add time tracking for some potential bottlenecks, run.py can now run without...

add time tracking for some potential bottlenecks, run.py can now run without training AND model_setup, if a model was stored locally

src/run_modules/post_processing.py

@@ -18,6 +18,7 @@ from src import statistics
 from src.plotting.postprocessing_plotting import plot_conditional_quantiles
 from src.plotting.postprocessing_plotting import PlotMonthlySummary, PlotStationMap, PlotClimatologicalSkillScore, PlotCompetitiveSkillScore
 from src.datastore import NameNotFoundInDataStore
+from src.helpers import TimeTracking
 
 
 class PostProcessing(RunEnvironment):
@@ -26,7 +27,7 @@ class PostProcessing(RunEnvironment):
         super().__init__()
         self.model: keras.Model = self._load_model()
         self.ols_model = None
-        self.batch_size: int = self.data_store.get("batch_size", "general.model")
+        self.batch_size: int = self.data_store.get_default("batch_size", "general.model", 64)
         self.test_data: DataGenerator = self.data_store.get("generator", "general.test")
         self.test_data_distributed = Distributor(self.test_data, self.model, self.batch_size)
         self.train_data: DataGenerator = self.data_store.get("generator", "general.train")
@@ -36,8 +37,14 @@ class PostProcessing(RunEnvironment):
         self._run()
 
     def _run(self):
-        self.train_ols_model()
-        preds_for_all_stations = self.make_prediction()
+        with TimeTracking():
+            self.train_ols_model()
+            logging.info("take a look on the next reported time measure. If this increases a lot, one should think to "
+                         "skip make_prediction() whenever it is possible to save time.")
+        with TimeTracking():
+            preds_for_all_stations = self.make_prediction()
+            logging.info("take a look on the next reported time measure. If this increases a lot, one should think to "
+                         "skip make_prediction() whenever it is possible to save time.")
         self.skill_scores = self.calculate_skill_scores()
         self.plot()