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Subsections
Syntax:
Take absolut value of all data values of signal s. If sample
values are complex, abs(s) returns the complex modulus (magnitude) of each sample.
6.20.3.2 acf
Syntax:
Input arguments:
- len -length of the fft (optional)
Autocorrelation function for real scalar signals, using fft (of length len). If len is ommited a default value is calculated. The maximum
of the calculated length is 128.
Syntax:
- acp(s, tau, past, maxdelay, maxdim, nref)
Input arguments:
- tau - proper delay time for s
- past - number of samples to exclude before and after
reference index (to avoid correlation effects)
- maxdelay - maximal delay (should be much smaller than the lenght of
s) (optional)
- maxdim - maximal dimension to use (optional)
- nref - number of reference points (optional)
Auto crossprediction function for real scalar signals for increasing
dimension. The default value for maxdelay is 25% of the input signal's
length. The default for maxdim is 8 and for nref it is 10% of the
input signal's length.
Syntax:
Input arguments:
- maxtau - maximal delay (should be much smaller than the lenght of
s) (optional)
- bins - number of bins used for histogram calculation
(optional)
Auto mutual information function for real scalar signals, can be used to
determine a proper delay time for time-delay reconstruction. The default value
for maxtau is 25% of the input signal's length. The default number of
bins is 128.
Syntax:
Input arguments:
Auto mutual information (average) function for real scalar signals using 128
equidistant partitions.
Syntax:
Input arguments:
- maxdim - analyze will not use a dimension higher than this limit
Try to do a automatic analysis procedure of a time series. The time series is
embedded using the first zero of the auto mutual information function for the
delay time.
Syntax:
- [rs, archetypes]=arch(s, na, mode='normalized')
Input arguments:
- na - number of generated archetypes
- mode - mode can be one of the following : 'normalized'
, 'mean', 'raw' (optional)
Archetypal analysis of column orientated data set:
- each row of data is one 'observation', e.g. the sample values of
all channels in a multichannel measurement at one point in time
- in mode 'normalized' each column of data is centered by removing its mean
and then normalized by dividing through its standard deviation before
the covariance matrix is calculated
- in mode 'mean' only the mean of every column of data is removed
- in mode 'raw' no preprocessing is applied to data
Default value for mode is 'normalized'.
Syntax:
Input arguments:
- s - data points (row vectors)
- bins - maximal number of partition per axis (optional)
Compute the boxcounting (capacity) dimension of a time-delay reconstructed
timeseries s for dimensions from 1 to D, where D is the
dimension of the input vectors using boxcounting approach. The default number
of bins is 100.
6.20.3.9 cao
Syntax:
- [E1, E2] = cao(s, maxdim, tau, NNR, Nref)
Input arguments:
- s - scalar input signal
- maxdim - maximal dimension
- tau - delay time
- NNR - number of nearest neighbor to use
- Nref - number of reference points (-1 means: use all points)
Estimate minimum embedding dimension using Cao's method.
The second output argument, E2, can be used to distinguish between
deterministic and random data.
Syntax:
Center signal by removing it's mean.
6.20.3.11 corrdim
Syntax:
Input arguments:
- s - data points (row vectors)
- bins - maximal number of partition per axis (optional)
Compute the correlation dimension of a time-delay reconstructed timeseries s
for dimensions from 1 to D, where D is the dimension of the input vectors
using boxcounting approach. The default number of bins is 100.
6.20.3.12 corrsum
Syntax:
- rs = corrsum(s, n, range, past, bins)
Input arguments:
- n - number of randomly chosen reference points (n == -1 means: use all points)
- range - maximal relative search radius (relative to attractor size) 0..1
- past - number of samples to exclude before and after each reference index
- bins - number of bins (optional)
Compute scaling of correlation sum for time-delay reconstructed timeseries s
(Grassberger-Proccacia Algorithm), using fast nearest neighbor search. Default
number of bins is 20.
6.20.3.13 corrsum2
Syntax:
- rs = corrsum2(s, npairs, range, past, bins)
Input arguments:
- npairs - number of pairs per bins
- range - maximal relative search radius (relative to attractor size) 0..1
- past - number of samples to exclude before and after each reference index
- bins - number of bins (optional), defaults to 32
Compute scaling of correlation sum for time-delay reconstructed timeseries s
(Grassberger-Proccacia Algorithm), using fast nearest neighbor search.
6.20.3.14 crosscorrdim
Syntax:
- rs = crosscorrdim(s, s2, n, range, past, bins)
Input arguments:
- n - number of randomly chosen reference points (n == -1 means : use all points)
- range - maximal relative search radius (relative to size of data set s2) 0..1
- past - number of samples to exclude before and after each reference index
- bins - number of bins (optional)
Compute scaling of cross-correlation sum for time-delay reconstructed timeseries s against
signal s2 (with same dimension as s), using fast nearest neighbor search.
Reference points are taken out of signal s, while neigbors are searched in
s2. The default number of bins is 32.
6.20.3.15 cut
Syntax:
- rs = cut(s, dim, start, stop)
Input arguments:
- dim - dimension along which the signal is cutted
- start - position where to start the cut
- stop - position where to stop (optional)
Cut a part of the signal. If stop is ommited only the data at start
is cutted.
6.20.3.16 db
Syntax:
Compute decibel values of signal relative to a reference value that is
determined by the signal's yunit values below dbmin are
set to dbmin. If dbmin is ommited it is set to -120.
6.20.3.17 delaytime
Syntax:
- tau = delaytime(s, maxdelay, past)
Input arguments:
- maxdelay - maximal delay time
- past - ?
Compute optimal delaytime for a scalar timeseries with method of Parlitz and Wichard.
6.20.3.18 diff
Syntax:
Compute the nth numerical derivative along dimension 1. s has be to sampled equidistantly.
6.20.3.19 dimensions
Syntax:
- [bc,in,co] = dimensions(s, bins)
Input arguments:
- s - data points (row vectors)
- bins - maximal number of partition per axis, default is 100
Output arguments:
- bc - scaling of boxes with partititon sizes
- in - scaling of information with partititon sizes
- co - scaling of correlation with partititon sizes
Compute boxcounting, information and correlation dimension of a time-delay
reconstructed timeseries s for dimensions from 1 to D,
where D is the dimension of the input vectors using boxcounting approach.
Scale data to be within 0 and 1. Give a sortiment of (integer)
partitionsizes with almost exponential behaviour.
6.20.3.20 display
6.20.3.21 embed
Syntax:
- emb = embed(s, dim, delay, shift, windowtype)
Input arguments:
- dim - embedding dimension
- delay - time delay (optional)
- shift - shift for two sequent time delay vectors (optional)
- windowtype - type of window (optional)
Output arguments:
- emb - n by dim array, each row contains the coordinates of
one point
Embeds signal s with embedding dimension dim and delay
delay (in samples). s must be a scalar time series. The default
values for dim and delay are equal to one. The default value for
windowtype is 'Rect', which is currently the only possible value.
6.20.3.22 fft
Syntax:
Output arguments:
- f - n by 2 array, the first column contains the magnitudes, the
second one the phases.
Fourier transform of scalar signal s.
6.20.3.23 filterbank
Syntax:
- filterbank(s, depth, filterlen)
Filter scalar signal s into
bands of equal bandwith,
using maximally flat filters.
6.20.3.24 firstmax
Syntax:
- [xpos, unit] = firstmax(s)
Give information about first local maximum of scalar signal s.
6.20.3.25 firstmin
Syntax:
- [xpos, unit] = firstmin(s)
Give information about first local minimum of scalar signal s.
6.20.3.26 firstzero
Syntax:
- [xpos, unit] = firstzero(s)
Give information about first zero of scalar signal s, using linear interpolation.
6.20.3.27 fracdims
Syntax:
- rs = fracdims(s, kmin, kmax, Nref, gstart, gend, past, steps)
- rs = fracdims(s, kmin, kmax, Nref, gstart, gend, past)
- rs = fracdims(s, kmin, kmax, Nref, gstart, gend)
Input arguments:
- kmin - minimal number of neighbors for each reference point
- kmax - maximal number of neighbors for each reference point
- Nref - number of randomly chosen reference points (n == -1 means : use all points)
- gstart - starting value for moments
- gend - end value for moments
- past - (optional) number of samples to exclude before and after each reference index, default is 0
- steps - (optional) number of moments to calculate, default is 32
Compute fractal dimension spectrum D(q) using moments of
neighbor distances for time-delay reconstructed timeseries s.
Do the main job - computing nearest neighbors for reference points.
6.20.3.28 getaxis
Syntax:
Get one of the currend xaxes.
6.20.3.29 gmi
Syntax:
- gmi(s, D, eps, NNR, len, Nref)
Input arguments:
- D -
- eps -
- NNR -
- len -
- Nref -
Generalized mutual information function for a scalar time series
6.20.3.30 histo
Syntax:
Histogram function using equidistantly spaced partitions.
6.20.3.31 infodim
Syntax:
Input arguments:
- s - data points (row vectors)
- bins - maximal number of partition per axis, default is 100
Compute the information dimension of a time-delay reconstructed
timeseries s for dimensions from 1 to D, where D is
the dimension of the input vectors. Using boxcounting approach.
Scale data to be within 0 and 1.
Give a sortiment of (integer) partitionsizes with almost exponential behaviour.
6.20.3.32 infodim2
Syntax:
- rs = infodim2(s, n, kmax, past)
Input arguments:
- n - number of randomly chosen reference points (n == -1 means : use all points)
- kmax - maximal number of neighbors for each reference point
- past - number of samples to exclude before and after each reference index
Compute scaling of moments of the nearest neighbor distances for
time-delay reconstructed timeseries s. This can be used to
calculate information dimension D1.
Numerically compute first derivative of
after
.
6.20.3.33 int
Syntax:
Numerical integration along dimension 1 signal s has to be sampled equidistantly.
6.20.3.34 intspikeint
Syntax:
Compute the interspike intervalls for a spiked scalar timeseries,
using transformation on ranked values.
6.20.3.35 intspikint
Syntax:
Compute the interspike intervalls for a spiked scalar timeseries,
using transformation on ranked values.
6.20.3.36 largelyap
Syntax:
- rs = largelyap(s, n, stepsahead, past, nnr)
Input arguments:
- n - number of randomly chosen reference points (-1 means: use all points)
- stepsahead - maximal length of prediction in samples
- past - exclude
- nnr - number of nearest neighbours (optional)
Output arguments:
Compute the largest lyapunov exponent of a time-delay reconstructed
timeseries s, using formula (1.5. of Nonlinear Time-Series
Analysis, Ulrich Parlitz 1998 [146]).
6.20.3.37 level_adaption
Syntax:
- level_adaption(s, timeconstants, dynamic_limit, threshold)
Each channel of signal s is independently divided by a scaling factor that
adapts to the current level of the samples in this channel. The adaption
process is simulated using a cascade of feedback loops (Püschel 1998) which
consists of low pass filters with time constants given as second argument
to this function. The number of time constants given determines the number
of feedback loops that are used.
Higher values for time constants will result in slower adaption speed.
Short time changes in the signal will be transmitted almost linearily.
In each feedback loop, a nonlinear compressing characteristic (see
Stefan Münkner 1993) limits the signal values to be within
[-dynamic_limit dynamic_limit]. A low value for dynamic_limit will
introduce nonlinear distortions to the signal.
To prevent the feedback loops from adapting to a zero level (in case all input
values are zero), a tiny threshold is given as 4th argument. The scaling factors
will not shrink below this threshold.
6.20.3.38 localdensity
Syntax:
- rs = localdensity(s, n, past)
Input arguments:
- n - number of nearest neighbour to compute
- past - a nearest neighbour is only valid if it is as least
past timesteps away from the reference point
past = 1 means: use all points but ref_point itself
Uses accelerated searching, distances are calculated with euclidian norm.
6.20.3.39 max
Syntax:
- [maximum, yunit, xpos, xunit] = max(s)
Give information about maximum of scalar signal s.
Example:
disp('maximum of signal : ')
disp(['y = ' num2str(m) ' ' label(yunit(s))]);
disp(['x = ' num2str(xpos) ' ' label(a)]);
6.20.3.40 medianfilt
Syntax:
Moving median filter of width len samples for a scalar time
series (len should be odd).
6.20.3.41 merge
Syntax:
- merge(signal1, signal2, dB)
- merge(signal1, signal2)
Input arguments:
- signal1, signal2 - Signals
- dB - energy ratio, (optional, default = 0)
Merges signal s1 and s2 into a new signal with energy
ration dB (in decibel) a positive value of dB increases
the amount of signal1 in the resulting signal.
