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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 os
import datasets
import pathlib
import glob
import pyarrow
import pprint
# 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 = {
"image_data": "./data/image_data.zip",
"cell_table": "./data/segmentation/cell_table.zip",
"deepcell_output": "./data/segmentation/deepcell_output.zip",
}
_URL_DATASET_CONFIGS = {
"nb1": {"image_data": _URL_DATA["image_data"]},
"nb2": {
"image_data": _URL_DATA["image_data"],
"segmentation/cell_table": _URL_DATA["cell_table"],
"segmentation/deepcell_output": _URL_DATA["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 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: dict[str, pathlib.Path]):
yield self.config.name, dataset_paths
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