How to use the visualqc.config.default_outlier_detection_method function in VisualQC

help_text_prepare = textwrap.dedent("""
    This flag enables batch-generation of 3d surface visualizations, prior to starting any review and rating operations.

    This makes the switch from one subject to the next, even more seamless (saving few seconds :) ).

    Default: False (required visualizations are generated only on demand, which can take 5-10 seconds for each subject).
    \n""")

    help_text_outlier_detection_method = textwrap.dedent("""
    Method used to detect the outliers.

    For more info, read http://scikit-learn.org/stable/modules/outlier_detection.html

    Default: {}.
    \n""".format(cfg.default_outlier_detection_method))

    help_text_outlier_fraction = textwrap.dedent("""
    Fraction of outliers expected in the given sample. Must be >= 1/n and <= (n-1)/n,
    where n is the number of samples in the current sample.

    For more info, read http://scikit-learn.org/stable/modules/outlier_detection.html

    Default: {}.
    \n""".format(cfg.default_outlier_fraction))

    help_text_outlier_feat_types = textwrap.dedent("""
    Type of features to be employed in training the outlier detection method.  It could be one of
    'cortical' (aparc.stats: mean thickness and other geometrical features from each cortical label),
    'subcortical' (aseg.stats: volumes of several subcortical structures),
    or 'both' (using both aseg and aparc stats).

Default: {}.
    \n""".format(default_num_rows))

    help_text_contour_color = textwrap.dedent("""
    Specifies the color to use for the contours overlaid on MRI (when vis_type requested prescribes contours). 
    Color can be specified in many ways as documented in https://matplotlib.org/users/colors.html
    Default: {}.
    \n""".format(cfg.default_contour_face_color))

    help_text_outlier_detection_method = textwrap.dedent("""
    Method used to detect the outliers.
    
    For more info, read http://scikit-learn.org/stable/modules/outlier_detection.html
    
    Default: {}.
    \n""".format(cfg.default_outlier_detection_method))

    help_text_outlier_fraction = textwrap.dedent("""
    Fraction of outliers expected in the given sample. Must be >= 1/n and <= (n-1)/n, 
    where n is the number of samples in the current sample.

    For more info, read http://scikit-learn.org/stable/modules/outlier_detection.html

    Default: {}.
    \n""".format(cfg.default_outlier_fraction))

    help_text_outlier_feat_types = textwrap.dedent("""
    Type of features to be employed in training the outlier detection method.  It could be one of  
    'cortical' (aparc.stats: mean thickness and other geometrical features from each cortical label), 
    'subcortical' (aseg.stats: volumes of several subcortical structures), 
    or 'both' (using both aseg and aparc stats).

in_out.add_argument("-b", "--bids_dir", action="store", dest="bids_dir",
                        default=None,
                        required=False, help=help_text_bids_dir)

    out_spec = parser.add_argument_group('Outputs',
                                         'options related to outputs')

    out_spec.add_argument("-o", "--out_dir", action="store", dest="out_dir",
                        required=False, help=help_text_out_dir,
                        default=None)

    outliers = parser.add_argument_group('Outlier detection',
                                         'options related to automatically detecting possible outliers')
    outliers.add_argument("-olm", "--outlier_method", action="store",
                          dest="outlier_method",
                          default=cfg.default_outlier_detection_method, required=False,
                          help=help_text_outlier_detection_method)

    outliers.add_argument("-olf", "--outlier_fraction", action="store",
                          dest="outlier_fraction",
                          default=cfg.default_outlier_fraction, required=False,
                          help=help_text_outlier_fraction)

    outliers.add_argument("-olt", "--outlier_feat_types", action="store",
                          dest="outlier_feat_types",
                          default=cfg.t1_mri_features_OLD, required=False,
                          help=help_text_outlier_feat_types)

    outliers.add_argument("-old", "--disable_outlier_detection", action="store_true",
                          dest="disable_outlier_detection",
                          required=False, help=help_text_disable_outlier_detection)

help_text_prepare = textwrap.dedent("""
    This flag does the heavy preprocessing first, prior to starting any review and rating operations.
    Heavy processing can include computation of registration quality metrics and outlier detection etc. 
    This makes the switch from one subject to the next, even more seamless (saving few seconds :) ).

    Default: False.
    \n""")

    help_text_outlier_detection_method = textwrap.dedent("""
    Method used to detect the outliers.

    For more info, read http://scikit-learn.org/stable/modules/outlier_detection.html

    Default: {}.
    \n""".format(cfg.default_outlier_detection_method))

    help_text_outlier_fraction = textwrap.dedent("""
    Fraction of outliers expected in the given sample. Must be >= 1/n and <= (n-1)/n, 
    where n is the number of samples in the current sample.

    For more info, read http://scikit-learn.org/stable/modules/outlier_detection.html

    Default: {}.
    \n""".format(cfg.default_outlier_fraction))

    help_text_outlier_feat_types = textwrap.dedent("""
    Type of features to be employed in training the outlier detection method.  
    It could be one of .

    Default: {}.
    \n""".format(cfg.alignment_features_OLD))

\n""".format(cfg.default_num_rows))

    help_text_prepare = textwrap.dedent("""
    This flag enables batch-generation of 3d surface visualizations, prior to starting any review and rating operations. 
    This makes the switch from one subject to the next, even more seamless (saving few seconds :) ).

