mne.io.Raw¶
- class mne.io.Raw(fname, allow_maxshield=False, preload=False, on_split_missing='raise', verbose=None)[source]¶
Raw data in FIF format.
- Parameters
- fname
str
| file-like The raw filename to load. For files that have automatically been split, the split part will be automatically loaded. Filenames not ending with
raw.fif
,raw_sss.fif
,raw_tsss.fif
,_meg.fif
,_eeg.fif
, or_ieeg.fif
(with or without an optional additional.gz
extension) will generate a warning. If a file-like object is provided, preloading must be used.Changed in version 0.18: Support for file-like objects.
- allow_maxshieldbool |
str
(defaultFalse
) If True, allow loading of data that has been recorded with internal active compensation (MaxShield). Data recorded with MaxShield should generally not be loaded directly, but should first be processed using SSS/tSSS to remove the compensation signals that may also affect brain activity. Can also be “yes” to load without eliciting a warning.
- preloadbool or
str
(defaultFalse
) Preload data into memory for data manipulation and faster indexing. If True, the data will be preloaded into memory (fast, requires large amount of memory). If preload is a string, preload is the file name of a memory-mapped file which is used to store the data on the hard drive (slower, requires less memory).
- on_split_missing
str
Can be
'raise'
(default) to raise an error,'warn'
to emit a warning, or'ignore'
to ignore when split file is missing.New in version 0.22.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only.
- fname
- Attributes
- info
dict
ch_names
list
ofstr
Channel names.
n_times
int
Number of time points.
times
ndarray
Time points.
- preloadbool
Indicates whether raw data are in memory.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only.
- info
Methods
__contains__
(ch_type)Check channel type membership.
__getitem__
(item)Get raw data and times.
__hash__
()Hash the object.
__len__
()Return the number of time points.
add_channels
(add_list[, force_update_info])Append new channels to the instance.
add_events
(events[, stim_channel, replace])Add events to stim channel.
add_proj
(projs[, remove_existing, verbose])Add SSP projection vectors.
add_reference_channels
(ref_channels)Add reference channels to data that consists of all zeros.
anonymize
([daysback, keep_his, verbose])Anonymize measurement information in place.
append
(raws[, preload])Concatenate raw instances as if they were continuous.
apply_function
(fun[, picks, dtype, n_jobs, …])Apply a function to a subset of channels.
apply_gradient_compensation
(grade[, verbose])Apply CTF gradient compensation.
apply_hilbert
([picks, envelope, n_jobs, …])Compute analytic signal or envelope for a subset of channels.
apply_proj
([verbose])Apply the signal space projection (SSP) operators to the data.
close
()Clean up the object.
copy
()Return copy of Raw instance.
crop
([tmin, tmax, include_tmax])Crop raw data file.
del_proj
([idx])Remove SSP projection vector.
describe
([data_frame])Describe channels (name, type, descriptive statistics).
drop_channels
(ch_names)Drop channel(s).
export
(fname[, fmt, verbose])Export Raw to external formats.
filter
(l_freq, h_freq[, picks, …])Filter a subset of channels.
Fix Elekta magnetometer coil types.
get_channel_types
([picks, unique, only_data_chs])Get a list of channel type for each channel.
get_data
([picks, start, stop, …])Get data in the given range.
Get a DigMontage from instance.
interpolate_bads
([reset_bads, mode, origin, …])Interpolate bad MEG and EEG channels.
load_bad_channels
([bad_file, force])Mark channels as bad from a text file.
load_data
([verbose])Load raw data.
notch_filter
(freqs[, picks, filter_length, …])Notch filter a subset of channels.
pick
(picks[, exclude])Pick a subset of channels.
pick_channels
(ch_names[, ordered])Pick some channels.
pick_types
([meg, eeg, stim, eog, ecg, emg, …])Pick some channels by type and names.
plot
([events, duration, start, n_channels, …])Plot raw data.
plot_projs_topomap
([ch_type, cmap, sensors, …])Plot SSP vector.
plot_psd
([fmin, fmax, tmin, tmax, proj, …])Plot the power spectral density across channels.
plot_psd_topo
([tmin, tmax, fmin, fmax, …])Plot channel-wise frequency spectra as topography.
plot_sensors
([kind, ch_type, title, …])Plot sensor positions.
rename_channels
(mapping[, allow_duplicates, …])Rename channels.
reorder_channels
(ch_names)Reorder channels.
resample
(sfreq[, npad, window, stim_picks, …])Resample all channels.
save
(fname[, picks, tmin, tmax, …])Save raw data to file.
savgol_filter
(h_freq[, verbose])Filter the data using Savitzky-Golay polynomial method.
set_annotations
(annotations[, emit_warning, …])Setter for annotations.
set_channel_types
(mapping[, verbose])Define the sensor type of channels.
set_eeg_reference
([ref_channels, …])Specify which reference to use for EEG data.
set_meas_date
(meas_date)Set the measurement start date.
set_montage
(montage[, match_case, …])Set EEG sensor configuration and head digitization.
time_as_index
(times[, use_rounding, origin])Convert time to indices.
to_data_frame
([picks, index, scalings, …])Export data in tabular structure as a pandas DataFrame.
- __contains__(ch_type)[source]¶
Check channel type membership.
- Parameters
- ch_type
str
Channel type to check for. Can be e.g. ‘meg’, ‘eeg’, ‘stim’, etc.
- ch_type
- Returns
- inbool
Whether or not the instance contains the given channel type.
Examples
Channel type membership can be tested as:
>>> 'meg' in inst True >>> 'seeg' in inst False
- __getitem__(item)[source]¶
Get raw data and times.
- Parameters
- item
tuple
or array_like See below for use cases.
- item
- Returns
Examples
Generally raw data is accessed as:
>>> data, times = raw[picks, time_slice]
To get all data, you can thus do either of:
>>> data, times = raw[:]
Which will be equivalent to:
>>> data, times = raw[:, :]
To get only the good MEG data from 10-20 seconds, you could do:
>>> picks = mne.pick_types(raw.info, meg=True, exclude='bads') >>> t_idx = raw.time_as_index([10., 20.]) >>> data, times = raw[picks, t_idx[0]:t_idx[1]]
- __len__()[source]¶
Return the number of time points.
- Returns
- len
int
The number of time points.
- len
Examples
This can be used as:
>>> len(raw) 1000
- property acqparser¶
The AcqParserFIF for the measurement info.
See also
- add_channels(add_list, force_update_info=False)[source]¶
Append new channels to the instance.
- Parameters
- add_list
list
A list of objects to append to self. Must contain all the same type as the current object.
- force_update_infobool
If True, force the info for objects to be appended to match the values in
self
. This should generally only be used when adding stim channels for which important metadata won’t be overwritten.New in version 0.12.
- add_list
- Returns
See also
Notes
If
self
is a Raw instance that has been preloaded into anumpy.memmap
instance, the memmap will be resized.Examples using
add_channels
:
- add_events(events, stim_channel=None, replace=False)[source]¶
Add events to stim channel.
- Parameters
- events
ndarray
, shape (n_events, 3) Events to add. The first column specifies the sample number of each event, the second column is ignored, and the third column provides the event value. If events already exist in the Raw instance at the given sample numbers, the event values will be added together.
- stim_channel
str
|None
Name of the stim channel to add to. If None, the config variable ‘MNE_STIM_CHANNEL’ is used. If this is not found, it will default to ‘STI 014’.
- replacebool
If True the old events on the stim channel are removed before adding the new ones.
- events
Notes
Data must be preloaded in order to add events.
Examples using
add_events
:
- add_proj(projs, remove_existing=False, verbose=None)[source]¶
Add SSP projection vectors.
- Parameters
- projs
list
List with projection vectors.
- remove_existingbool
Remove the projection vectors currently in the file.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- projs
- Returns
Examples using
add_proj
:
- add_reference_channels(ref_channels)[source]¶
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
- Returns
- property annotations¶
Annotations
for marking segments of data.
- anonymize(daysback=None, keep_his=False, verbose=None)[source]¶
Anonymize measurement information in place.
- Parameters
- daysback
int
|None
Number of days to subtract from all dates. If
None
(default), the acquisition date,info['meas_date']
, will be set toJanuary 1ˢᵗ, 2000
. This parameter is ignored ifinfo['meas_date']
isNone
(i.e., no acquisition date has been set).- keep_hisbool
If
True
,his_id
ofsubject_info
will not be overwritten. Defaults toFalse
.Warning
This could mean that
info
is not fully anonymized. Use with caution.- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only.
- daysback
- Returns
Notes
Removes potentially identifying information if it exists in
info
. Specifically for each of the following we use:- meas_date, file_id, meas_id
A default value, or as specified by
daysback
.
- subject_info
Default values, except for ‘birthday’ which is adjusted to maintain the subject age.
- experimenter, proj_name, description
Default strings.
- utc_offset
None
.
- proj_id
Zeros.
- proc_history
Dates use the
meas_date
logic, and experimenter a default string.
- helium_info, device_info
Dates use the
meas_date
logic, meta info uses defaults.
If
info['meas_date']
isNone
, it will remainNone
during processing the above fields.Operates in place.
New in version 0.13.0.
- append(raws, preload=None)[source]¶
Concatenate raw instances as if they were continuous.
Note
Boundaries of the raw files are annotated bad. If you wish to use the data as continuous recording, you can remove the boundary annotations after concatenation (see
mne.Annotations.delete()
).- Parameters
- raws
list
, orRaw
instance List of Raw instances to concatenate to the current instance (in order), or a single raw instance to concatenate.
- preloadbool,
str
, orNone
(defaultNone
) Preload data into memory for data manipulation and faster indexing. If True, the data will be preloaded into memory (fast, requires large amount of memory). If preload is a string, preload is the file name of a memory-mapped file which is used to store the data on the hard drive (slower, requires less memory). If preload is None, preload=True or False is inferred using the preload status of the instances passed in.
- raws
Examples using
append
:
- apply_function(fun, picks=None, dtype=None, n_jobs=1, channel_wise=True, verbose=None, **kwargs)[source]¶
Apply a function to a subset of channels.
The function
fun
is applied to the channels defined inpicks
. The raw object’s data is modified in-place. If the function returns a different data type (e.g.numpy.complex128
) it must be specified using thedtype
parameter, which causes the data type of all the data to change (even if the function is only applied to channels inpicks
). The object has to have the data loaded e.g. withpreload=True
orself.load_data()
.Note
If
n_jobs
> 1, more memory is required aslen(picks) * n_times
additional time points need to be temporarily stored in memory.Note
If the data type changes (
dtype != None
), more memory is required since the original and the converted data needs to be stored in memory.- Parameters
- fun
callable()
A function to be applied to the channels. The first argument of fun has to be a timeseries (
numpy.ndarray
). The function must operate on an array of shape(n_times,)
ifchannel_wise=True
and(len(picks), n_times)
otherwise. The function must return anndarray
shaped like its input.- picks
str
|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 data channels (excluding reference MEG channels). Note that channels ininfo['bads']
will be included if their names or indices are explicitly provided.- dtype
numpy.dtype
Data type to use after applying the function. If None (default) the data type is not modified.
- n_jobs
int
The number of jobs to run in parallel (default 1). Requires the joblib package.
- channel_wisebool
Whether to apply the function to each channel individually. If
False
, the function will be applied to all channels at once. DefaultTrue
.New in version 0.18.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.- **kwargs
dict
Additional keyword arguments to pass to
fun
.
- fun
- Returns
- selfinstance of
Raw
The raw object with transformed data.
- selfinstance of
- apply_gradient_compensation(grade, verbose=None)[source]¶
Apply CTF gradient compensation.
Warning
The compensation matrices are stored with single precision, so repeatedly switching between different of compensation (e.g., 0->1->3->2) can increase numerical noise, especially if data are saved to disk in between changing grades. It is thus best to only use a single gradient compensation level in final analyses.
- Parameters
- grade
int
CTF gradient compensation level.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- grade
- Returns
- rawinstance of
Raw
The modified Raw instance. Works in-place.
- rawinstance of
Examples using
apply_gradient_compensation
:
- apply_hilbert(picks=None, envelope=False, n_jobs=1, n_fft='auto', verbose=None)[source]¶
Compute analytic signal or envelope for a subset of channels.
- Parameters
- picks
str
|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 data channels (excluding reference MEG channels). Note that channels ininfo['bads']
will be included if their names or indices are explicitly provided.- envelopebool
Compute the envelope signal of each channel. Default False. See Notes.
- n_jobs
int
The number of jobs to run in parallel (default 1). Requires the joblib package.
- n_fft
int
|None
|str
Points to use in the FFT for Hilbert transformation. The signal will be padded with zeros before computing Hilbert, then cut back to original length. If None, n == self.n_times. If ‘auto’, the next highest fast FFT length will be use.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- picks
- Returns
Notes
Parameters
If
envelope=False
, the analytic signal for the channels defined inpicks
is computed and the data of the Raw object is converted to a complex representation (the analytic signal is complex valued).If
envelope=True
, the absolute value of the analytic signal for the channels defined inpicks
is computed, resulting in the envelope signal.If envelope=False, more memory is required since the original raw data as well as the analytic signal have temporarily to be stored in memory. If n_jobs > 1, more memory is required as
len(picks) * n_times
additional time points need to be temporaily stored in memory.Also note that the
n_fft
parameter will allow you to pad the signal with zeros before performing the Hilbert transform. This padding is cut off, but it may result in a slightly different result (particularly around the edges). Use at your own risk.Analytic signal
The analytic signal “x_a(t)” of “x(t)” is:
x_a = F^{-1}(F(x) 2U) = x + i y
where “F” is the Fourier transform, “U” the unit step function, and “y” the Hilbert transform of “x”. One usage of the analytic signal is the computation of the envelope signal, which is given by “e(t) = abs(x_a(t))”. Due to the linearity of Hilbert transform and the MNE inverse solution, the enevlope in source space can be obtained by computing the analytic signal in sensor space, applying the MNE inverse, and computing the envelope in source space.
Examples using
apply_hilbert
:
- apply_proj(verbose=None)[source]¶
Apply the signal space projection (SSP) operators to the data.
- Parameters
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- verbosebool,
- Returns
Notes
Once the projectors have been applied, they can no longer be removed. It is usually not recommended to apply the projectors at too early stages, as they are applied automatically later on (e.g. when computing inverse solutions). Hint: using the copy method individual projection vectors can be tested without affecting the original data. With evoked data, consider the following example:
projs_a = mne.read_proj('proj_a.fif') projs_b = mne.read_proj('proj_b.fif') # add the first, copy, apply and see ... evoked.add_proj(a).copy().apply_proj().plot() # add the second, copy, apply and see ... evoked.add_proj(b).copy().apply_proj().plot() # drop the first and see again evoked.copy().del_proj(0).apply_proj().plot() evoked.apply_proj() # finally keep both
Examples using
apply_proj
:
- property ch_names¶
Channel names.
- close()[source]¶
Clean up the object.
Does nothing for objects that close their file descriptors. Things like RawFIF will override this method.
- property compensation_grade¶
The current gradient compensation grade.
- copy()[source]¶
Return copy of Raw instance.
- Returns
- instinstance of
Raw
A copy of the instance.
- instinstance of
Examples using
copy
:
- crop(tmin=0.0, tmax=None, include_tmax=True)[source]¶
Crop raw data file.
Limit the data from the raw file to go between specific times. Note that the new tmin is assumed to be t=0 for all subsequently called functions (e.g., time_as_index, or Epochs). New first_samp and last_samp are set accordingly.
Thus function operates in-place on the instance. Use
mne.io.Raw.copy()
if operation on a copy is desired.- Parameters
- Returns
- rawinstance of
Raw
The cropped raw object, modified in-place.
- rawinstance of
Examples using
crop
:
- del_proj(idx='all')[source]¶
Remove SSP projection vector.
Note
The projection vector can only be removed if it is inactive (has not been applied to the data).
- Parameters
- Returns
Examples using
del_proj
:
- describe(data_frame=False)[source]¶
Describe channels (name, type, descriptive statistics).
- Parameters
- data_framebool
If True, return results in a pandas.DataFrame. If False, only print results. Columns ‘ch’, ‘type’, and ‘unit’ indicate channel index, channel type, and unit of the remaining five columns. These columns are ‘min’ (minimum), ‘Q1’ (first quartile or 25% percentile), ‘median’, ‘Q3’ (third quartile or 75% percentile), and ‘max’ (maximum).
- Returns
- result
None
|pandas.DataFrame
If data_frame=False, returns None. If data_frame=True, returns results in a pandas.DataFrame (requires pandas).
- result
- drop_channels(ch_names)[source]¶
Drop channel(s).
- Parameters
- Returns
See also
Notes
New in version 0.9.0.
Examples using
drop_channels
:
- export(fname, fmt='auto', verbose=None)[source]¶
Export Raw to external formats.
Supported formats: EEGLAB (set, uses
eeglabio
)Warning
Since we are exporting to external formats, there’s no guarantee that all the info will be preserved in the external format. To save in native MNE format (
.fif
) without information loss, usesave()
instead.- Parameters
- fname
str
Name of the output file.
- fmt‘auto’ | ‘eeglab’
Format of the export. Defaults to
'auto'
, which will infer the format from the filename extension. See supported formats above for more information.- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only.
- fname
Notes
For EEGLAB exports, channel locations are expanded to full EEGLAB format. For more details see
eeglabio.utils.cart_to_eeglab()
.
- property filenames¶
The filenames used.
- filter(l_freq, h_freq, picks=None, filter_length='auto', l_trans_bandwidth='auto', h_trans_bandwidth='auto', n_jobs=1, method='fir', iir_params=None, phase='zero', fir_window='hamming', fir_design='firwin', skip_by_annotation=('edge', 'bad_acq_skip'), pad='reflect_limited', verbose=None)[source]¶
Filter a subset of channels.
- Parameters
- l_freq
float
|None
For FIR filters, the lower pass-band edge; for IIR filters, the lower cutoff frequency. If None the data are only low-passed.
- h_freq
float
|None
For FIR filters, the upper pass-band edge; for IIR filters, the upper cutoff frequency. If None the data are only high-passed.
- picks
str
|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 data channels. Note that channels ininfo['bads']
will be included if their names or indices are explicitly provided.- filter_length
str
|int
Length of the FIR filter to use (if applicable):
‘auto’ (default): The filter length is chosen based on the size of the transition regions (6.6 times the reciprocal of the shortest transition band for fir_window=’hamming’ and fir_design=”firwin2”, and half that for “firwin”).
str: A human-readable time in units of “s” or “ms” (e.g., “10s” or “5500ms”) will be converted to that number of samples if
phase="zero"
, or the shortest power-of-two length at least that duration forphase="zero-double"
.int: Specified length in samples. For fir_design=”firwin”, this should not be used.
- l_trans_bandwidth
float
|str
Width of the transition band at the low cut-off frequency in Hz (high pass or cutoff 1 in bandpass). Can be “auto” (default) to use a multiple of
l_freq
:min(max(l_freq * 0.25, 2), l_freq)
Only used for
method='fir'
.- h_trans_bandwidth
float
|str
Width of the transition band at the high cut-off frequency in Hz (low pass or cutoff 2 in bandpass). Can be “auto” (default in 0.14) to use a multiple of
h_freq
:min(max(h_freq * 0.25, 2.), info['sfreq'] / 2. - h_freq)
Only used for
method='fir'
.- n_jobs
int
|str
Number of jobs to run in parallel. Can be ‘cuda’ if
cupy
is installed properly and method=’fir’.- method
str
‘fir’ will use overlap-add FIR filtering, ‘iir’ will use IIR forward-backward filtering (via filtfilt).
- iir_params
dict
|None
Dictionary of parameters to use for IIR filtering. If iir_params is None and method=”iir”, 4th order Butterworth will be used. For more information, see
mne.filter.construct_iir_filter()
.- phase
str
Phase of the filter, only used if
method='fir'
. Symmetric linear-phase FIR filters are constructed, and ifphase='zero'
(default), the delay of this filter is compensated for, making it non-causal. Ifphase=='zero-double'
, then this filter is applied twice, once forward, and once backward (also making it non-causal). If ‘minimum’, then a minimum-phase filter will be constricted and applied, which is causal but has weaker stop-band suppression.New in version 0.13.
- fir_window
str
The window to use in FIR design, can be “hamming” (default), “hann” (default in 0.13), or “blackman”.
New in version 0.15.
- fir_design
str
Can be “firwin” (default) to use
scipy.signal.firwin()
, or “firwin2” to usescipy.signal.firwin2()
. “firwin” uses a time-domain design technique that generally gives improved attenuation using fewer samples than “firwin2”.New in version 0.15.
- skip_by_annotation
str
|list
ofstr
If a string (or list of str), any annotation segment that begins with the given string will not be included in filtering, and segments on either side of the given excluded annotated segment will be filtered separately (i.e., as independent signals). The default (
('edge', 'bad_acq_skip')
will separately filter any segments that were concatenated bymne.concatenate_raws()
ormne.io.Raw.append()
, or separated during acquisition. To disable, provide an empty list. Only used ifinst
is raw.New in version 0.16..
- pad
str
The type of padding to use. Supports all
numpy.pad()
mode
options. Can also be “reflect_limited”, which pads with a reflected version of each vector mirrored on the first and last values of the vector, followed by zeros. Only used formethod='fir'
.- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- l_freq
- Returns
See also
Notes
Applies a zero-phase low-pass, high-pass, band-pass, or band-stop filter to the channels selected by
picks
. The data are modified inplace.The object has to have the data loaded e.g. with
preload=True
orself.load_data()
.l_freq
andh_freq
are the frequencies below which and above which, respectively, to filter out of the data. Thus the uses are:l_freq < h_freq
: band-pass filterl_freq > h_freq
: band-stop filterl_freq is not None and h_freq is None
: high-pass filterl_freq is None and h_freq is not None
: low-pass filter
self.info['lowpass']
andself.info['highpass']
are only updated with picks=None.Note
If n_jobs > 1, more memory is required as
len(picks) * n_times
additional time points need to be temporaily stored in memory.For more information, see the tutorials Background information on filtering and Filtering and resampling data and
mne.filter.create_filter()
.New in version 0.15.
Examples using
filter
:
- property first_samp¶
The first data sample.
- property first_time¶
The first time point (including first_samp but not meas_date).
- fix_mag_coil_types()[source]¶
Fix Elekta magnetometer coil types.
- Returns
- rawinstance of
Raw
The raw object. Operates in place.
- rawinstance of
Notes
This function changes magnetometer coil types 3022 (T1: SQ20483N) and 3023 (T2: SQ20483-A) to 3024 (T3: SQ20950N) in the channel definition records in the info structure.
Neuromag Vectorview systems can contain magnetometers with two different coil sizes (3022 and 3023 vs. 3024). The systems incorporating coils of type 3024 were introduced last and are used at the majority of MEG sites. At some sites with 3024 magnetometers, the data files have still defined the magnetometers to be of type 3022 to ensure compatibility with older versions of Neuromag software. In the MNE software as well as in the present version of Neuromag software coil type 3024 is fully supported. Therefore, it is now safe to upgrade the data files to use the true coil type.
Note
The effect of the difference between the coil sizes on the current estimates computed by the MNE software is very small. Therefore the use of mne_fix_mag_coil_types is not mandatory.
- get_channel_types(picks=None, unique=False, only_data_chs=False)[source]¶
Get a list of channel type for each channel.
- Parameters
- picks
str
|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 ininfo['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
.
- picks
- Returns
- channel_types
list
The channel types.
- channel_types
Examples using
get_channel_types
:
- get_data(picks=None, start=0, stop=None, reject_by_annotation=None, return_times=False, units=None, verbose=None)[source]¶
Get data in the given range.
- Parameters
- picks
str
|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 ininfo['bads']
will be included if their names or indices are explicitly provided.- start
int
The first sample to include. Defaults to 0.
- stop
int
|None
End sample (first not to include). If None (default), the end of the data is used.
- reject_by_annotation
None
| ‘omit’ | ‘NaN’ Whether to reject by annotation. If None (default), no rejection is done. If ‘omit’, segments annotated with description starting with ‘bad’ are omitted. If ‘NaN’, the bad samples are filled with NaNs.
- return_timesbool
Whether to return times as well. Defaults to False.
- units
str
|dict
|None
Specify the unit(s) that the data should be returned in. If
None
(default), the data is returned in the channel-type-specific default units, which are SI units (see Internal representation (units) and data channels). If a string, must be a sub-multiple of SI units that will be used to scale the data from all channels of the type associated with that unit. This only works if the data contains one channel type that has a unit (unitless channel types are left unchanged). For example if there are only EEG and STIM channels,units='uV'
will scale EEG channels to micro-Volts while STIM channels will be unchanged. Finally, if a dictionary is provided, keys must be channel types, and values must be units to scale the data of that channel type to. For exampledict(grad='fT/cm', mag='fT')
will scale the corresponding types accordingly, but all other channel types will remain in their channel-type-specific default unit.- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- picks
- Returns
Notes
New in version 0.14.0.
Examples using
get_data
:
- get_montage()[source]¶
Get a DigMontage from instance.
- Returns
- montage
None
|str
|DigMontage
A montage containing channel positions. If str or DigMontage is specified, the channel info will be updated with the channel positions. Default is None. For valid
str
values see documentation ofmne.channels.make_standard_montage()
. See also the documentation ofmne.channels.DigMontage
for more information.
- montage
- interpolate_bads(reset_bads=True, mode='accurate', origin='auto', method=None, exclude=(), verbose=None)[source]¶
Interpolate bad MEG and EEG channels.
Operates in place.
- Parameters
- reset_badsbool
If True, remove the bads from info.
- mode
str
Either
'accurate'
or'fast'
, determines the quality of the Legendre polynomial expansion used for interpolation of channels using the minimum-norm method.- originarray_like, shape (3,) |
str
Origin of the sphere in the head coordinate frame and in meters. Can be
'auto'
(default), which means a head-digitization-based origin fit.New in version 0.17.
- method
dict
Method to use for each channel type. Currently only the key “eeg” has multiple options:
"spline"
(default)Use spherical spline interpolation.
"MNE"
Use minimum-norm projection to a sphere and back. This is the method used for MEG channels.
The value for “meg” is “MNE”, and the value for “fnirs” is “nearest”. The default (None) is thus an alias for:
method=dict(meg="MNE", eeg="spline", fnirs="nearest")
New in version 0.21.
