{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "73e934a8-9027-4ad1-ad15-e68fba847768",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Anmelden und token holen\n",
    "from farmOS import farmOS\n",
    "\n",
    "hostname = \"farmos.wagframe.duckdns.org\"\n",
    "username = \"matze\"\n",
    "password = \"m6ChEHx5gMqgctr8dpb3fhATZbQS8hv4\"\n",
    "\n",
    "# Create the client.\n",
    "farm_client = farmOS(\n",
    "    hostname=hostname,\n",
    "    client_id = \"jupyter\", # Optional. The default oauth client_id \"farm\" is enabled on all farmOS servers.\n",
    ")\n",
    "import json\n",
    "# Authorize the client, save the token.\n",
    "# A scope can be specified, but will default to the default scope set when initializing the client.\n",
    "token = farm_client.authorize(username, password)\n",
    "#farm_client.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcf25e45-f887-4fd5-aed8-44090ed75680",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Excel Export\n",
    "import os\n",
    "# Definiere den Basispfad und den Dateinamen\n",
    "base_file_path = '/Users/Wir/Downloads/assets'\n",
    "file_extension = '.xlsx'\n",
    "file_index = 1\n",
    "file_path = f\"{base_file_path}_{file_index}{file_extension}\"\n",
    "\n",
    "# Überprüfe, ob die Datei bereits existiert und erhöhe den Index\n",
    "while os.path.exists(file_path):\n",
    "    file_index += 1\n",
    "    file_path = f\"{base_file_path}_{file_index}{file_extension}\"\n",
    "\n",
    "# Exportiere den DataFrame in eine Excel-Datei\n",
    "df.to_excel(file_path, index=False)\n",
    "print(f\"Die Datei wurde erfolgreich unter '{file_path}' gespeichert.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6022671-c929-4dbb-b60c-8d0ce17a1663",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#API Info\n",
    "\n",
    "info = farm_client.info()\n",
    "info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca74d822-4f5c-46d7-8129-31bbf28a1e00",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Zeige alle \"Land\" Daten\n",
    "import pandas as pd\n",
    "from shapely.geometry import Polygon\n",
    "from shapely import wkt  # for converting WKT string to shapely Polygon\n",
    "from pyproj import CRS, Transformer  # for coordinate transformation (if necessary)\n",
    "\n",
    "# Assuming this is your response\n",
    "response = farm_client.asset.get('land')\n",
    "all_land_assets = response['data']\n",
    "\n",
    "# Function to calculate area in hectares\n",
    "def calculate_area(geometry_wkt):\n",
    "    # Convert WKT (Well-Known Text) to Polygon\n",
    "    polygon = wkt.loads(geometry_wkt)\n",
    "\n",
    "    # Define the CRS (Coordinate Reference System) for the land's coordinates\n",
    "    crs_wgs84 = CRS(\"EPSG:4326\")  # WGS 84, latitude-longitude\n",
    "    crs_projected = CRS(\"EPSG:25832\")  # A projected CRS for accurate area calculation\n",
    "\n",
    "    # Transform coordinates to a projected CRS for area calculation\n",
    "    transformer = Transformer.from_crs(crs_wgs84, crs_projected, always_xy=True)\n",
    "    projected_polygon = Polygon([transformer.transform(*coord) for coord in polygon.exterior.coords])\n",
    "\n",
    "    # Calculate the area in square meters and convert to hectares\n",
    "    area_sq_meters = projected_polygon.area\n",
    "    area_hectares = area_sq_meters / 10_000  # 1 hectare = 10,000 square meters\n",
    "\n",
    "    # Round to 4 decimal places\n",
    "    return round(area_hectares, 4)\n",
    "\n",
    "\n",
    "land_asset_data = [\n",
    "    {\n",
    "        \"Name\": asset[\"attributes\"][\"name\"],\n",
    "        \"Status\": asset[\"attributes\"][\"status\"],\n",
    "        \"Created\": pd.to_datetime(asset[\"attributes\"][\"created\"]).strftime('%d.%m.%Y'),\n",
    "        \"Area (ha)\": calculate_area(asset[\"attributes\"][\"intrinsic_geometry\"][\"value\"]),\n",
    "        \"Asset type\": asset[\"type\"],\n",
