update EQNet event example generator and readme

.gitattributes

@@ -52,4 +52,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
-*.csv filter=lfs diff=lfs merge=lfs -text
+ncedc_eventid.h5 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -7,15 +7,11 @@ license: mit
 ## 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.
+Cite the NCEDC:
+"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.
+"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
@@ -66,7 +62,7 @@ Waveform data, metadata, or data products for this study were accessed through t
 - datasets
 - h5py
 - fsspec
-- pytorch
+- torch (for PyTorch)
 
 ### Usage
 Import the necessary packages:
@@ -74,6 +70,7 @@ Import the necessary packages:
 import h5py
 import numpy as np
 import torch
+from torch.utils.data import Dataset, IterableDataset, DataLoader
 from datasets import load_dataset
 ```
 We have 6 configurations for the dataset: 
@@ -88,28 +85,16 @@ We have 6 configurations for the dataset:
 
 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
+- `phase_pick`: the probability of the phase pick with shape `(3, nt)`, the first dimension is noise, P and S
+- `event_location`: the event location with shape `(4,)`, including latitude, longitude, depth and time
 - `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)`
+- `phase_pick`: the probability of the phase pick with shape `(n_station, 3, nt)`, the first dimension is noise, P and S
+- `event_center`: the probability of the event time with shape `(n_station, feature_nt)`, default feature time length is 512
+- `event_location`: the space-time coordinates of the event with shape `(n_staion, 4, feature_nt)`
+- `event_location_mask`: the probability mask of the event time with shape `(n_station, feature_nt)`
 - `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:
@@ -128,33 +113,70 @@ quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="station_test", split="t
 quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="event", split="train")
 ```
 
-#### Example loading the dataset
+#### Usage for `station`
+Then you can change the dataset into PyTorch format iterable dataset, and view the first sample:
 ```python
 quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="station_test", split="test")
+# for PyTorch DataLoader, we need to divide the dataset into several shards
+num_workers=4
+quakeflow_nc = quakeflow_nc.to_iterable_dataset(num_shards=num_workers)
+# because add examples formatting to get tensors when using the "torch" format
+# has not been implemented yet, we need to manually add the formatting when using iterable dataset
+# if you want to use dataset directly, just use
+# quakeflow_nc.with_format("torch")
+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, num_workers=num_workers)
+
+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
+```
+
+#### Usage for `event`
+
+Then you can change the dataset into PyTorch format dataset, and view the first sample (Don't forget to reorder the keys):
+```python
+quakeflow_nc = datasets.load_dataset("AI4EPS/quakeflow_nc", split="test", name="event_test")
+
+# for PyTorch DataLoader, we need to divide the dataset into several shards
+num_workers=4
+quakeflow_nc = quakeflow_nc.to_iterable_dataset(num_shards=num_workers)
+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():
-        if key == "data":
-            print(key, np.array(example[key]).shape)
-        else:
-            print(key, example[key])
+        print(key, example[key].shape, example[key].dtype)
     break
 
-# %%
-quakeflow_nc = quakeflow_nc.with_format("torch")
-dataloader = DataLoader(quakeflow_nc, batch_size=8, num_workers=0, collate_fn=lambda x: x)
+dataloader = DataLoader(quakeflow_nc, batch_size=1, num_workers=num_workers)
 
 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])
+    print(batch.keys())
+    for key in batch.keys():
+        print(key, batch[key].shape, batch[key].dtype)
     break
 ```

events.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:84166f6a0be6a02caeb8d11ed3495e5256db698c795dbb3db4d45d8b863313d8
-size 46863258

events_test.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:74b5bf132e23763f851035717a1baa92ab8fb73253138b640103390dce33e154
-size 1602217

events_train.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ef579400d9354ecaf142bdc7023291c952dbfc20d6bafab4715dff1774b3f7a5
-size 45261178

example.py

@@ -1,54 +0,0 @@
-# %%
-import datasets
-import numpy as np
-from torch.utils.data import DataLoader
-
-quakeflow_nc = datasets.load_dataset(
-    "AI4EPS/quakeflow_nc",
-    name="station",
-    split="train",
-    # name="station_test",
-    # split="test",
-    # download_mode="force_redownload",
-    trust_remote_code=True,
-    num_proc=36,
-)
-# quakeflow_nc = datasets.load_dataset(
-#     "./quakeflow_nc.py",
-#     name="station",
-#     split="train",
-#     # name="statoin_test",
-#     # split="test",
-#     num_proc=36,
-# )
-
-print(quakeflow_nc)
-
-# print the first sample of the iterable dataset
-for example in quakeflow_nc:
-    print("\nIterable dataset\n")
-    print(example)
-    print(example.keys())
-    for key in example.keys():
-        if key == "waveform":
-            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 dataset\n")
-    print(f"Batch size: {len(batch)}")
-    print(batch[0].keys())
-    for key in batch[0].keys():
-        if key == "waveform":
-            print(key, np.array(batch[0][key]).shape)
-        else:
-            print(key, batch[0][key])
-    break
-
-# %%

