for i, V in enumerate(coupling_values):
print 'calculating rates for coupling ' + str(V)
rates = []
for delta_E in delta_E_values:
evals, evecs = utils.sorted_eig(hamiltonian(delta_E, V))
print evals
exciton_reorg_energies = np.array([
os.exciton_reorg_energy(exciton, [reorg_energy, reorg_energy])
for exciton in evecs
])
print exciton_reorg_energies
rates.append(
os.modified_redfield_rates_general(
evals, evecs, np.array([g_site, scaling * g_site]),
np.array([g_site_dot, scaling * g_site_dot]),
np.array([g_site_dot_dot, scaling * g_site_dot_dot]),
np.array([reorg_energy, scaling * reorg_energy]), temperature,
time)[0][0, 1])
#rates.append(os.MRT_rates(hamiltonian(delta_E, V), np.array([reorg_energy, reorg_energy]), cutoff_freq, temperature, None)[0,1])
plt.subplot(1, coupling_values.size, i + 1)
rates_data.append(rates)
plt.loglog(delta_E_values,
np.array(rates) * utils.WAVENUMS_TO_INVERSE_PS,
label=V)
# plot extracted data from Ed's thesis
# xdata, ydata = np.loadtxt('../../data/thieved_data'+str(i)+'.txt', delimiter=', ', unpack=True)
# plt.loglog(xdata, ydata, color='red')
#s = interp.UnivariateSpline(xdata, ydata, k=2, s=None)
#plt.loglog(xdata, s(xdata), color='red')
time = np.linspace(0, time_interval, int(time_interval*2000))
lbf_coeffs = os.lbf_coeffs(reorg_energy, cutoff_freq, temperature, None, 0)
g_site = os.site_lbf_ed(time, lbf_coeffs)
g_site_dot = os.site_lbf_dot_ed(time, lbf_coeffs)
g_site_dot_dot = os.site_lbf_dot_dot_ed(time, lbf_coeffs)
scaling = 1.
for i,V in enumerate(coupling_values):
print 'calculating rates for coupling ' + str(V)
rates = []
for delta_E in delta_E_values:
evals, evecs = utils.sorted_eig(hamiltonian(delta_E, V))
print evals
exciton_reorg_energies = np.array([os.exciton_reorg_energy(exciton, [reorg_energy, reorg_energy]) for exciton in evecs])
print exciton_reorg_energies
rates.append(os.modified_redfield_rates_general(evals, evecs, np.array([g_site, scaling*g_site]), np.array([g_site_dot,scaling*g_site_dot]), np.array([g_site_dot_dot,scaling*g_site_dot_dot]), np.array([reorg_energy,scaling*reorg_energy]), temperature, time)[0][0,1])
#rates.append(os.MRT_rates(hamiltonian(delta_E, V), np.array([reorg_energy, reorg_energy]), cutoff_freq, temperature, None)[0,1])
plt.subplot(1, coupling_values.size, i+1)
rates_data.append(rates)
plt.loglog(delta_E_values, np.array(rates)*utils.WAVENUMS_TO_INVERSE_PS, label=V)
# plot extracted data from Ed's thesis
# xdata, ydata = np.loadtxt('../../data/thieved_data'+str(i)+'.txt', delimiter=', ', unpack=True)
# plt.loglog(xdata, ydata, color='red')
#s = interp.UnivariateSpline(xdata, ydata, k=2, s=None)
#plt.loglog(xdata, s(xdata), color='red')
plt.xlabel(r'$\Delta E$ (cm$^{-1}$)')
plt.ylabel(r'rate')
plt.ylim(0.01, 200)
plt.xlim(5,1000)
])
# print evals - exciton_reorg_energies
# evals,evecs = utils.sort_evals_evecs(evals-exciton_reorg_energies, evecs)
# print evals
# MRT_rates2 = os.modified_redfield_rates_general_unordered(evals-exciton_reorg_energies, evecs, lbf, lbf_dot, lbf_dot_dot, total_site_reorg_energy, temperature, time)
#
# liouvillian2 = MRT_rates2 - np.diag(np.sum(MRT_rates2, axis=0))
#
# print evals
# print MRT_rates2
print evals - exciton_reorg_energies
#MRT_rates,evals,evecs = os.modified_redfield_rates_general(evals-exciton_reorg_energies, evecs, lbf, lbf_dot, lbf_dot_dot, total_site_reorg_energy, temperature, time)
