Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4b8ad44a-5462-4c27-99a9-c95b862d142c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting analysis...\n",
      "Loaded lightsource/transformers_original.csv successfully.\n",
      "Columns in lightsource/transformers_original.csv: ['Transformer', 'scn_name', 'bus0', 'bus1', 'model', 'r', 'g', 'b', 's_nom', 's_nom_extendable', 's_nom_min', 's_nom_max', 's_max_pu', 'tap_ratio', 'tap_side', 'tap_position', 'phase_shift', 'build_year', 'lifetime', 'v_ang_min', 'v_ang_max', 'capital_cost', 'num_parallel', 'geom', 'topo', 'type', 's_nom_mod', 'sub_network', 'x_pu', 'r_pu', 'g_pu', 'b_pu', 'x_pu_eff', 'r_pu_eff', 's_nom_opt', 'x']\n",
      "--------------------------------------------------\n",
      "Loaded lightsource/transformers_updated.csv successfully.\n",
      "Columns in lightsource/transformers_updated.csv: ['Transformer', 'scn_name', 'bus0', 'bus1', 'model', 'r', 'g', 'b', 's_nom', 's_nom_extendable', 's_nom_min', 's_nom_max', 's_max_pu', 'tap_ratio', 'tap_side', 'tap_position', 'phase_shift', 'build_year', 'lifetime', 'v_ang_min', 'v_ang_max', 'capital_cost', 'num_parallel', 'geom', 'topo', 'type', 's_nom_mod', 'sub_network', 'x_pu', 'r_pu', 'g_pu', 'b_pu', 'x_pu_eff', 'r_pu_eff', 's_nom_opt', 'x']\n",
      "--------------------------------------------------\n",
      "Loaded lightsource/lines_original.csv successfully.\n",
      "Columns in lightsource/lines_original.csv: ['Unnamed: 0', 'bus0', 'bus1', 'type', 'carrier', 'x', 'r', 'g', 'b', 's_nom', 's_nom_extendable', 's_nom_min', 's_nom_max', 's_max_pu', 'build_year', 'lifetime', 'capital_cost', 'length', 'terrain_factor', 'num_parallel', 'v_ang_min', 'v_ang_max', 'geom', 's_nom_mod', 'sub_network', 'v_nom', 'x_pu', 'r_pu', 'b_pu', 'g_pu', 'x_pu_eff', 'r_pu_eff', 's_nom_opt']\n",
      "--------------------------------------------------\n",
      "Loaded lightsource/lines_updated.csv successfully.\n",
      "Columns in lightsource/lines_updated.csv: ['Unnamed: 0', 'bus0', 'bus1', 'type', 'carrier', 'x', 'r', 'g', 'b', 's_nom', 's_nom_extendable', 's_nom_min', 's_nom_max', 's_max_pu', 'build_year', 'lifetime', 'capital_cost', 'length', 'terrain_factor', 'num_parallel', 'v_ang_min', 'v_ang_max', 'geom', 's_nom_mod', 'sub_network', 'v_nom', 'x_pu', 'r_pu', 'b_pu', 'g_pu', 'x_pu_eff', 'r_pu_eff', 's_nom_opt']\n",
      "--------------------------------------------------\n",
      "\n",
      "Analyzing b for transformers...\n",
      "Extracted 'b' values for transformers. Count after cleaning: 544\n",
      "Extracted 'b' values for transformers. Count after cleaning: 544\n",
      "Corresponding values plot and statistics saved to lightsource/analysis_results/b\n",
      "Histogram and distribution statistics saved to lightsource/analysis_results/b\n",
      "No extreme changes found for transformers b\n",
      "\n",
      "Analyzing x for transformers...\n",
      "Extracted 'x' values for transformers. Count after cleaning: 544\n",
      "Extracted 'x' values for transformers. Count after cleaning: 544\n",
      "Corresponding values plot and statistics saved to lightsource/analysis_results/x\n",
      "Histogram and distribution statistics saved to lightsource/analysis_results/x\n",
      "No extreme changes found for transformers x\n",
      "\n",
      "Analyzing r for transformers...\n",
      "Extracted 'r' values for transformers. Count after cleaning: 544\n",
      "Extracted 'r' values for transformers. Count after cleaning: 544\n",
      "Corresponding values plot and statistics saved to lightsource/analysis_results/r\n",
      "Histogram and distribution statistics saved to lightsource/analysis_results/r\n",
