{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a53020a6-d40a-4940-8bde-53dd078a2008",
   "metadata": {},
   "source": [
    "# GridFM IEEE 30-Bus Data Generation and Analysis\n",
    "\n",
    "This notebook demonstrates how to generate, process, and analyze power grid data using the **GridFM DataKit and GraphKit frameworks**.\n",
    "\n",
    "The IEEE 30-Bus system is used as a benchmark electrical network where:\n",
    "\n",
    "- **Nodes represent buses (substations)**\n",
    "- **Edges represent transmission lines**\n",
    "- **Generators supply electrical power**\n",
    "- **Loads consume electrical power**\n",
    "\n",
    "The workflow implemented in this notebook includes:\n",
    "\n",
    "1. Setting up the GridFM environment\n",
    "2. Generating grid data using an interactive interface\n",
    "3. Loading the generated dataset\n",
    "4. Inspecting node and edge features\n",
    "5. Constructing graph structures for machine learning\n",
    "6. Creating a PyTorch Geometric graph object\n",
    "7. Visualizing the grid topology\n",
    "8. Analyzing the voltage profile of the network\n",
    "\n",
    "This pipeline prepares the dataset for **graph-based machine learning tasks such as voltage prediction, state estimation, and power flow modeling**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7114400-2a82-463f-be17-a5ff45cc6d23",
   "metadata": {},
   "source": [
    "## Environment Setup\n",
    "\n",
    "In this step, the required **GridFM libraries** are imported and verified.\n",
    "\n",
    "Two main packages are used:\n",
    "\n",
    "- **GridFM DataKit** – Responsible for dataset generation, grid simulation, and scenario creation.\n",
    "- **GridFM GraphKit** – Provides graph-based utilities and machine learning integration tools.\n",
    "\n",
    "The code also prints the installed version of DataKit and confirms that GraphKit has been loaded successfully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0cc6fa31-2cab-42b0-919f-c5897027340b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataKit Version: 0.1.0\n",
      "GraphKit module loaded successfully\n",
      "['__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', 'datasets', 'gridfm_graphkit', 'io', 'models']\n"
     ]
    }
   ],
   "source": [
    "import gridfm_datakit as datakit\n",
    "import gridfm_graphkit as graphkit\n",
    "\n",
    "print(f\"DataKit Version: {datakit.__version__}\")\n",
    "print(\"GraphKit module loaded successfully\")\n",
    "print(dir(graphkit))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb214fa0-3703-4f76-a9d0-3ed46650a500",
   "metadata": {},
   "source": [
    "## Interactive Grid Data Generation\n",
    "\n",
    "GridFM provides an **interactive dashboard** that allows users to generate power grid datasets directly inside the notebook.\n",
    "\n",
    "The dashboard allows configuration of:\n",
    "\n",
    "- Network topology\n",
    "- Load profiles\n",
    "- Generation parameters\n",
    "- Perturbation settings\n",
    "- Simulation scenarios\n",
    "\n",
    "Using this interface, synthetic but physically realistic power grid datasets can be generated for experimentation and machine learning research."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3d6398e4-ae1b-4a74-aa7b-6581fde12574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0cc54a9c65a241468013bda1cdbe7474",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value=\"<h3 style='color: #2196F3; margin: 10px 0;'>📡 Network Configuration</h3>\"), HTML(va…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ab7478dcd514f89854c9e662757253a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value=\"<h3 style='color: #4CAF50; margin: 10px 0;'>⚡ Load Configuration - Basic Parameters…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3153e9f1c346463e9b487e11ad84efcd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value=\"<p style='margin: 5px 0; color: #666;'>Parameters for searching the aggregated load…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c41c706d142b41678ef8381c2eab2e12",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value=\"<h3 style='color: #9C27B0; margin: 10px 0;'>🔌 Topology Perturbation Configuration</…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "778b75d17d184265921608976276df35",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value=\"<h3 style='color: #9C27B0; margin: 10px 0;'>🔋 Generation Perturbation Configuration…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "846937df056f43eca69b48fd53bddce8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value=\"<h3 style='color: #9C27B0; margin: 10px 0;'>🎛️ Line Admittance Perturbation Configu…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f91594c9a8024da9ad81a7b3226e8d0f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value=\"<h3 style='color: #795548; margin: 10px 0;'>⚙️ Execution Settings</h3>\"), HTML(valu…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61f75063e605404fb592ec0c76c47c28",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='user_config.yaml', description='Config Filename:', layout=Layout(width='400px'), placeholder='e.g.