/
channels.py
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channels.py
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# 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>
# Teon Brooks <teon.brooks@gmail.com>
# Daniel McCloy <dan.mccloy@gmail.com>
#
# License: BSD (3-clause)
import os
import os.path as op
import sys
import numpy as np
from scipy import sparse
from ..defaults import HEAD_SIZE_DEFAULT
from ..utils import (verbose, logger, warn, copy_function_doc_to_method_doc,
_check_preload, _validate_type, fill_doc, _check_option)
from ..io.compensator import get_current_comp
from ..io.constants import FIFF
from ..io.meas_info import anonymize_info, Info, MontageMixin
from ..io.pick import (channel_type, pick_info, pick_types, _picks_by_type,
_check_excludes_includes, _contains_ch_type,
channel_indices_by_type, pick_channels, _picks_to_idx,
_get_channel_types)
def _get_meg_system(info):
"""Educated guess for the helmet type based on channels."""
have_helmet = True
for ch in info['chs']:
if ch['kind'] == FIFF.FIFFV_MEG_CH:
# Only take first 16 bits, as higher bits store CTF grad comp order
coil_type = ch['coil_type'] & 0xFFFF
if coil_type == FIFF.FIFFV_COIL_NM_122:
system = '122m'
break
elif coil_type // 1000 == 3: # All Vectorview coils are 30xx
system = '306m'
break
elif (coil_type == FIFF.FIFFV_COIL_MAGNES_MAG or
coil_type == FIFF.FIFFV_COIL_MAGNES_GRAD):
nmag = np.sum([c['kind'] == FIFF.FIFFV_MEG_CH
for c in info['chs']])
system = 'Magnes_3600wh' if nmag > 150 else 'Magnes_2500wh'
break
elif coil_type == FIFF.FIFFV_COIL_CTF_GRAD:
system = 'CTF_275'
break
elif coil_type == FIFF.FIFFV_COIL_KIT_GRAD:
system = 'KIT'
break
elif coil_type == FIFF.FIFFV_COIL_BABY_GRAD:
system = 'BabySQUID'
break
elif coil_type == FIFF.FIFFV_COIL_ARTEMIS123_GRAD:
system = 'ARTEMIS123'
have_helmet = False
break
else:
system = 'unknown'
have_helmet = False
return system, have_helmet
def _get_ch_type(inst, ch_type, allow_ref_meg=False):
"""Choose a single channel type (usually for plotting).
Usually used in plotting to plot a single datatype, e.g. look for mags,
then grads, then ... to plot.
"""
if ch_type is None:
allowed_types = ['mag', 'grad', 'planar1', 'planar2', 'eeg', 'csd',
'fnirs_raw', 'fnirs_od', 'hbo', 'hbr', 'ecog', 'seeg']
allowed_types += ['ref_meg'] if allow_ref_meg else []
for type_ in allowed_types:
if isinstance(inst, Info):
if _contains_ch_type(inst, type_):
ch_type = type_
break
elif type_ in inst:
ch_type = type_
break
else:
raise RuntimeError('No plottable channel types found')
return ch_type
@verbose
def equalize_channels(instances, copy=True, verbose=None):
"""Equalize channel picks and ordering across multiple MNE-Python objects.
First, all channels that are not common to each object are dropped. Then,
using the first object in the list as a template, the channels of each
object are re-ordered to match the template. The end result is that all
given objects define the same channels, in the same order.
Parameters
----------
instances : list
A list of MNE-Python objects to equalize the channels for. Objects can
be of type Raw, Epochs, Evoked, AverageTFR, Forward, Covariance,
CrossSpectralDensity or Info.
copy : bool
When dropping and/or re-ordering channels, an object will be copied
when this parameter is set to ``True``. When set to ``False`` (the
default) the dropping and re-ordering of channels happens in-place.
.. versionadded:: 0.20.0
%(verbose)s
Returns
-------
equalized_instances : list
A list of MNE-Python objects that have the same channels defined in the
same order.
Notes
-----
This function operates inplace.
"""
from ..cov import Covariance
from ..io.base import BaseRaw
from ..io.meas_info import Info
from ..epochs import BaseEpochs
from ..evoked import Evoked
from ..forward import Forward
from ..time_frequency import _BaseTFR, CrossSpectralDensity
# Instances need to have a `ch_names` attribute and a `pick_channels`
# method that supports `ordered=True`.
allowed_types = (BaseRaw, BaseEpochs, Evoked, _BaseTFR, Forward,
Covariance, CrossSpectralDensity, Info)
allowed_types_str = ("Raw, Epochs, Evoked, TFR, Forward, Covariance, "
"CrossSpectralDensity or Info")
for inst in instances:
_validate_type(inst, allowed_types, "Instances to be modified",
allowed_types_str)
chan_template = instances[0].ch_names
logger.info('Identifying common channels ...')
channels = [set(inst.ch_names) for inst in instances]
common_channels = set(chan_template).intersection(*channels)
all_channels = set(chan_template).union(*channels)
dropped = list(set(all_channels - common_channels))
# Preserve the order of chan_template
order = np.argsort([chan_template.index(ch) for ch in common_channels])
common_channels = np.array(list(common_channels))[order].tolist()
# Update all instances to match the common_channels list
reordered = False
equalized_instances = []
for inst in instances:
# Only perform picking when needed
if inst.ch_names != common_channels:
if copy:
inst = inst.copy()
inst.pick_channels(common_channels, ordered=True)
if len(inst.ch_names) == len(common_channels):
reordered = True
equalized_instances.append(inst)
if dropped:
logger.info('Dropped the following channels:\n%s' % dropped)
elif reordered:
logger.info('Channels have been re-ordered.')
return equalized_instances
class ContainsMixin(object):
"""Mixin class for Raw, Evoked, Epochs."""
def __contains__(self, ch_type):
"""Check channel type membership.
Parameters
----------
ch_type : str
Channel type to check for. Can be e.g. 'meg', 'eeg', 'stim', etc.
Returns
-------
in : bool
Whether or not the instance contains the given channel type.
Examples
--------
Channel type membership can be tested as::
>>> 'meg' in inst # doctest: +SKIP
True
>>> 'seeg' in inst # doctest: +SKIP
False
"""
if ch_type == 'meg':
has_ch_type = (_contains_ch_type(self.info, 'mag') or
_contains_ch_type(self.info, 'grad'))
else:
has_ch_type = _contains_ch_type(self.info, ch_type)
return has_ch_type
@property
def compensation_grade(self):
"""The current gradient compensation grade."""
return get_current_comp(self.info)
@fill_doc
def get_channel_types(self, picks=None, unique=False, only_data_chs=False):
"""Get a list of channel type for each channel.
Parameters
----------
%(picks_all)s
unique : bool
Whether to return only unique channel types. Default is ``False``.
only_data_chs : bool
Whether to ignore non-data channels. Default is ``False``.
Returns
-------
channel_types : list
The channel types.
"""
return _get_channel_types(self.info, picks=picks, unique=unique,
only_data_chs=only_data_chs)
# XXX Eventually de-duplicate with _kind_dict of mne/io/meas_info.py
_human2fiff = {'ecg': FIFF.FIFFV_ECG_CH,
'eeg': FIFF.FIFFV_EEG_CH,
'emg': FIFF.FIFFV_EMG_CH,
'eog': FIFF.FIFFV_EOG_CH,
'exci': FIFF.FIFFV_EXCI_CH,
'ias': FIFF.FIFFV_IAS_CH,
'misc': FIFF.FIFFV_MISC_CH,
'resp': FIFF.FIFFV_RESP_CH,
'seeg': FIFF.FIFFV_SEEG_CH,
'stim': FIFF.FIFFV_STIM_CH,
'syst': FIFF.FIFFV_SYST_CH,
'bio': FIFF.FIFFV_BIO_CH,
'ecog': FIFF.FIFFV_ECOG_CH,
'fnirs_raw': FIFF.FIFFV_FNIRS_CH,
'fnirs_od': FIFF.FIFFV_FNIRS_CH,
'hbo': FIFF.FIFFV_FNIRS_CH,
'hbr': FIFF.FIFFV_FNIRS_CH}
_human2unit = {'ecg': FIFF.FIFF_UNIT_V,
'eeg': FIFF.FIFF_UNIT_V,
'emg': FIFF.FIFF_UNIT_V,
'eog': FIFF.FIFF_UNIT_V,
'exci': FIFF.FIFF_UNIT_NONE,
'ias': FIFF.FIFF_UNIT_NONE,
'misc': FIFF.FIFF_UNIT_V,
'resp': FIFF.FIFF_UNIT_NONE,
'seeg': FIFF.FIFF_UNIT_V,
'stim': FIFF.FIFF_UNIT_NONE,
'syst': FIFF.FIFF_UNIT_NONE,
'bio': FIFF.FIFF_UNIT_V,
'ecog': FIFF.FIFF_UNIT_V,
'fnirs_raw': FIFF.FIFF_UNIT_V,
'fnirs_od': FIFF.FIFF_UNIT_NONE,
'hbo': FIFF.FIFF_UNIT_MOL,
'hbr': FIFF.FIFF_UNIT_MOL}
_unit2human = {FIFF.FIFF_UNIT_V: 'V',
FIFF.FIFF_UNIT_T: 'T',
FIFF.FIFF_UNIT_T_M: 'T/m',
FIFF.FIFF_UNIT_MOL: 'M',
FIFF.FIFF_UNIT_NONE: 'NA',
FIFF.FIFF_UNIT_CEL: 'C'}
def _check_set(ch, projs, ch_type):
"""Ensure type change is compatible with projectors."""
new_kind = _human2fiff[ch_type]
if ch['kind'] != new_kind:
for proj in projs:
if ch['ch_name'] in proj['data']['col_names']:
raise RuntimeError('Cannot change channel type for channel %s '
'in projector "%s"'
% (ch['ch_name'], proj['desc']))
ch['kind'] = new_kind
class SetChannelsMixin(MontageMixin):
"""Mixin class for Raw, Evoked, Epochs."""
@verbose
def set_eeg_reference(self, ref_channels='average', projection=False,
ch_type='auto', verbose=None):
"""Specify which reference to use for EEG data.
Use this function to explicitly specify the desired reference for EEG.
This can be either an existing electrode or a new virtual channel.
This function will re-reference the data according to the desired
reference.
Parameters
----------
ref_channels : list of str | str
The name(s) of the channel(s) used to construct the reference. To
apply an average reference, specify ``'average'`` here (default).
If an empty list is specified, the data is assumed to already have
a proper reference and MNE will not attempt any re-referencing of
the data. Defaults to an average reference.
projection : bool
If ``ref_channels='average'`` this argument specifies if the
average reference should be computed as a projection (True) or not
(False; default). If ``projection=True``, the average reference is
added as a projection and is not applied to the data (it can be
applied afterwards with the ``apply_proj`` method). If
``projection=False``, the average reference is directly applied to
the data. If ``ref_channels`` is not ``'average'``, ``projection``
must be set to ``False`` (the default in this case).
ch_type : 'auto' | 'eeg' | 'ecog' | 'seeg'
The name of the channel type to apply the reference to. If 'auto',
the first channel type of eeg, ecog or seeg that is found (in that
order) will be selected.
.. versionadded:: 0.19
%(verbose_meth)s
Returns
-------
inst : instance of Raw | Epochs | Evoked
Data with EEG channels re-referenced. If ``ref_channels='average'``
and ``projection=True`` a projection will be added instead of
directly re-referencing the data.
