mne.time_frequency.AverageTFR#

class mne.time_frequency.AverageTFR(info, data, times, freqs, nave, comment=None, method=None, verbose=None)[source]#

Container for Time-Frequency data.

Can for example store induced power at sensor level or inter-trial coherence.

Parameters:

infomne.Info: The mne.Info object with information about the sensors and methods of measurement.
datandarray, shape (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.
naveint: The number of averaged TFRs.
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.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Attributes:

infomne.Info: The mne.Info object with information about the sensors and methods of measurement.
ch_nameslist: Channel names.
naveint: Number of averaged epochs.
datandarray, shape (n_channels, n_freqs, n_times): The data array.
timesndarray, shape (n_times,): Time vector 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.

Methods

`__add__`(tfr)	Add instances.
`__contains__`(ch_type)	Check channel type membership.
`__mul__`(a)	Multiply source instances.
`__sub__`(tfr)	Subtract instances.
`add_channels`(add_list[, force_update_info])	Append new channels to the instance.
`add_reference_channels`(ref_channels)	Add reference channels to data that consists of all zeros.
`apply_baseline`(baseline[, mode, verbose])	Baseline correct the data.
`copy`()	Return a copy of the instance.
`crop`([tmin, tmax, fmin, fmax, include_tmax])	Crop data to a given time interval in place.
`decimate`(decim[, offset, verbose])	Decimate the time-series data.
`drop_channels`(ch_names[, on_missing])	Drop channel(s).
`get_channel_types`([picks, unique, only_data_chs])	Get a list of channel type for each channel.
`pick`(picks[, exclude, verbose])	Pick a subset of channels.
`pick_channels`(ch_names[, ordered, verbose])	Warning LEGACY: New code should use inst.pick(...).
`pick_types`([meg, eeg, stim, eog, ecg, emg, ...])	Warning LEGACY: New code should use inst.pick(...).
`plot`([picks, baseline, mode, tmin, tmax, ...])	Plot TFRs as a two-dimensional image(s).
`plot_joint`([timefreqs, picks, baseline, ...])	Plot TFRs as a two-dimensional image with topomaps.
`plot_topo`([picks, baseline, mode, tmin, ...])	Plot TFRs in a topography with images.
`plot_topomap`([tmin, tmax, fmin, fmax, ...])	Plot topographic maps of specific time-frequency intervals of TFR data.
`reorder_channels`(ch_names)	Reorder channels.
`save`(fname[, overwrite, verbose])	Save TFR object to hdf5 file.
`shift_time`(tshift[, relative])	Shift time scale in epoched or evoked data.
`time_as_index`(times[, use_rounding])	Convert time to indices.
`to_data_frame`([picks, index, long_format, ...])	Export data in tabular structure as a pandas DataFrame.

__add__(tfr)[source]#: Add instances.

__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

__mul__(a)[source]#: Multiply source instances.

__sub__(tfr)[source]#: Subtract instances.

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 v0.12.

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.

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:

ref_channelsstr | list of str: Name of the electrode(s) which served as the reference in the recording. If a name is provided, a corresponding channel is added and its data is set to 0. This is useful for later re-referencing.

Returns:

instinstance of Raw | Epochs | Evoked: The modified instance.

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 | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

instinstance of AverageTFR: The modified instance.

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 v0.18.0.
fmaxfloat | None: Highest frequency of selection in Hz.

New in v0.18.0.
include_tmaxbool: If True (default), include tmax. If False, exclude tmax (similar to how Python indexing typically works).

New in v0.19.

Returns:

instinstance of AverageTFR: The modified instance.

decimate(decim, offset=0, *, verbose=None)[source]#

Decimate the time-series data.

Parameters:

decimint: 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.
offsetint: 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 v0.12.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

instMNE-object: The decimated object.

See also

mne.Epochs.resample
mne.io.Raw.resample

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 as inst.decimate(4).

If decim is 1, this method does not copy the underlying data.

New in v0.10.0.

References

drop_channels(ch_names, on_missing='raise')[source]#

Drop channel(s).

Parameters:

ch_namesiterable or str: Iterable (e.g. list) of channel name(s) or channel name to remove.
on_missing‘raise’ | ‘warn’ | ‘ignore’: Can be 'raise' (default) to raise an error, 'warn' to emit a warning, or 'ignore' to ignore when entries in ch_names are not present in the raw instance.

New in v0.23.0.

Returns:

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

See also

reorder_channels
pick_channels
pick_types

Notes

New in v0.9.0.

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

Get a list of channel type for each channel.

Parameters:

picksstr | array_like | 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.

pick(picks, exclude=(), *, verbose=None)[source]#

Pick a subset of channels.

Parameters:

picksstr | array_like | 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”).
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

New in v0.24.0.

Returns:

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

pick_channels(ch_names, ordered=None, *, verbose=None)[source]#

Warning

LEGACY: New code should use inst.pick(…).

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 v0.20.0.

Changed in version 1.5: The default changed from False in 1.4 to True in 1.5.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

New in v1.1.

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 v0.9.0.

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, temperature=False, gsr=False, eyetrack=False, include=(), exclude='bads', selection=None, verbose=None)[source]#

Warning

LEGACY: New code should use inst.pick(…).

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 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.
temperaturebool: Temperature channels.
gsrbool: Galvanic skin response channels.
eyetrackbool | str: Eyetracking channels. If True include all eyetracking channels. If False (default) include none. If string it can be ‘eyegaze’ (to include eye position channels) or ‘pupil’ (to include pupil-size 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, etc.) to this list of channel names.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

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

See also

pick_channels

Notes

New in v0.9.0.

plot(picks=None, baseline=None, mode='mean', tmin=None, tmax=None, fmin=None, fmax=None, vmin=None, vmax=None, cmap='RdBu_r', dB=False, colorbar=True, show=True, title=None, axes=None, layout=None, yscale='auto', mask=None, mask_style=None, mask_cmap='Greys', mask_alpha=0.1, combine=None, exclude=[], cnorm=None, verbose=None)[source]#

Plot TFRs as a two-dimensional image(s).

Parameters:

picksstr | array_like | 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 in info['bads'] will be included if their names or indices are explicitly provided.

baselineNone (default) or tuple, shape (2,)

The time interval to apply 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’) (default)
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’)

tminNone | float

The first time instant to display. If None the first time point available is used. Defaults to None.

tmaxNone | float

The last time instant to display. If None the last time point available is used. Defaults to None.

fminNone | float

The first frequency to display. If None the first frequency available is used. Defaults to None.

fmaxNone | float

The last frequency to display. If None the last frequency available is used. Defaults to None.

vminfloat | None

The minimum value an the color scale. If vmin is None, the data minimum value is used. Defaults to None.

vmaxfloat | None

The maximum value an the color scale. If vmax is None, the data maximum value is used. Defaults to None.

cmapmatplotlib colormap | ‘interactive’ | (colormap, bool)

The 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. If ‘interactive’, translates to (‘RdBu_r’, True). Defaults to ‘RdBu_r’.

Warning

Interactive mode works smoothly only for a small amount of images.

dBbool

If True, 10*log10 is applied to the data to get dB. Defaults to False.

colorbarbool

If true, colorbar will be added to the plot. Defaults to True.

showbool

Call pyplot.show() at the end. Defaults to True.

titlestr | ‘auto’ | None

String for title. Defaults to None (blank/no title). If ‘auto’, and combine is None, the title for each figure will be the channel name. If ‘auto’ and combine is not None, title states how many channels were combined into that figure and the method that was used for combine. If str, that String will be the title for each figure.

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 picks. If instance of Axes, there must be only one channel plotted. If combine is not None, axes must either be an instance of Axes, or a list of length 1.

layoutLayout | None

Layout instance specifying sensor positions. Used for interactive plotting of topographies on rectangle selection. If possible, the correct layout is inferred from the data.

yscale‘auto’ (default) | ‘linear’ | ‘log’

The scale of y (frequency) axis. ‘linear’ gives linear y axis, ‘log’ leads to log-spaced y axis and ‘auto’ detects if frequencies are log-spaced and only then sets the y axis to ‘log’.

