mne.Info#

class mne.Info(*args, **kwargs)[source]#

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, so new entries should not be manually added.

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).

Warning

This class should not be instantiated directly. To create a measurement information structure, use mne.create_info().

Parameters:

*argslist: Arguments.
**kwargsdict: Keyword arguments.

See also

mne.create_info

Notes

The following parameters have a nested structure.

chs list of dict:

calfloat
The calibration factor to bring the channels to physical units. Used in product with range to scale the data read from disk.

ch_namestr
The channel name.

coil_typeint
Coil type, e.g. FIFFV_COIL_MEG.

coord_frameint
The coordinate frame used, e.g. FIFFV_COORD_HEAD.

kindint
The kind of channel, e.g. FIFFV_EEG_CH.

locarray, 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.

lognoint
Logical channel number, conventions in the usage of this number vary.

rangefloat
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.

scannoint
Scanning order number, starting from 1.

unitint
The unit to use, e.g. FIFF_UNIT_T_M.

unit_mulint
Unit multipliers, most commonly FIFF_UNITM_NONE.
comps list of dict:

ctfkindint
CTF compensation grade.

colcalsndarray
Column calibrations.

matdict
A named matrix dictionary (with entries “data”, “col_names”, etc.) containing the compensation matrix.

rowcalsndarray
Row calibrations.

save_calibratedbool
Were the compensation data saved in calibrated form.
device_info dict:

typestr
Device type.

modelstr
Device model.

serialstr
Device serial.

sitestr
Device site.
dig list of dict:

kindint
The kind of channel, e.g. FIFFV_POINT_EEG, FIFFV_POINT_CARDINAL.

rarray, shape (3,)
3D position in m. and coord_frame.

identint
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_frameint
The coordinate frame used, e.g. FIFFV_COORD_HEAD.
events list of dict:

channelslist of int
Channel indices for the events.

listndarray, shape (n_events * 3,)
Events in triplets as number of samples, before, after.
file_id dict:

versionint
FIF format version, i.e. FIFFC_VERSION.

machidndarray, shape (2,)
Unique machine ID, usually derived from the MAC address.

secsint
Time in seconds.

usecsint
Time in microseconds.
helium_info dict:

he_level_rawfloat
Helium level (%) before position correction.

helium_levelfloat
Helium level (%) after position correction.

orig_file_guidstr
Original file GUID.

meas_datetuple of int
The helium level meas date.
hpi_meas list of dict:

creatorstr
Program that did the measurement.

sfreqfloat
Sample rate.

nchanint
Number of channels used.

naveint
Number of averages used.

ncoilint
Number of coils used.

first_sampint
First sample used.

last_sampint
Last sample used.

hpi_coilslist of dict
Coils, containing:

number: int
Coil number

epochndarray
Buffer containing one epoch and channel.

slopesndarray, shape (n_channels,)
HPI data.

corr_coeffndarray, shape (n_channels,)
HPI curve fit correlations.

coil_freqfloat
HPI coil excitation frequency
hpi_results list of dict:

dig_pointslist
Digitization points (see dig definition) for the HPI coils.

orderndarray, shape (ncoil,)
The determined digitization order.

usedndarray, shape (nused,)
The indices of the used coils.

momentsndarray, shape (ncoil, 3)
The coil moments.

goodnessndarray, shape (ncoil,)
The goodness of fits.

good_limitfloat
The goodness of fit limit.

dist_limitfloat
The distance limit.

acceptint
Whether or not the fit was accepted.

coord_transinstance of Transformation
The resulting MEG<->head transformation.
hpi_subsystem dict:

ncoilint
The number of coils.

event_channelstr
The event channel used to encode cHPI status (e.g., STI201).

hpi_coilslist 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:

versionint
FIF format version, i.e. FIFFC_VERSION.

machidndarray, shape (2,)
Unique machine ID, usually derived from the MAC address.

secsint
Time in seconds.

usecsint
Time in microseconds.
proc_history list of dict:

block_iddict
See id above.

datendarray, shape (2,)
2-element tuple of seconds and microseconds.

experimenterstr
Name of the person who ran the program.

creatorstr
Program that did the processing.

max_infodict
Maxwel filtering info, can contain:

sss_infodict
SSS processing information.

max_st
tSSS processing information.

sss_ctcdict
Cross-talk processing information.

sss_caldict
Fine-calibration information.

smartshielddict
MaxShield information. This dictionary is (always?) empty, but its presence implies that MaxShield was used during acquisition.
subject_info dict:

idint
Integer subject identifier.

his_idstr
String subject identifier.

last_namestr
Last name.

first_namestr
First name.

middle_namestr
Middle name.

birthdaytuple of int
Birthday in (year, month, day) format.

sexint
Subject sex (0=unknown, 1=male, 2=female).

handint
Handedness (1=right, 2=left, 3=ambidextrous).

Attributes:

acq_parsstr | None: MEG system acquisition parameters. See mne.AcqParserFIF for details.
acq_stimstr | None: MEG system stimulus parameters.
badslist of str: List of bad (noisy/broken) channels, by name. These channels will by default be ignored by many processing steps.
ch_nameslist of str: The names of the channels.
chslist of dict: A list of channel information dictionaries, one per channel. See Notes for more information.
command_linestr: Contains the command and arguments used to create the source space (used for source estimation).
compslist of dict: CTF software gradient compensation data. See Notes for more information.
ctf_head_tdict | None: The transformation from 4D/CTF head coordinates to Neuromag head coordinates. This is only present in 4D/CTF data.
custom_ref_appliedint: 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.
descriptionstr | None: String description of the recording.
dev_ctf_tdict | None: The transformation from device coordinates to 4D/CTF head coordinates. This is only present in 4D/CTF data.
dev_head_tdict | None: The device to head transformation.
device_infodict | None: Information about the acquisition device. See Notes for details.

New in version 0.19.
diglist of dict | None: The Polhemus digitization data in head coordinates. See Notes for more information.
eventslist of dict: Event list, sometimes extracted from the stim channels by Neuromag systems. In general this should not be used and mne.find_events() should be used for event processing. See Notes for more information.
experimenterstr | None: Name of the person that ran the experiment.
file_iddict | None: The FIF globally unique ID. See Notes for more information.
gantry_anglefloat | None: Tilt angle of the gantry in degrees.
helium_infodict | None: Information about the device helium. See Notes for details.

New in version 0.19.
highpassfloat: Highpass corner frequency in Hertz. Zero indicates a DC recording.
hpi_measlist of dict: HPI measurements that were taken at the start of the recording (e.g. coil frequencies). See Notes for details.
hpi_resultslist of dict: Head position indicator (HPI) digitization points and fit information (e.g., the resulting transform). See Notes for details.
hpi_subsystemdict | None: Information about the HPI subsystem that was used (e.g., event channel used for cHPI measurements). See Notes for details.
kit_system_idint: Identifies the KIT system.
line_freqfloat | None: Frequency of the power line in Hertz.
lowpassfloat: 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.
maxshieldbool: True if active shielding (IAS) was active during recording.
meas_datedatetime: The time (UTC) of the recording.

Changed in version 0.20: This is stored as a datetime object instead of a tuple of seconds/microseconds.
meas_filestr | None: Raw measurement file (used for source estimation).
meas_iddict | None: The ID assigned to this measurement by the acquisition system or during file conversion. Follows the same format as file_id.
mri_filestr | None: File containing the MRI to head transformation (used for source estimation).
mri_head_tdict | None: Transformation from MRI to head coordinates (used for source estimation).
mri_iddict | None: MRI unique ID (used for source estimation).
nchanint: Number of channels.
proc_historylist of dict: The MaxFilter processing history. See Notes for details.
proj_idint | None: ID number of the project the experiment belongs to.
proj_namestr | None: Name of the project the experiment belongs to.
projslist of Projection: List of SSP operators that operate on the data. See mne.Projection for details.
sfreqfloat: Sampling frequency in Hertz.
subject_infodict | None: Information about the subject. See Notes for details.
tempobject | None: Can be used to store temporary objects in an Info instance. It will not survive an I/O roundtrip.

New in version 0.24.
utc_offsetstr: “UTC offset of related meas_date (sHH:MM).

New in version 0.19.
working_dirstr: Working directory used when the source space was created (used for source estimation).
xplotter_layoutstr: Layout of the Xplotter (Neuromag system only).

Methods

`__contains__`(key, /)	True if the dictionary has the specified key, else False.
`__getitem__`	x.__getitem__(y) <==> x[y]
`__iter__`(/)	Implement iter(self).
`__len__`(/)	Return len(self).
`clear`()
`copy`()	Copy the instance.
`fromkeys`(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
`get`(key[, default])	Return the value for key if key is in the dictionary, else default.
`get_channel_types`([picks, unique, only_data_chs])	Get a list of channel type for each channel.
`get_montage`()	Get a DigMontage from instance.
`items`()
`keys`()
`normalize_proj`()	(Re-)Normalize projection vectors after subselection.
`pop`(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised
`popitem`(/)	Remove and return a (key, value) pair as a 2-tuple.
`set_montage`(montage[, match_case, ...])	Set EEG/sEEG/ECoG/DBS/fNIRS channel positions and digitization points.
`setdefault`(key[, default])	Insert key with a value of default if key is not in the dictionary.
`update`([other])	Update method using __setitem__().
`values`()

__contains__(key, /)#: True if the dictionary has the specified key, else False.

__getitem__()#: x.__getitem__(y) <==> x[y]

__iter__(/)#: Implement iter(self).

__len__(/)#: Return len(self).

clear() → None. Remove all items from D.#

property compensation_grade#: The current gradient compensation grade.

copy()[source]#

Copy the instance.

Returns:

infoinstance of Info: The copied info.

fromkeys(iterable, value=None, /)#: Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)#: Return the value for key if key is in the dictionary, else default.

get_channel_types(picks=None, unique=False, only_data_chs=False)[source]#

Get a list of channel type for each channel.

Parameters:

picksstr | list | slice | None: Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.
uniquebool: Whether to return only unique channel types. Default is False.
only_data_chsbool: Whether to ignore non-data channels. Default is False.

Returns:

channel_typeslist: The channel types.

get_montage()[source]#

Get a DigMontage from instance.

Returns:

montageNone | str | DigMontage: A montage containing channel positions. If a string or DigMontage is specified, the existing channel information will be updated with the channel positions from the montage. Valid strings are the names of the built-in montages that ship with MNE-Python; you can list those via mne.channels.get_builtin_montages(). If None (default), the channel positions will be removed from the Info.

items() → a set-like object providing a view on D's items#

keys() → a set-like object providing a view on D's keys#

normalize_proj()[source]#

(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.

mne.Info#

Examples using mne.Info#

Examples using `mne.Info`#