Merge remote-tracking branch 'origin/main' into pp/gaps_integration

Conflicts:
	sup3r/preprocessing/data_handling/base.py
	sup3r/utilities/regridder.py

README.rst

@@ -77,4 +77,4 @@ Brandon Benton, Grant Buster, Andrew Glaws, Ryan King. Super Resolution for Rene
 Acknowledgments
 ===============
 
-This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the DOE Grid Deployment Office (GDO), the DOE Advanced Scientific Computing Research (ASCR) program, the DOE Solar Energy Technologies Office (SETO), the United States Agency for International Development (USAID), and the Laboratory Directed Research and Development (LDRD) program at the National Renewable Energy Laboratory. The research was performed using computational resources sponsored by the Department of Energy's Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.
+This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the DOE Grid Deployment Office (GDO), the DOE Advanced Scientific Computing Research (ASCR) program, the DOE Solar Energy Technologies Office (SETO), the DOE Wind Energy Technologies Office (WETO), the United States Agency for International Development (USAID), and the Laboratory Directed Research and Development (LDRD) program at the National Renewable Energy Laboratory. The research was performed using computational resources sponsored by the Department of Energy's Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

requirements.txt

@@ -14,4 +14,3 @@ netCDF4==1.5.8
 dask
 sphinx
 pandas
-numpy==1.22

sup3r/bias/bias_calc.py

@@ -1,6 +1,7 @@
 """Utilities to calculate the bias correction factors for biased data that is
 going to be fed into the sup3r downscaling models. This is typically used to
 bias correct GCM data vs. some historical record like the WTK or NSRDB."""
+from abc import abstractmethod
 import copy
 import json
 import logging
@@ -120,8 +121,15 @@ def __init__(self,
  self.bias_tree = KDTree(self.bias_meta[['latitude', 'longitude']])
  self.bias_gid_raster = np.arange(lats.size)
  self.bias_gid_raster = self.bias_gid_raster.reshape(raster_shape)
+
+ self.out = None
+ self._init_out()
  logger.info('Finished initializing DataRetrievalBase.')
 
+ @abstractmethod
+ def _init_out(self):
+ """Initialize output arrays"""
+
  @property
  def meta(self):
  """Get a meta data dictionary on how these bias factors were
@@ -516,17 +524,8 @@ class LinearCorrection(DataRetrievalBase):
  NT = 1
  """size of the time dimension, 1 is no time-based bias correction"""
 
- def __init__(self, *args, **kwargs):
- """
- Parameters
- ----------
- *args : list
- Same positional args as DataRetrievalBase
- **kwargs : dict
- Same keyword args as DataRetrievalBase
- """
- super().__init__(*args, **kwargs)
-
+ def _init_out(self):
+ """Initialize output arrays"""
  keys = [f'{self.bias_feature}_scalar',
  f'{self.bias_feature}_adder',
  f'bias_{self.bias_feature}_mean',
@@ -903,16 +902,8 @@ class SkillAssessment(MonthlyLinearCorrection):
  PERCENTILES = (1, 5, 25, 50, 75, 95, 99)
  """Data percentiles to report."""
 
- def __init__(self, *args, **kwargs):
- """
- Parameters
- ----------
- *args : list
- Same positional args as DataRetrievalBase
- **kwargs : dict
- Same keyword args as DataRetrievalBase
- """
- super().__init__(*args, **kwargs)
+ def _init_out(self):
+ """Initialize output arrays"""
 
  monthly_keys = [f'{self.bias_feature}_scalar',
  f'{self.bias_feature}_adder',

sup3r/bias/bias_transforms.py

@@ -47,21 +47,18 @@ def get_spatial_bc_factors(lat_lon, feature_name, bias_fp, threshold=0.1):
  slice_x = slice(idx[0], idx[0] + lat_lon.shape[1])
 
  if diff.min() > threshold:
- msg = (
- 'The DataHandler top left coordinate of {} '
- 'appears to be {} away from the nearest '
- 'bias correction coordinate of {} from {}. '
- 'Cannot apply bias correction.'.format(
- lat_lon,
- diff.min(),
- lat_lon_bc[idy, idx],
- os.path.basename(bias_fp),
- )
- )
+ msg = ('The DataHandler top left coordinate of {} '
+ 'appears to be {} away from the nearest '
+ 'bias correction coordinate of {} from {}. '
+ 'Cannot apply bias correction.'.format(
+ lat_lon, diff.min(), lat_lon_bc[idy, idx],
+ os.path.basename(bias_fp),
+ ))
  logger.error(msg)
  raise RuntimeError(msg)
 
