test_model.py 文件源码

def test_model_bivariate(func, var, par):
    model = Model(func, var, par)
    x, dx = np.linspace(0, 10, 100, retstep=True, endpoint=False)
    u = np.cos(x * 2 * np.pi / 10)
    v = np.sin(x * 2 * np.pi / 10)
    fields = model.fields_template(x=x, u=u, v=v)
    parameters = dict(periodic=True, k1=1, k2=1)
    F = model.F(fields, parameters)
    J_sparse = model.J(fields, parameters)
    J_dense = model.J(fields, parameters, sparse=False)
    J_approx = model.F.diff_approx(fields, parameters)

    dxu = np.gradient(np.pad(u, 2, mode='wrap')) / dx
    dxxu = np.gradient(dxu) / dx
    dxu = dxu[2:-2]
    dxxu = dxxu[2:-2]

    dxv = np.gradient(np.pad(v, 2, mode='wrap')) / dx
    dxxv = np.gradient(dxv) / dx
    dxv = dxv[2:-2]
    dxxv = dxxv[2:-2]
    assert np.isclose(F, np.vstack([dxxv, dxxu]).flatten('F'),
                      atol=1E-3).all()
    assert np.isclose(J_approx, J_sparse.todense(), atol=1E-3).all()
    assert np.isclose(J_approx, J_dense, atol=1E-3).all()