@slow_test
def test_filters():
    """Test low-, band-, high-pass, and band-stop filters plus resampling
    """
    sfreq = 500
    sig_len_secs = 30

    a = rng.randn(2, sig_len_secs * sfreq)

    # let's test our catchers
    for fl in ['blah', [0, 1], 1000.5, '10ss', '10']:
        assert_raises(ValueError, band_pass_filter, a, sfreq, 4, 8,
                      filter_length=fl)
    for nj in ['blah', 0.5]:
        assert_raises(ValueError, band_pass_filter, a, sfreq, 4, 8, n_jobs=nj)
    # > Nyq/2
    assert_raises(ValueError, band_pass_filter, a, sfreq, 4, sfreq / 2.)
    assert_raises(ValueError, low_pass_filter, a, sfreq, sfreq / 2.)
    # check our short-filter warning:
    with warnings.catch_warnings(record=True) as w:
        # Warning for low attenuation
        band_pass_filter(a, sfreq, 1, 8, filter_length=1024)
        # Warning for too short a filter
        band_pass_filter(a, sfreq, 1, 8, filter_length='0.5s')
    assert_true(len(w) >= 2)

    # try new default and old default
    for fl in ['10s', '5000ms', None]:
        bp = band_pass_filter(a, sfreq, 4, 8, filter_length=fl)
        bs = band_stop_filter(a, sfreq, 4 - 0.5, 8 + 0.5, filter_length=fl)
        lp = low_pass_filter(a, sfreq, 8, filter_length=fl, n_jobs=2)
        hp = high_pass_filter(lp, sfreq, 4, filter_length=fl)
        assert_array_almost_equal(hp, bp, 2)
        assert_array_almost_equal(bp + bs, a, 1)

    # Overlap-add filtering with a fixed filter length
    filter_length = 8192
    bp_oa = band_pass_filter(a, sfreq, 4, 8, filter_length)
    bs_oa = band_stop_filter(a, sfreq, 4 - 0.5, 8 + 0.5, filter_length)
    lp_oa = low_pass_filter(a, sfreq, 8, filter_length)
    hp_oa = high_pass_filter(lp_oa, sfreq, 4, filter_length)
    assert_array_almost_equal(hp_oa, bp_oa, 2)
    # Our filters are no longer quite complementary with linear rolloffs :(
    # this is the tradeoff for stability of the filtering
    # obtained by directly using the result of firwin2 instead of
    # modifying it...
    assert_array_almost_equal(bp_oa + bs_oa, a, 1)

    # The two methods should give the same result
    # As filtering for short signals uses a circular convolution (FFT) and
    # the overlap-add filter implements a linear convolution, the signal
    # boundary will be slightly different and we ignore it
    n_edge_ignore = 0
    assert_array_almost_equal(hp[n_edge_ignore:-n_edge_ignore],
                              hp_oa[n_edge_ignore:-n_edge_ignore], 2)

    # and since these are low-passed, downsampling/upsampling should be close
    n_resamp_ignore = 10
    bp_up_dn = resample(resample(bp_oa, 2, 1, n_jobs=2), 1, 2, n_jobs=2)
    assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
                              bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
    # note that on systems without CUDA, this line serves as a test for a
    # graceful fallback to n_jobs=1
    bp_up_dn = resample(resample(bp_oa, 2, 1, n_jobs='cuda'), 1, 2,
                        n_jobs='cuda')
    assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
                              bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
    # test to make sure our resamling matches scipy's
    bp_up_dn = sp_resample(sp_resample(bp_oa, 2 * bp_oa.shape[-1], axis=-1,
                                       window='boxcar'),
                           bp_oa.shape[-1], window='boxcar', axis=-1)
    assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
                              bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)

    # make sure we don't alias
    t = np.array(list(range(sfreq * sig_len_secs))) / float(sfreq)
    # make sinusoid close to the Nyquist frequency
    sig = np.sin(2 * np.pi * sfreq / 2.2 * t)
    # signal should disappear with 2x downsampling
    sig_gone = resample(sig, 1, 2)[n_resamp_ignore:-n_resamp_ignore]
    assert_array_almost_equal(np.zeros_like(sig_gone), sig_gone, 2)

    # let's construct some filters
    iir_params = dict(ftype='cheby1', gpass=1, gstop=20)
    iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
    # this should be a third order filter
    assert_true(iir_params['a'].size - 1 == 3)
    assert_true(iir_params['b'].size - 1 == 3)
    iir_params = dict(ftype='butter', order=4)
    iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
    assert_true(iir_params['a'].size - 1 == 4)
    assert_true(iir_params['b'].size - 1 == 4)

    # check that picks work for 3d array with one channel and picks=[0]
    a = rng.randn(5 * sfreq, 5 * sfreq)
    b = a[:, None, :]

    with warnings.catch_warnings(record=True) as w:
        a_filt = band_pass_filter(a, sfreq, 4, 8)
        b_filt = band_pass_filter(b, sfreq, 4, 8, picks=[0])

    assert_array_equal(a_filt[:, None, :], b_filt)

    # check for n-dimensional case
    a = rng.randn(2, 2, 2, 2)
    assert_raises(ValueError, band_pass_filter, a, sfreq, Fp1=4, Fp2=8,
                  picks=np.array([0, 1]))

    # test that our overlap-add filtering doesn't introduce strange
    # artifacts (from mne_analyze mailing list 2015/06/25)
    N = 300
    sfreq = 100.
    lp = 10.
    sine_freq = 1.
    x = np.ones(N)
    x += np.sin(2 * np.pi * sine_freq * np.arange(N) / sfreq)
    with warnings.catch_warnings(record=True):  # filter attenuation
        x_filt = low_pass_filter(x, sfreq, lp, '1s')
    # the firwin2 function gets us this close
    assert_allclose(x, x_filt, rtol=1e-3, atol=1e-3)
dataset/ETHPy150Open mne-tools/mne-python/mne/tests/test_filter.py/test_filters/original