put m parameter first

tests/test_pdf_cmsshape.py

@@ -11,20 +11,20 @@
 import zfit_physics as zphys
 
 # specify globals here. Do NOT add any TensorFlow but just pure python
+m_true = 90.0
 beta_true = 0.2
 gamma_true = 0.3
-m_true = 90.0
 
 
-def create_cmsshape(gamma, beta, m, limits):
+def create_cmsshape(m, beta, gamma, limits):
     obs = zfit.Space("obs1", limits)
-    cmsshape = zphys.pdf.CMSShape(gamma=gamma, beta=beta, m=m, obs=obs)
+    cmsshape = zphys.pdf.CMSShape(m=m, beta=beta, gamma=gamma, obs=obs)
     return cmsshape, obs
 
 
 def test_cmsshape_pdf():
     # Test PDF here
-    cmsshape, _ = create_cmsshape(gamma=gamma_true, beta=beta_true, m=m_true, limits=(50, 130))
+    cmsshape, _ = create_cmsshape(m=m_true, beta=beta_true, gamma=gamma_true, limits=(50, 130))
     assert zfit.run(cmsshape.pdf(90.0)) == pytest.approx(
         cmsshape_numba.pdf(90.0, beta=beta_true, gamma=gamma_true, loc=m_true).item(), rel=1e-5
     )
@@ -43,7 +43,7 @@ def test_cmsshape_pdf():
 
 def test_cmsshape_integral():
     # Test CDF and integral here
-    cmsshape, obs = create_cmsshape(gamma=gamma_true, beta=beta_true, m=m_true, limits=(50, 130))
+    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))
     true_integral = 0.99999
@@ -66,10 +66,11 @@ def test_cmsshape_integral():
 
 # register the pdf here and provide sets of working parameter configurations
 def cmsshape_params_factory():
+    m = zfit.Parameter("m", m_true)
     beta = zfit.Parameter("beta", beta_true)
     gamma = zfit.Parameter("gamma", gamma_true)
-    m = zfit.Parameter("m", m_true)
-    return {"beta": beta, "gamma": gamma, "m": m}
+
+    return {"m": m, "beta": beta, "gamma": gamma}
 
 
 tester.register_pdf(pdf_class=zphys.pdf.CMSShape, params_factories=cmsshape_params_factory)

zfit_physics/models/pdf_cmsshape.py

@@ -8,14 +8,14 @@
 
 
 @z.function(wraps="tensor")
-def cmsshape_pdf_func(x, beta, gamma, m):
+def cmsshape_pdf_func(x, m, beta, gamma):
     """Calculate the CMSShape PDF.
 
     Args:
         x: value(s) for which the PDF will be calculated.
+        m: approximate center of the disribution.
         beta: steepness of the error function.
         gamma: steepness of the exponential distribution.
-        m: approximate center of the distribution.
 
     Returns:
         `tf.Tensor`: The calculated PDF values.
@@ -44,14 +44,14 @@ def cmsshape_pdf_func(x, beta, gamma, m):
 
 
 @z.function(wraps="tensor")
-def cmsshape_cdf_func(x, beta, gamma, m):
+def cmsshape_cdf_func(x, m, beta, gamma):
     """Analtical function for the CDF of the CMSShape distribution.
 
     Args:
         x: value(s) for which the CDF will be calculated.
+        m: approximate center of the distribution.
         beta: steepness of the error function.
         gamma: steepness of the exponential distribution.
-        m: approximate center of the distribution.
 
     Returns:
         `tf.Tensor`: The calculated CDF values.
@@ -81,11 +81,11 @@ def cmsshape_integral(limits: ztyping.SpaceType, params: dict, model) -> tf.Tens
         The calculated integral.
     """
     lower, upper = limits.limit1d
+    m = params["m"]
     beta = params["beta"]
     gamma = params["gamma"]
-    m = params["m"]
-    lower_cdf = cmsshape_cdf_func(x=lower, beta=beta, gamma=gamma, m=m)
-    upper_cdf = cmsshape_cdf_func(x=upper, beta=beta, gamma=gamma, m=m)
+    lower_cdf = cmsshape_cdf_func(x=lower, m=m, beta=beta, gamma=gamma)
+    upper_cdf = cmsshape_cdf_func(x=upper, m=m, beta=beta, gamma=gamma)
     return upper_cdf - lower_cdf
 
 
@@ -94,9 +94,9 @@ class CMSShape(zfit.pdf.BasePDF):
 
     def __init__(
         self,
+        m: ztyping.ParamTypeInput,
         beta: ztyping.ParamTypeInput,
         gamma: ztyping.ParamTypeInput,
-        m: ztyping.ParamTypeInput,
         obs: ztyping.ObsTypeInput,
         *,
         extended: Optional[ztyping.ExtendedInputType] = None,
@@ -116,9 +116,9 @@ def __init__(
         and [numba-stats](https://github.com/HDembinski/numba-stats/blob/main/src/numba_stats/cmsshape.py)
 
         Args:
+            m: Approximate center of the distribution.
             beta: Steepness of the error function.
             gamma: Steepness of the exponential distribution.
-            m: Approximate center of the distribution.
             obs: |@doc:pdf.init.obs| Observables of the
                model. This will be used as the default space of the PDF and,
                if not given explicitly, as the normalization range.
@@ -140,14 +140,14 @@ def __init__(
                the PDF for better identification.
                Has no programmatical functional purpose as identification. |@docend:pdf.init.name|
         """
-        params = {"beta": beta, "gamma": gamma, "m": m}
+        params = {"m": m, "beta": beta, "gamma": gamma}
         super().__init__(obs=obs, params=params, name=name, extended=extended, norm=norm)
 
     def _unnormalized_pdf(self, x: tf.Tensor) -> tf.Tensor:
+        m = self.params["m"]
         beta = self.params["beta"]
         gamma = self.params["gamma"]
-        m = self.params["m"]
-        return cmsshape_pdf_func(x, beta, gamma, m)
+        return cmsshape_pdf_func(x=x, m=m, beta=beta, gamma=gamma)
 
 
 cmsshape_integral_limits = Space(axes=(0,), limits=(((ANY_LOWER,),), ((ANY_UPPER,),)))