updated plot routines to use Legendre nodes

Author: Daniel Ruprecht

examples/acoustic_1d_imex/plotconvdata.py

@@ -2,6 +2,7 @@
 from matplotlib import pyplot as plt
 from pylab import rcParams
 from matplotlib.ticker import ScalarFormatter
+from subprocess import call
 
 fs     = 8
 order  = np.array([])
@@ -54,5 +55,8 @@
 plt.gca().get_xaxis().get_major_formatter().labelOnlyBase = False
 plt.gca().get_xaxis().set_major_formatter(ScalarFormatter())
 plt.show()
-fig.savefig('sdc_fwsw_convergence.pdf',bbox_inches='tight')
+filename = 'sdc_fwsw_convergence.pdf'
+fig.savefig(filename,bbox_inches='tight')
+call(["pdfcrop", filename, filename])
+
 

examples/acoustic_1d_imex/runconvergence.py

@@ -31,11 +31,11 @@
 
     # This comes as read-in for the problem class
     pparams = {}
-    pparams['nvars']     = [(2,250)]
+    pparams['nvars']     = [(2,400)]
     pparams['cadv']      = 0.05
     pparams['cs']        = 1.00
     pparams['order_adv'] = 5
-    pparams['waveno']    = 1
+    pparams['waveno']    = 2
 
     # This comes as read-in for the transfer operations
     tparams = {}
@@ -47,12 +47,11 @@
     description['problem_params']    = pparams
     description['dtype_u']           = mesh
     description['dtype_f']           = rhs_imex_mesh
-    description['collocation_class'] = collclass.CollGaussLobatto
+    description['collocation_class'] = collclass.CollGaussLegendre
     description['sweeper_class']     = imex_1st_order
+    description['do_coll_update']    = True
     description['level_params']      = lparams
     description['hook_class']        = plot_solution
-    #description['transfer_class'] = mesh_to_mesh_1d
-    #description['transfer_params'] = tparams
 
     Nsteps = [15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80]
 

examples/acoustic_1d_imex/runmultiscale.py

@@ -16,6 +16,7 @@
 
 from matplotlib import pyplot as plt
 from pylab import rcParams
+from subprocess import call
 
 fs = 8
 
@@ -56,11 +57,9 @@
     description['problem_params']    = pparams
     description['dtype_u']           = mesh
     description['dtype_f']           = rhs_imex_mesh
-    description['collocation_class'] = collclass.CollGaussRadau_Right
-    if sparams['maxiter']==2:
-      description['num_nodes']         = 2
-    else:
-      description['num_nodes']         = 3
+    description['collocation_class'] = collclass.CollGaussLegendre
+    # Number of nodes
+    description['num_nodes']         = 3
     description['sweeper_class']     = imex_1st_order
     description['level_params']      = lparams
     description['hook_class']        = plot_solution
@@ -137,4 +136,7 @@
     plt.legend(loc='upper left', fontsize=fs, prop={'size':fs})
     fig.gca().grid()
     #plt.show()
-    plt.gcf().savefig('fwsw-sdc-K'+str(sparams['maxiter'])+'-M'+str(description['num_nodes'])+'.pdf', bbox_inches='tight')
+    filename = 'sdc-fwsw-multiscale-K'+str(sparams['maxiter'])+'-M'+str(description['num_nodes'])+'.pdf'
+    plt.gcf().savefig(filename, bbox_inches='tight')
+    call(["pdfcrop", filename, filename])
+

examples/fwsw/plot_stifflimit_specrad.py

@@ -10,6 +10,7 @@
 
 from matplotlib import pyplot as plt
 from pylab import rcParams
+from subprocess import call
 
 if __name__ == "__main__":
 
@@ -20,7 +21,7 @@
     pparams['lambda_f'] = np.array([50.0*1j], dtype='complex')
     pparams['u0'] = 1.0
     swparams = {}
-    swparams['collocation_class'] = collclass.CollGaussLobatto
+    swparams['collocation_class'] = collclass.CollGaussLegendre
 
     nodes_v = np.arange(2,10)
     specrad = np.zeros((2,np.size(nodes_v)))
@@ -53,8 +54,10 @@
         # For Lobatto nodes, first column and row are all zeros, since q_1 = q_0; hence remove them
         QI = QI[1:,1:]
         Q  = Q[1:,1:]
-      # Eigenvalue of error propagation matrix in stiff limit: E = I - inv(QI)*Q
-      evals, evecs = np.linalg.eig( np.eye(nnodes-1) - np.linalg.inv(QI).dot(Q) )
+        # Eigenvalue of error propagation matrix in stiff limit: E = I - inv(QI)*Q
+        evals, evecs = np.linalg.eig( np.eye(nnodes-1) - np.linalg.inv(QI).dot(Q) )
+      else:
+        evals, evecs = np.linalg.eig( np.eye(nnodes) - np.linalg.inv(QI).dot(Q) )
       specrad[0,i] = np.linalg.norm( evals, np.inf )
 
   ### Plot result
@@ -71,5 +74,8 @@
     plt.yticks(fontsize=fs)
     plt.xticks(fontsize=fs)
     #plt.show()
-    fig.savefig('sdc_fwsw_stifflimit_specrad.pdf',bbox_inches='tight')
+    filename = 'sdc_fwsw_stifflimit_specrad.pdf'
+    fig.savefig(filename,bbox_inches='tight')
+    call(["pdfcrop", filename, filename])
+