401 lines
14 KiB
Plaintext
401 lines
14 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f512fba3-877f-411e-935c-0c878d478b2d",
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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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"import neofarm.lib as neo\n",
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"import importlib\n",
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"importlib.reload(neo)\n",
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"\n",
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"plant = neo.Plant.from_id(\"76f89f82-1238-43bb-b34f-796a92d491a2\")\n",
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"print(plant.name)\n",
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"\n",
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"\n",
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"seedings = neo.Seeding.get_list()\n",
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"for seeding in seedings:\n",
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" plant = seeding.getPlant()\n",
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" equipment = seeding.equipment[0]\n",
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" quantity1 = seeding.quantities[0]\n",
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" quantity2 = seeding.quantities[1]\n",
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" print(f\"name: {seeding.name}\")\n",
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" print(f\"timestamp: {seeding.timestamp}\")\n",
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" print(f\"plant name: {plant.name}\")\n",
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" print(f\"plant crop: {plant.crop[0].name}\")\n",
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" print(f\"plant loc: {plant.location[0].name}\")\n",
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" print(f\"equipment: {equipment.name}\")\n",
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" print(f\"isMovement: {seeding.isMovement}\")\n",
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" print(f\"Q1 type: {quantity1.type}\")\n",
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" print(f\"Q1 measure: {quantity1.measure}\")\n",
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" print(f\"Q1 value: {quantity1.value}\")\n",
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" print(f\"Q1 units: {quantity1.units.name}\")\n",
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" print(f\"Q1 inv adj: {quantity1.inventory_adjustment}\")\n",
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" print(f\"Q1 inv ass: {quantity1.inventory_asset.name}\")\n",
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" print(f\"Q2 type: {quantity2.type}\")\n",
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" print(f\"Q2 measure: {quantity2.measure}\")\n",
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" print(f\"Q2 value: {quantity2.value}\")\n",
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" print(f\"Q2 units: {quantity2.units.name}\")"
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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": "fdf49f3b-3949-4895-97fb-f5bd2484fce3",
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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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"import neofarm.lib as neo\n",
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"import importlib\n",
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"importlib.reload(neo)\n",
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"\n",
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"inputs = neo.Input.get_list()\n",
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"for element in inputs:\n",
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" #equipment = element.equipment[0]\n",
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" print(f\"name: {element.name}\")\n",
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" print(f\"timestamp: {element.timestamp}\")\n",
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" print(f\"plant name: {element.plant.name}\")\n",
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" print(f\"plant crop: {element.plant.crop[0].name}\")\n",
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" print(f\"plant loc: {element.plant.location[0].name}\")\n",
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" print(f\"equipment: {element.equipment[0].name}\")\n",
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" print(f\"isMovement: {element.isMovement}\")"
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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": "28598d92-24f2-47cb-bb19-9943800eefe1",
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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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"import neofarm.lib as neo\n",
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"import importlib\n",
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"importlib.reload(neo)\n",
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"\n",
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"data = neo.Harvest.get_list()\n",
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"for element in data:\n",
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" #equipment = element.equipment[0]\n",
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" print(f\"name: {element.name}\")\n",
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" print(f\"timestamp: {element.timestamp}\")\n",
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" print(f\"plant name: {element.plant.name}\")\n",
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" print(f\"plant crop: {element.plant.crop[0].name}\")\n",
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" print(f\"plant loc: {element.plant.location[0].name}\")\n",
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" print(f\"equipment: {element.equipment[0].name}\")\n",
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" print(f\"isMovement: {element.isMovement}\")\n",
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" print(f\"Name vom Inventory asset von der Quantity: {element.quantities[0].inventory_asset.name}\")"
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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": "a993570d-dea2-4bd6-80c0-b36297ce6a3a",
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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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"#Beispiel\n",
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"import neofarm.lib as neo\n",
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"\n",
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"if __name__ == \"__main__\":\n",
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" asset = neo.Asset.Plant.from_id(\"76f89f82-1238-43bb-b34f-796a92d491a2\")\n",
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" logs = asset.get_logs_of_type(neo.Log.Activity)\n",
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" for log in logs:\n",
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" print(log.name)"
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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": "3a33ffb1-afe7-44b5-863b-9779a95a2a9c",
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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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"# alle logs von einem Plant\n",
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"import neofarm.lib as neo\n",
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"from datetime import datetime\n",
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"\n",
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"if __name__ == \"__main__\":\n",
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" asset = neo.Asset.Plant.from_id(\"76f89f82-1238-43bb-b34f-796a92d491a2\")\n",
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" \n",
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" PlantName = \"W-Raps Helmacker Plant 24/25\" \n",
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" print(f\"PlantName: {PlantName}\")\n",
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" \n",
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" logs = asset.get_logs_of_type(neo.Log.Maintenance)\n",
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" for log in logs:\n",
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" print(\"Maintenance:\")\n",
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" print(f\"name: {log.name}\")\n",
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" formatted_date = datetime.fromisoformat(log.timestamp).strftime(\"%d.%m.%y %H:%M \") # Konvertiere in das gewünschte Format\n",
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" print(f\"timestamp: {formatted_date}\") \n",
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" print(f\"equipment: {log.equipment[0].name}\")\n",
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" \n",
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" logs = asset.get_logs_of_type(neo.Log.Seeding)\n",
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" for log in logs:\n",
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" print(\"Seeding:\")\n",
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" print(f\"name: {log.name}\")\n",
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" formatted_date = datetime.fromisoformat(log.timestamp).strftime(\"%d.%m.%y %H:%M \") # Konvertiere in das gewünschte Format\n",
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" print(f\"timestamp: {formatted_date}\")\n",
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" print(f\"equipment: {log.equipment[0].name}\")\n",
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" print(f\"plant loc: {log.location[0].name}\")\n",
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"\n",
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" logs = asset.get_logs_of_type(neo.Log.Input)\n",
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" for log in logs:\n",
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" print(\"Input:\")\n",
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" print(f\"name: {log.name}\")\n",
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" formatted_date = datetime.fromisoformat(log.timestamp).strftime(\"%d.%m.%y %H:%M \") # Konvertiere in das gewünschte Format\n",
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" print(f\"timestamp: {formatted_date}\")\n",
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" print(f\"equipment: {log.equipment[0].name}\")\n",
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"\n",
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" logs = asset.get_logs_of_type(neo.Log.Medical)\n",
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" for log in logs:\n",
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" print(\"Medical:\")\n",
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" print(f\"name: {log.name}\")\n",
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" formatted_date = datetime.fromisoformat(log.timestamp).strftime(\"%d.%m.%y %H:%M \") # Konvertiere in das gewünschte Format\n",
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" print(f\"timestamp: {formatted_date}\")\n",
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" print(f\"equipment: {log.equipment[0].name}\")\n",
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"\n",
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" logs = asset.get_logs_of_type(neo.Log.Harvest)\n",
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" for log in logs:\n",
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" print(\"Harvest:\")\n",
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" print(f\"name: {log.name}\")\n",
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" formatted_date = datetime.fromisoformat(log.timestamp).strftime(\"%d.%m.%y %H:%M \") # Konvertiere in das gewünschte Format\n",
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" print(f\"timestamp: {formatted_date}\")\n",
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" print(f\"equipment: {log.equipment[0].name}\")\n",
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"\n",
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" logs = asset.get_logs_of_type(neo.Log.Sale)\n",
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" for log in logs:\n",
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" print(\"Sale:\")\n",
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" print(f\"name: {log.name}\")\n",
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" formatted_date = datetime.fromisoformat(log.timestamp).strftime(\"%d.%m.%y %H:%M \") # Konvertiere in das gewünschte Format\n",
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" print(f\"timestamp: {formatted_date}\")"
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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": 4,
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"id": "ce30a9db-735e-4174-ad56-0f0079ab04c6",
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"PlantName: W-Raps Helmacker Plant 24/25\n"
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]
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},
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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>LogType</th>\n",
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" <th>Name</th>\n",
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" <th>Timestamp</th>\n",
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" <th>Equipment</th>\n",
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" <th>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>Maintenance</td>\n",
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" <td>Scheibeneggen Helmacker Maintenance 24/25</td>\n",
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" <td>28.07.24 11:34</td>\n",
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" <td>Scheibenegge Catros</td>\n",
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" <td></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>Seeding</td>\n",
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" <td>W-Raps Otello KWS Helmacker Seeding 24/25</td>\n",
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" <td>22.08.24 22:00</td>\n",
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" <td>Sähmaschine Cataya</td>\n",
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" <td>Helmacker</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>Input</td>\n",
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" <td>Innovert Raps Input 24/25</td>\n",
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" <td>12.10.24 13:18</td>\n",
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" <td>Spritze</td>\n",
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" <td></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>Input</td>\n",
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" <td>Innovert Raps Input 24/25</td>\n",
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" <td>03.11.24 23:00</td>\n",
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" <td>Spritze</td>\n",
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" <td></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>Medical</td>\n",
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" <td>Schneckenkorn Medical 24/25</td>\n",
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" <td>30.08.24 06:42</td>\n",
