1_add_env_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add environmental data\n",
    "This notebook is the code to add environmental data to the output of pypam\n",
    "For more information about this process please contact clea.parcerisas@vliz.be or check the documentation of both packages\n",
    "https://lifewatch-pypam.readthedocs.io/en/latest/\n",
    "https://github.com/lifewatch/bpnsdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cleap\\Envs\\wadden_soundscape\\lib\\site-packages\\geopandas\\_compat.py:124: UserWarning: The Shapely GEOS version (3.10.2-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.2\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "import os\n",
    "\n",
    "import geopandas\n",
    "import pandas as pd\n",
    "import xarray\n",
    "from tqdm import tqdm\n",
    "\n",
    "import bpnsdata\n",
    "\n",
    "import erddapy\n",
    "print(erddapy.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data_path = pathlib.Path('./data/raw_data')\n",
    "processed_data_path = pathlib.Path('./data/processed')\n",
    "metadata_path = raw_data_path.joinpath('data_summary_mda.csv')\n",
    "metadata = pd.read_csv(metadata_path, index_col=False)\n",
    "\n",
    "if not processed_data_path.exists():\n",
    "    os.mkdir(processed_data_path)\n",
    "\n",
    "env_vars = [\n",
    "\t\t\"shipping\",\n",
    "\t\t\"time\",\n",
    "\t\t\"bathymetry\"\n",
    "    ]\n",
    "\n",
    "\n",
    "# Change this to the columns you add in the csv!\n",
    "# YOU NEED TO KEEP deployment_name\n",
    "env_vars_from_csv = ['sediment_type', 'tide', 'deployment_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "     deployment_name  campaign instrument_name  instrument_sensitivity   \n0          NLOyster1         1       SoundTrap                  -170.0  \\\n1      NLOyster2High         1       SoundTrap                  -170.0   \n2       NLOyster2Low         1       SoundTrap                  -170.0   \n3            NLSand1         2       SoundTrap                  -170.0   \n4            NLSand2         2       SoundTrap                  -160.0   \n5      MusselbedHigh         3          uaural                  -164.0   \n6       MusselbedLow         3          uaural                  -164.0   \n7            MudHigh         4          uaural                  -164.0   \n8             MudLow         4          uaural                  -164.0   \n9        GeoReefHigh         5          uaural                  -164.0   \n10        GeoReefLow         5          uaural                  -164.0   \n11  MusselShellsHigh         6          uaural                  -164.0   \n12   MusselShellsLow         6          uaural                  -164.0   \n13    CoarseSandHigh         7          uaural                  -164.0   \n14     CoarseSandLow         7          uaural                  -164.0   \n\n    instrument_amp  instrument_Vpp         deployment_path timezone   \n0                0               2          0_NLOyster1.nc      UTC  \\\n1                0               2      1_NLOyster2High.nc      UTC   \n2                0               2       2_NLOyster2Low.nc      UTC   \n3                0               2            3_NLSand1.nc      UTC   \n4                0               2            4_NLSand2.nc      UTC   \n5               12               2      5_MusselbedHigh.nc      UTC   \n6               12               2       6_MusselbedLow.nc      UTC   \n7               12               2            7_MudHigh.nc      UTC   \n8               12               2             8_MudLow.nc      UTC   \n9               12               2        9_GeoReefHigh.nc      UTC   \n10              12               2        10_GeoReefLow.nc      UTC   \n11              12               2  11_MusselShellsHigh.nc      UTC   \n12              12               2   12_MusselShellsLow.nc      UTC   \n13              12               2    13_CoarseSandHigh.nc      UTC   \n14              12               2     14_CoarseSandLow.nc      UTC   \n\n    include_dirs  calibration  latitude  longitude  end_to_end_calibration   \n0              0           -1  53.06843    4.88076                     NaN  \\\n1              0           -1  53.06843    4.88076                     NaN   \n2              0           -1  53.06843    4.88076                     NaN   \n3              0           -1  53.06843    5.20231                     NaN   \n4              0           -1  53.06843    5.20231                     NaN   \n5              0           -1  53.06843    5.20231                     NaN   \n6              0           -1  53.06843    5.20231                     NaN   \n7              0           -1  53.06843    5.20231                     NaN   \n8              0           -1  53.06843    5.20231                     NaN   \n9              0           -1  53.06843    5.20231                     NaN   \n10             0           -1  53.06843    5.20231                     NaN   \n11             0           -1  53.06843    5.20231                     NaN   \n12             0           -1  53.06843    5.20231                     NaN   \n13             0           -1  53.06843    5.20231                     NaN   \n14             0           -1  53.06843    5.20231                     NaN   \n\n   sediment_type  tide new_var  \n0           reef   low       a  \n1           reef  high       a  \n2           reef   low       a  \n3           sand   low       a  \n4           sand   low       a  \n5         mussel  high       a  \n6         mussel   low       a  \n7            mud  high       a  \n8            mud   low       a  \n9        georeef  high       a  \n10       georeef   low       a  \n11        shells  high       a  \n12        shells   low       a  \n13    coarsesand  high       a  \n14    coarsesand   low       a  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>deployment_name</th>\n      <th>campaign</th>\n      <th>instrument_name</th>\n      <th>instrument_sensitivity</th>\n      <th>instrument_amp</th>\n      <th>instrument_Vpp</th>\n      <th>deployment_path</th>\n      <th>timezone</th>\n      <th>include_dirs</th>\n      <th>calibration</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>end_to_end_calibration</th>\n      <th>sediment_type</th>\n      <th>tide</th>\n      <th>new_var</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NLOyster1</td>\n      <td>1</td>\n      <td>SoundTrap</td>\n      <td>-170.0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0_NLOyster1.nc</td>\n      <td>UTC</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>53.06843</td>\n      <td>4.88076</td>\n      <td>NaN</td>\n      <td>reef</td>\n      <td>low</td>\n      <td>a</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NLOyster2High</td>\n      <td>1</td>\n      <td>SoundTrap</td>\n      <td>-170.0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1_NLOyster2High.nc</td>\n      <td>UTC</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>53.06843</td>\n      <td>4.88076</td>\n      <td>NaN</td>\n      <td>reef</td>\n      <td>high</td>\n      <td>a</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NLOyster2Low</td>\n      <td>1</td>\n      <td>SoundTrap</td>\n      <td>-170.0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2_NLOyster2Low.nc</td>\n      <td>UTC</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>53.06843</td>\n      <td>4.88076</td>\n      <td>NaN</td>\n      <td>reef</td>\n      <td>low</td>\n      <td>a</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NLSand1</td>\n      <td>2</td>\n      <td>SoundTrap</td>\n      <td>-170.0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>3_NLSand1.nc</td>\n      <td>UTC</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>NaN</td>\n      <td>sand</td>\n      <td>low</td>\n      <td>a</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NLSand2</td>\n      <td>2</td>\n      <td>SoundTrap</td>\n      <td>-160.0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>4_NLSand2.nc</td>\n      <td>UTC</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>NaN</td>\n      <td>sand</td>\n      <td>low</td>\n      <td>a</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>MusselbedHigh</td>\n      <td>3</td>\n      <td>uaural</td>\n      <td>-164.0</td>\n      <td>12</td>\n      <td>2</td>\n      <td>5_MusselbedHigh.nc</td>\n      <td>UTC</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>NaN</td>\n      <td>mussel</td>\n      <td>high</td>\n      <td>a</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>MusselbedLow</td>\n      <td>3</td>\n      <td>uaural</td>\n      <td>-164.0</td>\n      <td>12</td>\n      <td>2</td>\n      <td>6_MusselbedLow.nc</td>\n      <td>UTC</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>NaN</td>\n      <td>mussel</td>\n      <td>low</td>\n      <td>a</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>MudHigh</td>\n      <td>4</td>\n      <td>uaural</td>\n      <td>-164.0</td>\n      <td>12</td>\n      <td>2</td>\n      <td>7_MudHigh.nc</td>\n      <td>UTC</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>NaN</td>\n      <td>mud</td>\n      <td>high</td>\n      <td>a</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>MudLow</td>\n      <td>4</td>\n      <td>uaural</td>\n      <td>-164.0</td>\n      <td>12</td>\n      <td>2</td>\n      <td>8_MudLow.nc</td>\n      <td>UTC</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>NaN</td>\n      <td>mud</td>\n      <td>low</td>\n      <td>a</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>GeoReefHigh</td>\n      <td>5</td>\n      <td>uaural</td>\n      <td>-164.0</td>\n      <td>12</td>\n      <td>2</td>\n      <td>9_GeoReefHigh.nc</td>\n      <td>UTC</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>NaN</td>\n      <td>georeef</td>\n      <td>high</td>\n      <td>a</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>GeoReefLow</td>\n      <td>5</td>\n      <td>uaural</td>\n      <td>-164.0</td>\n      <td>12</td>\n      <td>2</td>\n      <td>10_GeoReefLow.nc</td>\n      <td>UTC</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>NaN</td>\n      <td>georeef</td>\n      <td>low</td>\n      <td>a</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>MusselShellsHigh</td>\n      <td>6</td>\n      <td>uaural</td>\n      <td>-164.0</td>\n      <td>12</td>\n      <td>2</td>\n      <td>11_MusselShellsHigh.nc</td>\n      <td>UTC</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>NaN</td>\n      <td>shells</td>\n      <td>high</td>\n      <td>a</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>MusselShellsLow</td>\n      <td>6</td>\n      <td>uaural</td>\n      <td>-164.0</td>\n      <td>12</td>\n      <td>2</td>\n      <td>12_MusselShellsLow.nc</td>\n      <td>UTC</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>NaN</td>\n      <td>shells</td>\n      <td>low</td>\n      <td>a</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>CoarseSandHigh</td>\n      <td>7</td>\n      <td>uaural</td>\n      <td>-164.0</td>\n      <td>12</td>\n      <td>2</td>\n      <td>13_CoarseSandHigh.nc</td>\n      <td>UTC</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>NaN</td>\n      <td>coarsesand</td>\n      <td>high</td>\n      <td>a</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>CoarseSandLow</td>\n      <td>7</td>\n      <td>uaural</td>\n      <td>-164.0</td>\n      <td>12</td>\n      <td>2</td>\n      <td>14_CoarseSandLow.nc</td>\n      <td>UTC</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>NaN</td>\n      <td>coarsesand</td>\n      <td>low</td>\n      <td>a</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
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      "  0%|          | 0/15 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data\\processed\\0_NLOyster1_env.nc\n",
      "NLOyster1 607 607\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
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      "data\\processed\\1_NLOyster2High_env.nc\n",
      "NLOyster2High 181 180\n"
     ]
    },
    {
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    },
    {
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      "data\\processed\\2_NLOyster2Low_env.nc\n",
      "NLOyster2Low 424 424\n"
     ]
    },
    {
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      " 20%|██        | 3/15 [00:23<01:30,  7.50s/it]"
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    },
    {
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      "data\\processed\\3_NLSand1_env.nc\n",
      "NLSand1 605 605\n"
     ]
    },
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    },
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      "data\\processed\\4_NLSand2_env.nc\n",
      "NLSand2 603 603\n"
     ]
    },
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     ]
    },
    {
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      "data\\processed\\5_MusselbedHigh_env.nc\n",
      "MusselbedHigh 366 366\n"
     ]
    },
    {
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      " 40%|████      | 6/15 [00:51<01:16,  8.51s/it]"
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    },
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      "data\\processed\\6_MusselbedLow_env.nc\n",
      "MusselbedLow 366 366\n"
     ]
    },
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      " 47%|████▋     | 7/15 [00:57<01:02,  7.76s/it]"
     ]
    },
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      "data\\processed\\7_MudHigh_env.nc\n",
      "MudHigh 427 427\n"
     ]
    },
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      "data\\processed\\8_MudLow_env.nc\n",
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      " 60%|██████    | 9/15 [01:12<00:44,  7.39s/it]"
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    },
    {
