class_docstr: Calculate and show a spectrogram
  
  Parameters
  ----------
  x : array-like
      1D signal to operate on. ``If len(x) < n_fft``, x will be
      zero-padded to length ``n_fft``.
  n_fft : int
      Number of FFT points. Much faster for powers of two.
  step : int | None
      Step size between calculations. If None, ``n_fft // 2``
      will be used.
  fs : float
      The sample rate of the data.
  window : str | None
      Window function to use. Can be ``'hann'`` for Hann window, or None
      for no windowing.
  normalize : bool
      Normalization of spectrogram values across frequencies.
  color_scale : {'linear', 'log'}
      Scale to apply to the result of the STFT.
      ``'log'`` will use ``10 * log10(power)``.
  cmap : str
      Colormap name.
  clim : str | tuple
      Colormap limits. Should be ``'auto'`` or a two-element tuple of
      min and max values.
code: def __init__(
      self,
      x=None,
      n_fft=256,
      step=None,
      fs=1.0,
      window="hann",
      normalize=False,
      color_scale="log",
      cmap="cubehelix",
      clim="auto",
  ):
      self._x = np.asarray(x)
      self._n_fft = int(n_fft)
      self._step = step
      self._fs = float(fs)
      self._window = window
      self._normalize = normalize
      self._color_scale = color_scale
  
      if clim == "auto":
          self._clim_auto = True
      else:
          self._clim_auto = False
  
      if not isinstance(
          self._color_scale, str
      ) or self._color_scale not in ("log", "linear"):
          raise ValueError('color_scale must be "linear" or "log"')
  
      data = self._calculate_spectrogram()
      super(SpectrogramVisual, self).__init__(
          data, clim=clim, cmap=cmap
      )
  
imports: import numpy
  from .image import ImageVisual
  from ..util.fourier import stft, fft_freqs
label_desc: Use image node_id 1 to create a new SpectrogramVisual object from inherited base classes: ImageVisual with example: obj = SpectrogramVisual(x, n_fft, step, fs, window, normalize, color_scale, cmap, clim)