function [outcomes,a,b,c,d] = fit_arm_nerve_practice('folder')
subdirs = dir('folder');
outcomes = [];
a = []; b = []; c = []; d = [];
for i=1:length(subdirs),
if subdirs(i).isdir&~strcmp(subdirs(i).name,'.')&~strcmp(subdirs(i).name,'..'),
filename = ['folder' filesep subdirs(i).name filesep 'mouse_arm_data.txt'];
if exist(filename),
disp(['Analyzing file ' filename '.']);
data = load(filename,'-ascii');
locations = data(1,:);
rawdata = data(2:end,:);
outcomes(i) = anova1([rawdata;],locations,'off');
if outcomes<0.05,
fo = fitoptions('Method','NonLinearLeastSquares','StartPoint',[1 1]);
gauss = fittype('a+b*exp(-((x-c).^2)/((2*d^2)))','options',fo);
gauss = setoptions(gauss,fo);
[gof] = fit(locations',rawdata',gauss);
[a,b,c,d] = gaussfit(x,y);
a(end+1) = a_;
b(end+1) = b_;
c(end+1) = c_;
d(end+1) = d_;
else,
a= NaN;
b= NaN;
c= NaN;
d= NaN;
end;
end;
end;
end;
