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brain-structure.py

@@ -2,13 +2,13 @@ import os
 import json
 import logging
 import datasets
+from pathlib import Path
 
 logger = logging.getLogger(__name__)
 
 _DESCRIPTION = """
-This dataset contains T1-weighted .nii.gz structural MRI scans in a BIDS-like arrangement.
-Each scan has an associated JSON sidecar with metadata, including a 'split' field indicating
-whether it's train, validation, or test.
+A collection of T1-weighted .nii.gz structural MRI scans in a BIDS-like arrangement,
+with JSON sidecar metadata indicating train/validation/test splits.
 """
 
 _CITATION = """
@@ -25,44 +25,40 @@ _CITATION = """
 _HOMEPAGE = "https://huggingface.co/datasets/radiata-ai/brain-structure"
 _LICENSE = "ODC-By v1.0"
 
+# The "resolve/main/data.zip" part ensures it grabs data.zip from your 'main' branch.
+_DATA_URL = "https://huggingface.co/datasets/radiata-ai/brain-structure/resolve/main/data.zip"
+
 
 class BrainStructureConfig(datasets.BuilderConfig):
-    """
-    Configuration class for the Brain-Structure dataset.
-    """
+    """Configuration for Brain-Structure dataset (if you need multiple, define them here)."""
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
 
 
 class BrainStructure(datasets.GeneratorBasedBuilder):
     """
-    A dataset loader for T1 .nii.gz files plus JSON sidecars indicating splits
-    (train, validation, test).
+    A dataset loader for T1 .nii.gz files plus JSON sidecars stored in a single ZIP.
 
-    Examples of how users typically load this dataset:
-
-    >>> from datasets import load_dataset
-    >>> ds_val = load_dataset("radiata-ai/brain-structure", split="validation", trust_remote_code=True)
-    >>> ds_train = load_dataset("./brain-structure", split="train")  # local clone
+    Usage:
+        ds_train = load_dataset("radiata-ai/brain-structure", split="train", trust_remote_code=True)
     """
 
     VERSION = datasets.Version("1.0.0")
-
     BUILDER_CONFIGS = [
         BrainStructureConfig(
-            name="all",
+            name="default",
             version=VERSION,
-            description="All structural MRI data in a BIDS-like arrangement, labeled with train/val/test splits."
-        ),
+            description="Structural MRIs with sidecar metadata. Splits (train/val/test) indicated in the sidecars.",
+        )
     ]
-    DEFAULT_CONFIG_NAME = "all"
+    DEFAULT_CONFIG_NAME = "default"
 
     def _info(self):
-        """
-        Returns DatasetInfo, including the feature schema and other metadata.
-        """
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
             features=datasets.Features(
                 {
                     "id": datasets.Value("string"),
@@ -88,86 +84,74 @@ class BrainStructure(datasets.GeneratorBasedBuilder):
                     },
                 }
             ),
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager: datasets.DownloadManager):
         """
-        Creates SplitGenerators for 'train', 'validation', and 'test'.
-        We locate the dataset files by referencing the directory of this script.
+        Downloads and extracts 'data.zip', then defines train/validation/test splits
+        by matching sidecars with 'split': 'train'/'validation'/'test'.
         """
-        data_dir = os.path.abspath(os.path.dirname(__file__))
-
-        logger.info(f"BrainStructure: scanning data in {data_dir}")
+        # Download and extract your single ZIP containing all subfolders
+        extracted_dir = dl_manager.download_and_extract(_DATA_URL)
+        # The ZIP will typically unzip into a folder named "data" or similar. We'll just scan everything inside.
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"data_dir": data_dir, "desired_split": "train"}
+                gen_kwargs={"data_dir": extracted_dir, "desired_split": "train"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"data_dir": data_dir, "desired_split": "validation"}
+                gen_kwargs={"data_dir": extracted_dir, "desired_split": "validation"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                gen_kwargs={"data_dir": data_dir, "desired_split": "test"}
+                gen_kwargs={"data_dir": extracted_dir, "desired_split": "test"},
             ),
         ]
 
