"""Functions to plot ICA specific data (besides topographies)."""
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import warnings
from functools import partial
import numpy as np
from scipy.stats import gaussian_kde
from .._fiff.meas_info import create_info
from .._fiff.pick import _picks_to_idx, pick_types
from .._fiff.proj import _has_eeg_average_ref_proj
from ..defaults import DEFAULTS, _handle_default
from ..utils import (
_reject_data_segments,
_validate_type,
fill_doc,
verbose,
)
from .epochs import plot_epochs_image
from .evoked import _butterfly_on_button_press, _butterfly_onpick
from .topomap import _plot_ica_topomap
from .utils import (
_compute_scalings,
_convert_psds,
_get_cmap,
_get_plot_ch_type,
_handle_precompute,
_make_event_color_dict,
plt_show,
)
@fill_doc
def plot_ica_sources(
ica,
inst,
picks=None,
start=None,
stop=None,
title=None,
show=True,
block=False,
show_first_samp=False,
show_scrollbars=True,
time_format="float",
precompute=None,
use_opengl=None,
*,
psd_args=None,
theme=None,
overview_mode=None,
splash=True,
):
"""Plot estimated latent sources given the unmixing matrix.
Typical usecases:
1. plot evolution of latent sources over time based on (Raw input)
2. plot latent source around event related time windows (Epochs input)
3. plot time-locking in ICA space (Evoked input)
Parameters
----------
ica : instance of mne.preprocessing.ICA
The ICA solution.
inst : instance of Raw, Epochs or Evoked
The object to plot the sources from.
%(picks_ica)s
start, stop : float | int | None
If ``inst`` is a `~mne.io.Raw` or an `~mne.Evoked` object, the first and
last time point (in seconds) of the data to plot. If ``inst`` is a
`~mne.io.Raw` object, ``start=None`` and ``stop=None`` will be
translated into ``start=0.`` and ``stop=3.``, respectively. For
`~mne.Evoked`, ``None`` refers to the beginning and end of the evoked
signal. If ``inst`` is an `~mne.Epochs` object, specifies the index of
the first and last epoch to show.
title : str | None
The window title. If None a default is provided.
show : bool
Show figure if True.
block : bool
Whether to halt program execution until the figure is closed.
Useful for interactive selection of components in raw and epoch
plotter. For evoked, this parameter has no effect. Defaults to False.
show_first_samp : bool
If True, show time axis relative to the ``raw.first_samp``.
%(show_scrollbars)s
%(time_format)s
%(precompute)s
%(use_opengl)s
psd_args : dict | None
Dictionary of arguments to pass to :meth:`~mne.Epochs.compute_psd` in
interactive mode. Ignored if ``inst`` is not supplied. If ``None``,
nothing is passed. Defaults to ``None``.
.. versionadded:: 1.9
%(theme_pg)s
.. versionadded:: 1.0
%(overview_mode)s
.. versionadded:: 1.1
%(splash)s
.. versionadded:: 1.6
Returns
-------
%(browser)s
Notes
-----
For raw and epoch instances, it is possible to select components for
exclusion by clicking on the line. The selected components are added to
``ica.exclude`` on close.
%(notes_2d_backend)s
.. versionadded:: 0.10.0
"""
from ..epochs import BaseEpochs
from ..evoked import Evoked
from ..io import BaseRaw
exclude = ica.exclude
picks = _picks_to_idx(ica.n_components_, picks, picks_on="components")
if isinstance(inst, BaseRaw | BaseEpochs):
fig = _plot_sources(
ica,
inst,
picks,
exclude,
start=start,
stop=stop,
show=show,
title=title,
block=block,
psd_args=psd_args,
show_first_samp=show_first_samp,
show_scrollbars=show_scrollbars,
time_format=time_format,
precompute=precompute,
use_opengl=use_opengl,
theme=theme,
overview_mode=overview_mode,
splash=splash,
)
elif isinstance(inst, Evoked):
if start is not None or stop is not None:
inst = inst.copy().crop(start, stop)
sources = ica.get_sources(inst)
fig = _plot_ica_sources_evoked(
evoked=sources,
picks=picks,
exclude=exclude,
title=title,
labels=getattr(ica, "labels_", None),
show=show,
ica=ica,
)
else:
raise ValueError("Data input must be of Raw or Epochs type")
return fig
def _create_properties_layout(figsize=None, fig=None):
"""Create main figure and axes layout used by plot_ica_properties."""
import matplotlib.pyplot as plt
if fig is not None and figsize is not None:
raise ValueError("Cannot specify both fig and figsize.")
if figsize is None:
figsize = [7.0, 6.0]
if fig is None:
fig = plt.figure(figsize=figsize, facecolor=[0.95] * 3)
axes_params = (
("topomap", [0.08, 0.5, 0.3, 0.45]),
("image", [0.5, 0.6, 0.45, 0.35]),
("erp", [0.5, 0.5, 0.45, 0.1]),
("spectrum", [0.08, 0.1, 0.32, 0.3]),
("variance", [0.5, 0.1, 0.45, 0.25]),
)
axes = [fig.add_axes(loc, label=name) for name, loc in axes_params]
return fig, axes
def _plot_ica_properties(
pick,
ica,
inst,
psds_mean,
freqs,
n_trials,
epoch_var,
plot_lowpass_edge,
epochs_src,
set_title_and_labels,
plot_std,
psd_ylabel,
spectrum_std,
log_scale,
topomap_args,
image_args,
fig,
axes,
kind,
dropped_indices,
):
"""Plot ICA properties (helper)."""
