{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Access Vessel CO2 Delayed QC (Parquet)\n",
    "\n",
    "This Jupyter notebook demonstrates how to access and plot vessel co2 delayed qc data, available as a [Parquet](https://parquet.apache.org) dataset stored on S3.\n",
    "\n",
    "🔗 More information about the dataset is available [in the AODN metadata catalogue](https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/63db5801-cc19-40ef-83b3-85ccba884cf7).\n",
    "\n",
    "📌 The source of truth for this notebook is maintained on [GitHub](https://github.com/aodn/aodn_cloud_optimised/tree/main/notebooks/vessel_co2_delayed_qc.ipynb).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_name = \"vessel_co2_delayed_qc\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install/Update packages and Load common functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using CPython 3.12.6 interpreter at: \u001b[36m/home/lbesnard/miniforge3/envs/AodnCloudOptimised/bin/python\u001b[39m\n",
      "Creating virtual environment at: \u001b[36m.venv\u001b[39m\n",
      "Activate with: \u001b[32msource .venv/bin/activate\u001b[39m\n",
      "\u001b[2mAudited \u001b[1m240 packages\u001b[0m \u001b[2min 61ms\u001b[0m\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Local version 0.2.1 is up to date (remote: 0.2.1)\n"
     ]
    }
   ],
   "source": [
    "import os, requests, importlib.util\n",
    "\n",
    "open('setup.py', 'w').write(requests.get('https://raw.githubusercontent.com/aodn/aodn_cloud_optimised/main/notebooks/setup.py').text)\n",
    "\n",
    "spec = importlib.util.spec_from_file_location(\"setup\", \"setup.py\")\n",
    "setup = importlib.util.module_from_spec(spec)\n",
    "spec.loader.exec_module(setup)\n",
    "\n",
    "setup.install_requirements()\n",
    "setup.load_dataquery()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from DataQuery import GetAodn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding Parquet Partitioning\n",
    "\n",
    "Parquet files can be **partitioned** by one or more columns, which means the data is physically organised into folders based on the values in those columns. This is similar to how databases use indexes to optimise query performance.\n",
    "\n",
    "Partitioning enables **faster filtering**: when you query data using a partitioned column, only the relevant subset of files needs to be read—improving performance significantly.\n",
    "\n",
    "For example, if a dataset is partitioned by `\"site_code\"`, `\"timestamp\"`, and `\"polygon\"`, filtering on `\"site_code\"` allows the system to skip unrelated files entirely.\n",
    "\n",
    "In this notebook, the `GetAodn` class includes built-in methods to efficiently filter data by **time** and **latitude/longitude** using the **timestamp** and **polygon** partitions. Other partitions can be used for filtering via the `scalar_filter`.\n",
    "\n",
    "Any filtering on columns that are **not** partitioned can be significantly slower, as all files may need to be scanned. However, the `GetAodn` class provides a `scalar_filter` method that lets you apply these filters at load time—before the data is fully read—helping reduce the size of the resulting DataFrame.\n",
    "\n",
    "Once the dataset is loaded, further filtering using Pandas is efficient and flexible.\n",
    "\n",
    "See further below in the notebook for examples of how to filter the data effectively.\n",
    "\n",
    "To view the actual partition columns for this dataset, run:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 138 ms, sys: 31 ms, total: 169 ms\n",
      "Wall time: 6.88 s\n"
     ]
    }
   ],
   "source": [
    "aodn = GetAodn()\n",
    "dname = f'{dataset_name}.parquet'\n",
    "%time aodn_dataset = aodn.get_dataset(dname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp: int32\n",
       "polygon: string\n",
       "platform_code: string"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aodn_dataset.dataset.partitioning.schema"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List unique partition values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['VLMJ', 'ZMFR']\n",
      "CPU times: user 10.6 ms, sys: 1.16 ms, total: 11.7 ms\n",
      "Wall time: 10.1 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "unique_partition_value = aodn_dataset.get_unique_partition_values('platform_code')\n",
    "print(list(unique_partition_value)[0:2])  # showing a subset only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise Spatial Extent of the dataset\n",
    "This section plots the polygons representing the areas where data is available. It helps to identify and create a bounding box around the regions containing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lbesnard/miniforge3/envs/AodnCloudOptimised/lib/python3.12/site-packages/cartopy/mpl/feature_artist.py:144: UserWarning: facecolor will have no effect as it has been defined as \"never\".\n",
      "  warnings.warn('facecolor will have no effect as it has been '\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aodn_dataset.plot_spatial_extent()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Temporal Extent of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similary to the spatial extent, we're retrieving the minimum and maximum timestamp partition values of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2008-01-11 09:32:35'), Timestamp('2024-04-18 20:07:33'))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aodn_dataset.get_temporal_extent()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Metadata\n",
    "\n",
    "For all Parquet datasets, we create a sidecar file named **_common_metadata** in the root of the dataset. This file contains both the dataset-level and variable-level attributes.  \n",
    "The metadata can be retrieved below as a dictionary, and it will also be included in the pandas DataFrame when using the `get_data` method from the `GetAodn` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-06-05 13:23:19,186 - aodn.GetAodn - INFO - Retrieving metadata for aodn-cloud-optimised/vessel_co2_delayed_qc.parquet\n",
      "2025-06-05 13:23:19,302 - aodn.GetAodn - WARNING - Old 'dataset_medata' deprecated key found in parquet schema. Renamed to 'global_attributes'. Dataset should be updated\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'TIME': {'type': 'timestamp[ns]',\n",
       "  'standard_name': 'time',\n",
       "  'long_name': 'analysis_time',\n",
       "  'axis': 'T',\n",
       "  'valid_min': 0.0,\n",
       "  'valid_max': 999999.0,\n",
       "  'ancillary_variables': 'TIME_quality_control'},\n",
       " 'TIME_quality_control': {'type': 'float',\n",
       "  'standard_name': 'time status_flag',\n",
       "  'long_name': 'Quality Control flag for time',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'LATITUDE': {'type': 'double',\n",
       "  'standard_name': 'latitude',\n",
       "  'long_name': 'latitude',\n",
       "  'units': 'degrees_north',\n",
       "  'axis': 'Y',\n",
       "  'valid_min': -90.0,\n",
       "  'valid_max': 90.0,\n",
       "  'reference_datum': 'geographical coordinates, WGS84 projection',\n",
       "  'ancillary_variables': 'LATITUDE_quality_control'},\n",
       " 'LATITUDE_quality_control': {'type': 'float',\n",
       "  'standard_name': 'latitude status_flag',\n",
       "  'long_name': 'Quality Control flag for latitude',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'LONGITUDE': {'type': 'double',\n",
       "  'standard_name': 'longitude',\n",
       "  'long_name': 'longitude',\n",
       "  'units': 'degrees_east',\n",
       "  'axis': 'X',\n",
       "  'valid_min': -180.0,\n",
       "  'valid_max': 180.0,\n",
       "  'reference_datum': 'geographical coordinates, WGS84 projection',\n",
       "  'ancillary_variables': 'LONGITUDE_quality_control'},\n",
       " 'LONGITUDE_quality_control': {'type': 'float',\n",
       "  'standard_name': 'longitude status_flag',\n",
       "  'long_name': 'Quality Control flag for longitude',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'TEMP': {'type': 'double',\n",
       "  'standard_name': 'sea_surface_temperature',\n",
       "  'long_name': 'sea surface temperature',\n",
       "  'units': 'degree_Celsius',\n",
       "  'valid_min': -2.0,\n",
       "  'valid_max': 40.0,\n",
       "  'ancillary_variables': 'TEMP_quality_control'},\n",
       " 'TEMP_quality_control': {'type': 'float',\n",
       "  'standard_name': 'sea_surface_temperature status_flag',\n",
       "  'long_name': 'Quality Control flag for sea_surface_temperature',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'TEMP_2': {'type': 'double',\n",
       "  'long_name': 'equilibrator water temperature',\n",
       "  'units': 'degree_Celsius',\n",
       "  'valid_min': -2.0,\n",
       "  'valid_max': 40.0,\n",
       "  'ancillary_variables': 'TEMP_2_quality_control'},\n",
       " 'TEMP_2_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for sea_surface_temperature',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'PSAL': {'type': 'double',\n",
       "  'standard_name': 'sea_surface_salinity',\n",
       "  'long_name': 'sea surface salinity',\n",
       "  'units': '1e-3',\n",
       "  'valid_min': 0.0,\n",
       "  'valid_max': 42.0,\n",
       "  'ancillary_variables': 'PSAL_quality_control'},\n",
       " 'PSAL_quality_control': {'type': 'float',\n",
       "  'standard_name': 'sea_surface_salinity status_flag',\n",
       "  'long_name': 'Quality Control flag for sea_surface_salinity',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'WSPD': {'type': 'double',\n",
       "  'standard_name': 'wind_speed',\n",
       "  'long_name': 'wind speed',\n",
       "  'units': 'm s-1',\n",
       "  'ancillary_variables': 'WSPD_quality_control'},\n",
       " 'WSPD_quality_control': {'type': 'float',\n",
       "  'standard_name': 'wind_speed status_flag',\n",
       "  'long_name': 'Quality Control flag for wind speed',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'WDIR': {'type': 'double',\n",
       "  'long_name': 'wind direction',\n",
       "  'units': 'degree',\n",
       "  'ancillary_variables': 'WDIR_quality_control',\n",
       "  'comment': 'true wind direction where 0 is North and 90 is East'},\n",
       " 'WDIR_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for wind direction',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'Press_Equil': {'type': 'double',\n",
       "  'long_name': 'equilibrator head space pressure',\n",
       "  'units': 'hPa',\n",
       "  'ancillary_variables': 'Press_Equil_quality_control'},\n",
       " 'Press_Equil_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for equilibrator head space pressure',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'Press_ATM': {'type': 'double',\n",
       "  'long_name': 'barometric pressure',\n",
       "  'units': 'hPa',\n",
       "  'ancillary_variables': 'Press_ATM_quality_control'},\n",
       " 'Press_ATM_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for barometric pressure',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'xCO2EQ_PPM': {'type': 'double',\n",
       "  'long_name': 'mole fraction of CO2 in the equilibrator head space (dry)',\n",
       "  'units': '1e-6',\n",
       "  'ancillary_variables': 'xCO2EQ_PPM_quality_control',\n",
       "  'comment': 'the unit 1e-6 is also called parts per million (ppm)'},\n",
       " 'xCO2EQ_PPM_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for xCO2EQ_PPM',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'xCO2ATM_PPM': {'type': 'double',\n",
       "  'long_name': 'mole fraction of CO2 in the atmosphere (dry) measured every 4 hours after standard runs',\n",
       "  'units': '1e-6',\n",
       "  'ancillary_variables': 'xCO2ATM_PPM_quality_control',\n",
       "  'comment': 'the unit 1e-6 is also called parts per million (ppm)'},\n",
       " 'xCO2ATM_PPM_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for xCO2ATM_PPM',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'xCO2ATM_PPM_INTERPOLATED': {'type': 'double',\n",
       "  'long_name': 'mole fraction of CO2 in the atmosphere (dry) measured every 4 hours after standard runs and values linearly interpolated to the times shown',\n",
       "  'units': '1e-6',\n",
       "  'ancillary_variables': 'xCO2ATM_PPM_INTERPOLATED_quality_control',\n",
       "  'comment': 'the unit 1e-6 is also called parts per million (ppm)'},\n",
