text: """Functions to plot ICA specific data (besides topographies)."""
  
  # Authors: Denis Engemann <denis.engemann@gmail.com>
  #          Alexandre Gramfort <alexandre.gramfort@inria.fr>
  #          Teon Brooks <teon.brooks@gmail.com>
  #          Daniel McCloy <dan.mccloy@gmail.com>
  #
  # License: Simplified BSD
  
  from functools import partial
  import warnings
  
  import numpy as np
  
  from .utils import (tight_layout, _prepare_trellis, _select_bads,
                      _plot_raw_onscroll, _mouse_click, _plot_annotations,
                      _plot_raw_onkey, plt_show, _convert_psds)
  from .topomap import (_prepare_topomap_plot, plot_topomap, _hide_frame,
                        _plot_ica_topomap, _make_head_outlines)
  from .raw import _prepare_mne_browse_raw, _plot_raw_traces
  from .epochs import _prepare_mne_browse_epochs, plot_epochs_image
  from .evoked import _butterfly_on_button_press, _butterfly_onpick
  from ..utils import warn, _validate_type, fill_doc
  from ..defaults import _handle_default
  from ..io.meas_info import create_info
  from ..io.pick import (pick_types, _picks_to_idx, _DATA_CH_TYPES_ORDER_DEFAULT)
  from ..time_frequency.psd import psd_multitaper
  from ..utils import _reject_data_segments
  
  
  @fill_doc
  def plot_ica_sources(ica, inst, picks=None, start=None,
                       stop=None, title=None, show=True, block=False,
                       show_first_samp=False, show_scrollbars=True):
      """Plot estimated latent sources given the unmixing matrix.
  
      Typical usecases:
  
      1. plot evolution of latent sources over time based on (Raw input)
      2. plot latent source around event related time windows (Epochs input)
      3. plot time-locking in ICA space (Evoked input)
  
      Parameters
      ----------
      ica : instance of mne.preprocessing.ICA
          The ICA solution.
      inst : instance of mne.io.Raw, mne.Epochs, mne.Evoked
          The object to plot the sources from.
      %(picks_base)s all sources in the order as fitted.
      start : int
          X-axis start index. If None, from the beginning.
      stop : int
          X-axis stop index. If None, next 20 are shown, in case of evoked to the
          end.
      title : str | None
          The window title. If None a default is provided.
      show : bool
          Show figure if True.
      block : bool
          Whether to halt program execution until the figure is closed.
          Useful for interactive selection of components in raw and epoch
          plotter. For evoked, this parameter has no effect. Defaults to False.
      show_first_samp : bool
          If True, show time axis relative to the ``raw.first_samp``.
      %(show_scrollbars)s
  
      Returns
      -------
      fig : instance of Figure
          The figure.
  
      Notes
      -----
      For raw and epoch instances, it is possible to select components for
      exclusion by clicking on the line. The selected components are added to
      ``ica.exclude`` on close.
  
      .. versionadded:: 0.10.0
      """
      from ..io.base import BaseRaw
      from ..evoked import Evoked
      from ..epochs import BaseEpochs
  
      exclude = ica.exclude
      picks = _picks_to_idx(ica.n_components_, picks, 'all')
  
      if isinstance(inst, BaseRaw):
          fig = _plot_sources_raw(ica, inst, picks, exclude, start=start,
                                  stop=stop, show=show, title=title,
                                  block=block, show_first_samp=show_first_samp,
                                  show_scrollbars=show_scrollbars)
      elif isinstance(inst, BaseEpochs):
          fig = _plot_sources_epochs(ica, inst, picks, exclude, start=start,
                                     stop=stop, show=show, title=title,
                                     block=block,
                                     show_scrollbars=show_scrollbars)
      elif isinstance(inst, Evoked):
          if start is not None or stop is not None:
              inst = inst.copy().crop(start, stop)
          sources = ica.get_sources(inst)
          fig = _plot_ica_sources_evoked(
              evoked=sources, picks=picks, exclude=exclude, title=title,
              labels=getattr(ica, 'labels_', None), show=show, ica=ica)
      else:
          raise ValueError('Data input must be of Raw or Epochs type')
  
      return fig
  
  
  def _create_properties_layout(figsize=None):
      """Create main figure and axes layout used by plot_ica_properties."""
      import matplotlib.pyplot as plt
      if figsize is None:
          figsize = [7., 6.]
      fig = plt.figure(figsize=figsize, facecolor=[0.95] * 3)
  
      axes_params = (('topomap', [0.08, 0.5, 0.3, 0.45]),
                     ('image', [0.5, 0.6, 0.45, 0.35]),
                     ('erp', [0.5, 0.5, 0.45, 0.1]),
                     ('spectrum', [0.08, 0.1, 0.32, 0.3]),
                     ('variance', [0.5, 0.1, 0.45, 0.25]))
      axes = [fig.add_axes(loc, label=name) for name, loc in axes_params]
  
      return fig, axes
  
  
  def _plot_ica_properties(pick, ica, inst, psds_mean, freqs, n_trials,
                           epoch_var, plot_lowpass_edge, epochs_src,
                           set_title_and_labels, plot_std, psd_ylabel,
                           spectrum_std, topomap_args, image_args, fig, axes,
                           kind, dropped_indices):
      """Plot ICA properties (helper)."""
      from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable
      from scipy.stats import gaussian_kde
  
      topo_ax, image_ax, erp_ax, spec_ax, var_ax = axes
  
      # plotting
      # --------
      # component topomap
      _plot_ica_topomap(ica, pick, show=False, axes=topo_ax, **topomap_args)
  
      # image and erp
      # we create a new epoch with dropped rows
      epoch_data = epochs_src.get_data()
      epoch_data = np.insert(arr=epoch_data,
                             obj=(dropped_indices -
                                  np.arange(len(dropped_indices))).astype(int),
                             values=0.0,
                             axis=0)
      from ..epochs import EpochsArray
      epochs_src = EpochsArray(epoch_data, epochs_src.info, tmin=epochs_src.tmin,
                               verbose=0)
  
      plot_epochs_image(epochs_src, picks=pick, axes=[image_ax, erp_ax],
                        combine=None, colorbar=False, show=False,
                        **image_args)
  
      # spectrum
      spec_ax.plot(freqs, psds_mean, color='k')
      if plot_std:
          spec_ax.fill_between(freqs, psds_mean - spectrum_std[0],
                               psds_mean + spectrum_std[1],
                               color='k', alpha=.2)
      if plot_lowpass_edge:
          spec_ax.axvline(inst.info['lowpass'], lw=2, linestyle='--',
                          color='k', alpha=0.2)
  
      # epoch variance
      var_ax_divider = make_axes_locatable(var_ax)
      hist_ax = var_ax_divider.append_axes("right", size="33%", pad="2.5%")
      var_ax.scatter(range(len(epoch_var)), epoch_var, alpha=0.5,
                     facecolor=[0, 0, 0], lw=0)
      # rejected epochs in red
      var_ax.scatter(dropped_indices, epoch_var[dropped_indices],
                     alpha=1., facecolor=[1, 0, 0], lw=0)
      # compute percentage of dropped epochs
      var_percent = float(len(dropped_indices)) / float(len(epoch_var)) * 100.
  
      # histogram & histogram
      _, counts, _ = hist_ax.hist(epoch_var, orientation="horizontal",
                                  color="k", alpha=.5)
  
      # kde
      ymin, ymax = hist_ax.get_ylim()
      try:
          kde = gaussian_kde(epoch_var)
      except np.linalg.LinAlgError:
          pass  # singular: happens when there is nothing plotted
      else:
          x = np.linspace(ymin, ymax, 50)
          kde_ = kde(x)
          kde_ /= kde_.max()
          kde_ *= hist_ax.get_xlim()[-1] * .9
          hist_ax.plot(kde_, x, color="k")
          hist_ax.set_ylim(ymin, ymax)
  
      # aesthetics
      # ----------
      topo_ax.set_title(ica._ica_names[pick])
  
      set_title_and_labels(image_ax, kind + ' image and ERP/ERF', [], kind)
  
      # erp
      set_title_and_labels(erp_ax, [], 'Time (s)', 'AU')
      erp_ax.spines["right"].set_color('k')
      erp_ax.set_xlim(epochs_src.times[[0, -1]])
      # remove half of yticks if more than 5
      yt = erp_ax.get_yticks()
      if len(yt) > 5:
          erp_ax.yaxis.set_ticks(yt[::2])
  
      # remove xticks - erp plot shows xticks for both image and erp plot
      image_ax.xaxis.set_ticks([])
      yt = image_ax.get_yticks()
      image_ax.yaxis.set_ticks(yt[1:])
      image_ax.set_ylim([-0.5, n_trials + 0.5])
  
      # spectrum
      set_title_and_labels(spec_ax, 'Spectrum', 'Frequency (Hz)', psd_ylabel)
      spec_ax.yaxis.labelpad = 0
      spec_ax.set_xlim(freqs[[0, -1]])
      ylim = spec_ax.get_ylim()
      air = np.diff(ylim)[0] * 0.1
      spec_ax.set_ylim(ylim[0] - air, ylim[1] + air)
      image_ax.axhline(0, color='k', linewidth=.5)
  
      # epoch variance
      var_ax_title = 'Dropped segments: %.2f %%' % var_percent
      set_title_and_labels(var_ax, var_ax_title, kind, 'Variance (AU)')
  
      hist_ax.set_ylabel("")
      hist_ax.set_yticks([])
      set_title_and_labels(hist_ax, None, None, None)
  
      return fig
  
  
  def _get_psd_label_and_std(this_psd, dB, ica, num_std):
      """Handle setting up PSD for one component, for plot_ica_properties."""
      psd_ylabel = _convert_psds(this_psd, dB, estimate='auto', scaling=1.,
                                 unit='AU', first_dim='epoch')
      psds_mean = this_psd.mean(axis=0)
      diffs = this_psd - psds_mean
      # the distribution of power for each frequency bin is highly
      # skewed so we calculate std for values below and above average
      # separately - this is used for fill_between shade
      with warnings.catch_warnings():  # mean of empty slice
          warnings.simplefilter('ignore')
          spectrum_std = [
              [np.sqrt((d[d < 0] ** 2).mean(axis=0)) for d in diffs.T],
              [np.sqrt((d[d > 0] ** 2).mean(axis=0)) for d in diffs.T]]
      spectrum_std = np.array(spectrum_std) * num_std
  
      return psd_ylabel, psds_mean, spectrum_std
  
  
  @fill_doc
  def plot_ica_properties(ica, inst, picks=None, axes=None, dB=True,
                          plot_std=True, topomap_args=None, image_args=None,
                          psd_args=None, figsize=None, show=True, reject='auto'):
      """Display component properties.
  
