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针对pulse-transit的工具

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"""Functions to plot raw M/EEG data."""
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from collections import OrderedDict
import numpy as np
from .._fiff.pick import _picks_to_idx, pick_channels, pick_types
from ..defaults import _handle_default
from ..filter import create_filter
from ..utils import _check_option, _get_stim_channel, _validate_type, legacy, verbose
from ..utils.spectrum import _split_psd_kwargs
from .utils import (
_check_cov,
_compute_scalings,
_get_channel_plotting_order,
_handle_decim,
_handle_precompute,
_make_event_color_dict,
_shorten_path_from_middle,
)
_RAW_CLIP_DEF = 1.5
@verbose
def plot_raw(
raw,
events=None,
duration=10.0,
start=0.0,
n_channels=20,
bgcolor="w",
color=None,
bad_color="lightgray",
event_color="cyan",
scalings=None,
remove_dc=True,
order=None,
show_options=False,
title=None,
show=True,
block=False,
highpass=None,
lowpass=None,
filtorder=4,
clipping=_RAW_CLIP_DEF,
show_first_samp=False,
proj=True,
group_by="type",
butterfly=False,
decim="auto",
noise_cov=None,
event_id=None,
show_scrollbars=True,
show_scalebars=True,
time_format="float",
precompute=None,
use_opengl=None,
picks=None,
*,
theme=None,
overview_mode=None,
splash=True,
verbose=None,
):
"""Plot raw data.
Parameters
----------
raw : instance of Raw
The raw data to plot.
events : array | None
Events to show with vertical bars.
duration : float
Time window (s) to plot. The lesser of this value and the duration
of the raw file will be used.
start : float
Initial time to show (can be changed dynamically once plotted). If
show_first_samp is True, then it is taken relative to
``raw.first_samp``.
n_channels : int
Number of channels to plot at once. Defaults to 20. The lesser of
``n_channels`` and ``len(raw.ch_names)`` will be shown.
Has no effect if ``order`` is 'position', 'selection' or 'butterfly'.
bgcolor : color object
Color of the background.
color : dict | color object | None
Color for the data traces. If None, defaults to::
dict(mag='darkblue', grad='b', eeg='k', eog='k', ecg='m',
emg='k', ref_meg='steelblue', misc='k', stim='k',
resp='k', chpi='k')
bad_color : color object
Color to make bad channels.
%(event_color)s
Defaults to ``'cyan'``.
%(scalings)s
remove_dc : bool
If True remove DC component when plotting data.
order : array of int | None
Order in which to plot data. If the array is shorter than the number of
channels, only the given channels are plotted. If None (default), all
channels are plotted. If ``group_by`` is ``'position'`` or
``'selection'``, the ``order`` parameter is used only for selecting the
channels to be plotted.
show_options : bool
If True, a dialog for options related to projection is shown.
title : str | None
The title of the window. If None, and either the filename of the
raw object or '<unknown>' will be displayed as title.
show : bool
Show figure if True.
block : bool
Whether to halt program execution until the figure is closed.
Useful for setting bad channels on the fly by clicking on a line.
May not work on all systems / platforms.
(Only Qt) If you run from a script, this needs to
be ``True`` or a Qt-eventloop needs to be started somewhere
else in the script (e.g. if you want to implement the browser
inside another Qt-Application).
highpass : float | None
Highpass to apply when displaying data.
lowpass : float | None
Lowpass to apply when displaying data.
If highpass > lowpass, a bandstop rather than bandpass filter
will be applied.
filtorder : int
Filtering order. 0 will use FIR filtering with MNE defaults.
Other values will construct an IIR filter of the given order
and apply it with :func:`~scipy.signal.filtfilt` (making the effective
order twice ``filtorder``). Filtering may produce some edge artifacts
(at the left and right edges) of the signals during display.
.. versionchanged:: 0.18
Support for ``filtorder=0`` to use FIR filtering.
clipping : str | float | None
If None, channels are allowed to exceed their designated bounds in
the plot. If "clamp", then values are clamped to the appropriate
range for display, creating step-like artifacts. If "transparent",
then excessive values are not shown, creating gaps in the traces.
If float, clipping occurs for values beyond the ``clipping`` multiple
of their dedicated range, so ``clipping=1.`` is an alias for
``clipping='transparent'``.
.. versionchanged:: 0.21
Support for float, and default changed from None to 1.5.
show_first_samp : bool
If True, show time axis relative to the ``raw.first_samp``.
proj : bool
Whether to apply projectors prior to plotting (default is ``True``).
Individual projectors can be enabled/disabled interactively (see
Notes). This argument only affects the plot; use ``raw.apply_proj()``
to modify the data stored in the Raw object.
%(group_by_browse)s
butterfly : bool
Whether to start in butterfly mode. Defaults to False.
decim : int | 'auto'
Amount to decimate the data during display for speed purposes.
You should only decimate if the data are sufficiently low-passed,
otherwise aliasing can occur. The 'auto' mode (default) uses
the decimation that results in a sampling rate least three times
larger than ``min(info['lowpass'], lowpass)`` (e.g., a 40 Hz lowpass
will result in at least a 120 Hz displayed sample rate).
noise_cov : instance of Covariance | str | None
Noise covariance used to whiten the data while plotting.
Whitened data channels are scaled by ``scalings['whitened']``,
and their 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
event_id : dict | None
Event IDs used to show at event markers (default None shows
the event numbers).
.. versionadded:: 0.16.0
%(show_scrollbars)s
%(show_scalebars)s
.. versionadded:: 0.20.0
%(time_format)s
%(precompute)s
%(use_opengl)s
%(picks_all)s
%(theme_pg)s
.. versionadded:: 1.0
%(overview_mode)s
.. versionadded:: 1.1
%(splash)s
.. versionadded:: 1.6
%(verbose)s
Returns
-------
%(browser)s
Notes
-----
The arrow keys (up/down/left/right) can typically be used to navigate
between channels and time ranges, but this depends on the backend
matplotlib is configured to use (e.g., mpl.use('TkAgg') should work). The
left/right arrows will scroll by 25%% of ``duration``, whereas
shift+left/shift+right will scroll by 100%% of ``duration``. The scaling
can be adjusted with - and + (or =) keys. The viewport dimensions can be
adjusted with page up/page down and home/end keys. Full screen mode can be
toggled with the F11 key, and scrollbars can be hidden/shown by pressing
'z'. Right-click a channel label to view its location. To mark or un-mark a
channel as bad, click on a channel label or a channel trace. The changes
will be reflected immediately in the raw object's ``raw.info['bads']``
entry.
If projectors are present, a button labelled "Prj" in the lower right
corner of the plot window opens a secondary control window, which allows
enabling/disabling specific projectors individually. This provides a means
of interactively observing how each projector would affect the raw data if
it were applied.
Annotation mode is toggled by pressing 'a', butterfly mode by pressing
'b', and whitening mode (when ``noise_cov is not None``) by pressing 'w'.
By default, the channel means are removed when ``remove_dc`` is set to
``True``. This flag can be toggled by pressing 'd'.
%(notes_2d_backend)s
"""
from ..annotations import _annotations_starts_stops
from ..io import BaseRaw
from ._figure import _get_browser
info = raw.info.copy()
sfreq = info["sfreq"]
projs = info["projs"]
# this will be an attr for which projectors are currently "on" in the plot
projs_on = np.full_like(projs, proj, dtype=bool)
# disable projs in info if user doesn't want to see them right away
if not proj:
with info._unlock():
info["projs"] = list()
# handle defaults / check arg validity
color = _handle_default("color", color)
scalings = _compute_scalings(scalings, raw, remove_dc=remove_dc, duration=duration)
if scalings["whitened"] == "auto":
scalings["whitened"] = 1.0
_validate_type(raw, BaseRaw, "raw", "Raw")
decim, picks_data = _handle_decim(info, decim, lowpass)
noise_cov = _check_cov(noise_cov, info)
units = _handle_default("units", None)
unit_scalings = _handle_default("scalings", None)
_check_option("group_by", group_by, ("selection", "position", "original", "type"))
# clipping
_validate_type(clipping, (None, "numeric", str), "clipping")
if isinstance(clipping, str):
_check_option(
"clipping", clipping, ("clamp", "transparent"), extra="when a string"
)
clipping = 1.0 if clipping == "transparent" else clipping
elif clipping is not None:
clipping = float(clipping)
# be forgiving if user asks for too much time
duration = min(raw.times[-1], float(duration))
# determine IIR filtering parameters
if highpass is not None and highpass <= 0:
raise ValueError(f"highpass must be > 0, got {highpass}")
if highpass is None and lowpass is None:
ba = filt_bounds = None
else:
filtorder = int(filtorder)
if filtorder == 0:
method = "fir"
iir_params = None
else:
method = "iir"
iir_params = dict(order=filtorder, output="sos", ftype="butter")
ba = create_filter(
np.zeros((1, int(round(duration * sfreq)))),
sfreq,
highpass,
lowpass,
method=method,
iir_params=iir_params,
)
filt_bounds = _annotations_starts_stops(
raw, ("edge", "bad_acq_skip"), invert=True
)
# compute event times in seconds
if events is not None:
event_times = (events[:, 0] - raw.first_samp).astype(float)
event_times /= sfreq
event_nums = events[:, 2]
else:
event_times = event_nums = None
# determine trace order
ch_names = np.array(raw.ch_names)
ch_types = np.array(raw.get_channel_types())
picks = _picks_to_idx(info, picks, none="all", exclude=())
order = _get_channel_plotting_order(order, ch_types, picks=picks)
n_channels = min(info["nchan"], n_channels, len(order))
# adjust order based on channel selection, if needed
selections = None
if group_by in ("selection", "position"):
selections = _setup_channel_selections(raw, group_by, order)
order = np.concatenate(list(selections.values()))
default_selection = list(selections)[0]
n_channels = len(selections[default_selection])
assert isinstance(order, np.ndarray)
assert order.dtype.kind == "i"
if order.size == 0:
raise RuntimeError("No channels found to plot")
# handle event colors
event_color_dict = _make_event_color_dict(event_color, events, event_id)
# handle first_samp
first_time = raw._first_time if show_first_samp else 0
start += first_time
event_id_rev = {v: k for k, v in (event_id or {}).items()}
# generate window title; allow instances without a filename (e.g., ICA)
if title is None:
title = "<unknown>"
fnames = raw._filenames.copy()
if len(fnames):
title = fnames.pop(0)
extra = f" ... (+ {len(fnames)} more)" if len(fnames) else ""
title = f"{title}{extra}"
if len(title) > 60:
title = _shorten_path_from_middle(title)
elif not isinstance(title, str):
raise TypeError(f"title must be None or a string, got a {type(title)}")
# gather parameters and initialize figure
_validate_type(use_opengl, (bool, None), "use_opengl")
precompute = _handle_precompute(precompute)
params = dict(
inst=raw,
info=info,
# channels and channel order
ch_names=ch_names,
ch_types=ch_types,
ch_order=order,
picks=order[:n_channels],
n_channels=n_channels,
picks_data=picks_data,
group_by=group_by,
ch_selections=selections,
# time
t_start=start,
duration=duration,
n_times=raw.n_times,
first_time=first_time,
time_format=time_format,
decim=decim,
# events
event_color_dict=event_color_dict,
event_times=event_times,
event_nums=event_nums,
event_id_rev=event_id_rev,
# preprocessing
projs=projs,
projs_on=projs_on,
apply_proj=proj,
remove_dc=remove_dc,
filter_coefs=ba,
filter_bounds=filt_bounds,
noise_cov=noise_cov,
# scalings
scalings=scalings,
units=units,
unit_scalings=unit_scalings,
# colors
ch_color_bad=bad_color,
ch_color_dict=color,
# display
butterfly=butterfly,
clipping=clipping,
scrollbars_visible=show_scrollbars,
scalebars_visible=show_scalebars,
window_title=title,
bgcolor=bgcolor,
# Qt-specific
precompute=precompute,
use_opengl=use_opengl,
theme=theme,
overview_mode=overview_mode,
splash=splash,
)
fig = _get_browser(show=show, block=block, **params)
return fig
@legacy(alt="Raw.compute_psd().plot()")
@verbose
def plot_raw_psd(
raw,
fmin=0,
fmax=np.inf,
tmin=None,
tmax=None,
proj=False,
n_fft=None,
n_overlap=0,
reject_by_annotation=True,
picks=None,
ax=None,
color="black",
xscale="linear",
area_mode="std",
area_alpha=0.33,
dB=True,
estimate="power",
show=True,
n_jobs=None,
average=False,
line_alpha=None,
spatial_colors=True,
sphere=None,
window="hamming",
exclude="bads",
verbose=None,
):
"""%(plot_psd_doc)s.
Parameters
----------
raw : instance of Raw
The raw object.
%(fmin_fmax_psd)s
%(tmin_tmax_psd)s
%(proj_psd)s
n_fft : int | None
Number of points to use in Welch FFT calculations. Default is ``None``,
which uses the minimum of 2048 and the number of time points.
n_overlap : int
The number of points of overlap between blocks. The default value
is 0 (no overlap).
%(reject_by_annotation_psd)s
%(picks_good_data_noref)s
%(ax_plot_psd)s
%(color_plot_psd)s
%(xscale_plot_psd)s
%(area_mode_plot_psd)s
%(area_alpha_plot_psd)s
%(dB_plot_psd)s
%(estimate_plot_psd)s
%(show)s
%(n_jobs)s
%(average_plot_psd)s
%(line_alpha_plot_psd)s
%(spatial_colors_psd)s
%(sphere_topomap_auto)s
%(window_psd)s
.. versionadded:: 0.22.0
exclude : list of str | 'bads'
Channels names to exclude from being shown. If 'bads', the bad channels
are excluded. Pass an empty list to plot all channels (including
channels marked "bad", if any).
.. versionadded:: 0.24.0
%(verbose)s
Returns
-------
fig : instance of Figure
Figure with frequency spectra of the data channels.
Notes
-----
%(notes_plot_*_psd_func)s
"""
from ..time_frequency import Spectrum
init_kw, plot_kw = _split_psd_kwargs(plot_fun=Spectrum.plot)
return raw.compute_psd(**init_kw).plot(**plot_kw)
@legacy(alt="Raw.compute_psd().plot_topo()")
@verbose
def plot_raw_psd_topo(
raw,
tmin=0.0,
tmax=None,
fmin=0.0,
fmax=100.0,
proj=False,
*,
n_fft=2048,
n_overlap=0,
dB=True,
layout=None,
color="w",
fig_facecolor="k",
axis_facecolor="k",
axes=None,
block=False,
show=True,
n_jobs=None,
verbose=None,
):
"""Plot power spectral density, separately for each channel.
Parameters
----------
raw : instance of io.Raw
The raw instance to use.
%(tmin_tmax_psd)s
%(fmin_fmax_psd_topo)s
%(proj_psd)s
n_fft : int
Number of points to use in Welch FFT calculations. Defaults to 2048.
n_overlap : int
The number of points of overlap between blocks. Defaults to 0
(no overlap).
%(dB_spectrum_plot_topo)s
layout : instance of Layout | None
Layout instance specifying sensor positions (does not need to be
specified for Neuromag data). If ``None`` (default), the layout is
inferred from the data.
color : str | tuple
A matplotlib-compatible color to use for the curves. Defaults to white.
fig_facecolor : str | tuple
A matplotlib-compatible color to use for the figure background.
Defaults to black.
axis_facecolor : str | tuple
A matplotlib-compatible color to use for the axis background.
Defaults to black.
%(axes_spectrum_plot_topo)s
block : bool
Whether to halt program execution until the figure is closed.
May not work on all systems / platforms. Defaults to False.
%(show)s
%(n_jobs)s
%(verbose)s
Returns
-------
fig : instance of matplotlib.figure.Figure
Figure distributing one image per channel across sensor topography.
"""
from ..time_frequency import Spectrum
init_kw, plot_kw = _split_psd_kwargs(plot_fun=Spectrum.plot_topo)
return raw.compute_psd(**init_kw).plot_topo(**plot_kw)
def _setup_channel_selections(raw, kind, order):
"""Get dictionary of channel groupings."""
from ..channels import (
_EEG_SELECTIONS,
_SELECTIONS,
_divide_to_regions,
read_vectorview_selection,
)
_check_option("group_by", kind, ("position", "selection"))
if kind == "position":
selections_dict = _divide_to_regions(raw.info)
keys = _SELECTIONS[1:] # omit 'Vertex'
else: # kind == 'selection'
from ..channels.channels import _get_ch_info
(
has_vv_mag,
has_vv_grad,
*_,
has_neuromag_122_grad,
has_csd_coils,
) = _get_ch_info(raw.info)
if not (has_vv_grad or has_vv_mag or has_neuromag_122_grad):
raise ValueError(
"order='selection' only works for Neuromag "
"data. Use order='position' instead."
)
selections_dict = OrderedDict()
# get stim channel (if any)
stim_ch = _get_stim_channel(None, raw.info, raise_error=False)
stim_ch = stim_ch if len(stim_ch) else [""]
stim_ch = pick_channels(raw.ch_names, stim_ch, ordered=False)
# loop over regions
keys = np.concatenate([_SELECTIONS, _EEG_SELECTIONS])
for key in keys:
channels = read_vectorview_selection(key, info=raw.info)
picks = pick_channels(raw.ch_names, channels, ordered=False)
picks = np.intersect1d(picks, order)
if not len(picks):
continue # omit empty selections
selections_dict[key] = np.concatenate([picks, stim_ch])
# add misc channels
misc = pick_types(
raw.info,
meg=False,
eeg=False,
stim=True,
eog=True,
ecg=True,
emg=True,
ref_meg=False,
misc=True,
resp=True,
chpi=True,
exci=True,
ias=True,
syst=True,
seeg=False,
bio=True,
ecog=False,
fnirs=False,
dbs=False,
temperature=True,
gsr=True,
exclude=(),
)
if len(misc) and np.isin(misc, order).any():
selections_dict["Misc"] = misc
return selections_dict