latest dev

1 files changed, 427 insertions, 172 deletions

diff --git a/src/Python/ccpi/reconstruction/FISTAReconstructor.py b/src/Python/ccpi/reconstruction/FISTAReconstructor.py
index ea96b53..85bfac5 100644
--- a/src/Python/ccpi/reconstruction/FISTAReconstructor.py
+++ b/src/Python/ccpi/reconstruction/FISTAReconstructor.py
@@ -21,10 +21,9 @@
 
 
 import numpy
-import h5py
 #from ccpi.reconstruction.parallelbeam import alg
 
-from ccpi.imaging.Regularizer import Regularizer
+#from ccpi.imaging.Regularizer import Regularizer
 from enum import Enum
 
 import astra
@@ -74,18 +73,34 @@ class FISTAReconstructor():
     # 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
+    def __init__(self, projector_geometry, output_geometry, input_sinogram,
+                 **kwargs):
+        # handle parmeters:
+        # obligatory parameters
+        self.pars = dict()
+        self.pars['projector_geometry'] = projector_geometry # proj_geom
+        self.pars['output_geometry'] = output_geometry       # vol_geom
+        self.pars['input_sinogram'] = input_sinogram         # sino
+        sliceZ, nangles, detectors = numpy.shape(input_sinogram)
+        self.pars['detectors'] = detectors
+        self.pars['number_of_angles'] = nangles
+        self.pars['SlicesZ'] = sliceZ
+        self.pars['output_volume'] = None
+
+        print (self.pars)
+        # handle optional input parameters (at instantiation)
         
         # Accepted input keywords
-        kw = ('number_of_iterations', 
+        kw = (
+              # mandatory fields
+              'projector_geometry',
+              'output_geometry',
+              'input_sinogram',
+              'detectors',
+              'number_of_angles',
+              'SlicesZ',
+              # optional fields
+              'number_of_iterations', 
               'Lipschitz_constant' , 
               'ideal_image' ,
               'weights' , 
@@ -93,7 +108,13 @@ class FISTAReconstructor():
               'initialize' , 
               'regularizer' , 
               'ring_lambda_R_L1',
-              'ring_alpha')
+              'ring_alpha',
+              'subsets',
+              'output_volume',
+              'os_subsets',
+              'os_indices',
+              'os_bins')
+        self.acceptedInputKeywords = list(kw)
         
         # handle keyworded parameters
         if kwargs is not None:
@@ -110,85 +131,160 @@ class FISTAReconstructor():
         if 'weights' in kwargs.keys():
             self.pars['weights'] = kwargs['weights']
         else:
-            self.pars['weights'] = numpy.ones(numpy.shape(self.params['input_sinogram']))
+            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()
+            self.pars['Lipschitz_constant'] = None
         
-        if not self.pars['ideal_image'] in kwargs.keys():
+        if not 'ideal_image' in kwargs.keys():
             self.pars['ideal_image'] = None
         
-        if not self.pars['region_of_interest'] :
+        if not 'region_of_interest'in kwargs.keys() :
             if self.pars['ideal_image'] == None:
-                pass
+                self.pars['region_of_interest'] = None
             else:
-                self.pars['region_of_interest'] = numpy.nonzero(self.pars['ideal_image']>0.0)
-            
-        if not self.pars['regularizer'] :
+                ## nonzero if the image is larger than m
+                fsm = numpy.frompyfunc(lambda x,m: 1 if x>m else 0, 2,1)
+                
+                self.pars['region_of_interest'] = fsm(self.pars['ideal_image'], 0)
+                
+        # the regularizer must be a correctly instantiated object    
+        if not 'regularizer' in kwargs.keys() :
             self.pars['regularizer'] = None
+
+        #RING REMOVAL
+        if not 'ring_lambda_R_L1' in kwargs.keys():
+            self.pars['ring_lambda_R_L1'] = 0
+        if not 'ring_alpha' in kwargs.keys():
+            self.pars['ring_alpha'] = 1
+
+        # ORDERED SUBSET
+        if not 'subsets' in kwargs.keys():
+            self.pars['subsets'] = 0
         else:
-            # 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
+            self.createOrderedSubsets()
+
+        if not 'initialize' in kwargs.keys():
+            self.pars['initialize'] = False
+
         
             
             
+    def setParameter(self, **kwargs):
+        '''set named parameter for the reconstructor engine
+        
+        raises Exception if the named parameter is not recognized
         
+        '''
+        for key , value in kwargs.items():
+            if key in self.acceptedInputKeywords:
+                self.pars[key] = value
+            else:
+                raise Exception('Wrong parameter {0} for '.format(key) +
+                                'reconstructor')
+    # setParameter
+
+    def getParameter(self, key):
+        if type(key) is str:
+            if key in self.acceptedInputKeywords:
+                return self.pars[key]
+            else:
+                raise Exception('Unrecongnised parameter: {0} '.format(key) )
+        elif type(key) is list:
+            outpars = []
+            for k in key:
+                outpars.append(self.getParameter(k))
+            return outpars
+        else:
+            raise Exception('Unhandled input {0}' .format(str(type(key))))
+            
+    
     def calculateLipschitzConstantWithPowerMethod(self):
         ''' using Power method (PM) to establish L constant'''
         
-        #N = params.vol_geom.GridColCount
-        N = self.pars['output_geometry'].GridColCount
-        proj_geom = self.params['projector_geometry']
-        vol_geom = self.params['output_geometry']
+        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'):
+            
+                               
+        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
+            #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.T[0])
+            sqweight = numpy.sqrt(weights[0])
             proj_geomT = proj_geom.copy();
-            proj_geomT.DetectorRowCount = 1;
+            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):
-                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)
+            #        [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, proj_geomT, vol_geomT);
+                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;
-                ### this line?
-                sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geomT, vol_geomT);
-                y = sqweight*y;
+                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);
+                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
-            #fprintf('%s \n', 'Calculating Lipshitz constant for 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.T[0])
+            sqweight = numpy.sqrt(weights[0])
             
             sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom);
             y = sqweight*y;
@@ -217,6 +313,7 @@ class FISTAReconstructor():
             #end
             #clear x1
             del x1
+
         
         return s
     
@@ -225,130 +322,288 @@ class FISTAReconstructor():
         if regularizer is not None:
             self.pars['regularizer'] = regularizer
         
+
+    def initialize(self):
+        # convenience variable storage
+        proj_geom = self.pars['projector_geometry']
+        vol_geom = self.pars['output_geometry']
+        sino = self.pars['input_sinogram']
+        
+        # a 'warm start' with SIRT method
+        # Create a data object for the reconstruction
+        rec_id = astra.matlab.data3d('create', '-vol',
+                                    vol_geom);
+        
+        #sinogram_id = astra_mex_data3d('create', '-proj3d', proj_geom, sino);
+        sinogram_id = astra.matlab.data3d('create', '-proj3d',
+                                          proj_geom,
+                                          sino)
+
+        sirt_config = astra.astra_dict('SIRT3D_CUDA')
+        sirt_config['ReconstructionDataId' ] = rec_id
+        sirt_config['ProjectionDataId'] = sinogram_id
+
+        sirt = astra.algorithm.create(sirt_config)
+        astra.algorithm.run(sirt, iterations=35)
+        X = astra.matlab.data3d('get', rec_id)
+
+        # clean up memory
+        astra.matlab.data3d('delete', rec_id)
+        astra.matlab.data3d('delete', sinogram_id)
+        astra.algorithm.delete(sirt)
+
+        
+
+        return X
+
+    def createOrderedSubsets(self, subsets=None):
+        if subsets is None:
+            try:
+                subsets = self.getParameter('subsets')
+            except Exception():
+                subsets = 0
+            #return subsets
+
+        angles = self.getParameter('projector_geometry')['ProjectionAngles'] 
+        
+        #binEdges = numpy.linspace(angles.min(),
+        #                          angles.max(),
+        #                          subsets + 1)
+        binsDiscr, binEdges = numpy.histogram(angles, bins=subsets)
+        # get rearranged subset indices
+        IndicesReorg = numpy.zeros((numpy.shape(angles)))
+        counterM = 0
+        for ii in range(binsDiscr.max()):
+            counter = 0
+            for jj in range(subsets):
+                curr_index = ii + jj  + counter
+                #print ("{0} {1} {2}".format(binsDiscr[jj] , ii, counterM))
+                if binsDiscr[jj] > ii:
+                    if (counterM < numpy.size(IndicesReorg)):
+                        IndicesReorg[counterM] = curr_index
+                    counterM = counterM + 1
+                    
+                counter = counter + binsDiscr[jj] - 1    
+                
+        # store the OS in parameters
+        self.setParameter(os_subsets=subsets,
+                          os_bins=binsDiscr,
+                          os_indices=IndicesReorg)
+            
+
+    def prepareForIteration(self):
+        print ("FISTA Reconstructor: prepare for iteration")
+        
+        self.residual_error = numpy.zeros((self.pars['number_of_iterations']))
+        self.objective = numpy.zeros((self.pars['number_of_iterations']))
+
+        #2D array (for 3D data) of sparse "ring" 
+        detectors, nangles, sliceZ  = numpy.shape(self.pars['input_sinogram'])
+        self.r = numpy.zeros((detectors, sliceZ), dtype=numpy.float)
+        # another ring variable
+        self.r_x = self.r.copy()
+
+        self.residual = numpy.zeros(numpy.shape(self.pars['input_sinogram']))
+        
+        if self.getParameter('Lipschitz_constant') is None:
+            self.pars['Lipschitz_constant'] = \
+                            self.calculateLipschitzConstantWithPowerMethod()
+        # errors vector (if the ground truth is given)
+        self.Resid_error = numpy.zeros((self.getParameter('number_of_iterations')));
+        # objective function values vector
+        self.objective = numpy.zeros((self.getParameter('number_of_iterations')));      
+        
+
+    # prepareForIteration
+
+    def iterate(self, Xin=None):
+        print ("FISTA Reconstructor: iterate")
+        
+        if Xin is None:    
+            if self.getParameter('initialize'):
+                X = self.initialize()
+            else:
+                N = vol_geom['GridColCount']
+                X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
+        else:
+            # copy by reference
+            X = Xin
+        # store the output volume in the parameters
+        self.setParameter(output_volume=X)
+        X_t = X.copy()
+        # convenience variable storage
+        proj_geom , vol_geom, sino , \
+          SlicesZ  = self.getParameter([ 'projector_geometry' ,
+                                                'output_geometry',
+                                                'input_sinogram',
+                                                'SlicesZ' ])
+                   
+        t = 1
+        
+        for i in range(self.getParameter('number_of_iterations')):
+            X_old = X.copy()
+            t_old = t
+            r_old = self.r.copy()
+            if self.getParameter('projector_geometry')['type'] == 'parallel' or \
+               self.getParameter('projector_geometry')['type'] == 'fanflat' or \
+               self.getParameter('projector_geometry')['type'] == 'fanflat_vec':
+                # if the geometry is parallel use slice-by-slice
+                # projection-backprojection routine
+                #sino_updt = zeros(size(sino),'single');
+                proj_geomT = proj_geom.copy()
+                proj_geomT['DetectorRowCount'] = 1
+                vol_geomT = vol_geom.copy()
+                vol_geomT['GridSliceCount'] = 1;
+                self.sino_updt = numpy.zeros(numpy.shape(sino), dtype=numpy.float)
+                for kkk in range(SlicesZ):
+                    sino_id, self.sino_updt[kkk] = \
+                             astra.creators.create_sino3d_gpu(
+                                 X_t[kkk:kkk+1], proj_geomT, vol_geomT)
+                    astra.matlab.data3d('delete', sino_id)
+            else:
+                # for divergent 3D geometry (watch the GPU memory overflow in
+                # ASTRA versions < 1.8)
+                #[sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom);
+                sino_id, self.sino_updt = astra.creators.create_sino3d_gpu(
+                    X_t, proj_geom, vol_geom)
+
+
+            ## RING REMOVAL
+            self.ringRemoval(i)
+            ## Projection/Backprojection Routine
+            self.projectionBackprojection(X, X_t)
+            astra.matlab.data3d('delete', sino_id)
+            ## REGULARIZATION
+            X = self.regularize(X)
+            ## Update Loop
+            X , X_t, t = self.updateLoop(i, X, X_old, r_old, t, t_old)
+            self.setParameter(output_volume=X)
+        return X
+    ## iterate
     
-    
+    def ringRemoval(self, i):
+        print ("FISTA Reconstructor: ring removal")
+        residual = self.residual
+        lambdaR_L1 , alpha_ring , weights , L_const , sino= \
+                   self.getParameter(['ring_lambda_R_L1',
+                                      'ring_alpha' , 'weights',
+                                      'Lipschitz_constant',
+                                      'input_sinogram'])
+        r_x = self.r_x
+        sino_updt = self.sino_updt
+        
+        SlicesZ, anglesNumb, Detectors = \
+                    numpy.shape(self.getParameter('input_sinogram'))
+        if lambdaR_L1 > 0 :
+             for kkk in range(anglesNumb):
+                 
+                 residual[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
+                                       ((sino_updt[:,kkk,:]).squeeze() - \
+                                        (sino[:,kkk,:]).squeeze() -\
+                                        (alpha_ring * r_x)
+                                        )
+             vec = residual.sum(axis = 1)
+             #if SlicesZ > 1:
+             #    vec = vec[:,1,:].squeeze()
+             self.r = (r_x - (1./L_const) * vec).copy()
+             self.objective[i] = (0.5 * (residual ** 2).sum())
 
