update data
Browse files- quakeflow_demo.py +12 -4
quakeflow_demo.py
CHANGED
@@ -41,10 +41,10 @@ class BatchBuilderConfig(datasets.BuilderConfig):
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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,
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super().__init__(**kwargs)
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self.batch_size = batch_size
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-
self.
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class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
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@@ -89,6 +89,8 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
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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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}
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)
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@@ -96,6 +98,8 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
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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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}
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)
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@@ -165,7 +169,11 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
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for i, station_id in enumerate(station_ids):
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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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elif (
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(self.config.name == "event")
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@@ -185,4 +193,4 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
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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}
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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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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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}
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)
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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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}
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)
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for i, station_id in enumerate(station_ids):
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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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}
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elif (
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(self.config.name == "event")
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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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