test_model.py 文件源码

def test_model_monovariate(func, var, par, k):
    model = Model(func, var, par)
    x, dx = np.linspace(0, 10, 100, retstep=True, endpoint=False)
    U = np.cos(x * 2 * np.pi / 10)
    fields = model.fields_template(x=x, U=U)
    parameters = dict(periodic=True, k=k)
    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]

    assert np.isclose(F, k * dxxU, 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()