phy-4660/lag/data/clag_analysis-origbins-noLFerrors-4368A.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 0x7f975aacfd50>"
      ]
     },
     "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/4368A.lc\"\n",
    "\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 0x7f97727f7210>"
      ]
     },
     "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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AG51jNQiHSw4XfI5VvlPwUABCd1Qexq78NwIl/SXU//fkKuFN5DsCK4DyLg87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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97727d4ed0>"
      ]
     },
     "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 4.361e-01 5.503e+01 inf -- -2.230e+02 -- 1 1 1 1 1 1 1 1\n",
      "   2 7.719e-01 5.459e+01 6.638e+01 -- -1.566e+02 -- 0.578708 0.567444 0.565767 0.565645 0.565269 0.564635 0.565645 0.563945\n",
      "   3 3.383e+00 5.393e+01 6.562e+01 -- -9.097e+01 -- 0.185507 0.1439 0.132758 0.130601 0.130916 0.129667 0.131085 0.12865\n",
      "   4 1.445e+00 5.277e+01 6.426e+01 -- -2.671e+01 -- -0.142659 -0.257806 -0.298121 -0.305924 -0.303305 -0.305147 -0.304059 -0.306574\n",
      "   5 5.917e-01 5.083e+01 6.196e+01 -- 3.525e+01 -- -0.348804 -0.608981 -0.725147 -0.744945 -0.737788 -0.740133 -0.740511 -0.742464\n",
      "   6 3.723e-01 4.792e+01 5.867e+01 -- 9.392e+01 -- -0.417419 -0.869413 -1.14636 -1.18533 -1.17119 -1.17501 -1.17867 -1.1784\n",
      "   7 2.715e-01 4.436e+01 5.444e+01 -- 1.484e+02 -- -0.421165 -1.0235 -1.55636 -1.61922 -1.59707 -1.60795 -1.61745 -1.61028\n",
      "   8 3.575e-01 4.073e+01 4.955e+01 -- 1.979e+02 -- -0.374308 -1.10343 -1.93497 -2.03239 -2.00811 -2.03747 -2.05653 -2.03339\n",
      "   9 5.926e-01 3.677e+01 4.460e+01 -- 2.425e+02 -- -0.240503 -1.1527 -2.25517 -2.39911 -2.40429 -2.4649 -2.49991 -2.44956\n",
      "  10 7.482e-01 3.119e+01 3.817e+01 -- 2.807e+02 -- -0.0979917 -1.17663 -2.4937 -2.68072 -2.78672 -2.88942 -2.95412 -2.86404\n",
      "  11 1.685e+00 2.383e+01 2.855e+01 -- 3.092e+02 -- -0.0246695 -1.17354 -2.62135 -2.85109 -3.13126 -3.28381 -3.42245 -3.27294\n",
      "  12 1.725e+00 1.552e+01 1.678e+01 -- 3.260e+02 -- 0.0169008 -1.15973 -2.6659 -2.93332 -3.39842 -3.56844 -3.89212 -3.66754\n",
      "  13 4.932e-01 7.866e+00 6.878e+00 -- 3.329e+02 -- 0.0460619 -1.14068 -2.67864 -2.97196 -3.56721 -3.67273 -4.2811 -4.0398\n",
      "  14 1.822e-01 2.994e+00 2.075e+00 -- 3.350e+02 -- 0.0687779 -1.12228 -2.6668 -2.98023 -3.66584 -3.7084 -4.35974 -4.38147\n",
      "  15 4.696e-02 1.029e+00 5.962e-01 -- 3.356e+02 -- 0.0813069 -1.11484 -2.65366 -2.96318 -3.74202 -3.72746 -4.24095 -4.67811\n",
      "  16 2.197e-02 3.378e-01 1.595e-01 -- 3.357e+02 -- 0.0842152 -1.11741 -2.64665 -2.94344 -3.80682 -3.72328 -4.23445 -4.89778\n",
      "  17 1.076e-02 1.040e-01 2.591e-02 -- 3.357e+02 -- 0.0848765 -1.11851 -2.639 -2.93736 -3.84761 -3.73244 -4.19543 -5.00538\n",
      "  18 2.514e-03 8.065e-02 3.505e-03 -- 3.357e+02 -- 0.0846132 -1.1201 -2.63699 -2.93391 -3.86186 -3.72437 -4.196 -5.05922\n",
      "  19 3.048e-03 6.689e-02 4.875e-04 -- 3.357e+02 -- 0.0844096 -1.12043 -2.63505 -2.93301 -3.87157 -3.72842 -4.18689 -5.06117\n",
      "  20 1.543e-03 4.977e-02 1.110e-04 -- 3.357e+02 -- 0.0843879 -1.12064 -2.63495 -2.93266 -3.87045 -3.72409 -4.18999 -5.0766\n",
      "  21 1.622e-03 4.180e-02 4.688e-05 -- 3.357e+02 -- 0.084282 -1.1207 -2.63451 -2.93251 -3.87392 -3.72714 -4.1865 -5.06876\n",
      "********************\n",
      "0.084282 -1.1207 -2.63451 -2.93251 -3.87392 -3.72714 -4.1865 -5.06876\n",
      "0.228004 0.210453 0.282191 0.221429 0.392762 0.234543 0.3158 0.979911\n",
      "0.000969367 -0.000244794 -0.00155556 0.00155923 0.0136076 0.0418006 -0.0299347 -0.0115783\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",
      "+++ 3.357e+02 3.353e+02 8.433e-02 3.123e-01 0.809 +++\n",
      "+++ 3.357e+02 3.349e+02 8.433e-02 4.263e-01 1.68 +++\n",
      "+++ 3.357e+02 3.351e+02 8.433e-02 3.693e-01 1.21 +++\n",
      "+++ 3.357e+02 3.352e+02 8.433e-02 3.408e-01 1.01 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 3.357e+02 3.353e+02 -1.121e+00 -9.103e-01 0.833 +++\n",
