Merge pull request dipy#3320 from jhlegarreta/MiscDocImprovements2

DOC: Miscellaneous documentation improvements (part 2)

dipy/align/streamlinear.py

@@ -582,7 +582,7 @@ def __init__(self, matopt, xopt, fopt, matopt_history, funcs, iterations):
 
         Parameters
         ----------
-        matrix : array,
+        matopt : array,
             4x4 affine matrix which transforms the moving to the static
             streamlines
 
@@ -592,7 +592,7 @@ def __init__(self, matopt, xopt, fopt, matopt_history, funcs, iterations):
         fopt : float,
             final value of the metric
 
-        matrix_history : array
+        matopt_history : array
             All transformation matrices created during the optimization
 
         funcs : int,
@@ -1057,7 +1057,9 @@ def slr_with_qbx(
     Parameters
     ----------
     static : Streamlines
-    moving : Streamlines
+        Fixed or reference set of streamlines.
+    moving : streamlines
+        Moving streamlines.
 
     x0 : str, optional.
         rigid, similarity or affine transformation model (default affine)
@@ -1076,20 +1078,19 @@ def slr_with_qbx(
         If True, logs information about optimization. Default: False
 
     greater_than : int, optional
-            Keep streamlines that have length greater than
-            this value (default 50)
+        Keep streamlines that have length greater than this value.
 
     less_than : int, optional
-            Keep streamlines have length less than this value (default 250)
+        Keep streamlines have length less than this value.
 
     qbx_thr : variable int
-            Thresholds for QuickBundlesX (default [40, 30, 20, 15])
+        Thresholds for QuickBundlesX.
 
     nb_pts : int, optional
-            Number of points for discretizing each streamline (default 20)
+        Number of points for discretizing each streamline.
 
     progressive : boolean, optional
-            (default True)
+       True to enable progressive registration.
 
     rng : np.random.Generator
         If None creates random generator in function.

dipy/data/fetcher.py

@@ -2261,7 +2261,7 @@ def read_bundles_2_subjects(
     bundles : array-like
         E.g., ['af.left', 'cst.right', 'cc_1']. See all the available bundles
         in the ``exp_bundles_maps/bundles_2_subjects`` directory of your
-        ``$HOME/.dipy`` folder.
+        ``DIPY_HOME`` of ``$HOME/.dipy`` folder.
 
     Returns
     -------
@@ -2618,7 +2618,8 @@ def fetch_hcp(
     profile_name : string, optional
         The name of the AWS profile used for access. Default: "hcp"
     path : string, optional
-        Path to save files into. Default: '~/.dipy'
+        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'
     aws_access_key_id : string, optional
@@ -2838,7 +2839,8 @@ def fetch_hbn(subjects, *, path=None, include_afq=False):
         For example: ["NDARAA948VFH", "NDAREK918EC2"].
 
     path : string, optional
-        Path to save files into. Default: '~/.dipy'
+        Path to save files into. Defaults to the value of the ``DIPY_HOME``
+        environment variable is set; otherwise, defaults to ``$HOME/.dipy``.
 
     include_afq : bool, optional
         Whether to include pyAFQ derivatives. Default: False

dipy/denoise/gibbs.py

@@ -2,7 +2,6 @@
 import multiprocessing as mp
 
 import numpy as np
-import scipy
 import scipy.fft
 
 from dipy.testing.decorators import warning_for_keywords

dipy/denoise/shift_twist_convolution.pyx

@@ -33,7 +33,7 @@ def convolve(odfs_sh, kernel, sh_order_max, *, test_mode=False, num_threads=None
         Number of threads to be used for OpenMP parallelization. If None
         (default) the value of OMP_NUM_THREADS environment variable is used
         if it is set, otherwise all available threads are used. If < 0 the
-        maximal number of threads minus |num_threads + 1| is used (enter -1 to
+        maximal number of threads minus $|num_threads + 1|$ is used (enter -1 to
         use as many threads as possible). 0 raises an error.
     normalize : boolean
         Apply max-normalization to the output such that its value range matches

dipy/reconst/csdeconv.py

@@ -102,8 +102,8 @@ def auto_response(
     until="1.4",
 )
 def response_from_mask(gtab, data, mask):
-    """Computation of single-shell single-tissue (ssst) response
-        function from a given mask.
+    """Computation of single-shell single-tissue (ssst) response function from a
+    given mask.
 
     Parameters
     ----------
@@ -679,7 +679,7 @@ def csdeconv(dwsignal, X, B_reg, tau=0.1, convergence=50, P=None):
 
                 solve $Qf_n = z$ using Cholesky decomposition
 
-    We'd like to thanks Donald Tournier for his help with describing and
+    We would like to thank Donald Tournier for his help with describing and
     implementing this algorithm.
 
