Exclude unnec import

1 files changed, 5 insertions, 6 deletions

diff --git a/Wrappers/Python/wip/demo_nexus.py b/Wrappers/Python/wip/demo_nexus.py
index a51ec2d..ffa5741 100755
--- a/Wrappers/Python/wip/demo_nexus.py
+++ b/Wrappers/Python/wip/demo_nexus.py
@@ -13,8 +13,7 @@ from ccpi.optimisation.algs import FISTA, FBPD, CGLS
 from ccpi.optimisation.funcs import Norm2sq, Norm1

 from ccpi.plugins.ops import CCPiProjectorSimple

 from ccpi.reconstruction.parallelbeam import alg as pbalg

-from ccpi.plugins.processors import CCPiForwardProjector, CCPiBackwardProjector

-from ccpi.processors import Normalizer , CenterOfRotationFinder , AcquisitionDataPadder

+from ccpi.processors import Normalizer, CenterOfRotationFinder, AcquisitionDataPadder

 from ccpi.io.reader import NexusReader

 

 # All external imports

@@ -86,12 +85,12 @@ print ("Initial guess")
 x_init = ImageData(geometry=ig, dimension_labels=['horizontal_x','horizontal_y','vertical'])

         

 # Run FISTA reconstruction for least squares without regularization

-print ("run FISTA for least squares")

+print ("Run FISTA for least squares")

 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.title('FISTA LS')

 plt.show()

 

 # Set up 1-norm function for FISTA least squares plus 1-norm regularisation

@@ -103,7 +102,7 @@ g0 = Norm1(lam)
 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.title('FISTA LS+1')

 plt.show()

 

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

@@ -111,7 +110,7 @@ print ("Run FBPD for least squares plus 1-norm regularisation")
 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.title('FBPD LS+1')

 plt.show()

 

 # Run CGLS, which should agree with the FISTA least squares