fwhm,filename=iraf_fwhm()

xpix,ypix=source_list(filename)

#ast=AstrometryNet()

#ast.api_key= 'iqmqwvazpvolmjmn'

data,header=fits.getdata(filename,header=True)

exposure_time=header['EXPOSURE']

sigma_clip = SigmaClip(sigma=3., maxiters=10)

bkg_estimator = MedianBackground()

bkg = Background2D(data, (10,10), filter_size=(3, 3),sigma_clip=sigma_clip, bkg_estimator=bkg_estimator)

back=bkg.background # this is the background we need for the background subtraction.

back2=np.median(bkg.background)

mask = data == 0

unit = u.electron / u.s

xdf_image = CCDData(data, unit=unit, meta=header, mask=mask)

norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch(), clip=False)

xdf_image_clipped = np.clip(xdf_image, 1e-4, None)

mean, median, std = sigma_clipped_stats(xdf_image.data, sigma=3.0, maxiters=20, mask=xdf_image.mask)

print('Finding the sources')

#daofind = DAOStarFinder(fwhm=fwhm, threshold=5*std) # 3 sigma above the background.

#sources = daofind(data - median)

#sources_findpeaks = find_peaks(xdf_image.data, mask=xdf_image.mask, threshold=30.*std, box_size=30, centroid_func=centroid_2dg)

#print('We have found:',len(sources),' sources')

#print(sources)

#print(sources['xcentroid'], sources['ycentroid'],sources['fwhm'])

#positions=sources['xcentroid'], sources['ycentroid']

positions=np.genfromtxt('co_ordinates_list_1.txt',unpack=True,usecols=(0,1))

#print(positions)

radii=[ fwhm,2*fwhm, 4*fwhm, 6*fwhm]

#positions=(sources['xcentroid'], sources['ycentroid'])

apertures = [CircularAperture(positions, r=r) for r in radii]

an_ap = CircularAnnulus(positions, r_in=8*fwhm, r_out=10*fwhm)

#apers = [apertures, annulus_apertures]

#bkg_sigma=mad_std(data)

effective_gain=exposure_time

error=calc_total_error(data,back,effective_gain)

#error=0.1*data

phot_table = aperture_photometry(data, apertures,error=error)

phot_table2=aperture_photometry(data,an_ap)

bkg_mean = phot_table2['aperture_sum'] / an_ap.area()

bkg_sum = bkg_mean * an_ap.area()

final_sum0=phot_table['aperture_sum_0']-bkg_sum

final_sum1=phot_table['aperture_sum_1']-bkg_sum

final_sum2=phot_table['aperture_sum_2']-bkg_sum

final_sum3=phot_table['aperture_sum_3']-bkg_sum

mag_0=-2.5*np.log10(final_sum0/exposure_time)+25

mag_1=-2.5*np.log10(final_sum1/exposure_time)+25

mag_2=-2.5*np.log10(final_sum2/exposure_time)+25

mag_3=-2.5*np.log10(final_sum3/exposure_time)+25

fig=plt.figure()

plt.imshow(data,cmap='gray',origin='lower',vmin=50,vmax=400)

colors=['red','green','yellow','blue']

for i in range(len(apertures)):

apertures[i].plot(color=colors[i], alpha=0.7)

an_ap.plot(color='green', alpha=0.7)

plt.show()

for i in range (len(phot_table)):

print(mag_0[i],mag_1[i],mag_2[i],mag_3[i])

'''

mag=-2.5*np.log10(final_sum/30)+25

flux=final_sum

flux_err=phot_table['aperture_sum_err_0']

mag_err=1.09*flux_err/flux

x=[phot.value for phot in phot_table['xcenter']]

y=[phot.value for phot in phot_table['ycenter']]

#with open('result.dat', 'w') as f:

#with open('out.txt', 'w') as f:

for i in range(len(x)):

print(x[i],y[i],'\t',mag[i],mag_err[i])

outfile=' '

for i in range (len(phot_table)):

outfile+=x[i]+ " "+ y[i]+" "+ mag[i]+" " +mag_err[i]

outfile+='\n'

out=open('result.txt','w')

out.write(outfile,overwrite=True)

out.close()

'''

'''

phot_table['xcenter']

wcs_header=ast.solve_from_image('bfc.fits')

wcs=WCS(wcs_header)

xpix=[phot.value for phot in phot_table['xcenter']]

ypix=[phot.value for phot in phot_table['ycenter']]

ra=np.zeros(len(xpix))

dec=np.zeros(len(ypix))

for i in range(len(ra)):

ra[i],dec[i]=wcs.all_pix2world(xpix[i],ypix[i],0)

plt.imshow(data,cmap='gray',vmin=30,vmax=300)

plt.show()

#print(phot_table)