No BSD License
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datool(varargin)
DATOOL Application M-file for datool.fig
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matedit(varargin)
MATEDIT matrix editor using GridLite ActiveX plugin
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checkdm(A,B)
CHECKDM check dimensional matrix for validity
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createab(RL,BaseVars)
CREATEAB creates A and B submatrices for dimensional analysis
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created(RL,dv,Din,DNames)
CREATED create D matrix for a given relevance list
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data2si(Xin,RL)
DATA2SI transform data to SI units
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data2si(Xin,RL)
DATA2SI transform data to SI units
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diman(RL,BaseVars,D)
DIMAN
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dinst()
Installs the Dimensional Analysis Toolbox for Matlab
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dtrans(XData,piset)
DTRANS transform data from x to pi domain
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getdv(RL,bv)
GETDV get list of dependent variables
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latex(piset,flag)
LATEXPI LaTeX output for pis
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numpi(RL)
NUMPI calculate number of pis and base variables
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pretty(piset)
PRETTY pretty output for pis
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rlist(RL,Name,Dimension,Facto...
RLIST manage relevance liste
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spheredemo()
SPHEREDEMO demo for dimensional analysis
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texfile(FILENAME,piset)
Save piset as a LaTeX file
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transfdemo()
SPHEREDEMO2 DA demo with data transformation
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unit2si(Units)
UNIT2SI converts derived units to SI
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xsort(d,piset,Names)
XSORT sort x-data for transform to pi-domain
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beamdemo.m
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blastdemo.m
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contents.m
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dguidemo.m
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oscdemo.m
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View all files
from
Dimensional Analysis Toolbox for MATLAB
by Steffen Brückner
The toolbox provides support for dimensional analysis.
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| numpi(RL)
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function [npi,nbase] = numpi(RL)
% NUMPI calculate number of pis and base variables
% [nPI,nBASE] = NUMPI(RL) calculates the number of
% dimensionless groups nPI and the number of base
% variables nBASE which are needed for the problem.
% The number of dimensionless groups equals the
% number of dependent variables.
% Dimensional Analysis Toolbox for Matlab
% Steffen Brckner, 2002-02-10
% check input arguments
msg = nargchk(1,1,nargin);
if msg
error(msg);
return;
end
% determine the dimensional matrix
D = [RL.Dimension];
% determin the rank of the dimensional matrix, which
% equals the number of base variables
nbase = rank(D);
% Well, the number of dimensionless groups is now
% given by the size of the dimensional matrix minus
% the number of base variables
npi = size(D,2) - nbase;
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