       " 'xCO2ATM_PPM_INTERPOLATED_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for xCO2ATM_PPM_INTERPOLATED',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'fCO2SW_UATM': {'type': 'double',\n",
       "  'long_name': 'fugacity of carbon dioxide at surface water salinity and temperature',\n",
       "  'units': 'microatmospheres',\n",
       "  'ancillary_variables': 'fCO2SW_UATM_quality_control'},\n",
       " 'fCO2SW_UATM_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for fCO2SW_UATM',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'fCO2ATM_UATM_INTERPOLATED': {'type': 'double',\n",
       "  'long_name': 'fugacity of CO2 in the atmosphere',\n",
       "  'units': 'microatmospheres',\n",
       "  'ancillary_variables': 'fCO2ATM_UATM_INTERPOLATED_quality_control'},\n",
       " 'fCO2ATM_UATM_INTERPOLATED_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for fCO2ATM_UATM_INTERPOLATED',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'DfCO2': {'type': 'double',\n",
       "  'long_name': 'Difference between fCO2SW and fCO2ATM',\n",
       "  'units': 'microatmospheres',\n",
       "  'ancillary_variables': 'DfCO2_quality_control'},\n",
       " 'DfCO2_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for DfCO2',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'LICORflow': {'type': 'double',\n",
       "  'long_name': 'Gas flow through infrared gas analyser',\n",
       "  'units': 'ml min-1',\n",
       "  'ancillary_variables': 'LICORflow_quality_control'},\n",
       " 'LICORflow_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for LICORflow',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'H2OFLOW': {'type': 'double',\n",
       "  'long_name': 'water flow to equilibrator',\n",
       "  'units': 'L min-1',\n",
       "  'ancillary_variables': 'H2OFLOW_quality_control'},\n",
       " 'H2OFLOW_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for H2OFLOW',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'SUBFLAG': {'type': 'float',\n",
       "  'long_name': 'secondary flags, only for questionable measurements, WOCE flag 3 (Pierrot et Al 2009)',\n",
       "  'valid_min': 1,\n",
       "  'valid_max': 10,\n",
       "  'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "  'flag_meanings': 'Outside_of_standard_range Questionable_or_interpolated_SST Questionable_EQU_temperature Anomalous_EQU_temperature-SST_+or-1degC Questionable_sea-surface_salinity Questionable_pressure Low_EQU_gas_flow Questionable_air_value Interpolated_standard Other_see_metadata',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005'},\n",
       " 'TYPE': {'type': 'string',\n",
       "  'long_name': 'measurement type (equilibrator, standard or atmosphere)',\n",
       "  'units': 'categorical'},\n",
       " 'timestamp': {'type': 'int64'},\n",
       " 'polygon': {'type': 'string'},\n",
       " 'platform_code': {'type': 'string'},\n",
       " 'cruise_id': {'type': 'string'},\n",
       " 'vessel_name': {'type': 'string'},\n",
       " 'filename': {'type': 'string'},\n",
       " 'global_attributes': {'metadata_uuid': '63db5801-cc19-40ef-83b3-85ccba884cf7',\n",
       "  'title': 'IMOS Underway CO2 dataset measured',\n",
       "  'principal_investigator': '',\n",
       "  'principal_investigator_email': '',\n",
       "  'featureType': 'trajectory'}}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata = aodn_dataset.get_metadata()\n",
    "metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Query and Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a TIME and BoundingBox filter\n",
    "\n",
    "This cell loads a subset of the dataset based on a time range and a spatial bounding box. The result is returned as a pandas DataFrame, and basic information about its structure is displayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10199 entries, 0 to 10198\n",
      "Data columns (total 44 columns):\n",
      " #   Column                                     Non-Null Count  Dtype         \n",
      "---  ------                                     --------------  -----         \n",
      " 0   TIME                                       10199 non-null  datetime64[ns]\n",
      " 1   TIME_quality_control                       10199 non-null  float32       \n",
      " 2   LATITUDE                                   10199 non-null  float64       \n",
      " 3   LATITUDE_quality_control                   10199 non-null  float32       \n",
      " 4   LONGITUDE                                  10199 non-null  float64       \n",
      " 5   LONGITUDE_quality_control                  10199 non-null  float32       \n",
      " 6   TEMP                                       10199 non-null  float64       \n",
      " 7   TEMP_quality_control                       10199 non-null  float32       \n",
      " 8   TEMP_2                                     10196 non-null  float64       \n",
      " 9   TEMP_2_quality_control                     10199 non-null  float32       \n",
      " 10  PSAL                                       10199 non-null  float64       \n",
      " 11  PSAL_quality_control                       10199 non-null  float32       \n",
      " 12  WSPD                                       9592 non-null   float64       \n",
      " 13  WSPD_quality_control                       10199 non-null  float32       \n",
      " 14  WDIR                                       10157 non-null  float64       \n",
      " 15  WDIR_quality_control                       10199 non-null  float32       \n",
      " 16  Press_Equil                                10199 non-null  float64       \n",
      " 17  Press_Equil_quality_control                10199 non-null  float32       \n",
      " 18  Press_ATM                                  10199 non-null  float64       \n",
      " 19  Press_ATM_quality_control                  10199 non-null  float32       \n",
      " 20  xCO2EQ_PPM                                 9931 non-null   float64       \n",
      " 21  xCO2EQ_PPM_quality_control                 10199 non-null  float32       \n",
      " 22  xCO2ATM_PPM                                268 non-null    float64       \n",
      " 23  xCO2ATM_PPM_quality_control                10199 non-null  float32       \n",
      " 24  xCO2ATM_PPM_INTERPOLATED                   10199 non-null  float64       \n",
      " 25  xCO2ATM_PPM_INTERPOLATED_quality_control   10199 non-null  float32       \n",
      " 26  fCO2SW_UATM                                9931 non-null   float64       \n",
      " 27  fCO2SW_UATM_quality_control                10199 non-null  float32       \n",
      " 28  fCO2ATM_UATM_INTERPOLATED                  10199 non-null  float64       \n",
      " 29  fCO2ATM_UATM_INTERPOLATED_quality_control  10199 non-null  float32       \n",
      " 30  DfCO2                                      9931 non-null   float64       \n",
      " 31  DfCO2_quality_control                      10199 non-null  float32       \n",
      " 32  LICORflow                                  10199 non-null  float64       \n",
      " 33  LICORflow_quality_control                  10199 non-null  float32       \n",
      " 34  H2OFLOW                                    10199 non-null  float64       \n",
      " 35  H2OFLOW_quality_control                    10199 non-null  float32       \n",
      " 36  SUBFLAG                                    0 non-null      float32       \n",
      " 37  TYPE                                       10199 non-null  object        \n",
      " 38  cruise_id                                  10199 non-null  object        \n",
      " 39  vessel_name                                10199 non-null  object        \n",
      " 40  filename                                   10199 non-null  object        \n",
      " 41  timestamp                                  10199 non-null  int32         \n",
      " 42  polygon                                    10199 non-null  object        \n",
      " 43  platform_code                              10199 non-null  object        \n",
      "dtypes: datetime64[ns](1), float32(19), float64(17), int32(1), object(6)\n",
      "memory usage: 2.6+ MB\n",
      "CPU times: user 264 ms, sys: 51.9 ms, total: 316 ms\n",
      "Wall time: 1.02 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = aodn_dataset.get_data(date_start='2020-12-23 10:14:00', date_end='2024-01-01 07:50:00', lat_min=-34, lat_max=-32, lon_min=150, lon_max=155)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Download Subsetted Data as CSV\n",
    "\n",
    "# This cell downloads the filtered dataset as a ZIP-compressed CSV file.  \n",
    "# The CSV includes metadata at the top as commented lines, and a `FileLink` object is returned to allow downloading directly from the notebook.\n",
    "\n",
    "\n",
    "df.aodn.download_as_csv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>TIME_quality_control</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LATITUDE_quality_control</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LONGITUDE_quality_control</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>TEMP_quality_control</th>\n",
       "      <th>TEMP_2</th>\n",
       "      <th>TEMP_2_quality_control</th>\n",
       "      <th>...</th>\n",
       "      <th>H2OFLOW</th>\n",
       "      <th>H2OFLOW_quality_control</th>\n",
       "      <th>SUBFLAG</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>cruise_id</th>\n",
       "      <th>vessel_name</th>\n",
       "      <th>filename</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>polygon</th>\n",
       "      <th>platform_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-05-10 18:05:01.000000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.999871</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.395629</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.435</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.66</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2021_V03</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc</td>\n",
       "      <td>1619827200</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-05-10 18:06:23.000000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.996769</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.399301</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.429</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.66</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2021_V03</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc</td>\n",
       "      <td>1619827200</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-05-10 18:07:41.999999744</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.993654</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.402956</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.438</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.67</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2021_V03</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc</td>\n",
       "      <td>1619827200</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-05-10 18:09:03.999999744</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.990543</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.406601</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.428</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.66</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2021_V03</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc</td>\n",
       "      <td>1619827200</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-05-10 18:10:25.000000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.987426</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.410262</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.432</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.66</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2021_V03</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc</td>\n",
       "      <td>1619827200</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10194</th>\n",
       "      <td>2023-11-01 16:37:56.000000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.852700</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.471700</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.172</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.45</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2023_V06</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc</td>\n",
       "      <td>1698796800</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10195</th>\n",
       "      <td>2023-11-01 16:39:14.000000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.850300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.470400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.164</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.45</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2023_V06</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc</td>\n",
       "      <td>1698796800</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10196</th>\n",
       "      <td>2023-11-01 16:40:34.000000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.847900</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.469200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.147</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.43</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2023_V06</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc</td>\n",
       "      <td>1698796800</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10197</th>\n",
       "      <td>2023-11-01 16:41:54.000000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.845800</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.468100</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.129</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.42</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2023_V06</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc</td>\n",
       "      <td>1698796800</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10198</th>\n",
       "      <td>2023-11-01 16:43:14.000000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.843700</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.467100</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.110</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.40</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2023_V06</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc</td>\n",
       "      <td>1698796800</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10199 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               TIME  TIME_quality_control   LATITUDE  \\\n",
       "0     2021-05-10 18:05:01.000000000                   2.0 -33.999871   \n",
       "1     2021-05-10 18:06:23.000000000                   2.0 -33.996769   \n",
       "2     2021-05-10 18:07:41.999999744                   2.0 -33.993654   \n",
       "3     2021-05-10 18:09:03.999999744                   2.0 -33.990543   \n",
       "4     2021-05-10 18:10:25.000000000                   2.0 -33.987426   \n",
       "...                             ...                   ...        ...   \n",
       "10194 2023-11-01 16:37:56.000000000                   2.0 -33.852700   \n",
       "10195 2023-11-01 16:39:14.000000000                   2.0 -33.850300   \n",
       "10196 2023-11-01 16:40:34.000000000                   2.0 -33.847900   \n",
       "10197 2023-11-01 16:41:54.000000000                   2.0 -33.845800   \n",
       "10198 2023-11-01 16:43:14.000000000                   2.0 -33.843700   \n",
       "\n",
       "       LATITUDE_quality_control   LONGITUDE  LONGITUDE_quality_control  \\\n",
       "0                           2.0  151.395629                        2.0   \n",
       "1                           2.0  151.399301                        2.0   \n",
       "2                           2.0  151.402956                        2.0   \n",
       "3                           2.0  151.406601                        2.0   \n",
       "4                           2.0  151.410262                        2.0   \n",
       "...                         ...         ...                        ...   \n",
       "10194                       2.0  151.471700                        2.0   \n",
       "10195                       2.0  151.470400                        2.0   \n",
       "10196                       2.0  151.469200                        2.0   \n",
       "10197                       2.0  151.468100                        2.0   \n",
       "10198                       2.0  151.467100                        2.0   \n",
       "\n",
       "         TEMP  TEMP_quality_control  TEMP_2  TEMP_2_quality_control  ...  \\\n",
       "0      20.435                   2.0   20.66                     2.0  ...   \n",
       "1      20.429                   2.0   20.66                     2.0  ...   \n",
       "2      20.438                   2.0   20.67                     2.0  ...   \n",
       "3      20.428                   2.0   20.66                     2.0  ...   \n",
       "4      20.432                   2.0   20.66                     2.0  ...   \n",
       "...       ...                   ...     ...                     ...  ...   \n",
       "10194  20.172                   2.0   20.45                     2.0  ...   \n",
       "10195  20.164                   2.0   20.45                     2.0  ...   \n",
       "10196  20.147                   2.0   20.43                     2.0  ...   \n",
       "10197  20.129                   2.0   20.42                     2.0  ...   \n",
       "10198  20.110                   2.0   20.40                     2.0  ...   \n",
       "\n",
       "       H2OFLOW  H2OFLOW_quality_control  SUBFLAG  TYPE   cruise_id  \\\n",
       "0         2.09                      2.0      NaN   EQU  IN2021_V03   \n",
       "1         2.09                      2.0      NaN   EQU  IN2021_V03   \n",
       "2         2.09                      2.0      NaN   EQU  IN2021_V03   \n",
       "3         2.09                      2.0      NaN   EQU  IN2021_V03   \n",
       "4         2.09                      2.0      NaN   EQU  IN2021_V03   \n",
       "...        ...                      ...      ...   ...         ...   \n",
       "10194     2.12                      2.0      NaN   EQU  IN2023_V06   \n",
       "10195     2.12                      2.0      NaN   EQU  IN2023_V06   \n",
       "10196     2.12                      2.0      NaN   EQU  IN2023_V06   \n",
       "10197     2.12                      2.0      NaN   EQU  IN2023_V06   \n",
       "10198     2.12                      2.0      NaN   EQU  IN2023_V06   \n",
       "\n",
       "           vessel_name                                         filename  \\\n",
       "0      RV Investigator  IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc   \n",
       "1      RV Investigator  IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc   \n",
       "2      RV Investigator  IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc   \n",
       "3      RV Investigator  IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc   \n",
       "4      RV Investigator  IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc   \n",
       "...                ...                                              ...   \n",
       "10194  RV Investigator  IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc   \n",
       "10195  RV Investigator  IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc   \n",
       "10196  RV Investigator  IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc   \n",
       "10197  RV Investigator  IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc   \n",
       "10198  RV Investigator  IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc   \n",
       "\n",
       "        timestamp                                            polygon  \\\n",
       "0      1619827200  0103000000010000000500000000000000002062400000...   \n",
       "1      1619827200  0103000000010000000500000000000000002062400000...   \n",
       "2      1619827200  0103000000010000000500000000000000002062400000...   \n",
       "3      1619827200  0103000000010000000500000000000000002062400000...   \n",
       "4      1619827200  0103000000010000000500000000000000002062400000...   \n",
       "...           ...                                                ...   \n",
       "10194  1698796800  0103000000010000000500000000000000002062400000...   \n",
       "10195  1698796800  0103000000010000000500000000000000002062400000...   \n",
       "10196  1698796800  0103000000010000000500000000000000002062400000...   \n",
       "10197  1698796800  0103000000010000000500000000000000002062400000...   \n",
       "10198  1698796800  0103000000010000000500000000000000002062400000...   \n",
       "\n",
       "       platform_code  \n",
       "0               VLMJ  \n",
       "1               VLMJ  \n",
       "2               VLMJ  \n",
       "3               VLMJ  \n",
       "4               VLMJ  \n",
       "...              ...  \n",
       "10194           VLMJ  \n",
       "10195           VLMJ  \n",
       "10196           VLMJ  \n",
       "10197           VLMJ  \n",
       "10198           VLMJ  \n",
       "\n",
       "[10199 rows x 44 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.collections import LineCollection\n",
    "import numpy as np\n",
    "\n",
    "df_sorted = df.sort_values('TIME')\n",
    "\n",
    "# Create a list of segments\n",
    "points = np.array([df_sorted['LONGITUDE'], df_sorted['LATITUDE']]).T.reshape(-1, 1, 2)\n",
    "segments = np.concatenate([points[:-1], points[1:]], axis=1)\n",
    "\n",
    "# Create a LineCollection with segments colored by temperature\n",
    "norm = plt.Normalize(df_sorted['TEMP'].min(), df_sorted['TEMP'].max())\n",
    "lc = LineCollection(segments, cmap='RdYlBu_r', norm=norm)\n",
    "lc.set_array(df_sorted['TEMP'])\n",
    "lc.set_linewidth(2)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.add_collection(lc)\n",
    "ax.autoscale()\n",
    "ax.set_xlabel(metadata['LONGITUDE']['standard_name'])\n",
    "ax.set_ylabel(metadata['LATITUDE']['standard_name'])\n",
    "ax.invert_yaxis()\n",
    "\n",
    "# Adding color bar\n",
    "cbar = plt.colorbar(lc, ax=ax)\n",
    "cbar.set_label('Temperature')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a TIME and scalar/number filter\n",
    "\n",
    "This cell filters the dataset by time range and a scalar value (from a Parquet partition) using the `scalar_filter` argument.  \n",
    "This leverages Parquet partitioning to apply efficient, server-side filtering, which significantly speeds up data loading."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 244944 entries, 0 to 244943\n",
      "Data columns (total 44 columns):\n",
      " #   Column                                     Non-Null Count   Dtype         \n",
      "---  ------                                     --------------   -----         \n",
      " 0   TIME                                       244944 non-null  datetime64[ns]\n",
      " 1   TIME_quality_control                       244944 non-null  float32       \n",
      " 2   LATITUDE                                   244944 non-null  float64       \n",
      " 3   LATITUDE_quality_control                   244944 non-null  float32       \n",
      " 4   LONGITUDE                                  244944 non-null  float64       \n",
      " 5   LONGITUDE_quality_control                  244944 non-null  float32       \n",
      " 6   TEMP                                       244680 non-null  float64       \n",
      " 7   TEMP_quality_control                       244944 non-null  float32       \n",
      " 8   TEMP_2                                     244780 non-null  float64       \n",
      " 9   TEMP_2_quality_control                     244944 non-null  float32       \n",
      " 10  PSAL                                       244944 non-null  float64       \n",
      " 11  PSAL_quality_control                       244944 non-null  float32       \n",
      " 12  WSPD                                       222686 non-null  float64       \n",
      " 13  WSPD_quality_control                       244944 non-null  float32       \n",
      " 14  WDIR                                       237361 non-null  float64       \n",
      " 15  WDIR_quality_control                       244944 non-null  float32       \n",
      " 16  Press_Equil                                244944 non-null  float64       \n",
      " 17  Press_Equil_quality_control                244944 non-null  float32       \n",
      " 18  Press_ATM                                  241825 non-null  float64       \n",
      " 19  Press_ATM_quality_control                  244944 non-null  float32       \n",
      " 20  xCO2EQ_PPM                                 236906 non-null  float64       \n",
      " 21  xCO2EQ_PPM_quality_control                 244944 non-null  float32       \n",
      " 22  xCO2ATM_PPM                                8038 non-null    float64       \n",
      " 23  xCO2ATM_PPM_quality_control                244944 non-null  float32       \n",
      " 24  xCO2ATM_PPM_INTERPOLATED                   244944 non-null  float64       \n",
      " 25  xCO2ATM_PPM_INTERPOLATED_quality_control   244944 non-null  float32       \n",
      " 26  fCO2SW_UATM                                236647 non-null  float64       \n",
      " 27  fCO2SW_UATM_quality_control                244944 non-null  float32       \n",
      " 28  fCO2ATM_UATM_INTERPOLATED                  241561 non-null  float64       \n",
      " 29  fCO2ATM_UATM_INTERPOLATED_quality_control  244944 non-null  float32       \n",
      " 30  DfCO2                                      233528 non-null  float64       \n",
      " 31  DfCO2_quality_control                      244944 non-null  float32       \n",
      " 32  LICORflow                                  244944 non-null  float64       \n",
      " 33  LICORflow_quality_control                  244944 non-null  float32       \n",
      " 34  H2OFLOW                                    244944 non-null  float64       \n",
      " 35  H2OFLOW_quality_control                    244944 non-null  float32       \n",
      " 36  SUBFLAG                                    0 non-null       float32       \n",
      " 37  TYPE                                       244944 non-null  object        \n",
      " 38  cruise_id                                  244944 non-null  object        \n",
      " 39  vessel_name                                244944 non-null  object        \n",
      " 40  filename                                   244944 non-null  object        \n",
      " 41  timestamp                                  244944 non-null  int32         \n",
      " 42  polygon                                    244944 non-null  object        \n",
      " 43  platform_code                              244944 non-null  object        \n",
      "dtypes: datetime64[ns](1), float32(19), float64(17), int32(1), object(6)\n",
      "memory usage: 63.5+ MB\n",
      "CPU times: user 1.7 s, sys: 493 ms, total: 2.19 s\n",
      "Wall time: 6.46 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = aodn_dataset.get_data(date_start='2020-01-31 10:14:00', date_end='2022-02-01 07:50:00', scalar_filter={'platform_code': \"VLMJ\"})\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7pYf5acfByefJ5z2gtEBjf3MfUiliQViyoLjqybIs/vqKtSysC/Hff9iMkwUlMzyzuYcb/uF/+PM3LmXjxtFVPdY0TTtZSSmQ41y+JNXMX/6kg5pJkM45KB+yLnjSPv4JR7CvvZfu7jzJTIH+ntGvFqqqiLHxrOJWCp2d9fz7HS/y6HP9BAWcv6qKLAZb9rRg21GSngnSAAS+kCRTDkr5dPWnMYBHt5tEQntp6im2/cp+xSt7O1hQGyMyxg0yX9y+i/5eD8OEkEiP6dxDPM+nPwNONs+yeSNHjc45ewlLl1Twf//f02zZnSRkR+gdTPPjX2/jnk37WdQQ46/fdjbVVRUlXVvTNG0qCUMgjPEFJWIWbJSgg5pJYFhghCX9aYOdrcmS2pCeQW97N13dgsHaHu59bhtD3QrDlBR8j/PX1TK3ruqYbaQyeVq7M6xfZnHaqcu54s+L+0f9NWtQSrH3QC979vTQlXToHEjhOgrwkNIAfAp5n3CQEf+R/uue3SjlEI8FqSoP01hfQazMZVVdNdHo0beEuOwNZ5DN7cDxYH5daTuGL5lfxuKGKAXHo6XHP+z1ysoKPvepy/jR/9zHvn0eLZ0OvmvR15enuzPJ/X96gBUL49gBl3AoTN2cCMGgR1l5gjNXzaWq8thL5DVN07STmw5qJoEVsFhUHcAKwvx4aTtSB0IWLb0CJRTP7R7E3NRO0ArQ0dXDQAYCtkVddQWGcYzMMeVQVRvFKXjsbe0d8ZIQgsULq1i8cGRg9Mq2PezrytPbn6UnLUi+pqaMsAAJfkEwmMqTzvu09OfxHYff+q1EIxbVZREqTYuqRpOVFeVUVBRHRnp602w/0Eve8WnvGeB9V5wy5p9Je+8Afb1pHA9sefR/uu/7qz+jvb2bH/zkGZq6k+QzkprqKHte6KFxTojBVIZI3ORA1xCWkATDKX7/cBOWIahJhKiujZNx8zRUxUjEAqyYH2NOfc2Y+6tpmjZRhBSIcU4/CT39pJWi38nTML8cgWLj+tKWFwcTivWnR9h8wCccLA4abt/dQ09OgQ9bdu3kDevmUVFx9OXRiXicwSFFtuBT8Ee34mjVysWsWjny2GDbAVpbQQSgLmrTm4Wc46NQmIYgX1AoBdm8x96OFHucAjQb/GxoH/NqYlSEQuzr6SBZUCgkgWBp9W6qy6qJxpvJFhwi4WOP9tTXV/P5G95MJpPhD/c8jyPCpHMCIT1My0YKiZfJE66JogxJwAPDlAymcnQOOUjlsHdPH8FQiN8/WMC2BKGASSxuYVk2ZRWC+vIENeE8y5cvPyzBWtM0bUKJ8Qc1+Dqo0UogXEXMMJAIXK+06rmrF9Rx1Z+voOmO/SybK+nYn6YnDyBQKDpaPTLZASo4elDjOA5OoYDjuBTyh0/XjNaixrkEA63U1c3jf/1NMel2z55mOlxJR2uajt4h+ofy5DywTEFeCMh7mIEgQxmHwaEcLZ05HE/huor+dIbv/XYLgUKWhavirF+4kFDo+Hs5lZWFWLO4koLrU1M2uimscDjMO99xAQDv+ovisZdffplMLsrO5k4MEaE3mWFoKIsvYH/zEOF4DHyJROF5HoYhcRyPglOgayCHKRRir0kgMIDreUQe7iPj+CysDlMbtTGqgiyuqsAiw/LlC0a1dF7TNO1YJiSnZgwjNbfccgt33nkn27dvJxQKcd555/H1r3+d5a/Zjfiiiy7ikUceGXHeRz7yEW6//fZx9XM8dFAzCeZGAvzs4WZWzw/w/E44q4RCt4ZhsHhBLVee28WmrTlSBoQsiQTq4vBnF8SYN3fOMduIRCKsXlxNOl+goqz0asDza6owvAwN9a8WEly8uIHFAK8pB9PVO8j2A/0cGEoz2JWiPwN5x0UJi0LBJ38wvusf8vjTC3tp6VTwYBtLFrayeF6MeNBmXizCvPkhXtmX5M3nNpLOutRWhunoSfPDOx7iyQNw1ooY9VWxku9n9epibtGZZxy+H1YymaSn3+Hxlw6gXIvBTJpM1qN3IIPyFAofpCQUsMgW8njKhYJPT28GP1fggITCNoNng+0o36Pw293UVsSJxmzCNtSUx2ioCrF69ZzinmCapmknoUceeYRrr72WM888E9d1+bu/+zs2btzIK6+8MmKhyIc+9CG+/OUvDz8Ph0srrjpRdFAzCfYkB3B9xc79OYIljtQA3PPUS3Rl4hihHNWWQdbxMBzFVX9WzYXnn3vc85ta2yk4BZTyMM3ScnsAHt/RRH9PigGnl7cd4301lQlqXpdsu/dAM5s7OsgOQtfB3RGGMjCUeXV5+P79g/R0Z1HKRyEouB4FF371yB5M02TtokpyzhBPNYEA8gWPyaohFY/HicehccHhlaAPHGiiqS9H04EhJEE6u4boGOjHl4KashBePg/CJBoySabz2LZACknvUIbeZBrH9bCsfhQGqV+9wryaEMr3qYgHiYcCxOuD1FqC005txDT1f01N0141ITk1B89PJkcuYAkEAgQCI7/43nPPPSOe/+d//ic1NTU899xzbNiwYfh4OBymrq5uXP2aSPo35yQ4f8l89jQNkiv4LFtW2iaQHV29PLclQ3d/hh4hOa9RMqc+xHsvfcNh//iOprIsTkH1kC/A0IBTUj8AwgGBXWNjBsY+hdW4oIHGBQ1ccdZ6dh7o4d6nWnhpVxd721/di6q2OkwyWUChQIAUxSm2vKPIFQq8fKAf1yugFKDAcLPMLbfZtm8/KxctLPm+RiubzdLb28uCBfNZsAA4beTrSim2bW9m6/4+TCOEU3Bo7UyRcfLkHUXeAaFcpBQ4+QKesImGDPqHcviOR1t3lmDAwN/pY5sm//1AKxVRg2giQCIapCISJFClmGdLli1dqPN3NG0Wmsjpp4aGhhHHv/jFL/KlL33pmOcODhbLaBxa/HHIT37yE/7rv/6Luro63vrWt/KFL3xhSkdrdFAzCVp60rT3uuQcxaMvNPE3G1eNuY29QynqasvY3dEPwuOLH3lLCT0RuK6Lj4cnS9uaAMCQMfK+zdBg6W0IIVi+sJrNewbp7M9TVxkHIThlWRX4ikdfOEA+XwxkPAmhglMMYpAEAyaeC+Uxl3jEIpCI8MjL7YiXJXawnfJYgI6eDsJDsPz0xRgK8p5PJueR9xVCeDg5j1zexQrYOI6DZQpyKYeCo4gETZTvMZR1OWVNPXc/0cyyOWG6+9JUJBRDnkXWTWJ6W6mMWFRWVDJU8JG+hy0NmgeTNFRGGcz42EaeWDDA0qXVRE0DEc9Qhs2OA/uw5Xxae/sRpk0ymaajJ4OhQCiQykNIScH38REM5RWD7Q4dVgakQCkPMHAe6KQiZhExBTUVEaojQWrm2Myvih72y0bTNO1Impubicfjw8+P90XZ930++clPcv7557Nmzav5FO9+97tZsGABc+bMYfPmzXz2s59lx44d3HnnnZPW9+PRQc0k6M5mKYuA6wrqS0z92Pn0Vg40QUPU5Iy1pbUxNDTEqnnVKOVTHi/9233AyGMEfMKh0vNyDvnLS5bwl5csOfz4m5aOeK6U4sVdexlMSVrbM7T3pRjIFMg7FD/kfYHyPbI5SCbTNLXBEND01F4a5pQTCVkMpHNkcopwQKJ8H98X+CkXwxKYyudA5xBuQbC4PoLvKQq+S09/ln1tSeZU2ti2TVd3FhFyMA2QyiaZ8xBDBVIFHzwP8MnkFftbUxgBCT40dWfpHMzi+T6pdAGEhZE3icW68aViMO+yqCbMypoY3c0HkGaMoXyKZFrhOAHiCYt0aoigHcJxJXYIUAbKg+7BPJYBQ55PTzqHYZjkXvYxpE8wYFEVD1EeC1AZN5gTlaxZs0Tn7mjaDDAhFYUPnl+cZo8f592vuvbaa9m6dSuPPfbYiOMf/vCHh/++du1a6uvrueSSS9izZw+LF49+z8OJpIOaSdBYXkZuURrXKbB6QZiuviGkn8cwLULBAIPpDD4CKQwcJ0/QEBimSTQSY2goiW1bFIImzb5HzIBtvaX9QzYMAzPg4mQdzHCA/QeacT2PgG2ilMAKJhB+GsMw8JSBxEf5HoZpU3B9pHLIFRz2NA+gpCAYOHHTHkIITls28j9FoVCgpWOAHXv7aOpN059xaO/JEgkJhij+h81mwPUUAdMABcoHx/EwfAgEinlJQgjcvIdAoJTCR+Ern2jQIpvNEbCL53rSI1IZIp/qZTBpMK8uQN5JkXE8graB8gSZfIG6ihit3QOEsi7xsjg55eP4xWXuQdsknfcIWZK84+JLyZa9/YTMMlAKIeJ097nUVpVTGzPwlKKQL7C1OY/vZHnD+RWkMlk8YWNgI/CR+Bi2Qb7g4imPkK0QCNLpPLmcT0vnIMpVeJ5P4LFOyqIW5dEAC2rCNMyLs3zByTP/rWna6ExkTs1YfPzjH+d3v/sdf/rTn5g379jpFGeffTYAu3fv1kHNTPLEs1t44QCUV0Ey6/ByRzPdyTQBw6IsEeDFXR0oISiLhEH49A9kWFhbQUNdhKb2JJGgxe+e9gDoA9yW0tJiPd9jy84OntuVwgHOXF5BAOjLF1CeoD/l8paz6glYBvu7stRW2OALXt4zwI4D3YRCFgXXpTspiAUEsUjpy8Ingm3bNM6voXH+q4Xw8vk8Ww+0Uf/UNnpa4Y0b15HL5qitD5P28swrS+AOFgsP2oEAkUgUlE9lZSX/8YtnKK8o5y8vHjly9KmriqNcgUAA0zSHl2T39fXR3NxMWXkFpmmSy2XJ5/NUV1VTXV1FoVBgX1s33QMw2J9iqODTM5gl5ypUrsBQqoAyBLGQRTF9SCCkQTQiKbgKqTw83yOXc2loDCEUFHwbOxBGAUJIGoIe/X39KAWRAAgzTH86S8QOkc9kEME4YQPABwRpx8NL+/RmC+zuSJJ7sYXKyB7KowEqauI01AZYWRWlsvLwxGhN02YvpRTXXXcdv/rVr3j44YdZtGjRcc958cUXAaivr5/k3h2dDmomwdnnr+VAcgs9/bBucQU+kHM9hJLk8h75vAsInICPMjxyeYe865At+GRdD5mXWFLgFpNKuOjcI29oeTyJeJxMPosjwFYgBeRcH8f38DyBAlSxlh++AuUXv/HnHa+Y3CodCq4ABPGwYv2y6gn6CU2cQCDAGcsWccayY/yHqy4/4uEPv/PMo54Six0+b1hRUXHMvBXbtlm+cO5rV7kfUTqTY8feFgYQuP0S35S4uQKGEgwU8uzu7MPryDC/oZrW9l7C4RhYRvHfhCcoq6jAlwLLMMjnciSsBKGAhes5FHJJ+guKYMzG9wyEKOCrAMKVBAI2Xs6n4Pp0DOXpzgzwys4CvxcesaDNguoyGmoizK80Wdhw7HIBmqadWCe6Ts21117LT3/6U+666y5isRgdHR0AJBIJQqEQe/bs4ac//SlvfvObqaysZPPmzXzqU59iw4YNrFu3blz9HA8d1EyC81bWU58I8tM/PMOCugSGNBl0cwSlQTxsEg0Vd6muTATwhEt2yCSaMIkGTeJlFhHDZOmcMNvbMrzlrCo+dNnK41zxyGpra/na3/4Z/33fK+xvHyQYNBGGicwKfNdjxbwEVVVRbEMWRwyiAfA8ysssyvtNDClwPJdEUPLBt6/gvFOmZjhxpomEg5y+5vC8otcqFAokUxnuf2kHJgl8z6A/mWYgnSedc8j7gFAEbQs8GMx5BAIR7JBC+Ir9vQWWzInjFXI0HchQWSFwDJccLioHoVCEvOdimwZG3iedcngp1cu25l6E5xMK7mdudRmLG8IsqLBpmKODHE2bSkJIxDgLeQox+lH/73znO0CxwN5r/fCHP+Tqq6/Gtm3uv/9+/u3f/o10Ok1DQwNXXnklf//3fz+uPo6XUEpNVsmPaSmZTJJIJBgcHBxTIpWmnUitnT3s6E/i9vjs704TDdlsbW7FEkGkkDy5e5Azl5XjOR4CBYaBKSVOocBQOkNfUlBfY5DPODgGRA2BMoNYhsSQxXyg3Z0Fls0Jo5RLPGqzqKac+jlB1tQlKCsrm+ofgaZNuRPxeXHoGq+86VRiVun1xgCGHI9V9784oz/f9EiNpk1Dc2urmFv7+l3aVzIwMMDmrn7Wr5lHW/sAfakMyYxDIe9BQOD5PtFwCMOmOEIXFpjKYX+bIpHIErTA9aEsFCSZcRAmeK5gMFNgc3M/L+53ucc/QFUswLJFdSytM1g6v7RaTJqmjd6ETD/pvZ80TZtOysrK2DA8ijIfgFQqRWt3mm1NvXT35ehNF3BTHq7vgS9Q+FSHBaIAwZBBIBBgYDDNioYI/T1DuKYgYULeNrFRKF/RnSzQ9EIzT9iCiN3E3ESYU5bVsWZxNYYxvm+TmqYdbqpWP003OqjRtBkuGo2yPBpl+aJXd4wfGBjguaZuDrQM0tejaMoX8AHLsvF9RXkiSkEaROwQ0pTs25+hrCxDWnmYQlGWiCOyDp4HQznBjvwQm5uSxB/bSX1ljBVLEpy5ZN4JLQOgaTOZDmpGRwc1mjYLlZWVcUlZGRxcpOD7Pi8faKPpQJIDvVmSWZ+c51NwXXB9FjSEeXFvilMWJXCdJAf2DVFRCZ1pSAQEZWVlyIBDtpBlX+cAzX1ZHni6nerKACtqQ1y4fgWmHsHRNG2S6aBG0zSklKxdNI+1r1kZv7u1lV37Btnbk6Gzt8D8mgiG8PBkkHkN4CmDSitLT6fP0FA/MihxM1BW7RFwHUwUHd0ufYM+T+x4ipoym6U1Qc49ZTGhUGjqblbTpiFhMAE5NRPUmZPYtApqfv/73/PlL3+ZzZs3EwwGufDCC/n1r389/HpTUxMf+9jHeOihh4hGo7zvfe/jlltu0Tsea1oJlsydy5K5xRpJvq/YdqCXTS+30N6TxPctPASO71JfH6bgQjAg6EjnaelWJGQewxZUJgwKjospFK29GZp7Mjy8bYB5VUGWzw9y4SlrjtMLTdNgYrdJmMmmzaf9L3/5Sz70oQ/xta99jYsvvhjXddm6devw657n8ed//ufU1dXxxBNP0N7eznvf+14sy+JrX/vaFPZc06Y/KQWrF1WxelFxxVV3X5KXXu5k2wGHvLDoyXjknTzlFZIqy0JIg76uDDs6PBoqcgRMH9uysO0w+Vyeve1D7G0Z5IGXsqyZY3HaskqWzm84Ti80TdOObVrUqXFdl4ULF3LzzTdzzTXXHPE9d999N295y1toa2ujtraYEHn77bfz2c9+lu7ubmx7dAmLuk6Npo1dZ1cXL+3uYUtTPx0DDmHLIpPPYxiKfF6SGvIICUW0GgxMHN8naEfZ0znInDIDy5Q01FSyuNbgvLWLCIfDU31LmnZcJ7JOzZ53nEnMGt84xJDjsvjOZ2b059u0GKl5/vnnaW1tRUrJaaedRkdHB6eeeiq33nrr8DbomzZtYu3atcMBDcCll17Kxz72MV5++WVOO+20I7adz+fJ5/PDz5PJ5OTejKbNQLU1NWysqWHjeZBK5/jN49vZ2+qTKfgEQybBYAFpWNimIDWQxjDBiAjqEzamcPE8lz3tfbR1Cx7f0cuq+XWcsjjCsobStgjRtJlG16kZnWkR1OzduxeAL33pS9x2220sXLiQf/mXf+Giiy5i586dVFRU0NHRMSKgAYafH9qz4khuueUWbr755snrvKbNMtFIkHdvPBWAzXta2bS5k6ZeD4RP3oNIRRjf8/H8HL4q4BYEkUScVC6DL8FzfF7Y18nL+xUViWbOWlTN2ac0IsTM/4Wsadr4jG8jiXH63Oc+hxDimI/t27fj+8WU7c9//vNceeWVnHHGGfzwhz9ECMHPf/7zcfXhpptuYnBwcPjR3Nw8EbemaRqwbvFcPvIXp3Pd21ayZlklVYk4EhAoXGUQCIYIlQXBMkinXDJ5RSAYxkQCit7BAvdv7+PWXz/Jzx/bTGd3z1TfkqZNCSHEcK2akh+z4IvBlI7UfPrTn+bqq68+5nsaGxtpb28HYNWqVcPHA4EAjY2NNDU1AVBXV8fTTz894tzOzs7h144mEAgQCARK6b6maaNUV1PNu2qqyeVyPPLcKzQP+PSkIZXKkncVUUOQiNiIAPT2Z/BckBJqKm2yuTyeL9jRlKa1r436cDMrGss4pfEYO7Nr2kwzAdNPjPf8aWBKg5rq6mqqq6uP+74zzjiDQCDAjh07uOCCCwBwHIf9+/ezYMECAM4991z+9//+33R1dVFTUwPAfffdRzweHxEMaZo2dYLBIJeefzoAe/bs4dldWfb0W+TcFL4FpqewLEEwAI6n2NHlErUkpoSackhlc+weyrO9LcXeNofLz15E0LKm+K40TTtZTIucmng8zkc/+lG++MUv0tDQwIIFC7j11lsB+Mu//EsANm7cyKpVq3jPe97DP/3TP9HR0cHf//3fc+211+qRGE07CS1evJjFixcD8OSWLexvTdGSFHQP5JAG2BgsqhQYtsG+Hof0oADPo8pSCAVtA1l+8sBWrrnsyIsANG0mEVIi5PgyRsZ7/nQwLYIagFtvvRXTNHnPe95DNpvl7LPP5sEHH6S8vBwAwzD43e9+x8c+9jHOPfdcIpEI73vf+/jyl788xT3XNO14zlm7lnPWFv/+2LPP0p402NmawjHByXnUWArTKtCSgZZ0MSvH2T9IMGjwq6ee5i/OPmtK+69pk03v/TQ606JOzYmk69Ro2sljT3MbDz2zmx5HkslKnEKGQCDArj6FDWDBnITBJ9+6luoy/f9VO7FOZJ2a5qsvIG6PbxwiWXBp+M/HZvTn27QZqdE0bfZZ3DCHxQ1zAHjgxc1s2wMDOThlgcm2tgJI6EjDjT/dwtvXwVvOPQdDb5ypabOWDmo0TZsWLjl1HZecCp0DSR5+bgfJIUF7Fg5WfOB3m+Hpfc9zztoIlyxfRFhvmqnNIHr6aXR0UKNp2rRSWxbn/7vkTN7U28ttd2ynDZBC4AtFZ9plyyv97GlOs3phBRevbsSYBb/Ix+qa2zcBIIDvffTcqe2MNioTUlF4Fizpnvmp0JqmzUiVlZX87789n0sWGsXCZEiQgpxj4Lnw/O4+fvjAKzy1fcdUd/Wk8tLufYAPygcBH/yPTVPdJU2bMHqkRtO0ae0dF61j5883M+gIFNDhQEKCUDadvRk6+xTtva9w8br5xGPRo7bT09NDc08PHb4knwlhmz5pp4CbLxAIh4gYAlyB48OSOQmqYwaV0yzZsm9ggG/e34FSgBAUC8wKPvy9p/iPD549xb3TjkVPP42ODmo0TZvWwuEwq8oFm3shaoJrw9kL4zy7L4mHwjLhlbY0uzte4dRlIeaYJrlwJe19Q6RzDgXPoz1lYKo8hvAQSqBMiNg+jlPAAYZ6B6iLhDnQN4jrG3QMpBlKZVg1L8SlZ66Z6h/BqCil+M0zB6gP+LTnD324FQMbe0p7po2KFOOvCKyDGk3TtJPfX73lTDb/6FkKrqIqBkEvyzkrEjzy3CB78xBUinTBZ9vjKSwJC2vSRAMBhOHjA54vAIXywBTFqZms64CvMDCRpo9A4XmQzXvsaktTHoEtzTkGczu49JQ5JOKxqf4xHNMfnt7PQzuGaCwTWJ7Cd+Xwh9z/0aM02gyhgxpN06Y9KSVzDBiwYXOXomUwz5xag1hVkHnZHJ39oEQxMdb1oWXAI2EVqK8yEEoRC5gIIGb5VAYk82ujRKQgm07iWgGCgSoKfp58PkMm7aJMyHuSQDjMvu4h/vOR7bxpbTWrFy6c4p/EkT3w7Cs8t6uf1XNj7OpMs6TGJhIMEQgYfOiSFVPdPW0U9PTT6OigRtO0GWHJIsWBHHTlIZGQZAoOAQOCts3SWkFTd4a+bPG9vqsYMlzyPS7vXhNkYUMVFRUVR2h17ohnaxbMH/773S9sZfO+DIZUZArwh+e76U8rLlh9cm20uau9m6d29yMNyGVSXLa6gkQAOnqHGMiArxRyFuzePO0ZsvgYbxsznA5qNE2bES4553QMw+T/PbQdhE8wFKQypKi0XEIhm+WXr6W1s5NfP9lEXwpCQXALisf25GiYP/ZNMS8/bQ0Nlc3c+2wHygcZDPDkzh4OdPbxjnNXEToJ6uR09fVx30u9ZF0ISigPmbz17MX84sm9NGfANATP7tnLWUsWT3VXNW1C6KBG07QZwTq4W/eVZ80jFjtyfktDXR1/+9Zqfvfcdp7fmcJC0ZcR/Ncj21nfGOQN69aO6Zpr5jdQE49x/4vt7O9NIT1B6wDc8cQOrjxvFbHQ1KXg+r7PXU/tYiAJ5WVl5LJDvPPCJdi2TXncpa0XFIrdrUnOWjJl3dRGS4rxJ/rOgumnmT8WpWnarHK0gOYQwzC44qzVXLI2ih0URKNB0mmfP23NcMejL5DJZMZ0vZqyMt514QqW1QpMS2CZkq604McPvcJLe/aM51bG5e7ndtKfFQgTCpkUbz2rnvrKSgBOra3CE4Cv6BrwyeXzU9ZPbXSE8WoBvtIfU30Xk08HNZqmzUobTlnNVRcvx/ZyoAADWvrg+w9u5UBz85jaEkLwzgvWc+bSMKZlEjQVqZzioS0DPLZz/6T0/1ge3byN/QOCjCsJmIJ1jSHWLlgw/Hp9fR1zyhIgA6QKHlvbuk94H7UxOjRSM97HDKeDGk3TZq25FWV88PK1LK23MEyDdLJAf1Lw6xe72NbcOeb2Llyzkr9YX48pFPhgBG3uer6X3z79Cv6hTaom2f72dp5vypEdSlFdFqGhNsab16877H1zygTCdZDKZ8e+3hPSN02bbDqo0TRtVguHw/x/l5zO6QsEhiEIWJJe1+CBl9v5+X2bcRxnTO3Nq67ir85ewJxq6Mr4VAZhe2uWH9//Mql0epLuomj7zp1849FO9g9Bc16yszfHufOOPB23ck4ZwrKRhkHXkDvmaTftBDPExDxmOJ0orGmaBmxcfwaL61r4w9ZeQoYiNyQ4YAh+8MAeaiWcuizIwlHWoamqqOCqDeX8/pldbG8dwhc2/XnBXS81cenyGqoO5raMV3t7O6/0D9CfdGhP2uzuzSIEGJZEuT5KwdBR8mXm1NRQGesimQuTdx12dfRxSmN4Qvp1suju6eXhLb3khINlu8yPR1i/bD62Pf1qKAsxAXVqZsHSfR3UaJqmHbR43jzeF4/zi8f3kTIk0pYMADlg78sZ6l55mTlhyUUXrTxuW0II3nLWMozntrCzTRCMmAym4A8v9XLR0gzzGxrG3D/H83h6527ahwokhyyGsjl8XIQQdCQ9wEcIA9fxi/s6CWjdn2TlUVZsL62N8syeXkzgleY+TmmcN+Y+lWrPnj3c93gz61dFWb9+/YS3r5Qi5cMBIJ+Gzr0pdgdzFDKCi87SS9hnKh3UaJqmvUY8HucDl5/C1j372NKZR6XA8X0cFG0eHMgqtv32ZRaYPmeeWU91VdUx27v8jLXMr27h0R1DCCnJuYrHmrJcEOhjfs2RCv69KpvN0jWY5vmuTga6HVJuAN/J4ikfZAFXieIvcSGpCkFzVuL7CiE4uGklXHD+0QOwBZVBNu0GiaQv7ZHL5QgGg2P/oY2R67rc9It9uHmFYQ2wdm2eQCAwodfo6k/yi5f7yOZ9srk8u3cn2aU82nf3sajWZMFrEqenBV18b1R0UKNpmnYEaxYvorE+w1PPNtGaglYfIpYk5SnyKF4swCtPtlNht3Px6ioWzq0/alur588jSBOP78vi+ZAqKO5/pYc1Pd2ctWr5Ye9vb2+nJS149kAXgzlFQPpIHywbXCiu1nJ9yiIxIkGfOWUwLxrGisT5xkMHwIdPnVdx3A/ueXV1VMXaGcwrpBlmd3sPaxZN/mjNb/+0BeErhCm4b7/gjW1tLFk0MZWYfd/nnx/ejQCyBR/TFBQcn5p6i8422Dvk8b37X+Er10yvoEZvkzA6OqjRNE07inA4zBs3FPdG6uzu4YFnu/ApTmvYCiygJ+Nz1+ZOarZ18eYzF5JIJI7Y1uL584kEOnmkaYDMkMD1FC+0KazAPk5b/OoH+q5du7jzlRyJiMRzBWXRMF4+gys8UHmqy0NUhD3mxYKsW3J41bx//atjj/68XmNtFU/s6sB10mxr8lkzybs8dPUP8dCuNFKCl/cwTMnedJ6Jqv/n+opD68x85WMIg6rKKImIged2MZCGl/ZO0MW0k44OajRN00ahtrqKd19exdDQEA8+coCkKdieU5gFRdAUHMgKfvFsMxuWZlg6/8ijNnW1tVwWDPK7LR1kc4pYVPLMgQJ9Qzu45NTiiE1bt0PWNOhLeUhgfZUkVhmmWjqsXbYU05zYX9sNZRLl+/iOT2tfFtd1J/wahyil+PUTr9BQFaM341Npw/K5Afo7PVgzMdewzVcrzPmeIpv1MExBMuUQSQQJRA26W9MopaZX4uxErF6aBaufZv4Em6Zp2gSKxWJc8ZY1vOeyNXz4tDhLq6BQgLAt6SrAfTv6+MUT2+jv7z/i+YlEgitOncv8aslQSiE82NUDv31yJwDrT1mADwQsiTQkFywqZ+OaJZy2auWkBBuL5s6lPGyhDAMsxfa9+yf8Goc8unUHXiCIUFnCUjC3SuB60JvzJvQ6hhCgwLQESMjmHHJ5j0g0SnlZhNqGOL95ZPuEXnPS6SXdo6KDGm1G6R8c5Ne/fYb/+u2ztLS2TnV3tBluwYIF/M3Fp/LGRSbCgrqowCtAb1py99ZuNr+y84jnRaNR/nzdUuaUgyuKGb39wD3P76W9p4eEVJg+1Jpw5zMdbH1526Tex+K5FZgBm4JvsD+lJuUaHZ2dtAyahII25TUR3ntxDRWVZVTUllFWeeytLcbqM29cyqWVkrAwyBVcPB/KqqIEgxZ2wCQWD7FX7wwxI+mgRpsx9rV28vlf7+TePniiz+er97ZyoLVtqrulzQJnnLaaa97QSJkCpEQpyLlwbwf8acsuWo8QYAshuOL0ZcwNSyJRcF2Dzgxsa3e5dL5FbQBMQ5JWikebfLaXUOF4tBaX2wg8LAOau1OTco1tvXlM0yQgAyRsiwvWLCMRC2BbgpA98YHUKesaqTaLkYuQYBomwYCNEmAZAmea7YN0KFF4vI+ZTgc12rS3a/duvvXHZ/nPh5sA8H2F7xV/Sb60o4VsNjuV3dNmiXA4zF9sWM3GpTbRqKCgICGgvQB/2Jnm+c2Hj9oIIXjbecuYHzaRCIRU9LuCVs/kjYtCOJ4i4CuGFDy8tZcHHz3yyM94Nc6bQ9AuLgPPO4Jt2yZ2ZOjn2zppdk1iYZNAwOD8BQl27dpH2DKJ2BZSTXxQI6UkYloEbZOgZSIo5vQIJIZl4ijBs89OoykoPf00Kjqo0aatfU1N/GTTC/z82QG6u6HbLQY0AL5fTBL8wwE14fUvNO1Yli9ZzFtX13JKraSAoDepcDzY3CfY9OJ+XNc97JwL1zUyP6xQvsCwoCcn2D5gcsXKKNEIlAUEqYDJyw488tiuSen3wooQoVAYX0oOuNaEtdvb20fQKm4/kbQsqi2L+vp6fAIEQgGCQZuy0OTUxllaHqAuZhEIGBgChDj4pcdXZHIeyen0CSgkyHE+xHS64dLM/DvUZiTH83hm/yD79xVoSnn05DykX0w2PDRKU/w7vPTSS+SciU1E1LRjSSQSnLt6CW9eaBL0IGwKgjHBjgL8cUsHQ0fYA+ri0xezss4AIVDCIKUU2/t83ra2DqlMbCAgBDs8uPuF3agJHt1YUhXD9woYUtLck5uwdncOKYKGxJQSUJyyMApAHolpSaQhSDA5IwiLF9UQtkyEUiAFucKrvwd8XyFDegHwTKODGm1aGkxncJ0CBzIuBdejQDGA8R0fCUhDIA8OtX7nuQLNPZO7kaCmHcmiRYt451l11ETA8UwsyyRvSh7e18fu3S2Hvf+sJQtoDBqYShEwFL2O4qnmIa48o5rFcUEWMHxoTsGvn9hF/ij7OpVi6YK5BAyFwCedVezaNf4RoV3N/RwKj2xLcHrAIB6PA+BYJnbQJBAyqa6anL2YotEoVeEAkWiA2soI8Yg9HD5VAPHo9BnFFYaYkMdMp4MabVpQSqGUYsveA+zb38LeHdtp6fPAB+H5JACFICzFcOEtAIorOunp3D8l/da0eDzOm89cygrLJCwFnvIYcgRP9bs8v2PPYe8/d918GkNQ8BRCCfpy8OCuJBevnke5BN8UBDyfzizc9ewBHHfiRiHnlptEIkGscJBueeQigqPlK0UXCmFIDEOyQEqWNlYPv57xigGaoQQhc/I+iuorbGxTkkrnEK5PNGQSCVmsqg8wPxSatOtOOCkm5jHD6bE37aRVcBxaW1ow7ABdg4P85ukBWnMQxOdbHz2fhiVptrUO8uLefSRSPruS0D18tsS0BEIIlIJEYmKXjGraWJ152jwa+gbYtH+IvPLA89jeJ+h7pokL1taM2HPprLULUNv2s3sQfCkwQvDE3k7edd5CHn95H9v6isF6XxZ+t3kPFy6soqJibJWEj2TNgvlse66FmAXN3X2wuKbktna3DOApEChCApbWvroDeG9vL4VcMbfINCUVFdVHa2ZcdjW1sH/QI2IIVMDC9H0s5bOyPEp9rEDVcfbt0qYfPVKjnZQGBwd5cU8LX767jS/etZcf39fDvkGXQt4lmff56v95FKVctjy9j6EuWLCimk7fL2YIA1FLIqWk3BD8w6XzWDVB+8po2njUVZSxYUEYy3PBFQRMk07g3i1dON6IMUbOXrmQuVIQDUHHoEfroOLuZ1q46NRlrCkHASQS0DIIf3yxG88b/4hNfSJIWUhgSpOCa9Lc3FxSOweae2lRCk9AWAiWJmxCrxkV6er3cAIS1yrm2UyG57bs5cmmAskhj4BUVNoG9eURVi5IcM7KapYtbpyU604avfppVHRQo52UHMfh7sfaQCmUgoHX/V/cheDn929lTwH2F+AnTxZreCghWF8L7z4lwBIbLjmjgTlz5kzBHWjakVVWVnLl6QtYXGGSATwgLQUPbm6juXlkXaWLzlrA4rIQUSkoAF1S8KeX9vOG05ZxziKT7hRkHUUGeHp307j7ZpomdSELyzIIhw0GHf/4J71OoVBgvwfKU3i+QipFVTw68jphhaF8TNOnahJmgJ7fdYDtQwIPsAT4OQ/CAeygTdAxp+WKSF2nZnT09JN20vE8j//6/V6acgolirUzEIf+UkwC9j3Fs+0K03o1Ll9XLVlSobjovPXEQhbrT52a/mva8Zimybmr5mM818R+BCEpGDAkTw4qRLifeZXlw+89paGGztZ9NCEIAM1ZuOeJfVx2XiPP79lBErDDgt1tDqctyI2YxirF4rogQ+0FPEPSnjHHvCXTnvYhHAU+YPrQWHb4B2l3n0PePbjSOHx4G+Px7NY2OrCwjTyK4sahRjyA6StsKVg3X09Fz2R6pEY76TyyZQuvOApfCqSUxWTf1wybHlqyLQ3BKXHFh88M8+33n847zm2gPmETC01cjQ1Nm0xnnTGf8+okBUsiDYX0YMeAw6ZdXSPet/GcRSwOFCdqsr6iFXh+5z6u3ricxqggm1FkXMWmbQfG3af6eBQ7ZBM0TLKIo+5hdSQDQymGDHAAS0IdUF5eftj7cr5ESYkvBV5u4kYPnt66m535LAO+IhK28cMQiAeJGwLpuKyOqBHTYNOKISfmMUq33HILZ555JrFYjJqaGt7+9rezY8eOEe/J5XJce+21VFZWEo1GufLKK+nsnLzK16OhgxrtpOK6LqZvFWtISIHyFep1qQLSEJi2JG7DOUtMzj3zNEKhEAvmzWP9+vVT03FNK9G8uXM5r1IS8SEUNsi40AE8vatrRJ7MG09bSMJXGEDIgu198PyWfSwOF4P8QAB68uMPEOLxODEEygcbRffA6Ctyb+3KknIgClRIWLWo8ojvywgP5XtIz2fJ4vEnOAO09/TSljZx0j5+JkefUoSMAIaEjIDlCUnDvNoJudaUMJiAnJrRX+6RRx7h2muv5cknn+S+++7DcRw2btxI+jU1lj71qU/x29/+lp///Oc88sgjtLW18Y53vGPi730M9PSTdlIxDIP/ev7VvWdc1z/456HRGSgUPGzb4LYPnYs5hm8emnayqq+vo6ZW8eSubroMEAr6DMk9TQNcVBMkEokA8OYz5/HHzS0MOlBQsCUtWB8LEA0XwIJ0Fp7fvJvT1y0ZV38aYj47BiDvQ292dIHSyzu6GEQhDIVpCObFjlx7ZiidARMMAwIwISMnvb29bO8o4IZtDENiGXki0iAvBSifiIIVC+rHfZ3Z5J577hnx/D//8z+pqanhueeeY8OGDQwODvL973+fn/70p1x88cUA/PCHP2TlypU8+eSTnHPOOVPRbT1So51chBAIwDAFvLbKqAIbH+n4VIcFl6wwMAyJEDM/8U2bHQwpOKuxnEpV3MjSO/hv+4muHN3dvQBYlsXFK2uxKP5fiYVgc6ZYo4mcxJAWbbnx79QYtQT+wXIIXY464tYOr+UrRQcgfPAdRdzxqSw7cp2b/Qe66Bj0yBc8bHPsichH8kRrng4UOSGotk3qrSieUgQ8H0tI1tdOTnG/E0mICUgUPvhvKplMjniMpojj4OAgwHDpgOeeew7HcXjTm940/J4VK1Ywf/58Nm3aNAk/gdHRQY120rnlymXc/LalLDmYqW8DDQasXSBYUGMzp9yip19QmMBqqpp2MrAsiw1Lawi/Jlg3gedT0NpaDGzC4TAbV5RTGYR0HlwlcKRNv2FgSkGHEgxkC+Pqx9w5cwigQIAtBfuauo75/hd2duAa4JqCILByQdlR35t1fSzXZyijSPaOP6h5Ztd+UgA+BFDkHI9uzwXPJy9hTdifGfVoJnBJd0NDA4lEYvhxyy23HPPSvu/zyU9+kvPPP581a4qp4x0dHdi2TVlZ2Yj31tbW0tHRMSk/gtHQ00/aSafiYGLhTR8eOXyZz+f59u9eIOsoHF/wx5d2c8XZY12boWknv4sXVbJ1Xy89wKEMhq15H7+5i4aGGsrKynhDo+T32/vBV5gSClIwBJQHPJ7b0cQlp45vCqomLmlLgQukjxF7HGhu5UBeoAyXcluyZm7smEumUxGLsO/guQ414xxA2bxrFy93mwQDeVzLptYXUBEk64ObzVGV82hc3TC+i5wsJqIi8MHzm5ubh7erAI67xP3aa69l69atPPbYY+O7/gmgR2q0aSMQCLB+cQJTWgjLYn+3Q/fAwFR3S9MmxZpFlVRycBJW+biez9MpxZ49xdUlZfE4F9RCyARXCHwEvuvi500GvADd3d3Hav64os6hjaEF3YWjf5i+0uXjei6e70PWpyYeOWa7blbRnxF0Zg2WL11Ycv86Ojp4pdvE9F2G8h51foFoPEBYSCKGwA4FOWP54SuvtGIy+GsfxwpqPv7xj/O73/2Ohx56iHnz5g0fr6uro1AoMPC638GdnZ3U1dVNVtePSwc12rTyhlNXUlFlIk1JwZU8un1gqrukaZNm7aJK1icMgp5Pxi0W6nulANsPLvlesGABZzREUQrKw1BuWoiAiZSCA33jm55dvrQelXPIZQq0pgvs23d4cb8te5tJAQKBzPusm3P8oZdkJk+ZUsyxJBGrtI+gfD7P001ZXBOQJgmhEKEIA65P0leYCBoDBtFo9LhtTRcnekNLpRQf//jH+dWvfsWDDz7IotdVZT/jjDOwLIsHHnhg+NiOHTtoamri3HPPnbD7His9/aRNOxeubOC79+0l5/qgHF5pNlnVMO/4J2raNFRTUcbSVCfPu8WCdiEp2GtInL3drG2sZkFtFSv7XHqlIJ9QFDIuMgDtGUUmkyEcLr26Xaer8IEGIBobOQLT39/PjpSgx3GJW5LlYXHc3JXunh4K+DjSI275mGZpH0EvHehHBEDki6UfFkYCHMgU8AUEIjZRJMvnHXk5+bQlZfEx3jZG6dprr+WnP/0pd911F7FYbDhPJpFIEAqFSCQSXHPNNVx//fVUVFQQj8e57rrrOPfcc6ds5RPokRptGlpaX8k5SyLMjfik0xkefWbqktI07USYP7+WS+bHqJeCjCnwlGKv47N5R3Eq6syVdcRNiTANbBTZrE9O+WzbeewE32MZHBzEtAxsS9JjycOWXj+1qx8nVaA2DNGgyRkrj//Fom9wiFxB4hZAidI+fjbt7aY7XcALhoiagtMbwvR5srgFgACRKnBmw/h2GNfgO9/5DoODg1x00UXU19cPP+64447h9/zrv/4rb3nLW7jyyivZsGEDdXV13HnnnVPYaz1So01TZy2pYvvePqSQdGbzPLVrN2cvHV9ipKadzGKRMEsrMyQHFTnXx/Rhm/LwN7dx6ro5nLe4mvv3dpMXHuGAje8r2hCcUeL12vscqkIG0vXxPIhGXh2pOdDeQUsygzJt8imL82vzoyqv4AUiREIZpLSpiY196XnzQIohBX4iRGgoixUx6e536REgfYUNrI5JLGsGVhU/wSM1Sh1/o9FgMMi3vvUtvvWtb42nVxNKj9Ro09L8mhqW1UdBKUzDZPN+d1T/CTVtOquqquKihgQVPqSUh3Rhb8DkxW3FEZtzakKE4lGED4Yh8KTg2W2lla3f7Ss8y8QL2VS8ppCeUoot7WkM00DiUhnOsnTxomO09KrebAE7KLGlIK7Gtpx7045O9g9kUYYkrCARC7G6IkQuYBGxDaQhKJOwZMkM3cD2UFAz3scMN/PvUJuxLj9rMRXlMexIiGSuwBO79051lzRt0gVsm3VzgiRcIGAS9BVNhuDFvT1Eo1FWhgS+IfAMAxU0abUluVxuzNcpsyRxCSEg/5rvC7vaOujzJUbAJGRLLlw8hiXTShIWJkFpEhKjmyjI5/M8sq2TLk8x4PhE8CkLSNbUhNidETiWJGEZREIGF62eulU32slh2gQ1CxcuLFZUfM3jH//xH0e8Z/PmzbzhDW8gGAzS0NDAP/3TP01Rb7UTIRaLcfrCBJGAJBCArftSZDKZqe6Wpk26RCLBxrX11PmKnCEwDEGHFBzo7KO+vpKqgIkyJRKF73rc3TI0pva3dyQxpUHAsrADFhcvrgZgaGiI/T0+VbZJRVmIUxdUEY+NftdrIS0s28IIWdTVHv+8dDrNc81JUrJY4dh3FJ7rc0pdgpd78qQVGELgmQbnVQdLTjyeFg7VqRnvY4abNkENwJe//GXa29uHH9ddd93wa8lkko0bN7JgwQKee+45br31Vr70pS/xH//xH1PYY22ynbW8AdPw8As+eVfy0O7x71KsadOBFIJT5oaoNATSkCQE7Mj6vLi7m/OXVCM9D9/1wRfg+mze3zvqtrMCpCmRpsQ2X/0g3NGVw49YEAoQyimW142+DozreRiWIGBCSBa/lBxLT08vz3RkKACmIbCkoE4ILlxSS//AIMM7xCmIAJWvq2w744gJmHoqMTl7OplWYW0sFjtqUZ+f/OQnFAoFfvCDH2DbNqtXr+bFF1/ktttu48Mf/vAJ7ql2opimyanzQjy9z8U0PHpTAVKZLNHw+DfJ07STXTwe5w0xxdamPjqUwnEVB3yf0IFOVknBVhewJDbQBext7qOx4fi7YhtSIgAfBaoY1HT3DpAxJCFfUVCKRWVBDGP0yb6724dQhgGiGIQcSzKZZO+AT14qFMWtFxaHBEvm1wCwJyuxpY/yi8vcz24oG3U/tJltWoVt//iP/0hlZSWnnXYat95664hN1jZt2sSGDRuw7VcT2i699FJ27NhBf3//UdvM5/OHbe6lTS9nLl9GdVmA8ooYhgHPHtBLvLXZQwpBY2UA01W4vg8e7C8IMlISsiX2oZkH6dMlJT09g8dtMygFUgpMKTAPFmzrywGWgWWblBuSxkXVY+pnMS1YIJXAUMeeBtnZXyAXNDA8kEIxV8CS+cXrPb2/l5TrU/DAEbAyxJiCq2lLJwqPyrS5w//1v/4XP/vZz3jooYf4yEc+wte+9jVuvPHG4dc7Ojqora0dcc6h58faXOuWW24ZsbFXQ8MM2SdkltmwrArl+CgXWno92traprpLmnbCRKNRzqi2iXsgTYErBB2GxDQgYIAFmJ5ESsFuV+B53nHbNAATgSUEL7YO0AkEgaCAlbWlFfQ7FMoca4pgV+sAecPEdHyMgGSOEixtLAY0LS0DZKRAymJ15RofamtnWJG9o9FBzahM6R1+7nOfOyz59/WP7du3A3D99ddz0UUXsW7dOj760Y/yL//yL/z7v//7qLZMP5abbrqJwcHB4Udzc/NE3Jp2gtVVJIiaPq6v8BA81zb21R6aNp0lEglOrbMJCIE0i7/aHUeh8grhK0zXwwWkUjT1po/ZlicBUZx58oHUwdVPueJhIqHSpncFoA4+jqSvb5BOIfAFBG2TucJgzeJXR4S6AWQxOAtKweoFs2hvp+Jw2zgfMz9ReEpzaj796U9z9dVXH/M9jY2NRzx+9tln47ou+/fvZ/ny5dTV1dHZObIew6Hnx9pcKxAIHHeHUm16OKUuyINNeUwkjgUdvQPUVZZNdbc07YQpLy9nvepjS1KRBywpCNvG8BBJCCizJdgWOwayLC87cnCysjzCrr40phQUHK8YhKjiZ+KystIK26UBBNjAgqO0sSurikX8hCAPrK55tX/PtAyQQeEriApJrQFyFow8aGMzpUFNdXU11dVjm5c95MUXX0RKSU1NMXHs3HPP5fOf/zyO4wxXk7zvvvtYvnw55eWzKJqfxebNm8f8wX105ST5rOKZfb28VQc12ixTUVHBCqePLXmIBuxiwq9fHBspC0g8aeJ5xefZXI5QMHjEdkzLwM/l6c8rghRHaeoVRCLHS/M93PbmfjKAPBhdHamN9p4BHAClQEAdYvgLZ2fnIHlAITCFIoBiwbxZ9nv9BFcUnq6mxR1u2rSJf/u3f+Oll15i7969/OQnP+FTn/oUf/M3fzMcsLz73e/Gtm2uueYaXn75Ze644w6+8Y1vcP31109x77UT6ewlcxEFF0d6JJXJyy8fvrOwps10tbUVrA7Z2KaB5SsQAiUFXXmf12bTNOUUvn/kyr6WgoF8ccQnAySAxfPKSuqPw8GpJ6UIHKHyt1KKPQeDJwXEFCya8+r+TR2uQh1KeEawpj5eUj+mNZ1TMyrTYkl3IBDgZz/7GV/60pfI5/MsWrSIT33qUyMClkQiwb333su1117LGWecQVVVFf/wD/+gl3PPMqGATcI08RC4wI4MrJ7qTmnaFKivjpLrTtEvBQEBSggsBSY+LhLwsYDmVI4F8cMTf/PJHGkBUQssAYlC6X0JmpIYkMU/4odOS2s/pgcZqQgKwdq5ZcOvbW4dYFBBQIALzBOzZLWTVpJpEdScfvrpPPnkk8d937p163j00UdPQI+0k9mZS+Lct3uIqA25Auw+0MySBXpVmzb7LKqOMtiZxKe4Aso3JWkPAkYxoDGkh5SK5vQQDZGRxfCSgC18Ci4IBdY4ZnvSovglQyJZHD88n6ZY/8bH8MFDIQ9ujtnbO0hKCkxf4QIJBQtKHC2a9iaiIvAsSBSe+WNR2qxTXl7Oojg4Lggp2NqtcBxnqrulaVPi1No40pBgGkghMAT4SmCaLtJQ+Ah85ZDMvFqja3vLAINAwAflQ124OH3Unukb8/Wb2gfJKIWrFIaCcPjwUSFhGRhhi0DYojr8aq2x1jz4ChACIWFp9Swuqqmnn0Zl5t+hNiutmV+NocDzFTkFm7e2T3WXNG3KVBrFX/Wm4GBgo3D8YnADLpZQpCmWQUin0/QcPG8ISZkAAoeWYo/9IyPjKyxfEfAV0j88n2ZwMEPQMogZgoQlWFwdBSA5NIQj4FAIVKeEXqmqHde0mH7StLEKhUI0GLDbLf4y7giA7/t6Cag2KzWUh/GSObKejykLmBJMKfGViy19wEPg05Vtpas/wqFa7SZQVl7Mv4ECggxQNqZrW7YkqoorsOQRCtQMSIHrg1IC8zWv7075FABxcFuFxvrE4SfPJnr106jM/DvUZq1TVtdRGSlWWM3kJS9s1VWGtdmrQgqCpsSSPgIP13fwVYCC72EIHyk8pOfgMAT4BA1BOVAXjCPpJij6CRk+g4XdY7rugBLkhEDYJnbw8HwaT/lI4WOZEmkVP5IGB7MEZLGujg+Mfh/wmUsIOSGPmW7m36E2a9m2zRxbIkWxrHqLEgwNDU11tzRtSsSjASCFIXwUCggiBQgMPOVhihxdmQxpBongUyY94mUB2jJ5QqaBYdgIYWKOcaSGgsdQ3qMv4xB3D18+bkowJCB9DFF8vc9XBGyTYMCk3BR6lEYbNR3UaDPamiXzCAmB74MjFDv26aBGm70MLHwVAaJIPEzhYAizmCjcn2fA8SmzDQyjFwvIeVnqghYxawHFajNZHJKj2jsKinkxphQEZTF4qT6YL3NI52AWPAHFenvMjUZwHIeCAIQgYArCps6SAEBMQJKwHqnRtOlvoVVcBWUj6A9DLj+OghuaNo3NjyXweXVZb8HPASkChkPG8KmNhIgEy0mEyrAivSAk2ayDFBaesvFUCF9JBt1R7pEXCDFEMRun4ghTTwO+IutD3hV4TrFfLRkXG4GHoODDgvLSNs+ccfTqp1GZ+XeozXprVs2lLgoFyyDrmzzSmqS1tXequ6VpU0KigAKDeZ+skyVgZMhkQLiC3vQQmVwWSwqECGMbPrFYaPjMomNtSTlS2nl1RCcaOHzERcochnTxRXFn8eJBAYbAlmAz8+uqjNqhOjXjfcxwelxPmxVWzakm2p+mv+DheooXkh7NmU7OWVo71V3TtBPK8hwG/DxBQ+D4Nr2ZXQQcn6BZoMKS+EaWgkyAEpQHBoCKQ2cC+WJQJBTpQpqIfex9oDIFd/jvUWtkFeCO/jQBQFguju8yNxBh/1AOVDE5WApBpS4crI2RHqnRZoWKkM38sCDjQ9ZTmBKssMWugSyu6x6/AU2bIQb8PJYUGBICxisETZ9ICCpDEA34xKxeQkYzZQEfBQw5xVWDFYG5SHGoWJ8kr0ZR+0lKqkMmdWGL4Os+bXwjT96X5AqSoFfcDkep4hiQFICCeOzIm23OSlJMwPTTzB+p0UGNNmtUlJXRWB6gylfEw5Jw0AIBHWldbVibPeplhKxr4KkeLAGmMPA88IVgIAcSH1NGiiMmSuKpFNlCFgBfKSRZDNGPKY6dm6aUIu/5uAcDFfs1Cb8tgwOklUSaPhHTpyoaozuZw6eYMIyCWnvmfwCPic6pGZWZf4ea9hq1GJTXxiiPBkEauL5CCP3LU5s9yhIh5thBHN8YDjhc4ZPzJL2+IOfFMEQFPhJQCCeF27cVAFtWEZAuQcMjYKTIe0fPTct7ajjzJmiO/KgRZhaTLKBIK4ltWXQdnKryDz7CQV09WBs7HdRos0owVvxF6fgSx1f4SjE3roe4tdlDCEHYhLBVjlIenutQHpB4ShAxPFLKwM+ZgIsa7MHqbyPa+zJqzx6iVhkI8FQAT1l46ugFLVOFAiYK5SuCxqsfNQUnjyVcbLOYx1Np+2RyaarsDL6TJVdwOXydlKZHakZn5t+hpr2GVyjgHNx/xoDRLuLQtBmluwAFL4AwTkWYFv35AlIUMIVP0FD0qiwDOYns6cAd3A0iB70PoxwHhUFxkso+OJpzZCkPsn5x1OW1+TSDTh9SWkghCQifuBWn10siLI+ySIYqM8d8/UXjcDqoGZWZf4ea9hqxcIjXLqhwlKKnPzVl/dG0qZAjC+zH9V2y3loSZpC07xOxy6kI+Ng0IUQvSWsxuYJPb7IbpQqozQ8DCw4GMyagyLr9h7WvlGLo0MonAVG7OPbSmu4k54PnKUwgaAbJugOvOU8SCeqARiudDmq0WWd1ZXEZasFX+ApSevGTNov05/diixakKCBFGwmzmUjoAuaEK4nbMJBJI1B43gCZqgxD+UYCKPIqCe4LBA90YmBhkMYghxQth10j7fp4B0dB45Y5nLem8BECCgqybi+Ol0SJFCZ5QCKAaDB6WHsaeqRmlGb+HWraEawuD+EpBUrR4fl0dCSnukuadkL05aMoESQuAtSGPepCBtIIE7bfgOH4pL3i/ksxy6QumMZeU02KEFlSICKIvscxVQEzl8VMp7CHsoddw5Q9LIj1k7BzJOxidNOfSmOpYrUbIXJEDQ/TTOF5eRQWCVlGdaj+RP4oppcZWnzPdV3uv/9+vvvd7w7vzdfW1kYqVdoIui6+p81KhpTEfMXgweetQN1UdkjTToDWdA+QQ5EghUMqGyAeX1p8UUgMcRaxwEOAQUBKcq7EIoUfMPEdhRAdIKsxt3ZDdRacLGT2oaxliEjZ8HUEfYTMHCETTFYBkPQLKMoQZAnKATBDKJXFtHKU0UDA0qudjklMwEjLSbb304EDB7jssstoamoin8/zZ3/2Z8RiMb7+9a+Tz+e5/fbbx9zmyXWHmnYCHfoVGhVgSMHAgM6t0WY2gYclfSyZQ+ITeH0oH4xREXgLSIHnSRwUlm8SWLiYnBWgUwByCGQzDEkYHICcC7ufGm7CV3kUuYPXi2AaAYacPYStDhB9QAuWJZEij+vbOF6YsKX3d5qNPvGJT7B+/Xr6+/sJhULDx//iL/6CBx54oKQ29UiNNmstrIuT6xoiK0Ag6HSgbKo7pWmTyBIGQkTx/SxImBeNHf4mKakIvJme3L0Yvo1ngHTy2FXVqN5OXOFj+vsgM0TxI0SC0zV8uq8Ghv9uiDgAihwB08cy2kEV8MniUw1CURFYPLk3PVNMRE7MSZZT8+ijj/LEE09g2/aI4wsXLqS1tbWkNnVQo81qQSEQgCldghZ0ppPURuJT3S1NmxTV4SqguDrpeEUnq4Ib6ck9jllox1FZQqEwweo6Cn09dLse9WGJSiYhWA4ih+rrQFbUIcQglpAoZSBEcQRGEETQgg8gIhhK4DEEzJvcG55JZmBQ4/s+nucddrylpYVY7AgB9yicXHeoaSfYwuoo0YBLMAgYPh56KZQ28422inZV8Hwc18VCkAJyVpCCZWPZJr1mChImFLJQcGH/CwfPSgJphEgjKX4wZfI1ZJ0KBB4GPggIGXVUBCon5f606WHjxo3827/92/BzIQSpVIovfvGLvPnNby6pTR3UaLOeMHzEwaWmQvh0ZbqnukuadtKoqfhrJGUYCpRTQBmCeDBE3A7Rm/FxZRp8AUYnquf/QbId0ikgOhw8ZdQOUl43g3mB4Q9h0UDIrJ3aG5tuhCgm+o7rcXKtfvrnf/5nHn/8cVatWkUul+Pd73738NTT17/+9ZLa1NNP2qw3J1JBe6YHgYcPuCPK82malqh6K3734/jGNrLhKL5pEchn8SotBjrTBK0kgahC5SPYZgrlZhCD9SQj20CBFFkkCksKkkpSZ9dM9S1NP4cCk/G2cRJpaGjgpZde4o477uCll14ilUpxzTXXcNVVV41IHB4LHdRoGlAsC2YUdwjGozvbTXWoeor7pGknD1l9PmLAJuptJ5nP4OUKVIZCuGVRhBVGBm18N4/v+LhGAJfNCGrwqSMgXIIG2CaEpB6h0cBxHFasWMHvfvc7rrrqKq666qoJaffkCts0bYrUh6sReAgcDOGjdG6Nph1GlJ0J5mnEAxEC0QryClIqQLq/h8LQEKZloADlZjHCZdjKRahmQlYxGVQSRKDzaEoywyoKW5ZFLpeb8HZPnjvUtClWDGbAVRaesujL9k11lzTtpONGluEHz0cKSdoUWHGbeH095sH9nZTyET7YAx1IVawmLIUgboEtFQhdZK8kxaS/8T9OItdeey1f//rXcd2J+xKpp5807aCaUD3tmR4kxc2788qf6i5p2gnjeR4prwspXBRJwMLxbTzfICRzKOnjK4WvDBLhLGTrqXFboKIeN9WHlXPw+vsQ8SgG4BayKM8hGIohPAdPSRy7FjvuIfGRJ1l+h3biPfPMMzzwwAPce++9rF27lkgkMuL1O++8c8xt6qBG015HQTG3RngMZAcoC5VNbYc0bRIlnQMIMhR8CxMfhXcwofQ1Qb3IIxBIIfGVBKWgvAYGDPBzxXojoQRWTR1ioB1VyCF9H9/JY5o2rgCBj1FopdDbjlnxJhQ2AePVhOGctwtIIwkjRRBDJRBG4oT/PE5aMzBRuKysjCuvvHJC29RBjaa9Rn24io5s5/C8bI78lPZH0yZDJp8hRxe+AkEW62BZA5fih4LAh+ExyyIx/EyAKlYSFokalFdApZuxquYgs/3gOiiKIZGSxWXEpu/jymKrHiBUB54PvtNCCBf8AgSCxbP8vmLVYaVAnoshyk7kj+bkNQODmh/+8IcT3qYOajTtdV79jmqgFAxlhoiFS6tuqWknk1w+R0q1IYQ8WJupWPkXBIbwcJV18J0+BvOJB4rTASmngEmEoHX4tq/CBCM+D2fwTgwhkXYIkctgCIVQCt9XSN/DkCa+MDCtMKgeIAIKVKYVDAVWebFB10XhIwCZz0Cw7AT8ZKaBGVhReDLooEbTXqcmVEt7pufgM4+0yBBDBzXa9KWUoq/Qgjg0uap81IicUYkUJmXGAgzj8DpNUevo+zMppRB7t5CvdIhIkKYFoQjKd/E5ODjgFa8rUUg3h+tHDw79OCCK4z+GUnhCIITEUApQKHYi8iEIlE/Yz0I7eSxatOiY1a337t075jZ1UKNpRyDwQBwatVHkcjmCweBUd0vTxqw33wWkAInCQAqBrxQCiQDKrMVHDGRGw2/eSeHX38bf/wryuncADsp1QCp830OYNn4hh2HaxZycgwEVyU5IRMEIgyzuR2WpCJbI4+NQnKSi2G/VBuigZiZOP33yk58c8dxxHF544QXuuecePvOZz5TUpg5qNO0IakM1dOY6DmYWSFyh69Zo00tbug8h8pgij5QW4ACH8mIklYGFJbethvop3P2feE/fU0waBpLfv5e+9RspP81BGQamYYAfIGyeAt7z4BSKozhYGJZEyDAKGziYeEwMyKDoAtc7mLFvgK3z2oAZGdR84hOfOOLxb33rWzz77LMltamDGk07AiEEYSLkGcSSGXzRh+ctK/kbraadKB2ZPiCPIggKXMBWDH+glZkNJf87Vp6L+9hdOPf+F+TSw8dF9Tzq3v4x5LIz8Lvbke5z+LQhyUJiHfQ8WwxcnDwIF0tejDCXF08uG3kNw30Q/KGDF8wXr6NL28wql19+OTfddFNJicQ6qNG0o4gFY+C0gnABh4y/lZhxylR3S9OOqCPdgy8kSjkHa6wVOBQN+CpPmTEXy7KO1cQxeTuepfDr76C6ml89GAxjbfwbzPOvQJjFto3auajW3yKxAQf6th/MvPco5vN4UNF49AupMuDQprISHdEcNBHF806y4ntH84tf/IKKioqSzi05qHn00Uf57ne/y549e/jFL37B3Llz+fGPf8yiRYu44IILSm1W004aQggQBQQgROkfBpo2WZRSdOZ68JWPwkAoH7BAFbf78JSBIER1qL7ka/g9bTi/+S7ey5tePSgExpmXYr/5/YjYkfJdzgfxCtAPdAIrgXqQAYjGEccIrpQaALdw8JkJoo/p8VE82cQETB+dXD/J0047bUSisFKKjo4Ouru7+fa3v11SmyUFNb/85S95z3vew1VXXcULL7xAPl+c8xwcHORrX/saf/jDH0rqjKadbCyxBI9DGfgGWbeVkDl3SvukaT25bnxV4NBON1LIYl0YVfwyfjDNhbmR0ovXqXwW54H/xn34l+A5w8flglVYf/G3GA3Ljn5yoB7yXRSDmiEI1ELtCoBjrnZRvg9uDdBaPCDiIPReUVPlT3/6E7feeivPPfcc7e3t/OpXv+Ltb3/78OtXX301P/rRj0acc+mll3LPPfeMqv0rrrhixL8HKSXV1dVcdNFFrFixoqQ+lxTUfPWrX+X222/nve99Lz/72c+Gj59//vl89atfLakjmnYyCpkRMm4cQefBmh4OoIMabWr05pvxlTz4ffv139o9wMQQPrXh0nfCVkrhPf8gzu++h0r2Dh8X8Qqst3wQ4/RLjhmYAIiqKlRyFaSHQFhA9rjnFE/0Qb3ymgMDED2/pPuYcaagTk06neaUU07hAx/4AO94xzuO+J7LLrtsRO5LIDD66cIvfelLY+rPaJQU1OzYsYMNGzYcdjyRSDAwMDDePmnaScWScRx/YPjbb8bdRdhcOrWd0maNZC5Jyi8gyGOJ4hYGCn94REYWd1JCCqgLj29U47VLtIcZFuaF78C65F2IYHjUbYl4PSodApUu1sVp3ok41ugOIBAo3ylOsyj/4PLv30Lle0q9pRlDCIkY5/TTofOTyeSI44FA4IjByOWXX87ll19+zDYDgQB1dYcXZRwNwzBob2+npqZmxPHe3l5qamrwPO8oZx5dST+huro6du/efdjxxx57jMbGYySAado0ZMlDOQM+AgfJwFR2R5slWtN9tKaTZPw04KGQeMrgUL3rQ+MeNaF66kLV1IWqS76WGuon/z//Su4b140IaIxV5xC88T+w//yaMQU0w/oC0JOE7k7oazl2H5RCHfqwVf5rklpPrmXIM0FDQwOJRGL4ccstt5Tc1sMPP0xNTQ3Lly/nYx/7GL29vcc/6SCl1BGP5/N5bNsuqT8ljdR86EMf4hOf+AQ/+MEPEELQ1tbGpk2buOGGG/jCF75QUkc07WQmqQfRhCQIZMh73QSM0j9ENO1okk4zBQ98IoCLUCEQOYo7MklMPARRKoKlrQ55LeW5uI//BuePPz5sibb99r/FWLF+fBfwKyG3A0I2BAaO/d5CN7i7wXPBMMDzwSsAApUaQkRneVXvCaxT09zcTDweHz48limj17rssst4xzvewaJFi9izZw9/93d/x+WXX86mTZuOWTbgm9/8ZrE7QvC9732PaDQ6/JrnefzpT386sTk1n/vc5/B9n0suuYRMJsOGDRsIBALccMMNXHfddSV1RNNOZkGzhrzXg6fyKAJAG6CDGm3iJAs9IPoAEMJGksYngkJioXCxqLarMM2JqcTh7XiWwl23ozqbXj14hCXa47J8Obz8bDHRODWA6upCvG6qYVj6KfB6wAoWR2q8QrH4nhCQfRyil42/P9PZBAY18Xh8RFBTqr/+678e/vvatWtZt24dixcv5uGHH+aSSy456nn/+q//ChRHam6//fYRAZBt2yxcuJDbb7+9pD6V9L9DCMHnP/95PvOZz7B7925SqRSrVq0aEW1p2kzjqxgKBx8JeGTcbsKmDmy08enN78UQxV2yxcGtmUxRwDZC5A6mFNSES1+S/XqlLdEujbBtlFkGhQ7wFXTthqMFNVjg5IoP4oAPVgNU/9m4c0lmhGlQp6axsZGqqip27959zKBm3759ALzxjW/kzjvvpLx84v7NjSvkt22bVatWTVRfNO2kFjIbSLnFjS4lLtCOHq3RSjVQ2DWcI+MpMA7unA3Fz56QSFAdGf+36UPGtUR7XBcuA9kE0gLnGHk1Xuo1H7omsBhRc/QPRu3k09LSQm9vL/X1owvCH3rooQnvw6iDmqMt5zqSO++8s6TOaNrJTpIAUkhhI8jj+P2vSSTWtOMbcvYiRBaBBCyKKb/FIZniQE2MuDVxIzPHXKL95x/EOP1ixHiXCh9L9Qro3FGcSiKLcl3EkabQ3HBxN2+hQHWDoUsnjDAFS7pTqdSIRUH79u3jxRdfpKKigoqKCm6++WauvPJK6urq2LNnDzfeeCNLlizh0ksvHfU1Wlpa+M1vfkNTUxOFQmHEa7fddtuY+gtjCGoSiVeLOCml+NWvfkUikWD9+mIi2XPPPcfAwMCYgp9S5PN5zj77bF566SVeeOEFTj311OHXNm/ezLXXXsszzzxDdXU11113HTfeeOOk9kebXcJmIwXvJYTI4yvw1X70DsLaaAw57UAxZwalEMIFlYODmzoKoiTso03NlGYil2iXStTXo3rKQBUgIKG7CeqPsErWaARnz6EdN8E9uarfTrkp2NDy2Wef5Y1vfOPw8+uvvx6A973vfXznO99h8+bN/OhHP2JgYIA5c+awceNGvvKVr4w68fiBBx7gbW97G42NjWzfvp01a9awf/9+lFKcfvrpY+rrIaMOal5bXOezn/0sf/VXfzUiwcfzPP72b/92QpKPjuXGG29kzpw5vPTSSyOOJ5NJNm7cyJve9CZuv/12tmzZwgc+8AHKysr48Ic/PKl90mYXhYlSafDAcF2QGTgBHw7a9DRY2A3kkUJSXJ5c3NJACh9TKFyVoDIwsVVzj7SLNhSXaFtXfARZdYJHQaoboesByAqItAOHBzWiZgWq6X5gIUROgQo9UjPVLrrooqMuuwb44x//OK72b7rpJm644QZuvvlmYrEYv/zlL6mpqeGqq67isstKSwwvKafmBz/4AY899tiIjGXDMLj++us577zzuPXWW0vqzPHcfffd3Hvvvfzyl7/k7rvvHvHaT37yEwqFAj/4wQ+wbZvVq1fz4osvctttt+mgRptQAWM1bmYT0htAIsA7AMGVU90t7STTk+sp7lItfAzBwdorh74p+9gswbYndrPGYy/R/hjGijMn9Hqj71gVuFUoD+jdj5h35CrBYv7HUQMDYJo6Ofj1pmCkZrJt27aN//7v/wbANE2y2SzRaJQvf/nLXHHFFXzsYx8bc5sl3aHrumzfvv2w49u3b8f3/VKaPK7Ozk4+9KEP8eMf/5hw+PBvxZs2bWLDhg0jCvZceuml7Nixg/7+/qO2m8/nSSaTIx6adjymlyjWrBGA0TPV3dFOMt25ZhRJPOUUZ1OUdTAF2ABqiFmrsc2JDWi8Hc+S+5eP4tx1+6sBTTCM9bYPE7zhu1MX0ACivhGlLLBA2AVUd9fR31tWhtAraQ93KKgZ7+MkEolEhvNo6uvr2bNnz/BrPT2l/V4taaTm/e9/P9dccw179uzhrLPOAuCpp57iH//xH3n/+99fUkeORSnF1VdfzUc/+lHWr1/P/v37D3tPR0cHixYtGnGstrZ2+LWjLRm75ZZbuPnmmye8z9oMF10J2eIyVZVPQX4LonztVPdKm2L9+X5clYODScBCOKBMPOFiUEbMmvgplaMv0d6I/eYPTOgS7fEQ0TkoelHCBbcDwcTmD2nTzznnnMNjjz3GypUrefOb38ynP/1ptmzZwp133sk555xTUpslBTX//M//TF1dHf/yL/9Ce3s7UIyyPvOZz/DpT3961O187nOf4+tf//ox37Nt2zbuvfdehoaGuOmmm0rp7jHddNNNw8lPUMzNaWhomPDraDOMEKh8AmgDJUAd/ZunNjv05FpRHBqpLn4jLmYjWFQGJn77mClbol0iVTYP9rwMGJDfBvXrprpL04sUE7D66eRKvr7ttttIpVIA3HzzzaRSKe644w6WLl1a0sonKDGokVJy4403cuONNw5P15SSIPzpT3+aq6+++pjvaWxs5MEHH2TTpk2HZVSvX7+eq666ih/96EfU1dXR2dk54vVDz4+12dbRNvLStOMyloDTfPCJg+prRejkxlmnL7+Tgg8mNggLgX9wabZNiAoiwciEXm/Kl2iXSlZDQRSXbKe68QsFZIn7+8xKMyynxvM8WlpaWLeuGNxGIpGSqwi/1rjrbY9ntVN1dTXV1ccvXvbNb36Tr371q8PP29rauPTSS7njjjs4++yzATj33HP5/Oc/j+M4WFaxvPd9993H8uXLJ7RaoaYdIuIJVO984AAIB9gD6KBmtujP70Qd/BUqkCCc4i7TFIvoVQVrJ/yafssuCr/6Nv7+l189aFiYG96B9aYTs0S7VLK6Gj9cDvmBYmDTtBeWlLa/jzb9GYbBxo0b2bZtG2VlZRPWbklBzaJFixDHKLe8d+/ekjt0JPPnzx/x/NB2DIsXL2bevHkAvPvd7+bmm2/mmmuu4bOf/Sxbt27lG9/4xvAeE5o2KcR8oAnwQfWgelsQlfOmulfaJOrJdSNFcedsMJHCxJLuwdEag5AsIxqY2ETXk26Jdqmq5kFnf3HLBG8/oIOaUZsG2ySM1Zo1a9i7d+9h+bDjUVJQ88lPfnLEc8dxeOGFF7jnnnv4zGc+MxH9GrNEIsG9997LtddeyxlnnEFVVRX/8A//oJdza5NKVFShehPAAMWlUK2ADmpmou5c86tPDu7RBHl8FQAEIaOGhJ048sklOmmXaJcqXw3KAhSkO4/7du01Ztj0E8BXv/pVbrjhBr7yla9wxhlnEImMnKotZSaopKDmE5/4xBGPf+tb3+LZZ58tpckxWbhw4RELAq1bt45HH3100q+vaSMtoBjUhIBBlPIQwjj2Kdq0kXG7yXuDQIDi6IyBh8AgBGQRlFMemPgp7iPuoh0IY106gbton2By5Tr8JzeBn4NMEr+tDTlnzlR3a3qYgUHNm9/8ZgDe9ra3jZj9UUohhMDzvDG3OTF72B90+eWXc9NNN42oPqxpM52obET1dgB7QNnQuxWqTpnqbmnjpFSalNOMwEGIECYOrjr0oSApt+dOSoE4v7cd567bRy7RBoyzLj2plmiXrKIOBprAcaCwH9BBzWw1pRtajsYvfvELKioqJrJJTZsezEXgtAISRCsqvwKhV9VNX6nNYPYTtuNkXRNBFkUIsJCYVAYnbsPJQ4pLtH+G+/AvXrdEeyXW2/8WY/7yCb/mlPDrwd0PtgXZ7qnuzbShhESNM4ge7/kT7cILL5zwNksKak477bTDhoo6Ojro7u7m29/+9oR1TtOmC5GoR/WWg8gDDmR3QEDX4ZhulNOKoBPoQrgWmEkgjoGJEpLqwMTXsJq2S7RLVdUI7nYoFCDbg/L9mXV/k0QpiVLjDGrGef5kePTRR/nud7/L3r17+fnPf87cuXP58Y9/zKJFi7jgggvG3F5JQc0VV1wxIqiRUlJdXc1FF13EihU6m12bpWQ5qFbAAH9wqnujjZEa2g1iLyrgIwgAeaRjEAokMOTEF8+D6b1Eu1Syqgq/RUI2CakUqn03Yu7JVShQOzF++ctf8p73vIerrrqK559/nnw+D8Dg4CBf+9rX+MMf/jDmNksKar70pS+VcpqmzWz2Ckj3QX4Q/D7UYAyx6NSp7pV2HH5mH6KwH+gHIwSuUQxs8uUI1mHI0IRf82hLtOWqs7Hf9lFk9TRZol2qrj7wu0DWQX5y9gucaRQGivEtQBjv+RPtq1/9Krfffjvvfe97+dnPfjZ8/Pzzzx9Rm24sSgpqDMOgvb2dmpqRe3f09vZSU1NTUsaypk13IhJBJSvA7QFCII++kao29VJuO4JeAvRiGEnwEuD3F/80GyE6cbUzDplxS7RLVRmHIQ9QkNGbCI/GTJx+2rFjBxs2bDjseCKRYGBgoKQ2SwpqjrScGoo7Xtu67LU2m0WXQeZFcE3I7UJZyxF6yepJJeumcFUL4IDwcewoIptFkgF1GkQaj1lctFTejuco3PWdw5dob/wbzAum5xLtkuV6wVZgh6BxzVT3RpsidXV17N69m4ULF444/thjj9HYWNqU75iCmm9+85sACCH43ve+N1zZF4r7OPzpT3/SOTXarCZiMdT+BqANgmHIb0cvWT15ZN0XKG42aQISpcATBsqYi5B1EDn+ti1jNeOXaI+Ru3sr9OeLmzNWBDDDMy9vaDKog1twjLeNk8mHPvQhPvGJT/CDH/wAIQRtbW1s2rSJG264gS984QsltTmmoObQlgNKKW6//XYM49X5Odu2Wbhw4YRsSKVp09rcU6H5AGRdyOxHDQ4iEhNbaVYbo+QWlJWHg4MhpijgqkNL7mOY4cUTfslZs0R7rAr7ihWZHRecic9Xmql8JfHHOX003vMn2uc+9zl83+eSSy4hk8mwYcMGAoEAN9xwA9ddd11JbY4pqNm3bx8Ab3zjG7nzzjv1RpGadgSiogZ1IA6iG6QBe1+A0y6a6m7NSmpwH6gWhOhGeCFMI4ErA4CJIeKEzYnf0uJoS7SJVWC/5RqM0y+Z3UuYcy4E7WKCdLneUmQ2E0Lw+c9/ns985jPs3r2bVCrFqlWrRswCjVVJOTWTUQVQ02YUuQDcJPguyCZdi2MKqIGngA7AKu6c7XtYHniGjVDLCJgTXxzxyEu0TcwNV87YJdpj1rcLOrcAAs5571T3ZtqYiaufDrFtm1gsRiwWG1dAA2MIaq6//nq+8pWvEIlEuP7664/53ttuu21cndK0aW/d2fD8TohYkElD61Zo0MX4TojkAKhtQDfgAqo43UEMvBUEjYmveq5SAxTu/iHeU7N0ifYoufk8FHwoXwNKYcRqjn+SBszM1U+u63LzzTfzzW9+k1QqBUA0GuW6667ji1/8IpY19uT5UQc1L7zwAo5TnBd+/vnnJ2V1gKbNFEIIVGI5DL4EBtCzWQc1J8JQC7AVRBqUSTGaMRBiPSQmYWsDvUR7bDwPaldB87OQ7MP3PAxzQnfrmbEUcgIShU+uoOa6667jzjvv5J/+6Z8499xzAdi0aRNf+tKX6O3t5Tvf+c6Y2xz1v6bXTjk9/PDDY76Qps061Stg6CVAgJ9B7d2DaJz4hFTtoKFHQKVB5ECBkDbKn4soWz8pl9NLtMfODIdxe/ZCOApI1IHnYfFZU90tbYr89Kc/5Wc/+xmXX3758LF169bR0NDAu971rpKCmpLCtg984AMMDQ0ddjydTvOBD3yglCY1bcYprniaB0iU8/+3d99hUpXnw8e/50zd3mFZ2KX3LiBZbBCJYExssSNgNPFnYsMW9U3sMdijwUQ0iS0xiUnsDSsBUYqUpbOwsOxStrF9d3baOc/7x8DICuLCznJ2Z+7Pdc117Zxz5pn7PMrMPU81US1rrQ4pugRqIbAMfCug+VOgGgiC0gAnkNshCY1ZXYbv+XvwPXdnq4TGNmEacXc8j2PyBZLQHEnOaPB6QbPD9q+sjqbLMNEj8uhMXC7XIWvUAPTt2/eY17w7pjt86aWXaGlpOeR4S0sLL7/88jEFIkRUyhiBcjpAeaGxHLN6n9URdRnK24xZU465fiHByu0EjcUEWzaiSpeg9ryG8hcAdUA12HyAGzQN6Ab2UyA5sou6KV8L/vdfwPvIz1qtOaP3Horrxnm4LrkFLTny43WiTu5YMBUqEEDVVeIv3vjdrxHhMTXtfXQm1113HQ888EB4zycILeL74IMPct111x1TmUfVmdnQ0IBSCqUUjY2NuN3u8DnDMHj//fcP2TpBiFim5eWh1sSF9oPSfFCyCDJ+YnVYnZ4KBiFYDZVLwawGtwsIADVgawLDA0Y6od9lCrSk/a9MAMcgiIvc+idKKYw1C0NTtOsPSkplivYxsaem4kvMhbrdKF2HxiqrQxIWWbNmDZ9++im9evVi9OjRAKxduxa/38/pp5/O+eefH7729ddfb1OZR5XUpKamomkamqYxaNChu6pqmsZ99913NEUKEf2cAyBYEvq7eTemz4fuivx04qhS+BLgg8pKSM4I5S5aHGjB0NL6Df5QF0ZcJmAAPSEpN+JhyBTtjqHS+0N5IRgOWLcQRk22OqROLxqndKempvKTn7T+kZeb275/x0eV1CxcuBClFN///vd57bXXSE//uqnV6XTSu3dvcmSfGyFa0YePx6xeDv5m0IDdG6D/OKvD6rRUTdn+v2yAD+yZYEsF/OAIouxpaGnD0dIjv6VBOAaZot2h9L6jUMVLUY314ErF29yMOyHB6rA6NaW0CEzp7lyzll944YWIl3lUSc1pp50GhFYWzs3NRZcmVyHaJmcElC5DBbyY+9ahS1Lz7XYtA6cGyoHW7SS04T9qPfivAxcyP+IU7XOuwTZUZupEgjMtDa8C3A7QPGj7dkGC7Bso2u+YFgjo3bs3AB6Ph9LSUvx+f6vzo0bJehxCtJL3PYyiZdDcAtUlBNILcAwcY3VUnY658SvwbYWAG82RCINPO27vLVO0jy97Xj+o2QmagqZKQJKaI4nGdWqqq6u5++67WbhwIZWVlZim2ep8TU3NUZd5TElNVVUVP/3pT/nggw8Oe94wjGMpVoiopTtdGI4eYO4ATUMrXwuS1ByqaRP4XKAHULZUdHfSd7+mnczqMgJvP4ux4ctWx20TpuH84U9lRlNHcTsABcEAeMu+8/JYF40bWs6cOZOioiKuuuoqunfvHpFFfY8pqZkzZw51dXUsX76cyZMn88Ybb1BRUcFvf/tbHn/88XYHJUQ00gZOhK+2QXw8NO1FBbxoDvd3vzBGGJuXQ9UuSHKjeQIwpGNbacK7aC/6b+iLdT89bwiO836JLU9aDjqUvTuYhaC7QrtZiJjz+eefs2TJkvDMp0g4pqTms88+46233mL8+PHouk7v3r35wQ9+QHJyMnPnzuWss86KWIBCRAt73mCCuwfAzsVocTmYhZ9gG/Ejq8PqPCo3gd8DjRoqvRe2zMwOeRuZot1JZPaBoo/A5obmUDeDx+NBrfiChMk/sDa2Tqn9s5/oZLOfhgwZctg179rjmJKa5ubm8Ho0aWlpVFVVMWjQIEaOHMnq1asjGqAQ0UTLGA71e8BvwO6NGEOmy943QLCqBOqKgAAEDfRB0zvkfb59ivb5OKZeJlO0jyN7txyC9c2QoCA+iebSEsynrwWCNC9bhDZpMvGnTrU6zE4jGje0/NOf/sQdd9zB3XffzYgRIw7ZwDI5OfmoyzymT9PBgwdTWFhInz59GD16NM8++yx9+vRh/vz59OgR+U3jhIgWtsHjMYr/B2YztHhg/SIYe7rVYVlv13Yw48DmhIRu6JndI1q8TNHupPqMhpIl+LYshTcWodmCqGQ7BAuhOhGQpOaAaBwonJqaSkNDA9///vdbHVdKoWnaMY3PPaak5sYbb6SsLDSw65577mH69On8/e9/x+l08tJLLx1LkULEjvRBULYG3HHg32V1NJ1DVVGo6ynBDam9IlZsaIr2OwQ+fFmmaHdG9X6UPx5qFSQ0o5kJ2NwBVPdMnHGyQGW0mzFjBg6Hg3/84x/WDhS+/PLLw3+PGzeOkpIStmzZQl5eHpkd1A8uRNQYdCrUbYRAACr3YGzfhK3/sIgUrZTC3LENSkshNQWyeoDb3WHjUyImQYeAOzS9N6NPRIoMTdGej6oo+fqgTNHuVNSgEeDbhXNwJv4KJzg8mJndcWblYviaMaqrcWVkWB1mpxCN3U8bNmxgzZo1DB48OGJltjmpufnmm9tc6BNPPHFMwQgRC2ypqRgJuah9xaBMqFgNEUhqjNWrwbYPGitg36dQqaCoO0y/NQJRdyzVWANBHewOtB7921WWTNHuOhyDTiSoG5B2JY7V/8N056Gq1xKsrAK7BlXFIEkNACY2zHYO9G3v6yNt/Pjx7Nq1y5qkZs2aNW26LhLNR0JEO9VjDFTtRJkKSrcSGFSJI7Odm8EmumDVOlDrQ5McNA2ohFULYPKsCETdMQLV+8DvB90Gugu7+9g2o5Qp2l2TfUB+6I8fzATA90U51OwAQ8OmRXZmjOhcrr/+em688UZuu+02Ro4cechA4WNZyLfNSc3ChQuPunAhxOHZ+40gULQE5akCTNi2CDIvbF+hPXJAVaIMI7ShtU1D63Ya2qmXf+dLraQ17II4F8o0IT71qF8vU7SjixGXBo5k8CuM7WXYI/cjvkuLxr2fLr74YgCuvPLK8DFN047/QGEhRPupEZPh81fQsMGezQTrarGntt7YyPB4UO+9DVO/jz3tyC05KuiDwN6DXqxQGf2gsRFSUjrgDiJDM+vArqEFDcg4urE/5u4i/G/8UaZoRxO/CbU+lK6gWlYaPiAaZz8VFxdHvExJaoSwiDNnCIH4dKgtB98e1Nb34cQZwP7Wh5Wfw46/hlZb/XgzwT4D0eP7wc5NkNMf02aH9Wvggp9jU34wdEg5FeoXf/0mO96F2iWYrt6ggpCSCI3u/eNXTFRWIuwqReveD5U+AFvP4z+1WfPUgwNwuqGNv0S/dYr20Ik4z/k/9KzIzaASx5fqdwKs/wKjpQm8HqvDER3owD6SkSRJjRBWyj0Bat4Bw4QN76HGXwKaTvDDF6BmESgzNDQmWAylpRj6p2iAvqcA6utDZfz3K4zkfNi3FHRz/1gaQANwgSMJAtWhcTYtTdASAH89GI1Ql4qm22FPOWrbQoLBOEhLQTmywK5D76Gwtx7HxIkdVwe+RsAJugL9yB9yMkU7+iV060ZDbS2YNjSz2upwOo1onP0E8Le//Y358+dTXFzM0qVL6d27N08++SR9+/blnHPOOeryJKkRwkL20acSXPtqqLVBQfAvMyB/Duz6OHSBSwfH/hkLAQPN0NBcOsrhR9M1VMAMNVTsWxK6Zn+jRWjAvgbEQWIS1DeA5oADu+DaUsGZDA3FEJeJMuPBMMDmQzWWh7qxXA7YsxKwESj5AC0xOVS+IwHSslABB0ppmBn9ITcPd0LCsVWC8oIeBM0O3bO/9bJvnaL9gxnYTzlXpmhHES2jV6gFs7GZxrVrSYrg3kBdVTTOfnrmmWe4++67mTNnDg8++GB4DE1qaipPPvmkJDVCdDWapkGgFsz92YgCvvoPBE2waaiWIDTv3+0v2Ynm0AGFMvYnJzoQML8uMGiCrqEMBaZCs++AIdfB2i1g0yFZgWoAwwvZ6VA+AeID0FgEzR5IigdPcygOmw42Oxj7W378jWAAzQ3QUhUa+xDww/bl8KWJV1codzqawwb2NFROMqopCM6eMGAA8d0PXSXYbGgItR5pOjid6IeZvitTtGOPUVGJXrIWNLA7vCjTlMHeUWjevHn8+c9/5txzz+Whhx4KHx8/fjy33npsS1FIUiOE1fJ+Ctv/uv+JBoE9AKHERCmUSWgmk8ePcu5vjbCFftGogBlKiDRCiYiuhbqfdEIDhf0BtNq96JMmHf69Bx34I7SBYLBwE9TsDnVPuVpCYxqa6sHnD72B2t+9pUwwA2Co0DgYXaECRqibSw+CsReqHeANgn8ZrNNpiYtHc7nR4lPwKztBrwHBALqxAzJSibf1azWMUflaCHz2L4L/kynasceJqUC32wm++BCeWTeQMGKK1UFZKhq7n4qLixk7duwhx10uF83NzYd5xXeTpEYIiznOOIPAM68B9Xz9ra6hkYKiLpSwaAD6/uEyB03LPJDQaFqoC0vbn9QcuEQlQlzbu4Xsg4cBh18IMFhZCcU7wGyCeIWq24NqbgRfAJprQ4mHzQZKC7XyKAOMQCjR8jSGutFUEFQLqroJ6vxolKMFDFTJLlqMDTR+thoSU7D5G3E0l0NT7dcBJKXjPOtKbOOmyq/2KKd//yTMvy5DYaJ7G0F13tl7x0s0zn7q27cvBQUFhwwYXrBgAUOHDj2mMiWpEaIz0JOB+q+7ofRs+PHt8NZrkOKEpiUQXpMujdDA2m5o7vWhYTT+/es5HEhmdAfoqWh9J6CnR2aGgb1bN+j27dPK/X4/xo6i0DTc4L5QLLtLoMUHKm5/kmOGZnMFTLDbUQE/pg00dDTNhIABu0rQqPi64PAU7UvR3Mc4bkd0KYn9T6AhLQfQ0Xp1R2XKRsmmUpgHzfQ71jI6g/vvv59bb72Vm2++mWuvvRav14tSihUrVvDPf/6TuXPn8pe//OWYypakRojOIP88WPqHUJcTgFGOMycHdc11+y+4GuODF2DwOLAFoftQ7HHxBLcWom38APqlQHkdJOuhMSpJvbBPnn1cb8HpdMKQb2/pAfCuWgWGD/ZtB38LbKxHUYcCNFMLLRroqcCId2FPSUbP6SdTtGNQIDEdPScbcGDaHST3kKQmmtx3331cc801/OxnPyMuLo7f/OY3eDweLrvsMnJycnjqqae45JJLjqlsSWqE6Ay69wcygCrQQdN6ESgrw3HQh7n9h1eGV9oMHxs0GDVwUHj1TZvNdsg1nYl73DgAXITG+PhUM8pTDyYw5WcEd++CRYtxnJQPA/rhzpZkJhY1rF4MDQG0OB17cpbV4XQKpvq6Ibc9ZRyNxYsX8+ijj7Jq1SrKysp44403OPfcc8PnlVLcc889/PnPf6auro6TTjqJZ555hoEDBx6xXHVQi9GMGTOYMWMGHo+HpqYmuh2hNbgtJKkRohNwdO9OYNZjaCs+guw8SEvH0e3QX6eHS1YOHLPZbN96TWdkmibKUxcahOxOwN2rF+5eveB7+VaHJiymB3WUW6G82/GXNlkdTqegItD9pI7y9c3NzYwePZorr7yS888//5DzjzzyCH/4wx946aWX6Nu3L3fddRfTpk1j06ZNuN3uI5b9zc+p+Ph44uPbvwK4JDVCdBKOuDjUqWcDEPRG/0Z+/sJCMDWwOyBexsqIr9kaazE1O6rRBXnt3OhVHLMzzzyTM88887DnlFI8+eST/OY3vwmvJ/Pyyy/TvXt33nzzze/sPho0aNB3/gCrqak56pglqRGiEznwj9wRF/17FuneEnAS2p07MdnqcEQnYqT1BGWHjJ4QPPpNDaNRJLufGhoaWh13uVy4XK6jKqu4uJjy8nKmTp0aPpaSksLEiRNZunTpdyY19913HykdsCedJDVCCEsoZQOCocHBnXjDTXH8pU+aRP2Cl8Fug4wMGmpqSE6P7UUWIzn7KTc3t9Xxe+65h3vvvfeoyiovLweg+zcW1ezevXv43JFccskl7R4/cziS1AghLKGt/wzNDSo1FZKki0F8TdM0VIIbTRnQWI/asxtiPKmJpF27dpGc/HXr6NG20rRXR47761wr8QghYoJ/6xeg21AtJporC72PbEQpWjPSuhM0dIItPgKpqVaHY7kDLTXtfQAkJye3ehxLUpOdHdqnraKiotXxioqK8Llvc7QDlo9Gl0lqzj77bPLy8nC73fTo0YOZM2eyd+/eVtesW7eOU045BbfbTW5uLo888ohF0Qohvk1g52a0rUsgqQVsAZTbgf07ZkqI2KN5PWg+L3r9Nuw+v9XhWM4gtCtJux4RjKdv375kZ2fz6aefho81NDSwfPly8vOPPIPRNM0O6XqCLpTUTJkyhX//+98UFhby2muvsX37di644ILw+YaGBs444wx69+7NqlWrePTRR7n33nt57rnnLIxaCHEwIxCAde9BRTWaNwGt/wgcky63OizRCdlGn4zN4SQYdOB/fT5132gREB2vqamJgoICCgoKgNDg4IKCAkpLS9E0jTlz5vDb3/6Wt99+m/Xr1zNr1ixycnJarWVzvHWZMTU33XRT+O/evXtzxx13cO655xIIBHA4HLzyyiv4/X6ef/55nE4nw4cPp6CggCeeeIKrr776W8v1+Xz4fL7w82+OChdCRNCGd9CDdZgZ8dAUgN6ndpl1dcTx5czrg7dbMrRkYAb9NL00j/gb78J5nMd/dBZWbJOwcuVKpkz5eiPRm2++GYDZs2fz4osv8qtf/Yrm5mauvvpq6urqOPnkk1mwYMF3rlHTkbpMS83BampqeOWVV5g0aRIOR2jX4qVLl3LqqaeGlmrfb9q0aRQWFlJbW/ttRTF37lxSUlLCj2+OChdCRIax5SvYsRacLnTDQJswHUfPAVaHJTqp+H5DCA4ah5aaSLB4F1RVUzXvQavDssyBKd3tfRyNyZMno5Q65PHiiy8CoQG/999/P+Xl5Xi9Xj755BMGDRoU+Zs/Cl0qqbn99ttJSEggIyOD0tJS3nrrrfC58vLyw04tO3Du29x5553U19eHH7t27eqY4IWIYcHGRtSOz8FmhFYQTu2OffApVoclOrlu3/8J/skXQs0+tKoi2LiGfWtWWh2WJSI5UDiaWZrU3HHHHWiadsTHli1bwtffdtttrFmzho8++gibzcasWbPaPYra5XIdMhJcCBFZ2urXQPeiTBMSkmD0xVaHJLqInhNPxjbpNFwp4E6Kw7b9K6tDEp2YpWNqbrnlFq644oojXtOvX7/w35mZmWRmZjJo0CCGDh1Kbm4uy5YtIz8/n+zs7MNOLQO+c3qZEKLjGEUroaYUXVOYmgM1cAr2TNmkULRd8phxqOZycMWhmhutDscSVmxo2RVZmtRkZWWRlXVsH26maQKEB/nm5+fz61//OjxwGODjjz9m8ODBpKWlRSZgIcRRCdbVou36ArQgSnOiZeVh6z/B6rBEF+M67ce0bFsNLU0Ye4tpbGwkKSnJ6rCOKys2tOyKusSYmuXLl/P0009TUFBASUkJn332GZdeein9+/cPz4e/7LLLcDqdXHXVVWzcuJFXX32Vp556KjxaWwhx/JmL/4VZW42ZmBjatHLUBd/9IiG+wW63E4xPJ+jXUcpNcMPnVockOqkukdTEx8fz+uuvc/rppzN48GCuuuoqRo0axaJFi8IrIaakpPDRRx9RXFzMuHHjuOWWW7j77ruPOJ1bCNFxfCs/QdWWoOp9qIZGzH6nY4uxX9cighJ7gGGADSj97r2Foo0MFG6bLrFOzciRI/nss8++87pRo0bx+eeSwQthNe+enfDVx5DggKAXUobj7D/K6rBEF6Z65MGOVaAMqN5tdTjHnYypaZsu0VIjhOhatC9eD3351DehXJk4v3+R1SGJLi7te/kEa/fhqa6jubKSqqoqq0MSnZAkNUKIiPJ99F/w7EOzmxAfD5PORNPko0a0j6Zp+HoMQhku8PrxfvWl1SEdV9L91DbySSOEiJhAIAA0gM8LBmjDx+IeMNrqsES06JUHygSlQ2VsLZRqxYrCXZEkNUKIiDH3bISanRD0ga8Z+4TzrQ5JRBF90Fg0DVQggFkpG1yKQ3WJgcJCiK5Bq94Hmo7qlgnZg7DZbFaHJLow0zSpKduN+d57aIMHk33yaezS7NhsoFVWUVFRccj2ONHKig0tuyJJaoQQkdNSBSg0pYNbEhrRPuVffo56+k7MxgDKMNg7aDzBhhqCcW5ciYn4V30JPzzP6jCPC0lq2kaSGiFE5GgmaAHAAcl5VkcjujhVug2a/didNmwuO57CFdAYBAX+eDuazRlDSY1M6W4LGVMjhIiclkawJ4BNCy2WJkQ76Lt2oxSggeEzcSQ40Gwatngbdl2Drxazc8kSq8MUnYgkNUKIyGloAMMPfkB3Wx2N6OLMtHTsbhuaroW+rQIKFDhcdjSbhvIZmHdda3WYx4WpwFCqXY9YaKmR7ichROSYAQgCNhvOHtJSI9pH++o9NE1DodDQ0Gyh5EYpUKZCOWwk/HOB1WEeF9L91DaS1AghIsK/fTtoCnQNEhKtDkdEAb3Zj6ZpaISSGd1uxwyY4LShkUjOmx8QHx9vdZiiE5GkRggRGc2lEPSHfkbbnFZHI6KBGfj6bx2MgIL+A7D98jqyT5yCbo+drzCZ/dQ2sfN/hBCiQylt/0qvygCX7MYt2keZJrrLBS2erw/abPT8wwvEp6dbF5hFJKlpGxkoLISIjMYGsNlB1yEt9r50RGRpuo572kUEgkFM08QMKAIN3phMaETbSUuNECIiVN0+8AbQTAOUfLSI9tNKN+JKicff6MUep+Hf47M6JMvIQOG2kU8eIURkBFogEEDZdLBnWR2NiAI+v4npD2B32SAIekLsfmWZRKD7iejPamL3/xAhRERpRgDi3KhgAPoMtDocEQVaknMxKpdgYKC8CgYOszok0cnJmBohREQoTwPK7wfsuJOSrQ5HRIH0WVdhBIOoFoWu69DUZHVIljHNyDyinbTUCCHazb+vEs2uQLeBO8HqcESUSEjPRM9IRG/woXQTc/g4q0OyjGkqzHYOimnv67sCSWqEEO2mKnaC6QBdgUMW3hORE3fFtbD0SzAM6JVhdTiWkSndbSPdT0KIdlOBalSwBRUMoBwOq8MR0WTkqeB0Qlw8WuUeq6MRnZwkNUKIdjPrmjGbFabXxHTIasIicrJ790YPauhNTWjeZqrWr7U6JEuYpsJo50O6n4QQog3M+jowNfCZkCLTuUVkaQN7w45CtNQM7Pbo/2I+HBlT0zbSUiOEaL/6OvD7wBeUMTUi4lIv+BkJJ0wkvlc2rpJNVocjOjFpqRFCtF/ACzYbKA3iu1kdjYg2polRtB7l96DFp8L0y6yO6LiTgcJtI0mNEKL9gkGwO0DXSRw4yOpoRJTR6vahtTRBwIfmjs3NUqX7qW2k+0kI0S7NW7ZAzV4I+CFRNhsUkad0e+iBHgML/Yv2kJYaIUS7aOVFkJQCAVCm2+pwRBQy4+Lx+UH5FZo3SCy21UhLTdtIUiOEaBezdAt4GtH2laOfM8PqcEQUciano9ncKBVEM2Kzg0GSmraJzf87hBAR0VRSAmV7QMWheg8jru9oq0MSUci7+ktMTzMqGEDPzrU6HNGJSUuNEOKYqaI1oCnQTLSk7miaZnVIIgrZBwzFOWQEZosXPSkWO59k9lNbSVIjhDhmxu6dYBrYTFDDR1kdjohSmtOFf8dW8DZDVYrV4VjCUKFVgdtbRrSTpEYIcUzqKith704wbRhxdlxDJ1odkohSRnEhWl0lBPxocXFWh2MJGVPTNpLUCCGOzbal+0flKUjKJi4+3uqIRJRSdTVodgdoGo7eA6wOR3RiktQIIY5NSSmggRGAoSOtjkZEMeeJk/Ht3YPyNKOSkq0OxxIqAi01SlpqhBDiUI0NDaGkxjTB4YAh46wOSUQ57/rVqOYmtKRkUn4yy+pwjjsZKNw2ktQIIY5e4ZfoLgWGhpnWjdTMTKsjElHMu2kNBIJgmth75FkdjujEJKkRQhw1296dKKcGfg3bgLFWhyOiXMWKFaCA5HT8A4eRYXVAFpCBwm0jSY0Q4qj4/X7YVYpmmuCww4ARVockopzNqYPdhgoEUb4mq8OxhGm2PykxzQgF04nJisJCiKPiLVhO0BsgaJqYcanE95IVXkXHUi1ejMbm0EKP8rUljkBaaoQQRyW4vQDMIDQF0QcMsTocEeUCgQDm2q+gcDOaywYDY7O701Cq3YvnyeJ7QgjxTbv2gD8ISmEOGmp1NCLK7f7Ln6CiGvQ4zNnXk/njc6wOyRKh7qf2lxHtJKkRQrTZvg3rINgCaGhJaWQMGmZ1SCKKFb/yZ9haANnZkJSOooXExESrwxKdmHROCiHaTG1cidJ0CPhRfQZZHY6IYsWvvgALP4GaBvSkRPTRI+h76c+sDssyB2Y/tffRVvfeey+aprV6DBnS+bubpaVGCNF2u7dBwEDpTsjta3U0Igr5fD72vvAMrFoKTgfYdMze/cicdY3VoVnKiindw4cP55NPPgk/t9s7f8rQ+SMUQnQKtbtLweMDpYHbSda4k6wOSUSZ5oZ6KuY9ila0GRU00NxxkNGN1Mt+RlJSktXhWUrR/hWFFaHXNzQ0tDrucrlwuVyHXG+328nOzm7Xex5vXaL7aefOnVx11VX07duXuLg4+vfvzz333BNaL+Mg69at45RTTsHtdpObm8sjjzxiUcRCRJ9gwXKwK1RLPSpPup5EZFVXV1P58D2wbTMqKQkNG4yeQO49j5CWlmZ1eFElNzeXlJSU8GPu3LmHvW7btm3k5OTQr18/ZsyYQWlp6XGO9Oh1iZaaLVu2YJomzz77LAMGDGDDhg38/Oc/p7m5mcceewwIZZ5nnHEGU6dOZf78+axfv54rr7yS1NRUrr76aovvQIiuz795A5ongOZIwNZX1qYRkVNbW0vd479Dr6tCCypUZSXqjHPod+X/WR1apxHJ7qddu3aRnPz1xqCHa6WZOHEiL774IoMHD6asrIz77ruPU045hQ0bNnTqVrMukdRMnz6d6dOnh5/369ePwsJCnnnmmXBS88orr+D3+3n++edxOp0MHz6cgoICnnjiiSMmNT6fD5/PF37+zWY5IQQ07KtC1daACqDsbtLHTrE6JBElWlpaqHnst1C2G1MF0DPS0CZ8j76X/9zq0DoVw1QY7UxqDrw+OTm5VVJzOGeeeWb471GjRjFx4kR69+7Nv//9b6666qp2xdGRukT30+HU19eTnp4efr506VJOPfVUnE5n+Ni0adMoLCyktrb2W8uZO3duq2a43Fz5BSrEIdatxr9kEcb2Eszc3of9ZSfE0fJ4PJT99m606rLQhpU2O+bUH0tC0wmlpqYyaNAgioqKrA7liLpkUlNUVMS8efP4v//7ummyvLyc7t27t7ruwPPy8vJvLevOO++kvr4+/Ni1a1fHBC1EF1a1ajmceDJk9yRhxASrwxFRYPvWrZT/9v/B1k0QMNHinXDyFPr/+FyrQ+uUjveU7m9qampi+/bt9OjRI4J3FXmWJjV33HHHIfPgv/nYsmVLq9fs2bOH6dOnc+GFF/Lzn7c/m3e5XOGmuLY0yQkRa+rr6lBlezH9fgK6jj5spNUhiS6uaMH78OD/Q5WVQUYGOFww/Sz6//QXVofWaZlKReTRVrfeeiuLFi1i586dfPnll5x33nnYbDYuvfTSDrzL9rN0TM0tt9zCFVdcccRr+vXrF/577969TJkyhUmTJvHcc8+1ui47O5uKiopWxw4872pT0oToTJpWLcMdZ8MwNQLdupOammp1SKKLUqbJ9icfgzUrUC4bmj+ACupoZ55Fv/Mvtzo8cZDdu3dz6aWXUl1dTVZWFieffDLLli0jKyvL6tCOyNKkJisrq80VtGfPHqZMmcK4ceN44YUX0PXWjUz5+fn8+te/JhAI4HA4APj4448ZPHiwTAcUoh30onXgdGL6vbiGjrE6HNFFlezYQeCph6C2FgIBUKBye6FdeAX9TjjB6vA6veO9+N6//vWvdr2XVbrEmJo9e/YwefJk8vLyeOyxx6iqqqK8vLzVWJnLLrsMp9PJVVddxcaNG3n11Vd56qmnuPnmmy2MXIiuLRAI0Fy4k5Z9jeAzsI+U8TTi2Lj2lEJzMxgmJCZA7gCy/9+D9JeEpk2sHlPTVXSJKd0ff/wxRUVFFBUV0atXr1bn1P4+wpSUFD766COuvfZaxo0bR2ZmJnfffbesUSNEO+xetgzT40OhMNKyyOvZ0+qQRBdUtHYtvPQXqKkGrxfOvZoBF15mdVgiCnWJpOaKK674zrE3EJpL//nnn3d8QELECGP9ejBNgoEAcUOGWh2O6KLMtasJrN8ALV4AhktCc9Ss2PupK+oSSY0Qwhq+7YXoaGC3Exw2yupwRBdUtHkzgXffRXlCCY32u0ctjqhrMg2FabQzqWnn67sCSWqEEIe1Y80a9IYGTA1ITmHAmDFWhyS6ENM02fz0PNhYAOW70ewatkmnMOiHP7Q6tK7JNFGm2e4yop0kNUKIw9I3bsD0BcCm4xgkG1iKtlFKsfXF52DTZsylX0J2Dnpeb+y6hn7Bxeg2m9UhiigmSY0Q4rBq160Dmx2b3UbcqLFWhyO6ABXws/l3D6B278Le3Az9BqKlpmIG/ei/uIGBI2XhxmNlqgiMqTmKxfe6KklqhBCH2L1zJ/bafZg2HeVw0vvEiVaHJDo5j8fDtrvuwL5hPSq3F8Ftm9FGjyPjmmtxZWe32qtPHD0ZKNw2ktQIIQ618ivswQCGy4netw826TIQ36Hw8Uex7ywlmJKOvboadfs9jLjgQqvDEjFGkhohxCGaNxagu1047Xbc42XBPXFkG3//BI4tmwjYHdiNIOb0HzFSEpqIkpaatpGkRgjRSm1tLeaeCjQzSNBUJEtSI46gevt27EEfpsOGphT+KVMZ+3+/tDqsqGMa7Z+SbRoRCqYTk6RGCNFKw/LlmMEA2OyQ3YOkpCSrQxKdWMvKFdh2bAcUWnwCY6+51uqQRAyTpEYI0UrDyuXgdGIPBokbO87qcEQnV71pCyoIus1N2oyZVocTtaT7qW0kqRFChPn9fuyVezFNP0FNwyFdT+I76GV7ME0Dpdlwj5Ap2x1FmQrVzqSkva/vCiSpEUKEVRaswdbowRbnQGVm0fMbG8gKcbDytWtC42nc8ZDVjaysLKtDEjFOkhohRFjV55+j48AZdODuP8zqcEQnV/PZZ7QEDZwtHhIG9rc6nKhmmiZmO7c5aO/ruwJJaoQQYa69pfhtBh7TwDZihNXhiE5MKUXzM38GDbwjBpI4ZrzVIUU1GVPTNpLUCCEAaNhZCnurcSXHQ3ISg8bJIGHx7TZv3kwwpyeU7IGmAH0lCe5QphmBXbpjIKnRrQ5ACNE57HnrfRqrW6hY8CXxg0dbHY7o7PbuJZjdg2BONqahWR2NEIC01AghgJIVK9j7n7fQ0lNImHwytu9JV4I4snTdRgAdrV9/ksaNsTqcqKci0P0ks5+EEFFv6/IVlP3+WVRyIjabg/gxo+g1YrjVYYlOzltTR8BuQ3l9pKSmWR1O1JMxNW0jSY0QMayxpobdj/0J6htwJbgwM9PpOfNiq8MSXUBzRQW4nOBy0mTEwPr7okuQpEaIGFbxn7cwNTt60CTgdjPwtmtJz8y0OizRBaj6elAaOiakJFgdTtSTxffaRpIaIWJUeXk5xQsW4tQdtNQ1MOCxe8gdNMjqsEQX4QoECfq8aEDGgAFWhxP1pPupbWT2kxAxqvyVdzAME1/QR9rUkxlwwlirQxJdiLexAcPhwO92UedyWR2OEIC01AgRswKlO3D4DfQEF/H50THbSSlFZWUl3bt3tzqUqGc0e0K//JXB4CFDrA4n6plGBNapaefruwJJaoSIQdsLCqjdUYozwY07KYFhZ59ldUjtUrpzJ8WP/pF+005n/Z//hmtQb7qdcyYjTz3F6tCils0fxOZworkcaJqsU9PRZJuEtpGkRogYVPfBEnS7TsA0SOjdC5vNZnVIx2R7URE7//YG3o8+RSW4Wb9gMfTtgbF+K7vWFrE3978kTpnAuIsuwO12Wx1u1CgtLcVrmOiahsMdZ3U4QoRJUiNEjDEMA8/mQjSviS3eRfK006wO6agEAgH+9+qr2FZtwr1zL77GBjSbhmZ3YjY3oZdXQV4vzBY/wZI9+P62m0UffEHyD05k1MUXkZAgM3Xaq+STz6BoG3q/vriTk60OJybIQOG2kaRGiBiz8YMP8NTVY3c7SO6exeBJ+QBsWbuOTcuWMfWHPyI5N8fiKA9Vt2cvq579G8GtxTi8Hrz1zXj2lqP37gXJbvImfw/93pvJyOnJ5r/+G/+a1bj8JgHDh1lfTf3Lb/H5fxeQcNI4ci44h/6DB1t9S11SS0sLLFtJQq88fC4XaVNPtTqkmGCqCCQ1SpIaIUSUSWj040xMINDUiDY0NIV78YIFVM68ERpgWXWAM/7ftRZHGeL3+1n3wUfse/9j2FNGvCsOX3MzCXYXPk2h5eaQesJI/N8bwYhzzw2/rvt9txD0+/n8T89hfLEGu9eD4baDadD8xUq2fbWe2jEjcJ07jZFjxlh2f13Rlmefh5pavC4XpseLa+hQq0OKCdJS0zaS1AgRY8x9DRi79hKsqKYmdxsAlefdCPvHEDbc9wewOKkp3LKFqv8soHHdBpS3kQRHHC0KfC0ebH6Fr3sa2qQTGHn5heTl5R22DLvTyZQ512HeYPLJ/Pmk13ip+eh/4LChV9ZS6QtiLlnKnhNH03/WpQyUGTzfqWjRYpoWLwFdx/D56DH7MjIyMqwOS4gwSWqEiDFaVjrxWd1xp6SS2mt/N1O/blBUaWlcHo+HL//1KmrlWtTuKoJBA5fdgc8XpLm5Ed0B7vGj4MQxnHLOOW0uV9d1zvjlLwFYeco46j9cjHvzDuqratCNFswVGyksuIvtY0cw6IrL6DdwYEfdYpcWCASoeOU/2JqaMAyFfsIoBvy4a8+a60qUoVDtnJLd3td3BZLUCBFzFGZQEQyCxx8EQNvXRPjjLgUW3/Rr/LXNJPfPoceoEeT8aFqHzZBqKqvgf7//C2zbTpwRxOtrwe1wQMDA5/VCVjcyT5tAztln0qtXr3a91/jTToPTTqNg2TJq//Ym9t27CHr92JWGuXQthWs2s+2EIQy44nL6yyq5rTTt3UtqTjca9u5Bj3MzaM51VocUU5TZ/u4jFf0zuiWpESLW6DYbzTt34amqpsXrofaGWnJ+/H0q/vEuhgkqIQ7v9l04nTZqllbg21PGxn+8xkl/eZKkpKSIxNDY2EjhO++x98MlsGM3SWlpNPuaCCgNDIXfqcGIgfS78EcMmTQpIu95sDHf+x5jvvc91n31FeX/fg9j41ZwmtgVBJatp3DVb9g6dhDDrr6S3n36RPz9u6Km1WsIFO3A7XLi69ubrJ6dbzC5EJLUCBFjUgf2Qzlt2FOSUKZBcnIy1Ws2offKQk+II2VgLi1eP7qyY365muZloOtQu62IpHZspVC4bgNlX3yOZ1sVxratKE8LmDokxqN7msEMQs8cHCcMYfSlF5KdnR3Buz68URMmMGrCBNYuXUrV6wsIFu4AI4geNAmu2sjmX95O4YRRDLnycvJ69+7weDqz5soqTJcDzekgbcQwq8OJOTJQuG0kqREixgSS4/DYNey9upE8ejj79u2j29Tv0bK9lIbtO4k/PR+zf1+Sk5JpKipC21OBArbe+zB5b//rqN5LmSbr//giTdtLqd6yBWVqBAMGzng7SiniE114vD7iTjoRe/5ITpo+vWNu+juMzs+H/HzWfvkle//zHsbWYmymhkNp+L5cw/qvNrAuPpmB50yhJM5ORs9etBhBTj75ZEvitYLy+fBVVEBKCu4uulhjV6ZME9XOFYHb+/quQJIaIWJM9359ievfB6fXR2BHKY0bNuFbuZHGtZtwdctk1GWX4NrfzbTvV9cQvPk+dAOczqNbkXfLli2UP/svqtesQ9PBaXNhj3PijtNJdDupccWT+MNTGPnj6Z1mr6bRkyYxetIkVi9cyL7X3ydQUg6aiT1oEigtYufzVRhuNxVooGsscPwFpSviEhJxpCbjUybxPTLxGDaSslPw9utDemoqxLkZPny41bfXLnU7d8LGQhgzEp8u2yKIzkmSGiFiUN+xY9lw1+OYQYP+M85DZWeSkDIRV0YKZdXV9Nmf1EydcRlffrgYT1kl8fFuqqqqyMrK+s7y173xJnuf+zfeyhq0pETMqgb82U56jByM153I4CvOJyO3fYN+O9IJU6bAlCms+uQTqt/+CNe+Boz0RBxV9RhKgaZjahoYQTA1vHWNBJqbCQYNbHsr8ZkmNqcNX3AReyorSKiuhM1rrL6t9tF0SE+Dffvw9ehhdTQxR2Y/tY0kNULEIHt2GpnTT0EBFUU70V3xGM1evJWNuFyuVtc6U9MIeHwE0GgqLj5iUqOU4qvHnqHuo4/RnW7ic7rh8beQe/NMJvx0ZgffVeSNmzoVpk5lz549lL76LimmQcm2nSTEu2gqr8Zp+vG3+FCmIhBQ2JSG3zBRCppaAtg2bcDu8WA6Nbb8fh5Dbrre6ls6ZomajYYBA8HTQGJ6utXhxBxlKlS7Zz9JUiOEiEINCToN9XUkpKVjN8HnD2BzONB0Gz0O+hVeXVlJzdJlkJKGOzMD/Qhl7ikpYcM9j6KVlYHLBUEDEhIY8ZtrGTBxYsffVAfq2bMnPW/+PwAON0R2d0kJ24uLSTChbuUGUgxF7e4yzC9WEHSC7nJTvXr98Q06whQaLqcD055GrxyZ+SQ6J0lqhIhBbnccusOG1+tFCxj4mluwAbakr8fNvP/OO+jP/wOK96KnNxIc2I+Efv0OW17Z0q/Y86cXUfsqUA4ndtPAHD6I/Htvj9g08M6sV+/e9DowO+r7kwFYOec+ykePwiwqRj/nHCY9cJt1AUZAoLGRoKajOWykZWZaHU7MUWYEup+kpUYIEY3M8iq8Ph/mZ6vY+tGX6DowZgApI4fgq6qh+N2P4dF5mHurQtfva6R5SxGZ3/gyCwaD/O/+J/GtXI1WWEzSmCE0G15yLriAYZdfZMGddR7eXXtJSkigMSmOMx69y+pw2s1ctwGaPTg78VioaCbdT20jSY0QMWjHuiKCX2wIPdk/y1MvKMI9fSrrfv8X6rYVQ1YGxDugaG/osvGt90Yq272bdb/7Pe7yavxeLwzti8PpYsBt1zPghBOO5+10SgFdw3Q4iR/WtWc9HaAPGgRGAJKTrQ4lNhlm6NHeMqKcJDVCxCC1aTumCTqgaWCagA7lq5dia9LQHQp9/RYcV17I2JdnUFtUQs5p+eHX7/hyOYVPPouxrxZDKWxx8Rh9ejLmkftwx8dbdl+dieaKA9ODGfBZHUq7FRUV4dMUut2B9o2B5EJ0JpLUCBGDpv7tcd7peUpoPxnjoBML1uI8fQIBTHre8yvyb7wK0zTJHjY0fMnHdz+Gb/lybKYKLTXsdtP94h8xZualx/9GOjGbDsrTgu5wUFpa+q27iXcFPp8PTSk0NGzIGjVWkO6ntpGkRogYZNftmBMHw1eF4A8dM4OhlpuWr1ZhGzmKUT+7BAjtcg1QXl7O2t8+jq20HIJBlMtJYlYG3f/vcoZ2wP5MXZ0jLYGW+jqavlhB9ZSLKDzrBzjcDppafKDruDOTcdscBLwenImJtDR5SEyJp8EWR2qqixa7jfSevahoasTtduN2uWg2gsQ7nTQ1N+NwufBXVzNg/PjDJkxb332PgWf9kIplq8jOH9/q3MJnngGPh8k334ymfXeSkuLx4AkGQdNwpaZErI5E28k6NW0jSY0QMciVmc6UB3/FwguvhSovANrQnti27kE1mKgvCih7+1OqS3bSa+ZPcHm8bPrN47RUlqEDCYkJtPTuyZgH7iA1Lc3am+mk0kYOw9RtNKzbBmnJ+HbsxOfzQzAI2zbjP2EMut2OhonfZgefgdftwPQG8Np1mgNBnC4nmCZBp51G08Q0FI0oNKeGp74Ze3EJRWNHsbWlBVO3YRomhsOOtq0E6qrZOnc+Zm01ekoS7qHDSHI5qa2qwPXFMpRS/O/dT1E/PJPup32P4SdO+NZ7qSsuRjkdoCB4pHn9QlhMkhohYlTqieNg2mnw9w/Rr/8J/OM1gsFQj5JSsO7ZV1Bxdra/9F9sTY3EDR4Auh1TUySeNYXTr7nK6lvo1AZdeQm16zfSvLWYxkAQEw3dMCAuHtwONCOIsuloug66jqmHunfQwNQ0dN2GpmugQCkTzCAKPdT5EzTR4xKgezJ89jkkumHocLBrEAhCnzzYrmDjVnSHjjMnF3/lPnx2GyxeCq79rTNbdkD8YirXrGVf0ssMmHkZPSZNDLfOHaACAUChdA3ldh7vqhRI91NbdZmk5sEHH+S9996joKAAp9NJXV3dIdeUlpbyi1/8goULF5KYmMjs2bOZO3cudnuXuU0hjpu4uDh+8pdHKfvVz8nK7M5b814DwDBCiY137Tr0ESPQ3XHYAiYEDJxJCeTechUjp0y2NPauQNN10kePJPnua0nwtLCvtoY+ians3LQB+vXEMSCPxIwUPNtLcCQkYDR4iE9xE/CYOJXCDARx2oFmD9g1dGXDbGrGadMINDRC0MTR50QCzgTo1QtqGwANzaGD0ul37WyKd5aQ406iYfUGmjUVSohOHo/6ahUAasxg8HhCyValwY6nnmbH/xYx6c7bsB20aWWPvv3Y7fiSltpa8PutqE5hmvtH9LezjKP0xz/+kUcffZTy8nJGjx7NvHnzOPHEE9sXRwfqMt/2fr+fCy+8kPz8fP76178ect4wDM466yyys7P58ssvKSsrY9asWTgcDn73u99ZELEQnZ/L5aLPyJFAKJE5+DMv9ak7Mf63Aq0ygbzzT6dhZQH97pxDTm6uRdF2TRO/sZryiDO+f9zeewhQWFiI88Lp+MvLycjIpKWyghMGzyW1dx6fvfQSbCyC+npYvwFsGuzaw+bnX2HEz2eFy2msr8P0enHGx+OwSUtNrHj11Ve5+eabmT9/PhMnTuTJJ59k2rRpFBYW0q1bN6vDOyxNKdWl2qNefPFF5syZc0hLzQcffMCPfvQj9u7dG97xd/78+dx+++1UVVXhdB7+H6LP58Pn+3rKZUNDA7m5udTX15Ms6zGIGPLJggVUnnUjzjtnc8qsC+g+aBAAu3fvpltGBnaX65BuCREdlFIsvvEW9CXLoU9v4rtlMfIPj4Y/NwuWLKHlrnux9e1D+ohRDLj5Bosj7hwaGhpISUnp0O+LA++Rf9FfsTvat1xCMOBh6b+vanO8EydOZMKECTz99NMAmKZJbm4u119/PXfccUe7YukoUfMJtXTpUkaOHBlOaACmTZtGQ0MDGzdu/NbXzZ07l5SUlPAjV36Fihg1dfp0LjMKOe++28MJDUCvXr1wxsVJQhPFNE3jtD88QdzE72HabDTW1LHyjTfC533LVhJMzsCsbSSpfx/rAo1hB8bUtPcBoUTp4MfBP+wP8Pv9rFq1iqlTp4aP6brO1KlTWbp06XG776MVNZ9S5eXlrRIaIPy8vLz8W1935513Ul9fH37s2rWrQ+MUorM7eCyFiC2OsSPBFYdq8eJbsBCA8rIyPKvWErTbMDQbvl6yTUJXl5ub2+rH/Ny5cw+5Zt++fRiGcdjv1SN9p1rN0jE1d9xxBw8//PARr9m8eTNDhgw54jXt4XK5cMkKmUIIwYDLLmDd8hV4/QaqvIpP/t/d5HXLIdFnEHS68GdnkTdOtsCwgjIUSo/MOjW7du1q1f0UTd+BliY1t9xyC1dcccURr+n3LbsCf1N2djYrVqxodayioiJ8TgghxJElJCbSMrAv5rZisDsw//IqO01wnDgaNhQz/LUXrA4xZkVySndycvJ3jqnJzMzEZrOFv0cPqKio6NTfqZYmNVlZWWRlZUWkrPz8fB588EEqKyvDo7I//vhjkpOTGTZsWETeQwghot3E63/J2joPTes2QHI8GCYqPQ3btFy6jRxhdXixy1Cgt3dDy7YnRU6nk3HjxvHpp59y7rnnAqGBwp9++inXXXdd++LoQF1mSndpaSk1NTWUlpZiGAYFBQUADBgwgMTERM444wyGDRvGzJkzeeSRRygvL+c3v/kN1157bVQ1rQkhREdKSEigz7VXsn3es6hhg3F6fbgH9GfArIusDk0cZzfffDOzZ89m/PjxnHjiiTz55JM0Nzfz05/+1OrQvlWXSWruvvtuXnrppfDzsWPHArBw4UImT56MzWbj3Xff5Re/+AX5+fkkJCQwe/Zs7r//fqtCFkKILiknN5ceDz/AuiVfkNsrD1ISSUxPtzqsmGbFisIXX3wxVVVV3H333ZSXlzNmzBgWLFhwyODhzqTLrVPT0Y7HugNCCCG6vuO5Ts34ac9gd8S1q6xgoIWVH/4iqr/fomZKtxBCCCFiW5fpfhJCCCFilWxo2TaS1AghhBCdnanC68y0p4xoJ91PQgghhIgK0lIjhBBCdHamGXq0t4woJ0mNEEII0ckpQ6G0yGyTEM2k+0kIIYQQUUFaaoQQQohOTmY/tY0kNUIIIUQnJ91PbSNJjRBCCNHZyUDhNpExNUIIIYSICtJSI4QQQnRy0v3UNpLUCCGEEJ2cUhEYKBwD+1dLUvMNB/6jNzQ0WByJEEKIzuzA98TxSBYMw9spyujsJKn5hsbGRgByc3MtjkQIIURX0NjYSEpKSoeU7XQ6yc7OZmPBbyJSXnZ2Nk6nMyJldUaaioX2qKNgmiZ79+4lKSkJTdMsjaWhoYHc3Fx27dpFcnKypbF0FlInh5I6aU3q41BSJ4eKRJ0opWhsbCQnJwdd77h5N16vF7/fH5GynE4nbrc7ImV1RtJS8w26rtOrVy+rw2glOTlZPoi+QerkUFInrUl9HErq5FDtrZOOaqE5mNvtjupEJJJkSrcQQgghooIkNUIIIYSICpLUdGIul4t77rkHl8tldSidhtTJoaROWpP6OJTUyaGkTqKTDBQWQgghRFSQlhohhBBCRAVJaoQQQggRFSSpEUIIIURUkKRGCCGEEFFBkppOYM+ePVx++eVkZGQQFxfHyJEjWblyZfi8Uoq7776bHj16EBcXx9SpU9m2bZuFEXcswzC466676Nu3L3FxcfTv358HHnig1f4q0V4nixcv5sc//jE5OTlomsabb77Z6nxb7r+mpoYZM2aQnJxMamoqV111FU1NTcfxLiLrSHUSCAS4/fbbGTlyJAkJCeTk5DBr1iz27t3bqoxYqpNvuuaaa9A0jSeffLLV8Wiqk7bUx+bNmzn77LNJSUkhISGBCRMmUFpaGj7v9Xq59tprycjIIDExkZ/85CdUVFQcx7sQ7SFJjcVqa2s56aSTcDgcfPDBB2zatInHH3+ctLS08DWPPPIIf/jDH5g/fz7Lly8nISGBadOm4fVG5+ZkDz/8MM888wxPP/00mzdv5uGHH+aRRx5h3rx54WuivU6am5sZPXo0f/zjHw97vi33P2PGDDZu3MjHH3/Mu+++y+LFi7n66quP1y1E3JHqxOPxsHr1au666y5Wr17N66+/TmFhIWeffXar62KpTg72xhtvsGzZMnJycg45F0118l31sX37dk4++WSGDBnC//73P9atW8ddd93VarXem266iXfeeYf//Oc/LFq0iL1793L++ecfr1sQ7aWEpW6//XZ18sknf+t50zRVdna2evTRR8PH6urqlMvlUv/85z+PR4jH3VlnnaWuvPLKVsfOP/98NWPGDKVU7NUJoN54443w87bc/6ZNmxSgvvrqq/A1H3zwgdI0Te3Zs+e4xd5Rvlknh7NixQoFqJKSEqVU7NbJ7t27Vc+ePdWGDRtU79691e9///vwuWiuk8PVx8UXX6wuv/zyb31NXV2dcjgc6j//+U/42ObNmxWgli5d2lGhigiSlhqLvf3224wfP54LL7yQbt26MXbsWP785z+HzxcXF1NeXs7UqVPDx1JSUpg4cSJLly61IuQON2nSJD799FO2bt0KwNq1a1myZAlnnnkmEJt1crC23P/SpUtJTU1l/Pjx4WumTp2KrussX778uMdshfr6ejRNIzU1FYjNOjFNk5kzZ3LbbbcxfPjwQ87HUp2Ypsl7773HoEGDmDZtGt26dWPixImtuqhWrVpFIBBo9W9ryJAh5OXlxcRnSzSQpMZiO3bs4JlnnmHgwIF8+OGH/OIXv+CGG27gpZdeAqC8vByA7t27t3pd9+7dw+eizR133MEll1zCkCFDcDgcjB07ljlz5jBjxgwgNuvkYG25//Lycrp169bqvN1uJz09PSbqyOv1cvvtt3PppZeGNyuMxTp5+OGHsdvt3HDDDYc9H0t1UllZSVNTEw899BDTp0/no48+4rzzzuP8889n0aJFQKg+nE5nOBE+IFY+W6KB7NJtMdM0GT9+PL/73e8AGDt2LBs2bGD+/PnMnj3b4uis8e9//5tXXnmFf/zjHwwfPpyCggLmzJlDTk5OzNaJaLtAIMBFF12EUopnnnnG6nAss2rVKp566ilWr16NpmlWh2M50zQBOOecc7jpppsAGDNmDF9++SXz58/ntNNOszI8ESHSUmOxHj16MGzYsFbHhg4dGh6Nn52dDXDI6PuKiorwuWhz2223hVtrRo4cycyZM7npppuYO3cuEJt1crC23H92djaVlZWtzgeDQWpqaqK6jg4kNCUlJXz88cfhVhqIvTr5/PPPqaysJC8vD7vdjt1up6SkhFtuuYU+ffoAsVUnmZmZ2O327/y89fv91NXVtbomVj5booEkNRY76aSTKCwsbHVs69at9O7dG4C+ffuSnZ3Np59+Gj7f0NDA8uXLyc/PP66xHi8ejwddb/2/ps1mC//SisU6OVhb7j8/P5+6ujpWrVoVvuazzz7DNE0mTpx43GM+Hg4kNNu2beOTTz4hIyOj1flYq5OZM2eybt06CgoKwo+cnBxuu+02PvzwQyC26sTpdDJhwoQjft6OGzcOh8PR6t9WYWEhpaWlMfHZEhWsHqkc61asWKHsdrt68MEH1bZt29Qrr7yi4uPj1d///vfwNQ899JBKTU1Vb731llq3bp0655xzVN++fVVLS4uFkXec2bNnq549e6p3331XFRcXq9dff11lZmaqX/3qV+Fror1OGhsb1Zo1a9SaNWsUoJ544gm1Zs2a8Eyettz/9OnT1dixY9Xy5cvVkiVL1MCBA9Wll15q1S2125HqxO/3q7PPPlv16tVLFRQUqLKysvDD5/OFy4ilOjmcb85+Uiq66uS76uP1119XDodDPffcc2rbtm1q3rx5ymazqc8//zxcxjXXXKPy8vLUZ599plauXKny8/NVfn6+VbckjpIkNZ3AO++8o0aMGKFcLpcaMmSIeu6551qdN01T3XXXXap79+7K5XKp008/XRUWFloUbcdraGhQN954o8rLy1Nut1v169dP/frXv2715RTtdbJw4UIFHPKYPXu2Uqpt919dXa0uvfRSlZiYqJKTk9VPf/pT1djYaMHdRMaR6qS4uPiw5wC1cOHCcBmxVCeHc7ikJprqpC318de//lUNGDBAud1uNXr0aPXmm2+2KqOlpUX98pe/VGlpaSo+Pl6dd955qqys7DjfiThWmlIHLdMqhBBCCNFFyZgaIYQQQkQFSWqEEEIIERUkqRFCCCFEVJCkRgghhBBRQZIaIYQQQkQFSWqEEEIIERUkqRFCCCFEVJCkRgghhBBRQZIaIbqIyZMnM2fOnKh5zyuuuIJzzz23Q8oWQsQmu9UBCCE6r9dffx2HwxF+3qdPH+bMmXPckyshhGgLSWqEEN8qPT3d6hCEEKLNpPtJiC6otraWWbNmkZaWRnx8PGeeeSbbtm0Ln3/xxRdJTU3lww8/ZOjQoSQmJjJ9+nTKysrC1wSDQW644QZSU1PJyMjg9ttvZ/bs2a26hA7ufpo8eTIlJSXcdNNNaJqGpmkA3HvvvYwZM6ZVfE8++SR9+vQJPzcMg5tvvjn8Xr/61a/45rZzpmkyd+5c+vbtS1xcHKNHj+a///1vZCpMCBETJKkRogu64oorWLlyJW+//TZLly5FKcUPf/hDAoFA+BqPx8Njjz3G3/72NxYvXkxpaSm33npr+PzDDz/MK6+8wgsvvMAXX3xBQ0MDb7755re+5+uvv06vXr24//77KSsra5UgfZfHH3+cF198keeff54lS5ZQU1PDG2+80eqauXPn8vLLLzN//nw2btzITTfdxOWXX86iRYvaXjFCiJgm3U9CdDHbtm3j7bff5osvvmDSpEkAvPLKK+Tm5vLmm29y4YUXAhAIBJg/fz79+/cH4LrrruP+++8PlzNv3jzuvPNOzjvvPACefvpp3n///W993/T0dGw2G0lJSWRnZx9VzE8++SR33nkn559/PgDz58/nww8/DJ/3+Xz87ne/45NPPiE/Px+Afv36sWTJEp599llOO+20o3o/IURskqRGiC5m8+bN2O12Jk6cGD6WkZHB4MGD2bx5c/hYfHx8OKEB6NGjB5WVlQDU19dTUVHBiSeeGD5vs9kYN24cpmlGNN76+nrKyspaxWu32xk/fny4C6qoqAiPx8MPfvCDVq/1+/2MHTs2ovEIIaKXJDVCRKmDZy0BaJp2yDiWSNB1/ZByD+4Ga4umpiYA3nvvPXr27NnqnMvlal+AQoiYIWNqhOhihg4dSjAYZPny5eFj1dXVFBYWMmzYsDaVkZKSQvfu3fnqq6/CxwzDYPXq1Ud8ndPpxDCMVseysrIoLy9vldgUFBS0eq8ePXq0ijcYDLJq1arw82HDhuFyuSgtLWXAgAGtHrm5uW26JyGEkJYaIbqYgQMHcs455/Dzn/+cZ599lqSkJO644w569uzJOeec0+Zyrr/+eubOncuAAQMYMmQI8+bNo7a2Njyr6XD69OnD4sWLueSSS3C5XGRmZjJ58mSqqqp45JFHuOCCC1iwYAEffPABycnJ4dfdeOONPPTQQwwcOJAhQ4bwxBNPUFdXFz6flJTErbfeyk033YRpmpx88snU19fzxRdfkJyczOzZs4+proQQsUVaaoTogl544QXGjRvHj370I/Lz81FK8f777x/S5XQkt99+O5deeimzZs0iPz+fxMREpk2bhtvt/tbX3H///ezcuZP+/fuTlZUFhFqO/vSnP/HHP/6R0aNHs2LFilazrABuueUWZs6cyezZs8nPzycpKSk8QPmABx54gLvuuou5c+cydOhQpk+fznvvvUffvn2PomaEELFMUx3RyS6E6HJM02To0KFcdNFFPPDAA1aHI4QQR026n4SIUSUlJXz00Uecdtpp+Hw+nn76aYqLi7nsssusDk0IIY6JdD8JEaN0XefFF19kwoQJnHTSSaxfv55PPvmEoUOHWh2aEEIcE+l+EkIIIURUkJYaIYQQQkQFSWqEEEIIERUkqRFCCCFEVJCkRgghhBBRQZIaIYQQQkQFSWqEEEIIERUkqRFCCCFEVJCkRgghhBBR4f8DCLq8+eUe8YEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sorted = df.sort_values('TIME')\n",
    "\n",
    "# Create a list of segments\n",
    "points = np.array([df_sorted['LONGITUDE'], df_sorted['LATITUDE']]).T.reshape(-1, 1, 2)\n",
    "segments = np.concatenate([points[:-1], points[1:]], axis=1)\n",
    "\n",
    "# Create a LineCollection with segments colored by temperature\n",
    "norm = plt.Normalize(df_sorted['TEMP'].min(), df_sorted['TEMP'].max())\n",
    "lc = LineCollection(segments, cmap='RdYlBu_r', norm=norm)\n",
    "lc.set_array(df_sorted['TEMP'])\n",
    "lc.set_linewidth(2)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.add_collection(lc)\n",
    "ax.autoscale()\n",
    "ax.set_xlabel(metadata['LONGITUDE']['standard_name'])\n",
    "ax.set_ylabel(metadata['LATITUDE']['standard_name'])\n",
    "ax.invert_yaxis()\n",
    "\n",
    "# Adding color bar\n",
    "cbar = plt.colorbar(lc, ax=ax)\n",
    "cbar.set_label('Temperature')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}