Owning Class: spectral analysis
Requires: MathScript RT Module
pspec_eign(x, order)
pspec_eign(x, order, fftsize)
pspec_eign(x, order, range)
pspec_eign(x, order, 'corr')
pspec_eign(x, order, fftsize, fs)
pspec_eign(x, order, fftsize, range)
pspec_eign(x, order, fftsize, 'corr')
pspec_eign(x, order, 'corr', range)
pspec_eign(x, order, fftsize, fs, 'corr')
pspec_eign(x, order, fftsize, fs, range)
pspec_eign(x, order, fftsize, 'corr', range)
pspec_eign(x, order, fftsize, fs, win, noverlap)
pspec_eign(x, order, fftsize, fs, win, 'corr')
pspec_eign(x, order, fftsize, fs, win, range)
pspec_eign(x, order, fftsize, fs, 'corr', range)
pspec_eign(x, order, fftsize, fs, win, noverlap, 'corr')
pspec_eign(x, order, fftsize, fs, win, noverlap, range)
pspec_eign(x, order, fftsize, fs, win, 'corr', range)
pspec_eign(x, order, fftsize, fs, win, noverlap, 'corr', range)
S = pspec_eign(x, order)
S = pspec_eign(x, order, fftsize)
S = pspec_eign(x, order, range)
S = pspec_eign(x, order, 'corr')
S = pspec_eign(x, order, fftsize, fs)
S = pspec_eign(x, order, fftsize, range)
S = pspec_eign(x, order, fftsize, 'corr')
S = pspec_eign(x, order, 'corr', range)
S = pspec_eign(x, order, fftsize, fs, 'corr')
S = pspec_eign(x, order, fftsize, fs, range)
S = pspec_eign(x, order, fftsize, 'corr', range)
S = pspec_eign(x, order, fftsize, fs, win, noverlap)
S = pspec_eign(x, order, fftsize, fs, win, 'corr')
S = pspec_eign(x, order, fftsize, fs, win, range)
S = pspec_eign(x, order, fftsize, fs, 'corr', range)
S = pspec_eign(x, order, fftsize, fs, win, noverlap, 'corr')
S = pspec_eign(x, order, fftsize, fs, win, noverlap, range)
S = pspec_eign(x, order, fftsize, fs, win, 'corr', range)
S = pspec_eign(x, order, fftsize, fs, win, noverlap, 'corr', range)
[S, w] = pspec_eign(x, order)
[S, w] = pspec_eign(x, order, fftsize)
[S, w] = pspec_eign(x, order, range)
[S, w] = pspec_eign(x, order, 'corr')
[S, w] = pspec_eign(x, order, fftsize, range)
[S, w] = pspec_eign(x, order, fftsize, 'corr')
[S, w] = pspec_eign(x, order, 'corr', range)
[S, w] = pspec_eign(x, order, fftsize, 'corr', range)
[S, f] = pspec_eign(x, order, fftsize, fs)
[S, f] = pspec_eign(x, order, fftsize, fs, 'corr')
[S, f] = pspec_eign(x, order, fftsize, fs, range)
[S, f] = pspec_eign(x, order, fftsize, fs, win, noverlap)
[S, f] = pspec_eign(x, order, fftsize, fs, win, 'corr')
[S, f] = pspec_eign(x, order, fftsize, fs, win, range)
[S, f] = pspec_eign(x, order, fftsize, fs, 'corr', range)
[S, f] = pspec_eign(x, order, fftsize, fs, win, noverlap, 'corr')
[S, f] = pspec_eign(x, order, fftsize, fs, win, noverlap, range)
[S, f] = pspec_eign(x, order, fftsize, fs, win, 'corr', range)
[S, f] = pspec_eign(x, order, fftsize, fs, win, noverlap, 'corr', range)
[S, w, evec, eval] = pspec_eign(x, order)
[S, w, evec, eval] = pspec_eign(x, order, fftsize)
[S, w, evec, eval] = pspec_eign(x, order, range)
[S, w, evec, eval] = pspec_eign(x, order, 'corr')
[S, w, evec, eval] = pspec_eign(x, order, fftsize, range)
[S, w, evec, eval] = pspec_eign(x, order, fftsize, 'corr')
[S, w, evec, eval] = pspec_eign(x, order, 'corr', range)
[S, w, evec, eval] = pspec_eign(x, order, fftsize, 'corr', range)
[S, f, evec, eval] = pspec_eign(x, order, fftsize, fs)
[S, f, evec, eval] = pspec_eign(x, order, fftsize, fs, 'corr')
[S, f, evec, eval] = pspec_eign(x, order, fftsize, fs, range)
[S, f, evec, eval] = pspec_eign(x, order, fftsize, fs, win, noverlap)
[S, f, evec, eval] = pspec_eign(x, order, fftsize, fs, win, 'corr')
[S, f, evec, eval] = pspec_eign(x, order, fftsize, fs, win, range)
[S, f, evec, eval] = pspec_eign(x, order, fftsize, fs, 'corr', range)
[S, f, evec, eval] = pspec_eign(x, order, fftsize, fs, win, noverlap, 'corr')
[S, f, evec, eval] = pspec_eign(x, order, fftsize, fs, win, noverlap, range)
[S, f, evec, eval] = pspec_eign(x, order, fftsize, fs, win, 'corr', range)
[S, f, evec, eval] = pspec_eign(x, order, fftsize, fs, win, noverlap, 'corr', range)
Legacy Name: peig
Uses the eigenvector method to calculate the pseudospectrum of an input signal. If you do not request an output, this function plots the pseudospectrum in the current plot window.
| Name | Description | ||||
|---|---|---|---|---|---|
| x | Specifies the input signal. x can be a real or complex vector or matrix. If you specify 'corr', x is a square autocorrelation matrix. Otherwise, x is a real matrix whose elements are stacked row-wise. | ||||
| order | Specifies the dimension information of the signal subspace. order can be a scalar or a vector of two elements. If order is a scalar, order specifies the dimension of the signal subspace. If order is a vector of two elements, order(2) separates the signal and noise subspace. All eigenvalues greater than order(2)*the smallest eigenvalue belong to the signal subspace, and the signal subspace dimension is less than or equal to order(1). | ||||
| fftsize | Specifies the length of the FFT to perform on the estimated eigenvectors of the noise subspace. The default is 256. | ||||
| fs | Specifies the sampling frequency of the input sequence in Hz. | ||||
| win | Specifies the window to apply to each row of the data matrix. win can be an integer or a vector. If win is an integer, LabVIEW applies a Hamming window with a length of win. If win is a vector, win represents the window whose length equals the number of columns in the data matrix. The default is 2*order - 1. | ||||
| noverlap | Specifies the number of points that overlap when LabVIEW creates the data matrix. The default is 0. | ||||
| 'corr' | Specifies that x is a square autocorrelation matrix. | ||||
| range | Specifies the range of the estimated pseudospectrum. range is a string that accepts the following values.
|
| Name | Description |
|---|---|
| S | Returns the estimated pseudospectrum of the input signal. If x is real and range is 'half', the length of S is fftsize / 2 + 1 for an even fftsize and (fftsize + 1) / 2 for an odd fftsize. Otherwise, the length of S is fftsize. |
| w | Returns the discrete frequency vector in radians per sample that corresponds to S. |
| f | Returns the frequencies in Hz at the points where LabVIEW evaluates S. |
| evec | Returns the matrix whose columns are noise subspace eigenvectors. |
| eval | Returns all the eigenvalues of the autocorrelation matrix of the input data. |
This function is supported in the LabVIEW Run-Time Engine only if you request an output from the function. This function can remain in your scripts when you build a stand-alone application or shared library, but if you do not request an output, the LabVIEW Run-Time Engine does not execute this function. If you request an output, the LabVIEW Run-Time Engine executes this function as normal.
fs = 1000;
t = 0:1/fs:0.2;
fftsize = 1024;
noise = randnormal(size(t));
x = sin(2*pi*100*t) + cos(2*pi*250*t) + noise;
S = pspec_eign(x, 4, fftsize, fs);
psd_burg
psd_covar
psd_mcovar
psd_periodogram
psd_welch
psd_yule
pspec_music