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# -*- coding: utf-8 -*-
# Copyright 2019 Science Technology Facilities Council
# Copyright 2019 University of Manchester
#
# This work is part of the Core Imaging Library developed by Science Technology
# Facilities Council and University of Manchester
#
# 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.txt
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from numbers import Number
import numpy
from ccpi.optimisation.operators import ScaledOperator
import functools
class BlockScaledOperator(ScaledOperator):
'''ScaledOperator
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:
:param operator (Operator): a Operator or LinearOperator
:param scalar (Number): a scalar multiplier
Example:
The scaled operator behaves like the following:
.. code-block:: python
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, operator, scalar, shape=None):
if shape is None:
shape = operator.shape
if isinstance(scalar, (list, tuple, numpy.ndarray)):
size = functools.reduce(lambda x,y:x*y, shape, 1)
if len(scalar) != size:
raise ValueError('Scalar and operators size do not match: {}!={}'
.format(len(scalar), len(operator)))
self.scalar = scalar[:]
print ("BlockScaledOperator ", self.scalar)
elif isinstance (scalar, Number):
self.scalar = scalar
else:
raise TypeError('expected scalar to be a number of an iterable: got {}'.format(type(scalar)))
self.operator = operator
self.shape = shape
def direct(self, x, out=None):
print ("BlockScaledOperator self.scalar", self.scalar)
#print ("self.scalar", self.scalar[0]* x.get_item(0).as_array())
return self.scalar * (self.operator.direct(x, out=out))
def adjoint(self, x, out=None):
if self.operator.is_linear():
return self.scalar * self.operator.adjoint(x, out=out)
else:
raise TypeError('Operator is not linear')
def norm(self, **kwargs):
return numpy.abs(self.scalar) * self.operator.norm(**kwargs)
def range_geometry(self):
return self.operator.range_geometry()
def domain_geometry(self):
return self.operator.domain_geometry()
@property
def T(self):
'''Return the transposed of self'''
return type(self)(self.operator.T, self.scalar)
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