/
evoked.py
1483 lines (1282 loc) · 55.4 KB
/
evoked.py
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# -*- coding: utf-8 -*-
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Matti Hämäläinen <msh@nmr.mgh.harvard.edu>
# Denis Engemann <denis.engemann@gmail.com>
# Andrew Dykstra <andrew.r.dykstra@gmail.com>
# Mads Jensen <mje.mads@gmail.com>
# Jona Sassenhagen <jona.sassenhagen@gmail.com>
#
# License: BSD-3-Clause
from copy import deepcopy
import numpy as np
from .baseline import rescale, _log_rescale, _check_baseline
from .channels.channels import (UpdateChannelsMixin,
SetChannelsMixin, InterpolationMixin)
from .channels.layout import _merge_ch_data, _pair_grad_sensors
from .defaults import (_INTERPOLATION_DEFAULT, _EXTRAPOLATE_DEFAULT,
_BORDER_DEFAULT)
from .filter import detrend, FilterMixin, _check_fun
from .utils import (check_fname, logger, verbose, warn, sizeof_fmt,
SizeMixin, copy_function_doc_to_method_doc, _validate_type,
fill_doc, _check_option, _build_data_frame,
_check_pandas_installed, _check_pandas_index_arguments,
_convert_times, _scale_dataframe_data, _check_time_format,
_check_preload, _check_fname, TimeMixin)
from .viz import (plot_evoked, plot_evoked_topomap, plot_evoked_field,
plot_evoked_image, plot_evoked_topo)
from .viz.evoked import plot_evoked_white, plot_evoked_joint
from .viz.topomap import _topomap_animation
from .io.constants import FIFF
from .io.open import fiff_open
from .io.tag import read_tag
from .io.tree import dir_tree_find
from .io.pick import pick_types, _picks_to_idx, _FNIRS_CH_TYPES_SPLIT
from .io.meas_info import (ContainsMixin, read_meas_info, write_meas_info,
_read_extended_ch_info, _rename_list,
_ensure_infos_match)
from .io.proj import ProjMixin
from .io.write import (start_and_end_file, start_block, end_block,
write_int, write_string, write_float_matrix,
write_id, write_float, write_complex_float_matrix)
from .io.base import _check_maxshield, _get_ch_factors
from .parallel import parallel_func
_aspect_dict = {
'average': FIFF.FIFFV_ASPECT_AVERAGE,
'standard_error': FIFF.FIFFV_ASPECT_STD_ERR,
'single_epoch': FIFF.FIFFV_ASPECT_SINGLE,
'partial_average': FIFF.FIFFV_ASPECT_SUBAVERAGE,
'alternating_subaverage': FIFF.FIFFV_ASPECT_ALTAVERAGE,
'sample_cut_out_by_graph': FIFF.FIFFV_ASPECT_SAMPLE,
'power_density_spectrum': FIFF.FIFFV_ASPECT_POWER_DENSITY,
'dipole_amplitude_cuvre': FIFF.FIFFV_ASPECT_DIPOLE_WAVE,
'squid_modulation_lower_bound': FIFF.FIFFV_ASPECT_IFII_LOW,
'squid_modulation_upper_bound': FIFF.FIFFV_ASPECT_IFII_HIGH,
'squid_gate_setting': FIFF.FIFFV_ASPECT_GATE,
}
_aspect_rev = {val: key for key, val in _aspect_dict.items()}
@fill_doc
class Evoked(ProjMixin, ContainsMixin, UpdateChannelsMixin, SetChannelsMixin,
InterpolationMixin, FilterMixin, TimeMixin, SizeMixin):
"""Evoked data.
Parameters
----------
fname : str
Name of evoked/average FIF file to load.
If None no data is loaded.
condition : int, or str
Dataset ID number (int) or comment/name (str). Optional if there is
only one data set in file.
proj : bool, optional
Apply SSP projection vectors.
kind : str
Either 'average' or 'standard_error'. The type of data to read.
Only used if 'condition' is a str.
allow_maxshield : bool | str (default False)
If True, allow loading of data that has been recorded with internal
active compensation (MaxShield). Data recorded with MaxShield should
generally not be loaded directly, but should first be processed using
SSS/tSSS to remove the compensation signals that may also affect brain
activity. Can also be "yes" to load without eliciting a warning.
%(verbose)s
Attributes
----------
%(info_not_none)s
ch_names : list of str
List of channels' names.
nave : int
Number of averaged epochs.
kind : str
Type of data, either average or standard_error.
comment : str
Comment on dataset. Can be the condition.
data : array of shape (n_channels, n_times)
Evoked response.
first : int
First time sample.
last : int
Last time sample.
tmin : float
The first time point in seconds.
tmax : float
The last time point in seconds.
times : array
Time vector in seconds. Goes from ``tmin`` to ``tmax``. Time interval
between consecutive time samples is equal to the inverse of the
sampling frequency.
baseline : None | tuple of length 2
This attribute reflects whether the data has been baseline-corrected
(it will be a ``tuple`` then) or not (it will be ``None``).
Notes
-----
Evoked objects can only contain the average of a single set of conditions.
"""
@verbose
def __init__(self, fname, condition=None, proj=True,
kind='average', allow_maxshield=False, *,
verbose=None): # noqa: D102
_validate_type(proj, bool, "'proj'")
# Read the requested data
fname = _check_fname(fname=fname, must_exist=True, overwrite='read')
self.info, self.nave, self._aspect_kind, self.comment, times, \
self.data, self.baseline = _read_evoked(fname, condition, kind,
allow_maxshield)
self._set_times(times)
self._raw_times = self.times.copy()
self._decim = 1
self._update_first_last()
self.preload = True
# project and baseline correct
if proj:
self.apply_proj()
self.filename = fname
@property
def kind(self):
"""The data kind."""
return _aspect_rev[self._aspect_kind]
@kind.setter
def kind(self, kind):
_check_option('kind', kind, list(_aspect_dict.keys()))
self._aspect_kind = _aspect_dict[kind]
@property
def data(self):
"""The data matrix."""
return self._data
@data.setter
def data(self, data):
"""Set the data matrix."""
self._data = data
@fill_doc
def get_data(self, picks=None, units=None, tmin=None, tmax=None):
"""Get evoked data as 2D array.
Parameters
----------
%(picks_all)s
%(units)s
tmin : float | None
Start time of data to get in seconds.
tmax : float | None
End time of data to get in seconds.
Returns
-------
data : ndarray, shape (n_channels, n_times)
A view on evoked data.
Notes
-----
.. versionadded:: 0.24
"""
picks = _picks_to_idx(self.info, picks, "all", exclude=())
start, stop = self._handle_tmin_tmax(tmin, tmax)
data = self.data[picks, start:stop]
if units is not None:
ch_factors = _get_ch_factors(self, units, picks)
data *= ch_factors[:, np.newaxis]
return data
@verbose
def apply_function(self, fun, picks=None, dtype=None, n_jobs=None,
verbose=None, **kwargs):
"""Apply a function to a subset of channels.
%(applyfun_summary_evoked)s
Parameters
----------
%(fun_applyfun_evoked)s
%(picks_all_data_noref)s
%(dtype_applyfun)s
%(n_jobs)s
%(verbose)s
%(kwargs_fun)s
Returns
-------
self : instance of Evoked
The evoked object with transformed data.
"""
_check_preload(self, 'evoked.apply_function')
picks = _picks_to_idx(self.info, picks, exclude=(), with_ref_meg=False)
if not callable(fun):
raise ValueError('fun needs to be a function')
data_in = self._data
if dtype is not None and dtype != self._data.dtype:
self._data = self._data.astype(dtype)
# check the dimension of the incoming evoked data
_check_option('evoked.ndim', self._data.ndim, [2])
parallel, p_fun, n_jobs = parallel_func(_check_fun, n_jobs)
if n_jobs == 1:
# modify data inplace to save memory
for idx in picks:
self._data[idx, :] = _check_fun(fun, data_in[idx, :], **kwargs)
else:
# use parallel function
data_picks_new = parallel(p_fun(
fun, data_in[p, :], **kwargs) for p in picks)
for pp, p in enumerate(picks):
self._data[p, :] = data_picks_new[pp]
return self
@verbose
def apply_baseline(self, baseline=(None, 0), *, verbose=None):
"""Baseline correct evoked data.
Parameters
----------
%(baseline_evoked)s
Defaults to ``(None, 0)``, i.e. beginning of the the data until
time point zero.
%(verbose)s
Returns
-------
evoked : instance of Evoked
The baseline-corrected Evoked object.
Notes
-----
Baseline correction can be done multiple times.
.. versionadded:: 0.13.0
"""
baseline = _check_baseline(baseline, times=self.times,
sfreq=self.info['sfreq'])
if self.baseline is not None and baseline is None:
raise ValueError('The data has already been baseline-corrected. '
'Cannot remove existing baseline correction.')
elif baseline is None:
# Do not rescale
logger.info(_log_rescale(None))
else:
# Actually baseline correct the data. Logging happens in rescale().
self.data = rescale(self.data, self.times, baseline, copy=False)
self.baseline = baseline
return self
@verbose
def save(self, fname, *, overwrite=False, verbose=None):
"""Save evoked data to a file.
Parameters
----------
fname : str
The name of the file, which should end with ``-ave.fif(.gz)`` or
``_ave.fif(.gz)``.
%(overwrite)s
%(verbose)s
Notes
-----
To write multiple conditions into a single file, use
`mne.write_evokeds`.
.. versionchanged:: 0.23
Information on baseline correction will be stored with the data,
and will be restored when reading again via `mne.read_evokeds`.
"""
write_evokeds(fname, self, overwrite=overwrite)
@verbose
def export(self, fname, fmt='auto', *, overwrite=False, verbose=None):
"""Export Evoked to external formats.
%(export_fmt_support_evoked)s
%(export_warning)s
Parameters
----------
%(fname_export_params)s
%(export_fmt_params_evoked)s
%(overwrite)s
%(verbose)s
Notes
-----
.. versionadded:: 1.1
%(export_warning_note_evoked)s
"""
from .export import export_evokeds
export_evokeds(fname, self, fmt, overwrite=overwrite, verbose=verbose)
def __repr__(self): # noqa: D105
max_comment_length = 1000
if len(self.comment) > max_comment_length:
comment = self.comment[:max_comment_length]
comment += "..."
else:
comment = self.comment
s = "'%s' (%s, N=%s)" % (comment, self.kind, self.nave)
s += ", %0.5g – %0.5g sec" % (self.times[0], self.times[-1])
s += ', baseline '
if self.baseline is None:
s += 'off'
else:
s += f'{self.baseline[0]:g} – {self.baseline[1]:g} sec'
if self.baseline != _check_baseline(
self.baseline, times=self.times, sfreq=self.info['sfreq'],
on_baseline_outside_data='adjust'):
s += ' (baseline period was cropped after baseline correction)'
s += ", %s ch" % self.data.shape[0]
s += ", ~%s" % (sizeof_fmt(self._size),)
return "<Evoked | %s>" % s
@property
def ch_names(self):
"""Channel names."""
