Merge branch 'pythonize' of https://github.com/vais-ral/CCPi-FISTA_Reconstruction into pythonize

 Conflicts:
	demos/exportDemoRD2Data.m
	main_func/regularizers_CPU/FGP_TV_core.h
	main_func/regularizers_CPU/LLT_model.c
	src/Python/test/readhd5.py
	src/Python/test_reconstructor.py
	src/Python/test_regularizers.py

7 files changed, 988 insertions, 50 deletions

diff --git a/src/Python/ccpi/fista/FISTAReconstructor.py b/src/Python/ccpi/fista/FISTAReconstructor.py
new file mode 100644
index 0000000..1e76815
--- /dev/null
+++ b/src/Python/ccpi/fista/FISTAReconstructor.py
@@ -0,0 +1,389 @@
+# -*- 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
+
+   
+    
+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):
+        # handle parmeters:
+        # obligatory parameters
+        self.pars = dict()
+        self.pars['projector_geometry'] = projector_geometry
+        self.pars['output_geometry'] = output_geometry
+        self.pars['input_sinogram'] = input_sinogram
+        detectors, nangles, sliceZ = numpy.shape(input_sinogram)
+        self.pars['detectors'] = detectors
+        self.pars['number_og_angles'] = nangles
+        self.pars['SlicesZ'] = sliceZ
+
+        print (self.pars)
+        # handle optional input parameters (at instantiation)
+        
+        # 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.pars['input_sinogram']))
+        if 'Lipschitz_constant' in kwargs.keys():
+            self.pars['Lipschitz_constant'] = kwargs['Lipschitz_constant']
+        else:
+            self.pars['Lipschitz_constant'] = self.calculateLipschitzConstantWithPowerMethod()
+        
+        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:
+                pass
+            else:
+                self.pars['region_of_interest'] = numpy.nonzero(self.pars['ideal_image']>0.0)
+            
+        if not 'regularizer' in kwargs.keys() :
+            self.pars['regularizer'] = None
+        else:
+            # the regularizer must be a correctly instantiated object
+            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
+        
+            
+            
+        
+    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])
+            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 getEntry(location, nx):
+    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"
+##fname = "/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/dendr.h5"
+##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
new file mode 100644
index 0000000..d29ac0d
--- /dev/null
+++ b/src/Python/ccpi/fista/Reconstructor.py
@@ -0,0 +1,425 @@
+# -*- 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)
+
+#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__.py b/src/Python/ccpi/fista/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/src/Python/ccpi/fista/__init__.py
diff --git a/src/Python/test/astra_test.py b/src/Python/test/astra_test.py
new file mode 100644
index 0000000..42c375a
--- /dev/null
+++ b/src/Python/test/astra_test.py
@@ -0,0 +1,85 @@
+import astra
+import numpy
+import filefun
+
+
+# read in the same data as the DemoRD2
+angles = filefun.dlmread("DemoRD2/angles.csv")
+darks_ar = filefun.dlmread("DemoRD2/darks_ar.csv", separator=",")
+flats_ar = filefun.dlmread("DemoRD2/flats_ar.csv", separator=",")
+
+if True:
+    Sino3D = numpy.load("DemoRD2/Sino3D.npy")
+else:
+    sino = filefun.dlmread("DemoRD2/sino_01.csv", separator=",")
+    a = map (lambda x:x, numpy.shape(sino))
+    a.append(20)
+
+    Sino3D = numpy.zeros(tuple(a), dtype="float")
+
+    for i in range(1,numpy.shape(Sino3D)[2]+1):
+        print("Read file DemoRD2/sino_%02d.csv" % i)
+        sino = filefun.dlmread("DemoRD2/sino_%02d.csv" % i, separator=",")
+        Sino3D.T[i-1] = sino.T
+    
+Weights3D = numpy.asarray(Sino3D, dtype="float")
+
+##angles_rad = angles*(pi/180); % conversion to radians
+##size_det = size(data_raw3D,1); % detectors dim
+##angSize = size(data_raw3D, 2); % angles dim
+##slices_tot = size(data_raw3D, 3); % no of slices
+##recon_size = 950; % reconstruction size
+
+
+angles_rad = angles * numpy.pi /180.
+size_det, angSize, slices_tot = numpy.shape(Sino3D)
+size_det, angSize, slices_tot = [int(i) for i in numpy.shape(Sino3D)]
+recon_size = 950
+Z_slices = 3;
+det_row_count = Z_slices;
+
+#proj_geom = astra_create_proj_geom('parallel3d', 1, 1,
+# det_row_count, size_det, angles_rad);
+
+detectorSpacingX = 1.0
+detectorSpacingY = detectorSpacingX
+proj_geom = astra.create_proj_geom('parallel3d',
+                                            detectorSpacingX,
+                                            detectorSpacingY,
+                                            det_row_count,
+                                            size_det,
+                                            angles_rad)
+
+#vol_geom = astra_create_vol_geom(recon_size,recon_size,Z_slices);
+vol_geom = astra.create_vol_geom(recon_size,recon_size,Z_slices);
+
+sino = numpy.zeros((size_det, angSize, slices_tot), dtype="float")
+
+#weights = ones(size(sino));
+weights = numpy.ones(numpy.shape(sino))
+
+#####################################################################
+## PowerMethod for Lipschitz constant
+
+N = vol_geom['GridColCount']
+x1 = numpy.random.rand(1,N,N)
+#sqweight = sqrt(weights(:,:,1));
+sqweight = numpy.sqrt(weights.T[0]).T
+##proj_geomT = proj_geom;
+proj_geomT = proj_geom.copy()
+##proj_geomT.DetectorRowCount = 1;
+proj_geomT['DetectorRowCount'] = 1
+##vol_geomT = vol_geom;
+vol_geomT = vol_geom.copy()
+##vol_geomT.GridSliceCount = 1;
+vol_geomT['GridSliceCount'] = 1
+
+##[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
+
+#sino_id, y = astra.create_sino3d_gpu(x1, proj_geomT, vol_geomT);
+sino_id, y = astra.create_sino(x1, proj_geomT, vol_geomT);
+
+##y = sqweight.*y;
+##astra_mex_data3d('delete', sino_id);
+        
+
diff --git a/src/Python/test/readhd5.py b/src/Python/test/readhd5.py
index b042341..10f25ed 100644
--- a/src/Python/test/readhd5.py
+++ b/src/Python/test/readhd5.py
@@ -25,6 +25,7 @@ angSize = numpy.asarray(nx.get('/angSize'), dtype=int)[0]
 angles_rad = numpy.asarray(nx.get('/angles_rad'))
 recon_size = numpy.asarray(nx.get('/recon_size'), dtype=int)[0]
 size_det = numpy.asarray(nx.get('/size_det'), dtype=int)[0]
+
 slices_tot = numpy.asarray(nx.get('/slices_tot'), dtype=int)[0]
 
 #from ccpi.viewer.CILViewer2D import CILViewer2D
diff --git a/src/Python/test_reconstructor.py b/src/Python/test_reconstructor.py
index 0fd08f5..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):
@@ -23,10 +23,10 @@ nx = h5py.File(filename, "r")
 entries = [entry for entry in nx['/'].keys()]
 print (entries)
 
-Sino3D = numpy.asarray(nx.get('/Sino3D'))
-Weights3D = numpy.asarray(nx.get('/Weights3D'))
+Sino3D = numpy.asarray(nx.get('/Sino3D'), dtype="float32")
+Weights3D = numpy.asarray(nx.get('/Weights3D'), dtype="float32")
 angSize = numpy.asarray(nx.get('/angSize'), dtype=int)[0]
-angles_rad = numpy.asarray(nx.get('/angles_rad'))
+angles_rad = numpy.asarray(nx.get('/angles_rad'), dtype="float32")
 recon_size = numpy.asarray(nx.get('/recon_size'), dtype=int)[0]
 size_det = numpy.asarray(nx.get('/size_det'), dtype=int)[0]
 slices_tot = numpy.asarray(nx.get('/slices_tot'), dtype=int)[0]
@@ -58,21 +58,23 @@ vol_geom = astra.creators.create_vol_geom( image_size_x,
 ## First pass the arguments to the FISTAReconstructor and test the
 ## Lipschitz constant
 
-#fistaRecon = FISTAReconstructor(proj_geom, vol_geom, Sino3D )
+fistaRecon = FISTAReconstructor(proj_geom, vol_geom, Sino3D , weights=Weights3D)
+print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
  #N = params.vol_geom.GridColCount
  
 pars = dict()
-pars['projector_geometry'] = proj_geom
-pars['output_geometry'] = vol_geom
-pars['input_sinogram'] = Sino3D
+pars['projector_geometry'] = proj_geom.copy()
+pars['output_geometry'] = vol_geom.copy()
+pars['input_sinogram'] = Sino3D.copy()
 sliceZ , nangles , detectors  = numpy.shape(Sino3D)
 pars['detectors'] = detectors
 pars['number_of_angles'] = nangles
 pars['SlicesZ'] = sliceZ
     
 
-pars['weights'] = numpy.ones(numpy.shape(pars['input_sinogram']))
-         
+#pars['weights'] = numpy.ones(numpy.shape(pars['input_sinogram']))
+pars['weights'] = Weights3D.copy()         
+
 N = pars['output_geometry']['GridColCount']
 proj_geom = pars['projector_geometry']
 vol_geom = pars['output_geometry']
@@ -82,7 +84,7 @@ SlicesZ = 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 = 15;# % number of iteration for the PM
+    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)
@@ -96,7 +98,8 @@ if (proj_geom['type'] == 'parallel') or (proj_geom['type'] == 'parallel3d'):
     #[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
     
     import matplotlib.pyplot as plt
-    fig = plt.figure()
+    fig = [] 
+    props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
     
     #a.set_title('Lipschitz')        
     for i in range(niter):
@@ -107,14 +110,27 @@ if (proj_geom['type'] == 'parallel') or (proj_geom['type'] == 'parallel3d'):
 #            y = sqweight.*y;
 #            astra_mex_data3d('delete', sino_id);
 #            astra_mex_data3d('delete', id);
-        print ("iteration {0}".format(i))
+        #print ("iteration {0}".format(i))
+        fig.append(plt.figure())
+
+        a=fig[-1].add_subplot(1,2,1)
+        a.text(0.05, 0.95, "iteration {0}, x1".format(i), transform=a.transAxes,
+               fontsize=14,verticalalignment='top', bbox=props)
+        
+        imgplot = plt.imshow(x1[0].copy())
+        
+        
         sino_id, y = astra.creators.create_sino3d_gpu(x1,
                                                   proj_geomT,
                                                   vol_geomT)
-        #a=fig.add_subplot(2,1,1)
-        #imgplot = plt.imshow(y[0])
+        a=fig[-1].add_subplot(1,2,2)
+        a.text(0.05, 0.95, "iteration {0}, y".format(i), 
+               transform=a.transAxes, fontsize=14,verticalalignment='top', 
+               bbox=props)
+        
+        imgplot = plt.imshow(y[0].copy())
         
-        y = sqweight * y # element wise multiplication
+        y = (sqweight * y) # element wise multiplication
         
         #b=fig.add_subplot(2,1,2)
         #imgplot = plt.imshow(x1[0])
@@ -122,15 +138,17 @@ if (proj_geom['type'] == 'parallel') or (proj_geom['type'] == 'parallel3d'):
         
         #astra_mex_data3d('delete', sino_id);
         astra.matlab.data3d('delete', sino_id)
+        del x1
             
-        idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, 
+        idx,x1 = astra.creators.create_backprojection3d_gpu((sqweight*y), 
                                                             proj_geomT,
-                                                            vol_geomT);
-        print ("shape {1} x1 {0}".format(x1.T[:4].T, numpy.shape(x1)))
+                                                            vol_geomT)
+        del y
+        
+                                                            
         s = numpy.linalg.norm(x1)
         ### this line?
-        x1 = x1/s;
-        print ("x1 {0}".format(x1.T[:4].T))
+        x1 = (x1/s)
         
 #        ### this line?
 #        sino_id, y = astra.creators.create_sino3d_gpu(x1, 
@@ -138,10 +156,13 @@ if (proj_geom['type'] == 'parallel') or (proj_geom['type'] == 'parallel3d'):
 #                                                      vol_geomT);
 #        y = sqweight * y;
         astra.matlab.data3d('delete', sino_id);
-        astra.matlab.data3d('delete', idx);
+        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...')
diff --git a/src/Python/test_regularizers.py b/src/Python/test_regularizers.py
index 5804897..e76262c 100644
--- a/src/Python/test_regularizers.py
+++ b/src/Python/test_regularizers.py
@@ -5,15 +5,15 @@ Created on Fri Aug  4 11:10:05 2017
 @author: ofn77899
 """
 
