- 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.
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)