#1863 fix scikits solvers

pybamm/solvers/algebraic_solver.py

@@ -67,7 +67,8 @@ def _integrate(self, model, t_eval, inputs_dict=None):
 
         y0 = model.y0
         if isinstance(y0, casadi.DM):
-            y0 = y0.full().flatten()
+            y0 = y0.full()
+        y0 = y0.flatten()
 
         # The casadi algebraic solver can read rhs equations, but leaves them unchanged
         # i.e. the part of the solution vector that corresponds to the differential

pybamm/solvers/scikits_ode_solver.py

@@ -98,8 +98,12 @@ def _integrate(self, model, t_eval, inputs_dict=None):
         events = model.terminate_events_eval
         jacobian = model.jac_rhs_eval
 
-        def eqsydot(t, y, return_ydot):
-            return_ydot[:] = derivs(t, y, inputs)
+        if model.convert_to_format == "casadi":
+            def eqsydot(t, y, return_ydot):
+                return_ydot[:] = derivs(t, y, inputs).full().flatten()
+        else:
+            def eqsydot(t, y, return_ydot):
+                return_ydot[:] = derivs(t, y, inputs).flatten()
 
         def rootfn(t, y, return_root):
             return_root[:] = [event(t, y, inputs) for event in events]

tests/unit/test_solvers/test_scikits_solvers.py

@@ -49,10 +49,10 @@ class Model:
             length_scales = {}
             convert_to_format = "python"
 
-            def residuals_eval(self, t, y, ydot, inputs):
-                return np.array([0.5 * np.ones_like(y[0]) - ydot[0], 2 * y[0] - y[1]])
+            def rhs_algebraic_eval(self, t, y, inputs):
+                return np.array([0.5 * np.ones_like(y[0]), 2 * y[0] - y[1]])
 
-            def jacobian_eval(self, t, y, inputs):
+            def jac_rhs_algebraic_eval(self, t, y, inputs):
                 return np.array([[0.0, 0.0], [2.0, -1.0]])
 
         model = Model()
@@ -101,12 +101,12 @@ class Model:
             convert_to_format = "python"
             len_rhs_and_alg = 2
 
-            def residuals_eval(self, t, y, ydot, inputs):
+            def rhs_algebraic_eval(self, t, y, inputs):
                 return np.array(
-                    [0.5 * np.ones_like(y[0]) - 4 * ydot[0], 2.0 * y[0] - y[1]]
+                    [0.5 * np.ones_like(y[0]), 2.0 * y[0] - y[1]]
                 )
 
-            def jacobian_eval(self, t, y, inputs):
+            def jac_rhs_algebraic_eval(self, t, y, inputs):
                 return np.array([[0.0, 0.0], [2.0, -1.0]])
 
         model = Model()