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')
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