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" <td>Schneckenkornstreuer Leinfelder</td>\n",
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" <td></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>Harvest</td>\n",
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" <td>W-Raps Helmacker Harvest 24/25</td>\n",
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" <td>20.10.24 10:11</td>\n",
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" <td>Mähdrescher Leinfelder</td>\n",
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" <td></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>Sale</td>\n",
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" <td>W-Raps Sale 24/25</td>\n",
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" <td>28.10.24 07:54</td>\n",
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" <td></td>\n",
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" <td></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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" LogType Name Timestamp \\\n",
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"0 Maintenance Scheibeneggen Helmacker Maintenance 24/25 28.07.24 11:34 \n",
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"1 Seeding W-Raps Otello KWS Helmacker Seeding 24/25 22.08.24 22:00 \n",
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"2 Input Innovert Raps Input 24/25 12.10.24 13:18 \n",
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"3 Input Innovert Raps Input 24/25 03.11.24 23:00 \n",
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"4 Medical Schneckenkorn Medical 24/25 30.08.24 06:42 \n",
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"5 Harvest W-Raps Helmacker Harvest 24/25 20.10.24 10:11 \n",
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"6 Sale W-Raps Sale 24/25 28.10.24 07:54 \n",
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"\n",
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" Equipment Location \n",
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"0 Scheibenegge Catros \n",
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"1 Sähmaschine Cataya Helmacker \n",
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"2 Spritze \n",
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"3 Spritze \n",
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"4 Schneckenkornstreuer Leinfelder \n",
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"5 Mähdrescher Leinfelder \n",
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"6 "
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"#alle logs von einem Plant in Tabelle\n",
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"import neofarm.lib as neo\n",
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"import pandas as pd\n",
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"from datetime import datetime\n",
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"\n",
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"if __name__ == \"__main__\":\n",
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" asset = neo.Asset.Plant.from_id(\"76f89f82-1238-43bb-b34f-796a92d491a2\")\n",
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" \n",
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" # Definiere den Pflanzennamen\n",
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" PlantName = \"W-Raps Helmacker Plant 24/25\"\n",
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" print(f\"PlantName: {PlantName}\")\n",
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" \n",
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" # Erstelle eine Liste, in der die Log-Daten für die Tabelle gesammelt werden\n",
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" data = []\n",
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"\n",
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" # Funktion, um die Logs zu verarbeiten und zur Tabelle hinzuzufügen\n",
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" def process_logs(logs, log_type):\n",
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" for log in logs:\n",
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" formatted_date = datetime.fromisoformat(log.timestamp).strftime(\"%d.%m.%y %H:%M\")\n",
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" equipment_name = log.equipment[0].name if log.equipment else \"\"\n",
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" location_name = log.location[0].name if hasattr(log, 'location') and log.location else \"\"\n",
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" data.append({\n",
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" #\"PlantName\": PlantName,\n",
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" \"LogType\": log_type,\n",
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" \"Name\": log.name,\n",
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" \"Timestamp\": formatted_date,\n",
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" \"Equipment\": equipment_name,\n",
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" \"Location\": location_name\n",
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" })\n",
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"\n",
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" # Verarbeite die verschiedenen Log-Typen\n",
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" process_logs(asset.get_logs_of_type(neo.Log.Maintenance), \"Maintenance\")\n",
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" process_logs(asset.get_logs_of_type(neo.Log.Seeding), \"Seeding\")\n",
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" process_logs(asset.get_logs_of_type(neo.Log.Input), \"Input\")\n",
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" process_logs(asset.get_logs_of_type(neo.Log.Medical), \"Medical\")\n",
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" process_logs(asset.get_logs_of_type(neo.Log.Harvest), \"Harvest\")\n",
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" process_logs(asset.get_logs_of_type(neo.Log.Sale), \"Sale\")\n",
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"\n",
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" # Erstelle ein DataFrame aus den gesammelten Daten und zeige es an\n",
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" df = pd.DataFrame(data)\n",
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" display(df)\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": null,
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|
"id": "1b5e32c3-d543-4449-b311-c7e373d99296",
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"metadata": {},
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|
"outputs": [],
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|
"source": []
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|
}
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|
],
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"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
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|
"language": "python",
|
|
"name": "python3"
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|
},
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|
"language_info": {
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|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
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|
},
|
|
"file_extension": ".py",
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|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
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|
"version": "3.12.5"
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}
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},
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|
"nbformat": 4,
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"nbformat_minor": 5
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}
|