#!/usr/bin/env python3
"""
Generate KdV-Burgers dataset and save to parquet files in chunks.
"""

import os
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
from burgers_dataset import KdvBurgersDataset

def generate_dataset_split(
    split_name="train", 
    num_samples=1000, 
    chunk_size=100,
    output_dir="data"
):
    """Generate a dataset split and save as chunked parquet files."""
    
    os.makedirs(output_dir, exist_ok=True)
    
    dataset = KdvBurgersDataset()
    num_chunks = (num_samples + chunk_size - 1) // chunk_size  # Ceiling division
    
    print(f"Generating {num_samples} {split_name} samples in {num_chunks} chunks...")
    
    dataset_iter = iter(dataset)
    chunk_data = None
    
    for i in range(num_samples):
        sample = next(dataset_iter)
        
        if chunk_data is None:
            # Initialize chunk data on first sample
            chunk_data = {key: [] for key in sample.keys()}
        
        # Add sample to current chunk
        for key, value in sample.items():
            chunk_data[key].append(value)
        
        # Save chunk when full or at end
        if (i + 1) % chunk_size == 0 or i == num_samples - 1:
            chunk_idx = i // chunk_size
            
            # Convert numpy arrays to lists for PyArrow compatibility
            table_data = {}
            for key, values in chunk_data.items():
                table_data[key] = [arr.tolist() for arr in values]
            
            # Convert to PyArrow table
            table = pa.table(table_data)
            
            # Save chunk
            filename = f"{split_name}-{chunk_idx:05d}-of-{num_chunks:05d}.parquet"
            filepath = os.path.join(output_dir, filename)
            pq.write_table(table, filepath)
            
            print(f"Saved chunk {chunk_idx + 1}/{num_chunks}: {filepath}")
            
            # Reset for next chunk
            chunk_data = {key: [] for key in sample.keys()}
    
    print(f"Generated {num_samples} {split_name} samples")
    return num_samples

if __name__ == "__main__":
    np.random.seed(42)
    
    # Generate train split
    generate_dataset_split("train", num_samples=1000, chunk_size=100)
    
    # Generate test split
    generate_dataset_split("test", num_samples=200, chunk_size=100)