def _esys_calc(self, evals_count):
hamiltonian_mat = self.hamiltonian()
evals, evecs = sp.linalg.eigh(hamiltonian_mat,
eigvals=(0, evals_count - 1),
eigvals_only=False)
evals, evecs = order_eigensystem(evals, evecs)
return evals, evecs
def _esys_calc(self, evals_count):
hamiltonian_mat = self.hamiltonian()
evals, evecs = sparse.linalg.eigsh(hamiltonian_mat, k=evals_count, return_eigenvectors=True, which='SA')
# TODO consider normalization of zeropi wavefunctions
# evecs /= np.sqrt(self.grid.grid_spacing())
evals, evecs = spec_utils.order_eigensystem(evals, evecs)
return evals, evecs
def _esys_calc(self, evals_count: int) -> Tuple[ndarray, ndarray]:
hamiltonian_mat = self.hamiltonian()
evals, evecs = sp.linalg.eigh(hamiltonian_mat,
eigvals_only=False,
eigvals=(0, evals_count - 1))
evals, evecs = order_eigensystem(evals, evecs)
return evals, evecs
def _esys_calc(self, evals_count: int, hamiltonian_mat: csc_matrix = None) -> Tuple[ndarray, ndarray]:
if hamiltonian_mat is None:
hamiltonian_mat = self.hamiltonian()
evals, evecs = sparse.linalg.eigsh(hamiltonian_mat, k=evals_count, sigma=0.0, which='LM',
return_eigenvectors=True)
evals, evecs = spec_utils.order_eigensystem(evals, evecs)
return evals, evecs
def _esys_calc(self, evals_count, hamiltonian_mat=None):
if hamiltonian_mat is None:
hamiltonian_mat = self.hamiltonian()
evals, evecs = sparse.linalg.eigsh(hamiltonian_mat,
k=evals_count,
return_eigenvectors=True,
which='SA')
evals, evecs = order_eigensystem(evals, evecs)
return evals, evecs
def _esys_calc(self, evals_count) -> Tuple[ndarray, ndarray]:
hamiltonian_mat = self.hamiltonian()
evals, evecs = sparse.linalg.eigsh(
hamiltonian_mat,
k=evals_count,
return_eigenvectors=True,
sigma=0.0,
which="LM",
v0=settings.RANDOM_ARRAY[:self.hilbertdim()],
)
evals, evecs = spec_utils.order_eigensystem(evals, evecs)
return evals, evecs
def _esys_calc(self, evals_count: int) -> Tuple[ndarray, ndarray]:
hamiltonian_mat = self.hamiltonian()
evals, evecs = sparse.linalg.eigsh(
hamiltonian_mat,
k=evals_count,
sigma=0.0,
which="LM",
return_eigenvectors=True,
)
# TODO consider normalization of zeropi wavefunctions
# evecs /= np.sqrt(self.grid.grid_spacing())
evals, evecs = spec_utils.order_eigensystem(evals, evecs)
return evals, evecs
def eigensys(self, evals_count: int = 2) -> Tuple[ndarray, ndarray]:
hamiltonian_mat = self.hamiltonian()
evals, evecs = sp.linalg.eigh(hamiltonian_mat, eigvals_only=False)
evals, evecs = order_eigensystem(evals, evecs)
return evals, evecs
