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-rwxr-xr-xWrappers/Python/ccpi/framework/BlockDataContainer.py50
-rw-r--r--Wrappers/Python/ccpi/optimisation/functions/BlockFunction.py63
-rw-r--r--Wrappers/Python/ccpi/optimisation/functions/FunctionOperatorComposition.py19
-rw-r--r--Wrappers/Python/ccpi/optimisation/functions/L1Norm.py141
-rw-r--r--Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py23
-rwxr-xr-xWrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py68
-rwxr-xr-xWrappers/Python/ccpi/optimisation/functions/ScaledFunction.py34
-rw-r--r--Wrappers/Python/ccpi/optimisation/functions/ZeroFunction.py (renamed from Wrappers/Python/ccpi/optimisation/functions/ZeroFun.py)18
-rw-r--r--Wrappers/Python/ccpi/optimisation/functions/__init__.py2
-rwxr-xr-xWrappers/Python/wip/pdhg_TV_denoising.py17
-rw-r--r--Wrappers/Python/wip/pdhg_TV_tomography2D.py4
11 files changed, 267 insertions, 172 deletions
diff --git a/Wrappers/Python/ccpi/framework/BlockDataContainer.py b/Wrappers/Python/ccpi/framework/BlockDataContainer.py
index afdf617..386cd87 100755
--- a/Wrappers/Python/ccpi/framework/BlockDataContainer.py
+++ b/Wrappers/Python/ccpi/framework/BlockDataContainer.py
@@ -263,13 +263,26 @@ class BlockDataContainer(object):
return type(self)(*[el.conjugate() for el in self.containers], shape=self.shape)
## reductions
+
def sum(self, *args, **kwargs):
return numpy.sum([ el.sum(*args, **kwargs) for el in self.containers])
+
def squared_norm(self):
y = numpy.asarray([el.squared_norm() for el in self.containers])
return y.sum()
+
def norm(self):
- return numpy.sqrt(self.squared_norm())
+ return numpy.sqrt(self.squared_norm())
+
+ def pnorm(self, p=2):
+
+ if p==1:
+ return sum(self.abs())
+ elif p==2:
+ return sum([el*el for el in self.containers]).sqrt()
+ else:
+ return ValueError('Not implemented')
+
def copy(self):
'''alias of clone'''
return self.clone()
@@ -428,4 +441,39 @@ class BlockDataContainer(object):
def __itruediv__(self, other):
'''Inline truedivision'''
return self.__idiv__(other)
+
+
+
+
+
+if __name__ == '__main__':
+
+ from ccpi.framework import ImageGeometry, BlockGeometry
+ import numpy
+
+ N, M = 2, 3
+ ig = ImageGeometry(N, M)
+ BG = BlockGeometry(ig, ig)
+
+ U = BG.allocate('random_int')
+
+
+ print ("test sum BDC " )
+ w = U[0].as_array() + U[1].as_array()
+ w1 = sum(U).as_array()
+ numpy.testing.assert_array_equal(w, w1)
+
+ print ("test sum BDC " )
+ z = numpy.sqrt(U[0].as_array()**2 + U[1].as_array()**2)
+ z1 = sum(U**2).sqrt().as_array()
+ numpy.testing.assert_array_equal(z, z1)
+
+
+
+ z2 = U.pnorm(2)
+
+
+
+
+
diff --git a/Wrappers/Python/ccpi/optimisation/functions/BlockFunction.py b/Wrappers/Python/ccpi/optimisation/functions/BlockFunction.py
index 8cce290..bf627a5 100644
--- a/Wrappers/Python/ccpi/optimisation/functions/BlockFunction.py
+++ b/Wrappers/Python/ccpi/optimisation/functions/BlockFunction.py
@@ -6,36 +6,35 @@ Created on Fri Mar 8 10:01:31 2019
@author: evangelos
"""
-import numpy as np
-
from ccpi.optimisation.functions import Function
from ccpi.framework import BlockDataContainer
from numbers import Number
class BlockFunction(Function):
- '''A Block vector of Functions
+ '''BlockFunction acts as a separable sum function, i.e.,
- .. math::
-
- f = [f_1,f_2,f_3]
- f([x_1,x_2,x_3]) = f_1(x_1) + f_2(x_2) + f_3(x_3)
+ f = [f_1,...,f_n]
+
+ f([x_1,...,x_n]) = f_1(x_1) + .... + f_n(x_n)
'''
def __init__(self, *functions):
- '''Creator'''
+
self.functions = functions
self.length = len(self.functions)
super(BlockFunction, self).__init__()
def __call__(self, x):
- '''evaluates the BlockFunction on the BlockDataContainer
+
+ '''Evaluates the BlockFunction at a BlockDataContainer x
:param: x (BlockDataContainer): must have as many rows as self.length
returns sum(f_i(x_i))
'''
+
if self.length != x.shape[0]:
raise ValueError('BlockFunction and BlockDataContainer have incompatible size')
t = 0
@@ -44,7 +43,12 @@ class BlockFunction(Function):
return t
def convex_conjugate(self, x):
- '''Convex_conjugate does not take into account the BlockOperator'''
+
+ ''' Evaluate convex conjugate of BlockFunction at x
+
+ returns sum(f_i^{*}(x_i))
+
+ '''
t = 0
for i in range(x.shape[0]):
t += self.functions[i].convex_conjugate(x.get_item(i))
@@ -52,7 +56,13 @@ class BlockFunction(Function):
def proximal_conjugate(self, x, tau, out = None):
- '''proximal_conjugate does not take into account the BlockOperator'''
+
+ ''' Evaluate Proximal Operator of tau * f(\cdot) at x
+
+ prox_{tau*f}(x) = sum_{i} prox_{tau*f_{i}}(x_{i})
+
+
+ '''
if out is not None:
if isinstance(tau, Number):
@@ -76,7 +86,14 @@ class BlockFunction(Function):
def proximal(self, x, tau, out = None):
- '''proximal does not take into account the BlockOperator'''
+
+ ''' Evaluate Proximal Operator of tau * f^{*}(\cdot) at x
+
+ prox_{tau*f^{*}}(x) = sum_{i} prox_{tau*f^{*}_{i}}(x_{i})
+
+
+ '''
+
out = [None]*self.length
if isinstance(tau, Number):
for i in range(self.length):
@@ -88,8 +105,19 @@ class BlockFunction(Function):
return BlockDataContainer(*out)
def gradient(self,x, out=None):
- '''FIXME: gradient returns pass'''
- pass
+
+ ''' Evaluate gradient of f at x: f'(x)
+
+ returns: BlockDataContainer [f_{1}'(x_{1}), ... , f_{n}'(x_{n})]
+
+ '''
+
+ out = [None]*self.length
+ for i in range(self.length):
+ out[i] = self.functions[i].gradient(x.get_item(i))
+
+ return BlockDataContainer(*out)
+
if __name__ == '__main__':
@@ -100,6 +128,7 @@ if __name__ == '__main__':
from ccpi.framework import ImageGeometry, BlockGeometry
from ccpi.optimisation.operators import Gradient, Identity, BlockOperator
import numpy
+ import numpy as np
ig = ImageGeometry(M, N)
@@ -131,11 +160,7 @@ if __name__ == '__main__':
numpy.testing.assert_array_almost_equal(res_no_out[1].as_array(), \
res_out[1].as_array(), decimal=4)
-
-
-
-
-
+
##########################################################################
diff --git a/Wrappers/Python/ccpi/optimisation/functions/FunctionOperatorComposition.py b/Wrappers/Python/ccpi/optimisation/functions/FunctionOperatorComposition.py
index 34b7e35..38bc458 100644
--- a/Wrappers/Python/ccpi/optimisation/functions/FunctionOperatorComposition.py
+++ b/Wrappers/Python/ccpi/optimisation/functions/FunctionOperatorComposition.py
@@ -6,32 +6,47 @@ Created on Fri Mar 8 09:55:36 2019
@author: evangelos
"""
-import numpy as np
-#from ccpi.optimisation.funcs import Function
from ccpi.optimisation.functions import Function
from ccpi.optimisation.functions import ScaledFunction
class FunctionOperatorComposition(Function):
+ ''' Function composition with Operator, i.e., f(Ax)
+
+ A: operator
+ f: function
+
+ '''
+
def __init__(self, operator, function):
+
super(FunctionOperatorComposition, self).__init__()
self.function = function
self.operator = operator
alpha = 1
+
if isinstance (function, ScaledFunction):
alpha = function.scalar
self.L = 2 * alpha * operator.norm()**2
def __call__(self, x):
+
+ ''' Evaluate FunctionOperatorComposition at x
+
+ returns f(Ax)
+
+ '''
return self.function(self.operator.direct(x))
+ #TODO do not know if we need it
def call_adjoint(self, x):
return self.function(self.operator.adjoint(x))
+
def convex_conjugate(self, x):
''' convex_conjugate does not take into account the Operator'''
diff --git a/Wrappers/Python/ccpi/optimisation/functions/L1Norm.py b/Wrappers/Python/ccpi/optimisation/functions/L1Norm.py
index 163eefa..4e53f2c 100644
--- a/Wrappers/Python/ccpi/optimisation/functions/L1Norm.py
+++ b/Wrappers/Python/ccpi/optimisation/functions/L1Norm.py
@@ -16,11 +16,82 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
-"""
-Created on Wed Mar 6 19:42:34 2019
-@author: evangelos
-"""
+
+from ccpi.optimisation.functions import Function
+from ccpi.optimisation.functions.ScaledFunction import ScaledFunction
+from ccpi.optimisation.operators import ShrinkageOperator
+
+
+class L1Norm(Function):
+
+ '''
+
+ Class: L1Norm
+
+ Cases: a) f(x) = ||x||_{1}
+
+ b) f(x) = ||x - b||_{1}
+
+ '''
+
+ def __init__(self, **kwargs):
+
+ super(L1Norm, self).__init__()
+ self.b = kwargs.get('b',None)
+
+ def __call__(self, x):
+
+ ''' Evaluate L1Norm at x: f(x) '''
+
+ y = x
+ if self.b is not None:
+ y = x - self.b
+ return y.abs().sum()
+
+ def gradient(self,x):
+ #TODO implement subgradient???
+ return ValueError('Not Differentiable')
+
+ def convex_conjugate(self,x):
+ #TODO implement Indicator infty???
+
+ y = 0
+ if self.b is not None:
+ y = 0 + (self.b * x).sum()
+ return y
+
+ def proximal(self, x, tau, out=None):
+
+ # TODO implement shrinkage operator, we will need it later e.g SplitBregman
+
+ if out is None:
+ if self.b is not None:
+ return self.b + ShrinkageOperator.__call__(self, x - self.b, tau)
+ else:
+ return ShrinkageOperator.__call__(self, x, tau)
+ else:
+ if self.b is not None:
+ out.fill(self.b + ShrinkageOperator.__call__(self, x - self.b, tau))
+ else:
+ out.fill(ShrinkageOperator.__call__(self, x, tau))
+
+ def proximal_conjugate(self, x, tau, out=None):
+
+ if out is None:
+ if self.b is not None:
+ return (x - tau*self.b).divide((x - tau*self.b).abs().maximum(1.0))
+ else:
+ return x.divide(x.abs().maximum(1.0))
+ else:
+ if self.b is not None:
+ out.fill((x - tau*self.b).divide((x - tau*self.b).abs().maximum(1.0)))
+ else:
+ out.fill(x.divide(x.abs().maximum(1.0)) )
+
+ def __rmul__(self, scalar):
+ return ScaledFunction(self, scalar)
+
#import numpy as np
##from ccpi.optimisation.funcs import Function
@@ -92,67 +163,7 @@ Created on Wed Mar 6 19:42:34 2019
#
###############################################################################
-from ccpi.optimisation.functions import Function
-from ccpi.optimisation.functions.ScaledFunction import ScaledFunction
-from ccpi.optimisation.operators import ShrinkageOperator
-
-
-class L1Norm(Function):
-
- def __init__(self, **kwargs):
-
- super(L1Norm, self).__init__()
- self.b = kwargs.get('b',None)
-
- def __call__(self, x):
-
- y = x
- if self.b is not None:
- y = x - self.b
- return y.abs().sum()
-
- def gradient(self,x):
- #TODO implement subgradient???
- return ValueError('Not Differentiable')
-
- def convex_conjugate(self,x):
- #TODO implement Indicator infty???
