#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)
from galight.data_process import DataProcess
zp = 31.4 - 2.5 * np.log10(
header['PHOTMJSR']
) #Calculate the correspondingly zp as DN/S #This is wrong! See 3_...
data_process = DataProcess(fov_image=data,
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.
#%%
data_process_list = []
for band in bands:
fitsFile = pyfits.open(glob.glob(object_id+'/*-cutout-HSC-{0}*.fits'.format(band))[0])
file_header0 = fitsFile[0].header
try:
FLUXMAG0 = file_header0['FLUXMAG0']
zp = 2.5 * np.log10(FLUXMAG0) # This is something Xuheng can't make sure.
except:
zp = 27.0
PSF_file = glob.glob(object_id+'/*-psf*HSC-{0}*.fits'.format(band))[0]
PSF = pyfits.getdata(PSF_file)
data_process = DataProcess(fov_image = fitsFile[1].data, fov_noise_map = fitsFile[3].data ** 0.5, target_pos = [ra, dec],
pos_type = 'wcs', header = fitsFile[1].header,
rm_bkglight = True, if_plot=False, zp = zp)
data_process.generate_target_materials(radius=None, #detect_tool='sep'
)
data_process.PSF_list = [PSF]
data_process_list.append(data_process)
# % Determining the common settings for all bands, including cutout radius and apertures.
l_idx = [i for i in range(len(bands)) if bands[i] == lband][0] #The first index to run
run_list = [i for i in range(len(bands))]
del(run_list[l_idx])
run_list = [l_idx] + run_list #The list define the order to run
cut_radius = np.median([int(len(data_process_list[i].target_stamp)/2) for i in range(len(data_process_list))])
for i in range(len(bands)):
data_process_list[i].generate_target_materials(radius=cut_radius, create_mask = False, nsigma=2.8, #detect_tool='sep',
exp_sz= 1.2, npixels = 15, if_plot=False)
data_process_list[i].checkout()
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()
fit_sepc.build_fitting_seq()
#Setting the fitting method and run.
fit_run = FittingProcess(fit_sepc)
fit_run.run(algorithm_list=['PSO', 'PSO'],
setting_list=[{
data_process_0 = DataProcess(fov_image=fov_image,
fov_noise_map=err_data,
target_pos=[float(QSO_RA),
float(QSO_DEC)],
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'],
FLUXMAG0 = file_header0['FLUXMAG0']
zp = 2.5 * np.log10(
FLUXMAG0) # This is something Xuheng can't make sure.
except:
zp = 27.0
PSF_file = glob.glob(object_id + '/*-psf*HSC-{0}*.fits'.format(band))[0]
PSF = pyfits.getdata(PSF_file)
data_process = DataProcess(fov_image=fitsFile[1].data,
fov_noise_map=fitsFile[3].data**0.5,
target_pos=[ra, dec],
pos_type='wcs',
header=fitsFile[1].header,
rm_bkglight=True,
if_plot=False,
zp=zp)
data_process.generate_target_materials(radius=None)
data_process.PSF_list = [PSF]
data_process_list.append(data_process)
#%% Determining the common settings for all bands, including cutout radius and apertures.
l_idx = [i for i in range(len(bands))
if bands[i] == lband][0] #The first index to run
run_list = [i for i in range(len(bands))]
del (run_list[l_idx])
run_list = [l_idx] + run_list #The list define the order to run
cut_radius = np.median([
int(len(data_process_list[i].target_stamp) / 2)
for i in range(len(data_process_list))
])
for i in range(len(bands)):
exp_map = exp * wht/mean_wht
# print(pixel_scale, exp, exp_map.max())
idx = [i for i in range(len(ra_dec_info)) if ID in ra_dec_info[i]][0]
# print(ID, idx)
RA, Dec = ra_dec_info[idx].split(' ')[:2]
RA, Dec = np.float(RA), np.float(Dec)
if ID == 'J2100-1715':
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]])
else:
PSF_file = glob.glob('hscdata_web_download' +
'/psf*{1}*-{0}*.fits'.format(band, 's20a'))[0]
PSF = pyfits.getdata(PSF_file)
data_process = DataProcess(fov_image=fitsFile[1].data,
fov_noise_map=fitsFile[3].data**0.5,
target_pos=[ra, dec],
pos_type='wcs',
header=fitsFile[1].header,
rm_bkglight=False,
if_plot=False,
zp=zp)
data_process.PSF_list = [PSF]
data_process.generate_target_materials(radius=cut_radius,
create_mask=False,
nsigma=2.8,
exp_sz=1.2,
npixels=25,
if_plot=False)
import copy
apr0 = copy.deepcopy(data_process.apertures[0])
apr1 = copy.deepcopy(data_process.apertures[0])
apr0.positions = np.array([48., 49.])
apr1.positions = np.array([56., 48.])
data_process.apertures = [apr0, apr1]
# if rerun != 's21a':
# picklename = 'fit_result/'+'dual_result-band-{0}-s21a.pkl'.format(band)
# s21_res = pickle.load(open(picklename,'rb'))
# data_process.target_stamp -= s21_res.image_host_list[0]
fit_sepc = FittingSpecify(data_process)
fit_sepc.prepare_fitting_seq(point_source_num=point_source_num,
supersampling_factor=3)
save_name = 'sim_result_bulge_n{1}_/round0_ID{0}_'.format(j, bulge_n)
data_process_0 = DataProcess(
fov_image=sim_image_noise,
fov_noise_map=rms,
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)
exp = astro_tools.read_fits_exp(fitsFile[0].header) #Read the exposure time
exp_map = exp * np.ones_like(fov_image_rebin) #* factor**2
from galight.data_process import DataProcess
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
