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tyler
2025-08-19 09:13:22 -07:00
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mne/__init__.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""MNE software for MEG and EEG data analysis."""
# PEP0440 compatible formatted version, see:
# https://www.python.org/dev/peps/pep-0440/
#
# Generic release markers:
# X.Y
# X.Y.Z # For bugfix releases
#
# Admissible pre-release markers:
# X.YaN # Alpha release
# X.YbN # Beta release
# X.YrcN # Release Candidate
# X.Y # Final release
#
# Dev branch marker is: 'X.Y.devN' where N is an integer.
#
import lazy_loader as lazy
try:
from importlib.metadata import version
__version__ = version("mne")
except Exception:
__version__ = "0.0.0"
(__getattr__, __dir__, __all__) = lazy.attach_stub(__name__, __file__)
# initialize logging
from .utils import set_log_level, set_log_file
set_log_level(None, False)
set_log_file()

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__all__ = [
"AcqParserFIF",
"Annotations",
"BaseEpochs",
"BiHemiLabel",
"Covariance",
"Dipole",
"DipoleFixed",
"Epochs",
"EpochsArray",
"Evoked",
"EvokedArray",
"Forward",
"Info",
"Label",
"MixedSourceEstimate",
"MixedVectorSourceEstimate",
"Projection",
"Report",
"SourceEstimate",
"SourceMorph",
"SourceSpaces",
"Transform",
"VectorSourceEstimate",
"VolSourceEstimate",
"VolVectorSourceEstimate",
"add_reference_channels",
"add_source_space_distances",
"annotations_from_events",
"apply_forward",
"apply_forward_raw",
"average_forward_solutions",
"beamformer",
"channel_indices_by_type",
"channel_type",
"channels",
"chpi",
"combine_evoked",
"commands",
"compute_covariance",
"compute_proj_epochs",
"compute_proj_evoked",
"compute_proj_raw",
"compute_rank",
"compute_raw_covariance",
"compute_source_morph",
"concatenate_epochs",
"concatenate_events",
"concatenate_raws",
"convert_forward_solution",
"coreg",
"count_annotations",
"count_events",
"create_default_subject",
"create_info",
"cuda",
"datasets",
"decimate_surface",
"decoding",
"defaults",
"dig_mri_distances",
"dipole",
"epochs",
"equalize_channels",
"event",
"events_from_annotations",
"export",
"extract_label_time_course",
"filter",
"find_events",
"find_layout",
"find_stim_steps",
"fit_dipole",
"forward",
"get_config",
"get_config_path",
"get_head_surf",
"get_meg_helmet_surf",
"get_montage_volume_labels",
"get_volume_labels_from_aseg",
"get_volume_labels_from_src",
"grade_to_tris",
"grade_to_vertices",
"grand_average",
"grow_labels",
"gui",
"head_to_mni",
"head_to_mri",
"inverse_sparse",
"io",
"label_sign_flip",
"labels_to_stc",
"make_ad_hoc_cov",
"make_bem_model",
"make_bem_solution",
"make_field_map",
"make_fixed_length_epochs",
"make_fixed_length_events",
"make_forward_dipole",
"make_forward_solution",
"make_sphere_model",
"match_channel_orders",
"merge_events",
"minimum_norm",
"morph_labels",
"morph_source_spaces",
"open_docs",
"open_report",
"parse_config",
"pick_channels",
"pick_channels_cov",
"pick_channels_forward",
"pick_channels_regexp",
"pick_events",
"pick_info",
"pick_types",
"pick_types_forward",
"preprocessing",
"random_parcellation",
"read_annotations",
"read_bem_solution",
"read_bem_surfaces",
"read_cov",
"read_dipole",
"read_epochs",
"read_epochs_eeglab",
"read_epochs_fieldtrip",
"read_epochs_kit",
"read_events",
"read_evoked_besa",
"read_evoked_fieldtrip",
"read_evokeds",
"read_evokeds_mff",
"read_forward_solution",
"read_freesurfer_lut",
"read_label",
"read_labels_from_annot",
"read_lta",
"read_morph_map",
"read_proj",
"read_reject_parameters",
"read_source_estimate",
"read_source_morph",
"read_source_spaces",
"read_surface",
"read_talxfm",
"read_trans",
"read_tri",
"read_vectorview_selection",
"rename_channels",
"report",
"scale_bem",
"scale_labels",
"scale_mri",
"scale_source_space",
"sensitivity_map",
"set_bipolar_reference",
"set_cache_dir",
"set_config",
"set_eeg_reference",
"set_log_file",
"set_log_level",
"set_memmap_min_size",
"setup_source_space",
"setup_volume_source_space",
"simulation",
"source_space",
"spatial_dist_adjacency",
"spatial_inter_hemi_adjacency",
"spatial_src_adjacency",
"spatial_tris_adjacency",
"spatio_temporal_dist_adjacency",
"spatio_temporal_src_adjacency",
"spatio_temporal_tris_adjacency",
"split_label",
"stats",
"stc_near_sensors",
"stc_to_label",
"surface",
"sys_info",
"time_frequency",
"transform_surface_to",
"use_coil_def",
"use_log_level",
"verbose",
"vertex_to_mni",
"viz",
"what",
"whiten_evoked",
"write_bem_solution",
"write_bem_surfaces",
"write_cov",
"write_events",
"write_evokeds",
"write_forward_solution",
"write_head_bem",
"write_label",
"write_labels_to_annot",
"write_proj",
"write_source_spaces",
"write_surface",
"write_trans",
]
from . import (
beamformer,
channels,
chpi,
commands,
coreg,
cuda,
datasets,
decoding,
defaults,
dipole,
epochs,
event,
export,
filter,
forward,
gui,
inverse_sparse,
io,
minimum_norm,
preprocessing,
report,
simulation,
source_space,
stats,
surface,
time_frequency,
viz,
)
from ._fiff.meas_info import Info, create_info
from ._fiff.pick import (
channel_indices_by_type,
channel_type,
pick_channels,
pick_channels_cov,
pick_channels_forward,
pick_channels_regexp,
pick_info,
pick_types,
pick_types_forward,
)
from ._fiff.proj import Projection
from ._fiff.reference import (
add_reference_channels,
set_bipolar_reference,
set_eeg_reference,
)
from ._fiff.what import what
from ._freesurfer import (
get_volume_labels_from_aseg,
head_to_mni,
head_to_mri,
read_freesurfer_lut,
read_lta,
read_talxfm,
vertex_to_mni,
)
from .annotations import (
Annotations,
annotations_from_events,
count_annotations,
events_from_annotations,
read_annotations,
)
from .bem import (
make_bem_model,
make_bem_solution,
make_sphere_model,
read_bem_solution,
read_bem_surfaces,
write_bem_solution,
write_bem_surfaces,
write_head_bem,
)
from .channels import (
equalize_channels,
find_layout,
read_vectorview_selection,
rename_channels,
)
from .coreg import (
create_default_subject,
scale_bem,
scale_labels,
scale_mri,
scale_source_space,
)
from .cov import (
Covariance,
compute_covariance,
compute_raw_covariance,
make_ad_hoc_cov,
read_cov,
whiten_evoked,
write_cov,
)
from .dipole import Dipole, DipoleFixed, fit_dipole, read_dipole
from .epochs import (
BaseEpochs,
Epochs,
EpochsArray,
concatenate_epochs,
make_fixed_length_epochs,
read_epochs,
)
from .event import (
AcqParserFIF,
concatenate_events,
count_events,
find_events,
find_stim_steps,
make_fixed_length_events,
merge_events,
pick_events,
read_events,
write_events,
)
from .evoked import Evoked, EvokedArray, combine_evoked, read_evokeds, write_evokeds
from .forward import (
Forward,
apply_forward,
apply_forward_raw,
average_forward_solutions,
convert_forward_solution,
make_field_map,
make_forward_dipole,
make_forward_solution,
read_forward_solution,
use_coil_def,
write_forward_solution,
)
from .io import (
read_epochs_fieldtrip,
read_evoked_besa,
read_evoked_fieldtrip,
read_evokeds_mff,
)
from .io.base import concatenate_raws, match_channel_orders
from .io.eeglab import read_epochs_eeglab
from .io.kit import read_epochs_kit
from .label import (
BiHemiLabel,
Label,
grow_labels,
label_sign_flip,
labels_to_stc,
morph_labels,
random_parcellation,
read_label,
read_labels_from_annot,
split_label,
stc_to_label,
write_label,
write_labels_to_annot,
)
from .misc import parse_config, read_reject_parameters
from .morph import (
SourceMorph,
compute_source_morph,
grade_to_vertices,
read_source_morph,
)
from .morph_map import read_morph_map
from .proj import (
compute_proj_epochs,
compute_proj_evoked,
compute_proj_raw,
read_proj,
sensitivity_map,
write_proj,
)
from .rank import compute_rank
from .report import Report, open_report
from .source_estimate import (
MixedSourceEstimate,
MixedVectorSourceEstimate,
SourceEstimate,
VectorSourceEstimate,
VolSourceEstimate,
VolVectorSourceEstimate,
extract_label_time_course,
grade_to_tris,
read_source_estimate,
spatial_dist_adjacency,
spatial_inter_hemi_adjacency,
spatial_src_adjacency,
spatial_tris_adjacency,
spatio_temporal_dist_adjacency,
spatio_temporal_src_adjacency,
spatio_temporal_tris_adjacency,
stc_near_sensors,
)
from .source_space._source_space import (
SourceSpaces,
add_source_space_distances,
get_volume_labels_from_src,
morph_source_spaces,
read_source_spaces,
setup_source_space,
setup_volume_source_space,
write_source_spaces,
)
from .surface import (
decimate_surface,
dig_mri_distances,
get_head_surf,
get_meg_helmet_surf,
get_montage_volume_labels,
read_surface,
read_tri,
write_surface,
)
from .transforms import Transform, read_trans, transform_surface_to, write_trans
from .utils import (
get_config,
get_config_path,
grand_average,
open_docs,
set_cache_dir,
set_config,
set_log_file,
set_log_level,
set_memmap_min_size,
sys_info,
use_log_level,
verbose,
)

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from .commands.utils import main
if __name__ == "__main__":
main()

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mne/_fiff/__init__.py Normal file
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"""Private module for FIF basic I/O routines."""
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
# All imports should be done directly to submodules, so we don't import
# anything here or use lazy_loader.

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import heapq
from collections import Counter
import numpy as np
from ..utils import Bunch, _check_fname, _validate_type, logger, verbose, warn
from .constants import FIFF, _coord_frame_named
from .tag import read_tag
from .tree import dir_tree_find
from .write import _safe_name_list, start_and_end_file, write_dig_points
_dig_kind_dict = {
"cardinal": FIFF.FIFFV_POINT_CARDINAL,
"hpi": FIFF.FIFFV_POINT_HPI,
"eeg": FIFF.FIFFV_POINT_EEG,
"extra": FIFF.FIFFV_POINT_EXTRA,
}
_dig_kind_ints = tuple(sorted(_dig_kind_dict.values()))
_dig_kind_proper = {
"cardinal": "Cardinal",
"hpi": "HPI",
"eeg": "EEG",
"extra": "Extra",
"unknown": "Unknown",
}
_dig_kind_rev = {val: key for key, val in _dig_kind_dict.items()}
_cardinal_kind_rev = {1: "LPA", 2: "Nasion", 3: "RPA", 4: "Inion"}
def _format_dig_points(dig, enforce_order=False):
"""Format the dig points nicely."""
if enforce_order and dig is not None:
# reorder points based on type:
# Fiducials/HPI, EEG, extra (headshape)
fids_digpoints = []
hpi_digpoints = []
eeg_digpoints = []
extra_digpoints = []
head_digpoints = []
# use a heap to enforce order on FIDS, EEG, Extra
for idx, digpoint in enumerate(dig):
ident = digpoint["ident"]
kind = digpoint["kind"]
# push onto heap based on 'ident' (for the order) for
# each of the possible DigPoint 'kind's
# keep track of 'idx' in case of any clashes in
# the 'ident' variable, which can occur when
# user passes in DigMontage + DigMontage
if kind == FIFF.FIFFV_POINT_CARDINAL:
heapq.heappush(fids_digpoints, (ident, idx, digpoint))
elif kind == FIFF.FIFFV_POINT_HPI:
heapq.heappush(hpi_digpoints, (ident, idx, digpoint))
elif kind == FIFF.FIFFV_POINT_EEG:
heapq.heappush(eeg_digpoints, (ident, idx, digpoint))
elif kind == FIFF.FIFFV_POINT_EXTRA:
heapq.heappush(extra_digpoints, (ident, idx, digpoint))
elif kind == FIFF.FIFFV_POINT_HEAD:
heapq.heappush(head_digpoints, (ident, idx, digpoint))
# now recreate dig based on sorted order
fids_digpoints.sort(), hpi_digpoints.sort()
eeg_digpoints.sort()
extra_digpoints.sort(), head_digpoints.sort()
new_dig = []
for idx, d in enumerate(
fids_digpoints
+ hpi_digpoints
+ extra_digpoints
+ eeg_digpoints
+ head_digpoints
):
new_dig.append(d[-1])
dig = new_dig
return [DigPoint(d) for d in dig] if dig is not None else dig
def _get_dig_eeg(dig):
return [d for d in dig if d["kind"] == FIFF.FIFFV_POINT_EEG]
def _count_points_by_type(dig):
"""Get the number of points of each type."""
occurrences = Counter([d["kind"] for d in dig])
return dict(
fid=occurrences[FIFF.FIFFV_POINT_CARDINAL],
hpi=occurrences[FIFF.FIFFV_POINT_HPI],
eeg=occurrences[FIFF.FIFFV_POINT_EEG],
extra=occurrences[FIFF.FIFFV_POINT_EXTRA],
)
_dig_keys = {"kind", "ident", "r", "coord_frame"}
class DigPoint(dict):
"""Container for a digitization point.
This is a simple subclass of the standard dict type designed to provide
a readable string representation.
Parameters
----------
kind : int
The kind of channel,
e.g. ``FIFFV_POINT_EEG``, ``FIFFV_POINT_CARDINAL``.
r : array, shape (3,)
3D position in m. and coord_frame.
ident : int
Number specifying the identity of the point.
e.g. ``FIFFV_POINT_NASION`` if kind is ``FIFFV_POINT_CARDINAL``,
or 42 if kind is ``FIFFV_POINT_EEG``.
coord_frame : int
The coordinate frame used, e.g. ``FIFFV_COORD_HEAD``.
"""
def __repr__(self): # noqa: D105
from ..transforms import _coord_frame_name
if self["kind"] == FIFF.FIFFV_POINT_CARDINAL:
id_ = _cardinal_kind_rev.get(self["ident"], "Unknown cardinal")
else:
id_ = _dig_kind_proper[_dig_kind_rev.get(self["kind"], "unknown")]
id_ = f"{id_} #{self['ident']}"
id_ = id_.rjust(10)
cf = _coord_frame_name(self["coord_frame"])
x, y, z = self["r"]
if "voxel" in cf:
pos = (f"({x:0.1f}, {y:0.1f}, {z:0.1f})").ljust(25)
else:
pos = (f"({x * 1e3:0.1f}, {y * 1e3:0.1f}, {z * 1e3:0.1f}) mm").ljust(25)
return f"<DigPoint | {id_} : {pos} : {cf} frame>"
# speed up info copy by only deep copying the mutable item
def __deepcopy__(self, memodict):
"""Make a deepcopy."""
return DigPoint(
kind=self["kind"],
r=self["r"].copy(),
ident=self["ident"],
coord_frame=self["coord_frame"],
)
def __eq__(self, other): # noqa: D105
"""Compare two DigPoints.
Two digpoints are equal if they are the same kind, share the same
coordinate frame and position.
"""
my_keys = ["kind", "ident", "coord_frame"]
if set(self.keys()) != set(other.keys()):
return False
elif any(self[_] != other[_] for _ in my_keys):
return False
else:
return np.allclose(self["r"], other["r"])
def _read_dig_fif(fid, meas_info, *, return_ch_names=False):
"""Read digitizer data from a FIFF file."""
isotrak = dir_tree_find(meas_info, FIFF.FIFFB_ISOTRAK)
dig = None
ch_names = None
if len(isotrak) == 0:
logger.info("Isotrak not found")
elif len(isotrak) > 1:
warn("Multiple Isotrak found")
else:
isotrak = isotrak[0]
coord_frame = FIFF.FIFFV_COORD_HEAD
dig = []
for k in range(isotrak["nent"]):
kind = isotrak["directory"][k].kind
pos = isotrak["directory"][k].pos
if kind == FIFF.FIFF_DIG_POINT:
tag = read_tag(fid, pos)
dig.append(tag.data)
elif kind == FIFF.FIFF_MNE_COORD_FRAME:
tag = read_tag(fid, pos)
coord_frame = _coord_frame_named.get(int(tag.data.item()))
elif kind == FIFF.FIFF_MNE_CH_NAME_LIST:
tag = read_tag(fid, pos)
ch_names = _safe_name_list(tag.data, "read", "ch_names")
for d in dig:
d["coord_frame"] = coord_frame
out = _format_dig_points(dig)
if return_ch_names:
out = (out, ch_names)
return out
@verbose
def write_dig(
fname, pts, coord_frame=None, *, ch_names=None, overwrite=False, verbose=None
):
"""Write digitization data to a FIF file.
Parameters
----------
fname : path-like
Destination file name.
pts : iterator of dict
Iterator through digitizer points. Each point is a dictionary with
the keys 'kind', 'ident' and 'r'.
coord_frame : int | str | None
If all the points have the same coordinate frame, specify the type
here. Can be None (default) if the points could have varying
coordinate frames.
ch_names : list of str | None
Channel names associated with the digitization points, if available.
.. versionadded:: 1.9
%(overwrite)s
.. versionadded:: 1.0
%(verbose)s
.. versionadded:: 1.0
"""
from ..transforms import _to_const
fname = _check_fname(fname, overwrite=overwrite)
if coord_frame is not None:
coord_frame = _to_const(coord_frame)
pts_frames = {pt.get("coord_frame", coord_frame) for pt in pts}
bad_frames = pts_frames - {coord_frame}
if len(bad_frames) > 0:
raise ValueError(
"Points have coord_frame entries that are incompatible with "
f"coord_frame={coord_frame}: {tuple(bad_frames)}."
)
_validate_type(ch_names, (None, list, tuple), "ch_names")
if ch_names is not None:
for ci, ch_name in enumerate(ch_names):
_validate_type(ch_name, str, f"ch_names[{ci}]")
with start_and_end_file(fname) as fid:
write_dig_points(
fid, pts, block=True, coord_frame=coord_frame, ch_names=ch_names
)
_cardinal_ident_mapping = {
FIFF.FIFFV_POINT_NASION: "nasion",
FIFF.FIFFV_POINT_LPA: "lpa",
FIFF.FIFFV_POINT_RPA: "rpa",
}
def _ensure_fiducials_head(dig):
# Ensure that there are all three fiducials in the head coord frame
fids = dict()
for d in dig:
if d["kind"] == FIFF.FIFFV_POINT_CARDINAL:
name = _cardinal_ident_mapping.get(d["ident"], None)
if name is not None:
fids[name] = d
radius = None
mults = dict(
lpa=[-1, 0, 0],
rpa=[1, 0, 0],
nasion=[0, 1, 0],
)
for ident, name in _cardinal_ident_mapping.items():
if name not in fids:
if radius is None:
radius = [
np.linalg.norm(d["r"])
for d in dig
if d["coord_frame"] == FIFF.FIFFV_COORD_HEAD
and not np.isnan(d["r"]).any()
]
if not radius:
return # can't complete, no head points
radius = np.mean(radius)
dig.append(
DigPoint(
kind=FIFF.FIFFV_POINT_CARDINAL,
ident=ident,
r=np.array(mults[name], float) * radius,
coord_frame=FIFF.FIFFV_COORD_HEAD,
)
)
# XXXX:
# This does something really similar to _read_dig_montage_fif but:
# - does not check coord_frame
# - does not do any operation that implies assumptions with the names
def _get_data_as_dict_from_dig(dig, exclude_ref_channel=True):
"""Obtain coordinate data from a Dig.
Parameters
----------
dig : list of dicts
A container of DigPoints to be added to the info['dig'].
Returns
-------
ch_pos : dict
The container of all relevant channel positions inside dig.
"""
# Split up the dig points by category
hsp, hpi, elp = list(), list(), list()
fids, dig_ch_pos_location = dict(), list()
dig = [] if dig is None else dig
for d in dig:
if d["kind"] == FIFF.FIFFV_POINT_CARDINAL:
fids[_cardinal_ident_mapping[d["ident"]]] = d["r"]
elif d["kind"] == FIFF.FIFFV_POINT_HPI:
hpi.append(d["r"])
elp.append(d["r"])
elif d["kind"] == FIFF.FIFFV_POINT_EXTRA:
hsp.append(d["r"])
elif d["kind"] == FIFF.FIFFV_POINT_EEG:
if d["ident"] != 0 or not exclude_ref_channel:
dig_ch_pos_location.append(d["r"])
dig_coord_frames = set([d["coord_frame"] for d in dig])
if len(dig_coord_frames) == 0:
dig_coord_frames = set([FIFF.FIFFV_COORD_HEAD])
if len(dig_coord_frames) != 1:
raise RuntimeError(
"Only single coordinate frame in dig is supported, "
f"got {dig_coord_frames}"
)
dig_ch_pos_location = np.array(dig_ch_pos_location)
dig_ch_pos_location.shape = (-1, 3) # empty will be (0, 3)
return Bunch(
nasion=fids.get("nasion", None),
lpa=fids.get("lpa", None),
rpa=fids.get("rpa", None),
hsp=np.array(hsp) if len(hsp) else None,
hpi=np.array(hpi) if len(hpi) else None,
elp=np.array(elp) if len(elp) else None,
dig_ch_pos_location=dig_ch_pos_location,
coord_frame=dig_coord_frames.pop(),
)
def _get_fid_coords(dig, raise_error=True):
fid_coords = Bunch(nasion=None, lpa=None, rpa=None)
fid_coord_frames = dict()
for d in dig:
if d["kind"] == FIFF.FIFFV_POINT_CARDINAL:
key = _cardinal_ident_mapping[d["ident"]]
fid_coords[key] = d["r"]
fid_coord_frames[key] = d["coord_frame"]
if len(fid_coord_frames) > 0 and raise_error:
if set(fid_coord_frames.keys()) != set(["nasion", "lpa", "rpa"]):
raise ValueError(
f"Some fiducial points are missing (got {fid_coord_frames.keys()})."
)
if len(set(fid_coord_frames.values())) > 1:
raise ValueError(
"All fiducial points must be in the same coordinate system "
f"(got {len(fid_coord_frames)})"
)
coord_frame = fid_coord_frames.popitem()[1] if fid_coord_frames else None
return fid_coords, coord_frame
def _coord_frame_const(coord_frame):
from ..transforms import _str_to_frame
if not isinstance(coord_frame, str) or coord_frame not in _str_to_frame:
raise ValueError(
f"coord_frame must be one of {sorted(_str_to_frame.keys())}, got "
f"{coord_frame}"
)
return _str_to_frame[coord_frame]
def _make_dig_points(
nasion=None,
lpa=None,
rpa=None,
hpi=None,
extra_points=None,
dig_ch_pos=None,
*,
coord_frame="head",
add_missing_fiducials=False,
):
"""Construct digitizer info for the info.
Parameters
----------
nasion : array-like | numpy.ndarray, shape (3,) | None
Point designated as the nasion point.
lpa : array-like | numpy.ndarray, shape (3,) | None
Point designated as the left auricular point.
rpa : array-like | numpy.ndarray, shape (3,) | None
Point designated as the right auricular point.
hpi : array-like | numpy.ndarray, shape (n_points, 3) | None
Points designated as head position indicator points.
extra_points : array-like | numpy.ndarray, shape (n_points, 3)
Points designed as the headshape points.
dig_ch_pos : dict
Dict of EEG channel positions.
coord_frame : str
The coordinate frame of the points. Usually this is "unknown"
for native digitizer space. Defaults to "head".
add_missing_fiducials : bool
If True, add fiducials to the dig points if they are not present.
Requires that coord_frame='head' and that lpa, nasion, and rpa are all
None.
Returns
-------
dig : list of dicts
A container of DigPoints to be added to the info['dig'].
"""
coord_frame = _coord_frame_const(coord_frame)
dig = []
if lpa is not None:
lpa = np.asarray(lpa)
if lpa.shape != (3,):
raise ValueError(f"LPA should have the shape (3,) instead of {lpa.shape}")
dig.append(
{
"r": lpa,
"ident": FIFF.FIFFV_POINT_LPA,
"kind": FIFF.FIFFV_POINT_CARDINAL,
"coord_frame": coord_frame,
}
)
if nasion is not None:
nasion = np.asarray(nasion)
if nasion.shape != (3,):
raise ValueError(
f"Nasion should have the shape (3,) instead of {nasion.shape}"
)
dig.append(
{
"r": nasion,
"ident": FIFF.FIFFV_POINT_NASION,
"kind": FIFF.FIFFV_POINT_CARDINAL,
"coord_frame": coord_frame,
}
)
if rpa is not None:
rpa = np.asarray(rpa)
if rpa.shape != (3,):
raise ValueError(f"RPA should have the shape (3,) instead of {rpa.shape}")
dig.append(
{
"r": rpa,
"ident": FIFF.FIFFV_POINT_RPA,
"kind": FIFF.FIFFV_POINT_CARDINAL,
"coord_frame": coord_frame,
}
)
if hpi is not None:
hpi = np.asarray(hpi)
if hpi.ndim != 2 or hpi.shape[1] != 3:
raise ValueError(
f"HPI should have the shape (n_points, 3) instead of {hpi.shape}"
)
for idx, point in enumerate(hpi):
dig.append(
{
"r": point,
"ident": idx + 1,
"kind": FIFF.FIFFV_POINT_HPI,
"coord_frame": coord_frame,
}
)
if extra_points is not None:
extra_points = np.asarray(extra_points)
if len(extra_points) and extra_points.shape[1] != 3:
raise ValueError(
"Points should have the shape (n_points, 3) instead of "
f"{extra_points.shape}"
)
for idx, point in enumerate(extra_points):
dig.append(
{
"r": point,
"ident": idx + 1,
"kind": FIFF.FIFFV_POINT_EXTRA,
"coord_frame": coord_frame,
}
)
if dig_ch_pos is not None:
idents = []
use_arange = False
for key, value in dig_ch_pos.items():
_validate_type(key, str, "dig_ch_pos")
try:
idents.append(int(key[-3:]))
except ValueError:
use_arange = True
_validate_type(value, (np.ndarray, list, tuple), "dig_ch_pos")
value = np.array(value, dtype=float)
dig_ch_pos[key] = value
if value.shape != (3,):
raise RuntimeError(
"The position should be a 1D array of 3 floats. "
f"Provided shape {value.shape}."
)
if use_arange:
idents = np.arange(1, len(dig_ch_pos) + 1)
for key, ident in zip(dig_ch_pos, idents):
dig.append(
{
"r": dig_ch_pos[key],
"ident": int(ident),
"kind": FIFF.FIFFV_POINT_EEG,
"coord_frame": coord_frame,
}
)
if add_missing_fiducials:
assert coord_frame == FIFF.FIFFV_COORD_HEAD
# These being none is really an assumption that if you have one you
# should have all three. But we can relax this later if necessary.
assert lpa is None
assert rpa is None
assert nasion is None
_ensure_fiducials_head(dig)
return _format_dig_points(dig)
def _call_make_dig_points(nasion, lpa, rpa, hpi, extra, convert=True):
from ..transforms import (
Transform,
apply_trans,
get_ras_to_neuromag_trans,
)
if convert:
neuromag_trans = get_ras_to_neuromag_trans(nasion, lpa, rpa)
nasion = apply_trans(neuromag_trans, nasion)
lpa = apply_trans(neuromag_trans, lpa)
rpa = apply_trans(neuromag_trans, rpa)
if hpi is not None:
hpi = apply_trans(neuromag_trans, hpi)
extra = apply_trans(neuromag_trans, extra).astype(np.float32)
else:
neuromag_trans = None
ctf_head_t = Transform(fro="ctf_head", to="head", trans=neuromag_trans)
info_dig = _make_dig_points(
nasion=nasion, lpa=lpa, rpa=rpa, hpi=hpi, extra_points=extra
)
return info_dig, ctf_head_t
##############################################################################
# From artemis123 (we have modified the function a bit)
def _artemis123_read_pos(nas, lpa, rpa, hpi, extra):
# move into MNE head coords
dig_points, _ = _call_make_dig_points(nas, lpa, rpa, hpi, extra)
return dig_points
##############################################################################
# From bti
def _make_bti_dig_points(
nasion,
lpa,
rpa,
hpi,
extra,
convert=False,
use_hpi=False,
bti_dev_t=False,
dev_ctf_t=False,
):
from ..transforms import (
Transform,
combine_transforms,
invert_transform,
)
_hpi = hpi if use_hpi else None
info_dig, ctf_head_t = _call_make_dig_points(nasion, lpa, rpa, _hpi, extra, convert)
if convert:
t = combine_transforms(
invert_transform(bti_dev_t), dev_ctf_t, "meg", "ctf_head"
)
dev_head_t = combine_transforms(t, ctf_head_t, "meg", "head")
else:
dev_head_t = Transform("meg", "head", trans=None)
return info_dig, dev_head_t, ctf_head_t # ctf_head_t should not be needed

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
from ..utils import fill_doc
from .constants import FIFF
def get_current_comp(info):
"""Get the current compensation in effect in the data."""
comp = None
first_comp = -1
for k, chan in enumerate(info["chs"]):
if chan["kind"] == FIFF.FIFFV_MEG_CH:
comp = int(chan["coil_type"]) >> 16
if first_comp < 0:
first_comp = comp
elif comp != first_comp:
raise ValueError("Compensation is not set equally on all MEG channels")
return comp
def set_current_comp(info, comp):
"""Set the current compensation in effect in the data."""
comp_now = get_current_comp(info)
for k, chan in enumerate(info["chs"]):
if chan["kind"] == FIFF.FIFFV_MEG_CH:
rem = chan["coil_type"] - (comp_now << 16)
chan["coil_type"] = int(rem + (comp << 16))
def _make_compensator(info, grade):
"""Auxiliary function for make_compensator."""
for k in range(len(info["comps"])):
if info["comps"][k]["kind"] == grade:
this_data = info["comps"][k]["data"]
# Create the preselector
presel = np.zeros((this_data["ncol"], info["nchan"]))
for col, col_name in enumerate(this_data["col_names"]):
ind = [k for k, ch in enumerate(info["ch_names"]) if ch == col_name]
if len(ind) == 0:
raise ValueError(f"Channel {col_name} is not available in data")
elif len(ind) > 1:
raise ValueError(f"Ambiguous channel {col_name}")
presel[col, ind[0]] = 1.0
# Create the postselector (zero entries for channels not found)
postsel = np.zeros((info["nchan"], this_data["nrow"]))
for c, ch_name in enumerate(info["ch_names"]):
ind = [
k for k, ch in enumerate(this_data["row_names"]) if ch == ch_name
]
if len(ind) > 1:
raise ValueError(f"Ambiguous channel {ch_name}")
elif len(ind) == 1:
postsel[c, ind[0]] = 1.0
# else, don't use it at all (postsel[c, ?] = 0.0) by allocation
this_comp = np.dot(postsel, np.dot(this_data["data"], presel))
return this_comp
raise ValueError(f"Desired compensation matrix (grade = {grade:d}) not found")
@fill_doc
def make_compensator(info, from_, to, exclude_comp_chs=False):
"""Return compensation matrix eg. for CTF system.
Create a compensation matrix to bring the data from one compensation
state to another.
Parameters
----------
%(info_not_none)s
from_ : int
Compensation in the input data.
to : int
Desired compensation in the output.
exclude_comp_chs : bool
Exclude compensation channels from the output.
Returns
-------
comp : array | None.
The compensation matrix. Might be None if no compensation
is needed (from == to).
"""
if from_ == to:
return None
# s_orig = s_from + C1*s_from = (I + C1)*s_from
# s_to = s_orig - C2*s_orig = (I - C2)*s_orig
# s_to = (I - C2)*(I + C1)*s_from = (I + C1 - C2 - C2*C1)*s_from
if from_ != 0:
C1 = _make_compensator(info, from_)
comp_from_0 = np.linalg.inv(np.eye(info["nchan"]) - C1)
if to != 0:
C2 = _make_compensator(info, to)
comp_0_to = np.eye(info["nchan"]) - C2
if from_ != 0:
if to != 0:
# This is mathematically equivalent, but has higher numerical
# error than using the inverse to always go to zero and back
# comp = np.eye(info['nchan']) + C1 - C2 - np.dot(C2, C1)
comp = np.dot(comp_0_to, comp_from_0)
else:
comp = comp_from_0
else:
# from == 0, to != 0 guaranteed here
comp = comp_0_to
if exclude_comp_chs:
pick = [
k for k, c in enumerate(info["chs"]) if c["kind"] != FIFF.FIFFV_REF_MEG_CH
]
if len(pick) == 0:
raise ValueError(
"Nothing remains after excluding the compensation channels"
)
comp = comp[pick, :]
return comp
# @verbose
# def compensate_to(data, to, verbose=None):
# """
# %
# % [newdata] = mne_compensate_to(data,to)
# %
# % Apply compensation to the data as desired
# %
# """
#
# newdata = data.copy()
# now = get_current_comp(newdata['info'])
#
# # Are we there already?
# if now == to:
# logger.info('Data are already compensated as desired')
#
# # Make the compensator and apply it to all data sets
# comp = make_compensator(newdata['info'], now, to)
# for k in range(len(newdata['evoked'])):
# newdata['evoked'][k]['epochs'] = np.dot(comp,
# newdata['evoked'][k]['epochs'])
#
# # Update the compensation info in the channel descriptors
# newdata['info']['chs'] = set_current_comp(newdata['info']['chs'], to)
# return newdata
# def set_current_comp(chs, value):
# """Set the current compensation value in the channel info structures
# """
# new_chs = chs
#
# lower_half = int('FFFF', 16) # hex2dec('FFFF')
# for k in range(len(chs)):
# if chs[k]['kind'] == FIFF.FIFFV_MEG_CH:
# coil_type = float(chs[k]['coil_type']) & lower_half
# new_chs[k]['coil_type'] = int(coil_type | (value << 16))
#
# return new_chs

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from copy import deepcopy
import numpy as np
from ..utils import _pl, logger, verbose
from .constants import FIFF
from .matrix import _read_named_matrix, write_named_matrix
from .tag import read_tag
from .tree import dir_tree_find
from .write import end_block, start_block, write_int
def _add_kind(one):
"""Convert CTF kind to MNE kind."""
if one["ctfkind"] == int("47314252", 16):
one["kind"] = 1
elif one["ctfkind"] == int("47324252", 16):
one["kind"] = 2
elif one["ctfkind"] == int("47334252", 16):
one["kind"] = 3
else:
one["kind"] = int(one["ctfkind"])
def _calibrate_comp(
comp, chs, row_names, col_names, mult_keys=("range", "cal"), flip=False
):
"""Get row and column cals."""
ch_names = [c["ch_name"] for c in chs]
row_cals = np.zeros(len(row_names))
col_cals = np.zeros(len(col_names))
for names, cals, inv in zip(
(row_names, col_names), (row_cals, col_cals), (False, True)
):
for ii in range(len(cals)):
p = ch_names.count(names[ii])
if p != 1:
raise RuntimeError(
f"Channel {names[ii]} does not appear exactly once "
f"in data, found {p:d} instance{_pl(p)}"
)
idx = ch_names.index(names[ii])
val = chs[idx][mult_keys[0]] * chs[idx][mult_keys[1]]
val = float(1.0 / val) if inv else float(val)
val = 1.0 / val if flip else val
cals[ii] = val
comp["rowcals"] = row_cals
comp["colcals"] = col_cals
comp["data"]["data"] = row_cals[:, None] * comp["data"]["data"] * col_cals[None, :]
@verbose
def read_ctf_comp(fid, node, chs, verbose=None):
"""Read the CTF software compensation data from the given node.
Parameters
----------
fid : file
The file descriptor.
node : dict
The node in the FIF tree.
chs : list
The list of channels from info['chs'] to match with
compensators that are read.
%(verbose)s
Returns
-------
compdata : list
The compensation data
"""
return _read_ctf_comp(fid, node, chs, None)
def _read_ctf_comp(fid, node, chs, ch_names_mapping):
"""Read the CTF software compensation data from the given node.
Parameters
----------
fid : file
The file descriptor.
node : dict
The node in the FIF tree.
chs : list
The list of channels from info['chs'] to match with
compensators that are read.
ch_names_mapping : dict | None
The channel renaming to use.
%(verbose)s
Returns
-------
compdata : list
The compensation data
"""
from .meas_info import _rename_comps
ch_names_mapping = dict() if ch_names_mapping is None else ch_names_mapping
compdata = []
comps = dir_tree_find(node, FIFF.FIFFB_MNE_CTF_COMP_DATA)
for node in comps:
# Read the data we need
mat = _read_named_matrix(fid, node, FIFF.FIFF_MNE_CTF_COMP_DATA)
for p in range(node["nent"]):
kind = node["directory"][p].kind
pos = node["directory"][p].pos
if kind == FIFF.FIFF_MNE_CTF_COMP_KIND:
tag = read_tag(fid, pos)
break
else:
raise Exception("Compensation type not found")
# Get the compensation kind and map it to a simple number
one = dict(ctfkind=tag.data.item())
del tag
_add_kind(one)
for p in range(node["nent"]):
kind = node["directory"][p].kind
pos = node["directory"][p].pos
if kind == FIFF.FIFF_MNE_CTF_COMP_CALIBRATED:
tag = read_tag(fid, pos)
calibrated = tag.data
break
else:
calibrated = False
one["save_calibrated"] = bool(calibrated)
one["data"] = mat
_rename_comps([one], ch_names_mapping)
if not calibrated:
# Calibrate...
_calibrate_comp(one, chs, mat["row_names"], mat["col_names"])
else:
one["rowcals"] = np.ones(mat["data"].shape[0], dtype=np.float64)
one["colcals"] = np.ones(mat["data"].shape[1], dtype=np.float64)
compdata.append(one)
if len(compdata) > 0:
logger.info(f" Read {len(compdata)} compensation matrices")
return compdata
###############################################################################
# Writing
def write_ctf_comp(fid, comps):
"""Write the CTF compensation data into a fif file.
Parameters
----------
fid : file
The open FIF file descriptor
comps : list
The compensation data to write
"""
if len(comps) <= 0:
return
# This is very simple in fact
start_block(fid, FIFF.FIFFB_MNE_CTF_COMP)
for comp in comps:
start_block(fid, FIFF.FIFFB_MNE_CTF_COMP_DATA)
# Write the compensation kind
write_int(fid, FIFF.FIFF_MNE_CTF_COMP_KIND, comp["ctfkind"])
if comp.get("save_calibrated", False):
write_int(fid, FIFF.FIFF_MNE_CTF_COMP_CALIBRATED, comp["save_calibrated"])
if not comp.get("save_calibrated", True):
# Undo calibration
comp = deepcopy(comp)
data = (
(1.0 / comp["rowcals"][:, None])
* comp["data"]["data"]
* (1.0 / comp["colcals"][None, :])
)
comp["data"]["data"] = data
write_named_matrix(fid, FIFF.FIFF_MNE_CTF_COMP_DATA, comp["data"])
end_block(fid, FIFF.FIFFB_MNE_CTF_COMP_DATA)
end_block(fid, FIFF.FIFFB_MNE_CTF_COMP)

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ..utils import logger
from .constants import FIFF
from .tag import find_tag, has_tag
from .write import (
end_block,
start_block,
write_float_matrix,
write_int,
write_name_list,
)
def _transpose_named_matrix(mat):
"""Transpose mat inplace (no copy)."""
mat["nrow"], mat["ncol"] = mat["ncol"], mat["nrow"]
mat["row_names"], mat["col_names"] = mat["col_names"], mat["row_names"]
mat["data"] = mat["data"].T
def _read_named_matrix(fid, node, matkind, indent=" ", transpose=False):
"""Read named matrix from the given node.
Parameters
----------
fid : file
The opened file descriptor.
node : dict
The node in the tree.
matkind : int
The type of matrix.
transpose : bool
If True, transpose the matrix. Default is False.
%(verbose)s
Returns
-------
mat: dict
The matrix data
"""
# Descend one level if necessary
if node["block"] != FIFF.FIFFB_MNE_NAMED_MATRIX:
for k in range(node["nchild"]):
if node["children"][k]["block"] == FIFF.FIFFB_MNE_NAMED_MATRIX:
if has_tag(node["children"][k], matkind):
node = node["children"][k]
break
else:
logger.info(
f"{indent}Desired named matrix (kind = {matkind}) not available"
)
return None
else:
if not has_tag(node, matkind):
logger.info(
f"{indent}Desired named matrix (kind = {matkind}) not available"
)
return None
# Read everything we need
tag = find_tag(fid, node, matkind)
if tag is None:
raise ValueError("Matrix data missing")
else:
data = tag.data
nrow, ncol = data.shape
tag = find_tag(fid, node, FIFF.FIFF_MNE_NROW)
if tag is not None and tag.data != nrow:
raise ValueError(
"Number of rows in matrix data and FIFF_MNE_NROW tag do not match"
)
tag = find_tag(fid, node, FIFF.FIFF_MNE_NCOL)
if tag is not None and tag.data != ncol:
raise ValueError(
"Number of columns in matrix data and FIFF_MNE_NCOL tag do not match"
)
tag = find_tag(fid, node, FIFF.FIFF_MNE_ROW_NAMES)
row_names = tag.data.split(":") if tag is not None else []
tag = find_tag(fid, node, FIFF.FIFF_MNE_COL_NAMES)
col_names = tag.data.split(":") if tag is not None else []
mat = dict(
nrow=nrow, ncol=ncol, row_names=row_names, col_names=col_names, data=data
)
if transpose:
_transpose_named_matrix(mat)
return mat
def write_named_matrix(fid, kind, mat):
"""Write named matrix from the given node.
Parameters
----------
fid : file
The opened file descriptor.
kind : int
The kind of the matrix.
matkind : int
The type of matrix.
"""
# let's save ourselves from disaster
n_tot = mat["nrow"] * mat["ncol"]
if mat["data"].size != n_tot:
ratio = n_tot / float(mat["data"].size)
if n_tot < mat["data"].size and ratio > 0:
ratio = 1 / ratio
raise ValueError(
f"Cannot write matrix: row ({mat['nrow']}) and column ({mat['ncol']}) "
f"total element ({n_tot}) mismatch with data size ({mat['data'].size}), "
f"appears to be off by a factor of {ratio:g}x"
)
start_block(fid, FIFF.FIFFB_MNE_NAMED_MATRIX)
write_int(fid, FIFF.FIFF_MNE_NROW, mat["nrow"])
write_int(fid, FIFF.FIFF_MNE_NCOL, mat["ncol"])
if len(mat["row_names"]) > 0:
# let's prevent unintentional stupidity
if len(mat["row_names"]) != mat["nrow"]:
raise ValueError('len(mat["row_names"]) != mat["nrow"]')
write_name_list(fid, FIFF.FIFF_MNE_ROW_NAMES, mat["row_names"])
if len(mat["col_names"]) > 0:
# let's prevent unintentional stupidity
if len(mat["col_names"]) != mat["ncol"]:
raise ValueError('len(mat["col_names"]) != mat["ncol"]')
write_name_list(fid, FIFF.FIFF_MNE_COL_NAMES, mat["col_names"])
write_float_matrix(fid, kind, mat["data"])
end_block(fid, FIFF.FIFFB_MNE_NAMED_MATRIX)

