diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index e6d6de152e42d44f271ba986b6645d2cd36b68d0..748476b814c6c54812df27274f54615bbf08d269 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -641,20 +641,32 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass): # pragma: no cover
plot_name = self.plot_name
for branch in self._data["branch"].unique():
self._set_title(model_name, branch)
- self._plot(branch=branch)
self.plot_name = f"{plot_name}_{branch}"
- self._save()
+ try:
+ self._plot(branch=branch)
+ self._save()
+ except ValueError as e:
+ logging.info(f"Did not plot {self.plot_name} because of {e}")
if len(set(separate_vars).intersection(self._data[self._x_name].unique())) > 0:
self.plot_name += '_separated'
- self._plot(branch=branch, separate_vars=separate_vars)
- self._save(bbox_inches='tight')
+ try:
+ self._plot(branch=branch, separate_vars=separate_vars)
+ self._save(bbox_inches='tight')
+ except ValueError as e:
+ logging.info(f"Did not plot {self.plot_name} because of {e}")
else:
- self._plot()
- self._save()
+ try:
+ self._plot()
+ self._save()
+ except ValueError as e:
+ logging.info(f"Did not plot {self.plot_name} because of {e}")
if len(set(separate_vars).intersection(self._data[self._x_name].unique())) > 0:
self.plot_name += '_separated'
- self._plot(separate_vars=separate_vars)
- self._save(bbox_inches='tight')
+ try:
+ self._plot(separate_vars=separate_vars)
+ self._save(bbox_inches='tight')
+ except ValueError as e:
+ logging.info(f"Did not plot {self.plot_name} because of {e}")
@staticmethod
def _set_bootstrap_type(boot_type):
@@ -696,11 +708,26 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass): # pragma: no cover
number_tags = self._get_number_tag(data.coords[self._x_name].values, split_by='_')
new_boot_coords = self._return_vars_without_number_tag(data.coords[self._x_name].values, split_by='_',
keep=1, as_unique=True)
- values = data.values.reshape((*data.shape[:3], len(number_tags), len(new_boot_coords)))
- data = xr.DataArray(values, coords={station_dim: data.coords[station_dim], self._x_name: new_boot_coords,
- "branch": number_tags, self._ahead_dim: data.coords[self._ahead_dim],
- self._boot_dim: data.coords[self._boot_dim]},
- dims=[station_dim, self._ahead_dim, self._boot_dim, "branch", self._x_name])
+ try:
+ values = data.values.reshape((*data.shape[:3], len(number_tags), len(new_boot_coords)))
+ data = xr.DataArray(values, coords={station_dim: data.coords[station_dim], self._x_name: new_boot_coords,
+ "branch": number_tags, self._ahead_dim: data.coords[self._ahead_dim],
+ self._boot_dim: data.coords[self._boot_dim]},
+ dims=[station_dim, self._ahead_dim, self._boot_dim, "branch", self._x_name])
+ except ValueError:
+ data_coll = []
+ for nr in number_tags:
+ filtered_coords = list(filter(lambda x: nr in x.split("_")[0], data.coords[self._x_name].values))
+ new_boot_coords = self._return_vars_without_number_tag(filtered_coords, split_by='_', keep=1,
+ as_unique=True)
+ sel_data = data.sel({self._x_name: filtered_coords})
+ values = sel_data.values.reshape((*data.shape[:3], 1, len(new_boot_coords)))
+ sel_data = xr.DataArray(values, coords={station_dim: data.coords[station_dim], self._x_name: new_boot_coords,
+ "branch": [nr], self._ahead_dim: data.coords[self._ahead_dim],
+ self._boot_dim: data.coords[self._boot_dim]},
+ dims=[station_dim, self._ahead_dim, self._boot_dim, "branch", self._x_name])
+ data_coll.append(sel_data)
+ data = xr.concat(data_coll, "branch")
else:
try:
new_boot_coords = self._return_vars_without_number_tag(data.coords[self._x_name].values, split_by='_',
@@ -713,7 +740,7 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass): # pragma: no cover
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]
- return data.to_dataframe("data").reset_index(level=np.arange(len(data.dims)).tolist())
+ return data.to_dataframe("data").reset_index(level=np.arange(len(data.dims)).tolist()).dropna()
@staticmethod
def _get_target_sampling(sampling, pos):
@@ -765,9 +792,10 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass): # pragma: no cover
def _plot_selected_variables(self, separate_vars: List, branch=None):
data = self._data if branch is None else self._data[self._data["branch"] == str(branch)]
- self.raise_error_if_separate_vars_do_not_exist(data, separate_vars, self._x_name)
+ self.raise_error_if_vars_do_not_exist(data, separate_vars, self._x_name, name="separate_vars")
all_variables = self._get_unique_values_from_column_of_df(data, self._x_name)
remaining_vars = helpers.remove_items(all_variables, separate_vars)
+ self.raise_error_if_vars_do_not_exist(data, remaining_vars, self._x_name, name="remaining_vars")
data_first = self._select_data(df=data, variables=separate_vars, column_name=self._x_name)
data_second = self._select_data(df=data, variables=remaining_vars, column_name=self._x_name)
@@ -843,9 +871,13 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass): # pragma: no cover
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, column_name):
- if not self._variables_exist_in_df(df=data, variables=separate_vars, column_name=column_name):
- raise ValueError(f"At least one entry of `separate_vars' does not exist in `self.data' ")
+ def raise_error_if_vars_do_not_exist(self, data, vars, column_name, name="separate_vars"):
+ if len(vars) == 0:
+ msg = f"No variables are given for `{name}' to check in `self.data' "
+ raise ValueError(msg)
+ if not self._variables_exist_in_df(df=data, variables=vars, column_name=column_name):
+ msg = f"At least one entry of `{name}' does not exist in `self.data' "
+ raise ValueError(msg)
@staticmethod
def _get_unique_values_from_column_of_df(df: pd.DataFrame, column_name: str) -> List: