Create Validate_sudokuCSV_V2.1.py

Validate_sudokuCSV_V2.1.py

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+# Filename: Validate_sudokuCSV_V2.1.py
+#
+# Description:
+# This script validates the 'solutions' column of a sudoku.csv file generated by
+# Generate_sudokuCSV_V2.1.py or a similar script. It mathematically checks if each
+# solved grid adheres to the rules of Sudoku.
+#
+# Key Logic:
+# 1.  **Loads Data:** Reads the 'sudoku.csv' file into a pandas DataFrame.
+# 2.  **Validates Each Solution:** For every row, it checks the 'solutions' grid to ensure:
+#     - Each row (1-9) contains exactly one of each digit from 1 to 9.
+#     - Each column (1-9) contains exactly one of each digit from 1 to 9.
+#     - Each 3x3 box (1-9) contains exactly one of each digit from 1 to 9.
+# 3.  **Reports Findings:** Prints a clear summary of how many grids are valid and invalid.
+# 4.  **Handles Invalid Grids (Colab-Friendly):**
+#     - If invalid grids are found, it saves their row indices to a file named
+#       `invalid_sudoku_indices.txt`.
+#     - It then instructs the user on how to clean the master CSV by changing a
+#       single configuration flag (`PERFORM_CLEANUP`) and re-running the script.
+# 5.  **Performs Cleanup (If Enabled):** If the `PERFORM_CLEANUP` flag is set to True
+#     and the index file exists, it will:
+#     - Move the invalid rows from 'sudoku.csv' to a new 'invalid_sudokus.csv' file.
+#     - Overwrite 'sudoku.csv' with a new, clean version.
+
+import pandas as pd
+import numpy as np
+import os
+import sys
+
+# ==============================================================================
+# === CONFIGURATION ===
+# ==============================================================================
+
+# --- Primary Action ---
+# Set this to True AFTER running the script once and seeing an 'invalid' report.
+# This will trigger the cleanup process on the next run.
+PERFORM_CLEANUP = False
+
+# --- File Paths ---
+INPUT_CSV_PATH = 'sudoku.csv'
+INVALID_INDICES_FILE = 'invalid_sudoku_indices.txt'
+INVALID_CSV_EXPORT_PATH = 'invalid_sudokus.csv'
+
+# ==============================================================================
+# === VALIDATION LOGIC ===
+# ==============================================================================
+
+def validate_solution_grid(grid_1d: np.ndarray) -> bool:
+    """
+    Mathematically validates a 1D numpy array representing a 9x9 Sudoku solution.
+
+    Args:
+        grid_1d: A numpy array of 81 integers.
+
+    Returns:
+        True if the grid is a valid Sudoku solution, False otherwise.
+    """
+    if grid_1d.shape[0] != 81 or np.any(grid_1d == 0):
+        # Must be a fully filled 81-cell grid
+        return False
+
+    grid = grid_1d.reshape(9, 9)
+    
+    # The set of digits {1, 2, 3, 4, 5, 6, 7, 8, 9}
+    required_set = set(range(1, 10))
+
+    # 1. Check all rows
+    for i in range(9):
+        if set(grid[i, :]) != required_set:
+            return False
+
+    # 2. Check all columns
+    for j in range(9):
+        if set(grid[:, j]) != required_set:
+            return False
+
+    # 3. Check all 3x3 boxes
+    for box_row_start in range(0, 9, 3):
+        for box_col_start in range(0, 9, 3):
+            box = grid[box_row_start:box_row_start+3, box_col_start:box_col_start+3]
+            if set(box.flatten()) != required_set:
+                return False
+
+    # If all checks pass, the grid is valid
+    return True
+
+# ==============================================================================
+# === MAIN EXECUTION SCRIPT ===
+# ==============================================================================
+
+def run_validation():
+    """Main function to run the validation and reporting process."""
+    print("--- Sudoku CSV Validator V2.1 ---")
+
+    if not os.path.exists(INPUT_CSV_PATH):
+        print(f"FATAL ERROR: The file '{INPUT_CSV_PATH}' was not found.")
+        print("Please make sure you have generated it using 'Generate_sudokuCSV_V2.1.py'.")
+        sys.exit(1)
+
+    print(f"Loading data from '{INPUT_CSV_PATH}'...")
+    try:
+        df = pd.read_csv(INPUT_CSV_PATH)
+        # Ensure columns are treated as strings to preserve leading zeros, then convert to numbers
+        df['solutions_str'] = df['solutions'].astype(str).str.zfill(81)
+    except Exception as e:
+        print(f"Error reading CSV file: {e}")
+        sys.exit(1)
+
+    print("Validating all solution grids...")
