def test_mass_scaling(self):
kwargs_fixed = {}
param = SpecialParam(kwargs_fixed=kwargs_fixed, mass_scaling=True, num_scale_factor=3)
kwargs = {'scale_factor': [0, 1, 2]}
args = param.setParams(kwargs)
assert len(args) == 3
num_param, param_list = param.num_param()
assert num_param == 3
kwargs_new, _ = param.getParams(args, i=0)
assert kwargs_new['scale_factor'][1] == 1
param = SpecialParam(kwargs_fixed=kwargs, mass_scaling=True, num_scale_factor=3)
kwargs_in = {'scale_factor': [9, 9, 9]}
args = param.setParams(kwargs_in)
assert len(args) == 0
kwargs_new, _ = param.getParams(args, i=0)
print(kwargs_new)
assert kwargs_new['scale_factor'][1] == 1
def test_delta_images(self):
param = SpecialParam(num_images=2, point_source_offset=True, kwargs_fixed={},
kwargs_lower={'delta_x_image': [-1, -1], 'delta_y_image': [-1, -1]},
kwargs_upper={'delta_x_image': [1, 1], 'delta_y_image': [1, 1]})
kwargs = {'delta_x_image': [0.5, 0.5], 'delta_y_image': [0.5, 0.5]}
args = param.setParams(kwargs_special=kwargs)
kwargs_new, _ = param.getParams(args, i=0)
print(kwargs_new)
assert kwargs_new['delta_x_image'][0] == kwargs['delta_x_image'][0]
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)
param_fixed = SpecialParam(Ddt_sampling=True, kwargs_fixed=self.kwargs, point_source_offset=True, num_images=2,
source_size=True)
kwargs_new, i = param_fixed.getParams(args=[], i=0)
kwargs_new['D_dt'] = self.kwargs['D_dt']
class TestParam(object):
def setup(self):
kwargs_fixed = {}
self.param = SpecialParam(Ddt_sampling=True, kwargs_fixed=kwargs_fixed, point_source_offset=True, num_images=2,
source_size=True, num_tau0=2)
self.kwargs = {'D_dt': 1988, 'delta_x_image': [0, 0], 'delta_y_image': [0, 0], 'source_size': 0.1, 'tau0_list': [0, 1]}
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)
param_fixed = SpecialParam(Ddt_sampling=True, kwargs_fixed=self.kwargs, point_source_offset=True, num_images=2,
source_size=True)
kwargs_new, i = param_fixed.getParams(args=[], i=0)
kwargs_new['D_dt'] = self.kwargs['D_dt']
def test_num_params(self):
num, list = self.param.num_param()
assert num == 8
def test_mass_scaling(self):
kwargs_fixed = {}
param = SpecialParam(kwargs_fixed=kwargs_fixed, mass_scaling=True, num_scale_factor=3)
kwargs = {'scale_factor': [0, 1, 2]}
args = param.setParams(kwargs)
assert len(args) == 3
num_param, param_list = param.num_param()
assert num_param == 3
kwargs_new, _ = param.getParams(args, i=0)
assert kwargs_new['scale_factor'][1] == 1
param = SpecialParam(kwargs_fixed=kwargs, mass_scaling=True, num_scale_factor=3)
kwargs_in = {'scale_factor': [9, 9, 9]}
args = param.setParams(kwargs_in)
assert len(args) == 0
kwargs_new, _ = param.getParams(args, i=0)
print(kwargs_new)
assert kwargs_new['scale_factor'][1] == 1
def test_delta_images(self):
param = SpecialParam(num_images=2, point_source_offset=True, kwargs_fixed={},
kwargs_lower={'delta_x_image': [-1, -1], 'delta_y_image': [-1, -1]},
kwargs_upper={'delta_x_image': [1, 1], 'delta_y_image': [1, 1]})
kwargs = {'delta_x_image': [0.5, 0.5], 'delta_y_image': [0.5, 0.5]}
args = param.setParams(kwargs_special=kwargs)
kwargs_new, _ = param.getParams(args, i=0)
print(kwargs_new)
assert kwargs_new['delta_x_image'][0] == kwargs['delta_x_image'][0]
class Param(object):
"""
class that handles the parameter constraints. In particular when different model profiles share joint constraints.
Options between same model classes:
'joint_lens_with_lens':list [[i_lens, k_lens, ['param_name1', 'param_name2', ...]], [...], ...],
joint parameter between two lens models
'joint_lens_light_with_lens_light':list [[i_lens_light, k_lens_light, ['param_name1', 'param_name2', ...]], [...], ...],
joint parameter between two lens light models, the second adopts the value of the first
'joint_source_with_source':list [[i_source, k_source, ['param_name1', 'param_name2', ...]], [...], ...],
joint parameter between two source surface brightness models, the second adopts the value of the first
Options between different model classes:
'joint_lens_with_light': list [[i_light, k_lens, ['param_name1', 'param_name2', ...]], [...], ...],
joint parameter between lens model and lens light model
'joint_source_with_point_source': list [[i_point_source, k_source], [...], ...],
joint position parameter between lens model and source light model
'joint_lens_light_with_point_source': list [[i_point_source, k_lens_light], [...], ...],
joint position parameter between lens model and lens light model
'joint_extinction_with_lens_light': list [[i_lens_light, k_extinction, ['param_name1', 'param_name2', ...]], [...], ...],
joint parameters between the lens surface brightness and the optical depth models
'joint_lens_with_source_light': [[i_source, k_lens, ['param_name1', 'param_name2', ...]], [...], ...],
joint parameter between lens model and source light model. Samples light model parameter only.
hierarchy is as follows:
1. Point source parameters are inferred
2. Lens light joint parameters are set
3. Lens model joint constraints are set
4. Lens model solver is applied
5. Joint source and point source is applied
"""
def __init__(self, kwargs_model,
kwargs_fixed_lens=None, kwargs_fixed_source=None, kwargs_fixed_lens_light=None, kwargs_fixed_ps=None,
kwargs_fixed_special=None, kwargs_fixed_extinction=None,
kwargs_lower_lens=None, kwargs_lower_source=None, kwargs_lower_lens_light=None, kwargs_lower_ps=None,
kwargs_lower_special=None, kwargs_lower_extinction=None,
kwargs_upper_lens=None, kwargs_upper_source=None, kwargs_upper_lens_light=None, kwargs_upper_ps=None,
kwargs_upper_special=None, kwargs_upper_extinction=None,
kwargs_lens_init=None, linear_solver=True, joint_lens_with_lens=[], joint_lens_light_with_lens_light=[],
joint_source_with_source=[], joint_lens_with_light=[], joint_source_with_point_source=[],
joint_lens_light_with_point_source=[], joint_extinction_with_lens_light=[],
joint_lens_with_source_light=[], mass_scaling_list=None, point_source_offset=False,
num_point_source_list=None, image_plane_source_list=None, solver_type='NONE', Ddt_sampling=None,
source_size=False, num_tau0=0):
"""
:return:
"""
self._lens_model_list = kwargs_model.get('lens_model_list', [])
self._source_light_model_list = kwargs_model.get('source_light_model_list', [])
self._lens_light_model_list = kwargs_model.get('lens_light_model_list', [])
self._point_source_model_list = kwargs_model.get('point_source_model_list', [])
self._optical_depth_model_list = kwargs_model.get('optical_depth_model_list', [])
lens_model_class, source_model_class, _, _, _ = class_creator.create_class_instances(all_models=True, **kwargs_model)
self._image2SourceMapping = Image2SourceMapping(lensModel=lens_model_class, sourceModel=source_model_class)
if kwargs_fixed_lens is None:
kwargs_fixed_lens = [{} for i in range(len(self._lens_model_list))]
if kwargs_fixed_source is None:
kwargs_fixed_source = [{} for i in range(len(self._source_light_model_list))]
if kwargs_fixed_lens_light is None:
kwargs_fixed_lens_light = [{} for i in range(len(self._lens_light_model_list))]
if kwargs_fixed_ps is None:
kwargs_fixed_ps = [{} for i in range(len(self._point_source_model_list))]
if kwargs_fixed_special is None:
kwargs_fixed_special = {}
self._joint_lens_with_lens = joint_lens_with_lens
self._joint_lens_light_with_lens_light = joint_lens_light_with_lens_light
self._joint_source_with_source = joint_source_with_source
self._joint_lens_with_light = joint_lens_with_light
self._joint_lens_with_source_light = joint_lens_with_source_light
self._joint_source_with_point_source = copy.deepcopy(joint_source_with_point_source)
for param_list in self._joint_source_with_point_source:
if len(param_list) == 2:
param_list.append(['center_x', 'center_y'])
self._joint_lens_light_with_point_source = copy.deepcopy(joint_lens_light_with_point_source)
for param_list in self._joint_lens_light_with_point_source:
if len(param_list) == 2:
param_list.append(['center_x', 'center_y'])
if mass_scaling_list is None:
mass_scaling_list = [False] * len(self._lens_model_list)
self._mass_scaling_list = mass_scaling_list
if 1 in self._mass_scaling_list:
self._num_scale_factor = np.max(self._mass_scaling_list)
self._mass_scaling = True
else:
self._num_scale_factor = 0
self._mass_scaling = False
self._point_source_offset = point_source_offset
if num_point_source_list is None:
num_point_source_list = [1] * len(self._point_source_model_list)
# Attention: if joint coordinates with other source profiles, only indicate one as bool
if image_plane_source_list is None:
image_plane_source_list = [False] * len(self._source_light_model_list)
self._image_plane_source_list = image_plane_source_list
try:
self._num_images = num_point_source_list[0]
except:
self._num_images = 0
self._solver_type = solver_type
if self._solver_type == 'NONE':
self._solver = False
else:
self._solver = True
self._solver_module = Solver(solver_type=self._solver_type, lensModel=lens_model_class,
num_images=self._num_images)
self._joint_extinction_with_lens_light = joint_extinction_with_lens_light
# fix parameters joint within the same model types
kwargs_fixed_lens_updated = self._add_fixed_lens(kwargs_fixed_lens, kwargs_lens_init)
kwargs_fixed_lens_updated = self._fix_joint_param(kwargs_fixed_lens_updated, self._joint_lens_with_lens)
kwargs_fixed_lens_updated = self._fix_joint_param(kwargs_fixed_lens_updated, self._joint_lens_with_source_light)
kwargs_fixed_lens_light_updated = self._fix_joint_param(kwargs_fixed_lens_light, self._joint_lens_light_with_lens_light)
kwargs_fixed_source_updated = self._fix_joint_param(kwargs_fixed_source, self._joint_source_with_source)
kwargs_fixed_ps_updated = copy.deepcopy(kwargs_fixed_ps)
kwargs_fixed_extinction_updated = self._fix_joint_param(kwargs_fixed_extinction, self._joint_extinction_with_lens_light)
# fix parameters joint with other model types
kwargs_fixed_lens_updated = self._fix_joint_param(kwargs_fixed_lens_updated, self._joint_lens_with_light)
kwargs_fixed_source_updated = self._fix_joint_param(kwargs_fixed_source_updated, self._joint_source_with_point_source)
kwargs_fixed_lens_light_updated = self._fix_joint_param(kwargs_fixed_lens_light_updated,
self._joint_lens_light_with_point_source)
self.lensParams = LensParam(self._lens_model_list, kwargs_fixed_lens_updated, num_images=self._num_images,
solver_type=self._solver_type, kwargs_lower=kwargs_lower_lens,
kwargs_upper=kwargs_upper_lens)
self.lensLightParams = LightParam(self._lens_light_model_list, kwargs_fixed_lens_light_updated, type='lens_light',
linear_solver=linear_solver, kwargs_lower=kwargs_lower_lens_light,
kwargs_upper=kwargs_upper_lens_light)
self.souceParams = LightParam(self._source_light_model_list, kwargs_fixed_source_updated, type='source_light',
linear_solver=linear_solver, kwargs_lower=kwargs_lower_source,
kwargs_upper=kwargs_upper_source)
self.pointSourceParams = PointSourceParam(self._point_source_model_list, kwargs_fixed_ps_updated,
num_point_source_list=num_point_source_list,
linear_solver=linear_solver, kwargs_lower=kwargs_lower_ps,
kwargs_upper=kwargs_upper_ps)
self.extinctionParams = LightParam(self._optical_depth_model_list, kwargs_fixed_extinction_updated,
kwargs_lower=kwargs_lower_extinction, kwargs_upper=kwargs_upper_extinction,
linear_solver=False)
self.specialParams = SpecialParam(Ddt_sampling=Ddt_sampling, mass_scaling=self._mass_scaling,
kwargs_fixed=kwargs_fixed_special, num_scale_factor=self._num_scale_factor,
kwargs_lower=kwargs_lower_special, kwargs_upper=kwargs_upper_special,
point_source_offset=self._point_source_offset, num_images=self._num_images,
source_size=source_size, num_tau0=num_tau0)
for lens_source_joint in self._joint_lens_with_source_light:
i_source = lens_source_joint[0]
if i_source in self._image_plane_source_list:
raise ValueError("linking a source light model with a lens model AND simultaneously parameterizing the"
" source position in the image plane is not valid!")
@property
def num_point_source_images(self):
return self._num_images
def args2kwargs(self, args, bijective=False):
"""
:param args: tuple of parameter values (float, strings, ...)
:return: keyword arguments sorted in lenstronomy conventions
"""
i = 0
args = np.atleast_1d(args)
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)
kwargs_special, i = self.specialParams.getParams(args, i)
kwargs_extinction, i = self.extinctionParams.getParams(args, i)
# update lens_light joint parameters
kwargs_lens_light = self._update_lens_light_joint_with_point_source(kwargs_lens_light, kwargs_ps)
kwargs_lens_light = self._update_joint_param(kwargs_lens_light, kwargs_lens_light, self._joint_lens_light_with_lens_light)
# update lens_light joint with lens model parameters
kwargs_lens = self._update_joint_param(kwargs_lens_light, kwargs_lens, self._joint_lens_with_light)
kwargs_lens = self._update_joint_param(kwargs_source, kwargs_lens, self._joint_lens_with_source_light)
# update extinction model with lens light model
kwargs_extinction = self._update_joint_param(kwargs_lens_light, kwargs_extinction, self._joint_extinction_with_lens_light)
# update lens model joint parameters (including scaling)
kwargs_lens = self._update_joint_param(kwargs_lens, kwargs_lens, self._joint_lens_with_lens)
kwargs_lens = self.update_lens_scaling(kwargs_special, kwargs_lens)
# update point source constraint solver
if self._solver is True:
x_pos, y_pos = kwargs_ps[0]['ra_image'], kwargs_ps[0]['dec_image']
kwargs_lens = self._solver_module.update_solver(kwargs_lens, x_pos, y_pos)
# update source joint with point source
kwargs_source = self._update_source_joint_with_point_source(kwargs_lens, kwargs_source, kwargs_ps, kwargs_special,
image_plane=bijective)
# update source joint with source
kwargs_source = self._update_joint_param(kwargs_source, kwargs_source, self._joint_source_with_source)
# optional revert lens_scaling for bijective
if bijective is True:
kwargs_lens = self.update_lens_scaling(kwargs_special, kwargs_lens, inverse=True)
kwargs_return = {'kwargs_lens': kwargs_lens, 'kwargs_source': kwargs_source,
'kwargs_lens_light': kwargs_lens_light, 'kwargs_ps': kwargs_ps,
'kwargs_special': kwargs_special, 'kwargs_extinction': kwargs_extinction}
return kwargs_return
def kwargs2args(self, kwargs_lens=None, kwargs_source=None, kwargs_lens_light=None, kwargs_ps=None,
kwargs_special=None, kwargs_extinction=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)
args += self.souceParams.setParams(kwargs_source)
args += self.lensLightParams.setParams(kwargs_lens_light)
args += self.pointSourceParams.setParams(kwargs_ps)
args += self.specialParams.setParams(kwargs_special)
args += self.extinctionParams.setParams(kwargs_extinction)
return args
def param_limits(self):
"""
:return: lower and upper limits of the arguments being sampled
"""
lower_limit = self.kwargs2args(kwargs_lens=self.lensParams.lower_limit,
kwargs_source=self.souceParams.lower_limit,
kwargs_lens_light=self.lensLightParams.lower_limit,
kwargs_ps=self.pointSourceParams.lower_limit,
kwargs_special=self.specialParams.lower_limit,
kwargs_extinction=self.extinctionParams.lower_limit)
upper_limit = self.kwargs2args(kwargs_lens=self.lensParams.upper_limit,
kwargs_source=self.souceParams.upper_limit,
kwargs_lens_light=self.lensLightParams.upper_limit,
kwargs_ps=self.pointSourceParams.upper_limit,
kwargs_special=self.specialParams.upper_limit,
kwargs_extinction=self.extinctionParams.upper_limit)
return lower_limit, upper_limit
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
_num, _list = self.specialParams.num_param()
num += _num
list += _list
_num, _list = self.extinctionParams.num_param()
num += _num
list += _list
return num, list
def num_param_linear(self):
"""
:return: number of linear basis set coefficients that are solved for
"""
num = 0
num += self.souceParams.num_param_linear()
num += self.lensLightParams.num_param_linear()
num += self.pointSourceParams.num_param_linear()
return num
def image2source_plane(self, kwargs_source, kwargs_lens, image_plane=False):
"""
maps the image plane position definition of the source plane
:param kwargs_source:
:param kwargs_lens:
:return:
"""
kwargs_source_copy = copy.deepcopy(kwargs_source)
for i, kwargs in enumerate(kwargs_source_copy):
if self._image_plane_source_list[i] is True and not image_plane:
if 'center_x' in kwargs:
x_mapped, y_mapped = self._image2SourceMapping.image2source(kwargs['center_x'], kwargs['center_y'],
kwargs_lens, index_source=i)
kwargs['center_x'] = x_mapped
kwargs['center_y'] = y_mapped
return kwargs_source_copy
def _update_source_joint_with_point_source(self, kwargs_lens_list, kwargs_source_list, kwargs_ps, kwargs_special, image_plane=False):
kwargs_source_list = self.image2source_plane(kwargs_source_list, kwargs_lens_list, image_plane=image_plane)
for setting in self._joint_source_with_point_source:
i_point_source, k_source, param_list = setting
if 'ra_source' in kwargs_ps[i_point_source]:
x_mapped = kwargs_ps[i_point_source]['ra_source']
y_mapped = kwargs_ps[i_point_source]['dec_source']
else:
x_pos, y_pos = kwargs_ps[i_point_source]['ra_image'], kwargs_ps[i_point_source]['dec_image']
#x_pos, y_pos = self.real_image_positions(x_pos, y_pos, kwargs_special)
x_mapped, y_mapped = self._image2SourceMapping.image2source(x_pos, y_pos, kwargs_lens_list,
index_source=k_source)
for param_name in param_list:
if param_name == 'center_x':
kwargs_source_list[k_source][param_name] = np.mean(x_mapped)
elif param_name == 'center_y':
kwargs_source_list[k_source][param_name] = np.mean(y_mapped)
else:
kwargs_source_list[k_source][param_name] = kwargs_ps[i_point_source][param_name]
return kwargs_source_list
def _update_lens_light_joint_with_point_source(self, kwargs_lens_light_list, kwargs_ps):
for setting in self._joint_lens_light_with_point_source:
i_point_source, k_lens_light, param_list = setting
if 'ra_image' in kwargs_ps[i_point_source]:
x_mapped = kwargs_ps[i_point_source]['ra_image']
y_mapped = kwargs_ps[i_point_source]['dec_image']
else:
raise ValueError("Joint lens light with point source not possible as point source is defined in the source plane!")
for param_name in param_list:
if param_name == 'center_x':
kwargs_lens_light_list[k_lens_light][param_name] = np.mean(x_mapped)
elif param_name == 'center_y':
kwargs_lens_light_list[k_lens_light][param_name] = np.mean(y_mapped)
return kwargs_lens_light_list
@staticmethod
def _update_joint_param(kwargs_list_1, kwargs_list_2, joint_setting_list):
"""
:param kwargs_list_1: list of keyword arguments
:param kwargs_list_2: list of keyword arguments
:param joint_setting_list: [[i_1, k_2, ['param_name1', 'param_name2', ...]], [...], ...]
:return: udated kwargs_list_2 with arguments from kwargs_list_1 as defined in joint_setting_list
"""
for setting in joint_setting_list:
i_1, k_2, param_list = setting
for param_name in param_list:
kwargs_list_2[k_2][param_name] = kwargs_list_1[i_1][param_name]
return kwargs_list_2
@staticmethod
def _fix_joint_param(kwargs_list_2, joint_setting_list):
"""
:param kwargs_list_2: list of keyword arguments
:param joint_setting_list: [[i_1, k_2, ['param_name1', 'param_name2', ...]], [...], ...]
:return: fixes entries in kwargs_list_2 that are joint with other kwargs_list as defined in joint_setting_list
"""
kwargs_list_2_update = copy.deepcopy(kwargs_list_2)
for setting in joint_setting_list:
i_1, k_2, param_list = setting
for param_name in param_list:
kwargs_list_2_update[k_2][param_name] = 0
return kwargs_list_2_update
def update_lens_scaling(self, kwargs_special, kwargs_lens, inverse=False):
"""
multiplies the scaling parameters of the profiles
:param args:
:param kwargs_lens:
:param i:
:param inverse:
:return:
"""
kwargs_lens_updated = copy.deepcopy(kwargs_lens)
if self._mass_scaling is False:
return kwargs_lens_updated
scale_factor_list = np.array(kwargs_special['scale_factor'])
if inverse is True:
scale_factor_list = 1. / np.array(kwargs_special['scale_factor'])
for i, kwargs in enumerate(kwargs_lens_updated):
if self._mass_scaling_list[i] is not False:
scale_factor = scale_factor_list[self._mass_scaling_list[i] - 1]
if 'theta_E' in kwargs:
kwargs['theta_E'] *= scale_factor
elif 'alpha_Rs' in kwargs:
kwargs['alpha_Rs'] *= scale_factor
elif 'alpha_1' in kwargs:
kwargs['alpha_1'] *= scale_factor
elif 'sigma0' in kwargs:
kwargs['sigma0'] *= scale_factor
elif 'k_eff' in kwargs:
kwargs['k_eff'] *= scale_factor
return kwargs_lens_updated
def _add_fixed_lens(self, kwargs_fixed, kwargs_init):
kwargs_fixed_update = copy.deepcopy(kwargs_fixed)
if self._solver is True:
if kwargs_init is None:
raise ValueError("kwargs_lens_init must be specified when the point source solver is enabled!")
kwargs_fixed_update = self._solver_module.add_fixed_lens(kwargs_fixed_update, kwargs_init)
return kwargs_fixed_update
def check_solver(self, kwargs_lens, kwargs_ps):
"""
test whether the image positions map back to the same source position
:param kwargs_lens:
:param kwargs_ps:
:return: Euclidean distance between the rayshooting of the image positions
"""
if self._solver is True:
image_x, image_y = kwargs_ps[0]['ra_image'], kwargs_ps[0]['dec_image']
dist = self._solver_module.check_solver(image_x, image_y, kwargs_lens)
return np.max(dist)
else:
return 0
def check_positive_flux(self, kwargs_source, kwargs_lens_light, kwargs_ps):
pos_bool_ps = self.pointSourceParams.check_positive_flux(kwargs_ps)
pos_bool_source = self.souceParams.check_positive_flux_profile(kwargs_source)
pos_bool_lens_light = self.lensLightParams.check_positive_flux_profile(kwargs_lens_light)
if pos_bool_ps is True and pos_bool_source is True and pos_bool_lens_light is True:
return True
else:
return False
#def real_image_positions(self, x, y, kwargs_special):
# """
# :param kwargs_ps: point source kwargs
# :param kwargs_special: special kwargs (or other kwargs)
# :return: position where time delays are evaluated and solver is solved for
# """
# if self._point_source_offset is True:
# delta_x, delta_y = kwargs_special['delta_x_image'], kwargs_special['delta_y_image']
# return x + delta_x, y + delta_y
# else:
# return x, y
def print_setting(self):
"""
prints the setting of the parameter class
:return:
"""
num, param_list = self.num_param()
num_linear = self.num_param_linear()
print("The following model options are chosen:")
print("Lens models:", self._lens_model_list)
print("Source models:", self._source_light_model_list)
print("Lens light models:", self._lens_light_model_list)
print("Point source models:", self._point_source_model_list)
print("===================")
print("The following parameters are being fixed:")
print("Lens:", self.lensParams.kwargs_fixed)
print("Source:", self.souceParams.kwargs_fixed)
print("Lens light:", self.lensLightParams.kwargs_fixed)
print("Point source:", self.pointSourceParams.kwargs_fixed)
print("===================")
print("Joint parameters for different models")
print("Joint lens with lens:", self._joint_lens_with_lens)
print("Joint lens light with lens light:", self._joint_lens_light_with_lens_light)
print("Joint source with source:", self._joint_source_with_source)
print("Joint lens with light:", self._joint_lens_with_light)
print("Joint source with point source:", self._joint_source_with_point_source)
print("Joint lens light with point source:", self._joint_lens_light_with_point_source)
print("===================")
print("Number of non-linear parameters being sampled: ", num)
print("Parameters being sampled: ", param_list)
print("Number of linear parameters being solved for: ", num_linear)