- assert dset_scalar in res.dsets and dset_adder in res.dsets
+ msg = (f'Either {dset_scalar} or {dset_adder} not found in {bias_fp}.')
+ assert dset_scalar in res.dsets and dset_adder in res.dsets, msg
  scalar = res[dset_scalar, slice_y, slice_x]
  adder = res[dset_adder, slice_y, slice_x]
  return scalar, adder
@@ -94,15 +91,14 @@ def global_linear_bc(input, scalar, adder, out_range=None):
  return out
 
 
-def local_linear_bc(
- input,
- lat_lon,
- feature_name,
- bias_fp,
- lr_padded_slice,
- out_range=None,
- smoothing=0,
-):
+def local_linear_bc(input,
+ lat_lon,
+ feature_name,
+ bias_fp,
+ lr_padded_slice,
+ out_range=None,
+ smoothing=0,
+ ):
  """Bias correct data using a simple annual (or multi-year) *scalar +adder
  method on a site-by-site basis.
 
@@ -156,10 +152,8 @@ def local_linear_bc(
  adder = adder[spatial_slice]
 
  if np.isnan(scalar).any() or np.isnan(adder).any():
- msg = (
- 'Bias correction scalar/adder values had NaNs for '
- f'"{feature_name}" from: {bias_fp}'
- )
+ msg = ('Bias correction scalar/adder values had NaNs for '
+ f'"{feature_name}" from: {bias_fp}')
  logger.warning(msg)
  warn(msg)
 
@@ -171,12 +165,12 @@ def local_linear_bc(
 
  if smoothing > 0:
  for idt in range(scalar.shape[-1]):
- scalar[..., idt] = gaussian_filter(
- scalar[..., idt], smoothing, mode='nearest'
- )
- adder[..., idt] = gaussian_filter(
- adder[..., idt], smoothing, mode='nearest'
- )
+ scalar[..., idt] = gaussian_filter(scalar[..., idt],
+  smoothing,
+  mode='nearest')
+ adder[..., idt] = gaussian_filter(adder[..., idt],
+  smoothing,
+  mode='nearest')
 
  out = input * scalar + adder
  if out_range is not None:
@@ -186,17 +180,16 @@ def local_linear_bc(
  return out
 
 
-def monthly_local_linear_bc(
- input,
- lat_lon,
- feature_name,
- bias_fp,
- lr_padded_slice,
- time_index,
- temporal_avg=True,
- out_range=None,
- smoothing=0,
-):
+def monthly_local_linear_bc(input,
+ lat_lon,
+ feature_name,
+ bias_fp,
+ lr_padded_slice,
+ time_index,
+ temporal_avg=True,
+ out_range=None,
+ smoothing=0,
+ ):
  """Bias correct data using a simple monthly *scalar +adder method on a
  site-by-site basis.
 
@@ -269,29 +262,25 @@ def monthly_local_linear_bc(
  scalar = np.repeat(scalar, input.shape[-1], axis=-1)
  adder = np.repeat(adder, input.shape[-1], axis=-1)
  if len(time_index.month.unique()) > 2:
- msg = (
- 'Bias correction method "monthly_local_linear_bc" was used '
- 'with temporal averaging over a time index with >2 months.'
- )
+ msg = ('Bias correction method "monthly_local_linear_bc" was used '
+ 'with temporal averaging over a time index with >2 months.')
  warn(msg)
  logger.warning(msg)
 
  if np.isnan(scalar).any() or np.isnan(adder).any():
- msg = (
- 'Bias correction scalar/adder values had NaNs for '
- f'"{feature_name}" from: {bias_fp}'
- )
+ msg = ('Bias correction scalar/adder values had NaNs for '
+ f'"{feature_name}" from: {bias_fp}')
  logger.warning(msg)
  warn(msg)
 
  if smoothing > 0:
  for idt in range(scalar.shape[-1]):
- scalar[..., idt] = gaussian_filter(
- scalar[..., idt], smoothing, mode='nearest'
- )
- adder[..., idt] = gaussian_filter(
- adder[..., idt], smoothing, mode='nearest'
- )
+ scalar[..., idt] = gaussian_filter(scalar[..., idt],
+  smoothing,
+  mode='nearest')
+ adder[..., idt] = gaussian_filter(adder[..., idt],
+  smoothing,
+  mode='nearest')
 
  out = input * scalar + adder
  if out_range is not None: