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