def test_force_positive_source_surface_brightness(self):
kwargs_likelihood = {'force_minimum_source_surface_brightness': True}
kwargs_model = {'source_light_model_list': ['SERSIC']}
kwargs_constraints = {}
param_class = Param(kwargs_model, **kwargs_constraints)
kwargs_data = sim_util.data_configure_simple(numPix=10, deltaPix=0.1, exposure_time=1, sigma_bkg=0.1)
data_class = ImageData(**kwargs_data)
kwargs_psf = {'psf_type': 'NONE'}
psf_class = PSF(**kwargs_psf)
kwargs_sersic = {'amp': -1., 'R_sersic': 0.1, 'n_sersic': 2, 'center_x': 0, 'center_y': 0}
source_model_list = ['SERSIC']
kwargs_source = [kwargs_sersic]
source_model_class = LightModel(light_model_list=source_model_list)
imageModel = ImageModel(data_class, psf_class, lens_model_class=None, source_model_class=source_model_class)
image_sim = sim_util.simulate_simple(imageModel, [], kwargs_source)
kwargs_data['image_data'] = image_sim
kwargs_data_joint = {'multi_band_list': [[kwargs_data, kwargs_psf, {}]], 'multi_band_type': 'single-band'}
likelihood = LikelihoodModule(kwargs_data_joint=kwargs_data_joint, kwargs_model=kwargs_model, param_class=param_class, **kwargs_likelihood)
logL, _ = likelihood.logL(args=param_class.kwargs2args(kwargs_source=kwargs_source), verbose=True)
assert logL <= -10**10
def test_time_delay_likelihood(self):
kwargs_likelihood = {'time_delay_likelihood': True,
}
likelihood = LikelihoodModule(kwargs_data_joint=self.kwargs_data, kwargs_model=self.kwargs_model, param_class=self.param_class, **kwargs_likelihood)
args = self.param_class.kwargs2args(kwargs_lens=self.kwargs_lens, kwargs_source=self.kwargs_source,
kwargs_lens_light=self.kwargs_lens_light, kwargs_ps=self.kwargs_ps, kwargs_special=self.kwargs_cosmo)
logL = likelihood.logL(args, verbose=True)
npt.assert_almost_equal(logL, -3097.189103539873, decimal=-1)
def test_pixelbased_modelling(self):
ss_source = 2
numPix_source = self.numPix*ss_source
n_scales = 3
kwargs_pixelbased = {
'source_interpolation': 'nearest',
'supersampling_factor_source': ss_source, # supersampling of pixelated source grid
# following choices are to minimize pixel solver runtime (not to get accurate reconstruction!)
'threshold_decrease_type': 'none',
'num_iter_source': 2,
'num_iter_lens': 2,
'num_iter_global': 2,
'num_iter_weights': 2,
}
kwargs_likelihood = {
'image_likelihood': True,
'kwargs_pixelbased': kwargs_pixelbased,
'check_positive_flux': True, # effectively not applied, activated for code coverage purposes
}
kernel = PSF(**self.kwargs_psf).kernel_point_source
kwargs_psf = {'psf_type': 'PIXEL', 'kernel_point_source': kernel}
kwargs_numerics = {'supersampling_factor': 1}
kwargs_data = {'multi_band_list': [[self.kwargs_band, kwargs_psf, kwargs_numerics]]}
kwargs_model = {
'lens_model_list': ['SPEP'],
'lens_light_model_list': ['SLIT_STARLETS'],
'source_light_model_list': ['SLIT_STARLETS'],
}
kwargs_fixed_source = [{'n_scales': n_scales, 'n_pixels': numPix_source**2, 'scale': 1, 'center_x': 0, 'center_y': 0}]
kwargs_fixed_lens_light = [{'n_scales': n_scales, 'n_pixels': self.numPix**2, 'scale': 1, 'center_x': 0, 'center_y': 0}]
kwargs_constraints = {'source_grid_offset': True}
param_class = Param(kwargs_model,
kwargs_fixed_source=kwargs_fixed_source,
kwargs_fixed_lens_light=kwargs_fixed_lens_light,
**kwargs_constraints)
likelihood = LikelihoodModule(kwargs_data_joint=kwargs_data, kwargs_model=kwargs_model,
param_class=param_class, **kwargs_likelihood)
kwargs_source = [{'amp': np.ones(n_scales*numPix_source**2)}]
kwargs_lens_light = [{'amp': np.ones(n_scales*self.numPix**2)}]
kwargs_special = {'delta_x_source_grid': 0, 'delta_y_source_grid': 0}
args = param_class.kwargs2args(kwargs_lens=self.kwargs_lens, kwargs_source=kwargs_source,
kwargs_lens_light=kwargs_lens_light, kwargs_special=kwargs_special)
logL = likelihood.logL(args, verbose=True)
num_data_evaluate = likelihood.num_data
npt.assert_almost_equal(logL/num_data_evaluate, -1/2., decimal=1)
def test_time_delay_likelihood(self):
kwargs_likelihood = {
'time_delay_likelihood': True,
'time_delays_measured': np.ones(4),
'time_delays_uncertainties': np.ones(4)
}
likelihood = LikelihoodModule(imSim_class=self.imageModel,
param_class=self.param_class,
**kwargs_likelihood)
args = self.param_class.kwargs2args(
kwargs_lens=self.kwargs_lens,
kwargs_source=self.kwargs_source,
kwargs_lens_light=self.kwargs_lens_light,
kwargs_ps=self.kwargs_ps,
kwargs_cosmo=self.kwargs_cosmo)
logL, _ = likelihood.logL(args)
npt.assert_almost_equal(logL, -3340.79, decimal=-1)
def likelihoodModule(self):
"""
:return: Likelihood() class instance reflecting the current state of Fittingsequence
"""
kwargs_model = self._updateManager.kwargs_model
kwargs_likelihood = self._updateManager.kwargs_likelihood
likelihoodModule = LikelihoodModule(self.kwargs_data_joint, kwargs_model, self.param_class, **kwargs_likelihood)
return likelihoodModule
def test_force_positive_source_surface_brightness(self):
kwargs_likelihood = {
'force_positive_source_surface_brightness': True,
'numPix_source': 10,
'deltaPix_source': 0.1
}
kwargs_model = {'source_light_model_list': ['SERSIC']}
kwargs_constraints = {}
param_class = Param(kwargs_model, **kwargs_constraints)
kwargs_data = sim_util.data_configure_simple(numPix=10,
deltaPix=0.1,
exposure_time=1,
sigma_bkg=0.1)
data_class = Data(kwargs_data)
kwargs_psf = {'psf_type': 'NONE'}
psf_class = PSF(kwargs_psf)
kwargs_sersic = {
'amp': -1.,
'R_sersic': 0.1,
'n_sersic': 2,
'center_x': 0,
'center_y': 0
}
source_model_list = ['SERSIC']
kwargs_source = [kwargs_sersic]
source_model_class = LightModel(light_model_list=source_model_list)
imageModel = ImageModel(data_class,
psf_class,
lens_model_class=None,
source_model_class=source_model_class)
image_sim = sim_util.simulate_simple(imageModel, [], kwargs_source)
data_class.update_data(image_sim)
likelihood = LikelihoodModule(imSim_class=imageModel,
param_class=param_class,
**kwargs_likelihood)
logL, _ = likelihood.logL(args=param_class.kwargs2args(
kwargs_source=kwargs_source))
assert logL <= -10**10
def likelihoodModule(self):
"""
:return: Likelihood() class instance reflecting the current state of Fittingsequence
"""
kwargs_model = self._updateManager.kwargs_model
kwargs_likelihood = self._updateManager.kwargs_likelihood
param_class = self._updateManager.param_class(self._lens_temp)
imSim_class = class_creator.create_multiband(self.multi_band_list,
**kwargs_model)
likelihoodModule = LikelihoodModule(imSim_class=imSim_class,
param_class=param_class,
**kwargs_likelihood)
return likelihoodModule
def setup(self):
# data specifics
sigma_bkg = 0.05 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 10 # cutout pixel size
deltaPix = 0.1 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.5 # full width half max of PSF
# PSF specification
kwargs_data = sim_util.data_configure_simple(numPix, deltaPix,
exp_time, sigma_bkg)
data_class = ImageData(**kwargs_data)
kwargs_psf_gaussian = {
'psf_type': 'GAUSSIAN',
'fwhm': fwhm,
'pixel_size': deltaPix
}
psf = PSF(**kwargs_psf_gaussian)
kwargs_psf = {
'psf_type': 'PIXEL',
'kernel_point_source': psf.kernel_point_source
}
psf_class = PSF(**kwargs_psf)
kwargs_spemd = {
'theta_E': 1.,
'gamma': 1.8,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}
lens_model_list = ['SPEP']
self.kwargs_lens = [kwargs_spemd]
lens_model_class = LensModel(lens_model_list=lens_model_list)
kwargs_sersic = {
'amp': 1.,
'R_sersic': 0.1,
'n_sersic': 2,
'center_x': 0,
'center_y': 0
}
# 'SERSIC_ELLIPSE': elliptical Sersic profile
kwargs_sersic_ellipse = {
'amp': 1.,
'R_sersic': .6,
'n_sersic': 3,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}
lens_light_model_list = ['SERSIC']
self.kwargs_lens_light = [kwargs_sersic]
lens_light_model_class = LightModel(
light_model_list=lens_light_model_list)
source_model_list = ['SERSIC_ELLIPSE']
self.kwargs_source = [kwargs_sersic_ellipse]
source_model_class = LightModel(light_model_list=source_model_list)
kwargs_numerics = {
'supersampling_factor': 1,
'supersampling_convolution': False,
'compute_mode': 'regular'
}
imageModel = ImageModel(data_class,
psf_class,
lens_model_class,
source_model_class,
lens_light_model_class,
kwargs_numerics=kwargs_numerics)
image_sim = sim_util.simulate_simple(imageModel, self.kwargs_lens,
self.kwargs_source,
self.kwargs_lens_light)
data_class.update_data(image_sim)
kwargs_data['image_data'] = image_sim
kwargs_data_joint = {
'multi_band_list': [[kwargs_data, kwargs_psf, kwargs_numerics]],
'multi_band_type': 'single-band'
}
self.data_class = data_class
self.psf_class = psf_class
kwargs_model = {
'lens_model_list': lens_model_list,
'source_light_model_list': source_model_list,
'lens_light_model_list': lens_light_model_list,
'fixed_magnification_list': [False],
}
self.kwargs_numerics = {'subgrid_res': 1, 'psf_subgrid': False}
kwargs_constraints = {
'image_plane_source_list': [False] * len(source_model_list)
}
kwargs_likelihood = {
'source_marg': False,
'position_uncertainty': 0.004,
'check_solver': False,
'solver_tolerance': 0.001,
}
self.param_class = Param(kwargs_model, **kwargs_constraints)
self.Likelihood = LikelihoodModule(kwargs_data_joint=kwargs_data_joint,
kwargs_model=kwargs_model,
param_class=self.param_class,
**kwargs_likelihood)
prior_means = self.param_class.kwargs2args(
kwargs_lens=self.kwargs_lens,
kwargs_source=self.kwargs_source,
kwargs_lens_light=self.kwargs_lens_light)
prior_sigmas = np.ones_like(prior_means) * 0.1
self.output_dir = 'test_nested_out'
self.sampler = MultiNestSampler(self.Likelihood,
prior_type='uniform',
prior_means=prior_means,
prior_sigmas=prior_sigmas,
output_dir=self.output_dir,
remove_output_dir=True)
def _run_pso(self,
n_particles,
n_iterations,
kwargs_fixed_lens,
kwargs_mean_lens,
kwargs_sigma_lens,
kwargs_fixed_source,
kwargs_mean_source,
kwargs_sigma_source,
kwargs_fixed_lens_light,
kwargs_mean_lens_light,
kwargs_sigma_lens_light,
kwargs_fixed_ps,
kwargs_mean_ps,
kwargs_sigma_ps,
kwargs_fixed_cosmo,
kwargs_mean_cosmo,
kwargs_sigma_cosmo,
threadCount=1,
mpi=False,
print_key='PSO',
sigma_factor=1,
compute_bool=None,
fix_solver=False):
# initialise mcmc classes
param_class = Param(self.kwargs_model,
self.kwargs_constraints,
kwargs_fixed_lens,
kwargs_fixed_source,
kwargs_fixed_lens_light,
kwargs_fixed_ps,
kwargs_fixed_cosmo,
self._lens_lower,
self._source_lower,
self._lens_light_lower,
self._ps_lower,
self._cosmo_lower,
self._lens_upper,
self._source_upper,
self._lens_light_upper,
self._ps_upper,
self._cosmo_upper,
kwargs_lens_init=kwargs_mean_lens,
fix_lens_solver=fix_solver)
init_pos = param_class.setParams(kwargs_mean_lens, kwargs_mean_source,
kwargs_mean_lens_light,
kwargs_mean_ps, kwargs_mean_cosmo)
sigma_start = param_class.setParams(kwargs_sigma_lens,
kwargs_sigma_source,
kwargs_sigma_lens_light,
kwargs_sigma_ps,
kwargs_sigma_cosmo)
lowerLimit = np.array(init_pos) - np.array(sigma_start) * sigma_factor
upperLimit = np.array(init_pos) + np.array(sigma_start) * sigma_factor
num_param, param_list = param_class.num_param()
# initialize ImSim() class
kwargs_likelihood = copy.deepcopy(self.kwargs_likelihood)
if compute_bool is not None:
kwargs_likelihood['bands_compute'] = compute_bool
imSim_class = class_creator.create_multiband(self.multi_band_list,
self.kwargs_model)
likelihoodModule = LikelihoodModule(
imSim_class=imSim_class,
param_class=param_class,
kwargs_likelihood=kwargs_likelihood)
# run PSO
mcmc_class = Sampler(likelihoodModule=likelihoodModule)
result, chain = mcmc_class.pso(n_particles,
n_iterations,
lowerLimit,
upperLimit,
init_pos=init_pos,
threadCount=threadCount,
mpi=mpi,
print_key=print_key)
lens_result, source_result, lens_light_result, ps_result, cosmo_result = param_class.getParams(
result, bijective=True)
return lens_result, source_result, lens_light_result, ps_result, cosmo_result, chain, param_list
def _mcmc_run(self,
n_burn,
n_run,
walkerRatio,
kwargs_fixed_lens,
kwargs_mean_lens,
kwargs_sigma_lens,
kwargs_fixed_source,
kwargs_mean_source,
kwargs_sigma_source,
kwargs_fixed_lens_light,
kwargs_mean_lens_light,
kwargs_sigma_lens_light,
kwargs_fixed_ps,
kwargs_mean_ps,
kwargs_sigma_ps,
kwargs_fixed_cosmo,
kwargs_mean_cosmo,
kwargs_sigma_cosmo,
threadCount=1,
mpi=False,
init_samples=None,
sigma_factor=1,
compute_bool=None,
fix_solver=False):
param_class = Param(self.kwargs_model,
self.kwargs_constraints,
kwargs_fixed_lens,
kwargs_fixed_source,
kwargs_fixed_lens_light,
kwargs_fixed_ps,
kwargs_fixed_cosmo,
self._lens_lower,
self._source_lower,
self._lens_light_lower,
self._ps_lower,
self._cosmo_lower,
self._lens_upper,
self._source_upper,
self._lens_light_upper,
self._ps_upper,
self._cosmo_upper,
kwargs_lens_init=kwargs_mean_lens,
fix_lens_solver=fix_solver)
# initialize ImSim() class
kwargs_likelihood = copy.deepcopy(self.kwargs_likelihood)
if compute_bool is not None:
kwargs_likelihood['bands_compute'] = compute_bool
imSim_class = class_creator.create_multiband(self.multi_band_list,
self.kwargs_model)
likelihoodModule = LikelihoodModule(
imSim_class=imSim_class,
param_class=param_class,
kwargs_likelihood=kwargs_likelihood)
# run PSO
mcmc_class = Sampler(likelihoodModule=likelihoodModule)
mean_start = param_class.setParams(kwargs_mean_lens,
kwargs_mean_source,
kwargs_mean_lens_light,
kwargs_mean_ps, kwargs_mean_cosmo)
sigma_start = param_class.setParams(kwargs_sigma_lens,
kwargs_sigma_source,
kwargs_sigma_lens_light,
kwargs_sigma_ps,
kwargs_sigma_cosmo)
num_param, param_list = param_class.num_param()
# run MCMC
if not init_samples is None:
initpos = ReusePositionGenerator(init_samples)
else:
initpos = None
samples, dist = mcmc_class.mcmc_CH(walkerRatio,
n_run,
n_burn,
mean_start,
np.array(sigma_start) *
sigma_factor,
threadCount=threadCount,
mpi=mpi,
init_pos=initpos)
return samples, param_list, dist
def setup(self):
self.SimAPI = Simulation()
# data specifics
sigma_bkg = 0.05 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 10 # cutout pixel size
deltaPix = 0.1 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.5 # full width half max of PSF
# PSF specification
kwargs_data = self.SimAPI.data_configure(numPix, deltaPix, exp_time,
sigma_bkg)
data_class = Data(kwargs_data)
kwargs_psf = self.SimAPI.psf_configure(psf_type='GAUSSIAN',
fwhm=fwhm,
kernelsize=11,
deltaPix=deltaPix,
truncate=3,
kernel=None)
kwargs_psf = self.SimAPI.psf_configure(
psf_type='PIXEL',
fwhm=fwhm,
kernelsize=11,
deltaPix=deltaPix,
truncate=6,
kernel=kwargs_psf['kernel_point_source'])
psf_class = PSF(kwargs_psf)
kwargs_spemd = {
'theta_E': 1.,
'gamma': 1.8,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}
lens_model_list = ['SPEP']
self.kwargs_lens = [kwargs_spemd]
lens_model_class = LensModel(lens_model_list=lens_model_list)
kwargs_sersic = {
'amp': 1.,
'R_sersic': 0.1,
'n_sersic': 2,
'center_x': 0,
'center_y': 0
}
# 'SERSIC_ELLIPSE': elliptical Sersic profile
kwargs_sersic_ellipse = {
'amp': 1.,
'R_sersic': .6,
'n_sersic': 3,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}
lens_light_model_list = ['SERSIC']
self.kwargs_lens_light = [kwargs_sersic]
lens_light_model_class = LightModel(
light_model_list=lens_light_model_list)
source_model_list = ['SERSIC_ELLIPSE']
self.kwargs_source = [kwargs_sersic_ellipse]
source_model_class = LightModel(light_model_list=source_model_list)
kwargs_numerics = {'subgrid_res': 1, 'psf_subgrid': False}
imageModel = ImageModel(data_class,
psf_class,
lens_model_class,
source_model_class,
lens_light_model_class,
kwargs_numerics=kwargs_numerics)
image_sim = self.SimAPI.simulate(imageModel, self.kwargs_lens,
self.kwargs_source,
self.kwargs_lens_light)
data_class.update_data(image_sim)
self.data_class = data_class
self.psf_class = psf_class
kwargs_model = {
'lens_model_list': lens_model_list,
'source_light_model_list': source_model_list,
'lens_light_model_list': lens_light_model_list,
'fixed_magnification_list': [False],
}
self.kwargs_numerics = {'subgrid_res': 1, 'psf_subgrid': False}
num_source_model = len(source_model_list)
kwargs_constraints = {
'joint_center_lens_light': False,
'joint_center_source_light': False,
'additional_images_list': [False],
'fix_to_point_source_list': [False] * num_source_model,
'image_plane_source_list': [False] * num_source_model,
'solver': False,
}
kwargs_likelihood = {
'source_marg': True,
'point_source_likelihood': False,
'position_uncertainty': 0.004,
'check_solver': False,
'solver_tolerance': 0.001,
}
self.param_class = Param(kwargs_model, kwargs_constraints)
self.Likelihood = LikelihoodModule(imSim_class=imageModel,
param_class=self.param_class,
kwargs_likelihood=kwargs_likelihood)
self.sampler = Sampler(likelihoodModule=self.Likelihood)
def setup(self):
np.random.seed(42)
# data specifics
sigma_bkg = 0.05 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 50 # cutout pixel size
deltaPix = 0.1 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.5 # full width half max of PSF
kwargs_model = {'lens_model_list': ['SPEP'],
'lens_light_model_list': ['SERSIC'],
'source_light_model_list': ['SERSIC'],
'point_source_model_list': ['SOURCE_POSITION'],
'fixed_magnification_list': [True]}
# PSF specification
kwargs_band = sim_util.data_configure_simple(numPix, deltaPix, exp_time, sigma_bkg)
data_class = ImageData(**kwargs_band)
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': fwhm, 'pixel_size': deltaPix}
psf_class = PSF(**kwargs_psf)
print(np.shape(psf_class.kernel_point_source), 'test kernel shape -')
kwargs_spep = {'theta_E': 1., 'gamma': 1.95, 'center_x': 0, 'center_y': 0, 'e1': 0.1, 'e2': 0.1}
self.kwargs_lens = [kwargs_spep]
