fghj

.gitignore

@@ -1,8 +1,2 @@
 .DS_Store
-.vscode/
-
-.python-version
-LICENSE.txt
-pyproject.toml
-requirements-dev.lock
-requirements.lock
+.vscode/

ark_example.py

@@ -14,27 +14,18 @@
 
 """
 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
-
+Ark Analysis: https://github.com/angelolab/ark-analysis
 """
 
+import json
+import os
+
 import datasets
 import pathlib
+import glob
+import tifffile
+import xarray as xr
+import numpy as np
 
 # Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
@@ -55,145 +46,84 @@ _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"
 
-_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"]
-    }
-}
+_URLS = {"base_dataset": f"{_URL_REPO}/data/input_data.zip"}
 
+"""
+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
+"""
 
-# Note: Name of the dataset usually match the script name with CamelCase instead of snake_case
+# 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("0.0.5")
+    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="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",
+            name="base_dataset",
             version=VERSION,
-            description="This configuration contains data used by notebook 3 - Cell Clustering (Pixie Pipeline #2).",
+            description="This dataset contains only the 12 FOVs.",
         ),
         datasets.BuilderConfig(
-            name="post_clustering",
+            name="dev_dataset",
             version=VERSION,
-            description="This configuration contains data used by notebook 4 - Post Clustering.",
+            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.",
         ),
-        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."
-        )
     ]
 
+    DEFAULT_CONFIG_NAME = (
+        "base_dataset"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    )
+
     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()):
+        if self.config.name == "base_dataset":
+            features = datasets.Features(
+                {
+                    "Channel Data": datasets.Sequence(datasets.Image()),
+                    "Channel Names": datasets.Sequence(datasets.Value("string")),
+                    "Data Path": datasets.Value("string"),
+                }
+            )
+        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
             features = datasets.Features(
-                {f: datasets.Value("string") for f in _URL_DATASET_CONFIGS[self.config.name].keys()}
+                {
+                    "sentence": datasets.Value("string"),
+                    "option2": datasets.Value("string"),
+                    "second_domain_answer": datasets.Value("string")
+                    # These are the features of your dataset like images, labels ...
+                }
             )
-        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,
@@ -211,21 +141,50 @@ class ArkExample(datasets.GeneratorBasedBuilder):
         )
 
     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
+        # 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=self.config.name,
+                name="base_dataset",
                 # These kwargs will be passed to _generate_examples
-                gen_kwargs={"dataset_paths": data_dirs},
+                gen_kwargs={"filepath": pathlib.Path(data_dir)},
             ),
         ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, dataset_paths):
-        yield self.config.name, dataset_paths
+    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 TMA FOV Name
+            fov_name = fp.stem
+
+            # Get all channels per TMA FOV
+            channel_paths = fp.glob("*.tiff")
+
+            chan_data = []
+            chan_names = []
+            for chan in channel_paths:
+                chan_name = chan.stem
+                chan_image: np.ndarray = tifffile.imread(chan)
+
+                chan_data.append(chan_image)
+                chan_names.append(chan_name)
+
+            if self.config.name == "base_dataset":
+                yield fov_name, {
+                    "Channel Data": chan_data,
+                    "Channel Names": chan_names,
+                    "Data Path": filepath.as_posix(),
+                }

data/image_data.zip

@@ -1,3 +0,0 @@
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-oid sha256:4d1f29e53a40bb162e795f68906095131d88647f70a6e080ca3bac749a3f6174
-size 373529148

data/{ez_seg_data.zip → input_data.zip}

@@ -1,3 +1,3 @@
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-oid sha256:7251a3c98e53ac00858560c1f234208228f14198b915aabd6715cac0c8d5d74c
-size 14749038
+oid sha256:37eed9f786570426ee8e7cbd9447ef98c82830b7fee064e56cb79b360887489a
+size 400326580

data/ome_tiff.zip

@@ -1,3 +0,0 @@
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-oid sha256:82d3278f7d3e27e128f3ec3607934b933f68e3a3aab1298ce1e6dc5d0cf402e2
-size 37043244

data/pixie/example_cell_output_dir.zip

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-oid sha256:a76c024385ae04c9f21229a053c3623429d9fa26241265fc304e92716eb5fe3d
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data/pixie/example_pixel_output_dir.zip

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-size 1384928027

data/post_clustering.zip

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-oid sha256:d969d298c2a42130f17955b13f5b1b2c1e8687c180e1e27ce9205bc9ea8d33a1
-size 9209730

data/segmentation/cell_table.zip

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-oid sha256:4c453a9a6fe6f21d411ecd9e60278e4360d8df4b876f9b382d2d139ae62ade5d
-size 16724377

data/segmentation/deepcell_output.zip

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-oid sha256:92c1bf023c58187264d31848addb602d8aeb7f7545ef0fa370c73a5f35325a15
-size 1571004

data/spatial_analysis/spatial_lda.zip

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-oid sha256:eb7bdab9c054d4fff8504c51e9b185e02a8cc13e15287bafdee2fbb7ddcd66b4
-size 3296785