def test_joint_lens_with_light(self):
kwargs_model = {'lens_model_list': ['CHAMELEON'], 'lens_light_model_list': ['CHAMELEON']}
i_light, k_lens = 0, 0
kwargs_constraints = {'joint_lens_with_light': [[i_light, k_lens, ['w_t', 'w_c', 'center_x', 'center_y', 'e1', 'e2']]]}
kwargs_lens = [{'alpha_1': 10}]
kwargs_lens_light = [{'amp': 1, 'w_t': 0.5, 'w_c': 0.1, 'center_x': 0, 'center_y': 0.3, 'e1': 0.1, 'e2': -0.2}]
param = Param(kwargs_model=kwargs_model, **kwargs_constraints)
args = param.kwargs2args(kwargs_lens=kwargs_lens, kwargs_lens_light=kwargs_lens_light)
kwargs_return = param.args2kwargs(args)
kwargs_lens_out = kwargs_return['kwargs_lens_light']
kwargs_lens_light_out = kwargs_return['kwargs_lens_light']
assert kwargs_lens_out[0]['w_c'] == kwargs_lens_light[0]['w_c']
assert kwargs_lens_light_out[0]['w_c'] == kwargs_lens_light[0]['w_c']
kwargs_model = {'lens_model_list': ['SIS'], 'lens_light_model_list': ['SERSIC']}
i_light, k_lens = 0, 0
kwargs_constraints = {'joint_lens_with_light': [[i_light, k_lens, ['center_x',
'center_y']]]} # list[[i_point_source, k_source, ['param_name1', 'param_name2', ...]], [
kwargs_lens = [{'theta_E': 1, 'center_x': 0, 'center_y': 0}]
kwargs_lens_light = [{'amp': 1, 'R_sersic': 0.5, 'n_sersic': 2, 'center_x': 1, 'center_y': 1}]
param = Param(kwargs_model=kwargs_model, **kwargs_constraints)
args = param.kwargs2args(kwargs_lens=kwargs_lens, kwargs_lens_light=kwargs_lens_light)
#kwargs_lens_out, kwargs_source_out, _, kwargs_ps_out, _ = param.args2kwargs(args)
kwargs_return = param.args2kwargs(args)
kwargs_lens_out = kwargs_return['kwargs_lens']
assert kwargs_lens_out[0]['theta_E'] == kwargs_lens[0]['theta_E']
assert kwargs_lens_out[0]['center_x'] == kwargs_lens_light[0]['center_x']
def test_joint_source_with_point_source(self):
kwargs_model = {'lens_model_list': ['SIS'], 'source_light_model_list': ['SERSIC'], 'point_source_model_list': ['SOURCE_POSITION']}
i_source, k_ps = 0, 0
kwargs_constraints = {'joint_source_with_point_source': [[k_ps, i_source]]} # list[[i_point_source, k_source, ['param_name1', 'param_name2', ...]], [
kwargs_lens = [{'theta_E': 1, 'center_x': 0, 'center_y': 0}]
kwargs_source = [{'amp': 1, 'n_sersic': 2, 'R_sersic': 0.3, 'center_x': 1, 'center_y': 1}]
kwargs_ps = [{'ra_source': 0.5, 'dec_source': 0.5}]
param = Param(kwargs_model=kwargs_model, **kwargs_constraints)
args = param.kwargs2args(kwargs_lens=kwargs_lens, kwargs_source=kwargs_source, kwargs_ps=kwargs_ps)
kwargs_return = param.args2kwargs(args)
kwargs_lens_out = kwargs_return['kwargs_lens']
kwargs_source_out = kwargs_return['kwargs_source']
#kwargs_lens_out, kwargs_source_out, _, kwargs_ps_out, _ = param.args2kwargs(args)
assert kwargs_lens_out[0]['theta_E'] == kwargs_lens[0]['theta_E']
assert kwargs_source_out[0]['center_x'] == kwargs_ps[0]['ra_source']
kwargs_model = {'lens_model_list': ['SIS'], 'source_light_model_list': ['SERSIC'], 'point_source_model_list': ['LENSED_POSITION']}
i_source, k_ps = 0, 0
kwargs_constraints = {'joint_source_with_point_source': [[k_ps, i_source]]} # list[[i_point_source, k_source, ['param_name1', 'param_name2', ...]], [
kwargs_lens = [{'theta_E': 1, 'center_x': 0, 'center_y': 0}]
kwargs_source = [{'amp': 1, 'n_sersic': 2, 'R_sersic': 0.3, 'center_x': 1, 'center_y': 1}]
kwargs_ps = [{'ra_image': [0.5], 'dec_image': [0.5]}]
param = Param(kwargs_model=kwargs_model, **kwargs_constraints)
args = param.kwargs2args(kwargs_lens=kwargs_lens, kwargs_source=kwargs_source, kwargs_ps=kwargs_ps)
#kwargs_lens_out, kwargs_source_out, _, kwargs_ps_out, _ = param.args2kwargs(args)
kwargs_return = param.args2kwargs(args)
kwargs_lens_out = kwargs_return['kwargs_lens']
kwargs_source_out = kwargs_return['kwargs_source']
assert kwargs_lens_out[0]['theta_E'] == kwargs_lens[0]['theta_E']
npt.assert_almost_equal(kwargs_source_out[0]['center_x'], -0.207, decimal=2)
def __init__(self,
multi_band_list,
kwargs_model,
kwargs_constraints,
kwargs_likelihood,
kwargs_params,
verbose=True):
self.multi_band_list = multi_band_list
self.kwargs_model = kwargs_model
self.kwargs_constraints = kwargs_constraints
self.kwargs_likelihood = kwargs_likelihood
self.kwargs_params = kwargs_params
self._verbose = verbose
self.fitting = Fitting(multi_band_list=self.multi_band_list,
kwargs_model=self.kwargs_model,
kwargs_constraints=self.kwargs_constraints,
kwargs_likelihood=self.kwargs_likelihood,
kwargs_params=self.kwargs_params)
self._param = Param(kwargs_model,
kwargs_constraints,
fix_lens_solver=True)
if 'source_model' in self.kwargs_params:
kwargs_init, _, kwargs_fixed, _, _ = self.kwargs_params[
'source_model']
else:
kwargs_init, kwargs_fixed = [], []
self._kwargs_source_init = copy.deepcopy(kwargs_init)
self._kwargs_source_fixed = copy.deepcopy(kwargs_fixed)
self._lens_temp, self._source_temp, self._lens_light_temp, self._ps_temp, self._cosmo_temp = self.fitting.init_kwargs(
)
def param_class(self):
"""
:return: instance of the Param class with the recent options and bounds
"""
kwargs_fixed_lens, kwargs_fixed_source, kwargs_fixed_lens_light, kwargs_fixed_ps, kwargs_fixed_special, kwargs_fixed_extinction = self.fixed_kwargs
kwargs_lower_lens, kwargs_lower_source, kwargs_lower_lens_light, kwargs_lower_ps, kwargs_lower_special, kwargs_lower_extinction = self._lower_kwargs
kwargs_upper_lens, kwargs_upper_source, kwargs_upper_lens_light, kwargs_upper_ps, kwargs_upper_special, kwargs_upper_extinction = self._upper_kwargs
kwargs_model = self.kwargs_model
kwargs_constraints = self.kwargs_constraints
lens_temp = self._kwargs_temp['kwargs_lens']
param_class = Param(kwargs_model,
kwargs_fixed_lens,
kwargs_fixed_source,
kwargs_fixed_lens_light,
kwargs_fixed_ps,
kwargs_fixed_special,
kwargs_fixed_extinction,
kwargs_lower_lens,
kwargs_lower_source,
kwargs_lower_lens_light,
kwargs_lower_ps,
kwargs_lower_special,
kwargs_lower_extinction,
kwargs_upper_lens,
kwargs_upper_source,
kwargs_upper_lens_light,
kwargs_upper_ps,
kwargs_upper_special,
kwargs_upper_extinction,
kwargs_lens_init=lens_temp,
**kwargs_constraints)
return param_class
def param_class(self, kwargs_lens):
"""
:return: instance of the Param class with the recent options and bounds
"""
kwargs_fixed_lens, kwargs_fixed_source, kwargs_fixed_lens_light, kwargs_fixed_ps, kwargs_fixed_cosmo = self.fixed_kwargs
kwargs_lower_lens, kwargs_lower_source, kwargs_lower_lens_light, kwargs_lower_ps, kwargs_lower_cosmo = self.lower_kwargs
kwargs_upper_lens, kwargs_upper_source, kwargs_upper_lens_light, kwargs_upper_ps, kwargs_upper_cosmo = self.upper_kwargs
kwargs_model = self.kwargs_model
kwargs_constraints = self.kwargs_constraints
param_class = Param(kwargs_model,
kwargs_fixed_lens,
kwargs_fixed_source,
kwargs_fixed_lens_light,
kwargs_fixed_ps,
kwargs_fixed_cosmo,
kwargs_lower_lens,
kwargs_lower_source,
kwargs_lower_lens_light,
kwargs_lower_ps,
kwargs_lower_cosmo,
kwargs_upper_lens,
kwargs_upper_source,
kwargs_upper_lens_light,
kwargs_upper_ps,
kwargs_upper_cosmo,
kwargs_lens_init=kwargs_lens,
**kwargs_constraints)
return param_class
def setup(self):
kwargs_model = {
'lens_model_list': ['SPEP'],
'source_light_model_list': ['GAUSSIAN'],
'lens_light_model_list': ['SERSIC'],
'point_source_model_list': ['LENSED_POSITION']
}
kwargs_param = {'num_point_source_list': [2]}
kwargs_fixed_lens = [{'gamma': 1.9}] #for SPEP lens
kwargs_fixed_source = [{
'sigma_x': 0.1,
'sigma_y': 0.1,
'center_x': 0.2,
'center_y': 0.2
}]
kwargs_fixed_ps = [{'ra_image': [-1, 1], 'dec_image': [-1, 1]}]
kwargs_fixed_lens_light = [{}]
kwargs_fixed_cosmo = [{}]
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_fixed_ps=kwargs_fixed_ps,
kwargs_fixed_cosmo=kwargs_fixed_cosmo,
**kwargs_param)
self.param_class.print_setting()
def setup(self):
kwargs_model = {
'lens_model_list': ['SPEP'],
'source_light_model_list': ['GAUSSIAN'],
'lens_light_model_list': ['SERSIC'],
'point_source_model_list': ['LENSED_POSITION'],
'multi_plane': True,
'lens_redshift_list': [0.5],
'z_source': 2,
'source_redshift_list': [0.5]
}
kwargs_param = {
'num_point_source_list': [2],
'lens_redshift_sampling_indexes': [0],
'source_redshift_sampling_indexes': [0],
'image_plane_source_list': [True]
}
kwargs_fixed_lens = [{'gamma': 1.9}] # for SPEP lens
kwargs_fixed_source = [{
'sigma': 0.1,
'center_x': 0.2,
'center_y': 0.2
}]
kwargs_fixed_ps = [{'ra_image': [-1, 1], 'dec_image': [-1, 1]}]
kwargs_fixed_lens_light = [{}]
kwargs_fixed_cosmo = [{}]
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_fixed_ps=kwargs_fixed_ps,
kwargs_fixed_special=kwargs_fixed_cosmo,
**kwargs_param)
self.param_class.print_setting()
def get_param_class(self, lens_name, model_id):
"""
Get `Param` instance for the lens model.
