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meas_info.py
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meas_info.py
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# -*- coding: utf-8 -*-
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Matti Hämäläinen <msh@nmr.mgh.harvard.edu>
# Teon Brooks <teon.brooks@gmail.com>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD-3-Clause
from collections import Counter, OrderedDict
from collections.abc import Mapping
import contextlib
from copy import deepcopy
import datetime
from io import BytesIO
import operator
from textwrap import shorten
import string
import numpy as np
from .pick import (channel_type, _get_channel_types,
get_channel_type_constants, pick_types, _contains_ch_type)
from .constants import FIFF, _coord_frame_named
from .open import fiff_open
from .tree import dir_tree_find
from .tag import (read_tag, find_tag, _ch_coord_dict, _update_ch_info_named,
_rename_list)
from .proj import (_read_proj, _write_proj, _uniquify_projs, _normalize_proj,
_proj_equal, Projection)
from .ctf_comp import _read_ctf_comp, write_ctf_comp
from .write import (start_and_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 (invert_transform, Transform, _coord_frame_name,
_ensure_trans, _frame_to_str)
from ..utils import (logger, verbose, warn, object_diff, _validate_type,
_stamp_to_dt, _dt_to_stamp, _pl, _is_numeric,
_check_option, _on_missing, _check_on_missing, fill_doc,
_check_fname, repr_html)
from ._digitization import (_format_dig_points, _dig_kind_proper, DigPoint,
_dig_kind_rev, _dig_kind_ints, _read_dig_fif)
from ._digitization import write_dig, _get_data_as_dict_from_dig
from .compensator import get_current_comp
from ..defaults import _handle_default
b = bytes # alias
_SCALAR_CH_KEYS = ('scanno', 'logno', 'kind', 'range', 'cal', 'coil_type',
'unit', 'unit_mul', 'coord_frame')
_ALL_CH_KEYS_SET = set(_SCALAR_CH_KEYS + ('loc', 'ch_name'))
# XXX we need to require these except when doing simplify_info
_MIN_CH_KEYS_SET = set(('kind', 'cal', 'unit', 'loc', 'ch_name'))
def _get_valid_units():
"""Get valid units according to the International System of Units (SI).
The International System of Units (SI, :footcite:`WikipediaSI`) is the
default system for describing units in the Brain Imaging Data Structure
(BIDS). For more information, see the BIDS specification
:footcite:`BIDSdocs` and the appendix "Units" therein.
References
----------
.. footbibliography::
"""
valid_prefix_names = ['yocto', 'zepto', 'atto', 'femto', 'pico', 'nano',
'micro', 'milli', 'centi', 'deci', 'deca', 'hecto',
'kilo', 'mega', 'giga', 'tera', 'peta', 'exa',
'zetta', 'yotta']
valid_prefix_symbols = ['y', 'z', 'a', 'f', 'p', 'n', u'µ', 'm', 'c', 'd',
'da', 'h', 'k', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y']
valid_unit_names = ['metre', 'kilogram', 'second', 'ampere', 'kelvin',
'mole', 'candela', 'radian', 'steradian', 'hertz',
'newton', 'pascal', 'joule', 'watt', 'coulomb', 'volt',
'farad', 'ohm', 'siemens', 'weber', 'tesla', 'henry',
'degree Celsius', 'lumen', 'lux', 'becquerel', 'gray',
'sievert', 'katal']
valid_unit_symbols = ['m', 'kg', 's', 'A', 'K', 'mol', 'cd', 'rad', 'sr',
'Hz', 'N', 'Pa', 'J', 'W', 'C', 'V', 'F', u'Ω', 'S',
'Wb', 'T', 'H', u'°C', 'lm', 'lx', 'Bq', 'Gy', 'Sv',
'kat']
# Valid units are all possible combinations of either prefix name or prefix
# symbol together with either unit name or unit symbol. E.g., nV for
# nanovolt
valid_units = []
valid_units += ([''.join([prefix, unit]) for prefix in valid_prefix_names
for unit in valid_unit_names])
valid_units += ([''.join([prefix, unit]) for prefix in valid_prefix_names
for unit in valid_unit_symbols])
valid_units += ([''.join([prefix, unit]) for prefix in valid_prefix_symbols
for unit in valid_unit_names])
valid_units += ([''.join([prefix, unit]) for prefix in valid_prefix_symbols
for unit in valid_unit_symbols])
# units are also valid without a prefix
valid_units += valid_unit_names
valid_units += valid_unit_symbols
# we also accept "n/a" as a unit, which is the default missing value in
# BIDS
valid_units += ["n/a"]
return tuple(valid_units)
@verbose
def _unique_channel_names(ch_names, max_length=None, verbose=None):
"""Ensure unique channel names."""
