{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import sys\n",
    "import getopt\n",
    "sys.path.insert(1,\"/usr/local/science/clag/\")\n",
    "import clag\n",
    "%pylab inline\n",
    "\n",
    "ref_file=\"lc/1367A.lc\"\n",
    "echo_file=\"lc/3465A.lc\"\n",
    "\n",
    "dt=0.01\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fqL = np.logspace(np.log10(0.0049999999),np.log10(0.62032418),9)\n",
    "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227,\n",
    "                0.10747115, 0.16658029, 0.25819945, 0.40020915,\n",
    "                0.62032418])\n",
    "nfq = len(fqL) - 1\n",
    "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n",
    "\n",
    "\n",
    "## load the first light curve\n",
    "ref_time, ref_strength, ref_strength_err = np.loadtxt(ref_file,\n",
    "                                                      skiprows=1).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## initialize the psd class for multiple light curves ##\n",
    "P1  = clag.clag('psd10r',\n",
    "                [ref_time], [ref_strength], [ref_strength_err],\n",
    "                dt, fqL)\n",
    "ref_psd = np.ones(nfq)\n",
    "ref_psd, ref_psd_err = clag.optimize(P1, ref_psd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ref_psd, ref_psd_err = clag.errors(P1, ref_psd, ref_psd_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xscale('log'); ylim(-4,2)\n",
    "errorbar(fqd, ref_psd, yerr=ref_psd_err, fmt='o', ms=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load second light curve\n",
    "echo_time, echo_strength, echo_strength_err = np.loadtxt(echo_file,skiprows=1).T\n",
    "P2  = clag.clag('psd10r', [echo_time], [echo_strength], [echo_strength_err], dt, fqL)\n",
    "echo_psd = np.ones(nfq)\n",
    "echo_psd, echo_psd_err = clag.optimize(P2, echo_psd)\n",
    "echo_psd, echo_psd_err = clag.errors(P2, echo_psd, echo_psd_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xscale('log'); ylim(-4,2)\n",
    "errorbar(fqd, echo_psd, yerr=echo_psd_err, fmt='o', ms=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Cx = clag.clag('cxd10r',\n",
    "\t\t\t\t[[ref_time,echo_time]], \n",
    "            \t[[ref_strength,echo_strength]],\n",
    "            \t[[ref_strength_err,echo_strength_err]], \n",
    "               dt, fqL, ref_psd, echo_psd)\n",
    "\n",
    "Cx_vals = np.concatenate( (ref_psd*0.7+echo_psd*0.4-0.3,ref_psd*0+0.1) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Cx_vals, Cx_err = clag.optimize(Cx, Cx_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xscale('log'); ylim(-4,2)\n",
    "# errorbar(fqd, Cx_vals, yerr=Cx_err, fmt='o', ms=10)\n",
    "Cx_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Cx_vals, Cx_err = clag.errors(Cx,Cx_vals,Cx_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}