notebooks/Sentinel-2/sentinel2_zonal_statistics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "990a0ac2-5c94-4027-99be-b7559ff085c6",
   "metadata": {},
   "source": [
    "# Zonal Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a49d267-55ed-4bc7-bd38-2fabf9618bbb",
   "metadata": {},
   "source": [
    "Author: [Lukas Valentin Graf](https://github.com/lukasValentin/lukasValentin) (2022-2023)\n",
    "\n",
    "## Learning Objectives\n",
    "\n",
    "In this notebook you will learn how to\n",
    "\n",
    "* calculate zonal statistics for agricultural field parcels\n",
    "* see how these statistics change over time\n",
    "\n",
    "## Tutorial Content\n",
    "\n",
    "This tutorial is introductorial level. \n",
    "\n",
    "Basic knowledge about [GeoPandas](https://geopandas.org/en/stable/) and EOdal might be helpful. If you are unfamiliar with EOdal you might check out [these notebooks](../General) first.\n",
    "\n",
    "If you don't know about zonal statistics you might find reading [this blog](https://up42.com/blog/an-introduction-to-zonal-statistics) helpful.\n",
    "\n",
    "To run this notebook no additional requirements in terms of software-setup are necessary. It connects, however, to [Microsoft Planetary Computer](https://planetarycomputer.microsoft.com/). No authentication is required but stable internet connection is an asset.\n",
    "\n",
    "The data required to run this notebook can be found [here](./../data)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eee46a24-0d77-4179-af4d-70ad656dae18",
   "metadata": {},
   "source": [
    "### Preparing the environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "21eb2d1c-83af-4079-a261-da1318064da3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scipy in /mnt/ides/Lukas/venvs/geo_notebooks/lib64/python3.10/site-packages (1.10.1)\n",
      "Requirement already satisfied: numpy<1.27.0,>=1.19.5 in /mnt/ides/Lukas/venvs/geo_notebooks/lib64/python3.10/site-packages (from scipy) (1.23.5)\n"
     ]
    }
   ],
   "source": [
    "!pip install scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "540bbfb8-b0e6-4e0b-b80c-b18544934e54",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "\n",
    "from datetime import datetime\n",
    "from eodal.config import get_settings\n",
    "from eodal.core.scene import SceneCollection\n",
    "from eodal.core.sensors.sentinel2 import Sentinel2\n",
    "from eodal.mapper.feature import Feature\n",
    "from eodal.mapper.filter import Filter\n",
    "from eodal.mapper.mapper import Mapper, MapperConfigs\n",
    "from scipy.stats import median_abs_deviation\n",
    "\n",
    "from pathlib import Path\n",
    "from typing import List\n",
    "\n",
    "Settings = get_settings()\n",
    "# set to False to use a local data archove\n",
    "Settings.USE_STAC = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "995711b0-02ee-4405-939c-5766a4994137",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The EOdal version is 0.2.0\n"
     ]
    }
   ],
   "source": [
    "# check EOdal version\n",
    "import eodal\n",
    "print(f'The EOdal version is {eodal.__version__}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ce5056-3c30-49c8-bf59-dcc363d186d1",
   "metadata": {},
   "source": [
    "### Defining custom metrics for zonal statistics\n",
    "\n",
    "EOdal makes use of the [rasterstats](https://pythonhosted.org/rasterstats/) package to calculate zonal statistics. The cool thing about `rasterstats` is its ability to take custom functions as an input for calculating user-defined statistical metrics. This means, users will have a lot of freedom choicing the metrics most suited to their needs.\n",
    "\n",
    "Below you find two examples of such custom functions. There are two important things to consider when writing user-defined functions:\n",
    "\n",
    "1. the *function name* should be unique and not have the same name as any existing numpy function. Therefore, using a prefix such as `my_` might be helpful.\n",
    "2. the *function should return a single scalar* as this is the definition of doing zonal statistics. *N* raster cells overlapping a geometry are aggregated by the function into a single value.\n",
    "\n",
    "The two functions below calculate the `median absolute deviation` and the `sum of the square` of the raster cell values. This has no deeper meaning and is just to show how such custom functions should be designed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2c82618d-f5b8-4f9a-a247-e9f21d67108c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def my_median_abs_deviation(x: np.ma.MaskedArray) -> float:\n",
    "    \"\"\"\n",
    "    Custom function to calculate the median absolute\n",
    "    deviation in `eodal.scene.get_feature_timeseries()`.\n",
    "\n",
    "    :param x:\n",
    "        array with raster values\n",
    "    :returns:\n",
    "        median absolute deviation value\n",
    "    \"\"\"\n",
    "    x = x.filled(np.nan)\n",
    "    return median_abs_deviation(x, nan_policy='omit', axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "de6ef9a3-a57a-4ed0-bc07-40e455f8caa4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def my_square_sum(x: np.ma.MaskedArray) -> float:\n",
    "    \"\"\"\n",
    "    Custom function returning sum(x**2)\n",
    "\n",
    "    :param x:\n",
    "        array with raster values\n",
    "    :returns:\n",
    "        median absolute deviation value\n",
    "    \"\"\"\n",
    "    x = x.filled(np.nan)\n",
    "    # important: the sum of nan is zero!\n",
    "    if np.isnan(x).all():\n",
    "        return np.nan\n",
    "    return np.nansum(x*x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86526e1e-0e53-4512-86ff-9bbc3d96686a",
   "metadata": {},
   "source": [
    "### Getting the Sentinel-2 Data\n",
    "\n",
    "This part of the notebook is essentially the same as in the [EOdal mapper notebook](../General/EOdal_Mapper.ipynb). We therefore don't repeat explainations here and recommened to have a look at the aforementioned notebook for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e3093965-5018-42df-bad0-9332f589be44",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def preprocess_sentinel2_scenes(\n",
    "    ds: Sentinel2,\n",
    "    target_resolution: int,\n",
    ") -> Sentinel2:\n",
    "    \"\"\"\n",
    "    Resample Sentinel-2 scenes and mask clouds, shadows, and snow\n",
    "    based on the Scene Classification Layer (SCL).\n",
    "\n",
    "    NOTE:\n",
    "        Depending on your needs, the pre-processing function can be\n",
    "        fully customized using the full power of EOdal and its\n",
    "        interfacing libraries!\n",
    "\n",
    "    :param target_resolution:\n",
    "        spatial target resolution to resample all bands to.\n",
    "    :returns:\n",
    "        resampled, cloud-masked Sentinel-2 scene.\n",
    "    \"\"\"\n",
    "    # resample scene\n",
    "    ds.resample(inplace=True, target_resolution=target_resolution)\n",
    "    # mask clouds, shadows, and snow\n",
    "    ds.mask_clouds_and_shadows(inplace=True)\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ba01bae5-7479-4019-aa8b-9e997fd3c26f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#%% user-inputs\n",
    "# -------------------------- Collection -------------------------------\n",
    "collection: str = 'sentinel2-msi'\n",
    "\n",
    "# ------------------------- Time Range ---------------------------------\n",
    "time_start: datetime = datetime(2022,3,1)  \t\t# year, month, day (incl.)\n",
    "time_end: datetime = datetime(2022,5,1)   \t\t# year, month, day (incl.)\n",
    "\n",
    "# ---------------------- Spatial Feature  ------------------------------\n",
    "geom: Path = Path('../../data/sample_polygons/ZH_Polygons_2020_ESCH_EPSG32632.shp')\n",
    "\n",
    "# ------------------------- Metadata Filters ---------------------------\n",
    "metadata_filters: List[Filter] = [\n",
    "    Filter('cloudy_pixel_percentage','<', 80),\n",
    "    Filter('processing_level', '==', 'Level-2A')\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "704cd55e-67e9-4664-a043-cfff943b9ba8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EOdal MapperConfig\n",
       "------------------\n",
       "Collection: sentinel2-msi\n",
       "Time Range: 2022-03-01 00:00:00 - 2022-05-01 00:00:00\n",
       "Feature:\n",
       "Name\t\tgeometry\n",
       "Geometry\tPOLYGON ((477318.674545628 5255957.44910519, 477461.8062640463 5255880.593509117, 477450.87168848683 5255864.685411847, 477443.1052694199 5255855.242571056, 477302.4762257559 5255930.758590063, 477318.674545628 5255957.44910519))\n",
       "EPSG Code\t32632\n",
       "Attributes\t{}\n",
       "Metadata Filters: [cloudy_pixel_percentage < 80, processing_level == Level-2A]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#%% query the scenes available (no I/O of scenes, this only fetches metadata)\n",
