examples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-07-31 08:29:17,754 - geosyspy.utils.oauth2_client - INFO - Authenticated\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from geosyspy import Geosys\n",
    "from dotenv import load_dotenv\n",
    "import datetime as dt\n",
    "from dateutil.relativedelta import relativedelta\n",
    "import logging\n",
    "from geosyspy.utils.constants import *\n",
    "from IPython.display import Image, display\n",
    "\n",
    "logger = logging.getLogger('geosyspy')\n",
    "logger.setLevel(logging.DEBUG)\n",
    "\n",
    "# read .env file\n",
    "load_dotenv()\n",
    "\n",
    "API_CLIENT_ID = os.getenv('API_CLIENT_ID')\n",
    "API_CLIENT_SECRET = os.getenv('API_CLIENT_SECRET')\n",
    "API_USERNAME = os.getenv('API_USERNAME')\n",
    "API_PASSWORD = os.getenv('API_PASSWORD')\n",
    "\n",
    "client = Geosys(API_CLIENT_ID, API_CLIENT_SECRET, API_USERNAME, API_PASSWORD, Env.PREPROD, Region.NA)\n",
    "\n",
    "polygon = \"POLYGON((-91.17523978603823 40.29787117039518,-91.17577285022956 40.29199489606421,-91.167613719932 40.29199489606421,-91.1673028670095 40.29867040193312,-91.17523978603823 40.29787117039518))\"\n",
    "today = dt.date.today()\n",
    "year_ago = dt.date.today() + relativedelta(months=-12)\n",
    "\n",
    "# available crop codes of the connected user\n",
    "crops = client.get_available_crops()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:18:21,028 - geosyspy.services.map_product_service - INFO - Calling APIs for coverage\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",
       "        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>coveragePercent</th>\n",
       "      <th>maps</th>\n",
       "      <th>image.id</th>\n",
       "      <th>image.availableBands</th>\n",
       "      <th>image.sensor</th>\n",
       "      <th>image.spatialResolution</th>\n",
       "      <th>image.date</th>\n",
       "      <th>seasonField.id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>sentinel-2-l2a|S2A_15TXE_20240606_0_L2A</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>SENTINEL_2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2024-06-06T17:02:38Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_024032_20240528_2024...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2024-05-28T16:41:01Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>sentinel-2-l2a|S2B_15TXE_20240512_0_L2A</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>SENTINEL_2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2024-05-12T17:02:32Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_024032_20240512_2024...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2024-05-12T16:41:14Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>sentinel-2-l2a|S2B_15TXE_20240412_0_L2A</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>SENTINEL_2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2024-04-12T17:02:33Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>sentinel-2-l2a|S2A_15TXE_20240407_0_L2A</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>SENTINEL_2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2024-04-07T17:02:31Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>sentinel-2-l2a|S2A_15TXE_20240328_0_L2A</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>SENTINEL_2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2024-03-28T17:02:33Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>sentinel-2-l2a|S2B_15TXE_20240323_0_L2A</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>SENTINEL_2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2024-03-23T17:02:33Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_024032_20240309_2024...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2024-03-09T16:41:51Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>sentinel-2-l2a|S2B_15TXE_20240303_0_L2A</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>SENTINEL_2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2024-03-03T17:02:29Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_025032_20240229_2024...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2024-02-29T16:48:00Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>sentinel-2-l2a|S2B_15TXE_20240222_0_L2A</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>SENTINEL_2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2024-02-22T17:02:33Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_024032_20240222_2024...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2024-02-22T16:41:44Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>sentinel-2-l2a|S2B_15TXE_20240212_0_L2A</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>SENTINEL_2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2024-02-12T17:02:33Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_024032_20240206_2024...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2024-02-06T16:41:59Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC08_L2SP_024032_20231212_2023...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-12-12T16:41:50Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_025032_20231211_2023...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-12-11T16:48:07Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_025032_20231125_2023...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-11-25T16:48:09Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_024032_20231118_2023...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-11-18T16:41:53Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC08_L2SP_025032_20231117_2023...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-11-17T16:48:04Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_025032_20231109_2023...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-11-09T16:48:02Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_024032_20231102_2023...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-11-02T16:41:47Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_024032_20231017_2023...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-10-17T16:41:55Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_024032_20231001_2023...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-10-01T16:41:44Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_024032_20230915_2023...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-09-15T16:41:45Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC08_L2SP_025032_20230914_2023...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-09-14T16:47:54Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC08_L2SP_024032_20230822_2023...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-08-22T16:41:39Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_024032_20230729_2023...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-07-29T16:41:20Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_025032_20230720_2023...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-07-20T16:47:27Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>100.0</td>\n",
       "      <td>[{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...</td>\n",
       "      <td>landsat-c2l2-sr|LC09_L2SP_025032_20230704_2023...</td>\n",
       "      <td>[Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...</td>\n",
       "      <td>LANDSAT_9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-07-04T16:47:22Z</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    coveragePercent                                               maps  \\\n",
       "0             100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "1             100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "2             100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "3             100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "4             100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "5             100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "6             100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "7             100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "8             100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "9             100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "10            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "11            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "12            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "13            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "14            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "15            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "16            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "17            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "18            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "19            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "20            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "21            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "22            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "23            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "24            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "25            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "26            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "27            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "28            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "29            100.0  [{'type': 'COLORCOMPOSITION'}, {'type': 'CVI'}...   \n",
       "\n",
       "                                             image.id  \\\n",
       "0             sentinel-2-l2a|S2A_15TXE_20240606_0_L2A   \n",
       "1   landsat-c2l2-sr|LC09_L2SP_024032_20240528_2024...   \n",
       "2             sentinel-2-l2a|S2B_15TXE_20240512_0_L2A   \n",
       "3   landsat-c2l2-sr|LC09_L2SP_024032_20240512_2024...   \n",
       "4             sentinel-2-l2a|S2B_15TXE_20240412_0_L2A   \n",
       "5             sentinel-2-l2a|S2A_15TXE_20240407_0_L2A   \n",
       "6             sentinel-2-l2a|S2A_15TXE_20240328_0_L2A   \n",
       "7             sentinel-2-l2a|S2B_15TXE_20240323_0_L2A   \n",
       "8   landsat-c2l2-sr|LC09_L2SP_024032_20240309_2024...   \n",
       "9             sentinel-2-l2a|S2B_15TXE_20240303_0_L2A   \n",
       "10  landsat-c2l2-sr|LC09_L2SP_025032_20240229_2024...   \n",
       "11            sentinel-2-l2a|S2B_15TXE_20240222_0_L2A   \n",
       "12  landsat-c2l2-sr|LC09_L2SP_024032_20240222_2024...   \n",
       "13            sentinel-2-l2a|S2B_15TXE_20240212_0_L2A   \n",
       "14  landsat-c2l2-sr|LC09_L2SP_024032_20240206_2024...   \n",
       "15  landsat-c2l2-sr|LC08_L2SP_024032_20231212_2023...   \n",
       "16  landsat-c2l2-sr|LC09_L2SP_025032_20231211_2023...   \n",
       "17  landsat-c2l2-sr|LC09_L2SP_025032_20231125_2023...   \n",
       "18  landsat-c2l2-sr|LC09_L2SP_024032_20231118_2023...   \n",
       "19  landsat-c2l2-sr|LC08_L2SP_025032_20231117_2023...   \n",
       "20  landsat-c2l2-sr|LC09_L2SP_025032_20231109_2023...   \n",
       "21  landsat-c2l2-sr|LC09_L2SP_024032_20231102_2023...   \n",
       "22  landsat-c2l2-sr|LC09_L2SP_024032_20231017_2023...   \n",
       "23  landsat-c2l2-sr|LC09_L2SP_024032_20231001_2023...   \n",
       "24  landsat-c2l2-sr|LC09_L2SP_024032_20230915_2023...   \n",
       "25  landsat-c2l2-sr|LC08_L2SP_025032_20230914_2023...   \n",
       "26  landsat-c2l2-sr|LC08_L2SP_024032_20230822_2023...   \n",
       "27  landsat-c2l2-sr|LC09_L2SP_024032_20230729_2023...   \n",
       "28  landsat-c2l2-sr|LC09_L2SP_025032_20230720_2023...   \n",
       "29  landsat-c2l2-sr|LC09_L2SP_025032_20230704_2023...   \n",
       "\n",
       "                                 image.availableBands image.sensor  \\\n",
       "0   [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...   SENTINEL_2   \n",
       "1   [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "2   [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...   SENTINEL_2   \n",
       "3   [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "4   [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...   SENTINEL_2   \n",
       "5   [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...   SENTINEL_2   \n",
       "6   [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...   SENTINEL_2   \n",
       "7   [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...   SENTINEL_2   \n",
       "8   [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "9   [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...   SENTINEL_2   \n",
       "10  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "11  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...   SENTINEL_2   \n",
       "12  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "13  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...   SENTINEL_2   \n",
       "14  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "15  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_8   \n",
       "16  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "17  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "18  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "19  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_8   \n",
       "20  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "21  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "22  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "23  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "24  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "25  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_8   \n",
       "26  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_8   \n",
       "27  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "28  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "29  [Blue, Green, Red, Nir, Swir1, Swir2, CoastalB...    LANDSAT_9   \n",
       "\n",
       "    image.spatialResolution            image.date seasonField.id  \n",
       "0                      10.0  2024-06-06T17:02:38Z           None  \n",
       "1                      30.0  2024-05-28T16:41:01Z           None  \n",
       "2                      10.0  2024-05-12T17:02:32Z           None  \n",
       "3                      30.0  2024-05-12T16:41:14Z           None  \n",
       "4                      10.0  2024-04-12T17:02:33Z           None  \n",
       "5                      10.0  2024-04-07T17:02:31Z           None  \n",
       "6                      10.0  2024-03-28T17:02:33Z           None  \n",
       "7                      10.0  2024-03-23T17:02:33Z           None  \n",
       "8                      30.0  2024-03-09T16:41:51Z           None  \n",
       "9                      10.0  2024-03-03T17:02:29Z           None  \n",
       "10                     30.0  2024-02-29T16:48:00Z           None  \n",
       "11                     10.0  2024-02-22T17:02:33Z           None  \n",
       "12                     30.0  2024-02-22T16:41:44Z           None  \n",
       "13                     10.0  2024-02-12T17:02:33Z           None  \n",
       "14                     30.0  2024-02-06T16:41:59Z           None  \n",
       "15                     30.0  2023-12-12T16:41:50Z           None  \n",
       "16                     30.0  2023-12-11T16:48:07Z           None  \n",
       "17                     30.0  2023-11-25T16:48:09Z           None  \n",
       "18                     30.0  2023-11-18T16:41:53Z           None  \n",
       "19                     30.0  2023-11-17T16:48:04Z           None  \n",
       "20                     30.0  2023-11-09T16:48:02Z           None  \n",
       "21                     30.0  2023-11-02T16:41:47Z           None  \n",
       "22                     30.0  2023-10-17T16:41:55Z           None  \n",
       "23                     30.0  2023-10-01T16:41:44Z           None  \n",
       "24                     30.0  2023-09-15T16:41:45Z           None  \n",
       "25                     30.0  2023-09-14T16:47:54Z           None  \n",
       "26                     30.0  2023-08-22T16:41:39Z           None  \n",
       "27                     30.0  2023-07-29T16:41:20Z           None  \n",
       "28                     30.0  2023-07-20T16:47:27Z           None  \n",
       "29                     30.0  2023-07-04T16:47:22Z           None  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get coverage for the polygon\n",
    "coverage_info_df, images_references = client.get_satellite_coverage_image_references(year_ago, today, collections=[SatelliteImageryCollection.SENTINEL_2, SatelliteImageryCollection.LANDSAT_8, SatelliteImageryCollection.LANDSAT_9],polygon=polygon)\n",
    "coverage_info_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get product from coverage\n",
    "season_field_id = 'y3mxlxx'\n",
    "image_id = 'sentinel-2-l2a|S2B_21JYM_20240429_0_L2A'\n",
    "indicator = 'INSEASON_NDVI'\n",
    "image_extension = \".png\"\n",
    "product = client.get_product(season_field_id, image_id, indicator, image_extension)\n",
    "if image_extension is not None:\n",
    "    display(Image(\"output\" + image_extension))\n",
    "else:\n",
    "    display(product)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:18:23,405 - geosyspy.services.vegetation_time_series_service - INFO - Calling APIs for aggregated time series\n",
      "c:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\geosyspy\\services\\vegetation_time_series_service.py:57: FutureWarning: Passing literal json to 'read_json' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  df = pd.read_json(json.dumps(dict_response))\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",
       "        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>index</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-06-14</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.642264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-15</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.652966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-16</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.663388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-17</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.673508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-18</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.683310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-09</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.485644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-10</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.494038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-11</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.504215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-12</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.516175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-13</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.529917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           index     value\n",
       "date                      \n",
       "2023-06-14  NDVI  0.642264\n",
       "2023-06-15  NDVI  0.652966\n",
       "2023-06-16  NDVI  0.663388\n",
       "2023-06-17  NDVI  0.673508\n",
       "2023-06-18  NDVI  0.683310\n",
       "...          ...       ...\n",
       "2024-06-09  NDVI  0.485644\n",
       "2024-06-10  NDVI  0.494038\n",
       "2024-06-11  NDVI  0.504215\n",
       "2024-06-12  NDVI  0.516175\n",
       "2024-06-13  NDVI  0.529917\n",
       "\n",
       "[366 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get aggregated NDVI time series\n",
    "client.get_time_series(year_ago, today, collection=SatelliteImageryCollection.MODIS, indicators=[\"NDVI\"],polygon=polygon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:18:25,268 - geosyspy.services.vegetation_time_series_service - INFO - Calling APIs for aggregated time series\n",
      "c:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\geosyspy\\services\\vegetation_time_series_service.py:57: FutureWarning: Passing literal json to 'read_json' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  df = pd.read_json(json.dumps(dict_response))\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>value</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-06-14</th>\n",
       "      <td>EVI</td>\n",
       "      <td>0.443899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-15</th>\n",
       "      <td>EVI</td>\n",
       "      <td>0.450635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-16</th>\n",
       "      <td>EVI</td>\n",
       "      <td>0.457103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-17</th>\n",
       "      <td>EVI</td>\n",
       "      <td>0.463278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-18</th>\n",
       "      <td>EVI</td>\n",
       "      <td>0.469148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-09</th>\n",
       "      <td>EVI</td>\n",
       "      <td>0.324216</td>\n",
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       "    <tr>\n",
       "      <th>2024-06-10</th>\n",
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       "      <th>2024-06-13</th>\n",
       "      <td>EVI</td>\n",
       "      <td>0.361241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           index     value\n",
       "date                      \n",
       "2023-06-14   EVI  0.443899\n",
       "2023-06-15   EVI  0.450635\n",
       "2023-06-16   EVI  0.457103\n",
       "2023-06-17   EVI  0.463278\n",
       "2023-06-18   EVI  0.469148\n",
       "...          ...       ...\n",
       "2024-06-09   EVI  0.324216\n",
       "2024-06-10   EVI  0.331350\n",
       "2024-06-11   EVI  0.339898\n",
       "2024-06-12   EVI  0.349862\n",
       "2024-06-13   EVI  0.361241\n",
       "\n",
       "[366 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get aggregated EVI time series\n",
    "client.get_time_series(year_ago, today, collection=SatelliteImageryCollection.MODIS, indicators=[\"EVI\"], polygon=polygon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precipitation.cumulative</th>\n",
       "      <th>precipitation.probabilities</th>\n",
       "      <th>temperature.standard</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-06-14T00:00:00Z</th>\n",
       "      <td>0.149606</td>\n",
       "      <td>90.0</td>\n",
       "      <td>65.786</td>\n",
       "      <td>POINT (0.0872845021171696 43.69457564315705)</td>\n",
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       "    <tr>\n",
       "      <th>2024-06-15T00:00:00Z</th>\n",
       "      <td>0.070866</td>\n",
       "      <td>80.0</td>\n",
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       "      <td>POINT (0.0872845021171696 43.69457564315705)</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2024-06-17T00:00:00Z</th>\n",
       "      <td>0.039370</td>\n",
       "      <td>39.4</td>\n",
       "      <td>69.350</td>\n",
       "      <td>POINT (0.0872845021171696 43.69457564315705)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-18T00:00:00Z</th>\n",
       "      <td>0.232283</td>\n",
       "      <td>70.0</td>\n",
       "      <td>68.918</td>\n",
       "      <td>POINT (0.0872845021171696 43.69457564315705)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-19T00:00:00Z</th>\n",
       "      <td>0.385827</td>\n",
       "      <td>70.0</td>\n",
       "      <td>63.410</td>\n",
       "      <td>POINT (0.0872845021171696 43.69457564315705)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      precipitation.cumulative  precipitation.probabilities  \\\n",
       "date                                                                          \n",
       "2024-06-14T00:00:00Z                  0.149606                         90.0   \n",
       "2024-06-15T00:00:00Z                  0.070866                         80.0   \n",
       "2024-06-16T00:00:00Z                  0.000000                         10.0   \n",
       "2024-06-17T00:00:00Z                  0.039370                         39.4   \n",
       "2024-06-18T00:00:00Z                  0.232283                         70.0   \n",
       "2024-06-19T00:00:00Z                  0.385827                         70.0   \n",
       "\n",
       "                      temperature.standard  \\\n",
       "date                                         \n",
       "2024-06-14T00:00:00Z                65.786   \n",
       "2024-06-15T00:00:00Z                61.736   \n",
       "2024-06-16T00:00:00Z                63.554   \n",
       "2024-06-17T00:00:00Z                69.350   \n",
       "2024-06-18T00:00:00Z                68.918   \n",
       "2024-06-19T00:00:00Z                63.410   \n",
       "\n",
       "                                                          Location  \n",
       "date                                                                \n",
       "2024-06-14T00:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T00:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-16T00:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-17T00:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-18T00:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-19T00:00:00Z  POINT (0.0872845021171696 43.69457564315705)  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get aggregated 'Forecast daily' time series\n",
    "indicators = ['Precipitation', 'Temperature','Date']\n",
    "point = \"POINT (0.0872845021171696 43.69457564315705)\"\n",
    "client.get_time_series(dt.date.today(), dt.date.today() + relativedelta(days=+5), collection=WeatherTypeCollection.WEATHER_FORECAST_DAILY, indicators=indicators,polygon=point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>precipitation.cumulative</th>\n",
       "      <th>precipitation.probabilities</th>\n",
       "      <th>temperature.standard</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
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       "      <th>2024-06-15T06:00:00Z</th>\n",
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       "      <th>2024-06-15T19:00:00Z</th>\n",
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       "      <td>54.59</td>\n",
       "      <td>POINT (0.0872845021171696 43.69457564315705)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-16T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.97</td>\n",
       "      <td>POINT (0.0872845021171696 43.69457564315705)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      precipitation.cumulative  precipitation.probabilities  \\\n",
       "date                                                                          \n",
       "2024-06-14T07:00:00Z                  0.000000                         14.6   \n",
       "2024-06-14T08:00:00Z                  0.000000                         10.0   \n",
       "2024-06-14T09:00:00Z                  0.000000                         10.0   \n",
       "2024-06-14T10:00:00Z                  0.000000                         18.2   \n",
       "2024-06-14T11:00:00Z                  0.000000                         17.8   \n",
       "2024-06-14T12:00:00Z                  0.000000                         18.1   \n",
       "2024-06-14T13:00:00Z                  0.000000                         10.0   \n",
       "2024-06-14T14:00:00Z                  0.000000                          0.0   \n",
       "2024-06-14T15:00:00Z                  0.000000                         22.3   \n",
       "2024-06-14T16:00:00Z                  0.003937                         38.0   \n",
       "2024-06-14T17:00:00Z                  0.003937                         60.4   \n",
       "2024-06-14T18:00:00Z                  0.015748                         82.3   \n",
       "2024-06-14T19:00:00Z                  0.027559                         90.0   \n",
       "2024-06-14T20:00:00Z                  0.047244                         90.0   \n",
       "2024-06-14T21:00:00Z                  0.047244                         90.0   \n",
       "2024-06-14T22:00:00Z                  0.039370                         80.0   \n",
       "2024-06-14T23:00:00Z                  0.015748                         80.0   \n",
       "2024-06-15T00:00:00Z                  0.003937                         55.1   \n",
       "2024-06-15T01:00:00Z                  0.003937                         32.3   \n",
       "2024-06-15T02:00:00Z                  0.000000                         26.9   \n",
       "2024-06-15T03:00:00Z                  0.000000                         17.2   \n",
       "2024-06-15T04:00:00Z                  0.000000                         17.1   \n",
       "2024-06-15T05:00:00Z                  0.000000                         19.5   \n",
       "2024-06-15T06:00:00Z                  0.000000                         17.6   \n",
       "2024-06-15T07:00:00Z                  0.000000                         20.9   \n",
       "2024-06-15T08:00:00Z                  0.000000                         21.1   \n",
       "2024-06-15T09:00:00Z                  0.000000                         10.0   \n",
       "2024-06-15T10:00:00Z                  0.000000                         10.0   \n",
       "2024-06-15T11:00:00Z                  0.000000                         10.0   \n",
       "2024-06-15T12:00:00Z                  0.000000                          0.0   \n",
       "2024-06-15T13:00:00Z                  0.000000                          0.0   \n",
       "2024-06-15T14:00:00Z                  0.000000                          0.0   \n",
       "2024-06-15T15:00:00Z                  0.000000                          0.0   \n",
       "2024-06-15T16:00:00Z                  0.000000                          0.0   \n",
       "2024-06-15T17:00:00Z                  0.000000                          0.0   \n",
       "2024-06-15T18:00:00Z                  0.000000                          0.0   \n",
       "2024-06-15T19:00:00Z                  0.000000                          0.0   \n",
       "2024-06-15T20:00:00Z                  0.000000                          0.0   \n",
       "2024-06-15T21:00:00Z                  0.000000                          0.0   \n",
       "2024-06-15T22:00:00Z                  0.000000                          0.0   \n",
       "2024-06-15T23:00:00Z                  0.000000                          0.0   \n",
       "2024-06-16T00:00:00Z                  0.000000                          0.0   \n",
       "\n",
       "                      temperature.standard  \\\n",
       "date                                         \n",
       "2024-06-14T07:00:00Z                 62.87   \n",
       "2024-06-14T08:00:00Z                 65.03   \n",
       "2024-06-14T09:00:00Z                 67.55   \n",
       "2024-06-14T10:00:00Z                 70.07   \n",
       "2024-06-14T11:00:00Z                 71.87   \n",
       "2024-06-14T12:00:00Z                 73.49   \n",
       "2024-06-14T13:00:00Z                 74.03   \n",
       "2024-06-14T14:00:00Z                 75.29   \n",
       "2024-06-14T15:00:00Z                 75.83   \n",
       "2024-06-14T16:00:00Z                 74.21   \n",
       "2024-06-14T17:00:00Z                 71.33   \n",
       "2024-06-14T18:00:00Z                 68.27   \n",
       "2024-06-14T19:00:00Z                 64.67   \n",
       "2024-06-14T20:00:00Z                 61.97   \n",
       "2024-06-14T21:00:00Z                 60.17   \n",
       "2024-06-14T22:00:00Z                 59.45   \n",
       "2024-06-14T23:00:00Z                 58.91   \n",
       "2024-06-15T00:00:00Z                 58.01   \n",
       "2024-06-15T01:00:00Z                 58.01   \n",
       "2024-06-15T02:00:00Z                 58.01   \n",
       "2024-06-15T03:00:00Z                 57.83   \n",
       "2024-06-15T04:00:00Z                 57.83   \n",
       "2024-06-15T05:00:00Z                 57.65   \n",
       "2024-06-15T06:00:00Z                 58.19   \n",
       "2024-06-15T07:00:00Z                 59.09   \n",
       "2024-06-15T08:00:00Z                 60.53   \n",
       "2024-06-15T09:00:00Z                 61.97   \n",
       "2024-06-15T10:00:00Z                 63.95   \n",
       "2024-06-15T11:00:00Z                 65.93   \n",
       "2024-06-15T12:00:00Z                 67.19   \n",
       "2024-06-15T13:00:00Z                 67.91   \n",
       "2024-06-15T14:00:00Z                 68.27   \n",
       "2024-06-15T15:00:00Z                 68.63   \n",
       "2024-06-15T16:00:00Z                 67.01   \n",
       "2024-06-15T17:00:00Z                 65.75   \n",
       "2024-06-15T18:00:00Z                 64.13   \n",
       "2024-06-15T19:00:00Z                 61.79   \n",
       "2024-06-15T20:00:00Z                 59.09   \n",
       "2024-06-15T21:00:00Z                 56.93   \n",
       "2024-06-15T22:00:00Z                 55.67   \n",
       "2024-06-15T23:00:00Z                 54.59   \n",
       "2024-06-16T00:00:00Z                 52.97   \n",
       "\n",
       "                                                          Location  \n",
       "date                                                                \n",
       "2024-06-14T07:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-14T08:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-14T09:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-14T10:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-14T11:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-14T12:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-14T13:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-14T14:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-14T15:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-14T16:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-14T17:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-14T18:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-14T19:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-14T20:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-14T21:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-14T22:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-14T23:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T00:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T01:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T02:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T03:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T04:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T05:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T06:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T07:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T08:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T09:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T10:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T11:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T12:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T13:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T14:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T15:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T16:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T17:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T18:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T19:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T20:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T21:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T22:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-15T23:00:00Z  POINT (0.0872845021171696 43.69457564315705)  \n",
       "2024-06-16T00:00:00Z  POINT (0.0872845021171696 43.69457564315705)  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get aggregated 'Forecast hourly' time series\n",
    "indicators = ['Precipitation', 'Temperature']\n",
    "point = \"POINT (0.0872845021171696 43.69457564315705)\"\n",
    "client.get_time_series(dt.date.today(), dt.date.today() + relativedelta(days=+2), collection=WeatherTypeCollection.WEATHER_FORECAST_HOURLY, indicators=indicators, polygon=point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precipitation.cumulative</th>\n",
       "      <th>temperature.standard</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01T00:00:00Z</th>\n",
       "      <td>0.350394</td>\n",
       "      <td>24.386</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-02T00:00:00Z</th>\n",
       "      <td>0.011811</td>\n",
       "      <td>6.008</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.874</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.776</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.548</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-06T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.460</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-07T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.586</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-08T00:00:00Z</th>\n",
       "      <td>0.031496</td>\n",
       "      <td>25.574</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-09T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.238</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-10T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.980</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-11T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.454</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-12T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.498</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-13T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>36.392</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-14T00:00:00Z</th>\n",
       "      <td>0.314961</td>\n",
       "      <td>31.334</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-15T00:00:00Z</th>\n",
       "      <td>0.236220</td>\n",
       "      <td>24.062</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-16T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.656</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-17T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.114</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-18T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.304</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-19T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.168</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-20T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.326</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-21T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.926</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-22T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.384</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-23T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.656</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-24T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.898</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-25T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.556</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-26T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.662</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-27T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.272</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-28T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.852</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-29T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.478</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
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       "      <th>2022-01-30T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.512</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
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       "    <tr>\n",
       "      <th>2022-01-31T00:00:00Z</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.894</td>\n",
       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
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       "      <th>2022-02-01T00:00:00Z</th>\n",
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       "      <td>POINT (-91.17139663702373 40.29511916279967)</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      precipitation.cumulative  temperature.standard  \\\n",
       "date                                                                   \n",
       "2022-01-01T00:00:00Z                  0.350394                24.386   \n",
       "2022-01-02T00:00:00Z                  0.011811                 6.008   \n",
       "2022-01-03T00:00:00Z                  0.000000                13.874   \n",
       "2022-01-04T00:00:00Z                  0.000000                30.776   \n",
       "2022-01-05T00:00:00Z                  0.000000                15.548   \n",
       "2022-01-06T00:00:00Z                  0.000000                 4.460   \n",
       "2022-01-07T00:00:00Z                  0.000000                 4.586   \n",
       "2022-01-08T00:00:00Z                  0.031496                25.574   \n",
       "2022-01-09T00:00:00Z                  0.000000                19.238   \n",
       "2022-01-10T00:00:00Z                  0.000000                15.980   \n",
       "2022-01-11T00:00:00Z                  0.000000                28.454   \n",
       "2022-01-12T00:00:00Z                  0.000000                38.498   \n",
       "2022-01-13T00:00:00Z                  0.000000                36.392   \n",
       "2022-01-14T00:00:00Z                  0.314961                31.334   \n",
       "2022-01-15T00:00:00Z                  0.236220                24.062   \n",
       "2022-01-16T00:00:00Z                  0.000000                15.656   \n",
       "2022-01-17T00:00:00Z                  0.000000                26.114   \n",
       "2022-01-18T00:00:00Z                  0.000000                34.304   \n",
       "2022-01-19T00:00:00Z                  0.000000                17.168   \n",
       "2022-01-20T00:00:00Z                  0.000000                 3.326   \n",
       "2022-01-21T00:00:00Z                  0.000000                 6.926   \n",
       "2022-01-22T00:00:00Z                  0.000000                26.384   \n",
       "2022-01-23T00:00:00Z                  0.000000                24.656   \n",
       "2022-01-24T00:00:00Z                  0.000000                25.898   \n",
       "2022-01-25T00:00:00Z                  0.000000                 7.556   \n",
       "2022-01-26T00:00:00Z                  0.000000                 9.662   \n",
       "2022-01-27T00:00:00Z                  0.000000                30.272   \n",
       "2022-01-28T00:00:00Z                  0.000000                17.852   \n",
       "2022-01-29T00:00:00Z                  0.000000                22.478   \n",
       "2022-01-30T00:00:00Z                  0.000000                24.512   \n",
       "2022-01-31T00:00:00Z                  0.000000                29.894   \n",
       "2022-02-01T00:00:00Z                  0.216535                38.642   \n",
       "\n",
       "                                                          Location  \n",
       "date                                                                \n",
       "2022-01-01T00:00:00Z  POINT (-91.17139663702373 40.29511916279967)  \n",
       "2022-01-02T00:00:00Z  POINT (-91.17139663702373 40.29511916279967)  \n",
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       "2022-01-21T00:00:00Z  POINT (-91.17139663702373 40.29511916279967)  \n",
       "2022-01-22T00:00:00Z  POINT (-91.17139663702373 40.29511916279967)  \n",
       "2022-01-23T00:00:00Z  POINT (-91.17139663702373 40.29511916279967)  \n",
       "2022-01-24T00:00:00Z  POINT (-91.17139663702373 40.29511916279967)  \n",
       "2022-01-25T00:00:00Z  POINT (-91.17139663702373 40.29511916279967)  \n",
       "2022-01-26T00:00:00Z  POINT (-91.17139663702373 40.29511916279967)  \n",
       "2022-01-27T00:00:00Z  POINT (-91.17139663702373 40.29511916279967)  \n",
       "2022-01-28T00:00:00Z  POINT (-91.17139663702373 40.29511916279967)  \n",
       "2022-01-29T00:00:00Z  POINT (-91.17139663702373 40.29511916279967)  \n",
       "2022-01-30T00:00:00Z  POINT (-91.17139663702373 40.29511916279967)  \n",
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       "2022-02-01T00:00:00Z  POINT (-91.17139663702373 40.29511916279967)  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get aggregated 'Historical daily' time series\n",
    "indicators = ['Precipitation', 'Temperature']\n",
    "start_date = dt.datetime.strptime(\"2022-01-01\", \"%Y-%m-%d\")\n",
    "end_date = dt.datetime.strptime(\"2022-02-01\", \"%Y-%m-%d\")\n",
    "client.get_time_series(start_date, end_date, collection=WeatherTypeCollection.WEATHER_HISTORICAL_DAILY, indicators=indicators,polygon=polygon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:18:29,143 - geosyspy.services.vegetation_time_series_service - INFO - Calling APIs for time series by the pixel\n",
      "2024-06-14 11:18:30,182 - geosyspy.services.vegetation_time_series_service - INFO - Computing X and Y coordinates per pixel... \n",
      "2024-06-14 11:18:30,199 - geosyspy.services.vegetation_time_series_service - INFO - Done ! \n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>value</th>\n",
       "      <th>pixel.id</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
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       "      <th>date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-06-14</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.649486</td>\n",
       "      <td>mh11v4i222j4657</td>\n",
       "      <td>-6.620275e+06</td>\n",
       "      <td>-1.664613e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-14</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.635043</td>\n",
       "      <td>mh11v4i221j4658</td>\n",
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       "    <tr>\n",
       "      <th>2023-06-15</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.660745</td>\n",
       "      <td>mh11v4i222j4657</td>\n",
       "      <td>-6.620275e+06</td>\n",
       "      <td>-1.664613e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-15</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.645188</td>\n",
       "      <td>mh11v4i221j4658</td>\n",
       "      <td>-6.620507e+06</td>\n",
       "      <td>-1.664636e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-16</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.671886</td>\n",
       "      <td>mh11v4i222j4657</td>\n",
       "      <td>-6.620275e+06</td>\n",
       "      <td>-1.664613e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-11</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.588776</td>\n",
       "      <td>mh11v4i221j4658</td>\n",
       "      <td>-6.620507e+06</td>\n",
       "      <td>-1.664636e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-12</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.412464</td>\n",
       "      <td>mh11v4i222j4657</td>\n",
       "      <td>-6.620275e+06</td>\n",
       "      <td>-1.664613e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-12</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.619886</td>\n",
       "      <td>mh11v4i221j4658</td>\n",
       "      <td>-6.620507e+06</td>\n",
       "      <td>-1.664636e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-13</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.405473</td>\n",
       "      <td>mh11v4i222j4657</td>\n",
       "      <td>-6.620275e+06</td>\n",
       "      <td>-1.664613e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-13</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.654361</td>\n",
       "      <td>mh11v4i221j4658</td>\n",
       "      <td>-6.620507e+06</td>\n",
       "      <td>-1.664636e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>732 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "           index     value         pixel.id             X             Y\n",
       "date                                                                   \n",
       "2023-06-14  NDVI  0.649486  mh11v4i222j4657 -6.620275e+06 -1.664613e+07\n",
       "2023-06-14  NDVI  0.635043  mh11v4i221j4658 -6.620507e+06 -1.664636e+07\n",
       "2023-06-15  NDVI  0.660745  mh11v4i222j4657 -6.620275e+06 -1.664613e+07\n",
       "2023-06-15  NDVI  0.645188  mh11v4i221j4658 -6.620507e+06 -1.664636e+07\n",
       "2023-06-16  NDVI  0.671886  mh11v4i222j4657 -6.620275e+06 -1.664613e+07\n",
       "...          ...       ...              ...           ...           ...\n",
