def test_dynamics(agg_list, energies):
# Adding the energies to the molecules. Neeed to be done before agg
with qr.energy_units("1/cm"):
for i, mol in enumerate(agg_list):
mol.set_energy(1, energies[i])
# Creation of the aggregate for dynamics. multiplicity can be 1
agg = qr.Aggregate(molecules=agg_list)
agg.set_coupling_by_dipole_dipole(epsr=1.21)
agg.build(mult=1)
agg.diagonalize()
# Creating a propagation axis length t13_ax plus padding with intervals 1
t2_prop_axis = qr.TimeAxis(0.0, 1000, 1)
# Generates the propagator to describe motion in the aggregate
prop_Redfield = agg.get_ReducedDensityMatrixPropagator(
t2_prop_axis,
relaxation_theory="stR",
time_dependent=False,
secular_relaxation=True
)
# Obtaining the density matrix
shp = agg.get_Hamiltonian().dim
rho_i1 = qr.ReducedDensityMatrix(dim=shp, name="Initial DM")
# Setting initial conditions
rho_i1.data[shp-1,shp-1] = 1.0
# Propagating the system along the t13_ax_ax time axis
rho_t1 = prop_Redfield.propagate(rho_i1, name="Redfield evo from agg")
rho_t1.plot(coherences=False, axis=[0,t2_prop_axis.length,0,1.0], show=True)
def calcTwoD(loopNum):
''' Caculates a 2d spectrum for both perpendicular and parael lasers
using aceto bands and accurate lineshapes'''
container = []
agg = qr.Aggregate(molecules=forAggregate)
with qr.energy_units('1/cm'):
for i in range(len(forAggregate)):
agg.monomers[i].set_energy(1, random.gauss(energy, staticDis))
agg.set_coupling_by_dipole_dipole(epsr=1.21)
agg.build(mult=2)
agg.diagonalize()
tcalc_para = qr.TwoDResponseCalculator(t1axis=t13, t2axis=t2s, t3axis=t13, system=agg)
tcalc_para.bootstrap(rwa, pad=padding, verbose=True, lab=labPara, printEigen=False, printResp='paraResp')#, printResp='paraResp'
twods_para = tcalc_para.calculate()
paraContainer = twods_para.get_TwoDSpectrumContainer()
container.append(paraContainer)
tcalc_perp = qr.TwoDResponseCalculator(t1axis=t13, t2axis=t2s, t3axis=t13, system=agg)
tcalc_perp.bootstrap(rwa, pad=padding, verbose=True, lab=labPerp, printEigen=False)#, printResp='perpResp'
twods_perp = tcalc_perp.calculate()
perpContainer = twods_perp.get_TwoDSpectrumContainer()
container.append(perpContainer)
return container
def calcTwoDMock(loopNum):
''' Calculates a2d spectrum container for all time points on t2 axis
for both perpendicular and parallel lasers. Uses quantarhei only and
speeds up runtime by using simple lineshapes'''
container = []
agg = qr.Aggregate(forAggregateMock)
with qr.energy_units('1/cm'):
for i in range(len(forAggregateMock)):
agg.monomers[i].set_energy(1, random.gauss(energy, staticDis))
agg.set_coupling_by_dipole_dipole()
agg.build(mult=2)
agg.diagonalize()
rT = agg.get_RelaxationTensor(t2s, relaxation_theory='stR')
eUt = qr.EvolutionSuperOperator(t2s, rT[1], rT[0]) # rT[1] = Hamiltonian
eUt.set_dense_dt(t2Step)
eUt.calculate(show_progress=False)
mscPara = qr.MockTwoDResponseCalculator(t13, t2s, t13)
mscPara.bootstrap(rwa=rwa, shape="Gaussian")
contPara = mscPara.calculate_all_system(agg, eUt, labParaMock, show_progress=True)
cont2DPara = contPara.get_TwoDSpectrumContainer(stype=qr.signal_TOTL)
container.append(cont2DPara)
mscPerp = qr.MockTwoDResponseCalculator(t13, t2s, t13)
mscPerp.bootstrap(rwa=rwa, shape="Gaussian")
contPerp = mscPerp.calculate_all_system(agg, eUt, labPerpMock, show_progress=True)
cont2DPerp = contPerp.get_TwoDSpectrumContainer(stype=qr.signal_TOTL)
container.append(cont2DPerp)
return container
def _temp_build_agg(self, agg_list = None , mult = 1, diagonalize = True):
if not agg_list:
agg_list = self.mol_list
agg = qr.Aggregate(molecules=agg_list)
agg.set_coupling_by_dipole_dipole(epsr=1.21)
agg.build(mult=mult)
if diagonalize:
agg.diagonalize()
return agg
def build_agg(self, agg_list = None, mult = 1, diagonalize = True):
if not agg_list:
agg_list = self.mol_list
agg = qr.Aggregate(molecules=agg_list)
agg.set_coupling_by_dipole_dipole(epsr=1.21)
agg.build(mult=mult)
if diagonalize:
agg.diagonalize()
self.agg = agg
self.num_mol = agg.nmono
self.rwa = agg.get_RWA_suggestion()
def main():
with qr.energy_units("1/cm"):
mol1 = qr.Molecule([0.0, 12000.0])
mol2 = qr.Molecule([0.0, 12100.0])
mol3 = qr.Molecule([0.0, 12100.0])
agg = qr.Aggregate([mol1, mol2, mol3])
m1 = qr.Mode(100)
mol1.add_Mode(m1)
m2 = qr.Mode(100)
mol2.add_Mode(m2)
m3 = qr.Mode(100)
mol3.add_Mode(m3)
agg.build(mult=1)
print(agg.Ntot)
def calcTwoD(n_loopsum): #
''' Caculates a 2d spectrum for both perpendicular and parael lasers
using aceto bands and accurate lineshapes'''
container = []
#energies0 = [energy - (100 * num_mol / 2) + i * 100 for i in range(num_mol)]
#energies0 = [energy] * num_mol
# Giving random energies to the moleucles according to a gauss dist
with qr.energy_units("1/cm"):
for i, mol in enumerate(for_agg):
mol.set_energy(1, random.gauss(energy, static_dis))
#mol.set_energy(1, energies0[i])
agg = qr.Aggregate(molecules=for_agg)
agg.set_coupling_by_dipole_dipole(epsr=1.21)
agg.build(mult=2)
print(np.diagonal(agg.HH[1:num_mol + 1, 1:num_mol + 1]))
agg.diagonalize()
rwa = agg.get_RWA_suggestion()
print(np.diagonal(agg.HH[1:num_mol + 1, 1:num_mol + 1]))
# Initialising the twod response calculator for the paralell laser
resp_calc_temp = qr.TwoDResponseCalculator(t1axis=t13_ax,
t2axis=t2_ax,
t3axis=t13_ax,
system=agg)
# Bootstrap is the place to add 0-padding to the response signal
# printEigen=True prints eigenvalues, printResp='string' prints response
# Response is calculated Converted into spectrum Stored in a container
for lab in labs:
resp_calc = copy.deepcopy(resp_calc_temp)
resp_calc.bootstrap(rwa, pad=padding, lab=lab)
resp_cont = resp_calc.calculate()
spec_cont = resp_cont.get_TwoDSpectrumContainer()
container.append(spec_cont)
return container
def getEigen():
print('nM', nM)
agg = qr.Aggregate(molecules=forAggregate)
for j in range(nM):
with qr.energy_units('1/cm'):
agg.monomers[j].set_energy(1, random.gauss(energies[j], staticDis))
agg.set_coupling_by_dipole_dipole(epsr=1.21)
agg.build() #mult=2
N = len(forAggregate)
H = agg.get_Hamiltonian()
print(H)
SS = H.diagonalize()
print(H)
trans1 = SS[1:N + 1, 1:N + 1]
H.undiagonalize()
hamil = agg.HH[1:N + 1, 1:N + 1]
with open('transData.txt', 'a') as f:
f.write('Hamiltonian\n')
#np.savetxt(f, agg.HH[1:N+1,1:N+1])
np.savetxt(f, hamil)
f.write('Transformation Matrix\n')
np.savetxt(f, trans1)
agg.diagonalize()
diag = agg.HH[1:N + 1, 1:N + 1]
with open('transData.txt', 'a') as f:
f.write('Diagonalized\n')
np.savetxt(f, agg.HH[1:N + 1, 1:N + 1])
f.write('Dipoles\n')
np.savetxt(f, agg.D2)
def calcTwoD(n_loopsum): #
''' Caculates a 2d spectrum for both perpendicular and parael lasers
using aceto bands and accurate lineshapes'''
#energies0 = [energy - (100 * nM / 2) + i * 100 for i in range(nM)]