merge_hdf5.py

@@ -1,65 +0,0 @@
-# %%
-import os
-
-import h5py
-import matplotlib.pyplot as plt
-from tqdm import tqdm
-
-# %%
-h5_dir = "waveform_h5"
-h5_out = "waveform.h5"
-h5_train = "waveform_train.h5"
-h5_test = "waveform_test.h5"
-
-# # %%
-# h5_dir = "waveform_h5"
-# h5_out = "waveform.h5"
-# h5_train = "waveform_train.h5"
-# h5_test = "waveform_test.h5"
-
-h5_files = sorted(os.listdir(h5_dir))
-train_files = h5_files[:-1]
-test_files = h5_files[-1:]
-# train_files = h5_files
-# train_files = [x for x in train_files if (x != "2014.h5") and (x not in [])]
-# test_files = []
-print(f"train files: {train_files}")
-print(f"test files: {test_files}")
-
-# %%
-with h5py.File(h5_out, "w") as fp:
-    # external linked file
-    for h5_file in h5_files:
-        with h5py.File(os.path.join(h5_dir, h5_file), "r") as f:
-            for event in tqdm(f.keys(), desc=h5_file, total=len(f.keys())):
-                if event not in fp:
-                    fp[event] = h5py.ExternalLink(os.path.join(h5_dir, h5_file), event)
-                else:
-                    print(f"{event} already exists")
-                    continue
-
-# %%
-with h5py.File(h5_train, "w") as fp:
-    # external linked file
-    for h5_file in train_files:
-        with h5py.File(os.path.join(h5_dir, h5_file), "r") as f:
-            for event in tqdm(f.keys(), desc=h5_file, total=len(f.keys())):
-                if event not in fp:
-                    fp[event] = h5py.ExternalLink(os.path.join(h5_dir, h5_file), event)
-                else:
-                    print(f"{event} already exists")
-                    continue
-
-# %%
-with h5py.File(h5_test, "w") as fp:
-    # external linked file
-    for h5_file in test_files:
-        with h5py.File(os.path.join(h5_dir, h5_file), "r") as f:
-            for event in tqdm(f.keys(), desc=h5_file, total=len(f.keys())):
-                if event not in fp:
-                    fp[event] = h5py.ExternalLink(os.path.join(h5_dir, h5_file), event)
-                else:
-                    print(f"{event} already exists")
-                    continue
-
-# %%

models/phasenet_picks.csv

@@ -1,3 +0,0 @@
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-oid sha256:b51df5987a2a05e44e0949b42d00a28692109da521911c55d2692ebfad0c54d7
-size 9355127

models/phasenet_plus_events.csv

@@ -1,3 +0,0 @@
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-oid sha256:f686ebf8da632b71a947e4ee884c76f30a313ae0e9d6e32d1f675828884a95f7
-size 7381331

models/phasenet_plus_picks.csv

@@ -1,3 +0,0 @@
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-oid sha256:83d241a54477f722cd032efe8368a653bba170e1abebf3d9097d7756cfd54b23
-size 9987053

models/phasenet_pt_picks.csv

@@ -1,3 +0,0 @@
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-oid sha256:bb7ea98484b5e6e1c4c79ea5eb1e38bce43e87b546fc6d29c72d187a6d8b1d00
-size 8715799

picks.csv

@@ -1,3 +0,0 @@
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-oid sha256:52f077ae9f94481d4b80f37c9f15038ee1e3636d5da2da3b1d4aaa2991879cc3
-size 422247029

picks_test.csv

@@ -1,3 +0,0 @@
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-oid sha256:bb09f0ac169bf451cfcfb4547359756cb1a53828bf4074971d9160a3aa171f38
-size 21850235

picks_train.csv

@@ -1,3 +0,0 @@
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-oid sha256:d22c5d5eb1c27a723525c657c1308a3b643d6f3e716eb1c43e064b7a87bb0819
-size 400397230

quakeflow_nc.py

@@ -17,13 +17,14 @@
 """QuakeFlow_NC: A dataset of earthquake waveforms organized by earthquake events and based on the HDF5 format."""
 
