(np.sum(c_alphas * c_betas ** 3) - np.sum(c_alphas ** 3 * c_betas)) * g_site_dot[k]
+ 2.0j * np.sum(c_betas ** 3 * c_alphas) * site_reorg_energy
)
** 2
)
for k, t in enumerate(time)
]
)
mixing_integral1 = []
mixing_integral2 = []
for i, delta_E in enumerate(delta_E_values):
evals, evecs = utils.sorted_eig(hamiltonian(delta_E, 20.0))
rates, abs_lineshapes, fl_lineshapes, mixing_function, time2 = os.modified_redfield_relaxation_rates(
hamiltonian(delta_E, 20.0), np.array([reorg_energy, reorg_energy]), cutoff_freq, None, temperature, 0, 0.5
)
# mixing_integral1.append(integrate.simps(mixing_function[0,1], time2[:-5]))
# mixing_integral2.append(integrate.simps(new_mixing_function(time, evecs[0], evecs[1], reorg_energy, site_lbf, site_lbf_dot, site_lbf_dot_dot)))
mixing_integral1.append(mixing_function[0, 1])
mixing_integral2.append(
new_mixing_function(time, evecs[0], evecs[1], reorg_energy, site_lbf, site_lbf_dot, site_lbf_dot_dot)
)
plt.plot(delta_E_values, mixing_integral1[0], label="1")
plt.plot(delta_E_values, mixing_integral2[0], label="2", linewidth=2, ls="--", color="red")
plt.legend()
# plt.plot(delta_E_values, [np.abs(mixing_integral1[i] - mixing_integral2[i]) for i in range(len(mixing_integral1))])
plt.show()
reorg_energy = 100. # wavenumbers
cutoff_freq = 53. # wavenumbers
temperature = 300.
'''
Code to calculate rates for various coupling strengths and energy gaps between the monomers in a dimer, then saves data to file for plotting
'''
rates_data = []
for i, V in enumerate(coupling_values):
print 'calculating rates for coupling ' + str(V)
rates = []
for delta_E in delta_E_values:
rates.append(
os.modified_redfield_relaxation_rates(
hamiltonian(delta_E, V), np.array([reorg_energy,
reorg_energy]), cutoff_freq,
None, temperature, 0)[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')
plt.xlabel(r'$\Delta E$ (cm$^{-1}$)')
plt.ylabel(r'rate')
reorg_energy = 100.0 # wavenumbers
cutoff_freq = 53.0 # wavenumbers
temperature = 300.0
"""
Code to calculate rates for various coupling strengths and energy gaps between the monomers in a dimer, then saves data to file for plotting
"""
rates_data = []
for i, V in enumerate(coupling_values):
print "calculating rates for coupling " + str(V)
rates = []
for delta_E in delta_E_values:
rates.append(
os.modified_redfield_relaxation_rates(
hamiltonian(delta_E, V), np.array([reorg_energy, reorg_energy]), cutoff_freq, None, temperature, 0
)[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")
plt.xlabel(r"$\Delta E$ (cm$^{-1}$)")
plt.ylabel(r"rate")
plt.ylim(0.01, 200)
plt.xlim(5, 1000)
plt.legend()
np.exp(2. * np.sum(c_alphas**2 * c_betas**2) *
(g_site[k] + 1.j * site_reorg_energy * t)) *
((np.sum(c_alphas**2 * c_betas**2) * g_site_dot_dot[k]) -
((np.sum(c_alphas * c_betas**3) - np.sum(c_alphas**3 * c_betas)) *
g_site_dot[k] +
2.j * np.sum(c_betas**3 * c_alphas) * site_reorg_energy)**2)
for k, t in enumerate(time)
])
mixing_integral1 = []
mixing_integral2 = []
for i, delta_E in enumerate(delta_E_values):
evals, evecs = utils.sorted_eig(hamiltonian(delta_E, 20.))
rates, abs_lineshapes, fl_lineshapes, mixing_function, time2 = os.modified_redfield_relaxation_rates(
hamiltonian(delta_E, 20.), np.array([reorg_energy, reorg_energy]),
cutoff_freq, None, temperature, 0, 0.5)
# mixing_integral1.append(integrate.simps(mixing_function[0,1], time2[:-5]))
# mixing_integral2.append(integrate.simps(new_mixing_function(time, evecs[0], evecs[1], reorg_energy, site_lbf, site_lbf_dot, site_lbf_dot_dot)))
mixing_integral1.append(mixing_function[0, 1])
mixing_integral2.append(
new_mixing_function(time, evecs[0], evecs[1], reorg_energy, site_lbf,
site_lbf_dot, site_lbf_dot_dot))
plt.plot(delta_E_values, mixing_integral1[0], label='1')
plt.plot(delta_E_values,
mixing_integral2[0],
label='2',
linewidth=2,
ls='--',
color='red')
