Option for partially filled and tests.

gext/main.py

@@ -23,6 +23,7 @@ class Extrapolator:
             "nsteps": 6,
             "descriptor": "distance",
             "fitting": "leastsquare",
+            "allow_partially_filled": True,
         }
 
         self.nelectrons = nelectrons
@@ -95,7 +96,10 @@ class Extrapolator:
     def guess(self, coords: np.ndarray, overlap = None) -> np.ndarray:
         """Get a new electronic density to be used as a guess."""
 
+        if self.options["allow_partially_filled"]:
             n = min(self.options["nsteps"], self.descriptors.count)
+        else:
+            n = self.options["nsteps"]
 
         prev_descriptors = self.descriptors.get(n)
         descriptor = self._compute_descriptor(coords)

tests/test_extrapolation.py

@@ -41,3 +41,35 @@ def test_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_partial_extrapolation(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
+    m = 5
+    assert n < nframes
+
+    # initialize an extrapolator
+    extrapolator = gext.Extrapolator(nelectrons, nbasis, natoms, nsteps=n)
+
+    # load data in the extrapolator up to index n - 1
+    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 n
+    guessed_density = extrapolator.guess(data["trajectory"][m], data["overlaps"][m])
+    coeff = data["coefficients"][m][:, :nelectrons//2]
+    density = coeff @ coeff.T
+
+    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