def load_file(file: str,
clip: float = 4,
it: int = 1,
apply_file_correction: bool = False) -> LightCurve:
"""
Loads and normalizes target content
:param file: Name of target including path
:return: LightCurve object
"""
if not os.path.exists(file):
raise IOError(ctext(f"File {file} doesn't exist!", error))
mprint(f"Reading data from {file} ...", log)
try:
data = np.loadtxt(file)
except ValueError:
data = read_csv(file)
data = np.array((data.time, data.flux))
if data.shape[0] > data.shape[1]:
data = data.T
if data.shape[0] == 2:
lc = LightCurve(time=data[0], flux=data[1])
else:
lc = LightCurve(time=data[0], flux=data[1], flux_err=data[2])
lc = lc.remove_nans()
if apply_file_correction:
lc.flux = lc.flux + float(np.amin(lc.flux)) + 10
lc = lc.remove_outliers(clip, maxiters=it)
lc = mag(lc)
lc = lc.remove_nans()
else:
if np.amax(np.abs(lc.flux)) > 10:
mprint(
f"It seems as if your flux isn't in magnitudes. Be aware, that SMURFS expects the flux in magnitudes. "
f"Continuing ...", warn)
if np.abs(np.median(lc.flux)) > 1:
mprint(
f"The median of your flux is {'%.2f' % np.median(lc.flux)}. To do a proper analysis, the median should "
f"be close to 0. Be aware, that this might cause issues. Continuing...",
warn)
mprint(
f"Total observation length: {'%.2f' % (lc.time[-1] - lc.time[0])} days.",
log)
mprint("Extracted data from target!", info)
return lc
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
