create detailed and reduced competitive skill score plots, /close #131

mlair/plotting/postprocessing_plotting.py

@@ -701,12 +701,23 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
         self._model_setup = model_setup
         self._labels = None
         self._data = self._prepare_data(data)
+        default_plot_name = self.plot_name
+        # draw full detail plot
+        self.plot_name = default_plot_name + "_full_detail"
         self._plot()
         self._save()
-        # draw also a vertical version
-        self.plot_name += "_vertical"
+        # draw also a vertical full detail version
+        self.plot_name = default_plot_name + "_full_detail_vertical"
         self._plot_vertical()
         self._save()
+        # draw default plot with only model comparison
+        self.plot_name = default_plot_name
+        self._plot(single_model_comparison=True)
+        self._save()
+        # draw also a vertical full detail version
+        self.plot_name = default_plot_name + "_vertical"
+        self._plot_vertical(single_model_comparison=True)
+        self._save()
 
     def _prepare_data(self, data: pd.DataFrame) -> pd.DataFrame:
         """
@@ -724,12 +735,13 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
         self._labels = [str(i) + "d" for i in data.index.levels[1].values]
         return data.stack(level=0).reset_index(level=2, drop=True).reset_index(name="data")
 
-    def _plot(self):
+    def _plot(self, single_model_comparison=False):
         """Plot skill scores of the comparisons."""
         size = max([len(np.unique(self._data.comparison)), 6])
         fig, ax = plt.subplots(figsize=(size, size * 0.8))
-        order = self._create_pseudo_order()
-        sns.boxplot(x="comparison", y="data", hue="ahead", data=self._data, whis=1., ax=ax, palette="Blues_d",
+        data = self._filter_comparisons(self._data) if single_model_comparison is True else self._data
+        order = self._create_pseudo_order(data)
+        sns.boxplot(x="comparison", y="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(y=0, color="grey", linewidth=.5)
@@ -740,11 +752,12 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
         ax.legend(handles, self._labels)
         plt.tight_layout()
 
-    def _plot_vertical(self):
+    def _plot_vertical(self, single_model_comparison=False):
         """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",
+        data = self._filter_comparisons(self._data) if single_model_comparison is True else self._data
+        order = self._create_pseudo_order(data)
+        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)
@@ -754,12 +767,17 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
         ax.legend(handles, self._labels)
         plt.tight_layout()
 
-    def _create_pseudo_order(self):
+    def _create_pseudo_order(self, data):
         """Provide first predefined elements and append all remaining."""
         first_elements = [f"{self._model_setup}-persi", "ols-persi", f"{self._model_setup}-ols"]
-        uniq, index = np.unique(first_elements + self._data.comparison.unique().tolist(), return_index=True)
+        first_elements = list(filter(lambda x: x in data.comparison.tolist(), first_elements))
+        uniq, index = np.unique(first_elements + data.comparison.unique().tolist(), return_index=True)
         return uniq[index.argsort()]
 
+    def _filter_comparisons(self, data):
+        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]:
         """
         Calculate axis limits from data (Can be used to set axis extend).