transformnd 0.4.1

ND coordinate transformations

transformnd

A library providing an API for coordinate transformations, as well as some common transforms. The goal is to allow downstream applications which require such transformations (e.g. image registration) to be generic over anything inheriting from transformnd.Transform.

The base classes and utilities are very lightweight with few dependencies, for use as an API; additional transforms and features use extras.

Heavily inspired by/ cribbed directly from Philipp Schlegel's work in navis; co-developed with xform as a red team prototype.

N coordinates in D dimensions are given as a numpy array of shape (N, D).

Transform subclasses which are restricted to certain dimensionalities can specify this in their ndim class variable. Instances of Transform subclasses can further restrict their ndim. Use self._validate_coords(coords) in the apply method to ensure the coordinates are of valid type and dimensions.

Additionally, transformnd provides an interface for transforming types other than NxD numpy arrays, and implements these adapters for a few common types.

See the tutorial here. It is a marimo notebook. Open it with uv run --group tutorial marimo edit examples/tutorial.py.

Implemented transforms

All transforms are accessed under the transformnd.transforms subpackage.

Transform	Extra	Description
Identity		No-op transformation
Translation		Add a constant translation to the input coordinates
Scale		Multiply the input coordinates by constant scale factor
Reflection		Reflect coordinates about arbitrary planes
MapAxis		Rearrange axes of the input coordinates
Affine		Multiply augmented coordinates by an affine transformation matrix. Can represent all of the above transformations. Can be composed with matrix multiplication aff2 @ aff1.
ByDimension		Apply different transformations to subsets of the input coordinates' dimensions
moving_least_squares.MovingLeastSquares	movingleastsquares	Landmark-based transformation.
thin_plate_splines.ThinPlateSplines	thinplatesplines	Landmark-based transformation.
vector_field.Coordinates	vectorfield for in-memory, vectorfield-dask for chunked	Look up output coordinates in a vector field indexed by the input coordinates
vector_field.Displacements	vectorfield, vectorfield-dask for chunked	Look up translations in a vector field indexed by the input coordinates, and add them to input coordinates

Arbitrary transforms can be composed into a TransformSequence with transform1 | transform2. A graph of transforms between defined spaces can be traversed using the TransformGraph.

Implemented adapters

• Numpy arrays of shape (..., D, ...) (transformnd.adapters.ReshapeAdapter)
• meshio.Mesh (transformnd.adapters.meshio.MeshAdapter)
• pandas.DataFrame (transformnd.adapters.pandas.PandasAdapter)
  • Takes a subset of columns as a coordinate array
• polars.DataFrame (transformnd.adapters.polars.PolarsAdapter)
  • Similar to the pandas adapter
  • Currently, only scalar columns are supported (e.g. not a single struct column with fields x, y, z)
• Geometries from shapely (transformnd.adapters.shapely.GeometryAdapter)
• Objects composed of transformable attributes (transformnd.adapters.AttrAdapter).

Additional transforms and adapters

Contributions of additional transforms and adapters are welcome! Even if they're only thin wrappers around an external library, the downstream ecosystem benefits from a consistent API.

Such external transformation libraries should be specified as "extras" (pyproject.toml:project.optional-dependencies), and be contained in a submodule so that they are not immediately imported with transformnd.

Alternatively, consider adopting transformnd's base classes in your own library, and have your transformation instantly compatible for downstream users.

Methods which MUST be implemented:

• __init__: should validate parameters and must call the super() constructor
• apply: should call _validate_coords method early to check that the given coordinates are the correct shape

Methods which SHOULD be implemented if applicable:

• to_device: if any of the transformation's parameters need to be placed on a specific device (e.g. affine matrices on the GPU)
• is_identity: if you can cheaply check whether your transformation is an identity transformation. The base class implementation returns False.
• to_affine: if your transformation can be represented as an affine matrix. The base class implementation returns None.
• invert: if your transformation can be inverted (default None if not)
  • This automatically implements __invert__ (the ~my_transform operator), which returns NotImplemented (probably raising NotImplementedError) if invert would return None.

Contributing

• Use uv for environment and dependency management.
  • uv sync to set up the environment.
• Use prek for running pre-commit hooks.
  • prek install-hooks && prek run --all-files to get started.
• Use just for common development tasks (format, lint, test, generate docs, run benchmarks).
  • just to list commands.
• Docs are generated with pdoc (use just doc) and hosted on ReadTheDocs
• just bump bumps the version, commits, and tags (but does not push); depends on schpet/changelog
• just repl starts an IPython shell with all dependencies installed

Thanks

Thanks to contributors

• Francesca Drummer
• Johannes Soltwedel
• Lorenzo Cerrone
• Maks Hess
• Silvia Maria Macrì