phy-4660/lag/data/clag_analysis-origbins-noLFerrors-5404A.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tvhMr3fCZ8Lp89zzkk4IHhw3vbrVPgqN3hXp1RoCTIhcg+4IZ3239oquHGZIZ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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f736504bd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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",
    "from scipy.stats import norm\n",
    "from scipy.stats import lognorm\n",
    "from scipy.optimize import curve_fit\n",
    "import numpy.fft\n",
    "\n",
    "ref_file=\"lc/1367A.lc\"\n",
    "echo_file=\"lc/5404A.lc\"\n",
    "\n",
    "dt = 0.01\n",
    "t1, l1, l1e = np.loadtxt(ref_file).T\n",
    "errorbar(t1, l1, yerr=l1e, fmt='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.005     ,  0.01861938,  0.04473305,  0.06933623,  0.10747115,\n",
       "        0.16658029,  0.25819945,  0.40020915,  0.62032418])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n",
    "                0.25819945, 0.40020915, 0.62032418])\n",
    "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n",
    "nfq = len(fqL) - 1\n",
    "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n",
    "\n",
    "\n",
    "fqL\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 4.342e-01 5.077e+01 inf -- -5.530e+02 -- 1 1 1 1 1 1 1 1\n",
      "   2 7.674e-01 5.065e+01 8.300e+01 -- -4.700e+02 -- 0.653018 0.587019 0.568277 0.567457 0.566281 0.566085 0.565773 0.566163\n",
      "   3 3.298e+00 5.043e+01 8.075e+01 -- -3.893e+02 -- 0.414806 0.209135 0.141159 0.13784 0.133393 0.132728 0.131612 0.132761\n",
      "   4 1.572e+00 5.010e+01 7.754e+01 -- -3.117e+02 -- 0.322539 -0.0834456 -0.273066 -0.284295 -0.297612 -0.299435 -0.302479 -0.300412\n",
      "   5 5.908e-01 4.964e+01 7.386e+01 -- -2.379e+02 -- 0.302357 -0.214604 -0.654658 -0.688754 -0.723838 -0.729279 -0.736418 -0.733888\n",
      "   6 3.713e-01 4.877e+01 6.953e+01 -- -1.683e+02 -- 0.284419 -0.200357 -0.96379 -1.05472 -1.13798 -1.15477 -1.17031 -1.16748\n",
      "   7 2.709e-01 4.671e+01 6.269e+01 -- -1.056e+02 -- 0.277768 -0.185001 -1.13047 -1.34026 -1.52128 -1.56845 -1.6043 -1.60101\n",
      "   8 2.135e-01 4.361e+01 5.281e+01 -- -5.282e+01 -- 0.277012 -0.185189 -1.16375 -1.49463 -1.83211 -1.9477 -2.03737 -2.03476\n",
      "   9 1.764e-01 3.767e+01 4.019e+01 -- -1.264e+01 -- 0.282161 -0.184207 -1.17891 -1.53049 -2.01851 -2.24569 -2.46562 -2.46922\n",
      "  10 1.508e-01 2.738e+01 2.645e+01 -- 1.382e+01 -- 0.289545 -0.182463 -1.18795 -1.52893 -2.08812 -2.41103 -2.87498 -2.90486\n",
      "  11 1.349e-01 1.468e+01 1.390e+01 -- 2.772e+01 -- 0.293547 -0.180898 -1.1897 -1.52803 -2.10944 -2.46526 -3.22702 -3.34288\n",
      "  12 1.358e-01 5.365e+00 5.378e+00 -- 3.309e+01 -- 0.295456 -0.179941 -1.19052 -1.52801 -2.11928 -2.48294 -3.46233 -3.79368\n",
      "  13 1.868e-01 1.338e+00 1.633e+00 -- 3.473e+01 -- 0.297315 -0.17923 -1.19104 -1.52666 -2.12491 -2.48975 -3.55567 -4.30889\n",
      "  14 6.248e-01 2.604e-01 4.517e-01 -- 3.518e+01 -- 0.299091 -0.178645 -1.191 -1.52463 -2.12805 -2.4915 -3.5672 -5.11363\n",
      "  15 2.744e+02 2.611e-01 7.022e-02 -- 3.525e+01 -- 0.300248 -0.178286 -1.19075 -1.52307 -2.12961 -2.49161 -3.56337 -8\n",
