test_signal.py 文件源码

def test_smooth_1d():
    for edge in ['m', 'c']:
        for N in [20,21]:
            # values in [9.0,11.0]
            x = rand(N) + 10
            mn = 9.0
            mx = 11.0
            for M in range(18,27):
                print "1d", edge, "N=%i, M=%i" %(N,M)
                xsm = smooth(x, gaussian(M,2.0), edge=edge)
                assert len(xsm) == N
                # (N,1) case
                xsm2 = smooth(x[:,None], gaussian(M,2.0)[:,None], edge=edge)
                assert np.allclose(xsm, xsm2[:,0], atol=1e-14, rtol=1e-12)
                # Smoothed signal should not go to zero if edge effects are handled
                # properly. Also assert proper normalization (i.e. smoothed signal
                # is "in the middle" of the noisy original data).
                assert xsm.min() >= mn
                assert xsm.max() <= mx
                assert mn <= xsm[0] <= mx
                assert mn <= xsm[-1] <= mx
            # convolution with delta peak produces same data exactly
            assert np.allclose(smooth(x, np.array([0.0,1,0]), edge=edge),x, atol=1e-14,
                               rtol=1e-12)