build a pyemu.Cov instance implied by Vario2d

        Parameters
        ----------
        x : (iterable of floats)
            x-coordinate locations
        y : (iterable of floats)
            y-coordinate locations
        names : (iterable of str)
            names of locations. If None, cov must not be None
        cov : (pyemu.Cov)
            an existing Cov instance.  Vario2d contribution is added to cov

        Returns
        -------
        cov : pyemu.Cov

        Note
        ----
        either names or cov must not be None.

def covariance_matrix(self,x,y,names=None,cov=None):
        """build a pyemu.Cov instance implied by Vario2d

        Parameters
        ----------
        x : (iterable of floats)
            x-coordinate locations
        y : (iterable of floats)
            y-coordinate locations
        names : (iterable of str)
            names of locations. If None, cov must not be None
        cov : (pyemu.Cov)
            an existing Cov instance.  Vario2d contribution is added to cov

        Returns
        -------
        cov : pyemu.Cov

        Note
        ----
        either names or cov must not be None.

        """
        if not isinstance(x,np.ndarray):
            x = np.array(x)
        if not isinstance(y,np.ndarray):
            y = np.array(y)
        assert x.shape[0] == y.shape[0]

        if names is not None:
            assert x.shape[0] == len(names)
            c = np.zeros((len(names),len(names)))
            np.fill_diagonal(c,self.contribution)
            cov = Cov(x=c,names=names)
        elif cov is not None:
            assert cov.shape[0] == x.shape[0]
            names = cov.row_names
            c = np.zeros((len(names),1)) + self.contribution
            cont = Cov(x=c,names=names,isdiagonal=True)
            cov += cont

        else:
            raise Exception("Vario2d.covariance_matrix() requires either" +
                            "names or cov arg")
        rc = self.rotation_coefs
        for i1,(n1,x1,y1) in enumerate(zip(names,x,y)):
            dx = x1 - x[i1+1:]
            dy = y1 - y[i1+1:]
            dxx,dyy = self._apply_rotation(dx,dy)
            h = np.sqrt(dxx*dxx + dyy*dyy)

            h[h<0.0] = 0.0
            h = self._h_function(h)
            if np.any(np.isnan(h)):
                raise Exception("nans in h for i1 {0}".format(i1))
            cov.x[i1,i1+1:] += h
        for i in range(len(names)):
            cov.x[i+1:,i] = cov.x[i,i+1:]
        return cov