QTR

Implemented quasi time reversible, improved options. Added a test for the quasi time reversible.

tests/test_extrapolation.py

@@ -9,10 +9,13 @@ import gext.grassmann
 import utils
 
 SMALL = 1e-10
-THRESHOLD = 1e-2
+THRESHOLD = 5e-2
 
 @pytest.mark.parametrize("datafile", ["urea.json", "glucose.json"])
-def test_extrapolation(datafile):
+@pytest.mark.parametrize("fitting", ["leastsquare", "qtr"])
+@pytest.mark.parametrize("regularization", [0.0, 1e-6, 5e-6])
+@pytest.mark.parametrize("descriptor", ["distance", "coulomb"])
+def test_extrapolation(datafile, fitting, regularization, descriptor):
 
     # load test data from json file
     data = utils.load_json(f"tests/{datafile}")
@@ -26,7 +29,9 @@ def test_extrapolation(datafile):
     assert n < nframes
 
     # initialize an extrapolator
-    extrapolator = gext.Extrapolator(nelectrons, nbasis, natoms, nsteps=n)
+    extrapolator = gext.Extrapolator(nelectrons, nbasis, natoms,
+        nsteps=n, fitting=fitting, fitting_regularization=regularization,
+        descriptor=descriptor)
 
     # load data in the extrapolator up to index n - 1
     for (coords, coeff, overlap) in zip(data["trajectory"][:n],
@@ -106,3 +111,37 @@ def test_coefficient_extrapolation(datafile):
     assert np.linalg.norm(guessed_density - density, ord=np.inf) < THRESHOLD
     assert np.linalg.norm(guessed_density - density, ord=np.inf) \
           /np.linalg.norm(density, ord=np.inf) < THRESHOLD
+
+@pytest.mark.parametrize("datafile", ["urea.json", "glucose.json"])
+def test_errors(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]
+
+    # amount of data we want to use for fitting
+    n = 9
+    assert n < nframes
+
+    # initialize an extrapolator
+    extrapolator = gext.Extrapolator(nelectrons, nbasis, natoms, nsteps=n)
+
+    with pytest.raises(ValueError):
+        extrapolator.guess(data["trajectory"][0])
+
+    # initialize a new extrapolator
+    extrapolator = gext.Extrapolator(nelectrons, nbasis, natoms, nsteps=n,
+        allow_partially_filled=False)
+
+    # load data in the extrapolator up to index m - 1
+    m = 4
+    for (coords, coeff, overlap) in zip(data["trajectory"][:m],
+            data["coefficients"][:m], data["overlaps"][:m]):
+        extrapolator.load_data(coords, coeff, overlap)
+
+    # check an extrapolation at index m
+    with pytest.raises(ValueError):
+        extrapolator.guess(data["trajectory"][m])