%SCRIPT FOR RUNNING THE CRA TOOLBOX WITHOUT USING THE GUI
%The model is written as a Matlab function.

%%%% THE FIRST THING TO DO FOR RUNNING THE SCRIPT IS TO ADD THE FOLDER
%%%% CLASSES AND THE FOLDER Examples TO THE PATH %%%%

%Parameters to be set by the user are:
% 1-model_name: name of the model in .xml format. A struct is created to
% store the following characteristics of an ODE model: nominal values and
% names of the parameters, initial conditions and names of variables, input
% values.
% 2-stop_time: final time point for the model simulation
% 3-step_size: time interval for the vector of time points
% 4- ode_solver: type of ode_solver for simulating the model (possible
% choices are: ode45, ode15s, ode23t, sundials, stochastic, explicit tau
% and implicit tau
% 5- Nr: number of independent realizations to perform 
% 6- LBpi: lower boundary of the Latin Hypercube Sampling for perturbation
% of the model parameter space
% 7- UBpi: upper boundary of the Latin Hypercube Sampling
% 8- Ns: number of samples of the Latin Hypercube (parameters n of the
% lhsdesign function)
% 9- variable_name: variable of the model to set as reference node to be
% measured
% 10- current_func: is the type of evaluation function. Currently, it is
% possible to choose among
% three evaluation functions: area under the curve, maximum value and time
% of maximum for the time behavior of the selected variables. The user can
% also define his evaluation function in a .m file by extending the
% abstract class EvaluationFunction
% 11- tail_size: number of samples to include in the upper and lower tail
% when computing the probability density function of the evaluation
% function
% 12- current_tm: is the method for computing the tails of the pdf of the
% evaluation function. Right now, it is possible to choose between two
% methods: sorted() which sorts the values of the evaluation function and
% selects the first and last samples according to tail_size; tmp_sum()
% computes the tails by selecting the upper and lower quartile. When using
% tmp_sum(), the parameter step_size needs also to be specified. Step_size
% is the step for computing the lower and upper quartile of the pdf in an
% iterative way, i.e. when the upper and lower tails do not have the number of
% samples specified by the user, the threshold is increased of a quantity
% equal to step_size and the calculation of the tails is repeated. 
% The user can also define his own method for the tails computation by
% extending the abstract class TailMethod().
% 13- folder: name of the folder to create where the results are saved

tic;

model_name='model_Peng2016_reduced';

stop_time=5;
step_size=0.1;
ode_solver='ode45';

%time axis for model simulation
time_axis=[0:step_size:stop_time]';

%parameters and initial conditions of the model
nominal_parameters=[0.6391 0.32343 0.1 6.7027 0.1 1.4262 1.0617 2.0964 5.0511 3.0776 1.4596 0.97883 0.73912 0.1 0.35453 0.14414 0.56469 0.40886 0.21382 0.1 0.65031 4.1119 0.4722];
parameters_name={'lambda_A','r_p1','r_a1','r_m','k_CX','r_p2','r_a2','alpha_XD','pigreco_D', 'alpha_DC','k_RC','mi_C','alpha_DR','pigreco_R','alpha_IR','alpha_XR','mi_R','alpha_CI','mi_I','alpha_DCDR','mi_D','pigreco_C','alpha_DRR'};

x0=[1 1 0 1 1 1 1 1 1 1 1 1];
num_observables=12;
observables_name={'A','X1','X2','D_m','C2','R2','I2','D_C','D_R','C1','R1','I1'};

model=struct('name',model_name,'odesolver',ode_solver,'time',time_axis,'stop',stop_time,'step',step_size,'nominal_parameters',nominal_parameters,'parameters_name',{parameters_name},'num_observables',num_observables,'observables_name',{observables_name},'initial_conditions',x0,'input',0,'total_proteins',0);

Nr=100;

LBpi=0.1;
UBpi=10;
Ns=10000;

variable_name='C1';
current_func=Area(); %current evaluation function
tail_size=10;  %number of samples for the lower and upper tail
step_size=0.1;  %this parameter needs to be defined only when current_tm=tmp_sum(step_size)
current_tm=sorted();

folder='modelPeng2016_results';

%model simulation for each sample of the Latin Hypercube
disp('Starting model simulation with perturbed parameters');
[AllResults,AllPerturbations]=start_simulation(model,Nr,LBpi,UBpi,Ns);
disp('All done! Model simulation completed!');

%computation of the MIRI for the chosen evaluation function and model
%variable
disp('Starting computation of the MIRI for each parameter...');
try
  compute_MIRI(model,variable_name,current_func,tail_size,current_tm,Nr,Ns,AllResults,AllPerturbations,folder)
catch ME
    break    
end
%plot and save probability density function of the evaluation function
disp('Plot of the probability density function of the chosen evaluation function');
plotpdf_evalfunc(folder,variable_name);

%plot and save conditional probability density functions of the parameters 
disp('Plot of the parameter probability density functions');
plotpdf_param(folder,variable_name,model,Nr);

toc;