Tweaked parameter for convergence plots

examples/acoustic_1d_imex/ProblemClass_conv.py

@@ -20,7 +20,7 @@
 from buildWave1DMatrix import getWave1DMatrix, getWave1DAdvectionMatrix
 
 def u_initial(x, k):
-    return np.sin(k*2.0*np.pi*x)
+    return np.sin(k*2.0*np.pi*x) + np.sin(2.0*np.pi*x)
 
 class acoustic_1d_imex(ptype):
     """

examples/acoustic_1d_imex/plotconvdata.py

@@ -45,17 +45,17 @@
   plt.loglog(nsteps_plot[ii,:], error_plot[ii,:], shape[ii], markersize=fs, color=color[ii], label='p='+str(int(order_plot[ii])))
 
 
-plt.legend(loc='upper right', fontsize=fs, prop={'size':fs})
+plt.legend(loc='lower left', fontsize=fs, prop={'size':fs})
 plt.xlabel('Number of time steps', fontsize=fs)
 plt.ylabel('Relative error', fontsize=fs, labelpad=2)
 plt.xlim([0.9*np.min(nsteps_plot), 1.1*np.max(nsteps_plot)])
-plt.ylim([1e-7, 1e1])
-plt.yticks([1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e0, 1e1],fontsize=fs)
-plt.xticks([25, 50, 100], fontsize=fs)
+plt.ylim([1e-5, 1e0])
+plt.yticks([1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e0],fontsize=fs)
+plt.xticks([30, 40, 60, 80], fontsize=fs)
 plt.gca().get_xaxis().get_major_formatter().labelOnlyBase = False
 plt.gca().get_xaxis().set_major_formatter(ScalarFormatter())
 plt.show()
-filename = 'sdc_fwsw_convergence.pdf'
+filename = 'sdc-fwsw-convergence.pdf'
 fig.savefig(filename,bbox_inches='tight')
 call(["pdfcrop", filename, filename])
 

examples/acoustic_1d_imex/runconvergence.py

@@ -31,15 +31,11 @@
 
     # This comes as read-in for the problem class
     pparams = {}
-    pparams['nvars']     = [(2,400)]
-    pparams['cadv']      = 0.05
+    pparams['nvars']     = [(2,300)]
+    pparams['cadv']      = 0.1
     pparams['cs']        = 1.00
     pparams['order_adv'] = 5
-    pparams['waveno']    = 2
-
-    # This comes as read-in for the transfer operations
-    tparams = {}
-    tparams['finter'] = True
+    pparams['waveno']    = 5
 
     # Fill description dictionary for easy hierarchy creation
     description = {}
@@ -53,7 +49,7 @@
     description['level_params']      = lparams
     description['hook_class']        = plot_solution
 
-    Nsteps = [15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80]
+    Nsteps = [30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80]
 
     for order in [2, 3, 4]:
 

examples/acoustic_1d_imex/runitererror.py

@@ -46,11 +46,11 @@
 
       # This comes as read-in for the problem class
       pparams = {}
-      pparams['nvars']     = [(2,250)]
-      pparams['cadv']      = 0.05
+      pparams['nvars']     = [(2,300)]
+      pparams['cadv']      = 0.1
       pparams['cs']        = cs_v[cs_ind]
       pparams['order_adv'] = 5
-      pparams['waveno']    = 1
+      pparams['waveno']    = 5
 
       # This comes as read-in for the transfer operations
       tparams = {}
@@ -72,7 +72,7 @@
       for nodes_ind in np.arange(np.size(nodes_v)):
         # setup parameters "in time"
         t0   = 0
-        Tend = 0.05
+        Tend = 0.025
         description['num_nodes'] = nodes_v[nodes_ind]
 
          # quickly generate block of steps
@@ -119,15 +119,15 @@
       plt.plot(x, y, shape[ii], markersize=fs-2, color=color[ii], label=r'$C_{\rm fast}$=%4.2f' % (cs_v[ii]*dt/P.dx))       
       #plt.plot(x, 0.0*y+avg_convrate[ii,0], '--', color=color[ii])
 
-    plt.legend(loc='upper right', fontsize=fs, prop={'size':fs})
+    plt.legend(loc='upper right', fontsize=fs, prop={'size':fs-2})
     plt.xlabel('Iteration', fontsize=fs)
     plt.ylabel(r'$|| r^{k+1} ||_{\infty}/|| r^k ||_{\infty}$', fontsize=fs, labelpad=2)
     plt.xlim([0, sparams['maxiter']])
-    plt.ylim([0, 0.8])
+    plt.ylim([0, 1.0])
     plt.yticks(fontsize=fs)
     plt.xticks(fontsize=fs)
     plt.show()
-    filename = 'sdc_fwsw_iteration.pdf'
+    filename = 'sdc-fwsw-iteration.pdf'
     fig.savefig(filename,bbox_inches='tight')
     call(["pdfcrop", filename, filename])
 

examples/fwsw/run_convergence.py

@@ -0,0 +1,73 @@
+from __future__ import print_function
+
+from pySDC import CollocationClasses as collclass
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from examples.fwsw.ProblemClass import swfw_scalar
+from pySDC.datatype_classes.complex_mesh import mesh, rhs_imex_mesh
+from pySDC.sweeper_classes.imex_1st_order import imex_1st_order
+import pySDC.PFASST_stepwise as mp
+from pySDC import Log
+
+
+
+if __name__ == "__main__":
+
+    # set global logger (remove this if you do not want the output at all)
+    logger = Log.setup_custom_logger('root')
+
+    num_procs = 1
+
+    # This comes as read-in for the level class
+    lparams = {}
+    lparams['restol'] = 1E-12
+
+    sparams = {}
+    sparams['maxiter'] = 4
+
+    # This comes as read-in for the problem class
+    pparams = {}
+    pparams['lambda_s'] = np.array([0.1j], dtype='complex')
+    pparams['lambda_f'] = np.array([1.0j], dtype='complex')
+    pparams['u0'] = 1
+
+    # Fill description dictionary for easy hierarchy creation
+    description = {}
+    description['problem_class'] = swfw_scalar
+    description['problem_params'] = pparams
+    description['dtype_u'] = mesh
+    description['dtype_f'] = rhs_imex_mesh
+    description['collocation_class'] = collclass.CollGaussLegendre
+    description['num_nodes'] = [3]
+    description['do_LU'] = False
+    description['sweeper_class'] = imex_1st_order
+    description['level_params'] = lparams
+
+    # quickly generate block of steps
+    MS = mp.generate_steps(num_procs,sparams,description)
+
+    Nsteps_v = np.array([1, 2, 4, 8, 10, 15, 20])
+    Tend = 1.0
+    t0   = 0
+
+    P = MS[0].levels[0].prob
+    uinit = P.u_exact(t0)
+    uex = P.u_exact(Tend)
+    error = np.zeros(np.size(Nsteps_v))
+    convline = np.zeros(np.size(Nsteps_v))
+
+    for j in range(0,np.size(Nsteps_v)):
+    # setup parameters "in time"
+      dt   = Tend/float(Nsteps_v[j])
+
+      # call main function to get things done...
+      uend,stats = mp.run_pfasst(MS,u0=uinit,t0=t0,dt=dt,Tend=Tend)
+      error[j] = np.abs(uend.values - uex.values)
+      convline[j] = error[j]*(float(Nsteps_v[j])/float(Nsteps_v[j]))**sparams['maxiter']
+
+    plt.figure()
+    plt.loglog(Nsteps_v, error, 'bo', markersize=12)
+    plt.loglog(Nsteps_v, convline, '-', color='k')
+    plt.show()