def generate_heom(reorg_energy):
print str(reorg_energy) + ' at ' + str(tu.getTime())
print ''
model_heom = DQDHEOMModelSparse(Gamma_L, Gamma_R, bias, T_c, beta=beta, environment=environment(reorg_energy, beta, K), \
K=K, tc=True, trunc_level=N)
heom_matrix = model_heom.heom_matrix()
np.savez('data/F2_reorg_energy_heom_data_K'+str(K)+'_vals_'+str(re_min)+'-'+str(re_max)+'_'+str(reorg_energy)+'.npz', reorg_energy=reorg_energy, \
indices=heom_matrix.indices, elements=heom_matrix.data, indptrs=heom_matrix.indptr)
def calculate_FCS(reorg_energy):
print str(reorg_energy) + ' at ' + str(tu.getTime())
data_pt = np.load('data/F2_reorg_energy_heom_data_K'+str(K)+'_vals_'+str(re_min)+'-'+str(re_max)+'_'+str(reorg_energy)+'.npz')
elements = data_pt['elements']
indices = data_pt['indices']
indptrs = data_pt['indptrs']
heom_matrix = sp.csr_matrix((elements, indices, indptrs), shape=shape)
solver = FCSSolver(heom_matrix, jump_matrix, dv_pops)
mean_heom = solver.mean()
F2_heom = solver.second_order_fano_factor()
#coh_heom[i] = np.abs(model_heom.heom_solver.extract_system_density_matrix(solver.ss)[1,2])
return [mean_heom, F2_heom, 0]
num_matsubara_freqs = 1
# model = DQDHEOMModel(Gamma_L, Gamma_R, bias, T_c, beta=beta[0], drude_reorg_energy=reorg_energy, drude_cutoff=cutoff, \
# num_matsubara_freqs=0, temperature_correction=True, sites_to_couple=np.array([0,1,1]))
bias_values = np.linspace(-1, 1, 100)
F2 = np.zeros((len(beta)+1, bias_values.size))
def calculate_F2(model):
solver = FCSSolver(model.heom_matrix(), model.jump_matrix(), model.dv_pops)
return solver.second_order_fano_factor(0)
from multiprocessing import Pool
pool = Pool(processes=4)
for j,B in enumerate(beta):
print 'for beta = ' + str(B) + ' at ' + str(tu.getTime())
models = []
for E in bias_values:
models.append(DQDHEOMModel(Gamma_L, Gamma_R, E, T_c, beta=B, drude_reorg_energy=reorg_energy, drude_cutoff=cutoff, \
num_matsubara_freqs=num_matsubara_freqs, temperature_correction=True, sites_to_couple=np.array([0,1,1])))
F2[j+1] = pool.map(calculate_F2, models)
pool.close(); pool.join()
print 'Saving...'
np.savez('../data/HEOM_F2_bias_drude_K_'+str(num_matsubara_freqs)+'.npz', F2=F2, bias_values=bias_values, \
beta=beta, temperature=temperature)
time_propagator = np.zeros((2, 2))
time_propagator[0, 0] = -k01
time_propagator[0, 1] = k10
time_propagator[1, 1] = -k10
time_propagator[1, 0] = k01
time_step = t[1] - t[0]
dynamics = te.liouvillian_time_evolution(np.array([1, 0]),
time_propagator,
t[-1],
time_step,
wave_nums=False)
return dynamics[:, 0]
print '[Calculating Forster rates...]'
print 'Started at: ' + str(time_utils.getTime())
for i, v in enumerate(reorg_energy_values):
#print 'Calculating rates for reorg energy: ' + str(v)
forster_time = np.linspace(0, 20., 16000)
lbf = os.site_lbf_ed(forster_time,
os.lbf_coeffs(v, cutoff_freq, temperature, None, 5))
forster_rates[i] = os.forster_rate(100., 0, v, v, lbf, lbf, 0, 0,
np.array([1., 0]), np.array([0, 1.]),
system_hamiltonian, forster_time)
print 'Finished at: ' + str(time_utils.getTime())
def calculate_HEOM(reorg_energy):
print reorg_energy
system_hamiltonian = np.array([[100., 20.], [20., 0]])
cutoff_freq = 53.
'''
Created on 1 Jun 2016
@author: rstones
'''
import numpy as np
import matplotlib.pyplot as plt
import quant_mech.time_evolution as te
import quant_mech.time_utils as tutils
from quant_mech.hierarchy_solver import HierarchySolver
np.set_printoptions(precision=3, linewidth=150, suppress=True)
print 'Calculating time evolution...'
start_time = tutils.getTime()
time_step = 0.01
duration = 5 # picoseconds
average_site_CT_energies = np.array([15260., 15190., 15000., 15100., 15030., 15020., 15992., 16132.])
# site-CT couplings
couplings = np.array([[0,150.,-42.,-55.,-6.,17.,0,0],
[0,0,-56.,-36.,20.,-2.,0,0],
[0,0,0,7.,46.,-4.,70.,0],
[0,0,0,0,-5.,37.,0,0],
[0,0,0,0,0,-3.,70.,0],
[0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,40.],
[0,0,0,0,0,0,0,0]])
system_hamiltonian = np.diag(average_site_CT_energies) + couplings + couplings.T
site_reorg_energies = np.array([total_site_reorg_energy, total_site_reorg_energy, total_site_reorg_energy, \
total_site_reorg_energy, total_site_reorg_energy, total_site_reorg_energy])
lbf_fn = '../../data/PSIIRC_lbfs_' + str(
int(temperature)) + 'K_site_reorg_' + str(
int(total_site_reorg_energy)) + '_cutoff_freq_' + str(
int(cutoff_freq)) + '_no_modes_data.npz'
#lbf_fn = '../../data/PSIIRC_lbfs_' + str(int(temperature)) + 'K_site_reorg_' + str(int(total_site_reorg_energy)) + '_single_mode_data.npz'
try:
data = np.load(lbf_fn)
lbf = data['lbf']
lbf_dot = data['lbf_dot']
lbf_dot_dot = data['lbf_dot_dot']
time = data['time']
except IOError:
print 'Starting lbf calculations at ' + str(time_utils.getTime())
lbf_coeffs = os.lbf_coeffs(site_drude_reorg_energy, cutoff_freq,
temperature, single_mode_params, 100)
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_dot_ed(time, lbf_coeffs)
print 'Finished calculating lbfs at ' + str(time_utils.getTime())
np.savez(lbf_fn,
lbf=lbf,
lbf_dot=lbf_dot,
lbf_dot_dot=lbf_dot_dot,
time=time)
# calculate MRT rates
evals, evecs = utils.sorted_eig(hamiltonian[:6, :6])
time = np.arange(0, duration+time_step, time_step)
system_hamiltonian = np.array([[100., 20.], [20., 0]])
cutoff_freq = 53.
temperature = 300.
init_state = np.array([[1., 0], [0, 0]])
hs = HierarchySolver(system_hamiltonian, 1., cutoff_freq, temperature)
# error function for least squares fit
def residuals(p, y, pops1, pops2):
k12, k21 = p
return y - (-k12*pops1 + k21*pops2)
print '[Calculating Forster rates...]'
print 'Started at: ' + str(time_utils.getTime())
for i,v in enumerate(reorg_energy_values):
#print 'Calculating rates for reorg energy: ' + str(v)
forster_time = np.linspace(0, 20., 16000)
lbf = os.site_lbf_ed(forster_time, os.lbf_coeffs(v, cutoff_freq, temperature, None, 5))
forster_rates[i] = os.forster_rate(100., 0, v, v, lbf, lbf, 0, 0, np.array([1., 0]), np.array([0, 1.]), system_hamiltonian, forster_time)
print 'Finished at: ' + str(time_utils.getTime())
def calculate_HEOM(reorg_energy):
system_hamiltonian = np.array([[100., 20.], [20., 0]])
cutoff_freq = 53.
temperature = 300.
init_state = np.array([[1., 0], [0, 0]])
time_step = 0.00005
duration = 5.
drude_reorg_energy = 0.015 # 0.00147
drude_cutoff = 50. # 5.
# OBOscillator(drude_reorg_energy, drude_cutoff, beta, K=K)
def environment(beta, K):
return [(), \
(OBOscillator(drude_reorg_energy, drude_cutoff, beta, K=K), UBOscillator(mode_freq, hr_factor, damping, beta, K=K),), \
(OBOscillator(drude_reorg_energy, drude_cutoff, beta, K=K), UBOscillator(mode_freq, hr_factor, damping, beta, K=K),)]
model = DQDHEOMModelSparse(Gamma_L, Gamma_R, bias, T_c, beta=beta[0], environment=environment(beta[0], K), \
K=K, tc=True, trunc_level=6)
bias_values = np.linspace(-10, 10, 20)
mean = np.zeros((len(beta)+1, bias_values.size))
F2 = np.zeros((len(beta)+1, bias_values.size))
print "Starting calculation at " + str(tu.getTime())
for j,B in enumerate(beta):
print 'for beta = ' + str(B) + ' at ' + str(tu.getTime())
model.beta = B
model.environment = environment(B, K)
for i,E in enumerate(bias_values):
print E
model.bias = E
solver = FCSSolver(model.heom_matrix(), model.jump_matrix(), model.dv_pops)
try:
mean[j+1,i] = solver.mean()
F2[j+1,i] = solver.second_order_fano_factor()
except RuntimeError:
print "SINGULAR ERROR!!!!!!!"
return np.array([[energy_gap / 2., coupling], [coupling,
-energy_gap / 2.]])
reorg_energy = 100.
cutoff_freq = 53.
temperature = 300.
energy_gap_values = np.logspace(0, 3, 10)
coupling = 20.
hamiltonians = [
hamiltonian(energy_gap, coupling) for energy_gap in energy_gap_values
]
print 'Calculating line broadening function at ' + str(time_utils.getTime())
time = np.linspace(0, 10, 1000000)
num_expansion_terms = 500
lbf_fn = '../../data/hierarchy_MRT_comparison_lbf_data.npz'
try:
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,
# mode_params = [] #[(1111., 0.0578, 50.)]
K = 0
environment = []
if mode_params: # assuming that there is a single identical mode on each site
environment = [(OBOscillator(reorg_energy, cutoff_freq, beta, K=K), UBOscillator(mode_params[0][0], mode_params[0][1], mode_params[0][2], beta, K=K)), \
(OBOscillator(reorg_energy, cutoff_freq, beta, K=K), UBOscillator(mode_params[0][0], mode_params[0][1], mode_params[0][2], beta, K=K))]
else:
environment = [(OBOscillator(reorg_energy, cutoff_freq, beta, K=K),),
(OBOscillator(reorg_energy, cutoff_freq, beta, K=K),)]
hs = HierarchySolver(system_hamiltonian, environment, beta, num_matsubara_freqs=K, temperature_correction=False)
#hs = HierarchySolverNonRenorm(system_hamiltonian, environment, beta, num_matsubara_freqs=K, temperature_correction=False)
hs.truncation_level = 30
print 'Calculating time evolution...'
start = tutils.getTime()
init_state = np.array([[1.,0],[0,0]])
#init_state = np.dot(hs.system_evectors.T, np.dot(init_state, hs.system_evectors))
hs.init_system_dm = init_state
dm_history, time = hs.calculate_time_evolution(time_step, duration)
#exciton_dm_history = hs.transform_to_exciton_basis(dm_history)
end = tutils.getTime()
print 'Calculation took ' + str(tutils.duration(end, start))
# convert time in inverse wavenums to picoseconds
time /= utils.WAVENUMS_TO_INVERSE_PS
fig,(ax1,ax2) = plt.subplots(1, 2)
# define model
def hamiltonian(energy_gap, coupling):
return np.array([[energy_gap/2., coupling],
[coupling, -energy_gap/2.]])
reorg_energy = 100.
cutoff_freq = 53.
temperature = 300.
energy_gap_values = np.logspace(0, 3, 10)
coupling = 20.
