[demos] use new greedy methods in demos

src/pymordemos/burgers_ei.py

@@ -86,7 +86,7 @@
 import numpy as np
 from pymor.tools.docopt import docopt
 
-from pymor.algorithms.greedy import greedy
+from pymor.algorithms.greedy import rb_greedy
 from pymor.algorithms.ei import interpolate_operators
 from pymor.analyticalproblems.burgers import burgers_problem_2d
 from pymor.discretizers.fv import discretize_instationary_fv
@@ -193,10 +193,10 @@ def main(args):
 
     reductor = InstationaryRBReductor(eim)
 
-    greedy_data = greedy(fom, reductor, fom.parameter_space.sample_uniformly(args['SNAPSHOTS']),
-                         use_estimator=False, error_norm=lambda U: np.max(fom.l2_norm(U)),
-                         extension_params={'method': 'pod'}, max_extensions=args['RBSIZE'],
-                         pool=pool)
+    greedy_data = rb_greedy(fom, reductor, fom.parameter_space.sample_uniformly(args['SNAPSHOTS']),
+                            use_estimator=False, error_norm=lambda U: np.max(fom.l2_norm(U)),
+                            extension_params={'method': 'pod'}, max_extensions=args['RBSIZE'],
+                            pool=pool)
 
     rom = greedy_data['rom']
 

src/pymordemos/parabolic_mor.py

@@ -193,8 +193,8 @@ def reduce_greedy(fom, reductor, snapshots, basis_size):
     training_set = fom.parameter_space.sample_uniformly(snapshots)
     pool = new_parallel_pool()
 
-    greedy_data = greedy(fom, reductor, training_set, max_extensions=basis_size, pool=pool,
-                         extension_params={'method': 'pod'})
+    greedy_data = rb_greedy(fom, reductor, training_set, max_extensions=basis_size, pool=pool,
+                            extension_params={'method': 'pod'})
 
     return greedy_data['rom']
 
@@ -203,9 +203,9 @@ def reduce_adaptive_greedy(fom, reductor, validation_mus, basis_size):
 
     pool = new_parallel_pool()
 
-    greedy_data = adaptive_greedy(fom, reductor, validation_mus=validation_mus,
-                                  extension_params={'method': 'pod'}, max_extensions=basis_size,
-                                  pool=pool)
+    greedy_data = rb_adaptive_greedy(fom, reductor, validation_mus=validation_mus,
+                                     extension_params={'method': 'pod'}, max_extensions=basis_size,
+                                     pool=pool)
 
     return greedy_data['rom']
 

src/pymordemos/thermalblock.py

@@ -388,14 +388,14 @@ def reduce_naive(fom, reductor, basis_size):
 def reduce_greedy(fom, reductor, snapshots_per_block,
                   extension_alg_name, max_extensions, use_estimator, pool):
 
-    from pymor.algorithms.greedy import greedy
+    from pymor.algorithms.greedy import rb_greedy
 
     # run greedy
     training_set = fom.parameter_space.sample_uniformly(snapshots_per_block)
-    greedy_data = greedy(fom, reductor, training_set,
-                         use_estimator=use_estimator, error_norm=fom.h1_0_semi_norm,
-                         extension_params={'method': extension_alg_name}, max_extensions=max_extensions,
-                         pool=pool)
+    greedy_data = rb_greedy(fom, reductor, training_set,
+                            use_estimator=use_estimator, error_norm=fom.h1_0_semi_norm,
+                            extension_params={'method': extension_alg_name}, max_extensions=max_extensions,
+                            pool=pool)
     rom = greedy_data['rom']
 
     # generate summary
@@ -417,13 +417,13 @@ def reduce_adaptive_greedy(fom, reductor, validation_mus,
                            extension_alg_name, max_extensions, use_estimator,
                            rho, gamma, theta, pool):
 
-    from pymor.algorithms.adaptivegreedy import adaptive_greedy
+    from pymor.algorithms.adaptivegreedy import rb_adaptive_greedy
 
