Revert to use "normalizer" as input name

See #43 (comment)

mlpp_lib/datasets.py

@@ -44,8 +44,8 @@ class DataModule:
         and targets are lists of names.
     filter: `DataFilter`, optional
         The object that handles the data filtering.
-    data_transformer: `DataTransformer`, optional
-        The object to transform data.
+    normalizer: `DataTransformer`, optional
+        The object to normalize data.
         Must be provided if `.setup("test")` is called.
     sample_weighting: list of str or str or xr.Dataset, optional
         Name(s) of the variable(s) used for weighting dataset samples or an xr.Dataset
@@ -63,7 +63,7 @@ def __init__(
         group_samples: Optional[dict[str:int]] = None,
         data_dir: Optional[str] = None,
         filter: Optional[DataFilter] = None,
-        data_transformer: Optional[DataTransformer] = None,
+        normalizer: Optional[DataTransformer] = None,
         sample_weighting: Optional[Sequence[Hashable] or Hashable or xr.Dataset] = None,
         thinning: Optional[Mapping[str, int]] = None,
     ):
@@ -75,7 +75,7 @@ def __init__(
         self.splitter = splitter
         self.group_samples = group_samples
         self.filter = filter
-        self.data_transformer = data_transformer
+        self.normalizer = normalizer
         self.sample_weighting = (
             list(sample_weighting)
             if isinstance(sample_weighting, str)
@@ -155,21 +155,21 @@ def apply_filter(self):
     def standardize(self, stage=None):
         LOGGER.info("Standardizing data.")
 
-        if self.data_transformer is None:
+        if self.normalizer is None:
             if stage == "test":
                 raise ValueError("Must provide standardizer for `test` stage.")
             else:
-                self.data_transformer = DataTransformer({"Identity": (list(self.train[0].data_vars), {})})
+                self.normalizer = DataTransformer({"Identity": (list(self.train[0].data_vars), {})})
 
 
         if stage == "fit" or stage is None:        
-            self.data_transformer.fit(self.train[0])
+            self.normalizer.fit(self.train[0])
             self.train = (
-                tuple(self.data_transformer.transform(self.train[0])) + self.train[1:]
+                tuple(self.normalizer.transform(self.train[0])) + self.train[1:]
             )
-            self.val = tuple(self.data_transformer.transform(self.val[0])) + self.val[1:]
+            self.val = tuple(self.normalizer.transform(self.val[0])) + self.val[1:]
         if stage == "test" or stage is None:
-            self.test = tuple(self.data_transformer.transform(self.test[0])) + self.test[1:]
+            self.test = tuple(self.normalizer.transform(self.test[0])) + self.test[1:]
 
     def as_datasets(self, stage=None):
         batch_dims = self.batch_dims

mlpp_lib/train.py

@@ -124,4 +124,4 @@ def train(
     for k in history:
         history[k] = list(map(float, history[k]))
 
-    return model, custom_objects, datamodule.data_transformer, history
+    return model, custom_objects, datamodule.normalizer, history