      "+++ 3.357e+02 3.349e+02 -1.121e+00 -8.050e-01 1.76 +++\n",
      "+++ 3.357e+02 3.351e+02 -1.121e+00 -8.576e-01 1.26 +++\n",
      "+++ 3.357e+02 3.352e+02 -1.121e+00 -8.839e-01 1.04 +++\n",
      "+++ 3.357e+02 3.353e+02 -1.121e+00 -8.971e-01 0.933 +++\n",
      "+++ 3.357e+02 3.353e+02 -1.121e+00 -8.905e-01 0.985 +++\n",
      "+++ 3.357e+02 3.352e+02 -1.121e+00 -8.872e-01 1.01 +++\n",
      "+++ 3.357e+02 3.352e+02 -1.121e+00 -8.889e-01 0.998 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 3.357e+02 3.353e+02 -2.635e+00 -2.352e+00 0.888 +++\n",
      "+++ 3.357e+02 3.348e+02 -2.635e+00 -2.211e+00 1.91 +++\n",
      "+++ 3.357e+02 3.351e+02 -2.635e+00 -2.282e+00 1.36 +++\n",
      "+++ 3.357e+02 3.352e+02 -2.635e+00 -2.317e+00 1.11 +++\n",
      "+++ 3.357e+02 3.352e+02 -2.635e+00 -2.335e+00 0.997 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 3.357e+02 3.353e+02 -2.933e+00 -2.711e+00 0.893 +++\n",
      "+++ 3.357e+02 3.348e+02 -2.933e+00 -2.600e+00 1.98 +++\n",
      "+++ 3.357e+02 3.351e+02 -2.933e+00 -2.656e+00 1.38 +++\n",
      "+++ 3.357e+02 3.352e+02 -2.933e+00 -2.683e+00 1.13 +++\n",
      "+++ 3.357e+02 3.352e+02 -2.933e+00 -2.697e+00 1.01 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 3.357e+02 3.356e+02 -3.872e+00 -3.676e+00 0.27 +++\n",
      "+++ 3.357e+02 3.354e+02 -3.872e+00 -3.578e+00 0.656 +++\n",
      "+++ 3.357e+02 3.353e+02 -3.872e+00 -3.529e+00 0.919 +++\n",
      "+++ 3.357e+02 3.352e+02 -3.872e+00 -3.505e+00 1.07 +++\n",
      "+++ 3.357e+02 3.352e+02 -3.872e+00 -3.517e+00 0.994 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 3.357e+02 3.356e+02 -3.725e+00 -3.608e+00 0.326 +++\n",
      "+++ 3.357e+02 3.354e+02 -3.725e+00 -3.549e+00 0.747 +++\n",
      "+++ 3.357e+02 3.352e+02 -3.725e+00 -3.520e+00 1.03 +++\n",
      "+++ 3.357e+02 3.353e+02 -3.725e+00 -3.535e+00 0.881 +++\n",
      "+++ 3.357e+02 3.353e+02 -3.725e+00 -3.527e+00 0.952 +++\n",
      "+++ 3.357e+02 3.353e+02 -3.725e+00 -3.524e+00 0.989 +++\n",
      "+++ 3.357e+02 3.352e+02 -3.725e+00 -3.522e+00 1.01 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 3.357e+02 3.356e+02 -4.189e+00 -4.030e+00 0.334 +++\n",
      "+++ 3.357e+02 3.353e+02 -4.189e+00 -3.951e+00 0.82 +++\n",
      "+++ 3.357e+02 3.352e+02 -4.189e+00 -3.912e+00 1.18 +++\n",
      "+++ 3.357e+02 3.353e+02 -4.189e+00 -3.931e+00 0.987 +++\n",
      "+++ 3.357e+02 3.352e+02 -4.189e+00 -3.922e+00 1.08 +++\n",
      "+++ 3.357e+02 3.352e+02 -4.189e+00 -3.927e+00 1.03 +++\n",
      "+++ 3.357e+02 3.352e+02 -4.189e+00 -3.929e+00 1.01 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 3.357e+02 3.355e+02 -5.077e+00 -4.580e+00 0.558 +++\n",
      "+++ 3.357e+02 3.348e+02 -5.077e+00 -4.331e+00 1.79 +++\n",
      "+++ 3.357e+02 3.352e+02 -5.077e+00 -4.456e+00 1.04 +++\n",
      "+++ 3.357e+02 3.354e+02 -5.077e+00 -4.518e+00 0.777 +++\n",
      "+++ 3.357e+02 3.353e+02 -5.077e+00 -4.487e+00 0.901 +++\n",
      "+++ 3.357e+02 3.353e+02 -5.077e+00 -4.471e+00 0.971 +++\n",
      "+++ 3.357e+02 3.352e+02 -5.077e+00 -4.463e+00 1.01 +++\n",
      "********************\n",
      "0.0843333 -1.12071 -2.63465 -2.93252 -3.87183 -3.72454 -4.18887 -5.07698\n",
      "0.256503 0.231827 0.299857 0.235295 0.354897 0.202775 0.259869 0.613662\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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kU0nOnKz+mY/042ne+ttv5eZ33Myrd7+aV7zhFbx696u5+R0389bffivpx9Mrv4nUhMKq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f975a60a390>"
      ]
     },
     "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 4.674e+03 1.067e+01 inf -- 3.891e+02 -- -0.107531 -0.949418 -2.21262 -2.52746 -3.30101 -3.40801 -4.17516 -6.83849 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
      "   3 1.101e+02 1.211e+01 2.423e+00 -- 3.915e+02 -- -0.0911837 -0.911368 -2.19473 -2.50464 -3.26635 -3.36119 -4.17858 -6.53849 0.0224314 0.13674 0.236622 0.214641 0.153941 0.141456 0.0860925 -2.65496\n",
      "   5 3.499e+02 1.345e+01 2.206e+00 -- 3.937e+02 -- -0.0745994 -0.879737 -2.17195 -2.48014 -3.23698 -3.32257 -4.18252 -6.83849 -0.0437031 0.16436 0.347414 0.307699 0.194659 0.170845 0.0713363 1.432\n",
