status: failed
duration: 0.048s
captured errors:
def test_big_delay():
        length_m = 3995
        delta_t = length_m / c_m_s
        delta_t = 1
        axB = mplfigB(Nrows=2)
    
        idx_ord = 100
>       arm1 = delay_algorithms.bessel_delay_ABCDE(delta_t, order=idx_ord)

../../src/wield/control/algorithms/statespace/dense/test/test_delay.py:71: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../../src/wield/control/algorithms/statespace/dense/delay_algorithms.py:67: in bessel_delay_ABCDE
    zdict, pdict, k = bessel_delay(delay_s, order=order, rescale=rescale)
../../src/wield/control/algorithms/statespace/dense/delay_algorithms.py:45: in bessel_delay
    z, p, k = scipy.signal.besselap(order, norm="delay")
/opt/conda/lib/python3.12/site-packages/scipy/signal/_filter_design.py:4914: in besselap
    p = 1/_bessel_zeros(N)
/opt/conda/lib/python3.12/site-packages/scipy/signal/_filter_design.py:4786: in _bessel_zeros
    x = _aberth(f, fp, x0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

f = <function _bessel_zeros.<locals>.f at 0x7f962761e480>
fp = <function _bessel_zeros.<locals>.fp at 0x7f962761e520>
x0 = array([-0.00058824-0.01j      , -0.00116471-0.01018214j,
       -0.00172941-0.01034076j, -0.00228235-0.01047634j,
    ...38j,
       -0.00228235+0.01047634j, -0.00172941+0.01034076j,
       -0.00116471+0.01018214j, -0.00058824+0.01j      ])
tol = 1e-15, maxiter = 50

    def _aberth(f, fp, x0, tol=1e-15, maxiter=50):
        """
        Given a function `f`, its first derivative `fp`, and a set of initial
        guesses `x0`, simultaneously find the roots of the polynomial using the
        Aberth-Ehrlich method.
    
        ``len(x0)`` should equal the number of roots of `f`.
    
        (This is not a complete implementation of Bini's algorithm.)
        """
    
        N = len(x0)
    
        x = array(x0, complex)
        beta = np.empty_like(x0)
    
        for iteration in range(maxiter):
            alpha = -f(x) / fp(x)  # Newton's method
    
            # Model "repulsion" between zeros
            for k in range(N):
                beta[k] = np.sum(1/(x[k] - x[k+1:]))
                beta[k] += np.sum(1/(x[k] - x[:k]))
    
            x += alpha / (1 + alpha * beta)
    
            if not all(np.isfinite(x)):
>               raise RuntimeError('Root-finding calculation failed')
E               RuntimeError: Root-finding calculation failed

/opt/conda/lib/python3.12/site-packages/scipy/signal/_filter_design.py:4751: RuntimeError