Revert "Merge branch 'feat_fixture'"

This reverts commit 807915f.

src/alchemlyb/__init__.py

@@ -54,8 +54,6 @@ def concat(objs, *args, **kwargs):
 
 
     .. versionadded:: 0.5.0"""
-    if isinstance(objs, pd.DataFrame):
-        return objs
     # Sanity check
     try:
         attrs = objs[0].attrs

src/alchemlyb/tests/test_convergence.py

@@ -8,8 +8,17 @@
 from alchemlyb.parsing import gmx
 
 
-def test_convergence_ti(gmx_benzene_Coulomb_dHdl):
-    convergence = forward_backward_convergence(gmx_benzene_Coulomb_dHdl, "TI")
+@pytest.fixture()
+def gmx_benzene():
+    dataset = load_benzene()
+    return [gmx.extract_dHdl(dhdl, T=300) for dhdl in dataset["data"]["Coulomb"]], [
+        gmx.extract_u_nk(dhdl, T=300) for dhdl in dataset["data"]["Coulomb"]
+    ]
+
+
+def test_convergence_ti(gmx_benzene):
+    dHdl, u_nk = gmx_benzene
+    convergence = forward_backward_convergence(dHdl, "TI")
     assert convergence.shape == (10, 5)
 
     assert convergence.loc[0, "Forward"] == pytest.approx(3.07, 0.01)
@@ -19,29 +28,31 @@ def test_convergence_ti(gmx_benzene_Coulomb_dHdl):
 
 
 @pytest.mark.parametrize("estimator", ["MBAR", "BAR"])
-def test_convergence_fep(gmx_benzene_Coulomb_u_nk, estimator):
-    convergence = forward_backward_convergence(gmx_benzene_Coulomb_u_nk, estimator)
+def test_convergence_fep(gmx_benzene, estimator):
+    dHdl, u_nk = gmx_benzene
+    convergence = forward_backward_convergence(u_nk, estimator)
     assert convergence.shape == (10, 5)
     assert convergence.loc[0, "Forward"] == pytest.approx(3.02, 0.01)
     assert convergence.loc[0, "Backward"] == pytest.approx(3.06, 0.01)
     assert convergence.loc[9, "Forward"] == pytest.approx(3.05, 0.01)
     assert convergence.loc[9, "Backward"] == pytest.approx(3.04, 0.01)
 
 
-def test_convergence_wrong_estimator(gmx_benzene_Coulomb_dHdl):
+def test_convergence_wrong_estimator(gmx_benzene):
+    dHdl, u_nk = gmx_benzene
     with pytest.raises(ValueError, match="is not available in"):
-        forward_backward_convergence(gmx_benzene_Coulomb_dHdl, "WWW")
+        forward_backward_convergence(u_nk, "WWW")
 
 
-def test_convergence_wrong_cases(gmx_benzene_Coulomb_u_nk):
+def test_convergence_wrong_cases(gmx_benzene):
+    dHdl, u_nk = gmx_benzene
     with pytest.warns(DeprecationWarning, match="Using lower-case strings for"):
-        forward_backward_convergence(gmx_benzene_Coulomb_u_nk, "mbar")
+        forward_backward_convergence(u_nk, "mbar")
 
 
-def test_convergence_method(gmx_benzene_Coulomb_u_nk):
-    convergence = forward_backward_convergence(
-        gmx_benzene_Coulomb_u_nk, "MBAR", num=2, method="adaptive"
-    )
+def test_convergence_method(gmx_benzene):
+    dHdl, u_nk = gmx_benzene
+    convergence = forward_backward_convergence(u_nk, "MBAR", num=2, method="adaptive")
     assert len(convergence) == 2
 
 
@@ -64,7 +75,12 @@ def test_cummean_long_none_integter():
 
 
 def test_R_c_converged():
-    data = pd.Series(data=[0] * 100)
+    data = pd.Series(
+        data=[
+            0,
+        ]
+        * 100
+    )
     data.attrs["temperature"] = 310
     data.attrs["energy_unit"] = "kcal/mol"
     value, running_average = fwdrev_cumavg_Rc(data)
@@ -80,16 +96,42 @@ def test_R_c_notconverged():
 
 
 def test_R_c_real():
-    data = pd.Series(data=np.hstack((range(10), [4.5] * 10)))
+    data = pd.Series(
+        data=np.hstack(
+            (
+                range(10),
+                [
+                    4.5,
+                ]
+                * 10,
+            )
+        )
+    )
     data.attrs["temperature"] = 310
     data.attrs["energy_unit"] = "kcal/mol"
     value, running_average = fwdrev_cumavg_Rc(data, tol=2.0)
     np.testing.assert_allclose(value, 0.35)
 
 
 def test_A_c_real():
-    data = pd.Series(data=np.hstack((range(10), [4.5] * 10)))
+    data = pd.Series(
+        data=np.hstack(
+            (
+                range(10),
+                [
+                    4.5,
+                ]
+                * 10,
+            )
+        )
+    )
     data.attrs["temperature"] = 310
     data.attrs["energy_unit"] = "kcal/mol"
-    value = A_c([data] * 2, tol=2.0)
+    value = A_c(
+        [
+            data,
+        ]
+        * 2,
+        tol=2.0,
+    )
     np.testing.assert_allclose(value, 0.65)