expected_counts[model.get_compartment_id(A)] = N_measurements * rateA / (rateB + rateE + rateA) expected_counts[model.get_compartment_id(B)] = N_measurements * rateB / (rateB + rateE + rateA) expected_counts[model.get_compartment_id(E)] = N_measurements * rateE / (rateB + rateE + rateA) expected_counts[model.get_compartment_id(C0)] = N_measurements * ((N-1)*rateA / (rateB + rateE + rateA) * probAC0) expected_counts[model.get_compartment_id(C1)] = N_measurements * ((N-1)*rateA / (rateB + rateE + rateA) * probAC1) expected_counts[model.get_compartment_id(D)] = N_measurements * ((N-1)*rateB / (rateB + rateE + rateA) * probBD) expected_counts[model.get_compartment_id(F)] = (N-1) * expected_counts[model.get_compartment_id(E)] expected_counts[model.get_compartment_id(S)] = N_measurements * N - expected_counts.sum() pl.bar(x+width/2, expected_counts, width) pl.xticks(x) pl.gca().set_xticklabels(model.compartments) pl.figure() ndx = np.where(expected_counts==0) counts[ndx] = 1 expected_counts[ndx] = 1 pl.plot(x, np.abs(1-counts/expected_counts)) from scipy.stats import entropy _counts = np.delete(counts,1) _exp_counts = np.delete(expected_counts,1)
# va='bottom',
# bbox={'facecolor':'w','edgecolor':'w','pad':0}
# )
#pl.xscale('log')
#pl.yscale('log')
ylim = pl.gca().get_ylim()
min_ylim = 10**np.floor(np.log(ylim[0]) / np.log(10))
max_ylim = 10**np.ceil(np.log(ylim[1]) / np.log(10))
if min_ylim < 1:
min_ylim = 1
#pl.ylim([min_ylim, max_ylim])
#if _r < n_row-1:
# [ x.set_visible(False) for x in ax[i].xaxis.get_major_ticks() ]
pl.xlim([0, 35])
pl.xticks([0, 10, 20, 30])
bp.strip_axis(pl.gca())
pl.gcf().tight_layout()
pl.gcf().subplots_adjust(wspace=0.34, hspace=0.3)
pl.gcf().savefig(
"model_fit_figures/linlin_shutdown_model_all_confirmed_fit_after_feb_12.png",
dpi=300)
pl.gcf().savefig("model_fit_figures/SI_Fig_06.png", dpi=300)
if not loaded_fits:
with open('fit_parameters/shutdown_model_all_provinces_after_feb_12.p',
'wb') as f:
pickle.dump(fit_parameters, f)
pl.show()