Datasets:
File size: 2,092 Bytes
4de3f1f 3acb188 4de3f1f 3acb188 4de3f1f | 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 | """QuakeFlow DAS: Read DAS event waveforms from HuggingFace.
Files are downloaded on first access and cached locally.
"""
# %%
import functools
import h5py
import numpy as np
from huggingface_hub import hf_hub_download, list_repo_files
try:
import torch
_TORCH_AVAILABLE = True
except ImportError:
_TORCH_AVAILABLE = False
REPO_ID = "AI4EPS/quakeflow_das"
def read_event(filepath):
"""Read a single DAS event HDF5 file."""
with h5py.File(filepath, "r") as f:
result = {"data": f["data"][:].astype(np.float32)}
for key, val in f["data"].attrs.items():
result[key] = val.decode("utf-8", errors="replace") if isinstance(val, bytes) else val
return result
@functools.lru_cache(maxsize=None)
def list_h5(subset):
"""List all .h5 files for a subset from the HuggingFace repo (cached)."""
prefix = f"{subset}/data/"
return sorted(f for f in list_repo_files(REPO_ID, repo_type="dataset") if f.startswith(prefix) and f.endswith(".h5"))
def download(repo_path):
"""Download a file from HuggingFace (cached after first download)."""
return hf_hub_download(REPO_ID, repo_path, repo_type="dataset", local_dir=".")
_base_class = torch.utils.data.Dataset if _TORCH_AVAILABLE else object
class DASDataset(_base_class):
"""PyTorch Dataset for DAS events. Downloads files on first access."""
def __init__(self, subset, max_events=None):
self.files = list_h5(subset)
if max_events is not None:
self.files = self.files[:max_events]
def __len__(self):
return len(self.files)
def __getitem__(self, idx):
filepath = download(self.files[idx])
return read_event(filepath)
# %% Example: iterate over events
if __name__ == "__main__":
for subset in ["ridgecrest_north", "arcata"]:
print(f"\n=== {subset} ===")
dataset = DASDataset(subset, max_events=3)
for i in range(len(dataset)):
event = dataset[i]
print(f" {event['event_id']}: shape={event['data'].shape}, mag={event.get('magnitude', 'N/A')}")
# %%
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