diff --git a/gext/fitting.py b/gext/fitting.py
index 9422b1530a6ea77ddaeaead55543b4f8fa94799f..c72614289fc596063d53cf0ce0de13f56f15cadd 100644
--- a/gext/fitting.py
+++ b/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))