def load_lightcurve(self, task):
"""
Load lightcurve from task ID or full task dictionary.
Parameters:
task (integer or dict):
Returns:
:class:`lightkurve.TessLightCurve`: Lightcurve for the star in question.
Raises:
ValueError: On invalid file format.
.. codeauthor:: Rasmus Handberg <*****@*****.**>
"""
logger = logging.getLogger(__name__)
# Find the relevant information in the TODO-list:
if not isinstance(task, dict) or task.get("lightcurve") is None:
if isinstance(task, dict):
priority = int(task['priority'])
else:
priority = int(task)
self.cursor.execute(
"SELECT * FROM todolist INNER JOIN diagnostics ON todolist.priority=diagnostics.priority WHERE todolist.priority=? LIMIT 1;",
(priority, ))
task = self.cursor.fetchone()
if task is None:
raise ValueError(
"Priority could not be found in the TODO list")
task = dict(task)
# Get the path of the FITS file:
fname = os.path.join(self.input_folder, task.get('lightcurve'))
logger.debug('Loading lightcurve: %s', fname)
# Load lightcurve file and create a TessLightCurve object:
if fname.endswith(('.fits.gz', '.fits')):
with fits.open(fname, mode='readonly', memmap=True) as hdu:
# Filter out invalid parts of the input lightcurve:
hdu = _filter_fits_hdu(hdu)
# Quality flags from the pixels:
pixel_quality = np.asarray(
hdu['LIGHTCURVE'].data['PIXEL_QUALITY'], dtype='int32')
# Corrections applied to timestamps:
timecorr = hdu['LIGHTCURVE'].data['TIMECORR']
# Create the QUALITY column and fill it with flags of bad data points:
quality = np.zeros_like(hdu['LIGHTCURVE'].data['TIME'],
dtype='int32')
bad_data = ~np.isfinite(hdu['LIGHTCURVE'].data['FLUX_RAW'])
bad_data |= (pixel_quality & TESSQualityFlags.DEFAULT_BITMASK
!= 0)
quality[bad_data] |= CorrectorQualityFlags.FlaggedBadData
# Create lightkurve object:
lc = TessLightCurve(
time=hdu['LIGHTCURVE'].data['TIME'],
flux=hdu['LIGHTCURVE'].data['FLUX_RAW'],
flux_err=hdu['LIGHTCURVE'].data['FLUX_RAW_ERR'],
centroid_col=hdu['LIGHTCURVE'].data['MOM_CENTR1'],
centroid_row=hdu['LIGHTCURVE'].data['MOM_CENTR2'],
quality=quality,
cadenceno=np.asarray(hdu['LIGHTCURVE'].data['CADENCENO'],
dtype='int32'),
time_format='btjd',
time_scale='tdb',
targetid=hdu[0].header.get('TICID'),
label=hdu[0].header.get('OBJECT'),
camera=hdu[0].header.get('CAMERA'),
ccd=hdu[0].header.get('CCD'),
sector=hdu[0].header.get('SECTOR'),
ra=hdu[0].header.get('RA_OBJ'),
dec=hdu[0].header.get('DEC_OBJ'),
quality_bitmask=CorrectorQualityFlags.DEFAULT_BITMASK,
meta={'data_rel': hdu[0].header.get('DATA_REL')})
# Apply manual exclude flag:
manexcl = manual_exclude(lc)
lc.quality[manexcl] |= CorrectorQualityFlags.ManualExclude
elif fname.endswith(('.noisy', '.sysnoise')): # pragma: no cover
data = np.loadtxt(fname)
# Quality flags from the pixels:
pixel_quality = np.asarray(data[:, 3], dtype='int32')
# Corrections applied to timestamps:
timecorr = np.zeros(data.shape[0], dtype='float32')
# Change the Manual Exclude flag, since the simulated data
# and the real TESS quality flags differ in the definition:
indx = (pixel_quality & 256 != 0)
pixel_quality[indx] -= 256
pixel_quality[indx] |= TESSQualityFlags.ManualExclude
# Create the QUALITY column and fill it with flags of bad data points:
quality = np.zeros(data.shape[0], dtype='int32')
bad_data = ~np.isfinite(data[:, 1])
bad_data |= (pixel_quality & TESSQualityFlags.DEFAULT_BITMASK != 0)
quality[bad_data] |= CorrectorQualityFlags.FlaggedBadData
# Create lightkurve object:
lc = TessLightCurve(
time=data[:, 0],
flux=data[:, 1],
flux_err=data[:, 2],
quality=quality,
cadenceno=np.arange(1, data.shape[0] + 1, dtype='int32'),
time_format='jd',
time_scale='tdb',
targetid=task['starid'],
label="Star%d" % task['starid'],
camera=task['camera'],
ccd=task['ccd'],
sector=2,
#ra=0,
#dec=0,
quality_bitmask=CorrectorQualityFlags.DEFAULT_BITMASK,
meta={})
else:
raise ValueError("Invalid file format")