     def _generate_examples(self, data_dir, desired_split):
         """
-        Recursively walks the directory structure, looking for JSON sidecar files
-        ending in '_scandata.json'. For each matching file, yields an example
-        if sidecar["split"] == desired_split.
-
-        The corresponding .nii.gz is located by prefix matching.
+        Recursively find sidecar JSONs with 'split' matching desired_split. 
+        For each, yield an example containing the .nii.gz path + metadata.
         """
         id_ = 0
-        for root, dirs, files in os.walk(data_dir):
-            for fname in files:
-                # If you only want "msub" files, you can add: if fname.startswith("msub") and ...
-                if fname.endswith("_scandata.json"):
-                    sidecar_path = os.path.join(root, fname)
-                    with open(sidecar_path, "r") as f:
-                        sidecar = json.load(f)
-
-                    if sidecar.get("split") == desired_split:
-                        # Find the .nii.gz prefix
-                        nii_prefix = fname.replace("_scandata.json", "_T1w")
-                        nii_filepath = None
-                        for potential_file in files:
-                            if potential_file.startswith(nii_prefix) and potential_file.endswith(".nii.gz"):
-                                nii_filepath = os.path.join(root, potential_file)
-                                break
-
-                        if not nii_filepath:
-                            logger.warning(f"No corresponding .nii.gz found for {sidecar_path}")
-                            continue
-
-                        yield id_, {
-                            "id": str(id_),
-                            "nii_filepath": nii_filepath,
-                            "metadata": {
-                                "split": sidecar.get("split", ""),
-                                "participant_id": sidecar.get("participant_id", ""),
-                                "session_id": sidecar.get("session_id", ""),
-                                "study": sidecar.get("study", ""),
-                                "age": sidecar.get("age", 0),
-                                "sex": sidecar.get("sex", ""),
-                                "clinical_diagnosis": sidecar.get("clinical_diagnosis", ""),
-                                "scanner_manufacturer": sidecar.get("scanner_manufacturer", ""),
-                                "scanner_model": sidecar.get("scanner_model", ""),
-                                "field_strength": sidecar.get("field_strength", ""),
-                                "image_quality_rating": float(sidecar.get("image_quality_rating", 0.0)),
-                                "total_intracranial_volume": float(sidecar.get("total_intracranial_volume", 0.0)),
-                                "license": sidecar.get("license", ""),
-                                "website": sidecar.get("website", ""),
-                                "citation": sidecar.get("citation", ""),
-                                "t1_file_name": sidecar.get("t1_file_name", ""),
-                                "radiata_id": sidecar.get("radiata_id", 0),
-                            },
-                        }
-                        id_ += 1
+        data_path = Path(data_dir)
+        for json_path in data_path.rglob("*_scandata.json"):
+            with open(json_path, "r") as f:
+                sidecar = json.load(f)
+
+            if sidecar.get("split") == desired_split:
+                # Build the matching NIfTI path
+                possible_nii_name = json_path.name.replace("_scandata.json", "_T1w")
+                # Look in the same folder for .nii.gz
+                nii_path = json_path.parent / f"{possible_nii_name}.nii.gz"
+
+                if not nii_path.is_file():
+                    logger.warning(f"No .nii.gz found for {json_path}")
+                    continue
+
+                yield id_, {
+                    "id": str(id_),
+                    "nii_filepath": str(nii_path),
+                    "metadata": {
+                        "split": sidecar.get("split", ""),
+                        "participant_id": sidecar.get("participant_id", ""),
+                        "session_id": sidecar.get("session_id", ""),
+                        "study": sidecar.get("study", ""),
+                        "age": sidecar.get("age", 0),
+                        "sex": sidecar.get("sex", ""),
+                        "clinical_diagnosis": sidecar.get("clinical_diagnosis", ""),
+                        "scanner_manufacturer": sidecar.get("scanner_manufacturer", ""),
+                        "scanner_model": sidecar.get("scanner_model", ""),
+                        "field_strength": sidecar.get("field_strength", ""),
+                        "image_quality_rating": float(sidecar.get("image_quality_rating", 0.0)),
+                        "total_intracranial_volume": float(sidecar.get("total_intracranial_volume", 0.0)),
+                        "license": sidecar.get("license", ""),
+                        "website": sidecar.get("website", ""),
+                        "citation": sidecar.get("citation", ""),
+                        "t1_file_name": sidecar.get("t1_file_name", ""),
+                        "radiata_id": sidecar.get("radiata_id", 0),
+                    },
+                }
+                id_ += 1

data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:23e28ac334816528008cb0a3da4256439776b0793da4b7210a752a8feb07a8a4
+size 8431394741