"""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 copy import deepcopy import warnings import numpy as np from .raw import _setup_channel_selections from ..defaults import _handle_default from ..utils import verbose, logger, warn, fill_doc, _check_option from ..io.meas_info import create_info, _validate_type from ..io.pick import (_get_channel_types, _picks_to_idx, _DATA_CH_TYPES_SPLIT, _VALID_CHANNEL_TYPES) from .utils import (tight_layout, _setup_vmin_vmax, plt_show, _check_cov, _handle_precompute, _compute_scalings, DraggableColorbar, _setup_cmap, _handle_decim, _set_title_multiple_electrodes, _make_combine_callable, _set_window_title, _make_event_color_dict, _get_channel_plotting_order) @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 µV 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 µV 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='µV', 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 `~mne.viz.plot_compare_evokeds` to style the evoked plot below the image. Defaults to an empty dictionary, meaning `~mne.viz.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 _validate_type(group_by, (dict, None), 'group_by') 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) # `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=False, 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)) with this_info._unlock(): 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 += f' ({_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: # ensure get_ylim works properly this_axes_dict['evoked'].figure.canvas.draw_idle() 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] if 'invert_y' in ts_args: args = args[::-1] ax.set_ylim(*args) 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() _set_window_title(this_fig, this_group) subplot2grid(shape, (0, 0), colspan=colspan, rowspan=rowspan, fig=this_fig) if evoked: subplot2grid(shape, (2, 0), colspan=colspan, rowspan=1, fig=this_fig) if colorbar: subplot2grid(shape, (0, 9), colspan=1, rowspan=rowspan, fig=this_fig) 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() _set_window_title(fig, 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( f'size of overlay_times parameter ({len(overlay_times)}) does ' f'not match the number of epochs ({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 ' f'time interval (between {times[0]} s and {times[-1]} s)') if callable(order): order = order(times, data) if order is not None: if len(order) != n_epochs: raise ValueError(f'If order is a {type(order).__name__}, its ' f'length ({len(order)}) must match the length of ' f'the data ({n_epochs}).') order = np.array(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.""" from matplotlib.ticker import AutoLocator 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 = [tmin, 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') if not evoked: ax_im.set_xlabel('Time (s)') 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(overlay_times, 0.5 + np.arange(n_epochs), 'k', linewidth=2) ax_im.set_xlim(tmin, 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(copy=False))}, picks=_picks, axes=ax['evoked'], combine=pass_combine, **ts_args) ax['evoked'].set_xlim(tmin, tmax) ax['evoked'].lines[0].set_clip_on(True) ax['evoked'].collections[0].set_clip_on(True) ax['evoked'].get_shared_x_axes().join(ax['evoked'], ax_im) # fix the axes for proper updating during interactivity loc = ax_im.xaxis.get_major_locator() ax['evoked'].xaxis.set_major_locator(loc) ax['evoked'].yaxis.set_major_locator(AutoLocator()) # 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) with warnings.catch_warnings(record=True): warnings.simplefilter('ignore') tight_layout(fig=fig) # finish plt_show(show) return fig def plot_drop_log(drop_log, threshold=0, n_max_plot=20, subject=None, color='lightgray', 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 | None The subject name to use in the title of the plot. If ``None``, do not display a subject name. .. versionchanged:: 0.23 Added support for ``None``. .. versionchanged:: 1.0 Defaults to ``None``. 