Some plotting tweaks

demos/krylov_sampling.py

@@ -3,6 +3,8 @@ import numpy as np
 import matplotlib.pyplot as plt
 from nifty4.sugar import create_power_operator
 
+np.random.seed(42)
+
 x_space = ift.RGSpace(1024)
 h_space = x_space.get_default_codomain()
 
@@ -69,23 +71,27 @@ plt.plot(d.val,'o', label="data")
 plt.plot(s_x.val,label="original")
 plt.plot(m_x.val, label="reconstruction")
 plt.legend()
-plt.show()
+plt.savefig('Krylov_reconstruction.png')
+plt.close()
 
+pltdict = {'alpha': .2, 'linewidth': .2}
 for i in range(N_samps):
-    plt.plot(sky(samps_old[i]).val, color = 'b')
-    plt.plot(sky(samps[i]).val, color = 'r')
+    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.legend()
-plt.show()
+plt.savefig('Krylov_samples.png')
+plt.close()
+
 D_hat_old = ift.Field.zeros(x_space).val
 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), color='b')
-plt.plot(np.sqrt(D_hat_new/N_samps), color='r')
-plt.plot(-np.sqrt(D_hat_old/N_samps), color='b')
-plt.plot(-np.sqrt(D_hat_new/N_samps), color='r')
-plt.plot((s_x-m_x).val, color='k')
-plt.show()
+plt.plot(np.sqrt(D_hat_old/N_samps), 'r--', label='Old 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')
+plt.plot((s_x-m_x).val, color='k', label='signal - mean')
+plt.legend()
+plt.savefig('Krylov_uncertainty.png')
+plt.close()