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Generalized Cross Correlation Estimators.

Istanciation Signatures:

gcc = GCC(sig1, sig2, fftlen)
gcc = GCC.from_spectra(spec1, spec2, onesided=True)

Estimators:

gcc.cc(), gcc.roth(), gcc.scot(), gcc.phat(), gcc.ht()

gcc.gamma12()

class gccestimating.GCC(sig1=None, sig2=None, fftlen=None)[source]

Returns a GCC instance.

Provides estimation methods for Generalized Cross Correlation.

Parameters:

sig1ndarray: First signal.
sig2ndarray: Second signal.
fftlenint or None: Length of fft to be computed. If None, it will be calculated automatically as next power of two.

Returns:

gccGCC

Examples

(Source code)

_images/index-1_00.png — (png, hires.png, pdf)

_images/index-1_01.png — (png, hires.png, pdf)

Attributes:

spec11: Returns auto power spectrum of first signal.
spec12: Returns cross power spectrum of first and second signal.
spec22: Returns auto power spectrum of second signal.

Methods

`Estimate`(name, sig, spec)	Data of an Estimate.
`cc`()	Returns GCC estimate
`coherence`()	Returns the coherence.
`eckart`(sig0, noise1, noise2)	Returns an eckart estimate.
`from_spectra`(spec1, spec2[, onesided])	Returns a GCC instance.
`gamma12`()	Returns gamma12 $gamma_{12}(f)$
`ht`()	Returns GCC HT estimate
`phat`()	Returns GCC PHAT estimate
`roth`()	Returns GCC Roth estimate
`scot`()	Returns GCC SCOT estimate

classmethod from_spectra(spec1, spec2, onesided=True)[source]

Returns a GCC instance.

Parameters:

spec1ndarray: First spectrum.
spec2ndarray: Second spectrum.
onesidedbool: If you provide twosided Spectra (e.g. of comples signals) set to False. Default is True.

Returns:

gccGCC

property spec11: Returns auto power spectrum of first signal.

property spec22: Returns auto power spectrum of second signal.

property spec12: Returns cross power spectrum of first and second signal.

class Estimate(name: str, sig: ndarray, spec: ndarray)[source]

Data of an Estimate. Instances are returned by estimators in GCC.

Parameters:

namestr: Name of the estimator.
signdarray: Estimator signal array (Rxy(t), Cross Correlation).
specndarray: Estimator spectrum (Rxy(f)).

Methods

index_to_lag

cc()[source]

Returns GCC estimate

$mathcal{F}^{-1} (S_{xy})$

roth()[source]

Returns GCC Roth estimate

$mathcal{F}^{-1} (S_{xy}/S_{xx})$

scot()[source]

Returns GCC SCOT estimate

Smoothed gamma12 Transformed GCC.

$mathcal{F}^{-1} (S_{xy}/sqrt{S_{xx}S_{yy}})$

gamma12()[source]: Returns gamma12 $gamma_{12}(f)$

coherence()[source]: Returns the coherence.

phat()[source]

Returns GCC PHAT estimate

PHAse Transformed GCC.

$mathcal{F}^{-1}(S_{xy}/|S_{xy}|)$

eckart(sig0, noise1, noise2)[source]

Returns an eckart estimate.

Parameters:

sig0ndarray: estimate of the actual signal to be correlated.
noise1ndarray: estimated noise in sig1.
noise2ndarray: estimated noise in sig2

Returns:

estmateGCC.Estimate

ht()[source]: Returns GCC HT estimate

gccestimating.corrlags(corrlen, samplerate=1)[source]

Returns array of lags.

Parameters:

corrlenint: Lenght of correlation function (usually 2N-1).
sampleratescalar

Returns:

lagsndarray

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Indices and tables