2 files changed, 108 insertions, 0 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/functions/Function.py b/Wrappers/Python/ccpi/optimisation/functions/Function.py
new file mode 100755
index 0000000..43ce900
--- /dev/null
+++ b/Wrappers/Python/ccpi/optimisation/functions/Function.py
@@ -0,0 +1,48 @@
+# -*- coding: utf-8 -*-
+#   This work is part of the Core Imaging Library developed by
+#   Visual Analytics and Imaging System Group of the Science Technology
+#   Facilities Council, STFC
+
+#   Copyright 2018-2019 Jakob Jorgensen, Daniil Kazantsev and Edoardo Pasca
+
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+
+#       http://www.apache.org/licenses/LICENSE-2.0
+
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   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.
+
+import warnings
+
+class Function(object):
+    '''Abstract class representing a function
+    
+    
+    '''
+    def __init__(self):
+        self.L = None
+    def __call__(self,x, out=None):
+        raise NotImplementedError
+    def call_adjoint(self, x, out=None):
+        raise NotImplementedError
+    def convex_conjugate(self, x, out=None):
+        raise NotImplementedError
+    def proximal_conjugate(self, x, tau, out = None):
+        raise NotImplementedError
+    def grad(self, x):
+        warnings.warn('''This method will disappear in following 
+        versions of the CIL. Use gradient instead''', DeprecationWarning)
+        return self.gradient(x, out=None)
+    def prox(self, x, tau):
+        warnings.warn('''This method will disappear in following 
+        versions of the CIL. Use proximal instead''', DeprecationWarning)
+        return self.proximal(x, out=None)
+    def gradient(self, x, out=None):
+        raise NotImplementedError
+    def proximal(self, x, tau, out=None):
+        raise NotImplementedError
+\ No newline at end of file
diff --git a/Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py b/Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py
new file mode 100755
index 0000000..f2e39fb
--- /dev/null
+++ b/Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py
@@ -0,0 +1,60 @@
+from numbers import Number
+import numpy
+
+class ScaledFunction(object):
+    '''ScaledFunction
+
+    A class to represent the scalar multiplication of an Operator with a scalar.
+    It holds an operator and a scalar. Basically it returns the multiplication
+    of the result of direct and adjoint of the operator with the scalar.
+    For the rest it behaves like the operator it holds.
+
+    Args:
+       operator (Operator): a Operator or LinearOperator
+       scalar (Number): a scalar multiplier
+    Example:
+       The scaled operator behaves like the following:
+       sop = ScaledOperator(operator, scalar)
+       sop.direct(x) = scalar * operator.direct(x)
+       sop.adjoint(x) = scalar * operator.adjoint(x)
+       sop.norm() = operator.norm()
+       sop.range_geometry() = operator.range_geometry()
+       sop.domain_geometry() = operator.domain_geometry()
+    '''
+    def __init__(self, function, scalar):
+        super(ScaledFunction, self).__init__()
+        self.L = None
+        if not isinstance (scalar, Number):
+            raise TypeError('expected scalar: got {}'.format(type(scalar)))
+        self.scalar = scalar
+        self.function = function
+
+    def __call__(self,x, out=None):
+        return self.scalar * self.function(x)
+
+    def call_adjoint(self, x, out=None):
+        return self.scalar * self.function.call_adjoint(x, out=out)
+
+    def convex_conjugate(self, x, out=None):
+        return self.scalar * self.function.convex_conjugate(x, out=out)
+
+    def proximal_conjugate(self, x, tau, out = None):
+        '''TODO check if this is mathematically correct'''
+        return self.function.proximal_conjugate(x, tau, out=out)
+
+    def grad(self, x):
+        warnings.warn('''This method will disappear in following 
+        versions of the CIL. Use gradient instead''', DeprecationWarning)
+        return self.gradient(x, out=None)
+
+    def prox(self, x, tau):
+        warnings.warn('''This method will disappear in following 
+        versions of the CIL. Use proximal instead''', DeprecationWarning)
+        return self.proximal(x, out=None)
+
+    def gradient(self, x, out=None):
+        return self.scalar * self.function.gradient(x, out=out)
+
+    def proximal(self, x, tau, out=None):
+        '''TODO check if this is mathematically correct'''
+        return self.function.proximal(x, tau, out=out)