diff options
| author | Edoardo Pasca <edo.paskino@gmail.com> | 2019-04-10 11:30:28 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2019-04-10 11:30:28 +0100 |
| commit | 58229d793fc2a24ff65d3128435e4351e3b7e73d (patch) | |
| tree | a0f9740d9371de94156c0e40f1e98bccf55dfd47 /Wrappers | |
| parent | e7a0152d17e03df79db6cd3cd50d07202f9d185d (diff) | |
| download | framework-58229d793fc2a24ff65d3128435e4351e3b7e73d.tar.gz framework-58229d793fc2a24ff65d3128435e4351e3b7e73d.tar.bz2 framework-58229d793fc2a24ff65d3128435e4351e3b7e73d.tar.xz framework-58229d793fc2a24ff65d3128435e4351e3b7e73d.zip | |
Direct adjoint out (#236)
* fix direct out
* test blockoperator adjoint and direct with out
* add missing method and code for memory optimisation
* untrack build directory
* fix imports
* fixed adjoint without out parameter
* fix adjoint operator of Gradient, added test
Diffstat (limited to 'Wrappers')
51 files changed, 397 insertions, 7597 deletions
diff --git a/Wrappers/Python/build/lib/ccpi/__init__.py b/Wrappers/Python/build/lib/ccpi/__init__.py deleted file mode 100644 index cf2d93d..0000000 --- a/Wrappers/Python/build/lib/ccpi/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# -*- 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 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.
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/framework/BlockDataContainer.py b/Wrappers/Python/build/lib/ccpi/framework/BlockDataContainer.py deleted file mode 100644 index 21ef3f0..0000000 --- a/Wrappers/Python/build/lib/ccpi/framework/BlockDataContainer.py +++ /dev/null @@ -1,337 +0,0 @@ -# -*- coding: utf-8 -*-
-"""
-Created on Tue Mar 5 16:04:45 2019
-
-@author: ofn77899
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
-import numpy
-from numbers import Number
-import functools
-from ccpi.framework import DataContainer
-#from ccpi.framework import AcquisitionData, ImageData
-#from ccpi.optimisation.operators import Operator, LinearOperator
-
-class BlockDataContainer(object):
- '''Class to hold DataContainers as column vector'''
- __array_priority__ = 1
- def __init__(self, *args, **kwargs):
- ''''''
- self.containers = args
- self.index = 0
- #shape = kwargs.get('shape', None)
- #if shape is None:
- # shape = (len(args),1)
- shape = (len(args),1)
- self.shape = shape
- #print (self.shape)
- n_elements = functools.reduce(lambda x,y: x*y, shape, 1)
- if len(args) != n_elements:
- raise ValueError(
- 'Dimension and size do not match: expected {} got {}'
- .format(n_elements, len(args)))
-
-
- def __iter__(self):
- '''BlockDataContainer is Iterable'''
- return self
- def next(self):
- '''python2 backwards compatibility'''
- return self.__next__()
- def __next__(self):
- try:
- out = self[self.index]
- except IndexError as ie:
- raise StopIteration()
- self.index+=1
- return out
-
- def is_compatible(self, other):
- '''basic check if the size of the 2 objects fit'''
-
- for i in range(len(self.containers)):
- if type(self.containers[i])==type(self):
- self = self.containers[i]
-
- if isinstance(other, Number):
- return True
- elif isinstance(other, list):
- for ot in other:
- if not isinstance(ot, (Number,\
- numpy.int, numpy.int8, numpy.int16, numpy.int32, numpy.int64,\
- numpy.float, numpy.float16, numpy.float32, numpy.float64, \
- numpy.complex)):
- raise ValueError('List/ numpy array can only contain numbers {}'\
- .format(type(ot)))
- return len(self.containers) == len(other)
- elif isinstance(other, numpy.ndarray):
- return len(self.containers) == len(other)
- elif issubclass(other.__class__, DataContainer):
- return self.get_item(0).shape == other.shape
- return len(self.containers) == len(other.containers)
-
- def get_item(self, row):
- if row > self.shape[0]:
- raise ValueError('Requested row {} > max {}'.format(row, self.shape[0]))
- return self.containers[row]
-
- def __getitem__(self, row):
- return self.get_item(row)
-
- def add(self, other, *args, **kwargs):
- if not self.is_compatible(other):
- raise ValueError('Incompatible for add')
- out = kwargs.get('out', None)
- #print ("args" , *args)
- if isinstance(other, Number):
- return type(self)(*[ el.add(other, *args, **kwargs) for el in self.containers], shape=self.shape)
- elif isinstance(other, list) or isinstance(other, numpy.ndarray):
- return type(self)(*[ el.add(ot, *args, **kwargs) for el,ot in zip(self.containers,other)], shape=self.shape)
- elif issubclass(other.__class__, DataContainer):
- # try to do algebra with one DataContainer. Will raise error if not compatible
- return type(self)(*[ el.add(other, *args, **kwargs) for el in self.containers], shape=self.shape)
-
- return type(self)(
- *[ el.add(ot, *args, **kwargs) for el,ot in zip(self.containers,other.containers)],
- shape=self.shape)
-
- def subtract(self, other, *args, **kwargs):
- if not self.is_compatible(other):
- raise ValueError('Incompatible for subtract')
- out = kwargs.get('out', None)
- if isinstance(other, Number):
- return type(self)(*[ el.subtract(other, *args, **kwargs) for el in self.containers], shape=self.shape)
- elif isinstance(other, list) or isinstance(other, numpy.ndarray):
- return type(self)(*[ el.subtract(ot, *args, **kwargs) for el,ot in zip(self.containers,other)], shape=self.shape)
- elif issubclass(other.__class__, DataContainer):
- # try to do algebra with one DataContainer. Will raise error if not compatible
- return type(self)(*[ el.subtract(other, *args, **kwargs) for el in self.containers], shape=self.shape)
- return type(self)(*[ el.subtract(ot, *args, **kwargs) for el,ot in zip(self.containers,other.containers)],
- shape=self.shape)
-
- def multiply(self, other, *args, **kwargs):
- if not self.is_compatible(other):
- raise ValueError('{} Incompatible for multiply'.format(other))
- out = kwargs.get('out', None)
- if isinstance(other, Number):
- return type(self)(*[ el.multiply(other, *args, **kwargs) for el in self.containers], shape=self.shape)
- elif isinstance(other, list):
- return type(self)(*[ el.multiply(ot, *args, **kwargs) for el,ot in zip(self.containers,other)], shape=self.shape)
- elif isinstance(other, numpy.ndarray):
- return type(self)(*[ el.multiply(ot, *args, **kwargs) for el,ot in zip(self.containers,other)], shape=self.shape)
- elif issubclass(other.__class__, DataContainer):
- # try to do algebra with one DataContainer. Will raise error if not compatible
- return type(self)(*[ el.multiply(other, *args, **kwargs) for el in self.containers], shape=self.shape)
- return type(self)(*[ el.multiply(ot, *args, **kwargs) for el,ot in zip(self.containers,other.containers)],
- shape=self.shape)
-
- def divide(self, other, *args, **kwargs):
- if not self.is_compatible(other):
- raise ValueError('Incompatible for divide')
- out = kwargs.get('out', None)
- if isinstance(other, Number):
- return type(self)(*[ el.divide(other, *args, **kwargs) for el in self.containers], shape=self.shape)
- elif isinstance(other, list) or isinstance(other, numpy.ndarray):
- return type(self)(*[ el.divide(ot, *args, **kwargs) for el,ot in zip(self.containers,other)], shape=self.shape)
- elif issubclass(other.__class__, DataContainer):
- # try to do algebra with one DataContainer. Will raise error if not compatible
- return type(self)(*[ el.divide(other, *args, **kwargs) for el in self.containers], shape=self.shape)
- return type(self)(*[ el.divide(ot, *args, **kwargs) for el,ot in zip(self.containers,other.containers)],
- shape=self.shape)
-
- def power(self, other, *args, **kwargs):
- if not self.is_compatible(other):
- raise ValueError('Incompatible for power')
- out = kwargs.get('out', None)
- if isinstance(other, Number):
- return type(self)(*[ el.power(other, *args, **kwargs) for el in self.containers], shape=self.shape)
- elif isinstance(other, list) or isinstance(other, numpy.ndarray):
- return type(self)(*[ el.power(ot, *args, **kwargs) for el,ot in zip(self.containers,other)], shape=self.shape)
- return type(self)(*[ el.power(ot, *args, **kwargs) for el,ot in zip(self.containers,other.containers)], shape=self.shape)
-
- def maximum(self,other, *args, **kwargs):
- if not self.is_compatible(other):
- raise ValueError('Incompatible for maximum')
- out = kwargs.get('out', None)
- if isinstance(other, Number):
- return type(self)(*[ el.maximum(other, *args, **kwargs) for el in self.containers], shape=self.shape)
- elif isinstance(other, list) or isinstance(other, numpy.ndarray):
- return type(self)(*[ el.maximum(ot, *args, **kwargs) for el,ot in zip(self.containers,other)], shape=self.shape)
- return type(self)(*[ el.maximum(ot, *args, **kwargs) for el,ot in zip(self.containers,other.containers)], shape=self.shape)
-
- ## unary operations
- def abs(self, *args, **kwargs):
- return type(self)(*[ el.abs(*args, **kwargs) for el in self.containers], shape=self.shape)
- def sign(self, *args, **kwargs):
- return type(self)(*[ el.sign(*args, **kwargs) for el in self.containers], shape=self.shape)
- def sqrt(self, *args, **kwargs):
- return type(self)(*[ el.sqrt(*args, **kwargs) for el in self.containers], shape=self.shape)
- def conjugate(self, out=None):
- 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())
- def copy(self):
- '''alias of clone'''
- return self.clone()
- def clone(self):
- return type(self)(*[el.copy() for el in self.containers], shape=self.shape)
- def fill(self, x):
- for el,ot in zip(self.containers, x):
- el.fill(ot)
-
- def __add__(self, other):
- return self.add( other )
- # __radd__
-
- def __sub__(self, other):
- return self.subtract( other )
- # __rsub__
-
- def __mul__(self, other):
- return self.multiply(other)
- # __rmul__
-
- def __div__(self, other):
- return self.divide(other)
- # __rdiv__
- def __truediv__(self, other):
- return self.divide(other)
-
- def __pow__(self, other):
- return self.power(other)
- # reverse operand
- def __radd__(self, other):
- '''Reverse addition
-
- to make sure that this method is called rather than the __mul__ of a numpy array
- the class constant __array_priority__ must be set > 0
- https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__
- '''
- return self + other
- # __radd__
-
- def __rsub__(self, other):
- '''Reverse subtraction
-
- to make sure that this method is called rather than the __mul__ of a numpy array
- the class constant __array_priority__ must be set > 0
- https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__
- '''
- return (-1 * self) + other
- # __rsub__
-
- def __rmul__(self, other):
- '''Reverse multiplication
-
- to make sure that this method is called rather than the __mul__ of a numpy array
- the class constant __array_priority__ must be set > 0
- https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__
- '''
- return self * other
- # __rmul__
-
- def __rdiv__(self, other):
- '''Reverse division
-
- to make sure that this method is called rather than the __mul__ of a numpy array
- the class constant __array_priority__ must be set > 0
- https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__
- '''
- return pow(self / other, -1)
- # __rdiv__
- def __rtruediv__(self, other):
- '''Reverse truedivision
-
- to make sure that this method is called rather than the __mul__ of a numpy array
- the class constant __array_priority__ must be set > 0
- https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__
- '''
- return self.__rdiv__(other)
-
- def __rpow__(self, other):
- '''Reverse power
-
- to make sure that this method is called rather than the __mul__ of a numpy array
- the class constant __array_priority__ must be set > 0
- https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__
- '''
- return other.power(self)
-
- def __iadd__(self, other):
- '''Inline addition'''
- if isinstance (other, BlockDataContainer):
- for el,ot in zip(self.containers, other.containers):
- el += ot
- elif isinstance(other, Number):
- for el in self.containers:
- el += other
- elif isinstance(other, list) or isinstance(other, numpy.ndarray):
- if not self.is_compatible(other):
- raise ValueError('Incompatible for __iadd__')
- for el,ot in zip(self.containers, other):
- el += ot
- return self
- # __iadd__
-
- def __isub__(self, other):
- '''Inline subtraction'''
- if isinstance (other, BlockDataContainer):
- for el,ot in zip(self.containers, other.containers):
- el -= ot
- elif isinstance(other, Number):
- for el in self.containers:
- el -= other
- elif isinstance(other, list) or isinstance(other, numpy.ndarray):
- if not self.is_compatible(other):
- raise ValueError('Incompatible for __isub__')
- for el,ot in zip(self.containers, other):
- el -= ot
- return self
- # __isub__
-
- def __imul__(self, other):
- '''Inline multiplication'''
- if isinstance (other, BlockDataContainer):
- for el,ot in zip(self.containers, other.containers):
- el *= ot
- elif isinstance(other, Number):
- for el in self.containers:
- el *= other
- elif isinstance(other, list) or isinstance(other, numpy.ndarray):
- if not self.is_compatible(other):
- raise ValueError('Incompatible for __imul__')
- for el,ot in zip(self.containers, other):
- el *= ot
- return self
- # __imul__
-
- def __idiv__(self, other):
- '''Inline division'''
- if isinstance (other, BlockDataContainer):
- for el,ot in zip(self.containers, other.containers):
- el /= ot
- elif isinstance(other, Number):
- for el in self.containers:
- el /= other
- elif isinstance(other, list) or isinstance(other, numpy.ndarray):
- if not self.is_compatible(other):
- raise ValueError('Incompatible for __idiv__')
- for el,ot in zip(self.containers, other):
- el /= ot
- return self
- # __rdiv__
- def __itruediv__(self, other):
- '''Inline truedivision'''
- return self.__idiv__(other)
-
diff --git a/Wrappers/Python/build/lib/ccpi/framework/BlockGeometry.py b/Wrappers/Python/build/lib/ccpi/framework/BlockGeometry.py deleted file mode 100644 index 0f43155..0000000 --- a/Wrappers/Python/build/lib/ccpi/framework/BlockGeometry.py +++ /dev/null @@ -1,38 +0,0 @@ -from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
-import numpy
-from numbers import Number
-import functools
-from ccpi.framework import BlockDataContainer
-#from ccpi.optimisation.operators import Operator, LinearOperator
-
-class BlockGeometry(object):
- '''Class to hold Geometry as column vector'''
- #__array_priority__ = 1
- def __init__(self, *args, **kwargs):
- ''''''
- self.geometries = args
- self.index = 0
- #shape = kwargs.get('shape', None)
- #if shape is None:
- # shape = (len(args),1)
- shape = (len(args),1)
- self.shape = shape
- #print (self.shape)
- n_elements = functools.reduce(lambda x,y: x*y, shape, 1)
- if len(args) != n_elements:
- raise ValueError(
- 'Dimension and size do not match: expected {} got {}'
- .format(n_elements, len(args)))
-
- def get_item(self, index):
- '''returns the Geometry in the BlockGeometry located at position index'''
- return self.geometries[index]
-
- def allocate(self, value=0, dimension_labels=None):
- containers = [geom.allocate(value) for geom in self.geometries]
- return BlockDataContainer(*containers)
-
diff --git a/Wrappers/Python/build/lib/ccpi/framework/__init__.py b/Wrappers/Python/build/lib/ccpi/framework/__init__.py deleted file mode 100644 index 229edb5..0000000 --- a/Wrappers/Python/build/lib/ccpi/framework/__init__.py +++ /dev/null @@ -1,26 +0,0 @@ -# -*- coding: utf-8 -*-
-"""
-Created on Tue Mar 5 16:00:18 2019
-
-@author: ofn77899
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
-import numpy
-import sys
-from datetime import timedelta, datetime
-import warnings
-from functools import reduce
-
-
-from .framework import DataContainer
-from .framework import ImageData, AcquisitionData
-from .framework import ImageGeometry, AcquisitionGeometry
-from .framework import find_key, message
-from .framework import DataProcessor
-from .framework import AX, PixelByPixelDataProcessor, CastDataContainer
-from .BlockDataContainer import BlockDataContainer
-from .BlockGeometry import BlockGeometry
diff --git a/Wrappers/Python/build/lib/ccpi/framework/framework.py b/Wrappers/Python/build/lib/ccpi/framework/framework.py deleted file mode 100644 index 07c2ead..0000000 --- a/Wrappers/Python/build/lib/ccpi/framework/framework.py +++ /dev/null @@ -1,1496 +0,0 @@ -# -*- 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 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. - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import numpy -import sys -from datetime import timedelta, datetime -import warnings -from functools import reduce -from numbers import Number - - -def find_key(dic, val): - """return the key of dictionary dic given the value""" - return [k for k, v in dic.items() if v == val][0] - -def message(cls, msg, *args): - msg = "{0}: " + msg - for i in range(len(args)): - msg += " {%d}" %(i+1) - args = list(args) - args.insert(0, cls.__name__ ) - - return msg.format(*args ) - - -class ImageGeometry(object): - RANDOM = 'random' - RANDOM_INT = 'random_int' - CHANNEL = 'channel' - ANGLE = 'angle' - VERTICAL = 'vertical' - HORIZONTAL_X = 'horizontal_x' - HORIZONTAL_Y = 'horizontal_y' - - def __init__(self, - voxel_num_x=0, - voxel_num_y=0, - voxel_num_z=0, - voxel_size_x=1, - voxel_size_y=1, - voxel_size_z=1, - center_x=0, - center_y=0, - center_z=0, - channels=1): - - self.voxel_num_x = voxel_num_x - self.voxel_num_y = voxel_num_y - self.voxel_num_z = voxel_num_z - self.voxel_size_x = voxel_size_x - self.voxel_size_y = voxel_size_y - self.voxel_size_z = voxel_size_z - self.center_x = center_x - self.center_y = center_y - self.center_z = center_z - self.channels = channels - - # this is some code repetition - if self.channels > 1: - if self.voxel_num_z>1: - self.length = 4 - self.shape = (self.channels, self.voxel_num_z, self.voxel_num_y, self.voxel_num_x) - dim_labels = [ImageGeometry.CHANNEL, ImageGeometry.VERTICAL, - ImageGeometry.HORIZONTAL_Y, ImageGeometry.HORIZONTAL_X] - else: - self.length = 3 - self.shape = (self.channels, self.voxel_num_y, self.voxel_num_x) - dim_labels = [ImageGeometry.CHANNEL, ImageGeometry.HORIZONTAL_Y, ImageGeometry.HORIZONTAL_X] - else: - if self.voxel_num_z>1: - self.length = 3 - self.shape = (self.voxel_num_z, self.voxel_num_y, self.voxel_num_x) - dim_labels = [ImageGeometry.VERTICAL, ImageGeometry.HORIZONTAL_Y, - ImageGeometry.HORIZONTAL_X] - else: - self.length = 2 - self.shape = (self.voxel_num_y, self.voxel_num_x) - dim_labels = [ImageGeometry.HORIZONTAL_Y, ImageGeometry.HORIZONTAL_X] - - self.dimension_labels = dim_labels - - def get_min_x(self): - return self.center_x - 0.5*self.voxel_num_x*self.voxel_size_x - - def get_max_x(self): - return self.center_x + 0.5*self.voxel_num_x*self.voxel_size_x - - def get_min_y(self): - return self.center_y - 0.5*self.voxel_num_y*self.voxel_size_y - - def get_max_y(self): - return self.center_y + 0.5*self.voxel_num_y*self.voxel_size_y - - def get_min_z(self): - if not self.voxel_num_z == 0: - return self.center_z - 0.5*self.voxel_num_z*self.voxel_size_z - else: - return 0 - - def get_max_z(self): - if not self.voxel_num_z == 0: - return self.center_z + 0.5*self.voxel_num_z*self.voxel_size_z - else: - return 0 - - def clone(self): - '''returns a copy of ImageGeometry''' - return ImageGeometry( - self.voxel_num_x, - self.voxel_num_y, - self.voxel_num_z, - self.voxel_size_x, - self.voxel_size_y, - self.voxel_size_z, - self.center_x, - self.center_y, - self.center_z, - self.channels) - def __str__ (self): - repres = "" - repres += "Number of channels: {0}\n".format(self.channels) - repres += "voxel_num : x{0},y{1},z{2}\n".format(self.voxel_num_x, self.voxel_num_y, self.voxel_num_z) - repres += "voxel_size : x{0},y{1},z{2}\n".format(self.voxel_size_x, self.voxel_size_y, self.voxel_size_z) - repres += "center : x{0},y{1},z{2}\n".format(self.center_x, self.center_y, self.center_z) - return repres - def allocate(self, value=0, dimension_labels=None, **kwargs): - '''allocates an ImageData according to the size expressed in the instance''' - out = ImageData(geometry=self) - if isinstance(value, Number): - if value != 0: - out += value - else: - if value == ImageGeometry.RANDOM: - seed = kwargs.get('seed', None) - if seed is not None: - numpy.random.seed(seed) - out.fill(numpy.random.random_sample(self.shape)) - elif value == ImageGeometry.RANDOM_INT: - seed = kwargs.get('seed', None) - if seed is not None: - numpy.random.seed(seed) - max_value = kwargs.get('max_value', 100) - out.fill(numpy.random.randint(max_value,size=self.shape)) - else: - raise ValueError('Value {} unknown'.format(value)) - if dimension_labels is not None: - if dimension_labels != self.dimension_labels: - return out.subset(dimensions=dimension_labels) - return out - # The following methods return 2 members of the class, therefore I - # don't think we need to implement them. - # Additionally using __len__ is confusing as one would think this is - # an iterable. - #def __len__(self): - # '''returns the length of the geometry''' - # return self.length - #def shape(self): - # '''Returns the shape of the array of the ImageData it describes''' - # return self.shape - -class AcquisitionGeometry(object): - RANDOM = 'random' - RANDOM_INT = 'random_int' - ANGLE_UNIT = 'angle_unit' - DEGREE = 'degree' - RADIAN = 'radian' - CHANNEL = 'channel' - ANGLE = 'angle' - VERTICAL = 'vertical' - HORIZONTAL = 'horizontal' - def __init__(self, - geom_type, - dimension, - angles, - pixel_num_h=0, - pixel_size_h=1, - pixel_num_v=0, - pixel_size_v=1, - dist_source_center=None, - dist_center_detector=None, - channels=1, - **kwargs - ): - """ - General inputs for standard type projection geometries - detectorDomain or detectorpixelSize: - If 2D - If scalar: Width of detector or single detector pixel - If 2-vec: Error - If 3D - If scalar: Width in both dimensions - If 2-vec: Vertical then horizontal size - grid - If 2D - If scalar: number of detectors - If 2-vec: error - If 3D - If scalar: Square grid that size - If 2-vec vertical then horizontal size - cone or parallel - 2D or 3D - parallel_parameters: ? - cone_parameters: - source_to_center_dist (if parallel: NaN) - center_to_detector_dist (if parallel: NaN) - standard or nonstandard (vec) geometry - angles - angles_format radians or degrees - """ - self.geom_type = geom_type # 'parallel' or 'cone' - self.dimension = dimension # 2D or 3D - self.angles = angles - num_of_angles = len (angles) - - self.dist_source_center = dist_source_center - self.dist_center_detector = dist_center_detector - - self.pixel_num_h = pixel_num_h - self.pixel_size_h = pixel_size_h - self.pixel_num_v = pixel_num_v - self.pixel_size_v = pixel_size_v - - self.channels = channels - self.angle_unit=kwargs.get(AcquisitionGeometry.ANGLE_UNIT, - AcquisitionGeometry.DEGREE) - if channels > 1: - if pixel_num_v > 1: - shape = (channels, num_of_angles , pixel_num_v, pixel_num_h) - dim_labels = [AcquisitionGeometry.CHANNEL , - AcquisitionGeometry.ANGLE , AcquisitionGeometry.VERTICAL , - AcquisitionGeometry.HORIZONTAL] - else: - shape = (channels , num_of_angles, pixel_num_h) - dim_labels = [AcquisitionGeometry.CHANNEL , - AcquisitionGeometry.ANGLE, AcquisitionGeometry.HORIZONTAL] - else: - if pixel_num_v > 1: - shape = (num_of_angles, pixel_num_v, pixel_num_h) - dim_labels = [AcquisitionGeometry.ANGLE , AcquisitionGeometry.VERTICAL , - AcquisitionGeometry.HORIZONTAL] - else: - shape = (num_of_angles, pixel_num_h) - dim_labels = [AcquisitionGeometry.ANGLE, AcquisitionGeometry.HORIZONTAL] - self.shape = shape - - self.dimension_labels = dim_labels - - def clone(self): - '''returns a copy of the AcquisitionGeometry''' - return AcquisitionGeometry(self.geom_type, - self.dimension, - self.angles, - self.pixel_num_h, - self.pixel_size_h, - self.pixel_num_v, - self.pixel_size_v, - self.dist_source_center, - self.dist_center_detector, - self.channels) - - def __str__ (self): - repres = "" - repres += "Number of dimensions: {0}\n".format(self.dimension) - repres += "angles: {0}\n".format(self.angles) - repres += "voxel_num : h{0},v{1}\n".format(self.pixel_num_h, self.pixel_num_v) - repres += "voxel size: h{0},v{1}\n".format(self.pixel_size_h, self.pixel_size_v) - repres += "geometry type: {0}\n".format(self.geom_type) - repres += "distance source-detector: {0}\n".format(self.dist_source_center) - repres += "distance center-detector: {0}\n".format(self.dist_source_center) - repres += "number of channels: {0}\n".format(self.channels) - return repres - def allocate(self, value=0, dimension_labels=None): - '''allocates an AcquisitionData according to the size expressed in the instance''' - out = AcquisitionData(geometry=self) - if isinstance(value, Number): - if value != 0: - out += value - else: - if value == AcquisitionData.RANDOM: - seed = kwargs.get('seed', None) - if seed is not None: - numpy.random.seed(seed) - out.fill(numpy.random.random_sample(self.shape)) - elif value == AcquisitionData.RANDOM_INT: - seed = kwargs.get('seed', None) - if seed is not None: - numpy.random.seed(seed) - max_value = kwargs.get('max_value', 100) - out.fill(numpy.random.randint(max_value,size=self.shape)) - else: - raise ValueError('Value {} unknown'.format(value)) - if dimension_labels is not None: - if dimension_labels != self.dimension_labels: - return out.subset(dimensions=dimension_labels) - return out - -class DataContainer(object): - '''Generic class to hold data - - Data is currently held in a numpy arrays''' - - def __init__ (self, array, deep_copy=True, dimension_labels=None, - **kwargs): - '''Holds the data''' - - self.shape = numpy.shape(array) - self.number_of_dimensions = len (self.shape) - self.dimension_labels = {} - self.geometry = None # Only relevant for AcquisitionData and ImageData - - if dimension_labels is not None and \ - len (dimension_labels) == self.number_of_dimensions: - for i in range(self.number_of_dimensions): - self.dimension_labels[i] = dimension_labels[i] - else: - for i in range(self.number_of_dimensions): - self.dimension_labels[i] = 'dimension_{0:02}'.format(i) - - if type(array) == numpy.ndarray: - if deep_copy: - self.array = array.copy() - else: - self.array = array - else: - raise TypeError('Array must be NumpyArray, passed {0}'\ - .format(type(array))) - - # finally copy the geometry - if 'geometry' in kwargs.keys(): - self.geometry = kwargs['geometry'] - else: - # assume it is parallel beam - pass - - def get_dimension_size(self, dimension_label): - if dimension_label in self.dimension_labels.values(): - acq_size = -1 - for k,v in self.dimension_labels.items(): - if v == dimension_label: - acq_size = self.shape[k] - return acq_size - else: - raise ValueError('Unknown dimension {0}. Should be one of'.format(dimension_label, - self.dimension_labels)) - def get_dimension_axis(self, dimension_label): - if dimension_label in self.dimension_labels.values(): - for k,v in self.dimension_labels.items(): - if v == dimension_label: - return k - else: - raise ValueError('Unknown dimension {0}. Should be one of'.format(dimension_label, - self.dimension_labels.values())) - - - def as_array(self, dimensions=None): - '''Returns the DataContainer as Numpy Array - - Returns the pointer to the array if dimensions is not set. - If dimensions is set, it first creates a new DataContainer with the subset - and then it returns the pointer to the array''' - if dimensions is not None: - return self.subset(dimensions).as_array() - return self.array - - - def subset(self, dimensions=None, **kw): - '''Creates a DataContainer containing a subset of self according to the - labels in dimensions''' - if dimensions is None: - if kw == {}: - return self.array.copy() - else: - reduced_dims = [v for k,v in self.dimension_labels.items()] - for dim_l, dim_v in kw.items(): - for k,v in self.dimension_labels.items(): - if v == dim_l: - reduced_dims.pop(k) - return self.subset(dimensions=reduced_dims, **kw) - else: - # check that all the requested dimensions are in the array - # this is done by checking the dimension_labels - proceed = True - unknown_key = '' - # axis_order contains the order of the axis that the user wants - # in the output DataContainer - axis_order = [] - if type(dimensions) == list: - for dl in dimensions: - if dl not in self.dimension_labels.values(): - proceed = False - unknown_key = dl - break - else: - axis_order.append(find_key(self.dimension_labels, dl)) - if not proceed: - raise KeyError('Subset error: Unknown key specified {0}'.format(dl)) - - # slice away the unwanted data from the array - unwanted_dimensions = self.dimension_labels.copy() - left_dimensions = [] - for ax in sorted(axis_order): - this_dimension = unwanted_dimensions.pop(ax) - left_dimensions.append(this_dimension) - #print ("unwanted_dimensions {0}".format(unwanted_dimensions)) - #print ("left_dimensions {0}".format(left_dimensions)) - #new_shape = [self.shape[ax] for ax in axis_order] - #print ("new_shape {0}".format(new_shape)) - command = "self.array[" - for i in range(self.number_of_dimensions): - if self.dimension_labels[i] in unwanted_dimensions.values(): - value = 0 - for k,v in kw.items(): - if k == self.dimension_labels[i]: - value = v - - command = command + str(value) - else: - command = command + ":" - if i < self.number_of_dimensions -1: - command = command + ',' - command = command + ']' - - cleaned = eval(command) - # cleaned has collapsed dimensions in the same order of - # self.array, but we want it in the order stated in the - # "dimensions". - # create axes order for numpy.transpose - axes = [] - for key in dimensions: - #print ("key {0}".format( key)) - for i in range(len( left_dimensions )): - ld = left_dimensions[i] - #print ("ld {0}".format( ld)) - if ld == key: - axes.append(i) - #print ("axes {0}".format(axes)) - - cleaned = numpy.transpose(cleaned, axes).copy() - - return type(self)(cleaned , True, dimensions) - - def fill(self, array, **dimension): - '''fills the internal numpy array with the one provided''' - if dimension == {}: - if issubclass(type(array), DataContainer) or\ - issubclass(type(array), numpy.ndarray): - if array.shape != self.shape: - raise ValueError('Cannot fill with the provided array.' + \ - 'Expecting {0} got {1}'.format( - self.shape,array.shape)) - if issubclass(type(array), DataContainer): - numpy.copyto(self.array, array.array) - else: - #self.array[:] = array - numpy.copyto(self.array, array) - else: - - command = 'self.array[' - i = 0 - for k,v in self.dimension_labels.items(): - for dim_label, dim_value in dimension.items(): - if dim_label == v: - command = command + str(dim_value) - else: - command = command + ":" - if i < self.number_of_dimensions -1: - command = command + ',' - i += 1 - command = command + "] = array[:]" - exec(command) - - - def check_dimensions(self, other): - return self.shape == other.shape - - ## algebra - def __add__(self, other, *args, **kwargs): - out = kwargs.get('out', None) - - if issubclass(type(other), DataContainer): - if self.check_dimensions(other): - out = self.as_array() + other.as_array() - return type(self)(out, - deep_copy=True, - dimension_labels=self.dimension_labels, - geometry=self.geometry) - else: - raise ValueError('Wrong shape: {0} and {1}'.format(self.shape, - other.shape)) - elif isinstance(other, (int, float, complex)): - return type(self)( - self.as_array() + other, - deep_copy=True, - dimension_labels=self.dimension_labels, - geometry=self.geometry) - else: - raise TypeError('Cannot {0} DataContainer with {1}'.format("add" , - type(other))) - # __add__ - - def __sub__(self, other): - if issubclass(type(other), DataContainer): - if self.check_dimensions(other): - out = self.as_array() - other.as_array() - return type(self)(out, - deep_copy=True, - dimension_labels=self.dimension_labels, - geometry=self.geometry) - else: - raise ValueError('__sub__ Wrong shape: {0} and {1}'.format(self.shape, - other.shape)) - elif isinstance(other, (int, float, complex)): - return type(self)(self.as_array() - other, - deep_copy=True, - dimension_labels=self.dimension_labels, - geometry=self.geometry) - else: - raise TypeError('Cannot {0} DataContainer with {1}'.format("subtract" , - type(other))) - # __sub__ - def __truediv__(self,other): - return self.