%(set_eeg_reference_see_also_notes)s
"""
from ..io.reference import set_eeg_reference
return set_eeg_reference(self, ref_channels=ref_channels, copy=False,
projection=projection, ch_type=ch_type)[0]
def _get_channel_positions(self, picks=None):
"""Get channel locations from info.
Parameters
----------
picks : str | list | slice | None
None gets good data indices.
Notes
-----
.. versionadded:: 0.9.0
"""
picks = _picks_to_idx(self.info, picks)
chs = self.info['chs']
pos = np.array([chs[k]['loc'][:3] for k in picks])
n_zero = np.sum(np.sum(np.abs(pos), axis=1) == 0)
if n_zero > 1: # XXX some systems have origin (0, 0, 0)
raise ValueError('Could not extract channel positions for '
'{} channels'.format(n_zero))
return pos
def _set_channel_positions(self, pos, names):
"""Update channel locations in info.
Parameters
----------
pos : array-like | np.ndarray, shape (n_points, 3)
The channel positions to be set.
names : list of str
The names of the channels to be set.
Notes
-----
.. versionadded:: 0.9.0
"""
if len(pos) != len(names):
raise ValueError('Number of channel positions not equal to '
'the number of names given.')
pos = np.asarray(pos, dtype=np.float)
if pos.shape[-1] != 3 or pos.ndim != 2:
msg = ('Channel positions must have the shape (n_points, 3) '
'not %s.' % (pos.shape,))
raise ValueError(msg)
for name, p in zip(names, pos):
if name in self.ch_names:
idx = self.ch_names.index(name)
self.info['chs'][idx]['loc'][:3] = p
else:
msg = ('%s was not found in the info. Cannot be updated.'
% name)
raise ValueError(msg)
@verbose
def set_channel_types(self, mapping, verbose=None):
"""Define the sensor type of channels.
Parameters
----------
mapping : dict
A dictionary mapping a channel to a sensor type (str), e.g.,
``{'EEG061': 'eog'}``.
%(verbose_meth)s
Returns
-------
inst : instance of Raw | Epochs | Evoked
The instance (modified in place).
.. versionchanged:: 0.20
Return the instance.
Notes
-----
The following sensor types are accepted:
ecg, eeg, emg, eog, exci, ias, misc, resp, seeg, stim, syst, ecog,
hbo, hbr, fnirs_raw, fnirs_od
.. versionadded:: 0.9.0
"""
ch_names = self.info['ch_names']
# first check and assemble clean mappings of index and name
unit_changes = dict()
for ch_name, ch_type in mapping.items():
if ch_name not in ch_names:
raise ValueError("This channel name (%s) doesn't exist in "
"info." % ch_name)
c_ind = ch_names.index(ch_name)
if ch_type not in _human2fiff:
raise ValueError('This function cannot change to this '
'channel type: %s. Accepted channel types '
'are %s.'
% (ch_type,
", ".join(sorted(_human2unit.keys()))))
# Set sensor type
_check_set(self.info['chs'][c_ind], self.info['projs'], ch_type)
unit_old = self.info['chs'][c_ind]['unit']
unit_new = _human2unit[ch_type]
if unit_old not in _unit2human:
raise ValueError("Channel '%s' has unknown unit (%s). Please "
"fix the measurement info of your data."
% (ch_name, unit_old))
if unit_old != _human2unit[ch_type]:
this_change = (_unit2human[unit_old], _unit2human[unit_new])
if this_change not in unit_changes:
unit_changes[this_change] = list()
unit_changes[this_change].append(ch_name)
self.info['chs'][c_ind]['unit'] = _human2unit[ch_type]
if ch_type in ['eeg', 'seeg', 'ecog']:
coil_type = FIFF.FIFFV_COIL_EEG
elif ch_type == 'hbo':
coil_type = FIFF.FIFFV_COIL_FNIRS_HBO
elif ch_type == 'hbr':
coil_type = FIFF.FIFFV_COIL_FNIRS_HBR
elif ch_type == 'fnirs_raw':
coil_type = FIFF.FIFFV_COIL_FNIRS_RAW
elif ch_type == 'fnirs_od':
coil_type = FIFF.FIFFV_COIL_FNIRS_OD
else:
coil_type = FIFF.FIFFV_COIL_NONE
self.info['chs'][c_ind]['coil_type'] = coil_type
msg = "The unit for channel(s) {0} has changed from {1} to {2}."
for this_change, names in unit_changes.items():
warn(msg.format(", ".join(sorted(names)), *this_change))
return self
def rename_channels(self, mapping):
"""Rename channels.
Parameters
----------
mapping : dict | callable
A dictionary mapping the old channel to a new channel name
e.g. {'EEG061' : 'EEG161'}. Can also be a callable function
that takes and returns a string (new in version 0.10.0).
Returns
-------
inst : instance of Raw | Epochs | Evoked
The instance (modified in place).
.. versionchanged:: 0.20
Return the instance.
Notes
-----
.. versionadded:: 0.9.0
"""
rename_channels(self.info, mapping)
return self
@verbose
def plot_sensors(self, kind='topomap', ch_type=None, title=None,
show_names=False, ch_groups=None, to_sphere=True,
axes=None, block=False, show=True, sphere=None,
verbose=None):
"""Plot sensor positions.
Parameters
----------
kind : str
Whether to plot the sensors as 3d, topomap or as an interactive
sensor selection dialog. Available options 'topomap', '3d',
'select'. If 'select', a set of channels can be selected
interactively by using lasso selector or clicking while holding
control key. The selected channels are returned along with the
figure instance. Defaults to 'topomap'.
ch_type : None | str
The channel type to plot. Available options 'mag', 'grad', 'eeg',
'seeg', 'ecog', 'all'. If ``'all'``, all the available mag, grad,
eeg, seeg and ecog channels are plotted. If None (default), then
channels are chosen in the order given above.
title : str | None
Title for the figure. If None (default), equals to ``'Sensor
positions (%%s)' %% ch_type``.
show_names : bool | array of str
Whether to display all channel names. If an array, only the channel
names in the array are shown. Defaults to False.
ch_groups : 'position' | array of shape (n_ch_groups, n_picks) | None
Channel groups for coloring the sensors. If None (default), default
coloring scheme is used. If 'position', the sensors are divided
into 8 regions. See ``order`` kwarg of :func:`mne.viz.plot_raw`. If
array, the channels are divided by picks given in the array.
.. versionadded:: 0.13.0
to_sphere : bool
Whether to project the 3d locations to a sphere. When False, the
sensor array appears similar as to looking downwards straight above
the subject's head. Has no effect when kind='3d'. Defaults to True.
.. versionadded:: 0.14.0
axes : instance of Axes | instance of Axes3D | None
Axes to draw the sensors to. If ``kind='3d'``, axes must be an
instance of Axes3D. If None (default), a new axes will be created.
.. versionadded:: 0.13.0
block : bool
Whether to halt program execution until the figure is closed.
Defaults to False.
.. versionadded:: 0.13.0
show : bool
Show figure if True. Defaults to True.
%(topomap_sphere_auto)s
%(verbose_meth)s
Returns
-------
fig : instance of Figure
Figure containing the sensor topography.
selection : list
A list of selected channels. Only returned if ``kind=='select'``.
See Also
--------
mne.viz.plot_layout
Notes
-----
This function plots the sensor locations from the info structure using
matplotlib. For drawing the sensors using mayavi see
:func:`mne.viz.plot_alignment`.
.. versionadded:: 0.12.0
"""
from ..viz.utils import plot_sensors
return plot_sensors(self.info, kind=kind, ch_type=ch_type, title=title,
show_names=show_names, ch_groups=ch_groups,
to_sphere=to_sphere, axes=axes, block=block,
show=show, sphere=sphere, verbose=verbose)
@copy_function_doc_to_method_doc(anonymize_info)
def anonymize(self, daysback=None, keep_his=False, verbose=None):
"""
.. versionadded:: 0.13.0
"""
anonymize_info(self.info, daysback=daysback, keep_his=keep_his,
verbose=verbose)
self.set_meas_date(self.info['meas_date']) # unify annot update
return self
def set_meas_date(self, meas_date):
"""Set the measurement start date.
Parameters
----------
meas_date : datetime | float | tuple | None
The new measurement date.
If datetime object, it must be timezone-aware and in UTC.
A tuple of (seconds, microseconds) or float (alias for
``(meas_date, 0)``) can also be passed and a datetime
object will be automatically created. If None, will remove
the time reference.
Returns
-------
inst : instance of Raw | Epochs | Evoked
The modified raw instance. Operates in place.
See Also
--------
mne.io.Raw.anonymize
Notes
-----
If you want to remove all time references in the file, call
:func:`mne.io.anonymize_info(inst.info) <mne.io.anonymize_info>`
after calling ``inst.set_meas_date(None)``.
.. versionadded:: 0.20
"""
from ..annotations import _handle_meas_date
meas_date = _handle_meas_date(meas_date)
self.info['meas_date'] = meas_date
if hasattr(self, 'annotations'):
self.annotations._orig_time = meas_date
return self
class UpdateChannelsMixin(object):
"""Mixin class for Raw, Evoked, Epochs, AverageTFR."""
@verbose
def pick_types(self, meg=True, eeg=False, stim=False, eog=False,
ecg=False, emg=False, ref_meg='auto', misc=False,
resp=False, chpi=False, exci=False, ias=False, syst=False,
seeg=False, dipole=False, gof=False, bio=False, ecog=False,
fnirs=False, csd=False, include=(), exclude='bads',
selection=None, verbose=None):
"""Pick some channels by type and names.
Parameters
----------
meg : bool | str
If True include all MEG channels. If False include None.
If string it can be 'mag', 'grad', 'planar1' or 'planar2' to select
only magnetometers, all gradiometers, or a specific type of
gradiometer.
eeg : bool
If True include EEG channels.
stim : bool
If True include stimulus channels.
eog : bool
If True include EOG channels.
ecg : bool
If True include ECG channels.
emg : bool
If True include EMG channels.
ref_meg : bool | str
If True include CTF / 4D reference channels. If 'auto', the
reference channels are only included if compensations are present.
misc : bool
If True include miscellaneous analog channels.
resp : bool
If True include response-trigger channel. For some MEG systems this
is separate from the stim channel.
chpi : bool
If True include continuous HPI coil channels.
exci : bool
Flux excitation channel used to be a stimulus channel.
ias : bool
Internal Active Shielding data (maybe on Triux only).
syst : bool
System status channel information (on Triux systems only).
seeg : bool
Stereotactic EEG channels.
dipole : bool
Dipole time course channels.
gof : bool
Dipole goodness of fit channels.
bio : bool
Bio channels.
ecog : bool
Electrocorticography channels.
fnirs : bool | str
Functional near-infrared spectroscopy channels. If True include all
fNIRS channels. If False (default) include none. If string it can
be 'hbo' (to include channels measuring oxyhemoglobin) or 'hbr' (to
include channels measuring deoxyhemoglobin).
csd : bool
EEG-CSD channels.
include : list of str
List of additional channels to include. If empty do not include
any.
exclude : list of str | str
List of channels to exclude. If 'bads' (default), exclude channels
in ``info['bads']``.
selection : list of str
Restrict sensor channels (MEG, EEG) to this list of channel names.
%(verbose_meth)s
Returns
-------
inst : instance of Raw, Epochs, or Evoked
The modified instance.
See Also
--------
pick_channels
Notes
-----
.. versionadded:: 0.9.0
"""
idx = pick_types(
self.info, meg=meg, eeg=eeg, stim=stim, eog=eog, ecg=ecg, emg=emg,
ref_meg=ref_meg, misc=misc, resp=resp, chpi=chpi, exci=exci,
ias=ias, syst=syst, seeg=seeg, dipole=dipole, gof=gof, bio=bio,
ecog=ecog, fnirs=fnirs, include=include, exclude=exclude,
selection=selection)
return self._pick_drop_channels(idx)
def pick_channels(self, ch_names, ordered=False):
"""Pick some channels.