hamiltonians = [hamiltonian(energy_gap, coupling) for energy_gap in energy_gap_values]
print 'Calculating line broadening function at ' + str(time_utils.getTime())
time = np.linspace(0, 10, 1000000)
num_expansion_terms = 500
lbf_fn = '../../data/hierarchy_MRT_comparison_lbf_data.npz'
try:
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 FCSSolver.stationary_state(hm, model_heom.dv_pops)[:9]
reorg_energy_values = np.logspace(-6, 0, 60)
F2_heom = np.zeros((len(beta), reorg_energy_values.size))
F2_pert = np.zeros((len(beta), reorg_energy_values.size))
coh_heom = np.zeros((len(beta), reorg_energy_values.size))
coh_pert = np.zeros((len(beta), reorg_energy_values.size))
mean_heom = np.zeros((len(beta), reorg_energy_values.size))
mean_pert = np.zeros((len(beta), reorg_energy_values.size))
for j,B in enumerate(beta):
print "calculating for beta = " + str(B)
model_heom.beta = B
model_pert.beta = B
for i,E in enumerate(reorg_energy_values):
print str(E) + ' at ' + str(tu.getTime())
model_heom.environment = environment(E, B, K)
try:
solver = FCSSolver(model_heom.heom_matrix(), model_heom.jump_matrix(), model_heom.dv_pops)
mean_heom[j,i] = solver.mean()
F2_heom[j,i] = solver.second_order_fano_factor()
coh_heom[j,i] = np.abs(model_heom.heom_solver.extract_system_density_matrix(solver.ss)[1,2])
except ArpackNoConvergence:
print "Convergence error!"
model_pert.spectral_density = drude_spectral_density(E, cutoff)
solver_pert = DenseFCSSolver(model_pert.liouvillian(), model_pert.jump_matrix(), np.array([1,1,1,0,0]))
mean_pert[j,i] = solver_pert.mean()
F2_pert[j,i] = solver_pert.second_order_fano_factor(0)
coh_pert[j,i] = np.abs(solver_pert.ss[3] + solver_pert.ss[4])
# fname = '../../data/HEOM_weak_coupling_F2_reorg_energy_drude_T2.7_N6_K7.npz'
# init_state[0,0] = 1. # exciton basis
# init_state = np.dot(hs.system_evectors, np.dot(init_state, hs.system_evectors.T)) # transform to site basis for HEOM calculation
# dm_history, time = hs.converged_time_evolution(init_state, truncation_level, truncation_level, time_step, duration)
# exciton_dm_history = hs.transform_to_exciton_basis(dm_history)
# np.savez('../../data/PSIIRC_HEOM_dynamics_reorg_energy_'+str(int(reorg_energy))+'_wavenums.npz', exciton_dm_history=exciton_dm_history, \
# time=time, init_state=init_state, reorg_energy=reorg_energy, cutoff_freq=cutoff_freq, temperature=temperature, \
# system_hamiltonian=system_hamiltonian)
# try different cutoff freqs
reorg_energy = 35.
cutoff_freq_values = [40., 70., 100.]
temperature = 300.
for cutoff_freq in cutoff_freq_values:
print 'Started calculating HEOM dynamics for cutoff_freq = ' + str(
cutoff_freq) + 'cm-1 at ' + str(tutils.getTime())
hs = HierarchySolver(system_hamiltonian, reorg_energy, cutoff_freq,
temperature)
init_state = np.zeros(system_hamiltonian.shape)
init_state[0, 0] = 1. # exciton basis
init_state = np.dot(hs.system_evectors,
np.dot(init_state, hs.system_evectors.T)
) # transform to site basis for HEOM calculation
dm_history, time = hs.converged_time_evolution(init_state,
truncation_level,
truncation_level, time_step,
duration)
exciton_dm_history = hs.transform_to_exciton_basis(dm_history)
np.savez('../../data/PSIIRC_HEOM_dynamics_cutoff_freq_'+str(int(cutoff_freq))+'_wavenums.npz', exciton_dm_history=exciton_dm_history, \
time=time, init_state=init_state, reorg_energy=reorg_energy, cutoff_freq=cutoff_freq, temperature=temperature, \
system_hamiltonian=system_hamiltonian)
for i,cutoff_freq in enumerate(cutoff_freqs):
total_site_reorg_energy = site_drude_reorg_energy# + single_mode_params[0][0]*single_mode_params[0][1]
site_reorg_energies = np.array([total_site_reorg_energy, total_site_reorg_energy, total_site_reorg_energy, \
total_site_reorg_energy, total_site_reorg_energy, total_site_reorg_energy])
lbf_fn = '../../data/PSIIRC_lbfs_' + str(int(temperature)) + 'K_site_reorg_' + str(int(total_site_reorg_energy)) + '_cutoff_freq_'+str(int(cutoff_freq))+'_no_modes_data.npz'
#lbf_fn = '../../data/PSIIRC_lbfs_' + str(int(temperature)) + 'K_site_reorg_' + str(int(total_site_reorg_energy)) + '_single_mode_data.npz'
try:
data = np.load(lbf_fn)
lbf = data['lbf']
lbf_dot = data['lbf_dot']
lbf_dot_dot = data['lbf_dot_dot']
time = data['time']
except IOError:
print 'Starting lbf calculations at ' + str(time_utils.getTime())
lbf_coeffs = os.lbf_coeffs(site_drude_reorg_energy, cutoff_freq, temperature, single_mode_params, 100)
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_dot_ed(time, lbf_coeffs)
print 'Finished calculating lbfs at ' + str(time_utils.getTime())
np.savez(lbf_fn, lbf=lbf, lbf_dot=lbf_dot, lbf_dot_dot=lbf_dot_dot, time=time)
# calculate MRT rates
evals,evecs = utils.sorted_eig(hamiltonian[:6,:6])
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
hs = HierarchySolver(system_hamiltonian,
reorg_energy,
cutoff_freq,
temperature,
jump_operators=jump_operators,
jump_rates=jump_rates)
# start_time = tutils.getTime()
# print 'Calculating steady state...'
# steady_state = hs.calculate_steady_state(6,6)
# print steady_state
#
# end_time = tutils.getTime()
# print 'Calculation took ' + str(tutils.duration(end_time, start_time))
start_time = tutils.getTime()
print 'Calculating time evolution...'
dm_history, time = hs.hierarchy_time_evolution(np.array([[0, 0, 0, 0],
[0, 1., 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]]),
8,
8,
0.005,
15.,
sparse=True)
end_time = tutils.getTime()
print 'Calculation took ' + str(tutils.duration(end_time, start_time))
print time.shape
print dm_history.shape
for i in range(4):
from quant_mech.OBOscillator import OBOscillator
import quant_mech.time_utils as tu
from scipy.sparse.linalg.eigen.arpack.arpack import ArpackNoConvergence
import sys
# get task_id
task_id = sys.argv[1]
try:
task_id = int(task_id)
except TypeError:
print 'ERROR: Task id was None'
sys.exit(0)
stride = 1
print 'JOB ' + str(task_id) + ': starting calculation at ' + str(tu.getTime())
Gamma_L = 0.1 # meV
Gamma_R = 2.5e-3 # meV
bias = 0.2
T_c = 0.1 # meV
temperature = [1.4, 2.7, 12.] # Kelvin
k_B = constants.physical_constants["Boltzmann constant in eV/K"][0] * 1.e3 # meV / Kelvin
beta = [1. / (k_B * T) for T in temperature][1:2]
beta = beta[0]
#reorg_energy = 0.000147
cutoff = 5. # meV
N = 6
K = 6
data = np.load('../data/F2_reorg_energy_heom_data_N'+str(N)+'_K'+str(K)+'.npz')
# print 'Started calculating HEOM dynamics for reorg_energy = ' + str(reorg_energy) + 'cm-1 at ' + str(tutils.getTime())
# hs = HierarchySolver(system_hamiltonian, reorg_energy, cutoff_freq, temperature)
# init_state = np.zeros(system_hamiltonian.shape)
# init_state[0,0] = 1. # exciton basis
# init_state = np.dot(hs.system_evectors, np.dot(init_state, hs.system_evectors.T)) # transform to site basis for HEOM calculation
# dm_history, time = hs.converged_time_evolution(init_state, truncation_level, truncation_level, time_step, duration)
# exciton_dm_history = hs.transform_to_exciton_basis(dm_history)
# np.savez('../../data/PSIIRC_HEOM_dynamics_reorg_energy_'+str(int(reorg_energy))+'_wavenums.npz', exciton_dm_history=exciton_dm_history, \
# time=time, init_state=init_state, reorg_energy=reorg_energy, cutoff_freq=cutoff_freq, temperature=temperature, \
# system_hamiltonian=system_hamiltonian)
# try different cutoff freqs
reorg_energy = 35.
cutoff_freq_values = [40., 70., 100.]
temperature = 300.
for cutoff_freq in cutoff_freq_values:
print 'Started calculating HEOM dynamics for cutoff_freq = ' + str(cutoff_freq) + 'cm-1 at ' + str(tutils.getTime())
hs = HierarchySolver(system_hamiltonian, reorg_energy, cutoff_freq, temperature)
init_state = np.zeros(system_hamiltonian.shape)
init_state[0,0] = 1. # exciton basis
init_state = np.dot(hs.system_evectors, np.dot(init_state, hs.system_evectors.T)) # transform to site basis for HEOM calculation
dm_history, time = hs.converged_time_evolution(init_state, truncation_level, truncation_level, time_step, duration)
exciton_dm_history = hs.transform_to_exciton_basis(dm_history)
np.savez('../../data/PSIIRC_HEOM_dynamics_cutoff_freq_'+str(int(cutoff_freq))+'_wavenums.npz', exciton_dm_history=exciton_dm_history, \
time=time, init_state=init_state, reorg_energy=reorg_energy, cutoff_freq=cutoff_freq, temperature=temperature, \
system_hamiltonian=system_hamiltonian)
print 'Calculations finished'
hs = HierarchySolver(
system_hamiltonian,
reorg_energy,
cutoff_freq,
beta,
underdamped_mode_params=mode_params,
num_matsubara_freqs=1,
temperature_correction=True,
sites_to_couple=np.array([1, 1]),
)
hs.truncation_level = 11
print hs.system_dimension ** 2 * hs.number_density_matrices()
print "Calculating time evolution..."
start = tutils.getTime()
init_state = np.array([[1.0, 0], [0, 0]])
# init_state = np.dot(hs.system_evectors.T, np.dot(init_state, hs.system_evectors))
# print init_state
hs.init_system_dm = init_state
hs.truncation_level = 11
dm_history, time = hs.calculate_time_evolution(time_step, duration)
# exciton_dm_history = hs.transform_to_exciton_basis(dm_history)
end = tutils.getTime()
print "Calculation took " + str(tutils.duration(end, start))
# convert time in inverse wavenums to picoseconds
time /= utils.WAVENUMS_TO_INVERSE_PS
def environment(beta, K):
return [(), \
(OBOscillator(reorg_energy, cutoff, beta, K=K),), \
(OBOscillator(reorg_energy, cutoff, beta, K=K),)]
model = DQDHEOMModelSparse(Gamma_L, Gamma_R, bias, T_c, beta=beta[0], environment=environment(beta[0], K), \
K=K, tc=True, trunc_level=5)
bias_values = np.linspace(-1, 1, 200) * 10
mean = np.zeros((len(beta)+1, bias_values.size))
F2 = np.zeros((len(beta)+1, bias_values.size))
#coherence = np.zeros((len(beta)+1, bias_values.size))
site_steady_states = np.zeros((len(beta)+1, bias_values.size, 9), dtype='complex128')
print "Starting calculation at " + str(tu.getTime())
for j,B in enumerate(beta):
print 'for beta = ' + str(B)
model.beta = B
model.environment = environment(B, K)
for i,E in enumerate(bias_values):
#print E
model.bias = E
solver = FCSSolver(model.heom_matrix(), model.jump_matrix(), model.dv_pops)
try:
mean[j+1,i] = solver.mean()
F2[j+1,i] = solver.second_order_fano_factor()
#coherence[j+1,i] = solver.ss[3].squeeze() + solver.ss[4].squeeze()
site_steady_states[j+1,i] = solver.ss[:9].squeeze()
except RuntimeError:
