Datasets:

ajthor
/

chladni

#!/usr/bin/env python3
"""
Generate Chladni plate dataset and save to parquet files in chunks.
"""

import os
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
from dataset import Chladni2DDataset


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 = Chladni2DDataset()
    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():
                if hasattr(values[0], 'tolist'):
                    # Handle numpy arrays
                    table_data[key] = [arr.tolist() for arr in values]
                else:
                    # Handle scalars and other types
                    table_data[key] = 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=10000, chunk_size=1000)

    # Generate test split
    generate_dataset_split("test", num_samples=2000, chunk_size=1000)

	#!/usr/bin/env python3
	"""
	Generate Chladni plate dataset and save to parquet files in chunks.
	"""

	import os
	import numpy as np
	import pyarrow as pa
	import pyarrow.parquet as pq
	from dataset import Chladni2DDataset


	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 = Chladni2DDataset()
	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():
	if hasattr(values[0], 'tolist'):
	# Handle numpy arrays
	table_data[key] = [arr.tolist() for arr in values]
	else:
	# Handle scalars and other types
	table_data[key] = 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=10000, chunk_size=1000)

	# Generate test split
	generate_dataset_split("test", num_samples=2000, chunk_size=1000)