refact: Moving PresRat to qdm sub-module

sup3r/bias/bias_calc.py

@@ -1092,22 +1092,6 @@ def get_linear_correction(bias_data, base_data, bias_feature, base_dset):
  return out
 
 
-class PresRat(DataRetrievalBase):
- """PresRat bias correction method (precipitation)
-
- The PresRat correction is defined as the combination of using the
- model-predicted change ratio, the treatment of zero-precipitation days,
- and the final correction factor (K) [Pierce2015]_.
-
- References
- ----------
- .. [Pierce2015] Pierce, D. W., Cayan, D. R., Maurer, E. P., Abatzoglou, J.
- T., & Hegewisch, K. C. (2015). Improved bias correction techniques for
- hydrological simulations of climate change. Journal of Hydrometeorology,
- 16(6), 2421-2442.
- """
-
-
 class SkillAssessment(MonthlyLinearCorrection):
  """Calculate historical skill of one dataset compared to another."""
 

sup3r/bias/qdm.py

@@ -495,3 +495,48 @@ def run(self,
  self.write_outputs(fp_out, self.out)
 
  return copy.deepcopy(self.out)
+
+
+class PresRat(QuantileDeltaMappingCorrection):
+ """PresRat bias correction method (precipitation)
+
+ The PresRat correction is defined as the combination of using the
+ model-predicted change ratio, the treatment of zero-precipitation days,
+ and the final correction factor (K) [Pierce2015]_.
+
+ References
+ ----------
+ .. [Pierce2015] Pierce, D. W., Cayan, D. R., Maurer, E. P., Abatzoglou, J.
+ T., & Hegewisch, K. C. (2015). Improved bias correction techniques for
+ hydrological simulations of climate change. Journal of Hydrometeorology,
+ 16(6), 2421-2442.
+ # Todo:
+ # - Identify Z_gf. (0.01 mm) Also have to save on output params
+ # - Estimate K = <x> / <hat{x}>
+ """
+
+ def correction_factor():
+ """Preserve the mean precipitation change (K factor)
+
+ When bias correcting, the mean can fall in a different quantile, thus
+ modifying the corrected mean if the shape of the distribution changes.
+ That effect is more pronounced in skewed distributions, such as
+ precipitation.
+ """
+ pass
+ self.bias_dh.bias_ti
+
+ def dev():
+ # Estimate means per month. Later will split this into resolution (n
+ # of chunks and window width such as 30 days) and end up with one
+ # value per month to be used later as the K numberator.
+ t = self.base_dh.time_index
+ t.month
+
+ base_data, base_ti = cls.get_base_data(base_fps,
+ base_dset,
+ base_gid,
+ base_handler,
+ daily_reduction=daily_reduction,
+ decimals=decimals,
+ base_dh_inst=base_dh_inst)