cleaning stage, leaving only FGP/ROF and related compilers, demos

5 files changed, 0 insertions, 1963 deletions

diff --git a/Wrappers/Python/ccpi/filters/Regularizer.py b/Wrappers/Python/ccpi/filters/Regularizer.py
deleted file mode 100644
index 4ca94f2..0000000
--- a/Wrappers/Python/ccpi/filters/Regularizer.py
+++ /dev/null
@@ -1,325 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Aug  8 14:26:00 2017
-
-@author: ofn77899
-"""
-
-from ccpi.filters import cpu_regularizers
-import numpy as np
-from enum import Enum
-import timeit
-
-class Regularizer():
-    '''Class to handle regularizer algorithms to be used during reconstruction
-    
-    Currently 5 CPU (OMP) regularization algorithms are available:
-        
-    1) SplitBregman_TV
-    2) FGP_TV
-    3) LLT_model
-    4) PatchBased_Regul
-    5) TGV_PD
-    
-    Usage:
-        the regularizer can be invoked as object or as static method
-        Depending on the actual regularizer the input parameter may vary, and 
-        a different default setting is defined.
-        reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
-
-        out = reg(input=u0, regularization_parameter=10., number_of_iterations=30,
-          tolerance_constant=1e-4, 
-          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-
-        out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10.,
-          number_of_iterations=30, tolerance_constant=1e-4, 
-          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-        
-        A number of optional parameters can be passed or skipped
-        out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
-
-    '''
-    class Algorithm(Enum):
-        SplitBregman_TV = cpu_regularizers.SplitBregman_TV
-        FGP_TV = cpu_regularizers.FGP_TV
-        LLT_model = cpu_regularizers.LLT_model
-        PatchBased_Regul = cpu_regularizers.PatchBased_Regul
-        TGV_PD = cpu_regularizers.TGV_PD
-    # Algorithm
-    
-    class TotalVariationPenalty(Enum):
-        isotropic = 0
-        l1 = 1
-    # TotalVariationPenalty
-        
-    def __init__(self , algorithm, debug = True):
-        self.setAlgorithm ( algorithm )
-        self.debug = debug
-    # __init__
-    
-    def setAlgorithm(self, algorithm):
-        self.algorithm = algorithm
-        self.pars = self.getDefaultParsForAlgorithm(algorithm)
-    # setAlgorithm
-        
-    def getDefaultParsForAlgorithm(self, algorithm):
-        pars = dict()
-        
-        if algorithm == Regularizer.Algorithm.SplitBregman_TV :
-            pars['algorithm'] = algorithm
-            pars['input'] = None
-            pars['regularization_parameter'] = None
-            pars['number_of_iterations'] = 35
-            pars['tolerance_constant'] = 0.0001
-            pars['TV_penalty'] = Regularizer.TotalVariationPenalty.isotropic
-            
-        elif algorithm == Regularizer.Algorithm.FGP_TV :
-            pars['algorithm'] = algorithm
-            pars['input'] = None
-            pars['regularization_parameter'] = None
-            pars['number_of_iterations'] = 50
-            pars['tolerance_constant'] = 0.001
-            pars['TV_penalty'] = Regularizer.TotalVariationPenalty.isotropic
-            
-        elif algorithm == Regularizer.Algorithm.LLT_model:
-            pars['algorithm'] = algorithm
-            pars['input'] = None
-            pars['regularization_parameter'] = None
-            pars['time_step'] = None
-            pars['number_of_iterations'] = None
-            pars['tolerance_constant'] = None
-            pars['restrictive_Z_smoothing'] = 0
-            
-        elif algorithm == Regularizer.Algorithm.PatchBased_Regul:
-            pars['algorithm'] = algorithm
-            pars['input'] = None
-            pars['searching_window_ratio'] = None
-            pars['similarity_window_ratio'] = None
-            pars['PB_filtering_parameter'] = None
-            pars['regularization_parameter'] = None
-            
-        elif algorithm == Regularizer.Algorithm.TGV_PD:
-            pars['algorithm'] = algorithm
-            pars['input'] = None
-            pars['first_order_term'] = None
-            pars['second_order_term'] = None
-            pars['number_of_iterations'] = None
-            pars['regularization_parameter'] = None
-            
-        else:
-            raise Exception('Unknown regularizer algorithm')
-
-        self.acceptedInputKeywords = pars.keys()
-            
-        return pars
-    # parsForAlgorithm
-    
-    def setParameter(self, **kwargs):
-        '''set named parameter for the regularization engine
-        
-        raises Exception if the named parameter is not recognized
-        Typical usage is:
-            
-        reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
-        reg.setParameter(input=u0)    
-        reg.setParameter(regularization_parameter=10.)
-        
-        it can be also used as
-        reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
-        reg.setParameter(input=u0 , regularization_parameter=10.)
-        '''
-        
-        for key , value in kwargs.items():
-            if key in self.pars.keys():
-                self.pars[key] = value
-            else:
-                raise Exception('Wrong parameter {0} for regularizer algorithm'.format(key))
-    # setParameter
-	
-    def getParameter(self, key):
-        if type(key) is str:
-            if key in self.acceptedInputKeywords:
-                return self.pars[key]
-            else:
-                raise Exception('Unrecongnised parameter: {0} '.format(key) )
-        elif type(key) is list:
-            outpars = []
-            for k in key:
-                outpars.append(self.getParameter(k))
-            return outpars
-        else:
-            raise Exception('Unhandled input {0}' .format(str(type(key))))
-        # getParameter
-	
-        
-    def __call__(self, input = None, regularization_parameter = None,
-                 output_all = False, **kwargs):
-        '''Actual call for the regularizer. 
-        
-        One can either set the regularization parameters first and then call the
-        algorithm or set the regularization parameter during the call (as 
-        is done in the static methods). 
-        '''
-        
-        if kwargs is not None:
-            for key, value in kwargs.items():
-                #print("{0} = {1}".format(key, value))                        
-                self.pars[key] = value
-                    
-        if input is not None: 
-            self.pars['input'] = input
-        if regularization_parameter is not None:
-            self.pars['regularization_parameter'] = regularization_parameter
-            
-        if self.debug:
-            print ("--------------------------------------------------")
-            for key, value in self.pars.items():
-                if key== 'algorithm' :
-                    print("{0} = {1}".format(key, value.__name__))
-                elif key == 'input':
-                    print("{0} = {1}".format(key, np.shape(value)))
-                else:
-                    print("{0} = {1}".format(key, value))
-        
-            
-        if None in self.pars:
-                raise Exception("Not all parameters have been provided")
-        
-        input = self.pars['input']
-        regularization_parameter = self.pars['regularization_parameter']
-        if self.algorithm == Regularizer.Algorithm.SplitBregman_TV :
-            ret = self.algorithm(input, regularization_parameter,
-                              self.pars['number_of_iterations'],
-                              self.pars['tolerance_constant'],
-                              self.pars['TV_penalty'].value )    
-        elif self.algorithm == Regularizer.Algorithm.FGP_TV :
-            ret = self.algorithm(input, regularization_parameter,
-                              self.pars['number_of_iterations'],
-                              self.pars['tolerance_constant'],
-                              self.pars['TV_penalty'].value )
-        elif self.algorithm == Regularizer.Algorithm.LLT_model :
-            #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
-            # no default
-            ret = self.algorithm(input, 
-                              regularization_parameter,
-                              self.pars['time_step'] , 
-                              self.pars['number_of_iterations'],
-                              self.pars['tolerance_constant'],
-                              self.pars['restrictive_Z_smoothing'] )
-        elif self.algorithm == Regularizer.Algorithm.PatchBased_Regul :
-            #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
-            # no default
-            ret = self.algorithm(input, regularization_parameter,
-                                  self.pars['searching_window_ratio'] , 
-                                  self.pars['similarity_window_ratio'] , 
-                                  self.pars['PB_filtering_parameter'])
-        elif self.algorithm == Regularizer.Algorithm.TGV_PD :
-            #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
-            # no default
-            if len(np.shape(input)) == 2:
-                ret = self.algorithm(input, regularization_parameter,
-                                  self.pars['first_order_term'] , 
-                                  self.pars['second_order_term'] , 
-                                  self.pars['number_of_iterations'])
-            elif len(np.shape(input)) == 3:
-                #assuming it's 3D
-                # run independent calls on each slice
-                out3d = input.copy()
-                for i in range(np.shape(input)[0]):
-                    out = self.algorithm(input[i], regularization_parameter,
-                                 self.pars['first_order_term'] , 
-                                 self.pars['second_order_term'] , 
-                                 self.pars['number_of_iterations'])
-                    # copy the result in the 3D image
-                    out3d[i] = out[0].copy()
-                # append the rest of the info that the algorithm returns
-                output = [out3d]
-                for i in range(1,len(out)):
-                    output.append(out[i])
-                ret = output
-                
-                
-            
-        if output_all:
-            return ret
-        else:
-            return ret[0]
-        
-    # __call__
-    
-    @staticmethod
-    def SplitBregman_TV(input, regularization_parameter , **kwargs):
-        start_time = timeit.default_timer()
-        reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
-        a = reg(input, regularization_parameter, **kwargs) 
-        txt = reg.printParametersToString()
-        txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        return a, reg.pars, txt
-        
-    @staticmethod
-    def FGP_TV(input, regularization_parameter , **kwargs):
-        start_time = timeit.default_timer()
-        reg = Regularizer(Regularizer.Algorithm.FGP_TV)
-        a = reg(input, regularization_parameter, **kwargs) 
-        txt = reg.printParametersToString()
-        txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        return a, reg.pars, txt
-    
-    @staticmethod
-    def LLT_model(input, regularization_parameter , time_step, number_of_iterations,
-                  tolerance_constant, restrictive_Z_smoothing=0):
-        start_time = timeit.default_timer()
-        reg = Regularizer(Regularizer.Algorithm.LLT_model)
-        a = reg(input, regularization_parameter, time_step=time_step, 
-                        number_of_iterations=number_of_iterations,
-                        tolerance_constant=tolerance_constant, 
