update script
Browse files- quakeflow_nc.py +58 -59
quakeflow_nc.py
CHANGED
@@ -29,10 +29,9 @@ import datasets
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# Find for instance the citation on arxiv or on the dataset repo/website
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_CITATION = """\
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@InProceedings{huggingface:dataset,
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title = {
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author={
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}
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year={2020}
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}
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"""
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@@ -52,9 +51,10 @@ _LICENSE = ""
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# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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_REPO = "https://huggingface.co/datasets/AI4EPS/quakeflow_nc/resolve/main/data"
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_URLS = {
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"
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"
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}
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class BatchBuilderConfig(datasets.BuilderConfig):
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@@ -81,7 +81,6 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
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feature_nt = 512
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feature_scale = int(nt / feature_nt)
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sampling_rate=100.0
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num_stations = 10
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# This is an example of a dataset with multiple configurations.
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# If you don't want/need to define several sub-sets in your dataset,
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@@ -97,24 +96,24 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
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# default config, you can change batch_size and num_stations_list when use `datasets.load_dataset`
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="
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datasets.BuilderConfig(name="
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]
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DEFAULT_CONFIG_NAME = "
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def _info(self):
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# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
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if self.config.name=="
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features=datasets.Features(
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{
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"waveform": datasets.Array3D(shape=(3, self.nt
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"phase_pick": datasets.Array3D(shape=(3, self.nt
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"event_location": datasets.Sequence(datasets.Value("float32")),
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"station_location": datasets.
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})
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elif self.config.name=="
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features=datasets.Features(
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{
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"waveform": datasets.Array3D(shape=(None, 3, self.nt), dtype='float32'),
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@@ -157,7 +156,7 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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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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@@ -169,14 +168,14 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
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# "split": "dev",
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# },
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# ),
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]
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@@ -185,55 +184,55 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
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def _generate_examples(self, filepath, split):
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# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
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-
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for file in filepath:
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with h5py.File(file, "r") as fp:
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# for event_id in sorted(list(fp.keys())):
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for event_id in fp.keys():
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event = fp[event_id]
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station_ids = list(event.keys())
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phase_pick = np.zeros_like(waveforms)
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attrs = event.attrs
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event_location = [attrs["longitude"], attrs["latitude"], attrs["depth_km"], attrs["event_time_index"]]
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station_location = []
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for i, sta_id in enumerate(station_ids):
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# trace_id = event_id + "/" + sta_id
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waveforms[:, :, i] = event[sta_id][:,:self.nt]
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attrs = event[sta_id].attrs
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p_picks = attrs["phase_index"][attrs["phase_type"] == "P"]
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s_picks = attrs["phase_index"][attrs["phase_type"] == "S"]
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phase_pick[:, :, i] = generate_label([p_picks, s_picks], nt=self.nt)
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station_location.append([attrs["longitude"], attrs["latitude"], -attrs["elevation_m"]/1e3])
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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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if self.config.name=="NCEDC":
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yield event_id, {
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"waveform": torch.from_numpy(waveforms).float(),
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"phase_pick": torch.from_numpy(phase_pick).float(),
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"event_location": torch.from_numpy(np.array(event_location)).float(),
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"station_location": torch.from_numpy(np.array(station_location)).float(),
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}
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elif self.config.name=="NCEDC_full_size":
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yield event_id, {
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"waveform": torch.from_numpy(waveforms).float().permute(2,0,1),
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"phase_pick": torch.from_numpy(phase_pick).float().permute(2,0,1),
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"event_location": torch.from_numpy(np.array(event_location)).float(),
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"station_location": torch.from_numpy(np.array(station_location)).float(),
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}
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def generate_label(phase_list, label_width=[150, 150], nt=8192):
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# Find for instance the citation on arxiv or on the dataset repo/website
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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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# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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_REPO = "https://huggingface.co/datasets/AI4EPS/quakeflow_nc/resolve/main/data"
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_FILENAMES = ["NC1970-1989.h5", "NC1990-1994.h5", "NC1995-1999.h5", "NC2000-2004.h5", "NC2005-2009.h5", "NC2010.h5", "NC2011.h5", "NC2012.h5", "NC2013.h5", "NC2014.h5", "NC2015.h5", "NC2016.h5", "NC2017.h5", "NC2018.h5", "NC2019.h5", "NC2020.h5"]
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_URLS = {
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"station": [f"{_REPO}/{x}" for f in _FILENAMES],
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"event": [f"{_REPO}/{x}" for x in _FILENAMES],
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}
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class BatchBuilderConfig(datasets.BuilderConfig):
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feature_nt = 512
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feature_scale = int(nt / feature_nt)
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sampling_rate=100.0
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# This is an example of a dataset with multiple configurations.
