ENH: Allow all polynomial library library options in trapping

(other than degree, which has to be 2)

pysindy/optimizers/trapping_sr3.py

@@ -822,13 +822,15 @@ def _reduce(self, x, y):
         self.objective_history = objective_history
 
 
-def _make_constraints(n_tgts: int):
+def _make_constraints(n_tgts: int, **kwargs):
     """Create constraints for the Quadratic terms in TrappingSR3.
 
     These are the constraints from equation 5 of the Trapping SINDy paper.
 
     Args:
         n_tgts: number of coordinates or modes for which you're fitting an ODE.
+        kwargs: Keyword arguments to PolynomialLibrary such as
+            ``include_bias``.
 
     Returns:
         A tuple of the constraint zeros, and a constraint matrix to multiply
@@ -841,7 +843,7 @@ def _make_constraints(n_tgts: int):
         reshaping.
     """
     n_terms = n_poly_features(n_tgts, degree=2, include_bias=False)
-    lib = PolynomialLibrary(2, include_bias=False).fit(np.zeros((1, n_tgts)))
+    lib = PolynomialLibrary(2, **kwargs).fit(np.zeros((1, n_tgts)))
     terms = [(t_ind, exps) for t_ind, exps in enumerate(lib.powers_)]
 
     # index of tgt -> index of its pure quadratic term

test/test_optimizers.py

@@ -1135,7 +1135,7 @@ def test_remove_and_decrement():
 
 def test_trapping_constraints():
     # x, y, x^2, xy, y^2
-    constraint_rhs, constraint_lhs = _make_constraints(2)
+    constraint_rhs, constraint_lhs = _make_constraints(2, include_bias=False)
     stable_coefs = np.array([[0, 0, 0, 1, -1], [0, 0, -1, 1, 0]])
     result = np.tensordot(constraint_lhs, stable_coefs, ((1, 2), (1, 0)))
     np.testing.assert_array_equal(constraint_rhs, result)