0DzuR09Go5n5p+bWahLZ7U5bcs7XRvc7924T5/55dO684U9GoglNaMtrM+sOdxpsfeKoxakv77P7Oh2LrMsS0/YG+vdm98jwYAuPGGhJOmGdZtlWRZLjMbATE8AQKMi9ISnTG1N//KVO1fNtLvf/Mir6hmKO/M/b3741VEH8jeCQRYZIY9tBwZ134Z9Fa+oAiabjXvTgdSkbW93FhkltLPbtLaXHnqEg36narTScz3d1bhHzE1X5L5kh55mZmY5lZ65Pn7WMgX8Pt390l49mWdGpttQLKk7X0xXE557ZCZ462wO6WR7m/wdL4ycRem29YBd6Tk9E8jlW2bUPRjTTXaV5yfOWpZVsbhwWrNmtEUUT1p6zlUNaV7fy2a1a96UdJDp7pIx1awnLZ6m3//dqXrki2/MqiD1Cif03JZ5bJu7BtQzFFck6HdmmhofPPlQNYX8enzrQd35wh5ts59j5nnmN60102FFezsAoJEQesJTrn7rKn12zeE64dDsDawm9Lx/Y5ek9A/7q+d3aiie1E32BtNGlEpZGoyzyAgj/fdftuiDP35En//1M7U+FKBuDceTetUOQ5bNmpzt7Z2umZ6m0nPulPFV+c1on5gN7qZacfnsNh1uh1sv7e7V/v6os8ToyHEsMSpk8YxWvfNYU+25cdTr/v6ZneobTmjhtGanxdw4e2W6xf3ul/YW/Pyewbi67TmVWaFnu73MyNXe/uMHtqg/mtCKOe06x75tw+fz6fhFUyRlt7hvcD13c+0we1d3JvRcb4fHbzxillYv6Cy5wrdaVs+fIim7vf1Ju8pz9fxOZya8MaezSX99anrG59due0lb7GpaNrfnN701fe6GAj7NavdWlS8AAOUg9ISnnHjoNH3szKUK5PzQ7V5sFAn69Z4TFupjZx4mSbrpwS3qq3ArnVcMxZOy7EI+2tthxJMp/fqJHZIyCx4AlG7Tvn5ZljS1JeQseZlsOprtmZ7DcafSc06JS4yMGXZwst+u9IwnU3rfDx7SR37yWFlV6Sa4WzbLVem5qy9riVH7OJYYjeYTb0hXe963YZ8e23Kg4PV++tBWSdL7T1o04meXMw9Pt58/vnXkAh4jmkzq/OMW6I1HzHZG/EjSLLvS07S39wzFdaP9Ju8nz1qWN5w0Le7uZUZmc/uy2W1Opae7vd0shTp8zsRvai/HkfM75PNJO7qHnFD9yW3px3nsIVPyfs5HzzhM01rDemXfgLP5nUrP/BZNb9HFJy/SlWtWjHgdAwBQzwg9URfcVY5vPXqepraGdc7KOTpsZqt6hxP6n4dfreHRTZyBWLq13eeTmkOEnkj784t7tX8gppntEb3+8Jm1PhygbjmB0Kz2SbvcJNPenqn0HE97uyRNt4Pj/QPpUOr5nb36yyv7dfvze3Trc2Mv9Clko6u9fYVT6dnntGZXsrXdOGR6i959/AJJ0j//6cW8oe3T27r1zPYehQN+XXjCghEfXzitRctmtSmZsnTfxn1572dWe5P+7cKjdcPFJ2RfbmZ62pWeNz2wRX3DCR0+uz1rfqVbZoN7t1IpK2tz+7JZ7U6l557eYaVSlobjSW2xlyitmOPtSuf2ppCWzEjPPDVht6n0PPaQqQU/55NnLZMkJe2vHzM98/P5fLr27Ufq8tOX1PpQAACoKEJP1AV3pefFJx8qSfL7fc7cqRvu35w1uL9RDEQzm9sn6y/kEyWWSOlfbn9J923Y5/wyVC9++Vh6mde7jpuvYIBv48B4bbSXGC2dpEuMpMwio77hhLPVe+44Kz1N6GlmerpnS/77nevH9b02HcylW5OXz27TkpmtCgV86o8mdJu9Gd3Me6y0v3/jcrWEA3ri1W79+skdIz5uqjzPWz1H0wtsu3/DinS15z2jtLjnM7s9M9OzdziuH61LL278+FlLC7agHzm/U63hgLr6o7rxwS1Zm9vnT2nWrPaI/L70Isiu/qhe3tuvZMrSlJaQZrXnP34vOWrBFEnpuZ6DsYQz17VQpackvf81h2jxjMyCKEJPAAAmF35bRl3obEn/UnbMwila7frl5m3HzNMh01r02iXT1DecqNXhTZgBe4mRWdyEynlme7e+c88m/f0vnlI9xcl7eod1z/r0L89mOy2A8XEqCCfpEiMpM9OzdyjT3j5vnNubpzvt7ZlKT2PTvgH97qmRweFYXtk3oJQldTQFNbM9olDAr6X2/FVz+xNR6Sml2/w/YVcKfu3WF7O20ncPxvR/T++UJH3g5EUFb+NMO/RcW+IbbGZ7+t6+qP77wS3qHU5o6aw2venIuQU/pykU0OfPO0KS9PVbX3Ke76Wz2uT3+xQM+J3b3dkz7MzzPHx2fVQ6m3D72R3denZ7j5IpS7M7Ik4Faz6hgF9XrjlckjSjLeK83gEAwORA6Im6cP5xC3TB8Qt03btWZ10eCvh1+9+fruvff5xm1kGVQqlM6NnGEqOKe+iV/ZKk1yye5tnFDfn86ontSlnSCYum6rCZkzeoASrhZbPZera3W3vLYVmWeoYKz702G9f7oglnVuJ4Kz3NXFQz0/P5nelKz6MXTpEk/b8/b1Q8mSrpNk017nJXMHdETiv2qnkTN4/y0lMWa8mMVnX1x/TNOzNLjf738e2KJlJaMafdmaWZz/GLpqq9KagDAzE9vb276Ps1Mz339kV1w7r0LM+Pv2HkzPNcf/WaQ3TOytmKJVP69t0vS8pe0mW+tru6h7TenpXq9dZ2w9ngvr1HT27rliQdu7Dwc2+ce+Qc/csFR+nb7zt2Ig8PAAB4EKEn6sLsjib967uP1hFzR/5i09zAVZCDsXR7ewtLjCruL3boefJh02t8JMWzLEu/fGy7JOnCE6nyBMrhbpte1qCVnqmUpWt//4Le+Z8PFNyonrsAKBL0a1rr+JY6mRbv/QNRxZMpp/34a+9arRltYW3dP6hfPb694Od3D8b0q8e368r/fVqPbE4vD3KWGLmC6cNdId1ELDFyCwf9uuZtqyRJ//WXLbr54Vf1nXte1o/sIPIDJy8atUoyFPDr9OXp2cultLjPaIvI50vPouwejGvJzFa95ah5Y36ez+fT188/SrM7Mm8EL3eNb5hrV/Hu7Bl2vj7L6yT0XDm3UwG/T3v7os6M2NFa2w2fz6d3n7Cwrv69BwAAlUHoCXiYWWTUGqbSs5KiiaSz3fa1S+rnl6DHth7U5q4BtYQDevPqwi2OAMYWjad00WsW6Q0rZtVlp8CuniF9/95NzjKfXIlkSp/932d004Nb9Mq+AT24aX/e64WD/qxFeXM7m8bd6jzDDj27+mN6eW+/YomU2iNBHT67XR894zBJ0rf+vFH90exxNC/u6tVf3fCwjv/Hu/TpXz6tWx7brot//IieePWgNjhLjDLB3QrXG6CrJ2iep9vpy2fq3FVzlExZ+uJvntW/3L5eu3qG1dEU1DuOmT/m57/B3uJ+dwmhZyjgd8YFSMVVeRpTW8P6j/ccI/NlXO4KjOe5Kz3tze31UunZHA44b1A8bSo9R6myBQAAIEkBPMy0t7fS3l5RT2/r0XA8pemt4bqq8DILjN5y1FxeE0CZOltC+uo7jqz1YYzb/Ru6dN2tL+mERVP1v3/zuqyPRRNJffJnT+m253cr4PfpX999lN52dOEqwc7mkIbsZYCjzUccS2aRUdSZt3nEvA75/T791WsX6UfrNmtnz7D+/udP6vsfOEEBv0+v7h/UB370sLP8aMWcdkWCfj29vUeX3vSognbQ5w7u3O3tqydonmeuq966Urt60zNPD5vRqiUzW/WGFbOL+l78+sNnyudLzyDd0zvszNUcy+yOiLr6ozp0eoveWkSVp9vrDpuhr7/rKD229YBOXTbDudx8fV/c3etshl9eR+MdjlrQ6VSoBvy+qn39AQBAfeK3ZsDDnO3tBFwVZeZ5vnbJ9LpY3mCYds9iWhwBNLaHNttziZdMy7p8MJbQR37yuO7f2KVwwK9vv/9YrVk1Z9Tb6mgOyi7609wp45vnKUkz7MrEvuGEnng1XU1/5Lx0KNUUCug/LzpO7/nBQ7rrxb267k8v6mNnLtUlNz6irv6YVs7t0HcuOk6LZ7RqMJbQ+3/4sJ6yq/kkaZmr0nNme0Qz2tKBoNnoPdHmTWnW7z52yrg+d3pbREcvmKKntnXrnpf26r0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      