DOC: Remove default values from docstrings

Remove default values from docstrings: reduces the maintenance burden
and avoids the risk of inconsistencies if the default value is changed
in the method signature but not the parameter docstring.

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

@@ -74,23 +74,22 @@ def bundlewarp(
         Target bundle that will be moved/registered to match the static bundle
 
     dist : float, optional
-        Precomputed distance matrix (default None)
+        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

@@ -251,13 +251,13 @@ 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, optional
@@ -498,7 +498,6 @@ def solve(self, design_matrix, measurements, *, check=False, **kwargs):
             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

@@ -42,7 +42,7 @@ cdef class EnhancementKernel:
             Diffusion time
         force_recompute : boolean, optional
             Always compute the look-up table even if it is available
-            in cache. Default is False.
+            in cache.
         orientations : integer or Sphere object, optional
             Specify the number of orientations to be used with
             electrostatic repulsion, or provide a Sphere object.

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/noise_estimate.py

@@ -178,23 +178,21 @@ def _piesno_3D(
 
     alpha : float, optional
         Probabilistic estimation threshold for the gamma function.
-        Default: 0.01.
 
     step : int, optional
-        number of initial estimates for sigma to try. Default: 100.
+        number of initial estimates for sigma to try.
 
     itermax : int, optional
         Maximum number of iterations to execute if convergence
-        is not reached. Default: 100.
+        is not reached.
 
     eps : float, optional
         Tolerance for the convergence criterion. Convergence is
         reached if two subsequent estimates are smaller than eps.
-        Default: 1e-5.
 
     return_mask : bool, optional
         If True, return a mask identifying all the pure noise voxel
-        that were found. Default: False.
+        that were found.
 
     initial_estimation : float, optional
         Upper bound for the initial estimation of sigma. default : None,
@@ -292,11 +290,11 @@ def estimate_sigma(arr, *, disable_background_masking=False, N=0):
     arr : 3D or 4D ndarray
         The array to be estimated
 
-    disable_background_masking : bool, default False
+    disable_background_masking : bool, optional
         If True, uses all voxels for the estimation, otherwise, only non-zeros
         voxels are used. Useful if the background is masked by the scanner.
 
-    N : int, default 0
+    N : int, optional
         Number of coils of the receiver array. Use N = 1 in case of a SENSE
         reconstruction (Philips scanners) or the number of coils for a GRAPPA
         reconstruction (Siemens and GE). Use 0 to disable the correction factor,

dipy/io/image.py

@@ -16,7 +16,7 @@ def load_nifti_data(fname, *, as_ndarray=True):
     as_ndarray: bool, optional
         convert nibabel ArrayProxy to a numpy.ndarray.
         If you want to save memory and delay this casting, just turn this
-        option to False (default: True)
+        option to False.
 
     Returns
     -------
@@ -48,18 +48,18 @@ def load_nifti(
         Full path to a nifti file.
 
     return_img : bool, optional
-        Whether to return the nibabel nifti img object. Default: False
+        Whether to return the nibabel nifti img object.
 
     return_voxsize: bool, optional
-        Whether to return the nifti header zooms. Default: False
+        Whether to return the nifti header zooms.
 
     return_coords : bool, optional
-        Whether to return the nifti header aff2axcodes. Default: False
+        Whether to return the nifti header aff2axcodes.
 
     as_ndarray: bool, optional
         convert nibabel ArrayProxy to a numpy.ndarray.
         If you want to save memory and delay this casting, just turn this
-        option to False (default: True)
+        option to False.
 
     Returns
     -------

dipy/nn/deepn4.py

@@ -160,7 +160,6 @@ def __init__(self, *, verbose=False):
         ----------
         verbose : bool, optional
             Whether to show information about the processing.
-            Default: False
         """
 
         if not have_tf:

dipy/nn/evac.py

@@ -317,7 +317,6 @@ def __init__(self, *, verbose=False):
         ----------
         verbose : bool, optional
             Whether to show information about the processing.
-            Default: False
         """
 
         if not have_tf:

dipy/nn/histo_resdnn.py

@@ -74,12 +74,11 @@ def __init__(self, *, sh_order_max=8, basis_type="tournier07", verbose=False):
             Maximum SH order (l) in the SH fit.  For ``sh_order_max``, there
             will be
             ``(sh_order_max + 1) * (sh_order_max + 2) / 2`` SH coefficients
-            for a symmetric basis. Default: 8
+            for a symmetric basis.
         basis_type : {'tournier07', 'descoteaux07'}, optional
             ``tournier07`` (default) or ``descoteaux07``.
         verbose : bool, optional
             Whether to show information about the processing.
-            Default: False
 
         References
         ----------

dipy/nn/synb0.py

@@ -159,7 +159,6 @@ def __init__(self, *, verbose=False):
         ----------
         verbose : bool, optional
             Whether to show information about the processing.
-            Default: False
         """
 
         if not have_tf:

dipy/reconst/csdeconv.py

@@ -224,9 +224,8 @@ def __init__(
             will be used as deconvolution kernel :footcite:p:`Tournier2007`.
         reg_sphere : Sphere, optional
             sphere used to build the regularization B matrix.
-            Default: 'symmetric362'.
         sh_order_max : int, optional
-            maximal spherical harmonics order (l). Default: 8
+            maximal spherical harmonics order (l).
         lambda_ : float, optional
             weight given to the constrained-positivity regularization part of
             the deconvolution equation (see :footcite:p:`Tournier2007`).
@@ -235,7 +234,7 @@ def __init__(
             fODF is assumed to be zero.  Ideally, tau should be set to
             zero. However, to improve the stability of the algorithm, tau is
             set to tau*100 % of the mean fODF amplitude (here, 10% by default)
-            (see :footcite:p:`Tournier2007`). Default: 0.1.
+            (see :footcite:p:`Tournier2007`).
         convergence : int, optional
             Maximum number of iterations to allow the deconvolution to
             converge.
@@ -757,7 +756,7 @@ def odf_deconv(odf_sh, R, B_reg, lambda_=1.0, tau=0.1, r2_term=False):
         ``(sh_order_max + 1)(sh_order_max + 2)/2``)
         SH basis matrix used for deconvolution
     lambda_ : float, optional
-         lambda parameter in minimization equation (default 1.0)
+         lambda parameter in minimization equation
     tau : float, optional
          threshold (``tau *max(fODF)``) controlling the amplitude below
          which the corresponding fODF is assumed to be zero.
@@ -880,7 +879,7 @@ def odf_sh_to_sharp(
     sh_order_max : int, optional
         maximal SH order ($l$) of the SH representation
     lambda_ : float, optional
-        lambda parameter (see odfdeconv) (default 1.0)
+        lambda parameter (see odfdeconv)
     tau : float, optional
         tau parameter in the L matrix construction (see odfdeconv)
     r2_term : bool, optional
@@ -1144,30 +1143,30 @@ def recursive_response(
         shape `data.shape[0:3]` and dtype=bool. Default: use the entire data
         array.
     sh_order_max : int, optional
-        maximal spherical harmonics order (l). Default: 8
+        maximal spherical harmonics order (l).
     peak_thr : float, optional
         peak threshold, how large the second peak can be relative to the first
         peak in order to call it a single fiber population
-        :footcite:p:`Tax2014`. Default: 0.01
+        :footcite:p:`Tax2014`.
     init_fa : float, optional
-        FA of the initial 'fat' response function (tensor). Default: 0.08
+        FA of the initial 'fat' response function (tensor).
     init_trace : float, optional
-        trace of the initial 'fat' response function (tensor). Default: 0.0021
+        trace of the initial 'fat' response function (tensor).
     iter : int, optional
-        maximum number of iterations for calibration. Default: 8.
+        maximum number of iterations for calibration.
     convergence : float, optional
         convergence criterion, maximum relative change of SH
-        coefficients. Default: 0.001.
+        coefficients.
     parallel : bool, optional
         Whether to use parallelization in peak-finding during the calibration
-        procedure. Default: True
+        procedure.
     num_processes : int, optional
         If `parallel` is True, the number of subprocesses to use
         (default multiprocessing.cpu_count()). If < 0 the maximal number of
         cores minus ``num_processes + 1`` is used (enter -1 to use as many
         cores as possible). 0 raises an error.
     sphere : Sphere, optional.
-        The sphere used for peak finding. Default: default_sphere.
+        The sphere used for peak finding.
 
     Returns
     -------