data_process.noise_map = err_data
try:
data_process.generate_target_materials(radius=None,
create_mask=False,
nsigma=1.5,
exp_sz=1.2,
npixels=15,
if_plot=False)
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])
file_header['CRPIX1'] = file_header['CRPIX1'] - qso_center[0] + int(
len(data_process_list[k].target_stamp) / 2)
file_header['CRPIX2'] = file_header['CRPIX2'] - qso_center[1] + int(
len(data_process_list[k].target_stamp) / 2)
wcs = WCS(file_header)
write_result = open(
fit_folder + 'fit_result_{0}-band.txt'.format(band_seq[k]), 'w')
write_result.write("#The fitting information for {0}:\n".format(
filename_list[k]))
#==============================================================================
# fitting the QSO as a BH (PS) + Sersic
#==============================================================================
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()
# write_result.write("\nmodel_PS_result: "+repr(ps_result_1))
# write_result.write("\n=======================================================\n")
# tag_name = tag + "_qso_final_plot"
# print(call("mv {0} {1}".format(tag_name+'.pdf', tag+"_chisq_"+repr(round(reduced_Chisq_1,1)))+'.pdf', shell=True))
# if band_seq[k] == 'I': #Get the parameters of I band to fix to the other band.
# fix_Re_list_1 = [[i, source_result_1[i]['R_sersic']] for i in range(len(source_result_1))]
# fix_n_list_1 = [[i, source_result_1[i]['n_sersic']] for i in range(len(source_result_1))]
# ps_param1 = _fit_sepc_1.ps_params
#==============================================================================
# fitting the QSO as a BHBH (PS+PS) + Sersic
#==============================================================================
print("fitting the QSO as {0} point sources + Sersic".format(len(arr_x)))
num_BHBH = max(len(arr_x), 2)
fit_time = 1
tag = 'fit_result_detect/{0}/fit_{2}-band_image2_PSPS+Sersic_fittime-{1}'.format(qsoid,fit_time+1, band_seq[k])
_fit_sepc_2 = FittingSpeficy(data_process_list[k])
_fit_sepc_2.prepare_fitting_seq(point_source_num = num_BHBH, fix_n_list= fix_n_list_2, fix_Re_list= fix_Re_list_2, ps_params = ps_param2, neighborhood_size = neighborhood_size, threshold = threshold)
_fit_sepc_2.build_fitting_seq()
if k == run_list[0]:
_fit_sepc_2.plot_fitting_sets(savename = 'fit_result_detect/{0}/fitting_used_aper.pdf'.format(qsoid),
show_plot=show_plot)
_fit_run_2 = FittingProcess(_fit_sepc_2, savename = tag)
_fit_run_2.run(algorithm_list = ['PSO'], setting_list= [pso_setting])
_fit_run_2.translate_result()
_fit_run_2.plot_final_qso_fit(target_ID = qsoid, save_plot = True, show_plot = show_plot)
source_result_2, ps_result_2 = _fit_run_2.final_result_galaxy, _fit_run_2.final_result_ps
host_mag = source_result_2[0]['magnitude']
AGN_mags = [ps_result_2[i]['magnitude'] for i in range(len(ps_result_2))]
if len(ps_result_2) == 2:
c_miss = np.sqrt((ps_result_2[0]['ra_image']-ps_result_2[1]['ra_image'])**2+(ps_result_2[0]['dec_image']-ps_result_2[1]['dec_image'])**2)
elif len(ps_result_2) > 2:
data_process_0.PSF_list = [psf_data]
data_process_0.checkout() #Check if all the materials is known.
#%%Start to produce the class and params for lens fitting.
# #Manually input another component:
# apertures_0 = copy.deepcopy(data_process_0.apertures)
# 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)
data_process_0.checkout() #Check if all the materials is known.
#%%Start to produce the class and params for lens fitting.
# #Manually input another component:
# apertures_0 = copy.deepcopy(data_process_0.apertures)
# 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)
qsoid)) == True and fit_data == True:
print("Fiting the: " + filename_list[k])
print(claim)
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)
data_process.generate_target_materials(
radius=None,
create_mask=False,
nsigma=2.8, #radius=None, the target size would be automated set.
exp_sz=1.2,
npixels=15,
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])
#%%