scipy.signal.ShortTimeFFT.

extent

ShortTimeFFT.extent(n, axes_seq='tf', center_bins=False)

Return minimum and maximum values time-frequency values.

A tuple with four floats (t0, t1, f0, f1) for ‘tf’ and (f0, f1, t0, t1) for ‘ft’ is returned describing the corners of the time-frequency domain of the stft. That tuple can be passed to matplotlib.pyplot.imshow as a parameter with the same name.

Parameters:

nint

Number of samples in input signal.

axes_seq{‘tf’, ‘ft’}

Return time extent first and then frequency extent or vice-versa.

center_bins: bool

If set (default False), the values of the time slots and frequency bins are moved from the side the middle. This is useful, when plotting the stft values as step functions, i.e., with no interpolation.

See also

matplotlib.pyplot.imshow

Display data as an image.

scipy.signal.ShortTimeFFT

Class this method belongs to.

Examples

Try it in your browser!

The following two plots illustrate the effect of the parameter center_bins: The grid lines represent the three time and the four frequency values of the STFT. The left plot, where (t0, t1, f0, f1) = (0, 3, 0, 4) is passed as parameter extent to imshow, shows the standard behavior of the time and frequency values being at the lower edge of the corrsponding bin. The right plot, with (t0, t1, f0, f1) = (-0.5, 2.5, -0.5, 3.5), shows that the bins are centered over the respective values when passing center_bins=True.

>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from scipy.signal import ShortTimeFFT
...
>>> n, m = 12, 6
>>> SFT = ShortTimeFFT.from_window('hann', fs=m, nperseg=m, noverlap=0)
>>> Sxx = SFT.stft(np.cos(np.arange(n)))  # produces a colorful plot
...
>>> fig, axx = plt.subplots(1, 2, tight_layout=True, figsize=(6., 4.))
>>> for ax_, center_bins in zip(axx, (False, True)):
...     ax_.imshow(abs(Sxx), origin='lower', interpolation=None, aspect='equal',
...                cmap='viridis', extent=SFT.extent(n, 'tf', center_bins))
...     ax_.set_title(f"{center_bins=}")
...     ax_.set_xlabel(f"Time ({SFT.p_num(n)} points, Δt={SFT.delta_t})")
...     ax_.set_ylabel(f"Frequency ({SFT.f_pts} points, Δf={SFT.delta_f})")
...     ax_.set_xticks(SFT.t(n))  # vertical grid line are timestamps
...     ax_.set_yticks(SFT.f)  # horizontal grid line are frequency values
...     ax_.grid(True)
>>> plt.show()

Note that the step-like behavior with the constant colors is caused by passing interpolation=None to imshow.

Go BackOpen In Tab