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