More plotting

demos/krylov_sampling.py

@@ -80,12 +80,17 @@ plt.legend()
 plt.savefig('Krylov_reconstruction.png')
 plt.close()
 
-pltdict = {'alpha': .2, 'linewidth': .2}
+pltdict = {'alpha': .3, 'linewidth': .2}
 for i in range(N_samps):
-    plt.plot(sky(samps_old[i]).val, color='b', **pltdict)
-    plt.plot(sky(samps[i]).val, color='r', **pltdict)
-plt.plot((s_x - m_x).val, color='k')
-plt.savefig('Krylov_samples.png')
+    if i == 0:
+        plt.plot(sky(samps_old[i]).val, color='b', **pltdict, label='Traditional samples')
+        plt.plot(sky(samps[i]).val, color='r', **pltdict, label='Krylov samples')
+    else:
+        plt.plot(sky(samps_old[i]).val, color='b', **pltdict)
+        plt.plot(sky(samps[i]).val, color='r', **pltdict)
+plt.plot((s_x - m_x).val, color='k', label='signal - mean')
+plt.legend()
+plt.savefig('Krylov_samples_residuals.png')
 plt.close()
 
 D_hat_old = ift.Field.zeros(x_space).val
@@ -93,10 +98,10 @@ D_hat_new = ift.Field.zeros(x_space).val
 for i in range(N_samps):
     D_hat_old += sky(samps_old[i]).val**2
     D_hat_new += sky(samps[i]).val**2
-plt.plot(np.sqrt(D_hat_old / N_samps), 'r--', label='Old uncertainty')
+plt.plot(np.sqrt(D_hat_old / N_samps), 'r--', label='Traditional uncertainty')
 plt.plot(-np.sqrt(D_hat_old / N_samps), 'r--')
 plt.fill_between(range(len(D_hat_new)), -np.sqrt(D_hat_new / N_samps), np.sqrt(
-    D_hat_new / N_samps), facecolor='0.5', alpha=0.5, label='New unvertainty')
+    D_hat_new / N_samps), facecolor='0.5', alpha=0.5, label='Krylov unvertainty')
 plt.plot((s_x - m_x).val, color='k', label='signal - mean')
 plt.legend()
 plt.savefig('Krylov_uncertainty.png')