clean up

.gitignore

@@ -82,6 +82,7 @@ src/
 *.log
 *.lock
 testdata.json
+output_*.nc
 
 # IPython
 .ipynb_checkpoints

rook/utils/weighted_average_utils.py

@@ -1,21 +1,14 @@
 import numpy as np
 
-import xarray as xr
-
 import collections
 
-from roocs_utils.parameter import collection_parameter
-from roocs_utils.parameter import dimension_parameter
-
 from roocs_utils.project_utils import derive_ds_id
 
 from daops.ops.base import Operation
 from daops.utils import normalise
 
 from clisops.ops import subset
 
-# from clisops.ops.average import average_over_dims as average
-
 
 def apply_weighted_mean(ds):
     # fix cftime calendar
@@ -33,44 +26,21 @@ def apply_weighted_mean(ds):
 
 
 class WeightedAverage(Operation):
-    def _resolve_params(self, collection, **params):
-        """
-        Resolve the input parameters to `self.params` and parameterise
-        collection parameter and set to `self.collection`.
-        """
-        dims = dimension_parameter.DimensionParameter(["latitude", "longitude"])
-        collection = collection_parameter.CollectionParameter(collection)
-
-        self.collection = collection
-        self.params = {
-            "dims": dims,
-            "ignore_undetected_dims": params.get("ignore_undetected_dims"),
-        }
-
     def _calculate(self):
-        config = {
-            "output_type": self._output_type,
-            "output_dir": self._output_dir,
-            "split_method": self._split_method,
-            "file_namer": self._file_namer,
-        }
-
-        self.params.update(config)
-
         new_collection = collections.OrderedDict()
 
         for dset in self.collection:
             ds_id = derive_ds_id(dset)
             new_collection[ds_id] = dset.file_paths
 
-        # Normalise (i.e. "fix") data inputs based on "character"
+        # Normalise data inputs
         norm_collection = normalise.normalise(
             new_collection, False  # self._apply_fixes
         )
 
         rs = normalise.ResultSet(vars())
 
-        # apply weights
+        # calculate weighted mean
         datasets = []
         for ds_id in norm_collection.keys():
             ds = norm_collection[ds_id]