Lint.

gext/fitting.py

 """Module which provides functionality to perform fitting."""
 
+import abc
 from typing import List
 import numpy as np
-import abc
 
 class AbstractFitting(abc.ABC):
 
+    """Base class for fitting schemes."""
+
     def __init__(self, **kwargs):
         self.set_options(**kwargs)
 
@@ -35,6 +37,7 @@ class LeastSquare(AbstractFitting):
     }
 
     def set_options(self, **kwargs):
+        """Set options for least square minimization"""
         self.options = {}
         for key, value in kwargs.items():
             if key in self.supported_options:
@@ -43,7 +46,7 @@ class LeastSquare(AbstractFitting):
                 raise ValueError(f"Unsupported option: {key}")
 
         for option, default_value in self.supported_options.items():
-            if not option in self.options:
+            if option not in self.options:
                 self.options[option] = default_value
 
         if self.options["regularization"] < 0 \
@@ -59,8 +62,10 @@ class LeastSquare(AbstractFitting):
 
 class QuasiTimeReversible(AbstractFitting):
 
-    def set_options(**kwargs):
-        """TODO"""
+    """Quasi time reversible fitting scheme. Not yet implemented."""
 
-    def compute(self):
+    def set_options(self, **kwargs):
+        """Set options for quasi time reversible fitting"""
+
+    def compute(self, vectors: List[np.ndarray], target: np.ndarray):
         """Time reversible least square minimization fitting."""

gext/main.py

@@ -108,7 +108,8 @@ class Extrapolator:
         gammas = self.gammas.get(n)
         gamma = self.fitting_calculator.linear_combination(gammas, fit_coefficients)
 
-        fit_descriptor = self.fitting_calculator.linear_combination(prev_descriptors, fit_coefficients)
+        fit_descriptor = self.fitting_calculator.linear_combination(
+            prev_descriptors, fit_coefficients)
 
         if self.options["verbose"]:
             print("error on descriptor:", \