def pgen_lsp(
        times,
        mags,
        errs,
        magsarefluxes=False,
        startp=None,
        endp=None,
        stepsize=1.0e-4,
        autofreq=True,
        nbestpeaks=5,
        periodepsilon=0.1,
        sigclip=10.0,
        nworkers=None,
        workchunksize=None,
        glspfunc=_glsp_worker_withtau,
        verbose=True
):
    '''This calculates the generalized Lomb-Scargle periodogram.

    Uses the algorithm from Zechmeister and Kurster (2009).

    Parameters
    ----------

    times,mags,errs : np.array
        The mag/flux time-series with associated measurement errors to run the
        period-finding on.

    magsarefluxes : bool
        If the input measurement values in `mags` and `errs` are in fluxes, set
        this to True.

    startp,endp : float or None
        The minimum and maximum periods to consider for the transit search.

    stepsize : float
        The step-size in frequency to use when constructing a frequency grid for
        the period search.

    autofreq : bool
        If this is True, the value of `stepsize` will be ignored and the
        :py:func:`astrobase.periodbase.get_frequency_grid` function will be used
        to generate a frequency grid based on `startp`, and `endp`. If these are
        None as well, `startp` will be set to 0.1 and `endp` will be set to
        `times.max() - times.min()`.

    nbestpeaks : int
        The number of 'best' peaks to return from the periodogram results,
        starting from the global maximum of the periodogram peak values.

    periodepsilon : float
        The fractional difference between successive values of 'best' periods
        when sorting by periodogram power to consider them as separate periods
        (as opposed to part of the same periodogram peak). This is used to avoid
        broad peaks in the periodogram and make sure the 'best' periods returned
        are all actually independent.

    sigclip : float or int or sequence of two floats/ints or None
        If a single float or int, a symmetric sigma-clip will be performed using
        the number provided as the sigma-multiplier to cut out from the input
        time-series.

        If a list of two ints/floats is provided, the function will perform an
        'asymmetric' sigma-clip. The first element in this list is the sigma
        value to use for fainter flux/mag values; the second element in this
        list is the sigma value to use for brighter flux/mag values. For
        example, `sigclip=[10., 3.]`, will sigclip out greater than 10-sigma
        dimmings and greater than 3-sigma brightenings. Here the meaning of
        "dimming" and "brightening" is set by *physics* (not the magnitude
        system), which is why the `magsarefluxes` kwarg must be correctly set.

        If `sigclip` is None, no sigma-clipping will be performed, and the
        time-series (with non-finite elems removed) will be passed through to
        the output.

    nworkers : int
        The number of parallel workers to use when calculating the periodogram.

    workchunksize : None or int
        If this is an int, will use chunks of the given size to break up the
        work for the parallel workers. If None, the chunk size is set to 1.

    glspfunc : Python function
        The worker function to use to calculate the periodogram. This can be
        used to make this function calculate the time-series sampling window
        function instead of the time-series measurements' GLS periodogram by
        passing in `_glsp_worker_specwindow` instead of the default
        `_glsp_worker_withtau` function.

    verbose : bool
        If this is True, will indicate progress and details about the frequency
        grid used for the period search.

    Returns
    -------

    dict
        This function returns a dict, referred to as an `lspinfo` dict in other
        astrobase functions that operate on periodogram results. This is a
        standardized format across all astrobase period-finders, and is of the
        form below::

            {'bestperiod': the best period value in the periodogram,
             'bestlspval': the periodogram peak associated with the best period,
             'nbestpeaks': the input value of nbestpeaks,
             'nbestlspvals': nbestpeaks-size list of best period peak values,
             'nbestperiods': nbestpeaks-size list of best periods,
             'lspvals': the full array of periodogram powers,
             'periods': the full array of periods considered,
             'method':'gls' -> the name of the period-finder method,
             'kwargs':{ dict of all of the input kwargs for record-keeping}}

    '''

    # get rid of nans first and sigclip
    stimes, smags, serrs = sigclip_magseries(times,
                                             mags,
                                             errs,
                                             magsarefluxes=magsarefluxes,
                                             sigclip=sigclip)

