def myfunc(subtraj):
start_time = subtraj * subtraj_increment
print("\nsubtraj ", subtraj)
print("start time = ", start_time)
print("end time = ", start_time + subtraj_len)
md_time = [i for i in range(subtraj_len)]
md_time = np.array(md_time) * params["dt"] * units.au2fs
# Obtain the subtrajectory
for i in range(start_time, start_time + subtraj_len):
hvib_mb_subtrajs[subtraj].append(hvib_mb[0][i])
hvib_sd_subtrajs[subtraj].append(hvib_sd[0][i])
# Compute mb energy gaps and decoherence times
params["nsteps"] = subtraj_len
params["init_times"] = [0]
tau_mb, rates_mb = decoherence_times.decoherence_times(
hvib_mb_subtrajs[subtraj])
dE_mb = decoherence_times.energy_gaps(hvib_mb_subtrajs[subtraj])
avg_deco_mb = tau_mb * units.au2fs
print("Finished gather data for MD time",
params["init_times"][0] + params["nsteps"], "steps.")
print("Dephasing time between mb states 0 and 1 is:",
tau_mb.get(0, 1) * units.au2fs, "fs")
# Compute sd energy gaps and decoherence times
params["nsteps"] = subtraj_len
params["init_times"] = [0]
tau_sd, rates_sd = decoherence_times.decoherence_times(
hvib_sd_subtrajs[subtraj])
dE_sd = decoherence_times.energy_gaps(hvib_sd_subtrajs[subtraj])
avg_deco_sd = tau_sd * units.au2fs
print("Finished gather data for MD time",
params["init_times"][0] + params["nsteps"], "steps.")
print("Dephasing time between sd states 0 and 1 is:",
tau_sd.get(0, 1) * units.au2fs, "fs")
# Compute mb energies
mb_subtraj_energy = []
params["nstates"] = 31
for mb_index in range(params["nstates"]):
mb_subtraj_energy.append([])
for step in range(params["nsteps"]):
mb_subtraj_energy[mb_index].append(
hvib_mb_subtrajs[subtraj][step].get(mb_index, mb_index).real -
hvib_mb_subtrajs[subtraj][step].get(0, 0).real)
mb_subtraj_energy = np.array(mb_subtraj_energy)
tmp_mb_energies.append(mb_subtraj_energy)
# Compute sd energies
params["nstates"] = 64
sd_subtraj_energy = []
for sd_index in range(params["nstates"]):
sd_subtraj_energy.append([])
for step in range(params["nsteps"]):
sd_subtraj_energy[sd_index].append(
hvib_sd_subtrajs[subtraj][step].get(sd_index, sd_index).real -
hvib_sd_subtrajs[subtraj][step].get(0, 0).real)
sd_subtraj_energy = np.array(sd_subtraj_energy)
tmp_sd_energies.append(sd_subtraj_energy)
###############################################
###############################################
# Now it is time to run the NAMD
params["T"] = 300.0
params["ntraj"] = 250
params["sh_method"] = 1
params["Boltz_opt"] = 1
params["nsteps"] = subtraj_len
params["init_times"] = [0]
istates = [14, 18]
for istate in istates:
params["istate"] = istate # Recall index from 0
### FSSH
params["decoherence_method"] = 0
start = time.time()
# mb fssh
params["outfile"] = "_out_mb_fssh_subtraj_" + str(
subtraj) + "_istate" + str(istate) + ".txt"
params["nstates"] = 31 # total number of mb electronic states
res_mb_fssh = step4.run([hvib_mb_subtrajs[subtraj]], params)
mb_nbra_namd = data_conv.MATRIX2nparray(res_mb_fssh)
tmp_fssh_mb.append(mb_nbra_namd)
mb_hot_energy_fssh = (mb_nbra_namd[:, 95] -
mb_nbra_namd[:, 1]) * units.au2ev
#sys.exit(0)
#sd fssh
params["outfile"] = "_out_sd_fssh_subtraj_" + str(
subtraj) + "_istate" + str(istate) + ".txt"
params["nstates"] = 64 # total number of sd electronic states
res_sd_fssh = step4.run([hvib_sd_subtrajs[subtraj]], params)
sd_nbra_namd = data_conv.MATRIX2nparray(res_sd_fssh)
tmp_fssh_sd.append(sd_nbra_namd)
sd_hot_energy_fssh = (sd_nbra_namd[:, 194] -
sd_nbra_namd[:, 1]) * units.au2ev
end = time.time()
print("Time to run NBRA NAMD = ", end - start)
### IDA
params["decoherence_method"] = 1
start = time.time()
# mb ida
params["outfile"] = "_out_mb_ida_subtraj_" + str(
subtraj) + "_istate" + str(istate) + ".txt"
params["nstates"] = 31 # total number of mb electronic states
res_mb_ida = step4.run([hvib_mb_subtrajs[subtraj]], params)
mb_nbra_namd = data_conv.MATRIX2nparray(res_mb_ida)
tmp_ida_mb.append(mb_nbra_namd)
mb_hot_energy_ida = (mb_nbra_namd[:, 95] -
mb_nbra_namd[:, 1]) * units.au2ev
#sd ida
params["outfile"] = "_out_sd_ida_subtraj_" + str(
subtraj) + "_istate" + str(istate) + ".txt"
params["nstates"] = 64 # total number of sd electronic states
res_sd_ida = step4.run([hvib_sd_subtrajs[subtraj]], params)
sd_nbra_namd = data_conv.MATRIX2nparray(res_sd_ida)
tmp_ida_sd.append(sd_nbra_namd)
sd_hot_energy_ida = (sd_nbra_namd[:, 194] -
sd_nbra_namd[:, 1]) * units.au2ev
end = time.time()
print("Time to run NBRA NAMD = ", end - start)
### mSDM
params["decoherence_method"] = 2
start = time.time()
# mb msdm
params["outfile"] = "_out_mb_msdm_subtraj_" + str(
subtraj) + "_istate" + str(istate) + ".txt"
params["nstates"] = 31 # total number of mb electronic states
res_mb_msdm = step4.run([hvib_mb_subtrajs[subtraj]], params)
mb_nbra_namd = data_conv.MATRIX2nparray(res_mb_msdm)
tmp_msdm_mb.append(mb_nbra_namd)
mb_hot_energy_msdm = (mb_nbra_namd[:, 95] -
mb_nbra_namd[:, 1]) * units.au2ev
#sd msdm
params["outfile"] = "_out_sd_msdm_subtraj_" + str(
subtraj) + "_istate" + str(istate) + ".txt"
params["nstates"] = 64 # total number of sd electronic states
res_sd_msdm = step4.run([hvib_sd_subtrajs[subtraj]], params)
sd_nbra_namd = data_conv.MATRIX2nparray(res_sd_msdm)
tmp_msdm_sd.append(sd_nbra_namd)
sd_hot_energy_msdm = (sd_nbra_namd[:, 194] -
sd_nbra_namd[:, 1]) * units.au2ev
end = time.time()
print("Time to run NBRA NAMD = ", end - start)
plt.figure(num=None,
figsize=(3.21, 2.41),
dpi=300,
edgecolor='black',
frameon=True)
plt.subplot(1, 1, 1)
plt.title('(CdSe)$_{33}$ MB traj' + str(subtraj) + ' istate' +
str(istate) + '',
fontsize=10)
plt.xlabel('Time, fs', fontsize=10)
plt.ylabel('Energy, eV', fontsize=10)
plt.ylim(0, 3.5)
plt.yticks([0, 1, 2, 3])
for sd_index in range(31):
plt.plot(md_time,
mb_subtraj_energy[sd_index] * units.au2ev,
label="",
linewidth=1,
color="gray")
plt.plot(md_time,
mb_hot_energy_fssh,
linewidth=2.5,
color="red",
label="FSSH")
plt.plot(md_time,
mb_hot_energy_ida,
linewidth=2.5,
color="green",
label="IDA")
plt.plot(md_time,
mb_hot_energy_msdm,
linewidth=2.5,
color="blue",
label="mSDM")
plt.tight_layout()
plt.legend(fontsize=8, ncol=3, loc="lower left")
plt.savefig("CdSe33_MB_Dyn_" + str(subtraj) + "_" + str(istate) +
".png",
dpi=300)
plt.figure(num=None,
figsize=(3.21, 2.41),
dpi=300,
edgecolor='black',
frameon=True)
plt.subplot(1, 1, 1)
plt.title('(CdSe)$_{33}$ SP traj' + str(subtraj) + ' istate' +
str(istate) + '',
fontsize=10)
plt.xlabel('Time, fs', fontsize=10)
plt.ylabel('Energy, eV', fontsize=10)
plt.ylim(0, 3.5)
plt.yticks([0, 1, 2, 3])
for sd_index in range(64):
plt.plot(md_time,
sd_subtraj_energy[sd_index] * units.au2ev,
label="",
linewidth=1,
color="gray")
plt.plot(md_time,
sd_hot_energy_fssh,
linewidth=2.5,
color="red",
label="FSSH")
plt.plot(md_time,
sd_hot_energy_ida,
linewidth=2.5,
color="green",
label="IDA")
plt.plot(md_time,
sd_hot_energy_msdm,
linewidth=2.5,
color="blue",
label="mSDM")
plt.tight_layout()
plt.legend(fontsize=8, ncol=3, loc="lower left")
plt.savefig("CdSe33_" + str(subtraj) + "_SP_Dyn_" + str(istate) +
".png",
dpi=300)
params["Hvib_im_prefix"] = "Hvib_ks_"; params["Hvib_im_suffix"] = "_im"
params["nfiles"] = 2000
params["nstates"] = 2 # total number of electronic states
params["init_times"] = [10000]
params["active_space"] = range(2) # indexing is from 0!
#params["active_space"] = range(0,int(params["nstates"]/2)) # indexing is from 0!
# Include H**O and up to the last electronic state
Hvib_ks = step4.get_Hvib2(params)
Hvib_ks[0][-1].show_matrix()
print ("Length of hvib is: ", len(Hvib_ks[0]))
#sys.exit(0)
# Compute energy gaps for states i,j in trajectory of length "t"
nsteps = params["nfiles"]
dE = decoherence_times.energy_gaps(Hvib_ks[0])
print(dE[-1].get(0,1)*units.au2ev)
#sys.exit(0)
# For computing influence spectra
params2 = {}
params2["dt"] = 1.0 # MD timestep in fs
params2["wspan"] = 200.0 # cm^-1
params2["dw"] = 1.0 # cm^-1
params2["do_output"] = False
params2["do_center"] = True
params2["acf_type"] = 1
params2["data_type"] = 0
W = np.array([w for w in range(int(params2["wspan"]))])
ACF, J2 = [], []