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot_station_timeseries(1, \"od550aer\", start=2009)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26f2da65-151e-41b0-88c7-8b0e5fd6455e",
   "metadata": {},
   "source": [
    "## Colocation\n",
    "We can now move on to colocation. For this we need some model data. We can use Aerocom data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c910ca5-f802-4eee-af08-6a4ddea0617b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_id = \"AEROCOM-MEAN-2x3_AP3-CTRL\"\n",
    "model_name = \"AEROCOM_MEAN\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "183beaab-196a-48af-932b-8ea8f53e7fe4",
   "metadata": {},
   "source": [
    "Now we can pass this, and our config into the Colocator. Now the config needs to be passed as the argument ```obs_config```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4188606-7081-4a53-ba68-2338851d0884",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pyaerocom.io.read_aeronet_invv3.ReadAeronetInvV3'> ['AeronetInvV3Lev2.daily', 'AeronetInvV3Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_invv2.ReadAeronetInvV2'> ['AeronetInvV2Lev2.daily', 'AeronetInvV2Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav2.ReadAeronetSdaV2'> ['AeronetSDAV2Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav3.ReadAeronetSdaV3'> ['AeronetSDAV3Lev1.5.daily', 'AeronetSDAV3Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv2.ReadAeronetSunV2'> ['AeronetSunV2Lev2.daily', 'AeronetSunV2Lev2.AP']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv3.ReadAeronetSunV3'> ['AeronetSunV3Lev1.5.daily', 'AeronetSunV3Lev1.5.AP', 'AeronetSunV3Lev2.daily', 'AeronetSunV3Lev2.AP']\n",
      "<class 'pyaerocom.io.read_earlinet.ReadEarlinet'> ['EARLINET']\n",
      "<class 'pyaerocom.io.read_ebas.ReadEbas'> ['EBASMC']\n",
      "<class 'pyaerocom.io.read_aasetal.ReadAasEtal'> ['GAWTADsubsetAasEtAl']\n",
      "<class 'pyaerocom.io.read_airnow.ReadAirNow'> ['AirNow']\n",
      "<class 'pyaerocom.io.read_eea_aqerep.ReadEEAAQEREP'> ['EEAAQeRep.NRT']\n",
      "<class 'pyaerocom.io.read_eea_aqerep_v2.ReadEEAAQEREP_V2'> ['EEAAQeRep.v2']\n",
      "<class 'pyaerocom.io.pyaro.read_pyaro.ReadPyaro'> ['aeronetsdareader', 'aeronetsunreader', 'csv_timeseries']\n",
      "<class 'pyaerocom.plugins.gaw.reader.ReadGAW'> ['DMS_AMS_CVO']\n",
      "<class 'pyaerocom.plugins.ghost.reader.ReadGhost'> ['GHOST.EEA.monthly', 'GHOST.EEA.hourly', 'GHOST.EEA.daily', 'GHOST.EBAS.monthly', 'GHOST.EBAS.hourly', 'GHOST.EBAS.daily']\n",
      "<class 'pyaerocom.plugins.mep.reader.ReadMEP'> ['MEP']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████| 9993/9993 [00:00<00:00, 47789.66it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pyaerocom.io.read_aeronet_invv3.ReadAeronetInvV3'> ['AeronetInvV3Lev2.daily', 'AeronetInvV3Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_invv2.ReadAeronetInvV2'> ['AeronetInvV2Lev2.daily', 'AeronetInvV2Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav2.ReadAeronetSdaV2'> ['AeronetSDAV2Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav3.ReadAeronetSdaV3'> ['AeronetSDAV3Lev1.5.daily', 'AeronetSDAV3Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv2.ReadAeronetSunV2'> ['AeronetSunV2Lev2.daily', 'AeronetSunV2Lev2.AP']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv3.ReadAeronetSunV3'> ['AeronetSunV3Lev1.5.daily', 'AeronetSunV3Lev1.5.AP', 'AeronetSunV3Lev2.daily', 'AeronetSunV3Lev2.AP']\n",
