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data/1990.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7fd8404fa0d8a6939c0daa76ccaed24e28ccf649eee8364c50a13c1689bb658f
+size 59532944

quakeflow_demo.py

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+from typing import List
+
+import datasets
+import fsspec
+import h5py
+import numpy as np
+import torch
+
+_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_nc/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: List, **kwargs):
+        super().__init__(**kwargs)
+        self.batch_size = batch_size
+        self.num_stations_list = num_stations_list
+
+
+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")
+
+    degree2km = 111.32
+    nt = 8192
+    feature_nt = 512
+    feature_scale = int(nt / feature_nt)
+    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"),
+                }
+            )
+
+        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"),
+                }
+            )
+
+        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:
+                    event_ids = list(fp.keys())
+                    for event_id in event_ids:
+                        event = fp[event_id]
+                        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")
+                            phase_pick = np.zeros_like(waveforms)
+                            station_attrs = event.attrs
+                            event_location = [
+                                station_attrs["longitude"],
+                                station_attrs["latitude"],
+                                station_attrs["depth_km"],
+                                station_attrs["event_time_index"],
+                            ]
+
+                            for i, sta_id in enumerate(station_ids):
+                                waveforms[:, : self.nt] = event[sta_id][:, : self.nt]
+                                station_attrs = event[sta_id].attrs
+
+                                yield f"{event_id}/{sta_id}", {"data": torch.from_numpy(waveforms).float()}
+
+                        elif (
+                            (self.config.name == "event")
+                            or (self.config.name == "event_train")
+                            or (self.config.name == "event_test")
+                        ):
+                            event_attrs = event.attrs
+
+                            is_sick = False
+                            for sta_id in station_ids:
+                                station_attrs = event[sta_id].attrs
+                                if (
+                                    station_attrs["phase_index"][station_attrs["phase_type"] == "P"]
+                                    == station_attrs["phase_index"][station_attrs["phase_type"] == "S"]
+                                ):
+                                    is_sick = True
+                                    break
+                            if is_sick:
+                                continue
+
+                            waveforms = np.zeros([len(station_ids), 3, self.nt], dtype="float32")
+
+                            for i, sta_id in enumerate(station_ids):
+                                waveforms[i, :, :] = event[sta_id][:, : self.nt]
+                                station_attrs = event[sta_id].attrs
+                                p_picks = station_attrs["phase_index"][station_attrs["phase_type"] == "P"]
+                                s_picks = station_attrs["phase_index"][station_attrs["phase_type"] == "S"]
+
+                            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": torch.from_numpy(waveforms).float()}