[81] | 1 | /* |
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| 2 | \file |
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| 3 | \brief Models for synchronous electric drive using IT++ and BDM |
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| 4 | \author Vaclav Smidl. |
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| 5 | |
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| 6 | ----------------------------------- |
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| 7 | BDM++ - C++ library for Bayesian Decision Making under Uncertainty |
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| 8 | |
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| 9 | Using IT++ for numerical operations |
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| 10 | ----------------------------------- |
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| 11 | */ |
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| 12 | |
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| 13 | #include <itpp/itbase.h> |
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| 14 | #include <stat/libFN.h> |
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| 15 | #include <estim/libKF.h> |
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| 16 | #include <estim/libPF.h> |
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| 17 | |
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| 18 | #include "pmsm.h" |
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| 19 | #include "simulator.h" |
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| 20 | |
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| 21 | #include "iopom.h" |
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| 22 | |
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| 23 | using namespace itpp; |
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| 24 | //!Extended Kalman filter with unknown \c Q |
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| 25 | |
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| 26 | void set_simulator_t ( double &Ww ) { |
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| 27 | |
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| 28 | if ( t>0.0002 ) x[8]=1.2; // 1A //0.2ZP |
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| 29 | if ( t>0.4 ) x[8]=10.8; // 9A |
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| 30 | if ( t>0.6 ) x[8]=25.2; // 21A |
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| 31 | |
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| 32 | if ( t>0.7 ) Ww=2.*M_PI*10.; |
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| 33 | if ( t>1.0 ) x[8]=1.2; // 1A |
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| 34 | if ( t>1.2 ) x[8]=10.8; // 9A |
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| 35 | if ( t>1.4 ) x[8]=25.2; // 21A |
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| 36 | |
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| 37 | if ( t>1.6 ) Ww=2.*M_PI*50.; |
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| 38 | if ( t>1.9 ) x[8]=1.2; // 1A |
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| 39 | if ( t>2.1 ) x[8]=10.8; // 9A |
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| 40 | if ( t>2.3 ) x[8]=25.2; // 21A |
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| 41 | |
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| 42 | if ( t>2.5 ) Ww=2.*M_PI*100; |
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| 43 | if ( t>2.8 ) x[8]=1.2; // 1A |
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| 44 | if ( t>3.0 ) x[8]=10.8; // 9A |
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| 45 | if ( t>3.2 ) x[8]=25.2; // 21A |
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| 46 | |
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| 47 | if ( t>3.4 ) Ww=2.*M_PI*150; |
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| 48 | if ( t>3.7 ) x[8]=1.2; // 1A |
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| 49 | if ( t>3.9 ) x[8]=10.8; // 9A |
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| 50 | if ( t>4.1 ) x[8]=25.2; // 21A |
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| 51 | |
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| 52 | if ( t>4.3 ) Ww=2.*M_PI*0; |
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| 53 | if ( t>4.8 ) x[8]=-1.2; // 1A |
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| 54 | if ( t>5.0 ) x[8]=-10.8; // 9A |
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| 55 | if ( t>5.2 ) x[8]=-25.2; // 21A |
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| 56 | |
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| 57 | if ( t>5.4 ) Ww=2.*M_PI* ( -10. ); |
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| 58 | if ( t>5.7 ) x[8]=-1.2; // 1A |
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| 59 | if ( t>5.9 ) x[8]=-10.8; // 9A |
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| 60 | if ( t>6.1 ) x[8]=-25.2; // 21A |
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| 61 | |
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| 62 | if ( t>6.3 ) Ww=2.*M_PI* ( -50. ); |
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| 63 | if ( t>6.7 ) x[8]=-1.2; // 1A |
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| 64 | if ( t>6.9 ) x[8]=-10.8; // 9A |
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| 65 | if ( t>7.1 ) x[8]=-25.2; // 21A |
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| 66 | |
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| 67 | if ( t>7.3 ) Ww=2.*M_PI* ( -100. ); |
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| 68 | if ( t>7.7 ) x[8]=-1.2; // 1A |
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| 69 | if ( t>7.9 ) x[8]=-10.8; // 9A |
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| 70 | if ( t>8.1 ) x[8]=-25.2; // 21A |
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| 71 | if ( t>8.3 ) x[8]=10.8; // 9A |
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| 72 | if ( t>8.5 ) x[8]=25.2; // 21A |
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| 73 | |
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| 74 | x[8]=0.0; |
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| 75 | } |
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| 76 | |
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| 77 | int main() { |
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| 78 | // Kalman filter |
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| 79 | int Ndat = 90000; |
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| 80 | double h = 1e-6; |
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| 81 | int Nsimstep = 125; |
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| 82 | |
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| 83 | mat Xt=zeros ( Ndat ,9 ); //true state from simulator |
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| 84 | mat XtM=zeros ( Ndat ,4 ); //true state from simulator |
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| 85 | mat XtF=zeros ( Ndat ,4 ); //true state from simulator |
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| 86 | mat XtO=zeros ( Ndat ,4 ); //true state from simulator |
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| 87 | mat Dt=zeros ( Ndat,4 ); //observation |
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| 88 | mat Qrec=zeros ( Ndat, 4*4 ); //full covariance matrix |
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| 89 | |
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| 90 | // SET SIMULATOR |
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| 91 | pmsmsim_set_parameters ( 0.28,0.003465,0.1989,0.0,4,1.5,0.04, 200., 3e-6, h ); |
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| 92 | double Ww = 0.0; |
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| 93 | vec dt ( 2 ); |
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| 94 | vec ut ( 2 ); |
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| 95 | vec xtm=zeros ( 4 ); |
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| 96 | vec xdif=zeros ( 4 ); |
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| 97 | vec xt ( 4 ); |
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| 98 | IMpmsm fxu; |
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| 99 | // Rs Ls dt Fmag(Ypm) kp p J Bf(Mz) |
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| 100 | fxu.set_parameters ( 0.28, 0.003465, Nsimstep*h, 0.1989, 1.5 ,4.0, 0.04, 0.0 ); |
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| 101 | OMpmsm hxu; |
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| 102 | mat Qt=zeros ( 4,4 ); |
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| 103 | |
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| 104 | // ESTIMATORS |
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| 105 | vec mu0= "0.0 0.0 0.0 0.0"; |
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| 106 | vec Qdiag ( "62 66 454 0.03" ); //zdenek: 0.01 0.01 0.0001 0.0001 |
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| 107 | vec Rdiag ( "100 100" ); //var(diff(xth)) = "0.034 0.034" |
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| 108 | mat Q =diag( Qdiag ); |
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| 109 | mat R =diag ( Rdiag ); |
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| 110 | EKFfull Efix ( rx,ry,ru ); |
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| 111 | Efix.set_est ( mu0, 1*eye ( 4 ) ); |
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| 112 | Efix.set_parameters ( &fxu,&hxu,Q,R); |
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| 113 | |
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| 114 | EKFfull Eop ( rx,ry,ru ); |
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| 115 | Eop.set_est ( mu0, 1*eye ( 4 ) ); |
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| 116 | Eop.set_parameters ( &fxu,&hxu,Q,R); |
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| 117 | |
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| 118 | epdf& Efix_ep = Efix._epdf(); |
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| 119 | epdf& Eop_ep = Eop._epdf(); |
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| 120 | |
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| 121 | |
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| 122 | for ( int tK=1;tK<Ndat;tK++ ) { |
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| 123 | //Number of steps of a simulator for one step of Kalman |
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| 124 | for ( int ii=0; ii<Nsimstep;ii++ ) { |
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| 125 | set_simulator_t ( Ww ); |
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| 126 | pmsmsim_step ( Ww ); |
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| 127 | }; |
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| 128 | // simulation via deterministic model |
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| 129 | ut ( 0 ) = KalmanObs[0]; |
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| 130 | ut ( 1 ) = KalmanObs[1]; |
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| 131 | dt ( 0 ) = KalmanObs[2]; |
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| 132 | dt ( 1 ) = KalmanObs[3]; |
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| 133 | |
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| 134 | xt = fxu.eval ( xtm,ut ); |
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| 135 | |
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| 136 | //Results: |
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| 137 | // in xt we have simulaton according to the model |
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| 138 | // in x we have "reality" |
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| 139 | |
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| 140 | xtm ( 0 ) =x[0];xtm ( 1 ) =x[1];xtm ( 2 ) =x[2];xtm ( 3 ) =x[3]; |
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| 141 | xdif=xtm-xt; |
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| 142 | if ( xdif ( 3 ) >pi ) xdif ( 3 )-=2*pi; |
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| 143 | if ( xdif ( 3 ) <-pi ) xdif ( 3 ) +=2*pi; |
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| 144 | |
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| 145 | //Rt = 0.9*Rt + xdif^2 |
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| 146 | Qt*=0.0; |
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| 147 | Qt += outer_product ( xdif,xdif ); //(x-xt)^2 |
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| 148 | Qrec.set_row ( tK, vec ( Qt._data(),16 ) ); |
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| 149 | |
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| 150 | //ESTIMATE |
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| 151 | Efix.bayes(concat(dt,ut)); |
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| 152 | // |
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| 153 | Eop.set_parameters ( &fxu,&hxu,(Qt+1e-16*eye(4)),(1*eye(2))); |
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| 154 | Eop.bayes(concat(dt,ut)); |
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| 155 | |
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| 156 | Xt.set_row ( tK,vec ( x,9 ) ); //vec from C-array |
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| 157 | XtM.set_row ( tK,xt ); //vec from C-array |
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| 158 | XtF.set_row ( tK,Efix_ep.mean() ); |
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| 159 | XtO.set_row ( tK,Eop_ep.mean() ); |
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| 160 | Dt.set_row ( tK, concat ( dt,ut ) ); |
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| 161 | } |
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| 162 | |
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| 163 | char tmpstr[200]; |
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| 164 | sprintf ( tmpstr,"%s/%s","variance/","format" ); |
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| 165 | ofstream form ( tmpstr ); |
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| 166 | form << "# Experimetal header file"<< endl; |
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| 167 | dirfile_write ( form,"variance/",Xt,"X","{isa isb om th }" ); |
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| 168 | dirfile_write ( form,"variance/",XtM,"Xsim","{isa isb om th }" ); |
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| 169 | dirfile_write ( form,"variance/",XtO,"XO","{isa isb om th }" ); |
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| 170 | dirfile_write ( form,"variance/",XtF,"XF","{isa isb om th }" ); |
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| 171 | dirfile_write ( form,"variance/",Dt,"D","{isa isb usa usb }" ); |
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| 172 | dirfile_write ( form,"variance",Qrec,"Q","{ }" ); |
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| 173 | |
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| 174 | return 0; |
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| 175 | } |
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