-
- y = 0
- if self.b is not None:
- y = 0 + (self.b * x).sum()
- return y
-
- def proximal(self, x, tau, out=None):
-
- # TODO implement shrinkage operator, we will need it later e.g SplitBregman
-
- if out is None:
- if self.b is not None:
- return self.b + ShrinkageOperator.__call__(self, x - self.b, tau)
- else:
- return ShrinkageOperator.__call__(self, x, tau)
- else:
- if self.b is not None:
- out.fill(self.b + ShrinkageOperator.__call__(self, x - self.b, tau))
- else:
- out.fill(ShrinkageOperator.__call__(self, x, tau))
-
- def proximal_conjugate(self, x, tau, out=None):
-
- if out is None:
- if self.b is not None:
- return (x - tau*self.b).divide((x - tau*self.b).abs().maximum(1.0))
- else:
- return x.divide(x.abs().maximum(1.0))
- else:
- if self.b is not None:
- out.fill((x - tau*self.b).divide((x - tau*self.b).abs().maximum(1.0)))
- else:
- out.fill(x.divide(x.abs().maximum(1.0)) )
-
- def __rmul__(self, scalar):
- return ScaledFunction(self, scalar)
+
diff --git a/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py b/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py
index 903dafa..7397cfb 100644
--- a/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py
+++ b/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py
@@ -17,19 +17,16 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-import numpy
from ccpi.optimisation.functions import Function
from ccpi.optimisation.functions.ScaledFunction import ScaledFunction
class L2NormSquared(Function):
- '''
-
- Class: L2NormSquared
-
- Cases: a) f(x) = ||x||^{2}
+ '''
+
+ Cases: a) f(x) = \|x\|^{2}_{2}
- b) f(x) = ||x - b||^{2}, b
+ b) f(x) = ||x - b||^{2}_{2}
'''
@@ -50,9 +47,7 @@ class L2NormSquared(Function):
except AttributeError as ae:
# added for compatibility with SIRF
return (y.norm()**2)
-
-
-
+
def gradient(self, x, out=None):
''' Evaluate gradient of L2NormSquared at x: f'(x) '''
@@ -127,11 +122,10 @@ class L2NormSquared(Function):
def __rmul__(self, scalar):
- ''' Allows multiplication of L2NormSquared with a scalar
+ ''' Multiplication of L2NormSquared with a scalar
Returns: ScaledFunction
-
-
+
'''
return ScaledFunction(self, scalar)
@@ -139,7 +133,8 @@ class L2NormSquared(Function):
if __name__ == '__main__':
-
+ from ccpi.framework import ImageGeometry
+ import numpy
# TESTS for L2 and scalar * L2
M, N, K = 2,3,5
diff --git a/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py b/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py
index 24c47f4..f524c5f 100755
--- a/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py
+++ b/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py
@@ -17,47 +17,48 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-import numpy as np
from ccpi.optimisation.functions import Function, ScaledFunction
-from ccpi.framework import DataContainer, ImageData, \
- ImageGeometry, BlockDataContainer
+from ccpi.framework import BlockDataContainer
+
import functools
-############################ mixed_L1,2NORM FUNCTIONS #####################
class MixedL21Norm(Function):
+
+ '''
+ f(x) = ||x||_{2,1} = \sum |x|_{2}
+ '''
+
def __init__(self, **kwargs):
super(MixedL21Norm, self).__init__()
self.SymTensor = kwargs.get('SymTensor',False)
- def __call__(self, x, out=None):
+ def __call__(self, x):
- ''' Evaluates L1,2Norm at point x
+ ''' Evaluates L2,1Norm at point x
:param: x is a BlockDataContainer
'''
if not isinstance(x, BlockDataContainer):
- raise ValueError('__call__ expected BlockDataContainer, got {}'.format(type(x)))
+ raise ValueError('__call__ expected BlockDataContainer, got {}'.format(type(x)))
+
if self.SymTensor:
+ #TODO fix this case
param = [1]*x.shape[0]
param[-1] = 2
tmp = [param[i]*(x[i] ** 2) for i in range(x.shape[0])]
- res = sum(tmp).sqrt().sum()
- else:
-
-# tmp = [ x[i]**2 for i in range(x.shape[0])]
- tmp = [ el**2 for el in x.containers ]
