1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979
|
"""Functions to plot ICA specific data (besides topographies)."""
from __future__ import print_function
# Authors: Denis Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Teon Brooks <teon.brooks@gmail.com>
#
# License: Simplified BSD
from functools import partial
from numbers import Integral
import numpy as np
from .utils import (tight_layout, _prepare_trellis, _select_bads,
_layout_figure, _plot_raw_onscroll, _mouse_click,
_helper_raw_resize, _plot_raw_onkey, plt_show)
from .topomap import (_prepare_topo_plot, plot_topomap, _hide_frame,
_plot_ica_topomap)
from .raw import _prepare_mne_browse_raw, _plot_raw_traces, _convert_psds
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
from ..defaults import _handle_default
from ..io.meas_info import create_info
from ..io.pick import pick_types
from ..externals.six import string_types
from ..time_frequency.psd import psd_multitaper
def plot_ica_sources(ica, inst, picks=None, exclude=None, start=None,
stop=None, title=None, show=True, block=False,
show_first_samp=False):
"""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 : int | array_like of int | None.
The components to be displayed. If None, plot will show the
sources in the order as fitted.
exclude : array_like of int
The components marked for exclusion. If None (default), ICA.exclude
will be used.
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 figure 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``.
Returns
-------
fig : instance of pyplot.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 is None:
exclude = ica.exclude
elif len(ica.exclude) > 0:
exclude = np.union1d(ica.exclude, exclude)
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)
elif isinstance(inst, BaseEpochs):
fig = _plot_sources_epochs(ica, inst, picks, exclude, start=start,
stop=stop, show=show, title=title,
block=block)
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):
"""Plot ICA properties (helper)."""
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
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.scatter(range(len(epoch_var)), epoch_var, alpha=0.5,
facecolor=[0, 0, 0], lw=0)
# aesthetics
# ----------
topo_ax.set_title(ica._ica_names[pick])
set_title_and_labels(image_ax, 'Epochs image and ERP/ERF', [], 'Epochs')
# erp
set_title_and_labels(erp_ax, [], 'Time (s)', 'AU\n')
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:
yt = yt[::2]
erp_ax.yaxis.set_ticks(yt)
# 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
set_title_and_labels(var_ax, 'Epochs variance', 'Epoch (index)', 'AU')
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
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):
"""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 : int | array-like of int | None
The components to be displayed. If None, plot will show the first
five sources. If more than one components were chosen in the picks,
each one will be plotted in a separate figure. Defaults to None.
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 in ERP/ERF and spectrum plots.
Defaults to True, which plots one standard deviation above/below.
If set to float allows to control how many standard deviations are
plotted. For example 2.5 will plot 2.5 standard deviation above/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 of size (2,) | None
Allows to control size of the figure. If None, the figure size
defaults to [7., 6.].
show : bool
Show figure if True.
Returns
-------
fig : list
List of matplotlib figures.
Notes
-----
.. versionadded:: 0.13
"""
from ..io.base import BaseRaw
from ..epochs import BaseEpochs
from ..preprocessing import ICA
# 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
picks = list(range(min(5, ica.n_components_))) if picks is None else picks
picks = [picks] if isinstance(picks, Integral) else picks
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)
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):
# break up continuous signal into segments
from ..epochs import _segment_raw
inst = _segment_raw(inst, segment_length=2., verbose=False,
preload=True)
epochs_src = ica.get_sources(inst)
ica_data = np.swapaxes(epochs_src.get_data()[:, 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)
# the actual plot
fig = _plot_ica_properties(
pick, ica, inst, psds_mean, freqs, ica_data.shape[1],
np.var(ica_data[idx], axis=1), plot_lowpass_edge,
epochs_src, set_title_and_labels, plot_std, psd_ylabel,
spectrum_std, topomap_args, image_args, fig, axes)
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 : int | array_like of int | None.
The components to be displayed. If None, plot will show the
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
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()
if picks is None:
picks = np.arange(evoked.data.shape[0])
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 = set([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 = dict((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
from matplotlib import patheffects
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 arrays
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 matplotlib.pyplot.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, string_types):
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, len(this_scores))
tight_layout(fig=fig)
plt_show(show)
return fig
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 : array-like of int | None (default)
Indices of channels to include (if None, all channels
are used that were included on 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 pyplot.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'
if picks is None:
picks = [inst.ch_names.index(k) for k in ica.ch_names]
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
ch_types_used = [k for k in ['mag', 'grad', 'eeg'] if k in ica]
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 pyplot.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 ({0})'.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 pyplot.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):
"""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
if picks is None:
picks = range(len(orig_data))
types = ['misc' for _ in picks]
picks = list(sorted(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=())
_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)
_layout_figure(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_resize = partial(_helper_raw_resize, params=params)
params['fig'].canvas.mpl_connect('resize_event', callback_resize)
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):
"""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 picks is None:
picks = list(range(ica.n_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)
params['label_click_fun'] = partial(_label_clicked, params=params)
_prepare_mne_browse_epochs(params, projs=list(), n_channels=20,
n_epochs=n_epochs, scalings=scalings,
title=title, picks=picks,
order=['misc', 'eog', 'ecg'])
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)
|