#plt.show()
#%%
#print('Star ID msky sky_std nsky nrej')
#for star_ID in range(2, 10) : # for debug
img_uint16 = np.array(img * 65536.0, dtype=np.uint16)
ronoise = hdu[0].header['RDNOISE']
#gain = 0.46599999070167542 # e/ADU
gain = hdu[0].header['GAIN']
N_star = len(DAOfound)
mag_ann = np.zeros(N_star)
merr_ann = np.zeros(N_star)
# aperture sum
apert_sum = APPHOT(img_uint16, DAOapert,
method='exact')['aperture_sum']
ap_area = DAOapert.area()
#print(apert_sum)
apert_result = 'ID, Msky, sky_std, Sky count Pixel_N, Sky reject Pixel_N, mag_ann, merr_ann\n'
for star_ID in range(0, N_stars)[10:12]:
# since our `DAOannul` has many elements :
mask_annul = (DAOannul.to_mask(method='center'))[star_ID]
mask_apert = (DAOapert.to_mask(method='center'))[star_ID]
# CAUTION!! YOU MUST USE 'center', NOT 'exact'!!!
cutimg = mask_annul.cutout(img)
#cutimg.tofile('{0!s}_DAOstarfinder_Star_Flux_pixel_value_starID_{1:04}.csv'.format(f_name[:-4], star_ID), sep=',')
df_cutimg = pd.DataFrame(cutimg * 65536.0, dtype=np.uint16)
ct=0
for j in day:
for i in num:
image = CCDData.read('V-{:}-{:}(2).fit'.format(j,i), hdu=0, unit='u.electron/u.s')
rad= 450
img=image.data/exp_t[ct]
x_cent= np.argwhere(img == img.max())[0][0]#원 그리는 센터
y_cent= np.argwhere(img == img.max())[0][1]
annul = [Circul(positions=(y_cent,x_cent), r_in=b0, r_out=b0+1) for b0 in range(1,rad+1)]
phot = APPHOT(img, annul)
time.sleep(1)
#Plot
semimaj = np.arange(1, rad+1)
counts = np.zeros(rad)
dcounts = np.zeros(rad)
for k in range(0, rad):
count = phot[phot.colnames[k+3]]
counts[k] = count
# phot.colnames = column names = "aperture_sum_X"
dcount = count /(annul[k].area())# count per pixel
if(dcount >0):
dcounts[k]=dcount
else :