ark_example.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.

"""
This dataset contains example data for running through the multiplexed imaging data pipeline in
Ark Analysis: https://github.com/angelolab/ark-analysis
"""

import datasets
import pathlib

# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Ark Analysis Example Dataset},
author={Angelo Lab},
year={2022}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This dataset contains 11 Field of Views (FOVs), each with 22 channels. 
"""

_HOMEPAGE = "https://github.com/angelolab/ark-analysis"

_LICENSE = "https://github.com/angelolab/ark-analysis/blob/main/LICENSE"

# 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)
# _URL_REPO = "https://huggingface.co/datasets/angelolab/ark_example"


_URL_DATA = {
    "image_data": "./data/image_data.zip",
    "cell_table": "./data/segmentation/cell_table.zip",
    "deepcell_output": "./data/segmentation/deepcell_output.zip",
    "example_pixel_output_dir": "./data/segmentation/example_pixel_output_dir.zip"
}

_URL_DATASET_CONFIGS = {
    "nb1": {"image_data": _URL_DATA["image_data"]},
    "nb2": {
        "image_data": _URL_DATA["image_data"],
        "cell_table": _URL_DATA["cell_table"],
        "deepcell_output": _URL_DATA["deepcell_output"],
    },
    "nb3": {
        "image_data": _URL_DATA["image_data"],
        "cell_table": _URL_DATA["cell_table"],
        "deepcelL_output": _URL_DATA["deepcell_output"],
        "example_pixel_output_dir": _URL_DATA["example_pixel_output_dir"]
    }
}


"""
Dataset Fov renaming:

TMA2_R8C3 -> fov0
TMA6_R4C5 -> fov1
TMA7_R5C4 -> fov2
TMA10_R7C3 -> fov3
TMA11_R9C6 -> fov4
TMA13_R8C5 -> fov5
TMA17_R9C2 -> fov6
TMA18_R9C2 -> fov7
TMA21_R2C5 -> fov8
TMA21_R12C6 -> fov9
TMA24_R9C1 -> fov10
"""

# Note: Name of the dataset usually match the script name with CamelCase instead of snake_case
class ArkExample(datasets.GeneratorBasedBuilder):
    """The Dataset consists of 11 FOVs"""

    VERSION = datasets.Version("0.0.2")

    # 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', 'nb1')
    # data = datasets.load_dataset('my_dataset', 'nb2')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="nb1",
            version=VERSION,
            description="This dataset contains only the 12 FOVs, and their 22 channels.",
        ),
        datasets.BuilderConfig(
            name="nb2",
            version=VERSION,
            description="This dataset is a superset of the nb1 and contains data from notebook 1 in order to start with notebook 2. \
                Therefore you can start at any notebook with this dataset.",
        ),
    ]

    def _info(self):
        # This is the name of the configuration selected in BUILDER_CONFIGS above
        if self.config.name in ["nb1", "nb2", "nb3", "nb4"]:
            features = datasets.Features(
                {f: datasets.Value("string") for f in _URL_DATASET_CONFIGS[self.config.name].keys()}
            )
        else:
            ValueError("dataset name is incorrect.")
        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 = _URL_DATASET_CONFIGS[self.config.name]
        data_dirs = {}
        for data_name, url in urls.items():
            dl_path = pathlib.Path(dl_manager.download_and_extract(url))
            data_dirs[data_name] = dl_path

        return [
            datasets.SplitGenerator(
                name=self.config.name,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"dataset_paths": data_dirs},
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, dataset_paths):
        yield self.config.name, dataset_paths