File size: 5,099 Bytes
48f4563 | 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 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | #!/usr/bin/env python3
"""
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
# ── 1. Build dataset ───────────────────────────────────────────────────
dataset = HDF5WaveformDataset(
h5_file=args.h5_input,
mode="three", # returns [T, 3] waveform per station-day
allowed_families=("HH", "BH", "EH", "HN"),
allowed_z_only_channels=("EHZ",),
allow_z_only=True,
replicate_z_only=True, # Z-only → [Z, Z, Z]
target_sampling_rate=100.0, # resample everything to 100 Hz
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()
# ── 2. Build DataLoader ────────────────────────────────────────────────
loader = DataLoader(
dataset,
batch_size=1,
shuffle=False,
num_workers=0, # 0 = single-process, safest for h5py
collate_fn=waveform_collate_fn,
)
# ── 3. Iterate and print ───────────────────────────────────────────────
for i, batch in enumerate(loader):
if i >= args.n_samples:
break
item = batch[0] # batch_size=1, so one item per batch
w = item["waveform"] # torch.Tensor [T, 3]
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}")
# Quick per-channel stats
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()
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