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# If you don't want/need to define several sub-sets in your dataset,
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# default config, you can change batch_size and num_stations_list when use `datasets.load_dataset`
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="station", version=VERSION, description="yield station-based samples one by one"),
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datasets.BuilderConfig(name="event", version=VERSION, description="yield event-based samples one by one"),
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]
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DEFAULT_CONFIG_NAME = "station" # It's not mandatory to have a default configuration. Just use one if it make sense.
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def _info(self):
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# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
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if self.config.name=="station":
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features=datasets.Features(
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{
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"waveform": datasets.Array3D(shape=(3, self.nt), dtype='float32'),
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"phase_pick": datasets.Array3D(shape=(3, self.nt), dtype='float32'),
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"event_location": datasets.Sequence(datasets.Value("float32")),
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"station_location": datasets.Sequence(datasets.Value("float32")),
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})
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elif self.config.name=="event":
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features=datasets.Features(
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{
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"waveform": datasets.Array3D(shape=(None, 3, self.nt), dtype='float32'),
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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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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# "split": "dev",
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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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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": files[-1:],
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"split": "test"
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},
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),
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]
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def _generate_examples(self, filepath, split):
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# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
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+
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for file in filepath:
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with h5py.File(file, "r") as fp:
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# for event_id in sorted(list(fp.keys())):
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for event_id in fp.keys():
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event = fp[event_id]
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station_ids = list(event.keys())
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if self.config.name=="station":
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waveforms = np.zeros([3, self.nt], dtype="float32")
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phase_pick = np.zeros_like(waveforms)
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attrs = event.attrs
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event_location = [attrs["longitude"], attrs["latitude"], attrs["depth_km"], attrs["event_time_index"]]
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for i, sta_id in enumerate(station_ids):
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waveforms[:, :self.nt] = event[sta_id][:,:self.nt]
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attrs = event[sta_id].attrs
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p_picks = attrs["phase_index"][attrs["phase_type"] == "P"]
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s_picks = attrs["phase_index"][attrs["phase_type"] == "S"]
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phase_pick[:, :self.nt] = generate_label([p_picks, s_picks], nt=self.nt)
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station_location = [attrs["longitude"], attrs["latitude"], -attrs["elevation_m"]/1e3]
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yield f"{event_id}/{sta_id}", {
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"waveform": torch.from_numpy(waveforms).float(),
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"phase_pick": torch.from_numpy(phase_pick).float(),
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"event_location": torch.from_numpy(np.array(event_location)).float(),
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"station_location": torch.from_numpy(np.array(station_location)).float(),
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}
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elif self.config.name=="event":
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waveforms = np.zeros([len(station_ids), 3, self.nt], dtype="float32")
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phase_pick = np.zeros_like(waveforms)
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attrs = event.attrs
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event_location = [attrs["longitude"], attrs["latitude"], attrs["depth_km"], attrs["event_time_index"]]
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station_location = []
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for i, sta_id in enumerate(station_ids):
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waveforms[i, :, :self.nt] = event[sta_id][:,:self.nt]
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attrs = event[sta_id].attrs
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p_picks = attrs["phase_index"][attrs["phase_type"] == "P"]
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s_picks = attrs["phase_index"][attrs["phase_type"] == "S"]
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phase_pick[i, :, :] = generate_label([p_picks, s_picks], nt=self.nt)
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station_location.append([attrs["longitude"], attrs["latitude"], -attrs["elevation_m"]/1e3])
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yield event_id, {
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"waveform": torch.from_numpy(waveforms).float(),
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"phase_pick": torch.from_numpy(phase_pick).float(),
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"event_location": torch.from_numpy(np.array(event_location)).float(),
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"station_location": torch.from_numpy(np.array(station_location)).float(),
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}
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def generate_label(phase_list, label_width=[150, 150], nt=8192):
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