neuron_morphology.transforms.affine_transform module

class neuron_morphology.transforms.affine_transform.AffineTransform(affine: Optional[Any] = None)

Bases: neuron_morphology.transforms.transform_base.TransformBase

Handles transformations to a pia/wm aligned coordinate frame.

Methods

from_dict(affine_dict, float])	Create an AffineTransform from a dict with keys and values.
from_list(affine_list)	Create an Affine Transform from a list
to_dict(self)	Create dictionary defining the transformation.
to_list(self)	Create a list defining the transformation.
transform(self, vector)	Apply this transform to (3,) point or (n,3) array-like of points.
transform_morphology(self, morphology, clone)	Apply this transform to all nodes in a morphology.

classmethod from_dict(affine_dict: Dict[str, float])

Create an AffineTransform from a dict with keys and values.

Parameters:

affine_dict: keys and values corresponding to the following

[[tvr_00 tvr_01 tvr_02 tvr_09]

[tvr_03 tvr_04 tvr_05 tvr_10] [tvr_06 tvr_07 tvr_08 tvr_11] [0 0 0 1]]

Returns:

AffineTransform object

classmethod from_list(affine_list: List[float])

Create an Affine Transform from a list

Parameters:

affine_list: list of tvr values corresponding to:

[[tvr_00 tvr_01 tvr_02 tvr_09]

[tvr_03 tvr_04 tvr_05 tvr_10] [tvr_06 tvr_07 tvr_08 tvr_11] [0 0 0 1]]

Returns:

AffineTransform object

to_dict(self) → Dict

Create dictionary defining the transformation.

Returns:

Dict with keys and values corresponding to the following:

[[tvr_00 tvr_01 tvr_02 tvr_09]

[tvr_03 tvr_04 tvr_05 tvr_10] [tvr_06 tvr_07 tvr_08 tvr_11] [0 0 0 1]]

to_list(self) → List

Create a list defining the transformation.

Returns:

List with values corresponding to the following:

[[tvr_00 tvr_01 tvr_02 tvr_09]

[tvr_03 tvr_04 tvr_05 tvr_10] [tvr_06 tvr_07 tvr_08 tvr_11] [0 0 0 1]]

transform(self, vector: Any) → numpy.ndarray

Apply this transform to (3,) point or (n,3) array-like of points.

Parameters:

vector: a (3,) array-like point or a (n,3) array-like array

of points to be transformed

Returns:

numpy.ndarray with same shape as input

transform_morphology(self, morphology: neuron_morphology.morphology.Morphology, clone: bool = False) → neuron_morphology.morphology.Morphology

Apply this transform to all nodes in a morphology.

Parameters:

morphology: a Morphology loaded from an swc file

Returns:

A Morphology

neuron_morphology.transforms.affine_transform.affine_from_transform(transform: Any)

Create affine transformation.

Parameters:

transformation: (3, 3) row major array-like transformation

Returns:

(4, 4) numpy.ndarray affine matrix

neuron_morphology.transforms.affine_transform.affine_from_transform_translation(transform: Union[Any, NoneType] = None, translation: Union[Any, NoneType] = None, translate_first: bool = False)

Create affine from linear transformation and translation.

Affine transformation of vector x -> Ax + b in 3D: [A, b

0, 0, 0, 1]

A is a 3x3 linear tranformation b is a 3x1 translation

Parameters:

transform: linear transformation (3, 3) array-like

translation: linear translation (3,) array-like

translate_first: apply the translation before the transform

Returns:

(4, 4) numpy.ndarray affine matrix

neuron_morphology.transforms.affine_transform.affine_from_translation(translation: Any)

Create an affine translation.

Parameters:

translation: array-like vector of x, y, and z translations

Returns:

(4, 4) numpy.ndarray affine matrix

neuron_morphology.transforms.affine_transform.rotation_from_angle(angle: float, axis: int = 2)

Create an affine matrix from a rotation about a specific axis.

Parameters:

angle: rotation angle in radians

axis: axis to rotate about, 0=x, 1=y, 2=z (default z axis)

Returns:

(3, 3) numpy.ndarray rotation matrix