Source code for lenstronomy.LensModel.Solver.solver
from lenstronomy.LensModel.Solver.solver2point import Solver2Point
from lenstronomy.LensModel.Solver.solver4point import Solver4Point
import numpy as np
__all__ = ['Solver']
[docs]class Solver(object):
"""
joint solve class to manage with type of solver to be executed and checks whether the requirements are fulfilled.
"""
def __init__(self, solver_type, lensModel, num_images):
"""
:param solver_type: string, option for specific solver type
see detailed instruction of the Solver4Point and Solver2Point classes
:param lensModel: instance of a LensModel() class
:param num_images: int, number of images to be solved for
"""
self._num_images = num_images
self._lensModel = lensModel
if self._num_images == 4:
self._solver = Solver4Point(lensModel, solver_type=solver_type)
elif self. _num_images == 2:
self._solver = Solver2Point(lensModel, solver_type=solver_type)
else:
raise ValueError("%s number of images is not valid. Use 2 or 4!" % self._num_images)
[docs] def constraint_lensmodel(self, x_pos, y_pos, kwargs_list, xtol=1.49012e-12):
"""
:param x_pos:
:param y_pos:
:param kwargs_list:
:return:
"""
return self._solver.constraint_lensmodel(x_pos, y_pos, kwargs_list, xtol=xtol)
[docs] def update_solver(self, kwargs_lens, x_pos, y_pos):
"""
:param kwargs_lens:
:param x_pos:
:param y_pos:
:return:
"""
if not len(x_pos) == self._num_images:
raise ValueError("Point source number %s must be as specified by the solver with number of images %s" %
(len(x_pos), self._num_images))
kwargs_lens, precision = self.constraint_lensmodel(x_pos, y_pos, kwargs_lens)
return kwargs_lens
[docs] def check_solver(self, image_x, image_y, kwargs_lens):
"""
returns the precision of the solver to match the image position
:param kwargs_lens: full lens model (including solved parameters)
:param image_x: point source in image
:param image_y: point source in image
:return: precision of Euclidean distances between the different rays arriving at the image positions
"""
source_x, source_y = self._lensModel.ray_shooting(image_x, image_y, kwargs_lens)
dist = np.sqrt((source_x - source_x[0]) ** 2 + (source_y - source_y[0]) ** 2)
return dist
[docs] def add_fixed_lens(self, kwargs_fixed_lens, kwargs_lens_init):
"""
returns kwargs that are kept fixed during run, depending on options
:param kwargs_fixed_lens: keyword argument list of fixed parameters (indicated by fitting argument of the user)
:param kwargs_lens_init: Initial values of the full lens model keyword arguments
:return: updated kwargs_fixed_lens, added fixed parameters being added (and replaced later on) by the
non-linear solver.
"""
kwargs_fixed_lens = self._solver.add_fixed_lens(kwargs_fixed_lens, kwargs_lens_init)
return kwargs_fixed_lens