add scripts

example.py

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+# %%
+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

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+# %%
+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
+
+# %%

quakeflow_nc.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# TODO: Address all TODOs and remove all explanatory comments
+# Lint as: python3
+"""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
+
+# 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}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+A dataset of earthquake waveforms organized by earthquake events and based on the HDF5 format.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_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",
+]
+_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:]],
+}
+
+
+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
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+
+# 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")
+
+    nt = 8192
+
+    # 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.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # 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"
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = (
+        "station_test"  # 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(
+                {
+                    "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"),
+                    "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(
+                {
+                    "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"))),
+                },
+            )
+        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,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # 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")
+
+    # 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.
+
+        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:
+                            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,
+                            }

upload.py

@@ -0,0 +1,11 @@
+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",
+)