      "Extreme changes analysis saved to lightsource/analysis_results/r\n",
      "\n",
      "Analyzing b for lines...\n",
      "Extracted 'b' values for lines. Count after cleaning: 10884\n",
      "Extracted 'b' values for lines. Count after cleaning: 10884\n",
      "Corresponding values plot and statistics saved to lightsource/analysis_results/b\n",
      "Histogram and distribution statistics saved to lightsource/analysis_results/b\n",
      "Extreme changes analysis saved to lightsource/analysis_results/b\n",
      "\n",
      "Analyzing x for lines...\n",
      "Extracted 'x' values for lines. Count after cleaning: 10884\n",
      "Extracted 'x' values for lines. Count after cleaning: 10884\n",
      "Corresponding values plot and statistics saved to lightsource/analysis_results/x\n",
      "Histogram and distribution statistics saved to lightsource/analysis_results/x\n",
      "Extreme changes analysis saved to lightsource/analysis_results/x\n",
      "\n",
      "Analyzing r for lines...\n",
      "Extracted 'r' values for lines. Count after cleaning: 10884\n",
      "Extracted 'r' values for lines. Count after cleaning: 10884\n",
      "Corresponding values plot and statistics saved to lightsource/analysis_results/r\n",
      "Histogram and distribution statistics saved to lightsource/analysis_results/r\n",
      "Extreme changes analysis saved to lightsource/analysis_results/r\n",
      "\n",
      "Analysis completed.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from matplotlib.colors import LogNorm\n",
    "\n",
    "\n",
    "# Configuration\n",
    "DATA_DIR = \"lightsource\"\n",
    "\n",
    "FILES = {\n",
    "    'transformers_original': 'transformers_original.csv',\n",
    "    'transformers_updated': 'transformers_updated.csv',\n",
    "    'lines_original': 'lines_original.csv',\n",
    "    'lines_updated': 'lines_updated.csv'\n",
    "}\n",
    "\n",
    "# Add 'b', 'x', and 'r' columns\n",
    "COLUMNS_TO_ANALYZE = {\n",
    "    'transformers': ['b', 'x', 'r'],\n",
    "    'lines': ['b', 'x', 'r']\n",
    "}\n",
    "\n",
    "IDENTIFIER_COLUMNS = {\n",
    "    'transformers': 'Transformer',\n",
    "    'lines': ['bus0', 'bus1', 'type']\n",
    "}\n",
    "\n",
    "# Create separate output directories for b, x, and r analyses\n",
    "OUTPUT_DIR_BASE = os.path.join(DATA_DIR, \"analysis_results\")\n",
    "os.makedirs(OUTPUT_DIR_BASE, exist_ok=True)\n",
    "\n",
    "\n",
    "def load_csv(file_path):\n",
    "    \"\"\"Load a CSV file into a pandas DataFrame.\"\"\"\n",
    "    try:\n",
    "        df = pd.read_csv(file_path)\n",
    "        print(f\"Loaded {file_path} successfully.\")\n",
    "        print(f\"Columns in {file_path}: {df.columns.tolist()}\")\n",
    "        print(\"-\" * 50)\n",
    "        return df\n",
    "    except Exception as e:\n",
    "        print(f\"Error loading {file_path}: {e}\")\n",
    "        return None\n",
    "\n",
    "\n",
    "def extract_column_values(df, component, column_name):\n",
    "    \"\"\"Extract the specified column from the DataFrame.\"\"\"\n",
    "    if column_name in df.columns:\n",
    "        values = pd.to_numeric(df[column_name], errors='coerce').dropna()\n",
    "        if values.empty:\n",
    "            print(f\"No valid numeric data found in column '{column_name}' for '{component}'.\")\n",
    "            raise ValueError(f\"No valid numeric data in '{column_name}' for '{component}'.\")\n",
    "        print(f\"Extracted '{column_name}' values for {component}. Count after cleaning: {values.count()}\")\n",
    "        return values\n",
    "    else:\n",
    "        available_cols = df.columns.tolist()\n",
    "        print(f\"Column '{column_name}' not found in the dataset for '{component}'. Available columns: {available_cols}\")\n",
