plt.savefig(os.path.join(pre_out, ex2Dfile))
#plt.close()
print("... saved into: ", ex2Dfile)
#
# If the plotting window is reasonable, we should cut the unnecessary data
# by trimming the 2D spectrum
#
with qr.energy_units("1/cm"):
cont.trimall_to(window=plot_window)
#
# Window function for subsequenty FFT
#
import quantarhei.functions as func
window = func.Tukey(time_so, r=tukey_r, sym=False)
#
# FFT with the window function
#
# Specify REPH, NONR or `total` to get different types of spectra
#
print("\nCalculating FFT of the 2D maps")
fcont = cont.fft(window=window, dtype="total", offset=t_offset)
#
# Have a look which frequencies we actually have
#
Ndat = len(fcont.axis.data)
print("\nNumber of frequency points:", Ndat)
print("In 1/cm they are:")
cont1.trimall_to(window=plot_window)
cont2.trimall_to(window=plot_window)
#
#
# Global fit
# cont_residue contains the oscillatory residue
#
#params_guess = []
#params_out, cont_residue = cont.global_fit_exponential(params_guess)
#
# Window function for subsequenty FFT
#
import quantarhei.functions as func
window = func.Tukey(t2axis, r=0.3, sym=False)
#
# FFT of the spectra
#
print("\nCalculating FFT of the 2D maps")
fcont1 = cont1.fft(window=window, offset=0.0)
fcont2 = cont2.fft(window=window, offset=0.0)
#
# Have a look which frequencies we actually have
#
#Ndat = len(fcont1.axis.data)
#print("\nNumber of frequency points:", Ndat)
#print("In 1/cm they are:")
#with qr.energy_units("1/cm"):
def run(omega,
HR,
dE,
JJ,
rate,
E0,
vib_loc="up",
use_vib=True,
stype=qr.signal_REPH,
make_movie=False,
save_eUt=False,
t2_save_pathways=[],
dname=None,
trimer=None,
disE=None):
"""Runs a complete set of simulations for a single set of parameters
If disE is not None it tries to run averaging over Gaussian energetic
disorder.
"""
if dname is None:
dname = "sim_" + vib_loc
use_trimer = trimer["useit"]
rate_sp = trimer["rate"]
#
# PARAMETERS FROM INPUT FILE
#
dip1 = INP.dip1 # [1.5, 0.0, 0.0]
dip2 = INP.dip2 # [-1.0, -1.0, 0.0]
width = INP.feature_width # 100.0
width2 = INP.feature_width2
normalize_maps_to_maximu = False
trim_maps = False
units = "1/cm"
with qr.energy_units(units):
data_descr = "_dO=" + str(dE) + "_omega=" + str(omega) + "_HR=" + str(
HR) + "_J=" + str(JJ)
if use_vib:
sys_char = "_vib"
else:
sys_char = "_ele"
data_ext = sys_char + ".png"
obj_ext = sys_char + ".qrp"
# parameters of the SP
if use_trimer:
E2 = trimer["E2"]
epsa = (E0 + E2) / 2.0
DE = trimer["DE"]
J2 = 0.5 * numpy.sqrt(((E0 - E2)**2) - (DE**2))
ESP2 = epsa + DE / 2.0
ESP1 = epsa - DE / 2.0
#
# Model system is a dimer of molecules
#
with qr.energy_units("1/cm"):
if not use_trimer:
mol1 = qr.Molecule([0.0, E0])
mol2 = qr.Molecule([0.0, E0 + dE])
print("Monomer 1 energy:", E0)
print("Monomer 2 energy:", E0 + dE)
else:
if disE is not None:
mol1 = qr.Molecule([0.0, ESP2 + disE[0]])
mol2 = qr.Molecule([0.0, E0 + dE + disE[1]])
print("Monomer 1 (SP_high) energy:", ESP2 + disE[0])
print("Monomer 2 (B) energy:", E0 + dE + disE[1])
else:
mol1 = qr.Molecule([0.0, ESP2])
mol2 = qr.Molecule([0.0, E0 + dE])
print("Monomer 1 (SP_high) energy:", ESP2)
print("Monomer 2 (B) energy:", E0 + dE)
if disE is not None:
mol3 = qr.Molecule([0.0, ESP1 + disE[2]])
print("Monomer 3 (SP_low) energy:", ESP1 + disE[2])
else:
mol3 = qr.Molecule([0.0, ESP1])
print("Monomer 3 (SP_low) energy:", ESP1)
mol3.set_transition_width((0, 1), width2)
mol3.set_dipole(0, 1, trimer["dipsp"])
mol1.set_transition_width((0, 1), width2)
mol1.set_dipole(0, 1, dip1)
mol2.set_transition_width((0, 1), width)
mol2.set_dipole(0, 1, dip2)
if use_trimer:
agg = qr.Aggregate([mol1, mol2, mol3])
else:
agg = qr.Aggregate([mol1, mol2])
if use_trimer:
with qr.energy_units("1/cm"):
agg.set_resonance_coupling(0, 1, JJ)
print("B - SP_high coupling:", JJ)
agg.set_resonance_coupling(0, 2, J2)
print("SP coupling:", J2)
else:
with qr.energy_units("1/cm"):
agg.set_resonance_coupling(0, 1, JJ)
#
# Electronic only aggregate
#
agg_el = agg.deepcopy()
#
# if nuclear vibrations are to be added, do it here
#
if use_vib:
with qr.energy_units("1/cm"):
mod1 = qr.Mode(omega)
mod2 = qr.Mode(omega)
if vib_loc == "down":
set_vib = [True, False]
elif vib_loc == "up":
set_vib = [False, True]
elif vib_loc == "both":
set_vib = [True, True]
else:
raise Exception("Unknown location of the vibrations")
if set_vib[0]:
print("Vibrations set for SP_high molecule")
mol1.add_Mode(mod1)
mod1.set_nmax(0, INP.no_g_vib)
mod1.set_nmax(1, INP.no_e_vib)
mod1.set_HR(1, HR)
if set_vib[1]:
print("Vibrations set for B molecule")
mol2.add_Mode(mod2)
mod2.set_nmax(0, INP.no_g_vib)
mod2.set_nmax(1, INP.no_e_vib)
mod2.set_HR(1, HR)
agg3 = agg.deepcopy()
agg.build(mult=1)
agg_el.build(mult=1)
HH = agg.get_Hamiltonian()
He = agg_el.get_Hamiltonian()
#
# Laboratory setup
#
lab = qr.LabSetup()
lab.set_polarizations(pulse_polarizations=[X, X, X],
detection_polarization=X)
t2_N_steps = INP.t2_N_steps
t2_time_step = INP.t2_time_step
time2 = qr.TimeAxis(0.0, t2_N_steps, t2_time_step)
cont_p = qr.TwoDResponseContainer(t2axis=time2)
cont_m = qr.TwoDResponseContainer(t2axis=time2)
#
# spectra will be indexed by the times in the time axis `time2`
#
cont_p.use_indexing_type(time2)
#
# We define two-time axes, which will be FFTed and will define
# the omega_1 and omega_3 axes of the 2D spectrum
#
t1_N_steps = INP.t1_N_steps
t1_time_step = INP.t1_time_step
t3_N_steps = INP.t3_N_steps
t3_time_step = INP.t3_time_step
t1axis = qr.TimeAxis(0.0, t1_N_steps, t1_time_step)
t3axis = qr.TimeAxis(0.0, t3_N_steps, t3_time_step)
#
# This calculator calculated 2D spectra from the effective width
#
msc = qr.MockTwoDResponseCalculator(t1axis, time2, t3axis)
with qr.energy_units("1/cm"):
msc.bootstrap(rwa=E0, shape="Gaussian")
#
# System-bath interaction including vibrational states
#
operators = []
rates = []
if use_trimer:
print("Relaxation rates: ", rate, rate_sp)
with qr.eigenbasis_of(He):
if He.data[3, 3] < He.data[2, 2]:
Exception("Electronic states not orderred!")
operators.append(qr.qm.ProjectionOperator(2, 3, dim=He.dim))
with qr.energy_units("1/cm"):
print("2<-3", He.data[2, 2], He.data[3, 3])
rates.append(rate)
print("Transfer time B -> SP:", 1.0 / rate)
if He.data[2, 2] < He.data[1, 1]:
Exception("Electronic states not orderred!")
operators.append(qr.qm.ProjectionOperator(1, 2, dim=He.dim))
with qr.energy_units("1/cm"):
print("1<-2", He.data[1, 1], He.data[2, 2])
rates.append(rate_sp)
print("Transfer time P+ -> P-:", 1.0 / rate_sp)
# include detailed balace
if detailed_balance:
with qr.eigenbasis_of(He):
T = INP.temperature #77.0
Den = (He.data[3, 3] - He.data[2, 2]) / (kB_int * T)
operators.append(qr.qm.ProjectionOperator(3, 2, dim=He.dim))
thermal_fac = numpy.exp(-Den)
rates.append(rate * thermal_fac)
else:
with qr.eigenbasis_of(He):
if He.data[2, 2] < He.data[1, 1]:
Exception("Electronic states not orderred!")
