Added option to run multiple approximations in one config file.

Snakefile

@@ -20,7 +20,7 @@ use rule * from ann_training as ANN_*
 
 module approximation:
     snakefile: DIR + '/approximation.smk'
-    config: config
+    config: {**config['Approximation'], 'data_directory': config['data_directory']}
 
 use rule * from approximation as DG_*
 

config.yaml

 data_directory: 'model-Feb14'
-plot_dir: 'fig-Feb14'
-plot_name: 'Separation-Test'
 random_seed: 1234
 
 # Parameter for Approximation with Troubled Cell Detection
-dg_parameter:
+Approximation:
+  plot_dir: 'fig-Feb14'
+
+  schemes:
+    Separation_Test:
       wave_speed: 1
       polynomial_degree: 2
       cfl_number: 0.2
@@ -36,8 +38,8 @@ dg_parameter:
         num_eval_points: 12
 
       update_scheme: 'SSPRK3'
-#  Test_Run:
-#    detector: 'Theoretical'
+    Test_Run:
+      detector: 'Theoretical'
 
 # Parameter for Training Data Generation
 ANN_Data:

workflows/approximation.smk

@@ -7,27 +7,31 @@ configfile: 'config.yaml'
 
 PLOTS = ['error', 'exact_and_approx', 'semilog_error', 'shock_tube']
 DIR = config['data_directory']
+SCHEMES = config['schemes']
 # if config['random_seed'] is not None:
 #     np.random.seed(config['random_seed'])
 
 rule all:
     input:
-        expand(config['plot_dir'] + '/{plot}/' + config['plot_name'] + '.pdf', plot=PLOTS)
+        expand(config['plot_dir'] + '/{plot}/{scheme}.pdf', plot=PLOTS, scheme=SCHEMES)
     default_target: True
 
+def get_ANN_model(wildcards):
+    if config['schemes'][wildcards.scheme]['detector'] == 'ArtificialNeuralNetwork':
+        return DIR + '/trained models/' \
+               + config['schemes'][wildcards.scheme]['detector_config']['model_state']
+    return []
+
 rule approximate_solution:
     input:
-        config['data_directory'] + '/trained models/'
-        + config['dg_parameter']['detector_config']['model_state']
-        if config['dg_parameter']['detector'] == 'ArtificialNeuralNetwork' else ''
+        get_ANN_model
     output:
-        expand(config['plot_dir'] + '/{plot}/' + config['plot_name'] + '.pdf', plot=PLOTS)
+        expand(config['plot_dir'] + '/{plot}/{{scheme}}.pdf', plot=PLOTS)
     params:
-        dg_params=config['dg_parameter'],
-        plot_dir=config['plot_dir'],
-        plot_name=config['plot_name']
+        dg_params=lambda wildcards: config['schemes'][wildcards.scheme],
+        plot_dir=config['plot_dir']
     log:
-        DIR+'/log/approximate_solution.log'
+        DIR+'/log/approximate_solution_{scheme}.log'
     run:
         with open(str(log), 'w') as logfile:
             sys.stdout = logfile
@@ -35,13 +39,14 @@ rule approximate_solution:
 
             tic = time.perf_counter()
 
+            if len(input) > 0:
                 params.dg_params['detector_config']['model_state'] = input
 
             print(params.dg_params)
             dg_scheme = DGScheme(plot_dir=params.plot_dir, **params.dg_params)
 
             dg_scheme.approximate()
-            dg_scheme.save_plots(params.plot_name)
+            dg_scheme.save_plots(wildcards.scheme)
 
             toc = time.perf_counter()
             print(f'Time: {toc - tic:0.4f}s')
\ No newline at end of file