added option to smooth spatial bias correction factors outside of the…

… active domain to alleviate the nearest neighbor seams

sup3r/bias/bias_calc.py

@@ -10,6 +10,7 @@
 import pandas as pd
 from scipy.spatial import KDTree
 from scipy.stats import ks_2samp
+from scipy.ndimage.filters import gaussian_filter
 from concurrent.futures import ProcessPoolExecutor, as_completed
 import rex
 from rex.utilities.fun_utils import get_fun_call_str
@@ -391,7 +392,7 @@ def write_outputs(self, fp_out, scalar, adder):
  .format(fp_out))
 
  def run(self, knn, threshold=0.6, fp_out=None, max_workers=None,
- daily_avg=True):
+ daily_avg=True, smoothing=0):
  """Run linear correction factor calculations for every site in the bias
  dataset
 
@@ -412,6 +413,10 @@ def run(self, knn, threshold=0.6, fp_out=None, max_workers=None,
  available.
  daily_avg : bool
  Flag to do temporal daily averaging of the base data.
+ smoothing : float
+ Option to smooth the scalar/adder data outside of the spatial
+ domain set by the threshold input. This alleviates the weird seams
+ far from the domain of interest.
 
  Returns
  -------
@@ -473,9 +478,20 @@ def run(self, knn, threshold=0.6, fp_out=None, max_workers=None,
  logger.info('Completed bias calculations for {} out of {} '
  'sites'.format(i + 1, len(futures)))
 
+ logger.info('Finished calculating bias corrections. '
+ 'Mean scalar: {:.3f} mean adder: {:.3f}'
+ .format(np.nanmean(scalar), np.nanmean(adder)))
+
+ nan_mask = np.isnan(scalar)
  scalar = nn_fill_array(scalar)
  adder = nn_fill_array(adder)
 
+ if smoothing > 0:
+ scalar[nan_mask] = gaussian_filter(scalar, smoothing,
+ mode='nearest')[nan_mask]
+ adder[nan_mask] = gaussian_filter(adder, smoothing,
+ mode='nearest')[nan_mask]
+
  self.write_outputs(fp_out, scalar, adder)
 
  return scalar, adder