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Next: 6.23 Class achse Up: 6.22 Class core Previous: 6.22.2 Attributes
Subsections

- 6.22.3.1 acf
- 6.22.3.2 amutual2
- 6.22.3.3 compare
- 6.22.3.4 core
- 6.22.3.5 data
- 6.22.3.6 db
- 6.22.3.7 diff
- 6.22.3.8 display
- 6.22.3.9 dlens
- 6.22.3.10 embed
- 6.22.3.11 filterbank
- 6.22.3.12 int
- 6.22.3.13 intermutual
- 6.22.3.14 isempty
- 6.22.3.15 medianfilt
- 6.22.3.16 minus
- 6.22.3.17 movav
- 6.22.3.18 multires
- 6.22.3.19 ndim
- 6.22.3.20 norm1
- 6.22.3.21 norm2
- 6.22.3.22 plus
- 6.22.3.23 rang
- 6.22.3.24 rms
- 6.22.3.25 scalogram
- 6.22.3.26 spec
- 6.22.3.27 spec2
- 6.22.3.28 surrogate1
- 6.22.3.29 surrogate2
- 6.22.3.30 surrogate3
- 6.22.3.31 uminus
- 6.22.3.32 vertcat

## 6.22.3 Member functions

###
6.22.3.1 acf

Syntax:

- `acf(cin, m)`

Input Arguments:

- `cin` core object
- `m` fft-length

`acf` calculates the autocorrelation function of `cin` via fft of length `m`.

###
6.22.3.2 amutual2

Syntax:

- `amutual(cin, len)`

Input Arguments:

- `cin` core object
- `len` maximal lag

`amutual2` calculates the mutual information of a time series against itself, with increasing lag uses equidistant partitioning to compute histograms.

###
6.22.3.3 compare

Syntax:

- `compare(c1,c2, tolerance)`

Input Arguments:

- `c1,c2` core object of two signals
- `tolerance` tolerance of the signals's RMS value (default tolerance=1e-6)

`compare` compare two signals whether they have equal values slight differences due to rounding errors are ignored depending on the value of tolerance when signals are found to be not equal, a zero is returned.

###
6.22.3.4 core

`core` class constructor Syntax:

- `c = core(arg)`

Input Arguments:

- `arg` double array

A core object contains the pure data part of a signal object.
Methods: `ndim dlens data`

###
6.22.3.5 data

Syntax:

- `d = data(c, varargin)`
- `c=core object`

Input Arguments:

- `varargin` - selector string for data-elements in matlab notation

Return signal's data values
With no extra arguments, data returns the data array of a signal object
Another possible call is : `data(signal, ':,:,1:20')`

###
6.22.3.6 db

Syntax:

- `cout = db(cin, ref, scf, dbmin)`

Input Arguments:

- `cin` - core object
- `ref` - reference value
- `scf` - scaling factor
- `dbmin` - minimal db-value

compute decibel values to reference value `ref` and scaling factor (10 or 20) `scf`

###
6.22.3.7 diff

Syntax:

- `cout = diff(cin, nth, delta)`

Input Arguments:

- `cin` - core object
- `nth` - number of derivations
- `delta` - time difference between to signal values

`nth` numerical derivative along dimension 1 when `data` was sampled equidistantly with samplerate = 1/`delta`

###
6.22.3.8 display

Syntax:

- `display(c)`

Input Arguments:

- `c` - core object

###
6.22.3.9 dlens

Syntax:

- `d=dlens(c, nr)`

Input Arguments:

- `c` - core object

returns sizes of dimensions (same as function 'size' under matlab)

###
6.22.3.10 embed

Syntax:

- `cout = embed(cin, dim, delay, shift, windowtype)`

Input Arguments:

- `cin` - core object
- `dim` - embed dimension
- `delay` - delay time in samples for time delay vectors
- `shift` - shift in samples for two sequent time delay vectors
- `windowtype` - type of window

Create time delay vectors with dimension `dim`, `delay` is measured in samples
The input must be a scalar time series
The result is a n by dim array, each row contains the coordinates of one point

###
6.22.3.11 filterbank

Syntax:

- `filterbank(cin,H,G,ORDER,BASIS)`

Input Arguments:

- `H` - lowpass filter
- `G` - highpass filter
- `ORDER` - indicates the type of tree:
  - `0` - band sorting according to the filter bank
  - `1` - band sorting according to the frequency decomposition
- `BASIS` - desired subband decomposition

calculates the Wavelet Packet Transform of cin. It can be obtained using a selection algorithm function. It may be switched from one format to another using CHFORMAT. The different bands are sorted according to ORDER and BASIS. If BASIS is omitted, the output is a matrix with the coefficients obtained from all the wavelet packet basis in the library. Each column in the matrix represents the outputs for a level in the tree. The first column is the original signal. If the length of X is not a power of 2, the columns are zero padded to fit the different lengths. Run the script 'BASIS' for help on the basis format.
See also: IWPK, CHFORMAT, PRUNEADD, PRUNENON, GROWADD, GROWNON.

###
6.22.3.12 int

Syntax:

- `cout = int(cin, delta)`

Input Arguments:

- `cin` - core object
- `delta` - time period between two data samples

numerical integration along dimension 1 when data was sampled equidistantly with samplerate = 1/delta

###
6.22.3.13 intermutual

Syntax:

- `intermutual(cin1,cin2,n)`

Input Arguments:

- `cin1,cin2` - core objects

Calculates the mutual information of cin1 and cin2.

###
6.22.3.14 isempty

Syntax:

- `r = isempty(s)`

Input Arguments:

- `s` - core object

test if core contains no (valid) data

###
6.22.3.15 medianfilt

Syntax:

- `medianfilt(cin,len)`

Input Arguments:

- `cin` - core object

moving median filter

###
6.22.3.16 minus

Syntax:

- `minus(c1,c2)`

Input Arguments:

- `c1,c2` - core objects

subtract c2 from each columns of c1

###
6.22.3.17 movav

Syntax:

- `movav(cin,len)`

Input Arguments:

- `cin` - core object
- `len` - average length

moving average

###
6.22.3.18 multires

Syntax:

- `multires(cin,h,rh,g,rg,sc)`

Input Arguments:

- `cin` - core object

###
6.22.3.19 ndim

Syntax:

- `ndim(c)`

Input Arguments:

- `c` - core object

return number of dimensions, a scalar value has 0 dimensions

###
6.22.3.20 norm1

Syntax:

- `cout = norm1(cin,low,upp)`

Input Arguments:

- `cin` - core object
- `low` - column number
- `upp` - column number

normalize each single column of a the core object to be within low,upp

###
6.22.3.21 norm2

Syntax:

- `cout = norm2(cin)`

Input Arguments:

- `cin` - core object

normalize signal by removing it's mean and dividing by the standard deviation

###
6.22.3.22 plus

Syntax:

- `plus(c1,c2)`

Input Arguments:

- `c1,c2` - core objects

add c2 to each columns of c1

###
6.22.3.23 rang

Syntax:

- `cout = rang(cin)`

Input Arguments:

- `cin` - core object

###
6.22.3.24 rms

Syntax:

- `cout = rms(cin)`

Input Arguments:

- `cin` - core object

compute root mean square value of each column of c1

###
6.22.3.25 scalogram

Syntax:

- `cout = scalogram(cin, smin, smax, sstep, tim)`

###
6.22.3.26 spec

Syntax:

- `cout = spec(cin)`

Input Arguments:

- `cin` - core object

compute power spectrum for real valued signals

###
6.22.3.27 spec2

Syntax:

- `cout = spec2(cin, fensterlen, fenster, vorschub)`

Input Arguments:

- `cin` - core object
- `fensterlen` - window size
- `fenster` - type of window
- `vorschub` - moving step

spectrogramm of data using short time fft

###
6.22.3.28 surrogate1

Syntax:

- `cout = surrogate1(cin)`

Input Arguments:

- `cin` - core object

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

###
6.22.3.29 surrogate2

Syntax:

- `cout = surrogate2(cin)`

Input Arguments:

- `cin` - core object

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

###
6.22.3.30 surrogate3

Syntax:

- `cout = surrogate3(cin)`

Input Arguments:

- `cin` - core object

create surrogate data for a scalar time series by permuting samples randomly

###
6.22.3.31 uminus

Syntax:

- `r = uminus(c)`

Input Arguments:

- `c` - core object

negate time series

###
6.22.3.32 vertcat

Syntax:

- `r = vertcat(c1,c2)`

Input Arguments:

- `c1,c2` - core objects

catenate two timeseries verticaly

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