Test the volscale line, even if we're only assuming a volume scale of…

… 1.0.
icecube · Aug 29, 2024 · a14a719 · a14a719
1 parent 5398f42
commit a14a719
Showing 1 changed file with 41 additions and 34 deletions.
diff --git a/tests/test_genie_weighter.py b/tests/test_genie_weighter.py
@@ -33,42 +33,49 @@ def test_genie(self):
         c1 = simweights.CircleInjector(300, 0, 1)
         p1 = simweights.PowerLaw(0, 1e3, 1e4)
 
-        weight = np.zeros(nevents, dtype=result_dtype)
-        weight["neu"] = pdgid
-        weight["pzv"] = coszen
-        weight["Ev"] = p1.ppf(np.linspace(0, 1, nevents))
-
         for event_weight in [1e-6, 1e-3, 1]:
-            weight["wght"] = event_weight
-
             for nfiles in [1, 5, 50]:
-                rows = nfiles * [
-                    (
-                        pdgid,
-                        nevents,
-                        1,
-                        c1.radius,
-                        np.arccos(c1.cos_zen_max),
-                        np.arccos(c1.cos_zen_min),
-                        p1.a,
-                        p1.b,
-                        p1.g,
-                    ),
-                ]
-                info = np.array(rows, dtype=info_dtype)
-                d = {"I3GenieResult": weight, "I3GenieInfo": info}
-
-                for flux in [0.1, 1, 10]:
-                    wobj = simweights.GenieWeighter(d)
-                    w = wobj.get_weights(flux)
-                    np.testing.assert_allclose(
-                        w.sum(),
-                        flux * event_weight * c1.etendue * p1.integral / nfiles,
-                    )
-                    E = d["I3GenieResult"]["Ev"]
-                    y, x = np.histogram(E, weights=w, bins=51, range=[p1.a, p1.b])
-                    Ewidth = np.ediff1d(x)
-                    np.testing.assert_allclose(y, flux * event_weight * Ewidth * c1.etendue / nfiles, 5e-3)
+                for include_volscale in [True, False]:
+                    result_dtype = [("neu", np.int32), ("pzv", np.float64), ("Ev", np.float64), ("wght", np.float64)]
+                    if include_volscale:
+                        result_dtype.append(("volscale", np.float64))
+
+                    weight = np.zeros(nevents, dtype=result_dtype)
+                    weight["neu"] = pdgid
+                    weight["pzv"] = coszen
+                    weight["Ev"] = p1.ppf(np.linspace(0, 1, nevents))
+                    weight["wght"] = event_weight
+
+                    if include_volscale:
+                        weight["volscale"] = 1
+
+                    rows = nfiles * [
+                        (
+                            pdgid,
+                            nevents,
+                            1,
+                            c1.radius,
+                            np.arccos(c1.cos_zen_max),
+                            np.arccos(c1.cos_zen_min),
+                            p1.a,
+                            p1.b,
+                            p1.g,
+                        ),
+                    ]
+                    info = np.array(rows, dtype=info_dtype)
+                    d = {"I3GenieResult": weight, "I3GenieInfo": info}
+
+                    for flux in [0.1, 1, 10]:
+                        wobj = simweights.GenieWeighter(d)
+                        w = wobj.get_weights(flux)
+                        np.testing.assert_allclose(
+                            w.sum(),
+                            flux * event_weight * c1.etendue * p1.integral / nfiles,
+                        )
+                        E = d["I3GenieResult"]["Ev"]
+                        y, x = np.histogram(E, weights=w, bins=51, range=[p1.a, p1.b])
+                        Ewidth = np.ediff1d(x)
+                        np.testing.assert_allclose(y, flux * event_weight * Ewidth * c1.etendue / nfiles, 5e-3)
 
         with self.assertRaises(TypeError):
             simweights.GenieWeighter(d, nfiles=10)