black formatted way too much

requirements.txt

@@ -1 +1 @@
-zfit >= 0.12
+zfit>=0.12

tests/test_pdf_cmsshape.py

@@ -1,5 +1,4 @@
 """Tests for CMSShape PDF."""
-
 import numpy as np
 import pytest
 import tensorflow as tf
@@ -27,17 +26,11 @@ def test_cmsshape_pdf():
     # Test PDF here
     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,
+        cmsshape_numba.pdf(90.0, beta=beta_true, gamma=gamma_true, loc=m_true).item(), rel=1e-5
     )
     np.testing.assert_allclose(
         cmsshape.pdf(tf.range(50.0, 130, 10_000)),
-        cmsshape_numba.pdf(
-            tf.range(50.0, 130, 10_000).numpy(),
-            beta=beta_true,
-            gamma=gamma_true,
-            loc=m_true,
-        ),
+        cmsshape_numba.pdf(tf.range(50.0, 130, 10_000).numpy(), beta=beta_true, gamma=gamma_true, loc=m_true),
         rtol=1e-5,
     )
     assert cmsshape.pdf(tf.range(50.0, 130, 10_000)) <= cmsshape.pdf(90.0)

tests/test_pdf_cruijff.py

@@ -26,12 +26,7 @@ def create_cruijff(mu, sigmal, alphal, sigmar, alphar, limits):
 def test_cruijff_pdf():
     # Test PDF here
     cruijff, _ = create_cruijff(
-        mu=mu_true,
-        sigmal=sigmal_true,
-        alphal=alphal_true,
-        sigmar=sigmar_true,
-        alphar=alphar_true,
-        limits=(50, 130),
+        mu=mu_true, sigmal=sigmal_true, alphal=alphal_true, sigmar=sigmar_true, alphar=alphar_true, limits=(50, 130)
     )
     assert cruijff.pdf(90.0, norm=False).numpy() == pytest.approx(
         cruijff_numba.density(
@@ -87,12 +82,7 @@ def test_cruijff_pdf():
 def test_cruijff_integral():
     # Test CDF and integral here
     cruijff, obs = create_cruijff(
-        mu=mu_true,
-        sigmal=sigmal_true,
-        alphal=alphal_true,
-        sigmar=sigmar_true,
-        alphar=alphar_true,
-        limits=(50, 130),
+        mu=mu_true, sigmal=sigmal_true, alphal=alphal_true, sigmar=sigmar_true, alphar=alphar_true, limits=(50, 130)
     )
     full_interval_numeric = cruijff.numeric_integrate(obs, norm=False).numpy()
     true_integral = 67.71494

tests/test_pdf_erfexp.py

@@ -46,25 +46,12 @@ def test_erfexp_pdf():
     )
     np.testing.assert_allclose(
         erfexp.pdf(tf.range(50.0, 130, 10_000), norm=False),
-        erfexp_numpy(
-            tf.range(50.0, 130, 10_000),
-            mu=mu_true,
-            beta=beta_true,
-            gamma=gamma_true,
-            n=n_true,
-        ),
+        erfexp_numpy(tf.range(50.0, 130, 10_000), mu=mu_true, beta=beta_true, gamma=gamma_true, n=n_true),
         rtol=1e-8,
     )
     np.testing.assert_allclose(
         erfexp.pdf(tf.range(50.0, 130, 10_000)),
-        erfexp_numpy(
-            tf.range(50.0, 130, 10_000),
-            mu=mu_true,
-            beta=beta_true,
-            gamma=gamma_true,
-            n=n_true,
-        )
-        / 71.18838,
+        erfexp_numpy(tf.range(50.0, 130, 10_000), mu=mu_true, beta=beta_true, gamma=gamma_true, n=n_true) / 71.18838,
         rtol=1e-8,
         atol=1e-8,
     )
@@ -81,13 +68,7 @@ def test_erfexp_pdf_random_params():
         erfexp, __ = create_erfexp(mu=mu_true, beta=beta_true, gamma=gamma_true, n=n_true, limits=(50, 130))
         np.testing.assert_allclose(
             erfexp.pdf(tf.range(50.0, 130, 10_000), norm=False),
-            erfexp_numpy(
-                tf.range(50.0, 130, 10_000),
-                mu=mu_true,
-                beta=beta_true,
-                gamma=gamma_true,
-                n=n_true,
-            ),
+            erfexp_numpy(tf.range(50.0, 130, 10_000), mu=mu_true, beta=beta_true, gamma=gamma_true, n=n_true),
             rtol=1e-5,
         )
 

zfit_physics/models/pdf_argus.py

@@ -1,5 +1,4 @@
 """ARGUS PDF (https://en.wikipedia.org/wiki/ARGUS_distribution)"""
-
 from typing import Optional
 
 import numpy as np

zfit_physics/models/pdf_cruijff.py

@@ -93,13 +93,7 @@ def __init__(
            the PDF for better identification.
            Has no programmatical functional purpose as identification. |@docend:pdf.init.name|
         """
-        params = {
-            "mu": mu,
-            "sigmal": sigmal,
-            "alphal": alphal,
-            "sigmar": sigmar,
-            "alphar": alphar,
-        }
+        params = {"mu": mu, "sigmal": sigmal, "alphal": alphal, "sigmar": sigmar, "alphar": alphar}
         super().__init__(obs=obs, params=params, extended=extended, norm=norm)
 
     def _unnormalized_pdf(self, x):

zfit_physics/models/pdf_example.py

@@ -2,7 +2,6 @@
 
 Create a module for each pdf that you add.
 """
-
 import zfit