Bug in anisotropic optimizer with recent update on numpy 2.4

[PFLeget]

@rmjarvis was reporting to me that after updating to numpy 2.4, treegp looks to fail unit test in Piff with this error

$ pytest test_gp_interp.py -k aniso
======================================= test session starts ========================================
platform darwin -- Python 3.12.2, pytest-7.4.3, pluggy-1.3.0
rootdir: /Users/Mike/rmjarvis/Piff
configfile: pyproject.toml
plugins: timeout-2.2.0, xdist-3.3.1, nbval-0.11.0, anyio-4.6.2
collected 3 items / 2 deselected / 1 selected                                                      

test_gp_interp.py F                                                                          [100%]

============================================= FAILURES =============================================
____________________________________ test_gp_interp_anisotropic ____________________________________

    @timer
    def test_gp_interp_anisotropic():
    
        if __name__ == "__main__":
            atol = 4e-2
            rtol = 3e-1
            nstars = [1600, 4000, 1600, 4000]
        else:
            atol = 4e-1
            rtol = 5e-1
            nstars = [160, 500, 160, 500]
    
        noise_level = 1e-4
    
        L1 = get_correlation_length_matrix(4., 0.3, 0.3)
        invL1 = np.linalg.inv(L1)
        L2 = get_correlation_length_matrix(20., 0.3, 0.3)
        invL2 = np.linalg.inv(L2)
    
        kernels = ["4e-4 * AnisotropicRBF(invLam={0!r})".format(invL1),
                   "4e-4 * AnisotropicRBF(invLam={0!r})".format(invL1),
                   "4e-4 * AnisotropicVonKarman(invLam={0!r})".format(invL2),
                   "4e-4 * AnisotropicVonKarman(invLam={0!r})".format(invL2)]
    
        optimizer = ['none',
                     'anisotropic',
                     'none',
                     'anisotropic']
    
        for i in range(len(kernels)):
    
            stars_training, stars_validation = make_gaussian_random_fields(
                    kernels[i], nstars[i], xlim=-20, ylim=20,
                    seed=30352010, vmax=4e-2,
                    noise_level=noise_level)
>           check_gp(stars_training, stars_validation, kernels[i],
                     optimizer[i], min_sep=0., max_sep=5., nbins=11,
                     l0=20., atol=atol, rtol=rtol, plotting=False)

test_gp_interp.py:385: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test_gp_interp.py:187: in check_gp
    interp.solve(stars=stars_training, logger=None)
../piff/gp_interp.py:260: in solve
    self._fit(X, y, y_err=y_err, logger=logger)
../piff/gp_interp.py:177: in _fit
    self.gps[i].solve()
../../../miniforge3/envs/py3.12/lib/python3.12/site-packages/treegp/gp_interp.py:253: in solve
    self.kernel = self._fit(
../../../miniforge3/envs/py3.12/lib/python3.12/site-packages/treegp/gp_interp.py:136: in _fit
    kernel = self._optimizer.optimizer(kernel)
../../../miniforge3/envs/py3.12/lib/python3.12/site-packages/treegp/two_pcf.py:443: in optimizer
    robust.minimize_minuit(p0=self.p0_robust_fit)
../../../miniforge3/envs/py3.12/lib/python3.12/site-packages/treegp/two_pcf.py:186: in minimize_minuit
    self._minimize_minuit(p0=p0)
../../../miniforge3/envs/py3.12/lib/python3.12/site-packages/treegp/two_pcf.py:160: in _minimize_minuit
    self.m.migrad()
../../../miniforge3/envs/py3.12/lib/python3.12/site-packages/iminuit/minuit.py:789: in migrad
    fm = _robust_low_level_fit(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

fcn = <iminuit._core.FCN object at 0x1342f7fb0>
state = <iminuit._core.MnUserParameterState object at 0x1342f5130>, ncall = 0
strategy = <iminuit._core.MnStrategy object at 0x1348770b0>, tolerance = 0.1, precision = None
iterate = 5, use_simplex = True

    def _robust_low_level_fit(
        fcn: FCN,
        state: MnUserParameterState,
        ncall: int,
        strategy: MnStrategy,
        tolerance: float,
        precision: Optional[float],
        iterate: int,
        use_simplex: bool,
    ) -> FunctionMinimum:
        # Automatically call Migrad up to `iterate` times if minimum is not valid.
        # This simple heuristic makes Migrad converge more often. Optionally,
        # one can interleave calls to Simplex and Migrad, which may also help.
        migrad = MnMigrad(fcn, state, strategy)
        if precision is not None:
            migrad.precision = precision
>       fm = migrad(ncall, tolerance)
E       RuntimeError: Unable to cast Python instance of type <class 'numpy.ndarray'> to C++ type 'double'

../../../miniforge3/envs/py3.12/lib/python3.12/site-packages/iminuit/minuit.py:2782: RuntimeError
===================================== short test summary info ======================================
FAILED test_gp_interp.py::test_gp_interp_anisotropic - RuntimeError: Unable to cast Python instance of type <class 'numpy.ndarray'> to C++ type 'double'
================================= 1 failed, 2 deselected in 1.35s ==================================

I need to reproduce this error and find what is going on.