scipy.signal.cwt(data, wavelet, widths)[source]

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.


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.


cwt: (M, N) ndarray

Will have shape of (len(data), len(widths)).


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


>>> 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)