我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用scipy.signal.hann()。
def dft_np(signal, hop_size=256, fft_size=512): n_hops = len(signal) // hop_size s = [] hann_win = hann(fft_size) for hop_i in range(n_hops): frame = signal[(hop_i * hop_size):(hop_i * hop_size + fft_size)] frame = np.pad(frame, (0, fft_size - len(frame)), 'constant') frame *= hann_win s.append(frame) s = np.array(s) N = s.shape[-1] k = np.reshape(np.linspace(0.0, 2 * np.pi / N * (N // 2), N // 2), [1, N // 2]) x = np.reshape(np.linspace(0.0, N - 1, N), [N, 1]) freqs = np.dot(x, k) reals = np.dot(s, np.cos(freqs)) * (2.0 / N) imags = np.dot(s, np.sin(freqs)) * (2.0 / N) return reals, imags
def _window_evoked(evoked, size): """Window evoked (size in seconds)""" if isinstance(size, (float, int)): lsize = rsize = float(size) else: lsize, rsize = size evoked = evoked.copy() sfreq = float(evoked.info['sfreq']) lsize = int(lsize * sfreq) rsize = int(rsize * sfreq) lhann = signal.hann(lsize * 2) rhann = signal.hann(rsize * 2) window = np.r_[lhann[:lsize], np.ones(len(evoked.times) - lsize - rsize), rhann[-rsize:]] evoked.data *= window[None, :] return evoked
def hanning(self, sig): """ Apply a Hanning window to the signal and return it """ han_window = signal.hann(len(sig)) return(sig*han_window)
def apply(self, data): axis = data.ndim - 1 out = resample(data, self.f, axis=axis, window=hann(M=data.shape[axis])) return out
def power_spectrum(signal: np.ndarray, fs: int, window_width: int, window_overlap: int) -> (np.ndarray, np.ndarray, np.ndarray): """ Computes the power spectrum of the specified signal. A periodic Hann window with the specified width and overlap is used. Parameters ---------- signal: numpy.ndarray The input signal fs: int Sampling frequency of the input signal window_width: int Width of the Hann windows in samples window_overlap: int Overlap between Hann windows in samples Returns ------- f: numpy.ndarray Array of frequency values for the first axis of the returned spectrogram t: numpy.ndarray Array of time values for the second axis of the returned spectrogram sxx: numpy.ndarray Power spectrogram of the input signal with axes [frequency, time] """ f, t, sxx = spectrogram(x=signal, fs=fs, window=hann(window_width, sym=False), noverlap=window_overlap, mode="magnitude") return f, t, (1.0 / window_width) * (sxx ** 2)
def get_ft_windows(): """ Retrieve the available windows to be applied on signal data before FT. @return: dict with keys being the window name and items being again a dict containing the actual function and the normalization factor to calculate correctly the amplitude spectrum in the Fourier Transform To find out the amplitude normalization factor check either the scipy implementation on https://github.com/scipy/scipy/blob/v0.15.1/scipy/signal/windows.py#L336 or just perform a sum of the window (oscillating parts of the window should be averaged out and constant offset factor will remain): MM=1000000 # choose a big number print(sum(signal.hanning(MM))/MM) """ win = {'none': {'func': np.ones, 'ampl_norm': 1.0}, 'hamming': {'func': signal.hamming, 'ampl_norm': 1.0/0.54}, 'hann': {'func': signal.hann, 'ampl_norm': 1.0/0.5}, 'blackman': {'func': signal.blackman, 'ampl_norm': 1.0/0.42}, 'triang': {'func': signal.triang, 'ampl_norm': 1.0/0.5}, 'flattop': {'func': signal.flattop, 'ampl_norm': 1.0/0.2156}, 'bartlett': {'func': signal.bartlett, 'ampl_norm': 1.0/0.5}, 'parzen': {'func': signal.parzen, 'ampl_norm': 1.0/0.375}, 'bohman': {'func': signal.bohman, 'ampl_norm': 1.0/0.4052847}, 'blackmanharris': {'func': signal.blackmanharris, 'ampl_norm': 1.0/0.35875}, 'nuttall': {'func': signal.nuttall, 'ampl_norm': 1.0/0.3635819}, 'barthann': {'func': signal.barthann, 'ampl_norm': 1.0/0.5}} return win
def _get_window(start, end): """Return window which has length as much as parameter start - end""" from scipy.signal import hann window = 1 - np.r_[hann(4)[:2], np.ones(np.abs(end - start) - 4), hann(4)[-2:]].T return window
def stft(signal, fs, nfft, overlap): #plotting time domain signal plt.figure(1) t = np.arange(0,len(signal)/fs, 1/fs) plt.plot(t,signal) plt.axis(xmax = 1) plt.xlabel('Time in seconds') plt.ylabel('Amplitude') plt.title('Speech signal') if not np.log2(nfft).is_integer(): nfft = nearestPow2(nfft) slength = len(signal) hop_size = np.int32(overlap * nfft) nFrames = int(np.round(len(signal)/(nfft-hop_size))) #zero padding to make signal length long enough to have nFrames signal = np.append(signal, np.zeros(nfft)) STFT = np.empty((nfft, nFrames)) segment = np.zeros(nfft) start = 0 for n in range(nFrames): segment = signal[start:start+nfft] * hann(nfft) padded_seg = np.append(segment,np.zeros(nfft)) spec = fftshift(fft(padded_seg)) spec = spec[len(spec)/2:] spec = abs(spec)/max(abs(spec)) powerspec = 20*np.log10(spec) STFT[:,n] = powerspec start = start + nfft - hop_size #plot spectrogram plt.figure(2) freq = (fs/(2*nfft)) * np.arange(0,nfft,1) time = np.arange(0,nFrames)*(slength/(fs*nFrames)) plt.imshow(STFT, extent = [0,max(time),0,max(freq)],origin='lower', cmap='jet', interpolation='nearest', aspect='auto') plt.ylabel('Frequency in Hz') plt.xlabel('Time in seconds') plt.axis([0,max(time),0,np.max(freq)]) plt.title('Spectrogram of speech') plt.show() return (STFT, time, freq)