    # get rid of zero errs
    nzind = npnonzero(serrs)
    stimes, smags, serrs = stimes[nzind], smags[nzind], serrs[nzind]


    # make sure there are enough points to calculate a spectrum
    if len(stimes) > 9 and len(smags) > 9 and len(serrs) > 9:

        # get the frequencies to use
        if startp:
            endf = 1.0/startp
        else:
            # default start period is 0.1 day
            endf = 1.0/0.1

        if endp:
            startf = 1.0/endp
        else:
            # default end period is length of time series
            startf = 1.0/(stimes.max() - stimes.min())

        # if we're not using autofreq, then use the provided frequencies
        if not autofreq:
            omegas = 2*pi_value*nparange(startf, endf, stepsize)
            if verbose:
                LOGINFO(
                    'using %s frequency points, start P = %.3f, end P = %.3f' %
                    (omegas.size, 1.0/endf, 1.0/startf)
                )
        else:
            # this gets an automatic grid of frequencies to use
            freqs = get_frequency_grid(stimes,
                                       minfreq=startf,
                                       maxfreq=endf)
            omegas = 2*pi_value*freqs
            if verbose:
                LOGINFO(
                    'using autofreq with %s frequency points, '
                    'start P = %.3f, end P = %.3f' %
                    (omegas.size, 1.0/freqs.max(), 1.0/freqs.min())
                )

        # map to parallel workers
        if (not nworkers) or (nworkers > NCPUS):
            nworkers = NCPUS
            if verbose:
                LOGINFO('using %s workers...' % nworkers)

        pool = Pool(nworkers)

        tasks = [(stimes, smags, serrs, x) for x in omegas]
        if workchunksize:
            lsp = pool.map(glspfunc, tasks, chunksize=workchunksize)
        else:
            lsp = pool.map(glspfunc, tasks)

        pool.close()
        pool.join()
        del pool

        lsp = nparray(lsp)
        periods = 2.0*pi_value/omegas

        # find the nbestpeaks for the periodogram: 1. sort the lsp array by
        # highest value first 2. go down the values until we find five
        # values that are separated by at least periodepsilon in period

        # make sure to filter out non-finite values of lsp

        finitepeakind = npisfinite(lsp)
        finlsp = lsp[finitepeakind]
        finperiods = periods[finitepeakind]

        # make sure that finlsp has finite values before we work on it
        try:

            bestperiodind = npargmax(finlsp)

        except ValueError:

            LOGERROR('no finite periodogram values '
                     'for this mag series, skipping...')
            return {'bestperiod':npnan,
                    'bestlspval':npnan,
                    'nbestpeaks':nbestpeaks,
                    'nbestlspvals':None,
                    'nbestperiods':None,
                    'lspvals':None,
                    'omegas':omegas,
                    'periods':None,
                    'method':'gls',
                    'kwargs':{'startp':startp,
                              'endp':endp,
                              'stepsize':stepsize,
                              'autofreq':autofreq,
                              'periodepsilon':periodepsilon,
                              'nbestpeaks':nbestpeaks,
                              'sigclip':sigclip}}

        sortedlspind = npargsort(finlsp)[::-1]
        sortedlspperiods = finperiods[sortedlspind]
        sortedlspvals = finlsp[sortedlspind]

        # now get the nbestpeaks
        nbestperiods, nbestlspvals, peakcount = (
            [finperiods[bestperiodind]],
            [finlsp[bestperiodind]],
            1
        )
        prevperiod = sortedlspperiods[0]

        # find the best nbestpeaks in the lsp and their periods
        for period, lspval in zip(sortedlspperiods, sortedlspvals):

            if peakcount == nbestpeaks:
                break
            perioddiff = abs(period - prevperiod)
            bestperiodsdiff = [abs(period - x) for x in nbestperiods]

            # print('prevperiod = %s, thisperiod = %s, '
            #       'perioddiff = %s, peakcount = %s' %
            #       (prevperiod, period, perioddiff, peakcount))

