Alle logs zu plant mit quantities nebeneinander in Excel exportiert

reporting.ipynb

@@ -5,6 +5,9 @@
    "execution_count": null,
    "id": "f512fba3-877f-411e-935c-0c878d478b2d",
    "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    },
     "scrolled": true
    },
    "outputs": [],
@@ -101,7 +104,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "a993570d-dea2-4bd6-80c0-b36297ce6a3a",
-   "metadata": {},
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
    "outputs": [],
    "source": [
     "#Beispiel\n",
@@ -116,62 +123,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "3a33ffb1-afe7-44b5-863b-9779a95a2a9c",
-   "metadata": {},
+   "metadata": {
-   "outputs": [
+    "jupyter": {
-    {
+     "source_hidden": true
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PlantName: W-Raps Helmacker Plant 24/25\n",
-      "Maintenance:\n",
-      "name:       Scheibeneggen Helmacker Maintenance 24/25\n",
-      "timestamp:  28.07.24 11:34 \n",
-      "equipment:  Scheibenegge Catros\n",
-      "quant_type: quantity--price\n",
-      "quant_measure: time\n",
-      "quant_value: 2\n",
-      "quant_inventory_adjustment: None\n",
-      "Seeding:\n",
-      "name:       W-Raps Otello KWS Helmacker Seeding 24/25\n",
-      "timestamp:  22.08.24 22:00 \n",
-      "equipment:  Sähmaschine Cataya\n",
-      "plant loc:  Helmacker\n",
-      "quant_type: quantity--standard\n",
-      "quant_measure: weight\n",
-      "quant_value: 50\n",
-      "quant_inventory_adjustment: decrement\n",
-      "Input:\n",
-      "name:       Innovert Raps Input 24/25\n",
-      "timestamp:  12.10.24 13:18 \n",
-      "equipment:  Spritze\n",
-      "quant_type: quantity--standard\n",
-      "quant_measure: volume\n",
-      "quant_value: 10\n",
-      "quant_inventory_adjustment: decrement\n",
-      "Input:\n",
-      "name:       Innovert Raps Input 24/25\n",
-      "timestamp:  03.11.24 23:00 \n",
-      "equipment:  Spritze\n",
-      "quant_type: quantity--standard\n",
-      "quant_measure: volume\n",
-      "quant_value: 10\n",
-      "quant_inventory_adjustment: decrement\n",
-      "Medical:\n",
-      "name:       Schneckenkorn Medical 24/25\n",
-      "timestamp:  30.08.24 06:42 \n",
-      "equipment:  Schneckenkornstreuer Leinfelder\n",
-      "Harvest:\n",
-      "name:       W-Raps Helmacker  Harvest 24/25\n",
-      "timestamp:  20.10.24 10:11 \n",
-      "equipment:  Mähdrescher Leinfelder\n",
-      "Sale:\n",
-      "name:       W-Raps Sale 24/25\n",
-      "timestamp:  28.10.24 07:54 \n"
-     ]
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# alle logs von einem Plant\n",
     "import neofarm.lib as neo\n",
@@ -179,10 +138,13 @@
     "\n",
     "if __name__ == \"__main__\":\n",
     "    asset = neo.Asset.Plant.from_id(\"76f89f82-1238-43bb-b34f-796a92d491a2\")\n",
-    "    \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",
@@ -190,16 +152,16 @@
     "        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.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].unit_price.decimal}\")\n",
+    "        print(f\"quant_value: {input.quantities[0].unit_price}\")\n",
     "\n",
     "    \t\n",
-    "        \n",
+    "#log Seeding       \n",
     "    logs = asset.get_logs_of_type(neo.Log.Seeding)\n",
     "    for log in logs:\n",
     "        print(\"Seeding:\")\n",
@@ -208,7 +170,7 @@
     "        print(f\"timestamp:  {formatted_date}\")\n",
     "        print(f\"equipment:  {log.equipment[0].name}\")\n",
     "        print(f\"plant loc:  {log.location[0].name}\")\n",
-    "\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",
@@ -216,7 +178,7 @@
     "        print(f\"quant_inventory_adjustment: {input.quantities[0].inventory_adjustment}\")\n",
     "    \t\n",
     "\n",
-    "        \n",
+    "#log Input        \n",
     "    logs = asset.get_logs_of_type(neo.Log.Input)\n",
     "    for log in logs:\n",
     "        print(\"Input:\")\n",
@@ -226,16 +188,14 @@
     "        print(f\"timestamp:  {formatted_date}\")\n",
     "        print(f\"equipment:  {log.equipment[0].name}\")\n",
     "\n",
-    "\n",
+    "#quantity\n",
-    "\n",
-    "\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",
-    "   \n",
+    "#log Medical\n",
     "    logs = asset.get_logs_of_type(neo.Log.Medical)\n",
     "    for log in logs:\n",
     "        print(\"Medical:\")\n",
@@ -244,6 +204,15 @@
     "        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",
@@ -252,19 +221,38 @@
     "        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}\")"
+    "        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": "ce30a9db-735e-4174-ad56-0f0079ab04c6",
-   "metadata": {},
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
    "outputs": [],
    "source": [
     "#alle logs von einem Plant in Tabelle\n",
@@ -314,6 +302,297 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "649e4d8e-c3a9-4d5f-9bdf-9427f416fca9",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "#Alle logs zu plant mit quantities untereinander\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 %H:%M\")\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",
+    "                    \"QuantInventoryAdjustment\": quantity.inventory_adjustment\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\": \"None\",\n",
+    "                    \"QuantMeasure\": \"None\",\n",
+    "                    \"QuantValue\": \"None\",\n",
+    "                    \"QuantInventoryAdjustment\": \"None\"\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": "ef3c14f0-b158-4be9-ae7d-fd3b81aeb4ff",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "#Alle logs zu plant mit quantities nebeneinander\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 %H:%M\")\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 \"\"\n",
+    "\n",
+    "            # Menge und weitere Details (bis zu zwei Mengen)\n",
+    "            quantities = getattr(log, 'quantities', [])\n",
+    "            \n",
+    "            # Initialisiere Standardwerte für die zweite Menge\n",
+    "            quant_type_2 = quant_measure_2 = quant_value_2 = quant_inventory_adjustment_2 = \"\"\n",
+    "\n",
+    "            if len(quantities) > 0:\n",
+    "                # Erste Menge vorhanden\n",
+    "                quant_type_1 = quantities[0].type\n",
+    "                quant_measure_1 = quantities[0].measure\n",
+    "                quant_value_1 = quantities[0].value\n",
+    "                quant_inventory_adjustment_1 = quantities[0].inventory_adjustment\n",
+    "            else:\n",
+    "                # Keine Mengenangaben vorhanden\n",
+    "                quant_type_1 = quant_measure_1 = quant_value_1 = quant_inventory_adjustment_1 = \"\"\n",
+    "            \n",
+    "            if len(quantities) > 1:\n",
+    "                # Zweite Menge vorhanden\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",
+    "\n",
+    "            # Daten zur Tabelle hinzufügen\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_1\": quant_type_1,\n",
+    "                \"QuantMeasure_1\": quant_measure_1,\n",
+    "                \"QuantValue_1\": quant_value_1,\n",
+    "                \"QuantInventoryAdjustment_1\": quant_inventory_adjustment_1,\n",
+    "                \"QuantType_2\": quant_type_2,\n",
+    "                \"QuantMeasure_2\": quant_measure_2,\n",
+    "                \"QuantValue_2\": quant_value_2,\n",
+    "                \"QuantInventoryAdjustment_2\": quant_inventory_adjustment_2\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": "c1b73098-08f3-43fd-9bf0-d3d628b15e0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Alle logs zu plant mit quantities nebeneinander in Excel exportiert\n",
+    "import neofarm.lib as neo\n",
+    "import pandas as pd\n",
+    "from datetime import datetime\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",
+    "# Spezifizierter Pfad zum Downloads-Ordner (Windows-Standardpfad)\n",
+    "downloads_folder = os.path.join(os.path.expanduser(\"~\"), \"Downloads\")\n",
+    "file_path = os.path.join(downloads_folder, \"PlantLogs.xlsx\")\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",
+    "    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",
+    "            # Entferne die Zeitzone vom Timestamp\n",
+    "            timestamp = datetime.fromisoformat(log.timestamp).replace(tzinfo=None)  # Sicherstellen, dass es timezone-unaware ist\n",
+    "            equipment_name = log.equipment[0].name if log.equipment else \"\"\n",
+    "            location_name = log.location[0].name if hasattr(log, 'location') and log.location else \"\"\n",
+    "\n",
+    "            quantities = getattr(log, 'quantities', [])\n",
+    "            quant_type_2 = quant_measure_2 = quant_value_2 = quant_inventory_adjustment_2 = \"\"\n",
+    "\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_inventory_adjustment_1 = quantities[0].inventory_adjustment\n",
+    "            else:\n",
+    "                quant_type_1 = quant_measure_1 = quant_value_1 = quant_inventory_adjustment_1 = \"\"\n",
+    "            \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",
+    "\n",
+    "            data.append({\n",
+    "                \"PlantName\": PlantName,\n",
+    "                \"LogType\": log_type,\n",
+    "                \"Name\": log.name,\n",
+    "                \"Timestamp\": timestamp,  # Direkte Speicherung des datetime-Objekts\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",
+    "                \"QuantInventoryAdjustment_1\": quant_inventory_adjustment_1,\n",
+    "                \"QuantType_2\": quant_type_2,\n",
+    "                \"QuantMeasure_2\": quant_measure_2,\n",
+    "                \"QuantValue_2\": quant_value_2,\n",
+    "                \"QuantInventoryAdjustment_2\": quant_inventory_adjustment_2\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\n",
+    "    df = pd.DataFrame(data)\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",
+    "            print(\"Der Export wurde abgebrochen.\")\n",
+    "        else:\n",
+    "            df.to_excel(file_path, index=False)\n",
+    "    else:\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",
+    "\n",
+    "    # Füge Autofilter hinzu\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",
+    "            # Setze den Zeilenumbruch\n",
+    "            cell.alignment = Alignment(wrap_text=True)  # Zeilenumbruch aktivieren\n",
+    "            \n",
+    "            # Formatieren der Timestamp-Spalte als Datum\n",
+    "            if col_letter == 'D':  # Angenommen, die Timestamp-Spalte ist Spalte D\n",
+    "                cell.number_format = 'DD.MM.YYYY'  # Setze das Datumsformat\n",
+    "            \n",
+    "            max_length = max(max_length, len(str(cell.value)) if cell.value else 0)\n",
+    "        adjusted_width = (max_length + 2) * 1.2  # Extra Puffer hinzufügen\n",
+    "        worksheet.column_dimensions[col_letter].width = adjusted_width\n",
+    "\n",
+    "    # Speichern Sie die Datei\n",
+    "    workbook.save(file_path)\n",
+    "    print(f\"Die Datei wurde erfolgreich nach '{file_path}' exportiert, mit Autofilter, Zeilenumbruch und angepasster Spaltenbreite.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ebcc62d0-4334-4c61-aaa2-14cd96e8a6f0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de57b155-d635-4367-9a56-90d033c3a922",
    "metadata": {},
    "outputs": [],
    "source": []