__div__(other) - - def __div__(self, other): - if issubclass(type(other), DataContainer): - if self.check_dimensions(other): - out = self.as_array() / other.as_array() - return type(self)(out, - deep_copy=True, - dimension_labels=self.dimension_labels, - geometry=self.geometry) - else: - raise ValueError('__div__ Wrong shape: {0} and {1}'.format(self.shape, - other.shape)) - elif isinstance(other, (int, float, complex)): - return type(self)(self.as_array() / other, - deep_copy=True, - dimension_labels=self.dimension_labels, - geometry=self.geometry) - else: - raise TypeError('Cannot {0} DataContainer with {1}'.format("divide" , - type(other))) - # __div__ - - def __pow__(self, other): - if issubclass(type(other), DataContainer): - if self.check_dimensions(other): - out = self.as_array() ** other.as_array() - return type(self)(out, - deep_copy=True, - dimension_labels=self.dimension_labels, - geometry=self.geometry) - else: - raise ValueError('__pow__ Wrong shape: {0} and {1}'.format(self.shape, - other.shape)) - elif isinstance(other, (int, float, complex)): - return type(self)(self.as_array() ** other, - deep_copy=True, - dimension_labels=self.dimension_labels, - geometry=self.geometry) - else: - raise TypeError('pow: Cannot {0} DataContainer with {1}'.format("power" , - type(other))) - # __pow__ - - def __mul__(self, other): - if issubclass(type(other), DataContainer): - if self.check_dimensions(other): - out = self.as_array() * other.as_array() - return type(self)(out, - deep_copy=True, - dimension_labels=self.dimension_labels, - geometry=self.geometry) - else: - raise ValueError('*:Wrong shape: {0} and {1}'.format(self.shape, - other.shape)) - elif isinstance(other, (int, float, complex,\ - numpy.int, numpy.int8, numpy.int16, numpy.int32, numpy.int64,\ - numpy.float, numpy.float16, numpy.float32, numpy.float64, \ - numpy.complex)): - return type(self)(self.as_array() * other, - deep_copy=True, - dimension_labels=self.dimension_labels, - geometry=self.geometry) - - else: - raise TypeError('Cannot {0} DataContainer with {1}'.format("multiply" , - type(other))) - # __mul__ - - # reverse operand - def __radd__(self, other): - return self + other - # __radd__ - - def __rsub__(self, other): - return (-1 * self) + other - # __rsub__ - - def __rmul__(self, other): - return self * other - # __rmul__ - - def __rdiv__(self, other): - print ("call __rdiv__") - return pow(self / other, -1) - # __rdiv__ - def __rtruediv__(self, other): - return self.__rdiv__(other) - - def __rpow__(self, other): - if isinstance(other, (int, float)) : - fother = numpy.ones(numpy.shape(self.array)) * other - return type(self)(fother ** self.array , - dimension_labels=self.dimension_labels, - geometry=self.geometry) - elif issubclass(type(other), DataContainer): - if self.check_dimensions(other): - return type(self)(other.as_array() ** self.array , - dimension_labels=self.dimension_labels, - geometry=self.geometry) - else: - raise ValueError('Dimensions do not match') - # __rpow__ - - # in-place arithmetic operators: - # (+=, -=, *=, /= , //=, - # must return self - - - - def __iadd__(self, other): - if isinstance(other, (int, float)) : - numpy.add(self.array, other, out=self.array) - elif issubclass(type(other), DataContainer): - if self.check_dimensions(other): - numpy.add(self.array, other.array, out=self.array) - else: - raise ValueError('Dimensions do not match') - return self - # __iadd__ - - def __imul__(self, other): - if isinstance(other, (int, float)) : - arr = self.as_array() - numpy.multiply(arr, other, out=arr) - elif issubclass(type(other), DataContainer): - if self.check_dimensions(other): - numpy.multiply(self.array, other.array, out=self.array) - else: - raise ValueError('Dimensions do not match') - return self - # __imul__ - - def __isub__(self, other): - if isinstance(other, (int, float)) : - numpy.subtract(self.array, other, out=self.array) - elif issubclass(type(other), DataContainer): - if self.check_dimensions(other): - numpy.subtract(self.array, other.array, out=self.array) - else: - raise ValueError('Dimensions do not match') - return self - # __isub__ - - def __idiv__(self, other): - return self.__itruediv__(other) - def __itruediv__(self, other): - if isinstance(other, (int, float)) : - numpy.divide(self.array, other, out=self.array) - elif issubclass(type(other), DataContainer): - if self.check_dimensions(other): - numpy.divide(self.array, other.array, out=self.array) - else: - raise ValueError('Dimensions do not match') - return self - # __idiv__ - - def __str__ (self, representation=False): - repres = "" - repres += "Number of dimensions: {0}\n".format(self.number_of_dimensions) - repres += "Shape: {0}\n".format(self.shape) - repres += "Axis labels: {0}\n".format(self.dimension_labels) - if representation: - repres += "Representation: \n{0}\n".format(self.array) - return repres - - def clone(self): - '''returns a copy of itself''' - - return type(self)(self.array, - dimension_labels=self.dimension_labels, - deep_copy=True, - geometry=self.geometry ) - - def get_data_axes_order(self,new_order=None): - '''returns the axes label of self as a list - - if new_order is None returns the labels of the axes as a sorted-by-key list - if new_order is a list of length number_of_dimensions, returns a list - with the indices of the axes in new_order with respect to those in - self.dimension_labels: i.e. - self.dimension_labels = {0:'horizontal',1:'vertical'} - new_order = ['vertical','horizontal'] - returns [1,0] - ''' - if new_order is None: - - axes_order = [i for i in range(len(self.shape))] - for k,v in self.dimension_labels.items(): - axes_order[k] = v - return axes_order - else: - if len(new_order) == self.number_of_dimensions: - axes_order = [i for i in range(self.number_of_dimensions)] - - for i in range(len(self.shape)): - found = False - for k,v in self.dimension_labels.items(): - if new_order[i] == v: - axes_order[i] = k - found = True - if not found: - raise ValueError('Axis label {0} not found.'.format(new_order[i])) - return axes_order - else: - raise ValueError('Expecting {0} axes, got {2}'\ - .format(len(self.shape),len(new_order))) - - - def copy(self): - '''alias of clone''' - return self.clone() - - ## binary operations - - def pixel_wise_binary(self, pwop, x2, *args, **kwargs): - out = kwargs.get('out', None) - if out is None: - if isinstance(x2, (int, float, complex)): - out = pwop(self.as_array() , x2 , *args, **kwargs ) - elif isinstance(x2, (numpy.int, numpy.int8, numpy.int16, numpy.int32, numpy.int64,\ - numpy.float, numpy.float16, numpy.float32, numpy.float64, \ - numpy.complex)): - out = pwop(self.as_array() , x2 , *args, **kwargs ) - elif issubclass(type(x2) , DataContainer): - out = pwop(self.as_array() , x2.as_array() , *args, **kwargs ) - return type(self)(out, - deep_copy=False, - dimension_labels=self.dimension_labels, - geometry=self.geometry) - - - elif issubclass(type(out), DataContainer) and issubclass(type(x2), DataContainer): - if self.check_dimensions(out) and self.check_dimensions(x2): - kwargs['out'] = out.as_array() - pwop(self.as_array(), x2.as_array(), *args, **kwargs ) - #return type(self)(out.as_array(), - # deep_copy=False, - # dimension_labels=self.dimension_labels, - # geometry=self.geometry) - return out - else: - raise ValueError(message(type(self),"Wrong size for data memory: ", out.shape,self.shape)) - elif issubclass(type(out), DataContainer) and isinstance(x2, (int,float,complex)): - if self.check_dimensions(out): - kwargs['out']=out.as_array() - pwop(self.as_array(), x2, *args, **kwargs ) - return out - else: - raise ValueError(message(type(self),"Wrong size for data memory: ", out.shape,self.shape)) - elif issubclass(type(out), numpy.ndarray): - if self.array.shape == out.shape and self.array.dtype == out.dtype: - kwargs['out'] = out - pwop(self.as_array(), x2, *args, **kwargs) - #return type(self)(out, - # deep_copy=False, - # dimension_labels=self.dimension_labels, - # geometry=self.geometry) - else: - raise ValueError (message(type(self), "incompatible class:" , pwop.__name__, type(out))) - - def add(self, other, *args, **kwargs): - return self.pixel_wise_binary(numpy.add, other, *args, **kwargs) - - def subtract(self, other, *args, **kwargs): - return self.pixel_wise_binary(numpy.subtract, other, *args, **kwargs) - - def multiply(self, other, *args, **kwargs): - return self.pixel_wise_binary(numpy.multiply, other, *args, **kwargs) - - def divide(self, other, *args, **kwargs): - return self.pixel_wise_binary(numpy.divide, other, *args, **kwargs) - - def power(self, other, *args, **kwargs): - return self.pixel_wise_binary(numpy.power, other, *args, **kwargs) - - def maximum(self, x2, *args, **kwargs): - return self.pixel_wise_binary(numpy.maximum, x2, *args, **kwargs) - - ## unary operations - def pixel_wise_unary(self, pwop, *args, **kwargs): - out = kwargs.get('out', None) - if out is None: - out = pwop(self.as_array() , *args, **kwargs ) - return type(self)(out, - deep_copy=False, - dimension_labels=self.dimension_labels, - geometry=self.geometry) - elif issubclass(type(out), DataContainer): - if self.check_dimensions(out): - kwargs['out'] = out.as_array() - pwop(self.as_array(), *args, **kwargs ) - else: - raise ValueError(message(type(self),"Wrong size for data memory: ", out.shape,self.shape)) - elif issubclass(type(out), numpy.ndarray): - if self.array.shape == out.shape and self.array.dtype == out.dtype: - kwargs['out'] = out - pwop(self.as_array(), *args, **kwargs) - else: - raise ValueError (message(type(self), "incompatible class:" , pwop.__name__, type(out))) - - def abs(self, *args, **kwargs): - return self.pixel_wise_unary(numpy.abs, *args, **kwargs) - - def sign(self, *args, **kwargs): - return self.pixel_wise_unary(numpy.sign, *args, **kwargs) - - def sqrt(self, *args, **kwargs): - return self.pixel_wise_unary(numpy.sqrt, *args, **kwargs) - - def conjugate(self, *args, **kwargs): - return self.pixel_wise_unary(numpy.conjugate, *args, **kwargs) - #def __abs__(self): - # operation = FM.OPERATION.ABS - # return self.callFieldMath(operation, None, self.mask, self.maskOnValue) - # __abs__ - - ## reductions - def sum(self, *args, **kwargs): - return self.as_array().sum(*args, **kwargs) - def squared_norm(self): - '''return the squared euclidean norm of the DataContainer viewed as a vector''' - #shape = self.shape - #size = reduce(lambda x,y:x*y, shape, 1) - #y = numpy.reshape(self.as_array(), (size, )) - return self.dot(self.conjugate()) - #return self.dot(self) - def norm(self): - '''return the euclidean norm of the DataContainer viewed as a vector''' - return numpy.sqrt(self.squared_norm()) - def dot(self, other, *args, **kwargs): - '''return the inner product of 2 DataContainers viewed as vectors''' - if self.shape == other.shape: - return numpy.dot(self.as_array().ravel(), other.as_array().ravel()) - else: - raise ValueError('Shapes are not aligned: {} != {}'.format(self.shape, other.shape)) - - - - - -class ImageData(DataContainer): - '''DataContainer for holding 2D or 3D DataContainer''' - - def __init__(self, - array = None, - deep_copy=False, - dimension_labels=None, - **kwargs): - - self.geometry = None - if array is None: - if 'geometry' in kwargs.keys(): - geometry = kwargs['geometry'] - self.geometry = geometry - channels = geometry.channels - horiz_x = geometry.voxel_num_x - horiz_y = geometry.voxel_num_y - vert = 1 if geometry.voxel_num_z is None\ - else geometry.voxel_num_z # this should be 1 for 2D - if dimension_labels is None: - if channels > 1: - if vert > 1: - shape = (channels, vert, horiz_y, horiz_x) - dim_labels = [ImageGeometry.CHANNEL, - ImageGeometry.VERTICAL, - ImageGeometry.HORIZONTAL_Y, - ImageGeometry.HORIZONTAL_X] - else: - shape = (channels , horiz_y, horiz_x) - dim_labels = [ImageGeometry.CHANNEL, - ImageGeometry.HORIZONTAL_Y, - ImageGeometry.HORIZONTAL_X] - else: - if vert > 1: - shape = (vert, horiz_y, horiz_x) - dim_labels = [ImageGeometry.VERTICAL, - ImageGeometry.HORIZONTAL_Y, - ImageGeometry.HORIZONTAL_X] - else: - shape = (horiz_y, horiz_x) - dim_labels = [ImageGeometry.HORIZONTAL_Y, - ImageGeometry.HORIZONTAL_X] - dimension_labels = dim_labels - else: - shape = [] - for dim in dimension_labels: - if dim == ImageGeometry.CHANNEL: - shape.append(channels) - elif dim == ImageGeometry.HORIZONTAL_Y: - shape.append(horiz_y) - elif dim == ImageGeometry.VERTICAL: - shape.append(vert) - elif dim == ImageGeometry.HORIZONTAL_X: - shape.append(horiz_x) - if len(shape) != len(dimension_labels): - raise ValueError('Missing {0} axes'.format( - len(dimension_labels) - len(shape))) - shape = tuple(shape) - - array = numpy.zeros( shape , dtype=numpy.float32) - super(ImageData, self).__init__(array, deep_copy, - dimension_labels, **kwargs) - - else: - raise ValueError('Please pass either a DataContainer, ' +\ - 'a numpy array or a geometry') - else: - if issubclass(type(array) , DataContainer): - # if the array is a DataContainer get the info from there - if not ( array.number_of_dimensions == 2 or \ - array.number_of_dimensions == 3 or \ - array.number_of_dimensions == 4): - raise ValueError('Number of dimensions are not 2 or 3 or 4: {0}'\ - .format(array.number_of_dimensions)) - - #DataContainer.__init__(self, array.as_array(), deep_copy, - # array.dimension_labels, **kwargs) - super(ImageData, self).__init__(array.as_array(), deep_copy, - array.dimension_labels, **kwargs) - elif issubclass(type(array) , numpy.ndarray): - if not ( array.ndim == 2 or array.ndim == 3 or array.ndim == 4 ): - raise ValueError( - 'Number of dimensions are not 2 or 3 or 4 : {0}'\ - .format(array.ndim)) - - if dimension_labels is None: - if array.ndim == 4: - dimension_labels = [ImageGeometry.CHANNEL, - ImageGeometry.VERTICAL, - ImageGeometry.HORIZONTAL_Y, - ImageGeometry.HORIZONTAL_X] - elif array.ndim == 3: - dimension_labels = [ImageGeometry.VERTICAL, - ImageGeometry.HORIZONTAL_Y, - ImageGeometry.HORIZONTAL_X] - else: - dimension_labels = [ ImageGeometry.HORIZONTAL_Y, - ImageGeometry.HORIZONTAL_X] - - #DataContainer.__init__(self, array, deep_copy, dimension_labels, **kwargs) - super(ImageData, self).__init__(array, deep_copy, - dimension_labels, **kwargs) - - # load metadata from kwargs if present - for key, value in kwargs.items(): - if (type(value) == list or type(value) == tuple) and \ - ( len (value) == 3 and len (value) == 2) : - if key == 'origin' : - self.origin = value - if key == 'spacing' : - self.spacing = value - - def subset(self, dimensions=None, **kw): - # FIXME: this is clearly not rigth - # it should be something like - # out = DataContainer.subset(self, dimensions, **kw) - # followed by regeneration of the proper geometry. - out = super(ImageData, self).subset(dimensions, **kw) - #out.geometry = self.recalculate_geometry(dimensions , **kw) - out.geometry = self.geometry - return out - - -class AcquisitionData(DataContainer): - '''DataContainer for holding 2D or 3D sinogram''' - - def __init__(self, - array = None, - deep_copy=True, - dimension_labels=None, - **kwargs): - self.geometry = None - if array is None: - if 'geometry' in kwargs.keys(): - geometry = kwargs['geometry'] - self.geometry = geometry - channels = geometry.channels - horiz = geometry.pixel_num_h - vert = geometry.pixel_num_v - angles = geometry.angles - num_of_angles = numpy.shape(angles)[0] - - if dimension_labels is None: - if channels > 1: - if vert > 1: - shape = (channels, num_of_angles , vert, horiz) - dim_labels = [AcquisitionGeometry.CHANNEL, - AcquisitionGeometry.ANGLE, - AcquisitionGeometry.VERTICAL, - AcquisitionGeometry.HORIZONTAL] - else: - shape = (channels , num_of_angles, horiz) - dim_labels = [AcquisitionGeometry.CHANNEL, - AcquisitionGeometry.ANGLE, - AcquisitionGeometry.HORIZONTAL] - else: - if vert > 1: - shape = (num_of_angles, vert, horiz) - dim_labels = [AcquisitionGeometry.ANGLE, - AcquisitionGeometry.VERTICAL, - AcquisitionGeometry.HORIZONTAL - ] - else: - shape = (num_of_angles, horiz) - dim_labels = [AcquisitionGeometry.ANGLE, - AcquisitionGeometry.HORIZONTAL - ] - - dimension_labels = dim_labels - else: - shape = [] - for dim in dimension_labels: - if dim == AcquisitionGeometry.CHANNEL: - shape.append(channels) - elif dim == AcquisitionGeometry.ANGLE: - shape.append(num_of_angles) - elif dim == AcquisitionGeometry.VERTICAL: - shape.append(vert) - elif dim == AcquisitionGeometry.HORIZONTAL: - shape.append(horiz) - if len(shape) != len(dimension_labels): - raise ValueError('Missing {0} axes.\nExpected{1} got {2}'\ - .format( - len(dimension_labels) - len(shape), - dimension_labels, shape) - ) - shape = tuple(shape) - - array = numpy.zeros( shape , dtype=numpy.float32) - super(AcquisitionData, self).__init__(array, deep_copy, - dimension_labels, **kwargs) - else: - - if issubclass(type(array) ,DataContainer): - # if the array is a DataContainer get the info from there - if not ( array.number_of_dimensions == 2 or \ - array.number_of_dimensions == 3 or \ - array.number_of_dimensions == 4): - raise ValueError('Number of dimensions are not 2 or 3 or 4: {0}'\ - .format(array.number_of_dimensions)) - - #DataContainer.__init__(self, array.as_array(), deep_copy, - # array.dimension_labels, **kwargs) - super(AcquisitionData, self).__init__(array.as_array(), deep_copy, - array.dimension_labels, **kwargs) - elif issubclass(type(array) ,numpy.ndarray): - if not ( array.ndim == 2 or array.ndim == 3 or array.ndim == 4 ): - raise ValueError( - 'Number of dimensions are not 2 or 3 or 4 : {0}'\ - .format(array.ndim)) - - if dimension_labels is None: - if array.ndim == 4: - dimension_labels = ['channel' ,'angle' , 'vertical' , - 'horizontal'] - elif array.ndim == 3: - dimension_labels = ['angle' , 'vertical' , - 'horizontal'] - else: - dimension_labels = ['angle' , - 'horizontal'] - - #DataContainer.__init__(self, array, deep_copy, dimension_labels, **kwargs) - super(AcquisitionData, self).__init__(array, deep_copy, - dimension_labels, **kwargs) - - -class DataProcessor(object): - '''Defines a generic DataContainer processor - - accepts DataContainer as inputs and - outputs DataContainer - additional attributes can be defined with __setattr__ - ''' - - def __init__(self, **attributes): - if not 'store_output' in attributes.keys(): - attributes['store_output'] = True - attributes['output'] = False - attributes['runTime'] = -1 - attributes['mTime'] = datetime.now() - attributes['input'] = None - for key, value in attributes.items(): - self.__dict__[key] = value - - - def __setattr__(self, name, value): - if name == 'input': - self.set_input(value) - elif name in self.__dict__.keys(): - self.__dict__[name] = value - self.__dict__['mTime'] = datetime.now() - else: - raise KeyError('Attribute {0} not found'.format(name)) - #pass - - def set_input(self, dataset): - if issubclass(type(dataset), DataContainer): - if self.check_input(dataset): - self.__dict__['input'] = dataset - else: - raise TypeError("Input type mismatch: got {0} expecting {1}"\ - .format(type(dataset), DataContainer)) - - def check_input(self, dataset): - '''Checks parameters of the input DataContainer - - Should raise an Error if the DataContainer does not match expectation, e.g. - if the expected input DataContainer is 3D and the Processor expects 2D. - ''' - raise NotImplementedError('Implement basic checks for input DataContainer') - - def get_output(self, out=None): - for k,v in self.__dict__.items(): - if v is None and k != 'output': - raise ValueError('Key {0} is None'.format(k)) - shouldRun = False - if self.runTime == -1: - shouldRun = True - elif self.mTime > self.runTime: - shouldRun = True - - # CHECK this - if self.store_output and shouldRun: - self.runTime = datetime.now() - try: - self.output = self.process(out=out) - return self.output - except TypeError as te: - self.output = self.process() - return self.output - self.runTime = datetime.now() - try: - return self.process(out=out) - except TypeError as te: - return self.process() - - - def set_input_processor(self, processor): - if issubclass(type(processor), DataProcessor): - self.__dict__['input'] = processor - else: - raise TypeError("Input type mismatch: got {0} expecting {1}"\ - .format(type(processor), DataProcessor)) - - def get_input(self): - '''returns the input DataContainer - - It is useful in the case the user has provided a DataProcessor as - input - ''' - if issubclass(type(self.input), DataProcessor): - dsi = self.input.get_output() - else: - dsi = self.input - return dsi - - def process(self, out=None): - raise NotImplementedError('process must be implemented') - - - - -class DataProcessor23D(DataProcessor): - '''Regularizers DataProcessor - ''' - - def check_input(self, dataset): - '''Checks number of dimensions input DataContainer - - Expected input is 2D or 3D - ''' - if dataset.number_of_dimensions == 2 or \ - dataset.number_of_dimensions == 3: - return True - else: - raise ValueError("Expected input dimensions is 2 or 3, got {0}"\ - .format(dataset.number_of_dimensions)) - -###### Example of DataProcessors - -class AX(DataProcessor): - '''Example DataProcessor - The AXPY routines perform a vector multiplication operation defined as - - y := a*x - where: - - a is a scalar - - x a DataContainer. - ''' - - def __init__(self): - kwargs = {'scalar':None, - 'input':None, - } - - #DataProcessor.__init__(self, **kwargs) - super(AX, self).__init__(**kwargs) - - def check_input(self, dataset): - return True - - def process(self, out=None): - - dsi = self.get_input() - a = self.scalar - if out is None: - y = DataContainer( a * dsi.as_array() , True, - dimension_labels=dsi.dimension_labels ) - #self.setParameter(output_dataset=y) - return y - else: - out.fill(a * dsi.as_array()) - - -###### Example of DataProcessors - -class CastDataContainer(DataProcessor): - '''Example DataProcessor - Cast a DataContainer array to a different type. - - y := a*x - where: - - a is a scalar - - x a DataContainer. - ''' - - def __init__(self, dtype=None): - kwargs = {'dtype':dtype, - 'input':None, - } - - #DataProcessor.__init__(self, **kwargs) - super(CastDataContainer, self).__init__(**kwargs) - - def check_input(self, dataset): - return True - - def process(self, out=None): - - dsi = self.get_input() - dtype = self.dtype - if out is None: - y = numpy.asarray(dsi.as_array(), dtype=dtype) - - return type(dsi)(numpy.asarray(dsi.as_array(), dtype=dtype), - dimension_labels=dsi.dimension_labels ) - else: - out.fill(numpy.asarray(dsi.as_array(), dtype=dtype)) - - - - - -class PixelByPixelDataProcessor(DataProcessor): - '''Example DataProcessor - - This processor applies a python function to each pixel of the DataContainer - - f is a python function - - x a DataSet. - ''' - - def __init__(self): - kwargs = {'pyfunc':None, - 'input':None, - } - #DataProcessor.__init__(self, **kwargs) - super(PixelByPixelDataProcessor, self).__init__(**kwargs) - - def check_input(self, dataset): - return True - - def process(self, out=None): - - pyfunc = self.pyfunc - dsi = self.get_input() - - eval_func = numpy.frompyfunc(pyfunc,1,1) - - - y = DataContainer( eval_func( dsi.as_array() ) , True, - dimension_labels=dsi.dimension_labels ) - return y - - - - -if __name__ == '__main__': - shape = (2,3,4,5) - size = shape[0] - for i in range(1, len(shape)): - size = size * shape[i] - #print("a refcount " , sys.getrefcount(a)) - a = numpy.asarray([i for i in range( size )]) - print("a refcount " , sys.getrefcount(a)) - a = numpy.reshape(a, shape) - print("a refcount " , sys.getrefcount(a)) - ds = DataContainer(a, False, ['X', 'Y','Z' ,'W']) - print("a refcount " , sys.getrefcount(a)) - print ("ds label {0}".format(ds.dimension_labels)) - subset = ['W' ,'X'] - b = ds.subset( subset ) - print("a refcount " , sys.getrefcount(a)) - print ("b label {0} shape {1}".format(b.dimension_labels, - numpy.shape(b.as_array()))) - c = ds.subset(['Z','W','X']) - print("a refcount " , sys.getrefcount(a)) - - # Create a ImageData sharing the array with c - volume0 = ImageData(c.as_array(), False, dimensions = c.dimension_labels) - volume1 = ImageData(c, False) - - print ("volume0 {0} volume1 {1}".format(id(volume0.array), - id(volume1.array))) - - # Create a ImageData copying the array from c - volume2 = ImageData(c.as_array(), dimensions = c.dimension_labels) - volume3 = ImageData(c) - - print ("volume2 {0} volume3 {1}".format(id(volume2.array), - id(volume3.array))) - - # single number DataSet - sn = DataContainer(numpy.asarray([1])) - - ax = AX() - ax.scalar = 2 - ax.set_input(c) - #ax.apply() - print ("ax in {0} out {1}".format(c.as_array().flatten(), - ax.get_output().as_array().flatten())) - - cast = CastDataContainer(dtype=numpy.float32) - cast.set_input(c) - out = cast.get_output() - out *= 0 - axm = AX() - axm.scalar = 0.5 - axm.set_input_processor(cast) - axm.get_output(out) - #axm.apply() - print ("axm in {0} out {1}".format(c.as_array(), axm.get_output().as_array())) - - # check out in DataSetProcessor - #a = numpy.asarray([i for i in range( size )]) - - - # create a PixelByPixelDataProcessor - - #define a python function which will take only one input (the pixel value) - pyfunc = lambda x: -x if x > 20 else x - clip = PixelByPixelDataProcessor() - clip.pyfunc = pyfunc - clip.set_input(c) - #clip.apply() - - print ("clip in {0} out {1}".format(c.as_array(), clip.get_output().as_array())) - - #dsp = DataProcessor() - #dsp.set_input(ds) - #dsp.input = a - # pipeline - - chain = AX() - chain.scalar = 0.5 - chain.set_input_processor(ax) - print ("chain in {0} out {1}".format(ax.get_output().as_array(), chain.get_output().as_array())) - - # testing arithmetic operations - - print (b) - print ((b+1)) - print ((1+b)) - - print (b) - print ((b*2)) - - print (b) - print ((2*b)) - - print (b) - print ((b/2)) - - print (b) - print ((2/b)) - - print (b) - print ((b**2)) - - print (b) - print ((2**b)) - - print (type(volume3 + 2)) - - s = [i for i in range(3 * 4 * 4)] - s = numpy.reshape(numpy.asarray(s), (3,4,4)) - sino = AcquisitionData( s ) - - shape = (4,3,2) - a = [i for i in range(2*3*4)] - a = numpy.asarray(a) - a = numpy.reshape(a, shape) - print (numpy.shape(a)) - ds = DataContainer(a, True, ['X', 'Y','Z']) - # this means that I expect the X to be of length 2 , - # y of length 3 and z of length 4 - subset = ['Y' ,'Z'] - b0 = ds.subset( subset ) - print ("shape b 3,2? {0}".format(numpy.shape(b0.as_array()))) - # expectation on b is that it is - # 3x2 cut at z = 0 - - subset = ['X' ,'Y'] - b1 = ds.subset( subset , Z=1) - print ("shape b 2,3? {0}".format(numpy.shape(b1.as_array()))) - - - - # create VolumeData from geometry - vgeometry = ImageGeometry(voxel_num_x=2, voxel_num_y=3, channels=2) - vol = ImageData(geometry=vgeometry) - - sgeometry = AcquisitionGeometry(dimension=2, angles=numpy.linspace(0, 180, num=20), - geom_type='parallel', pixel_num_v=3, - pixel_num_h=5 , channels=2) - sino = AcquisitionData(geometry=sgeometry) - sino2 = sino.clone() - - a0 = numpy.asarray([i for i in range(2*3*4)]) - a1 = numpy.asarray([2*i for i in range(2*3*4)]) - - - ds0 = DataContainer(numpy.reshape(a0,(2,3,4))) - ds1 = DataContainer(numpy.reshape(a1,(2,3,4))) - - numpy.testing.assert_equal(ds0.dot(ds1), a0.dot(a1)) - - a2 = numpy.asarray([2*i for i in range(2*3*5)]) - ds2 = DataContainer(numpy.reshape(a2,(2,3,5))) - -# # it should fail if the shape is wrong -# try: -# ds2.dot(ds0) -# self.assertTrue(False) -# except ValueError as ve: -# self.assertTrue(True) - diff --git a/Wrappers/Python/build/lib/ccpi/io/__init__.py b/Wrappers/Python/build/lib/ccpi/io/__init__.py deleted file mode 100644 index 9233d7a..0000000 --- a/Wrappers/Python/build/lib/ccpi/io/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# -*- 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 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.
