/*!
  \file
  \brief Models for synchronous electric drive using IT++ and BDM
  \author Vaclav Smidl.

  -----------------------------------
  BDM++ - C++ library for Bayesian Decision Making under Uncertainty

  Using IT++ for numerical operations
  -----------------------------------
*/

#include <itpp/itbase.h>
#include <stat/libFN.h>
#include <estim/libKF.h>
//#include <estim/libPF.h>
#include <math/chmat.h>

#include "pmsm.h"
#include "simulator.h"
#include "sim_profiles.h"

#include <stat/loggers.h>

using namespace itpp;
//!Extended Kalman filter with unknown \c Q

int main() {
	// Kalman filter
	int Ndat = 90000;
	double h = 1e-6;
	int Nsimstep = 125;

	dirfilelog L("exp/sim_var",1000);
	//memlog L(Ndat);
	
	// SET SIMULATOR
	pmsmsim_set_parameters ( 0.28,0.003465,0.1989,0.0,4,1.5,0.04, 200., 3e-6, h );
	double Ww = 0.0;
	vec dt ( 2 );
	vec ut ( 2 );
	vec xtm=zeros ( 4 );
	vec xdif=zeros ( 4 );
	vec xt ( 4 );
	vec ddif=zeros(2);
	IMpmsm fxu;
	//                  Rs    Ls        dt           Fmag(Ypm) kp   p    J     Bf(Mz)
	fxu.set_parameters ( 0.28, 0.003465, Nsimstep*h, 0.1989,   1.5 ,4.0, 0.04, 0.0 );
	OMpmsm hxu;
	mat Qt=zeros ( 4,4 );
	mat Rt=zeros ( 2,2 );

	// ESTIMATORS
	vec mu0= "0.0 0.0 0.0 0.0";
	vec Qdiag ( "62 66 454 0.03" ); //zdenek: 0.01 0.01 0.0001 0.0001
	vec Rdiag ( "100 100" ); //var(diff(xth)) = "0.034 0.034"
	mat Q =diag( Qdiag );
	mat R =diag ( Rdiag );
	EKFfull Efix ( rx,ry,ru );
	Efix.set_est ( mu0, 1*eye ( 4 )  );
	Efix.set_parameters ( &fxu,&hxu,Q,R);

	EKFfull Eop ( rx,ry,ru );
	Eop.set_est ( mu0, 1*eye ( 4 ) );
	Eop.set_parameters ( &fxu,&hxu,Q,R);

	EKFfull Edi ( rx,ry,ru );
	Edi.set_est ( mu0, 1*eye ( 4 ) );
	Edi.set_parameters ( &fxu,&hxu,Q,R);

	epdf& Efix_ep = Efix._epdf();
	epdf& Eop_ep = Eop._epdf();
	epdf& Edi_ep = Edi._epdf();
	
	//LOG
	RV rQ("10", "{Q }", "16","0");
	RV rR("11", "{R }", "4","0");
	RV rUD("12 13 14 15", "{u_isa u_isb i_isa i_isb }", ones_i(4),zeros_i(4));
	int X_log = L.add(rx,"X");
	int Efix_log = L.add(rx,"XF");
	int Eop_log = L.add(rx,"XO");
	int Edi_log = L.add(rx,"XD");
	int Q_log = L.add(rQ,"Q");
	int R_log = L.add(rR,"R");
	int D_log = L.add(rUD,"D");
	L.init();

	for ( int tK=1;tK<Ndat;tK++ ) {
		//Number of steps of a simulator for one step of Kalman
		for ( int ii=0; ii<Nsimstep;ii++ ) {
			sim_profile_steps1 ( Ww , true);
			pmsmsim_step ( Ww );
		};
		// simulation via deterministic model
		ut ( 0 ) = KalmanObs[0];
		ut ( 1 ) = KalmanObs[1];
		dt ( 0 ) = KalmanObs[2];
		dt ( 1 ) = KalmanObs[3];

		xt = fxu.eval ( xtm,ut );
		//Results:
		// in xt we have simulaton according to the model
		// in x we have "reality"
		xtm ( 0 ) =x[0];xtm ( 1 ) =x[1];xtm ( 2 ) =x[2];xtm ( 3 ) =x[3];
		xdif = xtm-xt;
		if ( xdif ( 3 ) >pi ) xdif ( 3 )-=2*pi;
		if ( xdif ( 3 ) <-pi ) xdif ( 3 ) +=2*pi;
		
		ddif = hxu.eval(xtm,ut) - dt;

		//Rt = 0.9*Rt + xdif^2
		Qt*=0.01;
		Qt += outer_product ( xdif,xdif ); //(x-xt)^2
		Rt*=0.01;
		Rt += outer_product ( ddif,ddif ); //(x-xt)^2

		//ESTIMATE
		Efix.bayes(concat(dt,ut));
		//
		Eop.set_parameters ( &fxu,&hxu,(Qt+1e-16*eye(4)),(Rt+1e-3*eye(2)));
		Eop.bayes(concat(dt,ut));
		//
		Edi.set_parameters ( &fxu,&hxu,(diag(diag(Qt))+1e-16*eye(4)), (diag(diag(Rt))+1e-3*eye(2)));
		Edi.bayes(concat(dt,ut));
		
		//LOG
		L.logit(X_log, 	vec(x,4)); //vec from C-array
		L.logit(Efix_log, Efix_ep.mean() ); 
		L.logit(Eop_log, 	Eop_ep.mean() ); 
		L.logit(Edi_log, 	Edi_ep.mean() ); 
		L.logit(Q_log, 	vec(Qt._data(),16) ); 
		L.logit(R_log,		vec(Rt._data(),4) ); 
		L.logit(D_log,	vec(KalmanObs,4) ); 
		
		L.step(false);
	}

	L.step(true);
	//L.itsave("sim_var.it");	
	

	return 0;
}