fixed errors with branched models

src/run_modules/post_processing.py

@@ -65,6 +65,8 @@ class PostProcessing(RunEnvironment):
         with TimeTracking(name="boot predictions"):
             bootstrap_predictions = self.model.predict_generator(generator=bootstraps.boot_strap_generator(),
                                                                  steps=bootstraps.get_boot_strap_generator_length())
+        if isinstance(bootstrap_predictions, list):
+            bootstrap_predictions = bootstrap_predictions[-1]
         bootstrap_meta = np.array(bootstraps.get_boot_strap_meta())
         variables = np.unique(bootstrap_meta[:, 0])
         for station in np.unique(bootstrap_meta[:, 1]):
@@ -211,7 +213,7 @@ class PostProcessing(RunEnvironment):
         return ols_prediction
 
     def _create_persistence_forecast(self, data, persistence_prediction, mean, std, transformation_method, normalised):
-        tmp_persi = data.observation.copy().sel({'window': 0})#.shift(datetime=1)
+        tmp_persi = data.observation.copy().sel({'window': 0})
         if not normalised:
             tmp_persi = statistics.apply_inverse_transformation(tmp_persi, mean, std, transformation_method)
         window_lead_time = self.data_store.get("window_lead_time", "general")
@@ -234,7 +236,9 @@ class PostProcessing(RunEnvironment):
         tmp_nn = self.model.predict(input_data)
         if not normalised:
             tmp_nn = statistics.apply_inverse_transformation(tmp_nn, mean, std, transformation_method)
-        if tmp_nn.ndim == 3:
+        if isinstance(tmp_nn, list):
+            nn_prediction.values = np.swapaxes(np.expand_dims(tmp_nn[-1], axis=1), 2, 0)
+        elif tmp_nn.ndim == 3:
             nn_prediction.values = np.swapaxes(np.expand_dims(tmp_nn[-1, ...], axis=1), 2, 0)
         elif tmp_nn.ndim == 2:
             nn_prediction.values = np.swapaxes(np.expand_dims(tmp_nn, axis=1), 2, 0)