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diff --git a/src/Python/ccpi/reconstruction/Reconstructor.py b/src/Python/ccpi/reconstruction/Reconstructor.py
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+# -*- 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()