/
prepare_lc.py
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/
prepare_lc.py
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"""
Load the Kepler light curve from FITS files. Also do a bit of preprocessing.
"""
import inputs as inp
def loadlc(files, usepdc=False, **kwargs):
"""
Load Kepler light curves.
Parameters
----------
files : list of strings
The locations of the light curves to load together.
usepdc : bool, optional
Set to True to load the PDC light curves instead of the
default SAP.
Returns
-------
time : ndarray
Kepler times of center of exposure
flux : ndarray
Kepler normalized fluxes
fluxerr : ndarray
Kepler flux errors
cadence : ndarray
Kepler cadence number
quarter : ndarray
Kepler quarter
quality : ndarray
Kepler quality flag
"""
import astropy.io.fits as pyfits
import numpy as np
# load the first file
ifile = files[0]
data = pyfits.getdata(ifile)
# get the times and fluxes
time = data['time']+54833e0
flux = data['sap_flux']
fluxerr = data['sap_flux_err']
if usepdc:
flux = data['pdcsap_flux']
fluxerr = data['pdcsap_flux_err']
# where the times and fluxes are finite
good = (np.isfinite(time) & np.isfinite(flux))
# get the good values of everything
time = time[good]
flux = flux[good]
fluxerr = fluxerr[good]
quality = data['sap_quality'][good]
cadence = data['cadenceno'][good]
# pull the quarter from the header and set it up as an array
quart = pyfits.getval(ifile, 'quarter', 0)
quarter = np.zeros(len(time)) + quart
# normalize the fluxes
fluxerr /= np.median(flux)
flux /= np.median(flux)
# add in subsequent files
for i in np.arange(len(files)-1)+1:
ifile = files[i]
data = pyfits.getdata(ifile)
# get the times and fluxes
itime = data['time']+54833e0
iflux = data['sap_flux']
ifluxerr = data['sap_flux_err']
if usepdc:
iflux = data['pdcsap_flux']
ifluxerr = data['pdcsap_flux_err']
# where the times and fluxes are finite
good = (np.isfinite(itime) & np.isfinite(iflux))
# get the good values of everything
itime = itime[good]
iflux = iflux[good]
ifluxerr = ifluxerr[good]
iquality = data['sap_quality'][good]
icadence = data['cadenceno'][good]
# pull the quarter from the header and set it up as an array
quart = pyfits.getval(ifile, 'quarter', 0)
iquarter = np.zeros(len(itime)) + quart
# normalize the fluxes
ifluxerr /= np.median(iflux)
iflux /= np.median(iflux)
time = np.concatenate((time, itime))
flux = np.concatenate((flux, iflux))
fluxerr = np.concatenate((fluxerr, ifluxerr))
quality = np.concatenate((quality, iquality))
cadence = np.concatenate((cadence, icadence))
quarter = np.concatenate((quarter, iquarter))
# guarantee the light curve in sequential order
order = np.argsort(time)
time = time[order]
flux = flux[order]
fluxerr = fluxerr[order]
quality = quality[order]
cadence = cadence[order]
quarter = quarter[order]
return time, flux, fluxerr, cadence, quarter, quality
def preparelc(KIC, dataloc=inp.keplerdata, fill=True,
badflags=(128, 2048), ignorelist=inp.baddata,
**kwargs):
"""
Load Kepler light curves, then process them for analysis.
Parameters
----------
KIC : int
The Kepler ID for the system to look at
dataloc : string, optional
Directory point to the location of the Kepler light curves.
Default can be changed in the module initialization.
fill : boolean, optional
Should we fill in all missing cadences? If true, will
interpolate times to all missing cadences and assign them
flux with np.inf errors. Necessary for QATS requiring
continuous data. Default True.
badflags : tuple, optional
Flags that can be set by Kepler that we should take seriously
and ignore.
Set all cadences with these flags to have infinite errors.
Default 128 and 2048.
ignorelist : string, optional
File containing regions of time to ignore. File contents should
be 2 columns, with start and end times (in times already
adjusted by inp.timeoffset). Defaults to the file listed in the
module.
Returns
-------
time : ndarray
Kepler times of center of exposure
flux : ndarray
Kepler normalized fluxes
fluxerr : ndarray
Kepler flux errors
cadence : ndarray
Cadence number, starting at 0
quarter : ndarray
Kepler quarter
quality : ndarray
Kepler quality flag
"""
from glob import glob
import numpy as np
from scipy import interpolate
# load the lightcurve from FITS files
KICstr = str(int(KIC))
files = glob(dataloc + 'kplr*' + KICstr + '*llc.fits')
time, flux, fluxerr, cad, quart, qual = loadlc(files, **kwargs)
time -= inp.timeoffset
# make sure cadences start at 0
cad -= cad[0]
if fill:
# fill in the missing cadences and interpolate their times and
# fluxes (though the flux errors will be infinite)
newcad = np.arange(cad[-1]+1)
time = np.interp(newcad, cad, time)
newfluxerr = newcad * 0. + np.inf
newfluxerr[cad] = fluxerr
# fill in the old fluxes, etc to the new grid
newflux = newcad * 0. + 1.
newflux[cad] = flux
newqual = newcad * 0
newqual[cad] = qual
# default to quarter -1 for filled in gaps
newquart = newcad * 0. - 1.
newquart[cad] = quart
cad = newcad
flux = newflux
fluxerr = newfluxerr
qual = newqual
quart = newquart
# fill in the infinite flux errors with interpolated values
# to make plotting look better
func = interpolate.interp1d(time[np.isfinite(fluxerr)],
flux[np.isfinite(fluxerr)],
bounds_error=False, fill_value=1.)
flux[~np.isfinite(fluxerr)] = func(time[~np.isfinite(fluxerr)])
# ignore the places with these bad flags
for ii in badflags:
bad = np.where(qual & ii)[0]
fluxerr[bad] = np.inf
# ignore these regions for whatever reason
if ignorelist is not None:
tstart, tend = np.loadtxt(ignorelist, unpack=True, ndmin=2)
for ii in np.arange(len(tstart)):
igsrch = np.where((time >= tstart[ii]) & (time <= tend[ii]))[0]
fluxerr[igsrch] = np.inf
return time, flux, fluxerr, cad, quart, qual