    "feature = Feature.from_geoseries(gpd.read_file(geom).geometry)\n",
    "mapper_configs = MapperConfigs(\n",
    "    collection=collection,\n",
    "    time_start=time_start,\n",
    "    time_end=time_end,\n",
    "    feature=feature,\n",
    "    metadata_filters=metadata_filters\n",
    ")\n",
    "mapper_configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9aaf3b23-9570-449a-9dca-7d2093e8d4ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_uri</th>\n",
       "      <th>scene_id</th>\n",
       "      <th>spacecraft_name</th>\n",
       "      <th>tile_id</th>\n",
       "      <th>sensing_date</th>\n",
       "      <th>cloudy_pixel_percentage</th>\n",
       "      <th>epsg</th>\n",
       "      <th>sensing_time</th>\n",
       "      <th>sun_azimuth_angle</th>\n",
       "      <th>sun_zenith_angle</th>\n",
       "      <th>geom</th>\n",
       "      <th>assets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>S2A_MSIL2A_20220305T102921_R108_T32TMT_2022030...</td>\n",
       "      <td>S2A_OPER_MSI_L2A_TL_ESRI_20220306T020615_A0349...</td>\n",
       "      <td>Sentinel-2A</td>\n",
       "      <td>32TMT</td>\n",
       "      <td>2022-03-05</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>S2B_MSIL2A_20220307T101759_R065_T32TMT_2022030...</td>\n",
       "      <td>S2B_OPER_MSI_L2A_TL_ESRI_20220308T005901_A0261...</td>\n",
       "      <td>Sentinel-2B</td>\n",
       "      <td>32TMT</td>\n",
       "      <td>2022-03-07</td>\n",
       "      <td>11.010902</td>\n",
       "      <td>32632</td>\n",
       "      <td>2022-03-07 10:17:59.024</td>\n",
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       "      <td>POLYGON ((7.74869 46.85849, 7.75737 46.88302, ...</td>\n",
       "      <td>{'AOT': {'href': 'https://sentinel2l2a01.blob....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>S2B_MSIL2A_20220310T102739_R108_T32TMT_2022031...</td>\n",
       "      <td>S2B_OPER_MSI_L2A_TL_ESRI_20220311T101831_A0261...</td>\n",
       "      <td>Sentinel-2B</td>\n",
       "      <td>32TMT</td>\n",
       "      <td>2022-03-10</td>\n",
       "      <td>0.632361</td>\n",
       "      <td>32632</td>\n",
       "      <td>2022-03-10 10:27:39.025</td>\n",
       "      <td>161.482896</td>\n",
       "      <td>52.994047</td>\n",
       "      <td>POLYGON ((7.66287 47.84591, 9.13047 47.85363, ...</td>\n",
       "      <td>{'AOT': {'href': 'https://sentinel2l2a01.blob....</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>S2B_MSIL2A_20220320T102639_R108_T32TMT_2022032...</td>\n",
       "      <td>S2B_OPER_MSI_L2A_TL_ESRI_20220321T005358_A0263...</td>\n",
       "      <td>Sentinel-2B</td>\n",
       "      <td>32TMT</td>\n",
       "      <td>2022-03-20</td>\n",
       "      <td>0.338798</td>\n",
       "      <td>32632</td>\n",
       "      <td>2022-03-20 10:26:39.024</td>\n",
       "      <td>161.262135</td>\n",
       "      <td>48.991058</td>\n",
       "      <td>POLYGON ((7.66287 47.84591, 9.13047 47.85363, ...</td>\n",
       "      <td>{'AOT': {'href': 'https://sentinel2l2a01.blob....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>S2A_MSIL2A_20220322T101711_R065_T32TMT_2022032...</td>\n",
       "      <td>S2A_OPER_MSI_L2A_TL_ESRI_20220323T030838_A0352...</td>\n",
       "      <td>Sentinel-2A</td>\n",
       "      <td>32TMT</td>\n",
       "      <td>2022-03-22</td>\n",
       "      <td>1.812012</td>\n",
       "      <td>32632</td>\n",
       "      <td>2022-03-22 10:17:11.024</td>\n",
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       "      <td>{'AOT': {'href': 'https://sentinel2l2a01.blob....</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>S2A_MSIL2A_20220325T102651_R108_T32TMT_2022032...</td>\n",
       "      <td>S2A_OPER_MSI_L2A_TL_ESRI_20220325T235850_A0352...</td>\n",
       "      <td>Sentinel-2A</td>\n",
       "      <td>32TMT</td>\n",
       "      <td>2022-03-25</td>\n",
       "      <td>0.079917</td>\n",
       "      <td>32632</td>\n",
       "      <td>2022-03-25 10:26:51.024</td>\n",
       "      <td>161.201517</td>\n",
       "      <td>46.981061</td>\n",
       "      <td>POLYGON ((7.66287 47.84591, 9.13047 47.85363, ...</td>\n",
       "      <td>{'AOT': {'href': 'https://sentinel2l2a01.blob....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>S2B_MSIL2A_20220327T101629_R065_T32TMT_2022032...</td>\n",
       "      <td>S2B_OPER_MSI_L2A_TL_ESRI_20220328T020908_A0264...</td>\n",
       "      <td>Sentinel-2B</td>\n",
       "      <td>32TMT</td>\n",
       "      <td>2022-03-27</td>\n",
       "      <td>1.837532</td>\n",
       "      <td>32632</td>\n",
       "      <td>2022-03-27 10:16:29.024</td>\n",
       "      <td>157.802681</td>\n",
       "      <td>46.782320</td>\n",
       "      <td>POLYGON ((7.75253 46.85851, 7.80383 47.00126, ...</td>\n",
       "      <td>{'AOT': {'href': 'https://sentinel2l2a01.blob....</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>S2A_MSIL2A_20220404T102601_R108_T32TMT_2022040...</td>\n",
       "      <td>S2A_OPER_MSI_L2A_TL_ESRI_20220404T222443_A0354...</td>\n",
       "      <td>Sentinel-2A</td>\n",
       "      <td>32TMT</td>\n",
       "      <td>2022-04-04</td>\n",
       "      <td>43.516320</td>\n",
       "      <td>32632</td>\n",
       "      <td>2022-04-04 10:26:01.024</td>\n",
       "      <td>161.009776</td>\n",
       "      <td>43.034004</td>\n",
       "      <td>POLYGON ((7.66287 47.84591, 9.13047 47.85363, ...</td>\n",
       "      <td>{'AOT': {'href': 'https://sentinel2l2a01.blob....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>S2B_MSIL2A_20220406T101559_R065_T32TMT_2022040...</td>\n",
       "      <td>S2B_OPER_MSI_L2A_TL_ESRI_20220407T005151_A0265...</td>\n",
       "      <td>Sentinel-2B</td>\n",
       "      <td>32TMT</td>\n",
       "      <td>2022-04-06</td>\n",
       "      <td>29.913694</td>\n",
       "      <td>32632</td>\n",
       "      <td>2022-04-06 10:15:59.025</td>\n",
       "      <td>157.386954</td>\n",
       "      <td>42.869859</td>\n",
       "      <td>POLYGON ((7.74970 46.85850, 7.80005 46.99889, ...</td>\n",
       "      <td>{'AOT': {'href': 'https://sentinel2l2a01.blob....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>S2B_MSIL2A_20220409T102559_R108_T32TMT_2022041...</td>\n",
       "      <td>S2B_OPER_MSI_L2A_TL_ESRI_20220410T091212_A0265...</td>\n",
       "      <td>Sentinel-2B</td>\n",
       "      <td>32TMT</td>\n",
       "      <td>2022-04-09</td>\n",
       "      <td>71.353978</td>\n",
       "      <td>32632</td>\n",
       "      <td>2022-04-09 10:25:59.024</td>\n",
       "      <td>160.861845</td>\n",
       "      <td>41.124933</td>\n",
       "      <td>POLYGON ((7.66287 47.84591, 9.13047 47.85363, ...</td>\n",
       "      <td>{'AOT': {'href': 'https://sentinel2l2a01.blob....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S2A_MSIL2A_20220411T101601_R065_T32TMT_2022041...</td>\n",
       "      <td>S2A_OPER_MSI_L2A_TL_ESRI_20220412T095122_A0355...</td>\n",
       "      <td>Sentinel-2A</td>\n",
       "      <td>32TMT</td>\n",
       "      <td>2022-04-11</td>\n",
       "      <td>57.239157</td>\n",
       "      <td>32632</td>\n",
       "      <td>2022-04-11 10:16:01.024</td>\n",
       "      <td>157.170910</td>\n",
       "      <td>40.977996</td>\n",
       "      <td>POLYGON ((7.75911 46.85855, 7.77000 46.88972, ...</td>\n",
       "      <td>{'AOT': {'href': 'https://sentinel2l2a01.blob....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S2A_MSIL2A_20220414T102601_R108_T32TMT_2022041...</td>\n",
       "      <td>S2A_OPER_MSI_L2A_TL_ESRI_20220415T043913_A0355...</td>\n",
       "      <td>Sentinel-2A</td>\n",
       "      <td>32TMT</td>\n",
       "      <td>2022-04-14</td>\n",
       "      <td>6.772228</td>\n",
       "      <td>32632</td>\n",
       "      <td>2022-04-14 10:26:01.024</td>\n",
       "      <td>160.747609</td>\n",
       "      <td>39.265160</td>\n",
       "      <td>POLYGON ((7.66287 47.84591, 9.13047 47.85363, ...</td>\n",
       "      <td>{'AOT': {'href': 'https://sentinel2l2a01.blob....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S2B_MSIL2A_20220419T102559_R108_T32TMT_2022041...</td>\n",
       "      <td>S2B_OPER_MSI_L2A_TL_ESRI_20220419T211927_A0267...</td>\n",
       "      <td>Sentinel-2B</td>\n",
       "      <td>32TMT</td>\n",
       "      <td>2022-04-19</td>\n",
       "      <td>27.082452</td>\n",
       "      <td>32632</td>\n",
       "      <td>2022-04-19 10:25:59.024</td>\n",
       "      <td>160.499090</td>\n",
       "      <td>37.491044</td>\n",
       "      <td>POLYGON ((7.66287 47.84591, 9.13047 47.85363, ...</td>\n",
       "      <td>{'AOT': {'href': 'https://sentinel2l2a01.blob....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S2A_MSIL2A_20220421T101601_R065_T32TMT_2022042...</td>\n",
       "      <td>S2A_OPER_MSI_L2A_TL_ESRI_20220422T064357_A0356...</td>\n",
       "      <td>Sentinel-2A</td>\n",
       "      <td>32TMT</td>\n",
       "      <td>2022-04-21</td>\n",
       "      <td>18.815301</td>\n",
       "      <td>32632</td>\n",
       "      <td>2022-04-21 10:16:01.025</td>\n",
       "      <td>156.552690</td>\n",
       "      <td>37.414054</td>\n",
       "      <td>POLYGON ((7.75156 46.85851, 7.80321 47.00268, ...</td>\n",
       "      <td>{'AOT': {'href': 'https://sentinel2l2a01.blob....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S2A_MSIL2A_20220501T101601_R065_T32TMT_2022050...</td>\n",
       "      <td>S2A_OPER_MSI_L2A_TL_ESRI_20220502T062204_A0358...</td>\n",
       "      <td>Sentinel-2A</td>\n",
       "      <td>32TMT</td>\n",
       "      <td>2022-05-01</td>\n",
       "      <td>49.001628</td>\n",
       "      <td>32632</td>\n",
       "      <td>2022-05-01 10:16:01.024</td>\n",
       "      <td>155.646557</td>\n",
       "      <td>34.222124</td>\n",
       "      <td>POLYGON ((7.75468 46.85852, 7.77605 46.91851, ...</td>\n",
       "      <td>{'AOT': {'href': 'https://sentinel2l2a01.blob....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          product_uri  \\\n",
       "14  S2A_MSIL2A_20220305T102921_R108_T32TMT_2022030...   \n",
       "13  S2B_MSIL2A_20220307T101759_R065_T32TMT_2022030...   \n",
       "12  S2B_MSIL2A_20220310T102739_R108_T32TMT_2022031...   \n",
       "11  S2B_MSIL2A_20220320T102639_R108_T32TMT_2022032...   \n",
       "10  S2A_MSIL2A_20220322T101711_R065_T32TMT_2022032...   \n",
       "9   S2A_MSIL2A_20220325T102651_R108_T32TMT_2022032...   \n",
       "8   S2B_MSIL2A_20220327T101629_R065_T32TMT_2022032...   \n",
       "7   S2A_MSIL2A_20220404T102601_R108_T32TMT_2022040...   \n",
       "6   S2B_MSIL2A_20220406T101559_R065_T32TMT_2022040...   \n",
       "5   S2B_MSIL2A_20220409T102559_R108_T32TMT_2022041...   \n",
       "4   S2A_MSIL2A_20220411T101601_R065_T32TMT_2022041...   \n",
       "3   S2A_MSIL2A_20220414T102601_R108_T32TMT_2022041...   \n",
       "2   S2B_MSIL2A_20220419T102559_R108_T32TMT_2022041...   \n",
       "1   S2A_MSIL2A_20220421T101601_R065_T32TMT_2022042...   \n",
       "0   S2A_MSIL2A_20220501T101601_R065_T32TMT_2022050...   \n",
       "\n",
       "                                             scene_id spacecraft_name tile_id  \\\n",
       "14  S2A_OPER_MSI_L2A_TL_ESRI_20220306T020615_A0349...     Sentinel-2A   32TMT   \n",
       "13  S2B_OPER_MSI_L2A_TL_ESRI_20220308T005901_A0261...     Sentinel-2B   32TMT   \n",
       "12  S2B_OPER_MSI_L2A_TL_ESRI_20220311T101831_A0261...     Sentinel-2B   32TMT   \n",
       "11  S2B_OPER_MSI_L2A_TL_ESRI_20220321T005358_A0263...     Sentinel-2B   32TMT   \n",
       "10  S2A_OPER_MSI_L2A_TL_ESRI_20220323T030838_A0352...     Sentinel-2A   32TMT   \n",
       "9   S2A_OPER_MSI_L2A_TL_ESRI_20220325T235850_A0352...     Sentinel-2A   32TMT   \n",
       "8   S2B_OPER_MSI_L2A_TL_ESRI_20220328T020908_A0264...     Sentinel-2B   32TMT   \n",
       "7   S2A_OPER_MSI_L2A_TL_ESRI_20220404T222443_A0354...     Sentinel-2A   32TMT   \n",
       "6   S2B_OPER_MSI_L2A_TL_ESRI_20220407T005151_A0265...     Sentinel-2B   32TMT   \n",
       "5   S2B_OPER_MSI_L2A_TL_ESRI_20220410T091212_A0265...     Sentinel-2B   32TMT   \n",
       "4   S2A_OPER_MSI_L2A_TL_ESRI_20220412T095122_A0355...     Sentinel-2A   32TMT   \n",
       "3   S2A_OPER_MSI_L2A_TL_ESRI_20220415T043913_A0355...     Sentinel-2A   32TMT   \n",
       "2   S2B_OPER_MSI_L2A_TL_ESRI_20220419T211927_A0267...     Sentinel-2B   32TMT   \n",
       "1   S2A_OPER_MSI_L2A_TL_ESRI_20220422T064357_A0356...     Sentinel-2A   32TMT   \n",
       "0   S2A_OPER_MSI_L2A_TL_ESRI_20220502T062204_A0358...     Sentinel-2A   32TMT   \n",
       "\n",
       "   sensing_date  cloudy_pixel_percentage   epsg            sensing_time  \\\n",
       "14   2022-03-05                 1.280222  32632 2022-03-05 10:29:21.024   \n",
       "13   2022-03-07                11.010902  32632 2022-03-07 10:17:59.024   \n",
       "12   2022-03-10                 0.632361  32632 2022-03-10 10:27:39.025   \n",
       "11   2022-03-20                 0.338798  32632 2022-03-20 10:26:39.024   \n",
       "10   2022-03-22                 1.812012  32632 2022-03-22 10:17:11.024   \n",
       "9    2022-03-25                 0.079917  32632 2022-03-25 10:26:51.024   \n",
       "8    2022-03-27                 1.837532  32632 2022-03-27 10:16:29.024   \n",
       "7    2022-04-04                43.516320  32632 2022-04-04 10:26:01.024   \n",
       "6    2022-04-06                29.913694  32632 2022-04-06 10:15:59.025   \n",
       "5    2022-04-09                71.353978  32632 2022-04-09 10:25:59.024   \n",
       "4    2022-04-11                57.239157  32632 2022-04-11 10:16:01.024   \n",
       "3    2022-04-14                 6.772228  32632 2022-04-14 10:26:01.024   \n",
       "2    2022-04-19                27.082452  32632 2022-04-19 10:25:59.024   \n",
       "1    2022-04-21                18.815301  32632 2022-04-21 10:16:01.025   \n",
       "0    2022-05-01                49.001628  32632 2022-05-01 10:16:01.024   \n",
       "\n",
       "    sun_azimuth_angle  sun_zenith_angle  \\\n",
       "14         161.667769         54.956746   \n",
       "13         158.604149         54.757018   \n",
       "12         161.482896         52.994047   \n",
       "11         161.262135         48.991058   \n",
       "10         158.020749         48.773217   \n",
       "9          161.201517         46.981061   \n",
       "8          157.802681         46.782320   \n",
       "7          161.009776         43.034004   \n",
       "6          157.386954         42.869859   \n",
       "5          160.861845         41.124933   \n",
       "4          157.170910         40.977996   \n",
       "3          160.747609         39.265160   \n",
       "2          160.499090         37.491044   \n",
       "1          156.552690         37.414054   \n",
       "0          155.646557         34.222124   \n",
       "\n",
       "                                                 geom  \\\n",
       "14  POLYGON ((7.66287 47.84591, 9.13047 47.85363, ...   \n",
       "13  POLYGON ((7.74869 46.85849, 7.75737 46.88302, ...   \n",
       "12  POLYGON ((7.66287 47.84591, 9.13047 47.85363, ...   \n",
       "11  POLYGON ((7.66287 47.84591, 9.13047 47.85363, ...   \n",
       "10  POLYGON ((7.74718 46.85848, 7.76226 46.90118, ...   \n",
       "9   POLYGON ((7.66287 47.84591, 9.13047 47.85363, ...   \n",
       "8   POLYGON ((7.75253 46.85851, 7.80383 47.00126, ...   \n",
       "7   POLYGON ((7.66287 47.84591, 9.13047 47.85363, ...   \n",
       "6   POLYGON ((7.74970 46.85850, 7.80005 46.99889, ...   \n",
       "5   POLYGON ((7.66287 47.84591, 9.13047 47.85363, ...   \n",
       "4   POLYGON ((7.75911 46.85855, 7.77000 46.88972, ...   \n",
       "3   POLYGON ((7.66287 47.84591, 9.13047 47.85363, ...   \n",
       "2   POLYGON ((7.66287 47.84591, 9.13047 47.85363, ...   \n",
       "1   POLYGON ((7.75156 46.85851, 7.80321 47.00268, ...   \n",
       "0   POLYGON ((7.75468 46.85852, 7.77605 46.91851, ...   \n",
       "\n",
       "                                               assets  \n",
       "14  {'AOT': {'href': 'https://sentinel2l2a01.blob....  \n",
       "13  {'AOT': {'href': 'https://sentinel2l2a01.blob....  \n",
       "12  {'AOT': {'href': 'https://sentinel2l2a01.blob....  \n",
       "11  {'AOT': {'href': 'https://sentinel2l2a01.blob....  \n",
       "10  {'AOT': {'href': 'https://sentinel2l2a01.blob....  \n",
       "9   {'AOT': {'href': 'https://sentinel2l2a01.blob....  \n",
       "8   {'AOT': {'href': 'https://sentinel2l2a01.blob....  \n",
       "7   {'AOT': {'href': 'https://sentinel2l2a01.blob....  \n",
       "6   {'AOT': {'href': 'https://sentinel2l2a01.blob....  \n",
       "5   {'AOT': {'href': 'https://sentinel2l2a01.blob....  \n",
       "4   {'AOT': {'href': 'https://sentinel2l2a01.blob....  \n",
       "3   {'AOT': {'href': 'https://sentinel2l2a01.blob....  \n",
       "2   {'AOT': {'href': 'https://sentinel2l2a01.blob....  \n",
       "1   {'AOT': {'href': 'https://sentinel2l2a01.blob....  \n",
       "0   {'AOT': {'href': 'https://sentinel2l2a01.blob....  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now, a new Mapper instance is created\n",
    "mapper = Mapper(mapper_configs)\n",
    "mapper.query_scenes()\n",
    "# the metadata is loaded into a GeoPandas GeoDataFrame\n",
    "mapper.metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1dd3a970-325a-4664-837a-f2b62b87e97e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-31 14:31:36,642 eodal        INFO     Starting extraction of sentinel2 scenes\n",
      "2023-03-31 14:31:56,016 eodal        INFO     Finished extraction of sentinel2 scenes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "EOdal SceneCollection\n",
       "----------------------\n",
       "# Scenes:    15\n",
       "Timestamps:    2022-03-05 10:29:21, 2022-03-07 10:17:59, 2022-03-10 10:27:39, 2022-03-20 10:26:39, 2022-03-22 10:17:11, 2022-03-25 10:26:51, 2022-03-27 10:16:29, 2022-04-04 10:26:01, 2022-04-06 10:15:59, 2022-04-09 10:25:59, 2022-04-11 10:16:01, 2022-04-14 10:26:01, 2022-04-19 10:25:59, 2022-04-21 10:16:01, 2022-05-01 10:16:01\n",
       "Scene Identifiers:    S2A_MSIL2A_20220305T102921_N0400_R108_T32TMT_20220306T020614.SAFE, S2B_MSIL2A_20220307T101759_N0400_R065_T32TMT_20220308T005859.SAFE, S2B_MSIL2A_20220310T102739_N0400_R108_T32TMT_20220311T101830.SAFE, S2B_MSIL2A_20220320T102639_N0400_R108_T32TMT_20220321T005356.SAFE, S2A_MSIL2A_20220322T101711_N0400_R065_T32TMT_20220323T030836.SAFE, S2A_MSIL2A_20220325T102651_N0400_R108_T32TMT_20220325T235848.SAFE, S2B_MSIL2A_20220327T101629_N0400_R065_T32TMT_20220328T020907.SAFE, S2A_MSIL2A_20220404T102601_N0400_R108_T32TMT_20220404T222441.SAFE, S2B_MSIL2A_20220406T101559_N0400_R065_T32TMT_20220407T005149.SAFE, S2B_MSIL2A_20220409T102559_N0400_R108_T32TMT_20220410T091210.SAFE, S2A_MSIL2A_20220411T101601_N0400_R065_T32TMT_20220412T095120.SAFE, S2A_MSIL2A_20220414T102601_N0400_R108_T32TMT_20220415T043911.SAFE, S2B_MSIL2A_20220419T102559_N0400_R108_T32TMT_20220419T211925.SAFE, S2A_MSIL2A_20220421T101601_N0400_R065_T32TMT_20220422T064356.SAFE, S2A_MSIL2A_20220501T101601_N0400_R065_T32TMT_20220502T062203.SAFE"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#%% load the scenes available from STAC\n",
    "scene_kwargs = {\n",
    "    'scene_constructor': Sentinel2.from_safe,\n",
    "    'scene_constructor_kwargs': {'band_selection': ['B02', 'B03', 'B04', 'B08']},\n",
    "    'scene_modifier': preprocess_sentinel2_scenes,\n",
    "    'scene_modifier_kwargs': {'target_resolution': 10}\n",
    "}\n",
    "mapper.load_scenes(scene_kwargs=scene_kwargs)\n",
    "scoll = mapper.data\n",
    "scoll"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e79e781d-3960-4ed5-8e7a-b1f1910dd707",
   "metadata": {},
   "source": [
    "### Calculationg zonal statistics\n",
    "\n",
    "With the cells above we have loaded multiple scenes at once into a `SceneCollection`. We can now either calculate the zonal statistics per scene (which would be done using `RasterCollection.band_summaries()`) or make use of the `SceneCollection.get_feature_timeseries()` method that calls `RasterCollection.band_summaries()` for each scene and stacks the results into a single `GeoDataFrame`.\n",
    "\n",
    "In the example below, we calculate two vegetation indices, namely the [NDVI](https://gisgeography.com/ndvi-normalized-difference-vegetation-index/) and the [MSAVI](https://eos.com/industries/agriculture/msavi/), and calculate zonal statistics per single field parcel and Sentinel-2 scene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9cad0fde-1ff3-4f85-9409-37625ed75e07",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# calculate spectral indices\n",
    "for _, scene in scoll:\n",
    "    scene.calc_si('NDVI', inplace=True)\n",
    "    scene.calc_si('MSAVI', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cee5b98d-09d4-4748-81ef-9ba564a3ead2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/ides/Lukas/venvs/geo_notebooks/lib/python3.10/site-packages/geopandas/array.py:1406: UserWarning: CRS not set for some of the concatenation inputs. Setting output's CRS as WGS 84 / UTM zone 32N (the single non-null crs provided).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>percentile_10</th>\n",
       "      <th>percentile_50</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>percentile_90</th>\n",
       "      <th>my_square_sum</th>\n",
       "      <th>my_median_abs_deviation</th>\n",
       "      <th>GIS_ID</th>\n",
       "      <th>NUTZUNGSCO</th>\n",
       "      <th>NUTZUNG</th>\n",
       "      <th>geometry</th>\n",
       "      <th>band_name</th>\n",
       "      <th>acquisition_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.601058</td>\n",
       "      <td>0.682138</td>\n",
       "      <td>0.682138</td>\n",
       "      <td>0.674655</td>\n",
       "      <td>0.746230</td>\n",
       "      <td>22.457724</td>\n",
       "      <td>0.045558</td>\n",
       "      <td>24748</td>\n",
       "      <td>0513</td>\n",
       "      <td>Winterweizen ohne Futterweizen swissgranum</td>\n",
       "      <td>POLYGON ((477318.675 5255957.449, 477461.806 5...</td>\n",
       "      <td>ndvi</td>\n",
       "      <td>2022-03-05 10:29:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.530281</td>\n",
       "      <td>0.530281</td>\n",
       "      <td>0.530281</td>\n",
       "      <td>0.530281</td>\n",
       "      <td>0.530281</td>\n",
       "      <td>0.281198</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32832</td>\n",
       "      <td>0613</td>\n",
       "      <td>Übrige Dauerwiesen (ohne Weiden)</td>\n",
       "      <td>MULTIPOLYGON (((477325.745 5255975.901, 477298...</td>\n",
       "      <td>ndvi</td>\n",
       "      <td>2022-03-05 10:29:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.742672</td>\n",
       "      <td>0.773386</td>\n",
       "      <td>0.773386</td>\n",
       "      <td>0.771569</td>\n",
       "      <td>0.802435</td>\n",
       "      <td>14.309157</td>\n",
       "      <td>0.014653</td>\n",
       "      <td>31448</td>\n",
       "      <td>0502</td>\n",
       "      <td>Wintergerste</td>\n",
       "      <td>POLYGON ((477376.484 5255819.502, 477386.601 5...</td>\n",
       "      <td>ndvi</td>\n",
       "      <td>2022-03-05 10:29:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.571276</td>\n",
       "      <td>0.612358</td>\n",
       "      <td>0.612358</td>\n",
       "      <td>0.615501</td>\n",
       "      <td>0.661844</td>\n",
       "      <td>4.561506</td>\n",
       "      <td>0.022253</td>\n",
       "      <td>26950</td>\n",
       "      <td>0502</td>\n",
       "      <td>Wintergerste</td>\n",
       "      <td>POLYGON ((477354.814 5255737.217, 477359.285 5...</td>\n",
       "      <td>ndvi</td>\n",
       "      <td>2022-03-05 10:29:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.513121</td>\n",
       "      <td>0.513121</td>\n",
       "      <td>0.513121</td>\n",
       "      <td>0.513121</td>\n",
       "      <td>0.513121</td>\n",
       "      <td>0.263293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32829</td>\n",
       "      <td>0513</td>\n",
       "      <td>Winterweizen ohne Futterweizen swissgranum</td>\n",
       "      <td>POLYGON ((477326.142 5255992.784, 477325.728 5...</td>\n",
       "      <td>ndvi</td>\n",
       "      <td>2022-03-05 10:29:21</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.654246</td>\n",
       "      <td>0.654246</td>\n",
       "      <td>0.654246</td>\n",
       "      <td>0.654246</td>\n",
       "      <td>0.654246</td>\n",
       "      <td>0.428038</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32832</td>\n",
       "      <td>0613</td>\n",
       "      <td>Übrige Dauerwiesen (ohne Weiden)</td>\n",
       "      <td>MULTIPOLYGON (((477325.745 5255975.901, 477298...</td>\n",
       "      <td>msavi</td>\n",
       "      <td>2022-05-01 10:16:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.802803</td>\n",
       "      <td>0.862528</td>\n",
       "      <td>0.862528</td>\n",
       "      <td>0.842220</td>\n",
       "      <td>0.873511</td>\n",
       "      <td>17.088790</td>\n",
       "      <td>0.011793</td>\n",
       "      <td>31448</td>\n",
       "      <td>0502</td>\n",
       "      <td>Wintergerste</td>\n",
       "      <td>POLYGON ((477376.484 5255819.502, 477386.601 5...</td>\n",
       "      <td>msavi</td>\n",
       "      <td>2022-05-01 10:16:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.550848</td>\n",
       "      <td>0.748971</td>\n",
       "      <td>0.748971</td>\n",
       "      <td>0.714749</td>\n",
       "      <td>0.794924</td>\n",
       "      <td>6.237393</td>\n",
       "      <td>0.047486</td>\n",
       "      <td>26950</td>\n",
       "      <td>0502</td>\n",
       "      <td>Wintergerste</td>\n",
       "      <td>POLYGON ((477354.814 5255737.217, 477359.285 5...</td>\n",
       "      <td>msavi</td>\n",
       "      <td>2022-05-01 10:16:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.721242</td>\n",
       "      <td>0.721242</td>\n",
       "      <td>0.721242</td>\n",
       "      <td>0.721242</td>\n",
       "      <td>0.721242</td>\n",
       "      <td>0.520191</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32829</td>\n",
       "      <td>0513</td>\n",
       "      <td>Winterweizen ohne Futterweizen swissgranum</td>\n",
       "      <td>POLYGON ((477326.142 5255992.784, 477325.728 5...</td>\n",
       "      <td>msavi</td>\n",
       "      <td>2022-05-01 10:16:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.256419</td>\n",
       "      <td>0.387333</td>\n",
       "      <td>0.387333</td>\n",
       "      <td>0.394701</td>\n",
       "      <td>0.515173</td>\n",
       "      <td>3.780683</td>\n",
       "      <td>0.078116</td>\n",
       "      <td>32148</td>\n",
       "      <td>0513</td>\n",
       "      <td>Winterweizen ohne Futterweizen swissgranum</td>\n",
       "      <td>POLYGON ((477335.920 5256001.676, 477335.895 5...</td>\n",
       "      <td>msavi</td>\n",
       "      <td>2022-05-01 10:16:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    percentile_10  percentile_50    median      mean  percentile_90  \\\n",
       "0        0.601058       0.682138  0.682138  0.674655       0.746230   \n",
       "1        0.530281       0.530281  0.530281  0.530281       0.530281   \n",
       "2        0.742672       0.773386  0.773386  0.771569       0.802435   \n",
       "3        0.571276       0.612358  0.612358  0.615501       0.661844   \n",
       "4        0.513121       0.513121  0.513121  0.513121       0.513121   \n",
       "..            ...            ...       ...       ...            ...   \n",
       "7        0.654246       0.654246  0.654246  0.654246       0.654246   \n",
       "8        0.802803       0.862528  0.862528  0.842220       0.873511   \n",
       "9        0.550848       0.748971  0.748971  0.714749       0.794924   \n",
       "10       0.721242       0.721242  0.721242  0.721242       0.721242   \n",
       "11       0.256419       0.387333  0.387333  0.394701       0.515173   \n",
       "\n",
       "    my_square_sum  my_median_abs_deviation GIS_ID NUTZUNGSCO  \\\n",
       "0       22.457724                 0.045558  24748       0513   \n",
       "1        0.281198                 0.000000  32832       0613   \n",
       "2       14.309157                 0.014653  31448       0502   \n",
       "3        4.561506                 0.022253  26950       0502   \n",
       "4        0.263293                 0.000000  32829       0513   \n",
       "..            ...                      ...    ...        ...   \n",
       "7        0.428038                 0.000000  32832       0613   \n",
       "8       17.088790                 0.011793  31448       0502   \n",
       "9        6.237393                 0.047486  26950       0502   \n",
       "10       0.520191                 0.000000  32829       0513   \n",
       "11       3.780683                 0.078116  32148       0513   \n",
       "\n",
       "                                       NUTZUNG  \\\n",
       "0   Winterweizen ohne Futterweizen swissgranum   \n",
       "1             Übrige Dauerwiesen (ohne Weiden)   \n",
       "2                                 Wintergerste   \n",
       "3                                 Wintergerste   \n",
       "4   Winterweizen ohne Futterweizen swissgranum   \n",
       "..                                         ...   \n",
       "7             Übrige Dauerwiesen (ohne Weiden)   \n",
       "8                                 Wintergerste   \n",
       "9                                 Wintergerste   \n",
       "10  Winterweizen ohne Futterweizen swissgranum   \n",
       "11  Winterweizen ohne Futterweizen swissgranum   \n",
       "\n",
       "                                             geometry band_name  \\\n",
       "0   POLYGON ((477318.675 5255957.449, 477461.806 5...      ndvi   \n",
       "1   MULTIPOLYGON (((477325.745 5255975.901, 477298...      ndvi   \n",
       "2   POLYGON ((477376.484 5255819.502, 477386.601 5...      ndvi   \n",
       "3   POLYGON ((477354.814 5255737.217, 477359.285 5...      ndvi   \n",
       "4   POLYGON ((477326.142 5255992.784, 477325.728 5...      ndvi   \n",
       "..                                                ...       ...   \n",
       "7   MULTIPOLYGON (((477325.745 5255975.901, 477298...     msavi   \n",
       "8   POLYGON ((477376.484 5255819.502, 477386.601 5...     msavi   \n",
       "9   POLYGON ((477354.814 5255737.217, 477359.285 5...     msavi   \n",
       "10  POLYGON ((477326.142 5255992.784, 477325.728 5...     msavi   \n",
       "11  POLYGON ((477335.920 5256001.676, 477335.895 5...     msavi   \n",
       "\n",
       "      acquisition_time  \n",
       "0  2022-03-05 10:29:21  \n",
       "1  2022-03-05 10:29:21  \n",
       "2  2022-03-05 10:29:21  \n",
       "3  2022-03-05 10:29:21  \n",
       "4  2022-03-05 10:29:21  \n",
       "..                 ...  \n",
       "7  2022-05-01 10:16:01  \n",
       "8  2022-05-01 10:16:01  \n",
       "9  2022-05-01 10:16:01  \n",
       "10 2022-05-01 10:16:01  \n",
       "11 2022-05-01 10:16:01  \n",
       "\n",
       "[144 rows x 13 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get time series of the field parcel using a bunch of statistical metrics\n",
    "ts = scoll.get_feature_timeseries(\n",
    "    vector_features=geom,\n",
    "    method=['percentile_10', 'percentile_50', 'median', 'mean', 'percentile_90', my_square_sum, my_median_abs_deviation],\n",
    "    band_selection=['ndvi', 'msavi']\n",
    ")\n",
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "add73bfc-1abc-4141-9f46-4b1652aa1a64",
   "metadata": {},
   "source": [
    "### Plotting Time Series\n",
    "\n",
    "We can now plot time series values per field parcel using e.g., the median NDVI and MSAVI value per parcel and Sentinel-2 acquisition date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "987d6ad9-db6b-4660-b821-1568f6c6ff60",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting seaborn\n",
      "  Downloading seaborn-0.12.2-py3-none-any.whl (293 kB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m293.3/293.3 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m[36m0:00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: numpy!=1.24.0,>=1.17 in /mnt/ides/Lukas/venvs/geo_notebooks/lib64/python3.10/site-packages (from seaborn) (1.23.5)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in /mnt/ides/Lukas/venvs/geo_notebooks/lib64/python3.10/site-packages (from seaborn) (3.7.0)\n",
      "Requirement already satisfied: pandas>=0.25 in /mnt/ides/Lukas/venvs/geo_notebooks/lib64/python3.10/site-packages (from seaborn) (1.5.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /mnt/ides/Lukas/venvs/geo_notebooks/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /mnt/ides/Lukas/venvs/geo_notebooks/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.0.9)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /mnt/ides/Lukas/venvs/geo_notebooks/lib64/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.0.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /mnt/ides/Lukas/venvs/geo_notebooks/lib64/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.4)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /mnt/ides/Lukas/venvs/geo_notebooks/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.38.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /mnt/ides/Lukas/venvs/geo_notebooks/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /mnt/ides/Lukas/venvs/geo_notebooks/lib64/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.4.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /mnt/ides/Lukas/venvs/geo_notebooks/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /mnt/ides/Lukas/venvs/geo_notebooks/lib/python3.10/site-packages (from pandas>=0.25->seaborn) (2022.7.1)\n",
      "Requirement already satisfied: six>=1.5 in /mnt/ides/Lukas/venvs/geo_notebooks/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n",
      "Installing collected packages: seaborn\n",
      "Successfully installed seaborn-0.12.2\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "09273569-d3c4-46c0-859b-2b641b13e8e3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.style.use('tableau-colorblind10')\n",
    "\n",
    "# convert timestamps to get a nicely formatted x axis\n",
    "ts.acquisition_time = pd.to_datetime(ts.acquisition_time)\n",
    "f, ax = plt.subplots(ncols=2, figsize=(20,10))\n",
    "ts_sis = ts.groupby(ts.band_name)\n",
    "\n",
    "idx = 0\n",
    "for si, ts_si in ts_sis:\n",
    "    sns.lineplot(x='acquisition_time', y='median', hue='GIS_ID', data=ts_si, ax=ax[idx])\n",
    "    ax[idx].set_title(si.upper())\n",
    "    idx += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a74bd40d-6e51-49cd-933d-8097c8592486",
   "metadata": {},
   "source": [
