def loadLC(folderName, downloadDir, errorIfNot2Min=True, dumpHeader=False, delimiter="|", fluxType="PDCSAP", normalised=True):
"""
Loads multiple and single Tess light curves and creates a Lightkurve object to store them in.
Multiple LCs are detected when the folderName string contains a delimiter.
:param folderName: name of the data folder
:param downloadDir: name of the root data folder
:param errorIfNot2Min: behaviour if cadence is not 2 min if `True` (default), raises an error, else warning
:param dumpHeader: if `True` prints the header of the data
:param delimiter: delimiter chosen to separate the data path, defualt: |
:param fluxType: SAP or PDCSAP
:param normalised: if `True` returns the median-normalised flux
"""
lc = None
if "|" in folderName:
folderNames = folderName.split(delimiter)
else:
folderNames = [folderName]
for folderName in folderNames:
imgFname = "{}_lc.fits".format(folderName)
imgFname = os.path.join(downloadDir, folderName, imgFname)
head = fits.getheader(imgFname)
sector = head["sector"]
if dumpHeader:
print(repr(head))
lightCurveData = Table.read(imgFname)
cadeances = np.nanmedian((lightCurveData["TIME"][1:] - lightCurveData["TIME"][:-1])*24*60)
if np.abs(cadeances-2.) < 0.5:
logger.info("Cadence is 2 min for {}".format(imgFname))
else:
if errorIfNot2Min:
raise RuntimeError("Cadence is {:1.1f} min for {}".format(cadeances, imgFname))
else:
logger.warning("Cadence is {:1.1f} min for {}".format(cadeances, imgFname))
lightCurveData["TIME"].unit = u.day
time = lightCurveData["TIME"]
flux = lightCurveData["{}_FLUX".format(fluxType)]
fluxErr = lightCurveData["{}_FLUX_ERR".format(fluxType)]
meta = {
"TIME": lightCurveData["TIME"],
"MOM_CENTR1": lightCurveData["MOM_CENTR1"],
"MOM_CENTR2": lightCurveData["MOM_CENTR2"],
"MOM_CENTR1_ERR": lightCurveData["MOM_CENTR1_ERR"],
"MOM_CENTR2_ERR": lightCurveData["MOM_CENTR2_ERR"],
"POS_CORR1": lightCurveData["POS_CORR1"],
"POS_CORR2": lightCurveData["POS_CORR2"],
}
lcTemp = LightCurve(time=time, flux=flux, flux_err=fluxErr, meta=meta)
lcTemp = lcTemp.remove_nans()
if normalised:
lcTemp = lcTemp.normalize()
if lc is None:
lc = lcTemp
sectors = sector
else:
lc = lc.append(lcTemp)
sectors = "{},{}".format(sectors, sector)
ids = np.argsort(lc.time)
lc = lc[ids]
return lc, sectors
def get_mlffi(tic=None,
ra=None,
dec=None,
sectors='all',
flux_type='corr',
out_sec=False):
"""
For use on tesseract only. Fetches FFI light curves made by Brian Powell.
Parameters
----------
tic : int or None
TIC ID of target. If None, both ra and dec must not be None.
ra : float or None
RA of target. If None, tic must not be None. If not None, dec must also
not be None.
dec : float or None
Dec of target. If None, tic must not be None. If not None, ra must also
not be None.
sectors : str or list or array
The desired sectors to make the light curve from. May be set to 'all' to
use all available light curves.
flux_type : str
The type of correction applied to the light curve. Options are 'raw',
'corr', and 'pca'.
out_sec : bool
Flag determining whether or not the sectors that the light curve was
generated from are output. If True, an additional output will be
expected.
Returns
-------
lc : 'LightCurve' object
The combined light curve.
sectors : array
The sectors from which the output light curve was generated.
"""
if not tic and not ra and not dec:
raise ValueError('Please provide valid input for either tic or ra/dec')
if sectors == 'all':
if not ra and not dec:
info = tessobs_info(tic=tic)
else:
info = tessobs_info(ra=ra, dec=dec)
sectors = list(set(info['sector']))
if not isinstance(sectors, list):
raise ValueError('Sectors must be a list!')
if not tic:
tic = coord_to_tic(ra, dec)
camera_arr = []
chip_arr = []
Tmag_arr = []
sects = sectors
if not os.path.isdir('/data/tessraid/bppowel1/'):
raise ValueError('Not on tesseract')
for i in range(len(sects)):
path = '/data/tessraid/bppowel1/tesslcs_sector_' + str(
sects[i]) + '_104'
lc_files = []
for (dirpath, dirnames, filenames) in os.walk(path):
for name in filenames:
lc_files.append(os.path.join(dirpath, name))
lc_files = [t for t in lc_files if 'tesslc_' in t]
tics = [int(t.split('.')[0].split('_')[-1]) for t in lc_files]
path = [s for s in lc_files if str(tic) in s]
try:
fp = open(str(path[0]), 'rb')
except:
print('Target not found in Sector %s' % sects[i])
sects.remove(sects[i]) #trouble line, may cause silent error
continue
data = pickle.load(fp)
fp.close()
Tmag_arr.append(data[2])
camera_arr.append(data[4])
chip_arr.append(data[5])
time = data[6]
flux_err = data[10]
if flux_type == 'corr':
flux = data[8]
elif flux_type == 'raw':
flux = data[7]
elif flux_type == 'pca':
flux = data[9]
if i == 0:
lc = LightCurve(time, flux, flux_err=flux_err)
else:
sec_lc = LightCurve(time, flux, flux_err=flux_err)
lc.append(sec_lc)
lc = lc.normalize()
lc.Tmag = Tmag_arr
lc.camera = camera_arr
lc.chip = chip_arr
if out_sec:
return lc, sects
else:
return lc
