def direct(self, x, out=None):
'''Returns E(v)'''
if out is None:
tmp = []
for i in range(self.domain_geometry().shape[0]):
for j in range(x.shape[0]):
self.FD.direction = i
tmp.append(self.FD.adjoint(x.get_item(j)))
tmp1 = [tmp[i] for i in self.order_ind]
res = [0.5 * sum(x) for x in zip(tmp, tmp1)]
return BlockDataContainer(*res)
else:
ind = 0
for i in range(self.domain_geometry().shape[0]):
for j in range(x.shape[0]):
self.FD.direction = i
self.FD.adjoint(x.get_item(j), out=out[ind])
ind+=1
out1 = BlockDataContainer(*[out[i] for i in self.order_ind])
out.fill( 0.5 * (out + out1) )
def test_axpby2(self):
# test axpby with BlockDataContainer and DataContainer
ig0 = ImageGeometry(2, 3, 4)
# ig1 = ImageGeometry(2,3,5)
data0 = ig0.allocate(-1)
data2 = ig0.allocate(1)
data1 = ig0.allocate(2)
# data3 = ig1.allocate(3)
cp0 = BlockDataContainer(data0, data2)
# cp1 = BlockDataContainer(data1,data3)
out = cp0 * 0. - 10
cp0.axpby(3, -2, data1, out)
# operation should be [ 3 * -1 + (-2) * 2 , 3 * 1 + (-2) * 2 ]
# output should be [ -7 , -1 ]
res0 = ig0.allocate(-7)
res2 = ig0.allocate(-1)
res = BlockDataContainer(res0, res2)
print("res0", res0.as_array())
print("res2", res2.as_array())
print("###############################")
print("out_0", out.get_item(0).as_array())
print("out_1", out.get_item(1).as_array())
self.assertBlockDataContainerEqual(out, res)
def test_mixedL12Norm(self):
M, N, K = 2, 3, 5
ig = ImageGeometry(voxel_num_x=M, voxel_num_y=N)
u1 = ig.allocate('random_int')
u2 = ig.allocate('random_int')
U = BlockDataContainer(u1, u2, shape=(2, 1))
# Define no scale and scaled
f_no_scaled = MixedL21Norm()
f_scaled = 1 * MixedL21Norm()
# call
a1 = f_no_scaled(U)
a2 = f_scaled(U)
self.assertNumpyArrayAlmostEqual(a1, a2)
tmp = [el**2 for el in U.containers]
self.assertBlockDataContainerEqual(BlockDataContainer(*tmp),
U.power(2))
z1 = f_no_scaled.proximal_conjugate(U, 1)
u3 = ig.allocate('random_int')
u4 = ig.allocate('random_int')
z3 = BlockDataContainer(u3, u4, shape=(2, 1))
f_no_scaled.proximal_conjugate(U, 1, out=z3)
self.assertBlockDataContainerEqual(z3, z1)
def adjoint(self, x, out=None):
'''Adjoint operation for the BlockOperator
BlockOperator may contain both LinearOperator and Operator
This method exists in BlockOperator as it is not known what type of
Operator it will contain.
BlockOperator work on BlockDataContainer, but they will work on DataContainers
and inherited classes by simple wrapping the input in a BlockDataContainer of shape (1,1)
Raises: ValueError if the contained Operators are not linear
'''
if not self.is_linear():
raise ValueError('Not all operators in Block are linear.')