      "   7 5.441e+02 1.532e+01 2.037e+00 -- 3.958e+02 -- -0.0584518 -0.853102 -2.14774 -2.456 -3.21197 -3.29032 -4.1871 -7.13849 -0.100019 0.185908 0.435633 0.382521 0.226208 0.192552 0.0562607 1.59836\n",
      "   9 1.849e+02 1.731e+01 1.886e+00 -- 3.977e+02 -- -0.0431336 -0.830436 -2.1241 -2.43328 -3.1906 -3.26311 -4.19225 -7.43849 -0.148048 0.203221 0.505675 0.442641 0.251144 0.209088 0.0403995 2.5906\n",
      "  11 1.731e+04 1.942e+01 1.753e+00 -- 3.994e+02 -- -0.0288471 -0.810973 -2.10201 -2.41247 -3.17227 -3.23993 -4.19795 -7.73849 -0.189141 0.217465 0.561595 0.491159 0.271137 0.221974 0.0236847 0.232263\n",
      "  13 6.528e+03 2.165e+01 1.632e+00 -- 4.011e+02 -- -0.0156722 -0.794137 -2.08185 -2.39369 -3.15646 -3.22006 -4.20417 -7.43849 -0.224437 0.229415 0.606654 0.53056 0.287329 0.232192 0.00612053 0.325107\n",
      "  15 4.527e+02 2.400e+01 1.520e+00 -- 4.026e+02 -- -0.003614 -0.779481 -2.06369 -2.37687 -3.14277 -3.20293 -4.21087 -7.13849 -0.25488 0.239608 0.643331 0.562767 0.300521 0.240394 -0.0123632 1.71181\n",
      "  17 2.052e+03 2.647e+01 1.426e+00 -- 4.040e+02 -- 0.00736587 -0.766656 -2.04744 -2.36188 -3.13087 -3.1881 -4.21799 -7.43849 -0.281243 0.248428 0.673473 0.58925 0.311289 0.247033 -0.0318346 -0.392158\n",
      "  18 3.773e+00 5.593e+03 1.514e+00 -- 4.055e+02 -- 0.106995 -0.653906 -1.90262 -2.22854 -3.02717 -3.05917 -4.29289 -4.43849 -0.510384 0.325715 0.923411 0.807986 0.399154 0.301171 -0.237126 -1.01765\n",
      "  20 2.849e+00 3.659e+03 5.484e+00 -- 4.110e+02 -- 0.107323 -0.654528 -1.90254 -2.23194 -3.02323 -3.064 -4.28509 -4.32949 -0.509195 0.328192 0.912135 0.803339 0.389116 0.289516 -0.32659 -0.962776\n",
      "  22 1.486e+00 2.444e+03 3.340e+00 -- 4.143e+02 -- 0.108022 -0.654908 -1.90278 -2.23432 -3.01757 -3.06737 -4.25844 -4.25945 -0.507022 0.331633 0.909715 0.784568 0.370291 0.289219 -0.419626 -0.914908\n",
      "  24 9.532e-01 1.559e+03 2.394e+00 -- 4.167e+02 -- 0.108513 -0.655239 -1.90317 -2.23664 -3.01314 -3.07039 -4.24064 -4.21341 -0.504547 0.334668 0.90699 0.766684 0.356598 0.288412 -0.48198 -0.887983\n",
      "  25 1.060e+00 2.012e+02 1.863e+00 -- 4.149e+02 -- 0.112015 -0.658052 -1.90797 -2.25721 -2.98205 -3.09702 -4.14368 -3.91937 -0.477027 0.36146 0.872856 0.608912 0.263183 0.274467 -0.941403 -0.711503\n",
      "  26 6.891e-01 5.834e+02 5.512e+00 -- 4.204e+02 -- 0.112132 -0.654668 -1.90638 -2.25379 -3.06056 -3.07914 -4.76266 -3.9152 -0.42166 0.404325 0.803237 0.671367 0.321541 0.247382 -1.93887 -0.377098\n",
      "  28 4.161e-01 2.170e+02 1.164e+00 -- 4.215e+02 -- 0.111002 -0.655061 -1.90694 -2.25331 -3.04803 -3.07818 -4.87251 -3.9149 -0.433955 0.397673 0.809209 0.653739 0.311798 0.264429 -1.87993 -0.373049\n",
      "  30 4.034e-01 1.252e+02 5.089e-01 -- 4.221e+02 -- 0.110361 -0.655324 -1.90727 -2.253 -3.04023 -3.0776 -4.95911 -3.91453 -0.440907 0.393109 0.812583 0.641828 0.30727 0.275431 -1.81241 -0.369329\n",
      "  32 4.173e-01 1.421e+02 2.508e-01 -- 4.223e+02 -- 0.109998 -0.655506 -1.90746 -2.25281 -3.03521 -3.07721 -5.02214 -3.91419 -0.44479 0.38991 0.814296 0.633472 0.305273 0.282683 -1.7393 -0.365774\n",
      "  33 2.844e+01 4.813e+05 3.489e+01 -- 3.874e+02 -- 0.108249 -0.656743 -1.90855 -2.25163 -3.003 -3.0745 -5.3963 -3.91113 -0.462731 0.368115 0.820327 0.574151 0.299794 0.329367 -1.01341 -0.331703\n",
      "  36 1.265e+01 2.320e+05 1.439e+01 -- 4.018e+02 -- 0.109865 -0.656548 -1.90848 -2.25209 -3.0047 -3.07472 -5.3663 -3.91105 -0.437958 0.375118 0.819209 0.579752 0.302019 0.324713 -1.30159 -0.331943\n",
      "  39 6.733e+00 1.667e+05 5.585e+00 -- 4.074e+02 -- 0.111231 -0.65638 -1.90842 -2.25246 -3.00615 -3.0749 -5.3363 -3.91099 -0.420135 0.380536 0.818248 0.584365 0.30392 0.320786 -1.46618 -0.33213\n",
      "  42 3.976e+00 1.333e+05 3.474e+00 -- 4.109e+02 -- 0.112481 -0.65623 -1.90836 -2.25279 -3.00744 -3.07507 -5.3063 -3.91093 -0.405052 0.385134 0.817406 0.588389 0.305606 0.31735 -1.56491 -0.332295\n",
      "  45 3.037e+00 1.091e+05 2.625e+00 -- 4.135e+02 -- 0.113648 -0.656095 -1.90831 -2.25307 -3.00859 -3.07521 -5.2763 -3.91087 -0.391718 0.389141 0.816665 0.591957 0.307115 0.314316 -1.62712 -0.332443\n",