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      "data\\processed\\9_GeoReefHigh_env.nc\n",
      "GeoReefHigh 549 549\n"
     ]
    },
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      "data\\processed\\10_GeoReefLow_env.nc\n",
      "GeoReefLow 427 427\n"
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    },
    {
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      "data\\processed\\11_MusselShellsHigh_env.nc\n",
      "MusselShellsHigh 549 549\n"
     ]
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    {
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      "data\\processed\\12_MusselShellsLow_env.nc\n",
      "MusselShellsLow 549 549\n"
     ]
    },
    {
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      " 87%|████████▋ | 13/15 [01:48<00:17,  8.83s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data\\processed\\13_CoarseSandHigh_env.nc\n",
      "CoarseSandHigh 549 549\n"
     ]
    },
    {
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      " 93%|█████████▎| 14/15 [01:58<00:09,  9.12s/it]"
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    },
    {
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     "output_type": "stream",
     "text": [
      "data\\processed\\14_CoarseSandLow_env.nc\n",
      "CoarseSandLow 549 549\n"
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    },
    {
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    }
   ],
   "source": [
    "# Define the seadatamanager\n",
    "manager = bpnsdata.SeaDataManager(env_vars)\n",
    "id_name = 'id'\n",
    "datetime_name = 'datetime'\n",
    "for i, row in tqdm(metadata.iterrows(), total=len(metadata)):\n",
    "    deployment_path = raw_data_path.joinpath('deployments/deployments', row['deployment_path'])\n",
    "    env_path = processed_data_path.joinpath(row['deployment_path'].replace('.nc', '_env.nc'))\n",
    "    print(env_path)\n",
    "\n",
    "    # Read the dataset\n",
    "    ds_deployment = xarray.open_dataset(deployment_path, engine='netcdf4')\n",
    "    # Get the time information from the dataset to get a pandas df\n",
    "    datetime_index = ds_deployment[datetime_name].to_index()\n",
    "    df = pd.DataFrame({\"datetime\": datetime_index.values, 'id': ds_deployment[id_name]})\n",
    "    df = df.drop_duplicates(\"datetime\")\n",
    "    print(metadata.iloc[i]['deployment_name'], len(datetime_index), len(df))\n",
    "    df.datetime = df.datetime.dt.tz_localize('UTC')\n",
    "\n",
    "    df['latitude'] = row['latitude']\n",
    "    df['longitude'] = row['longitude']\n",
    "\n",
    "    # Generate the location information\n",
    "    geodf = geopandas.GeoDataFrame(df, geometry=geopandas.points_from_xy(x=df['longitude'], y=df['latitude']), crs='EPSG:4326')\n",
    "    geodf_env = manager(geodf, datetime_column='datetime')\n",
    "\n",
    "    for env_var in env_vars_from_csv:\n",
    "        geodf_env[env_var] = row[env_var]\n",
    "\n",
    "    # Remove the UTC (xarray does not support it?)\n",
    "    dt = geodf_env.datetime.dt.tz_localize(None)\n",
    "    lat = geodf_env['geometry'].y\n",
    "    lon = geodf_env['geometry'].x\n",
    "    df_env = geodf_env.drop(columns=['geometry'])\n",
    "    env_ds = df_env.to_xarray()\n",
    "    env_ds = env_ds.assign_coords(coords={'lat': (id_name, lat), 'lon': (id_name, lon), 'datetime': (id_name, dt), id_name : ('index', df.id.values)})\n",
    "    env_ds = env_ds.swap_dims({'index': id_name})\n",
    "\n",
    "    new_ds = ds_deployment.merge(env_ds)\n",
    "    new_ds['season'] = new_ds[datetime_name].dt.isocalendar().week\n",
    "\n",
    "    encoding = {'file_path': {'dtype': 'unicode'},\n",
    "                'start_sample': {'dtype': int, '_FillValue': -1},\n",
    "                'end_sample': {'dtype': int, '_FillValue': -1},\n",
    "                'datetime': {'dtype': float, '_FillValue': -1},\n",
    "                'deployment_name': {'dtype': str,}}\n",
    "    new_ds.to_netcdf(env_path,  encoding=encoding)\n",
    "    # print(new_ds)\n",
    "    ds_deployment.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "                     datetime   id  latitude  longitude   \n0   2022-06-16 11:04:36+00:00    0  53.06843    5.20231  \\\n1   2022-06-16 11:04:46+00:00    1  53.06843    5.20231   \n2   2022-06-16 11:04:56+00:00    2  53.06843    5.20231   \n3   2022-06-16 11:05:06+00:00    3  53.06843    5.20231   \n4   2022-06-16 11:05:16+00:00    4  53.06843    5.20231   \n..                        ...  ...       ...        ...   \n544 2022-06-15 22:40:19+00:00  544  53.06843    5.20231   \n545 2022-06-15 22:40:29+00:00  545  53.06843    5.20231   \n546 2022-06-15 22:40:39+00:00  546  53.06843    5.20231   \n547 2022-06-15 22:40:49+00:00  547  53.06843    5.20231   \n548 2022-06-15 22:40:59+00:00  548  53.06843    5.20231   \n\n                     geometry  route_density  moon_phase   \n0    POINT (5.20231 53.06843)            8.0    3.630688  \\\n1    POINT (5.20231 53.06843)            8.0    3.630717   \n2    POINT (5.20231 53.06843)            8.0    3.630745   \n3    POINT (5.20231 53.06843)            8.0    3.630774   \n4    POINT (5.20231 53.06843)            8.0    3.630802   \n..                        ...            ...         ...   \n544  POINT (5.20231 53.06843)            8.0    3.502747   \n545  POINT (5.20231 53.06843)            8.0    3.502776   \n546  POINT (5.20231 53.06843)            8.0    3.502804   \n547  POINT (5.20231 53.06843)            8.0    3.502833   \n548  POINT (5.20231 53.06843)            8.0    3.502862   \n\n                day_moment  bathymetry sediment_type tide deployment_name  \n0                      Day      -1.555    coarsesand  low   CoarseSandLow  \n1                      Day      -1.555    coarsesand  low   CoarseSandLow  \n2                      Day      -1.555    coarsesand  low   CoarseSandLow  \n3                      Day      -1.555    coarsesand  low   CoarseSandLow  \n4                      Day      -1.555    coarsesand  low   CoarseSandLow  \n..                     ...         ...           ...  ...             ...  \n544  Astronomical twilight      -1.555    coarsesand  low   CoarseSandLow  \n545  Astronomical twilight      -1.555    coarsesand  low   CoarseSandLow  \n546  Astronomical twilight      -1.555    coarsesand  low   CoarseSandLow  \n547  Astronomical twilight      -1.555    coarsesand  low   CoarseSandLow  \n548  Astronomical twilight      -1.555    coarsesand  low   CoarseSandLow  \n\n[549 rows x 12 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>datetime</th>\n      <th>id</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>geometry</th>\n      <th>route_density</th>\n      <th>moon_phase</th>\n      <th>day_moment</th>\n      <th>bathymetry</th>\n      <th>sediment_type</th>\n      <th>tide</th>\n      <th>deployment_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2022-06-16 11:04:36+00:00</td>\n      <td>0</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>POINT (5.20231 53.06843)</td>\n      <td>8.0</td>\n      <td>3.630688</td>\n      <td>Day</td>\n      <td>-1.555</td>\n      <td>coarsesand</td>\n      <td>low</td>\n      <td>CoarseSandLow</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2022-06-16 11:04:46+00:00</td>\n      <td>1</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>POINT (5.20231 53.06843)</td>\n      <td>8.0</td>\n      <td>3.630717</td>\n      <td>Day</td>\n      <td>-1.555</td>\n      <td>coarsesand</td>\n      <td>low</td>\n      <td>CoarseSandLow</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2022-06-16 11:04:56+00:00</td>\n      <td>2</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>POINT (5.20231 53.06843)</td>\n      <td>8.0</td>\n      <td>3.630745</td>\n      <td>Day</td>\n      <td>-1.555</td>\n      <td>coarsesand</td>\n      <td>low</td>\n      <td>CoarseSandLow</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2022-06-16 11:05:06+00:00</td>\n      <td>3</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>POINT (5.20231 53.06843)</td>\n      <td>8.0</td>\n      <td>3.630774</td>\n      <td>Day</td>\n      <td>-1.555</td>\n      <td>coarsesand</td>\n      <td>low</td>\n      <td>CoarseSandLow</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2022-06-16 11:05:16+00:00</td>\n      <td>4</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>POINT (5.20231 53.06843)</td>\n      <td>8.0</td>\n      <td>3.630802</td>\n      <td>Day</td>\n      <td>-1.555</td>\n      <td>coarsesand</td>\n      <td>low</td>\n      <td>CoarseSandLow</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    </tr>\n    <tr>\n      <th>544</th>\n      <td>2022-06-15 22:40:19+00:00</td>\n      <td>544</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>POINT (5.20231 53.06843)</td>\n      <td>8.0</td>\n      <td>3.502747</td>\n      <td>Astronomical twilight</td>\n      <td>-1.555</td>\n      <td>coarsesand</td>\n      <td>low</td>\n      <td>CoarseSandLow</td>\n    </tr>\n    <tr>\n      <th>545</th>\n      <td>2022-06-15 22:40:29+00:00</td>\n      <td>545</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>POINT (5.20231 53.06843)</td>\n      <td>8.0</td>\n      <td>3.502776</td>\n      <td>Astronomical twilight</td>\n      <td>-1.555</td>\n      <td>coarsesand</td>\n      <td>low</td>\n      <td>CoarseSandLow</td>\n    </tr>\n    <tr>\n      <th>546</th>\n      <td>2022-06-15 22:40:39+00:00</td>\n      <td>546</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>POINT (5.20231 53.06843)</td>\n      <td>8.0</td>\n      <td>3.502804</td>\n      <td>Astronomical twilight</td>\n      <td>-1.555</td>\n      <td>coarsesand</td>\n      <td>low</td>\n      <td>CoarseSandLow</td>\n    </tr>\n    <tr>\n      <th>547</th>\n      <td>2022-06-15 22:40:49+00:00</td>\n      <td>547</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>POINT (5.20231 53.06843)</td>\n      <td>8.0</td>\n      <td>3.502833</td>\n      <td>Astronomical twilight</td>\n      <td>-1.555</td>\n      <td>coarsesand</td>\n      <td>low</td>\n      <td>CoarseSandLow</td>\n    </tr>\n    <tr>\n      <th>548</th>\n      <td>2022-06-15 22:40:59+00:00</td>\n      <td>548</td>\n      <td>53.06843</td>\n      <td>5.20231</td>\n      <td>POINT (5.20231 53.06843)</td>\n      <td>8.0</td>\n      <td>3.502862</td>\n      <td>Astronomical twilight</td>\n      <td>-1.555</td>\n      <td>coarsesand</td>\n      <td>low</td>\n      <td>CoarseSandLow</td>\n    </tr>\n  </tbody>\n</table>\n<p>549 rows × 12 columns</p>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geodf_env"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.collections.QuadMesh at 0x274d50c8760>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_ds['oct3'].plot(x='id', y='frequency')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x274d84f7d60>]"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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D6oaoOt+ZmZlSMliei4sLUlJSNIpH5dNPP8WaNWuwb98+HDhwACYmJujUqRM++OADuLm5IT8/HwsWLMCuXbtQXFyMxo0bIzAwEGZmZhpdf00+p/dff1XtaWZmZoVtMzIykJycXOX1T05OrjSBmTt3bqWPGat8/fXXCAkJqXSdr68v9uzZg8TEROTl5cHd3R3Hjx8HANStW1cqd9euXdUS2oCAANjb20v9s6r7LKk+R5p83jT5nlLF9KDjkXEwgXkMODo6Ij09HSUlJTA1NZWWq24GVenRowd++umnKtdX1d+hadOmMDU1rfB4rKq9u6r+FZUxMTHByJEjMXLkSKSmpuLIkSNYs2YNXnvtNfz1118VahYA4K233sK1a9fw5ZdfIjAwEBYWFsjLy8PWrVs1Pq62XF1d8f777yM8PBxRUVHYv38/1q9fD0dHR4SHhxskpvT0dLXq/dTUVAD3Epny7O3tERwcjLfffrvSfVV2XoHSxKGyL3OVqj4THh4eyM7ORlpamlqzZFxcHNzc3GBlZVXlPsurU6cOAODtt99GcHBwhfWV3XSr4ujoWOkj3A/6vaiMvb09Zs6ciZkzZ+LatWv47bffsGrVKsyfPx/r1q3Dhx9+iAMHDmD58uXo1KmTlJBU1dfkYaiaYFQedP2bNWum1gxXXmVJKFD6R8vIkSOrjOH+viLlYzt8+DC6deumlmhdunQJCoUCbdq0gZmZGRQKRaXjq5SUlEifkebNm+Py5csVtrlx44ZUU6XJ500Va1xcHNq2bau2DVD6PWVrawtXV9cKn5PU1FTk5ORo9V1GusenkB4DHTt2RHFxsdp4C4WFhZUOIlWeo6MjfH19q/x3/198KpaWlmjfvj0OHTqk9tflgQMHYG9vr1Yd/iDDhw+XxltwdnbGkCFDMHLkSNy9e1f6S+n+5oeIiAj07t0bISEh0o34zz//BKDb5i2VM2fOoFOnTjh37pz0ZTx9+nR4enoiISHhkWPSVPnrC5SOaePm5lZpn4Xg4GDExsaiefPmatd0165d+Omnn9QS3fI8PDyq/UxUlfh06tRJikmlsLAQhw8f1rhDt+r4zs7OiI+Pr/BZXLZs2QOfoirviSeeQHx8vNSUB5TW3p09e1bjfQClHVO7desmlc3DwwOvvPIKOnXqpHb9Q0JC0LNnTyl5uXDhAtLS0vR2/Q8cOABra2u15keV4OBg3L59G87Ozmrn8a+//sKGDRuqvP6NGzeu9vpXVbsqhMCcOXNw8OBBaVlOTg62bt2KDh06oG7durC1tUWHDh1w8OBBtSTmn3/+QW5urlRjGRoaipiYGERHR0vbREdHIyYmRvosafJ5c3d3R+PGjXHgwAG1WA8ePIhmzZpJSVznzp1x+PBhtZgOHDgAU1PTKmuByTBYA/MY6NixI0JDQ/Huu+8iNTUVbm5u+Prrr5GWllbpX2e6MHnyZLz00kt4/fXXMXToUJw5cwYbN27EjBkzpGr97OxsREdHo2nTplV2Fu7QoQO++OIL1KtXD4GBgUhMTMSmTZsQHBwsvadOnTo4ffo0Tp06hfbt28PPzw979uyBt7c3GjRogNOnT2PdunVQKBQPHPBO9Rf+3r174e/vjyZNmjywrG3btoWVlRXefvttvPbaa6hXrx7+/vtvXL58GWPGjAGAR4pJU5s2bYKlpSUCAgJw8OBB/PHHH1i2bFml244dOxa7du3C2LFjMW7cODg6OuKXX37B1q1bq202fFhubm545plnsGjRIhQUFKBZs2bYtGkT7t69K/UPAkr/ik5LS6uyH4qpqSmmT5+OefPmwdTUFD169MDdu3exatUqJCYmVtkkUpnBgwfj66+/xtSpUzF9+nTY2dlh9erVWicUbm5uaNCgARYuXIjs7Gw0bdoUFy5cwJEjRzBx4kQApdd/37592Lx5M1q0aIGoqCisXr1ap9d/3759cHZ2Rrdu3XDy5El89913mD59eqXNT0OGDMG3336Ll156CZMmTULDhg3x999/Y/369Rg1ahTMzc11EpOKk5MTBgwYgOXLl8PKygpOTk5Yu3YtkpKS1D6jb775JkaPHo1XXnkF48aNQ2pqKpYuXSqN5g0A/fv3x5o1a/DKK69gxowZAIBly5bB09NT6tSs6edtypQpmDNnDhwcHBAWFobffvsN+/btw6effiptM378ePz8888YP348XnrpJVy/fh2ffPIJnnvuOTRq1AhAaXJ06dIlNGjQoNqnFy9dugQLCwup31Vl34EP+h2gcoww9gwZQI8ePcSsWbOk17m5ueKDDz4QISEhIiAgQMydO1csXLhQbwPZCVE6eNXAgQOFt7e3CAsLExs3blRbf/z48QcOrlZUVCQ+//xz0bNnT+Hj4yM6duwo3nnnHZGWliZt88UXX4j27dsLf39/cevWLREfHy8mTpwogoKCRFBQkBg6dKjYtWuXePnll8XQoUPVjn38+HG14925c0cMHTpUeHt7i/DwcI3LGhsbK6ZOnSo6duwovL29xYABA8QPP/wgrX/YmKoagK78oGSq93333Xfi2WefFT4+PuKpp54S+/fvr3Y/cXFxYtq0aaJDhw7Cz89PPPXUU+LHH3/UuMzaKigoEB9++KHo2LGj8Pf3FyNGjBBnz55V22bWrFkaDST4888/i2eeeUb4+PiI4OBgMWnSJBEVFSWtr2pAwvsHc0tNTRUzZswQ7du3Fx06dBBLliwRr7/+utYD2SUlJYnZs2eL0NBQ4e3tLXr27ClWr14tDdCWnp4u3nzzTREcHCwCAgLEwIEDxVdffSXee+890blzZ1FcXKzRtVYp//utet/q1avFSy+9JHx9fUXv3r3F999/X+1+UlJSxJw5c0THjh2Fj4+P6NOnj1i/fr0Us65lZWWJ8PBw0blzZ9GuXTsxbtw4ERkZWWG7iIgIMWrUKOHn5yeCg4PF3LlzRWZmpto2CQkJYsqUKSIgIEB06NBBvPHGGyIxMVFtG00+b0IIsXnzZtGrVy/h4+Mj+vXrJ3bs2FFhm1OnTolhw4YJHx8f0aVLF7F06VJRWFgorVddgwcNFNijRw+1z1Zl34Ga/g6QEAohOHMdGc9nn32Gli1bqj1GSto5ceIExowZU20HSjnp3bu3WlMDVS8+Ph5PPvkkFi1ahCFDhhg7nMfWTz/9hLS0NEyYMMHYoTw22AeGjCYxMREHDhzQetAwQxFCoLi4+IH/+DeA7uzatQseHh7GDgMArz9pLicnB5s3b2afGANjHxgyGgcHB6xYsUJqR65pHvQYuUptqfmoCXx9fR9qIDl9OHnypNSHqTqs+SAbGxssWLBA7Wkm0j82IRFVIT09vcq5fcpr3ry5VmPbkDxkZ2drNFR848aNKx1Phoj0iwkMERERyQ77wBAREZHsMIEhIiIi2WECQ0RERLLDBIaIiIhkp9Y/Rp2amgVddlNWKABnZ3ud77emYPnkjeWTN5ZP3lg+3R7nQWp9AiME9HKi9bXfmoLlkzeWT95YPnlj+QyDTUhEREQkO0xgiIiISHaYwBAREZHsMIEhIiIi2WECQ0RERLLDBIaIiIhkhwkMERERyQ4TGCIiIpIdJjBEREQkO0xgiIiISHaYwBAREZHsMIEhIiIi2WEC8xDyCkuMHQIREdFjjQmMlv7vaCzazNuPf29kGDsUIiKix5bREpjt27fDy8urwr/WrVsDACZPnlxh3R9//GGscCWbTtwEAMzYedHIkRARET2+zIx14P79+6NLly7S6+LiYrz44ovo3r07ACAmJgZLlixBx44dpW3q1q1r6DCrlMNmJCIiIqMxWgJjZWUFKysr6fXatWshhMBbb72FwsJCxMfHw9fXFy4uLsYKsVpNHKwevBERERHpRY3oA5ORkYH169djxowZsLCwwLVr16BQKNCkSRNjh1YlC7MaceqIiIgeSzXiLrx582bUr18fffv2BQBcu3YNdnZ2ePvttxEaGopnn30WR44cMXKUREREVFMYrQlJRQiBH3/8EePHj5eWXbt2Dfn5+QgNDcWECRNw6NAhTJ48GVu2bIGvr69W+1codB2xYfZtLKoy1cayASyf3LF88sbyyZuhyqfp/hVCCKHfUKp37tw5vPDCC/j777+lTrpKpRJZWVlqnXYnTZoEFxcXLFiwwFihAgCazf4ZAODlao8D07saNRYiIqLHldFrYI4ePYr27durJSsmJiYVnjjy8PBAdHS01vtPTc2CPlK04pISpKRk6X7HRqZQAM7O9no7b8bG8skbyydvLJ+8Gap8quM8iNETmHPnzqFdu3Zqy2bPng2FQoFFixZJy6KiouDp6an1/oWAXk60vvZbU7B88sbyyRvLJ28sn2EYvRPv1atX0bJlS7VlYWFh2LNnD3bu3Im4uDisXLkSERERGDVqlJGiJCIioprE6DUwKSkpqFOnjtqy3r17Izw8HKtXr0ZCQgJatWqFDRs2oHHjxkaKkoiIiGoSoycw586dq3T5sGHDMGzYMANHQ0RERHJg9CYkIiIiIm0xgSEiIiLZYQJDREREssMEhoiIiGSHCQwRERHJDhMYIiIikh0mMERERCQ7TGCIiIhIdpjAEBERkewwgXlINWEiKyIioscVExgiIiKSHSYwREREJDtMYIiIiEh2mMCQGiEEjl5Nxt38ImOHQkREVCUmMKTmn+vpGL3xJF76/iyyC4qNHQ4REVGlmMCQmou3swAAcWl5mL//CpR83IqIiGogJjCk5nparvTz4ehUbDpxw4jREBERVY4JDKmJS88DAPRoVQ8AsPavOBy7lmrMkIiIiCpgAvOQBGpf04oQAnFlNTBTujTDUP+GEADe+yUKN8oSGyIiopqACQxJkrILkVekhJmJAo3rWmFGjxbwa1QH2QUleGvXReQUslMvERHVDExgSKKqfWnqbAMzUxOYm5rgf4PaoJ6tBWJTc/HB/v8g2KmXiIhqACYwJLmeVtpM5FHPTlpWz84S/3uqLcxMFPj9agq+OnnTWOERERFJmMCQ5EZ6aQ1MCxdbteV+jepg5pMtAQCrjl3H37FpBo+NiIioPCYwJFE9Qt3Cxa7CuiF+DfG0bwMIAO/+HIX4DHbqJSIi42ECQ5I4VRPSfTUwKjPDWsKnoT2yCooxc9cl5BWVGDI8IiIiCRMYAgDkFZXgTlYBgMprYADAwswE/xvUFk425ohOycHCA+zUS0RExsEE5iHVttu2apyXutZmcLS1qHK7+vaW+HhQW5iaKHDwSjK+i7hlqBCJiIgkTGAIwL1HqJs52Txw28DGdfFm9xYAgBV/XsOJuHS9xkZERHQ/JjAE4F7/F3dHa422HxbQEAO9XaEUwDt7LyMhM1+f4REREalhAkMAgLh0zWtgAEChUGB2z1Zo42qHzPxizNx1Efns1EtERAbCBIYA3BvEzl3DBAYALM1MsPiptnC0Nsd/yTn48NBVduolIiKDYAJDUJabxLGZk2ZNSCoN6lhh0aA2MFUA+y8n4YczCfoIkYiISA0TGEJSVgHyi5UwNVHAra6V1u8PauKAad08AACfHY7BxTtZug6RiIhIDRMYQlzZI9SN61rBzPThPhIvtHNDT896KBHAhn/idBkeERFRBUxgSKtHqKuiUCgwqXMzKAAcu5aG6JQcHUVHRERUEROYh1Sb+qpKj1Br2f/lfu5ONujeqh4A4Nt/4x85LiIioqowgSFpEkdtnkCqyosdGgMo7dB75y7HhiEiIv1gAkNSHxhNB7GrjnfDOghqUhclSoHNpznNABER6QcTmMdcXlEJEssmcdRFDQwAjOnQBACw49xtZOYV6WSfRERE5TGBeczdKOv/4mBtDgdrc53ss2MzR7RysUVekRI/RXJcGCIi0j0mMI+5e1MIPHrzkYpCoZBqYbacTuAUA0REpHNMYB5zUgdeR900H6n09HJBozqWSM8rwp6LiTrdNxEREROYx5yuHqG+n5mJAiPblz6R9O2/8ShW1qLnzomIyOiYwDzmdPkI9f2e8mkAB2tzJGTm4/f/knW+fyIienwxganB8otKsPn0LUzeGomtZxJQVKLU6f6VQuCGDh+hvp+VuSmeC2wEAPjq5E3OVE1ERDpjZuwAqKLsgmJsi7yN7/6NR3rZY8j/3szEdxHxmNjJHX1a14epieKRj6OaxNHsISdx1MSwgEb4+uRN/JecgxNx6XiimZNejkNERI8X1sDUIJl5RVj393U8tf4kVh6NRXpeERrVtcKYDo3hbGuBhMx8hO+7gpHfROBIdOoj12io+r80dnj4SRwfxMHaHE/7NQQAfHWK0wsQEZFusAbGSIpKlIhKzMaVpGz8l5yNK0k5iE7ORmFJaVLi7miNcU80Re/W9WFmosD4ju7YcvoWvj4Vj5iUXLy16yJ8G9bBlC7NENTE4aFiuPcIte77v5Q3IsgNP565hX9vZODSnSy0bWCv1+MREVHtxwTGCIQQGPf9WUQlZVdY18rFFuNCmqJHq3pqzUTW5qYYG9IUQ/wb4utT8fjh9C2cv30Xk7aew+j2jTGtm4fWcVwvq4FpquNHqO/XsI4Vereuj32Xk/D1qZv4eFBbvR6PiIhqPyYwRvBfcg6ikrJhZqJAsLsDPF3s4FXfDp717dDEwQoKRdX9W+pYmWNql+YYHtgIG47fwLbI29hy5hYmhzaDuZbNQHFpuh/EripjOjTBvstJ+P2/FNxIz0NTPXQaJiKixwf7wBjBnzGpAIDOzZ3w2RBfTOnSHD29XNDU0bra5KW8enaWmPVkSzhYm6OwROBKJbU5D6LPR6jv19LFFp2bO0EA+Pbfm3o/HhER1W5MYIzgz+jSBKZrC+dH2o9CoYBvw9L+JOcS7mr13tzCEiRlFwLQzyPUlRkTXDqw3c8XE5GSU2iQYxIRUe3EBOYhPewTQIlZBYhKyoYCQGiLR3+k2K9RHQDAeS0TmBtlHXgdrc1RV0eTOD5IoFtd+Da0R2GJwA+nbxnkmEREVDsxgTGwo2XNR76N6sDJxuKR9+dblsBEJtzVKqlSPUJtiP4vKuUnedwWmYDsgmKDHZuIiGoXJjAGdiRGN81HKt4N7GGqAJKzC5GYVaDx+1T9X5oaoP9LeV1bOqOZkzWyC0qw49xtgx6biIhqD6MlMNu3b4eXl1eFf61btwYAXLp0CcOGDYO/vz+GDh2KCxcuGCtUnckuKMa/NzIAAN10lMBYmZvCs74dAO36wcTpcQqB6pgoFBjdvrQW5vuIWygs1u30CERE9HgwWgLTv39/HDt2TPp3+PBhuLu7Y8yYMcjNzcWECRPQvn17bN++HYGBgZg4cSJyc3ONFa5OnIhLR7FSoKmjtU5nf1b1g9EmgbmeZphB7CrTt019uNhZICWnEPsuJxr8+EREJH9GS2CsrKzg4uIi/du9ezeEEHjrrbfwyy+/wNLSEm+//TZatGiBd955B7a2tti/f7+xwtWJI2VPH3XxcNb4cWlNaJvAqE3iaIQExsLMBC+0cwMAfHMqHkpO8khERFqqEX1gMjIysH79esyYMQMWFhaIjIxEUFCQdJNXKBRo164dzp49a9xAH0GxUuCv2DQAQLeWumk+UlF15P0vOQf5RSUP3D4pqwAFZZM4NtLTJI4P8oxfQ9hZmiIuPU9K7IiIiDRVI0bi3bx5M+rXr4++ffsCAJKTk9GyZUu1bZydnXH16lWt963Dio5H2ve5hEzczS9GXWsz+LnV0WlcDetYwsXOAsnZhbicmIV2D5gbSTUHUhMHa5ibqgeiikuf5w0A7K3MMCygETaduImvT91Ej1a6rZWqiqHKZywsn7yxfPLG8un2OA9i9ARGCIEff/wR48ePl5bl5eXBwkL9EWMLCwsUFmo/+Jmzs34mDjQxNUG9etXvWwiBP6+mYP+FOzh06Q4AoGebBmhQv47O42nfzAn7LtxBTGYhegdWH1fKlRQAgGdD+yrLoK/zVt6rPb3wXcQtXLidhZisIjzhoduaqeoYonzGxPLJG8snbyyfYRg9gTl//jwSExMxYMAAaZmlpWWFZKWwsBBWVto3d6SmZkEfXSxKSpRIScmqdputZxKw+Ldo6XUdKzMMbF3vge97GF71bLAPwD9XkzHMp3612168mQ4AaGhrUSEWhaL0w6mv86Z2LAADvV2xPfI2Pj90BS2H+ur3gDBs+YyB5ZM3lk/eWD7dHudBjJ7AHD16FO3bt0fdunWlZa6urkhJSVHbLiUlBfXrV39jrowQ0NuJftB+ryaXzk/UvkldvBjcBEFNHGBuaqKXePwa3uvIq1SKaptjVLNQuztZVxmLPs9beaOCGmPnudv4OzYd/yVlo5WLnf4PCsOVz1hYPnlj+eSN5TMMo3fiPXfuHNq1a6e2zN/fH2fOnJFGlhVC4PTp0/D39zdGiA8tLacIANDTywVPNHPSerZobXjVt4O5qQIZeUWIz8ivdlvVLNTujoZ/Aul+TRytEdaqHgDg61PxRo6GiIjkwugJzNWrVyt02O3bty/u3r2LDz/8ENHR0fjwww+Rl5eHfv36GSnKh5OWW9oMpospAx7EwswEbVwfPLFjTmHxvUkcDTiNQHXGBJcObHcoKgkJmdUnX0REREANSGBSUlJQp456p1Y7OzusXbsWERERGDJkCCIjI7Fu3TrY2Bi/xkAbqbmlNTBONoaZLNG34YPHg1GN/+JkY446VoaJ60HauNqjQ1MHlAjg+wjWwhAR0YMZvQ/MuXPnKl3u5+eHHTt2GDga3UrLKa3pcLbVfw0MAPi51cF3EcD521UnMKpJHA09hcCDvNihCU7dyMDO83cw/gl3OBgo6SMiInkyegJTW+UWliC/bJ4fRwPdjP0aljYhRSfnILugGHaWFS+v1P/FCCPwVifY3QFe9e1wJSkbW8/ewoROzYwdEhEZWX5RCVJzC5GeW4TUnCKk5RaW/iv7OTW3CGk5hUjLLYKZiQJPetbDIJ8GaONqZ5Bxpci4mMA8pAf1wFb1f7E0M4GNuakBIgLq2VmiUR1LJNwtwMXbWQhp5lhhm3tPINWsBEahUGBMh8Z45+cobD2TgNEdmsDaQOeNiAxDCIGs/CLcSM9DanahlISk55YmIallyYgqScnVYGTx8n6KvI2fIm+jRT0bDPJugH5t6xukDyIZBxMYPUkr6//ibGNu0L8EfBvVQcLdZJy7fbfSBEY1Cm+zGtKBt7wwTxe4HbuOW5n52H3+Dp4vmy+JiGouIQTu5hdLicf9SUhabrnXuUUo0HIGekszEzjZmMPJxgKONuZwtrGAk23paycbczjbWsDJxgKJWfnYezERf1xNQUxKLpYfuYYVR2PRxcMJA70boHNzR5jp8UlQMjwmMHqi6v/iZKD+Lyp+jergQFRypR151SZxrAGPUN/PzESBUe0b43+/ReO7iHgM9W/ILxwiIyhRCmTmFyEtpwip1TTbqGpOipXaDQpiY26qloRI/9tawFn12rZ0ma2FqUZ/BDZ3tsETzZyQlV+Mg1eSsOdCIi7eycLh6FQcjk6Fk405+rVxxSAfV7SoZ/uwp4ZqECYwemLIR6jLU81MfeH2XSiFgEm5X/zEskkczU0VaGikSRwfZKC3K9b/E4fbdwuw/dxtDPRuABsLNiURPariEqVaTYgqKUlVvS5Xc5KRVwQtcxLYW5pVkoSYw9Hm3mtnW3N4NnVGzt1cvQ2EZm9lhqH+jTDUvxFiUnKw50Ii9l1ORFpuEb6LiMd3EfFo28Aeg7xd0bu1S415GpO0xwRGTwz9CLVKSxc7WJmZILugBLGpuWp/aVwv68Db2MEaZiY1s4Oblbkpng90w+q/rmPJ7zFY8nsMnGzM4VbXCm4O1nCra4XGDlZwq2uNxg5WcLa1UEvSiB4nBcXKskTkXs1Iel7FviRpuYXIzC/Wat8KAHWtzSsmJWWv1WpObCxgYfbg2lKFArC2MEXOQ5ZXWy3q2eKN7h6Y2qUZ/opNx96Ld3D0Whou3cnCpTtZ+PRwDLq3rIdBPq7o0NQRpjX0e5EqxwRGT4zVhGRmooB3Q3tE3MzEuYS7agmM6hHqZjWsA+/9ngtshDO3MnH5ThYypbb1Ipy/XXEOKUszEzSqa1WW2KgnOA3rWMLKCB2BlUKgoFiJgiIl8otLn0YrKFJCQKCxg3WlT4cRqSiVAmk5hUjKKkRidgGSswukmpL0cjUoqTmFyCnUrpOrqQJwUPUdqbQvyb0aEwcbixr7h462zExN0K2lM7q1dEZabiH2Xy5tYopOycHBK8k4eCUZ9e0sMNDbFQO9G6BJDRtmgirHb1I9Kd+J19D8GtVBxM1MnE+4i2f8GkrLr0tTCNTsX047SzOsKJvYMSu/GAmZ+YjPzMOtjPL/5yPxbj4KipWITc1FbGpupfuqb2ehVnvTxNEabd2LocwvRH6REgXFSuQXlSUZZT8XFCvLXpfc26a4bHmlr9XfX1hSfd24s60F3B2t0czJBu5O1nB3soG7ozUa1rHiX4C1XLFSIDWnEElZBUjKLkBSdtnPWaWJSlJ2IZKzC1FYonlHV3NTBRyt73VmLV9Dcn+SUtfa/LGvsXSyscCIoMZ4oZ0bopKysedCIg5EJSEpuxBfnLiJL07cRKBbHQz0aYCeni5swq7BmMDoibH6wABVj8gbly6PGpjy7K3M4GVlBy/XipM8FpcocSerQC2xuZWZj/iMPNzKzEdOYUnpDSK7EGduVT24nz5ZmCpgaWYKK3MTlCiF9Jdzak4hTsdnVti2iZTY2KglObYW/FWt6QqKlUjOLkBiWXKSnFWIpLLXydmlP6fmFGrct8TJxhyu9paoZ2tRmpzc17fEycYCzjYWsLPUrJMrqVMoFGjjao82rvZ4vZsH/oxJxZ4Ld3AiLh1nbt3FmVt3sfT3aDzp6YJBPq4IdKvL81zD8FtRT1Q1ME62hq+BUSUwcel5yMgrgoN1aQz3BrGr2TUwmjIzNUFjB2s0drBGCNQfGRdCIDOvGLcy8xBfltiofr6dVYCs/CJYmZnC0swEVuYmpUmGmUnZ67Lllb4uTUakn8ver76v0vdYmJpUqFHJLihGXHoe4tJyEZeWi+tpeYhLz8WN9DwUlgjEpOQiJqVibZKLXWmtjXtZctPMqTS5cbW3fOz/ojaE7ILi0hqTrHK1JtmliUliWQ2Kpn1MTE0UcLG1QH17S9S3U/1vWfq/vQVaN3WCaWERzEz4BJ6hWJqZoJeXC3p5uSApqwA/X0rE3ouJuJGeh70XS39u7GCFQd4N0L9tfTSoUzMfgnjcMIHRk1RVHxhrw9fAONiYw93RGnHpebhw+y5CPZyRU1iMZNUkjjXwEWpdUygUcLAxh4ONObwb1im3HKhXzx4pKVlGmQ7eztIM3g3s4d3AXm15iVLg9t18xJUlNNfTchGXlofrablIyy1CclnTwr831WttLM1M0NTRGu6OZUmNsw0CPJSooxAcCFADQghk5BUhqay2RJWkJGYXIlnVzJNVqPGAapZmJnAtS0xcVEmJXblExd4STjZVN+MoFEA9RxujfT4JqG9viZdCmmJscBOcS7iL3Rfu4NcrKYjPyMfqv65jzV/XEeLuiEE+rujWsh4sNei8TPrBBEYPCoqVUuc6Y9TAAKUD2sWl5+FcQmkCU34SR3srXvaaxtREIdUmdYaT2rqs/OIKSU1ceh5upuehoFiJq8k5uJpc8bmO+nYWZbU1pcmNu2Npc1T9x6TWpnx/k+Ts0qRE6muiSlKyC1D0gD5LKvaWZqhvX5qYuNqV1pbUt7OEi/291/aWZmxmqCUUCgX83erC360u3gprid/+S8aeC4k4HZ+J43HpOB6XDntLM/Ru7YJBPg3QltMXGBzvZHqg6v9ibqqAvZGeOPFrVAd7LyZK/WCu19A5kOjB7K3M4NOwDnwaqs/aXqwUuJ2ZLyU019NycSM9FzfS80tv3GX9f07dyFB7n5WZyb2Ow3WtYGaigKlCAYWiNJFSoPR/E9UyhQIKhQKmJqVf6qYK1f8KmJgAJlDAxEQBEwVgoij3f2XLyn42NVFU2JfqWJW/DzApi9M8twjZBcVIzy2SakgqdIp9yP4mLnb3N+tYSM07rNF6fFmbm2KgdwMM9G6A+IzSZqWfLybiTlYBtkXexrbI2/BwtsFTPg0wMtQDTGMMgwmMHqj6vzhaG3YagfJ8ywa0u3g7C8VKUe4R6trR/4VKH5lv4miNJo7W6FK2TNVEFnMzDddVtTVpZX1u0nNxMyMf+cVKXEnKxpWkbKPGbwhV9TdxsbMobeop6yRrzhGfSUONHawxqXMzTOjkjlM3MrDnwh0cjk7FtdTS6QtWHo1Fp+ZOGOTtilAPJ44mrkdMYPRANQaMs4HHgCnPw9kGthamyCksQXRy9r0OvI9B/xcqHYDMr5G5NDKzSnGJErcy83E9LQ830nORmFUApSgdu0YpBJTKcj+XLS9RAgICJUoBIYCS8uuVAkqU/i+EQIm0r7J15faj9rOy3P5FaV+UEqWAAKo8zv2VKar+Ji5292pJ6t/3s6ONBR9NJ70wUSgQ4u6IEHdHZOUX49CVJOy5mIgLt7PwZ0wq/oxJhaO1Ofq1rY9B3g3Q0oXTF+gaExg9MOYj1ComCgV8G9XB8evpOJdwV5aPUJPumZmaSE8yAc7GDkcrQpQmMU7OdkhKzoJ5WTMUkbHZW5lhiH8jDA1ohPQS4Otj1/DzxdLpC76PuIXvI26hjasdBvk0QB9OX6AzTGAekqjmEYE0I00jcD+/hqUJTOStu/cmcWQTEsmUoqwvjLlp6aPqfEqHaqJWZePKvNq5Gf6+no49F0qnL7icmI3LidFYfjgG3cqmLwjm9AWPhAmMHqQaaRqB+6maD45eS703iSPHLyAi0jszUxN0beGMri2ckZ5biH2Xk7D3YiKuJufg0JVkHCqbvmBA2fQFTWv4COk1ERMYPagpNTDeDe2hAJBXVDoseRMHa2b7REQG5lhu+oIrZdMX7C+bvmDTiZvYdOImAtzqYJB3AzzpVY8jb2uIZ0kPVH1gnI3YBwYoHTStRT1bRKeUjhHC/i9ERMajUCjQ2tUerctPX3DxDo5fT8fZW3dx9tZdLP0jGmGeLhjk7Yp2jTl9QXWYwOiB9Bi1kWtggNJmJFUCw/4vREQ1g4WZCXp6uaBn2fQFv1xKxJ6y6Qt+Lhtnxq2uVdkM2a6cvqASfEBdD7ILSudEqVMDRrz1bXRvyHo+Qk1EVPPUt7fE2JCm+Oml9tgw3B+DfRvAxtwUtzLzsfbvODy1/iSm/nQOBy4nIV/DaS0eB8a/w9ZCqgTGzkij8Jbn16iu9DMHsSMiqrnKT18wo0cL/P5fCvZcvIOIm5k4EZeBE3EZsLM0RZ/W9THI2xVtG9g/1k1Mxr/D1jLFSiF1mrWrAR2xmjhYoW0De6TnFqJFPQ6kREQkB9bmphjg7YoB3q6IzyhtVtp73/QFzZ1tMMjbFf3auqKekZ96NQaN7rBjxozRescKhQJfffWV1u+Ti6qGoMgpq30BADtL48+dolAosGG4PwBwuHQiIhlq7GCNiZ2b4ZVO7vj3Rgb2XEzEH1dTEJuai8//jMX/qaYv8GmAUA+nx+a7XqME5uTJk3jppZdga6vZX/A5OTn48ssvHyUu2couLE1gLM1MaswcGI/Lh5mIqDYzUSgQ7O6IYHdHZBcU4+CVZOy9cAfnb2fh6LU0HL2WBgdrc/RrUx+DfFzRysXO2CHrlUYJjIWFBV555RU4OTlptNOUlBR89913jxSYXGUXlHawqgn9X4iIqHayszTDEL+GGOLXELGpudh78Q5+vpSE1JxCbD59C5tP30Lr+nYY5OOKPq3ro6618Z+K1TWN7rLnzp3TaGdFRUUwNzdHvXr1NH5PbSN14LUwfvMRERHVfs2dbfBaVw9MDm2O49fTsPtCIo7GpCIqKRtRv2dj+ZFr6NaidPqCEPfaM32B1tUEKSkpWLt2LaKjo1FSUlrbIIRAUVERYmJicOrUKZ0HKSc16QkkIiJ6fJiZKBDq4YxQD2dk5BZhX1QS9ly4g6vJOfj1v2T8+l/p9AX925aOLeMu88FNte4cMXfuXBw9ehS+vr44ffo0/P394eTkhHPnzuG1117TR4yycq8JiTUwRERkHA425nihnRu+HxOEb0e1w/OBjVDXygxJ2YX48uRNPLvpX4zffBa7zt9GTmHxg3dYA2ldTXDq1Cl88cUXCAwMxF9//YXu3bsjKCgI69atw59//vlQTyzVJqyBISKimsTL1Q5eri0xrasHjl5LxZ4LifjnehoiE+4iMuEulv4egyc962GQTwMENq4LE5mMLaP1XVYIAVdXVwBAy5YtcenSJQQFBaFfv37YuHGjzgOUG9VTSDVhDBgiIiIVCzMTPOnpgic9XZCcXYBfLiVh94U7pdMXXErCz5eS0Kjc9AUNa/j0BVo3IbVt2xa7du0CALRp0wZ//fUXACA+Pl63kdVwooqBYFRNSLZsQiIiohrKxc4SLwY3UZu+wNbCFAmZ+Vj3dxwGrz+JV388h/01ePoCrasJZsyYgUmTJsHa2hqDBw/Ghg0bMGjQICQkJOCpp57SR4yywiYkIiKSi/unL/jjagr2XLiDf29m4tSNDJy6UTp9QW+v0rFlujvXnLFltL7LBgUF4Y8//kB+fj4cHR2xbds2/Prrr3BwcEC/fv30EWONVFUTIceBISIiObI2N0X/tq7o39YVtzLvTV9w+24Btp+7je3nbqNl/Wj0b+1SI6YveKi7rKWlJSIiIhATEwMTExN4eXkhJCQEJiYc8fVeHxg2IRERkTy51bXGhE7NML6jOyJuZmDPhUT8fjUF0UnZ+DwpG/93NBZhni54r48nrM2Nc7/TOoG5du0aJkyYgLS0NDRr1gxKpRJxcXFo3Lgx1q9fjwYNGugjTtnIYRMSERHVEiYKBTo0dUSHpo6YVViMf25lYfPxOJxLuItDV5IxLqQpWroYZ6Jgre+y8+bNg5+fHxYsWCDNjZSVlYV33nkH8+bNw7p163QeZE30oE68HAeGiIhqEztLM7wQ3BS9PBwRm5qLzLwioyUvwEMkMBcuXMD27dvVJna0t7fH66+/jmeffVanwcmR1ITEGhgiIqqlmtWAUXwf6jFq1aPT5Z0/fx6tW7fWSVBycCerAAXFygrL782FxASGiIhIX7S+y3bq1AlLly7FyZMn0a5dO5iZmeHy5cvYu3cvBg0ahJUrV0rbTp06VafB1jQ7zt3G8HZu0utipUBeUWlSwyYkIiIi/dE6gTlx4gT8/PyQkZGB33//XVru7++PGzdu4MaNGwBKny2v7e7mF6m9VnXgBdiEREREpE9a32W/+eYbfcQhS/d35FX1f7E0M4G5KR8pJyIi0hetE5idO3dWu/7pp59+yFDk5/4HkaRpBDgGDBERkV5pncB8/vnnaq9LSkqQmpoKMzMz+Pn5PVYJzP04jQAREZFhaH2nLd/vRSUnJwfz5s2Dl5eXToKSi6pqYJjAEBER6ZdOOmrY2tritddew6ZNm3SxO9kQ93WCySnrA2PPJ5CIiIj0Smc9TaOioqBUVhwX5XHCJiQiIiLD0PpOO3r06AqPSOfk5ODKlSsYO3asruKShQpPIamakDiIHRERkV5pfacNCQmpsMzCwgJvvfUWOnbsqJOg5EpVA2PLJiQiIiK90jqBqe2j62qjQidezoNERERkEBxt7RFU2YTEBIaIiEivmMA8EvUMJquATyEREREZAhOYR1CxBkaVwLAGhoiISJ8e6k6bnp6OwsJCWFtbo06dOrqOSbay8tkHhoiIyBA0vtMePHgQ3377Lc6dO4eCggJpuZWVFXx8fPDiiy+iZ8+eWh28sLAQixYtwt69e2Fubo5nn30W06dPh0KhwOTJkyuM+rtmzRr06NFDq2Po0/2deLNYA0NERGQQGt1pN23ahJUrV2L8+PGYOnUqnJ2dYWFhgcLCQqSkpODff//F7Nmz8frrr2P06NEaH3zhwoU4ceIENm7ciJycHEyfPh2NGjXC8OHDERMTgyVLlqg9ml23bl3tS6hHVTYhWTGBISIi0ieN7rRffPEF/ve//1Vaw9KiRQuEhITAy8sLCxYs0DiBycjIwLZt27Bp0yb4+fkBAMaNG4fIyEgMGTIE8fHx8PX1hYuLixbFMSxRrg6moFiJwpLS16yBISIi0i+N7rT5+flo3Lhxtdu4uroiKytL4wNHRETAzs4OwcHB0rIJEyYAKJ2WQKFQoEmTJhrvz9hUtS8KADYWfAqJiIhInzRKYHr16oXZs2fj3XffRUBAAMzM7r1NqVTi7NmzCA8PR58+fTQ+8M2bN+Hm5oadO3dizZo1KCoqwpAhQzB58mRcu3YNdnZ2ePvtt3Hy5Ek0aNAAr732Grp166Z1Ae+b9UDnVPsvP4idqYmeD6pHqvLo+7wZC8snbyyfvLF88mao8mm6f40SmPfffx//+9//8PLLL6OkpAQODg5SH5iMjAyYmZlh8ODBmDNnjsYB5ubmIi4uDj/88AMWLVqE5ORkzJs3D9bW1sjNzUV+fj5CQ0MxYcIEHDp0CJMnT8aWLVvg6+ur8TEAwNnZXqvttWFpZY569Ur3fzO3NIGpY31vmZzp87zVBCyfvLF88sbyyVtNKZ9GCYyFhQXee+89vPXWW4iKikJycjLy8vJgaWkJV1dXtGnTBlZWVtod2MwM2dnZWLZsGdzc3AAACQkJ2Lx5M/bt24fRo0dLnXZbt26NixcvYuvWrVonMKmpWRU62+pKfl4RUlJKm83iE+8CAGzNTaRlcqRQlH449XnejInlkzeWT95YPnkzVPlUx3kQrXqbWltbIzAw8KGDKs/FxQWWlpZS8gIAzZs3x+3bt2FiYlLhiSMPDw9ER0drfRwhKj4tpCtKIaR93y03Bkxt+ODq87zVBCyfvLF88sbyyVtNKZ/ORuLNzMzEmDFjNN7e398fBQUFiI2NlZZdu3YNbm5umD17doXmqKioKHh4eOgqXJ3jKLxERESGo7MExtTUVK025UE8PDzQvXt3zJkzB1FRUTh69CjWrVuHF154AWFhYdizZw927tyJuLg4rFy5EhERERg1apSuwtW5LNVEjhwDhoiISO90dre1s7PDokWLtHrP0qVLsWDBArzwwguwtrbGyJEjMXr0aCgUCoSHh2P16tVISEhAq1atsGHDhgc+ym1o5avQOAovERGR4Rj1bmtvb4/FixdXum7YsGEYNmyYgSPSTvkmwGzORE1ERGQwnI1aRziRIxERkeFodLfVZnwXbZuR5EyUa0NiExIREZHhaFQD07RpU+zevRtnz57VczjyUnkTEhMYIiIifdPobjt58mQ0adIE7777Lj777DN4enrqOy7ZyeJM1ERERAajcR+YgQMHYvDgwXj//ff1GI68qD+FVPYYNWtgiIiI9E6ru+28efOQm5urr1hkjU1IREREhqPVU0impqawt68ZkzjVBKKsF0xhsRIFxUoATGCIiIgMgY9RPwJVE1J2YWntiwKALceBISIi0jsmMI9A1QUmt7C0/4uVuQlMFArjBURERPSYYALzKMoymOKS0h8sTHk6iYiIDIF3XB0oUpb2fzFjAkNERGQQD3XHjYiIwLRp0zB48GDcvn0b69atw88//6zr2Go8VSfeorIaGHMTNh8REREZgtYJzMGDBzFhwgS4ubkhNjYWxcXFMDMzw+zZs/H999/rI8YaS9WJt6iktAbG3JQJDBERkSFoncCsXLkS77//PmbNmgVT09InbsaNG4ePPvoImzZt0nmANZmqE2+xsvQnNiEREREZhtZ33Li4OAQEBFRY7ufnh8TERF3EJDtSDQybkIiIiAxC6wSmZcuWOHr0aIXlO3bsQMuWLXUSlFyoamBUfWBYA0NERGQYWg8bO2fOHEyaNAnHjx9HUVER1qxZg7i4OFy4cAGrV6/WR4w1lijrBFOkZCdeIiIiQ9K6yqB9+/bYv38/WrRogbCwMGRkZCAgIAC//PILOnbsqI8Ya7xiduIlIiIyKK1rYF599VXMmDEDr7/+uj7ikZV7TyGxCYmIiMiQtL7jnj59GmZmnLCwPHbiJSIiMiytM5ERI0Zg+vTpGD58OBo1agRLS0u19R06dNBZcDWd1IlX1QeGNTBEREQGoXUCs2rVKgDAvHnzKqxTKBS4fPnyo0clE1InXvaBISIiMiitE5ioqCh9xCFrxewDQ0REZFC84z4CpaoTr5J9YIiIiAyJCcwjuP8pJPaBISIiMgzecR/JfbNRsw8MERGRQTCBeQT3JnMsbUIyM+HpJCIiMoRHGtAlLS0N+/btg1KpRFhYGNzc3HQVlyxUbEJiDQwREZEhaJTA5OXlYfHixfjll18AAIMHD8bo0aMxfPhw5OXlQQiBpUuXYsOGDY/nODB8jJqIiMigNGrz+Oijj3D69GmEh4dj0aJFiI6OxnPPPYdOnTrhxIkTOHXqFAYPHozPP/9c3/HWKBUnc2QTEhERkSFoVAPz22+/Yc2aNfDz8wMABAQEoFOnThg1ahTMzc0BAOPGjcMzzzyjv0hrIFUTkmoyRzPWwBARERmERlUGaWlpaNCggfTayckJ1tbWcHR0lJbZ2dkhPz9f9xHWYPeakPgYNRERkSFpfMc1NTWtsEyheLxrHCpMJcCB7IiIiAxC46eQzpw5g7p160qvhRA4d+4c7ty5AwDIzMzUfXQ1HCdzJCIiMg6NE5ipU6dWWDZjxgy1149bjcz9fWD4FBIREZFhaJTAcALHyon7RuLlZI5ERESGwTvuI5AGspMeo2YNDBERkSFoVAPTunVrjZuHLl++/EgByQkHsiMiIjIOjRKYr7/+Wvr5/Pnz2LRpE1599VX4+vrC3Nwcly5dwsqVKzFmzBi9BVojSX1g2ImXiIjIkDRKYIKDg6Wf582bh//973/o3LmztKx169Zwc3PDnDlzMHbsWJ0HWVMppZF4VZM5sgaGiIjIELSuMkhKSoKzs3OF5dbW1rh7965OgpILDmRHRERkHFrfcbt37465c+fi9OnTyM3NRU5ODo4fP465c+eiX79++oixxmIfGCIiIuPQeBwYlQ8++ADh4eEYPXo0lKqmEzMzDB48GO+++67OA6zJVCPxFnMyRyIiIoPSOoGxs7PDsmXLMH/+fMTGxgIAmjdvDjs7O50HV9NJj1FzMkciIiKD0qjKwN/fH2lpaWrL7Ozs4OvrC19f3wrJS2pqKvz9/XUXZQ3FPjBERETGoVENTEFBATZs2AAbGxuNdpqbm4vCwsJHCkwWhIAQ4l4TEmtgiIiIDEKjBKZDhw44f/68Vjtu3779QwUkJ0pxr/8LwD4wREREhqJRAvPNN9/oOw5ZErjXfASwBoaIiMhQWGXwCIS414EX4GSOREREhsI77iO51/9FAYAVMERERIbBBOYRlDYh3RvETtMJL4mIiOjRMIF5BKJcJ14+Qk1ERGQ4Wt91i4uLsXnzZiQkJAAAPvvsMwwYMAAzZ85ERkaGruOr0cp34uVEjkRERIajdQLz8ccfY9WqVbh79y5+/fVXrF+/HoMHD8bt27exYMECfcRYYwkhyjUhsQaGiIjIULS+6/7yyy9YsWIFWrdujX379iE0NBQTJkxAeHg4Dh8+rNW+CgsLMX/+fHTo0AGdOnXCJ598Is0vdOnSJQwbNgz+/v4YOnQoLly4oG2oeicEUMRB7IiIiAxO6wQmLy8Pzs7OKC4uxp9//okePXoAAJRKJczMtJtaaeHChfj777+xceNGLFu2DFu3bsWWLVuQm5uLCRMmoH379ti+fTsCAwMxceJE5ObmahuuXgkAxayBISIiMjitJ3Ns164dlixZAjs7O+Tl5aFnz56IiorCggUL8MQTT2i8n4yMDGzbtg2bNm2Cn58fAGDcuHGIjIyEmZkZLC0t8fbbb0OhUOCdd97Bn3/+if3792PIkCHahqw3SiHYB4aIiMgItK42WLhwIYqKinDx4kUsWrQIzs7O2LdvH5ydnREeHq7xfiIiImBnZ4fg4GBp2YQJE7Bo0SJERkYiKChIeixZoVCgXbt2OHv2rLbh6l0ha2CIiIgMTusamIYNG2L16tVqy6ZPn671gW/evAk3Nzfs3LkTa9asQVFREYYMGYLJkycjOTkZLVu2VNve2dkZV69e1fo4+hyapfxIvJZmJno9lqGoylAbylIZlk/eWD55Y/nkzVDl03T/Wicwubm5+PHHH3Ht2rVKZ5xetGiRxvuJi4vDDz/8gEWLFiE5ORnz5s2DtbU18vLyYGFhoba9hYXFQ81w7exsr/V7NGVqagILG0sAgK2VOerV09+xDE2f560mYPnkjeWTN5ZP3mpK+bROYN58802cOXMGnTp1gpWV1cMf2MwM2dnZWLZsGdzc3AAACQkJ2Lx5M9zd3SskK4WFhQ91vNTULAjx4O0eRlFxCVLTcwAACqFESkqWfg5kQApF6YdTn+fNmFg+eWP55I3lkzdDlU91nAfROoE5ceIEvvjiCwQGBj5UYCouLi6wtLSUkhcAaN68OW7fvo3g4GCkpKSobZ+SkoL69etrfRwhoLcTLQAUFJc2IVmYmtSqD6w+z1tNwPLJG8snbyyfvNWU8mnd89TDwwP5+fmPfGB/f38UFBQgNjZWWnbt2jW4ubnB398fZ86ckcaEEULg9OnT8Pf3f+Tj6pIQQkpgLM3YiZeIiMhQtK6B+fjjjzF16lQMGjQIjRo1gomJ+o376aef1mg/Hh4e6N69O+bMmYP3338fycnJWLduHSZPnoy+ffti2bJl+PDDDzF8+HD88MMPyMvLQ79+/bQNV6+EAArL1cAQERGRYWidwGzduhVxcXHYvHkzLC0t1dYpFAqNExgAWLp0KRYsWIAXXngB1tbWGDlyJEaPHg2FQoG1a9ciPDwcW7duhZeXF9atWwcbGxttw9UrgXuPUVuwBoaIiMhgtE5gfvrpJ3zyySfo37//Ix/c3t4eixcvrnSdn58fduzY8cjH0KfSJqTSZi42IRERERmO1nddR0fHCmO0PK7UamDYhERERGQwWtfAhIeH44MPPsCUKVPQuHFjmJqaqq1v1KiRzoKr6dT6wLAGhoiIyGC0TmAmTpwIAHjppZekof6B0uYUhUKBy5cv6y66Gk4AKFCNxMsaGCIiIoPROoHZvXs3bG1t9RGL7AghWANDRERkBFonMJMmTcLKlSvRtm1bfcQjK0o2IRERERmF1nddExMTFBUV6SMW2RFCSE1IVkxgiIiIDEbrGpju3bvjpZdeQo8ePeDm5lZh0sWpU6fqLLiaTsmB7IiIiIxC6wTmypUr8Pb2RlJSEpKSktTWKWrrHOJV4EB2RERExqF1AvPNN99otN26deswfPhw1KlTR+ug5EJtLiTWwBARERmM3u66a9asQWZmpr52XyNwHBgiIiLj0NtdV9SEubb1TCkEm5CIiIiMgHfdRyAANiEREREZgdZ9YOieEqWAsqymiTUwREREhsO77iMwNVGUe4z68XoCi4iIyJiYwDwCa3NTFJaU1sBYsgaGiIjIYHjXfQSq/i8Am5CIiIgMSWd33YKCAty8eVN6PWzYMNjZ2elq9zVSflGJ9DM78RIRERmOzu66J0+eRO/evaXXc+fOhaOjo652XyPll9XAmChK+8MQERGRYbDaQAcsTE0eu2kUiIiIjIkJjA6wAy8REZFh8c6rA+bs/0JERGRQGg1kd+rUqQduc+XKlUcORq5YA0NERGRYGiUwo0eP1mhnj2s/ED5CTUREZFgaJTBRUVH6jkPW+Ag1ERGRYfHOqwOsgSEiIjIs3nl1gH1giIiIDIt3Xh1gAkNERGRYvPPqgAX7wBARERkU77w6wD4wREREhsU7rw7wKSQiIiLD4p1XB1gDQ0REZFi88+oA+8AQEREZFu+8OmBpztNIRERkSLzz6gD7wBARERkW77w6wD4wREREhsU7rw6wDwwREZFh8c6rA5Zmj+cs3ERERMbCBEYH2IRERERkWLzz6oClmamxQyAiInqsMIHRAT6FREREZFi88+qABfvAEBERGRQTGB3gU0hERESGxTuvDliyEy8REZFB8c6rA+zES0REZFhMYHTAwdrM2CEQERE9VpjA6EAdK3Njh0BERPRYYQKjA6YmfAqJiIjIkJjAEBERkewwgSEiIiLZYQJDREREssME5hG1b+pg7BCIiIgeO3z+9xG806sVuresZ+wwiIiIHjtMYB7B034NjR0CERHRY4lNSERERCQ7TGCIiIhIdoyawBw6dAheXl5q/6ZNmwYAmDx5coV1f/zxhzHDJSIiohrCqH1goqOj0aNHDyxYsEBaZmlpCQCIiYnBkiVL0LFjR2ld3bp1DR4jERER1TxGTWBiYmLg6ekJFxcXteWFhYWIj4+Hr69vhXVERERERm1CiomJQbNmzSosv3btGhQKBZo0aWL4oIiIiKjGM1oNjBACsbGxOHbsGNauXYuSkhL07dsX06ZNw7Vr12BnZ4e3334bJ0+eRIMGDfDaa6+hW7duWh9Hocd5FvW5b2NRlak2lg1g+eSO5ZM3lk/eDFU+TfdvtAQmISEBeXl5sLCwwPLlyxEfH4+FCxciPz8fDg4OyM/PR2hoKCZMmIBDhw5h8uTJ2LJlC3x9fbU6jrOzvZ5KANSrp799G5s+z1tNwPLJG8snbyyfvNWU8hktgXFzc8OJEydQt25dKBQKtGnTBkqlEjNnzsSZM2cwevRoqdNu69atcfHiRWzdulXrBCY1NQtC6KMEQEpKln52bEQKRemHU5/nzZhYPnlj+eSN5ZM3Q5VPdZwHMWonXgcHB7XXLVq0QEFBATIzM+Hk5KS2zsPDA9HR0VofQwjo7UTXxg+oij7PW03A8skbyydvLJ+81ZTyGa0T79GjRxESEoK8vDxp2eXLl+Hg4IDFixdjzpw5attHRUXBw8PD0GESERFRDWS0BCYwMBCWlpZ49913ce3aNRw5cgSLFy/G+PHjERYWhj179mDnzp2Ii4vDypUrERERgVGjRhkrXCIiIqpBjNaEZGdnh40bN+Kjjz7C0KFDYWtri+HDh2P8+PFQKBQIDw/H6tWrkZCQgFatWmHDhg1o3LixscIlIiKiGsSofWBatWqFTZs2Vbpu2LBhGDZsmIEjIiIiIjngZI5EREQkO0xgiIiISHaYwBAREZHsMIEhIiIi2WECQ0RERLLDBIaIiIhkhwkMERERyQ4TGCIiIpIdJjBEREQkO0xgiIiISHaYwBAREZHsMIEhIiIi2WECQ0RERLLDBIaIiIhkhwkMERERyQ4TGCIiIpIdJjBEREQkO0xgiIiISHaYwBAREZHsMIEhIiIi2WECQ0RERLLDBIaIiIhkhwkMERERyQ4TGCIiIpIdJjBEREQkO0xgiIiISHaYwBAREZHsMIEhIiIi2WECQ0RERLLDBOYheTjbGDsEIiKixxYTmIdkZ2lm7BCIiIgeW0xgiIiISHaYwBAREZHsMIEhIiIi2WECQ0RERLLDBIaIiIhkhwkMERERyQ4TGCIiIpIdJjBEREQkO0xgiIiISHaYwBAREZHsMIEhIiIi2WECQ0RERLLDBIaIiIhkhwkMERERyQ4TGCIiIpIdJjBEREQkO0xgiIiISHaYwBAREZHsMIEhIiIi2WECQ0RERLLDBIaIiIhkhwkMERERyQ4TGCIiIpIdoyYwhw4dgpeXl9q/adOmAQAuXbqEYcOGwd/fH0OHDsWFCxeMGSoRERHVIEZNYKKjo9GjRw8cO3ZM+rdw4ULk5uZiwoQJaN++PbZv347AwEBMnDgRubm5xgyXiIiIagijJjAxMTHw9PSEi4uL9K9OnTr45ZdfYGlpibfffhstWrTAO++8A1tbW+zfv9+Y4aqxMFUYOwQiIqLHltETmGbNmlVYHhkZiaCgICgUpUmCQqFAu3btcPbsWcMGWIn3+niieT1bzO3taexQiIiIHltGS2CEEIiNjcWxY8fQp08f9OzZE0uXLkVhYSGSk5NRv359te2dnZ1x584dI0V7z2DfBvjjre5o6mht7FCIiIgeW2bGOnBCQgLy8vJgYWGB5cuXIz4+HgsXLkR+fr60vDwLCwsUFhZqfRyFjlt6VPvT9X5rCpZP3lg+eWP55I3l0+1xHsRoCYybmxtOnDiBunXrQqFQoE2bNlAqlZg5cyaCg4MrJCuFhYWwsrLS+jjOzva6Ctkg+60pWD55Y/nkjeWTN5bPMIyWwACAg4OD2usWLVqgoKAALi4uSElJUVuXkpJSoVlJE6mpWRDiUaJUp1CUXjxd77emYPnkjeWTN5ZP3lg+3R7nQYyWwBw9ehRvvfUWDh8+DGvr0v4kly9fhoODA4KCgrB+/XoIIaBQKCCEwOnTpzFp0iStjyME9HKi9bXfmoLlkzeWT95YPnlj+QzDaJ14AwMDYWlpiXfffRfXrl3DkSNHsHjxYowfPx59+/bF3bt38eGHHyI6Ohoffvgh8vLy0K9fP2OFS0RERDWI0RIYOzs7bNy4EWlpaRg6dCjeeecdPP/88xg/fjzs7Oywdu1aREREYMiQIYiMjMS6detgY2NjrHCJiIioBjFqH5hWrVph06ZNla7z8/PDjh07DBwRERERyQEncyQiIiLZYQJDREREssMEhoiIiGSHCQwRERHJDhMYIiIikh0mMERERCQ7TGCIiIhIdow6DowhcDZq7bB88sbyyRvLJ28sn26P88DthKgJMxoQERERaY5NSERERCQ7TGCIiIhIdpjAEBERkewwgSEiIiLZYQJDREREssMEhoiIiGSHCQwRERHJDhMYIiIikh0mMERERCQ7TGC0UFBQgLlz56J9+/YIDQ3FF198YeyQqnXo0CF4eXmp/Zs2bRoA4NKlSxg2bBj8/f0xdOhQXLhwQe29e/fuRc+ePeHv748pU6YgLS1NWieEwNKlS/HEE08gODgYixcvhlKpNFi5CgsLMXDgQJw4cUJadvPmTYwdOxYBAQHo378/jh07pvaev//+GwMHDoS/vz/GjBmDmzdvqq3/8ssv0aVLFwQGBmLu3LnIy8uT1hn6uldWvoULF1a4lt9++620/lGuV3p6Ol577TUEBgYiLCwMu3bt0ku5EhMTMW3aNAQHB6NLly5YtGgRCgoKANSO61dd+WrD9YuLi8PLL7+MwMBAdO/eHRs2bJDW1YbrV135asP1K2/ChAmYPXu29Fqf9wO9lk+Qxj744AMxaNAgceHCBXHw4EERGBgo9u3bZ+ywqrRq1SoxceJEkZSUJP3LzMwUOTk5onPnzuLjjz8W0dHRYsGCBaJTp04iJydHCCFEZGSk8PPzEzt27BCXL18Wo0aNEhMmTJD2u3HjRtGtWzdx6tQp8c8//4jQ0FCxYcMGg5QpPz9fTJkyRXh6eorjx48LIYRQKpVi0KBBYsaMGSI6OlqsWbNG+Pv7i1u3bgkhhLh165YICAgQGzduFP/99594/fXXxcCBA4VSqRRCCLF//34RFBQkfv/9dxEZGSn69+8v5s+fLx3TkNe9svIJIcTYsWPF2rVr1a5lbm6uEOLRr9fEiRPFiy++KK5cuSK2bt0qfHx8RGRkpE7LpVQqxXPPPSfGjx8v/vvvP3Hq1CnRq1cv8fHHH9eK61dd+YSQ//UrKSkRvXv3FjNmzBCxsbHi8OHDol27dmL37t214vpVVz4h5H/9ytu7d6/w9PQUs2bNEkIIvd8P9Fk+JjAaysnJEb6+vmo3lf/7v/8To0aNMmJU1ZsxY4ZYtmxZheU//vijCAsLk75AlEql6NWrl9i2bZsQQoiZM2dKH24hhEhISBBeXl7ixo0bQgghunXrJm0rhBA7d+4UPXr00GdRhBBCXL16VTz11FNi0KBBajf4v//+WwQEBEi/cEII8eKLL4rPP/9cCCHE8uXL1a5Tbm6uCAwMlN4/YsQIaVshhDh16pTw8/MTubm5Br3uVZVPCCG6dOkijh49Wun7HuV6xcXFCU9PT3Hz5k1p/dy5c9X2pwvR0dHC09NTJCcnS8v27NkjQkNDa8X1q658Qsj/+iUmJorXX39dZGVlScumTJkiwsPDa8X1q658Qsj/+qmkp6eLrl27iqFDh0rH0Of9QN/lYxOShqKiolBcXIzAwEBpWVBQECIjIw3afKKNmJgYNGvWrMLyyMhIBAUFQVE25adCoUC7du1w9uxZaX379u2l7Rs2bIhGjRohMjISiYmJuH37Njp06CCtDwoKwq1bt5CUlKTX8pw8eRIhISHYsmVLhfK0bdsWNjY2ajFVVR5ra2t4e3vj7NmzKCkpwfnz59XWBwQEoKioCFFRUQa97lWVLzs7G4mJiZVey8rKp831ioyMRMOGDdG4cWO19WfOnNFp2VxcXLBhwwbUq1evQtlqw/Wrrny14frVr18fy5cvh52dHYQQiIiIwKlTpxAcHFwrrl915asN10/lf//7HwYPHoyWLVuqxa+v+4G+y8cERkPJyclwdHSEhYWFtKxevXooKChARkaG8QKrghACsbGxOHbsGPr06YOePXti6dKlKCwsRHJyMurXr6+2vbOzM+7cuQMASEpKqnJ9cnIyAKitV31pq96vLyNGjMDcuXNhbW2ttvxB5alu/d27d1FQUKC23szMDA4ODlJ5DXXdqypfTEwMFAoF1qxZg65du+Kpp57Cjh07pPWPcr2qOjeJiYk6LVudOnXQpUsX6bVSqcS3336LJ554olZcv+rKVxuuX3lhYWEYMWIEAgMD0adPn1px/aorX225fv/88w/+/fdfvPrqq2rL9Xk/0Hf5zHSyl8dAXl6e2i8RAOl1YWGhMUKqVkJCghTz8uXLER8fj4ULFyI/P7/KsqjKkZ+fX+X6/Px86XX5dYDxzsODylPd+srKU369EMLo1/3atWtQKBTw8PDAqFGjcOrUKbz33nuws7NDr169Hul6Pejc6cuSJUtw6dIl/PTTT/jyyy9r3fUrX76LFy/Wquv3+eefIyUlBe+//z4WLVpU637/7i+ft7e37K9fQUEBwsPDMW/ePFhZWamt0+f9QN/lYwKjIUtLywonXfX6/g9ETeDm5oYTJ06gbt26UCgUaNOmDZRKJWbOnIng4OBKy6IqR1Vltba2VvtwWlpaSj8DqFBzYCiWlpYV/hrTpDx16tSpUIby662trVFSUmL06/7000+jR48ecHBwAAC0bt0a169fx+bNm9GrV69Hul5VvVefZVuyZAm++uorfPrpp/D09Kx11+/+8rVq1apWXT9fX18ApTfFt956C0OHDlV7auj+GOR2/e4v3+nTp2V//VauXAkfHx+1WkKVB8VQk8vHJiQNubq6Ij09HcXFxdKy5ORkWFlZoU6dOkaMrGoODg5SuyYAtGjRAgUFBXBxcUFKSoratikpKVJVn6ura6XrXVxc4OrqCgBS1WH5n11cXPRSjgepKl5NyuPg4ABLS0u19cXFxcjIyJDKa+zrrlAopC9PFQ8PD6ka9lGuV3Xv1YcFCxZg06ZNWLJkCfr06VNt/HK8fpWVrzZcv5SUFPz6669qy1q2bImioqJH+j6pKdevuvJlZ2fL/vr9/PPP+PXXXxEYGIjAwEDs2bMHe/bsQWBg4CP9/hm7fExgNNSmTRuYmZlJHZsAICIiAr6+vjAxqXmn8ejRowgJCVH7y+jy5ctwcHCQOlEJIQCU9pc5ffo0/P39AQD+/v6IiIiQ3nf79m3cvn0b/v7+cHV1RaNGjdTWR0REoFGjRhXaOg3F398fFy9elKozVTFVVZ68vDxcunQJ/v7+MDExga+vr9r6s2fPwszMDK1bt64R1/2zzz7D2LFj1ZZFRUXBw8MDwKNdr4CAANy6dUut/1JERAQCAgJ0Xo6VK1fihx9+wCeffIIBAwZIy2vL9auqfLXh+sXHx2Pq1KlqfRcuXLgAJycnBAUFyf76VVe+b775RvbX75tvvsGePXuwc+dO7Ny5E2FhYQgLC8POnTvh7++vt/uB3sunk2eZHhPvvfeeGDBggIiMjBSHDh0S7dq1EwcOHDB2WJXKysoSXbp0EW+++aaIiYkRhw8fFqGhoWLdunUiKytLPPHEE2LBggXi6tWrYsGCBaJz587SY5CnT58W3t7eYuvWrdJz/xMnTpT2vXbtWhEaGiqOHz8ujh8/LkJDQ8UXX3xh0PKVf8y4uLhY9O/fX7zxxhviv//+E2vXrhUBAQHSOBQ3b94Uvr6+Yu3atdI4FIMGDZIeG9y7d69o166dOHTokIiMjBQDBgwQCxYskI5ljOtevnyRkZGibdu2YsOGDSIuLk589913wsfHR5w+fVoI8ejXa9y4cWLUqFHi8uXLYuvWrcLX11fn41BER0eLNm3aiE8//VRtLI2kpKRacf2qK19tuH7FxcViyJAhYty4ceLq1avi8OHDolOnTuLLL7+sFdevuvLVhut3v1mzZkmPMuv7fqDP8jGB0UJubq54++23RUBAgAgNDRWbNm0ydkjV+u+//8TYsWNFQECA6Ny5s1ixYoX0pREZGSmefvpp4evrK5599llx8eJFtfdu27ZNdOvWTQQEBIgpU6aItLQ0aV1xcbH46KOPRPv27UVISIhYsmSJtF9DuX+clOvXr4uRI0cKHx8fMWDAAPHXX3+pbX/48GHRu3dv4efnJ1588UVpDAOVtWvXio4dO4qgoCAxZ84ckZ+fL60zxnW/v3yHDh0SgwYNEr6+vqJv374VvsAf5XqlpKSIiRMnCl9fXxEWFib27Nmj8/KsXbtWeHp6VvpPCPlfvweVT+7XTwgh7ty5I6ZMmSLatWsnOnfuLFavXi3FIffr96Dy1YbrV175BEYI/d4P9Fk+hRBl9UZEREREMlHzOm8QERERPQATGCIiIpIdJjBEREQkO0xgiIiISHaYwBAREZHsMIEhIiIi2WECQ0RERLLDBIaIHslvv/2Grl27wt/fH0ePHjV2OET0mOBAdkT0SAYPHgxvb29MmTIFzs7ONXJ2diKqfcyMHQARyVtWVhaCgoLg5uZm7FCI6DHCJiQiemhhYWG4desW5s6di7CwMHh5eeH//u//0KFDB3zwwQcAgEOHDqF///7w9/fHs88+i5MnT0rvLywsxMKFC9G+fXt069YN+/btg5eXF+Lj4wEAXl5eOHHihLT99u3bERYWJr3+77//MHr0aPj5+aFPnz747rvvpHUrVqzAjBkzEB4ejnbt2qFjx45Yv369tL64uBiffPIJQkNDERQUhGnTpiE9PR27d+9GSEgIiouLpW0PHDiA7t27gxXWRDUHExgiemg//fQTGjRogLlz52L58uUAgNOnT2Pbtm0YM2YMoqKiMGvWLEyePBm7d+/GU089hVdeeQVxcXEAgOXLl+P333/H6tWrsWzZMqxYsULjY+fn5+OVV15BUFAQdu/ejVmzZmHVqlXYuXOntM2BAwdgaWmJHTt24OWXX8bSpUsRGxsLAPjss8+wY8cOfPTRR9iyZQtSU1MRHh6OJ598Evn5+Th+/Li0n3379qFfv35QKBSPftKISCeYwBDRQ3NycoKpqSns7e3h5OQEAHjxxRfRtGlTNGvWDBs3bsRzzz2HQYMGwd3dHWPGjEHXrl2xefNmKJVKbNu2DdOmTUOHDh3Qvn17zJw5U+Nj79mzB87OznjjjTfQrFkzhIWFYdKkSfj666+lbRwcHDBr1iy4u7tj/PjxcHBwwIULFyCEwNatWzF9+nR07doVLVu2xPz589GqVSvY2tqiR48e2L9/PwAgLy8PR44cwYABA3R78ojokbAPDBHpVPm+MDExMdi3bx+2bNkiLSsqKkJoaCjS09ORkZGBNm3aSOsCAwM1Ps61a9cQFRWl9p6SkhKYmppKrxs3bqz22tbWFsXFxdKxvb29pXUtW7bEa6+9BgAYOHAg3n33Xbz//vs4fPgw6tevDx8fH41jIyL9YwJDRDplaWkp/VxSUoJXXnkFTz/9tNo2VlZW0tNK5fuVWFhYVLvvkpIS6efi4mJ07NgR8+bNq3J7c3PzCsuEEDAzq/6rr2vXrigpKcGpU6dw4MAB9OvXr9rticjw2IRERHrTvHlzxMfHw93dXfq3ZcsW/Pnnn7C1tUXDhg1x/vx5aftLly6pvd/c3Bw5OTnS65s3b6rtOzY2Fo0bN5b2ffbsWXzzzTcPjKtOnTpwdHREVFSUtOzy5cvo2rUr8vPzYWFhgV69euHQoUP466+/2HxEVAMxgSEivRk7dix++eUXfP3117hx4wa+/PJLfPnll2jWrBkA4OWXX8Znn32Gv/76CxcuXMDChQvV3u/r64tvv/0W169fx2+//Ybt27dL65566ink5+dj3rx5iImJwZEjR/Dhhx/C2dlZo9hGjx6Nzz77DMePH8fVq1fx4YcfIiAgQKoZGjhwoNRJuVWrVro5IUSkM2xCIiK9CQgIwOLFi7FixQosXrwYTZs2xbJly9ChQwcAwMiRI5GSkoI333wTZmZmGDt2LC5fviy9/7333sO7776LgQMHwtfXF9OmTcOaNWsAAHZ2dli/fj0++ugjPP3003BwcMDIkSMxceJEjWKbMGECsrKy8MYbb6C4uBjdu3fHe++9J60PCQmBra0t+vfvr8MzQkS6wpF4iajGiI+Px5NPPonffvsNjRs3Nmos2dnZ6Ny5M/bu3YsmTZoYNRYiqog1MERE5QghcODAARw8eBCBgYFMXohqKCYwRETlKBQKLFmyBKampli9erWxwyGiKrAJiYiIiGSHTyERERGR7DCBISIiItlhAkNERESywwSGiIiIZIcJDBEREckOExgiIiKSHSYwREREJDtMYIiIiEh2mMAQERGR7Pw/DsDKLUPun7IAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_ds.sel(id=0)['oct3'].plot()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}