diff --git a/mlair/data_handler/input_bootstraps.py b/mlair/data_handler/input_bootstraps.py
index 187f09050bb39a953ac58c2b7fca54b6a207aed1..b8ad614f2317e804d415b23308df760f4dd8da7f 100644
--- a/mlair/data_handler/input_bootstraps.py
+++ b/mlair/data_handler/input_bootstraps.py
@@ -123,11 +123,12 @@ class BootstrapIteratorVariable(BootstrapIterator):
_X = list(map(lambda x: x.expand_dims({self.boot_dim: range(nboot)}, axis=-1), _X))
_Y = _Y.expand_dims({self.boot_dim: range(nboot)}, axis=-1)
for index in range(len(_X)):
- single_variable = _X[index].sel({self._dimension: [dimension]})
- bootstrapped_variable = self.apply_bootstrap_method(single_variable.values)
- bootstrapped_data = xr.DataArray(bootstrapped_variable, coords=single_variable.coords,
- dims=single_variable.dims)
- _X[index] = bootstrapped_data.combine_first(_X[index]).transpose(*_X[index].dims)
+ if dimension in _X[index].coords[self._dimension]:
+ single_variable = _X[index].sel({self._dimension: [dimension]})
+ bootstrapped_variable = self.apply_bootstrap_method(single_variable.values)
+ bootstrapped_data = xr.DataArray(bootstrapped_variable, coords=single_variable.coords,
+ dims=single_variable.dims)
+ _X[index] = bootstrapped_data.combine_first(_X[index]).transpose(*_X[index].dims)
self._position += 1
except IndexError:
raise StopIteration()
diff --git a/mlair/plotting/data_insight_plotting.py b/mlair/plotting/data_insight_plotting.py
index 82ab4c88557c2ab9f4364607f18079ff1261f5e6..8dbf4fc4b088d10efe5208aebc8b46e50b75360c 100644
--- a/mlair/plotting/data_insight_plotting.py
+++ b/mlair/plotting/data_insight_plotting.py
@@ -867,31 +867,46 @@ def f_proc(var, d_var, f_index, time_dim="datetime", use_last_value=True): # pr
def f_proc_2(g, m, pos, variables_dim, time_dim, f_index, use_last_value): # pragma: no cover
+
+ # load lazy data
+ id_classes = list(filter(lambda x: "id_class" in x, dir(g)))
+ for id_cls_name in id_classes:
+ id_cls = getattr(g, id_cls_name)
+ if hasattr(id_cls, "lazy"):
+ id_cls.load_lazy() if id_cls.lazy is True else None
+
raw_data_single = dict()
- if hasattr(g.id_class, "lazy"):
- g.id_class.load_lazy() if g.id_class.lazy is True else None
- if m == 0:
- d = g.id_class._data
- if d is None:
- window_dim = g.id_class.window_dim
- history = g.id_class.history
- last_entry = history.coords[window_dim][-1]
- d1 = history.sel({window_dim: last_entry}, drop=True)
- label = g.id_class.label
- first_entry = label.coords[window_dim][0]
- d2 = label.sel({window_dim: first_entry}, drop=True)
- d = (d1, d2)
- else:
- gd = g.id_class
- filter_sel = {"filter": gd.input_data.coords["filter"][m - 1]}
- d = (gd.input_data.sel(filter_sel), gd.target_data)
- d = d[pos] if isinstance(d, tuple) else d
- for var in d[variables_dim].values:
- d_var = d.loc[{variables_dim: var}].squeeze().dropna(time_dim)
- var_str, f, pgram = f_proc(var, d_var, f_index, use_last_value=use_last_value)
- raw_data_single[var_str] = [(f, pgram)]
- if hasattr(g.id_class, "lazy"):
- g.id_class.clean_up() if g.id_class.lazy is True else None
+ for dh in list(filter(lambda x: "unfiltered" not in x, id_classes)):
+ current_cls = getattr(g, dh)
+ if m == 0:
+ d = current_cls._data
+ if d is None:
+ window_dim = current_cls.window_dim
+ history = current_cls.history
+ last_entry = history.coords[window_dim][-1]
+ d1 = history.sel({window_dim: last_entry}, drop=True)
+ label = current_cls.label
+ first_entry = label.coords[window_dim][0]
+ d2 = label.sel({window_dim: first_entry}, drop=True)
+ d = (d1, d2)
+ else:
+ filter_sel = {"filter": current_cls.input_data.coords["filter"][m - 1]}
+ d = (current_cls.input_data.sel(filter_sel), current_cls.target_data)
+ d = d[pos] if isinstance(d, tuple) else d
+ for var in d[variables_dim].values:
+ d_var = d.loc[{variables_dim: var}].squeeze().dropna(time_dim)
+ var_str, f, pgram = f_proc(var, d_var, f_index, use_last_value=use_last_value)
+ if var_str not in raw_data_single.keys():
+ raw_data_single[var_str] = [(f, pgram)]
+ else:
+ raise KeyError(f"There are multiple pgrams for key {var_str}. Please check your data handler.")
+
+ # perform clean up
+ for id_cls_name in id_classes:
+ id_cls = getattr(g, id_cls_name)
+ if hasattr(id_cls, "lazy"):
+ id_cls.clean_up() if id_cls.lazy is True else None
+
return raw_data_single
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index c67645fbbe9dce184aca4522010b4929295fd2dd..5d687dc2c86cc3681dfaa6b103bd207593e969f6 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -285,13 +285,13 @@ class PostProcessing(RunEnvironment):
boot_skill_score = self.calculate_feature_importance_skill_scores(bootstrap_type=boot_type,
bootstrap_method=boot_method)
self.feature_importance_skill_scores[boot_type][boot_method] = boot_skill_score
- except (FileNotFoundError, ValueError):
+ except (FileNotFoundError, ValueError, OSError):
if _iter != 0:
- raise RuntimeError(f"calculate_feature_importance ({boot_type}, {boot_type}) was called for the "
- f"2nd time. This means, that something internally goes wrong. Please check "
- f"for possible errors")
- logging.info(f"Could not load all files for feature importance ({boot_type}, {boot_type}), restart "
- f"calculate_feature_importance with create_new_bootstraps=True.")
+ raise RuntimeError(f"calculate_feature_importance ({boot_type}, {boot_method}) was called for "
+ f"the 2nd time. This means, that something internally goes wrong. Please "
+ f"check for possible errors.")
+ logging.info(f"Could not load all files for feature importance ({boot_type}, {boot_method}), "
+ f"restart calculate_feature_importance with create_new_bootstraps=True.")
self.calculate_feature_importance(True, _iter=1, bootstrap_type=boot_type,
bootstrap_method=boot_method)