def _interpolate_bads_meg_fast(inst,
picks,
mode='accurate',
dots=None,
verbose=False):
"""Interpolate bad channels from data in good channels."""
# We can have pre-picked instances or not.
# And we need to handle it.
inst_picked = True
if len(inst.ch_names) > len(picks):
picked_info = pick_info(inst.info, picks)
dots = _pick_dots(dots, picks, picks)
inst_picked = False
else:
picked_info = inst.info.copy()
def get_picks_bad_good(info, picks_meg):
picks_good = [
p for p in picks_meg if info['ch_names'][p] not in info['bads']
]
# select the bad meg channel to be interpolated
if len(info['bads']) == 0:
picks_bad = []
else:
picks_bad = [
p for p in picks_meg if info['ch_names'][p] in info['bads']
]
return picks_meg, picks_good, picks_bad
picks_meg, picks_good, picks_bad = get_picks_bad_good(
picked_info, range(picked_info['nchan']))
# return without doing anything if there are no meg channels
if len(picks_meg) == 0 or len(picks_bad) == 0:
return
# we need to make sure that only meg channels are passed here
# as the MNE interpolation code is not fogriving.
# This is why we picked the info.
mapping = _fast_map_meg_channels(picked_info,
pick_from=picks_good,
pick_to=picks_bad,
dots=dots,
mode=mode)
# the downside is that the mapping matrix now does not match
# the unpicked info of the data.
# Since we may have picked the info, we need to double map
# the indices.
_, picks_good_, picks_bad_orig = get_picks_bad_good(inst.info, picks)
ch_names_a = [picked_info['ch_names'][pp] for pp in picks_bad]
ch_names_b = [inst.info['ch_names'][pp] for pp in picks_bad_orig]
assert ch_names_a == ch_names_b
if not inst_picked:
picks_good_ = [pp for pp in picks if pp in picks_good_]
assert len(picks_good_) == len(picks_good)
ch_names_a = [picked_info['ch_names'][pp] for pp in picks_good]
ch_names_b = [inst.info['ch_names'][pp] for pp in picks_good_]
assert ch_names_a == ch_names_b
_do_interp_dots(inst, mapping, picks_good_, picks_bad_orig)
def _interpolate_bads_eeg(inst, picks=None, verbose=None):
""" Interpolate bad EEG channels.
Operates in place.
Parameters
----------
inst : mne.io.Raw, mne.Epochs or mne.Evoked
The data to interpolate. Must be preloaded.
picks: np.ndarray, shape(n_channels, ) | list | None
The channel indices to be used for interpolation.
"""
from mne.bem import _fit_sphere
from mne.utils import logger, warn
from mne.channels.interpolation import _do_interp_dots
from mne.channels.interpolation import _make_interpolation_matrix
import numpy as np
if picks is None:
picks = pick_types(inst.info, meg=False, eeg=True, exclude=[])
bads_idx = np.zeros(len(inst.ch_names), dtype=np.bool)
goods_idx = np.zeros(len(inst.ch_names), dtype=np.bool)
inst.info._check_consistency()
bads_idx[picks] = [inst.ch_names[ch] in inst.info['bads'] for ch in picks]
if len(picks) == 0 or bads_idx.sum() == 0:
return
goods_idx[picks] = True
goods_idx[bads_idx] = False
pos = inst._get_channel_positions(picks)
# Make sure only good EEG are used
bads_idx_pos = bads_idx[picks]
goods_idx_pos = goods_idx[picks]
pos_good = pos[goods_idx_pos]
pos_bad = pos[bads_idx_pos]
# test spherical fit
radius, center = _fit_sphere(pos_good)
distance = np.sqrt(np.sum((pos_good - center)**2, 1))
distance = np.mean(distance / radius)
if np.abs(1. - distance) > 0.1:
warn('Your spherical fit is poor, interpolation results are '
'likely to be inaccurate.')
logger.info('Computing interpolation matrix from {0} sensor '
'positions'.format(len(pos_good)))
interpolation = _make_interpolation_matrix(pos_good, pos_bad)
logger.info('Interpolating {0} sensors'.format(len(pos_bad)))
_do_interp_dots(inst, interpolation, goods_idx, bads_idx)
def _interpolate_bads_eeg(inst, picks=None, verbose=None):
""" Interpolate bad EEG channels.
Operates in place.
Parameters
----------
inst : mne.io.Raw, mne.Epochs or mne.Evoked
The data to interpolate. Must be preloaded.
picks: np.ndarray, shape(n_channels, ) | list | None
The channel indices to be used for interpolation.
"""
from mne.bem import _fit_sphere
from mne.utils import logger, warn
from mne.channels.interpolation import _do_interp_dots
from mne.channels.interpolation import _make_interpolation_matrix
import numpy as np
if picks is None:
picks = pick_types(inst.info, meg=False, eeg=True, exclude=[])
bads_idx = np.zeros(len(inst.ch_names), dtype=np.bool)
goods_idx = np.zeros(len(inst.ch_names), dtype=np.bool)
inst.info._check_consistency()
bads_idx[picks] = [inst.ch_names[ch] in inst.info['bads'] for ch in picks]
if len(picks) == 0 or bads_idx.sum() == 0:
return
goods_idx[picks] = True
goods_idx[bads_idx] = False
pos = inst._get_channel_positions(picks)
# Make sure only good EEG are used
bads_idx_pos = bads_idx[picks]
goods_idx_pos = goods_idx[picks]
pos_good = pos[goods_idx_pos]
pos_bad = pos[bads_idx_pos]
# test spherical fit
radius, center = _fit_sphere(pos_good)
distance = np.sqrt(np.sum((pos_good - center) ** 2, 1))
distance = np.mean(distance / radius)
if np.abs(1. - distance) > 0.1:
warn('Your spherical fit is poor, interpolation results are '
'likely to be inaccurate.')
logger.info('Computing interpolation matrix from {0} sensor '
'positions'.format(len(pos_good)))
interpolation = _make_interpolation_matrix(pos_good, pos_bad)
logger.info('Interpolating {0} sensors'.format(len(pos_bad)))
_do_interp_dots(inst, interpolation, goods_idx, bads_idx)
def _interpolate_bads_meg_fast(inst, picks, mode='accurate',
dots=None, verbose=None):
"""Interpolate bad channels from data in good channels."""
# We can have pre-picked instances or not.
# And we need to handle it.
inst_picked = True
if len(inst.ch_names) > len(picks):
picked_info = pick_info(inst.info, picks)
dots = _pick_dots(dots, picks, picks)
inst_picked = False
else:
picked_info = inst.info.copy()
def get_picks_bad_good(info, picks_meg):
picks_good = [p for p in picks_meg
if info['ch_names'][p] not in info['bads']]
# select the bad meg channel to be interpolated
if len(info['bads']) == 0:
picks_bad = []
else:
picks_bad = [p for p in picks_meg
if info['ch_names'][p] in info['bads']]
return picks_meg, picks_good, picks_bad
picks_meg, picks_good, picks_bad = get_picks_bad_good(
picked_info, range(picked_info['nchan']))
# return without doing anything if there are no meg channels
if len(picks_meg) == 0 or len(picks_bad) == 0:
return
# we need to make sure that only meg channels are passed here
# as the MNE interpolation code is not fogriving.
# This is why we picked the info.
mapping = _fast_map_meg_channels(
picked_info, pick_from=picks_good, pick_to=picks_bad,
dots=dots, mode=mode)
# the downside is that the mapping matrix now does not match
# the unpicked info of the data.
# Since we may have picked the info, we need to double map
# the indices.
_, picks_good_, picks_bad_orig = get_picks_bad_good(
inst.info, picks)
ch_names_a = [picked_info['ch_names'][pp] for pp in picks_bad]
ch_names_b = [inst.info['ch_names'][pp] for pp in picks_bad_orig]
assert ch_names_a == ch_names_b
if not inst_picked:
picks_good_ = [pp for pp in picks if pp in picks_good_]
assert len(picks_good_) == len(picks_good)
ch_names_a = [picked_info['ch_names'][pp] for pp in picks_good]
ch_names_b = [inst.info['ch_names'][pp] for pp in picks_good_]
assert ch_names_a == ch_names_b
_do_interp_dots(inst, mapping, picks_good_, picks_bad_orig)
def _interpolate_bads_meg(inst, mode='accurate', origin=None, verbose=None):
"""Interpolate bad channels from data in good channels.
Parameters
----------
inst : mne.io.Raw, mne.Epochs or mne.Evoked
The data to interpolate. Must be preloaded.
mode : str
Either `'accurate'` or `'fast'`, determines the quality of the
Legendre polynomial expansion used for interpolation. `'fast'` should
be sufficient for most applications.
origin : None | list
If None, origin is set to sensor center of mass, otherwise use the
coordinates provided as origin. The old standard value is (0., 0., 0.04)
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).
"""
from mne.channels.interpolation import _do_interp_dots
picks_meg = pick_types(inst.info, meg=True, eeg=False, exclude=[])
picks_good = pick_types(inst.info, meg=True, eeg=False, exclude='bads')
meg_ch_names = [inst.info['ch_names'][p] for p in picks_meg]
bads_meg = [ch for ch in inst.info['bads'] if ch in meg_ch_names]
# select the bad meg channel to be interpolated
if len(bads_meg) == 0:
picks_bad = []
else:
picks_bad = pick_channels(inst.info['ch_names'], bads_meg, exclude=[])
# return without doing anything if there are no meg channels
if len(picks_meg) == 0 or len(picks_bad) == 0:
return
info_from = pick_info(inst.info, picks_good)
info_to = pick_info(inst.info, picks_bad)
if check_version('mne', min_version='0.21'):
from mne.forward import _map_meg_or_eeg_channels
mapping = _map_meg_or_eeg_channels(info_from,
info_to,
mode=mode,
origin=origin)
else:
from mne.forward import _map_meg_channels
mapping = _map_meg_channels(info_from,
info_to,
mode=mode,
origin=origin)
_do_interp_dots(inst, mapping, picks_good, picks_bad)
def _interpolate_bads_meg_fast(inst, mode='accurate', verbose=None):
"""Interpolate bad channels from data in good channels.
"""
from mne.channels.interpolation import _do_interp_dots
from mne import pick_types, pick_channels
picks_meg = pick_types(inst.info, meg=True, eeg=False, exclude=[])
ch_names = [inst.info['ch_names'][p] for p in picks_meg]
picks_good = pick_types(inst.info, meg=True, eeg=False, exclude='bads')
# select the bad meg channel to be interpolated
if len(inst.info['bads']) == 0:
picks_bad = []
else:
picks_bad = pick_channels(ch_names, inst.info['bads'], exclude=[])
# return without doing anything if there are no meg channels
if len(picks_meg) == 0 or len(picks_bad) == 0:
return
mapping = _fast_map_meg_channels(inst, picks_good, picks_bad, mode=mode)
_do_interp_dots(inst, mapping, picks_good, picks_bad)
def _interpolate_bads_meg_fast(inst, mode='accurate', verbose=None):
"""Interpolate bad channels from data in good channels.
"""
from mne.channels.interpolation import _do_interp_dots
from mne import pick_types, pick_channels
picks_meg = pick_types(inst.info, meg=True, eeg=False, exclude=[])
ch_names = [inst.info['ch_names'][p] for p in picks_meg]
picks_good = pick_types(inst.info, meg=True, eeg=False, exclude='bads')
# select the bad meg channel to be interpolated
if len(inst.info['bads']) == 0:
picks_bad = []
else:
picks_bad = pick_channels(ch_names, inst.info['bads'],
exclude=[])
# return without doing anything if there are no meg channels
if len(picks_meg) == 0 or len(picks_bad) == 0:
return
mapping = _fast_map_meg_channels(inst, picks_good, picks_bad, mode=mode)
_do_interp_dots(inst, mapping, picks_good, picks_bad)
def _interpolate_bads_eeg(inst, picks=None, verbose=False):
""" Interpolate bad EEG channels.
Operates in place.
Parameters
----------
inst : mne.io.Raw, mne.Epochs or mne.Evoked
The data to interpolate. Must be preloaded.
picks : str | list | slice | None
Channels to include for interpolation. Slices and lists of integers
will be interpreted as channel indices. In lists, channel *name*
strings (e.g., ``['EEG 01', 'EEG 02']``) will pick the given channels.
None (default) will pick all EEG channels. Note that channels in
``info['bads']`` *will be included* if their names or indices are
explicitly provided.
"""
from mne.bem import _fit_sphere
from mne.utils import logger, warn
from mne.channels.interpolation import _do_interp_dots
from mne.channels.interpolation import _make_interpolation_matrix
import numpy as np
inst.info._check_consistency()
if picks is None:
picks = pick_types(inst.info, meg=False, eeg=True, exclude=[])
else:
picks = _handle_picks(inst.info, picks)
bads_idx = np.zeros(len(inst.ch_names), dtype=np.bool)
goods_idx = np.zeros(len(inst.ch_names), dtype=np.bool)
bads_idx[picks] = [inst.ch_names[ch] in inst.info['bads'] for ch in picks]
if len(picks) == 0 or bads_idx.sum() == 0:
return
goods_idx[picks] = True
goods_idx[bads_idx] = False
pos = inst._get_channel_positions(picks)
# Make sure only good EEG are used
bads_idx_pos = bads_idx[picks]
goods_idx_pos = goods_idx[picks]
pos_good = pos[goods_idx_pos]
pos_bad = pos[bads_idx_pos]
# test spherical fit
radius, center = _fit_sphere(pos_good)
distance = np.sqrt(np.sum((pos_good - center)**2, 1))
distance = np.mean(distance / radius)
if np.abs(1. - distance) > 0.1:
warn('Your spherical fit is poor, interpolation results are '
'likely to be inaccurate.')
logger.info('Computing interpolation matrix from {0} sensor '
'positions'.format(len(pos_good)))
interpolation = _make_interpolation_matrix(pos_good, pos_bad)
logger.info('Interpolating {0} sensors'.format(len(pos_bad)))
_do_interp_dots(inst, interpolation, goods_idx, bads_idx)
def _interpolate_bads_meg(inst, mode='accurate', origin=None, verbose=None):
"""Interpolate bad channels from data in good channels.
Parameters
----------
inst : mne.io.Raw, mne.Epochs or mne.Evoked
The data to interpolate. Must be preloaded.
mode : str
Either `'accurate'` or `'fast'`, determines the quality of the
Legendre polynomial expansion used for interpolation. `'fast'` should
be sufficient for most applications.
origin : None | list
If None, origin is set to sensor center of mass, otherwise use the
coordinates provided as origin. The old standard value is (0., 0., 0.04)
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).
"""
picks_meg = pick_types(inst.info, meg=True, eeg=False, exclude=[])
picks_good = pick_types(inst.info, meg=True, eeg=False, exclude='bads')
meg_ch_names = [inst.info['ch_names'][p] for p in picks_meg]
bads_meg = [ch for ch in inst.info['bads'] if ch in meg_ch_names]
# select the bad meg channel to be interpolated
if len(bads_meg) == 0:
picks_bad = []
else:
picks_bad = pick_channels(inst.info['ch_names'], bads_meg, exclude=[])
# return without doing anything if there are no meg channels
if len(picks_meg) == 0 or len(picks_bad) == 0:
return
info_from = pick_info(inst.info, picks_good)
info_to = pick_info(inst.info, picks_bad)
if origin is None:
posvec = np.array([inst.info['chs'][p]['loc'][0:3] for p in picks_meg])
norvec = np.array(
[inst.info['chs'][p]['loc'][9:12] for p in picks_meg])
cogpos = np.mean(posvec, axis=0)
norsum = np.mean(norvec, axis=0)
anorm = np.sqrt(np.dot(norsum, norsum.T))
ndir = norsum / anorm
# push the position slightly (4cm) away from the helmet:
altpos = cogpos - 0.04 * ndir
print(">_interpolate_bads_meg\\DBG> cog(sens) = [%8.5f %8.5f %8.5f]" % \
(cogpos[0], cogpos[1], cogpos[2]))
print(">_interpolate_bads_meg\\DBG> alt(sens) = [%8.5f %8.5f %8.5f]" % \
(altpos[0], altpos[1], altpos[2]))
cogposhd = apply_trans(inst.info['dev_head_t']['trans'],
cogpos,
move=True)
altposhd = apply_trans(inst.info['dev_head_t']['trans'],
altpos,
move=True)
print(">_interpolate_bads_meg\\DBG> cog(hdcs) = [%8.5f %8.5f %8.5f]" % \
(cogposhd[0], cogposhd[1], cogposhd[2]))
print(">_interpolate_bads_meg\\DBG> alt(hdcs) = [%8.5f %8.5f %8.5f]" % \
(altposhd[0], altposhd[1], altposhd[2]))
print(">_interpolate_bads_meg\\DBG> calling _map_meg_channels(..., origin=(%8.5f %8.5f %8.5f))" % \
(altposhd[0], altposhd[1], altposhd[2]))
origin = (altposhd[0], altposhd[1], altposhd[2])
else:
origin = origin
mapping = _map_meg_channels(info_from, info_to, mode=mode, origin=origin)
_do_interp_dots(inst, mapping, picks_good, picks_bad)
