BYOM function call_deri.m (calculates the model output)
Syntax: [Xout TE] = call_deri(t,par,X0v)
- t the time vector
- par the parameter structure
- X0v a vector with initial states and one concentration (scenario number)
The output Xout provides a matrix with time in rows, and states in columns. This function calls derivatives.m. The optional output TE is the time at which an event takes place (specified using the events function). The events function is set up to catch discontinuities. It should be specified according to the problem you are simulating. If you want to use parameters that are (or influence) initial states, they have to be included in this function.
function [Xout, TE] = call_deri(t,par,X0v)
global glo % allow for global parameters in structure glo global zvd % global structure for zero-variate data
This part extracts optional settings for the ODE solver that can be set in the main script (defaults are set in prelim_checks). The useode option decides whether to calculate the model results using the ODEs in derivatives.m, or the analytical solution in simplefun.m. Using eventson=1 turns on the events handling. Also modify the sub-function at the bottom of this function! Further in this section, initial values can be determined by a parameter (overwrite parts of X0), and zero-variate data can be calculated. See the example BYOM files for more information.
useode = glo.useode; % calculate model using ODE solver (1) or analytical solution (0) eventson = glo.eventson; % events function on (1) or off (0) stiff = glo.stiff; % set to 1 or 2 to use a stiff solver instead of the standard one % Unpack the vector X0v, which is X0mat for one scenario X0 = X0v(2:end); % these are the intitial states for a scenario % % if needed, extract parameters from par that influence initial states in X0 % Ci0 = par.Ci0(1); % example: parameter for the internal concentration % X0(2) = Ci0; % put this parameter in the correct location of the initial vector % if needed, calculate model values for zero-variate data from parameter set if ~isempty(zvd) zvd.ku(3) = par.Piw(1) * par.ke(1); % add model prediction as third value end
This part calls the ODE solver (or the explicit model in simplefun.m) to calculate the output (the value of the state variables over time). There is generally no need to modify this part. The solver ode45 generally works well. For stiff problems, the solver might become very slow; you can try ode15s instead.
c = X0v(1); % the concentration (or scenario number) t = t(:); % force t to be a row vector (needed when useode=0) % specify options for the ODE solver options = odeset; % start with default options if eventson == 1 options = odeset(options, 'Events',@eventsfun); % add an events function end options = odeset(options, 'RelTol',1e-4,'AbsTol',1e-7); % specify tightened tolerances % options = odeset(options,'InitialStep',max(t)/1000,'MaxStep',max(t)/100); % specify smaller stepsize TE = 0; % dummy for time of events if useode == 1 % use the ODE solver to calculate the solution % call the ODE solver (use ode15s for stiff problems, escpecially for pulses) if isempty(options.Events) % if no events function is specified ... switch stiff case 0 [~,Xout] = ode45(@derivatives,t,X0,options,par,c); case 1 [~,Xout] = ode113(@derivatives,t,X0,options,par,c); case 2 [~,Xout] = ode15s(@derivatives,t,X0,options,par,c); end else % with an events functions ... additional output arguments for events: % TE catches the time of an event, YE the states at the event, and IE the number of the event switch stiff case 0 [~,Xout,TE,YE,IE] = ode45(@derivatives,t,X0,options,par,c); case 1 [~,Xout,TE,YE,IE] = ode113(@derivatives,t,X0,options,par,c); case 2 [~,Xout,TE,YE,IE] = ode15s(@derivatives,t,X0,options,par,c); end end else % alternatively, use an explicit function provided in simplefun! Xout = simplefun(t,X0,par,c); end if isempty(TE) || all(TE == 0) % if there is no event caught TE = +inf; % return infinity end
Xout contains a row for each state variable. It can be mapped to the data. If you need to transform the model values to match the data, do it here.
% Xout(:,1) = Xout(:,1).^3; % e.g., do something on first column, like cube it ... % % To obtain the output of the derivatives at each time point. The values in % % dXout might be used to replace values in Xout, if the data to be fitted % % are the changes (rates) instead of the state variable itself. % % dXout = zeros(size(Xout)); % initialise with zeros % for i = 1:length(t) % run through all time points % dXout(i,:) = derivatives(t(i),Xout(i,:),par,c); % % derivatives for each stage at each time % end
Modify this part of the code if eventson=1. This subfunction catches the 'events': in this case, it looks for the external concentration where degradation stops. This function should be adapted to the problem you are modelling (this one matches the byom_bioconc_... files). You can catch more events by making a vector out of values.
Note that the eventsfun has the same inputs, in the same sequence, as derivatives.m.
function [value,isterminal,direction] = eventsfun(t,X,par,c) Ct = par.Ct(1); % threshold external concentration where degradation stops nevents = 1; % number of events that we try to catch value = zeros(nevents,1); % initialise with zeros value(1) = X(1) - Ct; % thing to follow is external concentration (state 1) minus threshold isterminal = zeros(nevents,1); % do NOT stop the solver at an event direction = zeros(nevents,1); % catch ALL zero crossing when function is increasing or decreasing