add batched configuration

.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

@@ -5,58 +5,66 @@ 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.
+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: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
+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 = 2
-  |  |- Dataset: /nc71111584/NC.MCB..HH (shape:(3, 12000))
+  |  |-* num_stations = 5
+  |  |- Dataset: /nc100012/NC.MRS..EH (shape:(3, 12000))
   |  |  |- (dtype=float32)
-  |  |  |  |-* azimuth = 233.0
-  |  |  |  |-* component = ['E' 'N' 'Z']
-  |  |  |  |-* distance_km = 1.9
+  |  |  |  |-* azimuth = 265.0
+  |  |  |  |-* component = ['Z']
+  |  |  |  |-* distance_km = 39.1
   |  |  |  |-* dt_s = 0.01
-  |  |  |  |-* elevation_m = 2391.0
-  |  |  |  |-* emergence_angle = 159.0
-  |  |  |  |-* event_id = ['nc71111584' 'nc71111584']
-  |  |  |  |-* latitude = 37.6444
+  |  |  |  |-* elevation_m = 3680.0
+  |  |  |  |-* emergence_angle = 93.0
+  |  |  |  |-* event_id = ['nc100012' 'nc100012']
+  |  |  |  |-* latitude = 37.5107
   |  |  |  |-* location = 
-  |  |  |  |-* longitude = -118.8968
+  |  |  |  |-* longitude = -118.8822
   |  |  |  |-* network = NC
-  |  |  |  |-* phase_index = [3000 3101]
+  |  |  |  |-* phase_index = [3274 3802]
   |  |  |  |-* 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_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 = [2.82143   3.055604  1.8412642]
-  |  |  |  |-* station = MCB
+  |  |  |  |-* snr = [0.         0.         1.98844361]
+  |  |  |  |-* station = MRS
   |  |  |  |-* unit = 1e-6m/s
-  |  |- Dataset: /nc71111584/NC.MCB..HN (shape:(3, 12000))
+  |  |- Dataset: /nc100012/NN.BEN.N1.EH (shape:(3, 12000))
   |  |  |- (dtype=float32)
-  |  |  |  |-* azimuth = 233.0
-  |  |  |  |-* component = ['E' 'N' 'Z']
+  |  |  |  |-* 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
   ......
   ```
 
@@ -65,8 +73,7 @@ Waveform data, metadata, or data products for this study were accessed through t
 ### Requirements
 - datasets
 - h5py
-- fsspec
-- pytorch
+- torch (for PyTorch)
 
 ### Usage
 Import the necessary packages:
@@ -74,87 +81,126 @@ 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: 
-- "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:
+We have 2 configurations for the dataset: `NCEDC` and `NCEDC_full_size`. They all return event-based samples one by one. But `NCEDC` returns samples with 10 stations each, while `NCEDC_full_size` return samples with stations same as the original data.
+
+The sample of `NCEDC` is a dictionary with the following keys:
+- `waveform`: the waveform with shape `(3, nt, n_sta)`, the first dimension is 3 components, the second dimension is the number of time samples, the third dimension is the number of stations
+- `phase_pick`: the probability of the phase pick with shape `(3, nt, n_sta)`, 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 `(n_sta, 3)`, the first dimension is latitude, longitude and depth
+
+Because Huggingface datasets only support dynamic size on first dimension, so the sample of `NCEDC_full_size` is a dictionary with the following keys:
+- `waveform`: the waveform with shape `(n_sta, 3, nt)`, 
+- `phase_pick`: the probability of the phase pick with shape `(n_sta, 3, nt)`
+- `event_location`: the event location with shape `(4,)`
+- `station_location`: the station location with shape `(n_sta, 3)`, the first dimension is latitude, longitude and depth
+
+The default configuration is `NCEDC`. 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")
+# to load "NCEDC"
+quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", split="train")
 # or
-quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="station_test", split="test")
+quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="NCEDC", split="train")
+
+# to load "NCEDC_full_size"
+quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="NCEDC_full_size", split="train")
+```
 
-# to load "event" with train split
-quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="event", split="train")
+If you want to use the first several shards of the dataset, you can download the script `quakeflow_nc.py` and change the code as 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 (Don't forget to specify the argument `name`):
+```python
+# don't forget to specify the script path
+quakeflow_nc = datasets.load_dataset("path_to_script/quakeflow_nc.py", split="train")
+quakeflow_nc
 ```
 
