update transform methods to proplery work when external transformation parameetrs are preovided

mlair/data_handler/data_handler_single_station.py

@@ -195,7 +195,17 @@ class DataHandlerSingleStation(AbstractDataHandler):
             else:
                 raise NotImplementedError
 
-        def f_apply(data, method, mean=None, std=None, min=None, max=None):
+        def f_apply(data, method, **kwargs):
+            for k, v in kwargs.items():
+                if not (isinstance(v, xr.DataArray) or v is None):
+                    _, opts = statistics.min_max(data, dim)
+                    helper = xr.ones_like(opts['min'])
+                    kwargs[k] = helper * v
+            mean = kwargs.pop('mean', None)
+            std = kwargs.pop('std', None)
+            min = kwargs.pop('min', None)
+            max = kwargs.pop('max', None)
+
             if method == "standardise":
                 return statistics.standardise_apply(data, mean, std), {"mean": mean, "std": std, "method": method}
             elif method == "centre":

mlair/data_handler/default_data_handler.py

@@ -241,6 +241,8 @@ class DefaultDataHandler(AbstractDataHandler):
 
         * standardise (default, if method is not given)
         * centre
+        * min_max
+        * log
 
         ### mean and std estimation
 
@@ -256,14 +258,16 @@ class DefaultDataHandler(AbstractDataHandler):
 
         If mean and std are not None, the default data handler expects this parameters to match the data and applies
         this values to the data. Make sure that all dimensions and/or coordinates are in agreement.
+
+        ### min and max given
+        If min and max are not None, the default data handler expects this parameters to match the data and applies
+        this values to the data. Make sure that all dimensions and/or coordinates are in agreement.
         """
 
         sp_keys = {k: copy.deepcopy(kwargs[k]) for k in cls._requirements if k in kwargs}
-        transformation_dict = sp_keys.get("transformation", None)
-        if transformation_dict is None:
+        if "transformation" not in sp_keys.keys():
             return
-        if isinstance(transformation_dict, dict):  # tuple for (input, target) transformation
-            transformation_dict = copy.deepcopy(transformation_dict), copy.deepcopy(transformation_dict)
+        transformation_dict = ({}, {})
 
         def _inner():
             """Inner method that is performed in both serial and parallel approach."""