PlotCompetitiveSkillScore has now a horizontal and vertical version

mlair/plotting/postprocessing_plotting.py

@@ -702,6 +702,10 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
         self._data = self._prepare_data(data)
         self._plot()
         self._save()
+        # draw also a vertical version
+        self.plot_name += "_vertical"
+        self._plot_vertical()
+        self._save()
 
     def _prepare_data(self, data: pd.DataFrame) -> pd.DataFrame:
         """
@@ -720,7 +724,7 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
         return data.stack(level=0).reset_index(level=2, drop=True).reset_index(name="data")
 
     def _plot(self):
-        """Plot skill scores of the comparisons cnn-persi, ols-persi and cnn-ols."""
+        """Plot skill scores of the comparisons."""
         fig, ax = plt.subplots()
         order = self._create_pseudo_order()
         sns.boxplot(x="comparison", y="data", hue="ahead", data=self._data, whis=1., ax=ax, palette="Blues_d",
@@ -728,7 +732,22 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
                     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._ylim())
+        ax.set(ylabel="skill score", xlabel="competing models", title="summary of all stations", ylim=self._lim())
+        handles, _ = ax.get_legend_handles_labels()
+        plt.xticks(rotation=20)
+        ax.legend(handles, self._labels)
+        plt.tight_layout()
+
+    def _plot_vertical(self):
+        """Plot skill scores of the comparisons, but vertically aligned."""
+        fig, ax = plt.subplots()
+        order = self._create_pseudo_order()
+        sns.boxplot(y="comparison", x="data", hue="ahead", data=self._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())
         handles, _ = ax.get_legend_handles_labels()
         ax.legend(handles, self._labels)
         plt.tight_layout()
@@ -739,9 +758,9 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
         uniq, index = np.unique(first_elements + self._data.comparison.unique().tolist(), return_index=True)
         return uniq[index.argsort()]
 
-    def _ylim(self) -> Tuple[float, float]:
+    def _lim(self) -> Tuple[float, float]:
         """
-        Calculate y-axis limits from data.
+        Calculate axis limits from data (Can be used to set axis extend).
 
         Lower limit is the minimum of 0 and data's minimum (reduced by small subtrahend) and upper limit is data's
         maximum (increased by a small addend).