Merge pull request #242 from jhlegarreta/ModelDocstringFixes

DOC: Miscellaneous docstring fixes

src/eddymotion/model/_dipy.py

@@ -39,8 +39,8 @@
 
 
 def gp_prediction(
-    gtab: GradientTable | np.ndarray,
     model: GaussianProcessRegressor,
+    gtab: GradientTable | np.ndarray,
     mask: np.ndarray | None = None,
     return_std: bool = False,
 ) -> np.ndarray:
@@ -53,12 +53,14 @@ def gp_prediction(
 
     Parameters
     ----------
-    model: :obj:`~sklearn.gaussian_process.GaussianProcessRegressor`
+    model : :obj:`~sklearn.gaussian_process.GaussianProcessRegressor`
         A fitted GaussianProcessRegressor model.
     gtab : :obj:`~dipy.core.gradients.GradientTable` or :obj:`~np.ndarray`
         Gradient table with one or more orientations at which the GP will be evaluated.
-    mask: :obj:`numpy.ndarray`
+    mask : :obj:`numpy.ndarray`, optional
         A boolean mask indicating which voxels to use (optional).
+    return_std : bool, optional
+        Whether to return the standard deviation of the predicted signal.
 
     Returns
     -------
@@ -100,8 +102,16 @@ def __init__(
 
         Parameters
         ----------
-        kernel : :obj:`~sklearn.gaussian_process.kernels.Kernel`
+        kernel_model : :obj:`~sklearn.gaussian_process.kernels.Kernel`, optional
             Kernel model to calculate the GP's covariance matrix.
+        lambda_s : :obj:`float`, optional
+            Signal scale parameter determining the variability of the signal.
+        a : :obj:`float`, optional
+            Distance scale parameter determining how fast the covariance
+            decreases as one moves along the surface of the sphere. Must have a
+            positive value.
+        sigma_sq : :obj:`float`, optional
+            Uncertainty of the measured values.
 
         References
         ----------
@@ -136,11 +146,14 @@ def fit(
         ----------
         gtab : :obj:`~dipy.core.gradients.GradientTable` or :obj:`~np.ndarray`
             The gradient table corresponding to the training data.
-        y : :obj:`~numpy.ndarray`
+        data : :obj:`~numpy.ndarray`
             The measured signal from one voxel.
         mask : :obj:`~numpy.ndarray`
             A boolean array used to mark the coordinates in the data that
             should be analyzed that has the shape data.shape[:-1]
+        random_state: :obj:`int`, optional
+            Determines random number generation used to initialize the centers
+            of the kernel bounds.
 
         Returns
         -------
@@ -149,12 +162,14 @@ def fit(
 
         """
 
-        # Extract b-vecs: Scikit learn wants (n_samples, n_features)
-        # where n_features is 3, and n_samples the different diffusion orientations.
+        # Extract b-vecs: scikit-learn wants (n_samples, n_features)
+        # where n_features is 3, and n_samples the different diffusion-encoding
+        # gradient orientations.
         X = gtab.bvecs if hasattr(gtab, "bvecs") else np.asarray(gtab)
 
-        # Data must be shapes (n_samples, n_targets) where n_samples is
-        # the number of diffusion orientations, and n_targets is number of voxels.
+        # Data must have shape (n_samples, n_targets) where n_samples is
+        # the number of diffusion-encoding gradient orientations, and n_targets
+        # is number of voxels.
         y = (
             data[mask[..., None]] if mask is not None else np.reshape(data, (-1, data.shape[-1]))
         ).T
@@ -208,9 +223,9 @@ class GPFit:
 
     Attributes
     ----------
-    model: :obj:`~sklearn.gaussian_process.GaussianProcessRegressor`
+    model : :obj:`~sklearn.gaussian_process.GaussianProcessRegressor`
         The fitted Gaussian process regressor object.
-    mask: :obj:`~numpy.ndarray`
+    mask : :obj:`~numpy.ndarray`
         The boolean mask used during fitting (can be ``None``).
 
     """
@@ -225,10 +240,10 @@ def __init__(
 
         Parameters
         ----------
-        model: :obj:`~sklearn.gaussian_process.GaussianProcessRegressor`
+        model : :obj:`~sklearn.gaussian_process.GaussianProcessRegressor`
             The fitted Gaussian process regressor object.
-        mask: :obj:`~numpy.ndarray`
-            The boolean mask used during fitting (can be ``None``).
+        mask : :obj:`~numpy.ndarray`, optional
+            The boolean mask used during fitting.
 
         """
         self.model = model
@@ -252,7 +267,7 @@ def predict(
             A 3D or 4D array with the simulated gradient(s).
 
         """
-        return gp_prediction(gtab, self.model, mask=self.mask)
+        return gp_prediction(self.model, gtab, mask=self.mask)
 
 
 def _rasb2dipy(gradient):

src/eddymotion/model/_sklearn.py

@@ -199,9 +199,9 @@ def __call__(
 
         Parameters
         ----------
-        X: :obj:`~numpy.ndarray`
+        X : :obj:`~numpy.ndarray`
             Gradient table (X)
-        Y: :obj:`~numpy.ndarray`, optional
+        Y : :obj:`~numpy.ndarray`, optional
             Gradient table (Y, optional)
         eval_gradient : :obj:`bool`, optional
             Determines whether the gradient with respect to the log of
@@ -275,13 +275,13 @@ def __init__(
 
         Parameters
         ----------
-        a : :obj:`float`
+        a : :obj:`float`, optional
             Minimum angle in rads.
-        lambda_s : :obj:`float`
+        lambda_s : :obj:`float`, optional
             The :math:`\lambda_s` hyperparameter.
-        a_bounds : :obj:`tuple`
-            Bounds for the a parameter.
-        lambda_s_bounds : :obj:`tuple`
+        a_bounds : :obj:`tuple`, optional
+            Bounds for the ``a`` parameter.
+        lambda_s_bounds : :obj:`tuple`, optional
             Bounds for the :math:`\lambda_s` hyperparameter.
 
         """
@@ -306,9 +306,9 @@ def __call__(
 
         Parameters
         ----------
-        X: :obj:`~numpy.ndarray`
+        X : :obj:`~numpy.ndarray`
             Gradient table (X)
-        Y: :obj:`~numpy.ndarray`, optional
+        Y : :obj:`~numpy.ndarray`, optional
             Gradient table (Y, optional)
         eval_gradient : :obj:`bool`, optional
             Determines whether the gradient with respect to the log of
@@ -323,7 +323,7 @@ def __call__(
         K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims),\
                 optional
             The gradient of the kernel k(X, X) with respect to the log of the
-            hyperparameter of the kernel. Only returned when `eval_gradient`
+            hyperparameter of the kernel. Only returned when ``eval_gradient``
             is True.
 
         """
@@ -394,14 +394,13 @@ def compute_pairwise_angles(
     ----------
     X : {array-like, sparse matrix} of shape (n_samples_X, n_features)
         Input data.
-    Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), \
-            default=None
+    Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), optional
         Input data. If ``None``, the output will be the pairwise
         similarities between all samples in ``X``.
-    dense_output : :obj:`bool`, default=True
+    dense_output : :obj:`bool`, optional
         Whether to return dense output even when the input is sparse. If
         ``False``, the output is sparse if both input arrays are sparse.
-    closest_polarity : :obj:`bool`, default=True
+    closest_polarity : :obj:`bool`, optional
         ``True`` to consider the smallest of the two angles between the crossing
          lines resulting from reversing each vector pair.
 

src/eddymotion/registration/ants.py

@@ -73,7 +73,7 @@ def _to_nifti(
     filename : :obj:`os.pathlike`
         The file path where the NIfTI file will be saved.
     clip : :obj:`bool`, optional
-        Whether to apply clipping to the data before saving, by default True.
+        Whether to apply clipping to the data before saving.
 
     """
     data = np.squeeze(data)
@@ -144,7 +144,7 @@ def _get_ants_settings(settings: str = "b0-to-b0_level0") -> Path:
     Parameters
     ----------
     settings : :obj:`str`, optional
-        Name of the settings configuration, by default ``"b0-to-b0_level0"``.
+        Name of the settings configuration.
 
     Returns
     -------
@@ -225,13 +225,13 @@ def generate_command(
     moving_path : :obj:`os.pathlike`
         Path to the moving image.
     fixedmask_path : :obj:`os.pathlike` or :obj:`list`, optional
-        Path to the fixed image mask, by default None.
+        Path to the fixed image mask.
     movingmask_path : :obj:`os.pathlike` or :obj:`list`, optional
-        Path to the moving image mask, by default None.
+        Path to the moving image mask.
     init_affine : :obj:`os.pathlike`, optional
-        Initial affine transformation, by default None.
+        Initial affine transformation.
     default : :obj:`str`, optional
-        Default settings configuration, by default "b0-to-b0_level0".
+        Default settings configuration.
     **kwargs : :obj:`dict`
         Additional parameters for ANTs registration.
 

src/eddymotion/testing/simulations.py

@@ -89,7 +89,7 @@ def create_random_polar_coordinates(
     hsph_dirs : :obj:`int`
         Number of hemisphere directions.
     seed : :obj:`int`, optional
-        Seed for the random number generator, by default 1234.
+        Seed for the random number generator.
 
     Returns
     -------
@@ -114,9 +114,9 @@ def create_diffusion_encoding_gradient_dirs(
     hsph_dirs : :obj:`int`
         Number of hemisphere directions.
     iterations : :obj:`int`, optional
-        Number of iterations for charge dispersion, by default 5000.
+        Number of iterations for charge dispersion.
     seed : :obj:`int`, optional
-        Seed for the random number generator, by default 1234.
+        Seed for the random number generator.
 
     Returns
     -------
@@ -148,7 +148,7 @@ def create_single_shell_gradient_table(
     bval_shell : :obj:`float`
         Shell b-value.
     iterations : :obj:`int`, optional
-        Number of iterations for charge dispersion, by default 5000.
+        Number of iterations for charge dispersion.
 
     Returns
     -------