      "<class 'pyaerocom.io.read_earlinet.ReadEarlinet'> ['EARLINET']\n",
      "<class 'pyaerocom.io.read_ebas.ReadEbas'> ['EBASMC']\n",
      "<class 'pyaerocom.io.read_aasetal.ReadAasEtal'> ['GAWTADsubsetAasEtAl']\n",
      "<class 'pyaerocom.io.read_airnow.ReadAirNow'> ['AirNow']\n",
      "<class 'pyaerocom.io.read_eea_aqerep.ReadEEAAQEREP'> ['EEAAQeRep.NRT']\n",
      "<class 'pyaerocom.io.read_eea_aqerep_v2.ReadEEAAQEREP_V2'> ['EEAAQeRep.v2']\n",
      "<class 'pyaerocom.io.pyaro.read_pyaro.ReadPyaro'> ['aeronetsdareader', 'aeronetsunreader', 'csv_timeseries']\n",
      "<class 'pyaerocom.plugins.gaw.reader.ReadGAW'> ['DMS_AMS_CVO']\n",
      "<class 'pyaerocom.plugins.ghost.reader.ReadGhost'> ['GHOST.EEA.monthly', 'GHOST.EEA.hourly', 'GHOST.EEA.daily', 'GHOST.EBAS.monthly', 'GHOST.EBAS.hourly', 'GHOST.EBAS.daily']\n",
      "<class 'pyaerocom.plugins.mep.reader.ReadMEP'> ['MEP']\n",
      "<class 'pyaerocom.io.read_aeronet_invv3.ReadAeronetInvV3'> ['AeronetInvV3Lev2.daily', 'AeronetInvV3Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_invv2.ReadAeronetInvV2'> ['AeronetInvV2Lev2.daily', 'AeronetInvV2Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav2.ReadAeronetSdaV2'> ['AeronetSDAV2Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav3.ReadAeronetSdaV3'> ['AeronetSDAV3Lev1.5.daily', 'AeronetSDAV3Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv2.ReadAeronetSunV2'> ['AeronetSunV2Lev2.daily', 'AeronetSunV2Lev2.AP']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv3.ReadAeronetSunV3'> ['AeronetSunV3Lev1.5.daily', 'AeronetSunV3Lev1.5.AP', 'AeronetSunV3Lev2.daily', 'AeronetSunV3Lev2.AP']\n",
      "<class 'pyaerocom.io.read_earlinet.ReadEarlinet'> ['EARLINET']\n",
      "<class 'pyaerocom.io.read_ebas.ReadEbas'> ['EBASMC']\n",
      "<class 'pyaerocom.io.read_aasetal.ReadAasEtal'> ['GAWTADsubsetAasEtAl']\n",
      "<class 'pyaerocom.io.read_airnow.ReadAirNow'> ['AirNow']\n",
      "<class 'pyaerocom.io.read_eea_aqerep.ReadEEAAQEREP'> ['EEAAQeRep.NRT']\n",
      "<class 'pyaerocom.io.read_eea_aqerep_v2.ReadEEAAQEREP_V2'> ['EEAAQeRep.v2']\n",
      "<class 'pyaerocom.io.pyaro.read_pyaro.ReadPyaro'> ['aeronetsdareader', 'aeronetsunreader', 'csv_timeseries']\n",
      "<class 'pyaerocom.plugins.gaw.reader.ReadGAW'> ['DMS_AMS_CVO']\n",
      "<class 'pyaerocom.plugins.ghost.reader.ReadGhost'> ['GHOST.EEA.monthly', 'GHOST.EEA.hourly', 'GHOST.EEA.daily', 'GHOST.EBAS.monthly', 'GHOST.EBAS.hourly', 'GHOST.EBAS.daily']\n",
      "<class 'pyaerocom.plugins.mep.reader.ReadMEP'> ['MEP']\n",
      "Searching database for AEROCOM-MEAN-2x3_AP3-CTRL\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/lustre/storeB/project/aerocom/aerocom-users-database/AEROCOM-PHASE-III-2019/AEROCOM-MEAN-2x3_AP3-CTRL has subdir renamed. Using that one\n",
      "Found match for ID AEROCOM-MEAN-2x3_AP3-CTRL\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Meteorology config substring in data_id MEAN-2x3 needs to start with met.\n",
      "Unknown input variable mmrprcpoxn\n",
      "Unknown input variable mmrprcpoxs\n",
      "Unknown input variable mmrprcprdn\n",
      "Invalid long_name None for coord time in cube. Overwriting with Time\n",
      "Invalid long_name None for coord lat in cube. Overwriting with Center coordinates for latitudes\n",
      "Invalid long_name None for coord lon in cube. Overwriting with Center coordinates for longitudes\n",
      "The following variable combinations will be colocated\n",
      "MODEL-VAR\tOBS-VAR\n",
      "od550aer\tod550aer\n",
      "Running AEROCOM-MEAN-2x3_AP3-CTRL (od550aer) vs. aeronetsunreader (od550aer)\n",
