Merge pull request dipy#3317 from jhlegarreta/MiscDocImprovements

DOC: Miscellaneous documentation improvements

dipy/align/streamlinear.py

@@ -359,8 +359,7 @@ def __init__(
                     ``x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0])``
 
         method : str,
-            'L_BFGS_B' or 'Powell' optimizers can be used. Default is
-            'L_BFGS_B'.
+            'L_BFGS_B' or 'Powell' optimizers can be used.
 
         bounds : list of tuples or None,
             If method == 'L_BFGS_B' then we can use bounded optimization.
@@ -371,14 +370,13 @@ def __init__(
 
         verbose : bool, optional.
             If True, if True then information about the optimization is shown.
-            Default: False.
 
         options : None or dict,
             Extra options to be used with the selected method.
 
         evolution : boolean
             If True save the transformation for each iteration of the
-            optimizer. Default is False. Supported only with Scipy >= 0.11.
+            optimizer. Supported only with Scipy >= 0.11.
 
         num_threads : int, optional
             Number of threads to be used for OpenMP parallelization. If None
@@ -1057,7 +1055,7 @@ def slr_with_qbx(
         Moving streamlines.
 
     x0 : str, optional.
-        rigid, similarity or affine transformation model (default affine)
+        rigid, similarity or affine transformation model
 
     rm_small_clusters : int, optional
         Remove clusters that have less than `rm_small_clusters`
@@ -1067,10 +1065,9 @@ def slr_with_qbx(
 
     select_random : int, optional.
         If not, None selects a random number of streamlines to apply clustering
-        Default None.
 
     verbose : bool, optional
-        If True, logs information about optimization. Default: False
+        If True, logs information about optimization.
 
     greater_than : int, optional
         Keep streamlines that have length greater than this value.
@@ -1236,32 +1233,32 @@ def groupwise_slr(
         List with streamlines of the bundles to be registered.
 
     x0 : str, optional
-        rigid, similarity or affine transformation model. Default: affine.
+        rigid, similarity or affine transformation model.
 
     tol : float, optional
-        Tolerance value to be used to assume convergence. Default: 0.
+        Tolerance value to be used to assume convergence.
 
     max_iter : int, optional
         Maximum number of iterations. Depending on the number of bundles to be
-        registered this may need to be larger. Default: 20.
+        registered this may need to be larger.
 
     qbx_thr : variable int, optional
         Thresholds for Quickbundles used for clustering streamlines and reduce
         computational time. If None, no clustering is performed. Higher values
-        cluster streamlines into a smaller number of centroids. Default: [4].
+        cluster streamlines into a smaller number of centroids.
 
     nb_pts : int, optional
-        Number of points for discretizing each streamline. Default: 20.
+        Number of points for discretizing each streamline.
 
     select_random : int, optional
         Maximum number of streamlines for each bundle. If None, all the
-        streamlines are used. Default: 10000.
+        streamlines are used.
 
     verbose : bool, optional
-        If True, logs information. Default: False.
+        If True, logs information.
 
     rng : np.random.Generator
-        If None, creates random generator in function. Default: None.
+        If None, creates random generator in function.
 
     References
     ----------

dipy/align/streamwarp.py

@@ -73,24 +73,23 @@ def bundlewarp(
     moving : Streamlines
         Target bundle that will be moved/registered to match the static bundle
 
-    dist : float, optional.
-        Precomputed distance matrix (default None)
+    dist : float, optional
+        Precomputed distance matrix.
 
     alpha : float, optional
         Represents the trade-off between regularizing the deformation and
         having points match very closely. Lower value of alpha means high
-        deformations (default 0.3)
+        deformations.
 
     beta : int, optional
         Represents the strength of the interaction between points
-        Gaussian kernel size (default 20)
+        Gaussian kernel size.
 
     max_iter : int, optional
-        Maximum number of iterations for deformation process in ml-CPD method
-        (default 15)
+        Maximum number of iterations for deformation process in ml-CPD method.
 
     affine : boolean, optional
-        If False, use rigid registration as starting point (default True)
+        If False, use rigid registration as starting point.
 
