def eigsh(self, k=(0, 0, 0), n=10, gauge='R', eigvals_only=True, **kwargs):
r""" Calculates a subset of eigenvalues of the physical quantity (default 10)
Setup the quantity and overlap matrix with respect to
the given k-point and calculate a subset of the eigenvalues using the sparse algorithms.
All subsequent arguments gets passed directly to :code:`scipy.linalg.eigsh`
Parameters
----------
spin : int, optional
the spin-component to calculate the eigenvalue spectrum of, note that
this parameter is only valid for `Spin.POLARIZED` matrices.
"""
# We always request the smallest eigenvalues...
spin = kwargs.pop('spin', 0)
dtype = kwargs.pop('dtype', None)
kwargs.update({'which': kwargs.get('which', 'SM')})
if self.spin.kind == Spin.POLARIZED:
P = self.Pk(k=k, dtype=dtype, spin=spin, gauge=gauge)
else:
P = self.Pk(k=k, dtype=dtype, gauge=gauge)
if self.orthogonal:
return lin.eigsh(P, k=n, return_eigenvectors=not eigvals_only, **kwargs)
S = self.Sk(k=k, dtype=dtype, gauge=gauge)
return lin.eigsh(P, M=S, k=n, return_eigenvectors=not eigvals_only, **kwargs)
def eigsh(self, k=(0, 0, 0), n=10, gauge='R', eigvals_only=True, **kwargs):
r""" Calculates a subset of eigenvalues of the physical quantity (default 10)
Setup the quantity and overlap matrix with respect to
the given k-point and calculate a subset of the eigenvalues using the sparse algorithms.
All subsequent arguments gets passed directly to :code:`scipy.linalg.eigsh`
"""
# We always request the smallest eigenvalues...
kwargs.update({'which': kwargs.get('which', 'SM')})
dtype = kwargs.pop('dtype', None)
P = self.Pk(k=k, dtype=dtype, gauge=gauge)
if self.orthogonal:
return lin.eigsh(P,
k=n,
return_eigenvectors=not eigvals_only,
**kwargs)
S = self.Sk(k=k, dtype=dtype, gauge=gauge)
return lin.eigsh(P,
M=S,
k=n,
return_eigenvectors=not eigvals_only,
**kwargs)
