occupations[site].append((ads, Nsites))
#populate the lattice
for key, value in occupations.iteritems():
available_sites = sites_list[:]
for ads, Nsites in value:
site = kmos_sites[key]
species = kmos_species[ads]
chosen_sites = random.sample(available_sites, Nsites)
for elem in chosen_sites:
model._put(site=[elem[0], elem[1], 0, site], new_species=species)
available_sites.remove(elem)
model._adjust_database()
#check that lattice occupation is as expected
model.print_coverages()
atoms = model.get_atoms()
view(atoms)
#run and plot model as before...
#get TOF labels
tof_labels = model.get_tof_header().split(' ')
#get coverage labels
cov_labels = model.get_occupation_header().split(' ')
#Number of kmc steps taken in each sample
sample_step = 1e5
#Number of samples
occupations[site].append((ads,Nsites))
#populate the lattice
for key, value in occupations.iteritems():
available_sites = sites_list[:]
for ads,Nsites in value:
site = kmos_sites[key]
species = kmos_species[ads]
chosen_sites = random.sample(available_sites, Nsites)
for elem in chosen_sites:
model._put(site=[elem[0],elem[1],0,site], new_species=species)
available_sites.remove(elem)
model._adjust_database()
#check that lattice occupation is as expected
model.print_coverages()
atoms=model.get_atoms()
view(atoms)
#run and plot model as before...
#get TOF labels
tof_labels = model.get_tof_header().split(' ')
#get coverage labels
cov_labels = model.get_occupation_header().split(' ')
#Number of kmc steps taken in each sample
sample_step = 1e5
#Number of samples
