QSO Fitting software: Download here . (version to use if you get a solveh_banded error: here )

Script to download SDSS Stripe 82 Lightcurves: Download here .

Ascii list of candidate quasars: Download here .

Period finding code for QSOs: here .

Requires Python scipy and numpy.

See Butler & Bloom 2011 (AJ,141,93) for more details.


Note on usage:

[~/strip82]|1> import stripe82
[~/strip82]|2> a = stripe82.Stripe82()
[~/strip82]|3># to run with an abitrary ra,dec
[~/strip82]|4> a.q(ra,dec)

[~/strip82]|5> from qso_fit import qso_fit
[~/strip82]|6> qso_fit?
Type:           function
Base Class:     
String Form:    
Namespace:      Interactive
File:           /home/nrbutler/python_modules/qso_fit.py
Definition:     qso_fit(time, data, error, filter='r', sys_err=0.0, return_model=False)
Docstring:
    Best-fit qso model determined for Sesar Strip82, ugriz-bands (default r).
        See additional notes for underlying code qso_engine.
    
    Input:
        time - measurement times [days]
        data - measured magnitudes in single filter (also specified)
        error - uncertainty in measured magnitudes
    
    Output:
        chi^2/nu - classical variability measure
        chi^2_qso/nu - fit statistic
        chi^2_qso/nu_NULL - expected fit statistic for non-qso variable
    
        signif_qso - significance chi^2/nu1 (rule out qso)
        signif_vary - significance that source is variable at all 
        class - source type (ambiguous, not_qso, qso)
    
        model - time series prediction for each datum given all others (iff return_model==True)
        dmodel - model uncertainty, including uncertainty in data
    
    Note on use (i.e., how class is defined):
    
          (0) signif_vary < 3: ambiguous, else
          (1) signif_qso > 3: qso, else
          (2) signif_not_qso > 3: not_qso