suffixes = tuple(string.ascii_lowercase)
if max_length is not None:
ch_names[:] = [name[:max_length] for name in ch_names]
unique_ids = np.unique(ch_names, return_index=True)[1]
if len(unique_ids) != len(ch_names):
dups = {ch_names[x]
for x in np.setdiff1d(range(len(ch_names)), 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(ch_names) == ch_stem)[0]
# We need an extra character since we append '-'.
# np.ceil(...) is the maximum number of appended digits.
if max_length is not None:
n_keep = (
max_length - 1 - int(np.ceil(np.log10(len(overlaps)))))
else:
n_keep = np.inf
n_keep = min(len(ch_stem), n_keep)
ch_stem = ch_stem[:n_keep]
for idx, ch_idx in enumerate(overlaps):
# try idx first, then loop through lower case chars
for suffix in (idx,) + suffixes:
ch_name = ch_stem + '-%s' % suffix
if ch_name not in ch_names:
break
if ch_name not in ch_names:
ch_names[ch_idx] = ch_name
else:
raise ValueError('Adding a single alphanumeric for a '
'duplicate resulted in another '
'duplicate name %s' % ch_name)
return ch_names
class MontageMixin(object):
"""Mixin for Montage getting and setting."""
@fill_doc
def get_montage(self):
"""Get a DigMontage from instance.
Returns
-------
%(montage)s
"""
from ..channels.montage import make_dig_montage
info = self if isinstance(self, Info) else self.info
if info['dig'] is None:
return None
# obtain coord_frame, and landmark coords
# (nasion, lpa, rpa, hsp, hpi) from DigPoints
montage_bunch = _get_data_as_dict_from_dig(info['dig'])
coord_frame = _frame_to_str.get(montage_bunch.coord_frame)
# get the channel names and chs data structure
ch_names, chs = info['ch_names'], info['chs']
picks = pick_types(info, meg=False, eeg=True, seeg=True,
ecog=True, dbs=True, fnirs=True, exclude=[])
# channel positions from dig do not match ch_names one to one,
# so use loc[:3] instead
ch_pos = {ch_names[ii]: chs[ii]['loc'][:3] for ii in picks}
# fNIRS uses multiple channels for the same sensors, we use
# a private function to format these for dig montage.
fnirs_picks = pick_types(info, fnirs=True, exclude=[])
if len(ch_pos) == len(fnirs_picks):
ch_pos = _get_fnirs_ch_pos(info)
elif len(fnirs_picks) > 0:
raise ValueError("MNE does not support getting the montage "
"for a mix of fNIRS and other data types. "
"Please raise a GitHub issue if you "
"require this feature.")
# create montage
montage = make_dig_montage(
ch_pos=ch_pos,
coord_frame=coord_frame,
nasion=montage_bunch.nasion,
lpa=montage_bunch.lpa,
rpa=montage_bunch.rpa,
hsp=montage_bunch.hsp,
hpi=montage_bunch.hpi,
)
return montage
@verbose
def set_montage(self, montage, match_case=True, match_alias=False,
on_missing='raise', verbose=None):
"""Set %(montage_types)s channel positions and digitization points.
Parameters
----------
%(montage)s
%(match_case)s
%(match_alias)s
%(on_missing_montage)s
%(verbose)s
Returns
-------
inst : instance of Raw | Epochs | Evoked
The instance, modified in-place.
See Also
--------
mne.channels.make_standard_montage
mne.channels.make_dig_montage
mne.channels.read_custom_montage
Notes
-----
.. warning::
Only %(montage_types)s channels can have their positions set using
a montage. Other channel types (e.g., MEG channels) should have
their positions defined properly using their data reading
functions.
"""
# How to set up a montage to old named fif file (walk through example)
# https://gist.github.com/massich/f6a9f4799f1fbeb8f5e8f8bc7b07d3df
from ..channels.montage import _set_montage
info = self if isinstance(self, Info) else self.info
_set_montage(info, montage, match_case, match_alias, on_missing)
return self
class ContainsMixin(object):
"""Mixin class for Raw, Evoked, Epochs and Info."""
def __contains__(self, ch_type):
"""Check channel type membership.
Parameters
----------
ch_type : str
Channel type to check for. Can be e.g. 'meg', 'eeg', 'stim', etc.
Returns
-------
in : bool
Whether or not the instance contains the given channel type.
Examples
--------
Channel type membership can be tested as::
>>> 'meg' in inst # doctest: +SKIP
True
>>> 'seeg' in inst # doctest: +SKIP
False
"""
info = self if isinstance(self, Info) else self.info
if ch_type == 'meg':
has_ch_type = (_contains_ch_type(info, 'mag') or
_contains_ch_type(info, 'grad'))
else:
has_ch_type = _contains_ch_type(info, ch_type)
return has_ch_type
@property
def compensation_grade(self):
"""The current gradient compensation grade."""
info = self if isinstance(self, Info) else self.info
return get_current_comp(info)
@fill_doc
def get_channel_types(self, picks=None, unique=False, only_data_chs=False):
"""Get a list of channel type for each channel.
Parameters
----------
%(picks_all)s
unique : bool
Whether to return only unique channel types. Default is ``False``.
only_data_chs : bool
Whether to ignore non-data channels. Default is ``False``.
Returns
-------
channel_types : list
The channel types.
"""
info = self if isinstance(self, Info) else self.info
return _get_channel_types(info, picks=picks, unique=unique,
only_data_chs=only_data_chs)
def _format_trans(obj, key):
try:
t = obj[key]
except KeyError:
pass
else:
if t is not None:
obj[key] = Transform(t['from'], t['to'], t['trans'])
def _check_ch_keys(ch, ci, name='info["chs"]', check_min=True):
ch_keys = set(ch)
bad = sorted(ch_keys.difference(_ALL_CH_KEYS_SET))
if bad:
raise KeyError(
f'key{_pl(bad)} errantly present for {name}[{ci}]: {bad}')
if check_min:
bad = sorted(_MIN_CH_KEYS_SET.difference(ch_keys))
if bad:
raise KeyError(
f'key{_pl(bad)} missing for {name}[{ci}]: {bad}',)
# As options are added here, test_meas_info.py:test_info_bad should be updated
def _check_bads(bads):
_validate_type(bads, list, 'bads')
return bads
def _check_description(description):
_validate_type(description, (None, str), "info['description']")
return description
def _check_dev_head_t(dev_head_t):
_validate_type(dev_head_t, (Transform, None), "info['dev_head_t']")
if dev_head_t is not None:
dev_head_t = _ensure_trans(dev_head_t, 'meg', 'head')
return dev_head_t
def _check_experimenter(experimenter):
_validate_type(experimenter, (None, str), 'experimenter')
return experimenter
def _check_line_freq(line_freq):
_validate_type(line_freq, (None, 'numeric'), 'line_freq')
line_freq = float(line_freq) if line_freq is not None else line_freq
return line_freq
def _check_subject_info(subject_info):
_validate_type(subject_info, (None, dict), 'subject_info')
return subject_info
def _check_device_info(device_info):
_validate_type(device_info, (None, dict, ), 'device_info')
return device_info
def _check_helium_info(helium_info):
_validate_type(helium_info, (None, dict, ), 'helium_info')
return helium_info
class Info(dict, MontageMixin, ContainsMixin):
"""Measurement information.
This data structure behaves like a dictionary. It contains all metadata
that is available for a recording. However, its keys are restricted to
those provided by the
`FIF format specification <https://github.com/mne-tools/fiff-constants>`__,
so new entries should not be manually added.
.. note::
This class should not be instantiated directly via
``mne.Info(...)``. Instead, use :func:`mne.create_info` to create
measurement information from scratch.
.. warning::
The only entries that should be manually changed by the user are:
``info['bads']``, ``info['description']``, ``info['device_info']``
``info['dev_head_t']``, ``info['experimenter']``,
``info['helium_info']``, ``info['line_freq']``, ``info['temp']``,
and ``info['subject_info']``.
All other entries should be considered read-only, though they can be
modified by various MNE-Python functions or methods (which have
safeguards to ensure all fields remain in sync).
Parameters
----------
*args : list
Arguments.
**kwargs : dict
Keyword arguments.
Attributes
----------
acq_pars : str | None
MEG system acquisition 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.
command_line : str
Contains the command and arguments used to create the source space
(used for source estimation).
comps : list of dict
CTF software gradient compensation data.
See Notes for more information.
ctf_head_t : Transform | None
The transformation from 4D/CTF head coordinates to Neuromag head
coordinates. This is only present in 4D/CTF data.
custom_ref_applied : int
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 : Transform | None
The transformation from device coordinates to 4D/CTF head coordinates.
This is only present in 4D/CTF data.
dev_head_t : Transform | None
The device to head transformation.
device_info : dict | None
Information about the acquisition device. See Notes for details.
.. versionadded:: 0.19
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.
gantry_angle : float | None
Tilt angle of the gantry in degrees.
helium_info : dict | None
Information about the device helium. See Notes for details.
.. versionadded:: 0.19
highpass : float
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.
kit_system_id : int
Identifies the KIT system.
line_freq : float | None
Frequency of the power line in Hertz.
lowpass : float
Lowpass corner frequency in Hertz.
It is automatically set to half the sampling rate if there is
otherwise no low-pass applied to the data.
maxshield : bool
True if active shielding (IAS) was active during recording.
meas_date : datetime
The time (UTC) of the recording.
.. versionchanged:: 0.20
This is stored as a :class:`~python:datetime.datetime` object
instead of a tuple of seconds/microseconds.
meas_file : str | None
Raw measurement file (used for source estimation).
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``.
mri_file : str | None
File containing the MRI to head transformation (used for source
estimation).
mri_head_t : dict | None
Transformation from MRI to head coordinates (used for source
estimation).
mri_id : dict | None
MRI unique ID (used for source estimation).
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.
temp : object | None
Can be used to store temporary objects in an Info instance. It will not
survive an I/O roundtrip.
.. versionadded:: 0.24
utc_offset : str
"UTC offset of related meas_date (sHH:MM).
.. versionadded:: 0.19
working_dir : str
Working directory used when the source space was created (used for
source estimation).
xplotter_layout : str
Layout of the Xplotter (Neuromag system only).
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 commonly ``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.
* ``device_info`` dict:
type : str
Device type.
model : str
Device model.
serial : str
Device serial.
site : str
Device site.
* ``dig`` list of dict:
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``.
* ``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.
* ``helium_info`` dict:
he_level_raw : float
Helium level (%) before position correction.
helium_level : float
Helium level (%) after position correction.
orig_file_guid : str
Original file GUID.
meas_date : tuple of int
The helium level meas date.
* ``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).
* ``mri_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.
* ``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
acquisition.
* ``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, 3=ambidextrous).
weight : float
Weight in kilograms.
height : float
Height in meters.
"""
_attributes = {
'acq_pars': 'acq_pars cannot be set directly. '
'See mne.AcqParserFIF() for details.',
'acq_stim': 'acq_stim cannot be set directly.',
'bads': _check_bads,
'ch_names': 'ch_names cannot be set directly. '
'Please use methods inst.add_channels(), '
'inst.drop_channels(), inst.pick_channels(), '
'inst.rename_channels(), inst.reorder_channels() '
'and inst.set_channel_types() instead.',
'chs': 'chs cannot be set directly. '
'Please use methods inst.add_channels(), '
'inst.drop_channels(), inst.pick_channels(), '
'inst.rename_channels(), inst.reorder_channels() '
'and inst.set_channel_types() instead.',
'command_line': 'command_line cannot be set directly.',
'comps': 'comps cannot be set directly. '
'Please use method Raw.apply_gradient_compensation() '
'instead.',
'ctf_head_t': 'ctf_head_t cannot be set directly.',
'custom_ref_applied': 'custom_ref_applied cannot be set directly. '
'Please use method inst.set_eeg_reference() '
'instead.',
'description': _check_description,
'dev_ctf_t': 'dev_ctf_t cannot be set directly.',
'dev_head_t': _check_dev_head_t,
'device_info': _check_device_info,
'dig': 'dig cannot be set directly. '
'Please use method inst.set_montage() instead.',
'events': 'events cannot be set directly.',
'experimenter': _check_experimenter,
'file_id': 'file_id cannot be set directly.',
'gantry_angle': 'gantry_angle cannot be set directly.',
'helium_info': _check_helium_info,
'highpass': 'highpass cannot be set directly. '
'Please use method inst.filter() instead.',
'hpi_meas': 'hpi_meas can not be set directly.',
'hpi_results': 'hpi_results cannot be set directly.',
'hpi_subsystem': 'hpi_subsystem cannot be set directly.',
'kit_system_id': 'kit_system_id cannot be set directly.',
'line_freq': _check_line_freq,
'lowpass': 'lowpass cannot be set directly. '
'Please use method inst.filter() instead.',
'maxshield': 'maxshield cannot be set directly.',
'meas_date': 'meas_date cannot be set directly. '
'Please use method inst.set_meas_date() instead.',
'meas_file': 'meas_file cannot be set directly.',
'meas_id': 'meas_id cannot be set directly.',
'mri_file': 'mri_file cannot be set directly.',
'mri_head_t': 'mri_head_t cannot be set directly.',
'mri_id': 'mri_id cannot be set directly.',
'nchan': 'nchan cannot be set directly. '
'Please use methods inst.add_channels(), '
'inst.drop_channels(), and inst.pick_channels() instead.',
'proc_history': 'proc_history cannot be set directly.',
'proj_id': 'proj_id cannot be set directly.',
'proj_name': 'proj_name cannot be set directly.',
'projs': 'projs cannot be set directly. '
'Please use methods inst.add_proj() and inst.del_proj() '
'instead.',
'sfreq': 'sfreq cannot be set directly. '
'Please use method inst.resample() instead.',
'subject_info': _check_subject_info,
'temp': lambda x: x,
'utc_offset': 'utc_offset cannot be set directly.',
'working_dir': 'working_dir cannot be set directly.',
'xplotter_layout': 'xplotter_layout cannot be set directly.'
}
def __init__(self, *args, **kwargs):
self._unlocked = True
super().__init__(*args, **kwargs)
# Deal with h5io writing things as dict
for key in ('dev_head_t', 'ctf_head_t', 'dev_ctf_t'):
_format_trans(self, key)
for res in self.get('hpi_results', []):
_format_trans(res, 'coord_trans')
if self.get('dig', None) is not None and len(self['dig']):
if isinstance(self['dig'], dict): # needs to be unpacked
self['dig'] = _dict_unpack(self['dig'], _DIG_CAST)
if not isinstance(self['dig'][0], DigPoint):
self['dig'] = _format_dig_points(self['dig'])
if isinstance(self.get('chs', None), dict):
self['chs']['ch_name'] = [str(x) for x in np.char.decode(
self['chs']['ch_name'], encoding='utf8')]
self['chs'] = _dict_unpack(self['chs'], _CH_CAST)
for pi, proj in enumerate(self.get('projs', [])):
if not isinstance(proj, Projection):
self['projs'][pi] = Projection(**proj)
# Old files could have meas_date as tuple instead of datetime
try:
meas_date = self['meas_date']
except KeyError:
pass
else:
self['meas_date'] = _ensure_meas_date_none_or_dt(meas_date)
self._unlocked = False
def __getstate__(self):
"""Get state (for pickling)."""
return {'_unlocked': self._unlocked}
def __setstate__(self, state):
"""Set state (for pickling)."""
self._unlocked = state['_unlocked']
def __setitem__(self, key, val):
"""Attribute setter."""
# During unpickling, the _unlocked attribute has not been set, so
# let __setstate__ do it later and act unlocked now
unlocked = getattr(self, '_unlocked', True)
if key in self._attributes:
if isinstance(self._attributes[key], str):
if not unlocked:
raise RuntimeError(self._attributes[key])
else:
val = self._attributes[key](val) # attribute checker function
else:
raise RuntimeError(
f"Info does not support directly setting the key {repr(key)}. "
"You can set info['temp'] to store temporary objects in an "
"Info instance, but these will not survive an I/O round-trip.")
super().__setitem__(key, val)
def update(self, other=None, **kwargs):
"""Update method using __setitem__()."""
iterable = other.items() if isinstance(other, Mapping) else other
if other is not None:
for key, val in iterable:
self[key] = val
for key, val in kwargs.items():
self[key] = val
@contextlib.contextmanager
def _unlock(self, *, update_redundant=False, check_after=False):
"""Context manager unlocking access to attributes."""
# needed for nested _unlock()
state = self._unlocked if hasattr(self, '_unlocked') else False
self._unlocked = True
try:
yield
except Exception:
raise
else:
if update_redundant:
self._update_redundant()
if check_after:
self._check_consistency()
finally:
self._unlocked = state
def copy(self):
"""Copy the instance.
Returns
-------
info : instance of Info
The copied info.
"""
return 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."""
MAX_WIDTH = 68
strs = ['<Info | %s non-empty values']
non_empty = 0
titles = _handle_default('titles')
for k, v in self.items():
if k == 'ch_names':
if v:
entr = shorten(', '.join(v), MAX_WIDTH, placeholder=' ...')
else:
entr = '[]' # always show
non_empty -= 1 # don't count as non-empty
elif k == 'bads':
if v:
entr = '{} items ('.format(len(v))
entr += ', '.join(v)
entr = shorten(entr, MAX_WIDTH, placeholder=' ...') + ')'
else:
entr = '[]' # always show
non_empty -= 1 # don't count as non-empty
elif k == 'projs':
if v:
entr = ', '.join(p['desc'] + ': o%s' %
{0: 'ff', 1: 'n'}[p['active']] for p in v)
entr = shorten(entr, MAX_WIDTH, placeholder=' ...')
else:
entr = '[]' # always show projs
non_empty -= 1 # don't count as non-empty
elif k == 'meas_date':
if v is None:
entr = 'unspecified'
else:
entr = v.strftime('%Y-%m-%d %H:%M:%S %Z')
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'))
elif k == 'dig' and v is not None:
counts = Counter(d['kind'] for d in v)
counts = ['%d %s' % (counts[ii],
_dig_kind_proper[_dig_kind_rev[ii]])
for ii in _dig_kind_ints if ii in counts]
counts = (' (%s)' % (', '.join(counts))) if len(counts) else ''
entr = '%d item%s%s' % (len(v), _pl(len(v)), counts)
elif isinstance(v, Transform):
# show entry only for non-identity transform
if not np.allclose(v["trans"], np.eye(v["trans"].shape[0])):
frame1 = _coord_frame_name(v['from'])
frame2 = _coord_frame_name(v['to'])
entr = '%s -> %s transform' % (frame1, frame2)
else:
entr = ''
elif k in ['sfreq', 'lowpass', 'highpass']:
entr = '{:.1f} Hz'.format(v)