% Load patient data and select a specific patient
clear;
load('all_fitted_params.mat');
pain_report_all = [];

for jj=1:39
    % load patient IDs into 'filename'
    filename = patient_IDs{jj};
    % load patient reports for iterated stat/mech model fit
    patient_report_iter4 = csvread(strcat('Patient_report_fixk0 iteration4/',filename,'_report_fixk0.csv'));
    % load patient reports for first round of mech only fit
    patient_report_iter1 = csvread(strcat('Patient_report_fixk0/',filename,'_report_fixk0.csv'));
    % merge pain reports only (both first and fourth iteration)
    % file format is real report, iter1 predict, iter4 predict
    patient_report_both = [patient_report_iter1(2:3,:); patient_report_iter4(3,:)];
    % append this patient report to all previous ones
    pain_report_all = [pain_report_all, patient_report_both];   
end
       
csvwrite('pain_report_all.csv', pain_report_all);
% correlation between real and iter1 pain
corrcoef(pain_report_all(1,:), pain_report_all(2,:))
% correlation between real and iter4 pain
corrcoef(pain_report_all(1,:), pain_report_all(3,:))

%% This is for cross validation correlation
load('uhatstat_vs_uhat_cv.mat')
[R,P,RL,RU] = corrcoef(unnamed(:,1),unnamed(:,2))

figure
plot(unnamed(:,2),unnamed(:,1),'.k', 'MarkerSize',20)
xlabel('unmitigated pain u given by fitting mechanistic model to testing data','FontSize',12)
ylabel('predicted u using statistical model on training data','FontSize',12)
hold on
plot(linspace(0,10,100),linspace(0,10,100),'k','LineWidth',1)