def run_on_mdl(mdl, sim_options):
Niter, opto_levels, dt, fix_dim, avg_last_factor, both_pixels = [
sim_options[key] for key in [
'Niter', 'opto_levels', 'dt', 'fix_dim', 'avg_last_factor',
'both_pixels'
]
]
average_last = int(np.floor(Niter * avg_last_factor)) #/5
if both_pixels:
this_YY_opto = dyn.compute_steady_state_Model_multi_inj(
mdl,
Niter=Niter,
fix_dim=[fix_dim, fix_dim + mdl.nQ],
inj_mag=[opto_levels, opto_levels],
sim_type='inj',
dt=dt)
else:
this_YY_opto = dyn.compute_steady_state_Model(mdl,
Niter=Niter,
fix_dim=fix_dim,
inj_mag=opto_levels,
sim_type='inj',
dt=dt)
to_return = np.nanmean(this_YY_opto[:, :, -average_last:], 2)
return to_return
def run_on_mdl(mdl,sim_options):
Niter,opto_levels,dt = [sim_options[key] for key in ['Niter','opto_levels','dt']]
average_last = int(np.floor(Niter/5))
fix_dims = [[0,4],[1,5],[2,6],[3,7],None]
max_val = 0
Ny = 1
this_YY_opto = dyn.compute_steady_state_Model(mdl,Niter=Niter,fix_dim=fix_dims,max_val=max_val,Ny=Ny,sim_type='fix',dt=dt)
to_return = np.nanmean(this_YY_opto[:,:,:,-average_last:],3)
return to_return
def run_on_mdl(mdl, sim_options):
Niter, opto_levels, dt = [
sim_options[key] for key in ['Niter', 'opto_levels', 'dt']
]
average_last = int(np.floor(Niter / 5))
this_YY_opto = dyn.compute_steady_state_Model(mdl,
Niter=Niter,
fix_dim=2,
inj_mag=opto_levels,
sim_type='inj',
dt=dt)
to_return = np.nanmean(this_YY_opto[:, :, -average_last:], 2)
return to_return
def run_on_mdl(mdl, sim_options):
Niter, opto_levels, dt, fix_dim, avg_last_factor, sim_type = [
sim_options[key] for key in [
'Niter', 'opto_levels', 'dt', 'fix_dim', 'avg_last_factor',
'sim_type'
]
]
average_last = int(np.floor(Niter * avg_last_factor)) #/5
#print('sim type: %s'%sim_type)
this_YY_opto = dyn.compute_steady_state_Model(mdl,
Niter=Niter,
fix_dim=fix_dim,
inj_mag=opto_levels,
sim_type=sim_type,
dt=dt)
to_return = np.nanmean(this_YY_opto[:, :, -average_last:], 2)
return to_return