"""
NeoSyn - data_processor.py

Handles synthetic data generation using CTGAN,
distribution plots, and summaries for both original and synthetic data.

Author: Saivivek Katkuri
Date: June 2025
"""

import pandas as pd
import matplotlib.pyplot as plt
import os
from datetime import datetime
from sdv.single_table import CTGANSynthesizer
from sdv.metadata import SingleTableMetadata

def generate_synthetic_data(original_data: pd.DataFrame, num_samples: int = 10, output_dir: str = "generated") -> pd.DataFrame:
    """
    Generates synthetic data using CTGAN and saves as a Parquet file.

    Args:
        original_data (pd.DataFrame): Original data.
        num_samples (int): Number of synthetic samples to generate.
        output_dir (str): Directory to save synthetic data.

    Returns:
        pd.DataFrame: Synthetic data.
    """
    # Create metadata for the table
    metadata = SingleTableMetadata()
    metadata.detect_from_dataframe(data=original_data)

    # Create the CTGAN synthesizer
    synthesizer = CTGANSynthesizer(metadata)

    # Fit on original data
    synthesizer.fit(original_data)

    # Sample synthetic data
    synthetic_data = synthesizer.sample(num_rows=num_samples)

    # Save as Parquet
    os.makedirs(output_dir, exist_ok=True)
    timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
    output_path = os.path.join(output_dir, f"synthetic_data_{timestamp}.parquet")
    synthetic_data.to_parquet(output_path)

    return synthetic_data

def plot_distribution(original_data: pd.DataFrame, synthetic_data: pd.DataFrame, column: str, output_dir: str = "plots") -> str:
    """
    Plots the distribution of a specified column in both original and synthetic data.

    Args:
        original_data (pd.DataFrame): Original data.
        synthetic_data (pd.DataFrame): Synthetic data.
        column (str): Column to plot.
        output_dir (str): Directory to save the plot.

    Returns:
        str: Path to the saved plot image.
    """
    if column not in original_data.columns:
        raise ValueError(f"Column '{column}' not found in original data.")

    os.makedirs(output_dir, exist_ok=True)
    plt.figure(figsize=(10, 6))
    plt.hist(original_data[column], bins=30, alpha=0.5, label="Original")
    plt.hist(synthetic_data[column], bins=30, alpha=0.5, label="Synthetic")
    plt.title(f"Distribution of {column}")
    plt.xlabel(column)
    plt.ylabel("Frequency")
    plt.legend()
    plot_path = os.path.join(output_dir, f"{column}_distribution.png")
    plt.savefig(plot_path)
    plt.close()

    return plot_path

def summarize_data(data: pd.DataFrame) -> str:
    """
    Generates a textual summary of the data using pandas describe.

    Args:
        data (pd.DataFrame): Data to summarize.

    Returns:
        str: Textual summary.
    """
    return data.describe().to_string()