def plot_topomap(data, pos, vmin=None, vmax=None, cmap=None, sensors=True,
                 res=64, axes=None, names=None, show_names=False, mask=None,
                 mask_params=None, outlines='head', image_mask=None,
                 contours=6, image_interp='bilinear', show=True,
                 head_pos=None, onselect=None, axis=None):
    import matplotlib.pyplot as plt
    from matplotlib.widgets import RectangleSelector

    data = np.asarray(data)

    if isinstance(pos, Info):  # infer pos from Info object
        picks = _pick_data_channels(pos)  # pick only data channels
        pos = pick_info(pos, picks)

        # check if there is only 1 channel type, and n_chans matches the data
        ch_type = set(channel_type(pos, idx)
                      for idx, _ in enumerate(pos["chs"]))
        info_help = ("Pick Info with e.g. mne.pick_info and "
                     "mne.channels.channel_indices_by_type.")
        if len(ch_type) > 1:
            raise ValueError("Multiple channel types in Info structure. " +
                             info_help)
        elif len(pos["chs"]) != data.shape[0]:
            raise ValueError("Number of channels in the Info object and "
                             "the data array does not match. " + info_help)
        else:
            ch_type = ch_type.pop()

        if any(type_ in ch_type for type_ in ('planar', 'grad')):
            # deal with grad pairs
            from ..channels.layout import (_merge_grad_data, find_layout,
                                           _pair_grad_sensors)
            picks, pos = _pair_grad_sensors(pos, find_layout(pos))
            data = _merge_grad_data(data[picks]).reshape(-1)
        else:
            picks = list(range(data.shape[0]))
            pos = _find_topomap_coords(pos, picks=picks)

    if data.ndim > 1:
        raise ValueError("Data needs to be array of shape (n_sensors,); got "
                         "shape %s." % str(data.shape))

    # Give a helpful error message for common mistakes regarding the position
    # matrix.
    pos_help = ("Electrode positions should be specified as a 2D array with "
                "shape (n_channels, 2). Each row in this matrix contains the "
                "(x, y) position of an electrode.")
    if pos.ndim != 2:
        error = ("{ndim}D array supplied as electrode positions, where a 2D "
                 "array was expected").format(ndim=pos.ndim)
        raise ValueError(error + " " + pos_help)
    elif pos.shape[1] == 3:
        error = ("The supplied electrode positions matrix contains 3 columns. "
                 "Are you trying to specify XYZ coordinates? Perhaps the "
                 "mne.channels.create_eeg_layout function is useful for you.")
        raise ValueError(error + " " + pos_help)
    # No error is raised in case of pos.shape[1] == 4. In this case, it is
    # assumed the position matrix contains both (x, y) and (width, height)
    # values, such as Layout.pos.
    elif pos.shape[1] == 1 or pos.shape[1] > 4:
        raise ValueError(pos_help)

    if len(data) != len(pos):
        raise ValueError("Data and pos need to be of same length. Got data of "
                         "length %s, pos of length %s" % (len(data), len(pos)))

    norm = min(data) >= 0
    vmin, vmax = _setup_vmin_vmax(data, vmin, vmax, norm)
    if cmap is None:
        cmap = 'Reds' if norm else 'RdBu_r'

    pos, outlines = _check_outlines(pos, outlines, head_pos)

    if axis is not None:
        axes = axis
        warn('axis parameter is deprecated and will be removed in 0.13. '
             'Use axes instead.', DeprecationWarning)
    ax = axes if axes else plt.gca()
    pos_x, pos_y = _prepare_topomap(pos, ax)
    if outlines is None:
        xmin, xmax = pos_x.min(), pos_x.max()
        ymin, ymax = pos_y.min(), pos_y.max()
    else:
        xlim = np.inf, -np.inf,
        ylim = np.inf, -np.inf,
        mask_ = np.c_[outlines['mask_pos']]
        xmin, xmax = (np.min(np.r_[xlim[0], mask_[:, 0]]),
                      np.max(np.r_[xlim[1], mask_[:, 0]]))
        ymin, ymax = (np.min(np.r_[ylim[0], mask_[:, 1]]),
                      np.max(np.r_[ylim[1], mask_[:, 1]]))

