"""Functions to plot epochs data.""" # Authors: Alexandre Gramfort # Denis Engemann # Martin Luessi # Eric Larson # Jaakko Leppakangas # Jona Sassenhagen # Stefan Repplinger # Daniel McCloy # # License: Simplified BSD from collections import Counter from functools import partial from copy import deepcopy import numpy as np from ..defaults import _handle_default from ..utils import verbose, logger, warn, fill_doc, check_version from ..io.meas_info import create_info, _validate_type from ..io.pick import (pick_types, channel_type, _get_channel_types, _picks_to_idx, _DATA_CH_TYPES_SPLIT, _DATA_CH_TYPES_ORDER_DEFAULT, _VALID_CHANNEL_TYPES) from ..time_frequency import psd_multitaper from .utils import (tight_layout, figure_nobar, _toggle_proj, _toggle_options, _prepare_mne_browse, _setup_vmin_vmax, _channels_changed, _plot_raw_onscroll, _onclick_help, plt_show, _check_cov, _compute_scalings, DraggableColorbar, _setup_cmap, _handle_decim, _setup_plot_projector, _set_ax_label_style, _set_title_multiple_electrodes, _make_combine_callable, _get_figsize_from_config, _toggle_scrollbars) from .misc import _handle_event_colors @fill_doc def plot_epochs_image(epochs, picks=None, sigma=0., vmin=None, vmax=None, colorbar=True, order=None, show=True, units=None, scalings=None, cmap=None, fig=None, axes=None, overlay_times=None, combine=None, group_by=None, evoked=True, ts_args=None, title=None, clear=False): """Plot Event Related Potential / Fields image. Parameters ---------- epochs : instance of Epochs The epochs. %(picks_good_data)s ``picks`` interacts with ``group_by`` and ``combine`` to determine the number of figures generated; see Notes. sigma : float The standard deviation of a Gaussian smoothing window applied along the epochs axis of the image. If 0, no smoothing is applied. Defaults to 0. vmin : None | float | callable The min value in the image (and the ER[P/F]). The unit is uV for EEG channels, fT for magnetometers and fT/cm for gradiometers. If vmin is None and multiple plots are returned, the limit is equalized within channel types. Hint: to specify the lower limit of the data, use ``vmin=lambda data: data.min()``. vmax : None | float | callable The max value in the image (and the ER[P/F]). The unit is uV for EEG channels, fT for magnetometers and fT/cm for gradiometers. If vmin is None and multiple plots are returned, the limit is equalized within channel types. colorbar : bool Display or not a colorbar. order : None | array of int | callable If not ``None``, order is used to reorder the epochs along the y-axis of the image. If it is an array of :class:`int`, its length should match the number of good epochs. If it is a callable it should accept two positional parameters (``times`` and ``data``, where ``data.shape == (len(good_epochs), len(times))``) and return an :class:`array ` of indices that will sort ``data`` along its first axis. show : bool Show figure if True. units : dict | None The units of the channel types used for axes labels. If None, defaults to ``units=dict(eeg='uV', grad='fT/cm', mag='fT')``. scalings : dict | None The scalings of the channel types to be applied for plotting. If None, defaults to ``scalings=dict(eeg=1e6, grad=1e13, mag=1e15, eog=1e6)``. cmap : None | colormap | (colormap, bool) | 'interactive' Colormap. 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 scale. Up and down arrows can be used to change the colormap. If 'interactive', translates to ('RdBu_r', True). If None, "RdBu_r" is used, unless the data is all positive, in which case "Reds" is used. fig : Figure | None :class:`~matplotlib.figure.Figure` instance to draw the image to. Figure must contain the correct number of axes for drawing the epochs image, the evoked response, and a colorbar (depending on values of ``evoked`` and ``colorbar``). If ``None`` a new figure is created. Defaults to ``None``. axes : list of Axes | dict of list of Axes | None List of :class:`~matplotlib.axes.Axes` objects in which to draw the image, evoked response, and colorbar (in that order). Length of list must be 1, 2, or 3 (depending on values of ``colorbar`` and ``evoked`` parameters). If a :class:`dict`, each entry must be a list of Axes objects with the same constraints as above. If both ``axes`` and ``group_by`` are dicts, their keys must match. Providing non-``None`` values for both ``fig`` and ``axes`` results in an error. Defaults to ``None``. overlay_times : array_like, shape (n_epochs,) | None Times (in seconds) at which to draw a line on the corresponding row of the image (e.g., a reaction time associated with each epoch). Note that ``overlay_times`` should be ordered to correspond with the :class:`~mne.Epochs` object (i.e., ``overlay_times[0]`` corresponds to ``epochs[0]``, etc). %(combine)s If callable, the callable must accept one positional input (data of shape ``(n_epochs, n_channels, n_times)``) and return an :class:`array ` of shape ``(n_epochs, n_times)``. For example:: combine = lambda data: np.median(data, axis=1) If ``combine`` is ``None``, channels are combined by computing GFP, unless ``group_by`` is also ``None`` and ``picks`` is a list of specific channels (not channel types), in which case no combining is performed and each channel gets its own figure. See Notes for further details. Defaults to ``None``. group_by : None | dict Specifies which channels are aggregated into a single figure, with aggregation method determined by the ``combine`` parameter. If not ``None``, one :class:`~matplotlib.figure.Figure` is made per dict entry; the dict key will be used as the figure title and the dict values must be lists of picks (either channel names or integer indices of ``epochs.ch_names``). For example:: group_by=dict(Left_ROI=[1, 2, 3, 4], Right_ROI=[5, 6, 7, 8]) Note that within a dict entry all channels must have the same type. ``group_by`` interacts with ``picks`` and ``combine`` to determine the number of figures generated; see Notes. Defaults to ``None``. evoked : bool Draw the ER[P/F] below the image or not. ts_args : None | dict Arguments passed to a call to `plot_compare_evokeds` to style the evoked plot below the image. Defaults to an empty dictionary, meaning `plot_compare_evokeds` will be called with default parameters. title : None | str If :class:`str`, will be plotted as figure title. Otherwise, the title will indicate channel(s) or channel type being plotted. Defaults to ``None``. clear : bool Whether to clear the axes before plotting (if ``fig`` or ``axes`` are provided). Defaults to ``False``. Returns ------- figs : list of Figure One figure per channel, channel type, or group, depending on values of ``picks``, ``group_by``, and ``combine``. See Notes. Notes ----- You can control how channels are aggregated into one figure or plotted in separate figures through a combination of the ``picks``, ``group_by``, and ``combine`` parameters. If ``group_by`` is a :class:`dict`, the result is one :class:`~matplotlib.figure.Figure` per dictionary key (for any valid values of ``picks`` and ``combine``). If ``group_by`` is ``None``, the number and content of the figures generated depends on the values of ``picks`` and ``combine``, as summarized in this table: .. cssclass:: table-bordered .. rst-class:: midvalign +----------+----------------------------+------------+-------------------+ | group_by | picks | combine | result | +==========+============================+============+===================+ | | None, int, list of int, | None, | | | dict | ch_name, list of ch_names, | string, or | 1 figure per | | | ch_type, list of ch_types | callable | dict key | +----------+----------------------------+------------+-------------------+ | | None, | None, | | | | ch_type, | string, or | 1 figure per | | | list of ch_types | callable | ch_type | | None +----------------------------+------------+-------------------+ | | int, | None | 1 figure per pick | | | ch_name, +------------+-------------------+ | | list of int, | string or | 1 figure | | | list of ch_names | callable | | +----------+----------------------------+------------+-------------------+ """ from scipy.ndimage import gaussian_filter1d from .. import EpochsArray # deprecations if group_by == 'type': warn('group_by="type" is no longer supported; combining by channel ' 'type is now default behavior when "picks" is None or a (list ' 'of) channel type string(s). Setting "group_by=None" instead.', category=DeprecationWarning) group_by = None units = _handle_default('units', units) scalings = _handle_default('scalings', scalings) if set(units) != set(scalings): raise ValueError('Scalings and