def test_resample_scipy():
"""Test resampling against SciPy."""
n_jobs_test = (1, 'cuda')
for window in ('boxcar', 'hann'):
for N in (100, 101, 102, 103):
x = np.arange(N).astype(float)
err_msg = '%s: %s' % (N, window)
x_2_sp = sp_resample(x, 2 * N, window=window)
for n_jobs in n_jobs_test:
x_2 = resample(x, 2, 1, 0, window=window, n_jobs=n_jobs)
assert_allclose(x_2, x_2_sp, atol=1e-12, err_msg=err_msg)
new_len = int(round(len(x) * (1. / 2.)))
x_p5_sp = sp_resample(x, new_len, window=window)
for n_jobs in n_jobs_test:
x_p5 = resample(x, 1, 2, 0, window=window, n_jobs=n_jobs)
assert_allclose(x_p5, x_p5_sp, atol=1e-12, err_msg=err_msg)
def test_resample_scipy():
"""Test resampling against SciPy."""
n_jobs_test = (1, 'cuda')
for window in ('boxcar', 'hann'):
for N in (100, 101, 102, 103):
x = np.arange(N).astype(float)
err_msg = '%s: %s' % (N, window)
x_2_sp = sp_resample(x, 2 * N, window=window)
for n_jobs in n_jobs_test:
x_2 = resample(x, 2, 1, 0, window=window, n_jobs=n_jobs)
assert_allclose(x_2, x_2_sp, atol=1e-12, err_msg=err_msg)
new_len = int(round(len(x) * (1. / 2.)))
x_p5_sp = sp_resample(x, new_len, window=window)
for n_jobs in n_jobs_test:
x_p5 = resample(x, 1, 2, 0, window=window, n_jobs=n_jobs)
assert_allclose(x_p5, x_p5_sp, atol=1e-12, err_msg=err_msg)
def test_resample_clone():
rng = np.random.RandomState(20161029)
channels = [2, 5, 32]
times = [100, 1001, 5777]
new_times = [59, 256, 3000]
for ch in channels:
for t in times:
for nt in new_times:
x = rng.randn(ch, t)
sp_xf = sp_resample(x, nt, axis=-1)
cl_xf = cl_resample(x, nt, axis=-1)
assert np.allclose(sp_xf, cl_xf)
def resample(self, *args, **kwargs):
"""Resample time series object using scipy's resample
Parameters are those of scipy.signal.resample, with *num* (number of samples in resampled result) as the only mandatory parameter
Difference is that this takes object's time vector into account automatically
If single param (num) is a float, taken to be multiple for current number of frames.
"""
args = list(args)
args.reverse()
n = args.pop()
args.reverse()
if type(n) in [float, np.float16, np.float32, np.float64, np.float128]:
n = np.round(len(self) * n)
if 't' not in kwargs or kwargs['t'] == None:
kwargs['t'] = self.time
new_data, new_time = sp_resample(self, n, *args, axis=0, **kwargs)
return self.__class__(data=new_data, time=new_time)
def test_filters():
"""Test low-, band-, high-pass, and band-stop filters plus resampling."""