operators.append(qr.qm.ProjectionOperator(1, 2, dim=He.dim))
rates.append(rate)
# include detailed balace
if detailed_balance:
with qr.eigenbasis_of(He):
T = INP.temperature #77.0
Den = (He.data[2, 2] - He.data[1, 1]) / (kB_int * T)
operators.append(qr.qm.ProjectionOperator(2, 1, dim=He.dim))
thermal_fac = numpy.exp(-Den)
rates.append(rate * thermal_fac)
sbi = qr.qm.SystemBathInteraction(sys_operators=operators, rates=rates)
sbi.set_system(agg)
#
# Liouville form for relaxation
#
LF = qr.qm.ElectronicLindbladForm(HH, sbi, as_operators=True)
#
# Pure dephasing
#
p_deph = qr.qm.ElectronicPureDephasing(agg, dtype="Gaussian")
# we simplify calculations by converting dephasing to
# corresponding Lorentzian form
p_deph.convert_to("Lorentzian")
eUt = qr.qm.EvolutionSuperOperator(time2,
HH,
relt=LF,
pdeph=p_deph,
mode="all")
eUt.set_dense_dt(INP.fine_splitting)
#
# We calculate evolution superoperator
#
eUt.calculate(show_progress=False)
# save the evolution operator
if save_eUt:
eut_name = os.path.join(
dname, "eUt" + "_omega2=" + str(omega) + data_descr + obj_ext)
eUt.save(eut_name)
#
# Prepare aggregate with all states (including 2-EX band)
#
agg3.build(mult=2)
agg3.diagonalize()
pways = dict()
olow_cm = omega - INP.omega_uncertainty / 2.0
ohigh_cm = omega + INP.omega_uncertainty / 2.0
olow = qr.convert(olow_cm, "1/cm", "int")
ohigh = qr.convert(ohigh_cm, "1/cm", "int")
for t2 in time2.data:
# this could save some memory of pathways become too big
pways = dict()
print("T2 =", t2)
twod = msc.calculate_one_system(t2,
agg3,
eUt,
lab,
pways=pways,
dtol=1.0e-12,
selection=[["omega2", [olow, ohigh]]])
pws = pways[str(t2)]
npa = len(pws)
print(" p:", npa)
has_R = False
has_NR = False
for pw in pws:
if pw.pathway_type == "NR":
has_NR = True
elif pw.pathway_type == "R":
has_R = True
print(" R:", has_R, ", NR:", has_NR)
if t2 in t2_save_pathways:
pws_name = os.path.join(
dname, "pws_t2=" + str(t2) + "_omega2=" + str(omega) +
data_descr + obj_ext)
qr.save_parcel(pways[str(t2)], pws_name)
cont_p.set_spectrum(twod)
twod = msc.calculate_one_system(t2,
agg3,
eUt,
lab,
pways=pways,
dtol=1.0e-12,
selection=[["omega2", [-ohigh,
-olow]]])
pws = pways[str(t2)]
npa = len(pws)
print(" m:", npa)
has_R = False
has_NR = False
for pw in pws:
if pw.pathway_type == "NR":
has_NR = True
elif pw.pathway_type == "R":
has_R = True
print(" R:", has_R, ", NR:", has_NR)
if t2 in t2_save_pathways:
pws_name = os.path.join(
dname, "pws_t2=" + str(t2) + "_omega2=" + str(-omega) +
data_descr + obj_ext)
qr.save_parcel(pways[str(t2)], pws_name)
cont_m.set_spectrum(twod)
if make_movie:
with qr.energy_units("1/cm"):
cont_p.make_movie("mov.mp4")
#
# Save aggregate when a single calculation is done
#
if save_eUt:
fname = os.path.join(dname, "aggregate.qrp")
agg3.save(fname)
#
# Window function for subsequenty FFT
#
window = func.Tukey(time2, r=INP.tukey_window_r, sym=False)
#
# FFT with the window function
#
# Specify REPH, NONR or `total` to get different types of spectra
#
print("Calculating FFT of the 2D maps")
#fcont = cont.fft(window=window, dtype=stype) #, dpart="real", offset=0.0)
fcont_p_re = cont_p.fft(window=window, dtype=qr.signal_REPH)
fcont_p_nr = cont_p.fft(window=window, dtype=qr.signal_NONR)
fcont_p_to = cont_p.fft(window=window, dtype=qr.signal_TOTL)
if normalize_maps_to_maximu:
fcont_p_re.normalize2(dpart=qr.part_ABS)
fcont_p_nr.normalize2(dpart=qr.part_ABS)
fcont_p_to.normalize2(dpart=qr.part_ABS)
fcont_m_re = cont_m.fft(window=window, dtype=qr.signal_REPH)
fcont_m_nr = cont_m.fft(window=window, dtype=qr.signal_NONR)
fcont_m_to = cont_m.fft(window=window, dtype=qr.signal_TOTL)
if normalize_maps_to_maximu:
fcont_m_re.normalize2(dpart=qr.part_ABS)
fcont_m_nr.normalize2(dpart=qr.part_ABS)