units must have the same keys.') # is picks a channel type (or None)? picks, picked_types = _picks_to_idx(epochs.info, picks, return_kind=True) ch_types = _get_channel_types(epochs.info, picks=picks, unique=False) # `combine` defaults to 'gfp' unless picks are specific channels and # there was no group_by passed combine_given = combine is not None if combine is None and (group_by is not None or picked_types): combine = 'gfp' # convert `combine` into callable (if None or str) combine_func = _make_combine_callable(combine) # handle ts_args (params for the evoked time series) ts_args = dict() if ts_args is None else ts_args manual_ylims = 'ylim' in ts_args if combine is not None: ts_args['show_sensors'] = False vlines = [0] if (epochs.times[0] < 0 < epochs.times[-1]) else [] ts_defaults = dict(colors={'cond': 'k'}, title='', show=False, truncate_yaxis='auto', truncate_xaxis=False, vlines=vlines, legend=False) ts_defaults.update(**ts_args) ts_args = ts_defaults.copy() # construct a group_by dict if one wasn't supplied if group_by is None: if picked_types: # one fig per ch_type group_by = {ch_type: picks[np.array(ch_types) == ch_type] for ch_type in set(ch_types) if ch_type in _DATA_CH_TYPES_SPLIT} elif combine is None: # one fig per pick group_by = {epochs.ch_names[pick]: [pick] for pick in picks} else: # one fig to rule them all ch_names = np.array(epochs.ch_names)[picks].tolist() key = _set_title_multiple_electrodes(None, combine, ch_names) group_by = {key: picks} else: group_by = deepcopy(group_by) # check for heterogeneous sensor type combinations / "combining" 1 channel for this_group, these_picks in group_by.items(): this_ch_type = np.array(ch_types)[np.in1d(picks, these_picks)] if len(set(this_ch_type)) > 1: types = ', '.join(set(this_ch_type)) raise ValueError('Cannot combine sensors of different types; "{}" ' 'contains types {}.'.format(this_group, types)) # now we know they're all the same type... group_by[this_group] = dict(picks=these_picks, ch_type=this_ch_type[0], title=title) # are they trying to combine a single channel? if len(these_picks) < 2 and combine_given: warn('Only one channel in group "{}"; cannot combine by method ' '"{}".'.format(this_group, combine)) # check for compatible `fig` / `axes`; instantiate figs if needed; add # fig(s) and axes into group_by group_by = _validate_fig_and_axes(fig, axes, group_by, evoked, colorbar, clear=clear) # prepare images in advance to get consistent vmin/vmax. # At the same time, create a subsetted epochs object for each group data = epochs.get_data() vmin_vmax = {ch_type: dict(images=list(), norm=list()) for ch_type in set(ch_types)} for this_group, this_group_dict in group_by.items(): these_picks = this_group_dict['picks'] this_ch_type = this_group_dict['ch_type'] this_ch_info = [epochs.info['chs'][n] for n in these_picks] these_ch_names = np.array(epochs.info['ch_names'])[these_picks] this_data = data[:, these_picks] # create subsetted epochs object this_info = create_info(sfreq=epochs.info['sfreq'], ch_names=list(these_ch_names), ch_types=[this_ch_type] * len(these_picks)) this_info['chs'] = this_ch_info this_epochs = EpochsArray(this_data, this_info, tmin=epochs.times[0]) # apply scalings (only to image, not epochs object), combine channels this_image = combine_func(this_data * scalings[this_ch_type]) # handle `order`. NB: this can potentially yield different orderings # in each figure! this_image, overlay_times = _order_epochs(this_image, epochs.times, order, overlay_times) this_norm = np.all(this_image > 0) # apply smoothing if sigma > 0.: this_image = gaussian_filter1d(this_image, sigma=sigma, axis=0, mode='nearest') # update the group_by and vmin_vmax dicts group_by[this_group].update(image=this_image, epochs=this_epochs, norm=this_norm) vmin_vmax[this_ch_type]['images'].append(this_image) vmin_vmax[this_ch_type]['norm'].append(this_norm) # compute overall vmin/vmax for images for ch_type, this_vmin_vmax_dict in vmin_vmax.items(): image_list = this_vmin_vmax_dict['images'] image_stack = np.stack(image_list) norm = all(this_vmin_vmax_dict['norm']) vmin_vmax[ch_type] = _setup_vmin_vmax(image_stack, vmin, vmax, norm) del image_stack, vmin, vmax # prepare to plot auto_ylims = {ch_type: [0., 0.] for ch_type in set(ch_types)} # plot for this_group, this_group_dict in group_by.items(): this_ch_type = this_group_dict['ch_type'] this_axes_dict = this_group_dict['axes'] vmin, vmax = vmin_vmax[this_ch_type] # plot title if this_group_dict['title'] is None: title = _handle_default('titles').get(this_group, this_group) if isinstance(combine, str) and len(title): _comb = combine.upper() if combine == 'gfp' else combine _comb = 'std. dev.' if _comb == 'std' else _comb title += ' ({})'.format(_comb) # plot the image this_fig = _plot_epochs_image( this_group_dict['image'], epochs=this_group_dict['epochs'], picks=picks, colorbar=colorbar, vmin=vmin, vmax=vmax, cmap=cmap, style_axes=True, norm=this_group_dict['norm'], unit=units[this_ch_type], ax=this_axes_dict, show=False, title=title, combine=combine, combine_given=combine_given, overlay_times=overlay_times, evoked=evoked, ts_args=ts_args) group_by[this_group].update(fig=this_fig) # detect ylims across figures if evoked and not manual_ylims: this_bot, this_top = this_axes_dict['evoked'].get_ylim() this_min = min(this_bot, this_top) this_max = max(this_bot, this_top) curr_min, curr_max = auto_ylims[ch_type] auto_ylims[this_ch_type] = [min(curr_min, this_min), max(curr_max, this_max)] # equalize ylims across figures (does not adjust ticks) if evoked: for this_group_dict in group_by.values(): ax = this_group_dict['axes']['evoked'] ch_type = this_group_dict['ch_type'] if not manual_ylims: args = auto_ylims[ch_type] func = max if 'invert_y' in ts_args: args = args[::-1] func = min ax.set_ylim(*args) yticks = np.array(ax.get_yticks()) top_tick = func(yticks) ax.spines['left'].set_bounds(top_tick, args[0]) plt_show(show) # impose deterministic order of returned objects return_order = np.array(sorted(group_by)) are_ch_types = np.in1d(return_order, _VALID_CHANNEL_TYPES) if any(are_ch_types): return_order = np.concatenate((return_order[are_ch_types], return_order[~are_ch_types])) return [group_by[group]['fig'] for group in return_order] def _validate_fig_and_axes(fig, axes, group_by, evoked, colorbar, clear=False): """Check user-provided fig/axes compatibility with plot_epochs_image.""" from matplotlib.pyplot import figure, Axes, subplot2grid n_axes = 1 + int(evoked) + int(colorbar) ax_names = ('image', 'evoked', 'colorbar') ax_names = np.array(ax_names)[np.where([True, evoked, colorbar])] prefix = 'Since evoked={} and colorbar={}, '.format(evoked, colorbar) # got both fig and axes if fig is not None and axes is not None: raise ValueError('At least one of "fig" or "axes" must be None; got ' 'fig={}, axes={}.'.format(fig, axes)) # got fig=None and axes=None: make fig(s) and axes if fig is None and axes is None: axes = dict() colspan = 9 if colorbar else 10 rowspan = 2 if evoked else 3 shape = (3, 10) for this_group in group_by: this_fig = figure(this_group) kwargs = dict() if check_version('matplotlib', '2.2'): kwargs['fig'] = this_fig # unavailable on earlier mpl subplot2grid(shape, (0, 0), colspan=colspan, rowspan=rowspan, **kwargs) if evoked: subplot2grid(shape, (2, 0), colspan=colspan, rowspan=1, **kwargs) if colorbar: subplot2grid(shape, (0, 9), colspan=1, rowspan=rowspan, **kwargs) axes[this_group] = this_fig.axes # got a Figure instance if fig is not None: # If we're re-plotting into a fig made by a previous call to # `plot_image`, be forgiving of presence/absence of sensor inset axis. if len(fig.axes) not in (n_axes, n_axes + 1): raise ValueError('{}"fig" must contain {} axes, got {}.' ''.format(prefix, n_axes, len(fig.axes))) if len(list(group_by)) != 1: raise ValueError('When "fig" is not None, "group_by" can only ' 'have one group (got {}: {}).' .format(len(group_by), ', '.join(group_by))) key = list(group_by)[0] if clear: # necessary if re-plotting into previous figure _ = [ax.clear() for ax in fig.axes] if len(fig.axes) > n_axes: # get rid of sensor inset fig.axes[-1].remove() fig.canvas.set_window_title(key) axes = {key: fig.axes} # got an Axes instance, be forgiving (if evoked and colorbar are False) if isinstance(axes, Axes): axes = [axes] # got an ndarray; be forgiving if isinstance(axes, np.ndarray): axes = axes.ravel().tolist() # got a list of axes, make it a dict if isinstance(axes, list): if len(axes) != n_axes: raise ValueError('{}"axes" must be length {}, got {}.' ''.format(prefix, n_axes, len(axes))) # for list of axes to work, must be only one group if len(list(group_by)) != 1: raise ValueError('When axes is a list, can only plot one group ' '(got {} groups: {}).' .format(len(group_by), ', '.join(group_by))) key = list(group_by)[0] axes = {key: axes} # got a dict of lists of axes, make it dict of dicts if isinstance(axes, dict): # in theory a user could pass a dict of axes but *NOT* pass a group_by # dict, but that is forbidden in the docstring so it shouldn't happen. # The next test could fail in that case because we've constructed a # group_by dict and the user won't have known what keys we chose. if set(axes) != set(group_by): raise ValueError('If "axes" is a dict its keys ({}) must match ' 'the keys in "group_by" ({}).' .format(list(axes), list(group_by))) for this_group, this_axes_list in axes.items(): if len(this_axes_list) != n_axes: raise ValueError('{}each value in "axes" must be a list of {} ' 'axes, got {}.'