+ res = sum(tmp).sqrt().sum()
-# print(x.containers)
-# print(tmp)
-# print(type(sum(tmp)))
-# print(type(tmp))
- res = sum(tmp).sqrt().sum()
-# print(res)
- return res
+ else:
+
+ #tmp = [ el**2 for el in x.containers ]
+ #res = sum(tmp).sqrt().sum()
+ res = x.pnorm()
+
+ return res
def gradient(self, x, out=None):
return ValueError('Not Differentiable')
@@ -93,19 +94,28 @@ class MixedL21Norm(Function):
else:
if out is None:
- tmp = [ el*el for el in x.containers]
- res = sum(tmp).sqrt().maximum(1.0)
- frac = [el/res for el in x.containers]
- res = BlockDataContainer(*frac)
- return res
+# tmp = [ el*el for el in x.containers]
+# res = sum(tmp).sqrt().maximum(1.0)
+# frac = [el/res for el in x.containers]
+# res = BlockDataContainer(*frac)
+# return res
+
+ return x.divide(x.pnorm().maximum(1.0))
else:
- res1 = functools.reduce(lambda a,b: a + b*b, x.containers, x.get_item(0) * 0 )
- res = res1.sqrt().maximum(1.0)
- x.divide(res, out=out)
- #for i,el in enumerate(x.containers):
- # el.divide(res, out=out.get_item(i))
+
+# res1 = functools.reduce(lambda a,b: a + b*b, x.containers, x.get_item(0) * 0 )
+# res = res1.sqrt().maximum(1.0)
+# x.divide(res, out=out)
+ x.divide(x.pnorm().maximum(1.0), out=out)
+
def __rmul__(self, scalar):
+
+ ''' Multiplication of L2NormSquared with a scalar
+
+ Returns: ScaledFunction
+
+ '''
return ScaledFunction(self, scalar)
diff --git a/Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py b/Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py
index 3fbb858..cb85249 100755
--- a/Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py
+++ b/Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py
@@ -20,6 +20,7 @@ from numbers import Number
import numpy
class ScaledFunction(object):
+
'''ScaledFunction
A class to represent the scalar multiplication of an Function with a scalar.
@@ -48,12 +49,22 @@ class ScaledFunction(object):
def convex_conjugate(self, x):
'''returns the convex_conjugate of the scaled function '''
- # if out is None:
- # return self.scalar * self.function.convex_conjugate(x/self.scalar)
- # else:
- # out.fill(self.function.convex_conjugate(x/self.scalar))
- # out *= self.scalar
return self.scalar * self.function.convex_conjugate(x/self.scalar)
+
+ def gradient(self, x, out=None):
+ '''Returns the gradient of the function at x, if the function is differentiable'''
+ if out is None:
+ return self.scalar * self.function.gradient(x)
+ else:
+ out.fill( self.scalar * self.function.gradient(x) )
+
+ def proximal(self, x, tau, out=None):
+ '''This returns the proximal operator for the function at x, tau
+ '''
+ if out is None:
+ return self.function.proximal(x, tau*self.scalar)
+ else:
+ out.fill( self.function.proximal(x, tau*self.scalar) )
def proximal_conjugate(self, x, tau, out = None):
'''This returns the proximal operator for the function at x, tau
@@ -76,20 +87,7 @@ class ScaledFunction(object):
versions of the CIL. Use proximal instead''', DeprecationWarning)
return self.proximal(x, out=None)
- def gradient(self, x, out=None):
- '''Returns the gradient of the function at x, if the function is differentiable'''
- if out is None:
- return self.scalar * self.function.gradient(x)
- else:
- out.fill( self.scalar * self.function.gradient(x) )
- def proximal(self, x, tau, out=None):
- '''This returns the proximal operator for the function at x, tau
- '''
- if out is None:
- return self.function.proximal(x, tau*self.scalar)
- else:
- out.fill( self.function.proximal(x, tau*self.scalar) )
if __name__ == '__main__':
diff --git a/Wrappers/Python/ccpi/optimisation/functions/ZeroFun.py b/Wrappers/Python/ccpi/optimisation/functions/ZeroFunction.py
index 6d21acb..cce519a 100644
--- a/Wrappers/Python/ccpi/optimisation/functions/ZeroFun.py
+++ b/Wrappers/Python/ccpi/optimisation/functions/ZeroFunction.py
@@ -17,21 +17,24 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-import numpy as np
-#from ccpi.optimisation.funcs import Function
from ccpi.optimisation.functions import Function
-from ccpi.framework import DataContainer, ImageData
from ccpi.framework import BlockDataContainer
-class ZeroFun(Function):
+class ZeroFunction(Function):
+
+ ''' ZeroFunction: f(x) = 0
+
+
+ '''
def __init__(self):
- super(ZeroFun, self).__init__()
+ super(ZeroFunction, self).__init__()
def __call__(self,x):
return 0
def convex_conjugate(self, x):
+
''' This is the support function sup <x, x^{*}> which in fact is the
indicator function for the set = {0}
So 0 if x=0, or inf if x neq 0
@@ -56,8 +59,3 @@ class ZeroFun(Function):
return 0
else:
return 0
-
- def domain_geometry(self):
- pass
- def range_geometry(self):
- pass \ No newline at end of file
diff --git a/Wrappers/Python/ccpi/optimisation/functions/__init__.py b/Wrappers/Python/ccpi/optimisation/functions/__init__.py
index 65e8848..a82ee3e 100644
--- a/Wrappers/Python/ccpi/optimisation/functions/__init__.py
+++ b/Wrappers/Python/ccpi/optimisation/functions/__init__.py
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from .Function import Function
-from .ZeroFun import ZeroFun
+from .ZeroFunction import ZeroFunction
from .L1Norm import L1Norm
from .L2NormSquared import L2NormSquared
from .ScaledFunction import ScaledFunction
diff --git a/Wrappers/Python/wip/pdhg_TV_denoising.py b/Wrappers/Python/wip/pdhg_TV_denoising.py
index 9bd5221..d885bca 100755
--- a/Wrappers/Python/wip/pdhg_TV_denoising.py
+++ b/Wrappers/Python/wip/pdhg_TV_denoising.py
@@ -14,7 +14,7 @@ import matplotlib.pyplot as plt
from ccpi.optimisation.algorithms import PDHG, PDHG_old
from ccpi.optimisation.operators import BlockOperator, Identity, Gradient
-from ccpi.optimisation.functions import ZeroFun, L2NormSquared, \
+from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, \
MixedL21Norm, FunctionOperatorComposition, BlockFunction, ScaledFunction
from skimage.util import random_noise
@@ -25,8 +25,6 @@ def dt(steps):
#%%
-
-# ############################################################################
# Create phantom for TV denoising
N = 200
@@ -68,7 +66,7 @@ if method == '0':
f2 = 0.5 * L2NormSquared(b = noisy_data)
f = BlockFunction(f1, f2)
- g = ZeroFun()
+ g = ZeroFunction()
else:
@@ -90,8 +88,8 @@ print ("normK", normK)
sigma = 1
tau = 1/(sigma*normK**2)
-opt = {'niter':1000}
-opt1 = {'niter':1000, 'memopt': True}
+opt = {'niter':2000}
+opt1 = {'niter':2000, 'memopt': True}
#t1 = timer()
#res, time, primal, dual, pdgap = PDHG_old(f, g, operator, tau = tau, sigma = sigma, opt = opt)
@@ -140,14 +138,11 @@ plt.colorbar()
plt.subplot(1,3,2)
plt.imshow(res1.as_array())
plt.colorbar()
+
#plt.show()
-#plt.figure(figsize=(5,5))
-plt.subplot(1,3,3)
-plt.imshow(np.abs(res1.as_array()-res.as_array()))
-plt.colorbar()
-plt.show()
+
#=======
## opt = {'niter':2000, 'memopt': True}
#
diff --git a/Wrappers/Python/wip/pdhg_TV_tomography2D.py b/Wrappers/Python/wip/pdhg_TV_tomography2D.py
index 159f2ea..e0868f7 100644
--- a/Wrappers/Python/wip/pdhg_TV_tomography2D.py
+++ b/Wrappers/Python/wip/pdhg_TV_tomography2D.py
@@ -16,7 +16,7 @@ import matplotlib.pyplot as plt
from ccpi.optimisation.algorithms import PDHG, PDHG_old
from ccpi.optimisation.operators import BlockOperator, Identity, Gradient
-from ccpi.optimisation.functions import ZeroFun, L2NormSquared, \
+from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, \
MixedL21Norm, BlockFunction, ScaledFunction
from ccpi.astra.ops import AstraProjectorSimple
@@ -90,7 +90,7 @@ operator = BlockOperator(op1, op2, shape=(2,1) )
alpha = 50
f = BlockFunction( alpha * MixedL21Norm(), \
0.5 * L2NormSquared(b = noisy_data) )
-g = ZeroFun()
+g = ZeroFunction()
# Compute operator Norm
normK = operator.norm()
generated by cgit v1.2.3 (git 2.25.1) at 2024-11-16 02:54:30 +0000