mne.EpochsArray¶
- class mne.EpochsArray(data, info, events=None, tmin=0, event_id=None, reject=None, flat=None, reject_tmin=None, reject_tmax=None, baseline=None, proj=True, on_missing='raise', metadata=None, selection=None, verbose=None)[source]¶
Epochs object from numpy array.
- Parameters
- data
array
, shape (n_epochs, n_channels, n_times) The channels’ time series for each epoch. See notes for proper units of measure.
- infoinstance of
Info
Info dictionary. Consider using
create_info
to populate this structure.- events
None
|array
ofint
, shape (n_events, 3) The events typically returned by the read_events function. If some events don’t match the events of interest as specified by event_id, they will be marked as ‘IGNORED’ in the drop log. If None (default), all event values are set to 1 and event time-samples are set to range(n_epochs).
- tmin
float
Start time before event. If nothing provided, defaults to 0.
- event_id
int
|list
ofint
|dict
|None
The id of the event to consider. If dict, the keys can later be used to access associated events. Example: dict(auditory=1, visual=3). If int, a dict will be created with the id as string. If a list, all events with the IDs specified in the list are used. If None, all events will be used with and a dict is created with string integer names corresponding to the event id integers.
- reject
dict
|None
Reject epochs based on peak-to-peak signal amplitude (PTP), i.e. the absolute difference between the lowest and the highest signal value. In each individual epoch, the PTP is calculated for every channel. If the PTP of any one channel exceeds the rejection threshold, the respective epoch will be dropped.
The dictionary keys correspond to the different channel types; valid keys are:
'grad'
,'mag'
,'eeg'
,'eog'
, and'ecg'
.Example:
reject = dict(grad=4000e-13, # unit: T / m (gradiometers) mag=4e-12, # unit: T (magnetometers) eeg=40e-6, # unit: V (EEG channels) eog=250e-6 # unit: V (EOG channels) )
Note
Since rejection is based on a signal difference calculated for each channel separately, applying baseline correction does not affect the rejection procedure, as the difference will be preserved.
If
reject
isNone
(default), no rejection is performed.- flat
dict
|None
Rejection parameters based on flatness of signal. Valid keys are
'grad'
,'mag'
,'eeg'
,'eog'
,'ecg'
. The values are floats that set the minimum acceptable peak-to-peak amplitude (PTP). If the PTP is smaller than this threshold, the epoch will be dropped. IfNone
then no rejection is performed based on flatness of the signal.- reject_tminscalar |
None
Start of the time window used to reject epochs (with the default None, the window will start with tmin).
- reject_tmaxscalar |
None
End of the time window used to reject epochs (with the default None, the window will end with tmax).
- baseline
None
|tuple
of length 2 The time interval to consider as “baseline” when applying baseline correction. If
None
, do not apply baseline correction. If a tuple(a, b)
, the interval is betweena
andb
(in seconds), including the endpoints. Ifa
isNone
, the beginning of the data is used; and ifb
isNone
, it is set to the end of the interval. If(None, None)
, the entire time interval is used.Note
The baseline
(a, b)
includes both endpoints, i.e. all timepointst
such thata <= t <= b
.Correction is applied to each epoch and channel individually in the following way:
Calculate the mean signal of the baseline period.
Subtract this mean from the entire epoch.
Defaults to
None
, i.e. no baseline correction.- projbool | ‘delayed’
Apply SSP projection vectors. See
mne.Epochs
for details.- on_missing
str
See
mne.Epochs
docstring for details.- metadatainstance of
pandas.DataFrame
|None
See
mne.Epochs
docstring for details.New in version 0.16.
- selection
ndarray
|None
The selection compared to the original set of epochs. Can be None to use
np.arange(len(events))
.New in version 0.16.
- 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.
- data
See also
Notes
Proper units of measure:
V: eeg, eog, seeg, dbs, emg, ecg, bio, ecog
T: mag
T/m: grad
M: hbo, hbr
Am: dipole
AU: misc
- Attributes
Methods
__contains__
(ch_type)Check channel type membership.
__getitem__
(item)Return an Epochs object with a copied subset of epochs.
__hash__
()Hash the object.
__iter__
()Facilitate iteration over epochs.
__len__
()Return the number of epochs.
add_channels
(add_list[, force_update_info])Append new channels to the instance.
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.
apply_baseline
([baseline, verbose])Baseline correct epochs.
apply_function
(fun[, picks, dtype, n_jobs, …])Apply a function to a subset of channels.
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.
as_type
([ch_type, mode])Compute virtual epochs using interpolated fields.
average
([picks, method])Compute an average over epochs.
copy
()Return copy of Epochs instance.
crop
([tmin, tmax, include_tmax, verbose])Crop a time interval from the epochs.
decimate
(decim[, offset, verbose])Decimate the epochs.
del_proj
([idx])Remove SSP projection vector.
drop
(indices[, reason, verbose])Drop epochs based on indices or boolean mask.
drop_bad
([reject, flat, verbose])Drop bad epochs without retaining the epochs data.
drop_channels
(ch_names)Drop channel(s).
drop_log_stats
([ignore])Compute the channel stats based on a drop_log from Epochs.
equalize_event_counts
([event_ids, method])Equalize the number of trials in each condition.
export
(fname[, fmt, verbose])Export Epochs to external formats.
filter
(l_freq, h_freq[, picks, …])Filter a subset of channels.
get_channel_types
([picks, unique, only_data_chs])Get a list of channel type for each channel.
get_data
([picks, item])Get all epochs as a 3D array.
Get a DigMontage from instance.
interpolate_bads
([reset_bads, mode, origin, …])Interpolate bad MEG and EEG channels.
iter_evoked
([copy])Iterate over epochs as a sequence of Evoked objects.
Load the data if not already preloaded.
next
([return_event_id])Iterate over epoch data.
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
([picks, scalings, n_epochs, …])Visualize epochs.
plot_drop_log
([threshold, n_max_plot, …])Show the channel stats based on a drop_log from Epochs.
plot_image
([picks, sigma, vmin, vmax, …])Plot Event Related Potential / Fields image.
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_topomap
([bands, tmin, tmax, proj, …])Plot the topomap of the power spectral density across epochs.
plot_sensors
([kind, ch_type, title, …])Plot sensor positions.
plot_topo_image
([layout, sigma, vmin, vmax, …])Plot Event Related Potential / Fields image on topographies.
rename_channels
(mapping[, allow_duplicates, …])Rename channels.
reorder_channels
(ch_names)Reorder channels.
resample
(sfreq[, npad, window, n_jobs, pad, …])Resample data.
Reset the drop_log and selection entries.
save
(fname[, split_size, fmt, overwrite, …])Save epochs in a fif file.
savgol_filter
(h_freq[, verbose])Filter the data using Savitzky-Golay polynomial method.
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.
shift_time
(tshift[, relative])Shift time scale in epoched or evoked data.
standard_error
([picks])Compute standard error over epochs.
subtract_evoked
([evoked])Subtract an evoked response from each epoch.
time_as_index
(times[, use_rounding])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]¶
Return an Epochs object with a copied subset of epochs.
- Parameters
- item
slice
, array_like,str
, orlist
See below for use cases.
- item
- Returns
- epochsinstance of
Epochs
See below for use cases.
- epochsinstance of
Notes
Epochs can be accessed as
epochs[...]
in several ways:Integer or slice:
epochs[idx]
will return anEpochs
object with a subset of epochs chosen by index (supports single index and Python-style slicing).String:
epochs['name']
will return anEpochs
object comprising only the epochs labeled'name'
(i.e., epochs created around events with the label'name'
).If there are no epochs labeled
'name'
but there are epochs labeled with /-separated tags (e.g.'name/left'
,'name/right'
), thenepochs['name']
will select the epochs with labels that contain that tag (e.g.,epochs['left']
selects epochs labeled'audio/left'
and'visual/left'
, but not'audio_left'
).If multiple tags are provided as a single string (e.g.,
epochs['name_1/name_2']
), this selects epochs containing all provided tags. For example,epochs['audio/left']
selects'audio/left'
and'audio/quiet/left'
, but not'audio/right'
. Note that tag-based selection is insensitive to order: tags like'audio/left'
and'left/audio'
will be treated the same way when selecting via tag.List of strings:
epochs[['name_1', 'name_2', ... ]]
will return anEpochs
object comprising epochs that match any of the provided names (i.e., the list of names is treated as an inclusive-or condition). If none of the provided names match any epoch labels, aKeyError
will be raised.If epoch labels are /-separated tags, then providing multiple tags as separate list entries will likewise act as an inclusive-or filter. For example,
epochs[['audio', 'left']]
would select'audio/left'
,'audio/right'
, and'visual/left'
, but not'visual/right'
.Pandas query:
epochs['pandas query']
will return anEpochs
object with a subset of epochs (and matching metadata) selected by the query called withself.metadata.eval
, e.g.:epochs["col_a > 2 and col_b == 'foo'"]
would return all epochs whose associated
col_a
metadata was greater than two, and whosecol_b
metadata was the string ‘foo’. Query-based indexing only works if Pandas is installed andself.metadata
is apandas.DataFrame
.New in version 0.16.
- __iter__()[source]¶
Facilitate iteration over epochs.
This method resets the object iteration state to the first epoch.
Notes
This enables the use of this Python pattern:
>>> for epoch in epochs: >>> print(epoch)
Where
epoch
is given by successive outputs ofmne.Epochs.next()
.
- __len__()[source]¶
Return the number of epochs.
- Returns
- n_epochs
int
The number of remaining epochs.
- n_epochs
Notes
This function only works if bad epochs have been dropped.
Examples
This can be used as:
>>> epochs.drop_bad() >>> len(epochs) 43 >>> len(epochs.events) 43
- 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.
- 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
- 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
- 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.
- apply_baseline(baseline=(None, 0), *, verbose=None)[source]¶
Baseline correct epochs.
- Parameters
- baseline
None
|tuple
of length 2 The time interval to consider as “baseline” when applying baseline correction. If
None
, do not apply baseline correction. If a tuple(a, b)
, the interval is betweena
andb
(in seconds), including the endpoints. Ifa
isNone
, the beginning of the data is used; and ifb
isNone
, it is set to the end of the interval. If(None, None)
, the entire time interval is used.Note
The baseline
(a, b)
includes both endpoints, i.e. all timepointst
such thata <= t <= b
.Correction is applied to each epoch and channel individually in the following way:
Calculate the mean signal of the baseline period.
