🎨 Fix numpydoc format

specifically, "the colon must be preceded by a space, or omitted if the type is absent."

Ref https://numpydoc.readthedocs.io/en/latest/format.html

Author: shnizzedy

CPAC/anat_preproc/lesion_preproc.py

@@ -12,12 +12,12 @@ def inverse_lesion(lesion_path):
 
     Parameters
     ----------
-    lesion_path: str
+    lesion_path : str
         path to the nifti file to be checked and inverted if needed
 
     Returns
     -------
-    lesion_out: str
+    lesion_out : str
         path to the output file, if the lesion does not require to be inverted
         it returns the unchanged lesion_path input
     """

CPAC/generate_motion_statistics/generate_motion_statistics.py

@@ -469,9 +469,9 @@ def gen_power_parameters(subject_id, scan_id, fdp=None, fdj=None, dvars=None,
         subject name or id
     scan_id : string
         scan name or id
-    FDP_1D: string
+    FDP_1D : string
         framewise displacement(FD as per power et al., 2012) file path
-    FDJ_1D: string
+    FDJ_1D : string
         framewise displacement(FD as per jenkinson et al., 2002) file path
     threshold : float
         scrubbing threshold set in the configuration

CPAC/longitudinal_pipeline/longitudinal_preproc.py

@@ -64,7 +64,7 @@ def norm_transformation(input_mat):
     
     Parameters
     ----------
-    input_mat: str or numpy.ndarray
+    input_mat : str or numpy.ndarray
         Either the path to text file matrix or a matrix already imported.
 
     Returns
@@ -105,12 +105,12 @@ def template_convergence(mat_file, mat_type='matrix',
 
     Parameters
     ----------
-    mat_file: str
+    mat_file : str
         path to an fsl flirt matrix
-    mat_type: str
+    mat_type : str
         'matrix'(default), 'ITK'
         The type of matrix used to represent the transformations
-    convergence_threshold: float
+    convergence_threshold : float
         (numpy.finfo(np.float64).eps (default)) threshold for the convergence
         The threshold is how different from no transformation is the
         transformation matrix.
@@ -141,22 +141,22 @@ def create_temporary_template(input_brain_list, input_skull_list,
 
     Parameters
     ----------
-    input_brain_list: list of str
+    input_brain_list : list of str
         list of brain image paths
-    input_skull_list: list of str
+    input_skull_list : list of str
         list of skull image paths
-    output_brain_path: Nifti1Image
+    output_brain_path : Nifti1Image
         temporary longitudinal brain template
-    output_skull_path: Nifti1Image
+    output_skull_path : Nifti1Image
         temporary longitudinal skull template
-    avg_method: str
+    avg_method : str
         function names from numpy library such as 'median', 'mean', 'std' ...
 
     Returns
     -------
-    output_brain_path: Nifti1Image
+    output_brain_path : Nifti1Image
         temporary longitudinal brain template
-    output_skull_path: Nifti1Image
+    output_skull_path : Nifti1Image
         temporary longitudinal skull template
     """
 
@@ -189,29 +189,29 @@ def register_img_list(input_brain_list, ref_img, dof=12, interp='trilinear', cos
 
     Parameters
     ----------
-    input_brain_list: list of str
+    input_brain_list : list of str
         list of brain image paths
-    ref_img: str
+    ref_img : str
         path to the reference image to which the images will be registered
-    dof: integer (int of long)
+    dof : integer (int of long)
         number of transform degrees of freedom (FLIRT) (12 by default)
-    interp: str
+    interp : str
         ('trilinear' (default) or 'nearestneighbour' or 'sinc' or 'spline')
         final interpolation method used in reslicing
-    cost: str
+    cost : str
         ('mutualinfo' or 'corratio' (default) or 'normcorr' or 'normmi' or
          'leastsq' or 'labeldiff' or 'bbr')
         cost function
-    thread_pool: int or multiprocessing.dummy.Pool
+    thread_pool : int or multiprocessing.dummy.Pool
         (default 2) number of threads. You can also provide a Pool so the
         node will be added to it to be run.
-    duplicated_basename: boolean
+    duplicated_basename : boolean
         whether there exists duplicated basename which may happen in non-BIDS dataset
-    unique_id_list: list
+    unique_id_list : list
         a list of unique IDs in data
     Returns
     -------
-    node_list: list of Node
+    node_list : list of Node
         each Node 'node' has been run and
         node.inputs.out_file contains the path to the registered image
         node.inputs.out_matrix_file contains the path to the transformation

CPAC/longitudinal_pipeline/longitudinal_workflow.py

@@ -690,7 +690,7 @@ def connect_anat_preproc_inputs(strat, anat_preproc, strat_name,
         name of the strategy
     strat_nodes_list_list : list
         a list of strat_nodes_list
-    workflow: Workflow
+    workflow : Workflow
         main longitudinal workflow
 
     Returns

CPAC/network_centrality/utils.py

@@ -98,20 +98,20 @@ def check_centrality_params(method_option, threshold_option, threshold):
 
     Parameters
     ----------
-    method_option: str or int
+    method_option : str or int
         one of {m_options} or index of option
 
-    threshold_option: str
+    threshold_option : str
         one of {t_options} or index of option
 
     threshold: float
 
     Returns
     -------
-    method_option: str
+    method_option : str
         one of {m_options}
 
-    threshold_option: str
+    threshold_option : str
         one of {t_options}
     '''
 

CPAC/nuisance/utils/compcor.py

@@ -117,13 +117,13 @@ def _cosine_drift(period_cut, frametimes):
      Create a cosine drift matrix with periods greater or equals to period_cut
     Parameters
     ----------
-    period_cut: float
+    period_cut : float
          Cut period of the low-pass filter (in sec)
-    frametimes: array of shape(nscans)
+    frametimes : array of shape(nscans)
          The sampling times (in sec)
     Returns
     -------
-    cdrift:  array of shape(n_scans, n_drifts)
+    cdrift :  array of shape(n_scans, n_drifts)
              cosin drifts plus a constant regressor at cdrift[:,0]
     Ref: http://en.wikipedia.org/wiki/Discrete_cosine_transform DCT-II
     """
@@ -152,11 +152,11 @@ def _full_rank(X, cmax=1e15):
     to guarantee that the condition number is smaller than a given threshold.
     Parameters
     ----------
-    X: array of shape(nrows, ncols)
+    X : array of shape(nrows, ncols)
     cmax=1.e-15, float tolerance for condition number
     Returns
     -------
-    X: array of shape(nrows, ncols) after regularization
+    X : array of shape(nrows, ncols) after regularization
     cmax=1.e-15, float tolerance for condition number
     """
     U, s, V = fallback_svd(X, full_matrices=False)

CPAC/pipeline/nipype_pipeline_engine/engine.py

@@ -17,13 +17,13 @@ def _doctest_skiplines(docstring, lines_to_skip):
 
     Parameters
     ----------
-    docstring: str
+    docstring : str
 
-    lines_to_skip: set or list
+    lines_to_skip : set or list
 
     Returns
     -------
-    docstring: str
+    docstring : str
 
     Examples
     --------

CPAC/pypeer/peer.py

@@ -78,7 +78,7 @@ def motion_scrub(_ms_filename, _motion_threshold):
     ----------
     _ms_filename : string
         Pathname of the CSV file containing the framewise displacement per time point for a given fMRI scan
-    _motion_threshold  : float
+    _motion_threshold : float
         Threshold for high motion (framewise displacement, defined by Power et al. 2012)
     Returns
     -------

CPAC/qc/utils.py

@@ -1254,7 +1254,7 @@ def gen_motion_plt(motion_parameters):
     Parameters
     ----------
 
-    motion_parameters: string
+    motion_parameters : string
                     Motion Parameters file
 
     Returns
@@ -2319,16 +2319,16 @@ def jc(input1, input2):
 
     Parameters
     ----------
-    input1: array_like
+    input1 : array_like
             Input data containing objects. Can be any type but will be converted
             into binary: background where 0, object everywhere else.
-    input2: array_like
+    input2 : array_like
             Input data containing objects. Can be any type but will be converted
             into binary: background where 0, object everywhere else.
 
     Returns
     -------
-    jc: float
+    jc : float
         The Jaccard coefficient between the object(s) in `input1` and the
         object(s) in `input2`. It ranges from 0 (no overlap) to 1 (perfect overlap).
 

CPAC/registration/output_func_to_standard.py

@@ -33,35 +33,35 @@ def fsl_apply_transform_func_to_mni(
 
     Parameters
     ----------
-    workflow: Nipype workflow object
+    workflow : Nipype workflow object
         the workflow containing the resources involved
-    output_name: str
+    output_name : str
         what the name of the warped functional should be when written to the
         resource pool
-    func_key: string
+    func_key : string
         resource pool key correspoding to the node containing the 3D or 4D
         functional file to be written into MNI space, use 'leaf' for a
         leaf node
-    ref_key: string
+    ref_key : string
         resource pool key correspoding to the file path to the template brain
         used for functional-to-template registration
-    num_strat: int
+    num_strat : int
         the number of strategy objects
-    strat: C-PAC Strategy object
+    strat : C-PAC Strategy object
         a strategy with one or more resource pools
-    interpolation_method: str
+    interpolation_method : str
         which interpolation to use when applying the warps
-    distcor: boolean
+    distcor : boolean
         indicates whether a distortion correction transformation should be
         added to the transforms, this of course requires that a distortion
         correction map exist in the resource pool
-    map_node: boolean
+    map_node : boolean
         indicates whether a mapnode should be used, if TRUE func_key is
         expected to correspond to a list of resources that should each
         be written into standard space with the other parameters
-    func_ts: boolean
+    func_ts : boolean
         indicates whether the input image is a 4D time series
-    num_cpus: int
+    num_cpus : int
         the number of CPUs dedicated to each participant workflow - this
         is used to determine how to parallelize the warp application step