navis.xform¶

navis.xform(x, transform, affine_fallback=True, caching=True)[source]¶

Apply transform(s) to data.

Notes

For Neurons only: whether there is a change in units during transformation (e.g. nm -> um) is inferred by comparing distances between x/y/z coordinates before and after transform. This guesstimate is then used to convert .units and node/soma radii. This works reasonably well with base 10 increments (e.g. nm -> um) but is off with odd changes in units.

Parameters:

x (Neuron/List | Volume/Trimesh | numpy.ndarray | pandas.DataFrame) – Data to transform. Dataframe must contain ['x', 'y', 'z'] columns. Numpy array must be shape (N, 3).
transform (Transform/Sequence or list thereof) – Either a single transform or a transform sequence.
affine_fallback (bool) – In same cases the non-rigid transformation of points can fail - for example if points are outside the deformation field. If that happens, they will be returned as NaN. Unless affine_fallback is True, in which case we will apply only the rigid affine part of the transformation to at least get close to the correct coordinates.
caching (bool) –
If True, will (pre-)cache data for transforms whenever possible. Depending on the data and the type of transforms this can tremendously speed things up at the cost of increased memory usage:
- False = no upfront cost, lower memory footprint
- True = higher upfront cost, most definitely faster
Only applies if input is NeuronList and if transforms include H5 transform.

Returns:

Copy of input with transformed coordinates.

Return type:

same type as x

Examples

>>> import navis
>>> # Example neurons are in 8nm voxel space
>>> nl = navis.example_neurons()
>>> # Make a simple Affine transform to go from voxel to nanometers
>>> import numpy as np
>>> M = np.diag([8, 8, 8, 8])
>>> tr = navis.transforms.AffineTransform(M)
>>> # Apply the transform
>>> xf = navis.xform(nl, tr)