{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:red\">Continuous Wavelet Spectrum </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example for sine waves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First written on Nov,19, 2014:  \n",
    "Author: tmiyama at gmail"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wavelet anaysis tool by Torrence and Compo [1998] is very popular in climatology. However, Liu et al. [2007] has pointed out that the method by Torrence and Compo [1998] has a bias in favor of large scales.  \n",
    "  \n",
    "This notebook is the translation of Liu et al.'s matlab test program  \n",
    "http://ocgweb.marine.usf.edu/~liu/wavelet_test_ElNino3_YLiu.m  \n",
    "to python.\n",
    "\n",
    "This notebook use an artificial sine curve for a test correponding to Liu et al. [2007].\n",
    "\n",
    "Notebook for NINO3 SST is here: http://nbviewer.ipython.org/urls/dl.dropbox.com/s/ryeoz3wxrkg1hhz/wavelet_test_ElNino3_Liu.ipynb?dl=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__References:__  \n",
    "  \n",
    "* Liu, Y., X.S. Liang, and R.H. Weisberg, 2007: Rectification of the bias in the wavelet power spectrum. Journal of Atmospheric and Oceanic Technology, 24(12), 2093-2102. http://ocgweb.marine.usf.edu/~liu/wavelet.html\n",
    "   \n",
    "* Torrence, C., and G. P. Compo, 1998: A practical guide to wavelet analysis. Bull. Amer. Meteor. Soc., 79, 61–78. http://paos.colorado.edu/research/wavelets/ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the comment in the original matlab script.\n",
    "\n",
    "% Rectification of the bias in the Wavelet power spectrum with the data set  \n",
    "% (Nino3.dat) given by Torrence and Compo (1998).  This code is modified  \n",
    "% from wavetest.m, the example script provided by Torrence and Compo\n",
    "% (1998), to demonstrate how to rectify the bias in wavelet power spectrum.  \n",
    "% This code generates Figure 4 of Liu et al. (2007).  \n",
    "%  \n",
    "% Yonggang Liu, 2006.4.12  \n",
    "%  \n",
    "% E-mail:  yliu18@gmail.com  \n",
    "% http://ocgweb.marine.usf.edu/~liu/wavelet.html  \n",
    "%\n",
    "% References:  \n",
    "%\n",
    "% Liu, Y., X.S. Liang, and R.H. Weisberg, 2007: Rectification of the bias\n",
    "% in the wavelet power spectrum. Journal of Atmospheric and Oceanic \n",
    "% Technology, 24(12), 2093-2102.\n",
    "%   \n",
    "% Torrence, C., and G. P. Compo, 1998: A practical guide to wavelet \n",
    "% analysis. Bull. Amer. Meteor. Soc., 79, 61�78.\n",
    "%========================================================================  \n",
    "% Here starts with the original file header:\n",
    "  \n",
    "  \n",
    "%WAVETEST Example Matlab script for WAVELET, using NINO3 SST dataset  \n",
    "%  \n",
    "% See \"http://paos.colorado.edu/research/wavelets/\"  \n",
    "% Written January 1998 by C. Torrence  \n",
    "%  \n",
    "% Modified Oct 1999, changed Global Wavelet Spectrum (GWS) to be sideways,  \n",
    "%   changed all \"log\" to \"log2\", changed logarithmic axis on GWS  \n",
    "%   a normal axis.    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading modules and data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Read usual modules__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Read Wavelet module__\n",
    "This module is the translation from the matlab tools by Torrence and Compo (1998).  \n",
    "http://paos.colorado.edu/research/wavelets/software.html  \n",
    "\n",
    "See the bottom of this notebook for the inside of this module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from wavelet import wavelet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Construct sine waves__   \n",
    "Section 5 in Liu et al. [2007]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt=1./24. # 1 hour\n",
    "t1=1.\n",
    "t2=8.\n",
    "t3=32.\n",
    "t4=128.\n",
    "t5=365.\n",
    "n=365*24*13\n",
    "t=np.arange(n)*dt\n",
    "pi2=np.pi*2.\n",
    "\n",
    "#5 sine waves\n",
    "sst=np.sin(t/t1*pi2)+np.sin(t/t2*pi2)+np.sin(t/t3*pi2)+np.sin(t/t4*pi2)+np.sin(t/t5*pi2) \n",
    "\n",
    "time = t/365. + 1993.0   # construct time array\n",
    "xlim = [time[0],time[-1]]  # plotting range\n",
    "\n",
    "plt.plot(time,sst)\n",
    "xrange=plt.xlim(xlim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wavelet Spectrum by Torrence and Compo [1998]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Set wavelet parameters__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "pad = 1      # pad the time series with zeroes (recommended)\n",
    "dj = 1./8.    # this will do 8 sub-octaves per octave\n",
    "s0 = 6.*dt    # this says start at a scale of 6hour\n",
    "j1 = 12./dj    # this says do 12 powers-of-two with dj sub-octaves each\n",
    "mother = 'Morlet'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Wavelet transform__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "wave,period,scale,coi = wavelet(sst,dt,pad,dj,s0,j1,mother);\n",
    "power = (np.abs(wave))**2         # compute wavelet power spectrum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Global wavelet spectrum__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "global_ws = power.sum(axis=1)/float(n)   # time-average over all times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wavelet Spectrum by Liu et al [2007]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red\">__Bias rectification__  </span>\n",
    "  \n",
    "divided by scales  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "########################\n",
    "#  Spectrum\n",
    "########################\n",
    "powers=np.zeros_like(power)\n",
    "for k in range(len(scale)):\n",
    "    powers[k,:] = power[k,:]/scale[k]\n",
    "\n",
    "########################\n",
    "#  Global Spectrum\n",
    "########################\n",
    "global_wss = global_ws/scale   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PLOT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Corresponding to Fig. 2 in Liu et al. (2007)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#figure size\n",
    "fig=plt.figure(figsize=(10,10))\n",
    "\n",
    "# subplot positions\n",
    "width= 0.65\n",
    "height = 0.28;\n",
    "pos1a = [0.1, 0.75, width, 0.2]\n",
    "pos1b = [0.1, 0.4, width, height]\n",
    "pos1c = [0.79, 0.4, 0.18, height]\n",
    "pos1d = [0.1, 0.02, width, height]\n",
    "pos1e = [0.79,0.02, 0.18, height]\n",
    "#########################################\n",
    "#---- a) Original signal\n",
    "#########################################\n",
    "ax=fig.add_axes(pos1a)\n",
    "ax.plot(time,sst,\"r-\")\n",
    "ax.set_ylabel('')\n",
    "plt.title('a) Original Sine curve')\n",
    "\n",
    "#########################################\n",
    "#   b) Wavelet spectrum\n",
    "#########################################\n",
    "\n",
    "#--- Contour plot wavelet power spectrum\n",
    "bx=fig.add_axes(pos1b,sharex=ax) \n",
    "Yticks = 2.**(np.arange(np.int(np.log2(np.min(period))),np.int(np.log2(np.max(period)))+1))\n",
    "bx.contourf(time,np.log2(period),np.log2(power))\n",
    "bx.set_xlabel('Time (year)')\n",
    "bx.set_ylabel('Period (days)')\n",
    "import matplotlib.ticker as ticker\n",
    "ymajorLocator=ticker.FixedLocator(np.log2(Yticks))\n",
    "bx.yaxis.set_major_locator( ymajorLocator )\n",
    "ticks=bx.yaxis.set_ticklabels(Yticks)\n",
    "plt.title('b) Wavelet Power Spectrum')\n",
    "\n",
    "\n",
    "# cone-of-influence, anything \"below\" is dubious\n",
    "ts = time;\n",
    "coi_area = np.concatenate([[np.max(scale)], coi, [np.max(scale)],[np.max(scale)]])\n",
    "ts_area = np.concatenate([[ts[0]], ts, [ts[-1]] ,[ts[0]]]);\n",
    "L = bx.plot(ts_area,np.log2(coi_area),'k',linewidth=3)\n",
    "F=bx.fill(ts_area,np.log2(coi_area),'k',alpha=0.3,hatch=\"x\")\n",
    "\n",
    "#########################################\n",
    "#   c) Global Wavelet spectrum\n",
    "#########################################\n",
    "\n",
    "#--- Plot global wavelet spectrum\n",
    "cx=fig.add_axes(pos1c,sharey=bx)\n",
    "cx.plot(np.log2(global_ws),np.log2(period),\"r-\")\n",
    "ylim=cx.set_ylim(np.log2([period.min(),period.max()]))\n",
    "cx.invert_yaxis()\n",
    "plt.title('c) Global Wavelet Spectrum')\n",
    "xrangec=cx.set_xlim([0,1.25*np.max(np.log2(global_ws))])\n",
    "\n",
    "#########################################\n",
    "#   d) Wavelet spectrum\n",
    "#########################################\n",
    "\n",
    "#--- Contour plot wavelet power spectrum\n",
    "dx=fig.add_axes(pos1d,sharex=ax)\n",
    "dx.contourf(time,np.log2(period),np.log2(powers))\n",
    "dx.set_xlabel('Time (year)')\n",
    "dx.set_ylabel('Period (days)')\n",
    "ymajorLocator=ticker.FixedLocator(np.log2(Yticks))\n",
    "dx.yaxis.set_major_locator( ymajorLocator )\n",
    "dx.yaxis.set_ticklabels(Yticks)\n",
    "plt.title('d) Wavelet Power Spectrum')\n",
    "\n",
    "\n",
    "# cone-of-influence, anything \"below\" is dubios\n",
    "L = dx.plot(ts_area,np.log2(coi_area),'k',linewidth=3)\n",
    "F=dx.fill(ts_area,np.log2(coi_area),'k',alpha=0.3,hatch=\"x\")\n",
    "\n",
    "#########################################\n",
    "#   e) Global Wavelet spectrum\n",
    "#########################################\n",
    "\n",
    "#--- Plot global wavelet spectrum\n",
    "ex=fig.add_axes(pos1e,sharey=dx)\n",
    "ex.plot(np.log2(global_wss),np.log2(period),\"r-\")\n",
    "ylim=ex.set_ylim(np.log2([period.min(),period.max()]))\n",
    "ex.invert_yaxis()\n",
    "plt.title('e) Global Wavelet Spectrum')\n",
    "xrangec=ex.set_xlim([0,1.25*np.max(np.log2(global_wss))])\n",
    "\n",
    "#########################################\n",
    "#   Save fig\n",
    "#########################################\n",
    "plt.savefig(\"wavelet_test_sine.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List of Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "def wavelet(Y,dt,pad=0.,dj=0.25,s0=-1,J1=-1,mother=\"MORLET\",param=-1):\n",
      "    \"\"\"\n",
      "This function is the translation of wavelet.m by Torrence and Compo\n",
      "\n",
      "import wave_bases from wave_bases.py\n",
      "\n",
      "The following is the original comment in wavelet.m\n",
      "\n",
      "#WAVELET  1D Wavelet transform with optional singificance testing\n",
      "%\n",
      "%   [WAVE,PERIOD,SCALE,COI] = wavelet(Y,DT,PAD,DJ,S0,J1,MOTHER,PARAM)\n",
      "%\n",
      "%   Computes the wavelet transform of the vector Y (length N),\n",
      "%   with sampling rate DT.\n",
      "%\n",
      "%   By default, the Morlet wavelet (k0=6) is used.\n",
      "%   The wavelet basis is normalized to have total energy=1 at all scales.\n",
      "%\n",
      "%\n",
      "% INPUTS:\n",
      "%\n",
      "%    Y = the time series of length N.\n",
      "%    DT = amount of time between each Y value, i.e. the sampling time.\n",
      "%\n",
      "% OUTPUTS:\n",
      "%\n",
      "%    WAVE is the WAVELET transform of Y. This is a complex array\n",
      "%    of dimensions (N,J1+1). FLOAT(WAVE) gives the WAVELET amplitude,\n",
      "%    ATAN(IMAGINARY(WAVE),FLOAT(WAVE) gives the WAVELET phase.\n",
      "%    The WAVELET power spectrum is ABS(WAVE)^2.\n",
      "%    Its units are sigma^2 (the time series variance).\n",
      "%\n",
      "%\n",
      "% OPTIONAL INPUTS:\n",
      "% \n",
      "% *** Note *** setting any of the following to -1 will cause the default\n",
      "%               value to be used.\n",
      "%\n",
      "%    PAD = if set to 1 (default is 0), pad time series with enough zeroes to get\n",
      "%         N up to the next higher power of 2. This prevents wraparound\n",
      "%         from the end of the time series to the beginning, and also\n",
      "%         speeds up the FFT's used to do the wavelet transform.\n",
      "%         This will not eliminate all edge effects (see COI below).\n",
      "%\n",
      "%    DJ = the spacing between discrete scales. Default is 0.25.\n",
      "%         A smaller # will give better scale resolution, but be slower to plot.\n",
      "%\n",
      "%    S0 = the smallest scale of the wavelet.  Default is 2*DT.\n",
      "%\n",
      "%    J1 = the # of scales minus one. Scales range from S0 up to S0*2^(J1*DJ),\n",
      "%        to give a total of (J1+1) scales. Default is J1 = (LOG2(N DT/S0))/DJ.\n",
      "%\n",
      "%    MOTHER = the mother wavelet function.\n",
      "%             The choices are 'MORLET', 'PAUL', or 'DOG'\n",
      "%\n",
      "%    PARAM = the mother wavelet parameter.\n",
      "%            For 'MORLET' this is k0 (wavenumber), default is 6.\n",
      "%            For 'PAUL' this is m (order), default is 4.\n",
      "%            For 'DOG' this is m (m-th derivative), default is 2.\n",
      "%\n",
      "%\n",
      "% OPTIONAL OUTPUTS:\n",
      "%\n",
      "%    PERIOD = the vector of \"Fourier\" periods (in time units) that corresponds\n",
      "%           to the SCALEs.\n",
      "%\n",
      "%    SCALE = the vector of scale indices, given by S0*2^(j*DJ), j=0...J1\n",
      "%            where J1+1 is the total # of scales.\n",
      "%\n",
      "%    COI = if specified, then return the Cone-of-Influence, which is a vector\n",
      "%        of N points that contains the maximum period of useful information\n",
      "%        at that particular time.\n",
      "%        Periods greater than this are subject to edge effects.\n",
      "%        This can be used to plot COI lines on a contour plot by doing:\n",
      "%\n",
      "%              contour(time,log(period),log(power))\n",
      "%              plot(time,log(coi),'k')\n",
      "%\n",
      "%----------------------------------------------------------------------------\n",
      "%   Copyright (C) 1995-2004, Christopher Torrence and Gilbert P. Compo\n",
      "%\n",
      "%   This software may be used, copied, or redistributed as long as it is not\n",
      "%   sold and this copyright notice is reproduced on each copy made. This\n",
      "%   routine is provided as is without any express or implied warranties\n",
      "%   whatsoever.\n",
      "%\n",
      "% Notice: Please acknowledge the use of the above software in any publications:\n",
      "%    ``Wavelet software was provided by C. Torrence and G. Compo,\n",
      "%      and is available at URL: http://paos.colorado.edu/research/wavelets/''.\n",
      "%\n",
      "% Reference: Torrence, C. and G. P. Compo, 1998: A Practical Guide to\n",
      "%            Wavelet Analysis. <I>Bull. Amer. Meteor. Soc.</I>, 79, 61-78.\n",
      "%\n",
      "% Please send a copy of such publications to either C. Torrence or G. Compo:\n",
      "%  Dr. Christopher Torrence               Dr. Gilbert P. Compo\n",
      "%  Research Systems, Inc.                 Climate Diagnostics Center\n",
      "%  4990 Pearl East Circle                 325 Broadway R/CDC1\n",
      "%  Boulder, CO 80301, USA                 Boulder, CO 80305-3328, USA\n",
      "%  E-mail: chris[AT]rsinc[DOT]com         E-mail: compo[AT]colorado[DOT]edu\n",
      "%----------------------------------------------------------------------------\"\"\"  \n",
      "    #modules\n",
      "    import numpy as np\n",
      "    from wave_bases import wave_bases\n",
      "    \n",
      "    #set default\n",
      "    n1 = len(Y)\n",
      "    if (s0 == -1): s0=2.*dt\n",
      "    if (dj == -1): dj = 1./4.\n",
      "    if (J1 == -1): J1=np.fix((np.log(n1*dt/s0)/np.log(2))/dj)\n",
      "    if (mother == -1): mother = 'MORLET'\n",
      "    #print \"s0=\",s0\n",
      "    #print \"J1=\",J1\n",
      "\n",
      "    #....construct time series to analyze, pad if necessary\n",
      "    x = Y - np.mean(Y);\n",
      "    if (pad == 1):\n",
      "        base2 = np.fix(np.log(n1)/np.log(2) + 0.4999)   # power of 2 nearest to N\n",
      "        temp=np.zeros(int((2**(base2+1)-n1),))\n",
      "        x=np.concatenate((x,temp))\n",
      "    \n",
      "    n = len(x)\n",
      "\n",
      "    #....construct wavenumber array used in transform [Eqn(5)]\n",
      "    k = np.arange(1,np.fix(n/2)+1)\n",
      "    k = k*(2.*np.pi)/(n*dt)\n",
      "    k = np.concatenate((np.zeros((1,)),k, -k[-2::-1]));\n",
      "\n",
      "    #....compute FFT of the (padded) time series\n",
      "    f = np.fft.fft(x)    # [Eqn(3)]\n",
      "    \n",
      "    #....construct SCALE array & empty PERIOD & WAVE arrays\n",
      "    scale=np.array([s0*2**(i*dj) for i in range(0,int(J1)+1)])\n",
      "    period = scale.copy()\n",
      "    wave = np.zeros((int(J1)+1,n),dtype=np.complex)  # define the wavelet array  # make it complex\n",
      "    # loop through all scales and compute transform\n",
      "    for a1 in range(0,int(J1)+1):\n",
      "        daughter,fourier_factor,coi,dofmin=wave_bases(mother,k,scale[a1],param)\n",
      "        wave[a1,:] = np.fft.ifft(f*daughter)  # wavelet transform[Eqn(4)]\n",
      "    period = fourier_factor*scale\n",
      "    coi=coi*dt*np.concatenate(([1.E-5],np.arange(1.,(n1+1.)/2.-1),np.flipud(np.arange(1,n1/2.)),[1.E-5])) # COI [Sec.3g]\n",
      "    wave = wave[:,:n1]  # get rid of padding before returning\n",
      "    return wave,period,scale,coi\n",
      "    # end of code\n"
     ]
    }
   ],
   "source": [
    "!cat wavelet.py"
   ]
  },
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    {
     "name": "stdout",
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     "text": [
      "def wave_bases(mother,k,scale,param):\n",
      "    \"\"\"\n",
      "    This is translation of wave_bases.m by Torrence and Gilbert P. Compo\n",
      " \n",
      "    The folloing is the original README\n",
      " \n",
      "%    WAVE_BASES  1D Wavelet functions Morlet, Paul, or DOG\n",
      "%\n",
      "%  [DAUGHTER,FOURIER_FACTOR,COI,DOFMIN] = ...\n",
      "%      wave_bases(MOTHER,K,SCALE,PARAM);\n",
      "%\n",
      "%   Computes the wavelet function as a function of Fourier frequency,\n",
      "%   used for the wavelet transform in Fourier space.\n",
      "%   (This program is called automatically by WAVELET)\n",
      "%\n",
      "% INPUTS:\n",
      "%\n",
      "%    MOTHER = a string, equal to 'MORLET' or 'PAUL' or 'DOG'\n",
      "%    K = a vector, the Fourier frequencies at which to calculate the wavelet\n",
      "%    SCALE = a number, the wavelet scale\n",
      "%    PARAM = the nondimensional parameter for the wavelet function\n",
      "%\n",
      "% OUTPUTS:\n",
      "%\n",
      "%    DAUGHTER = a vector, the wavelet function\n",
      "%    FOURIER_FACTOR = the ratio of Fourier period to scale\n",
      "%    COI = a number, the cone-of-influence size at the scale\n",
      "%    DOFMIN = a number, degrees of freedom for each point in the wavelet power\n",
      "%             (either 2 for Morlet and Paul, or 1 for the DOG)\n",
      "%\n",
      "%----------------------------------------------------------------------------\n",
      "%   Copyright (C) 1995-1998, Christopher Torrence and Gilbert P. Compo\n",
      "%   University of Colorado, Program in Atmospheric and Oceanic Sciences.\n",
      "%   This software may be used, copied, or redistributed as long as it is not\n",
      "%   sold and this copyright notice is reproduced on each copy made.  This\n",
      "%   routine is provided as is without any express or implied warranties\n",
      "%   whatsoever.\n",
      "%----------------------------------------------------------------------------\n",
      "    \"\"\"\n",
      "    #import modules\n",
      "    import numpy as np\n",
      "\n",
      "    #\n",
      "    mother = mother.upper()\n",
      "    n = len(k)\n",
      "    # define Heaviside step function\n",
      "    def ksign(x):\n",
      "        y=np.zeros_like(x)\n",
      "        y[x>0]=1\n",
      "        return y\n",
      "    #\n",
      "    if mother=='MORLET':  #-----------------------------------  Morlet\n",
      "        if (param == -1): param = 6.\n",
      "        k0 = param\n",
      "        expnt = -(scale*k - k0)**2/2. *ksign(k)\n",
      "        norm = np.sqrt(scale*k[1])*(np.pi**(-0.25))*np.sqrt(n)    # total energy=N   [Eqn(7)]\n",
      "        daughter = norm*np.exp(expnt)\n",
      "        daughter = daughter*ksign(k)  # Heaviside step function\n",
      "        fourier_factor = (4.*np.pi)/(k0 + np.sqrt(2. + k0**2)) # Scale-->Fourier [Sec.3h]\n",
      "        coi = fourier_factor/np.sqrt(2)            # Cone-of-influence [Sec.3g]\n",
      "        dofmin = 2.                          # Degrees of freedom\n",
      "    elif mother=='PAUL': #--------------------------------  Paul\n",
      "        if (param == -1): param = 4.\n",
      "        m = param\n",
      "        expnt = -(scale*k)*ksign(k)\n",
      "        norm = np.sqrt(scale*k[1])*(2.**m/np.sqrt(m*np.prod(np.arange(2,2*m))))*np.sqrt(n)\n",
      "        daughter = norm*((scale*k)**m)*np.exp(expnt)\n",
      "        daughter = daughter*ksign(k)      # Heaviside step function\n",
      "        fourier_factor = 4*np.pi/(2.*m+1.)\n",
      "        coi = fourier_factor*np.sqrt(2)\n",
      "        dofmin = 2.\n",
      "    elif mother=='DOG':  #--------------------------------  DOG\n",
      "        if (param == -1): param = 2.\n",
      "        m = param\n",
      "        expnt = -(scale*k)**2 / 2.0\n",
      "        from scipy.special import gamma \n",
      "        norm = np.sqrt(scale*k[1]/gamma(m+0.5))*np.sqrt(n)\n",
      "        daughter = -norm*(1j**m)*((scale*k)**m)*np.exp(expnt);\n",
      "        fourier_factor = 2.*np.pi*np.sqrt(2./(2.*m+1.))\n",
      "        coi = fourier_factor/np.sqrt(2)\n",
      "        dofmin = 1.\n",
      "    else:\n",
      "        raise Exception(\"Mother must be one of MORLET,PAUL,DOG\")\n",
      "\n",
      "\n",
      "    return daughter,fourier_factor,coi,dofmin \n",
      "\n",
      "    # end of code\n"
     ]
    }
   ],
   "source": [
    "!cat wave_bases.py"
   ]
  },
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