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diff --git a/dummy.py b/dummy.py
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+# cil imports
+from cil.framework import ImageData, ImageGeometry
+from cil.framework import AcquisitionGeometry, AcquisitionData
+from cil.optimisation.algorithms import CGLS, SIRT
+
+import src.operators.ProjectionOperator as UfoProjectionOperator
+import cil.plugins.astra.operators.ProjectionOperator as AstraProjectionOperator
+import cil.plugins.astra.processors.FBP
+
+
+# External imports
+import tifffile
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import logging
+import time
+
+logging.basicConfig(level = logging.INFO)
+
+# set up default colour map for visualisation
+#cmap = "gray"
+
+# set the backend for FBP and the ProjectionOperator
+#device = 'gpu'
+
+def reconstruct():
+ # number of pixels
+ n_pixels = 4096
+ z_size = 1024
+ #slices=2
+ #rounding='int8'
+ #rounding='half'
+ #rounding='single'
+ #n_pixels = 1024
+
+ # Angles
+ angles = np.linspace(0, 180, n_pixels, endpoint=False, dtype=np.float32)
+
+
+ # Setup acquisition geometry
+ # with sufficient number of projections
+ ag = AcquisitionGeometry.create_Parallel3D()\
+                            .set_angles(angles)\
+                            .set_panel([n_pixels,z_size], pixel_size=1)
+ #ag = AcquisitionGeometry.create_Parallel2D()\
+ #                        .set_angles(angles)\
+ #                        .set_panel(n_pixels,pixel_size=1)
+ 
+ ag.set_labels(['vertical', 'angle', 'horizontal'])
+
+ # Setup image geometry
+ ig = ImageGeometry(voxel_num_x=n_pixels,
+                   voxel_num_y=n_pixels,
+                   voxel_num_z=z_size,
+                   voxel_size_x=1,
+                   voxel_size_y=1,
+                   voxel_size_z=1)
+
+
+ # Get phantom
+ #home='/ccpi/data/new/phantom/'
+ home='/ccpi/data/new/1024/phantom/'
+ #home='/ccpi/data/test/1024/slices/'
+# phantom = []
+# for i in sorted(os.listdir(home)):
+# for i in range(0, z_size):
+  #file = os.path.join(home,i)
+  #img = tifffile.imread(file) 
+#  img = np.random.random_sample((n_pixels,n_pixels)).astype(np.float32)
+#  if len(img.shape)==3:
+#     img = np.squeeze(img, axis=0)
+#  phantom.append(img)
+# phantom = np.asarray(phantom)
+ phantom = np.random.random_sample((z_size,n_pixels,n_pixels)).astype(np.float32)
+
+ print(phantom.shape)
+ phantom_reshaped = np.transpose(phantom, (0, 1, 2))
+ print(phantom_reshaped.shape)
+ phantom = ImageData(phantom_reshaped, deep_copy=False, geometry=ig)
+ phantom_arr = phantom.as_array()
+ tifffile.imsave('phantom_3d.tif', phantom_arr[:,:,0])
+
+ # Create projection operator using Astra-Toolbox.
+ out_file = "ufo_single.tif"
+ sino_file= "sino_single.tif"
+
+# Ufo-harish
+ A = UfoProjectionOperator(ig, ag, True, rounding, slices)
+# Ufo-farago
+# A = UfoProjectionOperator(ig, ag, False, rounding, slices)
+# Astra
+# A = AstraProjectionOperator(ig, ag, 'gpu')
+  
+ #A.dot_test(A)
+
+ # Create an acqusition data (numerically)
+ sino = A.direct(phantom)
+ sino_arr = sino.as_array()
+ tifffile.imsave(sino_file,sino_arr[0,:,:])
+ 
+ # back_projection
+ back_projection = A.adjoint(sino)
+
+ # initial estimate - zero array in this case
+ initial = ig.allocate(0)
+
+ # setup CGLS
+ f = open("single.txt", "a")
+ for i in range(20,21):
+    total_time=0
+    for j in range(0,1):
+       cgls = CGLS(initial=initial,
+                  operator=A,
+                  data=sino,
+                  tolerance=1e-16,
+                  max_iteration = 50,
+                  update_objective_interval = 1 )
+
+       # run N interations
+       start = time.time()
+       cgls.run(i, verbose=True)
+       end = time.time()
+       if(j>0):
+          total_time += (end-start)
+    f.write('Iteration {} Time {} \n'.format(i,str(total_time/3)))
+ f.close()
+ print("finished reconstruction in single precision")
+
+if __name__ == '__main__':
+ reconstruct()
+