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evoked.py
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evoked.py
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# -*- coding: utf-8 -*-
"""Functions to plot evoked M/EEG data (besides topographies)."""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# Cathy Nangini <cnangini@gmail.com>
# Mainak Jas <mainak@neuro.hut.fi>
# Daniel McCloy <dan.mccloy@gmail.com>
#
# License: Simplified BSD
from copy import deepcopy
from functools import partial
from itertools import cycle
from numbers import Integral
import numpy as np
from ..fixes import _is_last_row
from ..io.pick import (channel_type,
_VALID_CHANNEL_TYPES, channel_indices_by_type,
_DATA_CH_TYPES_SPLIT, _pick_inst, _get_channel_types,
_PICK_TYPES_DATA_DICT, _picks_to_idx, pick_info)
from ..defaults import _handle_default
from .utils import (_draw_proj_checkbox, tight_layout, _check_delayed_ssp,
plt_show, _process_times, DraggableColorbar, _setup_cmap,
_setup_vmin_vmax, _check_cov, _make_combine_callable,
_validate_if_list_of_axes, _triage_rank_sss,
_connection_line, _get_color_list, _setup_ax_spines,
_setup_plot_projector, _prepare_joint_axes, _check_option,
_set_title_multiple_electrodes, _check_time_unit,
_plot_masked_image, _trim_ticks, _set_window_title,
_prop_kw, _get_cmap)
from ..utils import (logger, _clean_names, warn, _pl, verbose, _validate_type,
_check_if_nan, _check_ch_locs, fill_doc, _is_numeric,
_to_rgb)
from .topo import _plot_evoked_topo
from .topomap import (_prepare_topomap_plot, plot_topomap, _get_pos_outlines,
_draw_outlines, _prepare_topomap, _set_contour_locator,
_check_sphere, _make_head_outlines)
from ..channels.layout import _pair_grad_sensors, find_layout
def _butterfly_onpick(event, params):
"""Add a channel name on click."""
params['need_draw'] = True
ax = event.artist.axes
ax_idx = np.where([ax is a for a in params['axes']])[0]
if len(ax_idx) == 0: # this can happen if ax param is used
return # let the other axes handle it
else:
ax_idx = ax_idx[0]
lidx = np.where([
line is event.artist for line in params['lines'][ax_idx]])[0][0]
ch_name = params['ch_names'][params['idxs'][ax_idx][lidx]]
text = params['texts'][ax_idx]
x = event.artist.get_xdata()[event.ind[0]]
y = event.artist.get_ydata()[event.ind[0]]
text.set_x(x)
text.set_y(y)
text.set_text(ch_name)
text.set_color(event.artist.get_color())
text.set_alpha(1.)
text.set_zorder(len(ax.lines)) # to make sure it goes on top of the lines
text.set_path_effects(params['path_effects'])
# do NOT redraw here, since for butterfly plots hundreds of lines could
# potentially be picked -- use on_button_press (happens once per click)
# to do the drawing
def _butterfly_on_button_press(event, params):
"""Only draw once for picking."""
if params['need_draw']:
event.canvas.draw()
else:
idx = np.where([event.inaxes is ax for ax in params['axes']])[0]
if len(idx) == 1:
text = params['texts'][idx[0]]
text.set_alpha(0.)
text.set_path_effects([])
event.canvas.draw()
params['need_draw'] = False
def _line_plot_onselect(xmin, xmax, ch_types, info, data, times, text=None,
psd=False, time_unit='s', sphere=None):
"""Draw topomaps from the selected area."""
import matplotlib.pyplot as plt
ch_types = [type_ for type_ in ch_types if type_ in ('eeg', 'grad', 'mag')]
if len(ch_types) == 0:
raise ValueError('Interactive topomaps only allowed for EEG '
'and MEG channels.')
if ('grad' in ch_types and
len(_pair_grad_sensors(info, topomap_coords=False,
raise_error=False)) < 2):
ch_types.remove('grad')
if len(ch_types) == 0:
return
vert_lines = list()
if text is not None:
text.set_visible(True)
ax = text.axes
vert_lines.append(ax.axvline(xmin, zorder=0, color='red'))
vert_lines.append(ax.axvline(xmax, zorder=0, color='red'))
fill = ax.axvspan(xmin, xmax, alpha=0.2, color='green')
evoked_fig = plt.gcf()
evoked_fig.canvas.draw()
evoked_fig.canvas.flush_events()
minidx = np.abs(times - xmin).argmin()
maxidx = np.abs(times - xmax).argmin()
fig, axarr = plt.subplots(1, len(ch_types), squeeze=False,
figsize=(3 * len(ch_types), 3))
for idx, ch_type in enumerate(ch_types):
if ch_type not in ('eeg', 'grad', 'mag'):
continue
picks, pos, merge_channels, _, ch_type, this_sphere, clip_origin = \
_prepare_topomap_plot(info, ch_type, sphere=sphere)
outlines = _make_head_outlines(this_sphere, pos, 'head', clip_origin)
if len(pos) < 2:
fig.delaxes(axarr[0][idx])
continue
this_data = data[picks, minidx:maxidx]
if merge_channels:
from ..channels.layout import _merge_ch_data
method = 'mean' if psd else 'rms'
this_data, _ = _merge_ch_data(this_data, ch_type, [],
method=method)
title = '%s %s' % (ch_type, method.upper())
else:
title = ch_type
this_data = np.average(this_data, axis=1)
axarr[0][idx].set_title(title)
# can be all negative for dB PSD
vlim = (min(this_data), max(this_data)) if psd else (None, None)
cmap = 'Reds' if psd else None
plot_topomap(this_data, pos, cmap=cmap, vlim=vlim,
axes=axarr[0][idx], show=False, sphere=this_sphere,
outlines=outlines)
unit = 'Hz' if psd else time_unit
fig.suptitle('Average over %.2f%s - %.2f%s' % (xmin, unit, xmax, unit),
y=0.1)
tight_layout(pad=2.0, fig=fig)
plt_show()
if text is not None:
text.set_visible(False)
close_callback = partial(_topo_closed, ax=ax, lines=vert_lines,
fill=fill)
fig.canvas.mpl_connect('close_event', close_callback)
evoked_fig.canvas.draw()
evoked_fig.canvas.flush_events()
def _topo_closed(events, ax, lines, fill):
"""Remove lines from evoked plot as topomap is closed."""
for line in lines:
ax.lines.remove(line)
ax.patches.remove(fill)
ax.get_figure().canvas.draw()
def _rgb(x, y, z):
"""Transform x, y, z values into RGB colors."""
rgb = np.array([x, y, z]).T
rgb -= np.nanmin(rgb, 0)
rgb /= np.maximum(np.nanmax(rgb, 0), 1e-16) # avoid div by zero
return rgb
def _plot_legend(pos, colors, axis, bads, outlines, loc, size=30):
"""Plot (possibly colorized) channel legends for evoked plots."""
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
axis.get_figure().canvas.draw()
bbox = axis.get_window_extent() # Determine the correct size.
ratio = bbox.width / bbox.height
ax = inset_axes(axis, width=str(size / ratio) + '%',
height=str(size) + '%', loc=loc)
ax.set_adjustable('box')
ax.set_aspect('equal')
_prepare_topomap(pos, ax, check_nonzero=False)
pos_x, pos_y = pos.T
ax.scatter(pos_x, pos_y, color=colors, s=size * .8, marker='.', zorder=1)
if bads:
bads = np.array(bads)
ax.scatter(pos_x[bads], pos_y[bads], s=size / 6, marker='.',
color='w', zorder=1)
_draw_outlines(ax, outlines)
def _check_spatial_colors(info, picks, spatial_colors):
"""Use spatial colors if channel locations exist."""
# NB: this assumes `picks`` has already been through _picks_to_idx()
# and it reflects *just the picks for the current subplot*
if spatial_colors == 'auto':
if len(picks) == 1:
spatial_colors = False
else:
spatial_colors = _check_ch_locs(info)
return spatial_colors
def _plot_evoked(evoked, picks=None, exclude='bads', unit=True, show=True,
ylim=None, proj=False, xlim='tight', hline=None,
units=None, scalings=None, titles=None, axes=None,
plot_type='butterfly', cmap=None,
gfp=False, window_title=None, spatial_colors=False,
selectable=True, zorder='unsorted',
noise_cov=None, colorbar=True, mask=None, mask_style=None,
mask_cmap=None, mask_alpha=.25, time_unit='s',
show_names=False, group_by=None, sphere=None, *,
highlight=None, draw=True):
"""Aux function for plot_evoked and plot_evoked_image (cf. docstrings).
Extra params are:
plot_type : str, value ('butterfly' | 'image')
The type of graph to plot: 'butterfly' plots each channel as a line
(x axis: time, y axis: amplitude). 'image' plots a 2D image where
color depicts the amplitude of each channel at a given time point
(x axis: time, y axis: channel). In 'image' mode, the plot is not
interactive.
draw : bool
If True, draw at the end.
"""
import matplotlib.pyplot as plt
_check_option('spatial_colors', spatial_colors, [True, False, 'auto'])
# For evoked.plot_image ...
# First input checks for group_by and axes if any of them is not None.
# Either both must be dicts, or neither.
# If the former, the two dicts provide picks and axes to plot them to.
# Then, we call this function recursively for each entry in `group_by`.
if plot_type == "image" and isinstance(group_by, dict):
if axes is None:
axes = dict()
for sel in group_by:
plt.figure()
axes[sel] = plt.axes()
if not isinstance(axes, dict):
raise ValueError("If `group_by` is a dict, `axes` must be "
"a dict of axes or None.")
_validate_if_list_of_axes(list(axes.values()))
remove_xlabels = any([_is_last_row(ax) for ax in axes.values()])
for sel in group_by: # ... we loop over selections
if sel not in axes:
raise ValueError(sel + " present in `group_by`, but not "
"found in `axes`")
ax = axes[sel]
# the unwieldy dict comp below defaults the title to the sel
title = ({channel_type(evoked.info, idx): sel
for idx in group_by[sel]} if titles is None else titles)
_plot_evoked(evoked, group_by[sel], exclude, unit, show, ylim,
proj, xlim, hline, units, scalings, title,
ax, plot_type, cmap=cmap, gfp=gfp,
window_title=window_title,
selectable=selectable, noise_cov=noise_cov,
colorbar=colorbar, mask=mask,
mask_style=mask_style, mask_cmap=mask_cmap,
mask_alpha=mask_alpha, time_unit=time_unit,
show_names=show_names,
sphere=sphere, draw=False,
spatial_colors=spatial_colors)
if remove_xlabels and not _is_last_row(ax):
ax.set_xticklabels([])
ax.set_xlabel("")
ims = [ax.images[0] for ax in axes.values()]
clims = np.array([im.get_clim() for im in ims])
min, max = clims.min(), clims.max()
for im in ims:
im.set_clim(min, max)
figs = [ax.get_figure() for ax in axes.values()]
if len(set(figs)) == 1:
return figs[0]
else:
return figs
elif isinstance(axes, dict):
raise ValueError("If `group_by` is not a dict, "
"`axes` must not be a dict either.")
time_unit, times = _check_time_unit(time_unit, evoked.times)
evoked = evoked.copy() # we modify info
info = evoked.info
if axes is not None and proj == 'interactive':
raise RuntimeError('Currently only single axis figures are supported'
' for interactive SSP selection.')
_check_option('gfp', gfp, [True, False, 'only'])
if highlight is not None:
highlight = np.array(highlight, dtype=float)
highlight = np.atleast_2d(highlight)
if highlight.shape[1] != 2:
raise ValueError(
f'"highlight" must be reshapable into a 2D array with shape '
f'(n, 2). Got {highlight.shape}.'
)
scalings = _handle_default('scalings', scalings)
titles = _handle_default('titles', titles)
units = _handle_default('units', units)
if plot_type == "image":
if ylim is not None and not isinstance(ylim, dict):
# The user called Evoked.plot_image() or plot_evoked_image(), the
# clim parameters of those functions end up to be the ylim here.
raise ValueError("`clim` must be a dict. "
"E.g. clim = dict(eeg=[-20, 20])")
picks = _picks_to_idx(info, picks, none='all', exclude=())
if len(picks) != len(set(picks)):
raise ValueError("`picks` are not unique. Please remove duplicates.")
bad_ch_idx = [info['ch_names'].index(ch) for ch in info['bads']
if ch in info['ch_names']]
if len(exclude) > 0:
if isinstance(exclude, str) and exclude == 'bads':
exclude = bad_ch_idx
elif (isinstance(exclude, list) and
all(isinstance(ch, str) for ch in exclude)):
exclude = [info['ch_names'].index(ch) for ch in exclude]
else:
raise ValueError(
'exclude has to be a list of channel names or "bads"')
picks = np.array([pick for pick in picks if pick not in exclude])
types = np.array(_get_channel_types(info, picks), str)
ch_types_used = list()
for this_type in _VALID_CHANNEL_TYPES:
if this_type in types:
ch_types_used.append(this_type)
fig = None
if axes is None:
fig, axes = plt.subplots(len(ch_types_used), 1)
fig.subplots_adjust(left=0.125, bottom=0.1, right=0.975, top=0.92,
hspace=0.63)
if isinstance(axes, plt.Axes):
axes = [axes]
fig.set_size_inches(6.4, 2 + len(axes))
if isinstance(axes, plt.Axes):
axes = [axes]
elif isinstance(axes, np.ndarray):
axes = list(axes)
if fig is None:
fig = axes[0].get_figure()
if window_title is not None:
_set_window_title(fig, window_title)
if len(axes) != len(ch_types_used):
raise ValueError('Number of axes (%g) must match number of channel '
'types (%d: %s)' % (len(axes), len(ch_types_used),
sorted(ch_types_used)))
_check_option('proj', proj, (True, False, 'interactive', 'reconstruct'))
noise_cov = _check_cov(noise_cov, info)
if proj == 'reconstruct' and noise_cov is not None:
raise ValueError('Cannot use proj="reconstruct" when noise_cov is not '
'None')
projector, whitened_ch_names = _setup_plot_projector(
info, noise_cov, proj=proj is True, nave=evoked.nave)
if len(whitened_ch_names) > 0:
unit = False
if projector is not None:
evoked.data[:] = np.dot(projector, evoked.data)
if proj == 'reconstruct':
evoked = evoked._reconstruct_proj()
if plot_type == 'butterfly':
_plot_lines(evoked.data, info, picks, fig, axes, spatial_colors, unit,
units, scalings, hline, gfp, types, zorder, xlim, ylim,
times, bad_ch_idx, titles, ch_types_used, selectable,
False, line_alpha=1., nave=evoked.nave,
time_unit=time_unit, sphere=sphere, highlight=highlight)
plt.setp(axes, xlabel=f'Time ({time_unit})')
elif plot_type == 'image':
for ai, (ax, this_type) in enumerate(zip(axes, ch_types_used)):
use_nave = evoked.nave if ai == 0 else None
this_picks = list(picks[types == this_type])
_plot_image(evoked.data, ax, this_type, this_picks, cmap, unit,
units, scalings, times, xlim, ylim, titles,
colorbar=colorbar, mask=mask, mask_style=mask_style,
mask_cmap=mask_cmap, mask_alpha=mask_alpha,
nave=use_nave, time_unit=time_unit,
show_names=show_names, ch_names=evoked.ch_names)
if proj == 'interactive':
_check_delayed_ssp(evoked)
params = dict(evoked=evoked, fig=fig, projs=info['projs'], axes=axes,
types=types, units=units, scalings=scalings, unit=unit,
ch_types_used=ch_types_used, picks=picks,
plot_update_proj_callback=_plot_update_evoked,
plot_type=plot_type)
_draw_proj_checkbox(None, params)
plt.setp(fig.axes[:len(ch_types_used) - 1], xlabel='')
if draw:
fig.canvas.draw() # for axes plots update axes.
plt_show(show)
return fig
def _plot_lines(data, info, picks, fig, axes, spatial_colors, unit, units,
scalings, hline, gfp, types, zorder, xlim, ylim, times,
bad_ch_idx, titles, ch_types_used, selectable, psd,
line_alpha, nave, time_unit, sphere, *, highlight):
"""Plot data as butterfly plot."""
from matplotlib import patheffects, pyplot as plt
from matplotlib.widgets import SpanSelector
assert len(axes) == len(ch_types_used)
texts = list()
idxs = list()
lines = list()
sphere = _check_sphere(sphere, info)
path_effects = [patheffects.withStroke(linewidth=2, foreground="w",
alpha=0.75)]
gfp_path_effects = [patheffects.withStroke(linewidth=5, foreground="w",
alpha=0.75)]
if selectable:
selectables = np.ones(len(ch_types_used), dtype=bool)
for type_idx, this_type in enumerate(ch_types_used):
idx = picks[types == this_type]
if len(idx) < 2 or (this_type == 'grad' and len(idx) < 4):
# prevent unnecessary warnings for e.g. EOG
if this_type in _DATA_CH_TYPES_SPLIT:
logger.info('Need more than one channel to make '
'topography for %s. Disabling interactivity.'
% (this_type,))
selectables[type_idx] = False
if selectable:
# Parameters for butterfly interactive plots
params = dict(axes=axes, texts=texts, lines=lines,
ch_names=info['ch_names'], idxs=idxs, 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))
for ai, (ax, this_type) in enumerate(zip(axes, ch_types_used)):
line_list = list() # 'line_list' contains the lines for this axes
if unit is False:
this_scaling = 1.0
ch_unit = 'NA' # no unit
else:
this_scaling = 1. if scalings is None else scalings[this_type]
ch_unit = units[this_type]
idx = list(picks[types == this_type])
idxs.append(idx)
if len(idx) > 0:
# Set amplitude scaling
D = this_scaling * data[idx, :]
_check_if_nan(D)
gfp_only = gfp == 'only'
if not gfp_only:
chs = [info['chs'][i] for i in idx]
locs3d = np.array([ch['loc'][:3] for ch in chs])
# _plot_psd can pass spatial_colors=color (e.g., "black") so
# we need to use "is True" here
_spat_col = _check_spatial_colors(info, idx, spatial_colors)
if (_spat_col is True and
not _check_ch_locs(info=info, picks=idx)):
warn('Channel locations not available. Disabling spatial '
'colors.')
_spat_col = selectable = False
if _spat_col is True and len(idx) != 1:
x, y, z = locs3d.T
colors = _rgb(x, y, z)
_handle_spatial_colors(colors, info, idx, this_type, psd,
ax, sphere)
else:
if isinstance(_spat_col, (tuple, str)):
col = [_spat_col]
else:
col = ['k']
colors = col * len(idx)
for i in bad_ch_idx:
if i in idx:
colors[idx.index(i)] = 'r'
if zorder == 'std':
# find the channels with the least activity
# to map them in front of the more active ones
z_ord = D.std(axis=1).argsort()
elif zorder == 'unsorted':
z_ord = list(range(D.shape[0]))
elif not callable(zorder):
error = ('`zorder` must be a function, "std" '
'or "unsorted", not {0}.')
raise TypeError(error.format(type(zorder)))
else:
z_ord = zorder(D)
# plot channels
for ch_idx, z in enumerate(z_ord):
line_list.append(
ax.plot(times, D[ch_idx], picker=True,
zorder=z + 1 if _spat_col else 1,
color=colors[ch_idx], alpha=line_alpha,
linewidth=0.5)[0])
line_list[-1].set_pickradius(3.)
# Plot GFP / RMS
if gfp:
if gfp in [True, 'only']:
if this_type == 'eeg':
this_gfp = D.std(axis=0, ddof=0)
label = 'GFP'
else:
this_gfp = np.linalg.norm(D, axis=0) / np.sqrt(len(D))
label = 'RMS'
gfp_color = 3 * (0.,) if spatial_colors is True else (0., 1.,
0.)
this_ylim = ax.get_ylim() if (ylim is None or this_type not in
ylim.keys()) else ylim[this_type]
if gfp_only:
y_offset = 0.
else:
y_offset = this_ylim[0]
this_gfp += y_offset
ax.fill_between(times, y_offset, this_gfp, color='none',
facecolor=gfp_color, zorder=1, alpha=0.2)
line_list.append(ax.plot(times, this_gfp, color=gfp_color,
zorder=3, alpha=line_alpha)[0])
ax.text(times[0] + 0.01 * (times[-1] - times[0]),
this_gfp[0] + 0.05 * np.diff(ax.get_ylim())[0],
label, zorder=4, color=gfp_color,
path_effects=gfp_path_effects)
for ii, line in zip(idx, line_list):
if ii in bad_ch_idx:
line.set_zorder(2)
if spatial_colors is True:
line.set_linestyle("--")
ax.set_ylabel(ch_unit)
texts.append(ax.text(0, 0, '', zorder=3,
verticalalignment='baseline',
horizontalalignment='left',
fontweight='bold', alpha=0,
clip_on=True))
if xlim is not None:
if xlim == 'tight':
xlim = (times[0], times[-1])
ax.set_xlim(xlim)
if ylim is not None and this_type in ylim:
ax.set_ylim(ylim[this_type])
ax.set(title=r'%s (%d channel%s)'
% (titles[this_type], len(D), _pl(len(D))))
if ai == 0:
_add_nave(ax, nave)
if hline is not None:
for h in hline:
c = ('grey' if spatial_colors is True else 'r')
ax.axhline(h, linestyle='--', linewidth=2, color=c)
# Plot highlights
if highlight is not None:
this_ylim = ax.get_ylim() if (ylim is None or this_type not in
ylim.keys()) else ylim[this_type]
for this_highlight in highlight:
ax.fill_betweenx(
this_ylim, this_highlight[0], this_highlight[1],
facecolor='orange', alpha=0.15, zorder=99
)
# Put back the y limits as fill_betweenx messes them up
ax.set_ylim(this_ylim)
lines.append(line_list)
if selectable:
for ax in np.array(axes)[selectables]:
if len(ax.lines) == 1:
continue
text = ax.annotate('Loading...', xy=(0.01, 0.1),
xycoords='axes fraction', fontsize=20,
color='green', zorder=3)
text.set_visible(False)
callback_onselect = partial(_line_plot_onselect,
ch_types=ch_types_used, info=info,
data=data, times=times, text=text,
psd=psd, time_unit=time_unit,
sphere=sphere)
blit = False if plt.get_backend() == 'MacOSX' else True
minspan = 0 if len(times) < 2 else times[1] - times[0]
rect_kw = _prop_kw('rect', dict(alpha=0.5, facecolor='red'))
ax._span_selector = SpanSelector(
ax, callback_onselect, 'horizontal', minspan=minspan,
useblit=blit, **rect_kw)
def _add_nave(ax, nave):
"""Add nave to axes."""
