def generate_result_states(params, times, psi0=None, opt=None):
if opt is None:
opt = Options()
opt.atol = 1e-6
opt.rtol = 1e-6
args = {'params': params}
c_ops = c_ops_gen_jc(params)
e_ops = []
H = construct_ham(params)
if psi0 is None:
psi0 = steadystate(H[0], c_ops)
opt.store_states = True
trace = mesolve(H, psi0, times, c_ops, e_ops, args=args, options=opt)
size = 2 * params.c_levels
state_array = np.zeros([len(trace.states), size, size], dtype=complex)
for i, state in enumerate(trace.states):
state_array[i] = state.full()
frame_content = [[trace.times, state_array, params]]
columns = ['times', 'states', 'params']
results_frame = pd.DataFrame(frame_content, columns=columns)
results_frame['fd'] = params.fd
results_frame.set_index('fd', inplace=True)
return results_frame
def generate_result(params, times, psi0=None, opt=None, e_ops=None):
if opt is None:
opt = Options()
opt.atol = 1e-6
opt.rtol = 1e-6
args = {'params': params}
if e_ops is None:
e_ops = e_ops_gen(params)
c_ops = c_ops_gen_jc(params)
H = construct_ham(params)
if psi0 is None:
psi0 = steadystate(H[0], c_ops)
trace = mesolve(H, psi0, times, c_ops, e_ops, args=args, options=opt)
results_frame = pd.DataFrame(trace.expect, index=times)
results_frame.index.name = 'time'
results_frame['fd'] = params.fd
results_frame.set_index('fd', inplace=True, append=True)
results_frame = results_frame.reorder_levels(['fd', 'time'])
return results_frame
