pybispectra.cfc.PPC#

class pybispectra.cfc.PPC(data: ndarray, freqs: ndarray, sampling_freq: int | float, times: ndarray | None = None, verbose: bool = True)[source]#

Class for computing phase-phase coupling (PPC).

Parameters:

datandarray of float, shape of [epochs, channels, frequencies, times]: Fourier coefficients of the time-frequency representation (TFR) of data.

Changed in version 1.3: Requires TFR coefficients instead of non-time-resolved coefficients.
freqsndarray of float, shape of [frequencies]: Frequencies (in Hz) in data. Frequencies are expected to be evenly spaced.
sampling_freqint | float: Sampling frequency (in Hz) of the data from which data was derived.
timesndarray, shape of [times] | None: Timepoints (in seconds) in data. If data has a times dimension and times = None, the time of the first sample in data is assumed to be 0 seconds.

Added in version 1.3.
verbosebool (default True): Whether or not to report the progress of the processing.

Attributes:

resultsResultsCFC: PPC results.
datandarray of float, shape of [epochs, channels, frequencies, times]: Fourier coefficients of the TFR of data.
freqsndarray of float, shape of [frequencies]: Frequencies (in Hz) in data.
sampling_freqint | float: Sampling frequency (in Hz) of the data from which data was derived.
timesndarray, shape of [times] | None: Timepoints (in seconds) in data.
verbosebool: Whether or not to report the progress of the processing.

Methods

`compute`([indices, f1s, f2s, times, n_jobs])	Compute PPC, averaged over epochs.
`copy`()	Return a copy of the object.

Compute PPC, averaged over epochs.

Parameters:

indicestuple of tuple of int, length of 2 | None (default None): Indices of the seed and target channels, respectively, to compute PPC between. If None, coupling between all channels is computed.
f1stuple of int or float, length of 2 | None (default None): Start and end lower frequencies to compute PPC on, respectively. If None, all frequencies are used.
f2stuple of int or float, length of 2 | None (default None): Start and end higher frequencies to compute PPC on, respectively. If None, all frequencies are used.
timestuple of int or float, length of 2 | None (default None): Start and end times (in seconds) to compute PPC on, respectively. If None, all timepoints are used.

Added in version 1.3.
n_jobsint (default 1): Number of jobs to run in parallel. If -1, all available CPUs are used.

Notes

PPC is computed as coherence between frequencies [1]

\(\textrm{PPC}(\textbf{x}_{f_1},\textbf{y}_{f_2})=\Large \langle\frac{| \langle\textbf{a}_x(f_1)\textbf{a}_y(f_2) e^{i(\boldsymbol{\varphi}_x(f_1) \frac{f_2}{f_1}-\boldsymbol{\varphi}_y(f_2))} \rangle_t|}{\langle\textbf{a}_x (f_1)\textbf{a}_y(f_2) \rangle_t}\rangle\) ,

where \(\textbf{a}(f)\) and \(\boldsymbol{\varphi}(f)\) are the amplitude and phase of a signal at a given frequency, respectively; \(f_1\) and \(f_2\) correspond to a lower and higher frequency, respectively; \(<>_t\) represents the average value over timepoints; and \(<>\) represents the average value over epochs.

PPC is computed between all values of f1s and f2s.

Warning

For values of f1s higher than f2s, a numpy.nan value is returned.

References

copy()[source]#: Return a copy of the object.