Editing User:Josiah425:TISEAN Package:Table of functions
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In reference to the TISEAN library alphabetical order of programs which is located [http://www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/docs/alphabetical.html| here]. | |||
In reference to the TISEAN library alphabetical order of programs which is located [http://www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/docs/alphabetical.html here]. | |||
{| class="wikitable" | |||
|- | |||
! 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 || Autocorrelation function || || | |||
|- | |||
|d2 || Correlation dimension d2 || I believe not, don't know || c | |||
|- | |||
|delay || Creates delay embedding || || | |||
|- | |||
|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 || || | |||
|- | |||
|ghkss || Nonlinear noise reduction || || | |||
|- | |||
|henon || Create a Hénon time series || || | |||
|- | |||
|histogram || Creates histograms || || | |||
|- | |||
|ikeda || Create an Ikeda time series || || | |||
|- | |||
|intervals || Event/intervcal conversion || || | |||
|- | |||
|lazy || Simple nonlinear noise reduction || || | |||
|- | |||
|lfo-ar || Locally first order model vs. global AR model (old ll-ar) || || | |||
|- | |||
|lfo-run || Iterate a locally first order model (old nstep) || || | |||
|- | |||
|lfo-test || Test a locally first order model (old onestep) || || | |||
|- | |||
|lorenz || Create a Lorenz time series || || | |||
|- | |||
|low121 || Time domain low pass filter || || | |||
|- | |||
|lyap_k || Maximal Lyapunov exponent with the Kantz algorithm || || | |||
|- | |||
|lyap_r || Maximal Lyapunov exponent with the Rosenstein algorithm || || | |||
|- | |||
|lyap_spec || Full spectrum of Lyapunov exponents || || | |||
|- | |||
|lzo-gm || Locally zeroth order model vs. global mean || || | |||
|- | |||
|lzo-run || Iterate a locally zeroth order model || || | |||
|- | |||
|lzo-test || Test a locally zeroth order model (old zeroth) || || | |||
|- | |||
|makenoise || Produce noise || || | |||
|- | |||
|mem_spec || Power spectrum using the maximum entropy principle || || | |||
|- | |||
|mutual || Estimate the mutual information || || | |||
|- | |||
|notch || Notch filter || || | |||
|- | |||
|nstat_z || Nonstationarity testing via cross-prediction || || | |||
|- | |||
|pca || Principle component analysis || || | |||
|- | |||
|poincare || Create Poincaré sections || || | |||
|- | |||
|polyback || Fit a polynomial model (backward elimination) || || | |||
|- | |||
|polynom || Fit a polynomial model || || | |||
|- | |||
|polynomp || Fit a polynomial model (reads terms to fit from file) || || | |||
|- | |||
|polypar || Creates parameter file for polynomp || || | |||
|- | |- | ||
| | |predict || Forecast discriminating statistics for surrogates || || | ||
| | |||
|- | |- | ||
|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 || || | ||
|- | |- | ||
| | |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 || || | ||
|- | |- | ||
| | |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 | |||
|xzero || Locally zeroth order cross-prediction | |||
|} | |} |