RA = 315.2279713 Dec = -17.25608967 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])
target_pos=[1170., 940.], pos_type='pixel', header=header, rm_bkglight=False, exptime=np.ones_like(data) * exptime, if_plot=False, zp=zp) #Gain value assuming as 1 data_process.generate_target_materials(radius=65, create_mask=False, nsigma=2.8, if_select_obj=False, exp_sz=1.2, npixels=15, if_plot=True) data_process.find_PSF(radius=30, user_option=True) data_process.plot_overview(label='Example', target_label=None) #Start to produce the class and params for lens fitting. from galight.fitting_specify import FittingSpecify # data_process.apertures = [] fit_sepc = FittingSpecify(data_process) fit_sepc.prepare_fitting_seq(point_source_num=1) #, fix_n_list= [[0,4],[1,1]]) fit_sepc.build_fitting_seq() #Plot the initial settings for fittings. fit_sepc.plot_fitting_sets() #Setting the fitting method and run. from galight.fitting_process import FittingProcess fit_run = FittingProcess(fit_sepc, savename='savename', fitting_level='norm')
pos_type='pixel', header=header, rm_bkglight=True, exptime=np.ones_like(data_sb) * exptime, if_plot=False, zp=zp) #Gain value assuming as 1 data_process.generate_target_materials(radius=rad * 0.8, create_mask=False, nsigma=2.8, if_select_obj=False, exp_sz=1.2, npixels=15, if_plot=False) data_process.plot_overview(label=filt + '_' + str(fov_cut_idx) + '_FOV', target_label=name[:7], ifsave=True, filename=filename + '_FOV', if_plot=if_plot) # data_process.find_PSF(radius = 50, user_option = True, psf_edge=10) data_process.apertures = [data_process.apertures[0]] info = {} for psf, name in [[psf_true, 'same_psf'], [psf_model, 'diff_psf']]: # for psf, name in [[psf_model,'diff_psf']]: plot_fit_name = filename + name + '_fit' data_process.PSF_list = [psf] #Start to produce the class and params for lens fitting. fit_sepc = FittingSpecify(data_process) fit_sepc.prepare_fitting_seq( point_source_num=1) #, fix_n_list= [[0,4],[1,1]]) fit_sepc.build_fitting_seq() #Plot the initial settings for fittings.