            # this ensures that this period is different from the last
            # period and from all the other existing best periods by
            # periodepsilon to make sure we jump to an entire different peak
            # in the periodogram
            if (perioddiff > (periodepsilon*prevperiod) and
                all(x > (periodepsilon*period) for x in bestperiodsdiff)):
                nbestperiods.append(period)
                nbestlspvals.append(lspval)
                peakcount = peakcount + 1

            prevperiod = period


        return {'bestperiod':finperiods[bestperiodind],
                'bestlspval':finlsp[bestperiodind],
                'nbestpeaks':nbestpeaks,
                'nbestlspvals':nbestlspvals,
                'nbestperiods':nbestperiods,
                'lspvals':lsp,
                'omegas':omegas,
                'periods':periods,
                'method':'gls',
                'kwargs':{'startp':startp,
                          'endp':endp,
                          'stepsize':stepsize,
                          'autofreq':autofreq,
                          'periodepsilon':periodepsilon,
                          'nbestpeaks':nbestpeaks,
                          'sigclip':sigclip}}

    else:

        LOGERROR('no good detections for these times and mags, skipping...')
        return {'bestperiod':npnan,
                'bestlspval':npnan,
                'nbestpeaks':nbestpeaks,
                'nbestlspvals':None,
                'nbestperiods':None,
                'lspvals':None,
                'omegas':None,
                'periods':None,
                'method':'gls',
                'kwargs':{'startp':startp,
                          'endp':endp,
                          'stepsize':stepsize,
                          'autofreq':autofreq,
                          'periodepsilon':periodepsilon,
                          'nbestpeaks':nbestpeaks,
                          'sigclip':sigclip}}
def pgen_lsp(
        times,
        mags,
        errs,
        magsarefluxes=False,
        startp=None,
        endp=None,
        stepsize=1.0e-4,
        autofreq=True,
        nbestpeaks=5,
        periodepsilon=0.1,
        sigclip=10.0,
        nworkers=None,
        workchunksize=None,
        glspfunc=_glsp_worker_withtau,
        verbose=True
):
    '''This calculates the generalized Lomb-Scargle periodogram.

    Uses the algorithm from Zechmeister and Kurster (2009).

    Parameters
    ----------

    times,mags,errs : np.array
        The mag/flux time-series with associated measurement errors to run the
        period-finding on.

    magsarefluxes : bool
        If the input measurement values in `mags` and `errs` are in fluxes, set
        this to True.

    startp,endp : float or None
        The minimum and maximum periods to consider for the transit search.

    stepsize : float
        The step-size in frequency to use when constructing a frequency grid for
        the period search.

    autofreq : bool
        If this is True, the value of `stepsize` will be ignored and the
        :py:func:`astrobase.periodbase.get_frequency_grid` function will be used
        to generate a frequency grid based on `startp`, and `endp`. If these are
        None as well, `startp` will be set to 0.1 and `endp` will be set to
        `times.max() - times.min()`.

    nbestpeaks : int
        The number of 'best' peaks to return from the periodogram results,
        starting from the global maximum of the periodogram peak values.

    periodepsilon : float
        The fractional difference between successive values of 'best' periods
        when sorting by periodogram power to consider them as separate periods
        (as opposed to part of the same periodogram peak). This is used to avoid
        broad peaks in the periodogram and make sure the 'best' periods returned
        are all actually independent.

    sigclip : float or int or sequence of two floats/ints or None
        If a single float or int, a symmetric sigma-clip will be performed using
        the number provided as the sigma-multiplier to cut out from the input
        time-series.

        If a list of two ints/floats is provided, the function will perform an
        'asymmetric' sigma-clip. The first element in this list is the sigma
        value to use for fainter flux/mag values; the second element in this
        list is the sigma value to use for brighter flux/mag values. For
        example, `sigclip=[10., 3.]`, will sigclip out greater than 10-sigma
        dimmings and greater than 3-sigma brightenings. Here the meaning of
        "dimming" and "brightening" is set by *physics* (not the magnitude
        system), which is why the `magsarefluxes` kwarg must be correctly set.

        If `sigclip` is None, no sigma-clipping will be performed, and the
        time-series (with non-finite elems removed) will be passed through to
        the output.

    nworkers : int
        The number of parallel workers to use when calculating the periodogram.

    workchunksize : None or int
        If this is an int, will use chunks of the given size to break up the
        work for the parallel workers. If None, the chunk size is set to 1.

    glspfunc : Python function
        The worker function to use to calculate the periodogram. This can be
        used to make this function calculate the time-series sampling window
        function instead of the time-series measurements' GLS periodogram by
        passing in `_glsp_worker_specwindow` instead of the default
        `_glsp_worker_withtau` function.

    verbose : bool
        If this is True, will indicate progress and details about the frequency
        grid used for the period search.

    Returns
    -------

    dict
        This function returns a dict, referred to as an `lspinfo` dict in other
        astrobase functions that operate on periodogram results. This is a
        standardized format across all astrobase period-finders, and is of the
        form below::

            {'bestperiod': the best period value in the periodogram,
             'bestlspval': the periodogram peak associated with the best period,
             'nbestpeaks': the input value of nbestpeaks,
             'nbestlspvals': nbestpeaks-size list of best period peak values,
             'nbestperiods': nbestpeaks-size list of best periods,
             'lspvals': the full array of periodogram powers,
             'periods': the full array of periods considered,
             'method':'gls' -> the name of the period-finder method,
             'kwargs':{ dict of all of the input kwargs for record-keeping}}

    '''

    # get rid of nans first and sigclip
    stimes, smags, serrs = sigclip_magseries(times,
                                             mags,
                                             errs,
                                             magsarefluxes=magsarefluxes,
                                             sigclip=sigclip)

    # get rid of zero errs
    nzind = npnonzero(serrs)
    stimes, smags, serrs = stimes[nzind], smags[nzind], serrs[nzind]


    # make sure there are enough points to calculate a spectrum
    if len(stimes) > 9 and len(smags) > 9 and len(serrs) > 9:

        # get the frequencies to use
        if startp:
            endf = 1.0/startp
        else:
            # default start period is 0.1 day
            endf = 1.0/0.1

        if endp:
            startf = 1.0/endp
        else:
            # default end period is length of time series
            startf = 1.0/(stimes.max() - stimes.min())

        # if we're not using autofreq, then use the provided frequencies
        if not autofreq:
            omegas = 2*pi_value*nparange(startf, endf, stepsize)
            if verbose:
                LOGINFO(
                    'using %s frequency points, start P = %.3f, end P = %.3f' %
                    (omegas.size, 1.0/endf, 1.0/startf)
                )
        else:
            # this gets an automatic grid of frequencies to use
            freqs = get_frequency_grid(stimes,
                                       minfreq=startf,
                                       maxfreq=endf)
            omegas = 2*pi_value*freqs
            if verbose:
                LOGINFO(
                    'using autofreq with %s frequency points, '
                    'start P = %.3f, end P = %.3f' %
                    (omegas.size, 1.0/freqs.max(), 1.0/freqs.min())
                )

        # map to parallel workers
        if (not nworkers) or (nworkers > NCPUS):
            nworkers = NCPUS
            if verbose:
                LOGINFO('using %s workers...' % nworkers)

        pool = Pool(nworkers)

        tasks = [(stimes, smags, serrs, x) for x in omegas]
        if workchunksize:
            lsp = pool.map(glspfunc, tasks, chunksize=workchunksize)
        else:
            lsp = pool.map(glspfunc, tasks)

        pool.close()
        pool.join()
        del pool

        lsp = nparray(lsp)
        periods = 2.0*pi_value/omegas

        # find the nbestpeaks for the periodogram: 1. sort the lsp array by
        # highest value first 2. go down the values until we find five
        # values that are separated by at least periodepsilon in period

        # make sure to filter out non-finite values of lsp

        finitepeakind = npisfinite(lsp)
        finlsp = lsp[finitepeakind]
        finperiods = periods[finitepeakind]

        # make sure that finlsp has finite values before we work on it
        try:

            bestperiodind = npargmax(finlsp)

        except ValueError:

            LOGERROR('no finite periodogram values '
                     'for this mag series, skipping...')
            return {'bestperiod':npnan,
                    'bestlspval':npnan,
                    'nbestpeaks':nbestpeaks,
                    'nbestlspvals':None,
                    'nbestperiods':None,
                    'lspvals':None,
                    'omegas':omegas,
                    'periods':None,
                    'method':'gls',
                    'kwargs':{'startp':startp,
                              'endp':endp,
                              'stepsize':stepsize,
                              'autofreq':autofreq,
                              'periodepsilon':periodepsilon,
                              'nbestpeaks':nbestpeaks,
                              'sigclip':sigclip}}

        sortedlspind = npargsort(finlsp)[::-1]
        sortedlspperiods = finperiods[sortedlspind]
        sortedlspvals = finlsp[sortedlspind]

        # now get the nbestpeaks
        nbestperiods, nbestlspvals, peakcount = (
            [finperiods[bestperiodind]],
            [finlsp[bestperiodind]],
            1
        )
        prevperiod = sortedlspperiods[0]

        # find the best nbestpeaks in the lsp and their periods
        for period, lspval in zip(sortedlspperiods, sortedlspvals):

            if peakcount == nbestpeaks:
                break
            perioddiff = abs(period - prevperiod)
            bestperiodsdiff = [abs(period - x) for x in nbestperiods]

            # print('prevperiod = %s, thisperiod = %s, '
            #       'perioddiff = %s, peakcount = %s' %
            #       (prevperiod, period, perioddiff, peakcount))

            # this ensures that this period is different from the last
            # period and from all the other existing best periods by
            # periodepsilon to make sure we jump to an entire different peak
            # in the periodogram
            if (perioddiff > (periodepsilon*prevperiod) and
                all(x > (periodepsilon*period) for x in bestperiodsdiff)):
                nbestperiods.append(period)
                nbestlspvals.append(lspval)
                peakcount = peakcount + 1

            prevperiod = period


        return {'bestperiod':finperiods[bestperiodind],
                'bestlspval':finlsp[bestperiodind],
                'nbestpeaks':nbestpeaks,
                'nbestlspvals':nbestlspvals,
                'nbestperiods':nbestperiods,
                'lspvals':lsp,
                'omegas':omegas,
                'periods':periods,
                'method':'gls',
                'kwargs':{'startp':startp,
                          'endp':endp,
                          'stepsize':stepsize,
                          'autofreq':autofreq,
                          'periodepsilon':periodepsilon,
                          'nbestpeaks':nbestpeaks,
                          'sigclip':sigclip}}

    else:

