#1477 flake8

pybamm/expression_tree/operations/evaluate.py

@@ -626,11 +626,10 @@ def get_sensitivities(self):
         jacobian_evaluate = jax.jacfwd(self._evaluate_jax, argnums=3 + n)
 
         self._sens_evaluate = jax.jit(jacobian_evaluate,
-                                     static_argnums=self._static_argnums)
+                                      static_argnums=self._static_argnums)
 
         return EvaluatorJaxSensitivities(self._sens_evaluate, self._constants)
 
-
     def debug(self, t=None, y=None, y_dot=None, inputs=None, known_evals=None):
         # generated code assumes y is a column vector
         if y is not None and y.ndim == 1:
@@ -688,6 +687,7 @@ def evaluate(self, t=None, y=None, y_dot=None, inputs=None, known_evals=None):
         else:
             return result
 
+
 class EvaluatorJaxSensitivities:
     def __init__(self, jac_evaluate, constants):
         self._jac_evaluate = jac_evaluate

pybamm/solvers/base_solver.py

@@ -349,6 +349,7 @@ def jacp(*args, **kwargs):
                             [p_diff]
                         )
                     # jacp should be a function that returns a dict of sensitivities
+
                     def jacp(*args, **kwargs):
                         return {k: v(*args, **kwargs)
                                 for k, v in jacp_dict.items()}
@@ -1327,6 +1328,7 @@ def function(self, t, y, inputs):
         else:
             return self._function(t, y, inputs=inputs, known_evals={})[0]
 
+
 class SensitivityCallable:
     """A class that will be called by the solver when integrating"""
 
@@ -1355,6 +1357,7 @@ def function(self, t, y, inputs):
                 self._function(t, y, inputs=inputs, known_evals={})
             return {k: v[0] for k, v in ret_with_known_evals.items()}
 
+
 class Residuals(SolverCallable):
     """Returns information about residuals at time t and state y"""
 

pybamm/solvers/idaklu_solver.py

@@ -293,8 +293,6 @@ def sensfn(resvalS, t, y, yp, yS, ypS):
 
             """
 
-            np = len(resvalS)
-            n = resvalS[0].shape[0]
             dFdy = model.jacobian_eval(t, y, inputs)
             dFdyd = mass_matrix
             dFdp = model.sensitivities_eval(t, y, inputs)

tests/unit/test_solvers/test_base_solver.py

@@ -362,7 +362,7 @@ def exact_diff_b(y, a, b):
                     use_inputs = inputs
 
                 sens = model.sensitivities_eval(
-                        t, y, use_inputs
+                    t, y, use_inputs
                 )
                 np.testing.assert_allclose(
                     sens['a'],