diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index 7080a6b6472716c60996e462887d0feaf1a6a9ba..33150fae46da2f588d8a212d5d4297fcf5478cfa 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -31,7 +31,7 @@ logging.getLogger('matplotlib').setLevel(logging.WARNING)
 
 
 @TimeTrackingWrapper
-class PlotMonthlySummary(AbstractPlotClass):
+class PlotMonthlySummary(AbstractPlotClass):  # pragma: no cover
     """
     Show a monthly summary over all stations for each lead time ("ahead") as box and whiskers plot.
 
@@ -138,7 +138,7 @@ class PlotMonthlySummary(AbstractPlotClass):
 
 
 @TimeTrackingWrapper
-class PlotConditionalQuantiles(AbstractPlotClass):
+class PlotConditionalQuantiles(AbstractPlotClass):  # pragma: no cover
     """
     Create cond.quantile plots as originally proposed by Murphy, Brown and Chen (1989) [But in log scale].
 
@@ -382,7 +382,7 @@ class PlotConditionalQuantiles(AbstractPlotClass):
 
 
 @TimeTrackingWrapper
-class PlotClimatologicalSkillScore(AbstractPlotClass):
+class PlotClimatologicalSkillScore(AbstractPlotClass):  # pragma: no cover
     """
     Create plot of climatological skill score after Murphy (1988) as box plot over all stations.
 
@@ -474,7 +474,7 @@ class PlotClimatologicalSkillScore(AbstractPlotClass):
 
 
 @TimeTrackingWrapper
-class PlotCompetitiveSkillScore(AbstractPlotClass):
+class PlotCompetitiveSkillScore(AbstractPlotClass):  # pragma: no cover
     """
     Create competitive skill score plot.
 
@@ -591,7 +591,7 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
 
 
 @TimeTrackingWrapper
-class PlotBootstrapSkillScore(AbstractPlotClass):
+class PlotBootstrapSkillScore(AbstractPlotClass):  # pragma: no cover
     """
     Create plot of climatological skill score after Murphy (1988) as box plot over all stations.
 
@@ -840,7 +840,7 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
 
 
 @TimeTrackingWrapper
-class PlotTimeSeries:
+class PlotTimeSeries:  # pragma: no cover
     """
     Create time series plot.
 
@@ -973,7 +973,7 @@ class PlotTimeSeries:
 
 
 @TimeTrackingWrapper
-class PlotSeparationOfScales(AbstractPlotClass):
+class PlotSeparationOfScales(AbstractPlotClass):  # pragma: no cover
 
     def __init__(self, collection: DataCollection, plot_folder: str = ".", time_dim="datetime", window_dim="window",
                  filter_dim="filter", target_dim="variables"):
@@ -999,7 +999,8 @@ class PlotSeparationOfScales(AbstractPlotClass):
             self._save()
 
 
-class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass):
+@TimeTrackingWrapper
+class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass):  # pragma: no cover
 
     def __init__(self, data: xr.DataArray, plot_folder: str = ".", model_type_dim: str = "type",
                  error_measure: str = "mse", error_unit: str = None, dim_name_boots: str = 'boots'):
@@ -1028,6 +1029,8 @@ class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass):
 
     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")
         self._n_boots = self._data_table.shape[0]
 
     def _apply_root(self):
@@ -1038,19 +1041,19 @@ class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass):
     def _plot(self, orientation: str = "v"):
         data_table = self._data_table
         n_boots = self._n_boots
-        size = max([len(np.unique(data_table.columns)), 6])
+        size = len(np.unique(data_table.columns))
         if orientation == "v":
-            figsize = (size, size * 0.8)
+            figsize, width = (size, 5), 0.4
         elif orientation == "h":
-            figsize = (size * 0.8, size)
+            figsize, width = (6, (1+.5*size)), 0.65
         else:
             raise ValueError(f"orientation must be `v' or `h' but is: {orientation}")
         fig, ax = plt.subplots(figsize=figsize)
         sns.boxplot(data=data_table, ax=ax, whis=1., color="white",
-                    showmeans=True, meanprops={"markersize": 3, "markeredgecolor": "k"},
-                    flierprops={"marker": "o", "markerfacecolor": "black", "markeredgecolor": "none","markersize": 3},
+                    showmeans=True, meanprops={"markersize": 6, "markeredgecolor": "k"},
+                    flierprops={"marker": "o", "markerfacecolor": "black", "markeredgecolor": "none", "markersize": 3},
                     boxprops={'facecolor': 'none', 'edgecolor': 'k'},
-                    width=.3, orient=orientation)
+                    width=width, orient=orientation)
         if orientation == "v":
             ax.set_ylabel(f"{self.error_measure} (in {self.error_unit})")
             ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
@@ -1058,7 +1061,7 @@ class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass):
             ax.set_xlabel(f"{self.error_measure} (in {self.error_unit})")
         else:
             raise ValueError(f"orientation must be `v' or `h' but is: {orientation}")
-        text_box = AnchoredText(f"n={n_boots}", frameon=True, loc=4, pad=0.5)
+        text_box = AnchoredText(f"n={n_boots}", frameon=True, loc=1, pad=0.5)
         plt.setp(text_box.patch, edgecolor='k', facecolor='w')
         ax.add_artist(text_box)
         plt.setp(ax.lines, color='k')