6.20.3.42 min
Syntax:
- [minimum, yunit, xpos, xunit] = min(s)
Give information about minimum of scalar signal s.
Example:
disp('minimum of signal : ')
disp(['y = ' num2str(m) ' ' label(yunit(s))]);
disp(['x = ' num2str(xpos) ' ' label(a)]);
6.20.3.43 minus
Syntax:
- rs=minus(s, offset)
- rs=minus(s1,s2)
Input arguments:
- s, s1, s2 - signal object
- offset - scalar value
Calculate difference of signals s1 and s2 or substract a
scalar value from s.
6.20.3.44 movav
Syntax:
- rs = movav(s, len, windowtype)
- rs = movav(s, len)
Moving average of width len (samples) along first dimension.
6.20.3.45 multires
Syntax:
- rs = multires(s) => scale=3
- rs = multires(s, scale)
Multires perform multiresolution analysis.
Y = MULTIRES (X,H,RH,G,RG,SC) obtains the SC successive details and
the low frequency approximation of signal in X from a multiresolution
scheme. The analysis lowpass filter H, synthesis lowpass filter
RH, analysis highpass filter G and synthesis
highpass filter RG are used to implement the scheme.
Results are given in a scale+1 channels. The first scale
channels are the details corresponding to the scales
to
the last row contains the
approximation at scale
. The original signal can be
restored by summing all the channels of the resulting signal.
6.20.3.46 nearneigh
Syntax:
- rs = nearneigh(s, n) => past=1
- rs = nearneigh(s, n, past)
Input arguments:
- n - number of nearest neighbour to compute
- past - a nearest neighbour is only valid if it is as least
past timesteps away from the reference point.
past = 1 means: use all points but ref_point itself
n nearest neighbour algorithm. Find n nearest neighbours
(in order of increasing distances) to each point in signal s
uses accelerated searching, distances are calculated with euclidian norm.
6.20.3.47 norm1
Syntax:
- rs=norm1(s) => low=0 , upp=1
- rs=norm1(s, low) => upp=1
- rs=norm1(s, low, upp)
Scale and move signal values to be within [low,upp].
6.20.3.48 norm2
Syntax:
Normalize signal by removing it's mean and dividing by the standard deviation.
6.20.3.49 pca
Syntax:
- [rs, eigvals, eigvecs] = pca(s) => mode='normalized' , maxpercent = 95
- [rs, eigvals, eigvecs] = pca(s, mode) => maxpercent = 95
- [rs, eigvals, eigvecs] = pca(s, mode, maxpercent)
Input arguments:
- each row of data is one 'observation', e.g. the sample values of
all channels in a multichannel measurement at one point in time
- mode can be one of the following : 'normalized' (default), 'mean', 'raw'
- in mode 'normalized' each column of data is centered by removing its mean
and then normalized by dividing through its standard deviation before
the covariance matrix is calculated
- in mode 'mean' only the mean of every column of data is removed
- in mode 'raw' no preprocessing is applied to data
- maxpercent gives the limit of the accumulated percentage of the resulting
eigenvalues, default is 95 %
Principal component analysis of column orientated data set.
6.20.3.50 plosivity
Syntax:
- rs = plosivity(s, blen) => flen=1 , thresh=0, windowtype = 'Rect'
- rs = plosivity(s, blen, flen) => thresh=0, windowtype = 'Rect'
- rs = plosivity(s, blen, flen, thresh) => windowtype = 'Rect'
- rs = plosivity(s, blen, flen, thresh, windowtype)
Compute plosivity of a spectrogram. See also: window for list of possible window types.
6.20.3.51 plus
Syntax:
- rs=plus(s, offset)
- rs=plus(s1, s2)
Add two signals s1 and s2 or add a scalar value offset to s.
6.20.3.52 poincare
Syntax:
Compute Poincare-section of an embedded time series the result is a
set of vector points with dimension DIM-1, when the input data
set of vectors had dimension DIM. The projection is done
orthogonal to the tangential vector at the vector with index.
6.20.3.53 power
Syntax:
Calculate squared magnitude of each sample.
6.20.3.54 predict
Syntax:
- rs = predict(s, dim, delay, len) => nnr=1
- rs = predict(s, dim, delay, len, nnr) => mode=0
- rs = predict(s, dim, delay, len, nnr, mode)
Input arguments:
- dim - dimension for time-delay reconstruction
- delay - delay time (in samples) for time-delay reconstruction
- len - length of prediction (number of output values)
- nnr - number of nearest neighbors to use (default is one)
- step - stepsize (in samples) (default is one)
- mode:
- 0 = Output vectors are the mean of the images of the nearest neighbors
- 1 = Output vectors are the distance weighted mean of the images of the nearest neighbors
- 2 = Output vectors are calculated based on the local flow using the mean of the images of the neighbors
- 3 = Output vectors are calculated based on the local flow using the weighted mean of the images of the neighbors
Local constant iterative prediction for scalar data, using fast nearest neighbor search.
Four methods of computing the prediction output are possible.
6.20.3.55 predict2
Syntax:
- rs = predict2(s, len, nnr, step, mode)
Input arguments:
- len - length of prediction (number of output values)
- nnr - number of nearest neighbors to use (default is one)
- step - stepsize (in samples) (default is one)
- mode:
- 0 = Output vectors are the mean of the images of the nearest neighbors
- 1 = Output vectors are the distance weighted mean of the images of the nearest neighbors
- 2 = Output vectors are calculated based on the local flow using the mean of the images of the neighbors
- 3 = Output vectors are calculated based on the local flow using
the weighted mean of the images of the neighbors
Local constant iterative prediction for phase space data (e.g.
data stemming from a time delay reconstruction of a
scalar time series), using fast nearest neighbor search.
Four methods of computing the prediction output are possible.
6.20.3.56 rang
Syntax:
Transform scalar time series to rang values.
6.20.3.57 removeaxis
Syntax:
Remove axis one of the current xaxes. No bound checking for dim.
6.20.3.58 return_time
Syntax:
- rs = return_time(s, nnr, maxT) => past=1
- rs = return_time(s, nnr, maxT, past)
- rs = return_time(s, nnr, maxT, past, N)
Input arguments:
- nnr - number of nearest neighbors
- maxT - maximal return time to consider
- past - a nearest neighbor is only valid if it is as least past timesteps away from the reference point
past = 1 means: use all points but tt ref_point itself
- N - number of reference indices
Compute histogram of return times.
6.20.3.59 reverse
Syntax:
Reverse signal along dimension 1.
6.20.3.60 rms
Syntax:
Calculate root mean square value for signal along dimension 1.
6.20.3.61 scale
Syntax:
Scale signal by factor f.
6.20.3.62 scalogram
Syntax:
- rs = scalogram(s) => scalemin=0.1
- rs = scalogram(s, scalemin) => scalemax=1
- rs = scalogram(s, scalemin, scalemax) => scalestep=0.1
- rs = scalogram(s, scalemin, scalemax, scalestep) => mlen=10
- rs = scalogram(s, scalemin, scalemax, scalestep, mlen)
Scalogram of signal s using morlet wavelet. See also: spec2.
6.20.3.63 setaxis
Syntax:
- s = setaxis(s, dim, achse)
Change one of the current xaxes.
6.20.3.64 setunit
Syntax:
Change unit of one of the current xaxes.
6.20.3.65 shift
Syntax:
- s = shift(s, distance) (dim=1)
- s = shift(s, distance, dim)
shift signal on axis No. dim by distance (measured in the unit of the axis) to the right
6.20.3.66 signal
Syntax:
- s = signal(array)
creates a new signal object from a data array array
the data inside the object can be retrieved with x = data(s);
- s = signal(array, achse1, achse2, ...)
creates a new signal object from a data array array, using achse1 etc. as xachse entries
- s = signal(array, unit1, unit2, ...)
creates a new signal object from a data array 'array', using unit1 etc. to create xachse objects
- s = signal(array, samplerate1, samplerate2, ...)
creates a new signal object from a data array array, using as xunit
's' (second) and scalar samplerate1 as samplerate(s)
A signal object contains signal data, that is a collection of real or complex valued samples.
A signal can be one or multi-dimensional. The number of dimensions is the number of axes that
are needed to describe the the data.
An example for an one-dimensional signal is a one-channel
measurement (timeseries), or the power spectrum of a one-channel measurement.
