print("fitting the QSO as one BH + Sersic ")
tag = fit_folder + 'fit_{0}-band_fit0_PS+Sersic'.format(band_seq[k])
_fit_sepc_0 = FittingSpeficy(data_process_list[k])
_fit_sepc_0.prepare_fitting_seq(point_source_num=1,
fix_n_list=fix_n_list_0,
fix_Re_list=fix_Re_list_0,
ps_params=ps_param0,
apertures_center_focus=True)
_fit_sepc_0.build_fitting_seq()
if k == run_list[0]:
_fit_sepc_0.plot_fitting_sets(
savename=fit_folder +
'fitting0_used_aper.pdf'.format(image_ID),
show_plot=show_plot)
_fit_run_0 = FittingProcess(_fit_sepc_0, savename=tag)
_fit_run_0.run(algorithm_list=['PSO'], setting_list=[pso_setting])
_fit_run_0.translate_result()
_fit_run_0.plot_final_qso_fit(target_ID=image_ID,
save_plot=True,
show_plot=show_plot)
source_result_0, ps_result_0 = _fit_run_0.final_result_galaxy, _fit_run_0.final_result_ps
host_mag, AGN_mag = source_result_0[0]['magnitude'], ps_result_0[0][
'magnitude']
c_miss = np.sqrt(
(source_result_0[0]['center_x'] - ps_result_0[0]['ra_image'])**2 +
(source_result_0[0]['center_y'] - ps_result_0[0]['dec_image'])**2)
reduced_Chisq_0 = _fit_run_0.reduced_Chisq
write_result.write(
"1. Fitting as a regular QSO,i.e. one PS + Sersic:\n")
write_result.write("Reduced Chisq: " + repr(round(reduced_Chisq_0, 3)))
except:
continue
data_process.PSF_list = [PSF]
data_process.checkout() #Check if all the materials is known.
#Start to produce the class and params for lens fitting.
fit_sepc = FittingSpeficy(data_process)
fit_sepc.prepare_fitting_seq(
point_source_num=1
) #, fix_n_list= [[0,4]], fix_center_list = [[0,0]])
fit_sepc.plot_fitting_sets()
fit_sepc.build_fitting_seq()
#Setting the fitting method and run.
fit_run = FittingProcess(fit_sepc,
savename='model_result/' +
ids_file[i].split('/')[-1][:-5])
fit_run.run(algorithm_list=['PSO'], setting_list=[None])
fit_run.plot_final_qso_fit(save_plot=True,
target_ID=ids_file[i].split('/')[-1][:-5])
fit_run.dump_result()
# print(fit_run.final_result_galaxy[0])
filename = 'model_result/{0}_host_result.txt'.format(Bla_type)
if_file = glob.glob(filename)
if if_file == []:
write_file = open(filename, 'w')
else:
write_file = open(filename, 'r+')
write_file.read()
write_file.write(ids_file[i].split('/')[-1][:-5] + ' ' +
str(fit_run.final_result_galaxy[0]) + '\n')
def condition_bulgedisk(kwargs_lens, kwargs_source, kwargs_lens_light,
kwargs_ps, kwargs_special, kwargs_extinction):
logL = 0
phi0, q0 = param_util.ellipticity2phi_q(kwargs_lens_light[1]['e1'],
kwargs_lens_light[1]['e2'])
phi1, q1 = param_util.ellipticity2phi_q(kwargs_lens_light[2]['e1'],
kwargs_lens_light[2]['e2'])
cond_0 = (kwargs_lens_light[1]['R_sersic'] >
kwargs_lens_light[2]['R_sersic'] * 0.9)
cond_1 = (kwargs_lens_light[1]['R_sersic'] <
kwargs_lens_light[2]['R_sersic'] * 0.15)
cond_2 = (q0 < q1)
if cond_0 or cond_1 or cond_2:
logL -= 10**15
return logL
#%%Fitting as disk + bulge:
data_process_1 = copy.deepcopy(data_process_0)
apertures = copy.deepcopy(data_process_1.apertures)
comp_id = 0 #Change the component (galaxy) id = 0 into to components (i.e., bulge + disk)
add_aperture0 = copy.deepcopy(apertures[comp_id])
add_aperture0.a, add_aperture0.b = add_aperture0.a / 3, add_aperture0.b / 3
apertures = apertures[:comp_id] + [add_aperture0] + apertures[comp_id:]
data_process_1.apertures = apertures #Pass apertures to the data
fit_sepc_1 = FittingSpeficy(data_process_1)
fit_sepc_1.prepare_fitting_seq(
point_source_num=1,
fix_n_list=[[0, 4], [
1, 1
]], #First component fix n = 4 (bluge), second one fix to 1 (disk).
fix_center_list=[[0, 0]],
condition=condition_bulgedisk)
# fit_sepc_1.plot_fitting_sets()
picklename = save_name + 'single_Sersic.pkl'
fit_run_0 = pickle.load(open(picklename, 'rb'))
fit_sepc_1.build_fitting_seq()
fit_sepc_1.build_fitting_seq()
fit_sepc_1.kwargs_params['source_model'][0][
1] = fit_run_0.final_result_galaxy[0]
fit_sepc_1.kwargs_params['source_model'][4][0][
'R_sersic'] = fit_run_0.final_result_galaxy[0]['R_sersic']
fit_sepc_1.kwargs_params['source_model'][4][1][
'R_sersic'] = fit_run_0.final_result_galaxy[0]['R_sersic'] * 2
fit_run_1 = FittingProcess(fit_sepc_1,
savename=save_name + 'bulge+disk_2nd')
fit_run_1.run(algorithm_list=['PSO'], setting_list=[None])
fit_run_1.plot_final_qso_fit()
bic_1 = fit_run_1.fitting_seq.bic
fit_run_1.dump_result()
print("BIC compare:", round(fit_run_0.fitting_seq.bic, 1), round(bic_1, 1))
#%%
filename = 'fit_result/' + 'BIC_result_2nd.txt'
if_file = glob.glob(filename)
if if_file == []:
write_file = open(filename, 'w')
else:
write_file = open(filename, 'r+')
write_file.read()
if fit_run_0.fitting_seq.bic < fit_run_1.fitting_seq.bic:
print(ID + " is a singel Sersic model; " + "glob Number: " + str(NO))
write_file.write(ID + " is a singel Sersic model;" + "glob Number: " +
str(NO))
else:
print(ID + " is a disk+bulge!!! " + "glob Number: " + str(NO))
write_file.write(ID + "is a disk+bulge!!! " + "glob Number: " +
str(NO))
write_file.write("\n")
write_file.close()
# add_aperture1 = copy.deepcopy(apertures_0[0])
# add_pos = [60, 60] #The position of the component.
# if isinstance(add_aperture1.positions[0],float):
# add_aperture1.positions = np.array(add_pos)
# elif isinstance(add_aperture1.positions[0],np.ndarray):
# add_aperture1.positions = np.array([add_pos])
# add_aperture1.a, add_aperture1.b = 2, 2 #define the a, b value of this component, i.e., Reff = sqrt(a^2 +b^2)
# apertures_0 = apertures_0 + [add_aperture1] #attach the added aperture into the group.
# data_process_0.apertures = apertures_0 #Pass apertures to the data
fit_sepc_0 = FittingSpeficy(data_process_0)
fit_sepc_0.prepare_fitting_seq(
point_source_num=1) #, fix_n_list= [[0,4]], fix_center_list = [[0,0]])
# fit_sepc_0.plot_fitting_sets()
fit_sepc_0.build_fitting_seq()
#Setting the fitting method and run.
fit_run_0 = FittingProcess(fit_sepc_0,
savename=save_name + 'single_Sersic')
fit_run_0.run(algorithm_list=['PSO'], setting_list=[None])
fit_run_0.plot_final_qso_fit()
bic_0 = fit_run_0.fitting_seq.bic
fit_run_0.dump_result()
#%%Fitting as disk + bulge:
data_process_1 = copy.deepcopy(data_process_0)
apertures = copy.deepcopy(data_process_1.apertures)
comp_id = 0 #Change the component (galaxy) id = 0 into to components (i.e., bulge + disk)
add_aperture0 = copy.deepcopy(apertures[comp_id])
add_aperture0.a, add_aperture0.b = add_aperture0.a / 3, add_aperture0.b / 3
apertures = apertures[:comp_id] + [add_aperture0] + apertures[comp_id:]
data_process_1.apertures = apertures #Pass apertures to the data
fit_sepc_1 = FittingSpeficy(data_process_1)
fit_sepc_1.prepare_fitting_seq(
print("Comparing the fitting Chisq:")
write_result = open(
'fit_result_detect/{0}/fit_result.txt'.format(qsoid), 'w')
write_result.write("#The fitting information:\n")
#==============================================================================
# fitting the QSO as a BH (PS) + Sersic
#==============================================================================
for ft in range(1): #The fitting rounds for each band
print("fitting the QSO as one BH + Sersic ")
fit_time = ft
tag = 'fit_result_detect/{0}/fit_image0_PS+Sersic_fittime-{1}'.format(
qsoid, fit_time + 1)
_fit_sepc = FittingSpeficy(data_process_list[k])
_fit_sepc.prepare_fitting_seq(point_source_num=1)
_fit_sepc.build_fitting_seq()
_fit_run = FittingProcess(_fit_sepc, savename=tag)
_fit_run.run(algorithm_list=['PSO'], setting_list=[None])
_fit_run.translate_result()
_fit_run.plot_final_qso_fit(target_ID=qsoid,
save_plot=True,
show_plot=show_plot)
source_result_0, ps_result_0 = _fit_run.final_result_galaxy, _fit_run.final_result_ps
host_mag, AGN_mag = source_result_0[0]['magnitude'], ps_result_0[
0]['magnitude']
c_miss = np.sqrt((source_result_0[0]['center_x'] -
ps_result_0[0]['ra_image'])**2 +
(source_result_0[0]['center_y'] -
ps_result_0[0]['dec_image'])**2)
reduced_Chisq_0 = _fit_run.reduced_Chisq
write_result.write(
"1. Fitting as a regular QSO,i.e. one PS + Sersic:\n")
if_plot=False) data_process.PSF_list = [PSF] data_process.checkout() #Check if all the materials is known. #%%Start to produce the class and params for lens fitting. from decomprofile.fitting_specify import FittingSpeficy fit_sepc = FittingSpeficy(data_process) fit_sepc.prepare_fitting_seq(point_source_num=0) fit_sepc.build_fitting_seq() #%%Setting the fitting method and run. from decomprofile.fitting_process import FittingProcess savename = image_id + '-' + band fit_run = FittingProcess(fit_sepc, savename=savename) fit_run.run(algorithm_list=['PSO'], setting_list=[None]) #Only PSO, not MCMC # fit_run.plot_all() fit_run.plot_final_galaxy_fit(target_ID=savename, show_plot=False) fit_run.translate_result() # fit_run.dump_result() #To save result as pickle file # print(fit_run.final_result_galaxy[0]) #%% filename_ascii = image_id + '_result.txt' _ascii = open(filename_ascii, 'w') _ascii.write(str(fit_run.final_result_galaxy[0])) _ascii.close() # #%% # # Test load pkl