def test_stft(): """Test stft and istft tight frame property.""" sfreq = 1000. # Hz f = 7. # Hz for T in [127, 128]: # try with even and odd numbers # Test with low frequency signal t = np.arange(T).astype(np.float) x = np.sin(2 * np.pi * f * t / sfreq) x = np.array([x, x + 1.]) wsize = 128 tstep = 4 X = stft(x, wsize, tstep) xp = istft(X, tstep, Tx=T) freqs = stftfreq(wsize, sfreq=1000) max_freq = freqs[np.argmax(np.sum(np.abs(X[0])**2, axis=1))] assert X.shape[1] == len(freqs) assert np.all(freqs >= 0.) assert np.abs(max_freq - f) < 1. assert_array_almost_equal(x, xp, decimal=6) # norm conservation thanks to tight frame property assert_almost_equal(np.sqrt(stft_norm2(X)), [linalg.norm(xx) for xx in x], decimal=6) # Test with random signal x = np.random.randn(2, T) wsize = 16 tstep = 8 X = stft(x, wsize, tstep) xp = istft(X, tstep, Tx=T) freqs = stftfreq(wsize, sfreq=1000) max_freq = freqs[np.argmax(np.sum(np.abs(X[0])**2, axis=1))] assert X.shape[1] == len(freqs) assert np.all(freqs >= 0.) assert_array_almost_equal(x, xp, decimal=6) # norm conservation thanks to tight frame property assert_almost_equal(np.sqrt(stft_norm2(X)), [linalg.norm(xx) for xx in x], decimal=6) # Try with empty array x = np.zeros((0, T)) X = stft(x, wsize, tstep) xp = istft(X, tstep, T) assert xp.shape == x.shape
def test_stft(): "Test stft and istft tight frame property" sfreq = 1000. # Hz f = 7. # Hz for T in [253, 256]: # try with even and odd numbers # Test with low frequency signal t = np.arange(T).astype(np.float) x = np.sin(2 * np.pi * f * t / sfreq) x = np.array([x, x + 1.]) wsize = 128 tstep = 4 X = stft(x, wsize, tstep) xp = istft(X, tstep, Tx=T) freqs = stftfreq(wsize, sfreq=1000) max_freq = freqs[np.argmax(np.sum(np.abs(X[0]) ** 2, axis=1))] assert_true(X.shape[1] == len(freqs)) assert_true(np.all(freqs >= 0.)) assert_true(np.abs(max_freq - f) < 1.) assert_array_almost_equal(x, xp, decimal=6) # norm conservation thanks to tight frame property assert_almost_equal(np.sqrt(stft_norm2(X)), [linalg.norm(xx) for xx in x], decimal=6) # Test with random signal x = np.random.randn(2, T) wsize = 16 tstep = 8 X = stft(x, wsize, tstep) xp = istft(X, tstep, Tx=T) freqs = stftfreq(wsize, sfreq=1000) max_freq = freqs[np.argmax(np.sum(np.abs(X[0]) ** 2, axis=1))] assert_true(X.shape[1] == len(freqs)) assert_true(np.all(freqs >= 0.)) assert_array_almost_equal(x, xp, decimal=6) # norm conservation thanks to tight frame property assert_almost_equal(np.sqrt(stft_norm2(X)), [linalg.norm(xx) for xx in x], decimal=6) # Try with empty array x = np.zeros((0, T)) X = stft(x, wsize, tstep) xp = istft(X, tstep, T) assert_true(xp.shape == x.shape)
def test_stft(): "Test stft and istft tight frame property" sfreq = 1000. # Hz f = 7. # Hz for T in [253, 256]: # try with even and odd numbers t = np.arange(T).astype(np.float) x = np.sin(2 * np.pi * f * t / sfreq) x = np.array([x, x + 1.]) wsize = 128 tstep = 4 X = stft(x, wsize, tstep) xp = istft(X, tstep, Tx=T) freqs = stftfreq(wsize, sfreq=1000) max_freq = freqs[np.argmax(np.sum(np.abs(X[0])**2, axis=1))] assert_true(X.shape[1] == len(freqs)) assert_true(np.all(freqs >= 0.)) assert_true(np.abs(max_freq - f) < 1.) assert_array_almost_equal(x, xp, decimal=6) # norm conservation thanks to tight frame property assert_almost_equal(np.sqrt(stft_norm2(X)), map(linalg.norm, x), decimal=2) # Try with empty array x = np.zeros((0, T)) X = stft(x, wsize, tstep) xp = istft(X, tstep, T) assert_true(xp.shape == x.shape)
def fit(self, data): """ Fit data Parameters --------- data : ndarray, shape (channels, times) """ print "First do stft" stft_ = stft(data, self.wsize, self.tstep) # bandpass filter if self.sfreq: freqs = stftfreq(self.wsize, self.sfreq) hpass, lpass = 0, len(freqs) if self.hpass: hpass = min(np.where(freqs >= self.hpass)[0]) if self.lpass: lpass = max(np.where(freqs <= self.lpass)[0]) self._freqs = freqs[hpass:lpass] stft_ = stft_[:, hpass:lpass, :] # store shape to retrieve it later self._stft_shape = stft_.shape # concatenate data data2d = self._concat(stft_) print "Whiten data" dewhitening, whitened = self._whiten(data2d) print "Do ICA" mixing_, ic_ = self._fastica(whitened) # sort according to objective value objectives = [] for i in range(ic_.shape[0]): component = ic_[i, :] g_ = np.log(1 + np.abs(component)**2) objectives.append(np.mean(g_)) indices = np.argsort(objectives)[::-1] sorted_ic = ic_[np.argsort(objectives), :] sorted_mixing = mixing_[:, np.argsort(objectives)] # store for retrieving self._mixing = sorted_mixing self._dewhitening = dewhitening self._source_stft = self._split(sorted_ic)