diff --git a/src/plotting/postprocessing_plotting.py b/src/plotting/postprocessing_plotting.py
index 5c72b47585ccde6017f2f7769f9e09258d3943ca..6014db100c350198ca70b5b89d3a4a0fb6106670 100644
--- a/src/plotting/postprocessing_plotting.py
+++ b/src/plotting/postprocessing_plotting.py
@@ -634,7 +634,7 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
name skill_score_clim_{extra_name_tag}{model_setup}.pdf and resolution of 500dpi.
"""
- def __init__(self, data: Dict, plot_folder: str = ".", model_setup: str = ""):
+ def __init__(self, data: Dict, plot_folder: str = ".", model_setup: str = "", separate_vars=None,):
"""
Sets attributes and create plot
:param data: dictionary with station names as keys and 2D xarrays as values, consist on axis ahead and terms.
@@ -642,11 +642,17 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
:param model_setup: architecture type to specify plot name (default "CNN")
"""
super().__init__(plot_folder, f"skill_score_bootstrap_{model_setup}")
+ if separate_vars is None:
+ separate_vars = ['o3']
self._labels = None
self._x_name = "boot_var"
self._data = self._prepare_data(data)
self._plot()
self._save()
+ self.plot_name += '_separated'
+ self._plot(separate_vars=separate_vars)
+ self._save(bbox_inches='tight')
+
def _prepare_data(self, data: Dict) -> pd.DataFrame:
"""
@@ -667,9 +673,107 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
"""
return "" if score_only else "terms and "
- def _plot(self):
+ def _plot(self, separate_vars: str = None):
"""
- Main plot function to plot climatological skill score.
+ Main plot function to plot boots.
+ """
+ if separate_vars is None:
+ self._plot_all_variables()
+ else:
+ self._plot_selected_variables(separate_vars)
+
+ def _plot_selected_variables(self, separate_vars: List[str] = ['o3']):
+ data = self._data
+ self.raise_error_if_separate_vars_do_not_exist(data, separate_vars)
+ all_variables = self._get_unique_values_from_column_of_df(data, 'boot_var')
+ remaining_vars = helpers.list_pop(all_variables, separate_vars)
+ data_first = self._select_data(df=data, variables=separate_vars, column_name='boot_var')
+ data_second = self._select_data(df=data, variables=remaining_vars, column_name='boot_var')
+
+ fig, ax = plt.subplots(nrows=1, ncols=2,
+ gridspec_kw={'width_ratios': [len(separate_vars),
+ len(remaining_vars)
+ ]
+ }
+ )
+ if len(separate_vars) > 1:
+ first_box_width = .8
+ else:
+ first_box_width = 2.
+
+ sns.boxplot(x=self._x_name, y="data", hue="ahead", data=data_first, ax=ax[0], whis=1., palette="Blues_d",
+ showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"},
+ flierprops={"marker": "."}, width=first_box_width
+ )
+ ax[0].set(ylabel=f"skill score", xlabel="")
+
+ sns.boxplot(x=self._x_name, y="data", hue="ahead", data=data_second, ax=ax[1], whis=1., palette="Blues_d",
+ showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"},
+ flierprops={"marker": "."},
+ )
+ ax[1].set(ylabel="", xlabel="")
+ ax[1].yaxis.tick_right()
+ handles, _ = ax[1].get_legend_handles_labels()
+ for sax in ax:
+ matplotlib.pyplot.sca(sax)
+ sax.axhline(y=0, color="grey", linewidth=.5)
+ plt.xticks(rotation=45, ha='right')
+ sax.legend_.remove()
+
+ fig.legend(handles, self._labels, loc='upper center', ncol=len(handles)+1,)
+
+ def align_yaxis(ax1, ax2):
+ """
+ Align zeros of the two axes, zooming them out by same ratio
+
+ This function is copy pasted from https://stackoverflow.com/a/41259922
+ """
+ axes = (ax1, ax2)
+ extrema = [ax.get_ylim() for ax in axes]
+ tops = [extr[1] / (extr[1] - extr[0]) for extr in extrema]
+ # Ensure that plots (intervals) are ordered bottom to top:
+ if tops[0] > tops[1]:
+ axes, extrema, tops = [list(reversed(l)) for l in (axes, extrema, tops)]
+
+ # How much would the plot overflow if we kept current zoom levels?
+ tot_span = tops[1] + 1 - tops[0]
+
+ b_new_t = extrema[0][0] + tot_span * (extrema[0][1] - extrema[0][0])
+ t_new_b = extrema[1][1] - tot_span * (extrema[1][1] - extrema[1][0])
+ axes[0].set_ylim(extrema[0][0], b_new_t)
+ axes[1].set_ylim(t_new_b, extrema[1][1])
+
+ align_yaxis(ax[0], ax[1])
+ align_yaxis(ax[0], ax[1])
+
+ # plt.savefig('MYBOOTTESTPLOT.pdf', bbox_inches='tight')
+
+
+ @staticmethod
+ def _select_data(df: pd.DataFrame, variables: List[str], column_name: str) -> pd.DataFrame:
+ for i, variable in enumerate(variables):
+ if i == 0:
+ selected_data = df.loc[df[column_name] == variable]
+ else:
+ tmp_var = df.loc[df[column_name] == variable]
+ selected_data = pd.concat([selected_data, tmp_var], axis=0)
+ return selected_data
+
+ def raise_error_if_separate_vars_do_not_exist(self, data, separate_vars):
+ if not self._variables_exist_in_df(df=data, variables=separate_vars):
+ raise ValueError(f"At least one entry of `separate_vars' does not exist in `self.data' ")
+
+ @staticmethod
+ def _get_unique_values_from_column_of_df(df: pd.DataFrame, column_name: str) -> List:
+ return list(df[column_name].unique())
+
+ def _variables_exist_in_df(self, df: pd.DataFrame, variables: List[str], column_name: str = 'boot_var'):
+ vars_in_df = set(self._get_unique_values_from_column_of_df(df, column_name))
+ return set(variables).issubset(vars_in_df)
+
+ def _plot_all_variables(self):
+ """
+
"""
fig, ax = plt.subplots()
sns.boxplot(x=self._x_name, y="data", hue="ahead", data=self._data, ax=ax, whis=1., palette="Blues_d",
diff --git a/src/run_modules/pre_processing.py b/src/run_modules/pre_processing.py
index 5731b7a6291b146681e976aa40fcb3d87a464c3b..aa80a4b82c720172c60a5808a04fe82e5e9e543b 100644
--- a/src/run_modules/pre_processing.py
+++ b/src/run_modules/pre_processing.py
@@ -111,7 +111,10 @@ class PreProcessing(RunEnvironment):
df_descr = df_nometa.iloc[:-2].astype('float32').describe(
percentiles=[.05, .1, .25, .5, .75, .9, .95]).astype('int32')
df_descr = pd.concat([df_nometa.loc[['# Samples']], df_descr]).T
- df_descr.rename(columns={"# Samples": "sum"}, inplace=True)
+ df_descr.rename(columns={"# Samples": "no. samples", "count": "no. stations"}, inplace=True)
+ df_descr_colnames = list(df_descr.columns)
+ df_descr_colnames = [df_descr_colnames[1]] + [df_descr_colnames[0]] + df_descr_colnames[2:]
+ df_descr = df_descr[df_descr_colnames]
column_format = self.create_column_format_for_tex(df_descr)
df_descr.to_latex(os.path.join(path, "station_describe_short.tex"), na_rep='---',
column_format=column_format)