      "<class 'pyaerocom.io.read_aeronet_invv3.ReadAeronetInvV3'> ['AeronetInvV3Lev2.daily', 'AeronetInvV3Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_invv2.ReadAeronetInvV2'> ['AeronetInvV2Lev2.daily', 'AeronetInvV2Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav2.ReadAeronetSdaV2'> ['AeronetSDAV2Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav3.ReadAeronetSdaV3'> ['AeronetSDAV3Lev1.5.daily', 'AeronetSDAV3Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv2.ReadAeronetSunV2'> ['AeronetSunV2Lev2.daily', 'AeronetSunV2Lev2.AP']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv3.ReadAeronetSunV3'> ['AeronetSunV3Lev1.5.daily', 'AeronetSunV3Lev1.5.AP', 'AeronetSunV3Lev2.daily', 'AeronetSunV3Lev2.AP']\n",
      "<class 'pyaerocom.io.read_earlinet.ReadEarlinet'> ['EARLINET']\n",
      "<class 'pyaerocom.io.read_ebas.ReadEbas'> ['EBASMC']\n",
      "<class 'pyaerocom.io.read_aasetal.ReadAasEtal'> ['GAWTADsubsetAasEtAl']\n",
      "<class 'pyaerocom.io.read_airnow.ReadAirNow'> ['AirNow']\n",
      "<class 'pyaerocom.io.read_eea_aqerep.ReadEEAAQEREP'> ['EEAAQeRep.NRT']\n",
      "<class 'pyaerocom.io.read_eea_aqerep_v2.ReadEEAAQEREP_V2'> ['EEAAQeRep.v2']\n",
      "<class 'pyaerocom.io.pyaro.read_pyaro.ReadPyaro'> ['aeronetsdareader', 'aeronetsunreader', 'csv_timeseries']\n",
      "<class 'pyaerocom.plugins.gaw.reader.ReadGAW'> ['DMS_AMS_CVO']\n",
      "<class 'pyaerocom.plugins.ghost.reader.ReadGhost'> ['GHOST.EEA.monthly', 'GHOST.EEA.hourly', 'GHOST.EEA.daily', 'GHOST.EBAS.monthly', 'GHOST.EBAS.hourly', 'GHOST.EBAS.daily']\n",
      "<class 'pyaerocom.plugins.mep.reader.ReadMEP'> ['MEP']\n",
      "<class 'pyaerocom.io.read_aeronet_invv3.ReadAeronetInvV3'> ['AeronetInvV3Lev2.daily', 'AeronetInvV3Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_invv2.ReadAeronetInvV2'> ['AeronetInvV2Lev2.daily', 'AeronetInvV2Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav2.ReadAeronetSdaV2'> ['AeronetSDAV2Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav3.ReadAeronetSdaV3'> ['AeronetSDAV3Lev1.5.daily', 'AeronetSDAV3Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv2.ReadAeronetSunV2'> ['AeronetSunV2Lev2.daily', 'AeronetSunV2Lev2.AP']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv3.ReadAeronetSunV3'> ['AeronetSunV3Lev1.5.daily', 'AeronetSunV3Lev1.5.AP', 'AeronetSunV3Lev2.daily', 'AeronetSunV3Lev2.AP']\n",
      "<class 'pyaerocom.io.read_earlinet.ReadEarlinet'> ['EARLINET']\n",
      "<class 'pyaerocom.io.read_ebas.ReadEbas'> ['EBASMC']\n",
      "<class 'pyaerocom.io.read_aasetal.ReadAasEtal'> ['GAWTADsubsetAasEtAl']\n",
      "<class 'pyaerocom.io.read_airnow.ReadAirNow'> ['AirNow']\n",
      "<class 'pyaerocom.io.read_eea_aqerep.ReadEEAAQEREP'> ['EEAAQeRep.NRT']\n",
      "<class 'pyaerocom.io.read_eea_aqerep_v2.ReadEEAAQEREP_V2'> ['EEAAQeRep.v2']\n",
      "<class 'pyaerocom.io.pyaro.read_pyaro.ReadPyaro'> ['aeronetsdareader', 'aeronetsunreader', 'csv_timeseries']\n",
      "<class 'pyaerocom.plugins.gaw.reader.ReadGAW'> ['DMS_AMS_CVO']\n",
      "<class 'pyaerocom.plugins.ghost.reader.ReadGhost'> ['GHOST.EEA.monthly', 'GHOST.EEA.hourly', 'GHOST.EEA.daily', 'GHOST.EBAS.monthly', 'GHOST.EBAS.hourly', 'GHOST.EBAS.daily']\n",
      "<class 'pyaerocom.plugins.mep.reader.ReadMEP'> ['MEP']\n",
      "<class 'pyaerocom.io.read_aeronet_invv3.ReadAeronetInvV3'> ['AeronetInvV3Lev2.daily', 'AeronetInvV3Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_invv2.ReadAeronetInvV2'> ['AeronetInvV2Lev2.daily', 'AeronetInvV2Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav2.ReadAeronetSdaV2'> ['AeronetSDAV2Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav3.ReadAeronetSdaV3'> ['AeronetSDAV3Lev1.5.daily', 'AeronetSDAV3Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv2.ReadAeronetSunV2'> ['AeronetSunV2Lev2.daily', 'AeronetSunV2Lev2.AP']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv3.ReadAeronetSunV3'> ['AeronetSunV3Lev1.5.daily', 'AeronetSunV3Lev1.5.AP', 'AeronetSunV3Lev2.daily', 'AeronetSunV3Lev2.AP']\n",
      "<class 'pyaerocom.io.read_earlinet.ReadEarlinet'> ['EARLINET']\n",
      "<class 'pyaerocom.io.read_ebas.ReadEbas'> ['EBASMC']\n",
      "<class 'pyaerocom.io.read_aasetal.ReadAasEtal'> ['GAWTADsubsetAasEtAl']\n",
      "<class 'pyaerocom.io.read_airnow.ReadAirNow'> ['AirNow']\n",
      "<class 'pyaerocom.io.read_eea_aqerep.ReadEEAAQEREP'> ['EEAAQeRep.NRT']\n",
      "<class 'pyaerocom.io.read_eea_aqerep_v2.ReadEEAAQEREP_V2'> ['EEAAQeRep.v2']\n",
      "<class 'pyaerocom.io.pyaro.read_pyaro.ReadPyaro'> ['aeronetsdareader', 'aeronetsunreader', 'csv_timeseries']\n",
      "<class 'pyaerocom.plugins.gaw.reader.ReadGAW'> ['DMS_AMS_CVO']\n",
      "<class 'pyaerocom.plugins.ghost.reader.ReadGhost'> ['GHOST.EEA.monthly', 'GHOST.EEA.hourly', 'GHOST.EEA.daily', 'GHOST.EBAS.monthly', 'GHOST.EBAS.hourly', 'GHOST.EBAS.daily']\n",
      "<class 'pyaerocom.plugins.mep.reader.ReadMEP'> ['MEP']\n",
      "Successfully loaded cache file /home/danielh/MyPyaerocom/_cache/danielh/aeronetsunreader_od550aer.pkl\n",
      "Successfully imported aeronetsunreader data\n",
      "<class 'pyaerocom.io.read_aeronet_invv3.ReadAeronetInvV3'> ['AeronetInvV3Lev2.daily', 'AeronetInvV3Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_invv2.ReadAeronetInvV2'> ['AeronetInvV2Lev2.daily', 'AeronetInvV2Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav2.ReadAeronetSdaV2'> ['AeronetSDAV2Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav3.ReadAeronetSdaV3'> ['AeronetSDAV3Lev1.5.daily', 'AeronetSDAV3Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv2.ReadAeronetSunV2'> ['AeronetSunV2Lev2.daily', 'AeronetSunV2Lev2.AP']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv3.ReadAeronetSunV3'> ['AeronetSunV3Lev1.5.daily', 'AeronetSunV3Lev1.5.AP', 'AeronetSunV3Lev2.daily', 'AeronetSunV3Lev2.AP']\n",
      "<class 'pyaerocom.io.read_earlinet.ReadEarlinet'> ['EARLINET']\n",
      "<class 'pyaerocom.io.read_ebas.ReadEbas'> ['EBASMC']\n",
      "<class 'pyaerocom.io.read_aasetal.ReadAasEtal'> ['GAWTADsubsetAasEtAl']\n",
      "<class 'pyaerocom.io.read_airnow.ReadAirNow'> ['AirNow']\n",
      "<class 'pyaerocom.io.read_eea_aqerep.ReadEEAAQEREP'> ['EEAAQeRep.NRT']\n",
      "<class 'pyaerocom.io.read_eea_aqerep_v2.ReadEEAAQEREP_V2'> ['EEAAQeRep.v2']\n",
      "<class 'pyaerocom.io.pyaro.read_pyaro.ReadPyaro'> ['aeronetsdareader', 'aeronetsunreader', 'csv_timeseries']\n",
      "<class 'pyaerocom.plugins.gaw.reader.ReadGAW'> ['DMS_AMS_CVO']\n",
      "<class 'pyaerocom.plugins.ghost.reader.ReadGhost'> ['GHOST.EEA.monthly', 'GHOST.EEA.hourly', 'GHOST.EEA.daily', 'GHOST.EBAS.monthly', 'GHOST.EBAS.hourly', 'GHOST.EBAS.daily']\n",
      "<class 'pyaerocom.plugins.mep.reader.ReadMEP'> ['MEP']\n",
      "<class 'pyaerocom.io.read_aeronet_invv3.ReadAeronetInvV3'> ['AeronetInvV3Lev2.daily', 'AeronetInvV3Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_invv2.ReadAeronetInvV2'> ['AeronetInvV2Lev2.daily', 'AeronetInvV2Lev1.5.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav2.ReadAeronetSdaV2'> ['AeronetSDAV2Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sdav3.ReadAeronetSdaV3'> ['AeronetSDAV3Lev1.5.daily', 'AeronetSDAV3Lev2.daily']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv2.ReadAeronetSunV2'> ['AeronetSunV2Lev2.daily', 'AeronetSunV2Lev2.AP']\n",
      "<class 'pyaerocom.io.read_aeronet_sunv3.ReadAeronetSunV3'> ['AeronetSunV3Lev1.5.daily', 'AeronetSunV3Lev1.5.AP', 'AeronetSunV3Lev2.daily', 'AeronetSunV3Lev2.AP']\n",
      "<class 'pyaerocom.io.read_earlinet.ReadEarlinet'> ['EARLINET']\n",
