lr_padded_slice=None default for bias_transforms

sup3r/bias/bias_transforms.py

@@ -203,7 +203,7 @@ def local_linear_bc(input,
  lat_lon,
  feature_name,
  bias_fp,
- lr_padded_slice,
+ lr_padded_slice=None,
  out_range=None,
  smoothing=0,
  ):
@@ -292,8 +292,8 @@ def monthly_local_linear_bc(input,
  lat_lon,
  feature_name,
  bias_fp,
- lr_padded_slice,
  time_index,
+ lr_padded_slice=None,
  temporal_avg=True,
  out_range=None,
  smoothing=0,
@@ -318,18 +318,18 @@ def monthly_local_linear_bc(input,
  datasets "{feature_name}_scalar" and "{feature_name}_adder" that are
  the full low-resolution shape of the forward pass input that will be
  sliced using lr_padded_slice for the current chunk.
+ time_index : pd.DatetimeIndex
+ DatetimeIndex object associated with the input data temporal axis
+ (assumed 3rd axis e.g. axis=2). Note that if this method is called as
+ part of a sup3r resolution forward pass, the time_index will be
+ included automatically for the current chunk.
  lr_padded_slice : tuple | None
  Tuple of length four that slices (spatial_1, spatial_2, temporal,
  features) where each tuple entry is a slice object for that axes.
  Note that if this method is called as part of a sup3r forward pass, the
  lr_padded_slice will be included automatically in the kwargs for the
  active chunk. If this is None, no slicing will be done and the full
  bias correction source shape will be used.
- time_index : pd.DatetimeIndex
- DatetimeIndex object associated with the input data temporal axis
- (assumed 3rd axis e.g. axis=2). Note that if this method is called as
- part of a sup3r resolution forward pass, the time_index will be
- included automatically for the current chunk.
  temporal_avg : bool
  Take the average scalars and adders for the chunk's time index, this
  will smooth the transition of scalars/adders from month to month if
@@ -403,7 +403,7 @@ def local_qdm_bc(data: np.array,
  base_dset: str,
  feature_name: str,
  bias_fp,
- lr_padded_slice,
+ lr_padded_slice=None,
  threshold=0.1,
  relative=True,
  no_trend=False):