fix flatsys "legacy" keywords + enhance legacy keyword processing

murrayrm · murrayrm · commit 8463f682bcc9 · 2025-02-02T20:06:43.000-08:00
diff --git a/control/config.py b/control/config.py
@@ -380,11 +380,12 @@ def use_legacy_defaults(version):
 # warning.  If both the old and new keyword are present, a ControlArgument
 # exception is raised.
 #
-def _process_legacy_keyword(kwargs, oldkey, newkey, newval):
+def _process_legacy_keyword(kwargs, oldkey, newkey, newval, warn_oldkey=True):
     if oldkey in kwargs:
-        warnings.warn(
-            f"keyword '{oldkey}' is deprecated; use '{newkey}'",
-            FutureWarning)
+        if warn_oldkey:
+            warnings.warn(
+                f"keyword '{oldkey}' is deprecated; use '{newkey}'",
+                FutureWarning, stacklevel=3)
         if newval is not None:
             raise ControlArgument(
                 f"duplicate keywords '{oldkey}' and '{newkey}'")
diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py
@@ -415,7 +415,10 @@ def point_to_point(
 
     # Process keyword arguments
     trajectory_constraints = _process_legacy_keyword(
-        kwargs, 'constraints', 'trajectory_constraints', trajectory_constraints)
+        kwargs, 'constraints', 'trajectory_constraints',
+        trajectory_constraints, warn_oldkey=False)
+    cost = _process_legacy_keyword(
+        kwargs, 'trajectory_cost', 'cost', cost, warn_oldkey=False)
 
     minimize_kwargs = {}
     minimize_kwargs['method'] = kwargs.pop('minimize_method', None)
diff --git a/control/tests/flatsys_test.py b/control/tests/flatsys_test.py
@@ -200,7 +200,8 @@ def test_kinematic_car_ocp(
                 'ignore', message="unable to solve", category=UserWarning)
             traj_ocp = fs.solve_flat_ocp(
                 vehicle_flat, timepts, x0, u0,
-                cost=traj_cost, constraints=input_constraints,
+                trajectory_cost=traj_cost,
+                trajectory_constraints=input_constraints,
                 terminal_cost=terminal_cost, basis=basis,
                 initial_guess=initial_guess,
                 minimize_kwargs={'method': method},
@@ -445,8 +446,8 @@ def test_flat_solve_ocp(self, basis):
             (sp.optimize.NonlinearConstraint, lambda x, u: x, lb, ub)]
         traj_nlconst = fs.solve_flat_ocp(
             flat_sys, timepts, x0, u0,
-            cost=trajectory_cost, terminal_cost=terminal_cost,
-            constraints=nl_constraints, basis=basis,
+            trajectory_cost=trajectory_cost, terminal_cost=terminal_cost,
+            trajectory_constraints=nl_constraints, basis=basis,
         )
         x_nlconst, u_nlconst = traj_nlconst.eval(timepts)
         np.testing.assert_almost_equal(x_const, x_nlconst)
@@ -685,7 +686,7 @@ def test_solve_flat_ocp_errors(self):
         # Try to optimize with insufficient degrees of freedom
         with pytest.raises(ValueError, match="basis set is too small"):
             traj = fs.solve_flat_ocp(
-                flat_sys, timepts, x0, u0, cost=cost_fcn,
+                flat_sys, timepts, x0, u0, trajectory_cost=cost_fcn,
                 basis=fs.PolyFamily(2))
 
         # Solve with the errors in the various input arguments
@@ -700,20 +701,21 @@ def test_solve_flat_ocp_errors(self):
         with pytest.raises(TypeError, match="must be a list"):
             traj = fs.solve_flat_ocp(
                 flat_sys, timepts, x0, u0, cost_fcn,
-                constraints=np.eye(2), basis=fs.PolyFamily(8))
+                trajectory_constraints=np.eye(2), basis=fs.PolyFamily(8))
 
         # Unknown constraint type
         with pytest.raises(TypeError, match="unknown constraint type"):
             traj = fs.solve_flat_ocp(
                 flat_sys, timepts, x0, u0, cost_fcn,
-                constraints=[(None, 0, 0, 0)], basis=fs.PolyFamily(8))
+                trajectory_constraints=[(None, 0, 0, 0)],
+                basis=fs.PolyFamily(8))
 
         # Method arguments, parameters
         traj_method = fs.solve_flat_ocp(
-            flat_sys, timepts, x0, u0, cost=cost_fcn,
+            flat_sys, timepts, x0, u0, trajectory_cost=cost_fcn,
             basis=fs.PolyFamily(6), minimize_method='slsqp')
         traj_kwarg = fs.solve_flat_ocp(
-            flat_sys, timepts, x0, u0, cost=cost_fcn,
+            flat_sys, timepts, x0, u0, trajectory_cost=cost_fcn,
             basis=fs.PolyFamily(6), minimize_kwargs={'method': 'slsqp'})
         np.testing.assert_allclose(
             traj_method.eval(timepts)[0], traj_kwarg.eval(timepts)[0],
