{
 "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": null,
   "id": "96784d50-f9bf-43a2-ae95-1e53f9c8ffb4",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [],
   "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": {
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   "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": null,
   "id": "d23c84ac-9a04-4019-9b0c-0c549620af35",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "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"
   ]
  },
  {
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   "execution_count": 5,
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   },
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      "                    \"links\": {\n",
      "                        \"related\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/file?resourceVersion=id%3A454\"\n",
      "                        },\n",
      "                        \"self\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/relationships/file?resourceVersion=id%3A454\"\n",
      "                        }\n",
      "                    }\n",
      "                },\n",
      "                \"image\": {\n",
      "                    \"data\": [],\n",
      "                    \"links\": {\n",
      "                        \"related\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/image?resourceVersion=id%3A454\"\n",
      "                        },\n",
      "                        \"self\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/relationships/image?resourceVersion=id%3A454\"\n",
      "                        }\n",
      "                    }\n",
      "                },\n",
      "                \"group\": {\n",
      "                    \"data\": [],\n",
      "                    \"links\": {\n",
      "                        \"related\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/group?resourceVersion=id%3A454\"\n",
      "                        },\n",
      "                        \"self\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/relationships/group?resourceVersion=id%3A454\"\n",
      "                        }\n",
      "                    }\n",
      "                },\n",
      "                \"location\": {\n",
      "                    \"data\": [],\n",
      "                    \"links\": {\n",
      "                        \"related\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/location?resourceVersion=id%3A454\"\n",
      "                        },\n",
      "                        \"self\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/relationships/location?resourceVersion=id%3A454\"\n",
      "                        }\n",
      "                    }\n",
      "                },\n",
      "                \"asset\": {\n",
      "                    \"data\": [\n",
      "                        {\n",
      "                            \"type\": \"asset--plant\",\n",
      "                            \"id\": \"c0371d6c-0b48-4256-96bc-2c75004e5636\",\n",
      "                            \"meta\": {\n",
      "                                \"drupal_internal__target_id\": 106\n",
      "                            }\n",
      "                        }\n",
      "                    ],\n",
      "                    \"links\": {\n",
      "                        \"related\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/asset?resourceVersion=id%3A454\"\n",
      "                        },\n",
      "                        \"self\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/relationships/asset?resourceVersion=id%3A454\"\n",
      "                        }\n",
      "                    }\n",
      "                },\n",
      "                \"category\": {\n",
      "                    \"data\": [],\n",
      "                    \"links\": {\n",
      "                        \"related\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/category?resourceVersion=id%3A454\"\n",
