gistfile1.txt

# Amici.py

def check_gradients(self, x: np.ndarray = None, *args, **kwargs):
       if x is None and 'petab_problem' in dir(self.amici_object_builder):
           x = self.amici_object_builder.petab_problem.x_nominal_scaled
           x_free = self.amici_object_builder.petab_problem.x_free_indices
       return ObjectiveBase.check_gradients(self, x=x, x_free=x_free, *args, **kwargs)
       
       
# Base.py
  def check_gradients(
        self,
        *args,
        x: np.ndarray = None, 
        x_free: Sequence[int] = None,
        rtol: float = 1e-2,
        atol: float = 1e-3,
        mode: Literal = None,
        multi_eps=None,
        **kwargs,
    ) -> bool:
        """Check if gradients match finite differences (FDs)

        Parameters
        ----------
        rtol: relative error tolerance
        atol: absolute error tolerance
        mode: function values or residuals
        objAbsoluteTolerance: absolute tolerance in sensitivity calculation
        objRelativeTolerance: relative tolerance in sensitivity calculation
        multi_eps: multiple test step width for FDs

        Returns
        -------
        bool
            Indicates whether gradients match (True) FDs or not (False)
        """
        par = np.asarray(x)
        free_indices = par[x_free]
        dfs = []
        modes = []

        if mode is None:
            modes = [MODE_FUN, MODE_RES]
        else:
            modes = [mode]

        if multi_eps is None:
            multi_eps = np.array([10**(-i) for i in range(3, 9)])

        for mode in modes:
            try:
                dfs.append(self.check_grad_multi_eps(
                            free_indices, *args, **kwargs,
                            mode=mode, multi_eps=multi_eps))
            except (RuntimeError, ValueError):
                # Might happen in case PEtab problem not well defined or
                # fails for specified tolerances in forward sensitivities
                return False

        return all([
            any([
                np.all((mode_df.rel_err.values < rtol) |
                       (mode_df.abs_err.values < atol)),
            ])
            for mode_df in dfs
        ])