New in v0.14.0.

maskndarray | None

An array of booleans of the same shape as the data. Entries of the data that correspond to False in the mask are plotted transparently. Useful for, e.g., masking for statistical significance.

New in v0.16.0.

mask_styleNone | ‘both’ | ‘contour’ | ‘mask’

If mask is not None: if 'contour', a contour line is drawn around the masked areas (True in mask). If 'mask', entries not True in mask are shown transparently. If 'both', both a contour and transparency are used. If None, defaults to 'both' if mask is not None, and is ignored otherwise.

New in v0.17.

mask_cmapmatplotlib colormap | (colormap, bool) | ‘interactive’

The colormap chosen for masked parts of the image (see below), if mask is not None. If None, cmap is reused. Defaults to 'Greys'. Not interactive. Otherwise, as cmap.

New in v0.17.

mask_alphafloat

A float between 0 and 1. If mask is not None, this sets the alpha level (degree of transparency) for the masked-out segments. I.e., if 0, masked-out segments are not visible at all. Defaults to 0.1.

New in v0.16.0.

combine‘mean’ | ‘rms’ | callable() | None

Type of aggregation to perform across selected channels. If None, plot one figure per selected channel. If a function, it must operate on an array of shape (n_channels, n_freqs, n_times) and return an array of shape (n_freqs, n_times).

Changed in version 1.3: Added support for callable.

excludelist of str | ‘bads’

Channels names to exclude from being shown. If ‘bads’, the bad channels are excluded. Defaults to an empty list.

cnormmatplotlib.colors.Normalize | None

How to normalize the colormap. If None, standard linear normalization is performed. If not None, vmin and vmax will be ignored. See Matplotlib docs for more details on colormap normalization, and the ERDs example for an example of its use.

New in v0.24.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

figslist of instances of matplotlib.figure.Figure: A list of figures containing the time-frequency power.

Examples using plot:

Overview of MEG/EEG analysis with MNE-Python

Frequency and time-frequency sensor analysis

Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)

Decoding in time-frequency space using Common Spatial Patterns (CSP)

plot_joint(timefreqs=None, picks=None, baseline=None, mode='mean', tmin=None, tmax=None, fmin=None, fmax=None, vmin=None, vmax=None, cmap='RdBu_r', dB=False, colorbar=True, show=True, title=None, yscale='auto', combine='mean', exclude=[], topomap_args=None, image_args=None, verbose=None)[source]#

Plot TFRs as a two-dimensional image with topomaps.

Parameters:

timefreqsNone | list of tuple | dict of tuple: The time-frequency point(s) for which topomaps will be plotted. See Notes.
picksstr | array_like | 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 in info['bads'] will be included if their names or indices are explicitly provided.
baselineNone (default) or tuple of length 2: The time interval to apply 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. If b is None, then b is set to the end of the interval. If baseline is equal to (None, None), the entire time interval is used.
modeNone | str: If str, must be one of ‘ratio’, ‘zscore’, ‘mean’, ‘percent’, ‘logratio’ and ‘zlogratio’. Do baseline correction with ratio (power is divided by mean power during baseline) or zscore (power is divided by standard deviation of power during baseline after subtracting the mean, power = [power - mean(power_baseline)] / std(power_baseline)), mean simply subtracts the mean power, percent is the same as applying ratio then mean, logratio is the same as mean but then rendered in log-scale, zlogratio is the same as zscore but data is rendered in log-scale first. If None no baseline correction is applied.
tmin, tmaxfloat | None: First and last times to include, in seconds. None uses the first or last time present in the data. Default is tmin=None, tmax=None (all times).
fmin, fmaxfloat: The lower- and upper-bound on frequencies of interest. Default is fmin=0, fmax=np.inf (spans all frequencies present in the data).
vminfloat | None: The minimum value of the color scale for the image (for topomaps, see topomap_args). If vmin is None, the data absolute minimum value is used.
vmaxfloat | None: The maximum value of the color scale for the image (for topomaps, see topomap_args). If vmax is None, the data absolute maximum value is used.
cmapmatplotlib colormap: The colormap to use.
dBbool: If True, 10*log10 is applied to the data to get dB.
colorbarbool: If true, colorbar will be added to the plot (relating to the topomaps). For user defined axes, the colorbar cannot be drawn. Defaults to True.
showbool: Call pyplot.show() at the end.
titlestr | None: String for title. Defaults to None (blank/no title).
yscale‘auto’ (default) | ‘linear’ | ‘log’: The scale of y (frequency) axis. ‘linear’ gives linear y axis, ‘log’ leads to log-spaced y axis and ‘auto’ detects if frequencies are log-spaced and only then sets the y axis to ‘log’.
combine‘mean’ | ‘rms’ | callable(): Type of aggregation to perform across selected channels. If a function, it must operate on an array of shape (n_channels, n_freqs, n_times) and return an array of shape (n_freqs, n_times).

Changed in version 1.3: Added support for callable.
excludelist of str | ‘bads’: Channels names to exclude from being shown. If ‘bads’, the bad channels are excluded. Defaults to an empty list, i.e., [].
topomap_argsNone | dict: A dict of kwargs that are forwarded to mne.viz.plot_topomap() to style the topomaps. axes and show are ignored. If times is not in this dict, automatic peak detection is used. Beyond that, if None, no customizable arguments will be passed. Defaults to None.
image_argsNone | dict: A dict of kwargs that are forwarded to AverageTFR.plot() to style the image. axes and show are ignored. Beyond that, if None, no customizable arguments will be passed. Defaults to None.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

figmatplotlib.figure.Figure: The figure containing the topography.

Notes

timefreqs has three different modes: tuples, dicts, and auto. For (list of) tuple(s) mode, each tuple defines a pair (time, frequency) in s and Hz on the TFR plot. For example, to look at 10 Hz activity 1 second into the epoch and 3 Hz activity 300 msec into the epoch,

timefreqs=((1, 10), (.3, 3))

If provided as a dictionary, (time, frequency) tuples are keys and (time_window, frequency_window) tuples are the values - indicating the width of the windows (centered on the time and frequency indicated by the key) to be averaged over. For example,

timefreqs={(1, 10): (0.1, 2)}

would translate into a window that spans 0.95 to 1.05 seconds, as well as 9 to 11 Hz. If None, a single topomap will be plotted at the absolute peak across the time-frequency representation.

New in v0.16.0.

Examples using plot_joint:

Frequency and time-frequency sensor analysis

plot_topo(picks=None, baseline=None, mode='mean', tmin=None, tmax=None, fmin=None, fmax=None, vmin=None, vmax=None, layout=None, cmap='RdBu_r', title=None, dB=False, colorbar=True, layout_scale=0.945, show=True, border='none', fig_facecolor='k', fig_background=None, font_color='w', yscale='auto', verbose=None)[source]#

Plot TFRs in a topography with images.

Parameters:

picksstr | array_like | slice | None

baselineNone (default) or tuple of length 2

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’)

tminNone | float

The first time instant to display. If None the first time point available is used.

tmaxNone | float

The last time instant to display. If None the last time point available is used.

fminNone | float

The first frequency to display. If None the first frequency available is used.

fmaxNone | float

The last frequency to display. If None the last frequency available is used.

vminfloat | None

The minimum value of the color scale. If vmin is None, the data minimum value is used.

vmaxfloat | None

The maximum value of the color scale. If vmax is None, the data maximum value is used.

layoutLayout | None

Layout instance specifying sensor positions. If possible, the correct layout is inferred from the data.

cmapmatplotlib colormap | str

The colormap to use. Defaults to ‘RdBu_r’.

titlestr

Title of the figure.

dBbool

If True, 10*log10 is applied to the data to get dB.

colorbarbool

If true, colorbar will be added to the plot.

layout_scalefloat

Scaling factor for adjusting the relative size of the layout on the canvas.

showbool

Call pyplot.show() at the end.

borderstr

Matplotlib borders style to be used for each sensor plot.

fig_facecolorcolor

The figure face color. Defaults to black.

fig_backgroundNone | array

A background image for the figure. This must be a valid input to matplotlib.pyplot.imshow. Defaults to None.

font_colorcolor

The color of tick labels in the colorbar. Defaults to white.

yscale‘auto’ (default) | ‘linear’ | ‘log’

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

figmatplotlib.figure.Figure: The figure containing the topography.

Examples using plot_topo:

Frequency and time-frequency sensor analysis

plot_topomap(tmin=None, tmax=None, fmin=0.0, fmax=inf, *, ch_type=None, baseline=None, mode='mean', sensors=True, show_names=False, mask=None, mask_params=None, contours=6, outlines='head', sphere=None, image_interp='cubic', extrapolate='auto', border='mean', res=64, size=2, cmap=None, vlim=(None, None), cnorm=None, colorbar=True, cbar_fmt='%1.1e', units=None, axes=None, show=True)[source]#

Plot topographic maps of specific time-frequency intervals of TFR data.

Parameters:

tmin, tmaxfloat | None

First and last times to include, in seconds. None uses the first or last time present in the data. Default is tmin=None, tmax=None (all times).

fmin, fmaxfloat

The lower- and upper-bound on frequencies of interest. Default is fmin=0, fmax=np.inf (spans all frequencies present in the data).

The channel type to plot. For 'grad', the gradiometers are collected in pairs and the mean for each pair is plotted. If None the first available channel type from order shown above is used. Defaults to None.

baselinetuple or list of length 2

Perform baseline correction by

subtracting the mean baseline power (‘mean’)

dividing by the mean baseline power (‘ratio’)

dividing by the mean baseline power and taking the log (‘logratio’)

subtracting the mean baseline power followed by dividing by the mean baseline power (‘percent’)

subtracting the mean baseline power and dividing by the standard deviation of the baseline power (‘zscore’)

dividing by the mean baseline power, taking the log, and dividing by the standard deviation of the baseline power (‘zlogratio’)

If None no baseline correction is applied.

sensorsbool | str

Whether to add markers for sensor locations. If str, should be a valid matplotlib format string (e.g., 'r+' for red plusses, see the Notes section of plot()). If True (the default), black circles will be used.

show_namesbool | callable()

If True, show channel names next to each sensor marker. If callable, channel names will be formatted using the callable; e.g., to delete the prefix ‘MEG ‘ from all channel names, pass the function lambda x: x.replace('MEG ', ''). If mask is not None, only non-masked sensor names will be shown.

maskndarray of bool, shape (n_channels, n_times) | None

Array indicating channel-time combinations to highlight with a distinct plotting style (useful for, e.g. marking which channels at which times a statistical test of the data reaches significance). Array elements set to True will be plotted with the parameters given in mask_params. Defaults to None, equivalent to an array of all False elements.

mask_paramsdict | None

Additional plotting parameters for plotting significant sensors. Default (None) equals:

dict(marker='o', markerfacecolor='w', markeredgecolor='k',
        linewidth=0, markersize=4)

contoursint | array_like

The number of contour lines to draw. If 0, no contours will be drawn. If a positive integer, that number of contour levels are chosen using the matplotlib tick locator (may sometimes be inaccurate, use array for accuracy). If array-like, the array values are used as the contour levels. The values should be in µV for EEG, fT for magnetometers and fT/m for gradiometers. If colorbar=True, the colorbar will have ticks corresponding to the contour levels. Default is 6.

outlines‘head’ | dict | None

The outlines to be drawn. If ‘head’, the default head scheme will be drawn. 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’.

The sphere parameters to use for the head outline. Can be array-like of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give just the radius (origin assumed 0, 0, 0). Can also be an instance of a spherical ConductorModel to use the origin and radius from that object. If 'auto' the sphere is fit to digitization points. If 'eeglab' the head circle is defined by EEG electrodes 'Fpz', 'Oz', 'T7', and 'T8' (if 'Fpz' is not present, it will be approximated from the coordinates of 'Oz'). None (the default) is equivalent to 'auto' when enough extra digitization points are available, and (0, 0, 0, 0.095) otherwise.

