diff --git a/mlair/configuration/defaults.py b/mlair/configuration/defaults.py index 3da91b18c9af86abaa9492f2bc7ed6c15dc9fe5e..0c8254b3f6bd3e37f31b5101f4e8852615807648 100644 --- a/mlair/configuration/defaults.py +++ b/mlair/configuration/defaults.py @@ -48,7 +48,7 @@ DEFAULT_CREATE_NEW_BOOTSTRAPS = False DEFAULT_NUMBER_OF_BOOTSTRAPS = 20 DEFAULT_PLOT_LIST = ["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore", "PlotTimeSeries", "PlotCompetitiveSkillScore", "PlotBootstrapSkillScore", "PlotConditionalQuantiles", - "PlotAvailability"] + "PlotAvailability", "PlotSeparationOfScales"] DEFAULT_SAMPLING = "daily" diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py index b32df1a650ff4af077090923e2a60eb6198693e9..3dc91cbd54094f116f0d959fb9c845751e998464 100644 --- a/mlair/run_modules/post_processing.py +++ b/mlair/run_modules/post_processing.py @@ -263,9 +263,10 @@ class PostProcessing(RunEnvironment): plot_list = self.data_store.get("plot_list", "postprocessing") time_dimension = self.data_store.get("time_dim") - PlotSeparationOfScales(self.test_data, plot_folder=self.plot_path) + if ("filter" in self.test_data[0].get_X(as_numpy=False)[0].coords) and ("PlotSeparationOfScales" in plot_list): + PlotSeparationOfScales(self.test_data, plot_folder=self.plot_path) - if self.bootstrap_skill_scores is not None and "PlotBootstrapSkillScore" in plot_list: + 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 "PlotConditionalQuantiles" in plot_list: diff --git a/run_mixed_sampling.py b/run_mixed_sampling.py index 5b377509edd9e6d14f07ef33557d2025ca520727..5288063ac583e8dad24e253c5ae16810b540c5c8 100644 --- a/run_mixed_sampling.py +++ b/run_mixed_sampling.py @@ -22,7 +22,6 @@ def main(parser_args): test_end="2011-12-31", stations=["DEBW107", "DEBW013"], epochs=100, - plot_list=["PlotAvailability"] ) workflow = DefaultWorkflow(**args) workflow.run()