Update the examples

Signed-off-by: GitHub Actions Bot <[email protected]>

Author: actions-user

examples/Power Flow Example.ipynb

@@ -447,8 +447,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "List of component types in result dataset and associated data types\n",
-      "{'ComponentType.node': <class 'numpy.ndarray'>, 'ComponentType.line': <class 'dict'>, 'ComponentType.sym_load': <class 'dict'>}\n",
+      "List of component types in result dataset\n",
+      "[<ComponentType.node: 'node'>, <ComponentType.line: 'line'>, <ComponentType.sym_load: 'sym_load'>]\n",
       "------node result------\n",
       "('id', 'energized', 'u_pu', 'u', 'u_angle', 'p', 'q')\n",
       "------line result attributes------\n",
@@ -471,8 +471,8 @@
     "    },\n",
     ")\n",
     "\n",
-    "print(\"List of component types in result dataset and associated data types\")\n",
-    "print({str(component_type): type(component_data) for component_type, component_data in output_data.items()})\n",
+    "print(\"List of component types in result dataset\")\n",
+    "print(list(output_data.keys()))\n",
     "print(\"------node result------\")\n",
     "print(output_data[ComponentType.node].dtype.names)\n",
     "print(\"------line result attributes------\")\n",
@@ -499,8 +499,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "List of component types in result dataset and associated data types\n",
-      "{'ComponentType.node': <class 'dict'>, 'ComponentType.line': <class 'dict'>, 'ComponentType.sym_load': <class 'dict'>, 'ComponentType.source': <class 'dict'>}\n",
+      "List of component types in result dataset\n",
+      "[<ComponentType.node: 'node'>, <ComponentType.line: 'line'>, <ComponentType.sym_load: 'sym_load'>, <ComponentType.source: 'source'>]\n",
       "------node result------\n",
       "['id', 'energized', 'u_pu', 'u', 'u_angle', 'p', 'q']\n",
       "------line result attributes------\n",
@@ -519,8 +519,8 @@
     "    output_component_types=ComponentAttributeFilterOptions.everything,  # all attributes for all component types as columns\n",
     ")\n",
     "\n",
-    "print(\"List of component types in result dataset and associated data types\")\n",
-    "print({str(component_type): type(component_data) for component_type, component_data in output_data.items()})\n",
+    "print(\"List of component types in result dataset\")\n",
+    "print(list(output_data.keys()))\n",
     "print(\"------node result------\")\n",
     "print(list(output_data[ComponentType.node].keys()))\n",
     "print(\"------line result attributes------\")\n",
@@ -1430,6 +1430,7 @@
     "# we run the batch calculation with continue_on_batch_error=True,\n",
     "#    it will return the results with partially valid data\n",
     "\n",
+    "\n",
     "output_data = model.calculate_power_flow(update_data=time_series_mutation, continue_on_batch_error=True)\n",
     "\n",
     "# print node data for u_pu, note that the data is rubbish for scenario 3 and 7\n",

examples/Validation Examples.ipynb

@@ -113,8 +113,8 @@
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIDWrongType\u001b[0m                               Traceback (most recent call last)",
       "Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Without validation\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mPowerGridModel\u001b[49m\u001b[43m(\u001b[49m\u001b[43merror_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m output_data \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mcalculate_state_estimation(symmetric\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
-      "File \u001b[0;32m~/pgm/power-grid-model/src/power_grid_model/core/power_grid_model.py:125\u001b[0m, in \u001b[0;36mPowerGridModel.__init__\u001b[0;34m(self, input_data, system_frequency)\u001b[0m\n\u001b[1;32m    123\u001b[0m prepared_input \u001b[38;5;241m=\u001b[39m prepare_input_view(_map_to_component_types(input_data))\n\u001b[1;32m    124\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model_ptr \u001b[38;5;241m=\u001b[39m pgc\u001b[38;5;241m.\u001b[39mcreate_model(system_frequency, input_data\u001b[38;5;241m=\u001b[39mprepared_input\u001b[38;5;241m.\u001b[39mget_dataset_ptr())\n\u001b[0;32m--> 125\u001b[0m \u001b[43massert_no_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    126\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_all_component_count \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m prepared_input\u001b[38;5;241m.\u001b[39mget_info()\u001b[38;5;241m.\u001b[39mtotal_elements()\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m v \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m}\n",
-      "File \u001b[0;32m~/pgm/power-grid-model/src/power_grid_model/core/error_handling.py:169\u001b[0m, in \u001b[0;36massert_no_error\u001b[0;34m(batch_size, decode_error)\u001b[0m\n\u001b[1;32m    167\u001b[0m error \u001b[38;5;241m=\u001b[39m find_error(batch_size\u001b[38;5;241m=\u001b[39mbatch_size, decode_error\u001b[38;5;241m=\u001b[39mdecode_error)\n\u001b[1;32m    168\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m error \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 169\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m error\n",
+      "File \u001b[0;32m~/pgm/power-grid-model/src/power_grid_model/_core/power_grid_model.py:125\u001b[0m, in \u001b[0;36mPowerGridModel.__init__\u001b[0;34m(self, input_data, system_frequency)\u001b[0m\n\u001b[1;32m    123\u001b[0m prepared_input \u001b[38;5;241m=\u001b[39m prepare_input_view(_map_to_component_types(input_data))\n\u001b[1;32m    124\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model_ptr \u001b[38;5;241m=\u001b[39m pgc\u001b[38;5;241m.\u001b[39mcreate_model(system_frequency, input_data\u001b[38;5;241m=\u001b[39mprepared_input\u001b[38;5;241m.\u001b[39mget_dataset_ptr())\n\u001b[0;32m--> 125\u001b[0m \u001b[43massert_no_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    126\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_all_component_count \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m prepared_input\u001b[38;5;241m.\u001b[39mget_info()\u001b[38;5;241m.\u001b[39mtotal_elements()\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m v \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m}\n",
+      "File \u001b[0;32m~/pgm/power-grid-model/src/power_grid_model/_core/error_handling.py:169\u001b[0m, in \u001b[0;36massert_no_error\u001b[0;34m(batch_size, decode_error)\u001b[0m\n\u001b[1;32m    167\u001b[0m error \u001b[38;5;241m=\u001b[39m find_error(batch_size\u001b[38;5;241m=\u001b[39mbatch_size, decode_error\u001b[38;5;241m=\u001b[39mdecode_error)\n\u001b[1;32m    168\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m error \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 169\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m error\n",
       "\u001b[0;31mIDWrongType\u001b[0m: Wrong type for object with id 4\n\nTry validate_input_data() or validate_batch_data() to validate your data.\n"
      ]
     }