nist_lpbf_scan_tracks.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.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments
"""NIST LPBF Scan Tracks"""

import os

import datasets
import pickle


# # 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 = """\
Dataset from https://doi.org/10.18434/M3C37Q
"""

# 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 = "MIT"

# 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)
_URLS = {
    "powder_single_track_radiant_temperature": "https://huggingface.co/datasets/ppak10/NIST-LPBF-Scan-Tracks/resolve/main/data/powder_plate_1_single_line/radiant_temperature.pkl",
    "powder_single_track_camera_signal": "https://huggingface.co/datasets/ppak10/NIST-LPBF-Scan-Tracks/resolve/main/data/powder_plate_1_single_line/camera_signal.pkl",
    "powder_multiple_track_radiant_temperature": "https://huggingface.co/datasets/ppak10/NIST-LPBF-Scan-Tracks/resolve/main/data/powder_plate_2_pad/radiant_temperature.pkl",
    "powder_multiple_track_camera_signal": "https://huggingface.co/datasets/ppak10/NIST-LPBF-Scan-Tracks/resolve/main/data/powder_plate_2_pad/camera_signal.pkl",
    "bare_single_track_radiant_temperature": "https://huggingface.co/datasets/ppak10/NIST-LPBF-Scan-Tracks/resolve/main/data/powder_plate_6_bare_single_line_195_w_800_mm_s/radiant_temperature.pkl",
    "bare_single_track_camera_signal": "https://huggingface.co/datasets/ppak10/NIST-LPBF-Scan-Tracks/resolve/main/data/powder_plate_6_bare_single_line_195_w_800_mm_s/camera_signal.pkl",
    "bare_multiple_track_radiant_temperature": "https://huggingface.co/datasets/ppak10/NIST-LPBF-Scan-Tracks/resolve/main/data/powder_plate_7_bare_pad_195_w_800_mm_s/radiant_temperature.pkl",
    "bare_multiple_track_camera_signal": "https://huggingface.co/datasets/ppak10/NIST-LPBF-Scan-Tracks/resolve/main/data/powder_plate_7_bare_pad_195_w_800_mm_s/camera_signal.pkl",
}


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class Dataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("0.0.1")

    # 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="powder_single_track_radiant_temperature",
            version=VERSION,
            description="Radiant temperature from single track raster with powder"
        ),
        datasets.BuilderConfig(
            name="powder_single_track_camera_signal",
            version=VERSION,
            description="Camera signal from single track raster with powder"
        ),
        datasets.BuilderConfig(
            name="powder_multiple_track_radiant_temperature",
            version=VERSION,
            description="Radiant temperature from multiple track raster with powder"
        ),
        datasets.BuilderConfig(
            name="powder_multiple_track_camera_signal",
            version=VERSION,
            description="Camera signal from multiple track raster with powder"
        ),
        datasets.BuilderConfig(
            name="bare_single_track_radiant_temperature",
            version=VERSION,
            description="Radiant temperature from single track raster without powder"
        ),
        datasets.BuilderConfig(
            name="bare_single_track_camera_signal",
            version=VERSION,
            description="Camera signal from single track raster without powder"
        ),
        datasets.BuilderConfig(
            name="bare_multiple_track_radiant_temperature",
            version=VERSION,
            description="Radiant temperature from multiple track raster without powder"
        ),
        datasets.BuilderConfig(
            name="bare_multiple_track_camera_signal",
            version=VERSION,
            description="Camera signal from multiple track raster without powder"
        ),
    ]

    DEFAULT_CONFIG_NAME = "powder_single_track_radiant_temperature"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        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 = datasets.Features({
                "sentence": datasets.Value("string"),
            }),
            # 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]
        downloaded_files = dl_manager.download_and_extract(_URLS)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    # "filepath": os.path.join(data_dir, "train.jsonl"),
                    # "split": "train",
                    "files": downloaded_files
                },
            ),
            # 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, files):
        # 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 index, file in enumerate(files):
            with open(file, "rb") as f:
                track = pickle.load(f)
            yield index, {
                "track": track
            }

        # with open(filepath, encoding="utf-8") as f:
        #     for key, row in enumerate(f):
        #         data = json.loads(row)
        #         if self.config.name == "raw":
        #             # Yields examples as (key, example) tuples
        #             yield key, {
        #                 "sentence": data["sentence"],
        #                 "option1": data["option1"],
        #                 "answer": "" if split == "test" else data["answer"],
        #             }
        #         else:
        #             yield key, {
        #                 "sentence": data["sentence"],
        #                 "option2": data["option2"],
        #                 "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
        #             }