class Param(object):
"""
"""
def __init__(self,
kwargs_model,
kwargs_constraints,
kwargs_fixed_lens,
kwargs_fixed_source,
kwargs_fixed_lens_light,
kwargs_fixed_ps,
kwargs_lens_init=None,
linear_solver=True):
"""
:return:
"""
n = len(kwargs_fixed_source)
num_point_source_list = kwargs_constraints.get('num_point_source_list',
[0] * n)
self._image_plane_source_list = kwargs_constraints.get(
'image_plane_source_list', [False] * n)
self._fix_to_point_source_list = kwargs_constraints.get(
'fix_to_point_source_list', [False] * n)
self._joint_center_source = kwargs_constraints.get(
'joint_center_source_light', False)
self._joint_center_lens_light = kwargs_constraints.get(
'joint_center_lens_light', False)
self._lens_model_list = kwargs_model.get('lens_model_list', ['NONE'])
self.lensModel = LensModel(
lens_model_list=self._lens_model_list,
z_source=kwargs_model.get('z_source', None),
redshift_list=kwargs_model.get('redshift_list', None),
multi_plane=kwargs_model.get('multi_plane', False))
try:
self._num_images = num_point_source_list[0]
except:
self._num_images = 0
self._solver = kwargs_constraints.get('solver', False)
if self._solver:
self._solver_type = kwargs_constraints.get('solver_type',
'PROFILE')
self._solver_module = Solver(solver_type=self._solver_type,
lensModel=self.lensModel,
num_images=self._num_images)
else:
self._solver_type = 'NONE'
kwargs_fixed_lens = self._add_fixed_lens(kwargs_fixed_lens,
kwargs_lens_init)
kwargs_fixed_source = self._add_fixed_source(kwargs_fixed_source)
kwargs_fixed_lens_light = self._add_fixed_lens_light(
kwargs_fixed_lens_light)
kwargs_fixed_ps = kwargs_fixed_ps
self.lensParams = LensParam(self._lens_model_list,
kwargs_fixed_lens,
num_images=self._num_images,
solver_type=self._solver_type)
source_light_model_list = kwargs_model.get('source_light_model_list',
['NONE'])
self.souceParams = LightParam(source_light_model_list,
kwargs_fixed_source,
type='source_light',
linear_solver=linear_solver)
lens_light_model_list = kwargs_model.get('lens_light_model_list',
['NONE'])
self.lensLightParams = LightParam(lens_light_model_list,
kwargs_fixed_lens_light,
type='lens_light',
linear_solver=linear_solver)
point_source_model_list = kwargs_model.get('point_source_model_list',
['NONE'])
self.pointSourceParams = PointSourceParam(
point_source_model_list,
kwargs_fixed_ps,
num_point_source_list=num_point_source_list,
linear_solver=linear_solver)
@property
def num_point_source_images(self):
return self._num_images
def getParams(self, args, bijective=False):
"""
:param args: tuple of parameter values (float, strings, ...(
:return: keyword arguments sorted
"""
i = 0
kwargs_lens, i = self.lensParams.getParams(args, i)
kwargs_source, i = self.souceParams.getParams(args, i)
kwargs_lens_light, i = self.lensLightParams.getParams(args, i)
kwargs_ps, i = self.pointSourceParams.getParams(args, i)
if self._solver:
kwargs_lens = self._update_solver(kwargs_lens, kwargs_ps)