# Add additional attributes to lightcurve object:
lc.pixel_quality = pixel_quality
lc.timecorr = timecorr
# Modify the "extra_columns" tuple of the lightkurve object:
# This is used internally in lightkurve to keep track of the columns in the
# object, and make sure they are propergated.
lc.extra_columns = tuple(
list(lc.extra_columns) + ['timecorr', 'pixel_quality'])
# Keep the original task in the metadata:
lc.meta['task'] = task
lc.meta['additional_headers'] = fits.Header()
if logger.isEnabledFor(logging.DEBUG):
with contextlib.redirect_stdout(LoggerWriter(
logger, logging.DEBUG)):
lc.show_properties()
return lc
def load_lightcurve(self, task, ver='RAW'):
"""
Load lightcurve from task ID or full task dictionary.
Parameters:
task (integer or dict):
Returns:
``lightkurve.TessLightCurve``: Lightcurve for the star in question.
Raises:
ValueError: On invalid file format.
.. codeauthor:: Rasmus Handberg <*****@*****.**>
"""
logger = logging.getLogger(__name__)
# Find the relevant information in the TODO-list:
if not isinstance(task, dict) or task.get("lightcurve") is None:
self.cursor.execute(
"SELECT * FROM todolist INNER JOIN diagnostics ON todolist.priority=diagnostics.priority WHERE todolist.priority=? LIMIT 1;",
(task, ))
task = self.cursor.fetchone()
if task is None:
raise ValueError(
"Priority could not be found in the TODO list")
task = dict(task)
# Get the path of the FITS file:
fname = os.path.join(self.input_folder, task.get('lightcurve'))
logger.debug('Loading lightcurve: %s', fname)
if fname.endswith('.fits') or fname.endswith('.fits.gz'):
with fits.open(fname, mode='readonly', memmap=True) as hdu:
# Quality flags from the pixels:
pixel_quality = np.asarray(
hdu['LIGHTCURVE'].data['PIXEL_QUALITY'], dtype='int32')
# Create the QUALITY column and fill it with flags of bad data points:
quality = np.zeros_like(hdu['LIGHTCURVE'].data['TIME'],
dtype='int32')
if ver == 'RAW':
LC = hdu['LIGHTCURVE'].data['FLUX_RAW']
LC_ERR = hdu['LIGHTCURVE'].data['FLUX_RAW_ERR'],
elif ver == 'CORR':
LC = hdu['LIGHTCURVE'].data['FLUX_CORR']
LC_ERR = hdu['LIGHTCURVE'].data['FLUX_CORR_ERR'],
bad_data = ~np.isfinite(LC)
bad_data |= (pixel_quality & TESSQualityFlags.DEFAULT_BITMASK
!= 0)
quality[bad_data] |= CorrectorQualityFlags.FlaggedBadData
# Create lightkurve object:
lc = TessLightCurve(
time=hdu['LIGHTCURVE'].data['TIME'],
flux=LC,
flux_err=LC_ERR,
centroid_col=hdu['LIGHTCURVE'].data['MOM_CENTR1'],
centroid_row=hdu['LIGHTCURVE'].data['MOM_CENTR2'],
quality=quality,
cadenceno=np.asarray(hdu['LIGHTCURVE'].data['CADENCENO'],
dtype='int32'),
time_format='btjd',
time_scale='tdb',
targetid=hdu[0].header.get('TICID'),
label=hdu[0].header.get('OBJECT'),
camera=hdu[0].header.get('CAMERA'),
ccd=hdu[0].header.get('CCD'),
sector=hdu[0].header.get('SECTOR'),
ra=hdu[0].header.get('RA_OBJ'),
dec=hdu[0].header.get('DEC_OBJ'),
quality_bitmask=CorrectorQualityFlags.DEFAULT_BITMASK,
meta={})
# Apply manual exclude flag:
manexcl = manual_exclude(lc)
lc.quality[manexcl] |= CorrectorQualityFlags.ManualExclude
else:
raise ValueError("Invalid file format")
# Add additional attributes to lightcurve object:
lc.pixel_quality = pixel_quality
# Keep the original task in the metadata:
lc.meta['task'] = task
lc.meta['additional_headers'] = fits.Header()
if logger.isEnabledFor(logging.DEBUG):
lc.show_properties()
return lc