    "        raise ValueError(f\"Column '{column_name}' not found in the dataset for '{component}'.\")\n",
    "\n",
    "\n",
    "def plot_corresponding_values(original_df, updated_df, component, column_name):\n",
    "    \"\"\"Plot corresponding values for original and updated datasets, excluding identical values.\"\"\"\n",
    "    identifier = IDENTIFIER_COLUMNS.get(component)\n",
    "    output_dir = os.path.join(OUTPUT_DIR_BASE, column_name)\n",
    "    os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "    if isinstance(identifier, list):\n",
    "        merged_df = pd.merge(original_df, updated_df, on=identifier, suffixes=('_original', '_updated'))\n",
    "    else:\n",
    "        merged_df = pd.merge(original_df, updated_df, on=identifier, suffixes=('_original', '_updated'))\n",
    "\n",
    "    # Filter out identical values\n",
    "    col_original = f'{column_name}_original'\n",
    "    col_updated = f'{column_name}_updated'\n",
    "\n",
    "    # Handle potential infinity and NaN values\n",
    "    merged_df[col_original] = pd.to_numeric(merged_df[col_original], errors='coerce')\n",
    "    merged_df[col_updated] = pd.to_numeric(merged_df[col_updated], errors='coerce')\n",
    "\n",
    "    # Remove inf, -inf, and nan values\n",
    "    merged_df = merged_df[~merged_df[col_original].isin([np.inf, -np.inf]) &\n",
    "                          ~merged_df[col_updated].isin([np.inf, -np.inf]) &\n",
    "                          ~merged_df[col_original].isna() &\n",
    "                          ~merged_df[col_updated].isna()]\n",
    "\n",
    "    different_values = merged_df[merged_df[col_original] != merged_df[col_updated]]\n",
    "\n",
    "    if different_values.empty:\n",
    "        print(f\"No different values found for {component} {column_name}.\")\n",
    "        return\n",
    "\n",
    "    plt.figure(figsize=(8, 8))\n",
    "\n",
    "    # Create scatter plot with difference magnitude coloring\n",
    "    difference_magnitude = abs(different_values[col_updated] - different_values[col_original])\n",
    "    scatter = plt.scatter(different_values[col_original],\n",
    "                          different_values[col_updated],\n",
    "                          c=difference_magnitude,\n",
    "                          cmap='viridis',\n",
    "                          alpha=0.6)\n",
    "\n",
    "    plt.colorbar(scatter, label='Absolute Difference')\n",
    "\n",
    "    # Add diagonal line\n",
    "    all_values = np.concatenate([different_values[col_original], different_values[col_updated]])\n",
    "    min_val, max_val = all_values.min(), all_values.max()\n",
    "    plt.plot([min_val, max_val], [min_val, max_val], 'r--', label='y = x')\n",
    "\n",
    "    plt.xlabel(f'Original {column_name}')\n",
    "    plt.ylabel(f'Updated {column_name}')\n",
    "    plt.title(f'Different {column_name} Values for {component.capitalize()}\\n(Excluding identical values)')\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plot_path = os.path.join(output_dir, f\"{component}_corresponding_{column_name}_plot.png\")\n",
    "    plt.savefig(plot_path)\n",
    "    plt.close()\n",
    "\n",
    "    # Save statistics to text file\n",
    "    stats_path = os.path.join(output_dir, f\"{component}_{column_name}_statistics.txt\")\n",
    "    with open(stats_path, 'w') as f:\n",
    "        f.write(f\"Statistics for {component} {column_name}:\\n\")\n",
    "        f.write(f\"Number of different values: {len(different_values)}\\n\")\n",
    "        f.write(f\"Number of identical values: {len(merged_df) - len(different_values)}\\n\")\n",