Subtract this mean from the entire epoch.
Defaults to
(None, 0)
, i.e. beginning of the the data until time point zero.- 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.
- baseline
- Returns
- epochsinstance of
Epochs
The baseline-corrected Epochs object.
- epochsinstance of
Notes
Baseline correction can be done multiple times, but can never be reverted once the data has been loaded.
New in version 0.10.0.
- 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 epochs 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 in each epoch individually. If
False
, the function will be applied to all epochs and channels at once. DefaultTrue
.- 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
Epochs
The epochs object with transformed data.
- selfinstance of
- 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.
- 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
- as_type(ch_type='grad', mode='fast')[source]¶
Compute virtual epochs using interpolated fields.
Warning
Using virtual epochs to compute inverse can yield unexpected results. The virtual channels have
'_v'
appended at the end of the names to emphasize that the data contained in them are interpolated.- Parameters
- Returns
- epochsinstance of
mne.EpochsArray
The transformed epochs object containing only virtual channels.
- epochsinstance of
Notes
This method returns a copy and does not modify the data it operates on. It also returns an EpochsArray instance.
New in version 0.20.0.
- average(picks=None, method='mean')[source]¶
Compute an average over epochs.
- 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. Note that channels ininfo['bads']
will be included if their names or indices are explicitly provided.- method
str
|callable()
How to combine the data. If “mean”/”median”, the mean/median are returned. Otherwise, must be a callable which, when passed an array of shape (n_epochs, n_channels, n_time) returns an array of shape (n_channels, n_time). Note that due to file type limitations, the kind for all these will be “average”.
- picks
- Returns
Notes
Computes an average of all epochs in the instance, even if they correspond to different conditions. To average by condition, do
epochs[condition].average()
for each condition separately.When picks is None and epochs contain only ICA channels, no channels are selected, resulting in an error. This is because ICA channels are not considered data channels (they are of misc type) and only data channels are selected when picks is None.
The
method
parameter allows e.g. robust averaging. For example, one could do:>>> from scipy.stats import trim_mean >>> trim = lambda x: trim_mean(x, 0.1, axis=0) >>> epochs.average(method=trim)
This would compute the trimmed mean.
Examples using
average
:
- property ch_names¶
Channel names.
- property compensation_grade¶
The current gradient compensation grade.
- copy()[source]¶
Return copy of Epochs instance.
- Returns
- epochsinstance of
Epochs
A copy of the object.
- epochsinstance of
- crop(tmin=None, tmax=None, include_tmax=True, verbose=None)[source]¶
Crop a time interval from the epochs.
- Parameters
- tmin
float
|None
Start time of selection in seconds.
- tmax
float
|None
End time of selection in seconds.
- include_tmaxbool
If True (default), include tmax. If False, exclude tmax (similar to how Python indexing typically works).
New in version 0.19.
- 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.
- tmin
- Returns
- epochsinstance of
Epochs
The cropped epochs object, modified in-place.
- epochsinstance of
Notes
Unlike Python slices, MNE time intervals by default include both their end points;
crop(tmin, tmax)
returns the intervaltmin <= t <= tmax
. Passinclude_tmax=False
to specify the half-open intervaltmin <= t < tmax
instead.
- decimate(decim, offset=0, verbose=None)[source]¶
Decimate the epochs.
- Parameters
- decim
int
Factor by which to subsample the data.
Warning
Low-pass filtering is not performed, this simply selects every Nth sample (where N is the value passed to
decim
), i.e., it compresses the signal (see Notes). If the data are not properly filtered, aliasing artifacts may occur.- offset
int
Apply an offset to where the decimation starts relative to the sample corresponding to t=0. The offset is in samples at the current sampling rate.
New in version 0.12.
- 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.
- decim
- Returns
- epochsinstance of
Epochs
The decimated Epochs object.
- epochsinstance of
Notes
For historical reasons,
decim
/ “decimation” refers to simply subselecting samples from a given signal. This contrasts with the broader signal processing literature, where decimation is defined as (quoting 1, p. 172; which cites 2):“… a general system for downsampling by a factor of M is the one shown in Figure 4.23. Such a system is called a decimator, and downsampling by lowpass filtering followed by compression [i.e, subselecting samples] has been termed decimation (Crochiere and Rabiner, 1983).”
Hence “decimation” in MNE is what is considered “compression” in the signal processing community.
Decimation can be done multiple times. For example,
inst.decimate(2).decimate(2)
will be the same asinst.decimate(4)
.If
decim
is 1, this method does not copy the underlying data.New in version 0.10.0.
References
- 1
Alan V. Oppenheim, Ronald W. Schafer, and John R. Buck. Discrete-Time Signal Processing. Prentice Hall, Upper Saddle River, NJ, 2 edition edition, 1999. ISBN 978-0-13-754920-7.
- 2
Ronald E. Crochiere and Lawrence R. Rabiner. Multirate Digital Signal Processing. Pearson, Englewood Cliffs, NJ, 1 edition edition, 1983. ISBN 978-0-13-605162-6.
- 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).
- drop(indices, reason='USER', verbose=None)[source]¶
Drop epochs based on indices or boolean mask.