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/io/reader.py b/Wrappers/Python/build/lib/ccpi/io/reader.py deleted file mode 100644 index 856f5e0..0000000 --- a/Wrappers/Python/build/lib/ccpi/io/reader.py +++ /dev/null @@ -1,500 +0,0 @@ -# -*- 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 Jakob Jorgensen, Daniil Kazantsev, Edoardo Pasca and Srikanth Nagella
-
-# 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.
-
-'''
-This is a reader module with classes for loading 3D datasets.
-
-@author: Mr. Srikanth Nagella
-'''
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
-from ccpi.framework import AcquisitionGeometry
-from ccpi.framework import AcquisitionData
-import numpy as np
-import os
-
-h5pyAvailable = True
-try:
- from h5py import File as NexusFile
-except:
- h5pyAvailable = False
-
-pilAvailable = True
-try:
- from PIL import Image
-except:
- pilAvailable = False
-
-class NexusReader(object):
- '''
- Reader class for loading Nexus files.
- '''
-
- def __init__(self, nexus_filename=None):
- '''
- This takes in input as filename and loads the data dataset.
- '''
- self.flat = None
- self.dark = None
- self.angles = None
- self.geometry = None
- self.filename = nexus_filename
- self.key_path = 'entry1/tomo_entry/instrument/detector/image_key'
- self.data_path = 'entry1/tomo_entry/data/data'
- self.angle_path = 'entry1/tomo_entry/data/rotation_angle'
-
- def get_image_keys(self):
- try:
- with NexusFile(self.filename,'r') as file:
- return np.array(file[self.key_path])
- except KeyError as ke:
- raise KeyError("get_image_keys: " , ke.args[0] , self.key_path)
-
-
- def load(self, dimensions=None, image_key_id=0):
- '''
- This is generic loading function of flat field, dark field and projection data.
- '''
- if not h5pyAvailable:
- raise Exception("Error: h5py is not installed")
- if self.filename is None:
- return
- try:
- with NexusFile(self.filename,'r') as file:
- image_keys = np.array(file[self.key_path])
- projections = None
- if dimensions == None:
- projections = np.array(file[self.data_path])
- result = projections[image_keys==image_key_id]
- return result
- else:
- #When dimensions are specified they need to be mapped to image_keys
- index_array = np.where(image_keys==image_key_id)
- projection_indexes = index_array[0][dimensions[0]]
- new_dimensions = list(dimensions)
- new_dimensions[0]= projection_indexes
- new_dimensions = tuple(new_dimensions)
- result = np.array(file[self.data_path][new_dimensions])
- return result
- except:
- print("Error reading nexus file")
- raise
-
- def load_projection(self, dimensions=None):
- '''
- Loads the projection data from the nexus file.
- returns: numpy array with projection data
- '''
- try:
- if 0 not in self.get_image_keys():
- raise ValueError("Projections are not in the data. Data Path " ,
- self.data_path)
- except KeyError as ke:
- raise KeyError(ke.args[0] , self.data_path)
- return self.load(dimensions, 0)
-
- def load_flat(self, dimensions=None):
- '''
- Loads the flat field data from the nexus file.
- returns: numpy array with flat field data
- '''
- try:
- if 1 not in self.get_image_keys():
- raise ValueError("Flats are not in the data. Data Path " ,
- self.data_path)
- except KeyError as ke:
- raise KeyError(ke.args[0] , self.data_path)
- return self.load(dimensions, 1)
-
- def load_dark(self, dimensions=None):
- '''
- Loads the Dark field data from the nexus file.
- returns: numpy array with dark field data
- '''
- try:
- if 2 not in self.get_image_keys():
- raise ValueError("Darks are not in the data. Data Path " ,
- self.data_path)
- except KeyError as ke:
- raise KeyError(ke.args[0] , self.data_path)
- return self.load(dimensions, 2)
-
- def get_projection_angles(self):
- '''
- This function returns the projection angles
- '''
- if not h5pyAvailable:
- raise Exception("Error: h5py is not installed")
- if self.filename is None:
- return
- try:
- with NexusFile(self.filename,'r') as file:
- angles = np.array(file[self.angle_path],np.float32)
- image_keys = np.array(file[self.key_path])
- return angles[image_keys==0]
- except:
- print("get_projection_angles Error reading nexus file")
- raise
-
-
- def get_sinogram_dimensions(self):
- '''
- Return the dimensions of the dataset
- '''
- if not h5pyAvailable:
- raise Exception("Error: h5py is not installed")
- if self.filename is None:
- return
- try:
- with NexusFile(self.filename,'r') as file:
- projections = file[self.data_path]
- image_keys = np.array(file[self.key_path])
- dims = list(projections.shape)
- dims[0] = dims[1]
- dims[1] = np.sum(image_keys==0)
- return tuple(dims)
- except:
- print("Error reading nexus file")
- raise
-
- def get_projection_dimensions(self):
- '''
- Return the dimensions of the dataset
- '''
- if not h5pyAvailable:
- raise Exception("Error: h5py is not installed")
- if self.filename is None:
- return
- try:
- with NexusFile(self.filename,'r') as file:
- try:
- projections = file[self.data_path]
- except KeyError as ke:
- raise KeyError('Error: data path {0} not found\n{1}'\
- .format(self.data_path,
- ke.args[0]))
- #image_keys = np.array(file[self.key_path])
- image_keys = self.get_image_keys()
- dims = list(projections.shape)
- dims[0] = np.sum(image_keys==0)
- return tuple(dims)
- except:
- print("Warning: Error reading image_keys trying accessing data on " , self.data_path)
- with NexusFile(self.filename,'r') as file:
- dims = file[self.data_path].shape
- return tuple(dims)
-
-
-
- def get_acquisition_data(self, dimensions=None):
- '''
- This method load the acquisition data and given dimension and returns an AcquisitionData Object
- '''
- data = self.load_projection(dimensions)
- dims = self.get_projection_dimensions()
- geometry = AcquisitionGeometry('parallel', '3D',
- self.get_projection_angles(),
- pixel_num_h = dims[2],
- pixel_size_h = 1 ,
- pixel_num_v = dims[1],
- pixel_size_v = 1,
- dist_source_center = None,
- dist_center_detector = None,
- channels = 1)
- return AcquisitionData(data, geometry=geometry,
- dimension_labels=['angle','vertical','horizontal'])
-
- def get_acquisition_data_subset(self, ymin=None, ymax=None):
- '''
- This method load the acquisition data and given dimension and returns an AcquisitionData Object
- '''
- if not h5pyAvailable:
- raise Exception("Error: h5py is not installed")
- if self.filename is None:
- return
- try:
-
-
- with NexusFile(self.filename,'r') as file:
- try:
- dims = self.get_projection_dimensions()
- except KeyError:
- pass
- dims = file[self.data_path].shape
- if ymin is None and ymax is None:
- data = np.array(file[self.data_path])
- else:
- if ymin is None:
- ymin = 0
- if ymax > dims[1]:
- raise ValueError('ymax out of range')
- data = np.array(file[self.data_path][:,:ymax,:])
- elif ymax is None:
- ymax = dims[1]
- if ymin < 0:
- raise ValueError('ymin out of range')
- data = np.array(file[self.data_path][:,ymin:,:])
- else:
- if ymax > dims[1]:
- raise ValueError('ymax out of range')
- if ymin < 0:
- raise ValueError('ymin out of range')
-
- data = np.array(file[self.data_path]
- [: , ymin:ymax , :] )
-
- except:
- print("Error reading nexus file")
- raise
-
-
- try:
- angles = self.get_projection_angles()
- except KeyError as ke:
- n = data.shape[0]
- angles = np.linspace(0, n, n+1, dtype=np.float32)
-
- if ymax-ymin > 1:
-
- geometry = AcquisitionGeometry('parallel', '3D',
- angles,
- pixel_num_h = dims[2],
- pixel_size_h = 1 ,
- pixel_num_v = ymax-ymin,
- pixel_size_v = 1,
- dist_source_center = None,
- dist_center_detector = None,
- channels = 1)
- return AcquisitionData(data, False, geometry=geometry,
- dimension_labels=['angle','vertical','horizontal'])
- elif ymax-ymin == 1:
- geometry = AcquisitionGeometry('parallel', '2D',
- angles,
- pixel_num_h = dims[2],
- pixel_size_h = 1 ,
- dist_source_center = None,
- dist_center_detector = None,
- channels = 1)
- return AcquisitionData(data.squeeze(), False, geometry=geometry,
- dimension_labels=['angle','horizontal'])
- def get_acquisition_data_slice(self, y_slice=0):
- return self.get_acquisition_data_subset(ymin=y_slice , ymax=y_slice+1)
- def get_acquisition_data_whole(self):
- with NexusFile(self.filename,'r') as file:
- try:
- dims = self.get_projection_dimensions()
- except KeyError:
- print ("Warning: ")
- dims = file[self.data_path].shape
-
- ymin = 0
- ymax = dims[1] - 1
-
- return self.get_acquisition_data_subset(ymin=ymin, ymax=ymax)
-
-
-
- def list_file_content(self):
- try:
- with NexusFile(self.filename,'r') as file:
- file.visit(print)
- except:
- print("Error reading nexus file")
- raise
- def get_acquisition_data_batch(self, bmin=None, bmax=None):
- if not h5pyAvailable:
- raise Exception("Error: h5py is not installed")
- if self.filename is None:
- return
- try:
-
-
- with NexusFile(self.filename,'r') as file:
- try:
- dims = self.get_projection_dimensions()
- except KeyError:
- dims = file[self.data_path].shape
- if bmin is None or bmax is None:
- raise ValueError('get_acquisition_data_batch: please specify fastest index batch limits')
-
- if bmin >= 0 and bmin < bmax and bmax <= dims[0]:
- data = np.array(file[self.data_path][bmin:bmax])
- else:
- raise ValueError('get_acquisition_data_batch: bmin {0}>0 bmax {1}<{2}'.format(bmin, bmax, dims[0]))
-
- except:
- print("Error reading nexus file")
- raise
-
-
- try:
- angles = self.get_projection_angles()[bmin:bmax]
- except KeyError as ke:
- n = data.shape[0]
- angles = np.linspace(0, n, n+1, dtype=np.float32)[bmin:bmax]
-
- if bmax-bmin > 1:
-
- geometry = AcquisitionGeometry('parallel', '3D',
- angles,
- pixel_num_h = dims[2],
- pixel_size_h = 1 ,
- pixel_num_v = bmax-bmin,
- pixel_size_v = 1,
- dist_source_center = None,
- dist_center_detector = None,
- channels = 1)
- return AcquisitionData(data, False, geometry=geometry,
- dimension_labels=['angle','vertical','horizontal'])
- elif bmax-bmin == 1:
- geometry = AcquisitionGeometry('parallel', '2D',
- angles,
- pixel_num_h = dims[2],
- pixel_size_h = 1 ,
- dist_source_center = None,
- dist_center_detector = None,
- channels = 1)
- return AcquisitionData(data.squeeze(), False, geometry=geometry,
- dimension_labels=['angle','horizontal'])
-
-
-
-class XTEKReader(object):
- '''
- Reader class for loading XTEK files
- '''
-
- def __init__(self, xtek_config_filename=None):
- '''
- This takes in the xtek config filename and loads the dataset and the
- required geometry parameters
- '''
- self.projections = None
- self.geometry = {}
- self.filename = xtek_config_filename
- self.load()
-
- def load(self):
- pixel_num_h = 0
- pixel_num_v = 0
- xpixel_size = 0
- ypixel_size = 0
- source_x = 0
- detector_x = 0
- with open(self.filename) as f:
- content = f.readlines()
- content = [x.strip() for x in content]
- for line in content:
- if line.startswith("SrcToObject"):
- source_x = float(line.split('=')[1])
- elif line.startswith("SrcToDetector"):
- detector_x = float(line.split('=')[1])
- elif line.startswith("DetectorPixelsY"):
- pixel_num_v = int(line.split('=')[1])
- #self.num_of_vertical_pixels = self.calc_v_alighment(self.num_of_vertical_pixels, self.pixels_per_voxel)
- elif line.startswith("DetectorPixelsX"):
- pixel_num_h = int(line.split('=')[1])
- elif line.startswith("DetectorPixelSizeX"):
- xpixel_size = float(line.split('=')[1])
- elif line.startswith("DetectorPixelSizeY"):
- ypixel_size = float(line.split('=')[1])
- elif line.startswith("Projections"):
- self.num_projections = int(line.split('=')[1])
- elif line.startswith("InitialAngle"):
- self.initial_angle = float(line.split('=')[1])
- elif line.startswith("Name"):
- self.experiment_name = line.split('=')[1]
- elif line.startswith("Scattering"):
- self.scattering = float(line.split('=')[1])
- elif line.startswith("WhiteLevel"):
- self.white_level = float(line.split('=')[1])
- elif line.startswith("MaskRadius"):
- self.mask_radius = float(line.split('=')[1])
-
- #Read Angles
- angles = self.read_angles()
- self.geometry = AcquisitionGeometry('cone', '3D', angles, pixel_num_h, xpixel_size, pixel_num_v, ypixel_size, -1 * source_x,
- detector_x - source_x,
- )
-
- def read_angles(self):
- """
- Read the angles file .ang or _ctdata.txt file and returns the angles
- as an numpy array.
- """
- input_path = os.path.dirname(self.filename)
- angles_ctdata_file = os.path.join(input_path, '_ctdata.txt')
- angles_named_file = os.path.join(input_path, self.experiment_name+'.ang')
- angles = np.zeros(self.num_projections,dtype='f')
- #look for _ctdata.txt
- if os.path.exists(angles_ctdata_file):
- #read txt file with angles
- with open(angles_ctdata_file) as f:
- content = f.readlines()
- #skip firt three lines
- #read the middle value of 3 values in each line as angles in degrees
- index = 0
- for line in content[3:]:
- self.angles[index]=float(line.split(' ')[1])
- index+=1
- angles = np.deg2rad(self.angles+self.initial_angle);
- elif os.path.exists(angles_named_file):
- #read the angles file which is text with first line as header
- with open(angles_named_file) as f:
- content = f.readlines()
- #skip first line
- index = 0
- for line in content[1:]:
- angles[index] = float(line.split(':')[1])
- index+=1
- angles = np.flipud(angles+self.initial_angle) #angles are in the reverse order
- else:
- raise RuntimeError("Can't find angles file")
- return angles
-
- def load_projection(self, dimensions=None):
- '''
- This method reads the projection images from the directory and returns a numpy array
- '''
- if not pilAvailable:
- raise('Image library pillow is not installed')
- if dimensions != None:
- raise('Extracting subset of data is not implemented')
- input_path = os.path.dirname(self.filename)
- pixels = np.zeros((self.num_projections, self.geometry.pixel_num_h, self.geometry.pixel_num_v), dtype='float32')
- for i in range(1, self.num_projections+1):
- im = Image.open(os.path.join(input_path,self.experiment_name+"_%04d"%i+".tif"))
- pixels[i-1,:,:] = np.fliplr(np.transpose(np.array(im))) ##Not sure this is the correct way to populate the image
-
- #normalising the data
- #TODO: Move this to a processor
- pixels = pixels - (self.white_level*self.scattering)/100.0
- pixels[pixels < 0.0] = 0.000001 # all negative values to approximately 0 as the std log of zero and non negative number is not defined
- return pixels
-
- def get_acquisition_data(self, dimensions=None):
- '''
- This method load the acquisition data and given dimension and returns an AcquisitionData Object
- '''
- data = self.load_projection(dimensions)
- return AcquisitionData(data, geometry=self.geometry)
-
diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/__init__.py b/Wrappers/Python/build/lib/ccpi/optimisation/__init__.py deleted file mode 100644 index cf2d93d..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# -*- 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 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.
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/Algorithm.py b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/Algorithm.py deleted file mode 100644 index ed95c3f..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/Algorithm.py +++ /dev/null @@ -1,158 +0,0 @@ -# -*- 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 2019 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 time -from numbers import Integral - -class Algorithm(object): - '''Base class for iterative algorithms - - provides the minimal infrastructure. - Algorithms are iterables so can be easily run in a for loop. They will - stop as soon as the stop cryterion is met. - The user is required to implement the set_up, __init__, update and - and update_objective methods - - A courtesy method run is available to run n iterations. The method accepts - a callback function that receives the current iteration number and the actual objective - value and can be used to trigger print to screens and other user interactions. The run - method will stop when the stopping cryterion is met. - ''' - - def __init__(self): - '''Constructor - - Set the minimal number of parameters: - iteration: current iteration number - max_iteration: maximum number of iterations - memopt: whether to use memory optimisation () - timing: list to hold the times it took to run each iteration - update_objectice_interval: the interval every which we would save the current - objective. 1 means every iteration, 2 every 2 iteration - and so forth. This is by default 1 and should be increased - when evaluating the objective is computationally expensive. - ''' - self.iteration = 0 - self.__max_iteration = 0 - self.__loss = [] - self.memopt = False - self.timing = [] - self.update_objective_interval = 1 - def set_up(self, *args, **kwargs): - '''Set up the algorithm''' - raise NotImplementedError() - def update(self): - '''A single iteration of the algorithm''' - raise NotImplementedError() - - def should_stop(self): - '''default stopping cryterion: number of iterations - - The user can change this in concrete implementatition of iterative algorithms.''' - return self.max_iteration_stop_cryterion() - - def max_iteration_stop_cryterion(self): - '''default stop cryterion for iterative algorithm: max_iteration reached''' - return self.iteration >= self.max_iteration - def __iter__(self): - '''Algorithm is an iterable''' - return self - def next(self): - '''Algorithm is an iterable - - python2 backwards compatibility''' - return self.__next__() - def __next__(self): - '''Algorithm is an iterable - - calling this method triggers update and update_objective - ''' - if self.should_stop(): - raise StopIteration() - else: - time0 = time.time() - self.update() - self.timing.append( time.time() - time0 ) - if self.iteration % self.update_objective_interval == 0: - self.update_objective() - self.iteration += 1 - def get_output(self): - '''Returns the solution found''' - return self.x - def get_last_loss(self): - '''Returns the last stored value of the loss function - - if update_objective_interval is 1 it is the value of the objective at the current - iteration. If update_objective_interval > 1 it is the last stored value. - ''' - return self.__loss[-1] - def get_last_objective(self): - '''alias to get_last_loss''' - return self.get_last_loss() - def update_objective(self): - '''calculates the objective with the current solution''' - raise NotImplementedError() - @property - def loss(self): - '''returns the list of the values of the objective during the iteration - - The length of this list may be shorter than the number of iterations run when - the update_objective_interval > 1 - ''' - return self.__loss - @property - def objective(self): - '''alias of loss''' - return self.loss - @property - def max_iteration(self): - '''gets the maximum number of iterations''' - return self.__max_iteration - @max_iteration.setter - def max_iteration(self, value): - '''sets the maximum number of iterations''' - assert isinstance(value, int) - self.__max_iteration = value - @property - def update_objective_interval(self): - return self.__update_objective_interval - @update_objective_interval.setter - def update_objective_interval(self, value): - if isinstance(value, Integral): - if value >= 1: - self.__update_objective_interval = value - else: - raise ValueError('Update objective interval must be an integer >= 1') - else: - raise ValueError('Update objective interval must be an integer >= 1') - def run(self, iterations, verbose=True, callback=None): - '''run n iterations and update the user with the callback if specified''' - if self.should_stop(): - print ("Stop cryterion has been reached.") - i = 0 - for _ in self: - if verbose and self.iteration % self.update_objective_interval == 0: - print ("Iteration {}/{}, objective {}".format(self.iteration, - self.max_iteration, self.get_last_objective()) ) - else: - if callback is not None: - callback(self.iteration, self.get_last_objective()) - i += 1 - if i == iterations: - break - diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/CGLS.py b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/CGLS.py deleted file mode 100644 index 7194eb8..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/CGLS.py +++ /dev/null @@ -1,87 +0,0 @@ -# -*- 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 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. -""" -Created on Thu Feb 21 11:11:23 2019 - -@author: ofn77899 -""" - -from ccpi.optimisation.algorithms import Algorithm -#from collections.abc import Iterable -class CGLS(Algorithm): - - '''Conjugate Gradient Least Squares algorithm - - Parameters: - x_init: initial guess - operator: operator for forward/backward projections - data: data to operate on - ''' - def __init__(self, **kwargs): - super(CGLS, self).__init__() - self.x = kwargs.get('x_init', None) - self.operator = kwargs.get('operator', None) - self.data = kwargs.get('data', None) - if self.x is not None and self.operator is not None and \ - self.data is not None: - print ("Calling from creator") - self.set_up(x_init =kwargs['x_init'], - operator=kwargs['operator'], - data =kwargs['data']) - - def set_up(self, x_init, operator , data ): - - self.r = data.copy() - self.x = x_init.copy() - - self.operator = operator - self.d = operator.adjoint(self.r) - - - self.normr2 = self.d.squared_norm() - #if isinstance(self.normr2, Iterable): - # self.normr2 = sum(self.normr2) - #self.normr2 = numpy.sqrt(self.normr2) - #print ("set_up" , self.normr2) - - def update(self): - - Ad = self.operator.direct(self.d) - #norm = (Ad*Ad).sum() - #if isinstance(norm, Iterable): - # norm = sum(norm) - norm = Ad.squared_norm() - - alpha = self.normr2/norm - self.x += (self.d * alpha) - self.r -= (Ad * alpha) - s = self.operator.adjoint(self.r) - - normr2_new = s.squared_norm() - #if isinstance(normr2_new, Iterable): - # normr2_new = sum(normr2_new) - #normr2_new = numpy.sqrt(normr2_new) - #print (normr2_new) - - beta = normr2_new/self.normr2 - self.normr2 = normr2_new - self.d = s + beta*self.d - - def update_objective(self): - self.loss.append(self.r.squared_norm())
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/FBPD.py b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/FBPD.py deleted file mode 100644 index 445ba7a..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/FBPD.py +++ /dev/null @@ -1,86 +0,0 @@ -# -*- 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 2019 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. -""" -Created on Thu Feb 21 11:09:03 2019 - -@author: ofn77899 -""" - -from ccpi.optimisation.algorithms import Algorithm -from ccpi.optimisation.functions import ZeroFun - -class FBPD(Algorithm): - '''FBPD Algorithm - - Parameters: - x_init: initial guess - f: constraint - g: data fidelity - h: regularizer - opt: additional algorithm - ''' - constraint = None - data_fidelity = None - regulariser = None - def __init__(self, **kwargs): - pass - def set_up(self, x_init, operator=None, constraint=None, data_fidelity=None,\ - regulariser=None, opt=None): - - # default inputs - if constraint is None: - self.constraint = ZeroFun() - else: - self.constraint = constraint - if data_fidelity is None: - data_fidelity = ZeroFun() - else: - self.data_fidelity = data_fidelity - if regulariser is None: - self.regulariser = ZeroFun() - else: - self.regulariser = regulariser - - # algorithmic parameters - - - # step-sizes - self.tau = 2 / (self.data_fidelity.L + 2) - self.sigma = (1/self.tau - self.data_fidelity.L/2) / self.regulariser.L - - self.inv_sigma = 1/self.sigma - - # initialization - self.x = x_init - self.y = operator.direct(self.x) - - - def update(self): - - # primal forward-backward step - x_old = self.x - self.x = self.x - self.tau * ( self.data_fidelity.grad(self.x) + self.operator.adjoint(self.y) ) - self.x = self.constraint.prox(self.x, self.tau); - - # dual forward-backward step - self.y = self.y + self.sigma * self.operator.direct(2*self.x - x_old); - self.y = self.y - self.sigma * self.regulariser.prox(self.inv_sigma*self.y, self.inv_sigma); - - # time and criterion - self.loss = self.constraint(self.x) + self.data_fidelity(self.x) + self.regulariser(self.operator.direct(self.x)) diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/FISTA.py b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/FISTA.py deleted file mode 100644 index 93ba178..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/FISTA.py +++ /dev/null @@ -1,121 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Thu Feb 21 11:07:30 2019 - -@author: ofn77899 -""" - -from ccpi.optimisation.algorithms import Algorithm -from ccpi.optimisation.functions import ZeroFun -import numpy - -class FISTA(Algorithm): - '''Fast Iterative Shrinkage-Thresholding Algorithm - - Beck, A. and Teboulle, M., 2009. A fast iterative shrinkage-thresholding - algorithm for linear inverse problems. - SIAM journal on imaging sciences,2(1), pp.183-202. - - Parameters: - x_init: initial guess - f: data fidelity - g: regularizer - h: - opt: additional algorithm - ''' - - def __init__(self, **kwargs): - '''initialisation can be done at creation time if all - proper variables are passed or later with set_up''' - super(FISTA, self).__init__() - self.f = None - self.g = None - self.invL = None - self.t_old = 1 - args = ['x_init', 'f', 'g', 'opt'] - for k,v in kwargs.items(): - if k in args: - args.pop(args.index(k)) - if len(args) == 0: - return self.set_up(kwargs['x_init'], - f=kwargs['f'], - g=kwargs['g'], - opt=kwargs['opt']) - - def set_up(self, x_init, f=None, g=None, opt=None): - - # default inputs - if f is None: - self.f = ZeroFun() - else: - self.f = f - if g is None: - g = ZeroFun() - self.g = g - else: - self.g = g - - # algorithmic parameters - if opt is None: - opt = {'tol': 1e-4, 'memopt':False} - - self.tol = opt['tol'] if 'tol' in opt.keys() else 1e-4 - memopt = opt['memopt'] if 'memopt' in opt.keys() else False - self.memopt = memopt - - # initialization - if memopt: - self.y = x_init.clone() - self.x_old = x_init.clone() - self.x = x_init.clone() - self.u = x_init.clone() - else: - self.x_old = x_init.copy() - self.y = x_init.copy() - - #timing = numpy.zeros(max_iter) - #criter = numpy.zeros(max_iter) - - - self.invL = 1/f.L - - self.t_old = 1 - - def update(self): - # algorithm loop - #for it in range(0, max_iter): - - if self.memopt: - # u = y - invL*f.grad(y) - # store the result in x_old - self.f.gradient(self.y, out=self.u) - self.u.__imul__( -self.invL ) - self.u.__iadd__( self.y ) - # x = g.prox(u,invL) - self.g.proximal(self.u, self.invL, out=self.x) - - self.t = 0.5*(1 + numpy.sqrt(1 + 4*(self.t_old**2))) - - # y = x + (t_old-1)/t*(x-x_old) - self.x.subtract(self.x_old, out=self.y) - self.y.__imul__ ((self.t_old-1)/self.t) - self.y.__iadd__( self.x ) - - self.x_old.fill(self.x) - self.t_old = self.t - - - else: - u = self.y - self.invL*self.f.grad(self.y) - - self.x = self.g.prox(u,self.invL) - - self.t = 0.5*(1 + numpy.sqrt(1 + 4*(self.t_old**2))) - - self.y = self.x + (self.t_old-1)/self.t*(self.x-self.x_old) - - self.x_old = self.x.copy() - self.t_old = self.t - - def update_objective(self): - self.loss.append( self.f(self.x) + self.g(self.x) )
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/GradientDescent.py b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/GradientDescent.py deleted file mode 100644 index f1e4132..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/GradientDescent.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- 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 2019 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. -""" -Created on Thu Feb 21 11:05:09 2019 - -@author: ofn77899 -""" -from ccpi.optimisation.algorithms import Algorithm - -class GradientDescent(Algorithm): - '''Implementation of Gradient Descent algorithm - ''' - - def __init__(self, **kwargs): - '''initialisation can be done at creation time if all - proper variables are passed or later with set_up''' - super(GradientDescent, self).__init__() - self.x = None - self.rate = 0 - self.objective_function = None - self.regulariser = None - args = ['x_init', 'objective_function', 'rate'] - for k,v in kwargs.items(): - if k in args: - args.pop(args.index(k)) - if len(args) == 0: - return self.set_up(x_init=kwargs['x_init'], - objective_function=kwargs['objective_function'], - rate=kwargs['rate']) - - def should_stop(self): - '''stopping cryterion, currently only based on number of iterations''' - return self.iteration >= self.max_iteration - - def set_up(self, x_init, objective_function, rate): - '''initialisation of the algorithm''' - self.x = x_init.copy() - self.objective_function = objective_function - self.rate = rate - self.loss.append(objective_function(x_init)) - self.iteration = 0 - try: - self.memopt = self.objective_function.memopt - except AttributeError as ae: - self.memopt = False - if self.memopt: - self.x_update = x_init.copy() - - def update(self): - '''Single iteration''' - if self.memopt: - self.objective_function.gradient(self.x, out=self.x_update) - self.x_update *= -self.rate - self.x += self.x_update - else: - self.x += -self.rate * self.objective_function.gradient(self.x) - - def update_objective(self): - self.loss.append(self.objective_function(self.x)) -
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/PDHG.py b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/PDHG.py deleted file mode 100644 index d0e27ae..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/PDHG.py +++ /dev/null @@ -1,155 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Mon Feb 4 16:18:06 2019 - -@author: evangelos -""" -from ccpi.optimisation.algorithms import Algorithm -from ccpi.framework import ImageData -import numpy as np -import matplotlib.pyplot as plt -import time -from ccpi.optimisation.operators import BlockOperator -from ccpi.framework import BlockDataContainer - - -import matplotlib.pyplot as plt - -class PDHG(Algorithm): - '''Primal Dual Hybrid Gradient''' - - def __init__(self, **kwargs): - super(PDHG, self).__init__() - self.f = kwargs.get('f', None) - self.operator = kwargs.get('operator', None) - self.g = kwargs.get('g', None) - self.tau = kwargs.get('tau', None) - self.sigma = kwargs.get('sigma', None) - - if self.f is not None and self.operator is not None and \ - self.g is not None: - print ("Calling from creator") - self.set_up(self.f, - self.operator, - self.g, - self.tau, - self.sigma) - - def set_up(self, f, g, operator, tau = None, sigma = None, opt = None, **kwargs): - # algorithmic parameters - - if sigma is None and tau is None: - raise ValueError('Need sigma*tau||K||^2<1') - - - self.x_old = self.operator.domain_geometry().allocate() - self.y_old = self.operator.range_geometry().allocate() - - self.xbar = self.x_old.copy() - #x_tmp = x_old - self.x = self.x_old.copy() - self.y = self.y_old.copy() - #y_tmp = y_old - #y = y_tmp - - # relaxation parameter - self.theta = 1 - - def update(self): - # Gradient descent, Dual problem solution - self.y_old += self.sigma * self.operator.direct(self.xbar) - self.y = self.f.proximal_conjugate(self.y_old, self.sigma) - - # Gradient ascent, Primal problem solution - self.x_old -= self.tau * self.operator.adjoint(self.y) - self.x = self.g.proximal(self.x_old, self.tau) - - #Update - #xbar = x + theta * (x - x_old) - self.xbar.fill(self.x) - self.xbar -= self.x_old - self.xbar *= self.theta - self.xbar += self.x - -# self.x_old.fill(self.x) -# self.y_old.fill(self.y) - self.y_old = self.y.copy() - self.x_old = self.x.copy() - #self.y = self.y_old - - def update_objective(self): - self.loss.append([self.f(self.operator.direct(self.x)) + self.g(self.x), - -(self.f.convex_conjugate(self.y) + self.g.convex_conjugate(- 1 * self.operator.adjoint(self.y))) - ]) - - - -def PDHG_old(f, g, operator, tau = None, sigma = None, opt = None, **kwargs): - - # algorithmic parameters - if opt is None: - opt = {'tol': 1e-6, 'niter': 500, 'show_iter': 100, \ - 'memopt': False} - - if sigma is None and tau is None: - raise ValueError('Need sigma*tau||K||^2<1') - - niter = opt['niter'] if 'niter' in opt.keys() else 1000 - tol = opt['tol'] if 'tol' in opt.keys() else 1e-4 - memopt = opt['memopt'] if 'memopt' in opt.keys() else False - show_iter = opt['show_iter'] if 'show_iter' in opt.keys() else False - stop_crit = opt['stop_crit'] if 'stop_crit' in opt.keys() else False - - - x_old = operator.domain_geometry().allocate() - y_old = operator.range_geometry().allocate() - - - xbar = x_old - x_tmp = x_old - x = x_old - - y_tmp = y_old - y = y_tmp - - # relaxation parameter - theta = 1 - - t = time.time() - - objective = [] - - - for i in range(niter): - - # Gradient descent, Dual problem solution - y_tmp = y_old + sigma * operator.direct(xbar) - y = f.proximal_conjugate(y_tmp, sigma) - - # Gradient ascent, Primal problem solution - x_tmp = x_old - tau * operator.adjoint(y) - x = g.proximal(x_tmp, tau) - - #Update - xbar = x + theta * (x - x_old) - - x_old = x - y_old = y - - if i%100==0: - - primal = f(operator.direct(x)) + g(x) - dual = -(f.convex_conjugate(y) + g(-1*operator.adjoint(y))) - print( i, primal, dual, primal-dual) - -# plt.imshow(x.as_array()) -# plt.show() -# print(f(operator.direct(x)) + g(x), i) - - t_end = time.time() - - return x, t_end - t, objective - - - diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/__init__.py b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/__init__.py deleted file mode 100644 index f562973..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/__init__.py +++ /dev/null @@ -1,32 +0,0 @@ -# -*- 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 2019 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. -""" -Created on Thu Feb 21 11:03:13 2019 - -@author: ofn77899 -""" - -from .Algorithm import Algorithm -from .CGLS import CGLS -from .GradientDescent import GradientDescent -from .FISTA import FISTA -from .FBPD import FBPD -from .PDHG import PDHG -from .PDHG import PDHG_old - diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/algs.py b/Wrappers/Python/build/lib/ccpi/optimisation/algs.py deleted file mode 100644 index 6b6ae2c..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/algs.py +++ /dev/null @@ -1,319 +0,0 @@ -# -*- 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 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 numpy -import time - -from ccpi.optimisation.functions import Function -from ccpi.optimisation.functions import ZeroFun -from ccpi.framework import ImageData -from ccpi.framework import AcquisitionData -from ccpi.optimisation.spdhg import spdhg -from ccpi.optimisation.spdhg import KullbackLeibler -from ccpi.optimisation.spdhg import KullbackLeiblerConvexConjugate - -def FISTA(x_init, f=None, g=None, opt=None): - '''Fast Iterative Shrinkage-Thresholding Algorithm - - Beck, A. and Teboulle, M., 2009. A fast iterative shrinkage-thresholding - algorithm for linear inverse problems. - SIAM journal on imaging sciences,2(1), pp.183-202. - - Parameters: - x_init: initial guess - f: data fidelity - g: regularizer - h: - opt: additional algorithm - ''' - # default inputs - if f is None: f = ZeroFun() - if g is None: g = ZeroFun() - - # algorithmic parameters - if opt is None: - opt = {'tol': 1e-4, 'iter': 1000, 'memopt':False} - - max_iter = opt['iter'] if 'iter' in opt.keys() else 1000 - tol = opt['tol'] if 'tol' in opt.keys() else 1e-4 - memopt = opt['memopt'] if 'memopt' in opt.keys() else False - - - # initialization - if memopt: - y = x_init.clone() - x_old = x_init.clone() - x = x_init.clone() - u = x_init.clone() - else: - x_old = x_init - y = x_init; - - timing = numpy.zeros(max_iter) - criter = numpy.zeros(max_iter) - - invL = 1/f.L - - t_old = 1 - - c = f(x_init) + g(x_init) - - # algorithm loop - for it in range(0, max_iter): - - time0 = time.time() - if memopt: - # u = y - invL*f.grad(y) - # store the result in x_old - f.gradient(y, out=u) - u.__imul__( -invL ) - u.__iadd__( y ) - # x = g.prox(u,invL) - g.proximal(u, invL, out=x) - - t = 0.5*(1 + numpy.sqrt(1 + 4*(t_old**2))) - - # y = x + (t_old-1)/t*(x-x_old) - x.subtract(x_old, out=y) - y.__imul__ ((t_old-1)/t) - y.__iadd__( x ) - - x_old.fill(x) - t_old = t - - - else: - u = y - invL*f.grad(y) - - x = g.prox(u,invL) - - t = 0.5*(1 + numpy.sqrt(1 + 4*(t_old**2))) - - y = x + (t_old-1)/t*(x-x_old) - - x_old = x.copy() - t_old = t - - # time and criterion - timing[it] = time.time() - time0 - criter[it] = f(x) + g(x); - - # stopping rule - #if np.linalg.norm(x - x_old) < tol * np.linalg.norm(x_old) and it > 10: - # break - - #print(it, 'out of', 10, 'iterations', end='\r'); - - #criter = criter[0:it+1]; - timing = numpy.cumsum(timing[0:it+1]); - - return x, it, timing, criter - -def FBPD(x_init, operator=None, constraint=None, data_fidelity=None,\ - regulariser=None, opt=None): - '''FBPD Algorithm - - Parameters: - x_init: initial guess - f: constraint - g: data fidelity - h: regularizer - opt: additional algorithm - ''' - # default inputs - if constraint is None: constraint = ZeroFun() - if data_fidelity is None: data_fidelity = ZeroFun() - if regulariser is None: regulariser = ZeroFun() - - # algorithmic parameters - if opt is None: - opt = {'tol': 1e-4, 'iter': 1000} - else: - try: - max_iter = opt['iter'] - except KeyError as ke: - opt[ke] = 1000 - try: - opt['tol'] = 1000 - except KeyError as ke: - opt[ke] = 1e-4 - tol = opt['tol'] - max_iter = opt['iter'] - memopt = opt['memopts'] if 'memopts' in opt.keys() else False - - # step-sizes - tau = 2 / (data_fidelity.L + 2) - sigma = (1/tau - data_fidelity.L/2) / regulariser.L - inv_sigma = 1/sigma - - # initialization - x = x_init - y = operator.direct(x); - - timing = numpy.zeros(max_iter) - criter = numpy.zeros(max_iter) - - - - - # algorithm loop - for it in range(0, max_iter): - - t = time.time() - - # primal forward-backward step - x_old = x; - x = x - tau * ( data_fidelity.grad(x) + operator.adjoint(y) ); - x = constraint.prox(x, tau); - - # dual forward-backward step - y = y + sigma * operator.direct(2*x - x_old); - y = y - sigma * regulariser.prox(inv_sigma*y, inv_sigma); - - # time and criterion - timing[it] = time.time() - t - criter[it] = constraint(x) + data_fidelity(x) + regulariser(operator.direct(x)) - - # stopping rule - #if np.linalg.norm(x - x_old) < tol * np.linalg.norm(x_old) and it > 10: - # break - - criter = criter[0:it+1] - timing = numpy.cumsum(timing[0:it+1]) - - return x, it, timing, criter - -def CGLS(x_init, operator , data , opt=None): - '''Conjugate Gradient Least Squares algorithm - - Parameters: - x_init: initial guess - operator: operator for forward/backward projections - data: data to operate on - opt: additional algorithm - ''' - - if opt is None: - opt = {'tol': 1e-4, 'iter': 1000} - else: - try: - max_iter = opt['iter'] - except KeyError as ke: - opt[ke] = 1000 - try: - opt['tol'] = 1000 - except KeyError as ke: - opt[ke] = 1e-4 - tol = opt['tol'] - max_iter = opt['iter'] - - r = data.copy() - x = x_init.copy() - - d = operator.adjoint(r) - - normr2 = (d**2).sum() - - timing = numpy.zeros(max_iter) - criter = numpy.zeros(max_iter) - - # algorithm loop - for it in range(0, max_iter): - - t = time.time() - - Ad = operator.direct(d) - alpha = normr2/( (Ad**2).sum() ) - x = x + alpha*d - r = r - alpha*Ad - s = operator.adjoint(r) - - normr2_new = (s**2).sum() - beta = normr2_new/normr2 - normr2 = normr2_new - d = s + beta*d - - # time and criterion - timing[it] = time.time() - t - criter[it] = (r**2).sum() - - return x, it, timing, criter - -def SIRT(x_init, operator , data , opt=None, constraint=None): - '''Simultaneous Iterative Reconstruction Technique - - Parameters: - x_init: initial guess - operator: operator for forward/backward projections - data: data to operate on - opt: additional algorithm - constraint: func of Indicator type specifying convex constraint. - ''' - - if opt is None: - opt = {'tol': 1e-4, 'iter': 1000} - else: - try: - max_iter = opt['iter'] - except KeyError as ke: - opt[ke] = 1000 - try: - opt['tol'] = 1000 - except KeyError as ke: - opt[ke] = 1e-4 - tol = opt['tol'] - max_iter = opt['iter'] - - # Set default constraint to unconstrained - if constraint==None: - constraint = Function() - - x = x_init.clone() - - timing = numpy.zeros(max_iter) - criter = numpy.zeros(max_iter) - - # Relaxation parameter must be strictly between 0 and 2. For now fix at 1.0 - relax_par = 1.0 - - # Set up scaling matrices D and M. - im1 = ImageData(geometry=x_init.geometry) - im1.array[:] = 1.0 - M = 1/operator.direct(im1) - del im1 - aq1 = AcquisitionData(geometry=M.geometry) - aq1.array[:] = 1.0 - D = 1/operator.adjoint(aq1) - del aq1 - - # algorithm loop - for it in range(0, max_iter): - t = time.time() - r = data - operator.direct(x) - - x = constraint.prox(x + relax_par * (D*operator.adjoint(M*r)),None) - - timing[it] = time.time() - t - if it > 0: - criter[it-1] = (r**2).sum() - - r = data - operator.direct(x) - criter[it] = (r**2).sum() - return x, it, timing, criter - diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/funcs.py b/Wrappers/Python/build/lib/ccpi/optimisation/funcs.py deleted file mode 100644 index efc465c..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/funcs.py +++ /dev/null @@ -1,272 +0,0 @@ -# -*- 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 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. - -from ccpi.optimisation.ops import Identity, FiniteDiff2D -import numpy -from ccpi.framework import DataContainer -import warnings -from ccpi.optimisation.functions import Function -def isSizeCorrect(data1 ,data2): - if issubclass(type(data1), DataContainer) and \ - issubclass(type(data2), DataContainer): - # check dimensionality - if data1.check_dimensions(data2): - return True - elif issubclass(type(data1) , numpy.ndarray) and \ - issubclass(type(data2) , numpy.ndarray): - return data1.shape == data2.shape - else: - raise ValueError("{0}: getting two incompatible types: {1} {2}"\ - .format('Function', type(data1), type(data2))) - return False -class Norm2(Function): - - def __init__(self, - gamma=1.0, - direction=None): - super(Norm2, self).__init__() - self.gamma = gamma; - self.direction = direction; - - def __call__(self, x, out=None): - - if out is None: - xx = numpy.sqrt(numpy.sum(numpy.square(x.as_array()), self.direction, - keepdims=True)) - else: - if isSizeCorrect(out, x): - # check dimensionality - if issubclass(type(out), DataContainer): - arr = out.as_array() - numpy.square(x.as_array(), out=arr) - xx = numpy.sqrt(numpy.sum(arr, self.direction, keepdims=True)) - - elif issubclass(type(out) , numpy.ndarray): - numpy.square(x.as_array(), out=out) - xx = numpy.sqrt(numpy.sum(out, self.direction, keepdims=True)) - else: - raise ValueError ('Wrong size: x{0} out{1}'.format(x.shape,out.shape) ) - - p = numpy.sum(self.gamma*xx) - - return p - - def prox(self, x, tau): - - xx = numpy.sqrt(numpy.sum( numpy.square(x.as_array()), self.direction, - keepdims=True )) - xx = numpy.maximum(0, 1 - tau*self.gamma / xx) - p = x.as_array() * xx - - return type(x)(p,geometry=x.geometry) - def proximal(self, x, tau, out=None): - if out is None: - return self.prox(x,tau) - else: - if isSizeCorrect(out, x): - # check dimensionality - if issubclass(type(out), DataContainer): - numpy.square(x.as_array(), out = out.as_array()) - xx = numpy.sqrt(numpy.sum( out.as_array() , self.direction, - keepdims=True )) - xx = numpy.maximum(0, 1 - tau*self.gamma / xx) - x.multiply(xx, out= out.as_array()) - - - elif issubclass(type(out) , numpy.ndarray): - numpy.square(x.as_array(), out=out) - xx = numpy.sqrt(numpy.sum(out, self.direction, keepdims=True)) - - xx = numpy.maximum(0, 1 - tau*self.gamma / xx) - x.multiply(xx, out= out) - else: - raise ValueError ('Wrong size: x{0} out{1}'.format(x.shape,out.shape) ) - - -class TV2D(Norm2): - - def __init__(self, gamma): - super(TV2D,self).__init__(gamma, 0) - self.op = FiniteDiff2D() - self.L = self.op.get_max_sing_val() - - -# Define a class for squared 2-norm -class Norm2sq(Function): - ''' - f(x) = c*||A*x-b||_2^2 - - which has - - grad[f](x) = 2*c*A^T*(A*x-b) - - and Lipschitz constant - - L = 2*c*||A||_2^2 = 2*s1(A)^2 - - where s1(A) is the largest singular value of A. - - ''' - - def __init__(self,A,b,c=1.0,memopt=False): - super(Norm2sq, self).__init__() - - self.A = A # Should be an operator, default identity - self.b = b # Default zero DataSet? - self.c = c # Default 1. - if memopt: - try: - self.range_tmp = A.range_geometry().allocate() - self.domain_tmp = A.domain_geometry().allocate() - self.memopt = True - except NameError as ne: - warnings.warn(str(ne)) - self.memopt = False - except NotImplementedError as nie: - print (nie) - warnings.warn(str(nie)) - self.memopt = False - else: - self.memopt = False - - # Compute the Lipschitz parameter from the operator if possible - # Leave it initialised to None otherwise - try: - self.L = 2.0*self.c*(self.A.norm()**2) - except AttributeError as ae: - pass - except NotImplementedError as noe: - pass - - #def grad(self,x): - # return self.gradient(x, out=None) - - def __call__(self,x): - #return self.c* np.sum(np.square((self.A.direct(x) - self.b).ravel())) - #if out is None: - # return self.c*( ( (self.A.direct(x)-self.b)**2).sum() ) - #else: - y = self.A.direct(x) - y.__isub__(self.b) - #y.__imul__(y) - #return y.sum() * self.c - try: - return y.squared_norm() * self.c - except AttributeError as ae: - # added for compatibility with SIRF - return (y.norm()**2) * self.c - - def gradient(self, x, out = None): - if self.memopt: - #return 2.0*self.c*self.A.adjoint( self.A.direct(x) - self.b ) - - self.A.direct(x, out=self.range_tmp) - self.range_tmp -= self.b - self.A.adjoint(self.range_tmp, out=out) - #self.direct_placehold.multiply(2.0*self.c, out=out) - out *= (self.c * 2.0) - else: - return (2.0*self.c)*self.A.adjoint( self.A.direct(x) - self.b ) - - - -# Box constraints indicator function. Calling returns 0 if argument is within -# the box. The prox operator is projection onto the box. Only implements one -# scalar lower and one upper as constraint on all elements. Should generalise -# to vectors to allow different constraints one elements. -class IndicatorBox(Function): - - def __init__(self,lower=-numpy.inf,upper=numpy.inf): - # Do nothing - super(IndicatorBox, self).__init__() - self.lower = lower - self.upper = upper - - - def __call__(self,x): - - if (numpy.all(x.array>=self.lower) and - numpy.all(x.array <= self.upper) ): - val = 0 - else: - val = numpy.inf - return val - - def prox(self,x,tau=None): - return (x.maximum(self.lower)).minimum(self.upper) - - def proximal(self, x, tau, out=None): - if out is None: - return self.prox(x, tau) - else: - if not x.shape == out.shape: - raise ValueError('Norm1 Incompatible size:', - x.shape, out.shape) - #(x.abs() - tau*self.gamma).maximum(0) * x.sign() - x.abs(out = out) - out.__isub__(tau*self.gamma) - out.maximum(0, out=out) - if self.sign_x is None or not x.shape == self.sign_x.shape: - self.sign_x = x.sign() - else: - x.sign(out=self.sign_x) - - out.__imul__( self.sign_x ) - -# A more interesting example, least squares plus 1-norm minimization. -# Define class to represent 1-norm including prox function -class Norm1(Function): - - def __init__(self,gamma): - super(Norm1, self).__init__() - self.gamma = gamma - self.L = 1 - self.sign_x = None - - def __call__(self,x,out=None): - if out is None: - return self.gamma*(x.abs().sum()) - else: - if not x.shape == out.shape: - raise ValueError('Norm1 Incompatible size:', - x.shape, out.shape) - x.abs(out=out) - return out.sum() * self.gamma - - def prox(self,x,tau): - return (x.abs() - tau*self.gamma).maximum(0) * x.sign() - - def proximal(self, x, tau, out=None): - if out is None: - return self.prox(x, tau) - else: - if isSizeCorrect(x,out): - # check dimensionality - if issubclass(type(out), DataContainer): - v = (x.abs() - tau*self.gamma).maximum(0) - x.sign(out=out) - out *= v - #out.fill(self.prox(x,tau)) - elif issubclass(type(out) , numpy.ndarray): - v = (x.abs() - tau*self.gamma).maximum(0) - out[:] = x.sign() - out *= v - #out[:] = self.prox(x,tau) - else: - raise ValueError ('Wrong size: x{0} out{1}'.format(x.shape,out.shape) ) diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/BlockFunction.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/BlockFunction.py deleted file mode 100644 index 81c16cd..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/functions/BlockFunction.py +++ /dev/null @@ -1,79 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Fri Mar 8 10:01:31 2019 - -@author: evangelos -""" - -import numpy as np -#from ccpi.optimisation.funcs import Function -from ccpi.optimisation.functions import Function -from ccpi.framework import BlockDataContainer -from numbers import Number - -class BlockFunction(Function): - '''A Block vector of Functions - - .. 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) - - ''' - 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 - - :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 - for i in range(x.shape[0]): - t += self.functions[i](x.get_item(i)) - return t - - def convex_conjugate(self, x): - '''Convex_conjugate does not take into account the BlockOperator''' - t = 0 - for i in range(x.shape[0]): - t += self.functions[i].convex_conjugate(x.get_item(i)) - return t - - - def proximal_conjugate(self, x, tau, out = None): - '''proximal_conjugate does not take into account the BlockOperator''' - out = [None]*self.length - if isinstance(tau, Number): - for i in range(self.length): - out[i] = self.functions[i].proximal_conjugate(x.get_item(i), tau) - else: - for i in range(self.length): - out[i] = self.functions[i].proximal_conjugate(x.get_item(i), tau.get_item(i)) - - return BlockDataContainer(*out) - - def proximal(self, x, tau, out = None): - '''proximal does not take into account the BlockOperator''' - 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) - - def gradient(self,x, out=None): - '''FIXME: gradient returns pass''' - pass
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/Function.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/Function.py deleted file mode 100644 index 82f24a6..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/functions/Function.py +++ /dev/null @@ -1,69 +0,0 @@ -# -*- 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 -from ccpi.optimisation.functions.ScaledFunction import ScaledFunction - -class Function(object): - '''Abstract class representing a function - - Members: - L is the Lipschitz constant of the gradient of the Function - ''' - def __init__(self): - self.L = None - - def __call__(self,x, out=None): - '''Evaluates the function at x ''' - raise NotImplementedError - - def gradient(self, x, out=None): - '''Returns the gradient of the function at x, if the function is differentiable''' - raise NotImplementedError - - def proximal(self, x, tau, out=None): - '''This returns the proximal operator for the function at x, tau''' - raise NotImplementedError - - def convex_conjugate(self, x, out=None): - '''This evaluates the convex conjugate of the function at x''' - raise NotImplementedError - - def proximal_conjugate(self, x, tau, out = None): - '''This returns the proximal operator for the convex conjugate of the function at x, tau''' - raise NotImplementedError - - def grad(self, x): - '''Alias of gradient(x,None)''' - 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): - '''Alias of proximal(x, tau, None)''' - warnings.warn('''This method will disappear in following - versions of the CIL. Use proximal instead''', DeprecationWarning) - return self.proximal(x, out=None) - - def __rmul__(self, scalar): - '''Defines the multiplication by a scalar on the left - - returns a ScaledFunction''' - return ScaledFunction(self, scalar) - diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/FunctionOperatorComposition.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/FunctionOperatorComposition.py deleted file mode 100644 index 34b7e35..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/functions/FunctionOperatorComposition.py +++ /dev/null @@ -1,65 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -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): - - 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): - - return self.function(self.operator.direct(x)) - - 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''' - return self.function.convex_conjugate(x) - - def proximal(self, x, tau, out=None): - - '''proximal does not take into account the Operator''' - - return self.function.proximal(x, tau, out=out) - - def proximal_conjugate(self, x, tau, out=None): - - ''' proximal conjugate does not take into account the Operator''' - - return self.function.proximal_conjugate(x, tau, out=out) - - def gradient(self, x, out=None): - - ''' Gradient takes into account the Operator''' - if out is None: - return self.operator.adjoint( - self.function.gradient(self.operator.direct(x)) - ) - else: - self.operator.adjoint( - self.function.gradient(self.operator.direct(x), - out=out) - ) - -
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/IndicatorBox.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/IndicatorBox.py deleted file mode 100644 index df8dc89..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/functions/IndicatorBox.py +++ /dev/null @@ -1,65 +0,0 @@ -# -*- 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. -from ccpi.optimisation.functions import Function -import numpy - -class IndicatorBox(Function): - '''Box constraints indicator function. - - Calling returns 0 if argument is within the box. The prox operator is projection onto the box. - Only implements one scalar lower and one upper as constraint on all elements. Should generalise - to vectors to allow different constraints one elements. -''' - - def __init__(self,lower=-numpy.inf,upper=numpy.inf): - # Do nothing - super(IndicatorBox, self).__init__() - self.lower = lower - self.upper = upper - - - def __call__(self,x): - - if (numpy.all(x.array>=self.lower) and - numpy.all(x.array <= self.upper) ): - val = 0 - else: - val = numpy.inf - return val - - def prox(self,x,tau=None): - return (x.maximum(self.lower)).minimum(self.upper) - - def proximal(self, x, tau, out=None): - if out is None: - return self.prox(x, tau) - else: - if not x.shape == out.shape: - raise ValueError('Norm1 Incompatible size:', - x.shape, out.shape) - #(x.abs() - tau*self.gamma).maximum(0) * x.sign() - x.abs(out = out) - out.__isub__(tau*self.gamma) - out.maximum(0, out=out) - if self.sign_x is None or not x.shape == self.sign_x.shape: - self.sign_x = x.sign() - else: - x.sign(out=self.sign_x) - - out.__imul__( self.sign_x ) diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/L1Norm.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/L1Norm.py deleted file mode 100644 index 5a47edd..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/functions/L1Norm.py +++ /dev/null @@ -1,92 +0,0 @@ -# -*- 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 Evangelos Papoutsellis 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. -""" -Created on Wed Mar 6 19:42:34 2019 - -@author: evangelos -""" - -import numpy as np -#from ccpi.optimisation.funcs import Function -from ccpi.optimisation.functions import Function -from ccpi.framework import DataContainer, ImageData, ImageGeometry - - -############################ L1NORM FUNCTIONS ############################# -class SimpleL1Norm(Function): - - def __init__(self, alpha=1): - - super(SimpleL1Norm, self).__init__() - self.alpha = alpha - - def __call__(self, x): - return self.alpha * x.abs().sum() - - def gradient(self,x): - return ValueError('Not Differentiable') - - def convex_conjugate(self,x): - return 0 - - def proximal(self, x, tau): - ''' Soft Threshold''' - return x.sign() * (x.abs() - tau * self.alpha).maximum(0) - - def proximal_conjugate(self, x, tau): - return x.divide((x.abs()/self.alpha).maximum(1.0)) - -class L1Norm(SimpleL1Norm): - - def __init__(self, alpha=1, **kwargs): - - super(L1Norm, self).__init__() - self.alpha = alpha - self.b = kwargs.get('b',None) - - def __call__(self, x): - - if self.b is None: - return SimpleL1Norm.__call__(self, x) - else: - return SimpleL1Norm.__call__(self, x - self.b) - - def gradient(self, x): - return ValueError('Not Differentiable') - - def convex_conjugate(self,x): - if self.b is None: - return SimpleL1Norm.convex_conjugate(self, x) - else: - return SimpleL1Norm.convex_conjugate(self, x) + (self.b * x).sum() - - def proximal(self, x, tau): - - if self.b is None: - return SimpleL1Norm.proximal(self, x, tau) - else: - return self.b + SimpleL1Norm.proximal(self, x - self.b , tau) - - def proximal_conjugate(self, x, tau): - - if self.b is None: - return SimpleL1Norm.proximal_conjugate(self, x, tau) - else: - return SimpleL1Norm.proximal_conjugate(self, x - tau*self.b, tau) -
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/L2NormSquared.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/L2NormSquared.py deleted file mode 100644 index 889d703..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/functions/L2NormSquared.py +++ /dev/null @@ -1,233 +0,0 @@ -# -*- 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 Evangelos Papoutsellis 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 numpy -from ccpi.optimisation.functions import Function -from ccpi.optimisation.functions.ScaledFunction import ScaledFunction -from ccpi.framework import DataContainer, ImageData, ImageGeometry - -############################ L2NORM FUNCTION ############################# -class L2NormSquared(Function): - - def __init__(self, **kwargs): - - ''' L2NormSquared class - f : ImageGeometry --> R - - Cases: f(x) = ||x||^{2}_{2} - f(x) = || x - b ||^{2}_{2} - - ''' - - #TODO need x, b to live in the same geometry if b is not None - - super(L2NormSquared, self).__init__() - self.b = kwargs.get('b',None) - - def __call__(self, x): - ''' Evaluates L2NormSq at point x''' - - y = x - if self.b is not None: -# x.subtract(self.b, out = x) - y = x - self.b -# else: -# y -# if out is None: -# return x.squared_norm() -# else: - try: - return y.squared_norm() - except AttributeError as ae: - # added for compatibility with SIRF - return (y.norm()**2) - - - - def gradient(self, x, out=None): - ''' Evaluates gradient of L2NormSq at point x''' - if out is not None: - out.fill(x) - if self.b is not None: - out -= self.b - out *= 2 - else: - y = x - if self.b is not None: -# x.subtract(self.b, out=x) - y = x - self.b - return 2*y - - - def convex_conjugate(self, x, out=None): - ''' Evaluate convex conjugate of L2NormSq''' - - tmp = 0 - if self.b is not None: -# tmp = (self.b * x).sum() - tmp = (x * self.b).sum() - - if out is None: - # FIXME: this is a number - return (1./4.) * x.squared_norm() + tmp - else: - # FIXME: this is a DataContainer - out.fill((1./4.) * x.squared_norm() + tmp) - - - def proximal(self, x, tau, out = None): - - ''' The proximal operator ( prox_\{tau * f\}(x) ) evaluates i.e., - argmin_x { 0.5||x - u||^{2} + tau f(x) } - ''' - - if out is None: - if self.b is not None: - return (x - self.b)/(1+2*tau) + self.b - else: - return x/(1+2*tau) - else: - out.fill(x) - if self.b is not None: - out -= self.b - out /= (1+2*tau) - if self.b is not None: - out += self.b - #out.fill((x - self.b)/(1+2*tau) + self.b) - #else: - # out.fill(x/(1+2*tau)) - - - def proximal_conjugate(self, x, tau, out=None): - - if out is None: - if self.b is not None: - # change the order cannot add ImageData + NestedBlock - return (-1* tau*self.b + x)/(1 + tau/2) - else: - return x/(1 + tau/2 ) - else: - if self.b is not None: - out.fill((x - tau*self.b)/(1 + tau/2)) - else: - out.fill(x/(1 + tau/2 )) - - def __rmul__(self, scalar): - return ScaledFunction(self, scalar) - - -if __name__ == '__main__': - - - # TESTS for L2 and scalar * L2 - - M, N, K = 2,3,5 - ig = ImageGeometry(voxel_num_x=M, voxel_num_y = N, voxel_num_z = K) - u = ig.allocate('random_int') - b = ig.allocate('random_int') - - # check grad/call no data - f = L2NormSquared() - a1 = f.gradient(u) - a2 = 2 * u - numpy.testing.assert_array_almost_equal(a1.as_array(), a2.as_array(), decimal=4) - numpy.testing.assert_equal(f(u), u.squared_norm()) - - # check grad/call with data - f1 = L2NormSquared(b=b) - b1 = f1.gradient(u) - b2 = 2 * (u-b) - - numpy.testing.assert_array_almost_equal(b1.as_array(), b2.as_array(), decimal=4) - numpy.testing.assert_equal(f1(u), (u-b).squared_norm()) - - #check convex conjuagate no data - c1 = f.convex_conjugate(u) - c2 = 1/4 * u.squared_norm() - numpy.testing.assert_equal(c1, c2) - - #check convex conjuagate with data - d1 = f1.convex_conjugate(u) - d2 = (1/4) * u.squared_norm() + (u*b).sum() - numpy.testing.assert_equal(d1, d2) - - # check proximal no data - tau = 5 - e1 = f.proximal(u, tau) - e2 = u/(1+2*tau) - numpy.testing.assert_array_almost_equal(e1.as_array(), e2.as_array(), decimal=4) - - # check proximal with data - tau = 5 - h1 = f1.proximal(u, tau) - h2 = (u-b)/(1+2*tau) + b - numpy.testing.assert_array_almost_equal(h1.as_array(), h2.as_array(), decimal=4) - - # check proximal conjugate no data - tau = 0.2 - k1 = f.proximal_conjugate(u, tau) - k2 = u/(1 + tau/2 ) - numpy.testing.assert_array_almost_equal(k1.as_array(), k2.as_array(), decimal=4) - - # check proximal conjugate with data - l1 = f1.proximal_conjugate(u, tau) - l2 = (u - tau * b)/(1 + tau/2 ) - numpy.testing.assert_array_almost_equal(l1.as_array(), l2.as_array(), decimal=4) - - - # check scaled function properties - - # scalar - scalar = 100 - f_scaled_no_data = scalar * L2NormSquared() - f_scaled_data = scalar * L2NormSquared(b=b) - - # call - numpy.testing.assert_equal(f_scaled_no_data(u), scalar*f(u)) - numpy.testing.assert_equal(f_scaled_data(u), scalar*f1(u)) - - # grad - numpy.testing.assert_array_almost_equal(f_scaled_no_data.gradient(u).as_array(), scalar*f.gradient(u).as_array(), decimal=4) - numpy.testing.assert_array_almost_equal(f_scaled_data.gradient(u).as_array(), scalar*f1.gradient(u).as_array(), decimal=4) - - # conj - numpy.testing.assert_almost_equal(f_scaled_no_data.convex_conjugate(u), \ - f.convex_conjugate(u/scalar) * scalar, decimal=4) - - numpy.testing.assert_almost_equal(f_scaled_data.convex_conjugate(u), \ - scalar * f1.convex_conjugate(u/scalar), decimal=4) - - # proximal - numpy.testing.assert_array_almost_equal(f_scaled_no_data.proximal(u, tau).as_array(), \ - f.proximal(u, tau*scalar).as_array()) - - - numpy.testing.assert_array_almost_equal(f_scaled_data.proximal(u, tau).as_array(), \ - f1.proximal(u, tau*scalar).as_array()) - - - # proximal conjugate - numpy.testing.assert_array_almost_equal(f_scaled_no_data.proximal_conjugate(u, tau).as_array(), \ - (u/(1 + tau/(2*scalar) )).as_array(), decimal=4) - - numpy.testing.assert_array_almost_equal(f_scaled_data.proximal_conjugate(u, tau).as_array(), \ - ((u - tau * b)/(1 + tau/(2*scalar) )).as_array(), decimal=4) - - - diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/MixedL21Norm.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/MixedL21Norm.py deleted file mode 100644 index 1c51236..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/functions/MixedL21Norm.py +++ /dev/null @@ -1,136 +0,0 @@ -# -*- 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 Evangelos Papoutsellis 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 numpy as np -from ccpi.optimisation.functions import Function, ScaledFunction -from ccpi.framework import DataContainer, ImageData, \ - ImageGeometry, BlockDataContainer - -############################ mixed_L1,2NORM FUNCTIONS ##################### -class MixedL21Norm(Function): - - def __init__(self, **kwargs): - - super(MixedL21Norm, self).__init__() - self.SymTensor = kwargs.get('SymTensor',False) - - def __call__(self, x, out=None): - - ''' Evaluates L1,2Norm at point x - - :param: x is a BlockDataContainer - - ''' - if self.SymTensor: - - 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 ] - -# print(x.containers) -# print(tmp) -# print(type(sum(tmp))) -# print(type(tmp)) - res = sum(tmp).sqrt().sum() -# print(res) - return res - - def gradient(self, x, out=None): - return ValueError('Not Differentiable') - - def convex_conjugate(self,x): - - ''' This is the Indicator function of ||\cdot||_{2, \infty} - which is either 0 if ||x||_{2, \infty} or \infty - ''' - return 0.0 - - def proximal(self, x, tau, out=None): - - ''' - For this we need to define a MixedL2,2 norm acting on BDC, - different form L2NormSquared which acts on DC - - ''' - - pass - - def proximal_conjugate(self, x, tau, out=None): - - if self.SymTensor: - - param = [1]*x.shape[0] - param[-1] = 2 - tmp = [param[i]*(x[i] ** 2) for i in range(x.shape[0])] - frac = [x[i]/(sum(tmp).sqrt()).maximum(1.0) for i in range(x.shape[0])] - res = BlockDataContainer(*frac) - - return res - -# tmp2 = np.sqrt(x.as_array()[0]**2 + x.as_array()[1]**2 + 2*x.as_array()[2]**2)/self.alpha -# res = x.divide(ImageData(tmp2).maximum(1.0)) - else: - - tmp = [ el*el for el in x] - res = (sum(tmp).sqrt()).maximum(1.0) - frac = [x[i]/res for i in range(x.shape[0])] - res = BlockDataContainer(*frac) - - return res - - def __rmul__(self, scalar): - return ScaledFunction(self, scalar) - -#class MixedL21Norm_tensor(Function): -# -# def __init__(self): -# print("feerf") -# -# -if __name__ == '__main__': - - 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 = 0.5 * MixedL21Norm() - - # call - - a1 = f_no_scaled(U) - a2 = f_scaled(U) - - z = f_no_scaled.proximal_conjugate(U, 1) - - f_no_scaled = MixedL21Norm() - - tmp = [el*el for el in U] - - - diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/Norm2Sq.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/Norm2Sq.py deleted file mode 100644 index b553d7c..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/functions/Norm2Sq.py +++ /dev/null @@ -1,98 +0,0 @@ -# -*- 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. -from ccpi.optimisation.functions import Function -import numpy -import warnings - -# Define a class for squared 2-norm -class Norm2sq(Function): - ''' - f(x) = c*||A*x-b||_2^2 - - which has - - grad[f](x) = 2*c*A^T*(A*x-b) - - and Lipschitz constant - - L = 2*c*||A||_2^2 = 2*s1(A)^2 - - where s1(A) is the largest singular value of A. - - ''' - - def __init__(self,A,b,c=1.0,memopt=False): - super(Norm2sq, self).__init__() - - self.A = A # Should be an operator, default identity - self.b = b # Default zero DataSet? - self.c = c # Default 1. - if memopt: - try: - self.range_tmp = A.range_geometry().allocate() - self.domain_tmp = A.domain_geometry().allocate() - self.memopt = True - except NameError as ne: - warnings.warn(str(ne)) - self.memopt = False - except NotImplementedError as nie: - print (nie) - warnings.warn(str(nie)) - self.memopt = False - else: - self.memopt = False - - # Compute the Lipschitz parameter from the operator if possible - # Leave it initialised to None otherwise - try: - self.L = 2.0*self.c*(self.A.norm()**2) - except AttributeError as ae: - pass - except NotImplementedError as noe: - pass - - #def grad(self,x): - # return self.gradient(x, out=None) - - def __call__(self,x): - #return self.c* np.sum(np.square((self.A.direct(x) - self.b).ravel())) - #if out is None: - # return self.c*( ( (self.A.direct(x)-self.b)**2).sum() ) - #else: - y = self.A.direct(x) - y.__isub__(self.b) - #y.__imul__(y) - #return y.sum() * self.c - try: - return y.squared_norm() * self.c - except AttributeError as ae: - # added for compatibility with SIRF - return (y.norm()**2) * self.c - - def gradient(self, x, out = None): - if self.memopt: - #return 2.0*self.c*self.A.adjoint( self.A.direct(x) - self.b ) - - self.A.direct(x, out=self.range_tmp) - self.range_tmp -= self.b - self.A.adjoint(self.range_tmp, out=out) - #self.direct_placehold.multiply(2.0*self.c, out=out) - out *= (self.c * 2.0) - else: - return (2.0*self.c)*self.A.adjoint( self.A.direct(x) - self.b ) diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/ScaledFunction.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/ScaledFunction.py deleted file mode 100644 index 046a4a6..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/functions/ScaledFunction.py +++ /dev/null @@ -1,91 +0,0 @@ -# -*- 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 Evangelos Papoutsellis 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.
-from numbers import Number
-import numpy
-
-class ScaledFunction(object):
- '''ScaledFunction
-
- A class to represent the scalar multiplication of an Function with a scalar.
- It holds a function and a scalar. Basically it returns the multiplication
- of the product of the function __call__, convex_conjugate and gradient with the scalar.
- For the rest it behaves like the function it holds.
-
- Args:
- function (Function): a Function or BlockOperator
- scalar (Number): a scalar multiplier
- Example:
- The scaled operator behaves like the following:
-
- '''
- 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):
- '''Evaluates the function at x '''
- return self.scalar * self.function(x)
-
- 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 proximal_conjugate(self, x, tau, out = None):
- '''This returns the proximal operator for the function at x, tau
- '''
- if out is None:
- return self.scalar * self.function.proximal_conjugate(x/self.scalar, tau/self.scalar)
- else:
- out.fill(self.scalar * self.function.proximal_conjugate(x/self.scalar, tau/self.scalar))
-
- def grad(self, x):
- '''Alias of gradient(x,None)'''
- 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):
- '''Alias of proximal(x, tau, None)'''
- 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):
- '''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) )
diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/ZeroFun.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/ZeroFun.py deleted file mode 100644 index 88d9b64..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/functions/ZeroFun.py +++ /dev/null @@ -1,60 +0,0 @@ -# -*- 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 Evangelos Papoutsellis 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 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): - - def __init__(self): - super(ZeroFun, 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 - ''' - - if x.shape[0]==1: - return x.maximum(0).sum() - else: - if isinstance(x, BlockDataContainer): - return x.get_item(0).maximum(0).sum() + x.get_item(1).maximum(0).sum() - else: - return x.maximum(0).sum() + x.maximum(0).sum() - - def proximal(self,x,tau, out=None): - if out is None: - return x.copy() - else: - out.fill(x) - - def proximal_conjugate(self, x, tau): - return 0 - - def domain_geometry(self): - pass - def range_geometry(self): - pass
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/__init__.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/__init__.py deleted file mode 100644 index 2ed36f5..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/functions/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# -*- coding: utf-8 -*- - -from .Function import Function -from .ZeroFun import ZeroFun -from .L1Norm import SimpleL1Norm, L1Norm -#from .L2NormSquared import L2NormSq, SimpleL2NormSq -from .L2NormSquared import L2NormSquared -from .BlockFunction import BlockFunction -from .ScaledFunction import ScaledFunction -from .FunctionOperatorComposition import FunctionOperatorComposition -from .MixedL21Norm import MixedL21Norm -from .IndicatorBox import IndicatorBox -from .Norm2Sq import Norm2sq diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/functions.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/functions.py deleted file mode 100644 index 8632920..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/functions/functions.py +++ /dev/null @@ -1,312 +0,0 @@ -# -*- coding: utf-8 -*- - -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Feb 7 13:10:56 2019 - -@author: evangelos -""" - -import numpy as np -#from ccpi.optimisation.funcs import Function -from ccpi.optimisation.functions import Function -from ccpi.framework import DataContainer, ImageData, ImageGeometry -from operators import CompositeDataContainer, Identity, CompositeOperator -from numbers import Number - - -############################ L2NORM FUNCTIONS ############################# -class SimpleL2NormSq(Function): - - def __init__(self, alpha=1): - - super(SimpleL2NormSq, self).__init__() - self.alpha = alpha - - def __call__(self, x): - return self.alpha * x.power(2).sum() - - def gradient(self,x): - return 2 * self.alpha * x - - def convex_conjugate(self,x): - return (1/4*self.alpha) * x.power(2).sum() - - def proximal(self, x, tau): - return x.divide(1+2*tau*self.alpha) - - def proximal_conjugate(self, x, tau): - return x.divide(1 + tau/2*self.alpha ) - - -class L2NormSq(SimpleL2NormSq): - - def __init__(self, A, b = None, alpha=1, **kwargs): - - super(L2NormSq, self).__init__(alpha=alpha) - self.alpha = alpha - self.A = A - self.b = b - - def __call__(self, x): - - if self.b is None: - return SimpleL2NormSq.__call__(self, self.A.direct(x)) - else: - return SimpleL2NormSq.__call__(self, self.A.direct(x) - self.b) - - def convex_conjugate(self, x): - - ''' The convex conjugate corresponds to the simple functional i.e., - f(x) = alpha * ||x - b||_{2}^{2} - ''' - - if self.b is None: - return SimpleL2NormSq.convex_conjugate(self, x) - else: - return SimpleL2NormSq.convex_conjugate(self, x) + (self.b * x).sum() - - def gradient(self, x): - - if self.b is None: - return 2*self.alpha * self.A.adjoint(self.A.direct(x)) - else: - return 2*self.alpha * self.A.adjoint(self.A.direct(x) - self.b) - - def proximal(self, x, tau): - - ''' The proximal operator corresponds to the simple functional i.e., - f(x) = alpha * ||x - b||_{2}^{2} - - argmin_x { 0.5||x - u||^{2} + tau f(x) } - ''' - - if self.b is None: - return SimpleL2NormSq.proximal(self, x, tau) - else: - return self.b + SimpleL2NormSq.proximal(self, x - self.b , tau) - - - def proximal_conjugate(self, x, tau): - - ''' The proximal operator corresponds to the simple convex conjugate - functional i.e., f^{*}(x^{) - argmin_x { 0.5||x - u||^{2} + tau f(x) } - ''' - if self.b is None: - return SimpleL2NormSq.proximal_conjugate(self, x, tau) - else: - return SimpleL2NormSq.proximal_conjugate(self, x - tau * self.b, tau) - - -############################ L1NORM FUNCTIONS ############################# -class SimpleL1Norm(Function): - - def __init__(self, alpha=1): - - super(SimpleL1Norm, self).__init__() - self.alpha = alpha - - def __call__(self, x): - return self.alpha * x.abs().sum() - - def gradient(self,x): - return ValueError('Not Differentiable') - - def convex_conjugate(self,x): - return 0 - - def proximal(self, x, tau): - ''' Soft Threshold''' - return x.sign() * (x.abs() - tau * self.alpha).maximum(1.0) - - def proximal_conjugate(self, x, tau): - return x.divide((x.abs()/self.alpha).maximum(1.0)) - -class L1Norm(SimpleL1Norm): - - def __init__(self, A, b = None, alpha=1, **kwargs): - - super(L1Norm, self).__init__() - self.alpha = alpha - self.A = A - self.b = b - - def __call__(self, x): - - if self.b is None: - return SimpleL1Norm.__call__(self, self.A.direct(x)) - else: - return SimpleL1Norm.__call__(self, self.A.direct(x) - self.b) - - def gradient(self, x): - return ValueError('Not Differentiable') - - def convex_conjugate(self,x): - if self.b is None: - return SimpleL1Norm.convex_conjugate(self, x) - else: - return SimpleL1Norm.convex_conjugate(self, x) + (self.b * x).sum() - - def proximal(self, x, tau): - - if self.b is None: - return SimpleL1Norm.proximal(self, x, tau) - else: - return self.b + SimpleL1Norm.proximal(self, x + self.b , tau) - - def proximal_conjugate(self, x, tau): - - if self.b is None: - return SimpleL1Norm.proximal_conjugate(self, x, tau) - else: - return SimpleL1Norm.proximal_conjugate(self, x - tau*self.b, tau) - - -############################ mixed_L1,2NORM FUNCTIONS ############################# -class mixed_L12Norm(Function): - - def __init__(self, A, b=None, alpha=1, **kwargs): - - super(mixed_L12Norm, self).__init__() - self.alpha = alpha - self.A = A - self.b = b - - self.sym_grad = kwargs.get('sym_grad',False) - - - - def gradient(self,x): - return ValueError('Not Differentiable') - - - def __call__(self,x): - - y = self.A.direct(x) - eucl_norm = ImageData(y.power(2).sum(axis=0)).sqrt() - eucl_norm.__isub__(self.b) - return eucl_norm.sum() * self.alpha - - def convex_conjugate(self,x): - return 0 - - def proximal_conjugate(self, x, tau): - - if self.b is None: - - if self.sym_grad: - tmp2 = np.sqrt(x.as_array()[0]**2 + x.as_array()[1]**2 + 2*x.as_array()[2]**2)/self.alpha - res = x.divide(ImageData(tmp2).maximum(1.0)) - else: - res = x.divide((ImageData(x.power(2).sum(axis=0)).sqrt()/self.alpha).maximum(1.0)) - - else: - res = (x - tau*self.b)/ ((x - tau*self.b)).abs().maximum(1.0) - - return res - - -#%% - -class ZeroFun(Function): - - def __init__(self): - super(ZeroFun, 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 - ''' - return x.maximum(0).sum() - - def proximal(self,x,tau): - return x.copy() - - def proximal_conjugate(self, x, tau): - return 0 - - -class CompositeFunction(Function): - - def __init__(self, *args): - self.functions = args - self.length = len(self.functions) - - def get_item(self, ind): - return self.functions[ind] - - def __call__(self,x): - - t = 0 - for i in range(self.length): - for j in range(x.shape[0]): - t +=self.functions[i](x.get_item(j)) - return t - - def convex_conjugate(self, x): - - z = 0 - t = 0 - for i in range(x.shape[0]): - t += self.functions[z].convex_conjugate(x.get_item(i)) - z += 1 - - return t - - def proximal_conjugate(self, x, tau, out = None): - - if isinstance(tau, Number): - tau = CompositeDataContainer(tau) - out = [None]*self.length - for i in range(self.length): - out[i] = self.functions[i].proximal(x.get_item(i), tau.get_item(0)) - return CompositeDataContainer(*out) - - - def proximal_conjugate(self, x, tau, out = None): - - if isinstance(tau, Number): - tau = CompositeDataContainer(tau) - out = [None]*self.length - for i in range(self.length): - out[i] = self.functions[i].proximal_conjugate(x.get_item(i), tau.get_item(0)) - return CompositeDataContainer(*out) - - - - -if __name__ == '__main__': - - N = 3 - ig = (N,N) - ag = ig - op1 = Gradient(ig) - op2 = Identity(ig, ag) - - # Form Composite Operator - operator = CompositeOperator((2,1), op1, op2 ) - - # Create functions - alpha = 1 - noisy_data = ImageData(np.random.randint(10, size=ag)) - f = CompositeFunction(L1Norm(op1,alpha), \ - L2NormSq(op2, noisy_data, c = 0.5, memopt = False) ) - - u = ImageData(np.random.randint(10, size=ig)) - uComp = CompositeDataContainer(u) - - print(f(uComp)) # This is f(Kx) = f1(K1*u) + f2(K2*u) - - f1 = L1Norm(op1,alpha) - f2 = L2NormSq(op2, noisy_data, c = 0.5, memopt = False) - - print(f1(u) + f2(u)) - - - diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/mixed_L12Norm.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/mixed_L12Norm.py deleted file mode 100644 index ffeb32e..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/functions/mixed_L12Norm.py +++ /dev/null @@ -1,56 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Wed Mar 6 19:43:12 2019 - -@author: evangelos -""" - -import numpy as np -#from ccpi.optimisation.funcs import Function -from ccpi.optimisation.functions import Function -from ccpi.framework import DataContainer, ImageData, ImageGeometry - -############################ mixed_L1,2NORM FUNCTIONS ############################# -class mixed_L12Norm(Function): - - def __init__(self, alpha, **kwargs): - - super(mixed_L12Norm, self).__init__() - - self.alpha = alpha - self.b = kwargs.get('b',None) - self.sym_grad = kwargs.get('sym_grad',False) - - def __call__(self,x): - - if self.b is None: - tmp1 = x - else: - tmp1 = x - self.b -# - if self.sym_grad: - tmp = np.sqrt(tmp1.as_array()[0]**2 + tmp1.as_array()[1]**2 + 2*tmp1.as_array()[2]**2) - else: - tmp = ImageData(tmp1.power(2).sum(axis=0)).sqrt() - - return self.alpha*tmp.sum() - - def gradient(self,x): - return ValueError('Not Differentiable') - - def convex_conjugate(self,x): - return 0 - - def proximal(self, x, tau): - pass - - def proximal_conjugate(self, x, tau): - - if self.sym_grad: - tmp2 = np.sqrt(x.as_array()[0]**2 + x.as_array()[1]**2 + 2*x.as_array()[2]**2)/self.alpha - res = x.divide(ImageData(tmp2).maximum(1.0)) - else: - res = x.divide((ImageData(x.power(2).sum(axis=0)).sqrt()/self.alpha).maximum(1.0)) - - return res diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/BlockOperator.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/BlockOperator.py deleted file mode 100644 index ee8f609..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/operators/BlockOperator.py +++ /dev/null @@ -1,223 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Thu Feb 14 12:36:40 2019 - -@author: ofn77899 -""" -#from ccpi.optimisation.ops import Operator -import numpy -from numbers import Number -import functools -from ccpi.framework import AcquisitionData, ImageData, BlockDataContainer -from ccpi.optimisation.operators import Operator, LinearOperator -from ccpi.optimisation.operators.BlockScaledOperator import BlockScaledOperator -from ccpi.framework import BlockGeometry - -class BlockOperator(Operator): - '''A Block matrix containing Operators - - The Block Framework is a generic strategy to treat variational problems in the - following form: - - .. math:: - - min Regulariser + Fidelity - - - BlockOperators have a generic shape M x N, and when applied on an - Nx1 BlockDataContainer, will yield and Mx1 BlockDataContainer. - Notice: BlockDatacontainer are only allowed to have the shape of N x 1, with - N rows and 1 column. - - User may specify the shape of the block, by default is a row vector - - Operators in a Block are required to have the same domain column-wise and the - same range row-wise. - ''' - __array_priority__ = 1 - def __init__(self, *args, **kwargs): - ''' - Class creator - - Note: - Do not include the `self` parameter in the ``Args`` section. - - Args: - :param: vararg (Operator): Operators in the block. - :param: shape (:obj:`tuple`, optional): If shape is passed the Operators in - vararg are considered input in a row-by-row fashion. - Shape and number of Operators must match. - - Example: - BlockOperator(op0,op1) results in a row block - BlockOperator(op0,op1,shape=(1,2)) results in a column block - ''' - self.operators = args - shape = kwargs.get('shape', None) - if shape is None: - shape = (len(args),1) - self.shape = shape - n_elements = functools.reduce(lambda x,y: x*y, shape, 1) - if len(args) != n_elements: - raise ValueError( - 'Dimension and size do not match: expected {} got {}' - .format(n_elements,len(args))) - # test if operators are compatible - if not self.column_wise_compatible(): - raise ValueError('Operators in each column must have the same domain') - if not self.row_wise_compatible(): - raise ValueError('Operators in each row must have the same range') - - def column_wise_compatible(self): - '''Operators in a Block should have the same domain per column''' - rows, cols = self.shape - compatible = True - for col in range(cols): - column_compatible = True - for row in range(1,rows): - dg0 = self.get_item(row-1,col).domain_geometry() - dg1 = self.get_item(row,col).domain_geometry() - column_compatible = dg0.__dict__ == dg1.__dict__ and column_compatible - compatible = compatible and column_compatible - return compatible - - def row_wise_compatible(self): - '''Operators in a Block should have the same range per row''' - rows, cols = self.shape - compatible = True - for row in range(rows): - row_compatible = True - for col in range(1,cols): - dg0 = self.get_item(row,col-1).range_geometry() - dg1 = self.get_item(row,col).range_geometry() - row_compatible = dg0.__dict__ == dg1.__dict__ and row_compatible - compatible = compatible and row_compatible - return compatible - - def get_item(self, row, col): - '''returns the Operator at specified row and col''' - if row > self.shape[0]: - raise ValueError('Requested row {} > max {}'.format(row, self.shape[0])) - if col > self.shape[1]: - raise ValueError('Requested col {} > max {}'.format(col, self.shape[1])) - - index = row*self.shape[1]+col - return self.operators[index] - - def norm(self): - norm = [op.norm()**2 for op in self.operators] - return numpy.sqrt(sum(norm)) - - 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 = [] - 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) - - 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 functools.reduce(lambda x, y: x and y.is_linear(), self.operators, True): - 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) - res = [] - for row in range(self.shape[1]): - for col in range(self.shape[0]): - if col == 0: - prod = self.get_item(row, col).adjoint(x_b.get_item(col)) - else: - prod += self.get_item(row, col).adjoint(x_b.get_item(col)) - res.append(prod) - if self.shape[1]==1: - return ImageData(*res) - else: - return BlockDataContainer(*res, shape=shape) - - def get_output_shape(self, xshape, adjoint=False): - sshape = self.shape[1] - oshape = self.shape[0] - if adjoint: - sshape = self.shape[0] - oshape = self.shape[1] - if sshape != xshape[0]: - raise ValueError('Incompatible shapes {} {}'.format(self.shape, xshape)) - return (oshape, xshape[-1]) - - def __rmul__(self, scalar): - '''Defines the left multiplication with a scalar - - Args: scalar (number or iterable containing numbers): - - Returns: a block operator with Scaled Operators inside''' - if isinstance (scalar, list) or isinstance(scalar, tuple) or \ - isinstance(scalar, numpy.ndarray): - if len(scalar) != len(self.operators): - raise ValueError('dimensions of scalars and operators do not match') - scalars = scalar - else: - scalars = [scalar for _ in self.operators] - # create a list of ScaledOperator-s - ops = [ v * op for v,op in zip(scalars, self.operators)] - #return BlockScaledOperator(self, scalars ,shape=self.shape) - return type(self)(*ops, shape=self.shape) - @property - def T(self): - '''Return the transposed of self - - input in a row-by-row''' - newshape = (self.shape[1], self.shape[0]) - oplist = [] - for col in range(newshape[1]): - for row in range(newshape[0]): - oplist.append(self.get_item(col,row)) - return type(self)(*oplist, shape=newshape) - - def domain_geometry(self): - '''returns the domain of the BlockOperator - - If the shape of the BlockOperator is (N,1) the domain is a ImageGeometry or AcquisitionGeometry. - Otherwise it is a BlockGeometry. - ''' - if self.shape[1] == 1: - # column BlockOperator - return self.get_item(0,0).domain_geometry() - else: - shape = (self.shape[0], 1) - return BlockGeometry(*[el.domain_geometry() for el in self.operators], - shape=shape) - - def range_geometry(self): - '''returns the range of the BlockOperator''' - shape = (self.shape[1], 1) - return BlockGeometry(*[el.range_geometry() for el in self.operators], - shape=shape) -if __name__ == '__main__': - pass diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/BlockScaledOperator.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/BlockScaledOperator.py deleted file mode 100644 index aeb6c53..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/operators/BlockScaledOperator.py +++ /dev/null @@ -1,67 +0,0 @@ -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: - 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, 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): - return numpy.abs(self.scalar) * self.operator.norm() - 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''' - #print ("transpose before" , self.shape) - #shape = (self.shape[1], self.shape[0]) - ##self.shape = shape - ##self.operator.shape = shape - #print ("transpose" , shape) - #return self - return type(self)(self.operator.T, self.scalar)
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/FiniteDifferenceOperator.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/FiniteDifferenceOperator.py deleted file mode 100644 index 24c4e4b..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/operators/FiniteDifferenceOperator.py +++ /dev/null @@ -1,322 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Fri Mar 1 22:51:17 2019 - -@author: evangelos -""" - -from ccpi.optimisation.operators import Operator -from ccpi.optimisation.ops import PowerMethodNonsquare -from ccpi.framework import ImageData, BlockDataContainer -import numpy as np - -class FiniteDiff(Operator): - - # Works for Neum/Symmetric & periodic boundary conditions - # TODO add central differences??? - # TODO not very well optimised, too many conditions - # TODO add discretisation step, should get that from imageGeometry - - # Grad_order = ['channels', 'direction_z', 'direction_y', 'direction_x'] - # Grad_order = ['channels', 'direction_y', 'direction_x'] - # Grad_order = ['direction_z', 'direction_y', 'direction_x'] - # Grad_order = ['channels', 'direction_z', 'direction_y', 'direction_x'] - - def __init__(self, gm_domain, gm_range=None, direction=0, bnd_cond = 'Neumann'): - '''''' - super(FiniteDiff, self).__init__() - '''FIXME: domain and range should be geometries''' - self.gm_domain = gm_domain - self.gm_range = gm_range - self.direction = direction - self.bnd_cond = bnd_cond - - # Domain Geometry = Range Geometry if not stated - if self.gm_range is None: - self.gm_range = self.gm_domain - # check direction and "length" of geometry - if self.direction + 1 > len(self.gm_domain.shape): - raise ValueError('Gradient directions more than geometry domain') - - #self.voxel_size = kwargs.get('voxel_size',1) - # this wrongly assumes a homogeneous voxel size - self.voxel_size = self.gm_domain.voxel_size_x - - - def direct(self, x, out=None): - - x_asarr = x.as_array() - x_sz = len(x.shape) - - if out is None: - out = np.zeros(x.shape) - - fd_arr = out - - ######################## Direct for 2D ############################### - if x_sz == 2: - - if self.direction == 1: - - np.subtract( x_asarr[:,1:], x_asarr[:,0:-1], out = fd_arr[:,0:-1] ) - - if self.bnd_cond == 'Neumann': - pass - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[:,0], x_asarr[:,-1], out = fd_arr[:,-1] ) - else: - raise ValueError('No valid boundary conditions') - - if self.direction == 0: - - np.subtract( x_asarr[1:], x_asarr[0:-1], out = fd_arr[0:-1,:] ) - - if self.bnd_cond == 'Neumann': - pass - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[0,:], x_asarr[-1,:], out = fd_arr[-1,:] ) - else: - raise ValueError('No valid boundary conditions') - - ######################## Direct for 3D ############################### - elif x_sz == 3: - - if self.direction == 0: - - np.subtract( x_asarr[1:,:,:], x_asarr[0:-1,:,:], out = fd_arr[0:-1,:,:] ) - - if self.bnd_cond == 'Neumann': - pass - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[0,:,:], x_asarr[-1,:,:], out = fd_arr[-1,:,:] ) - else: - raise ValueError('No valid boundary conditions') - - if self.direction == 1: - - np.subtract( x_asarr[:,1:,:], x_asarr[:,0:-1,:], out = fd_arr[:,0:-1,:] ) - - if self.bnd_cond == 'Neumann': - pass - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[:,0,:], x_asarr[:,-1,:], out = fd_arr[:,-1,:] ) - else: - raise ValueError('No valid boundary conditions') - - - if self.direction == 2: - - np.subtract( x_asarr[:,:,1:], x_asarr[:,:,0:-1], out = fd_arr[:,:,0:-1] ) - - if self.bnd_cond == 'Neumann': - pass - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[:,:,0], x_asarr[:,:,-1], out = fd_arr[:,:,-1] ) - else: - raise ValueError('No valid boundary conditions') - - ######################## Direct for 4D ############################### - elif x_sz == 4: - - if self.direction == 0: - np.subtract( x_asarr[1:,:,:,:], x_asarr[0:-1,:,:,:], out = fd_arr[0:-1,:,:,:] ) - - if self.bnd_cond == 'Neumann': - pass - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[0,:,:,:], x_asarr[-1,:,:,:], out = fd_arr[-1,:,:,:] ) - else: - raise ValueError('No valid boundary conditions') - - if self.direction == 1: - np.subtract( x_asarr[:,1:,:,:], x_asarr[:,0:-1,:,:], out = fd_arr[:,0:-1,:,:] ) - - if self.bnd_cond == 'Neumann': - pass - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[:,0,:,:], x_asarr[:,-1,:,:], out = fd_arr[:,-1,:,:] ) - else: - raise ValueError('No valid boundary conditions') - - if self.direction == 2: - np.subtract( x_asarr[:,:,1:,:], x_asarr[:,:,0:-1,:], out = fd_arr[:,:,0:-1,:] ) - - if self.bnd_cond == 'Neumann': - pass - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[:,:,0,:], x_asarr[:,:,-1,:], out = fd_arr[:,:,-1,:] ) - else: - raise ValueError('No valid boundary conditions') - - if self.direction == 3: - np.subtract( x_asarr[:,:,:,1:], x_asarr[:,:,:,0:-1], out = fd_arr[:,:,:,0:-1] ) - - if self.bnd_cond == 'Neumann': - pass - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[:,:,:,0], x_asarr[:,:,:,-1], out = fd_arr[:,:,:,-1] ) - else: - raise ValueError('No valid boundary conditions') - - else: - raise NotImplementedError - - res = out/self.voxel_size - return type(x)(res) - - def adjoint(self, x, out=None): - - x_asarr = x.as_array() - #x_asarr = x - x_sz = len(x.shape) - - if out is None: - out = np.zeros(x.shape) - - fd_arr = out - - ######################## Adjoint for 2D ############################### - if x_sz == 2: - - if self.direction == 1: - - np.subtract( x_asarr[:,1:], x_asarr[:,0:-1], out = fd_arr[:,1:] ) - - if self.bnd_cond == 'Neumann': - np.subtract( x_asarr[:,0], 0, out = fd_arr[:,0] ) - np.subtract( -x_asarr[:,-2], 0, out = fd_arr[:,-1] ) - - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[:,0], x_asarr[:,-1], out = fd_arr[:,0] ) - - else: - raise ValueError('No valid boundary conditions') - - if self.direction == 0: - - np.subtract( x_asarr[1:,:], x_asarr[0:-1,:], out = fd_arr[1:,:] ) - - if self.bnd_cond == 'Neumann': - np.subtract( x_asarr[0,:], 0, out = fd_arr[0,:] ) - np.subtract( -x_asarr[-2,:], 0, out = fd_arr[-1,:] ) - - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[0,:], x_asarr[-1,:], out = fd_arr[0,:] ) - - else: - raise ValueError('No valid boundary conditions') - - ######################## Adjoint for 3D ############################### - elif x_sz == 3: - - if self.direction == 0: - - np.subtract( x_asarr[1:,:,:], x_asarr[0:-1,:,:], out = fd_arr[1:,:,:] ) - - if self.bnd_cond == 'Neumann': - np.subtract( x_asarr[0,:,:], 0, out = fd_arr[0,:,:] ) - np.subtract( -x_asarr[-2,:,:], 0, out = fd_arr[-1,:,:] ) - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[0,:,:], x_asarr[-1,:,:], out = fd_arr[0,:,:] ) - else: - raise ValueError('No valid boundary conditions') - - if self.direction == 1: - np.subtract( x_asarr[:,1:,:], x_asarr[:,0:-1,:], out = fd_arr[:,1:,:] ) - - if self.bnd_cond == 'Neumann': - np.subtract( x_asarr[:,0,:], 0, out = fd_arr[:,0,:] ) - np.subtract( -x_asarr[:,-2,:], 0, out = fd_arr[:,-1,:] ) - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[:,0,:], x_asarr[:,-1,:], out = fd_arr[:,0,:] ) - else: - raise ValueError('No valid boundary conditions') - - if self.direction == 2: - np.subtract( x_asarr[:,:,1:], x_asarr[:,:,0:-1], out = fd_arr[:,:,1:] ) - - if self.bnd_cond == 'Neumann': - np.subtract( x_asarr[:,:,0], 0, out = fd_arr[:,:,0] ) - np.subtract( -x_asarr[:,:,-2], 0, out = fd_arr[:,:,-1] ) - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[:,:,0], x_asarr[:,:,-1], out = fd_arr[:,:,0] ) - else: - raise ValueError('No valid boundary conditions') - - ######################## Adjoint for 4D ############################### - elif x_sz == 4: - - if self.direction == 0: - np.subtract( x_asarr[1:,:,:,:], x_asarr[0:-1,:,:,:], out = fd_arr[1:,:,:,:] ) - - if self.bnd_cond == 'Neumann': - np.subtract( x_asarr[0,:,:,:], 0, out = fd_arr[0,:,:,:] ) - np.subtract( -x_asarr[-2,:,:,:], 0, out = fd_arr[-1,:,:,:] ) - - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[0,:,:,:], x_asarr[-1,:,:,:], out = fd_arr[0,:,:,:] ) - else: - raise ValueError('No valid boundary conditions') - - if self.direction == 1: - np.subtract( x_asarr[:,1:,:,:], x_asarr[:,0:-1,:,:], out = fd_arr[:,1:,:,:] ) - - if self.bnd_cond == 'Neumann': - np.subtract( x_asarr[:,0,:,:], 0, out = fd_arr[:,0,:,:] ) - np.subtract( -x_asarr[:,-2,:,:], 0, out = fd_arr[:,-1,:,:] ) - - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[:,0,:,:], x_asarr[:,-1,:,:], out = fd_arr[:,0,:,:] ) - else: - raise ValueError('No valid boundary conditions') - - - if self.direction == 2: - np.subtract( x_asarr[:,:,1:,:], x_asarr[:,:,0:-1,:], out = fd_arr[:,:,1:,:] ) - - if self.bnd_cond == 'Neumann': - np.subtract( x_asarr[:,:,0,:], 0, out = fd_arr[:,:,0,:] ) - np.subtract( -x_asarr[:,:,-2,:], 0, out = fd_arr[:,:,-1,:] ) - - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[:,:,0,:], x_asarr[:,:,-1,:], out = fd_arr[:,:,0,:] ) - else: - raise ValueError('No valid boundary conditions') - - if self.direction == 3: - np.subtract( x_asarr[:,:,:,1:], x_asarr[:,:,:,0:-1], out = fd_arr[:,:,:,1:] ) - - if self.bnd_cond == 'Neumann': - np.subtract( x_asarr[:,:,:,0], 0, out = fd_arr[:,:,:,0] ) - np.subtract( -x_asarr[:,:,:,-2], 0, out = fd_arr[:,:,:,-1] ) - - elif self.bnd_cond == 'Periodic': - np.subtract( x_asarr[:,:,:,0], x_asarr[:,:,:,-1], out = fd_arr[:,:,:,0] ) - else: - raise ValueError('No valid boundary conditions') - - else: - raise NotImplementedError - - res = out/self.voxel_size - return type(x)(-res) - - def range_geometry(self): - '''Returns the range geometry''' - return self.gm_range - - def domain_geometry(self): - '''Returns the domain geometry''' - return self.gm_domain - - def norm(self): - x0 = self.gm_domain.allocate() - x0.fill( np.random.random_sample(x0.shape) ) - self.s1, sall, svec = PowerMethodNonsquare(self, 25, x0) - return self.s1 - - - - -
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/GradientOperator.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/GradientOperator.py deleted file mode 100644 index e00de0c..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/operators/GradientOperator.py +++ /dev/null @@ -1,186 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Fri Mar 1 22:50:04 2019 - -@author: evangelos -""" - -from ccpi.optimisation.operators import Operator, LinearOperator, ScaledOperator -from ccpi.optimisation.ops import PowerMethodNonsquare -from ccpi.framework import ImageData, ImageGeometry, BlockGeometry, BlockDataContainer -import numpy -from ccpi.optimisation.operators import FiniteDiff, SparseFiniteDiff - -#%% - -class Gradient(LinearOperator): - - def __init__(self, gm_domain, bnd_cond = 'Neumann', **kwargs): - - super(Gradient, self).__init__() - - self.gm_domain = gm_domain # Domain of Grad Operator - - self.correlation = kwargs.get('correlation','Space') - - if self.correlation=='Space': - if self.gm_domain.channels>1: - self.gm_range = BlockGeometry(*[self.gm_domain for _ in range(self.gm_domain.length-1)] ) - self.ind = numpy.arange(1,self.gm_domain.length) - else: - self.gm_range = BlockGeometry(*[self.gm_domain for _ in range(self.gm_domain.length) ] ) - self.ind = numpy.arange(self.gm_domain.length) - elif self.correlation=='SpaceChannels': - if self.gm_domain.channels>1: - self.gm_range = BlockGeometry(*[self.gm_domain for _ in range(self.gm_domain.length)]) - self.ind = range(self.gm_domain.length) - else: - raise ValueError('No channels to correlate') - - self.bnd_cond = bnd_cond - - - def direct(self, x, out=None): - - tmp = self.gm_range.allocate() - - for i in range(tmp.shape[0]): - tmp.get_item(i).fill(FiniteDiff(self.gm_domain, direction = self.ind[i], bnd_cond = self.bnd_cond).direct(x)) - return tmp - - def adjoint(self, x, out=None): - - tmp = self.gm_domain.allocate() - for i in range(x.shape[0]): - tmp+=FiniteDiff(self.gm_domain, direction = self.ind[i], bnd_cond = self.bnd_cond).adjoint(x.get_item(i)) - return tmp - - - def domain_geometry(self): - return self.gm_domain - - def range_geometry(self): - return self.gm_range - - def norm(self): - - x0 = self.gm_domain.allocate('random') - self.s1, sall, svec = PowerMethodNonsquare(self, 10, x0) - return self.s1 - - def __rmul__(self, scalar): - return ScaledOperator(self, scalar) - - - def matrix(self): - - tmp = self.gm_range.allocate() - - mat = [] - for i in range(tmp.shape[0]): - - spMat = SparseFiniteDiff(self.gm_domain, direction=self.ind[i], bnd_cond=self.bnd_cond) - mat.append(spMat.matrix()) - - return BlockDataContainer(*mat) - - - def sum_abs_row(self): - - tmp = self.gm_range.allocate() - res = self.gm_domain.allocate() - for i in range(tmp.shape[0]): - spMat = SparseFiniteDiff(self.gm_domain, direction=self.ind[i], bnd_cond=self.bnd_cond) - res += spMat.sum_abs_row() - return res - - def sum_abs_col(self): - - tmp = self.gm_range.allocate() - res = [] - for i in range(tmp.shape[0]): - spMat = SparseFiniteDiff(self.gm_domain, direction=self.ind[i], bnd_cond=self.bnd_cond) - res.append(spMat.sum_abs_col()) - return BlockDataContainer(*res) - - -if __name__ == '__main__': - - - from ccpi.optimisation.operators import Identity, BlockOperator - - M, N = 2, 3 - ig = ImageGeometry(M, N) - arr = ig.allocate('random_int' ) - - # check direct of Gradient and sparse matrix - G = Gradient(ig) - G_sp = G.matrix() - - res1 = G.direct(arr) - res1y = numpy.reshape(G_sp[0].toarray().dot(arr.as_array().flatten('F')), ig.shape, 'F') - - print(res1[0].as_array()) - print(res1y) - - res1x = numpy.reshape(G_sp[1].toarray().dot(arr.as_array().flatten('F')), ig.shape, 'F') - - print(res1[1].as_array()) - print(res1x) - - #check sum abs row - conc_spmat = numpy.abs(numpy.concatenate( (G_sp[0].toarray(), G_sp[1].toarray() ))) - print(numpy.reshape(conc_spmat.sum(axis=0), ig.shape, 'F')) - print(G.sum_abs_row().as_array()) - - print(numpy.reshape(conc_spmat.sum(axis=1), ((2,) + ig.shape), 'F')) - - print(G.sum_abs_col()[0].as_array()) - print(G.sum_abs_col()[1].as_array()) - - # Check Blockoperator sum abs col and row - - op1 = Gradient(ig) - op2 = Identity(ig) - - B = BlockOperator( op1, op2) - - Brow = B.sum_abs_row() - Bcol = B.sum_abs_col() - - concB = numpy.concatenate( (numpy.abs(numpy.concatenate( (G_sp[0].toarray(), G_sp[1].toarray() ))), op2.matrix().toarray())) - - print(numpy.reshape(concB.sum(axis=0), ig.shape, 'F')) - print(Brow.as_array()) - - print(numpy.reshape(concB.sum(axis=1)[0:12], ((2,) + ig.shape), 'F')) - print(Bcol[1].as_array()) - - -# print(numpy.concatene(G_sp[0].toarray()+ )) -# print(G_sp[1].toarray()) -# -# d1 = G.sum_abs_row() -# print(d1.as_array()) -# -# d2 = G_neum.sum_abs_col() -## print(d2) -# -# -# ########################################################### - a = BlockDataContainer( BlockDataContainer(arr, arr), arr) - b = BlockDataContainer( BlockDataContainer(arr+5, arr+3), arr+2) - c = a/b - - print(c[0][0].as_array(), (arr/(arr+5)).as_array()) - print(c[0][1].as_array(), (arr/(arr+3)).as_array()) - print(c[1].as_array(), (arr/(arr+2)).as_array()) - - - a1 = BlockDataContainer( arr, BlockDataContainer(arr, arr)) -# -# c1 = arr + a -# c2 = arr + a -# c2 = a1 + arr -# diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/IdentityOperator.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/IdentityOperator.py deleted file mode 100644 index a58a296..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/operators/IdentityOperator.py +++ /dev/null @@ -1,79 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Wed Mar 6 19:30:51 2019 - -@author: evangelos -""" - -from ccpi.optimisation.operators import LinearOperator -import scipy.sparse as sp -import numpy as np -from ccpi.framework import ImageData - - -class Identity(LinearOperator): - - def __init__(self, gm_domain, gm_range=None): - - self.gm_domain = gm_domain - self.gm_range = gm_range - if self.gm_range is None: - self.gm_range = self.gm_domain - - super(Identity, self).__init__() - - def direct(self,x,out=None): - if out is None: - return x.copy() - else: - out.fill(x) - - def adjoint(self,x, out=None): - if out is None: - return x.copy() - else: - out.fill(x) - - def norm(self): - return 1.0 - - def domain_geometry(self): - return self.gm_domain - - def range_geometry(self): - return self.gm_range - - def matrix(self): - - return sp.eye(np.prod(self.gm_domain.shape)) - - def sum_abs_row(self): - - return self.gm_domain.allocate(1)#ImageData(np.array(np.reshape(abs(self.matrix()).sum(axis=0), self.gm_domain.shape, 'F'))) - - def sum_abs_col(self): - - return self.gm_domain.allocate(1)#ImageData(np.array(np.reshape(abs(self.matrix()).sum(axis=1), self.gm_domain.shape, 'F'))) - - -if __name__ == '__main__': - - from ccpi.framework import ImageGeometry - - M, N = 2, 3 - ig = ImageGeometry(M, N) - arr = ig.allocate('random_int') - - Id = Identity(ig) - d = Id.matrix() - print(d.toarray()) - - d1 = Id.sum_abs_col() - print(d1.as_array()) - - - - - -
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/LinearOperator.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/LinearOperator.py deleted file mode 100644 index e19304f..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/operators/LinearOperator.py +++ /dev/null @@ -1,22 +0,0 @@ -# -*- coding: utf-8 -*-
-"""
-Created on Tue Mar 5 15:57:52 2019
-
-@author: ofn77899
-"""
-
-from ccpi.optimisation.operators import Operator
-
-
-class LinearOperator(Operator):
- '''A Linear Operator that maps from a space X <-> Y'''
- def __init__(self):
- super(LinearOperator, self).__init__()
- def is_linear(self):
- '''Returns if the operator is linear'''
- return True
- def adjoint(self,x, out=None):
- '''returns the adjoint/inverse operation
-
- only available to linear operators'''
- raise NotImplementedError
diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/Operator.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/Operator.py deleted file mode 100644 index 2d2089b..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/operators/Operator.py +++ /dev/null @@ -1,30 +0,0 @@ -# -*- coding: utf-8 -*-
-"""
-Created on Tue Mar 5 15:55:56 2019
-
-@author: ofn77899
-"""
-from ccpi.optimisation.operators.ScaledOperator import ScaledOperator
-
-class Operator(object):
- '''Operator that maps from a space X -> Y'''
- def is_linear(self):
- '''Returns if the operator is linear'''
- return False
- def direct(self,x, out=None):
- '''Returns the application of the Operator on x'''
- raise NotImplementedError
- def norm(self):
- '''Returns the norm of the Operator'''
- raise NotImplementedError
- def range_geometry(self):
- '''Returns the range of the Operator: Y space'''
- raise NotImplementedError
- def domain_geometry(self):
- '''Returns the domain of the Operator: X space'''
- raise NotImplementedError
- def __rmul__(self, scalar):
- '''Defines the multiplication by a scalar on the left
-
- returns a ScaledOperator'''
- return ScaledOperator(self, scalar)
diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/ScaledOperator.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/ScaledOperator.py deleted file mode 100644 index adcc6d9..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/operators/ScaledOperator.py +++ /dev/null @@ -1,42 +0,0 @@ -from numbers import Number -import numpy - -class ScaledOperator(object): - '''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: - 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, operator, scalar): - super(ScaledOperator, self).__init__() - if not isinstance (scalar, Number): - raise TypeError('expected scalar: got {}'.format(type(scalar))) - self.scalar = scalar - self.operator = operator - def direct(self, x, out=None): - 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): - return numpy.abs(self.scalar) * self.operator.norm() - def range_geometry(self): - return self.operator.range_geometry() - def domain_geometry(self): - return self.operator.domain_geometry() diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/SparseFiniteDiff.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/SparseFiniteDiff.py deleted file mode 100644 index 1b88cba..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/operators/SparseFiniteDiff.py +++ /dev/null @@ -1,144 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Tue Apr 2 14:06:15 2019 - -@author: vaggelis -""" - -import scipy.sparse as sp -import numpy as np -from ccpi.framework import ImageData - -class SparseFiniteDiff(): - - def __init__(self, gm_domain, gm_range=None, direction=0, bnd_cond = 'Neumann'): - - super(SparseFiniteDiff, self).__init__() - self.gm_domain = gm_domain - self.gm_range = gm_range - self.direction = direction - self.bnd_cond = bnd_cond - - if self.gm_range is None: - self.gm_range = self.gm_domain - - self.get_dims = [i for i in gm_domain.shape] - - if self.direction + 1 > len(self.gm_domain.shape): - raise ValueError('Gradient directions more than geometry domain') - - def matrix(self): - - i = self.direction - - mat = sp.spdiags(np.vstack([-np.ones((1,self.get_dims[i])),np.ones((1,self.get_dims[i]))]), [0,1], self.get_dims[i], self.get_dims[i], format = 'lil') - - if self.bnd_cond == 'Neumann': - mat[-1,:] = 0 - elif self.bnd_cond == 'Periodic': - mat[-1,0] = 1 - - tmpGrad = mat if i == 0 else sp.eye(self.get_dims[0]) - - for j in range(1, self.gm_domain.length): - - tmpGrad = sp.kron(mat, tmpGrad ) if j == i else sp.kron(sp.eye(self.get_dims[j]), tmpGrad ) - - return tmpGrad - - def T(self): - return self.matrix().T - - def direct(self, x): - - x_asarr = x.as_array() - res = np.reshape( self.matrix() * x_asarr.flatten('F'), self.gm_domain.shape, 'F') - return type(x)(res) - - def adjoint(self, x): - - x_asarr = x.as_array() - res = np.reshape( self.matrix().T * x_asarr.flatten('F'), self.gm_domain.shape, 'F') - return type(x)(res) - - def sum_abs_row(self): - - res = np.array(np.reshape(abs(self.matrix()).sum(axis=0), self.gm_domain.shape, 'F')) - res[res==0]=1 - return ImageData(res) - - def sum_abs_col(self): - - res = np.array(np.reshape(abs(self.matrix()).sum(axis=1), self.gm_domain.shape, 'C')) - res[res==0]=1 - return ImageData(res) - -if __name__ == '__main__': - - from ccpi.framework import ImageGeometry - from ccpi.optimisation.operators import FiniteDiff - - # 2D - M, N= 2, 3 - ig = ImageGeometry(M, N) - arr = ig.allocate('random_int') - - for i in [0,1]: - - # Neumann - sFD_neum = SparseFiniteDiff(ig, direction=i, bnd_cond='Neumann') - G_neum = FiniteDiff(ig, direction=i, bnd_cond='Neumann') - - # Periodic - sFD_per = SparseFiniteDiff(ig, direction=i, bnd_cond='Periodic') - G_per = FiniteDiff(ig, direction=i, bnd_cond='Periodic') - - u_neum_direct = G_neum.direct(arr) - u_neum_sp_direct = sFD_neum.direct(arr) - np.testing.assert_array_almost_equal(u_neum_direct.as_array(), u_neum_sp_direct.as_array(), decimal=4) - - u_neum_adjoint = G_neum.adjoint(arr) - u_neum_sp_adjoint = sFD_neum.adjoint(arr) - np.testing.assert_array_almost_equal(u_neum_adjoint.as_array(), u_neum_sp_adjoint.as_array(), decimal=4) - - u_per_direct = G_neum.direct(arr) - u_per_sp_direct = sFD_neum.direct(arr) - np.testing.assert_array_almost_equal(u_per_direct.as_array(), u_per_sp_direct.as_array(), decimal=4) - - u_per_adjoint = G_per.adjoint(arr) - u_per_sp_adjoint = sFD_per.adjoint(arr) - np.testing.assert_array_almost_equal(u_per_adjoint.as_array(), u_per_sp_adjoint.as_array(), decimal=4) - - # 3D - M, N, K = 2, 3, 4 - ig3D = ImageGeometry(M, N, K) - arr3D = ig3D.allocate('random_int') - - for i in [0,1,2]: - - # Neumann - sFD_neum3D = SparseFiniteDiff(ig3D, direction=i, bnd_cond='Neumann') - G_neum3D = FiniteDiff(ig3D, direction=i, bnd_cond='Neumann') - - # Periodic - sFD_per3D = SparseFiniteDiff(ig3D, direction=i, bnd_cond='Periodic') - G_per3D = FiniteDiff(ig3D, direction=i, bnd_cond='Periodic') - - u_neum_direct3D = G_neum3D.direct(arr3D) - u_neum_sp_direct3D = sFD_neum3D.direct(arr3D) - np.testing.assert_array_almost_equal(u_neum_direct3D.as_array(), u_neum_sp_direct3D.as_array(), decimal=4) - - u_neum_adjoint3D = G_neum3D.adjoint(arr3D) - u_neum_sp_adjoint3D = sFD_neum3D.adjoint(arr3D) - np.testing.assert_array_almost_equal(u_neum_adjoint3D.as_array(), u_neum_sp_adjoint3D.as_array(), decimal=4) - - u_per_direct3D = G_neum3D.direct(arr3D) - u_per_sp_direct3D = sFD_neum3D.direct(arr3D) - np.testing.assert_array_almost_equal(u_per_direct3D.as_array(), u_per_sp_direct3D.as_array(), decimal=4) - - u_per_adjoint3D = G_per3D.adjoint(arr3D) - u_per_sp_adjoint3D = sFD_per3D.adjoint(arr3D) - np.testing.assert_array_almost_equal(u_per_adjoint3D.as_array(), u_per_sp_adjoint3D.as_array(), decimal=4) - -
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/SymmetrizedGradientOperator.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/SymmetrizedGradientOperator.py deleted file mode 100644 index d908e49..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/operators/SymmetrizedGradientOperator.py +++ /dev/null @@ -1,118 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Fri Mar 1 22:53:55 2019 - -@author: evangelos -""" - -from ccpi.optimisation.operators import Operator -from ccpi.optimisation.operators import FiniteDiff -from ccpi.optimisation.ops import PowerMethodNonsquare -from ccpi.framework import ImageData, DataContainer -import numpy as np - - -class SymmetrizedGradient(Operator): - - def __init__(self, gm_domain, gm_range, bnd_cond = 'Neumann', **kwargs): - - super(SymmetrizedGradient, self).__init__() - - self.gm_domain = gm_domain # Domain of Grad Operator - self.gm_range = gm_range # Range of Grad Operator - self.bnd_cond = bnd_cond # Boundary conditions of Finite Differences - - # Kwargs Default options - self.memopt = kwargs.get('memopt',False) - self.correlation = kwargs.get('correlation','Space') - - #TODO not tested yet, operator norm??? - self.voxel_size = kwargs.get('voxel_size',[1]*len(gm_domain)) - - - def direct(self, x, out=None): - - tmp = np.zeros(self.gm_range) - tmp[0] = FiniteDiff(self.gm_domain[1:], direction = 1, bnd_cond = self.bnd_cond).adjoint(x.as_array()[0]) - tmp[1] = FiniteDiff(self.gm_domain[1:], direction = 0, bnd_cond = self.bnd_cond).adjoint(x.as_array()[1]) - tmp[2] = 0.5 * (FiniteDiff(self.gm_domain[1:], direction = 0, bnd_cond = self.bnd_cond).adjoint(x.as_array()[0]) + - FiniteDiff(self.gm_domain[1:], direction = 1, bnd_cond = self.bnd_cond).adjoint(x.as_array()[1]) ) - - return type(x)(tmp) - - - def adjoint(self, x, out=None): - - tmp = np.zeros(self.gm_domain) - - tmp[0] = FiniteDiff(self.gm_domain[1:], direction = 1, bnd_cond = self.bnd_cond).direct(x.as_array()[0]) + \ - FiniteDiff(self.gm_domain[1:], direction = 0, bnd_cond = self.bnd_cond).direct(x.as_array()[2]) - - tmp[1] = FiniteDiff(self.gm_domain[1:], direction = 1, bnd_cond = self.bnd_cond).direct(x.as_array()[2]) + \ - FiniteDiff(self.gm_domain[1:], direction = 0, bnd_cond = self.bnd_cond).direct(x.as_array()[1]) - - return type(x)(tmp) - - def alloc_domain_dim(self): - return ImageData(np.zeros(self.gm_domain)) - - def alloc_range_dim(self): - return ImageData(np.zeros(self.range_dim)) - - def domain_dim(self): - return self.gm_domain - - def range_dim(self): - return self.gm_range - - def norm(self): -# return np.sqrt(4*len(self.domainDim())) - #TODO this takes time for big ImageData - # for 2D ||grad|| = sqrt(8), 3D ||grad|| = sqrt(12) - x0 = ImageData(np.random.random_sample(self.domain_dim())) - self.s1, sall, svec = PowerMethodNonsquare(self, 25, x0) - return self.s1 - - - -if __name__ == '__main__': - - ########################################################################### - ## Symmetrized Gradient - - N, M = 2, 3 - ig = (N,M) - ig2 = (2,) + ig - ig3 = (3,) + ig - u1 = DataContainer(np.random.randint(10, size=ig2)) - w1 = DataContainer(np.random.randint(10, size=ig3)) - - E = SymmetrizedGradient(ig2,ig3) - - d1 = E.direct(u1) - d2 = E.adjoint(w1) - - LHS = (d1.as_array()[0]*w1.as_array()[0] + \ - d1.as_array()[1]*w1.as_array()[1] + \ - 2*d1.as_array()[2]*w1.as_array()[2]).sum() - - RHS = (u1.as_array()[0]*d2.as_array()[0] + \ - u1.as_array()[1]*d2.as_array()[1]).sum() - - - print(LHS, RHS, E.norm()) - - -# - - - - - - - - - - -
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/ZeroOperator.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/ZeroOperator.py deleted file mode 100644 index a7c5f09..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/operators/ZeroOperator.py +++ /dev/null @@ -1,39 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Wed Mar 6 19:25:53 2019 - -@author: evangelos -""" - -import numpy as np -from ccpi.framework import ImageData -from ccpi.optimisation.operators import Operator - -class ZeroOp(Operator): - - def __init__(self, gm_domain, gm_range): - self.gm_domain = gm_domain - self.gm_range = gm_range - super(ZeroOp, self).__init__() - - def direct(self,x,out=None): - if out is None: - return ImageData(np.zeros(self.gm_range)) - else: - return ImageData(np.zeros(self.gm_range)) - - def adjoint(self,x, out=None): - if out is None: - return ImageData(np.zeros(self.gm_domain)) - else: - return ImageData(np.zeros(self.gm_domain)) - - def norm(self): - return 0 - - def domain_dim(self): - return self.gm_domain - - def range_dim(self): - return self.gm_range
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/__init__.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/__init__.py deleted file mode 100644 index 1c09faf..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/operators/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -# -*- coding: utf-8 -*-
-"""
-Created on Tue Mar 5 15:56:27 2019
-
-@author: ofn77899
-"""
-
-from .Operator import Operator
-from .LinearOperator import LinearOperator
-from .ScaledOperator import ScaledOperator
-from .BlockOperator import BlockOperator
-from .BlockScaledOperator import BlockScaledOperator
-
-
-from .FiniteDifferenceOperator import FiniteDiff
-from .GradientOperator import Gradient
-from .SymmetrizedGradientOperator import SymmetrizedGradient
-from .IdentityOperator import Identity
-from .ZeroOperator import ZeroOp
diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/ops.py b/Wrappers/Python/build/lib/ccpi/optimisation/ops.py deleted file mode 100644 index 6afb97a..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/ops.py +++ /dev/null @@ -1,294 +0,0 @@ -# -*- 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 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 numpy -from scipy.sparse.linalg import svds -from ccpi.framework import DataContainer -from ccpi.framework import AcquisitionData -from ccpi.framework import ImageData -from ccpi.framework import ImageGeometry -from ccpi.framework import AcquisitionGeometry -from numbers import Number -# Maybe operators need to know what types they take as inputs/outputs -# to not just use generic DataContainer - - -class Operator(object): - '''Operator that maps from a space X -> Y''' - def __init__(self, **kwargs): - self.scalar = 1 - def is_linear(self): - '''Returns if the operator is linear''' - return False - def direct(self,x, out=None): - raise NotImplementedError - def size(self): - # To be defined for specific class - raise NotImplementedError - def norm(self): - raise NotImplementedError - def allocate_direct(self): - '''Allocates memory on the Y space''' - raise NotImplementedError - def allocate_adjoint(self): - '''Allocates memory on the X space''' - raise NotImplementedError - def range_geometry(self): - raise NotImplementedError - def domain_geometry(self): - raise NotImplementedError - def __rmul__(self, other): - '''reverse multiplication of Operator with number sets the variable scalar in the Operator''' - assert isinstance(other, Number) - self.scalar = other - return self - -class LinearOperator(Operator): - '''Operator that maps from a space X -> Y''' - def is_linear(self): - '''Returns if the operator is linear''' - return True - def adjoint(self,x, out=None): - raise NotImplementedError - -class Identity(Operator): - def __init__(self): - self.s1 = 1.0 - self.L = 1 - super(Identity, self).__init__() - - def direct(self,x,out=None): - if out is None: - return x.copy() - else: - out.fill(x) - - def adjoint(self,x, out=None): - if out is None: - return x.copy() - else: - out.fill(x) - - def size(self): - return NotImplemented - - def get_max_sing_val(self): - return self.s1 - -class TomoIdentity(Operator): - def __init__(self, geometry, **kwargs): - super(TomoIdentity, self).__init__() - self.s1 = 1.0 - self.geometry = geometry - - def is_linear(self): - return True - def direct(self,x,out=None): - - if out is None: - if self.scalar != 1: - return x * self.scalar - return x.copy() - else: - if self.scalar != 1: - out.fill(x * self.scalar) - return - out.fill(x) - return - - def adjoint(self,x, out=None): - return self.direct(x, out) - - def size(self): - return NotImplemented - - def get_max_sing_val(self): - return self.s1 - def allocate_direct(self): - if issubclass(type(self.geometry), ImageGeometry): - return ImageData(geometry=self.geometry) - elif issubclass(type(self.geometry), AcquisitionGeometry): - return AcquisitionData(geometry=self.geometry) - else: - raise ValueError("Wrong geometry type: expected ImageGeometry of AcquisitionGeometry, got ", type(self.geometry)) - def allocate_adjoint(self): - return self.allocate_direct() - def range_geometry(self): - return self.geometry - def domain_geometry(self): - return self.geometry - - - -class FiniteDiff2D(Operator): - def __init__(self): - self.s1 = 8.0 - super(FiniteDiff2D, self).__init__() - - def direct(self,x, out=None): - '''Forward differences with Neumann BC.''' - # FIXME this seems to be working only with numpy arrays - - d1 = numpy.zeros_like(x.as_array()) - d1[:,:-1] = x.as_array()[:,1:] - x.as_array()[:,:-1] - d2 = numpy.zeros_like(x.as_array()) - d2[:-1,:] = x.as_array()[1:,:] - x.as_array()[:-1,:] - d = numpy.stack((d1,d2),0) - #x.geometry.voxel_num_z = 2 - return type(x)(d,False,geometry=x.geometry) - - def adjoint(self,x, out=None): - '''Backward differences, Neumann BC.''' - Nrows = x.get_dimension_size('horizontal_x') - Ncols = x.get_dimension_size('horizontal_y') - Nchannels = 1 - if len(x.shape) == 4: - Nchannels = x.get_dimension_size('channel') - zer = numpy.zeros((Nrows,1)) - xxx = x.as_array()[0,:,:-1] - # - h = numpy.concatenate((zer,xxx), 1) - h -= numpy.concatenate((xxx,zer), 1) - - zer = numpy.zeros((1,Ncols)) - xxx = x.as_array()[1,:-1,:] - # - v = numpy.concatenate((zer,xxx), 0) - v -= numpy.concatenate((xxx,zer), 0) - return type(x)(h + v, False, geometry=x.geometry) - - def size(self): - return NotImplemented - - def get_max_sing_val(self): - return self.s1 - -def PowerMethodNonsquareOld(op,numiters): - # Initialise random - # Jakob's - #inputsize = op.size()[1] - #x0 = ImageContainer(numpy.random.randn(*inputsize) - # Edo's - #vg = ImageGeometry(voxel_num_x=inputsize[0], - # voxel_num_y=inputsize[1], - # voxel_num_z=inputsize[2]) - # - #x0 = ImageData(geometry = vg, dimension_labels=['vertical','horizontal_y','horizontal_x']) - #print (x0) - #x0.fill(numpy.random.randn(*x0.shape)) - - x0 = op.create_image_data() - - s = numpy.zeros(numiters) - # Loop - for it in numpy.arange(numiters): - x1 = op.adjoint(op.direct(x0)) - x1norm = numpy.sqrt((x1**2).sum()) - #print ("x0 **********" ,x0) - #print ("x1 **********" ,x1) - s[it] = (x1*x0).sum() / (x0*x0).sum() - x0 = (1.0/x1norm)*x1 - return numpy.sqrt(s[-1]), numpy.sqrt(s), x0 - -#def PowerMethod(op,numiters): -# # Initialise random -# x0 = np.random.randn(400) -# s = np.zeros(numiters) -# # Loop -# for it in np.arange(numiters): -# x1 = np.dot(op.transpose(),np.dot(op,x0)) -# x1norm = np.sqrt(np.sum(np.dot(x1,x1))) -# s[it] = np.dot(x1,x0) / np.dot(x1,x0) -# x0 = (1.0/x1norm)*x1 -# return s, x0 - - -def PowerMethodNonsquare(op,numiters , x0=None): - # Initialise random - # Jakob's - # inputsize , outputsize = op.size() - #x0 = ImageContainer(numpy.random.randn(*inputsize) - # Edo's - #vg = ImageGeometry(voxel_num_x=inputsize[0], - # voxel_num_y=inputsize[1], - # voxel_num_z=inputsize[2]) - # - #x0 = ImageData(geometry = vg, dimension_labels=['vertical','horizontal_y','horizontal_x']) - #print (x0) - #x0.fill(numpy.random.randn(*x0.shape)) - - if x0 is None: - #x0 = op.create_image_data() - x0 = op.allocate_direct() - x0.fill(numpy.random.randn(*x0.shape)) - - s = numpy.zeros(numiters) - # Loop - for it in numpy.arange(numiters): - x1 = op.adjoint(op.direct(x0)) - #x1norm = numpy.sqrt((x1*x1).sum()) - x1norm = x1.norm() - #print ("x0 **********" ,x0) - #print ("x1 **********" ,x1) - s[it] = (x1*x0).sum() / (x0.squared_norm()) - x0 = (1.0/x1norm)*x1 - return numpy.sqrt(s[-1]), numpy.sqrt(s), x0 - -class LinearOperatorMatrix(Operator): - def __init__(self,A): - self.A = A - self.s1 = None # Largest singular value, initially unknown - super(LinearOperatorMatrix, self).__init__() - - def direct(self,x, out=None): - if out is None: - return type(x)(numpy.dot(self.A,x.as_array())) - else: - numpy.dot(self.A, x.as_array(), out=out.as_array()) - - - def adjoint(self,x, out=None): - if out is None: - return type(x)(numpy.dot(self.A.transpose(),x.as_array())) - else: - numpy.dot(self.A.transpose(),x.as_array(), out=out.as_array()) - - - def size(self): - return self.A.shape - - def get_max_sing_val(self): - # If unknown, compute and store. If known, simply return it. - if self.s1 is None: - self.s1 = svds(self.A,1,return_singular_vectors=False)[0] - return self.s1 - else: - return self.s1 - def allocate_direct(self): - '''allocates the memory to hold the result of adjoint''' - #numpy.dot(self.A.transpose(),x.as_array()) - M_A, N_A = self.A.shape - out = numpy.zeros((N_A,1)) - return DataContainer(out) - def allocate_adjoint(self): - '''allocate the memory to hold the result of direct''' - #numpy.dot(self.A.transpose(),x.as_array()) - M_A, N_A = self.A.shape - out = numpy.zeros((M_A,1)) - return DataContainer(out) diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/spdhg.py b/Wrappers/Python/build/lib/ccpi/optimisation/spdhg.py deleted file mode 100644 index 263a7cd..0000000 --- a/Wrappers/Python/build/lib/ccpi/optimisation/spdhg.py +++ /dev/null @@ -1,338 +0,0 @@ -# Copyright 2018 Matthias Ehrhardt, 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.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
-import numpy
-
-from ccpi.optimisation.funcs import Function
-from ccpi.framework import ImageData
-from ccpi.framework import AcquisitionData
-
-
-class spdhg():
- """Computes a saddle point with a stochastic PDHG.
-
- This means, a solution (x*, y*), y* = (y*_1, ..., y*_n) such that
-
- (x*, y*) in arg min_x max_y sum_i=1^n <y_i, A_i> - f*[i](y_i) + g(x)
-
- where g : X -> IR_infty and f[i] : Y[i] -> IR_infty are convex, l.s.c. and
- proper functionals. For this algorithm, they all may be non-smooth and no
- strong convexity is assumed.
-
- Parameters
- ----------
- f : list of functions
- Functionals Y[i] -> IR_infty that all have a convex conjugate with a
- proximal operator, i.e.
- f[i].convex_conj.prox(sigma[i]) : Y[i] -> Y[i].
- g : function
- Functional X -> IR_infty that has a proximal operator, i.e.
- g.prox(tau) : X -> X.
- A : list of functions
- Operators A[i] : X -> Y[i] that possess adjoints: A[i].adjoint
- x : primal variable, optional
- By default equals 0.
- y : dual variable, optional
- Part of a product space. By default equals 0.
- z : variable, optional
- Adjoint of dual variable, z = A^* y. By default equals 0 if y = 0.
- tau : scalar / vector / matrix, optional
- Step size for primal variable. Note that the proximal operator of g
- has to be well-defined for this input.
- sigma : scalar, optional
- Scalar / vector / matrix used as step size for dual variable. Note that
- the proximal operator related to f (see above) has to be well-defined
- for this input.
- prob : list of scalars, optional
- Probabilities prob[i] that a subset i is selected in each iteration.
- If fun_select is not given, then the sum of all probabilities must
- equal 1.
- A_norms : list of scalars, optional
- Norms of the operators in A. Can be used to determine the step sizes
- tau and sigma and the probabilities prob.
- fun_select : function, optional
- Function that selects blocks at every iteration IN -> {1,...,n}. By
- default this is serial sampling, fun_select(k) selects an index
- i \in {1,...,n} with probability prob[i].
-
- References
- ----------
- [CERS2018] A. Chambolle, M. J. Ehrhardt, P. Richtarik and C.-B. Schoenlieb,
- *Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling
- and Imaging Applications*. SIAM Journal on Optimization, 28(4), 2783-2808
- (2018) http://doi.org/10.1007/s10851-010-0251-1
-
- [E+2017] M. J. Ehrhardt, P. J. Markiewicz, P. Richtarik, J. Schott,
- A. Chambolle and C.-B. Schoenlieb, *Faster PET reconstruction with a
- stochastic primal-dual hybrid gradient method*. Wavelets and Sparsity XVII,
- 58 (2017) http://doi.org/10.1117/12.2272946.
-
- [EMS2018] M. J. Ehrhardt, P. J. Markiewicz and C.-B. Schoenlieb, *Faster
- PET Reconstruction with Non-Smooth Priors by Randomization and
- Preconditioning*. (2018) ArXiv: http://arxiv.org/abs/1808.07150
- """
-
- def __init__(self, f, g, A, x=None, y=None, z=None, tau=None, sigma=None,
- prob=None, A_norms=None, fun_select=None):
- # fun_select is optional and by default performs serial sampling
-
- if x is None:
- x = A[0].allocate_direct(0)
-
- if y is None:
- if z is not None:
- raise ValueError('y and z have to be defaulted together')
-
- y = [Ai.allocate_adjoint(0) for Ai in A]
- z = 0 * x.copy()
-
- else:
- if z is None:
- raise ValueError('y and z have to be defaulted together')
-
- if A_norms is not None:
- if tau is not None or sigma is not None or prob is not None:
- raise ValueError('Either A_norms or (tau, sigma, prob) must '
- 'be given')
-
- tau = 1 / sum(A_norms)
- sigma = [1 / nA for nA in A_norms]
- prob = [nA / sum(A_norms) for nA in A_norms]
-
- #uniform prob, needs different sigma and tau
- #n = len(A)
- #prob = [1./n] * n
-
- if fun_select is None:
- if prob is None:
- raise ValueError('prob was not determined')
-
- def fun_select(k):
- return [int(numpy.random.choice(len(A), 1, p=prob))]
-
- self.iter = 0
- self.x = x
-
- self.y = y
- self.z = z
-
- self.f = f
- self.g = g
- self.A = A
- self.tau = tau
- self.sigma = sigma
- self.prob = prob
- self.fun_select = fun_select
-
- # Initialize variables
- self.z_relax = z.copy()
- self.tmp = self.x.copy()
-
- def update(self):
- # select block
- selected = self.fun_select(self.iter)
-
- # update primal variable
- #tmp = (self.x - self.tau * self.z_relax).as_array()
- #self.x.fill(self.g.prox(tmp, self.tau))
- self.tmp = - self.tau * self.z_relax
- self.tmp += self.x
- self.x = self.g.prox(self.tmp, self.tau)
-
- # update dual variable and z, z_relax
- self.z_relax = self.z.copy()
- for i in selected:
- # save old yi
- y_old = self.y[i].copy()
-
- # y[i]= prox(tmp)
- tmp = y_old + self.sigma[i] * self.A[i].direct(self.x)
- self.y[i] = self.f[i].convex_conj.prox(tmp, self.sigma[i])
-
- # update adjoint of dual variable
- dz = self.A[i].adjoint(self.y[i] - y_old)
- self.z += dz
-
- # compute extrapolation
- self.z_relax += (1 + 1 / self.prob[i]) * dz
-
- self.iter += 1
-
-
-## Functions
-
-class KullbackLeibler(Function):
- def __init__(self, data, background):
- self.data = data
- self.background = background
- self.__offset = None
-
- def __call__(self, x):
- """Return the KL-diveregnce in the point ``x``.
-
- If any components of ``x`` is non-positive, the value is positive
- infinity.
-
- Needs one extra array of memory of the size of `prior`.
- """
-
- # define short variable names
- y = self.data
- r = self.background
-
- # Compute
- # sum(x + r - y + y * log(y / (x + r)))
- # = sum(x - y * log(x + r)) + self.offset
- # Assume that
- # x + r > 0
-
- # sum the result up
- obj = numpy.sum(x - y * numpy.log(x + r)) + self.offset()
-
- if numpy.isnan(obj):
- # In this case, some element was less than or equal to zero
- return numpy.inf
- else:
- return obj
-
- @property
- def convex_conj(self):
- """The convex conjugate functional of the KL-functional."""
- return KullbackLeiblerConvexConjugate(self.data, self.background)
-
- def offset(self):
- """The offset which is independent of the unknown."""
-
- if self.__offset is None:
- tmp = self.domain.element()
-
- # define short variable names
- y = self.data
- r = self.background
-
- tmp = self.domain.element(numpy.maximum(y, 1))
- tmp = r - y + y * numpy.log(tmp)
-
- # sum the result up
- self.__offset = numpy.sum(tmp)
-
- return self.__offset
-
-# def __repr__(self):
-# """to be added???"""
-# """Return ``repr(self)``."""
- # return '{}({!r}, {!r}, {!r})'.format(self.__class__.__name__,
- ## self.domain, self.data,
- # self.background)
-
-
-class KullbackLeiblerConvexConjugate(Function):
- """The convex conjugate of Kullback-Leibler divergence functional.
-
- Notes
- -----
- The functional :math:`F^*` with prior :math:`g>0` is given by:
-
- .. math::
- F^*(x)
- =
- \\begin{cases}
- \\sum_{i} \left( -g_i \ln(1 - x_i) \\right)
- & \\text{if } x_i < 1 \\forall i
- \\\\
- +\\infty & \\text{else}
- \\end{cases}
-
- See Also
- --------
- KullbackLeibler : convex conjugate functional
- """
-
- def __init__(self, data, background):
- self.data = data
- self.background = background
-
- def __call__(self, x):
- y = self.data
- r = self.background
-
- tmp = numpy.sum(- x * r - y * numpy.log(1 - x))
-
- if numpy.isnan(tmp):
- # In this case, some element was larger than or equal to one
- return numpy.inf
- else:
- return tmp
-
-
- def prox(self, x, tau, out=None):
- # Let y = data, r = background, z = x + tau * r
- # Compute 0.5 * (z + 1 - sqrt((z - 1)**2 + 4 * tau * y))
- # Currently it needs 3 extra copies of memory.
-
- if out is None:
- out = x.copy()
-
- # define short variable names
- try: # this should be standard SIRF/CIL mode
- y = self.data.as_array()
- r = self.background.as_array()
- x = x.as_array()
-
- try:
- taua = tau.as_array()
- except:
- taua = tau
-
- z = x + tau * r
-
- out.fill(0.5 * (z + 1 - numpy.sqrt((z - 1) ** 2 + 4 * taua * y)))
-
- return out
-
- except: # e.g. for NumPy
- y = self.data
- r = self.background
-
- try:
- taua = tau.as_array()
- except:
- taua = tau
-
- z = x + tau * r
-
- out[:] = 0.5 * (z + 1 - numpy.sqrt((z - 1) ** 2 + 4 * taua * y))
-
- return out
-
- @property
- def convex_conj(self):
- return KullbackLeibler(self.data, self.background)
-
-
-def mult(x, y):
- try:
- xa = x.as_array()
- except:
- xa = x
-
- out = y.clone()
- out.fill(xa * y.as_array())
-
- return out
diff --git a/Wrappers/Python/build/lib/ccpi/processors.py b/Wrappers/Python/build/lib/ccpi/processors.py deleted file mode 100644 index ccef410..0000000 --- a/Wrappers/Python/build/lib/ccpi/processors.py +++ /dev/null @@ -1,514 +0,0 @@ -# -*- 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 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 - -from ccpi.framework import DataProcessor, DataContainer, AcquisitionData,\ - AcquisitionGeometry, ImageGeometry, ImageData -from ccpi.reconstruction.parallelbeam import alg as pbalg -import numpy -from scipy import ndimage - -import matplotlib.pyplot as plt - - -class Normalizer(DataProcessor): - '''Normalization based on flat and dark - - This processor read in a AcquisitionData and normalises it based on - the instrument reading with and without incident photons or neutrons. - - Input: AcquisitionData - Parameter: 2D projection with flat field (or stack) - 2D projection with dark field (or stack) - Output: AcquisitionDataSetn - ''' - - def __init__(self, flat_field = None, dark_field = None, tolerance = 1e-5): - kwargs = { - 'flat_field' : flat_field, - 'dark_field' : dark_field, - # very small number. Used when there is a division by zero - 'tolerance' : tolerance - } - - #DataProcessor.__init__(self, **kwargs) - super(Normalizer, self).__init__(**kwargs) - if not flat_field is None: - self.set_flat_field(flat_field) - if not dark_field is None: - self.set_dark_field(dark_field) - - def check_input(self, dataset): - if dataset.number_of_dimensions == 3 or\ - dataset.number_of_dimensions == 2: - return True - else: - raise ValueError("Expected input dimensions is 2 or 3, got {0}"\ - .format(dataset.number_of_dimensions)) - - def set_dark_field(self, df): - if type(df) is numpy.ndarray: - if len(numpy.shape(df)) == 3: - raise ValueError('Dark Field should be 2D') - elif len(numpy.shape(df)) == 2: - self.dark_field = df - elif issubclass(type(df), DataContainer): - self.dark_field = self.set_dark_field(df.as_array()) - - def set_flat_field(self, df): - if type(df) is numpy.ndarray: - if len(numpy.shape(df)) == 3: - raise ValueError('Flat Field should be 2D') - elif len(numpy.shape(df)) == 2: - self.flat_field = df - elif issubclass(type(df), DataContainer): - self.flat_field = self.set_flat_field(df.as_array()) - - @staticmethod - def normalize_projection(projection, flat, dark, tolerance): - a = (projection - dark) - b = (flat-dark) - with numpy.errstate(divide='ignore', invalid='ignore'): - c = numpy.true_divide( a, b ) - c[ ~ numpy.isfinite( c )] = tolerance # set to not zero if 0/0 - return c - - @staticmethod - def estimate_normalised_error(projection, flat, dark, delta_flat, delta_dark): - '''returns the estimated relative error of the normalised projection - - n = (projection - dark) / (flat - dark) - Dn/n = (flat-dark + projection-dark)/((flat-dark)*(projection-dark))*(Df/f + Dd/d) - ''' - a = (projection - dark) - b = (flat-dark) - df = delta_flat / flat - dd = delta_dark / dark - rel_norm_error = (b + a) / (b * a) * (df + dd) - return rel_norm_error - - def process(self, out=None): - - projections = self.get_input() - dark = self.dark_field - flat = self.flat_field - - if projections.number_of_dimensions == 3: - if not (projections.shape[1:] == dark.shape and \ - projections.shape[1:] == flat.shape): - raise ValueError('Flats/Dark and projections size do not match.') - - - a = numpy.asarray( - [ Normalizer.normalize_projection( - projection, flat, dark, self.tolerance) \ - for projection in projections.as_array() ] - ) - elif projections.number_of_dimensions == 2: - a = Normalizer.normalize_projection(projections.as_array(), - flat, dark, self.tolerance) - y = type(projections)( a , True, - dimension_labels=projections.dimension_labels, - geometry=projections.geometry) - return y - - -class CenterOfRotationFinder(DataProcessor): - '''Processor to find the center of rotation in a parallel beam experiment - - This processor read in a AcquisitionDataSet and finds the center of rotation - based on Nghia Vo's method. https://doi.org/10.1364/OE.22.019078 - - Input: AcquisitionDataSet - - Output: float. center of rotation in pixel coordinate - ''' - - def __init__(self): - kwargs = { - - } - - #DataProcessor.__init__(self, **kwargs) - super(CenterOfRotationFinder, self).__init__(**kwargs) - - def check_input(self, dataset): - if dataset.number_of_dimensions == 3: - if dataset.geometry.geom_type == 'parallel': - return True - else: - raise ValueError('{0} is suitable only for parallel beam geometry'\ - .format(self.__class__.__name__)) - else: - raise ValueError("Expected input dimensions is 3, got {0}"\ - .format(dataset.number_of_dimensions)) - - - # ######################################################################### - # Copyright (c) 2015, UChicago Argonne, LLC. All rights reserved. # - # # - # Copyright 2015. UChicago Argonne, LLC. This software was produced # - # under U.S. Government contract DE-AC02-06CH11357 for Argonne National # - # Laboratory (ANL), which is operated by UChicago Argonne, LLC for the # - # U.S. Department of Energy. The U.S. Government has rights to use, # - # reproduce, and distribute this software. NEITHER THE GOVERNMENT NOR # - # UChicago Argonne, LLC MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR # - # ASSUMES ANY LIABILITY FOR THE USE OF THIS SOFTWARE. If software is # - # modified to produce derivative works, such modified software should # - # be clearly marked, so as not to confuse it with the version available # - # from ANL. # - # # - # Additionally, redistribution and use in source and binary forms, with # - # or without modification, are permitted provided that the following # - # conditions are met: # - # # - # * Redistributions of source code must retain the above copyright # - # notice, this list of conditions and the following disclaimer. # - # # - # * Redistributions in binary form must reproduce the above copyright # - # notice, this list of conditions and the following disclaimer in # - # the documentation and/or other materials provided with the # - # distribution. # - # # - # * Neither the name of UChicago Argonne, LLC, Argonne National # - # Laboratory, ANL, the U.S. Government, nor the names of its # - # contributors may be used to endorse or promote products derived # - # from this software without specific prior written permission. # - # # - # THIS SOFTWARE IS PROVIDED BY UChicago Argonne, LLC AND CONTRIBUTORS # - # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # - # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS # - # FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL UChicago # - # Argonne, LLC OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, # - # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, # - # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # - # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # - # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT # - # LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN # - # ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # - # POSSIBILITY OF SUCH DAMAGE. # - # ######################################################################### - - @staticmethod - def as_ndarray(arr, dtype=None, copy=False): - if not isinstance(arr, numpy.ndarray): - arr = numpy.array(arr, dtype=dtype, copy=copy) - return arr - - @staticmethod - def as_dtype(arr, dtype, copy=False): - if not arr.dtype == dtype: - arr = numpy.array(arr, dtype=dtype, copy=copy) - return arr - - @staticmethod - def as_float32(arr): - arr = CenterOfRotationFinder.as_ndarray(arr, numpy.float32) - return CenterOfRotationFinder.as_dtype(arr, numpy.float32) - - - - - @staticmethod - def find_center_vo(tomo, ind=None, smin=-40, smax=40, srad=10, step=0.5, - ratio=2., drop=20): - """ - Find rotation axis location using Nghia Vo's method. :cite:`Vo:14`. - - Parameters - ---------- - tomo : ndarray - 3D tomographic data. - ind : int, optional - Index of the slice to be used for reconstruction. - smin, smax : int, optional - Reference to the horizontal center of the sinogram. - srad : float, optional - Fine search radius. - step : float, optional - Step of fine searching. - ratio : float, optional - The ratio between the FOV of the camera and the size of object. - It's used to generate the mask. - drop : int, optional - Drop lines around vertical center of the mask. - - Returns - ------- - float - Rotation axis location. - - Notes - ----- - The function may not yield a correct estimate, if: - - - the sample size is bigger than the field of view of the camera. - In this case the ``ratio`` argument need to be set larger - than the default of 2.0. - - - there is distortion in the imaging hardware. If there's - no correction applied, the center of the projection image may - yield a better estimate. - - - the sample contrast is weak. Paganin's filter need to be applied - to overcome this. - - - the sample was changed during the scan. - """ - tomo = CenterOfRotationFinder.as_float32(tomo) - - if ind is None: - ind = tomo.shape[1] // 2 - _tomo = tomo[:, ind, :] - - - - # Reduce noise by smooth filters. Use different filters for coarse and fine search - _tomo_cs = ndimage.filters.gaussian_filter(_tomo, (3, 1)) - _tomo_fs = ndimage.filters.median_filter(_tomo, (2, 2)) - - # Coarse and fine searches for finding the rotation center. - if _tomo.shape[0] * _tomo.shape[1] > 4e6: # If data is large (>2kx2k) - #_tomo_coarse = downsample(numpy.expand_dims(_tomo_cs,1), level=2)[:, 0, :] - #init_cen = _search_coarse(_tomo_coarse, smin, smax, ratio, drop) - #fine_cen = _search_fine(_tomo_fs, srad, step, init_cen*4, ratio, drop) - init_cen = CenterOfRotationFinder._search_coarse(_tomo_cs, smin, - smax, ratio, drop) - fine_cen = CenterOfRotationFinder._search_fine(_tomo_fs, srad, - step, init_cen, - ratio, drop) - else: - init_cen = CenterOfRotationFinder._search_coarse(_tomo_cs, - smin, smax, - ratio, drop) - fine_cen = CenterOfRotationFinder._search_fine(_tomo_fs, srad, - step, init_cen, - ratio, drop) - - #logger.debug('Rotation center search finished: %i', fine_cen) - return fine_cen - - - @staticmethod - def _search_coarse(sino, smin, smax, ratio, drop): - """ - Coarse search for finding the rotation center. - """ - (Nrow, Ncol) = sino.shape - centerfliplr = (Ncol - 1.0) / 2.0 - - # Copy the sinogram and flip left right, the purpose is to - # make a full [0;2Pi] sinogram - _copy_sino = numpy.fliplr(sino[1:]) - - # This image is used for compensating the shift of sinogram 2 - temp_img = numpy.zeros((Nrow - 1, Ncol), dtype='float32') - temp_img[:] = sino[-1] - - # Start coarse search in which the shift step is 1 - listshift = numpy.arange(smin, smax + 1) - listmetric = numpy.zeros(len(listshift), dtype='float32') - mask = CenterOfRotationFinder._create_mask(2 * Nrow - 1, Ncol, - 0.5 * ratio * Ncol, drop) - for i in listshift: - _sino = numpy.roll(_copy_sino, i, axis=1) - if i >= 0: - _sino[:, 0:i] = temp_img[:, 0:i] - else: - _sino[:, i:] = temp_img[:, i:] - listmetric[i - smin] = numpy.sum(numpy.abs(numpy.fft.fftshift( - #pyfftw.interfaces.numpy_fft.fft2( - # numpy.vstack((sino, _sino))) - numpy.fft.fft2(numpy.vstack((sino, _sino))) - )) * mask) - minpos = numpy.argmin(listmetric) - return centerfliplr + listshift[minpos] / 2.0 - - @staticmethod - def _search_fine(sino, srad, step, init_cen, ratio, drop): - """ - Fine search for finding the rotation center. - """ - Nrow, Ncol = sino.shape - centerfliplr = (Ncol + 1.0) / 2.0 - 1.0 - # Use to shift the sinogram 2 to the raw CoR. - shiftsino = numpy.int16(2 * (init_cen - centerfliplr)) - _copy_sino = numpy.roll(numpy.fliplr(sino[1:]), shiftsino, axis=1) - if init_cen <= centerfliplr: - lefttake = numpy.int16(numpy.ceil(srad + 1)) - righttake = numpy.int16(numpy.floor(2 * init_cen - srad - 1)) - else: - lefttake = numpy.int16(numpy.ceil( - init_cen - (Ncol - 1 - init_cen) + srad + 1)) - righttake = numpy.int16(numpy.floor(Ncol - 1 - srad - 1)) - Ncol1 = righttake - lefttake + 1 - mask = CenterOfRotationFinder._create_mask(2 * Nrow - 1, Ncol1, - 0.5 * ratio * Ncol, drop) - numshift = numpy.int16((2 * srad) / step) + 1 - listshift = numpy.linspace(-srad, srad, num=numshift) - listmetric = numpy.zeros(len(listshift), dtype='float32') - factor1 = numpy.mean(sino[-1, lefttake:righttake]) - num1 = 0 - for i in listshift: - _sino = ndimage.interpolation.shift( - _copy_sino, (0, i), prefilter=False) - factor2 = numpy.mean(_sino[0,lefttake:righttake]) - _sino = _sino * factor1 / factor2 - sinojoin = numpy.vstack((sino, _sino)) - listmetric[num1] = numpy.sum(numpy.abs(numpy.fft.fftshift( - #pyfftw.interfaces.numpy_fft.fft2( - # sinojoin[:, lefttake:righttake + 1]) - numpy.fft.fft2(sinojoin[:, lefttake:righttake + 1]) - )) * mask) - num1 = num1 + 1 - minpos = numpy.argmin(listmetric) - return init_cen + listshift[minpos] / 2.0 - - @staticmethod - def _create_mask(nrow, ncol, radius, drop): - du = 1.0 / ncol - dv = (nrow - 1.0) / (nrow * 2.0 * numpy.pi) - centerrow = numpy.ceil(nrow / 2) - 1 - centercol = numpy.ceil(ncol / 2) - 1 - # added by Edoardo Pasca - centerrow = int(centerrow) - centercol = int(centercol) - mask = numpy.zeros((nrow, ncol), dtype='float32') - for i in range(nrow): - num1 = numpy.round(((i - centerrow) * dv / radius) / du) - (p1, p2) = numpy.int16(numpy.clip(numpy.sort( - (-num1 + centercol, num1 + centercol)), 0, ncol - 1)) - mask[i, p1:p2 + 1] = numpy.ones(p2 - p1 + 1, dtype='float32') - if drop < centerrow: - mask[centerrow - drop:centerrow + drop + 1, - :] = numpy.zeros((2 * drop + 1, ncol), dtype='float32') - mask[:,centercol-1:centercol+2] = numpy.zeros((nrow, 3), dtype='float32') - return mask - - def process(self, out=None): - - projections = self.get_input() - - cor = CenterOfRotationFinder.find_center_vo(projections.as_array()) - - return cor - - -class AcquisitionDataPadder(DataProcessor): - '''Normalization based on flat and dark - - This processor read in a AcquisitionData and normalises it based on - the instrument reading with and without incident photons or neutrons. - - Input: AcquisitionData - Parameter: 2D projection with flat field (or stack) - 2D projection with dark field (or stack) - Output: AcquisitionDataSetn - ''' - - def __init__(self, - center_of_rotation = None, - acquisition_geometry = None, - pad_value = 1e-5): - kwargs = { - 'acquisition_geometry' : acquisition_geometry, - 'center_of_rotation' : center_of_rotation, - 'pad_value' : pad_value - } - - super(AcquisitionDataPadder, self).__init__(**kwargs) - - def check_input(self, dataset): - if self.acquisition_geometry is None: - self.acquisition_geometry = dataset.geometry - if dataset.number_of_dimensions == 3: - return True - else: - raise ValueError("Expected input dimensions is 2 or 3, got {0}"\ - .format(dataset.number_of_dimensions)) - - def process(self, out=None): - projections = self.get_input() - w = projections.get_dimension_size('horizontal') - delta = w - 2 * self.center_of_rotation - - padded_width = int ( - numpy.ceil(abs(delta)) + w - ) - delta_pix = padded_width - w - - voxel_per_pixel = 1 - geom = pbalg.pb_setup_geometry_from_acquisition(projections.as_array(), - self.acquisition_geometry.angles, - self.center_of_rotation, - voxel_per_pixel ) - - padded_geometry = self.acquisition_geometry.clone() - - padded_geometry.pixel_num_h = geom['n_h'] - padded_geometry.pixel_num_v = geom['n_v'] - - delta_pix_h = padded_geometry.pixel_num_h - self.acquisition_geometry.pixel_num_h - delta_pix_v = padded_geometry.pixel_num_v - self.acquisition_geometry.pixel_num_v - - if delta_pix_h == 0: - delta_pix_h = delta_pix - padded_geometry.pixel_num_h = padded_width - #initialize a new AcquisitionData with values close to 0 - out = AcquisitionData(geometry=padded_geometry) - out = out + self.pad_value - - - #pad in the horizontal-vertical plane -> slice on angles - if delta > 0: - #pad left of middle - command = "out.array[" - for i in range(out.number_of_dimensions): - if out.dimension_labels[i] == 'horizontal': - value = '{0}:{1}'.format(delta_pix_h, delta_pix_h+w) - command = command + str(value) - else: - if out.dimension_labels[i] == 'vertical' : - value = '{0}:'.format(delta_pix_v) - command = command + str(value) - else: - command = command + ":" - if i < out.number_of_dimensions -1: - command = command + ',' - command = command + '] = projections.array' - #print (command) - else: - #pad right of middle - command = "out.array[" - for i in range(out.number_of_dimensions): - if out.dimension_labels[i] == 'horizontal': - value = '{0}:{1}'.format(0, w) - command = command + str(value) - else: - if out.dimension_labels[i] == 'vertical' : - value = '{0}:'.format(delta_pix_v) - command = command + str(value) - else: - command = command + ":" - if i < out.number_of_dimensions -1: - command = command + ',' - command = command + '] = projections.array' - #print (command) - #cleaned = eval(command) - exec(command) - return out
\ No newline at end of file diff --git a/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py b/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py index e9bd801..5bf96cc 100644 --- a/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py +++ b/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py @@ -8,15 +8,11 @@ Created on Mon Feb 4 16:18:06 2019 from ccpi.optimisation.algorithms import Algorithm from ccpi.framework import ImageData import numpy as np -import matplotlib.pyplot as plt import time from ccpi.optimisation.operators import BlockOperator from ccpi.framework import BlockDataContainer from ccpi.optimisation.functions import FunctionOperatorComposition - -import matplotlib.pyplot as plt - class PDHG(Algorithm): '''Primal Dual Hybrid Gradient''' diff --git a/Wrappers/Python/ccpi/optimisation/operators/BlockOperator.py b/Wrappers/Python/ccpi/optimisation/operators/BlockOperator.py index c6a7f95..1d77510 100755 --- a/Wrappers/Python/ccpi/optimisation/operators/BlockOperator.py +++ b/Wrappers/Python/ccpi/optimisation/operators/BlockOperator.py @@ -139,11 +139,16 @@ class BlockOperator(Operator): 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=tmp.get_item(col)) + self.get_item(row,col).direct( + x_b.get_item(col), + out=out.get_item(row)) else: - self.get_item(row,col).direct(x_b.get_item(col), out=out) - out+=tmp - + 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 adjoint(self, x, out=None): '''Adjoint operation for the BlockOperator @@ -156,36 +161,72 @@ class BlockOperator(Operator): Raises: ValueError if the contained Operators are not linear ''' - if not functools.reduce(lambda x, y: x and y.is_linear(), self.operators, True): + 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) - res = [] - for row in range(self.shape[1]): - for col in range(self.shape[0]): - if col == 0: - prod = self.get_item(row, col).adjoint(x_b.get_item(col)) - else: - prod += self.get_item(row, col).adjoint(x_b.get_item(col)) - res.append(prod) - if self.shape[1]==1: - return ImageData(*res) + 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: + return ImageData(*res) + else: + return BlockDataContainer(*res, shape=shape) else: - return BlockDataContainer(*res, shape=shape) - + #tmp = self.domain_geometry().allocate() + + for col in range(self.shape[1]): + for row in range(self.shape[0]): + if row == 0: + if issubclass(out.__class__, DataContainer): + 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): + 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 is_linear(self): + '''returns whether all the elements of the BlockOperator are linear''' + return functools.reduce(lambda x, y: x and y.is_linear(), self.operators, True) + def get_output_shape(self, xshape, adjoint=False): - sshape = self.shape[1] - oshape = self.shape[0] + '''returns the shape of the output BlockDataContainer + + A(N,M) direct u(M,1) -> N,1 + A(N,M)^T adjoint u(N,1) -> M,1 + ''' + rows , cols = self.shape + xrows, xcols = xshape + if xcols != 1: + raise ValueError('BlockDataContainer cannot have more than 1 column') if adjoint: - sshape = self.shape[0] - oshape = self.shape[1] - if sshape != xshape[0]: - raise ValueError('Incompatible shapes {} {}'.format(self.shape, xshape)) - return (oshape, xshape[-1]) - + if rows != xrows: + raise ValueError('Incompatible shapes {} {}'.format(self.shape, xshape)) + return (cols,xcols) + if cols != xrows: + raise ValueError('Incompatible shapes {} {}'.format((rows,cols), xshape)) + return (rows,xcols) + def __rmul__(self, scalar): '''Defines the left multiplication with a scalar diff --git a/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py b/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py index 54456cc..9c639df 100644 --- a/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py +++ b/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py @@ -71,7 +71,7 @@ class Gradient(LinearOperator): self.FD.direction=self.ind[i] self.FD.adjoint(x.get_item(i), out = tmp) # FiniteDiff(self.gm_domain, direction = self.ind[i], bnd_cond = self.bnd_cond).adjoint(x.get_item(i), out=tmp) - out-=tmp + out+=tmp else: tmp = self.gm_domain.allocate() for i in range(x.shape[0]): diff --git a/Wrappers/Python/ccpi/optimisation/operators/ScaledOperator.py b/Wrappers/Python/ccpi/optimisation/operators/ScaledOperator.py index adcc6d9..3203dde 100644 --- a/Wrappers/Python/ccpi/optimisation/operators/ScaledOperator.py +++ b/Wrappers/Python/ccpi/optimisation/operators/ScaledOperator.py @@ -28,10 +28,18 @@ class ScaledOperator(object): self.scalar = scalar self.operator = operator def direct(self, x, out=None): - return self.scalar * self.operator.direct(x, out=out) + if out is None: + return self.scalar * self.operator.direct(x, out=out) + else: + self.operator.direct(x, out=out) + out *= self.scalar def adjoint(self, x, out=None): if self.operator.is_linear(): - return self.scalar * self.operator.adjoint(x, out=out) + if out is None: + return self.scalar * self.operator.adjoint(x, out=out) + else: + self.operator.adjoint(x, out=out) + out *= self.scalar else: raise TypeError('Operator is not linear') def norm(self): @@ -40,3 +48,5 @@ class ScaledOperator(object): return self.operator.range_geometry() def domain_geometry(self): return self.operator.domain_geometry() + def is_linear(self): + return self.operator.is_linear() diff --git a/Wrappers/Python/test/test_Operator.py b/Wrappers/Python/test/test_Operator.py index 46e8c7c..6656d34 100644 --- a/Wrappers/Python/test/test_Operator.py +++ b/Wrappers/Python/test/test_Operator.py @@ -1,8 +1,15 @@ import unittest #from ccpi.optimisation.operators import Operator from ccpi.optimisation.ops import TomoIdentity -from ccpi.framework import ImageGeometry, ImageData +from ccpi.framework import ImageGeometry, ImageData, BlockDataContainer, DataContainer +from ccpi.optimisation.operators import BlockOperator, BlockScaledOperator import numpy +from timeit import default_timer as timer +from ccpi.framework import ImageGeometry +from ccpi.optimisation.operators import Gradient, Identity, SparseFiniteDiff + +def dt(steps): + return steps[-1] - steps[-2] class TestOperator(unittest.TestCase): def test_ScaledOperator(self): @@ -22,3 +29,312 @@ class TestOperator(unittest.TestCase): y = Id.direct(img) numpy.testing.assert_array_equal(y.as_array(), img.as_array()) + + +class TestBlockOperator(unittest.TestCase): + def assertBlockDataContainerEqual(self, container1, container2): + print ("assert Block Data Container Equal") + self.assertTrue(issubclass(container1.__class__, container2.__class__)) + for col in range(container1.shape[0]): + if issubclass(container1.get_item(col).__class__, DataContainer): + print ("Checking col ", col) + self.assertNumpyArrayEqual( + container1.get_item(col).as_array(), + container2.get_item(col).as_array() + ) + else: + self.assertBlockDataContainerEqual(container1.get_item(col),container2.get_item(col)) + + def assertNumpyArrayEqual(self, first, second): + res = True + try: + numpy.testing.assert_array_equal(first, second) + except AssertionError as err: + res = False + print(err) + self.assertTrue(res) + + def assertNumpyArrayAlmostEqual(self, first, second, decimal=6): + res = True + try: + numpy.testing.assert_array_almost_equal(first, second, decimal) + except AssertionError as err: + res = False + print(err) + print("expected " , second) + print("actual " , first) + + self.assertTrue(res) + + def test_BlockOperator(self): + + + 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()) + + 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.direct(u) + 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_timedifference(self): + + M, N ,W = 100, 512, 512 + ig = ImageGeometry(M, N, W) + arr = ig.allocate('random_int') + + G = Gradient(ig) + Id = Identity(ig) + + B = BlockOperator(G, Id) + + + # Nx1 case + u = ig.allocate('random_int') + steps = [timer()] + i = 0 + n = 25. + t1 = t2 = 0 + res = B.range_geometry().allocate() + + while (i < n): + print ("i ", i) + steps.append(timer()) + z1 = B.direct(u) + steps.append(timer()) + t = dt(steps) + #print ("B.direct(u) " ,t) + t1 += t/n + + steps.append(timer()) + B.direct(u, out = res) + steps.append(timer()) + t = dt(steps) + #print ("B.direct(u, out=res) " ,t) + t2 += t/n + i += 1 + + print ("Time difference ", t1,t2) + self.assertGreater(t1,t2) + + steps = [timer()] + i = 0 + #n = 50. + t1 = t2 = 0 + resd = B.domain_geometry().allocate() + z1 = B.direct(u) + #B.adjoint(z1, out=resd) + + print (type(res)) + while (i < n): + print ("i ", i) + steps.append(timer()) + w1 = B.adjoint(z1) + steps.append(timer()) + t = dt(steps) + #print ("B.adjoint(z1) " ,t) + t1 += t/n + + steps.append(timer()) + B.adjoint(z1, out=resd) + steps.append(timer()) + t = dt(steps) + #print ("B.adjoint(z1, out=res) " ,t) + t2 += t/n + i += 1 + + print ("Time difference ", t1,t2) + self.assertGreater(t1,t2) + + def test_BlockOperatorLinearValidity(self): + + + 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') + w = B.range_geometry().allocate(ImageGeometry.RANDOM_INT) + w1 = B.direct(u) + u1 = B.adjoint(w) + self.assertEqual((w * w1).sum() , (u1*u).sum()) + + + + diff --git a/Wrappers/Python/test/test_functions.py b/Wrappers/Python/test/test_functions.py index 54dfa57..19cb65f 100644 --- a/Wrappers/Python/test/test_functions.py +++ b/Wrappers/Python/test/test_functions.py @@ -19,7 +19,7 @@ from ccpi.optimisation.operators import Gradient #from ccpi.optimisation.functions import SimpleL2NormSq from ccpi.optimisation.functions import L2NormSquared -from ccpi.optimisation.functions import SimpleL1Norm +#from ccpi.optimisation.functions import SimpleL1Norm from ccpi.optimisation.functions import L1Norm from ccpi.optimisation.funcs import Norm2sq |