      "  47 5.206e+00 2.332e+06 1.467e+00 -- 4.120e+02 -- 0.124504 -0.654874 -1.90786 -2.25555 -3.0188 -3.07651 -4.9763 -3.91038 -0.272749 0.424266 0.810137 0.623655 0.320604 0.287511 -2.03571 -0.33378\n",
      "  49 3.404e+00 4.336e+05 5.127e+00 -- 4.171e+02 -- 0.114992 -0.655304 -1.90798 -2.25475 -3.01557 -3.07613 -5.08623 -3.9105 -0.414737 0.410238 0.811866 0.611592 0.316059 0.295817 -2.07572 -0.333075\n",
      "  51 2.831e+00 3.964e+04 3.716e+00 -- 4.209e+02 -- 0.120998 -0.654743 -1.90782 -2.25581 -3.01975 -3.07664 -4.90259 -3.91033 -0.273559 0.428183 0.809505 0.62668 0.3219 0.284945 -1.99017 -0.333892\n",
      "  53 9.648e-01 6.528e+03 2.044e+00 -- 4.229e+02 -- 0.111438 -0.655357 -1.90801 -2.25473 -3.01525 -3.07606 -5.0328 -3.91052 -0.350991 0.411981 0.811898 0.611992 0.315981 0.296109 -2.02358 -0.332869\n",
      "  55 6.664e-01 4.533e+03 5.395e-01 -- 4.234e+02 -- 0.115342 -0.655005 -1.90788 -2.25535 -3.01813 -3.07641 -4.91958 -3.91037 -0.317126 0.420377 0.810122 0.620816 0.319972 0.288893 -1.97289 -0.333205\n",
      "  57 1.352e-01 5.078e+03 2.047e-01 -- 4.236e+02 -- 0.113136 -0.655183 -1.90793 -2.25501 -3.01693 -3.07625 -4.95555 -3.91041 -0.338259 0.415156 0.810681 0.616936 0.318597 0.291917 -1.97334 -0.332782\n",
      "  58 4.391e+00 1.231e+05 9.307e+00 -- 4.143e+02 -- 0.121374 -0.654363 -1.90751 -2.25625 -3.02524 -3.0772 -4.67748 -3.90983 -0.292538 0.427891 0.804793 0.639809 0.331433 0.271874 -1.8091 -0.332351\n",
      "  60 7.456e-01 3.599e+03 8.350e+00 -- 4.227e+02 -- 0.106032 -0.655512 -1.90793 -2.25425 -3.01553 -3.07594 -4.84676 -3.91027 -0.421005 0.399168 0.809729 0.61079 0.31849 0.294571 -1.79863 -0.330294\n",
      "  62 1.392e-01 2.951e+03 7.707e-01 -- 4.235e+02 -- 0.109207 -0.655134 -1.90775 -2.25492 -3.01931 -3.07641 -4.74803 -3.91008 -0.389616 0.408214 0.807266 0.62061 0.323572 0.285397 -1.79761 -0.330504\n",
      "  64 4.741e-02 3.163e+03 7.625e-02 -- 4.235e+02 -- 0.109031 -0.655208 -1.90776 -2.25473 -3.01888 -3.07633 -4.75697 -3.91007 -0.39504 0.405859 0.807295 0.619007 0.323289 0.286476 -1.78732 -0.330095\n",
      "  65 7.877e-01 1.498e+04 9.182e-01 -- 4.245e+02 -- 0.112757 -0.655181 -1.90757 -2.25427 -3.02158 -3.07653 -4.69927 -3.9097 -0.391127 0.400991 0.803725 0.621415 0.329113 0.280586 -1.7026 -0.327321\n",
      "  67 7.769e-01 7.591e+03 8.463e-03 -- 4.245e+02 -- 0.111405 -0.655282 -1.9076 -2.25407 -3.02073 -3.07642 -4.71485 -3.90973 -0.421937 0.397325 0.804142 0.618422 0.327917 0.282676 -1.69714 -0.327119\n",
      "  69 8.439e-01 1.220e+04 6.394e-03 -- 4.245e+02 -- 0.112447 -0.655184 -1.90757 -2.25426 -3.02159 -3.07653 -4.69823 -3.9097 -0.389155 0.400927 0.803682 0.621315 0.329157 0.280545 -1.70166 -0.327268\n",
      "  71 8.438e-01 1.705e+03 1.926e-02 -- 4.244e+02 -- 0.111092 -0.655286 -1.9076 -2.25405 -3.02073 -3.07642 -4.71408 -3.90973 -0.421995 0.397181 0.804108 0.618265 0.327936 0.282675 -1.69617 -0.327066\n",
      "  73 9.456e-01 3.856e+03 3.704e-02 -- 4.244e+02 -- 0.112145 -0.655184 -1.90756 -2.25425 -3.02163 -3.07653 -4.69678 -3.90969 -0.386386 0.40098 0.80363 0.621306 0.329229 0.280449 -1.70102 -0.327224\n",
      "  75 9.484e-01 1.219e+04 5.405e-02 -- 4.243e+02 -- 0.110702 -0.655293 -1.9076 -2.25403 -3.0207 -3.07642 -4.71375 -3.90972 -0.422922 0.396955 0.804089 0.618033 0.327915 0.282736 -1.69523 -0.327011\n",
      "  77 1.088e+00 1.133e+04 7.281e-02 -- 4.243e+02 -- 0.111809 -0.655182 -1.90756 -2.25425 -3.02168 -3.07654 -4.69489 -3.90969 -0.382814 0.401142 0.803569 0.621375 0.329327 0.280303 -1.70068 -0.327188\n",
      "  79 1.081e+00 3.236e+04 9.055e-02 -- 4.242e+02 -- 0.110185 -0.655303 -1.9076 -2.25401 -3.02065 -3.07641 -4.71376 -3.90972 -0.424473 0.396658 0.804084 0.61773 0.327857 0.282854 -1.69434 -0.326954\n",
      "  81 1.256e+00 3.127e+04 1.020e-01 -- 4.241e+02 -- 0.1114 -0.655177 -1.90756 -2.25426 -3.02175 -3.07655 -4.69255 -3.90968 -0.378605 0.401398 0.803501 0.621505 0.329444 0.280115 -1.70061 -0.327157\n",
      "  83 1.218e+00 5.401e+04 1.109e-01 -- 4.240e+02 -- 0.1095 -0.655315 -1.9076 -2.25398 -3.02057 -3.0764 -4.71391 -3.90972 -0.426174 0.396317 0.804088 0.61737 0.32777 0.283014 -1.69355 -0.326894\n",
      "  85 1.416e+00 4.823e+04 1.019e-01 -- 4.239e+02 -- 0.11089 -0.655173 -1.90755 -2.25427 -3.02182 -3.07655 -4.68987 -3.90967 -0.374267 0.401708 0.803429 0.621658 0.329569 0.279907 -1.70078 -0.327127\n",
      "  87 1.324e+00 6.691e+04 9.205e-02 -- 4.238e+02 -- 0.108647 -0.655329 -1.9076 -2.25396 -3.02049 -3.07639 -4.71389 -3.90972 -0.427257 0.39599 0.804093 0.616994 0.327674 0.283184 -1.69292 -0.326831\n",
      "  89 1.518e+00 5.336e+04 5.901e-02 -- 4.237e+02 -- 0.110282 -0.655169 -1.90755 -2.25427 -3.02188 -3.07656 -4.68711 -3.90967 -0.370691 0.401992 0.803358 0.621766 0.329676 0.279722 -1.70104 -0.32709\n",
      "  91 1.363e+00 6.464e+04 3.320e-02 -- 4.237e+02 -- 0.107719 -0.655341 -1.9076 -2.25393 -3.02043 -3.07638 -4.71329 -3.90972 -0.426953 0.395764 0.804086 0.616671 0.327601 0.283309 -1.69252 -0.326767\n",
      "  93 1.526e+00 4.562e+04 6.692e-03 -- 4.237e+02 -- 0.109628 -0.65517 -1.90755 -2.25427 -3.02191 -3.07656 -4.6847 -3.90966 -0.368773 0.402148 0.8033 0.621751 0.329738 0.279613 -1.70118 -0.327043\n",
      "  95 1.323e+00 5.120e+04 3.216e-02 -- 4.237e+02 -- 0.106898 -0.655349 -1.9076 -2.25392 -3.0204 -3.07637 -4.71179 -3.90972 -0.425043 0.395712 0.804056 0.616471 0.327583 0.283338 -1.69238 -0.326705\n",
      "  97 1.445e+00 3.175e+04 5.704e-02 -- 4.238e+02 -- 0.109016 -0.655177 -1.90754 -2.25426 -3.0219 -3.07656 -4.68302 -3.90966 -0.368792 0.402111 0.80326 0.621574 0.329739 0.27961 -1.70101 -0.326981\n",
      "  99 1.229e+00 3.515e+04 6.982e-02 -- 4.239e+02 -- 0.106338 -0.655353 -1.9076 -2.25391 -3.02041 -3.07637 -4.70944 -3.90972 -0.4221 0.395834 0.804 0.616416 0.327632 0.283254 -1.69235 -0.326647\n",
      " 101 1.319e+00 1.840e+04 7.455e-02 -- 4.239e+02 -- 0.108527 -0.655187 -1.90754 -2.25424 -3.02185 -3.07655 -4.68208 -3.90966 -0.370206 0.401903 0.803237 0.621265 0.329691 0.279697 -1.70044 -0.326907\n",
      " 103 1.120e+00 2.184e+04 7.530e-02 -- 4.240e+02 -- 0.106072 -0.655352 -1.9076 -2.25391 -3.02046 -3.07638 -4.70665 -3.90971 -0.41905 0.396056 0.803924 0.616461 0.327731 0.283092 -1.69227 -0.326593\n",
      " 105 1.194e+00 8.302e+03 6.817e-02 -- 4.241e+02 -- 0.108199 -0.655199 -1.90755 -2.25421 -3.02179 -3.07654 -4.68162 -3.90966 -0.372137 0.401611 0.80322 0.620907 0.329627 0.279821 -1.69955 -0.326829\n",
      " 107 1.025e+00 1.258e+04 6.301e-02 -- 4.241e+02 -- 0.106023 -0.655349 -1.90759 -2.25392 -3.02053 -3.07638 -4.70391 -3.9097 -0.41658 0.396283 0.803843 0.616536 0.327848 0.282906 -1.69203 -0.326541\n",
      " 109 1.099e+00 1.871e+03 5.223e-02 -- 4.242e+02 -- 0.108019 -0.655211 -1.90755 -2.25419 -3.02173 -3.07653 -4.6813 -3.90966 -0.37389 0.401324 0.803202 0.620571 0.329573 0.279934 -1.69853 -0.326753\n",
      " 111 9.609e-01 7.026e+03 4.523e-02 -- 4.242e+02 -- 0.106089 -0.655347 -1.90759 -2.25392 -3.02058 -3.07639 -4.70155 -3.9097 -0.414971 0.396455 0.803767 0.616587 0.327956 0.28274 -1.69159 -0.326489\n",
      " 113 1.043e+00 2.556e+03 3.426e-02 -- 4.243e+02 -- 0.107944 -0.655219 -1.90755 -2.25416 -3.02169 -3.07653 -4.68086 -3.90965 -0.375095 0.401096 0.803178 0.6203 0.329543 0.280008 -1.69752 -0.326683\n",
      " 115 9.324e-01 4.787e+03 2.670e-02 -- 4.243e+02 -- 0.106189 -0.655346 -1.90759 -2.25391 -3.02063 -3.07639 -4.69965 -3.90969 -0.414229 0.396547 0.803702 0.616588 0.32804 0.282617 -1.69102 -0.326437\n",
      " 117 1.029e+00 3.041e+03 1.600e-02 -- 4.243e+02 -- 0.107925 -0.655226 -1.90755 -2.25415 -3.02167 -3.07652 -4.6802 -3.90965 -0.375607 0.400949 0.803146 0.620104 0.32954 0.280036 -1.69661 -0.326621\n",
      " 119 9.381e-01 5.862e+03 7.637e-03 -- 4.243e+02 -- 0.106265 -0.655346 -1.90758 -2.2539 -3.02065 -3.07639 -4.69822 -3.90969 -0.414257 0.396559 0.803649 0.616532 0.328095 0.282542 -1.69035 -0.326385\n",
      " 121 1.053e+00 1.792e+03 3.136e-03 -- 4.243e+02 -- 0.107922 -0.655229 -1.90754 -2.25413 -3.02167 -3.07652 -4.67926 -3.90965 -0.375397 0.400887 0.803107 0.619981 0.329563 0.280018 -1.69585 -0.326566\n",
