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my_npz_dataset.py

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+import os
+import numpy as np
+from datasets import Dataset, DatasetDict, Features, Array3D, Value, GeneratorBasedBuilder
+
+class MyNpzDatasetBuilder(GeneratorBasedBuilder):
+    def _info(self):
+        features = Features({
+            'sdf': Array3D(dtype='float64', shape=(512, 512)),  # Adjust dtype and shape according to your data
+            'mask': Array3D(dtype='int64', shape=(512, 512)),
+            're': Array3D(dtype='float64', shape=(512, 512)),
+            'u': Array3D(dtype='float64', shape=(512, 512)),
+            'v': Array3D(dtype='float64', shape=(512, 512)),
+            'p': Array3D(dtype='float64', shape=(512, 512)),
+        })
+        return datasets.DatasetInfo(
+            description="Flow Bench Dataset for SciML",
+            features=features,
+            supervised_keys=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(self.config.data_files)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"file_path": os.path.join(data_dir, "lid_driven_cavity_NS_512x512_X.npz")}
+            )
+        ]
+
+    def _generate_examples(self, file_path):
+        data = np.load(file_path)
+        
+        for idx in range(len(data['sdf'])):
+            yield idx, {
+                'sdf': data['sdf'][idx],
+                'mask': data['mask'][idx],
+                're': data['re'][idx],
+                'u': data['u'][idx],
+                'v': data['v'][idx],
+                'p': data['p'][idx],
+            }
+
+# Example usage
+if __name__ == "__main__":
+    from datasets import load_dataset
+
+    dataset = load_dataset("./my_npz_dataset.py", data_files={"train": "./fake_lid_driven_cavity_NS_512x512_X.npz"})
+    print(dataset)
+