Small changes and updates

docs/src/citing.rst

@@ -106,9 +106,9 @@ If you use the charge neutral semigrand canonical sampling or any related functi
 please cite the following work,
 
     Xie, F., Zhong, P., Barroso-Luque, L., Ouyang, B. & Ceder, G.
-    `Grand-canonical Monte-Carlo simulation methods for charge-decorated cluster
-    expansions <https://arxiv.org/abs/2210.01165>`_ [arxiv:2210.01165].
-
+    `Semigrand-canonical Monte-Carlo simulation methods for charge-decorated cluster
+    expansions <https://doi.org/10.1016/j.commatsci.2022.112000>`_
+    Computational Materials Science 218, 112000 (2023).
 
 Coulomb electrostatic interactions
 ==================================

setup.cfg

@@ -43,17 +43,19 @@ install_requires =
 [options.extras_require]
 docs =
     sphinx >= 5.3
-    pydata-sphinx-theme ==0.12.0
-    ipython ==8.2.0
-    nbsphinx ==0.8.10
-    nbsphinx-link ==1.3.0
-    nb2plots ==0.6.1
+    pydata-sphinx-theme >=0.12.0
+    ipython >=8.2.0
+    nbsphinx >=0.8.10
+    nbsphinx-link >=1.3.0
+    nb2plots >=0.6.1
 tests =
-    pytest ==7.2.0
-    pytest-cov ==4.0.0
+    pytest >=7.2.0
+    pytest-cov >=4.0.0
     scikit-learn >=1.1.2
     h5py >=3.6.0
     coverage
+    polytope
+    cvxpy
 dev =
     pre-commit >=2.12.1
     cython >=0.29.32

smol/moca/sampler/kernel.py

@@ -68,8 +68,7 @@ def __init__(
         self.natural_params = ensemble.natural_parameters
         self._seed = seed if seed is not None else np.random.SeedSequence().entropy
         self._rng = np.random.default_rng(self._seed)
-        self._compute_features = ensemble.compute_feature_vector
-        self._feature_change = ensemble.compute_feature_vector_change
+        self._ensemble = ensemble
         self.trace = StepTrace(accepted=np.array([True]))
         self._usher, self._bias = None, None
 
@@ -93,6 +92,11 @@ def __init__(
         # run a initial step to populate trace values
         _ = self.single_step(np.zeros(ensemble.num_sites, dtype=int))
 
+    @property
+    def ensemble(self):
+        """Return the ensemble instance."""
+        return self._ensemble
+
     @property
     def mcusher(self):
         """Get the MCUsher."""
@@ -157,7 +161,7 @@ def compute_initial_trace(self, occupancy):
         """
         trace = Trace()
         trace.occupancy = occupancy
-        trace.features = self._compute_features(occupancy)
+        trace.features = self.ensemble.compute_feature_vector(occupancy)
         # set scalar values into shape (1,) array for sampling consistency.
         trace.enthalpy = np.array([np.dot(self.natural_params, trace.features)])
         if self.bias is not None:
@@ -253,7 +257,9 @@ def single_step(self, occupancy):
         """
         step = self._usher.propose_step(occupancy)
         log_factor = self._usher.compute_log_priori_factor(occupancy, step)
-        self.trace.delta_trace.features = self._feature_change(occupancy, step)
+        self.trace.delta_trace.features = self.ensemble.compute_feature_vector_change(
+            occupancy, step
+        )
         self.trace.delta_trace.enthalpy = np.array(
             np.dot(self.natural_params, self.trace.delta_trace.features)
         )
@@ -303,7 +309,9 @@ def single_step(self, occupancy):
         """
         step = self._usher.propose_step(occupancy)
         log_factor = self._usher.compute_log_priori_factor(occupancy, step)
-        self.trace.delta_trace.features = self._feature_change(occupancy, step)
+        self.trace.delta_trace.features = self.ensemble.compute_feature_vector_change(
+            occupancy, step
+        )
         self.trace.delta_trace.enthalpy = np.array(
             np.dot(self.natural_params, self.trace.delta_trace.features)
         )
@@ -514,7 +522,9 @@ def single_step(self, occupancy):
         """
         bin_id = self._get_bin_id(self._current_enthalpy)
         step = self._usher.propose_step(occupancy)
-        self.trace.delta_trace.features = self._feature_change(occupancy, step)
+        self.trace.delta_trace.features = self.ensemble.compute_feature_vector_change(
+            occupancy, step
+        )
         self.trace.delta_trace.enthalpy = np.array(
             np.dot(self.natural_params, self.trace.delta_trace.features)
         )
@@ -612,7 +622,7 @@ def set_aux_state(self, occupancy, *args, **kwargs):
         This is necessary for WangLandau to work properly because
         it needs to store the current enthalpy and features.
         """
-        features = np.array(self._compute_features(occupancy))
+        features = np.array(self.ensemble.compute_feature_vector(occupancy))
         enthalpy = np.dot(features, self.natural_params)
         self._current_features = features
         self._current_enthalpy = enthalpy