Merge pull request #100 from SUNCAT-Center/compat-update-ase-3-21

To release compat update for ASE 3.19.1 to 3.21

CHANGELOG.md

@@ -1,5 +1,10 @@
 # dev
 
+# Version 0.7.0 (November 2022)
+
+- Compatibility updated for ASE 3.19.1 to 3.21.0
+- Bugfixes
+
 # Version 0.6.2 (March 2020)
 
 -   Added module for site featurization and GA feature selection.

catlearn/__init__.py

@@ -1 +1 @@
-__version__ = "0.6.2"
+__version__ = "0.7.0"

catlearn/optimize/mlmin.py

@@ -266,10 +266,11 @@ def run(self, fmax=0.05, steps=200, kernel='SQE', max_step=0.25,
             guess = self.ase_ini
             guess_pos = np.array(self.list_train[-1])
             guess.positions = guess_pos.reshape(-1, 3)
-            guess.set_calculator(ASECalc(gp=self.gp,
-                                         index_constraints=self.index_mask,
-                                         scaling_targets=self.u_prior)
-                                 )
+            guess.calc = ASECalc(
+                gp=self.gp,
+                index_constraints=self.index_mask,
+                scaling_targets=self.u_prior
+            )
 
             # Optimization in the predicted landscape:
             ml_opt = MDMin(guess, trajectory=None, logfile=None, dt=0.020)
@@ -333,7 +334,7 @@ def run(self, fmax=0.05, steps=200, kernel='SQE', max_step=0.25,
             eval_atom = self.ase_ini
             pos_atom = self.interesting_point
             eval_atom.positions = np.array(pos_atom).reshape((-1, 3))
-            eval_atom.set_calculator(self.ase_calc)
+            eval_atom.calc = self.ase_calc
             energy_atom = eval_atom.get_potential_energy(
                                                       force_consistent=self.fc)
             forces_atom = -eval_atom.get_forces().reshape(-1)

catlearn/optimize/tools.py

@@ -47,7 +47,7 @@ def plotneb(trajectory='ML_NEB_catlearn.traj', view_path=True):
                 xycoords='data', xytext=(x_pred[np.argmax(e_pred)], 0.0),
                 textcoords='data', arrowprops=prop_plots)
 
-    ax.annotate(s=str(np.round(e_barrier, 3))+' eV',
+    ax.annotate(str(np.round(e_barrier, 3))+' eV',
                 xy=(x_pred[np.argmax(e_pred)], np.max(e_pred)/1.65),
                 xycoords='data',
                 fontsize=15.0,

docs/changelog.md

@@ -1,5 +1,10 @@
 # Changelog
 
+# Version 0.7.0 (November 2022)
+
+- Compatibility updated for ASE 3.19.1 to 3.21.0
+- Bugfixes
+
 # Version 0.6.2 (March 2020)
 
 -   Added module for site featurization and GA feature selection.

requirements.txt

@@ -1,4 +1,4 @@
-ase>=3.17.0
+ase>=3.21.0
 click>=6.7
 cycler>=0.10.0
 decorator>=4.3.0

test/test_mlmin.py

@@ -17,7 +17,7 @@ class TestMLMin(unittest.TestCase):
     """ General test of the ML-Min algorithm."""
     def test_minimize(self):
         initial_structure = read('initial_mlmin.traj')
-        initial_structure.set_calculator(EMT())
+        initial_structure.calc = EMT()
 
         initial_opt = MLMin(initial_structure,
                             trajectory='mlmin_structures.traj')
@@ -41,7 +41,7 @@ def test_acquisition(self):
 
         # Test acquisition lcb:
         initial_structure = read('initial_mlmin.traj')
-        initial_structure.set_calculator(EMT())
+        initial_structure.calc = EMT()
 
         initial_opt = MLMin(initial_structure,
                             trajectory='mlmin_structures.traj')
@@ -62,7 +62,7 @@ def test_acquisition(self):
 
         # Test acquisition ucb:
         initial_structure = read('initial_mlmin.traj')
-        initial_structure.set_calculator(EMT())
+        initial_structure.calc = EMT()
 
         initial_opt = MLMin(initial_structure,
                             trajectory='mlmin_structures.traj')
@@ -86,7 +86,7 @@ def test_kernel(self):
 
         # Test kernel ARD fixed:
         initial_structure = read('initial_mlmin.traj')
-        initial_structure.set_calculator(EMT())
+        initial_structure.calc = EMT()
 
         initial_opt = MLMin(initial_structure,
                             trajectory='mlmin_structures.traj')
@@ -108,7 +108,7 @@ def test_kernel(self):
 
         # Test ARD SQE kernel:
         initial_structure = read('initial_mlmin.traj')
-        initial_structure.set_calculator(EMT())
+        initial_structure.calc = EMT()
 
         initial_opt = MLMin(initial_structure,
                             trajectory='mlmin_structures.traj')
@@ -128,3 +128,9 @@ def test_kernel(self):
         fmax_test = 0.00766
         np.testing.assert_array_almost_equal(fmax_opt, fmax_test, decimal=5)
 
+
+if __name__ == '__main__':
+    test_class = TestMLMin()
+    test_class.test_minimize()
+    test_class.test_acquisition()
+    test_class.test_kernel()