kwargs_source = self._update_source(kwargs_lens,
kwargs_source,
kwargs_ps,
image_plane=bijective)
return kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps
def setParams(self,
kwargs_lens,
kwargs_source,
kwargs_lens_light,
kwargs_ps,
bounds=None):
"""
inverse of getParam function
:param kwargs_lens: keyword arguments depending on model options
:param kwargs_source: keyword arguments depending on model options
:return: tuple of parameters
"""
args = self.lensParams.setParams(kwargs_lens, bounds=bounds)
args += self.souceParams.setParams(kwargs_source, bounds=bounds)
args += self.lensLightParams.setParams(kwargs_lens_light,
bounds=bounds)
args += self.pointSourceParams.setParams(kwargs_ps)
return args
def param_init(self, kwarg_mean_lens, kwarg_mean_source,
kwarg_mean_lens_light, kwarg_mean_ps):
"""
returns upper and lower bounds on the parameters used in the X2_chain function for MCMC/PSO starting
bounds are defined relative to the catalogue level image called in the class Data
might be migrated to the param class
"""
#inizialize mean and sigma limit arrays
mean, sigma = self.lensParams.param_init(kwarg_mean_lens)
_mean, _sigma = self.souceParams.param_init(kwarg_mean_source)
mean += _mean
sigma += _sigma
_mean, _sigma = self.lensLightParams.param_init(kwarg_mean_lens_light)
mean += _mean
sigma += _sigma
_mean, _sigma = self.pointSourceParams.param_init(kwarg_mean_ps)
mean += _mean
sigma += _sigma
return mean, sigma
def num_param(self):
"""
:return: number of parameters involved (int)
"""
num, list = self.lensParams.num_param()
_num, _list = self.souceParams.num_param()
num += _num
list += _list
_num, _list = self.lensLightParams.num_param()
num += _num
list += _list
_num, _list = self.pointSourceParams.num_param()
num += _num
list += _list
return num, list
def _update_solver(self, kwargs_lens, kwargs_ps):
kwargs_lens = self._solver_module.update_solver(kwargs_lens, kwargs_ps)
return kwargs_lens
def _update_source(self,
kwargs_lens_list,
kwargs_source_list,
kwargs_ps,
image_plane=False):
for i, kwargs in enumerate(kwargs_source_list):
if self._image_plane_source_list[i] and not image_plane:
if 'center_x' in kwargs:
x_mapped, y_mapped = self.lensModel.ray_shooting(
kwargs['center_x'], kwargs['center_y'],
kwargs_lens_list)
kwargs['center_x'] = x_mapped
kwargs['center_y'] = y_mapped
if self._fix_to_point_source_list[i]:
x_mapped, y_mapped = self.lensModel.ray_shooting(
kwargs_ps[0]['ra_image'], kwargs_ps[0]['dec_image'],
kwargs_lens_list)
if 'center_x' in kwargs:
kwargs['center_x'] = np.mean(x_mapped)
kwargs['center_y'] = np.mean(y_mapped)
if self._joint_center_source:
for i in range(1, len(kwargs_source_list)):
kwargs_source_list[i]['center_x'] = kwargs_source_list[0][
'center_x']
kwargs_source_list[i]['center_y'] = kwargs_source_list[0][
'center_y']
return kwargs_source_list
def _add_fixed_source(self, kwargs_fixed):
"""
add fixed parameters that will be determined through mitigaton of other parameters based on various options
:param kwargs_fixed:
:return:
"""
for i, kwargs in enumerate(kwargs_fixed):
kwargs = kwargs_fixed[i]
if self._fix_to_point_source_list[i]:
kwargs['center_x'] = 0
kwargs['center_y'] = 0
if self._joint_center_source:
if i > 0:
kwargs['center_x'] = 0
kwargs['center_y'] = 0
return kwargs_fixed
def _add_fixed_lens_light(self, kwargs_fixed):
"""
add fixed parameters that will be determined through mitigaton of other parameters based on various options
:param kwargs_fixed:
:return:
"""
if self._joint_center_lens_light:
for i, kwargs in enumerate(kwargs_fixed):
kwargs['center_x'] = 0
kwargs['center_y'] = 0
return kwargs_fixed
def _add_fixed_lens(self, kwargs_fixed, kwargs_init):
if self._solver:
kwargs_fixed = self._solver_module.add_fixed_lens(
kwargs_fixed, kwargs_init)
return kwargs_fixed
class TestParam(object):
def setup(self):
self.light_model_list = ['GAUSSIAN', 'MULTI_GAUSSIAN', 'SERSIC', 'SERSIC_ELLIPSE', 'DOUBLE_SERSIC',
'CORE_SERSIC', 'DOUBLE_CORE_SERSIC', 'BULDGE_DISK', 'SHAPELETS', 'HERNQUIST',
'HERNQUIST_ELLIPSE', 'PJAFFE', 'PJAFFE_ELLIPSE', 'UNIFORM', 'NONE'
]
self.kwargs = [
{'amp': 1., 'sigma_x': 1, 'sigma_y': 1., 'center_x': 0, 'center_y': 0}, # 'GAUSSIAN'
{'amp': [1., 2], 'sigma': [1, 3], 'center_x': 0, 'center_y': 0}, # 'MULTI_GAUSSIAN'
{'I0_sersic': 1, 'R_sersic': 0.5, 'n_sersic': 1, 'center_x': 0, 'center_y': 0}, # 'SERSIC'
{'I0_sersic': 1, 'R_sersic': 0.5, 'n_sersic': 1, 'q': 0.8, 'phi_G': 0.5, 'center_x': 0, 'center_y': 0}, # 'SERSIC_ELLIPSE'
{'I0_sersic': 1, 'R_sersic': 0.5, 'n_sersic': 1, 'q': 0.8, 'phi_G': 0.5, 'center_x': 0, 'center_y': 0,
'I0_2': 1, 'R_2': 0.05, 'n_2': 2, 'phi_G_2': 0, 'q_2': 1}, # 'DOUBLE_SERSIC'
{'I0_sersic': 1, 'R_sersic': 0.5, 'Re': 0.1, 'gamma': 2., 'n_sersic': 1, 'q': 0.8, 'phi_G': 0.5, 'center_x': 0, 'center_y': 0},
# 'CORE_SERSIC'
{'I0_sersic': 1, 'R_sersic': 0.5, 'Re': 0.1, 'gamma': 2., 'n_sersic': 1, 'q': 0.8, 'phi_G': 0.5,
'center_x': 0, 'center_y': 0, 'I0_2': 1, 'R_2': 0.05, 'n_2': 2, 'phi_G_2': 0, 'q_2': 1},
# 'DOUBLE_CORE_SERSIC'
{'I0_b': 1, 'R_b': 0.1, 'phi_G_b': 0, 'q_b': 1, 'I0_d': 2, 'R_d': 1, 'phi_G_d': 0.5, 'q_d': 0.7, 'center_x': 0, 'center_y': 0}, # BULDGE_DISK
{'amp': [1, 1, 1], 'beta': 0.5, 'n_max': 1, 'center_x': 0, 'center_y': 0}, # 'SHAPELETS'
{'sigma0': 1, 'Rs': 0.5, 'center_x': 0, 'center_y': 0}, # 'HERNQUIST'
{'sigma0': 1, 'Rs': 0.5, 'center_x': 0, 'center_y': 0, 'q': 0.8, 'phi_G': 0}, # 'HERNQUIST_ELLIPSE'
{'sigma0': 1, 'Ra': 1, 'Rs': 0.5, 'center_x': 0, 'center_y': 0}, # 'PJAFFE'
{'sigma0': 1, 'Ra': 1, 'Rs': 0.5, 'center_x': 0, 'center_y': 0, 'q': 0.8, 'phi_G': 0}, # 'PJAFFE_ELLIPSE'
{'mean': 1}, # 'UNIFORM'
{}, # 'NONE'
]
self.kwargs_sigma = [
{'amp_sigma': 1., 'sigma_x_sigma': 1, 'sigma_y_sigma': 1., 'center_x_sigma': 0, 'center_y_sigma': 0},
# 'GAUSSIAN'
{'amp_sigma': 1., 'sigma_sigma': 1, 'center_x_sigma': 0, 'center_y_sigma': 0},
# 'MULTI_GAUSSIAN'
{'I0_sersic_sigma': 1, 'R_sersic_sigma': 0.5, 'n_sersic_sigma': 1, 'center_x_sigma': 1, 'center_y_sigma': 1}, # 'SERSIC'
{'I0_sersic_sigma': 1, 'R_sersic_sigma': 0.5, 'n_sersic_sigma': 1, 'center_x_sigma': 1,
'center_y_sigma': 1, 'ellipse_sigma': 0.1}, # 'SERSIC_ELLIPSE'
{'I0_sersic_sigma': 1, 'R_sersic_sigma': 0.5, 'n_sersic_sigma': 1, 'center_x_sigma': 1,
'center_y_sigma': 1, 'ellipse_sigma': 0.1, 'I0_2_sigma': 1, 'R_2_sigma': 0.05, 'n_2_sigma': 2}, # 'DOUBLE_SERSIC'
{'I0_sersic_sigma': 1, 'R_sersic_sigma': 0.5, 'n_sersic_sigma': 1, 'center_x_sigma': 1,
'center_y_sigma': 1, 'ellipse_sigma': 0.1, 'Re_sigma': 0.01, 'gamma_sigma': 0.1}, # 'CORE_SERSIC'
{'I0_sersic_sigma': 1, 'R_sersic_sigma': 0.5, 'n_sersic_sigma': 1, 'center_x_sigma': 1,
'center_y_sigma': 1, 'ellipse_sigma': 0.1, 'Re_sigma': 0.01, 'gamma_sigma': 0.1, 'I0_2_sigma': 1, 'R_2_sigma': 0.05, 'n_2_sigma': 2}, # 'DOUBLE_CORE_SERSIC'
{'I0_b_sigma': 1, 'R_b_sigma': 0.1, 'ellipse_sigma': 0.1, 'I0_d_sigma': 2, 'R_d_sigma': 1,
'center_x_sigma': 0, 'center_y_sigma': 0}, # BULDGE_DISK
{'amp_sigma': 1, 'beta_sigma': 0.1, 'n_max_sigma': 1, 'center_x_sigma': 0, 'center_y_sigma': 0}, # 'SHAPELETS'
{'sigma0_sigma': 1, 'Rs_sigma': 0.5, 'center_x_sigma': 0, 'center_y_sigma': 0}, # 'HERNQUIST'
{'sigma0_sigma': 1, 'Rs_sigma': 0.5, 'center_x_sigma': 0, 'center_y_sigma': 0, 'ellipse_sigma': 0.1}, # 'HERNQUIST_ELLIPSE'
{'sigma0_sigma': 1, 'Ra_sigma': 0.1, 'Rs_sigma': 0.5, 'center_x_sigma': 0, 'center_y_sigma': 0}, # 'PJAFFE'
{'sigma0_sigma': 1, 'Ra_sigma': 0.1, 'Rs_sigma': 0.5, 'center_x_sigma': 0, 'center_y_sigma': 0, 'ellipse_sigma': 0.1}, # 'PJAFFE'
{'mean_sigma': 0.1}, # 'UNIFORM'
{}, # 'NONE'
]
self.kwargs_fixed = [{}, {'sigma': [1, 3]}, {}, {}, {}, {}, {}, {}, {'amp': [1, 1, 1]}, {}, {}, {}, {}, {}, {}
]
self.kwargs_mean = []
for i in range(len(self.light_model_list)):
kwargs_mean_k = self.kwargs[i].copy()
kwargs_mean_k.update(self.kwargs_sigma[i])
self.kwargs_mean.append(kwargs_mean_k)
self.param = LightParam(light_model_list=self.light_model_list,
kwargs_fixed=self.kwargs_fixed, type='source_light', linear_solver=False)
self.param_fixed = LightParam(light_model_list=self.light_model_list,
kwargs_fixed=self.kwargs, type='source_light', linear_solver=False)
def test_get_setParams(self):
args = self.param.setParams(self.kwargs)
kwargs_new, _ = self.param.getParams(args, i=0)
args_new = self.param.setParams(kwargs_new)
for k in range(len(args)):
npt.assert_almost_equal(args[k], args_new[k], decimal=8)
args = self.param_fixed.setParams(self.kwargs)
kwargs_new, _ = self.param_fixed.getParams(args, i=0)
args_new = self.param_fixed.setParams(kwargs_new)
for k in range(len(args)):
npt.assert_almost_equal(args[k], args_new[k], decimal=8)
def test_param_init(self):
mean, sigma = self.param.param_init(self.kwargs_mean)
assert mean[0] == 0
def test_num_params(self):
num, list = self.param.num_param()
assert num == 93
