Instruction:
How would you explain what the following Python 3 function does
def _pkl_phased_magseries_plot(
        checkplotdict,
        lspmethod,
        periodind,
        stimes, smags, serrs,
        varperiod, varepoch,
        lspmethodind=0,
        phasewrap=True,
        phasesort=True,
        phasebin=0.002,
        minbinelems=7,
        plotxlim=(-0.8,0.8),
        plotdpi=100,
        bestperiodhighlight=None,
        xgridlines=None,
        xliminsetmode=False,
        magsarefluxes=False,
        directreturn=False,
        overplotfit=None,
        verbose=True,
        override_pfmethod=None
):
    '''This returns the phased magseries plot PNG as base64 plus info as a dict.

    Parameters
    ----------

    checkplotdict : dict
        This is an existing checkplotdict to update. If it's None or
        `directreturn` = True, then the generated dict result for this magseries
        plot will be returned directly.

    lspmethod : str
        lspmethod is a string indicating the type of period-finding algorithm
        that produced the period. If this is not in
        `astrobase.plotbase.METHODSHORTLABELS`, it will be used verbatim. In
        most cases, this will come directly from the lspinfo dict produced by a
        period-finder function.

    periodind : int
        This is the index of the current periodogram period being operated
        on::

            If == 0 -> best period and `bestperiodhighlight` is applied if not
                       None
            If > 0   -> some other peak of the periodogram
            If == -1 -> special mode w/ no periodogram labels and enabled
                        highlight

    stimes,smags,serrs : np.array
        The mag/flux time-series arrays along with associated errors. These
        should all have been run through nan-stripping and sigma-clipping
        beforehand.

    varperiod : float or None
        The period to use for this phased light curve plot tile.

    varepoch : 'min' or float or list of lists or None
        The epoch to use for this phased light curve plot tile. If this is a
        float, will use the provided value directly. If this is 'min', will
        automatically figure out the time-of-minimum of the phased light
        curve. If this is None, will use the mimimum value of `stimes` as the
        epoch of the phased light curve plot. If this is a list of lists, will
        use the provided value of `lspmethodind` to look up the current
        period-finder method and the provided value of `periodind` to look up
        the epoch associated with that method and the current period. This is
        mostly only useful when `twolspmode` is True.

    phasewrap : bool
        If this is True, the phased time-series will be wrapped around
        phase 0.0.

    phasesort : bool
        If True, will sort the phased light curve in order of increasing phase.

    phasebin: float
        The bin size to use to group together measurements closer than this
        amount in phase. This is in units of phase. If this is a float, a
        phase-binned version of the phased light curve will be overplotted on
        top of the regular phased light curve.

    minbinelems : int
        The minimum number of elements required per phase bin to include it in
        the phased LC plot.

    plotxlim : sequence of two floats or None
        The x-range (min, max) of the phased light curve plot. If None, will be
        determined automatically.

    plotdpi : int
        The resolution of the output plot PNGs in dots per inch.

    bestperiodhighlight : str or None
        If not None, this is a str with a matplotlib color specification to use
        as the background color to highlight the phased light curve plot of the
        'best' period and epoch combination. If None, no highlight will be
        applied.

    xgridlines : list of floats or None
        If this is provided, must be a list of floats corresponding to the phase
        values where to draw vertical dashed lines as a means of highlighting
        these.

    xliminsetmode : bool
        If this is True, the generated phased light curve plot will use the
        values of `plotxlim` as the main plot x-axis limits (i.e. zoomed-in if
        `plotxlim` is a range smaller than the full phase range), and will show
        the full phased light curve plot as an smaller inset. Useful for
        planetary transit light curves.

    magsarefluxes : bool
        If True, indicates the input time-series is fluxes and not mags so the
        plot y-axis direction and range can be set appropriately.

    directreturn : bool
        If this set to True, will return only the dict corresponding to the
        phased LC plot for the input `periodind` and `lspmethod` and not return
        this result embedded in a checkplotdict.

    overplotfit : dict
        If this is provided, it must be a dict of the form returned by one of
        the astrobase.lcfit.fit_XXXXX_magseries functions. This can be used to
        overplot a light curve model fit on top of the phased light curve plot
        returned by this function. The `overplotfit` dict has the following
        form, including at least the keys listed here::

            {'fittype':str: name of fit method,
            'fitchisq':float: the chi-squared value of the fit,
            'fitredchisq':float: the reduced chi-squared value of the fit,
            'fitinfo':{'fitmags':array: model mags or fluxes from fit function},
            'magseries':{'times':array: times where the fitmags are evaluated}}

        `fitmags` and `times` should all be of the same size. The input
        `overplotfit` dict is copied over to the checkplotdict for each specific
        phased LC plot to save all of this information for use later.

    verbose : bool
        If True, will indicate progress and warn about problems.

    override_pfmethod : str or None
        This is used to set a custom label for the periodogram method. Normally,
        this is taken from the 'method' key in the input `lspinfo` dict, but if
        you want to override the output method name, provide this as a string
        here. This can be useful if you have multiple results you want to
        incorporate into a checkplotdict from a single period-finder (e.g. if
        you ran BLS over several period ranges separately).

    Returns
    -------

    dict
        Returns a dict of the following form::

            {lspmethod: {'plot': the phased LC plot as base64 str,
                         'period': the period used for this phased LC,
                         'epoch': the epoch used for this phased LC,
                         'phase': phase value array,
                         'phasedmags': mags/fluxes sorted in phase order,
                         'binphase': array of binned phase values,
                         'binphasedmags': mags/fluxes sorted in binphase order,
                         'phasewrap': value of the input `phasewrap` kwarg,
                         'phasesort': value of the input `phasesort` kwarg,
                         'phasebin': value of the input `phasebin` kwarg,
                         'minbinelems': value of the input `minbinelems` kwarg,
                         'plotxlim': value of the input `plotxlim` kwarg,
                         'lcfit': the provided `overplotfit` dict}}

        The dict is in this form because we can use Python dicts' `update()`
        method to update an existing checkplotdict. If `returndirect` is True,
        only the inner dict is returned.

    '''

    # open the figure instance
    phasedseriesfig = plt.figure(figsize=(7.5,4.8),dpi=plotdpi)

    plotvarepoch = None

    # figure out the epoch, if it's None, use the min of the time
    if varepoch is None:
        plotvarepoch = npmin(stimes)

    # if the varepoch is 'min', then fit a spline to the light curve
    # phased using the min of the time, find the fit mag minimum and use
    # the time for that as the varepoch
    elif isinstance(varepoch,str) and varepoch == 'min':

        try:
            spfit = spline_fit_magseries(stimes,
                                         smags,
                                         serrs,
                                         varperiod,
                                         magsarefluxes=magsarefluxes,
                                         sigclip=None,
                                         verbose=verbose)
            plotvarepoch = spfit['fitinfo']['fitepoch']
            if len(plotvarepoch) != 1:
                plotvarepoch = plotvarepoch[0]

        except Exception as e:

            LOGERROR('spline fit failed, trying SavGol fit')

            sgfit = savgol_fit_magseries(stimes,
                                         smags,
                                         serrs,
                                         varperiod,
                                         sigclip=None,
                                         magsarefluxes=magsarefluxes,
                                         verbose=verbose)
            plotvarepoch = sgfit['fitinfo']['fitepoch']
            if len(plotvarepoch) != 1:
                plotvarepoch = plotvarepoch[0]

        finally:

            if plotvarepoch is None:

                LOGERROR('could not find a min epoch time, '
                         'using min(times) as the epoch for '
                         'the phase-folded LC')

                plotvarepoch = npmin(stimes)

    # special case with varepoch lists per each period-finder method
    elif isinstance(varepoch, list):

        try:
            thisvarepochlist = varepoch[lspmethodind]
            plotvarepoch = thisvarepochlist[periodind]
        except Exception as e:
            LOGEXCEPTION(
                "varepoch provided in list form either doesn't match "
                "the length of nbestperiods from the period-finder "
                "result, or something else went wrong. using min(times) "
                "as the epoch instead"
            )
            plotvarepoch = npmin(stimes)

    # the final case is to use the provided varepoch directly
    else:
        plotvarepoch = varepoch

    if verbose:
        LOGINFO('plotting %s phased LC with period %s: %.6f, epoch: %.5f' %
                (lspmethod, periodind, varperiod, plotvarepoch))

    # make the plot title based on the lspmethod
    if periodind == 0:
        plottitle = '%s best period: %.6f d - epoch: %.5f' % (
            (METHODSHORTLABELS[lspmethod] if lspmethod in METHODSHORTLABELS
             else lspmethod),
            varperiod,
            plotvarepoch
        )
    elif periodind > 0:
        plottitle = '%s peak %s: %.6f d - epoch: %.5f' % (
            (METHODSHORTLABELS[lspmethod] if lspmethod in METHODSHORTLABELS
             else lspmethod),
            periodind+1,
            varperiod,
            plotvarepoch
        )
    elif periodind == -1:
        plottitle = '%s period: %.6f d - epoch: %.5f' % (
            lspmethod,
            varperiod,
            plotvarepoch
        )

    # phase the magseries
    phasedlc = phase_magseries(stimes,
                               smags,
                               varperiod,
                               plotvarepoch,
                               wrap=phasewrap,
                               sort=phasesort)
    plotphase = phasedlc['phase']
    plotmags = phasedlc['mags']

    # if we're supposed to bin the phases, do so
    if phasebin:

        binphasedlc = phase_bin_magseries(plotphase,
                                          plotmags,
                                          binsize=phasebin,
                                          minbinelems=minbinelems)
        binplotphase = binphasedlc['binnedphases']
        binplotmags = binphasedlc['binnedmags']

    else:
        binplotphase = None
        binplotmags = None

    # finally, make the phased LC plot
    plt.plot(plotphase,
             plotmags,
             marker='o',
             ms=2.0, ls='None',mew=0,
             color='gray',
             rasterized=True)

    # overlay the binned phased LC plot if we're making one
    if phasebin:
        plt.plot(binplotphase,
                 binplotmags,
                 marker='o',
                 ms=4.0, ls='None',mew=0,
                 color='#1c1e57',
                 rasterized=True)

    # if we're making a overplotfit, then plot the fit over the other stuff
    if overplotfit and isinstance(overplotfit, dict):

        fitmethod = overplotfit['fittype']
        fitredchisq = overplotfit['fitredchisq']

        plotfitmags = overplotfit['fitinfo']['fitmags']
        plotfittimes = overplotfit['magseries']['times']

        # phase the fit magseries
        fitphasedlc = phase_magseries(plotfittimes,
                                      plotfitmags,
                                      varperiod,
                                      plotvarepoch,
                                      wrap=phasewrap,
                                      sort=phasesort)
        plotfitphase = fitphasedlc['phase']
        plotfitmags = fitphasedlc['mags']

        plotfitlabel = (r'%s fit ${\chi}^2/{\mathrm{dof}} = %.3f$' %
                        (fitmethod, fitredchisq))

        # plot the fit phase and mags
        plt.plot(plotfitphase, plotfitmags,'k-',
                 linewidth=3, rasterized=True,label=plotfitlabel)

        plt.legend(loc='upper left', frameon=False)

    # flip y axis for mags
    if not magsarefluxes:
        plot_ylim = plt.ylim()
        plt.ylim((plot_ylim[1], plot_ylim[0]))

    # set the x axis limit
    if not plotxlim:
        plt.xlim((npmin(plotphase)-0.1,
                  npmax(plotphase)+0.1))
    else:
        plt.xlim((plotxlim[0],plotxlim[1]))

    # make a grid
    ax = plt.gca()
    if isinstance(xgridlines, (list, tuple)):
        ax.set_xticks(xgridlines, minor=False)

    plt.grid(color='#a9a9a9',
             alpha=0.9,
             zorder=0,
             linewidth=1.0,
             linestyle=':')

    # make the x and y axis labels
    plot_xlabel = 'phase'
    if magsarefluxes:
        plot_ylabel = 'flux'
    else:
        plot_ylabel = 'magnitude'

    plt.xlabel(plot_xlabel)
    plt.ylabel(plot_ylabel)

    # fix the yaxis ticks (turns off offset and uses the full
    # value of the yaxis tick)
    plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)
    plt.gca().get_xaxis().get_major_formatter().set_useOffset(False)

    # set the plot title
    plt.title(plottitle)

    # make sure the best period phased LC plot stands out
    if (periodind == 0 or periodind == -1) and bestperiodhighlight:
        if MPLVERSION >= (2,0,0):
            plt.gca().set_facecolor(bestperiodhighlight)
        else:
            plt.gca().set_axis_bgcolor(bestperiodhighlight)

    # if we're making an inset plot showing the full range
    if (plotxlim and isinstance(plotxlim, (list, tuple)) and
        len(plotxlim) == 2 and xliminsetmode is True):

        # bump the ylim of the plot so that the inset can fit in this axes plot
        axesylim = plt.gca().get_ylim()

        if magsarefluxes:
            plt.gca().set_ylim(
                axesylim[0],
                axesylim[1] + 0.5*npabs(axesylim[1]-axesylim[0])
            )
        else:
            plt.gca().set_ylim(
                axesylim[0],
                axesylim[1] - 0.5*npabs(axesylim[1]-axesylim[0])
            )

        # put the inset axes in
        inset = inset_axes(plt.gca(), width="40%", height="40%", loc=1)

        # make the scatter plot for the phased LC plot
        inset.plot(plotphase,
                   plotmags,
                   marker='o',
                   ms=2.0, ls='None',mew=0,
                   color='gray',
                   rasterized=True)

        if phasebin:
            # make the scatter plot for the phased LC plot
            inset.plot(binplotphase,
                       binplotmags,
                       marker='o',
                       ms=4.0, ls='None',mew=0,
                       color='#1c1e57',
                       rasterized=True)

        # show the full phase coverage
        if phasewrap:
            inset.set_xlim(-0.2,0.8)
        else:
            inset.set_xlim(-0.1,1.1)

        # flip y axis for mags
        if not magsarefluxes:
            inset_ylim = inset.get_ylim()
            inset.set_ylim((inset_ylim[1], inset_ylim[0]))

        # set the plot title
        inset.text(0.5,0.9,'full phased light curve',
                   ha='center',va='center',transform=inset.transAxes)
        # don't show axes labels or ticks
        inset.set_xticks([])
        inset.set_yticks([])

    # this is the output instance
    phasedseriespng = StrIO()
    phasedseriesfig.savefig(phasedseriespng,
                            # bbox_inches='tight',
                            pad_inches=0.0, format='png')
    plt.close()

    # encode the finderpng instance to base64
    phasedseriespng.seek(0)
    phasedseriesb64 = base64.b64encode(phasedseriespng.read())

    # close the stringio buffer
    phasedseriespng.close()

    # this includes a fitinfo dict if one is provided in overplotfit
    retdict = {
        'plot':phasedseriesb64,
        'period':varperiod,
        'epoch':plotvarepoch,
        'phase':plotphase,
        'phasedmags':plotmags,
        'binphase':binplotphase,
        'binphasedmags':binplotmags,
        'phasewrap':phasewrap,
        'phasesort':phasesort,
        'phasebin':phasebin,
        'minbinelems':minbinelems,
        'plotxlim':plotxlim,
        'lcfit':overplotfit,
    }

    # if we're returning stuff directly, i.e. not being used embedded within
    # the checkplot_dict function
    if directreturn or checkplotdict is None:

        return retdict

    # this requires the checkplotdict to be present already, we'll just update
    # it at the appropriate lspmethod and periodind
    else:

        if override_pfmethod:
            checkplotdict[override_pfmethod][periodind] = retdict
        else:
            checkplotdict[lspmethod][periodind] = retdict

        return checkplotdict

Response: This function returns the phased magseries plot PNG as base64 plus info as a dict.


Instruction:
Can you generate the documentation for the following Python 3 function
def prewhiten_magseries(times, mags, errs,
                        whitenperiod,
                        whitenparams,
                        sigclip=3.0,
                        magsarefluxes=False,
                        plotfit=None,
                        plotfitphasedlconly=True,
                        rescaletomedian=True):
    '''Removes a periodic sinusoidal signal generated using whitenparams from
    the input magnitude time series.

    Parameters
    ----------

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

    whitenperiod : float
        The period of the sinusoidal signal to remove.

    whitenparams : list of floats
        This contains the Fourier amplitude and phase coefficients of the
        sinusoidal signal to remove::

            [ampl_1, ampl_2, ampl_3, ..., ampl_X,
             pha_1, pha_2, pha_3, ..., pha_X]

        where `X` is the Fourier order. These are usually the output of a
        previous Fourier fit to the light curve (from
        :py:func:`astrobase.lcfit.sinusoidal.fourier_fit_magseries` for
        example).

    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, will treat the input values of `mags` as fluxes for purposes of
        plotting the fit and sig-clipping.

    plotfit : str or False
        If this is a string, this function will make a plot showing the effect
        of the pre-whitening on the mag/flux time-series and write the plot to
        the path specified here.

    plotfitphasedlconly : bool
        If True, will plot only the phased LC for showing the effect of
        pre-whitening, and skip plotting the unphased LC.

    rescaletomedian : bool
        If this is True, then we add back the constant median term of the
        magnitudes to the final pre-whitened mag series.

    Returns
    -------

    dict
        Returns a dict of the form::

            {'wtimes':times array after pre-whitening,
             'wphase':phase array after pre-whitening,
             'wmags':mags array after pre-whitening,
             'werrs':errs array after pre-whitening,
             'whitenparams':the input pre-whitening params used,
             'whitenperiod':the input pre-whitening period used,
             'fitplotfile':the output plot file if plotfit was set}

    '''

    stimes, smags, serrs = sigclip_magseries(times, mags, errs,
                                             sigclip=sigclip,
                                             magsarefluxes=magsarefluxes)

    median_mag = np.median(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)/whitenperiod -
        np.floor((stimes - mintime)/whitenperiod)
    )
    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]

    # now subtract the harmonic series from the phased LC
    # these are still in phase order
    wmags = pmags - _fourier_func(whitenparams, phase, pmags)

    # resort everything by time order
    wtimeorder = np.argsort(ptimes)
    wtimes = ptimes[wtimeorder]
    wphase = phase[wtimeorder]
    wmags = wmags[wtimeorder]
    werrs = perrs[wtimeorder]

    if rescaletomedian:
        wmags = wmags + median_mag

    # prepare the returndict
    returndict = {'wtimes':wtimes,  # these are in the new time order
                  'wphase':wphase,
                  'wmags':wmags,
                  'werrs':werrs,
                  'whitenparams':whitenparams,
                  'whitenperiod':whitenperiod}

    # make the fit plot if required
    if plotfit and (isinstance(plotfit, str) or isinstance(plotfit, Strio)):

        if plotfitphasedlconly:
            plt.figure(figsize=(10,4.8))
        else:
            plt.figure(figsize=(16,9.6))

        if plotfitphasedlconly:

            # phased series before whitening
            plt.subplot(121)
            plt.plot(phase,pmags,
                     marker='.',
                     color='k',
                     linestyle='None',
                     markersize=2.0,
                     markeredgewidth=0)

            if not magsarefluxes:
                plt.gca().invert_yaxis()
                plt.ylabel('magnitude')
            else:
                plt.ylabel('fluxes')

            plt.xlabel('phase')
            plt.title('phased LC before pre-whitening')

            # phased series after whitening
            plt.subplot(122)
            plt.plot(wphase,wmags,
                     marker='.',
                     color='g',
                     linestyle='None',
                     markersize=2.0,
                     markeredgewidth=0)

            if not magsarefluxes:
                plt.gca().invert_yaxis()
                plt.ylabel('magnitude')
            else:
                plt.ylabel('fluxes')

            plt.xlabel('phase')
            plt.title('phased LC after pre-whitening')

        else:

            # time series before whitening
            plt.subplot(221)
            plt.plot(stimes,smags,
                     marker='.',
                     color='k',
                     linestyle='None',
                     markersize=2.0,
                     markeredgewidth=0)

            if not magsarefluxes:
                plt.gca().invert_yaxis()
                plt.ylabel('magnitude')
            else:
                plt.ylabel('fluxes')

            plt.xlabel('JD')
            plt.title('LC before pre-whitening')

            # time series after whitening
            plt.subplot(222)
            plt.plot(wtimes,wmags,
                     marker='.',
                     color='g',
                     linestyle='None',
                     markersize=2.0,
                     markeredgewidth=0)

            if not magsarefluxes:
                plt.gca().invert_yaxis()
                plt.ylabel('magnitude')
            else:
                plt.ylabel('fluxes')

            plt.xlabel('JD')
            plt.title('LC after pre-whitening with period: %.6f' % whitenperiod)

            # phased series before whitening
            plt.subplot(223)
            plt.plot(phase,pmags,
                     marker='.',
                     color='k',
                     linestyle='None',
                     markersize=2.0,
                     markeredgewidth=0)

            if not magsarefluxes:
                plt.gca().invert_yaxis()
                plt.ylabel('magnitude')
            else:
                plt.ylabel('fluxes')

            plt.xlabel('phase')
            plt.title('phased LC before pre-whitening')

            # phased series after whitening
            plt.subplot(224)
            plt.plot(wphase,wmags,
                     marker='.',
                     color='g',
                     linestyle='None',
                     markersize=2.0,
                     markeredgewidth=0)

            if not magsarefluxes:
                plt.gca().invert_yaxis()
                plt.ylabel('magnitude')
            else:
                plt.ylabel('fluxes')

            plt.xlabel('phase')
            plt.title('phased LC after pre-whitening')

        plt.tight_layout()
        plt.savefig(plotfit, format='png', pad_inches=0.0)
        plt.close()

        if isinstance(plotfit, str) or isinstance(plotfit, Strio):
            returndict['fitplotfile'] = plotfit

    return returndict

Response: This function removes a periodic sinusoidal signal generated using whitenparams from the input magnitude time - series.


Instruction:
Here you have a function in Python 3, explain what it does
def mask_signal(times, mags, errs,
                signalperiod,
                signalepoch,
                magsarefluxes=False,
                maskphases=(0,0,0.5,1.0),
                maskphaselength=0.1,
                plotfit=None,
                plotfitphasedlconly=True,
                sigclip=30.0):
    '''This removes repeating signals in the magnitude time series.

    Useful for masking planetary transit signals in light curves to search for
    other variability.

    A small worked example of using this and `prewhiten_magseries` above:

    https://github.com/waqasbhatti/astrobase/issues/77#issuecomment-463803558

    Parameters
    ----------

    times,mags,errs : np.array
        The input mag/flux time-series to run the masking on.

    signalperiod : float
        The period of the signal to mask.

    signalepoch : float
        The epoch of the signal to mask.

    magsarefluxes : bool
        Set to True if `mags` is actually an array of fluxes.

    maskphases : sequence of floats
        This defines which phase values will be masked. For each item in this
        sequence, this function will mask a length of phase given by
        `maskphaselength` centered on each `maskphases` value, and remove all LC
        points in these regions from the light curve.

    maskphaselength : float
        The length in phase to mask for each phase value provided in
        `maskphases`.

    plotfit : str or None
        If provided as a str, indicates the output plot file.

    plotfitphasedlconly : bool
        If True, will only plot the effect of masking the signal as requested on
        the phased LC. If False, will also plot the unphased LC.

    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.

    '''

    stimes, smags, serrs = sigclip_magseries(times, mags, errs,
                                             sigclip=sigclip,
                                             magsarefluxes=magsarefluxes)

    # now phase the light curve using the period and epoch provided
    phases = (
        (stimes - signalepoch)/signalperiod -
        np.floor((stimes - signalepoch)/signalperiod)
    )

    # mask the requested phases using the mask length (in phase units)
    # this gets all the masks into one array
    masks = np.array([(np.abs(phases - x) > maskphaselength)
                      for x in maskphases])
    # this flattens the masks to a single array for all combinations
    masks = np.all(masks,axis=0)

    # apply the mask to the times, mags, and errs
    mphases = phases[masks]
    mtimes = stimes[masks]
    mmags = smags[masks]
    merrs = serrs[masks]

    returndict = {'mphases':mphases,
                  'mtimes':mtimes,
                  'mmags':mmags,
                  'merrs':merrs}

    # make the fit plot if required
    if plotfit and isinstance(plotfit, str) or isinstance(plotfit, Strio):

        if plotfitphasedlconly:
            plt.figure(figsize=(10,4.8))
        else:
            plt.figure(figsize=(16,9.6))

        if plotfitphasedlconly:

            # phased series before whitening
            plt.subplot(121)
            plt.plot(phases,smags,
                     marker='.',
                     color='k',
                     linestyle='None',
                     markersize=2.0,
                     markeredgewidth=0)

            if not magsarefluxes:
                plt.gca().invert_yaxis()
                plt.ylabel('magnitude')
            else:
                plt.ylabel('fluxes')

            plt.xlabel('phase')
            plt.title('phased LC before signal masking')

            # phased series after whitening
            plt.subplot(122)
            plt.plot(mphases,mmags,
                     marker='.',
                     color='g',
                     linestyle='None',
                     markersize=2.0,
                     markeredgewidth=0)

            if not magsarefluxes:
                plt.gca().invert_yaxis()
                plt.ylabel('magnitude')
            else:
                plt.ylabel('fluxes')

            plt.xlabel('phase')
            plt.title('phased LC after signal masking')

        else:

            # time series before whitening
            plt.subplot(221)
            plt.plot(stimes,smags,
                     marker='.',
                     color='k',
                     linestyle='None',
                     markersize=2.0,
                     markeredgewidth=0)

            if not magsarefluxes:
                plt.gca().invert_yaxis()
                plt.ylabel('magnitude')
            else:
                plt.ylabel('fluxes')

            plt.xlabel('JD')
            plt.title('LC before signal masking')

            # time series after whitening
            plt.subplot(222)
            plt.plot(mtimes,mmags,
                     marker='.',
                     color='g',
                     linestyle='None',
                     markersize=2.0,
                     markeredgewidth=0)

            if not magsarefluxes:
                plt.gca().invert_yaxis()
                plt.ylabel('magnitude')
            else:
                plt.ylabel('fluxes')

            plt.xlabel('JD')
            plt.title('LC after signal masking')

            # phased series before whitening
            plt.subplot(223)
            plt.plot(phases,smags,
                     marker='.',
                     color='k',
                     linestyle='None',
                     markersize=2.0,
                     markeredgewidth=0)

            if not magsarefluxes:
                plt.gca().invert_yaxis()
                plt.ylabel('magnitude')
            else:
                plt.ylabel('fluxes')

            plt.xlabel('phase')
            plt.title('phased LC before signal masking')

            # phased series after whitening
            plt.subplot(224)
            plt.plot(mphases,mmags,
                     marker='.',
                     color='g',
                     linestyle='None',
                     markersize=2.0,
                     markeredgewidth=0)

            if not magsarefluxes:
                plt.gca().invert_yaxis()
                plt.ylabel('magnitude')
            else:
                plt.ylabel('fluxes')

            plt.xlabel('phase')
            plt.title('phased LC after signal masking')

        plt.tight_layout()
        plt.savefig(plotfit, format='png', pad_inches=0.0)
        plt.close()

        if isinstance(plotfit, str) or isinstance(plotfit, Strio):
            returndict['fitplotfile'] = plotfit

    return returndict

Response: This function masks the signal in the time - series times and mags.


Instruction:
Can you tell what is the following Python 3 function doing
def find_lc_timegroups(lctimes, mingap=4.0):
    '''Finds gaps in the provided time-series and indexes them into groups.

    This finds the gaps in the provided `lctimes` array, so we can figure out
    which times are for consecutive observations and which represent gaps
    between seasons or observing eras.

    Parameters
    ----------

    lctimes : array-like
        This contains the times to analyze for gaps; assumed to be some form of
        Julian date.

    mingap : float
        This defines how much the difference between consecutive measurements is
        allowed to be to consider them as parts of different timegroups. By
        default it is set to 4.0 days.

    Returns
    -------

    tuple
        A tuple of the form: `(ngroups, [slice(start_ind_1, end_ind_1), ...])`
        is returned.  This contains the number of groups as the first element,
        and a list of Python `slice` objects for each time-group found. These
        can be used directly to index into the array of times to quickly get
        measurements associated with each group.

    '''

    lc_time_diffs = np.diff(lctimes)
    group_start_indices = np.where(lc_time_diffs > mingap)[0]

    if len(group_start_indices) > 0:

        group_indices = []

        for i, gindex in enumerate(group_start_indices):

            if i == 0:
                group_indices.append(slice(0,gindex+1))
            else:
                group_indices.append(slice(group_start_indices[i-1]+1,gindex+1))

        # at the end, add the slice for the last group to the end of the times
        # array
        group_indices.append(slice(group_start_indices[-1]+1,len(lctimes)))

    # if there's no large gap in the LC, then there's only one group to worry
    # about
    else:
        group_indices = [slice(0,len(lctimes))]

    return len(group_indices), group_indices

Response: Finds gaps in the provided time - series and indexes them into groups.


Instruction:
Here you have a function in Python 3, explain what it does
def normalize_magseries(times,
                        mags,
                        mingap=4.0,
                        normto='globalmedian',
                        magsarefluxes=False,
                        debugmode=False):
    '''This normalizes the magnitude time-series to a specified value.

    This is used to normalize time series measurements that may have large time
    gaps and vertical offsets in mag/flux measurement between these
    'timegroups', either due to instrument changes or different filters.

    NOTE: this works in-place! The mags array will be replaced with normalized
    mags when this function finishes.

    Parameters
    ----------

    times,mags : array-like
        The times (assumed to be some form of JD) and mags (or flux)
        measurements to be normalized.

    mingap : float
        This defines how much the difference between consecutive measurements is
        allowed to be to consider them as parts of different timegroups. By
        default it is set to 4.0 days.

    normto : {'globalmedian', 'zero'} or a float
        Specifies the normalization type::

          'globalmedian' -> norms each mag to the global median of the LC column
          'zero'         -> norms each mag to zero
          a float        -> norms each mag to this specified float value.

    magsarefluxes : bool
        Indicates if the input `mags` array is actually an array of flux
        measurements instead of magnitude measurements. If this is set to True,
        then:

        - if `normto` is 'zero', then the median flux is divided from each
          observation's flux value to yield normalized fluxes with 1.0 as the
          global median.

        - if `normto` is 'globalmedian', then the global median flux value
          across the entire time series is multiplied with each measurement.

        - if `norm` is set to a `float`, then this number is multiplied with the
          flux value for each measurement.

    debugmode : bool
        If this is True, will print out verbose info on each timegroup found.

    Returns
    -------

    times,normalized_mags : np.arrays
        Normalized magnitude values after normalization. If normalization fails
        for some reason, `times` and `normalized_mags` will both be None.

    '''

    ngroups, timegroups = find_lc_timegroups(times,
                                             mingap=mingap)

    # find all the non-nan indices
    finite_ind = np.isfinite(mags)

    if any(finite_ind):

        # find the global median
        global_mag_median = np.median(mags[finite_ind])

        # go through the groups and normalize them to the median for
        # each group
        for tgind, tg in enumerate(timegroups):

            finite_ind = np.isfinite(mags[tg])

            # find this timegroup's median mag and normalize the mags in
            # it to this median
            group_median = np.median((mags[tg])[finite_ind])

            if magsarefluxes:
                mags[tg] = mags[tg]/group_median
            else:
                mags[tg] = mags[tg] - group_median

            if debugmode:
                LOGDEBUG('group %s: elems %s, '
                         'finite elems %s, median mag %s' %
                         (tgind,
                          len(mags[tg]),
                          len(finite_ind),
                          group_median))

        # now that everything is normalized to 0.0, add the global median
        # offset back to all the mags and write the result back to the dict
        if isinstance(normto, str) and normto == 'globalmedian':

            if magsarefluxes:
                mags = mags * global_mag_median
            else:
                mags = mags + global_mag_median

        # if the normto is a float, add everything to that float and return
        elif isinstance(normto, float):

            if magsarefluxes:
                mags = mags * normto
            else:
                mags = mags + normto

        # anything else just returns the normalized mags as usual
        return times, mags

    else:
        LOGERROR('measurements are all nan!')
        return None, None

Response: This function normalizes the magnitude time - series measurements to a specified value.


Instruction:
Can you tell what is the following Python 3 function doing
def sigclip_magseries(times, mags, errs,
                      sigclip=None,
                      iterative=False,
                      niterations=None,
                      meanormedian='median',
                      magsarefluxes=False):
    '''Sigma-clips a magnitude or flux time-series.

    Selects the finite times, magnitudes (or fluxes), and errors from the passed
    values, and apply symmetric or asymmetric sigma clipping to them.

    Parameters
    ----------

    times,mags,errs : np.array
        The magnitude or flux time-series arrays to sigma-clip. This doesn't
        assume all values are finite or if they're positive/negative. All of
        these arrays will have their non-finite elements removed, and then will
        be sigma-clipped based on the arguments to this function.

        `errs` is optional. Set it to None if you don't have values for these. A
        'faked' `errs` array will be generated if necessary, which can be
        ignored in the output as well.

    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.

    iterative : bool
        If this is set to True, will perform iterative sigma-clipping. If
        `niterations` is not set and this is True, sigma-clipping is iterated
        until no more points are removed.

    niterations : int
        The maximum number of iterations to perform for sigma-clipping. If None,
        the `iterative` arg takes precedence, and `iterative=True` will
        sigma-clip until no more points are removed.  If `niterations` is not
        None and `iterative` is False, `niterations` takes precedence and
        iteration will occur for the specified number of iterations.

    meanormedian : {'mean', 'median'}
        Use 'mean' for sigma-clipping based on the mean value, or 'median' for
        sigma-clipping based on the median value.  Default is 'median'.

    magsareflux : bool
        True if your "mags" are in fact fluxes, i.e. if "fainter" corresponds to
        `mags` getting smaller.

    Returns
    -------

    (stimes, smags, serrs) : tuple
        The sigma-clipped and nan-stripped time-series.

    '''

    returnerrs = True

    # fake the errors if they don't exist
    # this is inconsequential to sigma-clipping
    # we don't return these dummy values if the input errs are None
    if errs is None:
        # assume 0.1% errors if not given
        # this should work for mags and fluxes
        errs = 0.001*mags
        returnerrs = False

    # filter the input times, mags, errs; do sigclipping and normalization
    find = npisfinite(times) & npisfinite(mags) & npisfinite(errs)
    ftimes, fmags, ferrs = times[find], mags[find], errs[find]

    # get the center value and stdev
    if meanormedian == 'median':  # stddev = 1.483 x MAD

        center_mag = npmedian(fmags)
        stddev_mag = (npmedian(npabs(fmags - center_mag))) * 1.483

    elif meanormedian == 'mean':

        center_mag = npmean(fmags)
        stddev_mag = npstddev(fmags)

    else:
        LOGWARNING("unrecognized meanormedian value given to "
                   "sigclip_magseries: %s, defaulting to 'median'" %
                   meanormedian)
        meanormedian = 'median'
        center_mag = npmedian(fmags)
        stddev_mag = (npmedian(npabs(fmags - center_mag))) * 1.483

    # sigclip next for a single sigclip value
    if sigclip and isinstance(sigclip, (float, int)):

        if not iterative and niterations is None:

            sigind = (npabs(fmags - center_mag)) < (sigclip * stddev_mag)

            stimes = ftimes[sigind]
            smags = fmags[sigind]
            serrs = ferrs[sigind]

        else:

            #
            # iterative version adapted from scipy.stats.sigmaclip
            #

            # First, if niterations is not set, iterate until covergence
            if niterations is None:

                delta = 1

                this_times = ftimes
                this_mags = fmags
                this_errs = ferrs

                while delta:

                    if meanormedian == 'mean':
                        this_center = npmean(this_mags)
                        this_stdev = npstddev(this_mags)
                    elif meanormedian == 'median':
                        this_center = npmedian(this_mags)
                        this_stdev = (
                            npmedian(npabs(this_mags - this_center))
                        ) * 1.483
                    this_size = this_mags.size

                    # apply the sigclip
                    tsi = (
                        (npabs(this_mags - this_center)) <
                        (sigclip * this_stdev)
                    )

                    # update the arrays
                    this_times = this_times[tsi]
                    this_mags = this_mags[tsi]
                    this_errs = this_errs[tsi]

                    # update delta and go to the top of the loop
                    delta = this_size - this_mags.size

            else:  # If iterating only a certain number of times

                this_times = ftimes
                this_mags = fmags
                this_errs = ferrs

                iter_num = 0
                delta = 1
                while iter_num < niterations and delta:

                    if meanormedian == 'mean':

                        this_center = npmean(this_mags)
                        this_stdev = npstddev(this_mags)

                    elif meanormedian == 'median':

                        this_center = npmedian(this_mags)
                        this_stdev = (npmedian(npabs(this_mags -
                                                     this_center))) * 1.483
                    this_size = this_mags.size

                    # apply the sigclip
                    tsi = (
                        (npabs(this_mags - this_center)) <
                        (sigclip * this_stdev)
                    )

                    # update the arrays
                    this_times = this_times[tsi]
                    this_mags = this_mags[tsi]
                    this_errs = this_errs[tsi]

                    # update the number of iterations and delta and
                    # go to the top of the loop
                    delta = this_size - this_mags.size
                    iter_num += 1

            # final sigclipped versions
            stimes, smags, serrs = this_times, this_mags, this_errs

    # this handles sigclipping for asymmetric +ve and -ve clip values
    elif sigclip and isinstance(sigclip, (list,tuple)) and len(sigclip) == 2:

        # sigclip is passed as [dimmingclip, brighteningclip]
        dimmingclip = sigclip[0]
        brighteningclip = sigclip[1]

        if not iterative and niterations is None:

            if magsarefluxes:
                nottoodimind = (
                    (fmags - center_mag) > (-dimmingclip*stddev_mag)
                )
                nottoobrightind = (
                    (fmags - center_mag) < (brighteningclip*stddev_mag)
                )
            else:
                nottoodimind = (
                    (fmags - center_mag) < (dimmingclip*stddev_mag)
                )
                nottoobrightind = (
                    (fmags - center_mag) > (-brighteningclip*stddev_mag)
                )

            sigind = nottoodimind & nottoobrightind

            stimes = ftimes[sigind]
            smags = fmags[sigind]
            serrs = ferrs[sigind]

        else:

            #
            # iterative version adapted from scipy.stats.sigmaclip
            #
            if niterations is None:

                delta = 1

                this_times = ftimes
                this_mags = fmags
                this_errs = ferrs

                while delta:

                    if meanormedian == 'mean':

                        this_center = npmean(this_mags)
                        this_stdev = npstddev(this_mags)

                    elif meanormedian == 'median':
                        this_center = npmedian(this_mags)
                        this_stdev = (npmedian(npabs(this_mags -
                                                     this_center))) * 1.483
                    this_size = this_mags.size

                    if magsarefluxes:
                        nottoodimind = (
                            (this_mags - this_center) >
                            (-dimmingclip*this_stdev)
                        )
                        nottoobrightind = (
                            (this_mags - this_center) <
                            (brighteningclip*this_stdev)
                        )
                    else:
                        nottoodimind = (
                            (this_mags - this_center) <
                            (dimmingclip*this_stdev)
                        )
                        nottoobrightind = (
                            (this_mags - this_center) >
                            (-brighteningclip*this_stdev)
                        )

                    # apply the sigclip
                    tsi = nottoodimind & nottoobrightind

                    # update the arrays
                    this_times = this_times[tsi]
                    this_mags = this_mags[tsi]
                    this_errs = this_errs[tsi]

                    # update delta and go to top of the loop
                    delta = this_size - this_mags.size

            else:  # If iterating only a certain number of times
                this_times = ftimes
                this_mags = fmags
                this_errs = ferrs

                iter_num = 0
                delta = 1

                while iter_num < niterations and delta:

                    if meanormedian == 'mean':
                        this_center = npmean(this_mags)
                        this_stdev = npstddev(this_mags)
                    elif meanormedian == 'median':
                        this_center = npmedian(this_mags)
                        this_stdev = (npmedian(npabs(this_mags -
                                                     this_center))) * 1.483
                    this_size = this_mags.size

                    if magsarefluxes:
                        nottoodimind = (
                            (this_mags - this_center) >
                            (-dimmingclip*this_stdev)
                        )
                        nottoobrightind = (
                            (this_mags - this_center) <
                            (brighteningclip*this_stdev)
                        )
                    else:
                        nottoodimind = (
                            (this_mags - this_center) < (dimmingclip*this_stdev)
                        )
                        nottoobrightind = (
                            (this_mags - this_center) >
                            (-brighteningclip*this_stdev)
                        )

                    # apply the sigclip
                    tsi = nottoodimind & nottoobrightind

                    # update the arrays
                    this_times = this_times[tsi]
                    this_mags = this_mags[tsi]
                    this_errs = this_errs[tsi]

                    # update the number of iterations and delta
                    # and go to top of the loop
                    delta = this_size - this_mags.size
                    iter_num += 1

            # final sigclipped versions
            stimes, smags, serrs = this_times, this_mags, this_errs

    else:

        stimes = ftimes
        smags = fmags
        serrs = ferrs

    if returnerrs:
        return stimes, smags, serrs
    else:
        return stimes, smags, None

Response: This function takes a finite times magnitudes or fluxes and applies a symmetric or asymmetric sigma - clipping to the passed time - series and returns the resulting array.


Instruction:
Given the following Python 3 function, write the documentation
def sigclip_magseries_with_extparams(times, mags, errs, extparams,
                                     sigclip=None,
                                     iterative=False,
                                     magsarefluxes=False):
    '''Sigma-clips a magnitude or flux time-series and associated measurement
    arrays.

    Selects the finite times, magnitudes (or fluxes), and errors from the passed
    values, and apply symmetric or asymmetric sigma clipping to them.  Uses the
    same array indices as these values to filter out the values of all arrays in
    the `extparams` list. This can be useful for simultaneously sigma-clipping a
    magnitude/flux time-series along with their associated values of external
    parameters, such as telescope hour angle, zenith distance, temperature, moon
    phase, etc.

    Parameters
    ----------

    times,mags,errs : np.array
        The magnitude or flux time-series arrays to sigma-clip. This doesn't
        assume all values are finite or if they're positive/negative. All of
        these arrays will have their non-finite elements removed, and then will
        be sigma-clipped based on the arguments to this function.

        `errs` is optional. Set it to None if you don't have values for these. A
        'faked' `errs` array will be generated if necessary, which can be
        ignored in the output as well.

    extparams : list of np.array
        This is a list of all external parameter arrays to simultaneously filter
        along with the magnitude/flux time-series. All of these arrays should
        have the same length as the `times`, `mags`, and `errs` arrays.

    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.

    iterative : bool
        If this is set to True, will perform iterative sigma-clipping. If
        `niterations` is not set and this is True, sigma-clipping is iterated
        until no more points are removed.

    magsareflux : bool
        True if your "mags" are in fact fluxes, i.e. if "fainter" corresponds to
        `mags` getting smaller.

    Returns
    -------

    (stimes, smags, serrs) : tuple
        The sigma-clipped and nan-stripped time-series in `stimes`, `smags`,
        `serrs` and the associated values of the `extparams` in `sextparams`.

    '''

    returnerrs = True

    # fake the errors if they don't exist
    # this is inconsequential to sigma-clipping
    # we don't return these dummy values if the input errs are None
    if errs is None:
        # assume 0.1% errors if not given
        # this should work for mags and fluxes
        errs = 0.001*mags
        returnerrs = False

    # filter the input times, mags, errs; do sigclipping and normalization
    find = npisfinite(times) & npisfinite(mags) & npisfinite(errs)
    ftimes, fmags, ferrs = times[find], mags[find], errs[find]

    # apply the same indices to the external parameters
    for epi, eparr in enumerate(extparams):
        extparams[epi] = eparr[find]

    # get the median and stdev = 1.483 x MAD
    median_mag = npmedian(fmags)
    stddev_mag = (npmedian(npabs(fmags - median_mag))) * 1.483

    # sigclip next for a single sigclip value
    if sigclip and isinstance(sigclip, (float, int)):

        if not iterative:

            sigind = (npabs(fmags - median_mag)) < (sigclip * stddev_mag)

            stimes = ftimes[sigind]
            smags = fmags[sigind]
            serrs = ferrs[sigind]

            # apply the same indices to the external parameters
            for epi, eparr in enumerate(extparams):
                extparams[epi] = eparr[sigind]

        else:

            #
            # iterative version adapted from scipy.stats.sigmaclip
            #
            delta = 1

            this_times = ftimes
            this_mags = fmags
            this_errs = ferrs

            while delta:

                this_median = npmedian(this_mags)
                this_stdev = (npmedian(npabs(this_mags - this_median))) * 1.483
                this_size = this_mags.size

                # apply the sigclip
                tsi = (npabs(this_mags - this_median)) < (sigclip * this_stdev)

                # update the arrays
                this_times = this_times[tsi]
                this_mags = this_mags[tsi]
                this_errs = this_errs[tsi]

                # apply the same indices to the external parameters
                for epi, eparr in enumerate(extparams):
                    extparams[epi] = eparr[tsi]

                # update delta and go to the top of the loop
                delta = this_size - this_mags.size

            # final sigclipped versions
            stimes, smags, serrs = this_times, this_mags, this_errs

    # this handles sigclipping for asymmetric +ve and -ve clip values
    elif sigclip and isinstance(sigclip, (list, tuple)) and len(sigclip) == 2:

        # sigclip is passed as [dimmingclip, brighteningclip]
        dimmingclip = sigclip[0]
        brighteningclip = sigclip[1]

        if not iterative:

            if magsarefluxes:
                nottoodimind = (
                    (fmags - median_mag) > (-dimmingclip*stddev_mag)
                )
                nottoobrightind = (
                    (fmags - median_mag) < (brighteningclip*stddev_mag)
                )
            else:
                nottoodimind = (
                    (fmags - median_mag) < (dimmingclip*stddev_mag)
                )
                nottoobrightind = (
                    (fmags - median_mag) > (-brighteningclip*stddev_mag)
                )

            sigind = nottoodimind & nottoobrightind

            stimes = ftimes[sigind]
            smags = fmags[sigind]
            serrs = ferrs[sigind]

            # apply the same indices to the external parameters
            for epi, eparr in enumerate(extparams):
                extparams[epi] = eparr[sigind]

        else:

            #
            # iterative version adapted from scipy.stats.sigmaclip
            #
            delta = 1

            this_times = ftimes
            this_mags = fmags
            this_errs = ferrs

            while delta:

                this_median = npmedian(this_mags)
                this_stdev = (npmedian(npabs(this_mags - this_median))) * 1.483
                this_size = this_mags.size

                if magsarefluxes:
                    nottoodimind = (
                        (this_mags - this_median) > (-dimmingclip*this_stdev)
                    )
                    nottoobrightind = (
                        (this_mags - this_median) < (brighteningclip*this_stdev)
                    )
                else:
                    nottoodimind = (
                        (this_mags - this_median) < (dimmingclip*this_stdev)
                    )
                    nottoobrightind = (
                        (this_mags - this_median) >
                        (-brighteningclip*this_stdev)
                    )

                # apply the sigclip
                tsi = nottoodimind & nottoobrightind

                # update the arrays
                this_times = this_times[tsi]
                this_mags = this_mags[tsi]
                this_errs = this_errs[tsi]

                # apply the same indices to the external parameters
                for epi, eparr in enumerate(extparams):
                    extparams[epi] = eparr[tsi]

                # update delta and go to top of the loop
                delta = this_size - this_mags.size

            # final sigclipped versions
            stimes, smags, serrs = this_times, this_mags, this_errs

    else:

        stimes = ftimes
        smags = fmags
        serrs = ferrs

    if returnerrs:
        return stimes, smags, serrs, extparams
    else:
        return stimes, smags, None, extparams

Response: This function will take a finite time - series and a magnitude or flux time - series and an external parameter list and apply the sigma - clipping to those values.


Instruction:
Make a summary of the following Python 3 code
def phase_magseries(times, mags, period, epoch, wrap=True, sort=True):
    '''Phases a magnitude/flux time-series using a given period and epoch.

    The equation used is::

        phase = (times - epoch)/period - floor((times - epoch)/period)

    This phases the given magnitude timeseries using the given period and
    epoch. If wrap is True, wraps the result around 0.0 (and returns an array
    that has twice the number of the original elements). If sort is True,
    returns the magnitude timeseries in phase sorted order.

    Parameters
    ----------

    times,mags : np.array
        The magnitude/flux time-series values to phase using the provided
        `period` and `epoch`. Non-fiinite values will be removed.

    period : float
        The period to use to phase the time-series.

    epoch : float
        The epoch to phase the time-series. This is usually the time-of-minimum
        or time-of-maximum of some periodic light curve
        phenomenon. Alternatively, one can use the minimum time value in
        `times`.

    wrap : bool
        If this is True, the returned phased time-series will be wrapped around
        phase 0.0, which is useful for plotting purposes. The arrays returned
        will have twice the number of input elements because of this wrapping.

    sort : bool
        If this is True, the returned phased time-series will be sorted in
        increasing phase order.

    Returns
    -------

    dict
        A dict of the following form is returned::

            {'phase': the phase values,
             'mags': the mags/flux values at each phase,
             'period': the input `period` used to phase the time-series,
             'epoch': the input `epoch` used to phase the time-series}

    '''

    # find all the finite values of the magnitudes and times
    finiteind = np.isfinite(mags) & np.isfinite(times)

    finite_times = times[finiteind]
    finite_mags = mags[finiteind]

    magseries_phase = (
        (finite_times - epoch)/period -
        np.floor(((finite_times - epoch)/period))
    )

    outdict = {'phase':magseries_phase,
               'mags':finite_mags,
               'period':period,
               'epoch':epoch}

    if sort:
        sortorder = np.argsort(outdict['phase'])
        outdict['phase'] = outdict['phase'][sortorder]
        outdict['mags'] = outdict['mags'][sortorder]

    if wrap:
        outdict['phase'] = np.concatenate((outdict['phase']-1.0,
                                           outdict['phase']))
        outdict['mags'] = np.concatenate((outdict['mags'],
                                          outdict['mags']))

    return outdict

Response: Phases a magnitude time - series using a given period and epoch.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def time_bin_magseries(times, mags,
                       binsize=540.0,
                       minbinelems=7):
    '''Bins the given mag/flux time-series in time using the bin size given.

    Parameters
    ----------

    times,mags : np.array
        The magnitude/flux time-series to bin in time. Non-finite elements will
        be removed from these arrays. At least 10 elements in each array are
        required for this function to operate.

    binsize : float
        The bin size to use to group together measurements closer than this
        amount in time. This is in seconds.

    minbinelems : int
        The minimum number of elements required per bin to include it in the
        output.

    Returns
    -------

    dict
        A dict of the following form is returned::

            {'jdbin_indices': a list of the index arrays into the nan-filtered
                              input arrays per each bin,
             'jdbins': list of bin boundaries for each bin,
             'nbins': the number of bins generated,
             'binnedtimes': the time values associated with each time bin;
                            this is the median of the times in each bin,
             'binnedmags': the mag/flux values associated with each time bin;
                           this is the median of the mags/fluxes in each bin}

    '''

    # check if the input arrays are ok
    if not(times.shape and mags.shape and len(times) > 9 and len(mags) > 9):

        LOGERROR("input time/mag arrays don't have enough elements")
        return

    # find all the finite values of the magnitudes and times
    finiteind = np.isfinite(mags) & np.isfinite(times)
    finite_times = times[finiteind]
    finite_mags = mags[finiteind]

    # convert binsize in seconds to JD units
    binsizejd = binsize/(86400.0)
    nbins = int(np.ceil((np.nanmax(finite_times) -
                         np.nanmin(finite_times))/binsizejd) + 1)

    minjd = np.nanmin(finite_times)
    jdbins = [(minjd + x*binsizejd) for x in range(nbins)]

    # make a KD-tree on the JDs so we can do fast distance calculations.  we
    # need to add a bogus y coord to make this a problem that KD-trees can
    # solve.
    time_coords = np.array([[x,1.0] for x in finite_times])
    jdtree = cKDTree(time_coords)
    binned_finite_timeseries_indices = []

    collected_binned_mags = {}

    for jd in jdbins:
        # find all bin indices close to within binsizejd of this point
        # using the cKDTree query. we use the p-norm = 1 (I think this
        # means straight-up pairwise distance? FIXME: check this)
        bin_indices = jdtree.query_ball_point(np.array([jd,1.0]),
                                              binsizejd/2.0, p=1.0)

        # if the bin_indices have already been collected, then we're
        # done with this bin, move to the next one. if they haven't,
        # then this is the start of a new bin.
        if (bin_indices not in binned_finite_timeseries_indices and
            len(bin_indices) >= minbinelems):

            binned_finite_timeseries_indices.append(bin_indices)

    # convert to ndarrays
    binned_finite_timeseries_indices = [np.array(x) for x in
                                        binned_finite_timeseries_indices]

    collected_binned_mags['jdbins_indices'] = binned_finite_timeseries_indices
    collected_binned_mags['jdbins'] = jdbins
    collected_binned_mags['nbins'] = len(binned_finite_timeseries_indices)

    # collect the finite_times
    binned_jd = np.array([np.median(finite_times[x])
                          for x in binned_finite_timeseries_indices])
    collected_binned_mags['binnedtimes'] = binned_jd
    collected_binned_mags['binsize'] = binsize

    # median bin the magnitudes according to the calculated indices
    collected_binned_mags['binnedmags'] = (
        np.array([np.median(finite_mags[x])
                  for x in binned_finite_timeseries_indices])
    )

    return collected_binned_mags

Response: Returns a dictionary of the time - series in time using the given bin size given.


Instruction:
Make a summary of the following Python 3 code
def phase_bin_magseries(phases, mags,
                        binsize=0.005,
                        minbinelems=7):
    '''Bins a phased magnitude/flux time-series using the bin size provided.

    Parameters
    ----------

    phases,mags : np.array
        The phased magnitude/flux time-series to bin in phase. Non-finite
        elements will be removed from these arrays. At least 10 elements in each
        array are required for this function to operate.

    binsize : float
        The bin size to use to group together measurements closer than this
        amount in phase. This is in units of phase.

    minbinelems : int
        The minimum number of elements required per bin to include it in the
        output.

    Returns
    -------

    dict
        A dict of the following form is returned::

            {'phasebin_indices': a list of the index arrays into the
                                 nan-filtered input arrays per each bin,
             'phasebins': list of bin boundaries for each bin,
             'nbins': the number of bins generated,
             'binnedphases': the phase values associated with each phase bin;
                            this is the median of the phase value in each bin,
             'binnedmags': the mag/flux values associated with each phase bin;
                           this is the median of the mags/fluxes in each bin}

    '''

    # check if the input arrays are ok
    if not(phases.shape and mags.shape and len(phases) > 10 and len(mags) > 10):

        LOGERROR("input time/mag arrays don't have enough elements")
        return

    # find all the finite values of the magnitudes and phases
    finiteind = np.isfinite(mags) & np.isfinite(phases)
    finite_phases = phases[finiteind]
    finite_mags = mags[finiteind]

    nbins = int(np.ceil((np.nanmax(finite_phases) -
                         np.nanmin(finite_phases))/binsize) + 1)

    minphase = np.nanmin(finite_phases)
    phasebins = [(minphase + x*binsize) for x in range(nbins)]

    # make a KD-tree on the PHASEs so we can do fast distance calculations.  we
    # need to add a bogus y coord to make this a problem that KD-trees can
    # solve.
    time_coords = np.array([[x,1.0] for x in finite_phases])
    phasetree = cKDTree(time_coords)
    binned_finite_phaseseries_indices = []

    collected_binned_mags = {}

    for phase in phasebins:

        # find all bin indices close to within binsize of this point using the
        # cKDTree query. we use the p-norm = 1 for pairwise Euclidean distance.
        bin_indices = phasetree.query_ball_point(np.array([phase,1.0]),
                                                 binsize/2.0, p=1.0)

        # if the bin_indices have already been collected, then we're
        # done with this bin, move to the next one. if they haven't,
        # then this is the start of a new bin.
        if (bin_indices not in binned_finite_phaseseries_indices and
            len(bin_indices) >= minbinelems):

            binned_finite_phaseseries_indices.append(bin_indices)

    # convert to ndarrays
    binned_finite_phaseseries_indices = [np.array(x) for x in
                                         binned_finite_phaseseries_indices]

    collected_binned_mags['phasebins_indices'] = (
        binned_finite_phaseseries_indices
    )
    collected_binned_mags['phasebins'] = phasebins
    collected_binned_mags['nbins'] = len(binned_finite_phaseseries_indices)

    # collect the finite_phases
    binned_phase = np.array([np.median(finite_phases[x])
                             for x in binned_finite_phaseseries_indices])
    collected_binned_mags['binnedphases'] = binned_phase
    collected_binned_mags['binsize'] = binsize

    # median bin the magnitudes according to the calculated indices
    collected_binned_mags['binnedmags'] = (
        np.array([np.median(finite_mags[x])
                  for x in binned_finite_phaseseries_indices])
    )

    return collected_binned_mags

Response: This function returns a dict of the phase - bin data for a set of phases and mags.


Instruction:
How would you code a function in Python 3 to
calculate the total SNR of a transit assuming gaussian uncertainties.

Response:
def get_snr_of_dip(times,
                   mags,
                   modeltimes,
                   modelmags,
                   atol_normalization=1e-8,
                   indsforrms=None,
                   magsarefluxes=False,
                   verbose=True,
                   transitdepth=None,
                   npoints_in_transit=None):
    '''Calculate the total SNR of a transit assuming gaussian uncertainties.

    `modelmags` gets interpolated onto the cadence of `mags`. The noise is
    calculated as the 1-sigma std deviation of the residual (see below).

    Following Carter et al. 2009::

        Q = sqrt( Γ T ) * δ / σ

    for Q the total SNR of the transit in the r->0 limit, where::

        r = Rp/Rstar,
        T = transit duration,
        δ = transit depth,
        σ = RMS of the lightcurve in transit.
        Γ = sampling rate

    Thus Γ * T is roughly the number of points obtained during transit.
    (This doesn't correctly account for the SNR during ingress/egress, but this
    is a second-order correction).

    Note this is the same total SNR as described by e.g., Kovacs et al. 2002,
    their Equation 11.

    NOTE: this only works with fluxes at the moment.

    Parameters
    ----------

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

    modeltimes,modelmags : np.array
        A transiting planet model, either from BLS, a trapezoid model, or a
        Mandel-Agol model.

    atol_normalization : float
        The absolute tolerance to which the median of the passed model fluxes
        must be equal to 1.

    indsforrms : np.array
        A array of bools of `len(mags)` used to select points for the RMS
        measurement. If not passed, the RMS of the entire passed timeseries is
        used as an approximation. Genearlly, it's best to use out of transit
        points, so the RMS measurement is not model-dependent.

    magsarefluxes : bool
        Currently forced to be True because this function only works with
        fluxes.

    verbose : bool
        If True, indicates progress and warns about problems.

    transitdepth : float or None
        If the transit depth is known, pass it in here. Otherwise, it is
        calculated assuming OOT flux is 1.

    npoints_in_transits : int or None
        If the number of points in transit is known, pass it in here. Otherwise,
        the function will guess at this value.

    Returns
    -------

    (snr, transit_depth, noise) : tuple
        The returned tuple contains the calculated SNR, transit depth, and noise
        of the residual lightcurve calculated using the relation described
        above.

    '''

    if magsarefluxes:
        if not np.isclose(np.nanmedian(modelmags), 1, atol=atol_normalization):
            raise AssertionError('snr calculation assumes modelmags are '
                                 'median-normalized')
    else:
        raise NotImplementedError(
            'need to implement a method for identifying in-transit points when'
            'mags are mags, and not fluxes'
        )

    if not transitdepth:
        # calculate transit depth from whatever model magnitudes are passed.
        transitdepth = np.abs(np.max(modelmags) - np.min(modelmags))

    # generally, mags (data) and modelmags are at different cadence.
    # interpolate modelmags onto the cadence of mags.
    if not len(mags) == len(modelmags):
        from scipy.interpolate import interp1d

        fn = interp1d(modeltimes, modelmags, kind='cubic', bounds_error=True,
                      fill_value=np.nan)

        modelmags = fn(times)

        if verbose:
            LOGINFO('interpolated model timeseries onto the data timeseries')

    subtractedmags = mags - modelmags

    if isinstance(indsforrms, np.ndarray):
        subtractedrms = np.std(subtractedmags[indsforrms])
        if verbose:
            LOGINFO('using selected points to measure RMS')
    else:
        subtractedrms = np.std(subtractedmags)
        if verbose:
            LOGINFO('using all points to measure RMS')

    def _get_npoints_in_transit(modelmags):
        # assumes median-normalized fluxes are input
        if np.nanmedian(modelmags) == 1:
            return len(modelmags[(modelmags != 1)])
        else:
            raise NotImplementedError

    if not npoints_in_transit:
        npoints_in_transit = _get_npoints_in_transit(modelmags)

    snr = np.sqrt(npoints_in_transit) * transitdepth/subtractedrms

    if verbose:

        LOGINFO('\npoints in transit: {:d}'.format(npoints_in_transit) +
                '\ndepth: {:.2e}'.format(transitdepth) +
                '\nrms in residual: {:.2e}'.format(subtractedrms) +
                '\n\t SNR: {:.2e}'.format(snr))

    return snr, transitdepth, subtractedrms


Instruction:
How would you explain what the following Python 3 function does
def estimate_achievable_tmid_precision(snr, t_ingress_min=10,
                                       t_duration_hr=2.14):
    '''Using Carter et al. 2009's estimate, calculate the theoretical optimal
    precision on mid-transit time measurement possible given a transit of a
    particular SNR.

    The relation used is::

        sigma_tc = Q^{-1} * T * sqrt(θ/2)

        Q = SNR of the transit.
        T = transit duration, which is 2.14 hours from discovery paper.
        θ = τ/T = ratio of ingress to total duration
                ~= (few minutes [guess]) / 2.14 hours

    Parameters
    ----------

    snr : float
        The measured signal-to-noise of the transit, e,g. from
        :py:func:`astrobase.periodbase.kbls.bls_stats_singleperiod` or from
        running the `.compute_stats()` method on an Astropy BoxLeastSquares
        object.

    t_ingress_min : float
        The ingress duration in minutes. This is t_I to t_II in Winn (2010)
        nomenclature.

    t_duration_hr : float
        The transit duration in hours. This is t_I to t_IV in Winn (2010)
        nomenclature.

    Returns
    -------

    float
        Returns the precision achievable for transit-center time as calculated
        from the relation above. This is in days.

    '''

    t_ingress = t_ingress_min*u.minute
    t_duration = t_duration_hr*u.hour

    theta = t_ingress/t_duration

    sigma_tc = (1/snr * t_duration * np.sqrt(theta/2))

    LOGINFO('assuming t_ingress = {:.1f}'.format(t_ingress))
    LOGINFO('assuming t_duration = {:.1f}'.format(t_duration))
    LOGINFO('measured SNR={:.2f}\n\t'.format(snr) +
            '-->theoretical sigma_tc = {:.2e} = {:.2e} = {:.2e}'.format(
                sigma_tc.to(u.minute), sigma_tc.to(u.hour), sigma_tc.to(u.day)))

    return sigma_tc.to(u.day).value

Response:
Estimate the theoretical optimal base time measurement possible given a transit of a
        and a particular SNR.


Instruction:
Can you write a function in Python 3 where it
gives a BLS period, epoch, and transit ingress/egress points (usually from :py:func:`astrobase.periodbase.kbls.bls_stats_singleperiod`), return the times within transit durations + `extra_maskfrac` of each transit. Optionally, can use the (more accurate) trapezoidal fit period and epoch, if it's passed. Useful for inspecting individual transits, and masking them out if desired. Parameters ---------- blsd : dict This is the dict returned by :py:func:`astrobase.periodbase.kbls.bls_stats_singleperiod`. time : np.array The times from the time-series of transit observations used to calculate the initial period. extra_maskfrac : float This is the separation from in-transit points you desire, in units of the transit duration. `extra_maskfrac = 0` if you just want points inside transit (see below). trapd : dict This is a dict returned by :py:func:`astrobase.lcfit.transits.traptransit_fit_magseries` containing the trapezoid transit model. nperiodint : int This indicates how many periods backwards/forwards to try and identify transits from the epochs reported in `blsd` or `trapd`. Returns ------- (tmids_obsd, t_starts, t_ends) : tuple of np.array The returned items are:: tmids_obsd (np.ndarray): best guess of transit midtimes in lightcurve. Has length number of transits in lightcurve. t_starts (np.ndarray): t_Is - extra_maskfrac*tdur, for t_Is transit first contact point. t_ends (np.ndarray): t_Is + extra_maskfrac*tdur, for t_Is transit first contact point.

Response:
def get_transit_times(
        blsd,
        time,
        extra_maskfrac,
        trapd=None,
        nperiodint=1000
):
    '''Given a BLS period, epoch, and transit ingress/egress points (usually
    from :py:func:`astrobase.periodbase.kbls.bls_stats_singleperiod`), return
    the times within transit durations + `extra_maskfrac` of each transit.

    Optionally, can use the (more accurate) trapezoidal fit period and epoch, if
    it's passed.  Useful for inspecting individual transits, and masking them
    out if desired.

    Parameters
    ----------

    blsd : dict
        This is the dict returned by
        :py:func:`astrobase.periodbase.kbls.bls_stats_singleperiod`.

    time : np.array
        The times from the time-series of transit observations used to calculate
        the initial period.

    extra_maskfrac : float
        This is the separation from in-transit points you desire, in units of
        the transit duration. `extra_maskfrac = 0` if you just want points
        inside transit (see below).

    trapd : dict
        This is a dict returned by
        :py:func:`astrobase.lcfit.transits.traptransit_fit_magseries` containing
        the trapezoid transit model.

    nperiodint : int
        This indicates how many periods backwards/forwards to try and identify
        transits from the epochs reported in `blsd` or `trapd`.

    Returns
    -------

    (tmids_obsd, t_starts, t_ends) : tuple of np.array
        The returned items are::

            tmids_obsd (np.ndarray): best guess of transit midtimes in
            lightcurve. Has length number of transits in lightcurve.

            t_starts (np.ndarray): t_Is - extra_maskfrac*tdur, for t_Is transit
            first contact point.

            t_ends (np.ndarray): t_Is + extra_maskfrac*tdur, for t_Is transit
            first contact point.

    '''

    if trapd:
        period = trapd['fitinfo']['finalparams'][0]
        t0 = trapd['fitinfo']['fitepoch']
        transitduration_phase = trapd['fitinfo']['finalparams'][3]
        tdur = period * transitduration_phase
    else:
        period = blsd['period']
        t0 = blsd['epoch']
        tdur = (
            period *
            (blsd['transegressbin']-blsd['transingressbin'])/blsd['nphasebins']
        )
        if not blsd['transegressbin'] > blsd['transingressbin']:

            raise NotImplementedError(
                'careful of the width. '
                'this edge case must be dealt with separately.'
            )

    tmids = [t0 + ix*period for ix in range(-nperiodint,nperiodint)]

    sel = (tmids > np.nanmin(time)) & (tmids < np.nanmax(time))
    tmids_obsd = np.array(tmids)[sel]

    t_Is = tmids_obsd - tdur/2
    t_IVs = tmids_obsd + tdur/2

    # focus on the times around transit
    t_starts = t_Is - extra_maskfrac * tdur
    t_ends = t_IVs + extra_maskfrac * tdur

    return tmids_obsd, t_starts, t_ends


Instruction:
How would you implement a function in Python 3 that
gets the transit start, middle, and end times for transits in a given time-series of observations. Parameters ---------- time,flux,err_flux : np.array The input flux time-series measurements and their associated measurement errors blsfit_savpath : str or None If provided as a str, indicates the path of the fit plot to make for a simple BLS model fit to the transit using the obtained period and epoch. trapfit_savpath : str or None If provided as a str, indicates the path of the fit plot to make for a trapezoidal transit model fit to the transit using the obtained period and epoch. 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 This is by default True for this function, since it works on fluxes only at the moment. nworkers : int The number of parallel BLS period-finder workers to use. extra_maskfrac : float This is the separation (N) from in-transit points you desire, in units of the transit duration. `extra_maskfrac = 0` if you just want points inside transit, otherwise:: t_starts = t_Is - N*tdur, t_ends = t_IVs + N*tdur Thus setting N=0.03 masks slightly more than the guessed transit duration. Returns ------- (tmids_obsd, t_starts, t_ends) : tuple The returned items are:: tmids_obsd (np.ndarray): best guess of transit midtimes in lightcurve. Has length number of transits in lightcurve. t_starts (np.ndarray): t_Is - extra_maskfrac*tdur, for t_Is transit first contact point. t_ends (np.ndarray): t_Is + extra_maskfrac*tdur, for t_Is transit first contact point.

Response:
def given_lc_get_transit_tmids_tstarts_tends(
        time,
        flux,
        err_flux,
        blsfit_savpath=None,
        trapfit_savpath=None,
        magsarefluxes=True,
        nworkers=1,
        sigclip=None,
        extra_maskfrac=0.03
):
    '''Gets the transit start, middle, and end times for transits in a given
    time-series of observations.

    Parameters
    ----------

    time,flux,err_flux : np.array
        The input flux time-series measurements and their associated measurement
        errors

    blsfit_savpath : str or None
        If provided as a str, indicates the path of the fit plot to make for a
        simple BLS model fit to the transit using the obtained period and epoch.

    trapfit_savpath : str or None
        If provided as a str, indicates the path of the fit plot to make for a
        trapezoidal transit model fit to the transit using the obtained period
        and epoch.

    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
        This is by default True for this function, since it works on fluxes only
        at the moment.

    nworkers : int
        The number of parallel BLS period-finder workers to use.

    extra_maskfrac : float
        This is the separation (N) from in-transit points you desire, in units
        of the transit duration. `extra_maskfrac = 0` if you just want points
        inside transit, otherwise::

            t_starts = t_Is - N*tdur, t_ends = t_IVs + N*tdur

        Thus setting N=0.03 masks slightly more than the guessed transit
        duration.

    Returns
    -------

    (tmids_obsd, t_starts, t_ends) : tuple
        The returned items are::

            tmids_obsd (np.ndarray): best guess of transit midtimes in
            lightcurve. Has length number of transits in lightcurve.

            t_starts (np.ndarray): t_Is - extra_maskfrac*tdur, for t_Is transit
            first contact point.

            t_ends (np.ndarray): t_Is + extra_maskfrac*tdur, for t_Is transit
            first contact point.

    '''

    # first, run BLS to get an initial epoch and period.
    endp = 1.05*(np.nanmax(time) - np.nanmin(time))/2

    blsdict = kbls.bls_parallel_pfind(time, flux, err_flux,
                                      magsarefluxes=magsarefluxes, startp=0.1,
                                      endp=endp, maxtransitduration=0.3,
                                      nworkers=nworkers, sigclip=sigclip)

    blsd = kbls.bls_stats_singleperiod(time, flux, err_flux,
                                       blsdict['bestperiod'],
                                       magsarefluxes=True, sigclip=sigclip,
                                       perioddeltapercent=5)
    #  plot the BLS model.
    if blsfit_savpath:
        make_fit_plot(blsd['phases'], blsd['phasedmags'], None,
                      blsd['blsmodel'], blsd['period'], blsd['epoch'],
                      blsd['epoch'], blsfit_savpath,
                      magsarefluxes=magsarefluxes)

    ingduration_guess = blsd['transitduration'] * 0.2  # a guesstimate.
    transitparams = [
        blsd['period'], blsd['epoch'], blsd['transitdepth'],
        blsd['transitduration'], ingduration_guess
    ]

    # fit a trapezoidal transit model; plot the resulting phased LC.
    if trapfit_savpath:
        trapd = traptransit_fit_magseries(time, flux, err_flux,
                                          transitparams,
                                          magsarefluxes=magsarefluxes,
                                          sigclip=sigclip,
                                          plotfit=trapfit_savpath)

    # use the trapezoidal model's epoch as the guess to identify (roughly) in
    # and out of transit points
    tmids, t_starts, t_ends = get_transit_times(blsd,
                                                time,
                                                extra_maskfrac,
                                                trapd=trapd)

    return tmids, t_starts, t_ends


Instruction:
Can you tell what is the following Python 3 function doing
def _pycompress_sqlitecurve(sqlitecurve, force=False):
    '''This just compresses the sqlitecurve. Should be independent of OS.

    '''

    outfile = '%s.gz' % sqlitecurve

    try:

        if os.path.exists(outfile) and not force:
            os.remove(sqlitecurve)
            return outfile

        else:

            with open(sqlitecurve,'rb') as infd:
                with gzip.open(outfile,'wb') as outfd:
                    shutil.copyfileobj(infd, outfd)

            if os.path.exists(outfile):
                os.remove(sqlitecurve)
                return outfile

    except Exception as e:
        return None

Response: This function compresses the sqlitecurve file and returns the filename of the compressed file.


Instruction:
Can you generate the documentation for the following Python 3 function
def _pyuncompress_sqlitecurve(sqlitecurve, force=False):
    '''This just uncompresses the sqlitecurve. Should be independent of OS.

    '''

    outfile = sqlitecurve.replace('.gz','')

    try:

        if os.path.exists(outfile) and not force:
            return outfile

        else:

            with gzip.open(sqlitecurve,'rb') as infd:
                with open(outfile,'wb') as outfd:
                    shutil.copyfileobj(infd, outfd)

            # do not remove the intput file yet
            if os.path.exists(outfile):
                return outfile

    except Exception as e:
        return None

Response: This just uncompresses the sqlitecurve. Should be independent of OS.


Instruction:
Can you generate the documentation for the following Python 3 function
def _parse_csv_header_lcc_csv_v1(headerlines):
    '''
    This parses the header of the LCC CSV V1 LC format.

    '''

    # the first three lines indicate the format name, comment char, separator
    commentchar = headerlines[1]
    separator = headerlines[2]

    headerlines = [x.lstrip('%s ' % commentchar) for x in headerlines[3:]]

    # next, find the indices of the various LC sections
    metadatastart = headerlines.index('OBJECT METADATA')
    columnstart = headerlines.index('COLUMN DEFINITIONS')
    lcstart = headerlines.index('LIGHTCURVE')

    metadata = ' ' .join(headerlines[metadatastart+1:columnstart-1])
    columns = ' ' .join(headerlines[columnstart+1:lcstart-1])
    metadata = json.loads(metadata)
    columns = json.loads(columns)

    return metadata, columns, separator

Response: This parses the header of the LCC CSV V1 LC format.


Instruction:
Here you have a function in Python 3, explain what it does
def read_csvlc(lcfile):
    '''This reads a HAT data server or LCC-Server produced CSV light curve
    into an lcdict.

    This will automatically figure out the format of the file
    provided. Currently, it can read:

    - legacy HAT data server CSV LCs (e.g. from
      https://hatsouth.org/planets/lightcurves.html) with an extension of the
      form: `.hatlc.csv.gz`.
    - all LCC-Server produced LCC-CSV-V1 LCs (e.g. from
      https://data.hatsurveys.org) with an extension of the form: `-csvlc.gz`.

    Parameters
    ----------

    lcfile : str
        The light curve file to read.

    Returns
    -------

    dict
        Returns an lcdict that can be read and used by many astrobase processing
        functions.

    '''

    # read in the file and split by lines
    if '.gz' in os.path.basename(lcfile):
        LOGINFO('reading gzipped HATLC: %s' % lcfile)
        infd = gzip.open(lcfile,'rb')
    else:
        LOGINFO('reading HATLC: %s' % lcfile)
        infd = open(lcfile,'rb')

    # this transparently reads LCC CSVLCs
    lcformat_check = infd.read(12).decode()
    if 'LCC-CSVLC' in lcformat_check:
        infd.close()
        return read_lcc_csvlc(lcfile)
    else:
        infd.seek(0)

    # below is reading the HATLC v2 CSV LCs

    lctext = infd.read().decode()  # argh Python 3
    infd.close()

    # figure out the header and get the LC columns
    lcstart = lctext.index('# LIGHTCURVE\n')
    lcheader = lctext[:lcstart+12]
    lccolumns = lctext[lcstart+13:].split('\n')
    lccolumns = [x for x in lccolumns if len(x) > 0]

    # initialize the lcdict and parse the CSV header
    lcdict = _parse_csv_header(lcheader)

    # tranpose the LC rows into columns
    lccolumns = [x.split(',') for x in lccolumns]
    lccolumns = list(zip(*lccolumns))  # argh more Python 3

    # write the columns to the dict
    for colind, col in enumerate(lcdict['columns']):

        if (col.split('_')[0] in LC_MAG_COLUMNS or
            col.split('_')[0] in LC_ERR_COLUMNS or
            col.split('_')[0] in LC_FLAG_COLUMNS):
            lcdict[col] = np.array([_smartcast(x,
                                               COLUMNDEFS[col.split('_')[0]][2])
                                    for x in lccolumns[colind]])

        elif col in COLUMNDEFS:
            lcdict[col] = np.array([_smartcast(x,COLUMNDEFS[col][2])
                                    for x in lccolumns[colind]])

        else:
            LOGWARNING('lcdict col %s has no formatter available' % col)
            continue

    return lcdict

Response: This function reads a HAT - L1 or LCC - CSV - V1 light curve file into a lcdict.