1 test fails: TypeError: unsupported operand type(s) for *: 'NoneType' and 'float'

[yurivict]

========================================================================================= FAILURES ==========================================================================================
__________________________________________________________________________________ TestWrappers.test_pinv ___________________________________________________________________________________

self = <tests.test_linalg.TestWrappers object at 0x1711c8c9ddd0>
matrices = <xarray.DataArray (batch: 10, experiment: 3, dim: 4, dim2: 4)> Size: 4kB
0.11 0.3897 1.4 2.2 0.3435 0.2578 0.4345 ... 0.3557 1.105 2.256 2.58 1.683 1.311
Dimensions without coordinates: batch, experiment, dim, dim2

    def test_pinv(self, matrices):
>       out = pinv(matrices, dims=("experiment", "dim"))

test_linalg.py:145: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../../stage/usr/local/lib/python3.11/site-packages/xarray_einstats/linalg.py:807: in pinv
    return xr.apply_ufunc(
/usr/local/lib/python3.11/site-packages/xarray/core/computation.py:1268: in apply_ufunc
    return apply_dataarray_vfunc(
/usr/local/lib/python3.11/site-packages/xarray/core/computation.py:312: in apply_dataarray_vfunc
    result_var = func(*data_vars)
/usr/local/lib/python3.11/site-packages/xarray/core/computation.py:821: in apply_variable_ufunc
    result_data = func(*input_data)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

a = array([[[[1.10014813e-01, 3.43493819e-01, 1.00096157e+00,
          2.66531379e-01],
         [9.07669713e-01, 1.79952...-01,
          3.49558767e-03],
         [1.81801768e+00, 2.39405153e+00, 1.10546137e+00,
          1.31130995e+00]]]])
rcond = array(None, dtype=object), hermitian = False

    @array_function_dispatch(_pinv_dispatcher)
    def pinv(a, rcond=1e-15, hermitian=False):
        """
        Compute the (Moore-Penrose) pseudo-inverse of a matrix.
    
        Calculate the generalized inverse of a matrix using its
        singular-value decomposition (SVD) and including all
        *large* singular values.
    
        .. versionchanged:: 1.14
           Can now operate on stacks of matrices
    
        Parameters
        ----------
        a : (..., M, N) array_like
            Matrix or stack of matrices to be pseudo-inverted.
        rcond : (...) array_like of float
            Cutoff for small singular values.
            Singular values less than or equal to
            ``rcond * largest_singular_value`` are set to zero.
            Broadcasts against the stack of matrices.
        hermitian : bool, optional
            If True, `a` is assumed to be Hermitian (symmetric if real-valued),
            enabling a more efficient method for finding singular values.
            Defaults to False.
    
            .. versionadded:: 1.17.0
    
        Returns
        -------
        B : (..., N, M) ndarray
            The pseudo-inverse of `a`. If `a` is a `matrix` instance, then so
            is `B`.
    
        Raises
        ------
        LinAlgError
            If the SVD computation does not converge.
    
        See Also
        --------
        scipy.linalg.pinv : Similar function in SciPy.
        scipy.linalg.pinvh : Compute the (Moore-Penrose) pseudo-inverse of a
                             Hermitian matrix.
    
        Notes
        -----
        The pseudo-inverse of a matrix A, denoted :math:`A^+`, is
        defined as: "the matrix that 'solves' [the least-squares problem]
        :math:`Ax = b`," i.e., if :math:`\\bar{x}` is said solution, then
        :math:`A^+` is that matrix such that :math:`\\bar{x} = A^+b`.
    
        It can be shown that if :math:`Q_1 \\Sigma Q_2^T = A` is the singular
        value decomposition of A, then
        :math:`A^+ = Q_2 \\Sigma^+ Q_1^T`, where :math:`Q_{1,2}` are
        orthogonal matrices, :math:`\\Sigma` is a diagonal matrix consisting
        of A's so-called singular values, (followed, typically, by
        zeros), and then :math:`\\Sigma^+` is simply the diagonal matrix
        consisting of the reciprocals of A's singular values
        (again, followed by zeros). [1]_
    
        References
        ----------
        .. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando,
               FL, Academic Press, Inc., 1980, pp. 139-142.
    
        Examples
        --------
        The following example checks that ``a * a+ * a == a`` and
        ``a+ * a * a+ == a+``:
    
        >>> a = np.random.randn(9, 6)
        >>> B = np.linalg.pinv(a)
        >>> np.allclose(a, np.dot(a, np.dot(B, a)))
        True
        >>> np.allclose(B, np.dot(B, np.dot(a, B)))
        True
    
        """
        a, wrap = _makearray(a)
        rcond = asarray(rcond)
        if _is_empty_2d(a):
            m, n = a.shape[-2:]
            res = empty(a.shape[:-2] + (n, m), dtype=a.dtype)
            return wrap(res)
        a = a.conjugate()
        u, s, vt = svd(a, full_matrices=False, hermitian=hermitian)
    
        # discard small singular values
>       cutoff = rcond[..., newaxis] * amax(s, axis=-1, keepdims=True)
E       TypeError: unsupported operand type(s) for *: 'NoneType' and 'float'

/usr/local/lib/python3.11/site-packages/numpy/linalg/linalg.py:2025: TypeError
===================================================================================== warnings summary ======================================================================================
tests/test_stats.py::TestMvNormal::test_rvs_method[vals0]
tests/test_stats.py::TestMvNormal::test_rvs_method[vals1]
  /usr/ports/devel/py-xarray-einstats/work-py311/stage/usr/local/lib/python3.11/site-packages/xarray_einstats/stats.py:384: PendingDeprecationWarning: The `dims` argument has been renamed to `dim`, and will be removed in the future. This renaming is taking place throughout xarray over the next few releases.
    return mean + xr.dot(cov_chol, samples, dims=dim2)

tests/test_stats.py::TestMvNormal::test_pdf_method[vals0]
tests/test_stats.py::TestMvNormal::test_pdf_method[vals1]
  /usr/ports/devel/py-xarray-einstats/work-py311/stage/usr/local/lib/python3.11/site-packages/xarray_einstats/stats.py:397: PendingDeprecationWarning: The `dims` argument has been renamed to `dim`, and will be removed in the future. This renaming is taking place throughout xarray over the next few releases.
    maha = np.square(xr.dot(x_mu.rename({dim1: dim2}), u_mat, dims=dim2)).sum(dim=dim1)

tests/test_stats.py: 20 warnings
  /usr/local/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1069: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
    else dtype(mean))

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
================================================================================== short test summary info ==================================================================================
SKIPPED [1] test_stats.py:193: rankdata doesn't support Dataset input and dims=None
============================================================= 1 failed, 263 passed, 1 skipped, 24 warnings in 192.08s (0:03:12) =============================================================
*** Error code 1

Version: 0.8.0 Python 3.11 FreeBSD 14.1

[OriolAbril]

Hi, sorry. I can't reproduce this (nor can the project's CI). Can you add the versions you are using for the dependencies of the project? numpy, scipy and xarray