/
evoked.py
1293 lines (1112 loc) · 48.7 KB
/
evoked.py
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# -*- coding: utf-8 -*-
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Matti Hamalainen <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
from .channels.channels import (ContainsMixin, UpdateChannelsMixin,
SetChannelsMixin, InterpolationMixin,
equalize_channels)
from .channels.layout import _merge_grad_data, _pair_grad_sensors
from .filter import detrend, FilterMixin
from .utils import (check_fname, logger, verbose, _time_mask, warn, sizeof_fmt,
SizeMixin, copy_function_doc_to_method_doc)
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 .externals.six import string_types
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 channel_type, pick_types, _pick_data_channels
from .io.meas_info import read_meas_info, write_meas_info
from .io.proj import ProjMixin
from .io.write import (start_file, start_block, end_file, end_block,
write_int, write_string, write_float_matrix,
write_id)
from .io.base import ToDataFrameMixin, TimeMixin, _check_maxshield
_aspect_dict = {'average': FIFF.FIFFV_ASPECT_AVERAGE,
'standard_error': FIFF.FIFFV_ASPECT_STD_ERR}
_aspect_rev = {str(FIFF.FIFFV_ASPECT_AVERAGE): 'average',
str(FIFF.FIFFV_ASPECT_STD_ERR): 'standard_error'}
class Evoked(ProjMixin, ContainsMixin, UpdateChannelsMixin,
SetChannelsMixin, InterpolationMixin, FilterMixin,
ToDataFrameMixin, TimeMixin, SizeMixin):
"""Evoked data.
Parameters
----------
fname : string
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 : bool, str, int, or None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
Attributes
----------
info : dict
Measurement info.
ch_names : list of string
List of channels' names.
nave : int
Number of averaged epochs.
kind : str
Type of data, either average or standard_error.
first : int
First time sample.
last : int
Last time sample.
comment : string
Comment on dataset. Can be the condition.
times : array
Array of time instants in seconds.
data : array of shape (n_channels, n_times)
Evoked response.
times : ndarray
Time vector in seconds. Goes from `tmin` to `tmax`. Time interval
between consecutive time samples is equal to the inverse of the
sampling frequency.
verbose : bool, str, int, or None.
See above.
Notes
-----
Evoked objects contain a single condition only.
"""
@verbose
def __init__(self, fname, condition=None, proj=True,
kind='average', allow_maxshield=False,
verbose=None): # noqa: D102
if not isinstance(proj, bool):
raise ValueError(r"'proj' must be 'True' or 'False'")
# Read the requested data
self.info, self.nave, self._aspect_kind, self.first, self.last, \
self.comment, self.times, self.data = _read_evoked(
fname, condition, kind, allow_maxshield)
self.kind = _aspect_rev.get(str(self._aspect_kind), 'Unknown')
self.verbose = verbose
self.preload = True
# project and baseline correct
if proj:
self.apply_proj()
@property
def data(self):
"""The data matrix."""
return self._data
@data.setter
def data(self, data):
"""Set the data matrix."""
self._data = data
@verbose
def apply_baseline(self, baseline=(None, 0), verbose=None):
"""Baseline correct evoked data.
Parameters
----------
baseline : tuple of length 2
The time interval to apply baseline correction. If None do not
apply it. If baseline is (a, b) the interval is between "a (s)" and
"b (s)". If a is None the beginning of the data is used and if b is
None then b is set to the end of the interval. If baseline is equal
to (None, None) all the time interval is used. Correction is
applied by computing mean of the baseline period and subtracting it
from the data. The baseline (a, b) includes both endpoints, i.e.
all timepoints t such that a <= t <= b.
verbose : bool, str, int, or None
If not None, override default verbose level (see
:func:`mne.verbose` and :ref:`Logging documentation <tut_logging>`
for more).
Returns
-------
evoked : instance of Evoked
The baseline-corrected Evoked object.
Notes
-----
Baseline correction can be done multiple times.