    "        f.write(f\"Percentage of different values: {(len(different_values) / len(merged_df)) * 100:.2f}%\\n\")\n",
    "        f.write(f\"Mean absolute difference: {difference_magnitude.mean():.6f}\\n\")\n",
    "        f.write(f\"Max absolute difference: {difference_magnitude.max():.6f}\\n\")\n",
    "\n",
    "    print(f\"Corresponding values plot and statistics saved to {output_dir}\")\n",
    "\n",
    "\n",
    "def plot_histogram_different_values(original, updated, component, column_name):\n",
    "    \"\"\"Create and save overlapping histograms for different values only.\"\"\"\n",
    "    output_dir = os.path.join(OUTPUT_DIR_BASE, column_name)\n",
    "    os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "    # Handle potential infinity and NaN values\n",
    "    original = pd.Series(original).replace([np.inf, -np.inf], np.nan).dropna()\n",
    "    updated = pd.Series(updated).replace([np.inf, -np.inf], np.nan).dropna()\n",
    "\n",
    "    # Create a mask for different values\n",
    "    mask = original != updated\n",
    "    original_diff = original[mask]\n",
    "    updated_diff = updated[mask]\n",
    "\n",
    "    if len(original_diff) == 0:\n",
    "        print(f\"No different values found for {component} {column_name} histogram.\")\n",
    "        return\n",
    "\n",
    "    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12))\n",
    "\n",
    "    # Regular histogram\n",
    "    sns.histplot(original_diff, color='blue', label='Original', kde=True, ax=ax1, alpha=0.6)\n",
    "    sns.histplot(updated_diff, color='orange', label='Updated', kde=True, ax=ax1, alpha=0.6)\n",
    "    ax1.set_title(f\"Distribution of Different {column_name} Values\\n(Linear Scale)\")\n",
    "    ax1.set_xlabel(column_name)\n",
    "    ax1.set_ylabel('Count')\n",
    "    ax1.legend()\n",
    "\n",
    "    # Log scale histogram for better visualization of small values\n",
    "    if (original_diff > 0).all() and (updated_diff > 0).all():\n",
    "        sns.histplot(np.log10(original_diff), color='blue', label='Original', kde=True, ax=ax2, alpha=0.6)\n",
    "        sns.histplot(np.log10(updated_diff), color='orange', label='Updated', kde=True, ax=ax2, alpha=0.6)\n",
    "        ax2.set_title(f\"Distribution of Different {column_name} Values\\n(Log Scale)\")\n",
    "        ax2.set_xlabel(f'log10({column_name})')\n",
    "        ax2.set_ylabel('Count')\n",
    "        ax2.legend()\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plot_path = os.path.join(output_dir, f\"{component}_{column_name}_histogram.png\")\n",
    "    plt.savefig(plot_path)\n",
    "    plt.close()\n",
    "\n",
    "    # Save distribution statistics to text file\n",
    "    stats_path = os.path.join(output_dir, f\"{component}_{column_name}_distribution_statistics.txt\")\n",
    "    with open(stats_path, 'w') as f:\n",
    "        f.write(f\"Distribution Statistics for {component} {column_name}:\\n\\n\")\n",
    "        f.write(\"Original Different Values:\\n\")\n",
    "        f.write(f\"Count: {len(original_diff)}\\n\")\n",
    "        f.write(f\"Mean: {original_diff.mean():.6f}\\n\")\n",
    "        f.write(f\"Median: {original_diff.median():.6f}\\n\")\n",
    "        f.write(f\"Std Dev: {original_diff.std():.6f}\\n\")\n",
    "        f.write(f\"Min: {original_diff.min():.6f}\\n\")\n",
    "        f.write(f\"Max: {original_diff.max():.6f}\\n\\n\")\n",
    "\n",
    "        f.write(\"Updated Different Values:\\n\")\n",
    "        f.write(f\"Count: {len(updated_diff)}\\n\")\n",
    "        f.write(f\"Mean: {updated_diff.mean():.6f}\\n\")\n",
    "        f.write(f\"Median: {updated_diff.median():.6f}\\n\")\n",
    "        f.write(f\"Std Dev: {updated_diff.std():.6f}\\n\")\n",
