Changelog

2.1.1 [2022-09-01]

:meth:`~oflibpytorch.Flow.unpad` introduced: effectively a helper function to undo :meth:`~oflibpytorch.Flow.pad`
:meth:`~oflibpytorch.Flow.from_transforms` signature extended by a ``padding'' argument: convenience when creating a padded flow field, automatically adjusting the shape and relevant transform parameters
:meth:`~oflibpytorch.Flow.select` parameter ``item'' can be ``None'', returning ``self''
:meth:`~oflibpytorch.Flow.get_padding` signature extended by an ``item'' argument, used to select an item in the batched flow. Returns a simple list of padding values, rather than a list of lists.
Minor performance improvement in :meth:`~oflibpytorch.Flow.combine`

:meth:`~oflibpytorch.Flow.combine` introduced: efficient, generalised combination of flows with any frame of reference :attr:`ref`
:meth:`~oflibpytorch.Flow.combine_with` improvements, but will become deprecated in a future release in favour of :meth:`~oflibpytorch.Flow.combine`
Test coverage improved
Documentation updated and extended

Major update, enhancing usability for deep learning applications.

Flow vectors and masks are now batched, meaning the shape is (N, H, W) instead of (H, W). This enables easy integration with any deep learning application or network, harnessing the efficiencies of batch-wise processing.
A differentiable PyTorch function to approximately replace :func:`scipy.interpolate.griddata` was implemented
A toolbox-wide boolean setting called PURE_PYTORCH has been introduced. If it is set to True, non-Torch operations are avoided as far as possible. Specifically, this means avoiding the slow Scipy-based function :func:`scipy.interpolate.griddata` in favour of a more approximate, but significantly faster PyTorch-only method that interpolates unstructured data on a defined regular grid.
If PURE_PYTORCH is set to True, all oflibpytorch methods that output a float torch tensor are differentiable, again allowing for easy integration with deep learning algorithms.
Some utility functions made available
Documentation and unit test updates
Minor bugfixes

Type of the flow attribute :attr:`device` changed from string to the :class:`torch.device` class
If the CUDA device index is left undefined, it defaults to torch.cuda.current_device(). This avoids ambiguities and possible CUDA device mismatches when working with multiple GPUs.

Introduced functions that largely replicate functionality of flow class methods, but for Torch tensor and NumPy array flow inputs
Documentation updated with above functions, some older errors corrected
Minor bugfixes
Bibtex citation to use to acknowledge the authors added

Fixed bug in visualise (range_max calculation)
Removed all usages of the torch tensor attribute :attr:`ndim` for improved backwards compatibility with older torch versions.
Removed print statement in test_utils
Minor documentation corrections, addition of this changelog

First full release