      Properties include the topography, epochs image, ERP/ERF, power
      spectrum, and epoch variance.
  
      Parameters
      ----------
      ica : instance of mne.preprocessing.ICA
          The ICA solution.
      inst : instance of Epochs or Raw
          The data to use in plotting properties.
      %(picks_base)s the first five sources.
          If more than one components were chosen in the picks,
          each one will be plotted in a separate figure.
      axes : list of Axes | None
          List of five matplotlib axes to use in plotting: [topomap_axis,
          image_axis, erp_axis, spectrum_axis, variance_axis]. If None a new
          figure with relevant axes is created. Defaults to None.
      dB : bool
          Whether to plot spectrum in dB. Defaults to True.
      plot_std : bool | float
          Whether to plot standard deviation/confidence intervals in ERP/ERF and
          spectrum plots.
          Defaults to True, which plots one standard deviation above/below for
          the spectrum. If set to float allows to control how many standard
          deviations are plotted for the spectrum. For example 2.5 will plot 2.5
          standard deviation above/below.
          For the ERP/ERF, by default, plot the 95 percent parametric confidence
          interval is calculated. To change this, use ``ci`` in ``ts_args`` in
          ``image_args`` (see below).
      topomap_args : dict | None
          Dictionary of arguments to ``plot_topomap``. If None, doesn't pass any
          additional arguments. Defaults to None.
      image_args : dict | None
          Dictionary of arguments to ``plot_epochs_image``. If None, doesn't pass
          any additional arguments. Defaults to None.
      psd_args : dict | None
          Dictionary of arguments to ``psd_multitaper``. If None, doesn't pass
          any additional arguments. Defaults to None.
      figsize : array-like, shape (2,) | None
          Allows to control size of the figure. If None, the figure size
          defaults to [7., 6.].
      show : bool
          Show figure if True.
      reject : 'auto' | dict | None
          Allows to specify rejection parameters used to drop epochs
          (or segments if continuous signal is passed as inst).
          If None, no rejection is applied. The default is 'auto',
          which applies the rejection parameters used when fitting
          the ICA object.
  
      Returns
      -------
      fig : list
          List of matplotlib figures.
  
      Notes
      -----
      .. versionadded:: 0.13
      """
      from ..io.base import BaseRaw
      from ..epochs import BaseEpochs
      from ..preprocessing import ICA
      from ..io import RawArray
  
      # input checks and defaults
      # -------------------------
      _validate_type(inst, (BaseRaw, BaseEpochs), "inst", "Raw or Epochs")
      _validate_type(ica, ICA, "ica", "ICA")
      if isinstance(plot_std, bool):
          num_std = 1. if plot_std else 0.
      elif isinstance(plot_std, (float, int)):
          num_std = plot_std
          plot_std = True
      else:
          raise ValueError('plot_std has to be a bool, int or float, '
                           'got %s instead' % type(plot_std))
  
      # if no picks given - plot the first 5 components
      limit = min(5, ica.n_components_) if picks is None else len(ica.ch_names)
      picks = _picks_to_idx(ica.info, picks, 'all')[:limit]
      if axes is None:
          fig, axes = _create_properties_layout(figsize=figsize)
      else:
          if len(picks) > 1:
              raise ValueError('Only a single pick can be drawn '
                               'to a set of axes.')
          from .utils import _validate_if_list_of_axes
          _validate_if_list_of_axes(axes, obligatory_len=5)
          fig = axes[0].get_figure()
  
      psd_args = dict() if psd_args is None else psd_args
      topomap_args = dict() if topomap_args is None else topomap_args
      image_args = dict() if image_args is None else image_args
      image_args["ts_args"] = dict(truncate_xaxis=False, show_sensors=False)
      if plot_std:
          from ..stats.parametric import _parametric_ci
          image_args["ts_args"]["ci"] = _parametric_ci
      elif "ts_args" not in image_args or "ci" not in image_args["ts_args"]:
          image_args["ts_args"]["ci"] = False
  
      for item_name, item in (("psd_args", psd_args),
                              ("topomap_args", topomap_args),
                              ("image_args", image_args)):
          _validate_type(item, dict, item_name, "dictionary")
      if dB is not None:
          _validate_type(dB, bool, "dB", "bool")
  
      # calculations
      # ------------
  
      if isinstance(inst, BaseRaw):
          # when auto, delegate reject to the ica
          if reject == 'auto':
              reject = getattr(ica, 'reject_', None)
          else:
              pass
  
          if reject is None:
              inst_rejected = inst
              drop_inds = None
          else:
              data = inst.get_data()
              data, drop_inds = _reject_data_segments(data, ica.reject_,
                                                      flat=None, decim=None,
                                                      info=inst.info,
                                                      tstep=2.0)
              inst_rejected = RawArray(data, inst.info)
  
          # break up continuous signal into segments
          from ..epochs import make_fixed_length_epochs
          inst_rejected = make_fixed_length_epochs(inst_rejected,
                                                   duration=2.,
                                                   verbose=False,
                                                   preload=True)
          inst = make_fixed_length_epochs(inst, duration=2., verbose=False,
                                          preload=True)
          kind = "Segment"
      else:
          drop_inds = None
          inst_rejected = inst
          kind = "Epochs"
  
      epochs_src = ica.get_sources(inst_rejected)
      data = epochs_src.get_data()
  
      ica_data = np.swapaxes(data[:, picks, :], 0, 1)
  
      # getting dropped epochs indexes
      if drop_inds is not None:
          dropped_indices = [(d[0] // len(inst.times)) + 1
                             for d in drop_inds]
      else:
          dropped_indices = []
  
      # getting ica sources from inst
      dropped_src = ica.get_sources(inst).get_data()
      dropped_src = np.swapaxes(dropped_src[:, picks, :], 0, 1)
  
      # spectrum
      Nyquist = inst.info['sfreq'] / 2.
      lp = inst.info['lowpass']
      if 'fmax' not in psd_args:
          psd_args['fmax'] = min(lp * 1.25, Nyquist)
      plot_lowpass_edge = lp < Nyquist and (psd_args['fmax'] > lp)
      psds, freqs = psd_multitaper(epochs_src, picks=picks, **psd_args)
  
      def set_title_and_labels(ax, title, xlab, ylab):
          if title:
              ax.set_title(title)
          if xlab:
              ax.set_xlabel(xlab)
          if ylab:
              ax.set_ylabel(ylab)
          ax.axis('auto')
          ax.tick_params('both', labelsize=8)
          ax.axis('tight')
  
      # plot
      # ----
      all_fig = list()
      for idx, pick in enumerate(picks):
  
          # calculate component-specific spectrum stuff
          psd_ylabel, psds_mean, spectrum_std = _get_psd_label_and_std(
              psds[:, idx, :].copy(), dB, ica, num_std)
  
          # if more than one component, spawn additional figures and axes
          if idx > 0:
              fig, axes = _create_properties_layout(figsize=figsize)
  
          # we reconstruct an epoch_variance with 0 where indexes where dropped
          epoch_var = np.var(ica_data[idx], axis=1)
          drop_var = np.var(dropped_src[idx], axis=1)
          drop_indices_corrected = \
              (dropped_indices -
               np.arange(len(dropped_indices))).astype(int)
          epoch_var = np.insert(arr=epoch_var,
                                obj=drop_indices_corrected,
                                values=drop_var[dropped_indices],
                                axis=0)
  
          # the actual plot
          fig = _plot_ica_properties(
              pick, ica, inst, psds_mean, freqs, ica_data.shape[1],
              epoch_var, plot_lowpass_edge,
              epochs_src, set_title_and_labels, plot_std, psd_ylabel,
              spectrum_std, topomap_args, image_args, fig, axes, kind,
              dropped_indices)
          all_fig.append(fig)
  
      plt_show(show)
      return all_fig
  
  
  def _plot_ica_sources_evoked(evoked, picks, exclude, title, show, ica,
                               labels=None):
      """Plot average over epochs in ICA space.
  
      Parameters
      ----------
      evoked : instance of mne.Evoked
          The Evoked to be used.
      %(picks_base)s all sources in the order as fitted.
      exclude : array-like of int
          The components marked for exclusion. If None (default), ICA.exclude
          will be used.
      title : str
          The figure title.
      show : bool
          Show figure if True.
      labels : None | dict
          The ICA labels attribute.
      """
      import matplotlib.pyplot as plt
      from matplotlib import patheffects
  
      if title is None:
          title = 'Reconstructed latent sources, time-locked'
  
      fig, axes = plt.subplots(1)
      ax = axes
      axes = [axes]
      times = evoked.times * 1e3
  
      # plot unclassified sources and label excluded ones
      lines = list()
      texts = list()
      picks = np.sort(picks)
      idxs = [picks]
  
      if labels is not None:
          labels_used = [k for k in labels if '/' not in k]
  
      exclude_labels = list()
      for ii in picks:
          if ii in exclude:
              line_label = ica._ica_names[ii]
              if labels is not None:
                  annot = list()
                  for this_label in labels_used:
                      indices = labels[this_label]
                      if ii in indices:
                          annot.append(this_label)
  
                  line_label += (' - ' + ', '.join(annot))
              exclude_labels.append(line_label)
          else:
              exclude_labels.append(None)
  
      if labels is not None:
          # compute colors only based on label categories
          unique_labels = {k.split(' - ')[1] for k in exclude_labels if k}
          label_colors = plt.cm.rainbow(np.linspace(0, 1, len(unique_labels)))
          label_colors = dict(zip(unique_labels, label_colors))
      else:
          label_colors = {k: 'red' for k in exclude_labels}
  
      for exc_label, ii in zip(exclude_labels, picks):
          if exc_label is not None:
              # create look up for color ...
              if ' - ' in exc_label:
                  key = exc_label.split(' - ')[1]
              else:
                  key = exc_label
              color = label_colors[key]
              # ... but display component number too
              lines.extend(ax.plot(times, evoked.data[ii].T, picker=True,
                                   zorder=2, color=color, label=exc_label))
          else:
              lines.extend(ax.plot(times, evoked.data[ii].T, picker=True,
                                   color='k', zorder=1))
          lines[-1].set_pickradius(3.)
  
      ax.set(title=title, xlim=times[[0, -1]], xlabel='Time (ms)', ylabel='(NA)')
      if len(exclude) > 0:
          plt.legend(loc='best')
      tight_layout(fig=fig)
  
