]
data_process_list, zp_list = [], []
for i in range(len(band_seq)):
# The pixel scale is all 0.168
fitsFile = pyfits.open(image_folder + filename_list[i])
fov_image = fitsFile[1].data
header = fitsFile[
1].header # if target position is add in WCS, the header should have the wcs information, i.e. header['EXPTIME']
err_data = fitsFile[3].data**0.5
file_header0 = fitsFile[0].header
zp = 27.0
data_process_i = DataProcess(fov_image=fov_image,
fov_noise_map=err_data,
target_pos=[image_RA, image_DEC],
pos_type='wcs',
header=header,
rm_bkglight=True,
if_plot=False,
zp=zp)
data_process_i.noise_map = err_data
try:
data_process_i.generate_target_materials(
radius=None,
radius_list=[15, 20, 25, 30, 35, 40],
create_mask=False,
nsigma=1.2,
exp_sz=1.2,
npixels=9,
if_plot=False)
except:
data_process_i.generate_target_materials(radius=40,
QSO_im, err_map, PSF, _, _, qso_center, fr_c_RA_DEC = [], [], [], [], [], [], []
run_list.remove(i)
data_process_list.append(None)
zp_list.append(None)
else:
fitsFile = pyfits.open(image_folder + filename_list[i])
fov_image = fitsFile[1].data
header = fitsFile[
1].header # if target position is add in WCS, the header should have the wcs information, i.e. header['EXPTIME']
err_data = fitsFile[3].data**0.5
file_header0 = fitsFile[0].header
zp = 27.0
data_process_i = DataProcess(fov_image=fov_image,
fov_noise_map=err_data,
target_pos=[image_RA, image_DEC],
pos_type='wcs',
header=header,
rm_bkglight=False,
if_plot=False,
zp=zp)
data_process_i.noise_map = err_data
data_process_list.append(data_process_i)
try:
fit_size = len(
pyfits.getdata(
glob.glob(files[0] + 'data-BHBH(host image)_I-band.fits')[0]))
except:
fit_size = 61
radius = int((fit_size - 1) / 2)
for j in run_list:
data_process_list[j].generate_target_materials(radius=radius,
phi1, q1 = param_util.ellipticity2phi_q(kwargs_lens_light[1]['e1'],
kwargs_lens_light[1]['e2'])
cond_0 = (kwargs_lens_light[0]['R_sersic'] >
kwargs_lens_light[1]['R_sersic'] * 0.9)
cond_1 = (kwargs_lens_light[0]['R_sersic'] <
kwargs_lens_light[1]['R_sersic'] * 0.15)
cond_2 = (q0 < q1)
if cond_0 or cond_1 or cond_2:
logL -= 10**15
return logL
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.
elif 'FSRQ' in ids_file[0]:
pos_file = '../../data/Fermi_fsrq.txt'
f = open(pos_file, "r")
pos_info = f.read()
pos_info = pos_info.split('\n') # Split in to \n
line = [j for j in range(len(pos_info)) if ids[i] in pos_info[j]][0]
test_id, QSO_RA, QSO_DEC = pos_info[line].split(' ')
QSO_RA = float(QSO_RA)
QSO_DEC = float(QSO_DEC)
if test_id != ids[i]:
raise ValueError(
"When load pos for the target, line for ID not right")
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.noise_map = err_data
try:
data_process.generate_target_materials(radius=None,
create_mask=False,
nsigma=1.5,
exp_sz=1.2,
npixels=15,
if_plot=False)
except:
continue
data_process.PSF_list = [PSF]
zp = 27.0
PSF = pyfits.getdata('./Reines/{0}_HSC-I_psf.fits'.format(ID))
if len(PSF) != 0 and PSF.shape[0] != PSF.shape[1]:
cut = int((PSF.shape[0] - PSF.shape[1])/2)
if cut>0:
PSF = PSF[cut:-cut,:]
elif cut<0:
PSF = PSF[:,-cut:cut]
PSF /= PSF.sum()
#%%Start to use decomprofile
from decomprofile.data_process import DataProcess
QSO_RA = RA
QSO_DEC = Dec
data_process = DataProcess(fov_image = fov_image, fov_noise_map = err_data, target_pos = [QSO_RA, QSO_DEC],
pos_type = 'wcs', header = header, target_ID = ID,
rm_bkglight = False, if_plot=True, zp = zp)
data_process.noise_map = err_data
data_process.generate_target_materials(radius=None, create_mask = False, nsigma=3,
radius_list = [120, 140, 160,180],
exp_sz= 1.2, npixels = 55, if_plot=True,
save_plot = True)
data_process.PSF_list = [PSF]
# data_process.checkout() #Check if all the materials is known.
# #Start to produce the class and params for lens fitting.
# from decomprofile.fitting_specify import FittingSpeficy