from typing import List import datasets import fsspec import h5py import numpy as np _CITATION = """\ @InProceedings{huggingface:dataset, title = {NCEDC dataset for QuakeFlow}, author={Zhu et al.}, year={2023} } """ _DESCRIPTION = """\ A dataset of earthquake waveforms organized by earthquake events and based on the HDF5 format. """ _HOMEPAGE = "" _LICENSE = "" _REPO = "https://huggingface.co/datasets/AI4EPS/quakeflow_demo/resolve/main/data" _FILES = ["1990.h5"] _URLS = { "station": [f"{_REPO}/{x}" for x in _FILES], "event": [f"{_REPO}/{x}" for x in _FILES], "station_train": [f"{_REPO}/{x}" for x in _FILES[:-1]], "event_train": [f"{_REPO}/{x}" for x in _FILES[:-1]], "station_test": [f"{_REPO}/{x}" for x in _FILES[-1:]], "event_test": [f"{_REPO}/{x}" for x in _FILES[-1:]], } class BatchBuilderConfig(datasets.BuilderConfig): """ yield a batch of event-based sample, so the number of sample stations can vary among batches Batch Config for QuakeFlow_NC :param batch_size: number of samples in a batch :param num_stations_list: possible number of stations in a batch """ def __init__(self, batch_size: int, num_stations: List, **kwargs): super().__init__(**kwargs) self.batch_size = batch_size self.num_stations = num_stations class QuakeFlow_NC(datasets.GeneratorBasedBuilder): """QuakeFlow_NC: A dataset of earthquake waveforms organized by earthquake events and based on the HDF5 format.""" VERSION = datasets.Version("1.1.0") nt = 12000 sampling_rate = 100.0 BUILDER_CONFIGS = [ datasets.BuilderConfig( name="station", version=VERSION, description="yield station-based samples one by one of whole dataset" ), datasets.BuilderConfig( name="event", version=VERSION, description="yield event-based samples one by one of whole dataset" ), datasets.BuilderConfig( name="station_train", version=VERSION, description="yield station-based samples one by one of training dataset", ), datasets.BuilderConfig( name="event_train", version=VERSION, description="yield event-based samples one by one of training dataset" ), datasets.BuilderConfig( name="station_test", version=VERSION, description="yield station-based samples one by one of test dataset" ), datasets.BuilderConfig( name="event_test", version=VERSION, description="yield event-based samples one by one of test dataset" ), ] DEFAULT_CONFIG_NAME = "station_test" def _info(self): if ( (self.config.name == "station") or (self.config.name == "station_train") or (self.config.name == "station_test") ): features = datasets.Features( { "data": datasets.Array2D(shape=(3, self.nt), dtype="float32"), "event_id": datasets.Value("string"), "station_id": datasets.Value("string"), "phase_type": datasets.Sequence(datasets.Value("string")), "phase_index": datasets.Sequence(datasets.Value("int32")), "snr": datasets.Sequence(datasets.Value("float32")), } ) elif (self.config.name == "event") or (self.config.name == "event_train") or (self.config.name == "event_test"): features = datasets.Features( { "data": datasets.Array3D(shape=(None, 3, self.nt), dtype="float32"), "event_id": datasets.Value("string"), "station_ids": datasets.Sequence(datasets.Value("string")), "phase_type": datasets.Sequence(datasets.Sequence(datasets.Value("string"))), "phase_index": datasets.Sequence(datasets.Sequence(datasets.Value("int32"))), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): urls = _URLS[self.config.name] files = dl_manager.download_and_extract(urls) print(files) if self.config.name == "station" or self.config.name == "event": return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": files[:-1], "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": files[-1:], "split": "test"}, ), ] elif self.config.name == "station_train" or self.config.name == "event_train": return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": files, "split": "train", }, ), ] elif self.config.name == "station_test" or self.config.name == "event_test": return [ datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": files, "split": "test"}, ), ] else: raise ValueError("config.name is not in BUILDER_CONFIGS") def _generate_examples(self, filepath, split): for file in filepath: with fsspec.open(file, "rb") as fs: with h5py.File(fs, "r") as fp: for event_id in sorted(list(fp.keys())): event = fp[event_id] event_attrs = event.attrs station_ids = list(event.keys()) if ( (self.config.name == "station") or (self.config.name == "station_train") or (self.config.name == "station_test") ): waveforms = np.zeros([3, self.nt], dtype="float32") for i, station_id in enumerate(station_ids): station_attrs = event[station_id].attrs waveforms[:, : self.nt] = event[station_id][:, : self.nt] yield f"{event_id}/{station_id}", { "data": waveforms, "event_id": event_id, "station_id": station_id, "phase_type": station_attrs["phase_type"], "phase_index": station_attrs["phase_index"], "snr": station_attrs["snr"], } elif ( (self.config.name == "event") or (self.config.name == "event_train") or (self.config.name == "event_test") ): station_attrs = event[station_id].attrs waveforms = np.zeros([len(station_ids), 3, self.nt], dtype="float32") phase_type = [] phase_index = [] for i, station_id in enumerate(station_ids): waveforms[i, :, :] = event[station_id][:, : self.nt] station_attrs = event[station_id].attrs phase_type.append(station_attrs["phase_type"]) phase_index.append(station_attrs["phase_index"]) std = np.std(waveforms, axis=1, keepdims=True) std[std == 0] = 1.0 waveforms = (waveforms - np.mean(waveforms, axis=1, keepdims=True)) / std waveforms = waveforms.astype(np.float32) yield event_id, {"data": waveforms, "event_id": event_id, "station_ids": station_ids}