      "                        },\n",
      "                        \"self\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/relationships/category?resourceVersion=id%3A454\"\n",
      "                        }\n",
      "                    }\n",
      "                },\n",
      "                \"quantity\": {\n",
      "                    \"data\": [\n",
      "                        {\n",
      "                            \"type\": \"quantity--standard\",\n",
      "                            \"id\": \"75ddbac7-e03e-41ab-814c-4aab1246ce3e\",\n",
      "                            \"meta\": {\n",
      "                                \"target_revision_id\": 216,\n",
      "                                \"drupal_internal__target_id\": 134\n",
      "                            }\n",
      "                        }\n",
      "                    ],\n",
      "                    \"links\": {\n",
      "                        \"related\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/quantity?resourceVersion=id%3A454\"\n",
      "                        },\n",
      "                        \"self\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/relationships/quantity?resourceVersion=id%3A454\"\n",
      "                        }\n",
      "                    }\n",
      "                },\n",
      "                \"owner\": {\n",
      "                    \"data\": [\n",
      "                        {\n",
      "                            \"type\": \"user--user\",\n",
      "                            \"id\": \"a93c675c-72b1-40ea-90ae-41df789b7607\",\n",
      "                            \"meta\": {\n",
      "                                \"drupal_internal__target_id\": 1\n",
      "                            }\n",
      "                        }\n",
      "                    ],\n",
      "                    \"links\": {\n",
      "                        \"related\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/owner?resourceVersion=id%3A454\"\n",
      "                        },\n",
      "                        \"self\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/relationships/owner?resourceVersion=id%3A454\"\n",
      "                        }\n",
      "                    }\n",
      "                },\n",
      "                \"equipment\": {\n",
      "                    \"data\": [\n",
      "                        {\n",
      "                            \"type\": \"asset--equipment\",\n",
      "                            \"id\": \"5faba68c-aa10-45b4-b1e0-d687ef1a8562\",\n",
      "                            \"meta\": {\n",
      "                                \"drupal_internal__target_id\": 79\n",
      "                            }\n",
      "                        }\n",
      "                    ],\n",
      "                    \"links\": {\n",
      "                        \"related\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/equipment?resourceVersion=id%3A454\"\n",
      "                        },\n",
      "                        \"self\": {\n",
      "                            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest/71c022be-53c6-481f-b787-da0d16cfbf6f/relationships/equipment?resourceVersion=id%3A454\"\n",
      "                        }\n",
      "                    }\n",
      "                }\n",
      "            }\n",
      "        }\n",
      "    ],\n",
      "    \"links\": {\n",
      "        \"self\": {\n",
      "            \"href\": \"https://farmos.wagframe.duckdns.org/api/log/harvest\"\n",
      "        }\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Beispiel Json print\n",
    "import json\n",
    "\n",
    "response = farm_client.resource.get('log', 'harvest')\n",
    "print(json.dumps(response, indent=4))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2300251-b38a-4aa8-abe0-1290572e2d3a",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W-Raps Helmacker Plant 24/25\n",
