coherence(x, y, fs=1.0, window='hann', nperseg=None, noverlap=None, nfft=None, detrend='constant', axis=-1)
Cxy = abs(Pxy)**2/(Pxx*Pyy)
, where :None:None:`Pxx` and :None:None:`Pyy` are power spectral density estimates of X and Y, and :None:None:`Pxy` is the cross spectral density estimate of X and Y.
An appropriate amount of overlap will depend on the choice of window and on your requirements. For the default Hann window an overlap of 50% is a reasonable trade off between accurately estimating the signal power, while not over counting any of the data. Narrower windows may require a larger overlap.
Time series of measurement values
Time series of measurement values
Sampling frequency of the x and y time series. Defaults to 1.0.
Desired window to use. If :None:None:`window` is a string or tuple, it is passed to get_window
to generate the window values, which are DFT-even by default. See get_window
for a list of windows and required parameters. If :None:None:`window` is array_like it will be used directly as the window and its length must be nperseg. Defaults to a Hann window.
Length of each segment. Defaults to None, but if window is str or tuple, is set to 256, and if window is array_like, is set to the length of the window.
Number of points to overlap between segments. If :None:None:`None`, noverlap = nperseg // 2
. Defaults to :None:None:`None`.
Length of the FFT used, if a zero padded FFT is desired. If :None:None:`None`, the FFT length is :None:None:`nperseg`. Defaults to :None:None:`None`.
Specifies how to detrend each segment. If detrend
is a string, it is passed as the :None:None:`type` argument to the detrend
function. If it is a function, it takes a segment and returns a detrended segment. If detrend
is :None:None:`False`, no detrending is done. Defaults to 'constant'.
Axis along which the coherence is computed for both inputs; the default is over the last axis (i.e. axis=-1
).
Estimate the magnitude squared coherence estimate, Cxy, of discrete-time signals X and Y using Welch's method.
csd
Cross spectral density by Welch's method.
lombscargle
Lomb-Scargle periodogram for unevenly sampled data
periodogram
Simple, optionally modified periodogram
welch
Power spectral density by Welch's method.
>>> from scipy import signal
... import matplotlib.pyplot as plt
... rng = np.random.default_rng()
Generate two test signals with some common features.
>>> fs = 10e3
... N = 1e5
... amp = 20
... freq = 1234.0
... noise_power = 0.001 * fs / 2
... time = np.arange(N) / fs
... b, a = signal.butter(2, 0.25, 'low')
... x = rng.normal(scale=np.sqrt(noise_power), size=time.shape)
... y = signal.lfilter(b, a, x)
... x += amp*np.sin(2*np.pi*freq*time)
... y += rng.normal(scale=0.1*np.sqrt(noise_power), size=time.shape)
Compute and plot the coherence.
>>> f, Cxy = signal.coherence(x, y, fs, nperseg=1024)
... plt.semilogy(f, Cxy)
... plt.xlabel('frequency [Hz]')
... plt.ylabel('Coherence')
... plt.show()
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.signal._spectral_py.csd
scipy.signal._spectral_py.coherence
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