def mahalanobis_covariance(x, y, diff_degree, amp, val, vec, symm=None):
    """
    Converts x and y to a matrix of covariances. x and y are assumed to have
    columns (long,lat,t). Parameters are:
    - t_gam_fun: A function returning a matrix of variogram values.
      Inputs will be the 't' columns of x and y, as well as kwds.
    - amp: The MS amplitude of realizations.
    - scale: Scales distance.
    - inc, ecc: Anisotropy parameters.
    - n_threads: Maximum number of threads available to function.
    - symm: Flag indicating whether matrix will be symmetric (optional).
    - kwds: Passed to t_gam_fun.
    
    Output value should never drop below -1. This happens when:
    -1 > -sf*c+k
    
    """
    # Allocate
    nx = x.shape[0]
    ny = y.shape[0]
    ndim = x.shape[1]

    C = np.asmatrix(np.empty((nx, ny), order="F"))

    # Figure out symmetry and threading
    if symm is None:
        symm = x is y

    n_threads = min(pm.get_threadpool_size(), nx * ny / 2000)

    if n_threads > 1:
        if not symm:
            bounds = np.linspace(0, ny, n_threads + 1)
        else:
            bounds = np.array(np.sqrt(np.linspace(0, ny * ny, n_threads + 1)), dtype=int)

    # Target function for threads
    def targ(C, x, y, symm, diff_degree, amp, val, vec, cmin, cmax):
        mahal(C, x, y, symm, diff_degree, gamma(diff_degree), amp, val, vec, cmin, cmax)

    # Dispatch threads
    if n_threads <= 1:
        mahal(C, x, y, symm, diff_degree, gamma(diff_degree), amp, val, vec, cmin=0, cmax=C.shape[1])
    else:
        thread_args = [(C, x, y, symm, diff_degree, amp, val, vec, bounds[i], bounds[i + 1]) for i in xrange(n_threads)]
        pm.map_noreturn(targ, thread_args)

    if symm:
        pm.gp.symmetrize(C)

        # eigs = np.linalg.eigh(C)
        # val,vec = eigs
        # if np.any(val<-1.e-3):
        #     raise RuntimeError, 'Negative eigenvalues: %s'%val[np.where(val<0)]

    return C
 def targ(C, x, y, symm, diff_degree, amp, val, vec, cmin, cmax):
     mahal(C, x, y, symm, diff_degree, gamma(diff_degree), amp, val, vec, cmin, cmax)