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 absolute = len([x for x in drop_log if len(x) if not any(y in ignore for y in x)]) n_epochs_before_drop = len([x for x in drop_log if not any(y in ignore for y in x)]) 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() title = (f'{absolute} of {n_epochs_before_drop} epochs removed ' f'({percent:.1f}%)') if subject is not None: title = f'{subject}: {title}' ax.set_title(title) 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 removed') ax.grid(axis='y') tight_layout(pad=1, fig=fig) plt_show(show) return fig @fill_doc def plot_epochs(epochs, picks=None, scalings=None, n_epochs=20, n_channels=20, title=None, events=None, event_color=None, order=None, show=True, block=False, decim='auto', noise_cov=None, butterfly=False, show_scrollbars=True, show_scalebars=True, epoch_colors=None, event_id=None, group_by='type', precompute=None, use_opengl=None, *, theme=None, overview_mode=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)s 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. You can use `~mne.viz.plot_events` as a legend for the colors. By default, the coloring scheme is the same. Defaults to ``None``. .. warning:: If the epochs have been resampled, the events no longer align with the data. .. versionadded:: 0.14.0 %(event_color)s Defaults to ``None``. 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 %(show_scalebars)s .. versionadded:: 0.24.0 epoch_colors : list of (n_epochs) list (of n_channels) | None Colors to use for individual epochs. If None, use default colors. event_id : dict | None Dictionary of event labels (e.g. 'aud_l') as keys and associated event integers as values. Useful when ``events`` contains event numbers not present in ``epochs.event_id`` (e.g., because of event subselection). Values in ``event_id`` will take precedence over those in ``epochs.event_id`` when there are overlapping keys. .. versionadded:: 0.20 %(group_by_browse)s %(precompute)s %(use_opengl)s %(theme_pg)s .. versionadded:: 1.0 %(overview_mode)s .. versionadded:: 1.1 Returns ------- %(browser)s 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. ``h`` key plots a histogram of peak-to-peak values along with the used rejection thresholds. Butterfly plot can be toggled with ``b`` key. Left 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 """ from ._figure import _get_browser epochs.drop_bad() info = epochs.info.copy() sfreq = info['sfreq'] projs = info['projs'] projs_on = np.full_like(projs, epochs.proj, dtype=bool) if not epochs.proj: with info._unlock(): info['projs'] = list() # handle defaults / check arg validity color = _handle_default('color', None) scalings = _compute_scalings(scalings, epochs) scalings = _handle_default('scalings_plot_raw', scalings) if scalings['whitened'] == 'auto': scalings['whitened'] = 1. units = _handle_default('units', None) unit_scalings = _handle_default('scalings', None) decim, picks_data = _handle_decim(epochs.info.copy(), decim, None) noise_cov = _check_cov(noise_cov, epochs.info) event_id_rev = {v: k for k, v in (event_id or {}).items()} _check_option('group_by', group_by, ('selection', 'position', 'original', 'type')) # validate epoch_colors _validate_type(epoch_colors, (list, None), 'epoch_colors') if epoch_colors is not None: if len(epoch_colors) != len(epochs.events): msg = ('epoch_colors must have length equal to the number of ' f'epochs ({len(epochs)}); got length {len(epoch_colors)}.') raise ValueError(msg) for ix, this_colors in enumerate(epoch_colors): _validate_type(this_colors, list, f'epoch_colors[{ix}]') if len(this_colors) != len(epochs.ch_names): msg = (f'epoch colors for epoch {ix} has length ' f'{len(this_colors)}, expected {len(epochs.ch_names)}.') raise ValueError(msg) # handle time dimension n_epochs = min(n_epochs, len(epochs)) n_times = len(epochs) * len(epochs.times) duration = n_epochs * len(epochs.times) / sfreq # NB: this includes start and end of data: boundary_times = np.arange(len(epochs) + 1) * len(epochs.times) / sfreq # events if events is not None: event_nums = events[:, 2] event_samps = events[:, 0] epoch_n_samps = len(epochs.times) # handle overlapping epochs (each event may show up in multiple places) boundaries = (epochs.events[:, [0]] + np.array([-1, 1]) * epochs.time_as_index(0)) in_bounds = np.logical_and(boundaries[:, [0]] <= event_samps, event_samps < boundaries[:, [1]]) event_ixs = [np.nonzero(a)[0] for a in in_bounds.T] warned = False event_times = list() event_numbers = list() for samp, num, _ixs in zip(event_samps, event_nums, event_ixs): relevant_epoch_events = epochs.events[:, 0][_ixs] if len(relevant_epoch_events) > 1 and not warned: logger.info('You seem to have overlapping epochs. Some event ' 'lines may be duplicated in the plot.') warned = True offsets = samp - relevant_epoch_events + epochs.time_as_index(0) this_event_times = (_ixs * epoch_n_samps + offsets) / sfreq event_times.extend(this_event_times) event_numbers.extend([num] * len(_ixs)) event_nums = np.array(event_numbers) event_times = np.array(event_times) else: event_nums = None event_times = None event_color_dict = _make_event_color_dict(event_color, events, event_id) # determine trace order picks = _picks_to_idx(info, picks) n_channels = min(n_channels, len(picks)) ch_names = np.array(epochs.ch_names) ch_types = np.array(epochs.get_channel_types()) order = _get_channel_plotting_order(order, ch_types, picks) selections = None if group_by in ('selection', 'position'): selections = _setup_channel_selections(epochs, group_by, order) order = np.concatenate(list(selections.values())) default_selection = list(selections)[0] n_channels = len(selections[default_selection]) # generate window title if title is None: title = epochs._name if title is None or len(title) == 0: title = 'Epochs' elif not isinstance(title, str): raise TypeError(f'title must be None or a string, got a {type(title)}') precompute = _handle_precompute(precompute) params = dict(inst=epochs, info=info, n_epochs=n_epochs, # channels and channel order ch_names=ch_names, ch_types=ch_types, ch_order=order, picks=order[:n_channels], n_channels=n_channels, picks_data=picks_data, group_by=group_by, ch_selections=selections, # time t_start=0, duration=duration, n_times=n_times, first_time=0, time_format='float', decim=decim, boundary_times=boundary_times, # events event_id_rev=event_id_rev, event_color_dict=event_color_dict, event_nums=event_nums, event_times=event_times, # preprocessing projs=projs, projs_on=projs_on, apply_proj=epochs.proj, remove_dc=True, filter_coefs=None, filter_bounds=None, noise_cov=noise_cov, use_noise_cov=noise_cov is not None, # scalings scalings=scalings, units=units, unit_scalings=unit_scalings, # colors ch_color_bad='lightgray', ch_color_dict=color, epoch_color_bad=(1, 0, 0), epoch_colors=epoch_colors, # display butterfly=butterfly, clipping=None, scrollbars_visible=show_scrollbars, scalebars_visible=show_scalebars, window_title=title, xlabel='Epoch number', # pyqtgraph-specific precompute=precompute, use_opengl=use_opengl, theme=theme, overview_mode=overview_mode) fig = _get_browser(show=show, block=block, **params) return 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=None, average=False, line_alpha=None, spatial_colors=True, sphere=None, exclude='bads', 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)s %(picks_plot_psd_good_data)s ax : instance of Axes | None Axes to plot into. If None, axes will be created. %(color_plot_psd)s %(xscale_plot_psd)s %(area_mode_plot_psd)s %(area_alpha_plot_psd)s %(dB_plot_psd)s %(estimate_plot_psd)s %(show)s %(n_jobs)s %(average_plot_psd)s %(line_alpha_plot_psd)s %(spatial_colors_plot_psd)s %(sphere_topomap_auto)s exclude : list of str | 'bads' Channels names to exclude from being shown. If 'bads', the bad channels are excluded. Pass an empty list to plot all channels (including channels marked "bad", if any). .. versionadded:: 0.24.0 %(verbose)s Returns ------- fig : instance of Figure Figure with frequency spectra of the data channels. """ from ._mpl_figure import _psd_figure # generate figure # epochs always use multitaper, not Welch, so no need to allow "window" # param above fig = _psd_figure( inst=epochs, proj=proj, picks=picks, axes=ax, tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax, sphere=sphere, xscale=xscale, dB=dB, average=average, estimate=estimate, area_mode=area_mode, line_alpha=line_alpha, area_alpha=area_alpha, color=color, spatial_colors=spatial_colors, n_jobs=n_jobs, bandwidth=bandwidth, adaptive=adaptive, low_bias=low_bias, normalization=normalization, window='hamming', exclude=exclude) plt_show(show) return fig