In [3]:

    

import json
import numpy as np
import os, sys
import pywt
import matplotlib.pyplot as plt


    

In [ ]:

    




    

In [63]:

    

t = np.linspace(0.5,2,128)
mu = 0
sigma = 0.25
y = 1/(t*np.sqrt(2*np.pi)*sigma) * np.exp(-1*(np.log(t - mu))**2/(2*sigma**2))

[(_,one_level_y)] = pywt.swt(y, 'db2', level=1)


    

In [51]:

    

plt.plot(y)
plt.show()

plt.plot(one_level_y)
plt.show()


    

In [60]:

    

mu = 0.1
sigma = 0.25
y_shifted = 1/(t*np.sqrt(2*np.pi)*sigma) * np.exp(-1*(np.log(t - mu))**2/(2*sigma**2)) 

[(_,one_level_y_shifted)] = pywt.swt(y_shifted, 'db2', level=1)


    

In [61]:

    

plt.plot(y_shifted)
plt.show()

plt.plot(one_level_y_shifted)
plt.show()


    

In [62]:

    

print(one_level_y)


    

    




[  2.81224947e-03  -2.03959074e-03  -2.16151183e-03  -2.25473934e-03
  -2.31487576e-03  -2.33844227e-03  -2.32300720e-03  -2.26726338e-03
  -2.17105413e-03  -2.03535041e-03  -1.86218427e-03  -1.65454526e-03
  -1.41624777e-03  -1.15177749e-03  -8.66125667e-04  -5.64618719e-04
  -2.52750639e-04   6.39759301e-05   3.80194947e-04   6.90804869e-04
   9.91076713e-04   1.27674091e-03   1.54405257e-03   1.78983570e-03
   2.01150751e-03   2.20708457e-03   2.37517289e-03   2.51494437e-03
   2.62610188e-03   2.70883565e-03   2.76377307e-03   2.79192443e-03
   2.79462621e-03   2.77348403e-03   2.73031653e-03   2.66710156e-03
   2.58592556e-03   2.48893695e-03   2.37830405e-03   2.25617775e-03
   2.12465930e-03   1.98577292e-03   1.84144339e-03   1.69347821e-03
   1.54355413e-03   1.39320765e-03   1.24382911e-03   1.09666002e-03
   9.52793181e-04   8.13175088e-04   6.78610460e-04   5.49768292e-04
   4.27189219e-04   3.11293827e-04   2.02391645e-04   1.00690554e-04
   6.30640077e-06  -8.07273711e-05  -1.60450235e-04  -2.32965369e-04
  -2.98430818e-04  -3.57051119e-04  -4.09069441e-04  -4.54760287e-04
  -4.94422783e-04  -5.28374558e-04  -5.56946230e-04  -5.80476470e-04
  -5.99307637e-04  -6.13781963e-04  -6.24238250e-04  -6.31009054e-04
  -6.34418317e-04  -6.34779415e-04  -6.32393589e-04  -6.27548715e-04
  -6.20518380e-04  -6.11561240e-04  -6.00920616e-04  -5.88824297e-04
  -5.75484535e-04  -5.61098188e-04  -5.45847003e-04  -5.29897999e-04
  -5.13403947e-04  -4.96503917e-04  -4.79323880e-04  -4.61977353e-04
  -4.44566067e-04  -4.27180654e-04  -4.09901343e-04  -3.92798649e-04
  -3.75934059e-04  -3.59360704e-04  -3.43124010e-04  -3.27262325e-04
  -3.11807530e-04  -2.96785608e-04  -2.82217202e-04  -2.68118125e-04
  -2.54499852e-04  -2.41369979e-04  -2.28732645e-04  -2.16588933e-04
  -2.04937238e-04  -1.93773602e-04  -1.83092033e-04  -1.72884783e-04
  -1.63142618e-04  -1.53855049e-04  -1.45010551e-04  -1.36596756e-04
  -1.28600627e-04  -1.21008618e-04  -1.13806812e-04  -1.06981045e-04
  -1.00517015e-04  -9.44003800e-05  -8.86168405e-05  -8.31522135e-05
  -7.79924950e-05  -7.31239132e-05  -6.85329737e-05  -6.42064974e-05
  -6.01316509e-05  -5.62959715e-05  -2.53795959e-02   9.37312967e-03]

In [59]:

    

print(one_level_y_shifted)


    

    




[ -6.52177097e-03  -2.36598046e-05  -3.79116493e-05  -5.84408568e-05
  -8.69298193e-05  -1.25114197e-04  -1.74653634e-04  -2.36982147e-04
  -3.13150998e-04  -4.03678788e-04  -5.08423646e-04  -6.26490725e-04
  -7.56185125e-04  -8.95016167e-04  -1.03975436e-03  -1.18653784e-03
  -1.33102108e-03  -1.46855559e-03  -1.59439035e-03  -1.70387902e-03
  -1.79268108e-03  -1.85694557e-03  -1.89346806e-03  -1.89981351e-03
  -1.87440110e-03  -1.81654892e-03  -1.72647942e-03  -1.60528831e-03
  -1.45488122e-03  -1.27788389e-03  -1.07753186e-03  -8.57546361e-04
  -6.22002640e-04  -3.75196728e-04  -1.21515842e-04   1.34683140e-04
   3.89183624e-04   6.38005089e-04   8.77483064e-04   1.10433246e-03
   1.31569415e-03   1.50916537e-03   1.68281494e-03   1.83518473e-03
   1.96527912e-03   2.07254411e-03   2.15683825e-03   2.21839701e-03
   2.25779260e-03   2.27589070e-03   2.27380586e-03   2.25285668e-03
   2.21452199e-03   2.16039898e-03   2.09216393e-03   2.01153612e-03
   1.92024540e-03   1.82000340e-03   1.71247883e-03   1.59927646e-03
   1.48192004e-03   1.36183870e-03   1.24035677e-03   1.11868666e-03
   9.97924506e-04   8.79048333e-04   7.62918306e-04   6.50278852e-04
   5.41762297e-04   4.37893752e-04   3.39096973e-04   2.45700956e-04
   1.57947047e-04   7.59963774e-05  -6.25606210e-08  -7.02063192e-05
  -1.34469024e-04  -1.92935119e-04  -2.45732404e-04  -2.93025440e-04
  -3.35009355e-04  -3.71904092e-04  -4.03949112e-04  -4.31398570e-04
  -4.54516954e-04  -4.73575188e-04  -4.88847177e-04  -5.00606781e-04
  -5.09125200e-04  -5.14668735e-04  -5.17496902e-04  -5.17860880e-04
  -5.16002252e-04  -5.12152019e-04  -5.06529856e-04  -4.99343593e-04
  -4.90788874e-04  -4.81048998e-04  -4.70294898e-04  -4.58685243e-04
  -4.46366656e-04  -4.33474005e-04  -4.20130776e-04  -4.06449502e-04
  -3.92532232e-04  -3.78471039e-04  -3.64348545e-04  -3.50238465e-04
  -3.36206152e-04  -3.22309152e-04  -3.08597738e-04  -2.95115448e-04
  -2.81899604e-04  -2.68981811e-04  -2.56388439e-04  -2.44141086e-04
  -2.32257017e-04  -2.20749574e-04  -2.09628574e-04  -1.98900669e-04
  -1.88569694e-04  -1.78636986e-04  -1.69101675e-04  -1.59960966e-04
  -1.51210383e-04  -1.42844007e-04   2.25532277e-02  -1.82426962e-02]

In [ ]: