fix tests with removed ops.py

6 files changed, 25 insertions, 78 deletions

diff --git a/Wrappers/Python/test/test_BlockDataContainer.py b/Wrappers/Python/test/test_BlockDataContainer.py
index a20f289..aeb8454 100755
--- a/Wrappers/Python/test/test_BlockDataContainer.py
+++ b/Wrappers/Python/test/test_BlockDataContainer.py
@@ -7,15 +7,9 @@ Created on Tue Mar  5 16:08:23 2019
 

 import unittest

 import numpy

-#from ccpi.plugins.ops import CCPiProjectorSimple

-from ccpi.optimisation.ops import PowerMethodNonsquare

-from ccpi.optimisation.ops import TomoIdentity

-from ccpi.optimisation.funcs import Norm2sq, Norm1

 from ccpi.framework import ImageGeometry, AcquisitionGeometry

 from ccpi.framework import ImageData, AcquisitionData

-#from ccpi.optimisation.algorithms import GradientDescent

 from ccpi.framework import BlockDataContainer, DataContainer

-#from ccpi.optimisation.Algorithms import CGLS

 import functools

 

 from ccpi.optimisation.operators import Gradient, Identity, BlockOperator

diff --git a/Wrappers/Python/test/test_BlockOperator.py b/Wrappers/Python/test/test_BlockOperator.py
index e1c05fb..601a58c 100644
--- a/Wrappers/Python/test/test_BlockOperator.py
+++ b/Wrappers/Python/test/test_BlockOperator.py
@@ -1,12 +1,12 @@
 import unittest
 from ccpi.optimisation.operators import BlockOperator
 from ccpi.framework import BlockDataContainer
-from ccpi.optimisation.ops import TomoIdentity
+from ccpi.optimisation.ops import Identity
 from ccpi.framework import ImageGeometry, ImageData
 import numpy
 from ccpi.optimisation.operators import FiniteDiff
 
-class TestOperator(TomoIdentity):
+class TestOperator(Identity):
     def __init__(self, *args, **kwargs):
         super(TestOperator, self).__init__(*args, **kwargs)
         self.range = kwargs.get('range', self.geometry)
@@ -21,7 +21,7 @@ class TestBlockOperator(unittest.TestCase):
                ImageGeometry(10,20,30) , \
                ImageGeometry(10,20,30) ]
         x = [ g.allocate() for g in ig ]
-        ops = [ TomoIdentity(g) for g in ig ]
+        ops = [ Identity(g) for g in ig ]
 
         K = BlockOperator(*ops)
         X = BlockDataContainer(x[0])
@@ -50,7 +50,7 @@ class TestBlockOperator(unittest.TestCase):
                 ImageGeometry(10,20,30) , \
                 ImageGeometry(10,20,30) ]
             x = [ g.allocate() for g in ig ]
-            ops = [ TomoIdentity(g) for g in ig ]
+            ops = [ Identity(g) for g in ig ]
             
             K = BlockOperator(*ops)
             self.assertTrue(False)
@@ -90,7 +90,7 @@ class TestBlockOperator(unittest.TestCase):
                ImageGeometry(10,20,30) , \
                ImageGeometry(10,20,30) ]
         x = [ g.allocate() for g in ig ]
-        ops = [ TomoIdentity(g) for g in ig ]
+        ops = [ Identity(g) for g in ig ]
 
         val = 1
         # test limit as non Scaled
@@ -121,7 +121,7 @@ class TestBlockOperator(unittest.TestCase):
                #ImageGeometry(10,20,30) , \
                ImageGeometry(2,3    ) ]
         x = [ g.allocate() for g in ig ]
-        ops = [ TomoIdentity(g) for g in ig ]
+        ops = [ Identity(g) for g in ig ]
 
 
         # test limit as non Scaled
@@ -158,7 +158,7 @@ class TestBlockOperator(unittest.TestCase):
         print (img.shape, ig.shape)
         self.assertTrue(img.shape == (30,20,10))
         self.assertEqual(img.sum(), 0)
-        Id = TomoIdentity(ig)
+        Id = Identity(ig)
         y = Id.direct(img)
         numpy.testing.assert_array_equal(y.as_array(), img.as_array())
 
@@ -167,7 +167,7 @@ class TestBlockOperator(unittest.TestCase):
 
         from ccpi.plugins.ops import CCPiProjectorSimple
         from ccpi.optimisation.ops import PowerMethodNonsquare
-        from ccpi.optimisation.ops import TomoIdentity
+        from ccpi.optimisation.operators import Identity
         from ccpi.optimisation.funcs import Norm2sq, Norm1
         from ccpi.framework import ImageGeometry, AcquisitionGeometry
         from ccpi.optimisation.Algorithms import GradientDescent
@@ -265,8 +265,8 @@ class TestBlockOperator(unittest.TestCase):
                                 ImageData(geometry=ig, dimension_labels=['horizontal_x','horizontal_y','vertical']))
         