 
-from typing import Dict, List, Optional, Tuple, Union
-
-import datasets
-import fsspec
 import h5py
 import numpy as np
 import torch
+from typing import Dict, List, Optional, Tuple, Union
+import fsspec
+
+import datasets
+
 
 # TODO: Add BibTeX citation
 # Find for instance the citation on arxiv or on the dataset repo/website
@@ -50,45 +51,24 @@ _LICENSE = ""
 # TODO: Add link to the official dataset URLs here
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_REPO = "https://huggingface.co/datasets/AI4EPS/quakeflow_nc/resolve/main/waveform_h5"
+_REPO = "https://huggingface.co/datasets/AI4EPS/quakeflow_nc/resolve/main/data"
 _FILES = [
-    "1987.h5",
-    "1988.h5",
-    "1989.h5",
-    "1990.h5",
-    "1991.h5",
-    "1992.h5",
-    "1993.h5",
-    "1994.h5",
-    "1995.h5",
-    "1996.h5",
-    "1997.h5",
-    "1998.h5",
-    "1999.h5",
-    "2000.h5",
-    "2001.h5",
-    "2002.h5",
-    "2003.h5",
-    "2004.h5",
-    "2005.h5",
-    "2006.h5",
-    "2007.h5",
-    "2008.h5",
-    "2009.h5",
-    "2010.h5",
-    "2011.h5",
-    "2012.h5",
-    "2013.h5",
-    "2014.h5",
-    "2015.h5",
-    "2016.h5",
-    "2017.h5",
-    "2018.h5",
-    "2019.h5",
-    "2020.h5",
-    "2021.h5",
-    "2022.h5",
-    "2023.h5",
+    "NC1970-1989.h5",
+    "NC1990-1994.h5",
+    "NC1995-1999.h5",
+    "NC2000-2004.h5",
+    "NC2005-2009.h5",
+    "NC2010.h5",
+    "NC2011.h5",
+    "NC2012.h5",
+    "NC2013.h5",
+    "NC2014.h5",
+    "NC2015.h5",
+    "NC2016.h5",
+    "NC2017.h5",
+    "NC2018.h5",
+    "NC2019.h5",
+    "NC2020.h5",
 ]
 _URLS = {
     "station": [f"{_REPO}/{x}" for x in _FILES],
@@ -104,10 +84,14 @@ class BatchBuilderConfig(datasets.BuilderConfig):
     """
     yield a batch of event-based sample, so the number of sample stations can vary among batches
     Batch Config for QuakeFlow_NC
+    :param batch_size: number of samples in a batch
+    :param num_stations_list: possible number of stations in a batch
     """
 
-    def __init__(self, **kwargs):
+    def __init__(self, batch_size: int, num_stations_list: List, **kwargs):
         super().__init__(**kwargs)
+        self.batch_size = batch_size
+        self.num_stations_list = num_stations_list
 
 
 # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
@@ -116,7 +100,11 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
 
     VERSION = datasets.Version("1.1.0")
 
+    degree2km = 111.32
     nt = 8192
+    feature_nt = 512
+    feature_scale = int(nt / feature_nt)
+    sampling_rate = 100.0
 
