{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "f512fba3-877f-411e-935c-0c878d478b2d", "metadata": { "jupyter": { "source_hidden": true }, "scrolled": true }, "outputs": [], "source": [ "import neofarm.lib as neo\n", "import importlib\n", "importlib.reload(neo)\n", "\n", "plant = neo.Plant.from_id(\"76f89f82-1238-43bb-b34f-796a92d491a2\")\n", "print(plant.name)\n", "\n", "\n", "seedings = neo.Seeding.get_list()\n", "for seeding in seedings:\n", " plant = seeding.getPlant()\n", " equipment = seeding.equipment[0]\n", " quantity1 = seeding.quantities[0]\n", " quantity2 = seeding.quantities[1]\n", " print(f\"name: {seeding.name}\")\n", " print(f\"timestamp: {seeding.timestamp}\")\n", " print(f\"plant name: {plant.name}\")\n", " print(f\"plant crop: {plant.crop[0].name}\")\n", " print(f\"plant loc: {plant.location[0].name}\")\n", " print(f\"equipment: {equipment.name}\")\n", " print(f\"isMovement: {seeding.isMovement}\")\n", " print(f\"Q1 type: {quantity1.type}\")\n", " print(f\"Q1 measure: {quantity1.measure}\")\n", " print(f\"Q1 value: {quantity1.value}\")\n", " print(f\"Q1 units: {quantity1.units.name}\")\n", " print(f\"Q1 inv adj: {quantity1.inventory_adjustment}\")\n", " print(f\"Q1 inv ass: {quantity1.inventory_asset.name}\")\n", " print(f\"Q2 type: {quantity2.type}\")\n", " print(f\"Q2 measure: {quantity2.measure}\")\n", " print(f\"Q2 value: {quantity2.value}\")\n", " print(f\"Q2 units: {quantity2.units.name}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "fdf49f3b-3949-4895-97fb-f5bd2484fce3", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "import neofarm.lib as neo\n", "import importlib\n", "importlib.reload(neo)\n", "\n", "inputs = neo.Input.get_list()\n", "for element in inputs:\n", " #equipment = element.equipment[0]\n", " print(f\"name: {element.name}\")\n", " print(f\"timestamp: {element.timestamp}\")\n", " print(f\"plant name: {element.plant.name}\")\n", " print(f\"plant crop: {element.plant.crop[0].name}\")\n", " print(f\"plant loc: {element.plant.location[0].name}\")\n", " print(f\"equipment: {element.equipment[0].name}\")\n", " print(f\"isMovement: {element.isMovement}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "28598d92-24f2-47cb-bb19-9943800eefe1", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "import neofarm.lib as neo\n", "import importlib\n", "importlib.reload(neo)\n", "\n", "data = neo.Harvest.get_list()\n", "for element in data:\n", " #equipment = element.equipment[0]\n", " print(f\"name: {element.name}\")\n", " print(f\"timestamp: {element.timestamp}\")\n", " print(f\"plant name: {element.plant.name}\")\n", " print(f\"plant crop: {element.plant.crop[0].name}\")\n", " print(f\"plant loc: {element.plant.location[0].name}\")\n", " print(f\"equipment: {element.equipment[0].name}\")\n", " print(f\"isMovement: {element.isMovement}\")\n", " print(f\"Name vom Inventory asset von der Quantity: {element.quantities[0].inventory_asset.name}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "a993570d-dea2-4bd6-80c0-b36297ce6a3a", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "#Beispiel\n", "import neofarm.lib as neo\n", "\n", "if __name__ == \"__main__\":\n", " asset = neo.Asset.Plant.from_id(\"76f89f82-1238-43bb-b34f-796a92d491a2\")\n", " logs = asset.get_logs_of_type(neo.Log.Activity)\n", " for log in logs:\n", " print(log.name)" ] }, { "cell_type": "code", "execution_count": null, "id": "3a33ffb1-afe7-44b5-863b-9779a95a2a9c", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "# alle logs von einem Plant\n", "import neofarm.lib as neo\n", "from datetime import datetime\n", "\n", "if __name__ == \"__main__\":\n", " asset = neo.Asset.Plant.from_id(\"76f89f82-1238-43bb-b34f-796a92d491a2\")\n", " \n", " PlantName = \"W-Raps Helmacker Plant 24/25\" \n", " print(f\"PlantName: {PlantName}\")\n", " \n", " logs = asset.get_logs_of_type(neo.Log.Maintenance)\n", " for log in logs:\n", " print(\"Maintenance:\")\n", " print(f\"name: {log.name}\")\n", " formatted_date = datetime.fromisoformat(log.timestamp).strftime(\"%d.%m.%y %H:%M \") # Konvertiere in das gewünschte Format\n", " print(f\"timestamp: {formatted_date}\") \n", " print(f\"equipment: {log.equipment[0].name}\")\n", " \n", " logs = asset.get_logs_of_type(neo.Log.Seeding)\n", " for log in logs:\n", " print(\"Seeding:\")\n", " print(f\"name: {log.name}\")\n", " formatted_date = datetime.fromisoformat(log.timestamp).strftime(\"%d.%m.%y %H:%M \") # Konvertiere in das gewünschte Format\n", " print(f\"timestamp: {formatted_date}\")\n", " print(f\"equipment: {log.equipment[0].name}\")\n", " print(f\"plant loc: {log.location[0].name}\")\n", "\n", " logs = asset.get_logs_of_type(neo.Log.Input)\n", " for log in logs:\n", " print(\"Input:\")\n", " print(f\"name: {log.name}\")\n", " formatted_date = datetime.fromisoformat(log.timestamp).strftime(\"%d.