      "<class 'pyaerocom.io.read_ebas.ReadEbas'> ['EBASMC']\n",
      "<class 'pyaerocom.io.read_aasetal.ReadAasEtal'> ['GAWTADsubsetAasEtAl']\n",
      "<class 'pyaerocom.io.read_airnow.ReadAirNow'> ['AirNow']\n",
      "<class 'pyaerocom.io.read_eea_aqerep.ReadEEAAQEREP'> ['EEAAQeRep.NRT']\n",
      "<class 'pyaerocom.io.read_eea_aqerep_v2.ReadEEAAQEREP_V2'> ['EEAAQeRep.v2']\n",
      "<class 'pyaerocom.io.pyaro.read_pyaro.ReadPyaro'> ['aeronetsdareader', 'aeronetsunreader', 'csv_timeseries']\n",
      "<class 'pyaerocom.plugins.gaw.reader.ReadGAW'> ['DMS_AMS_CVO']\n",
      "<class 'pyaerocom.plugins.ghost.reader.ReadGhost'> ['GHOST.EEA.monthly', 'GHOST.EEA.hourly', 'GHOST.EEA.daily', 'GHOST.EBAS.monthly', 'GHOST.EBAS.hourly', 'GHOST.EBAS.daily']\n",
      "<class 'pyaerocom.plugins.mep.reader.ReadMEP'> ['MEP']\n",
      "Cropping along time axis based on Timestamps\n",
      "Input filters {'latitude': [-89.0, 89.0], 'longitude': [-178.5, 178.5]} result in unchanged data object\n",
      "Ignoring station Cuiaba, var od550aer (aeronetsunreader): no data available in specified time interval 2010-01-01T00:00:00.000000 - 2010-12-31T23:59:59.000000\n",
      "Skipping meta index 0. Reason: DataCoverageError(\"Could not retrieve any valid data for station Cuiaba and input variables ['od550aer']\")\n",
      "Failed to convert to StationData Error: DataCoverageError(\"['od550aer'] data could not be retrieved for meta index (or station name) [0]\")\n",
      "Ignoring station GSFC, var od550aer (aeronetsunreader): no data available in specified time interval 2010-01-01T00:00:00.000000 - 2010-12-31T23:59:59.000000\n",
      "Skipping meta index 3. Reason: DataCoverageError(\"Could not retrieve any valid data for station GSFC and input variables ['od550aer']\")\n",
      "Failed to convert to StationData Error: DataCoverageError(\"['od550aer'] data could not be retrieved for meta index (or station name) [3]\")\n",
      "Input time frequency monthly equals current frequency of data. Resampling will be applied anyways which will introduce NaN values at missing time stamps\n",
      "Input time frequency monthly equals current frequency of data. Resampling will be applied anyways which will introduce NaN values at missing time stamps\n",
      "Colocation processing status for AEROCOM_MEAN vs. aeronetsunreader\n",
      "  Model Var   Obs Var   Status\n",
      "0  od550aer  od550aer  SUCCESS\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'od550aer': {'od550aer': pyaerocom.ColocatedData: data: <xarray.DataArray 'od550aer' (data_source: 2, time: 12, station_name: 2)>\n",
       "  array([[[0.10352023, 0.03902594],\n",
       "          [0.12133368, 0.06929725],\n",
       "          [0.05860459, 0.09757444],\n",
       "          [0.08603477,        nan],\n",
       "          [0.0838792 ,        nan],\n",
       "          [       nan,        nan],\n",
       "          [       nan, 0.11134255],\n",
       "          [1.04779657, 0.04434151],\n",
       "          [1.28173338, 0.05913538],\n",
       "          [0.49343804, 0.04293068],\n",
       "          [0.19978076, 0.02446057],\n",
       "          [0.16508896, 0.0554687 ]],\n",
       "  \n",
       "         [[0.07018041, 0.04110068],\n",
       "          [0.06417298, 0.06631772],\n",
       "          [0.0651281 , 0.06129938],\n",
       "          [0.07052724, 0.06702135],\n",
       "          [0.09388953, 0.06159478],\n",
       "          [0.09309519, 0.06192226],\n",
       "          [0.19007444, 0.10699583],\n",
       "          [0.80961418, 0.08990368],\n",
       "          [0.78322548, 0.05577568],\n",
       "          [0.30896598, 0.05242291],\n",
       "          [0.10184403, 0.02809396],\n",
       "          [0.08933712, 0.02757543]]])\n",
