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 test_add_Molecule(self): """(Aggregate) Testing add_Molecule() method """ agg = TestAggregate(name="dimer-2-env") mol = qr.Molecule(elenergies=[0.0, 1.0]) nmols1 = agg.nmono agg.add_Molecule(mol) nmols2 = agg.nmono self.assertEqual(nmols1 + 1, nmols2) self.assertEqual(len(agg.monomers), nmols2) self.assertLessEqual(len(agg.mnames), nmols2)
def circularAgg(numMol, dipoleStrength): proteinDis = 8.7 difference = 0.6 r = 5 while difference > 0.1: t = np.linspace(0, np.pi * 2, numMol + 1) x = r * np.cos(t) y = r * np.sin(t) circle = np.c_[x, y] artificialDis = math.sqrt(((circle[0][0] - circle[1][0])**2)\ + ((circle[0][1] - circle[1][1])**2)) difference = abs(artificialDis - proteinDis) if artificialDis > proteinDis: r = r - 0.1 elif artificialDis < proteinDis: r = r + 0.1 circle2 = np.delete(circle, numMol, 0) dipoles = np.empty([numMol, 2]) mag = math.sqrt((circle[0][0]**2) + (circle[0][1]**2)) for i in range(numMol): dipoles[i][0] = -circle2[i][1] dipoles[i][1] = circle2[i][0] dipoles[i][0] = dipoles[i][0] / mag dipoles[i][1] = dipoles[i][1] / mag dipoles[i][0] = dipoles[i][0] * dipoleStrength dipoles[i][1] = dipoles[i][1] * dipoleStrength print('positions\n', circle2) print('dipoles\n', dipoles) forAggregate = [] for i in range(numMol): molName = qr.Molecule() molName.position = [circle2[i][0], circle2[i][1], 0.0] molName.set_dipole(0, 1, [dipoles[i][0], dipoles[i][1], 0.0]) forAggregate.append(molName) print( '\nA list of molecules was generated. Positions are in a ' 'ring with ', proteinDis, ' Angstrom spacings. Dipoles are ' 'added running along the tangent of the ring. All in the ' 'same direction with ', dipoleStrength, ' dipoles (D)\n') #return circle2, dipoles return forAggregate
def get_molecules_circular(self, nM, dip = 5, dist = 8.7, dif = 0.6, verbose = True): self.dipole_strength = dip r = 5 while dif > 0.1: t = np.linspace(0, np.pi * 2, nM+1) x = r * np.cos(t) y = r * np.sin(t) circle = np.c_[x, y] artificialDis = math.sqrt(((circle[0][0] - circle[1][0])**2)\ + ((circle[0][1] - circle[1][1])**2)) dif = abs(artificialDis-dist) if artificialDis > dist: r = r - 0.1 elif artificialDis < dist: r = r + 0.1 circle2 = np.delete(circle, nM, 0) dipoles = np.empty([nM,2]) mag = math.sqrt((circle[0][0]**2) + (circle[0][1]**2)) for i in range(nM): dipoles[i][0] = -circle2[i][1] dipoles[i][1] = circle2[i][0] dipoles[i][0] = dipoles[i][0] / mag dipoles[i][1] = dipoles[i][1] / mag dipoles[i][0] = dipoles[i][0] * self.dipole_strength dipoles[i][1] = dipoles[i][1] * self.dipole_strength forAggregate = [] for i in range(nM): molName = qr.Molecule() molName.position = [circle2[i][0], circle2[i][1], 0.0] molName.set_dipole(0,1,[dipoles[i][0], dipoles[i][1], 0.0]) forAggregate.append(molName) if verbose: print('\nA list of molecules was generated. Positions are in a ' 'ring with ', dist, ' Angstrom spacings. Dipoles are ' 'added running along the tangent of the ring. All in the ' 'same direction with ', self.dipole_strength, ' dipoles (D)\n') #self.positions = circle2 #self.dipoles = dipoles self.mol_list = forAggregate
Absorption of a monomeric two-level molecule """ cfce_params1 = dict(ftype="OverdampedBrownian", reorg=20.0, cortime=100.0, T=100, matsubara=20) en = 12000.0 e_units = qr.energy_units("1/cm") with e_units: m = qr.Molecule("Molecule", [0.0, en]) with qr.energy_units("1/cm"): cfce1 = qr.CorrelationFunction(ta, cfce_params1) m.set_egcf((0, 1), cfce1) m.set_dipole(0, 1, [0.0, 1.0, 0.0]) a1 = qr.AbsSpect(ta, m) with qr.energy_units("1/cm"): a1.calculate(rwa=en) HH = m.get_Hamiltonian() with qr.frequency_units("1/cm"): print(HH) a1.plot(axis=[11500, 12500, 0, numpy.max(a1.data) * 1.1])
# -*- coding: utf-8 -*- import numpy import quantarhei as qr _show_plots_ = True _save_2D_ = True _use_disorder_ = True E0 = 12000.0 with qr.energy_units("1/cm"): # two two-level molecules m1 = qr.Molecule([0.0, E0]) # transitions will have Gaussian lineshape with a width specified here m1.set_transition_width((0, 1), 100.0) # we create an aggregate from the two molecules agg = qr.Aggregate(molecules=[m1]) # we set transition dipole moment orientations for the two molecules m1.set_dipole(0, 1, [1.0, 0.8, 0.8]) # time axes of the propagation in t1 and t3 times t2_axis = qr.TimeAxis(0.0, 100, 10.0) t1_axis = qr.TimeAxis(0.0, 100, 10.0) t3_axis = qr.TimeAxis(0.0, 100, 10.0) from quantarhei.spectroscopy.mocktwodcalculator \ import MockTwoDResponseCalculator as TwoDResponseCalculator from quantarhei.spectroscopy import X
# -*- coding: utf-8 -*- import quantarhei as qr en = [0.0, 1.0] M = qr.Molecule("My first two-level molecule", en) H = M.get_Hamiltonian() print(H)
# -*- coding: utf-8 -*- #<remove> _show_plots_ = False #</remove> import quantarhei as qr en = [0.0, 1.0] M = qr.Molecule(elenergies=en) H = M.get_Hamiltonian() print(H) print("version = ", qr.Manager().version)
fft_of = qr.signal_REPH # qr.signal_NONR, qr.signal_TOTL ############################################################################### # # # MODEL DEFINITION # # ############################################################################### # # Create a two-level molecule with one intra-molecular harmonic mode # with qr.energy_units("1/cm"): mol = qr.Molecule(elenergies=[0.0, E1]) mol.set_dipole(0, 1, [1.0, 0.0, 1.0]) 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
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("") print("***********************************************************") print("* *") print("* Quantarhei's HEOM implementation demo *") 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,
import copy import quantarhei as qr _show_plots_ = True _movie_ = False _save_2D_ = True ############################################################################### # # 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"):
h_shift = 0.0 sc_H = 1.0 sc_P = 1.0 else: offset = 275 offset_P = 400 #485.0 offset_P_M = offset_P + 100.0 h_shift = 85.0 sc_H = 0.79 sc_P = 0.75 # # Molecules # with qr.energy_units("1/cm"): PM = qr.Molecule([0.0, 11610.0 + offset_P_M], name="PM") PL = qr.Molecule([0.0, 11610.0 + offset_P], name="PL") BM = qr.Molecule([0.0, 12220.0 + offset], name="BM") BL = qr.Molecule([0.0, 12370.0 + offset], name="BL") HL = qr.Molecule([0.0, 13020.0 + offset - h_shift], name="HL") HM = qr.Molecule([0.0, 13150.0 + offset + h_shift], name="HM") # CT states are effectively represented as "new molecules" in the system PCT_M = qr.Molecule([0.0, 15200], name="PCT1") PCT_L = qr.Molecule([0.0, 13550], name="PCT2") # 13500 # # Transition dipole moment from Ref. 1 are scaled # dPM = numpy.array([0.8546, 0.5051, 0.1206]) * sc_P dPL = numpy.array([-0.9649, -0.0250, 0.2613]) * sc_P
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)
############################################################################### # # Define problem # ############################################################################### # # define molecules # Nmol = 5 Emol = 12500.0 mols = [] with qr.energy_units("1/cm"): for ii in range(Nmol): mol = qr.Molecule(elenergies=[0.0, Emol+20.0*numpy.random.randn()]) # mols.append(mol) # mol1 = Molecule(elenergies=[0.0, 12550.0]) # mol2 = Molecule(elenergies=[0.0, 12150.0]) # mol3 = Molecule(elenergies=[0.0, 12350.0]) #mol1.position = [0.0, 0.0, 0.0] ## dimer 1 #mol2.position = [0.0, 4.0, 0.0] ### dimer 2 ##mol2.position = [0.0, 0.0, 6.0] #mol1.set_dipole(0,1,[2.0, 0.0, 0.0]) #mol2.set_dipole(0,1,[2.0*numpy.sqrt(1.0/2.0), 2.0*numpy.sqrt(1.0/2.0), 0.0]) #mol3.set_dipole(0,1,[0.0, 2.0, 0.0]) for ii in range(Nmol):
# frequency of the vibrational mode omega = 110.0 # Huan-Rhys factor HR = 0.01 # transition width width = 80 E2 = E1 + Edelta print("") print("Molecular dimer") print("E1:", E1, "1/cm") print("E2:", E2, "1/cm (delta =", Edelta, ")") 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)
# -*- 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"):
# -*- coding: utf-8 -*- _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
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)
# Group of two-level molecules ex_energies = numpy.zeros(N_molecules) ex_energies[:] = 12000.0 mols = [] previous_position = numpy.zeros(3, dtype=qr.REAL) dist_max = 10.0 dist_min = 5.0 placed_molecules = [] dipole_length = 6.0 with qr.energy_units("1/cm"): qr.log_report("Generating molecules") qr.log_report("--------------------") for i_m in range(N_molecules): mol = qr.Molecule([0.0, ex_energies[i_m]]) mols.append(mol) qr.log_info("Molecule:", i_m) # Randomly oriented transition dipole moment vec = numpy.random.rand(3) - 0.5 * numpy.ones(3) vec = qr.normalize2(vec, dipole_length) mol.set_dipole(0, 1, vec) qr.log_info("Transition dipole moment:", vec) # Randomly placing the molecule placed = False while not placed: pos = numpy.random.rand(3) - 0.5 * numpy.ones(3) pos = qr.normalize2(pos, dist_max) pos_n = numpy.array(pos) + previous_position
print( """ ******************************************************************************* * * 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)
dipoles = np.empty([numMol, 2]) mag = math.sqrt((circle[0][0]**2) + (circle[0][1]**2)) for i in range(numMol): dipoles[i][0] = -circle[i][1] dipoles[i][1] = circle[i][0] dipoles[i][0] = dipoles[i][0] / mag dipoles[i][1] = dipoles[i][1] / mag dipoles[i][0] = dipoles[i][0] * dipoleStrength dipoles[i][1] = dipoles[i][1] * dipoleStrength energies = [12500] * numMol #energies = [12000, 12100, 12200, 12300, 12400, 12150, 12250, 12350] forAggregate = [] for i in range(numMol): molName = qr.Molecule() with qr.energy_units("1/cm"): molName.set_energy(1, energies[i]) #molName.set_energy(1, random.gauss(energies[i], staticDis)) molName.set_transition_environment((0, 1), cf) #molName.position = [0.0, i*10.0, 0.0] molName.position = [circle[i][0], circle[i][1], 0.0] #molName.set_dipole(0,1,[0.0, 10.0, 0.0]) molName.set_dipole(0, 1, [dipoles[i][0], dipoles[i][1], 0.0]) forAggregate.append(molName) ####################################################################### # Creation of the aggregate protein ####################################################################### ''' pdb_name = 'LH1'
# -*- coding: utf-8 -*- #<remove> _show_plots_ = False #</remove> import quantarhei as qr en = [0.0, 1.0] M = qr.Molecule(name="My first two-level molecule", elenergies=en) H = M.get_Hamiltonian() print(H) print("version = ", qr.Manager().version)
lab = qr.LabSetup() lab.set_polarizations(pulse_polarizations=[X, X, X], detection_polarization=X) ############################################################################### # # Calculation with a realistic lineshape using bath correlation function # ############################################################################### # # Set up molecules composing an aggregate # 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 #
Absorption of a monomeric two-level molecule """ cfce_params1 = dict(ftype="OverdampedBrownian", reorg=20.0, cortime=100.0, T=100,matsubara=20) en = 12000.0 e_units = qr.energy_units("1/cm") with e_units: m = qr.Molecule(name="Molecule",elenergies=[0.0,en]) with qr.energy_units("1/cm"): cfce1 = qr.CorrelationFunction(ta,cfce_params1) m.set_egcf((0,1),cfce1) m.set_dipole(0,1,[0.0, 1.0, 0.0]) ac = qr.AbsSpectrumCalculator(ta,m) with qr.energy_units("1/cm"): ac.bootstrap(rwa=en) a1 = ac.calculate() HH = m.get_Hamiltonian() if _show_plots_: