PS_Alaska.py

# 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.
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"""P and S phase arrivals dataset for Alaska"""

import h5py
import csv
import os

import datasets

_PSAlaska_DESCRIPTION = """

"""

_ManualPick_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

_ManualPick_DESCRIPTION = """\
This dataset includes P and S phases recorded by the broadband stations in the Alaska Peninsula
"""

_PNTFIter1_CITATION = """
"""

_PNTFIter1_DESCRIPTION = """
This dataset includes P and S phases predicted by the PhaseNet-TF using model trained by the manualpick dataset
"""

_PNTFIter1Combined_CITATION = """
"""

_PNTFIter1Combined_DESCRIPTION = """
This dataset includes all P and S phases from PNTFiter1 dataset and all false negative arrivals of manualpick dataset
"""

_Data_URL = "/mnt/scratch/jieyaqi/alaska/final/PS_Alaska"


class PSAlaskaConfig(datasets.BuilderConfig):

    def __init__(self, description, data_url, citation, **kwargs):
        """BuilderConfig for PS_Alaska.
        Args:
          features: `list[string]`, list of the features that will appear in the
            feature dict. Should not include "label".
          data_url: `string`, url to download the zip file from.
          citation: `string`, citation for the data set.
          url: `string`, url for information about the data set.
          label_classes: `list[string]`, the list of classes for the label if the
            label is present as a string. Non-string labels will be cast to either
            'False' or 'True'.
          **kwargs: keyword arguments forwarded to super.
        """
        super(PSAlaskaConfig, self).__init__(
            version = datasets.Version("1.0.0"),
            **kwargs)
        self.description = description
        self.data_url = data_url
        self.citation = citation


class PSAlaskaDataset(datasets.GeneratorBasedBuilder):
    """P and S phase arrivals dataset for Alaska"""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        PSAlaskaConfig(
            name="ManualPick",
            description=_ManualPick_DESCRIPTION,
            data_url=_Data_URL+"/ManualPick",
            citation=_ManualPick_CITATION,
        ),
        PSAlaskaConfig(
            name="PNTFIter1",
            description=_PNTFIter1_DESCRIPTION,
            data_url=_Data_URL+"/PNTFIter1",
            citation=_PNTFIter1_CITATION,
        ),
        PSAlaskaConfig(
            name="PNTFIter1_combined",
            description=_PNTFIter1Combined_DESCRIPTION,
            data_url=_Data_URL+"/PNTFIter1_combined",
            citation=_PNTFIter1Combined_CITATION,
        ),
    ]

    DEFAULT_CONFIG_NAME = "PNTFIter1_combined"
    

    def _info(self):

        return datasets.DatasetInfo(
            description=_PSAlaska_DESCRIPTION + self.config.description,
            features=datasets.Features(
                {
                    "begin_time": datasets.Value("string"),
                    "end_time": datasets.Value("string"),
                    "component": datasets.Sequence(datasets.Value('string')),
                    "dt_s": datasets.Value("float"),
                    "event_id": datasets.Value("string"),
                    "station": datasets.Value("string"),
                    "network": datasets.Value("string"),
                    "phase_index": datasets.Sequence(datasets.Value('int32')),
                    "phase_time": datasets.Sequence(datasets.Value('string')),
                    "phase_type": datasets.Sequence(datasets.Value('string')),
                    "waveform": datasets.Array2D(shape=(3, 24000), dtype='float32'),
                }
            ),
            supervised_keys=("waveform", "phase_type"),
            citation=self.config.citation,
        )


    def _split_generators(self, dl_manager):

        urls = self.config.data_url
        data_dir = dl_manager.download_and_extract(urls)
        stationf = dl_manager.download_and_extract(_Data_URL, 'stations.csv')
        stationl = []
        eventl = []
        waveform_files = {}
        with open(stationf, newline='') as csvfile:
            r = csv.reader(csvfile, delimiter=',')
            next(r)
            for row in r:
                stationl.append(row[-1])

        with open(os.path.join(data_dir, 'catalogs.csv'), newline='') as csvfile:
            r = csv.reader(csvfile, delimiter=',')
            next(r)
            for row in r:
                eventl.append(row[3])

        for e in eventl:
            waveform_files[e] = os.path.join(data_dir, 'waveform', f'{e}.h5')

        return [
            datasets.SplitGenerator(
                name="full",
                gen_kwargs={
                    "stations": stationl,
                    "events": eventl,
                    "waveform_files": waveform_files
                },
            ),
        ]


    def _generate_examples(self, stations, events, waveform_files):
        
        for e in events:
            f = h5py.File(waveform_files[e], 'r')
            for sta in f[e].keys():
                key = f'{e}_{sta}'
                meta = f[e][sta].attrs
                yield key, {
                    "begin_time": meta['begin_time'],
                    "end_time": meta['end_time'],
                    "component": meta['component'],
                    "dt_s": meta['dt_s'],
                    "event_id": meta['event_id'],
                    "station": meta['station'],
                    "network": meta['network'],
                    "phase_index": meta['phase_index'],
                    "phase_time": meta['phase_time'],
                    "phase_type": meta['phase_type'],
                    "waveform": f[e][sta]
                }
            f.close()