      "active\n"
     ]
    }
   ],
   "source": [
    "# Erhalte Pflanzdaten und speichere den Namen\n",
    "plant_id = \"76f89f82-1238-43bb-b34f-796a92d491a2\"\n",
    "plant = farm_client.asset.get_id('plant', plant_id)\n",
    "plant_name = plant.get('data', {}).get('attributes', {}).get('name', '')\n",
    "plant_status= plant.get('data', {}).get('attributes', {}).get('status', '')\n",
    "print (plant_name)\n",
    "print (plant_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a48c8376-158d-4877-b86e-fded7aab1b74",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Verschachtelte Abfrage test2\n",
    "from farmOS import farmOS    \n",
    "import pandas as pd\n",
    "import json\n",
    "\n",
    "def get_related_logs_of_type(asset, log_type): \n",
    "    response = farm_client.resource.get('log', 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",
    "def get_related_quantities_of_weight(log, weight):\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'] == weight:  # Korrektur: Verwendung von weight\n",
    "            ret.append(quant)\n",
    "    return ret\n",
    "\n",
    "# API-Anfrage, um alle Harvest-Logs zu erhalten\n",
    "response = farm_client.resource.get('log', 'harvest')\n",
    "\n",
    "logs = []\n",
    "for item in response.get('data', []):\n",
    "    # plant name und fläche holen\n",
    "    assets = item.get('relationships', {}).get('asset', {}).get('data', [])\n",
    "    plant_name = ''\n",
    "    plant_area = 0.0\n",
    "    plant_status = ''\n",
    "    measure = ''\n",
    "    weight_measure = ''\n",
    "\n",
    "    for asset in assets:\n",
    "        if asset['type'] == 'asset--plant':    \n",
    "            plant_id = asset['id']\n",
    "            \n",
    "            # Alle Seeding-Logs für das Pflanzasset abrufen\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",
    "                \n",
    "                for area_quant in area_quants:\n",
    "                    plant_area += float(area_quant.get('data', {}).get('attributes', {}).get('value', {}).get('decimal', 0))\n",
    "                    measure = area_quant.get('data', {}).get('attributes', {}).get('measure', '')\n",
    "\n",
    "                                                            \n",
    "                                                                 \n",
    "            for seeding in seedings:\n",
    "                weight_quants = get_related_quantities_of_weight(seeding, 'weight')\n",
    "                \n",
    "                for weight_quant in weight_quants:\n",
    "                    weight_measure = weight_quant.get('data', {}).get('attributes', {}).get('measure', '')\n",
    "                    \n",
    "\n",
    "            \n",
    "            # Pflanzendaten abrufen und Namen sowie Status speichern\n",
    "            plant = farm_client.asset.get_id('plant', plant_id)\n",
    "            plant_name = plant.get('data', {}).get('attributes', {}).get('name', '')\n",
    "            plant_status = plant.get('data', {}).get('attributes', {}).get('status', '')\n",
    "\n",
    "    # Log erstellen und relevante Felder extrahieren\n",
    "    log = {\n",
    "        'Plant': plant_name,\n",
    "        'Area': plant_area,\n",
    "        'Plant Status': plant_status,\n",
    "        'Quantity measure': measure,\n",
    "        'Weight measure': weight_measure,  # Hinzufügen von weight_measure zur Log-Ausgabe\n",
    "        'Timestamp': pd.to_datetime(item['attributes']['timestamp']).strftime('%d.%m.%Y') if 'timestamp' in item['attributes'] else None,\n",
    "        'Log Name': item['attributes'].get('name', ''),\n",
    "        'Log Status': item['attributes'].get('status', ''),\n",
    "        'Revision Created': pd.to_datetime(item['attributes'].get('revision_created')).strftime('%d.%m.%Y') if 'revision_created' in item['attributes'] else None,\n",
    "        'Link': item.get('links', {}).get('self', {}).get('href', ''),\n",
    "        'Is Movement': item['attributes'].get('is_movement', ''),\n",
    "    }\n",
    "    logs.append(log)\n",
    "\n",
    "# DataFrame aus den Harvest Logs erstellen\n",
    "df = pd.DataFrame(logs)\n",
    "df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b753f464-3a65-4824-a231-6614e156b9e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3 style='font-weight:bold;'>W-Raps Helmacker Plant 24/25</h3>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c6924 th {\n",
       "  font-weight: bold;\n",
       "  text-align: center;\n",
       "  white-space: break-spaces;\n",
       "  max-width: 100px;\n",
       "}\n",
       "#T_c6924 td {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c6924\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c6924_level0_col0\" class=\"col_heading level0 col0\" >PlantStatus</th>\n",
       "      <th id=\"T_c6924_level0_col1\" class=\"col_heading level0 col1\" >SeedingLog AreaMeasure Quant1</th>\n",
       "      <th id=\"T_c6924_level0_col2\" class=\"col_heading level0 col2\" >SeedingLog Area Quant1</th>\n",
       "      <th id=\"T_c6924_level0_col3\" class=\"col_heading level0 col3\" >SeedingLog AreaMeasure Quant0</th>\n",
       "      <th id=\"T_c6924_level0_col4\" class=\"col_heading level0 col4\" >SeedingLog AreaDecimal Quant0</th>\n",
       "      <th id=\"T_c6924_level0_col5\" class=\"col_heading level0 col5\" >Timestamp HarvestLog</th>\n",
       "      <th id=\"T_c6924_level0_col6\" class=\"col_heading level0 col6\" >LogName HarvestLog</th>\n",
       "      <th id=\"T_c6924_level0_col7\" class=\"col_heading level0 col7\" >LogStatus HarvestLog</th>\n",
       "      <th id=\"T_c6924_level0_col8\" class=\"col_heading level0 col8\" >IsMovement HarvestLog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c6924_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_c6924_row0_col0\" class=\"data row0 col0\" >active</td>\n",
       "      <td id=\"T_c6924_row0_col1\" class=\"data row0 col1\" >area</td>\n",
       "      <td id=\"T_c6924_row0_col2\" class=\"data row0 col2\" >3.020000</td>\n",
       "      <td id=\"T_c6924_row0_col3\" class=\"data row0 col3\" >weight</td>\n",
       "      <td id=\"T_c6924_row0_col4\" class=\"data row0 col4\" >50.000000</td>\n",
       "      <td id=\"T_c6924_row0_col5\" class=\"data row0 col5\" >20.10.2024</td>\n",
       "      <td id=\"T_c6924_row0_col6\" class=\"data row0 col6\" >W-Raps Helmacker  Harvest 24/25</td>\n",
       "      <td id=\"T_c6924_row0_col7\" class=\"data row0 col7\" >done</td>\n",
       "      <td id=\"T_c6924_row0_col8\" class=\"data row0 col8\" >False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x12cac636cf0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3 style='font-weight:bold;'>W-Raps Nachtweide Plant 24/25 </h3>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_4f0b9 th {\n",
       "  font-weight: bold;\n",
       "  text-align: center;\n",
       "  white-space: break-spaces;\n",
       "  max-width: 100px;\n",
       "}\n",
       "#T_4f0b9 td {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4f0b9\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4f0b9_level0_col0\" class=\"col_heading level0 col0\" >PlantStatus</th>\n",
       "      <th id=\"T_4f0b9_level0_col1\" class=\"col_heading level0 col1\" >SeedingLog AreaMeasure Quant1</th>\n",
       "      <th id=\"T_4f0b9_level0_col2\" class=\"col_heading level0 col2\" >SeedingLog Area Quant1</th>\n",
       "      <th id=\"T_4f0b9_level0_col3\" class=\"col_heading level0 col3\" >SeedingLog AreaMeasure Quant0</th>\n",
       "      <th id=\"T_4f0b9_level0_col4\" class=\"col_heading level0 col4\" >SeedingLog AreaDecimal Quant0</th>\n",
       "      <th id=\"T_4f0b9_level0_col5\" class=\"col_heading level0 col5\" >Timestamp HarvestLog</th>\n",
       "      <th id=\"T_4f0b9_level0_col6\" class=\"col_heading level0 col6\" >LogName HarvestLog</th>\n",