         # setup a tomo identity
-        Ibig = 1e5 * TomoIdentity(geometry=ig)
-        Ismall = 1e-5 * TomoIdentity(geometry=ig)
+        Ibig = 1e5 * Identity(ig)
+        Ismall = 1e-5 * Identity(ig)
         
         # composite operator
         Kbig = BlockOperator(A, Ibig, shape=(2,1))
@@ -362,4 +362,4 @@ class TestBlockOperator(unittest.TestCase):
         G1 = FiniteDiff(ig1, direction=2, bnd_cond = 'Periodic')
         print(ig1.shape==u1.shape)
         print (G1.norm())
-        numpy.testing.assert_allclose(G1.norm(), numpy.sqrt(4), atol=0.1)
\ No newline at end of file
+        numpy.testing.assert_allclose(G1.norm(), numpy.sqrt(4), atol=0.1)
diff --git a/Wrappers/Python/test/test_Gradient.py b/Wrappers/Python/test/test_Gradient.py
index 1d6485c..c6b2d2e 100755
--- a/Wrappers/Python/test/test_Gradient.py
+++ b/Wrappers/Python/test/test_Gradient.py
@@ -1,9 +1,5 @@
 import unittest
 import numpy
-#from ccpi.plugins.ops import CCPiProjectorSimple
-from ccpi.optimisation.ops import PowerMethodNonsquare
-from ccpi.optimisation.ops import TomoIdentity
-from ccpi.optimisation.funcs import Norm2sq, Norm1
 from ccpi.framework import ImageGeometry, AcquisitionGeometry
 from ccpi.framework import ImageData, AcquisitionData
 #from ccpi.optimisation.algorithms import GradientDescent
diff --git a/Wrappers/Python/test/test_algorithms.py b/Wrappers/Python/test/test_algorithms.py
index a35ffc1..669804e 100755
--- a/Wrappers/Python/test/test_algorithms.py
+++ b/Wrappers/Python/test/test_algorithms.py
@@ -12,7 +12,7 @@ from ccpi.framework import ImageData
 from ccpi.framework import AcquisitionData
 from ccpi.framework import ImageGeometry
 from ccpi.framework import AcquisitionGeometry
-from ccpi.optimisation.ops import TomoIdentity
+from ccpi.optimisation.operators import Identity
 from ccpi.optimisation.funcs import Norm2sq
 from ccpi.optimisation.algorithms import GradientDescent
 from ccpi.optimisation.algorithms import CGLS
@@ -26,7 +26,7 @@ class TestAlgorithms(unittest.TestCase):
     def setUp(self):
         #wget.download('https://github.com/DiamondLightSource/Savu/raw/master/test_data/data/24737_fd.nxs')
         #self.filename = '24737_fd.nxs'
-        # we use TomoIdentity as the operator and solve the simple least squares 
+        # we use Identity as the operator and solve the simple least squares 
         # problem for a random-valued ImageData or AcquisitionData b?  
         # Then we know the minimiser is b itself
         
@@ -48,7 +48,7 @@ class TestAlgorithms(unittest.TestCase):
         # fill with random numbers
         b.fill(numpy.random.random(x_init.shape))
         
-        identity = TomoIdentity(geometry=ig)
+        identity = Identity(ig)
         
         norm2sq = Norm2sq(identity, b)
         
@@ -66,7 +66,7 @@ class TestAlgorithms(unittest.TestCase):
         # fill with random numbers
         b.fill(numpy.random.random(x_init.shape))
         
-        identity = TomoIdentity(geometry=ig)
+        identity = Identity(ig)
         
         alg = CGLS(x_init=x_init, operator=identity, data=b)
         alg.max_iteration = 1
@@ -83,7 +83,7 @@ class TestAlgorithms(unittest.TestCase):
         x_init = ImageData(geometry=ig)
         x_init.fill(numpy.random.random(x_init.shape))
         
-        identity = TomoIdentity(geometry=ig)
+        identity = Identity(ig)
         
         norm2sq = Norm2sq(identity, b)
         norm2sq.L = 2 * norm2sq.c * identity.norm()**2
@@ -121,4 +121,4 @@ class TestAlgorithms(unittest.TestCase):
 
 if __name__ == '__main__':
     unittest.main()
- 
\ No newline at end of file
+ 
diff --git a/Wrappers/Python/test/test_functions.py b/Wrappers/Python/test/test_functions.py
index 05bdd7a..b428c12 100644
--- a/Wrappers/Python/test/test_functions.py
+++ b/Wrappers/Python/test/test_functions.py
@@ -17,15 +17,10 @@ from ccpi.framework import BlockDataContainer
 from numbers import Number
 from ccpi.optimisation.operators import Gradient
 
-#from ccpi.optimisation.functions import SimpleL2NormSq
 from ccpi.optimisation.functions import L2NormSquared
-#from ccpi.optimisation.functions import SimpleL1Norm
 from ccpi.optimisation.functions import L1Norm, MixedL21Norm
 
-from ccpi.optimisation.funcs import Norm2sq
-# from ccpi.optimisation.functions.L2NormSquared import SimpleL2NormSq, L2NormSq
-# from ccpi.optimisation.functions.L1Norm import SimpleL1Norm, L1Norm
-#from ccpi.optimisation.functions import mixed_L12Norm
+from ccpi.optimisation.functions import Norm2sq
 from ccpi.optimisation.functions import ZeroFunction
 
 from ccpi.optimisation.functions import FunctionOperatorComposition
diff --git a/Wrappers/Python/test/test_run_test.py b/Wrappers/Python/test/test_run_test.py
index c698032..9b4d53b 100755
--- a/Wrappers/Python/test/test_run_test.py
+++ b/Wrappers/Python/test/test_run_test.py
@@ -8,16 +8,15 @@ from ccpi.framework import ImageGeometry
 from ccpi.framework import AcquisitionGeometry
 from ccpi.optimisation.algorithms import FISTA
 #from ccpi.optimisation.algs import FBPD
-from ccpi.optimisation.funcs import Norm2sq
+from ccpi.optimisation.functions import Norm2sq
 from ccpi.optimisation.functions import ZeroFunction
 from ccpi.optimisation.funcs import Norm1
-from ccpi.optimisation.funcs import TV2D
 from ccpi.optimisation.funcs import Norm2
 
-from ccpi.optimisation.ops import LinearOperatorMatrix
-from ccpi.optimisation.ops import TomoIdentity
-from ccpi.optimisation.ops import Identity
-from ccpi.optimisation.ops import PowerMethodNonsquare
+from ccpi.optimisation.operators import LinearOperatorMatrix
+from ccpi.optimisation.operators import Identity
+#from ccpi.optimisation.ops import PowerMethodNonsquare
+from ccpi.optimisation.operators import LinearOperator
 
 
 import numpy.testing
@@ -220,7 +219,7 @@ class TestAlgorithms(unittest.TestCase):
 
             # Create object instances with the test data A and b.
             f = Norm2sq(A, b, c=0.5, memopt=True)
-            f.L = PowerMethodNonsquare(A, 25, x_init)[0]
+            f.L = LinearOperator.PowerMethod(A, 25, x_init)[0]
             print ("Lipschitz", f.L)
             g0 = ZeroFun()
 
@@ -289,7 +288,7 @@ class TestAlgorithms(unittest.TestCase):
             # Data fidelity term
             f_denoise = Norm2sq(I, y, c=0.5, memopt=True)
             x_init = ImageData(geometry=ig)
-            f_denoise.L = PowerMethodNonsquare(I, 25, x_init)[0]
+            f_denoise.L = LinearOperator.PowerMethod(I, 25, x_init)[0]
 
             # 1-norm regulariser
             lam1_denoise = 1.0
@@ -335,43 +334,6 @@ class TestAlgorithms(unittest.TestCase):
 
             self.assertNumpyArrayAlmostEqual(
                 x_fbpd1_denoise.array.flatten(), x1_denoise.value, 5)
-            x1_cvx = x1_denoise.value
-            x1_cvx.shape = (N, N)
-
-            # Now TV with FBPD
-            lam_tv = 0.1
-            gtv = TV2D(lam_tv)
-            gtv(gtv.op.direct(x_init_denoise))
-
-            opt_tv = {'tol': 1e-4, 'iter': 10000}
-
-            x_fbpdtv_denoise, itfbpdtv_denoise, timingfbpdtv_denoise,\
-                criterfbpdtv_denoise = \
-                FBPD(x_init_denoise, gtv.op, None, f_denoise, gtv, opt=opt_tv)
-            print(x_fbpdtv_denoise)
-            print(criterfbpdtv_denoise[-1])
-
-            # Compare to CVXPY
-
-            # Construct the problem.
-            xtv_denoise = Variable((N, N))
-            objectivetv_denoise = Minimize(
-                0.5*sum_squares(xtv_denoise - y.array) + lam_tv*tv(xtv_denoise))
-            probtv_denoise = Problem(objectivetv_denoise)
-
-            # The optimal objective is returned by prob.solve().
-            resulttv_denoise = probtv_denoise.solve(
-                verbose=False, solver=SCS, eps=1e-12)
-
-            # The optimal solution for x is stored in x.value and optimal objective value
-            # is in result as well as in objective.value
-            print("CVXPY least squares plus 1-norm solution and objective value:")
-            print(xtv_denoise.value)
-            print(objectivetv_denoise.value)
-
-            self.assertNumpyArrayAlmostEqual(
-                x_fbpdtv_denoise.as_array(), xtv_denoise.value, 1)
-
         else:
             self.assertTrue(cvx_not_installable)