Instruction:
Can you generate a brief explanation for the following Python 3 code
def scipylsp_parallel(times,
                      mags,
                      errs, # ignored but for consistent API
                      startp,
                      endp,
                      nbestpeaks=5,
                      periodepsilon=0.1, # 0.1
                      stepsize=1.0e-4,
                      nworkers=4,
                      sigclip=None,
                      timebin=None):
    '''
    This uses the LSP function from the scipy library, which is fast as hell. We
    try to make it faster by running LSP for sections of the omegas array in
    parallel.

    '''

    # make sure there are no nans anywhere
    finiteind = np.isfinite(mags) & np.isfinite(errs)
    ftimes, fmags, ferrs = times[finiteind], mags[finiteind], errs[finiteind]

    if len(ftimes) > 0 and len(fmags) > 0:

        # sigclip the lightcurve if asked to do so
        if sigclip:
            worktimes, workmags, _ = sigclip_magseries(ftimes,
                                                       fmags,
                                                       ferrs,
                                                       sigclip=sigclip)
            LOGINFO('ndet after sigclipping = %s' % len(worktimes))

        else:
            worktimes = ftimes
            workmags = fmags

        # bin the lightcurve if asked to do so
        if timebin:

            binned = time_bin_magseries(worktimes, workmags, binsize=timebin)
            worktimes = binned['binnedtimes']
            workmags = binned['binnedmags']

        # renormalize the working mags to zero and scale them so that the
        # variance = 1 for use with our LSP functions
        normmags = (workmags - np.median(workmags))/np.std(workmags)

        startf = 1.0/endp
        endf = 1.0/startp
        omegas = 2*np.pi*np.arange(startf, endf, stepsize)

        # partition the omegas array by nworkers
        tasks = []
        chunksize = int(float(len(omegas))/nworkers) + 1
        tasks = [omegas[x*chunksize:x*chunksize+chunksize]
                 for x in range(nworkers)]

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

        pool = Pool(nworkers)

        tasks = [(worktimes, normmags, x) for x in tasks]
        lsp = pool.map(parallel_scipylsp_worker, tasks)

        pool.close()
        pool.join()

        lsp = np.concatenate(lsp)
        periods = 2.0*np.pi/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 we only get finite lsp values
        finitepeakind = npisfinite(lsp)
        finlsp = lsp[finitepeakind]
        finperiods = periods[finitepeakind]

        bestperiodind = npargmax(finlsp)

        sortedlspind = np.argsort(finlsp)[::-1]
        sortedlspperiods = finperiods[sortedlspind]
        sortedlspvals = finlsp[sortedlspind]

        prevbestlspval = sortedlspvals[0]
        # 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 and
                all(x > periodepsilon 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':'sls'}

    else:

        LOGERROR('no good detections for these times and mags, skipping...')
        return {'bestperiod':npnan,
                'bestlspval':npnan,
                'nbestpeaks':nbestpeaks,
                'nbestlspvals':None,
                'nbestperiods':None,
                'lspvals':None,
                'periods':None,
                'method':'sls'}

Response: This function uses scipy lsp to run a set of functions in parallel.


Instruction:
Can you generate the documentation for the following Python 3 function
def _periodicfeatures_worker(task):
    '''
    This is a parallel worker for the drivers below.

    '''

    pfpickle, lcbasedir, outdir, starfeatures, kwargs = task

    try:

        return get_periodicfeatures(pfpickle,
                                    lcbasedir,
                                    outdir,
                                    starfeatures=starfeatures,
                                    **kwargs)

    except Exception as e:

        LOGEXCEPTION('failed to get periodicfeatures for %s' % pfpickle)

Response: This is a parallel worker for the drivers below. It is a parallel worker for the drivers below.


Instruction:
Can you tell what is the following Python 3 function doing
def parallel_periodicfeatures_lcdir(
        pfpkl_dir,
        lcbasedir,
        outdir,
        pfpkl_glob='periodfinding-*.pkl*',
        starfeaturesdir=None,
        fourierorder=5,
        # these are depth, duration, ingress duration
        transitparams=(-0.01,0.1,0.1),
        # these are depth, duration, depth ratio, secphase
        ebparams=(-0.2,0.3,0.7,0.5),
        pdiff_threshold=1.0e-4,
        sidereal_threshold=1.0e-4,
        sampling_peak_multiplier=5.0,
        sampling_startp=None,
        sampling_endp=None,
        timecols=None,
        magcols=None,
        errcols=None,
        lcformat='hat-sql',
        lcformatdir=None,
        sigclip=10.0,
        verbose=False,
        maxobjects=None,
        nworkers=NCPUS,
        recursive=True,
):
    '''This runs parallel periodicfeature extraction for a directory of
    periodfinding result pickles.

    Parameters
    ----------

    pfpkl_dir : str
        The directory containing the pickles to process.

    lcbasedir : str
        The directory where all of the associated light curve files are located.

    outdir : str
        The directory where all the output will be written.

    pfpkl_glob : str
        The UNIX file glob to use to search for period-finder result pickles in
        `pfpkl_dir`.

    starfeaturesdir : str or None
        The directory containing the `starfeatures-<objectid>.pkl` files for
        each object to use calculate neighbor proximity light curve features.

    fourierorder : int
        The Fourier order to use to generate sinusoidal function and fit that to
        the phased light curve.

    transitparams : list of floats
        The transit depth, duration, and ingress duration to use to generate a
        trapezoid planet transit model fit to the phased light curve. The period
        used is the one provided in `period`, while the epoch is automatically
        obtained from a spline fit to the phased light curve.

    ebparams : list of floats
        The primary eclipse depth, eclipse duration, the primary-secondary depth
        ratio, and the phase of the secondary eclipse to use to generate an
        eclipsing binary model fit to the phased light curve. The period used is
        the one provided in `period`, while the epoch is automatically obtained
        from a spline fit to the phased light curve.

    pdiff_threshold : float
        This is the max difference between periods to consider them the same.

    sidereal_threshold : float
        This is the max difference between any of the 'best' periods and the
        sidereal day periods to consider them the same.

    sampling_peak_multiplier : float
        This is the minimum multiplicative factor of a 'best' period's
        normalized periodogram peak over the sampling periodogram peak at the
        same period required to accept the 'best' period as possibly real.

    sampling_startp, sampling_endp : float
        If the `pgramlist` doesn't have a time-sampling Lomb-Scargle
        periodogram, it will be obtained automatically. Use these kwargs to
        control the minimum and maximum period interval to be searched when
        generating this periodogram.

    timecols : list of str or None
        The timecol keys to use from the lcdict in calculating the features.

    magcols : list of str or None
        The magcol keys to use from the lcdict in calculating the features.

    errcols : list of str or None
        The errcol keys to use from the lcdict in calculating the features.

    lcformat : str
        This is the `formatkey` associated with your light curve format, which
        you previously passed in to the `lcproc.register_lcformat`
        function. This will be used to look up how to find and read the light
        curves specified in `basedir` or `use_list_of_filenames`.

    lcformatdir : str or None
        If this is provided, gives the path to a directory when you've stored
        your lcformat description JSONs, other than the usual directories lcproc
        knows to search for them in. Use this along with `lcformat` to specify
        an LC format JSON file that's not currently registered with lcproc.

    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.

    verbose : bool
        If True, will indicate progress while working.

    maxobjects : int
        The total number of objects to process from `pfpkl_list`.

    nworkers : int
        The number of parallel workers to launch to process the input.

    Returns
    -------

    dict
        A dict containing key: val pairs of the input period-finder result and
        the output periodic feature result pickles for each input pickle is
        returned.

    '''

    try:
        formatinfo = get_lcformat(lcformat,
                                  use_lcformat_dir=lcformatdir)
        if formatinfo:
            (dfileglob, readerfunc,
             dtimecols, dmagcols, derrcols,
             magsarefluxes, normfunc) = formatinfo
        else:
            LOGERROR("can't figure out the light curve format")
            return None
    except Exception as e:
        LOGEXCEPTION("can't figure out the light curve format")
        return None

    fileglob = pfpkl_glob

    # now find the files
    LOGINFO('searching for periodfinding pickles in %s ...' % pfpkl_dir)

    if recursive is False:
        matching = glob.glob(os.path.join(pfpkl_dir, fileglob))

    else:
        # use recursive glob for Python 3.5+
        if sys.version_info[:2] > (3,4):

            matching = glob.glob(os.path.join(pfpkl_dir,
                                              '**',
                                              fileglob),recursive=True)

        # otherwise, use os.walk and glob
        else:

            # use os.walk to go through the directories
            walker = os.walk(pfpkl_dir)
            matching = []

            for root, dirs, _files in walker:
                for sdir in dirs:
                    searchpath = os.path.join(root,
                                              sdir,
                                              fileglob)
                    foundfiles = glob.glob(searchpath)

                    if foundfiles:
                        matching.extend(foundfiles)

    # now that we have all the files, process them
    if matching and len(matching) > 0:

        LOGINFO('found %s periodfinding pickles, getting periodicfeatures...' %
                len(matching))

        return parallel_periodicfeatures(
            matching,
            lcbasedir,
            outdir,
            starfeaturesdir=starfeaturesdir,
            fourierorder=fourierorder,
            transitparams=transitparams,
            ebparams=ebparams,
            pdiff_threshold=pdiff_threshold,
            sidereal_threshold=sidereal_threshold,
            sampling_peak_multiplier=sampling_peak_multiplier,
            sampling_startp=sampling_startp,
            sampling_endp=sampling_endp,
            timecols=timecols,
            magcols=magcols,
            errcols=errcols,
            lcformat=lcformat,
            lcformatdir=lcformatdir,
            sigclip=sigclip,
            verbose=verbose,
            maxobjects=maxobjects,
            nworkers=nworkers,
        )

    else:

        LOGERROR('no periodfinding pickles found in %s' % (pfpkl_dir))
        return None

Response: This function runs the parallel periodicfeature extraction for a directory of period - finder result pickles.


Instruction:
Explain what the following Python 3 code does
def angle_wrap(angle, radians=False):
    '''Wraps the input angle to 360.0 degrees.

    Parameters
    ----------

    angle : float
        The angle to wrap around 360.0 deg.

    radians : bool
        If True, will assume that the input is in radians. The output will then
        also be in radians.

    Returns
    -------

    float
        Wrapped angle. If radians is True: input is assumed to be in radians,
        output is also in radians.

    '''

    if radians:
        wrapped = angle % (2.0*pi_value)
        if wrapped < 0.0:
            wrapped = 2.0*pi_value + wrapped

    else:

        wrapped = angle % 360.0
        if wrapped < 0.0:
            wrapped = 360.0 + wrapped

    return wrapped

Response: Wraps the input angle to 360. 0 degrees.