    "        \"Is fixed\": asset[\"attributes\"][\"is_fixed\"],\n",
    "        \"land_type\": asset[\"attributes\"][\"land_type\"],\n",
    "        \"Is location\": asset[\"attributes\"][\"is_location\"]\n",
    "    }\n",
    "    for asset in all_land_assets\n",
    "]\n",
    "\n",
    "# Create a pandas DataFrame from the extracted data\n",
    "df = pd.DataFrame(land_asset_data)\n",
    "\n",
    "# Setze Pandas-Optionen für eine bessere Anzeige\n",
    "pd.set_option('display.max_columns', None)  # Alle Spalten anzeigen\n",
    "pd.set_option('display.max_colwidth', 50)   # Maximale Spaltenbreite setzen\n",
    "pd.set_option('display.width', 1000)        # Maximale Breite der Tabelle\n",
    "\n",
    "# Zeige die Tabelle an\n",
    "df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33c1b024-fb74-472c-b229-36d8041b1336",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Zeige alle \"Plant\" Daten\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "# Assuming this is your response\n",
    "response = farm_client.asset.get('plant')\n",
    "all_plant_assets = response['data']\n",
    "\n",
    "\n",
    "\n",
    "plant_asset_data = [\n",
    "    {\n",
    "        \"Name\": asset[\"attributes\"][\"name\"],\n",
    "        \"Status\": asset[\"attributes\"][\"status\"],\n",
    "        \"Created\": pd.to_datetime(asset[\"attributes\"][\"created\"]).strftime('%d.%m.%Y'),\n",
    "        \n",
    "    }\n",
    "    for asset in all_plant_assets\n",
    "]\n",
    "\n",
    "# Create a pandas DataFrame from the extracted data\n",
    "df = pd.DataFrame(plant_asset_data)\n",
    "\n",
    "# Setze Pandas-Optionen für eine bessere Anzeige\n",
    "pd.set_option('display.max_columns', None)  # Alle Spalten anzeigen\n",
    "pd.set_option('display.max_colwidth', 50)   # Maximale Spaltenbreite setzen\n",
    "pd.set_option('display.width', 1000)        # Maximale Breite der Tabelle\n",
    "\n",
    "# Zeige die Tabelle an\n",
    "df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d64e0dc4-a685-4a42-810c-cafd8f43b988",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Zeige alle \"equipment\" Daten\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "# Assuming this is your response\n",
    "response = farm_client.asset.get('equipment')\n",
    "all_equipment_assets = response['data']\n",
    "\n",
    "\n",
    "\n",
    "equipment_asset_data = [\n",
    "    {\n",
    "        \"Name\": asset[\"attributes\"][\"name\"],\n",
    "        \"Status\": asset[\"attributes\"][\"status\"],\n",
    "        \"Created\": pd.to_datetime(asset[\"attributes\"][\"created\"]).strftime('%d.%m.%Y'),\n",
    "        \n",
    "    }\n",
    "    for asset in all_equipment_assets\n",
    "]\n",
    "\n",
    "# Create a pandas DataFrame from the extracted data\n",
    "df = pd.DataFrame(equipment_asset_data)\n",
    "\n",
    "# Setze Pandas-Optionen für eine bessere Anzeige\n",
    "pd.set_option('display.max_columns', None)  # Alle Spalten anzeigen\n",
    "pd.set_option('display.max_colwidth', 50)   # Maximale Spaltenbreite setzen\n",
    "pd.set_option('display.width', 1000)        # Maximale Breite der Tabelle\n",
    "\n",
    "# Zeige die Tabelle an\n",
    "df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "718ed35b-39f5-4732-8967-8f204e41f653",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Zeige alle \"material\" Daten\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "# Assuming this is your response\n",
    "response = farm_client.asset.get('material')\n",
    "all_material_assets = response['data']\n",
    "\n",
    "\n",
    "\n",
    "material_asset_data = [\n",
    "    {\n",
    "        \"Name\": asset[\"attributes\"][\"name\"],\n",
    "        \"Status\": asset[\"attributes\"][\"status\"],\n",
    "        \"Created\": pd.to_datetime(asset[\"attributes\"][\"created\"]).strftime('%d.%m.%Y'),\n",
    "        \n",
    "    }\n",
    "    for asset in all_material_assets\n",