Note
The indices refer to the current set of undropped epochs rather than the complete set of dropped and undropped epochs. They are therefore not necessarily consistent with any external indices (e.g., behavioral logs). To drop epochs based on external criteria, do not use the
preload=True
flag when constructing an Epochs object, and call this method before calling themne.Epochs.drop_bad()
ormne.Epochs.load_data()
methods.- Parameters
- indices
array
ofint
or bool Set epochs to remove by specifying indices to remove or a boolean mask to apply (where True values get removed). Events are correspondingly modified.
- reason
str
Reason for dropping the epochs (‘ECG’, ‘timeout’, ‘blink’ etc). Default: ‘USER’.
- 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.
- indices
- Returns
- epochsinstance of
Epochs
The epochs with indices dropped. Operates in-place.
- epochsinstance of
- drop_bad(reject='existing', flat='existing', verbose=None)[source]¶
Drop bad epochs without retaining the epochs data.
Should be used before slicing operations.
Warning
This operation is slow since all epochs have to be read from disk. To avoid reading epochs from disk multiple times, use
mne.Epochs.load_data()
.- Parameters
- reject
dict
|str
|None
Reject epochs based on peak-to-peak signal amplitude (PTP), i.e. the absolute difference between the lowest and the highest signal value. In each individual epoch, the PTP is calculated for every channel. If the PTP of any one channel exceeds the rejection threshold, the respective epoch will be dropped.
The dictionary keys correspond to the different channel types; valid keys are:
'grad'
,'mag'
,'eeg'
,'eog'
, and'ecg'
.Example:
reject = dict(grad=4000e-13, # unit: T / m (gradiometers) mag=4e-12, # unit: T (magnetometers) eeg=40e-6, # unit: V (EEG channels) eog=250e-6 # unit: V (EOG channels) )
Note
Since rejection is based on a signal difference calculated for each channel separately, applying baseline correction does not affect the rejection procedure, as the difference will be preserved.
If
reject
isNone
, no rejection is performed. If'existing'
(default), then the rejection parameters set at instantiation are used.- flat
dict
|str
|None
Rejection parameters based on flatness of signal. Valid keys are
'grad'
,'mag'
,'eeg'
,'eog'
,'ecg'
. The values are floats that set the minimum acceptable peak-to-peak amplitude (PTP). If the PTP is smaller than this threshold, the epoch will be dropped. IfNone
then no rejection is performed based on flatness of the signal. If'existing'
, then the flat parameters set during epoch creation are 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.
- reject
- Returns
- epochsinstance of
Epochs
The epochs with bad epochs dropped. Operates in-place.
- epochsinstance of
Notes
Dropping bad epochs can be done multiple times with different
reject
andflat
parameters. However, once an epoch is dropped, it is dropped forever, so if more lenient thresholds may subsequently be applied,epochs.copy
should be used.
- drop_channels(ch_names)[source]¶
Drop channel(s).
- Parameters
- Returns
See also
Notes
New in version 0.9.0.
Examples using
drop_channels
:
- drop_log_stats(ignore=('IGNORED',))[source]¶
Compute the channel stats based on a drop_log from Epochs.
- Parameters
- ignore
list
The drop reasons to ignore.
- ignore
- Returns
- perc
float
Total percentage of epochs dropped.
- perc
See also
- equalize_event_counts(event_ids=None, method='mintime')[source]¶
Equalize the number of trials in each condition.
It tries to make the remaining epochs occurring as close as possible in time. This method works based on the idea that if there happened to be some time-varying (like on the scale of minutes) noise characteristics during a recording, they could be compensated for (to some extent) in the equalization process. This method thus seeks to reduce any of those effects by minimizing the differences in the times of the events within a
Epochs
instance. For example, if one event type occurred at time points[1, 2, 3, 4, 120, 121]
and the another one at[3.5, 4.5, 120.5, 121.5]
, this method would remove the events at times[1, 2]
for the first event type – and not the events at times[120, 121]
.- Parameters
- event_ids
None
|list
|dict
The event types to equalize.
If
None
(default), equalize the counts of all event types present in theEpochs
instance.If a list, each element can either be a string (event name) or a list of strings. In the case where one of the entries is a list of strings, event types in that list will be grouped together before equalizing trial counts across conditions.
If a dictionary, the keys are considered as the event names whose counts to equalize, i.e., passing
dict(A=1, B=2)
will have the same effect as passing['A', 'B']
. This is useful if you intend to pass anevent_id
dictionary that was used when creatingEpochs
.In the case where partial matching is used (using
/
in the event names), the event types will be matched according to the provided tags, that is, processing works as if theevent_ids
matched by the provided tags had been supplied instead. Theevent_ids
must identify non-overlapping subsets of the epochs.- method
str
If
'truncate'
, events will be truncated from the end of each type of events. If'mintime'
, timing differences between each event type will be minimized.
- event_ids
- Returns
Notes
For example (if
epochs.event_id
was{'Left': 1, 'Right': 2, 'Nonspatial':3}
:epochs.equalize_event_counts([[‘Left’, ‘Right’], ‘Nonspatial’])
would equalize the number of trials in the
'Nonspatial'
condition with the total number of trials in the'Left'
and'Right'
conditions combined.If multiple indices are provided (e.g.
'Left'
and'Right'
in the example above), it is not guaranteed that after equalization the conditions will contribute equally. E.g., it is possible to end up with 70'Nonspatial'
epochs, 69'Left'
and 1'Right'
.Changed in version 0.23: Default to equalizing all events in the passed instance if no event names were specified explicitly.
