{
 "cells": [
  {
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   "source": [
    "#alle Seeding logs\n",
    "import neofarm.lib as neo\n",
    "\n",
    "#plant = neo.Asset.Plant.from_id(\"76f89f82-1238-43bb-b34f-796a92d491a2\")\n",
    "#print(plant.name)\n",
    "\n",
    "\n",
    "seedings = neo.Log.Seeding.get_list()\n",
    "for seeding in seedings:\n",
    "    plant = seeding.plant\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": "c1bf2175-65f7-4704-8d39-15a08849c9ae",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
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   "outputs": [],
   "source": [
    "import neofarm.lib as neo\n",
    "\n",
    "#plant = neo.Asset.Plant.from_id(\"76f89f82-1238-43bb-b34f-796a92d491a2\")\n",
    "#print(plant.name)\n",
    "\n",
    "\n",
    "seedings = neo.Log.Purchase.get_list()\n",
    "for seeding in seedings:\n",
    "    plant = seeding.plant\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",
    "\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": {},
   "outputs": [],
   "source": [
    "import neofarm.lib as neo\n",
    "\n",
    "data = neo.Log.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": {},
   "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\n",
    "    PlantName = \"W-Raps Helmacker Plant 24/25\" \n",
    "    print(f\"PlantName: {PlantName}\")\n",
    "\n",
    "    \n",
    "#log Maintenance\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",
    "#quantity\n",
    "        input=neo.Log.Maintenance.from_id(log.id)\n",
    "        print(f\"quant_type: {input.quantities[0].type}\")\n",
    "        print(f\"quant_measure: {input.quantities[0].measure}\")\n",
    "        print(f\"quant_value: {input.quantities[0].value}\")\n",
    "        print(f\"quant_inventory_adjustment: {input.quantities[0].inventory_adjustment}\")\n",
    "        print(f\"quant_value: {input.quantities[0].value}\")\n",
    "        print(f\"unit_name: {input.quantities[0].units.name}\")\n",
    "        \n",
    "    \t\n",
    "#log Seeding       \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",
    "#quantity\n",
    "        input=neo.Log.Seeding.from_id(log.id)\n",
    "        print(f\"quant_type: {input.quantities[0].type}\")\n",
    "        print(f\"quant_measure: {input.quantities[0].measure}\")\n",
    "        print(f\"quant_value: {input.quantities[0].value}\")\n",
    "        print(f\"quant_inventory_adjustment: {input.quantities[0].inventory_adjustment}\")\n",
    "        print(f\"quant_inventory_name: {input.quantities[0].inventory_asset.name}\")\n",
    "\n",
    "#log Input        \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",
    "        #print(f\"name:       {log.id}\")\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",
    "#quantity\n",
    "        input=neo.Log.Input.from_id(log.id)\n",
    "        print(f\"quant_type: {input.quantities[0].type}\")\n",
    "        print(f\"quant_measure: {input.quantities[0].measure}\")\n",
    "        print(f\"quant_value: {input.quantities[0].value}\")\n",
    "        print(f\"quant_inventory_adjustment: {input.quantities[0].inventory_adjustment}\")\n",
    "\n",
    "#log Medical\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",
    "#quantity\n",
    "        input=neo.Log.Medical.from_id(log.id)\n",
    "        print(f\"quant_type: {input.quantities[0].type}\")\n",
    "        print(f\"quant_measure: {input.quantities[0].measure}\")\n",
    "        print(f\"quant_value: {input.quantities[0].value}\")\n",
    "        print(f\"quant_inventory_adjustment: {input.quantities[0].inventory_adjustment}\")\n",
    "\n",
    "\n",
    "#log Harvest    \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",
    "#quantity\n",
    "        input=neo.Log.Harvest.from_id(log.id)\n",
    "        print(f\"quant_type: {input.quantities[0].type}\")\n",
    "        print(f\"quant_measure: {input.quantities[0].measure}\")\n",
    "        print(f\"quant_value: {input.quantities[0].value}\")\n",
    "        print(f\"quant_inventory_adjustment: {input.quantities[0].inventory_adjustment}\")\n",
    "\n",
    "#log Sale    \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}\")\n",
    "\n",
    "#quantity\n",
    "        input=neo.Log.Sale.from_id(log.id)\n",
    "        print(f\"quant_type: {input.quantities[0].type}\")\n",
    "        print(f\"quant_measure: {input.quantities[0].measure}\")\n",
    "        print(f\"quant_value: {input.quantities[0].value}\")\n",
    "        print(f\"quant_inventory_adjustment: {input.quantities[0].inventory_adjustment}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de57b155-d635-4367-9a56-90d033c3a922",
   "metadata": {
    "jupyter": {
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   "outputs": [],
   "source": [
    "#Alle logs zu plant mit quantities untereinander 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 zur Sammlung der Daten\n",
    "    data = []\n",
    "\n",
    "    # Funktion, um Logs zu verarbeiten und die Daten zur Liste hinzuzufügen\n",
    "    def process_logs(logs, log_type):\n",
    "        for log in logs:\n",
    "            formatted_date = datetime.fromisoformat(log.timestamp).strftime(\"%d.%m.%Y\")\n",
