Merge branch 'main' of hf.co:datasets/AI4EPS/quakeflow_nc
Browse files- README.md +34 -60
- example.py +41 -0
- quakeflow_nc.py +28 -21
README.md
CHANGED
@@ -66,7 +66,7 @@ Waveform data, metadata, or data products for this study were accessed through t
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- datasets
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- h5py
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- fsspec
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### Usage
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Import the necessary packages:
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@@ -74,7 +74,6 @@ Import the necessary packages:
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import h5py
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import numpy as np
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import torch
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from torch.utils.data import Dataset, IterableDataset, DataLoader
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from datasets import load_dataset
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```
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We have 6 configurations for the dataset:
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The sample of `station` is a dictionary with the following keys:
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- `data`: the waveform with shape `(3, nt)`, the default time length is 8192
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- `
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- `station_location`: the station location with shape `(3,)`, including latitude, longitude and depth
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The sample of `event` is a dictionary with the following keys:
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- `data`: the waveform with shape `(n_station, 3, nt)`, the default time length is 8192
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- `station_location`: the space coordinates of the station with shape `(n_station, 3)`, including latitude, longitude and depth
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The default configuration is `station_test`. You can specify the configuration by argument `name`. For example:
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@@ -117,70 +128,33 @@ quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="station_test", split="t
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quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="event", split="train")
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```
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####
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Then you can change the dataset into PyTorch format iterable dataset, and view the first sample:
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```python
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quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="station_test", split="test")
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# for PyTorch DataLoader, we need to divide the dataset into several shards
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num_workers=4
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quakeflow_nc = quakeflow_nc.to_iterable_dataset(num_shards=num_workers)
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# because add examples formatting to get tensors when using the "torch" format
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# has not been implemented yet, we need to manually add the formatting when using iterable dataset
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# if you want to use dataset directly, just use
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# quakeflow_nc.with_format("torch")
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quakeflow_nc = quakeflow_nc.map(lambda x: {key: torch.from_numpy(np.array(value, dtype=np.float32)) for key, value in x.items()})
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try:
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isinstance(quakeflow_nc, torch.utils.data.IterableDataset)
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except:
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raise Exception("quakeflow_nc is not an IterableDataset")
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# print the first sample of the iterable dataset
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for example in quakeflow_nc:
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print("\nIterable test\n")
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print(example.keys())
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for key in example.keys():
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break
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for batch in dataloader:
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print("\nDataloader test\n")
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print(batch
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Then you can change the dataset into PyTorch format dataset, and view the first sample (Don't forget to reorder the keys):
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```python
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quakeflow_nc = datasets.load_dataset("AI4EPS/quakeflow_nc", split="test", name="event_test")
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# for PyTorch DataLoader, we need to divide the dataset into several shards
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num_workers=4
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quakeflow_nc = quakeflow_nc.to_iterable_dataset(num_shards=num_workers)
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quakeflow_nc = quakeflow_nc.map(lambda x: {key: torch.from_numpy(np.array(value, dtype=np.float32)) for key, value in x.items()})
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try:
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isinstance(quakeflow_nc, torch.utils.data.IterableDataset)
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except:
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raise Exception("quakeflow_nc is not an IterableDataset")
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# print the first sample of the iterable dataset
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for example in quakeflow_nc:
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print("\nIterable test\n")
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print(example.keys())
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for key in example.keys():
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print(key, example[key].shape, example[key].dtype)
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break
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dataloader = DataLoader(quakeflow_nc, batch_size=1, num_workers=num_workers)
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for batch in dataloader:
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print("\nDataloader test\n")
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print(batch.keys())
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for key in batch.keys():
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print(key, batch[key].shape, batch[key].dtype)
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break
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```
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- datasets
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- h5py
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- fsspec
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- pytorch
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### Usage
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Import the necessary packages:
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import h5py
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import numpy as np
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import torch
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from datasets import load_dataset
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```
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We have 6 configurations for the dataset:
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The sample of `station` is a dictionary with the following keys:
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- `data`: the waveform with shape `(3, nt)`, the default time length is 8192
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- `begin_time`: the begin time of the waveform data
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- `end_time`: the end time of the waveform data
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- `phase_time`: the phase arrival time
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- `phase_index`: the time point index of the phase arrival time
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- `phase_type`: the phase type
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- `phase_polarity`: the phase polarity in ('U', 'D', 'N')
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- `event_time`: the event time
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- `event_time_index`: the time point index of the event time
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- `event_location`: the event location with shape `(3,)`, including latitude, longitude, depth
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- `station_location`: the station location with shape `(3,)`, including latitude, longitude and depth
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The sample of `event` is a dictionary with the following keys:
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- `data`: the waveform with shape `(n_station, 3, nt)`, the default time length is 8192
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- `begin_time`: the begin time of the waveform data
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- `end_time`: the end time of the waveform data
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- `phase_time`: the phase arrival time with shape `(n_station,)`
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- `phase_index`: the time point index of the phase arrival time with shape `(n_station,)`
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- `phase_type`: the phase type with shape `(n_station,)`
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- `phase_polarity`: the phase polarity in ('U', 'D', 'N') with shape `(n_station,)`
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- `event_time`: the event time
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- `event_time_index`: the time point index of the event time
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- `event_location`: the space-time coordinates of the event with shape `(n_staion, 3)`
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- `station_location`: the space coordinates of the station with shape `(n_station, 3)`, including latitude, longitude and depth
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The default configuration is `station_test`. You can specify the configuration by argument `name`. For example:
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quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="event", split="train")
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```
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#### Example loading the dataset
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```python
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quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="station_test", split="test")
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# print the first sample of the iterable dataset
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for example in quakeflow_nc:
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print("\nIterable test\n")
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print(example.keys())
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for key in example.keys():
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if key == "data":
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print(key, np.array(example[key]).shape)
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else:
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print(key, example[key])
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break
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# %%
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quakeflow_nc = quakeflow_nc.with_format("torch")
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dataloader = DataLoader(quakeflow_nc, batch_size=8, num_workers=0, collate_fn=lambda x: x)
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for batch in dataloader:
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print("\nDataloader test\n")
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print(f"Batch size: {len(batch)}")
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print(batch[0].keys())
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for key in batch[0].keys():
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if key == "data":
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print(key, np.array(batch[0][key]).shape)
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else:
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print(key, batch[0][key])
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break
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```
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example.py
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# %%
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import numpy as np
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from datasets import load_dataset
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from torch.utils.data import DataLoader
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# quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="station_test", split="test")
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quakeflow_nc = load_dataset(
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"./quakeflow_nc.py",
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name="station_test",
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# name="event_test",
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split="test",
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download_mode="force_redownload",
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)
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# print the first sample of the iterable dataset
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for example in quakeflow_nc:
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print("\nIterable test\n")
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print(example.keys())
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for key in example.keys():
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if key == "data":
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print(key, np.array(example[key]).shape)
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else:
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print(key, example[key])
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break
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# %%
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quakeflow_nc = quakeflow_nc.with_format("torch")
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dataloader = DataLoader(quakeflow_nc, batch_size=8, num_workers=0, collate_fn=lambda x: x)
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for batch in dataloader:
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print("\nDataloader test\n")
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print(f"Batch size: {len(batch)}")
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print(batch[0].keys())
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for key in batch[0].keys():
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if key == "data":
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print(key, np.array(batch[0][key]).shape)
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else:
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print(key, batch[0][key])
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break
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# %%
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quakeflow_nc.py
CHANGED
@@ -50,7 +50,7 @@ _LICENSE = ""
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# TODO: Add link to the official dataset URLs here
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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/
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_FILES = [
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"1987.h5",
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"1988.h5",
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@@ -104,14 +104,10 @@ class BatchBuilderConfig(datasets.BuilderConfig):
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"""
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yield a batch of event-based sample, so the number of sample stations can vary among batches
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Batch Config for QuakeFlow_NC
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:param batch_size: number of samples in a batch
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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,
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super().__init__(**kwargs)
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self.batch_size = batch_size
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self.num_stations_list = num_stations_list
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# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
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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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-
"phase_pick": datasets.Array2D(shape=(3, self.nt), dtype="float32"),
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"phase_time": datasets.Sequence(datasets.Value("string")),
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"phase_index": datasets.Sequence(datasets.Value("int32")),
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"phase_type": datasets.Sequence(datasets.Value("string")),
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"phase_polarity": datasets.Sequence(datasets.Value("string")),
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"begin_time": datasets.Value("string"),
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"end_time": datasets.Value("string"),
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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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@@ -186,18 +183,21 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
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elif (self.config.name == "event") or (self.config.name == "event_train") or (self.config.name == "event_test"):
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features = datasets.Features(
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{
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"data": datasets.
