README.md

---
license: mit
---

# Quakeflow_NC

## Introduction
This dataset is part of the data from NCEDC (Northern California Earthquake Data Center) and is organised 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/))

```
Group: / len:10000
  |- Group: /nc100012 len:5
  |  |-* begin_time = 1987-05-08T00:15:48.890
  |  |-* depth_km = 7.04
  |  |-* end_time = 1987-05-08T00:17:48.890
  |  |-* event_id = nc100012
  |  |-* event_time = 1987-05-08T00:16:14.700
  |  |-* event_time_index = 2581
  |  |-* latitude = 37.5423
  |  |-* longitude = -118.4412
  |  |-* magnitude = 1.1
  |  |-* magnitude_type = D
  |  |-* num_stations = 5
  |  |- Dataset: /nc100012/NC.MRS..EH (shape:(3, 12000))
  |  |  |- (dtype=float32)
  |  |  |  |-* azimuth = 265.0
  |  |  |  |-* component = ['Z']
  |  |  |  |-* distance_km = 39.1
  |  |  |  |-* dt_s = 0.01
  |  |  |  |-* elevation_m = 3680.0
  |  |  |  |-* emergence_angle = 93.0
  |  |  |  |-* event_id = ['nc100012' 'nc100012']
  |  |  |  |-* latitude = 37.5107
  |  |  |  |-* location = 
  |  |  |  |-* longitude = -118.8822
  |  |  |  |-* network = NC
  |  |  |  |-* phase_index = [3274 3802]
  |  |  |  |-* phase_polarity = ['U' 'N']
  |  |  |  |-* phase_remark = ['IP' 'S']
  |  |  |  |-* phase_score = [1 1]
  |  |  |  |-* phase_time = ['1987-05-08T00:16:21.630' '1987-05-08T00:16:26.920']
  |  |  |  |-* phase_type = ['P' 'S']
  |  |  |  |-* snr = [0.         0.         1.98844361]
  |  |  |  |-* station = MRS
  |  |  |  |-* unit = 1e-6m/s
  |  |- Dataset: /nc100012/NN.BEN.N1.EH (shape:(3, 12000))
  |  |  |- (dtype=float32)
  |  |  |  |-* azimuth = 329.0
  |  |  |  |-* component = ['Z']
  |  |  |  |-* distance_km = 22.5
  |  |  |  |-* dt_s = 0.01
  |  |  |  |-* elevation_m = 2476.0
  |  |  |  |-* emergence_angle = 102.0
  |  |  |  |-* event_id = ['nc100012' 'nc100012']
  |  |  |  |-* latitude = 37.7154
  |  |  |  |-* location = N1
  |  |  |  |-* longitude = -118.5741
  |  |  |  |-* network = NN
  |  |  |  |-* phase_index = [3010 3330]
  |  |  |  |-* phase_polarity = ['U' 'N']
  |  |  |  |-* phase_remark = ['IP' 'S']
  |  |  |  |-* phase_score = [0 0]
  |  |  |  |-* phase_time = ['1987-05-08T00:16:18.990' '1987-05-08T00:16:22.190']
  |  |  |  |-* phase_type = ['P' 'S']
  |  |  |  |-* snr = [0.         0.         7.31356192]
  |  |  |  |-* station = BEN
  |  |  |  |-* unit = 1e-6m/s
  ......
  ```

## How to use

### Requirements
- datasets
- h5py
- torch (for PyTorch)

### Usage
```python
import h5py
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset, DataLoader
from datasets import load_dataset

# load dataset
# ATTENTION: Streaming(Iterable Dataset) is difficult to support because of the feature of HDF5
# So we recommend to directly load the dataset and convert it into iterable later
# The dataset is very large, so you need to wait for some time at the first time
quakeflow_nc = datasets.load_dataset("AI4EPS/quakeflow_nc", split="train")
quakeflow_nc
```
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:
```python
# change the 37 to the number of shards you want
_URLS = {
    "NCEDC": [f"{_REPO}/ncedc_event_dataset_{i:03d}.h5" for i in range(37)]
}
```
Then you can use the dataset like this:
```python
quakeflow_nc = datasets.load_dataset("./quakeflow_nc.py", split="train")
quakeflow_nc
```
Then you can change the dataset into PyTorch format iterable dataset, and view the first sample:
```python
quakeflow_nc = quakeflow_nc.to_iterable_dataset()
quakeflow_nc = quakeflow_nc.with_format("torch")
# because add examples formatting to get tensors when using the "torch" format
# has not been implemented yet, we need to manually add the formatting
quakeflow_nc = quakeflow_nc.map(lambda x: {key: torch.from_numpy(np.array(value, dtype=np.float32)) for key, value in x.items()})
try:
    isinstance(quakeflow_nc, torch.utils.data.IterableDataset)
except:
    raise Exception("quakeflow_nc is not an IterableDataset")

# print the first sample of the iterable dataset
for example in quakeflow_nc:
    print("\nIterable test\n")
    print(example.keys())
    for key in example.keys():
        print(key, example[key].shape, example[key].dtype)
    break

dataloader = DataLoader(quakeflow_nc, batch_size=4)

for batch in dataloader:
    print("\nDataloader test\n")
    print(batch.keys())
    for key in batch.keys():
        print(key, batch[key].shape, batch[key].dtype)
    break
```