if nave is not None:
ax.annotate(
r'N$_{\mathrm{ave}}$=%d' % nave, ha='left', va='bottom',
xy=(0, 1), xycoords='axes fraction',
xytext=(0, 5), textcoords='offset pixels')
def _handle_spatial_colors(colors, info, idx, ch_type, psd, ax, sphere):
"""Set up spatial colors."""
used_nm = np.array(_clean_names(info['ch_names']))[idx]
# find indices for bads
bads = [np.where(used_nm == bad)[0][0] for bad in info['bads'] if bad in
used_nm]
pos, outlines = _get_pos_outlines(info, idx, sphere=sphere)
loc = 1 if psd else 2 # Legend in top right for psd plot.
_plot_legend(pos, colors, ax, bads, outlines, loc)
def _plot_image(data, ax, this_type, picks, cmap, unit, units, scalings, times,
xlim, ylim, titles, colorbar=True, mask=None, mask_cmap=None,
mask_style=None, mask_alpha=.25, nave=None,
time_unit='s', show_names=False, ch_names=None):
"""Plot images."""
import matplotlib.pyplot as plt
assert time_unit is not None
if show_names == "auto":
if picks is not None:
show_names = "all" if len(picks) < 25 else True
else:
show_names = False
cmap = _setup_cmap(cmap)
ch_unit = units[this_type]
this_scaling = scalings[this_type]
if unit is False:
this_scaling = 1.0
ch_unit = 'NA' # no unit
if picks is not None:
data = data[picks]
if mask is not None:
mask = mask[picks]
# Show the image
# Set amplitude scaling
data = this_scaling * data
if ylim is None or this_type not in ylim:
vmax = np.abs(data).max()
vmin = -vmax
else:
vmin, vmax = ylim[this_type]
_check_if_nan(data)
im, t_end = _plot_masked_image(
ax, data, times, mask, yvals=None, cmap=cmap[0],
vmin=vmin, vmax=vmax, mask_style=mask_style, mask_alpha=mask_alpha,
mask_cmap=mask_cmap)
# ignore xlim='tight'; happens automatically with `extent` in imshow
xlim = None if xlim == 'tight' else xlim
if xlim is not None:
ax.set_xlim(xlim)
if colorbar:
cbar = plt.colorbar(im, ax=ax)
cbar.ax.set_title(ch_unit)
if cmap[1]:
ax.CB = DraggableColorbar(cbar, im)
ylabel = 'Channels' if show_names else 'Channel (index)'
t = titles[this_type] + ' (%d channel%s' % (len(data), _pl(data)) + t_end
ax.set(ylabel=ylabel, xlabel=f'Time ({time_unit})', title=t)
_add_nave(ax, nave)
yticks = np.arange(len(picks))
if show_names != 'all':
yticks = np.intersect1d(np.round(ax.get_yticks()).astype(int), yticks)
yticklabels = np.array(ch_names)[picks] if show_names else np.array(picks)
ax.set(yticks=yticks, yticklabels=yticklabels[yticks])
@verbose
def plot_evoked(evoked, picks=None, exclude='bads', unit=True, show=True,
ylim=None, xlim='tight', proj=False, hline=None, units=None,
scalings=None, titles=None, axes=None, gfp=False,
window_title=None, spatial_colors=False, zorder='unsorted',
selectable=True, noise_cov=None, time_unit='s', sphere=None,
*, highlight=None, verbose=None):
"""Plot evoked data using butterfly plots.
Left click to a line shows the channel name. Selecting an area by clicking
and holding left mouse button plots a topographic map of the painted area.
.. note:: If bad channels are not excluded they are shown in red.
Parameters
----------
evoked : instance of Evoked
The evoked data.
%(picks_all)s
exclude : list of str | 'bads'
Channels names to exclude from being shown. If 'bads', the
bad channels are excluded.
unit : bool
Scale plot with channel (SI) unit.
show : bool
Show figure if True.
ylim : dict | None
Y limits for plots (after scaling has been applied). e.g.
ylim = dict(eeg=[-20, 20])
Valid keys are eeg, mag, grad, misc. If None, the ylim parameter
for each channel equals the pyplot default.
xlim : 'tight' | tuple | None
X limits for plots.
%(proj_plot)s
hline : list of float | None
The values at which to show an horizontal line.
units : dict | None
The units of the channel types used for axes labels. If None,
defaults to ``dict(eeg='µV', grad='fT/cm', mag='fT')``.
scalings : dict | None
The scalings of the channel types to be applied for plotting. If None,
defaults to ``dict(eeg=1e6, grad=1e13, mag=1e15)``.
titles : dict | None
The titles associated with the channels. If None, defaults to
``dict(eeg='EEG', grad='Gradiometers', mag='Magnetometers')``.
axes : instance of Axes | list | None
The axes to plot to. If list, the list must be a list of Axes of
the same length as the number of channel types. If instance of
Axes, there must be only one channel type plotted.
gfp : bool | 'only'
Plot the global field power (GFP) or the root mean square (RMS) of the
data. For MEG data, this will plot the RMS. For EEG, it plots GFP,
i.e. the standard deviation of the signal across channels. The GFP is
equivalent to the RMS of an average-referenced signal.
- ``True``
Plot GFP or RMS (for EEG and MEG, respectively) and traces for all
channels.
- ``'only'``
Plot GFP or RMS (for EEG and MEG, respectively), and omit the
traces for individual channels.
The color of the GFP/RMS trace will be green if
``spatial_colors=False``, and black otherwise.
.. versionchanged:: 0.23
Plot GFP for EEG instead of RMS. Label RMS traces correctly as such.
window_title : str | None
The title to put at the top of the figure.
spatial_colors : bool | 'auto'
If True, the lines are color coded by mapping physical sensor
coordinates into color values. Spatially similar channels will have
similar colors. Bad channels will be dotted. If False, the good
channels are plotted black and bad channels red. If ``'auto'``, uses
True if channel locations are present, and False if channel locations
are missing or if the data contains only a single channel. Defaults to
``'auto'``.
zorder : str | callable
Which channels to put in the front or back. Only matters if
``spatial_colors`` is used.
If str, must be ``std`` or ``unsorted`` (defaults to ``unsorted``). If
``std``, data with the lowest standard deviation (weakest effects) will
be put in front so that they are not obscured by those with stronger
effects. If ``unsorted``, channels are z-sorted as in the evoked
instance.
If callable, must take one argument: a numpy array of the same
dimensionality as the evoked raw data; and return a list of
unique integers corresponding to the number of channels.
.. versionadded:: 0.13.0
selectable : bool
Whether to use interactive features. If True (default), it is possible
to paint an area to draw topomaps. When False, the interactive features
are disabled. Disabling interactive features reduces memory consumption
and is useful when using ``axes`` parameter to draw multiaxes figures.
.. versionadded:: 0.13.0
noise_cov : instance of Covariance | str | None
Noise covariance used to whiten the data while plotting.
Whitened data channel names are shown in italic.
Can be a string to load a covariance from disk.
See also :meth:`mne.Evoked.plot_white` for additional inspection
of noise covariance properties when whitening evoked data.
For data processed with SSS, the effective dependence between
magnetometers and gradiometers may introduce differences in scaling,
consider using :meth:`mne.Evoked.plot_white`.
.. versionadded:: 0.16.0
%(time_unit)s
.. versionadded:: 0.16
%(sphere_topomap_auto)s
highlight : array-like of float, shape(2,) | array-like of float, shape (n, 2) | None
Segments of the data to highlight by means of a light-yellow
background color. Can be used to put visual emphasis on certain
time periods. The time periods must be specified as ``array-like``
objects in the form of ``(t_start, t_end)`` in the unit given by the
``time_unit`` parameter.
Multiple time periods can be specified by passing an ``array-like``
object of individual time periods (e.g., for 3 time periods, the shape
of the passed object would be ``(3, 2)``. If ``None``, no highlighting
is applied.
.. versionadded:: 1.1
%(verbose)s
Returns
-------
fig : instance of matplotlib.figure.Figure
Figure containing the butterfly plots.
See Also
--------
mne.viz.plot_evoked_white
""" # noqa: E501
return _plot_evoked(
evoked=evoked, picks=picks, exclude=exclude, unit=unit, show=show,
ylim=ylim, proj=proj, xlim=xlim, hline=hline, units=units,
scalings=scalings, titles=titles, axes=axes, plot_type="butterfly",
gfp=gfp, window_title=window_title, spatial_colors=spatial_colors,
selectable=selectable, zorder=zorder, noise_cov=noise_cov,
time_unit=time_unit, sphere=sphere, highlight=highlight)
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], fig_background=None,
merge_grads=False, legend=True, axes=None,
background_color='w', noise_cov=None, exclude='bads',
show=True):
"""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 | color | 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
Y limits 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, misc.
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 float | None
The values at which to show a vertical line.
fig_background : None | ndarray
A background image for the figure. This must work with a call to
plt.imshow. Defaults to None.
merge_grads : bool
Whether to use RMS value of gradiometer pairs. Only works for Neuromag
data. Defaults to False.
legend : bool | int | str | 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.
background_color : color
Background color. Typically 'k' (black) or 'w' (white; default).
.. versionadded:: 0.15.0
noise_cov : instance of Covariance | str | None
Noise covariance used to whiten the data while plotting.
Whitened data channel names are shown in italic.
Can be a string to load a covariance from disk.
.. versionadded:: 0.16.0
exclude : list of str | 'bads'
Channels names to exclude from the plot. If 'bads', the
bad channels are excluded. By default, exclude is set to 'bads'.
show : bool
Show figure if True.
Returns
-------
fig : instance of matplotlib.figure.Figure
Images of evoked responses at sensor locations.
"""
if not type(evoked) in (tuple, list):
evoked = [evoked]
background_color = _to_rgb(background_color, name='background_color')
dark_background = np.mean(background_color) < 0.5
if dark_background:
fig_facecolor = background_color
axis_facecolor = background_color
font_color = 'w'
else:
fig_facecolor = background_color
axis_facecolor = background_color
font_color = 'k'
if isinstance(color, (tuple, list)):
if len(color) != len(evoked):
raise ValueError('Lists of evoked objects and colors'
' must have the same length')
elif color is None:
if dark_background:
color = ['w'] + _get_color_list()
else:
color = _get_color_list()
color = color * ((len(evoked) % len(color)) + 1)
color = color[:len(evoked)]
else:
if not isinstance(color, str):
raise ValueError(
'color must be of type tuple, list, str, or None.'
)
color = cycle([color])
return _plot_evoked_topo(evoked=evoked, layout=layout,
layout_scale=layout_scale, color=color,
border=border, ylim=ylim, scalings=scalings,
title=title, proj=proj, vline=vline,
fig_facecolor=fig_facecolor,
fig_background=fig_background,
axis_facecolor=axis_facecolor,
font_color=font_color,
merge_channels=merge_grads,
legend=legend, axes=axes, exclude=exclude,
show=show, noise_cov=noise_cov)
@fill_doc
def plot_evoked_image(evoked, picks=None, exclude='bads', unit=True,
show=True, clim=None, xlim='tight', proj=False,
units=None, scalings=None, titles=None, axes=None,
cmap='RdBu_r', colorbar=True, mask=None,
mask_style=None, mask_cmap="Greys", mask_alpha=.25,
time_unit='s', show_names="auto", group_by=None,
sphere=None):
"""Plot evoked data as images.
Parameters
----------
evoked : instance of Evoked
The evoked data.
%(picks_all)s
This parameter can also be used to set the order the channels
are shown in, as the channel image is sorted by the order of picks.
exclude : list of str | 'bads'
Channels names to exclude from being shown. If 'bads', the
bad channels are excluded.
unit : bool
Scale plot with channel (SI) unit.
show : bool
Show figure if True.
clim : dict | None
Color limits for plots (after scaling has been applied). e.g.
``clim = dict(eeg=[-20, 20])``.
Valid keys are eeg, mag, grad, misc. If None, the clim parameter
for each channel equals the pyplot default.
xlim : 'tight' | tuple | None
X limits for plots.
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.
units : dict | None
The units of the channel types used for axes labels. If None,
defaults to ``dict(eeg='µV', grad='fT/cm', mag='fT')``.
scalings : dict | None
The scalings of the channel types to be applied for plotting. If None,`
defaults to ``dict(eeg=1e6, grad=1e13, mag=1e15)``.
titles : dict | None