diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index b23807ae30f52f19581b188d19213fdc81c9258e..afbd00d0e84f1478c3b43bc57af19752e05eefef 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -1112,7 +1112,7 @@ class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass): # pragma: no cover
self.prepare_data(data)
# create all combinations to plot (h/v, utest/notest, single/multi)
- variants = list(itertools.product(*[["v", "h"], [True, False], ["single", "multi"]]))
+ variants = list(itertools.product(*[["v", "h"], [True, False], ["single", "multi", "panel"]]))
# plot raw metric (mse)
for orientation, utest, agg_type in variants:
@@ -1157,7 +1157,9 @@ class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass): # pragma: no cover
if apply_u_test is True and agg_type == "multi":
return # not implemented
data_table = self._data_table
- if self.ahead_dim not in data_table.index.names and agg_type == "multi":
+ if self.ahead_dim not in data_table.index.names and agg_type in ["multi", "panel"]:
+ return # nothing to do
+ if apply_u_test is True and agg_type == "panel":
return # nothing to do
n_boots = self._n_boots
size = len(np.unique(data_table.columns))
@@ -1177,7 +1179,7 @@ class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass): # pragma: no cover
showmeans=True, meanprops={"markersize": 6, "markeredgecolor": "k"},
flierprops={"marker": "o", "markerfacecolor": "black", "markeredgecolor": "none", "markersize": 3},
boxprops={'facecolor': 'none', 'edgecolor': 'k'}, width=width, orient=orientation)
- else:
+ elif agg_type == "multi":
xy = {"x": self.model_type_dim, "y": 0} if orientation == "v" else {"x": 0, "y": self.model_type_dim}
sns.boxplot(data=data_table.stack(self.model_type_dim).reset_index(), ax=ax, whis=1.5, palette=color_palette,
showmeans=True, meanprops={"markersize": 6, "markeredgecolor": "k", "markerfacecolor": "white"},
@@ -1187,33 +1189,68 @@ class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass): # pragma: no cover
_labels = [str(i) + self.sampling for i in data_table.index.levels[1].values]
handles, _ = ax.get_legend_handles_labels()
ax.legend(handles, _labels)
+ else:
+ xy = (self.model_type_dim, 0) if orientation == "v" else (0, self.model_type_dim)
+ col_or_row = {"col": self.ahead_dim} if orientation == "v" else {"row": self.ahead_dim}
+ aspect = 0.4 if orientation == "v" else 5./3.
+ ax = sns.FacetGrid(data_table.stack(self.model_type_dim).reset_index(), **col_or_row, aspect=aspect, height=5)
+ ax.map(sns.boxplot, *xy, whis=1.5, color="white", showmeans=True, order=data_table.mean().index.to_list(),
+ meanprops={"markersize": 6, "markeredgecolor": "k"},
+ flierprops={"marker": "o", "markerfacecolor": "black", "markeredgecolor": "none", "markersize": 3},
+ boxprops={'facecolor': 'none', 'edgecolor': 'k'}, width=width, orient=orientation)
- if orientation == "v":
- if apply_u_test:
- ax = self.set_significance_bars(asteriks, ax, data_table, orientation)
- ylims = list(ax.get_ylim())
- ax.set_ylim([ylims[0], ylims[1]*1.025])
- ax.set_ylabel(f"{self.error_measure} (in {self.error_unit})")
- ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
- elif orientation == "h":
- if apply_u_test:
- ax = self.set_significance_bars(asteriks, ax, data_table, orientation)
- ax.set_xlabel(f"{self.error_measure} (in {self.error_unit})")
- xlims = list(ax.get_xlim())
- ax.set_xlim([xlims[0], xlims[1] * 1.015])
+ _labels = [str(i) + self.sampling for i in data_table.index.levels[1].values]
+ for axi, title in zip(ax.axes.flatten(), _labels):
+ axi.set_title(title)
+ plt.setp(axi.lines, color='k')
+
+ if agg_type == "panel":
+ if orientation == "v":
+ for axi in ax.axes.flatten():
+ axi.set_xlabel(None)
+ axi.set_xticklabels(axi.get_xticklabels(), rotation=45)
+ ax.set_ylabels(f"{self.error_measure} (in {self.error_unit})")
+ loc = "upper left"
+ else:
+ for axi in ax.axes.flatten():
+ axi.set_ylabel(None)
+ ax.set_xlabels(f"{self.error_measure} (in {self.error_unit})")
+ loc = "upper right"
+ text = f"n={n_boots}"
+ if self.block_length is not None:
+ text = f"{self.block_length}, {text}"
+ if len(season) > 0:
+ text = f"{season}, {text}"
+ text_box = AnchoredText(text, frameon=True, loc=loc)
+ plt.setp(text_box.patch, edgecolor='k', facecolor='w')
+ ax.axes.flatten()[0].add_artist(text_box)
else:
- raise ValueError(f"orientation must be `v' or `h' but is: {orientation}")
- text = f"n={n_boots}"
- if self.block_length is not None:
- text = f"{self.block_length}, {text}"
- if len(season) > 0:
- text = f"{season}, {text}"
- loc = "lower left"
- text_box = AnchoredText(text, frameon=True, loc=loc, pad=0.5, bbox_to_anchor=(0., 1.0),
- bbox_transform=ax.transAxes)
- plt.setp(text_box.patch, edgecolor='k', facecolor='w')
- ax.add_artist(text_box)
- plt.setp(ax.lines, color='k')
+ if orientation == "v":
+ if apply_u_test:
+ ax = self.set_significance_bars(asteriks, ax, data_table, orientation)
+ ylims = list(ax.get_ylim())
+ ax.set_ylim([ylims[0], ylims[1]*1.025])
+ ax.set_ylabel(f"{self.error_measure} (in {self.error_unit})")
+ ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
+ elif orientation == "h":
+ if apply_u_test:
+ ax = self.set_significance_bars(asteriks, ax, data_table, orientation)
+ ax.set_xlabel(f"{self.error_measure} (in {self.error_unit})")
+ xlims = list(ax.get_xlim())
+ ax.set_xlim([xlims[0], xlims[1] * 1.015])
+ else:
+ raise ValueError(f"orientation must be `v' or `h' but is: {orientation}")
+ text = f"n={n_boots}"
+ if self.block_length is not None:
+ text = f"{self.block_length}, {text}"
+ if len(season) > 0:
+ text = f"{season}, {text}"
+ loc = "lower left"
+ text_box = AnchoredText(text, frameon=True, loc=loc, pad=0.5, bbox_to_anchor=(0., 1.0),
+ bbox_transform=ax.transAxes)
+ plt.setp(text_box.patch, edgecolor='k', facecolor='w')
+ ax.add_artist(text_box)
+ plt.setp(ax.lines, color='k')
plt.tight_layout()
self._save()
plt.close("all")