"""Functions to plot M/EEG data on topo (one axes per channel).""" # Authors: The MNE-Python contributors. # License: BSD-3-Clause # Copyright the MNE-Python contributors. from copy import deepcopy from functools import partial import numpy as np from scipy import ndimage from .._fiff.pick import _picks_to_idx, channel_type, pick_types from ..defaults import _handle_default from ..utils import Bunch, _check_option, _clean_names, _is_numeric, _to_rgb, fill_doc from .utils import ( DraggableColorbar, _check_cov, _check_delayed_ssp, _draw_proj_checkbox, _plot_masked_image, _setup_ax_spines, _setup_vmin_vmax, add_background_image, plt_show, ) @fill_doc def iter_topography( info, layout=None, on_pick=None, fig=None, fig_facecolor="k", axis_facecolor="k", axis_spinecolor="k", layout_scale=None, legend=False, ): """Create iterator over channel positions. This function returns a generator that unpacks into a series of matplotlib axis objects and data / channel indices, both corresponding to the sensor positions of the related layout passed or inferred from the channel info. Hence, this enables convenient topography plot customization. Parameters ---------- %(info_not_none)s layout : instance of mne.channels.Layout | None The layout to use. If None, layout will be guessed. on_pick : callable | None The callback function to be invoked on clicking one of the axes. Is supposed to instantiate the following API: ``function(axis, channel_index)``. fig : matplotlib.figure.Figure | None The figure object to be considered. If None, a new figure will be created. fig_facecolor : color The figure face color. Defaults to black. axis_facecolor : color The axis face color. Defaults to black. axis_spinecolor : color The axis spine color. Defaults to black. In other words, the color of the axis' edge lines. layout_scale : float | None Scaling factor for adjusting the relative size of the layout on the canvas. If None, nothing will be scaled. legend : bool If True, an additional axis is created in the bottom right corner that can be used to, e.g., construct a legend. The index of this axis will be -1. Returns ------- gen : generator A generator that can be unpacked into: ax : matplotlib.axis.Axis The current axis of the topo plot. ch_dx : int The related channel index. """ return _iter_topography( info, layout, on_pick, fig, fig_facecolor, axis_facecolor, axis_spinecolor, layout_scale, legend=legend, ) def _legend_axis(pos): """Add a legend axis to the bottom right.""" import matplotlib.pyplot as plt left, bottom = pos[:, 0].max(), pos[:, 1].min() # check if legend axis overlaps a data axis overlaps = False for _pos in pos: h_overlap = _pos[0] <= left <= (_pos[0] + _pos[2]) v_overlap = _pos[1] <= bottom <= (_pos[1] + _pos[3]) if h_overlap and v_overlap: overlaps = True break if overlaps: left += 1.2 * _pos[2] wid, hei = pos[-1, 2:] return plt.axes([left, bottom, wid, hei]) def _iter_topography( info, layout, on_pick, fig, fig_facecolor="k", axis_facecolor="k", axis_spinecolor="k", layout_scale=None, unified=False, img=False, axes=None, legend=False, ): """Iterate over topography. Has the same parameters as iter_topography, plus: unified : bool If False (default), multiple matplotlib axes will be used. If True, a single axis will be constructed. The former is useful for custom plotting, the latter for speed. """ from matplotlib import collections from matplotlib import pyplot as plt from ..channels.layout import find_layout if fig is None: # Don't use constrained layout because we place axes manually fig = plt.figure(layout=None) def format_coord_unified(x, y, pos=None, ch_names=None): """Update status bar with channel name under cursor.""" # find candidate channels (ones that are down and left from cursor) pdist = np.array([x, y]) - pos[:, :2] pind = np.where((pdist >= 0).all(axis=1))[0] if len(pind) > 0: # find the closest channel closest = pind[np.sum(pdist[pind, :] ** 2, axis=1).argmin()] # check whether we are inside its box in_box = (pdist[closest, :] < pos[closest, 2:]).all() else: in_box = False return ( f"{ch_names[closest]} (click to magnify)" if in_box else "No channel here" ) def format_coord_multiaxis(x, y, ch_name=None): """Update status bar with channel name under cursor.""" return f"{ch_name} (click to magnify)" fig.set_facecolor(fig_facecolor) if layout is None: layout = find_layout(info) if on_pick is not None: callback = partial(_plot_topo_onpick, show_func=on_pick) fig.canvas.mpl_connect("button_press_event", callback) pos = layout.pos.copy() if layout_scale: pos[:, :2] *= layout_scale ch_names = _clean_names(info["ch_names"]) iter_ch = [(x, y) for x, y in enumerate(layout.names) if y in ch_names] if unified: if axes is None: under_ax = plt.axes([0, 0, 1, 1]) under_ax.axis("off") else: under_ax = axes under_ax.format_coord = partial( format_coord_unified, pos=pos, ch_names=layout.names ) under_ax.set(xlim=[0, 1], ylim=[0, 1]) axs = list() for idx, name in iter_ch: ch_idx = ch_names.index(name) if not unified: # old, slow way ax = plt.axes(pos[idx]) ax.patch.set_facecolor(axis_facecolor) for spine in ax.spines.values(): spine.set_color(axis_spinecolor) if not legend: ax.set(xticklabels=[], yticklabels=[]) for tick in ax.get_xticklines() + ax.get_yticklines(): tick.set_visible(False) ax._mne_ch_name = name ax._mne_ch_idx = ch_idx ax._mne_ax_face_color = axis_facecolor ax.format_coord = partial(format_coord_multiaxis, ch_name=name) yield ax, ch_idx else: ax = Bunch( ax=under_ax, pos=pos[idx], data_lines=list(), _mne_ch_name=name, _mne_ch_idx=ch_idx, _mne_ax_face_color=axis_facecolor, ) axs.append(ax) if not unified and legend: ax = _legend_axis(pos) yield ax, -1 if unified: under_ax._mne_axs = axs # Create a PolyCollection for the axis backgrounds verts = np.transpose( [ pos[:, :2], pos[:, :2] + pos[:, 2:] * [1, 0], pos[:, :2] + pos[:, 2:], pos[:, :2] + pos[:, 2:] * [0, 1], ], [1, 0, 2], ) if not img: under_ax.add_collection( collections.PolyCollection( verts, facecolor=axis_facecolor, edgecolor=axis_spinecolor, linewidth=1.0, ) ) # Not needed for image plots. for ax in axs: yield ax, ax._mne_ch_idx def _plot_topo( info, times, show_func, click_func=None, layout=None, vmin=None, vmax=None, ylim=None, colorbar=None, border="none", axis_facecolor="k", fig_facecolor="k", cmap="RdBu_r", layout_scale=None, title=None, x_label=None, y_label=None, font_color="w", unified=False, img=False, axes=None, ): """Plot on sensor layout.""" import matplotlib.pyplot as plt if layout.kind == "custom": layout = deepcopy(layout) layout.pos[:, :2] -= layout.pos[:, :2].min(0) layout.pos[:, :2] /= layout.pos[:, :2].max(0) # prepare callbacks tmin, tmax = times[0], times[-1] click_func = show_func if click_func is None else click_func on_pick = partial( click_func, tmin=tmin, tmax=tmax, vmin=vmin, vmax=vmax, ylim=ylim, x_label=x_label, y_label=y_label, ) if axes is None: # Don't use constrained layout because we place axes manually fig = plt.figure(layout=None) axes = plt.axes([0.015, 0.025, 0.97, 0.95]) axes.set_facecolor(fig_facecolor) else: fig = axes.figure if colorbar: sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin, vmax)) sm.set_array(np.linspace(vmin, vmax)) cb = fig.colorbar( sm, ax=axes, pad=0.025, fraction=0.075, shrink=0.5, anchor=(-1, 0.5) ) cb_yticks = plt.getp(cb.ax.axes, "yticklabels") plt.setp(cb_yticks, color=font_color) axes.axis("off") my_topo_plot = _iter_topography( info, layout=layout, on_pick=on_pick, fig=fig, layout_scale=layout_scale, axis_spinecolor=border, axis_facecolor=axis_facecolor, fig_facecolor=fig_facecolor, unified=unified, img=img, axes=axes, ) for ax, ch_idx in my_topo_plot: if layout.kind == "Vectorview-all" and ylim is not None: ylim_ = ylim.get(channel_type(info, ch_idx)) else: ylim_ = ylim show_func(ax, ch_idx, tmin=tmin, tmax=tmax, vmin=vmin, vmax=vmax, ylim=ylim_) if title is not None: plt.figtext(0.03, 0.95, title, color=font_color, fontsize=15, va="top") return fig def _plot_topo_onpick(event, show_func): """Onpick callback that shows a single channel in a new figure.""" # make sure that the swipe gesture in OS-X doesn't open many figures orig_ax = event.inaxes import matplotlib.pyplot as plt try: if hasattr(orig_ax, "_mne_axs"): # in unified, single-axes mode x, y = event.xdata, event.ydata for ax in orig_ax._mne_axs: if ( x >= ax.pos[0] and y >= ax.pos[1] and x <= ax.pos[0] + ax.pos[2] and y <= ax.pos[1] + ax.pos[3] ): orig_ax = ax break else: # no axis found return elif not hasattr(orig_ax, "_mne_ch_idx"): # neither old nor new mode return ch_idx = orig_ax._mne_ch_idx face_color = orig_ax._mne_ax_face_color fig, ax = plt.subplots(1) plt.title(orig_ax._mne_ch_name) ax.set_facecolor(face_color) # allow custom function to override parameters show_func(ax, ch_idx) plt_show(fig=fig) except Exception as err: # matplotlib silently ignores exceptions in event handlers, # so we print # it here to know what went wrong print(err) raise def _compute_ax_scalings(bn, xlim, ylim): """Compute scale factors for a unified plot.""" if isinstance(ylim, dict): # Take the first (ymin, ymax) entry. ylim = next(iter(ylim.values())) pos = bn.pos bn.x_s = pos[2] / (xlim[1] - xlim[0]) bn.x_t = pos[0] - bn.x_s * xlim[0] bn.y_s = pos[3] / (ylim[1] - ylim[0]) bn.y_t = pos[1] - bn.y_s * ylim[0] def _imshow_tfr( ax, ch_idx, tmin, tmax, vmin, vmax, onselect, *, ylim=None, tfr=None, freq=None, x_label=None, y_label=None, colorbar=False, cmap=("RdBu_r", True), yscale="auto", mask=None, mask_style="both", mask_cmap="Greys", mask_alpha=0.1, cnorm=None, ): """Show time-frequency map as two-dimensional image.""" from matplotlib.widgets import RectangleSelector _check_option("yscale", yscale, ["auto", "linear", "log"]) cmap, interactive_cmap = cmap times = np.linspace(tmin, tmax, num=tfr[ch_idx].shape[1]) img, t_end = _plot_masked_image( ax, tfr[ch_idx], times, mask, yvals=freq, cmap=cmap, vmin=vmin, vmax=vmax, mask_style=mask_style, mask_alpha=mask_alpha, mask_cmap=mask_cmap, yscale=yscale, cnorm=cnorm, ) if x_label is not None: ax.set_xlabel(x_label) if y_label is not None: ax.set_ylabel(y_label) if colorbar: if isinstance(colorbar, DraggableColorbar): cbar = colorbar.cbar # this happens with multiaxes case else: cbar = ax.get_figure().colorbar(mappable=img, ax=ax) if interactive_cmap: ax.CB = DraggableColorbar(cbar, img, kind="tfr_image", ch_type=None) ax.RS = RectangleSelector(ax, onselect=onselect) # reference must be kept return t_end def _imshow_tfr_unified( bn, ch_idx, tmin, tmax, vmin, vmax, onselect, *, ylim=None, tfr=None, freq=None, vline=None, x_label=None, y_label=None, colorbar=False, picker=True, cmap="RdBu_r", title=None, hline=None, ): """Show multiple tfrs on topo using a single axes.""" _compute_ax_scalings(bn, (tmin, tmax), (freq[0], freq[-1])) ax = bn.ax data_lines = bn.data_lines extent = ( bn.x_t + bn.x_s * tmin, bn.x_t + bn.x_s * tmax, bn.y_t + bn.y_s * freq[0], bn.y_t + bn.y_s * freq[-1], ) data_lines.append( ax.imshow( tfr[ch_idx], extent=extent, aspect="auto", origin="lower", vmin=vmin, vmax=vmax, cmap=cmap, ) ) data_lines[-1].set_clip_box(_pos_to_bbox(bn.pos, ax)) def _plot_timeseries( ax, ch_idx, tmin, tmax, vmin, vmax, ylim, data, color, times, vline=None, x_label=None, y_label=None, colorbar=False, hline=None, hvline_color="w", labels=None, ): """Show time series on topo split across multiple axes.""" import matplotlib.pyplot as plt picker_flag = False for data_, color_, times_ in zip(data, color, times): if not picker_flag: # use large tol for picker so we can click anywhere in the axes line = ax.plot(times_, data_[ch_idx], color=color_, picker=True)[0] line.set_pickradius(1e9) picker_flag = True else: ax.plot(times_, data_[ch_idx], color=color_) def _format_coord(x, y, labels, ax): """Create status string based on cursor coordinates.""" # find indices for datasets near cursor (if any) tdiffs = [np.abs(tvec - x).min() for tvec in times] nearby = [k for k, tdiff in enumerate(tdiffs) if tdiff < (tmax - tmin) / 100] xlabel = ax.get_xlabel() xunit = ( xlabel[xlabel.find("(") + 1 : xlabel.find(")")] if "(" in xlabel and ")" in xlabel else "s" ) timestr = f"{x:6.3f} {xunit}: " if not nearby: return f"{timestr} Nothing here" labels = [""] * len(nearby) if labels is None else labels nearby_data = [(data[n], labels[n], times[n]) for n in nearby] ylabel = ax.get_ylabel() yunit = ( ylabel[ylabel.find("(") + 1 : ylabel.find(")")] if "(" in ylabel and ")" in ylabel else "" ) # try to estimate whether to truncate condition labels slen = 9 + len(xunit) + sum([12 + len(yunit) + len(label) for label in labels]) bar_width = (ax.figure.get_size_inches() * ax.figure.dpi)[0] / 5.5 # show labels and y values for datasets near cursor trunc_labels = bar_width < slen s = timestr for data_, label, tvec in nearby_data: idx = np.abs(tvec - x).argmin() s += f"{data_[ch_idx, idx]:7.2f} {yunit}" if trunc_labels: label = label if len(label) <= 10 else f"{label[:6]}..{label[-2:]}" s += f" [{label}] " if label else " " return s ax.format_coord = lambda x, y: _format_coord(x, y, labels=labels, ax=ax) def _cursor_vline(event): """Draw cursor (vertical line).""" ax = event.inaxes if not ax: return if ax._cursorline is not None: ax._cursorline.remove() ax._cursorline = ax.axvline(event.xdata, color=ax._cursorcolor) ax.figure.canvas.draw() def _rm_cursor(event): ax = event.inaxes if ax._cursorline is not None: ax._cursorline.remove() ax._cursorline = None ax.figure.canvas.draw() ax._cursorline = None # choose cursor color based on perceived brightness of background facecol = _to_rgb(ax.get_facecolor()) face_brightness = np.dot(facecol, [299, 587, 114]) ax._cursorcolor = "white" if face_brightness < 150 else "black" plt.connect("motion_notify_event", _cursor_vline) plt.connect("axes_leave_event", _rm_cursor) ymin, ymax = ax.get_ylim() # don't pass vline or hline here (this fxn doesn't do hvline_color): _setup_ax_spines(ax, [], tmin, tmax, ymin, ymax, hline=False) ax.figure.set_facecolor("k" if hvline_color == "w" else "w") ax.spines["bottom"].set_color(hvline_color) ax.spines["left"].set_color(hvline_color) ax.tick_params(axis="x", colors=hvline_color, which="both") ax.tick_params(axis="y", colors=hvline_color, which="both") ax.title.set_color(hvline_color) ax.xaxis.label.set_color(hvline_color) ax.yaxis.label.set_color(hvline_color) if x_label is not None: ax.set_xlabel(x_label) if y_label is not None: if isinstance(y_label, list): ax.set_ylabel(y_label[ch_idx]) else: ax.set_ylabel(y_label) if vline is not None: vline = [vline] if _is_numeric(vline) else vline for vline_ in vline: plt.axvline(vline_, color=hvline_color, linewidth=1.0, linestyle="--") if hline is not None: hline = [hline] if _is_numeric(hline) else hline for hline_ in hline: plt.axhline(hline_, color=hvline_color, linewidth=1.0, zorder=10) if colorbar: plt.colorbar() def _plot_timeseries_unified( bn, ch_idx, tmin, tmax, vmin, vmax, ylim, data, color, times, vline=None, x_label=None, y_label=None, colorbar=False, hline=None, hvline_color="w", ): """Show multiple time series on topo using a single axes.""" import matplotlib.pyplot as plt if not (ylim and not any(v is None for v in ylim)): ylim = [min(np.min(d) for d in data), max(np.max(d) for d in data)] # Translation and scale parameters to take data->under_ax normalized coords _compute_ax_scalings(bn, (tmin, tmax), ylim) pos = bn.pos data_lines = bn.data_lines ax = bn.ax for data_, color_, times_ in zip(data, color, times): data_lines.append( ax.plot( bn.x_t + bn.x_s * times_, bn.y_t + bn.y_s * data_[ch_idx], linewidth=0.5, color=color_, )[0] ) # Needs to be done afterward for some reason (probable matlotlib bug) data_lines[-1].set_clip_box(_pos_to_bbox(pos, ax)) if vline: vline = np.array(vline) * bn.x_s + bn.x_t ax.vlines( vline, pos[1], pos[1] + pos[3], color=hvline_color, linewidth=0.5, linestyle="--", ) if hline: hline = np.array(hline) * bn.y_s + bn.y_t ax.hlines(hline, pos[0], pos[0] + pos[2], color=hvline_color, linewidth=0.5) if x_label is not None: ax.text( pos[0] + pos[2] / 2.0, pos[1], x_label, horizontalalignment="center", verticalalignment="top", ) if y_label is not None: y_label = y_label[ch_idx] if isinstance(y_label, list) else y_label ax.text( pos[0], pos[1] + pos[3] / 2.0, y_label, horizontalignment="right", verticalalignment="middle", rotation=90, ) if colorbar: plt.colorbar() def _erfimage_imshow( ax, ch_idx, tmin, tmax, vmin, vmax, ylim=None, data=None, epochs=None, sigma=None, order=None, scalings=None, vline=None, x_label=None, y_label=None, colorbar=False, cmap="RdBu_r", vlim_array=None, ): """Plot erfimage on sensor topography.""" import matplotlib.pyplot as plt this_data = data[:, ch_idx, :] if vlim_array is not None: vmin, vmax = vlim_array[ch_idx] if callable(order): order = order(epochs.times, this_data) if order is not None: this_data = this_data[order] if sigma > 0.0: this_data = ndimage.gaussian_filter1d(this_data, sigma=sigma, axis=0) img = ax.imshow( this_data, extent=[tmin, tmax, 0, len(data)], aspect="auto", origin="lower", vmin=vmin, vmax=vmax, picker=True, cmap=cmap, interpolation="nearest", ) ax = plt.gca() if x_label is not None: ax.set_xlabel(x_label) if y_label is not None: ax.set_ylabel(y_label) if colorbar: plt.colorbar(mappable=img) def _erfimage_imshow_unified( bn, ch_idx, tmin, tmax, vmin, vmax, ylim=None, data=None, epochs=None, sigma=None, order=None, scalings=None, vline=None, x_label=None, y_label=None, colorbar=False, cmap="RdBu_r", vlim_array=None, ): """Plot erfimage topography using a single axis.""" _compute_ax_scalings(bn, (tmin, tmax), (0, len(epochs.events))) ax = bn.ax data_lines = bn.data_lines extent = ( bn.x_t + bn.x_s * tmin, bn.x_t + bn.x_s * tmax, bn.y_t, bn.y_t + bn.y_s * len(epochs.events), ) this_data = data[:, ch_idx, :] vmin, vmax = (None, None) if vlim_array is None else vlim_array[ch_idx] if callable(order): order = order(epochs.times, this_data) if order is not None: this_data = this_data[order] if sigma > 0.0: this_data = ndimage.gaussian_filter1d(this_data, sigma=sigma, axis=0) data_lines.append( ax.imshow( this_data, extent=extent, aspect="auto", origin="lower", vmin=vmin, vmax=vmax, picker=True, cmap=cmap, interpolation="nearest", ) ) def _plot_evoked_topo( evoked, layout=None, layout_scale=0.945, color=None, border="none", ylim=None, scalings=None, title=None, proj=False, vline=(0.0,), hline=(0.0,), fig_facecolor="k", fig_background=None, axis_facecolor="k", font_color="w", merge_channels=False, legend=True, axes=None, exclude="bads", show=True, noise_cov=None, ): """Plot 2D topography of evoked responses. Clicking on the plot of an individual sensor opens a new figure showing the evoked response for the selected sensor. Parameters ---------- evoked : list of Evoked | Evoked The evoked response to plot. layout : instance of Layout | None Layout instance specifying sensor positions (does not need to be specified for Neuromag data). If possible, the correct layout is inferred from the data. layout_scale : float Scaling factor for adjusting the relative size of the layout on the canvas. color : list of color objects | color object | None Everything matplotlib accepts to specify colors. If not list-like, the color specified will be repeated. If None, colors are automatically drawn. border : str Matplotlib borders style to be used for each sensor plot. ylim : dict | None ylim for plots (after scaling has been applied). The value determines the upper and lower subplot limits. e.g. ylim = dict(eeg=[-20, 20]). Valid keys are eeg, mag, grad. If None, the ylim parameter for each channel type is determined by the minimum and maximum peak. scalings : dict | None The scalings of the channel types to be applied for plotting. If None,` defaults to ``dict(eeg=1e6, grad=1e13, mag=1e15)``. title : str Title of the figure. proj : bool | 'interactive' If true SSP projections are applied before display. If 'interactive', a check box for reversible selection of SSP projection vectors will be shown. vline : list of floats | None The values at which to show a vertical line. hline : list of floats | None The values at which to show a horizontal line. fig_facecolor : color The figure face color. Defaults to black. fig_background : None | array A background image for the figure. This must be a valid input to `matplotlib.pyplot.imshow`. Defaults to None. axis_facecolor : color The face color to be used for each sensor plot. Defaults to black. font_color : color The color of text in the colorbar and title. Defaults to white. merge_channels : bool Whether to use RMS value of gradiometer pairs. Only works for Neuromag data. Defaults to False. legend : bool | int | string | tuple If True, create a legend based on evoked.comment. If False, disable the legend. Otherwise, the legend is created and the parameter value is passed as the location parameter to the matplotlib legend call. It can be an integer (e.g. 0 corresponds to upper right corner of the plot), a string (e.g. 'upper right'), or a tuple (x, y coordinates of the lower left corner of the legend in the axes coordinate system). See matplotlib documentation for more details. axes : instance of matplotlib Axes | None Axes to plot into. If None, axes will be created. noise_cov : instance of Covariance | str | None Noise covariance used to whiten the data while plotting. Whitened data channels names are shown in italic. Can be a string to load a covariance from disk. exclude : list of str | 'bads' Channels names to exclude from being shown. If 'bads', the bad channels are excluded. By default, exclude is set to 'bads'. show : bool Show figure if True. .. versionadded:: 0.16.0 Returns ------- fig : instance of matplotlib.figure.Figure Images of evoked responses at sensor locations """ import matplotlib.pyplot as plt from ..channels.layout import _merge_ch_data, _pair_grad_sensors, find_layout from ..cov import whiten_evoked if type(evoked) not in (tuple, list): evoked = [evoked] noise_cov = _check_cov(noise_cov, evoked[0].info) if noise_cov is not None: evoked = [whiten_evoked(e, noise_cov) for e in evoked] else: evoked = [e.copy() for e in evoked] info = evoked[0].info ch_names = evoked[0].ch_names scalings = _handle_default("scalings", scalings) if not all(e.ch_names == ch_names for e in evoked): raise ValueError("All evoked.picks must be the same") ch_names = _clean_names(ch_names) if merge_channels: picks = _pair_grad_sensors(info, topomap_coords=False, exclude=exclude) chs = list() for pick in picks[::2]: ch = info["chs"][pick] ch["ch_name"] = ch["ch_name"][:-1] + "X" chs.append(ch) with info._unlock(update_redundant=True, check_after=True): info["chs"] = chs info["bads"] = list() # Bads handled by pair_grad_sensors new_picks = list() for e in evoked: data, _ = _merge_ch_data(e.data[picks], "grad", []) if noise_cov is None: data *= scalings["grad"] e.data = data new_picks.append(range(len(data))) picks = new_picks types_used = ["grad"] unit = _handle_default("units")["grad"] if noise_cov is None else "NA" y_label = f"RMS amplitude ({unit})" if layout is None: layout = find_layout(info, exclude=exclude) else: layout = layout.pick( "all", exclude=_picks_to_idx( info, exclude if exclude != "bads" else info["bads"], exclude=(), allow_empty=True, ), ) if not merge_channels: # XXX. at the moment we are committed to 1- / 2-sensor-types layouts chs_in_layout = [ch_name for ch_name in ch_names if ch_name in layout.names] types_used = [channel_type(info, ch_names.index(ch)) for ch in chs_in_layout] # Using dict conversion to remove duplicates types_used = list(dict.fromkeys(types_used)) # remove possible reference meg channels types_used = [ types_used for types_used in types_used if types_used != "ref_meg" ] # one check for all vendors is_meg = len([x for x in types_used if x in ["mag", "grad"]]) > 0 is_nirs = ( len( [ x for x in types_used if x in ("hbo", "hbr", "fnirs_cw_amplitude", "fnirs_od") ] ) > 0 ) if is_meg: picks = [ pick_types(info, meg=kk, ref_meg=False, exclude=exclude) for kk in types_used ] elif is_nirs: picks = [ pick_types(info, fnirs=kk, ref_meg=False, exclude=exclude) for kk in types_used ] else: types_used_kwargs = {t: True for t in types_used} picks = [pick_types(info, meg=False, exclude=exclude, **types_used_kwargs)] assert isinstance(picks, list) and len(types_used) == len(picks) if noise_cov is None: for e in evoked: for pick, ch_type in zip(picks, types_used): e.data[pick] *= scalings[ch_type] if proj is True and all(e.proj is not True for e in evoked): evoked = [e.apply_proj() for e in evoked] elif proj == "interactive": # let it fail early. for e in evoked: _check_delayed_ssp(e) # Y labels for picked plots must be reconstructed y_label = list() for ch_idx in range(len(chs_in_layout)): if noise_cov is None: unit = _handle_default("units")[channel_type(info, ch_idx)] else: unit = "NA" y_label.append(f"Amplitude ({unit})") if ylim is None: # find minima and maxima over all evoked data for each channel pick ylim_ = dict() for ch_type, p in zip(types_used, picks): ylim_[ch_type] = [ min([e.data[p].min() for e in evoked]), max([e.data[p].max() for e in evoked]), ] elif isinstance(ylim, dict): ylim_ = _handle_default("ylim", ylim) ylim_ = {kk: ylim_[kk] for kk in types_used} else: raise TypeError(f"ylim must be None or a dict. Got {type(ylim)}.") data = [e.data for e in evoked] comments = [e.comment for e in evoked] times = [e.times for e in evoked] show_func = partial( _plot_timeseries_unified, data=data, color=color, times=times, vline=vline, hline=hline, hvline_color=font_color, ) click_func = partial( _plot_timeseries, data=data, color=color, times=times, vline=vline, hline=hline, hvline_color=font_color, labels=comments, ) time_min = min([t[0] for t in times]) time_max = max([t[-1] for t in times]) fig = _plot_topo( info=info, times=[time_min, time_max], show_func=show_func, click_func=click_func, layout=layout, colorbar=False, ylim=ylim_, cmap=None, layout_scale=layout_scale, border=border, fig_facecolor=fig_facecolor, font_color=font_color, axis_facecolor=axis_facecolor, title=title, x_label="Time (s)", y_label=y_label, unified=True, axes=axes, ) add_background_image(fig, fig_background) if legend is not False: legend_loc = 0 if legend is True else legend labels = [e.comment if e.comment else "Unknown" for e in evoked] handles = fig.axes[0].lines[: len(evoked)] legend = plt.legend( labels=labels, handles=handles, loc=legend_loc, prop={"size": 10} ) legend.get_frame().set_facecolor(axis_facecolor) txts = legend.get_texts() for txt, col in zip(txts, color): txt.set_color(col) if proj == "interactive": for e in evoked: _check_delayed_ssp(e) params = dict( evokeds=evoked, times=times, plot_update_proj_callback=_plot_update_evoked_topo_proj, projs=evoked[0].info["projs"], fig=fig, ) _draw_proj_checkbox(None, params) plt_show(show) return fig def _plot_update_evoked_topo_proj(params, bools): """Update topo sensor plots.""" evokeds = [e.copy() for e in params["evokeds"]] fig = params["fig"] projs = [proj for proj, b in zip(params["projs"], bools) if b] params["proj_bools"] = bools for e in evokeds: e.add_proj(projs, remove_existing=True) e.apply_proj() # make sure to only modify the time courses, not the ticks for ax in fig.axes[0]._mne_axs: for line, evoked in zip(ax.data_lines, evokeds): line.set_ydata(ax.y_t + ax.y_s * evoked.data[ax._mne_ch_idx]) fig.canvas.draw() def plot_topo_image_epochs( epochs, layout=None, sigma=0.0, vmin=None, vmax=None, colorbar=None, order=None, cmap="RdBu_r", layout_scale=0.95, title=None, scalings=None, border="none", fig_facecolor="k", fig_background=None, font_color="w", show=True, ): """Plot Event Related Potential / Fields image on topographies. Parameters ---------- epochs : instance of :class:`~mne.Epochs` The epochs. layout : instance of Layout System specific sensor positions. sigma : float The standard deviation of the Gaussian smoothing to apply along the epoch axis to apply in the image. If 0., no smoothing is applied. vmin : float The min value in the image. The unit is µV for EEG channels, fT for magnetometers and fT/cm for gradiometers. vmax : float The max value in the image. The unit is µV for EEG channels, fT for magnetometers and fT/cm for gradiometers. colorbar : bool | None Whether to display a colorbar or not. If ``None`` a colorbar will be shown only if all channels are of the same type. Defaults to ``None``. order : None | array of int | callable If not None, order is used to reorder the epochs on the y-axis of the image. If it's an array of int it should be of length the number of good epochs. If it's a callable the arguments passed are the times vector and the data as 2d array (data.shape[1] == len(times)). cmap : colormap Colors to be mapped to the values. layout_scale : float Scaling factor for adjusting the relative size of the layout on the canvas. title : str Title of the figure. scalings : dict | None The scalings of the channel types to be applied for plotting. If ``None``, defaults to ``dict(eeg=1e6, grad=1e13, mag=1e15)``. border : str Matplotlib borders style to be used for each sensor plot. fig_facecolor : color The figure face color. Defaults to black. fig_background : None | array A background image for the figure. This must be a valid input to :func:`matplotlib.pyplot.imshow`. Defaults to ``None``. font_color : color The color of tick labels in the colorbar. Defaults to white. show : bool Whether to show the figure. Defaults to ``True``. Returns ------- fig : instance of :class:`matplotlib.figure.Figure` Figure distributing one image per channel across sensor topography. Notes ----- In an interactive Python session, this plot will be interactive; clicking on a channel image will pop open a larger view of the image; this image will always have a colorbar even when the topo plot does not (because it shows multiple sensor types). """ from ..channels.layout import find_layout scalings = _handle_default("scalings", scalings) # make a copy because we discard non-data channels and scale the data epochs = epochs.copy().load_data() # use layout to subset channels present in epochs object if layout is None: layout = find_layout(epochs.info) ch_names = set(layout.names) & set(epochs.ch_names) idxs = [epochs.ch_names.index(ch_name) for ch_name in ch_names] epochs = epochs.pick(idxs) # get lists of channel type & scale coefficient ch_types = epochs.get_channel_types() scale_coeffs = [scalings.get(ch_type, 1) for ch_type in ch_types] # scale the data epochs._data *= np.array(scale_coeffs)[:, np.newaxis] data = epochs.get_data(copy=False) # get vlims for each channel type vlim_dict = dict() for ch_type in set(ch_types): this_data = data[:, np.where(np.array(ch_types) == ch_type)] vlim_dict[ch_type] = _setup_vmin_vmax(this_data, vmin, vmax) vlim_array = np.array([vlim_dict[ch_type] for ch_type in ch_types]) # only show colorbar if we have a single channel type if colorbar is None: colorbar = len(set(ch_types)) == 1 # if colorbar=True, we know we have only 1 channel type so all entries # in vlim_array are the same, just take the first one if colorbar and vmin is None and vmax is None: vmin, vmax = vlim_array[0] show_func = partial( _erfimage_imshow_unified, scalings=scale_coeffs, order=order, data=data, epochs=epochs, sigma=sigma, cmap=cmap, vlim_array=vlim_array, ) erf_imshow = partial( _erfimage_imshow, scalings=scale_coeffs, order=order, data=data, epochs=epochs, sigma=sigma, cmap=cmap, vlim_array=vlim_array, colorbar=True, ) fig = _plot_topo( info=epochs.info, times=epochs.times, click_func=erf_imshow, show_func=show_func, layout=layout, colorbar=colorbar, vmin=vmin, vmax=vmax, cmap=cmap, layout_scale=layout_scale, title=title, fig_facecolor=fig_facecolor, font_color=font_color, border=border, x_label="Time (s)", y_label="Epoch", unified=True, img=True, ) add_background_image(fig, fig_background) plt_show(show) return fig def _pos_to_bbox(pos, ax): """Convert layout position to bbox.""" import matplotlib.transforms as mtransforms return mtransforms.TransformedBbox( mtransforms.Bbox.from_bounds(*pos), ax.transAxes, )