kwargs_sersic = {'amp': 1/0.05**2., 'R_sersic': 0.1, 'n_sersic': 2, 'center_x': 0, 'center_y': 0}
# 'SERSIC_ELLIPSE': elliptical Sersic profile
kwargs_sersic_ellipse = {'amp': 1., 'R_sersic': .6, 'n_sersic': 3, 'center_x': 0, 'center_y': 0}
self.kwargs_lens_light = [kwargs_sersic]
self.kwargs_source = [kwargs_sersic_ellipse]
self.kwargs_ps = [{'ra_source': 0.55, 'dec_source': 0.02,
'source_amp': 1.}] # quasar point source position in the source plane and intrinsic brightness
self.kwargs_cosmo = {'D_dt': 1000}
kwargs_numerics = {'supersampling_factor': 1, 'supersampling_convolution': False}
lens_model_class, source_model_class, lens_light_model_class, point_source_class, extinction_class = class_creator.create_class_instances(**kwargs_model)
imageModel = ImageModel(data_class, psf_class, lens_model_class, source_model_class,
lens_light_model_class, point_source_class, extinction_class, kwargs_numerics=kwargs_numerics)
image_sim = sim_util.simulate_simple(imageModel, self.kwargs_lens, self.kwargs_source,
self.kwargs_lens_light, self.kwargs_ps)
ra_pos, dec_pos = imageModel.PointSource.image_position(kwargs_ps=self.kwargs_ps, kwargs_lens=self.kwargs_lens)
data_class.update_data(image_sim)
kwargs_band['image_data'] = image_sim
self.data_class = data_class
self.psf_class = psf_class
self.kwargs_model = kwargs_model
self.kwargs_numerics = {
'supersampling_factor': 1,
'supersampling_convolution': False}
kwargs_constraints = {
'num_point_source_list': [4],
'solver_type': 'NONE', # 'PROFILE', 'PROFILE_SHEAR', 'ELLIPSE', 'CENTER'
'Ddt_sampling': True
}
def condition_definition(kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps=None, kwargs_special=None, kwargs_extinction=None):
logL = 0
if kwargs_lens_light[0]['R_sersic'] > kwargs_source[0]['R_sersic']:
logL -= 10**15
return logL
kwargs_likelihood = {'force_no_add_image': True,
'source_marg': True,
'astrometric_likelihood': True,
'image_position_uncertainty': 0.004,
'check_matched_source_position': False,
'source_position_tolerance': 0.001,
'source_position_sigma': 0.001,
'check_positive_flux': True,
'flux_ratio_likelihood': True,
'prior_lens': [[0, 'theta_E', 1, 0.1]],
'custom_logL_addition': condition_definition,
'image_position_likelihood': True
}
self.kwargs_data = {'multi_band_list': [[kwargs_band, kwargs_psf, kwargs_numerics]], 'multi_band_type': 'single-band',
'time_delays_measured': np.ones(4),
'time_delays_uncertainties': np.ones(4),
'flux_ratios': np.ones(4),
'flux_ratio_errors': np.ones(4),
'ra_image_list': ra_pos,
'dec_image_list': dec_pos
}
self.param_class = Param(self.kwargs_model, **kwargs_constraints)
self.imageModel = ImageModel(data_class, psf_class, lens_model_class, source_model_class,
lens_light_model_class,
point_source_class, kwargs_numerics=kwargs_numerics)
self.Likelihood = LikelihoodModule(kwargs_data_joint=self.kwargs_data, kwargs_model=kwargs_model, param_class=self.param_class, **kwargs_likelihood)
self.kwargs_band = kwargs_band
self.kwargs_psf = kwargs_psf
self.numPix = numPix
class TestLikelihoodModule(object):
"""
test the fitting sequences
"""
def setup(self):
np.random.seed(42)
# data specifics
sigma_bkg = 0.05 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 50 # cutout pixel size
deltaPix = 0.1 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.5 # full width half max of PSF
kwargs_model = {'lens_model_list': ['SPEP'],
'lens_light_model_list': ['SERSIC'],
'source_light_model_list': ['SERSIC'],
'point_source_model_list': ['SOURCE_POSITION'],
'fixed_magnification_list': [True]}
# PSF specification
kwargs_band = sim_util.data_configure_simple(numPix, deltaPix, exp_time, sigma_bkg)
data_class = ImageData(**kwargs_band)
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': fwhm, 'pixel_size': deltaPix}
psf_class = PSF(**kwargs_psf)
print(np.shape(psf_class.kernel_point_source), 'test kernel shape -')
kwargs_spep = {'theta_E': 1., 'gamma': 1.95, 'center_x': 0, 'center_y': 0, 'e1': 0.1, 'e2': 0.1}
self.kwargs_lens = [kwargs_spep]
kwargs_sersic = {'amp': 1/0.05**2., 'R_sersic': 0.1, 'n_sersic': 2, 'center_x': 0, 'center_y': 0}
# 'SERSIC_ELLIPSE': elliptical Sersic profile
kwargs_sersic_ellipse = {'amp': 1., 'R_sersic': .6, 'n_sersic': 3, 'center_x': 0, 'center_y': 0}
self.kwargs_lens_light = [kwargs_sersic]
self.kwargs_source = [kwargs_sersic_ellipse]
self.kwargs_ps = [{'ra_source': 0.55, 'dec_source': 0.02,
'source_amp': 1.}] # quasar point source position in the source plane and intrinsic brightness
self.kwargs_cosmo = {'D_dt': 1000}
kwargs_numerics = {'supersampling_factor': 1, 'supersampling_convolution': False}
lens_model_class, source_model_class, lens_light_model_class, point_source_class, extinction_class = class_creator.create_class_instances(**kwargs_model)
imageModel = ImageModel(data_class, psf_class, lens_model_class, source_model_class,
lens_light_model_class, point_source_class, extinction_class, kwargs_numerics=kwargs_numerics)
image_sim = sim_util.simulate_simple(imageModel, self.kwargs_lens, self.kwargs_source,
self.kwargs_lens_light, self.kwargs_ps)
ra_pos, dec_pos = imageModel.PointSource.image_position(kwargs_ps=self.kwargs_ps, kwargs_lens=self.kwargs_lens)
data_class.update_data(image_sim)
kwargs_band['image_data'] = image_sim
self.data_class = data_class
self.psf_class = psf_class
self.kwargs_model = kwargs_model
self.kwargs_numerics = {
'supersampling_factor': 1,
'supersampling_convolution': False}
kwargs_constraints = {
'num_point_source_list': [4],
'solver_type': 'NONE', # 'PROFILE', 'PROFILE_SHEAR', 'ELLIPSE', 'CENTER'
'Ddt_sampling': True
}
def condition_definition(kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps=None, kwargs_special=None, kwargs_extinction=None):
logL = 0
if kwargs_lens_light[0]['R_sersic'] > kwargs_source[0]['R_sersic']:
logL -= 10**15
return logL
kwargs_likelihood = {'force_no_add_image': True,
'source_marg': True,
'astrometric_likelihood': True,
'image_position_uncertainty': 0.004,
'check_matched_source_position': False,
'source_position_tolerance': 0.001,
'source_position_sigma': 0.001,
'check_positive_flux': True,
'flux_ratio_likelihood': True,
'prior_lens': [[0, 'theta_E', 1, 0.1]],
'custom_logL_addition': condition_definition,
'image_position_likelihood': True
}
self.kwargs_data = {'multi_band_list': [[kwargs_band, kwargs_psf, kwargs_numerics]], 'multi_band_type': 'single-band',
'time_delays_measured': np.ones(4),
'time_delays_uncertainties': np.ones(4),
'flux_ratios': np.ones(4),
'flux_ratio_errors': np.ones(4),
'ra_image_list': ra_pos,
'dec_image_list': dec_pos
}
self.param_class = Param(self.kwargs_model, **kwargs_constraints)
self.imageModel = ImageModel(data_class, psf_class, lens_model_class, source_model_class,
lens_light_model_class,
point_source_class, kwargs_numerics=kwargs_numerics)
self.Likelihood = LikelihoodModule(kwargs_data_joint=self.kwargs_data, kwargs_model=kwargs_model, param_class=self.param_class, **kwargs_likelihood)
self.kwargs_band = kwargs_band
self.kwargs_psf = kwargs_psf
self.numPix = numPix
def test_logL(self):
args = self.param_class.kwargs2args(kwargs_lens=self.kwargs_lens, kwargs_source=self.kwargs_source,
kwargs_lens_light=self.kwargs_lens_light, kwargs_ps=self.kwargs_ps, kwargs_special=self.kwargs_cosmo)
logL = self.Likelihood.logL(args, verbose=True)
num_data_evaluate = self.Likelihood.num_data
npt.assert_almost_equal(logL/num_data_evaluate, -1/2., decimal=1)
def test_time_delay_likelihood(self):
kwargs_likelihood = {'time_delay_likelihood': True,
}
likelihood = LikelihoodModule(kwargs_data_joint=self.kwargs_data, kwargs_model=self.kwargs_model, param_class=self.param_class, **kwargs_likelihood)
args = self.param_class.kwargs2args(kwargs_lens=self.kwargs_lens, kwargs_source=self.kwargs_source,
kwargs_lens_light=self.kwargs_lens_light, kwargs_ps=self.kwargs_ps, kwargs_special=self.kwargs_cosmo)
logL = likelihood.logL(args, verbose=True)
npt.assert_almost_equal(logL, -3097.189103539873, decimal=-1)
def test_check_bounds(self):
penalty, bound_hit = self.Likelihood.check_bounds(args=[0, 1], lowerLimit=[1, 0], upperLimit=[2, 2],
verbose=True)
assert bound_hit
def test_pixelbased_modelling(self):
ss_source = 2
numPix_source = self.numPix*ss_source
n_scales = 3
kwargs_pixelbased = {
'source_interpolation': 'nearest',
'supersampling_factor_source': ss_source, # supersampling of pixelated source grid
# following choices are to minimize pixel solver runtime (not to get accurate reconstruction!)
'threshold_decrease_type': 'none',
'num_iter_source': 2,
'num_iter_lens': 2,
'num_iter_global': 2,
'num_iter_weights': 2,
}
kwargs_likelihood = {
'image_likelihood': True,
'kwargs_pixelbased': kwargs_pixelbased,
'check_positive_flux': True, # effectively not applied, activated for code coverage purposes
}
kernel = PSF(**self.kwargs_psf).kernel_point_source
kwargs_psf = {'psf_type': 'PIXEL', 'kernel_point_source': kernel}
kwargs_numerics = {'supersampling_factor': 1}
kwargs_data = {'multi_band_list': [[self.kwargs_band, kwargs_psf, kwargs_numerics]]}
kwargs_model = {
'lens_model_list': ['SPEP'],
'lens_light_model_list': ['SLIT_STARLETS'],
'source_light_model_list': ['SLIT_STARLETS'],
}
kwargs_fixed_source = [{'n_scales': n_scales, 'n_pixels': numPix_source**2, 'scale': 1, 'center_x': 0, 'center_y': 0}]
kwargs_fixed_lens_light = [{'n_scales': n_scales, 'n_pixels': self.numPix**2, 'scale': 1, 'center_x': 0, 'center_y': 0}]
kwargs_constraints = {'source_grid_offset': True}
param_class = Param(kwargs_model,
kwargs_fixed_source=kwargs_fixed_source,
kwargs_fixed_lens_light=kwargs_fixed_lens_light,
**kwargs_constraints)
likelihood = LikelihoodModule(kwargs_data_joint=kwargs_data, kwargs_model=kwargs_model,
param_class=param_class, **kwargs_likelihood)
kwargs_source = [{'amp': np.ones(n_scales*numPix_source**2)}]
kwargs_lens_light = [{'amp': np.ones(n_scales*self.numPix**2)}]
kwargs_special = {'delta_x_source_grid': 0, 'delta_y_source_grid': 0}
args = param_class.kwargs2args(kwargs_lens=self.kwargs_lens, kwargs_source=kwargs_source,
kwargs_lens_light=kwargs_lens_light, kwargs_special=kwargs_special)
logL = likelihood.logL(args, verbose=True)
num_data_evaluate = likelihood.num_data
npt.assert_almost_equal(logL/num_data_evaluate, -1/2., decimal=1)
class TestFittingSequence(object):
"""
test the fitting sequences
"""
def setup(self):
np.random.seed(42)
# data specifics
sigma_bkg = 0.05 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 50 # cutout pixel size
deltaPix = 0.1 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.5 # full width half max of PSF
# PSF specification
kwargs_data = sim_util.data_configure_simple(numPix, deltaPix,
exp_time, sigma_bkg)
data_class = Data(kwargs_data)
kwargs_psf = sim_util.psf_configure_simple(psf_type='GAUSSIAN',
fwhm=fwhm,
kernelsize=11,
deltaPix=deltaPix,
truncate=3,
kernel=None)
psf_class = PSF(kwargs_psf)
kwargs_spemd = {
'theta_E': 1.,
'gamma': 1.95,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}
lens_model_list = ['SPEP']
self.kwargs_lens = [kwargs_spemd]
lens_model_class = LensModel(lens_model_list=lens_model_list)
kwargs_sersic = {
'amp': 1 / 0.05**2.,
'R_sersic': 0.1,
'n_sersic': 2,
'center_x': 0,
'center_y': 0
}
# 'SERSIC_ELLIPSE': elliptical Sersic profile
kwargs_sersic_ellipse = {
'amp': 1.,
'R_sersic': .6,
'n_sersic': 3,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}
lens_light_model_list = ['SERSIC']
self.kwargs_lens_light = [kwargs_sersic]
lens_light_model_class = LightModel(
light_model_list=lens_light_model_list)
source_model_list = ['SERSIC_ELLIPSE']
self.kwargs_source = [kwargs_sersic_ellipse]
source_model_class = LightModel(light_model_list=source_model_list)
self.kwargs_ps = [
{
'ra_source': 0.55,
'dec_source': 0.02,
'source_amp': 1.
}
] # quasar point source position in the source plane and intrinsic brightness
self.kwargs_cosmo = {'D_dt': 1000}
point_source_list = ['SOURCE_POSITION']
point_source_class = PointSource(
point_source_type_list=point_source_list,
fixed_magnification_list=[True])
kwargs_numerics = {'subgrid_res': 1, 'psf_subgrid': False}
imageModel = ImageModel(data_class,
psf_class,
lens_model_class,
source_model_class,
lens_light_model_class,
point_source_class,
kwargs_numerics=kwargs_numerics)
image_sim = sim_util.simulate_simple(imageModel, self.kwargs_lens,
self.kwargs_source,
self.kwargs_lens_light,
self.kwargs_ps)
data_class.update_data(image_sim)
self.data_class = data_class
self.psf_class = psf_class
self.kwargs_model = {
'lens_model_list': lens_model_list,
'source_light_model_list': source_model_list,
'lens_light_model_list': lens_light_model_list,
'point_source_model_list': point_source_list,
}
self.kwargs_numerics = {'subgrid_res': 1, 'psf_subgrid': False}
kwargs_constraints = {
'num_point_source_list': [4],
'solver_type':
'NONE', # 'PROFILE', 'PROFILE_SHEAR', 'ELLIPSE', 'CENTER'
'cosmo_type': 'D_dt'
}
kwargs_likelihood = {
'force_no_add_image': True,
'source_marg': True,
'point_source_likelihood': True,
'position_uncertainty': 0.004,
'check_solver': True,
'solver_tolerance': 0.001,
'check_positive_flux': True,
}
self.param_class = Param(self.kwargs_model, **kwargs_constraints)
self.imageModel = ImageModel(data_class,
psf_class,
lens_model_class,
source_model_class,
lens_light_model_class,
point_source_class,
kwargs_numerics=kwargs_numerics)
self.Likelihood = LikelihoodModule(imSim_class=self.imageModel,
param_class=self.param_class,
**kwargs_likelihood)
def test_logL(self):
args = self.param_class.kwargs2args(
kwargs_lens=self.kwargs_lens,
kwargs_source=self.kwargs_source,
kwargs_lens_light=self.kwargs_lens_light,
kwargs_ps=self.kwargs_ps,
kwargs_cosmo=self.kwargs_cosmo)
logL, _ = self.Likelihood.logL(args)
num_data_evaluate = self.Likelihood.imSim.numData_evaluate()
npt.assert_almost_equal(logL / num_data_evaluate, -1 / 2., decimal=1)
def test_time_delay_likelihood(self):
kwargs_likelihood = {
'time_delay_likelihood': True,
'time_delays_measured': np.ones(4),
'time_delays_uncertainties': np.ones(4)
}
likelihood = LikelihoodModule(imSim_class=self.imageModel,
param_class=self.param_class,
**kwargs_likelihood)
args = self.param_class.kwargs2args(
kwargs_lens=self.kwargs_lens,
kwargs_source=self.kwargs_source,
kwargs_lens_light=self.kwargs_lens_light,
kwargs_ps=self.kwargs_ps,
kwargs_cosmo=self.kwargs_cosmo)
logL, _ = likelihood.logL(args)
npt.assert_almost_equal(logL, -3340.79, decimal=-1)
def test_solver(self):
# make simulation with point source positions in image plane
x_pos, y_pos = self.imageModel.PointSource.image_position(
self.kwargs_ps, self.kwargs_lens)
kwargs_ps = [{'ra_image': x_pos[0], 'dec_image': y_pos[0]}]
kwargs_likelihood = {
'source_marg': True,
'point_source_likelihood': True,
'position_uncertainty': 0.004,
'check_solver': True,
'solver_tolerance': 0.001,
'check_positive_flux': True,
'solver': True
}
#imageModel = ImageModel(self.data_class, self.psf_class, self.lens_model_class, self.source_model_class,
# self.lens_light_model_class,
# point_source_class, kwargs_numerics=kwargs_numerics)
def test_force_positive_source_surface_brightness(self):
kwargs_likelihood = {
'force_positive_source_surface_brightness': True,
'numPix_source': 10,
'deltaPix_source': 0.1
}
kwargs_model = {'source_light_model_list': ['SERSIC']}
kwargs_constraints = {}
param_class = Param(kwargs_model, **kwargs_constraints)
kwargs_data = sim_util.data_configure_simple(numPix=10,
deltaPix=0.1,
exposure_time=1,
sigma_bkg=0.1)
data_class = Data(kwargs_data)
kwargs_psf = {'psf_type': 'NONE'}
psf_class = PSF(kwargs_psf)
kwargs_sersic = {
'amp': -1.,
'R_sersic': 0.1,
'n_sersic': 2,
'center_x': 0,
'center_y': 0
}
source_model_list = ['SERSIC']
kwargs_source = [kwargs_sersic]
source_model_class = LightModel(light_model_list=source_model_list)
imageModel = ImageModel(data_class,
psf_class,
lens_model_class=None,
source_model_class=source_model_class)
image_sim = sim_util.simulate_simple(imageModel, [], kwargs_source)
data_class.update_data(image_sim)
likelihood = LikelihoodModule(imSim_class=imageModel,
param_class=param_class,
**kwargs_likelihood)
logL, _ = likelihood.logL(args=param_class.kwargs2args(
kwargs_source=kwargs_source))
assert logL <= -10**10
class TestLikelihoodModule(object):
"""
test the fitting sequences
"""
def setup(self):
np.random.seed(42)
# data specifics
sigma_bkg = 0.05 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 50 # cutout pixel size
deltaPix = 0.1 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.5 # full width half max of PSF
kwargs_model = {
'lens_model_list': ['SPEP'],
'lens_light_model_list': ['SERSIC'],
'source_light_model_list': ['SERSIC_ELLIPSE'],
'point_source_model_list': ['SOURCE_POSITION'],
'fixed_magnification_list': [True]
}
# PSF specification
kwargs_band = sim_util.data_configure_simple(numPix, deltaPix,
exp_time, sigma_bkg)
data_class = ImageData(**kwargs_band)
kwargs_psf = {
'psf_type': 'GAUSSIAN',
'fwhm': fwhm,
'pixel_size': deltaPix
}
psf_class = PSF(**kwargs_psf)
print(np.shape(psf_class.kernel_point_source), 'test kernel shape -')
kwargs_spemd = {
'theta_E': 1.,
'gamma': 1.95,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}
self.kwargs_lens = [kwargs_spemd]
kwargs_sersic = {
'amp': 1 / 0.05**2.,
'R_sersic': 0.1,
'n_sersic': 2,
'center_x': 0,
'center_y': 0
}
# 'SERSIC_ELLIPSE': elliptical Sersic profile
kwargs_sersic_ellipse = {
'amp': 1.,
'R_sersic': .6,
'n_sersic': 3,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}
self.kwargs_lens_light = [kwargs_sersic]
self.kwargs_source = [kwargs_sersic_ellipse]
self.kwargs_ps = [
{
'ra_source': 0.55,
'dec_source': 0.02,
'source_amp': 1.
}
] # quasar point source position in the source plane and intrinsic brightness
self.kwargs_cosmo = {'D_dt': 1000}
kwargs_numerics = {
'supersampling_factor': 1,
'supersampling_convolution': False,
'compute_mode': 'gaussian'
}
lens_model_class, source_model_class, lens_light_model_class, point_source_class, extinction_class = class_creator.create_class_instances(
**kwargs_model)
imageModel = ImageModel(data_class,
psf_class,
lens_model_class,
source_model_class,
lens_light_model_class,
point_source_class,
extinction_class,
kwargs_numerics=kwargs_numerics)
image_sim = sim_util.simulate_simple(imageModel, self.kwargs_lens,
self.kwargs_source,
self.kwargs_lens_light,
self.kwargs_ps)
ra_pos, dec_pos = imageModel.PointSource.image_position(
kwargs_ps=self.kwargs_ps, kwargs_lens=self.kwargs_lens)
data_class.update_data(image_sim)
kwargs_band['image_data'] = image_sim
self.data_class = data_class
self.psf_class = psf_class
self.kwargs_model = kwargs_model
self.kwargs_numerics = {
'supersampling_factor': 1,
'supersampling_convolution': False
}
kwargs_constraints = {
'num_point_source_list': [4],
'solver_type':
'NONE', # 'PROFILE', 'PROFILE_SHEAR', 'ELLIPSE', 'CENTER'
'Ddt_sampling': True
}
def condition_definition(kwargs_lens,
kwargs_source,
kwargs_lens_light,
kwargs_ps=None,
kwargs_special=None,
kwargs_extinction=None):
logL = 0
if kwargs_lens_light[0]['R_sersic'] > kwargs_source[0]['R_sersic']:
logL -= 10**15
return logL
kwargs_likelihood = {
'force_no_add_image': True,
'source_marg': True,
'astrometric_likelihood': True,
'image_position_uncertainty': 0.004,
'check_matched_source_position': False,
'source_position_tolerance': 0.001,
'source_position_sigma': 0.001,
'check_positive_flux': True,
'flux_ratio_likelihood': True,
'prior_lens': [[0, 'theta_E', 1, 0.1]],
'custom_logL_addition': condition_definition,
'image_position_likelihood': True
}
self.kwargs_data = {
'multi_band_list': [[kwargs_band, kwargs_psf, kwargs_numerics]],
'multi_band_type': 'single-band',
'time_delays_measured': np.ones(4),
'time_delays_uncertainties': np.ones(4),
'flux_ratios': np.ones(4),
'flux_ratio_errors': np.ones(4),
'ra_image_list': ra_pos,
'dec_image_list': dec_pos
}
self.param_class = Param(self.kwargs_model, **kwargs_constraints)
self.imageModel = ImageModel(data_class,
psf_class,
lens_model_class,
source_model_class,
lens_light_model_class,
point_source_class,
kwargs_numerics=kwargs_numerics)
self.Likelihood = LikelihoodModule(kwargs_data_joint=self.kwargs_data,
kwargs_model=kwargs_model,
param_class=self.param_class,
**kwargs_likelihood)
def test_logL(self):
args = self.param_class.kwargs2args(
kwargs_lens=self.kwargs_lens,
kwargs_source=self.kwargs_source,
kwargs_lens_light=self.kwargs_lens_light,
kwargs_ps=self.kwargs_ps,
kwargs_special=self.kwargs_cosmo)
logL = self.Likelihood.logL(args, verbose=True)
num_data_evaluate = self.Likelihood.num_data
npt.assert_almost_equal(logL / num_data_evaluate, -1 / 2., decimal=1)
def test_time_delay_likelihood(self):
kwargs_likelihood = {
'time_delay_likelihood': True,
}
likelihood = LikelihoodModule(kwargs_data_joint=self.kwargs_data,
kwargs_model=self.kwargs_model,
param_class=self.param_class,
**kwargs_likelihood)
args = self.param_class.kwargs2args(
kwargs_lens=self.kwargs_lens,
kwargs_source=self.kwargs_source,
kwargs_lens_light=self.kwargs_lens_light,
kwargs_ps=self.kwargs_ps,
kwargs_special=self.kwargs_cosmo)
logL = likelihood.logL(args, verbose=True)
npt.assert_almost_equal(logL, -3080.29, decimal=-1)
#def test_solver(self):
# make simulation with point source positions in image plane
# x_pos, y_pos = self.imageModel.PointSource.image_position(self.kwargs_ps, self.kwargs_lens)
# kwargs_ps = [{'ra_image': x_pos[0], 'dec_image': y_pos[0]}]
# kwargs_likelihood = {
# 'source_marg': True,
# 'astrometric_likelihood': True,
# 'position_uncertainty': 0.004,
# 'check_solver': True,
# 'solver_tolerance': 0.001,
# 'check_positive_flux': True,
# 'solver': True
# }
#imageModel = ImageModel(self.data_class, self.psf_class, self.lens_model_class, self.source_model_class,
# self.lens_light_model_class,
# point_source_class, kwargs_numerics=kwargs_numerics)
def test_force_positive_source_surface_brightness(self):
kwargs_likelihood = {'force_minimum_source_surface_brightness': True}
kwargs_model = {'source_light_model_list': ['SERSIC']}
kwargs_constraints = {}
param_class = Param(kwargs_model, **kwargs_constraints)
kwargs_data = sim_util.data_configure_simple(numPix=10,
deltaPix=0.1,
exposure_time=1,
background_rms=0.1)
data_class = ImageData(**kwargs_data)
kwargs_psf = {'psf_type': 'NONE'}
psf_class = PSF(**kwargs_psf)
kwargs_sersic = {
'amp': -1.,
'R_sersic': 0.1,
'n_sersic': 2,
'center_x': 0,
'center_y': 0
}
source_model_list = ['SERSIC']
kwargs_source = [kwargs_sersic]
source_model_class = LightModel(light_model_list=source_model_list)
imageModel = ImageModel(data_class,
psf_class,
lens_model_class=None,
source_model_class=source_model_class)
image_sim = sim_util.simulate_simple(imageModel, [], kwargs_source)
kwargs_data['image_data'] = image_sim
kwargs_data_joint = {
'multi_band_list': [[kwargs_data, kwargs_psf, {}]],
'multi_band_type': 'single-band'
}
likelihood = LikelihoodModule(kwargs_data_joint=kwargs_data_joint,
kwargs_model=kwargs_model,
param_class=param_class,
**kwargs_likelihood)
logL = likelihood.logL(
args=param_class.kwargs2args(kwargs_source=kwargs_source),
verbose=True)
assert logL <= -10**10
def simple_einstein_ring_likelihood_2d():
# data specifics
sigma_bkg = 0.05 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 10 # cutout pixel size
deltaPix = 0.1 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.5 # full width half max of PSF
# PSF specification
kwargs_data = sim_util.data_configure_simple(numPix, deltaPix, exp_time,
sigma_bkg)
data_class = ImageData(**kwargs_data)
kwargs_psf_gaussian = {
'psf_type': 'GAUSSIAN',
'fwhm': fwhm,
'pixel_size': deltaPix
}
psf = PSF(**kwargs_psf_gaussian)
kwargs_psf = {
'psf_type': 'PIXEL',
'kernel_point_source': psf.kernel_point_source
}
psf_class = PSF(**kwargs_psf)
kwargs_spemd = {
'theta_E': 1.,
'gamma': 1.8,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}
lens_model_list = ['SPEP']
kwargs_lens = [kwargs_spemd]
lens_model_class = LensModel(lens_model_list=lens_model_list)
kwargs_sersic = {
'amp': 1.,
'R_sersic': 0.1,
'n_sersic': 2,
'center_x': 0,
'center_y': 0
}
# 'SERSIC_ELLIPSE': elliptical Sersic profile
kwargs_sersic_ellipse = {
'amp': 1.,
'R_sersic': .6,
'n_sersic': 3,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}
lens_light_model_list = ['SERSIC']
kwargs_lens_light = [kwargs_sersic]
lens_light_model_class = LightModel(light_model_list=lens_light_model_list)
source_model_list = ['SERSIC_ELLIPSE']
kwargs_source = [kwargs_sersic_ellipse]
source_model_class = LightModel(light_model_list=source_model_list)
kwargs_numerics = {
'supersampling_factor': 1,
'supersampling_convolution': False,
'compute_mode': 'regular'
}
imageModel = ImageModel(data_class,
psf_class,
lens_model_class,
source_model_class,
lens_light_model_class,
kwargs_numerics=kwargs_numerics)
image_sim = sim_util.simulate_simple(imageModel, kwargs_lens,
kwargs_source, kwargs_lens_light)
data_class.update_data(image_sim)
kwargs_data['image_data'] = image_sim
kwargs_data_joint = {
'multi_band_list': [[kwargs_data, kwargs_psf, kwargs_numerics]],
'multi_band_type': 'single-band'
}
kwargs_model = {
'lens_model_list': lens_model_list,
'source_light_model_list': source_model_list,
'lens_light_model_list': lens_light_model_list,
'fixed_magnification_list': [False],
}
kwargs_constraints = {
'image_plane_source_list': [False] * len(source_model_list)
}
kwargs_likelihood = {
'source_marg': False,
'image_position_uncertainty': 0.004,
'check_matched_source_position': False,
'source_position_tolerance': 0.001,
'source_position_sigma': 0.001,
}
# reduce number of param to sample (for runtime)
kwargs_fixed_lens = [{'gamma': 1.8, 'center_y': 0, 'e1': 0.1, 'e2': 0.1}]
kwargs_lower_lens = [{'theta_E': 0.8, 'center_x': -0.1}]
kwargs_upper_lens = [{'theta_E': 1.2, 'center_x': 0.1}]
kwargs_fixed_source = [{
'R_sersic': 0.6,
'n_sersic': 3,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}]
kwargs_fixed_lens_light = [{
'R_sersic': 0.1,
'n_sersic': 2,
'center_x': 0,
'center_y': 0
}]
param_class = Param(kwargs_model,
kwargs_fixed_lens=kwargs_fixed_lens,
kwargs_fixed_source=kwargs_fixed_source,
kwargs_fixed_lens_light=kwargs_fixed_lens_light,
kwargs_lower_lens=kwargs_lower_lens,
kwargs_upper_lens=kwargs_upper_lens,
**kwargs_constraints)
likelihood = LikelihoodModule(kwargs_data_joint=kwargs_data_joint,
kwargs_model=kwargs_model,
param_class=param_class,
**kwargs_likelihood)
kwargs_truths = {
'kwargs_lens': kwargs_lens,
'kwargs_source': kwargs_source,
'kwargs_lens_light': kwargs_lens_light
}
return likelihood, kwargs_truths
def setup(self):
np.random.seed(42)
# data specifics
sigma_bkg = 0.05 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 50 # cutout pixel size
deltaPix = 0.1 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.5 # full width half max of PSF
# PSF specification
kwargs_data = sim_util.data_configure_simple(numPix, deltaPix,
exp_time, sigma_bkg)
data_class = Data(kwargs_data)
kwargs_psf = sim_util.psf_configure_simple(psf_type='GAUSSIAN',
fwhm=fwhm,
kernelsize=11,
deltaPix=deltaPix,
truncate=3,
kernel=None)
psf_class = PSF(kwargs_psf)
kwargs_spemd = {
'theta_E': 1.,
'gamma': 1.95,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}
lens_model_list = ['SPEP']
self.kwargs_lens = [kwargs_spemd]
lens_model_class = LensModel(lens_model_list=lens_model_list)
kwargs_sersic = {
'amp': 1 / 0.05**2.,
'R_sersic': 0.1,
'n_sersic': 2,
'center_x': 0,
'center_y': 0
}
# 'SERSIC_ELLIPSE': elliptical Sersic profile
kwargs_sersic_ellipse = {
'amp': 1.,
'R_sersic': .6,
'n_sersic': 3,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}
lens_light_model_list = ['SERSIC']
self.kwargs_lens_light = [kwargs_sersic]
lens_light_model_class = LightModel(
light_model_list=lens_light_model_list)
source_model_list = ['SERSIC_ELLIPSE']
self.kwargs_source = [kwargs_sersic_ellipse]
source_model_class = LightModel(light_model_list=source_model_list)
self.kwargs_ps = [
{
'ra_source': 0.55,
'dec_source': 0.02,
'source_amp': 1.
}
] # quasar point source position in the source plane and intrinsic brightness
self.kwargs_cosmo = {'D_dt': 1000}
point_source_list = ['SOURCE_POSITION']
point_source_class = PointSource(
point_source_type_list=point_source_list,
fixed_magnification_list=[True])
kwargs_numerics = {'subgrid_res': 1, 'psf_subgrid': False}
imageModel = ImageModel(data_class,
psf_class,
lens_model_class,
source_model_class,
lens_light_model_class,
point_source_class,
kwargs_numerics=kwargs_numerics)
image_sim = sim_util.simulate_simple(imageModel, self.kwargs_lens,
self.kwargs_source,
self.kwargs_lens_light,
self.kwargs_ps)
data_class.update_data(image_sim)
self.data_class = data_class
self.psf_class = psf_class
self.kwargs_model = {
'lens_model_list': lens_model_list,
'source_light_model_list': source_model_list,
'lens_light_model_list': lens_light_model_list,
'point_source_model_list': point_source_list,
}
self.kwargs_numerics = {'subgrid_res': 1, 'psf_subgrid': False}
kwargs_constraints = {
'num_point_source_list': [4],
'solver_type':
'NONE', # 'PROFILE', 'PROFILE_SHEAR', 'ELLIPSE', 'CENTER'
'cosmo_type': 'D_dt'
}
kwargs_likelihood = {
'force_no_add_image': True,
'source_marg': True,
'point_source_likelihood': True,
'position_uncertainty': 0.004,
'check_solver': True,
'solver_tolerance': 0.001,
'check_positive_flux': True,
}
self.param_class = Param(self.kwargs_model, **kwargs_constraints)
self.imageModel = ImageModel(data_class,
psf_class,
lens_model_class,
source_model_class,
lens_light_model_class,
point_source_class,
kwargs_numerics=kwargs_numerics)
self.Likelihood = LikelihoodModule(imSim_class=self.imageModel,
param_class=self.param_class,
**kwargs_likelihood)