def pickeig(w, v, nr, envs): x0 = linalg_helper._gen_x0(envs['v'], envs['xs']) idx = np.argmax(np.abs( np.dot(np.array(guess).conj(), np.array(x0).T)), axis=1) return lib.linalg_helper._eigs_cmplx2real(w, v, idx)
def pickeig(w, v, nr, envs): x0 = linalg_helper._gen_x0(envs['v'], envs['xs']) idx = np.argmax(np.abs( np.dot(np.array(guess).conj(), np.array(x0).T)), axis=1) return w[idx].real, v[:, idx].real, idx
def pickeig(w, v, nroots, envs): x0 = linalg_helper._gen_x0(envs['v'], envs['xs']) s = np.dot(np.asarray(guess_k).conj(), np.asarray(x0).T) snorm = np.einsum('pi,pi->i', s.conj(), s) idx = np.argsort(-snorm)[:nroots] return linalg_helper._eigs_cmplx2real(w, v, idx, real_eigenvectors=False)
def pickeig(w, v, nr, envs): x0 = linalg_helper._gen_x0(envs['v'], envs['xs']) idx = np.argmax( np.abs(np.dot(np.array(guess).conj(),np.array(x0).T)), axis=1 ) return w[idx].real, v[:,idx].real, idx
def pickeig(w, v, nr, envs): x0 = linalg_helper._gen_x0(envs['v'], envs['xs']) idx = np.argmax( np.abs(np.dot(np.array(guess).conj(),np.array(x0).T)), axis=1 ) return lib.linalg_helper._eigs_cmplx2real(w, v, idx)