    Default: False (required visualizations are generated only on demand, which can take 5-10 seconds for each subject).
    \n""")

    help_text_outlier_detection_method = textwrap.dedent("""
    Method used to detect the outliers.

    For more info, read http://scikit-learn.org/stable/modules/outlier_detection.html

    Default: {}.
    \n""".format(cfg.default_outlier_detection_method))

    help_text_outlier_fraction = textwrap.dedent("""
    Fraction of outliers expected in the given sample. Must be >= 1/n and <= (n-1)/n, 
    where n is the number of samples in the current sample.

    For more info, read http://scikit-learn.org/stable/modules/outlier_detection.html

    Default: {}.
    \n""".format(cfg.default_outlier_fraction))

    help_text_outlier_feat_types = textwrap.dedent("""
    Type of features to be employed in training the outlier detection method.  It could be one of  
    'cortical' (aparc.stats: mean thickness and other geometrical features from each cortical label), 
    'subcortical' (aseg.stats: volumes of several subcortical structures), 
    or 'both' (using both aseg and aparc stats).

in_out.add_argument("-n", "--name_pattern", action="store", dest="name_pattern",
                        default=cfg.default_name_pattern, required=False,
                        help=help_text_name_pattern)

    preproc = parser.add_argument_group('Preprocessing',
                                         'options related to preprocessing before review')

    preproc.add_argument("-np", "--no_preproc", action="store_true", dest="no_preproc",
                          required=False, help=help_text_no_preproc)

    outliers = parser.add_argument_group('Outlier detection',
                                         'options related to automatically detecting possible outliers')
    outliers.add_argument("-olm", "--outlier_method", action="store",
                          dest="outlier_method",
                          default=cfg.default_outlier_detection_method, required=False,
                          help=help_text_outlier_detection_method)

    outliers.add_argument("-olf", "--outlier_fraction", action="store",
                          dest="outlier_fraction",
                          default=cfg.default_outlier_fraction, required=False,
                          help=help_text_outlier_fraction)

    outliers.add_argument("-olt", "--outlier_feat_types", action="store",
                          dest="outlier_feat_types",
                          default=cfg.func_mri_features_OLD, required=False,
                          help=help_text_outlier_feat_types)

    outliers.add_argument("-old", "--disable_outlier_detection", action="store_true",
                          dest="disable_outlier_detection",
                          required=False, help=help_text_disable_outlier_detection)

default=default_vis_type, required=False,
                          help=help_text_vis_type)

    vis_args.add_argument("-c", "--contour_color", action="store", dest="contour_color",
                          default=cfg.default_contour_face_color, required=False,
                          help=help_text_contour_color)

    vis_args.add_argument("-a", "--alpha_set", action="store", dest="alpha_set",
                          metavar='alpha', nargs=2,
                          default=default_alpha_set,
                          required=False, help=help_text_alphas)

    outliers = parser.add_argument_group('Outlier detection',
                                         'options related to automatically detecting possible outliers')
    outliers.add_argument("-olm", "--outlier_method", action="store", dest="outlier_method",
                          default=cfg.default_outlier_detection_method, required=False,
                          help=help_text_outlier_detection_method)

    outliers.add_argument("-olf", "--outlier_fraction", action="store", dest="outlier_fraction",
                          default=cfg.default_outlier_fraction, required=False,
                          help=help_text_outlier_fraction)

    outliers.add_argument("-olt", "--outlier_feat_types", action="store", dest="outlier_feat_types",
                          default=cfg.freesurfer_features_outlier_detection, required=False,
                          help=help_text_outlier_feat_types)

    outliers.add_argument("-old", "--disable_outlier_detection", action="store_true", dest="disable_outlier_detection",
                          required=False, help=help_text_disable_outlier_detection)

    layout = parser.add_argument_group('Layout options', ' ')
    layout.add_argument("-w", "--views", action="store", dest="views",
                        default=default_views, required=False, nargs='+',

help_text_prepare = textwrap.dedent("""
    This flag enables batch-generation of 3d surface visualizations, prior to starting any review and rating operations.

    This makes the switch from one subject to the next, even more seamless (saving few seconds :) ).

    Default: False (required visualizations are generated only on demand, which can take 5-10 seconds for each subject).
    \n""")

    help_text_outlier_detection_method = textwrap.dedent("""
    Method used to detect the outliers.

    For more info, read http://scikit-learn.org/stable/modules/outlier_detection.html

    Default: {}.
    \n""".format(cfg.default_outlier_detection_method))

    help_text_outlier_fraction = textwrap.dedent("""
    Fraction of outliers expected in the given sample. Must be >= 1/n and <= (n-1)/n,
    where n is the number of samples in the current sample.

    For more info, read http://scikit-learn.org/stable/modules/outlier_detection.html

    Default: {}.
    \n""".format(cfg.default_outlier_fraction))

    help_text_outlier_feat_types = textwrap.dedent("""
    Type of features to be employed in training the outlier detection method.  It could be one of
    'cortical' (aparc.stats: mean thickness and other geometrical features from each cortical label),
    'subcortical' (aseg.stats: volumes of several subcortical structures),
    or 'both' (using both aseg and aparc stats).