…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8459839b25f4c8d881e09f919f4c7a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(button_style='info', description='Create YAML Config', style=ButtonStyle(), tooltip='Save the current c…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d36cc2f61e4649219c175f20ef1eeb3d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(button_style='success', description='Generate and Run Configuration', style=ButtonStyle(), tooltip='Cli…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5daaaa7f375a4fe498675612f6cd823a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HTML(value='<b>Output</b>')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49e0cf8bba044e048874afcba5a9dc94",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(layout=Layout(border_bottom='1px solid #bbb', border_left='1px solid #bbb', border_right='1px solid #bb…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from gridfm_datakit.interactive import interactive_interface\n",
    "\n",
    "# This launches the visual dashboard right inside your notebook!\n",
    "interactive_interface()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c0c113b-3552-4966-b396-5e6433198acf",
   "metadata": {},
   "source": [
    "## Load Generated Dataset\n",
    "\n",
    "After data generation, the dataset is stored in the output directory.\n",
    "\n",
    "The dataset contains three main components:\n",
    "\n",
    "- **Bus Data** – Node information including voltage levels and power demand.\n",
    "- **Branch Data** – Transmission line information representing physical connections between buses.\n",
    "- **Generator Data** – Information about power generators connected to the network.\n",
    "\n",
    "These datasets are stored in **Parquet format**, which allows efficient storage and fast loading of large tabular datasets.\n",
    "\n",
    "Once loaded, the shapes of the datasets are displayed along with a preview of the bus data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7d0dbedc-52ad-46b2-9c4b-b7ab47188ad0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded the Parquet dataset!\n",
      "----------------------------------------\n",
      "Graph Nodes (Buses data shape): (305400, 18)\n",
      "Graph Edges (Branches data shape): (417380, 27)\n",
      "Power Generators data shape: (61080, 16)\n",
      "\n",
      "--- Sneak Peek at the Node (Bus) Data ---\n",
      "   scenario  load_scenario_idx  bus         Pd         Qd          Pg  \\\n",
      "0         0                0.0    0   0.000000   0.000000  134.350135   \n",
      "1         0                0.0    1  16.079180   8.398549   92.000001   \n",
      "2         0                0.0    2   1.807999   0.837318    0.000000   \n",
      "3         0                0.0    3   5.687592   1.345527    0.000000   \n",
      "4         0                0.0    4  72.344317  14.204905    0.000000   \n",
      "\n",
      "          Qg        Vm        Va   PQ   PV  REF  vn_kv  min_vm_pu  max_vm_pu  \\\n",
      "0   0.100738  1.060000  0.000000  0.0  0.0  1.0  132.0       0.94       1.06   \n",
      "1  12.491767  1.047376 -2.335048  0.0  1.0  0.0  132.0       0.94       1.06   \n",
      "2   0.000000  1.031806 -4.600761  1.0  0.0  0.0  132.0       0.94       1.06   \n",
      "3   0.000000  1.023085 -5.571488  1.0  0.0  0.0  132.0       0.94       1.06   \n",
      "4  22.313328  1.021101 -8.681739  0.0  1.0  0.0  132.0       0.94       1.06   \n",
      "\n",
      "    GS   BS scenario_partition  \n",
      "0  0.0  0.0                  0  \n",
      "1  0.0  0.0                  0  \n",
      "2  0.0  0.0                  0  \n",
      "3  0.0  0.0                  0  \n",
      "4  0.0  0.0                  0  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "data_dir = \"./data_out/case30_ieee/raw\"\n",
    "\n",
    "bus_df = pd.read_parquet(os.path.join(data_dir, \"bus_data.parquet\"))\n",
    "branch_df = pd.read_parquet(os.path.join(data_dir, \"branch_data.parquet\"))\n",
    "gen_df = pd.read_parquet(os.path.join(data_dir, \"gen_data.parquet\"))\n",
    "\n",
    "print(\"Successfully loaded the Parquet dataset!\")\n",
    "print(\"-\" * 40)\n",
    "\n",
    "print(f\"Graph Nodes (Buses data shape): {bus_df.shape}\")\n",
    "print(f\"Graph Edges (Branches data shape): {branch_df.shape}\")\n",
    "print(f\"Power Generators data shape: {gen_df.shape}\")\n",
    "\n",
    "print(\"\\n--- Sneak Peek at the Node (Bus) Data ---\")\n",
    "print(bus_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "641c18b8-a71d-4d09-910b-cbb37e962c7f",
   "metadata": {},
   "source": [
    "## Inspect Bus Features\n",
    "\n",
    "This step lists all available columns in the **bus dataset**.\n",
    "\n",
    "Typical bus features include:\n",
    "\n",
    "- **Bus ID**\n",
    "- **Voltage Magnitude (Vm)**\n",
    "- **Voltage Angle (Va)**\n",
    "- **Active Power Demand (Pd)**\n",
    "- **Reactive Power Demand (Qd)**\n",
    "\n",
    "Understanding these features is important for selecting **input features and prediction targets** for machine learning models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "12481b41-d6af-47a6-82cb-1adb38e2328d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bus Columns: ['scenario', 'load_scenario_idx', 'bus', 'Pd', 'Qd', 'Pg', 'Qg', 'Vm', 'Va', 'PQ', 'PV', 'REF', 'vn_kv', 'min_vm_pu', 'max_vm_pu', 'GS', 'BS', 'scenario_partition']\n"
     ]
    }
   ],
   "source": [
    "print(\"Bus Columns:\", bus_df.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1e91279-ae5f-4170-9845-5d471a5d2f9b",
   "metadata": {},
   "source": [
    "## Inspect Branch Features\n",
    "\n",
    "Branch data represents **transmission lines connecting buses** in the electrical network.\n",
    "\n",
    "Important branch features typically include:\n",
    "\n",
    "- **From Bus** – Starting node of the transmission line\n",
    "- **To Bus** – Ending node of the transmission line\n",
    "- **Resistance (R)**\n",
    "- **Reactance (X)**\n",
    "- **Power Flow values**\n",
    "\n",
    "These features describe the **physical properties of transmission lines**, which are essential for modeling power flow in the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "120151b9-afa2-4a6d-b023-18b609c9e3c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Branch Columns: ['scenario', 'load_scenario_idx', 'idx', 'from_bus', 'to_bus', 'pf', 'qf', 'pt', 'qt', 'r', 'x', 'b', 'Yff_r', 'Yff_i', 'Yft_r', 'Yft_i', 'Ytf_r', 'Ytf_i', 'Ytt_r', 'Ytt_i', 'tap', 'shift', 'ang_min', 'ang_max', 'rate_a', 'br_status', 'scenario_partition']\n"
     ]
    }
   ],
   "source": [
    "print(\"Branch Columns:\", branch_df.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d8007e0-b987-44f6-b961-6c0d32f8b2bf",
   "metadata": {},
   "source": [
    "## Build Physical Edge Tensor\n",
    "\n",
    "To represent the power grid as a graph, we extract the connectivity information from the branch dataset.\n",
    "\n",
    "Each transmission line connects two buses:\n",
    "\n",
    "- **Source node (from_bus)**\n",
    "- **Target node (to_bus)**\n",
    "\n",
    "These connections are converted into a tensor called **edge_index**, which follows the coordinate format used in **Graph Neural Networks (GNNs)**.\n",
    "\n",
    "The tensor structure looks like:\n",
    "\n",
    "edge_index =  \n",
    "[ source_nodes ]  \n",
    "[ target_nodes ]\n",
    "\n",
    "This representation allows graph-based machine learning frameworks to efficiently process network connectivity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2f2e0642-1e4f-40a0-ae52-967d54882d46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edge Tensor Built Successfully!\n",
      "----------------------------------------\n",
      "Tensor Shape: torch.Size([2, 41]) (2 rows: 'From' and 'To', across all power lines)\n",
      "\n",
      "First 5 physical wire connections:\n",
      "tensor([[0, 0, 1, 2, 1],\n",
      "        [1, 2, 3, 3, 4]])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "scenario_0_branches = branch_df[branch_df['scenario'] == 0]\n",
    "\n",
    "source_nodes = scenario_0_branches['from_bus'].values\n",
    "target_nodes = scenario_0_branches['to_bus'].values\n",
    "\n",
    "source_nodes = source_nodes.astype(np.int64)\n",
    "target_nodes = target_nodes.astype(np.int64)\n",
    "\n",
    "edge_index = torch.tensor([source_nodes, target_nodes], dtype=torch.long)\n",
    "\n",
    "print(\"Edge Tensor Built Successfully!\")\n",
    "print(\"-\" * 40)\n",
    "print(f\"Tensor Shape: {edge_index.shape} (2 rows: 'From' and 'To', across all power lines)\")\n",
    "print(\"\\nFirst 5 physical wire connections:\")\n",
    "print(edge_index[:, :5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eed4df7-30ec-4dc3-847a-10c9c3a6f600",
   "metadata": {},
   "source": [
    "## Construct PyTorch Geometric Graph Object\n",
    "\n",
    "In this step, the dataset is converted into a **PyTorch Geometric Data object**, which is the standard format used for graph neural networks.\n",
    "\n",
    "The graph object contains:\n",
    "\n",
    "- **Node Features (x)** – Input variables used for learning  \n",
    "  - Active Power Demand (Pd)\n",
    "  - Reactive Power Demand (Qd)\n",
    "\n",
    "- **Edge Index** – Connectivity between nodes representing transmission lines\n",
    "\n",
    "- **Target Labels (y)** – Output variable to be predicted  \n",
    "  - Voltage Magnitude (Vm)\n",
    "\n",
    "This graph structure enables the application of **graph neural networks to power grid problems**, such as voltage prediction and state estimation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "93e705bc-8881-4efa-9875-9f666bcfd0ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Graph Object Built!\n",
      "----------------------------------------\n",
      "Data(x=[30, 2], edge_index=[2, 41], y=[30, 1])\n",
      "\n",
      "This object contains:\n",
      "- 30 Nodes (Substations)\n",
      "- 41 Edges (Power Lines)\n",
      "- 2 Input Features per node (Pd, Qd)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "from torch_geometric.data import Data \n",
    "\n",
    "scenario_0_buses = bus_df[bus_df['scenario'] == 0]\n",
    "\n",
    "x_features = scenario_0_buses[['Pd', 'Qd']].values\n",
    "x_tensor = torch.tensor(x_features, dtype=torch.float)\n",
    "\n",
    "y_target = scenario_0_buses[['Vm']].values\n",
    "y_tensor = torch.tensor(y_target, dtype=torch.float)\n",
    "\n",
    "source_nodes = scenario_0_branches['from_bus'].values.astype(np.int64)\n",
    "target_nodes = scenario_0_branches['to_bus'].values.astype(np.int64)\n",
    "edge_index = torch.tensor(np.array([source_nodes, target_nodes]), dtype=torch.long)\n",
    "\n",
    "grid_graph = Data(x=x_tensor, edge_index=edge_index, y=y_tensor)\n",
    "\n",
    "print(\"Final Graph Object Built!\")\n",
    "print(\"-\" * 40)\n",
    "print(grid_graph)\n",
    "print(f\"\\nThis object contains:\")\n",
    "print(f\"- {grid_graph.num_nodes} Nodes (Substations)\")\n",
    "print(f\"- {grid_graph.num_edges} Edges (Power Lines)\")\n",
    "print(f\"- {grid_graph.num_node_features} Input Features per node (Pd, Qd)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30d16c8b-ac7c-49d5-9954-2ffad0869251",
   "metadata": {},
   "source": [
    "## Network Visualization\n",
    "\n",
    "To better understand the structure of the IEEE 30-Bus system, the graph is visualized using **NetworkX**.\n",
    "\n",
    "The PyTorch Geometric graph object is first converted into a NetworkX graph.\n",
    "\n",
    "A **spring layout algorithm** is used to position the nodes in a visually balanced manner while preserving connectivity relationships.\n",
    "\n",
    "The visualization shows:\n",
    "\n",
    "- Nodes representing buses\n",
    "- Edges representing transmission lines\n",
    "- The overall topology of the electrical network\n",
    "\n",
    "This step helps verify that the graph was constructed correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "dcd858d6-49d4-4181-841e-debc8a408e1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "from torch_geometric.utils import to_networkx\n",
    "\n",
    "visual_graph = to_networkx(grid_graph, to_undirected=True)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.title(\"GridFM: IEEE 30-Bus Physical Topology\", fontsize=16, fontweight='bold')\n",
    "\n",
    "pos = nx.spring_layout(visual_graph, seed=42)\n",
    "\n",
    "nx.draw(\n",
    "    visual_graph,\n",
    "    pos,\n",
    "    with_labels=True,\n",
    "    node_color='dodgerblue',\n",
    "    node_size=700,\n",
    "    font_size=10,\n",
    "    font_color='white',\n",
    "    font_weight='bold',\n",
    "    edge_color='gray',\n",
    "    width=2.0\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9108ef83-d91b-4aa6-8a6e-fb5f146ce4fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "from torch_geometric.utils import to_networkx\n",
    "\n",
    "visual_graph = to_networkx(grid_graph, to_undirected=True)\n",
    "centrality = nx.degree_centrality(visual_graph)\n",
    "node_colors = [centrality[node] for node in visual_graph.nodes()]\n",
    "\n",
    "plt.figure(figsize=(10, 7), facecolor='#0B0E14') \n",
    "ax = plt.gca()\n",
    "ax.set_facecolor('#0B0E14')\n",
    "\n",
    "pos = nx.spring_layout(visual_graph, k=0.5, iterations=50, seed=42)\n",
    "\n",
    "nx.draw_networkx_edges(\n",
    "    visual_graph, pos, \n",
    "    width=2, \n",
    "    alpha=0.3, \n",
    "    edge_color='#00F2FF' \n",
    ")\n",
    "\n",
    "nodes = nx.draw_networkx_nodes(\n",
    "    visual_graph, pos,\n",
    "    node_size=600,\n",
    "    node_color=node_colors,\n",
    "    cmap=plt.cm.viridis,\n",
    "    edgecolors='#ffffff',\n",
    "    linewidths=1.5\n",
    ")\n",
    "\n",
    "nx.draw_networkx_labels(\n",
    "    visual_graph, pos,\n",
    "    font_size=10,\n",
    "    font_color='white',\n",
    "    font_weight='bold'\n",
    ")\n",
    "\n",
    "cbar = plt.colorbar(nodes, shrink=0.7)\n",
    "cbar.set_label('Bus Criticality (Centrality)', color='white', size=12)\n",
    "cbar.ax.yaxis.set_tick_params(color='white')\n",
    "plt.setp(plt.getp(cbar.ax.axes, 'yticklabels'), color='white')\n",
    "\n",
    "plt.title(\"IEEE 30-Bus System | Network Topology Analysis\", \n",
    "          color='#00F2FF', size=20, weight='bold', pad=20)\n",
    "plt.axis('off')\n",
    "\n",
    "plt.savefig(\"ieee_case30_centrality.png\", facecolor='#0B0E14', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02d074b6-ac1e-43ca-8255-a944190b3658",
   "metadata": {},
   "source": [
    "## Voltage Profile Analysis\n",
    "\n",
    "Voltage stability is a critical aspect of power system operation.\n",
    "\n",
    "This step analyzes the **Voltage Magnitude (Vm)** across all buses in a selected scenario.\n",
    "\n",
    "The voltage values are plotted against bus numbers and compared against standard operational limits:\n",
    "\n",
    "- **Lower Limit:** 0.95 p.u.\n",
    "- **Upper Limit:** 1.05 p.u.\n",
    "\n",
    "A shaded region between these limits indicates the acceptable operating range.\n",
    "\n",
    "This visualization helps identify:\n",
    "\n",
    "- Voltage deviations\n",
    "- Potential instability points\n",
    "- Buses operating outside safe voltage limits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "baae1a10-3a74-4f08-b187-9aa28bb7b8df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scenario: 0\n",
      "Buses found: 30\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "base_path = '/home/powerprofsenergyhub/Desktop/data_out/case30_ieee/raw'\n",
    "bus_file = os.path.join(base_path, 'bus_data.parquet')\n",
    "\n",
    "if os.path.exists(bus_file):\n",
    "    df_bus = pd.read_parquet(bus_file)\n",
    "    \n",
    "    first_scenario = df_bus['scenario'].unique()[0]\n",
    "    scenario_data = df_bus[df_bus['scenario'] == first_scenario].sort_values('bus')\n",
    "    \n",
    "    bus_ids = scenario_data['bus'].values\n",
    "    voltages = scenario_data['Vm'].values\n",
    "    \n",
    "    print(f\"Scenario: {first_scenario}\")\n",
    "    print(f\"Buses found: {len(bus_ids)}\")\n",
    "\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    plt.plot(bus_ids, voltages, marker='o', linestyle='-', color='lime', label='Voltage (Vm)')\n",
    "    \n",
    "    plt.axhline(y=0.95, color='red', linestyle=':', label='Lower Limit (0.95)')\n",
    "    plt.axhline(y=1.05, color='red', linestyle=':', label='Upper Limit (1.05)')\n",
    "    plt.fill_between(bus_ids, 0.95, 1.05, color='green', alpha=0.1)\n",
    "\n",
    "    plt.title(f\"IEEE 30-Bus System: Voltage Profile (Scenario: {first_scenario})\")\n",
    "    plt.xlabel(\"Bus Number\")\n",
    "    plt.ylabel(\"Voltage Magnitude (p.u.)\")\n",
    "    plt.xticks(bus_ids, rotation=90, fontsize=8)\n",
    "    plt.grid(alpha=0.2)\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "else:\n",
    "    print(\"File not found.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14536b9-1f2c-460e-8a05-f86a8bfb11dd",
   "metadata": {},
   "source": [
    "## Dataset Validation and Consistency Checks\n",
    "\n",
    "Before using the generated IEEE 30-bus dataset for machine learning or power system analysis, it is important to validate the physical and structural consistency of the data.\n",
    "\n",
    "GridFM DataKit provides several validation functions that verify whether the generated dataset satisfies fundamental power system constraints.\n",
    "\n",
    "These checks ensure that:\n",
    "\n",
    "- Electrical network parameters are physically consistent\n",
    "- Power flow values match computed results\n",
    "- Generator limits and bus types are valid\n",
    "- Power balance equations are satisfied\n",
    "- Scenario indexing remains consistent across all data files\n",
    "\n",
    "Running these validations helps detect issues in the generated dataset before proceeding with graph construction or machine learning tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3302d577-e226-48b4-a7d2-3774c7605792",
   "metadata": {},
   "source": [
    "### Preparing Data for Validation\n",
    "\n",
    "The GridFM validation utilities expect the generated dataset to be provided as a dictionary containing the main data tables:\n",
    "\n",
    "- **bus_data** – Bus-level electrical information\n",
    "- **branch_data** – Transmission line data\n",
    "- **gen_data** – Generator information\n",
    "\n",
    "These dataframes are combined into a dictionary structure that can be passed to the validation functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c771ac1d-3f1c-4114-9d5a-72dbee2d175e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y-Bus data loaded\n"
     ]
    },
    {
     "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>scenario</th>\n",
       "      <th>load_scenario_idx</th>\n",
       "      <th>index1</th>\n",
       "      <th>index2</th>\n",
       "      <th>G</th>\n",
       "      <th>B</th>\n",
       "      <th>scenario_partition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.724846</td>\n",
       "      <td>-21.584953</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.305911</td>\n",
       "      <td>15.943582</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.418935</td>\n",
       "      <td>5.688170</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-5.305911</td>\n",
       "      <td>15.943582</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.295142</td>\n",
       "      <td>-32.278440</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   scenario  load_scenario_idx  index1  index2          G          B  \\\n",
       "0         0                0.0       0       0   6.724846 -21.584953   \n",
       "1         0                0.0       0       1  -5.305911  15.943582   \n",
       "2         0                0.0       0       2  -1.418935   5.688170   \n",
       "3         0                0.0       1       0  -5.305911  15.943582   \n",
       "4         0                0.0       1       1  10.295142 -32.278440   \n",
       "\n",
       "  scenario_partition  \n",
       "0                  0  \n",
       "1                  0  \n",
       "2                  0  \n",
       "3                  0  \n",
       "4                  0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "y_bus_df = pd.read_parquet(os.path.join(data_dir, \"y_bus_data.parquet\"))\n",
    "\n",
    "print(\"Y-Bus data loaded\")\n",
    "y_bus_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c3349e63-9a6e-4691-9d07-36a9ff1762b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "runtime_df = pd.read_parquet(os.path.join(data_dir, \"runtime_data.parquet\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "03075943-5fab-4e46-9b0e-e3ef51f37498",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset dictionary prepared for validation.\n"
     ]
    }
   ],
   "source": [
    "from gridfm_datakit import validation\n",
    "\n",
    "generated_data = {\n",
    "    \"bus_data\": bus_df,\n",
    "    \"branch_data\": branch_df,\n",
    "    \"gen_data\": gen_df,\n",
    "    \"y_bus_data\": y_bus_df,\n",
    "    \"runtime_data\": runtime_df,\n",
    "    \"mode\": \"pf\",\n",
    "    \"sn_mva\": 100\n",
    "}\n",
    "\n",
    "print(\"Dataset dictionary prepared for validation.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42e1a129-c55a-47a0-b491-85477f01b420",
   "metadata": {},
   "source": [
    "### Y-Bus Diagonal Consistency\n",
    "\n",
    "The Y-Bus matrix represents the electrical admittance of the power grid.\n",
    "\n",
    "This validation ensures that the **diagonal entries of the Y-Bus matrix are consistent with the branch and bus data**.\n",
    "\n",
    "The test verifies whether the network admittance values correctly reflect the physical properties of the transmission lines connected to each bus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cd44dd08-3ed4-4766-a320-30ead10fdbda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Y-bus diagonal consistency: validating 305400 bus entries across 10180 scenarios\n",
      "Y-Bus diagonal consistency check passed.\n"
     ]
    }
   ],
   "source": [
    "validation.validate_ybus_diagonal_consistency(generated_data)\n",
    "print(\"Y-Bus diagonal consistency check passed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cbad34d-901f-47d3-b80f-1396ca5efeae",
   "metadata": {},
   "source": [
    "### Deactivated Transmission Line Validation\n",
    "\n",
    "This check verifies that any transmission line marked as **deactivated** has:\n",
    "\n",
    "- Zero admittance\n",
    "- Zero power flow\n",
    "\n",
    "This ensures that inactive power lines do not affect power flow computations or graph structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "db53a00e-da61-4392-8d20-8378cea56d51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Deactivated lines zero admittance: validating 8992 deactivated branches\n",
      "    Deactivated lines zero admittance: OK\n",
      "Deactivated line validation passed.\n"
     ]
    }
   ],
   "source": [
    "validation.validate_deactivated_lines_zero_admittance(generated_data)\n",
    "print(\"Deactivated line validation passed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b163f8d-7a9a-4646-91b0-5516e9aa5eaa",
   "metadata": {},
   "source": [
    "### Computed vs Stored Power Flow Validation\n",
    "\n",
    "This validation compares the stored power flow values in the dataset with the power flows recomputed from the electrical network parameters.\n",
    "\n",
    "The comparison is performed using the system base power (`sn_mva`).  \n",
    "For the IEEE 30-bus test system, the base power is **100 MVA**.\n",
    "\n",
    "If the stored values match the recomputed results within acceptable tolerance limits, the dataset is considered physically consistent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2f4a0056-cfe0-445b-9923-5f0b743218ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Validate computed vs stored power flows: validating 417380 branches across 10180 scenarios\n",
      "    Computed vs stored power flows: OK\n",
      "Power flow validation passed.\n"
     ]
    }
   ],
   "source": [
    "validation.validate_computed_vs_stored_power_flows(generated_data, sn_mva=100)\n",
    "print(\"Power flow validation passed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aecbfe9-eb88-490c-9ae3-9900b009277e",
   "metadata": {},
   "source": [
    "### Branch Loading Validation\n",
    "\n",
    "This validation verifies whether transmission line loading remains within acceptable limits.\n",
    "\n",
    "Branch loading is computed based on the apparent power flowing through each transmission line and compared against its thermal rating (`rate_a`).\n",
    "\n",
    "The validation behavior depends on the dataset generation mode:\n",
    "\n",
    "- **PF Mode (Power Flow)**  \n",
    "  Branch loading statistics are computed for analysis.\n",
    "\n",
    "- **OPF Mode (Optimal Power Flow)**  \n",
    "  The validation ensures that all branch flows respect their thermal limits.\n",
    "\n",
    "For the IEEE 30-bus dataset used in this notebook, the dataset operates in **Power Flow (PF) mode**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ab8f738a-e4d0-461c-b8a0-02d6313e5945",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Branch loading limits (pf mode): validating 408388 rated branches across 10180 scenarios\n",
      "    Binding loading constraints (>= 0.99): 5196 branches\n",
      "    Overloaded branches (> 1.0): 1477 branches\n",
      "    Branch loading limits (PF mode): statistics computed\n",