-#### Example loading the dataset
+#### Usage for `NCEDC`
+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")
+quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="NCEDC", split="train")
+quakeflow_nc = quakeflow_nc.to_iterable_dataset()
+# 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():
-        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=4)
 
 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
+```
+
+#### Usage for `NCEDC_full_size`
+
+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="train", name="NCEDC_full_size")
+
+# 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
+quakeflow_nc = quakeflow_nc.map(lambda x: {key: torch.from_numpy(np.array(value, dtype=np.float32)) for key, value in x.items()})
+def reorder_keys(example):
+    example["waveform"] = example["waveform"].permute(1,2,0).contiguous()
+    example["phase_pick"] = example["phase_pick"].permute(1,2,0).contiguous()
+    return example
+    
+quakeflow_nc = quakeflow_nc.map(reorder_keys)
+
+try:
+    isinstance(quakeflow_nc, torch.utils.data.IterableDataset)
+except:
+    raise Exception("quakeflow_nc is not an IterableDataset")
+
+data_loader = DataLoader(
+    quakeflow_nc,
+    batch_size=1,
+    num_workers=num_workers,
+)
+
+for batch in quakeflow_nc:
+    print("\nIterable test\n")
+    print(batch.keys())
+    for key in batch.keys():
+        print(key, batch[key].shape, batch[key].dtype)
+    break
+
+for batch in data_loader:
+    print("\nDataloader test\n")
+    print(batch.keys())
+    for key in batch.keys():
+        batch[key] = batch[key].squeeze(0)
+        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 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b51df5987a2a05e44e0949b42d00a28692109da521911c55d2692ebfad0c54d7
-size 9355127

models/phasenet_plus_events.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f686ebf8da632b71a947e4ee884c76f30a313ae0e9d6e32d1f675828884a95f7
-size 7381331

models/phasenet_plus_picks.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:83d241a54477f722cd032efe8368a653bba170e1abebf3d9097d7756cfd54b23
-size 9987053

models/phasenet_pt_picks.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bb7ea98484b5e6e1c4c79ea5eb1e38bce43e87b546fc6d29c72d187a6d8b1d00
-size 8715799

picks.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:52f077ae9f94481d4b80f37c9f15038ee1e3636d5da2da3b1d4aaa2991879cc3
-size 422247029

picks_test.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bb09f0ac169bf451cfcfb4547359756cb1a53828bf4074971d9160a3aa171f38
-size 21850235

picks_train.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d22c5d5eb1c27a723525c657c1308a3b643d6f3e716eb1c43e064b7a87bb0819
-size 400397230

quakeflow_nc.py

@@ -17,21 +17,22 @@
 """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 datasets
+
 
 # TODO: Add BibTeX citation
 # Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
 @InProceedings{huggingface:dataset,
-title = {NCEDC dataset for QuakeFlow},
-author={Zhu et al.},
-year={2023}
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
 }
 """
 
@@ -50,74 +51,38 @@ _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"
-_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",
-]
+_REPO = "https://huggingface.co/datasets/AI4EPS/quakeflow_nc/resolve/main/data"
 _URLS = {
-    "station": [f"{_REPO}/{x}" for x in _FILES],
-    "event": [f"{_REPO}/{x}" for x in _FILES],
-    "station_train": [f"{_REPO}/{x}" for x in _FILES[:-1]],
-    "event_train": [f"{_REPO}/{x}" for x in _FILES[:-1]],
-    "station_test": [f"{_REPO}/{x}" for x in _FILES[-1:]],
-    "event_test": [f"{_REPO}/{x}" for x in _FILES[-1:]],
+    "NCEDC": [f"{_REPO}/ncedc_event_dataset_{i:03d}.h5" for i in range(37)],
+    "NCEDC_full_size": [f"{_REPO}/ncedc_event_dataset_{i:03d}.h5" for i in range(37)],
 }
 
-
 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
 class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
     """QuakeFlow_NC: A dataset of earthquake waveforms organized by earthquake events and based on the HDF5 format."""
-
+    
     VERSION = datasets.Version("1.1.0")
 
+    degree2km = 111.32
     nt = 8192
-
+    feature_nt = 512
+    feature_scale = int(nt / feature_nt)
+    sampling_rate=100.0
+    num_stations = 10
+    
     # This is an example of a dataset with multiple configurations.
     # If you don't want/need to define several sub-sets in your dataset,
     # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
@@ -129,80 +94,36 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
     # You will be able to load one or the other configurations in the following list with
     # data = datasets.load_dataset('my_dataset', 'first_domain')
     # data = datasets.load_dataset('my_dataset', 'second_domain')
-
+    
     # default config, you can change batch_size and num_stations_list when use `datasets.load_dataset`
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="station", version=VERSION, description="yield station-based samples one by one of whole dataset"
-        ),
-        datasets.BuilderConfig(
-            name="event", version=VERSION, description="yield event-based samples one by one of whole dataset"
-        ),
-        datasets.BuilderConfig(
-            name="station_train",
-            version=VERSION,
-            description="yield station-based samples one by one of training dataset",
-        ),
-        datasets.BuilderConfig(
-            name="event_train", version=VERSION, description="yield event-based samples one by one of training dataset"
-        ),
-        datasets.BuilderConfig(
-            name="station_test", version=VERSION, description="yield station-based samples one by one of test dataset"
-        ),
-        datasets.BuilderConfig(
-            name="event_test", version=VERSION, description="yield event-based samples one by one of test dataset"
-        ),
+        datasets.BuilderConfig(name="NCEDC", version=VERSION, description="yield event-based samples one by one, the number of sample stations is fixed(default: 10)"),
+        datasets.BuilderConfig(name="NCEDC_full_size", version=VERSION, description="yield event-based samples one by one, the number of sample stations is the same as the number of stations in the event"),
     ]
 
-    DEFAULT_CONFIG_NAME = (
-        "station_test"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-    )
+    DEFAULT_CONFIG_NAME = "NCEDC"  # It's not mandatory to have a default configuration. Just use one if it make sense.
 
     def _info(self):
         # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if (
-            (self.config.name == "station")
-            or (self.config.name == "station_train")
-            or (self.config.name == "station_test")
-        ):
-            features = datasets.Features(
+        if self.config.name=="NCEDC":
+            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"),
+                    "waveform": datasets.Array3D(shape=(3, self.nt, self.num_stations), dtype='float32'),
+                    "phase_pick": datasets.Array3D(shape=(3, self.nt, self.num_stations), 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(
+                    "station_location": datasets.Array2D(shape=(self.num_stations, 3), dtype="float32"),
+                })
+            
+        elif self.config.name=="NCEDC_full_size":
+            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"),
+                    "waveform": datasets.Array3D(shape=(None, 3, self.nt), dtype='float32'),
+                    "phase_pick": datasets.Array3D(shape=(None, 3, self.nt), dtype='float32'),
                     "event_location": datasets.Sequence(datasets.Value("float32")),
-                    "station_location": datasets.Sequence(datasets.Sequence(datasets.Value("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,
@@ -228,135 +149,104 @@ 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)
-
-        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"},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={"filepath": files[-1:], "split": "test"},
-                ),
-            ]
-        elif self.config.name == "station_train" or self.config.name == "event_train":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={"filepath": files, "split": "train"},
-                ),
-            ]
-        elif self.config.name == "station_test" or self.config.name == "event_test":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={"filepath": files, "split": "test"},
-                ),
-            ]
-        else:
-            raise ValueError("config.name is not in BUILDER_CONFIGS")
+        files = dl_manager.download_and_extract(urls)
+        # files = ["./data/ncedc_event_dataset_000.h5"]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": files,
+                    "split": "train",
+                },
+            ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.VALIDATION,
+            #     # These kwargs will be passed to _generate_examples
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "dev.jsonl"),
+            #         "split": "dev",
+            #     },
+            # ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.TEST,
+            #     # These kwargs will be passed to _generate_examples
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "test.jsonl"),
+            #         "split": "test"
+            #     },
+            # ),
+        ]
+        
+   
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath, split):
         # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-
+        num_stations = self.num_stations
         for file in filepath:
-            print(f"\nReading {file}")
-            with fsspec.open(file, "rb") as fs:
-                with h5py.File(fs, "r") as fp:
-                    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:
+            with h5py.File(file, "r") as fp:
+                # for event_id in sorted(list(fp.keys())):
+                for event_id in fp.keys():
+                    event = fp[event_id]
+                    station_ids = list(event.keys())
+                    
+                    if self.config.name=="NCEDC":
+                        if len(station_ids) < num_stations:
                             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"]
-                                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,
-                                }
-
-                        elif (
-                            (self.config.name == "event")
-                            or (self.config.name == "event_train")
-                            or (self.config.name == "event_test")
-                        ):
-
-                            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]
-                                )
-                            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,
-                            }
+                        else:
+                            station_ids = np.random.choice(station_ids, num_stations, replace=False)
+
+                    waveforms = np.zeros([3, self.nt, len(station_ids)])
+                    phase_pick = np.zeros_like(waveforms)
+                    attrs = event.attrs
+                    event_location = [attrs["longitude"], attrs["latitude"], attrs["depth_km"], attrs["event_time_index"]]
+                    station_location = []
+                    
+                    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)
+                        station_location.append([attrs["longitude"], attrs["latitude"], -attrs["elevation_m"]/1e3])
+                        
+                    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)
+                    
+                    if self.config.name=="NCEDC":
+                        yield event_id, {
+                            "waveform": 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=="NCEDC_full_size":
+                        
+                        yield event_id, {
+                            "waveform": torch.from_numpy(waveforms).float().permute(2,0,1),
+                            "phase_pick": torch.from_numpy(phase_pick).float().permute(2,0,1),
+                            "event_location": torch.from_numpy(np.array(event_location)).float(),
+                            "station_location": torch.from_numpy(np.array(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",
-)

waveform.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:77fb8b0bb040e1412a183a217dcbc1aa03ceb86b42db39ac62afe922a1673889
-size 20016390

waveform_h5/1987.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8afb94aafbf79db2848ae9c2006385c782493a97e6c71c1b8abf97c5d53bfc9d
-size 7744528

waveform_h5/1988.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c1398baca3f539e52744f83625b1dbb6f117a32b8d7e97f6af02a1f452f0dedd
-size 46126800

waveform_h5/1989.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:533cd50fe365de8c050f0ffd4a90b697dc6b90cb86c8199ec0172316eab2ddaa
-size 48255208

waveform_h5/1990.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f5a282a9a8c47cf65d144368085470940660faeb0e77cea59fff16af68020d26
-size 60092656

waveform_h5/1991.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5ba897d96eb92e8684b52a206e94a500abfe0192930f971ce7b1319c0638d452
-size 62332336

waveform_h5/1992.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d00021f46956bf43192f8c59405e203f823f1f4202c720efa52c5029e8e880b8
-size 67360896

waveform_h5/1993.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:eec41dd0aa7b88c81fa9f9b5dbcaab80e1c7bc8f6c144bd81761941278c57b4f
-size 706087936

waveform_h5/1994.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b1cd002f20573636eaf101a30c5bac477edda201aba3af68be358756543ed48a
-size 609524864

waveform_h5/1995.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:948f19d71520a0dd25574be300f70e62c383e319b07a7d7182fca1dcfa9d61ee
-size 1728452872

waveform_h5/1996.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:23654b6f9c3a4c5a0aa56ed13ba04e943a94b458a51ac80ec1d418e9aa132840
-size 1752242680

waveform_h5/1997.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d1c0f4c8146fc8ff27c8a47a942b967a97bd2835346203e6de74ca55dd522616
-size 2661543208

waveform_h5/1998.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1afac9c1a33424b739d26261ac2e9a4520be9c86c57bae4c8fe1a7a422356e45
-size 2070489120

waveform_h5/1999.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2f2595a1919a5435148cdcf2cfa1501ce5edb53878d471500b13936f0f6f558c
-size 2300297608

waveform_h5/2000.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:250fd52d9f8dd17a8bfb58a3ecfef25d62b0a1adf67f6fe6f2b446e9f72caf7a
-size 434865160

waveform_h5/2001.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d70dea6156b32057760f91742f7a05a336e4f63b1f793408b5e7aad6a15551e5
-size 919203704

waveform_h5/2002.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f88c4c5960741a8d354db4a7324d56ef8750ab93aa1d9b11fc80d0c497d8d6ae
-size 2445812792

waveform_h5/2003.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:943d649f1a8a0e3989d2458be68fbf041058a581c4c73f8de39f1d50d3e7b35c
-size 3618485352

waveform_h5/2004.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ed1ba66e10ba5c165568ac13950a1728927ba49b33903a0df42c3d9965a16807
-size 6158740712

waveform_h5/2005.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c816d75b172148763b19e60c1469c106c1af1f906843c3d6d94e603e02c2b6cb
-size 2994468240

waveform_h5/2006.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:521e6b0ce262461f87b4b0a78ac6403cfbb597d6ace36e17f92354c456a30447
-size 2189511664

waveform_h5/2007.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ae6654c213fb4838d6a732b2c8d936bd799005b2a189d64f2d74e3767c0c503a
-size 4393926088

waveform_h5/2008.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d8163aee689448c260032df9b0ab9132a5b46f0fee88a4c1ca8f4492ec5534d6
-size 3964283536

waveform_h5/2009.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6702c2d3951ddf1034f1886a79e8c5a00dfa47c88c84048edc528f047a2337b5
-size 4162296168

waveform_h5/2010.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2f2de7c07f088a32ea7ae71c2107dfd121780a47d3e3f23e5c98ddb482c6ce71
-size 4547184704

waveform_h5/2011.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:520d62f3a94f1b4889f583196676fe2eccb6452807461afc93432dca930d6052
-size 5633641952

waveform_h5/2012.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:98b90529df4cbff7f21cd233d482454eaeac77b81117720ca7fe6c2697819071
-size 9520058832

waveform_h5/2013.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e6f1030ff4ebe488ef9072ec984c91024a8be4ecdbe7e9af47c6e65de942c2fe
-size 8380878704

waveform_h5/2014.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a63f5e6d7d5bca552dcc99053753603dfa3109a6a080f8402f843ef688927d4c
-size 12088815344

waveform_h5/2015.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:42be6994ad27eb8aee241f5edfb4ed0ee69aa3460397325cc858224ba9dd9721
-size 8536767520

waveform_h5/2016.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6e706aefd38170da41196974fc92e457d0dc56948a63640a37cea4a86a297843
-size 9287201016

waveform_h5/2017.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e20f8e5a3f5ec8927e5d44e722987461ef08c9ceb33ab982038528e9000d5323
-size 8627205152

waveform_h5/2018.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ad6e83734ff1e24ad91b17cb6656766861ae9fb30413948579d762acc092e66a
-size 7158598240