rng = np.random.RandomState(0)
sfreq = 100
sig_len_secs = 15
a = rng.randn(2, sig_len_secs * sfreq)
# let's test our catchers
for fl in ['blah', [0, 1], 1000.5, '10ss', '10']:
pytest.raises((ValueError, TypeError),
filter_data,
a,
sfreq,
4,
8,
None,
fl,
1.0,
1.0,
fir_design='firwin')
with pytest.raises(TypeError, match='got <class'):
filter_data(a,
sfreq,
4,
8,
None,
1000,
1.0,
1.0,
n_jobs=0.5,
phase='zero',
fir_design='firwin')
with pytest.raises(ValueError, match='Invalid value'):
filter_data(a,
sfreq,
4,
8,
None,
1000,
1.0,
1.0,
n_jobs='blah',
phase='zero',
fir_design='firwin')
pytest.raises(ValueError,
filter_data,
a,
sfreq,
4,
8,
None,
100,
1.,
1.,
fir_window='foo')
pytest.raises(ValueError,
filter_data,
a,
sfreq,
4,
8,
None,
10,
1.,
1.,
fir_design='firwin') # too short
# > Nyq/2
pytest.raises(ValueError,
filter_data,
a,
sfreq,
4,
sfreq / 2.,
None,
100,
1.0,
1.0,
fir_design='firwin')
pytest.raises(ValueError,
filter_data,
a,
sfreq,
-1,
None,
None,
100,
1.0,
1.0,
fir_design='firwin')
# these should work
create_filter(None, sfreq, None, None)
create_filter(a, sfreq, None, None, fir_design='firwin')
create_filter(a, sfreq, None, None, method='iir')
# check our short-filter warning:
with pytest.warns(RuntimeWarning, match='attenuation'):
# Warning for low attenuation
filter_data(a, sfreq, 1, 8, filter_length=256, fir_design='firwin2')
with pytest.warns(RuntimeWarning, match='Increase filter_length'):
# Warning for too short a filter
filter_data(a, sfreq, 1, 8, filter_length='0.5s', fir_design='firwin2')
# try new default and old default
freqs = fftfreq(a.shape[-1], 1. / sfreq)
A = np.abs(fft(a))
kwargs = dict(fir_design='firwin')
for fl in ['auto', '10s', '5000ms', 1024, 1023]:
bp = filter_data(a, sfreq, 4, 8, None, fl, 1.0, 1.0, **kwargs)
bs = filter_data(a, sfreq, 8 + 1.0, 4 - 1.0, None, fl, 1.0, 1.0,
**kwargs)
lp = filter_data(a,
sfreq,
None,
8,
None,
fl,
10,
1.0,
n_jobs=2,
**kwargs)
hp = filter_data(lp, sfreq, 4, None, None, fl, 1.0, 10, **kwargs)
assert_allclose(hp, bp, rtol=1e-3, atol=2e-3)
assert_allclose(bp + bs, a, rtol=1e-3, atol=1e-3)
# Sanity check ttenuation
mask = (freqs > 5.5) & (freqs < 6.5)
assert_allclose(np.mean(np.abs(fft(bp)[:, mask]) / A[:, mask]),
1.,
atol=0.02)
assert_allclose(np.mean(np.abs(fft(bs)[:, mask]) / A[:, mask]),
0.,
atol=0.2)
# now the minimum-phase versions
bp = filter_data(a,
sfreq,
4,
8,
None,
fl,
1.0,
1.0,
phase='minimum',
**kwargs)
bs = filter_data(a,
sfreq,
8 + 1.0,
4 - 1.0,
None,
fl,
1.0,
1.0,
phase='minimum',
**kwargs)
assert_allclose(np.mean(np.abs(fft(bp)[:, mask]) / A[:, mask]),
1.,
atol=0.11)
assert_allclose(np.mean(np.abs(fft(bs)[:, mask]) / A[:, mask]),
0.,
atol=0.3)
# and since these are low-passed, downsampling/upsampling should be close
n_resamp_ignore = 10
bp_up_dn = resample(resample(bp, 2, 1, n_jobs=2), 1, 2, n_jobs=2)
assert_array_almost_equal(bp[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=None
bp_up_dn = resample(resample(bp, 2, 1, n_jobs='cuda'), 1, 2, n_jobs='cuda')
assert_array_almost_equal(bp[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,
2 * bp.shape[-1],
axis=-1,
window='boxcar'),
bp.shape[-1],
window='boxcar',
axis=-1)
assert_array_almost_equal(bp[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, output='ba')
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter
assert iir_params['a'].size - 1 == 3
assert iir_params['b'].size - 1 == 3
iir_params = dict(ftype='butter', order=4, output='ba')