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"
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-
"
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-
"
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-
"
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"phase_polarity": datasets.Sequence(datasets.Value("string")),
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"begin_time": datasets.Value("string"),
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"end_time": datasets.Value("string"),
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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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)
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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@@ -224,7 +224,8 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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urls = _URLS[self.config.name]
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# files = dl_manager.download(urls)
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-
files = dl_manager.download_and_extract(urls)
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print(files)
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if self.config.name == "station" or self.config.name == "event":
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@@ -280,9 +281,12 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
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event_attrs["longitude"],
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event_attrs["latitude"],
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event_attrs["depth_km"],
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-
event_attrs["event_time_index"],
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]
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station_ids = list(event.keys())
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if (
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(self.config.name == "station")
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or (self.config.name == "station_train")
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@@ -307,6 +311,8 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
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"phase_polarity": phase_polarity,
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"begin_time": begin_time,
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"end_time": end_time,
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"event_location": event_location,
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"station_location": station_location,
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}
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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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330 |
-
phase_type.append(attrs["phase_type"])
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331 |
-
phase_time.append(attrs["phase_time"])
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332 |
-
phase_index.append(attrs["phase_index"])
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-
phase_polarity.append(attrs["phase_polarity"])
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station_location.append(
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[attrs["longitude"], attrs["latitude"], -attrs["elevation_m"] / 1e3]
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)
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337 |
-
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338 |
yield event_id, {
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"data": waveforms,
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"phase_time": phase_time,
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@@ -343,6 +348,8 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
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"phase_polarity": phase_polarity,
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"begin_time": begin_time,
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"end_time": end_time,
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"event_location": event_location,
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"station_location": station_location,
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}
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50 |
# TODO: Add link to the official dataset URLs here
|
51 |
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
|
52 |
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
|
53 |
+
_REPO = "https://huggingface.co/datasets/AI4EPS/quakeflow_nc/resolve/main/waveform_h5"
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54 |
_FILES = [
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"1987.h5",
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56 |
"1988.h5",
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104 |
"""
|
105 |
yield a batch of event-based sample, so the number of sample stations can vary among batches
|
106 |
Batch Config for QuakeFlow_NC
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107 |
"""
|
108 |
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109 |
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def __init__(self, **kwargs):
|
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super().__init__(**kwargs)
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# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
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|
168 |
features = datasets.Features(
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{
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170 |
"data": datasets.Array2D(shape=(3, self.nt), dtype="float32"),
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171 |
"phase_time": datasets.Sequence(datasets.Value("string")),
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"phase_index": datasets.Sequence(datasets.Value("int32")),
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"phase_type": datasets.Sequence(datasets.Value("string")),
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"phase_polarity": datasets.Sequence(datasets.Value("string")),
|
175 |
"begin_time": datasets.Value("string"),
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"end_time": datasets.Value("string"),
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177 |
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"event_time": datasets.Value("string"),
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"event_time_index": datasets.Value("int32"),
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179 |
"event_location": datasets.Sequence(datasets.Value("float32")),
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180 |
"station_location": datasets.Sequence(datasets.Value("float32")),
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181 |
},
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|
183 |
elif (self.config.name == "event") or (self.config.name == "event_train") or (self.config.name == "event_test"):
|
184 |
features = datasets.Features(
|
185 |
{
|
186 |
+
"data": datasets.Array3D(shape=(None, 3, self.nt), dtype="float32"),
|
187 |
+
"phase_time": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
|
188 |
+
"phase_index": datasets.Sequence(datasets.Sequence(datasets.Value("int32"))),
|
189 |
+
"phase_type": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
|
190 |
+
"phase_polarity": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
|
|
|
191 |
"begin_time": datasets.Value("string"),
|
192 |
"end_time": datasets.Value("string"),
|
193 |
+
"event_time": datasets.Value("string"),
|
194 |
+
"event_time_index": datasets.Value("int32"),
|
195 |
"event_location": datasets.Sequence(datasets.Value("float32")),
|
196 |
+
"station_location": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
|
197 |
},
|
198 |
)
|
199 |
+
else:
|
200 |
+
raise ValueError(f"config.name = {self.config.name} is not in BUILDER_CONFIGS")
|
201 |
|
202 |
return datasets.DatasetInfo(
|
203 |
# This is the description that will appear on the datasets page.
|
|
|
224 |
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
|
225 |
urls = _URLS[self.config.name]
|
226 |
# files = dl_manager.download(urls)
|
227 |
+
# files = dl_manager.download_and_extract(urls)
|
228 |
+
files = ["waveform_h5/1989.h5", "waveform_h5/1990.h5"]
|
229 |
print(files)
|
230 |
|
231 |
if self.config.name == "station" or self.config.name == "event":
|
|
|
281 |
event_attrs["longitude"],
|
282 |
event_attrs["latitude"],
|
283 |
event_attrs["depth_km"],
|
|
|
284 |
]
|
285 |
+
event_time = event_attrs["event_time"]
|
286 |
+
event_time_index = event_attrs["event_time_index"]
|
287 |
station_ids = list(event.keys())
|
288 |
+
if len(station_ids) == 0:
|
289 |
+
continue
|
290 |
if (
|
291 |
(self.config.name == "station")
|
292 |
or (self.config.name == "station_train")
|
|
|
311 |
"phase_polarity": phase_polarity,
|
312 |
"begin_time": begin_time,
|
313 |
"end_time": end_time,
|
314 |
+
"event_time": event_time,
|
315 |
+
"event_time_index": event_time_index,
|
316 |
"event_location": event_location,
|
317 |
"station_location": station_location,
|
318 |
}
|
|
|
333 |
for i, sta_id in enumerate(station_ids):
|
334 |
waveforms[i, :, : self.nt] = event[sta_id][:, : self.nt]
|
335 |
attrs = event[sta_id].attrs
|
336 |
+
phase_type.append(list(attrs["phase_type"]))
|
337 |
+
phase_time.append(list(attrs["phase_time"]))
|
338 |
+
phase_index.append(list(attrs["phase_index"]))
|
339 |
+
phase_polarity.append(list(attrs["phase_polarity"]))
|
340 |
station_location.append(
|
341 |
[attrs["longitude"], attrs["latitude"], -attrs["elevation_m"] / 1e3]
|
342 |
)
|
|
|
343 |
yield event_id, {
|
344 |
"data": waveforms,
|
345 |
"phase_time": phase_time,
|
|
|
348 |
"phase_polarity": phase_polarity,
|
349 |
"begin_time": begin_time,
|
350 |
"end_time": end_time,
|
351 |
+
"event_time": event_time,
|
352 |
+
"event_time_index": event_time_index,
|
353 |
"event_location": event_location,
|
354 |
"station_location": station_location,
|
355 |
}
|