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# scipy.signal.cwt

`scipy.signal.``cwt`(data, wavelet, widths)

  Continuous wavelet transform.

  Performs a continuous wavelet transform on data, using the wavelet function. A CWT performs a convolution with data using the wavelet function, which is characterized by a width parameter and length parameter.

  Parameters : | data : (N,) ndarray data on which to perform the transform. wavelet : function Wavelet function, which should take 2 arguments. The first argument is the number of points that the returned vector will have (len(wavelet(width,length)) == length). The second is a width parameter, defining the size of the wavelet (e.g. standard deviation of a gaussian). See `ricker`, which satisfies these requirements. widths : (M,) sequence Widths to use for transform.
  Returns : | cwt: (M, N) ndarray : Will have shape of (len(data), len(widths)).

  Notes

      >>> length = min(10 * width[ii], len(data))
      >>> cwt[ii,:] = scipy.signal.convolve(data, wavelet(width[ii],
      ...                                       length), mode='same')

  Examples

      >>> from scipy import signal
      >>> sig = np.random.rand(20) - 0.5
      >>> wavelet = signal.ricker
      >>> widths = np.arange(1, 11)
      >>> cwtmatr = signal.cwt(sig, wavelet, widths)

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