Merge pull request #277 from sibirrer/main

refactoring LOS classes and split into individual LOS and population

slsim/FalsePositives/false_positive.py

@@ -1,6 +1,7 @@
 import numpy as np
 from slsim.lens import Lens
 
+
 class FalsePositive(Lens):
     """Class to manage individual false positive. Here, false positives refer to a 
     configuration that includes an elliptical galaxy at the center with blue galaxies 
@@ -12,8 +13,7 @@ def __init__(
         deflector_class,
         cosmo,
         test_area=4 * np.pi,
-        los_config=None,
-        los_dict=None,
+        los_class=None,
     ):
         """
         :param source_class: A Source class instance or list of Source class instance
@@ -23,21 +23,16 @@ def __init__(
         :param cosmo: astropy.cosmology instance
         :param test_area: area of disk around one lensing galaxies to be investigated
             on (in arc-seconds^2).
-        :param los_config: LOSConfig instance which manages line-of-sight (LOS) effects
-         and Gaussian mixture models in a simulation or analysis context.
-        :param los_dict: line of sight dictionary (optional, takes these values instead
-         of drawing from distribution) Takes "gamma" = [gamma1, gamma2] and
-         "kappa" = kappa as entries
-        :type los_dict: dict
+        :param los_class: line of sight dictionary (optional, takes these values instead of drawing from distribution)
+        :type los_class: ~LOSIndividual() class object
         """
         Lens.__init__(self,
-            source_class=source_class,
-            deflector_class=deflector_class,
-            cosmo=cosmo,
-            test_area=test_area,
-            los_config=los_config,
-            los_dict=los_dict,
-            )
+                      source_class=source_class,
+                      deflector_class=deflector_class,
+                      cosmo=cosmo,
+                      test_area=test_area,
+                      los_class=los_class,
+                      )
 
     def lenstronomy_kwargs(self, band=None):
         """Generates lenstronomy dictionary conventions for the class object.
@@ -72,5 +67,4 @@ def lenstronomy_kwargs(self, band=None):
             "kwargs_lens_light": combined_kwargs_lens_light,
             "kwargs_ps": kwargs_ps,
         }
-
-        return kwargs_model, kwargs_params
+        return kwargs_model, kwargs_params

slsim/FalsePositives/false_positive_pop.py

@@ -1,6 +1,6 @@
 from slsim.FalsePositives.false_positive import FalsePositive
-from slsim.ParamDistributions.los_config import LOSConfig
 from slsim.lens_pop import draw_test_area
+from slsim.LOS.los_pop import LOSPop
 import random
 
 
@@ -15,22 +15,22 @@ def __init__(
         elliptical_galaxy_population,
         blue_galaxy_population,
         cosmo=None,
-        los_config=None,
+        los_pop=None,
         source_number_choice=[1, 2, 3],
         weights_for_source_number=None,
         test_area_factor=1
     ):
         """
         Args:
-        :param elliptical_galaxy_population: Deflector population as an deflectors class 
+        :param elliptical_galaxy_population: Deflector population as a deflectors class
          instance.
-        :param blue_galaxy_population: Source population as an sources class inatnce.
+        :param blue_galaxy_population: Source population as a sources class inatnce.
         :param cosmo: astropy.cosmology instance
-        :param los_config: LOSConfig instance which manages line-of-sight (LOS) effects
+        :param los_pop: LOSPop instance which manages line-of-sight (LOS) effects
          and Gaussian mixture models in a simulation or analysis context.
         :param source_number_choice: A list of integers to choose source number from. If
          None, defaults to [1, 2, 3].
-        :param weights: A list of weights corresponding to the probabilities of 
+        :param weights_for_source_number: A list of weights corresponding to the probabilities of
          selecting each value in source_number_choice. If None, all choices are equally 
          likely. Defaults to None.
         :param test_area_factor: A multiplicative factor of a test_area. A test area is 
@@ -44,9 +44,9 @@ def __init__(
         self._choice = source_number_choice
         self._weights = weights_for_source_number
         self._test_area_factor = test_area_factor
-        self.los_config = los_config
+        self.los_config = los_pop
         if self.los_config is None:
-            self.los_config = LOSConfig()
+            self.los_config = LOSPop()
 
     def draw_deflector(self):
         """Draw and prepare a deflector (lens) with tolerance-based z_max.

slsim/LOS/los_individual.py

@@ -0,0 +1,38 @@
+
+
+class LOSIndividual(object):
+    """
+    class to store the quantities of an individual line of sight
+    """
+    def __init__(self, kappa=None, gamma=None):
+        """
+
+        :param gamma: [gamma1, gamma2] (takes these values if present)
+        :type gamma: list of floats
+        :param kappa: convergence (takes this values if present)
+        :type kappa: float
+        """
+        if kappa is None:
+            kappa = 0
+        if gamma is None:
+            gamma = [0, 0]
+        self._kappa = kappa
+        self._gamma = gamma
+
+    @property
+    def convergence(self):
+        """
+        line of sight convergence
+
+        :return: kappa
+        """
+        return self._kappa
+
+    @property
+    def shear(self):
+        """
+        line of sight shear
+
+        :return: gamma1, gamma2
+        """
+        return self._gamma[0], self._gamma[1]

slsim/ParamDistributions/los_config.py → slsim/LOS/los_pop.py

@@ -1,9 +1,10 @@
 from slsim.ParamDistributions.gaussian_mixture_model import GaussianMixtureModel
 from slsim.ParamDistributions.kext_gext_distributions import LineOfSightDistribution
+from slsim.LOS.los_individual import LOSIndividual
 import numpy as np
 
 
-class LOSConfig(object):
+class LOSPop(object):
     """Configuration class for setting parameters related to line-of-sight (LOS) effects
     and Gaussian mixture models in a simulation or analysis context.
 
@@ -41,8 +42,6 @@ def __init__(
         nonlinear_los_bool=False,
         nonlinear_correction_path=None,
         no_correction_path=None,
-        gamma=None,
-        kappa=None,
     ):
         """
         :param los_bool: Boolean to include line-of-sight distortions, default is True.
@@ -61,10 +60,6 @@ def __init__(
         :type nonlinear_correction_path: str or None
         :param no_correction_path: Path to the no non-linear correction distributions stored in an H5 file, default is None.
         :type no_correction_path: str or None
-        :param gamma: [gamma1, gamma2] (takes these values if present)
-        :type gamma: list of floats
-        :param kappa: convergence (takes this values if present)
-        :type kappa: float
         """
 
         self.mixgauss_gamma = mixgauss_gamma
@@ -75,39 +70,31 @@ def __init__(
         self.nonlinear_los_bool = nonlinear_los_bool
         self.nonlinear_correction_path = nonlinear_correction_path
         self.no_correction_path = no_correction_path
-        if gamma is not None:
-            self._gamma = gamma
-        if kappa is not None:
-            self._kappa = kappa
 
-    def calculate_los_linear_distortions(self, source_redshift, deflector_redshift):
-        """Calculate line-of-sight distortions in shear and convergence.
+    def draw_los(self, source_redshift, deflector_redshift):
+        """Calculate line-of-sight distortions in shear and convergence for an individual realisation.
 
         :param source_redshift: redshift of the source galaxy object.
         :type source_redshift: float
         :param deflector_redshift: redshift of the deflector galaxy object.
         :type deflector_redshift: float
-        :return: kappa, gamma1, gamma2
+        :return: LOSIndividual class instance
         """
         if not self.los_bool:
-            return 0, 0, 0
-        if hasattr(self, "_gamma") and hasattr(self, "_kappa"):
-            return self._gamma[0], self._gamma[1], self._kappa
+            return LOSIndividual(kappa=0, gamma=[0, 0])
 
         if self.mixgauss_gamma and not self.nonlinear_los_bool:
-            if not hasattr(self, "_gamma"):
                 mixture = GaussianMixtureModel(
                     means=self.mixgauss_means,
                     stds=self.mixgauss_stds,
                     weights=self.mixgauss_weights,
                 )
-                gamma = np.abs(mixture.rvs(size=1))[0]
+                gamma_abs = np.abs(mixture.rvs(size=1))[0]
                 phi = 2 * np.pi * np.random.random()
-                gamma1 = gamma * np.cos(2 * phi)
-                gamma2 = gamma * np.sin(2 * phi)
-                self._gamma = [gamma1, gamma2]
-            if not hasattr(self, "_kappa"):
-                self._kappa = np.random.normal(loc=0, scale=0.05)
+                gamma1 = gamma_abs * np.cos(2 * phi)
+                gamma2 = gamma_abs * np.sin(2 * phi)
+                gamma = [gamma1, gamma2]
+                kappa = np.random.normal(loc=0, scale=0.05)
         elif self.mixgauss_gamma and self.nonlinear_los_bool:
             raise ValueError(
                 "Can only choose one method for external shear and convergence"
@@ -119,12 +106,12 @@ def calculate_los_linear_distortions(self, source_redshift, deflector_redshift):
                 nonlinear_correction_path=self.nonlinear_correction_path,
                 no_correction_path=self.no_correction_path,
             )
-            gamma, self._kappa = LOS.get_kappa_gamma(
+            gamma_abs, kappa = LOS.get_kappa_gamma(
                 z_source, z_lens, self.nonlinear_los_bool
             )
             phi = 2 * np.pi * np.random.random()
-            gamma1 = gamma * np.cos(2 * phi)
-            gamma2 = gamma * np.sin(2 * phi)
-            self._gamma = [gamma1, gamma2]
+            gamma1 = gamma_abs * np.cos(2 * phi)
+            gamma2 = gamma_abs * np.sin(2 * phi)
+            gamma = [gamma1, gamma2]
 
-        return self._gamma[0], self._gamma[1], self._kappa
+        return LOSIndividual(kappa=kappa, gamma=gamma)

slsim/lens.py

@@ -6,15 +6,16 @@
 from lenstronomy.LensModel.Solver.lens_equation_solver import (
     analytical_lens_model_support,
 )
-from slsim.ParamDistributions.los_config import LOSConfig
 from slsim.Util.param_util import ellipticity_slsim_to_lenstronomy
+from slsim.LOS.los_individual import LOSIndividual
 from lenstronomy.LightModel.light_model import LightModel
 from lenstronomy.Util import constants
 from lenstronomy.Util import data_util
 from lenstronomy.Util import util
 
 from slsim.lensed_system_base import LensedSystemBase
 
+
 class Lens(LensedSystemBase):
     """Class to manage individual lenses."""
 
@@ -26,8 +27,7 @@ def __init__(
         lens_equation_solver="lenstronomy_analytical",
         test_area=4 * np.pi,
         magnification_limit=0.01,
-        los_config=None,
-        los_dict=None,
+        los_class=None,
     ):
         """
 
@@ -63,11 +63,8 @@ def __init__(
         :param magnification_limit: absolute lensing magnification lower limit to
             register a point source (ignore highly de-magnified images)
         :type magnification_limit: float >= 0
-        :param los_config: LOSConfig instance which manages line-of-sight (LOS) effects
-         and Gaussian mixture models in a simulation or analysis context.
-        :param los_dict: line of sight dictionary (optional, takes these values instead of drawing from distribution)
-         Takes "gamma" = [gamma1, gamma2] and "kappa" = kappa as entries
-        :type los_dict: dict
+        :param los_class: line of sight dictionary (optional, takes these values instead of drawing from distribution)
+        :type los_class: ~LOSIndividual() class object
         """
         self.deflector = deflector_class
         self.cosmo = cosmo
@@ -77,7 +74,7 @@ def __init__(
 
         if isinstance(source_class, list):
             self.source = source_class
-            # choose a highest resdshift source to use conventionally use in lens
+            # chose a highest resdshift source to use conventionally use in lens
             #  mass model.
             self.max_redshift_source_class = max(
                 self.source, key=lambda obj: obj.redshift)
@@ -101,13 +98,9 @@ def __init__(
             z_source=float(self.max_redshift_source_class.redshift),
             cosmo=self.cosmo,
         )
-
-        self._los_linear_distortions_cache = None
-        self.los_config = los_config
-        if self.los_config is None:
-            if los_dict is None:
-                los_dict = {}
-            self.los_config = LOSConfig(**los_dict)
+        if los_class is None:
+            los_class = LOSIndividual()
+        self.los_class = los_class
 
     @property
     def image_number(self):
@@ -361,22 +354,31 @@ def source_redshift_list(self):
             source_redshifts.append(source.redshift)
         return source_redshifts
 
+    @property
+    def los_linear_distortions(self):
+        """Line-of-sight distortions in shear and convergence.
+
+        :return: kappa, gamma1, gamma2
+        """
+        kappa = self.los_class.convergence
+        gamma1, gamma2 = self.los_class.shear
+        return kappa, gamma1, gamma2
+
     @property
     def external_convergence(self):
         """
 
         :return: external convergence
         """
-        _, _, kappa_ext = self.los_linear_distortions
-        return kappa_ext
+        return self.los_class.convergence
 
     @property
     def external_shear(self):
         """
 
         :return: the absolute external shear
         """
-        gamma1, gamma2, _ = self.los_linear_distortions
+        gamma1, gamma2 = self.los_class.shear
         return (gamma1**2 + gamma2**2) ** 0.5
 
     @property
@@ -407,7 +409,7 @@ def _einstein_radius(self, source):
                             z_source=float(source.redshift),
                             cosmo=self.cosmo,
                         )
-            _, _, kappa_ext = self.los_linear_distortions
+            kappa_ext = self.los_class.convergence
             gamma_pl = self.deflector.halo_properties
             theta_E = _lens_cosmo.sis_sigma_v2theta_E(
                 float(self.deflector.velocity_dispersion(cosmo=self.cosmo))
@@ -449,24 +451,6 @@ def deflector_velocity_dispersion(self):
         """
         return self.deflector.velocity_dispersion(cosmo=self.cosmo)
 
-    @property
-    def los_linear_distortions(self):
-        if self._los_linear_distortions_cache is None:
-            self._los_linear_distortions_cache = (
-                self._calculate_los_linear_distortions()
-            )
-        return self._los_linear_distortions_cache
-
-    def _calculate_los_linear_distortions(self):
-        """Line-of-sight distortions in shear and convergence.
-
-        :return: kappa, gamma1, gamma2
-        """
-        return self.los_config.calculate_los_linear_distortions(
-            source_redshift=self.max_redshift_source_class.redshift,
-            deflector_redshift=self.deflector_redshift,
-        )
-
     def deflector_magnitude(self, band):
         """Apparent magnitude of the deflector for a given band.