H5Writer#

class pyvisgen.io.H5Writer(output_path: Path, dataset_type: str, **kwargs)[source]#

Bases: DataWriter

HDF5 file writer for pyvisgen datasets.

This writer saves data arrays to HDF5 files using the h5py library. Each sample is written to a separate .h5 file. The writer automatically crops images to half their height with a small overlap and validates array shapes before writing.

Parameters:

output_pathstr or Path: Directory path where HDF5 files will be written.
dataset_typestr: Type of dataset being written (e.g., ‘train’, ‘test’, ‘validation’). This is used in the output filename pattern.

Examples

>>> writer = H5Writer(output_path="./data", dataset_type="train")
>>> writer.write(x_data, y_data, index=0)

Or as a context manager:

>>> rng = np.random.default_rng()
>>>
>>> with H5Writer(output_path="./data", dataset_type="train") as writer:
...     x_data = rng.uniform(size=(5, 10, 2, 256, 256))
...     y_data = rng.uniform(size=(5, 10, 2, 256, 256))
...
...     for bundle_id, (x, y) in enumerate(zip(x_data, y_data)):
...         writer.write(x, y, index=bundle_id)

Methods Summary

write(x, y, index[, name_x, name_y, overlap])

Write FFT pair data to an HDF5 file.

Methods Documentation

write(x, y, index, name_x='x', name_y='y', overlap: int = 5, **kwargs) → None[source]#

Write FFT pair data to an HDF5 file.

Creates a new HDF5 file for each sample with pattern samp_{dataset_type}_{index}.h5. The input arrays are cropped to half their height (with 5 pixel overlap) and validated before writing.

Parameters:

xnp.ndarray: First array of the FFT pair with shape (batch, 2, height, width). Expected to have 4 dimensions with axis 1 of size 2.
ynp.ndarray: Second array of the FFT pair with shape (batch, 2, height, width). Expected to have 4 dimensions with axis 1 of size 2.
indexint: Bundle index used in the output filename.
overlapint, optional: Overlap parameter for extracting half-images. Default: 5.
name_xstr, optional: Key of the dataset for x array in the HDF5 file. Default: "x".
name_ystr, optional: Key of the dataset for y array in the HDF5 file. Default: "y".

Raises:

ValueError: If x or y arrays don’t have the expected shape (4 dimensions with axis 1 of size 2).

Examples

>>> rng = np.random.default_rng()
>>>
>>> with H5Writer(output_path="./data", dataset_type="train") as writer:
...     x_data = rng.uniform(size=(5, 10, 2, 256, 256))
...     y_data = rng.uniform(size=(5, 10, 2, 256, 256))
...
...     for bundle_id, (x, y) in enumerate(zip(x_data, y_data)):
...         writer.write(x, y, index=bundle_id)

H5Writer#

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