def test_stockwell_core():
"""Test stockwell transform"""
# adapted from
# http://vcs.ynic.york.ac.uk/docs/naf/intro/concepts/timefreq.html
sfreq = 1000.0 # make things easy to understand
dur = 0.5
onset, offset = 0.175, 0.275
n_samp = int(sfreq * dur)
t = np.arange(n_samp) / sfreq # make an array for time
pulse_freq = 15.
pulse = np.cos(2. * np.pi * pulse_freq * t)
pulse[0:int(onset * sfreq)] = 0. # Zero before our desired pulse
pulse[int(offset * sfreq):] = 0. # and zero after our desired pulse
width = 0.5
freqs = fftpack.fftfreq(len(pulse), 1. / sfreq)
fmin, fmax = 1.0, 100.0
start_f, stop_f = [np.abs(freqs - f).argmin() for f in (fmin, fmax)]
W = _precompute_st_windows(n_samp, start_f, stop_f, sfreq, width)
st_pulse = _st(pulse, start_f, W)
st_pulse = np.abs(st_pulse) ** 2
assert_equal(st_pulse.shape[-1], len(pulse))
st_max_freq = freqs[st_pulse.max(axis=1).argmax(axis=0)] # max freq
assert_allclose(st_max_freq, pulse_freq, atol=1.0)
assert_true(onset < t[st_pulse.max(axis=0).argmax(axis=0)] < offset)
# test inversion to FFT, by averaging local spectra, see eq. 5 in
# Moukadem, A., Bouguila, Z., Ould Abdeslam, D. and Alain Dieterlen.
# "Stockwell transform optimization applied on the detection of split in
# heart sounds."
width = 1.0
start_f, stop_f = 0, len(pulse)
W = _precompute_st_windows(n_samp, start_f, stop_f, sfreq, width)
y = _st(pulse, start_f, W)
# invert stockwell
y_inv = fftpack.ifft(np.sum(y, axis=1)).real
assert_array_almost_equal(pulse, y_inv)
def test_stockwell_core():
"""Test stockwell transform."""
# adapted from
# http://vcs.ynic.york.ac.uk/docs/naf/intro/concepts/timefreq.html
sfreq = 1000.0 # make things easy to understand
dur = 0.5
onset, offset = 0.175, 0.275
n_samp = int(sfreq * dur)
t = np.arange(n_samp) / sfreq # make an array for time
pulse_freq = 15.
pulse = np.cos(2. * np.pi * pulse_freq * t)
pulse[0:int(onset * sfreq)] = 0. # Zero before our desired pulse
pulse[int(offset * sfreq):] = 0. # and zero after our desired pulse
width = 0.5
freqs = fftpack.fftfreq(len(pulse), 1. / sfreq)
fmin, fmax = 1.0, 100.0
start_f, stop_f = [np.abs(freqs - f).argmin() for f in (fmin, fmax)]
W = _precompute_st_windows(n_samp, start_f, stop_f, sfreq, width)
st_pulse = _st(pulse, start_f, W)
st_pulse = np.abs(st_pulse)**2
assert_equal(st_pulse.shape[-1], len(pulse))
st_max_freq = freqs[st_pulse.max(axis=1).argmax(axis=0)] # max freq
assert_allclose(st_max_freq, pulse_freq, atol=1.0)
assert (onset < t[st_pulse.max(axis=0).argmax(axis=0)] < offset)
# test inversion to FFT, by averaging local spectra, see eq. 5 in
# Moukadem, A., Bouguila, Z., Ould Abdeslam, D. and Alain Dieterlen.
# "Stockwell transform optimization applied on the detection of split in
# heart sounds."
width = 1.0
start_f, stop_f = 0, len(pulse)
W = _precompute_st_windows(n_samp, start_f, stop_f, sfreq, width)
y = _st(pulse, start_f, W)
# invert stockwell
y_inv = fftpack.ifft(np.sum(y, axis=1)).real
assert_array_almost_equal(pulse, y_inv)
def test_stockwell_core():
"""Test stockwell transform"""
# taken from
# http://vcs.ynic.york.ac.uk/docs/naf/intro/concepts/timefreq.html
sfreq = 1000.0 # make things easy to understand
t = np.arange(sfreq) # make an array for time
t /= sfreq # scale it so it goes to 1, i.e. 1 sec of time
pulse_freq = 15.
pulse = np.cos(2. * np.pi * pulse_freq * t)
pulse[0:175] = 0. # Zero before our desired pulse
pulse[275:] = 0. # and zero after our desired pulse
width = 0.5
freqs = fftpack.fftfreq(len(pulse), 1. / sfreq)
fmin, fmax = 1.0, 100.0
start_f, stop_f = [np.abs(freqs - f).argmin() for f in (fmin, fmax)]
st_precomputed = _precompute_st_windows(1000, start_f, stop_f, sfreq, width)
st_pulse = _st(pulse, *st_precomputed)
st_pulse = np.abs(st_pulse) ** 2
assert_equals(st_pulse.shape[-1], len(pulse))
st_max_freq = st_pulse.max(axis=1).argmax(axis=0) # max freq
assert_equals(st_max_freq, pulse_freq)
assert_true(175 < st_pulse.max(axis=0).argmax(axis=0) < 275) # max time
# test inversion to FFT, by averaging local spectra, see eq. 5 in
# Moukadem, A., Bouguila, Z., Ould Abdeslam, D. and Alain Dieterlen.
# "Stockwell transform optimization applied on the detection of split in
# heart sounds."
width = 1.0
start_f, stop_f = 0, len(pulse)
st_precomputed = _precompute_st_windows(1000, start_f, stop_f, sfreq, width)
y = _st(pulse, *st_precomputed)
# invert stockwell
y_inv = fftpack.ifft(np.sum(y, axis=1)).real
assert_array_almost_equal(pulse, y_inv)
