Add tests on custom model definition and fitting

skgstat/tests/test_models.py

@@ -186,6 +186,21 @@ def adder(l, a):
         for r, c in zip(res, adder([1, 4, 8], 4)):
             self.assertEqual(r, c)
 
+    def test_sum_spherical(self):
+        @variogram
+        def sum_spherical(h, r1, c1, r2, c2, b1=0, b2=0):
+            return spherical(h, r1, c1, b1) + spherical(h, r2, c2, b2)
+
+        # Parameters for the two spherical models
+        params = [1, 0.3, 10, 0.7]
+
+        # Values at which we'll evaluate the function and its expected result
+        vals = [0, 1, 100]
+        res = [0, 0.3 + spherical(1, 10, 0.7), 1]
+
+        for r, c in zip(res, sum_spherical(vals, *params)):
+            self.assertEqual(r, c)
+
 
 if __name__=='__main__':
     unittest.main()

skgstat/tests/test_variogram.py

@@ -19,6 +19,7 @@
 from skgstat import OrdinaryKriging
 from skgstat import estimators
 from skgstat import plotting
+from skgstat.models import variogram, spherical, matern
 
 
 class TestSpatiallyCorrelatedData(unittest.TestCase):
@@ -61,7 +62,7 @@ def test_sparse_maxlag_30(self):
             self.assertAlmostEqual(x, y, places=3)
 
 
-class TestVariogramInstatiation(unittest.TestCase):
+class TestVariogramInstantiation(unittest.TestCase):
     def setUp(self):
         # set up default values, whenever c and v are not important
         np.random.seed(42)
@@ -949,6 +950,18 @@ def test_implicit_nugget(self):
 
         self.assertTrue(abs(V.parameters[-1] - 2.) < 1e-10)
 
+    def test_fit_custom_model(self):
+
+        # Define a custom variogram and run the fit
+        @variogram
+        def sum_spherical(h, r1, c1, r2, c2, b1, b2):
+            return spherical(h, r1, c1, b1) + spherical(h, r2, c2, b2)
+
+        V = Variogram(self.c, self.v, use_nugget=True, model=sum_spherical)
+
+        # Check that 6 parameters were found
+        assert len(V.cof) == 6
+
 
 class TestVariogramQualityMeasures(unittest.TestCase):
     def setUp(self):