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert iir_params['a'].size - 1 == 4
assert iir_params['b'].size - 1 == 4
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, which requires 2 SOS ((2, 6))
assert iir_params['sos'].shape == (2, 6)
iir_params = dict(ftype='butter', order=4, output='sos')
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert iir_params['sos'].shape == (2, 6)
# check that picks work for 3d array with one channel and picks=[0]
a = rng.randn(5 * sfreq, 5 * sfreq)
b = a[:, None, :]
a_filt = filter_data(a,
sfreq,
4,
8,
None,
400,
2.0,
2.0,
fir_design='firwin')
b_filt = filter_data(b,
sfreq,
4,
8, [0],
400,
2.0,
2.0,
fir_design='firwin')
assert_array_equal(a_filt[:, None, :], b_filt)
# check for n-dimensional case
a = rng.randn(2, 2, 2, 2)
with pytest.warns(RuntimeWarning, match='longer'):
pytest.raises(ValueError, filter_data, a, sfreq, 4, 8,
np.array([0, 1]), 100, 1.0, 1.0)
# check corner case (#4693)
want_length = int(round(_length_factors['hamming'] * 1000. / 0.5))
want_length += (want_length % 2 == 0)
assert want_length == 6601
h = create_filter(np.empty(10000),
1000.,
l_freq=None,
h_freq=55.,
h_trans_bandwidth=0.5,
method='fir',
phase='zero-double',
fir_design='firwin',
verbose=True)
assert len(h) == 6601
h = create_filter(np.empty(10000),
1000.,
l_freq=None,
h_freq=55.,
h_trans_bandwidth=0.5,
method='fir',
phase='zero',
fir_design='firwin',
filter_length='7s',
verbose=True)
assert len(h) == 7001
h = create_filter(np.empty(10000),
1000.,
l_freq=None,
h_freq=55.,
h_trans_bandwidth=0.5,
method='fir',
phase='zero-double',
fir_design='firwin',
filter_length='7s',
verbose=True)
assert len(h) == 8193 # next power of two
def test_filters():
"""Test low-, band-, high-pass, and band-stop filters plus resampling
"""
Fs = 500
sig_len_secs = 30
a = np.random.randn(2, sig_len_secs * Fs)
# let's test our catchers
for fl in ['blah', [0, 1], 1000.5, '10ss', '10']:
assert_raises(ValueError, band_pass_filter, a, Fs, 4, 8,
filter_length=fl)
for nj in ['blah', 0.5, 0]:
assert_raises(ValueError, band_pass_filter, a, Fs, 4, 8, n_jobs=nj)
assert_raises(ValueError, band_pass_filter, a, Fs, 4, Fs / 2.) # > Nyq/2
assert_raises(ValueError, low_pass_filter, a, Fs, Fs / 2.) # > Nyq/2
# check our short-filter warning:
with warnings.catch_warnings(record=True) as w:
# Warning for low attenuation
band_pass_filter(a, Fs, 1, 8, filter_length=1024)
# Warning for too short a filter
band_pass_filter(a, Fs, 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, Fs, 4, 8, filter_length=fl)
bs = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5, filter_length=fl)
lp = low_pass_filter(a, Fs, 8, filter_length=fl, n_jobs=2)
hp = high_pass_filter(lp, Fs, 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, Fs, 4, 8, filter_length)
bs_oa = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5, filter_length)
lp_oa = low_pass_filter(a, Fs, 8, filter_length)
hp_oa = high_pass_filter(lp_oa, Fs, 4, filter_length)
assert_array_almost_equal(hp_oa, bp_oa, 2)
assert_array_almost_equal(bp_oa + bs_oa, a, 2)
# 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(Fs * sig_len_secs))) / float(Fs)
# make sinusoid close to the Nyquist frequency
sig = np.sin(2 * np.pi * Fs / 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)
def test_filters():
"""Test low-, band-, high-pass, and band-stop filters plus resampling."""
sfreq = 100
sig_len_secs = 15
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, fl, 1., 1.)
for nj in ['blah', 0.5]:
assert_raises(ValueError, band_pass_filter, a, sfreq, 4, 8, 100,
1., 1., n_jobs=nj)
assert_raises(ValueError, band_pass_filter, a, sfreq, 4, 8, 100,
1., 1., fir_window='foo')
# > Nyq/2
assert_raises(ValueError, band_pass_filter, a, sfreq, 4, sfreq / 2.,
100, 1.0, 1.0)
assert_raises(ValueError, low_pass_filter, a, sfreq, sfreq / 2.,
100, 1.0)
# 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=256)
assert_true(any('attenuation' in str(ww.message) for ww in w))
with warnings.catch_warnings(record=True) as w:
# Warning for too short a filter
band_pass_filter(a, sfreq, 1, 8, filter_length='0.5s')
assert_true(any('Increase filter_length' in str(ww.message) for ww in w))
# try new default and old default
for fl in ['auto', '10s', '5000ms', 1024]:
bp = band_pass_filter(a, sfreq, 4, 8, fl, 1.0, 1.0)
bs = band_stop_filter(a, sfreq, 4 - 1.0, 8 + 1.0, fl, 1.0, 1.0)
lp = low_pass_filter(a, sfreq, 8, fl, 1.0, n_jobs=2)
hp = high_pass_filter(lp, sfreq, 4, fl, 1.0)
assert_array_almost_equal(hp, bp, 4)
assert_array_almost_equal(bp + bs, a, 4)
# and since these are low-passed, downsampling/upsampling should be close
n_resamp_ignore = 10
bp_up_dn = resample(resample(bp, 2, 1, n_jobs=2), 1, 2, n_jobs=2)
assert_array_almost_equal(bp[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, 2, 1, n_jobs='cuda'), 1, 2, n_jobs='cuda')
assert_array_almost_equal(bp[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, 2 * bp.shape[-1], axis=-1,
window='boxcar'),
bp.shape[-1], window='boxcar', axis=-1)
assert_array_almost_equal(bp[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, output='ba')
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter
assert_equal(iir_params['a'].size - 1, 3)
assert_equal(iir_params['b'].size - 1, 3)
iir_params = dict(ftype='butter', order=4, output='ba')
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert_equal(iir_params['a'].size - 1, 4)
assert_equal(iir_params['b'].size - 1, 4)
iir_params = dict(ftype='cheby1', gpass=1, gstop=20, output='sos')
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter, which requires 2 SOS ((2, 6))
assert_equal(iir_params['sos'].shape, (2, 6))
iir_params = dict(ftype='butter', order=4, output='sos')
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert_equal(iir_params['sos'].shape, (2, 6))
# check that picks work for 3d array with one channel and picks=[0]
a = rng.randn(5 * sfreq, 5 * sfreq)
b = a[:, None, :]
a_filt = band_pass_filter(a, sfreq, 4, 8, 400, 2.0, 2.0)
b_filt = band_pass_filter(b, sfreq, 4, 8, 400, 2.0, 2.0, picks=[0])
assert_array_equal(a_filt[:, None, :], b_filt)
# check for n-dimensional case
a = rng.randn(2, 2, 2, 2)
with warnings.catch_warnings(record=True): # filter too long
assert_raises(ValueError, band_pass_filter, a, sfreq, 4, 8, 100,
1.0, 1.0, picks=np.array([0, 1]))
def resample(sample_rate,samples,L=16000,new_sample_rate=8000): return sp_resample(samples, int(new_sample_rate / sample_rate * samples.shape[0]))
def test_filters():
"""Test low-, band-, high-pass, and band-stop filters plus resampling
"""
Fs = 500
sig_len_secs = 30
a = np.random.randn(2, sig_len_secs * Fs)
# let's test our catchers
for fl in ['blah', [0, 1], 1000.5, '10ss', '10']:
assert_raises(ValueError, band_pass_filter, a, Fs, 4, 8,
filter_length=fl)
for nj in ['blah', 0.5, 0]:
assert_raises(ValueError, band_pass_filter, a, Fs, 4, 8, n_jobs=nj)
# check our short-filter warning:
with warnings.catch_warnings(record=True) as w:
# Warning for low attenuation
band_pass_filter(a, Fs, 1, 8, filter_length=1024)
# Warning for too short a filter
band_pass_filter(a, Fs, 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, Fs, 4, 8, filter_length=fl)
bs = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5, filter_length=fl)
lp = low_pass_filter(a, Fs, 8, filter_length=fl, n_jobs=2)
hp = high_pass_filter(lp, Fs, 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, Fs, 4, 8, filter_length)
bs_oa = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5, filter_length)
lp_oa = low_pass_filter(a, Fs, 8, filter_length)
hp_oa = high_pass_filter(lp_oa, Fs, 4, filter_length)
assert_array_almost_equal(hp_oa, bp_oa, 2)
assert_array_almost_equal(bp_oa + bs_oa, a, 2)
# 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 * len(bp_oa), window='boxcar'),
len(bp_oa), window='boxcar')
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(range(Fs * sig_len_secs)) / float(Fs)
# make sinusoid close to the Nyquist frequency
sig = np.sin(2 * np.pi * Fs / 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)
def test_filters():
"""Test low-, band-, high-pass, and band-stop filters plus resampling."""
sfreq = 100
sig_len_secs = 15
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,
filter_data,
a,
sfreq,
4,
8,
None,
fl,
1.0,
1.0,
fir_design='firwin')
for nj in ['blah', 0.5]:
assert_raises(ValueError,
filter_data,
a,
sfreq,
4,
8,
None,
1000,
1.0,
1.0,
n_jobs=nj,
phase='zero',
fir_design='firwin')
assert_raises(ValueError,
filter_data,
a,
sfreq,
4,
8,
None,
100,
1.,
1.,
fir_window='foo')
assert_raises(ValueError,
filter_data,
a,
sfreq,
4,
8,
None,
10,
1.,
1.,
fir_design='firwin') # too short
# > Nyq/2
assert_raises(ValueError,
filter_data,
a,
sfreq,
4,
sfreq / 2.,
None,
100,
1.0,
1.0,
fir_design='firwin')
assert_raises(ValueError,
filter_data,
a,
sfreq,
-1,
None,
None,
100,
1.0,
1.0,
fir_design='firwin')
# these should work
create_filter(a, sfreq, None, None, fir_design='firwin')
create_filter(a, sfreq, None, None, method='iir')
# check our short-filter warning:
with warnings.catch_warnings(record=True) as w:
# Warning for low attenuation
filter_data(a, sfreq, 1, 8, filter_length=256, fir_design='firwin2')
assert_true(any('attenuation' in str(ww.message) for ww in w))
with warnings.catch_warnings(record=True) as w:
# Warning for too short a filter
filter_data(a, sfreq, 1, 8, filter_length='0.5s', fir_design='firwin2')
assert_true(any('Increase filter_length' in str(ww.message) for ww in w))
# try new default and old default
freqs = fftfreq(a.shape[-1], 1. / sfreq)
A = np.abs(fft(a))
kwargs = dict(fir_design='firwin')
for fl in ['auto', '10s', '5000ms', 1024, 1023]:
bp = filter_data(a, sfreq, 4, 8, None, fl, 1.0, 1.0, **kwargs)
bs = filter_data(a, sfreq, 8 + 1.0, 4 - 1.0, None, fl, 1.0, 1.0,
**kwargs)
lp = filter_data(a,
sfreq,
None,
8,
None,
fl,
10,
1.0,
n_jobs=2,
**kwargs)
hp = filter_data(lp, sfreq, 4, None, None, fl, 1.0, 10, **kwargs)
assert_allclose(hp, bp, rtol=1e-3, atol=1e-3)
assert_allclose(bp + bs, a, rtol=1e-3, atol=1e-3)
# Sanity check ttenuation
mask = (freqs > 5.5) & (freqs < 6.5)
assert_allclose(np.mean(np.abs(fft(bp)[:, mask]) / A[:, mask]),
1.,
atol=0.02)
assert_allclose(np.mean(np.abs(fft(bs)[:, mask]) / A[:, mask]),
0.,
atol=0.2)
# now the minimum-phase versions
bp = filter_data(a,
sfreq,
4,
8,
None,
fl,
1.0,
1.0,
phase='minimum',
**kwargs)
bs = filter_data(a,
sfreq,
8 + 1.0,
4 - 1.0,
None,
fl,
1.0,
1.0,
phase='minimum',
**kwargs)
assert_allclose(np.mean(np.abs(fft(bp)[:, mask]) / A[:, mask]),
1.,
atol=0.11)
assert_allclose(np.mean(np.abs(fft(bs)[:, mask]) / A[:, mask]),
0.,
atol=0.3)
# and since these are low-passed, downsampling/upsampling should be close
n_resamp_ignore = 10
bp_up_dn = resample(resample(bp, 2, 1, n_jobs=2), 1, 2, n_jobs=2)
assert_array_almost_equal(bp[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, 2, 1, n_jobs='cuda'), 1, 2, n_jobs='cuda')
assert_array_almost_equal(bp[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,
2 * bp.shape[-1],
axis=-1,
window='boxcar'),
bp.shape[-1],
