Update doc and cast floats

src/eko/scale_variations/exponentiated.py

@@ -1,51 +1,45 @@
 # -*- coding: utf-8 -*-
-r"""
-This module contains the scale variation for ``ModSV=exponentiated``.
-"""
+r"""Contains the scale variation for ``ModSV=exponentiated``."""
 import numba as nb
 
 from .. import beta
 
 
 @nb.njit(cache=True)
 def gamma_variation(gamma, order, nf, L):
-    """
-    Adjust the anomalous dimensions with the scale variations.
+    """Adjust the anomalous dimensions with the scale variations.
 
     Parameters
     ----------
-        gamma : numpy.ndarray
-            anomalous dimensions
-        order : tuple(int,int)
-            perturbation order
-        nf : int
-            number of active flavors
-        L : float
-            logarithmic ratio of factorization and renormalization scale
+    gamma : numpy.ndarray
+        anomalous dimensions
+    order : tuple(int,int)
+        perturbation order
+    nf : int
+        number of active flavors
+    L : float
+        logarithmic ratio of factorization and renormalization scale
 
     Returns
     -------
-        gamma : numpy.ndarray
-            adjusted anomalous dimensions
+    numpy.ndarray
+        adjusted anomalous dimensions
     """
-    # since we are modifying *in-place* be carefull, that the order matters!
+    # since we are modifying *in-place* be careful, that the order matters!
     # and indeed, we need to adjust the high elements first
     beta0 = beta.beta_qcd((2, 0), nf)
     beta1 = beta.beta_qcd((3, 0), nf)
+    beta2 = beta.beta_qcd((4, 0), nf)
     if order[0] >= 4:
         gamma[3] -= (
-            3 * beta0 * L * gamma[2]
-            + (2 * beta1 * L - 3 * beta0**2 * L**2) * gamma[1]
-            + (
-                beta.beta_qcd((4, 0), nf) * L
-                - 5 / 2 * beta1 * beta0 * L**2
-                + beta0**3 * L**3
-            )
+            3.0 * beta0 * L * gamma[2]
+            + (2.0 * beta1 * L - 3.0 * beta0**2 * L**2) * gamma[1]
+            + (beta2 * L - 5.0 / 2.0 * beta1 * beta0 * L**2 + beta0**3 * L**3)
             * gamma[0]
         )
     if order[0] >= 3:
         gamma[2] -= (
-            2 * beta0 * gamma[1] * L + (beta1 * L - beta0**2 * L**2) * gamma[0]
+            2.0 * beta0 * gamma[1] * L + (beta1 * L - beta0**2 * L**2) * gamma[0]
         )
     if order[0] >= 2:
         gamma[1] -= beta0 * gamma[0] * L