"""Functions to plot ICA specific data (besides topographies).""" # Authors: Denis Engemann # Alexandre Gramfort # Teon Brooks # Daniel McCloy # # License: Simplified BSD from functools import partial import warnings import numpy as np from .utils import (tight_layout, _make_event_color_dict, plt_show, _convert_psds, _compute_scalings, _handle_precompute) from .topomap import _plot_ica_topomap from .epochs import plot_epochs_image from .evoked import _butterfly_on_button_press, _butterfly_onpick from ..channels.channels import _get_ch_type from ..utils import _validate_type, fill_doc from ..defaults import _handle_default from ..io.meas_info import create_info from ..io.pick import pick_types, _picks_to_idx from ..time_frequency.psd import psd_multitaper from ..utils import _reject_data_segments, verbose @fill_doc def plot_ica_sources(ica, inst, picks=None, start=None, stop=None, title=None, show=True, block=False, show_first_samp=False, show_scrollbars=True, time_format='float', precompute=None, use_opengl=None, *, theme=None, overview_mode=None): """Plot estimated latent sources given the unmixing matrix. Typical usecases: 1. plot evolution of latent sources over time based on (Raw input) 2. plot latent source around event related time windows (Epochs input) 3. plot time-locking in ICA space (Evoked input) Parameters ---------- ica : instance of mne.preprocessing.ICA The ICA solution. inst : instance of mne.io.Raw, mne.Epochs, mne.Evoked The object to plot the sources from. %(picks_ica)s ``None`` (default) will pick all sources in the order fitted. start, stop : float | int | None If ``inst`` is a `~mne.io.Raw` or an `~mne.Evoked` object, the first and last time point (in seconds) of the data to plot. If ``inst`` is a `~mne.io.Raw` object, ``start=None`` and ``stop=None`` will be translated into ``start=0.`` and ``stop=3.``, respectively. For `~mne.Evoked`, ``None`` refers to the beginning and end of the evoked signal. If ``inst`` is an `~mne.Epochs` object, specifies the index of the first and last epoch to show. title : str | None The window title. If None a default is provided. show : bool Show figure if True. block : bool Whether to halt program execution until the figure is closed. Useful for interactive selection of components in raw and epoch plotter. For evoked, this parameter has no effect. Defaults to False. show_first_samp : bool If True, show time axis relative to the ``raw.first_samp``. %(show_scrollbars)s %(time_format)s %(precompute)s %(use_opengl)s %(theme_pg)s .. versionadded:: 1.0 %(overview_mode)s .. versionadded:: 1.1 Returns ------- %(browser)s Notes ----- For raw and epoch instances, it is possible to select components for exclusion by clicking on the line. The selected components are added to ``ica.exclude`` on close. .. versionadded:: 0.10.0 """ from ..io.base import BaseRaw from ..evoked import Evoked from ..epochs import BaseEpochs exclude = ica.exclude picks = _picks_to_idx(ica.n_components_, picks, 'all') if isinstance(inst, (BaseRaw, BaseEpochs)): fig = _plot_sources(ica, inst, picks, exclude, start=start, stop=stop, show=show, title=title, block=block, show_first_samp=show_first_samp, show_scrollbars=show_scrollbars, time_format=time_format, precompute=precompute, use_opengl=use_opengl, theme=theme, overview_mode=overview_mode) elif isinstance(inst, Evoked): if start is not None or stop is not None: inst = inst.copy().crop(start, stop) sources = ica.get_sources(inst) fig = _plot_ica_sources_evoked( evoked=sources, picks=picks, exclude=exclude, title=title, labels=getattr(ica, 'labels_', None), show=show, ica=ica) else: raise ValueError('Data input must be of Raw or Epochs type') return fig def _create_properties_layout(figsize=None, fig=None): """Create main figure and axes layout used by plot_ica_properties.""" import matplotlib.pyplot as plt if fig is not None and figsize is not None: raise ValueError('Cannot specify both fig and figsize.') if figsize is None: figsize = [7., 6.] if fig is None: fig = plt.figure(figsize=figsize, facecolor=[0.95] * 3) axes_params = (('topomap', [0.08, 0.5, 0.3, 0.45]), ('image', [0.5, 0.6, 0.45, 0.35]), ('erp', [0.5, 0.5, 0.45, 0.1]), ('spectrum', [0.08, 0.1, 0.32, 0.3]), ('variance', [0.5, 0.1, 0.45, 0.25])) axes = [fig.add_axes(loc, label=name) for name, loc in axes_params] return fig, axes def _plot_ica_properties(pick, ica, inst, psds_mean, freqs, n_trials, epoch_var, plot_lowpass_edge, epochs_src, set_title_and_labels, plot_std, psd_ylabel, spectrum_std, log_scale, topomap_args, image_args, fig, axes, kind, dropped_indices): """Plot ICA properties (helper).""" from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable from scipy.stats import gaussian_kde topo_ax, image_ax, erp_ax, spec_ax, var_ax = axes # plotting # -------- # component topomap _plot_ica_topomap(ica, pick, show=False, axes=topo_ax, **topomap_args) topo_ax._ch_type = _get_ch_type(ica, ch_type=None, allow_ref_meg=ica.allow_ref_meg) # image and erp # we create a new epoch with dropped rows epoch_data = epochs_src.get_data() epoch_data = np.insert(arr=epoch_data, obj=(dropped_indices - np.arange(len(dropped_indices))).astype(int), values=0.0, axis=0) from ..epochs import EpochsArray epochs_src = EpochsArray(epoch_data, epochs_src.info, tmin=epochs_src.tmin, verbose=0) plot_epochs_image(epochs_src, picks=pick, axes=[image_ax, erp_ax], combine=None, colorbar=False, show=False, **image_args) # spectrum spec_ax.plot(freqs, psds_mean, color='k') if plot_std: spec_ax.fill_between(freqs, psds_mean - spectrum_std[0], psds_mean + spectrum_std[1], color='k', alpha=.2) if plot_lowpass_edge: spec_ax.axvline(inst.info['lowpass'], lw=2, linestyle='--', color='k', alpha=0.2) # epoch variance var_ax_divider = make_axes_locatable(var_ax) hist_ax = var_ax_divider.append_axes("right", size="33%", pad="2.5%") var_ax.scatter(range(len(epoch_var)), epoch_var, alpha=0.5, facecolor=[0, 0, 0], lw=0) # rejected epochs in red var_ax.scatter(dropped_indices, epoch_var[dropped_indices], alpha=1., facecolor=[1, 0, 0], lw=0) # compute percentage of dropped epochs var_percent = float(len(dropped_indices)) / float(len(epoch_var)) * 100. # histogram & histogram _, counts, _ = hist_ax.hist(epoch_var, orientation="horizontal", color="k", alpha=.5) # kde ymin, ymax = hist_ax.get_ylim() try: kde = gaussian_kde(epoch_var) except np.linalg.LinAlgError: pass # singular: happens when there is nothing plotted else: x = np.linspace(ymin, ymax, 50) kde_ = kde(x) kde_ /= kde_.max() or 1. kde_ *= hist_ax.get_xlim()[-1] * .9 hist_ax.plot(kde_, x, color="k") hist_ax.set_ylim(ymin, ymax) # aesthetics # ---------- set_title_and_labels(image_ax, kind + ' image and ERP/ERF', [], kind) # erp set_title_and_labels(erp_ax, [], 'Time (s)', 'AU') erp_ax.spines["right"].set_color('k') erp_ax.set_xlim(epochs_src.times[[0, -1]]) # remove half of yticks if more than 5 yt = erp_ax.get_yticks() if len(yt) > 5: erp_ax.yaxis.set_ticks(yt[::2]) # remove xticks - erp plot shows xticks for both image and erp plot image_ax.xaxis.set_ticks([]) yt = image_ax.get_yticks() image_ax.yaxis.set_ticks(yt[1:]) image_ax.set_ylim([-0.5, n_trials + 0.5]) def _set_scale(ax, scale): """Set the scale of a matplotlib axis.""" ax.set_xscale(scale) ax.set_yscale(scale) ax.relim() ax.autoscale() # spectrum set_title_and_labels(spec_ax, 'Spectrum', 'Frequency (Hz)', psd_ylabel) spec_ax.yaxis.labelpad = 0 spec_ax.set_xlim(freqs[[0, -1]]) ylim = spec_ax.get_ylim() air = np.diff(ylim)[0] * 0.1 spec_ax.set_ylim(ylim[0] - air, ylim[1] + air) image_ax.axhline(0, color='k', linewidth=.5) if log_scale: _set_scale(spec_ax, 'log') # epoch variance var_ax_title = 'Dropped segments: %.2f %%' % var_percent set_title_and_labels(var_ax, var_ax_title, kind, 'Variance (AU)') hist_ax.set_ylabel("") hist_ax.set_yticks([]) set_title_and_labels(hist_ax, None, None, None) def _plot_ica_properties_on_press(event, ica, pick, topomap_args): """Handle keypress events for ica properties plot.""" import matplotlib.pyplot as plt fig = event.canvas.figure if event.key == 'escape': plt.close(fig) if event.key in ('t', 'l'): ax_labels = [ax.get_label() for ax in fig.axes] if event.key == 't': ax = fig.axes[ax_labels.index('topomap')] ax.clear() ch_types = list(set(ica.get_channel_types())) ch_type = \ ch_types[(ch_types.index(ax._ch_type) + 1) % len(ch_types)] _plot_ica_topomap(ica, pick, ch_type=ch_type, show=False, axes=ax, **topomap_args) ax._ch_type = ch_type elif event.key == 'l': ax = fig.axes[ax_labels.index('spectrum')] _set_scale(ax, 'linear' if ax.get_xscale() == 'log' else 'log') del ax fig.canvas.draw() # add keypress event handler fig.canvas.mpl_connect( 'key_press_event', lambda event: _plot_ica_properties_on_press( event, ica, pick, topomap_args)) return fig def _get_psd_label_and_std(this_psd, dB, ica, num_std): """Handle