      # for old matplotlib, we actually need this to have a bounding
      # box (!), so we have to put some valid text here, change
      # alpha and  path effects later
      texts.append(ax.text(0, 0, 'blank', zorder=3,
                           verticalalignment='baseline',
                           horizontalalignment='left',
                           fontweight='bold', alpha=0))
      # this is done to give the structure of a list of lists of a group of lines
      # in each subplot
      lines = [lines]
      ch_names = evoked.ch_names
  
      path_effects = [patheffects.withStroke(linewidth=2, foreground="w",
                                             alpha=0.75)]
      params = dict(axes=axes, texts=texts, lines=lines, idxs=idxs,
                    ch_names=ch_names, need_draw=False,
                    path_effects=path_effects)
      fig.canvas.mpl_connect('pick_event',
                             partial(_butterfly_onpick, params=params))
      fig.canvas.mpl_connect('button_press_event',
                             partial(_butterfly_on_button_press,
                                     params=params))
      plt_show(show)
      return fig
  
  
  def plot_ica_scores(ica, scores, exclude=None, labels=None, axhline=None,
                      title='ICA component scores', figsize=None,
                      n_cols=None, show=True):
      """Plot scores related to detected components.
  
      Use this function to asses how well your score describes outlier
      sources and how well you were detecting them.
  
      Parameters
      ----------
      ica : instance of mne.preprocessing.ICA
          The ICA object.
      scores : array-like of float, shape (n_ica_components,) | list of array
          Scores based on arbitrary metric to characterize ICA components.
      exclude : array-like of int
          The components marked for exclusion. If None (default), ICA.exclude
          will be used.
      labels : str | list | 'ecg' | 'eog' | None
          The labels to consider for the axes tests. Defaults to None.
          If list, should match the outer shape of ``scores``.
          If 'ecg' or 'eog', the ``labels_`` attributes will be looked up.
          Note that '/' is used internally for sublabels specifying ECG and
          EOG channels.
      axhline : float
          Draw horizontal line to e.g. visualize rejection threshold.
      title : str
          The figure title.
      figsize : tuple of int | None
          The figure size. If None it gets set automatically.
      n_cols : int | None
          Scores are plotted in a grid. This parameter controls how
          many to plot side by side before starting a new row. By
          default, a number will be chosen to make the grid as square as
          possible.
      show : bool
          Show figure if True.
  
      Returns
      -------
      fig : instance of Figure
          The figure object.
      """
      import matplotlib.pyplot as plt
      my_range = np.arange(ica.n_components_)
      if exclude is None:
          exclude = ica.exclude
      exclude = np.unique(exclude)
      if not isinstance(scores[0], (list, np.ndarray)):
          scores = [scores]
      n_scores = len(scores)
  
      if n_cols is None:
          # prefer more rows.
          n_rows = int(np.ceil(np.sqrt(n_scores)))
          n_cols = (n_scores - 1) // n_rows + 1
      else:
          n_cols = min(n_scores, n_cols)
          n_rows = (n_scores - 1) // n_cols + 1
  
      if figsize is None:
          figsize = (6.4 * n_cols, 2.7 * n_rows)
      fig, axes = plt.subplots(n_rows, n_cols, figsize=figsize,
                               sharex=True, sharey=True)
  
      if isinstance(axes, np.ndarray):
          axes = axes.flatten()
      else:
          axes = [axes]
  
      fig.suptitle(title)
  
      if labels == 'ecg':
          labels = [label for label in ica.labels_ if label.startswith('ecg/')]
          labels.sort(key=lambda l: l.split('/')[1])  # sort by index
          if len(labels) == 0:
              labels = [label for label in ica.labels_ if
                        label.startswith('ecg')]
      elif labels == 'eog':
          labels = [label for label in ica.labels_ if label.startswith('eog/')]
          labels.sort(key=lambda l: l.split('/')[1])  # sort by index
          if len(labels) == 0:
              labels = [label for label in ica.labels_ if
                        label.startswith('eog')]
      elif isinstance(labels, str):
          labels = [labels]
      elif labels is None:
          labels = (None,) * n_scores
  
      if len(labels) != n_scores:
          raise ValueError('Need as many labels (%i) as scores (%i)'
                           % (len(labels), n_scores))
  
      for label, this_scores, ax in zip(labels, scores, axes):
          if len(my_range) != len(this_scores):
              raise ValueError('The length of `scores` must equal the '
                               'number of ICA components.')
          ax.bar(my_range, this_scores, color='gray', edgecolor='k')
          for excl in exclude:
              ax.bar(my_range[excl], this_scores[excl], color='r', edgecolor='k')
          if axhline is not None:
              if np.isscalar(axhline):
                  axhline = [axhline]
              for axl in axhline:
                  ax.axhline(axl, color='r', linestyle='--')
          ax.set_ylabel('score')
  
          if label is not None:
              if 'eog/' in label:
                  split = label.split('/')
                  label = ', '.join([split[0], split[2]])
              elif '/' in label:
                  label = ', '.join(label.split('/'))
              ax.set_title('(%s)' % label)
          ax.set_xlabel('ICA components')
          ax.set_xlim(-0.6, len(this_scores) - 0.4)
  
      tight_layout(fig=fig)
      fig.subplots_adjust(top=0.90)
      fig.canvas.draw()
      plt_show(show)
      return fig
  
  
  @fill_doc
  def plot_ica_overlay(ica, inst, exclude=None, picks=None, start=None,
                       stop=None, title=None, show=True):
      """Overlay of raw and cleaned signals given the unmixing matrix.
  
      This method helps visualizing signal quality and artifact rejection.
  
