def makeOperator(operatorInput, expectedShape):
"""Internal. Takes a dense numpy array or a sparse matrix or
a function and makes an operator performing matrix * blockvector
products.
Examples
--------
>>> A = makeOperator( arrayA, (n, n) )
>>> vectorB = A( vectorX )
"""
if operatorInput is None:
def ident(x):
return x
operator = LinearOperator(expectedShape, ident, matmat=ident)
else:
operator = aslinearoperator(operatorInput)
if operator.shape != expectedShape:
raise ValueError('operator has invalid shape')
if sys.version_info[0] >= 3:
# special methods are looked up on the class -- so make a new one
operator.__class__ = CallableLinearOperator
else:
operator.__call__ = operator.matmat
return operator
def makeOperator( operatorInput, expectedShape ):
"""Internal. Takes a dense numpy array or a sparse matrix or
a function and makes an operator performing matrix * blockvector
products.
Examples
--------
>>> A = makeOperator( arrayA, (n, n) )
>>> vectorB = A( vectorX )
"""
if operatorInput is None:
def ident(x):
return x
operator = LinearOperator(expectedShape, ident, matmat=ident)
else:
operator = aslinearoperator(operatorInput)
if operator.shape != expectedShape:
raise ValueError('operator has invalid shape')
if sys.version_info[0] >= 3:
# special methods are looked up on the class -- so make a new one
operator.__class__ = CallableLinearOperator
else:
operator.__call__ = operator.matmat
return operator
def makeOperator(operatorInput, expectedShape):
"""Internal. Takes a dense numpy array or a sparse matrix or
a function and makes an operator performing matrix * blockvector
products.
Example
-------
>>> A = makeOperator( arrayA, (n, n) )
>>> vectorB = A( vectorX )
"""
if operatorInput is None:
def ident(x):
return x
operator = LinearOperator(expectedShape, ident, matmat=ident)
else:
operator = aslinearoperator(operatorInput)
if operator.shape != expectedShape:
raise ValueError('operator has invalid shape')
operator.__call__ = operator.matmat
return operator
def makeOperator( operatorInput, expectedShape ):
"""Internal. Takes a dense numpy array or a sparse matrix or
a function and makes an operator performing matrix * blockvector
products.
Example
-------
>>> A = makeOperator( arrayA, (n, n) )
>>> vectorB = A( vectorX )
"""
if operatorInput is None:
def ident(x):
return x
operator = LinearOperator(expectedShape, ident, matmat=ident)
else:
operator = aslinearoperator(operatorInput)
if operator.shape != expectedShape:
raise ValueError('operator has invalid shape')
operator.__call__ = operator.matmat
return operator
def Operator(f, size=None): """Create a stacked version of the function f, casted as a LinearOperator. size is the dimension of the operator f.""" if size == None: size = f.Size operator = LinearOperator((size,size), matvec = StackFunction(f), dtype=float64) operator.__call__ = lambda x: operator * x return operator
def Operator(f, size=None):
"""Create a stacked version of the function f, casted as a LinearOperator.
size is the dimension of the operator f."""
if size == None: size = f.Size
operator = LinearOperator((size, size),
matvec=StackFunction(f),
dtype=float64)
operator.__call__ = lambda x: operator * x
return operator