-                        restrictive_Z_smoothing=restrictive_Z_smoothing) 
-        
-        txt = reg.printParametersToString()
-        txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        return a, reg.pars, txt
-    
-    @staticmethod
-    def PatchBased_Regul(input, regularization_parameter,
-                        searching_window_ratio, 
-                        similarity_window_ratio,
-                        PB_filtering_parameter):
-        start_time = timeit.default_timer()
-        reg = Regularizer(Regularizer.Algorithm.PatchBased_Regul)   
-        a = reg(input, 
-                        regularization_parameter,
-                        searching_window_ratio=searching_window_ratio, 
-                        similarity_window_ratio=similarity_window_ratio,
-                        PB_filtering_parameter=PB_filtering_parameter )
-        txt = reg.printParametersToString()
-        txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        return a, reg.pars, txt
-    
-    @staticmethod
-    def TGV_PD(input, regularization_parameter , first_order_term, 
-               second_order_term, number_of_iterations):
-        start_time = timeit.default_timer()
-        
-        reg = Regularizer(Regularizer.Algorithm.TGV_PD)
-        a = reg(input, regularization_parameter, 
-                        first_order_term=first_order_term, 
-                        second_order_term=second_order_term,
-                        number_of_iterations=number_of_iterations) 
-        txt = reg.printParametersToString()
-        txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        
-        
-        return a, reg.pars, txt
-    
-    def printParametersToString(self):
-        txt = r''
-        for key, value in self.pars.items():
-            if key== 'algorithm' :
-                txt += "{0} = {1}".format(key, value.__name__)
-            elif key == 'input':
-                txt += "{0} = {1}".format(key, np.shape(value))
-            else:
-                txt += "{0} = {1}".format(key, value)
-            txt += '\n'
-        return txt
-        
diff --git a/Wrappers/Python/ccpi/reconstruction/AstraDevice.py b/Wrappers/Python/ccpi/reconstruction/AstraDevice.py
deleted file mode 100644
index 57435f8..0000000
--- a/Wrappers/Python/ccpi/reconstruction/AstraDevice.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import astra
-from ccpi.reconstruction.DeviceModel import DeviceModel
-import numpy
-
-class AstraDevice(DeviceModel):
-    '''Concrete class for Astra Device'''
-
-    def __init__(self,
-                 device_type,
-                 data_aquisition_geometry,
-                 reconstructed_volume_geometry):
-        
-        super(AstraDevice, self).__init__(device_type,
-                                          data_aquisition_geometry,
-                                          reconstructed_volume_geometry)
-
-        self.type = device_type
-        self.proj_geom = astra.creators.create_proj_geom(
-            device_type,
-            self.acquisition_data_geometry['detectorSpacingX'],
-            self.acquisition_data_geometry['detectorSpacingY'],
-            self.acquisition_data_geometry['cameraX'],
-            self.acquisition_data_geometry['cameraY'],
-            self.acquisition_data_geometry['angles'],
-            )
-        
-        self.vol_geom = astra.creators.create_vol_geom(
-            self.reconstructed_volume_geometry['X'],
-            self.reconstructed_volume_geometry['Y'],
-            self.reconstructed_volume_geometry['Z']
-            )
-        
-    def doForwardProject(self, volume):
-        '''Forward projects the volume according to the device geometry
-
-Uses Astra-toolbox
-'''
-              
-        try:
-            sino_id, y = astra.creators.create_sino3d_gpu(
-                volume, self.proj_geom, self.vol_geom)
-            astra.matlab.data3d('delete', sino_id)
-            return y
-        except Exception as e:
-            print(e)
-            print("Value Error: ", self.proj_geom, self.vol_geom)
-
-    def doBackwardProject(self, projections):
-        '''Backward projects the projections according to the device geometry
-
-Uses Astra-toolbox
-'''
-        idx, volume = \
-               astra.creators.create_backprojection3d_gpu(
-                   projections,
-                   self.proj_geom,
-                   self.vol_geom)
-        
-        astra.matlab.data3d('delete', idx)
-        return volume
-    
-    def createReducedDevice(self, proj_par={'cameraY' : 1} , vol_par={'Z':1}):
-        '''Create a new device based on the current device by changing some parameter
-
-VERY RISKY'''
-        acquisition_data_geometry = self.acquisition_data_geometry.copy()
-        for k,v in proj_par.items():
-            if k in acquisition_data_geometry.keys():
-                acquisition_data_geometry[k] = v
-        proj_geom =  [ 
-            acquisition_data_geometry['cameraX'],
-            acquisition_data_geometry['cameraY'],
-            acquisition_data_geometry['detectorSpacingX'],
-            acquisition_data_geometry['detectorSpacingY'],
-            acquisition_data_geometry['angles']
-            ]
-
-        reconstructed_volume_geometry = self.reconstructed_volume_geometry.copy()
-        for k,v in vol_par.items():
-            if k in reconstructed_volume_geometry.keys():
-                reconstructed_volume_geometry[k] = v
-        
-        vol_geom = [
-            reconstructed_volume_geometry['X'],
-            reconstructed_volume_geometry['Y'],
-            reconstructed_volume_geometry['Z']
-            ]
-        return AstraDevice(self.type, proj_geom, vol_geom)
-        
-
-        
-if __name__=="main":
-    a = AstraDevice()
-
-
diff --git a/Wrappers/Python/ccpi/reconstruction/DeviceModel.py b/Wrappers/Python/ccpi/reconstruction/DeviceModel.py
deleted file mode 100644
index eeb9a34..0000000
--- a/Wrappers/Python/ccpi/reconstruction/DeviceModel.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from abc import ABCMeta, abstractmethod
-from enum import Enum
-
-class DeviceModel(metaclass=ABCMeta):
-    '''Abstract class that defines the device for projection and backprojection
-
-This class defines the methods that must be implemented by concrete classes.
-
-    '''
-    
-    class DeviceType(Enum):
-        '''Type of device
-PARALLEL BEAM
-PARALLEL BEAM 3D
-CONE BEAM
-HELICAL'''
-        
-        PARALLEL = 'parallel'
-        PARALLEL3D = 'parallel3d'
-        CONE_BEAM = 'cone-beam'
-        HELICAL = 'helical'
-        
-    def __init__(self,
-                 device_type,
-                 data_aquisition_geometry,
-                 reconstructed_volume_geometry):
-        '''Initializes the class
-
-Mandatory parameters are:
-device_type from DeviceType Enum
-data_acquisition_geometry: tuple (camera_X, camera_Y, detectorSpacingX,
-                                  detectorSpacingY, angles)
-reconstructed_volume_geometry: tuple (dimX,dimY,dimZ)
-'''
-        self.device_geometry = device_type
-        self.acquisition_data_geometry = {
-            'cameraX':           data_aquisition_geometry[0],
-            'cameraY':           data_aquisition_geometry[1],
-            'detectorSpacingX' : data_aquisition_geometry[2],
-            'detectorSpacingY' : data_aquisition_geometry[3],
-            'angles' :           data_aquisition_geometry[4],}
-        self.reconstructed_volume_geometry = {
-            'X': reconstructed_volume_geometry[0] ,
-            'Y': reconstructed_volume_geometry[1] ,
-            'Z': reconstructed_volume_geometry[2] }
-
-    @abstractmethod
-    def doForwardProject(self, volume):
-        '''Forward projects the volume according to the device geometry'''
-        return NotImplemented
-
-    
-    @abstractmethod
-    def doBackwardProject(self, projections):
-        '''Backward projects the projections according to the device geometry'''
-        return NotImplemented
-
-    @abstractmethod
-    def createReducedDevice(self):
-        '''Create a Device to do forward/backward projections on 2D slices'''
-        return NotImplemented
-    
-
diff --git a/Wrappers/Python/ccpi/reconstruction/FISTAReconstructor.py b/Wrappers/Python/ccpi/reconstruction/FISTAReconstructor.py
deleted file mode 100644
index e40ad24..0000000
--- a/Wrappers/Python/ccpi/reconstruction/FISTAReconstructor.py
+++ /dev/null
@@ -1,882 +0,0 @@
-# -*- coding: utf-8 -*-
-###############################################################################
-#This work is part of the Core Imaging Library developed by
-#Visual Analytics and Imaging System Group of the Science Technology
-#Facilities Council, STFC
-#
-#Copyright 2017 Edoardo Pasca, Srikanth Nagella
-#Copyright 2017 Daniil Kazantsev
-#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
-#http://www.apache.org/licenses/LICENSE-2.0
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
-###############################################################################
-
-
-
-import numpy
-#from ccpi.reconstruction.parallelbeam import alg
-
-#from ccpi.imaging.Regularizer import Regularizer
-from enum import Enum
-
-import astra
-from ccpi.reconstruction.AstraDevice import AstraDevice
-
-   
-    
-class FISTAReconstructor():
-    '''FISTA-based reconstruction algorithm using ASTRA-toolbox
-    
-    '''
-    # <<<< FISTA-based reconstruction algorithm using ASTRA-toolbox >>>>
-    # ___Input___:
-    # params.[] file:
-    #       - .proj_geom (geometry of the projector) [required]
-    #       - .vol_geom (geometry of the reconstructed object) [required]
-    #       - .sino (vectorized in 2D or 3D sinogram) [required]
-    #       - .iterFISTA (iterations for the main loop, default 40)
-    #       - .L_const (Lipschitz constant, default Power method)                                                                                                    )
-    #       - .X_ideal (ideal image, if given)
-    #       - .weights (statisitcal weights, size of the sinogram)
-    #       - .ROI (Region-of-interest, only if X_ideal is given)
-    #       - .initialize (a 'warm start' using SIRT method from ASTRA)
-    #----------------Regularization choices------------------------
-    #       - .Regul_Lambda_FGPTV (FGP-TV regularization parameter)
-    #       - .Regul_Lambda_SBTV (SplitBregman-TV regularization parameter)
-    #       - .Regul_Lambda_TVLLT (Higher order SB-LLT regularization parameter)
-    #       - .Regul_tol (tolerance to terminate regul iterations, default 1.0e-04)
-    #       - .Regul_Iterations (iterations for the selected penalty, default 25)
-    #       - .Regul_tauLLT (time step parameter for LLT term)
-    #       - .Ring_LambdaR_L1 (regularization parameter for L1-ring minimization, if lambdaR_L1 > 0 then switch on ring removal)
-    #       - .Ring_Alpha (larger values can accelerate convergence but check stability, default 1)
-    #----------------Visualization parameters------------------------
-    #       - .show (visualize reconstruction 1/0, (0 default))
-    #       - .maxvalplot (maximum value to use for imshow[0 maxvalplot])
-    #       - .slice (for 3D volumes - slice number to imshow)
-    # ___Output___:
-    # 1. X - reconstructed image/volume
-    # 2. output - a structure with
-    #    - .Resid_error - residual error (if X_ideal is given)
-    #    - .objective: value of the objective function
-    #    - .L_const: Lipshitz constant to avoid recalculations
-    
-    # References:
-    # 1. "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse
-    # Problems" by A. Beck and M Teboulle
-    # 2. "Ring artifacts correction in compressed sensing..." by P. Paleo
-    # 3. "A novel tomographic reconstruction method based on the robust
-    # Student's t function for suppressing data outliers" D. Kazantsev et.al.
