update doc

README.md

@@ -2,4 +2,179 @@
 license: mit
 ---
 
-## CEED: *C*alifornia *E*arthquak*E* *D*ataset for Machine Learning and Cloud Computing
+## CEED: *C*alifornia *E*arthquak*E* *D*ataset for Machine Learning and Cloud Computing
+
+The California EarthquakE Dataset (CEED) is a dataset of earthquake waveforms and metadata for machine learning and cloud computing. 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/)
+
+```
+ Group: / len:60424
+  |- Group: /ci38457511 len:35
+  |  |-* begin_time = 2019-07-06T03:19:23.668000
+  |  |-* depth_km = 8.0
+  |  |-* end_time = 2019-07-06T03:21:23.668000
+  |  |-* event_id = ci38457511
+  |  |-* event_time = 2019-07-06T03:19:53.040000
+  |  |-* event_time_index = 2937
+  |  |-* latitude = 35.7695
+  |  |-* longitude = -117.5993
+  |  |-* magnitude = 7.1
+  |  |-* magnitude_type = w
+  |  |-* nt = 12000
+  |  |-* nx = 35
+  |  |-* sampling_rate = 100
+  |  |-* source = SC
+  |  |- Dataset: /ci38457511/CI.CCC..HH (shape:(3, 12000))
+  |  |  |- (dtype=float32)
+  |  |  |  |-* azimuth = 141.849479
+  |  |  |  |-* back_azimuth = 321.986302
+  |  |  |  |-* component = ENZ
+  |  |  |  |-* depth_km = -0.67
+  |  |  |  |-* distance_km = 34.471389
+  |  |  |  |-* dt_s = 0.01
+  |  |  |  |-* elevation_m = 670.0
+  |  |  |  |-* event_id = ['ci38457511' 'ci38457511' 'ci37260300']
+  |  |  |  |-* instrument = HH
+  |  |  |  |-* latitude = 35.52495
+  |  |  |  |-* local_depth_m = 0.0
+  |  |  |  |-* location = 
+  |  |  |  |-* longitude = -117.36453
+  |  |  |  |-* network = CI
+  |  |  |  |-* p_phase_index = 3575
+  |  |  |  |-* p_phase_polarity = U
+  |  |  |  |-* p_phase_score = 0.8
+  |  |  |  |-* p_phase_status = manual
+  |  |  |  |-* p_phase_time = 2019-07-06T03:19:59.422000
+  |  |  |  |-* phase_index = [ 3575  4184 11826]
+  |  |  |  |-* phase_picking_channel = ['HHZ' 'HNN' 'HHZ']
+  |  |  |  |-* phase_polarity = ['U' 'N' 'N']
+  |  |  |  |-* phase_remark = ['i' 'e' 'e']
+  |  |  |  |-* phase_score = [0.8 0.5 0.5]
+  |  |  |  |-* phase_status = manual
+  |  |  |  |-* phase_time = ['2019-07-06T03:19:59.422000' '2019-07-06T03:20:05.509000' '2019-07-06T03:21:21.928000']
+  |  |  |  |-* phase_type = ['P' 'S' 'P']
+  |  |  |  |-* s_phase_index = 4184
+  |  |  |  |-* s_phase_polarity = N
+  |  |  |  |-* s_phase_score = 0.5
+  |  |  |  |-* s_phase_status = manual
+  |  |  |  |-* s_phase_time = 2019-07-06T03:20:05.509000
+  |  |  |  |-* snr = [ 637.9865898   286.9100766  1433.04052911]
+  |  |  |  |-* station = CCC
+  |  |  |  |-* unit = 1e-6m/s
+  |  |- Dataset: /ci38457511/CI.CCC..HN (shape:(3, 12000))
+  |  |  |- (dtype=float32)
+  |  |  |  |-* azimuth = 141.849479
+  |  |  |  |-* back_azimuth = 321.986302
+  |  |  |  |-* component = ENZ
+  |  |  |  |-* depth_km = -0.67
+  |  |  |  |-* distance_km = 34.471389
+  |  |  |  |-* dt_s = 0.01
+  |  |  |  |-* elevation_m = 670.0
+  |  |  |  |-* event_id = ['ci38457511' 'ci38457511' 'ci37260300']
+  ......
+  ```
+
+## Getting Started
+
+### 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 complex 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
+ceed = load_dataset("AI4EPS/CEED", split="test")
+# or
+ceed = load_dataset("AI4EPS/CEED", name="station_test", split="test")
+
+# to load "event" with train split
+ceed = load_dataset("AI4EPS/CEED", name="event", split="train")
+```
+
+#### Example loading the dataset
+```python
+ceed = load_dataset("AI4EPS/CEED", name="station_test", split="test")
+
+# print the first sample of the iterable dataset
+for example in ceed:
+    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
+
+# %%
+ceed = ceed.with_format("torch")
+dataloader = DataLoader(ceed, 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
+```
+
+#### Extension
+
+If you want to introduce new features in to labels, we recommend to make a copy of `CEED.py` and modify the `_generate_examples` method. Check [AI4EPS/EQNet](https://github.com/AI4EPS/EQNet/blob/master/eqnet/data/quakeflow_nc.py) for an example. To load the dataset with your modified script, specify the path to the script in `load_dataset` function:
+```python
+ceed = load_dataset("path/to/your/CEED.py", name="station_test", split="test", trust_remote_code=True)
+```