`nlsam.smoothing`

Module Contents

Functions

`sh_smooth`(data, bvals, bvecs[, sh_order, ...])	Smooth the raw diffusion signal with spherical harmonics.
`_local_standard_deviation`(arr[, current_slice])	Standard deviation estimation from local patches.
`local_standard_deviation`(arr[, n_cores, verbose])	Standard deviation estimation from local patches.

Attributes

logger

nlsam.smoothing.logger

nlsam.smoothing.sh_smooth(data, bvals, bvecs, sh_order=4, b0_threshold=1.0, similarity_threshold=50, regul=0.006)

Smooth the raw diffusion signal with spherical harmonics.

datandarray: The diffusion data to smooth.
gtabgradient table object: Corresponding gradients table object to data.
b0_thresholdfloat, default 1.0: Threshold to consider this bval as a b=0 image.
sh_orderint, default 8: Order of the spherical harmonics to fit.
similarity_thresholdint, default 50: All bvalues such that |b_1 - b_2| < similarity_threshold will be considered as identical for smoothing purpose. Must be lower than 200.
regulfloat, default 0.006: Amount of regularization to apply to sh coefficients computation.

Returns:: pred_sig – The smoothed diffusion data, fitted through spherical harmonics.
Return type:: ndarray

nlsam.smoothing._local_standard_deviation(arr, current_slice=None)

Standard deviation estimation from local patches.

Estimates the local variance on patches by using convolutions to estimate the mean. This is the multiprocessed function.

Parameters:

arr (3D or 4D ndarray) – The array to be estimated
current_slice (numpy slice object) – current slice to evaluate if we are running in parallel

Returns:

sigma – Map of standard deviation of the noise.

Return type:

ndarray

nlsam.smoothing.local_standard_deviation(arr, n_cores=-1, verbose=False)