# continue
else:
print("cutout:", '{0}-band{1}'.format(folder,band))
fitsFile = pyfits.open(file)
#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
header0 = im[0].header
img_filter = header0['FILTER']
img_cam = header0['APERNAME'] #In JDAT'simulation it is 'DETECTOR'
exptime = header0['TEXPTIME'] #The assumed exp time.
from galight.tools.astro_tools import plt_fits
plt_fits(data)
#%% Model using galight
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
ra, dec = pos.ra.degree, pos.dec.degree
#%%
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',
kwargs_numerics=kwargs_numerics)
image_sim = imageModel.image(kwargs_lens_light=kwargs_light)
poisson = image_util.add_poisson(image_sim, exp_time=exp_time)
bkg = image_util.add_background(image_sim, sigma_bkd=background_rms)
image_noisy = image_sim + bkg + poisson
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.
file_header0 = fits[0].header
zp = 27.0 # This is something Xuheng can't make sure.
PSF = pyfits.getdata(PSF_filename)
# Start to use galight
picklename = save_name + ID + '_single_Sersic.pkl'
picklename_l = glob.glob(picklename)
if picklename_l != []:
fit_run_0 = pickle.load(open(picklename_l[0], 'rb'))
else:
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]
exp = astro_tools.read_fits_exp(fitsFile[0].header) #Read the exposure time
pixel_scale = astro_tools.read_pixel_scale(fitsFile[1].header) #Read pixel scale
mean_wht = exp * (pixel_scale/0.135)**2
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)
file_header0 = fitsFile[0].header
FLUXMAG0 = file_header0['FLUXMAG0']
zp = 2.5 * np.log10(FLUXMAG0) # This is something Xuheng can't make sure.
PSF = pyfits.getdata(
'../example_data/HSC/QSO/000017.88+002612.6_HSC-I_psf.fits')
#%%Start to use galight
from galight.data_process import DataProcess
QSO_RA = 0.07452999800443649
QSO_DEC = 0.4368380010128021
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)
data_process.generate_target_materials(radius=None,
create_mask=False,
nsigma=2.8,
exp_sz=1.2,
npixels=15,
if_plot=False)
data_process.PSF_list = [PSF]
data_process.checkout() #Check if all the materials is known.
#Plot and save sim details
plot_sim_name = filename + '_sim.pdf'
labels = ['org_gal_img', 'project_gal_img', 'conv_gal_img', 'add_ps_noise']
plt_many_fits([
hd_gal_img, project_gal_img, conv_project_gal_img,
noise_conv_project_qso_img
],
labels=labels,
savename=plot_sim_name,
if_plot=False)
#%%Obtain PSF stars:
data_process = DataProcess(fov_image=data_mock,
target_pos=pos,
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)
zp = 2.5 * np.log10(
FLUXMAG0) # This is something Xuheng can't make sure.
except:
zp = 27.0
PSF_file = glob.glob('hscdata_web_download' +
'/psf*{1}*-{0}*.fits'.format(band, rerun))
if PSF_file != []:
PSF_file = PSF_file[0]
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.])
#%%
fitsFile = pyfits.open('../example_data/HST/QSO/1147_final_drz.fits')
fov_image = fitsFile[1].data # check the back grounp
header = fitsFile[1].header # if target position is add in WCS, the header should have the wcs information, i.e. header['EXPTIME']
#%%
wht = fitsFile[2].data # The WHT map
import galight.tools.astro_tools as astro_tools
exp = astro_tools.read_fits_exp(fitsFile[0].header) #Read the exposure time
mean_wht = exp * (0.0642/0.135)**2
exp_map = exp * wht/mean_wht
#%%Start to use galight
from galight.data_process import DataProcess
data_process = DataProcess(fov_image = fov_image, target_pos = [1142., 637.], pos_type = 'pixel', header = header,
rm_bkglight = False, exptime = exp_map, if_plot=False, zp = 27.0)
data_process.generate_target_materials(radius=65, create_mask = True, nsigma=2.8, if_select_obj=True,
exp_sz= 1.2, npixels = 15, if_plot=True)
#%%PSF works.
data_process.find_PSF(radius = 30, user_option = True)
# data_process.find_PSF(radius = 50, PSF_pos_list = [[ 350., 1055.], [2078., 1910.]], user_option = False)
data_process.plot_overview(label = 'Example', target_label = None)
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))]) )
# data_process.psf_id_for_fitting = 11
data_process.checkout() #Check if all the materials is known.
from galight.data_process import DataProcess
from galight.tools.cutout_tools import psf_clean
psfs, FWHMs = [], []
for data_sb in data_sbs:
zp = -2.5 * np.log10(2.350443 * 10**(-5) * 0.01499**2 / 3631)
header0 = im[0].header
img_filter = header0['FILTER']
img_cam = header0['APERNAME'] #In JDAT'simulation it is 'DETECTOR'
exptime = header0['TEXPTIME'] #The assumed exp time.
plt_fits(data_sb)
# Obtain PSF stars:
# 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_sb,
target_pos=[1170., 940.],
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=60,
user_option=True,
psf_edge=150,
select_all=True)
# data_process_.plot_overview(label = 'Example', target_label = None)
FWHM_list = np.array(data_process_.PSF_FWHM_list)
POS_list = np.array(data_process_.PSF_pos_list)
psf_list = np.array(data_process_.PSF_list)
fwhm_bools = [FWHM_list < 4.3][0]
psf_list = psf_list[fwhm_bools]
flux_list_1d,
label_list_1d,
deltaPix=deltaPix,
target_ID='Simulation',
if_annuli=True)
#%%Model:
from galight.data_process import DataProcess
from galight.fitting_specify import FittingSpecify
from galight.fitting_process import FittingProcess
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.
# plt_fits(PSF_rebin)
pyfits.PrimaryHDU(fov_image_rebin).writeto(rebin_name + '.fits',
overwrite=True)
# #%%
import galight.tools.astro_tools as astro_tools
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
FLUXMAG0) # This is something Xuheng can't make sure.
print(file, zp)
#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)
PSF_loc = [35.29689501, -4.685896878]
data_process.find_PSF(
radius=20,
PSF_pos_list=[PSF_loc],
pos_type='wcs',
)
#Generate the fitting materials
data_process.generate_target_materials(radius=30,
create_mask=False,
nsigma=2.8,