Bridge example

gpexamples/gpbridge/superscript.py

@@ -7,6 +7,7 @@ from jive.app import main
 import jive.util.proputils as pu
 from quickviewer import QuickViewer
 from copy import deepcopy
+from plotutils import create_dat
 
 props = pu.parse_file('bridge.pro')
 
@@ -77,39 +78,53 @@ Phi = globdat['Phi']
 err = abs(u - Phi @ u_coarse)
 err_grad = abs(eps - Phi @ eps_c)
 
-QuickViewer(u_post, globdat, title=r'Posterior mean diplacement ($\bar u$)')
-
-QuickViewer(u, globdat, title=r'Exact displacement ($u$)')
-
-fig, (ax1, ax2) = plt.subplots(2, 1, tight_layout=True)
-QuickViewer(err, globdat, ax=ax1, title=r'Discretization error ($|u_f - u_c|$)')
-QuickViewer(std_u_post, globdat, ax=ax2, title=r'Posterior standard deviation ($\sqrt{\bar \Sigma_{ii}}$)')
-# plt.savefig(fname='img/'+props['init']['mesh']['file'].replace('.msh','').replace('beam_', '')+'.pdf')
+plt.figure(figsize=(8,2), tight_layout=True)
+ax = plt.gca()
+QuickViewer(u_post, globdat, ax=ax, colorbar=False, pdf=True, scale=10.0, title=r'Posterior mean displacement ($\mu_u$)')
+plt.savefig('img/posterior-mean-state0.pdf')
 plt.show()
 
-fig, (ax1, ax2) = plt.subplots(2, 1, tight_layout=True)
-QuickViewer(err_grad, globdat, ax=ax1, comp=0, title=r'Discretization error ($|\varepsilon_f^{xx} - \varepsilon_c^{xx}|$)')
-QuickViewer(std_u_post, globdat, ax=ax2, title=r'Posterior standard deviation ($\sqrt{\bar \Sigma_{ii}}$)')
-# plt.savefig(fname='img/'+props['init']['mesh']['file'].replace('.msh','').replace('beam_', '')+'.pdf')
+plt.figure(figsize=(8,2), tight_layout=True)
+ax = plt.gca()
+QuickViewer(std_u_post, globdat, ax=ax, colorbar=False, pdf=True, title=r'Posterior standard deviation ($\sigma_u$)')
+plt.savefig('img/posterior-std-state0.pdf')
 plt.show()
 
-fig, (ax1, ax2) = plt.subplots(2, 1, tight_layout=True)
-QuickViewer(err_grad, globdat, ax=ax1, comp=1, title=r'Discretization error ($|\varepsilon_f^{yy} - \varepsilon_c^{yy}|$)')
-QuickViewer(std_u_post, globdat, ax=ax2, title=r'Posterior standard deviation ($\sqrt{\bar \Sigma_{ii}}$)')
-# plt.savefig(fname='img/'+props['init']['mesh']['file'].replace('.msh','').replace('beam_', '')+'.pdf')
+plt.figure(figsize=(8,2), tight_layout=True)
+ax = plt.gca()
+QuickViewer(std_eps_post, globdat, ax=ax, colorbar=False, pdf=True, title=r'Posterior standard deviation ($\sigma_\varepsilon$)')
+plt.savefig('img/posterior-std-strain.pdf')
 plt.show()
 
-QuickViewer(std_u_post, globdat, title=r'Posterior standard deviation ($\sqrt{\bar \Sigma_{ii}}$)')
-
 for i, sample in enumerate(samples_u_prior.T[:3]):
-
-    QuickViewer(sample, globdat, scale=10.0, title=r'Prior samples from $u$ (sample {})'.format(i+1))
+    plt.figure(figsize=(8,2), tight_layout=True)
+    ax = plt.gca()
+    QuickViewer(sample, globdat, ax=ax, colorbar=False, pdf=True, scale=10.0, title=r'Prior samples from $u$ (sample {})'.format(i+1))
+    plt.savefig('img/samples/prior-state0-{}.pdf'.format(i+1))
+    plt.show()
+
+for i, sample in enumerate(samples_f_prior.T[:3]):
+    plt.figure(figsize=(8,2), tight_layout=True)
+    ax = plt.gca()
+    QuickViewer(sample, globdat, ax=ax, colorbar=False, pdf=True, title=r'Prior samples from $f$ (sample {})'.format(i+1))
+    plt.savefig('img/samples/prior-extForce-{}.pdf'.format(i+1))
+    plt.show()
 
 for i, sample in enumerate(samples_u_post.T[:3]):
-
-    QuickViewer(sample, globdat, scale=10.0, title=r'Posterior samples from $u$ (sample {})'.format(i+1))
-
-fine_list = ['post', 'r0', 'r1', 'r2', 'r3']
+    plt.figure(figsize=(8,2), tight_layout=True)
+    ax = plt.gca()
+    QuickViewer(sample, globdat, ax=ax, colorbar=False, pdf=True, scale=10.0, title=r'Posterior samples from $u$ (sample {})'.format(i+1))
+    plt.savefig('img/samples/posterior-state0-{}.pdf'.format(i+1))
+    plt.show()
+
+for i, sample in enumerate(samples_f_post.T[:3]):
+    plt.figure(figsize=(8,2), tight_layout=True)
+    ax = plt.gca()
+    QuickViewer(sample, globdat, ax=ax, colorbar=False, pdf=True, title=r'Posterior samples from $f$ (sample {})'.format(i+1))
+    plt.savefig('img/samples/posterior-extForce-{}.pdf'.format(i+1))
+    plt.show()
+
+fine_list = ['r0', 'r1', 'r2', 'r3']
 x_dict = {}
 u_dict = {}
 
@@ -160,12 +175,20 @@ for fineness in fine_list:
 plt.figure()
 
 for fineness in fine_list:
-    plt.plot(x_dict[fineness], u_dict[fineness], label=fineness)
+    h_inv = 2**int(fineness[-1])
+    if h_inv == 1:
+        label = r'h = 1'
+    else:
+        label = r'$h = \frac{1}{' + str(h_inv) + '}$'
+    
+    plt.plot(x_dict[fineness], u_dict[fineness], label=label)
     if fineness == 'post':
         u_bar = np.array(u_dict[fineness])
         std_u_bottom = np.array(std_u_bottom)
         std_u_bottom[0] = std_u_bottom[-1] = 0
         plt.fill_between(x_dict[fineness], u_bar - 2*std_u_bottom, u_bar + 2*std_u_bottom, alpha=0.3)
 
+    create_dat([x_dict[fineness],u_dict[fineness]], ['x', 'u_top'], 'results/' + fineness + '-top')
+    
 plt.legend()
 plt.show()