diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index 2a41aab81d7ed62b1b58af515d703a2281236645..e6d6de152e42d44f271ba986b6645d2cd36b68d0 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -1042,7 +1042,6 @@ class PlotSeparationOfScales(AbstractPlotClass):  # pragma: no cover
             data = dh.get_X(as_numpy=False)[0]
             station = dh.id_class.station[0]
             data = data.sel(Stations=station)
-            # plt.subplots()
             data.plot(x=self.time_dim, y=self.window_dim, col=self.filter_dim, row=self.target_dim, robust=True)
             self.plot_name = f"{orig_plot_name}_{station}"
             self._save()
@@ -1085,9 +1084,8 @@ class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass):  # pragma: no cover
         return data
 
     def prepare_data(self, data: xr.DataArray):
-        self._data_table = data.to_pandas()
-        if "persi" in self._data_table.columns:
-            self._data_table["persi"] = self._data_table.pop("persi")
+        data_table = data.to_pandas()
+        self._data_table = data_table[data_table.mean().sort_values().index]
         self._n_boots = self._data_table.shape[0]
 
     def _apply_root(self):
@@ -1102,7 +1100,7 @@ class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass):  # pragma: no cover
         if orientation == "v":
             figsize, width = (size, 5), 0.4
         elif orientation == "h":
-            figsize, width = (6, (1+.5*size)), 0.65
+            figsize, width = (7, (1+.5*size)), 0.65
         else:
             raise ValueError(f"orientation must be `v' or `h' but is: {orientation}")
         fig, ax = plt.subplots(figsize=figsize)
@@ -1119,7 +1117,8 @@ class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass):  # pragma: no cover
         else:
             raise ValueError(f"orientation must be `v' or `h' but is: {orientation}")
         text = f"n={n_boots}" if self.block_length is None else f"{self.block_length}, n={n_boots}"
-        text_box = AnchoredText(text, frameon=True, loc=1, pad=0.5)
+        loc = "upper right" if orientation == "h" else "upper left"
+        text_box = AnchoredText(text, frameon=True, loc=loc, pad=0.5)
         plt.setp(text_box.patch, edgecolor='k', facecolor='w')
         ax.add_artist(text_box)
         plt.setp(ax.lines, color='k')
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index 9d03f47172d80b2d06e3ea6f10f44b076883c9ef..7f2b3b59b17910ae2667e003a821fbadab755b85 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -427,7 +427,7 @@ class PostProcessing(RunEnvironment):
 
         :return: the model
         """
-        try:
+        try:  # is only available if a model was trained in training stage
             model = self.data_store.get("best_model")
         except NameNotFoundInDataStore:
             logging.info("No model was saved in data store. Try to load model from experiment path.")
diff --git a/mlair/run_modules/training.py b/mlair/run_modules/training.py
index 8d82afb4c002c660e6fb966945b2e383007d5b70..a38837dce041295d37fae1ea86ef2a215d51dc89 100644
--- a/mlair/run_modules/training.py
+++ b/mlair/run_modules/training.py
@@ -14,6 +14,7 @@ import psutil
 import pandas as pd
 
 from mlair.data_handler import KerasIterator
+from mlair.model_modules import AbstractModelClass
 from mlair.model_modules.keras_extensions import CallbackHandler
 from mlair.plotting.training_monitoring import PlotModelHistory, PlotModelLearningRate
 from mlair.run_modules.run_environment import RunEnvironment
@@ -67,7 +68,7 @@ class Training(RunEnvironment):
     def __init__(self):
         """Set up and run training."""
         super().__init__()
-        self.model: keras.Model = self.data_store.get("model", "model")
+        self.model: AbstractModelClass = self.data_store.get("model", "model")
         self.train_set: Union[KerasIterator, None] = None
         self.val_set: Union[KerasIterator, None] = None
         # self.test_set: Union[KerasIterator, None] = None