class TestSourceParam(object):
def setup(self):
self._param = SourceParam(sne_distribution='GAUSSIAN', sne_apparent_m_sampling=True, kwargs_fixed=None)
kwargs_fixed = {'mu_sne': 1, 'sigma_sne': 0.1}
self._param_fixed = SourceParam(sne_distribution='GAUSSIAN', sne_apparent_m_sampling=True, kwargs_fixed=kwargs_fixed)
def test_param_list(self):
param_list = self._param.param_list(latex_style=False)
assert len(param_list) == 2
param_list = self._param.param_list(latex_style=True)
assert len(param_list) == 2
param_list = self._param_fixed.param_list(latex_style=False)
assert len(param_list) == 0
param_list = self._param_fixed.param_list(latex_style=True)
assert len(param_list) == 0
def test_args2kwargs(self):
kwargs = {'mu_sne': 1, 'sigma_sne': 0.1}
args = self._param.kwargs2args(kwargs)
kwargs_new, i = self._param.args2kwargs(args, i=0)
args_new = self._param.kwargs2args(kwargs_new)
npt.assert_almost_equal(args_new, args)
args = self._param_fixed.kwargs2args(kwargs)
kwargs_new, i = self._param_fixed.args2kwargs(args, i=0)
args_new = self._param_fixed.kwargs2args(kwargs_new)
npt.assert_almost_equal(args_new, args)
def setup(self):
self._param = SourceParam(sne_distribution='GAUSSIAN',
sne_sampling=True,
kwargs_fixed=None)
kwargs_fixed = {'mu_sne': 1, 'sigma_sne': 0.1}
self._param_fixed = SourceParam(sne_distribution='GAUSSIAN',
sne_sampling=True,
kwargs_fixed=kwargs_fixed)
def __init__(self, cosmology, ppn_sampling=False, lambda_mst_sampling=False, lambda_mst_distribution='NONE',
anisotropy_sampling=False, anisotropy_model='OM', anisotropy_distribution='NONE',
kappa_ext_sampling=False, kappa_ext_distribution='NONE', lambda_ifu_sampling=False,
lambda_ifu_distribution='NONE', alpha_lambda_sampling=False, sigma_v_systematics=False,
sne_apparent_m_sampling=False, sne_distribution='GAUSSIAN',
log_scatter=False,
kwargs_lower_cosmo=None, kwargs_upper_cosmo=None,
kwargs_fixed_cosmo=None, kwargs_lower_lens=None, kwargs_upper_lens=None, kwargs_fixed_lens=None,
kwargs_lower_kin=None, kwargs_upper_kin=None, kwargs_fixed_kin=None,
kwargs_lower_source=None, kwargs_upper_source=None, kwargs_fixed_source=None):
"""
:param cosmology: string describing cosmological model
:param ppn_sampling: post-newtonian parameter sampling
:param lambda_mst_sampling: bool, if True adds a global mass-sheet transform parameter in the sampling
:param lambda_mst_distribution: string, distribution function of the MST transform
:param lambda_ifu_sampling: bool, if True samples a separate lambda_mst for a second (e.g. IFU) data set
independently
:param alpha_lambda_sampling: bool, if True samples a parameter alpha_lambda, which scales lambda_mst linearly
according to a predefined quantity of the lens
:param lambda_ifu_distribution: string, distribution function of the lambda_ifu parameter
:param anisotropy_sampling: bool, if True adds a global stellar anisotropy parameter that alters the single lens
kinematic prediction
:param anisotropy_distribution: string, indicating the distribution function of the anisotropy model
:param sne_apparent_m_sampling: boolean, if True, samples/queries SNe unlensed magnitude distribution
(not intrinsic magnitudes but apparent!)
:param sne_distribution: string, apparent non-lensed brightness distribution (in linear space).
Currently supports:
'GAUSSIAN': Gaussian distribution
:param sigma_v_systematics: bool, if True samples paramaters relative to systematics in the velocity dispersion
measurement
:param log_scatter: boolean, if True, samples the Gaussian scatter amplitude in log space (and thus flat prior in log)
"""
self._kin_param = KinParam(anisotropy_sampling=anisotropy_sampling, anisotropy_model=anisotropy_model,
distribution_function=anisotropy_distribution, log_scatter=log_scatter,
sigma_v_systematics=sigma_v_systematics, kwargs_fixed=kwargs_fixed_kin)
self._cosmo_param = CosmoParam(cosmology=cosmology, ppn_sampling=ppn_sampling, kwargs_fixed=kwargs_fixed_cosmo)
self._lens_param = LensParam(lambda_mst_sampling=lambda_mst_sampling,
lambda_mst_distribution=lambda_mst_distribution,
lambda_ifu_sampling=lambda_ifu_sampling,
lambda_ifu_distribution=lambda_ifu_distribution,
kappa_ext_sampling=kappa_ext_sampling,
kappa_ext_distribution=kappa_ext_distribution,
alpha_lambda_sampling=alpha_lambda_sampling,
log_scatter=log_scatter,
kwargs_fixed=kwargs_fixed_lens)
self._source_param = SourceParam(sne_apparent_m_sampling=sne_apparent_m_sampling, sne_distribution=sne_distribution,
kwargs_fixed=kwargs_fixed_source)
self._kwargs_upper_cosmo, self._kwargs_lower_cosmo = kwargs_upper_cosmo, kwargs_lower_cosmo
self._kwargs_upper_lens, self._kwargs_lower_lens = kwargs_upper_lens, kwargs_lower_lens
self._kwargs_upper_kin, self._kwargs_lower_kin = kwargs_upper_kin, kwargs_lower_kin
self._kwargs_upper_source, self._kwargs_lower_source = kwargs_upper_source, kwargs_lower_source
class ParamManager(object):
"""
class for managing the parameters involved
"""
def __init__(self,
cosmology,
ppn_sampling=False,
lambda_mst_sampling=False,
lambda_mst_distribution='NONE',
anisotropy_sampling=False,
anisotropy_model='OM',
anisotropy_distribution='NONE',
kappa_ext_sampling=False,
kappa_ext_distribution='NONE',
lambda_ifu_sampling=False,
lambda_ifu_distribution='NONE',
alpha_lambda_sampling=False,
sigma_v_systematics=False,
sne_sampling=False,
sne_distribution='GAUSSIAN',
log_scatter=False,
kwargs_lower_cosmo=None,
kwargs_upper_cosmo=None,
kwargs_fixed_cosmo=None,
kwargs_lower_lens=None,
kwargs_upper_lens=None,
kwargs_fixed_lens=None,
kwargs_lower_kin=None,
kwargs_upper_kin=None,
kwargs_fixed_kin=None,
kwargs_lower_source=None,
kwargs_upper_source=None,
kwargs_fixed_source=None):
"""
:param cosmology: string describing cosmological model
:param ppn_sampling: post-newtonian parameter sampling
:param lambda_mst_sampling: bool, if True adds a global mass-sheet transform parameter in the sampling
:param lambda_mst_distribution: string, distribution function of the MST transform
:param lambda_ifu_sampling: bool, if True samples a separate lambda_mst for a second (e.g. IFU) data set
independently
:param alpha_lambda_sampling: bool, if True samples a parameter alpha_lambda, which scales lambda_mst linearly
according to a predefined quantity of the lens
:param lambda_ifu_distribution: string, distribution function of the lambda_ifu parameter
:param anisotropy_sampling: bool, if True adds a global stellar anisotropy parameter that alters the single lens
kinematic prediction
:param anisotropy_distribution: string, indicating the distribution function of the anisotropy model
:param sne_sampling: boolean, if True, samples/queries SNe unlensed magnitude distribution
(not intrinsic magnitudes but apparent!)
:param sne_distribution: string, apparent non-lensed brightness distribution (in linear space).
Currently supports:
'GAUSSIAN': Gaussian distribution
:param sigma_v_systematics: bool, if True samples paramaters relative to systematics in the velocity dispersion
measurement
:param log_scatter: boolean, if True, samples the Gaussian scatter amplitude in log space (and thus flat prior in log)
"""
self._kin_param = KinParam(
anisotropy_sampling=anisotropy_sampling,
anisotropy_model=anisotropy_model,
distribution_function=anisotropy_distribution,
log_scatter=log_scatter,
sigma_v_systematics=sigma_v_systematics,
kwargs_fixed=kwargs_fixed_kin)
self._cosmo_param = CosmoParam(cosmology=cosmology,
ppn_sampling=ppn_sampling,
kwargs_fixed=kwargs_fixed_cosmo)
self._lens_param = LensParam(
lambda_mst_sampling=lambda_mst_sampling,
lambda_mst_distribution=lambda_mst_distribution,
lambda_ifu_sampling=lambda_ifu_sampling,
lambda_ifu_distribution=lambda_ifu_distribution,
kappa_ext_sampling=kappa_ext_sampling,
kappa_ext_distribution=kappa_ext_distribution,
alpha_lambda_sampling=alpha_lambda_sampling,
log_scatter=log_scatter,
kwargs_fixed=kwargs_fixed_lens)
self._source_param = SourceParam(sne_sampling=sne_sampling,
sne_distribution=sne_distribution,
kwargs_fixed=kwargs_fixed_source)
self._kwargs_upper_cosmo, self._kwargs_lower_cosmo = kwargs_upper_cosmo, kwargs_lower_cosmo
self._kwargs_upper_lens, self._kwargs_lower_lens = kwargs_upper_lens, kwargs_lower_lens
self._kwargs_upper_kin, self._kwargs_lower_kin = kwargs_upper_kin, kwargs_lower_kin
self._kwargs_upper_source, self._kwargs_lower_source = kwargs_upper_source, kwargs_lower_source
@property
def num_param(self):
"""
number of parameters being sampled
:return: integer
"""
return len(self.param_list())
def param_list(self, latex_style=False):
"""
:param latex_style: bool, if True returns strings in latex symbols, else in the convention of the sampler
:return: list of the free parameters being sampled in the same order as the sampling
"""
list_param = []
list_param += self._cosmo_param.param_list(latex_style=latex_style)
list_param += self._lens_param.param_list(latex_style=latex_style)
list_param += self._kin_param.param_list(latex_style=latex_style)
list_param += self._source_param.param_list(latex_style=latex_style)
return list_param
def args2kwargs(self, args):
"""
:param args: sampling argument list
:return: keyword argument list with parameter names
"""
i = 0
kwargs_cosmo, i = self._cosmo_param.args2kwargs(args, i=i)
kwargs_lens, i = self._lens_param.args2kwargs(args, i=i)
kwargs_kin, i = self._kin_param.args2kwargs(args, i=i)
kwargs_source, i = self._source_param.args2kwargs(args, i=i)
return kwargs_cosmo, kwargs_lens, kwargs_kin, kwargs_source
def kwargs2args(self,
kwargs_cosmo=None,
kwargs_lens=None,
kwargs_kin=None,
kwargs_source=None):
"""
:param kwargs_cosmo: keyword argument list of parameters for cosmology sampling
:param kwargs_lens: keyword argument list of parameters for lens model sampling
:param kwargs_kin: keyword argument list of parameters for kinematic sampling
:param kwargs_source: keyword arguments of parameters of source brightness
:return: sampling argument list in specified order
"""
args = []
args += self._cosmo_param.kwargs2args(kwargs_cosmo)
args += self._lens_param.kwargs2args(kwargs_lens)
args += self._kin_param.kwargs2args(kwargs_kin)
args += self._source_param.kwargs2args(kwargs_source)
return args
def cosmo(self, kwargs_cosmo):
"""
:param kwargs_cosmo: keyword arguments of parameters (can include others not used for the cosmology)
:return: astropy.cosmology instance
"""
return self._cosmo_param.cosmo(kwargs_cosmo)
@property
def param_bounds(self):
"""
:return: argument list of the hard bounds in the order of the sampling
"""
lower_limit = self.kwargs2args(kwargs_cosmo=self._kwargs_lower_cosmo,
kwargs_lens=self._kwargs_lower_lens,
kwargs_kin=self._kwargs_lower_kin,
kwargs_source=self._kwargs_lower_source)
upper_limit = self.kwargs2args(kwargs_cosmo=self._kwargs_upper_cosmo,
kwargs_lens=self._kwargs_upper_lens,
kwargs_kin=self._kwargs_upper_kin,
kwargs_source=self._kwargs_upper_source)
return lower_limit, upper_limit
def test_raise(self):
with self.assertRaises(ValueError):
param = SourceParam(sne_apparent_m_sampling=True, sne_distribution='BAD')