# For each step make St_ci
#print("Making St_ci matrices....")
for step in range( finish_time-start_time ):
s_ci = SD2CI[step].H() * S_sd_job[step] * SD2CI[step]
S_ci_job.append( s_ci )
for step in range( finish_time-start_time-1 ):
st_ci = SD2CI[step].H() * St_sd_job[step] * SD2CI[step+1]
St_ci_job.append( st_ci )
#print("\nNormalize CI basis before output")
step3.apply_orthonormalization_general( S_ci_job, St_ci_job )
params2 = {"do_state_reordering":2, "state_reordering_alpha":0}
step3.apply_state_reordering_general( St_ci_job, ci_midpoint_energies, params2 )
step3.apply_phase_correction_general( St_ci_job )
# Output CI data to res directory
#print("Outputting the CI data to the res directory..." )
for step in range( finish_time-start_time ):
S_ci_job[step].real().show_matrix("%s/S_ci_%d_re" % (res_dir, int(start_time+step)))
ci_energies_job_cmatrix[step].real().show_matrix("%s/E_ci_%d_re" % (res_dir, int(start_time+step)))
for step in range( finish_time-start_time-1 ):
St_ci_job[step].real().show_matrix("%s/St_ci_%d_re" % (res_dir, int(start_time+step)))
# Now, compute the CI NACs and compute the CI Hvib
#print("Computing and outputting the CI NACs...")
for step in range( finish_time-start_time-1 ):
ci_nacs = ( 0.5j / dt ) * CMATRIX ( ( St_ci_job[step] - St_ci_job[step].H() ).real() )
ci_hvib = ci_midpoint_energies[step] - ci_nacs
# 4.0 Compute the mid-point energies and time-overlaps according to the energy ordered SDs.
St_sd = []
E_sd = []
res_dir_sd = "res_sd"
os.system("rm -r "+res_dir_sd )
os.system("mkdir "+res_dir_sd )
for step in range(finish_time-start_time-1):
E_sd.append ( 0.5 * (E_sd_sorted[step] + E_sd_sorted[step+1]) )
St_sd.append( mapping.ovlp_mat_arb( basis_sorted[step], basis_sorted[step+1], St_ks[0][step], use_minimal=False ) )
####################
# 5.0. Apply state reordering and/or phase corrections to the St_sd data
params["do_state_reordering"] = 2
params["state_reordering_alpha"] = 0.0
step3.apply_state_reordering_general(St_sd, E_sd, params)
step3.apply_phase_correction_general( St_sd )
# 4. Form Hvib_sd
Hvib_sd = []
for step in range(finish_time-start_time-1):
hvib_sd = E_sd[step] + 0.5j/dt * (St_sd[step] - St_sd[step].H())
Hvib_sd.append( hvib_sd )
Hvib_sd[step].real().show_matrix("%s/Hvib_sd_%d_re" % (res_dir_sd, step))
Hvib_sd[step].imag().show_matrix("%s/Hvib_sd_%d_im" % (res_dir_sd, step))
St_sd[step].real().show_matrix("%s/St_%d_re" % (res_dir_sd, step))
#sys.exit(0)
params.update({ "data_re_prefix" : "E_ks_", "data_re_suffix" : "_re", "data_im_prefix" : "E_ks_", "data_im_suffix" : "_im" } )
E_ks = data_read.get_data_sets(params)
E_ks[0][-1].show_matrix()
# Fetching S_ks
params.update({ "data_re_prefix" : "S_ks_", "data_re_suffix" : "_re", "data_im_prefix" : "S_ks_", "data_im_suffix" : "_im" } )
S_ks = data_read.get_data_sets(params)
# Fetching St_ks
params.update({ "data_re_prefix" : "St_ks_", "data_re_suffix" : "_re", "data_im_prefix" : "St_ks_", "data_im_suffix" : "_im" } )
St_ks = data_read.get_data_sets(params)
St_ks[0][-2].show_matrix()
#sys.exit(0)
step3.apply_orthonormalization_general( S_ks[0], St_ks[0] )
step3.apply_phase_correction_general( St_ks[0] )
#sys.exit(0)
ks_homo_index = 28
min_band_ks = 28
max_band_ks = 29
ks_orbital_indicies = list( range( min_band_ks, max_band_ks + 1 ) )
sd_basis_states_unique = [
# Phi 0
[1, -1],
# Phi 1
[2, -1],
# Phi 2
[-2, 1],
]