     References
@@ -753,19 +753,19 @@ def odf_deconv(odf_sh, R, B_reg, lambda_=1.0, tau=0.1, r2_term=False):
     Parameters
     ----------
     odf_sh : ndarray (``(sh_order_max + 1)*(sh_order_max + 2)/2``,)
-         ndarray of SH coefficients for the ODF spherical function to be
-         deconvolved
+        ndarray of SH coefficients for the ODF spherical function to be
+        deconvolved
     R : ndarray (``(sh_order_max + 1)(sh_order_max + 2)/2``,
-         ``(sh_order_max + 1)(sh_order_max + 2)/2``)
-         SDT matrix in SH basis
+        ``(sh_order_max + 1)(sh_order_max + 2)/2``)
+        SDT matrix in SH basis
     B_reg : ndarray (``(sh_order_max + 1)(sh_order_max + 2)/2``,
-         ``(sh_order_max + 1)(sh_order_max + 2)/2``)
-         SH basis matrix used for deconvolution
+        ``(sh_order_max + 1)(sh_order_max + 2)/2``)
+        SH basis matrix used for deconvolution
     lambda_ : float
-         lambda parameter in minimization equation (default 1.0)
+        lambda parameter in minimization equation (default 1.0)
     tau : float
-         threshold (``tau *max(fODF)``) controlling the amplitude below
-         which the corresponding fODF is assumed to be zero.
+        threshold (``tau *max(fODF)``) controlling the amplitude below
+        which the corresponding fODF is assumed to be zero.
     r2_term : bool
          True if ODF is computed from model that uses the $r^2$ term in the
          integral.  Recall that Tuch's ODF (used in Q-ball Imaging [1]_) and

dipy/reconst/cti.py

@@ -105,7 +105,7 @@ def split_cti_params(cti_params):
 
     Parameters
     ----------
-        params: numpy.ndarray (..., 48)
+    cti_params: numpy.ndarray (..., 48)
         All parameters estimated from the correlation tensor model.
         Parameters are ordered as follows:
 
@@ -141,14 +141,14 @@ def cti_prediction(cti_params, gtab1, gtab2, S0=1):
     Parameters
     ----------
     cti_params: numpy.ndarray (..., 48)
-    All parameters estimated from the correlation tensor model.
-    Parameters are ordered as follows:
+        All parameters estimated from the correlation tensor model.
+        Parameters are ordered as follows:
 
-        1. Three diffusion tensor's eigenvalues
-        2. Three lines of the eigenvector matrix each containing the
-           first, second and third coordinates of the eigenvector
-        3. Fifteen elements of the kurtosis tensor
-        4. Twenty-One elements of the covariance tensor
+            1. Three diffusion tensor's eigenvalues
+            2. Three lines of the eigenvector matrix each containing the
+               first, second and third coordinates of the eigenvector
+            3. Fifteen elements of the kurtosis tensor
+            4. Twenty-One elements of the covariance tensor
 
     gtab1: dipy.core.gradients.GradientTable
         A GradientTable class instance for first DDE diffusion epoch
@@ -279,14 +279,14 @@ def predict(self, cti_params, S0=1):
         Parameters
         ----------
         cti_params: numpy.ndarray (..., 48)
-        All parameters estimated from the correlation tensor model.
-        Parameters are ordered as follows:
+            All parameters estimated from the correlation tensor model.
+            Parameters are ordered as follows:
 
-            1. Three diffusion tensor's eigenvalues
-            2. Three lines of the eigenvector matrix each containing the
-               first, second and third coordinates of the eigenvector
-            3. Fifteen elements of the kurtosis tensor
-            4. Twenty-One elements of the covariance tensor
+                1. Three diffusion tensor's eigenvalues
+                2. Three lines of the eigenvector matrix each containing the
+                   first, second and third coordinates of the eigenvector
+                3. Fifteen elements of the kurtosis tensor
+                4. Twenty-One elements of the covariance tensor
 
         gtab1: dipy.core.gradients.GradientTable
             A GradientTable class instance for first DDE diffusion epoch
@@ -342,16 +342,6 @@ def predict(self, gtab1, gtab2, S0=1):
 
         Parameters
         ----------
-        params: numpy.ndarray (...,43)
-                All parameters estimated from the correlation tensor model.
-                Parameters are ordered as follows:
-
-                1. Three diffusion tensor's eigenvalues
-                2. Three lines of the eigenvector matrix each containing the
-                   first, second and third coordinates of the eigenvector
-                3. Fifteen elements of the kurtosis tensor
-                4. Twenty-One elements of the covariance tensor
-
         gtab1: dipy.core.gradients.GradientTable
             A GradientTable class instance for first DDE diffusion epoch
         gtab2: dipy.core.gradients.GradientTable
@@ -569,14 +559,14 @@ def ls_fit_cti(
     Returns
     -------
     cti_params : array (48)
-    All parameters estimated from the diffusion kurtosis model for all N
-    voxels. Parameters are ordered as follows:
-
-        1) Three diffusion tensor eigenvalues.
-        2) Three blocks of three elements, containing the first second and
-            third coordinates of the diffusion tensor eigenvectors.
-        3) Fifteen elements of the kurtosis tensor.
-        4) Twenty One elements of the covariance tensor.
+        All parameters estimated from the diffusion kurtosis model for all N
+        voxels. Parameters are ordered as follows:
+
+            1) Three diffusion tensor eigenvalues.
+            2) Three blocks of three elements, containing the first second and
+                third coordinates of the diffusion tensor eigenvectors.
+            3) Fifteen elements of the kurtosis tensor.
+            4) Twenty One elements of the covariance tensor.
 
     """
     A = design_matrix