:param lens_name: name of the lens
:type lens_name: `str`
:param model_id: model run identifier
:type model_id: `str`
:return: `Param` instance
:rtype: `obj`
"""
self.load_output(lens_name, model_id=model_id)
config = ModelConfig(settings=self._model_settings)
kwargs_params = config.get_kwargs_params()
kwargs_model = config.get_kwargs_model()
kwargs_constraints = config.get_kwargs_constraints()
param = Param(
kwargs_model,
kwargs_params['lens_model'][2],
kwargs_params['source_model'][2],
kwargs_params['lens_light_model'][2],
kwargs_params['point_source_model'][2],
# kwargs_params['special'][2],
# kwargs_params['extinction_model'][2],
# kwargs_lens_init=kwargs_result['kwargs_lens'],
kwargs_lens_init=kwargs_params['lens_model'][0],
**kwargs_constraints)
return param
def test_get_cosmo(self):
kwargs_model = {'lens_model_list': ['SPEP'], 'source_light_model_list': ['GAUSSIAN'],
'lens_light_model_list': ['SERSIC'], 'point_source_model_list': ['LENSED_POSITION'],
}
kwargs_param = {'Ddt_sampling': True}
kwargs_fixed_lens = [{'gamma': 1.9}] # for SPEP lens
kwargs_fixed_source = [{'sigma_x': 0.1, 'sigma_y': 0.1, 'center_x': 0.2, 'center_y': 0.2}]
kwargs_fixed_ps = [{'ra_image': [-1, 1], 'dec_image': [-1, 1]}]
kwargs_fixed_lens_light = [{}]
kwargs_fixed_cosmo = {'D_dt': 1000}
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_fixed_ps=kwargs_fixed_ps,
kwargs_fixed_special=kwargs_fixed_cosmo, **kwargs_param)
kwargs_true_lens = [
{'theta_E': 1., 'gamma': 1.9, 'e1':0.01, 'e2':-0.01, 'center_x': 0., 'center_y': 0.}] # for SPEP lens
kwargs_true_source = [
{'amp': 1 * 2 * np.pi * 0.1 ** 2, 'center_x': 0.2, 'center_y': 0.2, 'sigma_x': 0.1, 'sigma_y': 0.1}]
kwargs_true_lens_light = [{'center_x': -0.06, 'center_y': 0.4, 'phi_G': 4.8,
'q': 0.86, 'n_sersic': 1.7,
'amp': 11.8, 'R_sersic': 0.697, 'phi_G_2': 0}]
kwargs_true_ps = [{'point_amp': [1, 1], 'ra_image': [-1, 1], 'dec_image': [-1, 1]}]
args = param_class.kwargs2args(kwargs_true_lens, kwargs_true_source,
kwargs_lens_light=kwargs_true_lens_light, kwargs_ps=kwargs_true_ps,
kwargs_special={'D_dt': 1000})
assert param_class.specialParams._D_dt_sampling is True
def test_joint_lens_with_light_dict(self):
kwargs_model = {
'lens_model_list': ['SHEAR'],
'lens_light_model_list': ['SERSIC']
}
i_light, k_lens = 0, 0
kwargs_constraints = {
'joint_lens_with_light':
[[i_light, k_lens, {
'ra_0': 'center_x',
'dec_0': 'center_y'
}]]
}
kwargs_lens = [{'gamma1': 0.05, 'gamma2': 0.06}]
kwargs_lens_light = [{
'amp': 1,
'R_sersic': 1,
'n_sersic': 4,
'center_x': 0.1,
'center_y': 0.3
}]
param = Param(kwargs_model=kwargs_model, **kwargs_constraints)
args = param.kwargs2args(kwargs_lens=kwargs_lens,
kwargs_lens_light=kwargs_lens_light)
kwargs_return = param.args2kwargs(args)
kwargs_lens_out = kwargs_return['kwargs_lens']
kwargs_lens_light_out = kwargs_return['kwargs_lens_light']
assert kwargs_lens_out[0]['gamma1'] == kwargs_lens[0]['gamma1']
assert kwargs_lens_out[0]['ra_0'] == kwargs_lens_light[0]['center_x']
assert kwargs_lens_out[0]['dec_0'] == kwargs_lens_light[0]['center_y']
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_logsampling(self):
kwargs_model = {
'lens_model_list': ['SIS'],
'source_light_model_list': ['SERSIC'],
'point_source_model_list': ['LENSED_POSITION'],
'lens_light_model_list': ['SERSIC']
}
kwargs_constraints = {'log_sampling_lens': [[0, ['theta_E']]]}
kwargs_lens = [{'theta_E': 0.1, 'center_x': 0, 'center_y': 0}]
kwargs_source = [{
'amp': 1,
'n_sersic': 2,
'R_sersic': 0.3,
'center_x': 1,
'center_y': 1
}]
kwargs_lens_light = [{
'amp': 1,
'n_sersic': 2,
'R_sersic': 0.3,
'center_x': 0.2,
'center_y': 0.2
}]
kwargs_ps = [{'ra_image': [0.5], 'dec_image': [0.5]}]
param = Param(kwargs_model=kwargs_model, **kwargs_constraints)
args = param.kwargs2args(kwargs_lens=kwargs_lens,
kwargs_source=kwargs_source,
kwargs_lens_light=kwargs_lens_light,
kwargs_ps=kwargs_ps)
kwargs_return = param.args2kwargs(args)
kwargs_lens_out = kwargs_return['kwargs_lens']
assert args[0] == -1
assert kwargs_lens_out[0]['theta_E'] == 0.1
def test_num_param(self):
num_param, list = self.param_class.num_param()
assert list[0] == 'theta_E_lens0'
assert num_param == 10
kwargs_model = {
'lens_model_list': ['SPEP'],
'source_light_model_list': ['GAUSSIAN'],
'lens_light_model_list': ['SERSIC'],
'point_source_model_list': ['LENSED_POSITION']
}
kwargs_param = {}
kwargs_fixed_lens = [{'gamma': 1.9}] # for SPEP lens
kwargs_fixed_source = [{
'sigma': 0.1,
'center_x': 0.2,
'center_y': 0.2
}]
kwargs_fixed_ps = [{'ra_image': [-1, 1], 'dec_image': [-1, 1]}]
kwargs_fixed_lens_light = [{}]
kwargs_fixed_cosmo = [{}]
param_class_linear = Param(kwargs_model,
kwargs_fixed_lens,
kwargs_fixed_source,
kwargs_fixed_lens_light,
kwargs_fixed_ps,
kwargs_fixed_cosmo,
linear_solver=True,
**kwargs_param)
num_param, list = param_class_linear.num_param()
assert list[0] == 'theta_E_lens0'
print(list)
assert len(list) == num_param
assert num_param == 9
def test_with_solver(self):
kwargs_model = {
'lens_model_list': ['SPEP'],
'source_light_model_list': ['SERSIC'],
'point_source_model_list': ['LENSED_POSITION']
}
i_lens_light, k_ps = 0, 0
kwargs_constraints = {
'solver_type': 'PROFILE',
'num_point_source_list': [4]
}
kwargs_lens = [{
'theta_E': 1,
'gamma': 2,
'e1': 0.1,
'e2': 0.1,
'center_x': 0,
'center_y': 0
}]
kwargs_source = [{
'amp': 1,
'n_sersic': 2,
'R_sersic': 0.3,
'center_x': 1,
'center_y': 1
}]
kwargs_lens_light = [{
'amp': 1,
'n_sersic': 2,
'R_sersic': 0.3,
'center_x': 0.2,
'center_y': 0.2
}]
lensModel = LensModel(lens_model_list=['SPEP'])
lensEquationSlover = LensEquationSolver(lensModel=lensModel)
x_image, y_image = lensEquationSlover.image_position_from_source(
sourcePos_x=0.0, sourcePos_y=0.01, kwargs_lens=kwargs_lens)
print(x_image, y_image, 'test')
kwargs_ps = [{'ra_image': x_image, 'dec_image': y_image}]
param = Param(kwargs_model=kwargs_model,
kwargs_lens_init=kwargs_lens,
**kwargs_constraints)
args = param.kwargs2args(kwargs_lens=kwargs_lens,
kwargs_source=kwargs_source,
kwargs_lens_light=kwargs_lens_light,
kwargs_ps=kwargs_ps)
#kwargs_lens_out, kwargs_source_out, kwargs_lens_light_out, kwargs_ps_out, _ = param.args2kwargs(args)
kwargs_return = param.args2kwargs(args)
kwargs_lens_out = kwargs_return['kwargs_lens']
kwargs_ps_out = kwargs_return['kwargs_ps']
dist = param.check_solver(kwargs_lens=kwargs_lens_out,
kwargs_ps=kwargs_ps_out)
npt.assert_almost_equal(dist, 0, decimal=10)
def test_raise(self):
kwargs_model = {
'lens_model_list': ['SIS'],
'source_light_model_list': ['SERSIC'],
'point_source_model_list': ['LENSED_POSITION'],
'lens_light_model_list': ['SERSIC']
}
kwargs_constraints = {
'log_sampling_lens': [[0, {'theta_E'}]]
} # wrong type, dict instead of list
with self.assertRaises(TypeError):
param = Param(kwargs_model=kwargs_model, **kwargs_constraints)
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_shapelet_lens(self):
kwargs_model = {'lens_model_list': ['SHAPELETS_CART'], 'source_light_model_list': [],
'lens_light_model_list': [], 'point_source_model_list': []}
kwargs_param = {'num_shapelet_lens': 6}
kwargs_fixed_lens = [{'beta': 1}] # for SPEP lens
kwargs_fixed_source = [{}]
kwargs_fixed_ps = [{}]
kwargs_fixed_lens_light = [{}]
kwargs_fixed_cosmo = [{}]
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_fixed_ps=kwargs_fixed_ps,
kwargs_fixed_special=kwargs_fixed_cosmo, **kwargs_param)
self.param_class.print_setting()
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 param_class(self):
"""
creating instance of lenstronomy Param() class. It uses the keyword arguments in self.kwargs_constraints as
__init__() arguments, as well as self.kwargs_model, and the set of kwargs_fixed___, kwargs_lower___,
kwargs_upper___ arguments for lens, lens_light, source, point source, extinction and special parameters.
:return: instance of the Param class with the recent options and bounds
"""
kwargs_fixed_lens, kwargs_fixed_source, kwargs_fixed_lens_light, kwargs_fixed_ps, kwargs_fixed_special, kwargs_fixed_extinction = self.fixed_kwargs
kwargs_lower_lens, kwargs_lower_source, kwargs_lower_lens_light, kwargs_lower_ps, kwargs_lower_special, kwargs_lower_extinction = self._lower_kwargs
kwargs_upper_lens, kwargs_upper_source, kwargs_upper_lens_light, kwargs_upper_ps, kwargs_upper_special, kwargs_upper_extinction = self._upper_kwargs
kwargs_model = self.kwargs_model
kwargs_constraints = self.kwargs_constraints
lens_temp = self._kwargs_temp['kwargs_lens']
param_class = Param(kwargs_model, kwargs_fixed_lens, kwargs_fixed_source,
kwargs_fixed_lens_light, kwargs_fixed_ps, kwargs_fixed_special, kwargs_fixed_extinction,
kwargs_lower_lens, kwargs_lower_source, kwargs_lower_lens_light, kwargs_lower_ps,
kwargs_lower_special, kwargs_lower_extinction,
kwargs_upper_lens, kwargs_upper_source, kwargs_upper_lens_light, kwargs_upper_ps,
kwargs_upper_special, kwargs_upper_extinction,
kwargs_lens_init=lens_temp, **kwargs_constraints)
return param_class
def postprocess_mcmc_chain(kwargs_result, samples, kwargs_model, fixed_lens_kwargs, fixed_ps_kwargs, fixed_src_light_kwargs, fixed_special_kwargs, kwargs_constraints, kwargs_fixed_lens_light=None, verbose=False, forward_modeling=False):
"""Postprocess the MCMC chain for making the chains consistent with the optimized lens model and converting parameters
Returns
-------
pandas.DataFrame
processed MCMC chain, where each row is a sample
"""
param = Param(kwargs_model, fixed_lens_kwargs, kwargs_fixed_ps=fixed_ps_kwargs, kwargs_fixed_source=fixed_src_light_kwargs, kwargs_fixed_special=fixed_special_kwargs, kwargs_fixed_lens_light=kwargs_fixed_lens_light, kwargs_lens_init=kwargs_result['kwargs_lens'], **kwargs_constraints)
if verbose:
param.print_setting()
n_samples = len(samples)
processed = []
for i in range(n_samples):
kwargs = {}
kwargs_out = param.args2kwargs(samples[i])
kwargs_lens_out, kwargs_special_out, kwargs_ps_out, kwargs_source_out, kwargs_lens_light_out = kwargs_out['kwargs_lens'], kwargs_out['kwargs_special'], kwargs_out['kwargs_ps'], kwargs_out['kwargs_source'], kwargs_out['kwargs_lens_light']
for k, v in kwargs_lens_out[0].items():
kwargs['lens_mass_{:s}'.format(k)] = v
for k, v in kwargs_lens_out[1].items():
kwargs['external_shear_{:s}'.format(k)] = v
if forward_modeling:
for k, v in kwargs_source_out[0].items():
kwargs['src_light_{:s}'.format(k)] = v
for k, v in kwargs_lens_light_out[0].items():
kwargs['lens_light_{:s}'.format(k)] = v
else:
kwargs['src_light_R_sersic'] = kwargs_source_out[0]['R_sersic']
if 'ra_source' in kwargs_ps_out[0]:
kwargs['src_light_center_x'] = kwargs_ps_out[0]['ra_source'] - kwargs_lens_out[0]['center_x']
kwargs['src_light_center_y'] = kwargs_ps_out[0]['dec_source'] - kwargs_lens_out[0]['center_y']
for k, v in kwargs_special_out.items():
kwargs[k] = v
processed.append(kwargs)
processed_df = pd.DataFrame(processed)
processed_df = metadata_utils.add_qphi_columns(processed_df)
processed_df = metadata_utils.add_gamma_psi_ext_columns(processed_df)
return processed_df
def test_mass_scaling(self):
kwargs_model = {'lens_model_list': ['SIS', 'NFW', 'NFW', 'SIS', 'SERSIC', 'HERNQUIST']}
kwargs_constraints = {'mass_scaling_list': [False, 1, 1, 1, 1, 1]}
kwargs_fixed_lens = [{}, {'alpha_Rs': 0.1}, {'alpha_Rs': 0.3}, {'theta_E': 0.1},
{'k_eff': 0.3}, {'sigma0': 1}]
kwargs_fixed_cosmo = {}
param_class = Param(kwargs_model, kwargs_fixed_lens=kwargs_fixed_lens, kwargs_fixed_special=kwargs_fixed_cosmo
, **kwargs_constraints)
kwargs_lens = [{'theta_E': 1, 'center_x': 0, 'center_y': 0},
{'alpha_Rs': 0.1, 'Rs': 5, 'center_x': 1., 'center_y': 0},
{'alpha_Rs': 0.1, 'Rs': 5, 'center_x': 0, 'center_y': 1.},
{'theta_E': 0.1, 'center_x': 3, 'center_y': 1.},
{'k_eff': 0.3, 'R_sersic': 1, 'n_sersic': 2, 'center_x': 3, 'center_y': 1.},
{'sigma0': 1, 'Rs': 1, 'center_x': 3, 'center_y': 1.}]
kwargs_source = []
kwargs_lens_light = []
kwargs_ps = []
mass_scale = 2
kwargs_cosmo = {'scale_factor': [mass_scale]}
args = param_class.kwargs2args(kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps, kwargs_special=kwargs_cosmo)
assert args[-1] == mass_scale
kwargs_return = param_class.args2kwargs(args)
kwargs_lens = kwargs_return['kwargs_lens']
print(kwargs_lens, 'test')
assert kwargs_lens[0]['theta_E'] == 1
assert kwargs_lens[1]['alpha_Rs'] == 0.1 * mass_scale
assert kwargs_lens[2]['alpha_Rs'] == 0.3 * mass_scale
assert kwargs_lens[3]['theta_E'] == 0.1 * mass_scale
assert kwargs_lens[4]['k_eff'] == 0.3 * mass_scale
assert kwargs_lens[5]['sigma0'] == 1 * mass_scale
kwargs_return = param_class.args2kwargs(args, bijective=True)
kwargs_lens = kwargs_return['kwargs_lens']
assert kwargs_lens[0]['theta_E'] == 1
assert kwargs_lens[1]['alpha_Rs'] == 0.1
assert kwargs_lens[2]['alpha_Rs'] == 0.3
def test_joint_lens_light_with_point_source(self):
kwargs_model = {
'lens_model_list': ['SIS'],
'source_light_model_list': ['SERSIC'],
'point_source_model_list': ['LENSED_POSITION'],
'lens_light_model_list': ['SERSIC']
}
i_lens_light, k_ps = 0, 0
kwargs_constraints = {
'joint_lens_light_with_point_source': [[k_ps, i_lens_light]]
} # list[[i_point_source, k_source, ['param_name1', 'param_name2', ...]], [
kwargs_lens = [{'theta_E': 1, 'center_x': 0, 'center_y': 0}]
kwargs_source = [{
'amp': 1,
'n_sersic': 2,
'R_sersic': 0.3,
'center_x': 1,
'center_y': 1
}]
kwargs_lens_light = [{
'amp': 1,
'n_sersic': 2,
'R_sersic': 0.3,
'center_x': 0.2,
'center_y': 0.2
}]
kwargs_ps = [{'ra_image': [0.5], 'dec_image': [0.5]}]
param = Param(kwargs_model=kwargs_model, **kwargs_constraints)
args = param.kwargs2args(kwargs_lens=kwargs_lens,
kwargs_source=kwargs_source,
kwargs_lens_light=kwargs_lens_light,
kwargs_ps=kwargs_ps)
kwargs_lens_out, kwargs_source_out, kwargs_lens_light_out, kwargs_ps_out, _ = param.args2kwargs(
args)
assert kwargs_lens_light_out[0]['center_x'] == kwargs_ps_out[0][
'ra_image']
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)
end_time = time.time()
print(end_time - start_time, 'total time needed for computation')
print(
'============ CONGRATULATION, YOUR JOB WAS SUCCESSFUL ================ '
)
#Save in pickle
fix_setting = [
fixed_lens, fixed_source, fixed_lens_light, None, fixed_cosmo
]
# cosmo = FlatLambdaCDM(H0=70, Om0=0.3, Ob0=0.) #!!!Wrong cosmos
# td_cosmo = TDCosmography(z_l, z_s, kwargs_model, cosmo_fiducial=cosmo)
# make instance of parameter class with given model options, constraints and fixed parameters #
param = Param(kwargs_model,
fixed_lens,
fixed_source,
fixed_lens_light,
None,
fixed_cosmo,
kwargs_lens_init=kwargs_result['kwargs_lens'],
**kwargs_constraints)
sampler_type, samples_mcmc, param_mcmc, dist_mcmc = chain_list[-1]
mcmc_new_list = []
labels_new = [r"$\gamma$", r"$D_{\Delta t}$", "H$_0$"]
for i in range(len(samples_mcmc)):
# transform the parameter position of the MCMC chain in a lenstronomy convention with keyword arguments #
kwargs_result = param.args2kwargs(samples_mcmc[i])
D_dt = kwargs_result['kwargs_special']['D_dt']
# fermat_pot = td_cosmo.fermat_potential(kwargs_result['kwargs_lens'], kwargs_result['kwargs_ps'])
# delta_fermat_12 = fermat_pot[0] - fermat_pot[2]
gamma = kwargs_result['kwargs_lens'][0]['gamma']
# phi_ext, gamma_ext = kwargs_result['kwargs_lens'][1]['gamma1'], kwargs_result['kwargs_lens'][1]['gamma2']
mcmc_new_list.append([gamma, D_dt, cal_h0(z_l, z_s, D_dt)])
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 test_general_scaling(self):
kwargs_model = {
'lens_model_list': ['PJAFFE', 'PJAFFE', 'NFW', 'PJAFFE', 'NFW']
}
# Scale Rs for two of the PJAFFEs, and sigma0 for a different set of PJAFFEs
# Scale alpha_Rs for the NFWs
kwargs_constraints = {
'general_scaling': {
'Rs': [1, False, False, 1, False],
'sigma0': [False, 1, False, 1, False],
'alpha_Rs': [False, False, 1, False, 1],
}
}
# PJAFFE: sigma0, Ra, Rs, center_x, center_y
# NFW: Rs, alpha_Rs, center_x, center_y
kwargs_fixed_lens = [
{
'Rs': 2.0,
'center_x': 1.0
},
{
'sigma0': 2.0,
'Ra': 2.0,
'Rs': 3.0,
'center_y': 1.5
},
{
'alpha_Rs': 0.1
},
{
'Ra': 0.1,
'center_x': 0,
'center_y': 0
},
{
'Rs': 3,
'center_x': -1,
'center_y': 3
},
]
kwargs_fixed_cosmo = {}
param_class = Param(kwargs_model,
kwargs_fixed_lens=kwargs_fixed_lens,
kwargs_fixed_special=kwargs_fixed_cosmo,
**kwargs_constraints)
kwargs_lens = [{
'sigma0': 3,
'Ra': 2,
'center_y': 5
}, {
'center_x': 1.
}, {
'Rs': 3,
'center_x': 0.0,
'center_y': -1.0
}, {
'sigma0': 3,
'Rs': 1.5
}, {
'alpha_Rs': 4
}]
kwargs_source = []
kwargs_lens_light = []
kwargs_ps = []
# Define the scaling and power for each parameter
kwargs_cosmo = {
'Rs_scale_factor': [2.0],
'Rs_scale_pow': [1.1],
'sigma0_scale_factor': [3],
'sigma0_scale_pow': [2.0],
'alpha_Rs_scale_factor': [0.3],
'alpha_Rs_scale_pow': [0.5],
}
args = param_class.kwargs2args(kwargs_lens,
kwargs_source,
kwargs_lens_light,
kwargs_ps,
kwargs_special=kwargs_cosmo)
num, names = param_class.num_param()
print(names)
print(args)
kwargs_return = param_class.args2kwargs(args)
kwargs_lens = kwargs_return['kwargs_lens']
print('kwargs_lens:', kwargs_lens)
npt.assert_almost_equal(kwargs_lens[0]['Rs'], 2.0 * 2.0**1.1)
npt.assert_almost_equal(kwargs_lens[0]['sigma0'], 3)
npt.assert_almost_equal(kwargs_lens[1]['Rs'], 3.0)
npt.assert_almost_equal(kwargs_lens[1]['sigma0'], 3.0 * 2.0**2.0)
npt.assert_almost_equal(kwargs_lens[2]['alpha_Rs'], 0.3 * 0.1**0.5)
npt.assert_almost_equal(kwargs_lens[3]['Rs'], 2.0 * 1.5**1.1)
npt.assert_almost_equal(kwargs_lens[3]['sigma0'], 3.0 * 3**2.0)
npt.assert_almost_equal(kwargs_lens[4]['alpha_Rs'], 0.3 * 4**0.5)
kwargs_return = param_class.args2kwargs(args, bijective=True)
kwargs_lens = kwargs_return['kwargs_lens']
npt.assert_almost_equal(kwargs_lens[0]['Rs'], 2.0)
npt.assert_almost_equal(kwargs_lens[1]['sigma0'], 2.0)
npt.assert_almost_equal(kwargs_lens[2]['alpha_Rs'], 0.1)
npt.assert_almost_equal(kwargs_lens[3]['Rs'], 1.5)
npt.assert_almost_equal(kwargs_lens[3]['sigma0'], 3)
npt.assert_almost_equal(kwargs_lens[4]['alpha_Rs'], 4)
def run(self, algorithm_list=['PSO', 'MCMC'], setting_list=[None, None]):
"""
Run the fitting. The algorithm_list and setting_list will be pass to 'fitting_kwargs()'
"""
self.fitting_kwargs(algorithm_list=algorithm_list,
setting_list=setting_list)
fitting_specify_class = self.fitting_specify_class
start_time = time.time()
chain_list = self.fitting_seq.fit_sequence(self.fitting_kwargs_list)
kwargs_result = self.fitting_seq.best_fit()
ps_result = kwargs_result['kwargs_ps']
source_result = kwargs_result['kwargs_lens_light']
if self.fitting_kwargs_list[-1][0] == 'MCMC':
self.sampler_type, self.samples_mcmc, self.param_mcmc, self.dist_mcmc = chain_list[
-1]
end_time = time.time()
print(round(end_time - start_time, 3),
'total time taken for the overall fitting (s)')
print(
'============ CONGRATULATION, YOUR JOB WAS SUCCESSFUL ================ '
)
from lenstronomy.ImSim.image_linear_solve import ImageLinearFit
imageLinearFit = ImageLinearFit(
data_class=fitting_specify_class.data_class,
psf_class=fitting_specify_class.psf_class,
source_model_class=fitting_specify_class.lightModel,
point_source_class=fitting_specify_class.pointSource,
kwargs_numerics=fitting_specify_class.kwargs_numerics)
image_reconstructed, error_map, _, _ = imageLinearFit.image_linear_solve(
kwargs_source=source_result, kwargs_ps=ps_result)
imageModel = fitting_specify_class.imageModel
image_host_list = [
] #The linear_solver before and after LensModelPlot could have different result for very faint sources.
for i in range(len(source_result)):
image_host_list.append(
imageModel.source_surface_brightness(source_result,
de_lensed=True,
unconvolved=False,
k=i))
image_ps_list = []
for i in range(len(ps_result)):
image_ps_list.append(imageModel.point_source(ps_result, k=i))
if self.fitting_kwargs_list[-1][0] == 'MCMC':
from lenstronomy.Sampling.parameters import Param
try:
kwargs_fixed_source = fitting_specify_class.kwargs_params[
'lens_light_model'][2]
except:
kwargs_fixed_source = None
try:
kwargs_fixed_ps = fitting_specify_class.kwargs_params[
'point_source_model'][2]
except:
kwargs_fixed_ps = None
param = Param(fitting_specify_class.kwargs_model,
kwargs_fixed_lens_light=kwargs_fixed_source,
kwargs_fixed_ps=kwargs_fixed_ps,
**fitting_specify_class.kwargs_constraints)
mcmc_flux_list = []
if len(fitting_specify_class.point_source_list) > 0:
qso_labels_new = [
"Quasar_{0} flux".format(i) for i in range(
len(fitting_specify_class.point_source_list))
]
galaxy_labels_new = [
"Galaxy_{0} flux".format(i)
for i in range(len(fitting_specify_class.light_model_list))
]
labels_flux = qso_labels_new + galaxy_labels_new
else:
labels_flux = [
"Galaxy_{0} flux".format(i)
for i in range(len(fitting_specify_class.light_model_list))
]
if len(self.samples_mcmc
) > 10000: #Only save maximum 10000 chain results.
trans_steps = [
len(self.samples_mcmc) - 10000,
len(self.samples_mcmc)
]
else:
trans_steps = [0, len(self.samples_mcmc)]
print("Start transfering the Params to fluxs...")
for i in range(trans_steps[0], trans_steps[1]):
kwargs_out = param.args2kwargs(self.samples_mcmc[i])
kwargs_light_source_out = kwargs_out['kwargs_lens_light']
kwargs_ps_out = kwargs_out['kwargs_ps']
image_reconstructed, _, _, _ = imageLinearFit.image_linear_solve(
kwargs_source=kwargs_light_source_out,
kwargs_ps=kwargs_ps_out)
flux_list_quasar = []
if len(fitting_specify_class.point_source_list) > 0:
for j in range(len(
fitting_specify_class.point_source_list)):
image_ps_j = fitting_specify_class.imageModel.point_source(
kwargs_ps_out, k=j)
flux_list_quasar.append(np.sum(image_ps_j))
flux_list_galaxy = []
for j in range(len(fitting_specify_class.light_model_list)):
image_j = fitting_specify_class.imageModel.source_surface_brightness(
kwargs_light_source_out, unconvolved=False, k=j)
flux_list_galaxy.append(np.sum(image_j))
mcmc_flux_list.append(flux_list_quasar + flux_list_galaxy)
if int(i / 1000) > int((i - 1) / 1000):
print(trans_steps[1] - trans_steps[0],
"MCMC samplers in total, finished translate:",
i - trans_steps[0])
self.mcmc_flux_list = np.array(mcmc_flux_list)
self.labels_flux = labels_flux
self.chain_list = chain_list
self.kwargs_result = kwargs_result
self.ps_result = ps_result
self.source_result = source_result
self.imageLinearFit = imageLinearFit
self.reduced_Chisq = imageLinearFit.reduced_chi2(
image_reconstructed, error_map)
self.image_host_list = image_host_list
self.image_ps_list = image_ps_list
self.translate_result()
}
]]
chain_list_mcmc = fitting_seq.fit_sequence(fitting_kwargs_list)
kwargs_result = fitting_seq.best_fit()
args_result = fitting_seq.param_class.kwargs2args(**kwargs_result)
logL = fitting_seq.likelihoodModule.logL(args_result, verbose=True)
sampler_type, samples_mcmc, param_mcmc, dist_mcmc = chain_list_mcmc[0]
# import the parameter handling class #
from lenstronomy.Sampling.parameters import Param
import lenstronomy.Util.param_util as param_util
param = Param(kwargs_model,
fixed_lens,
kwargs_fixed_ps=fixed_ps,
kwargs_fixed_special=fixed_special,
kwargs_lens_init=kwargs_result['kwargs_lens'],
**kwargs_constraints)
# the number of non-linear parameters and their names #
num_param, param_list = param.num_param()
lensModel = LensModel(kwargs_model['lens_model_list'])
lensModelExtensions = LensModelExtensions(lensModel=lensModel)
mcmc_new_list = []
if fix_gamma:
labels_new = [
r"$\theta_E$", r"$\phi_{lens}$", r"$q$", r"$D_{dt}$", r"$H_0$"
]
else:
labels_new = [