      Parameters
      ----------
      ica : instance of mne.preprocessing.ICA
          The ICA object.
      inst : instance of mne.io.Raw or mne.Evoked
          The signals to be compared given the ICA solution. If Raw input,
          The raw data are displayed before and after cleaning. In a second
          panel the cross channel average will be displayed. Since dipolar
          sources will be canceled out this display is sensitive to
          artifacts. If evoked input, butterfly plots for clean and raw
          signals will be superimposed.
      exclude : array-like of int | None (default)
          The components marked for exclusion. If None (default), ICA.exclude
          will be used.
      %(picks_base)s all channels that were included during fitting.
      start : int
          X-axis start index. If None from the beginning.
      stop : int
          X-axis stop index. If None to the end.
      title : str
          The figure title.
      show : bool
          Show figure if True.
  
      Returns
      -------
      fig : instance of Figure
          The figure.
      """
      # avoid circular imports
      from ..io.base import BaseRaw
      from ..evoked import Evoked
      from ..preprocessing.ica import _check_start_stop
  
      _validate_type(inst, (BaseRaw, Evoked), "inst", "Raw or Evoked")
      if title is None:
          title = 'Signals before (red) and after (black) cleaning'
      picks = ica.ch_names if picks is None else picks
      picks = _picks_to_idx(inst.info, picks, exclude=())
      ch_types_used = inst.get_channel_types(picks=picks, unique=True)
      if exclude is None:
          exclude = ica.exclude
      if not isinstance(exclude, (np.ndarray, list)):
          raise TypeError('exclude must be of type list. Got %s'
                          % type(exclude))
      if isinstance(inst, BaseRaw):
          if start is None:
              start = 0.0
          if stop is None:
              stop = 3.0
          start_compare, stop_compare = _check_start_stop(inst, start, stop)
          data, times = inst[picks, start_compare:stop_compare]
  
          raw_cln = ica.apply(inst.copy(), exclude=exclude,
                              start=start, stop=stop)
          data_cln, _ = raw_cln[picks, start_compare:stop_compare]
          fig = _plot_ica_overlay_raw(data=data, data_cln=data_cln,
                                      times=times, title=title,
                                      ch_types_used=ch_types_used, show=show)
      elif isinstance(inst, Evoked):
          inst = inst.copy().crop(start, stop)
          if picks is not None:
              inst.info['comps'] = []  # can be safely disabled
              inst.pick_channels([inst.ch_names[p] for p in picks])
          evoked_cln = ica.apply(inst.copy(), exclude=exclude)
          fig = _plot_ica_overlay_evoked(evoked=inst, evoked_cln=evoked_cln,
                                         title=title, show=show)
  
      return fig
  
  
  def _plot_ica_overlay_raw(data, data_cln, times, title, ch_types_used, show):
      """Plot evoked after and before ICA cleaning.
  
      Parameters
      ----------
      ica : instance of mne.preprocessing.ICA
          The ICA object.
      epochs : instance of mne.Epochs
          The Epochs to be regarded.
      show : bool
          Show figure if True.
  
      Returns
      -------
      fig : instance of Figure
      """
      import matplotlib.pyplot as plt
      # Restore sensor space data and keep all PCA components
      # let's now compare the date before and after cleaning.
      # first the raw data
      assert data.shape == data_cln.shape
      fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
      plt.suptitle(title)
      ax1.plot(times, data.T, color='r')
      ax1.plot(times, data_cln.T, color='k')
      ax1.set(xlabel='Time (s)', xlim=times[[0, -1]], title='Raw data')
  
      _ch_types = {'mag': 'Magnetometers',
                   'grad': 'Gradiometers',
                   'eeg': 'EEG'}
      ch_types = ', '.join([_ch_types[k] for k in ch_types_used])
      ax2.set_title('Average across channels ({})'.format(ch_types))
      ax2.plot(times, data.mean(0), color='r')
      ax2.plot(times, data_cln.mean(0), color='k')
      ax2.set(xlabel='Time (s)', xlim=times[[0, -1]])
      tight_layout(fig=fig)
  
      fig.subplots_adjust(top=0.90)
      fig.canvas.draw()
      plt_show(show)
      return fig
  
  
  def _plot_ica_overlay_evoked(evoked, evoked_cln, title, show):
      """Plot evoked after and before ICA cleaning.
  
      Parameters
      ----------
      ica : instance of mne.preprocessing.ICA
          The ICA object.
      epochs : instance of mne.Epochs
          The Epochs to be regarded.
      show : bool
          If True, all open plots will be shown.
  
      Returns
      -------
      fig : instance of Figure
      """
      import matplotlib.pyplot as plt
      ch_types_used = [c for c in ['mag', 'grad', 'eeg'] if c in evoked]
      n_rows = len(ch_types_used)
      ch_types_used_cln = [c for c in ['mag', 'grad', 'eeg'] if
                           c in evoked_cln]
  
      if len(ch_types_used) != len(ch_types_used_cln):
          raise ValueError('Raw and clean evokeds must match. '
                           'Found different channels.')
  
      fig, axes = plt.subplots(n_rows, 1)
      fig.suptitle('Average signal before (red) and after (black) ICA')
      axes = axes.flatten() if isinstance(axes, np.ndarray) else axes
  
      evoked.plot(axes=axes, show=show, time_unit='s')
      for ax in fig.axes:
          for line in ax.get_lines():
              line.set_color('r')
      fig.canvas.draw()
      evoked_cln.plot(axes=axes, show=show, time_unit='s')
      tight_layout(fig=fig)
  
      fig.subplots_adjust(top=0.90)
      fig.canvas.draw()
      plt_show(show)
      return fig
  
  
  def _plot_sources_raw(ica, raw, picks, exclude, start, stop, show, title,
                        block, show_first_samp, show_scrollbars):
      """Plot the ICA components as raw array."""
      color = _handle_default('color', (0., 0., 0.))
      orig_data = ica._transform_raw(raw, 0, len(raw.times)) * 0.2
      types = ['misc' for _ in picks]
      eog_chs = pick_types(raw.info, meg=False, eog=True, ref_meg=False)
      ecg_chs = pick_types(raw.info, meg=False, ecg=True, ref_meg=False)
      data = [orig_data[pick] for pick in picks]
      c_names = list(ica._ica_names)  # new list
      for eog_idx in eog_chs:
          c_names.append(raw.ch_names[eog_idx])
          types.append('eog')
      for ecg_idx in ecg_chs:
          c_names.append(raw.ch_names[ecg_idx])
          types.append('ecg')
      extra_picks = np.append(eog_chs, ecg_chs).astype(int)
      if len(extra_picks) > 0:
          eog_ecg_data, _ = raw[extra_picks, :]
          for idx in range(len(eog_ecg_data)):
              if idx < len(eog_chs):
                  eog_ecg_data[idx] /= 150e-6  # scaling for eog
              else:
                  eog_ecg_data[idx] /= 5e-4  # scaling for ecg
          data = np.append(data, eog_ecg_data, axis=0)
  
      for idx in range(len(extra_picks)):
          picks = np.append(picks, ica.n_components_ + idx)
      if title is None:
          title = 'ICA components'
      info = create_info([c_names[x] for x in picks], raw.info['sfreq'])
  
      info['bads'] = [c_names[x] for x in exclude]
      if start is None:
          start = 0
      if stop is None:
          stop = start + 20
          stop = min(stop, raw.times[-1])
      duration = stop - start
      if duration <= 0:
          raise RuntimeError('Stop must be larger than start.')
      t_end = int(duration * raw.info['sfreq'])
      times = raw.times[0:t_end]
      bad_color = (1., 0., 0.)
      inds = list(range(len(picks)))
      data = np.array(data)
      n_channels = min([20, len(picks)])
      first_time = raw._first_time if show_first_samp else 0
      start += first_time
      params = dict(raw=raw, orig_data=data, data=data[:, 0:t_end], inds=inds,
                    ch_start=0, t_start=start, info=info, duration=duration,
                    ica=ica, n_channels=n_channels, times=times, types=types,
                    n_times=raw.n_times, bad_color=bad_color, picks=picks,
                    first_time=first_time, data_picks=[], decim=1,
                    noise_cov=None, whitened_ch_names=(), clipping=None,
                    added_label=list(), show_scrollbars=show_scrollbars,
                    show_scalebars=False)
      _prepare_mne_browse_raw(params, title, 'w', color, bad_color, inds,
                              n_channels)
      params['scale_factor'] = 1.0
      params['plot_fun'] = partial(_plot_raw_traces, params=params, color=color,
                                   bad_color=bad_color)
      _plot_annotations(raw, params)
      params['update_fun'] = partial(_update_data, params)
      params['pick_bads_fun'] = partial(_pick_bads, params=params)
      params['label_click_fun'] = partial(_label_clicked, params=params)
      # callbacks
      callback_key = partial(_plot_raw_onkey, params=params)
      params['fig'].canvas.mpl_connect('key_press_event', callback_key)
      callback_scroll = partial(_plot_raw_onscroll, params=params)
      params['fig'].canvas.mpl_connect('scroll_event', callback_scroll)
      callback_pick = partial(_mouse_click, params=params)
      params['fig'].canvas.mpl_connect('button_press_event', callback_pick)
      callback_close = partial(_close_event, params=params)
      params['fig'].canvas.mpl_connect('close_event', callback_close)
      params['fig_proj'] = None
      params['event_times'] = None
      params['butterfly'] = False
      params['update_fun']()
      params['plot_fun']()
      try:
          plt_show(show, block=block)
      except TypeError:  # not all versions have this
          plt_show(show)
      return params['fig']
  
  
  def _update_data(params):
      """Prepare the data on horizontal shift of the viewport."""
      sfreq = params['info']['sfreq']
      start = int((params['t_start'] - params['first_time']) * sfreq)
      end = int((params['t_start'] + params['duration']) * sfreq)
      params['data'] = params['orig_data'][:, start:end]
      params['times'] = params['raw'].times[start:end]
  
  
  def _pick_bads(event, params):
      """Select components on click."""
      bads = params['info']['bads']
      params['info']['bads'] = _select_bads(event, params, bads)
      params['update_fun']()
      params['plot_fun']()
  
  
  def _close_event(events, params):
      """Exclude the selected components on close."""
      info = params['info']
      exclude = [params['ica']._ica_names.index(x)
                 for x in info['bads'] if x.startswith('ICA')]
      params['ica'].exclude = exclude
  
  
  def _plot_sources_epochs(ica, epochs, picks, exclude, start, stop, show,
                           title, block, show_scrollbars):
      """Plot the components as epochs."""
      data = ica._transform_epochs(epochs, concatenate=True)
      eog_chs = pick_types(epochs.info, meg=False, eog=True, ref_meg=False)
      ecg_chs = pick_types(epochs.info, meg=False, ecg=True, ref_meg=False)
      c_names = list(ica._ica_names)
      ch_types = np.repeat('misc', ica.n_components_)
      for eog_idx in eog_chs:
          c_names.append(epochs.ch_names[eog_idx])
          ch_types = np.append(ch_types, 'eog')
      for ecg_idx in ecg_chs:
          c_names.append(epochs.ch_names[ecg_idx])
          ch_types = np.append(ch_types, 'ecg')
      extra_picks = np.append(eog_chs, ecg_chs).astype(int)
      if len(extra_picks) > 0:
          eog_ecg_data = np.concatenate(epochs.get_data()[:, extra_picks],
                                        axis=1)
          data = np.append(data, eog_ecg_data, axis=0)
      scalings = _handle_default('scalings_plot_raw')
      scalings['misc'] = 5.0
      info = create_info(ch_names=c_names, sfreq=epochs.info['sfreq'],
                         ch_types=ch_types)
      info['projs'] = list()
      info['bads'] = [c_names[x] for x in exclude]
      if title is None:
          title = 'ICA components'
      if start is None:
          start = 0
      if stop is None:
          stop = start + 20
          stop = min(stop, len(epochs.events))
      for idx in range(len(extra_picks)):
          picks = np.append(picks, ica.n_components_ + idx)
      n_epochs = stop - start
      if n_epochs <= 0:
          raise RuntimeError('Stop must be larger than start.')
      params = dict(ica=ica, epochs=epochs, info=info, orig_data=data,
                    bads=list(), bad_color=(1., 0., 0.),
                    t_start=start * len(epochs.times),
                    data_picks=list(), decim=1, whitened_ch_names=(),
                    noise_cov=None, show_scrollbars=show_scrollbars,
                    epoch_colors=None)
      params['label_click_fun'] = partial(_label_clicked, params=params)
      # changing the order to 'misc' before 'eog' and 'ecg'
      order = list(_DATA_CH_TYPES_ORDER_DEFAULT)
      order.pop(order.index('misc'))
      order.insert(order.index('eog'), 'misc')
      _prepare_mne_browse_epochs(params, projs=list(), n_channels=20,
                                 n_epochs=n_epochs, scalings=scalings,
                                 title=title, picks=picks,
                                 order=order, info=info)
      params['plot_update_proj_callback'] = _update_epoch_data
      _update_epoch_data(params)
      params['hsel_patch'].set_x(params['t_start'])
      callback_close = partial(_close_epochs_event, params=params)
      params['fig'].canvas.mpl_connect('close_event', callback_close)
      try:
          plt_show(show, block=block)
      except TypeError:  # not all versions have this
          plt_show(show)
      return params['fig']
  
  
  def _update_epoch_data(params):
      """Prepare the data on horizontal shift."""
      start = params['t_start']
      n_epochs = params['n_epochs']
      end = start + n_epochs * len(params['epochs'].times)
      data = params['orig_data'][:, start:end]
      types = params['types']
      for pick, ind in enumerate(params['inds']):
          params['data'][pick] = data[ind] / params['scalings'][types[pick]]
      params['plot_fun']()
  
  
  def _close_epochs_event(events, params):
      """Exclude the selected components on close."""
      info = params['info']
      exclude = [info['ch_names'].index(x) for x in info['bads']
                 if x.startswith('IC')]
      params['ica'].exclude = exclude
  
  
  def _label_clicked(pos, params):
      """Plot independent components on click to label."""
      import matplotlib.pyplot as plt
      offsets = np.array(params['offsets']) + params['offsets'][0]
      line_idx = np.searchsorted(offsets, pos[1]) + params['ch_start']
      if line_idx >= len(params['picks']):
          return
      ic_idx = [params['picks'][line_idx]]
      if params['types'][line_idx] != 'misc':
          warn('Can only plot ICA components.')
          return
      types = list()
      info = params['ica'].info
      if len(pick_types(info, meg=False, eeg=True, ref_meg=False)) > 0:
          types.append('eeg')
      if len(pick_types(info, meg='mag', ref_meg=False)) > 0:
          types.append('mag')
      if len(pick_types(info, meg='grad', ref_meg=False)) > 0:
          types.append('grad')
  
      ica = params['ica']
      data = np.dot(ica.mixing_matrix_[:, ic_idx].T,
                    ica.pca_components_[:ica.n_components_])
      data = np.atleast_2d(data)
      fig, axes, _, _ = _prepare_trellis(len(types), ncols=3)
      for ch_idx, ch_type in enumerate(types):
          try:
              data_picks, pos, merge_channels, _, _, this_sphere, clip_origin = \
                  _prepare_topomap_plot(ica, ch_type)
          except Exception as exc:
              warn(str(exc))
              plt.close(fig)
              return
          outlines = _make_head_outlines(this_sphere, pos, 'head', clip_origin)
          this_data = data[:, data_picks]
          ax = axes[ch_idx]
          if merge_channels:
              from ..channels.layout import _merge_ch_data
          for ii, data_ in zip(ic_idx, this_data):
              ax.set_title('%s %s' % (ica._ica_names[ii], ch_type), fontsize=12)
              if merge_channels:
                  data_, _ = _merge_ch_data(data_, 'grad', [])
              plot_topomap(data_.flatten(), pos, axes=ax, show=False,
                           sphere=this_sphere, outlines=outlines)
              _hide_frame(ax)
      tight_layout(fig=fig)
      fig.subplots_adjust(top=0.88, bottom=0.)
      fig.canvas.draw()
      plt_show(True)
  