%m.%y %H:%M \") # Konvertiere in das gewünschte Format\n", " print(f\"timestamp: {formatted_date}\")\n", " print(f\"equipment: {log.equipment[0].name}\")\n", "\n", " logs = asset.get_logs_of_type(neo.Log.Medical)\n", " for log in logs:\n", " print(\"Medical:\")\n", " print(f\"name: {log.name}\")\n", " formatted_date = datetime.fromisoformat(log.timestamp).strftime(\"%d.%m.%y %H:%M \") # Konvertiere in das gewünschte Format\n", " print(f\"timestamp: {formatted_date}\")\n", " print(f\"equipment: {log.equipment[0].name}\")\n", "\n", " logs = asset.get_logs_of_type(neo.Log.Harvest)\n", " for log in logs:\n", " print(\"Harvest:\")\n", " print(f\"name: {log.name}\")\n", " formatted_date = datetime.fromisoformat(log.timestamp).strftime(\"%d.%m.%y %H:%M \") # Konvertiere in das gewünschte Format\n", " print(f\"timestamp: {formatted_date}\")\n", " print(f\"equipment: {log.equipment[0].name}\")\n", "\n", " logs = asset.get_logs_of_type(neo.Log.Sale)\n", " for log in logs:\n", " print(\"Sale:\")\n", " print(f\"name: {log.name}\")\n", " formatted_date = datetime.fromisoformat(log.timestamp).strftime(\"%d.%m.%y %H:%M \") # Konvertiere in das gewünschte Format\n", " print(f\"timestamp: {formatted_date}\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "ce30a9db-735e-4174-ad56-0f0079ab04c6", "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true, "source_hidden": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PlantName: W-Raps Helmacker Plant 24/25\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
LogTypeNameTimestampEquipmentLocation
0MaintenanceScheibeneggen Helmacker Maintenance 24/2528.07.24 11:34Scheibenegge Catros
1SeedingW-Raps Otello KWS Helmacker Seeding 24/2522.08.24 22:00Sähmaschine CatayaHelmacker
2InputInnovert Raps Input 24/2512.10.24 13:18Spritze
3InputInnovert Raps Input 24/2503.11.24 23:00Spritze
4MedicalSchneckenkorn Medical 24/2530.08.24 06:42Schneckenkornstreuer Leinfelder
5HarvestW-Raps Helmacker Harvest 24/2520.10.24 10:11Mähdrescher Leinfelder
6SaleW-Raps Sale 24/2528.10.24 07:54
\n", "
" ], "text/plain": [ " LogType Name Timestamp \\\n", "0 Maintenance Scheibeneggen Helmacker Maintenance 24/25 28.07.24 11:34 \n", "1 Seeding W-Raps Otello KWS Helmacker Seeding 24/25 22.08.24 22:00 \n", "2 Input Innovert Raps Input 24/25 12.10.24 13:18 \n", "3 Input Innovert Raps Input 24/25 03.11.24 23:00 \n", "4 Medical Schneckenkorn Medical 24/25 30.08.24 06:42 \n", "5 Harvest W-Raps Helmacker Harvest 24/25 20.10.24 10:11 \n", "6 Sale W-Raps Sale 24/25 28.10.24 07:54 \n", "\n", " Equipment Location \n", "0 Scheibenegge Catros \n", "1 Sähmaschine Cataya Helmacker \n", "2 Spritze \n", "3 Spritze \n", "4 Schneckenkornstreuer Leinfelder \n", "5 Mähdrescher Leinfelder \n", "6 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#alle logs von einem Plant in Tabelle\n", "import neofarm.lib as neo\n", "import pandas as pd\n", "from datetime import datetime\n", "\n", "if __name__ == \"__main__\":\n", " asset = neo.Asset.Plant.from_id(\"76f89f82-1238-43bb-b34f-796a92d491a2\")\n", " \n", " # Definiere den Pflanzennamen\n", " PlantName = \"W-Raps Helmacker Plant 24/25\"\n", " print(f\"PlantName: {PlantName}\")\n", " \n", " # Erstelle eine Liste, in der die Log-Daten für die Tabelle gesammelt werden\n", " data = []\n", "\n", " # Funktion, um die Logs zu verarbeiten und zur Tabelle hinzuzufügen\n", " def process_logs(logs, log_type):\n", " for log in logs:\n", " formatted_date = datetime.fromisoformat(log.timestamp).strftime(\"%d.%m.%y %H:%M\")\n", " equipment_name = log.equipment[0].name if log.equipment else \"\"\n", " location_name = log.location[0].name if hasattr(log, 'location') and log.location else \"\"\n", " data.append({\n", " #\"PlantName\": PlantName,\n", " \"LogType\": log_type,\n", " \"Name\": log.name,\n", " \"Timestamp\": formatted_date,\n", " \"Equipment\": equipment_name,\n", " \"Location\": location_name\n", " })\n", "\n", " # Verarbeite die verschiedenen Log-Typen\n", " process_logs(asset.get_logs_of_type(neo.Log.Maintenance), \"Maintenance\")\n", " process_logs(asset.get_logs_of_type(neo.Log.Seeding), \"Seeding\")\n", " process_logs(asset.get_logs_of_type(neo.Log.Input), \"Input\")\n", " process_logs(asset.get_logs_of_type(neo.Log.Medical), \"Medical\")\n", " process_logs(asset.get_logs_of_type(neo.Log.Harvest), \"Harvest\")\n", " process_logs(asset.get_logs_of_type(neo.Log.Sale), \"Sale\")\n", "\n", " # Erstelle ein DataFrame aus den gesammelten Daten und zeige es an\n", " df = pd.DataFrame(data)\n", " display(df)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "1b5e32c3-d543-4449-b311-c7e373d99296", "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 }