def get_sismo_data(ID):
"""
Retrieve CoRoT timeseries from a local data repository.
The output record array has fields 'HJD', 'flux', 'e_flux', 'flag'.
@param ID: ID of the target: either an integer (CoRoT ID), an SIMBAD-recognised
target name, or a valid CoRoT FITS file
@type ID: int or str
@return: data, header
@rtype: numpy recarray, dict
"""
#-- data on one target can be spread over multiple files: collect the
# data
data = []
if isinstance(ID, str) and os.path.isfile(ID):
header = pyfits.getheader(ID)
times, flux, error, flags = fits.read_corot(ID)
data.append([times, flux, error, flags])
else:
#-- resolve the target's name: it's either a target name or CoRoT ID.
try:
ID = int(ID)
except ValueError:
info = sesame.search(ID, db='S')
IDs = [alias for alias in info['alias'] if 'HD' in alias]
if len(IDs) != 1:
logger.error(
"Data retrieval for %s not possible. Reason: no HD number resolved"
% (ID))
return
ID = IDs[0]
#-- collect the files containing data on the target
#catfiles = config.glob((os.sep).join(['catalogs','corot','sismo']),'*.fits')
fn_sismo = os.path.join(cc.path.ivsdata, 'catalogs', 'corot', 'sismo',
'*.fits')
catfiles = glob.glob(fn_sismo)
catfiles.sort()
for catfile in catfiles:
try:
header = pyfits.getheader(catfile)
except IOError:
continue
if header['starname'] == ID or header['corotid'].replace(
' ', '') == '%s' % (ID):
times, flux, error, flags = fits.read_corot(catfile)
data.append([times, flux, error, flags])
#-- now make a record array and sort according to times
if not data:
raise ValueError(
'target {0} not in offline CoRoT data repository'.format(ID))
data = np.hstack(data)
data = np.rec.fromarrays(data,
dtype=[('HJD', '>f8'), ('flux', '>f8'),
('e_flux', '>f8'), ('flag', 'i')])
sa = np.argsort(data['HJD'])
return data[sa], header
def get_exo_data(ID, type_data='white'):
"""
Retrieve CoRoT timeseries from a remote data repository.
The output record array has fields 'HJD', 'flux', 'e_flux', 'flag'.
@param ID: ID of the target: either an integer (CoRoT ID), an SIMBAD-recognised
target name, or a valid CoRoT FITS file
@type ID: int or str
@param type_data: 'white' or 'colors' (if available!)
@type type_data: str
@return: data, header
@rtype: numpy recarray, dict
"""
cat, units, comms = get_exo_catalog()
header = None
data = []
if isinstance(ID, str) and os.path.isfile(ID):
header = pyfits.getheader(ID)
times, flux, error, flags = fits.read_corot(ID)
data.append([times, flux, error, flags])
else:
#-- resolve the target's name: it's either a target name or CoRoT ID.
try:
ID = int(ID)
except ValueError:
logger.error(
"Data retrieval for %s not possible. Reason: ID not resolved" %
(ID))
return None
#-- collect the files containing data on the target
for filename in cat['FileName'][cat['CoRoT'] == ID]:
url = urllib.URLopener()
filen, msg = url.retrieve(filename)
try:
header = pyfits.getheader(filen)
except IOError:
continue
times, flux, error, flags = fits.read_corot(filen,
type_data=type_data)
url.close()
data.append([times, flux, error, flags])
#-- now make a record array and sort according to times
if not data:
raise ValueError(
'target {0} not in online CoRoT data repository'.format(ID))
data = np.hstack(data)
data = np.rec.fromarrays(data,
dtype=[('HJD', '>f8'), ('flux', '>f8'),
('e_flux', '>f8'), ('flag', 'i')])
sa = np.argsort(data['HJD'])
return data[sa], header
def get_sismo_data(ID):
"""
Retrieve CoRoT timeseries from a local data repository.
The output record array has fields 'HJD', 'flux', 'e_flux', 'flag'.
@param ID: ID of the target: either an integer (CoRoT ID), an SIMBAD-recognised
target name, or a valid CoRoT FITS file
@type ID: int or str
@return: data, header
@rtype: numpy recarray, dict
"""
# -- data on one target can be spread over multiple files: collect the
# data
data = []
if isinstance(ID, str) and os.path.isfile(ID):
header = pyfits.getheader(ID)
times, flux, error, flags = fits.read_corot(ID)
data.append([times, flux, error, flags])
else:
# -- resolve the target's name: it's either a target name or CoRoT ID.
try:
ID = int(ID)
except ValueError:
info = sesame.search(ID, db="S")
IDs = [alias for alias in info["alias"] if "HD" in alias]
if len(IDs) != 1:
logger.error("Data retrieval for %s not possible. Reason: no HD number resolved" % (ID))
return
ID = IDs[0]
# -- collect the files containing data on the target
# catfiles = config.glob((os.sep).join(['catalogs','corot','sismo']),'*.fits')
fn_sismo = os.path.join(cc.path.ivsdata, "catalogs", "corot", "sismo", "*.fits")
catfiles = glob.glob(fn_sismo)
catfiles.sort()
for catfile in catfiles:
try:
header = pyfits.getheader(catfile)
except IOError:
continue
if header["starname"] == ID or header["corotid"].replace(" ", "") == "%s" % (ID):
times, flux, error, flags = fits.read_corot(catfile)
data.append([times, flux, error, flags])
# -- now make a record array and sort according to times
if not data:
raise ValueError("target {0} not in offline CoRoT data repository".format(ID))
data = np.hstack(data)
data = np.rec.fromarrays(data, dtype=[("HJD", ">f8"), ("flux", ">f8"), ("e_flux", ">f8"), ("flag", "i")])
sa = np.argsort(data["HJD"])
return data[sa], header
def get_exo_data(ID, type_data="white"):
"""
Retrieve CoRoT timeseries from a remote data repository.
The output record array has fields 'HJD', 'flux', 'e_flux', 'flag'.
@param ID: ID of the target: either an integer (CoRoT ID), an SIMBAD-recognised
target name, or a valid CoRoT FITS file
@type ID: int or str
@param type_data: 'white' or 'colors' (if available!)
@type type_data: str
@return: data, header
@rtype: numpy recarray, dict
"""
cat, units, comms = get_exo_catalog()
header = None
data = []
if isinstance(ID, str) and os.path.isfile(ID):
header = pyfits.getheader(ID)
times, flux, error, flags = fits.read_corot(ID)
data.append([times, flux, error, flags])
else:
# -- resolve the target's name: it's either a target name or CoRoT ID.
try:
ID = int(ID)
except ValueError:
logger.error("Data retrieval for %s not possible. Reason: ID not resolved" % (ID))
return None
# -- collect the files containing data on the target
for filename in cat["FileName"][cat["CoRoT"] == ID]:
url = urllib.URLopener()
filen, msg = url.retrieve(filename)
try:
header = pyfits.getheader(filen)
except IOError:
continue
times, flux, error, flags = fits.read_corot(filen, type_data=type_data)
url.close()
data.append([times, flux, error, flags])
# -- now make a record array and sort according to times
if not data:
raise ValueError("target {0} not in online CoRoT data repository".format(ID))
data = np.hstack(data)
data = np.rec.fromarrays(data, dtype=[("HJD", ">f8"), ("flux", ">f8"), ("e_flux", ">f8"), ("flag", "i")])
sa = np.argsort(data["HJD"])
return data[sa], header
