Datasets:

angelolab
/

ark_example

	# 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 json
	import os

	import datasets
	import pathlib
	import glob
	import tifffile

	# 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"

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


	_URLS = {"base_dataset": f"{_URL_REPO}/data/fovs.zip"}

	"""
	https://huggingface.co/docs/datasets/dataset_script
	https://huggingface.co/docs/datasets/share
	https://huggingface.co/datasets/allenai/wmt22_african/blob/main/wmt22_african.py
	https://huggingface.co/docs/datasets/repository_structure
	"""

	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class ArkExample(datasets.GeneratorBasedBuilder):
	"""The Dataset consists of 12 FOVs"""

	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', 'base_dataset')
	# data = datasets.load_dataset('my_dataset', 'dev_dataset')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="base_dataset",
	version=VERSION,
	description="This dataset contains only the 12 FOVs.",
	),
	datasets.BuilderConfig(
	name="dev_dataset",
	version=VERSION,
	description="This dataset is a superset of the base_dataset, and contains intermediate data for all notebooks. \
	Therefore you can start at any notebook with this dataset.",
	),
	]

	DEFAULT_CONFIG_NAME = "base_dataset" # 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 information and typings for the dataset
	if (
	self.config.name == "base_dataset"
	): # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"fov": datasets.Sequence({"channel": datasets.Image()}),
	}
	)
	else: # This is an example to show how to have different features for "first_domain" and "second_domain"
	features = datasets.Features(
	{
	"sentence": datasets.Value("string"),
	"option2": datasets.Value("string"),
	"second_domain_answer": datasets.Value("string")
	# These are the features of your dataset like images, labels ...
	}
	)
	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]
	data_dir = dl_manager.download_and_extract(urls)
	return [
	datasets.SplitGenerator(
	name="BASE_DATASET",
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": pathlib.Path(data_dir) / "fovs"
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath: pathlib.Path):
	# 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.

	file_paths = list(filepath.rglob("*"))

	for fp in file_paths:
	if fp.suffix in [".tiff", ".tif"]:

	image_data = tifffile.imread(fp, key=0)
	if self.config.name == "base_dataset":
	yield fp.parent.stem, {
	"chan": image_data,
	"path": fp
	}

	# with open(filepath, encoding="utf-8") as f:
	# for key, row in enumerate(f):
	# data = json.loads(row)
	# if self.config.name == "first_domain":
	# # 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"],
	# }