matplotlib.pyplot.cohere   matplotlib.pyplot.cohere(x, y, NFFT=256, Fs=2, Fc=0, detrend=<function detrend_none>, window=<function window_hanning>, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, *, data=None, **kwargs)[source]
 
Plot the coherence between x and y. Plot the coherence between x and y. Coherence is the normalized cross spectral density:  \[C_{xy} = \frac{|P_{xy}|^2}{P_{xx}P_{yy}}\]  Parameters 
 
Fsfloat, default: 2


The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit.  
windowcallable or ndarray, default: window_hanning


A function or a vector of length NFFT. To create window vectors see window_hanning, window_none, numpy.blackman, numpy.hamming, numpy.bartlett, scipy.signal, scipy.signal.get_window, etc. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment.  
sides{'default', 'onesided', 'twosided'}, optional


Which sides of the spectrum to return. 'default' is one-sided for real data and two-sided for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided.  
pad_toint, optional


The number of points to which the data segment is padded when performing the FFT. This can be different from NFFT, which specifies the number of data points used. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to NFFT  
NFFTint, default: 256


The number of data points used in each block for the FFT. A power 2 is most efficient. This should NOT be used to get zero padding, or the scaling of the result will be incorrect; use pad_to for this instead.  
detrend{'none', 'mean', 'linear'} or callable, default: 'none'


The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in MATLAB, where the detrend parameter is a vector, in Matplotlib it is a function. The mlab module defines detrend_none, detrend_mean, and detrend_linear, but you can use a custom function as well. You can also use a string to choose one of the functions: 'none' calls detrend_none. 'mean' calls detrend_mean. 'linear' calls detrend_linear.  
scale_by_freqbool, default: True


Whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility.  
noverlapint, default: 0 (no overlap)


The number of points of overlap between blocks.  
Fcint, default: 0


The center frequency of x, which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband.    Returns 
 
Cxy1-D array


The coherence vector.  
freqs1-D array


The frequencies for the elements in Cxy.    Other Parameters 
 
dataindexable object, optional


If given, the following parameters also accept a string s, which is interpreted as data[s] (unless this raises an exception): x, y  **kwargs

Keyword arguments control the Line2D properties:   
Property Description   
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array  
alpha scalar or None  
animated bool  
antialiased or aa bool  
clip_box Bbox  
clip_on bool  
clip_path Patch or (Path, Transform) or None  
color or c color  
dash_capstyle CapStyle or {'butt', 'projecting', 'round'}  
dash_joinstyle JoinStyle or {'miter', 'round', 'bevel'}  
dashes sequence of floats (on/off ink in points) or (None, None)  
data (2, N) array or two 1D arrays  
drawstyle or ds {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'  
figure Figure  
fillstyle {'full', 'left', 'right', 'bottom', 'top', 'none'}  
gid str  
in_layout bool  
label object  
linestyle or ls {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}  
linewidth or lw float  
marker marker style string, Path or MarkerStyle  
markeredgecolor or mec color  
markeredgewidth or mew float  
markerfacecolor or mfc color  
markerfacecoloralt or mfcalt color  
markersize or ms float  
markevery None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool]  
path_effects AbstractPathEffect  
picker float or callable[[Artist, Event], tuple[bool, dict]]  
pickradius float  
rasterized bool  
sketch_params (scale: float, length: float, randomness: float)  
snap bool or None  
solid_capstyle CapStyle or {'butt', 'projecting', 'round'}  
solid_joinstyle JoinStyle or {'miter', 'round', 'bevel'}  
transform unknown  
url str  
visible bool  
xdata 1D array  
ydata 1D array  
zorder float       References Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986)