diff --git a/mlair/helpers/statistics.py b/mlair/helpers/statistics.py
index ea5a9f05c8ff91a5cd6be678ad03d12b923a4bec..ad6a368fdf7980639802412201e964def80669b2 100644
--- a/mlair/helpers/statistics.py
+++ b/mlair/helpers/statistics.py
@@ -311,7 +311,6 @@ class SkillScores:
for iahead in ahead_names:
data = internal_data.sel(ahead=iahead)
- external_data = self.external_data.sel(ahead=iahead, type=[self.observation_name])
skill_score.loc[["CASE I", "AI", "BI", "CI"], iahead] = np.stack(self._climatological_skill_score(
data, mu_type=1, forecast_name=forecast_name, observation_name=self.observation_name).values.flatten())
@@ -319,14 +318,15 @@ class SkillScores:
skill_score.loc[["CASE II", "AII", "BII"], iahead] = np.stack(self._climatological_skill_score(
data, mu_type=2, forecast_name=forecast_name, observation_name=self.observation_name).values.flatten())
- # if external_data is not None:
- skill_score.loc[["CASE III", "AIII"], iahead] = np.stack(self._climatological_skill_score(
- data, mu_type=3, forecast_name=forecast_name, observation_name=self.observation_name,
- external_data=external_data).values.flatten())
+ if self.external_data is not None:
+ external_data = self.external_data.sel(ahead=iahead, type=[self.observation_name])
+ skill_score.loc[["CASE III", "AIII"], iahead] = np.stack(self._climatological_skill_score(
+ data, mu_type=3, forecast_name=forecast_name, observation_name=self.observation_name,
+ external_data=external_data).values.flatten())
- skill_score.loc[["CASE IV", "AIV", "BIV", "CIV"], iahead] = np.stack(self._climatological_skill_score(
- data, mu_type=4, forecast_name=forecast_name, observation_name=self.observation_name,
- external_data=external_data).values.flatten())
+ skill_score.loc[["CASE IV", "AIV", "BIV", "CIV"], iahead] = np.stack(self._climatological_skill_score(
+ data, mu_type=4, forecast_name=forecast_name, observation_name=self.observation_name,
+ external_data=external_data).values.flatten())
return skill_score
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index c810d3c5517643612f773bdbffc3e0e029d9150d..127066b87cdf507519836ff916681e4885053c09 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -407,7 +407,7 @@ class PostProcessing(RunEnvironment):
be found inside `forecast_path`.
"""
subset_type = subset.name
- logging.debug(f"start make_prediction for {subset_type}")
+ logging.info(f"start make_prediction for {subset_type}")
time_dimension = self.data_store.get("time_dim")
window_dim = self.data_store.get("window_dim")
subset_type = subset.name
@@ -627,7 +627,7 @@ class PostProcessing(RunEnvironment):
try:
file = os.path.join(path, f"forecasts_{str(station)}_train_val.nc")
return xr.open_dataarray(file)
- except (IndexError, KeyError):
+ except (IndexError, KeyError, FileNotFoundError):
return None
def _get_external_data(self, station: str, path: str) -> Union[xr.DataArray, None]:
@@ -642,9 +642,20 @@ class PostProcessing(RunEnvironment):
try:
file = os.path.join(path, f"forecasts_{str(station)}_test.nc")
return xr.open_dataarray(file)
- except (IndexError, KeyError):
+ except (IndexError, KeyError, FileNotFoundError):
return None
+ @staticmethod
+ def _combine_forecasts(forecast, competitor, dim="type"):
+ """
+ Combine forecast and competitor if both are xarray. If competitor is None, this returns forecasts and vise
+ versa.
+ """
+ try:
+ return xr.concat([forecast, competitor], dim=dim)
+ except (TypeError, AttributeError):
+ return forecast if competitor is None else competitor
+
def calculate_skill_scores(self) -> Tuple[Dict, Dict]:
"""
Calculate skill scores of NN forecast.
@@ -656,16 +667,20 @@ class PostProcessing(RunEnvironment):
:return: competitive and climatological skill scores
"""
path = self.data_store.get("forecast_path")
+ all_stations = self.data_store.get("stations")
skill_score_competitive = {}
skill_score_climatological = {}
- for station in self.test_data:
+ for station in all_stations:
external_data = self._get_external_data(station, path)
- competitor = self.load_competitors(str(station))
- combined = xr.concat([external_data, competitor], dim="type") if competitor is not None else external_data
- skill_score = statistics.SkillScores(combined, models=remove_items(list(combined.type.values), "obs"))
- skill_score_competitive[station] = skill_score.skill_scores(self.window_lead_time)
-
- internal_data = self._get_internal_data(station, path) # ToDo: check if external is still right?
- skill_score_climatological[station] = skill_score.climatological_skill_scores(
- internal_data, self.window_lead_time, forecast_name=self.forecast_indicator)
+ competitor = self.load_competitors(station)
+ combined = self._combine_forecasts(external_data, competitor, dim="type")
+ model_list = remove_items(list(combined.type.values), "obs") if combined is not None else None
+ skill_score = statistics.SkillScores(combined, models=model_list)
+ if external_data is not None:
+ skill_score_competitive[station] = skill_score.skill_scores(self.window_lead_time)
+
+ internal_data = self._get_internal_data(station, path)
+ if internal_data is not None:
+ skill_score_climatological[station] = skill_score.climatological_skill_scores(
+ internal_data, self.window_lead_time, forecast_name=self.forecast_indicator)
return skill_score_competitive, skill_score_climatological
diff --git a/mlair/run_modules/pre_processing.py b/mlair/run_modules/pre_processing.py
index f696b0065b1db2692110488bd41513cd74aca233..c40b20e2475e921e2e0eb5746f648589c95180b0 100644
--- a/mlair/run_modules/pre_processing.py
+++ b/mlair/run_modules/pre_processing.py
@@ -269,8 +269,9 @@ class PreProcessing(RunEnvironment):
output = [
pool.apply_async(f_proc, args=(data_handler, station, set_name, store_processed_data), kwds=kwargs)
for station in set_stations]
- for p in output:
+ for i, p in enumerate(output):
dh, s = p.get()
+ logging.info(f"...finished: {s} ({int((i + 1.) / len(output) * 100)}%)")
if dh is not None:
collection.add(dh)
valid_stations.append(s)
@@ -352,8 +353,7 @@ def f_proc(data_handler, station, name_affix, store, **kwargs):
the station that was used. This function must be implemented globally to work together with multiprocessing.
"""
try:
- res = data_handler.build(station, name_affix=name_affix, store_processed_data=store,
- **kwargs)
+ res = data_handler.build(station, name_affix=name_affix, store_processed_data=store, **kwargs)
except (AttributeError, EmptyQueryResult, KeyError, requests.ConnectionError, ValueError) as e:
logging.info(f"remove station {station} because it raised an error: {e}")
res = None