if not isinstance(x, BlockDataContainer):
x_b = BlockDataContainer(x)
else:
x_b = x
shape = self.get_output_shape(x_b.shape, adjoint=True)
if out is None:
res = []
for col in range(self.shape[1]):
for row in range(self.shape[0]):
if row == 0:
prod = self.get_item(row,
col).adjoint(x_b.get_item(row))
else:
prod += self.get_item(row,
col).adjoint(x_b.get_item(row))
res.append(prod)
if self.shape[1] == 1:
# the output is a single DataContainer, so we can take it out
return res[0]
else:
return BlockDataContainer(*res, shape=shape)
else:
for col in range(self.shape[1]):
for row in range(self.shape[0]):
if row == 0:
if issubclass(out.__class__, DataContainer) or \
( has_sirf and issubclass(out.__class__, SIRFDataContainer) ):
self.get_item(row, col).adjoint(x_b.get_item(row),
out=out)
else:
op = self.get_item(row, col)
self.get_item(row,
col).adjoint(x_b.get_item(row),
out=out.get_item(col))
else:
if issubclass(out.__class__, DataContainer) or \
( has_sirf and issubclass(out.__class__, SIRFDataContainer) ):
out += self.get_item(row, col).adjoint(
x_b.get_item(row))
else:
a = out.get_item(col)
a += self.get_item(row, col).adjoint(
x_b.get_item(row), )
def skiptest_BlockDataContainerShape(self):
print("test block data container")
ig0 = ImageGeometry(12, 42, 55, 32)
ig1 = ImageGeometry(12, 42, 55, 32)
data0 = ImageData(geometry=ig0)
data1 = ImageData(geometry=ig1) + 1
data2 = ImageData(geometry=ig0) + 2
data3 = ImageData(geometry=ig1) + 3
cp0 = BlockDataContainer(data0, data1)
cp1 = BlockDataContainer(data2, data3)
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp0.T.shape == transpose_shape)
def adjoint(self, x, out=None):
if out is None:
tmp = [None]*self.domain_geometry().shape[0]
i = 0
for k in range(self.domain_geometry().shape[0]):
tmp1 = 0
for j in range(self.domain_geometry().shape[0]):
self.FD.direction = j
tmp1 += self.FD.direct(x[i])
i+=1
tmp[k] = tmp1
return BlockDataContainer(*tmp)
else:
tmp = self.domain_geometry().allocate()
i = 0
for k in range(self.domain_geometry().shape[0]):
tmp1 = 0
for j in range(self.domain_geometry().shape[0]):
self.FD.direction = j
self.FD.direct(x[i], out=tmp[j])
i+=1
tmp1+=tmp[j]
out[k].fill(tmp1)
def proximal(self, x, tau, out=None):
r"""Proximal operator of the BlockFunction at x:
.. math:: \mathrm{prox}_{\tau F}(x) = (\mathrm{prox}_{\tau f_{i}}(x_{i}))_{i=1}^{m}
Parameter:
x : BlockDataContainer and must have as many rows as self.length
"""
if self.length != x.shape[0]:
raise ValueError(
'BlockFunction and BlockDataContainer have incompatible size')
if out is None:
out = [None] * self.length
if isinstance(tau, Number):
for i in range(self.length):
out[i] = self.functions[i].proximal(x.get_item(i), tau)
else:
for i in range(self.length):
out[i] = self.functions[i].proximal(
x.get_item(i), tau.get_item(i))
return BlockDataContainer(*out)
else:
if isinstance(tau, Number):
for i in range(self.length):
self.functions[i].proximal(x.get_item(i), tau, out[i])
else:
for i in range(self.length):
self.functions[i].proximal(x.get_item(i), tau.get_item(i),
out[i])
def skiptest_BlockDataContainerShapeArithmetic(self):
print("test block data container")
ig0 = ImageGeometry(2, 3, 4)
ig1 = ImageGeometry(2, 3, 4)
data0 = ImageData(geometry=ig0)
data1 = ImageData(geometry=ig1) + 1
data2 = ImageData(geometry=ig0) + 2
data3 = ImageData(geometry=ig1) + 3
cp0 = BlockDataContainer(data0, data1)
#cp1 = BlockDataContainer(data2,data3)
cp1 = cp0 + 1
self.assertTrue(cp1.shape == cp0.shape)
cp1 = cp0.T + 1
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = cp0.T - 1
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = (cp0.T + 1) * 2
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = (cp0.T + 1) / 2
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = cp0.T.power(2.2)
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = cp0.T.maximum(3)
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = cp0.T.abs()
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = cp0.T.sign()
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = cp0.T.sqrt()
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = cp0.T.conjugate()
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
def sum_abs_col(self):
res = []
for row in range(self.shape[0]):
for col in range(self.shape[1]):
if col == 0:
prod = self.get_item(row, col).sum_abs_col()
else:
prod += self.get_item(row, col).sum_abs_col()
res.append(prod)
return BlockDataContainer(*res)
def test_axpby(self):
# test axpby between BlockDataContainers
ig0 = ImageGeometry(2, 3, 4)
ig1 = ImageGeometry(2, 3, 5)
data0 = ig0.allocate(-1)
data2 = ig0.allocate(1)
data1 = ig0.allocate(2)
data3 = ig0.allocate(3)
cp0 = BlockDataContainer(data0, data2)
cp1 = BlockDataContainer(data1, data3)
out = cp0 * 0. - 10
cp0.axpby(3, -2, cp1, out, num_threads=4)
# operation should be [ 3 * -1 + (-2) * 2 , 3 * 1 + (-2) * 3 ]
# output should be [ -7 , -3 ]
res0 = ig0.allocate(-7)
res2 = ig0.allocate(-3)
res = BlockDataContainer(res0, res2)
print("res0", res0.as_array())
print("res2", res2.as_array())
print("###############################")
print("out_0", out.get_item(0).as_array())
print("out_1", out.get_item(1).as_array())
self.assertBlockDataContainerEqual(out, res)
def test_BlockDataContainer_fill(self):
print("test block data container")
ig0 = ImageGeometry(2, 3, 4)
ig1 = ImageGeometry(2, 3, 5)
data0 = ImageData(geometry=ig0)
data1 = ImageData(geometry=ig1) + 1
data2 = ImageData(geometry=ig0) + 2
data3 = ImageData(geometry=ig1) + 3
cp0 = BlockDataContainer(data0, data1)
#cp1 = BlockDataContainer(data2,data3)
cp2 = BlockDataContainer(data0 + 1, data1 + 1)
data0.fill(data2)
self.assertNumpyArrayEqual(data0.as_array(), data2.as_array())
data0 = ImageData(geometry=ig0)
for el, ot in zip(cp0, cp2):
print(el.shape, ot.shape)
cp0.fill(cp2)
self.assertBlockDataContainerEqual(cp0, cp2)
def test_NestedBlockDataContainer(self):
ig0 = ImageGeometry(2, 3, 4)
ig1 = ImageGeometry(2, 3, 5)
data0 = ig0.allocate(0)
data2 = ig0.allocate(1)
cp0 = BlockDataContainer(data0, data2)
#cp1 = BlockDataContainer(data2,data3)
nested = BlockDataContainer(cp0, data2, data2)
out = BlockDataContainer(BlockDataContainer(data0, data0), data0,
data0)
nested.divide(data2, out=out)
self.assertBlockDataContainerEqual(out, nested)
def sum_abs_row(self):
res = []
for row in range(self.shape[0]):
for col in range(self.shape[1]):
if col == 0:
prod = self.get_item(row, col).sum_abs_row()
else:
prod += self.get_item(row, col).sum_abs_row()
res.append(prod)
if self.shape[1] == 1:
tmp = sum(res)
return ImageData(tmp)
else:
return BlockDataContainer(*res)
def allocate(self, value=0, dimension_labels=None, **kwargs):
max_value = kwargs.get('max_value', 100)
symmetry = kwargs.get('symmetry', False)
containers = [
geom.allocate(value, max_value=max_value)
for geom in self.geometries
]
if symmetry == True:
# for 2x2
# [ ig11, ig12\
# ig21, ig22]
# Row-wise Order
if len(containers) == 4:
containers[1] = containers[2]
# for 3x3
# [ ig11, ig12, ig13\
# ig21, ig22, ig23\
# ig31, ig32, ig33]
elif len(containers) == 9:
containers[1] = containers[3]
containers[2] = containers[6]
containers[5] = containers[7]
# for 4x4
# [ ig11, ig12, ig13, ig14\
# ig21, ig22, ig23, ig24\
# ig31, ig32, ig33, ig34
# ig41, ig42, ig43, ig44]
elif len(containers) == 16:
containers[1] = containers[4]
containers[2] = containers[8]
containers[3] = containers[12]
containers[6] = containers[9]
containers[7] = containers[10]
containers[11] = containers[15]
return BlockDataContainer(*containers)
def gradient(self, x, out=None):
r"""Returns the value of the gradient of the BlockFunction function at x.
.. math:: F'(x) = [f_{1}'(x_{1}), ... , f_{m}'(x_{m})]
Parameter:
x : BlockDataContainer and must have as many rows as self.length
"""
if self.length != x.shape[0]:
raise ValueError(
'BlockFunction and BlockDataContainer have incompatible size')
out = [None] * self.length
for i in range(self.length):
out[i] = self.functions[i].gradient(x.get_item(i))
return BlockDataContainer(*out)
def test_axpby4(self):
# test axpby with nested BlockDataContainer
ig0 = ImageGeometry(2, 3, 4)
ig1 = ImageGeometry(2, 3, 5)
data0 = ig0.allocate(-1)
data2 = ig0.allocate(1)
# data1 = ig0.allocate(2)
data3 = ig1.allocate(3)
cp0 = BlockDataContainer(data0, data2)
cp1 = BlockDataContainer(cp0 * 0. + [2, -2], data3)
print(cp1.get_item(0).get_item(0).as_array())
print(cp1.get_item(0).get_item(1).as_array())
print(cp1.get_item(1).as_array())
print("###############################")
out = cp1 * 0.