       "2024-06-11  NDVI  0.588776  mh11v4i221j4658 -6.620507e+06 -1.664636e+07\n",
       "2024-06-12  NDVI  0.412464  mh11v4i222j4657 -6.620275e+06 -1.664613e+07\n",
       "2024-06-12  NDVI  0.619886  mh11v4i221j4658 -6.620507e+06 -1.664636e+07\n",
       "2024-06-13  NDVI  0.405473  mh11v4i222j4657 -6.620275e+06 -1.664613e+07\n",
       "2024-06-13  NDVI  0.654361  mh11v4i221j4658 -6.620507e+06 -1.664636e+07\n",
       "\n",
       "[732 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get satellite image time series for Modis NDVI\n",
    "client.get_satellite_image_time_series(year_ago, today, collections=[SatelliteImageryCollection.MODIS], indicators=[\"NDVI\"], polygon=polygon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:18:30,223 - geosyspy.services.map_product_service - INFO - Calling APIs for coverage\n",
      "c:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\geosyspy\\geosys.py:359: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
      "  df_coverage[\"image.date\"] = pd.to_datetime(\n",
      "2024-06-14 11:18:48,887 - geosyspy.geosys - INFO - The highest resolution's image grid size is (72,76)\n",
      "2024-06-14 11:18:48,932 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2B_15TXE_20240222_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:49,692 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2B_15TXE_20240303_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:49,743 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2B_15TXE_20240323_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:49,810 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2A_15TXE_20240328_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:49,877 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2A_15TXE_20240407_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:49,936 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2B_15TXE_20240412_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:49,991 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2B_15TXE_20240512_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:50,044 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2A_15TXE_20240606_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:50,103 - geosyspy.geosys - INFO - interpolating landsat-c2l2-sr|LC08_L2SP_024032_20230822_20230826_02_T1_SR to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:50,156 - geosyspy.geosys - INFO - interpolating landsat-c2l2-sr|LC08_L2SP_025032_20230914_20230919_02_T1_SR to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:50,208 - geosyspy.geosys - INFO - interpolating landsat-c2l2-sr|LC08_L2SP_025032_20231117_20231122_02_T1_SR to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:50,263 - geosyspy.geosys - INFO - interpolating landsat-c2l2-sr|LC08_L2SP_024032_20231212_20231215_02_T1_SR to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n"
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       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
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       "\n",
       "html[theme=dark],\n",
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       "\n",
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       "  display: block !important;\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
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       "\n",
       ".xr-header {\n",
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       "  padding-bottom: 6px;\n",
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       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
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       "\n",
       ".xr-sections {\n",
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       "  display: grid;\n",
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       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
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       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
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       "\n",
       ".xr-section-summary {\n",
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       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                  (band: 10, y: 76, x: 72, time: 13)\n",
       "Coordinates:\n",
       "  * band                     (band) &lt;U11 &#x27;Blue&#x27; &#x27;CoastalBlue&#x27; ... &#x27;Swir2&#x27;\n",
       "  * y                        (y) float64 40.3 40.3 40.3 ... 40.29 40.29 40.29\n",
       "  * x                        (x) float64 -91.18 -91.18 -91.18 ... -91.17 -91.17\n",
       "  * time                     (time) datetime64[ns] 2024-02-12T17:02:33 ... 20...\n",
       "    image.id                 (time) &lt;U59 &#x27;sentinel-2-l2a|S2B_15TXE_20240212_0...\n",
       "    image.sensor             (time) &lt;U10 &#x27;SENTINEL_2&#x27; ... &#x27;LANDSAT_8&#x27;\n",
       "    image.spatialResolution  (time) float64 10.0 10.0 10.0 ... 30.0 30.0 30.0\n",
       "    crs                      (time) &lt;U9 &#x27;EPSG:4326&#x27; &#x27;EPSG:4326&#x27; ... &#x27;EPSG:4326&#x27;\n",
       "Data variables:\n",
       "    reflectance              (time, band, y, x) float64 nan nan nan ... nan nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5f179b19-5712-4e9e-83ac-e0e224b6acb5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5f179b19-5712-4e9e-83ac-e0e224b6acb5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 10</li><li><span class='xr-has-index'>y</span>: 76</li><li><span class='xr-has-index'>x</span>: 72</li><li><span class='xr-has-index'>time</span>: 13</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bce1fff9-f987-4074-84c2-bd5375f965c9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bce1fff9-f987-4074-84c2-bd5375f965c9' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U11</div><div class='xr-var-preview xr-preview'>&#x27;Blue&#x27; &#x27;CoastalBlue&#x27; ... &#x27;Swir2&#x27;</div><input id='attrs-4c39b8c0-1965-47dc-9cc0-78f395411618' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c39b8c0-1965-47dc-9cc0-78f395411618' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3da6ea1-a614-440e-b3f1-66371d272bf9' class='xr-var-data-in' type='checkbox'><label for='data-f3da6ea1-a614-440e-b3f1-66371d272bf9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Blue&#x27;, &#x27;CoastalBlue&#x27;, &#x27;Green&#x27;, &#x27;Nir&#x27;, &#x27;Red&#x27;, &#x27;RedEdge1&#x27;, &#x27;RedEdge2&#x27;,\n",
       "       &#x27;RedEdge3&#x27;, &#x27;Swir1&#x27;, &#x27;Swir2&#x27;], dtype=&#x27;&lt;U11&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>40.3 40.3 40.3 ... 40.29 40.29</div><input id='attrs-f0393d9d-84c3-499b-ae76-bac51dee54d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0393d9d-84c3-499b-ae76-bac51dee54d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52d13f45-e7c7-41dc-a3cb-04369955cbb9' class='xr-var-data-in' type='checkbox'><label for='data-52d13f45-e7c7-41dc-a3cb-04369955cbb9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([40.298765, 40.298673, 40.298581, 40.29849 , 40.298398, 40.298306,\n",
       "       40.298214, 40.298122, 40.298031, 40.297939, 40.297847, 40.297755,\n",
       "       40.297664, 40.297572, 40.29748 , 40.297388, 40.297296, 40.297205,\n",
       "       40.297113, 40.297021, 40.296929, 40.296837, 40.296746, 40.296654,\n",
       "       40.296562, 40.29647 , 40.296379, 40.296287, 40.296195, 40.296103,\n",
       "       40.296011, 40.29592 , 40.295828, 40.295736, 40.295644, 40.295553,\n",
       "       40.295461, 40.295369, 40.295277, 40.295185, 40.295094, 40.295002,\n",
       "       40.29491 , 40.294818, 40.294726, 40.294635, 40.294543, 40.294451,\n",
       "       40.294359, 40.294268, 40.294176, 40.294084, 40.293992, 40.2939  ,\n",
       "       40.293809, 40.293717, 40.293625, 40.293533, 40.293441, 40.29335 ,\n",
       "       40.293258, 40.293166, 40.293074, 40.292983, 40.292891, 40.292799,\n",
       "       40.292707, 40.292615, 40.292524, 40.292432, 40.29234 , 40.292248,\n",
       "       40.292157, 40.292065, 40.291973, 40.291881])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-91.18 -91.18 ... -91.17 -91.17</div><input id='attrs-ac26551f-0c90-4aab-a08a-93e3face4385' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac26551f-0c90-4aab-a08a-93e3face4385' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d3d114b-3744-403a-bf87-f2bbf1289927' class='xr-var-data-in' type='checkbox'><label for='data-1d3d114b-3744-403a-bf87-f2bbf1289927' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-91.175823, -91.175703, -91.175583, -91.175463, -91.175342, -91.175222,\n",
       "       -91.175102, -91.174982, -91.174862, -91.174741, -91.174621, -91.174501,\n",
       "       -91.174381, -91.174261, -91.17414 , -91.17402 , -91.1739  , -91.17378 ,\n",
       "       -91.17366 , -91.173539, -91.173419, -91.173299, -91.173179, -91.173059,\n",
       "       -91.172938, -91.172818, -91.172698, -91.172578, -91.172458, -91.172337,\n",
       "       -91.172217, -91.172097, -91.171977, -91.171857, -91.171736, -91.171616,\n",
       "       -91.171496, -91.171376, -91.171256, -91.171135, -91.171015, -91.170895,\n",
       "       -91.170775, -91.170655, -91.170534, -91.170414, -91.170294, -91.170174,\n",
       "       -91.170054, -91.169933, -91.169813, -91.169693, -91.169573, -91.169453,\n",
       "       -91.169332, -91.169212, -91.169092, -91.168972, -91.168852, -91.168731,\n",
       "       -91.168611, -91.168491, -91.168371, -91.168251, -91.16813 , -91.16801 ,\n",
       "       -91.16789 , -91.16777 , -91.16765 , -91.167529, -91.167409, -91.167289])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-02-12T17:02:33 ... 2023-12-...</div><input id='attrs-9c7c41a2-e2b2-4307-a4b0-174c232f7b30' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c7c41a2-e2b2-4307-a4b0-174c232f7b30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-735f03d3-fdb8-4be1-b0c9-1419e274d971' class='xr-var-data-in' type='checkbox'><label for='data-735f03d3-fdb8-4be1-b0c9-1419e274d971' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-02-12T17:02:33.000000000&#x27;, &#x27;2024-02-22T17:02:33.000000000&#x27;,\n",
       "       &#x27;2024-03-03T17:02:29.000000000&#x27;, &#x27;2024-03-23T17:02:33.000000000&#x27;,\n",
       "       &#x27;2024-03-28T17:02:33.000000000&#x27;, &#x27;2024-04-07T17:02:31.000000000&#x27;,\n",
       "       &#x27;2024-04-12T17:02:33.000000000&#x27;, &#x27;2024-05-12T17:02:32.000000000&#x27;,\n",
       "       &#x27;2024-06-06T17:02:38.000000000&#x27;, &#x27;2023-08-22T16:41:39.000000000&#x27;,\n",
       "       &#x27;2023-09-14T16:47:54.000000000&#x27;, &#x27;2023-11-17T16:48:04.000000000&#x27;,\n",
       "       &#x27;2023-12-12T16:41:50.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>image.id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U59</div><div class='xr-var-preview xr-preview'>&#x27;sentinel-2-l2a|S2B_15TXE_202402...</div><input id='attrs-6881562d-5735-4a92-a427-0c077c86398e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6881562d-5735-4a92-a427-0c077c86398e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef34da79-0618-4913-945a-95f041687845' class='xr-var-data-in' type='checkbox'><label for='data-ef34da79-0618-4913-945a-95f041687845' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;sentinel-2-l2a|S2B_15TXE_20240212_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2B_15TXE_20240222_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2B_15TXE_20240303_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2B_15TXE_20240323_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2A_15TXE_20240328_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2A_15TXE_20240407_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2B_15TXE_20240412_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2B_15TXE_20240512_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2A_15TXE_20240606_0_L2A&#x27;,\n",
       "       &#x27;landsat-c2l2-sr|LC08_L2SP_024032_20230822_20230826_02_T1_SR&#x27;,\n",
       "       &#x27;landsat-c2l2-sr|LC08_L2SP_025032_20230914_20230919_02_T1_SR&#x27;,\n",
       "       &#x27;landsat-c2l2-sr|LC08_L2SP_025032_20231117_20231122_02_T1_SR&#x27;,\n",
       "       &#x27;landsat-c2l2-sr|LC08_L2SP_024032_20231212_20231215_02_T1_SR&#x27;],\n",
       "      dtype=&#x27;&lt;U59&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>image.sensor</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;SENTINEL_2&#x27; ... &#x27;LANDSAT_8&#x27;</div><input id='attrs-c86d4b1d-09e8-4dd3-9288-93f14dd0c8b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c86d4b1d-09e8-4dd3-9288-93f14dd0c8b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e4618b4-71a1-493b-bfe4-14e56ee099b6' class='xr-var-data-in' type='checkbox'><label for='data-9e4618b4-71a1-493b-bfe4-14e56ee099b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;,\n",
       "       &#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;,\n",
       "       &#x27;SENTINEL_2&#x27;, &#x27;LANDSAT_8&#x27;, &#x27;LANDSAT_8&#x27;, &#x27;LANDSAT_8&#x27;, &#x27;LANDSAT_8&#x27;],\n",
       "      dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>image.spatialResolution</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0 10.0 10.0 ... 30.0 30.0 30.0</div><input id='attrs-ac727f54-fb2a-47cb-abd7-e5a752ccccca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac727f54-fb2a-47cb-abd7-e5a752ccccca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d798ad3-f716-432e-8ac6-92ece554f625' class='xr-var-data-in' type='checkbox'><label for='data-5d798ad3-f716-432e-8ac6-92ece554f625' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([10., 10., 10., 10., 10., 10., 10., 10., 10., 30., 30., 30., 30.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;EPSG:4326&#x27; ... &#x27;EPSG:4326&#x27;</div><input id='attrs-f4cfebd4-85de-4990-a8de-c3b8b7a44f65' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4cfebd4-85de-4990-a8de-c3b8b7a44f65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fd95253-c7f2-40ef-8283-0174a004b4ee' class='xr-var-data-in' type='checkbox'><label for='data-1fd95253-c7f2-40ef-8283-0174a004b4ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;,\n",
       "       &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;,\n",
       "       &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a46b965f-e49c-4c53-8f58-634dbe4f4b99' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a46b965f-e49c-4c53-8f58-634dbe4f4b99' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>reflectance</span></div><div class='xr-var-dims'>(time, band, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-168566cb-4492-4ca6-9227-1b9cbd7ff446' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-168566cb-4492-4ca6-9227-1b9cbd7ff446' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7282748-6cd9-454e-8236-2e3d15fbe37e' class='xr-var-data-in' type='checkbox'><label for='data-b7282748-6cd9-454e-8236-2e3d15fbe37e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ..., 0.05423359,\n",
       "          0.05400065, 0.05391411],\n",
       "         [       nan,        nan,        nan, ..., 0.05419569,\n",
       "          0.05389266, 0.05384903],\n",
       "         ...,\n",
       "         [0.07032395, 0.07137213, 0.07347509, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [0.0696    , 0.07030983, 0.07262973, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan]],\n",
       "\n",
       "        [[       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ..., 0.12789002,\n",
       "          0.12276267, 0.12556721],\n",
       "         [       nan,        nan,        nan, ..., 0.12933223,\n",
       "          0.12466034, 0.12927862],\n",
       "...\n",
       "         [       nan,        nan, 0.29560945, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan]],\n",
       "\n",
       "        [[       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ..., 0.04626791,\n",
       "          0.04706443,        nan],\n",
       "         ...,\n",
       "         [       nan,        nan, 0.07049863, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1930ad20-84ea-4869-a788-a24037abdeb1' class='xr-section-summary-in' type='checkbox'  ><label for='section-1930ad20-84ea-4869-a788-a24037abdeb1' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1ded9116-f6d6-482c-9095-0d1bbb2ac01e' class='xr-index-data-in' type='checkbox'/><label for='index-1ded9116-f6d6-482c-9095-0d1bbb2ac01e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Blue&#x27;, &#x27;CoastalBlue&#x27;, &#x27;Green&#x27;, &#x27;Nir&#x27;, &#x27;Red&#x27;, &#x27;RedEdge1&#x27;, &#x27;RedEdge2&#x27;,\n",
       "       &#x27;RedEdge3&#x27;, &#x27;Swir1&#x27;, &#x27;Swir2&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7a56d334-b31e-485b-a5e8-c1af5e492dd9' class='xr-index-data-in' type='checkbox'/><label for='index-7a56d334-b31e-485b-a5e8-c1af5e492dd9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 40.29876496841197,  40.29867318419115, 40.298581399970324,\n",
       "         40.2984896157495, 40.298397831528675, 40.298306047307854,\n",
       "       40.298214263087026, 40.298122478866205,  40.29803069464538,\n",
       "        40.29793891042455,  40.29784712620373,   40.2977553419829,\n",
       "        40.29766355776208,  40.29757177354125,  40.29747998932043,\n",
       "         40.2973882050996,  40.29729642087878,  40.29720463665795,\n",
       "        40.29711285243713,   40.2970210682163,  40.29692928399548,\n",
       "       40.296837499774654,  40.29674571555383, 40.296653931333005,\n",
       "        40.29656214711218, 40.296470362891355, 40.296378578670534,\n",
       "       40.296286794449706, 40.296195010228885,  40.29610322600806,\n",
       "        40.29601144178723,  40.29591965756641,  40.29582787334558,\n",
       "        40.29573608912476,  40.29564430490393,  40.29555252068311,\n",
       "        40.29546073646228,  40.29536895224146,  40.29527716802063,\n",
       "        40.29518538379981,  40.29509359957898,  40.29500181535816,\n",
       "       40.294910031137334,  40.29481824691651, 40.294726462695685,\n",
       "       40.294634678474864, 40.294542894254036,  40.29445111003321,\n",
       "        40.29435932581239,  40.29426754159156,  40.29417575737074,\n",
       "        40.29408397314991,  40.29399218892909,  40.29390040470826,\n",
       "        40.29380862048744,  40.29371683626661,  40.29362505204579,\n",
       "        40.29353326782496,  40.29344148360414,  40.29334969938331,\n",
       "        40.29325791516249, 40.293166130941664,  40.29307434672084,\n",
       "       40.292982562500015, 40.292890778279194, 40.292798994058366,\n",
       "       40.292707209837545,  40.29261542561672,  40.29252364139589,\n",
       "        40.29243185717507,  40.29234007295424,  40.29224828873342,\n",
       "        40.29215650451259,  40.29206472029177,  40.29197293607094,\n",
       "        40.29188115185012],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e5cb051e-01be-479e-8b49-8bce8687b0e3' class='xr-index-data-in' type='checkbox'/><label for='index-e5cb051e-01be-479e-8b49-8bce8687b0e3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-91.17582314506241, -91.17570294646015, -91.17558274785787,\n",
       "       -91.17546254925561, -91.17534235065334, -91.17522215205108,\n",
       "        -91.1751019534488, -91.17498175484653, -91.17486155624427,\n",
       "       -91.17474135764199, -91.17462115903973, -91.17450096043746,\n",
       "        -91.1743807618352, -91.17426056323292, -91.17414036463066,\n",
       "       -91.17402016602838, -91.17389996742612, -91.17377976882385,\n",
       "       -91.17365957022159, -91.17353937161931, -91.17341917301705,\n",
       "       -91.17329897441478,  -91.1731787758125, -91.17305857721024,\n",
       "       -91.17293837860797,  -91.1728181800057, -91.17269798140343,\n",
       "       -91.17257778280117,  -91.1724575841989, -91.17233738559663,\n",
       "       -91.17221718699436,  -91.1720969883921, -91.17197678978982,\n",
       "       -91.17185659118756, -91.17173639258529, -91.17161619398303,\n",
       "       -91.17149599538075, -91.17137579677848, -91.17125559817622,\n",
       "       -91.17113539957394, -91.17101520097168,  -91.1708950023694,\n",
       "       -91.17077480376715, -91.17065460516487, -91.17053440656261,\n",
       "       -91.17041420796033, -91.17029400935807,  -91.1701738107558,\n",
       "       -91.17005361215354, -91.16993341355126, -91.16981321494899,\n",
       "       -91.16969301634673, -91.16957281774445, -91.16945261914219,\n",
       "       -91.16933242053992, -91.16921222193766, -91.16909202333538,\n",
       "       -91.16897182473312, -91.16885162613085, -91.16873142752858,\n",
       "       -91.16861122892631, -91.16849103032405, -91.16837083172177,\n",
       "       -91.16825063311951, -91.16813043451724, -91.16801023591496,\n",
       "        -91.1678900373127, -91.16776983871043, -91.16764964010817,\n",
       "       -91.16752944150589, -91.16740924290363, -91.16728904430136],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-592f4d50-987f-4817-a6aa-8f95d9555f53' class='xr-index-data-in' type='checkbox'/><label for='index-592f4d50-987f-4817-a6aa-8f95d9555f53' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2024-02-12 17:02:33&#x27;, &#x27;2024-02-22 17:02:33&#x27;,\n",
       "               &#x27;2024-03-03 17:02:29&#x27;, &#x27;2024-03-23 17:02:33&#x27;,\n",
       "               &#x27;2024-03-28 17:02:33&#x27;, &#x27;2024-04-07 17:02:31&#x27;,\n",
       "               &#x27;2024-04-12 17:02:33&#x27;, &#x27;2024-05-12 17:02:32&#x27;,\n",
       "               &#x27;2024-06-06 17:02:38&#x27;, &#x27;2023-08-22 16:41:39&#x27;,\n",
       "               &#x27;2023-09-14 16:47:54&#x27;, &#x27;2023-11-17 16:48:04&#x27;,\n",
       "               &#x27;2023-12-12 16:41:50&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0a9c22eb-0525-4efe-861b-bb2b23f9dc43' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0a9c22eb-0525-4efe-861b-bb2b23f9dc43' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                  (band: 10, y: 76, x: 72, time: 13)\n",
       "Coordinates:\n",
       "  * band                     (band) <U11 'Blue' 'CoastalBlue' ... 'Swir2'\n",
       "  * y                        (y) float64 40.3 40.3 40.3 ... 40.29 40.29 40.29\n",
       "  * x                        (x) float64 -91.18 -91.18 -91.18 ... -91.17 -91.17\n",
       "  * time                     (time) datetime64[ns] 2024-02-12T17:02:33 ... 20...\n",
       "    image.id                 (time) <U59 'sentinel-2-l2a|S2B_15TXE_20240212_0...\n",
       "    image.sensor             (time) <U10 'SENTINEL_2' ... 'LANDSAT_8'\n",
       "    image.spatialResolution  (time) float64 10.0 10.0 10.0 ... 30.0 30.0 30.0\n",
       "    crs                      (time) <U9 'EPSG:4326' 'EPSG:4326' ... 'EPSG:4326'\n",
       "Data variables:\n",
       "    reflectance              (time, band, y, x) float64 nan nan nan ... nan nan"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get satellite image time series for LANDSAT_8 and SENTINEL_2 Reflectance\n",
    "time_series_xarr = client.get_satellite_image_time_series(year_ago, today, collections=[SatelliteImageryCollection.SENTINEL_2, SatelliteImageryCollection.LANDSAT_8], indicators=[\"Reflectance\"],polygon=polygon)\n",
    "time_series_xarr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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   },
   "outputs": [
    {
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      "2024-06-14 11:18:50,392 - geosyspy.services.map_product_service - INFO - Calling APIs for coverage\n",
      "c:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\geosyspy\\geosys.py:359: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
      "  df_coverage[\"image.date\"] = pd.to_datetime(\n",
      "2024-06-14 11:18:59,056 - geosyspy.geosys - INFO - The highest resolution's image grid size is (72,76)\n",
      "2024-06-14 11:18:59,087 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2B_15TXE_20240222_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:59,134 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2B_15TXE_20240303_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:59,187 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2B_15TXE_20240323_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:59,226 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2A_15TXE_20240328_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:59,273 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2A_15TXE_20240407_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:59,320 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2B_15TXE_20240412_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:59,383 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2B_15TXE_20240512_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:59,430 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2A_15TXE_20240606_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:59,487 - geosyspy.geosys - INFO - interpolating landsat-c2l2-sr|LC08_L2SP_024032_20230822_20230826_02_T1_SR to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:59,540 - geosyspy.geosys - INFO - interpolating landsat-c2l2-sr|LC08_L2SP_025032_20230914_20230919_02_T1_SR to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:59,582 - geosyspy.geosys - INFO - interpolating landsat-c2l2-sr|LC08_L2SP_025032_20231117_20231122_02_T1_SR to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:18:59,636 - geosyspy.geosys - INFO - interpolating landsat-c2l2-sr|LC08_L2SP_024032_20231212_20231215_02_T1_SR to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n"
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       "    crs                      (time) &lt;U9 &#x27;EPSG:4326&#x27; &#x27;EPSG:4326&#x27; ... &#x27;EPSG:4326&#x27;\n",
       "Data variables:\n",
       "    ndvi                     (time, band, y, x) float64 nan nan nan ... nan nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6c29a989-4b7f-4be1-ae8e-ad3666133f00' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6c29a989-4b7f-4be1-ae8e-ad3666133f00' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 1</li><li><span class='xr-has-index'>y</span>: 76</li><li><span class='xr-has-index'>x</span>: 72</li><li><span class='xr-has-index'>time</span>: 13</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3ac0c399-c6e3-4ffb-a19f-0740b2a78dad' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3ac0c399-c6e3-4ffb-a19f-0740b2a78dad' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U4</div><div class='xr-var-preview xr-preview'>&#x27;ndvi&#x27;</div><input id='attrs-b2c112e4-1f62-421f-8b2d-ac7d7e59e7a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2c112e4-1f62-421f-8b2d-ac7d7e59e7a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7b535a6-27dd-4bd8-9768-66ba21ac6fa1' class='xr-var-data-in' type='checkbox'><label for='data-e7b535a6-27dd-4bd8-9768-66ba21ac6fa1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;ndvi&#x27;], dtype=&#x27;&lt;U4&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>40.3 40.3 40.3 ... 40.29 40.29</div><input id='attrs-ccf29abe-3201-4993-826e-934a25c6689a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ccf29abe-3201-4993-826e-934a25c6689a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-112fe4af-f5b8-49cc-8a02-beab026d68b7' class='xr-var-data-in' type='checkbox'><label for='data-112fe4af-f5b8-49cc-8a02-beab026d68b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([40.298765, 40.298673, 40.298581, 40.29849 , 40.298398, 40.298306,\n",
       "       40.298214, 40.298122, 40.298031, 40.297939, 40.297847, 40.297755,\n",
       "       40.297664, 40.297572, 40.29748 , 40.297388, 40.297296, 40.297205,\n",
       "       40.297113, 40.297021, 40.296929, 40.296837, 40.296746, 40.296654,\n",
       "       40.296562, 40.29647 , 40.296379, 40.296287, 40.296195, 40.296103,\n",
       "       40.296011, 40.29592 , 40.295828, 40.295736, 40.295644, 40.295553,\n",
       "       40.295461, 40.295369, 40.295277, 40.295185, 40.295094, 40.295002,\n",
       "       40.29491 , 40.294818, 40.294726, 40.294635, 40.294543, 40.294451,\n",
       "       40.294359, 40.294268, 40.294176, 40.294084, 40.293992, 40.2939  ,\n",
       "       40.293809, 40.293717, 40.293625, 40.293533, 40.293441, 40.29335 ,\n",
       "       40.293258, 40.293166, 40.293074, 40.292983, 40.292891, 40.292799,\n",
       "       40.292707, 40.292615, 40.292524, 40.292432, 40.29234 , 40.292248,\n",
       "       40.292157, 40.292065, 40.291973, 40.291881])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-91.18 -91.18 ... -91.17 -91.17</div><input id='attrs-03253318-7025-4a32-a801-1cddcac1bf79' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-03253318-7025-4a32-a801-1cddcac1bf79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-856ad0b9-8035-4991-9c08-9b5c50f6cf3d' class='xr-var-data-in' type='checkbox'><label for='data-856ad0b9-8035-4991-9c08-9b5c50f6cf3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-91.175823, -91.175703, -91.175583, -91.175463, -91.175342, -91.175222,\n",
       "       -91.175102, -91.174982, -91.174862, -91.174741, -91.174621, -91.174501,\n",
       "       -91.174381, -91.174261, -91.17414 , -91.17402 , -91.1739  , -91.17378 ,\n",
       "       -91.17366 , -91.173539, -91.173419, -91.173299, -91.173179, -91.173059,\n",
       "       -91.172938, -91.172818, -91.172698, -91.172578, -91.172458, -91.172337,\n",
       "       -91.172217, -91.172097, -91.171977, -91.171857, -91.171736, -91.171616,\n",
       "       -91.171496, -91.171376, -91.171256, -91.171135, -91.171015, -91.170895,\n",
       "       -91.170775, -91.170655, -91.170534, -91.170414, -91.170294, -91.170174,\n",
       "       -91.170054, -91.169933, -91.169813, -91.169693, -91.169573, -91.169453,\n",
       "       -91.169332, -91.169212, -91.169092, -91.168972, -91.168852, -91.168731,\n",
       "       -91.168611, -91.168491, -91.168371, -91.168251, -91.16813 , -91.16801 ,\n",
       "       -91.16789 , -91.16777 , -91.16765 , -91.167529, -91.167409, -91.167289])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-02-12T17:02:33 ... 2023-12-...</div><input id='attrs-f787738a-01e1-43a0-b1a0-b29df4deecfc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f787738a-01e1-43a0-b1a0-b29df4deecfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dccbe778-a569-437b-b287-3e86024101a3' class='xr-var-data-in' type='checkbox'><label for='data-dccbe778-a569-437b-b287-3e86024101a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-02-12T17:02:33.000000000&#x27;, &#x27;2024-02-22T17:02:33.000000000&#x27;,\n",
       "       &#x27;2024-03-03T17:02:29.000000000&#x27;, &#x27;2024-03-23T17:02:33.000000000&#x27;,\n",
       "       &#x27;2024-03-28T17:02:33.000000000&#x27;, &#x27;2024-04-07T17:02:31.000000000&#x27;,\n",
       "       &#x27;2024-04-12T17:02:33.000000000&#x27;, &#x27;2024-05-12T17:02:32.000000000&#x27;,\n",
       "       &#x27;2024-06-06T17:02:38.000000000&#x27;, &#x27;2023-08-22T16:41:39.000000000&#x27;,\n",
       "       &#x27;2023-09-14T16:47:54.000000000&#x27;, &#x27;2023-11-17T16:48:04.000000000&#x27;,\n",
       "       &#x27;2023-12-12T16:41:50.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>image.id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U59</div><div class='xr-var-preview xr-preview'>&#x27;sentinel-2-l2a|S2B_15TXE_202402...</div><input id='attrs-f2576e73-6865-45f1-bc23-4b1d726ca2af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2576e73-6865-45f1-bc23-4b1d726ca2af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f45bd8ee-5e96-48d7-9c9b-ec003f50dd27' class='xr-var-data-in' type='checkbox'><label for='data-f45bd8ee-5e96-48d7-9c9b-ec003f50dd27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;sentinel-2-l2a|S2B_15TXE_20240212_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2B_15TXE_20240222_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2B_15TXE_20240303_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2B_15TXE_20240323_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2A_15TXE_20240328_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2A_15TXE_20240407_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2B_15TXE_20240412_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2B_15TXE_20240512_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2A_15TXE_20240606_0_L2A&#x27;,\n",
       "       &#x27;landsat-c2l2-sr|LC08_L2SP_024032_20230822_20230826_02_T1_SR&#x27;,\n",
       "       &#x27;landsat-c2l2-sr|LC08_L2SP_025032_20230914_20230919_02_T1_SR&#x27;,\n",
       "       &#x27;landsat-c2l2-sr|LC08_L2SP_025032_20231117_20231122_02_T1_SR&#x27;,\n",
       "       &#x27;landsat-c2l2-sr|LC08_L2SP_024032_20231212_20231215_02_T1_SR&#x27;],\n",
       "      dtype=&#x27;&lt;U59&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>image.sensor</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;SENTINEL_2&#x27; ... &#x27;LANDSAT_8&#x27;</div><input id='attrs-73b0edcb-142c-4fb6-ae8b-3de1bc4e61b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73b0edcb-142c-4fb6-ae8b-3de1bc4e61b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8221554d-7914-4ac9-b0e3-aef0ec29409c' class='xr-var-data-in' type='checkbox'><label for='data-8221554d-7914-4ac9-b0e3-aef0ec29409c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;,\n",
       "       &#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;,\n",
       "       &#x27;SENTINEL_2&#x27;, &#x27;LANDSAT_8&#x27;, &#x27;LANDSAT_8&#x27;, &#x27;LANDSAT_8&#x27;, &#x27;LANDSAT_8&#x27;],\n",
       "      dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>image.spatialResolution</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0 10.0 10.0 ... 30.0 30.0 30.0</div><input id='attrs-e6511268-5c5e-4eb4-a60c-162865c7df60' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6511268-5c5e-4eb4-a60c-162865c7df60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e0f4f0a-942f-45e3-b87c-d145b9df8db3' class='xr-var-data-in' type='checkbox'><label for='data-6e0f4f0a-942f-45e3-b87c-d145b9df8db3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([10., 10., 10., 10., 10., 10., 10., 10., 10., 30., 30., 30., 30.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;EPSG:4326&#x27; ... &#x27;EPSG:4326&#x27;</div><input id='attrs-a0006ce6-96dc-4957-9a35-a80407e9b803' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a0006ce6-96dc-4957-9a35-a80407e9b803' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-470e781e-2ba0-43af-8b66-d168dd23f995' class='xr-var-data-in' type='checkbox'><label for='data-470e781e-2ba0-43af-8b66-d168dd23f995' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;,\n",
       "       &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;,\n",
       "       &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bd670315-f709-4c5e-b799-2105ccb64509' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bd670315-f709-4c5e-b799-2105ccb64509' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ndvi</span></div><div class='xr-var-dims'>(time, band, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-8e70c881-c8f1-4846-bd28-502d25dd878c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e70c881-c8f1-4846-bd28-502d25dd878c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-779eecdd-593f-4bde-8167-9531447d57f4' class='xr-var-data-in' type='checkbox'><label for='data-779eecdd-593f-4bde-8167-9531447d57f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ..., 0.27231219,\n",
       "          0.27640811, 0.28088948],\n",
       "         [       nan,        nan,        nan, ..., 0.26366559,\n",
       "          0.27777576, 0.28848463],\n",
       "         ...,\n",
       "         [0.19965565, 0.19601919, 0.1808095 , ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [0.17985614, 0.18064232, 0.18158512, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan]]],\n",
       "\n",
       "\n",
       "       [[[       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ..., 0.20906702,\n",
       "          0.23013029, 0.23378646],\n",
       "         [       nan,        nan,        nan, ..., 0.22073078,\n",
       "...\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan]]],\n",
       "\n",
       "\n",
       "       [[[       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ..., 0.28751645,\n",
       "          0.27999385,        nan],\n",
       "         ...,\n",
       "         [       nan,        nan, 0.25898298, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-99aaae57-0f9a-4ce0-961b-2759657ea597' class='xr-section-summary-in' type='checkbox'  ><label for='section-99aaae57-0f9a-4ce0-961b-2759657ea597' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-95a43548-70b4-4562-b0a4-96198cda1c75' class='xr-index-data-in' type='checkbox'/><label for='index-95a43548-70b4-4562-b0a4-96198cda1c75' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;ndvi&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-36b9242c-048e-44d9-8a47-894f41127db7' class='xr-index-data-in' type='checkbox'/><label for='index-36b9242c-048e-44d9-8a47-894f41127db7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 40.29876496841197,  40.29867318419115, 40.298581399970324,\n",
       "         40.2984896157495, 40.298397831528675, 40.298306047307854,\n",
       "       40.298214263087026, 40.298122478866205,  40.29803069464538,\n",
       "        40.29793891042455,  40.29784712620373,   40.2977553419829,\n",
       "        40.29766355776208,  40.29757177354125,  40.29747998932043,\n",
       "         40.2973882050996,  40.29729642087878,  40.29720463665795,\n",
       "        40.29711285243713,   40.2970210682163,  40.29692928399548,\n",
       "       40.296837499774654,  40.29674571555383, 40.296653931333005,\n",
       "        40.29656214711218, 40.296470362891355, 40.296378578670534,\n",
       "       40.296286794449706, 40.296195010228885,  40.29610322600806,\n",
       "        40.29601144178723,  40.29591965756641,  40.29582787334558,\n",
       "        40.29573608912476,  40.29564430490393,  40.29555252068311,\n",
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       "       40.294910031137334,  40.29481824691651, 40.294726462695685,\n",
       "       40.294634678474864, 40.294542894254036,  40.29445111003321,\n",
       "        40.29435932581239,  40.29426754159156,  40.29417575737074,\n",
       "        40.29408397314991,  40.29399218892909,  40.29390040470826,\n",
       "        40.29380862048744,  40.29371683626661,  40.29362505204579,\n",
       "        40.29353326782496,  40.29344148360414,  40.29334969938331,\n",
       "        40.29325791516249, 40.293166130941664,  40.29307434672084,\n",
       "       40.292982562500015, 40.292890778279194, 40.292798994058366,\n",
       "       40.292707209837545,  40.29261542561672,  40.29252364139589,\n",
       "        40.29243185717507,  40.29234007295424,  40.29224828873342,\n",
       "        40.29215650451259,  40.29206472029177,  40.29197293607094,\n",
       "        40.29188115185012],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e3ae5529-328a-47a3-b1f8-4d5e52325c93' class='xr-index-data-in' type='checkbox'/><label for='index-e3ae5529-328a-47a3-b1f8-4d5e52325c93' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-91.17582314506241, -91.17570294646015, -91.17558274785787,\n",
       "       -91.17546254925561, -91.17534235065334, -91.17522215205108,\n",
       "        -91.1751019534488, -91.17498175484653, -91.17486155624427,\n",
       "       -91.17474135764199, -91.17462115903973, -91.17450096043746,\n",
       "        -91.1743807618352, -91.17426056323292, -91.17414036463066,\n",
       "       -91.17402016602838, -91.17389996742612, -91.17377976882385,\n",
       "       -91.17365957022159, -91.17353937161931, -91.17341917301705,\n",
       "       -91.17329897441478,  -91.1731787758125, -91.17305857721024,\n",
       "       -91.17293837860797,  -91.1728181800057, -91.17269798140343,\n",
       "       -91.17257778280117,  -91.1724575841989, -91.17233738559663,\n",
       "       -91.17221718699436,  -91.1720969883921, -91.17197678978982,\n",
       "       -91.17185659118756, -91.17173639258529, -91.17161619398303,\n",
       "       -91.17149599538075, -91.17137579677848, -91.17125559817622,\n",
       "       -91.17113539957394, -91.17101520097168,  -91.1708950023694,\n",
       "       -91.17077480376715, -91.17065460516487, -91.17053440656261,\n",
       "       -91.17041420796033, -91.17029400935807,  -91.1701738107558,\n",
       "       -91.17005361215354, -91.16993341355126, -91.16981321494899,\n",
       "       -91.16969301634673, -91.16957281774445, -91.16945261914219,\n",
       "       -91.16933242053992, -91.16921222193766, -91.16909202333538,\n",
       "       -91.16897182473312, -91.16885162613085, -91.16873142752858,\n",
       "       -91.16861122892631, -91.16849103032405, -91.16837083172177,\n",
       "       -91.16825063311951, -91.16813043451724, -91.16801023591496,\n",
       "        -91.1678900373127, -91.16776983871043, -91.16764964010817,\n",
       "       -91.16752944150589, -91.16740924290363, -91.16728904430136],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7ce54201-b83c-4ad8-80c8-389e40242d84' class='xr-index-data-in' type='checkbox'/><label for='index-7ce54201-b83c-4ad8-80c8-389e40242d84' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2024-02-12 17:02:33&#x27;, &#x27;2024-02-22 17:02:33&#x27;,\n",
       "               &#x27;2024-03-03 17:02:29&#x27;, &#x27;2024-03-23 17:02:33&#x27;,\n",
       "               &#x27;2024-03-28 17:02:33&#x27;, &#x27;2024-04-07 17:02:31&#x27;,\n",
       "               &#x27;2024-04-12 17:02:33&#x27;, &#x27;2024-05-12 17:02:32&#x27;,\n",
       "               &#x27;2024-06-06 17:02:38&#x27;, &#x27;2023-08-22 16:41:39&#x27;,\n",
       "               &#x27;2023-09-14 16:47:54&#x27;, &#x27;2023-11-17 16:48:04&#x27;,\n",
       "               &#x27;2023-12-12 16:41:50&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d47931d-5060-4674-87af-e18ce0cf2662' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8d47931d-5060-4674-87af-e18ce0cf2662' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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       "<xarray.Dataset>\n",
       "Dimensions:                  (band: 1, y: 76, x: 72, time: 13)\n",
       "Coordinates:\n",
       "  * band                     (band) <U4 'ndvi'\n",
       "  * y                        (y) float64 40.3 40.3 40.3 ... 40.29 40.29 40.29\n",
       "  * x                        (x) float64 -91.18 -91.18 -91.18 ... -91.17 -91.17\n",
       "  * time                     (time) datetime64[ns] 2024-02-12T17:02:33 ... 20...\n",
       "    image.id                 (time) <U59 'sentinel-2-l2a|S2B_15TXE_20240212_0...\n",
       "    image.sensor             (time) <U10 'SENTINEL_2' ... 'LANDSAT_8'\n",
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      ]
     },
     "execution_count": 12,
     "metadata": {},
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    }
   ],
   "source": [
    "\n",
    "import xarray as xr\n",
    "# Get satellite image time series for LANDSAT_8 and SENTINEL_2 NDVI \n",
    "# list of available indicators: Reflectance, NDVI, EVI, GNDVI, NDWI, CVI, CVIn, LAI\n",
    "ndvi_time_series_xarr = client.get_satellite_image_time_series(year_ago, today, collections=[SatelliteImageryCollection.SENTINEL_2, SatelliteImageryCollection.LANDSAT_8], indicators=[\"ndvi\"], polygon=polygon)\n",
    "\n",
    "ndvi_time_series_xarr\n"
   ]
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  {
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   "execution_count": 13,
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    {
     "name": "stderr",
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     "text": [
      "2024-06-14 11:18:59,688 - geosyspy.services.map_product_service - INFO - Calling APIs for coverage\n",
      "c:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\geosyspy\\geosys.py:359: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
      "  df_coverage[\"image.date\"] = pd.to_datetime(\n",
      "2024-06-14 11:19:08,619 - geosyspy.geosys - INFO - The highest resolution's image grid size is (72,76)\n",
      "2024-06-14 11:19:08,674 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2B_15TXE_20240222_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:19:08,765 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2B_15TXE_20240303_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:19:08,940 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2B_15TXE_20240323_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:19:09,020 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2A_15TXE_20240328_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:19:09,092 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2A_15TXE_20240407_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:19:09,175 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2B_15TXE_20240412_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:19:09,253 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2B_15TXE_20240512_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:19:09,331 - geosyspy.geosys - INFO - interpolating sentinel-2-l2a|S2A_15TXE_20240606_0_L2A to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:19:09,402 - geosyspy.geosys - INFO - interpolating landsat-c2l2-sr|LC08_L2SP_024032_20230822_20230826_02_T1_SR to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:19:09,475 - geosyspy.geosys - INFO - interpolating landsat-c2l2-sr|LC08_L2SP_025032_20230914_20230919_02_T1_SR to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:19:09,559 - geosyspy.geosys - INFO - interpolating landsat-c2l2-sr|LC08_L2SP_025032_20231117_20231122_02_T1_SR to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n",
      "2024-06-14 11:19:09,638 - geosyspy.geosys - INFO - interpolating landsat-c2l2-sr|LC08_L2SP_024032_20231212_20231215_02_T1_SR to sentinel-2-l2a|S2B_15TXE_20240212_0_L2A's grid\n"
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       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                  (band: 1, y: 76, x: 72, time: 13)\n",
       "Coordinates:\n",
       "  * band                     (band) &lt;U4 &#x27;NDWI&#x27;\n",
       "  * y                        (y) float64 40.3 40.3 40.3 ... 40.29 40.29 40.29\n",
       "  * x                        (x) float64 -91.18 -91.18 -91.18 ... -91.17 -91.17\n",
       "  * time                     (time) datetime64[ns] 2024-02-12T17:02:33 ... 20...\n",
       "    image.id                 (time) &lt;U59 &#x27;sentinel-2-l2a|S2B_15TXE_20240212_0...\n",
       "    image.sensor             (time) &lt;U10 &#x27;SENTINEL_2&#x27; ... &#x27;LANDSAT_8&#x27;\n",
       "    image.spatialResolution  (time) float64 10.0 10.0 10.0 ... 30.0 30.0 30.0\n",
       "    crs                      (time) &lt;U9 &#x27;EPSG:4326&#x27; &#x27;EPSG:4326&#x27; ... &#x27;EPSG:4326&#x27;\n",
       "Data variables:\n",
       "    ndwi                     (time, band, y, x) float64 nan nan nan ... nan nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-668e4744-45e7-4fb4-a843-1b1d1d48f66e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-668e4744-45e7-4fb4-a843-1b1d1d48f66e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 1</li><li><span class='xr-has-index'>y</span>: 76</li><li><span class='xr-has-index'>x</span>: 72</li><li><span class='xr-has-index'>time</span>: 13</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5699abf7-46f2-4fa4-a29d-e041763c7842' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5699abf7-46f2-4fa4-a29d-e041763c7842' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U4</div><div class='xr-var-preview xr-preview'>&#x27;NDWI&#x27;</div><input id='attrs-b606535b-ad77-4ffc-bbff-beea40e07b0b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b606535b-ad77-4ffc-bbff-beea40e07b0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-938c23c4-e93a-4d8a-bcea-511939747c59' class='xr-var-data-in' type='checkbox'><label for='data-938c23c4-e93a-4d8a-bcea-511939747c59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;NDWI&#x27;], dtype=&#x27;&lt;U4&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>40.3 40.3 40.3 ... 40.29 40.29</div><input id='attrs-033fb2bc-c0b0-46ee-a1f1-2bdfaad2a5bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-033fb2bc-c0b0-46ee-a1f1-2bdfaad2a5bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46fc95cf-632e-4c1f-bcfb-3e1ff328044c' class='xr-var-data-in' type='checkbox'><label for='data-46fc95cf-632e-4c1f-bcfb-3e1ff328044c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([40.298765, 40.298673, 40.298581, 40.29849 , 40.298398, 40.298306,\n",
