Merge branch 'lenstronomy:main' into main

sibirrer · Aug 30, 2024 · 511ae21 · 511ae21
2 parents 5f83c92 + 5980c8b
commit 511ae21
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Showing 2 changed files with 27 additions and 14 deletions.
diff --git a/lenstronomy/LensModel/Profiles/cored_steep_ellipsoid.py b/lenstronomy/LensModel/Profiles/cored_steep_ellipsoid.py
@@ -46,8 +46,8 @@ class CSE(LensProfileBase):
         "s": 10000,
         "e1": 0.5,
         "e2": 0.5,
-        "center_x": -100,
-        "center_y": -100,
+        "center_x": 100,
+        "center_y": 100,
     }
 
     def __init__(self, axis="product_avg"):
@@ -175,9 +175,8 @@ class CSEMajorAxis(LensProfileBase):
         "A": 1000,
         "s": 10000,
         "q": 0.99999,
-        "e2": 0.5,
-        "center_x": -100,
-        "center_y": -100,
+        "center_x": 100,
+        "center_y": 100,
     }
 
     def function(self, x, y, a, s, q):
@@ -275,7 +274,7 @@ def function(self, x, y, a_list, s_list, q):
         :param q: axis ratio
         :return: lensing potential
         """
-        f_ = np.zeros_like(x)
+        f_ = np.zeros_like(x, dtype=float)
         for a, s in zip(a_list, s_list):
             f_ += self.major_axis_model.function(x, y, a, s, q)
         return f_
@@ -290,7 +289,7 @@ def derivatives(self, x, y, a_list, s_list, q):
         :param q: axis ratio
         :return: deflection in x- and y-direction
         """
-        f_x, f_y = np.zeros_like(x), np.zeros_like(y)
+        f_x, f_y = np.zeros_like(x, dtype=float), np.zeros_like(y, dtype=float)
         for a, s in zip(a_list, s_list):
             f_x_, f_y_ = self.major_axis_model.derivatives(x, y, a, s, q)
             f_x += f_x_
@@ -307,7 +306,11 @@ def hessian(self, x, y, a_list, s_list, q):
         :param q: axis ratio
         :return: hessian elements f_xx, f_xy, f_yx, f_yy
         """
-        f_xx, f_xy, f_yy = np.zeros_like(x), np.zeros_like(x), np.zeros_like(x)
+        f_xx, f_xy, f_yy = (
+            np.zeros_like(x, dtype=float),
+            np.zeros_like(x, dtype=float),
+            np.zeros_like(x, dtype=float),
+        )
         for a, s in zip(a_list, s_list):
             f_xx_, f_xy_, _, f_yy_ = self.major_axis_model.hessian(x, y, a, s, q)
             f_xx += f_xx_
@@ -346,9 +349,8 @@ class CSEProductAvg(LensProfileBase):
         "A": 1000,
         "s": 10000,
         "q": 0.99999,
-        "e2": 0.5,
-        "center_x": -100,
-        "center_y": -100,
+        "center_x": 100,
+        "center_y": 100,
     }
 
     def __init__(self):
@@ -425,7 +427,7 @@ def function(self, x, y, a_list, s_list, q):
         :param q: axis ratio
         :return: lensing potential
         """
-        f_ = np.zeros_like(x)
+        f_ = np.zeros_like(x, dtype=float)
         for a, s in zip(a_list, s_list):
             f_ += self.major_axis_model.function(x, y, a, s, q)
         return f_
@@ -440,7 +442,7 @@ def derivatives(self, x, y, a_list, s_list, q):
         :param q: axis ratio
         :return: deflection in x- and y-direction
         """
-        f_x, f_y = np.zeros_like(x), np.zeros_like(y)
+        f_x, f_y = np.zeros_like(x, dtype=float), np.zeros_like(y, dtype=float)
         for a, s in zip(a_list, s_list):
             f_x_, f_y_ = self.major_axis_model.derivatives(x, y, a, s, q)
             f_x += f_x_
@@ -457,7 +459,11 @@ def hessian(self, x, y, a_list, s_list, q):
         :param q: axis ratio
         :return: hessian elements f_xx, f_xy, f_yx, f_yy
         """
-        f_xx, f_xy, f_yy = np.zeros_like(x), np.zeros_like(x), np.zeros_like(x)
+        f_xx, f_xy, f_yy = (
+            np.zeros_like(x, dtype=float),
+            np.zeros_like(x, dtype=float),
+            np.zeros_like(x, dtype=float),
+        )
         for a, s in zip(a_list, s_list):
             f_xx_, f_xy_, _, f_yy_ = self.major_axis_model.hessian(x, y, a, s, q)
             f_xx += f_xx_

diff --git a/test/test_LensModel/test_Profiles/test_nfw_ellipse_cse.py b/test/test_LensModel/test_Profiles/test_nfw_ellipse_cse.py
@@ -44,6 +44,13 @@ def test_derivatives(self):
         npt.assert_almost_equal(f_x_cse_low / f_x_nfw, 1, decimal=2)
         npt.assert_almost_equal(f_y_cse_low, f_y_nfw, decimal=2)
 
+        x = 2
+        y = 2
+        f_x_nfw, f_y_nfw = self.nfw.derivatives(x, y, **kwargs)
+        f_x_cse, f_y_cse = self.nfw_cse.derivatives(x, y, e1=0, e2=0, **kwargs)
+        npt.assert_almost_equal(f_x_cse, f_x_nfw, decimal=5)
+        npt.assert_almost_equal(f_y_cse, f_y_nfw, decimal=5)
+
     def test_hessian(self):
         x = np.linspace(0.01, 2, 10)
         y = np.zeros_like(x)