mne.
Info
[source]¶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 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: |
|
---|
See also
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).
Methods
__contains__ ($self, key, /) |
True if D has a key k, else False. |
__getitem__ |
x.__getitem__(y) <==> x[y] |
__iter__ ($self, /) |
Implement iter(self). |
__len__ ($self, /) |
Return len(self). |
clear () |
|
copy () |
Copy the instance. |
fromkeys ($type, iterable[, value]) |
Returns a new dict with keys from iterable and values equal to value. |
get (k[,d]) |
|
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 () |
2-tuple; but raise KeyError if D is empty. |
setdefault (k[,d]) |
|
update ([E, ]**F) |
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k] |
values () |
__contains__
($self, key, /)¶True if D has a key k, else False.
__getitem__
()¶x.__getitem__(y) <==> x[y]
__iter__
($self, /)¶Implement iter(self).
__len__
($self, /)¶Return len(self).
clear
() → None. Remove all items from D.¶fromkeys
($type, iterable, value=None, /)¶Returns a new dict with keys from iterable and values equal to value.
get
(k[, d]) → D[k] if k in D, else d. d defaults to None.¶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.
pop
(k[, d]) → v, remove specified key and return the corresponding value.¶If key is not found, d is returned if given, otherwise KeyError is raised
popitem
() → (k, v), remove and return some (key, value) pair as a¶2-tuple; but raise KeyError if D is empty.
setdefault
(k[, d]) → D.get(k,d), also set D[k]=d if k not in D¶update
([E, ]**F) → None. Update D from dict/iterable E and F.¶If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values
() → an object providing a view on D's values¶