diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index 6373d3b61a4aa7ffb4ce782365b9ba1234761f0e..66679a22bcf0d6a2da4702bf6d41e0fb61b6f62a 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -854,7 +854,8 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass): # pragma: no cover
except NotImplementedError:
pass
_, sampling_letter = self._get_sampling(sampling, 1)
- self._labels = [str(i) + sampling_letter for i in data.coords[self._ahead_dim].values]
+ sampling_letter = {"d": "D", "h": "H"}.get(sampling_letter, sampling_letter)
+ self._labels = [sampling_letter + str(i) for i in data.coords[self._ahead_dim].values]
if station_dim not in data.dims:
data = data.expand_dims(station_dim)
self._number_of_bootstraps = np.unique(data.coords[self._boot_dim].values).shape[0]
@@ -1138,7 +1139,8 @@ class PlotTimeSeries: # pragma: no cover
color = sns.color_palette("Blues_d", self._window_lead_time).as_hex()
for ahead in data.coords[self._ahead_dim].values:
plot_data = data.sel({"type": self._model_name, self._ahead_dim: ahead}).drop(["type", self._ahead_dim]).squeeze().shift(index=ahead)
- label = f"{ahead}{self._sampling}"
+ sampling_letter = {"d": "D", "h": "H"}.get(self._sampling, self._sampling)
+ label = f"{sampling_letter}{ahead}"
ax.plot(plot_data, color=color[ahead - 1], label=label)
def _plot_obs(self, ax, data):
@@ -1264,6 +1266,7 @@ class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass): # pragma: no cover
return # nothing to do
n_boots = self._n_boots
error_label = self.error_measure if self.error_unit is None else f"{self.error_measure} (in {self.error_unit})"
+ sampling_letter = {"d": "D", "h": "H"}.get(self.sampling, self.sampling)
if agg_type == "single":
fig, ax = plt.subplots()
if self.ahead_dim in data_table.index.names:
@@ -1291,7 +1294,7 @@ class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass): # pragma: no cover
g.map(sns.kdeplot, 0)
g.add_legend(title="")
fig = plt.gcf()
- _labels = [str(i) + self.sampling for i in data_table.index.levels[1].values]
+ _labels = [sampling_letter + str(i) for i in data_table.index.levels[1].values]
for axi, title in zip(g.axes.flatten(), _labels):
axi.set_title(title)
for axi in g.axes.flatten():
@@ -1324,6 +1327,7 @@ class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass): # pragma: no cover
size = len(np.unique(data_table.columns))
asteriks = self.get_asteriks_from_mann_whitney_u_result if apply_u_test is True else None
color_palette = sns.color_palette("Blues_d", self._factor).as_hex()
+ sampling_letter = {"d": "D", "h": "H"}.get(self.sampling, self.sampling)
if orientation == "v":
figsize, width = (size, 5), 0.4
elif orientation == "h":
@@ -1348,7 +1352,7 @@ class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass): # pragma: no cover
flierprops={"marker": "o", "markerfacecolor": "black", "markeredgecolor": "none", "markersize": 3},
boxprops={'edgecolor': 'k'}, width=.8, orient=orientation, **xy, hue=self.ahead_dim)
- _labels = [str(i) + self.sampling for i in data_table.index.levels[1].values]
+ _labels = [sampling_letter + str(i) for i in data_table.index.levels[1].values]
handles, _ = ax.get_legend_handles_labels()
ax.legend(handles, _labels)
else:
@@ -1362,7 +1366,7 @@ class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass): # pragma: no cover
flierprops={"marker": "o", "markerfacecolor": "black", "markeredgecolor": "none", "markersize": 3},
boxprops={'facecolor': 'none', 'edgecolor': 'k'}, width=width, orient=orientation)
- _labels = [str(i) + self.sampling for i in data_table.index.levels[1].values]
+ _labels = [sampling_letter + str(i) 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')
@@ -1631,6 +1635,7 @@ class PlotSeasonalMSEStack(AbstractPlotClass):
def _plot(self, dim, split_ahead=True, sampling="daily", orientation="vertical"):
_, sampling_letter = self._get_sampling(sampling, 1)
+ sampling_letter = {"d": "D", "h": "H"}.get(sampling_letter, sampling_letter)
if split_ahead is False:
self.plot_name = self.plot_name_orig + "_total_" + orientation
data = self._data.mean(dim)
@@ -1658,7 +1663,7 @@ class PlotSeasonalMSEStack(AbstractPlotClass):
fig, ax = plt.subplots(1, n, sharey=True, figsize=(np.prod(m) / 0.8, 5))
for i, sel in enumerate(data.coords[dim].values):
data.sel({dim: sel}).to_pandas().T.plot.bar(ax=ax[i], stacked=True, cmap="Dark2", legend=False)
- label = str(sel) + sampling_letter
+ label = sampling_letter + str(sel)
ax[i].set_title(label)
ax[i].xaxis.label.set_visible(False)
self._set_bar_label(ax[i])
@@ -1669,7 +1674,7 @@ class PlotSeasonalMSEStack(AbstractPlotClass):
fig, ax = plt.subplots(n, 1, sharex=True, figsize=(6, np.prod(m) * 0.6))
for i, sel in enumerate(data.coords[dim].values):
data.sel({dim: sel}).to_pandas().T.plot.barh(ax=ax[i], stacked=True, cmap="Dark2", legend=False)
- label = str(sel) + sampling_letter
+ label = sampling_letter + str(sel)
ax[i].set_title(label)
ax[i].yaxis.label.set_visible(False)
self._set_bar_label(ax[i])
@@ -1763,7 +1768,8 @@ class PlotErrorsOnMap(AbstractPlotClass):
cbar_label = f"{error_long_name} (in {error_units})" if error_units is not None else error_long_name
plt.colorbar(cb, label=cbar_label)
self._adjust_extent(ax)
- title = model_type if ahead is None else f"{model_type} ({ahead}{self.sampling})"
+ sampling_letter = {"d": "D", "h": "H"}.get(self.sampling, self.sampling)
+ title = model_type if ahead is None else f"{model_type} ({sampling_letter}{ahead})"
plt.title(title)
plt.tight_layout()