def aov_theta(times, mags, errs, frequency,
              binsize=0.05, minbin=9):
    '''Calculates the Schwarzenberg-Czerny AoV statistic at a test frequency.

    Parameters
    ----------

    times,mags,errs : np.array
        The input time-series and associated errors.

    frequency : float
        The test frequency to calculate the theta statistic at.

    binsize : float
        The phase bin size to use.

    minbin : int
        The minimum number of items in a phase bin to consider in the
        calculation of the statistic.

    Returns
    -------

    theta_aov : float
        The value of the AoV statistic at the specified `frequency`.

    '''

    period = 1.0/frequency
    fold_time = times[0]

    phased = phase_magseries(times,
                             mags,
                             period,
                             fold_time,
                             wrap=False,
                             sort=True)

    phases = phased['phase']
    pmags = phased['mags']
    bins = nparange(0.0, 1.0, binsize)
    ndets = phases.size

    binnedphaseinds = npdigitize(phases, bins)

    bin_s1_tops = []
    bin_s2_tops = []
    binndets = []
    goodbins = 0

    all_xbar = npmedian(pmags)

    for x in npunique(binnedphaseinds):

        thisbin_inds = binnedphaseinds == x
        thisbin_mags = pmags[thisbin_inds]

        if thisbin_mags.size > minbin:

            thisbin_ndet = thisbin_mags.size
            thisbin_xbar = npmedian(thisbin_mags)

            # get s1
            thisbin_s1_top = (
                thisbin_ndet *
                (thisbin_xbar - all_xbar) *
                (thisbin_xbar - all_xbar)
            )

            # get s2
            thisbin_s2_top = npsum((thisbin_mags - all_xbar) *
                                   (thisbin_mags - all_xbar))

            bin_s1_tops.append(thisbin_s1_top)
            bin_s2_tops.append(thisbin_s2_top)
            binndets.append(thisbin_ndet)
            goodbins = goodbins + 1


    # turn the quantities into arrays
    bin_s1_tops = nparray(bin_s1_tops)
    bin_s2_tops = nparray(bin_s2_tops)
    binndets = nparray(binndets)

    # calculate s1 first
    s1 = npsum(bin_s1_tops)/(goodbins - 1.0)

    # then calculate s2
    s2 = npsum(bin_s2_tops)/(ndets - goodbins)

    theta_aov = s1/s2

    return theta_aov
def aov_theta(times, mags, errs, frequency,
              binsize=0.05, minbin=9):
    '''Calculates the Schwarzenberg-Czerny AoV statistic at a test frequency.

    Parameters
    ----------

    times,mags,errs : np.array
        The input time-series and associated errors.

    frequency : float
        The test frequency to calculate the theta statistic at.

    binsize : float
        The phase bin size to use.

    minbin : int
        The minimum number of items in a phase bin to consider in the
        calculation of the statistic.

    Returns
    -------

    theta_aov : float
        The value of the AoV statistic at the specified `frequency`.

    '''

    period = 1.0/frequency
    fold_time = times[0]

    phased = phase_magseries(times,
                             mags,
                             period,
                             fold_time,
                             wrap=False,
                             sort=True)

    phases = phased['phase']
    pmags = phased['mags']
    bins = nparange(0.0, 1.0, binsize)
    ndets = phases.size

    binnedphaseinds = npdigitize(phases, bins)

    bin_s1_tops = []
    bin_s2_tops = []
    binndets = []
    goodbins = 0

    all_xbar = npmedian(pmags)

    for x in npunique(binnedphaseinds):

        thisbin_inds = binnedphaseinds == x
        thisbin_mags = pmags[thisbin_inds]

        if thisbin_mags.size > minbin:

            thisbin_ndet = thisbin_mags.size
            thisbin_xbar = npmedian(thisbin_mags)

            # get s1
            thisbin_s1_top = (
                thisbin_ndet *
                (thisbin_xbar - all_xbar) *
                (thisbin_xbar - all_xbar)
            )

            # get s2
            thisbin_s2_top = npsum((thisbin_mags - all_xbar) *
                                   (thisbin_mags - all_xbar))

            bin_s1_tops.append(thisbin_s1_top)
            bin_s2_tops.append(thisbin_s2_top)
            binndets.append(thisbin_ndet)
            goodbins = goodbins + 1


    # turn the quantities into arrays
    bin_s1_tops = nparray(bin_s1_tops)
    bin_s2_tops = nparray(bin_s2_tops)
    binndets = nparray(binndets)

    # calculate s1 first
    s1 = npsum(bin_s1_tops)/(goodbins - 1.0)

    # then calculate s2
    s2 = npsum(bin_s2_tops)/(ndets - goodbins)

    theta_aov = s1/s2

    return theta_aov
def aov_theta ( times , mags , errs , frequency , binsize = 0.05 , minbin = 9 ) : period = 1.0 / frequency fold_time = times [ 0 ] phased = phase_magseries ( times , mags , period , fold_time , wrap = False , sort = True ) phases = phased [ 'phase' ] pmags = phased [ 'mags' ] bins = nparange ( 0.0 , 1.0 , binsize ) ndets = phases . size binnedphaseinds = npdigitize ( phases , bins ) bin_s1_tops = [ ] bin_s2_tops = [ ] binndets = [ ] goodbins = 0 all_xbar = npmedian ( pmags ) for x in npunique ( binnedphaseinds ) : thisbin_inds = binnedphaseinds == x thisbin_mags = pmags [ thisbin_inds ] if thisbin_mags . size > minbin : thisbin_ndet = thisbin_mags . size thisbin_xbar = npmedian ( thisbin_mags ) # get s1 thisbin_s1_top = ( thisbin_ndet * ( thisbin_xbar - all_xbar ) * ( thisbin_xbar - all_xbar ) ) # get s2 thisbin_s2_top = npsum ( ( thisbin_mags - all_xbar ) * ( thisbin_mags - all_xbar ) ) bin_s1_tops . append ( thisbin_s1_top ) bin_s2_tops . append ( thisbin_s2_top ) binndets . append ( thisbin_ndet ) goodbins = goodbins + 1 # turn the quantities into arrays bin_s1_tops = nparray ( bin_s1_tops ) bin_s2_tops = nparray ( bin_s2_tops ) binndets = nparray ( binndets ) # calculate s1 first s1 = npsum ( bin_s1_tops ) / ( goodbins - 1.0 ) # then calculate s2 s2 = npsum ( bin_s2_tops ) / ( ndets - goodbins ) theta_aov = s1 / s2 return theta_aov
Calculates the Schwarzenberg-Czerny AoV statistic at a test frequency.

    Parameters
    ----------

    times,mags,errs : np.array
        The input time-series and associated errors.

    frequency : float
        The test frequency to calculate the theta statistic at.

    binsize : float
        The phase bin size to use.

    minbin : int
        The minimum number of items in a phase bin to consider in the
        calculation of the statistic.

    Returns
    -------

    theta_aov : float
        The value of the AoV statistic at the specified `frequency`.
Calculates the Schwarzenberg - Czerny AoV statistic at a test frequency .