#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 :