def test_sampler_init(self, import_fixture):
_, likelihood = import_fixture
sampler = NestedSampler(likelihood, 'uniform', None, None, 1, 1)
try:
sampler = NestedSampler(likelihood, 'gaussian', None, None, 1, 1) # will raise an Error
except Exception as e:
assert isinstance(e, ValueError)
try:
sampler = NestedSampler(likelihood, 'some_type', None, None, 1, 1) # will raise an Error
except Exception as e:
assert isinstance(e, ValueError)
def import_fixture(simple_einstein_ring_likelihood):
"""
:param simple_einstein_ring_likelihood: fixture
:return:
"""
likelihood, kwargs_truths = simple_einstein_ring_likelihood
prior_means = likelihood.param.kwargs2args(**kwargs_truths)
prior_sigmas = np.ones_like(prior_means) * 0.1
sampler = NestedSampler(likelihood, 'gaussian', prior_means, prior_sigmas, 0.5, 0.5)
return sampler, likelihood
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': True,
'position_uncertainty': 0.004,
'check_solver': False,
'solver_tolerance': 0.001,
}
# reduce number of param to sample (for runtime)
kwargs_fixed_lens = [{
'gamma': 1.8,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}]
kwargs_lower_lens = [{'theta_E': 0.8}]
kwargs_upper_lens = [{'theta_E': 1.2}]
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
}]
self.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)
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.sampler = NestedSampler(self.Likelihood, 'gaussian', prior_means,
prior_sigmas, 0.5, 0.5)