Resolve bug in bar_model.delta_f_ creation (#397)

* fix bug introduced in PR #381: there was a change to creating the delta_f_ matrix,
  which resulted in the columns and indices being tuples that were in the wrong order 
  for single lambda computations.
* ensure that columns are in the correct order by explicitly sorting
* add a test for the delta_f_ columns

src/alchemlyb/estimators/bar_.py

@@ -100,25 +100,24 @@ def fit(self, u_nk):
             (len(groups.get_group(i)) if i in groups.groups else 0)
             for i in u_nk.columns
         ]
-        
+
         # Pull lambda states from indices
-        states = list(set( x[1:] for x in u_nk.index))
+        states = list(set(x[1:] if len(x[1:]) > 1 else x[1] for x in u_nk.index))
         for state in states:
-            if len(state) == 1:
-                state = state[0]
             if state not in self._states_:
                 raise ValueError(
                     f"Indexed lambda state, {state}, is not represented in u_nk columns:"
                     f" {self._states_}"
                 )
-
+        states.sort(key=lambda x: self._states_.index(x))
+
         # Now get free energy differences and their uncertainties for each step
         deltas = np.array([])
         d_deltas = np.array([])
         for k in range(len(N_k) - 1):
             if N_k[k] == 0 or N_k[k + 1] == 0:
                 continue
-            
+
             # get us from lambda step k
             uk = groups.get_group(self._states_[k])
             # get w_F
@@ -149,7 +148,7 @@ def fit(self, u_nk):
                 "To compute the free energy with BAR, ensure that values in u_nk exist"
                 f" for the columns:\n{states}."
             )
-        
+
         # build matrix of deltas between each state
         adelta = np.zeros((len(deltas) + 1, len(deltas) + 1))
         ad_delta = np.zeros_like(adelta)

src/alchemlyb/tests/test_fep_estimators.py

@@ -138,6 +138,50 @@ def test_states_(self, estimator):
             _estimator.states_ = 1
 
 
+def test_delta_f_columns(
+    gmx_benzene_Coulomb_u_nk,
+    gmx_expanded_ensemble_case_1,
+):
+    """Ensure columns are tuples when appropriate."""
+
+    bar_1lambda = BAR().fit(alchemlyb.concat(gmx_benzene_Coulomb_u_nk))
+    assert set(bar_1lambda.delta_f_.columns) == set([0.0, 0.25, 0.5, 0.75, 1.0])
+
+    bar_4lambda = BAR().fit(alchemlyb.concat(gmx_expanded_ensemble_case_1))
+    assert set(bar_4lambda.delta_f_.columns) == set(
+        [
+            (0.0, 0.1, 0.0, 0.0),
+            (0.0, 0.4, 0.0, 0.0),
+            (0.0, 1.0, 0.4, 0.002),
+            (0.0, 1.0, 0.0, 0.0001),
+            (0.0, 1.0, 0.1, 0.0002),
+            (0.0, 0.84, 0.0, 0.0),
+            (0.0, 0.68, 0.0, 0.0),
+            (0.0, 1.0, 0.84, 0.2),
+            (0.0, 1.0, 0.3, 0.001),
+            (0.0, 1.0, 0.2, 0.0004),
+            (0.0, 0.16, 0.0, 0.0),
+            (0.0, 1.0, 0.52, 0.01),
+            (0.0, 1.0, 0.92, 0.4),
+            (0.0, 0.76, 0.0, 0.0),
+            (0.0, 0.46, 0.0, 0.0),
+            (0.0, 1.0, 0.6, 0.02),
+            (0.0, 0.92, 0.0, 0.0),
+            (0.0, 0.6, 0.0, 0.0),
+            (0.0, 0.34, 0.0, 0.0),
+            (0.0, 1.0, 0.76, 0.1),
+            (0.0, 1.0, 1.0, 1.0),
+            (0.0, 0.05, 0.0, 0.0),
+            (0.0, 1.0, 0.48, 0.004),
+            (0.0, 0.0, 0.0, 0.0),
+            (0.0, 0.22, 0.0, 0.0),
+            (0.0, 0.52, 0.0, 0.0),
+            (0.0, 1.0, 0.68, 0.04),
+            (0.0, 0.28, 0.0, 0.0),
+        ]
+    )
+
+
 def test_bootstrap(gmx_benzene_Coulomb_u_nk):
     u_nk = alchemlyb.concat(gmx_benzene_Coulomb_u_nk)
     mbar = MBAR(n_bootstraps=2)