    "        f.write(f\"Min: {updated_diff.min():.6f}\\n\")\n",
    "        f.write(f\"Max: {updated_diff.max():.6f}\\n\")\n",
    "\n",
    "    print(f\"Histogram and distribution statistics saved to {output_dir}\")\n",
    "\n",
    "\n",
    "def identify_extreme_changes(original_df, updated_df, component, column_name):\n",
    "    \"\"\"Identify rows where updated value is >10x or <0.1x of original value.\"\"\"\n",
    "    output_dir = os.path.join(OUTPUT_DIR_BASE, column_name)\n",
    "    os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "    identifier = IDENTIFIER_COLUMNS.get(component)\n",
    "\n",
    "    if isinstance(identifier, list):\n",
    "        merged_df = pd.merge(original_df, updated_df, on=identifier, suffixes=('_original', '_updated'))\n",
    "    else:\n",
    "        merged_df = pd.merge(original_df, updated_df, on=identifier, suffixes=('_original', '_updated'))\n",
    "\n",
    "    col_original = f'{column_name}_original'\n",
    "    col_updated = f'{column_name}_updated'\n",
    "\n",
    "    # Handle potential infinity and NaN values\n",
    "    merged_df[col_original] = pd.to_numeric(merged_df[col_original], errors='coerce')\n",
    "    merged_df[col_updated] = pd.to_numeric(merged_df[col_updated], errors='coerce')\n",
    "\n",
    "    # Remove inf, -inf, and nan values\n",
    "    merged_df = merged_df[~merged_df[col_original].isin([np.inf, -np.inf]) &\n",
    "                          ~merged_df[col_updated].isin([np.inf, -np.inf]) &\n",
    "                          ~merged_df[col_original].isna() &\n",
    "                          ~merged_df[col_updated].isna()]\n",
    "\n",
    "    # Handle division by zero by filtering out rows where original value is zero\n",
    "    merged_df = merged_df[merged_df[col_original] != 0]\n",
    "\n",
    "    # Calculate the ratio\n",
    "    merged_df[f'{column_name}_ratio'] = merged_df[col_updated] / merged_df[col_original]\n",
    "\n",
    "    # Identify extreme changes\n",
    "    extreme_changes = merged_df[(merged_df[f'{column_name}_ratio'] > 10) |\n",
    "                                (merged_df[f'{column_name}_ratio'] < 0.1)]\n",
    "\n",
    "    if not extreme_changes.empty:\n",
    "        # Create figure with two subplots\n",
    "        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12))\n",
    "\n",
    "        # Regular scale plot\n",
    "        scatter1 = ax1.scatter(extreme_changes[col_original],\n",
    "                               extreme_changes[col_updated],\n",
    "                               c=extreme_changes[f'{column_name}_ratio'],\n",
    "                               cmap='coolwarm',\n",
    "                               alpha=0.7)\n",
    "        ax1.set_xlabel(f'Original {column_name}')\n",
    "        ax1.set_ylabel(f'Updated {column_name}')\n",
    "        ax1.set_title(f'Extreme Changes in {column_name} (Linear Scale)')\n",
    "        plt.colorbar(scatter1, ax=ax1, label='Ratio (Updated/Original)')\n",
    "\n",
    "        # Log scale plot\n",
    "        scatter2 = ax2.scatter(extreme_changes[col_original],\n",
    "                               extreme_changes[col_updated],\n",
    "                               c=extreme_changes[f'{column_name}_ratio'],\n",
    "                               cmap='coolwarm',\n",
    "                               alpha=0.7)\n",
    "        ax2.set_xscale('log')\n",
    "        ax2.set_yscale('log')\n",
    "        ax2.set_xlabel(f'Original {column_name}')\n",
    "        ax2.set_ylabel(f'Updated {column_name}')\n",
    "        ax2.set_title(f'Extreme Changes in {column_name} (Log Scale)')\n",
    "        plt.colorbar(scatter2, ax=ax2, label='Ratio (Updated/Original)')\n",
    "\n",
    "        plt.tight_layout()\n",
    "\n",
    "        plot_path = os.path.join(output_dir, f\"{component}_extreme_changes_{column_name}_plot.png\")\n",
    "        plt.savefig(plot_path)\n",
    "        plt.close()\n",
    "\n",
    "        # Save extreme changes to CSV\n",
    "        csv_path = os.path.join(output_dir, f\"{component}_extreme_changes_{column_name}.csv\")\n",
    "        extreme_changes.to_csv(csv_path, index=False)\n",
    "\n",
    "        # Save extreme changes statistics\n",
    "        stats_path = os.path.join(output_dir, f\"{component}_extreme_changes_{column_name}_statistics.txt\")\n",
    "        with open(stats_path, 'w') as f:\n",
    "            f.write(f\"Extreme Changes Statistics for {component} {column_name}:\\n\")\n",
    "            f.write(f\"Total number of extreme changes: {len(extreme_changes)}\\n\")\n",
    "            f.write(f\"Percentage of total values: {(len(extreme_changes) / len(merged_df)) * 100:.2f}%\\n\")\n",
    "            f.write(f\"Mean ratio: {extreme_changes[f'{column_name}_ratio'].mean():.6f}\\n\")\n",
    "            f.write(f\"Median ratio: {extreme_changes[f'{column_name}_ratio'].median():.6f}\\n\")\n",
    "            f.write(f\"Min ratio: {extreme_changes[f'{column_name}_ratio'].min():.6f}\\n\")\n",
    "            f.write(f\"Max ratio: {extreme_changes[f'{column_name}_ratio'].max():.6f}\\n\")\n",
    "\n",
    "        print(f\"Extreme changes analysis saved to {output_dir}\")\n",
    "    else:\n",
    "        print(f\"No extreme changes found for {component} {column_name}\")\n",
    "\n",
    "\n",
    "def main():\n",
    "    print(\"Starting analysis...\")\n",
    "\n",
    "    # Load all datasets\n",
    "    datasets = {}\n",
    "    for key, filename in FILES.items():\n",
    "        file_path = os.path.join(DATA_DIR, filename)\n",
    "        df = load_csv(file_path)\n",
    "        if df is not None:\n",
    "            datasets[key] = df\n",
    "\n",
    "    # Process each component and column\n",
    "    for component in ['transformers', 'lines']:\n",
    "        for column_name in COLUMNS_TO_ANALYZE[component]:\n",
    "            print(f\"\\nAnalyzing {column_name} for {component}...\")\n",
    "\n",
    "            original_key = f\"{component}_original\"\n",
    "            updated_key = f\"{component}_updated\"\n",
    "\n",
    "            if original_key in datasets and updated_key in datasets:\n",
    "                try:\n",
    "                    # Extract values\n",
    "                    original_values = extract_column_values(datasets[original_key], component, column_name)\n",
    "                    updated_values = extract_column_values(datasets[updated_key], component, column_name)\n",
    "\n",
    "                    # Create plots and analysis\n",
    "                    plot_corresponding_values(datasets[original_key], datasets[updated_key], component, column_name)\n",
    "                    plot_histogram_different_values(original_values, updated_values, component, column_name)\n",
    "                    identify_extreme_changes(datasets[original_key], datasets[updated_key], component, column_name)\n",
    "\n",
    "                except Exception as e:\n",
    "                    print(f\"Error processing {column_name} for {component}: {e}\")\n",
    "            else:\n",
    "                print(f\"Missing data for {component}\")\n",
    "\n",
    "    print(\"\\nAnalysis completed.\")\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15358744-0cef-4c98-9748-4b414f8c432e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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