       "  Coordinates:\n",
       "    * data_source   (data_source) <U25 'aeronetsunreader' 'AEROCOM-MEAN-2x3_AP3...\n",
       "    * time          (time) datetime64[ns] 2010-01-15 2010-02-15 ... 2010-12-15\n",
       "    * station_name  (station_name) <U13 'Alta_Floresta' 'Tucson'\n",
       "      latitude      (station_name) float64 -9.871 32.23\n",
       "      longitude     (station_name) float64 -56.1 -111.0\n",
       "      altitude      (station_name) float64 277.0 779.0\n",
       "  Attributes: (12/19)\n",
       "      data_source:     ['aeronetsunreader', 'AEROCOM-MEAN-2x3_AP3-CTRL']\n",
       "      var_name:        ['od550aer', 'od550aer']\n",
       "      var_name_input:  ['od550aer', 'od550aer']\n",
       "      ts_type:         monthly\n",
       "      filter_name:     ALL-wMOUNTAINS\n",
       "      ts_type_src:     ['daily', 'monthly']\n",
       "      ...              ...\n",
       "      pyaerocom:       0.14.0\n",
       "      min_num_obs:     None\n",
       "      resample_how:    mean\n",
       "      model_name:      AEROCOM_MEAN\n",
       "      obs_name:        aeronetsunreader\n",
       "      vert_code:       None}}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = pya.Colocator(\n",
    "    model_id=model_id,\n",
    "    obs_config=config,\n",
    "    obs_vars=\"od550aer\",\n",
    "    ts_type=\"monthly\",\n",
    "    model_ts_type_read=\"monthly\",\n",
    "    model_name=model_name,\n",
    ")\n",
    "\n",
    "col.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd6855a8-71c9-441e-a1e3-ba9190919304",
   "metadata": {},
   "source": [
    "And again we plot the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7e0af0d3-0913-4d47-8714-b23037d251bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Jan 2010-Dec 2010 (monthly)'}, xlabel='aeronetsunreader', ylabel='AEROCOM-MEAN-2x3_AP3-CTRL'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col.data[\"od550aer\"][\"od550aer\"].plot_scatter()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7990462-4741-44c4-81e6-b4ee332429c2",
   "metadata": {},
   "source": [
    "## Pyaro and Aeroval\n",
    "\n",
    "Aeroval works more or less in the same way as the examples above, the only change is that the config is passed as an argument when defining the observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7b8f5225-6dcc-40d3-a44d-a30f3985cbb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "OBS_GROUNDBASED = {\n",
    "    ################\n",
    "    #    Pyaro\n",
    "    ################\n",
    "    \"Pyaro-m\": dict(\n",
    "        obs_id=config.data_id,\n",
    "        config=config,\n",
    "        web_interface_name=\"Pyaro-d\",\n",
    "        obs_vars=[\"od550aer\"],\n",
    "        obs_vert_type=\"Column\",\n",
    "        ts_type=\"monthly\",\n",
    "    ),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec332a5-a852-49ba-962a-cc484393c739",
   "metadata": {},
   "source": [
    "Other that that, aeroval works in the same way as before. One thing to notice is that the ```obs_id``` must be identical to the data_id!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "877baadd-aa6c-4213-a303-1a3c94e50832",
   "metadata": {},
   "source": [
    "## Configuration Catalogs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b0a0fc-2e7a-435b-92b5-3fbab19db9f7",
   "metadata": {},
   "source": [
    "Configs can become large and complicated. Thus we don't want to have to rewrite these config each time. We have introduced a catalog system to save configurations. This is done in using YAML files.\n",
    "\n",
    "The plan is to have one default catalog that is distributed with pyaerocom, which holds the most used configurations, and the option to have personal catalogs which the user can make themselves.\n",
