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
pos_dissocL = orientation_data[2:5].reshape((1, 3))
pos_dissocS = orientation_data[5:].reshape((1, 3))
idsi = np.array([traj.species_name(j) for j in types[config_time_ind]])
indsL = np.char.equal(idsi, "L")
posLi = positions[config_time_ind][indsL]
indsC = np.char.equal(idsi, "C")
posCi = positions[config_time_ind][indsC]
#-----------------------------------------------------------------------------------
#### Set up a reaction-diffusion system ####
#-----------------------------------------------------------------------------------
# Create LSCModel object to get parameters
model = LSCmodel.LSCModel(nL, nC)
# Define simulation box
system = readdy.ReactionDiffusionSystem(model.boxsize, unit_system=None)
system.periodic_boundary_conditions = [True, True,
True] # non-periodic in z-direction
# Define species types for simulation
system.add_species("C",
model.D_C) # Crowding agent with diffusion constant D_C
system.add_species("L", model.D_L) # Ligand
system.add_species("S", model.D_S) # Ligand-receptor complex
# Define pairwise potentials
set_cutshifted_lj = lambda type1, type2, sigma: system.potentials.add_lennard_jones(
type1,
if args.nSteps:
n_steps = int(args.nSteps)
else:
n_steps = 1000000
if args.nThreads:
n_threads = int(args.nThreads)
else:
n_threads = 1
#-------------------------------------------------------------------------------------------------------
#### Set up a reaction-diffusion system and simulate ####
#-------------------------------------------------------------------------------------------------------
# Create LSCModel object to get parameters
model = LSCmodel.LSCModel(n_Ltotal, n_C)
# Define simulation box
system = readdy.ReactionDiffusionSystem(model.boxsize, unit_system=None)
system.periodic_boundary_conditions = [True, True,
True] # non-periodic in z-direction
# Define species types for simulation
system.add_species("C",
model.D_C) # Crowding agent with diffusion constant D_C
system.add_species("L", model.D_L) # Ligand
system.add_species("SL", model.D_SL) # Ligand-receptor complex
# Define pairwise potentials
set_cutshifted_lj = lambda type1, type2, sigma: system.potentials.add_lennard_jones(
type1,
int(box_size[0]), int(box_size[1]), int(box_size[2]), nLtag, nC)
if args.trajfile:
trajfile = args.trajfile
print("TRAJFILE={}".format(trajfile))
else:
trajfile = None
if args.savefile:
savefile = args.savefile
print("SAVEFILE={}".format(savefile))
else:
savefile = "unbound_reaction_event_density_nL_{}".format(nLtag)
if args.ncores:
n_cores = int(args.ncores)
else:
n_cores = 1
#----------------------------------------------------------------------------------------
# Create LSCmodel object
#----------------------------------------------------------------------------------------
model = LSCmodel.LSCModel(nLtag, nC)
tstart = time.time()
react_probs = model.calc_reaction_probs(rundir, trajfile, savefile,
n_cores)
print("Reaction Probability Computation Time = {}".format(time.time() -
tstart))