       "       40.298214, 40.298122, 40.298031, 40.297939, 40.297847, 40.297755,\n",
       "       40.297664, 40.297572, 40.29748 , 40.297388, 40.297296, 40.297205,\n",
       "       40.297113, 40.297021, 40.296929, 40.296837, 40.296746, 40.296654,\n",
       "       40.296562, 40.29647 , 40.296379, 40.296287, 40.296195, 40.296103,\n",
       "       40.296011, 40.29592 , 40.295828, 40.295736, 40.295644, 40.295553,\n",
       "       40.295461, 40.295369, 40.295277, 40.295185, 40.295094, 40.295002,\n",
       "       40.29491 , 40.294818, 40.294726, 40.294635, 40.294543, 40.294451,\n",
       "       40.294359, 40.294268, 40.294176, 40.294084, 40.293992, 40.2939  ,\n",
       "       40.293809, 40.293717, 40.293625, 40.293533, 40.293441, 40.29335 ,\n",
       "       40.293258, 40.293166, 40.293074, 40.292983, 40.292891, 40.292799,\n",
       "       40.292707, 40.292615, 40.292524, 40.292432, 40.29234 , 40.292248,\n",
       "       40.292157, 40.292065, 40.291973, 40.291881])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-91.18 -91.18 ... -91.17 -91.17</div><input id='attrs-937dcdd6-684d-426e-9c12-76bb6ea74b90' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-937dcdd6-684d-426e-9c12-76bb6ea74b90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16e3eaaa-561e-4f2c-b58e-9557bc642d3f' class='xr-var-data-in' type='checkbox'><label for='data-16e3eaaa-561e-4f2c-b58e-9557bc642d3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-91.175823, -91.175703, -91.175583, -91.175463, -91.175342, -91.175222,\n",
       "       -91.175102, -91.174982, -91.174862, -91.174741, -91.174621, -91.174501,\n",
       "       -91.174381, -91.174261, -91.17414 , -91.17402 , -91.1739  , -91.17378 ,\n",
       "       -91.17366 , -91.173539, -91.173419, -91.173299, -91.173179, -91.173059,\n",
       "       -91.172938, -91.172818, -91.172698, -91.172578, -91.172458, -91.172337,\n",
       "       -91.172217, -91.172097, -91.171977, -91.171857, -91.171736, -91.171616,\n",
       "       -91.171496, -91.171376, -91.171256, -91.171135, -91.171015, -91.170895,\n",
       "       -91.170775, -91.170655, -91.170534, -91.170414, -91.170294, -91.170174,\n",
       "       -91.170054, -91.169933, -91.169813, -91.169693, -91.169573, -91.169453,\n",
       "       -91.169332, -91.169212, -91.169092, -91.168972, -91.168852, -91.168731,\n",
       "       -91.168611, -91.168491, -91.168371, -91.168251, -91.16813 , -91.16801 ,\n",
       "       -91.16789 , -91.16777 , -91.16765 , -91.167529, -91.167409, -91.167289])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-02-12T17:02:33 ... 2023-12-...</div><input id='attrs-27d3a5cc-238a-4332-9433-dcc444b0529f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27d3a5cc-238a-4332-9433-dcc444b0529f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b33ea52-022b-4099-a11e-e6864058cf26' class='xr-var-data-in' type='checkbox'><label for='data-7b33ea52-022b-4099-a11e-e6864058cf26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-02-12T17:02:33.000000000&#x27;, &#x27;2024-02-22T17:02:33.000000000&#x27;,\n",
       "       &#x27;2024-03-03T17:02:29.000000000&#x27;, &#x27;2024-03-23T17:02:33.000000000&#x27;,\n",
       "       &#x27;2024-03-28T17:02:33.000000000&#x27;, &#x27;2024-04-07T17:02:31.000000000&#x27;,\n",
       "       &#x27;2024-04-12T17:02:33.000000000&#x27;, &#x27;2024-05-12T17:02:32.000000000&#x27;,\n",
       "       &#x27;2024-06-06T17:02:38.000000000&#x27;, &#x27;2023-08-22T16:41:39.000000000&#x27;,\n",
       "       &#x27;2023-09-14T16:47:54.000000000&#x27;, &#x27;2023-11-17T16:48:04.000000000&#x27;,\n",
       "       &#x27;2023-12-12T16:41:50.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>image.id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U59</div><div class='xr-var-preview xr-preview'>&#x27;sentinel-2-l2a|S2B_15TXE_202402...</div><input id='attrs-3754ab3f-4b2d-49d7-950a-79176363ca57' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3754ab3f-4b2d-49d7-950a-79176363ca57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac749625-ed7f-477f-a507-ac15c9b9ef7a' class='xr-var-data-in' type='checkbox'><label for='data-ac749625-ed7f-477f-a507-ac15c9b9ef7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;sentinel-2-l2a|S2B_15TXE_20240212_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2B_15TXE_20240222_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2B_15TXE_20240303_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2B_15TXE_20240323_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2A_15TXE_20240328_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2A_15TXE_20240407_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2B_15TXE_20240412_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2B_15TXE_20240512_0_L2A&#x27;,\n",
       "       &#x27;sentinel-2-l2a|S2A_15TXE_20240606_0_L2A&#x27;,\n",
       "       &#x27;landsat-c2l2-sr|LC08_L2SP_024032_20230822_20230826_02_T1_SR&#x27;,\n",
       "       &#x27;landsat-c2l2-sr|LC08_L2SP_025032_20230914_20230919_02_T1_SR&#x27;,\n",
       "       &#x27;landsat-c2l2-sr|LC08_L2SP_025032_20231117_20231122_02_T1_SR&#x27;,\n",
       "       &#x27;landsat-c2l2-sr|LC08_L2SP_024032_20231212_20231215_02_T1_SR&#x27;],\n",
       "      dtype=&#x27;&lt;U59&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>image.sensor</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;SENTINEL_2&#x27; ... &#x27;LANDSAT_8&#x27;</div><input id='attrs-e1bbf2e4-d2f0-4c4d-af33-e0f84a3edc70' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1bbf2e4-d2f0-4c4d-af33-e0f84a3edc70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1694b77-4ccf-4a3f-a861-8cfffbe85537' class='xr-var-data-in' type='checkbox'><label for='data-b1694b77-4ccf-4a3f-a861-8cfffbe85537' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;,\n",
       "       &#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;, &#x27;SENTINEL_2&#x27;,\n",
       "       &#x27;SENTINEL_2&#x27;, &#x27;LANDSAT_8&#x27;, &#x27;LANDSAT_8&#x27;, &#x27;LANDSAT_8&#x27;, &#x27;LANDSAT_8&#x27;],\n",
       "      dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>image.spatialResolution</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0 10.0 10.0 ... 30.0 30.0 30.0</div><input id='attrs-0a8e8c64-ed16-44eb-b1e0-53bb20f0cf41' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a8e8c64-ed16-44eb-b1e0-53bb20f0cf41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d62ef484-1fe9-4512-9621-2ed2a8830dec' class='xr-var-data-in' type='checkbox'><label for='data-d62ef484-1fe9-4512-9621-2ed2a8830dec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([10., 10., 10., 10., 10., 10., 10., 10., 10., 30., 30., 30., 30.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;EPSG:4326&#x27; ... &#x27;EPSG:4326&#x27;</div><input id='attrs-3f2196b9-c931-4e26-9995-ddf0be3aebe7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f2196b9-c931-4e26-9995-ddf0be3aebe7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64ba5c15-8eaa-4ae5-bb68-2c65aeb38fc4' class='xr-var-data-in' type='checkbox'><label for='data-64ba5c15-8eaa-4ae5-bb68-2c65aeb38fc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;,\n",
       "       &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;,\n",
       "       &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;, &#x27;EPSG:4326&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1838cc05-63e9-4ae6-bdc6-5b942e4d1946' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1838cc05-63e9-4ae6-bdc6-5b942e4d1946' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ndwi</span></div><div class='xr-var-dims'>(time, band, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-a29eb303-ae91-4c61-a827-b480a5e8c862' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a29eb303-ae91-4c61-a827-b480a5e8c862' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84487e40-df30-45d3-8603-7a9bf8f58c5c' class='xr-var-data-in' type='checkbox'><label for='data-84487e40-df30-45d3-8603-7a9bf8f58c5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ..., -0.26479748,\n",
       "          -0.26576534, -0.30052149],\n",
       "         [        nan,         nan,         nan, ..., -0.32563838,\n",
       "          -0.33777601, -0.29802307],\n",
       "         ...,\n",
       "         [-0.2327863 , -0.23809063, -0.22383597, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [-0.23798855, -0.23628324, -0.21716863, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan]]],\n",
       "\n",
       "\n",
       "       [[[        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ..., -0.28987667,\n",
       "          -0.29359233, -0.30182073],\n",
       "         [        nan,         nan,         nan, ..., -0.31638959,\n",
       "...\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan]]],\n",
       "\n",
       "\n",
       "       [[[        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ..., -0.20457768,\n",
       "          -0.21002629,         nan],\n",
       "         ...,\n",
       "         [        nan,         nan, -0.10402268, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d2fa7505-97fc-466a-a9bb-044518619593' class='xr-section-summary-in' type='checkbox'  ><label for='section-d2fa7505-97fc-466a-a9bb-044518619593' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9ea3b299-0695-4ce0-8c6b-0a6f5f88dbb5' class='xr-index-data-in' type='checkbox'/><label for='index-9ea3b299-0695-4ce0-8c6b-0a6f5f88dbb5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;NDWI&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-53a227cd-6bd3-454d-87c6-f0b8111a9a2f' class='xr-index-data-in' type='checkbox'/><label for='index-53a227cd-6bd3-454d-87c6-f0b8111a9a2f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 40.29876496841197,  40.29867318419115, 40.298581399970324,\n",
       "         40.2984896157495, 40.298397831528675, 40.298306047307854,\n",
       "       40.298214263087026, 40.298122478866205,  40.29803069464538,\n",
       "        40.29793891042455,  40.29784712620373,   40.2977553419829,\n",
       "        40.29766355776208,  40.29757177354125,  40.29747998932043,\n",
       "         40.2973882050996,  40.29729642087878,  40.29720463665795,\n",
       "        40.29711285243713,   40.2970210682163,  40.29692928399548,\n",
       "       40.296837499774654,  40.29674571555383, 40.296653931333005,\n",
       "        40.29656214711218, 40.296470362891355, 40.296378578670534,\n",
       "       40.296286794449706, 40.296195010228885,  40.29610322600806,\n",
       "        40.29601144178723,  40.29591965756641,  40.29582787334558,\n",
       "        40.29573608912476,  40.29564430490393,  40.29555252068311,\n",
       "        40.29546073646228,  40.29536895224146,  40.29527716802063,\n",
       "        40.29518538379981,  40.29509359957898,  40.29500181535816,\n",
       "       40.294910031137334,  40.29481824691651, 40.294726462695685,\n",
       "       40.294634678474864, 40.294542894254036,  40.29445111003321,\n",
       "        40.29435932581239,  40.29426754159156,  40.29417575737074,\n",
       "        40.29408397314991,  40.29399218892909,  40.29390040470826,\n",
       "        40.29380862048744,  40.29371683626661,  40.29362505204579,\n",
       "        40.29353326782496,  40.29344148360414,  40.29334969938331,\n",
       "        40.29325791516249, 40.293166130941664,  40.29307434672084,\n",
       "       40.292982562500015, 40.292890778279194, 40.292798994058366,\n",
       "       40.292707209837545,  40.29261542561672,  40.29252364139589,\n",
       "        40.29243185717507,  40.29234007295424,  40.29224828873342,\n",
       "        40.29215650451259,  40.29206472029177,  40.29197293607094,\n",
       "        40.29188115185012],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-10f35731-ff44-497c-b67b-b13db2a23028' class='xr-index-data-in' type='checkbox'/><label for='index-10f35731-ff44-497c-b67b-b13db2a23028' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-91.17582314506241, -91.17570294646015, -91.17558274785787,\n",
       "       -91.17546254925561, -91.17534235065334, -91.17522215205108,\n",
       "        -91.1751019534488, -91.17498175484653, -91.17486155624427,\n",
       "       -91.17474135764199, -91.17462115903973, -91.17450096043746,\n",
       "        -91.1743807618352, -91.17426056323292, -91.17414036463066,\n",
       "       -91.17402016602838, -91.17389996742612, -91.17377976882385,\n",
       "       -91.17365957022159, -91.17353937161931, -91.17341917301705,\n",
       "       -91.17329897441478,  -91.1731787758125, -91.17305857721024,\n",
       "       -91.17293837860797,  -91.1728181800057, -91.17269798140343,\n",
       "       -91.17257778280117,  -91.1724575841989, -91.17233738559663,\n",
       "       -91.17221718699436,  -91.1720969883921, -91.17197678978982,\n",
       "       -91.17185659118756, -91.17173639258529, -91.17161619398303,\n",
       "       -91.17149599538075, -91.17137579677848, -91.17125559817622,\n",
       "       -91.17113539957394, -91.17101520097168,  -91.1708950023694,\n",
       "       -91.17077480376715, -91.17065460516487, -91.17053440656261,\n",
       "       -91.17041420796033, -91.17029400935807,  -91.1701738107558,\n",
       "       -91.17005361215354, -91.16993341355126, -91.16981321494899,\n",
       "       -91.16969301634673, -91.16957281774445, -91.16945261914219,\n",
       "       -91.16933242053992, -91.16921222193766, -91.16909202333538,\n",
       "       -91.16897182473312, -91.16885162613085, -91.16873142752858,\n",
       "       -91.16861122892631, -91.16849103032405, -91.16837083172177,\n",
       "       -91.16825063311951, -91.16813043451724, -91.16801023591496,\n",
       "        -91.1678900373127, -91.16776983871043, -91.16764964010817,\n",
       "       -91.16752944150589, -91.16740924290363, -91.16728904430136],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8696c954-d8d7-447f-9d39-afd30e7f8c6b' class='xr-index-data-in' type='checkbox'/><label for='index-8696c954-d8d7-447f-9d39-afd30e7f8c6b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2024-02-12 17:02:33&#x27;, &#x27;2024-02-22 17:02:33&#x27;,\n",
       "               &#x27;2024-03-03 17:02:29&#x27;, &#x27;2024-03-23 17:02:33&#x27;,\n",
       "               &#x27;2024-03-28 17:02:33&#x27;, &#x27;2024-04-07 17:02:31&#x27;,\n",
       "               &#x27;2024-04-12 17:02:33&#x27;, &#x27;2024-05-12 17:02:32&#x27;,\n",
       "               &#x27;2024-06-06 17:02:38&#x27;, &#x27;2023-08-22 16:41:39&#x27;,\n",
       "               &#x27;2023-09-14 16:47:54&#x27;, &#x27;2023-11-17 16:48:04&#x27;,\n",
       "               &#x27;2023-12-12 16:41:50&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f2a02a5d-55ea-4abe-b269-9dd79e26dfe4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f2a02a5d-55ea-4abe-b269-9dd79e26dfe4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                  (band: 1, y: 76, x: 72, time: 13)\n",
       "Coordinates:\n",
       "  * band                     (band) <U4 'NDWI'\n",
       "  * y                        (y) float64 40.3 40.3 40.3 ... 40.29 40.29 40.29\n",
       "  * x                        (x) float64 -91.18 -91.18 -91.18 ... -91.17 -91.17\n",
       "  * time                     (time) datetime64[ns] 2024-02-12T17:02:33 ... 20...\n",
       "    image.id                 (time) <U59 'sentinel-2-l2a|S2B_15TXE_20240212_0...\n",
       "    image.sensor             (time) <U10 'SENTINEL_2' ... 'LANDSAT_8'\n",
       "    image.spatialResolution  (time) float64 10.0 10.0 10.0 ... 30.0 30.0 30.0\n",
       "    crs                      (time) <U9 'EPSG:4326' 'EPSG:4326' ... 'EPSG:4326'\n",
       "Data variables:\n",
       "    ndwi                     (time, band, y, x) float64 nan nan nan ... nan nan"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lai_time_series_xarr = client.get_satellite_image_time_series(year_ago, today, collections=[SatelliteImageryCollection.SENTINEL_2, SatelliteImageryCollection.LANDSAT_8], indicators=[\"NDWI\"],polygon=polygon)\n",
    "lai_time_series_xarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Les dimensions ne sont pas identiques.\n",
      "Coordinates:\n",
      "  * band                     (band) <U4 'ndvi'\n",
      "  * y                        (y) float64 40.3 40.3 40.3 ... 40.29 40.29 40.29\n",
      "  * x                        (x) float64 -91.18 -91.18 -91.18 ... -91.17 -91.17\n",
      "  * time                     (time) datetime64[ns] 2024-02-12T17:02:33 ... 20...\n",
      "    image.id                 (time) <U59 'sentinel-2-l2a|S2B_15TXE_20240212_0...\n",
      "    image.sensor             (time) <U10 'SENTINEL_2' ... 'LANDSAT_8'\n",
      "    image.spatialResolution  (time) float64 10.0 10.0 10.0 ... 30.0 30.0 30.0\n",
      "    crs                      (time) <U9 'EPSG:4326' 'EPSG:4326' ... 'EPSG:4326'\n",
      "Coordinates:\n",
      "  * band                     (band) <U4 'NDWI'\n",
      "  * y                        (y) float64 40.3 40.3 40.3 ... 40.29 40.29 40.29\n",
      "  * x                        (x) float64 -91.18 -91.18 -91.18 ... -91.17 -91.17\n",
      "  * time                     (time) datetime64[ns] 2024-02-12T17:02:33 ... 20...\n",
      "    image.id                 (time) <U59 'sentinel-2-l2a|S2B_15TXE_20240212_0...\n",
      "    image.sensor             (time) <U10 'SENTINEL_2' ... 'LANDSAT_8'\n",
      "    image.spatialResolution  (time) float64 10.0 10.0 10.0 ... 30.0 30.0 30.0\n",
      "    crs                      (time) <U9 'EPSG:4326' 'EPSG:4326' ... 'EPSG:4326'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.backends.zarr.ZarrStore at 0x280e7d58940>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if ndvi_time_series_xarr.coords == lai_time_series_xarr.coords:\n",
    "    print(\"Les dimensions sont identiques.\")\n",
    "else:\n",
    "    print(\"Les dimensions ne sont pas identiques.\")\n",
    "    print(ndvi_time_series_xarr.coords)\n",
    "    print(lai_time_series_xarr.coords)\n",
    "\n",
    "\n",
    "datacube = xr.merge([ndvi_time_series_xarr, lai_time_series_xarr])\n",
    "\n",
    "datacube.to_zarr(\"result.zarr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display cumulative NDVI from last result\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "polygon_ndvi_ds = ndvi_time_series_xarr['ndvi'].sortby('time')\n",
    "time_coords = polygon_ndvi_ds['time']\n",
    "\n",
    "# exclude Nan values to caluculate mean\n",
    "masked_dataarray = polygon_ndvi_ds.where(~np.isnan(polygon_ndvi_ds))\n",
    "\n",
    "# NDVI mean calculattion\n",
    "mean_ndvi = masked_dataarray.mean(dim=['x', 'y'])\n",
    "\n",
    "# Cumulative NDVI calculation \n",
    "polygon_cumul_ndvi = mean_ndvi.cumsum(dim='time')\n",
    "polygon_cumul_ndvi \n",
    "\n",
    "# build & display cumlative Ndvi graph\n",
    "plt.plot(time_coords, polygon_cumul_ndvi)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('NDVI Cumul')\n",
    "plt.title('NVDI Cumul by date')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x28100f3ddd0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "time_series_xarr.reflectance.clip(0,1).sel(band='Green').plot(x=\"x\", y=\"y\", col=\"time\", col_wrap=3, figsize=(20,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('2024-06-06T17:02:38Z',\n",
       "  'SENTINEL_2'): <geosyspy.image_reference.ImageReference at 0x280e7be3b10>,\n",
       " ('2024-05-28T16:41:01Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280cf0460d0>,\n",
       " ('2024-05-12T17:02:32Z',\n",
       "  'SENTINEL_2'): <geosyspy.image_reference.ImageReference at 0x280e7be2bd0>,\n",
       " ('2024-05-12T16:41:14Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280cf06a950>,\n",
       " ('2024-04-12T17:02:33Z',\n",
       "  'SENTINEL_2'): <geosyspy.image_reference.ImageReference at 0x280e7bcfed0>,\n",
       " ('2024-04-07T17:02:31Z',\n",
       "  'SENTINEL_2'): <geosyspy.image_reference.ImageReference at 0x280e7be3410>,\n",
       " ('2024-03-28T17:02:33Z',\n",
       "  'SENTINEL_2'): <geosyspy.image_reference.ImageReference at 0x280cf079710>,\n",
       " ('2024-03-23T17:02:33Z',\n",
       "  'SENTINEL_2'): <geosyspy.image_reference.ImageReference at 0x280e7be2110>,\n",
       " ('2024-03-09T16:41:51Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280cf07abd0>,\n",
       " ('2024-03-03T17:02:29Z',\n",
       "  'SENTINEL_2'): <geosyspy.image_reference.ImageReference at 0x280e7be2c10>,\n",
       " ('2024-02-29T16:48:00Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280e7bf3f90>,\n",
       " ('2024-02-22T17:02:33Z',\n",
       "  'SENTINEL_2'): <geosyspy.image_reference.ImageReference at 0x280e7bf2d90>,\n",
       " ('2024-02-22T16:41:44Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280e7bf1090>,\n",
       " ('2024-02-12T17:02:33Z',\n",
       "  'SENTINEL_2'): <geosyspy.image_reference.ImageReference at 0x280e7bf1790>,\n",
       " ('2024-02-06T16:41:59Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280e7bf3490>,\n",
       " ('2023-12-12T16:41:50Z',\n",
       "  'LANDSAT_8'): <geosyspy.image_reference.ImageReference at 0x280e7bf0c90>,\n",
       " ('2023-12-11T16:48:07Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280e7bf2790>,\n",
       " ('2023-11-25T16:48:09Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280e7bf1f10>,\n",
       " ('2023-11-18T16:41:53Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280e7bf9390>,\n",
       " ('2023-11-17T16:48:04Z',\n",
       "  'LANDSAT_8'): <geosyspy.image_reference.ImageReference at 0x280e7bf2590>,\n",
       " ('2023-11-09T16:48:02Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280e7bf2490>,\n",
       " ('2023-11-02T16:41:47Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280e7bf2350>,\n",
       " ('2023-10-17T16:41:55Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280e7bf2f90>,\n",
       " ('2023-10-01T16:41:44Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280e7bf2610>,\n",
       " ('2023-09-15T16:41:45Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280e7bf2750>,\n",
       " ('2023-09-14T16:47:54Z',\n",
       "  'LANDSAT_8'): <geosyspy.image_reference.ImageReference at 0x280e7bf1b10>,\n",
       " ('2023-08-22T16:41:39Z',\n",
       "  'LANDSAT_8'): <geosyspy.image_reference.ImageReference at 0x280e7bf2090>,\n",
       " ('2023-07-29T16:41:20Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280e7bf1e10>,\n",
       " ('2023-07-20T16:47:27Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280e7bf29d0>,\n",
       " ('2023-07-04T16:47:22Z',\n",
       "  'LANDSAT_9'): <geosyspy.image_reference.ImageReference at 0x280e7c0c490>}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_references"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:20:04,351 - geosyspy.geosys - INFO - writing to c:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\image_sentinel-2-l2a_S2B_21JYM_20240429_0_L2A_tiff.zip\n"
     ]
    }
   ],
   "source": [
    "# Download and save a specific image\n",
    "season_field_id = 'y3mxlxx'\n",
    "image_id = 'sentinel-2-l2a|S2B_21JYM_20240429_0_L2A'\n",
    "indicator = 'INSEASON_NDVI'\n",
    "client.download_image(season_field_id, None, image_id, indicator)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:20:08,479 - geosyspy.services.analytics_fabric_service - INFO - The schema GeosysPy_NDVI already exists.\n"
     ]
    }
   ],
   "source": [
    "# Define a data schema in Analytics Fabrics\n",
    "schema = {\n",
    "    \"NDVI\": \"double\"\n",
    "}\n",
    "schema_id = \"GeosysPy_NDVI\"\n",
    "client.create_schema_id(schema_id, schema)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:20:08,985 - geosyspy.services.analytics_fabric_service - INFO - Calling APIs for metrics\n",
      "2024-06-14 11:20:09,222 - geosyspy.services.analytics_fabric_service - INFO - No metrics found in Analytic Fabric with SchemaId: LAI_RADAR, SeasonField:mUPLeUseMwsGMQ3ugs7xE between:2022-01-24 and 2022-03-01  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get metrics in Analytics Fabric\n",
    "\n",
    "start_date = dt.datetime.strptime(\"2022-01-24\", \"%Y-%m-%d\")\n",
    "end_date = dt.datetime.strptime(\"2022-03-01\", \"%Y-%m-%d\")\n",
    "schema_id = \"LAI_RADAR\"\n",
    "polygon = \"POLYGON((-52.72591542 -18.7395779,-52.72604885 -18.73951122,-52.72603114 -18.73908689,-52.71556835 -18.72490316,-52.71391916 -18.72612966,-52.71362802 -18.72623726,-52.71086473 -18.72804231,-52.72083542 -18.74173696,-52.72118937 -18.74159174,-52.72139229 -18.7418552,-52.72600257 -18.73969719,-52.72591542 -18.7395779))\"\n",
    "client.get_metrics( schema_id, start_date, end_date,polygon=polygon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:20:09,524 - geosyspy.services.vegetation_time_series_service - INFO - Calling APIs for aggregated time series\n",
      "c:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\geosyspy\\services\\vegetation_time_series_service.py:57: FutureWarning: Passing literal json to 'read_json' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  df = pd.read_json(json.dumps(dict_response))\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",
       "    .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>index</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-30</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.881439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.879117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.876899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.874825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.872929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           index     value\n",
       "date                      \n",
       "2018-12-30  NDVI  0.881439\n",
       "2018-12-31  NDVI  0.879117\n",
       "2019-01-01  NDVI  0.876899\n",
       "2019-01-02  NDVI  0.874825\n",
       "2019-01-03  NDVI  0.872929"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get time serie\n",
    "start_date = dt.datetime.strptime(\"2018-12-30\", \"%Y-%m-%d\")\n",
    "end_date = dt.datetime.strptime(\"2019-12-31\", \"%Y-%m-%d\")\n",
    "df = client.get_time_series(start_date, end_date, collection=SatelliteImageryCollection.MODIS, indicators=[\"NDVI\"],polygon=polygon)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Timestamp': '2018-12-30 00:00:00', 'Values': {'NDVI': 0.88143934}},\n",
       " {'Timestamp': '2018-12-31 00:00:00', 'Values': {'NDVI': 0.8791165400000001}},\n",
       " {'Timestamp': '2019-01-01 00:00:00', 'Values': {'NDVI': 0.87689914}},\n",
       " {'Timestamp': '2019-01-02 00:00:00', 'Values': {'NDVI': 0.8748249100000001}},\n",
       " {'Timestamp': '2019-01-03 00:00:00', 'Values': {'NDVI': 0.87292918}}]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create structure before push values in Analytics Fabrics\n",
    "values = []\n",
    "for i in range(0,len(df)):\n",
    "    prop = {\n",
    "        \"Timestamp\": str(df[\"value\"].index[i]),\n",
    "        \"Values\": {\n",
    "            \"NDVI\": df[\"value\"].values[i]\n",
    "        }\n",
    "        }\n",
    "    values.append(prop)\n",
    "values[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Push metrics in Analytics Fabrics\n",
    "schema_id = \"GeosysPy_NDVI\"\n",
    "client.push_metrics(schema_id, values, polygon=polygon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:20:20,051 - geosyspy.services.analytics_fabric_service - INFO - Calling APIs for metrics\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values.NDVI</th>\n",
       "      <th>Schema.Id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-30T00:00:00Z</th>\n",
       "      <td>0.881439</td>\n",
       "      <td>GeosysPy_NDVI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31T00:00:00Z</th>\n",
       "      <td>0.879117</td>\n",
       "      <td>GeosysPy_NDVI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01T00:00:00Z</th>\n",
       "      <td>0.876899</td>\n",
       "      <td>GeosysPy_NDVI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02T00:00:00Z</th>\n",
       "      <td>0.874825</td>\n",
       "      <td>GeosysPy_NDVI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03T00:00:00Z</th>\n",
       "      <td>0.872929</td>\n",
       "      <td>GeosysPy_NDVI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-02T00:00:00Z</th>\n",
       "      <td>0.388090</td>\n",
       "      <td>GeosysPy_NDVI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-03T00:00:00Z</th>\n",
       "      <td>0.385102</td>\n",
       "      <td>GeosysPy_NDVI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-04T00:00:00Z</th>\n",
       "      <td>0.382602</td>\n",
       "      <td>GeosysPy_NDVI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-05T00:00:00Z</th>\n",
       "      <td>0.380549</td>\n",
       "      <td>GeosysPy_NDVI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-06T00:00:00Z</th>\n",
       "      <td>0.378900</td>\n",
       "      <td>GeosysPy_NDVI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Values.NDVI      Schema.Id\n",
       "date                                            \n",
       "2018-12-30T00:00:00Z     0.881439  GeosysPy_NDVI\n",
       "2018-12-31T00:00:00Z     0.879117  GeosysPy_NDVI\n",
       "2019-01-01T00:00:00Z     0.876899  GeosysPy_NDVI\n",
       "2019-01-02T00:00:00Z     0.874825  GeosysPy_NDVI\n",
       "2019-01-03T00:00:00Z     0.872929  GeosysPy_NDVI\n",
       "...                           ...            ...\n",
       "2019-04-02T00:00:00Z     0.388090  GeosysPy_NDVI\n",
       "2019-04-03T00:00:00Z     0.385102  GeosysPy_NDVI\n",
       "2019-04-04T00:00:00Z     0.382602  GeosysPy_NDVI\n",
       "2019-04-05T00:00:00Z     0.380549  GeosysPy_NDVI\n",
       "2019-04-06T00:00:00Z     0.378900  GeosysPy_NDVI\n",
       "\n",
       "[98 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get metrics in Analytics Fabrics\n",
    "start_date = dt.datetime.strptime(\"2018-01-01\", \"%Y-%m-%d\")\n",
    "end_date = dt.datetime.strptime(\"2022-04-01\", \"%Y-%m-%d\")\n",
    "client.get_metrics(schema_id, start_date, end_date, polygon=polygon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:20:21,682 - geosyspy.services.analytics_processor_service - INFO - Retry -- Task still running\n",
      "2024-06-14 11:20:23,934 - geosyspy.services.analytics_processor_service - INFO - Retry -- Task still running\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'s3://geosys-geosys-us/2tKecZgMyEP6EkddLxa1gV/mrts/5f2141ab13324d6abab1df96a2ad4316'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_date=\"2020-10-09\"\n",
    "end_date=\"2022-10-09\"\n",
    "sensors=[\"Sentinel_2\", \"Landsat_8\"]\n",
    "denoiser=True\n",
    "smoother=\"ww\"\n",
    "eoc=True\n",
    "aggregation=\"mean\"\n",
    "index=\"ndvi\"\n",
    "raw_data=True\n",
    "str_polygon=\"POLYGON ((-0.49881816 46.27330504, -0.49231649 46.27320122, -0.49611449 46.26983426, -0.49821735 46.27094671, -0.49881816 46.27330504))\"\n",
    "\n",
    "str_s3_path = client.get_mr_time_series(str_polygon, start_date, end_date, sensors, denoiser, smoother, eoc, aggregation, index, raw_data)\n",
    "str_s3_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:20:29,099 - geosyspy.services.analytics_processor_service - INFO - Retry -- Task still running\n",
      "2024-06-14 11:20:31,237 - geosyspy.services.analytics_processor_service - INFO - Retry -- Task still running\n",
      "2024-06-14 11:20:35,400 - geosyspy.services.analytics_processor_service - INFO - Retry -- Task still running\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'s3://geosys-geosys-us/2tKecZgMyEP6EkddLxa1gV/mrts/3234ad205d9d45a398c9153c3d1753b8'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str_polygon=\"POLYGON ((-0.49881816 46.27330504, -0.49231649 46.27320122, -0.49611449 46.26983426, -0.49821735 46.27094671, -0.49881816 46.27330504))\"\n",
    "\n",
    "str_s3_path = client.get_mr_time_series(str_polygon)\n",
    "str_s3_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "ClientError",
     "evalue": "An error occurred (InvalidToken) when calling the ListObjects operation: The provided token is malformed or otherwise invalid.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mClientError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[32], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnotebook_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# read MRTS outputs from AWS and plot result\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m dict_mrts \u001b[38;5;241m=\u001b[39m \u001b[43mread_mrts_from_awsS3\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstr_s3_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      5\u001b[0m plot_mrts_dict(dict_mrts)\n\u001b[0;32m      7\u001b[0m \u001b[38;5;66;03m### others useful functions\u001b[39;00m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;66;03m# plot_mrts_from_aws(str_s3_path) : plot MRTS outputs from AWS\u001b[39;00m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m# download MRTS outputs from AWS and read outputs downloaded : download_s3_files(str_s3_path), read_mrts_data(str_downloaded_path)\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\notebook_utils.py:162\u001b[0m, in \u001b[0;36mread_mrts_from_awsS3\u001b[1;34m(str_s3_path)\u001b[0m\n\u001b[0;32m    160\u001b[0m bucket \u001b[38;5;241m=\u001b[39m s3\u001b[38;5;241m.\u001b[39mBucket(bucket_name)\n\u001b[0;32m    161\u001b[0m s3 \u001b[38;5;241m=\u001b[39m boto3\u001b[38;5;241m.\u001b[39mclient(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms3\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m--> 162\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mbucket\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobjects\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mPrefix\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdirectory_name\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m    163\u001b[0m \u001b[43m    \u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43ms3\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_object\u001b[49m\u001b[43m(\u001b[49m\u001b[43mBucket\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbucket_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mKey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    164\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfile_content\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mBody\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\boto3\\resources\\collection.py:81\u001b[0m, in \u001b[0;36mResourceCollection.__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     78\u001b[0m limit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlimit\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m     80\u001b[0m count \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m---> 81\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mpage\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpages\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m     82\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mitem\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mpage\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m     83\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01myield\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mitem\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\boto3\\resources\\collection.py:171\u001b[0m, in \u001b[0;36mResourceCollection.pages\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    168\u001b[0m \u001b[38;5;66;03m# Now that we have a page iterator or single page of results\u001b[39;00m\n\u001b[0;32m    169\u001b[0m \u001b[38;5;66;03m# we start processing and yielding individual items.\u001b[39;00m\n\u001b[0;32m    170\u001b[0m count \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m--> 171\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mpage\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mpages\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m    172\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpage_items\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m    173\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mitem\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpage\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\botocore\\paginate.py:269\u001b[0m, in \u001b[0;36mPageIterator.__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    267\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_inject_starting_params(current_kwargs)\n\u001b[0;32m    268\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 269\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    270\u001b[0m     parsed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_extract_parsed_response(response)\n\u001b[0;32m    271\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m first_request:\n\u001b[0;32m    272\u001b[0m         \u001b[38;5;66;03m# The first request is handled differently.  We could\u001b[39;00m\n\u001b[0;32m    273\u001b[0m         \u001b[38;5;66;03m# possibly have a resume/starting token that tells us where\u001b[39;00m\n\u001b[0;32m    274\u001b[0m         \u001b[38;5;66;03m# to index into the retrieved page.\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\botocore\\paginate.py:357\u001b[0m, in \u001b[0;36mPageIterator._make_request\u001b[1;34m(self, current_kwargs)\u001b[0m\n\u001b[0;32m    356\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_make_request\u001b[39m(\u001b[38;5;28mself\u001b[39m, current_kwargs):\n\u001b[1;32m--> 357\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_method\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcurrent_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\botocore\\client.py:553\u001b[0m, in \u001b[0;36mClientCreator._create_api_method.<locals>._api_call\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    549\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m    550\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpy_operation_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m() only accepts keyword arguments.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    551\u001b[0m     )\n\u001b[0;32m    552\u001b[0m \u001b[38;5;66;03m# The \"self\" in this scope is referring to the BaseClient.\u001b[39;00m\n\u001b[1;32m--> 553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43moperation_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\botocore\\client.py:1009\u001b[0m, in \u001b[0;36mBaseClient._make_api_call\u001b[1;34m(self, operation_name, api_params)\u001b[0m\n\u001b[0;32m   1005\u001b[0m     error_code \u001b[38;5;241m=\u001b[39m error_info\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQueryErrorCode\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m error_info\u001b[38;5;241m.\u001b[39mget(\n\u001b[0;32m   1006\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCode\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1007\u001b[0m     )\n\u001b[0;32m   1008\u001b[0m     error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[1;32m-> 1009\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[0;32m   1010\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1011\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parsed_response\n",
      "\u001b[1;31mClientError\u001b[0m: An error occurred (InvalidToken) when calling the ListObjects operation: The provided token is malformed or otherwise invalid."
     ]
    }
   ],
   "source": [
    "from notebook_utils import *\n",
    "\n",
    "# read MRTS outputs from AWS and plot result\n",
    "dict_mrts = read_mrts_from_awsS3(str_s3_path)\n",
    "plot_mrts_dict(dict_mrts)\n",
    "\n",
    "### others useful functions\n",
    "# plot_mrts_from_aws(str_s3_path) : plot MRTS outputs from AWS\n",
    "# download MRTS outputs from AWS and read outputs downloaded : download_s3_files(str_s3_path), read_mrts_data(str_downloaded_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AGRIQUEST COMPUTATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\geosyspy\\services\\agriquest_service.py:99: FutureWarning: Passing literal json to 'read_json' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  df = pd.read_json(json.dumps(dict_response))\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>1</th>\n",
       "      <th>AMU</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Area (km2)</th>\n",
       "      <th>Start</th>\n",
       "      <th>End</th>\n",
       "      <th>Value(Fahrenheit)</th>\n",
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       "  </thead>\n",
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       "      <th>2</th>\n",
       "      <td>dm6zd7</td>\n",
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       "      <td>50.1815140859714</td>\n",
       "      <td>2076206.19350909</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/10/23</td>\n",
       "      <td>42.6537761214063</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pm71ed</td>\n",
       "      <td>-116.26967229539</td>\n",
       "      <td>49.3142983301064</td>\n",
       "      <td>1140818.84899762</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/10/23</td>\n",
       "      <td>40.9050440207924</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37ndaz</td>\n",
       "      <td>-121.431647767903</td>\n",
       "      <td>50.8957142214965</td>\n",
       "      <td>859701.520677822</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/10/23</td>\n",
       "      <td>41.1429031374504</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>apx5rz</td>\n",
       "      <td>-121.101796289793</td>\n",
       "      <td>49.5280358248754</td>\n",
       "      <td>849852.287689543</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/10/23</td>\n",
       "      <td>40.4459632826831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mp6qwd</td>\n",
       "      <td>-119.253577028055</td>\n",
       "      <td>50.1638042845403</td>\n",
       "      <td>1371497.13784112</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/10/23</td>\n",
       "      <td>43.2356649016597</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>38.9080077311573</td>\n",
       "      <td>42925.0353570637</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/10/23</td>\n",
       "      <td>59.7459764343867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2938</th>\n",
       "      <td>ldgd6n</td>\n",
       "      <td>-71.5907951395792</td>\n",
       "      <td>41.6946227800715</td>\n",
       "      <td>620540.139719753</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/10/23</td>\n",
       "      <td>54.7409374010456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2939</th>\n",
       "      <td>1kwkj7</td>\n",
       "      <td>-71.2771619559509</td>\n",
       "      <td>41.5393927681328</td>\n",
       "      <td>25993.6751949394</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/10/23</td>\n",
       "      <td>54.7747722154322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2940</th>\n",
       "      <td>z5r5ke</td>\n",
       "      <td>-71.37775593944</td>\n",
       "      <td>41.514830063034</td>\n",
       "      <td>6405.03372192102</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/10/23</td>\n",
       "      <td>54.764876751904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2941</th>\n",
       "      <td>xlxlb4</td>\n",
       "      <td>-71.5785472344162</td>\n",
       "      <td>41.1767522042168</td>\n",
       "      <td>6353.96791249767</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/10/23</td>\n",
       "      <td>54.9894450291958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2940 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "1        AMU                  X                 Y        Area (km2)     Start  \\\n",
       "2     dm6zd7  -120.609779853754  50.1815140859714  2076206.19350909  06/01/22   \n",
       "3     pm71ed   -116.26967229539  49.3142983301064  1140818.84899762  06/01/22   \n",
       "4     37ndaz  -121.431647767903  50.8957142214965  859701.520677822  06/01/22   \n",
       "5     apx5rz  -121.101796289793  49.5280358248754  849852.287689543  06/01/22   \n",
       "6     mp6qwd  -119.253577028055  50.1638042845403  1371497.13784112  06/01/22   \n",
       "...      ...                ...               ...               ...       ...   \n",
       "2937  7zrznz  -77.0172633740488  38.9080077311573  42925.0353570637  06/01/22   \n",
       "2938  ldgd6n  -71.5907951395792  41.6946227800715  620540.139719753  06/01/22   \n",
       "2939  1kwkj7  -71.2771619559509  41.5393927681328  25993.6751949394  06/01/22   \n",
       "2940  z5r5ke    -71.37775593944   41.514830063034  6405.03372192102  06/01/22   \n",
       "2941  xlxlb4  -71.5785472344162  41.1767522042168  6353.96791249767  06/01/22   \n",
       "\n",
       "1          End Value(Fahrenheit)  \n",
       "2     06/10/23  42.6537761214063  \n",
       "3     06/10/23  40.9050440207924  \n",
       "4     06/10/23  41.1429031374504  \n",
       "5     06/10/23  40.4459632826831  \n",
       "6     06/10/23  43.2356649016597  \n",
       "...        ...               ...  \n",
       "2937  06/10/23  59.7459764343867  \n",
       "2938  06/10/23  54.7409374010456  \n",
       "2939  06/10/23  54.7747722154322  \n",
       "2940  06/10/23   54.764876751904  \n",
       "2941  06/10/23  54.9894450291958  \n",
       "\n",
       "[2940 rows x 7 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a panda data frame from Agriquest Weather Data:\n",
    "# on all AMU of o block\n",
    "# on a specific period\n",
    "# for a specific weather indicator\n",
    "aq_weather_data_frame = client.get_agriquest_weather_block_data(start_date=\"2022-06-01\",\n",
    "                                                  end_date=\"2023-06-10\",\n",
    "                                                  block_code=AgriquestBlocks.AMU_NORTH_AMERICA,\n",
    "                                                  weather_type=AgriquestWeatherType.AVERAGE_TEMPERATURE)\n",
    "\n",
    "aq_weather_data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\geosyspy\\services\\agriquest_service.py:149: FutureWarning: Passing literal json to 'read_json' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  df = pd.read_json(json.dumps(dict_response))\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",
       "        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>1</th>\n",
       "      <th>AMU</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Area (km2)</th>\n",
       "      <th>Start</th>\n",
       "      <th>End</th>\n",
       "      <th>NDVI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aude</td>\n",
       "      <td>2.41362224200849</td>\n",
       "      <td>43.1032535837836</td>\n",
       "      <td>1567533.41901905</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>0.67292565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Haute-Loire</td>\n",
       "      <td>3.80629821371942</td>\n",
       "      <td>45.128115177836</td>\n",
       "      <td>1236418.34584859</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>0.7676143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bouches-du-Rhône</td>\n",
       "      <td>5.08604203461634</td>\n",
       "      <td>43.5432739455779</td>\n",
       "      <td>1295740.4639529</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>0.5370673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lot-et-Garonne</td>\n",
       "      <td>0.460747437025639</td>\n",
       "      <td>44.3679636789425</td>\n",
       "      <td>1330771.26129295</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>0.6660436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Creuse</td>\n",
       "      <td>2.01822963286571</td>\n",
       "      <td>46.0906204404038</td>\n",
       "      <td>1383585.42233085</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>0.7965648</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Alpes-de-Haute-Provence</td>\n",
       "      <td>6.24465390599831</td>\n",
       "      <td>44.1061172758651</td>\n",
       "      <td>1728081.03437926</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>0.66969323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Morbihan</td>\n",
       "      <td>-2.81218268011372</td>\n",
       "      <td>47.8475865265542</td>\n",
       "      <td>1693893.7322425</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>0.71398413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Doubs</td>\n",
       "      <td>6.36272222421339</td>\n",
       "      <td>47.1659635940096</td>\n",
       "      <td>1298913.913808</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>0.8028164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Jura</td>\n",
       "      <td>5.69737730693424</td>\n",
       "      <td>46.7293599950923</td>\n",
       "      <td>1247558.25175067</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>0.80481243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Gard</td>\n",
       "      <td>4.17986125357777</td>\n",
       "      <td>43.9936012526349</td>\n",
       "      <td>1451959.77356846</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>06/01/22</td>\n",
       "      <td>0.63843656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "1                       AMU                  X                 Y  \\\n",
       "2                      Aude   2.41362224200849  43.1032535837836   \n",
       "3               Haute-Loire   3.80629821371942   45.128115177836   \n",
       "4          Bouches-du-Rhône   5.08604203461634  43.5432739455779   \n",
       "5            Lot-et-Garonne  0.460747437025639  44.3679636789425   \n",
       "6                    Creuse   2.01822963286571  46.0906204404038   \n",
       "..                      ...                ...               ...   \n",
       "79  Alpes-de-Haute-Provence   6.24465390599831  44.1061172758651   \n",
       "80                 Morbihan  -2.81218268011372  47.8475865265542   \n",
       "81                    Doubs   6.36272222421339  47.1659635940096   \n",
       "82                     Jura   5.69737730693424  46.7293599950923   \n",
       "83                     Gard   4.17986125357777  43.9936012526349   \n",
       "\n",
       "1         Area (km2)     Start       End        NDVI  \n",
       "2   1567533.41901905  06/01/22  06/01/22  0.67292565  \n",
       "3   1236418.34584859  06/01/22  06/01/22   0.7676143  \n",
       "4    1295740.4639529  06/01/22  06/01/22   0.5370673  \n",
       "5   1330771.26129295  06/01/22  06/01/22   0.6660436  \n",
       "6   1383585.42233085  06/01/22  06/01/22   0.7965648  \n",
       "..               ...       ...       ...         ...  \n",
       "79  1728081.03437926  06/01/22  06/01/22  0.66969323  \n",
       "80   1693893.7322425  06/01/22  06/01/22  0.71398413  \n",
       "81    1298913.913808  06/01/22  06/01/22   0.8028164  \n",
       "82  1247558.25175067  06/01/22  06/01/22  0.80481243  \n",
       "83  1451959.77356846  06/01/22  06/01/22  0.63843656  \n",
       "\n",
       "[82 rows x 7 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a panda data frame from Agriquest NDVI Data:\n",
    "# on all AMU of o block\n",
    "# on a specific date\n",
    "# for a specific commodity (ALL_VEGETATION or ALL_CROPS)\n",
    "\n",
    "aq_ndvi_data_frame = client.get_agriquest_ndvi_block_data(day_of_measure=\"2022-06-01\",\n",
    "                                               commodity_code=AgriquestCommodityCode.ALL_CROPS,             \n",
    "                                               block_code=AgriquestBlocks.FRA_DEPARTEMENTS)\n",
    "\n",
    "aq_ndvi_data_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ANALYTICS COMPUTATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "_2ND_CORN",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[35], line 6\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Analytics processor - Historical Harvest\u001b[39;00m\n\u001b[0;32m      2\u001b[0m harvest_historical_analytics \u001b[38;5;241m=\u001b[39m client\u001b[38;5;241m.\u001b[39mget_harvest_analytics(\n\u001b[0;32m      3\u001b[0m     season_duration\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m215\u001b[39m,\n\u001b[0;32m      4\u001b[0m     season_start_day\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m      5\u001b[0m     season_start_month\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m,\n\u001b[1;32m----> 6\u001b[0m     crop\u001b[38;5;241m=\u001b[39m\u001b[43mcrops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_2ND_CORN\u001b[49m,\n\u001b[0;32m      7\u001b[0m     year\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2021\u001b[39m,\n\u001b[0;32m      8\u001b[0m     geometry\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPOLYGON ((-56.785919346530768 -21.208154463301554 ,  -56.79078750820733 -21.206043784434833 ,  -56.790973809206818 -21.206069651656232 ,  -56.791373799079636 -21.197107091323097 ,  -56.785129186971687 -21.196010916846863 ,  -56.781397554331065 -21.19535575112814 ,  -56.777108478217059 -21.202038412606473 ,  -56.778435977920665 -21.211398619037478 ,  -56.785919346530768 -21.208154463301554))\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m      9\u001b[0m     harvest_type\u001b[38;5;241m=\u001b[39mHarvest\u001b[38;5;241m.\u001b[39mHARVEST_HISTORICAL)\n\u001b[0;32m     10\u001b[0m harvest_historical_analytics\n",
      "File \u001b[1;32mC:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_3.11.2544.0_x64__qbz5n2kfra8p0\\Lib\\enum.py:786\u001b[0m, in \u001b[0;36mEnumType.__getattr__\u001b[1;34m(cls, name)\u001b[0m\n\u001b[0;32m    784\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_member_map_[name]\n\u001b[0;32m    785\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m--> 786\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(name) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[1;31mAttributeError\u001b[0m: _2ND_CORN"
     ]
    }
   ],
   "source": [
    "# Analytics processor - Historical Harvest\n",
    "harvest_historical_analytics = client.get_harvest_analytics(\n",
    "    season_duration=215,\n",
    "    season_start_day=1,\n",
    "    season_start_month=4,\n",
    "    crop=crops._2ND_CORN,\n",
    "    year=2021,\n",
    "    geometry=\"POLYGON ((-56.785919346530768 -21.208154463301554 ,  -56.79078750820733 -21.206043784434833 ,  -56.790973809206818 -21.206069651656232 ,  -56.791373799079636 -21.197107091323097 ,  -56.785129186971687 -21.196010916846863 ,  -56.781397554331065 -21.19535575112814 ,  -56.777108478217059 -21.202038412606473 ,  -56.778435977920665 -21.211398619037478 ,  -56.785919346530768 -21.208154463301554))\",\n",
    "    harvest_type=Harvest.HARVEST_HISTORICAL)\n",
    "harvest_historical_analytics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "_2ND_CORN",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[36], line 6\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Analytics processor - Inseason Harvest\u001b[39;00m\n\u001b[0;32m      2\u001b[0m harvest_in_season_analytics \u001b[38;5;241m=\u001b[39m client\u001b[38;5;241m.\u001b[39mget_harvest_analytics(\n\u001b[0;32m      3\u001b[0m     season_duration\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m215\u001b[39m,\n\u001b[0;32m      4\u001b[0m     season_start_day\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m      5\u001b[0m     season_start_month\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m,\n\u001b[1;32m----> 6\u001b[0m     crop\u001b[38;5;241m=\u001b[39m\u001b[43mcrops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_2ND_CORN\u001b[49m,\n\u001b[0;32m      7\u001b[0m     year\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2021\u001b[39m,\n\u001b[0;32m      8\u001b[0m     geometry\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPOLYGON ((-56.785919346530768 -21.208154463301554 ,  -56.79078750820733 -21.206043784434833 ,  -56.790973809206818 -21.206069651656232 ,  -56.791373799079636 -21.197107091323097 ,  -56.785129186971687 -21.196010916846863 ,  -56.781397554331065 -21.19535575112814 ,  -56.777108478217059 -21.202038412606473 ,  -56.778435977920665 -21.211398619037478 ,  -56.785919346530768 -21.208154463301554))\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m      9\u001b[0m     harvest_type\u001b[38;5;241m=\u001b[39mHarvest\u001b[38;5;241m.\u001b[39mHARVEST_IN_SEASON)\n\u001b[0;32m     11\u001b[0m harvest_in_season_analytics\n",
      "File \u001b[1;32mC:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_3.11.2544.0_x64__qbz5n2kfra8p0\\Lib\\enum.py:786\u001b[0m, in \u001b[0;36mEnumType.__getattr__\u001b[1;34m(cls, name)\u001b[0m\n\u001b[0;32m    784\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_member_map_[name]\n\u001b[0;32m    785\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m--> 786\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(name) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[1;31mAttributeError\u001b[0m: _2ND_CORN"
     ]
    }
   ],
   "source": [
    "# Analytics processor - Inseason Harvest\n",
    "harvest_in_season_analytics = client.get_harvest_analytics(\n",
    "    season_duration=215,\n",
    "    season_start_day=1,\n",
    "    season_start_month=4,\n",
    "    crop=crops._2ND_CORN,\n",
    "    year=2021,\n",
    "    geometry=\"POLYGON ((-56.785919346530768 -21.208154463301554 ,  -56.79078750820733 -21.206043784434833 ,  -56.790973809206818 -21.206069651656232 ,  -56.791373799079636 -21.197107091323097 ,  -56.785129186971687 -21.196010916846863 ,  -56.781397554331065 -21.19535575112814 ,  -56.777108478217059 -21.202038412606473 ,  -56.778435977920665 -21.211398619037478 ,  -56.785919346530768 -21.208154463301554))\",\n",
    "    harvest_type=Harvest.HARVEST_IN_SEASON)\n",
    "\n",
    "harvest_in_season_analytics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analytics processor - Inseason Emergence\n",
    "emergence_in_season_analytics = client.get_emergence_analytics(\n",
    "    season_duration=215,\n",
    "    season_start_day=1,\n",
    "    season_start_month=4,\n",
    "    crop=crops._2ND_CORN,\n",
    "    year=2021,\n",
    "    geometry=\"POLYGON ((-56.785919346530768 -21.208154463301554 ,  -56.79078750820733 -21.206043784434833 ,  -56.790973809206818 -21.206069651656232 ,  -56.791373799079636 -21.197107091323097 ,  -56.785129186971687 -21.196010916846863 ,  -56.781397554331065 -21.19535575112814 ,  -56.777108478217059 -21.202038412606473 ,  -56.778435977920665 -21.211398619037478 ,  -56.785919346530768 -21.208154463301554))\",\n",
    "    emergence_type=Emergence.EMERGENCE_IN_SEASON)\n",
    "\n",
    "emergence_in_season_analytics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analytics processor - Historical Emergence\n",
    "emergence_historical_analytics = client.get_emergence_analytics(\n",
    "    season_duration=215,\n",
    "    season_start_day=1,\n",
    "    season_start_month=4,\n",
    "    crop=crops._2ND_CORN,\n",
    "    year=2021,\n",
    "    geometry=\"POLYGON ((-56.785919346530768 -21.208154463301554 ,  -56.79078750820733 -21.206043784434833 ,  -56.790973809206818 -21.206069651656232 ,  -56.791373799079636 -21.197107091323097 ,  -56.785129186971687 -21.196010916846863 ,  -56.781397554331065 -21.19535575112814 ,  -56.777108478217059 -21.202038412606473 ,  -56.778435977920665 -21.211398619037478 ,  -56.785919346530768 -21.208154463301554))\",\n",
    "    emergence_type=Emergence.EMERGENCE_HISTORICAL)\n",
    "\n",
    "emergence_historical_analytics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:21:54,660 - geosyspy.services.analytics_processor_service - INFO - Retry -- Task still running\n",
      "2024-06-14 11:21:56,797 - geosyspy.services.analytics_processor_service - INFO - Retry -- Task still running\n",
      "2024-06-14 11:22:00,941 - geosyspy.services.analytics_fabric_service - INFO - Calling APIs for Latest metrics\n",
      "2024-06-14 11:22:01,103 - geosyspy.services.analytics_fabric_service - INFO - No Latest metrics found in Analytic Fabric with SchemaId: EMERGENCE_DELAY, SeasonField:2Kh2c4sGy2EkTXQv4LRWv2 \n"
     ]
    },
    {
     "data": {
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     "execution_count": 37,
     "metadata": {},
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    }
   ],
   "source": [
    "# Analytics processor - Emergence Delay\n",
    "emergence_delay_analytics = client.get_emergence_analytics(\n",
    "    season_duration=215,\n",
    "    season_start_day=1,\n",
    "    season_start_month=4,\n",
    "    crop=crops.CORN,\n",
    "    year=2021,\n",
    "    geometry=\"POLYGON ((-56.785919346530768 -21.208154463301554 ,  -56.79078750820733 -21.206043784434833 ,  -56.790973809206818 -21.206069651656232 ,  -56.791373799079636 -21.197107091323097 ,  -56.785129186971687 -21.196010916846863 ,  -56.781397554331065 -21.19535575112814 ,  -56.777108478217059 -21.202038412606473 ,  -56.778435977920665 -21.211398619037478 ,  -56.785919346530768 -21.208154463301554))\",\n",
    "    emergence_type=Emergence.EMERGENCE_DELAY)\n",
    "\n",
    "emergence_delay_analytics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "Task Status: Error, Content: b'{\"events\":[{\"level\":\"Information\",\"date\":\"2024-06-14T09:22:01.912Z\",\"source\":\"Analytics.geosys-analyticsprocessors-webapi\",\"message\":\"Processor called\",\"eventData\":{\"TaskId\":\"cde93d016b1c4625bc12b648603d2f79\",\"Error\":null}},{\"level\":\"Information\",\"date\":\"2024-06-14T09:22:01.955Z\",\"source\":\"AnalyticsProcessor.InseasonPotentialScore\",\"message\":\"Processor started\"},{\"level\":\"Error\",\"date\":\"2024-06-14T09:22:01.999Z\",\"source\":\"AnalyticsProcessor.InseasonPotentialScore\",\"message\":\"Unhandled exception\",\"eventData\":{\"error\":\"KeyError\",\"message\":\"\\'toi\\'\",\"stackTrace\":\"Traceback (most recent call last):\\\\n  File \\\\\"/cloudevents_endpoint.py\\\\\", line 109, in __start_task\\\\n    results = main(data)\\\\n  File \\\\\"/main.py\\\\\", line 19, in main\\\\n    model = get_model(input_json[\\'parameters\\'])\\\\n  File \\\\\"/main.py\\\\\", line 13, in get_model\\\\n    model = predict_model.HistoricalPotentialScore(input_json_parameters)\\\\n  File \\\\\"/historicalpotentialscore/models/predict_model.py\\\\\", line 210, in __init__\\\\n    self.load_dates()\\\\n  File \\\\\"/historicalpotentialscore/models/predict_model.py\\\\\", line 248, in load_dates\\\\n    self.season = Season(**self.parameters[\\'toi\\'][\\'season\\'])\\\\nKeyError: \\'toi\\'\\\\n\"}}],\"id\":\"cde93d016b1c4625bc12b648603d2f79\",\"taskId\":\"cde93d016b1c4625bc12b648603d2f79\",\"processorCode\":\"potential_score\",\"userId\":\"2tKecZgMyEP6EkddLxa1gV\",\"userLogin\":\"ETotain_US\",\"customerCode\":\"GEOSYS_US\",\"parametersProfile\":{\"code\":\"potential_score_default\"},\"parameters\":{\"nb_historical_years\":5,\"threshold\":0.7,\"season_duration\":200,\"season_start_day\":1,\"season_start_month\":10,\"end_date\":\"2022-03-06\"},\"status\":\"Error\",\"createdAt\":\"2024-06-14T09:22:01.654Z\",\"modifiedAt\":\"2024-06-14T09:22:01.999Z\",\"duration\":0}'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[38], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Analytics processor - Potential Score\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m potential_score_analytics \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_potential_score_analytics\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mend_date\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m2022-03-06\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnb_historical_years\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mseason_duration\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m200\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mseason_start_day\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mseason_start_month\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcrop\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mcrops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCORN\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43msowing_date\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m2021-10-01\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     10\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgeometry\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPOLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[0;32m     11\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m     13\u001b[0m potential_score_analytics\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\geosyspy\\geosys.py:1023\u001b[0m, in \u001b[0;36mGeosys.get_potential_score_analytics\u001b[1;34m(self, end_date, nb_historical_years, season_duration, season_start_day, season_start_month, sowing_date, crop, geometry, season_field_id)\u001b[0m\n\u001b[0;32m   1007\u001b[0m sf_unique_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__master_data_management_service\u001b[38;5;241m.\u001b[39mget_season_field_unique_id(\n\u001b[0;32m   1008\u001b[0m     season_field_id\n\u001b[0;32m   1009\u001b[0m )\n\u001b[0;32m   1011\u001b[0m task_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__analytics_processor_service\u001b[38;5;241m.\u001b[39mlaunch_potential_score_processor(\n\u001b[0;32m   1012\u001b[0m     end_date\u001b[38;5;241m=\u001b[39mend_date,\n\u001b[0;32m   1013\u001b[0m     nb_historical_years\u001b[38;5;241m=\u001b[39mnb_historical_years,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1020\u001b[0m     crop\u001b[38;5;241m=\u001b[39mcrop\u001b[38;5;241m.\u001b[39mvalue,\n\u001b[0;32m   1021\u001b[0m )\n\u001b[1;32m-> 1023\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheck_status_and_metrics\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPOTENTIAL_SCORE\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msf_unique_id\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\geosyspy\\geosys.py:706\u001b[0m, in \u001b[0;36mGeosys.check_status_and_metrics\u001b[1;34m(self, task_id, schema, sf_unique_id)\u001b[0m\n\u001b[0;32m    705\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcheck_status_and_metrics\u001b[39m(\u001b[38;5;28mself\u001b[39m, task_id, schema, sf_unique_id):\n\u001b[1;32m--> 706\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__analytics_processor_service\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait_and_check_task_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    707\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__analytics_fabric_service\u001b[38;5;241m.\u001b[39mget_lastest_metrics(sf_unique_id, schema)\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\retrying.py:56\u001b[0m, in \u001b[0;36mretry.<locals>.wrap.<locals>.wrapped_f\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[38;5;129m@six\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(f)\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped_f\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw):\n\u001b[1;32m---> 56\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mRetrying\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdkw\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\retrying.py:257\u001b[0m, in \u001b[0;36mRetrying.call\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    254\u001b[0m     attempt \u001b[38;5;241m=\u001b[39m Attempt(tb, attempt_number, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    256\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshould_reject(attempt):\n\u001b[1;32m--> 257\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mattempt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wrap_exception\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    259\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_after_attempts:\n\u001b[0;32m    260\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_after_attempts(attempt_number)\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\retrying.py:301\u001b[0m, in \u001b[0;36mAttempt.get\u001b[1;34m(self, wrap_exception)\u001b[0m\n\u001b[0;32m    299\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m RetryError(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m    300\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 301\u001b[0m         \u001b[43msix\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreraise\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    302\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    303\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\six.py:719\u001b[0m, in \u001b[0;36mreraise\u001b[1;34m(tp, value, tb)\u001b[0m\n\u001b[0;32m    717\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m value\u001b[38;5;241m.\u001b[39m__traceback__ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m tb:\n\u001b[0;32m    718\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m value\u001b[38;5;241m.\u001b[39mwith_traceback(tb)\n\u001b[1;32m--> 719\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m value\n\u001b[0;32m    720\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    721\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\retrying.py:251\u001b[0m, in \u001b[0;36mRetrying.call\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    248\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_before_attempts(attempt_number)\n\u001b[0;32m    250\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 251\u001b[0m     attempt \u001b[38;5;241m=\u001b[39m Attempt(\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m, attempt_number, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m    252\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m    253\u001b[0m     tb \u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39mexc_info()\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\geosyspy\\services\\analytics_processor_service.py:50\u001b[0m, in \u001b[0;36mAnalyticsProcessorService.wait_and_check_task_status\u001b[1;34m(self, task_id)\u001b[0m\n\u001b[0;32m     48\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTask still running\u001b[39m\u001b[38;5;124m\"\u001b[39m)  \u001b[38;5;66;03m# raise exception to retry\u001b[39;00m\n\u001b[0;32m     49\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 50\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTask Status: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtask_status\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Content: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresponse\u001b[38;5;241m.\u001b[39mcontent\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m )\n\u001b[0;32m     52\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m task_status\n",
      "\u001b[1;31mException\u001b[0m: Task Status: Error, Content: b'{\"events\":[{\"level\":\"Information\",\"date\":\"2024-06-14T09:22:01.912Z\",\"source\":\"Analytics.geosys-analyticsprocessors-webapi\",\"message\":\"Processor called\",\"eventData\":{\"TaskId\":\"cde93d016b1c4625bc12b648603d2f79\",\"Error\":null}},{\"level\":\"Information\",\"date\":\"2024-06-14T09:22:01.955Z\",\"source\":\"AnalyticsProcessor.InseasonPotentialScore\",\"message\":\"Processor started\"},{\"level\":\"Error\",\"date\":\"2024-06-14T09:22:01.999Z\",\"source\":\"AnalyticsProcessor.InseasonPotentialScore\",\"message\":\"Unhandled exception\",\"eventData\":{\"error\":\"KeyError\",\"message\":\"\\'toi\\'\",\"stackTrace\":\"Traceback (most recent call last):\\\\n  File \\\\\"/cloudevents_endpoint.py\\\\\", line 109, in __start_task\\\\n    results = main(data)\\\\n  File \\\\\"/main.py\\\\\", line 19, in main\\\\n    model = get_model(input_json[\\'parameters\\'])\\\\n  File \\\\\"/main.py\\\\\", line 13, in get_model\\\\n    model = predict_model.HistoricalPotentialScore(input_json_parameters)\\\\n  File \\\\\"/historicalpotentialscore/models/predict_model.py\\\\\", line 210, in __init__\\\\n    self.load_dates()\\\\n  File \\\\\"/historicalpotentialscore/models/predict_model.py\\\\\", line 248, in load_dates\\\\n    self.season = Season(**self.parameters[\\'toi\\'][\\'season\\'])\\\\nKeyError: \\'toi\\'\\\\n\"}}],\"id\":\"cde93d016b1c4625bc12b648603d2f79\",\"taskId\":\"cde93d016b1c4625bc12b648603d2f79\",\"processorCode\":\"potential_score\",\"userId\":\"2tKecZgMyEP6EkddLxa1gV\",\"userLogin\":\"ETotain_US\",\"customerCode\":\"GEOSYS_US\",\"parametersProfile\":{\"code\":\"potential_score_default\"},\"parameters\":{\"nb_historical_years\":5,\"threshold\":0.7,\"season_duration\":200,\"season_start_day\":1,\"season_start_month\":10,\"end_date\":\"2022-03-06\"},\"status\":\"Error\",\"createdAt\":\"2024-06-14T09:22:01.654Z\",\"modifiedAt\":\"2024-06-14T09:22:01.999Z\",\"duration\":0}'"
     ]
    }
   ],
   "source": [
    "# Analytics processor - Potential Score\n",
    "potential_score_analytics = client.get_potential_score_analytics(\n",
    "    end_date = \"2022-03-06\",\n",
    "    nb_historical_years = 5,\n",
    "    season_duration = 200, \n",
    "    season_start_day = 1, \n",
    "    season_start_month = 10, \n",
    "    crop = crops.CORN,\n",
    "    sowing_date = \"2021-10-01\",\n",
    "    geometry=\"POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))\"\n",
    ")\n",
    "\n",
    "potential_score_analytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:22:36,673 - geosyspy.services.analytics_processor_service - INFO - Retry -- Task still running\n",
      "2024-06-14 11:22:38,822 - geosyspy.services.analytics_fabric_service - INFO - Calling APIs for Latest metrics\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values.peak_found</th>\n",
       "      <th>Values.max_NDVI_val</th>\n",
       "      <th>Values.max_NDVI_date</th>\n",
       "      <th>Schema.Id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
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       "    <tr>\n",
       "      <th>2024-06-14T09:22:37.178Z</th>\n",
       "      <td>True</td>\n",
       "      <td>0.914025</td>\n",
       "      <td>2022-03-29</td>\n",
       "      <td>GREENNESS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Values.peak_found  Values.max_NDVI_val  \\\n",
       "date                                                               \n",
       "2024-06-14T09:22:37.178Z               True             0.914025   \n",
       "\n",
       "                         Values.max_NDVI_date  Schema.Id  \n",
       "date                                                      \n",
       "2024-06-14T09:22:37.178Z           2022-03-29  GREENNESS  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analytics processor - Greenness\n",
    "greenness_analytics = client.get_greenness_analytics(\n",
    "    start_date =\"2022-01-15\",\n",
    "    end_date = \"2022-05-31\",    \n",
    "    crop = crops.CORN,\n",
    "    sowing_date = \"2022-01-15\",\n",
    "    # WKT\n",
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    "    # GeoJson\n",
    "    #geometry='{\"type\": \"Polygon\",\"coordinates\": [[[ -54.260277780000003, -25.387777780000000 ],[ -54.260277780000003, -25.374444440000001 ],[ -54.259999999999998, -25.374166670000001 ],[ -54.259722220000000, -25.374444440000001 ],[ -54.259444440000003, -25.374444440000001 ],[ -54.258888890000001, -25.374722219999999 ],[ -54.258611110000004, -25.374722219999999 ],[ -54.258888890000001, -25.375000000000000 ],[ -54.258888890000001, -25.375555550000001 ],[ -54.258611110000004, -25.376111110000000 ],[ -54.258611110000004, -25.381944440000002 ],[ -54.258333329999999, -25.384166669999999 ],[ -54.256944439999998, -25.383611110000000 ],[ -54.256944439999998, -25.384166669999999 ],[ -54.257500000000000, -25.384166669999999 ],[ -54.257500000000000, -25.384444439999999 ],[ -54.257777779999998, -25.384166669999999 ],[ -54.258070160000003, -25.384158120000002 ],[ -54.258055560000003, -25.384444439999999 ],[ -54.258077300000004, -25.384722060000001 ],[ -54.257500000000000, -25.385277779999999 ],[ -54.256944439999998, -25.385000000000002 ],[ -54.256388890000004, -25.383611110000000 ],[ -54.254722219999998, -25.383055550000002 ],[ -54.254722219999998, -25.382500000000000 ],[ -54.254166670000004, -25.381944440000002 ],[ -54.254444440000000, -25.381666670000001 ],[ -54.254722219999998, -25.381666670000001 ],[ -54.254722219999998, -25.379444440000000 ],[ -54.252777780000002, -25.379444440000000 ],[ -54.252777780000002, -25.385833330000001 ],[ -54.254192230000001, -25.386153900000000 ],[ -54.253906700000002, -25.385892160000001 ],[ -54.253888889999999, -25.385000000000002 ],[ -54.254444440000000, -25.385555549999999 ],[ -54.254787100000001, -25.385820770000002 ],[ -54.254722219999998, -25.386111110000002 ],[ -54.260277780000003, -25.387777780000000 ]]]}',\n",
    ")\n",
    "\n",
    "greenness_analytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:22:40,746 - geosyspy.services.analytics_processor_service - INFO - Retry -- Task still running\n",
      "2024-06-14 11:22:43,005 - geosyspy.services.analytics_processor_service - INFO - Retry -- Task still running\n",
      "2024-06-14 11:22:47,205 - geosyspy.services.analytics_fabric_service - INFO - Calling APIs for Latest metrics\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",
       "        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>Values.date</th>\n",
       "      <th>Schema.Id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-06-14T09:22:43.483Z</th>\n",
       "      <td>2022-05-10</td>\n",
       "      <td>HARVEST_READINESS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Values.date          Schema.Id\n",
       "date                                                   \n",
       "2024-06-14T09:22:43.483Z  2022-05-10  HARVEST_READINESS"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analytics processor - Harvest Readiness\n",
    "harvest_readiness_analytics = client.get_harvest_readiness_analytics(\n",
    "    start_date =\"2022-01-15\",\n",
    "    end_date = \"2022-05-31\",    \n",
    "    crop = crops.CORN,\n",
    "    sowing_date = \"2022-01-15\",\n",
    "    geometry=\"POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))\"\n",
    ")\n",
    "\n",
    "harvest_readiness_analytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:22:48,424 - geosyspy.services.analytics_processor_service - INFO - Retry -- Task still running\n"
     ]
    },
    {
     "ename": "Exception",
     "evalue": "Task Status: Error, Content: b'{\"events\":[{\"level\":\"Information\",\"date\":\"2024-06-14T09:22:48.124Z\",\"source\":\"Analytics.geosys-analyticsprocessors-webapi\",\"message\":\"Processor called\",\"eventData\":{\"TaskId\":\"4518fc68b58f45978ff1f5f4c93ddc0b\",\"Error\":null}},{\"level\":\"Information\",\"date\":\"2024-06-14T09:22:48.141Z\",\"source\":\"AnalyticsProcessor.PlantedArea\",\"message\":\"Processor started\"},{\"level\":\"Information\",\"date\":\"2024-06-14T09:22:49.861Z\",\"source\":\"AnalyticsProcessor.PlantedArea\",\"message\":\"Computation finished\"},{\"level\":\"Information\",\"date\":\"2024-06-14T09:22:49.871Z\",\"source\":\"AnalyticsProcessor.PlantedArea\",\"message\":\"Format results for the callback\"},{\"level\":\"Information\",\"date\":\"2024-06-14T09:22:49.881Z\",\"source\":\"AnalyticsProcessor.PlantedArea\",\"message\":\"Callback called\"},{\"level\":\"Error\",\"date\":\"2024-06-14T09:22:49.957Z\",\"source\":\"AnalyticsProcessor.PlantedArea\",\"message\":\"Error while sending results to Analytics Fabric\",\"eventData\":{\"error\":\"{\\\\n  \\\\\"Errors\\\\\": {\\\\n    \\\\\"\\\\\": {\\\\n      \\\\\"\\\\\": [\\\\n        {\\\\n          \\\\\"Code\\\\\": \\\\\"BadRequest\\\\\",\\\\n          \\\\\"Message\\\\\": \\\\\"Property \\'planted_area\\' with value \\'\\' cannot be null. Property \\'planted_percentage\\' with value \\'\\' cannot be null. \\\\\"\\\\n        }\\\\n      ]\\\\n    }\\\\n  },\\\\n  \\\\\"Code\\\\\": \\\\\"model_validation_error\\\\\",\\\\n  \\\\\"Message\\\\\": \\\\\"Bad request: see the Errors field for details\\\\\"\\\\n}\"}},{\"level\":\"Error\",\"date\":\"2024-06-14T09:22:49.978Z\",\"source\":\"AnalyticsProcessor.PlantedArea\",\"message\":\"Unhandled exception\",\"eventData\":{\"error\":\"Exception\",\"message\":\"Error while sending results to Analytics Fabric\",\"stackTrace\":\"Traceback (most recent call last):\\\\n  File \\\\\"/cloudevents_endpoint.py\\\\\", line 112, in __start_task\\\\n    __process_callback(results, context)\\\\n  File \\\\\"/cloudevents_endpoint.py\\\\\", line 133, in __process_callback\\\\n    cloudevent_endpoint_callback(formatted_results, context)\\\\n  File \\\\\"/cloudevents_endpoint_callback.py\\\\\", line 22, in cloudevent_endpoint_callback\\\\n    __send_to_analytics_fabrik(process_results, context)\\\\n  File \\\\\"/cloudevents_endpoint_callback.py\\\\\", line 52, in __send_to_analytics_fabrik\\\\n    raise Exception(\\\\\"Error while sending results to Analytics Fabric\\\\\")\\\\nException: Error while sending results to Analytics Fabric\\\\n\"}}],\"id\":\"4518fc68b58f45978ff1f5f4c93ddc0b\",\"taskId\":\"4518fc68b58f45978ff1f5f4c93ddc0b\",\"processorCode\":\"planted_area\",\"userId\":\"2tKecZgMyEP6EkddLxa1gV\",\"userLogin\":\"ETotain_US\",\"customerCode\":\"GEOSYS_US\",\"parametersProfile\":{\"code\":\"planted_area_default\"},\"parameters\":{\"threshold\":30},\"status\":\"Error\",\"createdAt\":\"2024-06-14T09:22:48.048Z\",\"modifiedAt\":\"2024-06-14T09:22:49.978Z\",\"duration\":1}'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[41], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Analytics processor - Planted area\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m planted_area_analytics \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_planted_area_analytics\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstart_date\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m2022-01-15\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mend_date\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m2022-05-31\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m        \u001b[49m\n\u001b[0;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgeometry\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPOLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[0;32m      6\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m      8\u001b[0m planted_area_analytics\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\geosyspy\\geosys.py:1159\u001b[0m, in \u001b[0;36mGeosys.get_planted_area_analytics\u001b[1;34m(self, start_date, end_date, geometry, season_field_id)\u001b[0m\n\u001b[0;32m   1152\u001b[0m sf_unique_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__master_data_management_service\u001b[38;5;241m.\u001b[39mget_season_field_unique_id(\n\u001b[0;32m   1153\u001b[0m     season_field_id\n\u001b[0;32m   1154\u001b[0m )\n\u001b[0;32m   1156\u001b[0m task_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__analytics_processor_service\u001b[38;5;241m.\u001b[39mlaunch_planted_area_processor(\n\u001b[0;32m   1157\u001b[0m     start_date, end_date, sf_unique_id\n\u001b[0;32m   1158\u001b[0m )\n\u001b[1;32m-> 1159\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheck_status_and_metrics\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPLANTED_AREA\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msf_unique_id\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\geosyspy\\geosys.py:706\u001b[0m, in \u001b[0;36mGeosys.check_status_and_metrics\u001b[1;34m(self, task_id, schema, sf_unique_id)\u001b[0m\n\u001b[0;32m    705\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcheck_status_and_metrics\u001b[39m(\u001b[38;5;28mself\u001b[39m, task_id, schema, sf_unique_id):\n\u001b[1;32m--> 706\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__analytics_processor_service\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait_and_check_task_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    707\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__analytics_fabric_service\u001b[38;5;241m.\u001b[39mget_lastest_metrics(sf_unique_id, schema)\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\retrying.py:56\u001b[0m, in \u001b[0;36mretry.<locals>.wrap.<locals>.wrapped_f\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[38;5;129m@six\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(f)\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped_f\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw):\n\u001b[1;32m---> 56\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mRetrying\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdkw\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\retrying.py:257\u001b[0m, in \u001b[0;36mRetrying.call\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    254\u001b[0m     attempt \u001b[38;5;241m=\u001b[39m Attempt(tb, attempt_number, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    256\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshould_reject(attempt):\n\u001b[1;32m--> 257\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mattempt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wrap_exception\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    259\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_after_attempts:\n\u001b[0;32m    260\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_after_attempts(attempt_number)\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\retrying.py:301\u001b[0m, in \u001b[0;36mAttempt.get\u001b[1;34m(self, wrap_exception)\u001b[0m\n\u001b[0;32m    299\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m RetryError(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m    300\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 301\u001b[0m         \u001b[43msix\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreraise\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    302\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    303\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\six.py:719\u001b[0m, in \u001b[0;36mreraise\u001b[1;34m(tp, value, tb)\u001b[0m\n\u001b[0;32m    717\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m value\u001b[38;5;241m.\u001b[39m__traceback__ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m tb:\n\u001b[0;32m    718\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m value\u001b[38;5;241m.\u001b[39mwith_traceback(tb)\n\u001b[1;32m--> 719\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m value\n\u001b[0;32m    720\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    721\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\retrying.py:251\u001b[0m, in \u001b[0;36mRetrying.call\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    248\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_before_attempts(attempt_number)\n\u001b[0;32m    250\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 251\u001b[0m     attempt \u001b[38;5;241m=\u001b[39m Attempt(\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m, attempt_number, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m    252\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m    253\u001b[0m     tb \u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39mexc_info()\n",
      "File \u001b[1;32mc:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\geosyspy\\services\\analytics_processor_service.py:50\u001b[0m, in \u001b[0;36mAnalyticsProcessorService.wait_and_check_task_status\u001b[1;34m(self, task_id)\u001b[0m\n\u001b[0;32m     48\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTask still running\u001b[39m\u001b[38;5;124m\"\u001b[39m)  \u001b[38;5;66;03m# raise exception to retry\u001b[39;00m\n\u001b[0;32m     49\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 50\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTask Status: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtask_status\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Content: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresponse\u001b[38;5;241m.\u001b[39mcontent\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m )\n\u001b[0;32m     52\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m task_status\n",
      "\u001b[1;31mException\u001b[0m: Task Status: Error, Content: b'{\"events\":[{\"level\":\"Information\",\"date\":\"2024-06-14T09:22:48.124Z\",\"source\":\"Analytics.geosys-analyticsprocessors-webapi\",\"message\":\"Processor called\",\"eventData\":{\"TaskId\":\"4518fc68b58f45978ff1f5f4c93ddc0b\",\"Error\":null}},{\"level\":\"Information\",\"date\":\"2024-06-14T09:22:48.141Z\",\"source\":\"AnalyticsProcessor.PlantedArea\",\"message\":\"Processor started\"},{\"level\":\"Information\",\"date\":\"2024-06-14T09:22:49.861Z\",\"source\":\"AnalyticsProcessor.PlantedArea\",\"message\":\"Computation finished\"},{\"level\":\"Information\",\"date\":\"2024-06-14T09:22:49.871Z\",\"source\":\"AnalyticsProcessor.PlantedArea\",\"message\":\"Format results for the callback\"},{\"level\":\"Information\",\"date\":\"2024-06-14T09:22:49.881Z\",\"source\":\"AnalyticsProcessor.PlantedArea\",\"message\":\"Callback called\"},{\"level\":\"Error\",\"date\":\"2024-06-14T09:22:49.957Z\",\"source\":\"AnalyticsProcessor.PlantedArea\",\"message\":\"Error while sending results to Analytics Fabric\",\"eventData\":{\"error\":\"{\\\\n  \\\\\"Errors\\\\\": {\\\\n    \\\\\"\\\\\": {\\\\n      \\\\\"\\\\\": [\\\\n        {\\\\n          \\\\\"Code\\\\\": \\\\\"BadRequest\\\\\",\\\\n          \\\\\"Message\\\\\": \\\\\"Property \\'planted_area\\' with value \\'\\' cannot be null. Property \\'planted_percentage\\' with value \\'\\' cannot be null. \\\\\"\\\\n        }\\\\n      ]\\\\n    }\\\\n  },\\\\n  \\\\\"Code\\\\\": \\\\\"model_validation_error\\\\\",\\\\n  \\\\\"Message\\\\\": \\\\\"Bad request: see the Errors field for details\\\\\"\\\\n}\"}},{\"level\":\"Error\",\"date\":\"2024-06-14T09:22:49.978Z\",\"source\":\"AnalyticsProcessor.PlantedArea\",\"message\":\"Unhandled exception\",\"eventData\":{\"error\":\"Exception\",\"message\":\"Error while sending results to Analytics Fabric\",\"stackTrace\":\"Traceback (most recent call last):\\\\n  File \\\\\"/cloudevents_endpoint.py\\\\\", line 112, in __start_task\\\\n    __process_callback(results, context)\\\\n  File \\\\\"/cloudevents_endpoint.py\\\\\", line 133, in __process_callback\\\\n    cloudevent_endpoint_callback(formatted_results, context)\\\\n  File \\\\\"/cloudevents_endpoint_callback.py\\\\\", line 22, in cloudevent_endpoint_callback\\\\n    __send_to_analytics_fabrik(process_results, context)\\\\n  File \\\\\"/cloudevents_endpoint_callback.py\\\\\", line 52, in __send_to_analytics_fabrik\\\\n    raise Exception(\\\\\"Error while sending results to Analytics Fabric\\\\\")\\\\nException: Error while sending results to Analytics Fabric\\\\n\"}}],\"id\":\"4518fc68b58f45978ff1f5f4c93ddc0b\",\"taskId\":\"4518fc68b58f45978ff1f5f4c93ddc0b\",\"processorCode\":\"planted_area\",\"userId\":\"2tKecZgMyEP6EkddLxa1gV\",\"userLogin\":\"ETotain_US\",\"customerCode\":\"GEOSYS_US\",\"parametersProfile\":{\"code\":\"planted_area_default\"},\"parameters\":{\"threshold\":30},\"status\":\"Error\",\"createdAt\":\"2024-06-14T09:22:48.048Z\",\"modifiedAt\":\"2024-06-14T09:22:49.978Z\",\"duration\":1}'"
     ]
    }
   ],
   "source": [
    "# Analytics processor - Planted area\n",
    "planted_area_analytics = client.get_planted_area_analytics(\n",
    "    start_date =\"2022-01-15\",\n",
    "    end_date = \"2022-05-31\",        \n",
    "    geometry=\"POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))\"\n",
    ")\n",
    "\n",
    "planted_area_analytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ajd\\Documents\\GitHub\\GeosysPy\\.venv\\Lib\\site-packages\\urllib3\\connectionpool.py:1100: InsecureRequestWarning: Unverified HTTPS request is being made to host 'gis-services-pp.geosys.com'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#tls-warnings\n",
      "  warnings.warn(\n",
      "2024-06-14 11:23:12,693 - geosyspy.services.analytics_processor_service - INFO - Retry -- Task still running\n",
      "2024-06-14 11:23:14,847 - geosyspy.services.analytics_processor_service - INFO - Retry -- Task still running\n",
      "2024-06-14 11:23:19,041 - geosyspy.services.analytics_fabric_service - INFO - Calling APIs for Latest metrics\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values.emergence_date</th>\n",
       "      <th>Values.sowing_date</th>\n",
       "      <th>Values.zarc_start_date</th>\n",
       "      <th>Values.zarc_end_date</th>\n",
       "      <th>Values.status</th>\n",
       "      <th>Schema.Id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-06-14T09:23:14Z</th>\n",
       "      <td>2022-02-17</td>\n",
       "      <td>2022-01-28</td>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>False</td>\n",
       "      <td>ZARC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Values.emergence_date Values.sowing_date  \\\n",
       "date                                                            \n",
       "2024-06-14T09:23:14Z            2022-02-17         2022-01-28   \n",
       "\n",
       "                     Values.zarc_start_date Values.zarc_end_date  \\\n",
       "date                                                               \n",
       "2024-06-14T09:23:14Z             2023-08-01           2023-12-31   \n",
       "\n",
       "                      Values.status Schema.Id  \n",
       "date                                           \n",
       "2024-06-14T09:23:14Z          False      ZARC  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analytics processor - zarc\n",
    "zarc_analytics = client.get_zarc_analytics(\n",
    "    start_date_emergence= \"2022-01-15\",\n",
    "    end_date_emergence = \"2022-05-31\",\n",
    "    nb_days_sowing_emergence = 20,\n",
    "    crop =crops.CORN,    \n",
    "    soil_type = ZarcSoilType.NONE,\n",
    "    cycle = ZarcCycleType.NONE,    \n",
    "    geometry=\"POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))\"\n",
    ")\n",
    "\n",
    "zarc_analytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-14 11:23:25,705 - geosyspy.services.analytics_processor_service - INFO - Retry -- Task still running\n",
      "2024-06-14 11:23:27,853 - geosyspy.services.analytics_processor_service - INFO - Retry -- Task still running\n",
      "2024-06-14 11:23:32,057 - geosyspy.services.analytics_processor_service - INFO - Retry -- Task still running\n",
      "2024-06-14 11:23:40,241 - geosyspy.services.analytics_fabric_service - INFO - Calling APIs for Latest metrics\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values.crop_code</th>\n",
       "      <th>Values.crop_probability</th>\n",
       "      <th>Schema.Id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-06-14T09:23:32.87Z</th>\n",
       "      <td>SOYBEANS</td>\n",
       "      <td>{\"CORN\": 0.053, \"SOYBEANS\": 0.933, \"SUGARCANE\"...</td>\n",
       "      <td>CROP_IDENTIFICATION</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Values.crop_code  \\\n",
       "date                                       \n",
       "2024-06-14T09:23:32.87Z         SOYBEANS   \n",
       "\n",
       "                                                   Values.crop_probability  \\\n",
       "date                                                                         \n",
       "2024-06-14T09:23:32.87Z  {\"CORN\": 0.053, \"SOYBEANS\": 0.933, \"SUGARCANE\"...   \n",
       "\n",
       "                                   Schema.Id  \n",
       "date                                          \n",
       "2024-06-14T09:23:32.87Z  CROP_IDENTIFICATION  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analytics processor - crop id\n",
    "brazil_crop_id_analytics = client.get_brazil_crop_id_analytics(\n",
    "    start_date =\"2020-10-01\",\n",
    "    end_date = \"2021-05-31\",    \n",
    "    season = CropIdSeason.SEASON_1,\n",
    "    geometry=\"POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))\"\n",
    ")\n",
    "\n",
    "brazil_crop_id_analytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CORN</th>\n",
       "      <th>SOYBEANS</th>\n",
       "      <th>SUGARCANE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.053</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CORN  SOYBEANS  SUGARCANE\n",
       "0  0.053     0.933      0.013"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Display probability by crop value from last zarc analytics result\n",
    "data_dict = eval(brazil_crop_id_analytics.values[0][1])\n",
    "df_new = pd.DataFrame.from_records([data_dict])\n",
    "df_new\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download ok\n"
     ]
    }
   ],
   "source": [
    "# display difference map\n",
    "season_field_id = \"bgbrzez\"\n",
    "image_early = \"sentinel-2-l2a%7CS2B_13SGC_20230520_0_L2A\"\n",
    "image_late = \"sentinel-2-l2a%7CS2B_13SGC_20230530_0_L2A\"\n",
    "image_diff = client.download_image_difference_map(season_field_id=season_field_id,image_id_earliest=image_early,image_id_latest=image_late)\n",
    "if image_diff is not None:\n",
    "    print(\"download ok\")\n",
    "else:\n",
    "    print(\"download nok\")"
   ]
  }
 ],
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