def test_set_resonance_coupling_matrix(self):
"""(Aggregate) Testing setting the whole resonance coupling matrix
"""
agg = TestAggregate(name="dimer-2-env")
mat = [[0.0, 1.0],
[1.0, 0.0]]
agg.set_resonance_coupling_matrix(mat)
self.assertAlmostEqual(agg.resonance_coupling[1,0], 1.0)
self.assertAlmostEqual(agg.resonance_coupling[0,1], 1.0)
self.assertAlmostEqual(agg.resonance_coupling[0,0], 0.0)
self.assertAlmostEqual(agg.resonance_coupling[1,1], 0.0)
mat = ((0.0, 500.0),
(500.0, 0.0))
with qr.energy_units("1/cm"):
agg.set_resonance_coupling_matrix(mat)
inint = qr.convert(500, "1/cm", "int")
self.assertAlmostEqual(agg.resonance_coupling[1,0], inint)
self.assertAlmostEqual(agg.resonance_coupling[0,1], inint)
self.assertAlmostEqual(agg.resonance_coupling[0,0], 0.0)
self.assertAlmostEqual(agg.resonance_coupling[1,1], 0.0)
def set_cor_func(for_agg, params, ax_len = 10000):
ax_length = ax_len
# Needs to have small step for the dynamics to converge
t_ax_sd = qr.TimeAxis(0.0, ax_length, 1)
db = SpectralDensityDB()
# Parameters for spectral density. ODBO, Renger or Silbey
params = params
with qr.energy_units('1/cm'):
sd_low_freq = qr.SpectralDensity(t_ax_sd, params)
reorg = qr.convert(sd_low_freq.measure_reorganization_energy(), "int", "1/cm")
print("spectral density reorg -", reorg)
# Adding the high freq modes
sd_high_freq = db.get_SpectralDensity(t_ax_sd, "Wendling_JPCB_104_2000_5825")
ax = sd_low_freq.axis
sd_high_freq.axis = ax
sd_tot = sd_low_freq + sd_high_freq
cf = sd_tot.get_CorrelationFunction(temperature=params['T'], ta=t_ax_sd)
# Assigning the correlation function to the list of molecules
for mol in for_agg:
mol.set_transition_environment((0,1),cf)
def test_set_resonance_coupling(self):
"""(Aggregate) Testing resonance coupling setting with different units
"""
agg = TestAggregate(name="dimer-2-env")
agg.init_coupling_matrix()
agg.set_resonance_coupling(0, 1, 1.0)
self.assertAlmostEqual(1.0, agg.resonance_coupling[0,1])
self.assertAlmostEqual(1.0, agg.resonance_coupling[1,0])
with qr.energy_units("1/cm"):
agg.set_resonance_coupling(0, 1, 100.0)
self.assertAlmostEqual(qr.convert(100.0, "1/cm", "int"),
agg.resonance_coupling[0,1])
self.assertAlmostEqual(qr.convert(100.0, "1/cm", "int"),
agg.resonance_coupling[1,0])
def test_set_resonance_coupling(self):
"""(Aggregate) Testing resonance coupling setting with different units
"""
agg = TestAggregate(name="dimer-2-env")
agg.init_coupling_matrix()
agg.set_resonance_coupling(0, 1, 1.0)
self.assertAlmostEqual(1.0, agg.resonance_coupling[0, 1])
self.assertAlmostEqual(1.0, agg.resonance_coupling[1, 0])
with qr.energy_units("1/cm"):
agg.set_resonance_coupling(0, 1, 100.0)
self.assertAlmostEqual(qr.convert(100.0, "1/cm", "int"),
agg.resonance_coupling[0, 1])
self.assertAlmostEqual(qr.convert(100.0, "1/cm", "int"),
agg.resonance_coupling[1, 0])
def test_dipole_dipole_coupling(self):
"""(Aggregate) Testing dipole-dipole coupling calculation
"""
agg = TestAggregate(name="dimer-2-env")
coup1 = agg.dipole_dipole_coupling(0, 1)
coup2 = agg.dipole_dipole_coupling(0, 1, epsr=2.0)
self.assertAlmostEqual(coup1, coup2 * 2.0)
with qr.energy_units("1/cm"):
coup = agg.dipole_dipole_coupling(0, 1)
self.assertAlmostEqual(qr.convert(coup1, "int", "1/cm"), coup)
def test_get_resonance_coupling(self):
"""(Aggregate) Testing resonance coupling retrieval in different units
"""
agg = TestAggregate(name="dimer-2-env")
agg.init_coupling_matrix()
agg.set_resonance_coupling(0, 1, 1.0)
coup = agg.get_resonance_coupling(1,0)
self.assertAlmostEqual(coup, 1.0)
with qr.energy_units("1/cm"):
coup = agg.get_resonance_coupling(1,0)
self.assertAlmostEqual(coup, qr.convert(1.0, "int", "1/cm"))
with qr.energy_units("eV"):
coup = agg.get_resonance_coupling(1,0)
self.assertAlmostEqual(coup, qr.convert(1.0, "int", "eV"))
with qr.energy_units("THz"):
coup = agg.get_resonance_coupling(1,0)
self.assertAlmostEqual(coup, qr.convert(1.0, "int", "THz"))
def test_dipole_dipole_coupling(self):
"""(Aggregate) Testing dipole-dipole coupling calculation
"""
agg = TestAggregate(name="dimer-2-env")
coup1 = agg.dipole_dipole_coupling(0,1)
coup2 = agg.dipole_dipole_coupling(0,1, epsr=2.0)
self.assertAlmostEqual(coup1, coup2*2.0)
with qr.energy_units("1/cm"):
coup = agg.dipole_dipole_coupling(0,1)
self.assertAlmostEqual(qr.convert(coup1,"int","1/cm"), coup)
def test_get_resonance_coupling(self):
"""(Aggregate) Testing resonance coupling retrieval in different units
"""
agg = TestAggregate(name="dimer-2-env")
agg.init_coupling_matrix()
agg.set_resonance_coupling(0, 1, 1.0)
coup = agg.get_resonance_coupling(1, 0)
self.assertAlmostEqual(coup, 1.0)
with qr.energy_units("1/cm"):
coup = agg.get_resonance_coupling(1, 0)
self.assertAlmostEqual(coup, qr.convert(1.0, "int", "1/cm"))
with qr.energy_units("eV"):
coup = agg.get_resonance_coupling(1, 0)
self.assertAlmostEqual(coup, qr.convert(1.0, "int", "eV"))
with qr.energy_units("THz"):
coup = agg.get_resonance_coupling(1, 0)
self.assertAlmostEqual(coup, qr.convert(1.0, "int", "THz"))
def test_set_resonance_coupling_matrix(self):
"""(Aggregate) Testing setting the whole resonance coupling matrix
"""
agg = TestAggregate(name="dimer-2-env")
mat = [[0.0, 1.0], [1.0, 0.0]]
agg.set_resonance_coupling_matrix(mat)
self.assertAlmostEqual(agg.resonance_coupling[1, 0], 1.0)
self.assertAlmostEqual(agg.resonance_coupling[0, 1], 1.0)
self.assertAlmostEqual(agg.resonance_coupling[0, 0], 0.0)
self.assertAlmostEqual(agg.resonance_coupling[1, 1], 0.0)
mat = ((0.0, 500.0), (500.0, 0.0))
with qr.energy_units("1/cm"):
agg.set_resonance_coupling_matrix(mat)
inint = qr.convert(500, "1/cm", "int")
self.assertAlmostEqual(agg.resonance_coupling[1, 0], inint)
self.assertAlmostEqual(agg.resonance_coupling[0, 1], inint)
self.assertAlmostEqual(agg.resonance_coupling[0, 0], 0.0)
self.assertAlmostEqual(agg.resonance_coupling[1, 1], 0.0)
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)
# Aggregate with vibrational states, Hamiltonian generated up to single
# exciton states in a Two-particle approximation
# This object is used for calculation of dynamics
#
agg.build(mult=1, vibgen_approx="TPA")
