X-Git-Url: https://git.creatis.insa-lyon.fr/pubgit/?p=CreaPhase.git;a=blobdiff_plain;f=octave_packages%2Fnan-2.5.5%2Fhist2res.m;fp=octave_packages%2Fnan-2.5.5%2Fhist2res.m;h=9d805b192daefd50616d2d13d091d0ef5b984ebc;hp=0000000000000000000000000000000000000000;hb=f5f7a74bd8a4900f0b797da6783be80e11a68d86;hpb=1705066eceaaea976f010f669ce8e972f3734b05

diff --git a/octave_packages/nan-2.5.5/hist2res.m b/octave_packages/nan-2.5.5/hist2res.m
new file mode 100644
index 0000000..9d805b1
--- /dev/null
+++ b/octave_packages/nan-2.5.5/hist2res.m
@@ -0,0 +1,147 @@
+function [R]=hist2res(H,fun)
+% Evaluates Histogram data
+% [R]=hist2res(H)
+%
+% [y]=hist2res(H,fun)
+%	estimates fun-statistic
+%
+% fun	'mean'	mean
+%	'std'	standard deviation
+%	'var'	variance
+%	'sem'	standard error of the mean
+%	'rms'	root mean square
+%	'meansq' mean of squares
+%	'sum'	sum
+%	'sumsq'	sum of squares
+%	'CM#'	central moment of order #
+%	'skewness' skewness 
+%	'kurtosis' excess coefficient (Fisher kurtosis)
+%
+% see also: NaN/statistic
+%
+% REFERENCES:
+% [1] C.L. Nikias and A.P. Petropulu "Higher-Order Spectra Analysis" Prentice Hall, 1993.
+% [2] C.E. Shannon and W. Weaver "The mathematical theory of communication" University of Illinois Press, Urbana 1949 (reprint 1963).
+% [3] http://www.itl.nist.gov/
+% [4] http://mathworld.wolfram.com/
+
+% This program is free software; you can redistribute it and/or
+% modify it under the terms of the GNU General Public License
+% as published by the Free Software Foundation; either version 2
+% of the License, or (at your option) any later version.
+% 
+% This program is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+% 
+% You should have received a copy of the GNU General Public License
+% along with this program; if not, write to the Free Software
+% Foundation, Inc., 51 Franklin Street - Fifth Floor, Boston, MA 02110-1301, USA.
+
+%	$Id: hist2res.m 9387 2011-12-15 10:42:14Z schloegl $
+%	Copyright (c) 1996-2002,2006 by Alois Schloegl <alois.schloegl@gmail.com>
+%       This function is part of the NaN-toolbox
+%       http://pub.ist.ac.at/~schloegl/matlab/NaN/
+
+
+if strcmp(H.datatype,'HISTOGRAM'),
+
+elseif strcmp(H.datatype,'qc:histo')
+	HDR = H; 
+	if isfield(H,'THRESHOLD'),
+		TH  = H.THRESHOLD;
+	else
+		TH = repmat([-inf,inf],HDR.NS,1); 
+	end;
+	HIS = H.HIS; 
+
+	% remove overflowing samples
+	HIS.N = sumskipnan(HIS.H); 
+	for k = 1:size(HIS.H,2);
+		t = HIS.X(:,min(k,size(HIS.X,2))); 
+		HIS.H(xor(t<=min(TH(k,:)), t>=max(TH(k,:))),k) = 0; 
+	end; 
+	Nnew = sumskipnan(HIS.H); 
+	R.ratio_lost = 1-Nnew./HIS.N;
+	HIS.N = Nnew; 
+	  
+	% scale into physical values
+	if H.FLAG.UCAL,
+		%t = HIS.X;
+		%for k=1:length(HDR.InChanSelect),
+		%	HIS.X(:,k) = t(:,min(size(t,2),k))*HDR.Calib(k+1,k)+HDR.Calib(1,k);
+		%end;
+		HIS.X = [ones(size(HIS.X,1),1),repmat(HIS.X,1,size(HIS.H,2)./size(HIS.X,2))]*H.Calib;
+	end; 	
+	H = HIS; 
+else
+        fprintf(2,'ERROR: arg1 is not a histogram\n');
+        return;
+end;
+if nargin<2, fun=[]; end;
+
+global FLAG_implicit_unbiased_estimation; 
+%%% check whether FLAG was already defined 
+if ~exist('FLAG_implicit_unbiased_estimation','var'),
+	FLAG_implicit_unbiased_estimation=[];
+end;
+%%% set DEFAULT value of FLAG
+if isempty(FLAG_implicit_unbiased_estimation),
+	FLAG_implicit_unbiased_estimation=logical(1);
+end;
+
+sz 	= size(H.H)./size(H.X);
+R.N 	= sumskipnan(H.H,1);
+R.SUM 	= sumskipnan(H.H.*repmat(H.X,sz),1);
+R.SSQ 	= sumskipnan(H.H.*repmat(H.X.*H.X,sz),1);
+%R.S3P 	= sumskipnan(H.H.*repmat(H.X.^3,sz),1);	% sum of 3rd power
+R.S4P 	= sumskipnan(H.H.*repmat(H.X.^4,sz),1);	% sum of 4th power
+%R.S5P 	= sumskipnan(H.H.*repmat(H.X.^5,sz),1);	% sum of 5th power
+
+R.MEAN	= R.SUM./R.N;
+R.MSQ   = R.SSQ./R.N;
+R.RMS	= sqrt(R.MSQ);
+R.SSQ0  = R.SSQ-R.SUM.*R.MEAN;		% sum square of mean removed
+
+if FLAG_implicit_unbiased_estimation,
+    n1 	= max(R.N-1,0);			% in case of n=0 and n=1, the (biased) variance, STD and STE are INF
+else
+    n1	= R.N;
+end;
+
+R.VAR  	= R.SSQ0./n1;	     		% variance (unbiased) 
+R.STD  	= sqrt(R.VAR);		     	% standard deviation
+R.SEM  	= sqrt(R.SSQ0./(R.N.*n1)); 	% standard error of the mean
+R.SEV	= sqrt(n1.*(n1.*R.S4P./R.N+(R.N.^2-2*R.N+3).*(R.SSQ./R.N).^2)./(R.N.^3)); % standard error of the variance
+R.Coefficient_of_variation = R.STD./R.MEAN;
+
+R.CM2	= R.SSQ0./n1;
+x       = repmat(H.X,sz) - repmat(R.MEAN,size(H.X,1),1);
+R.CM3 	= sumskipnan(H.H.*(x.^3),1)./n1;
+R.CM4 	= sumskipnan(H.H.*(x.^4),1)./n1;
+%R.CM5 	= sumskipnan(H.H.*(x.^5),1)./n1;
+
+R.SKEWNESS = R.CM3./(R.STD.^3);
+R.KURTOSIS = R.CM4./(R.VAR.^2)-3;
+R.MAD = sumskipnan(H.H.*abs(x),1)./R.N; % mean absolute deviation
+
+H.PDF = H.H./H.N(ones(size(H.H,1),1),:);
+status=warning('off'); 
+R.ENTROPY = -sumskipnan(H.PDF.*log2(H.PDF),1);
+warning(status); 
+R.QUANT = repmat(min(diff(H.X,[],1)),1,size(H.H,2)/size(H.X,2));
+R.MAX = max(H.X); 
+R.MIN = min(H.X); 
+R.RANGE = R.MAX-R.MIN;
+
+if ~isempty(fun),
+        fun=upper(fun);
+	if strncmp(fun,'CM',2) 
+                oo = str2double(fun(3:length(fun)));
+                R = sumskipnan(H.PDF.*(x.^oo),1);
+    	else	            
+		R = getfield(R,fun);
+	end;
+end;
+