    "]\n",
    "\n",
    "# Create a pandas DataFrame from the extracted data\n",
    "df = pd.DataFrame(material_asset_data)\n",
    "\n",
    "# Setze Pandas-Optionen für eine bessere Anzeige\n",
    "pd.set_option('display.max_columns', None)  # Alle Spalten anzeigen\n",
    "pd.set_option('display.max_colwidth', 50)   # Maximale Spaltenbreite setzen\n",
    "pd.set_option('display.width', 1000)        # Maximale Breite der Tabelle\n",
    "\n",
    "# Zeige die Tabelle an\n",
    "df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e3ac788-1146-4aa3-a155-4f5d0c16ef02",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Zeige alle \"product\" Daten\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "# Assuming this is your response\n",
    "response = farm_client.asset.get('product')\n",
    "all_product_assets = response['data']\n",
    "\n",
    "\n",
    "\n",
    "product_asset_data = [\n",
    "    {\n",
    "        \"Name\": asset[\"attributes\"][\"name\"],\n",
    "        \"Status\": asset[\"attributes\"][\"status\"],\n",
    "        \"Created\": pd.to_datetime(asset[\"attributes\"][\"created\"]).strftime('%d.%m.%Y'),\n",
    "        \n",
    "    }\n",
    "    for asset in all_product_assets\n",
    "]\n",
    "\n",
    "# Create a pandas DataFrame from the extracted data\n",
    "df = pd.DataFrame(product_asset_data)\n",
    "\n",
    "# Setze Pandas-Optionen für eine bessere Anzeige\n",
    "pd.set_option('display.max_columns', None)  # Alle Spalten anzeigen\n",
    "pd.set_option('display.max_colwidth', 50)   # Maximale Spaltenbreite setzen\n",
    "pd.set_option('display.width', 1000)        # Maximale Breite der Tabelle\n",
    "\n",
    "# Zeige die Tabelle an\n",
    "df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96784d50-f9bf-43a2-ae95-1e53f9c8ffb4",
   "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>Name</th>\n",
       "      <th>Status</th>\n",
       "      <th>Created</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>W-Raps Saatgut Otello KWS Seed 08/24</td>\n",
       "      <td>active</td>\n",
       "      <td>14.10.2024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Name  Status     Created\n",
       "0  W-Raps Saatgut Otello KWS Seed 08/24  active  14.10.2024"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Zeige alle \"seed\" Daten\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "# Assuming this is your response\n",
    "response = farm_client.asset.get('seed')\n",
    "all_seed_assets = response['data']\n",
    "\n",
    "\n",
    "\n",
    "seed_asset_data = [\n",
    "    {\n",
    "        \"Name\": asset[\"attributes\"][\"name\"],\n",
    "        \"Status\": asset[\"attributes\"][\"status\"],\n",
    "        \"Created\": pd.to_datetime(asset[\"attributes\"][\"created\"]).strftime('%d.%m.%Y'),\n",
    "        \n",
    "    }\n",
    "    for asset in all_seed_assets\n",
    "]\n",
    "\n",
    "# Create a pandas DataFrame from the extracted data\n",
    "df = pd.DataFrame(seed_asset_data)\n",
    "\n",
    "# Setze Pandas-Optionen für eine bessere Anzeige\n",
    "pd.set_option('display.max_columns', None)  # Alle Spalten anzeigen\n",
    "pd.set_option('display.max_colwidth', 50)   # Maximale Spaltenbreite setzen\n",
    "pd.set_option('display.width', 1000)        # Maximale Breite der Tabelle\n",
    "\n",
    "# Zeige die Tabelle an\n",
    "df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22b12932-9e79-4cfe-ab03-9a76d93a7a3f",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Zeige alle \"harvest\" Daten\n",
    "import pandas as pd\n",
    "\n",
    "response = farm_client.log.get('harvest')\n",
    "all_harvest_assets = response['data']\n",
    "\n",
    "harvest_asset_data = [\n",
    "    {\n",
    "        \"Name\": asset[\"attributes\"][\"name\"],\n",
    "        \"Status\": asset[\"attributes\"][\"status\"],\n",
    "         \"Timestamp\": pd.to_datetime(asset[\"attributes\"][\"timestamp\"]).strftime('%d.%m.%Y'),\n",