-from ccpi.viewer.CILViewer2D import Converter
-import vtk
+#from ccpi.viewer.CILViewer2D import Converter
+#import vtk
 
 import matplotlib.pyplot as plt
 import numpy as np
 import os    
 from enum import Enum
 import timeit
-
+#from PIL import Image
 #from Regularizer import Regularizer
 from ccpi.imaging.Regularizer import Regularizer
 
@@ -46,12 +46,21 @@ def nrmse(im1, im2):
 # u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
 # u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
 
-filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif"
-reader = vtk.vtkTIFFReader()
-reader.SetFileName(os.path.normpath(filename))
-reader.Update()
-#vtk returns 3D images, let's take just the one slice there is as 2D
-Im = Converter.vtk2numpy(reader.GetOutput()).T[0]/255
+
+#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif"
+filename = r"/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/lena_gray_512.tif"
+#filename = r'/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif'
+
+#reader = vtk.vtkTIFFReader()
+#reader.SetFileName(os.path.normpath(filename))
+#reader.Update()
+Im = plt.imread(filename)                     
+#Im = Image.open('/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif')/255
+#img.show()
+Im = np.asarray(Im, dtype='float32')
+
+
+
 
 #imgplot = plt.imshow(Im)
 perc = 0.05
@@ -68,7 +77,7 @@ fig = plt.figure()
 
 a=fig.add_subplot(2,3,1)
 a.set_title('noise')
-imgplot = plt.imshow(u0)
+imgplot = plt.imshow(u0,cmap="gray")
 
 reg_output = []
 ##############################################################################
@@ -80,6 +89,7 @@ reg_output = []
 use_object = True
 if use_object:
     reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+    print (reg.pars)
     reg.setParameter(input=u0)    
     reg.setParameter(regularization_parameter=10.)
     # or 
@@ -113,7 +123,7 @@ props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
 # place a text box in upper left in axes coords
 a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(plotme)
+imgplot = plt.imshow(plotme,cmap="gray")
 
 ###################### FGP_TV #########################################
 # u = FGP_TV(single(u0), 0.05, 100, 1e-04);
@@ -131,8 +141,12 @@ textstr = out2[-1]
 props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
 # place a text box in upper left in axes coords
 a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-        verticalalignment='top', bbox=props)
+         verticalalignment='top', bbox=props)
 imgplot = plt.imshow(reg_output[-1][0])
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
 
 ###################### LLT_model #########################################
 # * u0 = Im + .03*randn(size(Im)); % adding noise
@@ -149,13 +163,16 @@ pars = out2[-2]
 reg_output.append(out2)
 
 a=fig.add_subplot(2,3,4)
+
 textstr = out2[-1]
+
 # these are matplotlib.patch.Patch properties
 props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
 # place a text box in upper left in axes coords
 a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-        verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0])
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
+
 
 # ###################### PatchBased_Regul #########################################
 # # Quick 2D denoising example in Matlab:   
@@ -164,9 +181,9 @@ imgplot = plt.imshow(reg_output[-1][0])
 # #   ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); 
 
 out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
-                           searching_window_ratio=3,
-                           similarity_window_ratio=1,
-                           PB_filtering_parameter=0.08)
+                       searching_window_ratio=3,
+                       similarity_window_ratio=1,
+                       PB_filtering_parameter=0.08)
 pars = out2[-2]
 reg_output.append(out2)
 
@@ -180,20 +197,20 @@ props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
 # place a text box in upper left in axes coords
 a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0])
+imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
 
 
-###################### TGV_PD #########################################
-# Quick 2D denoising example in Matlab:   
-#   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
-#   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-#   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
+# ###################### TGV_PD #########################################
+# # Quick 2D denoising example in Matlab:   
+# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+# #   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
 
 
 out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
-                          first_order_term=1.3,
-                          second_order_term=1,
-                          number_of_iterations=550)
+                           first_order_term=1.3,
+                           second_order_term=1,
+                           number_of_iterations=550)
 pars = out2[-2]
 reg_output.append(out2)
 
@@ -207,8 +224,8 @@ textstr = out2[-1]
 props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
 # place a text box in upper left in axes coords
 a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-        verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0])
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
 
 
 plt.show()