cp2 = out + [1, 3]
print(cp2.get_item(0).get_item(0).as_array())
print(cp2.get_item(0).get_item(1).as_array())
print(cp2.get_item(1).as_array())
cp2.axpby(3, -2, cp1, out, num_threads=4)
# output should be [ [ -1 , 7 ] , 3]
res0 = ig0.allocate(-1)
res2 = ig0.allocate(7)
res3 = ig1.allocate(3)
res = BlockDataContainer(BlockDataContainer(res0, res2), res3)
# print ("res0", res0.as_array())
# print ("res2", res2.as_array())
print("###############################")
# print ("out_0", out.get_item(0).as_array())
# print ("out_1", out.get_item(1).as_array())
self.assertBlockDataContainerEqual(out, res)
def direct(self, x, out=None):
'''Direct operation for the BlockOperator
BlockOperator work on BlockDataContainer, but they will work on DataContainers
and inherited classes by simple wrapping the input in a BlockDataContainer of shape (1,1)
'''
if not isinstance(x, BlockDataContainer):
x_b = BlockDataContainer(x)
else:
x_b = x
shape = self.get_output_shape(x_b.shape)
res = []
if out is None:
for row in range(self.shape[0]):
for col in range(self.shape[1]):
if col == 0:
prod = self.get_item(row,
col).direct(x_b.get_item(col))
else:
prod += self.get_item(row,
col).direct(x_b.get_item(col))
res.append(prod)
return BlockDataContainer(*res, shape=shape)
else:
tmp = self.range_geometry().allocate()
for row in range(self.shape[0]):
for col in range(self.shape[1]):
if col == 0:
self.get_item(row, col).direct(x_b.get_item(col),
out=out.get_item(row))
else:
a = out.get_item(row)
self.get_item(row, col).direct(x_b.get_item(col),
out=tmp.get_item(row))
a += tmp.get_item(row)
def test_Nested_BlockDataContainer(self):
print("test_Nested_BlockDataContainer")
ig0 = ImageGeometry(2, 3, 4)
ig1 = ImageGeometry(2, 3, 4)
# data0 = ImageData(geometry=ig0)
# data1 = ImageData(geometry=ig1) + 1
# data2 = ImageData(geometry=ig0) + 2
# data3 = ImageData(geometry=ig1) + 3
data0 = ig0.allocate(0.)
data1 = ig1.allocate(1.)
data2 = ig0.allocate(2.)
data3 = ig1.allocate(3.)
cp0 = BlockDataContainer(data0, data1)
cp1 = BlockDataContainer(data2, data3)
nbdc = BlockDataContainer(cp0, cp1)
nbdc2 = nbdc + 2
numpy.testing.assert_almost_equal(
nbdc2.get_item(0).get_item(0).as_array()[0][0][0], 2., decimal=5)
numpy.testing.assert_almost_equal(
nbdc2.get_item(0).get_item(1).as_array()[0][0][0], 3., decimal=5)
numpy.testing.assert_almost_equal(
nbdc2.get_item(1).get_item(0).as_array()[0][0][0], 4., decimal=5)
numpy.testing.assert_almost_equal(
nbdc2.get_item(1).get_item(1).as_array()[0][0][0], 5., decimal=5)
nbdc2 = 2 + nbdc
numpy.testing.assert_almost_equal(
nbdc2.get_item(0).get_item(0).as_array()[0][0][0], 2., decimal=5)
numpy.testing.assert_almost_equal(
nbdc2.get_item(0).get_item(1).as_array()[0][0][0], 3., decimal=5)
numpy.testing.assert_almost_equal(
nbdc2.get_item(1).get_item(0).as_array()[0][0][0], 4., decimal=5)
numpy.testing.assert_almost_equal(
nbdc2.get_item(1).get_item(1).as_array()[0][0][0], 5., decimal=5)
nbdc2 = nbdc * 2
numpy.testing.assert_almost_equal(
nbdc2.get_item(0).get_item(0).as_array()[0][0][0], 0., decimal=5)
numpy.testing.assert_almost_equal(
nbdc2.get_item(0).get_item(1).as_array()[0][0][0], 2., decimal=5)
numpy.testing.assert_almost_equal(
nbdc2.get_item(1).get_item(0).as_array()[0][0][0], 4., decimal=5)
numpy.testing.assert_almost_equal(
nbdc2.get_item(1).get_item(1).as_array()[0][0][0], 6., decimal=5)
nbdc2 = 2 * nbdc
numpy.testing.assert_almost_equal(
