def get_exp_files(pipeline, night, obj, filtr, exptime, proctype='InstCal',
prep_files=False):
"""
Get all of the exposures for a given night, object, and filter. This funpacks the
relevant exposure fits image, weight maps, and data quality masks and copies them into the
pipelines temp_path directory.
"""
from astropyp import index
sql = "select * from decam_obs where cal_date='{0}' and object like'{1}%'".format(night, obj)
sql += "and object not like '%%SLEW' and filter like '{0}%' and exptime='{1}'".format(
filtr, exptime
)
exposures = index.query(sql, pipeline.idx_connect_str).sort(['expnum'])
expnums = exposures['expnum'].unique()
# Get the filenames for the image. dqmask, and wtmap products for each exposure
exp_files = {expnum:{} for expnum in expnums}
all_exposures = []
for idx, row in exposures.iterrows():
sql = "select * from decam_files where EXPNUM={0} and proctype='{1}'".format(
row['expnum'], proctype)
files = index.query(sql, pipeline.idx_connect_str)
for fidx, file in files.iterrows():
fits_name = get_fits_name(file['EXPNUM'], file['PRODTYPE'])
fits_path = os.path.join(pipeline.temp_path, fits_name)
# Funpack the file
if prep_files:
funpack_file(file['filename'], fits_path)
exp_files[file['EXPNUM']][file['PRODTYPE']] = fits_path
if file['PRODTYPE']=='image':
all_exposures.append(fits_path)
# Create ahead files
if prep_files:
create_ahead(file['EXPNUM'], pipeline.temp_path)
return (exposures, expnums, exp_files, all_exposures)
def get_header_info(pipeline, cat_name, frame):
"""
Get the exposure time, airmass, and gain from a DECam fits header
Parameters
----------
pipeline: :class:`.Pipeline`
Object that contains parameters about a decam pipeline
cat_name: str
Name of a catalog that contains a list of sources found using decamtoyz
frame: int
CCD number of the header to load
Returns
-------
exptime: float
Exposure time of the image
airmass: float
Airmass (sec(zenith distance))
gain: float
Gain in electrons/adu
"""
from astropyp import index
from astropy.io import fits
import numpy as np
expnum = os.path.basename(cat_name).split('-')[0]
sql = "select * from decam_files where EXPNUM={0} and PRODTYPE='image'".format(int(expnum))
files = index.query(sql, pipeline.idx_connect_str)
header = fits.getheader(os.path.join(pipeline.paths['decam'], files.iloc[0].filename), ext=0)
exptime = header['exptime']
airmass = 1/np.cos(np.radians(header['ZD']))
header = fits.getheader(os.path.join(pipeline.paths['decam'], files.iloc[0].filename), ext=frame)
gain = header['arawgain']
return exptime, airmass, gain
def funpack_exposures(idx_connect_str, exposures, temp_path, proctype='InstCal'):
"""
Run funpack to unpack compressed fits images (fits.fz) so that the
AstrOmatic suite can read them. This also creates an ahead file for each
image that can be used by SCAMP containing the appropriate header
information
Parameters
----------
idx_connect_str: str
Connection to database containing decam observation index
exposures: `pandas.DataFrame`
DataFrame with information about the observations
temp_path: str
Temporary directory where files are stored for pipeline processing
proctype: str
PROCTYPE from DECam image header (processing type of product from
DECam community pipeline)
"""
from astropyp import index
expnums = exposures['expnum'].unique()
# Get the filenames for the image, dqmask, and wtmap products for each exposure
for idx, row in exposures.iterrows():
sql = "select * from decam_files where EXPNUM={0} and proctype='{1}'".format(
row['expnum'], proctype)
files = index.query(sql, idx_connect_str)
for fidx, file_info in files.iterrows():
fits_name = get_fits_name(file_info['EXPNUM'], file_info['PRODTYPE'])
fits_path = os.path.join(temp_path, fits_name)
funpack_file(file_info['filename'], fits_path)
if file_info['PRODTYPE']=='image':
create_ahead(file_info['EXPNUM'], temp_path)
def get_phot_params(catalog, mag_name, img_filename=None, expnum=None, proctype='InstCal',
connection=None, frame_colname='frame', flux_name='FLUX_PSF'):
"""
Get necessary parameters like airmass and exposure time from the FITS image headers
"""
import numpy as np
from astropyp import index
from astropy.io import fits
# Get fields from FITS header
if img_filename is None:
sql = "select * from decam_obs where EXPNUM={0} and PRODTYPE='image'".format(expnum)
sql += " and PROCTYPE='{0}'".format(proctype)
files = index.query(sql, connection)
# Make sure there is only one image with the correct expnum
if len(files)==0:
raise DecamPhotError("No files found with EXPNUM {0}".format(expnum))
elif len(files)>1:
raise DecamPhotError("Multiple files found with EXPNUM {0}".format(expnum))
img_filename = files[0]['filename']
hdulist = fits.open(img_filename, memmap=True)
header = hdulist[0].header
exptime = header['EXPTIME']
catalog['airmass'] = 1/np.cos(np.radians(header['ZD']))
catalog[mag_name] = np.nan
#catalog['filename'] = catalog_filename
frames = np.unique(catalog[frame_colname])
# add fields to catalog frame by frame
for frame in frames:
gain = hdulist[frame].header['arawgain']
logger.debug('frame {0}: gain={1}, exptime={2}'.format(frame, gain, exptime))
frame_condition = catalog[frame_colname]==frame
counts = np.array(catalog[flux_name]) * gain/exptime
catalog[flux_name][frame_condition & (counts==0)] = np.nan
catalog[mag_name][frame_condition] = -2.5*np.log10(counts[frame_condition])
catalog['ccd'] = catalog['frame']
return catalog
