Time delay now tested for SIE (#307)

Time delays tested for SIE, bug fix in time delay calculation

src/caustics/lenses/base.py

@@ -1076,7 +1076,7 @@ def time_delay(
             potential = self.potential(x, y, z_s)
             TD = TD - potential
         if geometric_time_delay:
-            ax, ay = self.physical_deflection_angle(x, y, z_s)
+            ax, ay = self.reduced_deflection_angle(x, y, z_s)
             fp = 0.5 * (ax**2 + ay**2)
             TD = TD + fp
 

tests/test_sie.py

@@ -1,8 +1,8 @@
 from math import pi
 from io import StringIO
 
-import lenstronomy.Util.param_util as param_util
 import torch
+import lenstronomy.Util.param_util as param_util
 from lenstronomy.LensModel.lens_model import LensModel
 from utils import lens_test_helper
 

tests/test_time_delay.py

@@ -0,0 +1,50 @@
+import torch
+import numpy as np
+import lenstronomy.Util.param_util as param_util
+from lenstronomy.LensModel.lens_model import LensModel
+
+import caustics
+
+import pytest
+
+
+@pytest.mark.parametrize("q", [0.5, 0.7, 0.9])
+@pytest.mark.parametrize("phi", [0.0, np.pi / 3, np.pi / 2])
+@pytest.mark.parametrize("bx,by", [(0.1, -0.05), (0.2, 0.1), (0.0, 0.0)])
+def test_time_delay_pointsource(q, phi, bx, by):
+
+    # configuration parameters
+    bx = torch.tensor(bx)
+    by = torch.tensor(by)
+    z_l = torch.tensor(0.5)
+    z_s = torch.tensor(1.0)
+
+    # Define caustics lens
+    cosmo = caustics.FlatLambdaCDM(name="cosmo")
+    lens = caustics.SIE(cosmology=cosmo, z_l=z_l, x0=0.0, y0=0.0, q=q, phi=phi, b=1.0)
+    x, y = lens.forward_raytrace(bx, by, z_s)
+
+    # Define lenstronomy lens
+    lens_model_list = ["SIE"]
+    lens_ls = LensModel(
+        lens_model_list=lens_model_list, z_lens=z_l.item(), z_source=z_s.item()
+    )
+    e1, e2 = param_util.phi_q2_ellipticity(phi=phi, q=q)
+    kwargs_ls = [{"theta_E": 1.0, "e1": e1, "e2": e2, "center_x": 0.0, "center_y": 0.0}]
+
+    # Compute time delay caustics
+    tdc = lens.time_delay(x, y, z_s).detach().cpu().numpy()
+    tdc = tdc - np.min(tdc)
+    np.sort(tdc)
+
+    # Compute time delay lenstronomy
+    time_delays = lens_ls.arrival_time(
+        x.detach().cpu().numpy(),
+        y.detach().cpu().numpy(),
+        kwargs_ls,
+    )
+    time_delays = time_delays - np.min(time_delays)
+    np.sort(time_delays)
+
+    # Compare time delays
+    assert np.allclose(tdc, time_delays, atol=1e-3)