.format(prefix, n_axes, len(this_axes_list))) # NB: next line assumes all axes in each list are in same figure group_by[this_group]['fig'] = this_axes_list[0].get_figure() group_by[this_group]['axes'] = {key: axis for key, axis in zip(ax_names, this_axes_list)} return group_by def _order_epochs(data, times, order=None, overlay_times=None): """Sort epochs image data (2D). Helper for plot_epochs_image.""" n_epochs = len(data) if overlay_times is not None: if len(overlay_times) != n_epochs: raise ValueError('size of overlay_times parameter ({}) does not ' 'match the number of epochs ({}).' .format(len(overlay_times), n_epochs)) overlay_times = np.array(overlay_times) times_min = np.min(overlay_times) times_max = np.max(overlay_times) if ((times_min < times[0]) or (times_max > times[-1])): warn('Some values in overlay_times fall outside of the epochs ' 'time interval (between %s s and %s s)' % (times[0], times[-1])) if callable(order): order = order(times, data) if order is not None: if len(order) != n_epochs: raise ValueError('If order is a {}, its length ({}) must match ' 'the length of the data ({}).' .format(type(order).__name__, len(order), n_epochs)) order = np.asarray(order) data = data[order] if overlay_times is not None: overlay_times = overlay_times[order] return data, overlay_times def _plot_epochs_image(image, style_axes=True, epochs=None, picks=None, vmin=None, vmax=None, colorbar=False, show=False, unit=None, cmap=None, ax=None, overlay_times=None, title=None, evoked=False, ts_args=None, combine=None, combine_given=False, norm=False): """Plot epochs image. Helper function for plot_epochs_image.""" if cmap is None: cmap = 'Reds' if norm else 'RdBu_r' tmin = epochs.times[0] tmax = epochs.times[-1] ax_im = ax['image'] fig = ax_im.get_figure() # draw the image cmap = _setup_cmap(cmap, norm=norm) n_epochs = len(image) extent = [1e3 * tmin, 1e3 * tmax, 0, n_epochs] im = ax_im.imshow(image, vmin=vmin, vmax=vmax, cmap=cmap[0], aspect='auto', origin='lower', interpolation='nearest', extent=extent) # optional things if style_axes: ax_im.set_title(title) ax_im.set_ylabel('Epochs') ax_im.axis('auto') ax_im.axis('tight') ax_im.axvline(0, color='k', linewidth=1, linestyle='--') if overlay_times is not None: ax_im.plot(1e3 * overlay_times, 0.5 + np.arange(n_epochs), 'k', linewidth=2) ax_im.set_xlim(1e3 * tmin, 1e3 * tmax) # draw the evoked if evoked: from . import plot_compare_evokeds pass_combine = (combine if combine_given else None) _picks = [0] if len(picks) == 1 else None # prevent applying GFP plot_compare_evokeds({'cond': list(epochs.iter_evoked())}, picks=_picks, axes=ax['evoked'], combine=pass_combine, **ts_args) ax['evoked'].set_xlim(tmin, tmax) # don't multiply by 1e3 here ax_im.set_xticks([]) # draw the colorbar if colorbar: from matplotlib.pyplot import colorbar as cbar this_colorbar = cbar(im, cax=ax['colorbar']) this_colorbar.ax.set_ylabel(unit, rotation=270, labelpad=12) if cmap[1]: ax_im.CB = DraggableColorbar(this_colorbar, im) tight_layout(fig=fig) # finish plt_show(show) return fig def plot_drop_log(drop_log, threshold=0, n_max_plot=20, subject='Unknown subj', color=(0.8, 0.8, 0.8), width=0.8, ignore=('IGNORED',), show=True): """Show the channel stats based on a drop_log from Epochs. Parameters ---------- drop_log : list of list Epoch drop log from Epochs.drop_log. threshold : float The percentage threshold to use to decide whether or not to plot. Default is zero (always plot). n_max_plot : int Maximum number of channels to show stats for. subject : str The subject name to use in the title of the plot. color : tuple | str Color to use for the bars. width : float Width of the bars. ignore : list The drop reasons to ignore. show : bool Show figure if True. Returns ------- fig : instance of matplotlib.figure.Figure The figure. """ import matplotlib.pyplot as plt from ..epochs import _drop_log_stats percent = _drop_log_stats(drop_log, ignore) if percent < threshold: logger.info('Percent dropped epochs < supplied threshold; not ' 'plotting drop log.') return scores = Counter([ch for d in drop_log for ch in d if ch not in ignore]) ch_names = np.array(list(scores.keys())) counts = np.array(list(scores.values())) # init figure, handle easy case (no drops) fig, ax = plt.subplots() ax.set_title('{}: {:.1f}%'.format(subject, percent)) if len(ch_names) == 0: ax.text(0.5, 0.5, 'No drops', ha='center', fontsize=14) return fig # count epochs that aren't fully caught by `ignore` n_used = sum([any(ch not in ignore for ch in d) or len(d) == 0 for d in drop_log]) # calc plot values n_bars = min(n_max_plot, len(ch_names)) x = np.arange(n_bars) y = 100 * counts / n_used order = np.flipud(np.argsort(y)) ax.bar(x, y[order[:n_bars]], color=color, width=width, align='center') ax.set_xticks(x) ax.set_xticklabels(ch_names[order[:n_bars]], rotation=45, size=10, horizontalalignment='right') ax.set_ylabel('% of epochs rejected') ax.grid(axis='y') tight_layout(pad=1, fig=fig) plt_show(show) return fig def _draw_epochs_axes(epoch_idx, good_ch_idx, bad_ch_idx, data, times, axes, title_str, axes_handler): """Handle drawing epochs axes.""" this = axes_handler[0] for ii, data_, ax in zip(epoch_idx, data, axes): for l, d in zip(ax.lines, data_[good_ch_idx]): l.set_data(times, d) if bad_ch_idx is not None: bad_lines = [ax.lines[k] for k in bad_ch_idx] for l, d in zip(bad_lines, data_[bad_ch_idx]): l.set_data(times, d) if title_str is not None: ax.set_title(title_str % ii, fontsize=12) ax.set_ylim(data.min(), data.max()) ax.set_yticks(list()) ax.set_xticks(list()) if vars(ax)[this]['reject'] is True: # memorizing reject for l in ax.lines: l.set_color((0.8, 0.8, 0.8)) ax.get_figure().canvas.draw() else: # forgetting previous reject for k in axes_handler: if k == this: continue if vars(ax).get(k, {}).get('reject', None) is True: for l in ax.lines[:len(good_ch_idx)]: l.set_color('k') if bad_ch_idx is not None: for l in ax.lines[-len(bad_ch_idx):]: l.set_color('r') ax.get_figure().canvas.draw() break def _epochs_navigation_onclick(event, params): """Handle epochs navigation click.""" import matplotlib.pyplot as plt p = params here = None if event.inaxes == p['back'].ax: here = 1 elif event.inaxes == p['next'].ax: here = -1 elif event.inaxes == p['reject-quit'].ax: if p['reject_idx']: p['epochs'].drop(p['reject_idx']) plt.close(p['fig']) plt.close(event.inaxes.get_figure()) if here is not None: p['idx_handler'].rotate(here) p['axes_handler'].rotate(here) this_idx = p['idx_handler'][0] _draw_epochs_axes(this_idx, p['good_ch_idx'], p['bad_ch_idx'], p['data'][this_idx], p['times'], p['axes'], p['title_str'], p['axes_handler']) # XXX don't ask me why p['axes'][0].get_figure().canvas.draw() def _epochs_axes_onclick(event, params): """Handle epochs axes click.""" reject_color = (0.8, 0.8, 0.8) ax = event.inaxes if event.inaxes is None: return p = params here = vars(ax)[p['axes_handler'][0]] if here.get('reject', None) is False: idx = here['idx'] if idx not in p['reject_idx']: p['reject_idx'].append(idx) for l in ax.lines: l.set_color(reject_color) here['reject'] = True elif here.get('reject', None) is True: idx = here['idx'] if idx in p['reject_idx']: p['reject_idx'].pop(p['reject_idx'].index(idx)) good_lines = [ax.lines[k] for k in p['good_ch_idx']] for l in good_lines: l.set_color('k') if p['bad_ch_idx'] is not None: bad_lines = ax.lines[-len(p['bad_ch_idx']):] for l in bad_lines: l.set_color('r') here['reject'] = False ax.get_figure().canvas.draw() @fill_doc def plot_epochs(epochs, picks=None, scalings=None, n_epochs=20, n_channels=20, title=None, events=None, event_colors=None, order=None, show=True, block=False, decim='auto', noise_cov=None, butterfly=False, show_scrollbars=True, epoch_colors=None): """Visualize epochs. Bad epochs can be marked with a left click on top of the epoch. Bad channels can be selected by clicking the channel name on the left side of the main axes. Calling this function drops all the selected bad epochs as well as bad epochs marked beforehand with rejection parameters. Parameters ---------- epochs : instance of Epochs The epochs object %(picks_good_data)s scalings : dict | 'auto' | None Scaling factors for the traces. If any fields in scalings are 'auto', the scaling factor is set to match the 99.5th percentile of a subset of the corresponding data. If scalings == 'auto', all scalings fields are set to 'auto'. If any fields are 'auto' and data is not preloaded, a subset of epochs up to 100mb will be loaded. If None, defaults to:: dict(mag=1e-12, grad=4e-11, eeg=20e-6, eog=150e-6, ecg=5e-4, emg=1e-3, ref_meg=1e-12, misc=1e-3, stim=1, resp=1, chpi=1e-4, whitened=10.) n_epochs : int The number of epochs per view. Defaults to 20. n_channels : int The number of channels per view. Defaults to 20. title : str | None The title of the window. If None, epochs name will be displayed. Defaults to None. events : None, array, shape (n_events, 3) Events to show with vertical bars. If events are provided, the epoch numbers are not shown to prevent overlap. You can toggle epoch numbering through options (press 'o' key). You can use :func:`mne.viz.plot_events` as a legend for the colors. By default, the coloring scheme is the same. .. warning:: If the epochs have been resampled, the events no longer align with the data. .. versionadded:: 0.14.0 event_colors : None, dict Dictionary of event_id value and its associated color. If None, colors are automatically drawn from a default list (cycled through if number of events longer than list of default colors). Uses the same coloring scheme as :func:`mne.viz.plot_events`. .. versionadded:: 0.14.0 order : array of str | None Order in which to plot channel types. .. versionadded:: 0.18.0 show : bool Show figure if True. Defaults to True block : bool Whether to halt program execution until the figure is closed. Useful for rejecting bad trials on the fly by clicking on an epoch. Defaults to False. decim : int | 'auto' Amount to decimate the data during display for speed purposes. You should only decimate if the data are sufficiently low-passed, otherwise aliasing can occur. The 'auto' mode (default) uses the decimation that results in a sampling rate at least three times larger than ``info['lowpass']`` (e.g., a 40 Hz lowpass will result in at least a 120 Hz displayed sample rate). .. versionadded:: 0.15.0 noise_cov : instance of Covariance | str | None Noise covariance used to whiten the data while plotting. Whitened data channels are scaled by ``scalings['whitened']``, and their channel names are shown in italic. Can be a string to load a covariance from disk. See also :meth:`mne.Evoked.plot_white` for additional inspection of noise covariance properties when whitening evoked data. For data processed with SSS, the effective dependence between magnetometers and gradiometers may introduce differences in scaling, consider using :meth:`mne.Evoked.plot_white`. .. versionadded:: 0.16.0 butterfly : bool Whether to directly call the butterfly view. .. versionadded:: 0.18.0 %(show_scrollbars)s epoch_colors : list of (n_epochs) list (of n_channels) | None Colors to use for individual epochs. If None, use default colors. Returns ------- fig : instance of matplotlib.figure.Figure The figure. Notes ----- The arrow keys (up/down/left/right) can be used to navigate between channels and epochs and the scaling can be adjusted with - and + (or =) keys, but this depends on the backend matplotlib is configured to use (e.g., mpl.use(``TkAgg``) should work). Full screen mode can be toggled with f11 key. The amount of epochs and channels per view can be adjusted with home/end and page down/page up keys. These can also be set through options dialog by pressing ``o`` key. ``h`` key plots a histogram of peak-to-peak values along with the used rejection thresholds. Butterfly plot can be toggled with ``b`` key. Right mouse click adds a vertical line to the plot. Click 'help' button at bottom left corner of the plotter to view all the options. .. versionadded:: 0.10.0 """ epochs.drop_bad() scalings = _compute_scalings(scalings, epochs) scalings = _handle_default('scalings_plot_raw', scalings) decim, data_picks = _handle_decim(epochs.info.copy(), decim, None) projs = epochs.info['projs'] noise_cov = _check_cov(noise_cov, epochs.info) params = dict(epochs=epochs, info=epochs.info.copy(), t_start=0., bad_color=(0.8, 0.8, 0.8), histogram=None, decim=decim, data_picks=data_picks, noise_cov=noise_cov, use_noise_cov=noise_cov is not None, show_scrollbars=show_scrollbars, epoch_colors=epoch_colors) params['label_click_fun'] = partial(_pick_bad_channels, params=params) _prepare_mne_browse_epochs(params, projs, n_channels, n_epochs, scalings, title, picks, events=events, order=order, event_colors=event_colors, butterfly=butterfly) _prepare_projectors(params) callback_close = partial(_close_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'] @verbose def plot_epochs_psd(epochs, fmin=0, fmax=np.inf, tmin=None, tmax=None, proj=False, bandwidth=None, adaptive=False, low_bias=True, normalization='length', picks=None, ax=None, color='black', xscale='linear', area_mode='std', area_alpha=0.33, dB=True, estimate='auto', show=True, n_jobs=1, average=None, line_alpha=None, spatial_colors=None, verbose=None): """%(plot_psd_doc)s. Parameters ---------- epochs : instance of Epochs The epochs object fmin : float Start frequency to consider. fmax : float End frequency to consider. tmin : float | None Start time to consider. tmax : float | None End time to consider. proj : bool Apply projection. bandwidth : float The bandwidth of the multi taper windowing function in Hz. The default value is a window half-bandwidth of 4. adaptive : bool Use adaptive weights to combine the tapered spectra into PSD (slow, use n_jobs >> 1 to speed up computation). low_bias : bool Only use tapers with more than 90%% spectral concentration within bandwidth. normalization : str Either "full" or "length" (default). If "full", the PSD will be normalized by the sampling rate as well as the length of the signal (as in nitime). %(plot_psd_picks_good_data)s ax : instance of Axes | None Axes to plot into. If None, axes will be created. %(plot_psd_color)s %(plot_psd_xscale)s %(plot_psd_area_mode)s %(plot_psd_area_alpha)s %(plot_psd_dB)s %(plot_psd_estimate)s %(show)s %(n_jobs)s %(plot_psd_average)s %(plot_psd_line_alpha)s %(plot_psd_spatial_colors)s %(verbose)s Returns ------- fig : instance of Figure Figure with frequency spectra of the data channels. """ # this chunk should be removed for 0.2 if average is False and spatial_colors is None: spatial_colors = True if spatial_colors is None: spatial_colors = False warn('spatial_colors defaults to False in 0.19 but will change to True' ' in 0.20. Set it explicitly to avoid this warning.', DeprecationWarning) if average is None: average = True warn('average defaults to True in 0.19 but will change to False' ' in 0.20. Set it explicitly to avoid this warning.', DeprecationWarning) from .utils import _set_psd_plot_params, _plot_psd fig, picks_list, titles_list, units_list, scalings_list, ax_list, \ make_label = _set_psd_plot_params(epochs.info, proj, picks, ax, area_mode) del ax psd_list = list() for picks in picks_list: # Multitaper used for epochs instead of Welch, because Welch chunks # the data; epoched data are by nature already chunked, however. psd, freqs = psd_multitaper(epochs, picks=picks, fmin=fmin, fmax=fmax, tmin=tmin, tmax=tmax, bandwidth=bandwidth, adaptive=adaptive, low_bias=low_bias, normalization=normalization, proj=proj, n_jobs=n_jobs) psd_list.append(np.mean(psd, axis=0)) fig = _plot_psd(epochs, fig, freqs, psd_list, picks_list, titles_list, units_list, scalings_list, ax_list, make_label, color, area_mode, area_alpha, dB, estimate, average, spatial_colors, xscale, line_alpha) plt_show(show) return fig def _prepare_mne_browse_epochs(params, projs, n_channels, n_epochs, scalings, title, picks, events=None, event_colors=None, order=None, butterfly=False, info=None): """Set up the mne_browse_epochs window.""" import matplotlib as mpl from matplotlib.collections import LineCollection from matplotlib.colors import colorConverter epochs = params['epochs'] info = info or epochs.info orig_epoch_times, epochs_events = epochs.times, epochs.events name = epochs._name del epochs # Reorganize channels picks = _picks_to_idx(info, picks) picks = sorted(picks) # channel type string for every channel types = [channel_type(info, ch) for ch in picks] # list of unique channel types ch_types = list(_get_channel_types(info)) if order is None: order = _DATA_CH_TYPES_ORDER_DEFAULT inds = [pick_idx for order_type in order for pick_idx, ch_type in zip(picks, types) if order_type == ch_type] if len(ch_types) > len(order): ch_missing = [ch_type for ch_type in ch_types if ch_type not in order] ch_missing = np.unique(ch_missing) raise RuntimeError('%s are in picks but not in order.' ' Please specify all channel types picked.' % (str(ch_missing))) types = sorted(types, key=order.index) if not len(inds) == len(picks): raise RuntimeError('Some channels not classified. Please' ' check your picks') ch_names = [params['info']['ch_names'][idx] for idx in inds] _validate_type(params['epoch_colors'], (list, None), 'epoch_colors') if params['epoch_colors'] is not None: if len(params['epoch_colors']) != len(params['epochs'].events): raise ValueError('epoch_colors must be list of len(epochs.events).' ' Got %s' % len(params['epoch_colors'])) for epoch_idx in range(len(params['epoch_colors'])): these_colors = params['epoch_colors'][epoch_idx] _validate_type(these_colors, list, 'epoch_colors[%s]' % (epoch_idx,)) if len(these_colors) != len(params['epochs'].ch_names): raise ValueError('epoch_colors for the %dth epoch ' 'has length %d, expected %d.' % (epoch_idx, len(these_colors), len(params['epochs'].ch_names))) params['epoch_colors'][epoch_idx] = \ [these_colors[idx] for idx in inds] # set up plotting n_epochs = min(n_epochs, len(epochs_events)) duration = len(orig_epoch_times) * n_epochs n_channels = min(n_channels, len(picks)) if title is None: title = name if title is None or len(title) == 0: title = '' color = _handle_default('color', None) figsize = _get_figsize_from_config() params['fig'] = figure_nobar(facecolor='w', figsize=figsize, dpi=80) params['fig'].canvas.set_window_title(title or 'Epochs') _prepare_mne_browse(params, xlabel='Epochs') ax = params['ax'] ax_hscroll = params['ax_hscroll'] ax_vscroll = params['ax_vscroll'] # add secondary x axis for annotations / event labels ax2 = ax.twiny() ax2.set_zorder(-1) ax2.set_axes_locator(ax.get_axes_locator()) # set axis lims ax.axis([0, duration, 0, 200]) ax2.axis([0, duration, 0, 200]) # populate vertical and horizontal scrollbars ax_vscroll.add_patch(mpl.patches.Rectangle((0, 0), 1, len(picks), facecolor='w', zorder=3)) for ci in range(len(picks)): if ch_names[ci] in params['info']['bads']: this_color = params['bad_color'] else: this_color = color[types[ci]] ax_vscroll.add_patch(mpl.patches.Rectangle((0, ci), 1, 1, facecolor=this_color, edgecolor=this_color, zorder=4)) vsel_patch = mpl.patches.Rectangle((0, 0), 1, n_channels, alpha=0.5, edgecolor='w', facecolor='w', zorder=5) ax_vscroll.add_patch(vsel_patch) ax_vscroll.set_ylim(len(types), 0) ax_vscroll.set_title('Ch.') # populate colors list type_colors = [colorConverter.to_rgba(color[c]) for c in types] colors = list() for color_idx in range(len(type_colors)): colors.append([type_colors[color_idx]] * len(epochs_events)) lines = list() n_times = len(orig_epoch_times) for ch_idx in range(n_channels): if len(colors) - 1 < ch_idx: break lc = LineCollection(list(), antialiased=True, linewidths=0.5, zorder=3, picker=3.) ax.add_collection(lc) lines.append(lc) data = np.zeros((params['info']['nchan'], len(orig_epoch_times) * n_epochs)) ylim = (25., 0.) # Hardcoded 25 because butterfly has max 5 rows (5*5=25). # make shells for plotting traces offset = ylim[0] / n_channels offsets = np.arange(n_channels) * offset + (offset / 2.) times = np.arange(len(orig_epoch_times) * len(epochs_events)) epoch_times = np.arange(0, len(times), n_times) ax.set_yticks(offsets) ax.set_ylim(ylim) ticks = epoch_times + 0.5 * n_times ax.set_xticks(ticks) ax2.set_xticks(ticks[:n_epochs]) labels = list(range(1, len(ticks) + 1)) # epoch numbers ax.set_xticklabels(labels) xlim = epoch_times[-1] + len(orig_epoch_times) ax_hscroll.set_xlim(0, xlim) vertline_t = ax_hscroll.text(0, 1, '', color='y', va='bottom', ha='right') # fit horizontal scroll bar ticks hscroll_ticks = np.arange(0, xlim, xlim / 7.0) hscroll_ticks = np.append(hscroll_ticks, epoch_times[-1]) hticks = list() for tick in hscroll_ticks: hticks.append(epoch_times.flat[np.abs(epoch_times - tick).argmin()]) hlabels = [x // n_times + 1 for x in hticks] ax_hscroll.set_xticks(hticks) ax_hscroll.set_xticklabels(hlabels) for epoch_idx in range(len(epoch_times)): ax_hscroll.add_patch(mpl.patches.Rectangle((epoch_idx * n_times, 0), n_times, 1, facecolor='w', edgecolor='w', alpha=0.6)) hsel_patch = mpl.patches.Rectangle((0, 0), duration, 1, edgecolor='k', facecolor=(0.75, 0.75, 0.75), alpha=0.25, linewidth=1, clip_on=False) ax_hscroll.add_patch(hsel_patch) text = ax.text(0, 0, 'blank', zorder=3, verticalalignment='baseline', ha='left', fontweight='bold') text.set_visible(False) epoch_nr = True if events is not None: event_set = set(events[:, 2]) event_colors = _handle_event_colors(event_set, event_colors, event_set) epoch_nr = False # epoch number off by default to avoid overlap for label in ax.xaxis.get_ticklabels(): label.set_visible(False) params.update({'ax': ax, 'ax2': ax2, 'ax_hscroll': ax_hscroll, 'ax_vscroll': ax_vscroll, 'vsel_patch': vsel_patch, 'hsel_patch': hsel_patch, 'lines': lines, # vertical lines for segmentation 'projs': projs, 'ch_names': ch_names, 'n_channels': n_channels, 'n_epochs': n_epochs, 'scalings': scalings, 'duration': duration, 'ch_start': 0, 'colors': colors, 'def_colors': type_colors, # don't change at runtime 'picks': picks, 'bads': np.array(list(), dtype=int), 'data': data, 'times': times, 'epoch_times': epoch_times, 'offsets': offsets, 'labels': labels, 'scale_factor': 1.0, 'butterfly_scale': 1.0, 'fig_proj': None, 'types': np.array(types), 'inds': inds, 'vert_lines': list(), 'vertline_t': vertline_t, 'butterfly': butterfly, 'text': text, 'fig_options': None, 'settings': [True, True, epoch_nr, True], 'image_plot': None, 'events': events, 'event_colors': event_colors, 'ev_lines': list(), 'ev_texts': list(), 'ann': list(), # list for butterfly view annotations 'order': order, 'ch_types': ch_types}) params['plot_fun'] = partial(_plot_traces, params=params) # Plot epoch_colors if params['epoch_colors'] is not None: for epoch_idx, epoch_color in enumerate(params['epoch_colors']): for ch_idx in range(len(params['ch_names'])): if epoch_color[ch_idx] is not None: params['colors'][ch_idx][epoch_idx] = \ colorConverter.to_rgba(epoch_color[ch_idx]) # plot on horizontal patch if all colors are same if epoch_color.count(epoch_color[0]) == len(epoch_color): params['ax_hscroll'].patches[epoch_idx].set_color( epoch_color[0]) params['ax_hscroll'].patches[epoch_idx].set_zorder(3) params['ax_hscroll'].patches[epoch_idx].set_edgecolor('w') # callbacks callback_scroll = partial(_plot_onscroll, params=params) params['fig'].canvas.mpl_connect('scroll_event', callback_scroll) callback_click = partial(_mouse_click, params=params) params['fig'].canvas.mpl_connect('button_press_event', callback_click) callback_key = partial(_plot_onkey, params=params) params['fig'].canvas.mpl_connect('key_press_event', callback_key) params['fig'].canvas.mpl_connect('pick_event', partial(_onpick, params=params)) params['callback_key'] = callback_key # Draw event lines for the first time. _plot_vert_lines(params) def _prepare_projectors(params): """Set up the projectors for epochs browser.""" import matplotlib as mpl epochs = params['epochs'] projs = params['projs'] if len(projs) > 0 and not epochs.proj: # set up proj button ax_button = params['fig'].add_axes(params['proj_button_pos']) ax_button.set_axes_locator(params['proj_button_locator']) opt_button = mpl.widgets.Button(ax_button, 'Proj') callback_option = partial(_toggle_options, params=params) opt_button.on_clicked(callback_option) params['opt_button'] = opt_button params['apply_proj'] = epochs.proj # As here code is shared with plot_evoked, some extra steps: # first the actual plot update function params['plot_update_proj_callback'] = _plot_update_epochs_proj # then the toggle handler callback_proj = partial(_toggle_proj, params=params) # store these for use by callbacks in the options figure params['callback_proj'] = callback_proj callback_proj('none') def _plot_traces(params): """Plot concatenated epochs.""" params['text'].set_visible(False) ax = params['ax'] butterfly = params['butterfly'] offsets = params['offsets'] lines = params['lines'] epochs = params['epochs'] if butterfly: ch_start = 0 n_channels = len(params['picks']) data = params['data'] * params['butterfly_scale'] _prepare_butterfly(params) else: ch_start = params['ch_start'] n_channels = params['n_channels'] data = params['data'] * params['scale_factor'] n_times = len(epochs.times) tick_list = list() start_idx = int(params['t_start'] / n_times) end = params['t_start'] + params['duration'] end_idx = int(end / n_times) xlabels = params['labels'][start_idx:] event_ids = params['epochs'].events[:, 2] params['ax2'].set_xticklabels(event_ids[start_idx:]) ax.set_xticklabels(xlabels) ylabels = ax.yaxis.get_ticklabels() # do the plotting for line_idx in range(n_channels): ch_idx = line_idx + ch_start if line_idx >= len(lines): break elif ch_idx < len(params['ch_names']): if butterfly: # determine offsets for signal traces ch_type = params['types'][ch_idx] chan_types_split = sorted(set(params['ch_types']) & set(_DATA_CH_TYPES_SPLIT), key=params['order'].index) ylim = ax.get_ylim()[0] ticks = np.arange( 0, ylim, ylim / (4 * max(len(chan_types_split), 1))) offset_pos = np.arange(2, len(chan_types_split) * 4, 4) if ch_type in chan_types_split: offset = ticks[offset_pos[chan_types_split.index(ch_type)]] else: lines[line_idx].set_segments(list()) offset = None else: tick_list += [params['ch_names'][ch_idx]] offset = offsets[line_idx] if offset is None: continue if params['inds'][ch_idx] in params['data_picks']: this_decim = params['decim'] else: this_decim = 1 this_data = data[ch_idx] # subtraction here gets correct orientation for flipped ylim ydata = offset - this_data xdata = params['times'][:params['duration']] num_epochs = np.min([params['n_epochs'], len(epochs.events)]) segments = np.split(np.array((xdata, ydata)).T, num_epochs) segments = [segment[::this_decim] for segment in segments] ch_name = params['ch_names'][ch_idx] if ch_name in params['info']['bads']: if not butterfly: this_color = params['bad_color'] ylabels[line_idx].set_color(this_color) this_color = np.tile((params['bad_color']), (num_epochs, 1)) for bad_idx in params['bads']: if bad_idx < start_idx or bad_idx >= end_idx: continue this_color[bad_idx - start_idx] = (1., 0., 0.) lines[line_idx].set_zorder(2) else: this_color = params['colors'][ch_idx][start_idx:end_idx] lines[line_idx].set_zorder(3) if not butterfly: ylabels[line_idx].set_color('black') lines[line_idx].set_segments(segments) lines[line_idx].set_color(this_color) else: lines[line_idx].set_segments(list()) # finalize plot ax.set_xlim(params['times'][0], params['times'][0] + params['duration'], False) params['ax2'].set_xlim(params['times'][0], params['times'][0] + params['duration'], False) if butterfly: # compute labels for ticks surrounding the trace offset factor = -1. / params['butterfly_scale'] scalings_default = _handle_default('scalings') chan_types_split = sorted(set(params['types']) & set(_DATA_CH_TYPES_SPLIT), key=params['order'].index) ylim = ax.get_ylim()[0] ticks = np.arange( 0, ylim + 1, ylim / (4 * max(len(chan_types_split), 1))) offset_pos = np.arange(2, (len(chan_types_split) * 4) + 1, 4) ax.set_yticks(ticks) labels = [''] * 20 labels = [0 if idx in range(2, len(labels), 4) else label for idx, label in enumerate(labels)] for idx_chan, chan_type in enumerate(chan_types_split): tick_top, tick_bottom = 1 + idx_chan * 4, 3 + idx_chan * 4 offset = ticks[offset_pos[idx_chan]] for tick_pos in [tick_top, tick_bottom]: tickoffset_diff = ticks[tick_pos] - offset labels[tick_pos] = (tickoffset_diff * params['scalings'][chan_type] * factor * scalings_default[chan_type]) # Heuristic to turn floats to ints where possible (e.g. -500.0 to -500) for li, label in enumerate(labels): if isinstance(label, float) and float(str(label)) != round(label): labels[li] = round(label, 2) ax.set_yticklabels(labels, fontsize=12, color='black') else: ax.set_yticklabels(tick_list, fontsize=12) _set_ax_label_style(ax, params) if params['events'] is not None: # vertical lines for events. _draw_event_lines(params) params['vsel_patch'].set_y(ch_start) params['fig'].canvas.draw() # XXX This is a hack to make sure this figure gets drawn last # so that when matplotlib goes to calculate bounds we don't get a # CGContextRef error on the MacOSX backend :( if params['fig_proj'] is not None: params['fig_proj'].canvas.draw() def _plot_update_epochs_proj(params, bools=None): """Deal with proj changed.""" if bools is not None: inds = np.where(bools)[0] params['info']['projs'] = [deepcopy(params['projs'][ii]) for ii in inds] params['proj_bools'] = bools epochs = params['epochs'] n_epochs = params['n_epochs'] params['projector'], params['whitened_ch_names'] = _setup_plot_projector( params['info'], params['noise_cov'], True, params['use_noise_cov']) start = int(params['t_start'] / len(epochs.times)) end = start + n_epochs if epochs.preload: data = np.concatenate(epochs.get_data()[start:end], axis=1) else: # this is faster than epochs.get_data()[start:end] when not preloaded data = np.concatenate(epochs[start:end].get_data(), axis=1) if params['projector'] is not None: data = np.dot(params['projector'], data) types = params['types'] for pick, ind in enumerate(params['inds']): ch_name = params['info']['ch_names'][ind] if ch_name in params['whitened_ch_names'] and \ ch_name not in params['info']['bads']: norm = params['scalings']['whitened'] else: norm = params['scalings'][types[pick]] params['data'][pick] = data[ind] / norm params['plot_fun']() def _handle_picks(epochs): """Handle picks.""" if any('ICA' in k for k in epochs.ch_names): picks = pick_types(epochs.info, misc=True, ref_meg=False, exclude=[]) else: picks = pick_types(epochs.info, meg=True, eeg=True, eog=True, ecg=True, seeg=True, ecog=True, ref_meg=False, fnirs=True, exclude=[]) return picks def _plot_window(value, params): """Deal with horizontal shift of the viewport.""" max_times = len(params['times']) - params['duration'] if value > max_times: value = len(params['times']) - params['duration'] if value < 0: value = 0 if params['t_start'] != value: params['t_start'] = value params['hsel_patch'].set_x(value) params['plot_update_proj_callback'](params) def _plot_vert_lines(params): """Plot vertical lines.""" ax = params['ax'] while len(ax.lines) > 0: ax.lines.pop() params['vert_lines'] = list() params['ev_lines'] = list() params['vertline_t'].set_text('') epochs = params['epochs'] if params['settings'][3]: # if zeroline visible t_zero = np.where(epochs.times == 0.)[0] if len(t_zero) == 1: # not True if tmin > 0 for event_idx in range(len(epochs.events)): pos = [event_idx * len(epochs.times) + t_zero[0], event_idx * len(epochs.times) + t_zero[0]] ax.plot(pos, ax.get_ylim(), 'g', zorder=4, alpha=0.4) for epoch_idx in range(len(epochs.events)): pos = [epoch_idx * len(epochs.times), epoch_idx * len(epochs.times)] ax.plot(pos, ax.get_ylim(), color='black', linestyle='--', zorder=2) if params['events'] is not None: _draw_event_lines(params) def _pick_bad_epochs(event, params): """Select / drop bad epochs.""" if 'ica' in params: pos = (event.xdata, event.ydata) _pick_bad_channels(pos, params) return n_times = len(params['epochs'].times) start_idx = int(params['t_start'] / n_times) xdata = event.xdata xlim = event.inaxes.get_xlim() epoch_idx = start_idx + int(xdata / (xlim[1] / params['n_epochs'])) total_epochs = len(params['epochs'].events) if epoch_idx > total_epochs - 1: return # remove bad epoch if epoch_idx in params['bads']: params['bads'] = params['bads'][(params['bads'] != epoch_idx)] for ch_idx in range(len(params['ch_names'])): params['colors'][ch_idx][epoch_idx] = params['def_colors'][ch_idx] params['ax_hscroll'].patches[epoch_idx].set_color('w') params['ax_hscroll'].patches[epoch_idx].set_zorder(2) params['plot_fun']() return # add bad epoch params['bads'] = np.append(params['bads'], epoch_idx) params['ax_hscroll'].patches[epoch_idx].set_color((1., 0., 0., 1.)) params['ax_hscroll'].patches[epoch_idx].set_zorder(3) params['ax_hscroll'].patches[epoch_idx].set_edgecolor('w') for ch_idx in range(len(params['ch_names'])): params['colors'][ch_idx][epoch_idx] = (1., 0., 0., 1.) params['plot_fun']() def _pick_bad_channels(pos, params): """Select bad channels.""" text, ch_idx = _label2idx(params, pos) if text is None: return if text in params['info']['bads']: while text in params['info']['bads']: params['info']['bads'].remove(text) color = params['def_colors'][ch_idx] params['ax_vscroll'].patches[ch_idx + 1].set_color(color) else: params['info']['bads'].append(text) color = params['bad_color'] params['ax_vscroll'].patches[ch_idx + 1].set_color(color) if 'ica' in params: params['plot_fun']() else: params['plot_update_proj_callback'](params) def _plot_onscroll(event, params): """Handle scroll events.""" if event.key == 'control': if event.step < 0: event.key = '-' else: event.key = '+' _plot_onkey(event, params) return if params['butterfly']: return _plot_raw_onscroll(event, params, len(params['ch_names'])) def _mouse_click(event, params): """Handle mouse click events.""" from matplotlib.pyplot import fignum_exists if event.inaxes is None: if params['butterfly'] or not params['settings'][0]: return ax = params['ax'] ylim = ax.get_ylim() pos = ax.transData.inverted().transform((event.x, event.y)) if pos[0] > 0 or pos[1] < 0 or pos[1] > ylim[0]: return if event.button == 1: # left click params['label_click_fun'](pos) elif event.button == 3: # right click if 'ica' not in params: _, ch_idx = _label2idx(params, pos) if ch_idx is None: return if channel_type(params['info'], ch_idx) not in ['mag', 'grad', 'eeg', 'eog']: logger.info('Event related fields / potentials only ' 'available for MEG and EEG channels.') return # check if the figure was already closed if (params['image_plot'] is not None and not fignum_exists(params['image_plot'].number)): params['image_plot'] = None fig = plot_epochs_image(params['epochs'], picks=params['inds'][ch_idx], fig=params['image_plot'], clear=True)[0] params['image_plot'] = fig elif event.button == 1: # left click # vertical scroll bar changed if event.inaxes == params['ax_vscroll']: if params['butterfly']: return # Don't let scrollbar go outside vertical scrollbar limits # XXX: floating point exception on some machines if this happens. ch_start = min( max(int(event.ydata) - params['n_channels'] // 2, 0), len(params['ch_names']) - params['n_channels']) if params['ch_start'] != ch_start: params['ch_start'] = ch_start params['plot_fun']() # horizontal scroll bar changed elif event.inaxes == params['ax_hscroll']: # find the closest epoch time times = params['epoch_times'] offset = 0.5 * params['n_epochs'] * len(params['epochs'].times) xdata = times.flat[np.abs(times - (event.xdata - offset)).argmin()] _plot_window(xdata, params) # main axes elif event.inaxes == params['ax']: _pick_bad_epochs(event, params) elif event.inaxes == params['ax'] and event.button == 2: # middle click params['fig'].canvas.draw() if params['fig_proj'] is not None: params['fig_proj'].canvas.draw() elif event.inaxes == params['ax'] and event.button == 3: # right click n_times = len(params['epochs'].times) xdata = int(event.xdata % n_times) prev_xdata = 0 if len(params['vert_lines']) > 0: prev_xdata = params['vert_lines'][0][0].get_data()[0][0] while len(params['vert_lines']) > 0: params['ax'].lines.remove(params['vert_lines'][0][0]) params['vert_lines'].pop(0) if prev_xdata == xdata: # lines removed params['vertline_t'].set_text('') params['plot_fun']() return ylim = params['ax'].get_ylim() for epoch_idx in range(params['n_epochs']): # plot lines pos = [epoch_idx * n_times + xdata, epoch_idx * n_times + xdata] params['vert_lines'].append(params['ax'].plot(pos, ylim, 'y', zorder=5)) params['vertline_t'].set_text('%0.3f' % params['epochs'].times[xdata]) params['plot_fun']() def _plot_onkey(event, params): """Handle key presses.""" import matplotlib.pyplot as plt if event.key == 'down': if params['butterfly']: return params['ch_start'] += params['n_channels'] _channels_changed(params, len(params['ch_names'])) elif event.key == 'up': if params['butterfly']: return params['ch_start'] -= params['n_channels'] _channels_changed(params, len(params['ch_names'])) elif event.key == 'left': sample = params['t_start'] - params['duration'] sample = np.max([0, sample]) _plot_window(sample, params) elif event.key == 'right': sample = params['t_start'] + params['duration'] sample = np.min([sample, params['times'][-1] - params['duration']]) times = params['epoch_times'] xdata = times.flat[np.abs(times - sample).argmin()] _plot_window(xdata, params) elif event.key == '-': if params['butterfly']: params['butterfly_scale'] /= 1.1 else: params['scale_factor'] /= 1.1 params['plot_fun']() elif event.key in ['+', '=']: if params['butterfly']: params['butterfly_scale'] *= 1.1 else: params['scale_factor'] *= 1.1 params['plot_fun']() elif event.key == 'f11': mng = plt.get_current_fig_manager() mng.full_screen_toggle() elif event.key == 'pagedown': if params['n_channels'] == 1 or params['butterfly']: return n_channels = params['n_channels'] - 1 ylim = params['ax'].get_ylim() offset = ylim[0] / n_channels params['offsets'] = np.arange(n_channels) * offset + (offset / 2.) params['n_channels'] = n_channels params['ax'].collections.pop() params['ax'].set_yticks(params['offsets']) params['lines'].pop() params['vsel_patch'].set_height(n_channels) params['plot_fun']() elif event.key == 'pageup': if params['butterfly']: return from matplotlib.collections import LineCollection n_channels = params['n_channels'] + 1 ylim = params['ax'].get_ylim() offset = ylim[0] / n_channels params['offsets'] = np.arange(n_channels) * offset + (offset / 2.) params['n_channels'] = n_channels lc = LineCollection(list(), antialiased=True, linewidths=0.5, zorder=3, picker=3.) params['ax'].add_collection(lc) params['ax'].set_yticks(params['offsets']) params['lines'].append(lc) params['vsel_patch'].set_height(n_channels) params['plot_fun']() elif event.key == 'home': n_epochs = params['n_epochs'] - 1 if n_epochs <= 0: return n_times = len(params['epochs'].times) ticks = params['epoch_times'] + 0.5 * n_times params['ax2'].set_xticks(ticks[:n_epochs]) params['n_epochs'] = n_epochs params['duration'] -= n_times params['hsel_patch'].set_width(params['duration']) params['data'] = params['data'][:, :-n_times] params['plot_update_proj_callback'](params) elif event.key == 'end': n_epochs = params['n_epochs'] + 1 n_times = len(params['epochs'].times) if n_times * n_epochs > len(params['times']): return ticks = params['epoch_times'] + 0.5 * n_times params['ax2'].set_xticks(ticks[:n_epochs]) params['n_epochs'] = n_epochs if len(params['vert_lines']) > 0: ax = params['ax'] pos = params['vert_lines'][0][0].get_data()[0] + params['duration'] params['vert_lines'].append(ax.plot(pos, ax.get_ylim(), 'y', zorder=4)) params['duration'] += n_times if params['t_start'] + params['duration'] > len(params['times']): params['t_start'] -= n_times params['hsel_patch'].set_x(params['t_start']) params['hsel_patch'].set_width(params['duration']) params['data'] = np.zeros((len(params['data']), params['duration'])) params['plot_update_proj_callback'](params) elif event.key == 'b': params['butterfly'] = not params['butterfly'] if params['fig_options'] is not None: plt.close(params['fig_options']) params['fig_options'] = None _prepare_butterfly(params) params['plot_fun']() elif event.key == 'w': params['use_noise_cov'] = not params['use_noise_cov'] _plot_update_epochs_proj(params) _plot_traces(params) elif event.key == 'o': if not params['butterfly']: _open_options(params) elif event.key == 'h': _plot_histogram(params) elif event.key == '?': _onclick_help(event, params) elif event.key == 'escape': plt.close(params['fig']) elif event.key == 'z': # zen mode: remove scrollbars and buttons _toggle_scrollbars(params) def _prepare_butterfly(params): """Set up butterfly plot.""" from matplotlib.collections import LineCollection import matplotlib as mpl if params['butterfly']: units = _handle_default('units') chan_types = sorted(set(params['types']) & set(params['order']), key=params['order'].index) if len(chan_types) < 1: return params['ax_vscroll'].set_visible(False) ax = params['ax'] labels = ax.yaxis.get_ticklabels() for label in labels: label.set_visible(True) offsets = np.arange(0, ax.get_ylim()[0], ax.get_ylim()[0] / (4 * len(chan_types))) ticks = offsets ticks = [ticks[x] if x < len(ticks) else 0 for x in range(20)] ax.set_yticks(ticks) used_types = 0 params['offsets'] = [ticks[2]] # checking which annotations are displayed and removing them ann = params['ann'] annotations = [child for child in params['ax2'].get_children() if isinstance(child, mpl.text.Annotation)] for annote in annotations: annote.remove() ann[:] = list() assert len(params['ann']) == 0 titles = _handle_default('titles') for chan_type in chan_types: unit = units[chan_type] pos = (0, 1 - (ticks[2 + 4 * used_types] / ax.get_ylim()[0])) ann.append(params['ax2'].annotate( '%s (%s)' % (titles[chan_type], unit), xy=pos, xytext=(-70, 0), ha='left', size=12, va='center', xycoords='axes fraction', rotation=90, textcoords='offset points')) used_types += 1 while len(params['lines']) < len(params['picks']): lc = LineCollection(list(), antialiased=True, linewidths=.5, zorder=3, picker=3.) ax.add_collection(lc) params['lines'].append(lc) else: # change back to default view labels = params['ax'].yaxis.get_ticklabels() for label in labels: label.set_visible(params['settings'][0]) params['ax_vscroll'].set_visible(True) while len(params['ax2'].texts) > 0: params['ax2'].texts.pop() n_channels = params['n_channels'] while len(params['lines']) > n_channels: params['ax'].collections.pop() params['lines'].pop() ylim = (25., 0.) params['ax'].set_ylim(ylim) offset = ylim[0] / n_channels params['offsets'] = np.arange(n_channels) * offset + (offset / 2.) params['ax'].set_yticks(params['offsets']) def _onpick(event, params): """Add a channel name on click.""" if event.mouseevent.button != 2 or not params['butterfly']: return # text label added with a middle mouse button lidx = np.where([l is event.artist for l in params['lines']])[0][0] text = params['text'] text.set_x(event.mouseevent.xdata) text.set_y(event.mouseevent.ydata) text.set_text(params['ch_names'][lidx]) text.set_visible(True) # do NOT redraw here, since for butterfly plots hundreds of lines could # potentially be picked -- use _mouse_click (happens once per click) # to do the drawing def _close_event(event, params): """Drop selected bad epochs (called on closing of the plot).""" params['epochs'].drop(params['bads']) params['epochs'].info['bads'] = params['info']['bads'] logger.info('Channels marked as bad: %s' % params['epochs'].info['bads']) def _update_channels_epochs(event, params): """Change the amount of channels and epochs per view.""" from matplotlib.collections import LineCollection # Channels n_channels = int(np.around(params['channel_slider'].val)) offset = params['ax'].get_ylim()[0] / n_channels params['offsets'] = np.arange(n_channels) * offset + (offset / 2.) while len(params['lines']) > n_channels: params['ax'].collections.pop() params['lines'].pop() while len(params['lines']) < n_channels: lc = LineCollection(list(), linewidths=0.5, antialiased=True, zorder=3, picker=3.) params['ax'].add_collection(lc) params['lines'].append(lc) params['ax'].set_yticks(params['offsets']) params['vsel_patch'].set_height(n_channels) params['n_channels'] = n_channels # Epochs n_epochs = int(np.around(params['epoch_slider'].val)) n_times = len(params['epochs'].times) ticks = params['epoch_times'] + 0.5 * n_times params['ax2'].set_xticks(ticks[:n_epochs]) params['n_epochs'] = n_epochs params['duration'] = n_times * n_epochs params['hsel_patch'].set_width(params['duration']) params['data'] = np.zeros((len(params['data']), params['duration'])) if params['t_start'] + n_times * n_epochs > len(params['times']): params['t_start'] = len(params['times']) - n_times * n_epochs params['hsel_patch'].set_x(params['t_start']) params['plot_update_proj_callback'](params) def _toggle_labels(label, params): """Toggle axis labels.""" if label == 'Channel names visible': params['settings'][0] = not params['settings'][0] labels = params['ax'].yaxis.get_ticklabels() for label in labels: label.set_visible(params['settings'][0]) elif label == 'Event-id visible': params['settings'][1] = not params['settings'][1] labels = params['ax2'].xaxis.get_ticklabels() for label in labels: label.set_visible(params['settings'][1]) elif label == 'Epoch-id visible': params['settings'][2] = not params['settings'][2] labels = params['ax'].xaxis.get_ticklabels() for label in labels: label.set_visible(params['settings'][2]) elif label == 'Zeroline visible': params['settings'][3] = not params['settings'][3] _plot_vert_lines(params) params['fig'].canvas.draw() if params['fig_proj'] is not None: params['fig_proj'].canvas.draw() def _open_options(params): """Open the option window.""" import matplotlib.pyplot as plt import matplotlib as mpl if params['fig_options'] is not None: # turn off options dialog plt.close(params['fig_options']) params['fig_options'] = None return width = 10 height = 3 fig_options = figure_nobar(figsize=(width, height), dpi=80) fig_options.canvas.set_window_title('View settings') params['fig_options'] = fig_options ax_channels = plt.axes([0.15, 0.1, 0.65, 0.1]) ax_epochs = plt.axes([0.15, 0.25, 0.65, 0.1]) ax_button = plt.axes([0.85, 0.1, 0.1, 0.25]) ax_check = plt.axes([0.15, 0.4, 0.4, 0.55]) plt.axis('off') params['update_button'] = mpl.widgets.Button(ax_button, 'Update') params['channel_slider'] = mpl.widgets.Slider(ax_channels, 'Channels', 1, len(params['ch_names']), valfmt='%0.0f', valinit=params['n_channels']) params['epoch_slider'] = mpl.widgets.Slider(ax_epochs, 'Epochs', 1, len(params['epoch_times']), valfmt='%0.0f', valinit=params['n_epochs']) params['checkbox'] = mpl.widgets.CheckButtons(ax_check, ['Channel names visible', 'Event-id visible', 'Epoch-id visible', 'Zeroline visible'], actives=params['settings']) update = partial(_update_channels_epochs, params=params) params['update_button'].on_clicked(update) labels_callback = partial(_toggle_labels, params=params) params['checkbox'].on_clicked(labels_callback) close_callback = partial(_settings_closed, params=params) params['fig_options'].canvas.mpl_connect('close_event', close_callback) try: params['fig_options'].canvas.draw() params['fig_options'].show(warn=False) if params['fig_proj'] is not None: params['fig_proj'].canvas.draw() except Exception: pass def _settings_closed(events, params): """Handle close event from settings dialog.""" params['fig_options'] = None def _plot_histogram(params): """Plot histogram of peak-to-peak values.""" import matplotlib.pyplot as plt epochs = params['epochs'] p2p = np.ptp(epochs.get_data(), axis=2) types = list() data = list() if 'eeg' in params['types']: eegs = np.array([p2p.T[i] for i, x in enumerate(params['types']) if x == 'eeg']) data.append(eegs.ravel()) types.append('eeg') if 'mag' in params['types']: mags = np.array([p2p.T[i] for i, x in enumerate(params['types']) if x == 'mag']) data.append(mags.ravel()) types.append('mag') if 'grad' in params['types']: grads = np.array([p2p.T[i] for i, x in enumerate(params['types']) if x == 'grad']) data.append(grads.ravel()) types.append('grad') params['histogram'] = plt.figure() scalings = _handle_default('scalings') units = _handle_default('units') titles = _handle_default('titles') colors = _handle_default('color') for idx in range(len(types)): ax = plt.subplot(len(types), 1, idx + 1) plt.xlabel(units[types[idx]]) plt.ylabel('Count') color = colors[types[idx]] rej = None if epochs.reject is not None and types[idx] in epochs.reject: rej = epochs.reject[types[idx]] * scalings[types[idx]] rng = [0., rej * 1.1] else: rng = None plt.hist(data[idx] * scalings[types[idx]], bins=100, color=color, range=rng) if rej is not None: ax.plot((rej, rej), (0, ax.get_ylim()[1]), color='r') plt.title(titles[types[idx]]) params['histogram'].suptitle('Peak-to-peak histogram', y=0.99) params['histogram'].subplots_adjust(hspace=0.6) try: params['histogram'].show(warn=False) except Exception: pass if params['fig_proj'] is not None: params['fig_proj'].canvas.draw() plt.tight_layout(h_pad=0.7, pad=2) def _label2idx(params, pos): """Handle click on labels (returns channel name and idx).""" labels = params['ax'].yaxis.get_ticklabels() offsets = np.array(params['offsets']) + params['offsets'][0] line_idx = np.searchsorted(offsets, pos[1]) text = labels[line_idx].get_text() if len(text) == 0: return None, None ch_idx = params['ch_start'] + line_idx return text, ch_idx def _draw_event_lines(params): """Draw event lines.""" epochs = params['epochs'] n_times = len(epochs.times) start_idx = int(params['t_start'] / n_times) color = params['event_colors'] ax = params['ax'] for ev_line in params['ev_lines']: ax.lines.remove(ev_line) # clear the view first for ev_text in params['ev_texts']: ax.texts.remove(ev_text) params['ev_texts'] = list() params['ev_lines'] = list() t_zero = np.where(epochs.times == 0.)[0] # idx of 0s if len(t_zero) == 0: t_zero = epochs.times[0] * -1 * epochs.info['sfreq'] # if tmin > 0 end = params['n_epochs'] + start_idx samp_times = params['events'][:, 0] for idx, event in enumerate(epochs.events[start_idx:end]): event_mask = ((event[0] - t_zero < samp_times) & (samp_times < event[0] + n_times - t_zero)) for ev in params['events'][event_mask]: if ev[0] == event[0]: # don't redraw the zeroline continue pos = [idx * n_times + ev[0] - event[0] + t_zero, idx * n_times + ev[0] - event[0] + t_zero] kwargs = {} if ev[2] not in color else {'color': color[ev[2]]} params['ev_lines'].append(ax.plot(pos, ax.get_ylim(), zorder=3, **kwargs)[0]) params['ev_texts'].append(ax.text(pos[0], ax.get_ylim()[0], ev[2], color=color[ev[2]], ha='center', va='top'))