tests pass

tests/test_add_mutation.py

@@ -92,7 +92,7 @@ def set_up_quant_trait_model():
     # mu = theta/(4*N)
     r = rho / (4 * N)
     Opt = fwdpy11.Optimum
-    GSSmo = fwdpy11.GSSmo(
+    GSSmo = fwdpy11.GaussianStabilizingSelection.single_trait(
         [Opt(when=0, optimum=0.0, VS=1.0), Opt(when=N, optimum=1.0, VS=1.0)]
     )
     a = fwdpy11.Additive(2.0, GSSmo)

tests/test_dgvalue_pointer_vector.py

@@ -31,7 +31,8 @@ def test_construct_from_empty_list(self):
 
     def test_invalid_dimensionality(self):
         a = fwdpy11.Additive(2.0)
-        mvgss = fwdpy11.MultivariateGSS(np.zeros(2), 10.0)
+        mvgss = fwdpy11.GaussianStabilizingSelection.pleiotropy(
+            [fwdpy11.PleiotropicOptima(np.zeros(2), 10.0, when=0)])
         mv = fwdpy11.StrictAdditiveMultivariateEffects(2, 0, mvgss)
         from fwdpy11._fwdpy11 import _dgvalue_pointer_vector
 

tests/test_genetic_values.py

@@ -8,9 +8,10 @@ class testAdditive(unittest.TestCase):
     def setUp(self):
         GN = fwdpy11.GaussianNoise
         self.w = fwdpy11.Additive(2.0)
-        self.t = fwdpy11.Additive(2.0, fwdpy11.GSS(0.0, 1.0))
+        self.t = fwdpy11.Additive(2.0, fwdpy11.GaussianStabilizingSelection.single_trait(
+            [fwdpy11.Optimum(0.0, 1.0, 0)]))
         self.tn = fwdpy11.Additive(
-            1.0, fwdpy11.GSS(0.0, 1.0), GN(mean=0.1, sd=2.0))
+            1.0, fwdpy11.GaussianStabilizingSelection.single_trait([fwdpy11.Optimum(0.0, 1.0, 0)]), GN(mean=0.1, sd=2.0))
 
     def testScaling(self):
         self.assertEqual(self.w.scaling, 2.0)
@@ -74,13 +75,15 @@ def testPickleTraitWithNoiseToFile(self):
 
 
 class testMultiplicative(unittest.TestCase):
-    @classmethod
+    @ classmethod
     def setUp(self):
         GN = fwdpy11.GaussianNoise
         self.w = fwdpy11.Multiplicative(2.0)
-        self.t = fwdpy11.Multiplicative(2.0, fwdpy11.GSS(0.0, 1.0))
+        self.t = fwdpy11.Multiplicative(
+            2.0, fwdpy11.GaussianStabilizingSelection.single_trait([fwdpy11.Optimum(0.0, 1.0)]))
         self.tn = fwdpy11.Multiplicative(
-            1.0, fwdpy11.GSS(0.0, 1.0), GN(mean=0.1, sd=2.0)
+            1.0, fwdpy11.GaussianStabilizingSelection.single_trait(
+                [fwdpy11.Optimum(0.0, 1.0, 0)]), GN(mean=0.1, sd=2.0)
         )
 
     def testScaling(self):
@@ -131,9 +134,10 @@ def testPickleTraitWithNoise(self):
 
 
 class testGBR(unittest.TestCase):
-    @classmethod
+    @ classmethod
     def setUp(self):
-        self.gss = fwdpy11.GSS(0.0, 1.0)
+        self.gss = fwdpy11.GaussianStabilizingSelection.single_trait(
+            [fwdpy11.Optimum(0.0, 1.0, 0)])
         self.gnoise = fwdpy11.GaussianNoise(mean=0.0, sd=1.0)
         self.nonoise = fwdpy11.NoNoise()
 
@@ -160,15 +164,5 @@ def testPicklingGSSGaussianNoise(self):
         self.assertEqual(type(self.gss), type(up.gvalue_to_fitness))
 
 
-class testGSS(unittest.TestCase):
-    @classmethod
-    def setUp(self):
-        self.x = fwdpy11.GSS(0.0, 1.0)
-
-    def testProperties(self):
-        self.assertEqual(self.x.optimum, 0.0)
-        self.assertEqual(self.x.VS, 1.0)
-
-
 if __name__ == "__main__":
     unittest.main()

tests/test_inherited_noise.py

@@ -41,7 +41,8 @@ def setUp(self):
                 [100] * 3, 1),
             "simlen": 3,
             "gvalue": fwdpy11.Additive(
-                2.0, fwdpy11.GSS(optimum=0.0, VS=1.0), InheritedNoise()
+                2.0, fwdpy11.GaussianStabilizingSelection.single_trait(
+                    [fwdpy11.Optimum(optimum=0.0, VS=1.0, when=0)]), InheritedNoise()
             ),
         }
 

tests/test_multivariate_effects.py

@@ -17,7 +17,7 @@ def set_up_quant_trait_model():
     po = []
     for i, j in enumerate(timepoints):
         po.append(PO(when=int(j), optima=optima[i, :], VS=1.0))
-    GSSmo = fwdpy11.MultivariateGSSmo(po)
+    GSSmo = fwdpy11.GaussianStabilizingSelection.pleiotropy(po)
     cmat = np.identity(ntraits)
     np.fill_diagonal(cmat, 0.1)
     a = fwdpy11.StrictAdditiveMultivariateEffects(ntraits, 0, GSSmo)

tests/test_pickling.py

@@ -63,7 +63,8 @@ def setUp(self):
         self.mu = self.theta / (4 * self.N)
         self.r = self.rho / (4 * self.N)
 
-        self.GSS = fwdpy11.GSS(VS=1.0, optimum=0.0)
+        self.GSS = fwdpy11.GaussianStabilizingSelection.single_trait(
+            [fwdpy11.Optimum(VS=1.0, optimum=0.0, when=0)])
         a = fwdpy11.Additive(2.0, self.GSS)
         demography = fwdpy11.ForwardDemesGraph.tubes([self.N],
                                                      burnin=100,

tests/test_python_genetic_values.py

@@ -93,8 +93,8 @@ def test_run(self):
             # ndemes=2, scaling=2, gvalue_to_fitness=
             ndemes=2,
             scaling=2,
-            gvalue_to_fitness=fwdpy11.GSS(
-                fwdpy11.Optimum(optimum=0.0, VS=1.0)),
+            gvalue_to_fitness=fwdpy11.GaussianStabilizingSelection.single_trait([
+                fwdpy11.Optimum(optimum=0.0, VS=1.0)]),
         )
         params = fwdpy11.ModelParams(**pdict)
         pop2 = fwdpy11.DiploidPopulation([1000, 1000], 1.0)
@@ -124,7 +124,8 @@ def test_run(self):
 
         import pynoise
 
-        GSS = fwdpy11.GSS(optimum=0.0, VS=1.0)
+        GSS = fwdpy11.fwdpy11.GaussianStabilizingSelection.single_trait(
+            [fwdpy11.Optimum(optimum=0.0, VS=1.0, when=0)])
         Noise = pynoise.PyNoise()
         pdict = build_model(GSS, Noise, 5, [1000, 1000])
         params = fwdpy11.ModelParams(**pdict)
@@ -202,7 +203,7 @@ def test_run(self):
         md = np.array(pop.diploid_metadata)
 
         pdict["gvalue"] = fwdpy11.Additive(
-            2.0, fwdpy11.GSS(optimum=0.0, VS=1.0))
+            2.0, fwdpy11.GaussianStabilizingSelection.single_trait([fwdpy11.Optimum(optimum=0.0, VS=1.0, when=0)]))
         params = fwdpy11.ModelParams(**pdict)
         pop2 = fwdpy11.DiploidPopulation(1000, 1.0)
         rng = fwdpy11.GSLrng(1010)

tests/test_record_genetic_value_matrix.py

@@ -38,7 +38,7 @@ def set_up_quant_trait_model():
     rho = 1.0
     r = rho / (4 * N)
     Opt = fwdpy11.Optimum
-    GSSmo = fwdpy11.GSSmo(
+    GSSmo = fwdpy11.GaussianStabilizingSelection.single_trait(
         [Opt(when=0, optimum=0.0, VS=1.0), Opt(
             when=N, optimum=1.0, VS=1.0)]
     )
@@ -72,7 +72,7 @@ def set_up_two_trait_quant_trait_model():
             when=N, optima=np.array([np.sqrt(2.0), 0]), VS=2.0
         ),
     ]
-    GSSmo = fwdpy11.MultivariateGSSmo(optima)
+    GSSmo = fwdpy11.GaussianStabilizingSelection.pleiotropy(optima)
     a = fwdpy11.StrictAdditiveMultivariateEffects(2, 0, GSSmo)
     vcov = np.identity(2)
     np.fill_diagonal(vcov, 0.25)

tests/test_recreate_pop_from_own_treeseq.py

@@ -69,7 +69,7 @@ def pop_rng_params():
         "recregions": [fwdpy11.BinomialInterval(0, L, 1)],
         "rates": (0.0, mu, None),
         "gvalue": fwdpy11.Additive(
-            scaling=2, gvalue_to_fitness=fwdpy11.GSS(optimum=optimum, VS=VS)
+            scaling=2, gvalue_to_fitness=fwdpy11.GaussianStabilizingSelection.single_trait([fwdpy11.Optimum(optimum=optimum, VS=VS, when=0)])
         ),
         "simlen": simlen,
         "prune_selected": False,

tests/test_tree_sequences.py

@@ -59,7 +59,7 @@ def set_up_quant_trait_model(simlen=1.0):
     # mu = theta/(4*N)
     r = rho / (4 * N)
     Opt = fwdpy11.Optimum
-    GSSmo = fwdpy11.GSSmo(
+    GSSmo = fwdpy11.GaussianStabilizingSelection.single_trait(
         [Opt(when=0, optimum=0.0, VS=1.0), Opt(when=N, optimum=1.0, VS=1.0)]
     )
     a = fwdpy11.Additive(2.0, GSSmo)
@@ -998,7 +998,8 @@ def setUpClass(self):
         self.nreps = 500
         self.mu = self.theta / (4 * self.N)
         self.r = self.rho / (4 * self.N)
-        self.GSS = fwdpy11.GSS(VS=1.0, optimum=0.0)
+        self.GSS = fwdpy11.fwdpy11.GaussianStabilizingSelection.single_trait(
+            [fwdpy11.Optimum(VS=1.0, optimum=0.0, when=0)])
         a = fwdpy11.Additive(2.0, self.GSS)
         self.p = {
             "nregions": [],
@@ -1031,7 +1032,8 @@ def testQtraitSim(self):
         rho = 1.0
         r = rho / (4 * N)
 
-        GSS = fwdpy11.GSS(VS=1.0, optimum=1.0)
+        GSS = fwdpy11.GaussianStabilizingSelection.single_trait(
+            [fwdpy11.Optimum(VS=1.0, optimum=1.0, when=0)])
         a = fwdpy11.Additive(2.0, GSS)
         p = {
             "nregions": [],
@@ -1115,7 +1117,8 @@ def testQtraitSim(self):
         rho = 1.0
         r = rho / (4 * N)
 
-        GSS = fwdpy11.GSS(VS=1.0, optimum=1.0)
+        GSS = fwdpy11.GaussianStabilizingSelection.single_trait(
+            [fwdpy11.Optimum(VS=1.0, optimum=1.0, when=0)])
         a = fwdpy11.Additive(2.0, GSS)
         p = {
             "nregions": [],
@@ -1208,7 +1211,8 @@ def setUpClass(self):
         self.mu = self.theta / (4 * self.N)
         self.r = self.rho / (4 * self.N)
 
-        self.GSS = fwdpy11.GSS(VS=1.0, optimum=0.0)
+        self.GSS = fwdpy11.GaussianStabilizingSelection.single_trait(
+            [fwdpy11.Optimum(VS=1.0, optimum=0.0, when=0)])
         a = fwdpy11.Additive(2.0, self.GSS)
         self.p = {
             "nregions": [],
@@ -1353,7 +1357,8 @@ def __call__(self, pop):
         self.rho = 1.0
         self.r = self.rho / (4 * self.N)
 
-        self.GSS = fwdpy11.GSS(VS=1.0, optimum=0.0)
+        self.GSS = fwdpy11.GaussianStabilizingSelection.single_trait(
+            [fwdpy11.Optimum(VS=1.0, optimum=0.0, when=0)])
         a = fwdpy11.Additive(2.0, self.GSS)
         self.p = {
             "nregions": [],

tests/test_tree_sequences_different_des_in_different_demes.py

@@ -135,7 +135,7 @@ def setUpClass(self):
             "demography": demography,
             "simlen": 100,
             "gvalue": fwdpy11.Additive(
-                ndemes=2, scaling=2, gvalue_to_fitness=fwdpy11.GSS(optimum=0.0, VS=1.0)
+                ndemes=2, scaling=2, gvalue_to_fitness=fwdpy11.GaussianStabilizingSelection.single_trait([fwdpy11.Optimum(optimum=0.0, VS=1.0, when=0)])
             ),
         }