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目录
Example 小波分析简介代码实验结果
Example 等值线图代码实验结果
参考资料
Example 小波分析
简介
一般情况下，小波周期图水平轴对应原来时间序列的时间，垂直轴代表变化的周期，颜色代表变化周期的强度。该图里，黄色代表变化周期的高强度。
在最上面的图中表示 2 个变量的时间序列（Alongstream Velocity、Cross-Stream Velocity）,下面的 2 张图中为该 2 个变量的小波分析，随着时间序列的变化，变化周期的强度变化。 
代码
from __future__ import division
import numpy
from matplotlib import pyplot
import pycwt as wavelet
from pycwt.helpers import find
url = 'http://paos.colorado.edu/research/wavelets/wave_idl/nino3sst.txt'
dat = numpy.genfromtxt(url, skip_header=19)
title = 'NINO3 Sea Surface Temperature'
label = 'NINO3 SST'
units = 'degC'
t0 = 1871.0
dt = 0.25 # 获取NINO数据
# We also create a time array in years.
N = dat.size
t = numpy.arange(0, N) * dt + t0
p = numpy.polyfit(t - t0, dat, 1)
dat_notrend = dat - numpy.polyval(p, t - t0)
std = dat_notrend.std() # Standard deviation
var = std ** 2 # Variance
dat_norm = dat_notrend / std # Normalized dataset
mother = wavelet.Morlet(6)
s0 = 2 * dt # Starting scale, in this case 2 * 0.25 years = 6 months
dj = 1 / 12 # Twelve sub-octaves per octaves
J = 7 / dj # Seven powers of two with dj sub-octaves
alpha, _, _ = wavelet.ar1(dat) # Lag-1 autocorrelation for red noise
wave, scales, freqs, coi, fft, fftfreqs = wavelet.cwt(dat_norm, dt, dj, s0, J,
mother)
iwave = wavelet.icwt(wave, scales, dt, dj, mother) * std
power = (numpy.abs(wave)) ** 2
fft_power = numpy.abs(fft) ** 2
period = 1 / freqs
signif, fft_theor = wavelet.significance(1.0, dt, scales, 0, alpha,
significance_level=0.95,
wavelet=mother)
sig95 = numpy.ones([1, N]) * signif[:, None]
sig95 = power / sig95
glbl_power = power.mean(axis=1)
dof = N - scales # Correction for padding at edges
glbl_signif, tmp = wavelet.significance(var, dt, scales, 1, alpha,
significance_level=0.95, dof=dof,
wavelet=mother)
sel = find((period >= 2) & (period |