diff options
| -rw-r--r-- | Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py | 14 | ||||
| -rwxr-xr-x | Wrappers/Python/test/test_Gradient.py | 15 |
2 files changed, 26 insertions, 3 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py b/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py index 6f32845..d98961b 100644 --- a/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py +++ b/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py @@ -110,10 +110,11 @@ class Gradient(LinearOperator): def range_geometry(self): return self.gm_range - def norm(self): + def norm(self, **kwargs): x0 = self.gm_domain.allocate('random') - self.s1, sall, svec = LinearOperator.PowerMethod(self, 10, x0) + iterations = kwargs.get('iterations', 10) + self.s1, sall, svec = LinearOperator.PowerMethod(self, iterations, x0) return self.s1 def __rmul__(self, scalar): @@ -160,14 +161,21 @@ if __name__ == '__main__': from ccpi.optimisation.operators import Identity, BlockOperator - M, N = 2, 3 + M, N = 20, 30 ig = ImageGeometry(M, N) arr = ig.allocate('random_int' ) # check direct of Gradient and sparse matrix G = Gradient(ig) + norm1 = G.norm(iterations=300) + print ("should be sqrt(8) {} {}".format(numpy.sqrt(8), norm1)) G_sp = G.matrix() + ig4 = ImageGeometry(M,N, channels=3) + G4 = Gradient(ig4, correlation=Gradient.CORRELATION_SPACECHANNEL) + norm4 = G4.norm(iterations=300) + print ("should be sqrt(12) {} {}".format(numpy.sqrt(12), norm4)) + res1 = G.direct(arr) res1y = numpy.reshape(G_sp[0].toarray().dot(arr.as_array().flatten('F')), ig.shape, 'F') diff --git a/Wrappers/Python/test/test_Gradient.py b/Wrappers/Python/test/test_Gradient.py index c6b2d2e..89f26eb 100755 --- a/Wrappers/Python/test/test_Gradient.py +++ b/Wrappers/Python/test/test_Gradient.py @@ -84,3 +84,18 @@ class TestGradient(unittest.TestCase): res = G2D.direct(u4) print(res[0].as_array()) print(res[1].as_array()) + + M, N = 20, 30 + ig = ImageGeometry(M, N) + arr = ig.allocate('random_int' ) + + # check direct of Gradient and sparse matrix + G = Gradient(ig) + norm1 = G.norm(iterations=300) + print ("should be sqrt(8) {} {}".format(numpy.sqrt(8), norm1)) + numpy.testing.assert_almost_equal(norm1, numpy.sqrt(8), decimal=2) + ig4 = ImageGeometry(M,N, channels=3) + G4 = Gradient(ig4, correlation=Gradient.CORRELATION_SPACECHANNEL) + norm4 = G4.norm(iterations=300) + print ("should be sqrt(12) {} {}".format(numpy.sqrt(12), norm4)) + numpy.testing.assert_almost_equal(norm4, numpy.sqrt(12), decimal=2) |