#633 updated klu to take in abs and rel tol

pybamm/solvers/klu_sparse_solver.py

@@ -20,29 +20,29 @@ def have_klu():
 class KLU(pybamm.DaeSolver):
     """Solve a discretised model, using sundials with the KLU sparse linear solver.
 
-    Parameters
+     Parameters
     ----------
-    method : str, optional
-        The method to use in solve_ivp (default is "BDF")
-    tolerance : float, optional
-        The tolerance for the solver (default is 1e-8). Set as the both reltol and
-        abstol in solve_ivp.
+    rtol : float, optional
+        The relative tolerance for the solver (default is 1e-6).
+    atol : float, optional
+        The absolute tolerance for the solver (default is 1e-6).
     root_method : str, optional
         The method to use to find initial conditions (default is "lm")
-    tolerance : float, optional
+    root_tol : float, optional
         The tolerance for the initial-condition solver (default is 1e-8).
     max_steps: int, optional
         The maximum number of steps the solver will take before terminating
         (default is 1000).
     """
 
     def __init__(
-        self, method="ida", tol=1e-8, root_method="lm", root_tol=1e-6, max_steps=1000
+        self, rtol=1e-6, atol=1e-6, root_method="lm", root_tol=1e-6, max_steps=1000
     ):
+
         if klu_spec is None:
             raise ImportError("KLU is not installed")
 
-        super().__init__(method, tol, root_method, root_tol, max_steps)
+        super().__init__("ida", rtol, atol, root_method, root_tol, max_steps)
 
     def integrate(self, residuals, y0, t_eval, events, mass_matrix, jacobian):
         """
@@ -71,8 +71,8 @@ def integrate(self, residuals, y0, t_eval, events, mass_matrix, jacobian):
         def eqsres(t, y, ydot, return_residuals):
             return_residuals[:] = residuals(t, y, ydot)
 
-        rtol = self.tol
-        atol = self.tol
+        rtol = self._rtol
+        atol = self._atol
 
         if jacobian:
             jac_y0_t0 = jacobian(t_eval[0], y0)