    "            equipment_name = log.equipment[0].name if log.equipment else \"None\"\n",
    "            location_name = log.location[0].name if hasattr(log, 'location') and log.location else \"None\"\n",
    "\n",
    "            # Menge und weitere Details (falls vorhanden)\n",
    "            quantities = getattr(log, 'quantities', [])\n",
    "            for quantity in quantities:\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",
    "                    \"QuantType\": quantity.type,\n",
    "                    \"QuantMeasure\": quantity.measure,\n",
    "                    \"QuantValue\": quantity.value,\n",
    "                    \"UnitName\": quantities[0].units.name,\n",
    "                    \"QuantInventoryAdjustment\": quantity.inventory_adjustment\n",
    "                    \n",
    "                })\n",
    "            # Falls keine Mengeninformationen vorhanden sind\n",
    "            if not quantities:\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",
    "                    \"QuantType\": \"\",\n",
    "                    \"QuantMeasure\": \"\",\n",
    "                    \"QuantValue\": \"\",\n",
    "                    \"UnitName\": quantities[0].units.name,\n",
    "                    \"QuantInventoryAdjustment\": \"\"\n",
    "                    \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": "3e3fe365-b32b-4210-847c-bea0b36bd34c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "946b0281-c77a-4a1e-8e91-5f06ba74afa5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6d12b59-4895-426e-9a30-c9cee801671e",
   "metadata": {
    "scrolled": true
   },
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   "source": [
    "#Master Report\n",
    "# Importieren der benötigten Bibliotheken\n",
    "import neofarm.lib as neo\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "def process_logs(asset_id, plant_name):\n",
    "    \"\"\"\n",
    "    Diese Funktion ruft alle Log-Daten für eine bestimmte Pflanzen-ID ab,\n",
    "    formatiert sie und organisiert sie in einer Liste von Dictionarys.\n",
    "    Schließlich gibt sie einen DataFrame zurück, der die aggregierten Log-Daten enthält.\n",
    "    \n",
    "    Parameter:\n",
    "        asset_id (str): Die ID der Pflanze, für die die Logs abgerufen werden sollen.\n",
    "        plant_name (str): Der Name der Pflanze.\n",
    "\n",
    "    Rückgabe:\n",
    "        pd.DataFrame: Ein DataFrame mit allen gesammelten Log-Daten zur angegebenen Pflanze.\n",
    "    \"\"\"\n",
    "    # Lade die Pflanze basierend auf ihrer ID\n",
    "    asset = neo.Asset.Plant.from_id(asset_id)\n",
    "    data = []  # Liste zum Sammeln der Log-Daten\n",
    "    \n",
    "    # Definieren der Log-Typen, die abgefragt werden sollen\n",
    "    log_types = [\n",
    "        (\"Maintenance\", neo.Log.Maintenance),\n",
    "        (\"Seeding\", neo.Log.Seeding),\n",
    "        (\"Input\", neo.Log.Input),\n",
    "        (\"Medical\", neo.Log.Medical),\n",
    "        (\"Harvest\", neo.Log.Harvest),\n",
    "        (\"Sale\", neo.Log.Sale),\n",
    "    ]\n",
    "    \n",
    "    # Durchlaufen jeder Log-Typ-Kombination und Abrufen der zugehörigen Log-Daten\n",
    "    for log_name, log_type in log_types:\n",
    "        logs = asset.get_logs_of_type(log_type)\n",
    "        \n",
    "        # Verarbeitung der einzelnen Logs für den aktuellen Log-Typ\n",
    "        for log in logs:\n",
    "            # Formatieren des Timestamps im deutschen Datumsformat\n",
    "            formatted_date = datetime.fromisoformat(log.timestamp).strftime(\"%d.%m.%Y\")\n",
    "            \n",
    "            # Abrufen des ersten Equipment-Namens oder Standardwert, falls nicht vorhanden\n",
    "            equipment_name = log.equipment[0].name if log.equipment else \"None\"\n",
    "\n",
    "                     \n",
    "            # Abrufen des ersten Location-Namens oder leer, falls nicht vorhanden\n",
    "            location_name = log.location[0].name if hasattr(log, 'location') and log.location else \"\"\n",
    "\n",
    "            # Abrufen des ersten inventory_asset-Namens oder leer, falls nicht vorhanden\n",
    "            inventory_asset_name = log.inventory_asset.name if hasattr(log, 'inventory_asset') and log.inventory_asset else \"\"\n",
    "\n",
    "     \n",
    "            # Abrufen aller Mengen (quantities) im Log\n",
    "            quantities = getattr(log, 'quantities', [])\n",
    "            \n",
    "            # Initialisieren von Platzhaltern für den Fall, dass weniger als zwei Mengen vorhanden sind\n",
    "            quant_type_2 = quant_measure_2 = quant_value_2 = quant_inventory_adjustment_2 = \"\"\n",
    "            quant_unit_name_1 = quant_unit_name_2 = \"\"\n",
    "\n",
    "            # Verarbeitung der ersten Menge, falls vorhanden\n",
    "            if len(quantities) > 0:\n",
    "                quant_type_1 = quantities[0].type\n",
    "                quant_measure_1 = quantities[0].measure\n",
    "                quant_value_1 = quantities[0].value\n",
    "                #quant_unit_price_1 = quantities[0].unit_price     \n",
    "                #quant_total_price = quantities[0].total_price\n",
    "                quant_inventory_adjustment_1 = quantities[0].inventory_adjustment\n",
    "               # quant_inventory_name_1 = quantities[0].inventory_asset\n",
    "                quant_unit_name_1 = quantities[0].units.name if quantities[0].units else \"\"\n",
    "                quant_inventory_name_1 = quantities[0].inventory_asset.name if quantities[0].inventory_asset else \"\"\n",
    "                \n",
    "            else:\n",
    "                # Leere Werte, falls keine Menge vorhanden ist\n",
    "                quant_type_1 = quant_measure_1 = quant_value_1 = quant_inventory_adjustment_1 = \"\"\n",
    "\n",
    "            # Verarbeitung der zweiten Menge, falls vorhanden\n",
    "            if len(quantities) > 1:\n",
    "                quant_type_2 = quantities[1].type\n",
    "                quant_measure_2 = quantities[1].measure\n",
    "                quant_value_2 = quantities[1].value\n",
    "                quant_inventory_adjustment_2 = quantities[1].inventory_adjustment\n",
    "                quant_unit_name_2 = quantities[1].units.name if quantities[1].units else \"\"\n",
    "\n",
    "            # Hinzufügen der gesammelten Daten für diesen Log als Dictionary in die Liste\n",
    "            data.append({\n",
    "                \"PlantName\": plant_name,\n",
    "                \"LogType\": log_name,\n",
    "                \"Name\": log.name,\n",
    "                \"Timestamp\": formatted_date,\n",
    "                \"Equipment\": equipment_name,\n",
    "                \"Location\": location_name,\n",
    "                \"QuantType_1\": quant_type_1,\n",
    "                \"QuantMeasure_1\": quant_measure_1,\n",
    "                \"QuantValue_1\": quant_value_1,\n",
    "                \"QuantUnitName1\": quant_unit_name_1,\n",
    "                \"QuantInventoryAdjustment_1\": quant_inventory_adjustment_1,\n",
    "                \"quant_inventory_name_1\": quant_inventory_name_1,\n",
    "                \"QuantType_2\": quant_type_2,\n",
    "                \"QuantMeasure_2\": quant_measure_2,\n",
    "                \"QuantValue_2\": quant_value_2,\n",
    "                \"QuantUnitName2\": quant_unit_name_2,\n",
    "                \"QuantInventoryAdjustment_2\": quant_inventory_adjustment_2\n",
    "            })\n",
    "    \n",
    "    # Rückgabe des DataFrames mit allen gesammelten Log-Daten\n",
    "    return pd.DataFrame(data)\n",
    "\n",
    "# Beispielaufruf zur Demonstration\n",
    "if __name__ == \"__main__\":\n",
    "    # ID und Name der Pflanze definieren\n",
    "    asset_id = \"76f89f82-1238-43bb-b34f-796a92d491a2\"\n",
    "    plant_name = \"W-Raps Helmacker Plant 24/25\"\n",
    "    \n",
    "    # Abrufen und Formatieren der Logs in einem DataFrame\n",
    "    df = process_logs(asset_id, plant_name)\n",
    "    \n",
    "    # Konvertieren des 'Timestamp' in ein Datum für die korrekte Anzeige und Berechnung\n",
    "    df['Timestamp'] = pd.to_datetime(df['Timestamp'], format=\"%d.%m.%Y\")\n",
    "    \n",
    "    # Anzeigen des DataFrames zur Veranschaulichung\n",
    "    display(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "172b3075-7435-40e1-bbb5-138e1ca48800",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#ExcelExport\n",
    "import pandas as pd\n",
    "import os\n",
    "from openpyxl import load_workbook\n",
    "from openpyxl.utils import get_column_letter\n",
    "from openpyxl.styles import Alignment\n",
    "\n",
    "def export_to_excel(df, file_path, date_column=\"D\"):\n",
    "    # Exportiere den DataFrame zu Excel\n",
    "    df.to_excel(file_path, index=False)\n",
    "    \n",
    "    # Lade die Arbeitsmappe, um Formatierungen hinzuzufügen\n",
    "    workbook = load_workbook(file_path)\n",
    "    worksheet = workbook.active\n",
    "    worksheet.auto_filter.ref = worksheet.dimensions\n",
    "\n",
    "    # Passen Sie die Spaltenbreite an und aktivieren Sie den Zeilenumbruch\n",
    "    for col in worksheet.columns:\n",
    "        max_length = 0\n",
    "        col_letter = get_column_letter(col[0].column)\n",
    "        for cell in col:\n",
    "            cell.alignment = Alignment(wrap_text=True)\n",
    "            \n",
    "            # Setze das Datumsformat für die angegebene Spalte\n",
    "            if col_letter == date_column:\n",
    "                cell.number_format = 'DD.MM.YYYY'\n",
    "                \n",
    "            max_length = max(max_length, len(str(cell.value)) if cell.value else 0)\n",
    "        \n",
    "        adjusted_width = (max_length + 2) * 1.0\n",
    "        worksheet.column_dimensions[col_letter].width = adjusted_width\n",
    "\n",
    "    workbook.save(file_path)\n",
    "    print(f\"Die Datei wurde erfolgreich nach '{file_path}' exportiert, mit Autofilter, Zeilenumbruch und angepasster Spaltenbreite.\")\n",
    "\n",
    "# Beispielaufruf\n",
    "if __name__ == \"__main__\":\n",
    "    downloads_folder = os.path.join(os.path.expanduser(\"~\"), \"Downloads\")\n",
    "    file_path = os.path.join(downloads_folder, \"PlantLogs.xlsx\")\n",
    "    \n",
    "    \n",
    "    # Prüfe, ob die Datei bereits existiert\n",
    "    if os.path.exists(file_path):\n",
    "        overwrite = input(f\"Die Datei '{file_path}' existiert bereits. Möchten Sie sie überschreiben? (ja/nein): \")\n",
    "        if overwrite.lower() == 'ja':\n",
    "            export_to_excel(df, file_path)\n",
    "        else:\n",
    "            print(\"Der Export wurde abgebrochen.\")\n",
    "    else:\n",
    "        export_to_excel(df, file_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc1f2524-ca06-426f-a402-9e61ec9ddfbe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "849c4af5-f0f9-47f4-b841-fa8dc3e27c15",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
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   },
   "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>timestamp</th>\n",
       "      <th>plant_name</th>\n",
       "      <th>plant_crop</th>\n",
       "      <th>plant_location</th>\n",
       "      <th>equipment</th>\n",
       "      <th>isMovement</th>\n",
       "      <th>Q1_type</th>\n",
       "      <th>Q1_measure</th>\n",
       "      <th>Q1_value</th>\n",
       "      <th>Q1_units</th>\n",
       "      <th>Q1_inventory_adjustment</th>\n",
       "      <th>Q1_inventory_asset</th>\n",
       "      <th>Q2_type</th>\n",
       "      <th>Q2_measure</th>\n",
       "      <th>Q2_value</th>\n",
       "      <th>Q2_units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>W-Raps Otello KWS Helmacker Seeding 24/25</td>\n",
       "      <td>22.08.2024</td>\n",
       "      <td>W-Raps Helmacker Plant 24/25</td>\n",
       "      <td>Raps</td>\n",
       "      <td>Helmacker</td>\n",
       "      <td>Sähmaschine Cataya</td>\n",
       "      <td>False</td>\n",
       "      <td>quantity--standard</td>\n",
       "      <td>weight</td>\n",
       "      <td>50</td>\n",
       "      <td>kg</td>\n",
       "      <td>decrement</td>\n",
       "      <td>W-Raps Saatgut Otello KWS Seed 08/24</td>\n",
       "      <td>quantity--standard</td>\n",
       "      <td>area</td>\n",
       "      <td>3.02</td>\n",
       "      <td>ha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>W-Raps Otello KWS Nachtweide Seeding 24/25</td>\n",
       "      <td>28.08.2024</td>\n",
       "      <td>W-Raps Nachtweide Plant 24/25</td>\n",
       "      <td>Raps</td>\n",
       "      <td>Nachtweide</td>\n",
       "      <td>Sähmaschine Cataya</td>\n",
       "      <td>False</td>\n",
       "      <td>quantity--standard</td>\n",
       "      <td>weight</td>\n",
       "      <td>66</td>\n",
       "      <td>kg</td>\n",
       "      <td>decrement</td>\n",
       "      <td>W-Raps Saatgut Otello KWS Seed 08/24</td>\n",
       "      <td>quantity--standard</td>\n",
       "      <td>area</td>\n",
       "      <td>4.5</td>\n",
       "      <td>ha</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         name   timestamp  \\\n",
       "0   W-Raps Otello KWS Helmacker Seeding 24/25  22.08.2024   \n",
       "1  W-Raps Otello KWS Nachtweide Seeding 24/25  28.08.2024   \n",
       "\n",
       "                       plant_name plant_crop plant_location  \\\n",
       "0    W-Raps Helmacker Plant 24/25       Raps      Helmacker   \n",
       "1  W-Raps Nachtweide Plant 24/25        Raps     Nachtweide   \n",
       "\n",
       "            equipment  isMovement             Q1_type Q1_measure Q1_value  \\\n",
       "0  Sähmaschine Cataya       False  quantity--standard     weight       50   \n",
       "1  Sähmaschine Cataya       False  quantity--standard     weight       66   \n",
       "\n",
       "  Q1_units Q1_inventory_adjustment                    Q1_inventory_asset  \\\n",
       "0       kg               decrement  W-Raps Saatgut Otello KWS Seed 08/24   \n",
       "1       kg               decrement  W-Raps Saatgut Otello KWS Seed 08/24   \n",
       "\n",
       "              Q2_type Q2_measure Q2_value Q2_units  \n",
       "0  quantity--standard       area     3.02       ha  \n",
       "1  quantity--standard       area      4.5       ha  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Alle Seeding Logs\n",
    "import neofarm.lib as neo\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "# Liste für die Sammlung der Seeding-Log-Daten\n",
    "data = []\n",
    "\n",
    "# Alle Seeding-Logs abrufen\n",
    "seedings = neo.Log.Seeding.get_list()\n",
    "for seeding in seedings:\n",
    "    plant = seeding.plant\n",
    "    equipment = seeding.equipment[0]\n",
    "    quantity1 = seeding.quantities[0]\n",
    "    quantity2 = seeding.quantities[1]\n",
    "    \n",
    "    # Seeding-Daten als Dictionary hinzufügen\n",
    "    data.append({\n",
    "        \"name\": seeding.name,\n",
    "        \"timestamp\": datetime.fromisoformat(seeding.timestamp).strftime(\"%d.%m.%Y\"),  # Konvertiere in das gewünschte Format\n",
    "        \"plant_name\": plant.name,\n",
    "        \"plant_crop\": plant.crop[0].name,\n",
    "        \"plant_location\": plant.location[0].name,\n",
    "        \"equipment\": equipment.name,\n",
    "        \"isMovement\": seeding.isMovement,\n",
    "        \"Q1_type\": quantity1.type,\n",
    "        \"Q1_measure\": quantity1.measure,\n",
    "        \"Q1_value\": quantity1.value,\n",
    "        \"Q1_units\": quantity1.units.name,\n",
    "        \"Q1_inventory_adjustment\": quantity1.inventory_adjustment,\n",
    "        \"Q1_inventory_asset\": quantity1.inventory_asset.name,\n",
    "        \"Q2_type\": quantity2.type,\n",
    "        \"Q2_measure\": quantity2.measure,\n",
    "        \"Q2_value\": quantity2.value,\n",
    "        \"Q2_units\": quantity2.units.name\n",
    "    })\n",
    "\n",
    "# DataFrame erstellen und anzeigen\n",
    "df = pd.DataFrame(data)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "27e54d8e-cda2-465c-88eb-963a0fff3c5d",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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   },
   "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>timestamp</th>\n",
       "      <th>plant_name</th>\n",
       "      <th>equipment</th>\n",
       "      <th>Q1_type</th>\n",
       "      <th>Q1_measure</th>\n",
       "      <th>Q1_value</th>\n",
       "      <th>Q1_units</th>\n",
       "      <th>Q1_inventory_adjustment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Scheibeneggen Helmacker Maintenance 24/25</td>\n",
       "      <td>28.07.2024</td>\n",
       "      <td>W-Raps Helmacker Plant 24/25</td>\n",
       "      <td>Scheibenegge Catros</td>\n",
       "      <td>quantity--price</td>\n",
       "      <td>time</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        name   timestamp  \\\n",
       "0  Scheibeneggen Helmacker Maintenance 24/25  28.07.2024   \n",
       "\n",
       "                     plant_name            equipment          Q1_type  \\\n",
       "0  W-Raps Helmacker Plant 24/25  Scheibenegge Catros  quantity--price   \n",
       "\n",
       "  Q1_measure Q1_value Q1_units Q1_inventory_adjustment  \n",
       "0       time        2        h                    None  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Alle Maintenance Logs\n",
    "import neofarm.lib as neo\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "# Liste für die Sammlung der Mainenance-Log-Daten\n",
    "data = []\n",
    "\n",
    "# Alle Maintenance-Logs abrufen\n",
    "maintenances = neo.Log.Maintenance.get_list()\n",
    "for maintenance in maintenances:\n",
    "    plant = maintenance.plant\n",
    "    equipment = maintenance.equipment[0]\n",
    "    quantity1 = maintenance.quantities[0]\n",
    "    \n",
    "    \n",
    "    # Maintenance-Daten als Dictionary hinzufügen\n",
    "    data.append({\n",
    "        \"name\": maintenance.name,\n",
    "        \"timestamp\": datetime.fromisoformat(maintenance.timestamp).strftime(\"%d.%m.%Y\"),  # Konvertiere in das gewünschte Format\n",
    "        \"plant_name\": plant.name,\n",
    "        \"equipment\": equipment.name,\n",
    "        \"Q1_type\": quantity1.type,\n",
    "        \"Q1_measure\": quantity1.measure,\n",
    "        \"Q1_value\": quantity1.value,\n",
    "        \"Q1_units\": quantity1.units.name,\n",
    "        \"Q1_inventory_adjustment\": quantity1.inventory_adjustment,\n",
    "\n",
    "\n",
    "    })\n",
    "\n",
    "# DataFrame erstellen und anzeigen\n",
    "df = pd.DataFrame(data)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e8e0222a-25c8-4f3f-a1e8-5e7753432c4b",
   "metadata": {
    "jupyter": {
     "source_hidden": true
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    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>timestamp</th>\n",
       "      <th>plant_name</th>\n",
       "      <th>plant_crop</th>\n",
       "      <th>plant_location</th>\n",
       "      <th>equipment</th>\n",
       "      <th>isMovement</th>\n",
       "      <th>Q1_type</th>\n",
       "      <th>Q1_measure</th>\n",
       "      <th>Q1_value</th>\n",
       "      <th>Q1_units</th>\n",
       "      <th>Q1_inventory_adjustment</th>\n",
       "      <th>Q1_inventory_asset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Innovert Raps Input 24/25</td>\n",
       "      <td>12.10.2024</td>\n",
       "      <td>W-Raps Helmacker Plant 24/25</td>\n",
       "      <td>Raps</td>\n",
       "      <td>Helmacker</td>\n",
       "      <td>Spritze</td>\n",
       "      <td>False</td>\n",
       "      <td>quantity--standard</td>\n",
       "      <td>volume</td>\n",
       "      <td>10</td>\n",
       "      <td>l</td>\n",
       "      <td>decrement</td>\n",
       "      <td>Innovert Raps Material 08/24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Innovert Raps Input 24/25</td>\n",
       "      <td>03.11.2024</td>\n",
       "      <td>W-Raps Helmacker Plant 24/25</td>\n",
       "      <td>Raps</td>\n",
       "      <td>Helmacker</td>\n",
       "      <td>Spritze</td>\n",
       "      <td>False</td>\n",
       "      <td>quantity--standard</td>\n",
       "      <td>volume</td>\n",
       "      <td>10</td>\n",
       "      <td>l</td>\n",
       "      <td>decrement</td>\n",
       "      <td>Innovert Raps Material 08/24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name   timestamp                    plant_name  \\\n",
       "0  Innovert Raps Input 24/25  12.10.2024  W-Raps Helmacker Plant 24/25   \n",
       "1  Innovert Raps Input 24/25  03.11.2024  W-Raps Helmacker Plant 24/25   \n",
       "\n",
       "  plant_crop plant_location equipment  isMovement             Q1_type  \\\n",
       "0       Raps      Helmacker   Spritze       False  quantity--standard   \n",
