How to fit 2-D vectors in PAW ?
Here is an example showing how to proceed:
MACRO FIT2D * * Macro to test 2-D fits of functions F(x1,x2) = Y to data. * ZONE 2 2 OPT NSTA H/DEL * VECT/DEL * NP= 3 VECT/CREA PAR([NP]) R 100.0 1.0 1.0 VECT/CREA STEP([NP]) R 1.0 1.0 1.0 VECT/CREA PMIN([NP]) R 1.0 0.0 1.0 VECT/CREA PMAX([NP]) R 1.0 1.0 1.0 * * Define number of bins in X and Y, then book vectors: * NX= 6 NY= 8 MINX= 0.0 MAXX= 3.0 MINY= 0.0 MAXY= 4.0 N= [NX]*[NY] VECT/CREA Y([N]) VECT/CREA EY([N]) VECT/CREA X([N],2) * * Make dummy data using random function HRNDM2. Observe that here the values to * be expected from the fits are defined: * | | FUN2 20 EXP(-0.5*(((1.5-X)**2+(2.0-Y)**2)/(3.**2))) 20 [MINX] [MAXX] _ 20 [MINY] [MAXY] 2D 30 'DATA' [NX] [MINX] [MAXX] [NY] [MINY] [MAXY] APPLICATION COMIS QUIT SUBROUTINE RNDM DO 10 I=1,10000 CALL HRNDM2(20,RX,RY) CALL HFILL(30,RX,RY,1.0) 10 CONTINUE END CALL RNDM END QUIT H/PL 30 LEGO GET/CON 30 Y GET/ERR 30 EY * * Put coordinates x1 and x2 into vector X: * BINWX= ([MAXX]-[MINX])/[NX] BINWY= ([MAXY]-[MINY])/[NY] DO I=1,[NY] DO J=1,[NX] ZX= $SIGMA( [MINX]+[BINWX]*([J]-0.5) ) ZY= $SIGMA( [MINY]+[BINWY]*([I]-0.5) ) N= $SIGMA( ([I]-1)*[NX]+[J] ) VECT/INP X([N],1) [ZX] VECT/INP X([N],2) [ZY] ENDDO ENDDO * * Do the fit: * V/IN PAR 100.0 1.0 1.0 V/FIT X Y EY FUNXY.FOR 0 [NP] PAR STEP PMIN PMAX | Plot result: P1= PAR(1) P2= PAR(2) P3= PAR(3) ZONE 1 2 2 S FUN2 50 [P1]*EXP(-0.5*((([P2]-X)**2+([P3]-Y)**2)/(3.**2))) [NX] [MINX] [MAXX] _ [NY] [MINY] [MAXY] ' ' H/PL 50 LEGO RETURN
Where the COMIS function FUNXY.FOR is:
FUNCTION FUNXY(X)
DIMENSION X(2)
COMMON/PAWPAR/PAR(3)
FUNXY= PAR(1)
& *EXP( -0.5*(((PAR(2)-X(1))**2+(PAR(3)-X(2))**2)/(3.**2)) )
END
PAW > exe fit2d********************************************** * * * Function minimization by SUBROUTINE HFITV * * Variable-metric method * * ID = 0 CHOPT = 0 * * * ********************************************** Convergence when estimated distance to minimum (EDM) .LT. 0.10E+01 FCN= 44.38704 FROM MIGRAD STATUS=CONVERGED 70 CALLS 71 TOTAL EDM= 0.92E-04 STRATEGY= 1 ERROR MATRIX ACCURATE EXT PARAMETER STEP FIRST NO. NAME VALUE ERROR SIZE DERIVATIVE 1 P1 232.45 2.4176 0.58806 0.23439E-04 2 P2 1.7379 0.10665 0.24700E-01 0.10854 3 P3 2.0745 0.81073E-01 0.19395E-01 -0.86093E-01 CHISQUARE = 0.9864E+00 NPFIT = 48 PAW >
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