from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable
topo_ax, image_ax, erp_ax, spec_ax, var_ax = axes
# plotting
# --------
# component topomap
_plot_ica_topomap(ica, pick, show=False, axes=topo_ax, **topomap_args)
topo_ax._ch_type = _get_plot_ch_type(
ica,
ch_type=None,
allow_ref_meg=ica.allow_ref_meg,
)
# image and erp
# we create a new epoch with dropped rows
epoch_data = epochs_src.get_data(copy=False)
epoch_data = np.insert(
arr=epoch_data,
obj=(dropped_indices - np.arange(len(dropped_indices))).astype(int),
values=0.0,
axis=0,
)
from ..epochs import EpochsArray
epochs_src = EpochsArray(
epoch_data, epochs_src.info, tmin=epochs_src.tmin, verbose=0
)
plot_epochs_image(
epochs_src,
picks=pick,
axes=[image_ax, erp_ax],
combine=None,
colorbar=False,
show=False,
**image_args,
)
# spectrum
spec_ax.plot(freqs, psds_mean, color="k")
if plot_std:
spec_ax.fill_between(
freqs,
psds_mean - spectrum_std[0],
psds_mean + spectrum_std[1],
color="k",
alpha=0.2,
)
if plot_lowpass_edge:
spec_ax.axvline(
inst.info["lowpass"], lw=2, linestyle="--", color="k", alpha=0.2
)
# epoch variance
var_ax_divider = make_axes_locatable(var_ax)
hist_ax = var_ax_divider.append_axes("right", size="33%", pad="2.5%")
var_ax.scatter(
range(len(epoch_var)), epoch_var, alpha=0.5, facecolor=[0, 0, 0], lw=0
)
# rejected epochs in red
var_ax.scatter(
dropped_indices,
epoch_var[dropped_indices],
alpha=1.0,
facecolor=[1, 0, 0],
lw=0,
)
# compute percentage of dropped epochs
var_percent = float(len(dropped_indices)) / float(len(epoch_var)) * 100.0
# histogram & histogram
_, counts, _ = hist_ax.hist(
epoch_var, orientation="horizontal", color="k", alpha=0.5
)
# kde
ymin, ymax = hist_ax.get_ylim()
try:
kde = gaussian_kde(epoch_var)
except np.linalg.LinAlgError:
pass # singular: happens when there is nothing plotted
else:
x = np.linspace(ymin, ymax, 50)
kde_ = kde(x)
kde_ /= kde_.max() or 1.0
kde_ *= hist_ax.get_xlim()[-1] * 0.9
hist_ax.plot(kde_, x, color="k")
hist_ax.set_ylim(ymin, ymax)
# aesthetics
# ----------
set_title_and_labels(image_ax, kind + " image and ERP/ERF", [], kind)
# erp
set_title_and_labels(erp_ax, [], "Time (s)", "AU")
erp_ax.spines["right"].set_color("k")
erp_ax.set_xlim(epochs_src.times[[0, -1]])
# remove half of yticks if more than 5
yt = erp_ax.get_yticks()
if len(yt) > 5:
erp_ax.yaxis.set_ticks(yt[::2])
# remove xticks - erp plot shows xticks for both image and erp plot
image_ax.xaxis.set_ticks([])
yt = image_ax.get_yticks()
image_ax.yaxis.set_ticks(yt[1:])
image_ax.set_ylim([-0.5, n_trials + 0.5])
def _set_scale(ax, scale):
"""Set the scale of a matplotlib axis."""
ax.set_xscale(scale)
ax.set_yscale(scale)
ax.relim()
ax.autoscale()
# spectrum
set_title_and_labels(spec_ax, "Spectrum", "Frequency (Hz)", psd_ylabel)
spec_ax.yaxis.labelpad = 0
spec_ax.set_xlim(freqs[[0, -1]])
ylim = spec_ax.get_ylim()
air = np.diff(ylim)[0] * 0.1
spec_ax.set_ylim(ylim[0] - air, ylim[1] + air)
image_ax.axhline(0, color="k", linewidth=0.5)
if log_scale:
_set_scale(spec_ax, "log")
# epoch variance
var_ax_title = f"Dropped segments: {var_percent:.2f} %"
set_title_and_labels(var_ax, var_ax_title, kind, "Variance (AU)")
hist_ax.set_ylabel("")
hist_ax.set_yticks([])
set_title_and_labels(hist_ax, None, None, None)
def _plot_ica_properties_on_press(event, ica, pick, topomap_args):
"""Handle keypress events for ica properties plot."""
import matplotlib.pyplot as plt
fig = event.canvas.figure
if event.key == "escape":
plt.close(fig)
if event.key in ("t", "l"):
ax_labels = [ax.get_label() for ax in fig.axes]
if event.key == "t":
ax = fig.axes[ax_labels.index("topomap")]
ax.clear()
ch_types = list(set(ica.get_channel_types()))
ch_type = ch_types[(ch_types.index(ax._ch_type) + 1) % len(ch_types)]
_plot_ica_topomap(
ica, pick, ch_type=ch_type, show=False, axes=ax, **topomap_args
)
ax._ch_type = ch_type
elif event.key == "l":
ax = fig.axes[ax_labels.index("spectrum")]
_set_scale(ax, "linear" if ax.get_xscale() == "log" else "log")
del ax
fig.canvas.draw()
# add keypress event handler
fig.canvas.mpl_connect(
"key_press_event",
lambda event: _plot_ica_properties_on_press(event, ica, pick, topomap_args),
)
return fig
def _get_psd_label_and_std(this_psd, dB, ica, num_std, *, estimate):
"""Handle setting up PSD for one component, for plot_ica_properties."""
psd_ylabel = _convert_psds(
this_psd, dB, estimate=estimate, scaling=1.0, unit="AU", first_dim="epoch"
)
psds_mean = this_psd.mean(axis=0)
diffs = this_psd - psds_mean
# the distribution of power for each frequency bin is highly
# skewed so we calculate std for values below and above average
# separately - this is used for fill_between shade
with warnings.catch_warnings(): # mean of empty slice
warnings.simplefilter("ignore")
spectrum_std = [
[np.sqrt((d[d < 0] ** 2).mean(axis=0)) for d in diffs.T],
[np.sqrt((d[d > 0] ** 2).mean(axis=0)) for d in diffs.T],
]
spectrum_std = np.array(spectrum_std) * num_std
return psd_ylabel, psds_mean, spectrum_std
@verbose
def plot_ica_properties(
ica,
inst,
picks=None,
axes=None,
dB=True,
plot_std=True,
log_scale=False,
topomap_args=None,
image_args=None,
psd_args=None,
figsize=None,
show=True,
reject="auto",
reject_by_annotation=True,
*,
estimate="power",
verbose=None,
):
"""Display component properties.
Properties include the topography, epochs image, ERP/ERF, power
spectrum, and epoch variance.
Parameters
----------
ica : instance of mne.preprocessing.ICA
The ICA solution.
inst : instance of Epochs or Raw
The data to use in plotting properties.
.. note::
You can interactively cycle through topographic maps for different
channel types by pressing :kbd:`T`.
picks : int | list of int | slice | None
Indices of the independent components (ICs) to visualize.
If an integer, represents the index of the IC to pick.
Multiple ICs can be selected using a list of int or a slice.
The indices are 0-indexed, so ``picks=1`` will pick the second
IC: ``ICA001``. ``None`` will pick the first 5 components.
axes : list of Axes | None
List of five matplotlib axes to use in plotting: [topomap_axis,
image_axis, erp_axis, spectrum_axis, variance_axis]. If None a new
figure with relevant axes is created. Defaults to None.
dB : bool
Whether to plot spectrum in dB. Defaults to True.
plot_std : bool | float
Whether to plot standard deviation/confidence intervals in ERP/ERF and
spectrum plots.
Defaults to True, which plots one standard deviation above/below for
the spectrum. If set to float allows to control how many standard
deviations are plotted for the spectrum. For example 2.5 will plot 2.5
standard deviation above/below.
For the ERP/ERF, by default, plot the 95 percent parametric confidence
interval is calculated. To change this, use ``ci`` in ``ts_args`` in
``image_args`` (see below).
log_scale : bool
Whether to use a logarithmic frequency axis to plot the spectrum.
Defaults to ``False``.
.. note::
You can interactively toggle this setting by pressing :kbd:`L`.
.. versionadded:: 1.1
topomap_args : dict | None
Dictionary of arguments to ``plot_topomap``. If None, doesn't pass any
additional arguments. Defaults to None.
image_args : dict | None
Dictionary of arguments to ``plot_epochs_image``. If None, doesn't pass
any additional arguments. Defaults to None.
psd_args : dict | None
Dictionary of arguments to :meth:`~mne.Epochs.compute_psd`. If
``None``, doesn't pass any additional arguments. Defaults to ``None``.
figsize : array-like, shape (2,) | None
Allows to control size of the figure. If None, the figure size
defaults to [7., 6.].
show : bool
Show figure if True.
reject : 'auto' | dict | None
Allows to specify rejection parameters used to drop epochs
(or segments if continuous signal is passed as inst).
If None, no rejection is applied. The default is 'auto',
which applies the rejection parameters used when fitting
the ICA object.
%(reject_by_annotation_raw)s
.. versionadded:: 0.21.0
%(estimate_plot_psd)s
.. versionadded:: 1.8.0
%(verbose)s
Returns
-------
fig : list
List of matplotlib figures.
Notes
-----
.. versionadded:: 0.13
"""
return _fast_plot_ica_properties(
ica,
inst,
picks=picks,
axes=axes,
dB=dB,
plot_std=plot_std,
log_scale=log_scale,
topomap_args=topomap_args,
image_args=image_args,
psd_args=psd_args,
figsize=figsize,
show=show,
reject=reject,
reject_by_annotation=reject_by_annotation,
verbose=verbose,
estimate=estimate,
precomputed_data=None,
)
def _fast_plot_ica_properties(
ica,
inst,
picks=None,
axes=None,
dB=True,
plot_std=True,
log_scale=False,
topomap_args=None,
image_args=None,
psd_args=None,
figsize=None,
show=True,
reject="auto",
precomputed_data=None,
reject_by_annotation=True,
*,
estimate="power",
verbose=None,
):
"""Display component properties."""
from ..preprocessing import ICA
# input checks and defaults
# -------------------------
_validate_type(ica, ICA, "ica", "ICA")
_validate_type(plot_std, (bool, "numeric"), "plot_std")
if isinstance(plot_std, bool):
num_std = 1.0 if plot_std else 0.0
else:
plot_std = True
num_std = float(plot_std)
limit = min(5, ica.n_components_) if picks is None else ica.n_components_
picks = _picks_to_idx(ica.n_components_, picks, picks_on="components")[:limit]
if axes is None:
fig, axes = _create_properties_layout(figsize=figsize)
else:
if len(picks) > 1:
raise ValueError("Only a single pick can be drawn to a set of axes.")
from .utils import _validate_if_list_of_axes
_validate_if_list_of_axes(axes, obligatory_len=5)
fig = axes[0].get_figure()
psd_args = dict() if psd_args is None else psd_args
topomap_args = dict() if topomap_args is None else topomap_args
image_args = dict() if image_args is None else image_args
image_args["ts_args"] = dict(truncate_xaxis=False, show_sensors=False)
if plot_std:
from ..stats.parametric import _parametric_ci
image_args["ts_args"]["ci"] = _parametric_ci
elif "ts_args" not in image_args or "ci" not in image_args["ts_args"]:
image_args["ts_args"]["ci"] = False
for item_name, item in (
("psd_args", psd_args),
("topomap_args", topomap_args),
("image_args", image_args),
):
_validate_type(item, dict, item_name, "dictionary")
_validate_type(dB, (bool, None), "dB")
_validate_type(log_scale, (bool, None), "log_scale")
# calculations
# ------------
if isinstance(precomputed_data, tuple):
kind, dropped_indices, epochs_src, data = precomputed_data
else:
kind, dropped_indices, epochs_src, data = _prepare_data_ica_properties(
inst, ica, reject_by_annotation, reject
)
del reject
ica_data = np.swapaxes(data[:, picks, :], 0, 1)
dropped_src = ica_data
# spectrum
Nyquist = inst.info["sfreq"] / 2.0
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)
spectrum = epochs_src.compute_psd(picks=picks, **psd_args)
# we've already restricted picks ↑↑↑↑↑↑↑↑↑↑↑
# in the spectrum object, so here we do picks=all ↓↓↓↓↓↓↓↓↓↓↓
psds, freqs = spectrum.get_data(return_freqs=True, picks="all", exclude=[])
# we also pass exclude=[] so that when this is called by right-clicking in
# a plot_sources() window on an ICA component name that has been marked as
# bad, we can still get a plot of it.
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,
estimate=estimate,
)
# if more than one component, spawn additional figures and axes
if idx > 0:
fig, axes = _create_properties_layout(figsize=figsize)
# we reconstruct an epoch_variance with 0 where indexes where dropped
epoch_var = np.var(ica_data[idx], axis=1)
drop_var = np.var(dropped_src[idx], axis=1)
drop_indices_corrected = (
dropped_indices - np.arange(len(dropped_indices))
).astype(int)
epoch_var = np.insert(
arr=epoch_var,
obj=drop_indices_corrected,
values=drop_var[dropped_indices],
axis=0,
)
# the actual plot
fig = _plot_ica_properties(
pick,
ica,
inst,
psds_mean,
freqs,
ica_data.shape[1],
epoch_var,
plot_lowpass_edge,
epochs_src,
set_title_and_labels,
plot_std,
psd_ylabel,
spectrum_std,
log_scale,
topomap_args,
image_args,
fig,
axes,
kind,
dropped_indices,
)
all_fig.append(fig)
plt_show(show)
return all_fig
def _prepare_data_ica_properties(inst, ica, reject_by_annotation=True, reject="auto"):
"""Prepare Epochs sources to plot ICA properties.
Parameters
----------
ica : instance of mne.preprocessing.ICA
The ICA solution.
inst : instance of Epochs or Raw
The data to use in plotting properties.
reject_by_annotation : bool, optional
[description], by default True
reject : str, optional
[description], by default 'auto'
Returns
-------
kind : str
"Segment" for BaseRaw and "Epochs" for BaseEpochs
dropped_indices : list
Dropped epochs indexes.
epochs_src : instance of Epochs
Segmented data of ICA sources.
data : array of shape (n_epochs, n_ica_sources, n_times)
A view on epochs ICA sources data.
"""
from ..epochs import BaseEpochs
from ..io import BaseRaw, RawArray
_validate_type(inst, (BaseRaw, BaseEpochs), "inst", "Raw or Epochs")
if isinstance(inst, BaseRaw):
# when auto, delegate reject to the ica
from ..epochs import make_fixed_length_epochs
if reject == "auto":
reject = ica.reject_
if reject is None:
drop_inds = None
dropped_indices = []
# break up continuous signal into segments
epochs_src = make_fixed_length_epochs(
ica.get_sources(inst),
duration=2,
preload=True,
reject_by_annotation=reject_by_annotation,
proj=False,
verbose=False,
)
else:
data = inst.get_data()
data, drop_inds = _reject_data_segments(
data, reject, flat=None, decim=None, info=inst.info, tstep=2.0
)
inst_rejected = RawArray(data, inst.info)
# break up continuous signal into segments
epochs_src = make_fixed_length_epochs(
ica.get_sources(inst_rejected),
duration=2,
preload=True,
reject_by_annotation=reject_by_annotation,
proj=False,
verbose=False,
)
# getting dropped epochs indexes
dropped_indices = [(d[0] // len(epochs_src.times)) + 1 for d in drop_inds]
kind = "Segment"
else:
drop_inds = None
epochs_src = ica.get_sources(inst)
dropped_indices = []
kind = "Epochs"
return kind, dropped_indices, epochs_src, epochs_src.get_data(copy=False)
def _plot_ica_sources_evoked(evoked, picks, exclude, title, show, ica, labels=None):
"""Plot average over epochs in ICA space.