Instruction:
Create a Python 3 function for
converting from decimal degrees to DD MM SS.

Response:
def decimal_to_dms(decimal_value):
    '''Converts from decimal degrees (for declination coords) to DD:MM:SS.

    Parameters
    ----------

    decimal_value : float
        A decimal value to convert to degrees, minutes, seconds sexagesimal
        format.

    Returns
    -------

    tuple
        A four element tuple is returned: (sign, HH, MM, SS.ssss...)

    '''

    if decimal_value < 0:
        negative = True
        dec_val = fabs(decimal_value)
    else:
        negative = False
        dec_val = decimal_value

    degrees = trunc(dec_val)
    minutes_deg = dec_val - degrees

    minutes_mm = minutes_deg * 60.0
    minutes_out = trunc(minutes_mm)
    seconds = (minutes_mm - minutes_out)*60.0

    if negative:
        degrees = degrees
        return '-', degrees, minutes_out, seconds
    else:
        return '+', degrees, minutes_out, seconds


Instruction:
Create a Python 3 function for
converting from decimal degrees to HHMMSS.

Response:
def decimal_to_hms(decimal_value):
    '''Converts from decimal degrees (for RA coords) to HH:MM:SS.

    Parameters
    ----------

    decimal_value : float
        A decimal value to convert to hours, minutes, seconds. Negative values
        will be wrapped around 360.0.

    Returns
    -------

    tuple
        A three element tuple is returned: (HH, MM, SS.ssss...)

    '''

    # wrap to 360.0
    if decimal_value < 0:
        dec_wrapped = 360.0 + decimal_value
    else:
        dec_wrapped = decimal_value

    # convert to decimal hours first
    dec_hours = dec_wrapped/15.0

    if dec_hours < 0:
        negative = True
        dec_val = fabs(dec_hours)
    else:
        negative = False
        dec_val = dec_hours

    hours = trunc(dec_val)
    minutes_hrs = dec_val - hours

    minutes_mm = minutes_hrs * 60.0
    minutes_out = trunc(minutes_mm)
    seconds = (minutes_mm - minutes_out)*60.0

    if negative:
        hours = -hours
        return hours, minutes_out, seconds
    else:
        return hours, minutes_out, seconds


Instruction:
Here you have a function in Python 3, explain what it does
def hms_str_to_tuple(hms_string):
    '''Converts a string of the form HH:MM:SS or HH MM SS to a tuple of the form
    (HH, MM, SS).

    Parameters
    ----------

    hms_string : str
        A RA coordinate string of the form 'HH:MM:SS.sss' or 'HH MM SS.sss'.

    Returns
    -------

    tuple
        A three element tuple is returned (HH, MM, SS.ssss...)

    '''

    if ':' in hms_string:
        separator = ':'
    else:
        separator = ' '

    hh, mm, ss = hms_string.split(separator)

    return int(hh), int(mm), float(ss)

Response:
Converts a string of the form HH MM SS to a tuple of the form
    HH MM SS. ssss.


Instruction:
Explain what the following Python 3 code does
def dms_str_to_tuple(dms_string):
    '''Converts a string of the form [+-]DD:MM:SS or [+-]DD MM SS to a tuple of
    the form (sign, DD, MM, SS).

    Parameters
    ----------

    dms_string : str
        A declination coordinate string of the form '[+-]DD:MM:SS.sss' or
        '[+-]DD MM SS.sss'. The sign in front of DD is optional. If it's not
        there, this function will assume that the coordinate string is a
        positive value.

    Returns
    -------

    tuple
        A four element tuple of the form: (sign, DD, MM, SS.ssss...).

    '''
    if ':' in dms_string:
        separator = ':'
    else:
        separator = ' '

    sign_dd, mm, ss = dms_string.split(separator)
    if sign_dd.startswith('+') or sign_dd.startswith('-'):
        sign, dd = sign_dd[0], sign_dd[1:]
    else:
        sign, dd = '+', sign_dd

    return sign, int(dd), int(mm), float(ss)

Response:
Converts a string of the form [ + DD MM SS or - SS. sss or + DD MM SS to a tuple of
    the form ( sign DD MM SS SS. ssss...


Instruction:
Can you tell what is the following Python 3 function doing
def hms_to_decimal(hours, minutes, seconds, returndeg=True):
    '''Converts from HH, MM, SS to a decimal value.

    Parameters
    ----------

    hours : int
        The HH part of a RA coordinate.

    minutes : int
        The MM part of a RA coordinate.

    seconds : float
        The SS.sss part of a RA coordinate.

    returndeg : bool
        If this is True, then will return decimal degrees as the output.
        If this is False, then will return decimal HOURS as the output.
        Decimal hours are sometimes used in FITS headers.

    Returns
    -------

    float
        The right ascension value in either decimal degrees or decimal hours
        depending on `returndeg`.

    '''

    if hours > 24:

        return None

    else:

        dec_hours = fabs(hours) + fabs(minutes)/60.0 + fabs(seconds)/3600.0

        if returndeg:

            dec_deg = dec_hours*15.0

            if dec_deg < 0:
                dec_deg = dec_deg + 360.0
            dec_deg = dec_deg % 360.0
            return dec_deg
        else:
            return dec_hours

Response: Converts from HH MM SS to decimal value.


Instruction:
Create a Python 3 function for
converting from DD MM : SS to decimal degrees.

Response:
def dms_to_decimal(sign, degrees, minutes, seconds):
    '''Converts from DD:MM:SS to a decimal value.

    Parameters
    ----------

    sign : {'+', '-', ''}
        The sign part of a Dec coordinate.

    degrees : int
        The DD part of a Dec coordinate.

    minutes : int
        The MM part of a Dec coordinate.

    seconds : float
        The SS.sss part of a Dec coordinate.

    Returns
    -------

    float
        The declination value in decimal degrees.

    '''

    dec_deg = fabs(degrees) + fabs(minutes)/60.0 + fabs(seconds)/3600.0

    if sign == '-':
        return -dec_deg
    else:
        return dec_deg


Instruction:
How would you explain what the following Python 3 function does
def great_circle_dist(ra1, dec1, ra2, dec2):
    '''Calculates the great circle angular distance between two coords.

    This calculates the great circle angular distance in arcseconds between two
    coordinates (ra1,dec1) and (ra2,dec2). This is basically a clone of GCIRC
    from the IDL Astrolib.

    Parameters
    ----------

    ra1,dec1 : float or array-like
        The first coordinate's right ascension and declination value(s) in
        decimal degrees.

    ra2,dec2 : float or array-like
        The second coordinate's right ascension and declination value(s) in
        decimal degrees.

    Returns
    -------

    float or array-like
        Great circle distance between the two coordinates in arseconds.

    Notes
    -----

    If (`ra1`, `dec1`) is scalar and (`ra2`, `dec2`) is scalar: the result is a
    float distance in arcseconds.

    If (`ra1`, `dec1`) is scalar and (`ra2`, `dec2`) is array-like: the result
    is an np.array with distance in arcseconds between (`ra1`, `dec1`) and each
    element of (`ra2`, `dec2`).

    If (`ra1`, `dec1`) is array-like and (`ra2`, `dec2`) is scalar: the result
    is an np.array with distance in arcseconds between (`ra2`, `dec2`) and each
    element of (`ra1`, `dec1`).

    If (`ra1`, `dec1`) and (`ra2`, `dec2`) are both array-like: the result is an
    np.array with the pair-wise distance in arcseconds between each element of
    the two coordinate lists. In this case, if the input array-likes are not the
    same length, then excess elements of the longer one will be ignored.

    '''

    # wrap RA if negative or larger than 360.0 deg
    in_ra1 = ra1 % 360.0
    in_ra1 = in_ra1 + 360.0*(in_ra1 < 0.0)
    in_ra2 = ra2 % 360.0
    in_ra2 = in_ra2 + 360.0*(in_ra1 < 0.0)

    # convert to radians
    ra1_rad, dec1_rad = np.deg2rad(in_ra1), np.deg2rad(dec1)
    ra2_rad, dec2_rad = np.deg2rad(in_ra2), np.deg2rad(dec2)

    del_dec2 = (dec2_rad - dec1_rad)/2.0
    del_ra2 = (ra2_rad - ra1_rad)/2.0
    sin_dist = np.sqrt(np.sin(del_dec2) * np.sin(del_dec2) +
                       np.cos(dec1_rad) * np.cos(dec2_rad) *
                       np.sin(del_ra2) * np.sin(del_ra2))

    dist_rad = 2.0 * np.arcsin(sin_dist)

    # return the distance in arcseconds
    return np.rad2deg(dist_rad)*3600.0

Response: Calculates the great circle angular distance between two coordinates.


Instruction:
Implement a function in Python 3 to
find the closest object in (`ra2`, `dec2`) to scalar coordinate pair (`ra1`, `dec1`) and returns the distance in arcseconds. This is a quick matcher that uses the `great_circle_dist` function to find the closest object in (`ra2`, `dec2`) within `match_radius` arcseconds to (`ra1`, `dec1`). (`ra1`, `dec1`) must be a scalar pair, while (`ra2`, `dec2`) must be array-likes of the same lengths. Parameters ---------- ra1,dec1 : float Coordinate of the object to find matches to. In decimal degrees. ra2,dec2 : array-like The coordinates that will be searched for matches. In decimal degrees. match_radius : float The match radius in arcseconds to use for the match. Returns ------- tuple A two element tuple like the following:: (True -> no match found or False -> found a match, minimum distance between target and list in arcseconds)

Response:
def xmatch_basic(ra1, dec1, ra2, dec2, match_radius=5.0):
    '''Finds the closest object in (`ra2`, `dec2`) to scalar coordinate pair
    (`ra1`, `dec1`) and returns the distance in arcseconds.

    This is a quick matcher that uses the `great_circle_dist` function to find
    the closest object in (`ra2`, `dec2`) within `match_radius` arcseconds to
    (`ra1`, `dec1`). (`ra1`, `dec1`) must be a scalar pair, while
    (`ra2`, `dec2`) must be array-likes of the same lengths.

    Parameters
    ----------

    ra1,dec1 : float
        Coordinate of the object to find matches to. In decimal degrees.

    ra2,dec2 : array-like
        The coordinates that will be searched for matches. In decimal degrees.

    match_radius : float
        The match radius in arcseconds to use for the match.

    Returns
    -------

    tuple
        A two element tuple like the following::

            (True -> no match found or False -> found a match,
             minimum distance between target and list in arcseconds)

    '''

    min_dist_arcsec = np.min(great_circle_dist(ra1,dec1,ra2,dec2))

    if (min_dist_arcsec < match_radius):
        return (True,min_dist_arcsec)
    else:
        return (False,min_dist_arcsec)


Instruction:
Can you tell what is the following Python 3 function doing
def xmatch_neighbors(ra1, dec1,
                     ra2, dec2,
                     match_radius=60.0,
                     includeself=False,
                     sortresults=True):
    '''Finds the closest objects in (`ra2`, `dec2`) to scalar coordinate pair
    (`ra1`, `dec1`) and returns the indices of the objects that match.

    This is a quick matcher that uses the `great_circle_dist` function to find
    the closest object in (`ra2`, `dec2`) within `match_radius` arcseconds to
    (`ra1`, `dec1`). (`ra1`, `dec1`) must be a scalar pair, while
    (`ra2`, `dec2`) must be array-likes of the same lengths.

    Parameters
    ----------

    ra1,dec1 : float
        Coordinate of the object to find matches to. In decimal degrees.

    ra2,dec2 : array-like
        The coordinates that will be searched for matches. In decimal degrees.

    match_radius : float
        The match radius in arcseconds to use for the match.

    includeself : bool
        If this is True, the object itself will be included in the match
        results.

    sortresults : bool
        If this is True, the match indices will be sorted by distance.

    Returns
    -------

    tuple
        A tuple like the following is returned::

            (True -> matches found or False -> no matches found,
             minimum distance between target and list,
             np.array of indices where list of coordinates is
             closer than `match_radius` arcseconds from the target,
             np.array of distances in arcseconds)

    '''

    dist = great_circle_dist(ra1,dec1,ra2,dec2)

    if includeself:
        match_dist_ind = np.where(dist < match_radius)

    else:
        # make sure we match only objects that are not the same as this object
        match_dist_ind = np.where((dist < match_radius) & (dist > 0.1))

    if len(match_dist_ind) > 0:
        match_dists = dist[match_dist_ind]
        dist_sort_ind = np.argsort(match_dists)

        if sortresults:
            match_dist_ind = (match_dist_ind[0])[dist_sort_ind]

        min_dist = np.min(match_dists)

        return (True,min_dist,match_dist_ind,match_dists[dist_sort_ind])

    else:
        return (False,)

Response: Finds the closest objects in ra1 to dec1 and returns the indices of the objects that are closer than match_radius arcseconds from the target.


Instruction:
Given the following Python 3 function, write the documentation
def equatorial_to_galactic(ra, decl, equinox='J2000'):
    '''This converts from equatorial coords to galactic coords.

    Parameters
    ----------

    ra : float or array-like
        Right ascension values(s) in decimal degrees.

    decl : float or array-like
        Declination value(s) in decimal degrees.

    equinox : str
        The equinox that the coordinates are measured at. This must be
        recognizable by Astropy's `SkyCoord` class.

    Returns
    -------

    tuple of (float, float) or tuple of (np.array, np.array)
        The galactic coordinates (l, b) for each element of the input
        (`ra`, `decl`).

    '''

    # convert the ra/decl to gl, gb
    radecl = SkyCoord(ra=ra*u.degree, dec=decl*u.degree, equinox=equinox)

    gl = radecl.galactic.l.degree
    gb = radecl.galactic.b.degree

    return gl, gb

Response: This converts from equatorial coordinates to galactic coordinates.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def generate_transit_lightcurve(
        times,
        mags=None,
        errs=None,
        paramdists={'transitperiod':sps.uniform(loc=0.1,scale=49.9),
                    'transitdepth':sps.uniform(loc=1.0e-4,scale=2.0e-2),
                    'transitduration':sps.uniform(loc=0.01,scale=0.29)},
        magsarefluxes=False,
):
    '''This generates fake planet transit light curves.

    Parameters
    ----------

    times : np.array
        This is an array of time values that will be used as the time base.

    mags,errs : np.array
        These arrays will have the model added to them. If either is
        None, `np.full_like(times, 0.0)` will used as a substitute and the model
        light curve will be centered around 0.0.

    paramdists : dict
        This is a dict containing parameter distributions to use for the
        model params, containing the following keys ::

            {'transitperiod', 'transitdepth', 'transitduration'}

        The values of these keys should all be 'frozen' scipy.stats distribution
        objects, e.g.:

        https://docs.scipy.org/doc/scipy/reference/stats.html#continuous-distributions
        The variability epoch will be automatically chosen from a uniform
        distribution between `times.min()` and `times.max()`.

        The ingress duration will be automatically chosen from a uniform
        distribution ranging from 0.05 to 0.5 of the transitduration.

        The transitdepth will be flipped automatically as appropriate if
        `magsarefluxes=True`.

    magsarefluxes : bool
        If the generated time series is meant to be a flux time-series, set this
        to True to get the correct sign of variability amplitude.

    Returns
    -------

    dict
        A dict of the form below is returned::

            {'vartype': 'planet',
             'params': {'transitperiod': generated value of period,
                        'transitepoch': generated value of epoch,
                        'transitdepth': generated value of transit depth,
                        'transitduration': generated value of transit duration,
                        'ingressduration': generated value of transit ingress
                                           duration},
             'times': the model times,
             'mags': the model mags,
             'errs': the model errs,
             'varperiod': the generated period of variability == 'transitperiod'
             'varamplitude': the generated amplitude of
                             variability == 'transitdepth'}

    '''

    if mags is None:
        mags = np.full_like(times, 0.0)

    if errs is None:
        errs = np.full_like(times, 0.0)

    # choose the epoch
    epoch = npr.random()*(times.max() - times.min()) + times.min()

    # choose the period, depth, duration
    period = paramdists['transitperiod'].rvs(size=1)
    depth = paramdists['transitdepth'].rvs(size=1)
    duration = paramdists['transitduration'].rvs(size=1)

    # figure out the ingress duration
    ingduration = npr.random()*(0.5*duration - 0.05*duration) + 0.05*duration

    # fix the transit depth if it needs to be flipped
    if magsarefluxes and depth < 0.0:
        depth = -depth
    elif not magsarefluxes and depth > 0.0:
        depth = -depth

    # generate the model
    modelmags, phase, ptimes, pmags, perrs = (
        transits.trapezoid_transit_func([period, epoch, depth,
                                         duration, ingduration],
                                        times,
                                        mags,
                                        errs)
    )

    # resort in original time order
    timeind = np.argsort(ptimes)
    mtimes = ptimes[timeind]
    mmags = modelmags[timeind]
    merrs = perrs[timeind]

    # return a dict with everything
    modeldict = {
        'vartype':'planet',
        'params':{x:np.asscalar(y) for x,y in zip(['transitperiod',
                                                   'transitepoch',
                                                   'transitdepth',
                                                   'transitduration',
                                                   'ingressduration'],
                                                  [period,
                                                   epoch,
                                                   depth,
                                                   duration,
                                                   ingduration])},
        'times':mtimes,
        'mags':mmags,
        'errs':merrs,
        # these are standard keys that help with later characterization of
        # variability as a function period, variability amplitude, object mag,
        # ndet, etc.
        'varperiod':period,
        'varamplitude':depth
    }

    return modeldict

Response: This function generates a fake planet transit light curve for the given time - base and time - series.


Instruction:
Explain what the following Python 3 code does
def generate_eb_lightcurve(
        times,
        mags=None,
        errs=None,
        paramdists={'period':sps.uniform(loc=0.2,scale=99.8),
                    'pdepth':sps.uniform(loc=1.0e-4,scale=0.7),
                    'pduration':sps.uniform(loc=0.01,scale=0.44),
                    'depthratio':sps.uniform(loc=0.01,scale=0.99),
                    'secphase':sps.norm(loc=0.5,scale=0.1)},
        magsarefluxes=False,
):
    '''This generates fake EB light curves.

    Parameters
    ----------

    times : np.array
        This is an array of time values that will be used as the time base.

    mags,errs : np.array
        These arrays will have the model added to them. If either is
        None, `np.full_like(times, 0.0)` will used as a substitute and the model
        light curve will be centered around 0.0.

    paramdists : dict
        This is a dict containing parameter distributions to use for the
        model params, containing the following keys ::

            {'period', 'pdepth', 'pduration', 'depthratio', 'secphase'}

        The values of these keys should all be 'frozen' scipy.stats distribution
        objects, e.g.:

        https://docs.scipy.org/doc/scipy/reference/stats.html#continuous-distributions
        The variability epoch will be automatically chosen from a uniform
        distribution between `times.min()` and `times.max()`.

        The `pdepth` will be flipped automatically as appropriate if
        `magsarefluxes=True`.

    magsarefluxes : bool
        If the generated time series is meant to be a flux time-series, set this
        to True to get the correct sign of variability amplitude.

    Returns
    -------

    dict
        A dict of the form below is returned::

            {'vartype': 'EB',
             'params': {'period': generated value of period,
                        'epoch': generated value of epoch,
                        'pdepth': generated value of priary eclipse depth,
                        'pduration': generated value of prim eclipse duration,
                        'depthratio': generated value of prim/sec eclipse
                                      depth ratio},
             'times': the model times,
             'mags': the model mags,
             'errs': the model errs,
             'varperiod': the generated period of variability == 'period'
             'varamplitude': the generated amplitude of
                             variability == 'pdepth'}

    '''

    if mags is None:
        mags = np.full_like(times, 0.0)

    if errs is None:
        errs = np.full_like(times, 0.0)

    # choose the epoch
    epoch = npr.random()*(times.max() - times.min()) + times.min()

    # choose the period, pdepth, duration, depthratio
    period = paramdists['period'].rvs(size=1)
    pdepth = paramdists['pdepth'].rvs(size=1)
    pduration = paramdists['pduration'].rvs(size=1)
    depthratio = paramdists['depthratio'].rvs(size=1)
    secphase = paramdists['secphase'].rvs(size=1)

    # fix the transit depth if it needs to be flipped
    if magsarefluxes and pdepth < 0.0:
        pdepth = -pdepth
    elif not magsarefluxes and pdepth > 0.0:
        pdepth = -pdepth

    # generate the model
    modelmags, phase, ptimes, pmags, perrs = (
        eclipses.invgauss_eclipses_func([period, epoch, pdepth,
                                         pduration, depthratio, secphase],
                                        times,
                                        mags,
                                        errs)
    )

    # resort in original time order
    timeind = np.argsort(ptimes)
    mtimes = ptimes[timeind]
    mmags = modelmags[timeind]
    merrs = perrs[timeind]

    # return a dict with everything
    modeldict = {
        'vartype':'EB',
        'params':{x:np.asscalar(y) for x,y in zip(['period',
                                                   'epoch',
                                                   'pdepth',
                                                   'pduration',
                                                   'depthratio'],
                                                  [period,
                                                   epoch,
                                                   pdepth,
                                                   pduration,
                                                   depthratio])},
        'times':mtimes,
        'mags':mmags,
        'errs':merrs,
        'varperiod':period,
        'varamplitude':pdepth,
    }

    return modeldict

Response: This function generates fake EB light curves for a set of time - base objects.


Instruction:
How would you explain what the following Python 3 function does
def generate_flare_lightcurve(
        times,
        mags=None,
        errs=None,
        paramdists={
            # flare peak amplitude from 0.01 mag to 1.0 mag above median.  this
            # is tuned for redder bands, flares are much stronger in bluer
            # bands, so tune appropriately for your situation.
            'amplitude':sps.uniform(loc=0.01,scale=0.99),
            # up to 5 flares per LC and at least 1
            'nflares':[1,5],
            # 10 minutes to 1 hour for rise stdev
            'risestdev':sps.uniform(loc=0.007, scale=0.04),
            # 1 hour to 4 hours for decay time constant
            'decayconst':sps.uniform(loc=0.04, scale=0.163)
        },
        magsarefluxes=False,
):
    '''This generates fake flare light curves.

    Parameters
    ----------

    times : np.array
        This is an array of time values that will be used as the time base.

    mags,errs : np.array
        These arrays will have the model added to them. If either is
        None, `np.full_like(times, 0.0)` will used as a substitute and the model
        light curve will be centered around 0.0.

    paramdists : dict
        This is a dict containing parameter distributions to use for the
        model params, containing the following keys ::

            {'amplitude', 'nflares', 'risestdev', 'decayconst'}

        The values of these keys should all be 'frozen' scipy.stats distribution
        objects, e.g.:

        https://docs.scipy.org/doc/scipy/reference/stats.html#continuous-distributions
        The `flare_peak_time` for each flare will be generated automatically
        between `times.min()` and `times.max()` using a uniform distribution.

        The `amplitude` will be flipped automatically as appropriate if
        `magsarefluxes=True`.

    magsarefluxes : bool
        If the generated time series is meant to be a flux time-series, set this
        to True to get the correct sign of variability amplitude.

    Returns
    -------

    dict
        A dict of the form below is returned::

            {'vartype': 'flare',
             'params': {'amplitude': generated value of flare amplitudes,
                        'nflares': generated value of number of flares,
                        'risestdev': generated value of stdev of rise time,
                        'decayconst': generated value of decay constant,
                        'peaktime': generated value of flare peak time},
             'times': the model times,
             'mags': the model mags,
             'errs': the model errs,
             'varamplitude': the generated amplitude of
                             variability == 'amplitude'}

    '''

    if mags is None:
        mags = np.full_like(times, 0.0)

    if errs is None:
        errs = np.full_like(times, 0.0)

    nflares = npr.randint(paramdists['nflares'][0],
                          high=paramdists['nflares'][1])

    # generate random flare peak times based on the number of flares
    flarepeaktimes = (
        npr.random(
            size=nflares
        )*(times.max() - times.min()) + times.min()
    )

    # now add the flares to the time-series
    params = {'nflares':nflares}

    for flareind, peaktime in zip(range(nflares), flarepeaktimes):

        # choose the amplitude, rise stdev and decay time constant
        amp = paramdists['amplitude'].rvs(size=1)
        risestdev = paramdists['risestdev'].rvs(size=1)
        decayconst = paramdists['decayconst'].rvs(size=1)

        # fix the transit depth if it needs to be flipped
        if magsarefluxes and amp < 0.0:
            amp = -amp
        elif not magsarefluxes and amp > 0.0:
            amp = -amp

        # add this flare to the light curve
        modelmags, ptimes, pmags, perrs = (
            flares.flare_model(
                [amp, peaktime, risestdev, decayconst],
                times,
                mags,
                errs
            )
        )

        # update the mags
        mags = modelmags

        # add the flare params to the modeldict
        params[flareind] = {'peaktime':peaktime,
                            'amplitude':amp,
                            'risestdev':risestdev,
                            'decayconst':decayconst}

    #
    # done with all flares
    #

    # return a dict with everything
    modeldict = {
        'vartype':'flare',
        'params':params,
        'times':times,
        'mags':mags,
        'errs':errs,
        'varperiod':None,
        # FIXME: this is complicated because we can have multiple flares
        # figure out a good way to handle this upstream
        'varamplitude':[params[x]['amplitude']
                        for x in range(params['nflares'])],
    }

    return modeldict

Response: This function generates a fake flare light curve for the given set of time - base entries.


Instruction:
Here you have a function in Python 3, explain what it does
def generate_rrab_lightcurve(
        times,
        mags=None,
        errs=None,
        paramdists={
            'period':sps.uniform(loc=0.45,scale=0.35),
            'fourierorder':[8,11],
            'amplitude':sps.uniform(loc=0.4,scale=0.5),
            'phioffset':np.pi,
        },
        magsarefluxes=False
):
    '''This generates fake RRab light curves.

    Parameters
    ----------

    times : np.array
        This is an array of time values that will be used as the time base.

    mags,errs : np.array
        These arrays will have the model added to them. If either is
        None, `np.full_like(times, 0.0)` will used as a substitute and the model
        light curve will be centered around 0.0.

    paramdists : dict
        This is a dict containing parameter distributions to use for the
        model params, containing the following keys ::

            {'period', 'fourierorder', 'amplitude'}

        The values of these keys should all be 'frozen' scipy.stats distribution
        objects, e.g.:

        https://docs.scipy.org/doc/scipy/reference/stats.html#continuous-distributions
        The variability epoch will be automatically chosen from a uniform
        distribution between `times.min()` and `times.max()`.

        The `amplitude` will be flipped automatically as appropriate if
        `magsarefluxes=True`.

    magsarefluxes : bool
        If the generated time series is meant to be a flux time-series, set this
        to True to get the correct sign of variability amplitude.

    Returns
    -------

    dict
        A dict of the form below is returned::

            {'vartype': 'RRab',
             'params': {'period': generated value of period,
                        'epoch': generated value of epoch,
                        'amplitude': generated value of amplitude,
                        'fourierorder': generated value of fourier order,
                        'fourieramps': generated values of fourier amplitudes,
                        'fourierphases': generated values of fourier phases},
             'times': the model times,
             'mags': the model mags,
             'errs': the model errs,
             'varperiod': the generated period of variability == 'period'
             'varamplitude': the generated amplitude of
                             variability == 'amplitude'}

    '''

    modeldict = generate_sinusoidal_lightcurve(times,
                                               mags=mags,
                                               errs=errs,
                                               paramdists=paramdists,
                                               magsarefluxes=magsarefluxes)
    modeldict['vartype'] = 'RRab'
    return modeldict

Response: This function generates fake RRab light curves for a set of time - base objects.


Instruction:
Explain what the following Python 3 code does
def make_fakelc(lcfile,
                outdir,
                magrms=None,
                randomizemags=True,
                randomizecoords=False,
                lcformat='hat-sql',
                lcformatdir=None,
                timecols=None,
                magcols=None,
                errcols=None):
    '''This preprocesses an input real LC and sets it up to be a fake LC.

    Parameters
    ----------

    lcfile : str
        This is an input light curve file that will be used to copy over the
        time-base. This will be used to generate the time-base for fake light
        curves to provide a realistic simulation of the observing window
        function.

    outdir : str
        The output directory where the the fake light curve will be written.

    magrms : dict
        This is a dict containing the SDSS r mag-RMS (SDSS rmag-MAD preferably)
        relation based on all light curves that the input lcfile is from. This
        will be used to generate the median mag and noise corresponding to the
        magnitude chosen for this fake LC.

    randomizemags : bool
        If this is True, then a random mag between the first and last magbin in
        magrms will be chosen as the median mag for this light curve. This
        choice will be weighted by the mag bin probability obtained from the
        magrms kwarg. Otherwise, the median mag will be taken from the input
        lcfile's lcdict['objectinfo']['sdssr'] key or a transformed SDSS r mag
        generated from the input lcfile's lcdict['objectinfo']['jmag'],
        ['hmag'], and ['kmag'] keys. The magrms relation for each magcol will be
        used to generate Gaussian noise at the correct level for the magbin this
        light curve's median mag falls into.

    randomizecoords : bool
        If this is True, will randomize the RA, DEC of the output fake object
        and not copy over the RA/DEC from the real input object.

    lcformat : str
        This is the `formatkey` associated with your input real light curve
        format, which you previously passed in to the `lcproc.register_lcformat`
        function. This will be used to look up how to find and read the light
        curve specified in `lcfile`.

    lcformatdir : str or None
        If this is provided, gives the path to a directory when you've stored
        your lcformat description JSONs, other than the usual directories lcproc
        knows to search for them in. Use this along with `lcformat` to specify
        an LC format JSON file that's not currently registered with lcproc.

    timecols : list of str or None
        The timecol keys to use from the input lcdict in generating the fake
        light curve. Fake LCs will be generated for each each
        timecol/magcol/errcol combination in the input light curve.

    magcols : list of str or None
        The magcol keys to use from the input lcdict in generating the fake
        light curve. Fake LCs will be generated for each each
        timecol/magcol/errcol combination in the input light curve.

    errcols : list of str or None
        The errcol keys to use from the input lcdict in generating the fake
        light curve. Fake LCs will be generated for each each
        timecol/magcol/errcol combination in the input light curve.

    Returns
    -------

    tuple
        A tuple of the following form is returned::

            (fakelc_fpath,
             fakelc_lcdict['columns'],
             fakelc_lcdict['objectinfo'],
             fakelc_lcdict['moments'])

    '''

    try:
        formatinfo = get_lcformat(lcformat,
                                  use_lcformat_dir=lcformatdir)
        if formatinfo:
            (fileglob, readerfunc,
             dtimecols, dmagcols, derrcols,
             magsarefluxes, normfunc) = formatinfo
        else:
            LOGERROR("can't figure out the light curve format")
            return None
    except Exception as e:
        LOGEXCEPTION("can't figure out the light curve format")
        return None

    # override the default timecols, magcols, and errcols
    # using the ones provided to the function
    if timecols is None:
        timecols = dtimecols
    if magcols is None:
        magcols = dmagcols
    if errcols is None:
        errcols = derrcols

    # read in the light curve
    lcdict = readerfunc(lcfile)
    if isinstance(lcdict, tuple) and isinstance(lcdict[0],dict):
        lcdict = lcdict[0]

    # set up the fakelcdict with a randomly assigned objectid
    fakeobjectid = sha512(npr.bytes(12)).hexdigest()[-8:]
    fakelcdict = {
        'objectid':fakeobjectid,
        'objectinfo':{'objectid':fakeobjectid},
        'columns':[],
        'moments':{},
        'origformat':lcformat,
    }

    # now, get the actual mag of this object and other info and use that to
    # populate the corresponding entries of the fakelcdict objectinfo
    if ('objectinfo' in lcdict and
        isinstance(lcdict['objectinfo'], dict)):

        objectinfo = lcdict['objectinfo']

        # get the RA
        if (not randomizecoords and 'ra' in objectinfo and
            objectinfo['ra'] is not None and
            np.isfinite(objectinfo['ra'])):

            fakelcdict['objectinfo']['ra'] = objectinfo['ra']

        else:

            # if there's no RA available, we'll assign a random one between 0
            # and 360.0
            LOGWARNING('%s: assigning a random right ascension' % lcfile)
            fakelcdict['objectinfo']['ra'] = npr.random()*360.0

        # get the DEC
        if (not randomizecoords and 'decl' in objectinfo and
            objectinfo['decl'] is not None and
            np.isfinite(objectinfo['decl'])):

            fakelcdict['objectinfo']['decl'] = objectinfo['decl']

        else:

            # if there's no DECL available, we'll assign a random one between
            # -90.0 and +90.0
            LOGWARNING(' %s: assigning a random declination' % lcfile)
            fakelcdict['objectinfo']['decl'] = npr.random()*180.0 - 90.0

        # get the SDSS r mag for this object
        # this will be used for getting the eventual mag-RMS relation later
        if ((not randomizemags) and 'sdssr' in objectinfo and
            objectinfo['sdssr'] is not None and
            np.isfinite(objectinfo['sdssr'])):

            fakelcdict['objectinfo']['sdssr'] = objectinfo['sdssr']

        # if the SDSS r is unavailable, but we have J, H, K: use those to get
        # the SDSS r by using transformations
        elif ((not randomizemags) and ('jmag' in objectinfo and
                                       objectinfo['jmag'] is not None and
                                       np.isfinite(objectinfo['jmag'])) and
              ('hmag' in objectinfo and
               objectinfo['hmag'] is not None and
               np.isfinite(objectinfo['hmag'])) and
              ('kmag' in objectinfo and
               objectinfo['kmag'] is not None and
               np.isfinite(objectinfo['kmag']))):

            LOGWARNING('used JHK mags to generate an SDSS r mag for %s' %
                       lcfile)
            fakelcdict['objectinfo']['sdssr'] = jhk_to_sdssr(
                objectinfo['jmag'],
                objectinfo['hmag'],
                objectinfo['kmag']
            )

        # if there are no mags available or we're specically told to randomize
        # them, generate a random mag between 8 and 16.0
        elif randomizemags and magrms:

            LOGWARNING(' %s: assigning a random mag weighted by mag '
                       'bin probabilities' % lcfile)

            magbins = magrms[magcols[0]]['binned_sdssr_median']
            binprobs = magrms[magcols[0]]['magbin_probabilities']

            # this is the center of the magbin chosen
            magbincenter = npr.choice(magbins,size=1,p=binprobs)

            # in this magbin, choose between center and -+ 0.25 mag
            chosenmag = (
                npr.random()*((magbincenter+0.25) - (magbincenter-0.25)) +
                (magbincenter-0.25)
            )

            fakelcdict['objectinfo']['sdssr'] = np.asscalar(chosenmag)

        # if there are no mags available at all, generate a random mag
        # between 8 and 16.0
        else:

            LOGWARNING(' %s: assigning a random mag from '
                       'uniform distribution between 8.0 and 16.0' % lcfile)

            fakelcdict['objectinfo']['sdssr'] = npr.random()*8.0 + 8.0

    # if there's no info available, generate fake info
    else:

        LOGWARNING('no object information found in %s, '
                   'generating random ra, decl, sdssr' %
                   lcfile)
        fakelcdict['objectinfo']['ra'] = npr.random()*360.0
        fakelcdict['objectinfo']['decl'] = npr.random()*180.0 - 90.0
        fakelcdict['objectinfo']['sdssr'] = npr.random()*8.0 + 8.0

    #
    # NOW FILL IN THE TIMES, MAGS, ERRS
    #

    # get the time columns
    for tcind, tcol in enumerate(timecols):

        if '.' in tcol:
            tcolget = tcol.split('.')
        else:
            tcolget = [tcol]

        if tcol not in fakelcdict:
            fakelcdict[tcol] = _dict_get(lcdict, tcolget)
            fakelcdict['columns'].append(tcol)

            # update the ndet with the first time column's size. it's possible
            # that different time columns have different lengths, but that would
            # be weird and we won't deal with it for now
            if tcind == 0:
                fakelcdict['objectinfo']['ndet'] = fakelcdict[tcol].size

    # get the mag columns
    for mcol in magcols:

        if '.' in mcol:
            mcolget = mcol.split('.')
        else:
            mcolget = [mcol]

        # put the mcol in only once
        if mcol not in fakelcdict:

            measuredmags = _dict_get(lcdict, mcolget)
            measuredmags = measuredmags[np.isfinite(measuredmags)]

            # if we're randomizing, get the mags from the interpolated mag-RMS
            # relation
            if (randomizemags and magrms and mcol in magrms and
                'interpolated_magmad' in magrms[mcol] and
                magrms[mcol]['interpolated_magmad'] is not None):

                interpfunc = magrms[mcol]['interpolated_magmad']
                lcmad = interpfunc(fakelcdict['objectinfo']['sdssr'])

                fakelcdict['moments'][mcol] = {
                    'median': fakelcdict['objectinfo']['sdssr'],
                    'mad': lcmad
                }

            # if we're not randomizing, get the median and MAD from the light
            # curve itself
            else:

                # we require at least 10 finite measurements
                if measuredmags.size > 9:

                    measuredmedian = np.median(measuredmags)
                    measuredmad = np.median(
                        np.abs(measuredmags - measuredmedian)
                    )
                    fakelcdict['moments'][mcol] = {'median':measuredmedian,
                                                   'mad':measuredmad}

                # if there aren't enough measurements in this LC, try to get the
                # median and RMS from the interpolated mag-RMS relation first
                else:

                    if (magrms and mcol in magrms and
                        'interpolated_magmad' in magrms[mcol] and
                        magrms[mcol]['interpolated_magmad'] is not None):

                        LOGWARNING(
                            'input LC %s does not have enough '
                            'finite measurements, '
                            'generating mag moments from '
                            'fakelc sdssr and the mag-RMS relation' % lcfile
                        )

                        interpfunc = magrms[mcol]['interpolated_magmad']
                        lcmad = interpfunc(fakelcdict['objectinfo']['sdssr'])

                        fakelcdict['moments'][mcol] = {
                            'median': fakelcdict['objectinfo']['sdssr'],
                            'mad': lcmad
                        }

                    # if we don't have the mag-RMS relation either, then we
                    # can't do anything for this light curve, generate a random
                    # MAD between 5e-4 and 0.1
                    else:

                        LOGWARNING(
                            'input LC %s does not have enough '
                            'finite measurements and '
                            'no mag-RMS relation provided '
                            'assigning a random MAD between 5.0e-4 and 0.1'
                            % lcfile
                        )

                        fakelcdict['moments'][mcol] = {
                            'median':fakelcdict['objectinfo']['sdssr'],
                            'mad':npr.random()*(0.1 - 5.0e-4) + 5.0e-4
                        }

            # the magnitude column is set to all zeros initially. this will be
            # filled in by the add_fakelc_variability function below
            fakelcdict[mcol] = np.full_like(_dict_get(lcdict, mcolget), 0.0)
            fakelcdict['columns'].append(mcol)

    # get the err columns
    for mcol, ecol in zip(magcols, errcols):

        if '.' in ecol:
            ecolget = ecol.split('.')
        else:
            ecolget = [ecol]

        if ecol not in fakelcdict:

            measurederrs = _dict_get(lcdict, ecolget)
            measurederrs = measurederrs[np.isfinite(measurederrs)]

            # if we're randomizing, get the errs from the interpolated mag-RMS
            # relation
            if (randomizemags and magrms and mcol in magrms and
                'interpolated_magmad' in magrms[mcol] and
                magrms[mcol]['interpolated_magmad'] is not None):

                interpfunc = magrms[mcol]['interpolated_magmad']
                lcmad = interpfunc(fakelcdict['objectinfo']['sdssr'])

                # the median of the errs = lcmad
                # the mad of the errs is 0.1 x lcmad
                fakelcdict['moments'][ecol] = {
                    'median': lcmad,
                    'mad': 0.1*lcmad
                }

            else:

                # we require at least 10 finite measurements
                # we'll calculate the median and MAD of the errs to use later on
                if measurederrs.size > 9:
                    measuredmedian = np.median(measurederrs)
                    measuredmad = np.median(
                        np.abs(measurederrs - measuredmedian)
                    )
                    fakelcdict['moments'][ecol] = {'median':measuredmedian,
                                                   'mad':measuredmad}
                else:

                    if (magrms and mcol in magrms and
                        'interpolated_magmad' in magrms[mcol] and
                        magrms[mcol]['interpolated_magmad'] is not None):

                        LOGWARNING(
                            'input LC %s does not have enough '
                            'finite measurements, '
                            'generating err moments from '
                            'the mag-RMS relation' % lcfile
                        )

                        interpfunc = magrms[mcol]['interpolated_magmad']
                        lcmad = interpfunc(fakelcdict['objectinfo']['sdssr'])

                        fakelcdict['moments'][ecol] = {
                            'median': lcmad,
                            'mad': 0.1*lcmad
                        }

                    # if we don't have the mag-RMS relation either, then we
                    # can't do anything for this light curve, generate a random
                    # MAD between 5e-4 and 0.1
                    else:

                        LOGWARNING(
                            'input LC %s does not have '
                            'enough finite measurements and '
                            'no mag-RMS relation provided, '
                            'generating errs randomly' % lcfile
                        )
                        fakelcdict['moments'][ecol] = {
                            'median':npr.random()*(0.01 - 5.0e-4) + 5.0e-4,
                            'mad':npr.random()*(0.01 - 5.0e-4) + 5.0e-4
                        }

            # the errors column is set to all zeros initially. this will be
            # filled in by the add_fakelc_variability function below.
            fakelcdict[ecol] = np.full_like(_dict_get(lcdict, ecolget), 0.0)
            fakelcdict['columns'].append(ecol)

    # add the timecols, magcols, errcols to the lcdict
    fakelcdict['timecols'] = timecols
    fakelcdict['magcols'] = magcols
    fakelcdict['errcols'] = errcols

    # generate an output file name
    fakelcfname = '%s-fakelc.pkl' % fakelcdict['objectid']
    fakelcfpath = os.path.abspath(os.path.join(outdir, fakelcfname))

    # write this out to the output directory
    with open(fakelcfpath,'wb') as outfd:
        pickle.dump(fakelcdict, outfd, protocol=pickle.HIGHEST_PROTOCOL)

    # return the fakelc path, its columns, info, and moments so we can put them
    # into a collection DB later on
    LOGINFO('real LC %s -> fake LC %s OK' % (lcfile, fakelcfpath))

    return (fakelcfpath, fakelcdict['columns'],
            fakelcdict['objectinfo'], fakelcdict['moments'])

Response: This function will preprocesses an input real LC and sets it up to be a fake LC and generates a fake LC.


Instruction:
Can you tell what is the following Python 3 function doing
def add_fakelc_variability(fakelcfile,
                           vartype,
                           override_paramdists=None,
                           magsarefluxes=False,
                           overwrite=False):
    '''This adds variability of the specified type to the fake LC.

    The procedure is (for each `magcol`):

    - read the fakelcfile, get the stored moments and vartype info

    - add the periodic variability specified in vartype and varparamdists. if
      `vartype == None`, then do nothing in this step. If `override_vartype` is
      not None, override stored vartype with specified vartype. If
      `override_varparamdists` provided, override with specified
      `varparamdists`. NOTE: the varparamdists must make sense for the vartype,
      otherwise, weird stuff will happen.

    - add the median mag level stored in `fakelcfile` to the time series

    - add Gaussian noise to the light curve as specified in `fakelcfile`

    - add a varinfo key and dict to the lcdict with `varperiod`, `varepoch`,
      `varparams`

    - write back to fake LC pickle

    - return the `varinfo` dict to the caller

    Parameters
    ----------

    fakelcfile : str
        The name of the fake LC file to process.

    vartype : str
        The type of variability to add to this fake LC file.

    override_paramdists : dict
        A parameter distribution dict as in the `generate_XX_lightcurve`
        functions above. If provided, will override the distribution stored in
        the input fake LC file itself.

    magsarefluxes : bool
        Sets if the variability amplitude is in fluxes and not magnitudes.

    overwite : bool
        This overwrites the input fake LC file with a new variable LC even if
        it's been processed before.

    Returns
    -------

    dict
        A dict of the following form is returned::

            {'objectid':lcdict['objectid'],
             'lcfname':fakelcfile,
             'actual_vartype':vartype,
             'actual_varparams':lcdict['actual_varparams']}

    '''

    # read in the fakelcfile
    lcdict = _read_pklc(fakelcfile)

    # make sure to bail out if this light curve already has fake variability
    # added
    if ('actual_vartype' in lcdict and
        'actual_varparams' in lcdict and
        not overwrite):
        LOGERROR('%s has existing variability type: %s '
                 'and params: %s and overwrite = False, '
                 'skipping this file...' %
                 (fakelcfile, lcdict['actual_vartype'],
                  repr(lcdict['actual_varparams'])))
        return None

    # get the times, mags, errs from this LC
    timecols, magcols, errcols = (lcdict['timecols'],
                                  lcdict['magcols'],
                                  lcdict['errcols'])

    # get the correct function to apply variability
    if vartype in VARTYPE_LCGEN_MAP:
        vargenfunc = VARTYPE_LCGEN_MAP[vartype]
    elif vartype is None:
        vargenfunc = None
    else:
        LOGERROR('unknown variability type: %s, choose from: %s' %
                 (vartype, repr(list(VARTYPE_LCGEN_MAP.keys()))))
        return None

    # 1. generate the variability, including the overrides if provided we do
    # this outside the loop below to get the period, etc. distributions once
    # only per object. NOTE: in doing so, we're assuming that the difference
    # between magcols is just additive and the timebases for each magcol are the
    # same; this is not strictly correct
    if vargenfunc is not None:
        if (override_paramdists is not None and
            isinstance(override_paramdists,dict)):

            variablelc = vargenfunc(lcdict[timecols[0]],
                                    paramdists=override_paramdists,
                                    magsarefluxes=magsarefluxes)

        else:

            variablelc = vargenfunc(lcdict[timecols[0]],
                                    magsarefluxes=magsarefluxes)

    # for nonvariables, don't execute vargenfunc, but return a similar dict
    # so we can add the required noise to it
    else:
        variablelc = {'vartype':None,
                      'params':None,
                      'times':lcdict[timecols[0]],
                      'mags':np.full_like(lcdict[timecols[0]], 0.0),
                      'errs':np.full_like(lcdict[timecols[0]], 0.0)}

    # now iterate over the time, mag, err columns
    for tcol, mcol, ecol in zip(timecols, magcols, errcols):

        times, mags, errs = lcdict[tcol], lcdict[mcol], lcdict[ecol]

        # 2. get the moments for this magcol
        mag_median = lcdict['moments'][mcol]['median']
        mag_mad = lcdict['moments'][mcol]['mad']

        # add up to 5 mmag of extra RMS for systematics and red-noise
        mag_rms = mag_mad*1.483

        err_median = lcdict['moments'][ecol]['median']
        err_mad = lcdict['moments'][ecol]['mad']
        err_rms = err_mad*1.483

        # 3. add the median level + gaussian noise
        magnoise = npr.normal(size=variablelc['mags'].size)*mag_rms
        errnoise = npr.normal(size=variablelc['errs'].size)*err_rms

        finalmags = mag_median + (variablelc['mags'] + magnoise)
        finalerrs = err_median + (variablelc['errs'] + errnoise)

        # 4. update these tcol, mcol, ecol values in the lcdict
        lcdict[mcol] = finalmags
        lcdict[ecol] = finalerrs

    #
    # all done with updating mags and errs
    #

    # 5. update the light curve with the variability info
    lcdict['actual_vartype'] = variablelc['vartype']
    lcdict['actual_varparams'] = variablelc['params']

    # these standard keys are set to help out later with characterizing recovery
    # rates by magnitude, period, amplitude, ndet, etc.
    if vartype is not None:
        lcdict['actual_varperiod'] = variablelc['varperiod']
        lcdict['actual_varamplitude'] = variablelc['varamplitude']
    else:
        lcdict['actual_varperiod'] = np.nan
        lcdict['actual_varamplitude'] = np.nan

    # 6. write back, making sure to do it safely
    tempoutf = '%s.%s' % (fakelcfile, md5(npr.bytes(4)).hexdigest()[-8:])
    with open(tempoutf, 'wb') as outfd:
        pickle.dump(lcdict, outfd, pickle.HIGHEST_PROTOCOL)

    if os.path.exists(tempoutf):
        shutil.copy(tempoutf, fakelcfile)
        os.remove(tempoutf)
    else:
        LOGEXCEPTION('could not write output light curve file to dir: %s' %
                     os.path.dirname(tempoutf))
        # fail here
        raise

    LOGINFO('object: %s, vartype: %s -> %s OK' % (
        lcdict['objectid'],
        vartype,
        fakelcfile)
    )

    return {'objectid':lcdict['objectid'],
            'lcfname':fakelcfile,
            'actual_vartype':vartype,
            'actual_varparams':lcdict['actual_varparams']}

Response: This function adds a variability to the fake LC file.


Instruction:
Given the following Python 3 function, write the documentation
def add_variability_to_fakelc_collection(simbasedir,
                                         override_paramdists=None,
                                         overwrite_existingvar=False):
    '''This adds variability and noise to all fake LCs in `simbasedir`.

    If an object is marked as variable in the `fakelcs-info`.pkl file in
    `simbasedir`, a variable signal will be added to its light curve based on
    its selected type, default period and amplitude distribution, the
    appropriate params, etc. the epochs for each variable object will be chosen
    uniformly from its time-range (and may not necessarily fall on a actual
    observed time). Nonvariable objects will only have noise added as determined
    by their params, but no variable signal will be added.

    Parameters
    ----------

    simbasedir : str
        The directory containing the fake LCs to process.

    override_paramdists : dict
        This can be used to override the stored variable parameters in each fake
        LC. It should be a dict of the following form::

            {'<vartype1>': {'<param1>: a scipy.stats distribution function or
                                       the np.random.randint function,
                            .
                            .
                            .
                            '<paramN>: a scipy.stats distribution function
                                       or the np.random.randint function}

        for any vartype in VARTYPE_LCGEN_MAP. These are used to override the
        default parameter distributions for each variable type.

    overwrite_existingvar : bool
        If this is True, then will overwrite any existing variability in the
        input fake LCs in `simbasedir`.

    Returns
    -------

    dict
        This returns a dict containing the fake LC filenames as keys and
        variability info for each as values.

    '''

    # open the fakelcs-info.pkl
    infof = os.path.join(simbasedir,'fakelcs-info.pkl')
    with open(infof, 'rb') as infd:
        lcinfo = pickle.load(infd)

    lclist = lcinfo['lcfpath']
    varflag = lcinfo['isvariable']
    vartypes = lcinfo['vartype']

    vartind = 0

    varinfo = {}

    # go through all the LCs and add the required type of variability
    for lc, varf, _lcind in zip(lclist, varflag, range(len(lclist))):

        # if this object is variable, add variability
        if varf:

            thisvartype = vartypes[vartind]

            if (override_paramdists and
                isinstance(override_paramdists, dict) and
                thisvartype in override_paramdists and
                isinstance(override_paramdists[thisvartype], dict)):

                thisoverride_paramdists = override_paramdists[thisvartype]
            else:
                thisoverride_paramdists = None

            varlc = add_fakelc_variability(
                lc, thisvartype,
                override_paramdists=thisoverride_paramdists,
                overwrite=overwrite_existingvar
            )
            varinfo[varlc['objectid']] = {'params': varlc['actual_varparams'],
                                          'vartype': varlc['actual_vartype']}

            # update vartind
            vartind = vartind + 1

        else:

            varlc = add_fakelc_variability(
                lc, None,
                overwrite=overwrite_existingvar
            )
            varinfo[varlc['objectid']] = {'params': varlc['actual_varparams'],
                                          'vartype': varlc['actual_vartype']}

    #
    # done with all objects
    #

    # write the varinfo back to the dict and fakelcs-info.pkl
    lcinfo['varinfo'] = varinfo

    tempoutf = '%s.%s' % (infof, md5(npr.bytes(4)).hexdigest()[-8:])
    with open(tempoutf, 'wb') as outfd:
        pickle.dump(lcinfo, outfd, pickle.HIGHEST_PROTOCOL)

    if os.path.exists(tempoutf):
        shutil.copy(tempoutf, infof)
        os.remove(tempoutf)
    else:
        LOGEXCEPTION('could not write output light curve file to dir: %s' %
                     os.path.dirname(tempoutf))
        # fail here
        raise

    return lcinfo

Response: This function adds variability and noise to all fake LCs in the specified directory.


Instruction:
Can you tell what is the following Python 3 function doing
def add_flare_model(flareparams,
                    times,
                    mags,
                    errs):
    '''This adds a flare model function to the input magnitude/flux time-series.

    Parameters
    ----------

    flareparams : list of float
        This defines the flare model::

            [amplitude,
             flare_peak_time,
             rise_gaussian_stdev,
             decay_time_constant]

        where:

        `amplitude`: the maximum flare amplitude in mags or flux. If flux, then
        amplitude should be positive. If mags, amplitude should be negative.

        `flare_peak_time`: time at which the flare maximum happens.

        `rise_gaussian_stdev`: the stdev of the gaussian describing the rise of
        the flare.

        `decay_time_constant`: the time constant of the exponential fall of the
        flare.

    times,mags,errs : np.array
        The input time-series of measurements and associated errors for which
        the model will be generated. The times will be used to generate
        model mags.

    magsarefluxes : bool
        Sets the correct direction of the flare amplitude (+ve) for fluxes if
        True and for mags (-ve) if False.

    Returns
    -------

    dict
        A dict of the form below is returned::

        {'times': the original times array
         'mags': the original mags + the flare model mags evaluated at times,
         'errs': the original errs array,
         'flareparams': the input list of flare params}

    '''

    modelmags, ftimes, fmags, ferrs = flares.flare_model(
        flareparams,
        times,
        mags,
        errs
    )

    return {'times':times,
            'mags':mags + modelmags,
            'errs':errs,
            'flareparams':flareparams}

Response: This function adds a flare model function to the input time - series.


Instruction:
Given the following Python 3 function, write the documentation
def _smooth_acf(acf, windowfwhm=7, windowsize=21):
    '''This returns a smoothed version of the ACF.

    Convolves the ACF with a Gaussian of given `windowsize` and `windowfwhm`.

    Parameters
    ----------

    acf : np.array
        The auto-correlation function array to smooth.

    windowfwhm : int
        The smoothing window Gaussian kernel's FWHM .

    windowsize : int
        The number of input points to apply the smoothing over.

    Returns
    -------

    np.array
        Smoothed version of the input ACF array.

    '''

    convkernel = Gaussian1DKernel(windowfwhm, x_size=windowsize)
    smoothed = convolve(acf, convkernel, boundary='extend')

    return smoothed

Response: This function returns a smoothed version of the ACF.


Instruction:
How would you explain what the following Python 3 function does
def _smooth_acf_savgol(acf, windowsize=21, polyorder=2):
    '''
    This returns a smoothed version of the ACF.

    This version uses the Savitsky-Golay smoothing filter.

    Parameters
    ----------

    acf : np.array
        The auto-correlation function array to smooth.

    windowsize : int
        The number of input points to apply the smoothing over.

    polyorder : int
        The order of the polynomial to use in the Savitsky-Golay filter.

    Returns
    -------

    np.array
        Smoothed version of the input ACF array.

    '''

    smoothed = savgol_filter(acf, windowsize, polyorder)

    return smoothed

Response: This function returns a smoothed version of the ACF array using the Savitsky - Golay smoothing filter.


Instruction:
Can you tell what is the following Python 3 function doing
def _get_acf_peakheights(lags, acf, npeaks=20, searchinterval=1):
    '''This calculates the relative peak heights for first npeaks in ACF.

    Usually, the first peak or the second peak (if its peak height > first peak)
    corresponds to the correct lag. When we know the correct lag, the period is
    then::

        bestperiod = time[lags == bestlag] - time[0]

    Parameters
    ----------

    lags : np.array
        An array of lags that the ACF is calculated at.

    acf : np.array
        The array containing the ACF values.

    npeaks : int
        THe maximum number of peaks to consider when finding peak heights.

    searchinterval : int
        From `scipy.signal.argrelmax`: "How many points on each side to use for
        the comparison to consider comparator(n, n+x) to be True." This
        effectively sets how many points on each of the current peak will be
        used to check if the current peak is the local maximum.

    Returns
    -------

    dict
        This returns a dict of the following form::

            {'maxinds':the indices of the lag array where maxes are,
             'maxacfs':the ACF values at each max,
             'maxlags':the lag values at each max,
             'mininds':the indices of the lag array where mins are,
             'minacfs':the ACF values at each min,
             'minlags':the lag values at each min,
             'relpeakheights':the relative peak heights of each rel. ACF peak,
             'relpeaklags':the lags at each rel. ACF peak found,
             'peakindices':the indices of arrays where each rel. ACF peak is,
             'bestlag':the lag value with the largest rel. ACF peak height,
             'bestpeakheight':the largest rel. ACF peak height,
             'bestpeakindex':the largest rel. ACF peak's number in all peaks}

    '''

    maxinds = argrelmax(acf, order=searchinterval)[0]
    maxacfs = acf[maxinds]
    maxlags = lags[maxinds]
    mininds = argrelmin(acf, order=searchinterval)[0]
    minacfs = acf[mininds]
    minlags = lags[mininds]

    relpeakheights = npzeros(npeaks)
    relpeaklags = npzeros(npeaks,dtype=npint64)
    peakindices = npzeros(npeaks,dtype=npint64)

    for peakind, mxi in enumerate(maxinds[:npeaks]):

        # check if there are no mins to the left
        # throw away this peak because it's probably spurious
        # (FIXME: is this OK?)
        if npall(mxi < mininds):
            continue

        leftminind = mininds[mininds < mxi][-1]  # the last index to the left
        rightminind = mininds[mininds > mxi][0]  # the first index to the right
        relpeakheights[peakind] = (
            acf[mxi] - (acf[leftminind] + acf[rightminind])/2.0
        )
        relpeaklags[peakind] = lags[mxi]
        peakindices[peakind] = peakind

    # figure out the bestperiod if possible
    if relpeakheights[0] > relpeakheights[1]:
        bestlag = relpeaklags[0]
        bestpeakheight = relpeakheights[0]
        bestpeakindex = peakindices[0]
    else:
        bestlag = relpeaklags[1]
        bestpeakheight = relpeakheights[1]
        bestpeakindex = peakindices[1]

    return {'maxinds':maxinds,
            'maxacfs':maxacfs,
            'maxlags':maxlags,
            'mininds':mininds,
            'minacfs':minacfs,
            'minlags':minlags,
            'relpeakheights':relpeakheights,
            'relpeaklags':relpeaklags,
            'peakindices':peakindices,
            'bestlag':bestlag,
            'bestpeakheight':bestpeakheight,
            'bestpeakindex':bestpeakindex}

Response: This function calculates the relative peak heights for the first npeaks in an ACF.


Instruction:
Implement a function in Python 3 to
plot the unsmoothed and smoothed ACF vs. lag plot.

Response:
def plot_acf_results(acfp, outfile, maxlags=5000, yrange=(-0.4,0.4)):
    '''
    This plots the unsmoothed/smoothed ACF vs lag.

    Parameters
    ----------

    acfp : dict
        This is the dict returned from `macf_period_find` below.

    outfile : str
        The output file the plot will be written to.

    maxlags: int
        The maximum number of lags to include in the plot.

    yrange : sequence of two floats
        The y-range of the ACF vs. lag plot to use.

    '''

    import matplotlib.pyplot as plt

    lags = acfp['acfresults']['lags'][:maxlags]
    smoothedacf = acfp['acf'][:maxlags]
    unsmoothedacf = acfp['acfresults']['acf'][:maxlags]

    acfparams = acfp['kwargs']['smoothfunckwargs'].copy()
    acfparams.update({'peakinterval': int(acfp['kwargs']['smoothacf']/2.0)})

    # plot the ACFs
    fig, ax1 = plt.subplots()

    # this is lags vs acf
    ax1.plot(lags, unsmoothedacf, label='unsmoothed ACF',color='#1f77b4')
    ax1.plot(lags, smoothedacf, label='smoothed ACF', color='#ff7f0e')

    ax1.set_xlim((0,maxlags))

    ax1.set_xlabel('lags')

    # overplot the identified peaks
    acfmaxinds = acfp['acfpeaks']['maxinds']

    for i, maxind in enumerate(acfmaxinds):
        if i == 0:
            ax1.axvline(maxind,
                        linewidth=2.0,
                        color='red',
                        ymin=0.2, ymax=0.3,
                        label='identified ACF peaks')
        else:
            ax1.axvline(maxind,
                        linewidth=2.0,
                        color='red',
                        ymin=0.2, ymax=0.3)

    plt.ylabel('ACF')
    plt.ylim(yrange)
    ax1.legend()
    plt.title('%s' % repr(acfparams))
    plt.tight_layout()
    plt.savefig(outfile)
    plt.close('all')

    return outfile


Instruction:
Can you generate the documentation for the following Python 3 function
def _autocorr_func1(mags, lag, maglen, magmed, magstd):
    '''Calculates the autocorr of mag series for specific lag.

    This version of the function is taken from: Kim et al. (`2011
    <https://dx.doi.org/10.1088/0004-637X/735/2/68>`_)

    Parameters
    ----------

    mags : np.array
        This is the magnitudes array. MUST NOT have any nans.

    lag : float
        The specific lag value to calculate the auto-correlation for. This MUST
        be less than total number of observations in `mags`.

    maglen : int
        The number of elements in the `mags` array.

    magmed : float
        The median of the `mags` array.

    magstd : float
        The standard deviation of the `mags` array.

    Returns
    -------

    float
        The auto-correlation at this specific `lag` value.

    '''

    lagindex = nparange(1,maglen-lag)
    products = (mags[lagindex] - magmed) * (mags[lagindex+lag] - magmed)
    acorr = (1.0/((maglen - lag)*magstd)) * npsum(products)

    return acorr

Response: Calculates the autocorr of mag series for specific lag.


Instruction:
Can you generate the documentation for the following Python 3 function
def _autocorr_func3(mags, lag, maglen, magmed, magstd):
    '''
    This is yet another alternative to calculate the autocorrelation.

    Taken from: `Bayesian Methods for Hackers by Cameron Pilon <http://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter3_MCMC/Chapter3.ipynb#Autocorrelation>`_

    (This should be the fastest method to calculate ACFs.)

    Parameters
    ----------

    mags : np.array
        This is the magnitudes array. MUST NOT have any nans.

    lag : float
        The specific lag value to calculate the auto-correlation for. This MUST
        be less than total number of observations in `mags`.

    maglen : int
        The number of elements in the `mags` array.

    magmed : float
        The median of the `mags` array.

    magstd : float
        The standard deviation of the `mags` array.

    Returns
    -------

    float
        The auto-correlation at this specific `lag` value.

    '''

    # from http://tinyurl.com/afz57c4
    result = npcorrelate(mags, mags, mode='full')
    result = result / npmax(result)

    return result[int(result.size / 2):]

Response: This function is a fastest alternative to calculate the autocorrelation for a given set of magnitudes.


Instruction:
Can you generate the documentation for the following Python 3 function
def autocorr_magseries(times, mags, errs,
                       maxlags=1000,
                       func=_autocorr_func3,
                       fillgaps=0.0,
                       filterwindow=11,
                       forcetimebin=None,
                       sigclip=3.0,
                       magsarefluxes=False,
                       verbose=True):
    '''This calculates the ACF of a light curve.

    This will pre-process the light curve to fill in all the gaps and normalize
    everything to zero. If `fillgaps = 'noiselevel'`, fills the gaps with the
    noise level obtained via the procedure above. If `fillgaps = 'nan'`, fills
    the gaps with `np.nan`.

    Parameters
    ----------

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

    maxlags : int
        The maximum number of lags to calculate.

    func : Python function
        This is a function to calculate the lags.

    fillgaps : 'noiselevel' or float
        This sets what to use to fill in gaps in the time series. If this is
        'noiselevel', will smooth the light curve using a point window size of
        `filterwindow` (this should be an odd integer), subtract the smoothed LC
        from the actual LC and estimate the RMS. This RMS will be used to fill
        in the gaps. Other useful values here are 0.0, and npnan.

    filterwindow : int
        The light curve's smoothing filter window size to use if
        `fillgaps='noiselevel`'.

    forcetimebin : None or float
        This is used to force a particular cadence in the light curve other than
        the automatically determined cadence. This effectively rebins the light
        curve to this cadence. This should be in the same time units as `times`.

    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.

    magsarefluxes : bool
        If your input measurements in `mags` are actually fluxes instead of
        mags, set this is True.

    verbose : bool
        If True, will indicate progress and report errors.

    Returns
    -------

    dict
        A dict of the following form is returned::

            {'itimes': the interpolated time values after gap-filling,
             'imags': the interpolated mag/flux values after gap-filling,
             'ierrs': the interpolated mag/flux values after gap-filling,
             'cadence': the cadence of the output mag/flux time-series,
             'minitime': the minimum value of the interpolated times array,
             'lags': the lags used to calculate the auto-correlation function,
             'acf': the value of the ACF at each lag used}

    '''

    # get the gap-filled timeseries
    interpolated = fill_magseries_gaps(times, mags, errs,
                                       fillgaps=fillgaps,
                                       forcetimebin=forcetimebin,
                                       sigclip=sigclip,
                                       magsarefluxes=magsarefluxes,
                                       filterwindow=filterwindow,
                                       verbose=verbose)

    if not interpolated:
        print('failed to interpolate light curve to minimum cadence!')
        return None

    itimes, imags = interpolated['itimes'], interpolated['imags'],

    # calculate the lags up to maxlags
    if maxlags:
        lags = nparange(0, maxlags)
    else:
        lags = nparange(itimes.size)

    series_stdev = 1.483*npmedian(npabs(imags))

    if func != _autocorr_func3:

        # get the autocorrelation as a function of the lag of the mag series
        autocorr = nparray([func(imags, x, imags.size, 0.0, series_stdev)
                            for x in lags])

    # this doesn't need a lags array
    else:

        autocorr = _autocorr_func3(imags, lags[0], imags.size,
                                   0.0, series_stdev)
        # return only the maximum number of lags
        if maxlags is not None:
            autocorr = autocorr[:maxlags]

    interpolated.update({'minitime':itimes.min(),
                         'lags':lags,
                         'acf':autocorr})

    return interpolated

Response: This function calculates the ACF of a light curve with the given time - series and associated errors.


Instruction:
Make a summary of the following Python 3 code
def aovhm_theta(times, mags, errs, frequency,
                nharmonics, magvariance):
    '''This calculates the harmonic AoV theta statistic for a frequency.

    This is a mostly faithful translation of the inner loop in `aovper.f90`. See
    the following for details:

    - http://users.camk.edu.pl/alex/
    - Schwarzenberg-Czerny (`1996
      <http://iopscience.iop.org/article/10.1086/309985/meta>`_)

    Schwarzenberg-Czerny (1996) equation 11::

        theta_prefactor = (K - 2N - 1)/(2N)
        theta_top = sum(c_n*c_n) (from n=0 to n=2N)
        theta_bot = variance(timeseries) - sum(c_n*c_n) (from n=0 to n=2N)

        theta = theta_prefactor * (theta_top/theta_bot)

        N = number of harmonics (nharmonics)
        K = length of time series (times.size)

    Parameters
    ----------

    times,mags,errs : np.array
        The input time-series to calculate the test statistic for. These should
        all be of nans/infs and be normalized to zero.

    frequency : float
        The test frequency to calculate the statistic for.

    nharmonics : int
        The number of harmonics to calculate up to.The recommended range is 4 to
        8.

    magvariance : float
        This is the (weighted by errors) variance of the magnitude time
        series. We provide it as a pre-calculated value here so we don't have to
        re-calculate it for every worker.

    Returns
    -------

    aov_harmonic_theta : float
        THe value of the harmonic AoV theta for the specified test `frequency`.

    '''

    period = 1.0/frequency

    ndet = times.size
    two_nharmonics = nharmonics + nharmonics

    # phase with test period
    phasedseries = phase_magseries_with_errs(
        times, mags, errs, period, times[0],
        sort=True, wrap=False
    )

    # get the phased quantities
    phase = phasedseries['phase']
    pmags = phasedseries['mags']
    perrs = phasedseries['errs']

    # this is sqrt(1.0/errs^2) -> the weights
    pweights = 1.0/perrs

    # multiply by 2.0*PI (for omega*time)
    phase = phase * 2.0 * pi_value

    # this is the z complex vector
    z = npcos(phase) + 1.0j*npsin(phase)

    # multiply phase with N
    phase = nharmonics * phase

    # this is the psi complex vector
    psi = pmags * pweights * (npcos(phase) + 1j*npsin(phase))

    # this is the initial value of z^n
    zn = 1.0 + 0.0j

    # this is the initial value of phi
    phi = pweights + 0.0j

    # initialize theta to zero
    theta_aov = 0.0

    # go through all the harmonics now up to 2N
    for _ in range(two_nharmonics):

        # this is <phi, phi>
        phi_dot_phi = npsum(phi * phi.conjugate())

        # this is the alpha_n numerator
        alpha = npsum(pweights * z * phi)

        # this is <phi, psi>. make sure to use npvdot and NOT npdot to get
        # complex conjugate of first vector as expected for complex vectors
        phi_dot_psi = npvdot(phi, psi)

        # make sure phi_dot_phi is not zero
        phi_dot_phi = npmax([phi_dot_phi, 10.0e-9])

        # this is the expression for alpha_n
        alpha = alpha / phi_dot_phi

        # update theta_aov for this harmonic
        theta_aov = (theta_aov +
                     npabs(phi_dot_psi) * npabs(phi_dot_psi) / phi_dot_phi)

        # use the recurrence relation to find the next phi
        phi = phi * z - alpha * zn * phi.conjugate()

        # update z^n
        zn = zn * z

    # done with all harmonics, calculate the theta_aov for this freq
    # the max below makes sure that magvariance - theta_aov > zero
    theta_aov = ( (ndet - two_nharmonics - 1.0) * theta_aov /
                  (two_nharmonics * npmax([magvariance - theta_aov,
                                           1.0e-9])) )

    return theta_aov

Response: This function calculates the harmonic AoV theta statistic for a specific test frequency.


Instruction:
Given the following Python 3 function, write the documentation
def open_default(self):
        '''
        This opens the database connection using the default database parameters
        given in the ~/.astrobase/astrobase.conf file.

        '''

        if HAVECONF:
            self.open(DBDATA, DBUSER, DBPASS, DBHOST)
        else:
            LOGERROR("no default DB connection config found in lcdb.conf, "
                     "this function won't work otherwise")

Response: This opens the default database connection using the default parameters in lcdb. conf.


Instruction:
Write a Python 3 function that can
set the database connection to autocommit.

Response:
def autocommit(self):
        '''
        This sets the database connection to autocommit. Must be called before
        any cursors have been instantiated.

        '''

        if len(self.cursors.keys()) == 0:
            self.connection.autocommit = True
        else:
            raise AttributeError('database cursors are already active, '
                                 'cannot switch to autocommit now')


Instruction:
Create a Python 3 function to
create a new DB cursor for the current DB connection using a randomly generated handle.

Response:
def newcursor(self, dictcursor=False):
        '''
        This creates a DB cursor for the current DB connection using a
        randomly generated handle. Returns a tuple with cursor and handle.

        Parameters
        ----------

        dictcursor : bool
            If True, returns a cursor where each returned row can be addressed
            as a dictionary by column name.

        Returns
        -------

        tuple
            The tuple is of the form (handle, psycopg2.Cursor instance).

        '''

        handle = hashlib.sha256(os.urandom(12)).hexdigest()

        if dictcursor:
            self.cursors[handle] = self.connection.cursor(
                cursor_factory=psycopg2.extras.DictCursor
            )
        else:
            self.cursors[handle] = self.connection.cursor()

            return (self.cursors[handle], handle)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def commit(self):
        '''
        This just calls the connection's commit method.

        '''
        if not self.connection.closed:
            self.connection.commit()
        else:
            raise AttributeError('postgres connection to %s is closed' %
                                 self.database)

Response: Commits the current state of the current resource.


Instruction:
Explain what the following Python 3 code does
def rollback(self):
        '''
        This just calls the connection's commit method.

        '''
        if not self.connection.closed:
            self.connection.rollback()
        else:
            raise AttributeError('postgres connection to %s is closed' %
                                 self.database)

Response: This just calls the connection s commit method.