      "  16 2.745e+02 2.782e-01 4.368e-04 -- 3.525e+01 -- 0.300566 -0.178158 -1.19062 -1.52252 -2.13009 -2.49151 -3.5617 -8\n",
      "  17 2.745e+02 2.805e-01 6.410e-05 -- 3.525e+01 -- 0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n",
      "********************\n",
      "0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n",
      "0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n",
      "-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n",
    "p1 = np.ones(nfq)\n",
    "p1, p1e = clag.optimize(P1, p1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t### errors for param 0 ###\n",
      "+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n",
      "+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n",
      "+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n",
      "+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n",
      "+++ 3.525e+01 3.475e+01 3.006e-01 5.545e-01 0.997 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n",
      "+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n",
      "+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n",
      "+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n",
      "+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n",
      "+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02   1 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n",
      "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n",
      "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n",
      "+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n",
      "+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 3.525e+01 3.482e+01 -1.522e+00 -1.345e+00 0.865 +++\n",
      "+++ 3.525e+01 3.432e+01 -1.522e+00 -1.257e+00 1.86 +++\n",
      "+++ 3.525e+01 3.459e+01 -1.522e+00 -1.301e+00 1.32 +++\n",
      "+++ 3.525e+01 3.471e+01 -1.522e+00 -1.323e+00 1.08 +++\n",
      "+++ 3.525e+01 3.476e+01 -1.522e+00 -1.334e+00 0.97 +++\n",
      "+++ 3.525e+01 3.473e+01 -1.522e+00 -1.329e+00 1.03 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.522e+00 -1.331e+00 0.998 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 3.525e+01 3.481e+01 -2.130e+00 -1.977e+00 0.874 +++\n",
      "+++ 3.525e+01 3.429e+01 -2.130e+00 -1.901e+00 1.91 +++\n",
      "+++ 3.525e+01 3.457e+01 -2.130e+00 -1.939e+00 1.35 +++\n",
      "+++ 3.525e+01 3.470e+01 -2.130e+00 -1.958e+00 1.1 +++\n",
      "+++ 3.525e+01 3.476e+01 -2.130e+00 -1.968e+00 0.983 +++\n",
      "+++ 3.525e+01 3.473e+01 -2.130e+00 -1.963e+00 1.04 +++\n",
      "+++ 3.525e+01 3.474e+01 -2.130e+00 -1.965e+00 1.01 +++\n",
      "+++ 3.525e+01 3.475e+01 -2.130e+00 -1.966e+00 0.997 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 3.525e+01 3.474e+01 -2.491e+00 -2.358e+00 1.01 +++\n",
      "+++ 3.525e+01 3.512e+01 -2.491e+00 -2.425e+00 0.263 +++\n",
      "+++ 3.525e+01 3.496e+01 -2.491e+00 -2.392e+00 0.579 +++\n",
      "+++ 3.525e+01 3.486e+01 -2.491e+00 -2.375e+00 0.781 +++\n",
      "+++ 3.525e+01 3.480e+01 -2.491e+00 -2.367e+00 0.893 +++\n",
      "+++ 3.525e+01 3.477e+01 -2.491e+00 -2.363e+00 0.952 +++\n",
      "+++ 3.525e+01 3.476e+01 -2.491e+00 -2.361e+00 0.982 +++\n",
