diff --git a/Daten_ohne_relationships.ipynb b/Daten_ohne_relationships.ipynb new file mode 100644 index 0000000..9c60714 --- /dev/null +++ b/Daten_ohne_relationships.ipynb @@ -0,0 +1,1086 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "73e934a8-9027-4ad1-ad15-e68fba847768", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "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": 3, + "id": "dcf25e45-f887-4fd5-aed8-44090ed75680", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Die Datei wurde erfolgreich unter '/Users/Wir/Downloads/assets_1.xlsx' gespeichert.\n" + ] + } + ], + "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": 2, + "id": "ca74d822-4f5c-46d7-8129-31bbf28a1e00", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameStatusCreatedArea (ha)Asset typeIs fixedland_typeIs location
0Helmackeractive06.05.20243.0321asset--landTruefieldTrue
1Neureutactive06.05.20244.1875asset--landTruefieldTrue
2Schusterinselactive06.05.20244.9563asset--landTruefieldTrue
3Kesselackeractive06.05.20242.1625asset--landTruefieldTrue
4Kapelleactive06.05.20245.6771asset--landTruefieldTrue
5Loehleactive06.05.20242.0614asset--landTruefieldTrue
6Kirchenbergactive06.05.20242.4701asset--landTruefieldTrue
7Nachtweideactive06.05.20243.0704asset--landTruefieldTrue
8Steinackeractive06.05.20242.6109asset--landTruefieldTrue
9Siegelwoerthactive06.05.20240.7707asset--landTruefieldTrue
10Waethleactive06.05.20241.4308asset--landTruefieldTrue
11Wolfsfeldactive06.05.20243.5169asset--landTruefieldTrue
12Grosser Ackeractive06.05.202412.5316asset--landTruefieldTrue
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" + ], + "text/plain": [ + " Name Status Created Area (ha) Asset type Is fixed land_type Is location\n", + "0 Helmacker active 06.05.2024 3.0321 asset--land True field True\n", + "1 Neureut active 06.05.2024 4.1875 asset--land True field True\n", + "2 Schusterinsel active 06.05.2024 4.9563 asset--land True field True\n", + "3 Kesselacker active 06.05.2024 2.1625 asset--land True field True\n", + "4 Kapelle active 06.05.2024 5.6771 asset--land True field True\n", + "5 Loehle active 06.05.2024 2.0614 asset--land True field True\n", + "6 Kirchenberg active 06.05.2024 2.4701 asset--land True field True\n", + "7 Nachtweide active 06.05.2024 3.0704 asset--land True field True\n", + "8 Steinacker active 06.05.2024 2.6109 asset--land True field True\n", + "9 Siegelwoerth active 06.05.2024 0.7707 asset--land True field True\n", + "10 Waethle active 06.05.2024 1.4308 asset--land True field True\n", + "11 Wolfsfeld active 06.05.2024 3.5169 asset--land True field True\n", + "12 Grosser Acker active 06.05.2024 12.5316 asset--land True field True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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 + } + }, + "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", + " \"Created\": pd.to_datetime(asset[\"attributes\"][\"created\"]).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", + " \"Created\": pd.to_datetime(asset[\"attributes\"][\"created\"]).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": 4, + "id": "3dab14bd-e7e6-4a5f-b838-c6498fb20f23", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Name Status Created\n", + "0 Innovert Raps Input 24/25 done 14.10.2024" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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", + " \"Created\": pd.to_datetime(asset[\"attributes\"][\"created\"]).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", + " \"Created\": pd.to_datetime(asset[\"attributes\"][\"created\"]).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", + " \"Created\": pd.to_datetime(asset[\"attributes\"][\"created\"]).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", + " \"Created\": pd.to_datetime(asset[\"attributes\"][\"created\"]).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", + "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", + " \"Created\": pd.to_datetime(asset[\"attributes\"][\"created\"]).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": 5, + "id": "d23c84ac-9a04-4019-9b0c-0c549620af35", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTimestampPlantStatusRevision Created
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" + ], + "text/plain": [ + " Name Timestamp Plant Status Revision Created\n", + "0 W-Raps Helmacker Harvest 24/25 2024-10-20T10:11:15+00:00 W-Raps Helmacker Plant 24/25 done 2024-10-21T10:56:27+00:00" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Versuch\n", + "import pandas as pd\n", + "\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", + " assets = log['relationships']['asset']['data']\n", + " for asset in assets:\n", + " if asset['id'] == 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(plant_id, 'seeding')\n", + " \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", + " 'Name': item['attributes']['name'],\n", + " 'Timestamp': item['attributes']['timestamp'],\n", + " 'Plant': plant_name,\n", + " \n", + " 'Status': item['attributes']['status'],\n", + " 'Revision Created': item['attributes']['revision_created'],\n", + " }\n", + " logs.append(log)\n", + "\n", + "\n", + "\n", + "# Erstelle ein Pandas DataFrame aus den extrahierten Daten\n", + "df = pd.DataFrame(logs)\n", + "\n", + "\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", + "df\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c74becaa-5899-4ad0-bb6e-5399cc5d0aa2", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}