Outsourced run command for SM rule 'approximate_solution' into script.

scripts/approximate_solution.py

+# -*- coding: utf-8 -*-
+"""Script to approximate solution with Discontinuous Galerkin.
+
+@author: Laura C. Kühle
+"""
+import sys
+import time
+
+from tcd.DG_Approximation import DGScheme
+
+
+def main() -> None:
+    """Approximate solution."""
+    with open(str(snakemake.log[0]), 'w', encoding='utf-8') as logfile:
+        sys.stdout = logfile
+        sys.stderr = logfile
+
+        tic = time.perf_counter()
+
+        if len(snakemake.input) > 0:
+            snakemake.params['dg_params']['detector_config']['model_state'] = \
+                snakemake.input[0]
+
+        print(snakemake.params['dg_params'])
+        dg_scheme = DGScheme(**snakemake.params['dg_params'])
+
+        dg_scheme.approximate(
+            data_file=snakemake.params['plot_dir'] + '/' +
+            snakemake.wildcards['scheme'])
+
+        toc = time.perf_counter()
+        print(f'Time: {toc-tic:0.4f}s')
+
+
+if __name__ == '__main__':
+    if "snakemake" in locals():
+        main()
+    else:
+        print('Not Defined.')

workflows/approximation.smk

@@ -3,21 +3,22 @@ import time
 
 from scripts.tcd import Initial_Condition
 from scripts.tcd import Quadrature
-from scripts.tcd.DG_Approximation import DGScheme
 from scripts.tcd.Plotting import plot_approximation_results
 
 configfile: 'config.yaml'
 
+
 PLOTS = ['error', 'exact_and_approx', 'semilog_error', 'shock_tube']
 DIR = config['data_dir']
 SCHEMES = config['schemes']
 
+
 rule all:
     input:
-        expand(DIR + '/fig/{plot}/{scheme}.pdf', plot=PLOTS,
-            scheme=SCHEMES)
+        expand(DIR + '/fig/{plot}/{scheme}.pdf', plot=PLOTS, scheme=SCHEMES)
     default_target: True
 
+
 def get_ANN_model(wildcards):
     if config['schemes'][wildcards.scheme]['detector'] == \
             'ArtificialNeuralNetwork':
@@ -25,6 +26,7 @@ def get_ANN_model(wildcards):
             wildcards.scheme]['detector_config']['model_state']
     return []
 
+
 rule approximate_solution:
     input:
         get_ANN_model
@@ -35,24 +37,9 @@ rule approximate_solution:
         plot_dir = DIR + '/fig'
     log:
         DIR+'/log/approximate_solution/{scheme}.log'
-    run:
-        with open(str(log), 'w') as logfile:
-            sys.stdout = logfile
-            sys.stderr = logfile
+    script:
+        '../scripts/approximate_solution.py'
 
-            tic = time.perf_counter()
-
-            if len(input) > 0:
-                params.dg_params['detector_config']['model_state'] = input
-
-            print(params.dg_params)
-            dg_scheme = DGScheme(**params.dg_params)
-
-            dg_scheme.approximate(
-                data_file=params.plot_dir+'/'+wildcards.scheme)
-
-            toc = time.perf_counter()
-            print(f'Time: {toc - tic:0.4f}s')
 
 rule plot_approximation_results:
     input: