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generate_demo_hdf5_file.py
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generate_demo_hdf5_file.py
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from typing import Literal
import h5py
import numpy as np
def generate_demo_hdf5_file(
filename: str = "demo.h5", total_size: int = 2_000_000
) -> None:
with h5py.File(filename, "w") as f:
# Simple datasets of various shapes, sizes, and dtypes
f.create_dataset(
"simple/1d_float32", data=np.random.rand(10000).astype(np.float32)
)
f.create_dataset(
"simple/2d_int16",
data=np.random.randint(-32768, 32767, size=(100, 100), dtype=np.int16),
)
f.create_dataset(
"simple/3d_uint8",
data=np.random.randint(0, 255, size=(50, 50, 50), dtype=np.uint8),
)
f.create_dataset(
"simple/4d_complex64",
data=np.random.rand(20, 20, 20, 20).astype(np.complex64),
)
# Structured dataset
dt = np.dtype([("id", np.int32), ("value", np.float32), ("flag", np.bool_)])
structured_data = np.array(
[(1, 55.5, True), (2, 70.2, False), (3, 65.8, True)], dtype=dt
)
structured_dataset: h5py.Dataset = f.create_dataset(
"structured", data=structured_data
)
# Dataset with attributes
dset_with_attrs: h5py.Dataset = f.create_dataset(
"with_attributes", data=np.random.rand(500, 500)
)
dset_with_attrs.attrs["description"] = "Random 500x500 matrix"
dset_with_attrs.attrs["created_by"] = "generate_demo_hdf5_file"
# Compressed dataset
f.create_dataset(
"compressed",
data=np.random.rand(1000, 1000),
compression="gzip",
compression_opts=9,
)
# Large dataset to reach total size
remaining_size: int = total_size - sum(
dset.nbytes for dset in f.values() if isinstance(dset, h5py.Dataset)
)
if remaining_size > 0:
shape: tuple[int, int] = (
int(np.sqrt(remaining_size / 8)),
) * 2 # Assuming 8 bytes per float64
f.create_dataset("large", data=np.random.rand(*shape))
print(f"Demo HDF5 file '{filename}' created successfully.")
if __name__ == "__main__":
generate_demo_hdf5_file()