# 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:
Fs : scalar

The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2.

window : callable or ndarray

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. The default is window_hanning(). 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'}

Specifies which sides of the spectrum to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided.

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

NFFT : int

The number of data points used in each block for the FFT. A power 2 is most efficient. The default value is 256. 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 : {'default', 'constant', 'mean', 'linear', 'none'} or callable

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. 'default', 'constant', and 'mean' call detrend_mean(). 'linear' calls detrend_linear(). 'none' calls detrend_none().

scale_by_freq : bool, optional

Specifies 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.

noverlap : int

The number of points of overlap between blocks. The default value is 0 (no overlap).

Fc : int

The center frequency of x (defaults to 0), 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:
Cxy : 1-D array

The coherence vector.

freqs : 1-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 float
animated bool
antialiased bool
clip_box Bbox
clip_on bool
clip_path [(Path, Transform) | Patch | None]
color color
contains callable
dash_capstyle {'butt', 'round', 'projecting'}
dash_joinstyle {'miter', 'round', 'bevel'}
dashes sequence of floats (on/off ink in points) or (None, None)
drawstyle {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}
figure Figure
fillstyle {'full', 'left', 'right', 'bottom', 'top', 'none'}
gid str
in_layout bool
label object
linestyle {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
linewidth float
marker unknown
markeredgecolor color
markeredgewidth float
markerfacecolor color
markerfacecoloralt color
markersize float
markevery unknown
path_effects AbstractPathEffect
picker float or callable[[Artist, Event], Tuple[bool, dict]]
pickradius float
rasterized bool or None
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
solid_capstyle {'butt', 'round', 'projecting'}
solid_joinstyle {'miter', 'round', 'bevel'}
transform matplotlib.transforms.Transform
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)

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 are replaced by data[<arg>]:

• All arguments with the following names: 'x', 'y'.

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