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diff --git a/Wrappers/Python/wip/test_reader_reconstr.py b/Wrappers/Python/wip/test_reader_reconstr.py
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+# -*- coding: utf-8 -*-

+"""

+Created on Wed Mar 21 14:26:21 2018

+

+@author: ofn77899

+"""

+

+from ccpi.framework import ImageData , AcquisitionData, ImageGeometry, AcquisitionGeometry

+from ccpi.reconstruction.algs import FISTA, FBPD, CGLS

+from ccpi.reconstruction.funcs import Norm2sq, Norm1

+from ccpi.reconstruction.ops import CCPiProjectorSimple

+from ccpi.reconstruction.parallelbeam import alg as pbalg

+from ccpi.processors import CCPiForwardProjector, CCPiBackwardProjector , \

+Normalizer , CenterOfRotationFinder , AcquisitionDataPadder

+

+from ccpi.io.reader import NexusReader

+

+import numpy

+import matplotlib.pyplot as plt

+

+import os

+

+def add_dimension(data, fill_with, axis, start=True):

+    delta = data.shape[data.get_dimension_axis(axis)] - fill_with.shape[fill_with.get_dimension_axis(axis)] 

+    command = 'data.array['

+    i = 0

+    for k,v in data.dimension_labels.items():

+        if axis == v:

+            if start:

+                command = command + str(delta) + ":"

+            else:

+                l = data.get_dimension_size(axis) - delta

+                command = command + "0:" + str(l)

+        else:

+            command = command + ":"

+            

+        if i < data.number_of_dimensions -1:

+            command = command + ','

+        i += 1

+    command = command + "] = fill_with.array[:]" 

+    #print (command)

+    exec(command)

+    #return command

+

+

+def avg_img(image):

+    shape = list(numpy.shape(image))

+    l = shape.pop(0)

+    avg = numpy.zeros(shape)

+    for i in range(l):

+        avg += image[i] / l

+    return avg

+    

+def setupCCPiGeometries(voxel_num_x, voxel_num_y, voxel_num_z, angles, counter):

+    vg = ImageGeometry(voxel_num_x=voxel_num_x,voxel_num_y=voxel_num_y, voxel_num_z=voxel_num_z)

+    Phantom_ccpi = ImageData(geometry=vg,

+                        dimension_labels=['horizontal_x','horizontal_y','vertical'])

+    #.subset(['horizontal_x','horizontal_y','vertical'])

+    # ask the ccpi code what dimensions it would like

+        

+    voxel_per_pixel = 1

+    geoms = pbalg.pb_setup_geometry_from_image(Phantom_ccpi.as_array(),

+                                                angles,

+                                                voxel_per_pixel )

+    

+    pg = AcquisitionGeometry('parallel',

+                              '3D',

+                              angles,

+                              geoms['n_h'], 1,

+                              geoms['n_v'], 1 #2D in 3D is a slice 1 pixel thick

+                              )

+    

+    center_of_rotation = Phantom_ccpi.get_dimension_size('horizontal_x') / 2

+    ad = AcquisitionData(geometry=pg,dimension_labels=['angle','vertical','horizontal'])

+    geoms_i = pbalg.pb_setup_geometry_from_acquisition(ad.as_array(),

+                                                angles,

+                                                center_of_rotation,

+                                                voxel_per_pixel )

+

+    #print (counter)

+    counter+=1

+    #print (geoms , geoms_i)

+    if counter < 4:

+        if (not ( geoms_i == geoms )):

+            print ("not equal and {0}".format(counter))

+            X = max(geoms['output_volume_x'], geoms_i['output_volume_x'])

+            Y = max(geoms['output_volume_y'], geoms_i['output_volume_y'])

+            Z = max(geoms['output_volume_z'], geoms_i['output_volume_z'])

+            return setupCCPiGeometries(X,Y,Z,angles, counter)

+        else:

+            print ("return geoms {0}".format(geoms))

+            return geoms

+    else:

+        print ("return geoms_i {0}".format(geoms_i))

+        return geoms_i

+

+reader = NexusReader(os.path.join(".." ,".." ,".." , "data" , "24737_fd.nxs" ))

+

+dims = reader.get_projection_dimensions()

+print (dims)

+

+flat = avg_img(reader.load_flat())

+dark = avg_img(reader.load_dark())

+

+data = reader.getAcquisitionData()

+norm = Normalizer(flat_field=flat, dark_field=dark)

+

+norm.set_input(reader.getAcquisitionData())

+

+cor = CenterOfRotationFinder()

+cor.set_input(norm.get_output())

+center_of_rotation = cor.get_output()

+voxel_per_pixel = 1

+

+padder = AcquisitionDataPadder(center_of_rotation=center_of_rotation)

+padder.set_input(norm.get_output())

+padded_data = padder.get_output()

+

+pg = padded_data.geometry

+geoms = pbalg.pb_setup_geometry_from_acquisition(padded_data.as_array(),

+                                                pg.angles,

+                                                center_of_rotation,

+                                                voxel_per_pixel )

+vg = ImageGeometry(voxel_num_x=geoms['output_volume_x'],

+                   voxel_num_y=geoms['output_volume_y'], 

+                   voxel_num_z=geoms['output_volume_z'])

+#data = numpy.reshape(reader.getAcquisitionData())

+print ("define projector")

+Cop = CCPiProjectorSimple(vg, pg)

+# Create least squares object instance with projector and data.

+print ("Create least squares object instance with projector and data.")

+f = Norm2sq(Cop,padded_data,c=0.5)

+print ("Initial guess")

+# Initial guess

+x_init = ImageData(geometry=vg, dimension_labels=['horizontal_x','horizontal_y','vertical'])

+#invL = 0.5

+#g = f.grad(x_init)

+#print (g)

+#u = x_init - invL*f.grad(x_init)

+        

+#%%

+print ("run FISTA")

+# Run FISTA for least squares without regularization

+opt = {'tol': 1e-4, 'iter': 10}

+x_fista0, it0, timing0, criter0 = FISTA(x_init, f, None, opt=opt)

+

+plt.imshow(x_fista0.subset(horizontal_x=80).array)

+plt.title('FISTA0')

+plt.show()

+

+# Now least squares plus 1-norm regularization

+lam = 0.1

+g0 = Norm1(lam)

+

+# Run FISTA for least squares plus 1-norm function.

+x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g0,opt=opt)

+

+plt.imshow(x_fista0.subset(horizontal_x=80).array)

+plt.title('FISTA1')

+plt.show()

+

+plt.semilogy(criter1)

+plt.show()

+

+# Run FBPD=Forward Backward Primal Dual method on least squares plus 1-norm

+x_fbpd1, it_fbpd1, timing_fbpd1, criter_fbpd1 = FBPD(x_init,None,f,g0,opt=opt)

+

+plt.imshow(x_fbpd1.subset(horizontal_x=80).array)

+plt.title('FBPD1')

+plt.show()

+

+plt.semilogy(criter_fbpd1)

+plt.show()

+

+# Now FBPD for least squares plus TV

+#lamtv = 1

+#gtv = TV2D(lamtv)

+

+#x_fbpdtv, it_fbpdtv, timing_fbpdtv, criter_fbpdtv = FBPD(x_init,None,f,gtv,opt=opt)

+

+#plt.imshow(x_fbpdtv.subset(vertical=0).array)

+#plt.show()

+

+#plt.semilogy(criter_fbpdtv)

+#plt.show()  

+

+

+# Run CGLS, which should agree with the FISTA0

+x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Cop, padded_data, opt=opt)

+

+plt.imshow(x_CGLS.subset(horizontal_x=80).array)

+plt.title('CGLS')

+plt.title('CGLS recon, compare FISTA0')

+plt.show()

+

+plt.semilogy(criter_CGLS)

+plt.title('CGLS criterion')

+plt.show()
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