我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用scipy.signal.blackman()。
def getFFT(signal,T): N = len(signal) xf = np.linspace(0.0, 1.0/(2.0*float(T)), N/2) w = blackman(N) ffts = [] sfft = fft(np.array(signal)*w) return xf,sfft
def plot_spectrum(sys): """ Plot the High Harmonic Generation spectrum """ # Power spectrum emitted is calculated using the Larmor formula # (https://en.wikipedia.org/wiki/Larmor_formula) # which says that the power emitted is proportional to the square of the acceleration # i.e., the RHS of the second Ehrenfest theorem N = len(sys.P_average_RHS) k = np.arange(N) # frequency range omegas = (k - N / 2) * np.pi / (0.5 * sys.t) # spectra of the spectrum = np.abs( # used windows fourier transform to calculate the spectra # rhttp://docs.scipy.org/doc/scipy/reference/tutorial/fftpack.html fftpack.fft((-1) ** k * blackman(N) * sys.P_average_RHS) ) ** 2 spectrum /= spectrum.max() plt.semilogy(omegas / sys.omega_laser, spectrum) plt.ylabel('spectrum (arbitrary units)') plt.xlabel('frequency / $\\omega_L$') plt.xlim([0, 45.]) plt.ylim([1e-15, 1.])
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 stft(time_signal, time_dim=None, size=1024, shift=256, window=signal.blackman, fading=True, window_length=None): """ Calculates the short time Fourier transformation of a multi channel multi speaker time signal. It is able to add additional zeros for fade-in and fade out and should yield an STFT signal which allows perfect reconstruction. :param time_signal: multi channel time signal. :param time_dim: Scalar dim of time. Default: None means the biggest dimension :param size: Scalar FFT-size. :param shift: Scalar FFT-shift. Typically shift is a fraction of size. :param window: Window function handle. :param fading: Pads the signal with zeros for better reconstruction. :param window_length: Sometimes one desires to use a shorter window than the fft size. In that case, the window is padded with zeros. The default is to use the fft-size as a window size. :return: Single channel complex STFT signal with dimensions frames times size/2+1. """ if time_dim is None: time_dim = np.argmax(time_signal.shape) # Pad with zeros to have enough samples for the window function to fade. if fading: pad = [(0, 0)] * time_signal.ndim pad[time_dim] = [size - shift, size - shift] time_signal = np.pad(time_signal, pad, mode='constant') # Pad with trailing zeros, to have an integral number of frames. frames = _samples_to_stft_frames(time_signal.shape[time_dim], size, shift) samples = _stft_frames_to_samples(frames, size, shift) pad = [(0, 0)] * time_signal.ndim pad[time_dim] = [0, samples - time_signal.shape[time_dim]] time_signal = np.pad(time_signal, pad, mode='constant') if window_length is None: window = window(size) else: window = window(window_length) window = np.pad(window, (0, size - window_length), mode='constant') time_signal_seg = segment_axis(time_signal, size, size - shift, axis=time_dim) letters = string.ascii_lowercase mapping = letters[:time_signal_seg.ndim] + ',' + letters[time_dim + 1] \ + '->' + letters[:time_signal_seg.ndim] return rfft(np.einsum(mapping, time_signal_seg, window), axis=time_dim + 1)
def istft(stft_signal, size=1024, shift=256, window=signal.blackman, fading=True, window_length=None): """ Calculated the inverse short time Fourier transform to exactly reconstruct the time signal. :param stft_signal: Single channel complex STFT signal with dimensions frames times size/2+1. :param size: Scalar FFT-size. :param shift: Scalar FFT-shift. Typically shift is a fraction of size. :param window: Window function handle. :param fading: Removes the additional padding, if done during STFT. :param window_length: Sometimes one desires to use a shorter window than the fft size. In that case, the window is padded with zeros. The default is to use the fft-size as a window size. :return: Single channel complex STFT signal :return: Single channel time signal. """ assert stft_signal.shape[1] == size // 2 + 1 if window_length is None: window = window(size) else: window = window(window_length) window = np.pad(window, (0, size - window_length), mode='constant') window = _biorthogonal_window_loopy(window, shift) # Why? Line created by Hai, Lukas does not know, why it exists. window *= size time_signal = scipy.zeros(stft_signal.shape[0] * shift + size - shift) for j, i in enumerate(range(0, len(time_signal) - size + shift, shift)): time_signal[i:i + size] += window * np.real(irfft(stft_signal[j])) # Compensate fade-in and fade-out if fading: time_signal = time_signal[ size - shift:len(time_signal) - (size - shift)] return time_signal
