another adjustment

mlair/plotting/postprocessing_plotting.py

@@ -762,8 +762,7 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
                     showmeans=True, meanprops={"markersize": 3, "markeredgecolor": "k"}, flierprops={"marker": "."},
                     order=order)
         ax.axhline(y=0, color="grey", linewidth=.5)
-
-        ax.set(ylabel="skill score", xlabel="competing models", title="summary of all stations", ylim=self._lim())
+        ax.set(ylabel="skill score", xlabel="competing models", title="summary of all stations", ylim=self._lim(data))
         handles, _ = ax.get_legend_handles_labels()
         plt.xticks(rotation=90)
         ax.legend(handles, self._labels)
@@ -777,9 +776,8 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
         sns.boxplot(y="comparison", x="data", hue="ahead", data=data, whis=1., ax=ax, palette="Blues_d",
                     showmeans=True, meanprops={"markersize": 3, "markeredgecolor": "k"}, flierprops={"marker": "."},
                     order=order)
-        # ax.axhline(x=0, color="grey", linewidth=.5)
         ax.axvline(x=0, color="grey", linewidth=.5)
-        ax.set(xlabel="skill score", ylabel="competing models", title="summary of all stations", xlim=self._lim())
+        ax.set(xlabel="skill score", ylabel="competing models", title="summary of all stations", xlim=self._lim(data))
         handles, _ = ax.get_legend_handles_labels()
         ax.legend(handles, self._labels)
         plt.tight_layout()
@@ -795,7 +793,8 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
         filtered_headers = list(filter(lambda x: "nn-" in x, data.comparison.unique()))
         return data[data.comparison.isin(filtered_headers)]
 
-    def _lim(self) -> Tuple[float, float]:
+    @staticmethod
+    def _lim(data) -> Tuple[float, float]:
         """
         Calculate axis limits from data (Can be used to set axis extend).
 
@@ -805,8 +804,8 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
         :return:
         """
         limit = 5
-        lower = np.max([-limit, np.min([0, helpers.float_round(self._data.min()[2], 2) - 0.1])])
-        upper = np.min([limit, helpers.float_round(self._data.max()[2], 2) + 0.1])
+        lower = np.max([-limit, np.min([0, helpers.float_round(data.min()[2], 2) - 0.1])])
+        upper = np.min([limit, helpers.float_round(data.max()[2], 2) + 0.1])
         return lower, upper