feat: added author name; minor adaption in variable names

chemotools/baseline/_air_pls.py

@@ -16,18 +16,28 @@
 
 """
 
+# Authors:
+# Pau Cabaneros
+# Niklas Zell <[email protected]>
+
+
+### Imports ###
+
+
 import logging
 from typing import Union
 
 import numpy as np
 from sklearn.base import BaseEstimator, OneToOneFeatureMixin, TransformerMixin
 from sklearn.utils.validation import check_is_fitted
 
-from chemotools.utils.check_inputs import check_input
 from chemotools.utils._whittaker_base import WhittakerLikeSolver
+from chemotools.utils.check_inputs import check_input
 
 logger = logging.getLogger(__name__)
 
+### Main Class ###
+
 
 # TODO: is polynomial_order actually differences and if so, is the description correct?
 class AirPls(
@@ -187,7 +197,7 @@ def _calculate_air_pls(self, x):
         # FIXME: work on full Arrays and use internal loop of ``whittaker_solve``
         for i in range(0, self.nr_iterations - 1):
             # the baseline is fitted using the Whittaker smoother framework
-            z, _ = self._solve_single_b_fixed_lam(b=x, w=w)
+            z, _ = self._solve_single_b_fixed_lam(rhs_b=x, weights=w)
             d = x - z
             dssn = np.abs(d[d < 0].sum())
 

chemotools/baseline/_ar_pls.py

@@ -18,6 +18,13 @@
 
 """
 
+# Authors:
+# Pau Cabaneros
+# Niklas Zell <[email protected]>
+
+
+### Imports ###
+
 import logging
 from numbers import Integral
 from typing import Union
@@ -26,11 +33,13 @@
 from sklearn.base import BaseEstimator, OneToOneFeatureMixin, TransformerMixin
 from sklearn.utils.validation import check_is_fitted, check_scalar
 
-from chemotools.utils.check_inputs import check_input
 from chemotools.utils._whittaker_base import WhittakerLikeSolver
+from chemotools.utils.check_inputs import check_input
 
 logger = logging.getLogger(__name__)
 
+### Main Class ###
+
 
 class ArPls(
     OneToOneFeatureMixin,
@@ -194,7 +203,7 @@ def _calculate_ar_pls(self, x):
         # FIXME: work on full Arrays and use internal loop of ``whittaker_solve``
         for _ in range(self.nr_iterations):
             # the baseline is fitted using the Whittaker smoother framework
-            z, _ = self._solve_single_b_fixed_lam(b=x, w=w)
+            z, _ = self._solve_single_b_fixed_lam(rhs_b=x, weights=w)
             d = x - z
 
             # if there is no data point below the baseline, the baseline is considered