fcont_m_to.normalize2(dpart=qr.part_ABS)
if trim_maps:
twin = INP.trim_maps_to
with qr.energy_units("1/cm"):
fcont_p_re.trimall_to(window=twin)
fcont_p_nr.trimall_to(window=twin)
fcont_p_to.trimall_to(window=twin)
show_omega = omega
with qr.frequency_units("1/cm"):
sp1_p_re, show_Npoint1 = fcont_p_re.get_nearest(show_omega)
sp2_p_re, show_Npoint2 = fcont_p_re.get_nearest(-show_omega)
sp1_p_nr, show_Npoint1 = fcont_p_nr.get_nearest(show_omega)
sp2_p_nr, show_Npoint2 = fcont_p_nr.get_nearest(-show_omega)
sp1_p_to, show_Npoint1 = fcont_p_to.get_nearest(show_omega)
sp2_p_to, show_Npoint2 = fcont_p_to.get_nearest(-show_omega)
sp1_m_re, show_Npoint1 = fcont_m_re.get_nearest(show_omega)
sp2_m_re, show_Npoint2 = fcont_m_re.get_nearest(-show_omega)
sp1_m_nr, show_Npoint1 = fcont_m_nr.get_nearest(show_omega)
sp2_m_nr, show_Npoint2 = fcont_m_nr.get_nearest(-show_omega)
sp1_m_to, show_Npoint1 = fcont_m_to.get_nearest(show_omega)
sp2_m_to, show_Npoint2 = fcont_m_to.get_nearest(-show_omega)
with qr.energy_units(units):
if show_plots:
#
# Spots to look at in detail
#
with qr.energy_units("1/cm"):
with qr.eigenbasis_of(He):
Ep_l = He.data[1, 1]
Ep_u = He.data[2, 2]
Ep = numpy.zeros((4, 2))
Ep[0, 0] = Ep_l
Ep[0, 1] = Ep_l
Ep[1, 0] = Ep_l
Ep[1, 1] = Ep_u
Ep[2, 0] = Ep_u
Ep[2, 1] = Ep_l
Ep[3, 0] = Ep_u
Ep[3, 1] = Ep_u
print("\nPlotting and saving spectrum at frequency:",
fcont_p_re.axis.data[show_Npoint1], units)
fftf_1 = os.path.join(
dname, "twod_fft" + data_descr + "_stype=REPH" + "_omega=" +
str(omega) + data_ext)
sp1_p_re.plot(Npos_contours=10,
spart=qr.part_ABS,
label="Rephasing\n $\omega=" + str(omega) +
"$ cm$^{-1}$",
text_loc=[0.05, 0.1],
show_states=[Ep_l, Ep_u, Ep_u + numpy.abs(omega)],
show_diagonal="-k")
sp1_p_re.savefig(fftf_1)
print("... saved into: ", fftf_1)
fftf_2 = os.path.join(
dname, "twod_fft" + data_descr + "_stype=NONR" + "_omega=" +
str(omega) + data_ext)
sp1_p_nr.plot(Npos_contours=10,
spart=qr.part_ABS,
label="Non-rephasing\n $\omega=" + str(omega) +
"$ cm$^{-1}$",
text_loc=[0.05, 0.1],
show_states=[Ep_l, Ep_u, Ep_u + numpy.abs(omega)],
show_diagonal="-k")
sp1_p_nr.savefig(fftf_2)
print("... saved into: ", fftf_2)
fftf_3 = os.path.join(
dname, "twod_fft" + data_descr + "_stype=tot" + "_omega=" +
str(omega) + data_ext)
sp1_p_to.plot(Npos_contours=10,
spart=qr.part_ABS,
label="Total\n $\omega=" + str(omega) +
"$ cm$^{-1}$",
text_loc=[0.05, 0.1],
show_states=[Ep_l, Ep_u, Ep_u + numpy.abs(omega)],
show_diagonal="-k")
sp1_p_to.savefig(fftf_3)
print("... saved into: ", fftf_3)
#
# Point evolutions at the expected peak positions
#
for ii in range(4):
points = fcont_p_re.get_point_evolution(
Ep[ii, 0], Ep[ii, 1], fcont_p_re.axis)
points.apply_to_data(numpy.abs)
if ii >= 3:
points.plot(show=True)
else:
points.plot(show=False)
print("\nPlotting and saving spectrum at frequency:",
fcont_m_re.axis.data[show_Npoint2], units)
fftf_4 = os.path.join(
dname, "twod_fft" + data_descr + "_stype=REPH" + "_omega=" +
str(-omega) + data_ext)
sp2_m_re.plot(Npos_contours=10,
spart=qr.part_ABS,
label="Rephasing\n $\omega=" + str(-omega) +
"$ cm$^{-1}$",
text_loc=[0.05, 0.1],
show_states=[Ep_l, Ep_u, Ep_u + numpy.abs(omega)],
show_diagonal="-k")
sp2_m_re.savefig(fftf_4)
print("... saved into: ", fftf_4)
fftf_5 = os.path.join(
dname, "twod_fft" + data_descr + "_stype=NONR" + "_omega=" +
str(-omega) + data_ext)
sp2_m_nr.plot(Npos_contours=10,
spart=qr.part_ABS,
label="Non-rephasing\n $\omega=" + str(-omega) +
"$ cm$^{-1}$",
text_loc=[0.05, 0.1],
show_states=[Ep_l, Ep_u, Ep_u + numpy.abs(omega)],
show_diagonal="-k")
sp2_m_nr.savefig(fftf_5)
print("... saved into: ", fftf_5)
fftf_6 = os.path.join(
dname, "twod_fft" + data_descr + "_stype=tot" + "_omega=" +
str(-omega) + data_ext)
