#slice a section of the total image around each source to carry out flux analysis. For this, the point cannot lie too close to the edge.
if x > 100 and y > 100 and x < (len(data[0]) - 100) and y < (
len(data) - 100): #cuts off edge noise within 100 pixels
index_y.append(y)
index_x.append(x)
data_set = data[int(y - 15):int(y + 15), int(x - 15):int(x + 15)]
centre = np.array(
[14.0, 14.0]) #the centre is fixed due to the sliced section
i_tot_flux, N = circ_apt_flux(data_set, centre, apt_size)
tot_flux.append(i_tot_flux) #find individual total flux
i_err_tot_flux = np.sqrt(i_tot_flux)
err_tot_flux.append(
i_err_tot_flux) #individual error in total flux
i_loc_backgrnd, Na = ann_ref(data_set, centre, apt_size, ann_size)
loc_backgrnd.append(i_loc_backgrnd) #individual background
i_source_flux = flux(data_set, centre, apt_size, ann_size)
source_flux.append(i_source_flux) #individual source flux
i_mag = header["MAGZPT"] - 2.5 * np.log10(i_source_flux)
mag.append(i_mag) #individual apparent magnitude
i_m_err = np.sqrt((header["MAGZRR"])**2 + (25 /
(4 * np.log(10)**2)) *
((i_tot_flux + i_loc_backgrnd / Na) /
(i_source_flux**2)))
m_err.append(i_m_err) #individual error in m
catalogue = [
index_x, index_y, tot_flux, err_tot_flux, source_flux, loc_backgrnd,
mag, m_err
]
zip(*catalogue
) #converts the catalogue into the right format to be saved as a csv
import numpy as np
from astropy.io import fits
hdulist = fits.open('A1_mosaic.fits')
header = hdulist[0].header
data = hdulist[0].data
hdulist.close()
from photometry import flux
data_small = data[1450:1500,1200:1230]
counts = flux(data_small)
m = header["MAGZPT"] -2.5*np.log10(counts)
print(m)
