def ConvCnstrMODMaskDcplOptions(opt=None, method='cns'):
"""A wrapper function that dynamically defines a class derived from
the Options class associated with one of the implementations of
the Convolutional Constrained MOD with Mask Decoupling problem,
and returns an object instantiated with the provided parameters.
The wrapper is designed to allow the appropriate object to be
created by calling this function using the same syntax as would be
used if it were a class. The specific implementation is selected
by use of an additional keyword argument 'method'. Valid values are
as specified in the documentation for :func:`ConvCnstrMODMaskDcpl`.
"""
# Assign base class depending on method selection argument
if method == 'ism':
base = ConvCnstrMODMaskDcpl_IterSM.Options
elif method == 'cg':
base = ConvCnstrMODMaskDcpl_CG.Options
elif method == 'cns':
base = ConvCnstrMODMaskDcpl_Consensus.Options
else:
raise ValueError('Unknown ConvCnstrMODMaskDcpl solver method %s' %
method)
# Nested class with dynamically determined inheritance
class ConvCnstrMODMaskDcplOptions(base):
def __init__(self, opt):
super(ConvCnstrMODMaskDcplOptions, self).__init__(opt)
# Allow pickling of objects of type ConvCnstrMODMaskDcplOptions
_fix_dynamic_class_lookup(ConvCnstrMODMaskDcplOptions, method)
# Return object of the nested class type
return ConvCnstrMODMaskDcplOptions(opt)
def ConvCnstrMODMaskDcpl(*args, **kwargs):
"""A wrapper function that dynamically defines a class derived from
one of the implementations of the Convolutional Constrained MOD
with Mask Decoupling problems, and returns an object instantiated
with the provided. parameters. The wrapper is designed to allow the
appropriate object to be created by calling this function using the
same syntax as would be used if it were a class. The specific
implementation is selected by use of an additional keyword
argument 'method'. Valid values are:
- ``'ism'`` :
Use the implementation defined in :class:`.ConvCnstrMODMaskDcpl_IterSM`.
This method works well for a small number of training images, but is
very slow for larger training sets.
- ``'cg'`` :
Use the implementation defined in :class:`.ConvCnstrMODMaskDcpl_CG`.
This method is slower than ``'ism'`` for small training sets, but has
better run time scaling as the training set grows.
- ``'cns'`` :
Use the implementation defined in
:class:`.ConvCnstrMODMaskDcpl_Consensus`. This method is the best choice
for large training sets.
The default value is ``'cns'``.
"""
# Extract method selection argument or set default
if 'method' in kwargs:
method = kwargs['method']
del kwargs['method']
else:
method = 'cns'
# Assign base class depending on method selection argument
if method == 'ism':
base = ConvCnstrMODMaskDcpl_IterSM
elif method == 'cg':
base = ConvCnstrMODMaskDcpl_CG
elif method == 'cns':
base = ConvCnstrMODMaskDcpl_Consensus
else:
raise ValueError('Unknown ConvCnstrMODMaskDcpl solver method %s' %
method)
# Nested class with dynamically determined inheritance
class ConvCnstrMODMaskDcpl(base):
def __init__(self, *args, **kwargs):
super(ConvCnstrMODMaskDcpl, self).__init__(*args, **kwargs)
# Allow pickling of objects of type ConvCnstrMODMaskDcpl
_fix_dynamic_class_lookup(ConvCnstrMODMaskDcpl, method)
# Return object of the nested class type
return ConvCnstrMODMaskDcpl(*args, **kwargs)