-    # D. Kazantsev, 2016-17
-    def __init__(self, projector_geometry,
-                 output_geometry,
-                 input_sinogram,
-                 device, 
-                 **kwargs):
-        # handle parmeters:
-        # obligatory parameters
-        self.pars = dict()
-        self.pars['projector_geometry'] = projector_geometry # proj_geom
-        self.pars['output_geometry'] = output_geometry       # vol_geom
-        self.pars['input_sinogram'] = input_sinogram         # sino
-        sliceZ, nangles, detectors = numpy.shape(input_sinogram)
-        self.pars['detectors'] = detectors
-        self.pars['number_of_angles'] = nangles
-        self.pars['SlicesZ'] = sliceZ
-        self.pars['output_volume'] = None
-        self.pars['device_model'] = device
-
-        self.use_device = True
-        
-        print (self.pars)
-        # handle optional input parameters (at instantiation)
-        
-        # Accepted input keywords
-        kw = (
-              # mandatory fields
-              'projector_geometry',
-              'output_geometry',
-              'input_sinogram',
-              'detectors',
-              'number_of_angles',
-              'SlicesZ',
-              # optional fields
-              'number_of_iterations', 
-              'Lipschitz_constant' , 
-              'ideal_image' ,
-              'weights' , 
-              'region_of_interest' , 
-              'initialize' , 
-              'regularizer' , 
-              'ring_lambda_R_L1',
-              'ring_alpha',
-              'subsets',
-              'output_volume',
-              'os_subsets',
-              'os_indices',
-              'os_bins',
-              'device_model',
-              'reduced_device_model')
-        self.acceptedInputKeywords = list(kw)
-        
-        # handle keyworded parameters
-        if kwargs is not None:
-            for key, value in kwargs.items():
-                if key in kw:
-                    #print("{0} = {1}".format(key, value))                        
-                    self.pars[key] = value
-                    
-        # set the default values for the parameters if not set
-        if 'number_of_iterations' in kwargs.keys():
-            self.pars['number_of_iterations'] = kwargs['number_of_iterations']
-        else:
-            self.pars['number_of_iterations'] = 40
-        if 'weights' in kwargs.keys():
-            self.pars['weights'] = kwargs['weights']
-        else:
-            self.pars['weights'] = \
-                                 numpy.ones(numpy.shape(
-                                     self.pars['input_sinogram']))
-        if 'Lipschitz_constant' in kwargs.keys():
-            self.pars['Lipschitz_constant'] = kwargs['Lipschitz_constant']
-        else:
-            self.pars['Lipschitz_constant'] = None
-        
-        if not 'ideal_image' in kwargs.keys():
-            self.pars['ideal_image'] = None
-        
-        if not 'region_of_interest'in kwargs.keys() :
-            if self.pars['ideal_image'] == None:
-                self.pars['region_of_interest'] = None
-            else:
-                ## nonzero if the image is larger than m
-                fsm = numpy.frompyfunc(lambda x,m: 1 if x>m else 0, 2,1)
-                
-                self.pars['region_of_interest'] = fsm(self.pars['ideal_image'], 0)
-                
-        # the regularizer must be a correctly instantiated object    
-        if not 'regularizer' in kwargs.keys() :
-            self.pars['regularizer'] = None
-
-        #RING REMOVAL
-        if not 'ring_lambda_R_L1' in kwargs.keys():
-            self.pars['ring_lambda_R_L1'] = 0
-        if not 'ring_alpha' in kwargs.keys():
-            self.pars['ring_alpha'] = 1
-
-        # ORDERED SUBSET
-        if not 'subsets' in kwargs.keys():
-            self.pars['subsets'] = 0
-        else:
-            self.createOrderedSubsets()
-
-        if not 'initialize' in kwargs.keys():
-            self.pars['initialize'] = False
-
-        reduced_device = device.createReducedDevice()
-        self.setParameter(reduced_device_model=reduced_device)
-
-        
-                
-    def setParameter(self, **kwargs):
-        '''set named parameter for the reconstructor engine
-        
-        raises Exception if the named parameter is not recognized
-        
-        '''
-        for key , value in kwargs.items():
-            if key in self.acceptedInputKeywords:
-                self.pars[key] = value
-            else:
-                raise Exception('Wrong parameter {0} for '.format(key) +
-                                'reconstructor')
-    # setParameter
-
-    def getParameter(self, key):
-        if type(key) is str:
-            if key in self.acceptedInputKeywords:
-                return self.pars[key]
-            else:
-                raise Exception('Unrecongnised parameter: {0} '.format(key) )
-        elif type(key) is list:
-            outpars = []
-            for k in key:
-                outpars.append(self.getParameter(k))
-            return outpars
-        else:
-            raise Exception('Unhandled input {0}' .format(str(type(key))))
-            
-    
-    def calculateLipschitzConstantWithPowerMethod(self):
-        ''' using Power method (PM) to establish L constant'''
-        
-        N = self.pars['output_geometry']['GridColCount']
-        proj_geom = self.pars['projector_geometry']
-        vol_geom = self.pars['output_geometry']
-        weights = self.pars['weights']
-        SlicesZ = self.pars['SlicesZ']
-        
-            
-                               
-        if (proj_geom['type'] == 'parallel') or \
-           (proj_geom['type'] == 'parallel3d'):
-            #% for parallel geometry we can do just one slice
-            #print('Calculating Lipshitz constant for parallel beam geometry...')
-            niter = 5;# % number of iteration for the PM
-            #N = params.vol_geom.GridColCount;
-            #x1 = rand(N,N,1);
-            x1 = numpy.random.rand(1,N,N)
-            #sqweight = sqrt(weights(:,:,1));
-            sqweight = numpy.sqrt(weights[0:1,:,:])
-            proj_geomT = proj_geom.copy();
-            proj_geomT['DetectorRowCount'] = 1;
-            vol_geomT = vol_geom.copy();
-            vol_geomT['GridSliceCount'] = 1;
-            
-            #[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
-            
-            
-            for i in range(niter):
-            #        [id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geomT, vol_geomT);
-            #            s = norm(x1(:));
-            #            x1 = x1/s;
-            #            [sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
-            #            y = sqweight.*y;
-            #            astra_mex_data3d('delete', sino_id);
-            #            astra_mex_data3d('delete', id);
-                #print ("iteration {0}".format(i))
-                                
-                sino_id, y = astra.creators.create_sino3d_gpu(x1,
-                                                          proj_geomT,
-                                                          vol_geomT)
-                
-                y = (sqweight * y).copy() # element wise multiplication
-                
-                #b=fig.add_subplot(2,1,2)
-                #imgplot = plt.imshow(x1[0])
-                #plt.show()
-                
-                #astra_mex_data3d('delete', sino_id);
-                astra.matlab.data3d('delete', sino_id)
-                del x1
-                    
-                idx,x1 = astra.creators.create_backprojection3d_gpu((sqweight*y).copy(), 
-                                                                    proj_geomT,
-                                                                    vol_geomT)
-                del y
-                
-                                                                    
-                s = numpy.linalg.norm(x1)
-                ### this line?
-                x1 = (x1/s).copy();
-                
-            #        ### this line?
