# 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 os import datasets import pathlib import glob # 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" _URL_DATA = { "input_data": "data/input_data.zip", "segmentation/cell_table": "data/segmentation/cell_table.zip", "segmentation/deepcell_output": "data/segmentation/deepcell_output.zip", } _URL_DATASET_CONFIGS = { "nb1": {"input_data": _URL_DATA["input_data"]}, "nb2": { "input_data": _URL_DATA["input_data"], "segmentation/cell_table": _URL_DATA["segmentation/cell_table"], "segmentation/deepcell_output": _URL_DATA["segmentation/deepcell_output"], }, } """ 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 """ # TODO: 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 == "nb1": features = datasets.Features({"Data Path": datasets.Value("string")}) elif self.config.name == "nb2": features = datasets.Features({"Data Path": datasets.Value("string")}) else: features = datasets.Features({"Data Path": datasets.Value("string")}) 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_dir = dl_manager.download_and_extract(urls) return [ datasets.SplitGenerator( name=self.config.name, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": pathlib.Path(data_dir)}, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath: pathlib.Path): # Get all TMA paths file_paths = list(pathlib.Path(filepath / "input_data").glob("*")) # Loop over all the TMAs for fp in file_paths: # Get the file Name fn = fp.stem if self.config.name == "fovs": yield fn, { "Data Path": filepath.as_posix(), }