added the option for the user to specify qa datsets with mapping from…

… sup3r output to source alias name

sup3r/qa/qa.py

@@ -26,6 +26,7 @@ class Sup3rQa:
 
  def __init__(self, source_file_paths, out_file_path, s_enhance, t_enhance,
  temporal_coarsening_method,
+ features=None,
  temporal_slice=slice(None),
  target=None,
  shape=None,
@@ -63,6 +64,11 @@ def __init__(self, source_file_paths, out_file_path, s_enhance, t_enhance,
  Subsample will take every t_enhance-th time step, average will
  average over t_enhance time steps, total will sum over t_enhance
  time steps
+ features : list | dict | None
+ Explicit list of features to validate. Can be a list of string
+ feature names, a dictionary mapping the sup3r output feature name
+ to the source_handler feature name (e.g. {'ghi': 'rsds'}), or None
+ for all features found in the out_file_path.
  temporal_slice : slice | tuple | list
  Slice defining size of full temporal domain. e.g. If we have 5
  files each with 5 time steps then temporal_slice = slice(None) will
@@ -134,14 +140,15 @@ def __init__(self, source_file_paths, out_file_path, s_enhance, t_enhance,
  self.t_enhance = t_enhance
  self._t_meth = temporal_coarsening_method
  self._out_fp = out_file_path
+ self._features = features
  self.qa_fp = qa_fp
  self.save_sources = save_sources
  self.output_handler = self.output_handler_class(self._out_fp)
 
  HandlerClass = get_input_handler_class(source_file_paths,
  input_handler)
  self.source_handler = HandlerClass(source_file_paths,
- self.features,
+ self.source_features,
  target=target,
  shape=shape,
  temporal_slice=temporal_slice,
@@ -160,7 +167,6 @@ def __enter__(self):
 
  def __exit__(self, type, value, traceback):
  self.close()
-
  if type is not None:
  raise
 
@@ -206,19 +212,34 @@ def features(self):
  -------
  list
  """
-
  # all lower case
  ignore = ('meta', 'time_index', 'times', 'xlat', 'xlong')
 
- if self.output_type == 'nc':
- features = list(self.output_handler.variables.keys())
- elif self.output_type == 'h5':
- features = self.output_handler.dsets
+ if self._features is None:
+ if self.output_type == 'nc':
+ features = list(self.output_handler.variables.keys())
+ elif self.output_type == 'h5':
+ features = self.output_handler.dsets
+ features = [f for f in features if f.lower() not in ignore]
 
- features = [f for f in features if f.lower() not in ignore]
+ elif isinstance(self._features, dict):
+ features = sorted(self._features.keys())
 
  return features
 
+ @property
+ def source_features(self):
+ """Get a list of feature names from the source input file, excluding
+ meta and time index datasets. This property considers the features
+ input mapping if a dictionary was provided, e.g. if
+ features={'ghi': 'rsds'}, this property will return ['rsds']"""
+ if isinstance(self._features, dict):
+ source_features = [self._features[f] for f in self.features]
+ else:
+ source_features = self.features
+
+ return source_features
+
  @property
  def output_type(self):
  """Get output data type