sp2_m_to.plot(Npos_contours=10,
spart=qr.part_ABS,
label="Total\n $\omega=" + str(-omega) +
"$ cm$^{-1}$",
text_loc=[0.05, 0.1],
show_states=[Ep_l, Ep_u, Ep_u + numpy.abs(omega)],
show_diagonal="-k")
sp2_m_to.savefig(fftf_6)
print("... saved into: ", fftf_6)
#
# Point evolutions at the expected peak positions
#
for ii in range(4):
points = fcont_p_re.get_point_evolution(
Ep[ii, 0], Ep[ii, 1], fcont_m_re.axis)
points.apply_to_data(numpy.abs)
if ii >= 3:
points.plot(show=True)
else:
points.plot(show=False)
save_containers = False
if save_containers:
fname = os.path.join(dname, "cont_p" + data_descr + obj_ext)
print("Saving container into: " + fname)
cont_p.save(fname)
fname = os.path.join(dname, "cont_m" + data_descr + obj_ext)
print("Saving container into: " + fname)
cont_m.save(fname)
import resource
memo = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / (1024 * 1024)
print("Memory usage: ", memo, "in MB")
return (sp1_p_re, sp1_p_nr, sp2_m_re, sp2_m_nr)
fgrn = os.path.join(drnm, "sp_mono_" + str(tg) + ".png")
sp.plot(show=False)
sp.savefig(fgrn)
print("Saving " + flnm)
sp.save_data(flnm)
if ext == "dat":
_data = numpy.loadtxt(flnm, dtype=complex)
print("max=", numpy.max(_data))
save_spectra(cont_tot, "dat")
#
# Window function for subsequenty FFT
#
window = func.Tukey(time2, r=tukey_window_r, sym=False)
#
# FFT with the window function
#
# Specify REPH, NONR or `total` to get different types of spectra
#
print("Calculating FFT of the 2D maps")
#fcont = cont.fft(window=window, dtype=stype) #, dpart="real", offset=0.0)
print("Positive frequency:")
fcont_p_re = cont_p.fft(window=window, dtype=qr.signal_REPH)
print("# 1/3")
fcont_p_nr = cont_p.fft(window=window, dtype=qr.signal_NONR)
print("# 2/3")
fcont_p_to = cont_p.fft(window=window, dtype=qr.signal_TOTL)
def run(omega,
HR,
dE,
JJ,
rate,
E0,
vib_loc="up",
use_vib=True,
detailed_balance=False,
temperature=77.0,
stype=qr.signal_REPH,
save_eUt=False,
t2_save_pathways=[],
dname=None,
trimer=None,
disE=None):
"""Runs a complete set of simulations for a single set of parameters
If disE is not None it tries to run averaging over Gaussian energetic
disorder.
"""
if dname is None:
dname = "sim_" + vib_loc
use_trimer = trimer["useit"]
rate_sp = trimer["rate"]
#
# PARAMETERS FROM INPUT FILE
#
dip1 = INP.dip1 # [1.5, 0.0, 0.0]
dip2 = INP.dip2 # [-1.0, -1.0, 0.0]
width = INP.feature_width # 100.0
width2 = INP.feature_width2
normalize_maps_to_maximu = False
trim_maps = False
units = "1/cm"
with qr.energy_units(units):
data_descr = "_dO="+str(dE)+"_omega="+str(omega)+ \
"_HR="+str(HR)+"_J="+str(JJ)
if use_vib:
sys_char = "_vib"
else:
sys_char = "_ele"
data_ext = sys_char + ".png"
obj_ext = sys_char + ".qrp"
# parameters of the SP
if use_trimer:
E2 = trimer["E2"]
epsa = (E0 + E2) / 2.0
DE = trimer["DE"]
J2 = 0.5 * numpy.sqrt(((E0 - E2)**2) - (DE**2))
ESP2 = epsa + DE / 2.0
ESP1 = epsa - DE / 2.0
#
# Model system is a dimer of molecules
#
with qr.energy_units("1/cm"):
if not use_trimer:
if disE is not None:
mol1 = qr.Molecule([0.0, E0 + disE[0]])
mol2 = qr.Molecule([0.0, E0 + dE + disE[1]])
print("Monomer 1 energy:", E0 + disE[0], "1/cm")
print("Monomer 2 energy:", E0 + dE + disE[1], "1/cm")
else:
mol1 = qr.Molecule([0.0, E0])
mol2 = qr.Molecule([0.0, E0 + dE])
print("Monomer 1 energy:", E0, "1/cm")
print("Monomer 2 energy:", E0 + dE, "1/cm")
else:
if disE is not None:
mol1 = qr.Molecule([0.0, ESP2 + disE[0]])
mol2 = qr.Molecule([0.0, E0 + dE + disE[1]])
print("Monomer 1 (SP_high) energy:", ESP2 + disE[0], "1/cm")
print("Monomer 2 (B) energy:", E0 + dE + disE[1], "1/cm")
else:
mol1 = qr.Molecule([0.0, ESP2])
mol2 = qr.Molecule([0.0, E0 + dE])
print("Monomer 1 (SP_high) energy:", ESP2, "1/cm")
