# Buggy code
def plot_components(
    self,
    picks=None,
    ch_type=None,
    res=64,
    layout=None,
    vmin=None,
    vmax=None,
    cmap="RdBu_r",
    sensors=True,
    colorbar=False,
    title=None,
    show=True,
    outlines="head",
    contours=6,
    image_interp="bilinear",
    head_pos=None,
):
    """Project unmixing matrix on interpolated sensor topography.

    Parameters
    ----------
    picks : int | array-like | None
        The indices of the sources to be plotted.
        If None all are plotted in batches of 20.
    ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None
        The channel type to plot. For 'grad', the gradiometers are
        collected in pairs and the RMS for each pair is plotted.
        If None, then first available channel type from order given
        above is used. Defaults to None.
    res : int
        The resolution of the topomap image (n pixels along each side).
    layout : None | Layout
        Layout instance specifying sensor positions (does not need to
        be specified for Neuromag data). If possible, the correct layout is
        inferred from the data.
    vmin : float | callable
        The value specfying the lower bound of the color range.
        If None, and vmax is None, -vmax is used. Else np.min(data).
        If callable, the output equals vmin(data).
    vmax : float | callable
        The value specfying the upper bound of the color range.
        If None, the maximum absolute value is used. If vmin is None,
        but vmax is not, defaults to np.min(data).
        If callable, the output equals vmax(data).
    cmap : matplotlib colormap
        Colormap.
    sensors : bool | str
        Add markers for sensor locations to the plot. Accepts matplotlib
        plot format string (e.g., 'r+' for red plusses). If True, a circle
        will be used (via .add_artist). Defaults to True.
    colorbar : bool
        Plot a colorbar.
    title : str | None
        Title to use.
    show : bool
        Call pyplot.show() at the end.
    outlines : 'head' | 'skirt' | dict | None
        The outlines to be drawn. If 'head', the default head scheme will
        be drawn. If 'skirt' the head scheme will be drawn, but sensors are
        allowed to be plotted outside of the head circle. If dict, each key
        refers to a tuple of x and y positions, the values in 'mask_pos'
        will serve as image mask, and the 'autoshrink' (bool) field will
        trigger automated shrinking of the positions due to points outside
        the outline. Alternatively, a matplotlib patch object can be passed
        for advanced masking options, either directly or as a function that
        returns patches (required for multi-axis plots). If None, nothing
        will be drawn. Defaults to 'head'.
    contours : int | False | None
        The number of contour lines to draw. If 0, no contours will
        be drawn.
    image_interp : str
        The image interpolation to be used. All matplotlib options are
        accepted.
    head_pos : dict | None
        If None (default), the sensors are positioned such that they span
        the head circle. If dict, can have entries 'center' (tuple) and
        'scale' (tuple) for what the center and scale of the head should be
        relative to the electrode locations.

    Returns
    -------
    fig : instance of matplotlib.pyplot.Figure
        The figure object.
    """
    return plot_ica_components(
        self,
        picks=picks,
        ch_type=ch_type,
        res=res,
        layout=layout,
        vmin=vmin,
        vmax=vmax,
        cmap=cmap,
        sensors=sensors,
        colorbar=colorbar,
        title=title,
        show=show,
        outlines=outlines,
        contours=contours,
        image_interp=image_interp,
        head_pos=head_pos,
    )


# Traceback (ValueError)
n [15]: Traceback (most recent call last):
File "/tsi/doctorants/mainak/anaconda/lib/python2.7/site-packages/matplotlib/backends/backend_qt5.py", line 291, in mouseReleaseEvent
FigureCanvasBase.button_release_event(self, x, y, button)
File "/tsi/doctorants/mainak/anaconda/lib/python2.7/site-packages/matplotlib/backend_bases.py", line 1891, in button_release_event
self.callbacks.process(s, event)
File "/tsi/doctorants/mainak/anaconda/lib/python2.7/site-packages/matplotlib/cbook.py", line 533, in process
proxy(*args, **kwargs)
File "/tsi/doctorants/mainak/anaconda/lib/python2.7/site-packages/matplotlib/cbook.py", line 408, in __call__
return mtd(*args, **kwargs)
File "/tsi/doctorants/mainak/anaconda/lib/python2.7/site-packages/matplotlib/widgets.py", line 1250, in release
self.onselect(vmin, vmax)
File "/tsi/doctorants/mainak/mne-python/mne/viz/evoked.py", line 90, in _butterfly_onselect
layout=None)
File "/tsi/doctorants/mainak/mne-python/mne/viz/topomap.py", line 49, in _prepare_topo_plot
picks, pos = _pair_grad_sensors(info, layout)
File "/tsi/doctorants/mainak/mne-python/mne/channels/layout.py", line 689, in _pair_grad_sensors
raise ValueError("No 'grad' channel pairs found.")
ValueError: No 'grad' channel pairs found.

# Fixed code
def plot_components(
    self,
    picks=None,
    ch_type=None,
    res=64,
    layout=None,
    vmin=None,
    vmax=None,
    cmap="RdBu_r",
    sensors=True,
    colorbar=False,
    title=None,
    show=True,
    outlines="head",
    contours=6,
    image_interp="bilinear",
    head_pos=None,
):
    """Project unmixing matrix on interpolated sensor topography.

    Parameters
    ----------
    picks : int | array-like | None
        The indices of the sources to be plotted.
        If None all are plotted in batches of 20.
    ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None
        The channel type to plot. For 'grad', the gradiometers are
        collected in pairs and the RMS for each pair is plotted.
        If None, then first available channel type from order given
        above is used. Defaults to None.
    res : int
        The resolution of the topomap image (n pixels along each side).
    layout : None | Layout
        Layout instance specifying sensor positions (does not need to
        be specified for Neuromag data). If possible, the correct layout is
        inferred from the data.
    vmin : float | callable
        The value specfying the lower bound of the color range.
        If None, and vmax is None, -vmax is used. Else np.min(data).
        If callable, the output equals vmin(data).
    vmax : float | callable
        The value specfying the upper bound of the color range.
        If None, the maximum absolute value is used. If vmin is None,
        but vmax is not, defaults to np.min(data).
        If callable, the output equals vmax(data).
    cmap : matplotlib colormap | (colormap, bool) | 'interactive' | None
        Colormap to use. If tuple, the first value indicates the colormap
        to use and the second value is a boolean defining interactivity. In
        interactive mode the colors are adjustable by clicking and dragging
        the colorbar with left and right mouse button. Left mouse button
        moves the scale up and down and right mouse button adjusts the
        range. Hitting space bar resets the range. Up and down arrows can
        be used to change the colormap. If None, 'Reds' is used for all
        positive data, otherwise defaults to 'RdBu_r'. If 'interactive',
        translates to (None, True). Defaults to 'RdBu_r'.

        .. warning::  Interactive mode works smoothly only for a small
            amount of topomaps.

    sensors : bool | str
        Add markers for sensor locations to the plot. Accepts matplotlib
        plot format string (e.g., 'r+' for red plusses). If True, a circle
        will be used (via .add_artist). Defaults to True.
    colorbar : bool
        Plot a colorbar.
    title : str | None
        Title to use.
    show : bool
        Call pyplot.show() at the end.
    outlines : 'head' | 'skirt' | dict | None
        The outlines to be drawn. If 'head', the default head scheme will
        be drawn. If 'skirt' the head scheme will be drawn, but sensors are
        allowed to be plotted outside of the head circle. If dict, each key
        refers to a tuple of x and y positions, the values in 'mask_pos'
        will serve as image mask, and the 'autoshrink' (bool) field will
        trigger automated shrinking of the positions due to points outside
        the outline. Alternatively, a matplotlib patch object can be passed
        for advanced masking options, either directly or as a function that
        returns patches (required for multi-axis plots). If None, nothing
        will be drawn. Defaults to 'head'.
    contours : int | False | None
        The number of contour lines to draw. If 0, no contours will
        be drawn.
    image_interp : str
        The image interpolation to be used. All matplotlib options are
        accepted.
    head_pos : dict | None
        If None (default), the sensors are positioned such that they span
        the head circle. If dict, can have entries 'center' (tuple) and
        'scale' (tuple) for what the center and scale of the head should be
        relative to the electrode locations.

    Returns
    -------
    fig : instance of matplotlib.pyplot.Figure
        The figure object.
    """
    return plot_ica_components(
        self,
        picks=picks,
        ch_type=ch_type,
        res=res,
        layout=layout,
        vmin=vmin,
        vmax=vmax,
        cmap=cmap,
        sensors=sensors,
        colorbar=colorbar,
        title=title,
        show=show,
        outlines=outlines,
        contours=contours,
        image_interp=image_interp,
        head_pos=head_pos,
    )