     Returns
     -------

dipy/core/gradients.py

@@ -39,7 +39,6 @@ def unique_bvals(bvals, bmag=None, rbvals=False):
 
     rbvals : bool, optional
         If True function also returns all individual rounded b-values.
-        Default: False
 
     Returns
     -------
@@ -1015,7 +1014,6 @@ def unique_bvals_magnitude(bvals, *, bmag=None, rbvals=False):
 
     rbvals : bool, optional
         If True function also returns all individual rounded b-values.
-        Default: False
 
     Returns
     -------

dipy/core/optimize.py

@@ -251,24 +251,24 @@ def sparse_nnls(
     X : ndarray. May be either sparse or dense. Shape (N, M)
        The regressors
 
-    momentum : float, optional (default: 1).
+    momentum : float, optional
         The persistence of the gradient.
 
-    step_size : float, optional (default: 0.01).
+    step_size : float, optional
         The increment of parameter update in each iteration
 
-    non_neg : Boolean, optional (default: True)
+    non_neg : Boolean, optional
         Whether to enforce non-negativity of the solution.
 
-    check_error_iter : int (default:10)
+    check_error_iter : int, optional
         How many rounds to run between error evaluation for
         convergence-checking.
 
-    max_error_checks : int (default: 10)
+    max_error_checks : int, optional
         Don't check errors more than this number of times if no improvement in
         r-squared is seen.
 
-    converge_on_sse : float (default: 0.99)
+    converge_on_sse : float, optional
       a percentage improvement in SSE that is required each time to say
       that things are still going well.
 
@@ -397,9 +397,9 @@ def __init__(self, m, *, A=None, L=None):
         ----------
         m : int
             Positive int indicating the number of regressors.
-        A : array (t = m + k + 1, p, p) (optional)
+        A : array (t = m + k + 1, p, p), optional
             Constraint matrices $A$.
-        L : array (m, m) (optional)
+        L : array (m, m), optional
             Regularization matrix $L$.
             Default: None.
 
@@ -493,12 +493,11 @@ def solve(self, design_matrix, measurements, *, check=False, **kwargs):
             Design matrix.
         measurements : array (n)
             Measurements.
-        check : boolean (optional)
+        check : boolean, optional
             If True check whether the unconstrained optimization solution
             already satisfies the constraints, before running the constrained
             optimization. This adds overhead, but can avoid unnecessary
             constrained optimization calls.
-            Default: False
         kwargs : keyword arguments
             Arguments passed to the CVXPY solve method.
 

dipy/data/fetcher.py

@@ -2605,14 +2605,14 @@ def fetch_hcp(
     subjects : list
         Each item is an integer, identifying one of the HCP subjects
     hcp_bucket : string, optional
-        The name of the HCP S3 bucket. Default: "hcp-openaccess"
+        The name of the HCP S3 bucket.
     profile_name : string, optional
-        The name of the AWS profile used for access. Default: "hcp"
+        The name of the AWS profile used for access.
     path : string, optional
         Path to save files into. Defaults to the value of the ``DIPY_HOME``
         environment variable is set; otherwise, defaults to ``$HOME/.dipy``.
     study : string, optional
-        Which HCP study to grab. Default: 'HCP_1200'
+        Which HCP study to grab.
     aws_access_key_id : string, optional
         AWS credentials to HCP AWS S3. Will only be used if `profile_name` is
         set to False.
@@ -2838,7 +2838,7 @@ def fetch_hbn(subjects, *, path=None, include_afq=False):
         environment variable is set; otherwise, defaults to ``$HOME/.dipy``.
 
     include_afq : bool, optional
-        Whether to include pyAFQ derivatives. Default: False
+        Whether to include pyAFQ derivatives
 
     Returns
     -------

dipy/denoise/enhancement_kernel.pyx

@@ -40,14 +40,14 @@ cdef class EnhancementKernel:
             Angular diffusion
         t : float
             Diffusion time
-        force_recompute : boolean
+        force_recompute : boolean, optional
             Always compute the look-up table even if it is available
-            in cache. Default is False.
-        orientations : integer or Sphere object
+            in cache.
+        orientations : integer or Sphere object, optional
             Specify the number of orientations to be used with
             electrostatic repulsion, or provide a Sphere object.
             The default sphere is 'repulsion100'.
-        verbose : boolean
+        verbose : boolean, optional
             Enable verbose mode.
 
         References

dipy/denoise/gibbs.py

@@ -250,14 +250,11 @@ def gibbs_removal(vol, *, slice_axis=2, n_points=3, inplace=True, num_processes=
         Matrix containing one volume (3D) or multiple (4D) volumes of images.
     slice_axis : int (0, 1, or 2)
         Data axis corresponding to the number of acquired slices.
-        Default is set to the third axis.
     n_points : int, optional
         Number of neighbour points to access local TV (see note).
-        Default is set to 3.
     inplace : bool, optional
         If True, the input data is replaced with results. Otherwise, returns
         a new array.
-        Default is set to True.
     num_processes : int or None, optional
         Split the calculation to a pool of children processes. This only
         applies to 3D or 4D `data` arrays. Default is 1. If < 0 the maximal

dipy/denoise/localpca.py

@@ -198,23 +198,23 @@ def genpca(
         Array of data to be denoised. The dimensions are (X, Y, Z, N), where N
         are the diffusion gradient directions. The first 3 dimension must have
         size >= 2 * patch_radius + 1 or size = 1.
-    sigma : float or 3D array (optional)
+    sigma : float or 3D array, optional
         Standard deviation of the noise estimated from the data. If no sigma
         is given, this will be estimated based on random matrix theory
         :footcite:p:`Veraart2016b`, :footcite:p:`Veraart2016c`.
-    mask : 3D boolean array (optional)
+    mask : 3D boolean array, optional
         A mask with voxels that are true inside the brain and false outside of
         it. The function denoises within the true part and returns zeros
         outside of those voxels.
-    patch_radius : int or 1D array (optional)
+    patch_radius : int or 1D array, optional
         The radius of the local patch to be taken around each voxel (in
         voxels). E.g. patch_radius=2 gives 5x5x5 patches.
-    pca_method : 'eig' or 'svd' (optional)
+    pca_method : 'eig' or 'svd', optional
         Use either eigenvalue decomposition (eig) or singular value
         decomposition (svd) for principal component analysis. The default
         method is 'eig' which is faster. However, occasionally 'svd' might be
         more accurate.
-    tau_factor : float (optional)
+    tau_factor : float, optional
         Thresholding of PCA eigenvalues is done by nulling out eigenvalues that
         are smaller than:
 
@@ -225,12 +225,12 @@ def genpca(
         $\tau_{factor}$ can be set to a predefined values (e.g. $\tau_{factor} =
         2.3$ :footcite:p:`Manjon2013`), or automatically calculated using random
         matrix theory (in case that $\tau_{factor}$ is set to None).
-    return_sigma : bool (optional)
+    return_sigma : bool, optional
         If true, the Standard deviation of the noise will be returned.
-    out_dtype : str or dtype (optional)
+    out_dtype : str or dtype, optional
         The dtype for the output array. Default: output has the same dtype as
         the input.
-    suppress_warning : bool (optional)
+    suppress_warning : bool, optional
         If true, suppress warning caused by patch_size < arr.shape[-1].
 
     Returns
@@ -413,30 +413,30 @@ def localpca(
     arr : 4D array
         Array of data to be denoised. The dimensions are (X, Y, Z, N), where N
         are the diffusion gradient directions.
-    sigma : float or 3D array (optional)
+    sigma : float or 3D array, optional
         Standard deviation of the noise estimated from the data. If not given,
         calculate using method in :footcite:t:`Manjon2013`.
-    mask : 3D boolean array (optional)
+    mask : 3D boolean array, optional
         A mask with voxels that are true inside the brain and false outside of
         it. The function denoises within the true part and returns zeros
         outside of those voxels.
-    patch_radius : int or 1D array (optional)
+    patch_radius : int or 1D array, optional
         The radius of the local patch to be taken around each voxel (in
         voxels). E.g. patch_radius=2 gives 5x5x5 patches.
     gtab: gradient table object (optional if sigma is provided)
         gradient information for the data gives us the bvals and bvecs of
         diffusion data, which is needed to calculate noise level if sigma is
         not provided.
-    patch_radius_sigma : int (optional)
+    patch_radius_sigma : int, optional
         The radius of the local patch to be taken around each voxel (in
         voxels) for estimating sigma. E.g. patch_radius_sigma=2 gives
         5x5x5 patches.
-    pca_method : 'eig' or 'svd' (optional)
+    pca_method : 'eig' or 'svd', optional
         Use either eigenvalue decomposition (eig) or singular value
         decomposition (svd) for principal component analysis. The default
         method is 'eig' which is faster. However, occasionally 'svd' might be
         more accurate.
-    tau_factor : float (optional)
+    tau_factor : float, optional
         Thresholding of PCA eigenvalues is done by nulling out eigenvalues that
         are smaller than:
 
@@ -449,16 +449,16 @@ def localpca(
         set to None, it will be automatically calculated using the
         Marcenko-Pastur distribution :footcite:p:`Veraart2016c`. Default: 2.3
         according to :footcite:t:`Manjon2013`.
-    return_sigma : bool (optional)
+    return_sigma : bool, optional
         If true, a noise standard deviation estimate based on the
         Marcenko-Pastur distribution is returned :footcite:p:`Veraart2016c`.
-    correct_bias : bool (optional)
+    correct_bias : bool, optional
         Whether to correct for bias due to Rician noise. This is an
         implementation of equation 8 in :footcite:p:`Manjon2013`.
-    out_dtype : str or dtype (optional)
+    out_dtype : str or dtype, optional
         The dtype for the output array. Default: output has the same dtype as
         the input.
-    suppress_warning : bool (optional)
+    suppress_warning : bool, optional
         If true, suppress warning caused by patch_size < arr.shape[-1].
 
     Returns
@@ -519,25 +519,25 @@ def mppca(
     arr : 4D array
         Array of data to be denoised. The dimensions are (X, Y, Z, N), where N
         are the diffusion gradient directions.
-    mask : 3D boolean array (optional)
+    mask : 3D boolean array, optional
         A mask with voxels that are true inside the brain and false outside of
         it. The function denoises within the true part and returns zeros
         outside of those voxels.
-    patch_radius : int or 1D array (optional)
+    patch_radius : int or 1D array, optional
         The radius of the local patch to be taken around each voxel (in
         voxels). E.g. patch_radius=2 gives 5x5x5 patches.
-    pca_method : 'eig' or 'svd' (optional)
+    pca_method : 'eig' or 'svd', optional
         Use either eigenvalue decomposition (eig) or singular value
         decomposition (svd) for principal component analysis. The default
         method is 'eig' which is faster. However, occasionally 'svd' might be
         more accurate.
-    return_sigma : bool (optional)
+    return_sigma : bool, optional
         If true, a noise standard deviation estimate based on the
         Marcenko-Pastur distribution is returned :footcite:p:`Veraart2016b`.
-    out_dtype : str or dtype (optional)
+    out_dtype : str or dtype, optional
         The dtype for the output array. Default: output has the same dtype as
         the input.
-    suppress_warning : bool (optional)
+    suppress_warning : bool, optional
         If true, suppress warning caused by patch_size < arr.shape[-1].
 
     Returns