cwt(data, wavelet, widths, dtype=None, **kwargs)
Performs a continuous wavelet transform on data
, using the :None:None:`wavelet` function. A CWT performs a convolution with data
using the :None:None:`wavelet` function, which is characterized by a width parameter and length parameter. The :None:None:`wavelet` function is allowed to be complex.
For non-symmetric, complex-valued wavelets, the input signal is convolved with the time-reversed complex-conjugate of the wavelet data [1].
length = min(10 * width[ii], len(data))
cwt[ii,:] = signal.convolve(data, np.conj(wavelet(length, width[ii],
**kwargs))[::-1], mode='same')
data on which to perform the transform.
Wavelet function, which should take 2 arguments. The first argument is the number of points that the returned vector will have (len(wavelet(length,width)) == 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 to use for transform.
The desired data type of output. Defaults to float64
if the output of :None:None:`wavelet` is real and complex128
if it is complex.
Keyword arguments passed to wavelet function.
Will have shape of (len(widths), len(data)).
Continuous wavelet transform.
>>> from scipy import signal
... import matplotlib.pyplot as plt
... t = np.linspace(-1, 1, 200, endpoint=False)
... sig = np.cos(2 * np.pi * 7 * t) + signal.gausspulse(t - 0.4, fc=2)
... widths = np.arange(1, 31)
... cwtmatr = signal.cwt(sig, signal.ricker, widths)
... plt.imshow(cwtmatr, extent=[-1, 1, 31, 1], cmap='PRGn', aspect='auto',
... vmax=abs(cwtmatr).max(), vmin=-abs(cwtmatr).max())
... plt.show()
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.signal._wavelets.morlet
scipy.signal._wavelets.cwt
scipy.signal._peak_finding.find_peaks_cwt
scipy.signal._wavelets.morlet2
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