    "Since `ts` is a `GeoDataFrame` all results can be also visualized on a map or saved as, e.g., GeoPackage file for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "711a7eff-d7d0-45a3-b7be-a6001fdfbe3f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qITsXHivTGw27d/16p47jwEkvDLxmZunIxxrdvjj7BxNXQkSkP0MF32pNffD1W51pgKrIErx/7Ed4rEzHxmOXW3yOx8p0dghRizDwmpnoYD+xSyAiarO7wbdPl7Y9KbKsuhYeK9MxZMN/DFQZmZvsPAU8Vqa3umNncfYPeOgf7O2l+2PgJSIiozkU+RRK48KQPXtQm65z6UY1PFamI+KT4waqjMSWlnMZHivT8cLeE22+llow7ARKsj4MvEREZHSBnp7aXt+2fOPZm1/Kh1dYuCX7/wePlemYsy/X4Nf2WJnOhzVRo7hKgwXhGoREZA0Uv43x/eNyU/oI2HoCknuuReavX1IWfrl1x+iv869T17D91DWU8HOD7sHAa4YkABpbte+FvSdQKucXMBFZhz0vDgMAlJWVwu9d/Ycq1KG+R8/c1ym3dWIMNRDAz43WCt36X+QoGn8WgSW3JwOvGdo5cZBBxjQREVkCT09PCJumQaWqxEMrPke1Rr/zPVamIyqoO5aFBhinQNLbX1O/Q+aFMrHLgMfKdAz1ccFnLweLXYpZUCgUmPLpWZwvrxK7FJNj4DVDoXJPYK/YVRARmd7Py+p7kJbs/x/+depai8/beOwyjv18HftnjTBSZdQchUKBgK3m11lz9OoNm+ntVSgUGLfnDH6uuG2U60d8chxbpgwxyrWNjYGXiIjMTuLY/kgc2x9Ay38lfrKoAv9v00F88/cQY5ZG9zDXkNsYj5XpyJ09CJ6eljsXJi3nMl79Ilfv34IYyt78UmwR56XbjIHXwuQoFAi04C9WIiJ93e2Z81udibLq2vsee768CgFrs5D72mhTlGaTsvMUFjvsLmDrCfRwaY/j0X8Su5RGbTiSj1X/vQiR8qxVY+C1MM9sPcHnzBORTcqPGQOg/olc93tIgaLyDpbs/5+2h5jaLmBtFhSVxl9hwRTuruksxhCHhOxcvHvsMgOtCBh4zVQHe6CqkY4MfpEQka2bEdQLM4J63Xc2+b9OXUPUEw9a9K+vxTRkw39w6Ua12GUYlcfKdGTPHmTQ35raQqC11N802wmCIIhdhDlSKht/EzWV+42LMseB9/b2Eri6OkKlqkRtbWOLqpEhsJ1Nh21tOm1p664r03G/b2LNvV829V5rras+NNbWCoUCgVtPNLocpi1wdZDifOzYFh2bkJ2LTccu22xb3WVOOaRLl44tOo49vGaKvRJERM0riQu776S2+/3q+n4rQWw8dhkbj10GALSzA355w3y+wbfV0s/+D4lfXRS7DLOhuq3Rfp4w0FovBl4LpFAoGIiJiH5T2kzo3XAkH9HBfg22t3TZM7Wgu1KEpfX+muoJZ5ZOjAdkkOkw8Fqgwf86oV2rkoiIgOzZgxDaxDCwN/97sUHgfegfrQ839/b+SgGcNqOlrhQKBfpuPXHfYR5EtoiB14y1s6vvWfgjsdbfIyIyV4Genhjq44KjV280ut9zZToU9wxtaOy9tTU0QIMxwBP9PEy2OP+o977CWWWlSV6L6K5+SVk4vdCylv5j4DVj3/9tkMUs6E1EJLbPXg6G18r0RmfIm3JM5t78Uuxt4tfjD3dyQHJ43xbPcs/OU+C1AzlWsyQYWQdLHCLDwGvGzOVXZERElqL4PuN5vVami76O+c8Vt5scekFExiMRuwAiIiJDampVhnt7ft8O7W2aYojILDDwWijfVfvFLoGIyOL0WZ0JoP7hFUTUegnZuWKXoBcGXjPX1H+Q6jZnrhERNcVB2vj20urfH2FpZ6JaiKzR3ZVKLAUDr5nLmT1I7BKIiCzO1RYs3VhiRk+LIiLjYuA1c5y4RkRkWG1Zg5eILBMDLxER2RRDrcFLZOsUCoXYJbQYA68FG/jOAbFLICKyaB9M5LAxotbqZ0FL7HEdXgvwVA83fHXpeoPtP1fcFqEay6RQKHBUUYPvf7mOgpJbKKu6g19ralGtrsUdTR3UmvqF6YXf/pBh2AHo5d4Bn0zuw+E5ZFbeP/YjZgT1QqjcE9grdjVElsmSps8z8FqAPS8Oa3Ih9dZQKBTIVQJfXylC4fUqlNy8DVW1GlVqDWpqNaitAzSCaZ9MRNZJAHC+vKrFTwx0dZAicbQ/JgV2N2pdZBukaPob8uLsH7g0GZENYeC1cIYMwkRiU93WYM6+XMzZ1/z6jg4SYHIfL7zz7EATVEaW6LkAL+zOLRa7DCIyAwy8RGSRbtcBu3OLsTu3+R/6JAACPDti5/jHObTChrzz7MAWfX60s+NENqLWGvXeVzgU+ZTYZTSLgddC3O9Xc0R0f3UAchQ3Wzy0wqO9PTaMHYDnRjxq3MLILOz/2yCEWtDkGyJzclZZKXYJLcLAayFOzx7U4m/WRNQ2pdW1CP/0OMI/Pd7ssQ5S4MV+3kgc298ElZExBLLXn8jq6R14fX190a5dO9jZ/f5QxqlTp2L58uUNjk1NTUVKSgqUSiW8vb0RHR2NkJAQAEBsbCzS09Mhlf7+/EcHBwecOnVK+/HHH3+MLVu2oLy8HL1798Ybb7wBf39/AEBBQQFWrlyJ/Px8uLi4YMaMGZgxY4b23P3792Pjxo0oKirCww8/jNdffx1Dhw7V93bNBn8NS2SebmuAf526hn+dutbssVy1wrxk5ynqV2kgIqvXqh7erKwseHt73/eY7OxsrFu3DsnJyZDL5di3bx/mz5+PzMxMdOvWDQAwZ84czJs3r9HzDx8+jOTkZGzevBne3t7YsmUL3n33Xbz33nuorq7GrFmzEBYWhu3bt+PChQuYNWsWHnroIYSGhuLs2bNYvHgxVq9ejVGjRiEjIwNz585FVlYWv8kQkWj0XbWiYzs7LBvpy9UEjGTuF6dwQf5nscsgsnhpOZfNfnUdow1pqKmpwcKFC9GvXz8AwKRJk7BmzRrk5ORoA+/9bN++HQsWLICvry8AYOHChdp9X3/9NdRqNRYuXAipVIrAwED85S9/wUcffYTQ0FCkpaUhODgYY8aMAQBMmTIFn3zyCfbt24fZs2cb4W6JiAzvplrA4uwfsDj7h2aPlQIY4uOCzWN68Qf7Fvr1DhdfJDKEv+/Ltc7Am5SUhJMnTwIARo4cidjYWDg6OuocExYWpvNxRUUFbt26BXd3d+2248ePIzs7G8XFxejZsyeWLFkCuVwOjUaD06dPY/z48Rg3bhwUCgUCAgKwcuVKeHt7Iz8/H71799YZDuHn54dPPvkEAJCfn4/g4GCd1/fz88PZs2dbfI8SiR0kErvmDzShQ3OGYNTm5scUEpHt0QA4evVGi3uPO7e3xz/HDsDTfcUPx1KpROdvU7K35wNHidpKA/P/WtI78AYGBiIoKAjx8fEoKSnB/PnzsWLFCqxZs6bJcwRBwLJly+Dv74+goCAAgI+PD6RSKSIjI+Hs7IxNmzZh5syZyMrKQl1dHe7cuYOMjAxs27YNDg4OWLJkCaKiopCWlgaVSgVnZ2ed13BxccGNGzdQV1cHlUoFFxcXnf3Ozs64cOFCi+/Tzc1RZ5yyORjp+igABl4iaruy3ybm4dPmj5VJgMl9H8LumSOMWlOnTu2Nev3GuLo6Nn8QETXL3L+W9A68H330kfbfTk5OeO211xAREYE333wTMpmswfFqtRqxsbG4ePEiduzYAYmk/ieAuXPn6hy3aNEifPHFFzh48KC2d3b27Nnw8vICAMTExOCZZ55BYWFhs0G0qf36BNjr1yvNroeXiEgMd+qA1NO/IPXvu1t0/MPOD+D95wMR+GDLeo+lUgk6dWqPiopqaDSGHWbQp4vjfZdNUqksY0klInMn1tdSS4N2m8fwent7o66uDuXl5dpweldNTQ0iIyNRXV2N1NTUBr2u95JKpfDy8oJSqUSnTp0AAB07dtR5HQAoLy+Hm5sbfv75Z53zVSoVXF1dIZFI4OrqCpVK1WC/m5tbi++rrk5AXR1XIici0tfPv9a0ePhVeyng4fQAvF3bo08XR/w/nwcNunJC6kT/+w7zqK3lOF4iQzD3ryW9Am9BQQEyMjIQExOj3VZYWAiZTIauXbvqHCsIAhYsWACZTIbNmzfDwcFBZ9+qVaswefJk9OzZE0B9T/DVq1fh4+ODDh06oEePHjh37hz69OkDALh2rX7JnwcffBByuRwffvghamtrYW9ffwu5ubkICAgAAMjlcpw7d06nnry8PIwdO1af2zVLuVyPl4isSLWmPiD//GsNjl5WYevJa8Be07y2tPlDiMhK6DXC2N3dHXv27EFKSgrUajUKCwuxfv16hIeHQyKRYPTo0dp1dDMyMnDp0iWsX79eJ+wC9UMLiouLER8fD6VSicrKSqxZswYymUy7Tu/zzz+PLVu24OLFi7h58yaSkpIwZMgQPPTQQwgODoajoyOSkpJQWVmJEydO4OOPP8a0adMA1K/KcPToUWRmZqKmpga7du3ClStXMGHCBAM0mbg4+5qIyDCeC/Bq/iAisgp69fB6eHhg27ZtWLt2LTZs2ABXV1eMGTMGUVFRAOp7e6uqqgAAaWlpuHr1KgYOHKhzjfHjxyMhIQEJCQlITExEWFgYNBoN5HI5UlJS0KFDBwDACy+8gBs3bmD69Om4c+cOBg8ejKSkJACATCbD1q1b8cYbbyAoKAju7u6IiYnB8OHDAQC9evXC2rVrkZSUhMWLF+PRRx/F1q1b0blz57a1FhERWY13nh3Y/EFEZBXsBEHgQNVGKJU3xS6hSV4r06ERuwgiIgtXGle/fGaOQoFQDhUjapO7X0+m1qVLx+YPgp5DGsg8FIv0SUVEZI3GJTPsElk7Bl4iIrJpt817cjkRGQADLxERERG1yYYj+WKXcF8MvBZqTE9OwCMiaq2lIx8TuwQiq7Lx25/ELuG+GHgtVMrzT4pdAhGRxYoO9gMAZOcpRK6EyDrcVJv3GggMvEREZLNe2MsJa0S2gIGXiIiIiKwaA68Fy509SOwSiIgsjljrhRKReBh4LRgfM0xE1HpdV6aLXQIRmQgDLxER2Yw+XRy1/zbvKTZEZEgMvBbuqR5uYpdARGQxDkU+BQB4/9iPIldCRKbEwGvh9rw4TOwSiIgszuLsH8QugYhMiIGXiIhswt3JagoF194lsjUMvFbAo7292CUQEZm1e98lA7Zy7V0iW8PAawXOxowRuwQiIrNWxN5dIqMz568vBl4iIrJqrg5S7b/Zu0tkPC/tKxC7hCYx8FoJafOHEBHZpPOxYwEACz47KXIlRNYtT3FT7BKaxMBrJYr55CAiogY2jw/Q/nt3brGIlRBZP43YBdwHAy8REVmtSYHdAQBefKoakU1j4CUiIqt07zJk5tzzRETGx8BrRXJnDxK7BCIis+DpKNP+mxPViIiB14p4enqKXQIRkVnIfW00ACDik+MiV0JE5oCBl4iIrMoHE3//bdfe/FIRKyEic8HAa2XeDu0tdglERKIKldf/tsuTE9WI6DcMvFZmRlAvsUsgIhLNvRPV6kSuhYjMBwMvERFZBV/3Dtp/c6IaEd2LgdcKPdXDTewSiIhM7pu/hwAA/pr6nciVEJG5YeC1QnteHCZ2CUREJpV9z7KMmRfKRKyEiMwRAy8REVm8wN+WZezKiWpE1AgGXivl0d5e7BKIiEzi3olqgsi1EJF5YuC1UmdjxohdAhGR0Q18sJP235yoRkRNYeAlIiKLtX/WCABA+K5vxS2EiMwaA68Vk4pdABGREeXeM1Htq0vXRayEiMwdA68VK/5tXBsRkTXy/G2imgcnqhFRM/Se2eTr64t27drBzs5Ou23q1KlYvnx5g2NTU1ORkpICpVIJb29vREdHIySkfp3E2NhYpKenQyr9vR/SwcEBp06dAgCMGjUKpaWlOq8zdOhQbNmyBQBw5MgRrF+/HoWFhXBzc8O8efMwYcIEAMA///lPvPfee7C31729//73v+jcubO+t0xERGbm3olqRETNadVU/qysLHh7e9/3mOzsbKxbtw7JycmQy+XYt28f5s+fj8zMTHTr1g0AMGfOHMybN6/Ja2zfvh2DBw9usP3y5cuYO3culi9fjgkTJuD777/HnDlz8Mgjj6Bv374AgPHjx2PVqlWtuT0iIjJjY3r+3nHBiWpE1BJGG9JQU1ODhQsXol+/frC3t8ekSZPg5OSEnJycNl/76NGj8PLywtSpUyGTyRAUFISnn34aaWlpbS/cytw7xo2IyBqkPP8kAGDUe1+JXAkRWYpWBd6kpCQMGzYMw4YNw/Lly1FZWdngmLCwMISHh2s/rqiowK1bt+Du7q7ddvz4cYwbNw4DBgxAeHg48vLydK6xc+dOjBw5EoMHD0Z0dDTKyuqfniMIAgRBd7VFNzc3FBQUaD8+f/48pkyZgieeeALPPvssvv3WNmfw3h3jRkRkDe79If6ssuH3HiKixugdeAMDAxEUFISsrCzs2LEDOTk5WLFixX3PEQQBy5Ytg7+/P4KCggAAPj4+6N69O7Zs2YKvv/4affv2xcyZM3H9ev1M28cffxwBAQH4/PPP8fnnn0OlUiE6OhoAEBQUhKKiIqSmpqKmpgbHjx/Hl19+iV9//RVAfcjz8fHBqlWr8M0332D8+PGIiIjATz/91PKGkdjB3l5iFX+IiKyBFIC394Owt5dwohqRmTLXjGMn/LGrVE+HDx9GREQEzpw5A5lM1mC/Wq1GbGwsCgoKsGPHDnTp0qXR62g0GgwfPhxRUVGYOnVqg/0XL17E2LFjceDAAXTv3h1ZWVnYsGEDSkpKEBQUhG7duuHw4cPIzMxs9PqTJ0/G0KFDsWDBghbdlyAIOhPmLNl7X5/F3E/PiF0GEVGbCJumAQByflKg3zsczkBkju5+nZqbNj9/1tvbG3V1dSgvL4eXl5fOvpqaGkRGRqK6uhqpqalwcXFp8jpSqRReXl5QKpVNvg4AlJWVoXv37hg9ejRGjx6t3f/WW2+ha9eu962zqWs35vr1Skgk1hF4w/s+wsBLRBbtxcAHoVLVD2Fg2CUyX3e/Tk3F1dWxRcfp9fvugoICrF69WmdbYWEhZDJZg7ApCAIWLFgAmUyGlJQUnbArCALeeustXLhwQbtNrVbj6tWr8PHxQVFREVasWAG1Wq3zOkD9UIhff/0VaWlpOuN4jx49iv79+wMANm/ejBMndGfuFhYWwsfHp8X3WlcnoLa2zmr+EBFZsqTxA1BbWwf5243/Fo+IzIO55hu9Aq+7uzv27NmDlJQUqNVqFBYWYv369QgPD4dEIsHo0aO16+hmZGTg0qVLWL9+PRwcHHSuY2dnh+LiYsTHx0OpVKKyshJr1qyBTCZDSEgIOnfujEOHDiEpKQk1NTUoKSlBYmIiQkJC0LVrV0ilUrz55ptITU2FRqPBrl27UFxcrB0KUVFRgfj4eFy9ehW3b9/Gv//9b1y5cgUTJ07U53atylM93MQugYioVUrveYjOL7fuiFgJEVkqvcfwnjx5EmvXrsWPP/4IV1dXjBkzBlFRUZDJZPD19UVycjKCg4Mxffp0nDx5UufBEkD9+rgJCQmoqKhAYmIiDh8+DI1GA7lcjqVLl6JHjx4A6ldZWLVqFfLy8tC+fXuMGDECMTEx6NixIwDgm2++QWJiIn755Rc8+uijWLZsGZ544gkAwJ07d7B27VpkZmaiuroavr6+WLx4sXaN3pZQKm/q0ywWwRonedj99kcCwM4OsJcAUgnQTiJFO4kd2reT4oF2UnRysEenB+zh6eQAd0cHPO7RCY96PoBAC1vFwt5eAldXR6hUlRbVc69QKPD83nM4p6xEmyYNkM1pZwf88kZ94LXG9zAia1Nq4qe8dunSsUXHtXnSmrWyxsBrTJYaxCyNLbRzWs5lxH55Fr/esc77I/3c/eaZlnMZc/blilwNETXHXANvmyetEREZ0qTA7pgU2L1Fx4bv+hZfX7oOjXFLIpFEBXXX/pthl4jagoGXiCzWnheHtei4HIUCM3bncPynhVkWGgAA6PnWFyJXQkSWjoGXiKxeoKcnTi8c3fyBABKyc7H1xGXcZrexqO79tSiHtxBRWzHwEhHdY1loAJaFBjQ7XpoT8Yynwz3fmThRjYgMgYGXiKgVPD09cSiyZat8cCKefi4vre/d3XAkX+RKiMhaMPASERmZPhPx/pr6HQ5cKLPZiXhvh/bW/vvN/14UsRIisiYMvEREZiTl+SdbdJy1TsSbEdQLAOCTwKEMRGQ4DLxERBbIGifiXY+foB0vbe61EpFlYeAlIrJydyfiNUehUOClfQU4o7hp8ol47u1//3bEiWpEZGgMvEREBKB+Il727JZPxIvJzMNNtWGicdmav0ClqsSCz04a5HpERPdi4CUiIr0Z8ol4257tq/337tzithdHRPQHDLxERGRUzT0Rz95eAgDosvxzE1RDRLZIInYBRERECoXCZpdiIyLjY+AlIiLReSV8JXYJRGTFGHiJiEhUL+76VuwSiMjKMfASEZGo9v9YJnYJRGTlGHiJiEg0XHOXiEyBgZeIiEShUCjELoGIbAQDLxERiSJg6wmxSyAiG8HAS0REJhe69b9il0BENoSBl4iITC5HcVPsEojIhjDwEhGRSXGiGhGZGgMvERGZTA4nqhGRCBh4iYjIZEI5UY2IRMDAS0REJjHwnQNil0BENoqBl4iITOLnittil0BENoqBl4iIjI4T1YhITAy8RERkVNl5nKhGROJi4CUiIqN6YS8nqhGRuBh4iYjIqHq4tBe7BCKycQy8RERkVJduVItdAhHZOAZeIiIymr+mfid2CUREDLxERGQ8z8t7iF0CEREDLxERGU+o3BOlcWFoZyd2JURkyxh4iYjI6H55Iwy5sweJXQYR2Si9A6+vry/69OkDuVyu/RMfH9/osampqQgNDUW/fv0wbtw4HDx4ULsvNjYWfn5+OtcZMGCAdv+oUaMavE5ERIR2/5EjRzBx4kT069cPTz31FD7//HOd196xYwdGjhyJgIAATJkyBefOndP3VomIyIA8Pet7ez3a24tdChHZmFa962RlZcHb2/u+x2RnZ2PdunVITk6GXC7Hvn37MH/+fGRmZqJbt24AgDlz5mDevHlNXmP79u0YPHhwg+2XL1/G3LlzsXz5ckyYMAHff/895syZg0ceeQR9+/bFf/7zH6xfvx6bN29G3759sX37dsyePRvZ2dno0KFDa26ZiIgM5GzMGAB8+hoRmY7RhjTU1NRg4cKF6NevH+zt7TFp0iQ4OTkhJyenzdc+evQovLy8MHXqVMhkMgQFBeHpp59GWloaAOCTTz7B5MmTMWTIELRv3x5z584FABw6dKjNr01ERIZRGheGoT4uYpdBRDagVT28SUlJOHnyJABg5MiRiI2NhaOjo84xYWFhOh9XVFTg1q1bcHd31247fvw4srOzUVxcjJ49e2LJkiWQy+Xa/Tt37kRsbCyqqqowZMgQLF++HJ07d4YgCBAEQef6bm5uOHXqFAAgPz8fY8aM0e6zs7PD448/jrNnz+LPf/5zi+5RIrGDRMJZFi0llUp0/ibjYDubDtvaNDL+NgJSqQTOS/aKXQoRGYC9vXm+Z+odeAMDAxEUFIT4+HiUlJRg/vz5WLFiBdasWdPkOYIgYNmyZfD390dQUBAAwMfHB1KpFJGRkXB2dsamTZswc+ZMZGVlwc3NDY8//jgCAgKQmJiIqqoqLF68GNHR0di9ezeCgoLw1ltvITU1FRMnTkROTg6+/PJLyGQyAIBKpYKLi4tODc7Ozrh+/XqL79PNzRF2dgy8+urUiU9UMgW2s+mwrU1D2DQNq778H17fXyB2KUTUBq6ujs0fJAI74Y9dpXo6fPgwIiIicObMGW3gvJdarUZsbCwKCgqwY8cOdOnSpdHraDQaDB8+HFFRUZg6dWqD/RcvXsTYsWNx4MABdO/eHVlZWdiwYQNKSkoQFBSEbt264fDhw8jMzESfPn2wadMmjBgxQnv+a6+9BqlUirfffrtF91Vefos9vHqQSiXo1Kk9KiqqodHUiV2O1WI7mw7b2nT+2NZuyz8XuyQiaqXr8RNM+notDdhtnirr7e2Nuro6lJeXw8vLS2dfTU0NIiMjUV1djdTU1Aa9rveSSqXw8vKCUqls8nUAoKysDN27d8fo0aMxevRo7f633noLXbt2BQC4urrixo0bOuerVCr06tWrxfdVVyegrq5NPwvYJI2mDrW1DAfGxnY2Hba16dxt69K4MOQoFAjdekLskohIT+b6fqnXQIuCggKsXr1aZ1thYSFkMpk2bN4lCAIWLFgAmUyGlJQUnbArCALeeustXLhwQbtNrVbj6tWr8PHxQVFREVasWAG1Wq3zOkD9UIhff/0VaWlpOuN4jx49iv79+wMA5HI5zp49q92n0WiQn5+PgIAAfW6XiIhEEvjbEmbtpWJXQkTWQK/A6+7ujj179iAlJQVqtRqFhYVYv349wsPDIZFIMHr0aO3EsYyMDFy6dAnr16+Hg4ODznXs7OxQXFyM+Ph4KJVKVFZWYs2aNZDJZAgJCUHnzp1x6NAhJCUloaamBiUlJUhMTERISAi6du0KqVSKN998E6mpqdBoNNi1axeKi4u1QyGee+45pKWl4fjx46iqqsK6devwwAMPYNSoUQZqNiIiMoWfl/GBFUTUdnoFXg8PD2zbtg1ffvklBg0ahJkzZ2LEiBF47bXXANT3wlZVVQEA0tLScPXqVQwcOFDn4RHLli0DACQkJODBBx9EWFgYRo4ciZ9++gkpKSno0KEDZDIZkpOTcf78eQwbNgyTJ09G9+7dsWrVKgCAk5MTNmzYgA8++AD9+vXD3r17sW3bNm0vc3BwMGJiYvD6669jyJAhOH36NLZt29YgeBMRkfm7+8CKHi6cQEhErdPmSWvWSqm8KXYJFsXeXgJXV0eoVJVmO37HGrCdTYdtbTr6tjUfWEFkvkrjwpo/yIC6dOnYouPMc7E0IiKiJpTGhWGin4fYZRCRBWHgJSIii7NlyhCT9yQRkeVi4CUiIotVGheGt0N7i10GEZk5Bl4iIrJoM4J6oTQuDFzBjIiawsBLRERWoTiOS5gRUeMYeImIyGrcXcLM1YH9vUT0OwZeIiKyOudjx3JSGxFpMfASEZHVKo0LQ6Bny9bpJCLrxcBLRERWLXv2SPb2Etk4Bl4iIrIJpXFheGWAt9hlEJEIGHiJiMhmJI7tz95eIhvEwEtERDanNC4MXMiByHYw8BIRkU26uoxPaSOyFQy8RERks+4+pY2IrBsDLxER2Tw+rILIujHwEhERof5hFR9M5KOJiawRAy8REdFvQuWeHOJAZIUYeImIiP6gNC4MDznJxC6DiAyEgZeIiKgRpxeORu5sDnEgsgYMvERERE3w9OQQByJrwMBLRETUjNK4MAR6dhS7DCJqJQZeIiKiFsiePZJDHIgsFAMvERFRC3GIA1HTPNrbi11Ckxh4iYiI9FQaF4YxPTuLXQaRWfngpf5il9AkBl4iIqJWSHn+Sfb2Et0j0NNT7BKaxMBLRETUBqVxYfxmSmTm+DVKRETURoq4MLwywFvsMoioCQy8REREBpA4tj+HOBCZKQZeIiIiAyqNC4MDv7sSmRV+SRIRERnY1eVheDu0t9hlENFvGHiJiIiMYEZQLw5xIDITDLxERERGVBoXho7t7MQug8iozP0znIGXiIjIyH5aMg4fTORjicl6PenjInYJ98XAS0REZAKhcj6WmKzXZy8Hi13Cfen90GNfX1+0a9cOdna/d15PnToVy5cvb3BsamoqUlJSoFQq4e3tjejoaISEhAAAYmNjkZ6eDqlUqj3ewcEBp06dAgCMGjUKpaWlOq8zdOhQbNmyBQBw7NgxJCUl4aeffoKjoyNGjx6NRYsWwcHBAXv37sWSJUvQrl07nXp2796NgIAAfW+ZiIjIYErjwtAvKQu/3LojdilENkPvwAsAWVlZ8Pa+/wLb2dnZWLduHZKTkyGXy7Fv3z7Mnz8fmZmZ6NatGwBgzpw5mDdvXpPX2L59OwYPHtxge3l5OSIiIhAbG4upU6eirKwMs2bNwoYNGxATEwMAGDhwIHbt2tWa2yMiIjKq0wtHI0ehQOjWE2KXQmQTjDakoaamBgsXLkS/fv1gb2+PSZMmwcnJCTk5OW2+dmFhIWpqajBhwgRIpVJ07doVw4YNQ35+ftsLJyIiMoFATw5xIDKVVgXepKQkDBs2DMOGDcPy5ctRWVnZ4JiwsDCEh4drP66oqMCtW7fg7u6u3Xb8+HGMGzcOAwYMQHh4OPLy8nSusXPnTowcORKDBw9GdHQ0ysrKAAB+fn7o2rUrPvzwQ9y5cwdFRUX45ptvMGLECO25xcXFmD59OgYMGIAxY8Zg3759rblVIiIioyqNC0OgZ0exyyCyanoPaQgMDERQUBDi4+NRUlKC+fPnY8WKFVizZk2T5wiCgGXLlsHf3x9BQUEAAB8fH0ilUkRGRsLZ2RmbNm3CzJkzkZWVBTc3Nzz++OMICAhAYmIiqqqqsHjxYkRHR2P37t3o0KEDNm3ahL/97W9YtWoVAGDs2LGYPn06AMDNzQ3du3dHdHQ0evbsiYMHDyImJgYeHh7a12+ORGIHicTcF9kwH1KpROdvMg62s+mwrU2HbQ0cmvsUFAoF/N49LnYpRK1ib2/eX792giAIbbnA4cOHERERgTNnzkAmkzXYr1arERsbi4KCAuzYsQNdunRp9DoajQbDhw9HVFQUpk6d2mD/xYsXMXbsWBw4cACdOnXCuHHjEBkZiUmTJkGpVGLRokXo27cvXn/99UavHx0dDXt7eyQlJbXovgRB0JkwR0REZAp2f98tdglEehM2TRO7hPtq1aS1e3l7e6Ourg7l5eXw8vLS2VdTU4PIyEhUV1cjNTUVLi4uTV5HKpXCy8sLSqWyydcBgLKyMhw9ehROTk6YNq2+cX18fPDKK69g0aJFTQZeb29vnD17tsX3df16JXt49SCVStCpU3tUVFRDo6kTuxyrxXY2Hba16bCtdV2Pn4AXd32L/T+WiV0KUYupVA2Ht5qCq6tji47TK/AWFBQgIyNDuxICUD+BTCaToWvXrjrHCoKABQsWQCaTYfPmzXBwcNDZt2rVKkyePBk9e/YEUN8TfPXqVfj4+KCoqAjbtm3D0qVLtUuLFRYWAqgPtz/88APq6nTfFNVqNSSS+u70Dz/8EK6urnj66ad16vTx8WnxvdbVCaira1Pnt03SaOpQW8tvWMbGdjYdtrXpsK1/9374kwAAj5XpIldC1DwHCcz+a1evARfu7u7Ys2cPUlJSoFarUVhYiPXr1yM8PBwSiQSjR4/WrqObkZGBS5cuYf369TphFwDs7OxQXFyM+Ph4KJVKVFZWYs2aNZDJZAgJCUHnzp1x6NAhJCUloaamBiUlJUhMTERISAi6du2KJ598EgqFAh999BHUajVKSkqwc+dO7aS12tpaxMfHo6CgAGq1Gl988QWOHDmiM4mOiIjI3JXGhZn9I1uJXhv+mNglNEvvMbwnT57E2rVr8eOPP8LV1RVjxoxBVFQUZDIZfH19kZycjODgYEyfPh0nT57UebAEAIwfPx4JCQmoqKhAYmIiDh8+DI1GA7lcjqVLl6JHjx4AgPPnz2PVqlXIy8tD+/btMWLECMTExKBjx/qZrEeOHMHGjRu1D54YMWIEFi1aBGdnZwiCgM2bN+PTTz+FSqXCI488gujoaAwfPrzF96lU3tSnWWyevb0Erq6OUKkqzf6nPEvGdjYdtrXpsK2bt2T///CvU9fELoOoUWIur9elS8tWOGnzpDVrxcCrH37DMg22s+mwrU2Hbd1yHOJA5sgSAq95ryFBREREWqVxYWjHMQ5EemPgJSIisiC/vBGGt0N7i10GkUVh4CUiIrIwM4J68bHERHpg4CUiIrJQDL1ELcPAS0RERERWjYGXiIjIQnHVBqKWYeAlIiKyQEv2/0/sEojg0V6vh/aKhoGXiIjIAvFBFGQOPnipv9gltAgDLxERkYV5kEMZyEwEenqKXUKLMPASERFZEIVCgVqxiyCyMAy8REREFiRg6wmxSyCyOAy8REREFuKvqd+JXQKRRWLgJSIishCZF8rELoHIIjHwEhERWYCunKhG1GoMvERERGZOoVBAELsIIgvGwEtERGTmOFGNzJGd2AXogYGXiIjIjI1N/lrsEoga9aSPi9gltBgDLxERkRk7WVQhdglEjfrs5WCxS2gxBl4iIiIz5cGJakQGwcBLRERkhnIUCrFLILIaDLxERERmKJQT1YgMhoGXiIiIiKwaAy8REZEZKo0LE7sEIqvBwEtERGSmSuPC8FQPN7HLILJ4DLxERERmbM+Lw9jbS9RGDLxEREQWoDQuzKKebEXWzcHCEqSFlUtERGS7SuLC8MoAb7HLIMJrwx8TuwS9MPASERFZkMSx/TnEgUQXHewndgl6YeAlIiKyQKVxYWjHMQ5ELcLAS0REZKF+eSMMb4f2FrsMIrPHwEtERGTBZgT14hAHomYw8BIREVmB0rgwdOQYBzKBaQFeYpegNwZeIiIiK/HTknH4YOIgscsgK/fOswPFLkFvDLxERERWJFTuySEOZDSW+rllr+8Jvr6+aNeuHezsfv+1ydSpU7F8+fIGx6ampiIlJQVKpRLe3t6Ijo5GSEgIACA2Nhbp6emQSqXa4x0cHHDq1CkAwKhRo1BaWqrzOkOHDsWWLVsAAMeOHUNSUhJ++uknODo6YvTo0Vi0aBEcHBwAAPv378fGjRtRVFSEhx9+GK+//jqGDh2q7+0SERFZpNK4MASszYKi8o7YpZCVsOQhM3oHXgDIysqCt/f9F77Ozs7GunXrkJycDLlcjn379mH+/PnIzMxEt27dAABz5szBvHnzmrzG9u3bMXjw4Abby8vLERERgdjYWEydOhVlZWWYNWsWNmzYgJiYGJw9exaLFy/G6tWrMWrUKGRkZGDu3LnIysqCp6dna26ZiIjI4uS+Nho5CgVCt54QuxSyAj8tGSd2Ca1mtCENNTU1WLhwIfr16wd7e3tMmjQJTk5OyMnJafO1CwsLUVNTgwkTJkAqlaJr164YNmwY8vPzAQBpaWkIDg7GmDFj8MADD2DKlCno1asX9u3b1+bXJiIisiSBnhziQG1n6WPDWxV4k5KSMGzYMAwbNgzLly9HZWVlg2PCwsIQHh6u/biiogK3bt2Cu7u7dtvx48cxbtw4DBgwAOHh4cjLy9O5xs6dOzFy5EgMHjwY0dHRKCsrAwD4+fmha9eu+PDDD3Hnzh0UFRXhm2++wYgRIwAA+fn58Pf317mWn58fzp4925rbJSIisnilcWHo08VR7DLIQoXKLfs35HoPaQgMDERQUBDi4+NRUlKC+fPnY8WKFVizZk2T5wiCgGXLlsHf3x9BQUEAAB8fH0ilUkRGRsLZ2RmbNm3CzJkzkZWVBTc3Nzz++OMICAhAYmIiqqqqsHjxYkRHR2P37t3o0KEDNm3ahL/97W9YtWoVAGDs2LGYPn06AEClUsHFxUWnBmdnZ1y4cKHF9ymR2EEisdyxKqYmlUp0/ibjYDubDtvadNjWpnN0wdOoqvoVXglfiV0KWZDr8RPELqHN7ARBENpygcOHDyMiIgJnzpyBTCZrsF+tViM2NhYFBQXYsWMHunTp0uh1NBoNhg8fjqioKEydOrXB/osXL2Ls2LE4cOAAOnXqhHHjxiEyMhKTJk2CUqnEokWL0LdvX7z++ut4+umn8dJLL2HatGna89955x18//33+OCDD1p0X4Ig6EyYIyIisgZ2f98tdglkQQb6OOPE4j+LXUabtWrS2r28vb1RV1eH8vJyeHnpLkRcU1ODyMhIVFdXIzU1tUGv672kUim8vLygVCqbfB0AKCsrw9GjR+Hk5KQNtD4+PnjllVewaNEivP7663B1dYVKpdI5X6VSwc3NrcX3df16JXt49SCVStCpU3tUVFRDo6kTuxyrxXY2Hba16bCtTefRNzPELoEszIG/jYRK1XDoqrlwdW3ZMB29Am9BQQEyMjIQExOj3VZYWAiZTIauXbvqHCsIAhYsWACZTIbNmzdrlwu7u2/VqlWYPHkyevbsCaC+J/jq1avw8fFBUVERtm3bhqVLl6Jdu3ba1wHqw+0PP/yAujrdN0W1Wg2JpP7XYXK5HOfOndPZn5eXh7Fjx7b4XuvqBNTVtanz2yZpNHWoreU3LGNjO5sO29p02NbGlZ2ngKpGI3YZZEFyZw+ymq9JvQZMubu7Y8+ePUhJSYFarUZhYSHWr1+P8PBwSCQSjB49WruObkZGBi5duoT169frhF0AsLOzQ3FxMeLj46FUKlFZWYk1a9ZAJpMhJCQEnTt3xqFDh5CUlISamhqUlJQgMTERISEh6Nq1K5588kkoFAp89NFHUKvVKCkpwc6dO7WT1qZMmYKjR48iMzMTNTU12LVrF65cuYIJEyYYpNGIiIgszQt7uTQZtVxpXJhVLeWq9xjekydPYu3atfjxxx/h6uqKMWPGICoqCjKZDL6+vkhOTkZwcDCmT5+OkydP6jxYAgDGjx+PhIQEVFRUIDExEYcPH4ZGo4FcLsfSpUvRo0cPAMD58+exatUq5OXloX379hgxYgRiYmLQsWNHAMCRI0ewceNG7YMnRowYgUWLFsHZ2RlA/TrASUlJKCoqwqOPPoply5ZhwIABLb5PpfKmPs1i8+ztJXB1dYRKVWk1Pw2aI7az6bCtTYdtbXweK9PFLoEsRFRQdywLDRC7jBbr0qVji45r86Q1a8XAqx9+wzINtrPpsK1Nh21tXAPfOYCfK26LXQZZAEtcr7mlgbfNk9aIiIjIPCkUCoZdatbm8QGYFNhd7DKMioGXiIjISgXwkcJ0HxIACgvs1W0NBl4iIiIrxHG7dD/Zswch0IompTWHgZeIiMjKhG79r9glkJnqYA9cXmobvbr3YuAlIiKyIgqFAjkKTrymhnJnD7Kqpcb0wcBLRERkRThul/7I01GG3NdGi12GqBh4iYiIrATH7dIfWeJSY8ag15PWiIiIyDz9NfU7sUsgMxLo2ZFh9x7s4SUiIrICmRfKxC6BzASDbkPs4SUiIrJwHMpAADDRz4Nhtwns4SUiIrJgngy7BPbqNoeBl4iIyEIlZOeiTuwiSFRLRz6G6GA/scswewy8REREFmrjsctil0AiYq9uyzHwEhERWSCO27VdH0wchFC5bT5AorUYeImIiCyMTwLDri1qZwf88gZ7dVuDgZeIiMiCvH/sR9zWiF0FmZotPxbYEBh4iYiILMji7B/ELoFMyFkmwYXX/yx2GRaPgZeIiMhCcNyubeGkNMPhgyeIiIgsgO+q/WKXQCbSw6U9w66BsYeXiIjIzGXnKaDiwF2bwKBrHOzhJSIiMnMv7D0hdglkZE/1cGPYNSL28BIREZkxjtu1fgy6xsceXiIiIjM18J0DYpdARvTKAG+GXRNhDy8REZEZUigU+LnitthlkJEw6JoWAy8REZEZCtjKcbvWaPP4AEwK7C52GTaHgZeIiMjMcNyu9ZEAULBXVzQMvERERGZkbPLXYpdABpY9exAC+VhgUTHwEhERmQmFQoGTRRVil0EG0l4K/LyMvbrmgIGXiIjITHDcrvUoXvYUHBw6ora2TuxSCFyWjIiIyCxw3K518HSU4Xr8BHhyCINZYQ8vERGRyCI+OS52CWQAXGrMfLGHl4iISGR780vFLoHaINCzI8OumWMPLxERkYg4lMGyMehaBvbwEhERieRBhl2LNdHPg2HXgrCHl4iISAQbjuSjVuwiqFUYdC2P3oHX19cX7dq1g52dnXbb1KlTsXz58gbHpqamIiUlBUqlEt7e3oiOjkZISAgAIDY2Funp6ZBKpdrjHRwccOrUKQDAqFGjUFpaqvM6Q4cOxZYtW/D55583eD1BEODh4YFDhw5h7969WLJkCdq1a6dzzO7duxEQEKDvLRMRERncm/+9KHYJpKelIx9DdLCf2GVQK7SqhzcrKwve3t73PSY7Oxvr1q1DcnIy5HI59u3bh/nz5yMzMxPdunUDAMyZMwfz5s1r8hrbt2/H4MGDG2yfMGECJkyYoLNt6dKlcHFx0X48cOBA7Nq1q+U3RUREZCIct2t52Ktr2Yw2hrempgYLFy5Ev379YG9vj0mTJsHJyQk5OTkGf60zZ87gyJEjmDNnjsGvTUREZEg9Ehl2LckHEwcx7FqBVvXwJiUl4eTJkwCAkSNHIjY2Fo6OjjrHhIXpfnJUVFTg1q1bcHd31247fvw4srOzUVxcjJ49e2LJkiWQy+Xa/Tt37kRsbCyqqqowZMgQLF++HJ07d25Qz9tvv42IiAg4OTlptxUXF2P69Ok4d+4cPDw8MHv2bIwfP77F9yiR2EEisWv+QAIASKUSnb/JONjOpsO2Nh1bausDZxS4pRa7CmqJdnZAyT8mtOpcW/qcthR6B97AwEAEBQUhPj4eJSUlmD9/PlasWIE1a9Y0eY4gCFi2bBn8/f0RFBQEAPDx8YFUKkVkZCScnZ2xadMmzJw5E1lZWXBzc8Pjjz+OgIAAJCYmoqqqCosXL0Z0dDR2796tc+1jx47h2rVrmDJlinabm5sbunfvjujoaPTs2RMHDx5ETEwMPDw8tK/fHDc3R53xw9QynTq1F7sEm8B2Nh22tenYQluHf8oHTFiC4mVPGeRJabbwOW0p7ARBENpygcOHDyMiIgJnzpyBTCZrsF+tViM2NhYFBQXYsWMHunTp0uh1NBoNhg8fjqioKEydOrXB/osXL2Ls2LE4cOAAunfvrt0eGRkJX19fREdH37fO6Oho2NvbIykpqUX3VV5+iz28epBKJejUqT0qKqqh0fC54cbCdjYdtrXp2Epbuy3/XOwSqBnODhIULmv78AVb+Zw2B66ujs0fBAMsS+bt7Y26ujqUl5fDy8tLZ19NTQ0iIyNRXV2N1NRUnUllfySVSuHl5QWlUtnk6wBAWVmZNvBWVVXhyJEjzYbdu+efPXu2ZTcFoK5OQF1dm34WsEkaTR1qa/nFbWxsZ9NhW5uONbd1wNossUugZuTOHgRPT0+Dfg5a8+e0pdFrcElBQQFWr16ts62wsBAymQxdu3bV2S4IAhYsWACZTIaUlBSdsCsIAt566y1cuHBBu02tVuPq1avw8fFBUVERVqxYAbX694FOhYWFAOqHQtx14sQJODk5wdfXV+e1P/zwQxw4cKBBnfeeS0REZAo5CgUUlXfELoOa0MOlPUrjwgwyhIHMl16B193dHXv27EFKSgrUajUKCwuxfv16hIeHQyKRYPTo0dp1dDMyMnDp0iWsX78eDg4OOtexs7NDcXEx4uPjoVQqUVlZiTVr1kAmkyEkJASdO3fGoUOHkJSUhJqaGpSUlCAxMREhISE6wbqgoACPPfZYgzpra2sRHx+PgoICqNVqfPHFFzhy5AjCw8Nb00ZEREStFrr1hNglUBNK48JwPPpPYpdBJqDXkAYPDw9s27YNa9euxYYNG+Dq6ooxY8YgKioKQH0valVVFQAgLS0NV69excCBA3WuMX78eCQkJCAhIQGJiYkICwuDRqOBXC5HSkoKOnToAABITk7GqlWrMGzYMLRv3x4jRoxATEyMzrWUSmWjwySmTZuGiooKzJ07FyqVCo888gjeffdd+Pv763O7REREbcL1ds3TUB8XfPZysNhlkAm1edKatVIqb4pdgkWxt5fA1dURKlUlxysZEdvZdNjWpmOtbR269b/IUfB7ibkxxZq61vo5bY66dOnYouO4QBwREZGBZecpGHbNzCsDvPkACRvW5lUaiIiISNcLezlu15ww6BIDLxERkQFx3K752Dw+AJMCu4tdBpkBBl4iIiIDYdg1DxIACvbq0j0YeImIiAyAYdc8ZM8ehECuqUt/wMBLRETURj4JDLtiay8FfjbAY4HJOjHwEhERtUHo1v/itkbsKmzb3ccCEzWFgZeIiKiV3j/2I5cfE5FHe3ucjRkjdhlkARh4iYiIWkGhUGBx9g9il2GzuNQY6YMPniAiImqFgK1ca1cMgZ4dGXZJb+zhJSIi0hNXZBAHgy61Fnt4iYiI9MCwa3oT/TwYdqlN2MNLRETUQl4MuybHoEuGwMBLRETUAkM2/Adcfcx0lo58DNHBfmKXQVaCgZeIiKgZCdm5uHSjWuwybAZ7dcnQGHiJiIjuQ6FQYOOxy2KXYRM+mDgIoXI+QIIMj4GXiIjoPrj8mPHZAyhiry4ZEQMvERFRE7gig/HxscBkCgy8REREjWDYNa6O7ezw05JxYpdBNoKBl4iI6A8Ydo2Lvbpkagy8RERE9/BdtV/sEqzWw50ccHLB02KXQTaIgZeIiOg3f039DqrbXG3XGLjUGImJjxYmIiICkJ2nQOaFMrHLsDpDfVwYdkl07OElIiIC8MJeLj9maAy6ZC4YeImIyOZxkpphbR4fgEmB3cUug0iLgZeIiGwaw67hOEiBq8vYq0vmh4GXiIiI2ozDF8iccdIaERHZNAa1tuGkNLIE7OElIiKbVxoXxqENrcCgS5aCgZeIiIj08sHEQQiV80lpZDkYeImIyKal5VzGnH25YpdhETq2s8NPS8aJXQaR3hh4iYjIZgWszYKi8o7YZVgEDl8gS8bAS0RENoljdltmop8HtkwZInYZRG3CwEtERDaHYbdl2KtL1kLvwOvr64t27drBzs5Ou23q1KlYvnx5g2NTU1ORkpICpVIJb29vREdHIyQkBAAQGxuL9PR0SKVS7fEODg44deoUAGDUqFEoLS3VeZ2hQ4diy5Yt+Pzzzxu8niAI8PDwwKFDhwAA+/fvx8aNG1FUVISHH34Yr7/+OoYOHarv7RIRkRV5/9iPWJz9g9hlmL3s2YMQ6MlJaWQ9WtXDm5WVBW9v7/sek52djXXr1iE5ORlyuRz79u3D/PnzkZmZiW7dugEA5syZg3nz5jV5je3bt2Pw4MENtk+YMAETJkzQ2bZ06VK4uLgAAM6ePYvFixdj9erVGDVqFDIyMjB37lxkZWXBk1/AREQ2qc/qTJRW14pdhlnzdJQh97XRYpdBZHBGe/BETU0NFi5ciH79+sHe3h6TJk2Ck5MTcnJyDP5aZ86cwZEjRzBnzhwAQFpaGoKDgzFmzBg88MADmDJlCnr16oV9+/YZ/LWJiMj8eaxMZ9htRmlcGMMuWa1WBd6kpCQMGzYMw4YNw/Lly1FZWdngmLCwMISHh2s/rqiowK1bt+Du7q7ddvz4cYwbNw4DBgxAeHg48vLydK6xc+dOjBw5EoMHD0Z0dDTKysoareftt99GREQEnJycAAD5+fnw9/fXOcbPzw9nz55tze0SEZEF43jd+4sK6s6xumT19B7SEBgYiKCgIMTHx6OkpATz58/HihUrsGbNmibPEQQBy5Ytg7+/P4KCggAAPj4+kEqliIyMhLOzMzZt2oSZM2ciKysLbm5uePzxxxEQEIDExERUVVVh8eLFiI6Oxu7du3WufezYMVy7dg1TpkzRblOpVNrhDXc5OzvjwoULLb5PicQOEold8wcSAEAqlej8TcbBdjYdtrXpGKutt393Hou+LDDoNa2JBEBZ/ASxy7BKfP8wP3aCIAhtucDhw4cRERGBM2fOQCaTNdivVqsRGxuLgoIC7NixA126dGn0OhqNBsOHD0dUVBSmTp3aYP/FixcxduxYHDhwAN27d9duj4yMhK+vL6Kjo7Xbnn76abz00kuYNm2adts777yD77//Hh988EGL7ksQBJ0Jc0REZDk8Yj6Gskotdhlmq3jZU5zTQjalzcuSeXt7o66uDuXl5fDy8tLZV1NTg8jISFRXVyM1NbVBr+u9pFIpvLy8oFQqm3wdACgrK9MG3qqqKhw5ckQn7AKAq6srVCqVzjaVSgU3N7cW39f165Xs4dWDVCpBp07tUVFRDY2mTuxyrBbb2XTY1qZj6LZ2W/5524uyUo93dkT+igmoqKiGStVwOCIZBt8/TMfV1bFFx+kVeAsKCpCRkYGYmBjttsLCQshkMnTt2lXnWEEQsGDBAshkMmzevBkODg46+1atWoXJkyejZ8+eAOp7gq9evQofHx8UFRVh27ZtWLp0Kdq1a6d9HaB+KMRdJ06cgJOTE3x9fXVeWy6X49y5czrb8vLyMHbs2Bbfa12dgLq6NnV+2ySNpg61tfziNja2s+mwrU3HEG3N8bpNK40Lg719/a/Y+XltGmxn86HX4BJ3d3fs2bMHKSkpUKvVKCwsxPr16xEeHg6JRILRo0dr19HNyMjApUuXsH79ep2wCwB2dnYoLi5GfHw8lEolKisrsWbNGshkMoSEhKBz5844dOgQkpKSUFNTg5KSEiQmJiIkJEQnWBcUFOCxxx5rUOeUKVNw9OhRZGZmoqamBrt27cKVK1caLGVGRETWYcORfIbdJrwd2puT0sjm6dXD6+HhgW3btmHt2rXYsGEDXF1dMWbMGERFRQGo74WtqqoCUL802NWrVzFw4ECda4wfPx4JCQlISEhAYmIiwsLCoNFoIJfLkZKSgg4dOgAAkpOTsWrVKgwbNgzt27fHiBEjdHqWAUCpVDY6TKJXr15Yu3YtkpKSsHjxYjz66KPYunUrOnfurM/tEhGRBfBdtR+q2xqxyzA77eyAX95g0CUCDDBpzVoplTfFLsGi2NtL4OrqCJWqkr++MSK2s+mwrU2nLW3NXt3G5c4e1OikNH5emwbb2XS6dOnYouPaPGmNiIhIDAy7DQ18sBP2zxohdhlEZoeBl4iILMqGI/l4878XxS7D7HCcLlHTGHiJiMhi9HzrC/x6h78ivtcHEwchVM41dYnuh4GXiIgsAocw6HJqB1xawl5dopZg4CUiIrPHsKuLwxeI9MPAS0REZishOxcbj10WuwyzMaZnZ6Q8/6TYZRBZHAZeIiIyS48mZuCmmitn3sVeXaLWY+AlIiKzwyEMv8uePQiBjaypS0Qtx8BLRERmQ6FQIGDrCbHLMAud29sjP2aM2GUQWQUGXiIiMhvj9pwRuwSzwOELRIbFwEtERGbj5IKntf/OzlPghb221dv7ygBvJI7tL3YZRFaHgZeIiMxSqNwTpXLdnk5rffCEHYAS9uoSGQ0DLxERWYwLr/+50e0KhQJD/33SIld1yJ09CJ6clEZkVAy8RERk8Tw9PfHTknFN7k/LuYzl2fkoq641YVX393AnB50hHERkPAy8RERk9SYFdsekwO4tOlahUOClfQXIVdyEsQZPcFIakWkx8BIREd3D09MT2bN1hxjkKBQI1WO5NGeZBJ/MGMD1c4nMBAMvERFRMwI9PXV6ZcN3fYt3/vQYx94SWQgGXiIiIj3teXGY2CUQkR4kYhdARERERGRMDLxEREREZNUYeImIiIjIqjHwEhEREZFVY+AlIiIiIqvGwEtEREREVo2Bl4iIiIisGgMvEREREVk1Bl4iIiIismoMvERERERk1Rh4iYiIiMiqMfASERERkVVj4CUiIiIiq8bAS0RERERWjYGXiIiIiKwaAy8RERERWTU7QRAEsYsgIiIiIjIW9vASERERkVVj4CUiIiIiq8bAS0RERERWjYGXiIiIiKwaAy8RERERWTUGXiIiIiKyagy8RERERGTVGHiJiIiIyKox8BIRERGRVWPgJSIiIiKrxsBr4xITE+Hr6wsAeO+99yCXy3X++Pv748UXXwQAvPzyyw32P/7449i0aRMA4Pbt23jjjTcwaNAg9OvXD1FRUbh+/br2ta5du4aZM2ciMDAQQUFBWLNmDerq6rT7jx07hrCwMMjlcvzpT39Cenq6CVvC+AzZ1gDw888/Y+LEiRg6dGiD1yooKMBzzz2HgIAABAcH4/3339fZv3//fjz99NOQy+X485//jKNHjxrxzk3L0J/TCQkJGDZsGJ544gm89NJL+PHHH7WvpVKpsGDBAvTv3x8DBw7E0qVLUVNTo93f3P+DpTNkW9+4cQOLFy9GUFAQnnjiCYSHhyMnJ0f7Wnz/MOz7x10HDx6Er68v/u///k+7zZbb2pDt/OKLL8Lf319nf1hYmPa1bLmdRSGQzcrPzxcGDRok9OrVq8lj/vrXvwq7d+9udN+NGzeEoUOHCj/88IMgCIIQHx8vjB07Vrhy5YpQXl4uzJo1S5g9e7YgCIJQV1cnjB8/Xli4cKFw48YN4eLFi8LIkSOFf//734IgCIJCoRD69u0r7NixQ6iqqhK++uorQS6XC2fOnDHwXYvD0G393XffCcOGDRPmzZsnPPnkkzrHVlVVCUOHDhXefvtt4datW8Lp06eFAQMGCAcOHBAEQRDy8vIEf39/Yf/+/UJ1dbXw8ccfC3379hWKi4sNdLfiMXQ7/+Mf/xCeffZZoaioSKiurhaWLl0qhISEaI+PiIgQXnzxRUGpVAoKhUJ49tlnhfj4eEEQmv9/sHSGbuuIiAghIiJCuH79ulBTUyMkJiYKgwYNEtRqNd8/DNzWd1VWVgqjRo0SAgMDhePHjwuCYNvv1YZu5xdeeEFIS0tr9FhbbmexMPDaKI1GI0yZMkV47733mvzizszMFMaNGyfU1tY2un/FihXCypUrBUEQBLVaLfTv31/4z3/+o91/8eJFoVevXoJCoRDOnDkj9O7dW1CpVNr9qampQmhoqCAIgpCcnCyEhYXpXH/+/PnC8uXL23KbZsHQbX33+IsXLwppaWkNAm9mZqYwaNAgnWutWbNGePnll7XXmjNnjs45U6ZMEbZs2dKq+zMXxmjndevWaYOAIAjCjz/+qP2cViqVgq+vr5Cfn6/df/jwYSEwMFC4fft2s/8PlswYbb1v3z7hl19+0X58/vx5vn8Ixmnru1atWiUsXbpUGDlypPbz3Fbb2hjtfL/Aa6vtLCYOabBRH374IR544AGMGzeu0f1qtRpJSUlYuHAhpFJpg/2XLl1CRkYG/v73vwMArly5glu3bsHf3197zKOPPor27dvj3LlzyM/Px0MPPQQXFxftfn9/f1y+fBm3bt1Cfn6+zrkA4Ofnh7NnzxrgbsVl6LYGgGeeeQaPPvpoo9fLz89H7969da51b1taa1sbo50XLFiAwYMHaz8uKipCu3bt4OTkhIKCAtjb22t//QnUf05XVVWhsLCw2f8HS2aMtg4LC8ODDz4IALh+/Tref/99PPHEE/Dw8OD7h4HbGgDOnz+PjIwMLFy4UGe7rba1sdo5MzMTTz/9NAYOHIiZM2fi559/BmC77SwmBl4bVFZWhnfffRcrVqxo8ph9+/bB2dkZw4cPb3T/li1bMGXKFLi5uQGoH8sIAM7OzjrHderUCdevX4dKpWqw7+7HKpWq0f0uLi46Y4AtkTHaujlNteWNGzdQV1cHlUql8yYL1P9fWHJbm6Kdf/31V7z55puYPn06HB0doVKp4OTkBInk97fRu+3e1Of8vf8PlsrYbf30008jKCgIV69excaNG2FnZ8f3DwO3tSAIiIuLw6uvvgpXV1ed422xrY3Vzo8++ih69uyJ3bt348CBA3B2dsasWbNw584dm2xnsTHw2qC33noLU6dORY8ePZo8ZteuXQgPD290X3l5Ob788kud/XZ2dk1ey87O7r7773d+c+eZO2O0dXNssa2N3c6lpaV48cUX4efnhwULFgBo++e8pTJ2Wx84cADHjh1D7969MW3aNFRVVdnk5zRgvLb+5JNPYG9vj4kTJzY4xxbb2ljtvGLFCixevBidO3eGm5sb/vGPf+DatWs4efKkTbaz2Bh4bcyxY8dw9uxZRERENHnM5cuXceHCBYSGhja6/6uvvkLPnj3RrVs37ba7P9XeuHFDu00QBNy4cQPu7u5wc3PT2Qf83ivs5uYGV1fXRve3tFfTHBmrrZvTVFu7urpCIpHA1dVV2/b37rfUtjZ2O1+5cgXPPfccBg4ciHXr1sHe3h5AfTvfvHkTGo1Ge+zddr3f5/zd/wdLZKrPaTc3NyxZsgRKpRJHjhzh+0cTWtPW169fx8aNG7Fy5cpGz7G1tjbl+7STkxNcXFygVCptrp3NgWW+61KrpaenQ6FQIDg4GIMHD9b+hD948GDs378fAHD06FH07t0bnTp1avQa3377rc64RgDw8fGBi4sLzp07p912/vx5qNVq9OnTB3K5HEVFRTpBKzc3F4899hgcHR0hl8t1zr27PyAgwCD3LQZjtXVz5HI5zp8/j9raWu22e9uysbbOy8uz2LY2Zjtfv34dL7/8MiZPnozly5frBFU/Pz/U1dXh/Pnz2m25ubno2LEjunfv3uz/gyUyVltXVVVh1KhRyMvL09leV1cHqVTK9w8DtvXhw4ehUqnw/PPPY/DgwRg8eDCKi4sRGRmJ+Ph4m2trY7XzrVu3sGLFCpSXl2u33R2q4OPjY3PtbBbEnjVHpnXjxg2huLhY++f06dNCr169hOLiYqGqqkoQBEFYunSpEBMT0+Q1/vSnPwmffvppg+1r164VxowZI1y5ckUoKysTXnrpJSE6Olq7f+rUqcKrr74q/Prrr8IPP/wgDB06VLu8S1lZmdC/f3/h/fffF6qqqoQvv/xSkMvlQkFBgWEbwISM2dZ3NbZKw+3bt4WRI0cKq1atEm7duiX83//9nxAYGCh8/fXXgiDUz36Xy+XaZcl27twp9O/fX1AqlQa4a9MzZjsvXbpUWLRoUZPnLViwQHjhhRcEpVIpXLt2TRg7dqywevVqQRCa/3+wRMZs61deeUV46aWXhNLSUqGmpkZ45513hIEDBwrl5eWCIPD9w1BtXVVVpXPd4uJiITg4WMjMzBRu3LghCIJttbUxP6efffZZ4dVXXxUqKioElUolREdHC88++6yg0WgEQbCtdjYHDLw27urVqw2WYPnb3/4mJCYmNnlOYGCgcPDgwQbbb9++LaxcuVIYMGCA0K9fP+0X+l3FxcXCrFmzhICAACEoKEj45z//qXP+yZMnhbCwMKFPnz5CaGiokJ2d3ca7My+GbOsZM2YIffr0Efz8/IRevXoJffr0Efr06SOcOHFCEIT65bOee+45QS6XCyNGjBBSU1N1zj9w4IAQGhoq9OnTRxg/frxw8uRJA9yheTBkO/fu3Vvw9/fXtu/dP5999pkgCIJQUVEhvPrqq0JgYKAwcOBA4R//+Idw+/Zt7fnN/T9YOkO2dXl5uRATEyMEBQUJ/fv3F8LDw4XTp09r9/P9w3Bt/Uf3LksmCLbd1oZs519++UWYO3euMHDgQGHIkCFCVFSUoFAotPttuZ3FYCcIgiB2LzMRERERkbFwDC8RERERWTUGXiIiIiKyagy8RERERGTVGHiJiIiIyKox8BIRERGRVWPgJSIiIiKrxsBLRERERFaNgZeIiIiIrBoDLxERERFZNQZeIiIiIrJqDLxEREREZNUYeImIiIjIqv1/W6/lRP5EDlwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.plot()"
   ]
  }
 ],
 "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}