%
%   read the strain data and compute the correlation matrix
%
load becker.dat
load bird.dat
load bormann_hammond.dat
load hackl.dat
load holt.dat
load kreemer.dat
load loveless_meade.dat
load mccaffrey.dat
load parsons.dat
load gpsgridder.dat
load shen.dat
load smith_konter.dat
load tape.dat
load tong.dat
load zeng.dat
load UCERF.dat
%
%   compute the variance of each
%
std(mccaffrey)		% 52 01
std(tape)		% 74 02
std(becker)		% 82 03
std(UCERF)		% 86 04
std(shen)		% 91 05
std(parsons)		% 92 06
std(bormann_hammond)	% 106 07
std(gpsgridder)		% 109 08
std(zeng)		% 112 09
std(holt)		% 126 10
std(hackl)		% 137 11
std(loveless_meade)	% 149 12
std(kreemer)		% 152 13
std(smith_konter)	% 175 14
std(tong)		% 178 15
std(bird)		% 187 16

%
%   load the A-matrix and compute the correlations
%
A=[mccaffrey,tape,becker,shen,parsons,bormann_hammond,gpsgridder,zeng,holt,hackl,loveless_meade,kreemer,smith_konter,tong,bird,UCERF];
R=corrcoef(A);
%
figure(1)
imagesc(R,[0.4,0.9]);colorbar;colormap(gray);axis('square')
%
%  load just the rough models
%
A2=[shen,bormann_hammond,gpsgridder,zeng,holt,loveless_meade,kreemer,smith_konter,tong,bird,UCERF];
R2=corrcoef(A2);
figure(2)
imagesc(R2,[0.4,0.9]);colorbar;colormap(gray);axis('square')
%
%  load just good the rough models
%
A3=[gpsgridder,zeng,holt,loveless_meade,kreemer,smith_konter,tong,bird];
R3=corrcoef(A3);
figure(3)
imagesc(R3,[0.4,0.9]);colorbar;colormap(gray);axis('square')