.. versionadded:: 0.13.0
"""
self.data = rescale(self.data, self.times, baseline, copy=False)
return self
def save(self, fname):
"""Save dataset to file.
Parameters
----------
fname : string
The name of the file, which should end with -ave.fif or
-ave.fif.gz.
Notes
-----
To write multiple conditions into a single file, use
:func:`mne.write_evokeds`.
"""
write_evokeds(fname, self)
def __repr__(self): # noqa: D105
_kind_swap = dict(average='mean', standard_error='SEM')
s = "'%s' (%s, N=%s)" % (self.comment, _kind_swap[self.kind],
self.nave)
s += ", [%0.5g, %0.5g] sec" % (self.times[0], self.times[-1])
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']
def crop(self, tmin=None, tmax=None):
"""Crop data to a given time interval.
Parameters
----------
tmin : float | None
Start time of selection in seconds.
tmax : float | None
End time of selection in seconds.
Returns
-------
evoked : instance of Evoked
The cropped Evoked object.
Notes
-----
Unlike Python slices, MNE time intervals include both their end points;
crop(tmin, tmax) returns the interval tmin <= t <= tmax.
"""
mask = _time_mask(self.times, tmin, tmax, sfreq=self.info['sfreq'])
self.times = self.times[mask]
self.first = int(self.times[0] * self.info['sfreq'])
self.last = len(self.times) + self.first - 1
self.data = self.data[:, mask]
return self
def decimate(self, decim, offset=0):
"""Decimate the evoked data.
.. note:: No filtering is performed. To avoid aliasing, ensure
your data are properly lowpassed.
Parameters
----------
decim : int
The amount to decimate data.
offset : int
Apply an offset to where the decimation starts relative to the
sample corresponding to t=0. The offset is in samples at the
current sampling rate.
Returns
-------
evoked : instance of Evoked
The decimated Evoked object.
See Also
--------
Epochs.decimate
Epochs.resample
mne.io.Raw.resample
Notes
-----
Decimation can be done multiple times. For example,
``evoked.decimate(2).decimate(2)`` will be the same as
``evoked.decimate(4)``.
.. versionadded:: 0.13.0
"""
decim, offset, new_sfreq = _check_decim(self.info, decim, offset)
start_idx = int(round(self.times[0] * (self.info['sfreq'] * decim)))
i_start = start_idx % decim + offset
decim_slice = slice(i_start, None, decim)
self.info['sfreq'] = new_sfreq
self.data = self.data[:, decim_slice].copy()
self.times = self.times[decim_slice].copy()
return self
def shift_time(self, tshift, relative=True):
"""Shift time scale in evoked data.
Parameters
----------
tshift : float
The amount of time shift to be applied if relative is True
else the first time point. When relative is True, positive value
of tshift moves the data forward while negative tshift moves it
backward.
relative : bool
If true, move the time backwards or forwards by specified amount.
Else, set the starting time point to the value of tshift.
Notes
-----
Maximum accuracy of time shift is 1 / evoked.info['sfreq']
"""
times = self.times
sfreq = self.info['sfreq']
offset = self.first if relative else 0
self.first = int(tshift * sfreq) + offset
self.last = self.first + len(times) - 1
self.times = np.arange(self.first, self.last + 1,
dtype=np.float) / sfreq
@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=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, 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=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)
@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, 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, show=show)
@copy_function_doc_to_method_doc(plot_evoked_topomap)
def plot_topomap(self, times="auto", ch_type=None, layout=None, vmin=None,
vmax=None, cmap=None, sensors=True, colorbar=True,
scalings=None, scaling_time=None, units=None, res=64,
size=1, cbar_fmt="%3.1f",
time_unit=None, time_format=None,
proj=False, show=True, show_names=False, title=None,
mask=None, mask_params=None, outlines='head',
contours=6, image_interp='bilinear', average=None,
head_pos=None, axes=None):
return plot_evoked_topomap(
self, times=times, ch_type=ch_type, layout=layout, vmin=vmin,
vmax=vmax, cmap=cmap, sensors=sensors, colorbar=colorbar,
scalings=scalings, scaling_time=scaling_time, 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, head_pos=head_pos,
axes=axes)
@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=1):
return plot_evoked_field(self, surf_maps, time=time,
time_label=time_label, n_jobs=n_jobs)
@copy_function_doc_to_method_doc(plot_evoked_white)
def plot_white(self, noise_cov, show=True, rank=None, time_unit=None,
verbose=None):
return plot_evoked_white(
self, noise_cov=noise_cov, rank=rank, show=show,
time_unit=time_unit, 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)
def animate_topomap(self, ch_type=None, times=None, frame_rate=None,
butterfly=False, blit=True, show=True, time_unit=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'.
If None, first available channel type from ('mag', 'grad', 'eeg')
is used. Defaults to None.
times : array of floats | 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
Returns
-------
fig : instance of matplotlib figure
The figure.
anim : instance of matplotlib 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)
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
-----
.. versionadded:: 0.9.0
"""
from .forward import _as_meg_type_evoked
return _as_meg_type_evoked(self, ch_type=ch_type, mode=mode)
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 : array-like of int | None
If None only MEG, EEG, SEEG, ECoG and fNIRS channels are detrended.
Returns
-------
evoked : instance of Evoked
The detrended evoked object.
"""
if picks is None:
picks = _pick_data_channels(self.info)
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
"""
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
out.comment = '-' + (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 : 'mag', 'grad', 'eeg', 'seeg', 'ecog', 'hbo', hbr', 'misc', None # noqa
The channel type to use. Defaults to None. If more than one sensor
Type is present in the data the channel type has to be explicitly
set.
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
"""
supported = ('mag', 'grad', 'eeg', 'seeg', 'ecog', 'misc', 'hbo',
'hbr', 'None')
data_picks = _pick_data_channels(self.info, with_ref_meg=False)
types_used = set([channel_type(self.info, idx) for idx in data_picks])
if str(ch_type) not in supported:
raise ValueError('Channel type must be `{supported}`. You gave me '
'`{ch_type}` instead.'
.format(ch_type=ch_type,
supported='` or `'.join(supported)))
elif ch_type is not None and ch_type not in types_used:
raise ValueError('Channel type `{ch_type}` not found in this '
'evoked object.'.format(ch_type=ch_type))
elif len(types_used) > 1 and ch_type is None:
raise RuntimeError('More than one sensor type found. `ch_type` '
'must not be `None`, pass a sensor type '
'value instead')
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 = 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 == 'ecog':
ecog = True
elif ch_type in ('hbo', 'hbr'):
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)
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_grad_data(data)
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
def _check_decim(info, decim, offset):
"""Check decimation parameters."""
if decim < 1 or decim != int(decim):
raise ValueError('decim must be an integer > 0')
decim = int(decim)
new_sfreq = info['sfreq'] / float(decim)
lowpass = info['lowpass']
if decim > 1 and lowpass is None:
warn('The measurement information indicates data is not low-pass '
'filtered. The decim=%i parameter will result in a sampling '
'frequency of %g Hz, which can cause aliasing artifacts.'
% (decim, new_sfreq))
elif decim > 1 and new_sfreq < 2.5 * lowpass:
warn('The measurement information indicates a low-pass frequency '
'of %g Hz. The decim=%i parameter will result in a sampling '
'frequency of %g Hz, which can cause aliasing artifacts.'
