example_dataset_updates

ark_example.py

@@ -14,18 +14,27 @@
 
 """
 This dataset contains example data for running through the multiplexed imaging data pipeline in
-Ark Analysis: https://github.com/angelolab/ark-analysis
-"""
+Ark Analysis: https://github.com/angelolab/ark-analysis.
+
 
-import json
-import os
+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
-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 = """\
@@ -46,35 +55,46 @@ _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_REPO = "https://huggingface.co/datasets/angelolab/ark_example"
 
 
-_URLS = {"base_dataset": f"{_URL_REPO}/data/input_data.zip"}
+_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",
+}
 
-"""
-Dataset Fov renaming:
+_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"],
+    },
+}
 
-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
+# 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("0.0.1")
+    VERSION = datasets.Version("0.0.3")
 
     # This is an example of a dataset with multiple configurations.
     # If you don't want/need to define several sub-sets in your dataset,
@@ -85,45 +105,42 @@ class ArkExample(datasets.GeneratorBasedBuilder):
     # 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",
+            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 dataset contains only the 12 FOVs.",
+            description="This configuration contains data used by notebook 2 - Pixel Clustering (Pixie Pipeline #1).",
         ),
         datasets.BuilderConfig(
-            name="dev_dataset",
+            name="cluster_cells",
             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.",
+            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.",
         ),
     ]
 
-    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 == "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"
+        if self.config.name in [
+            "segment_image_data",
+            "cluster_pixels",
+            "cluster_cells",
+            "post_clustering",
+        ]:
             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 ...
-                }
+                {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,
@@ -141,50 +158,21 @@ class ArkExample(datasets.GeneratorBasedBuilder):
         )
 
     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)
+        # 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="base_dataset",
+                name=self.config.name,
                 # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": pathlib.Path(data_dir)},
+                gen_kwargs={"dataset_paths": data_dirs},
             ),
         ]
 
     # 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 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(),
-                }
+    def _generate_examples(self, dataset_paths):
+        yield self.config.name, dataset_paths

data/{input_data.zip → image_data.zip}

@@ -1,3 +1,3 @@
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-size 400326580
+oid sha256:8aeaa113900f5067f2cef3d5533c3c0b6ae5538dd86f952193b6271135c0e2a0
+size 400230929

data/pixie/example_cell_output_dir.zip

@@ -0,0 +1,3 @@
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+oid sha256:4db7ebdd0aa88371ce348a4a5c5f851077cb3bffa1289c014173a0f1492c0006
+size 86425942

data/pixie/example_pixel_output_dir.zip

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+oid sha256:4b7f4a006fa021d0547592d3e290f122f342b1d6330107b812b0dd6bf5659a14
+size 1494868926

data/segmentation/cell_table.zip

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+oid sha256:82b8d88726130899575f97636539f601f0dedcead4b167cfc3a17964a9f4d76c
+size 15162294

data/segmentation/deepcell_output.zip

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+size 1552387