    "        \n",
    "    }\n",
    "    for asset in all_harvest_assets\n",
    "]\n",
    "\n",
    "# Create a pandas DataFrame from the extracted data\n",
    "df = pd.DataFrame(harvest_asset_data)\n",
    "\n",
    "# Setze Pandas-Optionen für eine bessere Anzeige\n",
    "pd.set_option('display.max_columns', None)  # Alle Spalten anzeigen\n",
    "pd.set_option('display.max_colwidth', 50)   # Maximale Spaltenbreite setzen\n",
    "pd.set_option('display.width', 1000)        # Maximale Breite der Tabelle\n",
    "\n",
    "# Zeige die Tabelle an\n",
    "df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d6a676c-94cb-4d9e-8c3b-24a40be73dea",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Zeige alle \"activity\" Daten\n",
    "import pandas as pd\n",
    "\n",
    "response = farm_client.log.get('activity')\n",
    "all_activity_assets = response['data']\n",
    "\n",
    "activity_asset_data = [\n",
    "    {\n",
    "        \"Name\": asset[\"attributes\"][\"name\"],\n",
    "        \"Status\": asset[\"attributes\"][\"status\"],\n",
    "         \"Timestamp\": pd.to_datetime(asset[\"attributes\"][\"timestamp\"]).strftime('%d.%m.%Y'),\n",
    "        \n",
    "    }\n",
    "    for asset in all_activity_assets\n",
    "]\n",
    "\n",
    "# Create a pandas DataFrame from the extracted data\n",
    "df = pd.DataFrame(activity_asset_data)\n",
    "\n",
    "# Setze Pandas-Optionen für eine bessere Anzeige\n",
    "pd.set_option('display.max_columns', None)  # Alle Spalten anzeigen\n",
    "pd.set_option('display.max_colwidth', 50)   # Maximale Spaltenbreite setzen\n",
    "pd.set_option('display.width', 1000)        # Maximale Breite der Tabelle\n",
    "\n",
    "# Zeige die Tabelle an\n",
    "df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dab14bd-e7e6-4a5f-b838-c6498fb20f23",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Zeige alle \"input\" Daten\n",
    "import pandas as pd\n",
    "\n",
    "response = farm_client.log.get('input')\n",
    "all_input_assets = response['data']\n",
    "\n",
    "input_asset_data = [\n",
    "    {\n",
    "        \"Name\": asset[\"attributes\"][\"name\"],\n",
    "        \"Status\": asset[\"attributes\"][\"status\"],\n",
    "         \"Timestamp\": pd.to_datetime(asset[\"attributes\"][\"timestamp\"]).strftime('%d.%m.%Y'),\n",
    "        \n",
    "    }\n",
    "    for asset in all_input_assets\n",
    "]\n",
    "\n",
    "# Create a pandas DataFrame from the extracted data\n",
    "df = pd.DataFrame(input_asset_data)\n",
    "\n",
    "# Setze Pandas-Optionen für eine bessere Anzeige\n",
    "pd.set_option('display.max_columns', None)  # Alle Spalten anzeigen\n",
    "pd.set_option('display.max_colwidth', 50)   # Maximale Spaltenbreite setzen\n",
    "pd.set_option('display.width', 1000)        # Maximale Breite der Tabelle\n",
    "\n",
    "# Zeige die Tabelle an\n",
    "df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "625d3e17-635c-4775-94e7-a0179cf690de",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Zeige alle \"maintenance\" Daten\n",
    "import pandas as pd\n",
    "\n",
    "response = farm_client.log.get('maintenance')\n",
    "all_maintenance_assets = response['data']\n",
    "\n",
    "maintenance_asset_data = [\n",
    "    {\n",
    "        \"Name\": asset[\"attributes\"][\"name\"],\n",
    "        \"Status\": asset[\"attributes\"][\"status\"],\n",
    "         \"Timestamp\": pd.to_datetime(asset[\"attributes\"][\"timestamp\"]).strftime('%d.%m.%Y'),\n",
    "        \n",
    "    }\n",
    "    for asset in all_maintenance_assets\n",
    "]\n",
    "\n",
    "# Create a pandas DataFrame from the extracted data\n",
    "df = pd.DataFrame(maintenance_asset_data)\n",
    "\n",
    "# Setze Pandas-Optionen für eine bessere Anzeige\n",
    "pd.set_option('display.max_columns', None)  # Alle Spalten anzeigen\n",
    "pd.set_option('display.max_colwidth', 50)   # Maximale Spaltenbreite setzen\n",
    "pd.set_option('display.width', 1000)        # Maximale Breite der Tabelle\n",
    "\n",
    "# Zeige die Tabelle an\n",
    "df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bad0e10e-a112-45b0-a454-672348389dea",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Zeige alle \"medical\" Daten\n",
    "import pandas as pd\n",
    "\n",
    "response = farm_client.log.get('medical')\n",
    "all_medical_assets = response['data']\n",
    "\n",
    "medical_asset_data = [\n",
    "    {\n",
    "        \"Name\": asset[\"attributes\"][\"name\"],\n",
    "        \"Status\": asset[\"attributes\"][\"status\"],\n",
    "         \"Timestamp\": pd.to_datetime(asset[\"attributes\"][\"timestamp\"]).strftime('%d.%m.%Y'),\n",
    "        \n",
    "    }\n",
    "    for asset in all_medical_assets\n",
    "]\n",
    "\n",
    "# Create a pandas DataFrame from the extracted data\n",
    "df = pd.DataFrame(medical_asset_data)\n",
    "\n",
    "# Setze Pandas-Optionen für eine bessere Anzeige\n",
    "pd.set_option('display.max_columns', None)  # Alle Spalten anzeigen\n",
    "pd.set_option('display.max_colwidth', 50)   # Maximale Spaltenbreite setzen\n",
    "pd.set_option('display.width', 1000)        # Maximale Breite der Tabelle\n",
    "\n",
    "# Zeige die Tabelle an\n",
    "df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a08ff50e-499c-497d-879c-85c0c9b107c2",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Zeige alle \"seeding\" Daten\n",
    "import pandas as pd\n",
    "\n",
    "response = farm_client.log.get('seeding')\n",
    "all_seeding_assets = response['data']\n",
    "\n",
    "seeding_asset_data = [\n",
    "    {\n",
    "        \"Name\": asset[\"attributes\"][\"name\"],\n",
    "        \"Status\": asset[\"attributes\"][\"status\"],\n",
    "        \"Timestamp\": pd.to_datetime(asset[\"attributes\"][\"timestamp\"]).strftime('%d.%m.%Y'),\n",
    "        \n",
    "    }\n",
    "    for asset in all_seeding_assets\n",
    "]\n",
    "\n",
    "# Create a pandas DataFrame from the extracted data\n",
    "df = pd.DataFrame(seeding_asset_data)\n",
    "\n",
    "# Setze Pandas-Optionen für eine bessere Anzeige\n",
    "pd.set_option('display.max_columns', None)  # Alle Spalten anzeigen\n",
    "pd.set_option('display.max_colwidth', 50)   # Maximale Spaltenbreite setzen\n",
    "pd.set_option('display.width', 1000)        # Maximale Breite der Tabelle\n",
    "\n",
    "# Zeige die Tabelle an\n",
    "df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64a933c4-75b3-4dd5-902c-5da2850d585f",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#filter auf einen harvest log\n",
    "response = farm_client.log.get('harvest')\n",
    "all_harvest_assets = response['data']\n",
    "\n",
    "harvest_asset_data = [\n",
    "    {\n",
    "        \"Name\": asset[\"attributes\"][\"name\"],\n",
    "        \"Status\": asset[\"attributes\"][\"status\"],\n",
    "        \"Timestamp\": pd.to_datetime(asset[\"attributes\"][\"timestamp\"]).strftime('%d.%m.%Y'),\n",
    "    }\n",
    "    for asset in all_harvest_assets\n",
    "]\n",
    "\n",
    "# Erstelle ein Pandas DataFrame aus den extrahierten Daten\n",
    "df = pd.DataFrame(harvest_asset_data)\n",
    "\n",
    "# Filtere den DataFrame, um nur den gewünschten Harvest anzuzeigen (enthält den Teilstring)\n",
    "df_filtered = df[df['Name'].str.contains(\"Helmacker\", na=False)]\n",
    "\n",
    "# Setze Pandas-Optionen für eine bessere Anzeige\n",
    "pd.set_option('display.max_columns', None)  # Alle Spalten anzeigen\n",
    "pd.set_option('display.max_colwidth', 50)   # Maximale Spaltenbreite setzen\n",
    "pd.set_option('display.width', 1000)        # Maximale Breite der Tabelle\n",
    "\n",
    "# Zeige die gefilterte Tabelle an\n",
    "df_filtered\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d23c84ac-9a04-4019-9b0c-0c549620af35",
   "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>Plant</th>\n",
