def test_sis_travel_time_new(self):
z_source = 1.5
z_lens = 0.5
lens_model_list = ['SIS', 'SIS']
redshift_list = [z_lens, 0.2]
lensModelMutli = MultiPlane(z_source=z_source,
lens_model_list=lens_model_list,
lens_redshift_list=redshift_list)
lensModel = LensModel(lens_model_list=lens_model_list)
kwargs_lens = [{
'theta_E': 1.,
'center_x': 0,
'center_y': 0
}, {
'theta_E': 0.,
'center_x': 0,
'center_y': 0
}]
dt = lensModelMutli.arrival_time(1., 0., kwargs_lens)
Dt = lensModelMutli._multi_plane_base._cosmo_bkg.ddt(z_lens=z_lens,
z_source=z_source)
fermat_pot = lensModel.fermat_potential(1, 0., kwargs_lens)
dt_simple = const.delay_arcsec2days(fermat_pot, Dt)
print(dt, dt_simple)
npt.assert_almost_equal(dt, dt_simple, decimal=8)
def logL_delay(self, kwargs_lens, kwargs_cosmo):
"""
routine to compute the log likelihood of the time delay distance
:param args:
:return:
"""
delay_arcsec = self.Multiband.fermat_potential(kwargs_lens, kwargs_cosmo)
D_dt_model = kwargs_cosmo['delay_dist']
delay_days = const.delay_arcsec2days(delay_arcsec, D_dt_model)
logL = self._logL_delays(delay_days, self.delays_measured, self.delays_errors)
return logL
def _lensing_potential2time_delay(self, potential, z_lens, z_source):
"""
transforms the lensing potential (in units arcsec^2) to a gravitational time-delay as measured at z=0
:param potential: lensing potential
:param z_lens: redshift of the deflector
:param z_source: redshift of source for the definition of the lensing quantities
:return: gravitational time-delay in units of days
"""
D_dt = self._cosmo_bkg.D_dt(z_lens, z_source)
delay_days = const.delay_arcsec2days(potential, D_dt)
return delay_days
def logL(self, kwargs_lens, kwargs_ps, kwargs_cosmo):
"""
routine to compute the log likelihood of the time delay distance
:param kwargs_lens: lens model kwargs list
:param kwargs_ps: point source kwargs list
:param kwargs_cosmo: cosmology and other kwargs
:return: log likelihood of the model given the time delay data
"""
x_pos, y_pos = self._pointSource.image_position(kwargs_ps=kwargs_ps, kwargs_lens=kwargs_lens, original_position=True)
x_pos, y_pos = x_pos[0], y_pos[0]
delay_arcsec = self._lensModel.fermat_potential(x_pos, y_pos, kwargs_lens)
D_dt_model = kwargs_cosmo['D_dt']
delay_days = const.delay_arcsec2days(delay_arcsec, D_dt_model)
logL = self._logL_delays(delay_days, self._delays_measured, self._delays_errors)
return logL
def logL_delay(self, kwargs_lens, kwargs_ps, kwargs_cosmo):
"""
routine to compute the log likelihood of the time delay distance
:param args:
:return:
"""
x_pos, y_pos = self.imSim.image_positions(kwargs_ps=kwargs_ps,
kwargs_lens=kwargs_lens)
x_pos, y_pos = self.param.real_image_positions(x_pos[0], y_pos[0],
kwargs_cosmo)
x_source, y_source = self.imSim.LensModel.ray_shooting(
x_pos, y_pos, kwargs_lens)
delay_arcsec = self.imSim.LensModel.fermat_potential(
x_pos, y_pos, x_source, y_source, kwargs_lens)
D_dt_model = kwargs_cosmo['D_dt']
delay_days = const.delay_arcsec2days(delay_arcsec, D_dt_model)
logL = self._logL_delays(delay_days, self._delays_measured,
self._delays_errors)
return logL
def logL(self, kwargs_lens, kwargs_ps, kwargs_cosmo):
"""
routine to compute the log likelihood of the time delay distance
:param kwargs_lens: lens model kwargs list
:param kwargs_ps: point source kwargs list
:param kwargs_cosmo: cosmology and other kwargs
:return: log likelihood of the model given the time delay data
"""
x_pos, y_pos = self._pointSource.image_position(kwargs_ps=kwargs_ps, kwargs_lens=kwargs_lens, original_position=True)
x_pos, y_pos = x_pos[0], y_pos[0]
if len(self._delays_measured) != (len(y_pos) - 1):
return -np.inf
if self.sort_images_by_dec:
increasing_dec_i = np.argsort(y_pos)
x_pos = x_pos[increasing_dec_i][self._abcd_ordering_i]
y_pos = y_pos[increasing_dec_i][self._abcd_ordering_i]
delay_arcsec = self._lensModel.fermat_potential(x_pos, y_pos, kwargs_lens)
D_dt_model = kwargs_cosmo['D_dt']
delay_days = const.delay_arcsec2days(delay_arcsec, D_dt_model)
logL = self._logL_delays(delay_days, self._delays_measured, self._delays_errors)
return logL