return self.info['ch_names']
@copy_function_doc_to_method_doc(plot_evoked)
def plot(self, 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):
return plot_evoked(
self, picks=picks, exclude=exclude, unit=unit, show=show,
ylim=ylim, proj=proj, xlim=xlim, hline=hline, units=units,
scalings=scalings, titles=titles, axes=axes, gfp=gfp,
window_title=window_title, spatial_colors=spatial_colors,
zorder=zorder, selectable=selectable, noise_cov=noise_cov,
time_unit=time_unit, sphere=sphere, highlight=highlight,
verbose=verbose)
@copy_function_doc_to_method_doc(plot_evoked_image)
def plot_image(self, 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=None, group_by=None, sphere=None):
return plot_evoked_image(
self, picks=picks, exclude=exclude, unit=unit, show=show,
clim=clim, xlim=xlim, proj=proj, units=units, scalings=scalings,
titles=titles, axes=axes, cmap=cmap, colorbar=colorbar, mask=mask,
mask_style=mask_style, mask_cmap=mask_cmap, mask_alpha=mask_alpha,
time_unit=time_unit, show_names=show_names, group_by=group_by,
sphere=sphere)
@copy_function_doc_to_method_doc(plot_evoked_topo)
def plot_topo(self, 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):
"""
Notes
-----
.. versionadded:: 0.10.0
"""
return plot_evoked_topo(
self, layout=layout, layout_scale=layout_scale,
color=color, border=border, ylim=ylim, scalings=scalings,
title=title, proj=proj, vline=vline, fig_background=fig_background,
merge_grads=merge_grads, legend=legend, axes=axes,
background_color=background_color, noise_cov=noise_cov,
exclude=exclude, show=show)
@copy_function_doc_to_method_doc(plot_evoked_topomap)
def plot_topomap(self, times="auto", ch_type=None, vmin=None,
vmax=None, cmap=None, sensors=True, colorbar=True,
scalings=None, units=None, res=64,
size=1, cbar_fmt="%3.1f",
time_unit='s', time_format=None,
proj=False, show=True, show_names=False, title=None,
mask=None, mask_params=None, outlines='head',
contours=6, image_interp=_INTERPOLATION_DEFAULT,
average=None, axes=None,
extrapolate=_EXTRAPOLATE_DEFAULT, sphere=None,
border=_BORDER_DEFAULT, nrows=1, ncols='auto'):
return plot_evoked_topomap(
self, times=times, ch_type=ch_type, vmin=vmin,
vmax=vmax, cmap=cmap, sensors=sensors, colorbar=colorbar,
scalings=scalings, units=units, res=res,
size=size, cbar_fmt=cbar_fmt, time_unit=time_unit,
time_format=time_format, proj=proj, show=show,
show_names=show_names, title=title, mask=mask,
mask_params=mask_params, outlines=outlines, contours=contours,
image_interp=image_interp, average=average, axes=axes,
extrapolate=extrapolate, sphere=sphere, border=border,
nrows=nrows, ncols=ncols)
@copy_function_doc_to_method_doc(plot_evoked_field)
def plot_field(self, surf_maps, time=None, time_label='t = %0.0f ms',
n_jobs=None, fig=None, vmax=None, n_contours=21,
*, interaction='terrain', verbose=None):
return plot_evoked_field(self, surf_maps, time=time,
time_label=time_label, n_jobs=n_jobs,
fig=fig, vmax=vmax, n_contours=n_contours,
interaction=interaction, verbose=verbose)
@copy_function_doc_to_method_doc(plot_evoked_white)
def plot_white(self, noise_cov, show=True, rank=None, time_unit='s',
sphere=None, axes=None, verbose=None):
return plot_evoked_white(
self, noise_cov=noise_cov, rank=rank, show=show,
time_unit=time_unit, sphere=sphere, axes=axes, verbose=verbose)
@copy_function_doc_to_method_doc(plot_evoked_joint)
def plot_joint(self, times="peaks", title='', picks=None,
exclude='bads', show=True, ts_args=None,
topomap_args=None):
return plot_evoked_joint(self, times=times, title=title, picks=picks,
exclude=exclude, show=show, ts_args=ts_args,
topomap_args=topomap_args)
@fill_doc
def animate_topomap(self, ch_type=None, times=None, frame_rate=None,
butterfly=False, blit=True, show=True, time_unit='s',
sphere=None, *, image_interp=_INTERPOLATION_DEFAULT,
extrapolate=_EXTRAPOLATE_DEFAULT, verbose=None):
"""Make animation of evoked data as topomap timeseries.
The animation can be paused/resumed with left mouse button.
Left and right arrow keys can be used to move backward or forward
in time.
Parameters
----------
ch_type : str | None
Channel type to plot. Accepted data types: 'mag', 'grad', 'eeg',
'hbo', 'hbr', 'fnirs_cw_amplitude',
'fnirs_fd_ac_amplitude', 'fnirs_fd_phase', and 'fnirs_od'.
If None, first available channel type from the above list is used.
