"""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 numpy as np from .utils import (tight_layout, _prepare_trellis, _select_bads, _plot_raw_onscroll, _mouse_click, _plot_raw_onkey, plt_show, _convert_psds) from .topomap import (_prepare_topo_plot, plot_topomap, _hide_frame, _plot_ica_topomap) from .raw import _prepare_mne_browse_raw, _plot_raw_traces from .epochs import _prepare_mne_browse_epochs, plot_epochs_image from .evoked import _butterfly_on_button_press, _butterfly_onpick from ..utils import warn, _validate_type, fill_doc from ..defaults import _handle_default from ..io.meas_info import create_info from ..io.pick import (pick_types, _picks_to_idx, _get_channel_types, _DATA_CH_TYPES_ORDER_DEFAULT) from ..time_frequency.psd import psd_multitaper from ..utils import _reject_data_segments @fill_doc def plot_ica_sources(ica, inst, picks=None, exclude='deprecated', start=None, stop=None, title=None, show=True, block=False, show_first_samp=False, show_scrollbars=True): """Plot estimated latent sources given the unmixing matrix. Typical usecases: 1. plot evolution of latent sources over time based on (Raw input) 2. plot latent source around event related time windows (Epochs input) 3. plot time-locking in ICA space (Evoked input) Parameters ---------- ica : instance of mne.preprocessing.ICA The ICA solution. inst : instance of mne.io.Raw, mne.Epochs, mne.Evoked The object to plot the sources from. %(picks_base)s all sources in the order as fitted. exclude : 'deprecated' The ``exclude`` parameter is deprecated and will be removed in version 0.20; specify excluded components using the ``ICA.exclude`` attribute instead. start : int X-axis start index. If None, from the beginning. stop : int X-axis stop index. If None, next 20 are shown, in case of evoked to the end. title : str | None The window title. If None a default is provided. show : bool Show figure if True. block : bool Whether to halt program execution until the figure is closed. Useful for interactive selection of components in raw and epoch plotter. For evoked, this parameter has no effect. Defaults to False. show_first_samp : bool If True, show time axis relative to the ``raw.first_samp``. %(show_scrollbars)s Returns ------- fig : instance of Figure The figure. Notes ----- For raw and epoch instances, it is possible to select components for exclusion by clicking on the line. The selected components are added to ``ica.exclude`` on close. .. versionadded:: 0.10.0 """ from ..io.base import BaseRaw from ..evoked import Evoked from ..epochs import BaseEpochs if exclude != 'deprecated': warn('The "exclude" parameter is deprecated and will be removed in ' 'version 0.20; specify excluded components using the ICA.exclude ' 'attribute instead. Provided value of {} will be ignored; falling' ' back to ICA.exclude'.format(exclude), DeprecationWarning) exclude = ica.exclude picks = _picks_to_idx(ica.n_components_, picks, 'all') if isinstance(inst, BaseRaw): fig = _plot_sources_raw(ica, inst, picks, exclude, start=start, stop=stop, show=show, title=title, block=block, show_first_samp=show_first_samp, show_scrollbars=show_scrollbars) elif isinstance(inst, BaseEpochs): fig = _plot_sources_epochs(ica, inst, picks, exclude, start=start, stop=stop, show=show, title=title, block=block, show_scrollbars=show_scrollbars) elif isinstance(inst, Evoked): if start is not None or stop is not None: inst = inst.copy().crop(start, stop) sources = ica.get_sources(inst) fig = _plot_ica_sources_evoked( evoked=sources, picks=picks, exclude=exclude, title=title, labels=getattr(ica, 'labels_', None), show=show, ica=ica) else: raise ValueError('Data input must be of Raw or Epochs type') return fig def _create_properties_layout(figsize=None): """Create main figure and axes layout used by plot_ica_properties.""" import matplotlib.pyplot as plt if figsize is None: figsize = [7., 