tomobar.supp.dicts#

tomobar.supp.dicts.dicts_check(self, _data_: dict, _algorithm_: Optional[dict] = None, _regularisation_: Optional[dict] = None, method_run: str = 'FISTA') → tuple[source]#

This function checks the given _data_, _algorithm_, and _regularisation_ dictionaries and populates parameters if required. Please note that the dictionaries are required for iterative methods only as for direct methods the input is an array explicitly given to the function as an argument. The most versatile method in terms of parametrisation is currently FISTA, therefore most of the keywords arguments bellow are applicable to it.

Parameters

  • _data_ (dict) – Data dictionary where data related items must be specified.

  • _algorithm_ (dict, optional) – Algorithm dictionary. Defaults to {}.

  • _regularisation_ (dict, optional) – Regularisation dictionary. Needed only for FISTA and ADMM algorithms. Defaults to {}.

  • method_run (str, optional) – The name of the method to be run. Defaults to “FISTA”.

Keyword Arguments

  • _data_['projection_norm_data'] (ndarray) – The -log(normalised) projection data as a 2D sinogram or as a 3D data array.

  • _data_['projection_raw_data'] (ndarray) – Raw data for PWLS and SWLS models. FISTA-related parameter.

  • _data_['data_axes_labels_order'] (list, None) – [“detY”, “angles”, “detX”].

  • _data_['OS_number'] (int) – The number of ordered subsets, if None or 1 then the classical (full data) algorithm. Defaults to 1.

  • _data_['huber_threshold'] (float) – Parameter for the Huber data fidelity (to supress outliers).

  • _data_['studentst_threshold] (float) – Parameter for Students’t data fidelity (to supress outliers).

  • _data_['ringGH_lambda'] (float) – Parameter for Group-Huber data model to supress full rings of the uniform intensity.

  • _data_['ringGH_accelerate'] (float) – Group-Huber data model acceleration factor (can lead to divergence). Defaults to 50.

  • _data_['beta_SWLS'] (float) – Regularisation parameter for stripe-weighted data model (ring artefacts removal) Defaults to 0.1.

  • _algorithm_['iterations'] (int) – The number of iterations for the reconstruction algorithm.

  • _algorithm_['nonnegativity'] (bool) – Enable nonnegativity for the solution. Defaults to False.

  • _algorithm_['recon_mask_radius'] (float) – Enables a circular mask cutoff in the reconstructed image. Defaults to 1.0.

  • _algorithm_['initialise'] (ndarray) – Initialise an algorithm with an array.

  • _algorithm_['lipschitz_const'] (float) – Lipschitz constant for the FISTA algorithm. If not provided it will be calculated for each method call.

  • _algorithm_['ADMM_rho_const'] (float) – Augmented Lagrangian parameter for the ADMM algorithm.

  • _algorithm_['ADMM_relax_par'] (float) – Over relaxation parameter for the convergence acceleration of the ADMM algorithm.

  • _algorithm_['tolerance'] (float) – Tolerance to terminate reconstruction algorithm iterations earlier. Defaults to 0.0.

  • _algorithm_['verbose'] (bool) – Switch on printing of iterations number and other messages. Defaults to False.

  • _regularisation_['method'] (str) – Select the regularisation method from the CCPi-regularisation toolkit. The supported methods listed: ROF_TV, FGP_TV, PD_TV, SB_TV, LLT_ROF, TGV, NDF, Diff4th, NLTV. If one also installed pypwt package for Wavelets then one can WAVELET regularisation by adding WAVELETS to any method above by appending “_WAVELETS” string to an existing regulariser. For instance, ROF_TV_WAVELETS would enable dual regularisation with ROF_TV and wavelets.

  • _regularisation_['regul_param'] (float) – The main regularisation parameter for regularisers to control the amount of smoothing. Defaults to 0.001.

  • _regularisation_['iterations'] (int) – The number of INNER iterations for regularisers. Defaults to 150.

  • _regularisation_['device_regulariser'] (str, int) – Select the device as ‘cpu’ or ‘gpu’. Or provide GPU index (integer) of a specific device.

  • _regularisation_['edge_threhsold'] (float) – Noise-related threshold parameter for NDF and DIFF4th (diffusion) regularisers.

  • _regularisation_['tolerance'] (float) – Tolerance to stop inner regularisation iterations prematurely.

  • _regularisation_['time_marching_step'] (float) – Time step parameter for convergence of gradient-based methods: ROF_TV,LLT_ROF,NDF,Diff4th.

  • _regularisation_['regul_param2'] (float) – The second regularisation parameter (LLT_ROF or when using WAVELETS in addition).

  • _regularisation_['TGV_alpha1'] (float) – The TGV penalty specific parameter for the 1st order term.

  • _regularisation_['TGV_alpha2'] (float) – The TGV penalty specific parameter for the 2nd order term.

  • _regularisation_['PD_LipschitzConstant'] (float) – The Primal-Dual (PD) penalty related parameter for convergence (PD_TV and TGV specific).

  • _regularisation_['NDF_penalty'] (str) – The NDF-method specific penalty type: Huber (default), Perona, Tukey.

  • _regularisation_['NLTV_H_i'] (ndarray) – The NLTV penalty related weights, the array of i-related indices.

  • _regularisation_['NLTV_H_j'] (ndarray) – The NLTV penalty related weights, the array of j-related indices.

  • _regularisation_['NLTV_Weights] (ndarray) – The NLTV-specific penalty type, the array of Weights.

  • _regularisation_['methodTV'] (int) – 0/1 - TV specific isotropic/anisotropic choice.

Returns

A tuple with three populated dictionaries (_data_, _algorithm_, _regularisation_).

Return type

tuple