Defaults to None.
times : array of float | None
The time points to plot. If None, 10 evenly spaced samples are
calculated over the evoked time series. Defaults to None.
frame_rate : int | None
Frame rate for the animation in Hz. If None,
frame rate = sfreq / 10. Defaults to None.
butterfly : bool
Whether to plot the data as butterfly plot under the topomap.
Defaults to False.
blit : bool
Whether to use blit to optimize drawing. In general, it is
recommended to use blit in combination with ``show=True``. If you
intend to save the animation it is better to disable blit.
Defaults to True.
show : bool
Whether to show the animation. Defaults to True.
time_unit : str
The units for the time axis, can be "ms" (default in 0.16)
or "s" (will become the default in 0.17).
.. versionadded:: 0.16
%(sphere_topomap_auto)s
%(image_interp_topomap)s
%(extrapolate_topomap)s
.. versionadded:: 0.22
%(verbose)s
Returns
-------
fig : instance of matplotlib.figure.Figure
The figure.
anim : instance of matplotlib.animation.FuncAnimation
Animation of the topomap.
Notes
-----
.. versionadded:: 0.12.0
"""
return _topomap_animation(
self, ch_type=ch_type, times=times, frame_rate=frame_rate,
butterfly=butterfly, blit=blit, show=show, time_unit=time_unit,
sphere=sphere, image_interp=image_interp,
extrapolate=extrapolate, verbose=verbose)
def as_type(self, ch_type='grad', mode='fast'):
"""Compute virtual evoked using interpolated fields.
.. Warning:: Using virtual evoked to compute inverse can yield
unexpected results. The virtual channels have ``'_v'`` appended
at the end of the names to emphasize that the data contained in
them are interpolated.
Parameters
----------
ch_type : str
The destination channel type. It can be 'mag' or 'grad'.
mode : str
Either ``'accurate'`` or ``'fast'``, determines the quality of the
Legendre polynomial expansion used. ``'fast'`` should be sufficient
for most applications.
Returns
-------
evoked : instance of mne.Evoked
The transformed evoked object containing only virtual channels.
Notes
-----
This method returns a copy and does not modify the data it
operates on. It also returns an EvokedArray instance.
.. versionadded:: 0.9.0
"""
from .forward import _as_meg_type_inst
return _as_meg_type_inst(self, ch_type=ch_type, mode=mode)
@fill_doc
def detrend(self, order=1, picks=None):
"""Detrend data.
This function operates in-place.
Parameters
----------
order : int
Either 0 or 1, the order of the detrending. 0 is a constant
(DC) detrend, 1 is a linear detrend.
%(picks_good_data)s
Returns
-------
evoked : instance of Evoked
The detrended evoked object.
"""
picks = _picks_to_idx(self.info, picks)
self.data[picks] = detrend(self.data[picks], order, axis=-1)
return self
def copy(self):
"""Copy the instance of evoked.
Returns
-------
evoked : instance of Evoked
A copy of the object.
"""
evoked = deepcopy(self)
return evoked
def __neg__(self):
"""Negate channel responses.
Returns
-------
evoked_neg : instance of Evoked
The Evoked instance with channel data negated and '-'
prepended to the comment.
"""
out = self.copy()
out.data *= -1
if out.comment is not None and ' + ' in out.comment:
out.comment = f'({out.comment})' # multiple conditions in evoked
out.comment = f'- {out.comment or "unknown"}'
return out
def get_peak(self, ch_type=None, tmin=None, tmax=None,
mode='abs', time_as_index=False, merge_grads=False,
return_amplitude=False):
"""Get location and latency of peak amplitude.
Parameters
----------
ch_type : str | None
The channel type to use. Defaults to None. If more than one channel
type is present in the data, this value **must** be provided.
tmin : float | None
The minimum point in time to be considered for peak getting.
If None (default), the beginning of the data is used.
tmax : float | None
The maximum point in time to be considered for peak getting.
If None (default), the end of the data is used.
mode : 'pos' | 'neg' | 'abs'
How to deal with the sign of the data. If 'pos' only positive
values will be considered. If 'neg' only negative values will
be considered. If 'abs' absolute values will be considered.