- exclude
list
|tuple
The channels to exclude from interpolation. If excluded a bad channel will stay in bads.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- Returns
Notes
New in version 0.9.0.
Examples using
interpolate_bads
:
- property last_samp¶
The last data sample.
- load_bad_channels(bad_file=None, force=False)[source]¶
Mark channels as bad from a text file.
This function operates mostly in the style of the C function
mne_mark_bad_channels
. Each line in the text file will be interpreted as a name of a bad channel.- Parameters
- bad_file
str
File name of the text file containing bad channels If bad_file = None, bad channels are cleared, but this is more easily done directly as raw.info[‘bads’] = [].
- forcebool
Whether or not to force bad channel marking (of those that exist) if channels are not found, instead of raising an error.
- bad_file
- load_data(verbose=None)[source]¶
Load raw data.
- Parameters
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- verbosebool,
- Returns
- rawinstance of
Raw
The raw object with data.
- rawinstance of
Notes
This function will load raw data if it was not already preloaded. If data were already preloaded, it will do nothing.
New in version 0.10.0.
Examples using
load_data
:
- property n_times¶
Number of time points.
- notch_filter(freqs, picks=None, filter_length='auto', notch_widths=None, trans_bandwidth=1.0, n_jobs=1, method='fir', iir_params=None, mt_bandwidth=None, p_value=0.05, phase='zero', fir_window='hamming', fir_design='firwin', pad='reflect_limited', verbose=None)[source]¶
Notch filter a subset of channels.
- Parameters
- freqs
float
|array
offloat
|None
Specific frequencies to filter out from data, e.g., np.arange(60, 241, 60) in the US or np.arange(50, 251, 50) in Europe. None can only be used with the mode ‘spectrum_fit’, where an F test is used to find sinusoidal components.
- picks
str
|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 data channels. Note that channels ininfo['bads']
will be included if their names or indices are explicitly provided.- filter_length
str
|int
Length of the FIR filter to use (if applicable):
‘auto’ (default): The filter length is chosen based on the size of the transition regions (6.6 times the reciprocal of the shortest transition band for fir_window=’hamming’ and fir_design=”firwin2”, and half that for “firwin”).
str: A human-readable time in units of “s” or “ms” (e.g., “10s” or “5500ms”) will be converted to that number of samples if
phase="zero"
, or the shortest power-of-two length at least that duration forphase="zero-double"
.int: Specified length in samples. For fir_design=”firwin”, this should not be used.
When
method=='spectrum_fit'
, this sets the effective window duration over which fits are computed. Seemne.filter.create_filter()
for options. Longer window lengths will give more stable frequency estimates, but require (potentially much) more processing and are not able to adapt as well to non-stationarities.The default in 0.21 is None, but this will change to
'10s'
in 0.22.- notch_widths
float
|array
offloat
|None
Width of each stop band (centred at each freq in freqs) in Hz. If None, freqs / 200 is used.
- trans_bandwidth
float
Width of the transition band in Hz. Only used for
method='fir'
.- n_jobs
int
|str
Number of jobs to run in parallel. Can be ‘cuda’ if
cupy
is installed properly and method=’fir’.- method
str
‘fir’ will use overlap-add FIR filtering, ‘iir’ will use IIR forward-backward filtering (via filtfilt).
- iir_params
dict
|None
Dictionary of parameters to use for IIR filtering. If iir_params is None and method=”iir”, 4th order Butterworth will be used. For more information, see
mne.filter.construct_iir_filter()
.- mt_bandwidth
float
|None
The bandwidth of the multitaper windowing function in Hz. Only used in ‘spectrum_fit’ mode.
- p_value
float
P-value to use in F-test thresholding to determine significant sinusoidal components to remove when method=’spectrum_fit’ and freqs=None. Note that this will be Bonferroni corrected for the number of frequencies, so large p-values may be justified.
- phase
str
Phase of the filter, only used if
method='fir'
. Symmetric linear-phase FIR filters are constructed, and ifphase='zero'
(default), the delay of this filter is compensated for, making it non-causal. Ifphase=='zero-double'
, then this filter is applied twice, once forward, and once backward (also making it non-causal). If ‘minimum’, then a minimum-phase filter will be constricted and applied, which is causal but has weaker stop-band suppression.New in version 0.13.
- fir_window
str
The window to use in FIR design, can be “hamming” (default), “hann” (default in 0.13), or “blackman”.
New in version 0.15.
- fir_design
str
Can be “firwin” (default) to use
scipy.signal.firwin()
, or “firwin2” to usescipy.signal.firwin2()
. “firwin” uses a time-domain design technique that generally gives improved attenuation using fewer samples than “firwin2”.New in version 0.15.
- pad
str
The type of padding to use. Supports all
numpy.pad()
mode
options. Can also be “reflect_limited”, which pads with a reflected version of each vector mirrored on the first and last values of the vector, followed by zeros. Only used formethod='fir'
. The default is'reflect_limited'
.New in version 0.15.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- freqs
- Returns
- rawinstance of
Raw
The raw instance with filtered data.
- rawinstance of
See also
Notes
Applies a zero-phase notch filter to the channels selected by “picks”. By default the data of the Raw object is modified inplace.
The Raw object has to have the data loaded e.g. with
preload=True
orself.load_data()
.Note
If n_jobs > 1, more memory is required as
len(picks) * n_times
additional time points need to be temporaily stored in memory.For details, see
mne.filter.notch_filter()
.Examples using
notch_filter
:
- pick(picks, exclude=())[source]¶
Pick a subset of channels.
- Parameters
- picks
str
|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 ininfo['bads']
will be included if their names or indices are explicitly provided.- exclude
list
|str
Set of channels to exclude, only used when picking based on types (e.g., exclude=”bads” when picks=”meg”).
- picks
- Returns
Examples using
pick
:
- pick_channels(ch_names, ordered=False)[source]¶
Pick some channels.
- Parameters
- Returns
See also
Notes
The channel names given are assumed to be a set, i.e. the order does not matter. The original order of the channels is preserved. You can use
reorder_channels
to set channel order if necessary.New in version 0.9.0.
Examples using
pick_channels
:
- pick_types(meg=False, eeg=False, stim=False, eog=False, ecg=False, emg=False, ref_meg='auto', misc=False, resp=False, chpi=False, exci=False, ias=False, syst=False, seeg=False, dipole=False, gof=False, bio=False, ecog=False, fnirs=False, csd=False, dbs=False, include=(), exclude='bads', selection=None, verbose=None)[source]¶
Pick some channels by type and names.
- Parameters
- megbool |
str
If True include MEG channels. If string it can be ‘mag’, ‘grad’, ‘planar1’ or ‘planar2’ to select only magnetometers, all gradiometers, or a specific type of gradiometer.
- eegbool
If True include EEG channels.
- stimbool
If True include stimulus channels.
- eogbool
If True include EOG channels.
- ecgbool
If True include ECG channels.
- emgbool
If True include EMG channels.
- ref_megbool |
str
If True include CTF / 4D reference channels. If ‘auto’, reference channels are included if compensations are present and
meg
is not False. Can also be the string options for themeg
parameter.- miscbool
If True include miscellaneous analog channels.
- respbool
If
True
include respiratory channels.- chpibool
If True include continuous HPI coil channels.
- excibool
Flux excitation channel used to be a stimulus channel.
- iasbool
Internal Active Shielding data (maybe on Triux only).
- systbool
System status channel information (on Triux systems only).
- seegbool
Stereotactic EEG channels.
- dipolebool
Dipole time course channels.
- gofbool
Dipole goodness of fit channels.
- biobool
Bio channels.
- ecogbool
Electrocorticography channels.
- fnirsbool |
str
Functional near-infrared spectroscopy channels. If True include all fNIRS channels. If False (default) include none. If string it can be ‘hbo’ (to include channels measuring oxyhemoglobin) or ‘hbr’ (to include channels measuring deoxyhemoglobin).
- csdbool
EEG-CSD channels.
- dbsbool
Deep brain stimulation channels.
- include
list
ofstr
List of additional channels to include. If empty do not include any.
- exclude
list
ofstr
|str
List of channels to exclude. If ‘bads’ (default), exclude channels in
info['bads']
.- selection
list
ofstr
Restrict sensor channels (MEG, EEG) to this list of channel names.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- megbool |
- Returns
See also
Notes
New in version 0.9.0.
Examples using
pick_types
:
- plot(events=None, duration=10.0, start=0.0, n_channels=20, bgcolor='w', color=None, bad_color=(0.8, 0.8, 0.8), event_color='cyan', scalings=None, remove_dc=True, order=None, show_options=False, title=None, show=True, block=False, highpass=None, lowpass=None, filtorder=4, clipping=1.5, show_first_samp=False, proj=True, group_by='type', butterfly=False, decim='auto', noise_cov=None, event_id=None, show_scrollbars=True, show_scalebars=True, verbose=None)[source]¶
Plot raw data.
- Parameters
- events
array
|None
Events to show with vertical bars.
- duration
float
Time window (s) to plot. The lesser of this value and the duration of the raw file will be used.
- start
float
Initial time to show (can be changed dynamically once plotted). If show_first_samp is True, then it is taken relative to
raw.first_samp
.- n_channels
int
Number of channels to plot at once. Defaults to 20. The lesser of
n_channels
andlen(raw.ch_names)
will be shown. Has no effect iforder
is ‘position’, ‘selection’ or ‘butterfly’.- bgcolorcolor object
Color of the background.
- color
dict
| color object |None
Color for the data traces. If None, defaults to:
dict(mag='darkblue', grad='b', eeg='k', eog='k', ecg='m', emg='k', ref_meg='steelblue', misc='k', stim='k', resp='k', chpi='k')
- bad_colorcolor object
Color to make bad channels.
- event_colorcolor object |
dict
|None
Color(s) to use for events. To show all events in the same color, pass any matplotlib-compatible color. To color events differently, pass a
dict
that maps event names or integer event numbers to colors (must include entries for all events, or include a “fallback” entry with key-1
). IfNone
, colors are chosen from the current Matplotlib color cycle. Defaults to'cyan'
.- scalings‘auto’ |
dict
|None
Scaling factors for the traces. If any fields in scalings are ‘auto’, the scaling factor is set to match the 99.5th percentile of a subset of the corresponding data. If scalings == ‘auto’, all scalings fields are set to ‘auto’. If any fields are ‘auto’ and data is not preloaded, a subset of times up to 100mb will be loaded. If None, defaults to:
dict(mag=1e-12, grad=4e-11, eeg=20e-6, eog=150e-6, ecg=5e-4, emg=1e-3, ref_meg=1e-12, misc=1e-3, stim=1, resp=1, chpi=1e-4, whitened=1e2)
- remove_dcbool
If True remove DC component when plotting data.
- order
array
ofint
|None
Order in which to plot data. If the array is shorter than the number of channels, only the given channels are plotted. If None (default), all channels are plotted. If
group_by
is'position'
or'selection'
, theorder
parameter is used only for selecting the channels to be plotted.- show_optionsbool
If True, a dialog for options related to projection is shown.
- title
str
|None
The title of the window. If None, and either the filename of the raw object or ‘<unknown>’ will be displayed as title.
- showbool
Show figure if True.
- blockbool
Whether to halt program execution until the figure is closed. Useful for setting bad channels on the fly by clicking on a line. May not work on all systems / platforms.
- highpass
float
|None
Highpass to apply when displaying data.
- lowpass
float
|None
Lowpass to apply when displaying data. If highpass > lowpass, a bandstop rather than bandpass filter will be applied.
- filtorder
int
Filtering order. 0 will use FIR filtering with MNE defaults. Other values will construct an IIR filter of the given order and apply it with
filtfilt()
(making the effective order twicefiltorder
). Filtering may produce some edge artifacts (at the left and right edges) of the signals during display.Changed in version 0.18: Support for
filtorder=0
to use FIR filtering.- clipping
str
|float
|None
If None, channels are allowed to exceed their designated bounds in the plot. If “clamp”, then values are clamped to the appropriate range for display, creating step-like artifacts. If “transparent”, then excessive values are not shown, creating gaps in the traces. If float, clipping occurs for values beyond the
clipping
multiple of their dedicated range, soclipping=1.
is an alias forclipping='transparent'
.Changed in version 0.21: Support for float, and default changed from None to 1.5.
- show_first_sampbool
If True, show time axis relative to the
raw.first_samp
.- projbool
Whether to apply projectors prior to plotting (default is
True
). Individual projectors can be enabled/disabled interactively (see Notes). This argument only affects the plot; useraw.apply_proj()
to modify the data stored in the Raw object.- group_by
str
How to group channels.
'type'
groups by channel type,'original'
plots in the order of ch_names,'selection'
uses Elekta’s channel groupings (only works for Neuromag data),'position'
groups the channels by the positions of the sensors.'selection'
and'position'
modes allow custom selections by using a lasso selector on the topomap. In butterfly mode,'type'
and'original'
group the channels by type, whereas'selection'
and'position'
use regional grouping.'type'
and'original'
modes are ignored whenorder
is notNone
. Defaults to'type'
.- butterflybool
Whether to start in butterfly mode. Defaults to False.
- decim
int
| ‘auto’ Amount to decimate the data during display for speed purposes. You should only decimate if the data are sufficiently low-passed, otherwise aliasing can occur. The ‘auto’ mode (default) uses the decimation that results in a sampling rate least three times larger than
min(info['lowpass'], lowpass)
(e.g., a 40 Hz lowpass will result in at least a 120 Hz displayed sample rate).- noise_covinstance of
Covariance
|str
|None
Noise covariance used to whiten the data while plotting. Whitened data channels are scaled by
scalings['whitened']
, and their channel names are shown in italic. Can be a string to load a covariance from disk. See alsomne.Evoked.plot_white()
for additional inspection of noise covariance properties when whitening evoked data. For data processed with SSS, the effective dependence between magnetometers and gradiometers may introduce differences in scaling, consider usingmne.Evoked.plot_white()
.New in version 0.16.0.
- event_id
dict
|None
Event IDs used to show at event markers (default None shows the event numbers).
New in version 0.16.0.
- show_scrollbarsbool
Whether to show scrollbars when the plot is initialized. Can be toggled after initialization by pressing z (“zen mode”) while the plot window is focused. Default is
True
.New in version 0.19.0.
- show_scalebarsbool
Whether or not to show the scale bars. Defaults to True.
New in version 0.20.0.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only.
- events
- Returns
- figinstance of
matplotlib.figure.Figure
Raw traces.
- figinstance of
Notes
The arrow keys (up/down/left/right) can typically be used to navigate between channels and time ranges, but this depends on the backend matplotlib is configured to use (e.g., mpl.use(‘TkAgg’) should work). The left/right arrows will scroll by 25% of
duration
, whereas shift+left/shift+right will scroll by 100% ofduration
. The scaling can be adjusted with - and + (or =) keys. The viewport dimensions can be adjusted with page up/page down and home/end keys. Full screen mode can be toggled with the F11 key, and scrollbars can be hidden/shown by pressing ‘z’. Right-click a channel label to view its location. To mark or un-mark a channel as bad, click on a channel label or a channel trace. The changes will be reflected immediately in the raw object’sraw.info['bads']
entry.If projectors are present, a button labelled “Prj” in the lower right corner of the plot window opens a secondary control window, which allows enabling/disabling specific projectors individually. This provides a means of interactively observing how each projector would affect the raw data if it were applied.
Annotation mode is toggled by pressing ‘a’, butterfly mode by pressing ‘b’, and whitening mode (when
noise_cov is not None
) by pressing ‘w’. By default, the channel means are removed whenremove_dc
is set toTrue
. This flag can be toggled by pressing ‘d’.Examples using
plot
:
- plot_projs_topomap(ch_type=None, cmap=None, sensors=True, colorbar=False, res=64, size=1, show=True, outlines='head', contours=6, image_interp='bilinear', axes=None, vlim=(None, None), sphere=None, extrapolate='auto', border='mean')[source]¶
Plot SSP vector.
- Parameters
- ch_type‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ |
None
|list
The channel type to plot. For ‘grad’, the gradiometers are collec- ted in pairs and the RMS for each pair is plotted. If None (default), it will return all channel types present. If a list of ch_types is provided, it will return multiple figures.
- cmapmatplotlib colormap | (colormap, bool) | ‘interactive’ |
None
Colormap to use. If tuple, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In interactive mode (only works if
colorbar=True
) the colors are adjustable by clicking and dragging the colorbar with left and right mouse button. Left mouse button moves the scale up and down and right mouse button adjusts the range. Hitting space bar resets the range. Up and down arrows can be used to change the colormap. If None (default), ‘Reds’ is used for all positive data, otherwise defaults to ‘RdBu_r’. If ‘interactive’, translates to (None, True).- sensorsbool |
str
Add markers for sensor locations to the plot. Accepts matplotlib plot format string (e.g., ‘r+’ for red plusses). If True, a circle will be used (via .add_artist). Defaults to True.
- colorbarbool
Plot a colorbar.
- res
int
The resolution of the topomap image (n pixels along each side).
- sizescalar
Side length of the topomaps in inches (only applies when plotting multiple topomaps at a time).
- showbool
Show figure if True.
- outlines‘head’ | ‘skirt’ |
dict
|None
The outlines to be drawn. If ‘head’, the default head scheme will be drawn. If ‘skirt’ the head scheme will be drawn, but sensors are allowed to be plotted outside of the head circle. If dict, each key refers to a tuple of x and y positions, the values in ‘mask_pos’ will serve as image mask. Alternatively, a matplotlib patch object can be passed for advanced masking options, either directly or as a function that returns patches (required for multi-axis plots). If None, nothing will be drawn. Defaults to ‘head’.
- contours
int
|array
offloat
The number of contour lines to draw. If 0, no contours will be drawn. When an integer, matplotlib ticker locator is used to find suitable values for the contour thresholds (may sometimes be inaccurate, use array for accuracy). If an array, the values represent the levels for the contours. Defaults to 6.
- image_interp
str
The image interpolation to be used. All matplotlib options are accepted.
- axesinstance of
Axes
|list
|None
The axes to plot to. If list, the list must be a list of Axes of the same length as the number of projectors. If instance of Axes, there must be only one projector. Defaults to None.
- vlim
tuple
of length 2 | ‘joint’ Colormap limits to use. If
tuple
, specifies the lower and upper bounds of the colormap (in that order); providingNone
for either of these will set the corresponding boundary at the min/max of the data (separately for each projector). The keyword value'joint'
will compute the colormap limits jointly across all provided projectors of the same channel type, using the min/max of the projector data. If vlim is'joint'
,info
must not beNone
. Defaults to(None, None)
.- sphere
float
| array_like |str
|None
The sphere parameters to use for the cartoon head. Can be array-like of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give the radius (origin assumed 0, 0, 0). Can also be a spherical ConductorModel, which will use the origin and radius. Can be “auto” to use a digitization-based fit. Can also be None (default) to use ‘auto’ when enough extra digitization points are available, and 0.095 otherwise. Currently the head radius does not affect plotting.
New in version 0.20.
- extrapolate
str
Options:
'box'
Extrapolate to four points placed to form a square encompassing all data points, where each side of the square is three times the range of the data in the respective dimension.
'local'
(default)Extrapolate only to nearby points (approximately to points closer than median inter-electrode distance). This will also set the mask to be polygonal based on the convex hull of the sensors.
'head'
Extrapolate out to the edges of the clipping circle. This will be on the head circle when the sensors are contained within the head circle, but it can extend beyond the head when sensors are plotted outside the head circle.
Changed in version 0.21:
The default was changed to
'local'
'local'
was changed to use a convex hull mask'head'
was changed to extrapolate out to the clipping circle.
New in version 0.20.
- border
float
| ‘mean’ Value to extrapolate to on the topomap borders. If
'mean'
(default), then each extrapolated point has the average value of its neighbours.New in version 0.20.
- ch_type‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ |
- Returns
- figinstance of
Figure
Figure distributing one image per channel across sensor topography.
- figinstance of
Examples using
plot_projs_topomap
:
- plot_psd(fmin=0, fmax=inf, tmin=None, tmax=None, proj=False, n_fft=None, n_overlap=0, reject_by_annotation=True, picks=None, ax=None, color='black', xscale='linear', area_mode='std', area_alpha=0.33, dB=True, estimate='auto', show=True, n_jobs=1, average=False, line_alpha=None, spatial_colors=True, sphere=None, window='hamming', verbose=None)[source]¶
Plot the power spectral density across channels.
Different channel types are drawn in sub-plots. When the data have been processed with a bandpass, lowpass or highpass filter, dashed lines (╎) indicate the boundaries of the filter. The line noise frequency is also indicated with a dashed line (⋮).
- Parameters
- fmin
float
Start frequency to consider.
- fmax
float
End frequency to consider.
- tmin
float
|None
Start time to consider.
- tmax
float
|None
End time to consider.
- projbool
Apply projection.
- n_fft
int
|None
Number of points to use in Welch FFT calculations. Default is None, which uses the minimum of 2048 and the number of time points.
- n_overlap
int
The number of points of overlap between blocks. The default value is 0 (no overlap).
- reject_by_annotationbool
Whether to omit bad segments from the data before fitting. If
True
(default), annotated segments whose description begins with'bad'
are omitted. IfFalse
, no rejection based on annotations is performed.Has no effect if
inst
is not amne.io.Raw
object.- picks
str
|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 good data channels. Note that channels ininfo['bads']
will be included if their names or indices are explicitly provided Cannot be None ifax
is supplied.If bothpicks
andax
are None separate subplots will be created for each standard channel type (mag
,grad
, andeeg
).- axinstance of
Axes
|None
Axes to plot into. If None, axes will be created.
- color
str
|tuple
A matplotlib-compatible color to use. Has no effect when spatial_colors=True.
- xscale
str
Can be ‘linear’ (default) or ‘log’.
- area_mode
str
|None
Mode for plotting area. If ‘std’, the mean +/- 1 STD (across channels) will be plotted. If ‘range’, the min and max (across channels) will be plotted. Bad channels will be excluded from these calculations. If None, no area will be plotted. If average=False, no area is plotted.
- area_alpha
float
Alpha for the area.
- dBbool
Plot Power Spectral Density (PSD), in units (amplitude**2/Hz (dB)) if
dB=True
, andestimate='power'
orestimate='auto'
. Plot PSD in units (amplitude**2/Hz) ifdB=False
and,estimate='power'
. Plot Amplitude Spectral Density (ASD), in units (amplitude/sqrt(Hz)), ifdB=False
andestimate='amplitude'
orestimate='auto'
. Plot ASD, in units (amplitude/sqrt(Hz) (db)), ifdB=True
andestimate='amplitude'
.- estimate
str
, {‘auto’, ‘power’, ‘amplitude’} Can be “power” for power spectral density (PSD), “amplitude” for amplitude spectrum density (ASD), or “auto” (default), which uses “power” when dB is True and “amplitude” otherwise.
- showbool
Show figure if True.
- n_jobs
int
The number of jobs to run in parallel (default 1). Requires the joblib package.
- averagebool
If False, the PSDs of all channels is displayed. No averaging is done and parameters area_mode and area_alpha are ignored. When False, it is possible to paint an area (hold left mouse button and drag) to plot a topomap.
- line_alpha
float
|None
Alpha for the PSD line. Can be None (default) to use 1.0 when
average=True
and 0.1 whenaverage=False
.- spatial_colorsbool
Whether to use spatial colors. Only used when
average=False
.- sphere
float
| array_like |str
|None
The sphere parameters to use for the cartoon head. Can be array-like of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give the radius (origin assumed 0, 0, 0). Can also be a spherical ConductorModel, which will use the origin and radius. Can be “auto” to use a digitization-based fit. Can also be None (default) to use ‘auto’ when enough extra digitization points are available, and 0.095 otherwise. Currently the head radius does not affect plotting.
New in version 0.20.
- window
str
|float
|tuple
Windowing function to use. See
scipy.signal.get_window()
.New in version 0.22.0.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only.
- fmin
- Returns
- figinstance of
Figure
Figure with frequency spectra of the data channels.
- figinstance of
Examples using
plot_psd
:
- plot_psd_topo(tmin=0.0, tmax=None, fmin=0, fmax=100, proj=False, n_fft=2048, n_overlap=0, layout=None, color='w', fig_facecolor='k', axis_facecolor='k', dB=True, show=True, block=False, n_jobs=1, axes=None, verbose=None)[source]¶
Plot channel-wise frequency spectra as topography.
- Parameters
- tmin
float
Start time for calculations. Defaults to zero.
- tmax
float
|None
End time for calculations. If None (default), the end of data is used.
- fmin
float
Start frequency to consider. Defaults to zero.
- fmax
float
End frequency to consider. Defaults to 100.
- projbool
Apply projection. Defaults to False.
- n_fft
int
Number of points to use in Welch FFT calculations. Defaults to 2048.
- n_overlap
int
The number of points of overlap between blocks. Defaults to 0 (no overlap).
- layoutinstance of
Layout
|None
Layout instance specifying sensor positions (does not need to be specified for Neuromag data). If None (default), the correct layout is inferred from the data.
- color
str
|tuple
A matplotlib-compatible color to use for the curves. Defaults to white.
- fig_facecolor
str
|tuple
A matplotlib-compatible color to use for the figure background. Defaults to black.
- axis_facecolor
str
|tuple
A matplotlib-compatible color to use for the axis background. Defaults to black.
- dBbool
If True, transform data to decibels. Defaults to True.
- showbool
Show figure if True. Defaults to True.
- blockbool
Whether to halt program execution until the figure is closed. May not work on all systems / platforms. Defaults to False.
- n_jobs
int
The number of jobs to run in parallel (default 1). Requires the joblib package.
- axesinstance of matplotlib
Axes
|None
Axes to plot into. If None, axes will be created.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only.
- tmin
- Returns
- figinstance of
matplotlib.figure.Figure
Figure distributing one image per channel across sensor topography.
- figinstance of
Examples using
plot_psd_topo
:
- plot_sensors(kind='topomap', ch_type=None, title=None, show_names=False, ch_groups=None, to_sphere=True, axes=None, block=False, show=True, sphere=None, verbose=None)[source]¶
Plot sensor positions.
- Parameters
- kind
str
Whether to plot the sensors as 3d, topomap or as an interactive sensor selection dialog. Available options ‘topomap’, ‘3d’, ‘select’. If ‘select’, a set of channels can be selected interactively by using lasso selector or clicking while holding control key. The selected channels are returned along with the figure instance. Defaults to ‘topomap’.
- ch_type
None
|str
The channel type to plot. Available options ‘mag’, ‘grad’, ‘eeg’, ‘seeg’, ‘dbs’, ‘ecog’, ‘all’. If
'all'
, all the available mag, grad, eeg, seeg, dbs, and ecog channels are plotted. If None (default), then channels are chosen in the order given above.- title
str
|None
Title for the figure. If None (default), equals to
'Sensor positions (%s)' % ch_type
.- show_namesbool |
array
ofstr
Whether to display all channel names. If an array, only the channel names in the array are shown. Defaults to False.
- ch_groups‘position’ |
array
of shape (n_ch_groups, n_picks) |None
Channel groups for coloring the sensors. If None (default), default coloring scheme is used. If ‘position’, the sensors are divided into 8 regions. See
order
kwarg ofmne.viz.plot_raw()
. If array, the channels are divided by picks given in the array.New in version 0.13.0.
- to_spherebool
Whether to project the 3d locations to a sphere. When False, the sensor array appears similar as to looking downwards straight above the subject’s head. Has no effect when kind=’3d’. Defaults to True.
New in version 0.14.0.
- axesinstance of
Axes
| instance ofAxes3D
|None
Axes to draw the sensors to. If
kind='3d'
, axes must be an instance of Axes3D. If None (default), a new axes will be created.New in version 0.13.0.
- blockbool
Whether to halt program execution until the figure is closed. Defaults to False.
New in version 0.13.0.
- showbool
Show figure if True. Defaults to True.
- sphere
float
| array_like |str
|None
The sphere parameters to use for the cartoon head. Can be array-like of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give the radius (origin assumed 0, 0, 0). Can also be a spherical ConductorModel, which will use the origin and radius. Can be “auto” to use a digitization-based fit. Can also be None (default) to use ‘auto’ when enough extra digitization points are available, and 0.095 otherwise. Currently the head radius does not affect plotting.
New in version 0.20.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- kind
- Returns
See also
Notes
This function plots the sensor locations from the info structure using matplotlib. For drawing the sensors using mayavi see
mne.viz.plot_alignment()
.New in version 0.12.0.
Examples using
plot_sensors
:
- property proj¶
Whether or not projections are active.
- rename_channels(mapping, allow_duplicates=False, verbose=None)[source]¶
Rename channels.
- Parameters
- mapping
dict
|callable()
A dictionary mapping the old channel to a new channel name e.g. {‘EEG061’ : ‘EEG161’}. Can also be a callable function that takes and returns a string.
Changed in version 0.10.0: Support for a callable function.
- allow_duplicatesbool
If True (default False), allow duplicates, which will automatically be renamed with
-N
at the end.New in version 0.22.0.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- mapping
- Returns
Notes
New in version 0.9.0.
Examples using
rename_channels
:
- reorder_channels(ch_names)[source]¶
Reorder channels.
- Parameters
- ch_names
list
The desired channel order.
- ch_names
- Returns
See also
Notes
Channel names must be unique. Channels that are not in
ch_names
are dropped.New in version 0.16.0.
Examples using
reorder_channels
:
- resample(sfreq, npad='auto', window='boxcar', stim_picks=None, n_jobs=1, events=None, pad='reflect_limited', verbose=None)[source]¶
Resample all channels.
If appropriate, an anti-aliasing filter is applied before resampling. See Resampling and decimating data for more information.
Warning
The intended purpose of this function is primarily to speed up computations (e.g., projection calculation) when precise timing of events is not required, as downsampling raw data effectively jitters trigger timings. It is generally recommended not to epoch downsampled data, but instead epoch and then downsample, as epoching downsampled data jitters triggers. For more, see this illustrative gist.
If resampling the continuous data is desired, it is recommended to construct events using the original data. The event onsets can be jointly resampled with the raw data using the ‘events’ parameter (a resampled copy is returned).
- Parameters
- sfreq
float
New sample rate to use.
- npad
int
|str
Amount to pad the start and end of the data. Can also be “auto” to use a padding that will result in a power-of-two size (can be much faster).
- window
str
|tuple
Frequency-domain window to use in resampling. See
scipy.signal.resample()
.- stim_picks
list
ofint
|None
Stim channels. These channels are simply subsampled or supersampled (without applying any filtering). This reduces resampling artifacts in stim channels, but may lead to missing triggers. If None, stim channels are automatically chosen using
mne.pick_types()
.- n_jobs
int
|str
Number of jobs to run in parallel. Can be ‘cuda’ if
cupy
is installed properly.- events2D
array
, shape (n_events, 3) |None
An optional event matrix. When specified, the onsets of the events are resampled jointly with the data. NB: The input events are not modified, but a new array is returned with the raw instead.
- pad
str
The type of padding to use. Supports all
numpy.pad()
mode
options. Can also be “reflect_limited”, which pads with a reflected version of each vector mirrored on the first and last values of the vector, followed by zeros. Only used formethod='fir'
. The default is'reflect_limited'
.New in version 0.15.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- sfreq
- Returns
See also
Notes
For some data, it may be more accurate to use
npad=0
to reduce artifacts. This is dataset dependent – check your data!For optimum performance and to make use of
n_jobs > 1
, the raw object has to have the data loaded e.g. withpreload=True
orself.load_data()
, but this increases memory requirements. The resulting raw object will have the data loaded into memory.
- save(fname, picks=None, tmin=0, tmax=None, buffer_size_sec=None, drop_small_buffer=False, proj=False, fmt='single', overwrite=False, split_size='2GB', split_naming='neuromag', verbose=None)[source]¶
Save raw data to file.
- Parameters
- fname
str
File name of the new dataset. This has to be a new filename unless data have been preloaded. Filenames should end with
raw.fif
(common raw data),raw_sss.fif
(Maxwell-filtered continuous data),raw_tsss.fif
(temporally signal-space-separated data),_meg.fif
(common MEG data),_eeg.fif
(common EEG data), or_ieeg.fif
(common intracranial EEG data). You may also append an additional.gz
suffix to enable gzip compression.- picks
str
|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 ininfo['bads']
will be included if their names or indices are explicitly provided.- tmin
float
Start time of the raw data to use in seconds (must be >= 0).
- tmax
float
End time of the raw data to use in seconds (cannot exceed data duration).
- buffer_size_sec
float
|None
Size of data chunks in seconds. If None (default), the buffer size of the original file is used.
- drop_small_bufferbool
Drop or not the last buffer. It is required by maxfilter (SSS) that only accepts raw files with buffers of the same size.
- projbool
If True the data is saved with the projections applied (active).
Note
If
apply_proj()
was used to apply the projections, the projectons will be active even ifproj
is False.- fmt‘single’ | ‘double’ | ‘int’ | ‘short’
Format to use to save raw data. Valid options are ‘double’, ‘single’, ‘int’, and ‘short’ for 64- or 32-bit float, or 32- or 16-bit integers, respectively. It is strongly recommended to use ‘single’, as this is backward-compatible, and is standard for maintaining precision. Note that using ‘short’ or ‘int’ may result in loss of precision, complex data cannot be saved as ‘short’, and neither complex data types nor real data stored as ‘double’ can be loaded with the MNE command-line tools. See raw.orig_format to determine the format the original data were stored in.
- overwritebool
If True (default False), overwrite the destination file if it exists. To overwrite original file (the same one that was loaded), data must be preloaded upon reading.
- split_size
str
|int
Large raw files are automatically split into multiple pieces. This parameter specifies the maximum size of each piece. If the parameter is an integer, it specifies the size in Bytes. It is also possible to pass a human-readable string, e.g., 100MB.
Note
Due to FIFF file limitations, the maximum split size is 2GB.
- split_naming{‘neuromag’ | ‘bids’}
Add the filename partition with the appropriate naming schema.
New in version 0.17.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- fname
Notes
If Raw is a concatenation of several raw files, be warned that only the measurement information from the first raw file is stored. This likely means that certain operations with external tools may not work properly on a saved concatenated file (e.g., probably some or all forms of SSS). It is recommended not to concatenate and then save raw files for this reason.
Examples using
save
:
- savgol_filter(h_freq, verbose=None)[source]¶
Filter the data using Savitzky-Golay polynomial method.
- Parameters
- h_freq
float
Approximate high cut-off frequency in Hz. Note that this is not an exact cutoff, since Savitzky-Golay filtering 1 is done using polynomial fits instead of FIR/IIR filtering. This parameter is thus used to determine the length of the window over which a 5th-order polynomial smoothing is used.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- h_freq
- Returns
See also
Notes
For Savitzky-Golay low-pass approximation, see:
New in version 0.9.0.
References
- 1
Abraham Savitzky and Marcel J. E. Golay. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8):1627–1639, 1964. doi:10.1021/ac60214a047.
Examples
>>> import mne >>> from os import path as op >>> evoked_fname = op.join(mne.datasets.sample.data_path(), 'MEG', 'sample', 'sample_audvis-ave.fif') >>> evoked = mne.read_evokeds(evoked_fname, baseline=(None, 0))[0] >>> evoked.savgol_filter(10.) # low-pass at around 10 Hz >>> evoked.plot()
- set_annotations(annotations, emit_warning=True, on_missing='raise', *, verbose=None)[source]¶
Setter for annotations.
This setter checks if they are inside the data range.
- Parameters
- annotationsinstance of
mne.Annotations
|None
Annotations to set. If None, the annotations is defined but empty.
- emit_warningbool
Whether to emit warnings when cropping or omitting annotations.
- on_missing
str
Can be
'raise'
(default) to raise an error,'warn'
to emit a warning, or'ignore'
to ignore when entries in ch_names are not present in the raw instance.New in version 0.23.0.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- annotationsinstance of
- Returns
- selfinstance of
Raw
The raw object with annotations.
- selfinstance of
Examples using
set_annotations
:
- set_channel_types(mapping, verbose=None)[source]¶
Define the sensor type of channels.
- Parameters
- mapping
dict
A dictionary mapping a channel to a sensor type (str), e.g.,
{'EEG061': 'eog'}
.- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- mapping
- Returns
Notes
The following sensor types are accepted:
ecg, eeg, emg, eog, exci, ias, misc, resp, seeg, dbs, stim, syst, ecog, hbo, hbr, fnirs_cw_amplitude, fnirs_fd_ac_amplitude, fnirs_fd_phase, fnirs_od
New in version 0.9.0.
Examples using
set_channel_types
:
- set_eeg_reference(ref_channels='average', projection=False, ch_type='auto', forward=None, verbose=None)[source]¶
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.
- Parameters
- ref_channels
list
ofstr
|str
Can be:
The name(s) of the channel(s) used to construct the reference.
'average'
to apply an average reference (default)'REST'
to use the Reference Electrode Standardization Technique infinity reference 2.An empty list, in which case MNE will not attempt any re-referencing of the data
- projectionbool
If
ref_channels='average'
this argument specifies if the average reference should be computed as a projection (True) or not (False; default). Ifprojection=True
, the average reference is added as a projection and is not applied to the data (it can be applied afterwards with theapply_proj
method). Ifprojection=False
, the average reference is directly applied to the data. Ifref_channels
is not'average'
,projection
must be set toFalse
(the default in this case).- ch_type‘auto’ | ‘eeg’ | ‘ecog’ | ‘seeg’ | ‘dbs’
The name of the channel type to apply the reference to. If ‘auto’, the first channel type of eeg, ecog, seeg or dbs that is found (in that order) will be selected.
New in version 0.19.
- forwardinstance of
Forward
|None
Forward solution to use. Only used with
ref_channels='REST'
.New in version 0.21.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- ref_channels
- Returns
See also
mne.set_bipolar_reference
Convenience function for creating bipolar references.
Notes
Some common referencing schemes and the corresponding value for the
ref_channels
parameter:- Average reference:
A new virtual reference electrode is created by averaging the current EEG signal by setting
ref_channels='average'
. Bad EEG channels are automatically excluded if they are properly set ininfo['bads']
.
- A single electrode:
Set
ref_channels
to a list containing the name of the channel that will act as the new reference, for exampleref_channels=['Cz']
.
- The mean of multiple electrodes:
A new virtual reference electrode is created by computing the average of the current EEG signal recorded from two or more selected channels. Set
ref_channels
to a list of channel names, indicating which channels to use. For example, to apply an average mastoid reference, when using the 10-20 naming scheme, setref_channels=['M1', 'M2']
.
- REST
The given EEG electrodes are referenced to a point at infinity using the lead fields in
forward
, which helps standardize the signals.
If a reference is requested that is not the average reference, this function removes any pre-existing average reference projections.
During source localization, the EEG signal should have an average reference.
In order to apply a reference, the data must be preloaded. This is not necessary if
ref_channels='average'
andprojection=True
.For an average or REST reference, bad EEG channels are automatically excluded if they are properly set in
info['bads']
.
New in version 0.9.0.
References
- 2
D. Yao. A method to standardize a reference of scalp EEG recordings to a point at infinity. Physiological Measurement, 22(4):693–711, 2001. doi:10.1088/0967-3334/22/4/305.
Examples using
set_eeg_reference
:
- set_meas_date(meas_date)[source]¶
Set the measurement start date.
- Parameters
- meas_date
datetime
|float
|tuple
|None
The new measurement date. If datetime object, it must be timezone-aware and in UTC. A tuple of (seconds, microseconds) or float (alias for
(meas_date, 0)
) can also be passed and a datetime object will be automatically created. If None, will remove the time reference.
- meas_date
- Returns
See also
Notes
If you want to remove all time references in the file, call
mne.io.anonymize_info(inst.info)
after callinginst.set_meas_date(None)
.New in version 0.20.
- set_montage(montage, match_case=True, match_alias=False, on_missing='raise', verbose=None)[source]¶
Set EEG sensor configuration and head digitization.
- Parameters
- montage
None
|str
|DigMontage
A montage containing channel positions. If str or DigMontage is specified, the channel info will be updated with the channel positions. Default is None. For valid
str
values see documentation ofmne.channels.make_standard_montage()
. See also the documentation ofmne.channels.DigMontage
for more information.- match_casebool
If True (default), channel name matching will be case sensitive.
New in version 0.20.
- match_aliasbool |
dict
Whether to use a lookup table to match unrecognized channel location names to their known aliases. If True, uses the mapping in
mne.io.constants.CHANNEL_LOC_ALIASES
. If adict
is passed, it will be used instead, and should map from non-standard channel names to names in the specifiedmontage
. Default isFalse
.New in version 0.23.
- on_missing
str
Can be
'raise'
(default) to raise an error,'warn'
to emit a warning, or'ignore'
to ignore when channels have missing coordinates.New in version 0.20.1.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.
- montage
- Returns
Notes
Operates in place.
Examples using
set_montage
:
- time_as_index(times, use_rounding=False, origin=None)[source]¶
Convert time to indices.
- Parameters
- timeslist-like |
float
|int
List of numbers or a number representing points in time.
- use_roundingbool
If True, use rounding (instead of truncation) when converting times to indices. This can help avoid non-unique indices.
- origin
datetime
|float
|int
|None
Time reference for times. If None,
times
are assumed to be relative tofirst_samp
.New in version 0.17.0.
- timeslist-like |
- Returns
- index
ndarray
Indices relative to
first_samp
corresponding to the times supplied.
- index
Examples using
time_as_index
:
- property times¶
Time points.
- to_data_frame(picks=None, index=None, scalings=None, copy=True, start=None, stop=None, long_format=False, time_format='ms')[source]¶
Export data in tabular structure as a pandas DataFrame.
Channels are converted to columns in the DataFrame. By default, an additional column “time” is added, unless
index
is notNone
(in which case time values form the DataFrame’s index).- Parameters
- picks
str
|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 ininfo['bads']
will be included if their names or indices are explicitly provided.- index‘time’ |
None
Kind of index to use for the DataFrame. If
None
, a sequential integer index (pandas.RangeIndex
) will be used. If'time'
, apandas.Float64Index
,pandas.Int64Index
,pandas.DatetimeIndex
, orpandas.TimedeltaIndex
will be used (depending on the value oftime_format
). Defaults toNone
.- scalings
dict
|None
Scaling factor applied to the channels picked. If
None
, defaults todict(eeg=1e6, mag=1e15, grad=1e13)
— i.e., converts EEG to µV, magnetometers to fT, and gradiometers to fT/cm.- copybool
If
True
, data will be copied. Otherwise data may be modified in place. Defaults toTrue
.- start
int
|None
Starting sample index for creating the DataFrame from a temporal span of the Raw object.
None
(the default) uses the first sample.- stop
int
|None
Ending sample index for creating the DataFrame from a temporal span of the Raw object.
None
(the default) uses the last sample.- long_formatbool
If True, the DataFrame is returned in long format where each row is one observation of the signal at a unique combination of time point and channel. For convenience, a
ch_type
column is added to facilitate subsetting the resulting DataFrame. Defaults toFalse
.- time_format
str
|None
Desired time format. If
None
, no conversion is applied, and time values remain as float values in seconds. If'ms'
, time values will be rounded to the nearest millisecond and converted to integers. If'timedelta'
, time values will be converted topandas.Timedelta
values. If'datetime'
, time values will be converted topandas.Timestamp
values, relative toraw.info['meas_date']
and offset byraw.first_samp
. Default is'ms'
in version 0.22, and will change toNone
in version 0.23.New in version 0.20.
- picks
- Returns
- dfinstance of
pandas.DataFrame
A dataframe suitable for usage with other statistical/plotting/analysis packages.
- dfinstance of
Examples using
to_data_frame
: