def apply(self, wf):
"""
Applies the operator to a wavefunction.
Parameters
----------
wf : A Wavefunction
The wavefunction you want to apply the operator.
Returns
-------
result : a Wavefunction
The wavefunction resulting of the operation. It has the same
shape (i.e. is in the same Hilbert space) as the one passed as
argument.
Raises
------
DMRGException
if `wf` is has not the correct dimensions as a matrix.
Examples
--------
>>> import numpy as np
>>> from dmrg101.core.operators import Operator
>>> from dmrg101.core.wavefunction import Wavefunction
>>> wf = Wavefunction(2, 2)
>>> wf.randomize()
>>> identity_operator = Operator(np.eye(2, 2), np.eye(2, 2))
>>> new_wf = identity_operator.apply(wf)
>>> np.array_equal(new_wf.as_matrix, wf.as_matrix)
True
"""
if wf.as_matrix.shape != ((self.left_dim, self.right_dim)):
raise DMRGException("Wavefunction does not fit.")
result = Wavefunction(self.left_dim, self.right_dim)
tmp = np.dot(self.right_op, wf.as_matrix.transpose())
result.as_matrix = self.parameter * np.dot(self.left_op,
tmp.transpose())
return result
def apply(self, wf):
"""
Applies the operator to a wavefunction.
Parameters
----------
wf : A Wavefunction
The wavefunction you want to apply the operator.
Returns
-------
result : a Wavefunction
The wavefunction resulting of the operation. It has the same
shape (i.e. is in the same Hilbert space) as the one passed as
argument.
Raises
------
DMRGException
if `wf` is has not the correct dimensions as a matrix.
Examples
--------
>>> import numpy as np
>>> from dmrg101.core.operators import Operator
>>> from dmrg101.core.wavefunction import Wavefunction
>>> wf = Wavefunction(2, 2)
>>> wf.randomize()
>>> identity_operator = Operator(np.eye(2, 2), np.eye(2, 2))
>>> new_wf = identity_operator.apply(wf)
>>> np.array_equal(new_wf.as_matrix, wf.as_matrix)
True
"""
if wf.as_matrix.shape != ((self.left_dim, self.right_dim)):
raise DMRGException("Wavefunction does not fit.")
result = Wavefunction(self.left_dim, self.right_dim)
tmp = np.dot(self.right_op, wf.as_matrix.transpose())
result.as_matrix = self.parameter * np.dot(self.left_op, tmp.transpose())
return result