       "      <th id=\"T_4f0b9_level0_col7\" class=\"col_heading level0 col7\" >LogStatus HarvestLog</th>\n",
       "      <th id=\"T_4f0b9_level0_col8\" class=\"col_heading level0 col8\" >IsMovement HarvestLog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4f0b9_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_4f0b9_row0_col0\" class=\"data row0 col0\" >active</td>\n",
       "      <td id=\"T_4f0b9_row0_col1\" class=\"data row0 col1\" >area</td>\n",
       "      <td id=\"T_4f0b9_row0_col2\" class=\"data row0 col2\" >4.500000</td>\n",
       "      <td id=\"T_4f0b9_row0_col3\" class=\"data row0 col3\" >weight</td>\n",
       "      <td id=\"T_4f0b9_row0_col4\" class=\"data row0 col4\" >66.000000</td>\n",
       "      <td id=\"T_4f0b9_row0_col5\" class=\"data row0 col5\" >28.10.2024</td>\n",
       "      <td id=\"T_4f0b9_row0_col6\" class=\"data row0 col6\" >W-Raps Nachtweide  Harvest 24/25</td>\n",
       "      <td id=\"T_4f0b9_row0_col7\" class=\"data row0 col7\" >done</td>\n",
       "      <td id=\"T_4f0b9_row0_col8\" class=\"data row0 col8\" >False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x12caf68afc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Verschachtelte Abfrage test3\n",
    "from farmOS import farmOS    \n",
    "import pandas as pd\n",
    "\n",
    "def get_related_logs_of_type(asset, log_type): \n",
    "    response = farm_client.resource.get('log', log_type)\n",
    "    return [log for log in response['data'] if any(log_asset['id'] == asset['id'] for log_asset in log['relationships']['asset']['data'])]\n",
    "\n",
    "def get_related_quantities(log, measure_type):\n",
    "    quantities = log['relationships']['quantity']['data']\n",
    "    return [farm_client.resource.get_id('quantity', 'standard', quant_ref['id']) for quant_ref in quantities if farm_client.resource.get_id('quantity', 'standard', quant_ref['id'])['data']['attributes']['measure'] == measure_type]\n",
    "\n",
    "\n",
    "\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",
    "# API-Anfrage, um alle Harvest-Logs zu erhalten\n",
    "response = farm_client.resource.get('log', 'harvest')\n",
    "\n",
    "logs = []\n",
    "for item in response.get('data', []):\n",
    "    assets = item.get('relationships', {}).get('asset', {}).get('data', [])\n",
    "    plant_name = ''\n",
    "    plant_area = 0.0\n",
    "    plant_status = ''\n",
    "    quantity_measure = ''\n",
    "    weight_measure = ''\n",
    "    weight_decimal = 0.0\n",
    "\n",
    "    for asset in assets:\n",
    "        if asset['type'] == 'asset--plant':    \n",
    "            plant_id = asset['id']\n",
    "            \n",
    "            # Alle Seeding-Logs für das Pflanzasset abrufen\n",
    "            seedings = get_related_logs_of_type(asset, 'seeding')\n",
    "            \n",
    "            # Fläche und Menge abrufen\n",
    "            for seeding in seedings:\n",
    "                area_quants = get_related_quantities(seeding, 'area')\n",
    "                for area_quant in area_quants:\n",
    "                    plant_area += float(area_quant.get('data', {}).get('attributes', {}).get('value', {}).get('decimal', 0))\n",
    "                    quantity_measure = area_quant.get('data', {}).get('attributes', {}).get('measure', '')\n",
    "\n",
    "                weight_quants = get_related_quantities(seeding, 'weight')\n",
    "                for weight_quant in weight_quants:\n",
    "                    weight_measure = weight_quant.get('data', {}).get('attributes', {}).get('measure', '')\n",
    "                    weight_decimal += float(weight_quant.get('data', {}).get('attributes', {}).get('value', {}).get('decimal', 0))\n",
    "                    \n",
    "            # Pflanzendaten abrufen\n",
    "            plant = farm_client.asset.get_id('plant', plant_id)\n",
    "            plant_name = plant.get('data', {}).get('attributes', {}).get('name', '')\n",
    "            plant_status = plant.get('data', {}).get('attributes', {}).get('status', '')\n",
    "\n",
    "    # Log erstellen und relevante Felder extrahieren\n",
    "    log = {\n",
    "        'PlantName': plant_name,\n",
    "        'PlantStatus': plant_status,\n",
    "        'SeedingLog AreaMeasure Quant1': quantity_measure,\n",
    "        'SeedingLog Area Quant1': plant_area,\n",
    "        'SeedingLog AreaMeasure Quant0': weight_measure,\n",
    "        'SeedingLog AreaDecimal Quant0' : weight_decimal,\n",
    "        'Timestamp HarvestLog': pd.to_datetime(item['attributes'].get('timestamp')).strftime('%d.%m.%Y') if 'timestamp' in item['attributes'] else None,\n",
    "        'LogName HarvestLog': item['attributes'].get('name', ''),\n",
    "        'LogStatus HarvestLog': item['attributes'].get('status', ''),\n",
    "        'IsMovement HarvestLog': item['attributes'].get('is_movement', ''),\n",
    "    }\n",
    "    logs.append(log)\n",
    "\n",
    "# DataFrame aus den Harvest Logs erstellen\n",
    "df = pd.DataFrame(logs)\n",
    "from IPython.display import display, HTML  # Für Jupyter Notebook geeignet\n",
    "\n",
    "# Gruppierung und Anzeige als HTML-Format\n",
    "grouped = df.groupby('PlantName')\n",
    "for plant, data in grouped:\n",
    "    display(HTML(f\"<h3 style='font-weight:bold;'>{plant}</h3>\"))  # Pflanzname als fettgedruckte Überschrift\n",
    "    \n",
    "    # Style der Tabelle anpassen\n",
    "    styled_table = data.drop(columns=['PlantName']).style.set_table_styles(\n",
    "        [\n",
    "            {'selector': 'th', 'props': [('font-weight', 'bold'), ('text-align', 'center'), ('white-space', 'break-spaces'), ('max-width', '100px')]},\n",
    "            {'selector': 'td', 'props': [('text-align', 'center')]}\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    display(styled_table)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3df54bf3-d8aa-4ec6-bb12-23b05f0aeb99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3 style='font-weight:bold;'>W-Raps Helmacker Plant 24/25</h3>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_3b1ce th {\n",
       "  font-weight: bold;\n",
       "  text-align: center;\n",
       "  white-space: break-spaces;\n",
       "  max-width: 100px;\n",
       "}\n",
       "#T_3b1ce td {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_3b1ce\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_3b1ce_level0_col0\" class=\"col_heading level0 col0\" >PlantStatus</th>\n",
       "      <th id=\"T_3b1ce_level0_col1\" class=\"col_heading level0 col1\" >SeedingLog AreaMeasure Quant1</th>\n",
       "      <th id=\"T_3b1ce_level0_col2\" class=\"col_heading level0 col2\" >SeedingLog Area Quant1</th>\n",
       "      <th id=\"T_3b1ce_level0_col3\" class=\"col_heading level0 col3\" >SeedingLog WeightMeasure Quant0</th>\n",
       "      <th id=\"T_3b1ce_level0_col4\" class=\"col_heading level0 col4\" >SeedingLog WeightDecimal Quant0</th>\n",
       "      <th id=\"T_3b1ce_level0_col5\" class=\"col_heading level0 col5\" >Timestamp HarvestLog</th>\n",
       "      <th id=\"T_3b1ce_level0_col6\" class=\"col_heading level0 col6\" >LogName HarvestLog</th>\n",
       "      <th id=\"T_3b1ce_level0_col7\" class=\"col_heading level0 col7\" >LogStatus HarvestLog</th>\n",
       "      <th id=\"T_3b1ce_level0_col8\" class=\"col_heading level0 col8\" >IsMovement HarvestLog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_3b1ce_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_3b1ce_row0_col0\" class=\"data row0 col0\" >active</td>\n",
       "      <td id=\"T_3b1ce_row0_col1\" class=\"data row0 col1\" >area</td>\n",
       "      <td id=\"T_3b1ce_row0_col2\" class=\"data row0 col2\" >3.020000</td>\n",
       "      <td id=\"T_3b1ce_row0_col3\" class=\"data row0 col3\" >weight</td>\n",
       "      <td id=\"T_3b1ce_row0_col4\" class=\"data row0 col4\" >50.000000</td>\n",
       "      <td id=\"T_3b1ce_row0_col5\" class=\"data row0 col5\" >20.10.2024</td>\n",
       "      <td id=\"T_3b1ce_row0_col6\" class=\"data row0 col6\" >W-Raps Helmacker  Harvest 24/25</td>\n",
       "      <td id=\"T_3b1ce_row0_col7\" class=\"data row0 col7\" >done</td>\n",
       "      <td id=\"T_3b1ce_row0_col8\" class=\"data row0 col8\" >False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x12caf68bad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3 style='font-weight:bold;'>W-Raps Nachtweide Plant 24/25 </h3>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_bb745 th {\n",
       "  font-weight: bold;\n",
       "  text-align: center;\n",
       "  white-space: break-spaces;\n",
       "  max-width: 100px;\n",
       "}\n",
       "#T_bb745 td {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_bb745\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_bb745_level0_col0\" class=\"col_heading level0 col0\" >PlantStatus</th>\n",
       "      <th id=\"T_bb745_level0_col1\" class=\"col_heading level0 col1\" >SeedingLog AreaMeasure Quant1</th>\n",
       "      <th id=\"T_bb745_level0_col2\" class=\"col_heading level0 col2\" >SeedingLog Area Quant1</th>\n",
       "      <th id=\"T_bb745_level0_col3\" class=\"col_heading level0 col3\" >SeedingLog WeightMeasure Quant0</th>\n",
       "      <th id=\"T_bb745_level0_col4\" class=\"col_heading level0 col4\" >SeedingLog WeightDecimal Quant0</th>\n",
       "      <th id=\"T_bb745_level0_col5\" class=\"col_heading level0 col5\" >Timestamp HarvestLog</th>\n",
       "      <th id=\"T_bb745_level0_col6\" class=\"col_heading level0 col6\" >LogName HarvestLog</th>\n",
       "      <th id=\"T_bb745_level0_col7\" class=\"col_heading level0 col7\" >LogStatus HarvestLog</th>\n",
       "      <th id=\"T_bb745_level0_col8\" class=\"col_heading level0 col8\" >IsMovement HarvestLog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_bb745_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_bb745_row0_col0\" class=\"data row0 col0\" >active</td>\n",
       "      <td id=\"T_bb745_row0_col1\" class=\"data row0 col1\" >area</td>\n",
       "      <td id=\"T_bb745_row0_col2\" class=\"data row0 col2\" >4.500000</td>\n",
       "      <td id=\"T_bb745_row0_col3\" class=\"data row0 col3\" >weight</td>\n",
       "      <td id=\"T_bb745_row0_col4\" class=\"data row0 col4\" >66.000000</td>\n",
       "      <td id=\"T_bb745_row0_col5\" class=\"data row0 col5\" >28.10.2024</td>\n",
       "      <td id=\"T_bb745_row0_col6\" class=\"data row0 col6\" >W-Raps Nachtweide  Harvest 24/25</td>\n",
       "      <td id=\"T_bb745_row0_col7\" class=\"data row0 col7\" >done</td>\n",
       "      <td id=\"T_bb745_row0_col8\" class=\"data row0 col8\" >False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x12caf68a450>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testtttt\n",
    "from farmOS import farmOS    \n",
    "import pandas as pd\n",
    "\n",
    "def get_related_logs_of_type(asset, log_type): \n",
    "    response = farm_client.resource.get('log', log_type)\n",
    "    return [log for log in response['data'] if any(log_asset['id'] == asset['id'] for log_asset in log['relationships']['asset']['data'])]\n",
    "\n",
    "def get_related_quantities(log, measure_type):\n",
    "    quantities = log['relationships']['quantity']['data']\n",
    "    return [farm_client.resource.get_id('quantity', 'standard', quant_ref['id']) \n",
    "            for quant_ref in quantities \n",
    "            if farm_client.resource.get_id('quantity', 'standard', quant_ref['id'])['data']['attributes']['measure'] == measure_type]\n",
    "\n",
    "def get_related_taxonomy_units(taxonomy_term, measure):\n",
    "    taxonomy = taxonomy_term['relationships']['units']['data']\n",
    "    ret = []\n",
    "    for tax_ref in taxonomy:\n",
    "        tax_id = tax_ref['id']\n",
    "        quant = farm_client.resource.get_id('quantity', 'standard', tax_id)\n",
    "        if quant['data']['attributes']['measure'] == measure:\n",
    "            ret.append(quant)\n",
    "    return ret\n",
    "\n",
    "# API-Anfrage, um alle Harvest-Logs zu erhalten\n",
    "response = farm_client.resource.get('log', 'harvest')\n",
    "\n",
    "logs = []\n",
    "for item in response.get('data', []):\n",
    "    assets = item.get('relationships', {}).get('asset', {}).get('data', [])\n",
    "    plant_name = ''\n",
    "    plant_area = 0.0\n",
    "    plant_status = ''\n",
    "    quantity_measure = ''\n",
    "    weight_measure = ''\n",
    "    weight_decimal = 0.0\n",
    "\n",
    "    for asset in assets:\n",
    "        if asset['type'] == 'asset--plant':    \n",
    "            plant_id = asset['id']\n",
    "            \n",
    "            # Alle Seeding-Logs für das Pflanzasset abrufen\n",
    "            seedings = get_related_logs_of_type(asset, 'seeding')\n",
    "            \n",
    "            # Fläche und Menge abrufen\n",
    "            for seeding in seedings:\n",
    "                area_quants = get_related_quantities(seeding, 'area')\n",
    "                for area_quant in area_quants:\n",
    "                    plant_area += float(area_quant.get('data', {}).get('attributes', {}).get('value', {}).get('decimal', 0))\n",
    "                    quantity_measure = area_quant.get('data', {}).get('attributes', {}).get('measure', '')\n",
    "\n",
    "                weight_quants = get_related_quantities(seeding, 'weight')\n",
    "                for weight_quant in weight_quants:\n",
    "                    weight_measure = weight_quant.get('data', {}).get('attributes', {}).get('measure', '')\n",
    "                    weight_decimal += float(weight_quant.get('data', {}).get('attributes', {}).get('value', {}).get('decimal', 0))\n",
    "                    \n",
    "            # Pflanzendaten abrufen\n",
    "            plant = farm_client.asset.get_id('plant', plant_id)\n",
    "            plant_name = plant.get('data', {}).get('attributes', {}).get('name', '')\n",
    "            plant_status = plant.get('data', {}).get('attributes', {}).get('status', '')\n",
    "\n",
    "    # Log erstellen und relevante Felder extrahieren\n",
    "    log = {\n",
    "        'PlantName': plant_name,\n",
    "        'PlantStatus': plant_status,\n",
    "        'SeedingLog AreaMeasure Quant1': quantity_measure,\n",
    "        'SeedingLog Area Quant1': plant_area,\n",
    "        'SeedingLog WeightMeasure Quant0': weight_measure,\n",
    "        'SeedingLog WeightDecimal Quant0' : weight_decimal,\n",
    "        'Timestamp HarvestLog': pd.to_datetime(item['attributes'].get('timestamp')).strftime('%d.%m.%Y') if 'timestamp' in item['attributes'] else None,\n",
    "        'LogName HarvestLog': item['attributes'].get('name', ''),\n",
    "        'LogStatus HarvestLog': item['attributes'].get('status', ''),\n",
    "        'IsMovement HarvestLog': item['attributes'].get('is_movement', ''),\n",
    "    }\n",
    "    logs.append(log)\n",
    "\n",
    "# DataFrame aus den Harvest Logs erstellen\n",
    "df = pd.DataFrame(logs)\n",
    "from IPython.display import display, HTML  # Für Jupyter Notebook geeignet\n",
    "\n",
    "# Gruppierung und Anzeige als HTML-Format\n",
    "grouped = df.groupby('PlantName')\n",
    "for plant, data in grouped:\n",
    "    display(HTML(f\"<h3 style='font-weight:bold;'>{plant}</h3>\"))  # Pflanzname als fettgedruckte Überschrift\n",
    "    \n",
    "    # Style der Tabelle anpassen\n",
    "    styled_table = data.drop(columns=['PlantName']).style.set_table_styles(\n",
    "        [\n",
    "            {'selector': 'th', 'props': [('font-weight', 'bold'), ('text-align', 'center'), ('white-space', 'break-spaces'), ('max-width', '100px')]},\n",
    "            {'selector': 'td', 'props': [('text-align', 'center')]}\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    display(styled_table)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da71b53e-0621-47af-84ca-11e266ec8c2e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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