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, **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. **kwargs : Keyword arguments control the Line2D properties of the coherence plot: Property Description agg_filter unknown alpha float (0.0 transparent through 1.0 opaque) animated [True | False] antialiased or aa [True | False] axes an Axes instance clip_box a matplotlib.transforms.Bbox instance clip_on [True | False] 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 matplotlib.figure.Figure instance fillstyle [‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’] gid an id string label string or anything printable with ‘%s’ conversion. 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 unknown picker float distance in points or callable pick function fn(artist, event) pickradius float distance in points rasterized [True | False | None] sketch_params unknown snap unknown solid_capstyle [‘butt’ | ‘round’ | ‘projecting’] solid_joinstyle [‘miter’ | ‘round’ | ‘bevel’] transform a matplotlib.transforms.Transform instance url a url string visible [True | False] xdata 1D array ydata 1D array zorder any number

Returns:

The return value is a tuple (Cxy, f), where f are the frequencies of the coherence vector. kwargs are applied to the lines.

References

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

Examples

(Source code, png, pdf)

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’.