def run_bllz_wrapper( hvib, istate, outfile_name, params ):
"""
A small wrapper function to run the bllz dynamics.
hvib ( list of list of matrices ): The vibronic hamiltonian for all timesteps
istate ( int ): Index of the initial state. This index is from 0
outfile ( string ): The name of the output file
params ( dict ): A dictionary of important dynamical parameters
returns: A list of energy values that is the electronic energy vs time. Returned energy is in eV
"""
params["outfile"] = outfile_name
params["nstates"] = hvib[0].num_of_rows
params["istate"] = istate
res_bllz, P = lz.run( [hvib], params )
bllz_namd = data_conv.MATRIX2nparray( res_bllz )
# Recall that for bllz, we use the schrodiger not the surface hopping energy
energy_bllz = ( bllz_namd[:,3*params["nstates"]+1] - bllz_namd[:,1] )*units.au2ev
# for bllz surface hopping energy
#energy_bllz = ( bllz_namd[:,3*params["nstates"]+2] - bllz_namd[:,1] )*units.au2ev
return energy_bllz
def test_lz(Hvib, params, expected_result):
"""Tests that the LZ probabilities are computed correctly according to the NBRA BLSH scheme"""
params["istate"] = 1 # From 0
params["T"] = 300.0 # Temperature, K
params["target_space"] = 1
params["gap_min_exception"] = 0
params["Boltz_opt_BL"] = 1 # Option to incorporate hte frustrated hops into BL probabilities
params["outfile"] = "_out_Markov_.txt" # output file
params["evolve_Markov"] = True # Rely on the Markov approach
params["evolve_TSH"] = False # don't care about TSH
params["ntraj"] = 1 # how many stochastic trajectories
params["init_times"] = [0] # starting points for sub-trajectories
params["do_output"] = True # request to print the results into a file
params["do_return"] = False # request to not store the date in the operating memory
params["return_probabilities"] = True
res, P = lz.run(Hvib, params)
computed_result = 0
for t in range(len(P[0])):
if P[0][t].get(0,0) != 1:
computed_result = P[0][t].get(0,1); #print (computed_result); sys.exit(0)
assert expected_result == round(computed_result, 4)
break
#====================== One case =====================
# Looking on the "SH" populations - NBRA-TSH approach
params["gap_min_exception"] = 0
params["Boltz_opt"] = 1 # Option for the frustrated hops acceptance/rejection
params["Boltz_opt_BL"] = 0 # Option to incorporate hte frustrated hops into BL probabilities
params["outfile"] = "_out_TSH_.txt" # output file
params["evolve_Markov"] = False # don't propagate Markov
params["evolve_TSH"] = True # Rely on propagating trajectories
params["ntraj"] = 100 # how many stochastic trajectories
start = time.time()
res = lz.run(Hvib, params)
end = time.time()
print("Time to run TSH = ", end - start)
"""
#====================== Another case =====================
# Looking on the "SE" populations - Markov chain approach
params["target_space"] = 1
params["gap_min_exception"] = 0
params["Boltz_opt"] = 0 # Option for the frustrated hops acceptance/rejection
params[
"Boltz_opt_BL"] = 1 # Option to incorporate hte frustrated hops into BL probabilities
params["outfile"] = "_out_Markov_.txt" # output file
params["evolve_Markov"] = True # Rely on the Markov approach
params["evolve_TSH"] = False # don't care about TSH
params["ntraj"] = 1 # how many stochastic trajectories
start = time.time()
res = lz.run(Hvib, params)
end = time.time()
print("Time to run Markov = ", end - start)