    "\n",
    "The default catalog is found in the data folder of pyaerocom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5c1adb9b-e4b9-41e5-996f-8495a05e13a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/danielh/Documents/pyaerocom/pyaerocom/pyaerocom/data/pyaro_catalogs/default.yaml')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PyaroConfig._DEFAULT_CATALOG"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a6bd101-506d-40a6-865e-7b1858cd1c85",
   "metadata": {},
   "source": [
    "And looks like this"
   ]
  },
  {
   "cell_type": "raw",
   "id": "dca02d29-433e-408f-a253-876fd53bed5d",
   "metadata": {},
   "source": [
    "aeronetsun_test:\n",
    "  data_id: aeronetsunreader\n",
    "  filename_or_obj_or_url: https://pyaerocom.met.no/pyaro-suppl/testdata/aeronetsun_testdata.csv\n",
    "  filters: {}\n",
    "  name_map: {}\n",
    "\n",
    "aeronetsun_od550aer:\n",
    "  data_id: aeronetsunreader\n",
    "  filename_or_obj_or_url: https://pyaerocom.met.no/pyaro-suppl/testdata/aeronetsun_testdata.csv\n",
    "  filters:\n",
    "    variables:\n",
    "      include:\n",
    "      - AOD_550nm\n",
    "  name_map:\n",
    "    AOD_550nm: od550aer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa6bfba7-8eca-4385-81f6-e054422875de",
   "metadata": {},
   "source": [
    "The ```PyaroConfig``` class comes with methods to look at the available configs. This method can take the path to a personal catalog. When no path is used, only the default catalog will be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "12c04375-1c0d-4e3c-92d2-2f5275e9ce06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['aeronetsun_test', 'aeronetsun_od550aer']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PyaroConfig.list_configs()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "096d4386-b92a-4fda-aae1-44f276a4d460",
   "metadata": {},
   "source": [
    "From this list we can load a config. As with the list function, you need to add the path to the personal catalog if you want to use it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bcff88dd-e7ce-40e4-b79a-1018b3b0bd68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PyaroConfig(data_id='aeronetsunreader', filename_or_obj_or_url='https://pyaerocom.met.no/pyaro-suppl/testdata/aeronetsun_testdata.csv', filters={'variables': {'include': ['AOD_550nm']}}, name_map={'AOD_550nm': 'od550aer'})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config = PyaroConfig.load(\"aeronetsun_od550aer\")\n",
    "config"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a713f74-5918-4135-bf34-a914a7aa1b6f",
   "metadata": {},
   "source": [
    "If you want to save the current config, use can use the ```save``` method together with a unique name for the config. This name needs to be unique!\n",
    "\n",
    "You can add a path to where you want to save the catalog. If no path is given, the current working directory is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "67f15630-31d7-46f8-844e-a2f3577b492a",
   "metadata": {},
   "outputs": [],
   "source": [
    "config.save(\"aeronetsun_personal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a8e74367-2266-49c5-ab7f-77346125cecf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updating with private catalog catalog.yaml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['aeronetsun_test', 'aeronetsun_od550aer', 'aeronetsun_personal']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "PyaroConfig.list_configs(Path(\"./catalog.yaml\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f27d02b-a881-4bc7-baf0-6be4d9934066",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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