tests improvements and base model for gamma_pl adapted

sibirrer · Jul 6, 2024 · d36c598 · d36c598
1 parent 314b6fb
commit d36c598
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Showing 4 changed files with 11 additions and 4 deletions.
diff --git a/hierarc/LensPosterior/base_config.py b/hierarc/LensPosterior/base_config.py
@@ -118,4 +118,5 @@ def __init__(
             gamma_in_scaling=gamma_in_scaling,
             log_m2l_scaling=log_m2l_scaling,
             gamma_pl_scaling=gamma_pl_scaling,
+            gamma_pl_mean=gamma,
         )
diff --git a/hierarc/LensPosterior/kin_scaling_config.py b/hierarc/LensPosterior/kin_scaling_config.py
@@ -13,6 +13,7 @@ def __init__(
         gamma_in_scaling=None,
         log_m2l_scaling=None,
         gamma_pl_scaling=None,
+        gamma_pl_mean=None,
     ):
         """
 
@@ -22,6 +23,7 @@ def __init__(
         :param gamma_in_scaling: array of gamma_in parameter to be interpolated (optional, otherwise None)
         :param log_m2l_scaling: array of log_m2l parameter to be interpolated (optional, otherwise None)
         :param gamma_pl_scaling: array of power-law density profile slopes to be interpolated (optional, otherwise None)
+        :param gamma_pl_mean: mean gamma_pl upon which the covariances are calculated
         """
         self._r_eff = r_eff
         self._anisotropy_model = anisotropy_model
@@ -51,6 +53,7 @@ def __init__(
         self._gamma_in_scaling = gamma_in_scaling
         self._log_m2l_scaling = log_m2l_scaling
         self._gamma_pl_scaling = gamma_pl_scaling
+        self._gamma_pl_mean = gamma_pl_mean
 
         if gamma_in_scaling is not None:
             self._param_name_list.append("gamma_in")
@@ -101,7 +104,7 @@ def kwargs_lens_base(self):
         if "log_m2l" in self._param_name_list:
             kwargs_base["log_m2l"] = np.mean(self._log_m2l_scaling)
         if "gamma_pl" in self._param_name_list:
-            kwargs_base["gamma_pl"] = np.mean(self._gamma_pl_scaling)
+            kwargs_base["gamma_pl"] = self._gamma_pl_mean
         return kwargs_base
 
     @property

diff --git a/hierarc/Likelihood/hierarchy_likelihood.py b/hierarc/Likelihood/hierarchy_likelihood.py
@@ -306,7 +306,7 @@ def log_likelihood_single(
         kwargs_kin_draw = self._aniso_distribution.draw_anisotropy(**kwargs_kin)
         kwargs_param = {**kwargs_lens_draw, **kwargs_kin_draw}
         kin_scaling = self.kin_scaling(kwargs_param)
-
+        print(kwargs_lens_draw, 'test kwargs_lens_draw')
         lnlikelihood = self.log_likelihood(
             ddt_,
             dd_,

diff --git a/test/test_LensPosterior/test_kin_constraints.py b/test/test_LensPosterior/test_kin_constraints.py
@@ -2,6 +2,7 @@
 from lenstronomy.Analysis.kinematics_api import KinematicsAPI
 from hierarc.Likelihood.hierarchy_likelihood import LensLikelihood
 import numpy.testing as npt
+import numpy as np
 import pytest
 import unittest
 
@@ -105,15 +106,17 @@ def test_likelihoodconfiguration_om(self):
             kwargs_aperture=kwargs_aperture,
             kwargs_seeing=kwargs_seeing,
             anisotropy_model=anisotropy_model,
+            gamma_pl_scaling=np.linspace(1.8, 2.2, 5),
             **kwargs_kin_api_settings
         )
 
         kwargs_likelihood = kin_constraints.hierarchy_configuration(num_sample_model=5)
         kwargs_likelihood["normalized"] = False
-        ln_class = LensLikelihood(**kwargs_likelihood)
+        ln_class = LensLikelihood(gamma_pl_index=0, **kwargs_likelihood)
         kwargs_kin = {"a_ani": 1}
+        kwargs_lens = {"gamma_pl_list": [gamma]}
         ln_likelihood = ln_class.lens_log_likelihood(
-            cosmo, kwargs_lens={}, kwargs_kin=kwargs_kin
+            cosmo, kwargs_lens=kwargs_lens, kwargs_kin=kwargs_kin
         )
         npt.assert_almost_equal(ln_likelihood, 0, decimal=1)