Unified print commands.

ANN_Data_Generator.py

@@ -197,8 +197,8 @@ class TrainingDataGenerator:
         """
         # print(type(init_cond_list))
         troubled_indicator = 'without' if is_smooth else 'with'
-        print('Calculating data ' + troubled_indicator + ' troubled cells...')
-        print('Samples to complete:', num_samples)
+        print(f'Calculating data {troubled_indicator} troubled cells...')
+        print(f'Samples to complete: {num_samples}')
         tic = time.perf_counter()
 
         num_datapoints = stencil_len
@@ -241,11 +241,11 @@ class TrainingDataGenerator:
 
             count += 1
             if count % 1000 == 0:
-                print(str(count) + ' samples completed.')
+                print(f'{count} samples completed.')
 
         toc = time.perf_counter()
-        print('Finished calculating data ' + troubled_indicator +
-              ' troubled cells!')
+        print(f'Finished calculating data {troubled_indicator} '
+              f'troubled cells!')
         print(f'Calculation time: {toc - tic:0.4f}s\n')
 
         # Set output data

ANN_Training.py

@@ -138,7 +138,7 @@ class ModelTrainer:
         # Train with validation
         if verbose:
             print('\nTraining model...')
-            print('Number of epochs:', num_epochs)
+            print(f'Number of epochs: {num_epochs}')
         tic_train = time.perf_counter()
         for epoch in range(num_epochs):
             self._model.train()
@@ -305,7 +305,7 @@ def evaluate_models(models: dict, directory: str, num_iterations: int = 100,
 
     # Train models for evaluation
     print('\nTraining models with 5-fold cross validation...')
-    print('Number of iterations:', num_iterations)
+    print(f'Number of iterations: {num_iterations}')
     tic_train = time.perf_counter()
     classification_stats = {}
     for iteration in range(num_iterations):
@@ -330,7 +330,7 @@ def evaluate_models(models: dict, directory: str, num_iterations: int = 100,
                             result[measure])
         # Report status
         if iteration+1 % max(10, 10*(num_iterations//100)):
-            print(iteration+1, 'iterations completed.')
+            print(f'{iteration+1} iterations completed.')
     toc_train = time.perf_counter()
     print('Finished training models with 5-fold cross validation!')
     print(f'Training time: {toc_train - tic_train:0.4f}s\n')

Plotting.py

@@ -461,9 +461,9 @@ def plot_results(projection: ndarray, troubled_cell_history: list,
     plot_semilog_error(grid, pointwise_error)
     plot_error(grid, exact, approx)
 
-    print('p =', basis.polynomial_degree)
-    print('N =', mesh.num_cells)
-    print('maximum error =', max_error)
+    print(f'p = {basis.polynomial_degree}')
+    print(f'N = {mesh.num_cells}')
+    print(f'maximum error = {max_error:e}')
 
 
 def _check_colors(colors: dict) -> dict: