To help you get started, we've collected the most common ways that nitransforms is being used within popular public projects.

poldracklab / niworkflows / niworkflows / interfaces / nitransforms.py View on Github

def concatenate_xfms(
    in_files, out_file, out_inv=None, reference=None, moving=None, fmt="itk"
):
    """Concatenate linear transforms."""
    xfm = TransformChain(
        [load_affine(f, fmt=XFM_FMT[Path(f).suffix]) for f in in_files]
    ).asaffine()
    if reference is not None and not xfm.reference:
        xfm.reference = reference

    xfm.to_filename(out_file, moving=moving, fmt=fmt)

    if out_inv is not None:
        inv_xfm = ~xfm
        if moving is not None:
            inv_xfm.reference = moving
        inv_xfm.to_filename(out_inv, moving=reference, fmt=fmt)

View more ways to use nitransforms

NiTransforms

A development repo for nipy/nibabel#656

About

Spatial transforms formalize mappings between coordinates of objects in biomedical images. Transforms typically are the outcome of image registration methodologies, which estimate the alignment between two images. Image registration is a prominent task present in nearly all standard image processing and analysis pipelines. The proliferation of software implementations of image registration methodologies has resulted in a spread of data structures and file formats used to preserve and communicate transforms. This segregation of formats precludes the compatibility between tools and endangers the reproducibility of results. We propose a software tool capable of converting between formats and resampling images to apply transforms generated by the most popular neuroimaging packages and libraries (AFNI, FSL, FreeSurfer, ITK, and SPM). The proposed software is subject to continuous integration tests to check the compatibility with each supported tool after every change to the code base. Compatibility between software tools and imaging formats is a necessary bridge to ensure the reproducibility of results and enable the optimization and evaluation of current image processing and analysis workflows.

Integration with NiBabel

NiTransforms started as a feature-repo spun off of NiBabel. Shortly after starting with nipy/nibabel#656, it became apparent that it was going to build up in a humongous PR nobody would be able to review as thoroughly as it would require. Also, NiTransforms has many connections to BIDS/BIDS-Derivatives and its X5 format specification for transforms, which falls outside of the current scope of NiBabel.

The plan is to make it an isolated tool, and once it is pertinent, proceed with the integration into NiBabel. Once this repository is ready for integration, we will define what can go into NiBabel (presumably everything, except perhaps some final details of the X5 implementation, although NiBabel will support the data structure at least logically). This is to say that the chances that NiTransforms is integrated into NiBabel are high and scheduled to happen in ~2022 Q2.