Add dataset management classes and loading script for HuggingFace integration

requirements.txt

@@ -1,4 +1,5 @@
 gradio
 huggingface[cli]
 numpy
-fsspec
+fsspec
+datasets

test.py

@@ -0,0 +1,77 @@
+import os
+import numpy as np
+import datasets
+
+_CITATION = r"""
+@misc{test2025,
+  title={Test Dataset},
+  author={Your Name},
+  year={2025},
+  howpublished={\url{https://huggingface.co/datasets/DLSCA/test}}
+}
+"""
+
+_DESCRIPTION = """
+A test dataset using local numpy arrays for HuggingFace Datasets.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/DLSCA/test"
+_LICENSE = "MIT"
+
+class TestDownloadManager(datasets.DownloadManager):
+    def __init__(self, data_dir):
+        self.data_dir = data_dir
+
+    def download_and_extract(self, url_or_urls):
+        # No download needed, just return the local data dir
+        return self.data_dir
+
+class TestDataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "trace": datasets.features.Sequence(
+                        datasets.Value("int8"), length=20971
+                    ),
+                    "label0": datasets.Value("int32"),
+                    "label1": datasets.Value("int32"),
+                    "label2": datasets.Value("int32"),
+                    "label3": datasets.Value("int32"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # Use the provided data_dir from load_dataset
+        data_dir = dl_manager.manual_dir if dl_manager.manual_dir else dl_manager.data_dir
+        traces_path = os.path.join(data_dir, "traces.npy")
+        labels_path = os.path.join(data_dir, "labels.npy")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "traces_path": traces_path,
+                    "labels_path": labels_path,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, traces_path, labels_path):
+        traces = np.load(traces_path)
+        labels = np.load(labels_path)
+        for idx, (trace, label) in enumerate(zip(traces, labels)):
+            yield idx, {
+                "trace": trace.tolist(),
+                "label0": int(label[0]),
+                "label1": int(label[1]),
+                "label2": int(label[2]),
+                "label3": int(label[3]),
+            }

test_dataset.py

@@ -0,0 +1,16 @@
+from datasets import load_dataset
+
+def main():
+    # Load the dataset from the local script
+    ds = load_dataset(
+        'test.py',
+        data_dir='data',
+        split='train',
+        trust_remote_code=True,
+    )
+    print(ds)
+    print(ds[0])  # Show the first example
+    print('Features:', ds.features)
+
+if __name__ == "__main__":
+    main()