      " 123 9.747e-01 1.055e+04 1.263e-02 -- 4.243e+02 -- 0.106279 -0.655349 -1.90758 -2.25389 -3.02065 -3.07639 -4.69719 -3.90968 -0.414927 0.396498 0.803609 0.616418 0.328121 0.282516 -1.68965 -0.326333\n",
      " 125 1.111e+00 5.278e+03 2.340e-02 -- 4.243e+02 -- 0.107901 -0.655231 -1.90754 -2.25413 -3.02169 -3.07652 -4.67804 -3.90964 -0.374485 0.400906 0.803062 0.619925 0.329608 0.279959 -1.69529 -0.326518\n",
      " 127 1.036e+00 1.896e+04 3.343e-02 -- 4.242e+02 -- 0.106203 -0.655354 -1.90758 -2.25388 -3.02064 -3.07639 -4.69648 -3.90968 -0.416081 0.396375 0.80358 0.616251 0.328119 0.282535 -1.68894 -0.32628\n",
      " 129 1.195e+00 1.424e+04 4.252e-02 -- 4.242e+02 -- 0.107834 -0.655231 -1.90754 -2.25412 -3.02172 -3.07652 -4.67654 -3.90964 -0.372961 0.400999 0.803011 0.619921 0.32967 0.279866 -1.6949 -0.326475\n",
      " 131 1.114e+00 3.012e+04 5.112e-02 -- 4.241e+02 -- 0.106017 -0.655361 -1.90758 -2.25386 -3.02061 -3.07638 -4.69598 -3.90968 -0.417517 0.396205 0.80356 0.616037 0.328093 0.28259 -1.68827 -0.326225\n",
      " 133 1.291e+00 2.480e+04 5.501e-02 -- 4.241e+02 -- 0.107702 -0.655229 -1.90754 -2.25412 -3.02175 -3.07653 -4.67479 -3.90964 -0.371025 0.401147 0.802956 0.619954 0.329744 0.279748 -1.69469 -0.326435\n",
      " 135 1.192e+00 4.132e+04 5.866e-02 -- 4.240e+02 -- 0.105716 -0.655369 -1.90758 -2.25384 -3.02057 -3.07637 -4.69557 -3.90968 -0.418932 0.396012 0.803546 0.615793 0.328051 0.282668 -1.68765 -0.326169\n",
      " 137 1.381e+00 3.378e+04 5.372e-02 -- 4.240e+02 -- 0.107499 -0.655228 -1.90753 -2.25412 -3.0218 -3.07653 -4.67287 -3.90963 -0.369006 0.401322 0.802898 0.619996 0.32982 0.279621 -1.6946 -0.326396\n",
      " 139 1.253e+00 4.876e+04 4.978e-02 -- 4.239e+02 -- 0.105319 -0.655378 -1.90758 -2.25383 -3.02052 -3.07637 -4.69506 -3.90968 -0.419968 0.395831 0.803531 0.615542 0.328005 0.282751 -1.68713 -0.326112\n",
      " 141 1.442e+00 3.792e+04 3.551e-02 -- 4.239e+02 -- 0.107235 -0.655227 -1.90753 -2.25412 -3.02183 -3.07653 -4.67093 -3.90963 -0.367349 0.401482 0.802843 0.620014 0.329885 0.279508 -1.69456 -0.326353\n",
      " 143 1.281e+00 4.985e+04 2.509e-02 -- 4.239e+02 -- 0.104881 -0.655386 -1.90758 -2.25381 -3.02048 -3.07636 -4.69427 -3.90968 -0.420306 0.3957 0.803511 0.615319 0.327969 0.282811 -1.68673 -0.326054\n",
      " 145 1.457e+00 3.617e+04 6.649e-03 -- 4.239e+02 -- 0.106937 -0.655229 -1.90753 -2.25412 -3.02185 -3.07653 -4.66916 -3.90962 -0.366454 0.401581 0.802793 0.619973 0.329929 0.279431 -1.69448 -0.326305\n",
      " 147 1.271e+00 4.504e+04 5.304e-03 -- 4.239e+02 -- 0.104484 -0.655393 -1.90758 -2.2538 -3.02046 -3.07635 -4.69308 -3.90967 -0.419842 0.395651 0.803481 0.615152 0.327958 0.282828 -1.68645 -0.325997\n",
      " 149 1.426e+00 3.019e+04 2.056e-02 -- 4.239e+02 -- 0.106651 -0.655233 -1.90753 -2.25411 -3.02185 -3.07653 -4.6677 -3.90962 -0.366499 0.401591 0.802752 0.619855 0.329943 0.279405 -1.69426 -0.32625\n",
      " 151 1.228e+00 3.710e+04 2.895e-02 -- 4.239e+02 -- 0.1042 -0.655397 -1.90758 -2.25379 -3.02046 -3.07635 -4.69149 -3.90967 -0.418762 0.395689 0.803437 0.615054 0.327979 0.28279 -1.68624 -0.325941\n",
      " 153 1.364e+00 2.275e+04 3.671e-02 -- 4.240e+02 -- 0.106417 -0.655239 -1.90753 -2.2541 -3.02182 -3.07653 -4.66661 -3.90962 -0.367337 0.40151 0.802719 0.619668 0.329931 0.279426 -1.69385 -0.326189\n",
      " 155 1.169e+00 2.891e+04 3.977e-02 -- 4.240e+02 -- 0.104061 -0.655399 -1.90758 -2.25378 -3.02048 -3.07635 -4.68964 -3.90967 -0.417432 0.395789 0.803383 0.615013 0.328027 0.282709 -1.68604 -0.325888\n",
      " 157 1.292e+00 1.595e+04 4.051e-02 -- 4.240e+02 -- 0.10626 -0.655247 -1.90753 -2.25408 -3.02179 -3.07652 -4.66581 -3.90962 -0.368619 0.40137 0.802691 0.61944 0.329904 0.279475 -1.69327 -0.326124\n",
      " 159 1.112e+00 2.222e+04 3.910e-02 -- 4.241e+02 -- 0.104051 -0.655399 -1.90758 -2.25378 -3.02051 -3.07635 -4.68771 -3.90966 -0.41623 0.395911 0.803323 0.615002 0.32809 0.282604 -1.68578 -0.325836\n",
      " 161 1.228e+00 1.084e+04 3.547e-02 -- 4.241e+02 -- 0.106183 -0.655255 -1.90753 -2.25406 -3.02176 -3.07651 -4.66515 -3.90961 -0.369965 0.40121 0.802665 0.619207 0.329875 0.279531 -1.69259 -0.326059\n",
      " 163 1.067e+00 1.774e+04 3.132e-02 -- 4.241e+02 -- 0.104126 -0.655399 -1.90757 -2.25377 -3.02054 -3.07636 -4.6859 -3.90966 -0.4154 0.396019 0.803264 0.614993 0.328155 0.2825 -1.68543 -0.325784\n",
      " 165 1.185e+00 7.799e+03 2.564e-02 -- 4.242e+02 -- 0.106169 -0.655262 -1.90753 -2.25404 -3.02173 -3.07651 -4.6645 -3.90961 -0.37108 0.401067 0.802637 0.618998 0.329856 0.279574 -1.69187 -0.325996\n",
      " 167 1.042e+00 1.568e+04 2.005e-02 -- 4.242e+02 -- 0.104234 -0.6554 -1.90757 -2.25377 -3.02057 -3.07636 -4.68431 -3.90965 -0.415037 0.39609 0.803209 0.614964 0.328211 0.282413 -1.685 -0.325733\n",
      " 169 1.166e+00 6.928e+03 1.359e-02 -- 4.242e+02 -- 0.106193 -0.655267 -1.90753 -2.25403 -3.02171 -3.0765 -4.66377 -3.90961 -0.371795 0.400963 0.802606 0.618827 0.32985 0.279594 -1.69119 -0.325938\n",
      " 171 1.038e+00 1.607e+04 7.134e-03 -- 4.242e+02 -- 0.104336 -0.655401 -1.90757 -2.25376 -3.02059 -3.07636 -4.68298 -3.90965 -0.415146 0.396113 0.803161 0.614903 0.328252 0.282352 -1.6845 -0.325682\n",
      " 173 1.173e+00 8.268e+03 6.275e-04 -- 4.242e+02 -- 0.106228 -0.655271 -1.90753 -2.25401 -3.02171 -3.0765 -4.66288 -3.90961 -0.372036 0.400909 0.80257 0.6187 0.329861 0.279586 -1.69059 -0.325883\n",
      " 175 1.055e+00 1.886e+04 6.252e-03 -- 4.242e+02 -- 0.104399 -0.655404 -1.90757 -2.25375 -3.02059 -3.07636 -4.6819 -3.90965 -0.415663 0.396089 0.80312 0.614807 0.328275 0.28232 -1.68397 -0.325631\n",
      " 177 1.202e+00 1.171e+04 1.208e-02 -- 4.242e+02 -- 0.10625 -0.655274 -1.90752 -2.25401 -3.02171 -3.0765 -4.66183 -3.9096 -0.371805 0.400905 0.80253 0.618615 0.329886 0.279551 -1.69008 -0.325833\n",
      " 179 1.088e+00 2.375e+04 1.876e-02 -- 4.242e+02 -- 0.1044 -0.655408 -1.90757 -2.25374 -3.02058 -3.07635 -4.68102 -3.90964 -0.416481 0.396024 0.803087 0.614677 0.328281 0.282317 -1.68342 -0.325578\n",
      " 181 1.247e+00 1.681e+04 2.282e-02 -- 4.242e+02 -- 0.106242 -0.655275 -1.90752 -2.254 -3.02172 -3.0765 -4.66061 -3.9096 -0.371173 0.400948 0.802486 0.618565 0.329922 0.279494 -1.68969 -0.325786\n",
      " 183 1.130e+00 2.993e+04 2.820e-02 -- 4.241e+02 -- 0.104331 -0.655413 -1.90757 -2.25373 -3.02057 -3.07635 -4.68028 -3.90964 -0.417458 0.395928 0.803059 0.614518 0.328272 0.282337 -1.68288 -0.325526\n",
      " 185 1.300e+00 2.258e+04 2.907e-02 -- 4.241e+02 -- 0.106194 -0.655276 -1.90752 -2.25399 -3.02174 -3.0765 -4.65925 -3.9096 -0.370276 0.401025 0.802439 0.618537 0.329966 0.279422 -1.6894 -0.325741\n",
      " 187 1.174e+00 3.603e+04 3.195e-02 -- 4.241e+02 -- 0.104189 -0.655419 -1.90757 -2.25371 -3.02054 -3.07634 -4.6796 -3.90964 -0.418419 0.395818 0.803034 0.614341 0.328254 0.282371 -1.68237 -0.325472\n",
      " 189 1.350e+00 2.756e+04 2.844e-02 -- 4.240e+02 -- 0.106101 -0.655277 -1.90752 -2.25399 -3.02177 -3.0765 -4.6578 -3.90959 -0.36931 0.401118 0.802391 0.618516 0.330012 0.279345 -1.68917 -0.325696\n",
      " 191 1.209e+00 4.041e+04 2.805e-02 -- 4.240e+02 -- 0.103991 -0.655425 -1.90757 -2.2537 -3.02052 -3.07634 -4.67888 -3.90964 -0.419178 0.395713 0.80301 0.614159 0.328232 0.282407 -1.68192 -0.325418\n",
      " 193 1.386e+00 3.040e+04 2.035e-02 -- 4.240e+02 -- 0.10597 -0.655278 -1.90752 -2.25399 -3.02178 -3.0765 -4.65634 -3.90959 -0.368498 0.401205 0.802344 0.618484 0.330052 0.279274 -1.68898 -0.32565\n",
      " 195 1.228e+00 4.191e+04 1.710e-02 -- 4.240e+02 -- 0.103766 -0.655431 -1.90757 -2.25369 -3.02049 -3.07633 -4.67804 -3.90964 -0.419581 0.395634 0.802983 0.613991 0.328214 0.282435 -1.68153 -0.325363\n",
      " 197 1.400e+00 3.045e+04 7.090e-03 -- 4.240e+02 -- 0.105817 -0.65528 -1.90751 -2.25398 -3.02179 -3.0765 -4.65495 -3.90959 -0.368037 0.401264 0.8023 0.618424 0.330081 0.27922 -1.68878 -0.325601\n",
      " 199 1.228e+00 4.042e+04 2.670e-03 -- 4.240e+02 -- 0.103554 -0.655436 -1.90757 -2.25367 -3.02048 -3.07633 -4.67701 -3.90964 -0.419574 0.395594 0.802951 0.613849 0.328208 0.282441 -1.6812 -0.325309\n",
      " 201 1.391e+00 2.809e+04 6.909e-03 -- 4.240e+02 -- 0.105665 -0.655283 -1.90751 -2.25397 -3.02179 -3.0765 -4.65372 -3.90958 -0.368037 0.40128 0.802261 0.618327 0.330095 0.279192 -1.68851 -0.325549\n",
