def do_Circ_phot(pos, FWHM, ap_min=3., ap_factor=1.5, rin=None, rout=None, \
sky_nsigma=3., sky_iter=10):
'''
In rigorous manner, we should use
error = sqrt (flux / epadu + area * stdev**2 + area**2 * stdev**2 / nsky)
as in http://stsdas.stsci.edu/cgi-bin/gethelp.cgi?phot .
Here flux == aperture_sum - sky, i.e., flux from image_reduc.
stdev should include sky error AND ronoise.
'''
if rin == None:
rin = 4 * FWHM
if rout == None:
rout = 6 * FWHM
N = len(pos)
if pos.ndim == 1:
N = 1
an = CircAn(pos, r_in=rin, r_out=rout)
ap_size = np.max([ap_min, ap_factor*FWHM])
aperture = CircAp(pos, r=ap_size)
flux = aperture.do_photometry(image_reduc, method='exact')[0]
# do phot and get sum from aperture. [0] is sum and [1] is error.
#For test:
#N=len(pos_star_fit)
#an = CircAn(pos_star_fit, r_in=4*FWHM_moffat, r_out=6*FWHM_moffat)
#ap_size = 1.5*FWHM_moffat
#aperture = CircAp(pos_star_fit, r=ap_size)
#flux = aperture.do_photometry(image_reduc, method='exact')[0]
flux_ss = np.zeros(N)
error = np.zeros(N)
for i in range(0, N):
mask_an = (an.to_mask(method='center'))[i]
# cf: test = mask_an.cutout(image_reduc) <-- will make cutout image.
sky_an = mask_an.apply(image_reduc)
all_sky = sky_an[np.nonzero(sky_an)]
# only annulus region will be saved as np.ndarray
msky, stdev, nsky, nrej = sky_fit(all_sky, method='Mode', mode_option='sex')
area = aperture.area()
flux_ss[i] = flux[i] - msky*area # sky subtracted flux
error[i] = np.sqrt( flux_ss[i]/gain \
+ area * stdev**2 \
+ area**2 * stdev**2 / nsky )
# To know rejected number, uncomment the following.
# plt.imshow(sky_an, cmap='gray_r', vmin=-20)
# plt.colorbar()
# plt.show()
# mask_ap = (aperture.to_mask(method='exact'))[i]
# star_ap = mask_ap.apply(image_reduc)
# plt.imshow(star_ap)
# plt.colorbar()
# plt.show()
# plt.cla()
if pos.ndim > 1:
print('\t[{x:7.2f}, {y:7.2f}], {nsky:3d} {nrej:3d} {msky:7.2f} {stdev:7.2f} {flux:7.1f} {ferr:3.1f}'.format(\
x=pos[i][0], y=pos[i][1], \
nsky=nsky, nrej=nrej, msky=msky, stdev=stdev,\
flux=flux_ss[i], ferr=error[i]))
return flux_ss, error
if len(DAOfound) == 0:
print('No star was founded using DAOStarFinder\n' * 3)
else:
# Use the object "found" for aperture photometry:
print(len(DAOfound), 'stars were founded')
#print('DAOfound \n', DAOfound)
DAOfound.pprint(max_width=1800)
# save XY coordinates:
DAOfound.write(dir_name + f_name[:-5] + '_DAOStarFinder.csv',
overwrite=True,
format='ascii.fast_csv')
DAOcoord = (DAOfound['xcentroid'], DAOfound['ycentroid'])
# Save apertures as circular, 4 pixel radius, at each (X, Y)
DAOapert = CircAp(DAOcoord, r=4.)
#print('DAOapert\n ', DAOapert)
DAOimgXY = np.array(DAOcoord)
#print('DAOimgXY \n', DAOimgXY)
plt.figure(figsize=(12, 12))
ax = plt.gca()
im = plt.imshow(img, vmax=0.35, origin='lower')
DAOapert.plot(color='red', lw=2., alpha=0.7)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="3%", pad=0.05)
plt.colorbar(im, cax=cax)
plt.show()
#%%
print('No star was founded using DAOStarFinder\n' * 3)
else:
# Use the object "found" for aperture photometry:
N_stars = len(DAOfound)
print(N_stars, 'star(s) is(are) founded')
#print('DAOfound \n', DAOfound)
#DAOfound.pprint(max_width=1800)
# save XY coordinates:
DAOfound.write(f_name[:-4] + '_DAOStarFinder.csv',
overwrite=True,
format='ascii.fast_csv')
DAOcoord = (DAOfound['xcentroid'], DAOfound['ycentroid'])
DAOannul = CircAn(positions=DAOcoord, r_in=4 * FWHM, r_out=6 * FWHM)
# Save apertures as circular, 4 pixel radius, at each (X, Y)
DAOapert = CircAp(DAOcoord, r=4.)
#print('DAOapert\n ', DAOapert)
DAOimgXY = np.array(DAOcoord)
#print('DAOimgXY \n', DAOimgXY)
plt.figure(figsize=(16, 12))
ax = plt.gca()
im = plt.imshow(img, vmax=thresh * 4, origin='lower')
DAOannul.plot(color='red', lw=2., alpha=0.7)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="3%", pad=0.05)
plt.colorbar(im, cax=cax)
plt.savefig(f_name[:-4] +
'_DAOstarfinder_Annulus_result_all_stars.png',
overwrite=True)
print('No star was founded using DAOStarFinder\n' * 3)
else:
# Use the object "found" for aperture photometry:
N_stars = len(DAOfound)
print(N_stars, 'star(s) is(are) founded')
#print('DAOfound \n', DAOfound)
#DAOfound.pprint(max_width=1800)
# save XY coordinates:
DAOfound.write(f_name[:-4] + '_DAOStarFinder.csv',
overwrite=True,
format='ascii.fast_csv')
DAOcoord = (DAOfound['xcentroid'], DAOfound['ycentroid'])
DAOannul = CircAn(positions=DAOcoord, r_in=4 * FWHM, r_out=6 * FWHM)
# Save apertures as circular, 4 pixel radius, at each (X, Y)
DAOapert = CircAp(DAOcoord, r=2.0 * FWHM)
#print('DAOapert\n ', DAOapert)
DAOimgXY = np.array(DAOcoord)
#print('DAOimgXY \n', DAOimgXY)
plt.figure(figsize=(16, 12))
ax = plt.gca()
im = plt.imshow(img, vmax=thresh * 4, origin='lower')
DAOannul.plot(color='red', lw=2., alpha=0.7)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="3%", pad=0.05)
plt.colorbar(im, cax=cax)
plt.savefig(f_name[:-4] +
'_DAOstarfinder_Annulus_result_all_stars.png',
overwrite=True)
from photutils import DAOStarFinder
from photutils import CircularAperture as CircAp
find = DAOStarFinder(
fwhm=FWHM,
threshold=thresh,
sharplo=0.2,
sharphi=1.0, # default values
roundlo=-1.0,
roundhi=1.0, # default values
sigma_radius=1.5, # default values
ratio=1.0, # 1.0: circular gaussian
exclude_border=True) # To exclude sources near edges
# The DAOStarFinder object ("find") gets at least one input: the image.
# Then it returns the astropy table which contains the aperture photometry results:
found = find(img)
# Use the object "found" for aperture photometry:
# save XY coordinates:
coord = (found['xcentroid'], found['ycentroid'])
# Save apertures as circular, 4 pixel radius, at each (X, Y)
apert = CircAp(coord, r=4.)
# Draw image and overplot apertures:
plt.figure(figsize=(10, 10))
plt.imshow(img, vmax=6550)
apert.plot(color='red', lw=2., alpha=0.7)
plt.colorbar()
plt.show()