Octave Forge 4.0.3



Greetings from a Matlab refugee,
Longtime Matlab user, I have just shifted to Octave (to save $ at a small company I just joined).
First, I downloaded Octave 4.0.3 to my laptop, 64-bit Windows 10 Toshiba. This installed perfectly.
Second, I attempted to install the control package, several ways, with no success. My error messages are shown below:
First method
>> pkg install -forge control
error: get_forge_pkg: could not read URL, please verify internet connection
error: called from
get_forge_pkg at line 77 column 7
get_forge_download at line 26 column 12
pkg at line 393 column 29
>>
Second method
>> pkg install control-3.0.0.tar.gz
make: C:/Program: Command not found
make: Entering directory `/tmp/oct-VDJ2TK/control/src'
make: C:/Program: Command not found
make: C:/Program: Command not found
make: C:/Program: Command not found
make: C:/Program: Command not found
/bin/sh: C:/Program: No such file or directory
make: *** [slicotlibrary.a] Error 127
tar -xzf slicot.tar.gz
mkdir sltmp
mv slicot/src/*.f ./sltmp
mv slicot/src_aux/*.f ./sltmp
cp TG04BX.fortran ./sltmp/TG04BX.f
cp TB01ZD.fortran ./sltmp/TB01ZD.f
cd sltmp; C:/Program Files/Octave/octave-4.0.3/bin/mkoctfile-4.0.3.exe -c *.f
make: Leaving directory `/tmp/oct-VDJ2TK/control/src'
pkg: error running `make' for the control package.
error: called from 'configure_make' in file C:Program FilesOctaveoctave-4.0.3
shareoctave4.0.3mpkgprivateconfigure_make.m near line 96, column 9
>>
Third idea
I have tried to research this subject. I read that the control package used to be part of the Octave release but then got pulled out into an external product. But, I notice that my 4.0.3 install appears to have a fully populated control folder out there on my disk. Is there some way to get Octave to realize that and start using it?
Fourth idea
If some earlier Octave release can get me going, I'd be willing to switch versions. I just want the package to run, does not have to be the latest greatest.
Would anyone have an idea how to resolve this, by any method? Thanks, thanks, thanks, for your helpful suggestions!
Sincerely,
b
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31 Signal Processing

This chapter describes the signal processing and fast Fouriertransform functions available in Octave. Fast Fourier transforms arecomputed with the FFTW or FFTPACK libraries depending on howOctave is built.

Built-in Function: fft(x)
Built-in Function: fft(x, n)
Built-in Function: fft(x, n, dim)

Compute the discrete Fourier transform of A usinga Fast Fourier Transform (FFT) algorithm.

The FFT is calculated along the first non-singleton dimension of thearray. Thus if x is a matrix, fft (x) computes theFFT for each column of x.

If called with two arguments, n is expected to be an integerspecifying the number of elements of x to use, or an emptymatrix to specify that its value should be ignored. If n islarger than the dimension along which the FFT is calculated, thenx is resized and padded with zeros. Otherwise, if n issmaller than the dimension along which the FFT is calculated, thenx is truncated.

If called with three arguments, dim is an integer specifying thedimension of the matrix along which the FFT is performed

See also:ifft, fft2, fftn, fftw.

Built-in Function: ifft(x)
Built-in Function: ifft(x, n)
Built-in Function: ifft(x, n, dim)

Compute the inverse discrete Fourier transform of Ausing a Fast Fourier Transform (FFT) algorithm.

The inverse FFT is calculated along the first non-singleton dimensionof the array. Thus if x is a matrix, fft (x) computesthe inverse FFT for each column of x.

If called with two arguments, n is expected to be an integerspecifying the number of elements of x to use, or an emptymatrix to specify that its value should be ignored. If n islarger than the dimension along which the inverse FFT is calculated, thenx is resized and padded with zeros. Otherwise, if n issmaller than the dimension along which the inverse FFT is calculated,then x is truncated.

If called with three arguments, dim is an integer specifying thedimension of the matrix along which the inverse FFT is performed

See also:fft, ifft2, ifftn, fftw.

Built-in Function: fft2(A)
Built-in Function: fft2(A, m, n)

Compute the two-dimensional discrete Fourier transform of A usinga Fast Fourier Transform (FFT) algorithm.

The optional arguments m and n may be used specify the number ofrows and columns of A to use. If either of these is larger than thesize of A, A is resized and padded with zeros.

If A is a multi-dimensional matrix, each two-dimensional sub-matrixof A is treated separately.

Octave Forge 4.0.3

See also:ifft2, fft, fftn, fftw.

Built-in Function: ifft2(A)
Built-in Function: ifft2(A, m, n)

Compute the inverse two-dimensional discrete Fourier transform of Ausing a Fast Fourier Transform (FFT) algorithm.

The optional arguments m and n may be used specify the number ofrows and columns of A to use. If either of these is larger than thesize of A, A is resized and padded with zeros.

If A is a multi-dimensional matrix, each two-dimensional sub-matrixof A is treated separately

See also:fft2, ifft, ifftn, fftw.

Built-in Function: fftn(A)
Built-in Function: fftn(A, size)

Compute the N-dimensional discrete Fourier transform of A usinga Fast Fourier Transform (FFT) algorithm.

The optional vector argument size may be used specify the dimensionsof the array to be used. If an element of size is smaller than thecorresponding dimension of A, then the dimension of A istruncated prior to performing the FFT. Otherwise, if an element ofsize is larger than the corresponding dimension then A isresized and padded with zeros.

See also:ifftn, fft, fft2, fftw.

Built-in Function: ifftn(A)
Built-in Function: ifftn(A, size)

Compute the inverse N-dimensional discrete Fourier transform of Ausing a Fast Fourier Transform (FFT) algorithm.

The optional vector argument size may be used specify the dimensionsof the array to be used. If an element of size is smaller than thecorresponding dimension of A, then the dimension of A istruncated prior to performing the inverse FFT. Otherwise, if an element ofsize is larger than the corresponding dimension then A isresized and padded with zeros.

See also:fftn, ifft, ifft2, fftw.

Octave uses the FFTW libraries to perform FFT computations. When Octavestarts up and initializes the FFTW libraries, they read a system widefile (on a Unix system, it is typically /etc/fftw/wisdom) thatcontains information useful to speed up FFT computations. Thisinformation is called the wisdom. The system-wide file allowswisdom to be shared between all applications using the FFTW libraries.

Use the fftw function to generate and save wisdom. Using theutilities provided together with the FFTW libraries(fftw-wisdom on Unix systems), you can even add wisdomgenerated by Octave to the system-wide wisdom file.

Loadable Function: method =fftw('planner')
Loadable Function: fftw('planner', method)
Loadable Function: wisdom =fftw('dwisdom')
Loadable Function: fftw('dwisdom', wisdom)
Loadable Function: fftw('threads', nthreads)
Loadable Function: nthreads =fftw('threads')

Manage FFTW wisdom data.

Wisdom data can be used to significantly accelerate the calculation of theFFTs, but implies an initial cost in its calculation. When the FFTWlibraries are initialized, they read a system wide wisdom file (typically in/etc/fftw/wisdom), allowing wisdom to be shared between applicationsother than Octave. Alternatively, the fftw function can be used toimport wisdom. For example,

will save the existing wisdom used by Octave to the string wisdom.This string can then be saved to a file and restored using the saveand load commands respectively. This existing wisdom can bere-imported as follows

If wisdom is an empty string, then the wisdom used is cleared.

During the calculation of Fourier transforms further wisdom is generated.The fashion in which this wisdom is generated is also controlled bythe fftw function. There are five different manners in which thewisdom can be treated:

'estimate'

Specifies that no run-time measurement of the optimal means ofcalculating a particular is performed, and a simple heuristic is usedto pick a (probably sub-optimal) plan. The advantage of this method isthat there is little or no overhead in the generation of the plan, whichis appropriate for a Fourier transform that will be calculated once.

'measure'

In this case a range of algorithms to perform the transform is consideredand the best is selected based on their execution time.

'patient'

Similar to 'measure', but a wider range of algorithms isconsidered.

'exhaustive'

Like 'measure', but all possible algorithms that may be used totreat the transform are considered.

'hybrid'

As run-time measurement of the algorithm can be expensive, this is acompromise where 'measure' is used for transforms up to the sizeof 8192 and beyond that the 'estimate' method is used.

The default method is 'estimate'. The current method canbe queried with

or set by using

Note that calculated wisdom will be lost when restarting Octave. However,the wisdom data can be reloaded if it is saved to a file as describedabove. Saved wisdom files should not be used on different platforms sincethey will not be efficient and the point of calculating the wisdom is lost.

The number of threads used for computing the plans and executing thetransforms can be set with

Note that octave must be compiled with multi-threaded FFTW support forthis feature. The number of processors available to the current process isused per default.

See also:fft, ifft, fft2, ifft2, fftn, ifftn.

Function File: fftconv(x, y)
Function File: fftconv(x, y, n)

Convolve two vectors using the FFT for computation.

c = fftconv (x, y) returns a vector of length equal tolength (x) + length (y) - 1. If x and yare the coefficient vectors of two polynomials, the returned value is thecoefficient vector of the product polynomial.

The computation uses the FFT by calling the function fftfilt. Ifthe optional argument n is specified, an N-point FFT is used.

See also:deconv, conv, conv2.

Function File: fftfilt(b, x)
Function File: fftfilt(b, x, n)

Filter x with the FIR filter b using the FFT.

If x is a matrix, filter each column of the matrix.

Given the optional third argument, n, fftfilt uses theoverlap-add method to filter x with b using an N-point FFT.The FFT size must be an even power of 2 and must be greater than or equal tothe length of b. If the specified n does not meet thesecriteria, it is automatically adjusted to the nearest value that does.

See also:filter, filter2.

Built-in Function: y =filter(b, a, x)
Built-in Function: [y, sf] =filter(b, a, x, si)
Built-in Function: [y, sf] =filter(b, a, x, [], dim)
Built-in Function: [y, sf] =filter(b, a, x, si, dim)

Apply a 1-D digital filter to the data x.

filter returns the solution to the following linear, time-invariantdifference equation:

whereN=length(a)-1 and M=length(b)-1.The result is calculated over the first non-singleton dimension of xor over dim if supplied.

An equivalent form of the equation is:

where c = a/a(1) and d = b/a(1).

If the fourth argument si is provided, it is taken as theinitial state of the system and the final state is returned assf. The state vector is a column vector whose length isequal to the length of the longest coefficient vector minus one.If si is not supplied, the initial state vector is set to allzeros.

In terms of the Z Transform, y is the result of passing thediscrete-time signal x through a system characterized by the followingrational system function:

See also:filter2, fftfilt, freqz.

Function File: y =filter2(b, x)
Function File: y =filter2(b, x, shape)

Apply the 2-D FIR filter b to x.

If the argument shape is specified, return an array of the desiredshape. Possible values are:

'full'

pad x with zeros on all sides before filtering.

'same'

unpadded x (default)

'valid'

trim x after filtering so edge effects are no included.

Note this is just a variation on convolution, with the parameters reversedand b rotated 180 degrees.

See also:conv2.

Function File: [h, w] =freqz(b, a, n, 'whole')
Function File: [h, w] =freqz(b)
Function File: [h, w] =freqz(b, a)
Function File: [h, w] =freqz(b, a, n)
Function File: h =freqz(b, a, w)
Function File: [h, w] =freqz(…, Fs)
Function File: freqz(…)

Return the complex frequency response h of the rational IIR filterwhose numerator and denominator coefficients are b and a,respectively.

The response is evaluated at n angular frequencies between 0 and2*pi.

The output value w is a vector of the frequencies.

If a is omitted, the denominator is assumed to be 1 (thiscorresponds to a simple FIR filter).

If n is omitted, a value of 512 is assumed. For fastest computation,n should factor into a small number of small primes.

If the fourth argument, 'whole', is omitted the response isevaluated at frequencies between 0 andpi.

freqz (b, a, w)

Evaluate the response at the specific frequencies in the vector w.The values for w are measured in radians.

[…] = freqz (…, Fs)

Return frequencies in Hz instead of radians assuming a sampling rateFs. If you are evaluating the response at specific frequenciesw, those frequencies should be requested in Hz rather than radians.

freqz (…)

Plot the magnitude and phase response of h rather than returning them.

See also:freqz_plot.

Function File: freqz_plot(w, h)
Function File: freqz_plot(w, h, freq_norm)

Plot the magnitude and phase response of h.

If the optional freq_norm argument is true, the frequency vectorw is in units of normalized radians. If freq_norm is false, ornot given, then w is measured in Hertz.

