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README.md

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+---
+license: mit
+---
+
+# Quakeflow_NC
+
+## Introduction
+This dataset is part of the data (1970-2020) 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, and you can find more information about the format at [AI4EPS](https://ai4eps.github.io/homepage/ml4earth/seismic_event_format1/))
+
+Cite the NCEDC and PhaseNet:
+
+Zhu, W., & Beroza, G. C. (2018). PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method. arXiv preprint arXiv:1803.03211.
+
+NCEDC (2014), Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC.
+
+Acknowledge the NCEDC:
+
+Waveform data, metadata, or data products for this study were accessed through the Northern California Earthquake Data Center (NCEDC), doi:10.7932/NCEDC.
+
+```
+ Group: / len:16227
+  |- Group: /nc71111584 len:2
+  |  |-* begin_time = 2020-01-02T07:01:19.620
+  |  |-* depth_km = 3.69
+  |  |-* end_time = 2020-01-02T07:03:19.620
+  |  |-* event_id = nc71111584
+  |  |-* event_time = 2020-01-02T07:01:48.240
+  |  |-* event_time_index = 2862
+  |  |-* latitude = 37.6545
+  |  |-* longitude = -118.8798
+  |  |-* magnitude = -0.15
+  |  |-* magnitude_type = D
+  |  |-* num_stations = 2
+  |  |- Dataset: /nc71111584/NC.MCB..HH (shape:(3, 12000))
+  |  |  |- (dtype=float32)
+  |  |  |  |-* azimuth = 233.0
+  |  |  |  |-* component = ['E' 'N' 'Z']
+  |  |  |  |-* distance_km = 1.9
+  |  |  |  |-* dt_s = 0.01
+  |  |  |  |-* elevation_m = 2391.0
+  |  |  |  |-* emergence_angle = 159.0
+  |  |  |  |-* event_id = ['nc71111584' 'nc71111584']
+  |  |  |  |-* latitude = 37.6444
+  |  |  |  |-* location = 
+  |  |  |  |-* longitude = -118.8968
+  |  |  |  |-* network = NC
+  |  |  |  |-* phase_index = [3000 3101]
+  |  |  |  |-* phase_polarity = ['U' 'N']
+  |  |  |  |-* phase_remark = ['IP' 'ES']
+  |  |  |  |-* phase_score = [1 2]
+  |  |  |  |-* phase_time = ['2020-01-02T07:01:49.620' '2020-01-02T07:01:50.630']
+  |  |  |  |-* phase_type = ['P' 'S']
+  |  |  |  |-* snr = [2.82143   3.055604  1.8412642]
+  |  |  |  |-* station = MCB
+  |  |  |  |-* unit = 1e-6m/s
+  |  |- Dataset: /nc71111584/NC.MCB..HN (shape:(3, 12000))
+  |  |  |- (dtype=float32)
+  |  |  |  |-* azimuth = 233.0
+  |  |  |  |-* component = ['E' 'N' 'Z']
+  ......
+  ```
+
+## How to use
+
+### Requirements
+- datasets
+- h5py
+- fsspec
+- pytorch
+
+### Usage
+Import the necessary packages:
+```python
+import h5py
+import numpy as np
+import torch
+from datasets import load_dataset
+```
+We have 6 configurations for the dataset: 
+- "station"
+- "event"
+- "station_train"
+- "event_train"
+- "station_test"
+- "event_test"
+
+"station" yields station-based samples one by one, while "event" yields event-based samples one by one. The configurations with no suffix are the full dataset, while the configurations with suffix "_train" and "_test" only have corresponding split of the full dataset. Train split contains data from 1970 to 2019, while test split contains data in 2020.
+
+The sample of `station` is a dictionary with the following keys:
+- `data`: the waveform with shape `(3, nt)`, the default time length is 8192
+- `begin_time`: the begin time of the waveform data
+- `end_time`: the end time of the waveform data
+- `phase_time`: the phase arrival time
+- `phase_index`: the time point index of the phase arrival time
+- `phase_type`: the phase type
+- `phase_polarity`: the phase polarity in ('U', 'D', 'N')
+- `event_time`: the event time
+- `event_time_index`: the time point index of the event time
+- `event_location`: the event location with shape `(3,)`, including latitude, longitude, depth
+- `station_location`: the station location with shape `(3,)`, including latitude, longitude and depth
+
+The sample of `event` is a dictionary with the following keys:
+- `data`: the waveform with shape `(n_station, 3, nt)`, the default time length is 8192
+- `begin_time`: the begin time of the waveform data
+- `end_time`: the end time of the waveform data
+- `phase_time`: the phase arrival time with shape `(n_station,)`
+- `phase_index`: the time point index of the phase arrival time with shape `(n_station,)`
+- `phase_type`: the phase type with shape `(n_station,)`
+- `phase_polarity`: the phase polarity in ('U', 'D', 'N') with shape `(n_station,)`
+- `event_time`: the event time
+- `event_time_index`: the time point index of the event time
+- `event_location`: the space-time coordinates of the event with shape `(n_staion, 3)`
+- `station_location`: the space coordinates of the station with shape `(n_station, 3)`, including latitude, longitude and depth
+
+The default configuration is `station_test`. You can specify the configuration by argument `name`. For example:
+```python
+# 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
+
+# to load "station_test" with test split
+quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", split="test")
+# or
+quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="station_test", split="test")
+
+# to load "event" with train split
+quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="event", split="train")
+```
+
+#### Example loading the dataset
+```python
+quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="station_test", split="test")
+
+# 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():
+        if key == "data":
+            print(key, np.array(example[key]).shape)
+        else:
+            print(key, example[key])
+    break
+
+# %%
+quakeflow_nc = quakeflow_nc.with_format("torch")
+dataloader = DataLoader(quakeflow_nc, batch_size=8, num_workers=0, collate_fn=lambda x: x)
+
+for batch in dataloader:
+    print("\nDataloader test\n")
+    print(f"Batch size: {len(batch)}")
+    print(batch[0].keys())
+    for key in batch[0].keys():
+        if key == "data":
+            print(key, np.array(batch[0][key]).shape)
+        else:
+            print(key, batch[0][key])
+    break
+```