Lint.

gext/fitting.py

@@ -61,11 +61,11 @@ class LeastSquare(AbstractFitting):
         """Given a set of vectors and a target return the fitting
         coefficients."""
         matrix = np.array(vectors).T
-        A = matrix.T @ matrix
+        a = matrix.T @ matrix
         b = matrix.T @ target
         if self.options["regularization"] > 0.0:
-            A += np.identity(len(b))*self.options["regularization"]
-        coefficients = np.linalg.solve(A, b)
+            a += np.identity(len(b))*self.options["regularization"]
+        coefficients = np.linalg.solve(a, b)
         return np.array(coefficients, dtype=np.float64)
 
 class QuasiTimeReversible(AbstractFitting):
@@ -98,12 +98,12 @@ class QuasiTimeReversible(AbstractFitting):
         else:
             time_reversible_matrix = matrix[:, :q//2+1] + matrix[:, :q//2-1:-1]
 
-        A = time_reversible_matrix.T @ time_reversible_matrix
+        a = time_reversible_matrix.T @ time_reversible_matrix
         b = time_reversible_matrix.T @ (target + past_target)
 
         if self.options["regularization"] > 0.0:
-            A += np.identity(len(b))*self.options["regularization"]
-        coefficients = np.linalg.solve(A, b)
+            a += np.identity(len(b))*self.options["regularization"]
+        coefficients = np.linalg.solve(a, b)
 
         if q == 1:
             full_coefficients = np.concatenate(([-1.0], coefficients))