mne.time_frequency.EpochsTFR¶

class mne.time_frequency.EpochsTFR(info, data, times, freqs, comment=None, method=None, events=None, event_id=None, metadata=None, verbose=None)[source]¶

Container for Time-Frequency data on epochs.

Can for example store induced power at sensor level.

Parameters

infoInfo: The measurement info.
datandarray, shape (n_epochs, n_channels, n_freqs, n_times): The data.
timesndarray, shape (n_times,): The time values in seconds.
freqsndarray, shape (n_freqs,): The frequencies in Hz.
commentstr | None, default None: Comment on the data, e.g., the experimental condition.
methodstr | None, default None: Comment on the method used to compute the data, e.g., morlet wavelet.
eventsndarray, shape (n_events, 3) | None: The events as stored in the Epochs class. If None (default), all event values are set to 1 and event time-samples are set to range(n_epochs).
event_iddict | None: Example: dict(auditory=1, visual=3). They keys can be used to access associated events. If None, all events will be used and a dict is created with string integer names corresponding to the event id integers.
metadatainstance of pandas.DataFrame | None: A pandas.DataFrame containing pertinent information for each trial. See mne.Epochs for further details.
verbosebool, str, int, or None: 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.

Attributes

infoinstance of Info: Measurement info.
ch_nameslist: Channel names.
datandarray, shape (n_epochs, n_channels, n_freqs, n_times): The data array.
timesndarray, shape (n_times,): The time values in seconds.
freqsndarray, shape (n_freqs,): The frequencies in Hz.
commentstr: Comment on dataset. Can be the condition.
methodstr | None, default None: Comment on the method used to compute the data, e.g., morlet wavelet.
eventsndarray, shape (n_events, 3) | None: Array containing sample information as event_id
event_iddict | None: Names of conditions correspond to event_ids
metadatapandas.DataFrame, shape (n_events, n_cols) | None: Get the metadata.
Notes
—–
.. versionadded:: 0.13.0

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.
`apply_baseline`(baseline[, mode, verbose])	Baseline correct the data.
`average`()	Average the data across epochs.
`copy`()	Return a copy of the instance.
`crop`([tmin, tmax, fmin, fmax, include_tmax])	Crop data to a given time interval in place.
`drop_channels`(ch_names)	Drop channel(s).
`get_channel_types`([picks, unique, only_data_chs])	Get a list of channel type for each channel.
`get_montage`()	Get a DigMontage from instance.
`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.
`reorder_channels`(ch_names)	Reorder channels.
`save`(fname[, overwrite])	Save TFR object to hdf5 file.

__contains__(ch_type)[source]¶

Check channel type membership.

Parameters

ch_typestr: Channel type to check for. Can be e.g. ‘meg’, ‘eeg’, ‘stim’, etc.

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

itemslice, array_like, str, or list: See below for use cases.

Returns

epochsinstance of Epochs: See below for use cases.

Notes

Epochs can be accessed as epochs[...] in several ways:

Integer or slice: epochs[idx] will return an Epochs object with a subset of epochs chosen by index (supports single index and Python-style slicing).
String: epochs['name'] will return an Epochs 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'), then epochs['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 an Epochs 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, a KeyError 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 an Epochs object with a subset of epochs (and matching metadata) selected by the query called with self.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 whose col_b metadata was the string ‘foo’. Query-based indexing only works if Pandas is installed and self.metadata is a pandas.DataFrame.

New in version 0.16.

__hash__()[source]¶

Hash the object.

Returns

hashint: The hash

__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 of mne.Epochs.next().

__len__()[source]¶

Return the number of epochs.

Returns

n_epochsint: The number of remaining 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_listlist: 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.

Returns

instinstance of Raw, Epochs, or Evoked: The modified instance.

See also

drop_channels

Notes

If self is a Raw instance that has been preloaded into a numpy.memmap instance, the memmap will be resized.

apply_baseline(baseline, mode='mean', verbose=None)[source]¶

Baseline correct the data.

Parameters

baselinearray_like, shape (2,)

The time interval to apply rescaling / baseline correction. If None do not apply it. If baseline is (a, b) the interval is between “a (s)” and “b (s)”. If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal to (None, None) all the time interval is used.

Perform baseline correction by

subtracting the mean of baseline values (‘mean’)
dividing by the mean of baseline values (‘ratio’)
dividing by the mean of baseline values and taking the log (‘logratio’)
subtracting the mean of baseline values followed by dividing by the mean of baseline values (‘percent’)
subtracting the mean of baseline values and dividing by the standard deviation of baseline values (‘zscore’)
dividing by the mean of baseline values, taking the log, and dividing by the standard deviation of log baseline values (‘zlogratio’)

verbosebool, str, int, or None

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

instinstance of AverageTFR: The modified instance.

Examples using apply_baseline:

average()[source]¶

Average the data across epochs.

Returns

aveinstance of AverageTFR: The averaged data.

Examples using average:

property ch_names¶: Channel names.

property compensation_grade¶: The current gradient compensation grade.

copy()[source]¶

Return a copy of the instance.

Returns

copyinstance of EpochsTFR | instance of AverageTFR: A copy of the instance.

crop(tmin=None, tmax=None, fmin=None, fmax=None, include_tmax=True)[source]¶

Crop data to a given time interval in place.

Parameters

tminfloat | None: Start time of selection in seconds.
tmaxfloat | None: End time of selection in seconds.
fminfloat | None: Lowest frequency of selection in Hz.

New in version 0.18.0.
fmaxfloat | None: Highest frequency of selection in Hz.

New in version 0.18.0.
include_tmaxbool: If True (default), include tmax. If False, exclude tmax (similar to how Python indexing typically works).

New in version 0.19.

Returns

instinstance of AverageTFR: The modified instance.

Examples using crop:

drop_channels(ch_names)[source]¶

Drop channel(s).

Parameters

ch_namesiterable or str: Iterable (e.g. list) of channel name(s) or channel name to remove.

Returns

instinstance of Raw, Epochs, or Evoked: The modified instance.

See also

reorder_channels
pick_channels
pick_types

Notes

New in version 0.9.0.

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

Get a list of channel type for each channel.

Parameters

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

Returns

channel_typeslist: The channel types.

get_montage()[source]¶

Get a DigMontage from instance.

Returns

montageNone | str | DigMontage: A montage containing channel positions. If str or DigMontage is specified, the channel info will be updated with the channel positions. Default is None. See also the documentation of mne.channels.DigMontage for more information.

property metadata¶: Get the metadata.

next(return_event_id=False)[source]¶

Iterate over epoch data.

Parameters

return_event_idbool: If True, return both the epoch data and an event_id.

Returns

epocharray of shape (n_channels, n_times): The epoch data.
event_idint: The event id. Only returned if return_event_id is True.

pick(picks, exclude=())[source]¶

Pick a subset of channels.

Parameters

picksstr | list | slice | None: Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.
excludelist | str: Set of channels to exclude, only used when picking based on types (e.g., exclude=”bads” when picks=”meg”).

Returns

instinstance of Raw, Epochs, or Evoked: The modified instance.

pick_channels(ch_names, ordered=False)[source]¶

Pick some channels.

Parameters

ch_nameslist: The list of channels to select.
orderedbool: If True (default False), ensure that the order of the channels in the modified instance matches the order of ch_names.

New in version 0.20.0.

Returns

instinstance of Raw, Epochs, or Evoked: The modified instance.

See also

drop_channels
pick_types
reorder_channels

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, 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 the meg parameter.
miscbool: If True include miscellaneous analog channels.
respbool: If True include response-trigger channel. For some MEG systems this is separate from the stim channel.
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.
includelist of str: List of additional channels to include. If empty do not include any.
excludelist of str | str: List of channels to exclude. If ‘bads’ (default), exclude channels in info['bads'].
selectionlist of str: Restrict sensor channels (MEG, EEG) to this list of channel names.
verbosebool, str, int, or None: 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