QSO Period Finding software: Download here .

Requires Python scipy and numpy.

See Zheng et al. 2016 (arXiv:1512.08730) for more details.

Note on usage:

Definition:  lomb(time, signal, error, f1, df, numf, sys_err=0.0, ltau=2.0, lvar=-2.6, do_fit=False)
C version of lomb_scargle

    time: time vector
    signal: data vector
    error: data uncertainty vector
    sys_err: systematic error (scalar) to be added in quadrature to error
    f1: starting frequency
    df: frequency step
    numf: number of frequencies to consider

    ltau,lvar: DRW model parameters, initial guesses if do_fit=True
    (will be estimated if do_fit=True)

    psd: power spectrum on frequency grid: f1,f1+df,...,f1+numf*df

[~/strip82]|1> from lomb_scargle_red_fix import lomb
[~/strip82]|2> (t,r,dr,mags) = load_dat(id,filter='r')
[~/strip82]|3> res = qso_fit(t,r,dr,filter='r')
[~/strip82]|4> t-=t.min()
[~/strip82]|5> Xmax = t.max()
[~/strip82]|6> df = 0.1/Xmax; f1 = 2./Xmax; f2 = 0.1;
[~/strip82]|7> numf = int((f2-f1)/df)
[~/strip82]|8> psd,lvar,ltau = lomb(t,r,dr,f1,df,numf,lvar=res['lvar'],ltau=res['ltau'])
Note: the qso_fit software can be found here .