Moved plotting into separate function.

DG_Approximation.py

@@ -3,7 +3,8 @@
 @author: Laura C. Kühle
 
 Urgent:
-TODO: Move plotting into separate function
+TODO: Move plotting into separate function -> Done
+TODO: Move plotting into separate rule
 TODO: Adapt TCD from Soraya
     (Dropbox->...->TEST_troubled-cell-detector->Troubled_Cell_Detector)
 TODO: Add verbose output
@@ -201,22 +202,19 @@ class DGScheme:
             self._polynomial_degree, self._num_grid_cells, self._detector,
             self._limiter)
 
-    def approximate(self, data_dir, data_name):
+    def approximate(self, data_file):
         """Approximates projection.
 
         Initializes projection and evolves it in time. Each time step consists
         of three parts: A projection update, a troubled-cell detection,
         and limiting based on the detected cells.
 
-        At final time, result and error plots are
-        generated and, if verbose flag is set, also displayed.
+        At final time, results are saved in JSON file.
 
         Attributes
         ----------
-        data_dir: str
-            Path to directory in which data is saved.
-        data_name : str
-            Name of data.
+        data_file: str
+            Path to file in which data will be saved.
 
         """
         projection = self._do_initial_projection(self._init_cond)
@@ -254,12 +252,28 @@ class DGScheme:
                         for key in approx_stats.keys()}
 
         # Save approximation results in JSON format
-        with open(data_dir + '/' + data_name + '.json', 'w') \
+        with open(data_file + '.json', 'w') \
                 as json_file:
             json_file.write(json.dumps(approx_stats))
 
+    def plot_approximation_results(self, data_file, directory, plot_name):
+        """Plots given approximation results.
+
+        Generates plots based on given data, sets plot directory if not
+        already existing, and saves plots.
+
+        Parameters
+        ----------
+        data_file: str
+            Path to data file for plotting.
+        directory: str
+            Path to directory in which plots will be saved.
+        plot_name : str
+            Name of plot.
+
+        """
         # Read approximation results
-        with open(data_dir + '/' + data_name + '.json') as json_file:
+        with open(data_file + '.json') as json_file:
             approx_stats = json.load(json_file)
 
         # Decode all ndarrays by converting lists
@@ -270,32 +284,18 @@ class DGScheme:
         # and any detector-dependant plots
         self._detector.plot_results(**approx_stats)
 
-    def save_plots(self, plot_dir, plot_name):
-        """Saves plotted results.
-
-        Sets plot directory, if not already existing, and saves plots
-        generated during the last approximation.
-
-        Parameters
-        ----------
-        plot_dir: str
-            Path to directory in which plots are saved.
-        plot_name : str
-            Name of plot.
-
-        """
         # Set paths for plot files if not existing already
-        if not os.path.exists(plot_dir):
-            os.makedirs(plot_dir)
+        if not os.path.exists(directory):
+            os.makedirs(directory)
 
         # Save plots
         for identifier in plt.get_figlabels():
             # Set path for figure directory if not existing already
-            if not os.path.exists(plot_dir + '/' + identifier):
-                os.makedirs(plot_dir + '/' + identifier)
+            if not os.path.exists(directory + '/' + identifier):
+                os.makedirs(directory + '/' + identifier)
 
             plt.figure(identifier)
-            plt.savefig(plot_dir + '/' + identifier + '/' +
+            plt.savefig(directory + '/' + identifier + '/' +
                         plot_name + '.pdf')
 
     def _reset(self):

config.yaml

-data_dir: 'model-Soraya_Mar02'
+data_dir: 'model-Mar14'
 random_seed: 1234
 
 # Parameter for Approximation with Troubled Cell Detection
 Approximation:
-  plot_dir: 'fig-Soraya_Mar02'
+  plot_dir: 'fig-Mar14'
 
   schemes:
     Separation_Test:
@@ -21,7 +21,7 @@ Approximation:
       detector_config:
         fold_len: 16
         whisker_len: 3
-        add_reconstructions: False
+        add_reconstructions: True
         model_state: 'Adam.model.pt'
 
       init_cond: 'Sine'
@@ -52,7 +52,7 @@ ANN_Data:
   smooth_troubled_balance: 0.5
 
   stencil_length: 3
-  add_reconstructions : False
+  add_reconstructions : True
 
   # Initial Conditions for Training Data
   functions:
@@ -87,7 +87,7 @@ ANN_Training:
       threshold: 1.0e-5
       batch_size: 500
       model: ThreeLayerReLu
-      model_config: {input_size: 3}
+      model_config: {}
       loss_function: BCELoss
       optimizer: Adam
     SGD:
@@ -95,7 +95,7 @@ ANN_Training:
       threshold: 1.0e-5
       batch_size: 500
       model: ThreeLayerReLu
-      model_config: {input_size: 3}
+      model_config: {}
       loss_function: BCELoss
       optimizer: SGD
 

workflows/approximation.smk

@@ -48,10 +48,11 @@ rule approximate_solution:
             print(params.dg_params)
             dg_scheme = DGScheme(**params.dg_params)
 
-            dg_scheme.approximate(data_dir=params.plot_dir,
-                data_name=wildcards.scheme)
-            dg_scheme.save_plots(plot_dir=params.plot_dir,
-                plot_name=wildcards.scheme)
+            dg_scheme.approximate(
+                data_file=params.plot_dir+'/'+wildcards.scheme)
+            dg_scheme.plot_approximation_results(directory=params.plot_dir,
+                plot_name=wildcards.scheme,
+                data_file=params.plot_dir+'/'+wildcards.scheme)
 
             toc = time.perf_counter()
             print(f'Time: {toc - tic:0.4f}s')
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