/
meas_info.py
1959 lines (1751 loc) · 73 KB
/
meas_info.py
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# -*- coding: utf-8 -*-
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Matti Hamalainen <msh@nmr.mgh.harvard.edu>
# Teon Brooks <teon.brooks@gmail.com>
#
# License: BSD (3-clause)
from collections import Counter
from copy import deepcopy
import datetime
import os.path as op
import re
import numpy as np
from scipy import linalg
from .pick import channel_type
from .constants import FIFF
from .open import fiff_open
from .tree import dir_tree_find
from .tag import read_tag, find_tag
from .proj import _read_proj, _write_proj, _uniquify_projs, _normalize_proj
from .ctf_comp import read_ctf_comp, write_ctf_comp
from .write import (start_file, end_file, start_block, end_block,
write_string, write_dig_points, write_float, write_int,
write_coord_trans, write_ch_info, write_name_list,
write_julian, write_float_matrix, write_id, DATE_NONE)
from .proc_history import _read_proc_history, _write_proc_history
from ..transforms import _to_const
from ..transforms import invert_transform
from ..utils import logger, verbose, warn, object_diff, _validate_type
from .. import __version__
from ..externals.six import b, BytesIO, string_types, text_type
from .compensator import get_current_comp
_kind_dict = dict(
eeg=(FIFF.FIFFV_EEG_CH, FIFF.FIFFV_COIL_EEG, FIFF.FIFF_UNIT_V),
mag=(FIFF.FIFFV_MEG_CH, FIFF.FIFFV_COIL_VV_MAG_T3, FIFF.FIFF_UNIT_T),
grad=(FIFF.FIFFV_MEG_CH, FIFF.FIFFV_COIL_VV_PLANAR_T1, FIFF.FIFF_UNIT_T_M),
ref_meg=(FIFF.FIFFV_REF_MEG_CH, FIFF.FIFFV_COIL_VV_MAG_T3,
FIFF.FIFF_UNIT_T),
misc=(FIFF.FIFFV_MISC_CH, FIFF.FIFFV_COIL_NONE, FIFF.FIFF_UNIT_NONE),
stim=(FIFF.FIFFV_STIM_CH, FIFF.FIFFV_COIL_NONE, FIFF.FIFF_UNIT_V),
eog=(FIFF.FIFFV_EOG_CH, FIFF.FIFFV_COIL_NONE, FIFF.FIFF_UNIT_V),
ecg=(FIFF.FIFFV_ECG_CH, FIFF.FIFFV_COIL_NONE, FIFF.FIFF_UNIT_V),
emg=(FIFF.FIFFV_EMG_CH, FIFF.FIFFV_COIL_NONE, FIFF.FIFF_UNIT_V),
seeg=(FIFF.FIFFV_SEEG_CH, FIFF.FIFFV_COIL_EEG, FIFF.FIFF_UNIT_V),
bio=(FIFF.FIFFV_BIO_CH, FIFF.FIFFV_COIL_NONE, FIFF.FIFF_UNIT_V),
ecog=(FIFF.FIFFV_ECOG_CH, FIFF.FIFFV_COIL_EEG, FIFF.FIFF_UNIT_V),
hbo=(FIFF.FIFFV_FNIRS_CH, FIFF.FIFFV_COIL_FNIRS_HBO, FIFF.FIFF_UNIT_MOL),
hbr=(FIFF.FIFFV_FNIRS_CH, FIFF.FIFFV_COIL_FNIRS_HBR, FIFF.FIFF_UNIT_MOL)
)
def _summarize_str(st):
"""Make summary string."""
return st[:56][::-1].split(',', 1)[-1][::-1] + ', ...'
def _stamp_to_dt(stamp):
"""Convert timestamp to datetime object in Windows-friendly way."""
# The min on windows is 86400
stamp = [int(s) for s in stamp]
if len(stamp) == 1: # In case there is no microseconds information
stamp.append(0)
return (datetime.datetime.utcfromtimestamp(stamp[0]) +
datetime.timedelta(0, 0, stamp[1])) # day, sec, μs
# XXX Eventually this should be de-duplicated with the MNE-MATLAB stuff...
class Info(dict):
"""Measurement information.
This data structure behaves like a dictionary. It contains all metadata
that is available for a recording.
This class should not be instantiated directly. To create a measurement
information strucure, use :func:`mne.create_info`.
The only entries that should be manually changed by the user are
``info['bads']`` and ``info['description']``. All other entries should
be considered read-only, or should be modified by functions or methods.
Parameters
----------
acq_pars : str | None
MEG system acquition parameters.
See :class:`mne.AcqParserFIF` for details.
acq_stim : str | None
MEG system stimulus parameters.
bads : list of str
List of bad (noisy/broken) channels, by name. These channels will by
default be ignored by many processing steps.
ch_names : list of str
The names of the channels.
chs : list of dict
A list of channel information dictionaries, one per channel.
See Notes for more information.
comps : list of dict
CTF software gradient compensation data.
See Notes for more information.
ctf_head_t : dict | None
The transformation from 4D/CTF head coordinates to Neuromag head
coordinates. This is only present in 4D/CTF data.
custom_ref_applied : bool
Whether a custom (=other than average) reference has been applied to
the EEG data. This flag is checked by some algorithms that require an
average reference to be set.
description : str | None
String description of the recording.
dev_ctf_t : dict | None
The transformation from device coordinates to 4D/CTF head coordinates.
This is only present in 4D/CTF data.
dev_head_t : dict | None
The device to head transformation.
dig : list of dict | None
The Polhemus digitization data in head coordinates.
See Notes for more information.
events : list of dict
Event list, sometimes extracted from the stim channels by Neuromag
systems. In general this should not be used and
:func:`mne.find_events` should be used for event processing.
See Notes for more information.
experimenter : str | None
Name of the person that ran the experiment.
file_id : dict | None
The FIF globally unique ID. See Notes for more information.
highpass : float | None
Highpass corner frequency in Hertz. Zero indicates a DC recording.
hpi_meas : list of dict
HPI measurements that were taken at the start of the recording
(e.g. coil frequencies).
See Notes for details.
hpi_results : list of dict
Head position indicator (HPI) digitization points and fit information
(e.g., the resulting transform).
See Notes for details.
hpi_subsystem : dict | None
Information about the HPI subsystem that was used (e.g., event
channel used for cHPI measurements).
See Notes for details.
line_freq : float | None
Frequency of the power line in Hertz.
gantry_angle : float | None
Tilt angle of the gantry in degrees.
lowpass : float | None
Lowpass corner frequency in Hertz.
meas_date : list of int
The first element of this list is a UNIX timestamp (seconds since
1970-01-01 00:00:00) denoting the date and time at which the
measurement was taken. The second element is the additional number of
microseconds.
meas_id : dict | None
The ID assigned to this measurement by the acquisition system or
during file conversion. Follows the same format as ``file_id``.
nchan : int
Number of channels.
proc_history : list of dict
The MaxFilter processing history.
See Notes for details.
proj_id : int | None
ID number of the project the experiment belongs to.
proj_name : str | None
Name of the project the experiment belongs to.
projs : list of Projection
List of SSP operators that operate on the data.
See :class:`mne.Projection` for details.
sfreq : float
Sampling frequency in Hertz.
subject_info : dict | None
Information about the subject.
See Notes for details.
See Also
--------
mne.create_info
Notes
-----
The following parameters have a nested structure.
* ``chs`` list of dict:
cal : float
The calibration factor to bring the channels to physical
units. Used in product with ``range`` to scale the data read
from disk.
ch_name : str
The channel name.
coil_type : int
Coil type, e.g. ``FIFFV_COIL_MEG``.
coord_frame : int
The coordinate frame used, e.g. ``FIFFV_COORD_HEAD``.
kind : int
The kind of channel, e.g. ``FIFFV_EEG_CH``.
loc : array, shape (12,)
Channel location. For MEG this is the position plus the
normal given by a 3x3 rotation matrix. For EEG this is the
position followed by reference position (with 6 unused).
The values are specified in device coordinates for MEG and in
head coordinates for EEG channels, respectively.
logno : int
Logical channel number, conventions in the usage of this
number vary.
range : float
The hardware-oriented part of the calibration factor.
This should be only applied to the continuous raw data.
Used in product with ``cal`` to scale data read from disk.
scanno : int
Scanning order number, starting from 1.
unit : int
The unit to use, e.g. ``FIFF_UNIT_T_M``.
unit_mul : int
Unit multipliers, most commontly ``FIFF_UNITM_NONE``.
* ``comps`` list of dict:
ctfkind : int
CTF compensation grade.
colcals : ndarray
Column calibrations.
mat : dict
A named matrix dictionary (with entries "data", "col_names", etc.)
containing the compensation matrix.
rowcals : ndarray
Row calibrations.
save_calibrated : bool
Were the compensation data saved in calibrated form.
* ``dig`` dict:
kind : int
Digitization kind, e.g. ``FIFFV_POINT_EXTRA``.
ident : int
Identifier.
r : ndarary, shape (3,)
Position.
coord_frame : int
Coordinate frame, e.g. ``FIFFV_COORD_HEAD``.
* ``events`` list of dict:
channels : list of int
Channel indices for the events.
list : ndarray, shape (n_events * 3,)
Events in triplets as number of samples, before, after.
* ``file_id`` dict:
version : int
FIF format version, i.e. ``FIFFC_VERSION``.
machid : ndarray, shape (2,)
Unique machine ID, usually derived from the MAC address.
secs : int
Time in seconds.
usecs : int
Time in microseconds.
* ``hpi_meas`` list of dict:
creator : str
Program that did the measurement.
sfreq : float
Sample rate.
nchan : int
Number of channels used.
nave : int
Number of averages used.
ncoil : int
Number of coils used.
first_samp : int
First sample used.
last_samp : int
Last sample used.
hpi_coils : list of dict
Coils, containing:
number: int
Coil number
epoch : ndarray
Buffer containing one epoch and channel.
slopes : ndarray, shape (n_channels,)
HPI data.
corr_coeff : ndarray, shape (n_channels,)
HPI curve fit correlations.
coil_freq : float
HPI coil excitation frequency
* ``hpi_results`` list of dict:
dig_points : list
Digitization points (see ``dig`` definition) for the HPI coils.
order : ndarray, shape (ncoil,)
The determined digitization order.
used : ndarray, shape (nused,)
The indices of the used coils.
moments : ndarray, shape (ncoil, 3)
The coil moments.
goodness : ndarray, shape (ncoil,)
The goodness of fits.
good_limit : float
The goodness of fit limit.
dist_limit : float
The distance limit.
accept : int
Whether or not the fit was accepted.
coord_trans : instance of Transformation
The resulting MEG<->head transformation.
* ``hpi_subsystem`` dict:
ncoil : int
The number of coils.
event_channel : str
The event channel used to encode cHPI status (e.g., STI201).
hpi_coils : list of ndarray
List of length ``ncoil``, each 4-element ndarray contains the
event bits used on the event channel to indicate cHPI status
(using the first element of these arrays is typically
sufficient).
* ``proc_history`` list of dict:
block_id : dict
See ``id`` above.
date : ndarray, shape (2,)
2-element tuple of seconds and microseconds.
experimenter : str
Name of the person who ran the program.
creator : str
Program that did the processing.
max_info : dict
Maxwel filtering info, can contain:
sss_info : dict
SSS processing information.
max_st
tSSS processing information.
sss_ctc : dict
Cross-talk processing information.
sss_cal : dict
Fine-calibration information.
smartshield : dict
MaxShield information. This dictionary is (always?) empty,
but its presence implies that MaxShield was used during
acquisiton.
* ``subject_info`` dict:
id : int
Integer subject identifier.
his_id : str
String subject identifier.
last_name : str
Last name.
first_name : str
First name.
middle_name : str
Middle name.
birthday : tuple of int
Birthday in (year, month, day) format.
sex : int
Subject sex (0=unknown, 1=male, 2=female).
hand : int
Handedness (1=right, 2=left).
"""
def copy(self):
"""Copy the instance.
Returns
-------
info : instance of Info
The copied info.
"""
return Info(deepcopy(self))
def normalize_proj(self):
"""(Re-)Normalize projection vectors after subselection.
Applying projection after sub-selecting a set of channels that
were originally used to compute the original projection vectors
can be dangerous (e.g., if few channels remain, most power was
in channels that are no longer picked, etc.). By default, mne
will emit a warning when this is done.
This function will re-normalize projectors to use only the
remaining channels, thus avoiding that warning. Only use this
function if you're confident that the projection vectors still
adequately capture the original signal of interest.
"""
_normalize_proj(self)
def __repr__(self):
"""Summarize info instead of printing all."""
strs = ['<Info | %s non-empty fields']
non_empty = 0
for k, v in self.items():
if k in ['bads', 'ch_names']:
entr = (', '.join(b for ii, b in enumerate(v) if ii < 10)
if v else '0 items')
if len(v) > 10:
# get rid of of half printed ch names
entr = _summarize_str(entr)
elif k == 'projs' and v:
entr = ', '.join(p['desc'] + ': o%s' %
{0: 'ff', 1: 'n'}[p['active']] for p in v)
if len(entr) >= 56:
entr = _summarize_str(entr)
elif k == 'meas_date' and np.iterable(v):
if np.array_equal(v, DATE_NONE):
entr = 'unspecified'
else:
# first entry in meas_date is meaningful
entr = (_stamp_to_dt(v).strftime('%Y-%m-%d %H:%M:%S') +
' GMT')
elif k == 'kit_system_id' and v is not None:
from .kit.constants import KIT_SYSNAMES
entr = '%i (%s)' % (v, KIT_SYSNAMES.get(v, 'unknown'))
else:
this_len = (len(v) if hasattr(v, '__len__') else
('%s' % v if v is not None else None))
entr = (('%d items' % this_len) if isinstance(this_len, int)
else ('%s' % this_len if this_len else ''))
if entr:
non_empty += 1
entr = ' | ' + entr
if k == 'chs':
ch_types = [channel_type(self, idx) for idx in range(len(v))]
ch_counts = Counter(ch_types)
entr += " (%s)" % ', '.join("%s: %d" % (ch_type.upper(), count)
for ch_type, count
in ch_counts.items())
strs.append('%s : %s%s' % (k, type(v).__name__, entr))
if k in ['sfreq', 'lowpass', 'highpass']:
strs[-1] += ' Hz'
strs_non_empty = sorted(s for s in strs if '|' in s)
strs_empty = sorted(s for s in strs if '|' not in s)
st = '\n '.join(strs_non_empty + strs_empty)
st += '\n>'
st %= non_empty
return st
def _check_consistency(self):
"""Do some self-consistency checks and datatype tweaks."""
missing = [bad for bad in self['bads'] if bad not in self['ch_names']]
if len(missing) > 0:
raise RuntimeError('bad channel(s) %s marked do not exist in info'
% (missing,))
chs = [ch['ch_name'] for ch in self['chs']]
if len(self['ch_names']) != len(chs) or any(
ch_1 != ch_2 for ch_1, ch_2 in zip(self['ch_names'], chs)) or \
self['nchan'] != len(chs):
raise RuntimeError('info channel name inconsistency detected, '
'please notify mne-python developers')
# make sure we have the proper datatypes
for key in ('sfreq', 'highpass', 'lowpass'):
if self.get(key) is not None:
self[key] = float(self[key])
# make sure channel names are not too long
self._check_ch_name_length()
# make sure channel names are unique
unique_ids = np.unique(self['ch_names'], return_index=True)[1]
if len(unique_ids) != self['nchan']:
dups = set(self['ch_names'][x]
for x in np.setdiff1d(range(self['nchan']), unique_ids))
warn('Channel names are not unique, found duplicates for: '
'%s. Applying running numbers for duplicates.' % dups)
for ch_stem in dups:
overlaps = np.where(np.array(self['ch_names']) == ch_stem)[0]
n_keep = min(len(ch_stem),
14 - int(np.ceil(np.log10(len(overlaps)))))
ch_stem = ch_stem[:n_keep]
for idx, ch_idx in enumerate(overlaps):
ch_name = ch_stem + '-%s' % idx
assert ch_name not in self['ch_names']
self['ch_names'][ch_idx] = ch_name
self['chs'][ch_idx]['ch_name'] = ch_name
# make sure required the compensation channels are present
comps_bad, comps_missing = _bad_chans_comp(self, self['ch_names'])
if comps_bad:
msg = 'Compensation channel(s) %s do not exist in info'
raise RuntimeError(msg % (comps_missing,))
if 'filename' in self:
warn('the "filename" key is misleading\
and info should not have it')
def _check_ch_name_length(self):
"""Check that channel names are sufficiently short."""
bad_names = list()
for ch in self['chs']:
if len(ch['ch_name']) > 15:
bad_names.append(ch['ch_name'])
ch['ch_name'] = ch['ch_name'][:15]
if len(bad_names) > 0:
warn('%d channel names are too long, have been truncated to 15 '
'characters:\n%s' % (len(bad_names), bad_names))
self._update_redundant()
def _update_redundant(self):
"""Update the redundant entries."""
self['ch_names'] = [ch['ch_name'] for ch in self['chs']]
self['nchan'] = len(self['chs'])
def _simplify_info(info):
"""Return a simplified info structure to speed up picking."""
chs = [{key: ch[key]
for key in ('ch_name', 'kind', 'unit', 'coil_type', 'loc')}
for ch in info['chs']]
sub_info = Info(chs=chs, bads=info['bads'], comps=info['comps'])
sub_info._update_redundant()
return sub_info
@verbose
def read_fiducials(fname, verbose=None):
"""Read fiducials from a fiff file.
Parameters
----------
fname : str
The filename to read.
verbose : bool, str, int, or None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
Returns
-------
pts : list of dicts
List of digitizer points (each point in a dict).
coord_frame : int
The coordinate frame of the points (one of
mne.io.constants.FIFF.FIFFV_COORD_...)
"""
fid, tree, _ = fiff_open(fname)
with fid:
isotrak = dir_tree_find(tree, FIFF.FIFFB_ISOTRAK)
isotrak = isotrak[0]
pts = []
coord_frame = FIFF.FIFFV_COORD_HEAD
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)
pts.append(tag.data)
elif kind == FIFF.FIFF_MNE_COORD_FRAME:
tag = read_tag(fid, pos)
coord_frame = tag.data[0]
# coord_frame is not stored in the tag
for pt in pts:
pt['coord_frame'] = coord_frame
return pts, coord_frame
@verbose
def write_fiducials(fname, pts, coord_frame=FIFF.FIFFV_COORD_UNKNOWN,
verbose=None):
"""Write fiducials to a fiff file.
Parameters
----------
fname : str
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
The coordinate frame of the points (one of
mne.io.constants.FIFF.FIFFV_COORD_...).
verbose : bool, str, int, or None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
"""
write_dig(fname, pts, coord_frame)
def write_dig(fname, pts, coord_frame=None):
"""Write digitization data to a FIF file.
Parameters
----------
fname : str
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.
"""
if coord_frame is not None:
coord_frame = _to_const(coord_frame)
pts_frames = set((pt.get('coord_frame', coord_frame) for pt in pts))
bad_frames = pts_frames - set((coord_frame,))
if len(bad_frames) > 0:
raise ValueError(
'Points have coord_frame entries that are incompatible with '
'coord_frame=%i: %s.' % (coord_frame, str(tuple(bad_frames))))
with start_file(fname) as fid:
write_dig_points(fid, pts, block=True, coord_frame=coord_frame)
end_file(fid)
def _read_dig_fif(fid, meas_info):
"""Read digitizer data from a FIFF file."""
isotrak = dir_tree_find(meas_info, FIFF.FIFFB_ISOTRAK)
dig = None
if len(isotrak) == 0:
logger.info('Isotrak not found')
elif len(isotrak) > 1:
warn('Multiple Isotrak found')
else:
isotrak = isotrak[0]
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)
dig[-1]['coord_frame'] = FIFF.FIFFV_COORD_HEAD
return dig
def _read_dig_points(fname, comments='%', unit='auto'):
"""Read digitizer data from a text file.
If fname ends in .hsp or .esp, the function assumes digitizer files in [m],
otherwise it assumes space-delimited text files in [mm].
Parameters
----------
fname : str
The filepath of space delimited file with points, or a .mat file
(Polhemus FastTrak format).
comments : str
The character used to indicate the start of a comment;
Default: '%'.
unit : 'auto' | 'm' | 'cm' | 'mm'
Unit of the digitizer files (hsp and elp). If not 'm', coordinates will
be rescaled to 'm'. Default is 'auto', which assumes 'm' for *.hsp and
*.elp files and 'mm' for *.txt files, corresponding to the known
Polhemus export formats.
Returns
-------
dig_points : np.ndarray, shape (n_points, 3)
Array of dig points in [m].
"""
if unit not in ('auto', 'm', 'mm', 'cm'):
raise ValueError('unit must be one of "auto", "m", "mm", or "cm"')
_, ext = op.splitext(fname)
if ext == '.elp' or ext == '.hsp':
with open(fname) as fid:
file_str = fid.read()
value_pattern = r"\-?\d+\.?\d*e?\-?\d*"
coord_pattern = r"({0})\s+({0})\s+({0})\s*$".format(value_pattern)
if ext == '.hsp':
coord_pattern = '^' + coord_pattern
points_str = [m.groups() for m in re.finditer(coord_pattern, file_str,
re.MULTILINE)]
dig_points = np.array(points_str, dtype=float)
elif ext == '.mat': # like FastScan II
from scipy.io import loadmat
dig_points = loadmat(fname)['Points'].T
else:
dig_points = np.loadtxt(fname, comments=comments, ndmin=2)
if unit == 'auto':
unit = 'mm'
if dig_points.shape[1] > 3:
warn('Found %d columns instead of 3, using first 3 for XYZ '
'coordinates' % (dig_points.shape[1],))
dig_points = dig_points[:, :3]
if dig_points.shape[-1] != 3:
err = 'Data must be (n, 3) instead of %s' % (dig_points.shape,)
raise ValueError(err)
if unit == 'mm':
dig_points /= 1000.
elif unit == 'cm':
dig_points /= 100.
return dig_points
def _write_dig_points(fname, dig_points):
"""Write points to text file.
Parameters
----------
fname : str
Path to the file to write. The kind of file to write is determined
based on the extension: '.txt' for tab separated text file.
dig_points : numpy.ndarray, shape (n_points, 3)
Points.
"""
_, ext = op.splitext(fname)
dig_points = np.asarray(dig_points)
if (dig_points.ndim != 2) or (dig_points.shape[1] != 3):
err = ("Points must be of shape (n_points, 3), "
"not %s" % (dig_points.shape,))
raise ValueError(err)
if ext == '.txt':
with open(fname, 'wb') as fid:
version = __version__
now = datetime.datetime.now().strftime("%I:%M%p on %B %d, %Y")
fid.write(b("% Ascii 3D points file created by mne-python version "
"{version} at {now}\n".format(version=version,
now=now)))
fid.write(b("% {N} 3D points, "
"x y z per line\n".format(N=len(dig_points))))
np.savetxt(fid, dig_points, delimiter='\t', newline='\n')
else:
msg = "Unrecognized extension: %r. Need '.txt'." % ext
raise ValueError(msg)
def _make_dig_points(nasion=None, lpa=None, rpa=None, hpi=None,
extra_points=None, dig_ch_pos=None):
"""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.
Returns
-------
dig : list
List of digitizer points to be added to the info['dig'].
"""
dig = []
if lpa is not None:
lpa = np.asarray(lpa)
if lpa.shape != (3,):
raise ValueError('LPA should have the shape (3,) instead of %s'
% (lpa.shape,))
dig.append({'r': lpa, 'ident': FIFF.FIFFV_POINT_LPA,
'kind': FIFF.FIFFV_POINT_CARDINAL,
'coord_frame': FIFF.FIFFV_COORD_HEAD})
if nasion is not None:
nasion = np.asarray(nasion)
if nasion.shape != (3,):
raise ValueError('Nasion should have the shape (3,) instead of %s'
% (nasion.shape,))
dig.append({'r': nasion, 'ident': FIFF.FIFFV_POINT_NASION,
'kind': FIFF.FIFFV_POINT_CARDINAL,
'coord_frame': FIFF.FIFFV_COORD_HEAD})
if rpa is not None:
rpa = np.asarray(rpa)
if rpa.shape != (3,):
raise ValueError('RPA should have the shape (3,) instead of %s'
% (rpa.shape,))
dig.append({'r': rpa, 'ident': FIFF.FIFFV_POINT_RPA,
'kind': FIFF.FIFFV_POINT_CARDINAL,
'coord_frame': FIFF.FIFFV_COORD_HEAD})
if hpi is not None:
hpi = np.asarray(hpi)
if hpi.ndim != 2 or hpi.shape[1] != 3:
raise ValueError('HPI should have the shape (n_points, 3) instead '
'of %s' % (hpi.shape,))
for idx, point in enumerate(hpi):
dig.append({'r': point, 'ident': idx + 1,
'kind': FIFF.FIFFV_POINT_HPI,
'coord_frame': FIFF.FIFFV_COORD_HEAD})
if extra_points is not None:
extra_points = np.asarray(extra_points)
if extra_points.shape[1] != 3:
raise ValueError('Points should have the shape (n_points, 3) '
'instead of %s' % (extra_points.shape,))
for idx, point in enumerate(extra_points):
dig.append({'r': point, 'ident': idx + 1,
'kind': FIFF.FIFFV_POINT_EXTRA,
'coord_frame': FIFF.FIFFV_COORD_HEAD})
if dig_ch_pos is not None:
keys = sorted(dig_ch_pos.keys())
try: # use the last 3 as int if possible (e.g., EEG001->1)
idents = [int(key[-3:]) for key in keys]
except ValueError: # and if any conversion fails, simply use arange
idents = np.arange(1, len(keys) + 1)
for key, ident in zip(keys, idents):
dig.append({'r': dig_ch_pos[key], 'ident': ident,
'kind': FIFF.FIFFV_POINT_EEG,
'coord_frame': FIFF.FIFFV_COORD_HEAD})
return dig
@verbose
def read_info(fname, verbose=None):
"""Read measurement info from a file.
Parameters
----------
fname : str
File name.
verbose : bool, str, int, or None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
Returns
-------
info : instance of Info
Measurement information for the dataset.
"""
f, tree, _ = fiff_open(fname)
with f as fid:
info = read_meas_info(fid, tree)[0]
return info
def read_bad_channels(fid, node):
"""Read bad channels.
Parameters
----------
fid : file
The file descriptor.
node : dict
The node of the FIF tree that contains info on the bad channels.
Returns
-------
bads : list
A list of bad channel's names.
"""
nodes = dir_tree_find(node, FIFF.FIFFB_MNE_BAD_CHANNELS)
bads = []
if len(nodes) > 0:
for node in nodes:
tag = find_tag(fid, node, FIFF.FIFF_MNE_CH_NAME_LIST)
if tag is not None and tag.data is not None:
bads = tag.data.split(':')
return bads
@verbose
def read_meas_info(fid, tree, clean_bads=False, verbose=None):
"""Read the measurement info.
Parameters
----------
fid : file
Open file descriptor.
tree : tree
FIF tree structure.
clean_bads : bool
If True, clean info['bads'] before running consistency check.
Should only be needed for old files where we did not check bads
before saving.
verbose : bool, str, int, or None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
Returns
-------
info : instance of Info
Info on dataset.
meas : dict
Node in tree that contains the info.
"""
# Find the desired blocks
meas = dir_tree_find(tree, FIFF.FIFFB_MEAS)
if len(meas) == 0:
raise ValueError('Could not find measurement data')
if len(meas) > 1:
raise ValueError('Cannot read more that 1 measurement data')
meas = meas[0]
meas_info = dir_tree_find(meas, FIFF.FIFFB_MEAS_INFO)
if len(meas_info) == 0:
raise ValueError('Could not find measurement info')
if len(meas_info) > 1:
raise ValueError('Cannot read more that 1 measurement info')
meas_info = meas_info[0]
# Read measurement info
dev_head_t = None
ctf_head_t = None
dev_ctf_t = None
meas_date = None
highpass = None
lowpass = None
nchan = None
sfreq = None
chs = []
experimenter = None
description = None
proj_id = None
proj_name = None
line_freq = None
gantry_angle = None
custom_ref_applied = False
xplotter_layout = None
kit_system_id = None
for k in range(meas_info['nent']):
kind = meas_info['directory'][k].kind
pos = meas_info['directory'][k].pos
if kind == FIFF.FIFF_NCHAN:
tag = read_tag(fid, pos)
nchan = int(tag.data)
elif kind == FIFF.FIFF_SFREQ:
tag = read_tag(fid, pos)
sfreq = float(tag.data)
elif kind == FIFF.FIFF_CH_INFO:
tag = read_tag(fid, pos)
chs.append(tag.data)
elif kind == FIFF.FIFF_LOWPASS:
tag = read_tag(fid, pos)
if not np.isnan(tag.data):
lowpass = float(tag.data)
elif kind == FIFF.FIFF_HIGHPASS:
tag = read_tag(fid, pos)
if not np.isnan(tag.data):
highpass = float(tag.data)
elif kind == FIFF.FIFF_MEAS_DATE:
tag = read_tag(fid, pos)
meas_date = tag.data
elif kind == FIFF.FIFF_COORD_TRANS:
tag = read_tag(fid, pos)
cand = tag.data
if cand['from'] == FIFF.FIFFV_COORD_DEVICE and \
cand['to'] == FIFF.FIFFV_COORD_HEAD:
dev_head_t = cand
elif cand['from'] == FIFF.FIFFV_COORD_HEAD and \
cand['to'] == FIFF.FIFFV_COORD_DEVICE:
# this reversal can happen with BabyMEG data
dev_head_t = invert_transform(cand)
elif cand['from'] == FIFF.FIFFV_MNE_COORD_CTF_HEAD and \
cand['to'] == FIFF.FIFFV_COORD_HEAD:
ctf_head_t = cand
elif cand['from'] == FIFF.FIFFV_MNE_COORD_CTF_DEVICE and \
cand['to'] == FIFF.FIFFV_MNE_COORD_CTF_HEAD:
dev_ctf_t = cand
elif kind == FIFF.FIFF_EXPERIMENTER:
tag = read_tag(fid, pos)
experimenter = tag.data
elif kind == FIFF.FIFF_DESCRIPTION:
tag = read_tag(fid, pos)
description = tag.data
elif kind == FIFF.FIFF_PROJ_ID:
tag = read_tag(fid, pos)
proj_id = tag.data
elif kind == FIFF.FIFF_PROJ_NAME:
tag = read_tag(fid, pos)
proj_name = tag.data
elif kind == FIFF.FIFF_LINE_FREQ:
tag = read_tag(fid, pos)
line_freq = float(tag.data)
elif kind == FIFF.FIFF_GANTRY_ANGLE:
tag = read_tag(fid, pos)
gantry_angle = float(tag.data)
elif kind in [FIFF.FIFF_MNE_CUSTOM_REF, 236]: # 236 used before v0.11
tag = read_tag(fid, pos)
custom_ref_applied = bool(tag.data)
elif kind == FIFF.FIFF_XPLOTTER_LAYOUT:
tag = read_tag(fid, pos)
xplotter_layout = str(tag.data)
elif kind == FIFF.FIFF_MNE_KIT_SYSTEM_ID:
tag = read_tag(fid, pos)
kit_system_id = int(tag.data)
# Check that we have everything we need
if nchan is None:
raise ValueError('Number of channels is not defined')
if sfreq is None:
raise ValueError('Sampling frequency is not defined')
if len(chs) == 0:
raise ValueError('Channel information not defined')