- export(fname, fmt='auto', verbose=None)[source]¶
Export Epochs 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 filename¶
The filename.
- 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='edge', 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.
- 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
- get_data(picks=None, item=None)[source]¶
Get all epochs as a 3D array.
- 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.- item
slice
| array_like |str
|list
|None
The items to get. See
mne.Epochs.__getitem__()
for a description of valid options. This can be substantially faster for obtaining an ndarray than__getitem__()
for repeated access on large Epochs objects. None (default) is an alias forslice(None)
.New in version 0.20.
- picks
- Returns
- data
array
of shape (n_epochs, n_channels, n_times) A view on epochs data.
- data
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
:
- iter_evoked(copy=False)[source]¶
Iterate over epochs as a sequence of Evoked objects.
The Evoked objects yielded will each contain a single epoch (i.e., no averaging is performed).
This method resets the object iteration state to the first epoch.
- Parameters
- copybool
If False copies of data and measurement info will be omitted to save time.
- load_data()[source]¶
Load the data if not already preloaded.
- Returns
- epochsinstance of
Epochs
The epochs object.
- epochsinstance of
Notes
This function operates in-place.
New in version 0.10.0.
- property metadata¶
Get the metadata.
- 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
- 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.
- 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.
- plot(picks=None, scalings=None, n_epochs=20, n_channels=20, title=None, events=None, event_color=None, order=None, show=True, block=False, decim='auto', noise_cov=None, butterfly=False, show_scrollbars=True, epoch_colors=None, event_id=None, group_by='type')[source]¶
Visualize epochs.
Bad epochs can be marked with a left click on top of the epoch. Bad channels can be selected by clicking the channel name on the left side of the main axes. Calling this function drops all the selected bad epochs as well as bad epochs marked beforehand with rejection parameters.
- 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 good data channels. Note that channels ininfo['bads']
will be included if their names or indices are explicitly provided.- scalings
dict
| ‘auto’ |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 epochs up to 100 Mb 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=10.)
- n_epochs
int
The number of epochs per view. Defaults to 20.
- n_channels
int
The number of channels per view. Defaults to 20.
- title
str
|None
The title of the window. If None, epochs name will be displayed. Defaults to None.
- events
None
|array
, shape (n_events, 3) Events to show with vertical bars. You can use
plot_events
as a legend for the colors. By default, the coloring scheme is the same. Defaults toNone
.Warning
If the epochs have been resampled, the events no longer align with the data.
New in version 0.14.0.
- 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 toNone
.- order
array
ofstr
|None
Order in which to plot channel types.
New in version 0.18.0.
- showbool
Show figure if True. Defaults to True.
- blockbool
Whether to halt program execution until the figure is closed. Useful for rejecting bad trials on the fly by clicking on an epoch. 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 at least three times larger than
info['lowpass']
(e.g., a 40 Hz lowpass will result in at least a 120 Hz displayed sample rate).New in version 0.15.0.
- 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.
- butterflybool
Whether to directly call the butterfly view.
New in version 0.18.0.
- 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.
- epoch_colors
list
of (n_epochs)list
(of n_channels) |None
Colors to use for individual epochs. If None, use default colors.
- event_id
dict
|None
Dictionary of event labels (e.g. ‘aud_l’) as keys and associated event integers as values. Useful when
events
contains event numbers not present inepochs.event_id
(e.g., because of event subselection). Values inevent_id
will take precedence over those inepochs.event_id
when there are overlapping keys.New in version 0.20.
- 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'
.
- picks
- Returns
- figinstance of
matplotlib.figure.Figure
The figure.
- figinstance of
Notes
The arrow keys (up/down/left/right) can be used to navigate between channels and epochs and the scaling can be adjusted with - and + (or =) keys, but this depends on the backend matplotlib is configured to use (e.g., mpl.use(
TkAgg
) should work). Full screen mode can be toggled with f11 key. The amount of epochs and channels per view can be adjusted with home/end and page down/page up keys. These can also be set through options dialog by pressingo
key.h
key plots a histogram of peak-to-peak values along with the used rejection thresholds. Butterfly plot can be toggled withb
key. Right mouse click adds a vertical line to the plot. Click ‘help’ button at bottom left corner of the plotter to view all the options.New in version 0.10.0.
Examples using
plot
:
- plot_drop_log(threshold=0, n_max_plot=20, subject='Unknown subj', color=(0.9, 0.9, 0.9), width=0.8, ignore=('IGNORED',), show=True)[source]¶
Show the channel stats based on a drop_log from Epochs.
- Parameters
- threshold
float
The percentage threshold to use to decide whether or not to plot. Default is zero (always plot).
- n_max_plot
int
Maximum number of channels to show stats for.
- subject
str
|None
The subject name to use in the title of the plot. If
None
, do not display a subject name.Changed in version 0.23: Added support for
None
.- color
tuple
|str
Color to use for the bars.
- width
float
Width of the bars.
- ignore
list
The drop reasons to ignore.
- showbool
Show figure if True.
- threshold
- Returns
- figinstance of
matplotlib.figure.Figure
The figure.
- figinstance of
- plot_image(picks=None, sigma=0.0, vmin=None, vmax=None, colorbar=True, order=None, show=True, units=None, scalings=None, cmap=None, fig=None, axes=None, overlay_times=None, combine=None, group_by=None, evoked=True, ts_args=None, title=None, clear=False)[source]¶
Plot Event Related Potential / Fields image.
- 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 good data channels. Note that channels ininfo['bads']
will be included if their names or indices are explicitly provided.picks
interacts withgroup_by
andcombine
to determine the number of figures generated; see Notes.- sigma
float
The standard deviation of a Gaussian smoothing window applied along the epochs axis of the image. If 0, no smoothing is applied. Defaults to 0.
- vmin
None
|float
|callable()
The min value in the image (and the ER[P/F]). The unit is µV for EEG channels, fT for magnetometers and fT/cm for gradiometers. If vmin is None and multiple plots are returned, the limit is equalized within channel types. Hint: to specify the lower limit of the data, use
vmin=lambda data: data.min()
.- vmax
None
|float
|callable()
The max value in the image (and the ER[P/F]). The unit is µV for EEG channels, fT for magnetometers and fT/cm for gradiometers. If vmin is None and multiple plots are returned, the limit is equalized within channel types.
- colorbarbool
Display or not a colorbar.
- order
None
|array
ofint
|callable()
If not
None
, order is used to reorder the epochs along the y-axis of the image. If it is an array ofint
, its length should match the number of good epochs. If it is a callable it should accept two positional parameters (times
anddata
, wheredata.shape == (len(good_epochs), len(times))
) and return anarray
of indices that will sortdata
along its first axis.- showbool
Show figure if True.
- units
dict
|None
The units of the channel types used for axes labels. If None, defaults to
units=dict(eeg='µV', grad='fT/cm', mag='fT')
.- scalings
dict
|None
The scalings of the channel types to be applied for plotting. If None, defaults to
scalings=dict(eeg=1e6, grad=1e13, mag=1e15, eog=1e6)
.- cmap
None
| colormap | (colormap, bool) | ‘interactive’ Colormap. If tuple, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In interactive mode 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 scale. Up and down arrows can be used to change the colormap. If ‘interactive’, translates to (‘RdBu_r’, True). If None, “RdBu_r” is used, unless the data is all positive, in which case “Reds” is used.
- fig
Figure
|None
Figure
instance to draw the image to. Figure must contain the correct number of axes for drawing the epochs image, the evoked response, and a colorbar (depending on values ofevoked
andcolorbar
). IfNone
a new figure is created. Defaults toNone
.- axes
list
ofAxes
|dict
oflist
ofAxes
|None
List of
Axes
objects in which to draw the image, evoked response, and colorbar (in that order). Length of list must be 1, 2, or 3 (depending on values ofcolorbar
andevoked
parameters). If adict
, each entry must be a list of Axes objects with the same constraints as above. If bothaxes
andgroup_by
are dicts, their keys must match. Providing non-None
values for bothfig
andaxes
results in an error. Defaults toNone
.- overlay_timesarray_like, shape (n_epochs,) |
None
Times (in seconds) at which to draw a line on the corresponding row of the image (e.g., a reaction time associated with each epoch). Note that
overlay_times
should be ordered to correspond with theEpochs
object (i.e.,overlay_times[0]
corresponds toepochs[0]
, etc).- combine
None
|str
|callable()
How to combine information across channels. If a
str
, must be one of ‘mean’, ‘median’, ‘std’ (standard deviation) or ‘gfp’ (global field power). If callable, the callable must accept one positional input (data of shape(n_epochs, n_channels, n_times)
) and return anarray
of shape(n_epochs, n_times)
. For example:combine = lambda data: np.median(data, axis=1)
If
combine
isNone
, channels are combined by computing GFP, unlessgroup_by
is alsoNone
andpicks
is a list of specific channels (not channel types), in which case no combining is performed and each channel gets its own figure. See Notes for further details. Defaults toNone
.- group_by
None
|dict
Specifies which channels are aggregated into a single figure, with aggregation method determined by the
combine
parameter. If notNone
, oneFigure
is made per dict entry; the dict key will be used as the figure title and the dict values must be lists of picks (either channel names or integer indices ofepochs.ch_names
). For example:group_by=dict(Left_ROI=[1, 2, 3, 4], Right_ROI=[5, 6, 7, 8])
Note that within a dict entry all channels must have the same type.
group_by
interacts withpicks
andcombine
to determine the number of figures generated; see Notes. Defaults toNone
.- evokedbool
Draw the ER[P/F] below the image or not.
- ts_args
None
|dict
Arguments passed to a call to
plot_compare_evokeds
to style the evoked plot below the image. Defaults to an empty dictionary, meaningplot_compare_evokeds
will be called with default parameters.- title
None
|str
If
str
, will be plotted as figure title. Otherwise, the title will indicate channel(s) or channel type being plotted. Defaults toNone
.- clearbool
Whether to clear the axes before plotting (if
fig
oraxes
are provided). Defaults toFalse
.
- picks
- Returns
Notes
You can control how channels are aggregated into one figure or plotted in separate figures through a combination of the
picks
,group_by
, andcombine
parameters. Ifgroup_by
is adict
, the result is oneFigure
per dictionary key (for any valid values ofpicks
andcombine
). Ifgroup_by
isNone
, the number and content of the figures generated depends on the values ofpicks
andcombine
, as summarized in this table:group_by
picks
combine
result
dict
None, int, list of int, ch_name, list of ch_names, ch_type, list of ch_types
None, string, or callable
1 figure per dict key
None
None, ch_type, list of ch_types
None, string, or callable
1 figure per ch_type
int, ch_name, list of int, list of ch_names
None
1 figure per pick
string or callable
1 figure
- 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
- plot_psd(fmin=0, fmax=inf, tmin=None, tmax=None, proj=False, bandwidth=None, adaptive=False, low_bias=True, normalization='length', 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, 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.
- bandwidth
float
The bandwidth of the multi taper windowing function in Hz. The default value is a window half-bandwidth of 4.
- adaptivebool
Use adaptive weights to combine the tapered spectra into PSD (slow, use n_jobs >> 1 to speed up computation).
- low_biasbool
Only use tapers with more than 90% spectral concentration within bandwidth.
- normalization
str
Either “full” or “length” (default). If “full”, the PSD will be normalized by the sampling rate as well as the length of the signal (as in nitime).
- 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.
- 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
- plot_psd_topomap(bands=None, tmin=None, tmax=None, proj=False, bandwidth=None, adaptive=False, low_bias=True, normalization='length', ch_type=None, cmap=None, agg_fun=None, dB=True, n_jobs=1, normalize=False, cbar_fmt='auto', outlines='head', axes=None, show=True, sphere=None, vlim=(None, None), verbose=None)[source]¶
Plot the topomap of the power spectral density across epochs.
- Parameters
- bands
list
oftuple
|None
The frequencies or frequency ranges to plot. Length-2 tuples specify a single frequency and a subplot title (e.g.,
(6.5, 'presentation rate')
); length-3 tuples specify lower and upper band edges and a subplot title. IfNone
(the default), expands to:bands = [(0, 4, 'Delta'), (4, 8, 'Theta'), (8, 12, 'Alpha'), (12, 30, 'Beta'), (30, 45, 'Gamma')]
In bands where a single frequency is provided, the topomap will reflect the single frequency bin that is closest to the provided value.
- tmin
float
|None
Start time to consider.
- tmax
float
|None
End time to consider.
- projbool
Apply projection.
- bandwidth
float
The bandwidth of the multi taper windowing function in Hz. The default value is a window half-bandwidth of 4 Hz.
- adaptivebool
Use adaptive weights to combine the tapered spectra into PSD (slow, use n_jobs >> 1 to speed up computation).
- low_biasbool
Only use tapers with more than 90% spectral concentration within bandwidth.
- normalization
str
Either “full” or “length” (default). If “full”, the PSD will be normalized by the sampling rate as well as the length of the signal (as in nitime).
- ch_type‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ |
None
The channel type to plot. For ‘grad’, the gradiometers are collected in pairs and the mean for each pair is plotted. If None, then first available channel type from order given above is used. Defaults to None.
- 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 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. IfNone
,'Reds'
is used for data that is either all-positive or all-negative, and'RdBu_r'
is used otherwise.'interactive'
is equivalent to(None, True)
. Defaults toNone
.- agg_fun
callable()
The function used to aggregate over frequencies. Defaults to
numpy.sum()
ifnormalize=True
, elsenumpy.mean()
.- dBbool
If
True
, transform data to decibels (with10 * np.log10(data)
) following the application ofagg_fun
. Ignored ifnormalize=True
.- n_jobs
int
The number of jobs to run in parallel (default 1). Requires the joblib package.
- normalizebool
If True, each band will be divided by the total power. Defaults to False.
- cbar_fmt
str
Format string for the colorbar tick labels. If
'auto'
, is equivalent to ‘%0.3f’ ifdB=False
and ‘%0.1f’ ifdB=True
. Defaults to'auto'
.- 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’.
- axes
list
ofAxes
|None
List of axes to plot consecutive topographies to. If
None
the axes will be created automatically. Defaults toNone
.- showbool
Show figure if 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.
- vlim
tuple
of length 2 | ‘joint’ Colormap limits to use. If a
tuple
of floats, specifies the lower and upper bounds of the colormap (in that order); providingNone
for either entry will set the corresponding boundary at the min/max of the data (separately for each topomap). Elements of thetuple
may also be callable functions which take in aNumPy array
and return a scalar. Ifvlim='joint'
, will compute the colormap limits jointly across all topomaps of the same channel type, using the min/max of the data. Defaults to(None, None)
.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.
- bands
- Returns
- figinstance of
Figure
Figure distributing one image per channel across sensor topography.
- figinstance of
- 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.
- plot_topo_image(layout=None, sigma=0.0, vmin=None, vmax=None, colorbar=None, order=None, cmap='RdBu_r', layout_scale=0.95, title=None, scalings=None, border='none', fig_facecolor='k', fig_background=None, font_color='w', show=True)[source]¶
Plot Event Related Potential / Fields image on topographies.
- Parameters
- layoutinstance of
Layout
System specific sensor positions.
- sigma
float
The standard deviation of the Gaussian smoothing to apply along the epoch axis to apply in the image. If 0., no smoothing is applied.
- vmin
float
The min value in the image. The unit is µV for EEG channels, fT for magnetometers and fT/cm for gradiometers.