nbdc2.get_item(0).get_item(0).as_array()[0][0][0], 0., decimal=5)
numpy.testing.assert_almost_equal(
nbdc2.get_item(0).get_item(1).as_array()[0][0][0], 2., decimal=5)
numpy.testing.assert_almost_equal(
nbdc2.get_item(1).get_item(0).as_array()[0][0][0], 4., decimal=5)
numpy.testing.assert_almost_equal(
nbdc2.get_item(1).get_item(1).as_array()[0][0][0], 6., decimal=5)
nbdc2 = nbdc / 2
numpy.testing.assert_almost_equal(
nbdc2.get_item(0).get_item(0).as_array()[0][0][0], 0., decimal=5)
numpy.testing.assert_almost_equal(
nbdc2.get_item(0).get_item(1).as_array()[0][0][0], .5, decimal=5)
numpy.testing.assert_almost_equal(
nbdc2.get_item(1).get_item(0).as_array()[0][0][0], 1., decimal=5)
numpy.testing.assert_almost_equal(
nbdc2.get_item(1).get_item(1).as_array()[0][0][0],
3. / 2,
decimal=5)
c5 = nbdc.get_item(0).power(2).sum()
c5a = nbdc.power(2).sum()
print("sum", c5a, c5)
cp0 = BlockDataContainer(data0, data2)
a = cp0 * data2
b = data2 * cp0
self.assertBlockDataContainerEqual(a, b)
print("test_Nested_BlockDataContainer OK")
# Setup and run the simple CGLS algorithm x_init = ig.allocate() cgls1 = CGLS(x_init=x_init, operator=Aop, data=noisy_data) cgls1.max_iteration = 20 cgls1.update_objective_interval = 5 cgls1.run(20, verbose=True) # Setup and run the regularised CGLS algorithm (Tikhonov with Identity) x_init = ig.allocate() alpha1 = 50 op1 = Identity(ig) block_op1 = BlockOperator(Aop, alpha1 * op1, shape=(2, 1)) block_data1 = BlockDataContainer(noisy_data, op1.range_geometry().allocate()) cgls2 = CGLS(x_init=x_init, operator=block_op1, data=block_data1) cgls2.max_iteration = 200 cgls2.update_objective_interval = 10 cgls2.run(200, verbose=True) # Setup and run the regularised CGLS algorithm (Tikhonov with Gradient) x_init = ig.allocate() alpha2 = 25 op2 = Gradient(ig) block_op2 = BlockOperator(Aop, alpha2 * op2, shape=(2, 1)) block_data2 = BlockDataContainer(noisy_data, op2.range_geometry().allocate())
def test_BlockOperator(self):
print ("test_BlockOperator")
M, N = 3, 4
ig = ImageGeometry(M, N)
arr = ig.allocate('random_int')
G = Gradient(ig)
Id = Identity(ig)
B = BlockOperator(G, Id)
# Nx1 case
u = ig.allocate('random_int')
z1 = B.direct(u)
res = B.range_geometry().allocate()
#res = z1.copy()
B.direct(u, out=res)
print (type(z1), type(res))
print (z1.shape)
print(z1[0][0].as_array())
print(res[0][0].as_array())
self.assertBlockDataContainerEqual(z1, res)
# for col in range(z1.shape[0]):
# a = z1.get_item(col)
# b = res.get_item(col)
# if isinstance(a, BlockDataContainer):
# for col2 in range(a.shape[0]):
# self.assertNumpyArrayEqual(
# a.get_item(col2).as_array(),
# b.get_item(col2).as_array()
# )
# else:
# self.assertNumpyArrayEqual(
# a.as_array(),
# b.as_array()
# )
z1 = B.range_geometry().allocate(ImageGeometry.RANDOM_INT)
res1 = B.adjoint(z1)
res2 = B.domain_geometry().allocate()
B.adjoint(z1, out=res2)
self.assertNumpyArrayEqual(res1.as_array(), res2.as_array())
BB = BlockOperator( Id, 2 * Id)
B = BlockOperator( BB, Id )
v = B.domain_geometry().allocate()
B.adjoint(res,out=v)
vv = B.adjoint(res)
el1 = B.get_item(0,0).adjoint(z1.get_item(0)) +\
B.get_item(1,0).adjoint(z1.get_item(1))
print ("el1" , el1.as_array())
print ("vv" , vv.as_array())
print ("v" , v.as_array())
self.assertNumpyArrayEqual(v.as_array(),vv.as_array())
# test adjoint
print ("############ 2x1 #############")
BB = BlockOperator( Id, 2 * Id)
u = ig.allocate(1)
z1 = BB.direct(u)
print ("z1 shape {} one\n{} two\n{}".format(z1.shape,
z1.get_item(0).as_array(),
z1.get_item(1).as_array()))
res = BB.range_geometry().allocate(0)
BB.direct(u, out=res)
print ("res shape {} one\n{} two\n{}".format(res.shape,
res.get_item(0).as_array(),
res.get_item(1).as_array()))
self.assertNumpyArrayEqual(z1.get_item(0).as_array(),
u.as_array())
self.assertNumpyArrayEqual(z1.get_item(1).as_array(),
2 * u.as_array())
self.assertNumpyArrayEqual(res.get_item(0).as_array(),
u.as_array())
self.assertNumpyArrayEqual(res.get_item(1).as_array(),
2 * u.as_array())
x1 = BB.adjoint(z1)
print("adjoint x1\n",x1.as_array())
res1 = BB.domain_geometry().allocate()
BB.adjoint(z1, out=res1)
print("res1\n",res1.as_array())
self.assertNumpyArrayEqual(x1.as_array(),
res1.as_array())
self.assertNumpyArrayEqual(x1.as_array(),
5 * u.as_array())
self.assertNumpyArrayEqual(res1.as_array(),
5 * u.as_array())
#################################################
print ("############ 2x2 #############")
BB = BlockOperator( Id, 2 * Id, 3 * Id, Id, shape=(2,2))
B = BB
u = ig.allocate(1)
U = BlockDataContainer(u,u)
z1 = B.direct(U)
print ("z1 shape {} one\n{} two\n{}".format(z1.shape,
z1.get_item(0).as_array(),
z1.get_item(1).as_array()))
self.assertNumpyArrayEqual(z1.get_item(0).as_array(),
3 * u.as_array())
self.assertNumpyArrayEqual(z1.get_item(1).as_array(),
4 * u.as_array())
res = B.range_geometry().allocate()
B.direct(U, out=res)
self.assertNumpyArrayEqual(res.get_item(0).as_array(),
3 * u.as_array())
self.assertNumpyArrayEqual(res.get_item(1).as_array(),
4 * u.as_array())
x1 = B.adjoint(z1)
# this should be [15 u, 10 u]
el1 = B.get_item(0,0).adjoint(z1.get_item(0)) + B.get_item(1,0).adjoint(z1.get_item(1))
el2 = B.get_item(0,1).adjoint(z1.get_item(0)) + B.get_item(1,1).adjoint(z1.get_item(1))
shape = B.get_output_shape(z1.shape, adjoint=True)
print ("shape ", shape)
out = B.domain_geometry().allocate()
for col in range(B.shape[1]):
for row in range(B.shape[0]):
if row == 0:
el = B.get_item(row,col).adjoint(z1.get_item(row))
else:
el += B.get_item(row,col).adjoint(z1.get_item(row))
out.get_item(col).fill(el)
print ("el1 " , el1.as_array())
print ("el2 " , el2.as_array())
print ("out shape {} one\n{} two\n{}".format(out.shape,
out.get_item(0).as_array(),
out.get_item(1).as_array()))
self.assertNumpyArrayEqual(out.get_item(0).as_array(),
15 * u.as_array())
self.assertNumpyArrayEqual(out.get_item(1).as_array(),
10 * u.as_array())
res2 = B.domain_geometry().allocate()
#print (res2, res2.as_array())
B.adjoint(z1, out = res2)
#print ("adjoint",x1.as_array(),"\n",res2.as_array())
self.assertNumpyArrayEqual(
out.get_item(0).as_array(),
res2.get_item(0).as_array()
)
self.assertNumpyArrayEqual(
out.get_item(1).as_array(),
res2.get_item(1).as_array()
)
if True:
#B1 = BlockOperator(Id, Id, Id, Id, shape=(2,2))
B1 = BlockOperator(G, Id)
U = ig.allocate(ImageGeometry.RANDOM_INT)
#U = BlockDataContainer(u,u)
RES1 = B1.range_geometry().allocate()
Z1 = B1.direct(U)
B1.direct(U, out = RES1)
self.assertBlockDataContainerEqual(Z1,RES1)
print("U", U.as_array())
print("Z1", Z1[0][0].as_array())
print("RES1", RES1[0][0].as_array())
print("Z1", Z1[0][1].as_array())
print("RES1", RES1[0][1].as_array())
def test_BlockDataContainer(self):
print("test block data container")
ig0 = ImageGeometry(2, 3, 4)
ig1 = ImageGeometry(2, 3, 5)
# data0 = ImageData(geometry=ig0)
# data1 = ImageData(geometry=ig1) + 1
data0 = ig0.allocate(0.)
data1 = ig1.allocate(1.)
# data2 = ImageData(geometry=ig0) + 2
# data3 = ImageData(geometry=ig1) + 3
data2 = ig0.allocate(2.)
data3 = ig1.allocate(3.)
cp0 = BlockDataContainer(data0, data1)
cp1 = BlockDataContainer(data2, data3)
cp2 = BlockDataContainer(data0 + 1, data2 + 1)
d = cp2 + data0
self.assertEqual(d.get_item(0).as_array()[0][0][0], 1)
try:
d = cp2 + data1
self.assertTrue(False)
except ValueError as ve:
print(ve)
self.assertTrue(True)
d = cp2 - data0
self.assertEqual(d.get_item(0).as_array()[0][0][0], 1)
try:
d = cp2 - data1
self.assertTrue(False)
except ValueError as ve:
print(ve)
self.assertTrue(True)
d = cp2 * data2
self.assertEqual(d.get_item(0).as_array()[0][0][0], 2)
try:
d = cp2 * data1
self.assertTrue(False)
except ValueError as ve:
print(ve)
self.assertTrue(True)
a = [(el, ot) for el, ot in zip(cp0.containers, cp1.containers)]
print(a[0][0].shape)
#cp2 = BlockDataContainer(*a)
cp2 = cp0.add(cp1)
self.assertEqual(cp2.get_item(0).as_array()[0][0][0], 2.)
self.assertEqual(cp2.get_item(1).as_array()[0][0][0], 4.)
cp2 = cp0 + cp1
self.assertTrue(cp2.get_item(0).as_array()[0][0][0] == 2.)
self.assertTrue(cp2.get_item(1).as_array()[0][0][0] == 4.)
cp2 = cp0 + 1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
1.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
2.,
decimal=5)
cp2 = cp0 + [1, 2]
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
1.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
3.,
decimal=5)
cp2 += cp1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
+3.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
+6.,
decimal=5)
cp2 += 1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
+4.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
+7.,
decimal=5)
cp2 += [-2, -1]
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
2.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
6.,
decimal=5)
cp2 = cp0.subtract(cp1)
assert (cp2.get_item(0).as_array()[0][0][0] == -2.)
assert (cp2.get_item(1).as_array()[0][0][0] == -2.)
cp2 = cp0 - cp1
assert (cp2.get_item(0).as_array()[0][0][0] == -2.)
assert (cp2.get_item(1).as_array()[0][0][0] == -2.)
cp2 = cp0 - 1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
-1.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
0,
decimal=5)
cp2 = cp0 - [1, 2]
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
-1.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
-1.,
decimal=5)
cp2 -= cp1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
-3.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
-4.,
decimal=5)
cp2 -= 1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
-4.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
-5.,
decimal=5)
cp2 -= [-2, -1]
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
-2.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
-4.,
decimal=5)
cp2 = cp0.multiply(cp1)
assert (cp2.get_item(0).as_array()[0][0][0] == 0.)
assert (cp2.get_item(1).as_array()[0][0][0] == 3.)
cp2 = cp0 * cp1
assert (cp2.get_item(0).as_array()[0][0][0] == 0.)
assert (cp2.get_item(1).as_array()[0][0][0] == 3.)
cp2 = cp0 * 2
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
0.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
2,
decimal=5)
cp2 = 2 * cp0
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
0.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
2,
decimal=5)
cp2 = cp0 * [3, 2]
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
0.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
2.,
decimal=5)
cp2 = cp0 * numpy.asarray([3, 2])
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
0.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
2.,
decimal=5)
cp2 = [3, 2] * cp0
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
0.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
2.,
decimal=5)
cp2 = numpy.asarray([3, 2]) * cp0
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
0.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
2.,
decimal=5)
try:
cp2 = [3, 2, 3] * cp0
#numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 0. , decimal=5)
#numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 2., decimal = 5)
self.assertTrue(False)
except ValueError as ve:
print(ve)
self.assertTrue(True)
cp2 *= cp1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
0,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
+6.,
decimal=5)
cp2 *= 1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
0.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
+6.,
decimal=5)
cp2 *= [-2, -1]
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
0.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
-6.,
decimal=5)
try:
cp2 *= [2, 3, 5]
self.assertTrue(False)
except ValueError as ve:
print(ve)
self.assertTrue(True)
cp2 = cp0.divide(cp1)
assert (cp2.get_item(0).as_array()[0][0][0] == 0.)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
1. / 3.,
decimal=4)
cp2 = cp0 / cp1
assert (cp2.get_item(0).as_array()[0][0][0] == 0.)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
1. / 3.,
decimal=4)
cp2 = cp0 / 2
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
0.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
0.5,
decimal=5)
cp2 = cp0 / [3, 2]
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
0.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
0.5,
decimal=5)
cp2 = cp0 / numpy.asarray([3, 2])
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
0.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
0.5,
decimal=5)
cp3 = numpy.asarray([3, 2]) / (cp0 + 1)
numpy.testing.assert_almost_equal(cp3.get_item(0).as_array()[0][0][0],
3.,
decimal=5)
numpy.testing.assert_almost_equal(cp3.get_item(1).as_array()[0][0][0],
1,
decimal=5)
cp2 += 1
cp2 /= cp1
# TODO fix inplace division
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
1. / 2,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
1.5 / 3.,
decimal=5)
cp2 /= 1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
0.5,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
0.5,
decimal=5)
cp2 /= [-2, -1]
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
-0.5 / 2.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
-0.5,
decimal=5)
####
cp2 = cp0.power(cp1)
assert (cp2.get_item(0).as_array()[0][0][0] == 0.)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
1.,
decimal=4)
cp2 = cp0**cp1
assert (cp2.get_item(0).as_array()[0][0][0] == 0.)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
1.,
decimal=4)
cp2 = cp0**2
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
0.,
decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
1.,
decimal=5)
cp2 = cp0.maximum(cp1)
assert (cp2.get_item(0).as_array()[0][0][0] == cp1.get_item(
0).as_array()[0][0][0])
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
cp2.get_item(1).as_array()[0][0][0],
decimal=4)
cp2 = cp0.abs()
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0],
0.,
decimal=4)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0],
1.,
decimal=4)
cp2 = cp0.subtract(cp1)
s = cp2.sign()
numpy.testing.assert_almost_equal(s.get_item(0).as_array()[0][0][0],
-1.,
decimal=4)
numpy.testing.assert_almost_equal(s.get_item(1).as_array()[0][0][0],
-1.,
decimal=4)
cp2 = cp0.add(cp1)
s = cp2.sqrt()
numpy.testing.assert_almost_equal(s.get_item(0).as_array()[0][0][0],
numpy.sqrt(2),
decimal=4)
numpy.testing.assert_almost_equal(s.get_item(1).as_array()[0][0][0],
numpy.sqrt(4),
decimal=4)
s = cp0.sum()
size = functools.reduce(lambda x, y: x * y, data1.shape, 1)
print("size", size)
numpy.testing.assert_almost_equal(s, 0 + size, decimal=4)
s0 = 1
s1 = 1
for i in cp0.get_item(0).shape:
s0 *= i
for i in cp0.get_item(1).shape:
s1 *= i
dev = 'gpu'
else:
dev = 'cpu'
Aop = AstraProjectorSimple(ig, ag, dev)
sin = Aop.direct(data)
noisy_data = AcquisitionData(sin.as_array() + np.random.normal(0, 3, ig.shape))
# Setup and run the CGLS algorithm
alpha = 50
Grad = Gradient(ig)
# Form Tikhonov as a Block CGLS structure
op_CGLS = BlockOperator(Aop, alpha * Grad, shape=(2, 1))
block_data = BlockDataContainer(noisy_data, Grad.range_geometry().allocate())
x_init = ig.allocate()
cgls = CGLS(x_init=x_init, operator=op_CGLS, data=block_data)
cgls.max_iteration = 1000
cgls.update_objective_interval = 200
cgls.run(1000, verbose=False)
#Setup and run the PDHG algorithm
# Create BlockOperator
op_PDHG = BlockOperator(Grad, Aop, shape=(2, 1))
# Create functions
f1 = 0.5 * alpha**2 * L2NormSquared()
f2 = 0.5 * L2NormSquared(b=noisy_data)
f = BlockFunction(f1, f2)
Gy = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann')
d1 = abs(Gx.matrix()).toarray().sum(axis=0)
d2 = abs(Gy.matrix()).toarray().sum(axis=0)
d3 = abs(Id.matrix()).toarray().sum(axis=0)
d_res = numpy.reshape(d1 + d2 + d3, ig.shape, 'F')
print(d_res)
#
z1 = abs(Gx.matrix()).toarray().sum(axis=1)
z2 = abs(Gy.matrix()).toarray().sum(axis=1)
z3 = abs(Id.matrix()).toarray().sum(axis=1)
#
z_res = BlockDataContainer(BlockDataContainer(ImageData(numpy.reshape(z2, ig.shape, 'F')),\
ImageData(numpy.reshape(z1, ig.shape, 'F'))),\
ImageData(numpy.reshape(z3, ig.shape, 'F')))
#
ttt = B.sum_abs_col()
#
#TODO this is not working
# numpy.testing.assert_array_almost_equal(z_res[0][0].as_array(), ttt[0][0].as_array(), decimal=4)
# numpy.testing.assert_array_almost_equal(z_res[0][1].as_array(), ttt[0][1].as_array(), decimal=4)
# numpy.testing.assert_array_almost_equal(z_res[1].as_array(), ttt[1].as_array(), decimal=4)
u = ig.allocate('random_int')
z1 = B.direct(u)
res = B.range_geometry().allocate()
B.direct(u, out=res)