setting up PSD for one component, for plot_ica_properties.""" psd_ylabel = _convert_psds(this_psd, dB, estimate='auto', scaling=1., unit='AU', first_dim='epoch') psds_mean = this_psd.mean(axis=0) diffs = this_psd - psds_mean # the distribution of power for each frequency bin is highly # skewed so we calculate std for values below and above average # separately - this is used for fill_between shade with warnings.catch_warnings(): # mean of empty slice warnings.simplefilter('ignore') spectrum_std = [ [np.sqrt((d[d < 0] ** 2).mean(axis=0)) for d in diffs.T], [np.sqrt((d[d > 0] ** 2).mean(axis=0)) for d in diffs.T]] spectrum_std = np.array(spectrum_std) * num_std return psd_ylabel, psds_mean, spectrum_std @verbose def plot_ica_properties(ica, inst, picks=None, axes=None, dB=True, plot_std=True, log_scale=False, topomap_args=None, image_args=None, psd_args=None, figsize=None, show=True, reject='auto', reject_by_annotation=True, *, verbose=None): """Display component properties. Properties include the topography, epochs image, ERP/ERF, power spectrum, and epoch variance. Parameters ---------- ica : instance of mne.preprocessing.ICA The ICA solution. inst : instance of Epochs or Raw The data to use in plotting properties. .. note:: You can interactively cycle through topographic maps for different channel types by pressing :kbd:`T`. picks : str | list | slice | None Components to include. Slices and lists of integers will be interpreted as component indices. ``None`` (default) will use the first five components. Each component will be plotted in a separate figure. axes : list of Axes | None List of five matplotlib axes to use in plotting: [topomap_axis, image_axis, erp_axis, spectrum_axis, variance_axis]. If None a new figure with relevant axes is created. Defaults to None. dB : bool Whether to plot spectrum in dB. Defaults to True. plot_std : bool | float Whether to plot standard deviation/confidence intervals in ERP/ERF and spectrum plots. Defaults to True, which plots one standard deviation above/below for the spectrum. If set to float allows to control how many standard deviations are plotted for the spectrum. For example 2.5 will plot 2.5 standard deviation above/below. For the ERP/ERF, by default, plot the 95 percent parametric confidence interval is calculated. To change this, use ``ci`` in ``ts_args`` in ``image_args`` (see below). log_scale : bool Whether to use a logarithmic frequency axis to plot the spectrum. Defaults to ``False``. .. note:: You can interactively toggle this setting by pressing :kbd:`L`. .. versionadded:: 1.1 topomap_args : dict | None Dictionary of arguments to ``plot_topomap``. If None, doesn't pass any additional arguments. Defaults to None. image_args : dict | None Dictionary of arguments to ``plot_epochs_image``. If None, doesn't pass any additional arguments. Defaults to None. psd_args : dict | None Dictionary of arguments to ``psd_multitaper``. If None, doesn't pass any additional arguments. Defaults to None. figsize : array-like, shape (2,) | None Allows to control size of the figure. If None, the figure size defaults to [7., 6.]. show : bool Show figure if True. reject : 'auto' | dict | None Allows to specify rejection parameters used to drop epochs (or segments if continuous signal is passed as inst). If None, no rejection is applied. The default is 'auto', which applies the rejection parameters used when fitting the ICA object. %(reject_by_annotation_raw)s .. versionadded:: 0.21.0 %(verbose)s Returns ------- fig : list List of matplotlib figures. Notes ----- .. versionadded:: 0.13 """ return _fast_plot_ica_properties(ica, inst, picks=picks, axes=axes, dB=dB, plot_std=plot_std, log_scale=log_scale, topomap_args=topomap_args, image_args=image_args, psd_args=psd_args, figsize=figsize, show=show, reject=reject, reject_by_annotation=reject_by_annotation, verbose=verbose, precomputed_data=None) def _fast_plot_ica_properties(ica, inst, picks=None, axes=None, dB=True, plot_std=True, log_scale=False, topomap_args=None, image_args=None, psd_args=None, figsize=None, show=True, reject='auto', precomputed_data=None, reject_by_annotation=True, *, verbose=None): """Display component properties.""" from ..preprocessing import ICA # input checks and defaults # ------------------------- _validate_type(ica, ICA, "ica", "ICA") _validate_type(plot_std, (bool, 'numeric'), 'plot_std') if