      "+++ 3.525e+01 3.475e+01 -2.491e+00 -2.360e+00 0.997 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 3.525e+01 3.507e+01 -3.561e+00 -3.413e+00 0.363 +++\n",
      "+++ 3.525e+01 3.484e+01 -3.561e+00 -3.339e+00 0.814 +++\n",
      "+++ 3.525e+01 3.468e+01 -3.561e+00 -3.301e+00 1.13 +++\n",
      "+++ 3.525e+01 3.477e+01 -3.561e+00 -3.320e+00 0.962 +++\n",
      "+++ 3.525e+01 3.473e+01 -3.561e+00 -3.311e+00 1.04 +++\n",
      "+++ 3.525e+01 3.475e+01 -3.561e+00 -3.315e+00   1 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 3.525e+01 3.524e+01 -8.000e+00 -6.000e+00 0.0178 +++\n",
      "+++ 3.525e+01 3.515e+01 -8.000e+00 -5.000e+00 0.187 +++\n",
      "+++ 3.525e+01 3.494e+01 -8.000e+00 -4.500e+00 0.618 +++\n",
      "+++ 3.525e+01 3.466e+01 -8.000e+00 -4.250e+00 1.18 +++\n",
      "+++ 3.525e+01 3.482e+01 -8.000e+00 -4.375e+00 0.851 +++\n",
      "+++ 3.525e+01 3.475e+01 -8.000e+00 -4.312e+00   1 +++\n",
      "********************\n",
      "0.300605 -0.178131 -1.19059 -1.52241 -2.13019 -2.49148 -3.56144 -8\n",
      "0.253864 0.205597 0.228999 0.191096 0.1638 0.131949 0.246154 3.6875\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "p1, p1e = clag.errors(P1, p1, p1e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OIZVKcf36darVKrlcjhs3bpDNZrtdlhQp9yRIkqQg9yRI0gnivkW11CsMCZJ0AkOAhpWH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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7388dbb2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xscale('log'); ylim(-4,2)\n",
    "errorbar(fqd, p1, yerr=p1e, fmt='o', ms=10, color=\"black\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tbI7VOThQdCDvc6xynYKHPBC+o/IwcuW/ISjqLaL+vydXCW8i3xFYAZRvacRJ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\nREREREREREREREREREQkq/4vwbptSNrRFIEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7364bb1710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t2, l2, l2e = np.loadtxt(echo_file).T\n",
    "errorbar(t1, l1, yerr=l1e, fmt='o', color=\"green\")\n",
    "errorbar(t2, l2, yerr=l2e, fmt='o', color=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 4.610e-01 9.098e+01 inf -- 1.744e+02 -- 1 1 1 1 1 1 1 1\n",
      "   2 8.066e-01 8.980e+01 1.014e+02 -- 2.758e+02 -- 0.764458 0.592778 0.539049 0.562235 0.572289 0.566582 0.574488 0.570186\n",
      "   3 4.133e+00 8.890e+01 1.012e+02 -- 3.771e+02 -- 0.390803 0.167503 0.104265 0.129187 0.143015 0.13475 0.14757 0.142553\n",
      "   4 7.028e+01 8.821e+01 1.004e+02 -- 4.775e+02 -- 0.00503816 -0.253372 -0.326662 -0.304159 -0.285875 -0.29734 -0.280456 -0.284431\n",
      "   5 8.140e-01 8.698e+01 9.903e+01 -- 5.765e+02 -- -0.34905 -0.657519 -0.755903 -0.73766 -0.713908 -0.729735 -0.710276 -0.71228\n",
      "   6 3.770e-01 8.522e+01 9.618e+01 -- 6.727e+02 -- -0.633175 -1.02654 -1.18245 -1.1696 -1.13984 -1.16159 -1.14079 -1.13949\n",
      "   7 2.735e-01 8.339e+01 9.226e+01 -- 7.650e+02 -- -0.806027 -1.33498 -1.60319 -1.59717 -1.56166 -1.59163 -1.57085 -1.56469\n",
      "   8 2.138e-01 8.120e+01 8.777e+01 -- 8.527e+02 -- -0.862131 -1.54146 -2.00828 -2.01427 -1.9772 -2.01825 -2.00048 -1.98894\n",
      "   9 1.754e-01 7.801e+01 8.241e+01 -- 9.351e+02 -- -0.842095 -1.59954 -2.37394 -2.40556 -2.38506 -2.43954 -2.42825 -2.41302\n",
      "  10 1.483e-01 7.298e+01 7.502e+01 -- 1.010e+03 -- -0.783896 -1.56622 -2.64497 -2.73499 -2.78365 -2.85462 -2.85094 -2.83628\n",
      "  11 1.272e-01 6.498e+01 6.421e+01 -- 1.074e+03 -- -0.728704 -1.54164 -2.75253 -2.94491 -3.15964 -3.25681 -3.26391 -3.25681\n",
      "  12 1.111e-01 5.364e+01 4.985e+01 -- 1.124e+03 -- -0.697013 -1.52135 -2.76748 -3.01573 -3.47965 -3.61977 -3.65793 -3.67111\n",
      "  13 9.921e-02 3.928e+01 3.311e+01 -- 1.157e+03 -- -0.68286 -1.50549 -2.78198 -3.01943 -3.7022 -3.88826 -4.01576 -4.07908\n",
      "  14 8.742e-02 2.316e+01 1.815e+01 -- 1.175e+03 -- -0.67696 -1.49524 -2.79447 -3.00982 -3.82195 -4.00921 -4.30702 -4.48377\n",
      "  15 6.880e-02 9.460e+00 7.667e+00 -- 1.183e+03 -- -0.675273 -1.48938 -2.80142 -2.99712 -3.87606 -4.02132 -4.49151 -4.87573\n",
      "  16 3.697e-02 2.241e+00 2.003e+00 -- 1.185e+03 -- -0.675444 -1.48628 -2.80573 -2.98594 -3.90153 -4.01347 -4.55974 -5.21119\n",
      "  17 6.073e-03 2.318e-01 2.232e-01 -- 1.185e+03 -- -0.675725 -1.48454 -2.80827 -2.9785 -3.91588 -4.0101 -4.56645 -5.40386\n",
      "  18 8.426e-04 9.306e-02 4.878e-03 -- 1.185e+03 -- -0.675636 -1.48368 -2.80908 -2.97402 -3.92351 -4.00944 -4.56338 -5.43668\n",
      "  19 4.159e-04 3.811e-02 3.790e-04 -- 1.185e+03 -- -0.675401 -1.48333 -2.809 -2.97151 -3.92669 -4.00953 -4.56255 -5.4346\n",
      "  20 1.919e-04 1.760e-02 7.212e-05 -- 1.185e+03 -- -0.675286 -1.48317 -2.80889 -2.97028 -3.92799 -4.00961 -4.56253 -5.43514\n",
      "********************\n",
      "-0.675286 -1.48317 -2.80889 -2.97028 -3.92799 -4.00961 -4.56253 -5.43514\n",
      "0.231904 0.203388 0.246283 0.177512 0.182405 0.144335 0.166268 0.423476\n",
      "0.000864464 0.00208153 0.00125836 0.0175976 -0.0171085 -0.00187558 -1.56774e-05 2.12549e-05\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)\n",
    "p2 = np.ones(nfq)\n",
    "p2, p2e = clag.optimize(P2, p2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t### errors for param 0 ###\n",
      "+++ 1.185e+03 1.185e+03 -6.752e-01 -4.433e-01 0.84 +++\n",
      "+++ 1.185e+03 1.184e+03 -6.752e-01 -3.274e-01 1.76 +++\n",
      "+++ 1.185e+03 1.185e+03 -6.752e-01 -3.854e-01 1.27 +++\n",
      "+++ 1.185e+03 1.185e+03 -6.752e-01 -4.144e-01 1.04 +++\n",
      "+++ 1.185e+03 1.185e+03 -6.752e-01 -4.288e-01 0.94 +++\n",
      "+++ 1.185e+03 1.185e+03 -6.752e-01 -4.216e-01 0.992 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 1.185e+03 1.185e+03 -1.483e+00 -1.280e+00 0.943 +++\n",
      "+++ 1.185e+03 1.184e+03 -1.483e+00 -1.178e+00   2 +++\n",
      "+++ 1.185e+03 1.185e+03 -1.483e+00 -1.229e+00 1.43 +++\n",
      "+++ 1.185e+03 1.185e+03 -1.483e+00 -1.254e+00 1.18 +++\n",
      "+++ 1.185e+03 1.185e+03 -1.483e+00 -1.267e+00 1.06 +++\n",
      "+++ 1.185e+03 1.185e+03 -1.483e+00 -1.273e+00 0.999 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.563e+00 0.987 +++\n",
      "+++ 1.185e+03 1.184e+03 -2.809e+00 -2.439e+00 2.1 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.501e+00 1.5 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.532e+00 1.23 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.547e+00 1.11 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.555e+00 1.05 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.559e+00 1.02 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.561e+00   1 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.792e+00 0.612 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.703e+00 1.31 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.748e+00 0.934 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.726e+00 1.12 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.737e+00 1.02 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.742e+00 0.978 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.740e+00   1 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 1.185e+03 1.185e+03 -3.929e+00 -3.746e+00 0.715 +++\n",
      "+++ 1.185e+03 1.185e+03 -3.929e+00 -3.655e+00 1.61 +++\n",
      "+++ 1.185e+03 1.185e+03 -3.929e+00 -3.701e+00 1.12 +++\n",
      "+++ 1.185e+03 1.185e+03 -3.929e+00 -3.723e+00 0.906 +++\n",
      "+++ 1.185e+03 1.185e+03 -3.929e+00 -3.712e+00 1.01 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 1.185e+03 1.185e+03 -4.010e+00 -3.865e+00 0.991 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 1.185e+03 1.185e+03 -4.563e+00 -4.396e+00 1.01 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 1.185e+03 1.185e+03 -5.435e+00 -5.223e+00 0.386 +++\n",
      "+++ 1.185e+03 1.185e+03 -5.435e+00 -5.118e+00 0.993 +++\n",
      "********************\n",
      "-0.675245 -1.48309 -2.80886 -2.96971 -3.92857 -4.00963 -4.56252 -5.43512\n",
      "0.253646 0.209743 0.248217 0.230185 0.21667 0.144334 0.166265 0.317595\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "p2, p2e = clag.errors(P2, p2, p2e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K5/N0dnYyPT295JhcLkcul6Orq4vR0er3QgiFQqQeTZHNZuk/3M+Tjxe6QEY3R7m9/Xb6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7365078710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xscale('log'); ylim(-6,2)\n",
    "errorbar(fqd, p1, yerr=p1e, fmt='o', ms=10, color=\"green\")\n",
    "errorbar(fqd, p2, yerr=p2e, fmt='o', ms=10, color=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 3.996e+02 1.150e+01 inf -- 1.244e+03 -- -0.48732 -1.13061 -2.29972 -2.54606 -3.32938 -3.55056 -4.36198 -7.01756 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
      "   3 1.146e+02 1.274e+01 2.644e+00 -- 1.247e+03 -- -0.46592 -1.09839 -2.27929 -2.51606 -3.2945 -3.53263 -4.40205 -7.31756 0.173375 0.179509 0.201787 0.171316 0.150055 0.152867 0.0990853 -2.1868\n",
      "   5 6.107e+01 1.422e+01 2.363e+00 -- 1.249e+03 -- -0.445096 -1.06944 -2.25767 -2.48924 -3.26504 -3.51627 -4.44162 -7.01756 0.23367 0.240665 0.283299 0.22705 0.187337 0.196036 0.0982347 -2.11553\n",
      "   7 2.105e+01 1.585e+01 2.131e+00 -- 1.251e+03 -- -0.425484 -1.04389 -2.23659 -2.46561 -3.24012 -3.50157 -4.48059 -6.71756 0.283283 0.288529 0.348404 0.27129 0.21583 0.231595 0.0974289 -2.46919\n",
      "   9 2.358e+01 1.743e+01 1.909e+00 -- 1.253e+03 -- -0.40737 -1.02151 -2.21687 -2.44489 -3.21894 -3.48846 -4.51886 -6.41756 0.324291 0.326635 0.400676 0.306939 0.238059 0.261125 0.0966177 -1.38312\n",
      "  11 4.074e+01 1.911e+01 1.751e+00 -- 1.255e+03 -- -0.390833 -1.00195 -2.19886 -2.42677 -3.20086 -3.47683 -4.55632 -6.71756 0.358383 0.357437 0.443033 0.336042 0.255704 0.285884 0.0957404 1.63909\n",
      "  13 1.370e+01 2.129e+01 1.587e+00 -- 1.256e+03 -- -0.375838 -0.984839 -2.18261 -2.41092 -3.18537 -3.46656 -4.59286 -6.41756 0.386914 0.382651 0.477698 0.360091 0.269847 0.306761 0.095098 2.03338\n",
      "  15 1.158e+01 2.351e+01 1.408e+00 -- 1.258e+03 -- -0.362294 -0.96986 -2.16805 -2.39704 -3.17203 -3.45749 -4.62837 -6.11756 0.410934 0.403523 0.506367 0.380155 0.281317 0.324474 0.0944395 -1.46371\n",
      "  16 1.607e+03 7.266e+02 8.010e+00 -- 1.266e+03 -- -0.240266 -0.83846 -2.03814 -2.27523 -3.05681 -3.37736 -4.97188 -8 0.614202 0.577977 0.745974 0.548634 0.37576 0.476151 0.0813258 0.437418\n",
      "  17 9.038e+02 4.128e+00 3.834e+00 -- 1.270e+03 -- -0.232783 -0.852575 -2.04678 -2.29283 -3.08252 -3.39513 -5.04543 -8 0.548929 0.498486 0.710668 0.487552 0.327531 0.450717 -0.157389 -0.565487\n",
      "  18 2.647e+02 5.497e-01 2.932e-02 -- 1.270e+03 -- -0.23322 -0.852233 -2.04339 -2.2905 -3.07964 -3.39057 -5.07095 -8 0.552004 0.51698 0.698282 0.491552 0.331349 0.451833 -0.156166 -2.69909\n",
      "  19 9.652e-01 2.219e+00 6.531e-01 -- 1.269e+03 -- -0.233246 -0.852217 -2.0438 -2.29075 -3.08035 -3.39107 -5.06163 -5 0.551423 0.515009 0.700327 0.490665 0.330402 0.452209 -0.176384 -0.814953\n",
      "  20 7.789e+00 7.955e-01 6.402e-01 -- 1.270e+03 -- -0.233412 -0.852282 -2.04423 -2.29033 -3.07948 -3.38989 -5.09639 -5.98364 0.551849 0.515428 0.700779 0.489124 0.333636 0.45985 -0.205833 -1.60153\n",
      "  21 8.557e+01 4.082e-02 1.163e-02 -- 1.270e+03 -- -0.233294 -0.852207 -2.04379 -2.29075 -3.08047 -3.39073 -5.05794 -7.0122 0.551775 0.515347 0.699486 0.490646 0.330835 0.451633 -0.186288 -1.50956\n",
      "  22 1.000e+03 9.633e-02 1.325e-03 -- 1.270e+03 -- -0.233263 -0.852211 -2.04374 -2.29072 -3.08027 -3.39085 -5.06491 -8 0.551627 0.515234 0.700132 0.49056 0.330468 0.452479 -0.17545 1.26429\n",
      "  23 6.947e+01 7.583e-02 5.012e-04 -- 1.270e+03 -- -0.23325 -0.852208 -2.04374 -2.29072 -3.08029 -3.39091 -5.06372 -8 0.551664 0.515214 0.700074 0.490679 0.330402 0.452326 -0.175132 2.98034\n",
      "  24 1.062e+00 2.147e+00 6.150e-01 -- 1.269e+03 -- -0.23325 -0.852208 -2.04374 -2.29072 -3.08028 -3.39091 -5.06387 -5 0.551636 0.515215 0.70009 0.490675 0.330405 0.452326 -0.175244 -0.97755\n",
      "  25 5.159e+00 7.693e-01 6.227e-01 -- 1.270e+03 -- -0.233412 -0.852294 -2.04429 -2.29036 -3.07951 -3.38938 -5.09276 -5.9203 0.551804 0.515509 0.700612 0.48922 0.334134 0.458766 -0.237317 -2.01536\n",
      "  26 3.256e+00 2.577e+00 8.259e-01 -- 1.269e+03 -- -0.233292 -0.852208 -2.04377 -2.29076 -3.08053 -3.3906 -5.0573 -4.88481 0.551755 0.515396 0.699418 0.490641 0.330803 0.451361 -0.185258 0.154178\n",
      "  27 8.306e+00 8.513e-01 7.419e-01 -- 1.269e+03 -- -0.2334 -0.85217 -2.04371 -2.2903 -3.07979 -3.394 -5.12588 -5.6438 0.551608 0.514436 0.70228 0.489167 0.329463 0.464863 0.0259555 0.65621\n",
      "  28 3.018e+02 2.512e-01 7.557e-02 -- 1.270e+03 -- -0.23325 -0.852177 -2.04375 -2.29075 -3.08018 -3.3918 -5.06768 -7.58414 0.552082 0.51496 0.699848 0.491275 0.330113 0.452176 -0.189638 -1.62114\n",
      "  29 9.648e+02 1.054e-01 5.781e-05 -- 1.270e+03 -- -0.233258 -0.852207 -2.04374 -2.29072 -3.08024 -3.39094 -5.06451 -8 0.551479 0.515219 0.700113 0.49073 0.330409 0.452292 -0.172337 -0.754914\n",
      "********************\n",
      "-0.233258 -0.852207 -2.04374 -2.29072 -3.08024 -3.39094 -5.06451 -8 0.551479 0.515219 0.700113 0.49073 0.330409 0.452292 -0.172337 -0.754914\n",
      "0.0271527 0.0112859 0.0294371 0.0238447 0.0419821 0.0761252 1.60955 678.342 0.189103 0.111853 0.196777 0.152506 0.193957 0.252076 3.72644 1583.62\n",
      "0.0121717 -0.0302322 -0.0279866 -0.0393548 -0.105374 -0.0168605 -0.00293106 -0.000917936 0.00679344 -3.13658e-05 -4.94438e-05 -0.00117381 0.00319937 0.00794062 0.00314398 -0.000234878\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "Cx = clag.clag('cxd10r', [[t1,t2]], [[l1,l2]], [[l1e,l2e]], dt, fqL, p1, p2)\n",
    "p  = np.concatenate( ((p1+p2)*0.5-0.3,p1*0+0.1) ) # a  good starting point generally\n",
    "p, pe = clag.optimize(Cx, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phi, phie = p[nfq:], pe[nfq:]\n",
    "lag, lage = phi/(2*np.pi*fqd), phie/(2*np.pi*fqd)    \n",
    "cx, cxe   = p[:nfq], pe[:nfq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.0966594 ,  2.8412865 ,  2.00075511,  0.90476609,  0.39301911,\n",
       "        0.34709626, -0.08532525, -0.24113739])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f736317bf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "xscale('log'); ylim(-10,15)\n",
    "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n",
    "\n",
    "lag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7363203250>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0Pwa+AkwEDgMuy+H49fo48FTZumuIYOsU4IbU+i8BLxJlXJmsWwA8SNROzUrW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\nPLHuYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7362fc36d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "\n",
    "# Define model function to be used to fit to the data above:\n",
    "def tophat_time(x, *p):\n",
    "    mean, width = p\n",
    "    if x>(mean+width): y=0\n",
    "    if x<(mean-width): y=0\n",
    "    if x==(mean+width) | x==(mean-width): y=5\n",
    "    return y\n",
    "\n",
    "def tophat_freq(f, *pars):\n",
    "    A,T,t0 = pars\n",
    "    #return A*T*sinc(pi*f*T)*exp(-i*2*pi*f*t0)\n",
    "    return A*T*sinc(pi*f*T)*cos(2*pi*f*t0)\n",
    "\n",
    "x=np.logspace(fqd[0],fqd[-1],200)\n",
    "\n",
    "# p0 is the initial guess for the fitting coefficients\n",
    "p0 = [3, 3, 3]\n",
    "coeff, var_matrix = curve_fit(tophat_freq, fqd, lag, p0)\n",
    "fit = tophat_freq(fqd, *coeff)\n",
    "\n",
    "\n",
    "mpl.rcParams['xtick.labelsize']=12\n",
    "mpl.rcParams['ytick.labelsize']=12\n",
    "xscale('log'); xlim(.009,.6)\n",
    "xlabel(\"Fourier Frequency (days$^{-1}$)\",fontsize=20)\n",
    "ylabel(\"Time Lag (days)\",fontsize=20)\n",
    "\n",
    "\n",
    "errorbar(fqd, lag, yerr=lage, fmt='o', ms=5,color=\"black\")\n",
    "plot(fqd,fit)"
   ]
  },
  {
   "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": {
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