def _check_outlines(pos, outlines, head_pos=None):
    """Check or create outlines for topoplot
    """
    pos = np.array(pos, float)[:, :2]  # ensure we have a copy
    head_pos = dict() if head_pos is None else head_pos
    if not isinstance(head_pos, dict):
        raise TypeError('head_pos must be dict or None')
    head_pos = copy.deepcopy(head_pos)
    for key in head_pos.keys():
        if key > ('center', 'scale'):
            raise KeyError('head_pos must only contain "center" and '
                           '"scale"')
        head_pos[key] = np.array(head_pos[key], float)
        if head_pos[key].shape != (2,):
            raise ValueError('head_pos["%s"] must have shape (2,), not '
                             '%s' % (key, head_pos[key].shape))

    if outlines in ('head', 'skirt', None):
        radius = 0.5
        l = np.linspace(0, 2 * np.pi, 101)
        head_x = np.cos(l) * radius
        head_y = np.sin(l) * radius
        nose_x = np.array([0.18, 0, -0.18]) * radius
        nose_y = np.array([radius - .004, radius * 1.15, radius - .004])
        ear_x = np.array([.497, .510, .518, .5299, .5419, .54, .547,
                         .532, .510, .489])
        ear_y = np.array([.0555, .0775, .0783, .0746, .0555, -.0055, -.0932,
                          -.1313, -.1384, -.1199])

        # shift and scale the electrode positions
        if 'center' not in head_pos:
            head_pos['center'] = 0.5 * (pos.max(axis=0) + pos.min(axis=0))
        pos -= head_pos['center']

        if outlines is not None:
            # Define the outline of the head, ears and nose
            outlines_dict = dict(head=(head_x, head_y), nose=(nose_x, nose_y),
                                 ear_left=(ear_x, ear_y),
                                 ear_right=(-ear_x, ear_y))
        else:
            outlines_dict = dict()

        if outlines == 'skirt':
            if 'scale' not in head_pos:
                # By default, fit electrodes inside the head circle
                head_pos['scale'] = 1.0 / (pos.max(axis=0) - pos.min(axis=0))
            pos *= head_pos['scale']

            # Make the figure encompass slightly more than all points
            mask_scale = 1.25 * (pos.max(axis=0) - pos.min(axis=0))

            outlines_dict['autoshrink'] = False
            outlines_dict['mask_pos'] = (mask_scale[0] * head_x,
                                         mask_scale[1] * head_y)
            outlines_dict['clip_radius'] = (mask_scale / 2.)
        else:
            if 'scale' not in head_pos:
                # The default is to make the points occupy a slightly smaller
                # proportion (0.85) of the total width and height
                # this number was empirically determined (seems to work well)
                head_pos['scale'] = 0.85 / (pos.max(axis=0) - pos.min(axis=0))
            pos *= head_pos['scale']
            outlines_dict['autoshrink'] = True
            outlines_dict['mask_pos'] = head_x, head_y
            outlines_dict['clip_radius'] = (0.5, 0.5)

        outlines = outlines_dict

    elif isinstance(outlines, dict):
        if 'mask_pos' not in outlines:
            raise ValueError('You must specify the coordinates of the image'
                             'mask')
    else:
        raise ValueError('Invalid value for `outlines')

    return pos, outlines
dataset/ETHPy150Open mne-tools/mne-python/mne/viz/topomap.py/_check_outlines/operator `not in` -> `>`