format the docstring

src/regmod/optimizer.py

@@ -28,6 +28,7 @@ def scipy_optimize(
     -------
     NDArray
         Optimal solution.
+
     """
     x0 = np.zeros(model.size) if x0 is None else x0
     bounds = model.uvec.T
@@ -88,6 +89,7 @@ def set_trim_weights(model: "Model", index: NDArray, mask: float):
         Index where the weights need to be set.
     mask : float
         Value of the weights to set.
+
     """
     weights = np.ones(model.data.num_obs)
     weights[index] = mask
@@ -106,6 +108,7 @@ def trimming(optimize: Callable) -> Callable:
     -------
     Callable
         Trimming optimization solver.
+
     """
 
     def optimize_with_trimming(

src/regmod/prior.py

@@ -97,6 +97,7 @@ class GaussianPrior(Prior):
         Raised when size of the standard deviation vector doesn't match.
     ValueError
         Raised when any value in standard deviation vector is non-positive.
+
     """
 
     mean: NDArray = field(default=0.0, repr=False)
@@ -151,6 +152,7 @@ class UniformPrior(Prior):
         Raised when size of the upper bound vector doesn't match.
     ValueError
         Raised if lower bound is greater than upper bound.
+
     """
 
     lb: NDArray = field(default=-np.inf, repr=False)
@@ -195,6 +197,7 @@ class LinearPrior:
     -------
     is_empty()
         Indicate if the prior is empty.
+
     """
 
     mat: NDArray = field(default_factory=lambda: np.empty(shape=(0, 1)), repr=False)
@@ -258,6 +261,7 @@ class SplinePrior(LinearPrior):
     -------
     attach_spline(spline)
         Attach the spline to process the domain.
+
     """
 
     size: int = 100
@@ -283,6 +287,7 @@ def attach_spline(self, spline: XSpline):
         ----------
         spline : XSpline
             Spline used to create the linear mapping for the prior.
+
         """
         knots_lb = spline.knots[0]
         knots_ub = spline.knots[-1]

src/regmod/utils.py

@@ -36,6 +36,7 @@ def default_vec_factory(
     Notes
     -----
     This function should be replaced in the next version.
+
     """
 
     if vec is None or np.isscalar(vec):
@@ -66,6 +67,7 @@ def check_size(vec: NDArray, size: int, vec_name: str = "vec") -> None:
     Notes
     -----
     This function should be replaced in the next version.
+
     """
     assert len(vec) == size, f"{vec_name} must length {size}."
 
@@ -104,6 +106,7 @@ class SplineSpecs:
     -------
     create_spline(vec)
         Create the spline from given vector as the data.
+
     """
 
     knots: NDArray
@@ -146,6 +149,7 @@ def create_spline(self, vec: NDArray | None = None) -> XSpline:
         -------
         XSpline
             Spline object.
+
         """
         if self.knots_type == "abs":
             knots = self.knots
@@ -198,6 +202,7 @@ def sizes_to_slices(sizes: list[int]) -> list[slice]:
     [0, 1, 2, 3, 4, 5]
     >>> [x[s] for s in slices]
     [[0], [1, 2], [3, 4, 5]]
+
     """
     sizes = np.asarray(sizes).astype(int)
     if any(sizes < 0):