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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

  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`.