+    valid_indices = []
+    invalid_indices = []
+
+    for index, row in df.iterrows():
+        try:
+            # Convert the string of digits into a numpy array of integers
+            solution_grid_1d = np.array(list(map(int, row['solutions_str'])))
+            if validate_solution_grid(solution_grid_1d):
+                valid_indices.append(index)
+            else:
+                invalid_indices.append(index)
+        except (ValueError, TypeError):
+            # Handle cases where a row might be malformed
+            invalid_indices.append(index)
+
+    # --- Report Findings ---
+    num_valid = len(valid_indices)
+    num_invalid = len(invalid_indices)
+    total_grids = len(df)
+
+    print("\n--- VALIDATION REPORT ---")
+    print(f"Total grids scanned: {total_grids}")
+    print(f"  => Valid solutions:   {num_valid}")
+    print(f"  => Invalid solutions: {num_invalid}")
+    print("-------------------------\n")
+
+    if num_invalid > 0:
+        print(f"Found {num_invalid} invalid grids. Saving their indices to '{INVALID_INDICES_FILE}'.")
+        # Save indices to the text file, one index per line
+        with open(INVALID_INDICES_FILE, 'w') as f:
+            for index in invalid_indices:
+                f.write(f"{index}\n")
+
+        print("\n*** ACTION REQUIRED ***")
+        print(f"To clean your '{INPUT_CSV_PATH}', please follow these steps:")
+        print("1. Open this script ('Validate_sudokuCSV_V2.1.py') in the editor.")
+        print("2. Change the configuration flag at the top from 'PERFORM_CLEANUP = False' to 'PERFORM_CLEANUP = True'.")
+        print("3. Re-run this script.")
+        print("This will move the invalid entries to 'invalid_sudokus.csv' and create a clean 'sudoku.csv'.")
+    else:
+        print("Congratulations! All solution grids in the CSV are valid.")
+        # If no invalid grids were found, remove the old index file if it exists
+        if os.path.exists(INVALID_INDICES_FILE):
+            os.remove(INVALID_INDICES_FILE)
+
+
+def run_cleanup():
+    """Main function to perform the cleanup process."""
+    print("--- Sudoku CSV Cleanup Utility ---")
+    print(f"PERFORM_CLEANUP is set to True. Attempting to clean '{INPUT_CSV_PATH}'.")
+
+    if not os.path.exists(INVALID_INDICES_FILE):
+        print(f"ERROR: The file '{INVALID_INDICES_FILE}' was not found.")
+        print("Please run the script with 'PERFORM_CLEANUP = False' first to generate the list of invalid indices.")
+        sys.exit(1)
+
+    print(f"Loading master data from '{INPUT_CSV_PATH}'...")
+    df = pd.read_csv(INPUT_CSV_PATH)
+
+    print(f"Loading invalid indices from '{INVALID_INDICES_FILE}'...")
+    with open(INVALID_INDICES_FILE, 'r') as f:
+        invalid_indices = [int(line.strip()) for line in f]
+
+    print(f"Found {len(invalid_indices)} indices to remove.")
+
+    # Separate the DataFrame into invalid and valid parts
+    df_invalid = df.loc[invalid_indices]
+    df_valid = df.drop(invalid_indices)
+
+    # --- Perform the file operations ---
+    # 1. Export invalid puzzles and their solutions
+    print(f"Saving {len(df_invalid)} invalid entries to '{INVALID_CSV_EXPORT_PATH}'...")
+    df_invalid.to_csv(INVALID_CSV_EXPORT_PATH, index=False)
+
+    # 2. Overwrite the original file with only the valid data
+    print(f"Overwriting '{INPUT_CSV_PATH}' with {len(df_valid)} valid entries...")
+    df_valid.to_csv(INPUT_CSV_PATH, index=False)
+
+    # 3. Clean up the index file
+    os.remove(INVALID_INDICES_FILE)
+
+    print("\n--- Cleanup Complete! ---")
+    print(f"Your '{INPUT_CSV_PATH}' is now clean.")
+    print(f"The invalid entries have been moved to '{INVALID_CSV_EXPORT_PATH}'.")
+    print("Set 'PERFORM_CLEANUP = False' before running validation again.")
+
+
+if __name__ == '__main__':
+    if PERFORM_CLEANUP:
+        run_cleanup()
+    else:
+        run_validation()