Instruction:
Create a Python 3 function for
adding catalog info to objectinfo key of all pklcs in pklcdir.

Response:
def pklc_fovcatalog_objectinfo(
        pklcdir,
        fovcatalog,
        fovcatalog_columns=[0,1,2,
                            6,7,
                            8,9,
                            10,11,
                            13,14,15,16,
                            17,18,19,
                            20,21],
        fovcatalog_colnames=['objectid','ra','decl',
                             'jmag','jmag_err',
                             'hmag','hmag_err',
                             'kmag','kmag_err',
                             'bmag','vmag','rmag','imag',
                             'sdssu','sdssg','sdssr',
                             'sdssi','sdssz'],
        fovcatalog_colformats=('U20,f8,f8,'
                               'f8,f8,'
                               'f8,f8,'
                               'f8,f8,'
                               'f8,f8,f8,f8,'
                               'f8,f8,f8,'
                               'f8,f8')
):
    '''Adds catalog info to objectinfo key of all pklcs in lcdir.

    If fovcatalog, fovcatalog_columns, fovcatalog_colnames are provided, uses
    them to find all the additional information listed in the fovcatalog_colname
    keys, and writes this info to the objectinfo key of each lcdict. This makes
    it easier for astrobase tools to work on these light curve.

    The default set up for fovcatalog is to use a text file generated by the
    HATPI pipeline before auto-calibrating a field. The format is specified as
    above in _columns,  _colnames, and _colformats.

    '''

    if fovcatalog.endswith('.gz'):
        catfd = gzip.open(fovcatalog)
    else:
        catfd = open(fovcatalog)

    # read the catalog using the colformats, etc.
    fovcat = np.genfromtxt(catfd,
                           usecols=fovcatalog_columns,
                           names=fovcatalog_colnames,
                           dtype=fovcatalog_colformats)
    catfd.close()

    pklclist = sorted(glob.glob(os.path.join(pklcdir, '*HAT*-pklc.pkl')))

    updatedpklcs, failedpklcs = [], []

    for pklc in pklclist:

        lcdict = read_hatpi_pklc(pklc)
        objectid = lcdict['objectid']

        catind = np.where(fovcat['objectid'] == objectid)

        # if we found catalog info for this object, put it into objectinfo
        if len(catind) > 0 and catind[0]:

            lcdict['objectinfo'].update(
                {x:y for x,y in zip(
                    fovcatalog_colnames,
                    [np.asscalar(fovcat[z][catind]) for
                     z in fovcatalog_colnames]
                )
                }
            )

            # write the LC back to the pickle (tempfile for atomicity)
            with open(pklc+'-tmp','wb') as outfd:
                pickle.dump(lcdict, outfd, pickle.HIGHEST_PROTOCOL)

            # overwrite previous once we know it exists
            if os.path.exists(pklc+'-tmp'):
                shutil.move(pklc+'-tmp',pklc)

                LOGINFO('updated %s with catalog info for %s at %.3f, %.3f OK' %
                        (pklc, objectid,
                         lcdict['objectinfo']['ra'],
                         lcdict['objectinfo']['decl']))

                updatedpklcs.append(pklc)

        # otherwise, do nothing
        else:
            failedpklcs.append(pklc)

    # end of pklclist processing
    return updatedpklcs, failedpklcs


Instruction:
Given the following Python 3 function, write the documentation
def _base64_to_file(b64str, outfpath, writetostrio=False):
    '''This converts the base64 encoded string to a file.

    Parameters
    ----------

    b64str : str
        A base64 encoded strin that is the output of `base64.b64encode`.

    outfpath : str
        The path to where the file will be written. This should include an
        appropriate extension for the file (e.g. a base64 encoded string that
        represents a PNG should have its `outfpath` end in a '.png') so the OS
        can open these files correctly.

    writetostrio : bool
        If this is True, will return a StringIO object with the binary stream
        decoded from the base64-encoded input string `b64str`. This can be
        useful to embed these into other files without having to write them to
        disk.

    Returns
    -------

    str or StringIO object
        If `writetostrio` is False, will return the output file's path as a
        str. If it is True, will return a StringIO object directly. If writing
        the file fails in either case, will return None.

    '''

    try:

        filebytes = base64.b64decode(b64str)

        # if we're writing back to a stringio object
        if writetostrio:

            outobj = StrIO(filebytes)
            return outobj

        # otherwise, we're writing to an actual file
        else:

            with open(outfpath,'wb') as outfd:
                outfd.write(filebytes)

            if os.path.exists(outfpath):
                return outfpath
            else:
                LOGERROR('could not write output file: %s' % outfpath)
                return None

    except Exception as e:

        LOGEXCEPTION('failed while trying to convert '
                     'b64 string to file %s' % outfpath)
        return None

Response: This function converts the base64 encoded string to a file.


Instruction:
Here you have a function in Python 3, explain what it does
def get_phased_quantities(stimes, smags, serrs, period):
    '''Does phase-folding for the mag/flux time-series given a period.

    Given finite and sigma-clipped times, magnitudes, and errors, along with the
    period at which to phase-fold the data, perform the phase-folding and
    return the phase-folded values.

    Parameters
    ----------

    stimes,smags,serrs : np.array
        The sigma-clipped and finite input mag/flux time-series arrays to
        operate on.

    period : float
        The period to phase the mag/flux time-series at. stimes.min() is used as
        the epoch value to fold the times-series around.

    Returns
    -------

    (phase, pmags, perrs, ptimes, mintime) : tuple
        The tuple returned contains the following items:

        - `phase`: phase-sorted values of phase at each of stimes
        - `pmags`: phase-sorted magnitudes at each phase
        - `perrs`: phase-sorted errors
        - `ptimes`: phase-sorted times
        - `mintime`: earliest time in stimes.

    '''

    # phase the mag series using the given period and faintest mag time
    # mintime = stimes[npwhere(smags == npmax(smags))]

    # phase the mag series using the given period and epoch = min(stimes)
    mintime = np.min(stimes)

    # calculate the unsorted phase, then sort it
    iphase = (stimes - mintime)/period - np.floor((stimes - mintime)/period)
    phasesortind = np.argsort(iphase)

    # these are the final quantities to use for the Fourier fits
    phase = iphase[phasesortind]
    pmags = smags[phasesortind]
    perrs = serrs[phasesortind]

    # get the times sorted in phase order (useful to get the fit mag minimum
    # with respect to phase -- the light curve minimum)
    ptimes = stimes[phasesortind]

    return phase, pmags, perrs, ptimes, mintime

Response: Does phase - folding for the mag - time - series given a period. Returns the final quantities of the mag - time - series.


Instruction:
Here you have a function in Python 3, explain what it does
def make_fit_plot(phase, pmags, perrs, fitmags,
                  period, mintime, magseriesepoch,
                  plotfit,
                  magsarefluxes=False,
                  wrap=False,
                  model_over_lc=False):
    '''This makes a plot of the LC model fit.

    Parameters
    ----------

    phase,pmags,perrs : np.array
        The actual mag/flux time-series.

    fitmags : np.array
        The model fit time-series.

    period : float
        The period at which the phased LC was generated.

    mintime : float
        The minimum time value.

    magseriesepoch : float
        The value of time around which the phased LC was folded.

    plotfit : str
        The name of a file to write the plot to.

    magsarefluxes : bool
        Set this to True if the values in `pmags` and `fitmags` are actually
        fluxes.

    wrap : bool
        If True, will wrap the phased LC around 0.0 to make some phased LCs
        easier to look at.

    model_over_lc : bool
        Usually, this function will plot the actual LC over the model LC. Set
        this to True to plot the model over the actual LC; this is most useful
        when you have a very dense light curve and want to be able to see how it
        follows the model.

    Returns
    -------

    Nothing.

    '''

    # set up the figure
    plt.close('all')
    plt.figure(figsize=(8,4.8))

    if model_over_lc:
        model_z = 100
        lc_z = 0
    else:
        model_z = 0
        lc_z = 100

    if not wrap:

        plt.plot(phase, fitmags, linewidth=3.0, color='red',zorder=model_z)
        plt.plot(phase,pmags,
                 marker='o',
                 markersize=1.0,
                 linestyle='none',
                 rasterized=True, color='k',zorder=lc_z)

        # set the x axis ticks and label
        plt.gca().set_xticks(
            [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
        )

    else:
        plt.plot(np.concatenate([phase-1.0,phase]),
                 np.concatenate([fitmags,fitmags]),
                 linewidth=3.0,
                 color='red',zorder=model_z)
        plt.plot(np.concatenate([phase-1.0,phase]),
                 np.concatenate([pmags,pmags]),
                 marker='o',
                 markersize=1.0,
                 linestyle='none',
                 rasterized=True, color='k',zorder=lc_z)

        plt.gca().set_xlim((-0.8,0.8))
        # set the x axis ticks and label
        plt.gca().set_xticks(
            [-0.8,-0.7,-0.6,-0.5,-0.4,-0.3,-0.2,-0.1,
             0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8]
        )

    # set the y axis limit and label
    ymin, ymax = plt.ylim()
    if not magsarefluxes:
        plt.gca().invert_yaxis()
        plt.ylabel('magnitude')
    else:
        plt.ylabel('flux')

    plt.xlabel('phase')
    plt.title('period: %.6f, folded at %.6f, fit epoch: %.6f' %
              (period, mintime, magseriesepoch))
    plt.savefig(plotfit)
    plt.close()

Response: This function makes a plot of the model fit of the phased LC model.


Instruction:
Here you have a function in Python 3, explain what it does
def objectlist_radeclbox(radeclbox,
                         gaia_mirror=None,
                         columns=('source_id',
                                  'ra','dec',
                                  'phot_g_mean_mag',
                                  'l','b',
                                  'parallax, parallax_error',
                                  'pmra','pmra_error',
                                  'pmdec','pmdec_error'),
                         extra_filter=None,
                         returnformat='csv',
                         forcefetch=False,
                         cachedir='~/.astrobase/gaia-cache',
                         verbose=True,
                         timeout=15.0,
                         refresh=2.0,
                         maxtimeout=300.0,
                         maxtries=3,
                         complete_query_later=True):

    '''This queries the GAIA TAP service for a list of objects in an equatorial
    coordinate box.

    Parameters
    ----------

    radeclbox : sequence of four floats
        This defines the box to search in::

            [ra_min, ra_max, decl_min, decl_max]

    gaia_mirror : {'gaia','heidelberg','vizier'} or None
        This is the key used to select a GAIA catalog mirror from the
        `GAIA_URLS` dict above. If set, the specified mirror will be used. If
        None, a random mirror chosen from that dict will be used.

    columns : sequence of str
        This indicates which columns from the GAIA table to request for the
        objects found within the search radius.

    extra_filter: str or None
        If this is provided, must be a valid ADQL filter string that is used to
        further filter the cone-search results.

    returnformat : {'csv','votable','json'}
        The returned file format to request from the GAIA catalog service.

    forcefetch : bool
        If this is True, the query will be retried even if cached results for
        it exist.

    cachedir : str
        This points to the directory where results will be downloaded.

    verbose : bool
        If True, will indicate progress and warn of any issues.

    timeout : float
        This sets the amount of time in seconds to wait for the service to
        respond to our initial request.

    refresh : float
        This sets the amount of time in seconds to wait before checking if the
        result file is available. If the results file isn't available after
        `refresh` seconds have elapsed, the function will wait for `refresh`
        seconds continuously, until `maxtimeout` is reached or the results file
        becomes available.

    maxtimeout : float
        The maximum amount of time in seconds to wait for a result to become
        available after submitting our query request.

    maxtries : int
        The maximum number of tries (across all mirrors tried) to make to either
        submit the request or download the results, before giving up.

    completequerylater : bool
        If set to True, a submitted query that does not return a result before
        `maxtimeout` has passed will be cancelled but its input request
        parameters and the result URL provided by the service will be saved. If
        this function is then called later with these same input request
        parameters, it will check if the query finally finished and a result is
        available. If so, will download the results instead of submitting a new
        query. If it's not done yet, will start waiting for results again. To
        force launch a new query with the same request parameters, set the
        `forcefetch` kwarg to True.

    Returns
    -------

    dict
        This returns a dict of the following form::

            {'params':dict of the input params used for the query,
             'provenance':'cache' or 'new download',
             'result':path to the file on disk with the downloaded data table}

    '''

    # this was generated using the awesome query generator at:
    # https://gea.esac.esa.int/archive/

    # NOTE: here we don't resolve the table name right away. this is because
    # some of the GAIA mirrors use different table names, so we leave the table
    # name to be resolved by the lower level tap_query function. this is done by
    # the {{table}} construct.
    query = (
        "select {columns} from {{table}} where "
        "CONTAINS(POINT('ICRS',{{table}}.ra, {{table}}.dec),"
        "BOX('ICRS',{ra_center:.5f},{decl_center:.5f},"
        "{ra_width:.5f},{decl_height:.5f}))=1"
        "{extra_filter_str}"
    )

    ra_min, ra_max, decl_min, decl_max = radeclbox
    ra_center = (ra_max + ra_min)/2.0
    decl_center = (decl_max + decl_min)/2.0
    ra_width = ra_max - ra_min
    decl_height = decl_max - decl_min

    if extra_filter is not None:
        extra_filter_str = ' and %s ' % extra_filter
    else:
        extra_filter_str = ''

    formatted_query = query.format(columns=', '.join(columns),
                                   extra_filter_str=extra_filter_str,
                                   ra_center=ra_center,
                                   decl_center=decl_center,
                                   ra_width=ra_width,
                                   decl_height=decl_height)

    return tap_query(formatted_query,
                     gaia_mirror=gaia_mirror,
                     returnformat=returnformat,
                     forcefetch=forcefetch,
                     cachedir=cachedir,
                     verbose=verbose,
                     timeout=timeout,
                     refresh=refresh,
                     maxtimeout=maxtimeout,
                     maxtries=maxtries,
                     complete_query_later=complete_query_later)

Response: This function returns a list of objects in an equatorial object list.


Instruction:
How would you explain what the following Python 3 function does
def generalized_lsp_value(times, mags, errs, omega):
    '''Generalized LSP value for a single omega.

