enh: apply suggestions from code review

Co-authored-by: Jon Haitz Legarreta Gorroño <[email protected]>

src/eddymotion/model/dipy.py

@@ -329,7 +329,7 @@ def compute_spherical_covariance(
 
     _ensure_positive_scale(a)
 
-    return np.where(theta <= a, 1 - 3 * theta / (2 * a) + theta ** 3 / (2 * a ** 3), 0)
+    return np.where(theta <= a, 1 - 3 * theta / (2 * a) + theta**3 / (2 * a**3), 0)
 
 
 def compute_derivative(
@@ -367,9 +367,9 @@ def compute_derivative(
     min_angles = theta > a
 
     if weighting == "spherical":
-        deriv_a = 1.5 * (theta[min_angles] / a ** 2 - theta[min_angles] ** 3 / a ** 4)
+        deriv_a = 1.5 * (theta[min_angles] / a**2 - theta[min_angles] ** 3 / a**4)
     elif weighting == "exponential":
-        deriv_a = np.exp(-theta[min_angles] / a) * theta[min_angles] / a ** 2
+        deriv_a = np.exp(-theta[min_angles] / a) * theta[min_angles] / a**2
     else:
         raise ValueError(f"Unknown kernel weighting '{weighting}'.")
 
@@ -464,6 +464,27 @@ def __init__(
         a_bounds: tuple[float, float] = (1e-5, np.pi),
         sigma_sq_bounds: tuple[float, float] = (1e-5, 1e4),
     ):
+        r"""
+        Initialize a kernel with pairwise angles.
+
+        Parameters
+        ----------
+        weighting : :obj:`str`
+            The type of kernel to build (either "exponential", "sphere", or "test").
+        lambda_s : :obj:`float`
+            The :math:`\lambda_s` hyperparameter.
+        a : :obj:`float`
+            Minimum angle in rads.
+        sigma_sq : :obj:`float`
+            Error allowed in collinear orientations.
+        lambda_s_bounds : :obj:`tuple`
+            Bounds for the :math:`\lambda_s` hyperparameter.
+        a_bounds : :obj:`tuple`
+            Bounds for the a parameter.
+        sigma_sq_bounds : :obj:`tuple`
+            Bounds for the error parameter.
+
+        """
         self._weighting = weighting  # For __repr__
         self.lambda_s = lambda_s
         self.a = a
@@ -492,9 +513,9 @@ def __call__(self, gtab_X, gtab_Y=None, eval_gradient=False):
         ----------
         gtab_X: :obj:`~dipy.core.gradients.GradientTable`
             Gradient table (X)
-        gtab_Y: :obj:`~dipy.core.gradients.GradientTable`
+        gtab_Y: :obj:`~dipy.core.gradients.GradientTable`, optional
             Gradient table (Y, optional)
-        eval_gradient : :obj:`bool`
+        eval_gradient : :obj:`bool`, optional
             Determines whether the gradient with respect to the log of
             the kernel hyperparameter is computed.
             Only supported when gtab_Y is ``None``.