MRT_rates = os.modified_redfield_rates_general(
evals - exciton_reorg_energies, evecs, lbf, lbf_dot, lbf_dot_dot,
total_site_reorg_energy, temperature, time)[0]
print evals
print MRT_rates
# time evolution
liouvillian = MRT_rates - np.diag(np.sum(MRT_rates, axis=0))
# print liouvillian
# for col in liouvillian.T:
# print np.sum(col)
init_state = np.array([1., 0, 0, 0, 0, 0])
dv_dynamics = te.liouvillian_time_evolution(init_state, liouvillian,
duration, time_step)
1.5*total_site_reorg_energy])
MRT_rates2 = os.modified_redfield_rates_general_unordered(evals-exciton_reorg_energies, evecs,
np.array([lbf,lbf,lbf,lbf,lbf,lbf,1.5*lbf]), \
np.array([lbf_dot,lbf_dot,lbf_dot,lbf_dot,lbf_dot,lbf_dot,1.5*lbf_dot]), \
np.array([lbf_dot_dot,lbf_dot_dot,lbf_dot_dot,lbf_dot_dot,lbf_dot_dot,lbf_dot_dot,1.5*lbf_dot_dot]), \
reorgs,\
temperature, time)
liouvillian2 = MRT_rates2 - np.diag(np.sum(MRT_rates2, axis=0))
print evals
print MRT_rates2
MRT_rates, evals, evecs = os.modified_redfield_rates_general(
evals - exciton_reorg_energies, evecs, lbf, lbf_dot, lbf_dot_dot,
total_site_reorg_energy, temperature, time)
print evals
print MRT_rates
MRT_rates3,evals,evecs = os.modified_redfield_rates_general(evals, evecs,\
np.array([lbf,lbf,lbf,lbf,lbf,lbf,1.5*lbf]),\
np.array([lbf_dot,lbf_dot,lbf_dot,lbf_dot,lbf_dot,lbf_dot,1.5*lbf_dot]),\
np.array([lbf_dot_dot,lbf_dot_dot,lbf_dot_dot,lbf_dot_dot,lbf_dot_dot,lbf_dot_dot,1.5*lbf_dot_dot]),\
reorgs,\
temperature, time)
print MRT_rates3
# time evolution
liouvillian = MRT_rates - np.diag(np.sum(MRT_rates, axis=0))
lbf = os.site_lbf_ed(time, lbf_coeffs)
lbf_dot = os.site_lbf_dot_ed(time, lbf_coeffs)
lbf_dot_dot = os.site_lbf_dot_ed(time, lbf_coeffs)
np.savez(lbf_fn,
lbf=lbf,
lbf_dot=lbf_dot,
lbf_dot_dot=lbf_dot_dot,
time=time)
print 'Calculating MRT rates at ' + str(time_utils.getTime())
MRT_rates = np.zeros(energy_gap_values.size)
for i, H in enumerate(hamiltonians):
evals, evecs = utils.sorted_eig(H)
MRT_rates[i] = os.modified_redfield_rates_general(evals, evecs, lbf,
lbf_dot, lbf_dot_dot,
reorg_energy,
temperature, time)[0][0,
1]
# define function for HEOM calculation pool
def HEOM_calculation(hamiltonian):
reorg_energy = 100.
cutoff_freq = 53.
temperature = 300.
init_state = np.array([[1., 0], [0, 0]])
duration = 5.
time_step = 0.00005
hs = HierarchySolver(hamiltonian, reorg_energy, cutoff_freq, temperature)
data = np.load(lbf_fn)
lbf = data['lbf']
lbf_dot = data['lbf_dot']
lbf_dot_dot = data['lbf_dot_dot']
except IOError:
lbf_coeffs = os.lbf_coeffs(reorg_energy, cutoff_freq, temperature, None, num_expansion_terms)
lbf = os.site_lbf_ed(time, lbf_coeffs)
lbf_dot = os.site_lbf_dot_ed(time, lbf_coeffs)
lbf_dot_dot = os.site_lbf_dot_ed(time, lbf_coeffs)
np.savez(lbf_fn, lbf=lbf, lbf_dot=lbf_dot, lbf_dot_dot=lbf_dot_dot, time=time)
print 'Calculating MRT rates at ' + str(time_utils.getTime())
MRT_rates = np.zeros(energy_gap_values.size)
for i,H in enumerate(hamiltonians):
evals,evecs = utils.sorted_eig(H)
MRT_rates[i] = os.modified_redfield_rates_general(evals, evecs, lbf, lbf_dot, lbf_dot_dot, reorg_energy, temperature, time)[0][0,1]
# define function for HEOM calculation pool
def HEOM_calculation(hamiltonian):
reorg_energy = 100.
cutoff_freq = 53.
temperature = 300.
init_state = np.array([[1., 0], [0, 0]])
duration = 5.
time_step = 0.00005
hs = HierarchySolver(hamiltonian, reorg_energy, cutoff_freq, temperature)
HEOM_history, time = hs.hierarchy_time_evolution(init_state, 18, time_step, duration)
return HEOM_history
exciton_reorg_energies = np.array([os.exciton_reorg_energy(exciton, site_reorg_energies) for exciton in evecs]) # print evals - exciton_reorg_energies # evals,evecs = utils.sort_evals_evecs(evals-exciton_reorg_energies, evecs) # print evals # MRT_rates2 = os.modified_redfield_rates_general_unordered(evals-exciton_reorg_energies, evecs, lbf, lbf_dot, lbf_dot_dot, total_site_reorg_energy, temperature, time) # # liouvillian2 = MRT_rates2 - np.diag(np.sum(MRT_rates2, axis=0)) # # print evals # print MRT_rates2 print evals - exciton_reorg_energies #MRT_rates,evals,evecs = os.modified_redfield_rates_general(evals-exciton_reorg_energies, evecs, lbf, lbf_dot, lbf_dot_dot, total_site_reorg_energy, temperature, time) MRT_rates = os.modified_redfield_rates_general(evals-exciton_reorg_energies, evecs, lbf, lbf_dot, lbf_dot_dot, total_site_reorg_energy, temperature, time)[0] print evals print MRT_rates # time evolution liouvillian = MRT_rates - np.diag(np.sum(MRT_rates, axis=0)) # print liouvillian # for col in liouvillian.T: # print np.sum(col) init_state = np.array([1., 0, 0, 0, 0, 0]) dv_dynamics = te.liouvillian_time_evolution(init_state, liouvillian, duration, time_step) dv_dynamics = np.array(dv_dynamics) dynamics[i] = dv_dynamics
total_site_reorg_energy, total_site_reorg_energy, total_site_reorg_energy,\
1.5*total_site_reorg_energy])
MRT_rates2 = os.modified_redfield_rates_general_unordered(evals-exciton_reorg_energies, evecs,
np.array([lbf,lbf,lbf,lbf,lbf,lbf,1.5*lbf]), \
np.array([lbf_dot,lbf_dot,lbf_dot,lbf_dot,lbf_dot,lbf_dot,1.5*lbf_dot]), \
np.array([lbf_dot_dot,lbf_dot_dot,lbf_dot_dot,lbf_dot_dot,lbf_dot_dot,lbf_dot_dot,1.5*lbf_dot_dot]), \
reorgs,\
temperature, time)
liouvillian2 = MRT_rates2 - np.diag(np.sum(MRT_rates2, axis=0))
print evals
print MRT_rates2
MRT_rates,evals,evecs = os.modified_redfield_rates_general(evals-exciton_reorg_energies, evecs, lbf, lbf_dot, lbf_dot_dot, total_site_reorg_energy, temperature, time)
print evals
print MRT_rates
MRT_rates3,evals,evecs = os.modified_redfield_rates_general(evals, evecs,\
np.array([lbf,lbf,lbf,lbf,lbf,lbf,1.5*lbf]),\
np.array([lbf_dot,lbf_dot,lbf_dot,lbf_dot,lbf_dot,lbf_dot,1.5*lbf_dot]),\
np.array([lbf_dot_dot,lbf_dot_dot,lbf_dot_dot,lbf_dot_dot,lbf_dot_dot,lbf_dot_dot,1.5*lbf_dot_dot]),\
reorgs,\
temperature, time)
print MRT_rates3
# time evolution
liouvillian = MRT_rates - np.diag(np.sum(MRT_rates, axis=0))
# print liouvillian
