MatchingData#

class MatchingData(target, template, template_mask=None, target_mask=None, invert_target=False, rotations=None)[source]#

Bases: object

Contains data required for template matching.

Parameters:

targetnp.ndarray or tme.density.Density: Target data.
templatenp.ndarray or tme.density.Density: Template data.
target_masknp.ndarray or tme.density.Density, optional: Target mask data.
template_masknp.ndarray or tme.density.Density, optional: Template mask data.
invert_targetbool, optional: Whether to invert the target before template matching..
rotations: np.ndarray, optional: Template rotations to sample. Can be a single (d, d) or a stack (n, d, d) of rotation matrices where d is the dimension of the template.

Examples

The following achieves the minimal definition of a MatchingData instance.

>>> import numpy as np
>>> from tme.matching_data import MatchingData
>>> target = np.random.rand(50,40,60)
>>> template = target[15:25, 10:20, 30:40]
>>> matching_data = MatchingData(target=target, template=template)

Attributes

`MatchingData.rotations`	Return stored rotation matrices.
`MatchingData.target`	Return the target.
`MatchingData.target_filter`	Returns the composable target filter.
`MatchingData.target_mask`	Return the target mask.
`MatchingData.template`	Return the reversed template.
`MatchingData.template_filter`	Returns the composable template filter.
`MatchingData.template_mask`	Return the reversed template mask.

Methods

`MatchingData.fourier_padding`([pad_fourier])	Computes efficient shape four Fourier transforms and potential associated shifts.
`MatchingData.subset_array`(arr, arr_slice, ...)	Extract a subset of the input array according to the given slice and apply padding.
`MatchingData.subset_by_slice`([target_slice, ...])	Subset class instance based on slices.
`MatchingData.target_padding`([pad_target])	Computes padding for the target based on the template's shape.
`MatchingData.to_backend`()	Transfer and convert types of class instance's data arrays to the current backend