Function test_triinterp

lib/matplotlib/tests/test_triangulation.py:428–520

()

def test_triinterp():
    # Test points within triangles of masked triangulation.
    x, y = np.meshgrid(np.arange(4), np.arange(4))
    x = x.ravel()
    y = y.ravel()
    z = 1.23*x - 4.79*y
    triangles = [[0, 1, 4], [1, 5, 4], [1, 2, 5], [2, 6, 5], [2, 3, 6],
                 [3, 7, 6], [4, 5, 8], [5, 9, 8], [5, 6, 9], [6, 10, 9],
                 [6, 7, 10], [7, 11, 10], [8, 9, 12], [9, 13, 12], [9, 10, 13],
                 [10, 14, 13], [10, 11, 14], [11, 15, 14]]
    mask = np.zeros(len(triangles))
    mask[8:10] = 1
    triang = mtri.Triangulation(x, y, triangles, mask)
    linear_interp = mtri.LinearTriInterpolator(triang, z)
    cubic_min_E = mtri.CubicTriInterpolator(triang, z)
    cubic_geom = mtri.CubicTriInterpolator(triang, z, kind='geom')

    xs = np.linspace(0.25, 2.75, 6)
    ys = [0.25, 0.75, 2.25, 2.75]
    xs, ys = np.meshgrid(xs, ys)  # Testing arrays with array.ndim = 2
    for interp in (linear_interp, cubic_min_E, cubic_geom):
        zs = interp(xs, ys)
        assert_array_almost_equal(zs, (1.23*xs - 4.79*ys))

    # Test points outside triangulation.
    xs = [-0.25, 1.25, 1.75, 3.25]
    ys = xs
    xs, ys = np.meshgrid(xs, ys)
    for interp in (linear_interp, cubic_min_E, cubic_geom):
        zs = linear_interp(xs, ys)
        assert_array_equal(zs.mask, [[True]*4]*4)

    # Test mixed configuration (outside / inside).
    xs = np.linspace(0.25, 1.75, 6)
    ys = [0.25, 0.75, 1.25, 1.75]
    xs, ys = np.meshgrid(xs, ys)
    for interp in (linear_interp, cubic_min_E, cubic_geom):
        zs = interp(xs, ys)
        matest.assert_array_almost_equal(zs, (1.23*xs - 4.79*ys))
        mask = (xs >= 1) * (xs <= 2) * (ys >= 1) * (ys <= 2)
        assert_array_equal(zs.mask, mask)

    # 2nd order patch test: on a grid with an 'arbitrary shaped' triangle,
    # patch test shall be exact for quadratic functions and cubic
    # interpolator if *kind* = user
    (a, b, c) = (1.23, -4.79, 0.6)

    def quad(x, y):
        return a*(x-0.5)**2 + b*(y-0.5)**2 + c*x*y

    def gradient_quad(x, y):
        return (2*a*(x-0.5) + c*y, 2*b*(y-0.5) + c*x)

    x = np.array([0.2, 0.33367, 0.669, 0., 1., 1., 0.])
    y = np.array([0.3, 0.80755, 0.4335, 0., 0., 1., 1.])
    triangles = np.array([[0, 1, 2], [3, 0, 4], [4, 0, 2], [4, 2, 5],
                          [1, 5, 2], [6, 5, 1], [6, 1, 0], [6, 0, 3]])
    triang = mtri.Triangulation(x, y, triangles)