      "Branch loading validation completed.\n"
     ]
    }
   ],
   "source": [
    "validation.validate_branch_loading_opf_mode(generated_data)\n",
    "print(\"Branch loading validation completed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f0d6704-0c6b-4eeb-b84b-133ad7d9599a",
   "metadata": {},
   "source": [
    "### Deactivated Generator Validation\n",
    "\n",
    "Generators that are marked as **inactive** must have zero power output.\n",
    "\n",
    "This validation ensures that inactive generators do not produce power in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a24702be-aae0-4e05-83fe-619627a08555",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Deactivated generators zero output: validating 1188 deactivated generators\n",
      "    Deactivated generators zero output: OK\n",
      "Deactivated generator validation passed.\n"
     ]
    }
   ],
   "source": [
    "validation.validate_deactivated_generators_zero_output(generated_data)\n",
    "print(\"Deactivated generator validation passed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7644468-0f7c-45fa-aa6e-4898cf8ddca2",
   "metadata": {},
   "source": [
    "### Generator Operating Limits\n",
    "\n",
    "This validation verifies that generator outputs remain within their specified limits.\n",
    "\n",
    "Each generator must operate within its defined:\n",
    "\n",
    "- Minimum generation capacity\n",
    "- Maximum generation capacity\n",
    "\n",
    "Violations could indicate unrealistic operating conditions in the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e143e8ed-56eb-4497-9c4d-94b737e76925",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Generator limits: validating 49712 active generators (mode: pf)\n",
      "    Binding P limits: 39890 at minimum, 44806 at maximum\n",
      "    Generator limits: OK\n",
      "Generator limit validation passed.\n"
     ]
    }
   ],
   "source": [
    "validation.validate_generator_limits(generated_data)\n",
    "print(\"Generator limit validation passed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cf6032b-4250-4905-b40b-a7e89fda23e0",
   "metadata": {},
   "source": [
    "### Voltage Magnitude Limits Validation\n",
    "\n",
    "Voltage magnitude must remain within safe operational bounds.\n",
    "\n",
    "Typical power system limits are:\n",
    "\n",
    "- Lower limit: **0.95 per unit**\n",
    "- Upper limit: **1.05 per unit**\n",
    "\n",
    "This validation checks whether all bus voltages remain within the allowed range in OPF mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6e74bcb6-bf6a-4e8e-b9c3-26ca86185dc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Voltage magnitude limits: skipped (not in OPF mode)\n",
      "Voltage magnitude validation passed.\n"
     ]
    }
   ],
   "source": [
    "validation.validate_voltage_magnitude_limits_opf_mode(generated_data)\n",
    "print(\"Voltage magnitude validation passed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "692ee44a-e2bc-463e-8cd7-699f08f7e1f5",
   "metadata": {},
   "source": [
    "### Bus-Level Generation Consistency\n",
    "\n",
    "The total power generation recorded at each bus must equal the sum of the generators connected to that bus.\n",
    "\n",
    "This validation verifies that the aggregated generator outputs match the bus-level generation values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6905e58e-e8c1-4679-89c6-4570c3cf9a95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Bus generation consistency: validating 305400 bus entries across 10180 scenarios\n",
      "    Bus generation consistency: OK\n",
      "Bus generation consistency validation passed.\n"
     ]
    }
   ],
   "source": [
    "validation.validate_bus_generation_consistency(generated_data)\n",
    "print(\"Bus generation consistency validation passed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19e6dec4-94ac-4f92-a42a-5fdb8b4c0498",
   "metadata": {},
   "source": [
    "### Power Balance Equation Validation\n",
    "\n",
    "Power systems must obey **Kirchhoff's Current Law (KCL)**.\n",
    "\n",
    "At every bus:\n",
    "\n",
    "Power Generated – Power Consumed = Net Power Flow\n",
    "\n",
    "This validation checks whether the power balance equations hold across all buses in every scenario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "04ab5101-0bc3-4461-9154-dea7bf0d6b20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Power balance equations (Kirchhoff's Law): validating 305400 bus entries across 10180 scenarios\n",
      "    Power balance equations (Kirchhoff's Law): OK\n",
      "Power balance validation passed.\n"
     ]
    }
   ],
   "source": [
    "validation.validate_power_balance_equations(generated_data ,sn_mva = 100)\n",
    "print(\"Power balance validation passed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27c74dd7-cb0c-4c02-b816-f5b367e04f09",
   "metadata": {},
   "source": [
    "### Constant Cost Generator Validation\n",
    "\n",
    "Some generators have cost functions containing only a constant term.\n",
    "\n",
    "These generators should not be perturbed or modified across scenarios.\n",
    "\n",
    "This validation ensures that their cost coefficients remain identical across all generated scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "15b95983-171d-4ba3-ab4b-59b0c0144a10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Constant cost generators unchanged: validating 4 constant-cost generators across 10180 scenarios\n",
      "    Constant cost generators unchanged: OK\n",
      "Constant generator cost validation passed.\n"
     ]
    }
   ],
   "source": [
    "validation.validate_constant_cost_generators_unchanged(generated_data)\n",
    "print(\"Constant generator cost validation passed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3aff013-fb47-495b-b438-c2cb72abfc03",
   "metadata": {},
   "source": [
    "### Bus Type and Generator Consistency\n",
    "\n",
    "Power system buses can be classified as:\n",
    "\n",
    "- **PQ Bus** – Load bus (no active generators)\n",
    "- **PV Bus** – Voltage-controlled bus (must have generators)\n",
    "- **REF Bus** – Slack/reference bus (must have at least one generator)\n",
    "\n",
    "This validation ensures that bus types are consistent with the presence or absence of generators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ce8a5c6c-a7e1-4fe1-935e-289f9513b2ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Bus type-generator consistency: validating 305400 bus entries across 10180 scenarios\n",
      "    Bus type-generator consistency: validated 49712 PV, 245508 PQ, 10180 REF bus entries\n",
      "    Bus type-generator consistency: OK\n",
      "Bus type validation passed.\n"
     ]
    }
   ],
   "source": [
    "validation.validate_bus_type_generator_consistency(generated_data)\n",
    "print(\"Bus type validation passed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5927ba7-81d0-45e2-87b1-815307b2489e",
   "metadata": {},
   "source": [
    "### Scenario Index Consistency\n",
    "\n",
    "Multiple scenarios may exist in the dataset to represent different grid conditions.\n",
    "\n",
    "This validation checks whether the scenario indices remain consistent across all dataset files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2ab010a3-a753-41bc-8950-b4be6a56330c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Scenario indexing consistency: validating 10180 scenarios across 4 data files\n",
      "    Scenario indexing consistency: OK\n",
      "Scenario indexing validation passed.\n"
     ]
    }
   ],
   "source": [
    "validation.validate_scenario_indexing_consistency(generated_data)\n",
    "print(\"Scenario indexing validation passed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d3f6b26-d3ef-43c0-9f25-7a3250197548",
   "metadata": {},
   "source": [
    "### Bus Index Consistency\n",
    "\n",
    "Bus identifiers must remain consistent across all dataset components.\n",
    "\n",
    "This validation ensures that the same bus indices appear correctly in:\n",
    "\n",
    "- Bus data\n",
    "- Branch data\n",
    "- Generator data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "977888c9-b588-4a58-b3e2-d6a305b92ba5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Bus indexing consistency: validating 30 buses across 3 data files\n",
      "    Bus indexing consistency: OK\n",
      "Bus indexing validation passed.\n"
     ]
    }
   ],
   "source": [
    "validation.validate_bus_indexing_consistency(generated_data)\n",
    "print(\"Bus indexing validation passed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "246c1e5c-b4e9-43ea-b04c-67f8f4398567",
   "metadata": {},
   "source": [
    "### Dataset Completeness Validation\n",
    "\n",
    "The final validation ensures that all required data fields are present and correctly populated.\n",
    "\n",
    "This check verifies the completeness of the dataset before it is used for machine learning or power system analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "52ae5e3e-2c27-43be-81fd-cd800f97f6b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Data completeness: validating 1906036 total entries across 4 data files\n",
      "    Data completeness: OK (all required columns present and NaN-free)\n",
      "Data completeness validation passed.\n"
     ]
    }
   ],
   "source": [
    "validation.validate_data_completeness(generated_data)\n",
    "print(\"Data completeness validation passed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9d73db-05f8-4fad-bc7a-72061a02435d",
   "metadata": {},
   "source": [
    "## Validation Summary\n",
    "\n",
    "All validation checks were executed to verify the physical and structural correctness of the generated IEEE 30-Bus dataset.\n",
    "\n",
    "Successful completion of these validations confirms that:\n",
    "\n",
    "- The power grid simulation data is physically consistent\n",
    "- Generator and transmission line parameters are valid\n",
    "- Power balance equations hold\n",
    "- Scenario and indexing structures are correct\n",
    "\n",
    "The dataset is therefore ready for further processing, graph construction, and machine learning experimentation."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}