pos_type='wcs',
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
#Load the fov image data:
fov_image = fitsFile[1].data # check the back grounp
#Derive the header informaion, might be used to obtain the pixel scale and the exposure time.
header = fitsFile[1].header # if target position is add in WCS, the header should have the wcs information, i.e. header['EXPTIME']
#Derive the fov noise level map:
err_data= fitsFile[3].data ** 0.5
#Load the PSF data:
PSF_files = glob.glob('psf*{0}*.fits'.format(HSCband))
PSF = pyfits.getdata(PSF_files[0])
data_process = DataProcess(fov_image = fov_image, fov_noise_map = err_data, target_pos = [QSO_RA, QSO_DEC],
pos_type = 'wcs', header = header,
rm_bkglight = True, if_plot=False, zp = zp)
#Generate the fitting materials
data_process.generate_target_materials(radius=120, create_mask = False, nsigma=2.8,
exp_sz= 1.5, npixels = 40, if_plot=False)
# data_process.apertures = [] #Assuming there is no host (i.e., as constant.) #!!!
#Manually input the PSF:
data_process.PSF_list = [PSF]
print('{0}-band{1}'.format(folder,band))
file_header = copy.deepcopy(fitsFile[1].header)
file_header['CRPIX1'] = file_header['CRPIX1']-data_process.target_pos[0]+len(data_process.target_stamp)/2
file_header['CRPIX2'] = file_header['CRPIX2']-data_process.target_pos[1]+len(data_process.target_stamp)/2
pyfits.PrimaryHDU(data_process.target_stamp,header=file_header).writeto('{0}-band{1}.fits'.format(folder,band) ,overwrite=True)
pyfits.PrimaryHDU(data_process.noise_map,header=file_header).writeto('{0}-band{1}_noise.fits'.format(folder,band) ,overwrite=True)
plt.imshow(image_noisy, origin='lower')
plt.show()
#%%
from galight.data_process import DataProcess
from galight.fitting_specify import FittingSpecify
from galight.fitting_process import FittingProcess
data_process = DataProcess(fov_image=image_noisy,
target_pos=[25, 25],
pos_type='pixel',
exptime=100,
rm_bkglight=False,
if_plot=False,
zp=25)
data_process.PSF_list = [psf_class.kernel_pixel]
data_process.generate_target_materials(radius=25,
create_mask=False,
nsigma=2.8,
exp_sz=1,
npixels=10,
if_plot=True,
bkg_std=background_rms)
data_process.deltaPix = 1
data_process.checkout() #Check if all the materials is known.
fit_sepc = FittingSpecify(data_process)
fit_sepc.prepare_fitting_seq(
point_source_num=0, sersic_major_axis=False
) #, fix_n_list= [[0,4]], fix_center_list = [[0,0]])
fit_sepc.plot_fitting_sets()
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=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 = 50, user_option = True, psf_edge=10)
data_process.plot_overview(label='Example', target_label=None)
data_process.PSF_list = [psf_list[0]]
#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')
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.
# fit_sepc.plot_fitting_sets()
#
fit_run = FittingProcess(fit_sepc,
savename=plot_fit_name,
fitting_level='norm')
fit_run.run(algorithm_list=['PSO', 'PSO'])
if fit_run.reduced_Chisq > 1.3:
fit_run = FittingProcess(fit_sepc,
savename=plot_fit_name,
target_pos=[len(sim_image_noise) / 2,
len(sim_image_noise) / 2],
pos_type='pixel',
header=None,
rm_bkglight=False,
if_plot=False,
zp=zp)
data_process_0.deltaPix = deltaPix
data_process_0.generate_target_materials(radius=None,
create_mask=False,
nsigma=2.8,
exp_sz=1.2,
npixels=15,
if_plot=False)
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 = FittingSpecify(data_process_0)
fit_sepc_0.prepare_fitting_seq(
data_process = DataProcess(fov_image=fov_image_rebin,
target_pos=[318.0, 239.6],
pos_type='pixel',
rm_bkglight=True,
exptime=exp_map,
if_plot=False,
zp=27.0)
data_process.generate_target_materials(radius=len(fov_image_rebin) / 2,
create_mask=False,
nsigma=2.8,
if_select_obj=False,
exp_sz=1.2,
npixels=300,
if_plot=False,
bkg_std=bkg_std)
data_process.PSF_list = [PSF_rebin]
data_process.deltaPix = 1
data_process.noise_map[np.isnan(data_process.noise_map)] = bkg_std
from galight.tools.measure_tools import mask_obj
import copy
apertures = copy.deepcopy(data_process.apertures)
apertures[0].a, apertures[0].b = apertures[0].a * expzs, apertures[0].b * expzs
apertures[1].a, apertures[1].b = apertures[1].a * expzs, apertures[1].b * expzs
masks = mask_obj(data_process.target_stamp, apertures, sum_mask=True)
masks = 1 - masks
data_process.target_mask = masks
# plt.imshow(masks, origin='lower')
# plt.show()
#%%