| |
| """ |
| Minimal HDF5WaveformDataset usage example. |
| |
| Run: |
| python example_dataloader.py --h5_input path/to/data.h5 |
| """ |
|
|
| import argparse |
| import numpy as np |
| import sys |
| from pathlib import Path |
| from torch.utils.data import DataLoader |
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parents[1])) |
|
|
| from utils.hdf5_waveform_dataset import HDF5WaveformDataset, waveform_collate_fn |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--h5_input", default="data/hdf5/continuous_waveform_usa_20190701.h5", |
| help="HDF5 file, directory, or glob pattern") |
| parser.add_argument("--n_samples", type=int, default=3, |
| help="Number of samples to print") |
| parser.add_argument("--response_json", default="data/response/instrument_responses.json", |
| help="Instrument-response JSON used with --remove_response") |
| parser.add_argument("--remove_response", action="store_true", |
| help="Remove the native instrument response before resampling") |
| parser.add_argument("--response_output", default="VEL", |
| help="Physical output unit for response removal: DISP, VEL, or ACC") |
| parser.add_argument("--response_pre_filt", nargs=4, type=float, default=None, |
| metavar=("F1", "F2", "F3", "F4"), |
| help="Four-corner pre-filter passed to ObsPy remove_response") |
| parser.add_argument("--response_water_level", type=float, default=60.0, |
| help="ObsPy water level; use a negative value to pass None") |
| args = parser.parse_args() |
| water_level = None if args.response_water_level < 0 else args.response_water_level |
|
|
| |
| dataset = HDF5WaveformDataset( |
| h5_file=args.h5_input, |
| mode="three", |
| allowed_families=("HH", "BH", "EH", "HN"), |
| allowed_z_only_channels=("EHZ",), |
| allow_z_only=True, |
| replicate_z_only=True, |
| target_sampling_rate=100.0, |
| instrument_response_json=args.response_json if args.remove_response else None, |
| remove_instrument_response=args.remove_response, |
| response_output=args.response_output, |
| response_pre_filt=tuple(args.response_pre_filt) if args.response_pre_filt else None, |
| response_water_level=water_level, |
| ) |
|
|
| print(f"HDF5 files : {len(dataset.h5_files)}") |
| print(f"Total samples: {len(dataset)}") |
| print() |
|
|
| |
| loader = DataLoader( |
| dataset, |
| batch_size=1, |
| shuffle=False, |
| num_workers=0, |
| collate_fn=waveform_collate_fn, |
| ) |
|
|
| |
| for i, batch in enumerate(loader): |
| if i >= args.n_samples: |
| break |
|
|
| item = batch[0] |
|
|
| w = item["waveform"] |
| sr = item["sampling_rate"] |
| duration_sec = w.shape[0] / sr if sr and sr > 0 else float("nan") |
|
|
| print(f"── Sample {i + 1} ──────────────────────────────────────────") |
| print(f" station_id : {item['station_id']}") |
| print(f" network : {item['station_info'].get('network', '')}." |
| f"{item['station_info'].get('station', '')}") |
| print(f" channels : {item['channels']}") |
| print(f" starttime : {item['starttime']}") |
| print(f" sampling_rate : {sr} Hz") |
| print(f" waveform shape: {tuple(w.shape)} " |
| f"({duration_sec:.1f} s × 3 components)") |
| print(f" waveform dtype: {w.dtype}") |
| print(f" Z-only : {item.get('is_z_only', False)}") |
| if args.remove_response: |
| print(f" response : {item.get('instrument_processing', {})}") |
| print(f" location : " |
| f"lon={item['station_info'].get('longitude', float('nan')):.4f} " |
| f"lat={item['station_info'].get('latitude', float('nan')):.4f}") |
| |
| for c, name in enumerate(["E/1", "N/2", "Z/3"]): |
| ch = w[:, c].numpy() |
| print(f" ch[{name}] " |
| f"min={float(np.min(ch)):+.3e} " |
| f"max={float(np.max(ch)):+.3e} " |
| f"std={float(np.std(ch)):.3e}") |
| print() |
|
|
| dataset.close() |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