     # This is an example of a dataset with multiple configurations.
     # If you don't want/need to define several sub-sets in your dataset,
@@ -165,44 +153,30 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
             or (self.config.name == "station_train")
             or (self.config.name == "station_test")
         ):
-            features = datasets.Features(
+            features=datasets.Features(
                 {
-                    "id": datasets.Value("string"),
-                    "event_id": datasets.Value("string"),
-                    "station_id": datasets.Value("string"),
-                    "waveform": datasets.Array2D(shape=(3, self.nt), dtype="float32"),
-                    "phase_time": datasets.Sequence(datasets.Value("string")),
-                    "phase_index": datasets.Sequence(datasets.Value("int32")),
-                    "phase_type": datasets.Sequence(datasets.Value("string")),
-                    "phase_polarity": datasets.Sequence(datasets.Value("string")),
-                    "begin_time": datasets.Value("string"),
-                    "end_time": datasets.Value("string"),
-                    "event_time": datasets.Value("string"),
-                    "event_time_index": datasets.Value("int32"),
+                    "data": datasets.Array2D(shape=(3, self.nt), dtype='float32'),
+                    "phase_pick": datasets.Array2D(shape=(3, self.nt), dtype='float32'),
                     "event_location": datasets.Sequence(datasets.Value("float32")),
                     "station_location": datasets.Sequence(datasets.Value("float32")),
-                },
-            )
-        elif (self.config.name == "event") or (self.config.name == "event_train") or (self.config.name == "event_test"):
-            features = datasets.Features(
+                })
+            
+        elif (
+            (self.config.name == "event") 
+            or (self.config.name == "event_train") 
+            or (self.config.name == "event_test")
+        ):
+            features=datasets.Features(
                 {
-                    "event_id": datasets.Value("string"),
-                    "waveform": datasets.Array3D(shape=(None, 3, self.nt), dtype="float32"),
-                    "phase_time": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
-                    "phase_index": datasets.Sequence(datasets.Sequence(datasets.Value("int32"))),
-                    "phase_type": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
-                    "phase_polarity": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
-                    "begin_time": datasets.Value("string"),
-                    "end_time": datasets.Value("string"),
-                    "event_time": datasets.Value("string"),
-                    "event_time_index": datasets.Value("int32"),
-                    "event_location": datasets.Sequence(datasets.Value("float32")),
-                    "station_location": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
-                },
+                    "data": datasets.Array3D(shape=(None, 3, self.nt), dtype='float32'),
+                    "phase_pick": datasets.Array3D(shape=(None, 3, self.nt), dtype='float32'),
+                    "event_center" : datasets.Array2D(shape=(None, self.feature_nt), dtype='float32'),
+                    "event_location": datasets.Array3D(shape=(None, 4, self.feature_nt), dtype='float32'),
+                    "event_location_mask": datasets.Array2D(shape=(None, self.feature_nt), dtype='float32'),
+                    "station_location": datasets.Array2D(shape=(None, 3), dtype="float32"),
+                }
             )
-        else:
-            raise ValueError(f"config.name = {self.config.name} is not in BUILDER_CONFIGS")
-
+            
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
@@ -228,20 +202,18 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
         urls = _URLS[self.config.name]
         # files = dl_manager.download(urls)
-        if "bucket" not in self.storage_options:
-            files = dl_manager.download_and_extract(urls)
-        else:
-            files = [f"{self.storage_options['bucket']}/{x}" for x in _FILES]
-            # files = [f"/nfs/quakeflow_dataset/NC/quakeflow_nc/waveform_h5/{x}" for x in _FILES][-3:]
-        print("Files:\n", "\n".join(sorted(files)))
-        print(self.storage_options)
+        files = dl_manager.download_and_extract(urls)
+        print(files)
 
         if self.config.name == "station" or self.config.name == "event":
             return [
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN,
                     # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": files[:-1], "split": "train"},
+                    gen_kwargs={
+                        "filepath": files[:-1],
+                        "split": "train",
+                    },
                 ),
                 datasets.SplitGenerator(
                     name=datasets.Split.TEST,
@@ -252,7 +224,10 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
             return [
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN,
-                    gen_kwargs={"filepath": files, "split": "train"},
+                    gen_kwargs={
+                        "filepath": files,
+                        "split": "train",
+                    },
                 ),
             ]
         elif self.config.name == "station_test" or self.config.name == "event_test":
@@ -271,92 +246,156 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
         # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
 
         for file in filepath:
-            print(f"\nReading {file}")
             with fsspec.open(file, "rb") as fs:
                 with h5py.File(fs, "r") as fp:
+                    # for event_id in sorted(list(fp.keys())):
                     event_ids = list(fp.keys())
                     for event_id in event_ids:
                         event = fp[event_id]
-                        event_attrs = event.attrs
-                        begin_time = event_attrs["begin_time"]
-                        end_time = event_attrs["end_time"]
-                        event_location = [
-                            event_attrs["longitude"],
-                            event_attrs["latitude"],
-                            event_attrs["depth_km"],
-                        ]
-                        event_time = event_attrs["event_time"]
-                        event_time_index = event_attrs["event_time_index"]
                         station_ids = list(event.keys())
-                        if len(station_ids) == 0:
-                            continue
                         if (
                             (self.config.name == "station")
                             or (self.config.name == "station_train")
                             or (self.config.name == "station_test")
                         ):
-                            waveform = np.zeros([3, self.nt], dtype="float32")
-
-                            for i, station_id in enumerate(station_ids):
-                                waveform[:, : self.nt] = event[station_id][:, : self.nt]
-                                attrs = event[station_id].attrs
-                                phase_type = attrs["phase_type"]
-                                phase_time = attrs["phase_time"]
-                                phase_index = attrs["phase_index"]
-                                phase_polarity = attrs["phase_polarity"]
+                            waveforms = np.zeros([3, self.nt], dtype="float32")
+                            phase_pick = np.zeros_like(waveforms)
+                            attrs = event.attrs
+                            event_location = [
+                                attrs["longitude"],
+                                attrs["latitude"],
+                                attrs["depth_km"],
+                                attrs["event_time_index"],
+                            ]
+                            
+                            for i, sta_id in enumerate(station_ids):
+                                waveforms[:, : self.nt] = event[sta_id][:, :self.nt]
+                                # waveforms[:, : self.nt] = event[sta_id][: self.nt, :].T
+                                attrs = event[sta_id].attrs
+                                p_picks = attrs["phase_index"][attrs["phase_type"] == "P"]
+                                s_picks = attrs["phase_index"][attrs["phase_type"] == "S"]
+                                # phase_pick[:, :self.nt] = generate_label([p_picks, s_picks], nt=self.nt)
                                 station_location = [attrs["longitude"], attrs["latitude"], -attrs["elevation_m"] / 1e3]
 
-                                yield f"{event_id}/{station_id}", {
-                                    "id": f"{event_id}/{station_id}",
-                                    "event_id": event_id,
-                                    "station_id": station_id,
-                                    "waveform": waveform,
-                                    "phase_time": phase_time,
-                                    "phase_index": phase_index,
-                                    "phase_type": phase_type,
-                                    "phase_polarity": phase_polarity,
-                                    "begin_time": begin_time,
-                                    "end_time": end_time,
-                                    "event_time": event_time,
-                                    "event_time_index": event_time_index,
-                                    "event_location": event_location,
-                                    "station_location": station_location,
+                                yield f"{event_id}/{sta_id}", {
+                                    "data": torch.from_numpy(waveforms).float(),
+                                    "phase_pick": torch.from_numpy(phase_pick).float(),
+                                    "event_location": torch.from_numpy(np.array(event_location)).float(),
+                                    "station_location": torch.from_numpy(np.array(station_location)).float(),
                                 }
 
+
                         elif (
                             (self.config.name == "event")
                             or (self.config.name == "event_train")
                             or (self.config.name == "event_test")
                         ):
+                            event_attrs = event.attrs
+
+                            # avoid stations with P arrival equals S arrival
+                            is_sick = False
+                            for sta_id in station_ids:
+                                attrs = event[sta_id].attrs
+                                if attrs["phase_index"][attrs["phase_type"] == "P"] == attrs["phase_index"][attrs["phase_type"] == "S"]:
+                                    is_sick = True
+                                    break
+                            if is_sick:
+                                continue
+                            
+                            waveforms = np.zeros([len(station_ids), 3, self.nt], dtype="float32")
+                            phase_pick = np.zeros_like(waveforms)
+                            event_center = np.zeros([len(station_ids), self.nt])
+                            event_location = np.zeros([len(station_ids), 4, self.nt])
+                            event_location_mask = np.zeros([len(station_ids), self.nt])
+                            station_location = np.zeros([len(station_ids), 3])
+
+                            for i, sta_id in enumerate(station_ids):
+                                # trace_id = event_id + "/" + sta_id
+                                waveforms[i, :, :] = event[sta_id][:, :self.nt]
+                                attrs = event[sta_id].attrs
+                                p_picks = attrs["phase_index"][attrs["phase_type"] == "P"]
+                                s_picks = attrs["phase_index"][attrs["phase_type"] == "S"]
+                                phase_pick[i, :, :] = generate_label([p_picks, s_picks], nt=self.nt)
+
+                                ## TODO: how to deal with multiple phases
+                                # center = (attrs["phase_index"][::2] + attrs["phase_index"][1::2])/2.0
+                                ## assuming only one event with both P and S picks
+                                c0 = ((p_picks) + (s_picks)) / 2.0 # phase center
+                                c0_width = ((s_picks - p_picks) * self.sampling_rate / 200.0).max() if p_picks!=s_picks else 50
+                                dx = round(
+                                    (event_attrs["longitude"] - attrs["longitude"])
+                                    * np.cos(np.radians(event_attrs["latitude"]))
+                                    * self.degree2km,
+                                    2,
+                                )
+                                dy = round(
+                                    (event_attrs["latitude"] - attrs["latitude"])
+                                    * self.degree2km,
+                                    2,
+                                )
+                                dz = round(
+                                    event_attrs["depth_km"] + attrs["elevation_m"] / 1e3,
+                                    2,
+                                )
 
-                            waveform = np.zeros([len(station_ids), 3, self.nt], dtype="float32")
-                            phase_type = []
-                            phase_time = []
-                            phase_index = []
-                            phase_polarity = []
-                            station_location = []
-
-                            for i, station_id in enumerate(station_ids):
-                                waveform[i, :, : self.nt] = event[station_id][:, : self.nt]
-                                attrs = event[station_id].attrs
-                                phase_type.append(list(attrs["phase_type"]))
-                                phase_time.append(list(attrs["phase_time"]))
-                                phase_index.append(list(attrs["phase_index"]))
-                                phase_polarity.append(list(attrs["phase_polarity"]))
-                                station_location.append(
-                                    [attrs["longitude"], attrs["latitude"], -attrs["elevation_m"] / 1e3]
+                                event_center[i, :] = generate_label(
+                                    [
+                                        # [c0 / self.feature_scale],
+                                        c0,
+                                    ],
+                                    label_width=[
+                                        c0_width,
+                                    ],
+                                    # label_width=[
+                                    #     10,
+                                    # ],
+                                    # nt=self.feature_nt,
+                                    nt=self.nt,
+                                )[1, :]
+                                mask = event_center[i, :] >= 0.5
+                                event_location[i, 0, :] = (
+                                    np.arange(self.nt) - event_attrs["event_time_index"]
+                                ) / self.sampling_rate
+                                # event_location[0, :, i] = (np.arange(self.feature_nt) - 3000 / self.feature_scale) / self.sampling_rate
+                                # print(event_location[i, 1:, mask].shape, event_location.shape, event_location[i][1:, mask].shape)
+                                event_location[i][1:, mask] = np.array([dx, dy, dz])[:, np.newaxis]
+                                event_location_mask[i, :] = mask
+
+                                ## station location
+                                station_location[i, 0] = round(
+                                    attrs["longitude"]
+                                    * np.cos(np.radians(attrs["latitude"]))
+                                    * self.degree2km,
+                                    2,
                                 )
+                                station_location[i, 1] = round(attrs["latitude"] * self.degree2km, 2)
+                                station_location[i, 2] =  round(-attrs["elevation_m"]/1e3, 2)
+
+                            std = np.std(waveforms, axis=1, keepdims=True)
+                            std[std == 0] = 1.0
+                            waveforms = (waveforms - np.mean(waveforms, axis=1, keepdims=True)) / std
+                            waveforms = waveforms.astype(np.float32)
+
                             yield event_id, {
-                                "event_id": event_id,
-                                "waveform": waveform,
-                                "phase_time": phase_time,
-                                "phase_index": phase_index,
-                                "phase_type": phase_type,
-                                "phase_polarity": phase_polarity,
-                                "begin_time": begin_time,
-                                "end_time": end_time,
-                                "event_time": event_time,
-                                "event_time_index": event_time_index,
-                                "event_location": event_location,
-                                "station_location": station_location,
+                                "data": torch.from_numpy(waveforms).float(),
+                                "phase_pick": torch.from_numpy(phase_pick).float(),
+                                "event_center": torch.from_numpy(event_center[:, ::self.feature_scale]).float(),
+                                "event_location": torch.from_numpy(event_location[:, :, ::self.feature_scale]).float(),
+                                "event_location_mask": torch.from_numpy(event_location_mask[:, ::self.feature_scale]).float(),
+                                "station_location": torch.from_numpy(station_location).float(),
                             }
+
+
+def generate_label(phase_list, label_width=[150, 150], nt=8192):
+    target = np.zeros([len(phase_list) + 1, nt], dtype=np.float32)
+
+    for i, (picks, w) in enumerate(zip(phase_list, label_width)):
+        for phase_time in picks:
+            t = np.arange(nt) - phase_time
+            gaussian = np.exp(-(t**2) / (2 * (w / 6) ** 2))
+            gaussian[gaussian < 0.1] = 0.0
+            target[i + 1, :] += gaussian
+
+    target[0:1, :] = np.maximum(0, 1 - np.sum(target[1:, :], axis=0, keepdims=True))
+
+    return target

upload.py

@@ -1,11 +0,0 @@
-from huggingface_hub import HfApi
-
-api = HfApi()
-
-# Upload all the content from the local folder to your remote Space.
-# By default, files are uploaded at the root of the repo
-api.upload_folder(
-    folder_path="./",
-    repo_id="AI4EPS/quakeflow_nc",
-    repo_type="space",
-)

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