-            #        sino_id, y = astra.creators.create_sino3d_gpu(x1, 
-            #                                                      proj_geomT, 
-            #                                                      vol_geomT);
-            #        y = sqweight * y;
-                astra.matlab.data3d('delete', sino_id);
-                astra.matlab.data3d('delete', idx)
-                print ("iteration {0} s= {1}".format(i,s))
-                
-            #end
-            del proj_geomT
-            del vol_geomT
-            #plt.show()
-        else:
-            #% divergen beam geometry
-            print('Calculating Lipshitz constant for divergen beam geometry...')
-            niter = 8; #% number of iteration for PM
-            x1 = numpy.random.rand(SlicesZ , N , N);
-            #sqweight = sqrt(weights);
-            sqweight = numpy.sqrt(weights[0])
-            
-            sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom);
-            y = sqweight*y;
-            #astra_mex_data3d('delete', sino_id);
-            astra.matlab.data3d('delete', sino_id);
-            
-            for i in range(niter):
-                #[id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geom, vol_geom);
-                idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, 
-                                                                    proj_geom, 
-                                                                    vol_geom)
-                s = numpy.linalg.norm(x1)
-                ### this line?
-                x1 = x1/s;
-                ### this line?
-                #[sino_id, y] = astra_create_sino3d_gpu(x1, proj_geom, vol_geom);
-                sino_id, y = astra.creators.create_sino3d_gpu(x1, 
-                                                              proj_geom, 
-                                                              vol_geom);
-                
-                y = sqweight*y;
-                #astra_mex_data3d('delete', sino_id);
-                #astra_mex_data3d('delete', id);
-                astra.matlab.data3d('delete', sino_id);
-                astra.matlab.data3d('delete', idx);
-            #end
-            #clear x1
-            del x1
-
-        
-        return s
-    
-    
-    def setRegularizer(self, regularizer):
-        if regularizer is not None:
-            self.pars['regularizer'] = regularizer
-        
-
-    def initialize(self):
-        # convenience variable storage
-        proj_geom = self.pars['projector_geometry']
-        vol_geom = self.pars['output_geometry']
-        sino = self.pars['input_sinogram']
-        
-        # a 'warm start' with SIRT method
-        # Create a data object for the reconstruction
-        rec_id = astra.matlab.data3d('create', '-vol',
-                                    vol_geom);
-        
-        #sinogram_id = astra_mex_data3d('create', '-proj3d', proj_geom, sino);
-        sinogram_id = astra.matlab.data3d('create', '-proj3d',
-                                          proj_geom,
-                                          sino)
-
-        sirt_config = astra.astra_dict('SIRT3D_CUDA')
-        sirt_config['ReconstructionDataId' ] = rec_id
-        sirt_config['ProjectionDataId'] = sinogram_id
-
-        sirt = astra.algorithm.create(sirt_config)
-        astra.algorithm.run(sirt, iterations=35)
-        X = astra.matlab.data3d('get', rec_id)
-
-        # clean up memory
-        astra.matlab.data3d('delete', rec_id)
-        astra.matlab.data3d('delete', sinogram_id)
-        astra.algorithm.delete(sirt)
-
-        
-
-        return X
-
-    def createOrderedSubsets(self, subsets=None):
-        if subsets is None:
-            try:
-                subsets = self.getParameter('subsets')
-            except Exception():
-                subsets = 0
-            #return subsets
-        else:
-            self.setParameter(subsets=subsets)
-            
-
-        angles = self.getParameter('projector_geometry')['ProjectionAngles'] 
-        
-        #binEdges = numpy.linspace(angles.min(),
-        #                          angles.max(),
-        #                          subsets + 1)
-        binsDiscr, binEdges = numpy.histogram(angles, bins=subsets)
-        # get rearranged subset indices
-        IndicesReorg = numpy.zeros((numpy.shape(angles)), dtype=numpy.int32)
-        counterM = 0
-        for ii in range(binsDiscr.max()):
-            counter = 0
-            for jj in range(subsets):
-                curr_index = ii + jj  + counter
-                #print ("{0} {1} {2}".format(binsDiscr[jj] , ii, counterM))
-                if binsDiscr[jj] > ii:
-                    if (counterM < numpy.size(IndicesReorg)):
-                        IndicesReorg[counterM] = curr_index
-                    counterM = counterM + 1
-                    
-                counter = counter + binsDiscr[jj] - 1    
-                
-        # store the OS in parameters
-        self.setParameter(os_subsets=subsets,
-                          os_bins=binsDiscr,
-                          os_indices=IndicesReorg)
-            
-
-    def prepareForIteration(self):
-        print ("FISTA Reconstructor: prepare for iteration")
-        
-        self.residual_error = numpy.zeros((self.pars['number_of_iterations']))
-        self.objective = numpy.zeros((self.pars['number_of_iterations']))
-
-        #2D array (for 3D data) of sparse "ring" 
-        detectors, nangles, sliceZ  = numpy.shape(self.pars['input_sinogram'])
-        self.r = numpy.zeros((detectors, sliceZ), dtype=numpy.float)
-        # another ring variable
-        self.r_x = self.r.copy()
-
-        self.residual = numpy.zeros(numpy.shape(self.pars['input_sinogram']))
-        
-        if self.getParameter('Lipschitz_constant') is None:
-            self.pars['Lipschitz_constant'] = \
-                            self.calculateLipschitzConstantWithPowerMethod()
-        # errors vector (if the ground truth is given)
-        self.Resid_error = numpy.zeros((self.getParameter('number_of_iterations')));
-        # objective function values vector
-        self.objective = numpy.zeros((self.getParameter('number_of_iterations')));      
-        
-
-    # prepareForIteration
-
-    def iterate (self, Xin=None):
-        if self.getParameter('subsets') == 0:
-            return self.iterateStandard(Xin)
-        else:
-            return self.iterateOrderedSubsets(Xin)
-        
-    def iterateStandard(self, Xin=None):
-        print ("FISTA Reconstructor: iterate")
-        
-        if Xin is None:    
-            if self.getParameter('initialize'):
-                X = self.initialize()
-            else:
-                N = vol_geom['GridColCount']
-                X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
-        else:
-            # copy by reference
-            X = Xin
-        # store the output volume in the parameters
-        self.setParameter(output_volume=X)
-        X_t = X.copy()
-        # convenience variable storage
-        proj_geom , vol_geom, sino , \
-          SlicesZ , ring_lambda_R_L1 , weights = \
-                            self.getParameter([ 'projector_geometry' ,
-                                                'output_geometry',
-                                                'input_sinogram',
-                                                'SlicesZ' ,
-                                                'ring_lambda_R_L1',
-                                                'weights'])
-                   
-        t = 1
-
-        device = self.getParameter('device_model')
-        reduced_device = self.getParameter('reduced_device_model')
-        
-        for i in range(self.getParameter('number_of_iterations')):
-            print("iteration", i)
-            X_old = X.copy()
-            t_old = t
-            r_old = self.r.copy()
-            pg = self.getParameter('projector_geometry')['type']
-            if pg == 'parallel' or \
-               pg == 'fanflat' or \
-               pg == 'fanflat_vec':
-                # if the geometry is parallel use slice-by-slice
-                # projection-backprojection routine
-                #sino_updt = zeros(size(sino),'single');
-                
-                if self.use_device :
-                    self.sino_updt = numpy.zeros(numpy.shape(sino), dtype=numpy.float)
-                    
-                    for kkk in range(SlicesZ):
-                        self.sino_updt[kkk] = \
-                            reduced_device.doForwardProject( X_t[kkk:kkk+1] )
-                else:
-                    proj_geomT = proj_geom.copy()
-                    proj_geomT['DetectorRowCount'] = 1
-                    vol_geomT = vol_geom.copy()
-                    vol_geomT['GridSliceCount'] = 1;
-                    self.sino_updt = numpy.zeros(numpy.shape(sino), dtype=numpy.float)
-                    for kkk in range(SlicesZ):
-                        sino_id, self.sino_updt[kkk] = \
-                                 astra.creators.create_sino3d_gpu(
-                                     X_t[kkk:kkk+1], proj_geomT, vol_geomT)
-                        astra.matlab.data3d('delete', sino_id)
-            else:
-                # for divergent 3D geometry (watch the GPU memory overflow in
