compileOdeData.m

function Data = compileOdeData(Data)

GuildInfo = Data.GuildInfo;

% New variables resized to avoid unnecessary computation and memory usage
Data.c = vertcat(Data.Guilds(GuildInfo.iProducerGuilds).c);
Data.r = vertcat(Data.Guilds(GuildInfo.iProducerGuilds).igr);
%Data.r = vertcat(Data.Guilds(GuildInfo.iProducerGuilds).newigrEx);
Data.bx = vertcat(Data.Guilds(GuildInfo.iConsumerGuilds).bx);
Data.ax = vertcat(Data.Guilds(GuildInfo.iFeederGuilds).ax); 
Data.x = vertcat(Data.Guilds(GuildInfo.iConsumerGuilds).mbr);
Data.omega = Data.omega(GuildInfo.iFeederGuilds,GuildInfo.iFoodGuilds);

%Data.B0 = Data.B0(GuildInfo.iFeederGuilds,GuildInfo.iFoodGuilds);%1500
%Data.B0 = Data.B0_1(GuildInfo.iFeederGuilds,GuildInfo.iFoodGuilds); %all 1500 except by birds
Data.B0 = Data.B0_6(GuildInfo.iFeederGuilds,GuildInfo.iFoodGuilds); %B0 by TL
%Data.B0 = Data.B0_opt(GuildInfo.iFeederGuilds,GuildInfo.iFoodGuilds); %randoms B0


%Data.B0 = Data.B0_3(GuildInfo.iFeederGuilds,GuildInfo.iFoodGuilds);
%%Ineficiencia en el consumo solo para herbivoros

Data.communityMatrix = Data.communityMatrix(GuildInfo.iFeederGuilds,GuildInfo.iFoodGuilds);

Data.d = Data.d(GuildInfo.iFeederGuilds,GuildInfo.iFoodGuilds);
%Data.d = Data.d_1(GuildInfo.iFeederGuilds,GuildInfo.iFoodGuilds); %d=corrected


Data.q = Data.q(GuildInfo.iFeederGuilds,GuildInfo.iFoodGuilds);
Data.e = Data.e(GuildInfo.iFeederGuilds,GuildInfo.iFoodGuilds);
Data.y = Data.y(GuildInfo.iFeederGuilds,GuildInfo.iFoodGuilds);

Data.B0_pow_q = Data.B0.^Data.q;
Data.B0_pow_q = Data.B0_pow_q.*Data.communityMatrix;

switch Data.K.type
    case 'Constant'
        %%%K = repmat(Data.K.mean,1,Data.nYearsFwd); this is the original
        %%%but with the changes MATLAB suggest to me that change to
        %%%repmat(A,[M,N])
        K = repmat(Data.K.mean,[1,Data.nYearsFwd]);
                
    case 'White noise'
        K = zeros(1,Data.nYearsFwd);
        for i = 1:Data.nYearsFwd
            while K(i) <= 0
                K(i) = normrnd(Data.K.mean,Data.K.standard_deviation,1,1);
            end
        end
    case 'AR(1)'
        K = zeros(1,Data.nYearsFwd);
        phi = Data.K.autocorrelation;
        c = Data.K.mean*(1-phi);
        sigma_e = Data.K.standard_deviation*sqrt(1-phi^2);
        
        while K(1) <= 0
            K(1) = normrnd(Data.K.mean,Data.K.standard_deviation,1,1);
            for i = 2:Data.nYearsFwd
                while K(i) <= 0
                    K(i) = c + phi*K(i-1) + normrnd(0,sigma_e,1,1);
                end
            end
        end
end
Data.K.values = K;