window='boxcar',
axis=-1)
assert_array_almost_equal(bp[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, output='ba')
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter
assert_equal(iir_params['a'].size - 1, 3)
assert_equal(iir_params['b'].size - 1, 3)
iir_params = dict(ftype='butter', order=4, output='ba')
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert_equal(iir_params['a'].size - 1, 4)
assert_equal(iir_params['b'].size - 1, 4)
iir_params = dict(ftype='cheby1', gpass=1, gstop=20, output='sos')
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter, which requires 2 SOS ((2, 6))
assert_equal(iir_params['sos'].shape, (2, 6))
iir_params = dict(ftype='butter', order=4, output='sos')
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert_equal(iir_params['sos'].shape, (2, 6))
# check that picks work for 3d array with one channel and picks=[0]
a = rng.randn(5 * sfreq, 5 * sfreq)
b = a[:, None, :]
a_filt = filter_data(a,
sfreq,
4,
8,
None,
400,
2.0,
2.0,
fir_design='firwin')
b_filt = filter_data(b,
sfreq,
4,
8, [0],
400,
2.0,
2.0,
fir_design='firwin')
assert_array_equal(a_filt[:, None, :], b_filt)
# check for n-dimensional case
a = rng.randn(2, 2, 2, 2)
with warnings.catch_warnings(record=True): # filter too long
assert_raises(ValueError, filter_data, a, sfreq, 4, 8,
np.array([0, 1]), 100, 1.0, 1.0)
def test_filters():
"""Test low-, band-, high-pass, and band-stop filters plus resampling
"""
sfreq = 500
sig_len_secs = 30
a = np.random.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 = np.random.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 = np.random.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)
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)
def test_filters():
"""Test low-, band-, high-pass, and band-stop filters plus resampling
"""
Fs = 500
sig_len_secs = 30
# Filtering of short signals (filter length = len(a))
a = np.random.randn(2, sig_len_secs * Fs)
bp = band_pass_filter(a, Fs, 4, 8)
bs = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5)
lp = low_pass_filter(a, Fs, 8)
hp = high_pass_filter(lp, Fs, 4)
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, Fs, 4, 8, filter_length)
bs_oa = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5, filter_length)
lp_oa = low_pass_filter(a, Fs, 8, filter_length)
hp_oa = high_pass_filter(lp_oa, Fs, 4, filter_length)
assert_array_almost_equal(hp_oa, bp_oa, 2)
assert_array_almost_equal(bp_oa + bs_oa, a, 2)
# 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 * len(bp_oa), window='boxcar'),
len(bp_oa), window='boxcar')
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(range(Fs * sig_len_secs)) / float(Fs)
# make sinusoid close to the Nyquist frequency
sig = np.sin(2 * np.pi * Fs / 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)
def test_filters():
"""Test low-, band-, high-pass, and band-stop filters plus resampling."""