-                # ASTRA versions < 1.8)
-                #[sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom);
-                
-                if self.use_device:
-                    self.sino_updt = device.doForwardProject(X_t)
-                else:
-                    sino_id, self.sino_updt = astra.creators.create_sino3d_gpu(
-                        X_t, proj_geom, vol_geom)
-                    astra.matlab.data3d('delete', sino_id)
-
-
-            ## RING REMOVAL
-            if ring_lambda_R_L1 != 0:
-                self.ringRemoval(i)
-            else:
-                self.residual = weights * (self.sino_updt - sino)
-                self.objective[i] = 0.5 * numpy.linalg.norm(self.residual)
-                #objective(i) = 0.5*norm(residual(:)); % for the objective function output
-            ## Projection/Backprojection Routine
-            X, X_t = self.projectionBackprojection(X, X_t)
-            
-            ## REGULARIZATION
-            Y = self.regularize(X)
-            X = Y.copy()
-            ## Update Loop
-            X , X_t, t = self.updateLoop(i, X, X_old, r_old, t, t_old)
-
-            print ("t" , t)
-            print ("X min {0} max {1}".format(X_t.min(),X_t.max()))
-            self.setParameter(output_volume=X)
-        return X
-    ## iterate
-    
-    def ringRemoval(self, i):
-        print ("FISTA Reconstructor: ring removal")
-        residual = self.residual
-        lambdaR_L1 , alpha_ring , weights , L_const , sino= \
-                   self.getParameter(['ring_lambda_R_L1',
-                                      'ring_alpha' , 'weights',
-                                      'Lipschitz_constant',
-                                      'input_sinogram'])
-        r_x = self.r_x
-        sino_updt = self.sino_updt
-        
-        SlicesZ, anglesNumb, Detectors = \
-                    numpy.shape(self.getParameter('input_sinogram'))
-        if lambdaR_L1 > 0 :
-             for kkk in range(anglesNumb):
-                 
-                 residual[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
-                                       ((sino_updt[:,kkk,:]).squeeze() - \
-                                        (sino[:,kkk,:]).squeeze() -\
-                                        (alpha_ring * r_x)
-                                        )
-             vec = residual.sum(axis = 1)
-             #if SlicesZ > 1:
-             #    vec = vec[:,1,:].squeeze()
-             self.r = (r_x - (1./L_const) * vec).copy()
-             self.objective[i] = (0.5 * (residual ** 2).sum())
-
-    def projectionBackprojection(self, X, X_t):
-        print ("FISTA Reconstructor: projection-backprojection routine")
-        
-        # a few useful variables
-        SlicesZ, anglesNumb, Detectors = \
-                    numpy.shape(self.getParameter('input_sinogram'))
-        residual = self.residual
-        proj_geom , vol_geom , L_const = \
-                  self.getParameter(['projector_geometry' ,
-                                                  'output_geometry',
-                                                  'Lipschitz_constant'])
-        
-        device, reduced_device = self.getParameter(['device_model',
-                                                    'reduced_device_model'])
-        
-        if self.getParameter('projector_geometry')['type'] == 'parallel' or \
-           self.getParameter('projector_geometry')['type'] == 'fanflat' or \
-           self.getParameter('projector_geometry')['type'] == 'fanflat_vec':
-            # if the geometry is parallel use slice-by-slice
-            # projection-backprojection routine
-            #sino_updt = zeros(size(sino),'single');
-            x_temp = numpy.zeros(numpy.shape(X),dtype=numpy.float32)
-                
-            if self.use_device:
-                proj_geomT = proj_geom.copy()
-                proj_geomT['DetectorRowCount'] = 1
-                vol_geomT = vol_geom.copy()
-                vol_geomT['GridSliceCount'] = 1;
-                
-                for kkk in range(SlicesZ):
-                    
-                    x_id, x_temp[kkk] = \
-                             astra.creators.create_backprojection3d_gpu(
-                                 residual[kkk:kkk+1],
-                                 proj_geomT, vol_geomT)
-                    astra.matlab.data3d('delete', x_id)
-            else:
-                for kkk in range(SliceZ):
-                    x_temp[kkk] = \
-                        reduced_device.doBackwardProject(residual[kkk:kkk+1])
-        else:
-            if self.use_device:
-                x_id, x_temp = \
-                  astra.creators.create_backprojection3d_gpu(
-                      residual, proj_geom, vol_geom)
-                astra.matlab.data3d('delete', x_id)
-            else:
-                x_temp = \
-                    device.doBackwardProject(residual)
-                       
-
-        X = X_t - (1/L_const) * x_temp
-        #astra.matlab.data3d('delete', sino_id)
-        return (X , X_t)
-        
-
-    def regularize(self, X , output_all=False):
-        #print ("FISTA Reconstructor: regularize")
-        
-        regularizer = self.getParameter('regularizer')
-        if regularizer is not None:
-            return regularizer(input=X,
-                               output_all=output_all)
-        else:
-            return X
-
-    def updateLoop(self, i, X, X_old, r_old, t, t_old):
-        print ("FISTA Reconstructor: update loop")
-        lambdaR_L1 = self.getParameter('ring_lambda_R_L1')
-            
-        t = (1 + numpy.sqrt(1 + 4 * t**2))/2
-        X_t = X + (((t_old -1)/t) * (X - X_old))
-
-        if lambdaR_L1 > 0:
-            self.r = numpy.max(
-                numpy.abs(self.r) - lambdaR_L1 , 0) * \
-                numpy.sign(self.r)
-            self.r_x = self.r + \
-                             (((t_old-1)/t) * (self.r - r_old))
-
-        if self.getParameter('region_of_interest') is None:
-            string = 'Iteration Number {0} | Objective {1} \n'
-            print (string.format( i, self.objective[i]))
-        else:
-            ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
-                                                    'ideal_image')
-            
-            Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
-            string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
-            print (string.format(i,Resid_error[i], self.objective[i]))
-        return (X , X_t, t)
-
-    def iterateOrderedSubsets(self, Xin=None):
-        print ("FISTA Reconstructor: Ordered Subsets iterate")
-        
-        if Xin is None:    
-            if self.getParameter('initialize'):
-                X = self.initialize()
-            else:
-                N = vol_geom['GridColCount']
-                X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
-        else:
-            # copy by reference
-            X = Xin
-        # store the output volume in the parameters
-        self.setParameter(output_volume=X)
-        X_t = X.copy()
-
-        # some useful constants
-        proj_geom ,    vol_geom, sino , \
-          SlicesZ,     weights , alpha_ring ,\
-          lambdaR_L1 , L_const , iterFISTA         = self.getParameter(
-            ['projector_geometry' , 'output_geometry', 'input_sinogram',
-             'SlicesZ' ,            'weights',         'ring_alpha' ,
-             'ring_lambda_R_L1',    'Lipschitz_constant',
-             'number_of_iterations'])
-
-            
-        # errors vector (if the ground truth is given)
-        Resid_error = numpy.zeros((iterFISTA));
-        # objective function values vector
-        #objective = numpy.zeros((iterFISTA));
-        objective = self.objective
-
-          
-        t = 1
-
-        ## additional for 
-        proj_geomSUB = proj_geom.copy()
-        self.residual2 = numpy.zeros(numpy.shape(sino))
-        residual2 = self.residual2
-        sino_updt_FULL = self.residual.copy()
-        r_x = self.r.copy()
-
-        print ("starting iterations")
-        ##    % Outer FISTA iterations loop
-        for i in range(self.getParameter('number_of_iterations')):
-            # With OS approach it becomes trickier to correlate independent
-            # subsets, hence additional work is required one solution is to
-            # work with a full sinogram at times
-
-            r_old = self.r.copy()
-            t_old = t
-            SlicesZ, anglesNumb, Detectors = \
-                        numpy.shape(self.getParameter('input_sinogram'))        ## https://github.com/vais-ral/CCPi-FISTA_Reconstruction/issues/4
-            if (i > 1 and lambdaR_L1 > 0) :
-                for kkk in range(anglesNumb):
-                     
-                     residual2[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
-                                           ((sino_updt_FULL[:,kkk,:]).squeeze() - \
-                                            (sino[:,kkk,:]).squeeze() -\
-                                            (alpha_ring * r_x)
-                                            )
-                
-                vec = self.residual.sum(axis = 1)
-                #if SlicesZ > 1:
-                #    vec = vec[:,1,:] # 1 or 0?
