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8573d70 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | 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)
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