diff --git a/src/run_modules/experiment_setup.py b/src/run_modules/experiment_setup.py
index 56c22a81e48421438816855770b7477e84e3a8d8..53ef2c61af61f6f2005f6ee7fde81eb1cfefc340 100644
--- a/src/run_modules/experiment_setup.py
+++ b/src/run_modules/experiment_setup.py
@@ -20,6 +20,8 @@ DEFAULT_VAR_ALL_DICT = {'o3': 'dma8eu', 'relhum': 'average_values', 'temp': 'max
'v': 'average_values', 'no': 'dma8eu', 'no2': 'dma8eu', 'cloudcover': 'average_values',
'pblheight': 'maximum'}
DEFAULT_TRANSFORMATION = {"scope": "data", "method": "standardise", "mean": "estimate"}
+DEFAULT_PLOT_LIST = ["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore", "PlotTimeSeries",
+ "PlotCompetitiveSkillScore", "PlotBootstrapSkillScore", "plot_conditional_quantiles"]
class ExperimentSetup(RunEnvironment):
@@ -34,7 +36,8 @@ class ExperimentSetup(RunEnvironment):
limit_nan_fill=None, train_start=None, train_end=None, val_start=None, val_end=None, test_start=None,
test_end=None, use_all_stations_on_all_data_sets=True, trainable=None, fraction_of_train=None,
experiment_path=None, plot_path=None, forecast_path=None, overwrite_local_data=None, sampling="daily",
- create_new_model=None, bootstrap_path=None, permute_data_on_training=None, transformation=None):
+ create_new_model=None, bootstrap_path=None, permute_data_on_training=None, transformation=None,
+ evaluate_bootstraps=True, plot_list=None):
# create run framework
super().__init__()
@@ -115,6 +118,10 @@ class ExperimentSetup(RunEnvironment):
# use all stations on all data sets (train, val, test)
self._set_param("use_all_stations_on_all_data_sets", use_all_stations_on_all_data_sets, default=True)
+ # set post-processing instructions
+ self._set_param("evaluate_bootstraps", evaluate_bootstraps, scope="general.postprocessing")
+ self._set_param("plot_list", plot_list, default=DEFAULT_PLOT_LIST, scope="general.postprocessing")
+
def _set_param(self, param: str, value: Any, default: Any = None, scope: str = "general") -> None:
if value is None and default is not None:
value = default
diff --git a/src/run_modules/post_processing.py b/src/run_modules/post_processing.py
index 0a61ee4f07d0c6eccf698aa16d3de9d7275e75f6..c7ac3f10799d4329e8744d6ea69286c57119645a 100644
--- a/src/run_modules/post_processing.py
+++ b/src/run_modules/post_processing.py
@@ -50,7 +50,8 @@ class PostProcessing(RunEnvironment):
self.make_prediction()
logging.info("take a look on the next reported time measure. If this increases a lot, one should think to "
"skip make_prediction() whenever it is possible to save time.")
- self.bootstrap_skill_scores = self.create_boot_straps()
+ if self.data_store.get("evaluate_bootstraps", "general.postprocessing"):
+ self.bootstrap_skill_scores = self.create_boot_straps()
self.skill_scores = self.calculate_skill_scores()
self.plot()
@@ -124,19 +125,29 @@ class PostProcessing(RunEnvironment):
logging.debug("Run plotting routines...")
path = self.data_store.get("forecast_path", "general")
- plot_conditional_quantiles(self.test_data.stations, pred_name="CNN", ref_name="obs",
- forecast_path=path, plot_name_affix="cali-ref", plot_folder=self.plot_path)
- plot_conditional_quantiles(self.test_data.stations, pred_name="obs", ref_name="CNN",
- forecast_path=path, plot_name_affix="like-bas", plot_folder=self.plot_path)
- PlotStationMap(generators={'b': self.test_data}, plot_folder=self.plot_path)
- PlotMonthlySummary(self.test_data.stations, path, r"forecasts_%s_test.nc", self.target_var,
- plot_folder=self.plot_path)
- PlotClimatologicalSkillScore(self.skill_scores[1], plot_folder=self.plot_path, model_setup="CNN")
- PlotClimatologicalSkillScore(self.skill_scores[1], plot_folder=self.plot_path, score_only=False,
- extra_name_tag="all_terms_", model_setup="CNN")
- PlotCompetitiveSkillScore(self.skill_scores[0], plot_folder=self.plot_path, model_setup="CNN")
- PlotBootstrapSkillScore(self.bootstrap_skill_scores, plot_folder=self.plot_path, model_setup="CNN")
- PlotTimeSeries(self.test_data.stations, path, r"forecasts_%s_test.nc", plot_folder=self.plot_path, sampling=self._sampling)
+ plot_list = self.data_store.get("plot_list", "general.postprocessing")
+
+ if "plot_conditional_quantiles" in plot_list:
+ plot_conditional_quantiles(self.test_data.stations, pred_name="CNN", ref_name="obs",
+ forecast_path=path, plot_name_affix="cali-ref", plot_folder=self.plot_path)
+ plot_conditional_quantiles(self.test_data.stations, pred_name="obs", ref_name="CNN",
+ forecast_path=path, plot_name_affix="like-bas", plot_folder=self.plot_path)
+ if "PlotStationMap" in plot_list:
+ PlotStationMap(generators={'b': self.test_data}, plot_folder=self.plot_path)
+ if "PlotMonthlySummary" in plot_list:
+ PlotMonthlySummary(self.test_data.stations, path, r"forecasts_%s_test.nc", self.target_var,
+ plot_folder=self.plot_path)
+ if "PlotClimatologicalSkillScore" in plot_list:
+ PlotClimatologicalSkillScore(self.skill_scores[1], plot_folder=self.plot_path, model_setup="CNN")
+ PlotClimatologicalSkillScore(self.skill_scores[1], plot_folder=self.plot_path, score_only=False,
+ extra_name_tag="all_terms_", model_setup="CNN")
+ if "PlotCompetitiveSkillScore" in plot_list:
+ PlotCompetitiveSkillScore(self.skill_scores[0], plot_folder=self.plot_path, model_setup="CNN")
+ if self.bootstrap_skill_scores is not None and "PlotBootstrapSkillScore" in plot_list:
+ PlotBootstrapSkillScore(self.bootstrap_skill_scores, plot_folder=self.plot_path, model_setup="CNN")
+ if "PlotTimeSeries" in plot_list:
+ PlotTimeSeries(self.test_data.stations, path, r"forecasts_%s_test.nc", plot_folder=self.plot_path,
+ sampling=self._sampling)
def calculate_test_score(self):
test_score = self.model.evaluate_generator(generator=self.test_data_distributed.distribute_on_batches(),