def _rhs(t, rho, L_list):
# _rhs being a staticmethod enables the propagator to be pickled (for
# parallelization)
out = np.zeros(rho.shape[0], dtype=complex)
L = L_list[0][0]
L_coeff = L_list[0][1]
spmvpy_csr(L.data, L.indices, L.indptr, rho, L_coeff, out)
for n in range(1, len(L_list)):
L = L_list[n][0]
L_coeff = L_list[n][1]
spmvpy_csr(L.data, L.indices, L.indptr, rho, L_coeff, out)
return out
def psi_list_td_with_state(t, psi, H_list_and_args):
H_list = H_list_and_args[0]
args = H_list_and_args[1]
H = H_list[0][0]
H_td = H_list[0][1]
out = np.zeros(psi.shape[0], dtype=complex)
spmvpy_csr(H.data, H.indices, H.indptr, psi, H_td(t, args), out)
for n in range(1, len(H_list)):
#
# args[n][0] = the sparse data for a Qobj in operator form
# args[n][1] = function callback giving the coefficient
#
H = H_list[n][0]
H_td = H_list[n][1]
spmvpy_csr(H.data, H.indices, H.indptr, psi, H_td(t, args), out)
return out
def psi_list_td_with_state(t, psi, L_List_and_args):
L_List = L_List_and_args[0]
args = L_List_and_args[1]
L = L_List[0][0]
tdfunc = L_List[0][1]
out = np.zeros(psi.shape[0],dtype=complex)
spmvpy_csr(L.data, L.indices, L.indptr, psi, tdfunc(t, psi, args), out)
for n in range(1, len(L_List)):
#
# args[n][0] = the sparse data for a Qobj in operator form
# args[n][1] = function callback giving the coefficient
#
L = L_List[n][0]
tdfunc = L_List[n][1]
spmvpy_csr(L.data, L.indices, L.indptr, psi, tdfunc(t, psi, args), out)
return out
def psi_list_td(t, psi, H_list_and_args):
H_list = H_list_and_args[0]
args = H_list_and_args[1]
H = H_list[0][0]
H_td = H_list[0][1]
out = np.zeros(psi.shape[0],dtype=complex)
spmvpy_csr(H.data, H.indices, H.indptr, psi, H_td(t, args), out)
for n in range(1, len(H_list)):
#
# args[n][0] = the sparse data for a Qobj in operator form
# args[n][1] = function callback giving the coefficient
#
H = H_list[n][0]
H_td = H_list[n][1]
spmvpy_csr(H.data, H.indices, H.indptr, psi, H_td(t, args), out)
return out
def drho_list_td(t, rho, L_list, args):
out = np.zeros(rho.shape[0], dtype=complex)
L = L_list[0][0]
L_td = L_list[0][1]
spmvpy_csr(L.data, L.indices, L.indptr, rho, L_td(t, args), out)
for n in range(1, len(L_list)):
#
# L_args[n][0] = the sparse data for a Qobj in super-operator form
# L_args[n][1] = function callback giving the coefficient
#
L = L_list[n][0]
L_td = L_list[n][1]
if L_list[n][2]:
spmvpy_csr(L.data, L.indices, L.indptr, rho, L_td(t, args)**2, out)
else:
spmvpy_csr(L.data, L.indices, L.indptr, rho, L_td(t, args), out)
return out
def drho_list_td(t, rho, L_list, args):
out = np.zeros(rho.shape[0],dtype=complex)
L = L_list[0][0]
L_td = L_list[0][1]
spmvpy_csr(L.data, L.indices, L.indptr,
rho, L_td(t, args), out)
for n in range(1, len(L_list)):
#
# L_args[n][0] = the sparse data for a Qobj in super-operator form
# L_args[n][1] = function callback giving the coefficient
#
L = L_list[n][0]
L_td = L_list[n][1]
if L_list[n][2]:
spmvpy_csr(L.data, L.indices, L.indptr,
rho, L_td(t, args)**2, out)
else:
spmvpy_csr(L.data, L.indices, L.indptr,
rho, L_td(t, args), out)
return out