Rename 'norm_range' to 'norm' and fix argus integration

Updated the usage and references of 'norm_range' to 'norm' across the codebase for consistency. Added detailed checks in the ARGUS PDF model to handle specific parameter cases and enhanced the test functions to reflect these changes.

src/zfit_physics/models/pdf_argus.py

@@ -69,7 +69,7 @@ def __init__(
             \mathrm{Argus}(m, m_0, c, p) = m \cdot \left[ 1 - \left( \frac{m}{m_0} \right)^2 \right]^p
             \cdot \exp\left[ c \cdot \left(1 - \left(\frac{m}{m_0}\right)^2 \right) \right]
 
-        and normalized to one over the `norm_range` (which defaults to `obs`).
+        and normalized to one over the `norm` (which defaults to `obs`).
 
         The implementation follows the `RooFit version <https://root.cern.ch/doc/master/classRooArgusBG.html>`_
 
@@ -114,19 +114,36 @@ def __init__(
         Returns:
             `tf.Tensor`: the values matching the (broadcasted) shapes of the input
         """
+
         params = {"m0": m0, "c": c, "p": p}
         super().__init__(obs=obs, name=name, params=params, extended=extended, norm=norm, label=label)
-
-    _N_OBS = 1
-
-    @zfit.supports()
-    def _unnormalized_pdf(self, x, params):
+        if isinstance(p, zfit.param.ConstantParameter):
+            p_is_half = np.isclose(p.static_value, 0.5)
+        else:
+            try:
+                p_is_half = np.isclose(p, 0.5)
+            except NotImplementedError:
+                p_is_half = False  # we cannot know, unfortunately
+
+        if isinstance(c, zfit.param.ConstantParameter):
+            c_is_negative = c.static_value < 0
+        else:
+            try:
+                c_is_negative = c < 0
+            except NotImplementedError:
+                c_is_negative = False
+
+        self._argus_p_is_half = p_is_half
+        self._argus_c_is_positive = c_is_negative
+
+    @zfit.supports(norm=False)
+    def _pdf(self, x, norm, params):
         """
         Calculation of ARGUS PDF value
         (Docs: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.argus.html)
         """
         m = x[0]
-
+        del norm
         m0 = params["m0"]
         c = params["c"]
         p = params["p"]
@@ -191,12 +208,10 @@ def argus_integral_p_half_func(lower, upper, c, m0):
 
 
 def argus_integral_p_half(limits, params, model):
-    del model
-    p = params["p"]
-    if not isinstance(p, zfit.param.ConstantParameter) or not np.isclose(p.static_value, 0.5):
-        raise zfit.exception.AnalyticIntegralNotImplementedError()
     c = params["c"]
-    if not isinstance(c, zfit.param.ConstantParameter) or c.static_value > 0:
+    if not model._argus_p_is_half:
+        raise zfit.exception.AnalyticIntegralNotImplementedError()
+    if not model._argus_c_is_positive:
         raise zfit.exception.AnalyticIntegralNotImplementedError()
 
     m0 = params["m0"]

src/zfit_physics/models/pdf_kde.py

@@ -118,5 +118,5 @@ def dist_kwargs():
         )
 
     # @zfit.supports()
-    # def _analytic_integrate(self, limits, norm_range):
+    # def _analytic_integrate(self, limits, norm):
     #     raise AnalyticIntegralNotImplementedError

tests/test_pdf_argus.py

@@ -14,7 +14,7 @@
 param2_true = 1.2
 
 
-def test_standard():
+def test_standard_argus():
     # test special properties  here
     obs = zfit.Space("obs1", (-2, 6))
 
@@ -24,8 +24,8 @@ def test_standard():
     upper = 5.0
     argus_pdf = argus.pdf(tf.linspace(lower, upper, 1000001))
     assert pytest.approx(zfit.run(tf.reduce_mean(argus_pdf) * (upper - lower)), 4e-2) == 1.0
-    analytic_integral = zfit.run(argus.analytic_integrate(obs, norm_range=False))
-    numeric_integral = zfit.run(argus.numeric_integrate(obs, norm_range=False))
+    analytic_integral = zfit.run(argus.analytic_integrate(obs, norm=False))
+    numeric_integral = zfit.run(argus.numeric_integrate(obs, norm=False))
     assert pytest.approx(analytic_integral, 4e-2) == numeric_integral
 
 

tests/test_pdf_cmsshape.py

@@ -44,8 +44,8 @@ def test_cmsshape_pdf():
 def test_cmsshape_integral():
     # Test CDF and integral here
     cmsshape, obs = create_cmsshape(m=m_true, beta=beta_true, gamma=gamma_true, limits=(50, 130))
-    full_interval_analytic = zfit.run(cmsshape.analytic_integrate(obs, norm_range=False))
-    full_interval_numeric = zfit.run(cmsshape.numeric_integrate(obs, norm_range=False))
+    full_interval_analytic = zfit.run(cmsshape.analytic_integrate(obs, norm=False))
+    full_interval_numeric = zfit.run(cmsshape.numeric_integrate(obs, norm=False))
     true_integral = 0.99999
     numba_stats_full_integral = cmsshape_numba.cdf(
         130, beta=beta_true, gamma=gamma_true, loc=m_true
@@ -55,8 +55,8 @@ def test_cmsshape_integral():
     assert full_interval_analytic == pytest.approx(numba_stats_full_integral, 1e-8)
     assert full_interval_numeric == pytest.approx(numba_stats_full_integral, 1e-8)
 
-    analytic_integral = zfit.run(cmsshape.analytic_integrate(limits=(80, 100), norm_range=False))
-    numeric_integral = zfit.run(cmsshape.numeric_integrate(limits=(80, 100), norm_range=False))
+    analytic_integral = zfit.run(cmsshape.analytic_integrate(limits=(80, 100), norm=False))
+    numeric_integral = zfit.run(cmsshape.numeric_integrate(limits=(80, 100), norm=False))
     numba_stats_integral = cmsshape_numba.cdf(100, beta=beta_true, gamma=gamma_true, loc=m_true) - cmsshape_numba.cdf(
         80, beta=beta_true, gamma=gamma_true, loc=m_true
     )

tests/test_pdf_relbw.py

@@ -34,14 +34,14 @@ def test_relbw_pdf():
 def test_relbw_integral():
     # Test CDF and integral here
     relbw, obs = create_relbw(m_true, gamma_true, limits=(0, 200))
-    full_interval_analytic = zfit.run(relbw.analytic_integrate(obs, norm_range=False))
-    full_interval_numeric = zfit.run(relbw.numeric_integrate(obs, norm_range=False))
+    full_interval_analytic = zfit.run(relbw.analytic_integrate(obs, norm=False))
+    full_interval_numeric = zfit.run(relbw.numeric_integrate(obs, norm=False))
     true_integral = 0.99888
     assert full_interval_analytic == pytest.approx(true_integral, 1e-4)
     assert full_interval_numeric == pytest.approx(true_integral, 1e-2)
 
-    analytic_integral = zfit.run(relbw.analytic_integrate(limits=(50, 100), norm_range=False))
-    numeric_integral = zfit.run(relbw.numeric_integrate(limits=(50, 100), norm_range=False))
+    analytic_integral = zfit.run(relbw.analytic_integrate(limits=(50, 100), norm=False))
+    numeric_integral = zfit.run(relbw.numeric_integrate(limits=(50, 100), norm=False))
     assert analytic_integral == pytest.approx(numeric_integral, 0.01)