use refactore code

7 files changed, 1 insertions, 948 deletions

diff --git a/src/Python/ccpi/fista/FISTAReconstructor.pyc b/src/Python/ccpi/fista/FISTAReconstructor.pyc
deleted file mode 100644
index ecc4d7d..0000000
--- a/src/Python/ccpi/fista/FISTAReconstructor.pyc
+++ /dev/null
diff --git a/src/Python/ccpi/fista/FISTAReconstructor.py~ b/src/Python/ccpi/fista/FISTAReconstructor.py~
deleted file mode 100644
index 6c7024d..0000000
--- a/src/Python/ccpi/fista/FISTAReconstructor.py~
+++ /dev/null
@@ -1,349 +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 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, **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)
-
-
-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()
-
-
diff --git a/src/Python/ccpi/fista/Reconstructor.py~ b/src/Python/ccpi/fista/Reconstructor.py~
deleted file mode 100644
index ba67327..0000000
--- a/src/Python/ccpi/fista/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()
diff --git a/src/Python/ccpi/fista/__init__.pyc b/src/Python/ccpi/fista/__init__.pyc
deleted file mode 100644
index 719e264..0000000
--- a/src/Python/ccpi/fista/__init__.pyc
+++ /dev/null
diff --git a/src/Python/ccpi/fista/__pycache__/FISTAReconstructor.cpython-35.pyc b/src/Python/ccpi/fista/__pycache__/FISTAReconstructor.cpython-35.pyc
deleted file mode 100644
index 84f16e2..0000000
--- a/src/Python/ccpi/fista/__pycache__/FISTAReconstructor.cpython-35.pyc
+++ /dev/null
diff --git a/src/Python/ccpi/fista/__pycache__/__init__.cpython-35.pyc b/src/Python/ccpi/fista/__pycache__/__init__.cpython-35.pyc
deleted file mode 100644
index 90c23ff..0000000
--- a/src/Python/ccpi/fista/__pycache__/__init__.cpython-35.pyc
+++ /dev/null
diff --git a/src/Python/test_reconstructor.py b/src/Python/test_reconstructor.py
index 6f46e96..a4a622b 100644
--- a/src/Python/test_reconstructor.py
+++ b/src/Python/test_reconstructor.py
@@ -9,7 +9,7 @@ Based on DemoRD2.m
 import h5py
 import numpy
 
-from ccpi.reconstruction_dev.FISTAReconstructor import FISTAReconstructor
+from ccpi.fista.FISTAReconstructor import FISTAReconstructor
 import astra
 
 ##def getEntry(nx, location):