344 lines
13 KiB
Plaintext
344 lines
13 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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"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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"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": 6,
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"id": "3a33ffb1-afe7-44b5-863b-9779a95a2a9c",
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"metadata": {},
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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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"Maintenance:\n",
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"name: Scheibeneggen Helmacker Maintenance 24/25\n",
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"timestamp: 28.07.24 11:34 \n",
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"equipment: Scheibenegge Catros\n",
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"quant_type: quantity--price\n",
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"quant_measure: time\n",
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"quant_value: 2\n",
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"quant_inventory_adjustment: None\n",
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"Seeding:\n",
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"name: W-Raps Otello KWS Helmacker Seeding 24/25\n",
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"timestamp: 22.08.24 22:00 \n",
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"equipment: Sähmaschine Cataya\n",
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"plant loc: Helmacker\n",
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"quant_type: quantity--standard\n",
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"quant_measure: weight\n",
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"quant_value: 50\n",
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"quant_inventory_adjustment: decrement\n",
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"Input:\n",
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"name: Innovert Raps Input 24/25\n",
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"timestamp: 12.10.24 13:18 \n",
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"equipment: Spritze\n",
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"quant_type: quantity--standard\n",
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"quant_measure: volume\n",
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"quant_value: 10\n",
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"quant_inventory_adjustment: decrement\n",
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"Input:\n",
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"name: Innovert Raps Input 24/25\n",
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"timestamp: 03.11.24 23:00 \n",
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"equipment: Spritze\n",
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"quant_type: quantity--standard\n",
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"quant_measure: volume\n",
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"quant_value: 10\n",
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"quant_inventory_adjustment: decrement\n",
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"Medical:\n",
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"name: Schneckenkorn Medical 24/25\n",
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"timestamp: 30.08.24 06:42 \n",
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"equipment: Schneckenkornstreuer Leinfelder\n",
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"Harvest:\n",
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"name: W-Raps Helmacker Harvest 24/25\n",
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"timestamp: 20.10.24 10:11 \n",
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"equipment: Mähdrescher Leinfelder\n",
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"Sale:\n",
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"name: W-Raps Sale 24/25\n",
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"timestamp: 28.10.24 07:54 \n"
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]
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}
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],
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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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" input=neo.Log.Maintenance.from_id(log.id)\n",
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" print(f\"quant_type: {input.quantities[0].type}\")\n",
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" print(f\"quant_measure: {input.quantities[0].measure}\")\n",
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" print(f\"quant_value: {input.quantities[0].value}\")\n",
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" print(f\"quant_inventory_adjustment: {input.quantities[0].inventory_adjustment}\")\n",
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" #print(f\"quant_value: {input.quantities[0].unit_price.decimal}\")\n",
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"\n",
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" \t\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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" input=neo.Log.Seeding.from_id(log.id)\n",
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" print(f\"quant_type: {input.quantities[0].type}\")\n",
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" print(f\"quant_measure: {input.quantities[0].measure}\")\n",
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" print(f\"quant_value: {input.quantities[0].value}\")\n",
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" print(f\"quant_inventory_adjustment: {input.quantities[0].inventory_adjustment}\")\n",
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" \t\n",
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"\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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" #print(f\"name: {log.id}\")\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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"\n",
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"\n",
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"\n",
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" input=neo.Log.Input.from_id(log.id)\n",
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" print(f\"quant_type: {input.quantities[0].type}\")\n",
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" print(f\"quant_measure: {input.quantities[0].measure}\")\n",
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" print(f\"quant_value: {input.quantities[0].value}\")\n",
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" print(f\"quant_inventory_adjustment: {input.quantities[0].inventory_adjustment}\")\n",
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"\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": null,
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"id": "ce30a9db-735e-4174-ad56-0f0079ab04c6",
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"metadata": {},
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"outputs": [],
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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": "649e4d8e-c3a9-4d5f-9bdf-9427f416fca9",
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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": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"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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}
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