Python Sparse data Analysis Package external MRI plugin.

Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the gallery for the big picture.

class mri.operators.proximity.weighted.WeightedSparseThreshold(weights, coeffs_shape, weight_type='scale_based', zero_weight_coarse=True, linear=<modopt.opt.linear.Identity object>, **kwargs)[source]¶

This is a weighted version of SparseThreshold in ModOpt. When chosen scale_based, it allows the users to specify an array of weights W[i] and each weight is assigen to respective scale i. Also, custom weights can be defined. Note that the weights on coarse scale is always set to 0

Parameters

weights : numpy.ndarray

Input array of weights or a tuple holding base weight W and power P

coeffs_shape : tuple

The shape of linear coefficients

weight_type : string ‘custom’ | ‘scale_based’ | ‘custom_scale’,

default ‘scale_based’ Mode of operation of proximity: custom -> custom array of weights scale_based -> custom weights applied per scale

zero_weight_coarse : bool, default True

linear : object, default Identity()

Linear operator, to be used in cost function evaluation