6.] fig = plt.figure(figsize=figsize, facecolor=[0.95] * 3) axes_params = (('topomap', [0.08, 0.5, 0.3, 0.45]), ('image', [0.5, 0.6, 0.45, 0.35]), ('erp', [0.5, 0.5, 0.45, 0.1]), ('spectrum', [0.08, 0.1, 0.32, 0.3]), ('variance', [0.5, 0.1, 0.45, 0.25])) axes = [fig.add_axes(loc, label=name) for name, loc in axes_params] return fig, axes def _plot_ica_properties(pick, ica, inst, psds_mean, freqs, n_trials, epoch_var, plot_lowpass_edge, epochs_src, set_title_and_labels, plot_std, psd_ylabel, spectrum_std, topomap_args, image_args, fig, axes, kind, dropped_indices): """Plot ICA properties (helper).""" from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable from scipy.stats import gaussian_kde topo_ax, image_ax, erp_ax, spec_ax, var_ax = axes # plotting # -------- # component topomap _plot_ica_topomap(ica, pick, show=False, axes=topo_ax, **topomap_args) # image and erp # we create a new epoch with dropped rows epoch_data = epochs_src.get_data() epoch_data = np.insert(arr=epoch_data, obj=(dropped_indices - np.arange(len(dropped_indices))).astype(int), values=0.0, axis=0) from ..epochs import EpochsArray epochs_src = EpochsArray(epoch_data, epochs_src.info, 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 kde = gaussian_kde(epoch_var) ymin, ymax = hist_ax.get_ylim() x = np.linspace(ymin, ymax, 50) kde_ = kde(x) kde_ /= kde_.max() kde_ *= hist_ax.get_xlim()[-1] * .9 hist_ax.plot(kde_, x, color="k") hist_ax.set_ylim(ymin, ymax) # aesthetics # ---------- topo_ax.set_title(ica._ica_names[pick]) set_title_and_labels(image_ax, kind + ' image and ERP/ERF', [], kind) # erp set_title_and_labels(erp_ax, [], 'Time (s)', 'AU') erp_ax.spines["right"].set_color('k') erp_ax.set_xlim(epochs_src.times[[0, -1]]) # remove half of yticks if more than 5 yt = erp_ax.get_yticks() if len(yt) > 5: erp_ax.yaxis.set_ticks(yt[::2]) # remove xticks - erp plot shows xticks for both image and erp plot image_ax.xaxis.set_ticks([]) yt = image_ax.get_yticks() image_ax.yaxis.set_ticks(yt[1:]) image_ax.set_ylim([-0.5, n_trials + 0.5]) # spectrum set_title_and_labels(spec_ax, 'Spectrum', 'Frequency (Hz)', psd_ylabel) spec_ax.yaxis.labelpad = 0 spec_ax.set_xlim(freqs[[0, -1]]) ylim = spec_ax.get_ylim() air = np.diff(ylim)[0] * 0.1 spec_ax.set_ylim(ylim[0] - air, ylim[1] + air) image_ax.axhline(0, color='k', linewidth=.5) # epoch variance var_ax_title = 'Dropped segments: %.2f %%' % var_percent set_title_and_labels(var_ax, var_ax_title, kind, 'Variance (AU)') hist_ax.set_ylabel("") hist_ax.set_yticks([]) set_title_and_labels(hist_ax, None, None, None) return fig def _get_psd_label_and_std(this_psd, dB, ica, num_std): """Handle setting up PSD for one component, for plot_ica_properties.""" psd_ylabel = _convert_psds(this_psd, dB, estimate='auto', scaling=1., unit='AU', ch_names=ica.ch_names) 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 spectrum_std = [ [np.sqrt((d[d < 0] ** 2).mean(axis=0)) for d in diffs.T], [np.sqrt((d[d > 0] ** 2).mean(axis=0)) for d in diffs.T]] spectrum_std = np.array(spectrum_std) * num_std return psd_ylabel, psds_mean, spectrum_std @fill_doc def plot_ica_properties(ica, inst, picks=None, axes=None, dB=True, plot_std=True, topomap_args=None, image_args=None, psd_args=None, figsize=None, show=True, reject='auto'): """Display component properties. Properties include the topography, epochs image, ERP/ERF, power spectrum, and epoch variance. Parameters ---------- ica : instance of mne.preprocessing.ICA The ICA solution. inst: instance of Epochs or Raw The data to use in plotting properties. %(picks_base)s the first five sources. If more than one components were chosen in the picks, each one will be plotted in a separate figure. axes: list of matplotlib axes | None List of five matplotlib axes to use in plotting: [topomap_axis, image_axis, erp_axis, spectrum_axis, variance_axis]. If None a new figure with relevant axes is created. Defaults to None. dB: bool Whether to plot spectrum in dB. Defaults to True. plot_std: bool | float Whether to plot standard deviation/confidence intervals in ERP/ERF and spectrum plots. Defaults to True, which plots one standard deviation above/below for the spectrum. If set to float allows to control how many standard deviations are plotted for the spectrum. For example 2.5 will plot 2.5 standard deviation above/below. For the ERP/ERF, by default, plot the 95 percent parametric confidence interval is calculated. To change this, use ``ci`` in ``ts_args`` in ``image_args`` (see below). topomap_args : dict | None Dictionary of arguments to ``plot_topomap``. If None, doesn't pass any additional arguments. Defaults to None. image_args : dict | None Dictionary of arguments to ``plot_epochs_image``. If None, doesn't pass any additional arguments. Defaults to None. psd_args : dict | None Dictionary of arguments to ``psd_multitaper``. If None, doesn't pass any additional arguments. Defaults to None. figsize : array-like, shape (2,) | None Allows to control size of the figure. If None, the figure size defaults to [7., 6.]. show : bool Show figure if True. reject : 'auto' | dict | None Allows to specify rejection parameters used to drop epochs (or segments if continuous signal is passed as inst). If None, no rejection is applied. The default is 'auto', which applies the rejection parameters used when fitting the ICA object. Returns ------- fig : list List of matplotlib figures. Notes ----- .. versionadded:: 0.13 """ from ..io.base import BaseRaw from ..epochs import BaseEpochs from ..preprocessing import ICA from ..io import RawArray # input checks and defaults # ------------------------- _validate_type(inst, (BaseRaw, BaseEpochs), "inst", "Raw or Epochs") _validate_type(ica, ICA, "ica", "ICA") if isinstance(plot_std, bool): num_std = 1. if plot_std else 0. elif isinstance(plot_std, (float, int)): num_std = plot_std plot_std = True else: raise ValueError('plot_std has to be a bool, int or float, ' 'got %s instead' % type(plot_std)) # if no picks given - plot the first 5 components limit = min(5, ica.n_components_) if picks is None else len(ica.ch_names) picks = _picks_to_idx(ica.info, picks, 'all')[:limit] if axes is None: fig, axes = _create_properties_layout(figsize=figsize) else: if len(picks) > 1: raise ValueError('Only a single pick can be drawn ' 'to a set of axes.') from .utils import _validate_if_list_of_axes _validate_if_list_of_axes(axes, obligatory_len=5) fig = axes[0].get_figure() psd_args = dict() if psd_args is None else psd_args topomap_args = dict() if topomap_args is None else topomap_args image_args = dict() if image_args is None else image_args image_args["ts_args"] = dict(truncate_xaxis=False, show_sensors=False) if plot_std: from ..stats.parametric import _parametric_ci image_args["ts_args"]["ci"] = _parametric_ci elif "ts_args" not in image_args or "ci" not in image_args["ts_args"]: image_args["ts_args"]["ci"] = False for item_name, item in (("psd_args", psd_args), ("topomap_args", topomap_args), ("image_args", image_args)): _validate_type(item, dict, item_name, "dictionary") if dB is not None: _validate_type(dB, bool, "dB", "bool") # calculations # ------------ if isinstance(inst, BaseRaw): # when auto, delegate reject to the ica if reject == 'auto': reject = getattr(ica, 'reject_', None) else: pass if reject is None: inst_rejected = inst drop_inds = None else: data = inst.get_data() data, drop_inds = _reject_data_segments(data, ica.reject_, flat=None, decim=None, info=inst.info, tstep=2.0) inst_rejected = RawArray(data, inst.info) # break up continuous signal into segments from ..epochs import _segment_raw inst_rejected = _segment_raw(inst_rejected, segment_length=2., verbose=False, preload=True) inst = _segment_raw(inst, segment_length=2., verbose=False, preload=True) kind = "Segment" else: drop_inds = None inst_rejected = inst kind = "Epochs" epochs_src = ica.get_sources(inst_rejected) data = epochs_src.get_data() ica_data = np.swapaxes(data[:, picks, :], 0, 1) # getting dropped epochs indexes if drop_inds is not None: dropped_indices = [(d[0] // len(inst.times)) + 1 for d in drop_inds] else: dropped_indices = [] # getting ica sources from inst dropped_src = ica.get_sources(inst).get_data() dropped_src = np.swapaxes(dropped_src[:, picks, :], 0, 1) # spectrum Nyquist = inst.info['sfreq'] / 2. lp = inst.info['lowpass'] if 'fmax' not in psd_args: psd_args['fmax'] = min(lp * 1.25, Nyquist) plot_lowpass_edge = lp < Nyquist and (psd_args['fmax'] > lp) psds, freqs = psd_multitaper(epochs_src, picks=picks, **psd_args) def set_title_and_labels(ax, title, xlab, ylab): if title: ax.set_title(title) if xlab: ax.set_xlabel(xlab) if ylab: ax.set_ylabel(ylab) ax.axis('auto') ax.tick_params('both', labelsize=8) ax.axis('tight') # plot # ---- all_fig = list() for idx, pick in enumerate(picks): # calculate component-specific spectrum stuff psd_ylabel, psds_mean, spectrum_std = _get_psd_label_and_std( psds[:, idx, :].copy(), dB, ica, num_std) # if more than one component, spawn additional figures and axes if idx > 0: fig, axes = _create_properties_layout(figsize=figsize) # we reconstruct an epoch_variance with 0 where indexes where dropped epoch_var = np.var(ica_data[idx], axis=1) drop_var = np.var(dropped_src[idx], axis=1) drop_indices_corrected = \ (dropped_indices - np.arange(len(dropped_indices))).astype(int) epoch_var = np.insert(arr=epoch_var, obj=drop_indices_corrected, values=drop_var[dropped_indices], axis=0) # the actual plot fig = _plot_ica_properties( pick, ica, inst, psds_mean, freqs, ica_data.shape[1], epoch_var, plot_lowpass_edge, epochs_src, set_title_and_labels, plot_std, psd_ylabel, spectrum_std, topomap_args, image_args, fig, axes, kind, dropped_indices) all_fig.append(fig) plt_show(show) return all_fig def _plot_ica_sources_evoked(evoked, picks, exclude, title, show, ica, labels=None): """Plot average over epochs in ICA space. Parameters ---------- evoked : instance of mne.Evoked The Evoked to be used. %(picks_base)s all sources in the order as fitted. exclude : array-like of int The components marked for exclusion. If None (default), ICA.exclude will be used. title : str The figure title. show : bool Show figure if True. labels : None | dict The ICA labels attribute. """ import matplotlib.pyplot as plt from matplotlib import patheffects if title is None: title = 'Reconstructed latent sources, time-locked' fig, axes = plt.subplots(1) ax = axes axes = [axes] times = evoked.times * 1e3 # plot unclassified sources and label excluded ones lines = list() texts = list() picks = np.sort(picks) idxs = [picks] if labels is not None: labels_used = [k for k in labels if '/' not in k] exclude_labels = list() for ii in picks: if ii in exclude: line_label = ica._ica_names[ii] if labels is not None: annot = list() for this_label in labels_used: indices = labels[this_label] if ii in indices: annot.append(this_label) line_label += (' - ' + ', '.join(annot)) exclude_labels.append(line_label) else: exclude_labels.append(None) if labels is not None: # compute colors only based on label categories unique_labels = {k.split(' - ')[1] for k in exclude_labels if k} label_colors = plt.cm.rainbow(np.linspace(0, 1, len(unique_labels))) label_colors = dict(zip(unique_labels, label_colors)) else: label_colors = {k: 'red' for k in exclude_labels} for exc_label, ii in zip(exclude_labels, picks): if exc_label is not None: # create look up for color ... if ' - ' in exc_label: key = exc_label.split(' - ')[1] else: key = exc_label color = label_colors[key] # ... but display component number too lines.extend(ax.plot(times, evoked.data[ii].T, picker=3., zorder=2, color=color, label=exc_label)) else: lines.extend(ax.plot(times, evoked.data[ii].T, picker=3., color='k', zorder=1)) ax.set(title=title, xlim=times[[0, -1]], xlabel='Time (ms)', ylabel='(NA)') if len(exclude) > 0: plt.legend(loc='best') tight_layout(fig=fig) # for old matplotlib, we actually need this to have a bounding # box (!), so we have to put some valid text here, change # alpha and path effects later texts.append(ax.text(0, 0, 'blank', zorder=3, verticalalignment='baseline', horizontalalignment='left', fontweight='bold', alpha=0)) # this is done to give the structure of a list of lists of a group of lines # in each subplot lines = [lines] ch_names = evoked.ch_names path_effects = [patheffects.withStroke(linewidth=2, foreground="w", alpha=0.75)] params = dict(axes=axes, texts=texts, lines=lines, idxs=idxs, ch_names=ch_names, need_draw=False, path_effects=path_effects) fig.canvas.mpl_connect('pick_event', partial(_butterfly_onpick, params=params)) fig.canvas.mpl_connect('button_press_event', partial(_butterfly_on_button_press, params=params)) plt_show(show) return fig def plot_ica_scores(ica, scores, exclude=None, labels=None, axhline=None, title='ICA component scores', figsize=None, 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. 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_rows = len(scores) if figsize is None: figsize = (6.4, 2.7 * n_rows) fig, axes = plt.subplots(n_rows, figsize=figsize, sharex=True, sharey=True) if isinstance(axes, np.ndarray): axes = axes.flatten() else: axes = [axes] axes[0].set_title(title) if labels == 'ecg': labels = [l for l in ica.labels_ if l.startswith('ecg/')] elif labels == 'eog': labels = [l for l in ica.labels_ if l.startswith('eog/')] labels.sort(key=lambda l: l.split('/')[1]) # sort by index elif isinstance(labels, str): if len(axes) > 1: raise ValueError('Need as many labels as axes (%i)' % len(axes)) labels = [labels] elif isinstance(labels, (tuple, list)): if len(labels) != len(axes): raise ValueError('Need as many labels as axes (%i)' % len(axes)) elif labels is None: labels = (None,) * n_rows 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) plt_show(show) return fig @fill_doc def plot_ica_overlay(ica, inst, exclude=None, picks=None, start=None, stop=None, title=None, show=True): """Overlay of raw and cleaned signals given the unmixing matrix. This method helps visualizing signal quality and artifact rejection. Parameters ---------- ica : instance of mne.preprocessing.ICA The ICA object. inst : instance of mne.io.Raw or mne.Evoked The signals to be compared given the ICA solution. If Raw input, The raw data are displayed before and after cleaning. In a second panel the cross channel average will be displayed. Since dipolar sources will be canceled out this display is sensitive to artifacts. If evoked input, butterfly plots for clean and raw signals will be superimposed. exclude : array-like of int | None (default) The components marked for exclusion. If None (default), ICA.exclude will be used. %(picks_base)s all channels that were included during fitting. start : int X-axis start index. If None from the beginning. stop : int X-axis stop index. If None to the end. title : str The figure title. show : bool Show figure if True. Returns ------- fig : instance of Figure The figure. """ # avoid circular imports from ..io.base import BaseRaw from ..evoked import Evoked from ..preprocessing.ica import _check_start_stop _validate_type(inst, (BaseRaw, Evoked), "inst", "Raw or Evoked") if title is None: title = 'Signals before (red) and after (black) cleaning' picks = ica.ch_names if picks is None else picks picks = _picks_to_idx(inst.info, picks, exclude=()) ch_types_used = _get_channel_types(inst.info, picks=picks, unique=True) if exclude is None: exclude = ica.exclude if not isinstance(exclude, (np.ndarray, list)): raise TypeError('exclude must be of type list. Got %s' % type(exclude)) if isinstance(inst, BaseRaw): if start is None: start = 0.0 if stop is None: stop = 3.0 start_compare, stop_compare = _check_start_stop(inst, start, stop) data, times = inst[picks, start_compare:stop_compare] raw_cln = ica.apply(inst.copy(), exclude=exclude, start=start, stop=stop) data_cln, _ = raw_cln[picks, start_compare:stop_compare] fig = _plot_ica_overlay_raw(data=data, data_cln=data_cln, times=times, title=title, ch_types_used=ch_types_used, show=show) elif isinstance(inst, Evoked): inst = inst.copy().crop(start, stop) if picks is not None: inst.info['comps'] = [] # can be safely disabled inst.pick_channels([inst.ch_names[p] for p in picks]) evoked_cln = ica.apply(inst.copy(), exclude=exclude) fig = _plot_ica_overlay_evoked(evoked=inst, evoked_cln=evoked_cln, title=title, show=show) return fig def _plot_ica_overlay_raw(data, data_cln, times, title, ch_types_used, show): """Plot evoked after and before ICA cleaning. Parameters ---------- ica : instance of mne.preprocessing.ICA The ICA object. epochs : instance of mne.Epochs The Epochs to be regarded. show : bool Show figure if True. Returns ------- fig : instance of Figure """ import matplotlib.pyplot as plt # Restore sensor space data and keep all PCA components # let's now compare the date before and after cleaning. # first the raw data assert data.shape == data_cln.shape fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) plt.suptitle(title) ax1.plot(times, data.T, color='r') ax1.plot(times, data_cln.T, color='k') ax1.set(xlabel='Time (s)', xlim=times[[0, -1]], title='Raw data') _ch_types = {'mag': 'Magnetometers', 'grad': 'Gradiometers', 'eeg': 'EEG'} ch_types = ', '.join([_ch_types[k] for k in ch_types_used]) ax2.set_title('Average across channels ({})'.format(ch_types)) ax2.plot(times, data.mean(0), color='r') ax2.plot(times, data_cln.mean(0), color='k') ax2.set(xlabel='Time (s)', xlim=times[[0, -1]]) tight_layout(fig=fig) fig.subplots_adjust(top=0.90) fig.canvas.draw() plt_show(show) return fig def _plot_ica_overlay_evoked(evoked, evoked_cln, title, show): """Plot evoked after and before ICA cleaning. Parameters ---------- ica : instance of mne.preprocessing.ICA The ICA object. epochs : instance of mne.Epochs The Epochs to be regarded. show : bool If True, all open plots will be shown. Returns ------- fig : instance of Figure """ import matplotlib.pyplot as plt ch_types_used = [c for c in ['mag', 'grad', 'eeg'] if c in evoked] n_rows = len(ch_types_used) ch_types_used_cln = [c for c in ['mag', 'grad', 'eeg'] if c in evoked_cln] if len(ch_types_used) != len(ch_types_used_cln): raise ValueError('Raw and clean evokeds must match. ' 'Found different channels.') fig, axes = plt.subplots(n_rows, 1) fig.suptitle('Average signal before (red) and after (black) ICA') axes = axes.flatten() if isinstance(axes, np.ndarray) else axes evoked.plot(axes=axes, show=show, time_unit='s') for ax in fig.axes: for l in ax.get_lines(): l.set_color('r') fig.canvas.draw() evoked_cln.plot(axes=axes, show=show, time_unit='s') tight_layout(fig=fig) fig.subplots_adjust(top=0.90) fig.canvas.draw() plt_show(show) return fig def _plot_sources_raw(ica, raw, picks, exclude, start, stop, show, title, block, show_first_samp, show_scrollbars): """Plot the ICA components as raw array.""" color = _handle_default('color', (0., 0., 0.)) orig_data = ica._transform_raw(raw, 0, len(raw.times)) * 0.2 types = ['misc' for _ in picks] eog_chs = pick_types(raw.info, meg=False, eog=True, ref_meg=False) ecg_chs = pick_types(raw.info, meg=False, ecg=True, ref_meg=False) data = [orig_data[pick] for pick in picks] c_names = list(ica._ica_names) # new list for eog_idx in eog_chs: c_names.append(raw.ch_names[eog_idx]) types.append('eog') for ecg_idx in ecg_chs: c_names.append(raw.ch_names[ecg_idx]) types.append('ecg') extra_picks = np.append(eog_chs, ecg_chs).astype(int) if len(extra_picks) > 0: eog_ecg_data, _ = raw[extra_picks, :] for idx in range(len(eog_ecg_data)): if idx < len(eog_chs): eog_ecg_data[idx] /= 150e-6 # scaling for eog else: eog_ecg_data[idx] /= 5e-4 # scaling for ecg data = np.append(data, eog_ecg_data, axis=0) for idx in range(len(extra_picks)): picks = np.append(picks, ica.n_components_ + idx) if title is None: title = 'ICA components' info = create_info([c_names[x] for x in picks], raw.info['sfreq']) info['bads'] = [c_names[x] for x in exclude] if start is None: start = 0 if stop is None: stop = start + 20 stop = min(stop, raw.times[-1]) duration = stop - start if duration <= 0: raise RuntimeError('Stop must be larger than start.') t_end = int(duration * raw.info['sfreq']) times = raw.times[0:t_end] bad_color = (1., 0., 0.) inds = list(range(len(picks))) data = np.array(data) n_channels = min([20, len(picks)]) first_time = raw._first_time if show_first_samp else 0 start += first_time params = dict(raw=raw, orig_data=data, data=data[:, 0:t_end], inds=inds, ch_start=0, t_start=start, info=info, duration=duration, ica=ica, n_channels=n_channels, times=times, types=types, n_times=raw.n_times, bad_color=bad_color, picks=picks, first_time=first_time, data_picks=[], decim=1, noise_cov=None, whitened_ch_names=(), clipping=None, use_scalebars=False, show_scrollbars=show_scrollbars) _prepare_mne_browse_raw(params, title, 'w', color, bad_color, inds, n_channels) params['scale_factor'] = 1.0 params['plot_fun'] = partial(_plot_raw_traces, params=params, color=color, bad_color=bad_color) params['update_fun'] = partial(_update_data, params) params['pick_bads_fun'] = partial(_pick_bads, params=params) params['label_click_fun'] = partial(_label_clicked, params=params) # callbacks callback_key = partial(_plot_raw_onkey, params=params) params['fig'].canvas.mpl_connect('key_press_event', callback_key) callback_scroll = partial(_plot_raw_onscroll, params=params) params['fig'].canvas.mpl_connect('scroll_event', callback_scroll) callback_pick = partial(_mouse_click, params=params) params['fig'].canvas.mpl_connect('button_press_event', callback_pick) callback_close = partial(_close_event, params=params) params['fig'].canvas.mpl_connect('close_event', callback_close) params['fig_proj'] = None params['event_times'] = None params['butterfly'] = False params['update_fun']() params['plot_fun']() try: plt_show(show, block=block) except TypeError: # not all versions have this plt_show(show) return params['fig'] def _update_data(params): """Prepare the data on horizontal shift of the viewport.""" sfreq = params['info']['sfreq'] start = int((params['t_start'] - params['first_time']) * sfreq) end = int((params['t_start'] + params['duration']) * sfreq) params['data'] = params['orig_data'][:, start:end] params['times'] = params['raw'].times[start:end] def _pick_bads(event, params): """Select components on click.""" bads = params['info']['bads'] params['info']['bads'] = _select_bads(event, params, bads) params['update_fun']() params['plot_fun']() def _close_event(events, params): """Exclude the selected components on close.""" info = params['info'] exclude = [params['ica']._ica_names.index(x) for x in info['bads'] if x.startswith('ICA')] params['ica'].exclude = exclude def _plot_sources_epochs(ica, epochs, picks, exclude, start, stop, show, title, block, show_scrollbars): """Plot the components as epochs.""" data = ica._transform_epochs(epochs, concatenate=True) eog_chs = pick_types(epochs.info, meg=False, eog=True, ref_meg=False) ecg_chs = pick_types(epochs.info, meg=False, ecg=True, ref_meg=False) c_names = list(ica._ica_names) ch_types = np.repeat('misc', ica.n_components_) for eog_idx in eog_chs: c_names.append(epochs.ch_names[eog_idx]) ch_types = np.append(ch_types, 'eog') for ecg_idx in ecg_chs: c_names.append(epochs.ch_names[ecg_idx]) ch_types = np.append(ch_types, 'ecg') extra_picks = np.append(eog_chs, ecg_chs).astype(int) if len(extra_picks) > 0: eog_ecg_data = np.concatenate(epochs.get_data()[:, extra_picks], axis=1) data = np.append(data, eog_ecg_data, axis=0) scalings = _handle_default('scalings_plot_raw') scalings['misc'] = 5.0 info = create_info(ch_names=c_names, sfreq=epochs.info['sfreq'], ch_types=ch_types) info['projs'] = list() info['bads'] = [c_names[x] for x in exclude] if title is None: title = 'ICA components' if start is None: start = 0 if stop is None: stop = start + 20 stop = min(stop, len(epochs.events)) for idx in range(len(extra_picks)): picks = np.append(picks, ica.n_components_ + idx) n_epochs = stop - start if n_epochs <= 0: raise RuntimeError('Stop must be larger than start.') params = dict(ica=ica, epochs=epochs, info=info, orig_data=data, bads=list(), bad_color=(1., 0., 0.), t_start=start * len(epochs.times), data_picks=list(), decim=1, whitened_ch_names=(), noise_cov=None, show_scrollbars=show_scrollbars, epoch_colors=None) params['label_click_fun'] = partial(_label_clicked, params=params) # changing the order to 'misc' before 'eog' and 'ecg' order = list(_DATA_CH_TYPES_ORDER_DEFAULT) order.pop(order.index('misc')) order.insert(order.index('eog'), 'misc') _prepare_mne_browse_epochs(params, projs=list(), n_channels=20, n_epochs=n_epochs, scalings=scalings, title=title, picks=picks, order=order, info=info) params['plot_update_proj_callback'] = _update_epoch_data _update_epoch_data(params) params['hsel_patch'].set_x(params['t_start']) callback_close = partial(_close_epochs_event, params=params) params['fig'].canvas.mpl_connect('close_event', callback_close) try: plt_show(show, block=block) except TypeError: # not all versions have this plt_show(show) return params['fig'] def _update_epoch_data(params): """Prepare the data on horizontal shift.""" start = params['t_start'] n_epochs = params['n_epochs'] end = start + n_epochs * len(params['epochs'].times) data = params['orig_data'][:, start:end] types = params['types'] for pick, ind in enumerate(params['inds']): params['data'][pick] = data[ind] / params['scalings'][types[pick]] params['plot_fun']() def _close_epochs_event(events, params): """Exclude the selected components on close.""" info = params['info'] exclude = [info['ch_names'].index(x) for x in info['bads'] if x.startswith('IC')] params['ica'].exclude = exclude def _label_clicked(pos, params): """Plot independent components on click to label.""" import matplotlib.pyplot as plt offsets = np.array(params['offsets']) + params['offsets'][0] line_idx = np.searchsorted(offsets, pos[1]) + params['ch_start'] if line_idx >= len(params['picks']): return ic_idx = [params['picks'][line_idx]] if params['types'][line_idx] != 'misc': warn('Can only plot ICA components.') return types = list() info = params['ica'].info if len(pick_types(info, meg=False, eeg=True, ref_meg=False)) > 0: types.append('eeg') if len(pick_types(info, meg='mag', ref_meg=False)) > 0: types.append('mag') if len(pick_types(info, meg='grad', ref_meg=False)) > 0: types.append('grad') ica = params['ica'] data = np.dot(ica.mixing_matrix_[:, ic_idx].T, ica.pca_components_[:ica.n_components_]) data = np.atleast_2d(data) fig, axes = _prepare_trellis(len(types), max_col=3) for ch_idx, ch_type in enumerate(types): try: data_picks, pos, merge_grads, _, _ = _prepare_topo_plot(ica, ch_type, None) except Exception as exc: warn(exc) plt.close(fig) return this_data = data[:, data_picks] ax = axes[ch_idx] if merge_grads: from ..channels.layout import _merge_grad_data for ii, data_ in zip(ic_idx, this_data): ax.set_title('%s %s' % (ica._ica_names[ii], ch_type), fontsize=12) data_ = _merge_grad_data(data_) if merge_grads else data_ plot_topomap(data_.flatten(), pos, axes=ax, show=False) _hide_frame(ax) tight_layout(fig=fig) fig.subplots_adjust(top=0.88, bottom=0.) fig.canvas.draw() plt_show(True)