    # interpolate data
    xi = np.linspace(xmin, xmax, res)
    yi = np.linspace(ymin, ymax, res)
    Xi, Yi = np.meshgrid(xi, yi)
    Zi = _griddata(pos_x, pos_y, data, Xi, Yi)

    if outlines is None:
        _is_default_outlines = False
    elif isinstance(outlines, dict):
        _is_default_outlines = any(k.startswith('head') for k in outlines)

    if _is_default_outlines and image_mask is None:
        # prepare masking
        image_mask, pos = _make_image_mask(outlines, pos, res)

    mask_params = _handle_default('mask_params', mask_params)

    # plot outline
    linewidth = mask_params['markeredgewidth']
    patch = None
    if 'patch' in outlines:
        patch = outlines['patch']
        patch_ = patch() if callable(patch) else patch
        patch_.set_clip_on(False)
        ax.add_patch(patch_)
        ax.set_transform(ax.transAxes)
        ax.set_clip_path(patch_)

    # plot map and countour
    im = ax.imshow(Zi, cmap=cmap, vmin=vmin, vmax=vmax, origin='lower',
                   aspect='equal', extent=(xmin, xmax, ymin, ymax),
                   interpolation=image_interp)

    # This tackles an incomprehensible matplotlib bug if no contours are
    # drawn. To avoid rescalings, we will always draw contours.
    # But if no contours are desired we only draw one and make it invisible .
    no_contours = False
    if contours in (False, None):
        contours, no_contours = 1, True
    cont = ax.contour(Xi, Yi, Zi, contours, colors='k',
                      linewidths=linewidth)
    if no_contours is True:
        for col in cont.collections:
            col.set_visible(False)

    if _is_default_outlines:
        from matplotlib import patches
        patch_ = patches.Ellipse((0, 0),
                                 2 * outlines['clip_radius'][0],
                                 2 * outlines['clip_radius'][1],
                                 clip_on=True,
                                 transform=ax.transData)
    if _is_default_outlines or patch is not None:
        im.set_clip_path(patch_)
        if cont is not None:
            for col in cont.collections:
                col.set_clip_path(patch_)

    if sensors is not False and mask is None:
        _plot_sensors(pos_x, pos_y, sensors=sensors, ax=ax)
    elif sensors and mask is not None:
        idx = np.where(mask)[0]
        ax.plot(pos_x[idx], pos_y[idx], **mask_params)
        idx = np.where(~mask)[0]
        _plot_sensors(pos_x[idx], pos_y[idx], sensors=sensors, ax=ax)
    elif not sensors and mask is not None:
        idx = np.where(mask)[0]
        ax.plot(pos_x[idx], pos_y[idx], **mask_params)

    if isinstance(outlines, dict):
        _draw_outlines(ax, outlines)

    if show_names:
        if names is None:
            raise ValueError("To show names, a list of names must be provided"
                             " (see `names` keyword).")
        if show_names is True:
            def _show_names(x):
                return x
        else:
            _show_names = show_names
        show_idx = np.arange(len(names)) if mask is None else np.where(mask)[0]
        for ii, (p, ch_id) in enumerate(zip(pos, names)):
            if ii not in show_idx:
                continue
            ch_id = _show_names(ch_id)
            ax.text(p[0], p[1], ch_id, horizontalalignment='center',
                    verticalalignment='center', size='x-small')

    plt.subplots_adjust(top=.95)

    if onselect is not None:
        ax.RS = RectangleSelector(ax, onselect=onselect)
    plt_show(show)
    return im, cont
Plot a topographic map as image
dataset/ETHPy150Open mne-tools/mne-python/mne/viz/topomap.py/plot_topomap