def count_badpixels(self, thresh=10000, prescan=False):
amps = common.valid_amps_list()
if prescan:
return dict([(amp, int(np.sum(self.prescan_cutouts[amp] > thresh))) for amp in amps])
else:
return dict([(amp, int(np.sum(self.overscan_cutouts[amp] > thresh))) for amp in amps])
def fit_dark_scaling(im, dark_guess_scaled, extname):
# im needs to be bias-subtracted !!!!!!!!!
# im should have overscan stripped out
# dark should have overscan stripped out
# dark should be scaled according to exptime and best guess of
# T dependence so that the additional scaling fit out by this routine
# is a perturbation
sh_im = im.shape
assert((sh_im[0] == 1032) and (sh_im[1] == 2048))
sh_dark = dark_guess_scaled.shape
assert((sh_dark[0] == 1032) and (sh_dark[1] == 2048))
amps = common.valid_amps_list()
rescale_factors = np.zeros(4)
ncalls = np.zeros(4, dtype=int)
# did optimizer exit successfully or not
success = np.zeros(4, dtype=bool)
for i, amp in enumerate(amps):
res = fit_dark_scaling_1amp(im, dark_guess_scaled, amp, extname)
x = res.x
rescale_factors[i] = x[0]
ncalls[i] = res.nfev
success[i] = res.success
return rescale_factors, ncalls, success
def __init__(self, image):
# image should be a 2D numpy array with dimensions
# 2248 x 1032 in the case of DESI GFA cameras
par = common.gfa_misc_params()
sh = image.shape
assert(sh[0] == par['height_with_prescan_overscan'])
assert(sh[1] == par['width_with_prescan_overscan'])
amps = common.valid_amps_list()
self.overscan_cutouts = {}
self.prescan_cutouts = {}
for amp in amps:
bdy = common.overscan_bdy_coords(amp)
self.overscan_cutouts[amp] = image[bdy['y_l']:bdy['y_u'], bdy['x_l']:bdy['x_u']]
bdy = common.prescan_bdy_coords(amp)
self.prescan_cutouts[amp] = image[bdy['y_l']:bdy['y_u'], bdy['x_l']:bdy['x_u']]
self.n_badpix_overscan = self.count_badpixels()
self.n_badpix_prescan = self.count_badpixels(prescan=True)
# still per-amp but summing prescan and overscan counts together
self.n_badpix = dict([(amp, self.n_badpix_overscan[amp] + self.n_badpix_prescan[amp]) for amp in amps])
# including all amps and lumping together prescan and overscan
self.n_badpix_all = np.sum([n for n in self.n_badpix.values()])
# units are raw ADU
self.overscan_medians = dict([(amp, np.median(self.overscan_cutouts[amp])) for amp in amps])
# units are raw ADU
self.prescan_medians = dict([(amp, np.median(self.prescan_cutouts[amp])) for amp in amps])
def estimate_sky_mag(self, careful_sky=False, flatfielding_on=True):
# calculate sky brightness in mag per sq asec
# this is meant to be run on the reduced image in ADU
assert(self.are_pixels_calibrated(flatfielding_on=flatfielding_on))
sky_adu_per_pixel = self.estimate_sky_level(careful_sky=careful_sky,
flatfielding_on=flatfielding_on)
acttime = self.time_s_for_dark
sky_mag = sky.adu_to_surface_brightness(sky_adu_per_pixel,
acttime, self.extname)
if self.sky_level_adu_per_amp is not None:
amps = common.valid_amps_list()
self.sky_mag_per_amp = [sky.adu_to_surface_brightness(self.sky_level_adu_per_amp[amp], acttime, self.extname) for amp in amps]
print(self.extname + ' sky mag per square asec AB : ' +
'{:.3f}'.format(sky_mag))
self.sky_mag = sky_mag
# calculate sky mag for just upper 8 rows (lumping together amps G and H)
self.sky_mag_upper()
return sky_mag
def prescan_overscan_ccds_table(tab, exp):
# add information about bad pixels in overscan/prescan to
# CCDs table, should be useful in identifying reasons for problematic
# reductions...
tab['npix_bad_total'] = [
exp.images[extname].overscan.n_badpix_all for extname in tab['extname']
]
ampnames = common.valid_amps_list()
npix_bad_per_amp = np.zeros((len(tab), len(ampnames)), dtype=int)
overscan_medians = np.zeros((len(tab), len(ampnames)), dtype='float32')
prescan_medians = np.zeros((len(tab), len(ampnames)), dtype='float32')
for i, t in enumerate(tab):
npix_bad_per_amp[i, :] = np.array([
exp.images[t['extname']].overscan.n_badpix[amp] for amp in ampnames
])
overscan_medians[i, :] = np.array([
exp.images[t['extname']].overscan.overscan_medians[amp]
for amp in ampnames
])
prescan_medians[i, :] = np.array([
exp.images[t['extname']].overscan.prescan_medians[amp]
for amp in ampnames
])
tab['npix_bad_per_amp'] = npix_bad_per_amp
tab['overscan_medians_adu'] = overscan_medians
tab['prescan_medians_adu'] = prescan_medians
def median_zenith_camera_zeropoint(extname):
# wraps zenith_zeropoint_photometric_1amp
# eventually want to do a better job of dealing with amp-to-amp
# zeropoint variation so this is hopefully a temporary hack
amps = common.valid_amps_list()
zps = [zenith_zeropoint_photometric_1amp(extname, amp) for amp in amps]
return np.median(zps)
def subtract_bias(self):
print('Attempting to subtract bias...')
for extname in self.images.keys():
if self.images[extname] is not None:
self.images[extname].image = self.images[extname].image - \
load_calibs.read_bias_image(extname)
amps = common.valid_amps_list()
for amp in amps:
bdy = common.amp_bdy_coords(amp)
self.images[extname].image[bdy['y_l']:bdy['y_u'],
bdy['x_l']:bdy['x_u']] -= \
self.images[extname].overscan.overscan_medians[amp]
self.images[extname].bias_subtracted = True
self.images[extname].calc_variance_e_squared()
def estimate_sky_level(self, careful_sky=False, flatfielding_on=True):
# do something dumb for now, return to this later with something
# more sophisticated, possibly involving segmentation
# and/or finding the mode
assert(self.are_pixels_calibrated(flatfielding_on=flatfielding_on))
if careful_sky:
if self.segmap is None:
self.set_segmap()
self.sky_level_adu = np.median(self.image[self.segmap.array == 0])
else:
self.sky_level_adu = np.median(self.image)
self.sky_level_adu_per_amp = {}
for amp in common.valid_amps_list():
bdy = common.amp_bdy_coords(amp)
self.sky_level_adu_per_amp[amp] = np.median(self.image[bdy['y_l']:bdy['y_u'], bdy['x_l']:bdy['x_u']])
return self.sky_level_adu
def assemble_ccds_table(tab, catalog, exp, outdir, proc_obj, cube_index=None,
ps1=None, det_sn_thresh=5.0, sky_mags=True,
minimal=False, mjdrange=None):
nrows = len(tab)
tab['extname'] = tab['camera']
tab['contrast'] = [exp.images[extname].header['CONTRAST'] for extname in tab['camera']]
if minimal:
return tab
if sky_mags:
tab['sky_mag_ab'] = [exp.images[extname].sky_mag for extname in tab['camera']]
tab['sky_mag_ab_subregion'] = [exp.images[extname].sky_mag_upper for extname in tab['camera']]
amps = common.valid_amps_list()
if sky_mags:
sky_mag_ab_per_amp = np.zeros((nrows, len(amps)), dtype='float32') # 4 amps
for i, extname in enumerate(tab['camera']):
for j in range(len(amps)):
_mag = np.nan if exp.images[extname].sky_mag_per_amp is None else exp.images[extname].sky_mag_per_amp[j]
sky_mag_ab_per_amp[i, j] = _mag
tab['sky_mag_ab_per_amp'] = sky_mag_ab_per_amp
tab['petal_loc'] = np.array([common.gfa_extname_to_gfa_number(extname) for extname in tab['camera']], dtype='uint8')
tab['expid'] = [exp.images[extname].header['EXPID'] for extname in tab['camera']]
# should work except if early versions of guide cubes lacked
# MJD information...
tab['mjd'] = [exp.images[extname].try_retrieve_meta_keyword('MJD-OBS', placeholder=0.0) for extname in tab['camera']]
eph = util.load_lst()
tab['lst_deg'] = [util.interp_ephemeris(t['mjd'], eph=eph) for t in tab]
tab['moon_illumination'] = [util.interp_ephemeris(t['mjd'], eph=eph, colname='MPHASE') for t in tab]
tab['program'] = [str(exp.images[extname].try_retrieve_meta_keyword('PROGRAM', placeholder='')) for extname in tab['camera']]
tab['skyra'] = [exp.images[extname].try_retrieve_meta_keyword('SKYRA', placeholder=np.nan) for extname in tab['camera']]
tab['skydec'] = [exp.images[extname].try_retrieve_meta_keyword('SKYDEC', placeholder=np.nan) for extname in tab['camera']]
# zenith distance using approximate center of the field given by SKYRA, SKYDEC
tab['zenith_dist_deg'] = [util._zenith_distance(t['skyra'], t['skydec'], t['lst_deg']) for t in tab]
tab['domshutl'] = np.array(nrows*[exp.try_retrieve_header_card('DOMSHUTL', placeholder='')], dtype='U8')
tab['domshutu'] = np.array(nrows*[exp.try_retrieve_header_card('DOMSHUTU', placeholder='')], dtype='U8')
tab['pmcover'] = exp.try_retrieve_header_card('PMCOVER', placeholder='')
tab['moonra'] = exp.try_retrieve_header_card('MOONRA', placeholder=np.nan)
tab['moondec'] = exp.try_retrieve_header_card('MOONDEC', placeholder=np.nan)
if np.isnan(tab['moonra'][0]):
tab['moonra'] = util.interp_ephemeris(tab['mjd'][0], eph=eph,
colname='MOONRA')
if np.isnan(tab['moondec'][0]):
tab['moondec'] = util.interp_ephemeris(tab['mjd'][0], eph=eph,
colname='MOONDEC')
tab['moon_zd_deg'] = util._zenith_distance(tab['moonra'][0],
tab['moondec'][0],
tab['lst_deg'][0])
tab['t_c_for_dark'] = [exp.images[extname].t_c_for_dark for extname in tab['camera']]
tab['t_c_for_dark_is_guess'] = [int(exp.images[extname].t_c_for_dark_is_guess) for extname in tab['camera']]
tab['time_s_for_dark'] = [exp.images[extname].time_s_for_dark for extname in tab['camera']]
tab['night'] = exp.try_retrieve_header_card('NIGHT', placeholder='')
tab['focus'] = exp.try_retrieve_header_card('FOCUS', placeholder='')
tab['exptime'] = [exp.images[extname].try_retrieve_meta_keyword('EXPTIME', placeholder=np.nan) for extname in tab['camera']]
# hack for acquisition images like guide-00074954-0000.fits.fz
# to make sure their _ccds table ends up listing cube_index 0
# rather than NaN
is_0000_acq_file = proc_obj.fname_in.find('-0000.fits.fz') != -1
if is_0000_acq_file:
tab['cube_index'] = 0
else:
tab['cube_index'] = np.nan if cube_index is None else int(cube_index)
if cube_index == -1:
tab['coadd_index_start'] = [exp.images[extname].coadd_index_range[0] for extname in tab['camera']]
tab['coadd_index_end'] = [exp.images[extname].coadd_index_range[1] for extname in tab['camera']]
tab['coadd_mjdobs_min'] = [exp.bintables[extname]['MJD-OBS'][ind] for extname, ind in zip(tab['camera'], tab['coadd_index_start'])]
tab['coadd_mjdobs_max'] = [exp.bintables[extname]['MJD-OBS'][ind] for extname, ind in zip(tab['camera'], tab['coadd_index_end'])]
tab['racen'] = np.zeros(len(tab), dtype=float)
tab['deccen'] = np.zeros(len(tab), dtype=float)
tab['fname_raw'] = proc_obj.fname_in
tab['gitrev'] = proc_obj.gitrev
tab['fiber_fracflux'] = [(exp.images[extname].psf.fiber_fracflux if exp.images[extname].psf is not None else np.nan) for extname in tab['camera']]
tab['fiber_fracflux_elg'] = [(exp.images[extname].psf.fiber_fracflux_elg if exp.images[extname].psf is not None else np.nan) for extname in tab['camera']]
tab['fiber_fracflux_bgs'] = [(exp.images[extname].psf.fiber_fracflux_bgs if exp.images[extname].psf is not None else np.nan) for extname in tab['camera']]
tab['n_sources_for_psf'] = [(exp.images[extname].psf.nstars if exp.images[extname].psf is not None else 0) for extname in tab['camera']]
# this pertains to aperture _3 which is 1.5 asec radius
tab['aper_corr_fac'] = [(exp.images[extname].psf.aper_corr_fac if exp.images[extname].psf is not None else np.nan) for extname in tab['camera']]
tab['xcentroid_psf'] = [(exp.images[extname].psf.xcen_flux_weighted if exp.images[extname].psf is not None else np.nan) for extname in tab['camera']]
tab['ycentroid_psf'] = [(exp.images[extname].psf.ycen_flux_weighted if exp.images[extname].psf is not None else np.nan) for extname in tab['camera']]
tab['psf_fwhm_pix'] = [(exp.images[extname].psf.moffat_fwhm_pix if exp.images[extname].psf is not None else np.nan) for extname in tab['camera']]
tab['psf_fwhm_asec'] = [(exp.images[extname].psf.moffat_fwhm_asec if exp.images[extname].psf is not None else np.nan) for extname in tab['camera']]
tab['psf_centroid_cbox'] = [(float(exp.images[extname].psf.cbox) if exp.images[extname].psf is not None else np.nan) for extname in tab['camera']]
tab['psf_centroid_failed'] = [(exp.images[extname].psf.psf_centroiding_failed if exp.images[extname].psf is not None else 0) for extname in tab['camera']]
tab['radprof_fwhm_asec'] = [(exp.images[extname].psf.radprof_fwhm_asec if exp.images[extname].psf is not None else np.nan) for extname in tab['camera']]
# is 0 the best placeholder value here when no PSF exists?
tab['psf_centroiding_flag'] = [(exp.images[extname].psf.psf_centroiding_flag if exp.images[extname].psf is not None else 0) for extname in tab['camera']]
tab['psf_asymmetry_ratio'] = [(exp.images[extname].psf.psf_asymmetry_ratio if exp.images[extname].psf is not None else np.float32(np.nan)) for extname in tab['camera']]
tab['psf_asymmetry_numerator'] = [(exp.images[extname].psf.psf_asymmetry_numerator if exp.images[extname].psf is not None else np.float32(np.nan)) for extname in tab['camera']]
tab['psf_asymmetry_denominator'] = [(exp.images[extname].psf.psf_asymmetry_denominator if exp.images[extname].psf is not None else np.float32(np.nan)) for extname in tab['camera']]
tab['psf_total_flux'] = [(exp.images[extname].psf.psf_total_flux if exp.images[extname].psf is not None else np.float32(np.nan)) for extname in tab['camera']]
radprof_ccds_table(tab, exp)
for i, extname in enumerate(tab['camera']):
racen, deccen = ccd_center_radec(exp.images[extname].wcs)
tab['racen'][i] = racen
tab['deccen'][i] = deccen
tab['mountha_header'] = exp.try_retrieve_header_card('MOUNTHA', placeholder=np.nan)
tab['mountdec_header'] = exp.try_retrieve_header_card('MOUNTDEC', placeholder=np.nan)
tab['ha_deg'] = [util._get_ha(t['skyra'], t['lst_deg'], t['mountdec_header']) for t in tab]
tab['ha_deg_per_gfa'] = [util._get_ha(t['racen'], t['lst_deg'], t['mountdec_header']) for t in tab]
tab['moon_sep_deg'] = util.moon_separation(tab['moonra'], tab['moondec'],
tab['racen'], tab['deccen'])
# per-camera zenith distance -- will only be accurate to the extent that
# each camera's WCS recalibration succeeded
tab['zd_deg_per_gfa'] = [util._zenith_distance(t['racen'], t['deccen'], t['lst_deg']) for t in tab]
tab['header_airmass'] = exp.try_retrieve_header_card('AIRMASS', placeholder=np.nan)
# this should make the airmass properly evolve
# with time for guide cube outputs
tab['airmass'] = 1.0/np.cos(tab['zenith_dist_deg']/(180.0/np.pi))
# this per-camera version of airmass should also evolve
# properly with time for guide cube outputs
tab['airmass_per_gfa'] = 1.0/np.cos(tab['zd_deg_per_gfa']/(180.0/np.pi))
tab['zp_adu_per_s'] = [exp.images[extname].compute_zeropoint(ps1) for extname in tab['camera']]
tab['n_stars_for_zp'] = [(np.sum(ps1['use_for_zp'] & (ps1['extname'] == extname)).astype(int) if 'use_for_zp' in ps1.colnames else 0) for extname in tab['camera']]
tab['transparency'] = [util.transparency_from_zeropoint(tab[i]['zp_adu_per_s'], tab[i]['airmass_per_gfa'], tab[i]['camera']) for i in range(len(tab))]
par = common.gfa_misc_params()
tab['kterm'] = np.float32(par['kterm'])
tab['fracflux_nominal_pointsource'] = np.float32(par['fracflux_nominal_pointsource'])
tab['fracflux_nominal_elg'] = np.float32(par['fracflux_nominal_elg'])
tab['fracflux_nominal_bgs'] = np.float32(par['fracflux_nominal_bgs'])
tab['det_sn_thresh'] = det_sn_thresh
prescan_overscan_ccds_table(tab, exp)
high_level_ccds_metrics(tab, catalog, exp)
astrom_ccds_table(tab, exp)
dark_current_ccds_table(tab, exp)
if mjdrange is not None:
tab['req_mjd_min'] = mjdrange[0]
tab['req_mjd_max'] = mjdrange[1]
return tab
def assemble_ccds_table(tab,
catalog,
exp,
outdir,
proc_obj,
cube_index=None,
ps1=None,
det_sn_thresh=5.0,
sky_mags=True,
minimal=False):
nrows = len(tab)
tab['extname'] = tab['camera']
tab['contrast'] = [
exp.images[extname].header['CONTRAST'] for extname in tab['camera']
]
if minimal:
return tab
if sky_mags:
tab['sky_mag_ab'] = [
exp.images[extname].sky_mag for extname in tab['camera']
]
amps = common.valid_amps_list()
if sky_mags:
sky_mag_ab_per_amp = np.zeros((nrows, len(amps)),
dtype='float32') # 4 amps
for i, extname in enumerate(tab['camera']):
for j in range(len(amps)):
_mag = np.nan if exp.images[
extname].sky_mag_per_amp is None else exp.images[
extname].sky_mag_per_amp[j]
sky_mag_ab_per_amp[i, j] = _mag
tab['sky_mag_ab_per_amp'] = sky_mag_ab_per_amp
tab['petal_loc'] = np.array([
common.gfa_extname_to_gfa_number(extname) for extname in tab['camera']
],
dtype='uint8')
tab['expid'] = [
exp.images[extname].header['EXPID'] for extname in tab['camera']
]
# should work except if early versions of guide cubes lacked
# MJD information...
tab['mjd'] = [
exp.images[extname].try_retrieve_meta_keyword('MJD-OBS',
placeholder=0.0)
for extname in tab['camera']
]
eph = util.load_lst()
tab['lst_deg'] = [util.interp_lst(t['mjd'], eph=eph) for t in tab]
tab['program'] = [
str(exp.images[extname].try_retrieve_meta_keyword('PROGRAM',
placeholder=''))
for extname in tab['camera']
]
tab['skyra'] = [
exp.images[extname].try_retrieve_meta_keyword('SKYRA',
placeholder=np.nan)
for extname in tab['camera']
]
tab['skydec'] = [
exp.images[extname].try_retrieve_meta_keyword('SKYDEC',
placeholder=np.nan)
for extname in tab['camera']
]
# zenith distance using approximate center of the field given by SKYRA, SKYDEC
tab['zenith_dist_deg'] = [
util._zenith_distance(t['skyra'], t['skydec'], t['lst_deg'])
for t in tab
]
tab['domshutl'] = np.array(
nrows * [exp.try_retrieve_header_card('DOMSHUTL', placeholder='')],
dtype='U8')
tab['domshutu'] = np.array(
nrows * [exp.try_retrieve_header_card('DOMSHUTU', placeholder='')],
dtype='U8')
tab['pmcover'] = exp.try_retrieve_header_card('PMCOVER', placeholder='')
tab['moonra'] = exp.try_retrieve_header_card('MOONRA', placeholder=np.nan)
tab['moondec'] = exp.try_retrieve_header_card('MOONDEC',
placeholder=np.nan)
tab['t_c_for_dark'] = [
exp.images[extname].t_c_for_dark for extname in tab['camera']
]
tab['t_c_for_dark_is_guess'] = [
int(exp.images[extname].t_c_for_dark_is_guess)
for extname in tab['camera']
]
tab['time_s_for_dark'] = [
exp.images[extname].time_s_for_dark for extname in tab['camera']
]
tab['night'] = exp.try_retrieve_header_card('NIGHT', placeholder='')
tab['focus'] = exp.try_retrieve_header_card('FOCUS', placeholder='')
tab['exptime'] = [
exp.images[extname].try_retrieve_meta_keyword('EXPTIME',
placeholder=np.nan)
for extname in tab['camera']
]
tab['cube_index'] = np.nan if cube_index is None else int(cube_index)
if cube_index == -1:
tab['coadd_index_start'] = [
exp.images[extname].coadd_index_range[0]
for extname in tab['camera']
]
tab['coadd_index_end'] = [
exp.images[extname].coadd_index_range[1]
for extname in tab['camera']
]
tab['racen'] = np.zeros(len(tab), dtype=float)
tab['deccen'] = np.zeros(len(tab), dtype=float)
tab['fname_raw'] = proc_obj.fname_in
tab['gitrev'] = proc_obj.gitrev
tab['fiber_fracflux'] = [(exp.images[extname].psf.fiber_fracflux if
exp.images[extname].psf is not None else np.nan)
for extname in tab['camera']]
tab['n_sources_for_psf'] = [(exp.images[extname].psf.nstars
if exp.images[extname].psf is not None else 0)
for extname in tab['camera']]
# this pertains to aperture _3 which is 1.5 asec radius
tab['aper_corr_fac'] = [(exp.images[extname].psf.aper_corr_fac if
exp.images[extname].psf is not None else np.nan)
for extname in tab['camera']]
tab['xcentroid_psf'] = [(exp.images[extname].psf.xcen_flux_weighted if
exp.images[extname].psf is not None else np.nan)
for extname in tab['camera']]
tab['ycentroid_psf'] = [(exp.images[extname].psf.ycen_flux_weighted if
exp.images[extname].psf is not None else np.nan)
for extname in tab['camera']]
tab['psf_fwhm_pix'] = [(exp.images[extname].psf.moffat_fwhm_pix
if exp.images[extname].psf is not None else np.nan)
for extname in tab['camera']]
tab['psf_fwhm_asec'] = [(exp.images[extname].psf.moffat_fwhm_asec if
exp.images[extname].psf is not None else np.nan)
for extname in tab['camera']]
tab['psf_centroid_cbox'] = [
(float(exp.images[extname].psf.cbox)
if exp.images[extname].psf is not None else np.nan)
for extname in tab['camera']
]
tab['psf_centroid_failed'] = [
(exp.images[extname].psf.psf_centroiding_failed
if exp.images[extname].psf is not None else 0)
for extname in tab['camera']
]
for i, extname in enumerate(tab['camera']):
racen, deccen = ccd_center_radec(exp.images[extname].wcs)
tab['racen'][i] = racen
tab['deccen'][i] = deccen
tab['mountha_header'] = exp.try_retrieve_header_card('MOUNTHA',
placeholder=np.nan)
tab['mountdec_header'] = exp.try_retrieve_header_card('MOUNTDEC',
placeholder=np.nan)
tab['ha_deg'] = [
util._get_ha(t['skyra'], t['lst_deg'], t['mountdec_header'])
for t in tab
]
tab['ha_deg_per_gfa'] = [
util._get_ha(t['racen'], t['lst_deg'], t['mountdec_header'])
for t in tab
]
tab['moon_sep_deg'] = util.moon_separation(tab['moonra'], tab['moondec'],
tab['racen'], tab['deccen'])
# per-camera zenith distance -- will only be accurate to the extent that
# each camera's WCS recalibration succeeded
tab['zd_deg_per_gfa'] = [
util._zenith_distance(t['racen'], t['deccen'], t['lst_deg'])
for t in tab
]
tab['header_airmass'] = exp.try_retrieve_header_card('AIRMASS',
placeholder=np.nan)
# this should make the airmass properly evolve
# with time for guide cube outputs
tab['airmass'] = 1.0 / np.cos(tab['zenith_dist_deg'] / (180.0 / np.pi))
# this per-camera version of airmass should also evolve
# properly with time for guide cube outputs
tab['airmass_per_gfa'] = 1.0 / np.cos(tab['zd_deg_per_gfa'] /
(180.0 / np.pi))
tab['zp_adu_per_s'] = [
exp.images[extname].compute_zeropoint(ps1) for extname in tab['camera']
]
tab['transparency'] = [
util.transparency_from_zeropoint(tab[i]['zp_adu_per_s'],
tab[i]['airmass_per_gfa'],
tab[i]['camera'])
for i in range(len(tab))
]
tab['det_sn_thresh'] = det_sn_thresh
prescan_overscan_ccds_table(tab, exp)
high_level_ccds_metrics(tab, catalog, exp)
astrom_ccds_table(tab, exp)
dark_current_ccds_table(tab, exp)
return tab
