diff options
| -rwxr-xr-x | Wrappers/Python/ccpi/framework/BlockDataContainer.py | 1 | ||||
| -rw-r--r-- | Wrappers/Python/wip/CGLS_tikhonov.py | 25 |
2 files changed, 12 insertions, 14 deletions
diff --git a/Wrappers/Python/ccpi/framework/BlockDataContainer.py b/Wrappers/Python/ccpi/framework/BlockDataContainer.py index d509d25..b9f5c5f 100755 --- a/Wrappers/Python/ccpi/framework/BlockDataContainer.py +++ b/Wrappers/Python/ccpi/framework/BlockDataContainer.py @@ -96,7 +96,6 @@ class BlockDataContainer(object): shape=self.shape)
def multiply(self, other, *args, **kwargs):
- print ("BlockDataContainer" , other)
self.is_compatible(other)
out = kwargs.get('out', None)
if isinstance(other, Number):
diff --git a/Wrappers/Python/wip/CGLS_tikhonov.py b/Wrappers/Python/wip/CGLS_tikhonov.py index f247896..e9bbcd9 100644 --- a/Wrappers/Python/wip/CGLS_tikhonov.py +++ b/Wrappers/Python/wip/CGLS_tikhonov.py @@ -11,8 +11,7 @@ import matplotlib.pyplot as plt import numpy from ccpi.framework import BlockDataContainer from ccpi.optimisation.operators import BlockOperator -from ccpi.optimisation.operators.BlockOperator import BlockLinearOperator - + # Set up phantom size N x N x vert by creating ImageGeometry, initialising the # ImageData object with this geometry and empty array and finally put some # data into its array, and display one slice as image. @@ -128,26 +127,26 @@ simplef.L = 0.00003 gd = GradientDescent( x_init=x_init, objective_function=simplef, rate=simplef.L) -gd.max_iteration = 10 +gd.max_iteration = 50 Kbig.direct(X_init) Kbig.adjoint(B) cg = CGLS() cg.set_up(X_init, Kbig, B ) -cg.max_iteration = 5 +cg.max_iteration = 10 cgsmall = CGLS() cgsmall.set_up(X_init, Ksmall, B ) -cgsmall.max_iteration = 5 +cgsmall.max_iteration = 10 cgs = CGLS() cgs.set_up(x_init, A, b ) -cgs.max_iteration = 6 +cgs.max_iteration = 10 cgok = CGLS() cgok.set_up(X_init, Kok, B ) -cgok.max_iteration = 6 +cgok.max_iteration = 10 # # #out.__isub__(B) #out2 = K.adjoint(out) @@ -176,22 +175,22 @@ cgok.run(10, verbose=True) # print ("iteration {} {}".format(cgs.iteration, cgs.get_current_loss())) # # fig = plt.figure() -plt.subplot(1,6,1) +plt.subplot(2,3,1) plt.imshow(Phantom.subset(vertical=0).as_array()) plt.title('Simulated Phantom') -plt.subplot(1,6,2) +plt.subplot(2,3,2) plt.imshow(gd.get_output().subset(vertical=0).as_array()) plt.title('Simple Gradient Descent') -plt.subplot(1,6,3) +plt.subplot(2,3,3) plt.imshow(cgs.get_output().subset(vertical=0).as_array()) plt.title('Simple CGLS') -plt.subplot(1,6,4) +plt.subplot(2,3,5) plt.imshow(cg.get_output().get_item(0).subset(vertical=0).as_array()) plt.title('Composite CGLS\nbig lambda') -plt.subplot(1,6,5) +plt.subplot(2,3,6) plt.imshow(cgsmall.get_output().get_item(0).subset(vertical=0).as_array()) plt.title('Composite CGLS\nsmall lambda') -plt.subplot(1,6,6) +plt.subplot(2,3,4) plt.imshow(cgok.get_output().get_item(0).subset(vertical=0).as_array()) plt.title('Composite CGLS\nok lambda') plt.show() |