def smepdpsolve_generic(ssdata, options, progress_bar): """ For internal use. .. note:: Experimental. """ if debug: print(inspect.stack()[0][3]) N_store = len(ssdata.tlist) N_substeps = ssdata.nsubsteps N = N_store * N_substeps dt = (ssdata.tlist[1] - ssdata.tlist[0]) / N_substeps NT = ssdata.ntraj data = Odedata() data.solver = "smepdpsolve" data.times = ssdata.tlist data.expect = np.zeros((len(ssdata.e_ops), N_store), dtype=complex) data.jump_times = [] data.jump_op_idx = [] # Liouvillian for the deterministic part. # needs to be modified for TD systems L = liouvillian_fast(ssdata.H, ssdata.c_ops) progress_bar.start(ssdata.ntraj) for n in range(ssdata.ntraj): progress_bar.update(n) rho_t = mat2vec(ssdata.rho0.full()).ravel() states_list, jump_times, jump_op_idx = \ _smepdpsolve_single_trajectory(data, L, dt, ssdata.tlist, N_store, N_substeps, rho_t, ssdata.c_ops, ssdata.e_ops) data.states.append(states_list) data.jump_times.append(jump_times) data.jump_op_idx.append(jump_op_idx) progress_bar.finished() # average density matrices if options.average_states and np.any(data.states): data.states = [sum(state_list).unit() for state_list in data.states] # average data.expect = data.expect / ssdata.ntraj # standard error if NT > 1: data.se = (data.ss - NT * (data.expect ** 2)) / (NT * (NT - 1)) else: data.se = None return data
def sepdpsolve_generic(ssdata, options, progress_bar): """ For internal use. .. note:: Experimental. """ if debug: print(inspect.stack()[0][3]) N_store = len(ssdata.tlist) N_substeps = ssdata.nsubsteps N = N_store * N_substeps dt = (ssdata.tlist[1] - ssdata.tlist[0]) / N_substeps NT = ssdata.ntraj data = Odedata() data.solver = "sepdpsolve" data.times = ssdata.tlist data.expect = np.zeros((len(ssdata.e_ops), N_store), dtype=complex) data.ss = np.zeros((len(ssdata.e_ops), N_store), dtype=complex) data.jump_times = [] data.jump_op_idx = [] # effective hamiltonian for deterministic part Heff = ssdata.H for c in ssdata.c_ops: Heff += -0.5j * c.dag() * c progress_bar.start(ssdata.ntraj) for n in range(ssdata.ntraj): progress_bar.update(n) psi_t = ssdata.psi0.full().ravel() states_list, jump_times, jump_op_idx = \ _sepdpsolve_single_trajectory(Heff, dt, ssdata.tlist, N_store, N_substeps, psi_t, ssdata.c_ops, ssdata.e_ops, data) data.states.append(states_list) data.jump_times.append(jump_times) data.jump_op_idx.append(jump_op_idx) progress_bar.finished() # average density matrices if options.average_states and np.any(data.states): data.states = [sum(state_list).unit() for state_list in data.states] # average data.expect = data.expect / NT # standard error if NT > 1: data.se = (data.ss - NT * (data.expect ** 2)) / (NT * (NT - 1)) else: data.se = None # convert complex data to real if hermitian data.expect = [np.real(data.expect[n,:]) if e.isherm else data.expect[n,:] for n, e in enumerate(ssdata.e_ops)] return data
def smesolve_generic(ssdata, options, progress_bar): """ internal .. note:: Experimental. """ if debug: print(inspect.stack()[0][3]) N_store = len(ssdata.tlist) N_substeps = ssdata.nsubsteps N = N_store * N_substeps dt = (ssdata.tlist[1] - ssdata.tlist[0]) / N_substeps NT = ssdata.ntraj data = Odedata() data.solver = "smesolve" data.times = ssdata.tlist data.expect = np.zeros((len(ssdata.e_ops), N_store), dtype=complex) data.ss = np.zeros((len(ssdata.e_ops), N_store), dtype=complex) data.noise = [] data.measurement = [] # pre-compute suporoperator operator combinations that are commonly needed # when evaluating the RHS of stochastic master equations A_ops = [] for c_idx, c in enumerate(ssdata.sc_ops): n = c.dag() * c A_ops.append([spre(c).data, spost(c).data, spre(c.dag()).data, spost(c.dag()).data, spre(n).data, spost(n).data, (spre(c) * spost(c.dag())).data, lindblad_dissipator(c, data_only=True)]) s_e_ops = [spre(e) for e in ssdata.e_ops] # Liouvillian for the deterministic part. # needs to be modified for TD systems L = liouvillian_fast(ssdata.H, ssdata.c_ops) progress_bar.start(ssdata.ntraj) for n in range(ssdata.ntraj): progress_bar.update(n) rho_t = mat2vec(ssdata.state0.full()).ravel() noise = ssdata.noise[n] if ssdata.noise else None states_list, dW, m = _smesolve_single_trajectory( L, dt, ssdata.tlist, N_store, N_substeps, rho_t, A_ops, s_e_ops, data, ssdata.rhs, ssdata.d1, ssdata.d2, ssdata.d2_len, ssdata.homogeneous, ssdata.distribution, ssdata.args, store_measurement=ssdata.store_measurement, store_states=ssdata.store_states, noise=noise) data.states.append(states_list) data.noise.append(dW) data.measurement.append(m) progress_bar.finished() # average density matrices if options.average_states and np.any(data.states): data.states = [sum(state_list).unit() for state_list in data.states] # average data.expect = data.expect / NT # standard error if NT > 1: data.se = (data.ss - NT * (data.expect ** 2)) / (NT * (NT - 1)) else: data.se = None # convert complex data to real if hermitian data.expect = [np.real(data.expect[n,:]) if e.isherm else data.expect[n,:] for n, e in enumerate(ssdata.e_ops)] return data
def ssesolve_generic(ssdata, options, progress_bar): """ internal .. note:: Experimental. """ if debug: print(inspect.stack()[0][3]) N_store = len(ssdata.tlist) N_substeps = ssdata.nsubsteps N = N_store * N_substeps dt = (ssdata.tlist[1] - ssdata.tlist[0]) / N_substeps NT = ssdata.ntraj data = Odedata() data.solver = "ssesolve" data.times = ssdata.tlist data.expect = np.zeros((len(ssdata.e_ops), N_store), dtype=complex) data.ss = np.zeros((len(ssdata.e_ops), N_store), dtype=complex) data.noise = [] data.measurement = [] # pre-compute collapse operator combinations that are commonly needed # when evaluating the RHS of stochastic Schrodinger equations A_ops = [] for c_idx, c in enumerate(ssdata.sc_ops): A_ops.append([c.data, (c + c.dag()).data, (c - c.dag()).data, (c.dag() * c).data]) progress_bar.start(ssdata.ntraj) for n in range(ssdata.ntraj): progress_bar.update(n) psi_t = ssdata.state0.full().ravel() noise = ssdata.noise[n] if ssdata.noise else None states_list, dW, m = _ssesolve_single_trajectory( ssdata.H, dt, ssdata.tlist, N_store, N_substeps, psi_t, A_ops, ssdata.e_ops, data, ssdata.rhs_func, ssdata.d1, ssdata.d2, ssdata.d2_len, ssdata.homogeneous, ssdata.distribution, ssdata.args, store_measurement=ssdata.store_measurement, noise=noise) data.states.append(states_list) data.noise.append(dW) data.measurement.append(m) progress_bar.finished() # average density matrices if options.average_states and np.any(data.states): data.states = [sum(state_list).unit() for state_list in data.states] # average data.expect = data.expect / NT # standard error if NT > 1: data.se = (data.ss - NT * (data.expect ** 2)) / (NT * (NT - 1)) else: data.se = None # convert complex data to real if hermitian data.expect = [np.real(data.expect[n,:]) if e.isherm else data.expect[n,:] for n, e in enumerate(ssdata.e_ops)] return data
def smepdpsolve_generic(ssdata, options, progress_bar): """ For internal use. .. note:: Experimental. """ if debug: print(inspect.stack()[0][3]) N_store = len(ssdata.tlist) N_substeps = ssdata.nsubsteps N = N_store * N_substeps dt = (ssdata.tlist[1] - ssdata.tlist[0]) / N_substeps NT = ssdata.ntraj data = Odedata() data.solver = "smepdpsolve" data.times = ssdata.tlist data.expect = np.zeros((len(ssdata.e_ops), N_store), dtype=complex) data.jump_times = [] data.jump_op_idx = [] # Liouvillian for the deterministic part. # needs to be modified for TD systems L = liouvillian_fast(ssdata.H, ssdata.c_ops) progress_bar.start(ssdata.ntraj) for n in range(ssdata.ntraj): progress_bar.update(n) rho_t = mat2vec(ssdata.rho0.full()).ravel() states_list, jump_times, jump_op_idx = \ _smepdpsolve_single_trajectory(L, dt, ssdata.tlist, N_store, N_substeps, rho_t, ssdata.c_ops, ssdata.e_ops, data) data.states.append(states_list) data.jump_times.append(jump_times) data.jump_op_idx.append(jump_op_idx) progress_bar.finished() # average density matrices if options.average_states and np.any(data.states): data.states = [sum(state_list).unit() for state_list in data.states] # average data.expect = data.expect / ssdata.ntraj # standard error if NT > 1: data.se = (data.ss - NT * (data.expect ** 2)) / (NT * (NT - 1)) else: data.se = None return data