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from gzip import GzipFile
from io import SEEK_SET, BytesIO
from pathlib import Path
import numpy as np
from scipy.sparse import issparse
from ..utils import _check_fname, _file_like, _validate_type, logger, verbose, warn
from .constants import FIFF
from .tag import Tag, _call_dict_names, _matrix_info, _read_tag_header, read_tag
from .tree import dir_tree_find, make_dir_tree
class _NoCloseRead:
"""Create a wrapper that will not close when used as a context manager."""
def __init__(self, fid):
self.fid = fid
def __enter__(self):
return self.fid
def __exit__(self, type_, value, traceback):
return
def close(self):
return
def seek(self, offset, whence=SEEK_SET):
return self.fid.seek(offset, whence)
def read(self, size=-1):
return self.fid.read(size)
def _fiff_get_fid(fname):
"""Open a FIF file with no additional parsing."""
if _file_like(fname):
logger.debug("Using file-like I/O")
fid = _NoCloseRead(fname)
fid.seek(0)
else:
_validate_type(fname, Path, "fname", extra="or file-like")
if fname.suffixes[-1] == ".gz":
logger.debug("Using gzip I/O")
fid = GzipFile(fname, "rb") # Open in binary mode
else:
logger.debug("Using normal I/O")
fid = open(fname, "rb") # Open in binary mode
return fid
def _get_next_fname(fid, fname, tree):
"""Get the next filename in split files."""
_validate_type(fname, (Path, None), "fname")
nodes_list = dir_tree_find(tree, FIFF.FIFFB_REF)
next_fname = None
for nodes in nodes_list:
next_fname = None
for ent in nodes["directory"]:
if ent.kind == FIFF.FIFF_REF_ROLE:
tag = read_tag(fid, ent.pos)
role = int(tag.data.item())
if role != FIFF.FIFFV_ROLE_NEXT_FILE:
next_fname = None
break
if ent.kind not in (FIFF.FIFF_REF_FILE_NAME, FIFF.FIFF_REF_FILE_NUM):
continue
# If we can't resolve it, assume/hope it's in the current directory
if fname is None:
fname = Path().resolve()
if ent.kind == FIFF.FIFF_REF_FILE_NAME:
tag = read_tag(fid, ent.pos)
next_fname = fname.parent / tag.data
if ent.kind == FIFF.FIFF_REF_FILE_NUM:
# Some files don't have the name, just the number. So
# we construct the name from the current name.
if next_fname is not None:
continue
next_num = read_tag(fid, ent.pos).data.item()
base = fname.name
idx = base.find(".")
idx2 = base.rfind("-")
num_str = base[idx2 + 1 : idx]
if not num_str.isdigit():
idx2 = -1
if idx2 < 0 and next_num == 1:
# this is the first file, which may not be numbered
next_fname = (
fname.parent / f"{base[:idx]}-{next_num:d}.{base[idx + 1 :]}"
)
continue
next_fname = (
fname.parent / f"{base[:idx2]}-{next_num:d}.{base[idx + 1 :]}"
)
if next_fname is not None:
break
return next_fname
@verbose
def fiff_open(fname, preload=False, verbose=None):
"""Open a FIF file.
Parameters
----------
fname : path-like | fid
Name of the fif file, or an opened file (will seek back to 0).
preload : bool
If True, all data from the file is read into a memory buffer. This
requires more memory, but can be faster for I/O operations that require
frequent seeks.
%(verbose)s
Returns
-------
fid : file
The file descriptor of the open file.
tree : fif tree
The tree is a complex structure filled with dictionaries,
lists and tags.
directory : list
A list of tags.
"""
fid = _fiff_get_fid(fname)
try:
return _fiff_open(fname, fid, preload)
except Exception:
fid.close()
raise
def _fiff_open(fname, fid, preload):
# do preloading of entire file
if preload:
# note that StringIO objects instantiated this way are read-only,
# but that's okay here since we are using mode "rb" anyway
with fid as fid_old:
fid = BytesIO(fid_old.read())
tag = _read_tag_header(fid, 0)
# Check that this looks like a fif file
prefix = f"file {repr(fname)} does not"
if tag.kind != FIFF.FIFF_FILE_ID:
raise ValueError(f"{prefix} start with a file id tag")
if tag.type != FIFF.FIFFT_ID_STRUCT:
raise ValueError(f"{prefix} start with a file id tag")
if tag.size != 20:
raise ValueError(f"{prefix} start with a file id tag")
tag = read_tag(fid, tag.next_pos)
if tag.kind != FIFF.FIFF_DIR_POINTER:
raise ValueError(f"{prefix} have a directory pointer")
# Read or create the directory tree
logger.debug(f" Creating tag directory for {fname}...")
dirpos = int(tag.data.item())
read_slow = True
if dirpos > 0:
dir_tag = read_tag(fid, dirpos)
if dir_tag is None or dir_tag.data is None:
fid.seek(0, 2) # move to end of file
size = fid.tell()
extra = "" if size > dirpos else f" > file size {size}"
warn(
"FIF tag directory missing at the end of the file "
f"(at byte {dirpos}{extra}), possibly corrupted file: {fname}"
)
else:
directory = dir_tag.data
read_slow = False
if read_slow:
pos = 0
fid.seek(pos, 0)
directory = list()
while pos is not None:
tag = _read_tag_header(fid, pos)
if tag is None:
break # HACK : to fix file ending with empty tag...
pos = tag.next_pos
directory.append(tag)
tree, _ = make_dir_tree(fid, directory, indent=1)
logger.debug("[done]")
# Back to the beginning
fid.seek(0)
return fid, tree, directory
@verbose
def show_fiff(
fname,
indent=" ",
read_limit=np.inf,
max_str=30,
output=str,
tag=None,
*,
show_bytes=False,
verbose=None,
):
"""Show FIFF information.
This function is similar to mne_show_fiff.
Parameters
----------
fname : path-like
Filename to evaluate.
indent : str
How to indent the lines.
read_limit : int
Max number of bytes of data to read from a tag. Can be np.inf
to always read all data (helps test read completion).
max_str : int
Max number of characters of string representation to print for
each tag's data.
output : type
Either str or list. str is a convenience output for printing.
tag : int | None
Provide information about this tag. If None (default), all information
is shown.
show_bytes : bool
If True (default False), print the byte offsets of each tag.
%(verbose)s
Returns
-------
contents : str
The contents of the file.
"""
if output not in [list, str]:
raise ValueError("output must be list or str")
if isinstance(tag, str): # command mne show_fiff passes string
tag = int(tag)
fname = _check_fname(fname, "read", True)
f, tree, _ = fiff_open(fname)
# This gets set to 0 (unknown) by fiff_open, but FIFFB_ROOT probably
# makes more sense for display
tree["block"] = FIFF.FIFFB_ROOT
with f as fid:
out = _show_tree(
fid,
tree,
indent=indent,
level=0,
read_limit=read_limit,
max_str=max_str,
tag_id=tag,
show_bytes=show_bytes,
)
if output is str:
out = "\n".join(out)
return out
def _find_type(value, fmts=("FIFF_",), exclude=("FIFF_UNIT",)):
"""Find matching values."""
value = int(value)
vals = [
k
for k, v in FIFF.items()
if v == value
and any(fmt in k for fmt in fmts)
and not any(exc in k for exc in exclude)
]
if len(vals) == 0:
vals = ["???"]
return vals
def _show_tree(
fid,
tree,
indent,
level,
read_limit,
max_str,
tag_id,
*,
show_bytes=False,
):
"""Show FIFF tree."""
this_idt = indent * level
next_idt = indent * (level + 1)
# print block-level information
found_types = "/".join(_find_type(tree["block"], fmts=["FIFFB_"]))
out = [f"{this_idt}{str(int(tree['block'])).ljust(4)} = {found_types}"]
tag_found = False
if tag_id is None or out[0].strip().startswith(str(tag_id)):
tag_found = True
if tree["directory"] is not None:
kinds = [ent.kind for ent in tree["directory"]] + [-1]
types = [ent.type for ent in tree["directory"]]
sizes = [ent.size for ent in tree["directory"]]
poss = [ent.pos for ent in tree["directory"]]
counter = 0
good = True
for k, kn, size, pos, type_ in zip(kinds[:-1], kinds[1:], sizes, poss, types):
if not tag_found and k != tag_id:
continue
tag = Tag(kind=k, type=type_, size=size, next=FIFF.FIFFV_NEXT_NONE, pos=pos)
if read_limit is None or size <= read_limit:
try:
tag = read_tag(fid, pos)
except Exception:
good = False
if kn == k:
# don't print if the next item is the same type (count 'em)
counter += 1
else:
if show_bytes:
at = f" @{pos}"
else:
at = ""
# find the tag type
this_type = _find_type(k, fmts=["FIFF_"])
# prepend a count if necessary
prepend = "x" + str(counter + 1) + ": " if counter > 0 else ""
postpend = ""
# print tag data nicely
if tag.data is not None:
postpend = " = " + str(tag.data)[:max_str]
if isinstance(tag.data, np.ndarray):
if tag.data.size > 1:
postpend += " ... array size=" + str(tag.data.size)
elif isinstance(tag.data, dict):
postpend += " ... dict len=" + str(len(tag.data))
elif isinstance(tag.data, str):
postpend += " ... str len=" + str(len(tag.data))
elif isinstance(tag.data, list | tuple):
postpend += " ... list len=" + str(len(tag.data))
elif issparse(tag.data):
postpend += (
f" ... sparse ({tag.data.getformat()}) shape="
f"{tag.data.shape}"
)
else:
postpend += " ... type=" + str(type(tag.data))
postpend = ">" * 20 + f"BAD @{pos}" if not good else postpend
matrix_info = _matrix_info(tag)
if matrix_info is not None:
_, type_, _, _ = matrix_info
type_ = _call_dict_names.get(type_, f"?{type_}?")
this_type = "/".join(this_type)
out += [
f"{next_idt}{prepend}{str(k).ljust(4)} = "
f"{this_type}{at} ({size}b {type_}) {postpend}"
]
out[-1] = out[-1].replace("\n", "")
counter = 0
good = True
if tag_id in kinds:
tag_found = True
if not tag_found:
out = [""]
level = -1 # removes extra indent
# deal with children
for branch in tree["children"]:
out += _show_tree(
fid,
branch,
indent,
level + 1,
read_limit,
max_str,
tag_id,
show_bytes=show_bytes,
)
return out

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
from ..fixes import _csc_array_cast
from ..utils import _check_fname, warn
from .constants import FIFF
from .open import fiff_open, read_tag
from .tag import _float_item, _int_item, find_tag
from .tree import dir_tree_find
from .write import (
_safe_name_list,
end_block,
start_block,
write_float,
write_float_matrix,
write_float_sparse,
write_id,
write_int,
write_int_matrix,
write_name_list_sanitized,
write_string,
)
_proc_map = dict( # ID, caster, writer
parent_file_id=(
FIFF.FIFF_PARENT_FILE_ID,
dict,
write_id,
),
block_id=(
FIFF.FIFF_BLOCK_ID,
dict,
write_id,
),
parent_block_id=(
FIFF.FIFF_PARENT_BLOCK_ID,
dict,
write_id,
),
date=(
FIFF.FIFF_MEAS_DATE,
lambda d: tuple(int(dd) for dd in d),
write_int,
),
experimenter=(
FIFF.FIFF_EXPERIMENTER,
str,
write_string,
),
creator=(
FIFF.FIFF_CREATOR,
str,
write_string,
),
)
def _read_proc_history(fid, tree):
"""Read processing history from fiff file.
This function reads the SSS info, the CTC correction and the
calibaraions from the SSS processing logs inside af a raw file
(C.f. Maxfilter v2.2 manual (October 2010), page 21)::
104 = { 900 = proc. history
104 = { 901 = proc. record
103 = block ID
204 = date
212 = scientist
113 = creator program
104 = { 502 = SSS info
264 = SSS task
263 = SSS coord frame
265 = SSS origin
266 = SSS ins.order
267 = SSS outs.order
268 = SSS nr chnls
269 = SSS components
278 = SSS nfree
243 = HPI g limit 0.98
244 = HPI dist limit 0.005
105 = } 502 = SSS info
104 = { 504 = MaxST info
264 = SSS task
272 = SSST subspace correlation
279 = SSST buffer length
105 = }
104 = { 501 = CTC correction
103 = block ID
204 = date
113 = creator program
800 = CTC matrix
3417 = proj item chs
105 = } 501 = CTC correction
104 = { 503 = SSS finecalib.
270 = SSS cal chnls
271 = SSS cal coeff
105 = } 503 = SSS finecalib.
105 = } 901 = proc. record
105 = } 900 = proc. history
"""
proc_history = dir_tree_find(tree, FIFF.FIFFB_PROCESSING_HISTORY)
out = list()
if len(proc_history) > 0:
proc_history = proc_history[0]
proc_records = dir_tree_find(proc_history, FIFF.FIFFB_PROCESSING_RECORD)
for proc_record in proc_records:
record = dict()
for i_ent in range(proc_record["nent"]):
kind = proc_record["directory"][i_ent].kind
pos = proc_record["directory"][i_ent].pos
for key, (id_, cast, _) in _proc_map.items():
if kind == id_:
tag = read_tag(fid, pos)
record[key] = cast(tag.data)
break
else:
warn(f"Unknown processing history item {kind}")
record["max_info"] = _read_maxfilter_record(fid, proc_record)
iass = dir_tree_find(proc_record, FIFF.FIFFB_IAS)
if len(iass) > 0:
# XXX should eventually populate this
ss = [dict() for _ in range(len(iass))]
record["ias"] = ss
if len(record["max_info"]) > 0:
out.append(record)
return out
def _write_proc_history(fid, info):
"""Write processing history to file."""
if len(info["proc_history"]) > 0:
start_block(fid, FIFF.FIFFB_PROCESSING_HISTORY)
for record in info["proc_history"]:
start_block(fid, FIFF.FIFFB_PROCESSING_RECORD)
for key, (id_, _, writer) in _proc_map.items():
if key in record:
writer(fid, id_, record[key])
_write_maxfilter_record(fid, record["max_info"])
if "ias" in record:
for _ in record["ias"]:
start_block(fid, FIFF.FIFFB_IAS)
# XXX should eventually populate this
end_block(fid, FIFF.FIFFB_IAS)
end_block(fid, FIFF.FIFFB_PROCESSING_RECORD)
end_block(fid, FIFF.FIFFB_PROCESSING_HISTORY)
_sss_info_keys = (
"job",
"frame",
"origin",
"in_order",
"out_order",
"nchan",
"components",
"nfree",
"hpi_g_limit",
"hpi_dist_limit",
)
_sss_info_ids = (
FIFF.FIFF_SSS_JOB,
FIFF.FIFF_SSS_FRAME,
FIFF.FIFF_SSS_ORIGIN,
FIFF.FIFF_SSS_ORD_IN,
FIFF.FIFF_SSS_ORD_OUT,
FIFF.FIFF_SSS_NMAG,
FIFF.FIFF_SSS_COMPONENTS,
FIFF.FIFF_SSS_NFREE,
FIFF.FIFF_HPI_FIT_GOOD_LIMIT,
FIFF.FIFF_HPI_FIT_DIST_LIMIT,
)
_sss_info_writers = (
write_int,
write_int,
write_float,
write_int,
write_int,
write_int,
write_int,
write_int,
write_float,
write_float,
)
_sss_info_casters = (
_int_item,
_int_item,
np.array,
_int_item,
_int_item,
_int_item,
np.array,
_int_item,
_float_item,
_float_item,
)
_max_st_keys = ("job", "subspcorr", "buflen")
_max_st_ids = (FIFF.FIFF_SSS_JOB, FIFF.FIFF_SSS_ST_CORR, FIFF.FIFF_SSS_ST_LENGTH)
_max_st_writers = (write_int, write_float, write_float)
_max_st_casters = (_int_item, _float_item, _float_item)
_sss_ctc_keys = ("block_id", "date", "creator", "decoupler")
_sss_ctc_ids = (
FIFF.FIFF_BLOCK_ID,
FIFF.FIFF_MEAS_DATE,
FIFF.FIFF_CREATOR,
FIFF.FIFF_DECOUPLER_MATRIX,
)
_sss_ctc_writers = (write_id, write_int, write_string, write_float_sparse)
_sss_ctc_casters = (dict, np.array, str, _csc_array_cast)
_sss_cal_keys = ("cal_chans", "cal_corrs")
_sss_cal_ids = (FIFF.FIFF_SSS_CAL_CHANS, FIFF.FIFF_SSS_CAL_CORRS)
_sss_cal_writers = (write_int_matrix, write_float_matrix)
_sss_cal_casters = (np.array, np.array)
def _read_ctc(fname):
"""Read cross-talk correction matrix."""
fname = _check_fname(fname, overwrite="read", must_exist=True)
f, tree, _ = fiff_open(fname)
with f as fid:
sss_ctc = _read_maxfilter_record(fid, tree)["sss_ctc"]
bad_str = f"Invalid cross-talk FIF: {fname}"
if len(sss_ctc) == 0:
raise ValueError(bad_str)
node = dir_tree_find(tree, FIFF.FIFFB_DATA_CORRECTION)[0]
comment = find_tag(fid, node, FIFF.FIFF_COMMENT).data
if comment != "cross-talk compensation matrix":
raise ValueError(bad_str)
sss_ctc["creator"] = find_tag(fid, node, FIFF.FIFF_CREATOR).data
sss_ctc["date"] = find_tag(fid, node, FIFF.FIFF_MEAS_DATE).data
return sss_ctc
def _read_maxfilter_record(fid, tree):
"""Read maxfilter processing record from file."""
sss_info_block = dir_tree_find(tree, FIFF.FIFFB_SSS_INFO) # 502
sss_info = dict()
if len(sss_info_block) > 0:
sss_info_block = sss_info_block[0]
for i_ent in range(sss_info_block["nent"]):
kind = sss_info_block["directory"][i_ent].kind
pos = sss_info_block["directory"][i_ent].pos
for key, id_, cast in zip(_sss_info_keys, _sss_info_ids, _sss_info_casters):
if kind == id_:
tag = read_tag(fid, pos)
sss_info[key] = cast(tag.data)
break
max_st_block = dir_tree_find(tree, FIFF.FIFFB_SSS_ST_INFO) # 504
max_st = dict()
if len(max_st_block) > 0:
max_st_block = max_st_block[0]
for i_ent in range(max_st_block["nent"]):
kind = max_st_block["directory"][i_ent].kind
pos = max_st_block["directory"][i_ent].pos
for key, id_, cast in zip(_max_st_keys, _max_st_ids, _max_st_casters):
if kind == id_:
tag = read_tag(fid, pos)
max_st[key] = cast(tag.data)
break
sss_ctc_block = dir_tree_find(tree, FIFF.FIFFB_CHANNEL_DECOUPLER) # 501
sss_ctc = dict()
if len(sss_ctc_block) > 0:
sss_ctc_block = sss_ctc_block[0]
for i_ent in range(sss_ctc_block["nent"]):
kind = sss_ctc_block["directory"][i_ent].kind
pos = sss_ctc_block["directory"][i_ent].pos
for key, id_, cast in zip(_sss_ctc_keys, _sss_ctc_ids, _sss_ctc_casters):
if kind == id_:
tag = read_tag(fid, pos)
sss_ctc[key] = cast(tag.data)
break
else:
if kind == FIFF.FIFF_PROJ_ITEM_CH_NAME_LIST:
tag = read_tag(fid, pos)
chs = _safe_name_list(tag.data, "read", "proj_items_chs")
# This list can null chars in the last entry, e.g.:
# [..., 'MEG2642', 'MEG2643', 'MEG2641\x00 ... \x00']
chs[-1] = chs[-1].split("\x00")[0]
sss_ctc["proj_items_chs"] = chs
sss_cal_block = dir_tree_find(tree, FIFF.FIFFB_SSS_CAL) # 503
sss_cal = dict()
if len(sss_cal_block) > 0:
sss_cal_block = sss_cal_block[0]
for i_ent in range(sss_cal_block["nent"]):
kind = sss_cal_block["directory"][i_ent].kind
pos = sss_cal_block["directory"][i_ent].pos
for key, id_, cast in zip(_sss_cal_keys, _sss_cal_ids, _sss_cal_casters):
if kind == id_:
tag = read_tag(fid, pos)
sss_cal[key] = cast(tag.data)
break
max_info = dict(sss_info=sss_info, sss_ctc=sss_ctc, sss_cal=sss_cal, max_st=max_st)
return max_info
def _write_maxfilter_record(fid, record):
"""Write maxfilter processing record to file."""
sss_info = record["sss_info"]
if len(sss_info) > 0:
start_block(fid, FIFF.FIFFB_SSS_INFO)
for key, id_, writer in zip(_sss_info_keys, _sss_info_ids, _sss_info_writers):
if key in sss_info:
writer(fid, id_, sss_info[key])
end_block(fid, FIFF.FIFFB_SSS_INFO)
max_st = record["max_st"]
if len(max_st) > 0:
start_block(fid, FIFF.FIFFB_SSS_ST_INFO)
for key, id_, writer in zip(_max_st_keys, _max_st_ids, _max_st_writers):
if key in max_st:
writer(fid, id_, max_st[key])
end_block(fid, FIFF.FIFFB_SSS_ST_INFO)
sss_ctc = record["sss_ctc"]
if len(sss_ctc) > 0: # dict has entries
start_block(fid, FIFF.FIFFB_CHANNEL_DECOUPLER)
for key, id_, writer in zip(_sss_ctc_keys, _sss_ctc_ids, _sss_ctc_writers):
if key in sss_ctc:
writer(fid, id_, sss_ctc[key])
if "proj_items_chs" in sss_ctc:
write_name_list_sanitized(
fid,
FIFF.FIFF_PROJ_ITEM_CH_NAME_LIST,
sss_ctc["proj_items_chs"],
"proj_items_chs",
)
end_block(fid, FIFF.FIFFB_CHANNEL_DECOUPLER)
sss_cal = record["sss_cal"]
if len(sss_cal) > 0:
start_block(fid, FIFF.FIFFB_SSS_CAL)
for key, id_, writer in zip(_sss_cal_keys, _sss_cal_ids, _sss_cal_writers):
if key in sss_cal:
writer(fid, id_, sss_cal[key])
end_block(fid, FIFF.FIFFB_SSS_CAL)

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
from ..defaults import DEFAULTS
from ..utils import (
_check_option,
_check_preload,
_on_missing,
_validate_type,
fill_doc,
logger,
pinv,
verbose,
warn,
)
from .constants import FIFF
from .meas_info import _check_ch_keys
from .pick import _ELECTRODE_CH_TYPES, pick_channels, pick_channels_forward, pick_types
from .proj import _has_eeg_average_ref_proj, make_eeg_average_ref_proj, setup_proj
def _check_before_reference(inst, ref_from, ref_to, ch_type):
"""Prepare instance for referencing."""
# Check to see that data is preloaded
_check_preload(inst, "Applying a reference")
ch_type = _get_ch_type(inst, ch_type)
ch_dict = {**{type_: True for type_ in ch_type}, "meg": False, "ref_meg": False}
eeg_idx = pick_types(inst.info, **ch_dict)
if ref_to is None:
ref_to = [inst.ch_names[i] for i in eeg_idx]
extra = "EEG channels found"
else:
extra = "channels supplied"
if len(ref_to) == 0:
raise ValueError(f"No {extra} to apply the reference to")
_check_ssp(inst, ref_from + ref_to)
# If the reference touches EEG/ECoG/sEEG/DBS electrodes, note in the
# info that a non-CAR has been applied.
ref_to_channels = pick_channels(inst.ch_names, ref_to, ordered=True)
if len(np.intersect1d(ref_to_channels, eeg_idx)) > 0:
with inst.info._unlock():
inst.info["custom_ref_applied"] = FIFF.FIFFV_MNE_CUSTOM_REF_ON
return ref_to
def _check_ssp(inst, ref_items):
"""Check for SSPs that may block re-referencing."""
projs_to_remove = []
for i, proj in enumerate(inst.info["projs"]):
# Remove any average reference projections
if (
proj["desc"] == "Average EEG reference"
or proj["kind"] == FIFF.FIFFV_PROJ_ITEM_EEG_AVREF
):
logger.info("Removing existing average EEG reference projection.")
# Don't remove the projection right away, but do this at the end of
# this loop.
projs_to_remove.append(i)
# Inactive SSPs may block re-referencing
elif (
not proj["active"]
and len([ch for ch in ref_items if ch in proj["data"]["col_names"]]) > 0
):
raise RuntimeError(
"Inactive signal space projection (SSP) operators are "
"present that operate on sensors involved in the desired "
"referencing scheme. These projectors need to be applied "
"using the apply_proj() method function before the desired "
"reference can be set."
)
for i in projs_to_remove:
del inst.info["projs"][i]
# Need to call setup_proj after changing the projs:
inst._projector, _ = setup_proj(inst.info, add_eeg_ref=False, activate=False)
def _check_before_dict_reference(inst, ref_dict):
"""Prepare instance for dict-based referencing."""
# Check to see that data is preloaded
_check_preload(inst, "Applying a reference")
# Promote all values to list-like. This simplifies our logic and also helps catch
# self-referencing cases like `{"Cz": ["Cz"]}`
_refdict = {k: [v] if isinstance(v, str) else list(v) for k, v in ref_dict.items()}
# Check that keys are strings and values are lists-of-strings
key_types = {type(k) for k in _refdict}
value_types = {type(v) for val in _refdict.values() for v in val}
for elem_name, elem in dict(key=key_types, value=value_types).items():
if bad_elem := elem - {str}:
raise TypeError(
f"{elem_name.capitalize()}s in the ref_channels dict must be strings. "
f"Your dict has {elem_name}s of type "
f'{", ".join(map(lambda x: x.__name__, bad_elem))}.'
)
# Check that keys are valid channels and values are lists-of-valid-channels
ch_set = set(inst.ch_names)
bad_ch_set = set(inst.info["bads"])
keys = set(_refdict)
values = set(sum(_refdict.values(), []))
for elem_name, elem in dict(key=keys, value=values).items():
if bad_elem := elem - ch_set:
raise ValueError(
f'ref_channels dict contains invalid {elem_name}(s) '
f'({", ".join(bad_elem)}) '
"that are not names of channels in the instance."
)
# Check that values are not bad channels
if bad_elem := elem.intersection(bad_ch_set):
warn(
f"ref_channels dict contains {elem_name}(s) "
f"({', '.join(bad_elem)}) "
"that are marked as bad channels."
)
_check_ssp(inst, keys.union(values))
# Check for self-referencing
self_ref = [[k] == v for k, v in _refdict.items()]
if any(self_ref):
which = np.array(list(_refdict))[np.nonzero(self_ref)]
for ch in which:
warn(f"Channel {ch} is self-referenced, which will nullify the channel.")
# Check that channel types match. First unpack list-like vals into separate items:
pairs = [(k, v) for k in _refdict for v in _refdict[k]]
ch_type_map = dict(zip(inst.ch_names, inst.get_channel_types()))
mismatch = [ch_type_map[k] != ch_type_map[v] for k, v in pairs]
if any(mismatch):
mismatch_pairs = np.array(pairs)[mismatch]
for k, v in mismatch_pairs:
warn(
f"Channel {k} ({ch_type_map[k]}) is referenced to channel {v} which is "
f"a different channel type ({ch_type_map[v]})."
)
# convert channel names to indices
keys_ix = pick_channels(inst.ch_names, list(_refdict), ordered=True)
vals_ix = (pick_channels(inst.ch_names, v, ordered=True) for v in _refdict.values())
return dict(zip(keys_ix, vals_ix))
def _apply_reference(inst, ref_from, ref_to=None, forward=None, ch_type="auto"):
"""Apply a custom EEG referencing scheme."""
ref_to = _check_before_reference(inst, ref_from, ref_to, ch_type)
# Compute reference
if len(ref_from) > 0:
# this is guaranteed below, but we should avoid the crazy pick_channels
# behavior that [] gives all. Also use ordered=True just to make sure
# that all supplied channels actually exist.
assert len(ref_to) > 0
ref_names = ref_from
ref_from = pick_channels(inst.ch_names, ref_from, ordered=True)
ref_to = pick_channels(inst.ch_names, ref_to, ordered=True)
data = inst._data
ref_data = data[..., ref_from, :].mean(-2, keepdims=True)
data[..., ref_to, :] -= ref_data
ref_data = ref_data[..., 0, :]
# REST
if forward is not None:
# use ch_sel and the given forward
forward = pick_channels_forward(forward, ref_names, ordered=True)
# 1-3. Compute a forward (G) and avg-ref'ed data (done above)
G = forward["sol"]["data"]
assert G.shape[0] == len(ref_names)
# 4. Compute the forward (G) and average-reference it (Ga):
Ga = G - np.mean(G, axis=0, keepdims=True)
# 5. Compute the Ga_inv by SVD
Ga_inv = pinv(Ga, rtol=1e-6)
# 6. Compute Ra = (G @ Ga_inv) in eq (8) from G and Ga_inv
Ra = G @ Ga_inv
# 7-8. Compute Vp = Ra @ Va; then Vpa=average(Vp)
Vpa = np.mean(Ra @ data[..., ref_from, :], axis=-2, keepdims=True)
data[..., ref_to, :] += Vpa
else:
ref_data = None
return inst, ref_data
def _apply_dict_reference(inst, ref_dict):
"""Apply a dict-based custom EEG referencing scheme."""
# this converts all keys to channel indices and all values to arrays of ch. indices:
ref_dict = _check_before_dict_reference(inst, ref_dict)
data = inst._data
orig_data = data.copy()
for ref_to, ref_from in ref_dict.items():
ref_data = orig_data[..., ref_from, :].mean(-2, keepdims=True)
data[..., [ref_to], :] -= ref_data
with inst.info._unlock():
inst.info["custom_ref_applied"] = FIFF.FIFFV_MNE_CUSTOM_REF_ON
return inst, None
@fill_doc
def add_reference_channels(inst, ref_channels, copy=True):
"""Add reference channels to data that consists of all zeros.
Adds reference channels to data that were not included during recording.
This is useful when you need to re-reference your data to different
channels. These added channels will consist of all zeros.
Parameters
----------
inst : instance of Raw | Epochs | Evoked
Instance of Raw or Epochs with EEG channels and reference channel(s).
%(ref_channels)s
copy : bool
Specifies whether the data will be copied (True) or modified in-place
(False). Defaults to True.
Returns
-------
inst : instance of Raw | Epochs | Evoked
Data with added EEG reference channels.
Notes
-----
.. warning::
When :ref:`re-referencing <tut-set-eeg-ref>`,
make sure to apply the montage using :meth:`mne.io.Raw.set_montage`
only after calling this function. Applying a montage will only set
locations of channels that exist at the time it is applied.
"""
from ..epochs import BaseEpochs
from ..evoked import Evoked
from ..io import BaseRaw
# Check to see that data is preloaded
_check_preload(inst, "add_reference_channels")
_validate_type(ref_channels, (list, tuple, str), "ref_channels")
if isinstance(ref_channels, str):
ref_channels = [ref_channels]
for ch in ref_channels:
if ch in inst.info["ch_names"]:
raise ValueError(f"Channel {ch} already specified in inst.")
# Once CAR is applied (active), don't allow adding channels
if _has_eeg_average_ref_proj(inst.info, check_active=True):
raise RuntimeError("Average reference already applied to data.")
if copy:
inst = inst.copy()
if isinstance(inst, BaseRaw | Evoked):
data = inst._data
refs = np.zeros((len(ref_channels), data.shape[1]))
data = np.vstack((data, refs))
inst._data = data
elif isinstance(inst, BaseEpochs):
data = inst._data
x, y, z = data.shape
refs = np.zeros((x * len(ref_channels), z))
data = np.vstack((data.reshape((x * y, z), order="F"), refs))
data = data.reshape(x, y + len(ref_channels), z, order="F")
inst._data = data
else:
raise TypeError(
f"inst should be Raw, Epochs, or Evoked instead of {type(inst)}."
)
nchan = len(inst.info["ch_names"])
if inst.info.get("dig", None) is not None:
# A montage has been set. Try to infer location of reference channels.
# "zeroth" EEG electrode dig points is reference
ref_dig_loc = [
dl
for dl in inst.info["dig"]
if (dl["kind"] == FIFF.FIFFV_POINT_EEG and dl["ident"] == 0)
]
if len(ref_channels) > 1 or len(ref_dig_loc) != len(ref_channels):
ref_dig_array = np.full(12, np.nan)
warn(
"Location for this channel is unknown or ambiguous; consider calling "
"set_montage() after adding new reference channels if needed. "
"Applying a montage will only set locations of channels that "
"exist at the time it is applied."
)
else: # n_ref_channels == 1 and a single ref digitization exists
ref_dig_array = np.concatenate(
(ref_dig_loc[0]["r"], ref_dig_loc[0]["r"], np.zeros(6))
)
# Replace the (possibly new) Ref location for each channel
for idx in pick_types(inst.info, meg=False, eeg=True, exclude=[]):
inst.info["chs"][idx]["loc"][3:6] = ref_dig_loc[0]["r"]
else:
# If no montage has ever been set, we cannot even try to infer a location.
ref_dig_array = np.full(12, np.nan)
for ch in ref_channels:
chan_info = {
"ch_name": ch,
"coil_type": FIFF.FIFFV_COIL_EEG,
"kind": FIFF.FIFFV_EEG_CH,
"logno": nchan + 1,
"scanno": nchan + 1,
"cal": 1,
"range": 1.0,
"unit_mul": FIFF.FIFF_UNITM_NONE,
"unit": FIFF.FIFF_UNIT_V,
"coord_frame": FIFF.FIFFV_COORD_HEAD,
"loc": ref_dig_array,
}
inst.info["chs"].append(chan_info)
inst.info._update_redundant()
range_ = np.arange(1, len(ref_channels) + 1)
if isinstance(inst, BaseRaw):
inst._cals = np.hstack((inst._cals, [1] * len(ref_channels)))
for pi, picks in enumerate(inst._read_picks):
inst._read_picks[pi] = np.concatenate([picks, np.max(picks) + range_])
elif isinstance(inst, BaseEpochs):
picks = inst.picks
inst.picks = np.concatenate([picks, np.max(picks) + range_])
inst.info._check_consistency()
set_eeg_reference(inst, ref_channels=ref_channels, copy=False, verbose=False)
return inst
_ref_dict = {
FIFF.FIFFV_MNE_CUSTOM_REF_ON: "on",
FIFF.FIFFV_MNE_CUSTOM_REF_OFF: "off",
FIFF.FIFFV_MNE_CUSTOM_REF_CSD: "CSD",
}
def _check_can_reref(inst):
from ..epochs import BaseEpochs
from ..evoked import Evoked
from ..io import BaseRaw
_validate_type(inst, (BaseRaw, BaseEpochs, Evoked), "Instance")
current_custom = inst.info["custom_ref_applied"]
if current_custom not in (
FIFF.FIFFV_MNE_CUSTOM_REF_ON,
FIFF.FIFFV_MNE_CUSTOM_REF_OFF,
):
raise RuntimeError(
"Cannot set new reference on data with custom reference type "
f"{_ref_dict[current_custom]!r}"
)
@verbose
def set_eeg_reference(
inst,
ref_channels="average",
copy=True,
projection=False,
ch_type="auto",
forward=None,
*,
joint=False,
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.
Note that it is also possible to re-reference the signal using a
Laplacian (LAP) "reference-free" transformation using the
:func:`.compute_current_source_density` function.
Parameters
----------
inst : instance of Raw | Epochs | Evoked
Instance of Raw or Epochs with EEG channels and reference channel(s).
%(ref_channels_set_eeg_reference)s
copy : bool
Specifies whether the data will be copied (True) or modified in-place
(False). Defaults to True.
%(projection_set_eeg_reference)s
%(ch_type_set_eeg_reference)s
%(forward_set_eeg_reference)s
%(joint_set_eeg_reference)s
%(verbose)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.
ref_data : array
Array of reference data subtracted from EEG channels. This will be
``None`` if ``projection=True``, or if ``ref_channels`` is ``"REST"`` or a
:class:`dict`.
%(set_eeg_reference_see_also_notes)s
"""
from ..forward import Forward
_check_can_reref(inst)
if isinstance(ref_channels, dict):
logger.info("Applying a custom dict-based reference.")
return _apply_dict_reference(inst, ref_channels)
ch_type = _get_ch_type(inst, ch_type)
if projection: # average reference projector
if ref_channels != "average":
raise ValueError(
'Setting projection=True is only supported for ref_channels="average", '
f"got {ref_channels!r}."
)
# We need verbose='error' here in case we add projs sequentially
if _has_eeg_average_ref_proj(inst.info, ch_type=ch_type, verbose="error"):
warn(
"An average reference projection was already added. The data "
"has been left untouched."
)
else:
# Creating an average reference may fail. In this case, make
# sure that the custom_ref_applied flag is left untouched.
custom_ref_applied = inst.info["custom_ref_applied"]
try:
with inst.info._unlock():
inst.info["custom_ref_applied"] = FIFF.FIFFV_MNE_CUSTOM_REF_OFF
if joint:
inst.add_proj(
make_eeg_average_ref_proj(
inst.info, ch_type=ch_type, activate=False
)
)
else:
for this_ch_type in ch_type:
inst.add_proj(
make_eeg_average_ref_proj(
inst.info, ch_type=this_ch_type, activate=False
)
)
except Exception:
with inst.info._unlock():
inst.info["custom_ref_applied"] = custom_ref_applied
raise
# If the data has been preloaded, projections will no
# longer be automatically applied.
if inst.preload:
logger.info(
"Average reference projection was added, "
"but has not been applied yet. Use the "
"apply_proj method to apply it."
)
return inst, None
del projection # not used anymore
inst = inst.copy() if copy else inst
ch_dict = {**{type_: True for type_ in ch_type}, "meg": False, "ref_meg": False}
ch_sel = [inst.ch_names[i] for i in pick_types(inst.info, **ch_dict)]
if ref_channels == "REST":
_validate_type(forward, Forward, 'forward when ref_channels="REST"')
else:
forward = None # signal to _apply_reference not to do REST
if ref_channels in ("average", "REST"):
logger.info(f"Applying {ref_channels} reference.")
ref_channels = ch_sel
if ref_channels == []:
logger.info("EEG data marked as already having the desired reference.")
else:
logger.info(
"Applying a custom "
f"{tuple(DEFAULTS['titles'][type_] for type_ in ch_type)} "
"reference."
)
return _apply_reference(inst, ref_channels, ch_sel, forward, ch_type=ch_type)
def _get_ch_type(inst, ch_type):
_validate_type(ch_type, (str, list, tuple), "ch_type")
valid_ch_types = ("auto",) + _ELECTRODE_CH_TYPES
if isinstance(ch_type, str):
_check_option("ch_type", ch_type, valid_ch_types)
if ch_type != "auto":
ch_type = [ch_type]
elif isinstance(ch_type, list | tuple):
for type_ in ch_type:
_validate_type(type_, str, "ch_type")
_check_option("ch_type", type_, valid_ch_types[1:])
ch_type = list(ch_type)
# if ch_type is 'auto', search through list to find first reasonable
# reference-able channel type.
if ch_type == "auto":
for type_ in _ELECTRODE_CH_TYPES:
if type_ in inst:
ch_type = [type_]
logger.info(
f"{DEFAULTS['titles'][type_]} channel type selected for "
"re-referencing"
)
break
# if auto comes up empty, or the user specifies a bad ch_type.
else:
raise ValueError("No EEG, ECoG, sEEG or DBS channels found to rereference.")
return ch_type
@verbose
def set_bipolar_reference(
inst,
anode,
cathode,
ch_name=None,
ch_info=None,
drop_refs=True,
copy=True,
on_bad="warn",
verbose=None,
):
"""Re-reference selected channels using a bipolar referencing scheme.
A bipolar reference takes the difference between two channels (the anode
minus the cathode) and adds it as a new virtual channel. The original
channels will be dropped by default.
Multiple anodes and cathodes can be specified, in which case multiple
virtual channels will be created. The 1st cathode will be subtracted
from the 1st anode, the 2nd cathode from the 2nd anode, etc.
By default, the virtual channels will be annotated with channel-info and
-location of the anodes and coil types will be set to EEG_BIPOLAR.
Parameters
----------
inst : instance of Raw | Epochs | Evoked
Data containing the unreferenced channels.
anode : str | list of str
The name(s) of the channel(s) to use as anode in the bipolar reference.
cathode : str | list of str
The name(s) of the channel(s) to use as cathode in the bipolar
reference.
ch_name : str | list of str | None
The channel name(s) for the virtual channel(s) containing the resulting
signal. By default, bipolar channels are named after the anode and
cathode, but it is recommended to supply a more meaningful name.
ch_info : dict | list of dict | None
This parameter can be used to supply a dictionary (or a dictionary for
each bipolar channel) containing channel information to merge in,
overwriting the default values. Defaults to None.
drop_refs : bool
Whether to drop the anode/cathode channels from the instance.
copy : bool
Whether to operate on a copy of the data (True) or modify it in-place
(False). Defaults to True.
on_bad : str
If a bipolar channel is created from a bad anode or a bad cathode, mne
warns if on_bad="warns", raises ValueError if on_bad="raise", and does
nothing if on_bad="ignore". For "warn" and "ignore", the new bipolar
channel will be marked as bad. Defaults to on_bad="warns".
%(verbose)s
Returns
-------
inst : instance of Raw | Epochs | Evoked
Data with the specified channels re-referenced.
See Also
--------
set_eeg_reference : Convenience function for creating an EEG reference.
Notes
-----
1. If the anodes contain any EEG channels, this function removes
any pre-existing average reference projections.
2. During source localization, the EEG signal should have an average
reference.
3. The data must be preloaded.
.. versionadded:: 0.9.0
"""
from ..epochs import BaseEpochs, EpochsArray
from ..evoked import EvokedArray
from ..io import BaseRaw, RawArray
from .meas_info import create_info
_check_can_reref(inst)
if not isinstance(anode, list):
anode = [anode]
if not isinstance(cathode, list):
cathode = [cathode]
if len(anode) != len(cathode):
raise ValueError(
f"Number of anodes (got {len(anode)}) must equal the number "
f"of cathodes (got {len(cathode)})."
)
if ch_name is None:
ch_name = [f"{a}-{c}" for (a, c) in zip(anode, cathode)]
elif not isinstance(ch_name, list):
ch_name = [ch_name]
if len(ch_name) != len(anode):
raise ValueError(
"Number of channel names must equal the number of "
f"anodes/cathodes (got {len(ch_name)})."
)
# Check for duplicate channel names (it is allowed to give the name of the
# anode or cathode channel, as they will be replaced).
for ch, a, c in zip(ch_name, anode, cathode):
if ch not in [a, c] and ch in inst.ch_names:
raise ValueError(
f'There is already a channel named "{ch}", please '
"specify a different name for the bipolar "
"channel using the ch_name parameter."
)
if ch_info is None:
ch_info = [{} for _ in anode]
elif not isinstance(ch_info, list):
ch_info = [ch_info]
if len(ch_info) != len(anode):
raise ValueError(
"Number of channel info dictionaries must equal the "
"number of anodes/cathodes."
)
if copy:
inst = inst.copy()
anode = _check_before_reference(
inst, ref_from=cathode, ref_to=anode, ch_type="auto"
)
# Create bipolar reference channels by multiplying the data
# (channels x time) with a matrix (n_virtual_channels x channels)
# and add them to the instance.
multiplier = np.zeros((len(anode), len(inst.ch_names)))
for idx, (a, c) in enumerate(zip(anode, cathode)):
multiplier[idx, inst.ch_names.index(a)] = 1
multiplier[idx, inst.ch_names.index(c)] = -1
ref_info = create_info(
ch_names=ch_name,
sfreq=inst.info["sfreq"],
ch_types=inst.get_channel_types(picks=anode),
)
# Update "chs" in Reference-Info.
for ch_idx, (an, info) in enumerate(zip(anode, ch_info)):
_check_ch_keys(info, ch_idx, name="ch_info", check_min=False)
an_idx = inst.ch_names.index(an)
# Copy everything from anode (except ch_name).
an_chs = {k: v for k, v in inst.info["chs"][an_idx].items() if k != "ch_name"}
ref_info["chs"][ch_idx].update(an_chs)
# Set coil-type to bipolar.
ref_info["chs"][ch_idx]["coil_type"] = FIFF.FIFFV_COIL_EEG_BIPOLAR
# Update with info from ch_info-parameter.
ref_info["chs"][ch_idx].update(info)
# Set other info-keys from original instance.
pick_info = {
k: v
for k, v in inst.info.items()
if k not in ["chs", "ch_names", "bads", "nchan", "sfreq"]
}
with ref_info._unlock():
ref_info.update(pick_info)
# Rereferencing of data.
ref_data = multiplier @ inst._data
if isinstance(inst, BaseRaw):
ref_inst = RawArray(ref_data, ref_info, first_samp=inst.first_samp, copy=None)
elif isinstance(inst, BaseEpochs):
ref_inst = EpochsArray(
ref_data,
ref_info,
events=inst.events,
tmin=inst.tmin,
event_id=inst.event_id,
metadata=inst.metadata,
)
else:
ref_inst = EvokedArray(
ref_data,
ref_info,
tmin=inst.tmin,
comment=inst.comment,
nave=inst.nave,
kind="average",
)
# Add referenced instance to original instance.
inst.add_channels([ref_inst], force_update_info=True)
# Handle bad channels.
bad_bipolar_chs = []
for ch_idx, (a, c) in enumerate(zip(anode, cathode)):
if a in inst.info["bads"] or c in inst.info["bads"]:
bad_bipolar_chs.append(ch_name[ch_idx])
# Add warnings if bad channels are present.
if bad_bipolar_chs:
msg = f"Bipolar channels are based on bad channels: {bad_bipolar_chs}."
_on_missing(on_bad, msg)
inst.info["bads"] += bad_bipolar_chs
added_channels = ", ".join([name for name in ch_name])
logger.info(f"Added the following bipolar channels:\n{added_channels}")
for attr_name in ["picks", "_projector"]:
setattr(inst, attr_name, None)
# Drop remaining channels.
if drop_refs:
drop_channels = list((set(anode) | set(cathode)) & set(inst.ch_names))
inst.drop_channels(drop_channels)
return inst

523
mne/_fiff/tag.py Normal file
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@@ -0,0 +1,523 @@
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import html
import re
import struct
from dataclasses import dataclass
from functools import partial
from typing import Any
import numpy as np
from scipy.sparse import csc_array, csr_array
from ..utils import _check_option, warn
from ..utils.numerics import _julian_to_date
from .constants import (
FIFF,
_ch_coil_type_named,
_ch_kind_named,
_ch_unit_mul_named,
_ch_unit_named,
_dig_cardinal_named,
_dig_kind_named,
)
##############################################################################
# HELPERS
@dataclass
class Tag:
"""Tag in FIF tree structure."""
kind: int
type: int
size: int
next: int
pos: int
data: Any = None
def __eq__(self, tag): # noqa: D105
return int(
self.kind == tag.kind
and self.type == tag.type
and self.size == tag.size
and self.next == tag.next
and self.pos == tag.pos
and self.data == tag.data
)
@property
def next_pos(self):
"""The next tag position."""
if self.next == FIFF.FIFFV_NEXT_SEQ: # 0
return self.pos + 16 + self.size
elif self.next > 0:
return self.next
else: # self.next should be -1 if we get here
return None # safest to return None so that things like fid.seek die
def _frombuffer_rows(fid, tag_size, dtype=None, shape=None, rlims=None):
"""Get a range of rows from a large tag."""
if shape is not None:
item_size = np.dtype(dtype).itemsize
if not len(shape) == 2:
raise ValueError("Only implemented for 2D matrices")
want_shape = np.prod(shape)
have_shape = tag_size // item_size
if want_shape != have_shape:
raise ValueError(
f"Wrong shape specified, requested {want_shape} but got "
f"{have_shape}"
)
if not len(rlims) == 2:
raise ValueError("rlims must have two elements")
n_row_out = rlims[1] - rlims[0]
if n_row_out <= 0:
raise ValueError("rlims must yield at least one output")
row_size = item_size * shape[1]
# # of bytes to skip at the beginning, # to read, where to end
start_skip = int(rlims[0] * row_size)
read_size = int(n_row_out * row_size)
end_pos = int(fid.tell() + tag_size)
# Move the pointer ahead to the read point
fid.seek(start_skip, 1)
# Do the reading
out = np.frombuffer(fid.read(read_size), dtype=dtype)
# Move the pointer ahead to the end of the tag
fid.seek(end_pos)
else:
out = np.frombuffer(fid.read(tag_size), dtype=dtype)
return out
def _loc_to_coil_trans(loc):
"""Convert loc vector to coil_trans."""
assert loc.shape[-1] == 12
coil_trans = np.zeros(loc.shape[:-1] + (4, 4))
coil_trans[..., :3, 3] = loc[..., :3]
coil_trans[..., :3, :3] = np.reshape(
loc[..., 3:], loc.shape[:-1] + (3, 3)
).swapaxes(-1, -2)
coil_trans[..., -1, -1] = 1.0
return coil_trans
def _coil_trans_to_loc(coil_trans):
"""Convert coil_trans to loc."""
coil_trans = coil_trans.astype(np.float64)
return np.roll(coil_trans.T[:, :3], 1, 0).flatten()
def _loc_to_eeg_loc(loc):
"""Convert a loc to an EEG loc."""
if not np.isfinite(loc[:3]).all():
raise RuntimeError("Missing EEG channel location")
if np.isfinite(loc[3:6]).all() and (loc[3:6]).any():
return np.array([loc[0:3], loc[3:6]]).T
else:
return loc[0:3][:, np.newaxis].copy()
##############################################################################
# READING FUNCTIONS
# None of these functions have docstring because it's more compact that way,
# and hopefully it's clear what they do by their names and variable values.
# See ``read_tag`` for variable descriptions. Return values are implied
# by the function names.
def _read_tag_header(fid, pos):
"""Read only the header of a Tag."""
fid.seek(pos, 0)
s = fid.read(16)
if len(s) != 16:
where = fid.tell() - len(s)
extra = f" in file {fid.name}" if hasattr(fid, "name") else ""
warn(f"Invalid tag with only {len(s)}/16 bytes at position {where}{extra}")
return None
# struct.unpack faster than np.frombuffer, saves ~10% of time some places
kind, type_, size, next_ = struct.unpack(">iIii", s)
return Tag(kind, type_, size, next_, pos)
def _read_matrix(fid, tag, shape, rlims):
"""Read a matrix (dense or sparse) tag."""
# This should be easy to implement (see _frombuffer_rows)
# if we need it, but for now, it's not...
if shape is not None or rlims is not None:
raise ValueError("Row reading not implemented for matrices yet")
matrix_coding, matrix_type, bit, dtype = _matrix_info(tag)
pos = tag.pos + 16
fid.seek(pos + tag.size - 4, 0)
if matrix_coding == "dense":
# Find dimensions and return to the beginning of tag data
ndim = int(np.frombuffer(fid.read(4), dtype=">i4").item())
fid.seek(-(ndim + 1) * 4, 1)
dims = np.frombuffer(fid.read(4 * ndim), dtype=">i4")[::-1]
#
# Back to where the data start
#
fid.seek(pos, 0)
if ndim > 3:
raise Exception(
"Only 2 or 3-dimensional matrices are supported at this time"
)
data = fid.read(int(bit * dims.prod()))
data = np.frombuffer(data, dtype=dtype)
# Note: we need the non-conjugate transpose here
if matrix_type == FIFF.FIFFT_COMPLEX_FLOAT:
data = data.view(">c8")
elif matrix_type == FIFF.FIFFT_COMPLEX_DOUBLE:
data = data.view(">c16")
data.shape = dims
else:
# Find dimensions and return to the beginning of tag data
ndim = int(np.frombuffer(fid.read(4), dtype=">i4").item())
fid.seek(-(ndim + 2) * 4, 1)
dims = np.frombuffer(fid.read(4 * (ndim + 1)), dtype=">i4")
if ndim != 2:
raise Exception("Only two-dimensional matrices are supported at this time")
# Back to where the data start
fid.seek(pos, 0)
nnz = int(dims[0])
nrow = int(dims[1])
ncol = int(dims[2])
# We need to make a copy so that we can own the data, otherwise we get:
# _sparsetools.csr_sort_indices(len(self.indptr) - 1, self.indptr,
# E ValueError: WRITEBACKIFCOPY base is read-only
data = np.frombuffer(fid.read(bit * nnz), dtype=dtype).astype(np.float32)
shape = (dims[1], dims[2])
if matrix_coding == "sparse CCS":
tmp_indices = fid.read(4 * nnz)
indices = np.frombuffer(tmp_indices, dtype=">i4")
tmp_ptr = fid.read(4 * (ncol + 1))
indptr = np.frombuffer(tmp_ptr, dtype=">i4")
swap = nrow
klass = csc_array
else:
assert matrix_coding == "sparse RCS", matrix_coding
tmp_indices = fid.read(4 * nnz)
indices = np.frombuffer(tmp_indices, dtype=">i4")
tmp_ptr = fid.read(4 * (nrow + 1))
indptr = np.frombuffer(tmp_ptr, dtype=">i4")
swap = ncol
klass = csr_array
if indptr[-1] > len(indices) or np.any(indptr < 0):
# There was a bug in MNE-C that caused some data to be
# stored without byte swapping
indices = np.concatenate(
(
np.frombuffer(tmp_indices[: 4 * (swap + 1)], dtype=">i4"),
np.frombuffer(tmp_indices[4 * (swap + 1) :], dtype="<i4"),
)
)
indptr = np.frombuffer(tmp_ptr, dtype="<i4")
data = klass((data, indices, indptr), shape=shape)
return data
def _read_simple(fid, tag, shape, rlims, dtype):
"""Read simple datatypes from tag (typically used with partial)."""
return _frombuffer_rows(fid, tag.size, dtype=dtype, shape=shape, rlims=rlims)
def _read_string(fid, tag, shape, rlims):
"""Read a string tag."""
# Always decode to ISO 8859-1 / latin1 (FIFF standard).
d = _frombuffer_rows(fid, tag.size, dtype=">c", shape=shape, rlims=rlims)
string = str(d.tobytes().decode("latin1", "ignore"))
if re.search(r"&#[0-9a-fA-F]{6};", string):
string = html.unescape(string)
return string
def _read_complex_float(fid, tag, shape, rlims):
"""Read complex float tag."""
# data gets stored twice as large
if shape is not None:
shape = (shape[0], shape[1] * 2)
d = _frombuffer_rows(fid, tag.size, dtype=">f4", shape=shape, rlims=rlims)
d = d.view(">c8")
return d
def _read_complex_double(fid, tag, shape, rlims):
"""Read complex double tag."""
# data gets stored twice as large
if shape is not None:
shape = (shape[0], shape[1] * 2)
d = _frombuffer_rows(fid, tag.size, dtype=">f8", shape=shape, rlims=rlims)
d = d.view(">c16")
return d
def _read_id_struct(fid, tag, shape, rlims):
"""Read ID struct tag."""
return dict(
version=int(np.frombuffer(fid.read(4), dtype=">i4").item()),
machid=np.frombuffer(fid.read(8), dtype=">i4"),
secs=int(np.frombuffer(fid.read(4), dtype=">i4").item()),
usecs=int(np.frombuffer(fid.read(4), dtype=">i4").item()),
)
def _read_dig_point_struct(fid, tag, shape, rlims):
"""Read dig point struct tag."""
kind = int(np.frombuffer(fid.read(4), dtype=">i4").item())
kind = _dig_kind_named.get(kind, kind)
ident = int(np.frombuffer(fid.read(4), dtype=">i4").item())
if kind == FIFF.FIFFV_POINT_CARDINAL:
ident = _dig_cardinal_named.get(ident, ident)
return dict(
kind=kind,
ident=ident,
r=np.frombuffer(fid.read(12), dtype=">f4"),
coord_frame=FIFF.FIFFV_COORD_UNKNOWN,
)
def _read_coord_trans_struct(fid, tag, shape, rlims):
"""Read coord trans struct tag."""
from ..transforms import Transform
fro = int(np.frombuffer(fid.read(4), dtype=">i4").item())
to = int(np.frombuffer(fid.read(4), dtype=">i4").item())
rot = np.frombuffer(fid.read(36), dtype=">f4").reshape(3, 3)
move = np.frombuffer(fid.read(12), dtype=">f4")
trans = np.r_[np.c_[rot, move], np.array([[0], [0], [0], [1]]).T]
data = Transform(fro, to, trans)
fid.seek(48, 1) # Skip over the inverse transformation
return data
_ch_coord_dict = {
FIFF.FIFFV_MEG_CH: FIFF.FIFFV_COORD_DEVICE,
FIFF.FIFFV_REF_MEG_CH: FIFF.FIFFV_COORD_DEVICE,
FIFF.FIFFV_EEG_CH: FIFF.FIFFV_COORD_HEAD,
FIFF.FIFFV_ECOG_CH: FIFF.FIFFV_COORD_HEAD,
FIFF.FIFFV_SEEG_CH: FIFF.FIFFV_COORD_HEAD,
FIFF.FIFFV_DBS_CH: FIFF.FIFFV_COORD_HEAD,
FIFF.FIFFV_FNIRS_CH: FIFF.FIFFV_COORD_HEAD,
}
def _read_ch_info_struct(fid, tag, shape, rlims):
"""Read channel info struct tag."""
d = dict(
scanno=int(np.frombuffer(fid.read(4), dtype=">i4").item()),
logno=int(np.frombuffer(fid.read(4), dtype=">i4").item()),
kind=int(np.frombuffer(fid.read(4), dtype=">i4").item()),
range=float(np.frombuffer(fid.read(4), dtype=">f4").item()),
cal=float(np.frombuffer(fid.read(4), dtype=">f4").item()),
coil_type=int(np.frombuffer(fid.read(4), dtype=">i4").item()),
# deal with really old OSX Anaconda bug by casting to float64
loc=np.frombuffer(fid.read(48), dtype=">f4").astype(np.float64),
# unit and exponent
unit=int(np.frombuffer(fid.read(4), dtype=">i4").item()),
unit_mul=int(np.frombuffer(fid.read(4), dtype=">i4").item()),
)
# channel name
ch_name = np.frombuffer(fid.read(16), dtype=">c")
ch_name = ch_name[: np.argmax(ch_name == b"")].tobytes()
d["ch_name"] = ch_name.decode()
# coil coordinate system definition
_update_ch_info_named(d)
return d
def _update_ch_info_named(d):
d["coord_frame"] = _ch_coord_dict.get(d["kind"], FIFF.FIFFV_COORD_UNKNOWN)
d["kind"] = _ch_kind_named.get(d["kind"], d["kind"])
d["coil_type"] = _ch_coil_type_named.get(d["coil_type"], d["coil_type"])
d["unit"] = _ch_unit_named.get(d["unit"], d["unit"])
d["unit_mul"] = _ch_unit_mul_named.get(d["unit_mul"], d["unit_mul"])
def _read_old_pack(fid, tag, shape, rlims):
"""Read old pack tag."""
offset = float(np.frombuffer(fid.read(4), dtype=">f4").item())
scale = float(np.frombuffer(fid.read(4), dtype=">f4").item())
data = np.frombuffer(fid.read(tag.size - 8), dtype=">i2")
data = data * scale # to float64
data += offset
return data
def _read_dir_entry_struct(fid, tag, shape, rlims):
"""Read dir entry struct tag."""
pos = tag.pos + 16
entries = list()
for offset in range(1, tag.size // 16):
ent = _read_tag_header(fid, pos + offset * 16)
# The position of the real tag on disk is stored in the "next" entry within the
# directory, so we need to overwrite ent.pos. For safety let's also overwrite
# ent.next to point nowhere
ent.pos, ent.next = ent.next, FIFF.FIFFV_NEXT_NONE
entries.append(ent)
return entries
def _read_julian(fid, tag, shape, rlims):
"""Read julian tag."""
return _julian_to_date(int(np.frombuffer(fid.read(4), dtype=">i4").item()))
# Read types call dict
_call_dict = {
FIFF.FIFFT_STRING: _read_string,
FIFF.FIFFT_COMPLEX_FLOAT: _read_complex_float,
FIFF.FIFFT_COMPLEX_DOUBLE: _read_complex_double,
FIFF.FIFFT_ID_STRUCT: _read_id_struct,
FIFF.FIFFT_DIG_POINT_STRUCT: _read_dig_point_struct,
FIFF.FIFFT_COORD_TRANS_STRUCT: _read_coord_trans_struct,
FIFF.FIFFT_CH_INFO_STRUCT: _read_ch_info_struct,
FIFF.FIFFT_OLD_PACK: _read_old_pack,
FIFF.FIFFT_DIR_ENTRY_STRUCT: _read_dir_entry_struct,
FIFF.FIFFT_JULIAN: _read_julian,
}
_call_dict_names = {
FIFF.FIFFT_STRING: "str",
FIFF.FIFFT_COMPLEX_FLOAT: "c8",
FIFF.FIFFT_COMPLEX_DOUBLE: "c16",
FIFF.FIFFT_ID_STRUCT: "ids",
FIFF.FIFFT_DIG_POINT_STRUCT: "dps",
FIFF.FIFFT_COORD_TRANS_STRUCT: "cts",
FIFF.FIFFT_CH_INFO_STRUCT: "cis",
FIFF.FIFFT_OLD_PACK: "op_",
FIFF.FIFFT_DIR_ENTRY_STRUCT: "dir",
FIFF.FIFFT_JULIAN: "jul",
FIFF.FIFFT_VOID: "nul", # 0
}
# Append the simple types
_simple_dict = {
FIFF.FIFFT_BYTE: ">B",
FIFF.FIFFT_SHORT: ">i2",
FIFF.FIFFT_INT: ">i4",
FIFF.FIFFT_USHORT: ">u2",
FIFF.FIFFT_UINT: ">u4",
FIFF.FIFFT_FLOAT: ">f4",
FIFF.FIFFT_DOUBLE: ">f8",
FIFF.FIFFT_DAU_PACK16: ">i2",
}
for key, dtype in _simple_dict.items():
_call_dict[key] = partial(_read_simple, dtype=dtype)
_call_dict_names[key] = dtype
def read_tag(fid, pos, shape=None, rlims=None):
"""Read a Tag from a file at a given position.
Parameters
----------
fid : file
The open FIF file descriptor.
pos : int
The position of the Tag in the file.
shape : tuple | None
If tuple, the shape of the stored matrix. Only to be used with
data stored as a vector (not implemented for matrices yet).
rlims : tuple | None
If tuple, the first (inclusive) and last (exclusive) rows to retrieve.
Note that data are assumed to be stored row-major in the file. Only to
be used with data stored as a vector (not implemented for matrices
yet).
Returns
-------
tag : Tag
The Tag read.
"""
tag = _read_tag_header(fid, pos)
if tag is None:
return tag
if tag.size > 0:
if _matrix_info(tag) is not None:
tag.data = _read_matrix(fid, tag, shape, rlims)
else:
# All other data types
try:
fun = _call_dict[tag.type]
except KeyError:
raise Exception(f"Unimplemented tag data type {tag.type}") from None
tag.data = fun(fid, tag, shape, rlims)
return tag
def find_tag(fid, node, findkind):
"""Find Tag in an open FIF file descriptor.
Parameters
----------
fid : file-like
Open file.
node : dict
Node to search.
findkind : int
Tag kind to find.
Returns
-------
tag : instance of Tag
The first tag found.
"""
if node["directory"] is not None:
for subnode in node["directory"]:
if subnode.kind == findkind:
return read_tag(fid, subnode.pos)
return None
def has_tag(node, kind):
"""Check if the node contains a Tag of a given kind."""
for d in node["directory"]:
if d.kind == kind:
return True
return False
def _rename_list(bads, ch_names_mapping):
return [ch_names_mapping.get(bad, bad) for bad in bads]
def _int_item(x):
return int(x.item())
def _float_item(x):
return float(x.item())
def _matrix_info(tag):
matrix_coding = tag.type & 0xFFFF0000
if matrix_coding == 0 or tag.size == 0:
return None
matrix_type = tag.type & 0x0000FFFF
matrix_coding_dict = {
FIFF.FIFFT_MATRIX: "dense",
FIFF.FIFFT_MATRIX | FIFF.FIFFT_SPARSE_CCS_MATRIX: "sparse CCS",
FIFF.FIFFT_MATRIX | FIFF.FIFFT_SPARSE_RCS_MATRIX: "sparse RCS",
}
_check_option("matrix_coding", matrix_coding, list(matrix_coding_dict))
matrix_coding = matrix_coding_dict[matrix_coding]
matrix_bit_dtype = {
FIFF.FIFFT_INT: (4, ">i4"),
FIFF.FIFFT_JULIAN: (4, ">i4"),
FIFF.FIFFT_FLOAT: (4, ">f4"),
FIFF.FIFFT_DOUBLE: (8, ">f8"),
FIFF.FIFFT_COMPLEX_FLOAT: (8, ">f4"),
FIFF.FIFFT_COMPLEX_DOUBLE: (16, ">f8"),
}
_check_option("matrix_type", matrix_type, list(matrix_bit_dtype))
bit, dtype = matrix_bit_dtype[matrix_type]
return matrix_coding, matrix_type, bit, dtype

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ..utils import logger, verbose
from .constants import FIFF
from .tag import read_tag
def dir_tree_find(tree, kind):
"""Find nodes of the given kind from a directory tree structure.
Parameters
----------
tree : dict
Directory tree.
kind : int
Kind to find.
Returns
-------
nodes : list
List of matching nodes.
"""
nodes = []
if isinstance(tree, list):
for t in tree:
nodes += dir_tree_find(t, kind)
else:
# Am I desirable myself?
if tree["block"] == kind:
nodes.append(tree)
# Search the subtrees
for child in tree["children"]:
nodes += dir_tree_find(child, kind)
return nodes
@verbose
def make_dir_tree(fid, directory, start=0, indent=0, verbose=None):
"""Create the directory tree structure."""
if directory[start].kind == FIFF.FIFF_BLOCK_START:
tag = read_tag(fid, directory[start].pos)
block = tag.data.item()
else:
block = 0
start_separate = False
this = start
tree = dict()
tree["block"] = block
tree["id"] = None
tree["parent_id"] = None
tree["nent"] = 0
tree["nchild"] = 0
tree["directory"] = directory[this]
tree["children"] = []
while this < len(directory):
if directory[this].kind == FIFF.FIFF_BLOCK_START:
if this != start:
if not start_separate:
start_separate = True
logger.debug(" " * indent + f"start {{ {block}")
child, this = make_dir_tree(fid, directory, this, indent + 1)
tree["nchild"] += 1
tree["children"].append(child)
elif directory[this].kind == FIFF.FIFF_BLOCK_END:
tag = read_tag(fid, directory[start].pos)
if tag.data == block:
break
else:
tree["nent"] += 1
if tree["nent"] == 1:
tree["directory"] = list()
tree["directory"].append(directory[this])
# Add the id information if available
if block == 0:
if directory[this].kind == FIFF.FIFF_FILE_ID:
tag = read_tag(fid, directory[this].pos)
tree["id"] = tag.data
else:
if directory[this].kind == FIFF.FIFF_BLOCK_ID:
tag = read_tag(fid, directory[this].pos)
tree["id"] = tag.data
elif directory[this].kind == FIFF.FIFF_PARENT_BLOCK_ID:
tag = read_tag(fid, directory[this].pos)
tree["parent_id"] = tag.data
this += 1
# Eliminate the empty directory
if tree["nent"] == 0:
tree["directory"] = None
content = f"block = {tree['block']} nent = {tree['nent']} nchild = {tree['nchild']}"
if start_separate:
logger.debug(" " * indent + f"end }} {content}")
else:
logger.debug(" " * indent + content)
last = this
return tree, last

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import os
import os.path as op
from pathlib import Path
import numpy as np
from .constants import FIFF
from .meas_info import _get_valid_units
def _check_orig_units(orig_units):
"""Check original units from a raw file.
Units that are close to a valid_unit but not equal can be remapped to fit
into the valid_units. All other units that are not valid will be replaced
with "n/a".
Parameters
----------
orig_units : dict
Dictionary mapping channel names to their units as specified in
the header file. Example: {'FC1': 'nV'}
Returns
-------
orig_units_remapped : dict
Dictionary mapping channel names to their VALID units as specified in
the header file. Invalid units are now labeled "n/a".
Example: {'FC1': 'nV', 'Hfp3erz': 'n/a'}
"""
if orig_units is None:
return
valid_units = _get_valid_units()
valid_units_lowered = [unit.lower() for unit in valid_units]
orig_units_remapped = dict(orig_units)
for ch_name, unit in orig_units.items():
# Be lenient: we ignore case for now.
if unit.lower() in valid_units_lowered:
continue
# Common "invalid units" can be remapped to their valid equivalent
remap_dict = dict()
remap_dict["uv"] = "µV"
remap_dict["μv"] = "µV" # greek letter mu vs micro sign. use micro
remap_dict["\x83\xeav"] = "µV" # for shift-jis mu, use micro
if unit.lower() in remap_dict:
orig_units_remapped[ch_name] = remap_dict[unit.lower()]
continue
# Some units cannot be saved, they are invalid: assign "n/a"
orig_units_remapped[ch_name] = "n/a"
return orig_units_remapped
def _find_channels(ch_names, ch_type="EOG"):
"""Find EOG channel."""
substrings = (ch_type,)
substrings = [s.upper() for s in substrings]
if ch_type == "EOG":
substrings = ("EOG", "EYE")
eog_idx = [
idx
for idx, ch in enumerate(ch_names)
if any(substring in ch.upper() for substring in substrings)
]
return eog_idx
def _mult_cal_one(data_view, one, idx, cals, mult):
"""Take a chunk of raw data, multiply by mult or cals, and store."""
one = np.asarray(one, dtype=data_view.dtype)
assert data_view.shape[1] == one.shape[1], (
data_view.shape[1],
one.shape[1],
) # noqa: E501
if mult is not None:
assert mult.ndim == one.ndim == 2
data_view[:] = mult @ one[idx]
else:
assert cals is not None
if isinstance(idx, slice):
data_view[:] = one[idx]
else:
# faster than doing one = one[idx]
np.take(one, idx, axis=0, out=data_view)
data_view *= cals
def _blk_read_lims(start, stop, buf_len):
"""Deal with indexing in the middle of a data block.
Parameters
----------
start : int
Starting index.
stop : int
Ending index (exclusive).
buf_len : int
Buffer size in samples.
Returns
-------
block_start_idx : int
The first block to start reading from.
r_lims : list
The read limits.
d_lims : list
The write limits.
Notes
-----
Consider this example::
>>> start, stop, buf_len = 2, 27, 10
+---------+---------+---------
File structure: | buf0 | buf1 | buf2 |
+---------+---------+---------
File time: 0 10 20 30
+---------+---------+---------
Requested time: 2 27
| |
blockstart blockstop
| |
start stop
We need 27 - 2 = 25 samples (per channel) to store our data, and
we need to read from 3 buffers (30 samples) to get all of our data.
On all reads but the first, the data we read starts at
the first sample of the buffer. On all reads but the last,
the data we read ends on the last sample of the buffer.
We call ``this_data`` the variable that stores the current buffer's data,
and ``data`` the variable that stores the total output.
On the first read, we need to do this::
>>> data[0:buf_len-2] = this_data[2:buf_len] # doctest: +SKIP
On the second read, we need to do::
>>> data[1*buf_len-2:2*buf_len-2] = this_data[0:buf_len] # doctest: +SKIP
On the final read, we need to do::
>>> data[2*buf_len-2:3*buf_len-2-3] = this_data[0:buf_len-3] # doctest: +SKIP
This function encapsulates this logic to allow a loop over blocks, where
data is stored using the following limits::
>>> data[d_lims[ii, 0]:d_lims[ii, 1]] = this_data[r_lims[ii, 0]:r_lims[ii, 1]] # doctest: +SKIP
""" # noqa: E501
# this is used to deal with indexing in the middle of a sampling period
assert all(isinstance(x, int) for x in (start, stop, buf_len))
block_start_idx = start // buf_len
block_start = block_start_idx * buf_len
last_used_samp = stop - 1
block_stop = last_used_samp - last_used_samp % buf_len + buf_len
read_size = block_stop - block_start
n_blk = read_size // buf_len + (read_size % buf_len != 0)
start_offset = start - block_start
end_offset = block_stop - stop
d_lims = np.empty((n_blk, 2), int)
r_lims = np.empty((n_blk, 2), int)
for bi in range(n_blk):
# Triage start (sidx) and end (eidx) indices for
# data (d) and read (r)
if bi == 0:
d_sidx = 0
r_sidx = start_offset
else:
d_sidx = bi * buf_len - start_offset
r_sidx = 0
if bi == n_blk - 1:
d_eidx = stop - start
r_eidx = buf_len - end_offset
else:
d_eidx = (bi + 1) * buf_len - start_offset
r_eidx = buf_len
d_lims[bi] = [d_sidx, d_eidx]
r_lims[bi] = [r_sidx, r_eidx]
return block_start_idx, r_lims, d_lims
def _file_size(fname):
"""Get the file size in bytes."""
with open(fname, "rb") as f:
f.seek(0, os.SEEK_END)
return f.tell()
def _read_segments_file(
raw,
data,
idx,
fi,
start,
stop,
cals,
mult,
dtype,
n_channels=None,
offset=0,
trigger_ch=None,
):
"""Read a chunk of raw data."""
if n_channels is None:
n_channels = raw._raw_extras[fi]["orig_nchan"]
n_bytes = np.dtype(dtype).itemsize
# data_offset and data_left count data samples (channels x time points),
# not bytes.
data_offset = n_channels * start * n_bytes + offset
data_left = (stop - start) * n_channels
# Read up to 100 MB of data at a time, block_size is in data samples
block_size = ((int(100e6) // n_bytes) // n_channels) * n_channels
block_size = min(data_left, block_size)
with open(raw.filenames[fi], "rb", buffering=0) as fid:
fid.seek(data_offset)
# extract data in chunks
for sample_start in np.arange(0, data_left, block_size) // n_channels:
count = min(block_size, data_left - sample_start * n_channels)
block = np.fromfile(fid, dtype, count)
if block.size != count:
raise RuntimeError(
f"Incorrect number of samples ({block.size} != {count}), please "
"report this error to MNE-Python developers"
)
block = block.reshape(n_channels, -1, order="F")
n_samples = block.shape[1] # = count // n_channels
sample_stop = sample_start + n_samples
if trigger_ch is not None:
stim_ch = trigger_ch[start:stop][sample_start:sample_stop]
block = np.vstack((block, stim_ch))
data_view = data[:, sample_start:sample_stop]
_mult_cal_one(data_view, block, idx, cals, mult)
def read_str(fid, count=1):
"""Read string from a binary file in a python version compatible way."""
dtype = np.dtype(f">S{count}")
string = fid.read(dtype.itemsize)
data = np.frombuffer(string, dtype=dtype)[0]
bytestr = b"".join([data[0 : data.index(b"\x00") if b"\x00" in data else count]])
return str(bytestr.decode("ascii")) # Return native str type for Py2/3
def _create_chs(ch_names, cals, ch_coil, ch_kind, eog, ecg, emg, misc):
"""Initialize info['chs'] for eeg channels."""
chs = list()
for idx, ch_name in enumerate(ch_names):
if ch_name in eog or idx in eog:
coil_type = FIFF.FIFFV_COIL_NONE
kind = FIFF.FIFFV_EOG_CH
elif ch_name in ecg or idx in ecg:
coil_type = FIFF.FIFFV_COIL_NONE
kind = FIFF.FIFFV_ECG_CH
elif ch_name in emg or idx in emg:
coil_type = FIFF.FIFFV_COIL_NONE
kind = FIFF.FIFFV_EMG_CH
elif ch_name in misc or idx in misc:
coil_type = FIFF.FIFFV_COIL_NONE
kind = FIFF.FIFFV_MISC_CH
else:
coil_type = ch_coil
kind = ch_kind
chan_info = {
"cal": cals[idx],
"logno": idx + 1,
"scanno": idx + 1,
"range": 1.0,
"unit_mul": FIFF.FIFF_UNITM_NONE,
"ch_name": ch_name,
"unit": FIFF.FIFF_UNIT_V,
"coord_frame": FIFF.FIFFV_COORD_HEAD,
"coil_type": coil_type,
"kind": kind,
"loc": np.zeros(12),
}
if coil_type == FIFF.FIFFV_COIL_EEG:
chan_info["loc"][:3] = np.nan
chs.append(chan_info)
return chs
def _construct_bids_filename(base, ext, part_idx, validate=True):
"""Construct a BIDS compatible filename for split files."""
# insert index in filename
dirname = op.dirname(base)
base = op.basename(base)
deconstructed_base = base.split("_")
if len(deconstructed_base) < 2 and validate:
raise ValueError(
"Filename base must end with an underscore followed "
f"by the modality (e.g., _eeg or _meg), got {base}"
)
suffix = deconstructed_base[-1]
base = "_".join(deconstructed_base[:-1])
use_fname = f"{base}_split-{part_idx + 1:02}_{suffix}{ext}"
if dirname:
use_fname = op.join(dirname, use_fname)
return use_fname
def _make_split_fnames(fname, n_splits, split_naming):
"""Make a list of split filenames."""
if n_splits == 1:
fname = Path(fname)
return [fname]
res = []
base, ext = op.splitext(fname)
for i in range(n_splits):
if split_naming == "neuromag":
path = Path(f"{base}-{i:d}{ext}" if i else fname)
res.append(path)
else:
assert split_naming == "bids"
path = Path(_construct_bids_filename(base, ext, i))
res.append(path)
return res

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from collections import OrderedDict
from inspect import signature
from ..utils import _check_fname, logger
def what(fname):
"""Try to determine the type of the FIF file.
Parameters
----------
fname : path-like
The filename. Should end in ``.fif`` or ``.fif.gz``.
Returns
-------
what : str | None
The type of the file. Will be 'unknown' if it could not be determined.
Notes
-----
.. versionadded:: 0.19
"""
from ..bem import read_bem_solution, read_bem_surfaces
from ..cov import read_cov
from ..epochs import read_epochs
from ..event import read_events
from ..evoked import read_evokeds
from ..forward import read_forward_solution
from ..io import read_raw_fif
from ..minimum_norm import read_inverse_operator
from ..preprocessing import read_ica
from ..proj import read_proj
from ..source_space import read_source_spaces
from ..transforms import read_trans
from .meas_info import read_fiducials
fname = _check_fname(fname, overwrite="read", must_exist=True)
checks = OrderedDict()
checks["raw"] = read_raw_fif
checks["ica"] = read_ica
checks["epochs"] = read_epochs
checks["evoked"] = read_evokeds
checks["forward"] = read_forward_solution
checks["inverse"] = read_inverse_operator
checks["src"] = read_source_spaces
checks["bem solution"] = read_bem_solution
checks["bem surfaces"] = read_bem_surfaces
checks["cov"] = read_cov
checks["transform"] = read_trans
checks["events"] = read_events
checks["fiducials"] = read_fiducials
checks["proj"] = read_proj
for what, func in checks.items():
args = signature(func).parameters
assert "verbose" in args, func
kwargs = dict(verbose="error")
if "preload" in args:
kwargs["preload"] = False
try:
func(fname, **kwargs)
except Exception as exp:
logger.debug(f"Not {what}: {exp}")
else:
return what
return "unknown"

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import datetime
import os.path as op
import re
import time
import uuid
from contextlib import contextmanager
from gzip import GzipFile
import numpy as np
from scipy.sparse import csc_array, csr_array
from ..utils import _file_like, _validate_type, logger
from ..utils.numerics import _date_to_julian
from .constants import FIFF
# We choose a "magic" date to store (because meas_date is obligatory)
# to treat as meas_date=None. This one should be impossible for systems
# to write -- the second field is microseconds, so anything >= 1e6
# should be moved into the first field (seconds).
DATE_NONE = (0, 2**31 - 1)
def _write(fid, data, kind, data_size, FIFFT_TYPE, dtype):
"""Write data."""
if isinstance(data, np.ndarray):
data_size *= data.size
# XXX for string types the data size is used as
# computed in ``write_string``.
fid.write(np.array(kind, dtype=">i4").tobytes())
fid.write(np.array(FIFFT_TYPE, dtype=">i4").tobytes())
fid.write(np.array(data_size, dtype=">i4").tobytes())
fid.write(np.array(FIFF.FIFFV_NEXT_SEQ, dtype=">i4").tobytes())
fid.write(np.array(data, dtype=dtype).tobytes())
def _get_split_size(split_size):
"""Convert human-readable bytes to machine-readable bytes."""
if isinstance(split_size, str):
exp = dict(MB=20, GB=30).get(split_size[-2:], None)
if exp is None:
raise ValueError('split_size has to end with either "MB" or "GB"')
split_size = int(float(split_size[:-2]) * 2**exp)
if split_size > 2147483648:
raise ValueError("split_size cannot be larger than 2GB")
return split_size
_NEXT_FILE_BUFFER = 1048576 # 2 ** 20 extra cushion for last post-data tags
def write_nop(fid, last=False):
"""Write a FIFF_NOP."""
fid.write(np.array(FIFF.FIFF_NOP, dtype=">i4").tobytes())
fid.write(np.array(FIFF.FIFFT_VOID, dtype=">i4").tobytes())
fid.write(np.array(0, dtype=">i4").tobytes())
next_ = FIFF.FIFFV_NEXT_NONE if last else FIFF.FIFFV_NEXT_SEQ
fid.write(np.array(next_, dtype=">i4").tobytes())
INT32_MAX = 2147483647
def write_int(fid, kind, data):
"""Write a 32-bit integer tag to a fif file."""
data_size = 4
data = np.asarray(data)
if data.dtype.kind not in "uib" and data.size > 0:
raise TypeError(
f"Cannot safely write data kind {kind} with dtype {data.dtype} as int",
)
max_val = data.max() if data.size > 0 else 0
if max_val > INT32_MAX:
raise TypeError(
f"Value {max_val} exceeds maximum allowed ({INT32_MAX}) for tag {kind}"
)
data = data.astype(">i4").T
_write(fid, data, kind, data_size, FIFF.FIFFT_INT, ">i4")
def write_double(fid, kind, data):
"""Write a double-precision floating point tag to a fif file."""
data_size = 8
data = np.array(data, dtype=">f8").T
_write(fid, data, kind, data_size, FIFF.FIFFT_DOUBLE, ">f8")
def write_float(fid, kind, data):
"""Write a single-precision floating point tag to a fif file."""
data_size = 4
data = np.array(data, dtype=">f4").T
_write(fid, data, kind, data_size, FIFF.FIFFT_FLOAT, ">f4")
def write_dau_pack16(fid, kind, data):
"""Write a dau_pack16 tag to a fif file."""
data_size = 2
data = np.array(data, dtype=">i2").T
_write(fid, data, kind, data_size, FIFF.FIFFT_DAU_PACK16, ">i2")
def write_complex64(fid, kind, data):
"""Write a 64 bit complex floating point tag to a fif file."""
data_size = 8
data = np.array(data, dtype=">c8").T
_write(fid, data, kind, data_size, FIFF.FIFFT_COMPLEX_FLOAT, ">c8")
def write_complex128(fid, kind, data):
"""Write a 128 bit complex floating point tag to a fif file."""
data_size = 16
data = np.array(data, dtype=">c16").T
_write(fid, data, kind, data_size, FIFF.FIFFT_COMPLEX_FLOAT, ">c16")
def write_julian(fid, kind, data):
"""Write a Julian-formatted date to a FIF file."""
assert isinstance(data, datetime.date), type(data)
data_size = 4
jd = _date_to_julian(data)
data = np.array(jd, dtype=">i4")
_write(fid, data, kind, data_size, FIFF.FIFFT_JULIAN, ">i4")
def write_string(fid, kind, data):
"""Write a string tag."""
try:
str_data = str(data).encode("latin1")
except UnicodeEncodeError:
str_data = str(data).encode("latin1", errors="xmlcharrefreplace")
data_size = len(str_data) # therefore compute size here
if data_size > 0:
_write(fid, str_data, kind, data_size, FIFF.FIFFT_STRING, ">S")
def write_name_list(fid, kind, data):
"""Write a colon-separated list of names.
Parameters
----------
data : list of strings
"""
write_string(fid, kind, ":".join(data))
def write_name_list_sanitized(fid, kind, lst, name):
"""Write a sanitized, colon-separated list of names."""
write_string(fid, kind, _safe_name_list(lst, "write", name))
def _safe_name_list(lst, operation, name):
if operation == "write":
assert isinstance(lst, list | tuple | np.ndarray), type(lst)
if any("{COLON}" in val for val in lst):
raise ValueError(f'The substring "{{COLON}}" in {name} not supported.')
return ":".join(val.replace(":", "{COLON}") for val in lst)
else:
# take a sanitized string and return a list of strings
assert operation == "read"
assert lst is None or isinstance(lst, str)
if not lst: # None or empty string
return []
return [val.replace("{COLON}", ":") for val in lst.split(":")]
def write_float_matrix(fid, kind, mat):
"""Write a single-precision floating-point matrix tag."""
_write_matrix_data(fid, kind, mat, FIFF.FIFFT_FLOAT)
def write_double_matrix(fid, kind, mat):
"""Write a double-precision floating-point matrix tag."""
_write_matrix_data(fid, kind, mat, FIFF.FIFFT_DOUBLE)
def write_int_matrix(fid, kind, mat):
"""Write integer 32 matrix tag."""
_write_matrix_data(fid, kind, mat, FIFF.FIFFT_INT)
def write_complex_float_matrix(fid, kind, mat):
"""Write complex 64 matrix tag."""
_write_matrix_data(fid, kind, mat, FIFF.FIFFT_COMPLEX_FLOAT)
def write_complex_double_matrix(fid, kind, mat):
"""Write complex 128 matrix tag."""
_write_matrix_data(fid, kind, mat, FIFF.FIFFT_COMPLEX_DOUBLE)
def _write_matrix_data(fid, kind, mat, data_type):
dtype = {
FIFF.FIFFT_FLOAT: ">f4",
FIFF.FIFFT_DOUBLE: ">f8",
FIFF.FIFFT_COMPLEX_FLOAT: ">c8",
FIFF.FIFFT_COMPLEX_DOUBLE: ">c16",
FIFF.FIFFT_INT: ">i4",
}[data_type]
dtype = np.dtype(dtype)
data_size = dtype.itemsize * mat.size + 4 * (mat.ndim + 1)
matrix_type = data_type | FIFF.FIFFT_MATRIX
fid.write(np.array(kind, dtype=">i4").tobytes())
fid.write(np.array(matrix_type, dtype=">i4").tobytes())
fid.write(np.array(data_size, dtype=">i4").tobytes())
fid.write(np.array(FIFF.FIFFV_NEXT_SEQ, dtype=">i4").tobytes())
fid.write(np.array(mat, dtype=dtype).tobytes())
dims = np.empty(mat.ndim + 1, dtype=np.int32)
dims[: mat.ndim] = mat.shape[::-1]
dims[-1] = mat.ndim
fid.write(np.array(dims, dtype=">i4").tobytes())
check_fiff_length(fid)
def get_machid():
"""Get (mostly) unique machine ID.
Returns
-------
ids : array (length 2, int32)
The machine identifier used in MNE.
"""
mac = f"{uuid.getnode():012x}".encode() # byte conversion for Py3
mac = re.findall(b"..", mac) # split string
mac += [b"00", b"00"] # add two more fields
# Convert to integer in reverse-order (for some reason)
from codecs import encode
mac = b"".join([encode(h, "hex_codec") for h in mac[::-1]])
ids = np.flipud(np.frombuffer(mac, np.int32, count=2))
return ids
def get_new_file_id():
"""Create a new file ID tag."""
secs, usecs = divmod(time.time(), 1.0)
secs, usecs = int(secs), int(usecs * 1e6)
return {
"machid": get_machid(),
"version": FIFF.FIFFC_VERSION,
"secs": secs,
"usecs": usecs,
}
def write_id(fid, kind, id_=None):
"""Write fiff id."""
id_ = _generate_meas_id() if id_ is None else id_
data_size = 5 * 4 # The id comprises five integers
fid.write(np.array(kind, dtype=">i4").tobytes())
fid.write(np.array(FIFF.FIFFT_ID_STRUCT, dtype=">i4").tobytes())
fid.write(np.array(data_size, dtype=">i4").tobytes())
fid.write(np.array(FIFF.FIFFV_NEXT_SEQ, dtype=">i4").tobytes())
# Collect the bits together for one write
arr = np.array(
[id_["version"], id_["machid"][0], id_["machid"][1], id_["secs"], id_["usecs"]],
dtype=">i4",
)
fid.write(arr.tobytes())
def start_block(fid, kind):
"""Write a FIFF_BLOCK_START tag."""
write_int(fid, FIFF.FIFF_BLOCK_START, kind)
def end_block(fid, kind):
"""Write a FIFF_BLOCK_END tag."""
write_int(fid, FIFF.FIFF_BLOCK_END, kind)
def start_file(fname, id_=None):
"""Open a fif file for writing and writes the compulsory header tags.
Parameters
----------
fname : path-like | fid
The name of the file to open. It is recommended
that the name ends with .fif or .fif.gz. Can also be an
already opened file.
id_ : dict | None
ID to use for the FIFF_FILE_ID.
"""
if _file_like(fname):
logger.debug(f"Writing using {type(fname)} I/O")
fid = fname
fid.seek(0)
else:
fname = str(fname)
if op.splitext(fname)[1].lower() == ".gz":
logger.debug("Writing using gzip")
# defaults to compression level 9, which is barely smaller but much
# slower. 2 offers a good compromise.
fid = GzipFile(fname, "wb", compresslevel=2)
else:
logger.debug("Writing using normal I/O")
fid = open(fname, "wb")
# Write the compulsory items
write_id(fid, FIFF.FIFF_FILE_ID, id_)
write_int(fid, FIFF.FIFF_DIR_POINTER, -1)
write_int(fid, FIFF.FIFF_FREE_LIST, -1)
return fid
@contextmanager
def start_and_end_file(fname, id_=None):
"""Start and (if successfully written) close the file."""
with start_file(fname, id_=id_) as fid:
yield fid
end_file(fid) # we only hit this line if the yield does not err
def check_fiff_length(fid, close=True):
"""Ensure our file hasn't grown too large to work properly."""
if fid.tell() > 2147483648: # 2 ** 31, FIFF uses signed 32-bit locations
if close:
fid.close()
raise OSError(
"FIFF file exceeded 2GB limit, please split file, reduce"
" split_size (if possible), or save to a different "
"format"
)
def end_file(fid):
"""Write the closing tags to a fif file and closes the file."""
write_nop(fid, last=True)
check_fiff_length(fid)
fid.close()
def write_coord_trans(fid, trans):
"""Write a coordinate transformation structure."""
data_size = 4 * 2 * 12 + 4 * 2
fid.write(np.array(FIFF.FIFF_COORD_TRANS, dtype=">i4").tobytes())
fid.write(np.array(FIFF.FIFFT_COORD_TRANS_STRUCT, dtype=">i4").tobytes())
fid.write(np.array(data_size, dtype=">i4").tobytes())
fid.write(np.array(FIFF.FIFFV_NEXT_SEQ, dtype=">i4").tobytes())
fid.write(np.array(trans["from"], dtype=">i4").tobytes())
fid.write(np.array(trans["to"], dtype=">i4").tobytes())
# The transform...
rot = trans["trans"][:3, :3]
move = trans["trans"][:3, 3]
fid.write(np.array(rot, dtype=">f4").tobytes())
fid.write(np.array(move, dtype=">f4").tobytes())
# ...and its inverse
trans_inv = np.linalg.inv(trans["trans"])
rot = trans_inv[:3, :3]
move = trans_inv[:3, 3]
fid.write(np.array(rot, dtype=">f4").tobytes())
fid.write(np.array(move, dtype=">f4").tobytes())
def write_ch_info(fid, ch):
"""Write a channel information record to a fif file."""
data_size = 4 * 13 + 4 * 7 + 16
fid.write(np.array(FIFF.FIFF_CH_INFO, dtype=">i4").tobytes())
fid.write(np.array(FIFF.FIFFT_CH_INFO_STRUCT, dtype=">i4").tobytes())
fid.write(np.array(data_size, dtype=">i4").tobytes())
fid.write(np.array(FIFF.FIFFV_NEXT_SEQ, dtype=">i4").tobytes())
# Start writing fiffChInfoRec
fid.write(np.array(ch["scanno"], dtype=">i4").tobytes())
fid.write(np.array(ch["logno"], dtype=">i4").tobytes())
fid.write(np.array(ch["kind"], dtype=">i4").tobytes())
fid.write(np.array(ch["range"], dtype=">f4").tobytes())
fid.write(np.array(ch["cal"], dtype=">f4").tobytes())
fid.write(np.array(ch["coil_type"], dtype=">i4").tobytes())
fid.write(np.array(ch["loc"], dtype=">f4").tobytes()) # writing 12 values
# unit and unit multiplier
fid.write(np.array(ch["unit"], dtype=">i4").tobytes())
fid.write(np.array(ch["unit_mul"], dtype=">i4").tobytes())
# Finally channel name
ch_name = ch["ch_name"][:15]
fid.write(np.array(ch_name, dtype=">c").tobytes())
fid.write(b"\0" * (16 - len(ch_name)))
def write_dig_points(fid, dig, block=False, coord_frame=None, *, ch_names=None):
"""Write a set of digitizer data points into a fif file."""
if dig is not None:
data_size = 5 * 4
if block:
start_block(fid, FIFF.FIFFB_ISOTRAK)
if coord_frame is not None:
write_int(fid, FIFF.FIFF_MNE_COORD_FRAME, coord_frame)
for d in dig:
fid.write(np.array(FIFF.FIFF_DIG_POINT, ">i4").tobytes())
fid.write(np.array(FIFF.FIFFT_DIG_POINT_STRUCT, ">i4").tobytes())
fid.write(np.array(data_size, dtype=">i4").tobytes())
fid.write(np.array(FIFF.FIFFV_NEXT_SEQ, ">i4").tobytes())
# Start writing fiffDigPointRec
fid.write(np.array(d["kind"], ">i4").tobytes())
fid.write(np.array(d["ident"], ">i4").tobytes())
fid.write(np.array(d["r"][:3], ">f4").tobytes())
if ch_names is not None:
write_name_list_sanitized(
fid, FIFF.FIFF_MNE_CH_NAME_LIST, ch_names, "ch_names"
)
if block:
end_block(fid, FIFF.FIFFB_ISOTRAK)
def write_float_sparse_rcs(fid, kind, mat):
"""Write a single-precision sparse compressed row matrix tag."""
return write_float_sparse(fid, kind, mat, fmt="csr")
def write_float_sparse(fid, kind, mat, fmt="auto"):
"""Write a single-precision floating-point sparse matrix tag."""
if fmt == "auto":
fmt = "csr" if isinstance(mat, csr_array) else "csc"
need = csr_array if fmt == "csr" else csc_array
matrix_type = getattr(FIFF, f"FIFFT_SPARSE_{fmt[-1].upper()}CS_MATRIX")
_validate_type(mat, need, "sparse")
matrix_type = matrix_type | FIFF.FIFFT_MATRIX | FIFF.FIFFT_FLOAT
nnzm = mat.nnz
nrow = mat.shape[0]
data_size = 4 * nnzm + 4 * nnzm + 4 * (nrow + 1) + 4 * 4
fid.write(np.array(kind, dtype=">i4").tobytes())
fid.write(np.array(matrix_type, dtype=">i4").tobytes())
fid.write(np.array(data_size, dtype=">i4").tobytes())
fid.write(np.array(FIFF.FIFFV_NEXT_SEQ, dtype=">i4").tobytes())
fid.write(np.array(mat.data, dtype=">f4").tobytes())
fid.write(np.array(mat.indices, dtype=">i4").tobytes())
fid.write(np.array(mat.indptr, dtype=">i4").tobytes())
dims = [nnzm, mat.shape[0], mat.shape[1], 2]
fid.write(np.array(dims, dtype=">i4").tobytes())
check_fiff_length(fid)
def _generate_meas_id():
"""Generate a new meas_id dict."""
id_ = dict()
id_["version"] = FIFF.FIFFC_VERSION
id_["machid"] = get_machid()
id_["secs"], id_["usecs"] = DATE_NONE
return id_

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"""Freesurfer handling functions."""
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import os.path as op
from gzip import GzipFile
from pathlib import Path
import numpy as np
from ._fiff.constants import FIFF
from ._fiff.meas_info import read_fiducials
from .surface import _read_mri_surface, read_surface
from .transforms import (
Transform,
_ensure_trans,
apply_trans,
combine_transforms,
invert_transform,
read_ras_mni_t,
)
from .utils import (
_check_fname,
_check_option,
_import_nibabel,
_validate_type,
get_subjects_dir,
logger,
verbose,
)
def _check_subject_dir(subject, subjects_dir):
"""Check that the Freesurfer subject directory is as expected."""
subjects_dir = Path(get_subjects_dir(subjects_dir, raise_error=True))
for img_name in ("T1", "brain", "aseg"):
if not (subjects_dir / subject / "mri" / f"{img_name}.mgz").is_file():
raise ValueError(
"Freesurfer recon-all subject folder "
"is incorrect or improperly formatted, "
f"got {subjects_dir / subject}"
)
return subjects_dir / subject
def _get_aseg(aseg, subject, subjects_dir):
"""Check that the anatomical segmentation file exists and load it."""
nib = _import_nibabel("load aseg")
subjects_dir = Path(get_subjects_dir(subjects_dir, raise_error=True))
if aseg == "auto": # use aparc+aseg if auto
aseg = _check_fname(
subjects_dir / subject / "mri" / "aparc+aseg.mgz",
overwrite="read",
must_exist=False,
)
if not aseg: # if doesn't exist use wmparc
aseg = subjects_dir / subject / "mri" / "wmparc.mgz"
else:
aseg = subjects_dir / subject / "mri" / f"{aseg}.mgz"
_check_fname(aseg, overwrite="read", must_exist=True)
aseg = nib.load(aseg)
aseg_data = np.array(aseg.dataobj)
return aseg, aseg_data
def _reorient_image(img, axcodes="RAS"):
"""Reorient an image to a given orientation.
Parameters
----------
img : instance of SpatialImage
The MRI image.
axcodes : tuple | str
The axis codes specifying the orientation, e.g. "RAS".
See :func:`nibabel.orientations.aff2axcodes`.
Returns
-------
img_data : ndarray
The reoriented image data.
vox_ras_t : ndarray
The new transform from the new voxels to surface RAS.
Notes
-----
.. versionadded:: 0.24
"""
nib = _import_nibabel("reorient MRI image")
orig_data = np.array(img.dataobj).astype(np.float32)
# reorient data to RAS
ornt = nib.orientations.axcodes2ornt(
nib.orientations.aff2axcodes(img.affine)
).astype(int)
ras_ornt = nib.orientations.axcodes2ornt(axcodes)
ornt_trans = nib.orientations.ornt_transform(ornt, ras_ornt)
img_data = nib.orientations.apply_orientation(orig_data, ornt_trans)
orig_mgh = nib.MGHImage(orig_data, img.affine)
aff_trans = nib.orientations.inv_ornt_aff(ornt_trans, img.shape)
vox_ras_t = np.dot(orig_mgh.header.get_vox2ras_tkr(), aff_trans)
return img_data, vox_ras_t
def _mri_orientation(orientation):
"""Get MRI orientation information from an image.
Parameters
----------
orientation : str
Orientation that you want. Can be "axial", "sagittal", or "coronal".
Returns
-------
axis : int
The dimension of the axis to take slices over when plotting.
x : int
The dimension of the x axis.
y : int
The dimension of the y axis.
Notes
-----
.. versionadded:: 0.21
.. versionchanged:: 0.24
"""
_check_option("orientation", orientation, ("coronal", "axial", "sagittal"))
axis = dict(coronal=1, axial=2, sagittal=0)[orientation]
x, y = sorted(set([0, 1, 2]).difference(set([axis])))
return axis, x, y
def _get_mri_info_data(mri, data):
# Read the segmentation data using nibabel
if data:
_import_nibabel("load MRI atlas data")
out = dict()
_, out["vox_mri_t"], out["mri_ras_t"], dims, _, mgz = _read_mri_info(
mri, return_img=True
)
out.update(
mri_width=dims[0], mri_height=dims[1], mri_depth=dims[1], mri_volume_name=mri
)
if data:
assert mgz is not None
out["mri_vox_t"] = invert_transform(out["vox_mri_t"])
out["data"] = np.asarray(mgz.dataobj)
return out
def _get_mgz_header(fname):
"""Adapted from nibabel to quickly extract header info."""
fname = _check_fname(fname, overwrite="read", must_exist=True, name="MRI image")
if fname.suffix != ".mgz":
raise OSError("Filename must end with .mgz")
header_dtd = [
("version", ">i4"),
("dims", ">i4", (4,)),
("type", ">i4"),
("dof", ">i4"),
("goodRASFlag", ">i2"),
("delta", ">f4", (3,)),
("Mdc", ">f4", (3, 3)),
("Pxyz_c", ">f4", (3,)),
]
header_dtype = np.dtype(header_dtd)
with GzipFile(fname, "rb") as fid:
hdr_str = fid.read(header_dtype.itemsize)
header = np.ndarray(shape=(), dtype=header_dtype, buffer=hdr_str)
# dims
dims = header["dims"].astype(int)
dims = dims[:3] if len(dims) == 4 else dims
# vox2ras_tkr
delta = header["delta"]
ds = np.array(delta, float)
ns = np.array(dims * ds) / 2.0
v2rtkr = np.array(
[
[-ds[0], 0, 0, ns[0]],
[0, 0, ds[2], -ns[2]],
[0, -ds[1], 0, ns[1]],
[0, 0, 0, 1],
],
dtype=np.float32,
)
# ras2vox
d = np.diag(delta)
pcrs_c = dims / 2.0
Mdc = header["Mdc"].T
pxyz_0 = header["Pxyz_c"] - np.dot(Mdc, np.dot(d, pcrs_c))
M = np.eye(4, 4)
M[0:3, 0:3] = np.dot(Mdc, d)
M[0:3, 3] = pxyz_0.T
header = dict(dims=dims, vox2ras_tkr=v2rtkr, vox2ras=M, zooms=header["delta"])
return header
def _get_atlas_values(vol_info, rr):
# Transform MRI coordinates (where our surfaces live) to voxels
rr_vox = apply_trans(vol_info["mri_vox_t"], rr)
good = (
(rr_vox >= -0.5) & (rr_vox < np.array(vol_info["data"].shape, int) - 0.5)
).all(-1)
idx = np.round(rr_vox[good].T).astype(np.int64)
values = np.full(rr.shape[0], np.nan)
values[good] = vol_info["data"][tuple(idx)]
return values
def get_volume_labels_from_aseg(mgz_fname, return_colors=False, atlas_ids=None):
"""Return a list of names and colors of segmented volumes.
Parameters
----------
mgz_fname : path-like
Filename to read. Typically ``aseg.mgz`` or some variant in the
freesurfer pipeline.
return_colors : bool
If True returns also the labels colors.
atlas_ids : dict | None
A lookup table providing a mapping from region names (str) to ID values
(int). Can be None to use the standard Freesurfer LUT.
.. versionadded:: 0.21.0
Returns
-------
label_names : list of str
The names of segmented volumes included in this mgz file.
label_colors : list of str
The RGB colors of the labels included in this mgz file.
See Also
--------
read_freesurfer_lut
Notes
-----
.. versionchanged:: 0.21.0
The label names are now sorted in the same order as their corresponding
values in the MRI file.
.. versionadded:: 0.9.0
"""
nib = _import_nibabel("load MRI atlas data")
mgz_fname = _check_fname(
mgz_fname, overwrite="read", must_exist=True, name="mgz_fname"
)
atlas = nib.load(mgz_fname)
data = np.asarray(atlas.dataobj) # don't need float here
want = np.unique(data)
if atlas_ids is None:
atlas_ids, colors = read_freesurfer_lut()
elif return_colors:
raise ValueError("return_colors must be False if atlas_ids are provided")
# restrict to the ones in the MRI, sorted by label name
keep = np.isin(list(atlas_ids.values()), want)
keys = sorted(
(key for ki, key in enumerate(atlas_ids.keys()) if keep[ki]),
key=lambda x: atlas_ids[x],
)
if return_colors:
colors = [colors[k] for k in keys]
out = keys, colors
else:
out = keys
return out
##############################################################################
# Head to MRI volume conversion
@verbose
def head_to_mri(
pos,
subject,
mri_head_t,
subjects_dir=None,
*,
kind="mri",
unscale=False,
verbose=None,
):
"""Convert pos from head coordinate system to MRI ones.
Parameters
----------
pos : array, shape (n_pos, 3)
The coordinates (in m) in head coordinate system.
%(subject)s
mri_head_t : instance of Transform
MRI<->Head coordinate transformation.
%(subjects_dir)s
kind : str
The MRI coordinate frame kind, can be ``'mri'`` (default) for
FreeSurfer surface RAS or ``'ras'`` (default in 1.2) to use MRI RAS
(scanner RAS).
.. versionadded:: 1.2
unscale : bool
For surrogate MRIs (e.g., scaled using ``mne coreg``), if True
(default False), use the MRI scaling parameters to obtain points in
the original/surrogate subject's MRI space.
.. versionadded:: 1.2
%(verbose)s
Returns
-------
coordinates : array, shape (n_pos, 3)
The MRI RAS coordinates (in mm) of pos.
Notes
-----
This function requires nibabel.
"""
from .coreg import read_mri_cfg
_validate_type(kind, str, "kind")
_check_option("kind", kind, ("ras", "mri"))
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
t1_fname = subjects_dir / subject / "mri" / "T1.mgz"
head_mri_t = _ensure_trans(mri_head_t, "head", "mri")
if kind == "ras":
_, _, mri_ras_t, _, _ = _read_mri_info(t1_fname)
head_ras_t = combine_transforms(head_mri_t, mri_ras_t, "head", "ras")
head_dest_t = head_ras_t
else:
assert kind == "mri"
head_dest_t = head_mri_t
pos_dest = apply_trans(head_dest_t, pos)
# unscale if requested
if unscale:
params = read_mri_cfg(subject, subjects_dir)
pos_dest /= params["scale"]
pos_dest *= 1e3 # mm
return pos_dest
##############################################################################
# Surface to MNI conversion
@verbose
def vertex_to_mni(vertices, hemis, subject, subjects_dir=None, verbose=None):
"""Convert the array of vertices for a hemisphere to MNI coordinates.
Parameters
----------
vertices : int, or list of int
Vertex number(s) to convert.
hemis : int, or list of int
Hemisphere(s) the vertices belong to.
%(subject)s
subjects_dir : str, or None
Path to ``SUBJECTS_DIR`` if it is not set in the environment.
%(verbose)s
Returns
-------
coordinates : array, shape (n_vertices, 3)
The MNI coordinates (in mm) of the vertices.
"""
singleton = False
if not isinstance(vertices, list) and not isinstance(vertices, np.ndarray):
singleton = True
vertices = [vertices]
if not isinstance(hemis, list) and not isinstance(hemis, np.ndarray):
hemis = [hemis] * len(vertices)
if not len(hemis) == len(vertices):
raise ValueError("hemi and vertices must match in length")
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
surfs = [subjects_dir / subject / "surf" / f"{h}.white" for h in ["lh", "rh"]]
# read surface locations in MRI space
rr = [read_surface(s)[0] for s in surfs]
# take point locations in MRI space and convert to MNI coordinates
xfm = read_talxfm(subject, subjects_dir)
xfm["trans"][:3, 3] *= 1000.0 # m->mm
data = np.array([rr[h][v, :] for h, v in zip(hemis, vertices)])
if singleton:
data = data[0]
return apply_trans(xfm["trans"], data)
##############################################################################
# Volume to MNI conversion
@verbose
def head_to_mni(pos, subject, mri_head_t, subjects_dir=None, verbose=None):
"""Convert pos from head coordinate system to MNI ones.
Parameters
----------
pos : array, shape (n_pos, 3)
The coordinates (in m) in head coordinate system.
%(subject)s
mri_head_t : instance of Transform
MRI<->Head coordinate transformation.
%(subjects_dir)s
%(verbose)s
Returns
-------
coordinates : array, shape (n_pos, 3)
The MNI coordinates (in mm) of pos.
Notes
-----
This function requires either nibabel.
"""
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
# before we go from head to MRI (surface RAS)
head_mni_t = combine_transforms(
_ensure_trans(mri_head_t, "head", "mri"),
read_talxfm(subject, subjects_dir),
"head",
"mni_tal",
)
return apply_trans(head_mni_t, pos) * 1000.0
@verbose
def get_mni_fiducials(subject, subjects_dir=None, verbose=None):
"""Estimate fiducials for a subject.
Parameters
----------
%(subject)s
%(subjects_dir)s
%(verbose)s
Returns
-------
fids_mri : list
List of estimated fiducials (each point in a dict), in the order
LPA, nasion, RPA.
Notes
-----
This takes the ``fsaverage-fiducials.fif`` file included with MNE—which
contain the LPA, nasion, and RPA for the ``fsaverage`` subject—and
transforms them to the given FreeSurfer subject's MRI space.
The MRI of ``fsaverage`` is already in MNI Talairach space, so applying
the inverse of the given subject's MNI Talairach affine transformation
(``$SUBJECTS_DIR/$SUBJECT/mri/transforms/talairach.xfm``) is used
to estimate the subject's fiducial locations.
For more details about the coordinate systems and transformations involved,
see https://surfer.nmr.mgh.harvard.edu/fswiki/CoordinateSystems and
:ref:`tut-source-alignment`.
"""
# Eventually we might want to allow using the MNI Talairach with-skull
# transformation rather than the standard brain-based MNI Talaranch
# transformation, and/or project the points onto the head surface
# (if available).
fname_fids_fs = (
Path(__file__).parent / "data" / "fsaverage" / "fsaverage-fiducials.fif"
)
# Read fsaverage fiducials file and subject Talairach.
fids, coord_frame = read_fiducials(fname_fids_fs)
assert coord_frame == FIFF.FIFFV_COORD_MRI
if subject == "fsaverage":
return fids # special short-circuit for fsaverage
mni_mri_t = invert_transform(read_talxfm(subject, subjects_dir))
for f in fids:
f["r"] = apply_trans(mni_mri_t, f["r"])
return fids
@verbose
def estimate_head_mri_t(subject, subjects_dir=None, verbose=None):
"""Estimate the head->mri transform from fsaverage fiducials.
A subject's fiducials can be estimated given a Freesurfer ``recon-all``
by transforming ``fsaverage`` fiducials using the inverse Talairach
transform, see :func:`mne.coreg.get_mni_fiducials`.
Parameters
----------
%(subject)s
%(subjects_dir)s
%(verbose)s
Returns
-------
%(trans_not_none)s
"""
from .channels.montage import compute_native_head_t, make_dig_montage
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
lpa, nasion, rpa = get_mni_fiducials(subject, subjects_dir)
montage = make_dig_montage(
lpa=lpa["r"], nasion=nasion["r"], rpa=rpa["r"], coord_frame="mri"
)
return invert_transform(compute_native_head_t(montage))
def _get_affine_from_lta_info(lines):
"""Get the vox2ras affine from lta file info."""
volume_data = np.loadtxt([line.split("=")[1] for line in lines])
# get the size of the volume (number of voxels), slice resolution.
# the matrix of directional cosines and the ras at the center of the bore
dims, deltas, dir_cos, center_ras = (
volume_data[0],
volume_data[1],
volume_data[2:5],
volume_data[5],
)
dir_cos_delta = dir_cos.T * deltas
vol_center = (dir_cos_delta @ dims[:3]) / 2
affine = np.eye(4)
affine[:3, :3] = dir_cos_delta
affine[:3, 3] = center_ras - vol_center
return affine
@verbose
def read_lta(fname, verbose=None):
"""Read a Freesurfer linear transform array file.
Parameters
----------
fname : path-like
The transform filename.
%(verbose)s
Returns
-------
affine : ndarray
The affine transformation described by the lta file.
"""
_check_fname(fname, "read", must_exist=True)
with open(fname) as fid:
lines = fid.readlines()
# 0 is linear vox2vox, 1 is linear ras2ras
trans_type = int(lines[0].split("=")[1].strip()[0])
assert trans_type in (0, 1)
affine = np.loadtxt(lines[5:9])
if trans_type == 1:
return affine
src_affine = _get_affine_from_lta_info(lines[12:18])
dst_affine = _get_affine_from_lta_info(lines[21:27])
# don't compute if src and dst are already identical
if np.allclose(src_affine, dst_affine):
return affine
ras2ras = src_affine @ np.linalg.inv(affine) @ np.linalg.inv(dst_affine)
affine = np.linalg.inv(np.linalg.inv(src_affine) @ ras2ras @ src_affine)
return affine
@verbose
def read_talxfm(subject, subjects_dir=None, verbose=None):
"""Compute MRI-to-MNI transform from FreeSurfer talairach.xfm file.
Parameters
----------
%(subject)s
%(subjects_dir)s
%(verbose)s
Returns
-------
mri_mni_t : instance of Transform
The affine transformation from MRI to MNI space for the subject.
"""
# Adapted from freesurfer m-files. Altered to deal with Norig
# and Torig correctly
subjects_dir = get_subjects_dir(subjects_dir)
# Setup the RAS to MNI transform
ras_mni_t = read_ras_mni_t(subject, subjects_dir)
ras_mni_t["trans"][:3, 3] /= 1000.0 # mm->m
# We want to get from Freesurfer surface RAS ('mri') to MNI ('mni_tal').
# This file only gives us RAS (non-zero origin) ('ras') to MNI ('mni_tal').
# Se we need to get the ras->mri transform from the MRI headers.
# To do this, we get Norig and Torig
# (i.e. vox_ras_t and vox_mri_t, respectively)
path = subjects_dir / subject / "mri" / "orig.mgz"
if not path.is_file():
path = subjects_dir / subject / "mri" / "T1.mgz"
if not path.is_file():
raise OSError(f"mri not found: {path}")
_, _, mri_ras_t, _, _ = _read_mri_info(path)
mri_mni_t = combine_transforms(mri_ras_t, ras_mni_t, "mri", "mni_tal")
return mri_mni_t
def _check_mri(mri, subject, subjects_dir) -> str:
"""Check whether an mri exists in the Freesurfer subject directory."""
_validate_type(mri, "path-like", mri)
mri = Path(mri)
if mri.is_file() and mri.name != mri:
return str(mri)
elif not mri.is_file():
if subject is None:
raise FileNotFoundError(
f"MRI file {mri!r} not found and no subject provided."
)
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
mri = subjects_dir / subject / "mri" / mri
if not mri.is_file():
raise FileNotFoundError(
f"MRI file {mri!r} not found in the subjects directory "
f"{subjects_dir!r} for subject {subject}."
)
if mri.name == mri:
raise OSError(
f"Ambiguous filename - found {mri!r} in current folder. "
"If this is correct prefix name with relative or absolute path."
)
return str(mri)
def _read_mri_info(path, units="m", return_img=False, use_nibabel=False):
# This is equivalent but 100x slower, so only use nibabel if we need to
# (later):
if use_nibabel:
nib = _import_nibabel()
hdr = nib.load(path).header
n_orig = hdr.get_vox2ras()
t_orig = hdr.get_vox2ras_tkr()
dims = hdr.get_data_shape()
zooms = hdr.get_zooms()[:3]
else:
hdr = _get_mgz_header(path)
n_orig = hdr["vox2ras"]
t_orig = hdr["vox2ras_tkr"]
dims = hdr["dims"]
zooms = hdr["zooms"]
# extract the MRI_VOXEL to RAS (non-zero origin) transform
vox_ras_t = Transform("mri_voxel", "ras", n_orig)
# extract the MRI_VOXEL to MRI transform
vox_mri_t = Transform("mri_voxel", "mri", t_orig)
# construct the MRI to RAS (non-zero origin) transform
mri_ras_t = combine_transforms(invert_transform(vox_mri_t), vox_ras_t, "mri", "ras")
assert units in ("m", "mm")
if units == "m":
conv = np.array([[1e-3, 1e-3, 1e-3, 1]]).T
# scaling and translation terms
vox_ras_t["trans"] *= conv
vox_mri_t["trans"] *= conv
# just the translation term
mri_ras_t["trans"][:, 3:4] *= conv
out = (vox_ras_t, vox_mri_t, mri_ras_t, dims, zooms)
if return_img:
nibabel = _import_nibabel()
out += (nibabel.load(path),)
return out
def read_freesurfer_lut(fname=None):
"""Read a Freesurfer-formatted LUT.
Parameters
----------
fname : path-like | None
The filename. Can be None to read the standard Freesurfer LUT.
Returns
-------
atlas_ids : dict
Mapping from label names to IDs.
colors : dict
Mapping from label names to colors.
"""
lut = _get_lut(fname)
names, ids = lut["name"], lut["id"]
colors = np.array([lut["R"], lut["G"], lut["B"], lut["A"]], float).T
atlas_ids = dict(zip(names, ids))
colors = dict(zip(names, colors))
return atlas_ids, colors
def _get_lut(fname=None):
"""Get a FreeSurfer LUT."""
if fname is None:
fname = Path(__file__).parent / "data" / "FreeSurferColorLUT.txt"
_check_fname(fname, "read", must_exist=True)
dtype = [
("id", "<i8"),
("name", "U"),
("R", "<i8"),
("G", "<i8"),
("B", "<i8"),
("A", "<i8"),
]
lut = {d[0]: list() for d in dtype}
with open(fname) as fid:
for line in fid:
line = line.strip()
if line.startswith("#") or not line:
continue
line = line.split()
if len(line) != len(dtype):
raise RuntimeError(f"LUT is improperly formatted: {fname}")
for d, part in zip(dtype, line):
lut[d[0]].append(part)
lut = {d[0]: np.array(lut[d[0]], dtype=d[1]) for d in dtype}
assert len(lut["name"]) > 0
lut["name"] = [str(name) for name in lut["name"]]
return lut
@verbose
def _get_head_surface(surf, subject, subjects_dir, bem=None, verbose=None):
"""Get a head surface from the Freesurfer subject directory.
Parameters
----------
surf : str
The name of the surface 'auto', 'head', 'outer_skin', 'head-dense'
or 'seghead'.
%(subject)s
%(subjects_dir)s
bem : mne.bem.ConductorModel | None
The conductor model that stores information about the head surface.
%(verbose)s
Returns
-------
head_surf : dict | None
A dictionary with keys 'rr', 'tris', 'ntri', 'use_tris', 'np'
and 'coord_frame' that store information for mesh plotting and other
useful information about the head surface.
Notes
-----
.. versionadded: 0.24
"""
from .bem import _bem_find_surface, read_bem_surfaces
_check_option("surf", surf, ("auto", "head", "outer_skin", "head-dense", "seghead"))
if surf in ("auto", "head", "outer_skin"):
if bem is not None:
try:
return _bem_find_surface(bem, "head")
except RuntimeError:
logger.info(
"Could not find the surface for "
"head in the provided BEM model, "
"looking in the subject directory."
)
if subject is None:
if surf == "auto":
return
raise ValueError(
"To plot the head surface, the BEM/sphere"
" model must contain a head surface "
'or "subject" must be provided (got '
"None)"
)
subject_dir = op.join(get_subjects_dir(subjects_dir, raise_error=True), subject)
if surf in ("head-dense", "seghead"):
try_fnames = [
op.join(subject_dir, "bem", f"{subject}-head-dense.fif"),
op.join(subject_dir, "surf", "lh.seghead"),
]
else:
try_fnames = [
op.join(subject_dir, "bem", "outer_skin.surf"),
op.join(subject_dir, "bem", "flash", "outer_skin.surf"),
op.join(subject_dir, "bem", f"{subject}-head-sparse.fif"),
op.join(subject_dir, "bem", f"{subject}-head.fif"),
]
for fname in try_fnames:
if op.exists(fname):
logger.info(f"Using {op.basename(fname)} for head surface.")
if op.splitext(fname)[-1] == ".fif":
return read_bem_surfaces(fname, on_defects="warn")[0]
else:
return _read_mri_surface(fname)
raise OSError(
"No head surface found for subject "
f"{subject} after trying:\n" + "\n".join(try_fnames)
)
@verbose
def _get_skull_surface(surf, subject, subjects_dir, bem=None, verbose=None):
"""Get a skull surface from the Freesurfer subject directory.
Parameters
----------
surf : str
The name of the surface 'outer' or 'inner'.
%(subject)s
%(subjects_dir)s
bem : mne.bem.ConductorModel | None
The conductor model that stores information about the skull surface.
%(verbose)s
Returns
-------
skull_surf : dict | None
A dictionary with keys 'rr', 'tris', 'ntri', 'use_tris', 'np'
and 'coord_frame' that store information for mesh plotting and other
useful information about the head surface.
Notes
-----
.. versionadded: 0.24
"""
from .bem import _bem_find_surface
if bem is not None:
try:
return _bem_find_surface(bem, surf + "_skull")
except RuntimeError:
logger.info(
"Could not find the surface for "
"skull in the provided BEM model, "
"looking in the subject directory."
)
subjects_dir = Path(get_subjects_dir(subjects_dir, raise_error=True))
fname = _check_fname(
subjects_dir / subject / "bem" / (surf + "_skull.surf"),
overwrite="read",
must_exist=True,
name=f"{surf} skull surface",
)
return _read_mri_surface(fname)
def _estimate_talxfm_rigid(subject, subjects_dir):
from .coreg import _trans_from_params, fit_matched_points
xfm = read_talxfm(subject, subjects_dir)
# XYZ+origin + halfway
pts_tal = np.concatenate([np.eye(4)[:, :3], np.eye(3) * 0.5])
pts_subj = apply_trans(invert_transform(xfm), pts_tal)
# we fit with scaling enabled, but then discard it (we just need
# the rigid-body components)
params = fit_matched_points(pts_subj, pts_tal, scale=3, out="params")
rigid = _trans_from_params((True, True, False), params[:6])
return rigid

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
from scipy.signal import get_window
from .utils import _ensure_int, logger, verbose
###############################################################################
# Class for interpolation between adjacent points
class _Interp2:
r"""Interpolate between two points.
Parameters
----------
control_points : array, shape (n_changes,)
The control points (indices) to use.
values : callable | array, shape (n_changes, ...)
Callable that takes the control point and returns a list of
arrays that must be interpolated.
interp : str
Can be 'zero', 'linear', 'hann', or 'cos2' (same as hann).
Notes
-----
This will process data using overlapping windows of potentially
different sizes to achieve a constant output value using different
2-point interpolation schemes. For example, for linear interpolation,
and window sizes of 6 and 17, this would look like::
1 _ _
|\ / '-. .-'
| \ / '-. .-'
| x |-.-|
| / \ .-' '-.
|/ \_.-' '-.
0 +----|----|----|----|---
0 5 10 15 20 25
"""
def __init__(self, control_points, values, interp="hann"):
# set up interpolation
self.control_points = np.array(control_points, int).ravel()
if not np.array_equal(np.unique(self.control_points), self.control_points):
raise ValueError("Control points must be sorted and unique")
if len(self.control_points) == 0:
raise ValueError("Must be at least one control point")
if not (self.control_points >= 0).all():
raise ValueError(
f"All control points must be positive (got {self.control_points[:3]})"
)
if isinstance(values, np.ndarray):
values = [values]
if isinstance(values, list | tuple):
for v in values:
if not (v is None or isinstance(v, np.ndarray)):
raise TypeError(
'All entries in "values" must be ndarray or None, got '
f"{type(v)}"
)
if v is not None and v.shape[0] != len(self.control_points):
raise ValueError(
"Values, if provided, must be the same length as the number of "
f"control points ({len(self.control_points)}), got {v.shape[0]}"
)
use_values = values
def val(pt):
idx = np.where(control_points == pt)[0][0]
return [v[idx] if v is not None else None for v in use_values]
values = val
self.values = values
self.n_last = None
self._position = 0 # start at zero
self._left_idx = 0
self._left = self._right = self._use_interp = None
known_types = ("cos2", "linear", "zero", "hann")
if interp not in known_types:
raise ValueError(f'interp must be one of {known_types}, got "{interp}"')
self._interp = interp
def feed_generator(self, n_pts):
"""Feed data and get interpolators as a generator."""
self.n_last = 0
n_pts = _ensure_int(n_pts, "n_pts")
original_position = self._position
stop = self._position + n_pts
logger.debug(f"Feed {n_pts} ({self._position}-{stop})")
used = np.zeros(n_pts, bool)
if self._left is None: # first one
logger.debug(f" Eval @ 0 ({self.control_points[0]})")
self._left = self.values(self.control_points[0])
if len(self.control_points) == 1:
self._right = self._left
n_used = 0
# Left zero-order hold condition
if self._position < self.control_points[self._left_idx]:
n_use = min(self.control_points[self._left_idx] - self._position, n_pts)
logger.debug(f" Left ZOH {n_use}")
this_sl = slice(None, n_use)
assert used[this_sl].size == n_use
assert not used[this_sl].any()
used[this_sl] = True
yield [this_sl, self._left, None, None]
self._position += n_use
n_used += n_use
self.n_last += 1
# Standard interpolation condition
stop_right_idx = np.where(self.control_points >= stop)[0]
if len(stop_right_idx) == 0:
stop_right_idx = [len(self.control_points) - 1]
stop_right_idx = stop_right_idx[0]
left_idxs = np.arange(self._left_idx, stop_right_idx)
self.n_last += max(len(left_idxs) - 1, 0)
for bi, left_idx in enumerate(left_idxs):
if left_idx != self._left_idx or self._right is None:
if self._right is not None:
assert left_idx == self._left_idx + 1
self._left = self._right
self._left_idx += 1
self._use_interp = None # need to recreate it
eval_pt = self.control_points[self._left_idx + 1]
logger.debug(f" Eval @ {self._left_idx + 1} ({eval_pt})")
self._right = self.values(eval_pt)
assert self._right is not None
left_point = self.control_points[self._left_idx]
right_point = self.control_points[self._left_idx + 1]
if self._use_interp is None:
interp_span = right_point - left_point
if self._interp == "zero":
self._use_interp = None
elif self._interp == "linear":
self._use_interp = np.linspace(
1.0, 0.0, interp_span, endpoint=False
)
else: # self._interp in ('cos2', 'hann'):
self._use_interp = np.cos(
np.linspace(0, np.pi / 2.0, interp_span, endpoint=False)
)
self._use_interp *= self._use_interp
n_use = min(stop, right_point) - self._position
if n_use > 0:
logger.debug(
f" Interp {self._interp} {n_use} ({left_point}-{right_point})"
)
interp_start = self._position - left_point
assert interp_start >= 0
if self._use_interp is None:
this_interp = None
else:
this_interp = self._use_interp[interp_start : interp_start + n_use]
assert this_interp.size == n_use
this_sl = slice(n_used, n_used + n_use)
assert used[this_sl].size == n_use
assert not used[this_sl].any()
used[this_sl] = True
yield [this_sl, self._left, self._right, this_interp]
self._position += n_use
n_used += n_use
# Right zero-order hold condition
if self.control_points[self._left_idx] <= self._position:
n_use = stop - self._position
if n_use > 0:
logger.debug(f" Right ZOH {n_use}")
this_sl = slice(n_pts - n_use, None)
assert not used[this_sl].any()
used[this_sl] = True
assert self._right is not None
yield [this_sl, self._right, None, None]
self._position += n_use
n_used += n_use
self.n_last += 1
assert self._position == stop
assert n_used == n_pts
assert used.all()
assert self._position == original_position + n_pts
def feed(self, n_pts):
"""Feed data and get interpolated values."""
# Convenience function for assembly
out_arrays = None
for o in self.feed_generator(n_pts):
if out_arrays is None:
out_arrays = [
np.empty(v.shape + (n_pts,)) if v is not None else None
for v in o[1]
]
for ai, arr in enumerate(out_arrays):
if arr is not None:
if o[3] is None:
arr[..., o[0]] = o[1][ai][..., np.newaxis]
else:
arr[..., o[0]] = o[1][ai][..., np.newaxis] * o[3] + o[2][ai][
..., np.newaxis
] * (1.0 - o[3])
assert out_arrays is not None
return out_arrays
###############################################################################
# Constant overlap-add processing class
def _check_store(store):
if isinstance(store, np.ndarray):
store = [store]
if isinstance(store, list | tuple) and all(
isinstance(s, np.ndarray) for s in store
):
store = _Storer(*store)
if not callable(store):
raise TypeError(f"store must be callable, got type {type(store)}")
return store
class _COLA:
r"""Constant overlap-add processing helper.
Parameters
----------
process : callable
A function that takes a chunk of input data with shape
``(n_channels, n_samples)`` and processes it.
store : callable | ndarray
A function that takes a completed chunk of output data.
Can also be an ``ndarray``, in which case it is treated as the
output data in which to store the results.
n_total : int
The total number of samples.
n_samples : int
The number of samples per window.
n_overlap : int
The overlap between windows.
window : str
The window to use. Default is "hann".
tol : float
The tolerance for COLA checking.
Notes
-----
This will process data using overlapping windows to achieve a constant
output value. For example, for ``n_total=27``, ``n_samples=10``,
``n_overlap=5`` and ``window='triang'``::
1 _____ _______
| \ /\ /\ /
| \ / \ / \ /
| x x x
| / \ / \ / \
| / \/ \/ \
0 +----|----|----|----|----|-
0 5 10 15 20 25
This produces four windows: the first three are the requested length
(10 samples) and the last one is longer (12 samples). The first and last
window are asymmetric.
"""
@verbose
def __init__(
self,
process,
store,
n_total,
n_samples,
n_overlap,
sfreq,
window="hann",
tol=1e-10,
*,
verbose=None,
):
n_samples = _ensure_int(n_samples, "n_samples")
n_overlap = _ensure_int(n_overlap, "n_overlap")
n_total = _ensure_int(n_total, "n_total")
if n_samples <= 0:
raise ValueError(f"n_samples must be > 0, got {n_samples}")
if n_overlap < 0:
raise ValueError(f"n_overlap must be >= 0, got {n_overlap}")
if n_total < 0:
raise ValueError(f"n_total must be >= 0, got {n_total}")
self._n_samples = int(n_samples)
self._n_overlap = int(n_overlap)
del n_samples, n_overlap
if n_total < self._n_samples:
raise ValueError(
f"Number of samples per window ({self._n_samples}) must be at "
f"most the total number of samples ({n_total})"
)
if not callable(process):
raise TypeError(f"process must be callable, got type {type(process)}")
self._process = process
self._step = self._n_samples - self._n_overlap
self._store = _check_store(store)
self._idx = 0
self._in_buffers = self._out_buffers = None
# Create our window boundaries
window_name = window if isinstance(window, str) else "custom"
self._window = get_window(
window, self._n_samples, fftbins=(self._n_samples - 1) % 2
)
self._window /= _check_cola(
self._window, self._n_samples, self._step, window_name, tol=tol
)
self.starts = np.arange(0, n_total - self._n_samples + 1, self._step)
self.stops = self.starts + self._n_samples
delta = n_total - self.stops[-1]
self.stops[-1] = n_total
sfreq = float(sfreq)
pl = "s" if len(self.starts) != 1 else ""
logger.info(
f" Processing {len(self.starts):4d} data chunk{pl} of (at least) "
f"{self._n_samples / sfreq:0.1f} s with "
f"{self._n_overlap / sfreq:0.1f} s overlap and {window_name} windowing"
)
del window, window_name
if delta > 0:
logger.info(
f" The final {delta / sfreq} s will be lumped into the final window"
)
@property
def _in_offset(self):
"""Compute from current processing window start and buffer len."""
return self.starts[self._idx] + self._in_buffers[0].shape[-1]
@verbose
def feed(self, *datas, verbose=None, **kwargs):
"""Pass in a chunk of data."""
# Append to our input buffer
if self._in_buffers is None:
self._in_buffers = [None] * len(datas)
if len(datas) != len(self._in_buffers):
raise ValueError(
f"Got {len(datas)} array(s), needed {len(self._in_buffers)}"
)
for di, data in enumerate(datas):
if not isinstance(data, np.ndarray) or data.ndim < 1:
raise TypeError(
f"data entry {di} must be an 2D ndarray, got {type(data)}"
)
if self._in_buffers[di] is None:
# In practice, users can give large chunks, so we use
# dynamic allocation of the in buffer. We could save some
# memory allocation by only ever processing max_len at once,
# but this would increase code complexity.
self._in_buffers[di] = np.empty(data.shape[:-1] + (0,), data.dtype)
if (
data.shape[:-1] != self._in_buffers[di].shape[:-1]
or self._in_buffers[di].dtype != data.dtype
):
raise TypeError(
f"data must dtype {self._in_buffers[di].dtype} and "
f"shape[:-1]=={self._in_buffers[di].shape[:-1]}, got dtype "
f"{data.dtype} shape[:-1]={data.shape[:-1]}"
)
logger.debug(
f" + Appending {self._in_offset:d}->"
f"{self._in_offset + data.shape[-1]:d}"
)
self._in_buffers[di] = np.concatenate([self._in_buffers[di], data], -1)
if self._in_offset > self.stops[-1]:
raise ValueError(
f"data (shape {data.shape}) exceeded expected total buffer size ("
f"{self._in_offset} > {self.stops[-1]})"
)
# Check to see if we can process the next chunk and dump outputs
while self._idx < len(self.starts) and self._in_offset >= self.stops[self._idx]:
start, stop = self.starts[self._idx], self.stops[self._idx]
this_len = stop - start
this_window = self._window.copy()
if self._idx == len(self.starts) - 1:
this_window = np.pad(
self._window, (0, this_len - len(this_window)), "constant"
)
for offset in range(self._step, len(this_window), self._step):
n_use = len(this_window) - offset
this_window[offset:] += self._window[:n_use]
if self._idx == 0:
for offset in range(self._n_samples - self._step, 0, -self._step):
this_window[:offset] += self._window[-offset:]
logger.debug(f" * Processing {start}->{stop}")
this_proc = [in_[..., :this_len].copy() for in_ in self._in_buffers]
if not all(
proc.shape[-1] == this_len == this_window.size for proc in this_proc
):
raise RuntimeError("internal indexing error")
outs = self._process(*this_proc, **kwargs)
if self._out_buffers is None:
max_len = np.max(self.stops - self.starts)
self._out_buffers = [
np.zeros(o.shape[:-1] + (max_len,), o.dtype) for o in outs
]
for oi, out in enumerate(outs):
out *= this_window
self._out_buffers[oi][..., : stop - start] += out
self._idx += 1
if self._idx < len(self.starts):
next_start = self.starts[self._idx]
else:
next_start = self.stops[-1]
delta = next_start - self.starts[self._idx - 1]
for di in range(len(self._in_buffers)):
self._in_buffers[di] = self._in_buffers[di][..., delta:]
logger.debug(f" - Shifting input/output buffers by {delta:d} samples")
self._store(*[o[..., :delta] for o in self._out_buffers])
for ob in self._out_buffers:
ob[..., :-delta] = ob[..., delta:]
ob[..., -delta:] = 0.0
def _check_cola(win, nperseg, step, window_name, tol=1e-10):
"""Check whether the Constant OverLap Add (COLA) constraint is met."""
# adapted from SciPy
binsums = np.sum(
[win[ii * step : (ii + 1) * step] for ii in range(nperseg // step)], axis=0
)
if nperseg % step != 0:
binsums[: nperseg % step] += win[-(nperseg % step) :]
const = np.median(binsums)
deviation = np.max(np.abs(binsums - const))
if deviation > tol:
raise ValueError(
f"segment length {nperseg:d} with step {step:d} for {window_name} window "
"type does not provide a constant output "
f"({100 * deviation / const:g}% deviation)"
)
return const
class _Storer:
"""Store data in chunks."""
def __init__(self, *outs, picks=None):
for oi, out in enumerate(outs):
if not isinstance(out, np.ndarray) or out.ndim < 1:
raise TypeError(f"outs[oi] must be >= 1D ndarray, got {out}")
self.outs = outs
self.idx = 0
self.picks = picks
def __call__(self, *outs):
if len(outs) != len(self.outs) or not all(
out.shape[-1] == outs[0].shape[-1] for out in outs
):
raise ValueError("Bad outs")
idx = (Ellipsis,)
if self.picks is not None:
idx += (self.picks,)
stop = self.idx + outs[0].shape[-1]
idx += (slice(self.idx, stop),)
for o1, o2 in zip(self.outs, outs):
o1[idx] = o2
self.idx = stop

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"""Utility functions to baseline-correct data."""
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
from .utils import _check_option, _validate_type, logger, verbose
def _log_rescale(baseline, mode="mean"):
"""Log the rescaling method."""
if baseline is not None:
_check_option(
"mode",
mode,
["logratio", "ratio", "zscore", "mean", "percent", "zlogratio"],
)
msg = f"Applying baseline correction (mode: {mode})"
else:
msg = "No baseline correction applied"
return msg
@verbose
def rescale(data, times, baseline, mode="mean", copy=True, picks=None, verbose=None):
"""Rescale (baseline correct) data.
Parameters
----------
data : array
It can be of any shape. The only constraint is that the last
dimension should be time.
times : 1D array
Time instants is seconds.
%(baseline_rescale)s
mode : 'mean' | 'ratio' | 'logratio' | 'percent' | 'zscore' | 'zlogratio'
Perform baseline correction by
- subtracting the mean of baseline values ('mean')
- dividing by the mean of baseline values ('ratio')
- dividing by the mean of baseline values and taking the log
('logratio')
- subtracting the mean of baseline values followed by dividing by
the mean of baseline values ('percent')
- subtracting the mean of baseline values and dividing by the
standard deviation of baseline values ('zscore')
- dividing by the mean of baseline values, taking the log, and
dividing by the standard deviation of log baseline values
('zlogratio')
copy : bool
Whether to return a new instance or modify in place.
picks : list of int | None
Data to process along the axis=-2 (None, default, processes all).
%(verbose)s
Returns
-------
data_scaled: array
Array of same shape as data after rescaling.
"""
if copy:
data = data.copy()
if verbose is not False:
msg = _log_rescale(baseline, mode)
logger.info(msg)
if baseline is None or data.shape[-1] == 0:
return data
bmin, bmax = baseline
if bmin is None:
imin = 0
else:
imin = np.where(times >= bmin)[0]
if len(imin) == 0:
raise ValueError(
f"bmin is too large ({bmin}), it exceeds the largest time value"
)
imin = int(imin[0])
if bmax is None:
imax = len(times)
else:
imax = np.where(times <= bmax)[0]
if len(imax) == 0:
raise ValueError(
f"bmax is too small ({bmax}), it is smaller than the smallest time "
"value"
)
imax = int(imax[-1]) + 1
if imin >= imax:
raise ValueError(
f"Bad rescaling slice ({imin}:{imax}) from time values {bmin}, {bmax}"
)
# technically this is inefficient when `picks` is given, but assuming
# that we generally pick most channels for rescaling, it's not so bad
mean = np.mean(data[..., imin:imax], axis=-1, keepdims=True)
if mode == "mean":
def fun(d, m):
d -= m
elif mode == "ratio":
def fun(d, m):
d /= m
elif mode == "logratio":
def fun(d, m):
d /= m
np.log10(d, out=d)
elif mode == "percent":
def fun(d, m):
d -= m
d /= m
elif mode == "zscore":
def fun(d, m):
d -= m
d /= np.std(d[..., imin:imax], axis=-1, keepdims=True)
elif mode == "zlogratio":
def fun(d, m):
d /= m
np.log10(d, out=d)
d /= np.std(d[..., imin:imax], axis=-1, keepdims=True)
if picks is None:
fun(data, mean)
else:
for pi in picks:
fun(data[..., pi, :], mean[..., pi, :])
return data
def _check_baseline(baseline, times, sfreq, on_baseline_outside_data="raise"):
"""Check if the baseline is valid and adjust it if requested.
``None`` values inside ``baseline`` will be replaced with ``times[0]`` and
``times[-1]``.
Parameters
----------
baseline : array-like, shape (2,) | None
Beginning and end of the baseline period, in seconds. If ``None``,
assume no baseline and return immediately.
times : array
The time points.
sfreq : float
The sampling rate.
on_baseline_outside_data : 'raise' | 'info' | 'adjust'
What to do if the baseline period exceeds the data.
If ``'raise'``, raise an exception (default).
If ``'info'``, log an info message.
If ``'adjust'``, adjust the baseline such that it is within the data range.
Returns
-------
(baseline_tmin, baseline_tmax) | None
The baseline with ``None`` values replaced with times, and with adjusted times
if ``on_baseline_outside_data='adjust'``; or ``None``, if ``baseline`` is
``None``.
"""
if baseline is None:
return None
_validate_type(baseline, "array-like")
baseline = tuple(baseline)
if len(baseline) != 2:
raise ValueError(
f"baseline must have exactly two elements (got {len(baseline)})."
)
tmin, tmax = times[0], times[-1]
tstep = 1.0 / float(sfreq)
# check default value of baseline and `tmin=0`
if baseline == (None, 0) and tmin == 0:
raise ValueError(
"Baseline interval is only one sample. Use `baseline=(0, 0)` if this is "
"desired."
)
baseline_tmin, baseline_tmax = baseline
if baseline_tmin is None:
baseline_tmin = tmin
baseline_tmin = float(baseline_tmin)
if baseline_tmax is None:
baseline_tmax = tmax
baseline_tmax = float(baseline_tmax)
if baseline_tmin > baseline_tmax:
raise ValueError(
f"Baseline min ({baseline_tmin}) must be less than baseline max ("
f"{baseline_tmax})"
)
if (baseline_tmin < tmin - tstep) or (baseline_tmax > tmax + tstep):
msg = (
f"Baseline interval [{baseline_tmin}, {baseline_tmax}] s is outside of "
f"epochs data [{tmin}, {tmax}] s. Epochs were probably cropped."
)
if on_baseline_outside_data == "raise":
raise ValueError(msg)
elif on_baseline_outside_data == "info":
logger.info(msg)
elif on_baseline_outside_data == "adjust":
if baseline_tmin < tmin - tstep:
baseline_tmin = tmin
if baseline_tmax > tmax + tstep:
baseline_tmax = tmax
return baseline_tmin, baseline_tmax

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""Beamformers for source localization."""
import lazy_loader as lazy
(__getattr__, __dir__, __all__) = lazy.attach_stub(__name__, __file__)

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mne/beamformer/__init__.pyi Normal file
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__all__ = [
"Beamformer",
"apply_dics",
"apply_dics_csd",
"apply_dics_epochs",
"apply_dics_tfr_epochs",
"apply_lcmv",
"apply_lcmv_cov",
"apply_lcmv_epochs",
"apply_lcmv_raw",
"make_dics",
"make_lcmv",
"make_lcmv_resolution_matrix",
"rap_music",
"read_beamformer",
"trap_music",
]
from ._compute_beamformer import Beamformer, read_beamformer
from ._dics import (
apply_dics,
apply_dics_csd,
apply_dics_epochs,
apply_dics_tfr_epochs,
make_dics,
)
from ._lcmv import (
apply_lcmv,
apply_lcmv_cov,
apply_lcmv_epochs,
apply_lcmv_raw,
make_lcmv,
)
from ._rap_music import rap_music, trap_music
from .resolution_matrix import make_lcmv_resolution_matrix

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mne/beamformer/_compute_beamformer.py Normal file
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"""Functions shared between different beamformer types."""
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from copy import deepcopy
import numpy as np
from .._fiff.proj import Projection, make_projector
from ..cov import Covariance, make_ad_hoc_cov
from ..forward.forward import _restrict_forward_to_src_sel, is_fixed_orient
from ..minimum_norm.inverse import _get_vertno, _prepare_forward
from ..source_space._source_space import label_src_vertno_sel
from ..time_frequency.csd import CrossSpectralDensity
from ..utils import (
_check_option,
_check_src_normal,
_import_h5io_funcs,
_pl,
_reg_pinv,
_sym_mat_pow,
check_fname,
logger,
verbose,
warn,
)
def _check_proj_match(proj, filters):
"""Check whether SSP projections in data and spatial filter match."""
proj_data, _, _ = make_projector(proj, filters["ch_names"])
if not np.allclose(
proj_data, filters["proj"], atol=np.finfo(float).eps, rtol=1e-13
):
raise ValueError(
"The SSP projections present in the data "
"do not match the projections used when "
"calculating the spatial filter."
)
def _check_src_type(filters):
"""Check whether src_type is in filters and set custom warning."""
if "src_type" not in filters:
filters["src_type"] = None
warn_text = (
"The spatial filter does not contain src_type and a robust "
"guess of src_type is not possible without src. Consider "
"recomputing the filter."
)
return filters, warn_text
def _prepare_beamformer_input(
info,
forward,
label=None,
pick_ori=None,
noise_cov=None,
rank=None,
pca=False,
loose=None,
combine_xyz="fro",
exp=None,
limit=None,
allow_fixed_depth=True,
limit_depth_chs=False,
):
"""Input preparation common for LCMV, DICS, and RAP-MUSIC."""
_check_option("pick_ori", pick_ori, ("normal", "max-power", "vector", None))
# Restrict forward solution to selected vertices
if label is not None:
_, src_sel = label_src_vertno_sel(label, forward["src"])
forward = _restrict_forward_to_src_sel(forward, src_sel)
if loose is None:
loose = 0.0 if is_fixed_orient(forward) else 1.0
# TODO: Deduplicate with _check_one_ch_type, should not be necessary
# (DICS hits this code path, LCMV does not)
if noise_cov is None:
noise_cov = make_ad_hoc_cov(info, std=1.0)
(
forward,
info_picked,
gain,
_,
orient_prior,
_,
trace_GRGT,
noise_cov,
whitener,
) = _prepare_forward(
forward,
info,
noise_cov,
"auto",
loose,
rank=rank,
pca=pca,
use_cps=True,
exp=exp,
limit_depth_chs=limit_depth_chs,
combine_xyz=combine_xyz,
limit=limit,
allow_fixed_depth=allow_fixed_depth,
)
is_free_ori = not is_fixed_orient(forward) # could have been changed
nn = forward["source_nn"]
if is_free_ori: # take Z coordinate
nn = nn[2::3]
nn = nn.copy()
vertno = _get_vertno(forward["src"])
if forward["surf_ori"]:
nn[...] = [0, 0, 1] # align to local +Z coordinate
if pick_ori is not None and not is_free_ori:
raise ValueError(
f"Normal or max-power orientation (got {pick_ori!r}) can only be picked "
"when a forward operator with free orientation is used."
)
if pick_ori == "normal" and not forward["surf_ori"]:
raise ValueError(
"Normal orientation can only be picked when a forward operator oriented in "
"surface coordinates is used."
)
_check_src_normal(pick_ori, forward["src"])
del forward, info
# Undo the scaling that MNE prefers
scale = np.sqrt((noise_cov["eig"] > 0).sum() / trace_GRGT)
gain /= scale
if orient_prior is not None:
orient_std = np.sqrt(orient_prior)
else:
orient_std = np.ones(gain.shape[1])
# Get the projector
proj, _, _ = make_projector(info_picked["projs"], info_picked["ch_names"])
return (is_free_ori, info_picked, proj, vertno, gain, whitener, nn, orient_std)
def _reduce_leadfield_rank(G):
"""Reduce the rank of the leadfield."""
# decompose lead field
u, s, v = np.linalg.svd(G, full_matrices=False)
# backproject, omitting one direction (equivalent to setting the smallest
# singular value to zero)
G = np.matmul(u[:, :, :-1], s[:, :-1, np.newaxis] * v[:, :-1, :])
return G
def _sym_inv_sm(x, reduce_rank, inversion, sk):
"""Symmetric inversion with single- or matrix-style inversion."""
if x.shape[1:] == (1, 1):
with np.errstate(divide="ignore", invalid="ignore"):
x_inv = 1.0 / x
x_inv[~np.isfinite(x_inv)] = 1.0
else:
assert x.shape[1:] == (3, 3)
if inversion == "matrix":
x_inv = _sym_mat_pow(x, -1, reduce_rank=reduce_rank)
# Reapply source covariance after inversion
x_inv *= sk[:, :, np.newaxis]
x_inv *= sk[:, np.newaxis, :]
else:
# Invert for each dipole separately using plain division
diags = np.diagonal(x, axis1=1, axis2=2)
assert not reduce_rank # guaranteed earlier
with np.errstate(divide="ignore"):
diags = 1.0 / diags
# set the diagonal of each 3x3
x_inv = np.zeros_like(x)
for k in range(x.shape[0]):
this = diags[k]
# Reapply source covariance after inversion
this *= sk[k] * sk[k]
x_inv[k].flat[::4] = this
return x_inv
def _compute_beamformer(
G,
Cm,
reg,
n_orient,
weight_norm,
pick_ori,
reduce_rank,
rank,
inversion,
nn,
orient_std,
whitener,
):
"""Compute a spatial beamformer filter (LCMV or DICS).
For more detailed information on the parameters, see the docstrings of
`make_lcmv` and `make_dics`.
Parameters
----------
G : ndarray, shape (n_dipoles, n_channels)
The leadfield.
Cm : ndarray, shape (n_channels, n_channels)
The data covariance matrix.
reg : float
Regularization parameter.
n_orient : int
Number of dipole orientations defined at each source point
weight_norm : None | 'unit-noise-gain' | 'nai'
The weight normalization scheme to use.
pick_ori : None | 'normal' | 'max-power'
The source orientation to compute the beamformer in.
reduce_rank : bool
Whether to reduce the rank by one during computation of the filter.
rank : dict | None | 'full' | 'info'
See compute_rank.
inversion : 'matrix' | 'single'
The inversion scheme to compute the weights.
nn : ndarray, shape (n_dipoles, 3)
The source normals.
orient_std : ndarray, shape (n_dipoles,)
The std of the orientation prior used in weighting the lead fields.
whitener : ndarray, shape (n_channels, n_channels)
The whitener.
Returns
-------
W : ndarray, shape (n_dipoles, n_channels)
The beamformer filter weights.
"""
_check_option(
"weight_norm",
weight_norm,
["unit-noise-gain-invariant", "unit-noise-gain", "nai", None],
)
# Whiten the data covariance
Cm = whitener @ Cm @ whitener.T.conj()
# Restore to properly Hermitian as large whitening coefs can have bad
# rounding error
Cm[:] = (Cm + Cm.T.conj()) / 2.0
assert Cm.shape == (G.shape[0],) * 2
s, _ = np.linalg.eigh(Cm)
if not (s >= -s.max() * 1e-7).all():
# This shouldn't ever happen, but just in case
warn(
"data covariance does not appear to be positive semidefinite, "
"results will likely be incorrect"
)
# Tikhonov regularization using reg parameter to control for
# trade-off between spatial resolution and noise sensitivity
# eq. 25 in Gross and Ioannides, 1999 Phys. Med. Biol. 44 2081
Cm_inv, loading_factor, rank = _reg_pinv(Cm, reg, rank)
assert orient_std.shape == (G.shape[1],)
n_sources = G.shape[1] // n_orient
assert nn.shape == (n_sources, 3)
logger.info(f"Computing beamformer filters for {n_sources} source{_pl(n_sources)}")
n_channels = G.shape[0]
assert n_orient in (3, 1)
Gk = np.reshape(G.T, (n_sources, n_orient, n_channels)).transpose(0, 2, 1)
assert Gk.shape == (n_sources, n_channels, n_orient)
sk = np.reshape(orient_std, (n_sources, n_orient))
del G, orient_std
_check_option("reduce_rank", reduce_rank, (True, False))
# inversion of the denominator
_check_option("inversion", inversion, ("matrix", "single"))
if (
inversion == "single"
and n_orient > 1
and pick_ori == "vector"
and weight_norm == "unit-noise-gain-invariant"
):
raise ValueError(
'Cannot use pick_ori="vector" with inversion="single" and '
'weight_norm="unit-noise-gain-invariant"'
)
if reduce_rank and inversion == "single":
raise ValueError(
'reduce_rank cannot be used with inversion="single"; '
'consider using inversion="matrix" if you have a '
"rank-deficient forward model (i.e., from a sphere "
"model with MEG channels), otherwise consider using "
"reduce_rank=False"
)
if n_orient > 1:
_, Gk_s, _ = np.linalg.svd(Gk, full_matrices=False)
assert Gk_s.shape == (n_sources, n_orient)
if not reduce_rank and (Gk_s[:, 0] > 1e6 * Gk_s[:, 2]).any():
raise ValueError(
"Singular matrix detected when estimating spatial filters. "
"Consider reducing the rank of the forward operator by using "
"reduce_rank=True."
)
del Gk_s
#
# 1. Reduce rank of the lead field
#
if reduce_rank:
Gk = _reduce_leadfield_rank(Gk)
def _compute_bf_terms(Gk, Cm_inv):
bf_numer = np.matmul(Gk.swapaxes(-2, -1).conj(), Cm_inv)
bf_denom = np.matmul(bf_numer, Gk)
return bf_numer, bf_denom
#
# 2. Reorient lead field in direction of max power or normal
#
if pick_ori == "max-power":
assert n_orient == 3
_, bf_denom = _compute_bf_terms(Gk, Cm_inv)
if weight_norm is None:
ori_numer = np.eye(n_orient)[np.newaxis]
ori_denom = bf_denom
else:
# compute power, cf Sekihara & Nagarajan 2008, eq. 4.47
ori_numer = bf_denom
# Cm_inv should be Hermitian so no need for .T.conj()
ori_denom = np.matmul(
np.matmul(Gk.swapaxes(-2, -1).conj(), Cm_inv @ Cm_inv), Gk
)
ori_denom_inv = _sym_inv_sm(ori_denom, reduce_rank, inversion, sk)
ori_pick = np.matmul(ori_denom_inv, ori_numer)
assert ori_pick.shape == (n_sources, n_orient, n_orient)
# pick eigenvector that corresponds to maximum eigenvalue:
eig_vals, eig_vecs = np.linalg.eig(ori_pick.real) # not Hermitian!
# sort eigenvectors by eigenvalues for picking:
order = np.argsort(np.abs(eig_vals), axis=-1)
# eig_vals = np.take_along_axis(eig_vals, order, axis=-1)
max_power_ori = eig_vecs[np.arange(len(eig_vecs)), :, order[:, -1]]
assert max_power_ori.shape == (n_sources, n_orient)
# set the (otherwise arbitrary) sign to match the normal
signs = np.sign(np.sum(max_power_ori * nn, axis=1, keepdims=True))
signs[signs == 0] = 1.0
max_power_ori *= signs
# Compute the lead field for the optimal orientation,
# and adjust numer/denom
Gk = np.matmul(Gk, max_power_ori[..., np.newaxis])
n_orient = 1
else:
max_power_ori = None
if pick_ori == "normal":
Gk = Gk[..., 2:3]
n_orient = 1
#
# 3. Compute numerator and denominator of beamformer formula (unit-gain)
#
bf_numer, bf_denom = _compute_bf_terms(Gk, Cm_inv)
assert bf_denom.shape == (n_sources,) + (n_orient,) * 2
assert bf_numer.shape == (n_sources, n_orient, n_channels)
del Gk # lead field has been adjusted and should not be used anymore
#
# 4. Invert the denominator
#
# Here W is W_ug, i.e.:
# G.T @ Cm_inv / (G.T @ Cm_inv @ G)
bf_denom_inv = _sym_inv_sm(bf_denom, reduce_rank, inversion, sk)
assert bf_denom_inv.shape == (n_sources, n_orient, n_orient)
W = np.matmul(bf_denom_inv, bf_numer)
assert W.shape == (n_sources, n_orient, n_channels)
del bf_denom_inv, sk
#
# 5. Re-scale filter weights according to the selected weight_norm
#
# Weight normalization is done by computing, for each source::
#
# W_ung = W_ug / sqrt(W_ug @ W_ug.T)
#
# with W_ung referring to the unit-noise-gain (weight normalized) filter
# and W_ug referring to the above-calculated unit-gain filter stored in W.
if weight_norm is not None:
# Three different ways to calculate the normalization factors here.
# Only matters when in vector mode, as otherwise n_orient == 1 and
# they are all equivalent.
#
# In MNE < 0.21, we just used the Frobenius matrix norm:
#
# noise_norm = np.linalg.norm(W, axis=(1, 2), keepdims=True)
# assert noise_norm.shape == (n_sources, 1, 1)
# W /= noise_norm
#
# Sekihara 2008 says to use sqrt(diag(W_ug @ W_ug.T)), which is not
# rotation invariant:
if weight_norm in ("unit-noise-gain", "nai"):
noise_norm = np.matmul(W, W.swapaxes(-2, -1).conj()).real
noise_norm = np.reshape( # np.diag operation over last two axes
noise_norm, (n_sources, -1, 1)
)[:, :: n_orient + 1]
np.sqrt(noise_norm, out=noise_norm)
noise_norm[noise_norm == 0] = np.inf
assert noise_norm.shape == (n_sources, n_orient, 1)
W /= noise_norm
else:
assert weight_norm == "unit-noise-gain-invariant"
# Here we use sqrtm. The shortcut:
#
# use = W
#
# ... does not match the direct route (it is rotated!), so we'll
# use the direct one to match FieldTrip:
use = bf_numer
inner = np.matmul(use, use.swapaxes(-2, -1).conj())
W = np.matmul(_sym_mat_pow(inner, -0.5), use)
noise_norm = 1.0
if weight_norm == "nai":
# Estimate noise level based on covariance matrix, taking the
# first eigenvalue that falls outside the signal subspace or the
# loading factor used during regularization, whichever is largest.
if rank > len(Cm):
# Covariance matrix is full rank, no noise subspace!
# Use the loading factor as noise ceiling.
if loading_factor == 0:
raise RuntimeError(
"Cannot compute noise subspace with a full-rank "
"covariance matrix and no regularization. Try "
"manually specifying the rank of the covariance "
"matrix or using regularization."
)
noise = loading_factor
else:
noise, _ = np.linalg.eigh(Cm)
noise = noise[-rank]
noise = max(noise, loading_factor)
W /= np.sqrt(noise)
W = W.reshape(n_sources * n_orient, n_channels)
logger.info("Filter computation complete")
return W, max_power_ori
def _compute_power(Cm, W, n_orient):
"""Use beamformer filters to compute source power.
Parameters
----------
Cm : ndarray, shape (n_channels, n_channels)
Data covariance matrix or CSD matrix.
W : ndarray, shape (nvertices*norient, nchannels)
Beamformer weights.
Returns
-------
power : ndarray, shape (nvertices,)
Source power.
"""
n_sources = W.shape[0] // n_orient
Wk = W.reshape(n_sources, n_orient, W.shape[1])
source_power = np.trace(
(Wk @ Cm @ Wk.conj().transpose(0, 2, 1)).real, axis1=1, axis2=2
)
return source_power
class Beamformer(dict):
"""A computed beamformer.
Notes
-----
.. versionadded:: 0.17
"""
def copy(self):
"""Copy the beamformer.
Returns
-------
beamformer : instance of Beamformer
A deep copy of the beamformer.
"""
return deepcopy(self)
def __repr__(self): # noqa: D105
n_verts = sum(len(v) for v in self["vertices"])
n_channels = len(self["ch_names"])
if self["subject"] is None:
subject = "unknown"
else:
subject = f'"{self["subject"]}"'
out = "<Beamformer | {}, subject {}, {} vert, {} ch".format(
self["kind"],
subject,
n_verts,
n_channels,
)
if self["pick_ori"] is not None:
out += f', {self["pick_ori"]} ori'
if self["weight_norm"] is not None:
out += f', {self["weight_norm"]} norm'
if self.get("inversion") is not None:
out += f', {self["inversion"]} inversion'
if "rank" in self:
out += f', rank {self["rank"]}'
out += ">"
return out
@verbose
def save(self, fname, overwrite=False, verbose=None):
"""Save the beamformer filter.
Parameters
----------
fname : path-like
The filename to use to write the HDF5 data.
Should end in ``'-lcmv.h5'`` or ``'-dics.h5'``.
%(overwrite)s
%(verbose)s
"""
_, write_hdf5 = _import_h5io_funcs()
ending = f'-{self["kind"].lower()}.h5'
check_fname(fname, self["kind"], (ending,))
csd_orig = None
try:
if "csd" in self:
csd_orig = self["csd"]
self["csd"] = self["csd"].__getstate__()
write_hdf5(fname, self, overwrite=overwrite, title="mnepython")
finally:
if csd_orig is not None:
self["csd"] = csd_orig
def read_beamformer(fname):
"""Read a beamformer filter.
Parameters
----------
fname : path-like
The filename of the HDF5 file.
Returns
-------
filter : instance of Beamformer
The beamformer filter.
"""
read_hdf5, _ = _import_h5io_funcs()
beamformer = read_hdf5(fname, title="mnepython")
if "csd" in beamformer:
beamformer["csd"] = CrossSpectralDensity(**beamformer["csd"])
# h5io seems to cast `bool` to `int` on round-trip, probably a bug
# we should fix at some point (if possible -- could be HDF5 limitation)
for key in ("normalize_fwd", "is_free_ori", "is_ssp"):
if key in beamformer:
beamformer[key] = bool(beamformer[key])
for key in ("data_cov", "noise_cov"):
if beamformer.get(key) is not None:
for pi, p in enumerate(beamformer[key]["projs"]):
p = Projection(**p)
p["active"] = bool(p["active"])
beamformer[key]["projs"][pi] = p
beamformer[key] = Covariance(
*[
beamformer[key].get(arg)
for arg in (
"data",
"names",
"bads",
"projs",
"nfree",
"eig",
"eigvec",
"method",
"loglik",
)
]
)
return Beamformer(beamformer)
def _proj_whiten_data(M, proj, filters):
if filters.get("is_ssp", True):
# check whether data and filter projs match
_check_proj_match(proj, filters)
if filters["whitener"] is None:
M = np.dot(filters["proj"], M)
if filters["whitener"] is not None:
M = np.dot(filters["whitener"], M)
return M

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"""Dynamic Imaging of Coherent Sources (DICS)."""
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
from .._fiff.pick import pick_channels, pick_info
from ..channels import equalize_channels
from ..forward import _subject_from_forward
from ..minimum_norm.inverse import _check_depth, _check_reference, combine_xyz
from ..rank import compute_rank
from ..source_estimate import _get_src_type, _make_stc
from ..time_frequency import EpochsTFR
from ..time_frequency.tfr import _check_tfr_complex
from ..utils import (
_check_channels_spatial_filter,
_check_one_ch_type,
_check_option,
_check_rank,
_validate_type,
logger,
verbose,
warn,
)
from ._compute_beamformer import (
Beamformer,
_check_src_type,
_compute_beamformer,
_compute_power,
_prepare_beamformer_input,
_proj_whiten_data,
)
@verbose
def make_dics(
info,
forward,
csd,
reg=0.05,
noise_csd=None,
label=None,
pick_ori=None,
rank=None,
weight_norm=None,
reduce_rank=False,
depth=1.0,
real_filter=True,
inversion="matrix",
verbose=None,
):
"""Compute a Dynamic Imaging of Coherent Sources (DICS) spatial filter.
This is a beamformer filter that can be used to estimate the source power
at a specific frequency range :footcite:`GrossEtAl2001`. It does this by
constructing a spatial filter for each source point.
The computation of these filters is very similar to those of the LCMV
beamformer (:func:`make_lcmv`), but instead of operating on a covariance
matrix, the CSD matrix is used. When applying these filters to a CSD matrix
(see :func:`apply_dics_csd`), the source power can be estimated for each
source point.
Parameters
----------
%(info_not_none)s
forward : instance of Forward
Forward operator.
csd : instance of CrossSpectralDensity
The data cross-spectral density (CSD) matrices. A source estimate is
performed for each frequency or frequency-bin defined in the CSD
object.
reg : float
The regularization to apply to the cross-spectral density before
computing the inverse.
noise_csd : instance of CrossSpectralDensity | None
Noise cross-spectral density (CSD) matrices. If provided, whitening
will be done. The noise CSDs need to have been computed for the same
frequencies as the data CSDs. Providing noise CSDs is mandatory if you
mix sensor types, e.g. gradiometers with magnetometers or EEG with
MEG.
.. versionadded:: 0.20
label : Label | None
Restricts the solution to a given label.
%(pick_ori_bf)s
%(rank_none)s
.. versionadded:: 0.17
%(weight_norm)s
Defaults to ``None``, in which case no normalization is performed.
%(reduce_rank)s
%(depth)s
real_filter : bool
If ``True``, take only the real part of the cross-spectral-density
matrices to compute real filters.
.. versionchanged:: 0.23
Version 0.23 an earlier used ``real_filter=False`` as the default,
as of version 0.24 ``True`` is the default.
%(inversion_bf)s
.. versionchanged:: 0.21
Default changed to ``'matrix'``.
%(verbose)s
Returns
-------
filters : instance of Beamformer
Dictionary containing filter weights from DICS beamformer.
Contains the following keys:
'kind' : str
The type of beamformer, in this case 'DICS'.
'weights' : ndarray, shape (n_frequencies, n_weights)
For each frequency, the filter weights of the beamformer.
'csd' : instance of CrossSpectralDensity
The data cross-spectral density matrices used to compute the
beamformer.
'ch_names' : list of str
Channels used to compute the beamformer.
'proj' : ndarray, shape (n_channels, n_channels)
Projections used to compute the beamformer.
'vertices' : list of ndarray
Vertices for which the filter weights were computed.
'n_sources' : int
Number of source location for which the filter weight were
computed.
'subject' : str
The subject ID.
'pick-ori' : None | 'max-power' | 'normal' | 'vector'
The orientation in which the beamformer filters were computed.
'inversion' : 'single' | 'matrix'
Whether the spatial filters were computed for each dipole
separately or jointly for all dipoles at each vertex using a
matrix inversion.
'weight_norm' : None | 'unit-noise-gain'
The normalization of the weights.
'src_type' : str
Type of source space.
'source_nn' : ndarray, shape (n_sources, 3)
For each source location, the surface normal.
'is_free_ori' : bool
Whether the filter was computed in a fixed direction
(pick_ori='max-power', pick_ori='normal') or not.
'whitener' : None | ndarray, shape (n_channels, n_channels)
Whitening matrix, provided if whitening was applied to the
covariance matrix and leadfield during computation of the
beamformer weights.
'max-power-ori' : ndarray, shape (n_sources, 3) | None
When pick_ori='max-power', this fields contains the estimated
direction of maximum power at each source location.
See Also
--------
apply_dics_csd
Notes
-----
The original reference is :footcite:`GrossEtAl2001`. See
:footcite:`vanVlietEtAl2018` for a tutorial style paper on the topic.
The DICS beamformer is very similar to the LCMV (:func:`make_lcmv`)
beamformer and many of the parameters are shared. However,
:func:`make_dics` and :func:`make_lcmv` currently have different defaults
for these parameters, which were settled on separately through extensive
practical use case testing (but not necessarily exhaustive parameter space
searching), and it remains to be seen how functionally interchangeable they
could be.
The default setting reproduce the DICS beamformer as described in
:footcite:`vanVlietEtAl2018`::
inversion='single', weight_norm=None, depth=1.
To use the :func:`make_lcmv` defaults, use::
inversion='matrix', weight_norm='unit-noise-gain-invariant', depth=None
For more information about ``real_filter``, see the
supplemental information from :footcite:`HippEtAl2011`.
References
----------
.. footbibliography::
""" # noqa: E501
rank = _check_rank(rank)
_check_option("pick_ori", pick_ori, [None, "vector", "normal", "max-power"])
_check_option("inversion", inversion, ["single", "matrix"])
_validate_type(weight_norm, (str, None), "weight_norm")
frequencies = [np.mean(freq_bin) for freq_bin in csd.frequencies]
n_freqs = len(frequencies)
_, _, allow_mismatch = _check_one_ch_type("dics", info, forward, csd, noise_csd)
# remove bads so that equalize_channels only keeps all good
info = pick_info(info, pick_channels(info["ch_names"], [], info["bads"]))
info, forward, csd = equalize_channels([info, forward, csd])
csd, noise_csd = _prepare_noise_csd(csd, noise_csd, real_filter)
depth = _check_depth(depth, "depth_sparse")
if inversion == "single":
depth["combine_xyz"] = False
(
is_free_ori,
info,
proj,
vertices,
G,
whitener,
nn,
orient_std,
) = _prepare_beamformer_input(
info,
forward,
label,
pick_ori,
noise_cov=noise_csd,
rank=rank,
pca=False,
**depth,
)
# Compute ranks
csd_int_rank = []
if not allow_mismatch:
noise_rank = compute_rank(noise_csd, info=info, rank=rank)
for i in range(len(frequencies)):
csd_rank = compute_rank(
csd.get_data(index=i, as_cov=True), info=info, rank=rank
)
if not allow_mismatch:
for key in csd_rank:
if key not in noise_rank or csd_rank[key] != noise_rank[key]:
raise ValueError(
f"{key} data rank ({csd_rank[key]}) did not match the noise "
f"rank ({noise_rank.get(key, None)})"
)
csd_int_rank.append(sum(csd_rank.values()))
del noise_csd
ch_names = list(info["ch_names"])
logger.info("Computing DICS spatial filters...")
Ws = []
max_oris = []
for i, freq in enumerate(frequencies):
if n_freqs > 1:
logger.info(
" computing DICS spatial filter at "
f"{round(freq, 2)} Hz ({i + 1}/{n_freqs})"
)
Cm = csd.get_data(index=i)
# XXX: Weird that real_filter happens *before* whitening, which could
# make things complex again...?
if real_filter:
Cm = Cm.real
# compute spatial filter
n_orient = 3 if is_free_ori else 1
W, max_power_ori = _compute_beamformer(
G,
Cm,
reg,
n_orient,
weight_norm,
pick_ori,
reduce_rank,
rank=csd_int_rank[i],
inversion=inversion,
nn=nn,
orient_std=orient_std,
whitener=whitener,
)
Ws.append(W)
max_oris.append(max_power_ori)
Ws = np.array(Ws)
if pick_ori == "max-power":
max_oris = np.array(max_oris)
else:
max_oris = None
src_type = _get_src_type(forward["src"], vertices)
subject = _subject_from_forward(forward)
is_free_ori = is_free_ori if pick_ori in [None, "vector"] else False
n_sources = np.sum([len(v) for v in vertices])
filters = Beamformer(
kind="DICS",
weights=Ws,
csd=csd,
ch_names=ch_names,
proj=proj,
vertices=vertices,
n_sources=n_sources,
subject=subject,
pick_ori=pick_ori,
inversion=inversion,
weight_norm=weight_norm,
src_type=src_type,
source_nn=forward["source_nn"].copy(),
is_free_ori=is_free_ori,
whitener=whitener,
max_power_ori=max_oris,
)
return filters
def _prepare_noise_csd(csd, noise_csd, real_filter):
if noise_csd is not None:
csd, noise_csd = equalize_channels([csd, noise_csd])
# Use the same noise CSD for all frequencies
if len(noise_csd.frequencies) > 1:
noise_csd = noise_csd.mean()
noise_csd = noise_csd.get_data(as_cov=True)
if real_filter:
noise_csd["data"] = noise_csd["data"].real
return csd, noise_csd
def _apply_dics(data, filters, info, tmin, tfr=False):
"""Apply DICS spatial filter to data for source reconstruction."""
if isinstance(data, np.ndarray) and data.ndim == (2 + tfr):
data = [data]
one_epoch = True
else:
one_epoch = False
Ws = filters["weights"]
one_freq = len(Ws) == 1
subject = filters["subject"]
# compatibility with 0.16, add src_type as None if not present:
filters, warn_text = _check_src_type(filters)
for i, M in enumerate(data):
if not one_epoch:
logger.info(f"Processing epoch : {i + 1}")
# Apply SSPs
if not tfr: # save computation, only compute once
M_w = _proj_whiten_data(M, info["projs"], filters)
stcs = []
for j, W in enumerate(Ws):
if tfr: # must compute for each frequency
M_w = _proj_whiten_data(M[:, j], info["projs"], filters)
# project to source space using beamformer weights
sol = np.dot(W, M_w)
if filters["is_free_ori"] and filters["pick_ori"] != "vector":
logger.info("combining the current components...")
sol = combine_xyz(sol)
tstep = 1.0 / info["sfreq"]
stcs.append(
_make_stc(
sol,
vertices=filters["vertices"],
src_type=filters["src_type"],
tmin=tmin,
tstep=tstep,
subject=subject,
vector=(filters["pick_ori"] == "vector"),
source_nn=filters["source_nn"],
warn_text=warn_text,
)
)
if one_freq:
yield stcs[0]
else:
yield stcs
logger.info("[done]")
@verbose
def apply_dics(evoked, filters, verbose=None):
"""Apply Dynamic Imaging of Coherent Sources (DICS) beamformer weights.
Apply Dynamic Imaging of Coherent Sources (DICS) beamformer weights
on evoked data.
.. warning:: The result of this function is meant as an intermediate step
for further processing (such as computing connectivity). If
you are interested in estimating source time courses, use an
LCMV beamformer (:func:`make_lcmv`, :func:`apply_lcmv`)
instead. If you are interested in estimating spectral power at
the source level, use :func:`apply_dics_csd`.
.. warning:: This implementation has not been heavily tested so please
report any issues or suggestions.
Parameters
----------
evoked : Evoked
Evoked data to apply the DICS beamformer weights to.
filters : instance of Beamformer
DICS spatial filter (beamformer weights)
Filter weights returned from :func:`make_dics`.
%(verbose)s
Returns
-------
stc : SourceEstimate | VolSourceEstimate | list
Source time courses. If the DICS beamformer has been computed for more
than one frequency, a list is returned containing for each frequency
the corresponding time courses.
See Also
--------
apply_dics_epochs
apply_dics_tfr_epochs
apply_dics_csd
""" # noqa: E501
_check_reference(evoked)
info = evoked.info
data = evoked.data
tmin = evoked.times[0]
sel = _check_channels_spatial_filter(evoked.ch_names, filters)
data = data[sel]
stc = _apply_dics(data=data, filters=filters, info=info, tmin=tmin)
return next(stc)
@verbose
def apply_dics_epochs(epochs, filters, return_generator=False, verbose=None):
"""Apply Dynamic Imaging of Coherent Sources (DICS) beamformer weights.
Apply Dynamic Imaging of Coherent Sources (DICS) beamformer weights
on single trial data.
.. warning:: The result of this function is meant as an intermediate step
for further processing (such as computing connectivity). If
you are interested in estimating source time courses, use an
LCMV beamformer (:func:`make_lcmv`, :func:`apply_lcmv`)
instead. If you are interested in estimating spectral power at
the source level, use :func:`apply_dics_csd`.
.. warning:: This implementation has not been heavily tested so please
report any issue or suggestions.
Parameters
----------
epochs : Epochs
Single trial epochs.
filters : instance of Beamformer
DICS spatial filter (beamformer weights)
Filter weights returned from :func:`make_dics`. The DICS filters must
have been computed for a single frequency only.
return_generator : bool
Return a generator object instead of a list. This allows iterating
over the stcs without having to keep them all in memory.
%(verbose)s
Returns
-------
stc: list | generator of (SourceEstimate | VolSourceEstimate)
The source estimates for all epochs.
See Also
--------
apply_dics
apply_dics_tfr_epochs
apply_dics_csd
"""
_check_reference(epochs)
if len(filters["weights"]) > 1:
raise ValueError(
"This function only works on DICS beamformer weights that have "
"been computed for a single frequency. When calling make_dics(), "
"make sure to use a CSD object with only a single frequency (or "
"frequency-bin) defined."
)
info = epochs.info
tmin = epochs.times[0]
sel = _check_channels_spatial_filter(epochs.ch_names, filters)
data = epochs.get_data(sel)
stcs = _apply_dics(data=data, filters=filters, info=info, tmin=tmin)
if not return_generator:
stcs = list(stcs)
return stcs
@verbose
def apply_dics_tfr_epochs(epochs_tfr, filters, return_generator=False, verbose=None):
"""Apply Dynamic Imaging of Coherent Sources (DICS) beamformer weights.
Apply Dynamic Imaging of Coherent Sources (DICS) beamformer weights
on single trial time-frequency data.
Parameters
----------
epochs_tfr : EpochsTFR
Single trial time-frequency epochs.
filters : instance of Beamformer
DICS spatial filter (beamformer weights)
Filter weights returned from :func:`make_dics`.
return_generator : bool
Return a generator object instead of a list. This allows iterating
over the stcs without having to keep them all in memory.
%(verbose)s
Returns
-------
stcs : list of list of (SourceEstimate | VectorSourceEstimate | VolSourceEstimate)
The source estimates for all epochs (outside list) and for
all frequencies (inside list).
See Also
--------
apply_dics
apply_dics_epochs
apply_dics_csd
""" # noqa E501
_validate_type(epochs_tfr, EpochsTFR)
_check_tfr_complex(epochs_tfr)
if filters["pick_ori"] == "vector":
warn(
"Using a vector solution to compute power will lead to "
"inaccurate directions (only in the first quadrent) "
"because power is a strictly positive (squared) metric. "
"Using singular value decomposition (SVD) to determine "
"the direction is not yet supported in MNE."
)
sel = _check_channels_spatial_filter(epochs_tfr.ch_names, filters)
data = epochs_tfr.data[:, sel, :, :]
stcs = _apply_dics(data, filters, epochs_tfr.info, epochs_tfr.tmin, tfr=True)
if not return_generator:
stcs = [[stc for stc in tfr_stcs] for tfr_stcs in stcs]
return stcs
@verbose
def apply_dics_csd(csd, filters, verbose=None):
"""Apply Dynamic Imaging of Coherent Sources (DICS) beamformer weights.
Apply a previously computed DICS beamformer to a cross-spectral density
(CSD) object to estimate source power in time and frequency windows
specified in the CSD object :footcite:`GrossEtAl2001`.
.. note:: Only power can computed from the cross-spectral density, not
complex phase-amplitude, so vector DICS filters will be
converted to scalar source estimates since power is strictly
positive and so 3D directions cannot be combined meaningfully
(the direction would be confined to the positive quadrant).
Parameters
----------
csd : instance of CrossSpectralDensity
The data cross-spectral density (CSD) matrices. A source estimate is
performed for each frequency or frequency-bin defined in the CSD
object.
filters : instance of Beamformer
DICS spatial filter (beamformer weights)
Filter weights returned from `make_dics`.
%(verbose)s
Returns
-------
stc : SourceEstimate
Source power with frequency instead of time.
frequencies : list of float
The frequencies for which the source power has been computed. If the
data CSD object defines frequency-bins instead of exact frequencies,
the mean of each bin is returned.
See Also
--------
apply_dics
apply_dics_epochs
apply_dics_tfr_epochs
References
----------
.. footbibliography::
""" # noqa: E501
ch_names = filters["ch_names"]
vertices = filters["vertices"]
n_orient = 3 if filters["is_free_ori"] else 1
subject = filters["subject"]
whitener = filters["whitener"]
n_sources = filters["n_sources"]
# If CSD is summed over multiple frequencies, take the average frequency
frequencies = [np.mean(dfreq) for dfreq in csd.frequencies]
n_freqs = len(frequencies)
source_power = np.zeros((n_sources, len(csd.frequencies)))
# Ensure the CSD is in the same order as the weights
csd_picks = [csd.ch_names.index(ch) for ch in ch_names]
logger.info("Computing DICS source power...")
for i, freq in enumerate(frequencies):
if n_freqs > 1:
logger.info(
" applying DICS spatial filter at "
f"{round(freq, 2)} Hz ({i + 1}/{n_freqs})"
)
Cm = csd.get_data(index=i)
Cm = Cm[csd_picks, :][:, csd_picks]
W = filters["weights"][i]
# Whiten the CSD
Cm = np.dot(whitener, np.dot(Cm, whitener.conj().T))
source_power[:, i] = _compute_power(Cm, W, n_orient)
logger.info("[done]")
# compatibility with 0.16, add src_type as None if not present:
filters, warn_text = _check_src_type(filters)
return (
_make_stc(
source_power,
vertices=vertices,
src_type=filters["src_type"],
tmin=0.0,
tstep=1.0,
subject=subject,
warn_text=warn_text,
),
frequencies,
)

503
mne/beamformer/_lcmv.py Normal file
View File

@@ -0,0 +1,503 @@
"""Compute Linearly constrained minimum variance (LCMV) beamformer."""
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
from .._fiff.meas_info import _simplify_info
from .._fiff.pick import pick_channels_cov, pick_info
from ..forward import _subject_from_forward
from ..minimum_norm.inverse import _check_depth, _check_reference, combine_xyz
from ..rank import compute_rank
from ..source_estimate import _get_src_type, _make_stc
from ..utils import (
_check_channels_spatial_filter,
_check_info_inv,
_check_one_ch_type,
logger,
verbose,
)
from ._compute_beamformer import (
Beamformer,
_check_src_type,
_compute_beamformer,
_compute_power,
_prepare_beamformer_input,
_proj_whiten_data,
)
@verbose
def make_lcmv(
info,
forward,
data_cov,
reg=0.05,
noise_cov=None,
label=None,
pick_ori=None,
rank="info",
weight_norm="unit-noise-gain-invariant",
reduce_rank=False,
depth=None,
inversion="matrix",
verbose=None,
):
"""Compute LCMV spatial filter.
Parameters
----------
%(info_not_none)s
Specifies the channels to include. Bad channels (in ``info['bads']``)
are not used.
forward : instance of Forward
Forward operator.
data_cov : instance of Covariance
The data covariance.
reg : float
The regularization for the whitened data covariance.
noise_cov : instance of Covariance
The noise covariance. If provided, whitening will be done. Providing a
noise covariance is mandatory if you mix sensor types, e.g.
gradiometers with magnetometers or EEG with MEG.
.. note::
If ``noise_cov`` is ``None`` and ``weight_norm='unit-noise-gain'``,
the unit noise is assumed to be 1 in SI units, e.g., 1 T for
magnetometers, 1 V for EEG, so resulting amplitudes will be tiny.
Consider using :func:`mne.make_ad_hoc_cov` to provide a
``noise_cov`` to set noise values that are more reasonable for
neural data or using ``weight_norm='nai'`` for weight-normalized
beamformer output that is scaled by a noise estimate.
label : instance of Label
Restricts the LCMV solution to a given label.
%(pick_ori_bf)s
- ``'vector'``
Keeps the currents for each direction separate
%(rank_info)s
%(weight_norm)s
Defaults to ``'unit-noise-gain-invariant'``.
%(reduce_rank)s
%(depth)s
.. versionadded:: 0.18
%(inversion_bf)s
.. versionadded:: 0.21
%(verbose)s
Returns
-------
filters : instance of Beamformer
Dictionary containing filter weights from LCMV beamformer.
Contains the following keys:
'kind' : str
The type of beamformer, in this case 'LCMV'.
'weights' : array
The filter weights of the beamformer.
'data_cov' : instance of Covariance
The data covariance matrix used to compute the beamformer.
'noise_cov' : instance of Covariance | None
The noise covariance matrix used to compute the beamformer.
'whitener' : None | ndarray, shape (n_channels, n_channels)
Whitening matrix, provided if whitening was applied to the
covariance matrix and leadfield during computation of the
beamformer weights.
'weight_norm' : str | None
Type of weight normalization used to compute the filter
weights.
'pick-ori' : None | 'max-power' | 'normal' | 'vector'
The orientation in which the beamformer filters were computed.
'ch_names' : list of str
Channels used to compute the beamformer.
'proj' : array
Projections used to compute the beamformer.
'is_ssp' : bool
If True, projections were applied prior to filter computation.
'vertices' : list
Vertices for which the filter weights were computed.
'is_free_ori' : bool
If True, the filter was computed with free source orientation.
'n_sources' : int
Number of source location for which the filter weight were
computed.
'src_type' : str
Type of source space.
'source_nn' : ndarray, shape (n_sources, 3)
For each source location, the surface normal.
'proj' : ndarray, shape (n_channels, n_channels)
Projections used to compute the beamformer.
'subject' : str
The subject ID.
'rank' : int
The rank of the data covariance matrix used to compute the
beamformer weights.
'max-power-ori' : ndarray, shape (n_sources, 3) | None
When pick_ori='max-power', this fields contains the estimated
direction of maximum power at each source location.
'inversion' : 'single' | 'matrix'
Whether the spatial filters were computed for each dipole
separately or jointly for all dipoles at each vertex using a
matrix inversion.
Notes
-----
The original reference is :footcite:`VanVeenEtAl1997`.
To obtain the Sekihara unit-noise-gain vector beamformer, you should use
``weight_norm='unit-noise-gain', pick_ori='vector'`` followed by
:meth:`vec_stc.project('pca', src) <mne.VectorSourceEstimate.project>`.
.. versionchanged:: 0.21
The computations were extensively reworked, and the default for
``weight_norm`` was set to ``'unit-noise-gain-invariant'``.
References
----------
.. footbibliography::
"""
# check number of sensor types present in the data and ensure a noise cov
info = _simplify_info(info, keep=("proc_history",))
noise_cov, _, allow_mismatch = _check_one_ch_type(
"lcmv", info, forward, data_cov, noise_cov
)
# XXX we need this extra picking step (can't just rely on minimum norm's
# because there can be a mismatch. Should probably add an extra arg to
# _prepare_beamformer_input at some point (later)
picks = _check_info_inv(info, forward, data_cov, noise_cov)
info = pick_info(info, picks)
data_rank = compute_rank(data_cov, rank=rank, info=info)
noise_rank = compute_rank(noise_cov, rank=rank, info=info)
for key in data_rank:
if (
key not in noise_rank or data_rank[key] != noise_rank[key]
) and not allow_mismatch:
raise ValueError(
f"{key} data rank ({data_rank[key]}) did not match the noise rank ("
f"{noise_rank.get(key, None)})"
)
del noise_rank
rank = data_rank
logger.info(f"Making LCMV beamformer with rank {rank}")
del data_rank
depth = _check_depth(depth, "depth_sparse")
if inversion == "single":
depth["combine_xyz"] = False
(
is_free_ori,
info,
proj,
vertno,
G,
whitener,
nn,
orient_std,
) = _prepare_beamformer_input(
info,
forward,
label,
pick_ori,
noise_cov=noise_cov,
rank=rank,
pca=False,
**depth,
)
ch_names = list(info["ch_names"])
data_cov = pick_channels_cov(data_cov, include=ch_names)
Cm = data_cov._get_square()
if "estimator" in data_cov:
del data_cov["estimator"]
rank_int = sum(rank.values())
del rank
# compute spatial filter
n_orient = 3 if is_free_ori else 1
W, max_power_ori = _compute_beamformer(
G,
Cm,
reg,
n_orient,
weight_norm,
pick_ori,
reduce_rank,
rank_int,
inversion=inversion,
nn=nn,
orient_std=orient_std,
whitener=whitener,
)
# get src type to store with filters for _make_stc
src_type = _get_src_type(forward["src"], vertno)
# get subject to store with filters
subject_from = _subject_from_forward(forward)
# Is the computed beamformer a scalar or vector beamformer?
is_free_ori = is_free_ori if pick_ori in [None, "vector"] else False
is_ssp = bool(info["projs"])
filters = Beamformer(
kind="LCMV",
weights=W,
data_cov=data_cov,
noise_cov=noise_cov,
whitener=whitener,
weight_norm=weight_norm,
pick_ori=pick_ori,
ch_names=ch_names,
proj=proj,
is_ssp=is_ssp,
vertices=vertno,
is_free_ori=is_free_ori,
n_sources=forward["nsource"],
src_type=src_type,
source_nn=forward["source_nn"].copy(),
subject=subject_from,
rank=rank_int,
max_power_ori=max_power_ori,
inversion=inversion,
)
return filters
def _apply_lcmv(data, filters, info, tmin):
"""Apply LCMV spatial filter to data for source reconstruction."""
if isinstance(data, np.ndarray) and data.ndim == 2:
data = [data]
return_single = True
else:
return_single = False
W = filters["weights"]
for i, M in enumerate(data):
if len(M) != len(filters["ch_names"]):
raise ValueError("data and picks must have the same length")
if not return_single:
logger.info(f"Processing epoch : {i + 1}")
M = _proj_whiten_data(M, info["projs"], filters)
# project to source space using beamformer weights
vector = False
if filters["is_free_ori"]:
sol = np.dot(W, M)
if filters["pick_ori"] == "vector":
vector = True
else:
logger.info("combining the current components...")
sol = combine_xyz(sol)
else:
# Linear inverse: do computation here or delayed
if M.shape[0] < W.shape[0] and filters["pick_ori"] != "max-power":
sol = (W, M)
else:
sol = np.dot(W, M)
tstep = 1.0 / info["sfreq"]
# compatibility with 0.16, add src_type as None if not present:
filters, warn_text = _check_src_type(filters)
yield _make_stc(
sol,
vertices=filters["vertices"],
tmin=tmin,
tstep=tstep,
subject=filters["subject"],
vector=vector,
source_nn=filters["source_nn"],
src_type=filters["src_type"],
warn_text=warn_text,
)
logger.info("[done]")
@verbose
def apply_lcmv(evoked, filters, *, verbose=None):
"""Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights.
Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights
on evoked data.
Parameters
----------
evoked : Evoked
Evoked data to invert.
filters : instance of Beamformer
LCMV spatial filter (beamformer weights).
Filter weights returned from :func:`make_lcmv`.
%(verbose)s
Returns
-------
stc : SourceEstimate | VolSourceEstimate | VectorSourceEstimate
Source time courses.
See Also
--------
make_lcmv, apply_lcmv_raw, apply_lcmv_epochs, apply_lcmv_cov
Notes
-----
.. versionadded:: 0.18
"""
_check_reference(evoked)
info = evoked.info
data = evoked.data
tmin = evoked.times[0]
sel = _check_channels_spatial_filter(evoked.ch_names, filters)
data = data[sel]
stc = _apply_lcmv(data=data, filters=filters, info=info, tmin=tmin)
return next(stc)
@verbose
def apply_lcmv_epochs(epochs, filters, *, return_generator=False, verbose=None):
"""Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights.
Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights
on single trial data.
Parameters
----------
epochs : Epochs
Single trial epochs.
filters : instance of Beamformer
LCMV spatial filter (beamformer weights)
Filter weights returned from :func:`make_lcmv`.
return_generator : bool
Return a generator object instead of a list. This allows iterating
over the stcs without having to keep them all in memory.
%(verbose)s
Returns
-------
stc: list | generator of (SourceEstimate | VolSourceEstimate)
The source estimates for all epochs.
See Also
--------
make_lcmv, apply_lcmv_raw, apply_lcmv, apply_lcmv_cov
"""
_check_reference(epochs)
info = epochs.info
tmin = epochs.times[0]
sel = _check_channels_spatial_filter(epochs.ch_names, filters)
data = epochs.get_data(sel)
stcs = _apply_lcmv(data=data, filters=filters, info=info, tmin=tmin)
if not return_generator:
stcs = [s for s in stcs]
return stcs
@verbose
def apply_lcmv_raw(raw, filters, start=None, stop=None, *, verbose=None):
"""Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights.
Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights
on raw data.
Parameters
----------
raw : mne.io.Raw
Raw data to invert.
filters : instance of Beamformer
LCMV spatial filter (beamformer weights).
Filter weights returned from :func:`make_lcmv`.
start : int
Index of first time sample (index not time is seconds).
stop : int
Index of first time sample not to include (index not time is seconds).
%(verbose)s
Returns
-------
stc : SourceEstimate | VolSourceEstimate
Source time courses.
See Also
--------
make_lcmv, apply_lcmv_epochs, apply_lcmv, apply_lcmv_cov
"""
_check_reference(raw)
info = raw.info
sel = _check_channels_spatial_filter(raw.ch_names, filters)
data, times = raw[sel, start:stop]
tmin = times[0]
stc = _apply_lcmv(data=data, filters=filters, info=info, tmin=tmin)
return next(stc)
@verbose
def apply_lcmv_cov(data_cov, filters, verbose=None):
"""Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights.
Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights
to a data covariance matrix to estimate source power.
Parameters
----------
data_cov : instance of Covariance
Data covariance matrix.
filters : instance of Beamformer
LCMV spatial filter (beamformer weights).
Filter weights returned from :func:`make_lcmv`.
%(verbose)s
Returns
-------
stc : SourceEstimate | VolSourceEstimate
Source power.
See Also
--------
make_lcmv, apply_lcmv, apply_lcmv_epochs, apply_lcmv_raw
"""
sel = _check_channels_spatial_filter(data_cov.ch_names, filters)
sel_names = [data_cov.ch_names[ii] for ii in sel]
data_cov = pick_channels_cov(data_cov, sel_names)
n_orient = filters["weights"].shape[0] // filters["n_sources"]
# Need to project and whiten along both dimensions
data = _proj_whiten_data(data_cov["data"].T, data_cov["projs"], filters)
data = _proj_whiten_data(data.T, data_cov["projs"], filters)
del data_cov
source_power = _compute_power(data, filters["weights"], n_orient)
# compatibility with 0.16, add src_type as None if not present:
filters, warn_text = _check_src_type(filters)
return _make_stc(
source_power,
vertices=filters["vertices"],
src_type=filters["src_type"],
tmin=0.0,
tstep=1.0,
subject=filters["subject"],
source_nn=filters["source_nn"],
warn_text=warn_text,
)

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"""Compute a Recursively Applied and Projected MUltiple Signal Classification (RAP-MUSIC).""" # noqa
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
from scipy import linalg
from .._fiff.pick import pick_channels_forward, pick_info
from ..fixes import _safe_svd
from ..forward import convert_forward_solution, is_fixed_orient
from ..inverse_sparse.mxne_inverse import _make_dipoles_sparse
from ..minimum_norm.inverse import _log_exp_var
from ..utils import _check_info_inv, fill_doc, logger, verbose
from ._compute_beamformer import _prepare_beamformer_input
@fill_doc
def _apply_rap_music(
data, info, times, forward, noise_cov, n_dipoles=2, picks=None, use_trap=False
):
"""RAP-MUSIC or TRAP-MUSIC for evoked data.
Parameters
----------
data : array, shape (n_channels, n_times)
Evoked data.
%(info_not_none)s
times : array
Times.
forward : instance of Forward
Forward operator.
noise_cov : instance of Covariance
The noise covariance.
n_dipoles : int
The number of dipoles to estimate. The default value is 2.
picks : list of int
Caller ensures this is a list of int.
use_trap : bool
Use the TRAP-MUSIC variant if True (default False).
Returns
-------
dipoles : list of instances of Dipole
The dipole fits.
explained_data : array | None
Data explained by the dipoles using a least square fitting with the
selected active dipoles and their estimated orientation.
"""
info = pick_info(info, picks)
del picks
# things are much simpler if we avoid surface orientation
align = forward["source_nn"].copy()
if forward["surf_ori"] and not is_fixed_orient(forward):
forward = convert_forward_solution(forward, surf_ori=False)
is_free_ori, info, _, _, G, whitener, _, _ = _prepare_beamformer_input(
info, forward, noise_cov=noise_cov, rank=None
)
forward = pick_channels_forward(forward, info["ch_names"], ordered=True)
del info
# whiten the data (leadfield already whitened)
M = np.dot(whitener, data)
del data
_, eig_vectors = linalg.eigh(np.dot(M, M.T))
phi_sig = eig_vectors[:, -n_dipoles:]
n_orient = 3 if is_free_ori else 1
G.shape = (G.shape[0], -1, n_orient)
gain = forward["sol"]["data"].copy()
gain.shape = G.shape
n_channels = G.shape[0]
A = np.empty((n_channels, n_dipoles))
gain_dip = np.empty((n_channels, n_dipoles))
oris = np.empty((n_dipoles, 3))
poss = np.empty((n_dipoles, 3))
G_proj = G.copy()
phi_sig_proj = phi_sig.copy()
idxs = list()
for k in range(n_dipoles):
subcorr_max = -1.0
source_idx, source_ori, source_pos = 0, [0, 0, 0], [0, 0, 0]
for i_source in range(G.shape[1]):
Gk = G_proj[:, i_source]
subcorr, ori = _compute_subcorr(Gk, phi_sig_proj)
if subcorr > subcorr_max:
subcorr_max = subcorr
source_idx = i_source
source_ori = ori
source_pos = forward["source_rr"][i_source]
if n_orient == 3 and align is not None:
surf_normal = forward["source_nn"][3 * i_source + 2]
# make sure ori is aligned to the surface orientation
source_ori *= np.sign(source_ori @ surf_normal) or 1.0
if n_orient == 1:
source_ori = forward["source_nn"][i_source]
idxs.append(source_idx)
if n_orient == 3:
Ak = np.dot(G[:, source_idx], source_ori)
else:
Ak = G[:, source_idx, 0]
A[:, k] = Ak
oris[k] = source_ori
poss[k] = source_pos
logger.info(f"source {k + 1} found: p = {source_idx}")
if n_orient == 3:
logger.info("ori = {} {} {}".format(*tuple(oris[k])))
projection = _compute_proj(A[:, : k + 1])
G_proj = np.einsum("ab,bso->aso", projection, G)
phi_sig_proj = np.dot(projection, phi_sig)
if use_trap:
phi_sig_proj = phi_sig_proj[:, -(n_dipoles - k) :]
del G, G_proj
sol = linalg.lstsq(A, M)[0]
if n_orient == 3:
X = sol[:, np.newaxis] * oris[:, :, np.newaxis]
X.shape = (-1, len(times))
else:
X = sol
gain_active = gain[:, idxs]
if n_orient == 3:
gain_dip = (oris * gain_active).sum(-1)
idxs = np.array(idxs)
active_set = np.array([[3 * idxs, 3 * idxs + 1, 3 * idxs + 2]]).T.ravel()
else:
gain_dip = gain_active[:, :, 0]
active_set = idxs
gain_active = whitener @ gain_active.reshape(gain.shape[0], -1)
assert gain_active.shape == (n_channels, X.shape[0])
explained_data = gain_dip @ sol
M_estimate = whitener @ explained_data
_log_exp_var(M, M_estimate)
tstep = np.median(np.diff(times)) if len(times) > 1 else 1.0
dipoles = _make_dipoles_sparse(
X, active_set, forward, times[0], tstep, M, gain_active, active_is_idx=True
)
for dipole, ori in zip(dipoles, oris):
signs = np.sign((dipole.ori * ori).sum(-1, keepdims=True))
dipole.ori *= signs
dipole.amplitude *= signs[:, 0]
logger.info("[done]")
return dipoles, explained_data
def _compute_subcorr(G, phi_sig):
"""Compute the subspace correlation."""
Ug, Sg, Vg = _safe_svd(G, full_matrices=False)
# Now we look at the actual rank of the forward fields
# in G and handle the fact that it might be rank defficient
# eg. when using MEG and a sphere model for which the
# radial component will be truly 0.
rank = np.sum(Sg > (Sg[0] * 1e-6))
if rank == 0:
return 0, np.zeros(len(G))
rank = max(rank, 2) # rank cannot be 1
Ug, Sg, Vg = Ug[:, :rank], Sg[:rank], Vg[:rank]
tmp = np.dot(Ug.T.conjugate(), phi_sig)
Uc, Sc, _ = _safe_svd(tmp, full_matrices=False)
X = np.dot(Vg.T / Sg[None, :], Uc[:, 0]) # subcorr
return Sc[0], X / np.linalg.norm(X)
def _compute_proj(A):
"""Compute the orthogonal projection operation for a manifold vector A."""
U, _, _ = _safe_svd(A, full_matrices=False)
return np.identity(A.shape[0]) - np.dot(U, U.T.conjugate())
def _rap_music(evoked, forward, noise_cov, n_dipoles, return_residual, use_trap):
"""RAP-/TRAP-MUSIC implementation."""
info = evoked.info
data = evoked.data
times = evoked.times
picks = _check_info_inv(info, forward, data_cov=None, noise_cov=noise_cov)
data = data[picks]
dipoles, explained_data = _apply_rap_music(
data, info, times, forward, noise_cov, n_dipoles, picks, use_trap
)
if return_residual:
residual = evoked.copy().pick([info["ch_names"][p] for p in picks])
residual.data -= explained_data
active_projs = [p for p in residual.info["projs"] if p["active"]]
for p in active_projs:
p["active"] = False
residual.add_proj(active_projs, remove_existing=True)
residual.apply_proj()
return dipoles, residual
else:
return dipoles
@verbose
def rap_music(
evoked,
forward,
noise_cov,
n_dipoles=5,
return_residual=False,
*,
verbose=None,
):
"""RAP-MUSIC source localization method.
Compute Recursively Applied and Projected MUltiple SIgnal Classification
(RAP-MUSIC) :footcite:`MosherLeahy1999,MosherLeahy1996` on evoked data.
.. note:: The goodness of fit (GOF) of all the returned dipoles is the
same and corresponds to the GOF of the full set of dipoles.
Parameters
----------
evoked : instance of Evoked
Evoked data to localize.
forward : instance of Forward
Forward operator.
noise_cov : instance of Covariance
The noise covariance.
n_dipoles : int
The number of dipoles to look for. The default value is 5.
return_residual : bool
If True, the residual is returned as an Evoked instance.
%(verbose)s
Returns
-------
dipoles : list of instance of Dipole
The dipole fits.
residual : instance of Evoked
The residual a.k.a. data not explained by the dipoles.
Only returned if return_residual is True.
See Also
--------
mne.fit_dipole
mne.beamformer.trap_music
Notes
-----
.. versionadded:: 0.9.0
References
----------
.. footbibliography::
"""
return _rap_music(evoked, forward, noise_cov, n_dipoles, return_residual, False)
@verbose
def trap_music(
evoked,
forward,
noise_cov,
n_dipoles=5,
return_residual=False,
*,
verbose=None,
):
"""TRAP-MUSIC source localization method.
Compute Truncated Recursively Applied and Projected MUltiple SIgnal Classification
(TRAP-MUSIC) :footcite:`Makela2018` on evoked data.
.. note:: The goodness of fit (GOF) of all the returned dipoles is the
same and corresponds to the GOF of the full set of dipoles.
Parameters
----------
evoked : instance of Evoked
Evoked data to localize.
forward : instance of Forward
Forward operator.
noise_cov : instance of Covariance
The noise covariance.
n_dipoles : int
The number of dipoles to look for. The default value is 5.
return_residual : bool
If True, the residual is returned as an Evoked instance.
%(verbose)s
Returns
-------
dipoles : list of instance of Dipole
The dipole fits.
residual : instance of Evoked
The residual a.k.a. data not explained by the dipoles.
Only returned if return_residual is True.
See Also
--------
mne.fit_dipole
mne.beamformer.rap_music
Notes
-----
.. versionadded:: 1.4
References
----------
.. footbibliography::
"""
return _rap_music(evoked, forward, noise_cov, n_dipoles, return_residual, True)

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"""Compute resolution matrix for beamformers."""
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
from .._fiff.pick import pick_channels, pick_channels_forward, pick_info
from ..evoked import EvokedArray
from ..utils import fill_doc, logger
from ._lcmv import apply_lcmv
@fill_doc
def make_lcmv_resolution_matrix(filters, forward, info):
"""Compute resolution matrix for LCMV beamformer.
Parameters
----------
filters : instance of Beamformer
Dictionary containing filter weights from LCMV beamformer
(see mne.beamformer.make_lcmv).
forward : instance of Forward
Forward Solution with leadfield matrix.
%(info_not_none)s Used to compute LCMV filters.
Returns
-------
resmat : array, shape (n_dipoles_lcmv, n_dipoles_fwd)
Resolution matrix (filter matrix multiplied to leadfield from
forward solution). Numbers of rows (n_dipoles_lcmv) and columns
(n_dipoles_fwd) may differ by a factor depending on orientation
constraints of filter and forward solution, respectively (e.g. factor 3
for free dipole orientation versus factor 1 for scalar beamformers).
"""
# don't include bad channels from noise covariance matrix
bads_filt = filters["noise_cov"]["bads"]
ch_names = filters["noise_cov"]["names"]
# good channels
ch_names = [c for c in ch_names if (c not in bads_filt)]
# adjust channels in forward solution
forward = pick_channels_forward(forward, ch_names, ordered=True)
# get leadfield matrix from forward solution
leadfield = forward["sol"]["data"]
# get the filter weights for beamformer as matrix
filtmat = _get_matrix_from_lcmv(filters, forward, info)
# compute resolution matrix
resmat = filtmat.dot(leadfield)
logger.info(f"Dimensions of LCMV resolution matrix: {resmat.shape}.")
return resmat
def _get_matrix_from_lcmv(filters, forward, info, verbose=None):
"""Get inverse matrix for LCMV beamformer.
Returns
-------
invmat : array, shape (n_dipoles, n_channels)
Inverse matrix associated with LCMV beamformer filters.
"""
# number of channels for identity matrix
info = pick_info(info, pick_channels(info["ch_names"], filters["ch_names"]))
n_chs = len(info["ch_names"])
# create identity matrix as input for inverse operator
# set elements to zero for non-selected channels
id_mat = np.eye(n_chs)
# convert identity matrix to evoked data type (pretending it's an epochs
evo_ident = EvokedArray(id_mat, info=info, tmin=0.0)
# apply beamformer to identity matrix
stc_lcmv = apply_lcmv(evo_ident, filters, verbose=verbose)
# turn source estimate into numpsy array
invmat = stc_lcmv.data
return invmat

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mne/channels/__init__.py Normal file
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"""Module dedicated to manipulation of channels.
Can be used for setting of sensor locations used for processing and plotting.
"""
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import lazy_loader as lazy
(__getattr__, __dir__, __all__) = lazy.attach_stub(__name__, __file__)

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mne/channels/__init__.pyi Normal file
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__all__ = [
"DigMontage",
"Layout",
"_EEG_SELECTIONS",
"_SELECTIONS",
"_divide_to_regions",
"combine_channels",
"compute_dev_head_t",
"compute_native_head_t",
"equalize_channels",
"find_ch_adjacency",
"find_layout",
"fix_mag_coil_types",
"generate_2d_layout",
"get_builtin_ch_adjacencies",
"get_builtin_montages",
"make_1020_channel_selections",
"make_dig_montage",
"make_eeg_layout",
"make_grid_layout",
"make_standard_montage",
"read_ch_adjacency",
"read_custom_montage",
"read_dig_captrak",
"read_dig_dat",
"read_dig_egi",
"read_dig_fif",
"read_dig_hpts",
"read_dig_localite",
"read_dig_polhemus_isotrak",
"read_layout",
"read_polhemus_fastscan",
"read_vectorview_selection",
"rename_channels",
"transform_to_head",
"unify_bad_channels",
]
from .channels import (
_EEG_SELECTIONS,
_SELECTIONS,
_divide_to_regions,
combine_channels,
equalize_channels,
find_ch_adjacency,
fix_mag_coil_types,
get_builtin_ch_adjacencies,
make_1020_channel_selections,
read_ch_adjacency,
read_vectorview_selection,
rename_channels,
unify_bad_channels,
)
from .layout import (
Layout,
find_layout,
generate_2d_layout,
make_eeg_layout,
make_grid_layout,
read_layout,
)
from .montage import (
DigMontage,
compute_dev_head_t,
compute_native_head_t,
get_builtin_montages,
make_dig_montage,
make_standard_montage,
read_custom_montage,
read_dig_captrak,
read_dig_dat,
read_dig_egi,
read_dig_fif,
read_dig_hpts,
read_dig_localite,
read_dig_polhemus_isotrak,
read_polhemus_fastscan,
transform_to_head,
)

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mne/channels/_dig_montage_utils.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
from ..utils import Bunch, _check_fname, _soft_import, warn
def _read_dig_montage_egi(
fname,
_scaling,
_all_data_kwargs_are_none,
):
if not _all_data_kwargs_are_none:
raise ValueError(
"hsp, hpi, elp, point_names, fif must all be None if egi is not None"
)
_check_fname(fname, overwrite="read", must_exist=True)
defusedxml = _soft_import("defusedxml", "reading EGI montages")
root = defusedxml.ElementTree.parse(fname).getroot()
ns = root.tag[root.tag.index("{") : root.tag.index("}") + 1]
sensors = root.find(f"{ns}sensorLayout/{ns}sensors")
fids = dict()
dig_ch_pos = dict()
fid_name_map = {
"Nasion": "nasion",
"Right periauricular point": "rpa",
"Left periauricular point": "lpa",
}
for s in sensors:
name, number, kind = s[0].text, int(s[1].text), int(s[2].text)
coordinates = np.array([float(s[3].text), float(s[4].text), float(s[5].text)])
coordinates *= _scaling
# EEG Channels
if kind == 0:
dig_ch_pos[f"EEG {number:03d}"] = coordinates
# Reference
elif kind == 1:
dig_ch_pos[f"EEG {len(dig_ch_pos) + 1:03d}"] = coordinates
# Fiducials
elif kind == 2:
fid_name = fid_name_map[name]
fids[fid_name] = coordinates
# Unknown
else:
warn(
f"Unknown sensor type {kind} detected. Skipping sensor..."
"Proceed with caution!"
)
return Bunch(
# EGI stuff
nasion=fids["nasion"],
lpa=fids["lpa"],
rpa=fids["rpa"],
ch_pos=dig_ch_pos,
coord_frame="unknown",
)
def _parse_brainvision_dig_montage(fname, scale):
FID_NAME_MAP = {"Nasion": "nasion", "RPA": "rpa", "LPA": "lpa"}
defusedxml = _soft_import("defusedxml", "reading BrainVision montages")
root = defusedxml.ElementTree.parse(fname).getroot()
sensors = root.find("CapTrakElectrodeList")
fids, dig_ch_pos = dict(), dict()
for s in sensors:
name = s.find("Name").text
is_fid = name in FID_NAME_MAP
coordinates = scale * np.array(
[float(s.find("X").text), float(s.find("Y").text), float(s.find("Z").text)]
)
# Fiducials
if is_fid:
fids[FID_NAME_MAP[name]] = coordinates
# EEG Channels
else:
dig_ch_pos[name] = coordinates
return dict(
# BVCT stuff
nasion=fids["nasion"],
lpa=fids["lpa"],
rpa=fids["rpa"],
ch_pos=dig_ch_pos,
coord_frame="unknown",
)

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mne/channels/_standard_montage_utils.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import csv
import os.path as op
from collections import OrderedDict
from functools import partial
import numpy as np
from .._freesurfer import get_mni_fiducials
from ..transforms import _sph_to_cart
from ..utils import _pl, _soft_import, warn
from . import __file__ as _CHANNELS_INIT_FILE
from .montage import make_dig_montage
MONTAGE_PATH = op.join(op.dirname(_CHANNELS_INIT_FILE), "data", "montages")
_str = "U100"
# In standard_1020, T9=LPA, T10=RPA, Nasion is the same as Iz with a
# sign-flipped Y value
def _egi_256(head_size):
fname = op.join(MONTAGE_PATH, "EGI_256.csd")
montage = _read_csd(fname, head_size)
ch_pos = montage._get_ch_pos()
# For this cap, the Nasion is the frontmost electrode,
# LPA/RPA we approximate by putting 75% of the way (toward the front)
# between the two electrodes that are halfway down the ear holes
nasion = ch_pos["E31"]
lpa = 0.75 * ch_pos["E67"] + 0.25 * ch_pos["E94"]
rpa = 0.75 * ch_pos["E219"] + 0.25 * ch_pos["E190"]
fids_montage = make_dig_montage(
coord_frame="unknown",
nasion=nasion,
lpa=lpa,
rpa=rpa,
)
montage += fids_montage # add fiducials to montage
return montage
def _easycap(basename, head_size):
fname = op.join(MONTAGE_PATH, basename)
montage = _read_theta_phi_in_degrees(fname, head_size, add_fiducials=True)
return montage
def _hydrocel(basename, head_size):
fname = op.join(MONTAGE_PATH, basename)
return _read_sfp(fname, head_size)
def _str_names(ch_names):
return [str(ch_name) for ch_name in ch_names]
def _safe_np_loadtxt(fname, **kwargs):
out = np.genfromtxt(fname, **kwargs)
ch_names = _str_names(out["f0"])
others = tuple(out[f"f{ii}"] for ii in range(1, len(out.dtype.fields)))
return (ch_names,) + others
def _biosemi(basename, head_size):
fname = op.join(MONTAGE_PATH, basename)
fid_names = ("Nz", "LPA", "RPA")
return _read_theta_phi_in_degrees(fname, head_size, fid_names)
def _mgh_or_standard(basename, head_size, coord_frame="unknown"):
fid_names = ("Nz", "LPA", "RPA")
fname = op.join(MONTAGE_PATH, basename)
ch_names_, pos = [], []
with open(fname) as fid:
# Ignore units as we will scale later using the norms anyway
for line in fid:
if "Positions\n" in line:
break
pos = []
for line in fid:
if "Labels\n" in line:
break
pos.append(list(map(float, line.split())))
for line in fid:
if not line or not set(line) - {" "}:
break
ch_names_.append(line.strip(" ").strip("\n"))
pos = np.array(pos) / 1000.0
ch_pos = _check_dupes_odict(ch_names_, pos)
nasion, lpa, rpa = (ch_pos.pop(n) for n in fid_names)
if head_size is None:
scale = 1.0
else:
scale = head_size / np.median(np.linalg.norm(pos, axis=1))
for value in ch_pos.values():
value *= scale
# if we are in MRI/MNI coordinates, we need to replace nasion, LPA, and RPA
# with those of fsaverage for ``trans='fsaverage'`` to work
if coord_frame == "mri":
lpa, nasion, rpa = (x["r"].copy() for x in get_mni_fiducials("fsaverage"))
nasion *= scale
lpa *= scale
rpa *= scale
return make_dig_montage(
ch_pos=ch_pos, coord_frame=coord_frame, nasion=nasion, lpa=lpa, rpa=rpa
)
standard_montage_look_up_table = {
"EGI_256": _egi_256,
"easycap-M1": partial(_easycap, basename="easycap-M1.txt"),
"easycap-M10": partial(_easycap, basename="easycap-M10.txt"),
"easycap-M43": partial(_easycap, basename="easycap-M43.txt"),
"GSN-HydroCel-128": partial(_hydrocel, basename="GSN-HydroCel-128.sfp"),
"GSN-HydroCel-129": partial(_hydrocel, basename="GSN-HydroCel-129.sfp"),
"GSN-HydroCel-256": partial(_hydrocel, basename="GSN-HydroCel-256.sfp"),
"GSN-HydroCel-257": partial(_hydrocel, basename="GSN-HydroCel-257.sfp"),
"GSN-HydroCel-32": partial(_hydrocel, basename="GSN-HydroCel-32.sfp"),
"GSN-HydroCel-64_1.0": partial(_hydrocel, basename="GSN-HydroCel-64_1.0.sfp"),
"GSN-HydroCel-65_1.0": partial(_hydrocel, basename="GSN-HydroCel-65_1.0.sfp"),
"biosemi128": partial(_biosemi, basename="biosemi128.txt"),
"biosemi16": partial(_biosemi, basename="biosemi16.txt"),
"biosemi160": partial(_biosemi, basename="biosemi160.txt"),
"biosemi256": partial(_biosemi, basename="biosemi256.txt"),
"biosemi32": partial(_biosemi, basename="biosemi32.txt"),
"biosemi64": partial(_biosemi, basename="biosemi64.txt"),
"mgh60": partial(_mgh_or_standard, basename="mgh60.elc", coord_frame="mri"),
"mgh70": partial(_mgh_or_standard, basename="mgh70.elc", coord_frame="mri"),
"standard_1005": partial(
_mgh_or_standard, basename="standard_1005.elc", coord_frame="mri"
),
"standard_1020": partial(
_mgh_or_standard, basename="standard_1020.elc", coord_frame="mri"
),
"standard_alphabetic": partial(
_mgh_or_standard, basename="standard_alphabetic.elc", coord_frame="mri"
),
"standard_postfixed": partial(
_mgh_or_standard, basename="standard_postfixed.elc", coord_frame="mri"
),
"standard_prefixed": partial(
_mgh_or_standard, basename="standard_prefixed.elc", coord_frame="mri"
),
"standard_primed": partial(
_mgh_or_standard, basename="standard_primed.elc", coord_frame="mri"
),
"artinis-octamon": partial(
_mgh_or_standard, coord_frame="mri", basename="artinis-octamon.elc"
),
"artinis-brite23": partial(
_mgh_or_standard, coord_frame="mri", basename="artinis-brite23.elc"
),
"brainproducts-RNP-BA-128": partial(
_easycap, basename="brainproducts-RNP-BA-128.txt"
),
}
def _read_sfp(fname, head_size):
"""Read .sfp BESA/EGI files."""
# fname has been already checked
fid_names = ("FidNz", "FidT9", "FidT10")
options = dict(dtype=(_str, "f4", "f4", "f4"))
ch_names, xs, ys, zs = _safe_np_loadtxt(fname, **options)
# deal with "headshape"
mask = np.array([ch_name == "headshape" for ch_name in ch_names], bool)
hsp = np.stack([xs[mask], ys[mask], zs[mask]], axis=-1)
mask = ~mask
pos = np.stack([xs[mask], ys[mask], zs[mask]], axis=-1)
ch_names = [ch_name for ch_name, m in zip(ch_names, mask) if m]
ch_pos = _check_dupes_odict(ch_names, pos)
del xs, ys, zs, ch_names
# no one grants that fid names are there.
nasion, lpa, rpa = (ch_pos.pop(n, None) for n in fid_names)
if head_size is not None:
scale = head_size / np.median(np.linalg.norm(pos, axis=-1))
for value in ch_pos.values():
value *= scale
nasion = nasion * scale if nasion is not None else None
lpa = lpa * scale if lpa is not None else None
rpa = rpa * scale if rpa is not None else None
return make_dig_montage(
ch_pos=ch_pos, coord_frame="unknown", nasion=nasion, rpa=rpa, lpa=lpa, hsp=hsp
)
def _read_csd(fname, head_size):
# Label, Theta, Phi, Radius, X, Y, Z, off sphere surface
options = dict(
comments="//", dtype=(_str, "f4", "f4", "f4", "f4", "f4", "f4", "f4")
)
ch_names, _, _, _, xs, ys, zs, _ = _safe_np_loadtxt(fname, **options)
pos = np.stack([xs, ys, zs], axis=-1)
if head_size is not None:
pos *= head_size / np.median(np.linalg.norm(pos, axis=1))
return make_dig_montage(ch_pos=_check_dupes_odict(ch_names, pos))
def _check_dupes_odict(ch_names, pos):
"""Warn if there are duplicates, then turn to ordered dict."""
ch_names = list(ch_names)
dups = OrderedDict((ch_name, ch_names.count(ch_name)) for ch_name in ch_names)
dups = OrderedDict((ch_name, count) for ch_name, count in dups.items() if count > 1)
n = len(dups)
if n:
dups = ", ".join(f"{ch_name} ({count})" for ch_name, count in dups.items())
warn(
f"Duplicate channel position{_pl(n)} found, the last will be "
f"used for {dups}"
)
return OrderedDict(zip(ch_names, pos))
def _read_elc(fname, head_size):
"""Read .elc files.
The `.elc` files are so-called "asa electrode files". ASA here stands for
Advances Source Analysis, and is a software package developed and sold by
the ANT Neuro company. They provide a device for sensor digitization, called
'xensor', which produces the `.elc` files.
Parameters
----------
fname : str
File extension is expected to be '.elc'.
head_size : float | None
The size of the head in [m]. If none, returns the values read from the
file with no modification.
Returns
-------
montage : instance of DigMontage
The montage units are [m].
"""
fid_names = ("Nz", "LPA", "RPA")
with open(fname) as fid:
# Read units
# _read_elc does require to detect the units. (see _mgh_or_standard)
for line in fid:
if "UnitPosition" in line:
units = line.split()[1]
scale = dict(m=1.0, mm=1e-3)[units]
break
else:
raise RuntimeError(f"Could not detect units in file {fname}")
for line in fid:
if "Positions\n" in line:
break
# Read positions
new_style = False
pos = []
for line in fid:
if "Labels\n" in line:
break
if ":" in line:
# Of the 'new' format: `E01 : 5.288 -3.658 119.693`
pos.append(list(map(float, line.split(":")[1].split())))
new_style = True
else:
# Of the 'old' format: `5.288 -3.658 119.693`
pos.append(list(map(float, line.split())))
# Read labels
ch_names_ = []
for line in fid:
if not line or not set(line) - {" "}:
break
if new_style:
# Not sure how this format would deal with spaces in channel labels,
# but none of my test files had this, so let's wait until it comes up.
parsed = line.strip(" ").strip("\n").split()
else:
parsed = [line.strip(" ").strip("\n")]
ch_names_.extend(parsed)
pos = np.array(pos) * scale
if head_size is not None:
pos *= head_size / np.median(np.linalg.norm(pos, axis=1))
ch_pos = _check_dupes_odict(ch_names_, pos)
nasion, lpa, rpa = (ch_pos.pop(n, None) for n in fid_names)
return make_dig_montage(
ch_pos=ch_pos, coord_frame="unknown", nasion=nasion, lpa=lpa, rpa=rpa
)
def _read_theta_phi_in_degrees(fname, head_size, fid_names=None, add_fiducials=False):
ch_names, theta, phi = _safe_np_loadtxt(
fname, skip_header=1, dtype=(_str, "i4", "i4")
)
if add_fiducials:
# Add fiducials based on 10/20 spherical coordinate definitions
# http://chgd.umich.edu/wp-content/uploads/2014/06/
# 10-20_system_positioning.pdf
# extrapolated from other sensor coordinates in the Easycap layouts
# https://www.easycap.de/wp-content/uploads/2018/02/
# Easycap-Equidistant-Layouts.pdf
assert fid_names is None
fid_names = ["Nasion", "LPA", "RPA"]
ch_names.extend(fid_names)
theta = np.append(theta, [115, -115, 115])
phi = np.append(phi, [90, 0, 0])
radii = np.full(len(phi), head_size)
pos = _sph_to_cart(np.array([radii, np.deg2rad(phi), np.deg2rad(theta)]).T)
ch_pos = _check_dupes_odict(ch_names, pos)
nasion, lpa, rpa = None, None, None
if fid_names is not None:
nasion, lpa, rpa = (ch_pos.pop(n, None) for n in fid_names)
return make_dig_montage(
ch_pos=ch_pos, coord_frame="unknown", nasion=nasion, lpa=lpa, rpa=rpa
)
def _read_elp_besa(fname, head_size):
# This .elp is not the same as polhemus elp. see _read_isotrak_elp_points
dtype = np.dtype("S8, S8, f8, f8, f8")
data = np.loadtxt(fname, dtype=dtype)
ch_names = data["f1"].astype(str).tolist()
az = data["f2"]
horiz = data["f3"]
radius = np.abs(az / 180.0)
az = np.deg2rad(np.array([h if a >= 0.0 else 180 + h for h, a in zip(horiz, az)]))
pol = radius * np.pi
rad = data["f4"] / 100
pos = _sph_to_cart(np.array([rad, az, pol]).T)
if head_size is not None:
pos *= head_size / np.median(np.linalg.norm(pos, axis=1))
ch_pos = _check_dupes_odict(ch_names, pos)
fid_names = ("Nz", "LPA", "RPA")
# No one grants that the fid names actually exist.
nasion, lpa, rpa = (ch_pos.pop(n, None) for n in fid_names)
return make_dig_montage(ch_pos=ch_pos, nasion=nasion, lpa=lpa, rpa=rpa)
def _read_brainvision(fname, head_size):
# 'BrainVision Electrodes File' format
# Based on BrainVision Analyzer coordinate system: Defined between
# standard electrode positions: X-axis from T7 to T8, Y-axis from Oz to
# Fpz, Z-axis orthogonal from XY-plane through Cz, fit to a sphere if
# idealized (when radius=1), specified in millimeters
defusedxml = _soft_import("defusedxml", "reading BrainVision montages")
root = defusedxml.ElementTree.parse(fname).getroot()
ch_names = [s.text for s in root.findall("./Electrode/Name")]
theta = [float(s.text) for s in root.findall("./Electrode/Theta")]
pol = np.deg2rad(np.array(theta))
phi = [float(s.text) for s in root.findall("./Electrode/Phi")]
az = np.deg2rad(np.array(phi))
rad = [float(s.text) for s in root.findall("./Electrode/Radius")]
rad = np.array(rad) # specified in mm
pos = _sph_to_cart(np.array([rad, az, pol]).T)
if head_size is not None:
pos *= head_size / np.median(np.linalg.norm(pos, axis=1))
return make_dig_montage(ch_pos=_check_dupes_odict(ch_names, pos))
def _read_xyz(fname):
"""Import EEG channel locations from CSV, TSV, or XYZ files.
CSV and TSV files should have columns 4 columns containing
ch_name, x, y, and z. Each row represents one channel.
XYZ files should have 5 columns containing
count, x, y, z, and ch_name. Each row represents one channel
CSV files should be separated by commas, TSV and XYZ files should be
separated by tabs.
Parameters
----------
fname : str
Name of the file to read channel locations from.
Returns
-------
montage : instance of DigMontage
The montage.
"""
ch_names = []
pos = []
file_format = op.splitext(fname)[1].lower()
with open(fname) as f:
if file_format != ".xyz":
f.readline() # skip header
delimiter = "," if file_format == ".csv" else "\t"
for row in csv.reader(f, delimiter=delimiter):
if file_format == ".xyz":
_, x, y, z, ch_name, *_ = row
ch_name = ch_name.strip() # deals with variable tab size
else:
ch_name, x, y, z, *_ = row
ch_names.append(ch_name)
pos.append((x, y, z))
d = _check_dupes_odict(ch_names, np.array(pos, dtype=float))
return make_dig_montage(ch_pos=d)

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-42.27 42.33 -39.99 31.80
001 -4.09 10.91 4.00 3.00 MLC11-2622
002 -7.25 8.87 4.00 3.00 MLC12-2622
003 -10.79 7.43 4.00 3.00 MLC13-2622
004 -14.40 5.31 4.00 3.00 MLC14-2622
005 -17.45 2.88 4.00 3.00 MLC15-2622
006 -19.94 -0.21 4.00 3.00 MLC16-2622
007 -22.30 -3.88 4.00 3.00 MLC17-2622
008 -7.70 5.16 4.00 3.00 MLC21-2622
009 -11.18 3.69 4.00 3.00 MLC22-2622
010 -14.17 1.40 4.00 3.00 MLC23-2622
011 -16.42 -1.52 4.00 3.00 MLC24-2622
012 -18.64 -4.88 4.00 3.00 MLC25-2622
013 -12.55 -2.00 4.00 3.00 MLC31-2622
014 -15.13 -5.41 4.00 3.00 MLC32-2622
015 -9.57 0.28 4.00 3.00 MLC41-2622
016 -11.51 -5.56 4.00 3.00 MLC42-2622
017 -4.04 4.58 4.00 3.00 MLC51-2622
018 -6.04 1.35 4.00 3.00 MLC52-2622
019 -8.79 -3.34 4.00 3.00 MLC53-2622
020 -8.32 -7.10 4.00 3.00 MLC54-2622
021 -6.60 -10.22 4.00 3.00 MLC55-2622
022 -4.01 -1.76 4.00 3.00 MLC61-2622
023 -5.55 -4.97 4.00 3.00 MLC62-2622
024 -3.74 -8.12 4.00 3.00 MLC63-2622
025 -7.63 28.14 4.00 3.00 MLF11-2622
026 -12.92 27.01 4.00 3.00 MLF12-2622
027 -18.14 25.41 4.00 3.00 MLF13-2622
028 -23.34 23.65 4.00 3.00 MLF14-2622
029 -4.64 25.47 4.00 3.00 MLF21-2622
030 -9.22 24.68 4.00 3.00 MLF22-2622
031 -13.60 23.41 4.00 3.00 MLF23-2622
032 -18.31 21.53 4.00 3.00 MLF24-2622
033 -22.68 19.69 4.00 3.00 MLF25-2622
034 -6.57 22.14 4.00 3.00 MLF31-2622
035 -10.75 21.22 4.00 3.00 MLF32-2622
036 -15.16 19.49 4.00 3.00 MLF33-2622
037 -19.01 17.57 4.00 3.00 MLF34-2622
038 -22.93 15.25 4.00 3.00 MLF35-2622
039 -4.25 19.38 4.00 3.00 MLF41-2622
040 -8.17 18.80 4.00 3.00 MLF42-2622
041 -12.29 17.37 4.00 3.00 MLF43-2622
042 -15.93 15.49 4.00 3.00 MLF44-2622
043 -19.89 13.39 4.00 3.00 MLF45-2622
044 -24.12 10.50 4.00 3.00 MLF46-2622
045 -5.48 16.15 4.00 3.00 MLF51-2622
046 -9.58 15.10 4.00 3.00 MLF52-2622
047 -13.17 13.43 4.00 3.00 MLF53-2622
048 -16.66 11.39 4.00 3.00 MLF54-2622
049 -20.76 9.06 4.00 3.00 MLF55-2622
050 -24.71 5.73 4.00 3.00 MLF56-2622
051 -7.17 12.78 4.00 3.00 MLF61-2622
052 -10.58 11.08 4.00 3.00 MLF62-2622
053 -13.93 9.16 4.00 3.00 MLF63-2622
054 -17.37 7.29 4.00 3.00 MLF64-2622
055 -20.83 4.87 4.00 3.00 MLF65-2622
056 -23.40 1.59 4.00 3.00 MLF66-2622
057 -25.90 -2.51 4.00 3.00 MLF67-2622
058 -6.96 -27.32 4.00 3.00 MLO11-2622
059 -11.88 -25.97 4.00 3.00 MLO12-2622
060 -16.48 -23.69 4.00 3.00 MLO13-2622
061 -20.64 -20.44 4.00 3.00 MLO14-2622
062 -4.82 -30.75 4.00 3.00 MLO21-2622
063 -10.11 -29.77 4.00 3.00 MLO22-2622
064 -15.52 -27.87 4.00 3.00 MLO23-2622
065 -20.40 -24.85 4.00 3.00 MLO24-2622
066 -7.92 -33.45 4.00 3.00 MLO31-2622
067 -13.84 -31.94 4.00 3.00 MLO32-2622
068 -19.61 -29.16 4.00 3.00 MLO33-2622
069 -24.70 -25.44 4.00 3.00 MLO34-2622
070 -5.16 -36.86 4.00 3.00 MLO41-2622
071 -11.67 -35.84 4.00 3.00 MLO42-2622
072 -17.98 -33.55 4.00 3.00 MLO43-2622
073 -23.91 -30.00 4.00 3.00 MLO44-2622
074 -8.79 -39.34 4.00 3.00 MLO51-2622
075 -15.83 -37.54 4.00 3.00 MLO52-2622
076 -22.47 -34.34 4.00 3.00 MLO53-2622
077 -4.98 -13.36 4.00 3.00 MLP11-2622
078 -10.20 -10.01 4.00 3.00 MLP12-2622
079 -3.80 -16.69 4.00 3.00 MLP21-2622
080 -8.73 -13.30 4.00 3.00 MLP22-2622
081 -13.58 -8.80 4.00 3.00 MLP23-2622
082 -5.66 -19.72 4.00 3.00 MLP31-2622
083 -8.41 -16.83 4.00 3.00 MLP32-2622
084 -12.08 -14.80 4.00 3.00 MLP33-2622
085 -15.13 -11.95 4.00 3.00 MLP34-2622
086 -17.18 -8.63 4.00 3.00 MLP35-2622
087 -9.92 -20.16 4.00 3.00 MLP41-2622
088 -13.37 -18.09 4.00 3.00 MLP42-2622
089 -16.59 -15.58 4.00 3.00 MLP43-2622
090 -19.06 -11.87 4.00 3.00 MLP44-2622
091 -20.87 -8.06 4.00 3.00 MLP45-2622
092 -4.02 -24.07 4.00 3.00 MLP51-2622
093 -8.77 -23.79 4.00 3.00 MLP52-2622
094 -12.92 -22.08 4.00 3.00 MLP53-2622
095 -16.83 -19.50 4.00 3.00 MLP54-2622
096 -20.23 -16.32 4.00 3.00 MLP55-2622
097 -22.76 -11.97 4.00 3.00 MLP56-2622
098 -24.58 -7.58 4.00 3.00 MLP57-2622
099 -27.14 12.98 4.00 3.00 MLT11-2622
100 -28.19 7.51 4.00 3.00 MLT12-2622
101 -28.08 2.09 4.00 3.00 MLT13-2622
102 -28.56 -5.98 4.00 3.00 MLT14-2622
103 -26.96 -11.17 4.00 3.00 MLT15-2622
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336 -1.963487 1.749684 0.069221 0.051916 COMNT
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259
mne/channels/data/layouts/EGI256.lout Normal file
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126
mne/channels/data/layouts/KIT-125.lout Normal file
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158
mne/channels/data/layouts/KIT-157.lout Normal file
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161 -0.547000000 -0.5000000000 0.03188472676 0.02713339699 COMNT
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028 0.61 0.87 0.02 0.04 MEG 028
029 0.66 0.84 0.02 0.04 MEG 029
030 0.59 0.76 0.02 0.04 MEG 030
031 0.39 0.62 0.02 0.04 MEG 031
032 0.55 0.85 0.02 0.04 MEG 032
033 0.28 0.39 0.02 0.04 MEG 033
034 0.37 0.52 0.02 0.04 MEG 034
035 0.36 0.59 0.02 0.04 MEG 035
036 0.38 0.70 0.02 0.04 MEG 036
037 0.07 0.87 0.02 0.04 MEG 037
038 0.24 0.61 0.02 0.04 MEG 038
039 0.32 0.68 0.02 0.04 MEG 039
040 0.30 0.81 0.02 0.04 MEG 040
041 0.43 0.96 0.02 0.04 MEG 041
042 0.55 0.95 0.02 0.04 MEG 042
043 0.42 0.74 0.02 0.04 MEG 043
044 0.56 0.72 0.02 0.04 MEG 044
045 0.47 0.76 0.02 0.04 MEG 045
046 0.52 0.75 0.02 0.04 MEG 046
047 0.45 0.85 0.02 0.04 MEG 047
048 0.40 0.79 0.02 0.04 MEG 048
049 0.24 0.79 0.02 0.04 MEG 049
050 0.21 0.46 0.02 0.04 MEG 050
051 0.32 0.76 0.02 0.04 MEG 051
052 0.20 0.63 0.02 0.04 MEG 052
053 0.27 0.33 0.02 0.04 MEG 053
054 0.17 0.74 0.02 0.04 MEG 054
055 0.05 0.65 0.02 0.04 MEG 055
056 0.28 0.63 0.02 0.04 MEG 056
057 0.70 0.62 0.02 0.04 MEG 057
058 0.94 0.38 0.02 0.04 MEG 058
059 0.91 0.73 0.02 0.04 MEG 059
060 0.82 0.93 0.02 0.04 MEG 060
061 0.93 0.63 0.02 0.04 MEG 061
062 0.75 0.78 0.02 0.04 MEG 062
063 0.69 0.78 0.02 0.04 MEG 063
064 0.43 0.00 0.02 0.04 MEG 064
065 0.18 0.40 0.02 0.04 MEG 065
066 0.19 0.29 0.02 0.04 MEG 066
067 0.15 0.56 0.02 0.04 MEG 067
068 0.33 0.53 0.02 0.04 MEG 068
069 0.35 0.47 0.02 0.04 MEG 069
070 0.25 0.89 0.02 0.04 MEG 070
071 0.24 0.53 0.02 0.04 MEG 071
072 0.16 0.95 0.02 0.04 MEG 072
073 0.67 0.75 0.02 0.04 MEG 073
074 0.74 0.86 0.02 0.04 MEG 074
075 0.81 0.71 0.02 0.04 MEG 075
076 0.78 0.62 0.02 0.04 MEG 076
077 0.65 0.65 0.02 0.04 MEG 077
078 0.83 0.81 0.02 0.04 MEG 078
079 0.82 0.53 0.02 0.04 MEG 079
080 0.78 0.36 0.02 0.04 MEG 080
081 0.56 0.65 0.02 0.04 MEG 081
082 0.35 0.74 0.02 0.04 MEG 082
083 0.21 0.71 0.02 0.04 MEG 083
084 0.12 0.75 0.02 0.04 MEG 084
085 0.11 0.66 0.02 0.04 MEG 085
086 0.21 0.92 0.02 0.04 MEG 086
087 0.13 0.96 0.02 0.04 MEG 087
088 0.03 0.76 0.02 0.04 MEG 088
089 0.66 0.89 0.02 0.04 MEG 089
090 0.61 0.93 0.02 0.04 MEG 090
091 0.63 0.79 0.02 0.04 MEG 091
092 0.71 0.84 0.02 0.04 MEG 092
093 0.44 0.91 0.02 0.04 MEG 093
094 0.56 0.89 0.02 0.04 MEG 094
095 0.42 0.68 0.02 0.04 MEG 095
096 0.54 0.79 0.02 0.04 MEG 096
097 0.11 0.86 0.02 0.04 MEG 097
098 0.14 0.36 0.02 0.04 MEG 098
099 0.32 0.60 0.02 0.04 MEG 099
100 0.25 0.45 0.02 0.04 MEG 100
101 0.19 0.54 0.02 0.04 MEG 101
102 0.27 0.85 0.02 0.04 MEG 102
103 0.27 0.75 0.02 0.04 MEG 103
104 0.01 0.64 0.02 0.04 MEG 104
105 0.69 0.68 0.02 0.04 MEG 105
106 0.88 0.82 0.02 0.04 MEG 106
107 0.45 0.80 0.02 0.04 MEG 107
108 0.50 0.86 0.02 0.04 MEG 108
109 0.36 0.80 0.02 0.04 MEG 109
110 0.49 0.96 0.02 0.04 MEG 110
111 0.37 0.93 0.02 0.04 MEG 111
112 0.32 0.90 0.02 0.04 MEG 112
113 0.07 0.42 0.02 0.04 MEG 113
114 0.73 0.72 0.02 0.04 MEG 114
115 0.19 0.12 0.02 0.04 MEG 115
116 0.01 0.51 0.02 0.04 MEG 116
117 0.07 0.29 0.02 0.04 MEG 117
118 0.16 0.47 0.02 0.04 MEG 118
119 0.22 0.33 0.02 0.04 MEG 119
120 0.10 0.54 0.02 0.04 MEG 120
121 0.78 0.89 0.02 0.04 MEG 121
122 0.87 0.63 0.02 0.04 MEG 122
123 0.86 0.72 0.02 0.04 MEG 123
124 0.77 0.70 0.02 0.04 MEG 124
125 0.63 0.71 0.02 0.04 MEG 125
126 0.89 0.27 0.02 0.04 MEG 126
127 0.97 0.62 0.02 0.04 MEG 127
128 0.83 0.62 0.02 0.04 MEG 128
129 0.77 0.11 0.02 0.04 MEG 129
130 0.86 0.95 0.02 0.04 MEG 130
131 0.71 0.42 0.02 0.04 MEG 131
132 0.78 0.53 0.02 0.04 MEG 132
133 0.65 0.57 0.02 0.04 MEG 133
134 0.16 0.67 0.02 0.04 MEG 134
135 0.29 0.71 0.02 0.04 MEG 135
136 0.16 0.23 0.02 0.04 MEG 136
137 0.82 0.34 0.02 0.04 MEG 137
138 0.87 0.52 0.02 0.04 MEG 138
139 0.81 0.22 0.02 0.04 MEG 139
140 0.90 0.40 0.02 0.04 MEG 140
141 0.97 0.49 0.02 0.04 MEG 141
142 0.74 0.30 0.02 0.04 MEG 142
143 0.81 0.44 0.02 0.04 MEG 143
144 0.95 0.75 0.02 0.04 MEG 144
145 0.13 0.19 0.02 0.04 MEG 145
146 0.28 0.56 0.02 0.04 MEG 146
147 0.74 0.15 0.02 0.04 MEG 147
148 0.10 0.33 0.02 0.04 MEG 148
149 0.35 0.02 0.02 0.04 MEG 149
150 0.03 0.39 0.02 0.04 MEG 150
151 0.27 0.06 0.02 0.04 MEG 151
152 0.31 0.43 0.02 0.04 MEG 152
153 0.77 0.26 0.02 0.04 MEG 153
154 0.67 0.10 0.02 0.04 MEG 154
155 0.76 0.44 0.02 0.04 MEG 155
156 0.83 0.18 0.02 0.04 MEG 156
157 0.61 0.02 0.02 0.04 MEG 157
158 0.91 0.86 0.02 0.04 MEG 158
159 0.92 0.51 0.02 0.04 MEG 159
160 0.86 0.30 0.02 0.04 MEG 160
161 0.44 0.12 0.02 0.04 MEG 161
162 0.37 0.30 0.02 0.04 MEG 162
163 0.30 0.17 0.02 0.04 MEG 163
164 0.36 0.25 0.02 0.04 MEG 164
165 0.41 0.22 0.02 0.04 MEG 165
166 0.31 0.28 0.02 0.04 MEG 166
167 0.05 0.53 0.02 0.04 MEG 167
168 0.08 0.76 0.02 0.04 MEG 168
169 0.69 0.24 0.02 0.04 MEG 169
170 0.57 0.18 0.02 0.04 MEG 170
171 0.50 0.17 0.02 0.04 MEG 171
172 0.64 0.20 0.02 0.04 MEG 172
173 0.65 0.42 0.02 0.04 MEG 173
174 0.69 0.53 0.02 0.04 MEG 174
175 0.61 0.44 0.02 0.04 MEG 175
176 0.70 0.32 0.02 0.04 MEG 176
177 0.44 0.17 0.02 0.04 MEG 177
178 0.38 0.18 0.02 0.04 MEG 178
179 0.32 0.22 0.02 0.04 MEG 179
180 0.44 0.06 0.02 0.04 MEG 180
181 0.22 0.16 0.02 0.04 MEG 181
182 0.36 0.07 0.02 0.04 MEG 182
183 0.28 0.11 0.02 0.04 MEG 183
184 0.42 0.27 0.02 0.04 MEG 184
185 0.52 0.32 0.02 0.04 MEG 185
186 0.57 0.33 0.02 0.04 MEG 186
187 0.47 0.32 0.02 0.04 MEG 187
188 0.62 0.37 0.02 0.04 MEG 188
189 0.73 0.49 0.02 0.04 MEG 189
190 0.67 0.36 0.02 0.04 MEG 190
191 0.74 0.57 0.02 0.04 MEG 191
192 0.64 0.49 0.02 0.04 MEG 192
193 0.59 0.06 0.02 0.04 MEG 193
194 0.52 -0.00 0.02 0.04 MEG 194
195 0.58 0.29 0.02 0.04 MEG 195
196 0.53 0.27 0.02 0.04 MEG 196
197 0.47 0.26 0.02 0.04 MEG 197
198 0.34 0.39 0.02 0.04 MEG 198
199 0.42 0.33 0.02 0.04 MEG 199
200 0.38 0.35 0.02 0.04 MEG 200
201 0.53 0.22 0.02 0.04 MEG 201
202 0.59 0.24 0.02 0.04 MEG 202
203 0.65 0.27 0.02 0.04 MEG 203
204 0.27 0.26 0.02 0.04 MEG 204
205 0.51 0.11 0.02 0.04 MEG 205
206 0.65 0.15 0.02 0.04 MEG 206
207 0.51 0.05 0.02 0.04 MEG 207
208 0.69 0.05 0.02 0.04 MEG 208

158
mne/channels/data/layouts/KIT-AS-2008.lout Normal file
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@@ -0,0 +1,158 @@
0.00 0.00 0.04 0.02
000 0.43 0.98 0.10 0.05 MEG 001
001 0.38 0.96 0.10 0.05 MEG 002
002 0.32 0.92 0.10 0.05 MEG 003
003 0.44 0.93 0.10 0.05 MEG 004
004 0.39 0.91 0.10 0.05 MEG 005
005 0.45 0.88 0.10 0.05 MEG 006
006 0.36 0.82 0.10 0.05 MEG 007
007 0.32 0.78 0.10 0.05 MEG 008
008 0.33 0.68 0.10 0.05 MEG 009
009 0.40 0.79 0.10 0.05 MEG 010
010 0.36 0.74 0.10 0.05 MEG 011
011 0.48 0.78 0.10 0.05 MEG 012
012 0.39 0.71 0.10 0.05 MEG 013
013 0.37 0.66 0.10 0.05 MEG 014
014 0.48 0.72 0.10 0.05 MEG 015
015 0.44 0.69 0.10 0.05 MEG 016
016 0.28 0.57 0.10 0.05 MEG 017
017 0.29 0.51 0.10 0.05 MEG 018
018 0.32 0.45 0.10 0.05 MEG 019
019 0.40 0.36 0.10 0.05 MEG 020
020 0.46 0.44 0.10 0.05 MEG 021
021 0.33 0.60 0.10 0.05 MEG 022
022 0.34 0.53 0.10 0.05 MEG 023
023 0.41 0.42 0.10 0.05 MEG 024
024 0.46 0.51 0.10 0.05 MEG 025
025 0.38 0.59 0.10 0.05 MEG 026
026 0.50 0.38 0.10 0.05 MEG 027
027 0.41 0.48 0.10 0.05 MEG 028
028 0.42 0.56 0.10 0.05 MEG 029
029 0.51 0.49 0.10 0.05 MEG 030
030 0.46 0.58 0.10 0.05 MEG 031
031 0.47 0.64 0.10 0.05 MEG 032
032 0.12 0.99 0.10 0.05 MEG 033
033 0.07 0.90 0.10 0.05 MEG 034
034 0.11 0.88 0.10 0.05 MEG 035
035 0.13 0.77 0.10 0.05 MEG 036
036 0.16 0.97 0.10 0.05 MEG 037
037 0.07 0.78 0.10 0.05 MEG 038
038 0.20 0.94 0.10 0.05 MEG 039
039 0.16 0.86 0.10 0.05 MEG 040
040 0.10 0.67 0.10 0.05 MEG 041
041 0.25 0.90 0.10 0.05 MEG 042
042 0.20 0.83 0.10 0.05 MEG 043
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047 0.25 0.69 0.10 0.05 MEG 048
048 0.00 0.52 0.10 0.05 MEG 049
049 0.02 0.40 0.10 0.05 MEG 050
050 0.07 0.30 0.10 0.05 MEG 051
051 0.12 0.20 0.10 0.05 MEG 052
052 0.05 0.53 0.10 0.05 MEG 053
053 0.07 0.42 0.10 0.05 MEG 054
054 0.16 0.24 0.10 0.05 MEG 055
055 0.10 0.56 0.10 0.05 MEG 056
056 0.15 0.37 0.10 0.05 MEG 057
057 0.16 0.56 0.10 0.05 MEG 058
058 0.17 0.48 0.10 0.05 MEG 059
059 0.20 0.40 0.10 0.05 MEG 060
060 0.21 0.48 0.10 0.05 MEG 061
061 0.28 0.34 0.10 0.05 MEG 062
062 0.24 0.61 0.10 0.05 MEG 063
063 0.30 0.39 0.10 0.05 MEG 064
064 0.67 0.93 0.10 0.05 MEG 065
065 0.62 0.96 0.10 0.05 MEG 066
066 0.56 0.98 0.10 0.05 MEG 067
067 0.50 0.99 0.10 0.05 MEG 068
068 0.60 0.86 0.10 0.05 MEG 069
069 0.56 0.93 0.10 0.05 MEG 070
070 0.71 0.72 0.10 0.05 MEG 071
071 0.65 0.83 0.10 0.05 MEG 072
072 0.56 0.88 0.10 0.05 MEG 073
073 0.65 0.76 0.10 0.05 MEG 074
074 0.56 0.83 0.10 0.05 MEG 075
075 0.64 0.66 0.10 0.05 MEG 076
076 0.62 0.71 0.10 0.05 MEG 077
077 0.53 0.78 0.10 0.05 MEG 078
078 0.57 0.68 0.10 0.05 MEG 079
079 0.53 0.72 0.10 0.05 MEG 080
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085 0.61 0.42 0.10 0.05 MEG 086
086 0.67 0.53 0.10 0.05 MEG 087
087 0.69 0.61 0.10 0.05 MEG 088
088 0.56 0.45 0.10 0.05 MEG 089
089 0.60 0.48 0.10 0.05 MEG 090
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091 0.56 0.51 0.10 0.05 MEG 092
092 0.59 0.55 0.10 0.05 MEG 093
093 0.51 0.55 0.10 0.05 MEG 094
094 0.54 0.58 0.10 0.05 MEG 095
095 0.54 0.64 0.10 0.05 MEG 096
096 1.00 0.69 0.10 0.05 MEG 097
097 0.97 0.81 0.10 0.05 MEG 098
098 0.93 0.92 0.10 0.05 MEG 099
099 0.87 1.00 0.10 0.05 MEG 100
100 0.93 0.80 0.10 0.05 MEG 101
101 0.83 0.97 0.10 0.05 MEG 102
102 0.89 0.68 0.10 0.05 MEG 103
103 0.84 0.87 0.10 0.05 MEG 104
104 0.79 0.94 0.10 0.05 MEG 105
105 0.85 0.68 0.10 0.05 MEG 106
106 0.83 0.76 0.10 0.05 MEG 107
107 0.76 0.91 0.10 0.05 MEG 108
108 0.74 0.76 0.10 0.05 MEG 109
109 0.76 0.81 0.10 0.05 MEG 110
110 0.76 0.69 0.10 0.05 MEG 111
111 0.71 0.83 0.10 0.05 MEG 112
112 0.88 0.22 0.10 0.05 MEG 113
113 0.94 0.32 0.10 0.05 MEG 114
114 0.98 0.42 0.10 0.05 MEG 115
115 1.00 0.54 0.10 0.05 MEG 116
116 0.84 0.26 0.10 0.05 MEG 117
117 0.93 0.45 0.10 0.05 MEG 118
118 0.95 0.56 0.10 0.05 MEG 119
119 0.81 0.30 0.10 0.05 MEG 120
120 0.85 0.38 0.10 0.05 MEG 121
121 0.81 0.41 0.10 0.05 MEG 122
122 0.83 0.49 0.10 0.05 MEG 123
123 0.85 0.58 0.10 0.05 MEG 124
124 0.73 0.35 0.10 0.05 MEG 125
125 0.79 0.49 0.10 0.05 MEG 126
126 0.74 0.46 0.10 0.05 MEG 127
127 0.77 0.61 0.10 0.05 MEG 128
128 0.20 0.12 0.10 0.05 MEG 129
129 0.37 0.02 0.10 0.05 MEG 130
130 0.46 0.00 0.10 0.05 MEG 131
131 0.30 0.11 0.10 0.05 MEG 132
132 0.47 0.06 0.10 0.05 MEG 133
133 0.25 0.21 0.10 0.05 MEG 134
134 0.32 0.17 0.10 0.05 MEG 135
135 0.39 0.13 0.10 0.05 MEG 136
136 0.29 0.26 0.10 0.05 MEG 137
137 0.41 0.19 0.10 0.05 MEG 138
138 0.47 0.18 0.10 0.05 MEG 139
139 0.39 0.26 0.10 0.05 MEG 140
140 0.50 0.22 0.10 0.05 MEG 141
141 0.33 0.29 0.10 0.05 MEG 142
142 0.45 0.29 0.10 0.05 MEG 143
143 0.50 0.28 0.10 0.05 MEG 144
144 0.65 0.03 0.10 0.05 MEG 145
145 0.82 0.13 0.10 0.05 MEG 146
146 0.55 0.06 0.10 0.05 MEG 147
147 0.71 0.12 0.10 0.05 MEG 148
148 0.62 0.14 0.10 0.05 MEG 149
149 0.69 0.18 0.10 0.05 MEG 150
150 0.76 0.23 0.10 0.05 MEG 151
151 0.54 0.18 0.10 0.05 MEG 152
152 0.61 0.20 0.10 0.05 MEG 153
153 0.73 0.27 0.10 0.05 MEG 154
154 0.63 0.25 0.10 0.05 MEG 155
155 0.56 0.28 0.10 0.05 MEG 156
156 0.67 0.35 0.10 0.05 MEG 157

158
mne/channels/data/layouts/KIT-UMD-3.lout Normal file
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-25.00 28.00 -21.35 23.75
000 -23.42 20.48 3.20 2.40 MEG 001
001 -22.32 15.16 3.20 2.40 MEG 002
002 -24.20 10.24 3.20 2.40 MEG 003
003 -25.00 5.27 3.20 2.40 MEG 004
004 -24.75 -0.21 3.20 2.40 MEG 005
005 -23.41 -5.22 3.20 2.40 MEG 006
006 -22.35 -11.37 3.20 2.40 MEG 007
007 -14.06 -15.64 3.20 2.40 MEG 008
008 -15.12 -18.15 3.20 2.40 MEG 009
009 -11.26 -20.73 3.20 2.40 MEG 010
010 -6.28 -20.94 3.20 2.40 MEG 011
011 -2.04 -21.35 3.20 2.40 MEG 012
012 2.04 -21.35 3.20 2.40 MEG 013
013 6.28 -20.94 3.20 2.40 MEG 014
014 11.26 -20.73 3.20 2.40 MEG 015
015 15.12 -18.15 3.20 2.40 MEG 016
016 19.41 -14.06 3.20 2.40 MEG 017
017 22.35 -11.37 3.20 2.40 MEG 018
018 24.06 -3.70 3.20 2.40 MEG 019
019 24.23 1.80 3.20 2.40 MEG 020
020 24.80 5.19 3.20 2.40 MEG 021
021 22.03 13.42 3.20 2.40 MEG 022
022 21.58 16.68 3.20 2.40 MEG 023
023 23.42 20.48 3.20 2.40 MEG 024
024 20.15 19.33 3.20 2.40 MEG 025
025 7.46 -2.58 3.20 2.40 MEG 026
026 22.86 7.70 3.20 2.40 MEG 027
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028 19.70 -8.80 3.20 2.40 MEG 029
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032 1.80 -18.87 3.20 2.40 MEG 033
033 -1.80 -18.87 3.20 2.40 MEG 034
034 -10.12 -18.16 3.20 2.40 MEG 035
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037 -19.70 -8.80 3.20 2.40 MEG 038
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040 -7.46 -2.58 3.20 2.40 MEG 041
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042 -16.84 18.53 3.20 2.40 MEG 043
043 -18.55 14.46 3.20 2.40 MEG 044
044 -20.31 10.64 3.20 2.40 MEG 045
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047 -17.20 -6.26 3.20 2.40 MEG 048
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050 -5.79 -18.42 3.20 2.40 MEG 051
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052 -8.25 6.10 3.20 2.40 MEG 053
053 5.79 -18.42 3.20 2.40 MEG 054
054 8.92 -15.60 3.20 2.40 MEG 055
055 16.21 -11.50 3.20 2.40 MEG 056
056 17.20 -6.26 3.20 2.40 MEG 057
057 20.62 -2.66 3.20 2.40 MEG 058
058 -6.11 13.61 3.20 2.40 MEG 059
059 20.31 10.64 3.20 2.40 MEG 060
060 17.58 15.92 3.20 2.40 MEG 061
061 16.84 18.53 3.20 2.40 MEG 062
062 13.49 18.47 3.20 2.40 MEG 063
063 15.28 13.32 3.20 2.40 MEG 064
064 -4.11 11.13 3.20 2.40 MEG 065
065 19.39 7.54 3.20 2.40 MEG 066
066 17.50 3.47 3.20 2.40 MEG 067
067 -6.54 8.57 3.20 2.40 MEG 068
068 11.44 -8.04 3.20 2.40 MEG 069
069 12.41 -13.14 3.20 2.40 MEG 070
070 8.16 -13.13 3.20 2.40 MEG 071
071 -7.60 2.77 3.20 2.40 MEG 072
072 1.62 -16.14 3.20 2.40 MEG 073
073 -6.80 0.14 3.20 2.40 MEG 074
074 -5.40 -15.93 3.20 2.40 MEG 075
075 -8.16 -13.13 3.20 2.40 MEG 076
076 -12.41 -13.14 3.20 2.40 MEG 077
077 -14.81 -8.97 3.20 2.40 MEG 078
078 -3.23 -2.94 3.20 2.40 MEG 079
079 -17.50 3.47 3.20 2.40 MEG 080
080 -19.39 7.54 3.20 2.40 MEG 081
081 4.03 -2.84 3.20 2.40 MEG 082
082 -15.28 13.32 3.20 2.40 MEG 083
083 -13.49 18.47 3.20 2.40 MEG 084
084 -12.29 15.99 3.20 2.40 MEG 085
085 -16.74 10.63 3.20 2.40 MEG 086
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205
mne/channels/data/layouts/Vectorview-grad.lout Normal file
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103
mne/channels/data/layouts/Vectorview-grad_norm.lout Normal file
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083 15.05 3.30 4.00 3.00 MEG 083
084 14.29 7.20 4.00 3.00 MEG 084
085 12.81 11.43 4.00 3.00 MEG 085
086 9.96 14.67 4.00 3.00 MEG 086
087 6.46 17.06 4.00 3.00 MEG 087
088 2.60 18.73 4.00 3.00 MEG 088
089 -1.60 22.21 4.00 3.00 MEG 089
090 -5.83 21.82 4.00 3.00 MEG 090
091 -9.75 20.43 4.00 3.00 MEG 091
092 -13.45 18.45 4.00 3.00 MEG 092
093 -16.67 15.62 4.00 3.00 MEG 093
094 -19.33 12.13 4.00 3.00 MEG 094
095 -20.94 7.82 4.00 3.00 MEG 095
096 -21.81 3.65 4.00 3.00 MEG 096
097 -22.23 -1.27 4.00 3.00 MEG 097
098 -21.14 -5.87 4.00 3.00 MEG 098
099 -20.30 -9.97 4.00 3.00 MEG 099
100 -18.46 -13.84 4.00 3.00 MEG 100
101 -16.07 -17.08 4.00 3.00 MEG 101
102 -12.88 -19.71 4.00 3.00 MEG 102
103 -9.34 -21.89 4.00 3.00 MEG 103
104 -5.64 -23.02 4.00 3.00 MEG 104
105 -1.72 -23.54 4.00 3.00 MEG 105
106 2.48 -23.24 4.00 3.00 MEG 106
107 6.42 -22.00 4.00 3.00 MEG 107
108 9.86 -20.19 4.00 3.00 MEG 108
109 13.22 -17.32 4.00 3.00 MEG 109
110 15.75 -14.15 4.00 3.00 MEG 110
111 17.67 -10.19 4.00 3.00 MEG 111
112 18.65 -6.08 4.00 3.00 MEG 112
113 19.69 -1.27 4.00 3.00 MEG 113
114 19.27 3.70 4.00 3.00 MEG 114
115 18.30 8.05 4.00 3.00 MEG 115
116 16.46 12.48 4.00 3.00 MEG 116
117 13.74 15.93 4.00 3.00 MEG 117
118 10.41 18.72 4.00 3.00 MEG 118
119 6.64 20.69 4.00 3.00 MEG 119
120 2.67 22.02 4.00 3.00 MEG 120
121 -1.74 25.41 4.00 3.00 MEG 121
122 -6.59 24.84 4.00 3.00 MEG 122
123 -11.16 23.37 4.00 3.00 MEG 123
124 -15.46 21.07 4.00 3.00 MEG 124
125 -19.25 17.84 4.00 3.00 MEG 125
126 -22.45 13.89 4.00 3.00 MEG 126
127 -24.89 8.96 4.00 3.00 MEG 127
128 -26.13 4.36 4.00 3.00 MEG 128
129 -26.65 -1.22 4.00 3.00 MEG 129
130 -25.30 -6.36 4.00 3.00 MEG 130
131 -24.16 -11.45 4.00 3.00 MEG 131
132 -21.98 -15.88 4.00 3.00 MEG 132
133 -18.81 -19.82 4.00 3.00 MEG 133
134 -15.20 -22.99 4.00 3.00 MEG 134
135 -11.11 -25.29 4.00 3.00 MEG 135
136 -6.51 -26.74 4.00 3.00 MEG 136
137 -1.86 -27.28 4.00 3.00 MEG 137
138 3.17 -26.90 4.00 3.00 MEG 138
139 7.79 -25.55 4.00 3.00 MEG 139
140 12.07 -23.15 4.00 3.00 MEG 140
141 15.93 -20.09 4.00 3.00 MEG 141
142 19.04 -16.25 4.00 3.00 MEG 142
143 21.39 -11.67 4.00 3.00 MEG 143
144 22.75 -6.58 4.00 3.00 MEG 144
145 23.99 -1.23 4.00 3.00 MEG 145
146 23.36 4.49 4.00 3.00 MEG 146
147 22.02 9.37 4.00 3.00 MEG 147
148 19.51 14.31 4.00 3.00 MEG 148
149 16.20 18.23 4.00 3.00 MEG 149
150 12.16 21.54 4.00 3.00 MEG 150
151 7.85 23.69 4.00 3.00 MEG 151
152 3.16 25.01 4.00 3.00 MEG 152
153 -23.01 18.82 4.00 3.00 MEG 153
154 -26.06 15.31 4.00 3.00 MEG 154
155 -28.76 10.18 4.00 3.00 MEG 155
156 -31.71 3.39 4.00 3.00 MEG 156
157 -32.05 -2.89 4.00 3.00 MEG 157
158 -31.42 -8.67 4.00 3.00 MEG 158
159 -26.22 -15.24 4.00 3.00 MEG 159
160 -23.31 -19.72 4.00 3.00 MEG 160
161 -19.33 -23.66 4.00 3.00 MEG 161
162 -14.75 -26.73 4.00 3.00 MEG 162
163 -9.92 -28.91 4.00 3.00 MEG 163
164 -4.52 -30.10 4.00 3.00 MEG 164
165 1.25 -30.15 4.00 3.00 MEG 165
166 6.17 -29.40 4.00 3.00 MEG 166
167 11.43 -27.39 4.00 3.00 MEG 167
168 16.20 -24.37 4.00 3.00 MEG 168
169 20.37 -20.27 4.00 3.00 MEG 169
170 23.54 -15.56 4.00 3.00 MEG 170
171 28.66 -8.94 4.00 3.00 MEG 171
172 29.46 -3.00 4.00 3.00 MEG 172
173 29.04 3.51 4.00 3.00 MEG 173
174 25.94 10.77 4.00 3.00 MEG 174
175 23.08 15.80 4.00 3.00 MEG 175
176 19.78 19.54 4.00 3.00 MEG 176
177 -26.70 20.52 4.00 3.00 MEG 177
178 -29.66 16.81 4.00 3.00 MEG 178
179 -32.55 11.68 4.00 3.00 MEG 179
180 -32.47 -13.23 4.00 3.00 MEG 180
181 -27.63 -19.12 4.00 3.00 MEG 181
182 -23.75 -23.89 4.00 3.00 MEG 182
183 -18.94 -27.77 4.00 3.00 MEG 183
184 -13.64 -30.59 4.00 3.00 MEG 184
185 -7.93 -32.70 4.00 3.00 MEG 185
186 -2.12 -33.31 4.00 3.00 MEG 186
187 4.06 -32.74 4.00 3.00 MEG 187
188 10.04 -31.14 4.00 3.00 MEG 188
189 15.57 -28.41 4.00 3.00 MEG 189
190 20.44 -24.69 4.00 3.00 MEG 190
191 24.62 -19.81 4.00 3.00 MEG 191
192 29.49 -13.87 4.00 3.00 MEG 192
193 29.48 12.54 4.00 3.00 MEG 193
194 26.49 17.54 4.00 3.00 MEG 194
195 23.28 21.40 4.00 3.00 MEG 195
196 -36.84 4.15 4.00 3.00 MEG 196
197 -37.22 -3.16 4.00 3.00 MEG 197
198 -36.14 -9.68 4.00 3.00 MEG 198
199 -28.42 -23.63 4.00 3.00 MEG 199
200 -23.68 -28.05 4.00 3.00 MEG 200
201 -18.03 -31.89 4.00 3.00 MEG 201
202 -11.97 -34.42 4.00 3.00 MEG 202
203 -5.32 -35.88 4.00 3.00 MEG 203
204 1.03 -36.08 4.00 3.00 MEG 204
205 7.92 -35.00 4.00 3.00 MEG 205
206 13.99 -32.64 4.00 3.00 MEG 206
207 19.78 -29.06 4.00 3.00 MEG 207
208 24.79 -24.52 4.00 3.00 MEG 208
209 33.39 -10.13 4.00 3.00 MEG 209
210 34.62 -3.11 4.00 3.00 MEG 210
211 34.23 4.57 4.00 3.00 MEG 211
212 -32.38 19.14 4.00 3.00 MEG 212
213 -35.90 13.21 4.00 3.00 MEG 213
214 -36.70 -14.70 4.00 3.00 MEG 214
215 -32.93 -22.44 4.00 3.00 MEG 215
216 -28.17 -28.07 4.00 3.00 MEG 216
217 -22.65 -32.41 4.00 3.00 MEG 217
218 -16.53 -35.71 4.00 3.00 MEG 218
219 -9.52 -37.92 4.00 3.00 MEG 219
220 -2.58 -38.82 4.00 3.00 MEG 220
221 4.65 -38.54 4.00 3.00 MEG 221
222 11.78 -36.65 4.00 3.00 MEG 222
223 18.43 -33.60 4.00 3.00 MEG 223
224 24.26 -29.21 4.00 3.00 MEG 224
225 29.52 -23.44 4.00 3.00 MEG 225
226 33.73 -15.36 4.00 3.00 MEG 226
227 33.02 14.20 4.00 3.00 MEG 227
228 29.24 19.93 4.00 3.00 MEG 228
229 -36.80 18.24 4.00 3.00 MEG 229
230 -40.03 12.76 4.00 3.00 MEG 230
231 -41.35 5.03 4.00 3.00 MEG 231
232 -41.79 -3.17 4.00 3.00 MEG 232
233 -40.48 -10.59 4.00 3.00 MEG 233
234 -32.92 -26.79 4.00 3.00 MEG 234
235 -27.40 -32.12 4.00 3.00 MEG 235
236 -20.92 -36.72 4.00 3.00 MEG 236
237 -14.11 -39.49 4.00 3.00 MEG 237
238 -6.76 -41.18 4.00 3.00 MEG 238
239 1.45 -41.40 4.00 3.00 MEG 239
240 8.96 -40.25 4.00 3.00 MEG 240
241 16.27 -37.84 4.00 3.00 MEG 241
242 22.75 -33.68 4.00 3.00 MEG 242
243 29.08 -28.20 4.00 3.00 MEG 243
244 37.59 -11.05 4.00 3.00 MEG 244
245 39.12 -3.16 4.00 3.00 MEG 245
246 38.59 5.47 4.00 3.00 MEG 246
247 37.16 13.60 4.00 3.00 MEG 247
248 33.62 18.93 4.00 3.00 MEG 248

258
mne/channels/data/montages/EGI_256.csd Normal file
View File

@@ -0,0 +1,258 @@
// MatLab Sphere coordinates [degrees] Cartesian coordinates
// Label Theta Phi Radius X Y Z off sphere surface
E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011
E2 44.600 -0.880 1.000 0.7119 0.7021 -0.0154 0.00000000000000000
E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000
E4 58.200 21.800 1.000 0.4893 0.7891 0.3714 -0.00000000000000011
E5 64.200 33.600 1.000 0.3625 0.7499 0.5534 0.00000000000000000
E6 70.800 45.500 1.000 0.2305 0.6619 0.7133 -0.00000000000000022
E7 77.200 56.700 1.000 0.1216 0.5354 0.8358 0.00000000000000000
E8 90.000 67.700 1.000 0.0000 0.3795 0.9252 0.00000000000000000
E9 127.300 78.300 1.000 -0.1229 0.1613 0.9792 0.00000000000000000
E10 51.200 -9.080 1.000 0.6188 0.7696 -0.1578 0.00000000000000000
E11 58.800 2.370 1.000 0.5176 0.8546 0.0414 0.00000000000000000
E12 66.800 14.300 1.000 0.3817 0.8907 0.2470 -0.00000000000000011
E13 73.800 25.200 1.000 0.2524 0.8689 0.4258 0.00000000000000022
E14 81.360 36.400 1.000 0.1209 0.7958 0.5934 0.00000000000000022
E15 90.000 46.900 1.000 0.0000 0.6833 0.7302 0.00000000000000000
E16 102.800 56.700 1.000 -0.1216 0.5354 0.8358 0.00000000000000000
E17 128.200 66.600 1.000 -0.2456 0.3121 0.9178 -0.00000000000000011
E18 66.600 -4.970 1.000 0.3957 0.9143 -0.0866 -0.00000000000000011
E19 74.000 4.680 1.000 0.2747 0.9581 0.0816 -0.00000000000000011
E20 81.960 15.700 1.000 0.1346 0.9532 0.2706 0.00000000000000022
E21 90.000 26.400 1.000 0.0000 0.8957 0.4446 0.00000000000000000
E22 98.640 36.400 1.000 -0.1209 0.7958 0.5934 0.00000000000000022
E23 109.200 45.500 1.000 -0.2305 0.6619 0.7133 0.00000000000000000
E24 127.200 54.200 1.000 -0.3537 0.4659 0.8111 0.00000000000000000
E25 82.540 -3.260 1.000 0.1296 0.9899 -0.0569 0.00000000000000000
E26 90.000 5.370 1.000 0.0000 0.9956 0.0936 0.00000000000000000
E27 98.040 15.700 1.000 -0.1346 0.9532 0.2706 0.00000000000000022
E28 106.200 25.200 1.000 -0.2524 0.8689 0.4258 0.00000000000000022
E29 115.800 33.600 1.000 -0.3625 0.7499 0.5534 0.00000000000000000
E30 128.800 41.200 1.000 -0.4715 0.5864 0.6587 0.00000000000000000
E31 90.000 -11.000 1.000 0.0000 0.9816 -0.1908 0.00000000000000000
E32 97.460 -3.260 1.000 -0.1296 0.9899 -0.0569 0.00000000000000000
E33 106.000 4.680 1.000 -0.2747 0.9581 0.0816 -0.00000000000000011
E34 113.200 14.300 1.000 -0.3817 0.8907 0.2470 -0.00000000000000022
E35 121.800 21.800 1.000 -0.4893 0.7891 0.3714 -0.00000000000000011
E36 128.500 30.200 1.000 -0.5380 0.6764 0.5030 0.00000000000000022
E37 113.400 -4.970 1.000 -0.3957 0.9143 -0.0866 0.00000000000000000
E38 121.200 2.370 1.000 -0.5176 0.8546 0.0414 0.00000000000000000
E39 128.300 11.000 1.000 -0.6084 0.7704 0.1908 0.00000000000000000
E40 135.300 20.800 1.000 -0.6645 0.6576 0.3551 0.00000000000000000
E41 140.600 32.000 1.000 -0.6553 0.5383 0.5299 0.00000000000000000
E42 144.500 44.000 1.000 -0.5856 0.4177 0.6947 0.00000000000000000
E43 151.000 54.800 1.000 -0.5042 0.2795 0.8171 0.00000000000000000
E44 163.200 66.400 1.000 -0.3833 0.1157 0.9164 0.00000000000000000
E45 197.000 77.300 1.000 -0.2102 -0.0643 0.9755 0.00000000000000000
E46 128.800 -9.080 1.000 -0.6188 0.7696 -0.1578 0.00000000000000000
E47 135.400 -0.880 1.000 -0.7119 0.7021 -0.0154 -0.00000000000000011
E48 142.500 8.460 1.000 -0.7847 0.6021 0.1471 0.00000000000000000
E49 149.200 19.400 1.000 -0.8102 0.4830 0.3322 0.00000000000000000
E50 155.300 32.200 1.000 -0.7688 0.3536 0.5329 0.00000000000000000
E51 162.400 44.200 1.000 -0.6834 0.2168 0.6972 0.00000000000000000
E52 173.500 54.500 1.000 -0.5770 0.0657 0.8141 0.00000000000000000
E53 197.000 65.600 1.000 -0.3951 -0.1208 0.9107 0.00000000000000000
E54 142.300 -14.000 1.000 -0.7677 0.5934 -0.2419 0.00000000000000000
E55 149.100 -4.100 1.000 -0.8559 0.5122 -0.0715 0.00000000000000000
E56 156.700 7.130 1.000 -0.9113 0.3925 0.1241 0.00000000000000022
E57 163.200 19.500 1.000 -0.9024 0.2725 0.3338 0.00000000000000000
E58 169.700 31.600 1.000 -0.8380 0.1523 0.5240 0.00000000000000000
E59 179.500 43.000 1.000 -0.7313 0.0064 0.6820 0.00000000000000000
E60 197.000 53.000 1.000 -0.5755 -0.1760 0.7986 0.00000000000000000
E61 158.000 -17.200 1.000 -0.8857 0.3579 -0.2957 -0.00000000000000022
E62 165.100 -5.730 1.000 -0.9615 0.2558 -0.0998 0.00000000000000022
E63 171.400 6.890 1.000 -0.9816 0.1485 0.1200 0.00000000000000022
E64 177.200 19.000 1.000 -0.9444 0.0462 0.3256 0.00000000000000000
E65 184.300 31.100 1.000 -0.8539 -0.0642 0.5165 0.00000000000000000
E66 196.000 39.900 1.000 -0.7374 -0.2115 0.6414 0.00000000000000000
E67 167.300 -27.900 1.000 -0.8621 0.1943 -0.4679 0.00000000000000000
E68 172.300 -17.500 1.000 -0.9451 0.1278 -0.3007 0.00000000000000000
E69 179.500 -6.970 1.000 -0.9926 0.0087 -0.1213 0.00000000000000000
E70 185.400 5.990 1.000 -0.9901 -0.0936 0.1044 0.00000000000000022
E71 191.000 18.700 1.000 -0.9298 -0.1807 0.3206 0.00000000000000000
E72 197.000 28.500 1.000 -0.8404 -0.2569 0.4772 0.00000000000000000
E73 174.500 -38.200 1.000 -0.7822 0.0753 -0.6184 0.00000000000000022
E74 193.000 -6.630 1.000 -0.9679 -0.2234 -0.1155 0.00000000000000000
E75 199.000 7.590 1.000 -0.9372 -0.3227 0.1321 0.00000000000000000
E76 205.000 19.800 1.000 -0.8527 -0.3976 0.3387 -0.00000000000000011
E77 209.000 31.900 1.000 -0.7425 -0.4116 0.5284 0.00000000000000000
E78 214.000 43.600 1.000 -0.6004 -0.4050 0.6896 0.00000000000000000
E79 221.000 55.600 1.000 -0.4264 -0.3707 0.8251 -0.00000000000000011
E80 233.000 67.400 1.000 -0.2313 -0.3069 0.9232 0.00000000000000000
E81 -90.000 78.400 1.000 0.0000 -0.2011 0.9796 -0.00000000000000011
E82 183.900 -45.800 1.000 -0.6956 -0.0474 -0.7169 0.00000000000000000
E83 205.000 -15.000 1.000 -0.8754 -0.4082 -0.2588 0.00000000000000000
E84 206.000 -3.510 1.000 -0.8971 -0.4375 -0.0612 -0.00000000000000022
E85 213.000 10.000 1.000 -0.8259 -0.5364 0.1736 -0.00000000000000011
E86 218.000 22.700 1.000 -0.7270 -0.5680 0.3859 0.00000000000000000
E87 225.000 35.300 1.000 -0.5771 -0.5771 0.5779 0.00000000000000000
E88 232.000 46.800 1.000 -0.4214 -0.5394 0.7290 -0.00000000000000011
E89 245.000 56.900 1.000 -0.2308 -0.4949 0.8377 -0.00000000000000011
E90 -90.000 67.500 1.000 0.0000 -0.3827 0.9239 0.00000000000000000
E91 195.000 -50.900 1.000 -0.6092 -0.1632 -0.7760 0.00000000000000000
E92 203.000 -42.700 1.000 -0.6765 -0.2872 -0.6782 -0.00000000000000022
E93 211.000 -32.500 1.000 -0.7229 -0.4344 -0.5373 0.00000000000000000
E94 212.000 -23.100 1.000 -0.7801 -0.4874 -0.3923 0.00000000000000000
E95 216.000 -12.400 1.000 -0.7901 -0.5741 -0.2147 0.00000000000000000
E96 221.000 0.666 1.000 -0.7547 -0.6560 0.0116 0.00000000000000000
E97 228.000 12.900 1.000 -0.6522 -0.7244 0.2233 0.00000000000000022
E98 233.000 24.900 1.000 -0.5459 -0.7244 0.4210 0.00000000000000000
E99 241.000 36.400 1.000 -0.3902 -0.7040 0.5934 0.00000000000000000
E100 251.000 46.900 1.000 -0.2225 -0.6460 0.7302 0.00000000000000000
E101 -90.000 44.200 1.000 0.0000 -0.7169 0.6972 0.00000000000000000
E102 211.000 -47.800 1.000 -0.5758 -0.3460 -0.7408 0.00000000000000000
E103 217.000 -39.900 1.000 -0.6127 -0.4617 -0.6414 0.00000000000000000
E104 223.000 -29.500 1.000 -0.6365 -0.5936 -0.4924 0.00000000000000000
E105 224.000 -20.500 1.000 -0.6738 -0.6507 -0.3502 0.00000000000000000
E106 228.000 -8.840 1.000 -0.6612 -0.7343 -0.1537 0.00000000000000022
E107 235.000 2.900 1.000 -0.5728 -0.8181 0.0506 0.00000000000000000
E108 242.000 14.600 1.000 -0.4543 -0.8544 0.2521 0.00000000000000000
E109 248.000 25.700 1.000 -0.3375 -0.8355 0.4337 -0.00000000000000011
E110 257.000 36.000 1.000 -0.1820 -0.7883 0.5878 0.00000000000000000
E111 226.000 -43.800 1.000 -0.5014 -0.5192 -0.6921 0.00000000000000000
E112 230.000 -36.300 1.000 -0.5180 -0.6174 -0.5920 0.00000000000000000
E113 235.000 -25.900 1.000 -0.5160 -0.7369 -0.4368 -0.00000000000000022
E114 235.000 -17.500 1.000 -0.5470 -0.7812 -0.3007 0.00000000000000000
E115 244.000 -6.240 1.000 -0.4358 -0.8935 -0.1087 0.00000000000000000
E116 251.000 4.850 1.000 -0.3244 -0.9421 0.0845 0.00000000000000000
E117 258.000 15.500 1.000 -0.2004 -0.9426 0.2672 0.00000000000000000
E118 263.000 25.200 1.000 -0.1103 -0.8981 0.4258 0.00000000000000000
E119 -90.000 33.400 1.000 0.0000 -0.8348 0.5505 -0.00000000000000011
E120 237.000 -41.100 1.000 -0.4104 -0.6320 -0.6574 0.00000000000000022
E121 242.000 -33.400 1.000 -0.3919 -0.7371 -0.5505 -0.00000000000000022
E122 247.000 -23.400 1.000 -0.3586 -0.8448 -0.3971 0.00000000000000000
E123 252.000 -11.200 1.000 -0.3031 -0.9329 -0.1942 0.00000000000000000
E124 257.000 -3.660 1.000 -0.2245 -0.9724 -0.0638 0.00000000000000022
E125 264.000 5.580 1.000 -0.1040 -0.9898 0.0972 0.00000000000000000
E126 -90.000 15.400 1.000 0.0000 -0.9641 0.2656 0.00000000000000000
E127 -83.000 25.200 1.000 0.1103 -0.8981 0.4258 0.00000000000000000
E128 -77.000 36.000 1.000 0.1820 -0.7883 0.5878 0.00000000000000000
E129 -71.000 46.900 1.000 0.2225 -0.6460 0.7302 0.00000000000000000
E130 -65.000 56.900 1.000 0.2308 -0.4949 0.8377 -0.00000000000000011
E131 -53.000 67.400 1.000 0.2313 -0.3069 0.9232 0.00000000000000000
E132 -17.000 77.300 1.000 0.2102 -0.0643 0.9755 0.00000000000000000
E133 248.000 -36.400 1.000 -0.3015 -0.7463 -0.5934 0.00000000000000022
E134 253.000 -30.700 1.000 -0.2514 -0.8223 -0.5105 -0.00000000000000011
E135 258.000 -19.400 1.000 -0.1961 -0.9226 -0.3322 -0.00000000000000011
E136 265.000 -12.900 1.000 -0.0850 -0.9711 -0.2233 0.00000000000000000
E137 -90.000 -5.280 1.000 0.0000 -0.9958 -0.0920 0.00000000000000000
E138 -84.000 5.580 1.000 0.1040 -0.9898 0.0972 -0.00000000000000022
E139 -78.000 15.500 1.000 0.2004 -0.9426 0.2672 -0.00000000000000011
E140 -68.000 25.700 1.000 0.3375 -0.8355 0.4337 -0.00000000000000011
E141 -61.000 36.400 1.000 0.3902 -0.7040 0.5934 0.00000000000000000
E142 -52.000 46.800 1.000 0.4214 -0.5394 0.7290 0.00000000000000000
E143 -41.000 55.600 1.000 0.4264 -0.3707 0.8251 0.00000000000000000
E144 -17.000 65.600 1.000 0.3951 -0.1208 0.9107 0.00000000000000000
E145 258.000 -35.800 1.000 -0.1686 -0.7933 -0.5850 0.00000000000000000
E146 264.000 -29.600 1.000 -0.0909 -0.8647 -0.4939 0.00000000000000000
E147 -90.000 -22.100 1.000 0.0000 -0.9265 -0.3762 0.00000000000000000
E148 -85.000 -12.900 1.000 0.0850 -0.9711 -0.2233 0.00000000000000000
E149 -77.000 -3.660 1.000 0.2245 -0.9724 -0.0638 0.00000000000000022
E150 -71.000 4.850 1.000 0.3244 -0.9421 0.0845 -0.00000000000000022
E151 -62.000 14.600 1.000 0.4543 -0.8544 0.2521 0.00000000000000000
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36
mne/channels/data/montages/GSN-HydroCel-32.sfp Normal file
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67
mne/channels/data/montages/GSN-HydroCel-64_1.0.sfp Normal file
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68
mne/channels/data/montages/GSN-HydroCel-65_1.0.sfp Normal file
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48
mne/channels/data/montages/artinis-brite23.elc Normal file
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@@ -0,0 +1,48 @@
# ASA optode file
ReferenceLabel avg
UnitPosition mm
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76.10 -0.29 31.24
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32
mne/channels/data/montages/artinis-octamon.elc Normal file
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# ASA optode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 13
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17.84 84.96 -10.84
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Labels
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132
mne/channels/data/montages/biosemi128.txt Normal file
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D30 -80.5 36
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LPA -115 0
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20
mne/channels/data/montages/biosemi16.txt Normal file
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@@ -0,0 +1,20 @@
Site Theta Phi
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164
mne/channels/data/montages/biosemi160.txt Normal file
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LPA -115 0
RPA 115 0

260
mne/channels/data/montages/biosemi256.txt Normal file
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@@ -0,0 +1,260 @@
Site Theta Phi
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C6 55.2 -15
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C9 73.6 -30
C10 82.8 -33.75
C11 92 -33.75
C12 101.2 -22.5
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C14 82.8 -22.5
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C21 64.4 -6
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C23 46 0
C24 36.8 0
C25 27.6 6
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C28 55.2 15
C29 64.4 6
C30 73.6 6
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D3 27.6 30
D4 36.8 36
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D6 64.4 18
D7 73.6 18
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D15 64.4 30
D16 55.2 30
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D18 55.2 45
D19 64.4 42
D20 73.6 42
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D23 101.2 45
D24 92 56.25
D25 82.8 56.25
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D27 64.4 54
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D29 64.4 66
D30 73.6 66
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E4 36.8 54
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E6 46 75
E7 55.2 75
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E9 73.6 78
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E12 92 90
E13 82.8 90
E14 73.6 90
E15 64.4 90
E16 55.2 90
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H12 -101.2 45
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H24 -46 60
H25 -55.2 60
H26 -64.4 66
H27 -73.6 66
H28 -82.8 67.5
H29 -92 67.5
H30 -101.2 67.5
H31 -110.4 66
H32 -119.6 66
Nz 115 90
LPA -115 0
RPA 115 0

36
mne/channels/data/montages/biosemi32.txt Normal file
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@@ -0,0 +1,36 @@
Site Theta Phi
Fp1 -92 -72
AF3 -74 -65
F7 -92 -36
F3 -60 -51
FC1 -32 -45
FC5 -72 -21
T7 -92 0
C3 -46 0
CP1 -32 45
CP5 -72 21
P7 -92 36
P3 -60 51
Pz 46 -90
PO3 -74 65
O1 -92 72
Oz 92 -90
O2 92 -72
PO4 74 -65
P4 60 -51
P8 92 -36
CP6 72 -21
CP2 32 -45
C4 46 0
T8 92 0
FC6 72 21
FC2 32 45
F4 60 51
F8 92 36
AF4 74 65
Fp2 92 72
Fz 46 90
Cz 0 0
Nz 115 90
LPA -115 0
RPA 115 0

68
mne/channels/data/montages/biosemi64.txt Normal file
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@@ -0,0 +1,68 @@
Site Theta Phi
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AF7 -92 -54
AF3 -74 -65
F1 -50 -68
F3 -60 -51
F5 -75 -41
F7 -92 -36
FT7 -92 -18
FC5 -72 -21
FC3 -50 -28
FC1 -32 -45
C1 -23 0
C3 -46 0
C5 -69 0
T7 -92 0
TP7 -92 18
CP5 -72 21
CP3 -50 28
CP1 -32 45
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P5 -75 41
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P9 -115 36
PO7 -92 54
PO3 -74 65
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Iz 115 -90
Oz 92 -90
POz 69 -90
Pz 46 -90
CPz 23 -90
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Fp2 92 72
AF8 92 54
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AFz 69 90
Fz 46 90
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FT8 92 18
FC6 72 21
FC4 50 28
FC2 32 45
FCz 23 90
Cz 0 0
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C6 69 0
T8 92 0
TP8 92 -18
CP6 72 -21
CP4 50 -28
CP2 32 -45
P2 50 -68
P4 60 -51
P6 75 -41
P8 92 -36
P10 115 -36
PO8 92 -54
PO4 74 -65
O2 92 -72
Nz 115 90
LPA -115 0
RPA 115 0

131
mne/channels/data/montages/brainproducts-RNP-BA-128.txt Normal file
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@@ -0,0 +1,131 @@
Name Theta Phi
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F7 -90 -36
F9 -113 -36
FC5 -69 -21
FC1 -31 -46
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T7 -90 0
CP5 -69 21
CP1 -31 46
Pz 45 -90
P3 -60 51
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Oz 90 -90
O2 90 -72
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P8 90 -36
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CP2 31 -46
CP6 69 -21
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C4 45 0
Cz 0 0
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FC6 69 21
F10 113 36
F8 90 36
F4 60 51
Fp2 90 72
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AF3 -74 -68
AFz 67 90
F1 -49 -68
F5 -74 -41
FT7 -90 -18
FC3 -49 -29
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CPz 22 -90
CP4 49 -29
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POO2 79 -82
PPO10h 101 -45
PPO4h 62 -67
CPP4h 46 -48
TPP8h 81 -29
TP10 113 -18
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CCP2h 16 -45
FCC4h 35 19
FTT8h 79 10
FT10 113 18
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FFC2h 35 73
AFF4h 62 67
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AFF1h -57 -82
AFF5h -72 -55
FFT7h -81 -29
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FCC1h -16 -45
FCC5h -57 -12
TTP7h -79 10
CCP3h -35 19
CPP1h -35 73
CPP5h -62 35
TPP9h -101 27
PPO5h -72 55
PPO1h -57 82
POO9h -101 63
OI1h -101 81
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POO10h 101 -63
PPO2h 57 -82
PPO6h 72 -55
TPP10h 101 -27
CPP6h 62 -35
CPP2h 35 -73
CCP4h 35 -19
TTP8h 79 -10
FCC6h 57 12
FCC2h 16 45
FFC4h 46 48
FFT8h 81 29
AFF6h 72 55
AFF2h 57 82
AFp2 79 82
FCz 23 90
Fpz 90 90

75
mne/channels/data/montages/easycap-M1.txt Normal file
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@@ -0,0 +1,75 @@
Site Theta Phi
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Fp2 92 72
F3 -60 -51
F4 60 51
C3 -46 0
C4 46 0
P3 -60 51
P4 60 -51
O1 -92 72
O2 92 -72
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F8 92 36
T7 -92 0
T8 92 0
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P8 92 -36
Fz 46 90
Cz 0 0
Pz 46 -90
F1 -50 -68
F2 50 68
FC1 -32 -45
FC2 32 45
C1 -23 0
C2 23 0
CP1 -32 45
CP2 32 -45
P1 -50 68
P2 50 -68
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AF4 74 65
FC3 -53 -33
FC4 53 33
CP3 -52 33
CP4 52 -33
PO3 -74 65
PO4 74 -65
F5 -75 -41
F6 75 41
FC5 -72 -21
FC6 72 21
C5 -69 0
C6 69 0
CP5 -72 21
CP6 72 -21
P5 -75 41
P6 75 -41
AF7 -92 -54
AF8 92 54
FT7 -92 -18
FT8 92 18
TP7 -92 18
TP8 92 -18
PO7 -92 54
PO8 92 -54
F9 -115 -36
F10 115 36
FT9 -115 -18
FT10 115 18
TP9 -115 18
TP10 115 -18
P9 -115 36
P10 115 -36
PO9 -115 54
PO10 115 -54
O9 -115 72
O10 115 -72
Fpz 92 90
AFz 69 90
FCz 23 90
CPz 23 -90
POz 69 -90
Oz 92 -90
Iz 115 -90

62
mne/channels/data/montages/easycap-M10.txt Normal file
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@@ -0,0 +1,62 @@
Site Theta Phi
1 0 0
2 23 90
3 23 30
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6 -23 30
7 -23 -30
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9 46 66
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22 69 42
23 69 18
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30 -69 30
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32 -69 -18
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34 -69 -66
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37 92 45
38 92 22
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46 -92 22
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55 115 -65
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60 -115 -10
61 -115 -35

65
mne/channels/data/montages/easycap-M43.txt Normal file
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@@ -0,0 +1,65 @@
Site Theta Phi
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Ref 0 0

132
mne/channels/data/montages/mgh60.elc Normal file
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@@ -0,0 +1,132 @@
# ASA electrode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 63
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85.7939 -20.0093 -48.0310
0.0083 86.8110 -39.9830
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55.7433 69.6568 -10.7550
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73.0431 44.4217 -12.0000
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79.5341 19.9357 24.4380
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0.4009 -9.1670 100.2440
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67.1179 -10.9003 63.5800
83.4559 -12.7763 29.2080
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73.0557 -73.0683 -2.5400
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55.6666 -97.6251 2.7300
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29.8426 -112.1560 8.8000
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Labels
LPA
RPA
Nz
EEG001
EEG002
EEG003
EEG004
EEG005
EEG006
EEG007
EEG008
EEG009
EEG010
EEG011
EEG012
EEG013
EEG014
EEG015
EEG016
EEG017
EEG018
EEG019
EEG020
EEG021
EEG022
EEG023
EEG024
EEG025
EEG026
EEG027
EEG028
EEG029
EEG030
EEG031
EEG032
EEG033
EEG034
EEG035
EEG036
EEG037
EEG038
EEG039
EEG040
EEG041
EEG042
EEG043
EEG044
EEG045
EEG046
EEG047
EEG048
EEG049
EEG050
EEG051
EEG052
EEG053
EEG054
EEG055
EEG056
EEG057
EEG058
EEG059
EEG060

152
mne/channels/data/montages/mgh70.elc Normal file
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@@ -0,0 +1,152 @@
# ASA electrode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 73
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698
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# ASA electrode file
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200
mne/channels/data/montages/standard_1020.elc Normal file
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@@ -0,0 +1,200 @@
# ASA electrode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 97
Positions
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85.7939 -20.0093 -48.0310
0.0083 86.8110 -39.9830
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0.1123 88.2470 -1.7130
29.8723 84.8959 -7.0800
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46.5843 73.8078 6.0340
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73.0557 -73.0683 -2.5400
73.8947 -74.3903 -41.2200
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49.8196 -99.4461 21.7270
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85.7939 -45.0093 -68.0310
-86.0761 -24.9897 -67.9860
85.7939 -25.0093 -68.0310
Labels
LPA
RPA
Nz
Fp1
Fpz
Fp2
AF9
AF7
AF5
AF3
AF1
AFz
AF2
AF4
AF6
AF8
AF10
F9
F7
F5
F3
F1
Fz
F2
F4
F6
F8
F10
FT9
FT7
FC5
FC3
FC1
FCz
FC2
FC4
FC6
FT8
FT10
T9
T7
C5
C3
C1
Cz
C2
C4
C6
T8
T10
TP9
TP7
CP5
CP3
CP1
CPz
CP2
CP4
CP6
TP8
TP10
P9
P7
P5
P3
P1
Pz
P2
P4
P6
P8
P10
PO9
PO7
PO5
PO3
PO1
POz
PO2
PO4
PO6
PO8
PO10
O1
Oz
O2
O9
Iz
O10
T3
T5
T4
T6
M1
M2
A1
A2

142
mne/channels/data/montages/standard_alphabetic.elc Normal file
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@@ -0,0 +1,142 @@
# ASA electrode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 68
Positions
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85.7939 -20.0093 -48.0310
0.0083 86.8110 -39.9830
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29.8723 84.8959 -7.0800
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0.2313 80.7710 35.4170
35.7123 77.7259 21.9560
55.7433 69.6568 -10.7550
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0.3122 58.5120 66.4620
29.5142 57.6019 59.5400
51.8362 54.3048 40.8140
67.9142 49.8297 16.3670
73.0431 44.4217 -12.0000
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0.3761 27.3900 88.6680
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62.2931 23.7228 55.6300
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37.6720 -9.6241 88.4120
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73.0557 -73.0683 -2.5400
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85.7939 -45.0093 -68.0310
-86.0761 -24.9897 -67.9860
85.7939 -25.0093 -68.0310
Labels
LPA
RPA
Nz
Fp1
Fpz
Fp2
B3
B1
Bz
B2
B4
F7
F5
F3
F1
Fz
F2
F4
F6
F8
D7
D5
D3
D1
Dz
D2
D4
D6
D8
T3
C5
C3
C1
Cz
C2
C4
C6
T4
E7
E5
E3
E1
Ez
E2
E4
E6
E8
T5
P5
P3
P1
Pz
P2
P4
P6
T6
H3
H1
Hz
H2
H4
O1
Oz
O2
M1
M2
A1
A2

212
mne/channels/data/montages/standard_postfixed.elc Normal file
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@@ -0,0 +1,212 @@
# ASA electrode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 103
Positions
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85.7939 -20.0093 -48.0310
0.0083 86.8110 -39.9830
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0.1123 88.2470 -1.7130
29.8723 84.8959 -7.0800
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0.2313 80.7710 35.4170
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55.7433 69.6568 -10.7550
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37.6720 -9.6241 88.4120
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83.4559 -12.7763 29.2080
85.0799 -15.0203 -9.4900
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67.8877 -75.9043 28.0910
73.0557 -73.0683 -2.5400
73.8947 -74.3903 -41.2200
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49.8196 -99.4461 21.7270
55.6666 -97.6251 2.7300
54.9876 -98.0911 -35.5410
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29.8426 -112.1560 8.8000
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29.7416 -114.2600 -29.2560
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85.7939 -45.0093 -68.0310
-86.0761 -24.9897 -67.9860
85.7939 -25.0093 -68.0310
Labels
LPA
RPA
Nz
Fp1
Fpz
Fp2
F7a
F5a
F3a
F1a
Fza
F2a
F4a
F6a
F8a
F7
F5
F3
F1
Fz
F2
F4
F6
F8
F7p
F5p
F3p
F1p
Fzp
F2p
F4p
F6p
F8p
T1
T3a
C5a
C3a
C1a
Cza
C2a
C4a
C6a
T4a
T2
T3
C5
C3
C1
Cz
C2
C4
C6
T4
T3p
C5p
C3p
C1p
Czp
C2p
C4p
C6p
T4p
T5a
P5a
P3a
P1a
Pza
P2a
P4a
P6a
T6a
Cb1a
T5
P5
P3
P1
Pz
P2
P4
P6
T6
Cb2a
Cb1
O1a
P5p
P3p
P1p
Pzp
P2p
P4p
P6p
O2a
Cb2
O1
Oz
O2
Cb1p
Iz
Cb2p
M1
M2
A1
A2

160
mne/channels/data/montages/standard_prefixed.elc Normal file
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@@ -0,0 +1,160 @@
# ASA electrode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 77
Positions
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85.7939 -20.0093 -48.0310
0.0083 86.8110 -39.9830
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0.1123 88.2470 -1.7130
29.8723 84.8959 -7.0800
55.7433 69.6568 -10.7550
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0.2313 80.7710 35.4170
35.7123 77.7259 21.9560
55.7433 69.6568 -10.7550
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0.3122 58.5120 66.4620
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51.8362 54.3048 40.8140
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73.0431 44.4217 -12.0000
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0.3761 27.3900 88.6680
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85.0799 -15.0203 -9.4900
85.5599 -16.3613 -48.2710
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73.8947 -74.3903 -41.2200
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0.0045 -118.5650 -23.0780
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85.7939 -45.0093 -68.0310
-86.0761 -24.9897 -67.9860
85.7939 -25.0093 -68.0310
Labels
LPA
RPA
Nz
Fp1
Fpz
Fp2
aF3
aF1
aFz
aF2
aF4
F7
F5
F3
F1
Fz
F2
F4
F6
F8
iT1
T1
pF5
pF3
pF1
pFz
pF2
pF4
pF6
T2
iT2
iT3
T3
C5
C3
C1
Cz
C2
C4
C6
T4
iT4
T3A
pC5
pC3
pC1
pCz
pC2
pC4
pC6
T4A
iT5
T5
P5
P3
P1
Pz
P2
P4
P6
T6
iT6
pO5
pO3
pO1
pOz
pO2
pO4
pO6
O1
Oz
O2
Iz
M1
M2
A1
A2

212
mne/channels/data/montages/standard_primed.elc Normal file
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# ASA electrode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 103
Positions
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85.7939 -20.0093 -48.0310
0.0083 86.8110 -39.9830
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0.1123 88.2470 -1.7130
29.8723 84.8959 -7.0800
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-18.4717 79.9041 32.7520
0.2313 80.7710 35.4170
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35.7123 77.7259 21.9560
46.5843 73.8078 6.0340
55.7433 69.6568 -10.7550
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0.3122 58.5120 66.4620
29.5142 57.6019 59.5400
51.8362 54.3048 40.8140
67.9142 49.8297 16.3670
73.0431 44.4217 -12.0000
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0.3761 27.3900 88.6680
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66.6118 -46.6372 65.5800
83.3218 -46.1013 31.2060
85.5488 -45.5453 -7.1300
-73.0093 -73.7657 -40.9980
-72.4343 -73.4527 -2.4870
-67.2723 -76.2907 28.3820
-53.0073 -78.7878 55.9400
-28.6203 -80.5249 75.4360
0.3247 -81.1150 82.6150
31.9197 -80.4871 76.7160
55.6667 -78.5602 56.5610
67.8877 -75.9043 28.0910
73.0557 -73.0683 -2.5400
73.8947 -74.3903 -41.2200
-54.9104 -98.0448 -35.4650
-54.8404 -97.5279 2.7920
-48.4244 -99.3408 21.5990
-36.5114 -100.8529 37.1670
-18.9724 -101.7680 46.5360
0.2156 -102.1780 50.6080
19.8776 -101.7930 46.3930
36.7816 -100.8491 36.3970
49.8196 -99.4461 21.7270
55.6666 -97.6251 2.7300
54.9876 -98.0911 -35.5410
-29.4134 -112.4490 8.8390
0.1076 -114.8920 14.6570
29.8426 -112.1560 8.8000
-29.8184 -114.5700 -29.2160
0.0045 -118.5650 -23.0780
29.7416 -114.2600 -29.2560
-86.0761 -44.9897 -67.9860
85.7939 -45.0093 -68.0310
-86.0761 -24.9897 -67.9860
85.7939 -25.0093 -68.0310
Labels
LPA
RPA
Nz
Fp1
Fpz
Fp2
F7'
F5'
F3'
F1'
Fz'
F2'
F4'
F6'
F8'
F7
F5
F3
F1
Fz
F2
F4
F6
F8
F7''
F5''
F3''
F1''
Fz''
F2''
F4''
F6''
F8''
T1
T3'
C5'
C3'
C1'
Cz'
C2'
C4'
C6'
T4'
T2
T3
C5
C3
C1
Cz
C2
C4
C6
T4
T3''
C5''
C3''
C1''
Cz''
C2''
C4''
C6''
T4''
T5'
P5'
P3'
P1'
Pz'
P2'
P4'
P6'
T6'
Cb1'
T5
P5
P3
P1
Pz
P2
P4
P6
T6
Cb2'
Cb1
O1'
P5''
P3''
P1''
Pz''
P2''
P4''
P6''
O2'
Cb2
O1
Oz
O2
Cb1''
Iz
Cb2''
M1
M2
A1
A2

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""Neighbor definitions for clustering permutation analysis."""
# This is a selection of files from http://fieldtrip.fcdonders.nl/template
# Additional definitions can be obtained through the FieldTrip software.
# For additional information on how these definitions were computed, please
# consider the related fieldtrip documentation:
# http://fieldtrip.fcdonders.nl/template/neighbours.
#
# KIT neighbor files were computed with ft_prepare_neighbours using the
# triangulation method.

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