-                r_x = self.r_x
-                # update ring variable
-                self.r = (r_x - (1./L_const) * vec).copy()
-
-            # subset loop
-            counterInd = 1
-            geometry_type = self.getParameter('projector_geometry')['type']
-            angles = self.getParameter('projector_geometry')['ProjectionAngles']
-
-            for ss in range(self.getParameter('subsets')):
-                #print ("Subset {0}".format(ss))
-                X_old = X.copy()
-                t_old = t
-                
-                # the number of projections per subset
-                numProjSub = self.getParameter('os_bins')[ss]
-                CurrSubIndices = self.getParameter('os_indices')\
-                                 [counterInd:counterInd+numProjSub]
-                #print ("Len CurrSubIndices {0}".format(numProjSub))
-                mask = numpy.zeros(numpy.shape(angles), dtype=bool)
-                #cc = 0
-                for j in range(len(CurrSubIndices)):
-                    mask[int(CurrSubIndices[j])] = True
-                proj_geomSUB['ProjectionAngles'] = angles[mask]
-                
-                if self.use_device:
-                    device = self.getParameter('device_model')\
-                             .createReducedDevice(
-                                 proj_par={'angles':angles[mask]},
-                                 vol_par={})
-        
-                shape = list(numpy.shape(self.getParameter('input_sinogram')))
-                shape[1] = numProjSub
-                sino_updt_Sub = numpy.zeros(shape)
-                if geometry_type == 'parallel' or \
-                   geometry_type == 'fanflat' or \
-                   geometry_type == 'fanflat_vec' :
-
-                    for kkk in range(SlicesZ):
-                        if self.use_device:
-                            sinoT = device.doForwardProject(X_t[kkk:kkk+1])
-                        else:
-                            sino_id, sinoT = astra.creators.create_sino3d_gpu (
-                                X_t[kkk:kkk+1] , proj_geomSUB, vol_geom)
-                            astra.matlab.data3d('delete', sino_id)
-                        sino_updt_Sub[kkk] = sinoT.T.copy()
-                        
-                else:
-                    # for 3D geometry (watch the GPU memory overflow in
-                    # ASTRA < 1.8)
-                    if self.use_device:
-                        sino_updt_Sub = device.doForwardProject(X_t)
-                        
-                    else:
-                        sino_id, sino_updt_Sub = \
-                             astra.creators.create_sino3d_gpu (X_t, proj_geomSUB, vol_geom)
-                        
-                        astra.matlab.data3d('delete', sino_id)
-        
-                #print ("shape(sino_updt_Sub)",numpy.shape(sino_updt_Sub))
-                if lambdaR_L1 > 0 :
-                    ## RING REMOVAL
-                    #print ("ring removal")
-                    residualSub , sino_updt_Sub, sino_updt_FULL = \
-                        self.ringRemovalOrderedSubsets(ss,
-                                                       counterInd,
-                                                       sino_updt_Sub,
-                                                       sino_updt_FULL)
-                else:
-                    #PWLS model
-                    #print ("PWLS model")
-                    residualSub = weights[:,CurrSubIndices,:] * \
-                                  ( sino_updt_Sub - \
-                                    sino[:,CurrSubIndices,:].squeeze() )
-                    objective[i] = 0.5 * numpy.linalg.norm(residualSub)
-
-                # projection/backprojection routine
-                if geometry_type == 'parallel' or \
-                   geometry_type == 'fanflat' or \
-                   geometry_type == 'fanflat_vec' :
-                    # if geometry is 2D use slice-by-slice projection-backprojection
-                    # routine
-                    x_temp = numpy.zeros(numpy.shape(X), dtype=numpy.float32)
-                    for kkk in range(SlicesZ):
-                        if self.use_device:
-                            x_temp[kkk] = device.doBackwardProject(
-                                residualSub[kkk:kkk+1])
-                        else:
-                            x_id, x_temp[kkk] = \
-                                     astra.creators.create_backprojection3d_gpu(
-                                         residualSub[kkk:kkk+1],
-                                         proj_geomSUB, vol_geom)
-                            astra.matlab.data3d('delete', x_id)
-                        
-                else:
-                    if self.use_device:
-                        x_temp = device.doBackwardProject(
-                            residualSub)
-                    else:
-                        x_id, x_temp = \
-                          astra.creators.create_backprojection3d_gpu(
-                              residualSub, proj_geomSUB, vol_geom)
-
-                        astra.matlab.data3d('delete', x_id)
-                
-                X = X_t - (1/L_const) * x_temp
-                
-                ## REGULARIZATION
-                X = self.regularize(X)
-                
-                ## Update subset Loop
-                t = (1 + numpy.sqrt(1 + 4 * t**2))/2
-                X_t = X + (((t_old -1)/t) * (X - X_old))
-            # FINAL
-            ## update iteration loop
-            if lambdaR_L1 > 0:
-                self.r = numpy.max(
-                    numpy.abs(self.r) - lambdaR_L1 , 0) * \
-                    numpy.sign(self.r)
-                self.r_x = self.r + \
-                                 (((t_old-1)/t) * (self.r - r_old))
-
-            if self.getParameter('region_of_interest') is None:
-                string = 'Iteration Number {0} | Objective {1} \n'
-                print (string.format( i, self.objective[i]))
-            else:
-                ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
-                                                        'ideal_image')
-                
-                Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
-                string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
-                print (string.format(i,Resid_error[i], self.objective[i]))    
-            print("X min {0} max {1}".format(X.min(),X.max()))
-            self.setParameter(output_volume=X)
-            counterInd = counterInd + numProjSub
-
-        return X
-    
-    def ringRemovalOrderedSubsets(self, ss,counterInd,
-                                  sino_updt_Sub, sino_updt_FULL):
-        residual = self.residual
-        r_x = self.r_x
-        weights , alpha_ring , sino = \
-                self.getParameter( ['weights', 'ring_alpha', 'input_sinogram'])
-        numProjSub = self.getParameter('os_bins')[ss]
-        CurrSubIndices = self.getParameter('os_indices')\
-                         [counterInd:counterInd+numProjSub]
-
-        shape = list(numpy.shape(self.getParameter('input_sinogram')))
-        shape[1] = numProjSub
-            
-        residualSub = numpy.zeros(shape)
-
-        for kkk in range(numProjSub):
-            #print ("ring removal indC ... {0}".format(kkk))
-            indC = int(CurrSubIndices[kkk])
-            residualSub[:,kkk,:] = weights[:,indC,:].squeeze() * \
-                (sino_updt_Sub[:,kkk,:].squeeze() - \
-                sino[:,indC,:].squeeze() - alpha_ring * r_x)
-            # filling the full sinogram
-            sino_updt_FULL[:,indC,:] = sino_updt_Sub[:,kkk,:].squeeze()
-
-        return (residualSub , sino_updt_Sub, sino_updt_FULL)
-
-
diff --git a/Wrappers/Python/ccpi/reconstruction/Reconstructor.py b/Wrappers/Python/ccpi/reconstruction/Reconstructor.py
deleted file mode 100644
index 2ad8a44..0000000
--- a/Wrappers/Python/ccpi/reconstruction/Reconstructor.py
+++ /dev/null
@@ -1,598 +0,0 @@
-# -*- coding: utf-8 -*-
-###############################################################################
-#This work is part of the Core Imaging Library developed by
-#Visual Analytics and Imaging System Group of the Science Technology
-#Facilities Council, STFC
-#
-#Copyright 2017 Edoardo Pasca, Srikanth Nagella
-#Copyright 2017 Daniil Kazantsev
-#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
-#http://www.apache.org/licenses/LICENSE-2.0
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
-###############################################################################
-
-
-
-import numpy
-import h5py
-from ccpi.reconstruction.parallelbeam import alg
-
-from Regularizer import Regularizer
-from enum import Enum
-
-import astra
-
-
-class Reconstructor:
-    
-    class Algorithm(Enum):
-        CGLS = alg.cgls
-        CGLS_CONV = alg.cgls_conv
-        SIRT = alg.sirt
-        MLEM = alg.mlem
-        CGLS_TICHONOV = alg.cgls_tikhonov
-        CGLS_TVREG = alg.cgls_TVreg
-        FISTA = 'fista'
-        
-    def __init__(self, algorithm = None, projection_data = None,
-                 angles = None, center_of_rotation = None , 
-                 flat_field = None, dark_field = None, 
-                 iterations = None, resolution = None, isLogScale = False, threads = None, 
-                 normalized_projection = None):
-    
-        self.pars = dict()
-        self.pars['algorithm'] = algorithm
-        self.pars['projection_data'] = projection_data
-        self.pars['normalized_projection'] = normalized_projection
-        self.pars['angles'] = angles
-        self.pars['center_of_rotation'] = numpy.double(center_of_rotation)
-        self.pars['flat_field'] = flat_field
-        self.pars['iterations'] = iterations
-        self.pars['dark_field'] = dark_field
-        self.pars['resolution'] = resolution
-        self.pars['isLogScale'] = isLogScale
-        self.pars['threads'] = threads
-        if (iterations != None):
-            self.pars['iterationValues'] = numpy.zeros((iterations)) 
-        
-        if projection_data != None and dark_field != None and flat_field != None:
-            norm = self.normalize(projection_data, dark_field, flat_field, 0.1)
-            self.pars['normalized_projection'] = norm
-            
-    
-    def setPars(self, parameters):
-        keys = ['algorithm','projection_data' ,'normalized_projection', \
-                'angles' , 'center_of_rotation' , 'flat_field', \
-                'iterations','dark_field' , 'resolution', 'isLogScale' , \
-                'threads' , 'iterationValues', 'regularize']
-        
-        for k in keys:
-            if k not in parameters.keys():
-                self.pars[k] = None
-            else:
-                self.pars[k] = parameters[k]
-                
-        
-    def sanityCheck(self):
-        projection_data = self.pars['projection_data']
-        dark_field = self.pars['dark_field']
-        flat_field = self.pars['flat_field']
-        angles = self.pars['angles']
-        
-        if projection_data != None and dark_field != None and \
-            angles != None and flat_field != None:
-            data_shape =  numpy.shape(projection_data)
-            angle_shape = numpy.shape(angles)
-            
-            if angle_shape[0] != data_shape[0]:
-                #raise Exception('Projections and angles dimensions do not match: %d vs %d' % \
-                #                (angle_shape[0] , data_shape[0]) )
-                return (False , 'Projections and angles dimensions do not match: %d vs %d' % \
-                                (angle_shape[0] , data_shape[0]) )
-            
-            if data_shape[1:] != numpy.shape(flat_field):
-                #raise Exception('Projection and flat field dimensions do not match')
-                return (False , 'Projection and flat field dimensions do not match')
-            if data_shape[1:] != numpy.shape(dark_field):
-                #raise Exception('Projection and dark field dimensions do not match')
-                return (False , 'Projection and dark field dimensions do not match')
-            
-            return (True , '' )
-        elif self.pars['normalized_projection'] != None:
-            data_shape =  numpy.shape(self.pars['normalized_projection'])
-            angle_shape = numpy.shape(angles)
-            
-            if angle_shape[0] != data_shape[0]:
-                #raise Exception('Projections and angles dimensions do not match: %d vs %d' % \
-                #                (angle_shape[0] , data_shape[0]) )
-                return (False , 'Projections and angles dimensions do not match: %d vs %d' % \
-                                (angle_shape[0] , data_shape[0]) )
-            else:
-                return (True , '' )
-        else:
-            return (False , 'Not enough data')
-            
-    def reconstruct(self, parameters = None):
-        if parameters != None:
-            self.setPars(parameters)
-        
-        go , reason = self.sanityCheck()
-        if go:
-            return self._reconstruct()
-        else:
-            raise Exception(reason)
-            
-            
-    def _reconstruct(self, parameters=None):
-        if parameters!=None:
-            self.setPars(parameters)
-        parameters = self.pars
-        
-        if parameters['algorithm'] != None and \
-           parameters['normalized_projection'] != None and \
-           parameters['angles'] != None and \
-           parameters['center_of_rotation'] != None and \
-           parameters['iterations'] != None and \
-           parameters['resolution'] != None and\
-           parameters['threads'] != None and\
-           parameters['isLogScale'] != None:
-               
-               
-           if parameters['algorithm'] in (Reconstructor.Algorithm.CGLS,
-                        Reconstructor.Algorithm.MLEM, Reconstructor.Algorithm.SIRT):
-               #store parameters
-               self.pars = parameters
-               result = parameters['algorithm'](
-                           parameters['normalized_projection'] ,
-                           parameters['angles'],
-                           parameters['center_of_rotation'],
-                           parameters['resolution'],
-                           parameters['iterations'],
-                           parameters['threads'] ,
-                           parameters['isLogScale']
-                           )
-               return result
-           elif parameters['algorithm'] in (Reconstructor.Algorithm.CGLS_CONV,
-                          Reconstructor.Algorithm.CGLS_TICHONOV, 
-                          Reconstructor.Algorithm.CGLS_TVREG) :
-               self.pars = parameters
-               result = parameters['algorithm'](
-                           parameters['normalized_projection'] ,
-                           parameters['angles'],
-                           parameters['center_of_rotation'],
-                           parameters['resolution'],
-                           parameters['iterations'],
-                           parameters['threads'] ,
-                           parameters['regularize'],
-                           numpy.zeros((parameters['iterations'])),
-                           parameters['isLogScale']
-                           )
-               
-           elif parameters['algorithm'] == Reconstructor.Algorithm.FISTA:
-               pass
-             
-        else:
-           if parameters['projection_data'] != None and \
-                     parameters['dark_field'] != None and \
-                     parameters['flat_field'] != None:
-               norm = self.normalize(parameters['projection_data'],
-                                   parameters['dark_field'], 
-                                   parameters['flat_field'], 0.1)
-               self.pars['normalized_projection'] = norm
-               return self._reconstruct(parameters)
-              
-                
-                
-    def _normalize(self, projection, dark, flat, def_val=0):
-        a = (projection - dark)
-        b = (flat-dark)
-        with numpy.errstate(divide='ignore', invalid='ignore'):
-            c = numpy.true_divide( a, b )
-            c[ ~ numpy.isfinite( c )] = def_val  # set to not zero if 0/0 
-        return c
-    
-    def normalize(self, projections, dark, flat, def_val=0):
-        norm = [self._normalize(projection, dark, flat, def_val) for projection in projections]
-        return numpy.asarray (norm, dtype=numpy.float32)
-        
-    
-    
-class FISTA():
-    '''FISTA-based reconstruction algorithm using ASTRA-toolbox
-    
-    '''
-    # <<<< FISTA-based reconstruction algorithm using ASTRA-toolbox >>>>
-    # ___Input___:
-    # params.[] file:
-    #       - .proj_geom (geometry of the projector) [required]
-    #       - .vol_geom (geometry of the reconstructed object) [required]
-    #       - .sino (vectorized in 2D or 3D sinogram) [required]
-    #       - .iterFISTA (iterations for the main loop, default 40)
-    #       - .L_const (Lipschitz constant, default Power method)                                                                                                    )
-    #       - .X_ideal (ideal image, if given)
-    #       - .weights (statisitcal weights, size of the sinogram)
-    #       - .ROI (Region-of-interest, only if X_ideal is given)
-    #       - .initialize (a 'warm start' using SIRT method from ASTRA)
-    #----------------Regularization choices------------------------
-    #       - .Regul_Lambda_FGPTV (FGP-TV regularization parameter)
-    #       - .Regul_Lambda_SBTV (SplitBregman-TV regularization parameter)
-    #       - .Regul_Lambda_TVLLT (Higher order SB-LLT regularization parameter)
-    #       - .Regul_tol (tolerance to terminate regul iterations, default 1.0e-04)
-    #       - .Regul_Iterations (iterations for the selected penalty, default 25)
-    #       - .Regul_tauLLT (time step parameter for LLT term)
-    #       - .Ring_LambdaR_L1 (regularization parameter for L1-ring minimization, if lambdaR_L1 > 0 then switch on ring removal)
-    #       - .Ring_Alpha (larger values can accelerate convergence but check stability, default 1)
-    #----------------Visualization parameters------------------------
-    #       - .show (visualize reconstruction 1/0, (0 default))
-    #       - .maxvalplot (maximum value to use for imshow[0 maxvalplot])
-    #       - .slice (for 3D volumes - slice number to imshow)
-    # ___Output___:
-    # 1. X - reconstructed image/volume
-    # 2. output - a structure with
-    #    - .Resid_error - residual error (if X_ideal is given)
-    #    - .objective: value of the objective function
-    #    - .L_const: Lipshitz constant to avoid recalculations
-    
-    # References:
-    # 1. "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse
-    # Problems" by A. Beck and M Teboulle
-    # 2. "Ring artifacts correction in compressed sensing..." by P. Paleo
-    # 3. "A novel tomographic reconstruction method based on the robust
-    # Student's t function for suppressing data outliers" D. Kazantsev et.al.
-    # D. Kazantsev, 2016-17
-    def __init__(self, projector_geometry, output_geometry, input_sinogram, **kwargs):
-        self.params = dict()
-        self.params['projector_geometry'] = projector_geometry
-        self.params['output_geometry'] = output_geometry
-        self.params['input_sinogram'] = input_sinogram
-        detectors, nangles, sliceZ = numpy.shape(input_sinogram)
-        self.params['detectors'] = detectors
-        self.params['number_og_angles'] = nangles
-        self.params['SlicesZ'] = sliceZ
-        
-        # Accepted input keywords
-        kw = ('number_of_iterations', 'Lipschitz_constant' , 'ideal_image' ,
-              'weights' , 'region_of_interest' , 'initialize' , 
-              'regularizer' , 
-              'ring_lambda_R_L1',
-              'ring_alpha')
-        
-        # handle keyworded parameters
-        if kwargs is not None:
-            for key, value in kwargs.items():
-                if key in kw:
-                    #print("{0} = {1}".format(key, value))                        
-                    self.pars[key] = value
-                    
-        # set the default values for the parameters if not set
-        if 'number_of_iterations' in kwargs.keys():
-            self.pars['number_of_iterations'] = kwargs['number_of_iterations']
-        else:
-            self.pars['number_of_iterations'] = 40
-        if 'weights' in kwargs.keys():
-            self.pars['weights'] = kwargs['weights']
-        else:
-            self.pars['weights'] = numpy.ones(numpy.shape(self.params['input_sinogram']))
-        if 'Lipschitz_constant' in kwargs.keys():
-            self.pars['Lipschitz_constant'] = kwargs['Lipschitz_constant']
-        else:
-            self.pars['Lipschitz_constant'] = self.calculateLipschitzConstantWithPowerMethod()
-        
-        if not self.pars['ideal_image'] in kwargs.keys():
-            self.pars['ideal_image'] = None
-        
-        if not self.pars['region_of_interest'] :
-            if self.pars['ideal_image'] == None:
-                pass
-            else:
-                self.pars['region_of_interest'] = numpy.nonzero(self.pars['ideal_image']>0.0)
-            
-        if not self.pars['regularizer'] :
-            self.pars['regularizer'] = None
-        else:
-            # the regularizer must be a correctly instantiated object
-            if not self.pars['ring_lambda_R_L1']:
-                self.pars['ring_lambda_R_L1'] = 0
-            if not self.pars['ring_alpha']:
-                self.pars['ring_alpha'] = 1
-        
-            
-            
-        
-    def calculateLipschitzConstantWithPowerMethod(self):
-        ''' using Power method (PM) to establish L constant'''
-        
-        #N = params.vol_geom.GridColCount
-        N = self.pars['output_geometry'].GridColCount
-        proj_geom = self.params['projector_geometry']
-        vol_geom = self.params['output_geometry']
-        weights = self.pars['weights']
-        SlicesZ = self.pars['SlicesZ']
-        
-        if (proj_geom['type'] == 'parallel') or (proj_geom['type'] == 'parallel3d'):
-            #% for parallel geometry we can do just one slice
-            #fprintf('%s \n', 'Calculating Lipshitz constant for parallel beam geometry...');
-            niter = 15;# % number of iteration for the PM
-            #N = params.vol_geom.GridColCount;
-            #x1 = rand(N,N,1);
-            x1 = numpy.random.rand(1,N,N)
-            #sqweight = sqrt(weights(:,:,1));
-            sqweight = numpy.sqrt(weights.T[0])
-            proj_geomT = proj_geom.copy();
-            proj_geomT.DetectorRowCount = 1;
-            vol_geomT = vol_geom.copy();
-            vol_geomT['GridSliceCount'] = 1;
-            
-            
-            for i in range(niter):
-                if i == 0:
-                    #[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
-                    sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geomT, vol_geomT);
-                    y = sqweight * y # element wise multiplication
-                    #astra_mex_data3d('delete', sino_id);
-                    astra.matlab.data3d('delete', sino_id)
-                    
-                idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, proj_geomT, vol_geomT);
-                s = numpy.linalg.norm(x1)
-                ### this line?
-                x1 = x1/s;
-                ### this line?
-                sino_id, y = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
-                y = sqweight*y;
-                astra.matlab.data3d('delete', sino_id);
-                astra.matlab.data3d('delete', idx);
-            #end
-            del proj_geomT
-            del vol_geomT
-        else:
-            #% divergen beam geometry
-            #fprintf('%s \n', 'Calculating Lipshitz constant for divergen beam geometry...');
-            niter = 8; #% number of iteration for PM
-            x1 = numpy.random.rand(SlicesZ , N , N);
-            #sqweight = sqrt(weights);
-            sqweight = numpy.sqrt(weights.T[0])
-            
-            sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom);
-            y = sqweight*y;
-            #astra_mex_data3d('delete', sino_id);
-            astra.matlab.data3d('delete', sino_id);
-            
-            for i in range(niter):
-                #[id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geom, vol_geom);
-                idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, 
-                                                                    proj_geom, 
-                                                                    vol_geom)
-                s = numpy.linalg.norm(x1)
-                ### this line?
-                x1 = x1/s;
-                ### this line?
-                #[sino_id, y] = astra_create_sino3d_gpu(x1, proj_geom, vol_geom);
-                sino_id, y = astra.creators.create_sino3d_gpu(x1, 
-                                                              proj_geom, 
-                                                              vol_geom);
-                
-                y = sqweight*y;
-                #astra_mex_data3d('delete', sino_id);
-                #astra_mex_data3d('delete', id);
-                astra.matlab.data3d('delete', sino_id);
-                astra.matlab.data3d('delete', idx);
-            #end
-            #clear x1
-            del x1
-        
-        return s
-    
-    
-    def setRegularizer(self, regularizer):
-        #if regularizer
-        self.pars['regularizer'] = regularizer
-        
-    
-    
-
-
-def getEntry(location):
-    for item in nx[location].keys():
-        print (item)
-
-
-print ("Loading Data")
-
-##fname = "D:\\Documents\\Dataset\\IMAT\\20170419_crabtomo\\crabtomo\\Sample\\IMAT00005153_crabstomo_Sample_000.tif"
-####ind = [i * 1049 for i in range(360)]
-#### use only 360 images
-##images = 200
-##ind = [int(i * 1049 / images) for i in range(images)]
-##stack_image = dxchange.reader.read_tiff_stack(fname, ind, digit=None, slc=None)
-
-#fname = "D:\\Documents\\Dataset\\CGLS\\24737_fd.nxs"
-fname = "C:\\Users\\ofn77899\\Documents\\CCPi\\CGLS\\24737_fd_2.nxs"
-nx = h5py.File(fname, "r")
-
-# the data are stored in a particular location in the hdf5
-for item in nx['entry1/tomo_entry/data'].keys():
-    print (item)
-
-data = nx.get('entry1/tomo_entry/data/rotation_angle')
-angles = numpy.zeros(data.shape)
-data.read_direct(angles)
-print (angles)
-# angles should be in degrees
-
-data = nx.get('entry1/tomo_entry/data/data')
-stack = numpy.zeros(data.shape)
-data.read_direct(stack)
-print (data.shape)
-
-print ("Data Loaded")
-
-
-# Normalize
-data = nx.get('entry1/tomo_entry/instrument/detector/image_key')
-itype = numpy.zeros(data.shape)
-data.read_direct(itype)
-# 2 is dark field
-darks = [stack[i] for i in range(len(itype)) if itype[i] == 2 ]
-dark = darks[0]
-for i in range(1, len(darks)):
-    dark += darks[i]
-dark = dark / len(darks)
-#dark[0][0] = dark[0][1]
-
-# 1 is flat field
-flats = [stack[i] for i in range(len(itype)) if itype[i] == 1 ]
-flat = flats[0]
-for i in range(1, len(flats)):
-    flat += flats[i]
-flat = flat / len(flats)
-#flat[0][0] = dark[0][1]
-
-
-# 0 is projection data
-proj = [stack[i] for i in range(len(itype)) if itype[i] == 0 ]
-angle_proj = [angles[i] for i in range(len(itype)) if itype[i] == 0 ]
-angle_proj = numpy.asarray (angle_proj)
-angle_proj = angle_proj.astype(numpy.float32)
-
-# normalized data are
-# norm = (projection - dark)/(flat-dark)
-
-def normalize(projection, dark, flat, def_val=0.1):
-    a = (projection - dark)
-    b = (flat-dark)
-    with numpy.errstate(divide='ignore', invalid='ignore'):
-        c = numpy.true_divide( a, b )
-        c[ ~ numpy.isfinite( c )] = def_val  # set to not zero if 0/0 
-    return c
-    
-
-norm = [normalize(projection, dark, flat) for projection in proj]
-norm = numpy.asarray (norm)
-norm = norm.astype(numpy.float32)
-
-#recon = Reconstructor(algorithm = Algorithm.CGLS, normalized_projection = norm,
-#                 angles = angle_proj, center_of_rotation = 86.2 , 
-#                 flat_field = flat, dark_field = dark, 
-#                 iterations = 15, resolution = 1, isLogScale = False, threads = 3)
-
-#recon = Reconstructor(algorithm = Reconstructor.Algorithm.CGLS, projection_data = proj,
-#                 angles = angle_proj, center_of_rotation = 86.2 , 
-#                 flat_field = flat, dark_field = dark, 
-#                 iterations = 15, resolution = 1, isLogScale = False, threads = 3)
-#img_cgls = recon.reconstruct()
-#
-#pars = dict()
-#pars['algorithm'] = Reconstructor.Algorithm.SIRT
-#pars['projection_data'] = proj
-#pars['angles'] = angle_proj
-#pars['center_of_rotation'] = numpy.double(86.2)
-#pars['flat_field'] = flat
-#pars['iterations'] = 15
-#pars['dark_field'] = dark
-#pars['resolution'] = 1
-#pars['isLogScale'] = False
-#pars['threads'] = 3
-#
-#img_sirt = recon.reconstruct(pars)
-#
-#recon.pars['algorithm'] = Reconstructor.Algorithm.MLEM
-#img_mlem = recon.reconstruct()
-
-############################################################
-############################################################
-#recon.pars['algorithm'] = Reconstructor.Algorithm.CGLS_CONV
-#recon.pars['regularize'] = numpy.double(0.1)
-#img_cgls_conv = recon.reconstruct()
-
-niterations = 15
-threads = 3
-
-img_cgls = alg.cgls(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-img_mlem = alg.mlem(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-img_sirt = alg.sirt(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-
-iteration_values = numpy.zeros((niterations,))
-img_cgls_conv = alg.cgls_conv(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
-                              iteration_values, False)
-print ("iteration values %s" % str(iteration_values))
-
-iteration_values = numpy.zeros((niterations,))
-img_cgls_tikhonov = alg.cgls_tikhonov(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
-                                      numpy.double(1e-5), iteration_values , False)
-print ("iteration values %s" % str(iteration_values))
-iteration_values = numpy.zeros((niterations,))
-img_cgls_TVreg = alg.cgls_TVreg(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
-                                      numpy.double(1e-5), iteration_values , False)
-print ("iteration values %s" % str(iteration_values))
-
-
-##numpy.save("cgls_recon.npy", img_data)
-import matplotlib.pyplot as plt
-fig, ax = plt.subplots(1,6,sharey=True)
-ax[0].imshow(img_cgls[80])
-ax[0].axis('off')  # clear x- and y-axes
-ax[1].imshow(img_sirt[80])
-ax[1].axis('off')  # clear x- and y-axes
-ax[2].imshow(img_mlem[80])
-ax[2].axis('off')  # clear x- and y-axesplt.show()
-ax[3].imshow(img_cgls_conv[80])
-ax[3].axis('off')  # clear x- and y-axesplt.show()
-ax[4].imshow(img_cgls_tikhonov[80])
-ax[4].axis('off')  # clear x- and y-axesplt.show()
-ax[5].imshow(img_cgls_TVreg[80])
-ax[5].axis('off')  # clear x- and y-axesplt.show()
-
-
-plt.show()
-
-#viewer = edo.CILViewer()
-#viewer.setInputAsNumpy(img_cgls2)
-#viewer.displaySliceActor(0)
-#viewer.startRenderLoop()
-
-import vtk
-
-def NumpyToVTKImageData(numpyarray):
-    if (len(numpy.shape(numpyarray)) == 3):
-        doubleImg = vtk.vtkImageData()
-        shape = numpy.shape(numpyarray)
-        doubleImg.SetDimensions(shape[0], shape[1], shape[2])
-        doubleImg.SetOrigin(0,0,0)
-        doubleImg.SetSpacing(1,1,1)
-        doubleImg.SetExtent(0, shape[0]-1, 0, shape[1]-1, 0, shape[2]-1)
-        #self.img3D.SetScalarType(vtk.VTK_UNSIGNED_SHORT, vtk.vtkInformation())
-        doubleImg.AllocateScalars(vtk.VTK_DOUBLE,1)
-        
-        for i in range(shape[0]):
-            for j in range(shape[1]):
-                for k in range(shape[2]):
-                    doubleImg.SetScalarComponentFromDouble(
-                        i,j,k,0, numpyarray[i][j][k])
-    #self.setInput3DData( numpy_support.numpy_to_vtk(numpyarray) )
-        # rescale to appropriate VTK_UNSIGNED_SHORT
-        stats = vtk.vtkImageAccumulate()
-        stats.SetInputData(doubleImg)
-        stats.Update()
-        iMin = stats.GetMin()[0]
-        iMax = stats.GetMax()[0]
-        scale = vtk.VTK_UNSIGNED_SHORT_MAX / (iMax - iMin)
-
-        shiftScaler = vtk.vtkImageShiftScale ()
-        shiftScaler.SetInputData(doubleImg)
-        shiftScaler.SetScale(scale)
-        shiftScaler.SetShift(iMin)
-        shiftScaler.SetOutputScalarType(vtk.VTK_UNSIGNED_SHORT)
-        shiftScaler.Update()
-        return shiftScaler.GetOutput()
-        
-#writer = vtk.vtkMetaImageWriter()
-#writer.SetFileName(alg + "_recon.mha")
-#writer.SetInputData(NumpyToVTKImageData(img_cgls2))
-#writer.Write()