matplotlib.axes.Axes.cohere

Axes.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 is it 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:	**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 contains unknown 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 matplotlib.transforms.Transform url str visible bool xdata 1D array ydata 1D array zorder float

Notes

Note

In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments can also be string s, which is interpreted as data[s] (unless this raises an exception): x, y.

Objects passed as data must support item access (data[s]) and membership test (s in data).

References

Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986)

Examples using matplotlib.axes.Axes.cohere