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