| 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} | |