status: failed
duration: 0.051s
Captured stdout call
zpk(z=2π·[-0.1591549430918953], p=2π·[], k=0.1)
captured errors:
zpk = ((-1,), (), 0.1)

    @pytest.mark.parametrize('zpk', [
        ((0,), (), 0.1),
        ((), (0,), 0.1),
        ((-1,), (), 0.1),
        ((), (-1,), 0.1),
        ((0, 0,), (10, 10), 0.1),
        ((10, 10), (0, 0), 0.1),
        ((-1, -1,), (10, 10), 0.1),
        ((10, 10), (-1, -1), 0.1),
        ((0, ), (10, 10), 0.1),
        ((10, 10), (0,), 0.1),
        ((-1, ), (10, 10), 0.1),
        ((10, 10), (-1, ), 0.1),
    ])
    def test_ZPK_various(zpk):
        """
        Test the conversions to and from ZPK representation and statespace representation
        using a delay filter
        """
        delta_t = 1
        axB = mplfigB(Nrows=2)
        F_Hz = logspaced(0.01 / delta_t, 2 / delta_t, 1000)
    
        filt = SISO.zpk(zpk, fiducial_rtol=1e-7)
        print(filt)
    
        xfer1 = filt.fresponse(f=F_Hz).tf
        axB.ax0.semilogx(F_Hz, abs(xfer1), label="Direct ZPK")
        axB.ax1.plot(F_Hz, np.angle(xfer1, deg=True))
    
        xfer2 = (filt * filt).fresponse(f=F_Hz).tf
        axB.ax0.semilogx(F_Hz, abs(xfer2), label="ZPK self product")
        axB.ax1.plot(F_Hz, np.angle(xfer2, deg=True))
    
>       filt_ss = filt.asSS * 4

../../src/wield/control/SISO/test/test_SISO_delay.py:194: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../../src/wield/control/SISO/zpk.py:219: in asSS
    self._SS = algorithm_choice.algo_run(
../../src/wield/control/utilities/algorithm_choice.py:174: in algo_run
    ret = func(*args, **kwargs)
../../src/wield/control/SISO/algorithms_zpk2ss.py:81: in zpk2ss_chain_poly
    statesp = ss.SISOStateSpace(
../../src/wield/control/SISO/ss.py:130: in __init__
    self.__init_internal__(
../../src/wield/control/utilities/__init__.py:35: in wrap
    ret = self.method(inst, *args, **kw)
../../src/wield/control/SISO/ss.py:163: in __init_internal__
    self.test_fresponse(
../../src/wield/control/SISO/siso.py:45: in test_fresponse
    self_response = self.fresponse(**fiducial.domain_kw())
../../src/wield/control/SISO/ss.py:287: in fresponse
    tf = self.ss.fresponse_raw(f=f, w=w, s=s, z=z, **kwargs)[..., 0, 0]
../../src/wield/control/ss_bare/ss.py:288: in fresponse_raw
    return algorithm_choice.algo_run(
../../src/wield/control/utilities/algorithm_choice.py:174: in algo_run
    ret = func(*args, **kwargs)
../../src/wield/control/ss_bare/algorithms_xfers.py:21: in ss2fresponse_laub
    ss = ss.balanceA(which='ABC')
../../src/wield/control/ss_bare/ss.py:574: in balanceA
    Ascale, (sca, P) = scipy.linalg.matrix_balance(
/opt/conda/lib/python3.12/site-packages/scipy/linalg/_basic.py:1662: in matrix_balance
    A = np.atleast_2d(_asarray_validated(A, check_finite=True))
/opt/conda/lib/python3.12/site-packages/scipy/_lib/_util.py:321: in _asarray_validated
    a = toarray(a)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = array([[0. , 1. ],
       [0.5, nan]]), dtype = None, order = None

    @set_module('numpy')
    def asarray_chkfinite(a, dtype=None, order=None):
        """Convert the input to an array, checking for NaNs or Infs.
    
        Parameters
        ----------
        a : array_like
            Input data, in any form that can be converted to an array.  This
            includes lists, lists of tuples, tuples, tuples of tuples, tuples
            of lists and ndarrays.  Success requires no NaNs or Infs.
        dtype : data-type, optional
            By default, the data-type is inferred from the input data.
        order : {'C', 'F', 'A', 'K'}, optional
            Memory layout.  'A' and 'K' depend on the order of input array a.
            'C' row-major (C-style),
            'F' column-major (Fortran-style) memory representation.
            'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise
            'K' (keep) preserve input order
            Defaults to 'C'.
    
        Returns
        -------
        out : ndarray
            Array interpretation of `a`.  No copy is performed if the input
            is already an ndarray.  If `a` is a subclass of ndarray, a base
            class ndarray is returned.
    
        Raises
        ------
        ValueError
            Raises ValueError if `a` contains NaN (Not a Number) or Inf (Infinity).
    
        See Also
        --------
        asarray : Create and array.
        asanyarray : Similar function which passes through subclasses.
        ascontiguousarray : Convert input to a contiguous array.
        asfarray : Convert input to a floating point ndarray.
        asfortranarray : Convert input to an ndarray with column-major
                         memory order.
        fromiter : Create an array from an iterator.
        fromfunction : Construct an array by executing a function on grid
                       positions.
    
        Examples
        --------
        Convert a list into an array.  If all elements are finite
        ``asarray_chkfinite`` is identical to ``asarray``.
    
        >>> a = [1, 2]
        >>> np.asarray_chkfinite(a, dtype=float)
        array([1., 2.])
    
        Raises ValueError if array_like contains Nans or Infs.
    
        >>> a = [1, 2, np.inf]
        >>> try:
        ...     np.asarray_chkfinite(a)
        ... except ValueError:
        ...     print('ValueError')
        ...
        ValueError
    
        """
        a = asarray(a, dtype=dtype, order=order)
        if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all():
>           raise ValueError(
                "array must not contain infs or NaNs")
E           ValueError: array must not contain infs or NaNs

/opt/conda/lib/python3.12/site-packages/numpy/lib/function_base.py:630: ValueError