Parameters
----------
evoked : instance of mne.Evoked
The Evoked to be used.
%(picks_base)s all sources in the order as fitted.
exclude : array-like of int
The components marked for exclusion. If None (default), ICA.exclude
will be used.
title : str
The figure title.
show : bool
Show figure if True.
labels : None | dict
The ICA labels attribute.
"""
import matplotlib.pyplot as plt
from matplotlib import patheffects
if title is None:
title = "Reconstructed latent sources, time-locked"
fig, axes = plt.subplots(1, layout="constrained")
ax = axes
axes = [axes]
times = evoked.times * 1e3
# plot unclassified sources and label excluded ones
lines = list()
texts = list()
picks = np.sort(picks)
idxs = [picks]
if labels is not None:
labels_used = [k for k in labels if "/" not in k]
exclude_labels = list()
for ii in picks:
if ii in exclude:
line_label = ica._ica_names[ii]
if labels is not None:
annot = list()
for this_label in labels_used:
indices = labels[this_label]
if ii in indices:
annot.append(this_label)
if annot:
line_label += " – " + ", ".join(annot) # Unicode en-dash
exclude_labels.append(line_label)
else:
exclude_labels.append(None)
label_props = [("k", "-") if lb is None else ("r", "-") for lb in exclude_labels]
styles = ["-", "--", ":", "-."]
if labels is not None:
# differentiate categories by linestyle and components by color
col_lbs = [it for it in exclude_labels if it is not None]
cmap = _get_cmap("tab10", len(col_lbs))
unique_labels = set()
for label in exclude_labels:
if label is None:
continue
elif " – " in label:
unique_labels.add(label.split(" – ")[1])
else:
unique_labels.add("")
# Determine up to 4 different styles for n categories
cat_styles = dict(
zip(
unique_labels,
map(
lambda ux: styles[int(ux % len(styles))], range(len(unique_labels))
),
)
)
for label_idx, label in enumerate(exclude_labels):
if label is not None:
color = cmap(col_lbs.index(label))
if " – " in label:
label_name = label.split(" – ")[1]
else:
label_name = ""
style = cat_styles[label_name]
label_props[label_idx] = (color, style)
for pick_idx, (exc_label, pick) in enumerate(zip(exclude_labels, picks)):
color, style = label_props[pick_idx]
# ensure traces of excluded components are plotted on top
zorder = 2 if exc_label is None else 10
lines.extend(
ax.plot(
times,
evoked.data[pick].T,
picker=True,
zorder=zorder,
color=color,
linestyle=style,
label=exc_label,
)
)
lines[-1].set_pickradius(3.0)
ax.set(title=title, xlim=times[[0, -1]], xlabel="Time (ms)", ylabel="(NA)")
leg_lines_labels = list(
zip(
*[
(line, label)
for line, label in zip(lines, exclude_labels)
if label is not None
]
)
)
if len(leg_lines_labels):
leg_lines, leg_labels = leg_lines_labels
ax.legend(leg_lines, leg_labels, loc="best")
texts.append(
ax.text(
0,
0,
"",
zorder=3,
verticalalignment="baseline",
horizontalalignment="left",
fontweight="bold",
alpha=0,
)
)
# this is done to give the structure of a list of lists of a group of lines
# in each subplot
lines = [lines]
ch_names = evoked.ch_names
path_effects = [patheffects.withStroke(linewidth=2, foreground="w", alpha=0.75)]
params = dict(
axes=axes,
texts=texts,
lines=lines,
idxs=idxs,
ch_names=ch_names,
need_draw=False,
path_effects=path_effects,
)
fig.canvas.mpl_connect("pick_event", partial(_butterfly_onpick, params=params))
fig.canvas.mpl_connect(
"button_press_event", partial(_butterfly_on_button_press, params=params)
)
plt_show(show)
return fig
def plot_ica_scores(
ica,
scores,
exclude=None,
labels=None,
axhline=None,
title="ICA component scores",
figsize=None,
n_cols=None,
show=True,
):
"""Plot scores related to detected components.
Use this function to asses how well your score describes outlier
sources and how well you were detecting them.
Parameters
----------
ica : instance of mne.preprocessing.ICA
The ICA object.
scores : array-like of float, shape (n_ica_components,) | list of array
Scores based on arbitrary metric to characterize ICA components.
exclude : array-like of int
The components marked for exclusion. If None (default), ICA.exclude
will be used.
labels : str | list | 'ecg' | 'eog' | None
The labels to consider for the axes tests. Defaults to None.
If list, should match the outer shape of ``scores``.
If 'ecg' or 'eog', the ``labels_`` attributes will be looked up.
Note that '/' is used internally for sublabels specifying ECG and
EOG channels.
axhline : float
Draw horizontal line to e.g. visualize rejection threshold.
title : str
The figure title.
figsize : tuple of int | None
The figure size. If None it gets set automatically.
n_cols : int | None
Scores are plotted in a grid. This parameter controls how
many to plot side by side before starting a new row. By
default, a number will be chosen to make the grid as square as
possible.
show : bool
Show figure if True.
Returns
-------
fig : instance of Figure
The figure object.
"""
import matplotlib.pyplot as plt
my_range = np.arange(ica.n_components_)
if exclude is None:
exclude = ica.exclude
exclude = np.unique(exclude)
if not isinstance(scores[0], list | np.ndarray):
scores = [scores]
n_scores = len(scores)
if n_cols is None:
# prefer more rows.
n_rows = int(np.ceil(np.sqrt(n_scores)))
n_cols = (n_scores - 1) // n_rows + 1
else:
n_cols = min(n_scores, n_cols)
n_rows = (n_scores - 1) // n_cols + 1
if figsize is None:
figsize = (6.4 * n_cols, 2.7 * n_rows)
fig, axes = plt.subplots(
n_rows, n_cols, figsize=figsize, sharex=True, sharey=True, layout="constrained"
)
if isinstance(axes, np.ndarray):
axes = axes.flatten()
else:
axes = [axes]
fig.suptitle(title)
if labels == "ecg":
labels = [label for label in ica.labels_ if label.startswith("ecg/")]
labels.sort(key=lambda label: label.split("/")[1]) # sort by index
if len(labels) == 0:
labels = [label for label in ica.labels_ if label.startswith("ecg")]
elif labels == "eog":
labels = [label for label in ica.labels_ if label.startswith("eog/")]
labels.sort(key=lambda label: label.split("/")[1]) # sort by index
if len(labels) == 0:
labels = [label for label in ica.labels_ if label.startswith("eog")]
elif isinstance(labels, str):
labels = [labels]
elif labels is None:
labels = (None,) * n_scores
if len(labels) != n_scores:
raise ValueError(f"Need as many labels ({len(labels)}) as scores ({n_scores})")
for label, this_scores, ax in zip(labels, scores, axes):
if len(my_range) != len(this_scores):
raise ValueError(
"The length of `scores` must equal the number of ICA components."
)
ax.bar(my_range, this_scores, color="gray", edgecolor="k")
for excl in exclude:
ax.bar(my_range[excl], this_scores[excl], color="r", edgecolor="k")
if axhline is not None:
if np.isscalar(axhline):
axhline = [axhline]
for axl in axhline:
ax.axhline(axl, color="r", linestyle="--")
ax.set_ylabel("score")
if label is not None:
if "eog/" in label:
split = label.split("/")
label = ", ".join([split[0], split[2]])
elif "/" in label:
label = ", ".join(label.split("/"))
ax.set_title(f"({label})")
ax.set_xlabel("ICA components")
ax.set_xlim(-0.6, len(this_scores) - 0.4)
fig.canvas.draw()
plt_show(show)
return fig
@verbose
def plot_ica_overlay(
ica,
inst,
exclude=None,
picks=None,
start=None,
stop=None,
title=None,
show=True,
n_pca_components=None,
*,
on_baseline="warn",
verbose=None,
):
"""Overlay of raw and cleaned signals given the unmixing matrix.
This method helps visualizing signal quality and artifact rejection.
Parameters
----------
ica : instance of mne.preprocessing.ICA
The ICA object.
inst : instance of Raw or Evoked
The signal to plot. If `~mne.io.Raw`, the raw data per channel type is displayed
before and after cleaning. A second panel with the RMS for MEG sensors and the
:term:`GFP` for EEG sensors is displayed. If `~mne.Evoked`, butterfly traces for
signals before and after cleaning will be superimposed.
exclude : array-like of int | None (default)
The components marked for exclusion. If ``None`` (default), the components
listed in ``ICA.exclude`` will be used.
%(picks_base)s all channels that were included during fitting.
start, stop : float | None
The first and last time point (in seconds) of the data to plot. If
``inst`` is a `~mne.io.Raw` object, ``start=None`` and ``stop=None``
will be translated into ``start=0.`` and ``stop=3.``, respectively. For
`~mne.Evoked`, ``None`` refers to the beginning and end of the evoked
signal.
%(title_none)s
%(show)s
%(n_pca_components_apply)s
.. versionadded:: 0.22
%(on_baseline_ica)s
%(verbose)s
Returns
-------
fig : instance of Figure
The figure.
"""
# avoid circular imports
from ..evoked import Evoked
from ..io import BaseRaw
from ..preprocessing.ica import _check_start_stop
if ica.current_fit == "unfitted":
raise RuntimeError("You need to fit the ICA first")
_validate_type(inst, (BaseRaw, Evoked), "inst", "Raw or Evoked")
if title is None:
title = "Signals before (red) and after (black) cleaning"
picks = ica.ch_names if picks is None else picks
picks = _picks_to_idx(inst.info, picks, exclude=())
if exclude is None:
exclude = ica.exclude
if not isinstance(exclude, np.ndarray | list):
raise TypeError(f"exclude must be of type list. Got {type(exclude)}")
if isinstance(inst, BaseRaw):
start = 0.0 if start is None else start
stop = 3.0 if stop is None else stop
start, stop = _check_start_stop(inst, start, stop)
raw_cln = ica.apply(
inst.copy(),
exclude=exclude,
start=start,
stop=stop,
n_pca_components=n_pca_components,
)
fig = _plot_ica_overlay_raw(
raw=inst,
raw_cln=raw_cln,
picks=picks,
start=start,
stop=stop,
title=title,
show=show,
)
else:
assert isinstance(inst, Evoked)
inst = inst.copy().crop(start, stop)
if picks is not None:
with inst.info._unlock():
inst.info["comps"] = [] # can be safely disabled
inst.pick([inst.ch_names[p] for p in picks])
evoked_cln = ica.apply(
inst.copy(),
exclude=exclude,
n_pca_components=n_pca_components,
on_baseline=on_baseline,
)
fig = _plot_ica_overlay_evoked(
evoked=inst, evoked_cln=evoked_cln, title=title, show=show
)
return fig
def _plot_ica_overlay_raw(*, raw, raw_cln, picks, start, stop, title, show):
"""Plot evoked after and before ICA cleaning.
Parameters
----------
raw : Raw
Raw data before exclusion of ICs.
raw_cln : Raw
Data after exclusion of ICs.
picks : array of shape (n_channels_selected,)
Array of selected channel indices.
start : int
Start time to plot in samples.
stop : int
Stop time to plot in samples.
title : str
Title of the figure(s).
show : bool
Show figure if True.
Returns
-------
fig : instance of Figure
"""
import matplotlib.pyplot as plt
ch_types = raw.get_channel_types(picks=picks, unique=True)
for ch_type in ch_types:
if ch_type in ("mag", "grad"):
fig, ax = plt.subplots(3, 1, sharex=True, layout="constrained")
elif ch_type == "eeg" and not _has_eeg_average_ref_proj(
raw.info, check_active=True
):
fig, ax = plt.subplots(3, 1, sharex=True, layout="constrained")
else:
fig, ax = plt.subplots(2, 1, sharex=True, layout="constrained")
fig.suptitle(title)
# select sensors and retrieve data array
picks_by_type = _picks_to_idx(raw.info, ch_type, exclude=())
picks_ = np.intersect1d(picks, picks_by_type)
data, times = raw[picks_, start:stop]
data_cln, _ = raw_cln[picks_, start:stop]
# plot all sensors of the same type together
ax[0].plot(times, data.T, color="r")
ax[0].plot(times, data_cln.T, color="k")
_ch_type = DEFAULTS["titles"].get(ch_type, ch_type)
ax[0].set(xlabel="Time (s)", xlim=times[[0, -1]], title=f"Raw {_ch_type} data")
# second plot for M/EEG using GFP or RMS
if ch_type == "eeg": # Global Field Power
ax[1].plot(times, np.std(data, axis=0), color="r")
ax[1].plot(times, np.std(data_cln, axis=0), color="k")
ax[1].set(
xlabel="Time (s)",
xlim=times[[0, -1]],
title=f"{_ch_type} Global Field Power",
)
elif ch_type in ("mag", "grad"): # RMS
ax[1].plot(times, np.sqrt((data**2).mean(axis=0)), color="r")
ax[1].plot(times, np.sqrt((data_cln**2).mean(axis=0)), color="k")
ax[1].set(xlabel="Time (s)", xlim=times[[0, -1]], title=f"{_ch_type} RMS")
# last plot with the average across all channels of the same type
if ch_type != "eeg" or not _has_eeg_average_ref_proj(
raw.info, check_active=True
):
ax[-1].plot(times, data.mean(axis=0), color="r")
ax[-1].plot(times, data_cln.mean(axis=0), color="k")
ax[-1].set(
xlabel="Time (s)",
xlim=times[[0, -1]],
title=f"Average across {_ch_type} channels",
)
plt_show(show)
return fig
def _plot_ica_overlay_evoked(evoked, evoked_cln, title, show):
"""Plot evoked after and before ICA cleaning.
Parameters
----------
evoked : instance of mne.Evoked
The Evoked before IC rejection.
evoked_cln : instance of mne.Evoked
The Evoked after IC rejection.
title : str | None
The title of the figure.
show : bool
If True, all open plots will be shown.
Returns
-------
fig : instance of Figure
"""
import matplotlib.pyplot as plt
ch_types_used = [c for c in ["mag", "grad", "eeg"] if c in evoked]
n_rows = len(ch_types_used)
ch_types_used_cln = [c for c in ["mag", "grad", "eeg"] if c in evoked_cln]
if len(ch_types_used) != len(ch_types_used_cln):
raise ValueError("Raw and clean evokeds must match. Found different channels.")
fig, axes = plt.subplots(n_rows, 1, layout="constrained")
if title is None:
title = "Average signal before (red) and after (black) ICA"
fig.suptitle(title)
axes = axes.flatten() if isinstance(axes, np.ndarray) else axes
evoked.plot(axes=axes, show=False, time_unit="s", spatial_colors=False)
for ax in fig.axes:
for line in ax.get_lines():
line.set_color("r")
fig.canvas.draw()
evoked_cln.plot(axes=axes, show=False, time_unit="s", spatial_colors=False)
fig.canvas.draw()
plt_show(show)
return fig
def _plot_sources(
ica,
inst,
picks,
exclude,
start,
stop,
show,
title,
block,
show_scrollbars,
show_first_samp,
time_format,
precompute,
use_opengl,
*,
psd_args,
theme=None,
overview_mode=None,
splash=True,
):
"""Plot the ICA components as a RawArray or EpochsArray."""
from ..epochs import BaseEpochs, EpochsArray
from ..io import BaseRaw, RawArray
from ._figure import _get_browser
# handle defaults / check arg validity
is_raw = isinstance(inst, BaseRaw)
is_epo = isinstance(inst, BaseEpochs)
sfreq = inst.info["sfreq"]
color = _handle_default("color", (0.0, 0.0, 0.0))
units = _handle_default("units", None)
scalings = (
_compute_scalings(None, inst)
if is_raw
else _handle_default("scalings_plot_raw")
)
scalings["misc"] = 5.0
scalings["whitened"] = 1.0
unit_scalings = _handle_default("scalings", None)
# data
if is_raw:
data = ica._transform_raw(inst, 0, len(inst.times))[picks]
else:
data = ica._transform_epochs(inst, concatenate=True)[picks]
# events
if is_epo:
event_id_rev = {v: k for k, v in inst.event_id.items()}
event_nums = inst.events[:, 2]
event_color_dict = _make_event_color_dict(None, inst.events, inst.event_id)
# channel properties / trace order / picks
ch_names = list(ica._ica_names) # copy
ch_types = ["misc" for _ in picks]
# add EOG/ECG channels if present
eog_chs = pick_types(inst.info, meg=False, eog=True, ref_meg=False)
ecg_chs = pick_types(inst.info, meg=False, ecg=True, ref_meg=False)
for eog_idx in eog_chs:
ch_names.append(inst.ch_names[eog_idx])
ch_types.append("eog")
for ecg_idx in ecg_chs:
ch_names.append(inst.ch_names[ecg_idx])
ch_types.append("ecg")
extra_picks = np.concatenate((eog_chs, ecg_chs)).astype(int)
if len(extra_picks):
if is_raw:
eog_ecg_data, _ = inst[extra_picks, :]
else:
eog_ecg_data = np.concatenate(inst.get_data(extra_picks), axis=1)
data = np.append(data, eog_ecg_data, axis=0)
picks = np.concatenate((picks, ica.n_components_ + np.arange(len(extra_picks))))
ch_order = np.arange(len(picks))
n_channels = min([20, len(picks)])
ch_names_picked = [ch_names[x] for x in picks]
# create info
info = create_info(ch_names_picked, sfreq, ch_types=ch_types)
with info._unlock():
info["meas_date"] = inst.info["meas_date"]
info["bads"] = [ch_names[x] for x in exclude if x in picks]
if is_raw:
inst_array = RawArray(data, info, inst.first_samp)
inst_array._annotations = inst.annotations
else:
data = data.reshape(-1, len(inst), len(inst.times)).swapaxes(0, 1)
inst_array = EpochsArray(data, info)
# handle time dimension
start = 0 if start is None else start
_last = inst.times[-1] if is_raw else len(inst.events)
stop = min(start + 20, _last) if stop is None else stop
first_time = inst._first_time if show_first_samp else 0
if is_raw:
duration = stop - start
start += first_time
else:
n_epochs = stop - start
total_epochs = len(inst)
epoch_n_times = len(inst.times)
n_epochs = min(n_epochs, total_epochs)
n_times = total_epochs * epoch_n_times
duration = n_epochs * epoch_n_times / sfreq
event_times = (
np.arange(total_epochs) * epoch_n_times + inst.time_as_index(0)
) / sfreq
# NB: this includes start and end of data:
boundary_times = np.arange(total_epochs + 1) * epoch_n_times / sfreq
if duration <= 0:
raise RuntimeError("Stop must be larger than start.")
# misc
bad_color = "lightgray"
title = "ICA components" if title is None else title
precompute = _handle_precompute(precompute)
params = dict(
inst=inst_array,
ica=ica,
ica_inst=inst,
info=info,
# channels and channel order
ch_names=np.array(ch_names_picked),
ch_types=np.array(ch_types),
ch_order=ch_order,
picks=picks,
n_channels=n_channels,
picks_data=list(),
# time
t_start=start if is_raw else boundary_times[start],
duration=duration,
n_times=inst.n_times if is_raw else n_times,
first_time=first_time,
time_format=time_format,
decim=1,
# events
event_times=None if is_raw else event_times,
# preprocessing
projs=list(),
projs_on=np.array([], dtype=bool),
apply_proj=False,
remove_dc=True, # for EOG/ECG
filter_coefs=None,
filter_bounds=None,
noise_cov=None,
# scalings
scalings=scalings,
units=units,
unit_scalings=unit_scalings,
# colors
ch_color_bad=bad_color,
ch_color_dict=color,
# display
butterfly=False,
clipping=None,
scrollbars_visible=show_scrollbars,
scalebars_visible=False,
window_title=title,
precompute=precompute,
use_opengl=use_opengl,
theme=theme,
overview_mode=overview_mode,
psd_args=psd_args,
splash=splash,
)
if is_epo:
params.update(
n_epochs=n_epochs,
boundary_times=boundary_times,
event_id_rev=event_id_rev,
event_color_dict=event_color_dict,
event_nums=event_nums,
epoch_color_bad=(1, 0, 0),
epoch_colors=None,
xlabel="Epoch number",
)
fig = _get_browser(show=show, block=block, **params)
return fig
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