See also:freqz.

Function File: sinc(x)

Compute the sinc function.

Returnsin (pi*x) / (pi*x).

Function File: b =unwrap(x)
Function File: b =unwrap(x, tol)
Function File: b =unwrap(x, tol, dim)

Unwrap radian phases by adding multiples of 2*pi as appropriate to removejumps greater than tol.

tol defaults to pi.

Unwrap will work along the dimension dim. If dimis unspecified it defaults to the first non-singleton dimension.

Function File: [a, b] =arch_fit(y, x, p, iter, gamma, a0, b0)

Fit an ARCH regression model to the time series y using the scoringalgorithm in Engle’s original ARCH paper.

The model is

in which e(t) is N(0, h(t)), given a time-series vectory up to time t-1 and a matrix of (ordinary) regressors xup to t. The order of the regression of the residual variance isspecified by p.

If invoked as arch_fit (y, k, p) with a positiveinteger k, fit an ARCH(k, p) process, i.e., do the abovewith the t-th row of x given by

Optionally, one can specify the number of iterations iter, theupdating factor gamma, and initial values a0 and b0for the scoring algorithm.

Function File: arch_rnd(a, b, t)

Simulate an ARCH sequence of length t with AR coefficients b andCH coefficients a.

The result y(t) follows the model

where e(t), given y up to time t-1, isN(0, h(t)), with

Function File: [pval, lm] =arch_test(y, x, p)

For a linear regression model

perform a Lagrange Multiplier (LM) test of the null hypothesis of noconditional heteroscedascity against the alternative of CH(p).

I.e., the model is

given y up to t-1 and x up to t,e(t) is N(0, h(t)) with

and the null is a(1)a(p) 0.

If the second argument is a scalar integer, k, perform the sametest in a linear autoregression model of order k, i.e., with

as the t-th row of x.

Under the null, LM approximately has a chisquare distribution withp degrees of freedom and pval is the p-value (1minus the CDF of this distribution at LM) of the test.

If no output argument is given, the p-value is displayed.

Function File: arma_rnd(a, b, v, t, n)

Return a simulation of the ARMA model.

The ARMA model is defined by

in which k is the length of vector a, l is the length ofvector b and e is Gaussian white noise with variance v.The function returns a vector of length t.

The optional parameter n gives the number of dummy x(i)used for initialization, i.e., a sequence of length t+n isgenerated and x(n+1:t+n) is returned. If nis omitted, n = 100 is used.

Function File: autoreg_matrix(y, k)

Given a time series (vector) y, return a matrix with ones in the firstcolumn and the first k lagged values of y in the other columns.

In other words, for t > k,[1, y(t-1), …, y(t-k)] is thet-th row of the result.

The resulting matrix may be used as a regressor matrix in autoregressions.

Function File: bartlett(m)

Return the filter coefficients of a Bartlett (triangular) window of lengthm.

For a definition of the Bartlett window see, e.g.,A.V. Oppenheim & R. W. Schafer,Discrete-Time Signal Processing.

Function File: blackman(m)
Function File: blackman(m, 'periodic')
Function File: blackman(m, 'symmetric')

Return the filter coefficients of a Blackman window of length m.

If the optional argument 'periodic' is given, the periodic formof the window is returned. This is equivalent to the window of lengthm+1 with the last coefficient removed. The optional argument'symmetric' is equivalent to not specifying a second argument.

For a definition of the Blackman window, see, e.g.,A.V. Oppenheim & R. W. Schafer,Discrete-Time Signal Processing.

Function File: detrend(x, p)

If x is a vector, detrend (x, p) removes thebest fit of a polynomial of order p from the data x.

If x is a matrix, detrend (x, p) does the samefor each column in x.

The second argument p is optional. If it is not specified, a value of1 is assumed. This corresponds to removing a linear trend.

The order of the polynomial can also be given as a string, in which casep must be either 'constant' (corresponds to p=0)or 'linear' (corresponds to p=1).

See also:polyfit.

Function File: [d, dd] =diffpara(x, a, b)

Return the estimator d for the differencing parameter of anintegrated time series.

The frequencies from [2*pi*a/t, 2*pi*b/T] are used for theestimation. If b is omitted, the interval[2*pi/T, 2*pi*a/T] is used. If both b and a are omittedthen a = 0.5 * sqrt (T) and b = 1.5 * sqrt (T) is used, whereT is the sample size. If x is a matrix, the differencingparameter of each column is estimated.

The estimators for all frequencies in the intervals described above isreturned in dd.

The value of d is simply the mean of dd.

Reference: P.J. Brockwell & R.A. Davis. Time Series:Theory and Methods. Springer 1987.

Function File: durbinlevinson(c, oldphi, oldv)

Perform one step of the Durbin-Levinson algorithm.

The vector c specifies the autocovariances[gamma_0, …, gamma_t] from lag 0 to t, oldphispecifies the coefficients based on c(t-1) and oldvspecifies the corresponding error.

If oldphi and oldv are omitted, all steps from 1 to t ofthe algorithm are performed.

Function File: fftshift(x)
Function File: fftshift(x, dim)

Perform a shift of the vector x, for use with the fft andifft functions, in order the move the frequency 0 to the center ofthe vector or matrix.

If x is a vector of N elements corresponding to N timesamples spaced by dt, thenfftshift (fft (x)) corresponds to frequencies

where df = 1 / dt.

If x is a matrix, the same holds for rows and columns. If xis an array, then the same holds along each dimension.

The optional dim argument can be used to limit the dimension alongwhich the permutation occurs.

See also:ifftshift.

Function File: ifftshift(x)
Function File: ifftshift(x, dim)

Undo the action of the fftshift function.

For even length x, fftshift is its own inverse, but odd lengthsdiffer slightly.

See also:fftshift.

Function File: fractdiff(x, d)

Compute the fractional differences (1-L)^d x where Ldenotes the lag-operator and d is greater than -1.

Function File: hamming(m)
Function File: hamming(m, 'periodic')
Function File: hamming(m, 'symmetric')

Return the filter coefficients of a Hamming window of length m.

If the optional argument 'periodic' is given, the periodic formof the window is returned. This is equivalent to the window of lengthm+1 with the last coefficient removed. The optional argument'symmetric' is equivalent to not specifying a second argument.

For a definition of the Hamming window see, e.g.,A.V. Oppenheim & R. W. Schafer,Discrete-Time Signal Processing.

Function File: hanning(m)
Function File: hanning(m, 'periodic')
Function File: hanning(m, 'symmetric')

Return the filter coefficients of a Hanning window of length m.

If the optional argument 'periodic' is given, the periodic formof the window is returned. This is equivalent to the window of lengthm+1 with the last coefficient removed. The optional argument'symmetric' is equivalent to not specifying a second argument.

For a definition of the Hanning window see, e.g.,A.V. Oppenheim & R. W. Schafer,Discrete-Time Signal Processing.

Function File: hurst(x)
Octave Forge 4.0.3

Estimate the Hurst parameter of sample x via the rescaled rangestatistic.

If x is a matrix, the parameter is estimated for every column.

Function File: pp =pchip(x, y)
Function File: yi =pchip(x, y, xi)

Return the Piecewise Cubic Hermite Interpolating Polynomial (pchip) ofpoints x and y.

If called with two arguments, return the piecewise polynomial ppthat may be used with ppval to evaluate the polynomial at specificpoints.

When called with a third input argument, pchip evaluates the pchippolynomial at the points xi. The third calling form is equivalent toppval (pchip (x, y), xi).

The variable x must be a strictly monotonic vector (either increasingor decreasing) of length n.

y can be either a vector or array. If y is a vector then itmust be the same length n as x. If y is an array thenthe size of y must have the form[s1, s2, …, sk, n]The array is reshaped internally to a matrix where the leading dimension isgiven bys1 * s2 * … * skand each row of this matrix is then treated separately. Note that this isexactly opposite to interp1 but is done for MATLAB compatibility.

See also:spline, ppval, mkpp, unmkpp.

Function File: [Pxx, w] =periodogram(x)
Function File: [Pxx, w] =periodogram(x, win)
Function File: [Pxx, w] =periodogram(x, win, nfft)
Function File: [Pxx, f] =periodogram(x, win, nfft, Fs)
Function File: [Pxx, f] =periodogram(…, 'range')
Function File: periodogram(…)

Return the periodogram (Power Spectral Density) of x.

The possible inputs are:

x

data vector. If x is real-valued a one-sided spectrum is estimated.If x is complex-valued, or 'range' specifies'twosided', the full spectrum is estimated.

win

window weight data. If window is empty or unspecified a default rectangularwindow is used. Otherwise, the window is applied to the signal(x .* win) before computing the periodogram. The windowdata must be a vector of the same length as x.

Octave Forge 4.0.3 Pro

nfft

number of frequency bins. The default is 256 or the next higher power of2 greater than the length of x(max (256, 2.^nextpow2 (length (x)))). If nfft is greaterthan the length of the input then x will be zero-padded to the lengthof nfft.

Fs

sampling rate. The default is 1.

range

range of spectrum. 'onesided' computes spectrum from[0..nfft/2+1]. 'twosided' computes spectrum from[0..nfft-1].

The optional second output w are the normalized angular frequencies.For a one-sided calculation w is in the range [0, pi] if nfftis even and [0, pi) if nfft is odd. Similarly, for a two-sidedcalculation w is in the range [0, 2*pi] or [0, 2*pi) depending onnfft.

If a sampling frequency is specified, Fs, then the output frequenciesf will be in the range [0, Fs/2] or [0, Fs/2) forone-sided calculations. For two-sided calculations the range will be[0, Fs).

When called with no outputs the periodogram is immediately plotted in thecurrent figure window.

See also:fft.

Function File: sinetone(freq, rate, sec, ampl)

Return a sinetone of frequency freq with a length of secseconds at sampling rate rate and with amplitude ampl.

The arguments freq and ampl may be vectors of common size.

The defaults are rate = 8000, sec = 1, and ampl = 64.

See also:sinewave.

Function File: sinewave(m, n, d)

Return an m-element vector with i-th element given bysin (2 * pi * (i+d-1) / n).

The default value for d is 0 and the default value for n ism.

See also:sinetone.

Function File: spectral_adf(c)
Function File: spectral_adf(c, win)
Function File: spectral_adf(c, win, b)

Return the spectral density estimator given a vector of autocovariancesc, window name win, and bandwidth, b.

The window name, e.g., 'triangle' or 'rectangle' isused to search for a function called win_lw.

If win is omitted, the triangle window is used.

If b is omitted, 1 / sqrt (length (x)) is used.

See also:spectral_xdf.

Function File: spectral_xdf(x)
Function File: spectral_xdf(x, win)
Function File: spectral_xdf(x, win, b)

Return the spectral density estimator given a data vector x, windowname win, and bandwidth, b.

The window name, e.g., 'triangle' or 'rectangle' is used tosearch for a function called win_sw.

If win is omitted, the triangle window is used.

If b is omitted, 1 / sqrt (length (x)) is used.

See also:spectral_adf.

Function File: spencer(x)

Return Spencer’s 15 point moving average of each column of x.

Function File: y =stft(x)
Function File: y =stft(x, win_size)
Function File: y =stft(x, win_size, inc)
Function File: y =stft(x, win_size, inc, num_coef)
Function File: y =stft(x, win_size, inc, num_coef, win_type)
Function File: [y, c] =stft(…)

Compute the short-time Fourier transform of the vector x withnum_coef coefficients by applying a window of win_size datapoints and an increment of inc points.

Before computing the Fourier transform, one of the following windowsis applied:

'hanning'

win_type = 1

'hamming'

win_type = 2

'rectangle'

win_type = 3

The window names can be passed as strings or by the win_type number.

The following defaults are used for unspecified arguments:win_size = 80, inc = 24, num_coef = 64, andwin_type = 1.

y = stft (x, …) returns the absolute values of theFourier coefficients according to the num_coef positive frequencies.

[y, c] = stft (x, …) returns the entireSTFT-matrix y and a 3-element vector c containing the windowsize, increment, and window type, which is needed by the synthesisfunction.

See also:synthesis.

Function File: x =synthesis(y, c)

Compute a signal from its short-time Fourier transform y and a3-element vector c specifying window size, increment, and window type.

The values y and c can be derived by

See also:stft.

Function File: [a, v] =yulewalker(c)

Fit an AR (p)-model with Yule-Walker estimates given a vector c ofautocovariances [gamma_0, …, gamma_p].

Returns the AR coefficients, a, and the variance of white noise,v.

Octave Forge 4.0.3 Version

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