- vmax
float
The max value in the image. The unit is µV for EEG channels, fT for magnetometers and fT/cm for gradiometers.
- colorbarbool |
None
Whether to display a colorbar or not. If
None
a colorbar will be shown only if all channels are of the same type. Defaults toNone
.- order
None
|array
ofint
|callable()
If not None, order is used to reorder the epochs on the y-axis of the image. If it’s an array of int it should be of length the number of good epochs. If it’s a callable the arguments passed are the times vector and the data as 2d array (data.shape[1] == len(times)).
- cmapcolormap
Colors to be mapped to the values.
- layout_scale
float
Scaling factor for adjusting the relative size of the layout on the canvas.
- title
str
Title of the figure.
- scalings
dict
|None
The scalings of the channel types to be applied for plotting. If
None
, defaults todict(eeg=1e6, grad=1e13, mag=1e15)
.- border
str
Matplotlib borders style to be used for each sensor plot.
- fig_facecolorcolor
The figure face color. Defaults to black.
- fig_background
None
|array
A background image for the figure. This must be a valid input to
matplotlib.pyplot.imshow()
. Defaults toNone
.- font_colorcolor
The color of tick labels in the colorbar. Defaults to white.
- showbool
Whether to show the figure. Defaults to
True
.
- layoutinstance of
- Returns
- figinstance of
matplotlib.figure.Figure
Figure distributing one image per channel across sensor topography.
- figinstance of
Notes
In an interactive Python session, this plot will be interactive; clicking on a channel image will pop open a larger view of the image; this image will always have a colorbar even when the topo plot does not (because it shows multiple sensor types).
- 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.
- 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.
- resample(sfreq, npad='auto', window='boxcar', n_jobs=1, pad='edge', verbose=None)[source]¶
Resample data.
If appropriate, an anti-aliasing filter is applied before resampling. See Resampling and decimating data for more information.
Note
Data must be loaded.
- 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()
.- n_jobs
int
|str
Number of jobs to run in parallel. Can be ‘cuda’ if
cupy
is installed properly.- 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'edge'
, which pads with the edge values of each vector.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!
- reset_drop_log_selection()[source]¶
Reset the drop_log and selection entries.
This method will simplify
self.drop_log
andself.selection
so that they are meaningless (tuple of empty tuples and increasing integers, respectively). This can be useful when concatenating many Epochs instances, asdrop_log
can accumulate many entries which can become problematic when saving.
- save(fname, split_size='2GB', fmt='single', overwrite=False, verbose=True)[source]¶
Save epochs in a fif file.
- Parameters
- fname
str
The name of the file, which should end with -epo.fif or -epo.fif.gz.
- 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.
New in version 0.10.0.
- fmt
str
Format to save data. Valid options are ‘double’ or ‘single’ for 64- or 32-bit float, or for 128- or 64-bit complex numbers respectively. Note: Data are processed with double precision. Choosing single-precision, the saved data will slightly differ due to the reduction in precision.
New in version 0.17.
- 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. This defaults to True in 0.18 but will change to False in 0.19.
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.
- fname
Notes
Bad epochs will be dropped before saving the epochs to disk.
- 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 3 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
- 3
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_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.
- 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 4.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
- 4
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.
- 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.
- shift_time(tshift, relative=True)[source]¶
Shift time scale in epoched or evoked data.
- Parameters
- tshift
float
The (absolute or relative) time shift in seconds. If
relative
is True, positive tshift increases the time value associated with each sample, while negative tshift decreases it.- relativebool
If True, increase or decrease time values by
tshift
seconds. Otherwise, shift the time values such that the time of the first sample equalstshift
.
- tshift
- Returns
- epochsinstance of
Epochs
The modified Epochs instance.
- epochsinstance of
Notes
This method allows you to shift the time values associated with each data sample by an arbitrary amount. It does not resample the signal or change the data values in any way.
- standard_error(picks=None)[source]¶
Compute standard error over epochs.
- 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. Note that channels ininfo['bads']
will be included if their names or indices are explicitly provided.
- picks
- Returns
- evokedinstance of
Evoked
The standard error over epochs.
- evokedinstance of
- subtract_evoked(evoked=None)[source]¶
Subtract an evoked response from each epoch.
Can be used to exclude the evoked response when analyzing induced activity, see e.g. [1].
- Parameters
- Returns
- selfinstance of
Epochs
The modified instance (instance is also modified inplace).
- selfinstance of
References
- 1
David et al. “Mechanisms of evoked and induced responses in MEG/EEG”, NeuroImage, vol. 31, no. 4, pp. 1580-1591, July 2006.
- property times¶
Time vector in seconds.
- property tmax¶
Last time point.
- property tmin¶
First time point.
- to_data_frame(picks=None, index=None, scalings=None, copy=True, 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, additional columns “time”, “epoch” (epoch number), and “condition” (epoch event description) are added, unless
index
is notNone
(in which case the columns specified inindex
will be used to form the DataFrame’s index instead).- 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
str
|list
ofstr
|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
, orpandas.TimedeltaIndex
will be used (depending on the value oftime_format
). If a list of two or more string values, apandas.MultiIndex
will be created. Valid string values are ‘time’, ‘epoch’, and ‘condition’. 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
.- 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, channel, epoch number, and condition. 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. 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