Add tests

src/alchemlyb/tests/test_convergence.py

@@ -2,7 +2,7 @@
 import pandas as pd
 import pytest
 
-from alchemlyb.convergence import forward_backward_convergence, fwdrev_cumavg_Rc, A_c
+from alchemlyb.convergence import forward_backward_convergence, fwdrev_cumavg_Rc, A_c, moving_average
 from alchemlyb.convergence.convergence import _cummean
 
 
@@ -25,6 +25,14 @@ def test_convergence_fep(gmx_benzene_Coulomb_u_nk, estimator):
     assert convergence.loc[9, "Forward"] == pytest.approx(3.05, 0.01)
     assert convergence.loc[9, "Backward"] == pytest.approx(3.04, 0.01)
 
+@pytest.mark.parametrize("estimator", ["MBAR"])
+def test_moving_average_fep(gmx_benzene_Coulomb_u_nk, estimator):
+    df_avg = moving_average(gmx_benzene_Coulomb_u_nk, estimator)
+    assert df_avg.shape == (9, 2)
+    assert df_avg.loc[0, "FE"] == pytest.approx(3.01, 0.01)
+    assert df_avg.loc[0, "FE_Error"] == pytest.approx(0.067, 0.01)
+    assert df_avg.loc[8, "FE"] == pytest.approx(3.10, 0.01)
+    assert df_avg.loc[8, "FE_Error"] == pytest.approx(0.066, 0.01)
 
 def test_convergence_wrong_estimator(gmx_benzene_Coulomb_dHdl):
     with pytest.raises(ValueError, match="is not available in"):

src/alchemlyb/tests/test_visualisation.py

@@ -8,7 +8,7 @@
 import alchemlyb
 from alchemlyb.convergence import forward_backward_convergence
 from alchemlyb.estimators import MBAR, TI, BAR
-from alchemlyb.visualisation import plot_convergence
+from alchemlyb.visualisation import plot_convergence, plot_moving_average
 from alchemlyb.visualisation.dF_state import plot_dF_state
 from alchemlyb.visualisation.mbar_matrix import plot_mbar_overlap_matrix
 from alchemlyb.visualisation.ti_dhdl import plot_ti_dhdl
@@ -217,6 +217,28 @@ def test_plot_convergence_final_nan():
     assert isinstance(ax, matplotlib.axes.Axes)
     plt.close(ax.figure)
 
+def test_plot_moving_average(gmx_benzene_Coulomb_u_nk):
+    data_list = gmx_benzene_Coulomb_u_nk
+    fe = []
+    fe_error = []
+    num_points = 10
+    for i in range(1, num_points + 1):
+        slice = int(len(data_list[0]) / num_points * i)
+        u_nk_coul = alchemlyb.concat([data[:slice] for data in data_list])
+        estimate = MBAR().fit(u_nk_coul)
+        fe.append(estimate.delta_f_.loc[0, 1])
+        fe_error.append(estimate.d_delta_f_.loc[0, 1])
+
+    df = pd.DataFrame(
+        data={
+            "FE": fe,
+            "FE_Error": fe_error,
+        }
+    )
+    df.attrs = estimate.delta_f_.attrs
+    ax = plot_moving_average(df)
+    assert isinstance(ax, matplotlib.axes.Axes)
+    plt.close(ax.figure)
 
 class Test_Units:
     @staticmethod