TomobarRecon

>>> disp -avv

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  TomobarRecon3d(savu.plugins.reconstructions.tomobar_recon_3D)                    
  A wrapper around TOmographic MOdel-BAsed Reconstruction (ToMoBAR) software for     
  advanced iterative image reconstruction using _3D_ capabilities of regularisation. 
  The plugin will run on one cluster node, i.e. it can be slow.                      
    1)               init_vol : None
    Dataset to use as volume initialiser (doesn't currently work with preview).
    2)                preview : []
    A slice list of required frames.
    3)                    log : True
    Take the log of the data before reconstruction (True or False).
    4)     centre_of_rotation : 453.5
    Centre of rotation to use for the reconstruction.
    5)            in_datasets : []
    Create a list of the dataset(s) to process.
    6)       ring_accelerator : 50.0
    Acceleration constant for ring removal (use with care).
    7)            output_size : auto
    The dimension of the reconstructed volume (only X-Y dimension).
    8)          nonnegativity : ENABLE
    Nonnegativity constraint, choose Enable or None.
    9)         regularisation : ROF_TV
    To regularise choose methods ROF_TV, FGP_TV, SB_TV, LLT_ROF, NDF, Diff4th.
   10)             iterations : 15
    Number of outer iterations for FISTA method.
   11)             edge_param : 0.01
    Edge (noise) related parameter, relevant for NDF and Diff4th.
   12)                  ratio : 0.95
    Ratio of the m2asks diameter in pixels to the smallest edge size along given
    axis.
   13)               log_func : np.nan_to_num(-np.log(sino))
    Override the default log function.
   14)   regularisation_parameter2 : 0.005
    Regularisation (smoothing) value for LLT_ROF method.
   15)            NDF_penalty : Huber
    NDF specific penalty type Huber, Perona, Tukey.
   16)              tolerance : 1e-10
    Tolerance to stop outer iterations earlier.
   17)           ordersubsets : 6
    The number of ordered-subsets to accelerate reconstruction.
   18)           out_datasets : []
    Create a list of the dataset(s) to create.
   19)             centre_pad : False
    Pad the sinogram to centre it in order to fill the reconstructed volume ROI for
    asthetic purposes. NB: Only available for selected algorithms and will be ignored
    otherwise. WARNING: This will significantly increase the size of the data and the
    time to compute the reconstruction).
   20)   regularisation_iterations : 400
    The number of regularisation iterations.
   21)   regularisation_parameter : 0.0002
    Regularisation (smoothing) value, higher the value stronger the smoothing effect.
   22)             force_zero : [None, None]
    Set any values in the reconstructed image outside of this range to zero.
   23)              vol_shape : fixed
    Override the size of the reconstuction volume with an integer value.
   24)          converg_const : power
    Lipschitz constant, can be set to a scalar value or automatic calculation using
    power methods.
   25)   time_marching_parameter : 0.002
    Time marching parameter, relevant for (ROF_TV, LLT_ROF, NDF, Diff4th) penalties.
   26)           datafidelity : LS
    Data fidelity, Least Squares (LS) or PWLS.
   27)              outer_pad : False
    Pad the sinogram width to fill the reconstructed volume for asthetic purposes.
    Choose from True (defaults to sqrt(2)), False or float <= 2.1. NB: Only available
    for selected algorithms and will be ignored otherwise. WARNING: This will
    increase the size of the data and the time to compute the reconstruction).
   28)          ring_variable : 0.0
    Regularisation variable for ring removal.

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