pybispectra.cfc.PPC#
- class pybispectra.cfc.PPC(data: ndarray, freqs: ndarray, sampling_freq: int | float, verbose: bool = True)[source]#
Class for computing phase-phase coupling (PPC).
- Parameters:
- data
ndarray
offloat
, shape of [epochs, channels, frequencies] Fourier coefficients.
- freqs
ndarray
offloat
, shape of [frequencies] Frequencies (in Hz) in
data
. Frequencies are expected to be evenly spaced.- sampling_freq
int
|float
Sampling frequency (in Hz) of the data from which
data
was derived.- verbose
bool
(defaultTrue
) Whether or not to report the progress of the processing.
- data
- Attributes:
results
ResultsCFC
PPC results.
- data
ndarray
offloat
, shape of [epochs, channels, frequencies] Fourier coefficients.
- freqs
ndarray
offloat
, shape of [frequencies] Frequencies (in Hz) in
data
.- sampling_freq
int
|float
Sampling frequency (in Hz) of the data from which
data
was derived.- verbose
bool
Whether or not to report the progress of the processing.
Methods
compute
([indices, f1s, f2s, n_jobs])Compute PPC, averaged over epochs.
copy
()Return a copy of the object.
- compute(indices: tuple[tuple[int]] | None = None, f1s: tuple[int | float] | None = None, f2s: tuple[int | float] | None = None, n_jobs: int = 1) None [source]#
Compute PPC, averaged over epochs.
- Parameters:
- indices
tuple
oftuple
ofint
, length of 2 |None
(defaultNone
) Indices of the seed and target channels, respectively, to compute PPC between. If
None
, coupling between all channels is computed.- f1s
tuple
ofint
orfloat
, length of 2 |None
(defaultNone
) Start and end lower frequencies to compute PPC on, respectively. If
None
, all frequencies are used.- f2s
tuple
ofint
orfloat
, length of 2 |None
(defaultNone
) Start and end higher frequencies to compute PPC on, respectively. If
None
, all frequencies are used.- n_jobs
int
(default1
) Number of jobs to run in parallel. If
-1
, all available CPUs are used.
- indices
Notes
PPC is computed as coherence between frequencies [1]
\(\textrm{PPC}(\textbf{x}_{f_1},\textbf{y}_{f_2})=\Large \frac{|\langle \textbf{a}_x(f_1)\textbf{a}_y(f_2) e^{i(\boldsymbol{\varphi}_x(f_1)\frac{f_2} {f_1}-\boldsymbol{\varphi}_y(f_2))} \rangle|}{\langle\textbf{a}_x(f_1) \textbf{a}_y(f_2) \rangle}\) ,
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; and \(<>\) represents the average value over epochs.
PPC is computed between all values of
f1s
andf2s
. If any value off1s
is higher thanf2s
, anumpy.nan
value is returned.References
- property results: ResultsCFC#
PPC results.