'center_y':
obj[i][0][1] * pix_scale + 0.5
})
source_params_3 = [
kwargs_source_init, kwargs_source_sigma, fixed_source,
kwargs_lower_source, kwargs_upper_source
]
print("fitting the QSO as {0} small Sersic + Sersic".format(
len(arr_x)))
source_result_3, image_host_3, error_map_3, reduced_Chisq_3 = fit_galaxy(
QSO_img,
psf_ave=psf,
psf_std=None,
background_rms=background_rms_list[k],
source_params=source_params_3,
galaxy_msk=None,
pix_sz=pix_scale,
no_MCMC=True,
galaxy_std=QSO_std,
tag=tag,
deep_seed='very_deep',
pltshow=pltshow,
return_Chisq=True)
# host_mag = -2.5*np.log10(image_host_3[len(arr_x)+1].sum()) + zp_list[k]
host_mag = -2.5 * np.log10(
image_host_3[len(arr_x)].sum()) + zp_list[k]
AGN_mags = [
-2.5 * np.log10(image_host_3[i].sum()) + zp_list[k]
for i in range(len(arr_x))
]
if len(arr_x) == 2:
c_miss = np.sqrt((source_result_3[0]['center_x'] -
galaxy_msk = None
if fitting_strategy == 'boost_galaxy_center_rms':
galaxy_std[galaxy_img == galaxy_img[img_c - 3:img_c + 4,
img_c - 3:img_c + 4].max()] = 10**6
elif fitting_strategy == 'add_galaxy_center_mask':
galaxy_msk = np.ones_like(galaxy_img)
galaxy_msk[galaxy_img == galaxy_img[img_c - 3:img_c + 4,
img_c - 3:img_c + 4].max()] = 0
tag = 'RESULTS/fit_image_{0}'.format(filename_list[k].split('.fits')[0])
source_result, image_host, error_map, reduced_Chisq = fit_galaxy(
galaxy_img,
psf_ave=psf,
psf_std=None,
background_rms=background_rms_list[k],
source_params=source_params,
galaxy_msk=galaxy_msk,
pix_sz=pix_scale,
no_MCMC=True,
galaxy_std=galaxy_std,
tag=tag,
deep_seed=deep_seed,
pltshow=pltshow,
return_Chisq=True)
if band_seq[k] == 'I':
Iband_inf = source_result # in order to save the I band source_reslt to fix Reff and n for other band.
if pltshow == 0:
plot_compare = False
fits_plot = False
else:
plot_compare = True
fits_plot = True