        LOGERROR('no good detections for these times and mags, skipping...')
        return {'bestperiod':npnan,
                'bestlspval':npnan,
                'nbestpeaks':nbestpeaks,
                'nbestlspvals':None,
                'nbestperiods':None,
                'lspvals':None,
                'omegas':None,
                'periods':None,
                'method':'gls',
                'kwargs':{'startp':startp,
                          'endp':endp,
                          'stepsize':stepsize,
                          'autofreq':autofreq,
                          'periodepsilon':periodepsilon,
                          'nbestpeaks':nbestpeaks,
                          'sigclip':sigclip}}
def pgen_lsp ( times , mags , errs , magsarefluxes = False , startp = None , endp = None , stepsize = 1.0e-4 , autofreq = True , nbestpeaks = 5 , periodepsilon = 0.1 , sigclip = 10.0 , nworkers = None , workchunksize = None , glspfunc = _glsp_worker_withtau , verbose = True ) : # get rid of nans first and sigclip stimes , smags , serrs = sigclip_magseries ( times , mags , errs , magsarefluxes = magsarefluxes , sigclip = sigclip ) # get rid of zero errs nzind = npnonzero ( serrs ) stimes , smags , serrs = stimes [ nzind ] , smags [ nzind ] , serrs [ nzind ] # make sure there are enough points to calculate a spectrum if len ( stimes ) > 9 and len ( smags ) > 9 and len ( serrs ) > 9 : # get the frequencies to use if startp : endf = 1.0 / startp else : # default start period is 0.1 day endf = 1.0 / 0.1 if endp : startf = 1.0 / endp else : # default end period is length of time series startf = 1.0 / ( stimes . max ( ) - stimes . min ( ) ) # if we're not using autofreq, then use the provided frequencies if not autofreq : omegas = 2 * pi_value * nparange ( startf , endf , stepsize ) if verbose : LOGINFO ( 'using %s frequency points, start P = %.3f, end P = %.3f' % ( omegas . size , 1.0 / endf , 1.0 / startf ) ) else : # this gets an automatic grid of frequencies to use freqs = get_frequency_grid ( stimes , minfreq = startf , maxfreq = endf ) omegas = 2 * pi_value * freqs if verbose : LOGINFO ( 'using autofreq with %s frequency points, ' 'start P = %.3f, end P = %.3f' % ( omegas . size , 1.0 / freqs . max ( ) , 1.0 / freqs . min ( ) ) ) # map to parallel workers if ( not nworkers ) or ( nworkers > NCPUS ) : nworkers = NCPUS if verbose : LOGINFO ( 'using %s workers...' % nworkers ) pool = Pool ( nworkers ) tasks = [ ( stimes , smags , serrs , x ) for x in omegas ] if workchunksize : lsp = pool . map ( glspfunc , tasks , chunksize = workchunksize ) else : lsp = pool . map ( glspfunc , tasks ) pool . close ( ) pool . join ( ) del pool lsp = nparray ( lsp ) periods = 2.0 * pi_value / omegas # find the nbestpeaks for the periodogram: 1. sort the lsp array by # highest value first 2. go down the values until we find five # values that are separated by at least periodepsilon in period # make sure to filter out non-finite values of lsp finitepeakind = npisfinite ( lsp ) finlsp = lsp [ finitepeakind ] finperiods = periods [ finitepeakind ] # make sure that finlsp has finite values before we work on it try : bestperiodind = npargmax ( finlsp ) except ValueError : LOGERROR ( 'no finite periodogram values ' 'for this mag series, skipping...' ) return { 'bestperiod' : npnan , 'bestlspval' : npnan , 'nbestpeaks' : nbestpeaks , 'nbestlspvals' : None , 'nbestperiods' : None , 'lspvals' : None , 'omegas' : omegas , 'periods' : None , 'method' : 'gls' , 'kwargs' : { 'startp' : startp , 'endp' : endp , 'stepsize' : stepsize , 'autofreq' : autofreq , 'periodepsilon' : periodepsilon , 'nbestpeaks' : nbestpeaks , 'sigclip' : sigclip } } sortedlspind = npargsort ( finlsp ) [ : : - 1 ] sortedlspperiods = finperiods [ sortedlspind ] sortedlspvals = finlsp [ sortedlspind ] # now get the nbestpeaks nbestperiods , nbestlspvals , peakcount = ( [ finperiods [ bestperiodind ] ] , [ finlsp [ bestperiodind ] ] , 1 ) prevperiod = sortedlspperiods [ 0 ] # find the best nbestpeaks in the lsp and their periods for period , lspval in zip ( sortedlspperiods , sortedlspvals ) : if peakcount == nbestpeaks : break perioddiff = abs ( period - prevperiod ) bestperiodsdiff = [ abs ( period - x ) for x in nbestperiods ] # print('prevperiod = %s, thisperiod = %s, ' #       'perioddiff = %s, peakcount = %s' % #       (prevperiod, period, perioddiff, peakcount)) # this ensures that this period is different from the last # period and from all the other existing best periods by # periodepsilon to make sure we jump to an entire different peak # in the periodogram if ( perioddiff > ( periodepsilon * prevperiod ) and all ( x > ( periodepsilon * period ) for x in bestperiodsdiff ) ) : nbestperiods . append ( period ) nbestlspvals . append ( lspval ) peakcount = peakcount + 1 prevperiod = period return { 'bestperiod' : finperiods [ bestperiodind ] , 'bestlspval' : finlsp [ bestperiodind ] , 'nbestpeaks' : nbestpeaks , 'nbestlspvals' : nbestlspvals , 'nbestperiods' : nbestperiods , 'lspvals' : lsp , 'omegas' : omegas , 'periods' : periods , 'method' : 'gls' , 'kwargs' : { 'startp' : startp , 'endp' : endp , 'stepsize' : stepsize , 'autofreq' : autofreq , 'periodepsilon' : periodepsilon , 'nbestpeaks' : nbestpeaks , 'sigclip' : sigclip } } else : LOGERROR ( 'no good detections for these times and mags, skipping...' ) return { 'bestperiod' : npnan , 'bestlspval' : npnan , 'nbestpeaks' : nbestpeaks , 'nbestlspvals' : None , 'nbestperiods' : None , 'lspvals' : None , 'omegas' : None , 'periods' : None , 'method' : 'gls' , 'kwargs' : { 'startp' : startp , 'endp' : endp , 'stepsize' : stepsize , 'autofreq' : autofreq , 'periodepsilon' : periodepsilon , 'nbestpeaks' : nbestpeaks , 'sigclip' : sigclip } }
This calculates the generalized Lomb-Scargle periodogram.

    Uses the algorithm from Zechmeister and Kurster (2009).

    Parameters
    ----------

    times,mags,errs : np.array
        The mag/flux time-series with associated measurement errors to run the
        period-finding on.

    magsarefluxes : bool
        If the input measurement values in `mags` and `errs` are in fluxes, set
        this to True.

    startp,endp : float or None
        The minimum and maximum periods to consider for the transit search.

    stepsize : float
        The step-size in frequency to use when constructing a frequency grid for
        the period search.

    autofreq : bool
        If this is True, the value of `stepsize` will be ignored and the
        :py:func:`astrobase.periodbase.get_frequency_grid` function will be used
        to generate a frequency grid based on `startp`, and `endp`. If these are
        None as well, `startp` will be set to 0.1 and `endp` will be set to
        `times.max() - times.min()`.

    nbestpeaks : int
        The number of 'best' peaks to return from the periodogram results,
        starting from the global maximum of the periodogram peak values.

    periodepsilon : float
        The fractional difference between successive values of 'best' periods
        when sorting by periodogram power to consider them as separate periods
        (as opposed to part of the same periodogram peak). This is used to avoid
        broad peaks in the periodogram and make sure the 'best' periods returned
        are all actually independent.

    sigclip : float or int or sequence of two floats/ints or None
        If a single float or int, a symmetric sigma-clip will be performed using
        the number provided as the sigma-multiplier to cut out from the input
        time-series.

        If a list of two ints/floats is provided, the function will perform an
        'asymmetric' sigma-clip. The first element in this list is the sigma
        value to use for fainter flux/mag values; the second element in this
        list is the sigma value to use for brighter flux/mag values. For
        example, `sigclip=[10., 3.]`, will sigclip out greater than 10-sigma
        dimmings and greater than 3-sigma brightenings. Here the meaning of
        "dimming" and "brightening" is set by *physics* (not the magnitude
        system), which is why the `magsarefluxes` kwarg must be correctly set.

        If `sigclip` is None, no sigma-clipping will be performed, and the
        time-series (with non-finite elems removed) will be passed through to
        the output.

    nworkers : int
        The number of parallel workers to use when calculating the periodogram.

    workchunksize : None or int
        If this is an int, will use chunks of the given size to break up the
        work for the parallel workers. If None, the chunk size is set to 1.

    glspfunc : Python function
        The worker function to use to calculate the periodogram. This can be
        used to make this function calculate the time-series sampling window
        function instead of the time-series measurements' GLS periodogram by
        passing in `_glsp_worker_specwindow` instead of the default
        `_glsp_worker_withtau` function.

    verbose : bool
        If this is True, will indicate progress and details about the frequency
        grid used for the period search.

    Returns
    -------

    dict
        This function returns a dict, referred to as an `lspinfo` dict in other
        astrobase functions that operate on periodogram results. This is a
        standardized format across all astrobase period-finders, and is of the
        form below::

            {'bestperiod': the best period value in the periodogram,
             'bestlspval': the periodogram peak associated with the best period,
             'nbestpeaks': the input value of nbestpeaks,
             'nbestlspvals': nbestpeaks-size list of best period peak values,
             'nbestperiods': nbestpeaks-size list of best periods,
             'lspvals': the full array of periodogram powers,
             'periods': the full array of periods considered,
             'method':'gls' -> the name of the period-finder method,
             'kwargs':{ dict of all of the input kwargs for record-keeping}}
This calculates the generalized Lomb - Scargle periodogram .