transform.py 文件源码

def compute_optimal_scales(self):
        """Form a set of scales to use in the wavelet transform.

        For non-orthogonal wavelet analysis, one can use an
        arbitrary set of scales.

        It is convenient to write the scales as fractional powers of
        two:

            s_j = s_0 * 2 ** (j * dj), j = 0, 1, ..., J

            J = (1 / dj) * log2(N * dt / s_0)

        s0 - smallest resolvable scale
        J - largest scale

        choose s0 so that the equivalent Fourier period is 2 * dt.

        The choice of dj depends on the width in spectral space of
        the wavelet function. For the morlet, dj=0.5 is the largest
        that still adequately samples scale. Smaller dj gives finer
        scale resolution.
        """
        dt = self.dt
        # resolution
        dj = self.dj
        # smallest resolvable scale, chosen so that the equivalent
        # fourier period is approximately 2dt
        s0 = self.s0

        # Largest scale
        J = int((1 / dj) * np.log2(self.N * dt / s0))

        sj = s0 * 2 ** (dj * np.arange(0, J + 1))
        return sj

    # TODO: use np.frompyfunc on this
    # TODO: can we just replace it with fftfreqs?