#
# agg2 object will be used for calculation of 2D spectra
# so it needs to know how to represent the spectra (what width it should have).
# It also has to be built with
# two-exciton states (in two-particle approximation)
#
for mol_name in transition_widths:
width = qr.convert(transition_widths[mol_name], "1/cm", "int")
mol = agg2.get_Molecule_by_name(mol_name)
mol.set_transition_width((0,1), width)
print("Aggregate has ", agg2.nmono, "single excited electronic states")
agg2.build(mult=2, vibgen_approx="TPA")
print("and ", agg2.Ntot, " (electro-vibrational) states in total")
print("Number of single exciton states :", agg2.Nb[0]+agg2.Nb[1])
qr.save_parcel(agg2, os.path.join(pre_out,"agg2_built.qrp"))
#
# Electronic aggregate is built with single exciton states only
import numpy
import quantarhei as qr
qr.Manager().gen_conf.legacy_relaxation = True
print("Preparing a model system:")
with qr.energy_units("1/cm"):
mol1 = qr.Molecule([0.0, 12010])
mol2 = qr.Molecule([0.0, 12000])
mol3 = qr.Molecule([0.0, 12100])
mol4 = qr.Molecule([0.0, 12110])
agg = qr.Aggregate([mol1, mol2, mol3, mol4])
agg.set_resonance_coupling(2, 3, qr.convert(100.0, "1/cm", "int"))
agg.set_resonance_coupling(1, 3, qr.convert(100.0, "1/cm", "int"))
agg.set_resonance_coupling(1, 2, qr.convert(0.0, "1/cm", "int"))
qr.save_parcel(agg, "agg.qrp")
agg2 = qr.load_parcel("agg.qrp")
agg2.build()
H = agg2.get_Hamiltonian()
print("...done")
print("Setting up Lindblad form relaxation:")
Ndim = 5
with qr.eigenbasis_of(H):
K12 = qr.qm.ProjectionOperator(1, 2, dim=Ndim)
def test_LindbladWithVibrations_dynamics_comp(self):
"""Compares Lindblad dynamics of a system with vibrations calculated from propagator and superoperator
"""
# Aggregate
import quantarhei as qr
time = qr.TimeAxis(0.0, 1000, 1.0)
# create a model
with qr.energy_units("1/cm"):
me1 = qr.Molecule([0.0, 12100.0])
me2 = qr.Molecule([0.0, 12000.0])
me3 = qr.Molecule([0.0, 12900.0])
agg_el = qr.Aggregate([me1, me2, me3])
agg_el.set_resonance_coupling(0, 1,
qr.convert(150, "1/cm", to="int"))
agg_el.set_resonance_coupling(1, 2, qr.convert(50,
"1/cm",
to="int"))
m1 = qr.Molecule([0.0, 12100.0])
m2 = qr.Molecule([0.0, 12000.0])
m3 = qr.Molecule([0.0, 12900.0])
mod1 = qr.Mode(frequency=qr.convert(100, "1/cm", "int"))
m1.add_Mode(mod1)
mod1.set_HR(1, 0.01)
agg = qr.Aggregate([m1, m2, m3])
agg.set_resonance_coupling(0, 1, qr.convert(150, "1/cm", to="int"))
agg.set_resonance_coupling(1, 2, qr.convert(50, "1/cm", to="int"))
agg_el.build()
agg.build()
hame = agg_el.get_Hamiltonian()
ham = agg.get_Hamiltonian()
# calculate relaxation tensor
with qr.eigenbasis_of(hame):
#
# Operator describing relaxation
#
K = qr.qm.ProjectionOperator(1, 2, dim=hame.dim)
#
# System bath interaction with prescribed rate
#
from quantarhei.qm import SystemBathInteraction
sbi = SystemBathInteraction(sys_operators=[K],
rates=(1.0 / 100.0, ))
sbi.set_system(agg) #agg.set_SystemBathInteraction(sbi)
with qr.eigenbasis_of(ham):
#
# Corresponding Lindblad form
#
from quantarhei.qm import ElectronicLindbladForm
LF = ElectronicLindbladForm(ham, sbi, as_operators=True)
#
# Evolution of reduced density matrix
#
prop = qr.ReducedDensityMatrixPropagator(time, ham, LF)
#
# Evolution by superoperator
#
eSO = qr.qm.EvolutionSuperOperator(time, ham, LF)
eSO.set_dense_dt(5)
eSO.calculate()
# compare the two propagations
pairs = [(5, 4), (5, 5), (6, 5), (7, 5)]
for p in pairs:
rho_i1 = qr.ReducedDensityMatrix(dim=ham.dim)
rho_i1.data[p[0], p[1]] = 1.0
rho_t1 = prop.propagate(rho_i1)
exp_rho_t2 = eSO.data[:, :, :, p[0], p[1]]
#import matplotlib.pyplot as plt
#plt.plot(rho_t1.TimeAxis.data, numpy.real(rho_t1.data[:,p[0],p[1]]), "-r")
#plt.plot(rho_t1.TimeAxis.data, numpy.real(exp_rho_t2[:,p[0],p[1]]), "--g")
#plt.show()
#for kk in range(rho_t1.TimeAxis.length):
# print(kk, numpy.real(rho_t1.data[kk,p[0],p[1]]),numpy.real(exp_rho_t2[kk,p[0],p[1]]))
#numpy.testing.assert_allclose(RRT.data, rtd)
numpy.testing.assert_allclose(numpy.real(rho_t1.data[:, :, :]),
numpy.real(exp_rho_t2[:, :, :]),
rtol=5.0e-2,
atol=1.0e-3)
# Create an aggregate
#
agg = qr.Aggregate(molecules=[mol])
agg.build()
#
# Time axes and the calculator
#
t1axis = qr.TimeAxis(0.0, 1000, 10.0)
t3axis = qr.TimeAxis(0.0, 1000, 10.0)
t2axis = qr.TimeAxis(0.0, Nt2, dt2)
# FIXME: TwoDResponseCalculator
msc = qr.MockTwoDResponseCalculator(t1axis, t2axis, t3axis)
msc.bootstrap(rwa=qr.convert(E1,"1/cm","int"),
all_positive=False, shape="Gaussian")
#
# Laboratory setup
#
lab = LabSetup()
lab.set_polarizations(pulse_polarizations=[X,X,X], detection_polarization=X)
#
# Hamiltonian is required
#
H = agg.get_Hamiltonian()
#
with qr.energy_units("1/cm"):
agg.set_resonance_coupling_matrix(J_Matrix[0:3, 0:3])
#agg.save("RC_Model_40_4_adjusted_CT_no_environment_unbuilt.hdf5")
qr.save_parcel(
agg,
os.path.join(pre_out,
"RC_Model_40_4_adjusted_CT_no_environment_unbuilt.qrp"))
# In[3]:
# check that units were set correctly
rc = agg.resonance_coupling[1, 0]
with qr.energy_units("1/cm"):
print(qr.convert(rc, "int"))
with qr.energy_units("1/cm"):
print(agg.get_resonance_coupling(1, 0))
# In[4]:
# Bath correlation function
time = qr.TimeAxis(0.0, 1000, 1.0)
cfA_params = dict(ftype="OverdampedBrownian",
reorg=190,
cortime=80,
T=77,
matsubara=100)
cfH_params = dict(ftype="OverdampedBrownian",
#
# In[37]:
frac_electronic.report_on_expansion(3, N=4)
#
# State 3 is a clear anti-symmetric mixture of P(+) and B
#
# In[38]:
N1 = frac_electronic.Nbe[1]
print("Energies in 1/cm:")
print([
qr.convert(frac_electronic.HH[i, i], "int", "1/cm")
for i in range(1, N1 + 1)
])
# In[39]:
#
# Transition dipoles square
#
print("Transition dipoles square:")
print(frac_electronic.D2[1:N1 + 1, 0])
# In[40]:
#frac.save("fraction_45_2_vibrations_CT_built.hdf5")
qr.save_parcel(frac,
def run(omega, HR, dE, JJ, rate, vib_loc="up", use_vib=True,
stype=qr.signal_REPH, make_movie=False):
"""Runs a complete set of simulations for a single set of parameters
"""
#
# FIXED PARAMETERS
#
E0 = 10000.0
dip1 = [1.5, 0.0, 0.0]
dip2 = [-1.0, -1.0, 0.0]
width = 100.0
#rate = 1.0/50.0
normalize_maps_to_maximu = False
trim_maps = False
units = "1/cm"
with qr.energy_units(units):
data_descr = "_dO="+str(dE-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"
#
# Model system is a dimer of molecules
#
with qr.energy_units("1/cm"):
mol1 = qr.Molecule([0.0, E0])
mol2 = qr.Molecule([0.0, E0+dE])
mod1 = qr.Mode(omega)
mod2 = qr.Mode(omega)
mol1.set_transition_width((0,1), qr.convert(width, "1/cm", "int"))
mol1.set_dipole(0,1, dip1)
mol2.set_transition_width((0,1), qr.convert(width, "1/cm", "int"))
mol2.set_dipole(0,1, dip2)
agg = qr.Aggregate([mol1, mol2])
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:
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]:
mol1.add_Mode(mod1)
mod1.set_nmax(0, 3)
mod1.set_nmax(1, 3)
mod1.set_HR(1, HR)
if set_vib[1]:
mol2.add_Mode(mod2)
mod2.set_nmax(0, 3)
mod2.set_nmax(1, 3)
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()
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
#
# Laboratory setup
#
lab = qr.LabSetup()
lab.set_polarizations(pulse_polarizations=[X,X,X],
detection_polarization=X)
time2 = qr.TimeAxis(0.0, 100, 10.0)
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 = 100
t1_time_step = 10.0
t3_N_steps = 100
t3_time_step = 10.0
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 = []
with qr.eigenbasis_of(He):
operators.append(qr.qm.ProjectionOperator(1, 2, dim=He.dim))
rates.append(rate)
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")
eUt = qr.qm.EvolutionSuperOperator(time2, HH, relt=LF, pdeph=p_deph,
mode="all")
eUt.set_dense_dt(10)
#
# We calculate evolution superoperator
#
eUt.calculate(show_progress=False)
#
# Prepare aggregate with all states (including 2-EX band)
#
agg3.build(mult=2)
agg3.diagonalize()
pways = dict()
t2_save_pathways = [300.0]
olow = qr.convert(omega-30.0, "1/cm", "int")
ohigh = qr.convert(omega+30.0, "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,
