removed slow create_boot_straps() implementation

src/run_modules/post_processing.py

@@ -55,17 +55,14 @@ class PostProcessing(RunEnvironment):
         if self.data_store.get("evaluate_bootstraps", "general.postprocessing"):
             bootstrap_path = self.data_store.get("bootstrap_path", "general")
             BootStraps(self.test_data, bootstrap_path, 20)
-            with TimeTracking(name="split (refac_1)"):
+            with TimeTracking(name="split (refac_1): create_boot_straps_refac_2()"):
                 self.create_boot_straps_refac_2()
                 self.bootstrap_skill_scores = self.calculate_bootstrap_skill_scores()
-            with TimeTracking(name="split (refac)"):
+            with TimeTracking(name="split (refac): create_boot_straps_refac()"):
                 self.create_boot_straps_refac()
                 self.bootstrap_skill_scores = self.calculate_bootstrap_skill_scores()
-            with TimeTracking(name="merged"):
+            with TimeTracking(name="merged: combined_boot_forecast_and_skill()"):
                 self.bootstrap_skill_scores = self.combined_boot_forecast_and_skill()
-            with TimeTracking(name="original version"):
-                self.create_boot_straps()
-                self.bootstrap_skill_scores = self.calculate_bootstrap_skill_scores()
 
 
         # skill scores
@@ -74,43 +71,6 @@ class PostProcessing(RunEnvironment):
         # plotting
         # self.plot()
 
-    def create_boot_straps(self):
-        # forecast
-        with TimeTracking(name="boot predictions"):
-            bootstrap_path = self.data_store.get("bootstrap_path", "general")
-            forecast_path = self.data_store.get("forecast_path", "general")
-            window_lead_time = self.data_store.get("window_lead_time", "general")
-            bootstraps = BootStraps(self.test_data, bootstrap_path, 20)
-            # make bootstrap predictions
-            logging.info("predictions")
-            bootstrap_predictions = self.model.predict_generator(generator=bootstraps.boot_strap_generator(),
-                                                                 steps=bootstraps.get_boot_strap_generator_length(),
-                                                                 use_multiprocessing=True)
-
-            if isinstance(bootstrap_predictions, list):
-                bootstrap_predictions = bootstrap_predictions[-1]
-            # get bootstrap prediction meta data
-            bootstrap_meta = np.array(bootstraps.get_boot_strap_meta())
-            # save bootstrap predictions separately for each station and variable combination
-            variables = np.unique(bootstrap_meta[:, 0])
-            for station in np.unique(bootstrap_meta[:, 1]):
-                logging.info(station)
-                coords = None
-                for boot in variables:
-                    # store each variable - station - combination
-                    ind = np.all(bootstrap_meta == [boot, station], axis=1)
-                    length = sum(ind)
-                    sel = bootstrap_predictions[ind].reshape((length, window_lead_time, 1))
-                    coords = (range(length), range(1, window_lead_time + 1))
-                    tmp = xr.DataArray(sel, coords=(*coords, [boot]), dims=["index", "ahead", "type"])
-                    file_name = os.path.join(forecast_path, f"bootstraps_{boot}_{station}.nc")
-                    tmp.to_netcdf(file_name)
-                # store also true labels for each station
-                labels = bootstraps.get_labels(station).reshape((length, window_lead_time, 1))
-                file_name = os.path.join(forecast_path, f"bootstraps_labels_{station}.nc")
-                labels = xr.DataArray(labels, coords=(*coords, ["obs"]), dims=["index", "ahead", "type"])
-                labels.to_netcdf(file_name)
-
     def create_boot_straps_refac(self):
         # forecast
         with TimeTracking(name="boot predictions"):