An example for a two-dimensional signal is a twelve-channel measurement, with one
time axis and a 'channel' axis. Another example for a two-dimensional signal is
a short time spectrogramm of a time series, where we have a time axis and a frequency axis.
Each axis can have a physical unit(e.g. 's' or 'Hz'), a starting point and a step value.
E.g. if a time-series is sampled with 1000 Hz, beginning at 1 min 12 sec, the unit is 's',
the starting point is 72 and the step value (delta) is 0.001.
But not only the axes have physical units, also the sample value themselve can have a
unit, maybe 'V' or 'Pa', depending on what the sampled data represent (=> yunit)
All units are stored as objects of class 'unit', all axes are stored as objects
of class 'achse' (this somewhat peculiar name was chosen because of conflicts with
reserved matlab keywords 'axis' and 'axes', which otherwise would have been the first choice).
Example for creating a 2-dimensional signal with y-unit set to 'Volt', the first dimension's unit is
'second' (time), the second dimension's unit is 'n' (Channels).
Examples:
tmp = rand(100, 10);
s = signal(tmp, unit('s'), unit('n'));
s = setyunit(s, unit('V'));
s = addcomment(s, 'Example signal with two dimensions')
- Loading from disk
s = signal(filename)
loads a previously stored signal object
- Importing from other file formats:
ASCII: s = signal('data/spalte1.dat', 'ASCII')
WAVE: s = signal('data/Sounds/hat.wav', 'WAVE')
AU (SUN AUDIO): s = signal('data/Sounds/hat.au', 'AU')
(old) NLD-Format : s = signal('test.nld', 'NLD')
6.20.3.67 spacing
- v = spacing(s) (dim=1)
- v = spacing(s, dim)
return spacing values for xaxis nr. dim
6.20.3.68 spec
Syntax:
compute power spectrum for real valued scalar signals. Multivariate signals are
accepted but may produce unwanted results as only the spectrum of the first
column is returned.
6.20.3.69 spec2
Syntax:
Input Arguments:
- fensterlen - size of window (optional)
- fenster - window type (optional)
- vorschub - shift in samples (optional)
spectrogramm of signal s using short time fft
Examples:
view(spec2(sine(10000, 1000, 8000), 512, 'Hanning'))
6.20.3.70 stts
Syntax:
- rs = stts(s, I) (J=0, K=1, L=1)
- rs = stts(s, I, J) (K=1, L=1)
- rs = stts(s, I, J, K) (L=1)
- rs = stts(s, I, J, K, L)
Input Arguments:
- s - input data set of N snapshots of length M, given as N by M matrix
- I - number of spatial neighbours
- J - number of temporal neighbours (in the past)
- K - spatial shift (= spatial delay)
- L - temporal delay
Spatiotemporal prediction conforming to U. Parlitz, NONLINEAR
TIME-SERIES ANALYSIS Chapter 1.10.2.1.
6.20.3.71 sttserror
Syntax:
Input Arguments:
- s1 - original signal
- s2 - predicted signal
compute error function for prediction of spatial-temporal systems
see U. Parlitz "Nonlinear Time Series Analysis", Section 1.10.2.2 Eq. 1.10
6.20.3.72 surrogate1
Syntax:
create surrogate data for a scalar time series by randomizing phases of fourier spectrum
see : James Theiler et al.'Using Surrogate Data to Detect Nonlinearity in Time Series', APPENDIX : ALGORITHM I
6.20.3.73 surrogate2
Syntax:
create surrogate data for a scalar time series
see : James Theiler et al.'Using Surrogate Data to Detect Nonlinearity in Time Series', APPENDIX : ALGORITHM II
6.20.3.74 surrogate3
Syntax:
create surrogate data for a scalar time series by permuting samples randomly
6.20.3.75 surrogate_test
Syntax:
- rs=surrogate_test(s, ntests, method,func)
Input Arguments:
- s - has to be a real, scalar signal
- ntests - is the number of surrogate data sets to create
- method - method to generate surrogate data sets:
- 1: surrogate1
- 2: surrogate2
- 3: surrogate3
- func - string with matlab-code, have to return a signal
object with a scalar time series. The data to process is a signal
object referred by the qualifier s (see example).
Output Arguments:
- rs is a signal object with a three dimensional time
series. The first component is the result of the func function
applied to the original data set s. The second component is the
mean of the result of the func function applied to the ntests surrogate data sets. The third component is the standard
deviation. There is a special plothint ('surrerrorbar') for the view function to
show this result in the common way.
surrogate_test runs an automatic surrogate data test task. It
generates ntests surrogate data sets an performs the func
function to each set. func is a string with matlab-code who
returns a signal s with a scalar time series.
Example:
st = surrogate_test(s, 10, 1, 1, 'largelyap(embed(s,3,1,1), 128,20,10);');
6.20.3.76 swap
Syntax:
- rs = swap(s) (exchange dimension 1 and dimension 2)
- rs = swap(s, dim1, dim2)
Exchange signal's dimensions (and axes)
6.20.3.77 takens_estimator
Syntax:
- D2 = takens_estimator2(s, n, range, past)
Input Arguments:
- n - number of randomly chosen reference points (n == -1 means : use all points)
- range - maximal relative search radius (relative to attractor size) 0..1
- past - number of samples to exclude before and after each reference index
Takens estimator for correlation dimension
6.20.3.78 tc3
Syntax:
Input Arguments:
- tau - see explaination below
- n - number of surrogate data sets to generate
- method - method to generate the surrogate data sets:
- 1: surrogate1
- 2: surrogate2
- 3: surrogate3
Output Arguments:
- rs is a row vector, returned as signal object. The first
item is the
value for the original data set s. The
following n values are the
values for the generated
surrogates. There exist a special plothint ('surrbar') for the
view function to show this kind of result in the common way.
This function calculates a special value for the original data set and
the n generated surrogate data sets. The
value is
defined as followed:
In terms of surrogate data test this is a test statistics for higher
order moments. The original tc3 function is located under
utils/tc3.m and use simple matlab
vectors.
6.20.3.79 trend
Syntax:
trend correction
calculate moving average of width len (samples) for a scalar time series
(len should be odd) and remove the result from the input signal
6.20.3.80 trev
Syntax:
- rs = trev(s,tau,n,method)
Input Arguments:
- tau - see explaination below
- n - number of surrogate data sets to generate
- method - method to generate the surrogate data sets:
- 1: surrogate1
- 2: surrogate2
- 3: surrogate3
Output Arguments:
- rs is a row vector, returned as signal object. The first
item is the
value for the original data set s. The
following n values are the
values for the generated
surrogates. There exist a special plothint ('surrbar') for the
view function to show this kind of result in the common way.
This function calculates a special value for the original data set and
the n generated surrogate data sets. The
value is
defined as followed:
In terms of surrogate data test this is a test statistics for
time reversibility. The original trev function is located under
utils/trev.m and use simple matlab
vectors.
6.20.3.81 upsample
Syntax:
- rs = upsample(s, factor, method)
Input Arguments:
- method may be one of the following :
- 'fft'
- 'spline'
- 'akima'
- 'nearest'
- 'linear'
- 'cubic'
- s has be to sampled equidistantly for fft interpolation
Change sample rate of signal s by one-dimensional interpolation
6.20.3.82 view
Syntax:
- view(signal) (fontsize=12)
- view(signal, fontsize)
- view(signal, fontsize, figurehandle)
Signal viewer that decides from the signal's attributes which kind of plot to
produce, using the signal's plothint entry to get a hint which kind of plot to produce
Possible plothints are:
- 'graph'
- 'bar'
- 'surrbar'
- 'surrerrorbar'
- 'points'
- 'xyplot'
- 'xypoints'
- 'scatter'
- '3dcurve'
- '3dpoints'
- 'spectrogram'
- 'image'
- 'multigraph'
- 'multipoints'
- 'subplotgraph'
6.20.3.83 write
Syntax:
- write(s, filename) (writes in TSTOOL's own file format)
- write(s, filename, 'ASCII')
- write(s, filename, 'WAV') (RIFF WAVE FORMAT)
- write(s, filename, 'AU') (SUN AUDIO FORMAT)
- write(s, filename, 'NLD') (old NLD FORMAT)
- write(s, filename, 'SIPP') (si++ file format)
writes a signal object to file filename (uses matlab's file format)
Next: 6.21 Class description
Up: 6.20 Class signal
Previous: 6.20.2 Attributes
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