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, hold=None, data=None, **kwargs)

Plot the coherence between x and y.

Plot the coherence between x and y. Coherence is the normalized cross spectral density:

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.

pad_to : integer

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 : integer

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 pylab 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 : boolean, 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 : integer

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

Fc : integer

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:

The return value is a tuple (Cxy, f), where f are the

frequencies of the coherence vector.

kwargs are applied to the lines.

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 (0.0 transparent through 1.0 opaque)
animated	bool
antialiased or aa	bool
clip_box	a Bbox instance
clip_on	bool
clip_path	[(Path, Transform) | Patch | None]
color or c	any matplotlib color
contains	a callable function
dash_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
dash_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
dashes	sequence of on/off ink in points
drawstyle	[‘default’ | ‘steps’ | ‘steps-pre’ | ‘steps-mid’ | ‘steps-post’]
figure	a Figure instance
fillstyle	[‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’]
gid	an id string
label	object
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float value in points
marker	A valid marker style
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markerfacecoloralt or mfcalt	any matplotlib color
markersize or ms	float
markevery	[None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects	AbstractPathEffect
picker	float distance in points or callable pick function fn(artist, event)
pickradius	float distance in points
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	a matplotlib.transforms.Transform instance
url	a url string
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’.