Added a test for time reversibility.

tests/test_descriptor_fitting.py

@@ -88,3 +88,44 @@ def test_quasi_time_reversible(datafile, regularization):
         fitted_target = fitting_calculator.linear_combination(vectors, fit_coefficients)
         error = np.linalg.norm(target - fitted_target, ord=np.inf)
         assert error < THRESHOLD
+
+@pytest.mark.parametrize("datafile", ["urea.json", "glucose.json"])
+def test_time_reversibility(datafile):
+
+    # load test data from json file
+    data = utils.load_json(f"tests/{datafile}")
+    nelectrons = data["nelectrons"]
+    natoms = data["trajectory"].shape[1]
+    nbasis = data["overlaps"].shape[1]
+    nframes = data["trajectory"].shape[0]
+
+    # initialize an extrapolator
+    extrapolator = gext.Extrapolator(nelectrons, nbasis, natoms,
+        nsteps=nframes, fitting="qtr")
+
+    # load data in the extrapolator
+    for (coords, coeff, overlap) in zip(data["trajectory"],
+            data["coefficients"], data["overlaps"]):
+        extrapolator.load_data(coords, coeff, overlap)
+
+    descriptors = extrapolator.descriptors.get(10)
+
+    # we symmetrize the future and past targets (remember that it is
+    # quasi time reversible, not exactly time reversible)
+    target = descriptors[0] + descriptors[-1]
+    descriptors[0] = target
+    descriptors[-1] = target
+
+    fitting_calculator = extrapolator.fitting_calculator
+
+    # fit the future target
+    fit_coefficients = fitting_calculator.fit(descriptors[:-1], descriptors[-1])
+    fitted_target = fitting_calculator.linear_combination(descriptors[:-1], fit_coefficients)
+
+    # fit the past target
+    reversed_descriptors = list(reversed(descriptors))
+    fit_coefficients = fitting_calculator.fit(reversed_descriptors[:-1], reversed_descriptors[-1])
+    fitted_target_reverse = fitting_calculator.linear_combination(reversed_descriptors[:-1], fit_coefficients)
+
+    # check the time reversibility
+    assert np.linalg.norm(fitted_target - fitted_target_reverse, ord=np.inf) < SMALL