# 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.""" | |
import csv | |
import json | |
import os | |
import h5py | |
import numpy as np | |
import torch | |
import fsspec | |
from glob import glob | |
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 = {A great new dataset}, | |
author={huggingface, Inc. | |
}, | |
year={2020} | |
} | |
""" | |
# 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/data" | |
_URLS = { | |
"NCEDC": [f"{_REPO}/ncedc_event_dataset_{i:03d}.h5" for i in range(37)] | |
} | |
# 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") | |
# 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') | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig(name="NCEDC", version=VERSION, description="This part of my dataset covers a first domain"), | |
] | |
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 | |
features=datasets.Features( | |
{ | |
"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.Value("float32")], | |
"station_location": datasets.Array2D(shape=(self.num_stations, 3), dtype="float32"), | |
} | |
) | |
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) | |
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" | |
# }, | |
# ), | |
] | |
degree2km = 111.32 | |
nt = 8192 | |
feature_nt = 512 | |
feature_scale = int(nt / feature_nt) | |
sampling_rate=100.0 | |
num_stations = 10 | |
# 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: | |
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 len(station_ids) < num_stations: | |
continue | |
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) | |
yield event_id, { | |
"waveform": torch.from_numpy(waveforms).float(), | |
"phase_pick": torch.from_numpy(phase_pick).float(), | |
"event_location": event_location, | |
"station_location": station_location, | |
} | |
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 |