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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}
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