gamma = params.gamma = 3.0 omega = params.omega = 0.001 params.collapse = False metric = 'nuc' # make file identifiers base_name, name_mod = test_flu.get_fname(params) #remove year base_name = '_'.join(base_name.split('_')[:1] + base_name.split('_')[2:]) base_name = base_name.replace('_????', '') # load data (with Koel boost and without), save in dictionary prediction_distances = {} normed_distances = {} for boost in [0.0, 0.5, 1.0]: params.boost = boost years, tmp_pred, tmp_normed = AU.load_prediction_data(params, metric) prediction_distances.update(tmp_pred) normed_distances.update(tmp_normed) ################################################################################## ## main figure 3c ################################################################################## # make figure plt.figure(figsize=(12, 6)) # plot line for random expection plt.plot([min(years) - 0.5, max(years) + 0.5], [1, 1], lw=2, c='k') # add shaded boxes and optimal and L&L predictions for yi, year in enumerate(years): plt.gca().add_patch( plt.Rectangle([year - 0.5, 0.2],
plt.savefig(figure_folder + 'Fig4_s2_' + base_name + '_polarizer_revised' + ff) ################################################################################## ## Fig 5: compare bootstrap distributions of prediction results ## Bootstrapping is over years ## ################################################################################## # load fitness prediction data params.boost = 0.0 params.gamma = 3.0 params.omega = 0.1 params.diffusion = 0.5 params.collapse = False years_I, prediction_distances_I, normed_distances_I = AU.load_prediction_data( params, metric) plotted_methods = { m: normed_distances_I[m] for m in [('expansion, internal nodes', 0.0, 'growth'), ('L&L', 0.0, r'L\&L'), ('ladder rank', 0.0, 'ladder rank')] } ti_ext_normed = tau_i + 2 ti_int_normed = tau_i + 2 + len(mem_time_scale) plotted_methods.update({ ('polarizer', tau, 'external'): (normed_distance[:, ti_ext_normed].mean(), AU.boot_strap(normed_distance[:, ti_ext_normed], n=1000)), ('polarizer', tau, 'internal'):
gamma = params.gamma = 3.0 omega = params.omega = 0.001 params.collapse = False metric = 'nuc' # make file identifiers base_name, name_mod = test_flu.get_fname(params) #remove year base_name = '_'.join(base_name.split('_')[:1]+base_name.split('_')[2:]) base_name = base_name.replace('_????','') # load data (with Koel boost and without), save in dictionary prediction_distances={} normed_distances={} for boost in [0.0,0.5,1.0]: params.boost = boost years,tmp_pred, tmp_normed = AU.load_prediction_data(params, metric) prediction_distances.update(tmp_pred) normed_distances.update(tmp_normed) ################################################################################## ## main figure 3c ################################################################################## # make figure plt.figure(figsize = (12,6)) # plot line for random expection plt.plot([min(years)-0.5,max(years)+0.5], [1,1], lw=2, c='k') # add shaded boxes and optimal and L&L predictions for yi,year in enumerate(years): plt.gca().add_patch(plt.Rectangle([year-0.5, 0.2], 1.0, 1.8, color='k', alpha=0.05*(1+np.mod(year,2)))) plt.plot([year-0.5, year+0.5], [prediction_distances[('minimal',boost,'minimal')][yi],
################################################################################## ## Fig 5: compare bootstrap distributions of prediction results ## Bootstrapping is over years ## ################################################################################## # load fitness prediction data params.boost = 0.0 params.gamma = 3.0 params.omega = 0.1 params.diffusion = 0.5 params.collapse=False years_I,prediction_distances_I, normed_distances_I = AU.load_prediction_data(params, metric) plotted_methods = {m:normed_distances_I[m] for m in [('expansion, internal nodes', 0.0, 'growth'), ('L&L', 0.0, r'L\&L'), ('ladder rank',0.0, 'ladder rank')]} ti_ext_normed = tau_i+2 ti_int_normed = tau_i+2+len(mem_time_scale) plotted_methods.update({('polarizer',tau,'external'):(normed_distance[:,ti_ext_normed].mean(), AU.boot_strap(normed_distance[:,ti_ext_normed], n=1000)), ('polarizer',tau,'internal'):(normed_distance[:,ti_int_normed].mean(),AU.boot_strap(normed_distance[:,ti_int_normed], n=1000))}) tick_labels = { ('fitness,internal nodes', 0.0, 'pred(I)'):'internal', ('fitness,terminal nodes', 0.0, 'pred(T)'):'terminal', ('expansion, internal nodes', 0.0, 'growth'):'growth', ('L&L', 0.0, r'L\&L'):r'L\&L', ('ladder rank',0.0, 'ladder rank'):'ladder rank', ('polarizer',tau,'external'):r'terminal $\tau='+str(tau)+'$',