User:Josiah425:TISEAN Package:Table of functions: Difference between revisions

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|lazy || Simple nonlinear noise reduction || There is not || FORTRAN
|lazy || Simple nonlinear noise reduction || There is not || FORTRAN
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|lfo-ar || Locally first order model vs. global AR model (old ll-ar) || ||
|lfo-ar || Locally first order model vs. global AR model (old ll-ar) || Does not exist || C
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|lfo-run || Iterate a locally first order model (old nstep) || ||
|lfo-run || Iterate a locally first order model (old nstep) || Does not exist || C
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|lfo-test || Test a locally first order model (old onestep) || ||
|lfo-test || Test a locally first order model (old onestep) || Does not exist || C
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|lorenz || Create a Lorenz time series ||  ||
|lorenz || Create a Lorenz time series ||  ||
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|low121 || Time domain low pass filter ||  ||
|low121 || Time domain low pass filter ||  ||
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|lyap_k || Maximal Lyapunov exponent with the Kantz algorithm || ||
|lyap_k || Maximal Lyapunov exponent with the Kantz algorithm || Does not exist || C
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|lyap_r || Maximal Lyapunov exponent with the Rosenstein algorithm || ||
|lyap_r || Maximal Lyapunov exponent with the Rosenstein algorithm || Does not exist || C
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|lyap_spec || Full spectrum of Lyapunov exponents || ||
|lyap_spec || Full spectrum of Lyapunov exponents || Does not exist || C
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|lzo-gm || Locally zeroth order model vs. global mean || ||
|lzo-gm || Locally zeroth order model vs. global mean || Does not exist || C
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|lzo-run || Iterate a locally zeroth order model || ||
|lzo-run || Iterate a locally zeroth order model || Does not exist || C
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|lzo-test || Test a locally zeroth order model (old zeroth) || ||
|lzo-test || Test a locally zeroth order model (old zeroth) || Doest not exist || C
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|makenoise || Produce noise ||  ||
|makenoise || Produce noise ||  ||
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|poincare || Create Poincaré sections ||  ||
|poincare || Create Poincaré sections ||  ||
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|polyback || Fit a polynomial model (backward elimination) || ||
|polyback || Fit a polynomial model (backward elimination) || polyfit, detrend, wpolyfit || I do not know if they work the same way, but it does seem so, written in C
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|polynom || Fit a polynomial model || ||
|polynom || Fit a polynomial model || same as above || same as above
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|polynomp || Fit a polynomial model (reads terms to fit from file) ||  ||
|polynomp || Fit a polynomial model (reads terms to fit from file) || same as above || same as above
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|polypar || Creates parameter file for polynomp || ||
|polypar || Creates parameter file for polynomp || same as above || same as above
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|predict || Forecast discriminating statistics for surrogates || Does not exist || FORTRAN
|predict || Forecast discriminating statistics for surrogates || Does not exist || FORTRAN
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|randomize_spikespec_exp_event || Surrogate data preserving event time power spectrum ||  ||
|randomize_spikespec_exp_event || Surrogate data preserving event time power spectrum ||  ||
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|rbf || Radial basis functions fit || ||
|rbf || Radial basis functions fit || Does not exist || C
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|recurr || Creates a recurrence plot ||  ||
|recurr || Creates a recurrence plot ||  ||
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|xrecur || Cross-recurrence Plot ||  ||
|xrecur || Cross-recurrence Plot ||  ||
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|xzero || Locally zeroth order cross-prediction
|xzero || Locally zeroth order cross-prediction || Does not exist || C
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Revision as of 01:02, 3 April 2015

In reference to the TISEAN library alphabetical order of programs which is located here.

Program Name Program Description Corresponding Octave Function Comments
arima-model Fit and possibly iterate an ARIMA model There is 'aar' in TSA but cannot determine if this is different or not This is a c-file that can be wrapped in C++/mfile/octfile code
ar-model Fit and possibly iterate an Autoregessive model Same as above C; see also: aarmam, adim, amarma, mvaar from TSA
ar-run Iterate an Autoregessive model Same as above FORTRAN
av-d2 Simply smooth output of d2 Same as above C
boxcount Renyi Entopies of Qth order There most likely is none C
c1 Fixed mass estimation of D1 Most likely is none FORTRAN
c2d Get local slopes from correlation integral Most likely none FORTRAN
c2g Gaussian kernel of C2
c2t Takens estimator of D2 Most likely 'rmle' from tsa FORTRAN
choose Choose rows and/or columns from a data file Does not need to be ported ------
compare Compares two data sets If 'rms' exists in Octave no need for port FORTRAN
corr, autocorr Autocorrelation function There is 'acorf' in tsa but i don't know if is the same corr -C, autocorr (faster according to documentation) - FORTRAN
d2 Correlation dimension d2 I believe not, don't know c
delay Creates delay embedding Most likely does not exist C
endtoend Determine end-to-end mismatch
events Interval/event conversion
extrema Determine the extrema of a time series
false_nearest The false nearest neighbor algorithm Does not exist C
ghkss Nonlinear noise reduction There is not C
henon Create a Hénon time series There is not To m-file; already ported
histogram Creates histograms
ikeda Create an Ikeda time series
intervals Event/intervcal conversion
lazy Simple nonlinear noise reduction There is not FORTRAN
lfo-ar Locally first order model vs. global AR model (old ll-ar) Does not exist C
lfo-run Iterate a locally first order model (old nstep) Does not exist C
lfo-test Test a locally first order model (old onestep) Does not exist C
lorenz Create a Lorenz time series
low121 Time domain low pass filter
lyap_k Maximal Lyapunov exponent with the Kantz algorithm Does not exist C
lyap_r Maximal Lyapunov exponent with the Rosenstein algorithm Does not exist C
lyap_spec Full spectrum of Lyapunov exponents Does not exist C
lzo-gm Locally zeroth order model vs. global mean Does not exist C
lzo-run Iterate a locally zeroth order model Does not exist C
lzo-test Test a locally zeroth order model (old zeroth) Doest not exist C
makenoise Produce noise
mem_spec Power spectrum using the maximum entropy principle
mutual Estimate the mutual information Does not exist C
notch Notch filter
nstat_z Nonstationarity testing via cross-prediction
pca, pc Principle component analysis 'pcacov' if likely the equivalent pca - C, pc - FORTRAN
poincare Create Poincaré sections
polyback Fit a polynomial model (backward elimination) polyfit, detrend, wpolyfit I do not know if they work the same way, but it does seem so, written in C
polynom Fit a polynomial model same as above same as above
polynomp Fit a polynomial model (reads terms to fit from file) same as above same as above
polypar Creates parameter file for polynomp same as above same as above
predict Forecast discriminating statistics for surrogates Does not exist FORTRAN
randomize General constraint randomization (surrogates)
randomize_spikeauto_exp_random Surrogate data preserving event time autocorrelations
randomize_spikespec_exp_event Surrogate data preserving event time power spectrum
rbf Radial basis functions fit Does not exist C
recurr Creates a recurrence plot
resample Resamples data There is 'resample' in Octave, but I believe it does something else C
rescale Rescale data set This should be in Octave, cannot find... C
rms Rescale data set and get mean, variance and data interval This should be in Octave, cannot find... FORTRNAN
sav_gol Savitzky-Golay filter
spectrum Power spectrum using FFT
spikeauto Autocorrelation function of event times
spikespec Power spectrum of event times
stp Creates a space-time separation plot
surrogates Creates surrogate data
timerev Time reversal discrimating statistics for surrogates
upo Finds unstable periodic orbits and estimates their stability No corresponding function in Octave FORTRAN
upoembed Takes the output of upo and create data files out of it
wiener Wiener filter
xc2 Cross-correlation integral
xcor Cross-correlations
xrecur Cross-recurrence Plot
xzero Locally zeroth order cross-prediction Does not exist C
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