print("Monomer 2 (B) energy:", E0 + dE, "1/cm")
if disE is not None:
mol3 = qr.Molecule([0.0, ESP1 + disE[2]])
print("Monomer 3 (SP_low) energy:", ESP1 + disE[2], "1/cm")
else:
mol3 = qr.Molecule([0.0, ESP1])
print("Monomer 3 (SP_low) energy:", ESP1, "1/cm")
mol3.set_transition_width((0, 1), width2)
mol3.set_dipole(0, 1, trimer["dipsp"])
mol1.set_transition_width((0, 1), width2)
mol1.set_dipole(0, 1, dip1)
mol2.set_transition_width((0, 1), width)
mol2.set_dipole(0, 1, dip2)
if use_trimer:
agg = qr.Aggregate([mol1, mol2, mol3])
else:
agg = qr.Aggregate([mol1, mol2])
if use_trimer:
with qr.energy_units("1/cm"):
agg.set_resonance_coupling(0, 1, JJ)
print("B - SP_high coupling:", JJ, "1/cm")
agg.set_resonance_coupling(0, 2, J2)
print("SP coupling:", J2, "1/cm")
else:
with qr.energy_units("1/cm"):
agg.set_resonance_coupling(0, 1, JJ)
#
# Electronic only aggregate
#
agg_el = agg.deepcopy()
#
# if nuclear vibrations are to be added, do it here
#
if use_vib:
with qr.energy_units("1/cm"):
mod1 = qr.Mode(omega)
mod2 = qr.Mode(omega)
if vib_loc == "down":
set_vib = [True, False]
elif vib_loc == "up":
set_vib = [False, True]
elif vib_loc == "both":
set_vib = [True, True]
else:
raise Exception("Unknown location of the vibrations")
if set_vib[0]:
print("Vibrations set for SP_high molecule")
mol1.add_Mode(mod1)
mod1.set_nmax(0, INP.vibmode["no_g_vib"])
mod1.set_nmax(1, INP.vibmode["no_e_vib"])
mod1.set_HR(1, HR)
if set_vib[1]:
print("Vibrations set for B molecule")
mol2.add_Mode(mod2)
mod2.set_nmax(0, INP.vibmode["no_g_vib"])
mod2.set_nmax(1, INP.vibmode["no_e_vib"])
mod2.set_HR(1, HR)
#
# Before we build the aggregate, we make its copy to have an unbuilt version
#
agg3 = agg.deepcopy()
aggA = agg.deepcopy()
#
# here we build the complete aggregate and its electronic only version
#
agg.build(mult=1)
agg_el.build(mult=1)
aggA.build(mult=1)
# total Hamiltonian
HH = agg.get_Hamiltonian()
# electronic Hamiltonian
He = agg_el.get_Hamiltonian()
#
# Laboratory setup
#
lab = qr.LabSetup()
lab.set_polarizations(pulse_polarizations=[X, X, X],
detection_polarization=X)
#
# Time axis for the calculation of excited state evolution
#
t2_N_steps = INP.t2_N_steps
t2_time_step = INP.t2_time_step
time2 = qr.TimeAxis(0.0, t2_N_steps, t2_time_step)
#
# Containers for 2D maps with positive and negative frequencies
#
cont_p = qr.TwoDResponseContainer(t2axis=time2)
cont_m = qr.TwoDResponseContainer(t2axis=time2)
cont_tot = qr.TwoDResponseContainer(t2axis=time2)
#
# spectra will be indexed by the times in the time axis `time2`
#
cont_p.use_indexing_type(time2)
cont_m.use_indexing_type(time2)
cont_tot.use_indexing_type(time2)
#
# We define two-time axes, which will be FFTed and will define
# the omega_1 and omega_3 axes of the 2D spectrum
#
t1_N_steps = INP.t1_N_steps
t1_time_step = INP.t1_time_step
t3_N_steps = INP.t3_N_steps
t3_time_step = INP.t3_time_step
t1axis = qr.TimeAxis(0.0, t1_N_steps, t1_time_step)
t3axis = qr.TimeAxis(0.0, t3_N_steps, t3_time_step)
#
# This calculator calculated 2D spectra from the effective width
#
msc = qr.MockTwoDResponseCalculator(t1axis, time2, t3axis)
with qr.energy_units("1/cm"):
msc.bootstrap(rwa=E0, shape="Gaussian")
#
# System-bath interaction including vibrational states
#
operators = []
rates = []
print("Relaxation rates: ", rate, rate_sp, "1/fs")
with qr.eigenbasis_of(He):
if use_trimer:
if He.data[3, 3] < He.data[2, 2]:
Exception("Electronic states not orderred!")
operators.append(qr.qm.ProjectionOperator(2, 3, dim=He.dim))
with qr.energy_units("1/cm"):
print("2 <- 3 : energies = ", He.data[2, 2], He.data[3, 3],
"1/cm")
rates.append(rate)
print("Transfer time 2 <- 3:", 1.0 / rate, "fs")
if He.data[2, 2] < He.data[1, 1]:
Exception("Electronic states not orderred!")
operators.append(qr.qm.ProjectionOperator(1, 2, dim=He.dim))
with qr.energy_units("1/cm"):
print("1 <- 2 : energies = ", He.data[1, 1], He.data[2, 2],
"1/cm")
rates.append(rate_sp)
print("Transfer time 1 <- 2", 1.0 / rate_sp, "fs")
else:
if He.data[2, 2] < He.data[1, 1]:
Exception("Electronic states not orderred!")
operators.append(qr.qm.ProjectionOperator(1, 2, dim=He.dim))
with qr.energy_units("1/cm"):
print("1 <- 2 : energies = ", He.data[1, 1], He.data[2, 2],
"1/cm")
rates.append(rate_sp)
print("Transfer time 1 <- 2", 1.0 / rate, "fs")
# include detailed balace
if detailed_balance:
if use_trimer:
with qr.eigenbasis_of(He):
T = temperature #77.0
Den = (He.data[3, 3] - He.data[2, 2]) / (kB_int * T)
operators.append(qr.qm.ProjectionOperator(3, 2, dim=He.dim))
thermal_fac = numpy.exp(-Den)
rates.append(rate * thermal_fac)
Den = (He.data[2, 2] - He.data[1, 1]) / (kB_int * T)
operators.append(qr.qm.ProjectionOperator(2, 1, dim=He.dim))
thermal_fac = numpy.exp(-Den)
rates.append(rate_sp * thermal_fac)
else:
with qr.eigenbasis_of(He):
T = temperature #77.0
Den = (He.data[2, 2] - He.data[1, 1]) / (kB_int * T)
operators.append(qr.qm.ProjectionOperator(2, 1, dim=He.dim))
thermal_fac = numpy.exp(-Den)
rates.append(rate * thermal_fac)
sbi = qr.qm.SystemBathInteraction(sys_operators=operators, rates=rates)
sbi.set_system(agg)
#
# Lindblad form for relaxation
#
LF = qr.qm.ElectronicLindbladForm(HH, sbi, as_operators=True)
#
# Pure dephasing
#
p_deph = qr.qm.ElectronicPureDephasing(agg, dtype="Gaussian")
# we simplify calculations by converting dephasing to
# corresponding Lorentzian form
p_deph.convert_to("Lorentzian")
eUt = qr.qm.EvolutionSuperOperator(time2,
HH,
relt=LF,
pdeph=p_deph,
mode="all")
eUt.set_dense_dt(INP.fine_splitting)
print("---")
#
# We calculate evolution superoperator
#
eUt.calculate(show_progress=False)
# save the evolution operator
if save_eUt:
eut_name = os.path.join(
dname, "eUt" + "_omega2=" + str(omega) + data_descr + obj_ext)
eUt.save(eut_name)
#
# Prepare aggregate with all states (including 2-EX band)
#
agg3.build(mult=2)
agg3.diagonalize()
agg_el.diagonalize()
print("")
print("Square of the transition dipoles")
print(agg_el.D2)
print("")
print("---")
#
# calculation of absorption spectrum
#
time1 = qr.TimeAxis(0.0, 1000, 5.0)
absc = qr.MockAbsSpectrumCalculator(time1, system=aggA)
with qr.energy_units("1/cm"):
absc.bootstrap(rwa=E0)
spctrm = absc.calculate()
spctrm.normalize2()
with qr.energy_units("1/cm"):
spctrm.plot(show=False, axis=[10500.0, 13500.0, 0.0, 1.1])
spctrm.savefig(os.path.join(dname, "abs.png"))
spctrm.save_data(os.path.join(dname, "abs.dat"))
pways = dict()
olow_cm = omega - INP.omega_uncertainty / 2.0
ohigh_cm = omega + INP.omega_uncertainty / 2.0
olow = qr.convert(olow_cm, "1/cm", "int")
ohigh = qr.convert(ohigh_cm, "1/cm", "int")
for t2 in time2.data:
# this could save some memory of pathways become too big
pways = dict()
print("T2 =", t2, "fs (of T2_max =", time2.max, "fs)")
twod = msc.calculate_one_system(t2,
agg3,
eUt,
lab,
pways=pways,
dtol=1.0e-12,
selection=[["omega2", [olow, ohigh]]])
pws = pways[str(t2)]
npa = len(pws)
has_R = False
has_NR = False
for pw in pws:
if pw.pathway_type == "NR":
has_NR = True
elif pw.pathway_type == "R":
has_R = True
if t2 in t2_save_pathways:
pws_name = os.path.join(
dname, "pws_t2=" + str(t2) + "_omega2=" + str(omega) +
data_descr + obj_ext)
qr.save_parcel(pways[str(t2)], pws_name)
cont_p.set_spectrum(twod)
twod = msc.calculate_one_system(t2,
agg3,
eUt,
lab,
pways=pways,
dtol=1.0e-12,
selection=[["omega2", [-ohigh,
-olow]]])
pws = pways[str(t2)]
npa = len(pws)
#print(" m:", npa)
has_R = False
has_NR = False
for pw in pws:
if pw.pathway_type == "NR":
has_NR = True
elif pw.pathway_type == "R":
has_R = True
#print(" R:", has_R, ", NR:", has_NR)
if t2 in t2_save_pathways:
pws_name = os.path.join(
dname, "pws_t2=" + str(t2) + "_omega2=" + str(-omega) +
data_descr + obj_ext)
qr.save_parcel(pways[str(t2)], pws_name)
cont_m.set_spectrum(twod)
# calculation without pre-selecting pathways
twod = msc.calculate_one_system(t2,
agg3,
eUt,
lab,
pways=pways,
dtol=1.0e-12)
cont_tot.set_spectrum(twod)
# saving total spectrum to a directory for further analysis
try:
saveit = INP.total_spectrum["save_it"]
except:
saveit = False
if saveit:
try:
dform = INP.total_spectrum["data_format"]
except:
dform = "dat"
try:
stp = INP.total_spectrum["spectra_type"]
if stp == "TOTL":
stype = qr.signal_TOTL
elif stp == "REPH":
stype = qr.signal_REPH
elif stp == "NONR":
stype = qr.signal_NONR
else:
raise Exception("Wrong signal type")
except:
stype = qr.signal_REPH
save_spectra(cont_tot, ext=dform, dir=dname, stype=stype)
#
# Save aggregate when a single calculation is done
#
if save_eUt:
fname = os.path.join(dname, "aggregate.qrp")
agg3.save(fname)
#
# Window function for subsequenty FFT
#
window = func.Tukey(time2, r=INP.tukey_window_r, sym=False)
#
# FFT with the window function
#
# Specify REPH, NONR or `total` to get different types of spectra
#
print("Calculating FFT of the 2D maps")
#fcont = cont.fft(window=window, dtype=stype) #, dpart="real", offset=0.0)
fcont_p_re = cont_p.fft(window=window, dtype=qr.signal_REPH)
fcont_p_nr = cont_p.fft(window=window, dtype=qr.signal_NONR)
fcont_p_to = cont_p.fft(window=window, dtype=qr.signal_TOTL)
if normalize_maps_to_maximu:
fcont_p_re.normalize2(dpart=qr.part_ABS)
fcont_p_nr.normalize2(dpart=qr.part_ABS)
fcont_p_to.normalize2(dpart=qr.part_ABS)
fcont_m_re = cont_m.fft(window=window, dtype=qr.signal_REPH)
fcont_m_nr = cont_m.fft(window=window, dtype=qr.signal_NONR)
fcont_m_to = cont_m.fft(window=window, dtype=qr.signal_TOTL)
if normalize_maps_to_maximu:
fcont_m_re.normalize2(dpart=qr.part_ABS)
fcont_m_nr.normalize2(dpart=qr.part_ABS)
fcont_m_to.normalize2(dpart=qr.part_ABS)
if trim_maps:
twin = INP.trim_maps_to
with qr.energy_units("1/cm"):
fcont_p_re.trimall_to(window=twin)
fcont_p_nr.trimall_to(window=twin)
fcont_p_to.trimall_to(window=twin)
show_omega = omega
with qr.frequency_units("1/cm"):
sp1_p_re, show_Npoint1 = fcont_p_re.get_nearest(show_omega)
sp2_p_re, show_Npoint2 = fcont_p_re.get_nearest(-show_omega)
sp1_p_nr, show_Npoint1 = fcont_p_nr.get_nearest(show_omega)
sp2_p_nr, show_Npoint2 = fcont_p_nr.get_nearest(-show_omega)
sp1_p_to, show_Npoint1 = fcont_p_to.get_nearest(show_omega)
sp2_p_to, show_Npoint2 = fcont_p_to.get_nearest(-show_omega)
sp1_m_re, show_Npoint1 = fcont_m_re.get_nearest(show_omega)
sp2_m_re, show_Npoint2 = fcont_m_re.get_nearest(-show_omega)
sp1_m_nr, show_Npoint1 = fcont_m_nr.get_nearest(show_omega)
sp2_m_nr, show_Npoint2 = fcont_m_nr.get_nearest(-show_omega)
sp1_m_to, show_Npoint1 = fcont_m_to.get_nearest(show_omega)
sp2_m_to, show_Npoint2 = fcont_m_to.get_nearest(-show_omega)
sstm = platform.system()
#print(sstm)
if sstm != "Windows":
import resource
memo = resource.getrusage(
resource.RUSAGE_SELF).ru_maxrss / (1024 * 1024)
print("Memory usage: ", memo, "in MB")
return (sp1_p_re, sp1_p_nr, sp2_m_re, sp2_m_nr)
#
# twod spectrum object is under revision - use d__data instead of data if you want to reach the raw data
#
sp = cont.get_spectrum(10.0)
sp.set_data_flag("total")
print(sp.d__data[33, 30])
# In[23]:
#
# Window function for subsequenty FFT
#
import quantarhei.functions as func
window = func.Tukey(time_so, r=0.3, sym=False)
#
# FFT with the window function
#
# Specify REPH, NONR or `total` to get different types of spectra
#
fcont = cont.fft(window=window, dtype="REPH")
# In[24]:
#
# Here we save all results
#
# save container with spectra