def stft(time_signal, time_dim=None, size=1024, shift=256, window=signal.blackman, fading=True, window_length=None): """ Calculates the short time Fourier transformation of a multi channel multi speaker time signal. It is able to add additional zeros for fade-in and fade out and should yield an STFT signal which allows perfect reconstruction. :param time_signal: multi channel time signal. :param time_dim: Scalar dim of time. Default: None means the biggest dimension :param size: Scalar FFT-size. :param shift: Scalar FFT-shift. Typically shift is a fraction of size. :param window: Window function handle. :param fading: Pads the signal with zeros for better reconstruction. :param window_length: Sometimes one desires to use a shorter window than the fft size. In that case, the window is padded with zeros. The default is to use the fft-size as a window size. :return: Single channel complex STFT signal with dimensions frames times size/2+1. """ if time_dim is None: time_dim = np.argmax(time_signal.shape) ''' # Pad with zeros to have enough samples for the window function to fade. if fading: pad = [(0, 0)] * time_signal.ndim pad[time_dim] = [size - shift, size - shift] time_signal = np.pad(time_signal, pad, mode='constant') ''' # Pad with trailing zeros, to have an integral number of frames. frames = _samples_to_stft_frames(time_signal.shape[time_dim], size, shift) samples = _stft_frames_to_samples(frames, size, shift) pad = [(0, 0)] * time_signal.ndim pad[time_dim] = [0, samples - time_signal.shape[time_dim]] time_signal = np.pad(time_signal, pad, mode='constant') if window_length is None: window = window(size) else: window = window(window_length) window = np.pad(window, (0, size - window_length), mode='constant') time_signal_seg = segment_axis(time_signal, size, size - shift, axis=time_dim) letters = string.ascii_lowercase mapping = letters[:time_signal_seg.ndim] + ',' + letters[time_dim + 1] \ + '->' + letters[:time_signal_seg.ndim] return rfft(np.einsum(mapping, time_signal_seg, window), axis=time_dim + 1)
def istft(stft_signal, signal_len, size=1024, shift=256, window=signal.blackman, fading=True, window_length=None): """ Calculated the inverse short time Fourier transform to exactly reconstruct the time signal. :param stft_signal: Single channel complex STFT signal with dimensions frames times size/2+1. :param size: Scalar FFT-size. :param shift: Scalar FFT-shift. Typically shift is a fraction of size. :param window: Window function handle. :param fading: Removes the additional padding, if done during STFT. :param window_length: Sometimes one desires to use a shorter window than the fft size. In that case, the window is padded with zeros. The default is to use the fft-size as a window size. :return: Single channel complex STFT signal :return: Single channel time signal. """ assert stft_signal.shape[1] == size // 2 + 1 if window_length is None: window = window(size) else: window = window(window_length) window = np.pad(window, (0, size - window_length), mode='constant') window = _biorthogonal_window_loopy(window, shift) # Why? Line created by Hai, Lukas does not know, why it exists. window *= size time_signal = scipy.zeros(stft_signal.shape[0] * shift + size - shift) for j, i in enumerate(range(0, len(time_signal) - size + shift, shift)): time_signal[i:i + size] += window * np.real(irfft(stft_signal[j])) time_signal = time_signal[0:signal_len] # Compensate fade-in and fade-out #if fading: # time_signal = time_signal[ # size - shift:len(time_signal) - (size - shift)] return time_signal
