scipy.signal.stft¶

scipy.signal.
stft
(x, fs=1.0, window='hann', nperseg=256, noverlap=None, nfft=None, detrend=False, return_onesided=True, boundary='zeros', padded=True, axis=1)[source]¶ Compute the Short Time Fourier Transform (STFT).
STFTs can be used as a way of quantifying the change of a nonstationary signal’s frequency and phase content over time.
Parameters: x : array_like
Time series of measurement values
fs : float, optional
Sampling frequency of the x time series. Defaults to 1.0.
window : str or tuple or array_like, optional
Desired window to use. If window is a string or tuple, it is passed to
get_window
to generate the window values, which are DFTeven by default. Seeget_window
for a list of windows and required parameters. If window is array_like it will be used directly as the window and its length must be nperseg. Defaults to a Hann window.nperseg : int, optional
Length of each segment. Defaults to 256.
noverlap : int, optional
Number of points to overlap between segments. If None,
noverlap = nperseg // 2
. Defaults to None. When specified, the COLA constraint must be met (see Notes below).nfft : int, optional
Length of the FFT used, if a zero padded FFT is desired. If None, the FFT length is nperseg. Defaults to None.
detrend : str or function or False, optional
return_onesided : bool, optional
If True, return a onesided spectrum for real data. If False return a twosided spectrum. Note that for complex data, a twosided spectrum is always returned. Defaults to True.
boundary : str or None, optional
Specifies whether the input signal is extended at both ends, and how to generate the new values, in order to center the first windowed segment on the first input point. This has the benefit of enabling reconstruction of the first input point when the employed window function starts at zero. Valid options are
['even', 'odd', 'constant', 'zeros', None]
. Defaults to ‘zeros’, for zero padding extension. I.e.[1, 2, 3, 4]
is extended to[0, 1, 2, 3, 4, 0]
fornperseg=3
.padded : bool, optional
Specifies whether the input signal is zeropadded at the end to make the signal fit exactly into an integer number of window segments, so that all of the signal is included in the output. Defaults to True. Padding occurs after boundary extension, if boundary is not None, and padded is True, as is the default.
axis : int, optional
Axis along which the STFT is computed; the default is over the last axis (i.e.
axis=1
).Returns: f : ndarray
Array of sample frequencies.
t : ndarray
Array of segment times.
Zxx : ndarray
STFT of x. By default, the last axis of Zxx corresponds to the segment times.
See also
istft
 Inverse Short Time Fourier Transform
check_COLA
 Check whether the Constant OverLap Add (COLA) constraint is met
welch
 Power spectral density by Welch’s method.
spectrogram
 Spectrogram by Welch’s method.
csd
 Cross spectral density by Welch’s method.
lombscargle
 LombScargle periodogram for unevenly sampled data
Notes
In order to enable inversion of an STFT via the inverse STFT in
istft
, the signal windowing must obey the constraint of “Constant OverLap Add” (COLA), and the input signal must have complete windowing coverage (i.e.(x.shape[axis]  nperseg) % (npersegnoverlap) == 0
). The padded argument may be used to accomplish this.The COLA constraint ensures that every point in the input data is equally weighted, thereby avoiding aliasing and allowing full reconstruction. Whether a choice of window, nperseg, and noverlap satisfy this constraint can be tested with
check_COLA
.New in version 0.19.0.
References
[R301] Oppenheim, Alan V., Ronald W. Schafer, John R. Buck “DiscreteTime Signal Processing”, Prentice Hall, 1999. [R302] Daniel W. Griffin, Jae S. Limdt “Signal Estimation from Modified Short Fourier Transform”, IEEE 1984, 10.1109/TASSP.1984.1164317 Examples
>>> from scipy import signal >>> import matplotlib.pyplot as plt
Generate a test signal, a 2 Vrms sine wave whose frequency is slowly modulated around 3kHz, corrupted by white noise of exponentially decreasing magnitude sampled at 10 kHz.
>>> fs = 10e3 >>> N = 1e5 >>> amp = 2 * np.sqrt(2) >>> noise_power = 0.01 * fs / 2 >>> time = np.arange(N) / float(fs) >>> mod = 500*np.cos(2*np.pi*0.25*time) >>> carrier = amp * np.sin(2*np.pi*3e3*time + mod) >>> noise = np.random.normal(scale=np.sqrt(noise_power), ... size=time.shape) >>> noise *= np.exp(time/5) >>> x = carrier + noise
Compute and plot the STFT’s magnitude.
>>> f, t, Zxx = signal.stft(x, fs, nperseg=1000) >>> plt.pcolormesh(t, f, np.abs(Zxx), vmin=0, vmax=amp) >>> plt.title('STFT Magnitude') >>> plt.ylabel('Frequency [Hz]') >>> plt.xlabel('Time [sec]') >>> plt.show()