Defaults to 'abs'.
time_as_index : bool
Whether to return the time index instead of the latency in seconds.
merge_grads : bool
If True, compute peak from merged gradiometer data.
return_amplitude : bool
If True, return also the amplitude at the maximum response.
.. versionadded:: 0.16
Returns
-------
ch_name : str
The channel exhibiting the maximum response.
latency : float | int
The time point of the maximum response, either latency in seconds
or index.
amplitude : float
The amplitude of the maximum response. Only returned if
return_amplitude is True.
.. versionadded:: 0.16
""" # noqa: E501
supported = ('mag', 'grad', 'eeg', 'seeg', 'dbs', 'ecog', 'misc',
'None') + _FNIRS_CH_TYPES_SPLIT
types_used = self.get_channel_types(unique=True, only_data_chs=True)
_check_option('ch_type', str(ch_type), supported)
if ch_type is not None and ch_type not in types_used:
raise ValueError(
f'Channel type "{ch_type}" not found in this evoked object.'
)
elif len(types_used) > 1 and ch_type is None:
raise RuntimeError(
'Multiple data channel types found. Please pass the "ch_type" '
'parameter.'
)
if merge_grads:
if ch_type != 'grad':
raise ValueError('Channel type must be "grad" for merge_grads')
elif mode == 'neg':
raise ValueError('Negative mode (mode=neg) does not make '
'sense with merge_grads=True')
meg = eeg = misc = seeg = dbs = ecog = fnirs = False
picks = None
if ch_type in ('mag', 'grad'):
meg = ch_type
elif ch_type == 'eeg':
eeg = True
elif ch_type == 'misc':
misc = True
elif ch_type == 'seeg':
seeg = True
elif ch_type == 'dbs':
dbs = True
elif ch_type == 'ecog':
ecog = True
elif ch_type in _FNIRS_CH_TYPES_SPLIT:
fnirs = ch_type
if ch_type is not None:
if merge_grads:
picks = _pair_grad_sensors(self.info, topomap_coords=False)
else:
picks = pick_types(self.info, meg=meg, eeg=eeg, misc=misc,
seeg=seeg, ecog=ecog, ref_meg=False,
fnirs=fnirs, dbs=dbs)
data = self.data
ch_names = self.ch_names
if picks is not None:
data = data[picks]
ch_names = [ch_names[k] for k in picks]
if merge_grads:
data, _ = _merge_ch_data(data, ch_type, [])
ch_names = [ch_name[:-1] + 'X' for ch_name in ch_names[::2]]
ch_idx, time_idx, max_amp = _get_peak(data, self.times, tmin,
tmax, mode)
out = (ch_names[ch_idx], time_idx if time_as_index else
self.times[time_idx])
if return_amplitude:
out += (max_amp,)
return out
@verbose
def to_data_frame(self, picks=None, index=None,
scalings=None, copy=True, long_format=False,
time_format=None, *, verbose=None):
"""Export data in tabular structure as a pandas DataFrame.
Channels are converted to columns in the DataFrame. By default,
an additional column "time" is added, unless ``index='time'``
(in which case time values form the DataFrame's index).
Parameters
----------
%(picks_all)s
%(index_df_evk)s
Defaults to ``None``.
%(scalings_df)s
%(copy_df)s
%(long_format_df_raw)s
%(time_format_df)s
.. versionadded:: 0.20
%(verbose)s
Returns
-------
%(df_return)s
"""
# check pandas once here, instead of in each private utils function
pd = _check_pandas_installed() # noqa
# arg checking
valid_index_args = ['time']
valid_time_formats = ['ms', 'timedelta']
index = _check_pandas_index_arguments(index, valid_index_args)
time_format = _check_time_format(time_format, valid_time_formats)
# get data
picks = _picks_to_idx(self.info, picks, 'all', exclude=())
data = self.data[picks, :]
times = self.times
data = data.T
if copy:
data = data.copy()
data = _scale_dataframe_data(self, data, picks, scalings)
# prepare extra columns / multiindex
mindex = list()
times = _convert_times(self, times, time_format)
mindex.append(('time', times))
# build DataFrame
df = _build_data_frame(self, data, picks, long_format, mindex, index,
default_index=['time'])
return df
@fill_doc
class EvokedArray(Evoked):
"""Evoked object from numpy array.
Parameters
----------
data : array of shape (n_channels, n_times)
The channels' evoked response. See notes for proper units of measure.
%(info_not_none)s Consider using :func:`mne.create_info` to populate this
structure.
tmin : float
Start time before event. Defaults to 0.
comment : str
Comment on dataset. Can be the condition. Defaults to ''.
nave : int
Number of averaged epochs. Defaults to 1.
kind : str
Type of data, either average or standard_error. Defaults to 'average'.
%(baseline_evoked)s
Defaults to ``None``, i.e. no baseline correction.
.. versionadded:: 0.23
%(verbose)s
See Also
--------
EpochsArray, io.RawArray, create_info
Notes
-----
Proper units of measure:
* V: eeg, eog, seeg, dbs, emg, ecg, bio, ecog
* T: mag
* T/m: grad
* M: hbo, hbr
* Am: dipole
* AU: misc
"""
@verbose
def __init__(self, data, info, tmin=0., comment='', nave=1, kind='average',
baseline=None, *, verbose=None): # noqa: D102
dtype = np.complex128 if np.iscomplexobj(data) else np.float64
data = np.asanyarray(data, dtype=dtype)
if data.ndim != 2:
raise ValueError('Data must be a 2D array of shape (n_channels, '
'n_samples), got shape %s' % (data.shape,))
if len(info['ch_names']) != np.shape(data)[0]:
raise ValueError('Info (%s) and data (%s) must have same number '
'of channels.' % (len(info['ch_names']),
np.shape(data)[0]))
self.data = data
self.first = int(round(tmin * info['sfreq']))
self.last = self.first + np.shape(data)[-1] - 1
self._set_times(np.arange(self.first, self.last + 1,
dtype=np.float64) / info['sfreq'])
self._raw_times = self.times.copy()
self._decim = 1
self.info = info.copy() # do not modify original info
self.nave = nave
self.kind = kind
self.comment = comment
self.picks = None
self.preload = True
self._projector = None
_validate_type(self.kind, "str", "kind")
if self.kind not in _aspect_dict:
raise ValueError('unknown kind "%s", should be "average" or '
'"standard_error"' % (self.kind,))
self._aspect_kind = _aspect_dict[self.kind]
self.baseline = baseline
if self.baseline is not None: # omit log msg if not baselining
self.apply_baseline(self.baseline)
def _get_entries(fid, evoked_node, allow_maxshield=False):
"""Get all evoked entries."""
comments = list()
aspect_kinds = list()
for ev in evoked_node:
for k in range(ev['nent']):
my_kind = ev['directory'][k].kind
pos = ev['directory'][k].pos
if my_kind == FIFF.FIFF_COMMENT:
tag = read_tag(fid, pos)
comments.append(tag.data)
my_aspect = _get_aspect(ev, allow_maxshield)[0]
for k in range(my_aspect['nent']):
my_kind = my_aspect['directory'][k].kind
pos = my_aspect['directory'][k].pos
if my_kind == FIFF.FIFF_ASPECT_KIND:
tag = read_tag(fid, pos)
aspect_kinds.append(int(tag.data))
comments = np.atleast_1d(comments)
aspect_kinds = np.atleast_1d(aspect_kinds)
if len(comments) != len(aspect_kinds) or len(comments) == 0:
fid.close()
raise ValueError('Dataset names in FIF file '
'could not be found.')
t = [_aspect_rev[a] for a in aspect_kinds]
t = ['"' + c + '" (' + tt + ')' for tt, c in zip(t, comments)]
t = '\n'.join(t)
return comments, aspect_kinds, t
def _get_aspect(evoked, allow_maxshield):
"""Get Evoked data aspect."""
is_maxshield = False
aspect = dir_tree_find(evoked, FIFF.FIFFB_ASPECT)
if len(aspect) == 0:
_check_maxshield(allow_maxshield)
aspect = dir_tree_find(evoked, FIFF.FIFFB_IAS_ASPECT)
is_maxshield = True
if len(aspect) > 1:
logger.info('Multiple data aspects found. Taking first one.')
return aspect[0], is_maxshield
def _get_evoked_node(fname):
"""Get info in evoked file."""
f, tree, _ = fiff_open(fname)
with f as fid:
_, meas = read_meas_info(fid, tree, verbose=False)
evoked_node = dir_tree_find(meas, FIFF.FIFFB_EVOKED)
return evoked_node
def _check_evokeds_ch_names_times(all_evoked):
evoked = all_evoked[0]
ch_names = evoked.ch_names
for ii, ev in enumerate(all_evoked[1:]):
if ev.ch_names != ch_names:
if set(ev.ch_names) != set(ch_names):
raise ValueError(
"%s and %s do not contain the same channels." % (evoked,
ev))
else:
warn("Order of channels differs, reordering channels ...")
ev = ev.copy()
ev.reorder_channels(ch_names)
all_evoked[ii + 1] = ev
if not np.max(np.abs(ev.times - evoked.times)) < 1e-7:
raise ValueError("%s and %s do not contain the same time instants"
% (evoked, ev))
return all_evoked
def combine_evoked(all_evoked, weights):
"""Merge evoked data by weighted addition or subtraction.
Each `~mne.Evoked` in ``all_evoked`` should have the same channels and the
same time instants. Subtraction can be performed by passing
``weights=[1, -1]``.
.. Warning::
Other than cases like simple subtraction mentioned above (where all
weights are -1 or 1), if you provide numeric weights instead of using
``'equal'`` or ``'nave'``, the resulting `~mne.Evoked` object's
``.nave`` attribute (which is used to scale noise covariance when
applying the inverse operator) may not be suitable for inverse imaging.
Parameters
----------
all_evoked : list of Evoked
The evoked datasets.
weights : list of float | 'equal' | 'nave'
The weights to apply to the data of each evoked instance, or a string
describing the weighting strategy to apply: ``'nave'`` computes
sum-to-one weights proportional to each object's ``nave`` attribute;
``'equal'`` weights each `~mne.Evoked` by ``1 / len(all_evoked)``.
Returns
-------
evoked : Evoked
The new evoked data.
Notes
-----
.. versionadded:: 0.9.0
"""
naves = np.array([evk.nave for evk in all_evoked], float)
if isinstance(weights, str):
_check_option('weights', weights, ['nave', 'equal'])
if weights == 'nave':
weights = naves / naves.sum()
else:
weights = np.ones_like(naves) / len(naves)
else:
weights = np.array(weights, float)
if weights.ndim != 1 or weights.size != len(all_evoked):
raise ValueError('weights must be the same size as all_evoked')
# cf. https://en.wikipedia.org/wiki/Weighted_arithmetic_mean, section on
# "weighted sample variance". The variance of a weighted sample mean is:
#
# σ² = w₁² σ₁² + w₂² σ₂² + ... + wₙ² σₙ²
#
# We estimate the variance of each evoked instance as 1 / nave to get:
#
# σ² = w₁² / nave₁ + w₂² / nave₂ + ... + wₙ² / naveₙ
#
# And our resulting nave is the reciprocal of this:
new_nave = 1. / np.sum(weights ** 2 / naves)
# This general formula is equivalent to formulae in Matti's manual
# (pp 128-129), where:
# new_nave = sum(naves) when weights='nave' and
# new_nave = 1. / sum(1. / naves) when weights are all 1.
all_evoked = _check_evokeds_ch_names_times(all_evoked)
evoked = all_evoked[0].copy()
# use union of bad channels
bads = list(set(b for e in all_evoked for b in e.info['bads']))
evoked.info['bads'] = bads
evoked.data = sum(w * e.data for w, e in zip(weights, all_evoked))
evoked.nave = new_nave
comment = ''
for idx, (w, e) in enumerate(zip(weights, all_evoked)):
# pick sign
sign = '' if w >= 0 else '-'
# format weight
weight = '' if np.isclose(abs(w), 1.) else f'{abs(w):0.3f}'
# format multiplier
multiplier = ' × ' if weight else ''
# format comment
if e.comment is not None and ' + ' in e.comment: # multiple conditions
this_comment = f'({e.comment})'