moved lr/hr normalization to separate methods in dual data handling

sup3r/preprocessing/data_handling/dual_data_handling.py

@@ -254,32 +254,74 @@ def normalize(self, means=None, stds=None, max_workers=None):
  if stds is None:
  stds = self.stds
 
+ self._normalize_lr(means, stds)
+ self._normalize_hr(means, stds)
+
+ def _normalize_lr(self, means, stds):
+ """Normalize the low-resolution data features including in the
+ low-res data handler
+
+ Note that self.lr_data is usually a unique regridded array but if
+ regridding was not performed then it is just a sliced *view* of
+ self.lr_dh.data and the super().normalize() operation will have applied
+ to that data already.
+
+ Parameters
+ ----------
+ means : dict | none
+ Dictionary of means for all features with keys: feature names and
+ values: mean values. If this is None, the self.means attribute will
+ be used. If this is not None, this DataHandler object means
+ attribute will be updated.
+ stds : dict | none
+ dictionary of standard deviation values for all features with keys:
+ feature names and values: standard deviations. If this is None, the
+ self.stds attribute will be used. If this is not None, this
+ DataHandler object stds attribute will be updated.
+ """
+
  logger.info('Normalizing low resolution data features='
  f'{self.lr_dh.features}')
  super().normalize(means=means, stds=stds,
  features=self.lr_dh.features,
  max_workers=self.lr_dh.norm_workers)
 
  if id(self.lr_dh.data) != id(self.lr_data.base):
- # self.lr_data is usually a unique regridded array but if
- # regridding was not performed then it is just a sliced view of
- # self.lr_dh.data and the super().normalize() operation will have
- # applied to that data already.
  self.lr_dh.normalize(means=means, stds=stds,
  features=self.lr_dh.features,
  max_workers=self.lr_dh.norm_workers)
  else:
  self.lr_dh._is_normalized = True
 
+ def _normalize_hr(self, means, stds):
+ """Normalize the high-resolution data features including in the
+ high-res data handler
+
+ Note that self.hr_data is usually just a sliced *view* of
+ self.hr_dh.data but if the *view* is broken then it will have to be
+ normalized too
+
+ Parameters
+ ----------
+ means : dict | none
+ Dictionary of means for all features with keys: feature names and
+ values: mean values. If this is None, the self.means attribute will
+ be used. If this is not None, this DataHandler object means
+ attribute will be updated.
+ stds : dict | none
+ dictionary of standard deviation values for all features with keys:
+ feature names and values: standard deviations. If this is None, the
+ self.stds attribute will be used. If this is not None, this
+ DataHandler object stds attribute will be updated.
+ """
+
  logger.info('Normalizing high resolution data features='
  f'{self.hr_dh.features}')
  self.hr_dh.normalize(means=means, stds=stds,
  features=self.hr_dh.features,
  max_workers=self.hr_dh.norm_workers)
 
  if id(self.hr_data.base) != id(self.hr_dh.data):
- # self.hr_data is usually just a sliced view of self.hr_dh.data
- # but if the view is broken then it will have to be normalized too
  mean_arr = np.array([means[fn] for fn in self.hr_dh.features])
  std_arr = np.array([stds[fn] for fn in self.hr_dh.features])
  self.hr_data = (self.hr_data - mean_arr) / std_arr