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
class TestFittingSequence(object):
"""
test the fitting sequences
"""
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 test_pso(self):
n_particles = 2
n_iterations = 2
result, chain = self.sampler.pso(n_particles,
n_iterations,
lower_start=None,
upper_start=None,
threadCount=1,
init_pos=None,
mpi=False,
print_key='PSO')
assert len(result) == 16
def test_mcmc_emcee(self):
n_walkers = 36
n_run = 2
n_burn = 2
mean_start = self.param_class.setParams(
kwargs_lens=self.kwargs_lens,
kwargs_source=self.kwargs_source,
kwargs_lens_light=self.kwargs_lens_light)
sigma_start = np.ones_like(mean_start) * 0.1
samples = self.sampler.mcmc_emcee(n_walkers,
n_run,
n_burn,
mean_start,
sigma_start,
mpi=False)
assert len(samples) == n_walkers * n_run
def test_mcmc_CH(self):
walkerRatio = 2
n_run = 2
n_burn = 2
mean_start = self.param_class.setParams(
kwargs_lens=self.kwargs_lens,
kwargs_source=self.kwargs_source,
kwargs_lens_light=self.kwargs_lens_light)
sigma_start = np.ones_like(mean_start) * 0.1
self.sampler.mcmc_CH(walkerRatio,
n_run,
n_burn,
mean_start,
sigma_start,
threadCount=1,
init_pos=None,
mpi=False)
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
class TestFittingSequence(object):
"""
test the fitting sequences
"""
def setup(self):
np.random.seed(42)
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 = 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 = 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)
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.,
'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 = self.SimAPI.simulate(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
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,
'cosmo_type': 'D_dt'
}
self.kwargs_numerics = {'subgrid_res': 1, 'psf_subgrid': False}
kwargs_constraints = {
'num_point_source_list': [4],
'additional_images_list': [True],
'solver': False,
'solver_type':
'PROFILE_SHEAR', # 'PROFILE', 'PROFILE_SHEAR', 'ELLIPSE', 'CENTER'
}
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(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=kwargs_likelihood)
def test_logL(self):
args = self.param_class.setParams(
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=kwargs_likelihood)
args = self.param_class.setParams(
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, -3313.79, 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
}
