healpy.projaxes.HpxCartesianAxes.cohere

HpxCartesianAxes.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.

Call signature:

cohere(x, y, NFFT=256, Fs=2, Fc=0, detrend = mlab.detrend_none,
       window = mlab.window_hanning, noverlap=0, pad_to=None,
       sides='default', scale_by_freq=None, **kwargs)

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

Keyword arguments:

NFFT: integer
The number of data points used in each block for the FFT. Must be even; 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.
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.
detrend: 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.
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.
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 psd (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
sides: [ ‘default’ | ‘onesided’ | ‘twosided’ ]
Specifies which sides of the PSD 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 PSD, while ‘twosided’ forces two-sided.
scale_by_freq: boolean
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.

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)

kwargs control the Line2D properties of the coherence plot:

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: ['-' | '--' | '-.' | ':' | 'None' | ' ' | ''] and any drawstyle in combination with a linestyle, e.g., 'steps--'. linewidth or lw: float value in points lod: [True | False] marker: unknown 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 | integer | (startind, stride) 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

Example: