Added angular binning option to recipes [version:1.0.3]

crow/__init__.py

@@ -8,4 +8,4 @@
 from .recipes.binned_grid import GridBinnedClusterRecipe
 from .recipes.binned_parent import BinnedClusterRecipe
 
-__version__ = "1.0.2"
+__version__ = "1.0.4"

crow/recipes/binned_exact.py

@@ -308,9 +308,10 @@ def evaluate_theory_prediction_lensing_profile(
         self,
         z_edges,
         log_proxy_edges,
-        radius_centers,
+        distance_centers,
         sky_area: float,
         average_on: None | ClusterProperty = None,
+        distance_units: str = "mpc",
     ) -> float:
         """Evaluate the theory prediction for this cluster recipe.
 
@@ -320,7 +321,20 @@ def evaluate_theory_prediction_lensing_profile(
         """
         assert len(log_proxy_edges) == 2, "log_proxy_edges should be size 2"
         assert len(z_edges) == 2, "z_edges should be size 2"
-
+        if distance_units.lower() == "arcmin":
+            distance_centers_rad = distance_centers * np.pi / (180.0 * 60.0)
+            z_bin_mean = (z_edges[1] + z_edges[0]) / 2.0
+            a = 1.0 / (1.0 + z_bin_mean)
+            radius_centers = (
+                self.cluster_theory.cosmo.angular_diameter_distance(a)
+                * distance_centers_rad
+            )
+        elif distance_units.lower() == "mpc":
+            radius_centers = distance_centers
+        else:
+            raise ValueError(
+                f"Unknown distance_units='{distance_units}'. Expected 'arcmin' or 'mpc'."
+            )
         if self.purity == None:
             self.integrator.integral_bounds = [
                 self.mass_interval,

crow/recipes/binned_grid.py

@@ -357,14 +357,28 @@ def evaluate_theory_prediction_lensing_profile(
         self,
         z_edges: tuple[float, float],
         log_proxy_edges: tuple[float, float],
-        radius_centers: np.ndarray,
+        distance_centers: np.ndarray,
         sky_area: float,
         average_on: None | ClusterProperty = None,
+        distance_units: str = "mpc",
     ) -> float:
         r"""Evaluate the theoretical prediction for the average lensing profile
         (..:math:`\langle\Delta\Sigma(R)\rangle` or ..:math:`\langle g_t(R)\rangle`)
         in the provided bin."""
-
+        if distance_units.lower() == "arcmin":
+            distance_centers_rad = distance_centers * np.pi / (180.0 * 60.0)
+            z_bin_mean = (z_edges[1] + z_edges[0]) / 2.0
+            a = 1.0 / (1.0 + z_bin_mean)
+            radius_centers = (
+                self.cluster_theory.cosmo.angular_diameter_distance(a)
+                * distance_centers_rad
+            )
+        elif distance_units.lower() == "mpc":
+            radius_centers = distance_centers
+        else:
+            raise ValueError(
+                f"Unknown distance_units='{distance_units}'. Expected 'arcmin' or 'mpc'."
+            )
         if not (average_on & (ClusterProperty.DELTASIGMA | ClusterProperty.SHEAR)):
             # Raise a ValueError if the necessary flags are not present
             raise ValueError(

crow/recipes/binned_parent.py

@@ -97,9 +97,10 @@ def evaluate_theory_prediction_lensing_profile(
         self,
         z_edges,
         mass_proxy_edges,
-        radius_centers,
+        distance_centers,
         sky_area: float,
         average_on: None | ClusterProperty = None,
+        distance_units: str = "mpc",
     ) -> float:
         """Evaluate the theory prediction for this cluster recipe.
 

tests/test_recipe_binned_shear_profile.py

@@ -255,6 +255,58 @@ def test_evaluates_theory_prediction_returns_value(
     assert prediction_c > 0
 
 
+def test_evaluates_theory_prediction_different_bins(
+    binned_exact_deltasigma: ExactBinnedClusterRecipe,
+    binned_grid_gt: GridBinnedClusterRecipe,
+):
+
+    mass_proxy_edges = (2, 5)
+    mass_proxy_edges_err = (2, 5, 6)
+    z_edges = (0.5, 1)
+    z_edges_err = (0.5, 1.0, 1.2)
+    radius_center = np.atleast_1d(1.5)
+    sky_area = 360**2
+    average_on = ClusterProperty.DELTASIGMA
+
+    z_bin_mean = (z_edges[1] + z_edges[0]) / 2.0
+    a = 1.0 / (1.0 + z_bin_mean)
+    angular_center_arcmin = (
+        radius_center
+        / binned_exact_deltasigma.cluster_theory.cosmo.angular_diameter_distance(a)
+        * 180.0
+        / np.pi
+        * 60.0
+    )
+    # test exact deltasigma
+    prediction_rad = binned_exact_deltasigma.evaluate_theory_prediction_lensing_profile(
+        z_edges, mass_proxy_edges, radius_center, sky_area, average_on, "mpc"
+    )
+    prediction_ang = binned_exact_deltasigma.evaluate_theory_prediction_lensing_profile(
+        z_edges, mass_proxy_edges, angular_center_arcmin, sky_area, average_on, "arcmin"
+    )
+    rel_tol = 1.0e-4
+    assert np.all(np.abs(prediction_rad - prediction_ang) < rel_tol)
+    # test grid reduced shear
+    average_on = ClusterProperty.SHEAR
+    prediction_rad = binned_grid_gt.evaluate_theory_prediction_lensing_profile(
+        z_edges, mass_proxy_edges, radius_center, sky_area, average_on, "mpc"
+    )
+    prediction_ang = binned_grid_gt.evaluate_theory_prediction_lensing_profile(
+        z_edges, mass_proxy_edges, angular_center_arcmin, sky_area, average_on, "arcmin"
+    )
+    rel_tol = 1.0e-4
+    assert np.all(np.abs(prediction_rad - prediction_ang) < rel_tol)
+
+    with pytest.raises(ValueError, match="Unknown distance_units='rad'"):
+        binned_exact_deltasigma.evaluate_theory_prediction_lensing_profile(
+            z_edges, mass_proxy_edges, radius_center, sky_area, average_on, "rad"
+        )
+    with pytest.raises(ValueError, match="Unknown distance_units='rad'"):
+        binned_grid_gt.evaluate_theory_prediction_lensing_profile(
+            z_edges, mass_proxy_edges, radius_center, sky_area, average_on, "rad"
+        )
+
+
 def test_grid_shear_matches_exact_within_tolerance(
     binned_exact_deltasigma: ExactBinnedClusterRecipe,
     binned_grid_deltasigma: GridBinnedClusterRecipe,