sfreq = 100
sig_len_secs = 15
a = rng.randn(2, sig_len_secs * sfreq)
# let's test our catchers
for fl in ['blah', [0, 1], 1000.5, '10ss', '10']:
pytest.raises(ValueError, filter_data, a, sfreq, 4, 8, None, fl,
1.0, 1.0, fir_design='firwin')
for nj in ['blah', 0.5]:
pytest.raises(ValueError, filter_data, a, sfreq, 4, 8, None, 1000,
1.0, 1.0, n_jobs=nj, phase='zero', fir_design='firwin')
pytest.raises(ValueError, filter_data, a, sfreq, 4, 8, None, 100,
1., 1., fir_window='foo')
pytest.raises(ValueError, filter_data, a, sfreq, 4, 8, None, 10,
1., 1., fir_design='firwin') # too short
# > Nyq/2
pytest.raises(ValueError, filter_data, a, sfreq, 4, sfreq / 2., None,
100, 1.0, 1.0, fir_design='firwin')
pytest.raises(ValueError, filter_data, a, sfreq, -1, None, None,
100, 1.0, 1.0, fir_design='firwin')
# these should work
create_filter(None, sfreq, None, None)
create_filter(a, sfreq, None, None, fir_design='firwin')
create_filter(a, sfreq, None, None, method='iir')
# check our short-filter warning:
with pytest.warns(RuntimeWarning, match='attenuation'):
# Warning for low attenuation
filter_data(a, sfreq, 1, 8, filter_length=256, fir_design='firwin2')
with pytest.warns(RuntimeWarning, match='Increase filter_length'):
# Warning for too short a filter
filter_data(a, sfreq, 1, 8, filter_length='0.5s', fir_design='firwin2')
# try new default and old default
freqs = fftfreq(a.shape[-1], 1. / sfreq)
A = np.abs(fft(a))
kwargs = dict(fir_design='firwin')
for fl in ['auto', '10s', '5000ms', 1024, 1023]:
bp = filter_data(a, sfreq, 4, 8, None, fl, 1.0, 1.0, **kwargs)
bs = filter_data(a, sfreq, 8 + 1.0, 4 - 1.0, None, fl, 1.0, 1.0,
**kwargs)
lp = filter_data(a, sfreq, None, 8, None, fl, 10, 1.0, n_jobs=2,
**kwargs)
hp = filter_data(lp, sfreq, 4, None, None, fl, 1.0, 10, **kwargs)
assert_allclose(hp, bp, rtol=1e-3, atol=1e-3)
assert_allclose(bp + bs, a, rtol=1e-3, atol=1e-3)
# Sanity check ttenuation
mask = (freqs > 5.5) & (freqs < 6.5)
assert_allclose(np.mean(np.abs(fft(bp)[:, mask]) / A[:, mask]),
1., atol=0.02)
assert_allclose(np.mean(np.abs(fft(bs)[:, mask]) / A[:, mask]),
0., atol=0.2)
# now the minimum-phase versions
bp = filter_data(a, sfreq, 4, 8, None, fl, 1.0, 1.0,
phase='minimum', **kwargs)
bs = filter_data(a, sfreq, 8 + 1.0, 4 - 1.0, None, fl, 1.0, 1.0,
phase='minimum', **kwargs)
assert_allclose(np.mean(np.abs(fft(bp)[:, mask]) / A[:, mask]),
1., atol=0.11)
assert_allclose(np.mean(np.abs(fft(bs)[:, mask]) / A[:, mask]),
0., atol=0.3)
# and since these are low-passed, downsampling/upsampling should be close
n_resamp_ignore = 10
bp_up_dn = resample(resample(bp, 2, 1, n_jobs=2), 1, 2, n_jobs=2)
assert_array_almost_equal(bp[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, 2, 1, n_jobs='cuda'), 1, 2, n_jobs='cuda')
assert_array_almost_equal(bp[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, 2 * bp.shape[-1], axis=-1,
window='boxcar'),
bp.shape[-1], window='boxcar', axis=-1)
assert_array_almost_equal(bp[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, output='ba')
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter
assert iir_params['a'].size - 1 == 3
assert iir_params['b'].size - 1 == 3
iir_params = dict(ftype='butter', order=4, output='ba')
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert iir_params['a'].size - 1 == 4
assert iir_params['b'].size - 1 == 4
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, which requires 2 SOS ((2, 6))
assert iir_params['sos'].shape == (2, 6)
iir_params = dict(ftype='butter', order=4, output='sos')
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert iir_params['sos'].shape == (2, 6)
# check that picks work for 3d array with one channel and picks=[0]
a = rng.randn(5 * sfreq, 5 * sfreq)
b = a[:, None, :]
a_filt = filter_data(a, sfreq, 4, 8, None, 400, 2.0, 2.0,
fir_design='firwin')
b_filt = filter_data(b, sfreq, 4, 8, [0], 400, 2.0, 2.0,
fir_design='firwin')
assert_array_equal(a_filt[:, None, :], b_filt)
# check for n-dimensional case
a = rng.randn(2, 2, 2, 2)
with pytest.warns(RuntimeWarning, match='longer'):
pytest.raises(ValueError, filter_data, a, sfreq, 4, 8,
np.array([0, 1]), 100, 1.0, 1.0)
# check corner case (#4693)
h = create_filter(
np.empty(10000), 1000., l_freq=None, h_freq=55.,
h_trans_bandwidth=0.5, method='fir', phase='zero-double',
fir_design='firwin', verbose=True)
assert len(h) == 6601
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,
fl,
1.0,
1.0,
phase='zero')
for nj in ['blah', 0.5]:
assert_raises(ValueError,
band_pass_filter,
a,
sfreq,
4,
8,
100,
1.0,
1.0,
n_jobs=nj,
phase='zero')
# > Nyq/2
assert_raises(ValueError,
band_pass_filter,
a,
sfreq,
4,
sfreq / 2.,
100,
1.0,
1.0,
phase='zero')
assert_raises(ValueError,
low_pass_filter,
a,
sfreq,
sfreq / 2.,
100,
1.0,
phase='zero')
# 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, phase='zero')
assert_true(any('attenuation' in str(ww.message) for ww in w))
with warnings.catch_warnings(record=True) as w:
# Warning for too short a filter
band_pass_filter(a, sfreq, 1, 8, filter_length='0.5s', phase='zero')
assert_true(any('Increase filter_length' in str(ww.message) for ww in w))
# try new default and old default
for fl in ['auto', '10s', '5000ms', 8192]:
bp = band_pass_filter(a, sfreq, 4, 8, fl, 1.0, 1.0, phase='zero')
bs = band_stop_filter(a,
sfreq,
4 - 1.0,
8 + 1.0,
fl,
1.0,
1.0,
phase='zero')
lp = low_pass_filter(a, sfreq, 8, fl, 1.0, n_jobs=2, phase='zero')
hp = high_pass_filter(lp, sfreq, 4, fl, 1.0, phase='zero')
assert_array_almost_equal(hp, bp, 5)
assert_array_almost_equal(bp + bs, a, 5)
# and since these are low-passed, downsampling/upsampling should be close
n_resamp_ignore = 10
bp_up_dn = resample(resample(bp, 2, 1, n_jobs=2), 1, 2, n_jobs=2)
assert_array_almost_equal(bp[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, 2, 1, n_jobs='cuda'), 1, 2, n_jobs='cuda')
assert_array_almost_equal(bp[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,
2 * bp.shape[-1],
axis=-1,
window='boxcar'),
bp.shape[-1],
window='boxcar',
axis=-1)
assert_array_almost_equal(bp[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, output='ba')
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter
assert_equal(iir_params['a'].size - 1, 3)
assert_equal(iir_params['b'].size - 1, 3)
iir_params = dict(ftype='butter', order=4, output='ba')
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert_equal(iir_params['a'].size - 1, 4)
assert_equal(iir_params['b'].size - 1, 4)
iir_params = dict(ftype='cheby1', gpass=1, gstop=20, output='sos')
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter, which requires 2 SOS ((2, 6))
assert_equal(iir_params['sos'].shape, (2, 6))
iir_params = dict(ftype='butter', order=4, output='sos')
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert_equal(iir_params['sos'].shape, (2, 6))
# check that picks work for 3d array with one channel and picks=[0]
a = rng.randn(5 * sfreq, 5 * sfreq)
b = a[:, None, :]
a_filt = band_pass_filter(a, sfreq, 4, 8, 1000, 2.0, 2.0, phase='zero')
b_filt = band_pass_filter(b,
sfreq,
4,
8,
1000,
2.0,
2.0,
picks=[0],
phase='zero')
assert_array_equal(a_filt[:, None, :], b_filt)
# check for n-dimensional case
a = rng.randn(2, 2, 2, 2)
with warnings.catch_warnings(record=True): # filter too long
assert_raises(ValueError,
band_pass_filter,
a,
sfreq,
4,
8,
100,
1.0,
1.0,
picks=np.array([0, 1]),
phase='zero')
def resample(self, *args, **kwargs):
if 't' not in kwargs or kwargs['t']==None:
kwargs['t'] = self.time
new_data,new_time = sp_resample(self, *args, **kwargs)
return self.__class__(data=new_data, time=new_time)