New in v0.20.

Changed in version 1.1: Added 'eeglab' option.

image_interpstr

The image interpolation to be used. Options are 'cubic' (default) to use scipy.interpolate.CloughTocher2DInterpolator, 'nearest' to use scipy.spatial.Voronoi or 'linear' to use scipy.interpolate.LinearNDInterpolator.

extrapolatestr

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 for MEG sensors)
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' (default for non-MEG sensors)
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' for MEG sensors.
'local' was changed to use a convex hull mask
'head' was changed to extrapolate out to the clipping circle.

borderfloat | ‘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 v0.20.

resint

The resolution of the topomap image (number of pixels along each side).

sizefloat

Side length of each subplot in inches.

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. If None, '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 to None.

Warning

Interactive mode works smoothly only for a small amount of topomaps. Interactive mode is disabled by default for more than 2 topomaps.

vlimtuple of length 2

Colormap limits to use. If a tuple of floats, specifies the lower and upper bounds of the colormap (in that order); providing None for either entry will set the corresponding boundary at the min/max of the data. Defaults to (None, None).

New in v1.2.

cnormmatplotlib.colors.Normalize | None

New in v1.2.

colorbarbool

Plot a colorbar in the rightmost column of the figure.

cbar_fmtstr

Formatting string for colorbar tick labels. See Format Specification Mini-Language for details.

unitsstr | None

The units to use for the colorbar label. Ignored if colorbar=False. If None the label will be “AU” indicating arbitrary units. Default is None.

axesinstance of Axes | None

The axes to plot to. If None, a new Figure will be created. Default is None.

showbool

Show the figure if True.

Returns:

figmatplotlib.figure.Figure: The figure containing the topography.

Examples using plot_topomap:

Frequency and time-frequency sensor analysis

reorder_channels(ch_names)[source]#

Reorder channels.

Parameters:

ch_nameslist: The desired channel order.

Returns:

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

See also

drop_channels
pick_types
pick_channels

Notes

Channel names must be unique. Channels that are not in ch_names are dropped.

New in v0.16.0.

save(fname, overwrite=False, *, verbose=None)[source]#

Save TFR object to hdf5 file.

Parameters:

fnamepath-like: The file name, which should end with -tfr.h5.
overwritebool: If True (default False), overwrite the destination file if it exists.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

See also

read_tfrs, write_tfrs

shift_time(tshift, relative=True)[source]#

Shift time scale in epoched or evoked data.

Parameters:

tshiftfloat: 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 equals tshift.

Returns:

epochsMNE-object: The modified instance.

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.

time_as_index(times, use_rounding=False)[source]#

Convert time to indices.

Parameters:

timeslist-like | float | int: List of numbers or a number representing points in time.
use_roundingbool: If True, use rounding (instead of truncation) when converting times to indices. This can help avoid non-unique indices.

Returns:

indexndarray: Indices corresponding to the times supplied.

property times#: Time vector in seconds.

property tmax#: Last time point.

property tmin#: First time point.

to_data_frame(picks=None, index=None, long_format=False, time_format=None, *, verbose=None)[source]#

Export data in tabular structure as a pandas DataFrame.

Channels are converted to columns in the DataFrame. By default, additional columns 'time', 'freq', 'epoch', and 'condition' (epoch event description) are added, unless index is not None (in which case the columns specified in index will be used to form the DataFrame’s index instead). 'epoch', and 'condition' are not supported for AverageTFR.

Parameters:

picksstr | array_like | 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.
indexstr | list of str | None: Kind of index to use for the DataFrame. If None, a sequential integer index (pandas.RangeIndex) will be used. If 'time', a pandas.Index or pandas.TimedeltaIndex will be used (depending on the value of time_format). If a list of two or more string values, a pandas.MultiIndex will be created. Valid string values are 'time', 'freq', 'epoch', and 'condition' for EpochsTFR and 'time' and 'freq' for AverageTFR. Defaults to None.
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 to False.
time_formatstr | 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 to pandas.Timedelta values. Default is None.

New in v0.23.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns: