fix: add mass*width factor to relBW w/o ff

src/tensorwaves/physics/helicity_formalism/amplitude.py

@@ -418,10 +418,15 @@ def __call__(self, dataset: dict) -> tf.Tensor:
         return self._call_wrapper(dataset)
 
     def _without_form_factor(self, dataset: dict) -> tf.Tensor:
-        return relativistic_breit_wigner(
-            dataset[self._dynamics_props.inv_mass_name],
-            self._dynamics_props.resonance_mass,
-            self._dynamics_props.resonance_width,
+        mass0 = self._dynamics_props.resonance_mass
+        gamma0 = self._dynamics_props.resonance_width
+        return (
+            relativistic_breit_wigner(
+                dataset[self._dynamics_props.inv_mass_name],
+                self._dynamics_props.resonance_mass,
+                self._dynamics_props.resonance_width,
+            )
+            * atfi.complex(mass0 * gamma0, atfi.const(0.0))
         )
 
     def _with_form_factor(self, dataset: dict) -> tf.Tensor:

tests/data/test_generate.py

@@ -1,3 +1,6 @@
+# cspell:ignore tolist
+from pprint import pprint
+
 import numpy as np
 import pytest
 
@@ -11,27 +14,29 @@
 
 def test_generate_data(data_sample: np.ndarray):
     sub_sample = data_sample[:, :5, :]
+    print("Expected list, get by running pytest with the -s flag")
+    pprint(np.round(sub_sample, decimals=11).tolist())
     assert pytest.approx(sub_sample) == [
         [
-            [-0.802315863797, 0.178700594344, -1.124458521281, 1.392857355870],
-            [-0.893880825493, 0.014200873801, 0.794094652102, 1.195747009822],
-            [1.095206560829, -0.407826696013, -0.712210827094, 1.368592082066],
-            [-0.111597827921, -0.855676181548, 1.088009508016, 1.388668604239],
-            [0.494199311030, -0.459114111102, -0.652173184220, 0.938268931731],
+            [-1.11655564285, -0.65098757279, -0.29889930779, 1.32658287329],
+            [0.6614259842, -0.67081160216, 0.91253074716, 1.31155819645],
+            [-0.89388082549, 0.0142008738, 0.7940946521, 1.19574700982],
+            [1.09520656083, -0.40782669601, -0.71221082709, 1.36859208207],
+            [-0.91362335417, 0.65774093748, -0.03330470173, 1.12625040612],
         ],
         [
-            [-0.013664538258, -0.063677496239, 0.790709134631, 0.804786440778],
-            [-0.047509140382, 0.019741853435, -0.928869183224, 0.940033805392],
-            [-0.671293601965, 0.030434499723, 0.885454524023, 1.119736491083],
-            [-0.108188868818, 0.073259826346, 0.136621115257, 0.232284091501],
-            [-0.053620536930, 0.820055795500, -0.423278811086, 0.934211088656],
+            [0.99646172468, 0.95461505668, 0.29199575453, 1.4169354722],
+            [-0.12901200642, 0.8756471174, -0.77276118186, 1.18270053384],
+            [-0.04750914038, 0.01974185344, -0.92886918322, 0.94003380539],
+            [-0.67129360197, 0.03043449972, 0.88545452402, 1.11973649108],
+            [0.68138748966, -1.08237744503, -0.3566346599, 1.33462985945],
         ],
         [
-            [0.815980402056, -0.115023098105, 0.333749386650, 0.899256203351],
-            [0.941389965876, -0.033942727236, 0.134774531122, 0.961119184785],
-            [-0.423912958864, 0.377392196290, -0.173243696929, 0.608571426849],
-            [0.219786696740, 0.782416355201, -1.224630623273, 1.475947304258],
-            [-0.440578774100, -0.360941684398, 1.075451995307, 1.224419979611],
+            [0.12009391817, -0.3036274839, 0.00690355325, 0.35338165451],
+            [-0.53241397778, -0.20483551524, -0.1397695653, 0.60264126971],
+            [0.94138996588, -0.03394272724, 0.13477453112, 0.96111918479],
+            [-0.42391295886, 0.37739219629, -0.17324369693, 0.60857142685],
+            [0.2322358645, 0.42463650754, 0.38993936163, 0.63601973443],
         ],
     ]
 

tests/optimizer/test_minuit.py

@@ -17,14 +17,14 @@ def test_optimize(fit_result: dict, free_parameters: dict):
         par_values = result["parameter_values"]
         par_errors = result["parameter_errors"]
         assert set(par_values) == set(free_parameters)
-        assert pytest.approx(result["log_likelihood"]) == -19182.154685316204
-        assert pytest.approx(par_values["Width_f(0)(500)"]) == 0.58052984362816
-        assert pytest.approx(par_errors["Width_f(0)(500)"]) == 0.01413164631479
+        assert pytest.approx(result["log_likelihood"]) == -12961.752837852626
+        assert pytest.approx(par_values["Width_f(0)(500)"]) == 0.5546203338476
+        assert pytest.approx(par_errors["Width_f(0)(500)"]) == 0.00981961384643
         assert (
             pytest.approx(par_values["Position_f(0)(980)"])
-            == 0.9895840830306458
+            == 0.9897319832688249
         )
         assert (
             pytest.approx(par_errors["Position_f(0)(980)"])
-            == 0.0004395998899025785
+            == 0.0007052210121507604
         )