       "1       Raps      Helmacker   Spritze       False  quantity--standard   \n",
       "\n",
       "  Q1_measure Q1_value Q1_units Q1_inventory_adjustment  \\\n",
       "0     volume       10        l               decrement   \n",
       "1     volume       10        l               decrement   \n",
       "\n",
       "             Q1_inventory_asset  \n",
       "0  Innovert Raps Material 08/24  \n",
       "1  Innovert Raps Material 08/24  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Alle Input Logs\n",
    "import neofarm.lib as neo\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "# Liste für die Sammlung der Input-Log-Daten\n",
    "data = []\n",
    "\n",
    "# Alle Input-Logs abrufen\n",
    "inputs = neo.Log.Input.get_list()\n",
    "for input in inputs:\n",
    "    plant = input.plant\n",
    "    equipment = input.equipment[0]\n",
    "    quantity1 = input.quantities[0]\n",
    "\n",
    "    \n",
    "    # Input-Daten als Dictionary hinzufügen\n",
    "    data.append({\n",
    "        \"name\": input.name,\n",
    "        \"timestamp\": datetime.fromisoformat(input.timestamp).strftime(\"%d.%m.%Y\"),  # Konvertiere in das gewünschte Format\n",
    "        \"plant_name\": plant.name,\n",
    "        \"plant_crop\": plant.crop[0].name,\n",
    "        \"plant_location\": plant.location[0].name,\n",
    "        \"equipment\": equipment.name,\n",
    "        \"isMovement\": input.isMovement,\n",
    "        \"Q1_type\": quantity1.type,\n",
    "        \"Q1_measure\": quantity1.measure,\n",
    "        \"Q1_value\": quantity1.value,\n",
    "        \"Q1_units\": quantity1.units.name,\n",
    "        \"Q1_inventory_adjustment\": quantity1.inventory_adjustment,\n",
    "        \"Q1_inventory_asset\": quantity1.inventory_asset.name,\n",
    "\n",
    "    })\n",
    "\n",
    "# DataFrame erstellen und anzeigen\n",
    "df = pd.DataFrame(data)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "318b15bd-4ea4-4eb6-b434-00900f1528dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6356e491-8615-40eb-ad75-a21cb517afc2",
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>plant_name</th>\n",
       "      <th>plant_crop</th>\n",
       "      <th>plant_location</th>\n",
       "      <th>equipment</th>\n",
       "      <th>isMovement</th>\n",
       "      <th>Q1_type</th>\n",
       "      <th>Q1_measure</th>\n",
       "      <th>Q1_value</th>\n",
       "      <th>Q1_units</th>\n",
       "      <th>Q1_inventory_adjustment</th>\n",
       "      <th>Q1_inventory_asset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Schneckenkorn Medical 24/25</td>\n",
       "      <td>30.08.2024</td>\n",
       "      <td>W-Raps Helmacker Plant 24/25</td>\n",
       "      <td>Raps</td>\n",
       "      <td>Helmacker</td>\n",
       "      <td>Schneckenkornstreuer Leinfelder</td>\n",
       "      <td>False</td>\n",
       "      <td>quantity--standard</td>\n",
       "      <td>weight</td>\n",
       "      <td>10</td>\n",
       "      <td>kg</td>\n",
       "      <td>decrement</td>\n",
       "      <td>Schneckenkorn Material 08/24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name   timestamp                    plant_name  \\\n",
       "0  Schneckenkorn Medical 24/25  30.08.2024  W-Raps Helmacker Plant 24/25   \n",
       "\n",
       "  plant_crop plant_location                        equipment  isMovement  \\\n",
       "0       Raps      Helmacker  Schneckenkornstreuer Leinfelder       False   \n",
       "\n",
       "              Q1_type Q1_measure Q1_value Q1_units Q1_inventory_adjustment  \\\n",
       "0  quantity--standard     weight       10       kg               decrement   \n",
       "\n",
       "             Q1_inventory_asset  \n",
       "0  Schneckenkorn Material 08/24  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Alle Medical Logs\n",
    "import neofarm.lib as neo\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "# Liste für die Sammlung der Medical-Log-Daten\n",
    "data = []\n",
    "\n",
    "# Alle Medical-Logs abrufen\n",
    "medicals = neo.Log.Medical.get_list()\n",
    "for medical in medicals:\n",
    "    plant = medical.plant\n",
    "    equipment = medical.equipment[0]\n",
    "    quantity1 = medical.quantities[0]\n",
    "\n",
    "    \n",
    "    # Medical-Daten als Dictionary hinzufügen\n",
    "    data.append({\n",
    "        \"name\": medical.name,\n",
    "        \"timestamp\": datetime.fromisoformat(medical.timestamp).strftime(\"%d.%m.%Y\"),  # Konvertiere in das gewünschte Format\n",
    "        \"plant_name\": plant.name,\n",
    "        \"plant_crop\": plant.crop[0].name,\n",
    "        \"plant_location\": plant.location[0].name,\n",
    "        \"equipment\": equipment.name,\n",
    "        \"isMovement\": medical.isMovement,\n",
    "        \"Q1_type\": quantity1.type,\n",
    "        \"Q1_measure\": quantity1.measure,\n",
    "        \"Q1_value\": quantity1.value,\n",
    "        \"Q1_units\": quantity1.units.name,\n",
    "        \"Q1_inventory_adjustment\": quantity1.inventory_adjustment,\n",
    "        \"Q1_inventory_asset\": quantity1.inventory_asset.name,\n",
    "\n",
    "    })\n",
    "\n",
    "# DataFrame erstellen und anzeigen\n",
    "df = pd.DataFrame(data)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ee597e4c-7275-4cda-8cc6-2daf82dbd4d8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>timestamp</th>\n",
       "      <th>plant_name</th>\n",
       "      <th>plant_crop</th>\n",
       "      <th>plant_location</th>\n",
       "      <th>equipment</th>\n",
       "      <th>isMovement</th>\n",
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       "      <th>Q1_value</th>\n",
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       "      <th>Q1_inventory_adjustment</th>\n",
       "      <th>Q1_inventory_asset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>W-Raps Helmacker  Harvest 24/25</td>\n",
       "      <td>20.10.2024</td>\n",
       "      <td>W-Raps Helmacker Plant 24/25</td>\n",
       "      <td>Raps</td>\n",
       "      <td>Helmacker</td>\n",
       "      <td>Mähdrescher Leinfelder</td>\n",
       "      <td>False</td>\n",
       "      <td>quantity--standard</td>\n",
       "      <td>weight</td>\n",
       "      <td>5</td>\n",
       "      <td>to</td>\n",
       "      <td>increment</td>\n",
       "      <td>W-Raps Product 08/24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>W-Raps Nachtweide  Harvest 24/25</td>\n",
       "      <td>28.10.2024</td>\n",
       "      <td>W-Raps Nachtweide Plant 24/25</td>\n",
       "      <td>Raps</td>\n",
       "      <td>Nachtweide</td>\n",
       "      <td>Mähdrescher Leinfelder</td>\n",
       "      <td>False</td>\n",
       "      <td>quantity--standard</td>\n",
       "      <td>weight</td>\n",
       "      <td>7.8</td>\n",
       "      <td>to</td>\n",
       "      <td>increment</td>\n",
       "      <td>W-Raps Product 08/24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               name   timestamp  \\\n",
       "0   W-Raps Helmacker  Harvest 24/25  20.10.2024   \n",
       "1  W-Raps Nachtweide  Harvest 24/25  28.10.2024   \n",
       "\n",
       "                       plant_name plant_crop plant_location  \\\n",
       "0    W-Raps Helmacker Plant 24/25       Raps      Helmacker   \n",
       "1  W-Raps Nachtweide Plant 24/25        Raps     Nachtweide   \n",
       "\n",
       "                equipment  isMovement             Q1_type Q1_measure Q1_value  \\\n",
       "0  Mähdrescher Leinfelder       False  quantity--standard     weight        5   \n",
       "1  Mähdrescher Leinfelder       False  quantity--standard     weight      7.8   \n",
       "\n",
       "  Q1_units Q1_inventory_adjustment    Q1_inventory_asset  \n",
       "0       to               increment  W-Raps Product 08/24  \n",
       "1       to               increment  W-Raps Product 08/24  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Alle Harvest Logs\n",
    "import neofarm.lib as neo\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "# Liste für die Sammlung der Harvest-Log-Daten\n",
    "data = []\n",
    "\n",
    "# Alle Harvest-Logs abrufen\n",
    "harvests = neo.Log.Harvest.get_list()\n",
    "for harvest in harvests:\n",
    "    plant = harvest.plant\n",
    "    equipment = harvest.equipment[0]\n",
    "    quantity1 = harvest.quantities[0]\n",
    "\n",
    "    \n",
    "    # Harvest-Daten als Dictionary hinzufügen\n",
    "    data.append({\n",
    "        \"name\": harvest.name,\n",
    "        \"timestamp\": datetime.fromisoformat(harvest.timestamp).strftime(\"%d.%m.%Y\"),  # Konvertiere in das gewünschte Format\n",
    "        \"plant_name\": plant.name,\n",
    "        \"plant_crop\": plant.crop[0].name,\n",
    "        \"plant_location\": plant.location[0].name,\n",
    "        \"equipment\": equipment.name,\n",
    "        \"isMovement\": harvest.isMovement,\n",
    "        \"Q1_type\": quantity1.type,\n",
    "        \"Q1_measure\": quantity1.measure,\n",
    "        \"Q1_value\": quantity1.value,\n",
    "        \"Q1_units\": quantity1.units.name,\n",
    "        \"Q1_inventory_adjustment\": quantity1.inventory_adjustment,\n",
    "        \"Q1_inventory_asset\": quantity1.inventory_asset.name,\n",
    "\n",
    "    })\n",
    "\n",
    "# DataFrame erstellen und anzeigen\n",
    "df = pd.DataFrame(data)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3279d05b-5bae-497e-891d-3337844904f7",
   "metadata": {
    "scrolled": 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>timestamp</th>\n",
       "      <th>plant_name</th>\n",
       "      <th>plant_crop</th>\n",
       "      <th>plant_location</th>\n",
       "      <th>equipment</th>\n",
       "      <th>isMovement</th>\n",
       "      <th>Q1_type</th>\n",
       "      <th>Q1_measure</th>\n",
       "      <th>Q1_value</th>\n",
       "      <th>Q1_units</th>\n",
       "      <th>Q1_inventory_adjustment</th>\n",
       "      <th>Q1_inventory_asset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>W-Raps Sale 24/25</td>\n",
       "      <td>28.10.2024</td>\n",
       "      <td>W-Raps Helmacker Plant 24/25</td>\n",
       "      <td>Raps</td>\n",
       "      <td>Helmacker</td>\n",
       "      <td>Mähdrescher Leinfelder</td>\n",
       "      <td>False</td>\n",
       "      <td>quantity--price</td>\n",
       "      <td>weight</td>\n",
       "      <td>5</td>\n",
       "      <td>to</td>\n",
       "      <td>decrement</td>\n",
