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license: mit |
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# Quakeflow_NC |
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## Introduction |
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This dataset is part of the data from [NCEDC (Northern California Earthquake Data Center)](https://ncedc.org/index.html) and is organized as several HDF5 files. The dataset structure is shown below: (File [ncedc_event_dataset_000.h5.txt](./ncedc_event_dataset_000.h5.txt) shows the structure of the firsr shard of the dataset, and you can find more information about the format at [AI4EPS](https://ai4eps.github.io/homepage/ml4earth/seismic_event_format1/)) |
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Cite the NCEDC: |
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"NCEDC (2014), Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC." |
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Acknowledge the NCEDC: |
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"Waveform data, metadata, or data products for this study were accessed through the Northern California Earthquake Data Center (NCEDC), doi:10.7932/NCEDC." |
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``` |
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Group: / len:10000 |
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|- Group: /nc100012 len:5 |
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| |-* begin_time = 1987-05-08T00:15:48.890 |
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| |-* depth_km = 7.04 |
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| |-* end_time = 1987-05-08T00:17:48.890 |
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| |-* event_id = nc100012 |
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| |-* event_time = 1987-05-08T00:16:14.700 |
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| |-* event_time_index = 2581 |
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| |-* latitude = 37.5423 |
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| |-* longitude = -118.4412 |
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| |-* magnitude = 1.1 |
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| |-* magnitude_type = D |
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| |-* num_stations = 5 |
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| |- Dataset: /nc100012/NC.MRS..EH (shape:(3, 12000)) |
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| | |- (dtype=float32) |
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| | | |-* azimuth = 265.0 |
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| | | |-* component = ['Z'] |
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| | | |-* distance_km = 39.1 |
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| | | |-* dt_s = 0.01 |
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| | | |-* elevation_m = 3680.0 |
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| | | |-* emergence_angle = 93.0 |
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| | | |-* event_id = ['nc100012' 'nc100012'] |
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| | | |-* latitude = 37.5107 |
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| | | |-* location = |
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| | | |-* longitude = -118.8822 |
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| | | |-* network = NC |
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| | | |-* phase_index = [3274 3802] |
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| | | |-* phase_polarity = ['U' 'N'] |
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| | | |-* phase_remark = ['IP' 'S'] |
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| | | |-* phase_score = [1 1] |
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| | | |-* phase_time = ['1987-05-08T00:16:21.630' '1987-05-08T00:16:26.920'] |
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| | | |-* phase_type = ['P' 'S'] |
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| | | |-* snr = [0. 0. 1.98844361] |
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| | | |-* station = MRS |
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| | | |-* unit = 1e-6m/s |
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| |- Dataset: /nc100012/NN.BEN.N1.EH (shape:(3, 12000)) |
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| | |- (dtype=float32) |
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| | | |-* azimuth = 329.0 |
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| | | |-* component = ['Z'] |
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| | | |-* distance_km = 22.5 |
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| | | |-* dt_s = 0.01 |
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| | | |-* elevation_m = 2476.0 |
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| | | |-* emergence_angle = 102.0 |
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| | | |-* event_id = ['nc100012' 'nc100012'] |
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| | | |-* latitude = 37.7154 |
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| | | |-* location = N1 |
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| | | |-* longitude = -118.5741 |
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| | | |-* network = NN |
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| | | |-* phase_index = [3010 3330] |
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| | | |-* phase_polarity = ['U' 'N'] |
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| | | |-* phase_remark = ['IP' 'S'] |
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| | | |-* phase_score = [0 0] |
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| | | |-* phase_time = ['1987-05-08T00:16:18.990' '1987-05-08T00:16:22.190'] |
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| | | |-* phase_type = ['P' 'S'] |
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| | | |-* snr = [0. 0. 7.31356192] |
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| | | |-* station = BEN |
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| | | |-* unit = 1e-6m/s |
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...... |
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``` |
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## How to use |
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### Requirements |
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- datasets |
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- h5py |
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- torch (for PyTorch) |
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### Usage |
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```python |
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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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# load dataset |
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# ATTENTION: Streaming(Iterable Dataset) is difficult to support because of the feature of HDF5 |
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# So we recommend to directly load the dataset and convert it into iterable later |
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# The dataset is very large, so you need to wait for some time at the first time |
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quakeflow_nc = datasets.load_dataset("AI4EPS/quakeflow_nc", split="train") |
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quakeflow_nc |
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``` |
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If you want to use the first several shards of the dataset, you can download the script `quakeflow_nc.py` and change the code below: |
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```python |
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# change the 37 to the number of shards you want |
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_URLS = { |
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"NCEDC": [f"{_REPO}/ncedc_event_dataset_{i:03d}.h5" for i in range(37)] |
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} |
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``` |
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Then you can use the dataset like this: |
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```python |
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quakeflow_nc = datasets.load_dataset("./quakeflow_nc.py", split="train") |
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quakeflow_nc |
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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 = quakeflow_nc.to_iterable_dataset() |
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quakeflow_nc = quakeflow_nc.with_format("torch") |
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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 |
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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=4) |
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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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``` |