% (lowpass, decim, new_sfreq)) # > 50% nyquist lim
offset = int(offset)
if not 0 <= offset < decim:
raise ValueError('decim must be at least 0 and less than %s, got '
'%s' % (decim, offset))
return decim, offset, new_sfreq
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 : instance of Info
Info dictionary. Consider using ``create_info`` to populate
this structure.
tmin : float
Start time before event. Defaults to 0.
comment : string
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'.
verbose : bool, str, int, or None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
Notes
-----
Proper units of measure:
* V: eeg, eog, seeg, emg, ecg, bio, ecog
* T: mag
* T/m: grad
* M: hbo, hbr
* Am: dipole
* AU: misc
See Also
--------
EpochsArray, io.RawArray, create_info
"""
@verbose
def __init__(self, data, info, tmin=0., comment='', nave=1, kind='average',
verbose=None): # noqa: D102
dtype = np.complex128 if np.any(np.iscomplex(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)')
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
# XXX: this should use round and be tested
self.first = int(tmin * info['sfreq'])
self.last = self.first + np.shape(data)[-1] - 1
self.times = np.arange(self.first, self.last + 1,
dtype=np.float) / info['sfreq']
self.info = info.copy() # do not modify original info
self.nave = nave
self.kind = kind
self.comment = comment
self.picks = None
self.verbose = verbose
self.preload = True
self._projector = None
if not isinstance(self.kind, string_types):
raise TypeError('kind must be a string, not "%s"' % (type(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]
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.get(str(a), 'Unknown') 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_SMSH_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 grand_average(all_evoked, interpolate_bads=True):
"""Make grand average of a list evoked data.
The function interpolates bad channels based on `interpolate_bads`
parameter. If `interpolate_bads` is True, the grand average
file will contain good channels and the bad channels interpolated
from the good MEG/EEG channels.
The grand_average.nave attribute will be equal the number
of evoked datasets used to calculate the grand average.
Note: Grand average evoked shall not be used for source localization.
Parameters
----------
all_evoked : list of Evoked data
The evoked datasets.
interpolate_bads : bool
If True, bad MEG and EEG channels are interpolated.
Returns
-------
grand_average : Evoked
The grand average data.
Notes
-----
.. versionadded:: 0.9.0
"""
# check if all elements in the given list are evoked data
if not all(isinstance(e, Evoked) for e in all_evoked):
raise ValueError("Not all the elements in list are evoked data")
# Copy channels to leave the original evoked datasets intact.
all_evoked = [e.copy() for e in all_evoked]
# Interpolates if necessary
if interpolate_bads:
all_evoked = [e.interpolate_bads() if len(e.info['bads']) > 0
else e for e in all_evoked]
equalize_channels(all_evoked) # apply equalize_channels
# make grand_average object using combine_evoked
grand_average = combine_evoked(all_evoked, weights='equal')
# change the grand_average.nave to the number of Evokeds
grand_average.nave = len(all_evoked)
# change comment field
grand_average.comment = "Grand average (n = %d)" % grand_average.nave
return grand_average
def _check_evokeds_ch_names_times(all_evoked):
evoked = all_evoked[0]
ch_names = evoked.ch_names
for ev in all_evoked[1:]:
if ev.ch_names != ch_names:
raise ValueError(
"%s and %s do not contain the same channels" % (evoked, 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))
def combine_evoked(all_evoked, weights):
"""Merge evoked data by weighted addition or subtraction.
Data should have the same channels and the same time instants.
Subtraction can be performed by passing negative weights (e.g., [1, -1]).
Parameters
----------
all_evoked : list of Evoked
The evoked datasets.
weights : list of float | str
The weights to apply to the data of each evoked instance.
Can also be ``'nave'`` to weight according to evoked.nave,
or ``"equal"`` to use equal weighting (each weighted as ``1/N``).
Returns
-------
evoked : Evoked
The new evoked data.
Notes
-----
.. versionadded:: 0.9.0
"""
evoked = all_evoked[0].copy()
if isinstance(weights, string_types):
if weights not in ('nave', 'equal'):
raise ValueError('weights must be a list of float, or "nave" or '
'"equal"')
if weights == 'nave':
weights = np.array([e.nave for e in all_evoked], float)
weights /= weights.sum()
else: # == 'equal'
weights = [1. / len(all_evoked)] * len(all_evoked)
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')
_check_evokeds_ch_names_times(all_evoked)
# use union of bad channels
bads = list(set(evoked.info['bads']).union(*(ev.info['bads']
for ev in all_evoked[1:])))
evoked.info['bads'] = bads
evoked.data = sum(w * e.data for w, e in zip(weights, all_evoked))
# We should set nave based on how variances change when summing Gaussian
# random variables. From:
#
# https://en.wikipedia.org/wiki/Weighted_arithmetic_mean
#
# We know that the variance of a weighted sample mean is:
#
# σ^2 = w_1^2 σ_1^2 + w_2^2 σ_2^2 + ... + w_n^2 σ_n^2
#
# We estimate the variance of each evoked instance as 1 / nave to get:
#
# σ^2 = w_1^2 / nave_1 + w_2^2 / nave_2 + ... + w_n^2 / nave_n
#
# And our resulting nave is the reciprocal of this:
evoked.nave = max(int(round(
1. / sum(w ** 2 / e.nave for w, e in zip(weights, all_evoked)))), 1)
evoked.comment = ' + '.join('%0.3f * %s' % (w, e.comment or 'unknown')
for w, e in zip(weights, all_evoked))
return evoked
@verbose
def read_evokeds(fname, condition=None, baseline=None, kind='average',
proj=True, allow_maxshield=False, verbose=None):
"""Read evoked dataset(s).
Parameters
----------
fname : string
The file name, which should end with -ave.fif or -ave.fif.gz.
condition : int or str | list of int or str | None
The index or list of indices of the evoked dataset to read. FIF files
can contain multiple datasets. If None, all datasets are returned as a
list.
baseline : None (default) or tuple of length 2
The time interval to apply baseline correction. If None do not apply
it. If baseline is (a, b) the interval is between "a (s)" and "b (s)".
If a is None the beginning of the data is used and if b is None then b
is set to the end of the interval. If baseline is equal to (None, None)
all the time interval is used. Correction is applied by computing mean
of the baseline period and subtracting it from the data. The baseline
(a, b) includes both endpoints, i.e. all timepoints t such that
a <= t <= b.
kind : str
Either 'average' or 'standard_error', the type of data to read.
proj : bool
If False, available projectors won't be applied to the data.
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 : bool, str, int, or None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
Returns
-------
evoked : Evoked (if condition is int or str) or list of Evoked (if
condition is None or list)
The evoked dataset(s).
See Also
--------
write_evokeds
"""
check_fname(fname, 'evoked', ('-ave.fif', '-ave.fif.gz',
'_ave.fif', '_ave.fif.gz'))
logger.info('Reading %s ...' % fname)
return_list = True
if condition is None:
evoked_node = _get_evoked_node(fname)
condition = range(len(evoked_node))
elif not isinstance(condition, list):
condition = [condition]
return_list = False
out = [Evoked(fname, c, kind=kind, proj=proj,
allow_maxshield=allow_maxshield,
verbose=verbose).apply_baseline(baseline)
for c in condition]
return out if return_list else out[0]
def _read_evoked(fname, condition=None, kind='average', allow_maxshield=False):
"""Read evoked data from a FIF file."""
if fname is None:
raise ValueError('No evoked filename specified')
f, tree, _ = fiff_open(fname)
with f as fid:
# Read the measurement info
info, meas = read_meas_info(fid, tree, clean_bads=True)
# Locate the data of interest
processed = dir_tree_find(meas, FIFF.FIFFB_PROCESSED_DATA)
if len(processed) == 0:
raise ValueError('Could not find processed data')
evoked_node = dir_tree_find(meas, FIFF.FIFFB_EVOKED)
if len(evoked_node) == 0:
raise ValueError('Could not find evoked data')
# find string-based entry
if isinstance(condition, string_types):
if kind not in _aspect_dict.keys():
raise ValueError('kind must be "average" or '
'"standard_error"')
comments, aspect_kinds, t = _get_entries(fid, evoked_node,
allow_maxshield)
goods = (np.in1d(comments, [condition]) &