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')
fit_run.run(algorithm_list=['PSO', 'PSO'])
fit_run.plot_final_galaxy_fit()
print(fit_run.final_result_galaxy[0])
print(fit_run.final_result_ps[0])
for i in run_list:
band = bands[i]
print("Staring fitting band-"+band+"... ... ...")
data_process_list[i].apertures = apertures #Pass apertures to the data
fit_sepc_l[i] = FittingSpecify(data_process_list[i])
fix_n_list, fix_Re_list = None, None
if i != l_idx:
if fix_n == True:
fix_n_list = [[0,fit_run_l[l_idx].final_result_galaxy[0]['n_sersic'] ]]
if fix_re == True:
fix_Re_list = [[0,fit_run_l[l_idx].final_result_galaxy[0]['R_sersic'] ]]
fit_sepc_l[i].prepare_fitting_seq(point_source_num = point_source_num, supersampling_factor=3,
fix_n_list= fix_n_list, fix_Re_list=fix_Re_list)
fit_sepc_l[i].plot_fitting_sets(object_id+'/fitconfig-band-{0}.png'.format(band))
fit_sepc_l[i].build_fitting_seq()
fit_run_l[i] = FittingProcess(fit_sepc_l[i], savename = object_id+'/result-band-{0}'.format(band), fitting_level=fitting_level)
fit_run_l[i].run(algorithm_list = ['PSO'], setting_list=[None])
# fit_run.plot_all(target_ID = object_id)
if fit_run_l[i].image_ps_list != []:
fit_run_l[i].plot_final_qso_fit(save_plot=True, target_ID= object_id +'-'+ band )
else:
fit_run_l[i].plot_final_galaxy_fit(save_plot=True, target_ID= object_id +'-'+ band )
fit_run_l[i].cal_astrometry()
fit_run_l[i].dump_result()
residual = fit_run_l[i].flux_2d_out['data'] - fit_run_l[i].flux_2d_out['model']
pyfits.PrimaryHDU(residual).writeto(object_id+'/residual(data-model).fits',overwrite=True)
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=[{
'sigma_scale': 1.,
'n_particles': 50,
'n_iterations': 50
}] * 2)
fit_run.plot_final_galaxy_fit()
# fit_run.dump_result()
print(phi / np.pi * 180, fit_run.final_result_galaxy[0]['phi_G'] / np.pi * 180)
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
}] * 2)
fit_run_0.plot_final_qso_fit()
bic_0 = fit_run_0.fitting_seq.bic
fit_run_0.dump_result()
#%%
fit_run_0.plot_final_qso_fit()
# fit_run_0.fitting_specify_class.plot_fitting_sets()
# obj_id = int(input('Which obj to measure using statmorph?\n'))
# morph = fit_run_0.cal_statmorph(obj_id=obj_id, if_plot=True)
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)
fit_sepc.plot_fitting_sets(
object_id + '_fitconfig-band-{0}-{1}.png'.format(band, rerun))
fit_sepc.build_fitting_seq()
# if rerun != 's19a':
# fit_sepc.kwargs_params['point_source_model'] = s21_res.fitting_specify_class.kwargs_params['point_source_model']
fit_run = FittingProcess(fit_sepc,
savename=object_id +
'_result-band-{0}-{1}'.format(band, rerun),
fitting_level=fitting_level)
fit_run.run(algorithm_list=['PSO', 'PSO'], setting_list=[None, None])
fit_run.plot_final_qso_fit(save_plot=True,
target_ID=object_id + '-' + band)
fit_run.dump_result()
data_process.apertures = apertures #Pass apertures to the data
from galight.tools.measure_tools import plot_data_apertures
plot_data_apertures(data_process.target_stamp, apertures)
#%%Start to produce the class and params for lens fitting.
from galight.fitting_specify import FittingSpecify
fit_sepc = FittingSpecify(data_process)
fit_sepc.prepare_fitting_seq(
point_source_num=1,
fix_n_list=[
[0, 4], [1, 1]
], #fix_Re_list= [[0,0.2], [1,1.14]], #First component fix n = 4 (bluge), second one fix to 1 (disk).
fix_center_list=[[0, 0]])
fit_sepc.plot_fitting_sets()
fit_sepc.build_fitting_seq()
# #%%Setting the fitting method and run.
from galight.fitting_process import FittingProcess
fit_run = FittingProcess(fit_sepc, savename='test_bulge_disk.pkl')
fit_run.run()
fit_run.plot_all()
fit_run.dump_result()
print(fit_run.final_result_galaxy[:2])
if fix_re == True:
fix_Re_list = [[
0, fit_run_l[l_idx].final_result_galaxy[0]['R_sersic']
]]
fit_sepc_l[i].prepare_fitting_seq(
point_source_num=point_source_num,
supersampling_factor=3,
ps_pix_center_list=ps_pix_center_list,
fix_n_list=fix_n_list,
fix_Re_list=fix_Re_list)
fit_sepc_l[i].plot_fitting_sets(out_dir +
'/fitconfig-band-{0}.png'.format(band),
show_plot=show_plot)
fit_sepc_l[i].build_fitting_seq()
fit_run_l[i] = FittingProcess(fit_sepc_l[i],
savename=out_dir +
'/result-band-{0}'.format(band),
fitting_level=fitting_level)
fit_run_l[i].run(algorithm_list=['PSO'], setting_list=[None])
for j in range(5): #!!!Will be removed after Lenstrnomy debug.
if np.sum(fit_run_l[i].image_host_list[0]) != 0:
continue
else:
cut_radius = cut_radius - 1
data_process_list[i].generate_target_materials(
radius=cut_radius)
data_process_list[i].checkout()
data_process_list[
i].apertures = apertures #Pass apertures to the data
fit_sepc_ = FittingSpecify(data_process_list[i])
fit_sepc_.prepare_fitting_seq(
point_source_num=point_source_num,
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,
fitting_level='deep')
fit_run.run(algorithm_list=['PSO', 'PSO'])
info['inferred_host_flux_' +
name] = fit_run.final_result_galaxy[0]['flux_within_frame']
info['inferred_magnitude_' +
name] = fit_run.final_result_galaxy[0]['magnitude']
info['inferred_R_sersic_' +
name] = fit_run.final_result_galaxy[0]['R_sersic']
info['inferred_n_sersic_' +
name] = fit_run.final_result_galaxy[0]['n_sersic']
# data_process.apertures = []
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()
#%%Setting the fitting method and run.
from galight.fitting_process import FittingProcess
fit_run = FittingProcess(fit_sepc, savename = 'savename', fitting_level='norm')
fit_run.run(algorithm_list = ['PSO', 'MCMC','MCMC'], setting_list=[None,{'n_burn': 100, 'n_run': 100, 'walkerRatio': 10,'sigma_scale': .1},
{'n_burn': 200, 'n_run': 200, 'walkerRatio': 10,'sigma_scale': .1}])
# setting_list = [{'sigma_scale': 1., 'n_particles': 100, 'n_iterations': 100}, {'n_burn': 100, 'n_run': 100, 'walkerRatio': 10,'sigma_scale': .1}])
fit_run.plot_all()
fit_run.dump_result()
print(fit_run.final_result_galaxy[0])
#%%
# Test load pkl
import pickle
picklename = 'savename.pkl'
fitting_run_class = pickle.load(open(picklename,'rb'))
fitting_run_class.run_diag()
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=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 galight.fitting_specify import FittingSpecify
fit_sepc = FittingSpecify(data_process)
fit_sepc.prepare_fitting_seq(
point_source_num=1,
supersampling_factor=3) #, 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.
from galight.fitting_process import FittingProcess
fit_run = FittingProcess(fit_sepc, savename='HSC_QSO', fitting_level='deep')
fit_run.run(algorithm_list=['PSO'], setting_list=[None])
# fit_run.plot_all()
# fit_run.dump_result()
print(fit_run.final_result_galaxy[0])
# plt.imshow(data_process.segm_deblend, origin='lower')
# plt.colorbar()
# plt.show()
#%%Start to produce the class and params for lens fitting.
from galight.fitting_specify import FittingSpecify
fit_sepc = FittingSpecify(data_process)
fit_sepc.prepare_fitting_seq(
point_source_num=1,
supersampling_factor=3) #, 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.
from galight.fitting_process import FittingProcess
fit_run = FittingProcess(fit_sepc, savename='HSC_QSO', fitting_level='deep')
# fit_run.run(algorithm_list = ['PSO'], setting_list=[None])
# # fit_run.plot_all()
# fit_run.dump_result()
# print(fit_run.final_result_galaxy[0])
#%%
import pickle
from galight.tools.asymmetry_tools import Measure_asy
from galight.tools.astro_tools import plt_fits
fit_run_pkl = fit_run
obj_id = 0
asy_class = Measure_asy(
fit_run_pkl,
seg_cal_reg='or',
add_aperture0.a, add_aperture0.b = add_aperture0.a / 6, add_aperture0.a / 4
apertures = apertures[:comp_id + 1] + [add_aperture0
] + apertures[comp_id + 1:]
data_process_1.apertures = apertures #Pass apertures to the data
fit_sepc_1 = FittingSpecify(data_process_1)
fit_sepc_1.prepare_fitting_seq(
point_source_num=
0, #fix_n_list= [[0,1]], #First component fix n = 4 (bluge), second one fix to 1 (disk).
condition=condition_diskbulge)
fit_sepc_1.plot_fitting_sets()
fit_sepc_1.build_fitting_seq()
# fit_sepc_1.kwargs_params['lens_light_model'][0][0] = fit_run_0.final_result_galaxy[0] #Input to bulge
fit_run_1 = FittingProcess(fit_sepc_1, savename=save_name + 'bulge+disk')
fit_run_1.run(algorithm_list=['PSO', 'PSO'],
setting_list=[{
'sigma_scale': 1.,
'n_particles': 150,
'n_iterations': 150
}] * 2)
bic_1 = fit_run_1.fitting_seq.bic
fit_run_1.dump_result()
print(fit_run_1.final_result_galaxy[0]['q'],
fit_run_1.final_result_galaxy[1]['q'])
fit_run_1.plot_final_galaxy_fit(target_ID=ID)
bulge1 = fit_run_1.image_host_list[1]
disk1 = fit_run_1.image_host_list[0]
B2T = np.sum(bulge1) / np.sum(bulge1 + disk1)
# 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(
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 + 'single_Sersic')
fit_run_0.run(algorithm_list=['PSO'], setting_list=[None])
fit_run_0.plot_final_qso_fit()
bic_0 = fit_run_0.fitting_seq.bic
fit_run_0.dump_result()
#%%Fitting as disk + bulge:
data_process_1 = copy.deepcopy(data_process_0)
apertures = copy.deepcopy(data_process_1.apertures)
comp_id = 0 #Change the component (galaxy) id = 0 into to components (i.e., bulge + disk)
add_aperture0 = copy.deepcopy(apertures[comp_id])
add_aperture0.a, add_aperture0.b = add_aperture0.a / 3, add_aperture0.b / 3
apertures = apertures[:comp_id] + [add_aperture0] + apertures[comp_id:]
data_process_1.apertures = apertures #Pass apertures to the data
fit_sepc_1 = FittingSpecify(data_process_1)
fit_sepc_1.prepare_fitting_seq(
#Setting the fitting method and run.
import glob, pickle
if glob.glob(savename + '*pkl') == []:
# apertures = copy.deepcopy(data_process.apertures)
# apertures = [copy.deepcopy(apertures[0])] + apertures
# apertures[0].a, apertures[0].b = apertures[0].a*0.2, apertures[0].b*0.2
# data_process.apertures = apertures
from galight.fitting_specify import FittingSpecify
fit_sepc = FittingSpecify(data_process)
fit_sepc.prepare_fitting_seq(
point_source_num=0) #, fix_n_list= [[0,4],[1,1]])
fit_sepc.build_fitting_seq()
fit_sepc.plot_fitting_sets(
) #The apertures shows how the images will be modelled.
from galight.fitting_process import FittingProcess
fit_run = FittingProcess(fit_sepc, savename=savename, fitting_level='deep')
fit_run.run(algorithm_list=['PSO'], setting_list=[None])
fit_run.dump_result()
else:
fit_run = pickle.load(open(glob.glob(savename + '*.pkl')[0], 'rb'))
fit_run.fitting_specify_class.plot_fitting_sets()
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
fit_run.plot_all()
# fit_run.plot_final_galaxy_fit()
res = (fit_run.flux_2d_out['data'] - fit_run.flux_2d_out['model']) #* masks
if_plot=False)
plt_fits(data_process.PSF_list[0])
# data_process.apertures = [] #Assuming there is no host (i.e., as constant.) #!!!
# Manually input the PSF:
# data_process.PSF_list = [PSF]
# Check if all the materials is given, if so to pass to the next step.
data_process.checkout() #Check if all the materials is known.
fit_sepc = FittingSpecify(data_process)
# fit_sepc.prepare_fitting_seq(point_source_num = 2)
fit_sepc.prepare_fitting_seq(point_source_num=2,
ps_pix_center_list=[[-2.0, -1.0],
[6.0, -1.0]])
# if fit_sepc.kwargs_params['point_source_model'][0][0] == fit_sepc.kwargs_params['point_source_model'][0][1]:
# fit_sepc.prepare_fitting_seq(point_source_num = 2, ps_pix_center_list= [[-2.0, -1.0], [6.0, -1.0]])
# print(file)
fit_sepc.build_fitting_seq()
fit_sepc.plot_fitting_sets()
# if rerun != 's19a':
# fit_sepc.kwargs_params['point_source_model'] = s21_res.fitting_specify_class.kwargs_params['point_source_model']
fit_run = FittingProcess(fit_sepc,
savename=name +
'-{0}-band{1}'.format(folder, band),
fitting_level='norm')
fit_run.run(algorithm_list=['PSO', 'PSO'], setting_list=[None, None])
fit_run.plot_final_qso_fit(save_plot=True,
target_ID=folder + '-' + band)
fit_run.dump_result()