File size: 2,675 Bytes
3452229 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | #!/usr/bin/env python3
"""
Generate shallow water PDE dataset and save to parquet files in chunks.
"""
import os
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
from shallow_water_dataset import ShallowWaterDataset
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)
# Create shallow water dataset
dataset = ShallowWaterDataset(
Nphi=128, # Smaller grid for faster generation
Ntheta=64,
stop_sim_time=600, # Shorter simulations for dataset generation
save_interval=10, # Save every 1 hours
)
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 isinstance(values[0], np.ndarray):
table_data[key] = [
arr.tolist() if hasattr(arr, "tolist") else arr
for arr in values
]
else:
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=1000, chunk_size=5)
# Generate test split
generate_dataset_split("test", num_samples=200, chunk_size=5)
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