header=header,
rm_bkglight=True,
if_plot=False,
zp=zp)
data_process_0.noise_map = err_data
data_process_0.generate_target_materials(radius=None,
detect_tool='sep',
create_mask=False,
nsigma=2.8,
exp_sz=1.2,
npixels=15,
if_plot=True)
data_process_0.PSF_list = [PSF]
data_process_0.checkout() #Check if all the materials is known.
fit_sepc_0 = FittingSpecify(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 + ID + '_single_Sersic')
fit_run_0.run(algorithm_list=['PSO', 'PSO'],
setting_list=[{
'sigma_scale': 1.,
'n_particles': 50,
'n_iterations': 50
}] * 2)
fov_image = fitsFile[1].data # check the back grounp
data_process = DataProcess(fov_image = fov_image, target_pos = [RA, Dec], pos_type = 'wcs', header = header,
rm_bkglight = True, exptime = exp_map, if_plot=False, zp = 25.9463) #!!! zp use F160W for now
data_process.generate_target_materials(radius=20, cut_kernel = 'center_bright', create_mask = False,
# detect_tool = 'sep', if_select_obj= True, nsigma=2.5, thresh = 2.5, exp_sz= 1.2, npixels = 15,
if_plot=False)
#Lines used to find PSF and set the PSF_loc_dic.
data_process.find_PSF(radius = 30, user_option = True, if_filter=True, psf_edge =30)
# data_process.profiles_compare(norm_pix = 5, if_annuli=False, y_log = False,
# prf_name_list = (['target'] + ['PSF{0}'.format(i) for i in range(len(data_process.PSF_list))]) )
# PSF_loc = PSF_loc_dic[str(i)]
data_process.plot_overview(label = ID, target_label = None)
# data_process.find_PSF(radius = 30, PSF_pos_list = [PSF_loc])
data_process.checkout()
#Start to produce the class and params for lens fitting.
# data_process.apertures = []
print(ID, i)
fit_sepc = FittingSpecify(data_process)
fit_sepc.prepare_fitting_seq(point_source_num = 1) #, fix_n_list= [[0,4],[1,1]])
# psf_error_map = np.ones_like(data_process.PSF_list[data_process.psf_id_for_fitting]) *0.01 # It is in the variance unit (std^2).
# fit_sepc.prepare_fitting_seq(point_source_num = 1, psf_error_map = psf_error_map)
fit_sepc.build_fitting_seq()
#Plot the initial settings for fittings.
fit_sepc.plot_fitting_sets()
# fit_run = FittingProcess(fit_sepc, savename = 'pkl_files/'+ ID+'_'+str(i), fitting_level='deep')
# fit_run.run(algorithm_list = ['PSO'], setting_list=[None])
# # setting_list = [{'sigma_scale': 1., 'n_particles': 100, 'n_iterations': 100}, {'n_burn': 100, 'n_run': 100, 'walkerRatio': 10,'sigma_scale': .1}])
# # fit_run.plot_all()