+    def projectionBackprojection(self, X, X_t):
+        print ("FISTA Reconstructor: projection-backprojection routine")
+        
+        # a few useful variables
+        SlicesZ, anglesNumb, Detectors = \
+                    numpy.shape(self.getParameter('input_sinogram'))
+        residual = self.residual
+        proj_geom , vol_geom , L_const = \
+                  self.getParameter(['projector_geometry' ,
+                                                  'output_geometry',
+                                                  'Lipschitz_constant'])
+        
+        
+        if self.getParameter('projector_geometry')['type'] == 'parallel' or \
+           self.getParameter('projector_geometry')['type'] == 'fanflat' or \
+           self.getParameter('projector_geometry')['type'] == 'fanflat_vec':
+            # if the geometry is parallel use slice-by-slice
+            # projection-backprojection routine
+            #sino_updt = zeros(size(sino),'single');
+            proj_geomT = proj_geom.copy()
+            proj_geomT['DetectorRowCount'] = 1
+            vol_geomT = vol_geom.copy()
+            vol_geomT['GridSliceCount'] = 1;
+            x_temp = numpy.zeros(numpy.shape(X),dtype=numpy.float32)
+            
+            for kkk in range(SlicesZ):
+                
+                x_id, x_temp[kkk] = \
+                         astra.creators.create_backprojection3d_gpu(
+                             residual[kkk:kkk+1],
+                             proj_geomT, vol_geomT)
+                astra.matlab.data3d('delete', x_id)
+        else:
+            x_id, x_temp = \
+                  astra.creators.create_backprojection3d_gpu(
+                      residual, proj_geom, vol_geom)            
+
+        X = X_t - (1/L_const) * x_temp
+        #astra.matlab.data3d('delete', sino_id)
+        astra.matlab.data3d('delete', x_id)
+
+    def regularize(self, X):
+        print ("FISTA Reconstructor: regularize")
+        
+        regularizer = self.getParameter('regularizer')
+        if regularizer is not None:
+            return regularizer(input=X)
+        else:
+            return X
+
+    def updateLoop(self, i, X, X_old, r_old, t, t_old):
+        print ("FISTA Reconstructor: update loop")
+        lambdaR_L1 = self.getParameter('ring_lambda_R_L1')
+        if lambdaR_L1 > 0:
+            self.r = numpy.max(
+                numpy.abs(self.r) - lambdaR_L1 , 0) * \
+                numpy.sign(self.r)
+        t = (1 + numpy.sqrt(1 + 4 * t**2))/2
+        X_t = X + (((t_old -1)/t) * (X - X_old))
+
+        if lambdaR_L1 > 0:
+            self.r_x = self.r + \
+                             (((t_old-1)/t) * (self.r - r_old))
+
+        if self.getParameter('region_of_interest') is None:
+            string = 'Iteration Number {0} | Objective {1} \n'
+            print (string.format( i, self.objective[i]))
+        else:
+            ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
+                                                    'ideal_image')
+            
+            Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
+            string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
+            print (string.format(i,Resid_error[i], self.objective[i]))
+        return (X , X_t, t)
 
-def 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"
-##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()
-##
+    def os_iterate(self, Xin=None):
+        print ("FISTA Reconstructor: iterate")
+        
+        if Xin is None:    
+            if self.getParameter('initialize'):
+                X = self.initialize()
+            else:
+                N = vol_geom['GridColCount']
+                X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
+        else:
+            # copy by reference
+            X = Xin
+        # store the output volume in the parameters
+        self.setParameter(output_volume=X)
+        X_t = X.copy()
 
+        # some useful constants
+        proj_geom , vol_geom, sino , \
+          SlicesZ, weights , alpha_ring ,
+          lambdaR_L1 , L_const = self.getParameter(
+            ['projector_geometry' , 'output_geometry',
+             'input_sinogram', 'SlicesZ' ,  'weights', 'ring_alpha' ,
+             'ring_lambda_R_L1', 'Lipschitz_constant'])