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a standalone fork of nipy/nibabel#656

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Including tests, and considering how to operate (i.e., parallelization) with very long timeseries (say 400 timepoints).

(Propagation of @sgiavasis 's PR to @jmarabotto 's fork. Original PR message copied below) Started at the NiPreps hackathon at MIT in May 2024. Thanks again to @jmarabotto and @oesteban...

Re-implements serialization (i.e., splitting a 4D transformation into 3D transformations). For time-dependent transforms (e.g., when also slice-time correcting), parallelization will require a different partition (spatial and/or temporal windowing).

We are currently propagating the ArrayProxy reported dtype unless an explicit output_dtype is set. It would be more efficient to allow the user set a low memory flag such that...

One of the things I knowingly let drop from the ``apply()`` outsourcing (#195) was the parallelization of resampling. @jmarabotto - do you think this is something you can try to...

Addition of new file vis.py. Calls nitransform's nonlinear DenseFieldTransform class to illustrate corresponding transform objects through: - Distorted image and grid - Quiver plot coloured according to Diffusion Scalar map...

https://github.com/nipy/nitransforms/pull/188#pullrequestreview-1760979277

Is it possible to write a nonlinear transform into itk format (nifti or h5) using nitransforms? Or is the only supported output format x5?