def single_star_photometry(img,
xc,
yc,
bkg,
filename,
images_info,
upper_limit=False):
shortfile = filename.split('.fits')[0]
ttexp = images_info.loc[0, 'EXPTIME']
print('exptime = ' + str(ttexp))
nanmask = pd.isnull(img)
ap = 0.4 / images_info.loc[0, 'PIXSCALE']
filt = images_info.loc[images_info['FILENAME'] == filename,
'FILTER'].values[0]
vegazpt = config.VEGA_ZEROPTS[filt]
pos = [float(xc), float(yc)]
apertures = CircularAperture(pos, r=ap)
sigma_clip = SigmaClip(sigma=3.)
bkg_estimator = MedianBackground()
bkgimg = Background2D(img, (20, 20),
filter_size=(3, 3),
sigma_clip=sigma_clip,
bkg_estimator=bkg_estimator)
error = calc_total_error(
img,
bkgimg.background,
effective_gain=images_info.loc[images_info['FILENAME'] == filename,
'CCDGAIN'].values[0])
# error = calc_total_error(img, bkgimg.background, effective_gain=config.GAIN[instrument])
#For sky annuli background
#sky_inner = 0.8/images_info.loc[0,'PIXSCALE']
#sky_outer = 0.9/images_info.loc[0,'PIXSCALE']
#skyannuli = CircularAnnulus(pos, r_in=sky_inner, r_out=sky_outer)
#phot_apers = [apertures, skyannuli]
#phot_table2 = Table(aperture_photometry(img, phot_apers, method='exact', error=error, mask=nanmask)).to_pandas()
#For multiapertures background
phot = aperture_photometry(img, apertures, method='exact',
mask=nanmask) #error=error,
phot = Table(phot).to_pandas()
phot = phot.rename(columns={'aperture_sum': 'APERTURE_FLUX'
}) #,'aperture_sum_err':'APERTURE_FLUX_ERR'
phot['BKG_FLUX'] = bkg[0]
phot['BKG_FLUX_ERR'] = bkg[1]
phot['APERTURE_FLUX_ERR'] = np.sqrt(phot['APERTURE_FLUX'] / ttexp +
2 * phot['BKG_FLUX_ERR']**2)
phot = phot.apply(pd.to_numeric, errors='ignore')
if upper_limit == True:
print('this is true')
print('background flux error = ' + str(bkg[1]))
phot['STAR_FLUX'] = 3 * bkg[1]
#3 sigma = 3 * 1.4826 * MAD since distribution is Gaussian - No more MAD, just STD
phot['STAR_FLUX_ERR'] = np.nan
else:
#no need to divide over area because aperture areas for both star flux and backgroun flux are the same
phot['STAR_FLUX'] = phot['APERTURE_FLUX'] - phot['BKG_FLUX']
phot['STAR_FLUX_ERR'] = np.sqrt(phot['APERTURE_FLUX_ERR']**2 +
phot['BKG_FLUX_ERR']**2)
#bkg_mean = phot_table2['aperture_sum_1'] / skyannuli.area
#bkg_starap_sum = bkg_mean * apertures.area
#final_sum = phot_table2['aperture_sum_0']-bkg_starap_sum
#phot_table2['bg_subtracted_star_counts'] = final_sum
#bkg_mean_err = phot_table2['aperture_sum_err_1'] / skyannuli.area
#bkg_sum_err = bkg_mean_err * apertures.area
#phot_table2['bg_sub_star_cts_err'] = np.sqrt((phot_table2['aperture_sum_err_0']**2)+(bkg_sum_err**2))
#phot['STAR_FLUX_ANNULI'] = final_sum
#phot['STAR_FLUX_ERR_ANNULI'] = phot_table2['bg_sub_star_cts_err']
phot['VEGAMAG'] = vegazpt - 2.5 * np.log10(phot['STAR_FLUX'])
phot['VEGAMAG_UNC'] = 1.0857 * phot['STAR_FLUX_ERR'] / phot['STAR_FLUX']
#phot['VEGAMAG_UNC_APPHOT_ONLY'] = 1.0857 * phot['APERTURE_FLUX_ERR'] / phot['STAR_FLUX']
#phot['VEGAMAG_ANNULI'] = vegazpt - 2.5 * np.log10(phot['STAR_FLUX_ANNULI'])
#phot['VEGAMAG_UNC_ANNULI'] = 1.0857 * phot['STAR_FLUX_ERR_ANNULI'] / phot['STAR_FLUX_ANNULI']
phot.to_csv('phot_table_' + str(shortfile) + '.csv')
return phot