       "      <td>W-Raps Product 08/24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                name   timestamp                    plant_name plant_crop  \\\n",
       "0  W-Raps Sale 24/25  28.10.2024  W-Raps Helmacker Plant 24/25       Raps   \n",
       "\n",
       "  plant_location               equipment  isMovement          Q1_type  \\\n",
       "0      Helmacker  Mähdrescher Leinfelder       False  quantity--price   \n",
       "\n",
       "  Q1_measure Q1_value Q1_units Q1_inventory_adjustment    Q1_inventory_asset  \n",
       "0     weight        5       to               decrement  W-Raps Product 08/24  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Alle Sale Logs\n",
    "import neofarm.lib as neo\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "# Liste für die Sammlung der Sale-Log-Daten\n",
    "data = []\n",
    "\n",
    "# Alle Sale-Logs abrufen\n",
    "sales = neo.Log.Sale.get_list()\n",
    "for sale in sales:\n",
    "    plant = sale.plant\n",
    "  \n",
    "    quantity1 = sale.quantities[0]\n",
    "\n",
    "    \n",
    "    # Sale-Daten als Dictionary hinzufügen\n",
    "    data.append({\n",
    "        \"name\": sale.name,\n",
    "        \"timestamp\": datetime.fromisoformat(sale.timestamp).strftime(\"%d.%m.%Y\"),  # Konvertiere in das gewünschte Format\n",
    "        \"plant_name\": plant.name,\n",
    "        \"plant_crop\": plant.crop[0].name,\n",
    "        \"plant_location\": plant.location[0].name,\n",
    "        \"equipment\": equipment.name,\n",
    "        \"isMovement\": sale.isMovement,\n",
    "        \"Q1_type\": quantity1.type,\n",
    "        \"Q1_measure\": quantity1.measure,\n",
    "        \"Q1_value\": quantity1.value,\n",
    "        \"Q1_units\": quantity1.units.name,\n",
    "        \"Q1_inventory_adjustment\": quantity1.inventory_adjustment,\n",
    "        \"Q1_inventory_asset\": quantity1.inventory_asset.name,\n",
    "\n",
    "    })\n",
    "\n",
    "# DataFrame erstellen und anzeigen\n",
    "df = pd.DataFrame(data)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d6be8923-8eb0-4d3b-927a-af15fe2c0ba0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[21], line 15\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m purchase \u001b[38;5;129;01min\u001b[39;00m purchases:\n\u001b[0;32m     14\u001b[0m     quantity1 \u001b[38;5;241m=\u001b[39m purchase\u001b[38;5;241m.\u001b[39mquantities[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m---> 15\u001b[0m     quantity2 \u001b[38;5;241m=\u001b[39m \u001b[43mpurchase\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquantities\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[0;32m     18\u001b[0m     \u001b[38;5;66;03m# Purchase-Daten als Dictionary hinzufügen\u001b[39;00m\n\u001b[0;32m     19\u001b[0m     data\u001b[38;5;241m.\u001b[39mappend({\n\u001b[0;32m     20\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: purchase\u001b[38;5;241m.\u001b[39mname,\n\u001b[0;32m     21\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimestamp\u001b[39m\u001b[38;5;124m\"\u001b[39m: datetime\u001b[38;5;241m.\u001b[39mfromisoformat(purchase\u001b[38;5;241m.\u001b[39mtimestamp)\u001b[38;5;241m.\u001b[39mstrftime(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mm.\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m\"\u001b[39m),  \u001b[38;5;66;03m# Konvertiere in das gewünschte Format\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     34\u001b[0m \n\u001b[0;32m     35\u001b[0m     })\n",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "#Alle Purchase Logs Baustelle!!\n",
    "import neofarm.lib as neo\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "# Liste für die Sammlung der Purchase-Log-Daten\n",
    "data = []\n",
    "\n",
    "# Alle Purchase-Logs abrufen\n",
    "purchases = neo.Log.Purchase.get_list()\n",
    "for purchase in purchases:\n",
    "   \n",
    "  \n",
    "    quantity1 = purchase.quantities[0]\n",
    "    quantity2 = purchase.quantities[1]\n",
    "\n",
    "    \n",
    "    # Purchase-Daten als Dictionary hinzufügen\n",
    "    data.append({\n",
    "        \"name\": purchase.name,\n",
    "        \"timestamp\": datetime.fromisoformat(purchase.timestamp).strftime(\"%d.%m.%Y\"),  # Konvertiere in das gewünschte Format\n",
    "        \"Q1_type\": quantity1.type,\n",
    "        \"Q1_measure\": quantity1.measure,\n",
    "        \"Q1_value\": quantity1.value,\n",
    "        \"Q1_units\": quantity1.units.name,\n",
    "        \"Q1_inventory_adjustment\": quantity1.inventory_adjustment,\n",
    "        \"Q1_inventory_asset\": quantity1.inventory_asset.name,\n",
    "        \"Q2_type\": quantity2.type,\n",
    "        \"Q2_measure\": quantity2.measure,\n",
    "        \"Q2_value\": quantity2.value,\n",
    "        \"Q2_units\": quantity2.units.name,\n",
    "        \"Q2_inventory_adjustment\": quantity2.inventory_adjustment,\n",
    "        \"Q2_inventory_asset\": quantity2.inventory_asset.name\n",
    "\n",
    "    })\n",
    "\n",
    "# DataFrame erstellen und anzeigen\n",
    "df = pd.DataFrame(data)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d63742e3-04a4-4cc0-972f-3edd868325cc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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