    The relations used are::

        P(w) = (1/YY) * (YC*YC/CC + YS*YS/SS)

        where: YC, YS, CC, and SS are all calculated at T

        and where: tan 2omegaT = 2*CS/(CC - SS)

        and where:

        Y = sum( w_i*y_i )
        C = sum( w_i*cos(wT_i) )
        S = sum( w_i*sin(wT_i) )

        YY = sum( w_i*y_i*y_i ) - Y*Y
        YC = sum( w_i*y_i*cos(wT_i) ) - Y*C
        YS = sum( w_i*y_i*sin(wT_i) ) - Y*S

        CpC = sum( w_i*cos(w_T_i)*cos(w_T_i) )
        CC = CpC - C*C
        SS = (1 - CpC) - S*S
        CS = sum( w_i*cos(w_T_i)*sin(w_T_i) ) - C*S

    Parameters
    ----------

    times,mags,errs : np.array
        The time-series to calculate the periodogram value for.

    omega : float
        The frequency to calculate the periodogram value at.

    Returns
    -------

    periodogramvalue : float
        The normalized periodogram at the specified test frequency `omega`.

    '''

    one_over_errs2 = 1.0/(errs*errs)

    W = npsum(one_over_errs2)
    wi = one_over_errs2/W

    sin_omegat = npsin(omega*times)
    cos_omegat = npcos(omega*times)

    sin2_omegat = sin_omegat*sin_omegat
    cos2_omegat = cos_omegat*cos_omegat
    sincos_omegat = sin_omegat*cos_omegat

    # calculate some more sums and terms
    Y = npsum( wi*mags )
    C = npsum( wi*cos_omegat )
    S = npsum( wi*sin_omegat )

    CpS = npsum( wi*sincos_omegat )
    CpC = npsum( wi*cos2_omegat )
    CS = CpS - C*S
    CC = CpC - C*C
    SS = 1 - CpC - S*S  # use SpS = 1 - CpC

    # calculate tau
    tan_omega_tau_top = 2.0*CS
    tan_omega_tau_bottom = CC - SS
    tan_omega_tau = tan_omega_tau_top/tan_omega_tau_bottom
    tau = nparctan(tan_omega_tau)/(2.0*omega)

    YpY = npsum( wi*mags*mags)

    YpC = npsum( wi*mags*cos_omegat )
    YpS = npsum( wi*mags*sin_omegat )

    # SpS = npsum( wi*sin2_omegat )

    # the final terms
    YY = YpY - Y*Y
    YC = YpC - Y*C
    YS = YpS - Y*S

    periodogramvalue = (YC*YC/CC + YS*YS/SS)/YY

    return periodogramvalue

Response: Generalized LSP value for a single omega.


Instruction:
Explain what the following Python 3 code does
def generalized_lsp_value_withtau(times, mags, errs, omega):
    '''Generalized LSP value for a single omega.

    This uses tau to provide an arbitrary time-reference point.

    The relations used are::

        P(w) = (1/YY) * (YC*YC/CC + YS*YS/SS)

        where: YC, YS, CC, and SS are all calculated at T

        and where: tan 2omegaT = 2*CS/(CC - SS)

        and where:

        Y = sum( w_i*y_i )
        C = sum( w_i*cos(wT_i) )
        S = sum( w_i*sin(wT_i) )

        YY = sum( w_i*y_i*y_i ) - Y*Y
        YC = sum( w_i*y_i*cos(wT_i) ) - Y*C
        YS = sum( w_i*y_i*sin(wT_i) ) - Y*S

        CpC = sum( w_i*cos(w_T_i)*cos(w_T_i) )
        CC = CpC - C*C
        SS = (1 - CpC) - S*S
        CS = sum( w_i*cos(w_T_i)*sin(w_T_i) ) - C*S

    Parameters
    ----------

    times,mags,errs : np.array
        The time-series to calculate the periodogram value for.

    omega : float
        The frequency to calculate the periodogram value at.

    Returns
    -------

    periodogramvalue : float
        The normalized periodogram at the specified test frequency `omega`.

    '''

    one_over_errs2 = 1.0/(errs*errs)

    W = npsum(one_over_errs2)
    wi = one_over_errs2/W

    sin_omegat = npsin(omega*times)
    cos_omegat = npcos(omega*times)

    sin2_omegat = sin_omegat*sin_omegat
    cos2_omegat = cos_omegat*cos_omegat
    sincos_omegat = sin_omegat*cos_omegat

    # calculate some more sums and terms
    Y = npsum( wi*mags )
    C = npsum( wi*cos_omegat )
    S = npsum( wi*sin_omegat )

    CpS = npsum( wi*sincos_omegat )
    CpC = npsum( wi*cos2_omegat )
    CS = CpS - C*S
    CC = CpC - C*C
    SS = 1 - CpC - S*S  # use SpS = 1 - CpC

    # calculate tau
    tan_omega_tau_top = 2.0*CS
    tan_omega_tau_bottom = CC - SS
    tan_omega_tau = tan_omega_tau_top/tan_omega_tau_bottom
    tau = nparctan(tan_omega_tau)/(2.0*omega)

    # now we need to calculate all the bits at tau
    sin_omega_tau = npsin(omega*(times - tau))
    cos_omega_tau = npcos(omega*(times - tau))
    sin2_omega_tau = sin_omega_tau*sin_omega_tau
    cos2_omega_tau = cos_omega_tau*cos_omega_tau
    sincos_omega_tau = sin_omega_tau*cos_omega_tau

    C_tau = npsum(wi*cos_omega_tau)
    S_tau = npsum(wi*sin_omega_tau)

    CpS_tau = npsum( wi*sincos_omega_tau )
    CpC_tau = npsum( wi*cos2_omega_tau )
    CS_tau = CpS_tau - C_tau*S_tau
    CC_tau = CpC_tau - C_tau*C_tau
    SS_tau = 1 - CpC_tau - S_tau*S_tau  # use SpS = 1 - CpC

    YpY = npsum( wi*mags*mags)

    YpC_tau = npsum( wi*mags*cos_omega_tau )
    YpS_tau = npsum( wi*mags*sin_omega_tau )

    # SpS = npsum( wi*sin2_omegat )

    # the final terms
    YY = YpY - Y*Y
    YC_tau = YpC_tau - Y*C_tau
    YS_tau = YpS_tau - Y*S_tau

    periodogramvalue = (YC_tau*YC_tau/CC_tau + YS_tau*YS_tau/SS_tau)/YY

    return periodogramvalue

Response: Generalized LSP value for a single omega.


Instruction:
Make a summary of the following Python 3 code
def specwindow_lsp_value(times, mags, errs, omega):
    '''This calculates the peak associated with the spectral window function
    for times and at the specified omega.

    NOTE: this is classical Lomb-Scargle, not the Generalized
    Lomb-Scargle. `mags` and `errs` are silently ignored since we're calculating
    the periodogram of the observing window function. These are kept to present
    a consistent external API so the `pgen_lsp` function below can call this
    transparently.

    Parameters
    ----------

    times,mags,errs : np.array
        The time-series to calculate the periodogram value for.

    omega : float
        The frequency to calculate the periodogram value at.

    Returns
    -------

    periodogramvalue : float
        The normalized periodogram at the specified test frequency `omega`.

    '''

    norm_times = times - times.min()

    tau = (
        (1.0/(2.0*omega)) *
        nparctan( npsum(npsin(2.0*omega*norm_times)) /
                  npsum(npcos(2.0*omega*norm_times)) )
    )

    lspval_top_cos = (npsum(1.0 * npcos(omega*(norm_times-tau))) *
                      npsum(1.0 * npcos(omega*(norm_times-tau))))
    lspval_bot_cos = npsum( (npcos(omega*(norm_times-tau))) *
                            (npcos(omega*(norm_times-tau))) )

    lspval_top_sin = (npsum(1.0 * npsin(omega*(norm_times-tau))) *
                      npsum(1.0 * npsin(omega*(norm_times-tau))))
    lspval_bot_sin = npsum( (npsin(omega*(norm_times-tau))) *
                            (npsin(omega*(norm_times-tau))) )

    lspval = 0.5 * ( (lspval_top_cos/lspval_bot_cos) +
                     (lspval_top_sin/lspval_bot_sin) )

    return lspval

Response:
This function calculates the peak associated with the spectral window function
    for times and at the specified omega.


Instruction:
How would you explain what the following Python 3 function does
def pgen_lsp(
        times,
        mags,
        errs,
        magsarefluxes=False,
        startp=None,
        endp=None,
        stepsize=1.0e-4,
        autofreq=True,
        nbestpeaks=5,
        periodepsilon=0.1,
        sigclip=10.0,
        nworkers=None,
        workchunksize=None,
        glspfunc=_glsp_worker_withtau,
        verbose=True
):
    '''This calculates the generalized Lomb-Scargle periodogram.

    Uses the algorithm from Zechmeister and Kurster (2009).

    Parameters
    ----------

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    Returns
    -------

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

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

    '''

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

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

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

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

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

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

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

        pool = Pool(nworkers)

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

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

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

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

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

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

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

            bestperiodind = npargmax(finlsp)

        except ValueError:

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

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

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

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

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

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

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

            prevperiod = period

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

    else:

        LOGERROR('no good detections for these times and mags, skipping...')
        return {'bestperiod':npnan,
                'bestlspval':npnan,
                'nbestpeaks':nbestpeaks,
                'nbestlspvals':None,
                'nbestperiods':None,
                'lspvals':None,
                'omegas':None,
                'periods':None,
                'method':'gls',
                'kwargs':{'startp':startp,
                          'endp':endp,
                          'stepsize':stepsize,
                          'autofreq':autofreq,
                          'periodepsilon':periodepsilon,
                          'nbestpeaks':nbestpeaks,
                          'sigclip':sigclip}}

Response: This function calculates the generalized Lomb - Scargle periodogram for the given time - series.


Instruction:
Explain what the following Python 3 code does
def specwindow_lsp(
        times,
        mags,
        errs,
        magsarefluxes=False,
        startp=None,
        endp=None,
        stepsize=1.0e-4,
        autofreq=True,
        nbestpeaks=5,
        periodepsilon=0.1,
        sigclip=10.0,
        nworkers=None,
        glspfunc=_glsp_worker_specwindow,
        verbose=True
):
    '''This calculates the spectral window function.

    Wraps the `pgen_lsp` function above to use the specific worker for
    calculating the window-function.

    Parameters
    ----------

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

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

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

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

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

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

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

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

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

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

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

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

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

    Returns
    -------

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

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

    '''

    # run the LSP using glsp_worker_specwindow as the worker
    lspres = pgen_lsp(
        times,
        mags,
        errs,
        magsarefluxes=magsarefluxes,
        startp=startp,
        endp=endp,
        autofreq=autofreq,
        nbestpeaks=nbestpeaks,
        periodepsilon=periodepsilon,
        stepsize=stepsize,
        nworkers=nworkers,
        sigclip=sigclip,
        glspfunc=glspfunc,
        verbose=verbose
    )

    # update the resultdict to indicate we're a spectral window function
    lspres['method'] = 'win'

    if lspres['lspvals'] is not None:

        # renormalize the periodogram to between 0 and 1 like the usual GLS.
        lspmax = npnanmax(lspres['lspvals'])

        if npisfinite(lspmax):

            lspres['lspvals'] = lspres['lspvals']/lspmax
            lspres['nbestlspvals'] = [
                x/lspmax for x in lspres['nbestlspvals']
            ]
            lspres['bestlspval'] = lspres['bestlspval']/lspmax

    return lspres

Response: This function calculates the spectral window function for a set of time - series times mags and errs.


Instruction:
How would you explain what the following Python 3 function does
def get_new_apikey(lcc_server):
    '''This gets a new API key from the specified LCC-Server.

    NOTE: this only gets an anonymous API key. To get an API key tied to a user
    account (and associated privilege level), see the `import_apikey` function
    below.

    Parameters
    ----------

    lcc_server : str
        The base URL of the LCC-Server from where the API key will be fetched.

    Returns
    -------

    (apikey, expiry) : tuple
        This returns a tuple with the API key and its expiry date.

    '''

    USERHOME = os.path.expanduser('~')
    APIKEYFILE = os.path.join(USERHOME,
                              '.astrobase',
                              'lccs',
                              'apikey-%s' % lcc_server.replace(
                                  'https://',
                                  'https-'
                              ).replace(
                                  'http://',
                                  'http-'
                              ))

    # url for getting an API key
    url = '%s/api/key' % lcc_server

    # get the API key
    resp = urlopen(url)

    if resp.code == 200:

        respdict = json.loads(resp.read())

    else:

        LOGERROR('could not fetch the API key from LCC-Server at: %s' %
                 lcc_server)
        LOGERROR('the HTTP status code was: %s' % resp.status_code)
        return None

    #
    # now that we have an API key dict, get the API key out of it and write it
    # to the APIKEYFILE
    #
    apikey = respdict['result']['apikey']
    expires = respdict['result']['expires']

    # write this to the apikey file

    if not os.path.exists(os.path.dirname(APIKEYFILE)):
        os.makedirs(os.path.dirname(APIKEYFILE))

    with open(APIKEYFILE,'w') as outfd:
        outfd.write('%s %s\n' % (apikey, expires))

    # chmod it to the correct value
    os.chmod(APIKEYFILE, 0o100600)

    LOGINFO('key fetched successfully from: %s. expires on: %s' % (lcc_server,
                                                                   expires))
    LOGINFO('written to: %s' % APIKEYFILE)

    return apikey, expires

Response: This function gets a new API key from the specified LCC - Server and creates a new apikey file.


Instruction:
Can you tell what is the following Python 3 function doing
def submit_post_searchquery(url, data, apikey):
    '''This submits a POST query to an LCC-Server search API endpoint.

    Handles streaming of the results, and returns the final JSON stream. Also
    handles results that time out.

    Parameters
    ----------

    url : str
        The URL of the search API endpoint to hit. This is something like
        `https://data.hatsurveys.org/api/conesearch`

    data : dict
        A dict of the search query parameters to pass to the search service.

    apikey : str
        The API key to use to access the search service. API keys are required
        for all POST request made to an LCC-Server's API endpoints.

    Returns
    -------

    (status_flag, data_dict, dataset_id) : tuple
        This returns a tuple containing the status of the request: ('complete',
        'failed', 'background', etc.), a dict parsed from the JSON result of the
        request, and a dataset ID, which can be used to reconstruct the URL on
        the LCC-Server where the results can be browsed.

    '''

    # first, we need to convert any columns and collections items to broken out
    # params
    postdata = {}

    for key in data:

        if key == 'columns':
            postdata['columns[]'] = data[key]
        elif key == 'collections':
            postdata['collections[]'] = data[key]
        else:
            postdata[key] = data[key]

    # do the urlencode with doseq=True
    # we also need to encode to bytes
    encoded_postdata = urlencode(postdata, doseq=True).encode()

    # if apikey is not None, add it in as an Authorization: Bearer [apikey]
    # header
    if apikey:
        headers = {'Authorization':'Bearer: %s' % apikey}
    else:
        headers = {}

    LOGINFO('submitting search query to LCC-Server API URL: %s' % url)

    try:

        # hit the server with a POST request
        req = Request(url, data=encoded_postdata, headers=headers)
        resp = urlopen(req)

        if resp.code == 200:

            # we'll iterate over the lines in the response
            # this works super-well for ND-JSON!
            for line in resp:

                data = json.loads(line)
                msg = data['message']
                status = data['status']

                if status != 'failed':
                    LOGINFO('status: %s, %s' % (status, msg))
                else:
                    LOGERROR('status: %s, %s' % (status, msg))

                # here, we'll decide what to do about the query

                # completed query or query sent to background...
                if status in ('ok','background'):

                    setid = data['result']['setid']
                    # save the data pickle to astrobase lccs directory
                    outpickle = os.path.join(os.path.expanduser('~'),
                                             '.astrobase',
                                             'lccs',
                                             'query-%s.pkl' % setid)
                    if not os.path.exists(os.path.dirname(outpickle)):
                        os.makedirs(os.path.dirname(outpickle))

                    with open(outpickle,'wb') as outfd:
                        pickle.dump(data, outfd, pickle.HIGHEST_PROTOCOL)
                        LOGINFO('saved query info to %s, use this to '
                                'download results later with '
                                'retrieve_dataset_files' % outpickle)

                    # we're done at this point, return
                    return status, data, data['result']['setid']

                # the query probably failed...
                elif status == 'failed':

                    # we're done at this point, return
                    return status, data, None

        # if the response was not OK, then we probably failed
        else:

            try:
                data = json.load(resp)
                msg = data['message']

                LOGERROR(msg)
                return 'failed', None, None

            except Exception as e:

                LOGEXCEPTION('failed to submit query to %s' % url)
                return 'failed', None, None

    except HTTPError as e:

        LOGERROR('could not submit query to LCC API at: %s' % url)
        LOGERROR('HTTP status code was %s, reason: %s' % (e.code, e.reason))
        return 'failed', None, None

Response: This function submits a POST request to an LCC - Server search API endpoint and returns the final JSON stream that can be used to reconstruct the URL on the LCC - Server where the results can be browsed.


Instruction:
Can you tell what is the following Python 3 function doing
def object_info(lcc_server, objectid, db_collection_id):
    '''This gets information on a single object from the LCC-Server.

    Returns a dict with all of the available information on an object, including
    finding charts, comments, object type and variability tags, and
    period-search results (if available).

    If you have an LCC-Server API key present in `~/.astrobase/lccs/` that is
    associated with an LCC-Server user account, objects that are visible to this
    user will be returned, even if they are not visible to the public. Use this
    to look up objects that have been marked as 'private' or 'shared'.

    NOTE: you can pass the result dict returned by this function directly into
    the `astrobase.checkplot.checkplot_pickle_to_png` function, e.g.::

        astrobase.checkplot.checkplot_pickle_to_png(result_dict,
                                                    'object-%s-info.png' %
                                                    result_dict['objectid'])

    to generate a quick PNG overview of the object information.

    Parameters
    ----------

    lcc_server : str
        This is the base URL of the LCC-Server to talk to.

    objectid : str
        This is the unique database ID of the object to retrieve info for. This
        is always returned as the `db_oid` column in LCC-Server search results.

    db_collection_id : str
        This is the collection ID which will be searched for the object. This is
        always returned as the `collection` column in LCC-Server search results.

    Returns
    -------

    dict
        A dict containing the object info is returned. Some important items in
        the result dict:

        - `objectinfo`: all object magnitude, color, GAIA cross-match, and
          object type information available for this object

        - `objectcomments`: comments on the object's variability if available

        - `varinfo`: variability comments, variability features, type tags,
          period and epoch information if available

        - `neighbors`: information on the neighboring objects of this object in
          its parent light curve collection

        - `xmatch`: information on any cross-matches to external catalogs
          (e.g. KIC, EPIC, TIC, APOGEE, etc.)

        - `finderchart`: a base-64 encoded PNG image of the object's DSS2 RED
          finder chart. To convert this to an actual PNG, try the function:
          `astrobase.checkplot.pkl_io._b64_to_file`.

        - `magseries`: a base-64 encoded PNG image of the object's light
          curve. To convert this to an actual PNG, try the function:
          `astrobase.checkplot.pkl_io._b64_to_file`.

        - `pfmethods`: a list of period-finding methods applied to the object if
          any. If this list is present, use the keys in it to get to the actual
          period-finding results for each method. These will contain base-64
          encoded PNGs of the periodogram and phased light curves using the best
          three peaks in the periodogram, as well as period and epoch
          information.

    '''

    urlparams = {
        'objectid':objectid,
        'collection':db_collection_id
    }

    urlqs = urlencode(urlparams)
    url = '%s/api/object?%s' % (lcc_server, urlqs)

    try:

        LOGINFO(
            'getting info for %s in collection %s from %s' % (
                objectid,
                db_collection_id,
                lcc_server
            )
        )

        # check if we have an API key already
        have_apikey, apikey, expires = check_existing_apikey(lcc_server)

        # if not, get a new one
        if not have_apikey:
            apikey, expires = get_new_apikey(lcc_server)

        # if apikey is not None, add it in as an Authorization: Bearer [apikey]
        # header
        if apikey:
            headers = {'Authorization':'Bearer: %s' % apikey}
        else:
            headers = {}

        # hit the server
        req = Request(url, data=None, headers=headers)
        resp = urlopen(req)
        objectinfo = json.loads(resp.read())['result']
        return objectinfo

    except HTTPError as e:

        if e.code == 404:

            LOGERROR(
                'additional info for object %s not '
                'found in collection: %s' % (objectid,
                                             db_collection_id)
            )

        else:

            LOGERROR('could not retrieve object info, '
                     'URL used: %s, error code: %s, reason: %s' %
                     (url, e.code, e.reason))

        return None

Response: This function gets the basic information on a single object from the LCC - Server.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def list_recent_datasets(lcc_server, nrecent=25):
    '''This lists recent publicly visible datasets available on the LCC-Server.

    If you have an LCC-Server API key present in `~/.astrobase/lccs/` that is
    associated with an LCC-Server user account, datasets that belong to this
    user will be returned as well, even if they are not visible to the public.

    Parameters
    ----------

    lcc_server : str
        This is the base URL of the LCC-Server to talk to.

    nrecent : int
        This indicates how many recent public datasets you want to list. This is
        always capped at 1000.

    Returns
    -------

    list of dicts
        Returns a list of dicts, with each dict containing info on each dataset.

    '''

    urlparams = {'nsets':nrecent}
    urlqs = urlencode(urlparams)

    url = '%s/api/datasets?%s' % (lcc_server, urlqs)

    try:

        LOGINFO(
            'getting list of recent publicly '
            'visible and owned datasets from %s' % (
                lcc_server,
            )
        )

        # check if we have an API key already
        have_apikey, apikey, expires = check_existing_apikey(lcc_server)

        # if not, get a new one
        if not have_apikey:
            apikey, expires = get_new_apikey(lcc_server)

        # if apikey is not None, add it in as an Authorization: Bearer [apikey]
        # header
        if apikey:
            headers = {'Authorization':'Bearer: %s' % apikey}
        else:
            headers = {}

        # hit the server
        req = Request(url, data=None, headers=headers)
        resp = urlopen(req)
        recent_datasets = json.loads(resp.read())['result']
        return recent_datasets

    except HTTPError as e:

        LOGERROR('could not retrieve recent datasets list, '
                 'URL used: %s, error code: %s, reason: %s' %
                 (url, e.code, e.reason))

        return None

Response: This function returns a list of recent publicly visible datasets available on the LCC - Server.


Instruction:
Can you tell what is the following Python 3 function doing
def stetson_kindex(fmags, ferrs):
    '''This calculates the Stetson K index (a robust measure of the kurtosis).

    Parameters
    ----------

    fmags,ferrs : np.array
        The input mag/flux time-series to process. Must have no non-finite
        elems.

    Returns
    -------

    float
        The Stetson K variability index.

    '''

    # use a fill in value for the errors if they're none
    if ferrs is None:
        ferrs = npfull_like(fmags, 0.005)

    ndet = len(fmags)

    if ndet > 9:

        # get the median and ndet
        medmag = npmedian(fmags)

        # get the stetson index elements
        delta_prefactor = (ndet/(ndet - 1))
        sigma_i = delta_prefactor*(fmags - medmag)/ferrs

        stetsonk = (
            npsum(npabs(sigma_i))/(npsqrt(npsum(sigma_i*sigma_i))) *
            (ndet**(-0.5))
        )

        return stetsonk

    else:

        LOGERROR('not enough detections in this magseries '
                 'to calculate stetson K index')
        return npnan

Response: This calculates the Stetson K index for a robust measure of the kurtosis.


Instruction:
Can you generate the documentation for the following Python 3 function
def nonperiodic_lightcurve_features(times, mags, errs, magsarefluxes=False):
    '''This calculates the following nonperiodic features of the light curve,
    listed in Richards, et al. 2011):

    - amplitude
    - beyond1std
    - flux_percentile_ratio_mid20
    - flux_percentile_ratio_mid35
    - flux_percentile_ratio_mid50
    - flux_percentile_ratio_mid65
    - flux_percentile_ratio_mid80
    - linear_trend
    - max_slope
    - median_absolute_deviation
    - median_buffer_range_percentage
    - pair_slope_trend
    - percent_amplitude
    - percent_difference_flux_percentile
    - skew
    - stdev
    - timelength
    - mintime
    - maxtime

    Parameters
    ----------

    times,mags,errs : np.array
        The input mag/flux time-series to process.

    magsarefluxes : bool
        If True, will treat values in `mags` as fluxes instead of magnitudes.

    Returns
    -------

    dict
        A dict containing all of the features listed above.

    '''

    # remove nans first
    finiteind = npisfinite(times) & npisfinite(mags) & npisfinite(errs)
    ftimes, fmags, ferrs = times[finiteind], mags[finiteind], errs[finiteind]

    # remove zero errors
    nzind = npnonzero(ferrs)
    ftimes, fmags, ferrs = ftimes[nzind], fmags[nzind], ferrs[nzind]

    ndet = len(fmags)

    if ndet > 9:

        # calculate the moments
        moments = lightcurve_moments(ftimes, fmags, ferrs)

        # calculate the flux measures
        fluxmeasures = lightcurve_flux_measures(ftimes, fmags, ferrs,
                                                magsarefluxes=magsarefluxes)

        # calculate the point-to-point measures
        ptpmeasures = lightcurve_ptp_measures(ftimes, fmags, ferrs)

        # get the length in time
        mintime, maxtime = npmin(ftimes), npmax(ftimes)
        timelength = maxtime - mintime

        # get the amplitude
        series_amplitude = 0.5*(npmax(fmags) - npmin(fmags))

        # calculate the linear fit to the entire mag series
        fitcoeffs = nppolyfit(ftimes, fmags, 1, w=1.0/(ferrs*ferrs))
        series_linear_slope = fitcoeffs[1]

        # roll fmags by 1
        rolled_fmags = nproll(fmags,1)

        # calculate the magnitude ratio (from the WISE paper)
        series_magratio = (
            (npmax(fmags) - moments['median']) / (npmax(fmags) - npmin(fmags) )
        )

        # this is the dictionary returned containing all the measures
        measures = {
            'ndet':fmags.size,
            'mintime':mintime,
            'maxtime':maxtime,
            'timelength':timelength,
            'amplitude':series_amplitude,
            'ndetobslength_ratio':ndet/timelength,
            'linear_fit_slope':series_linear_slope,
            'magnitude_ratio':series_magratio,
        }
        if moments:
            measures.update(moments)
        if ptpmeasures:
            measures.update(ptpmeasures)
        if fluxmeasures:
            measures.update(fluxmeasures)

        return measures

    else:

        LOGERROR('not enough detections in this magseries '
                 'to calculate non-periodic features')
        return None

Response: This function calculates the non - periodic light curve features.


Instruction:
Here you have a function in Python 3, explain what it does
def gilliland_cdpp(times, mags, errs,
                   windowlength=97,
                   polyorder=2,
                   binsize=23400,  # in seconds: 6.5 hours for classic CDPP
                   sigclip=5.0,
                   magsarefluxes=False,
                   **kwargs):
    '''This calculates the CDPP of a timeseries using the method in the paper:

    Gilliland, R. L., Chaplin, W. J., Dunham, E. W., et al. 2011, ApJS, 197, 6
    (http://adsabs.harvard.edu/abs/2011ApJS..197....6G)

    The steps are:

    - pass the time-series through a Savitsky-Golay filter.

      - we use `scipy.signal.savgol_filter`, `**kwargs` are passed to this.

      - also see: http://scipy.github.io/old-wiki/pages/Cookbook/SavitzkyGolay.

      - the `windowlength` is the number of LC points to use (Kepler uses 2 days
        = (1440 minutes/day / 30 minutes/LC point) x 2 days = 96 -> 97 LC
        points).

      - the `polyorder` is a quadratic by default.

    - subtract the smoothed time-series from the actual light curve.

    - sigma clip the remaining LC.

    - get the binned mag series by averaging over 6.5 hour bins, only retaining
      bins with at least 7 points.

    - the standard deviation of the binned averages is the CDPP.

    - multiply this by 1.168 to correct for over-subtraction of white-noise.

    Parameters
    ----------

    times,mags,errs : np.array
        The input mag/flux time-series to calculate CDPP for.

    windowlength : int
        The smoothing window size to use.

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

    binsize : int
        The bin size to use for binning the light curve.

    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 True, indicates the input time-series is fluxes and not mags.

    kwargs : additional kwargs
        These are passed directly to `scipy.signal.savgol_filter`.

    Returns
    -------

    float
        The calculated CDPP value.

    '''

    # if no errs are given, assume 0.1% errors
    if errs is None:
        errs = 0.001*mags

    # get rid of nans first
    find = npisfinite(times) & npisfinite(mags) & npisfinite(errs)
    ftimes = times[find]
    fmags = mags[find]
    ferrs = errs[find]

    if ftimes.size < (3*windowlength):
        LOGERROR('not enough LC points to calculate CDPP')
        return npnan

    # now get the smoothed mag series using the filter
    # kwargs are provided to the savgol_filter function
    smoothed = savgol_filter(fmags, windowlength, polyorder, **kwargs)
    subtracted = fmags - smoothed

    # sigclip the subtracted light curve
    stimes, smags, serrs = sigclip_magseries(ftimes, subtracted, ferrs,
                                             magsarefluxes=magsarefluxes)

    # bin over 6.5 hour bins and throw away all bins with less than 7 elements
    binned = time_bin_magseries_with_errs(stimes, smags, serrs,
                                          binsize=binsize,
                                          minbinelems=7)
    bmags = binned['binnedmags']

    # stdev of bin mags x 1.168 -> CDPP
    cdpp = npstd(bmags) * 1.168

    return cdpp

Response: This function calculates the CDPP of a time - series using the Gilliland method.


Instruction:
Here you have a function in Python 3, explain what it does
def all_nonperiodic_features(times, mags, errs,
                             magsarefluxes=False,
                             stetson_weightbytimediff=True):
    '''This rolls up the feature functions above and returns a single dict.

    NOTE: this doesn't calculate the CDPP to save time since binning and
    smoothing takes a while for dense light curves.

    Parameters
    ----------

    times,mags,errs : np.array
        The input mag/flux time-series to calculate CDPP for.

    magsarefluxes : bool
        If True, indicates `mags` is actually an array of flux values.

    stetson_weightbytimediff : bool
        If this is True, the Stetson index for any pair of mags will be
        reweighted by the difference in times between them using the scheme in
        Fruth+ 2012 and Zhange+ 2003 (as seen in Sokolovsky+ 2017)::

            w_i = exp(- (t_i+1 - t_i)/ delta_t )

    Returns
    -------

    dict
        Returns a dict with all of the variability features.

    '''

    # remove nans first
    finiteind = npisfinite(times) & npisfinite(mags) & npisfinite(errs)
    ftimes, fmags, ferrs = times[finiteind], mags[finiteind], errs[finiteind]

    # remove zero errors
    nzind = npnonzero(ferrs)
    ftimes, fmags, ferrs = ftimes[nzind], fmags[nzind], ferrs[nzind]

    xfeatures = nonperiodic_lightcurve_features(times, mags, errs,
                                                magsarefluxes=magsarefluxes)
    stetj = stetson_jindex(ftimes, fmags, ferrs,
                           weightbytimediff=stetson_weightbytimediff)
    stetk = stetson_kindex(fmags, ferrs)

    xfeatures.update({'stetsonj':stetj,
                      'stetsonk':stetk})

    return xfeatures

Response: This function rolls up the feature functions above and returns a single dict with all non - periodic features.


Instruction:
Can you generate the documentation for the following Python 3 function
def _bls_runner(times,
                mags,
                nfreq,
                freqmin,
                stepsize,
                nbins,
                minduration,
                maxduration):
    '''This runs the pyeebls.eebls function using the given inputs.

    Parameters
    ----------

    times,mags : np.array
        The input magnitude time-series to search for transits.

    nfreq : int
        The number of frequencies to use when searching for transits.

    freqmin : float
        The minimum frequency of the period-search -> max period that will be
        used for the search.

    stepsize : float
        The step-size in frequency to use to generate a frequency-grid.

    nbins : int
        The number of phase bins to use.

    minduration : float
        The minimum fractional transit duration that will be considered.

    maxduration : float
        The maximum fractional transit duration that will be considered.

    Returns
    -------

    dict
        Returns a dict of the form::

            {
                'power':           the periodogram power array,
                'bestperiod':      the best period found,
                'bestpower':       the highest peak of the periodogram power,
                'transdepth':      transit depth found by eebls.f,
                'transduration':   transit duration found by eebls.f,
                'transingressbin': transit ingress bin found by eebls.f,
                'transegressbin':  transit egress bin found by eebls.f,
            }

    '''

    workarr_u = npones(times.size)
    workarr_v = npones(times.size)

    blsresult = eebls(times, mags,
                      workarr_u, workarr_v,
                      nfreq, freqmin, stepsize,
                      nbins, minduration, maxduration)

    return {'power':blsresult[0],
            'bestperiod':blsresult[1],
            'bestpower':blsresult[2],
            'transdepth':blsresult[3],
            'transduration':blsresult[4],
            'transingressbin':blsresult[5],
            'transegressbin':blsresult[6]}

Response: This function runs the pyeebls. eebls function and returns the output of the pyeebls. eebls function.


Instruction:
Given the following Python 3 function, write the documentation
def bls_serial_pfind(
        times, mags, errs,
        magsarefluxes=False,
        startp=0.1,  # search from 0.1 d to...
        endp=100.0,  # ... 100.0 d -- don't search full timebase
        stepsize=5.0e-4,
        mintransitduration=0.01,  # minimum transit length in phase
        maxtransitduration=0.4,   # maximum transit length in phase
        nphasebins=200,
        autofreq=True,  # figure out f0, nf, and df automatically
        periodepsilon=0.1,
        nbestpeaks=5,
        sigclip=10.0,
        verbose=True,
        get_stats=True,
):
    '''Runs the Box Least Squares Fitting Search for transit-shaped signals.

    Based on eebls.f from Kovacs et al. 2002 and python-bls from Foreman-Mackey
    et al. 2015. This is the serial version (which is good enough in most cases
    because BLS in Fortran is fairly fast). If nfreq > 5e5, this will take a
    while.

    Parameters
    ----------

    times,mags,errs : np.array
        The magnitude/flux time-series to search for transits.

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

    startp,endp : float
        The minimum and maximum periods to consider for the transit search.

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

    mintransitduration,maxtransitduration : float
        The minimum and maximum transitdurations (in units of phase) to consider
        for the transit search.

    nphasebins : int
        The number of phase bins to use in the period search.

    autofreq : bool
        If this is True, the values of `stepsize` and `nphasebins` will be
        ignored, and these, along with a frequency-grid, will be determined
        based on the following relations::

            nphasebins = int(ceil(2.0/mintransitduration))
            if nphasebins > 3000:
                nphasebins = 3000

            stepsize = 0.25*mintransitduration/(times.max()-times.min())

            minfreq = 1.0/endp
            maxfreq = 1.0/startp
            nfreq = int(ceil((maxfreq - minfreq)/stepsize))

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

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

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

    get_stats : bool
        If True, runs :py:func:`.bls_stats_singleperiod` for each of the best
        periods in the output and injects the output into the output dict so you
        only have to run this function to get the periods and their stats.

    Returns
    -------

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

            {'bestperiod': the best period value in the periodogram,
             'bestlspval': the periodogram peak associated with the best period,
             'nbestpeaks': the input value of nbestpeaks,
             'nbestlspvals': nbestpeaks-size list of best period peak values,
             'nbestperiods': nbestpeaks-size list of best periods,
             'stats': BLS stats for each best period,
             'lspvals': the full array of periodogram powers,
             'frequencies': the full array of frequencies considered,
             'periods': the full array of periods considered,
             'blsresult': the result dict from the eebls.f wrapper function,
             'stepsize': the actual stepsize used,
             'nfreq': the actual nfreq used,
             'nphasebins': the actual nphasebins used,
             'mintransitduration': the input mintransitduration,
             'maxtransitduration': the input maxtransitdurations,
             'method':'bls' -> the name of the period-finder method,
             'kwargs':{ dict of all of the input kwargs for record-keeping}}

    '''

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

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

        # if we're setting up everything automatically
        if autofreq:

            # figure out the best number of phasebins to use
            nphasebins = int(npceil(2.0/mintransitduration))
            if nphasebins > 3000:
                nphasebins = 3000

            # use heuristic to figure out best timestep
            stepsize = 0.25*mintransitduration/(stimes.max()-stimes.min())

            # now figure out the frequencies to use
            minfreq = 1.0/endp
            maxfreq = 1.0/startp
            nfreq = int(npceil((maxfreq - minfreq)/stepsize))

            # say what we're using
            if verbose:
                LOGINFO('min P: %s, max P: %s, nfreq: %s, '
                        'minfreq: %s, maxfreq: %s' % (startp, endp, nfreq,
                                                      minfreq, maxfreq))
                LOGINFO('autofreq = True: using AUTOMATIC values for '
                        'freq stepsize: %s, nphasebins: %s, '
                        'min transit duration: %s, max transit duration: %s' %
                        (stepsize, nphasebins,
                         mintransitduration, maxtransitduration))

        else:

            minfreq = 1.0/endp
            maxfreq = 1.0/startp
            nfreq = int(npceil((maxfreq - minfreq)/stepsize))

            # say what we're using
            if verbose:
                LOGINFO('min P: %s, max P: %s, nfreq: %s, '
                        'minfreq: %s, maxfreq: %s' % (startp, endp, nfreq,
                                                      minfreq, maxfreq))
                LOGINFO('autofreq = False: using PROVIDED values for '
                        'freq stepsize: %s, nphasebins: %s, '
                        'min transit duration: %s, max transit duration: %s' %
                        (stepsize, nphasebins,
                         mintransitduration, maxtransitduration))

        if nfreq > 5.0e5:

            if verbose:
                LOGWARNING('more than 5.0e5 frequencies to go through; '
                           'this will take a while. '
                           'you might want to use the '
                           'periodbase.bls_parallel_pfind function instead')

        if minfreq < (1.0/(stimes.max() - stimes.min())):

            if verbose:
                LOGWARNING('the requested max P = %.3f is larger than '
                           'the time base of the observations = %.3f, '
                           ' will make minfreq = 2 x 1/timebase'
                           % (endp, stimes.max() - stimes.min()))
            minfreq = 2.0/(stimes.max() - stimes.min())
            if verbose:
                LOGINFO('new minfreq: %s, maxfreq: %s' %
                        (minfreq, maxfreq))

        # run BLS
        try:

            blsresult = _bls_runner(stimes,
                                    smags,
                                    nfreq,
                                    minfreq,
                                    stepsize,
                                    nphasebins,
                                    mintransitduration,
                                    maxtransitduration)

            # find the peaks in the BLS. this uses wavelet transforms to
            # smooth the spectrum and find peaks. a similar thing would be
            # to do a convolution with a gaussian kernel or a tophat
            # function, calculate d/dx(result), then get indices where this
            # is zero
            # blspeakinds = find_peaks_cwt(blsresults['power'],
            #                              nparray([2.0,3.0,4.0,5.0]))

            frequencies = minfreq + nparange(nfreq)*stepsize
            periods = 1.0/frequencies
            lsp = blsresult['power']

            # find the nbestpeaks for the periodogram: 1. sort the lsp array
            # by highest value first 2. go down the values until we find
            # five values that are separated by at least periodepsilon in
            # period
            # make sure to get only the finite peaks in the periodogram
            # this is needed because BLS may produce infs for some peaks
            finitepeakind = npisfinite(lsp)
            finlsp = lsp[finitepeakind]
            finperiods = periods[finitepeakind]

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

                bestperiodind = npargmax(finlsp)

            except ValueError:

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

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

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

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

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

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

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

                prevperiod = period

            # generate the return dict
            resultdict = {
                'bestperiod':finperiods[bestperiodind],
                'bestlspval':finlsp[bestperiodind],
                'nbestpeaks':nbestpeaks,
                'nbestlspvals':nbestlspvals,
                'nbestperiods':nbestperiods,
                'lspvals':lsp,
                'frequencies':frequencies,
                'periods':periods,
                'blsresult':blsresult,
                'stepsize':stepsize,
                'nfreq':nfreq,
                'nphasebins':nphasebins,
                'mintransitduration':mintransitduration,
                'maxtransitduration':maxtransitduration,
                'method':'bls',
                'kwargs':{'startp':startp,
                          'endp':endp,
                          'stepsize':stepsize,
                          'mintransitduration':mintransitduration,
                          'maxtransitduration':maxtransitduration,
                          'nphasebins':nphasebins,
                          'autofreq':autofreq,
                          'periodepsilon':periodepsilon,
                          'nbestpeaks':nbestpeaks,
                          'sigclip':sigclip,
                          'magsarefluxes':magsarefluxes}
            }

            # get stats if requested
            if get_stats:
                resultdict['stats'] = []
                for bp in nbestperiods:

                    if verbose:
                        LOGINFO("Getting stats for best period: %.6f" % bp)

                    this_pstats = bls_stats_singleperiod(
                        times, mags, errs, bp,
                        magsarefluxes=resultdict['kwargs']['magsarefluxes'],
                        sigclip=resultdict['kwargs']['sigclip'],
                        nphasebins=resultdict['nphasebins'],
                        mintransitduration=resultdict['mintransitduration'],
                        maxtransitduration=resultdict['maxtransitduration'],
                        verbose=verbose,
                    )
                    resultdict['stats'].append(this_pstats)

            return resultdict

        except Exception as e:

            LOGEXCEPTION('BLS failed!')
            return {'bestperiod':npnan,
                    'bestlspval':npnan,
                    'nbestpeaks':nbestpeaks,
                    'nbestlspvals':None,
                    'nbestperiods':None,
                    'lspvals':None,
                    'periods':None,
                    'blsresult':None,
                    'stepsize':stepsize,
                    'nfreq':nfreq,
                    'nphasebins':nphasebins,
                    'mintransitduration':mintransitduration,
                    'maxtransitduration':maxtransitduration,
                    'method':'bls',
                    'kwargs':{'startp':startp,
                              'endp':endp,
                              'stepsize':stepsize,
                              'mintransitduration':mintransitduration,
                              'maxtransitduration':maxtransitduration,
                              'nphasebins':nphasebins,
                              'autofreq':autofreq,
                              'periodepsilon':periodepsilon,
                              'nbestpeaks':nbestpeaks,
                              'sigclip':sigclip,
                              'magsarefluxes':magsarefluxes}}

    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,
                'blsresult':None,
                'stepsize':stepsize,
                'nfreq':None,
                'nphasebins':None,
                'mintransitduration':mintransitduration,
                'maxtransitduration':maxtransitduration,
                'method':'bls',
                'kwargs':{'startp':startp,
                          'endp':endp,
                          'stepsize':stepsize,
                          'mintransitduration':mintransitduration,
                          'maxtransitduration':maxtransitduration,
                          'nphasebins':nphasebins,
                          'autofreq':autofreq,
                          'periodepsilon':periodepsilon,
                          'nbestpeaks':nbestpeaks,
                          'sigclip':sigclip,
                          'magsarefluxes':magsarefluxes}}

Response: This function runs the Box Least Squares Fitting Search for transit - shaped signals.