%
%   read the strain data and compute the correlation matrix
%
load becker.dat
load bird.dat
load bormann_hammond.dat
load freed.dat
load hackl.dat
load harvard.dat
load holt.dat
load hooks.dat
load kedar_baxter.dat
load kreemer.dat
load mccaffrey.dat
load parsons.dat
load pollitz.dat
load shen.dat
load smith_konter.dat
load tape.dat
load zeng.dat
load UCERF.dat
%
%   compute the variance of each
%
std(kedar_baxter) % 62   01
std(freed)        % 64   02
std(hooks)	  % 73   03
std(tape)         % 74   04
std(pollitz)      % 80   05
std(becker)       % 82   06
std(shen)	  % 86   07
std(bormann_hammond) % 91 07
std(parsons)      % 93   09
std(zeng)         % 96   10
std(holt)         % 111  11
std(kreemer)      % 114  12
std(smith_konter) % 123  13
std(hackl)	  % 135  14
std(harvard)      % 147  15
std(mccaffrey)    % 147  16
std(bird)         % 147  17
std(UCERF)        % 92   18
%
%   load the A-matrix and compute the correlations
%
A=[kedar_baxter,freed,hooks,tape,pollitz,becker,shen,bormann_hammond,parsons,zeng,holt,kreemer,smith_konter,hackl,harvard,mccaffrey,bird,UCERF];
R=corrcoef(A);
%
figure(1)
imagesc(R,[0.4,0.9]);colorbar;colormap(gray);axis('square')
%
%  load just the rough models
%
A2=[bormann_hammond,zeng,holt,kreemer,smith_konter,hackl,harvard,mccaffrey,bird,UCERF];
R2=corrcoef(A2);
figure(2)
imagesc(R2,[0.4,0.9]);colorbar;colormap(gray);axis('square')

