1087 lines
34 KiB
Plaintext
1087 lines
34 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "73e934a8-9027-4ad1-ad15-e68fba847768",
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"metadata": {
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"jupyter": {
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"source_hidden": true
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},
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"#Anmelden und token holen\n",
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"from farmOS import farmOS\n",
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"\n",
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"hostname = \"farmos.wagframe.duckdns.org\"\n",
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"username = \"matze\"\n",
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"password = \"m6ChEHx5gMqgctr8dpb3fhATZbQS8hv4\"\n",
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"\n",
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"# Create the client.\n",
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"farm_client = farmOS(\n",
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" hostname=hostname,\n",
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" client_id = \"jupyter\", # Optional. The default oauth client_id \"farm\" is enabled on all farmOS servers.\n",
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")\n",
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"import json\n",
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"# Authorize the client, save the token.\n",
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"# A scope can be specified, but will default to the default scope set when initializing the client.\n",
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"token = farm_client.authorize(username, password)\n",
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"#farm_client.info()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "dcf25e45-f887-4fd5-aed8-44090ed75680",
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Die Datei wurde erfolgreich unter '/Users/Wir/Downloads/assets_1.xlsx' gespeichert.\n"
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]
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}
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],
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"source": [
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"#Excel Export\n",
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"import os\n",
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"# Definiere den Basispfad und den Dateinamen\n",
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"base_file_path = '/Users/Wir/Downloads/assets'\n",
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"file_extension = '.xlsx'\n",
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"file_index = 1\n",
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"file_path = f\"{base_file_path}_{file_index}{file_extension}\"\n",
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"\n",
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"# Überprüfe, ob die Datei bereits existiert und erhöhe den Index\n",
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"while os.path.exists(file_path):\n",
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" file_index += 1\n",
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" file_path = f\"{base_file_path}_{file_index}{file_extension}\"\n",
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"\n",
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"# Exportiere den DataFrame in eine Excel-Datei\n",
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"df.to_excel(file_path, index=False)\n",
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"print(f\"Die Datei wurde erfolgreich unter '{file_path}' gespeichert.\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d6022671-c929-4dbb-b60c-8d0ce17a1663",
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"#API Info\n",
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"\n",
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"info = farm_client.info()\n",
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"info"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "ca74d822-4f5c-46d7-8129-31bbf28a1e00",
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"metadata": {
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true,
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"source_hidden": true
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},
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Name</th>\n",
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" <th>Status</th>\n",
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" <th>Created</th>\n",
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" <th>Area (ha)</th>\n",
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" <th>Asset type</th>\n",
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" <th>Is fixed</th>\n",
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" <th>land_type</th>\n",
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" <th>Is location</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Helmacker</td>\n",
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" <td>active</td>\n",
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" <td>06.05.2024</td>\n",
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" <td>3.0321</td>\n",
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" <td>asset--land</td>\n",
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" <td>True</td>\n",
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" <td>field</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Neureut</td>\n",
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" <td>active</td>\n",
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" <td>06.05.2024</td>\n",
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" <td>4.1875</td>\n",
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" <td>asset--land</td>\n",
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" <td>True</td>\n",
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" <td>field</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Schusterinsel</td>\n",
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" <td>active</td>\n",
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" <td>06.05.2024</td>\n",
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" <td>4.9563</td>\n",
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" <td>asset--land</td>\n",
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" <td>True</td>\n",
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" <td>field</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Kesselacker</td>\n",
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" <td>active</td>\n",
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" <td>06.05.2024</td>\n",
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" <td>2.1625</td>\n",
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" <td>asset--land</td>\n",
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" <td>True</td>\n",
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" <td>field</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Kapelle</td>\n",
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" <td>active</td>\n",
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" <td>06.05.2024</td>\n",
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" <td>5.6771</td>\n",
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" <td>asset--land</td>\n",
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" <td>True</td>\n",
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" <td>field</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>Loehle</td>\n",
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" <td>active</td>\n",
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" <td>06.05.2024</td>\n",
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" <td>2.0614</td>\n",
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" <td>asset--land</td>\n",
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" <td>True</td>\n",
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" <td>field</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>Kirchenberg</td>\n",
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" <td>active</td>\n",
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" <td>06.05.2024</td>\n",
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" <td>2.4701</td>\n",
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" <td>asset--land</td>\n",
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" <td>True</td>\n",
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" <td>field</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>Nachtweide</td>\n",
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" <td>active</td>\n",
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" <td>06.05.2024</td>\n",
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" <td>3.0704</td>\n",
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" <td>asset--land</td>\n",
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" <td>True</td>\n",
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" <td>field</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>Steinacker</td>\n",
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" <td>active</td>\n",
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" <td>06.05.2024</td>\n",
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" <td>2.6109</td>\n",
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" <td>asset--land</td>\n",
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" <td>True</td>\n",
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" <td>field</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>Siegelwoerth</td>\n",
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" <td>active</td>\n",
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" <td>06.05.2024</td>\n",
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" <td>0.7707</td>\n",
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" <td>asset--land</td>\n",
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" <td>True</td>\n",
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" <td>field</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <td>Waethle</td>\n",
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" <td>active</td>\n",
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" <td>06.05.2024</td>\n",
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" <td>1.4308</td>\n",
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" <td>asset--land</td>\n",
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" <td>True</td>\n",
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" <td>field</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11</th>\n",
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" <td>Wolfsfeld</td>\n",
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" <td>active</td>\n",
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" <td>06.05.2024</td>\n",
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" <td>3.5169</td>\n",
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" <td>asset--land</td>\n",
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" <td>True</td>\n",
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" <td>field</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>12</th>\n",
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" <td>Grosser Acker</td>\n",
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" <td>active</td>\n",
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" <td>06.05.2024</td>\n",
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" <td>12.5316</td>\n",
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" <td>asset--land</td>\n",
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" <td>True</td>\n",
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" <td>field</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Name Status Created Area (ha) Asset type Is fixed land_type Is location\n",
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"0 Helmacker active 06.05.2024 3.0321 asset--land True field True\n",
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"1 Neureut active 06.05.2024 4.1875 asset--land True field True\n",
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"2 Schusterinsel active 06.05.2024 4.9563 asset--land True field True\n",
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"3 Kesselacker active 06.05.2024 2.1625 asset--land True field True\n",
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"4 Kapelle active 06.05.2024 5.6771 asset--land True field True\n",
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"5 Loehle active 06.05.2024 2.0614 asset--land True field True\n",
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"6 Kirchenberg active 06.05.2024 2.4701 asset--land True field True\n",
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"7 Nachtweide active 06.05.2024 3.0704 asset--land True field True\n",
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"8 Steinacker active 06.05.2024 2.6109 asset--land True field True\n",
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"9 Siegelwoerth active 06.05.2024 0.7707 asset--land True field True\n",
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"10 Waethle active 06.05.2024 1.4308 asset--land True field True\n",
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"11 Wolfsfeld active 06.05.2024 3.5169 asset--land True field True\n",
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"12 Grosser Acker active 06.05.2024 12.5316 asset--land True field True"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"#Zeige alle \"Land\" Daten\n",
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"import pandas as pd\n",
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"from shapely.geometry import Polygon\n",
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"from shapely import wkt # for converting WKT string to shapely Polygon\n",
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"from pyproj import CRS, Transformer # for coordinate transformation (if necessary)\n",
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"\n",
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"# Assuming this is your response\n",
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"response = farm_client.asset.get('land')\n",
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"all_land_assets = response['data']\n",
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"\n",
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"# Function to calculate area in hectares\n",
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"def calculate_area(geometry_wkt):\n",
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" # Convert WKT (Well-Known Text) to Polygon\n",
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" polygon = wkt.loads(geometry_wkt)\n",
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"\n",
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" # Define the CRS (Coordinate Reference System) for the land's coordinates\n",
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" crs_wgs84 = CRS(\"EPSG:4326\") # WGS 84, latitude-longitude\n",
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" crs_projected = CRS(\"EPSG:25832\") # A projected CRS for accurate area calculation\n",
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"\n",
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" # Transform coordinates to a projected CRS for area calculation\n",
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" transformer = Transformer.from_crs(crs_wgs84, crs_projected, always_xy=True)\n",
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" projected_polygon = Polygon([transformer.transform(*coord) for coord in polygon.exterior.coords])\n",
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"\n",
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" # Calculate the area in square meters and convert to hectares\n",
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" area_sq_meters = projected_polygon.area\n",
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" area_hectares = area_sq_meters / 10_000 # 1 hectare = 10,000 square meters\n",
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"\n",
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" # Round to 4 decimal places\n",
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" return round(area_hectares, 4)\n",
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"\n",
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"\n",
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"land_asset_data = [\n",
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" {\n",
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" \"Name\": asset[\"attributes\"][\"name\"],\n",
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" \"Status\": asset[\"attributes\"][\"status\"],\n",
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" \"Created\": pd.to_datetime(asset[\"attributes\"][\"created\"]).strftime('%d.%m.%Y'),\n",
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" \"Area (ha)\": calculate_area(asset[\"attributes\"][\"intrinsic_geometry\"][\"value\"]),\n",
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" \"Asset type\": asset[\"type\"],\n",
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" \"Is fixed\": asset[\"attributes\"][\"is_fixed\"],\n",
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" \"land_type\": asset[\"attributes\"][\"land_type\"],\n",
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" \"Is location\": asset[\"attributes\"][\"is_location\"]\n",
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" }\n",
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" for asset in all_land_assets\n",
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"]\n",
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"\n",
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"# Create a pandas DataFrame from the extracted data\n",
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"df = pd.DataFrame(land_asset_data)\n",
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"\n",
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"# Setze Pandas-Optionen für eine bessere Anzeige\n",
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"pd.set_option('display.max_columns', None) # Alle Spalten anzeigen\n",
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"pd.set_option('display.max_colwidth', 50) # Maximale Spaltenbreite setzen\n",
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"pd.set_option('display.width', 1000) # Maximale Breite der Tabelle\n",
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"\n",
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"# Zeige die Tabelle an\n",
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"df\n",
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"\n",
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"\n"
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]
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},
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{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "33c1b024-fb74-472c-b229-36d8041b1336",
|
|
"metadata": {
|
|
"jupyter": {
|
|
"source_hidden": true
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
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"#Zeige alle \"Plant\" Daten\n",
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"import pandas as pd\n",
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"\n",
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"\n",
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"# Assuming this is your response\n",
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"response = farm_client.asset.get('plant')\n",
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"all_plant_assets = response['data']\n",
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"\n",
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"\n",
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"\n",
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"plant_asset_data = [\n",
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" {\n",
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" \"Name\": asset[\"attributes\"][\"name\"],\n",
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" \"Status\": asset[\"attributes\"][\"status\"],\n",
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" \"Created\": pd.to_datetime(asset[\"attributes\"][\"created\"]).strftime('%d.%m.%Y'),\n",
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" \n",
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" }\n",
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" for asset in all_plant_assets\n",
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"]\n",
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"\n",
|
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"# Create a pandas DataFrame from the extracted data\n",
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"df = pd.DataFrame(plant_asset_data)\n",
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"\n",
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"# Setze Pandas-Optionen für eine bessere Anzeige\n",
|
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"pd.set_option('display.max_columns', None) # Alle Spalten anzeigen\n",
|
|
"pd.set_option('display.max_colwidth', 50) # Maximale Spaltenbreite setzen\n",
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"pd.set_option('display.width', 1000) # Maximale Breite der Tabelle\n",
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"\n",
|
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"# Zeige die Tabelle an\n",
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"df\n",
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"\n",
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"\n"
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]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "d64e0dc4-a685-4a42-810c-cafd8f43b988",
|
|
"metadata": {
|
|
"jupyter": {
|
|
"source_hidden": true
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
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"#Zeige alle \"equipment\" Daten\n",
|
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"import pandas as pd\n",
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"\n",
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"\n",
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"# Assuming this is your response\n",
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"response = farm_client.asset.get('equipment')\n",
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"all_equipment_assets = response['data']\n",
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"\n",
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"\n",
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"\n",
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"equipment_asset_data = [\n",
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" {\n",
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" \"Name\": asset[\"attributes\"][\"name\"],\n",
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" \"Status\": asset[\"attributes\"][\"status\"],\n",
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" \"Created\": pd.to_datetime(asset[\"attributes\"][\"created\"]).strftime('%d.%m.%Y'),\n",
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" \n",
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" }\n",
|
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" for asset in all_equipment_assets\n",
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"]\n",
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"\n",
|
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"# Create a pandas DataFrame from the extracted data\n",
|
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"df = pd.DataFrame(equipment_asset_data)\n",
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"\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": [
|
|
"<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>Innovert Raps Input 24/25</td>\n",
|
|
" <td>done</td>\n",
|
|
" <td>14.10.2024</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"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",
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"\n",
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"# Setze Pandas-Optionen für eine bessere Anzeige\n",
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"pd.set_option('display.max_columns', None) # Alle Spalten anzeigen\n",
|
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"pd.set_option('display.max_colwidth', 50) # Maximale Spaltenbreite setzen\n",
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"pd.set_option('display.width', 1000) # Maximale Breite der Tabelle\n",
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"\n",
|
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"# Zeige die gefilterte Tabelle an\n",
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"df_filtered\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "d23c84ac-9a04-4019-9b0c-0c549620af35",
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"metadata": {
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true,
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"source_hidden": true
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Name</th>\n",
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" <th>Timestamp</th>\n",
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" <th>Plant</th>\n",
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" <th>Status</th>\n",
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" <th>Revision Created</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>W-Raps Helmacker Harvest 24/25</td>\n",
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" <td>2024-10-20T10:11:15+00:00</td>\n",
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" <td>W-Raps Helmacker Plant 24/25</td>\n",
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" <td>done</td>\n",
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" <td>2024-10-21T10:56:27+00:00</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
|
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" Name Timestamp Plant Status Revision Created\n",
|
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"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"
|
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]
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},
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"execution_count": 5,
|
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"metadata": {},
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"output_type": "execute_result"
|
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}
|
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],
|
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"source": [
|
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"#Versuch\n",
|
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"import pandas as pd\n",
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"\n",
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"\n",
|
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"def get_related_logs_of_type(asset, type): \n",
|
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" response = farm_client.resource.get('log', type)\n",
|
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" ret = []\n",
|
|
" for log in response['data']:\n",
|
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" assets = log['relationships']['asset']['data']\n",
|
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" for asset in assets:\n",
|
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" if asset['id'] == id:\n",
|
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" ret.append(log)\n",
|
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" 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"
|
|
]
|
|
},
|
|
{
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"metadata": {},
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"outputs": [],
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