Parameters
----------
ch_names : list
The list of channels to select.
ordered : bool
If True (default False), ensure that the order of the channels in
the modified instance matches the order of ``ch_names``.
.. versionadded:: 0.20.0
Returns
-------
inst : instance of Raw, Epochs, or Evoked
The modified instance.
See Also
--------
drop_channels
pick_types
reorder_channels
Notes
-----
The channel names given are assumed to be a set, i.e. the order
does not matter. The original order of the channels is preserved.
You can use ``reorder_channels`` to set channel order if necessary.
.. versionadded:: 0.9.0
"""
return self._pick_drop_channels(
pick_channels(self.info['ch_names'], ch_names, ordered=ordered))
@fill_doc
def pick(self, picks, exclude=()):
"""Pick a subset of channels.
Parameters
----------
%(picks_all)s
exclude : list | str
Set of channels to exclude, only used when picking based on
types (e.g., exclude="bads" when picks="meg").
Returns
-------
inst : instance of Raw, Epochs, or Evoked
The modified instance.
"""
picks = _picks_to_idx(self.info, picks, 'all', exclude,
allow_empty=False)
return self._pick_drop_channels(picks)
def reorder_channels(self, ch_names):
"""Reorder channels.
Parameters
----------
ch_names : list
The desired channel order.
Returns
-------
inst : instance of Raw, Epochs, or Evoked
The modified instance.
See Also
--------
drop_channels
pick_types
pick_channels
Notes
-----
Channel names must be unique. Channels that are not in ``ch_names``
are dropped.
.. versionadded:: 0.16.0
"""
_check_excludes_includes(ch_names)
idx = list()
for ch_name in ch_names:
ii = self.ch_names.index(ch_name)
if ii in idx:
raise ValueError('Channel name repeated: %s' % (ch_name,))
idx.append(ii)
return self._pick_drop_channels(idx)
def drop_channels(self, ch_names):
"""Drop channel(s).
Parameters
----------
ch_names : iterable or str
Iterable (e.g. list) of channel name(s) or channel name to remove.
Returns
-------
inst : instance of Raw, Epochs, or Evoked
The modified instance.
See Also
--------
reorder_channels
pick_channels
pick_types
Notes
-----
.. versionadded:: 0.9.0
"""
if isinstance(ch_names, str):
ch_names = [ch_names]
try:
all_str = all([isinstance(ch, str) for ch in ch_names])
except TypeError:
raise ValueError("'ch_names' must be iterable, got "
"type {} ({}).".format(type(ch_names), ch_names))
if not all_str:
raise ValueError("Each element in 'ch_names' must be str, got "
"{}.".format([type(ch) for ch in ch_names]))
missing = [ch for ch in ch_names if ch not in self.ch_names]
if len(missing) > 0:
msg = "Channel(s) {0} not found, nothing dropped."
raise ValueError(msg.format(", ".join(missing)))
bad_idx = [self.ch_names.index(ch) for ch in ch_names
if ch in self.ch_names]
idx = np.setdiff1d(np.arange(len(self.ch_names)), bad_idx)
return self._pick_drop_channels(idx)
def _pick_drop_channels(self, idx):
# avoid circular imports
from ..time_frequency import AverageTFR, EpochsTFR
_check_preload(self, 'adding, dropping, or reordering channels')
if getattr(self, 'picks', None) is not None:
self.picks = self.picks[idx]
if hasattr(self, '_cals'):
self._cals = self._cals[idx]
pick_info(self.info, idx, copy=False)
if getattr(self, '_projector', None) is not None:
self._projector = self._projector[idx][:, idx]
# All others (Evoked, Epochs, Raw) have chs axis=-2
axis = -3 if isinstance(self, (AverageTFR, EpochsTFR)) else -2
self._data = self._data.take(idx, axis=axis)
return self
def add_channels(self, add_list, force_update_info=False):
"""Append new channels to the instance.
Parameters
----------
add_list : list
A list of objects to append to self. Must contain all the same
type as the current object.
force_update_info : bool
If True, force the info for objects to be appended to match the
values in `self`. This should generally only be used when adding
stim channels for which important metadata won't be overwritten.
.. versionadded:: 0.12
Returns
-------
inst : instance of Raw, Epochs, or Evoked
The modified instance.
See Also
--------
drop_channels
Notes
-----
If ``self`` is a Raw instance that has been preloaded into a
:obj:`numpy.memmap` instance, the memmap will be resized.
"""
# avoid circular imports
from ..io import BaseRaw, _merge_info
from ..epochs import BaseEpochs
_validate_type(add_list, (list, tuple), 'Input')
# Object-specific checks
for inst in add_list + [self]:
_check_preload(inst, "adding channels")
if isinstance(self, BaseRaw):
con_axis = 0
comp_class = BaseRaw
elif isinstance(self, BaseEpochs):
con_axis = 1
comp_class = BaseEpochs
else:
con_axis = 0
comp_class = type(self)
for inst in add_list:
_validate_type(inst, comp_class, 'All input')
data = [inst._data for inst in [self] + add_list]
# Make sure that all dimensions other than channel axis are the same
compare_axes = [i for i in range(data[0].ndim) if i != con_axis]
shapes = np.array([dat.shape for dat in data])[:, compare_axes]
for shape in shapes:
if not ((shapes[0] - shape) == 0).all():
raise AssertionError('All data dimensions except channels '
'must match, got %s != %s'
% (shapes[0], shape))
del shapes
# Create final data / info objects
infos = [self.info] + [inst.info for inst in add_list]
new_info = _merge_info(infos, force_update_to_first=force_update_info)
# Now update the attributes
if isinstance(self._data, np.memmap) and con_axis == 0 and \
sys.platform != 'darwin': # resizing not available--no mremap
# Use a resize and fill in other ones
out_shape = (sum(d.shape[0] for d in data),) + data[0].shape[1:]
n_bytes = np.prod(out_shape) * self._data.dtype.itemsize
self._data.flush()
self._data.base.resize(n_bytes)
self._data = np.memmap(self._data.filename, mode='r+',
dtype=self._data.dtype, shape=out_shape)
assert self._data.shape == out_shape
assert self._data.nbytes == n_bytes
offset = len(data[0])
for d in data[1:]:
this_len = len(d)
self._data[offset:offset + this_len] = d
offset += this_len
else:
self._data = np.concatenate(data, axis=con_axis)
self.info = new_info
if isinstance(self, BaseRaw):
self._cals = np.concatenate([getattr(inst, '_cals')
for inst in [self] + add_list])
return self
class InterpolationMixin(object):
"""Mixin class for Raw, Evoked, Epochs."""
@verbose
def interpolate_bads(self, reset_bads=True, mode='accurate',
origin='auto', verbose=None):
"""Interpolate bad MEG and EEG channels.
Operates in place.
Parameters
----------
reset_bads : bool
If True, remove the bads from info.
mode : str
Either ``'accurate'`` or ``'fast'``, determines the quality of the
Legendre polynomial expansion used for interpolation of MEG
channels.
origin : array-like, shape (3,) | str
Origin of the sphere in the head coordinate frame and in meters.
Can be ``'auto'`` (default), which means a head-digitization-based
origin fit.
.. versionadded:: 0.17
%(verbose_meth)s
Returns
-------
inst : instance of Raw, Epochs, or Evoked
The modified instance.
Notes
-----
.. versionadded:: 0.9.0
"""
from ..bem import _check_origin
from .interpolation import _interpolate_bads_eeg, _interpolate_bads_meg
_check_preload(self, "interpolation")
if len(self.info['bads']) == 0: