tensorpac.utils.BinAmplitude

class tensorpac.utils.BinAmplitude(x, sf, f_pha=[2, 4], f_amp=[60, 80], n_bins=18, dcomplex='hilbert', cycle=(3, 6), width=7, edges=None, n_jobs=-1)

Bin the amplitude according to the phase.

Parameters

xarray_like

Array of data of shape (n_epochs, n_times)

sffloat

The sampling frequency

f_phatuple, list | [2, 4]

List of two floats describing the frequency bounds for extracting the phase

f_amptuple, list | [60, 80]

List of two floats describing the frequency bounds for extracting the amplitude

n_binsint | 18

Number of bins to use to binarize the phase and the amplitude

dcomplex{‘wavelet’, ‘hilbert’}

Method for the complex definition. Use either ‘hilbert’ or ‘wavelet’.

cycletuple | (3, 6)

Control the number of cycles for filtering (only if dcomplex is ‘hilbert’). Should be a tuple of integers where the first one refers to the number of cycles for the phase and the second for the amplitude [2].

widthint | 7

Width of the Morlet’s wavelet.

edgesint | None

Number of samples to discard to avoid edge effects due to filtering

__init__(x, sf, f_pha=[2, 4], f_amp=[60, 80], n_bins=18, dcomplex='hilbert', cycle=(3, 6), width=7, edges=None, n_jobs=-1)

Init.

Methods

__init__(x, sf[, f_pha, f_amp, n_bins, …])	Init.
filter(sf, x[, ftype, keepfilt, edges, n_jobs])	Filt the data in the specified frequency bands.
plot([unit, normalize])	Plot the amplitude.
show()	Show the figure.

Attributes

amplitude	Get the amplitude value.
cycle	Get the cycle value.
dcomplex	Get the dcomplex value.
f_amp	Vector of amplitudes of shape (n_amp, 2).
f_pha	Vector of phases of shape (n_pha, 2).
phase	Get the phase value.
width	Get the width value.
xvec	Vector of phases of shape (n_pha,) use for plotting.
yvec	Vector of amplitudes of shape (n_amp,) use for plotting.

filter(sf, x, ftype='phase', keepfilt=False, edges=None, n_jobs=-1)

Filt the data in the specified frequency bands.

Parameters

sffloat

The sampling frequency.

xarray_like

Array of data of shape (n_epochs, n_times)

ftype{‘phase’, ‘amplitude’}

Specify if you want to extract phase (‘phase’) or the amplitude (‘amplitude’).

n_jobsint | -1

Number of jobs to compute PAC in parallel. For very large data, set this parameter to 1 in order to prevent large memory usage.

keepfiltbool | False

Specify if you only want the filtered data (True). This parameter is only available with dcomplex=’hilbert’ and not wavelet.

edgesint | None

Number of samples to discard to avoid edge effects due to filtering

Returns

xfiltarray_like

The filtered data of shape (n_freqs, n_epochs, n_times)

plot(unit='rad', normalize=False, **kw)

Plot the amplitude.

Parameters

unit{‘rad’, ‘deg’}

The unit to use for the phase. Use either ‘deg’ for degree or ‘rad’ for radians

normalizebool | None

Normalize the histogram by the maximum

kwdict | {}

Additional inputs are passed to the matplotlib.pyplot.bar function

Returns

axMatplotlib axis

The matplotlib axis that contains the figure

show()

Show the figure.

property amplitude

Get the amplitude value.

property cycle

Get the cycle value.

property dcomplex

Get the dcomplex value.

property f_amp

Vector of amplitudes of shape (n_amp, 2).

property f_pha

Vector of phases of shape (n_pha, 2).

property phase

Get the phase value.

property width

Get the width value.

property xvec

Vector of phases of shape (n_pha,) use for plotting.

property yvec

Vector of amplitudes of shape (n_amp,) use for plotting.