Simplify __init__.py

README.md

@@ -89,10 +89,10 @@ heatwave_evaluation_list = [
     ),
 ]
 # Load in the EWB default list of event cases
-case_metadata = ewb.cases.load_ewb_events_yaml_into_case_list()
+case_metadata = ewb.cases.load_cases()
 
 # Create the evaluation class, with cases and evaluation objects declared
-ewb_instance = ewb.evaluation(
+ewb_instance = ewb.evaluate.ExtremeWeatherBench(
     case_metadata=case_metadata,
     evaluation_objects=heatwave_evaluation_list,
 )

data_prep/ibtracs_bounds.py

@@ -541,7 +541,7 @@ def update_cases_with_storm_bounds(storm_bounds, all_storms_df):
     """
     logger.info("Updating cases with storm bounds...")
 
-    cases_all = ewb.cases.load_ewb_events_yaml_into_case_list()
+    cases_all = ewb.cases.load_cases()
     cases_new = cases_all.copy()
 
     # Update the yaml cases with storm bounds from IBTrACS data

docs/examples/applied_ar.py

@@ -9,11 +9,11 @@
 
 # Load case data from the default events.yaml
 # Users can also define their own cases_dict structure
-case_yaml = ewb.load_cases()
+case_yaml = ewb.cases.load_cases()
 
 
 # ERA5 target
-era5_target = ewb.targets.ERA5(
+era5_target = ewb.inputs.ERA5(
     variables=[
         ewb.derived.AtmosphericRiverVariables(
             output_variables=["atmospheric_river_land_intersection"]
@@ -22,15 +22,15 @@
 )
 
 # Forecast (HRES)
-hres_forecast = ewb.forecasts.ZarrForecast(
+hres_forecast = ewb.inputs.ZarrForecast(
     source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
     name="HRES",
     variables=[
         ewb.derived.AtmosphericRiverVariables(
             output_variables=["atmospheric_river_land_intersection"]
         )
     ],
-    variable_mapping=ewb.HRES_metadata_variable_mapping,
+    variable_mapping=ewb.inputs.HRES_metadata_variable_mapping,
 )
 
 grap_forecast = ewb.inputs.get_cira_icechunk(
@@ -56,7 +56,7 @@
 )
 # Create a list of evaluation objects for atmospheric river
 ar_evaluation_objects = [
-    ewb.EvaluationObject(
+    ewb.inputs.EvaluationObject(
         event_type="atmospheric_river",
         metric_list=[
             ewb.metrics.CriticalSuccessIndex(),
@@ -66,7 +66,7 @@
         target=era5_target,
         forecast=hres_forecast,
     ),
-    ewb.EvaluationObject(
+    ewb.inputs.EvaluationObject(
         event_type="atmospheric_river",
         metric_list=[
             ewb.metrics.CriticalSuccessIndex(),
@@ -76,7 +76,7 @@
         target=era5_target,
         forecast=grap_forecast,
     ),
-    ewb.EvaluationObject(
+    ewb.inputs.EvaluationObject(
         event_type="atmospheric_river",
         metric_list=[
             ewb.metrics.CriticalSuccessIndex(),
@@ -91,7 +91,7 @@
 if __name__ == "__main__":
     # Initialize ExtremeWeatherBench; will only run on cases with event_type
     # atmospheric_river
-    ar_ewb = ewb.evaluation(
+    ar_ewb = ewb.evaluate.ExtremeWeatherBench(
         case_metadata=case_yaml,
         evaluation_objects=ar_evaluation_objects,
     )

docs/examples/applied_freeze.py

@@ -9,23 +9,23 @@
 
 # Load case data from the default events.yaml
 # Users can also define their own cases_dict structure
-case_yaml = ewb.load_cases()
+case_yaml = ewb.cases.load_cases()
 
 
 # ERA5 target
-era5_freeze_target = ewb.targets.ERA5(
+era5_freeze_target = ewb.inputs.ERA5(
     variables=["surface_air_temperature"],
     chunks=None,
 )
 
 # GHCN target
-ghcn_freeze_target = ewb.targets.GHCN(variables=["surface_air_temperature"])
+ghcn_freeze_target = ewb.inputs.GHCN(variables=["surface_air_temperature"])
 
 # Forecast (FCNv2) using helper in defaults
 fcnv2_forecast = ewb.defaults.cira_fcnv2_freeze_forecast
 
 # Load the climatology for DurationMeanError
-climatology = ewb.get_climatology(quantile=0.15)
+climatology = ewb.defaults.get_climatology(quantile=0.15)
 
 # Define the metrics
 metrics_list = [
@@ -36,13 +36,13 @@
 
 # Create a list of evaluation objects for freeze
 freeze_evaluation_object = [
-    ewb.EvaluationObject(
+    ewb.inputs.EvaluationObject(
         event_type="freeze",
         metric_list=metrics_list,
         target=ghcn_freeze_target,
         forecast=fcnv2_forecast,
     ),
-    ewb.EvaluationObject(
+    ewb.inputs.EvaluationObject(
         event_type="freeze",
         metric_list=metrics_list,
         target=era5_freeze_target,
@@ -52,7 +52,7 @@
 
 if __name__ == "__main__":
     # Initialize ExtremeWeatherBench runner instance
-    freeze_ewb = ewb.evaluation(
+    freeze_ewb = ewb.evaluate.ExtremeWeatherBench(
         case_metadata=case_yaml,
         evaluation_objects=freeze_evaluation_object,
     )

docs/examples/applied_heatwave.py

@@ -9,31 +9,31 @@
 
 # Load case data from the default events.yaml
 # Users can also define their own cases_dict structure
-case_yaml = ewb.load_cases()
+case_yaml = ewb.cases.load_cases()
 
 # Define targets
 # ERA5 target
-era5_heatwave_target = ewb.targets.ERA5(
+era5_heatwave_target = ewb.inputs.ERA5(
     variables=["surface_air_temperature"],
     chunks=None,
 )
 
 # GHCN target
-ghcn_heatwave_target = ewb.targets.GHCN(
+ghcn_heatwave_target = ewb.inputs.GHCN(
     variables=["surface_air_temperature"],
 )
 
 # Define forecast (HRES)
-hres_forecast = ewb.forecasts.ZarrForecast(
+hres_forecast = ewb.inputs.ZarrForecast(
     name="hres_forecast",
     source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
     variables=["surface_air_temperature"],
-    variable_mapping=ewb.HRES_metadata_variable_mapping,
+    variable_mapping=ewb.inputs.HRES_metadata_variable_mapping,
     preprocess=ewb.defaults.preprocess_heatwave_forecast_dataset,
 )
 
 # Load the climatology for DurationMeanError
-climatology = ewb.get_climatology(quantile=0.85)
+climatology = ewb.defaults.get_climatology(quantile=0.85)
 
 # Define the metrics
 metrics_list = [
@@ -45,13 +45,13 @@
 
 # Create a list of evaluation objects for heatwave
 heatwave_evaluation_object = [
-    ewb.EvaluationObject(
+    ewb.inputs.EvaluationObject(
         event_type="heat_wave",
         metric_list=metrics_list,
         target=ghcn_heatwave_target,
         forecast=hres_forecast,
     ),
-    ewb.EvaluationObject(
+    ewb.inputs.EvaluationObject(
         event_type="heat_wave",
         metric_list=metrics_list,
         target=era5_heatwave_target,
@@ -60,7 +60,7 @@
 ]
 if __name__ == "__main__":
     # Initialize ExtremeWeatherBench
-    heatwave_ewb = ewb.evaluation(
+    heatwave_ewb = ewb.evaluate.ExtremeWeatherBench(
         case_metadata=case_yaml,
         evaluation_objects=heatwave_evaluation_object,
     )

docs/examples/applied_severe.py

@@ -8,25 +8,25 @@
 
 
 # Load case data from the default events.yaml
-case_yaml = ewb.load_cases()
+case_yaml = ewb.cases.load_cases()
 
 # Subset to one severe convection case for example
 case_list = [n for n in case_yaml if n.case_id_number == 305]
 
 # Define PPH target
-pph_target = ewb.targets.PPH(
+pph_target = ewb.inputs.PPH(
     variables=["practically_perfect_hindcast"],
 )
 
 # Define LSR target
-lsr_target = ewb.targets.LSR()
+lsr_target = ewb.inputs.LSR()
 
 # Define HRES forecast
-hres_forecast = ewb.forecasts.ZarrForecast(
+hres_forecast = ewb.inputs.ZarrForecast(
     name="hres_forecast",
     source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
     variables=[ewb.derived.CravenBrooksSignificantSevere(layer_depth=100)],
-    variable_mapping=ewb.HRES_metadata_variable_mapping,
+    variable_mapping=ewb.inputs.HRES_metadata_variable_mapping,
     storage_options={"remote_options": {"anon": True}},
 )
 
@@ -58,7 +58,7 @@
 # Define evaluation objects for severe convection:
 # One evaluation object for PPH
 pph_evaluation_objects = [
-    ewb.EvaluationObject(
+    ewb.inputs.EvaluationObject(
         event_type="severe_convection",
         metric_list=pph_metrics,
         target=pph_target,
@@ -68,7 +68,7 @@
 
 # One evaluation object for LSR
 lsr_evaluation_objects = [
-    ewb.EvaluationObject(
+    ewb.inputs.EvaluationObject(
         event_type="severe_convection",
         metric_list=lsr_metrics,
         target=lsr_target,
@@ -78,7 +78,7 @@
 
 if __name__ == "__main__":
     # Initialize ExtremeWeatherBench with both evaluation objects
-    severe_ewb = ewb.evaluation(
+    severe_ewb = ewb.evaluate.ExtremeWeatherBench(
         case_metadata=case_list,
         evaluation_objects=lsr_evaluation_objects + pph_evaluation_objects,
     )

docs/examples/applied_tc.py

@@ -8,22 +8,22 @@
 
 
 # Load the case collection from the YAML file
-case_yaml = ewb.load_cases()
+case_yaml = ewb.cases.load_cases()
 
 # Select single case (TC Ida)
 case_list = [n for n in case_yaml if n.case_id_number == 220]
 
 # Define IBTrACS target, no arguments needed as defaults are sufficient
-ibtracs_target = ewb.targets.IBTrACS()
+ibtracs_target = ewb.inputs.IBTrACS()
 
 # # Define HRES forecast
-hres_forecast = ewb.forecasts.ZarrForecast(
+hres_forecast = ewb.inputs.ZarrForecast(
     name="hres_forecast",
     source="gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr",
     # Define tropical cyclone track derived variable to include in the forecast
     variables=[ewb.derived.TropicalCycloneTrackVariables()],
     # Define metadata variable mapping for HRES forecast
-    variable_mapping=ewb.HRES_metadata_variable_mapping,
+    variable_mapping=ewb.inputs.HRES_metadata_variable_mapping,
     storage_options={"remote_options": {"anon": True}},
     # Preprocess the HRES forecast to include geopotential thickness calculation
     preprocess=ewb.defaults.preprocess_hres_tc_forecast_dataset,
@@ -63,21 +63,21 @@
 # the relevant cases inside the events YAML file
 tc_evaluation_object = [
     # HRES forecast
-    ewb.EvaluationObject(
+    ewb.inputs.EvaluationObject(
         event_type="tropical_cyclone",
         metric_list=composite_landfall_metrics,
         target=ibtracs_target,
         forecast=hres_forecast,
     ),
     # Pangu forecast
-    ewb.EvaluationObject(
+    ewb.inputs.EvaluationObject(
         event_type="tropical_cyclone",
         metric_list=composite_landfall_metrics,
         target=ibtracs_target,
         forecast=pangu_forecast,
     ),
     # FCNv2 forecast
-    ewb.EvaluationObject(
+    ewb.inputs.EvaluationObject(
         event_type="tropical_cyclone",
         metric_list=composite_landfall_metrics,
         target=ibtracs_target,
@@ -87,7 +87,7 @@
 
 if __name__ == "__main__":
     # Initialize ExtremeWeatherBench
-    tc_ewb = ewb.evaluation(
+    tc_ewb = ewb.evaluate.ExtremeWeatherBench(
         case_metadata=case_list,
         evaluation_objects=tc_evaluation_object,
     )

docs/examples/example_config.py

@@ -10,22 +10,22 @@
 import extremeweatherbench as ewb
 
 # Define targets (observation data)
-era5_heatwave_target = ewb.targets.ERA5(
+era5_heatwave_target = ewb.inputs.ERA5(
     variables=["surface_air_temperature"],
     chunks=None,
 )
 
 # Define forecasts
-fcnv2_forecast = ewb.forecasts.KerchunkForecast(
+fcnv2_forecast = ewb.inputs.KerchunkForecast(
     name="fcnv2_forecast",
     source="gs://extremeweatherbench/FOUR_v200_GFS.parq",
     variables=["surface_air_temperature"],
-    variable_mapping=ewb.CIRA_metadata_variable_mapping,
+    variable_mapping=ewb.inputs.CIRA_metadata_variable_mapping,
 )
 
 # Define evaluation objects
 evaluation_objects = [
-    ewb.EvaluationObject(
+    ewb.inputs.EvaluationObject(
         event_type="heat_wave",
         metric_list=[
             ewb.metrics.MaximumMeanAbsoluteError(),
@@ -38,7 +38,7 @@
 
 # Load case data from the default events.yaml
 # Users can also define their own cases_dict structure
-cases_list = ewb.load_cases()
+cases_list = ewb.cases.load_cases()
 
 # Alternatively, users could define custom cases like this:
 # cases_list = [