fix: Pipeline requires explicit arguments

sup3r/bias/bias_calc.py

@@ -1204,12 +1204,21 @@ def __init__(self,
  base_fps,
  bias_fps,
  bias_fut_fps,
- *args,
+ base_dset,
+ bias_feature,
+ distance_upper_bound=None,
+ target=None,
+ shape=None,
+ base_handler='Resource',
+ bias_handler='DataHandlerNCforCC',
+ base_handler_kwargs=None,
+ bias_handler_kwargs=None,
+ decimals=None,
+ match_zero_rate=False,
  n_quantiles=101,
  dist="empirical",
  sampling="linear",
- log_base=10,
- **kwargs):
+ log_base=10):
  """
  Parameters
  ----------
@@ -1229,9 +1238,52 @@ def __init__(self,
  future in Cannon et. al. (2015)). This is the dataset that would be
  corrected, while `bias_fsp` is used to provide a transformation map
  with the baseline data.
- *(kw)args :
- For aditional arguments, check :class:`DataRetrievalBase`.
- *Note: The following arguments must be keyworded arguments.*
+ base_dset : str
+ A single dataset from the base_fps to retrieve. In the case of wind
+ components, this can be U_100m or V_100m which will retrieve
+ windspeed and winddirection and derive the U/V component.
+ bias_feature : str
+ This is the biased feature from bias_fps to retrieve. This should
+ be a single feature name corresponding to base_dset
+ distance_upper_bound : float
+ Upper bound on the nearest neighbor distance in decimal degrees.
+ This should be the approximate resolution of the low-resolution
+ bias data. None (default) will calculate this based on the median
+ distance between points in bias_fps
+ target : tuple
+ (lat, lon) lower left corner of raster to retrieve from bias_fps.
+ If None then the lower left corner of the full domain will be used.
+ shape : tuple
+ (rows, cols) grid size to retrieve from bias_fps. If None then the
+ full domain shape will be used.
+ base_handler : str
+ Name of rex resource handler or sup3r.preprocessing.data_handling
+ class to be retrieved from the rex/sup3r library. If a
+ sup3r.preprocessing.data_handling class is used, all data will be
+ loaded in this class' initialization and the subsequent bias
+ calculation will be done in serial
+ bias_handler : str
+ Name of the bias data handler class to be retrieved from the
+ sup3r.preprocessing.data_handling library.
+ base_handler_kwargs : dict | None
+ Optional kwargs to send to the initialization of the base_handler
+ class
+ bias_handler_kwargs : dict | None
+ Optional kwargs to send to the initialization of the bias_handler
+ class
+ decimals : int | None
+ Option to round bias and base data to this number of
+ decimals, this gets passed to np.around(). If decimals
+ is negative, it specifies the number of positions to
+ the left of the decimal point.
+ match_zero_rate : bool
+ Option to fix the frequency of zero values in the biased data. The
+ lowest percentile of values in the biased data will be set to zero
+ to match the percentile of zeros in the base data. If
+ SkillAssessment is being run and this is True, the distributions
+ will not be mean-centered. This helps resolve the issue where
+ global climate models produce too many days with small
+ precipitation totals e.g., the "drizzle problem" [Polade2014]_.
  dist : str, default="empirical",
  Define the type of distribution, which can be "empirical" or any
  parametric distribution defined in "scipy".
@@ -1280,7 +1332,19 @@ def __init__(self,
  self.sampling = sampling
  self.log_base = log_base
 
- super().__init__(base_fps, bias_fps, *args, **kwargs)
+ super().__init__(base_fps=base_fps,
+ bias_fps=bias_fps,
+ base_dset=base_dset,
+ bias_feature=bias_feature,
+ distance_upper_bound=distance_upper_bound,
+ target=target,
+ shape=shape,
+ base_handler=base_handler,
+ bias_handler=bias_handler,
+ base_handler_kwargs=base_handler_kwargs,
+ bias_handler_kwargs=bias_handler_kwargs,
+ decimals=decimals,
+ match_zero_rate=match_zero_rate)
 
  self.bias_fut_fps = bias_fut_fps