       "      <th>Area</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Log Name</th>\n",
       "      <th>Status</th>\n",
       "      <th>Revision Created</th>\n",
       "      <th>Link</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>W-Raps Helmacker Plant 24/25</td>\n",
       "      <td>3.12</td>\n",
       "      <td>20.10.2024</td>\n",
       "      <td>W-Raps Helmacker  Harvest 24/25</td>\n",
       "      <td>done</td>\n",
       "      <td>21.10.2024</td>\n",
       "      <td>https://farmos.wagframe.duckdns.org/api/log/ha...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Plant  Area   Timestamp                         Log Name Status Revision Created                                               Link\n",
       "0  W-Raps Helmacker Plant 24/25  3.12  20.10.2024  W-Raps Helmacker  Harvest 24/25   done       21.10.2024  https://farmos.wagframe.duckdns.org/api/log/ha..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Versuch\n",
    "import pandas as pd\n",
    "\n",
    "def get_related_logs_of_type(asset, type): \n",
    "    response = farm_client.resource.get('log', type)\n",
    "    ret = []\n",
    "    for log in response['data']:\n",
    "        log_assets = log['relationships']['asset']['data']\n",
    "        for log_asset in log_assets:\n",
    "            if log_asset['id'] == asset['id']:\n",
    "                ret.append(log)\n",
    "    return ret\n",
    "\n",
    "def get_related_quantities_of_measure(log, measure):\n",
    "    quantities = log['relationships']['quantity']['data']\n",
    "    ret = []\n",
    "    for quant_ref in quantities:\n",
    "        quant_id = quant_ref['id']\n",
    "        quant = farm_client.resource.get_id('quantity', 'standard', quant_id)\n",
    "        if quant['data']['attributes']['measure'] == measure:\n",
    "            ret.append(quant)\n",
    "    return ret\n",
    "\n",
    "# Beispiel für eine API-Abfrage mit farm_client\n",
    "response = farm_client.resource.get('log', 'harvest')\n",
    "\n",
    "logs = []\n",
    "for item in response['data']:\n",
    "    # plant name und fläche holen\n",
    "    assets = item['relationships']['asset']['data']\n",
    "    plant_name = ''\n",
    "    plant_area = 0.0\n",
    "    for asset in assets:\n",
    "        if asset['type'] == 'asset--plant':    \n",
    "            plant_id = asset['id']\n",
    "            seedings = get_related_logs_of_type(asset, 'seeding')\n",
    "            for seeding in seedings:\n",
    "                area_quants = get_related_quantities_of_measure(seeding, 'area')\n",
    "                for area_quant in area_quants:\n",
    "                    plant_area += float(area_quant['data']['attributes']['value']['decimal'])\n",
    "            plant = farm_client.asset.get_id('plant', plant_id)\n",
    "            plant_name = plant['data']['attributes']['name']\n",
    "            \n",
    "    \n",
    "    # log aufbauen\n",
    "    log = {\n",
    "        \n",
    "        \n",
    "        'Plant': plant_name,\n",
    "        'Area': plant_area,\n",
    "        \n",
    "        'Timestamp': pd.to_datetime(item[\"attributes\"][\"timestamp\"]).strftime('%d.%m.%Y'),\n",
    "        'Log Name': item['attributes']['name'],\n",
    "        'Status': item['attributes']['status'],\n",
    "        'Revision Created': pd.to_datetime(item[\"attributes\"][\"revision_created\"]).strftime('%d.%m.%Y'),\n",
    "        'Link': item['links']['self']['href']\n",
    "    }\n",
    "    logs.append(log)\n",
    "\n",
    "# Erstelle ein DataFrame aus den Harvest Logs\n",
    "df = pd.DataFrame(logs)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "434b8acb-89da-4ac0-8a36-0cb70df6db11",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e6566de-ba49-4566-87c1-37ed165aaff9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c4d4aa6-0a3b-4860-b186-965e827f8eed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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