Removed unnecessary xr_engine parameter

Author: bnaul

cesium_app/handlers/feature.py

@@ -97,8 +97,7 @@ def post(self):
         computed_fset = executor.submit(featurize.assemble_featureset,
                                         all_features, all_time_series)
         imputed_fset = executor.submit(featureset.Featureset.impute, computed_fset)
-        future = executor.submit(xr.Dataset.to_netcdf, imputed_fset,
-                                 fset_path, engine=cfg['xr_engine'])
+        future = executor.submit(xr.Dataset.to_netcdf, imputed_fset, fset_path)
         fset.task_id = future.key
         fset.save()
 

cesium_app/handlers/model.py

@@ -57,7 +57,7 @@ def _build_model_compute_statistics(fset_path, model_type, model_params,
         `params_to_optimize` is None or is an empty dict, this will be an empty
         dict.
     '''
-    fset = featureset.from_netcdf(fset_path, engine=cfg['xr_engine'])
+    fset = featureset.from_netcdf(fset_path)
     computed_model = build_model.build_model_from_featureset(
         featureset=fset, model_type=model_type,
         model_parameters=model_params,

cesium_app/handlers/prediction.py

@@ -102,8 +102,7 @@ def post(self):
         model_data = executor.submit(joblib.load, model.file.uri)
         predset = executor.submit(cesium.predict.model_predictions,
                                   fset_data, model_data)
-        future = executor.submit(xr.Dataset.to_netcdf, predset,
-                                 prediction_path, engine=cfg['xr_engine'])
+        future = executor.submit(xr.Dataset.to_netcdf, predset, prediction_path)
 
         prediction.task_id = future.key
         prediction.save()

cesium_app/models.py

@@ -198,7 +198,7 @@ def display_info(self):
         info['featureset_name'] = self.model.featureset.name
         if self.task_id is None:
             try:
-                with xr.open_dataset(self.file.uri, engine=cfg['xr_engine']) as pset:
+                with xr.open_dataset(self.file.uri) as pset:
                     info['results'] = pset.load()
             except (RuntimeError, OSError):
                 info['results'] = None

cesium_app/plot.py

@@ -26,7 +26,7 @@ def feature_scatterplot(fset_path, features_to_plot):
         Returns (fig.data, fig.layout) where `fig` is an instance of
         `plotly.tools.FigureFactory`.
     """
-    with featureset.from_netcdf(fset_path, engine=cfg['xr_engine']) as fset:
+    with featureset.from_netcdf(fset_path) as fset:
         feat_df = fset.to_dataframe()
         feat_df = feat_df[features_to_plot]
 
@@ -46,7 +46,7 @@ def feature_scatterplot(fset_path, features_to_plot):
 
 
 #def prediction_heatmap(pred_path):
-#    with xr.open_dataset(pred_path, engine=cfg['xr_engine']) as pset:
+#    with xr.open_dataset(pred_path) as pset:
 #        pred_df = pd.DataFrame(pset.prediction.values, index=pset.name,
 #                               columns=pset.class_label.values)
 #    pred_labels = pred_df.idxmax(axis=1)

cesium_app/tests/fixtures.py

@@ -88,7 +88,7 @@ def create_test_featureset(project, label_type='class'):
     fset_data = fixtures.sample_featureset(5, 1, features_to_use, targets)
     fset_path = pjoin(cfg['paths']['features_folder'],
                       '{}.nc'.format(str(uuid.uuid4())))
-    fset_data.to_netcdf(fset_path, engine=cfg['xr_engine'])
+    fset_data.to_netcdf(fset_path)
     f, created = m.File.create_or_get(uri=fset_path)
     fset = m.Featureset.create(name='test_featureset', file=f, project=project,
                                features_list=features_to_use,
@@ -127,7 +127,7 @@ def create_test_model(fset, model_type='RandomForestClassifier'):
             "loss": "hinge"},
         "LinearRegressor": {
             "fit_intercept": True}}
-    with featureset.from_netcdf(fset.file.uri, engine=cfg['xr_engine']) as fset_data:
+    with featureset.from_netcdf(fset.file.uri) as fset_data:
         model_data = build_model.build_model_from_featureset(fset_data,
                                                              model_type=model_type)
         model_path = pjoin(cfg['paths']['models_folder'],
@@ -157,12 +157,12 @@ def create_test_prediction(dataset, model):
         The model to use to create prediction.
 
     """
-    with featureset.from_netcdf(model.featureset.file.uri, engine=cfg['xr_engine']) as fset_data:
+    with featureset.from_netcdf(model.featureset.file.uri) as fset_data:
         model_data = joblib.load(model.file.uri)
         pred_data = predict.model_predictions(fset_data.load(), model_data)
     pred_path = pjoin(cfg['paths']['predictions_folder'],
                       '{}.nc'.format(str(uuid.uuid4())))
-    pred_data.to_netcdf(pred_path, engine=cfg['xr_engine'])
+    pred_data.to_netcdf(pred_path)
     f, created = m.File.create_or_get(uri=pred_path)
     pred = m.Prediction.create(file=f, dataset=dataset, project=dataset.project,
                                model=model, finished=datetime.datetime.now())