isinstance(plot_std, bool): num_std = 1. if plot_std else 0. else: plot_std = True num_std = float(plot_std) # if no picks given - plot the first 5 components limit = min(5, ica.n_components_) if picks is None else len(ica.ch_names) picks = _picks_to_idx(ica.info, picks, 'all')[:limit] if axes is None: fig, axes = _create_properties_layout(figsize=figsize) else: if len(picks) > 1: raise ValueError('Only a single pick can be drawn ' 'to a set of axes.') from .utils import _validate_if_list_of_axes _validate_if_list_of_axes(axes, obligatory_len=5) fig = axes[0].get_figure() psd_args = dict() if psd_args is None else psd_args topomap_args = dict() if topomap_args is None else topomap_args image_args = dict() if image_args is None else image_args image_args["ts_args"] = dict(truncate_xaxis=False, show_sensors=False) if plot_std: from ..stats.parametric import _parametric_ci image_args["ts_args"]["ci"] = _parametric_ci elif "ts_args" not in image_args or "ci" not in image_args["ts_args"]: image_args["ts_args"]["ci"] = False for item_name, item in (("psd_args", psd_args), ("topomap_args", topomap_args), ("image_args", image_args)): _validate_type(item, dict, item_name, "dictionary") _validate_type(dB, (bool, None), "dB") _validate_type(log_scale, (bool, None), "log_scale") # calculations # ------------ if isinstance(precomputed_data, tuple): kind, dropped_indices, epochs_src, data = precomputed_data else: kind, dropped_indices, epochs_src, data = _prepare_data_ica_properties( inst, ica, reject_by_annotation, reject) ica_data = np.swapaxes(data[:, picks, :], 0, 1) dropped_src = ica_data # spectrum Nyquist = inst.info['sfreq'] / 2. lp = inst.info['lowpass'] if 'fmax' not in psd_args: psd_args['fmax'] = min(lp * 1.25, Nyquist) plot_lowpass_edge = lp < Nyquist and (psd_args['fmax'] > lp) psds, freqs = psd_multitaper(epochs_src, picks=picks, **psd_args) def set_title_and_labels(ax, title, xlab, ylab): if title: ax.set_title(title) if xlab: ax.set_xlabel(xlab) if ylab: ax.set_ylabel(ylab) ax.axis('auto') ax.tick_params('both', labelsize=8) ax.axis('tight') # plot # ---- all_fig = list() for idx, pick in enumerate(picks): # calculate component-specific spectrum stuff psd_ylabel, psds_mean, spectrum_std = _get_psd_label_and_std( psds[:, idx, :].copy(), dB, ica, num_std) # if more than one component, spawn additional figures and axes if idx > 0: fig, axes = _create_properties_layout(figsize=figsize) # we reconstruct an epoch_variance with 0 where indexes where dropped epoch_var = np.var(ica_data[idx], axis=1) drop_var = np.var(dropped_src[idx], axis=1) drop_indices_corrected = \ (dropped_indices - np.arange(len(dropped_indices))).astype(int) epoch_var = np.insert(arr=epoch_var, obj=drop_indices_corrected, values=drop_var[dropped_indices], axis=0) # the actual plot fig = _plot_ica_properties( pick, ica, inst, psds_mean, freqs, ica_data.shape[1], epoch_var, plot_lowpass_edge, epochs_src, set_title_and_labels, plot_std, psd_ylabel, spectrum_std, log_scale, topomap_args, image_args, fig, axes, kind, dropped_indices) all_fig.append(fig) plt_show(show) return all_fig def _prepare_data_ica_properties(inst, ica, reject_by_annotation=True, reject='auto'): """Prepare Epochs sources to plot ICA properties. Parameters ---------- ica : instance of mne.preprocessing.ICA The ICA solution. inst : instance of Epochs or Raw The data to use in plotting properties. reject_by_annotation : bool, optional [description], by default True reject : str, optional [description], by default 'auto' Returns ------- kind : str "Segment" for BaseRaw and "Epochs" for BaseEpochs dropped_indices : list Dropped epochs indexes. epochs_src : instance of Epochs Segmented data of ICA sources. data : array of shape (n_epochs, n_ica_sources, n_times) A view on epochs ICA sources data. """ from ..io.base import BaseRaw from ..io import RawArray from ..epochs import BaseEpochs _validate_type(inst, (BaseRaw, BaseEpochs), "inst", "Raw or Epochs") if isinstance(inst, BaseRaw): # when auto, delegate reject to the ica from ..epochs import make_fixed_length_epochs if reject == 'auto': reject = getattr(ica, 'reject_', None) if reject is None: drop_inds = None dropped_indices = [] # break up continuous signal into segments epochs_src = make_fixed_length_epochs( ica.get_sources(inst), duration=2, preload=True, reject_by_annotation=reject_by_annotation, proj=False, verbose=False) else: data = inst.get_data() data, drop_inds = _reject_data_segments(data, ica.reject_, flat=None, decim=None, info=inst.info, tstep=2.0) inst_rejected = RawArray(data, inst.info) # break up continuous signal into segments epochs_src = make_fixed_length_epochs( ica.get_sources(inst_rejected), duration=2, preload=True, reject_by_annotation=reject_by_annotation, proj=False, verbose=False) # getting dropped epochs indexes dropped_indices = [(d[0] // len(epochs_src.times)) + 1 for d in drop_inds] kind = "Segment" else: drop_inds = None epochs_src = ica.get_sources(inst) dropped_indices = [] kind = "Epochs" return kind, dropped_indices, epochs_src, epochs_src.get_data() def _plot_ica_sources_evoked(evoked, picks, exclude, title, show, ica, labels=None): """Plot average over epochs in ICA space. Parameters ---------- evoked : instance of mne.Evoked The Evoked to be used. %(picks_base)s all sources in the order as fitted. exclude : array-like of int The components marked for exclusion. If None (default), ICA.exclude will be used. title : str The figure title. show : bool Show figure if True. labels : None | dict The ICA labels attribute. """ import matplotlib.pyplot as plt from matplotlib import patheffects if title is None: title = 'Reconstructed latent sources, time-locked' fig, axes = plt.subplots(1) ax = axes axes = [axes] times = evoked.times * 1e3 # plot unclassified sources and label excluded ones lines = list() texts = list() picks = np.sort(picks) idxs = [picks] if labels is not None: labels_used = [k for k in labels if '/' not in k] exclude_labels = list() for ii in picks: if ii in exclude: line_label = ica._ica_names[ii] if labels is not None: annot = list() for this_label in labels_used: indices = labels[this_label] if ii in indices: annot.append(this_label) if annot: line_label += (' – ' + ', '.join(annot)) # Unicode en-dash exclude_labels.append(line_label) else: exclude_labels.append(None) label_props = [('k', '-') if lb is None else ('r', '-') for lb in exclude_labels] styles = ['-', '--', ':', '-.'] if labels is not None: # differentiate categories by linestyle and components by color col_lbs = [it for it in exclude_labels if it is not None] cmap = plt.get_cmap('tab10', len(col_lbs)) unique_labels = set() for label in exclude_labels: if label is None: continue elif ' – ' in label: unique_labels.add(label.split(' – ')[1]) else: unique_labels.add('') # Determine up to 4 different styles for n categories cat_styles = dict(zip(unique_labels, map(lambda ux: styles[int(ux % len(styles))], range(len(unique_labels))))) for label_idx, label in enumerate(exclude_labels): if label is not None: color = cmap(col_lbs.index(label)) if ' – ' in label: label_name = label.split(' – ')[1] else: label_name = '' style = cat_styles[label_name] label_props[label_idx] = (color, style) for exc_label, ii in zip(exclude_labels, picks): color, style = label_props[ii] # ensure traces of excluded components are plotted on top zorder = 2 if exc_label is None else 10 lines.extend(ax.plot(times, evoked.data[ii].T, picker=True, zorder=zorder, color=color, linestyle=style, label=exc_label)) lines[-1].set_pickradius(3.) ax.set(title=title, xlim=times[[0, -1]], xlabel='Time (ms)', ylabel='(NA)') if len(exclude) > 0: plt.legend(loc='best') tight_layout(fig=fig) texts.append(ax.text(0, 0, '', zorder=3, verticalalignment='baseline', horizontalalignment='left', fontweight='bold', alpha=0)) # this is done to give the structure of a list of lists of a group of lines # in each subplot lines = [lines] ch_names = evoked.ch_names path_effects = [patheffects.withStroke(linewidth=2, foreground="w", alpha=0.75)] params = dict(axes=axes, texts=texts, lines=lines, idxs=idxs, ch_names=ch_names, need_draw=False, path_effects=path_effects) fig.canvas.mpl_connect('pick_event', partial(_butterfly_onpick, params=params)) fig.canvas.mpl_connect('button_press_event', partial(_butterfly_on_button_press, params=params)) plt_show(show) return fig def plot_ica_scores(ica, scores, exclude=None, labels=None, axhline=None, title='ICA component scores', figsize=None, n_cols=None, show=True): """Plot scores related to detected components. Use this function to asses how well your score describes outlier sources and how well you were detecting them. Parameters ---------- ica : instance of mne.preprocessing.ICA The ICA object. scores : array-like of float, shape (n_ica_components,) | list of array Scores based on arbitrary metric to characterize ICA components. exclude : array-like of int The components marked for exclusion. If None (default), ICA.exclude will be used. labels : str | list | 'ecg' | 'eog' | None The labels to consider for the axes tests. Defaults to None. If list, should match the outer shape of ``scores``. If 'ecg' or 'eog', the ``labels_`` attributes will be looked up. Note that '/' is used internally for sublabels specifying ECG and EOG channels. axhline : float Draw horizontal line to e.g. visualize rejection threshold. title : str The figure title. figsize : tuple of int | None The figure size. If None it gets set automatically. n_cols : int | None Scores are plotted in a grid. This parameter controls how many to plot side by side before starting a new row. By default, a number will be chosen to make the grid as square as possible. show : bool Show figure if True. Returns ------- fig : instance of Figure The figure object. """ import matplotlib.pyplot as plt my_range = np.arange(ica.n_components_) if exclude is None: exclude = ica.exclude exclude = np.unique(exclude) if not isinstance(scores[0], (list, np.ndarray)): scores = [scores] n_scores = len(scores) if n_cols is None: # prefer more rows. n_rows = int(np.ceil(np.sqrt(n_scores))) n_cols = (n_scores - 1) // n_rows + 1 else: n_cols = min(n_scores, n_cols) n_rows = (n_scores - 1) // n_cols + 1 if figsize is None: figsize = (6.4 * n_cols, 2.7 * n_rows) fig, axes = plt.subplots(n_rows, n_cols, figsize=figsize, sharex=True, sharey=True) if isinstance(axes, np.ndarray): axes = axes.flatten() else: axes = [axes] fig.suptitle(title) if labels == 'ecg': labels = [label for label in ica.labels_ if label.startswith('ecg/')] labels.sort(key=lambda l: l.split('/')[1]) # sort by index if len(labels) == 0: labels = [label for label in ica.labels_ if label.startswith('ecg')] elif labels == 'eog': labels = [label for label in ica.labels_ if label.startswith('eog/')] labels.sort(key=lambda l: l.split('/')[1]) # sort by index if len(labels) == 0: labels = [label for label in ica.labels_ if label.startswith('eog')] elif isinstance(labels, str): labels = [labels] elif labels is None: labels = (None,) * n_scores if len(labels) != n_scores: raise ValueError('Need as many labels (%i) as scores (%i)' % (len(labels), n_scores)) for label, this_scores, ax in zip(labels, scores, axes): if len(my_range) != len(this_scores): raise ValueError('The length of `scores` must equal the ' 'number of ICA components.') ax.bar(my_range, this_scores, color='gray', edgecolor='k') for excl in exclude: ax.bar(my_range[excl], this_scores[excl], color='r', edgecolor='k') if axhline is not None: if np.isscalar(axhline): axhline = [axhline] for axl in axhline: ax.axhline(axl, color='r', linestyle='--') ax.set_ylabel('score') if label is not None: if 'eog/' in label: split = label.split('/') label = ', '.join([split[0], split[2]]) elif '/' in label: label = ', '.join(label.split('/')) ax.set_title('(%s)' % label) ax.set_xlabel('ICA components') ax.set_xlim(-0.6, len(this_scores) - 0.4) tight_layout(fig=fig) adjust_top = 0.8 if len(fig.axes) == 1 else 0.9 fig.subplots_adjust(top=adjust_top) fig.canvas.draw() plt_show(show) return fig @fill_doc def plot_ica_overlay(ica, inst, exclude=None, picks=None, start=None, stop=None, title=None, show=True, n_pca_components=None): """Overlay of raw and cleaned signals given the unmixing matrix. This method helps visualizing signal quality and artifact rejection. Parameters ---------- ica : instance of mne.preprocessing.ICA The ICA object. inst : instance of mne.io.Raw or mne.Evoked The signal to plot. If `~mne.io.Raw`, the raw data is displayed before and after cleaning. In a second panel, the cross-channel average will be displayed. Since dipolar sources will be canceled out, this representation is sensitive to artifacts. If `~mne.Evoked`, butterfly traces for signals before and after cleaning will be superimposed. exclude : array-like of int | None (default) The components marked for exclusion. If ``None`` (default), ICA.exclude will be used. %(picks_base)s all channels that were included during fitting. start, stop : float | None The first and last time point (in seconds) of the data to plot. If ``inst`` is a `~mne.io.Raw` object, ``start=None`` and ``stop=None`` will be translated into ``start=0.`` and ``stop=3.``, respectively. For `~mne.Evoked`, ``None`` refers to the beginning and end of the evoked signal. %(title_none)s %(show)s %(n_pca_components_apply)s .. versionadded:: 0.22 Returns ------- fig : instance of Figure The figure. """ # avoid circular imports from ..io.base import BaseRaw from ..evoked import Evoked from ..preprocessing.ica import _check_start_stop if ica.current_fit == 'unfitted': raise RuntimeError('You need to fit the ICA first') _validate_type(inst, (BaseRaw, Evoked), "inst", "Raw or Evoked") if title is None: title = 'Signals before (red) and after (black) cleaning' picks = ica.ch_names if picks is None else picks picks = _picks_to_idx(inst.info, picks, exclude=()) ch_types_used = inst.get_channel_types(picks=picks, unique=True) if exclude is None: exclude = ica.exclude if not isinstance(exclude, (np.ndarray, list)): raise TypeError('exclude must be of type list. Got %s' % type(exclude)) if isinstance(inst, BaseRaw): if start is None: start = 0.0 if stop is None: stop = 3.0 start_compare, stop_compare = _check_start_stop(inst, start, stop) data, times = inst[picks, start_compare:stop_compare] raw_cln = ica.apply(inst.copy(), exclude=exclude, start=start, stop=stop, n_pca_components=n_pca_components) data_cln, _ = raw_cln[picks, start_compare:stop_compare] fig = _plot_ica_overlay_raw(data=data, data_cln=data_cln, times=times, title=title, ch_types_used=ch_types_used, show=show) else: assert isinstance(inst, Evoked) inst = inst.copy().crop(start, stop) if picks is not None: with inst.info._unlock(): inst.info['comps'] = [] # can be safely disabled inst.pick_channels([inst.ch_names[p] for p in picks]) evoked_cln = ica.apply(inst.copy(), exclude=exclude, n_pca_components=n_pca_components) fig = _plot_ica_overlay_evoked(evoked=inst, evoked_cln=evoked_cln, title=title, show=show) return fig def _plot_ica_overlay_raw(data, data_cln, times, title, ch_types_used, show): """Plot evoked after and before ICA cleaning. Parameters ---------- ica : instance of mne.preprocessing.ICA The ICA object. epochs : instance of mne.Epochs The Epochs to be regarded. show : bool Show figure if True. Returns ------- fig : instance of Figure """ import matplotlib.pyplot as plt # Restore sensor space data and keep all PCA components # let's now compare the date before and after cleaning. # first the raw data assert data.shape == data_cln.shape fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) plt.suptitle(title) ax1.plot(times, data.T, color='r') ax1.plot(times, data_cln.T, color='k') ax1.set(xlabel='Time (s)', xlim=times[[0, -1]], title='Raw data') _ch_types = {'mag': 'Magnetometers', 'grad': 'Gradiometers', 'eeg': 'EEG'} ch_types = ', '.join([_ch_types[k] for k in ch_types_used]) ax2.set_title('Average across channels ({})'.format(ch_types)) ax2.plot(times, data.mean(0), color='r') ax2.plot(times, data_cln.mean(0), color='k') ax2.set(xlabel='Time (s)', xlim=times[[0, -1]]) tight_layout(fig=fig) fig.subplots_adjust(top=0.90) fig.canvas.draw() plt_show(show) return fig def _plot_ica_overlay_evoked(evoked, evoked_cln, title, show): """Plot evoked after and before ICA cleaning. Parameters ---------- evoked : instance of mne.Evoked The Evoked before IC rejection. evoked_cln : instance of mne.Evoked The Evoked after IC rejection. title : str | None The title of the figure. show : bool If True, all open plots will be shown. Returns ------- fig : instance of Figure """ import matplotlib.pyplot as plt ch_types_used = [c for c in ['mag', 'grad', 'eeg'] if c in evoked] n_rows = len(ch_types_used) ch_types_used_cln = [c for c in ['mag', 'grad', 'eeg'] if c in evoked_cln] if len(ch_types_used) != len(ch_types_used_cln): raise ValueError('Raw and clean evokeds must match. ' 'Found different channels.') fig, axes = plt.subplots(n_rows, 1) if title is None: title = 'Average signal before (red) and after (black) ICA' fig.suptitle(title) axes = axes.flatten() if isinstance(axes, np.ndarray) else axes evoked.plot(axes=axes, show=False, time_unit='s') for ax in fig.axes: for line in ax.get_lines(): line.set_color('r') fig.canvas.draw() evoked_cln.plot(axes=axes, show=False, time_unit='s') tight_layout(fig=fig) fig.subplots_adjust(top=0.90) fig.canvas.draw() plt_show(show) return fig def _plot_sources(ica, inst, picks, exclude, start, stop, show, title, block, show_scrollbars, show_first_samp, time_format, precompute, use_opengl, *, theme=None, overview_mode=None): """Plot