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Resolve "release v1.2.0"

Merged Resolve "release v1.2.0"
release_v1.2.0 into master
Merged Ghost User requested to merge release_v1.2.0 into master
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mlair/run_modules/post_processing.py
+ 77
− 39
@@ -239,6 +239,7 @@ class PostProcessing(RunEnvironment):
model = keras.models.load_model(model_name, custom_objects=model_class.custom_objects)
return model
# noinspection PyBroadException
def plot(self):
"""
Create all plots.
@@ -263,46 +264,83 @@ class PostProcessing(RunEnvironment):
plot_list = self.data_store.get("plot_list", "postprocessing")
time_dimension = self.data_store.get("time_dim")
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):
PlotBootstrapSkillScore(self.bootstrap_skill_scores, plot_folder=self.plot_path, model_setup="CNN")
if "PlotConditionalQuantiles" in plot_list:
PlotConditionalQuantiles(self.test_data.keys(), data_pred_path=path, plot_folder=self.plot_path)
if "PlotStationMap" in plot_list:
if self.data_store.get("hostname")[:2] in self.data_store.get("hpc_hosts") or self.data_store.get(
"hostname")[:6] in self.data_store.get("hpc_hosts"):
logging.warning(
f"Skip 'PlotStationMap` because running on a hpc node: {self.data_store.get('hostname')}")
else:
gens = [(self.train_data, {"marker": 5, "ms": 9}),
(self.val_data, {"marker": 6, "ms": 9}),
(self.test_data, {"marker": 4, "ms": 9})]
PlotStationMap(generators=gens, plot_folder=self.plot_path)
gens = [(self.train_val_data, {"marker": 8, "ms": 9}),
(self.test_data, {"marker": 9, "ms": 9})]
PlotStationMap(generators=gens, plot_folder=self.plot_path, plot_name="station_map_var")
if "PlotMonthlySummary" in plot_list:
PlotMonthlySummary(self.test_data.keys(), 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 "PlotTimeSeries" in plot_list:
PlotTimeSeries(self.test_data.keys(), path, r"forecasts_%s_test.nc", plot_folder=self.plot_path,
sampling=self._sampling)
if "PlotAvailability" in plot_list:
avail_data = {"train": self.train_data, "val": self.val_data, "test": self.test_data}
PlotAvailability(avail_data, plot_folder=self.plot_path, time_dimension=time_dimension)
if "PlotAvailabilityHistogram" in plot_list:
avail_data = {"train": self.train_data, "val": self.val_data, "test": self.test_data}
PlotAvailabilityHistogram(avail_data, plot_folder=self.plot_path, )# time_dimension=time_dimension)
try:
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)
except Exception as e:
logging.error(f"Could not create plot PlotSeparationOfScales due to the following error: {e}")
try:
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")
except Exception as e:
logging.error(f"Could not create plot PlotBootstrapSkillScore due to the following error: {e}")
try:
if "PlotConditionalQuantiles" in plot_list:
PlotConditionalQuantiles(self.test_data.keys(), data_pred_path=path, plot_folder=self.plot_path)
except Exception as e:
logging.error(f"Could not create plot PlotConditionalQuantiles due to the following error: {e}")
try:
if "PlotStationMap" in plot_list:
if self.data_store.get("hostname")[:2] in self.data_store.get("hpc_hosts") or self.data_store.get(
"hostname")[:6] in self.data_store.get("hpc_hosts"):
logging.warning(
f"Skip 'PlotStationMap` because running on a hpc node: {self.data_store.get('hostname')}")
else:
gens = [(self.train_data, {"marker": 5, "ms": 9}),
(self.val_data, {"marker": 6, "ms": 9}),
(self.test_data, {"marker": 4, "ms": 9})]
PlotStationMap(generators=gens, plot_folder=self.plot_path)
gens = [(self.train_val_data, {"marker": 8, "ms": 9}),
(self.test_data, {"marker": 9, "ms": 9})]
PlotStationMap(generators=gens, plot_folder=self.plot_path, plot_name="station_map_var")
except Exception as e:
logging.error(f"Could not create plot PlotStationMap due to the following error: {e}")
try:
if "PlotMonthlySummary" in plot_list:
PlotMonthlySummary(self.test_data.keys(), path, r"forecasts_%s_test.nc", self.target_var,
plot_folder=self.plot_path)
except Exception as e:
logging.error(f"Could not create plot PlotMonthlySummary due to the following error: {e}")
try:
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")
except Exception as e:
logging.error(f"Could not create plot PlotClimatologicalSkillScore due to the following error: {e}")
try:
if "PlotCompetitiveSkillScore" in plot_list:
PlotCompetitiveSkillScore(self.skill_scores[0], plot_folder=self.plot_path, model_setup="CNN")
except Exception as e:
logging.error(f"Could not create plot PlotCompetitiveSkillScore due to the following error: {e}")
try:
if "PlotTimeSeries" in plot_list:
PlotTimeSeries(self.test_data.keys(), path, r"forecasts_%s_test.nc", plot_folder=self.plot_path,
sampling=self._sampling)
except Exception as e:
logging.error(f"Could not create plot PlotTimeSeries due to the following error: {e}")
try:
if "PlotAvailability" in plot_list:
avail_data = {"train": self.train_data, "val": self.val_data, "test": self.test_data}
PlotAvailability(avail_data, plot_folder=self.plot_path, time_dimension=time_dimension)
except Exception as e:
logging.error(f"Could not create plot PlotAvailability due to the following error: {e}")
try:
if "PlotAvailabilityHistogram" in plot_list:
avail_data = {"train": self.train_data, "val": self.val_data, "test": self.test_data}
PlotAvailabilityHistogram(avail_data, plot_folder=self.plot_path, ) # time_dimension=time_dimension)
except Exception as e:
logging.error(f"Could not create plot PlotAvailabilityHistogram due to the following error: {e}")
def calculate_test_score(self):
"""Evaluate test score of model and save locally."""