     # run greedy
-    greedy_data = adaptive_greedy(fom, reductor, validation_mus=-validation_mus,
-                                  use_estimator=use_estimator, error_norm=fom.h1_0_semi_norm,
-                                  extension_params={'method': extension_alg_name}, max_extensions=max_extensions,
-                                  rho=rho, gamma=gamma, theta=theta, pool=pool)
+    greedy_data = rb_adaptive_greedy(fom, reductor, validation_mus=-validation_mus,
+                                     use_estimator=use_estimator, error_norm=fom.h1_0_semi_norm,
+                                     extension_params={'method': extension_alg_name}, max_extensions=max_extensions,
+                                     rho=rho, gamma=gamma, theta=theta, pool=pool)
     rom = greedy_data['rom']
 
     # generate summary

src/pymordemos/thermalblock_adaptive.py

@@ -71,7 +71,7 @@
 
 from pymor.tools.docopt import docopt
 
-from pymor.algorithms.adaptivegreedy import adaptive_greedy
+from pymor.algorithms.adaptivegreedy import rb_adaptive_greedy
 from pymor.algorithms.error import reduction_error_analysis
 from pymor.analyticalproblems.thermalblock import thermal_block_problem
 from pymor.core.pickle import dump
@@ -142,7 +142,7 @@ def thermalblock_demo(args):
     reductor = reductors[args['--reductor']]
 
     pool = new_parallel_pool(ipython_num_engines=args['--ipython-engines'], ipython_profile=args['--ipython-profile'])
-    greedy_data = adaptive_greedy(
+    greedy_data = rb_adaptive_greedy(
         fom, reductor,
         validation_mus=args['--validation-mus'],
         rho=args['--rho'],

src/pymordemos/thermalblock_gui.py

@@ -48,7 +48,7 @@
     from Qt import QtWidgets
 except ImportError as e:
     raise QtMissing()
-from pymor.algorithms.greedy import greedy
+from pymor.algorithms.greedy import rb_greedy
 from pymor.analyticalproblems.thermalblock import thermal_block_problem
 from pymor.discretizers.cg import discretize_stationary_cg
 from pymor.gui.gl import ColorBarWidget, GLPatchWidget
@@ -166,10 +166,10 @@ def _first(self):
         product = self.m.h1_0_semi_product if args['--product'] == 'h1' else None
         reductor = CoerciveRBReductor(self.m, product=product)
 
-        greedy_data = greedy(self.m, reductor,
-                             self.m.parameter_space.sample_uniformly(args['SNAPSHOTS']),
-                             use_estimator=True, error_norm=self.m.h1_0_semi_norm,
-                             max_extensions=args['RBSIZE'])
+        greedy_data = rb_greedy(self.m, reductor,
+                                self.m.parameter_space.sample_uniformly(args['SNAPSHOTS']),
+                                use_estimator=True, error_norm=self.m.h1_0_semi_norm,
+                                max_extensions=args['RBSIZE'])
         self.rom, self.reductor = greedy_data['rom'], reductor
         self.first = False
 

src/pymordemos/thermalblock_simple.py

@@ -242,10 +242,10 @@ def reduce_greedy(fom, reductor, snapshots, basis_size):
     training_set = fom.parameter_space.sample_uniformly(snapshots)
     pool = new_parallel_pool()
 
-    greedy_data = greedy(fom, reductor, training_set,
-                         extension_params={'method': 'gram_schmidt'},
-                         max_extensions=basis_size,
-                         pool=pool)
+    greedy_data = rb_greedy(fom, reductor, training_set,
+                            extension_params={'method': 'gram_schmidt'},
+                            max_extensions=basis_size,
+                            pool=pool)
 
     return greedy_data['rom']
 
@@ -254,10 +254,10 @@ def reduce_adaptive_greedy(fom, reductor, validation_mus, basis_size):
 
     pool = new_parallel_pool()
 
-    greedy_data = adaptive_greedy(fom, reductor, validation_mus=-validation_mus,
-                                  extension_params={'method': 'gram_schmidt'},
-                                  max_extensions=basis_size,
-                                  pool=pool)
+    greedy_data = rb_adaptive_greedy(fom, reductor, validation_mus=-validation_mus,
+                                     extension_params={'method': 'gram_schmidt'},
+                                     max_extensions=basis_size,
+                                     pool=pool)
 
     return greedy_data['rom']