selection=[["omega2",[olow, ohigh]]])
print("Number of pathways used for omega2 =",omega,":",
len(pways[str(t2)]))
if t2 in t2_save_pathways:
pws_name = os.path.join("sim_"+vib_loc, "pws_t2="+str(t2)+
"_omega2="+str(omega)+data_descr+data_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,
selection=[["omega2",[-ohigh, -olow]]])
print("Number of pathways used for omega2 =",-omega,":",
len(pways[str(t2)]))
if t2 in t2_save_pathways:
pws_name = os.path.join("sim_"+vib_loc, "pws_t2="+str(t2)+
"_omega2="+str(-omega)+data_descr+data_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")
fname = os.path.join("sim_"+vib_loc, "pways.qrp")
qr.save_parcel(pways, fname)
fname = os.path.join("sim_"+vib_loc, "aggregate.qrp")
agg3.save(fname)
#
# Window function for subsequenty FFT
#
window = func.Tukey(time2, r=0.3, 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=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 = [9500, 11000, 9500, 11000]
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
#
# Have a look which frequencies we actually have
#
# Ndat = len(fcont_re.axis.data)
# print("\nNumber of frequency points:", Ndat)
# print("In 1/cm they are:")
# with qr.energy_units("1/cm"):
# for k_i in range(Ndat):
# print(k_i, fcont_re.axis.data[k_i])
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)
# units = "1/cm"
# with qr.energy_units(units):
#
# data_descr = "_dO="+str(dE-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"
with qr.energy_units(units):
print("\nPlotting and saving spectrum at frequency:",
fcont_p_re.axis.data[show_Npoint1], units)
fftf_1 = os.path.join("sim_"+vib_loc, "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("sim_"+vib_loc, "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("sim_"+vib_loc, "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
#
if show_plots:
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("sim_"+vib_loc, "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("sim_"+vib_loc, "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("sim_"+vib_loc, "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)
if show_plots:
#
# 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)
#points.apply_to_data(numpy.abs)
#points.plot()
# saving containers
# fname = os.path.join("sim_"+vib_loc,"fcont_re"+data_descr+obj_ext)
# print("Saving container into: "+fname)
# fcont_p_re.save(fname)
# fname = os.path.join("sim_"+vib_loc,"fcont_nr"+data_descr+obj_ext)
# print("Saving container into: "+fname)
# fcont_p_nr.save(fname)
# fname = os.path.join("sim_"+vib_loc,"fcont_to"+data_descr+obj_ext)
# print("Saving container into: "+fname)
# fcont_p_to.save(fname)
fname = os.path.join("sim_"+vib_loc,"cont_p"+data_descr+obj_ext)
print("Saving container into: "+fname)
cont_p.save(fname)
fname = os.path.join("sim_"+vib_loc,"cont_m"+data_descr+obj_ext)
print("Saving container into: "+fname)
cont_m.save(fname)
return (sp1_p_re, sp1_p_nr, sp2_m_re, sp2_m_nr)
with qr.energy_units("1/cm"):
agg.set_resonance_coupling_matrix(J_Matrix[0:3,0:3])
#agg.save("RC_Model_40_4_adjusted_CT_no_environment_unbuilt.hdf5")
qr.save_parcel(agg, os.path.join(pre_out,
"RC_Model_40_4_adjusted_CT_no_environment_unbuilt.qrp"))
# In[3]:
# check that units were set correctly
rc = agg.resonance_coupling[1,0]
with qr.energy_units("1/cm"):
print(qr.convert(rc, "int"))
with qr.energy_units("1/cm"):
print(agg.get_resonance_coupling(1,0))
# In[4]:
# Bath correlation function
time = qr.TimeAxis(0.0, 1000, 1.0)
cfA_params = dict(ftype="OverdampedBrownian",
reorg=190, cortime=80, T=77, matsubara=100)
cfH_params = dict(ftype="OverdampedBrownian",
reorg=200, cortime=100, T=77, matsubara=100)
#
# Set the correlation function to the transitions on the molecules
#
m1.set_transition_environment((0, 1), cf)
m2.set_transition_environment((0, 1), cf)
#
# Build the aggregate
#
agg.build()
#
# Calculate absorption spectrum
#
asc = qr.AbsSpectrumCalculator(time, system=agg)
asc.bootstrap(rwa=qr.convert(10050, "1/cm", "int"), lab=lab)
abs1 = asc.calculate(raw=False)
abs1.normalize2()
#
# Plot the absorption spectrum
#
with qr.energy_units("1/cm"):
abs1.plot(axis=[9000, 12000, 0.0, 1.01], color="b", show=False)
###############################################################################
#
# Calculation using effective Gaussian lineshape
#
###############################################################################
import quantarhei as qr
qr.Manager().gen_conf.legacy_relaxation = True
print("Preparing a model system:")
with qr.energy_units("1/cm"):
mol1 = qr.Molecule([0.0, 12000])
mol2 = qr.Molecule([0.0, 12000])
mol3 = qr.Molecule([0.0, 12100])
mol4 = qr.Molecule([0.0, 12100])
agg = qr.Aggregate([mol1, mol2, mol3, mol4])
agg.set_resonance_coupling(2,3,qr.convert(100.0,"1/cm","int"))
agg.set_resonance_coupling(1,3,qr.convert(100.0,"1/cm","int"))
with tempfile.TemporaryDirectory() as tdir:
path = os.path.join(tdir,"agg.qrp")
qr.save_parcel(agg,path)
agg2 = qr.load_parcel(path)
agg2.build()
H = agg2.get_Hamiltonian()
print("...done")
print("Setting up Lindblad form relaxation:")
Ndim = 5
with qr.energy_units('1/cm'):
sd_low_freq = qr.SpectralDensity(t_ax_sd, params)
# Adding the high freq modes
sd_high_freq = db.get_SpectralDensity(t_ax_sd, "Wendling_JPCB_104_2000_5825")
ax = sd_low_freq.axis
sd_high_freq.axis = ax
sd_tot = sd_low_freq + sd_high_freq
cf = sd_tot.get_CorrelationFunction(temperature=temperature, ta=t_ax_sd)
# Assigning the correlation function to the list of molecules
for mol in for_agg:
mol.set_transition_environment((0, 1), cf)
if _test_:
reorg = qr.convert(sd_low_freq.get_reorganization_energy(), "int", "1/cm")
print("input_reorg - ", reorg)
with qr.energy_units("1/cm"):
sd_tot.plot(show=True, axis=[0, 2000, 0.0, np.max(sd_tot.data)])
cf.plot(show=True)
#######################################################################
# Dynamics
#######################################################################
# Test the setup by calculating the dynamics at same and diff energies
if _test_:
energies1 = [energy] * num_mol
energies2 = [energy - (100 * num_mol / 2)\
+ i * 100 for i in range(num_mol)]
print("Fitted population decay time (exciton basis): ",
1.0 / popt_p_e[1])
print("Fitted population decay time (site basis): ",
1.0 / popt_p_s[1])
else:
popt_p_e = [0.0, 10000.0]
popt_p_s = [0.0, 10000.0]
if J == 0.0:
rat = 0.0
balance = 0.0
else:
rat = popt_p_e[1]
kBT = qr.core.units.kB_int * Temperature
print("kBT = ", qr.convert(kBT, "int", "1/cm"), "cm^-1")
with qr.eigenbasis_of(ham):
balance = rat * numpy.exp(
-(ham.data[2, 2] - ham.data[1, 1]) / kBT)
print("Uphill decay time from canonical detailed balance: ",
1.0 / balance)
rate_predicted = 0.5 * balance + 0.5 * rat
print(
"Prediction for the decoherence time" +
" based on population transfer:", 1.0 / rate_predicted)
#
# Exponential fitting of opt_coh and ext_coh
#
#
# Integration over disorder
#
for e1 in es1:
for e2 in es2:
e_shift = e1 - Ecalc
DE = e2 - e1
# get the spectrum with DE and shift it by e_shift
(N_DE, err) = des.locate(DE)
#print(DE, N_DE, weight(e1, E1, width_dis[0])*
# weight(e2, E2, width_dis[1]))
twod_a = spects[N_DE].deepcopy()
twod_a.shift_energy(qr.convert(e_shift,"1/cm","int"))
data[:,:] += weight(e1, E1, width_dis[0])* \
weight(e2, E2, width_dis[1])*twod_a.data[:,:]
twod_a.data[:,:] = data[:,:]/(N1_1*N1_2)
with qr.energy_units("1/cm"):
twod_a.plot(spart=qr.part_ABS)
if INP.save_fig:
twod_a.savefig(INP.fig_file)
if INP.save_spectrum:
twod_a.save(INP.spectrum_file)
# set bath correlation functions to the molecules
for i_m in range(N_molecules):
mols[i_m].set_transition_environment((0, 1), cf)
# aggregate of molecules
agg = qr.Aggregate(mols)
agg.set_coupling_by_dipole_dipole()
# Building the aggregate
qr.log_report("Building aggregate")
agg.build()
qr.log_report("...done")
qr.log_detail("Resonance coupling matrix: ")
qr.log_detail(qr.convert(agg.resonance_coupling, "int", "1/cm"),
use_indent=False)
# Dimension of the problem
HH = agg.get_Hamiltonian()
Nr = HH.dim
qr.log_detail("Hamiltonian has a rank:", Nr)
benchmark_report["Dimension"] = Nr
qr.log_report("Calculating Relaxation tensor:")
t1 = time.time()
(RT, ham) = agg.get_RelaxationTensor(timea,
relaxation_theory="standard_Redfield",
as_operators=True)
t2 = time.time()
def test_LindbladWithVibrations_dynamics_comp(self):
"""Compares Lindblad dynamics of a system with vibrations calculated from propagator and superoperator
"""
# Aggregate
import quantarhei as qr
time = qr.TimeAxis(0.0, 1000, 1.0)
# create a model
with qr.energy_units("1/cm"):
me1 = qr.Molecule([0.0, 12100.0])
me2 = qr.Molecule([0.0, 12000.0])
me3 = qr.Molecule([0.0, 12900.0])
agg_el = qr.Aggregate([me1, me2, me3])
agg_el.set_resonance_coupling(0, 1, qr.convert(150, "1/cm", to="int"))
agg_el.set_resonance_coupling(1, 2, qr.convert(50, "1/cm", to="int"))
m1 = qr.Molecule([0.0, 12100.0])
m2 = qr.Molecule([0.0, 12000.0])
m3 = qr.Molecule([0.0, 12900.0])
mod1 = qr.Mode(frequency=qr.convert(100, "1/cm", "int"))
m1.add_Mode(mod1)
mod1.set_HR(1, 0.01)
agg = qr.Aggregate([m1, m2, m3])
agg.set_resonance_coupling(0, 1, qr.convert(150, "1/cm", to="int"))
agg.set_resonance_coupling(1, 2, qr.convert(50, "1/cm", to="int"))
agg_el.build()
agg.build()
hame = agg_el.get_Hamiltonian()
ham = agg.get_Hamiltonian()
# calculate relaxation tensor
with qr.eigenbasis_of(hame):
#
# Operator describing relaxation
#
K = qr.qm.ProjectionOperator(1, 2, dim=hame.dim)
#
# System bath interaction with prescribed rate
#
from quantarhei.qm import SystemBathInteraction
sbi = SystemBathInteraction(sys_operators=[K], rates=(1.0/100.0,))
sbi.set_system(agg) #agg.set_SystemBathInteraction(sbi)
with qr.eigenbasis_of(ham):
#
# Corresponding Lindblad form
#
from quantarhei.qm import ElectronicLindbladForm
LF = ElectronicLindbladForm(ham, sbi, as_operators=True)
#
# Evolution of reduced density matrix
#
prop = qr.ReducedDensityMatrixPropagator(time, ham, LF)
#
# Evolution by superoperator
#
eSO = qr.qm.EvolutionSuperOperator(time, ham, LF)
eSO.set_dense_dt(5)
eSO.calculate()
# compare the two propagations
pairs = [(5,4), (5,5), (6,5), (7,5)]
for p in pairs:
rho_i1 = qr.ReducedDensityMatrix(dim=ham.dim)
rho_i1.data[p[0],p[1]] = 1.0
rho_t1 = prop.propagate(rho_i1)
exp_rho_t2 = eSO.data[:,:,:,p[0],p[1]]
#import matplotlib.pyplot as plt
#plt.plot(rho_t1.TimeAxis.data, numpy.real(rho_t1.data[:,p[0],p[1]]), "-r")
#plt.plot(rho_t1.TimeAxis.data, numpy.real(exp_rho_t2[:,p[0],p[1]]), "--g")
#plt.show()
#for kk in range(rho_t1.TimeAxis.length):
# print(kk, numpy.real(rho_t1.data[kk,p[0],p[1]]),numpy.real(exp_rho_t2[kk,p[0],p[1]]))
#numpy.testing.assert_allclose(RRT.data, rtd)
numpy.testing.assert_allclose(numpy.real(rho_t1.data[:,:,:]),
numpy.real(exp_rho_t2[:,:,:]),
rtol=5.0e-2,
atol=1.0e-3)
eUt = qr.qm.EvolutionSuperOperator(time2,
HH,
relt=LF,
pdeph=p_deph,
mode="all")
eUt.set_dense_dt(fine_splitting)
#
# We calculate evolution superoperator
#
eUt.calculate(show_progress=False)
olow_cm = omega - 10.0 / 2.0
ohigh_cm = omega + 10.0 / 2.0
olow = qr.convert(olow_cm, "1/cm", "int")
ohigh = qr.convert(ohigh_cm, "1/cm", "int")
print("---")
print("Calculating 2D spectra")
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,
agg,
eUt,
lab,
# set bath correlation functions to the molecules
for i_m in range(N_molecules):
mols[i_m].set_transition_environment((0, 1), cf)
# aggregate of molecules
agg = qr.Aggregate(mols)
agg.set_coupling_by_dipole_dipole()
# Building the aggregate
qr.log_report("Building aggregate")
agg.build()
qr.log_report("...done")
qr.log_detail("Resonance coupling matrix: ")
qr.log_detail(qr.convert(agg.resonance_coupling, "int", "1/cm"),
use_indent=False)
# Dimension of the problem
HH = agg.get_Hamiltonian()
Nr = HH.dim
qr.log_detail("Hamiltonian has a rank:", Nr)
benchmark_report["Dimension"] = Nr
qr.log_report("Calculating Relaxation tensor:")
t1 = time.time()
(RT, ham) = agg.get_RelaxationTensor(timea,
relaxation_theory="standard_Redfield",
as_operators=True)
t2 = time.time()
Np = 13
ex2Dfile = "test.png"
plot_window = [11000, 13500, 11000, 13500]
t1axis = qr.TimeAxis(0.0, 1000, 1.0)
t2axis = qr.TimeAxis(0.0, 100, 10.0)
t3axis = qr.TimeAxis(0.0, 1000, 1.0)
pws = qr.load_parcel(os.path.join(pre_in,filename))
print(len(pws))
mscal = qr.MockTwoDSpectrumCalculator(t1axis, t2axis, t3axis)
mscal.bootstrap(rwa=qr.convert(12200,"1/cm","int"))
pw = pws[Np]
mscal.set_pathways([pw])
twod = mscal.calculate()
eUt = qr.load_parcel(os.path.join(pre_in,"eUt.qrp"))
oset = qr.load_parcel(os.path.join(dirn,"A_saved_state.qrp"))
with qr.energy_units("1/cm"):
print(pw)
twod.plot(window=plot_window, Npos_contours=10,
stype="total", spart="real")
plt.show()
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)
print("Fitted population decay time (exciton basis): ",
1.0/popt_p_e[1])
print("Fitted population decay time (site basis): ",
1.0/popt_p_s[1])
else:
popt_p_e = [0.0, 10000.0]
popt_p_s = [0.0, 10000.0]
if J == 0.0:
rat = 0.0
balance = 0.0
else:
rat = popt_p_e[1]
kBT = qr.core.units.kB_int*Temperature
print("kBT = ", qr.convert(kBT,"int","1/cm"),"cm^-1")
with qr.eigenbasis_of(ham):
balance = rat*numpy.exp(-(ham.data[2,2]-ham.data[1,1])/kBT)
print("Uphill decay time from canonical detailed balance: ",
1.0/balance)
rate_predicted = 0.5*balance + 0.5*rat
print("Prediction for the decoherence time"+
" based on population transfer:", 1.0/rate_predicted)
#
# Exponential fitting of opt_coh and ext_coh
#
# Define Quantarhei DFunction which can fit itself
# Create an aggregate # agg = qr.Aggregate(molecules=[mol]) agg.build() # # Time axes and the calculator # t1axis = qr.TimeAxis(0.0, 1000, 10.0) t3axis = qr.TimeAxis(0.0, 1000, 10.0) t2axis = qr.TimeAxis(0.0, Nt2, dt2) # FIXME: TwoDResponseCalculator msc = qr.MockTwoDResponseCalculator(t1axis, t2axis, t3axis) msc.bootstrap(rwa=qr.convert(E1, "1/cm", "int"), shape="Gaussian") # # Laboratory setup # lab = LabSetup() lab.set_polarizations(pulse_polarizations=[X, X, X], detection_polarization=X) # # Hamiltonian is required # H = agg.get_Hamiltonian() # # Evolution superoperator
# Aggregate with vibrational states, Hamiltonian generated up to single
# exciton states in a Two-particle approximation
# This object is used for calculation of dynamics
#
agg.build(mult=1, vibgen_approx="TPA")
#
# agg2 object will be used for calculation of 2D spectra
# so it needs to know how to represent the spectra (what width it should have).
# It also has to be built with
# two-exciton states (in two-particle approximation)
#
for mol_name in transition_widths:
width = qr.convert(transition_widths[mol_name], "1/cm", "int")
mol = agg2.get_Molecule_by_name(mol_name)
mol.set_transition_width((0, 1), width)
print("Aggregate has ", agg2.nmono, "single excited electronic states")
agg2.build(mult=2, vibgen_approx="TPA")
print("and ", agg2.Ntot, " (electro-vibrational) states in total")
print("Number of single exciton states :", agg2.Nb[0] + agg2.Nb[1])
qr.save_parcel(agg2, os.path.join(pre_out, "agg2_built.qrp"))
#
# Electronic aggregate is built with single exciton states only
#
# This object is used for calculation of dynamics
#
agg.build(mult=1, vibgen_approx="TPA")
#
# width of the spectral features in 1/cm
#
wincm = 500
#
# agg2 object will be used for calculation of 2D spectra
# so it needs to know how to represent the spectra (what
# width it should have), and it has to be built with
# two-exciton states (in two-particle approximation)
#
width = qr.convert(wincm, "1/cm", "int")
PM = agg2.get_Molecule_by_name("PM")
PM.set_transition_width((0, 1), width)
PL = agg2.get_Molecule_by_name("PL")
PL.set_transition_width((0, 1), width)
width = qr.convert(wincm, "1/cm", "int")
BM = agg2.get_Molecule_by_name("BM")
BM.set_transition_width((0, 1), width)
BL = agg2.get_Molecule_by_name("BL")
BL.set_transition_width((0, 1), width)
width = qr.convert(wincm, "1/cm", "int")
PCT1 = agg2.get_Molecule_by_name("PCT1")
PCT1.set_transition_width((0, 1), width)
PCT2 = agg2.get_Molecule_by_name("PCT2")