#energies0 = [energy] * nM
# Giving random energies to the moleucles according to a gauss dist
with qr.energy_units("1/cm"):
for i, mol in enumerate(for_agg):
mol.set_energy(1, random.gauss(energy, static_dis))
#mol.set_energy(1, energies0[i])
agg = qr.Aggregate(molecules=for_agg)
agg.set_coupling_by_dipole_dipole(epsr=1.21)
agg.build(mult=1)
HH = agg.get_Hamiltonian()
pig_ens = np.diagonal(HH.data[1:nM+1,1:nM+1])\
/ (2.0*const.pi*const.c*1.0e-13)
en_order = np.argsort(pig_ens)
SS = HH.diagonalize()
eig_vecs = np.transpose(SS[1:nM + 1, 1:nM + 1])
state_ens = np.diagonal(HH.data[1:nM+1,1:nM+1])\
/ (2.0*const.pi*const.c*1.0e-13)
agg.diagonalize()
dips = agg.D2[0][1:nM + 1]
dip_order = np.flip(np.argsort(dips))
state_data = {
'pig_ens': pig_ens,
'en_order': en_order,
'eig_vecs': eig_vecs,
'state_ens': state_ens,
'dips': dips,
'dip_order': dip_order
}
return state_data
print("* *")
print("***********************************************************")
###############################################################################
#
# Model system definition
#
###############################################################################
# Three molecules
with qr.energy_units("1/cm"):
m1 = qr.Molecule([0.0, 10100.0])
m2 = qr.Molecule([0.0, 10300.0])
m3 = qr.Molecule([0.0, 10000.0])
# Aggregate is built from the molecules
agg = qr.Aggregate([m1, m2, m3])
# Couplings between them are set
with qr.energy_units("1/cm"):
agg.set_resonance_coupling(0, 1, 80.0)
agg.set_resonance_coupling(0, 2, 100.0)
# Interaction with the bath is set through bath correlation functions
timea = qr.TimeAxis(0.0, 500, 1.0)
cpar1 = dict(ftype="OverdampedBrownian-HighTemperature",
reorg=50,
cortime=50,
T=300)
cpar2 = dict(ftype="OverdampedBrownian-HighTemperature",
reorg=50,
cortime=50,
* J =""", J, "1/cm\n*", """
*******************************************************************************
""")
###########################################################################
#
# Model system definition
#
###########################################################################
# Two molecules
with qr.energy_units("1/cm"):
m1 = qr.Molecule([0.0, E1])
m2 = qr.Molecule([0.0, E2])
# Aggregate is built from the molecules
agg = qr.Aggregate([m1, m2])
# Couplings between them are set
with qr.energy_units("1/cm"):
agg.set_resonance_coupling(0, 1, J)
# Interaction with the bath is set through bath correlation functions
timea = qr.TimeAxis(0.0, N, dt)
cpar1 = dict(ftype="OverdampedBrownian-HighTemperature",
reorg=lamb,
cortime=tc,
T=Temperature)
#cpar2 = dict(ftype="OverdampedBrownian-HighTemperature", reorg=50,
# cortime=50, T=300)
with qr.energy_units("1/cm"):
# -*- coding: utf-8 -*-
#<remove>
_show_plots_ = False
#</remove>
import quantarhei as qr
en = [0.0, 1.0]
m1 = qr.Molecule(name="Mol1", elenergies=en)
m2 = qr.Molecule(name="Mol2", elenergies=en)
ag = qr.Aggregate(name="Homodimer")
ag.add_Molecule(m1)
ag.add_Molecule(m2)
ag.set_resonance_coupling(0, 1, 0.1)
ag.build(mult=1)
H = ag.get_Hamiltonian()
#with qr.energy_units("1/cm"):
# print(H)
#
# Here we test generation of states with 3 level molecules
#
en = [0.0, 10100.0] #, 20200.0]
with qr.energy_units("1/cm"):
_show_plots_ = False
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):
#######################################################################
# Getting aggregate data
#######################################################################
file_name = 'data/eigen_data.txt'
f = open(file_name, "w+")
f.close()
for i in range(n_per_loop):
for_agg_copy = for_agg
with qr.energy_units("1/cm"):
for i, mol in enumerate(for_agg_copy):
mol.set_energy(1, random.gauss(energy, static_dis))
agg = qr.Aggregate(molecules=for_agg_copy)
agg.set_coupling_by_dipole_dipole(epsr=1.21)
agg.build()
save_eigen_data(agg=agg, file=file_name)
pig_en, state_en, eig_vecs, state_dips, dip_order, en_order = extracting_eigen_data(
file_name)
#######################################################################
# Analysis
#######################################################################
print('dipoles')
print(state_dips[0])
print('order')
matsubara=20)
with qr.energy_units("1/cm"):
cfce1 = qr.CorrelationFunction(ta, cfce_params1)
cfce2 = qr.CorrelationFunction(ta, cfce_params2)
m1 = qr.Molecule("M1", [0.0, 12100])
m1.set_dipole(0, 1, [0.0, 3.0, 0.0])
m1.set_transition_environment((0, 1), cfce1)
m1.position = [0.0, 0.0, 0.0]
m2 = qr.Molecule("M1", [0.0, 12000])
m2.set_dipole(0, 1, [0.0, 1.0, 1.0])
m2.position = [5.0, 0.0, 0.0]
m2.set_transition_environment((0, 1), cfce2)
# create an aggregate
AG = qr.Aggregate("TestAggregate")
#AG.set_egcf_matrix(cm)
# fill the cluster with monomers
AG.add_Molecule(m1)
AG.add_Molecule(m2)
# setting coupling by dipole-dipole formula
AG.set_coupling_by_dipole_dipole()
AG.build()
HH = AG.get_Hamiltonian()
(RR, ham) = AG.get_RelaxationTensor(ta, relaxation_theory="standard_Redfield")
a2 = qr.AbsSpect(ta, AG, relaxation_tensor=RR)
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)
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)
# # CT states are dark # PCT_M.set_dipole(1, 0, [0.0, 0.0, 0.0]) PCT_L.set_dipole(1, 0, [0.0, 0.0, 0.0]) molecules = [PM, PL, BM, BL, HL, HM, PCT_M, PCT_L] # saving molecules without environment qr.save_parcel(molecules, os.path.join(pre_out, "molecules.qrp")) # # Here we build the RC as an aggregate of molecules # mol3 = [PM, PL, BM] agg = qr.Aggregate(molecules=mol3) # # Exciton interaction matrix # # values from Ref. 1 JP_77K_Jordanides = 575.0 JP_77K = JP_77K_Jordanides # # Fitted values of the model with CT states # starting values of the manual search of best parameters are # taken from Ref. 2 # if jordanides:
with qr.energy_units("1/cm"):
cfce1 = qr.CorrelationFunction(ta,cfce_params1)
cfce2 = qr.CorrelationFunction(ta,cfce_params2)
m1 = qr.Molecule([0.0, 12100], name="M1")
m1.set_dipole(0,1,[0.0,3.0,0.0])
m1.set_transition_environment((0,1),cfce1)
m1.position = [0.0,0.0,0.0]
m2 = qr.Molecule([0.0, 12000], name="M2")
m2.set_dipole(0,1,[0.0,1.0,1.0])
m2.position = [5.0,0.0,0.0]
m2.set_transition_environment((0,1),cfce2)
# create an aggregate
AG = qr.Aggregate(name="TestAggregate")
#AG.set_egcf_matrix(cm)
# fill the cluster with monomers
AG.add_Molecule(m1)
AG.add_Molecule(m2)
# setting coupling by dipole-dipole formula
AG.set_coupling_by_dipole_dipole()
AG.build()
HH = AG.get_Hamiltonian()
(RR,ham) = AG.get_RelaxationTensor(ta,
relaxation_theory="standard_Redfield")
def calcTwoD(n_loopsum): #
''' Caculates a 2d spectrum for both perpendicular and parael lasers
using aceto bands and accurate lineshapes'''
resp_container = []
#energies0 = [energy - (100 * num_mol / 2) + i * 100 for i in range(num_mol)]
#energies0 = [energy] * num_mol
# Giving random energies to the moleucles according to a gauss dist
with qr.energy_units("1/cm"):
for i, mol in enumerate(for_agg):
mol.set_energy(1, random.gauss(energy, static_dis))
#mol.set_energy(1, energies0[i])
agg = qr.Aggregate(molecules=for_agg)
agg_rates = copy.deepcopy(agg)
agg_rates.set_coupling_by_dipole_dipole(epsr=1.21)
agg_rates.build(mult=2)
if _forster_:
KK = agg_rates.get_FoersterRateMatrix()
else:
KK = agg_rates.get_RedfieldRateMatrix()
agg.set_coupling_by_dipole_dipole(epsr=1.21)
agg.build(mult=2)
rwa = agg.get_RWA_suggestion()
HH = agg_rates.get_Hamiltonian()
pig_ens = np.diagonal(HH.data[1:num_mol+1,1:num_mol+1])\
/ (2.0*const.pi*const.c*1.0e-13)
en_order = np.argsort(pig_ens)
SS = HH.diagonalize()
eig_vecs = np.transpose(SS[1:num_mol + 1, 1:num_mol + 1])
state_ens = np.diagonal(HH.data[1:num_mol+1,1:num_mol+1])\
/ (2.0*const.pi*const.c*1.0e-13)
agg_rates.diagonalize()
dips = agg_rates.D2[0][1:num_mol + 1]
dip_order = np.flip(np.argsort(dips))
# Initialising the twod response calculator for the paralell laser
resp_calc_temp = qr.TwoDResponseCalculator(t1axis=t13_ax,
t2axis=t2_ax,
t3axis=t13_ax,
system=agg,
rate_matrix=KK)
# keep_resp saves the reponse int he object. write_resp writes to numpy
# Response is calculated Converted into spectrum Stored in a container
for i, lab in enumerate(labs):
resp_calc = copy.deepcopy(resp_calc_temp)
resp_calc.bootstrap(
rwa, lab=lab, verbose=False,
keep_resp=True) #write_resp = save_dir + las_pol[i] + '_resp',
resp_cont = resp_calc.calculate()
resp_container.append(resp_calc.responses)
state_data = {
'pig_ens': pig_ens,
'en_order': en_order,
'eig_vecs': eig_vecs,
'state_ens': state_ens,
'dips': dips,
'dip_order': dip_order,
'rwa': rwa
}
return resp_container, state_data
with qr.energy_units("1/cm"):
mol1 = qr.Molecule([0.0, E1])
mol1.set_dipole(0, 1, [1.0, 0.0, 0.0])
mol1.set_transition_width((0, 1), width)
mod = qr.Mode(omega)
mol1.add_Mode(mod)
mod.set_nmax(0, 2)
mod.set_nmax(1, 2)
mod.set_HR(1, HR)
mol2 = qr.Molecule([0.0, E2])
mol2.set_dipole(0, 1, numpy.array([1.0, 1.0, 0.0]) / numpy.sqrt(2.0))
mol2.set_transition_width((0, 1), width)
agg = qr.Aggregate(molecules=[mol1, mol2])
agg.set_resonance_coupling(0, 1, JJ)
agg.build(mult=2)
agg.diagonalize()
H = agg.get_Hamiltonian()
#with qr.energy_units("1/cm"):
# print(H)
lab = qr.LabSetup()
lab.set_polarizations(pulse_polarizations=[X, X, X], detection_polarization=X)
#print(qr.convert(agg.Wd,"int","1/cm"))
energies2 = [energy - (100 * num_mol / 2) + i * 100 for i in range(num_mol)]
test_dynamics(agg_list=forAggregate, energies=energies1)
test_dynamics(agg_list=forAggregate, energies=energies2)
#######################################################################
# Calculation of the twoD PARA
#######################################################################
# Arrays for the direction on the laser plane
a_0 = np.array([1.0, 0.0, 0.0], dtype=np.float64)
a_90 = np.array([0.0, 1.0, 0.0], dtype=np.float64)
# Checks if mock is selected and creates agregate and laser conditions
if _mock_:
# Creation of aggregate from mockAggList (multiplicity must be 2)
aggMock = qr.Aggregate(molecules=forAggregateMock)
aggMock.set_coupling_by_dipole_dipole(epsr=1.21)
aggMock.build(mult=2)
aggMock.diagonalize()
rwa = aggMock.get_RWA_suggestion()
# quantarhei lab setup for mock calculator
labParaMock = qr.LabSetup()
labPerpMock = qr.LabSetup()
labParaMock.set_polarizations(pulse_polarizations=[a_0,a_0,a_0],
detection_polarization=a_0)
labPerpMock.set_polarizations(pulse_polarizations=[a_0,a_0,a_90],
detection_polarization=a_90)
else:
# Aggregate for twodcalculator (multiplicity must be 2)
agg = qr.Aggregate(molecules=forAggregate)
#
# MODEL: Simple dimer of molecules
#
###############################################################################
with qr.energy_units("1/cm"):
# two two-level molecules
m1 = qr.Molecule([0.0, 12000.0])
m2 = qr.Molecule([0.0, 12300.0])
# transitions will have Gaussian lineshape with a width specified here
m1.set_transition_width((0, 1), 150.0)
m2.set_transition_width((0, 1), 150.0)
# we create an aggregate from the two molecules
agg = qr.Aggregate(molecules=[m1, m2])
# we set transition dipole moment orientations for the two molecules
m1.set_dipole(0, 1, [1.0, 0.8, 0.8])
m2.set_dipole(0, 1, [0.8, 0.8, 0.0])
# resonance coupling is set by hand
with qr.energy_units("1/cm"):
agg.set_resonance_coupling(0, 1, 100.0)
# we copy the aggregate before it is built. For the calculation of 2D
# spectrum, we need to build the aggregate so that it contains two-exciton
# states. But those are irrelevant for single exciton excited state dynamics
# so we make two identical aggregates, one with single-excitons only, and
# one with two-excitons.
agg_2D = copy.copy(agg)
mod = qr.Mode(frequency=omega)
mol.add_Mode(mod)
mod.set_nmax(0, Ng)
mod.set_nmax(1, Ne)
mod.set_HR(1, hr_factor)
mol.set_transition_width((0, 1), width)
#
# 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")
#
#
with qr.energy_units("1/cm"):
# molecule 1
# molecular states
m1 = qr.Molecule([0.0, 10000.0])
# transition dipole moment
m1.set_dipole(0, 1, [1.0, 0.0, 0.0])
# molecule 2
m2 = qr.Molecule([0.0, 11000.0])
m2.set_dipole(0, 1, [1.0, 0.0, 0.0])
#
# Create aggregate of the two molecules
#
agg = qr.Aggregate(molecules=[m1, m2])
#
# Define time span of the calculation
#
time = qr.TimeAxis(0.0, 10000, 1.0)
#
# Define bath correlation function
#
cpar = dict(ftype="OverdampedBrownian", cortime=30, reorg=200, T=300)
with qr.energy_units("1/cm"):
cf = qr.CorrelationFunction(time, cpar)
#
# Set the correlation function to the transitions on the molecules
##
## Set system-bath interaction
##
#mol1.set_transition_environment((0,1),cf)
#mol2.set_transition_environment((0,1),cf)
#mol3.set_transition_environment((0,1),cf)
for ii in range(Nmol):
mols[ii].set_transition_environment((0,1), cf)
#
# Creating aggregate
#
#agg = Aggregate(name="Dimer", molecules=[mol1, mol2, mol3])
agg = qr.Aggregate(molecules=mols)
#agg.set_coupling_by_dipole_dipole()
with qr.energy_units("1/cm"):
#agg.set_resonance_coupling(0,1,-100.0)
#agg.set_resonance_coupling(1,2,-100.0)
if Nmol > 1:
for ii in range(Nmol-1):
agg.set_resonance_coupling(ii,ii+1,-100.0)
agg.set_resonance_coupling(0,Nmol-1,-100.0)
print(agg.get_resonance_coupling(0,1))
agg.build(mult=2)
print(agg.get_Hamiltonian())
with qr.energy_units("1/cm"):
# Bath correlation functions
timea = qr.TimeAxis(0.0, 1000, 1.0)
cfpar = dict(ftype="OverdampedBrownian",
reorg=30,
cortime=100,
T=300,
matsubara=100)
with qr.energy_units("1/cm"):
cf = qr.CorrelationFunction(timea, cfpar)
# 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
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)