the ICA components as a RawArray or EpochsArray.""" from ._figure import _get_browser from .. import EpochsArray, BaseEpochs from ..io import RawArray, BaseRaw # handle defaults / check arg validity is_raw = isinstance(inst, BaseRaw) is_epo = isinstance(inst, BaseEpochs) sfreq = inst.info['sfreq'] color = _handle_default('color', (0., 0., 0.)) units = _handle_default('units', None) scalings = (_compute_scalings(None, inst) if is_raw else _handle_default('scalings_plot_raw')) scalings['misc'] = 5. scalings['whitened'] = 1. unit_scalings = _handle_default('scalings', None) # data if is_raw: data = ica._transform_raw(inst, 0, len(inst.times))[picks] else: data = ica._transform_epochs(inst, concatenate=True)[picks] # events if is_epo: event_id_rev = {v: k for k, v in inst.event_id.items()} event_nums = inst.events[:, 2] event_color_dict = _make_event_color_dict(None, inst.events, inst.event_id) # channel properties / trace order / picks ch_names = list(ica._ica_names) # copy ch_types = ['misc' for _ in picks] # add EOG/ECG channels if present eog_chs = pick_types(inst.info, meg=False, eog=True, ref_meg=False) ecg_chs = pick_types(inst.info, meg=False, ecg=True, ref_meg=False) for eog_idx in eog_chs: ch_names.append(inst.ch_names[eog_idx]) ch_types.append('eog') for ecg_idx in ecg_chs: ch_names.append(inst.ch_names[ecg_idx]) ch_types.append('ecg') extra_picks = np.concatenate((eog_chs, ecg_chs)).astype(int) if len(extra_picks): if is_raw: eog_ecg_data, _ = inst[extra_picks, :] else: eog_ecg_data = np.concatenate(inst.get_data(extra_picks), axis=1) data = np.append(data, eog_ecg_data, axis=0) picks = np.concatenate( (picks, ica.n_components_ + np.arange(len(extra_picks)))) ch_order = np.arange(len(picks)) n_channels = min([20, len(picks)]) ch_names_picked = [ch_names[x] for x in picks] # create info info = create_info(ch_names_picked, sfreq, ch_types=ch_types) with info._unlock(): info['meas_date'] = inst.info['meas_date'] info['bads'] = [ch_names[x] for x in exclude if x in picks] if is_raw: inst_array = RawArray(data, info, inst.first_samp) inst_array.set_annotations(inst.annotations) else: data = data.reshape(-1, len(inst), len(inst.times)).swapaxes(0, 1) inst_array = EpochsArray(data, info) # handle time dimension start = 0 if start is None else start _last = inst.times[-1] if is_raw else len(inst.events) stop = min(start + 20, _last) if stop is None else stop first_time = inst._first_time if show_first_samp else 0 if is_raw: duration = stop - start start += first_time else: n_epochs = stop - start total_epochs = len(inst) epoch_n_times = len(inst.times) n_epochs = min(n_epochs, total_epochs) n_times = total_epochs * epoch_n_times duration = n_epochs * epoch_n_times / sfreq event_times = (np.arange(total_epochs) * epoch_n_times + inst.time_as_index(0)) / sfreq # NB: this includes start and end of data: boundary_times = np.arange(total_epochs + 1) * epoch_n_times / sfreq if duration <= 0: raise RuntimeError('Stop must be larger than start.') # misc bad_color = 'lightgray' title = 'ICA components' if title is None else title precompute = _handle_precompute(precompute) params = dict(inst=inst_array, ica=ica, ica_inst=inst, info=info, # channels and channel order ch_names=np.array(ch_names_picked), ch_types=np.array(ch_types), ch_order=ch_order, picks=picks, n_channels=n_channels, picks_data=list(), # time t_start=start if is_raw else boundary_times[start], duration=duration, n_times=inst.n_times if is_raw else n_times, first_time=first_time, time_format=time_format, decim=1, # events event_times=None if is_raw else event_times, # preprocessing projs=list(), projs_on=np.array([], dtype=bool), apply_proj=False, remove_dc=True, # for EOG/ECG filter_coefs=None, filter_bounds=None, noise_cov=None, # scalings scalings=scalings, units=units, unit_scalings=unit_scalings, # colors ch_color_bad=bad_color, ch_color_dict=color, # display butterfly=False, clipping=None, scrollbars_visible=show_scrollbars, scalebars_visible=False, window_title=title, precompute=precompute, use_opengl=use_opengl, theme=theme, overview_mode=overview_mode, ) if is_epo: params.update(n_epochs=n_epochs, boundary_times=boundary_times, event_id_rev=event_id_rev, event_color_dict=event_color_dict, event_nums=event_nums, epoch_color_bad=(1, 0, 0), epoch_colors=None, xlabel='Epoch number') fig = _get_browser(show=show, block=block, **params) return fig