pro guessvals1,colvec,errvec,f1,f2,gmrs,rmis,flags,sbad ; this routine uses the valid data in colvec (where index array sbad ; defines the positions of the bad data) to estimate plausible values ; for the missing data. Errors for data interpolated in this fashion are ; set to a very large value. ; The components of integer array flags are set to reflect which data ; are missing. ; ; On input, arrays f1, f2 and vectors grms, rmis are the result of running ; the fit_colors program, with results saved in ~/d/stardat/guessvals_in.sav. ; ; colvec contains the observed magnitudes in this order: ; {u,g,r,i,z,J,H,K,Gred,D51} ; constants bigerr=99.9 ; error for missing data di=[1,2,3,4,5,6,7,9] ; do only these indices -- not u and Gred ncol=n_elements(colvec) good=lonarr(ncol)+1. good(sbad)=0 sgood=where(good eq 1) ; deal with common cases, in which either (g,r) or (r,i) are known ; Prefer to base judgement on r-i, since this gives smaller errors on avg. cv0=colvec ; for debugging if(good(2) ne 0 and good(3) ne 0) then begin ; r-i is defined cv=fltarr(10) for i=0,7 do begin cv(di(i))=colvec(2)+interpol(f2(*,i),rmis,(colvec(2)-colvec(3))) endfor colvec(sbad)=cv(sbad) goto,fini endif if(good(1) ne 0 and good(2) ne 0) then begin ; g-r is defined cv=fltarr(10) for i=0,7 do begin cv(di(i))=colvec(2)+interpol(f1(*,i),gmrs,(colvec(1)-colvec(2))) endfor colvec(sbad)=cv(sbad) goto,fini endif ; if previous things fail, set everything to same mag as 1st good value, ; barring u if(max(sgood) gt 0) then begin sg1=where(good(1:*) eq 1) colvec(sbad)=colvec(min(sg1)+1) endif else begin colvec(sbad)=colvec(0)-1. ; If must use u, subtract 1 from all other mags endelse fini: ; set flags flags=intarr(4) if(good(0) eq 0) then flags(0)=1 if(good(5) eq 0 or good(6) eq 0 or good(7) eq 0) then flags(1)=1 if(good(4) eq 0) then flags(2)=1 if(good(1) eq 0 or good(2) eq 0 or good(3) eq 0 or good(8) eq 0 or good(9) \$ eq 0) then flags(3)=1 end