def test_equal_original(almost_equal_decimals = 5):
    rad, L, Gtest, Htest, phi = _load_test_data()

    H, G, Hvv, Hsvals, Gsvals = riemann_metric( phi, laplacian = L, n_dim = 2, invert_h = True )
    n = phi.shape[ 0 ]
    assert_array_almost_equal( Htest, H, almost_equal_decimals )

    tol = np.mean( Gtest[:,0,0])*10**(-almost_equal_decimals )
    assert_allclose( Gtest, G, tol)
#    assert_array_max_ulp( Gtest, G, almost_equal_decimals )
    # this assertion fails because Gtest is generally asymmetric. G is
    # mostly symmetric but not always. I suspect this is due to the
    # numerical errors, as many of these 2x2 matrices are very poorly
    # conditioned. What to do? Perhaps generate another matlab test set
    # with better condition numbers...
Loads the results from a matlab run and checks that our results
dataset/ETHPy150Open mmp2/megaman/megaman/geometry/tests/test_rmetric.py/test_equal_original