Add delta-learning test.

tests/test_delta_learning.py

@@ -0,0 +1,43 @@
+import os
+import shlex
+import shutil
+import subprocess
+import tempfile
+
+
+def test_delta_learning():
+    """
+    Make sure that the server can run using two backends for the in vacuo
+    vacuo calculation. The first is the "reference" backend, the second
+    applies delta learning corrections.
+    """
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # Copy files to temporary directory.
+        shutil.copyfile("tests/input/adp.parm7", tmpdir + "/adp.parm7")
+        shutil.copyfile("tests/input/adp.rst7", tmpdir + "/adp.rst7")
+        shutil.copyfile("tests/input/emle_sp.in", tmpdir + "/emle_sp.in")
+
+        # Copy the current environment to a new dictionary.
+        env = os.environ.copy()
+
+        # Set environment variables.
+        env["EMLE_BACKEND"] = "torchani,xtb"
+        env["EMLE_ENERGY_FREQUENCY"] = "1"
+
+        # Create the sander command.
+        command = "sander -O -i emle_sp.in -p adp.parm7 -c adp.rst7 -o emle.out"
+
+        process = subprocess.run(
+            shlex.split(command),
+            cwd=tmpdir,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            env=env,
+        )
+
+        # Make sure that the process exited successfully.
+        assert process.returncode == 0
+
+        # Make sure that an energy file is written.
+        assert os.path.isfile(tmpdir + "/emle_energy.txt")