def parfunc(x,ntraj):
kaoi = x[0]
nCi = x[1]
models.append(LSCmodel.LSCModel(nL,nCi))
dt = models[-1].dt
basedir = "./boxsize_5_5_5/run_bulk_nL{}_nC{}/trajectory_{}".format(nL,nCi,traj_number)
dissocdatafile = basedir+"/accepted_dissociation_moves.txt"
unbounddata_template = basedir+"/unbound_simulations_fine_output/unbound_reaction_event_density_nL_{}_*.npy".format(nL)
# Load reaction probability data for each timepoint for each trajectory
unbound_data_zipfile = "./boxsize_5_5_5/run_bulk_nL{}_nC{}".format(nL,nCi)+"/unbound_output_combined.zip"
coarse = 10
tstart = pytime.time()
#out = qan.process_quasireversible_simulations(kaoi,kdo,dt,dissocdatafile,unbound_data_files,coarse)
out = qan.process_quasireversible_simulations(kaoi,kdo,dt,dissocdatafile,unbound_data_zipfile,coarse,
zipped=True,target_surv_prob=target_surv_prob,nsteps=nsteps,ntraj=ntraj)
print("Processed run nC = {}, kao = {}, ntraj = {} in {:.2f} min".format(nCi,kaoi,ntraj,(pytime.time()-tstart)/60.))
# Save output to .npz file for plotting
out['nC'] = nCi
out['kao'] = kaoi
out['kdo'] = kdo
if isinstance(ntraj,tuple):
outfilename = "./analyzed_data/analysis_out_nC_{}_kao_{}_kdo_{}_targetprob_{}_nsteps_{}_ntraj_{}_{}.npz".format(
nCi,kaoi,kdo,target_surv_prob,nsteps,ntraj[1]-ntraj[0],ntraj[0])
else:
outfilename = "./analyzed_data/analysis_out_nC_{}_kao_{}_kdo_{}_targetprob_{}_nsteps_{}_ntraj_{}.npz".format(
nCi,kaoi,kdo,target_surv_prob,nsteps,ntraj)
np.savez(outfilename,out)
return 1
def parfunc(x):
kaoi = x[0]
nCi = x[1]
models.append(LSCmodel.LSCModel(nL, nCi))
dt = models[-1].dt
basedir = "./rundir/run_bulk_nL{}_nC{}/trajectory_{}".format(
nL, nCi, traj_number)
dissocdatafile = basedir + "/accepted_dissociation_moves.txt"
unbounddata_template = basedir + "/unbound_simulations_fine_output/unbound_reaction_event_density_nL_{}_*.npy".format(
nL)
# Load reaction probability data for each timepoint for each trajectory
unbound_data_files = []
for datai in glob.glob(unbounddata_template):
unbound_data_files.append(datai)
unbound_data_zipfile = basedir + "/unbound_output.zip"
coarse = 10
target_surv_prob = 1e-4 # where to extrapolate to with exponential function
tstart = pytime.time()
#out = qan.process_quasireversible_simulations(kaoi,kdo,dt,dissocdatafile,unbound_data_files,coarse)
out = qan.process_quasireversible_simulations(
kaoi,
kdo,
dt,
dissocdatafile,
unbound_data_zipfile,
coarsening=coarse,
zipped=True,
target_surv_prob=target_surv_prob)
print("Processed run nC = {}, kao = {} in {:.2f} min".format(
nCi, kaoi, (pytime.time() - tstart) / 60.))
return out
def parfunc(kaoi):
return qan.process_quasireversible_simulations(kaoi,kdo,dt,dissoc_data_file,unbound_data_files)