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.


	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

	"""

	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_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/pixie/example_pixel_output_dir.zip",
	"example_cell_output_dir": "./data/pixie/example_cell_output_dir.zip",
	"spatial_lda": "./data/spatial_analysis/spatial_lda.zip",
	"post_clustering": "./data/post_clustering.zip",
	"ome_tiff": "./data/ome_tiff.zip",
	"ez_seg_data": "./data/ez_seg_data.zip"
	}

	_URL_DATASET_CONFIGS = {
	"segment_image_data": {"image_data": _URL_DATA["image_data"]},
	"cluster_pixels": {
	"image_data": _URL_DATA["image_data"],
	"cell_table": _URL_DATA["cell_table"],
	"deepcell_output": _URL_DATA["deepcell_output"],
	},
	"cluster_cells": {
	"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"],
	},
	"post_clustering": {
	"image_data": _URL_DATA["image_data"],
	"cell_table": _URL_DATA["cell_table"],
	"deepcell_output": _URL_DATA["deepcell_output"],
	"example_cell_output_dir": _URL_DATA["example_cell_output_dir"],
	},
	"fiber_segmentation": {
	"image_data": _URL_DATA["image_data"],
	},
	"LDA_preprocessing": {
	"image_data": _URL_DATA["image_data"],
	"cell_table": _URL_DATA["cell_table"],
	},
	"LDA_training_inference": {
	"image_data": _URL_DATA["image_data"],
	"cell_table": _URL_DATA["cell_table"],
	"spatial_lda": _URL_DATA["spatial_lda"],
	},
	"neighborhood_analysis": {
	"image_data": _URL_DATA["image_data"],
	"cell_table": _URL_DATA["cell_table"],
	"deepcell_output": _URL_DATA["deepcell_output"],
	},
	"pairwise_spatial_enrichment": {
	"image_data": _URL_DATA["image_data"],
	"cell_table": _URL_DATA["cell_table"],
	"deepcell_output": _URL_DATA["deepcell_output"],
	"post_clustering": _URL_DATA["post_clustering"],
	},
	"ome_tiff": {
	"ome_tiff": _URL_DATA["ome_tiff"],
	},
	"ez_seg_data": {
	"ez_seg_data": _URL_DATA["ez_seg_data"]
	}
	}


	# 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("0.0.5")

	# You will be able to load one or the other configurations in the following list with
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="segment_image_data",
	version=VERSION,
	description="This configuration contains data used by notebook 1 - Segment Image Data.",
	),
	datasets.BuilderConfig(
	name="cluster_pixels",
	version=VERSION,
	description="This configuration contains data used by notebook 2 - Pixel Clustering (Pixie Pipeline #1).",
	),
	datasets.BuilderConfig(
	name="cluster_cells",
	version=VERSION,
	description="This configuration contains data used by notebook 3 - Cell Clustering (Pixie Pipeline #2).",
	),
	datasets.BuilderConfig(
	name="post_clustering",
	version=VERSION,
	description="This configuration contains data used by notebook 4 - Post Clustering.",
	),
	datasets.BuilderConfig(
	name="fiber_segmentation",
	version=VERSION,
	description="This configuration contains data used by the Fiber Segmentation Notebook.",
	),
	datasets.BuilderConfig(
	name="LDA_preprocessing",
	version=VERSION,
	description="This configuration contains data used by the Spatial LDA - Preprocessing Notebook."
	),
	datasets.BuilderConfig(
	name="LDA_training_inference",
	version=VERSION,
	description="This configuration contains data used by the Spatial LDA - Training and Inference Notebook."
	),
	datasets.BuilderConfig(
	name="neighborhood_analysis",
	version=VERSION,
	description="This configuration contains data used by the Neighborhood Analysis Notebook."
	),
	datasets.BuilderConfig(
	name="pairwise_spatial_enrichment",
	version=VERSION,
	description="This configuration contains data used by the Pairwise Spatial Enrichment Notebook."
	),
	datasets.BuilderConfig(
	name="ome_tiff",
	version=VERSION,
	description="This configuration contains an OME-TIFF format of FOV1. Intended to be used with the OME-TIFF Conversion Notebook."
	),
	datasets.BuilderConfig(
	name="ez_seg_data",
	version=VERSION,
	description="This configuration contains the data used by the ezSegmenter notebook."
	)
	]

	def _info(self):
	# This is the name of the configuration selected in BUILDER_CONFIGS above
	if self.config.name in list(_URL_DATASET_CONFIGS.keys()):
	features = datasets.Features(
	{f: datasets.Value("string") for f in _URL_DATASET_CONFIGS[self.config.name].keys()}
	)
	else:
	ValueError(f"Dataset name is incorrect, options include {list(_URL_DATASET_CONFIGS.keys())}")
	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):
	# This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
	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