      " 203 1.211e+00 3.681e+04 1.046e-02 -- 4.240e+02 -- 0.10339 -0.65544 -1.90756 -2.25367 -3.02047 -3.07633 -4.67579 -3.90963 -0.419218 0.395598 0.802913 0.613741 0.328217 0.282421 -1.68092 -0.325255\n",
      " 205 1.362e+00 2.436e+04 1.738e-02 -- 4.240e+02 -- 0.105534 -0.655288 -1.90751 -2.25396 -3.02178 -3.07649 -4.65266 -3.90958 -0.368467 0.401249 0.802225 0.618193 0.330096 0.279187 -1.68816 -0.325494\n",
      " 207 1.182e+00 3.237e+04 1.877e-02 -- 4.240e+02 -- 0.103297 -0.655443 -1.90756 -2.25366 -3.02048 -3.07633 -4.67442 -3.90963 -0.418664 0.395637 0.802869 0.613665 0.328241 0.282377 -1.68064 -0.325203\n",
      " 209 1.325e+00 2.040e+04 2.221e-02 -- 4.240e+02 -- 0.105441 -0.655293 -1.90751 -2.25395 -3.02177 -3.07649 -4.65177 -3.90958 -0.369187 0.401182 0.802193 0.618034 0.330086 0.279201 -1.68772 -0.325436\n",
      " 211 1.150e+00 2.823e+04 2.124e-02 -- 4.241e+02 -- 0.103274 -0.655445 -1.90756 -2.25365 -3.02049 -3.07632 -4.67299 -3.90963 -0.418095 0.395695 0.802822 0.613609 0.328274 0.282318 -1.68036 -0.325151\n",
      " 213 1.288e+00 1.706e+04 2.176e-02 -- 4.241e+02 -- 0.105391 -0.655299 -1.90751 -2.25394 -3.02175 -3.07649 -4.65098 -3.90958 -0.370016 0.401097 0.802163 0.617865 0.330073 0.279223 -1.68722 -0.325378\n",
      " 215 1.123e+00 2.514e+04 1.884e-02 -- 4.241e+02 -- 0.103305 -0.655447 -1.90756 -2.25365 -3.02051 -3.07632 -4.67159 -3.90962 -0.417666 0.395753 0.802774 0.61356 0.328311 0.282255 -1.68003 -0.3251\n",
      " 217 1.259e+00 1.485e+04 1.739e-02 -- 4.241e+02 -- 0.105376 -0.655304 -1.90751 -2.25392 -3.02173 -3.07648 -4.65022 -3.90958 -0.370776 0.401013 0.802132 0.617703 0.330062 0.279242 -1.68669 -0.32532\n",
      " 219 1.105e+00 2.348e+04 1.310e-02 -- 4.241e+02 -- 0.103366 -0.655448 -1.90756 -2.25364 -3.02052 -3.07632 -4.67027 -3.90962 -0.41747 0.395796 0.802727 0.613505 0.328345 0.282197 -1.67967 -0.325049\n",
      " 221 1.244e+00 1.401e+04 1.065e-02 -- 4.241e+02 -- 0.105386 -0.655309 -1.90751 -2.25391 -3.02171 -3.07648 -4.64944 -3.90957 -0.371342 0.400944 0.8021 0.617559 0.330058 0.279251 -1.68616 -0.325265\n",
      " 223 1.099e+00 2.340e+04 5.564e-03 -- 4.241e+02 -- 0.10343 -0.65545 -1.90756 -2.25363 -3.02053 -3.07632 -4.66909 -3.90962 -0.417537 0.395814 0.802684 0.613436 0.328372 0.282152 -1.67926 -0.324998\n",
      " 225 1.243e+00 1.459e+04 2.885e-03 -- 4.241e+02 -- 0.105406 -0.655313 -1.90751 -2.2539 -3.02171 -3.07647 -4.64859 -3.90957 -0.371646 0.400902 0.802065 0.617438 0.330062 0.279246 -1.68566 -0.325211\n",
      " 227 1.105e+00 2.483e+04 2.557e-03 -- 4.241e+02 -- 0.103477 -0.655453 -1.90755 -2.25362 -3.02053 -3.07632 -4.66805 -3.90961 -0.417854 0.395806 0.802645 0.613347 0.328389 0.282123 -1.67882 -0.324947\n",
      " 229 1.256e+00 1.645e+04 4.768e-03 -- 4.241e+02 -- 0.105422 -0.655316 -1.90751 -2.25389 -3.0217 -3.07647 -4.64766 -3.90957 -0.371669 0.400889 0.802028 0.617342 0.330075 0.279225 -1.68522 -0.32516\n",
      " 231 1.121e+00 2.751e+04 9.997e-03 -- 4.241e+02 -- 0.10349 -0.655456 -1.90755 -2.25361 -3.02053 -3.07632 -4.66714 -3.90961 -0.418357 0.395771 0.802609 0.613237 0.328396 0.28211 -1.67838 -0.324896\n",
      " 233 1.279e+00 1.926e+04 1.104e-02 -- 4.241e+02 -- 0.105421 -0.655318 -1.90751 -2.25388 -3.02171 -3.07647 -4.64663 -3.90957 -0.371441 0.400903 0.801989 0.617267 0.330095 0.27919 -1.68484 -0.325111\n",
      " 235 1.144e+00 3.091e+04 1.553e-02 -- 4.241e+02 -- 0.10346 -0.65546 -1.90755 -2.2536 -3.02051 -3.07631 -4.66631 -3.90961 -0.418965 0.395718 0.802578 0.613111 0.328395 0.282112 -1.67793 -0.324844\n",
      " 237 1.308e+00 2.246e+04 1.471e-02 -- 4.241e+02 -- 0.105396 -0.65532 -1.9075 -2.25387 -3.02172 -3.07647 -4.64552 -3.90956 -0.371043 0.400939 0.801948 0.617207 0.33012 0.279146 -1.68451 -0.325063\n",
      " 239 1.168e+00 3.433e+04 1.790e-02 -- 4.241e+02 -- 0.103389 -0.655465 -1.90755 -2.25359 -3.0205 -3.07631 -4.66553 -3.90961 -0.419576 0.395655 0.802548 0.612973 0.328387 0.282122 -1.6775 -0.324792\n",
      " 241 1.336e+00 2.532e+04 1.481e-02 -- 4.241e+02 -- 0.105346 -0.655322 -1.9075 -2.25387 -3.02173 -3.07647 -4.64436 -3.90956 -0.370581 0.400986 0.801906 0.617153 0.330146 0.279097 -1.68423 -0.325016\n",
      " 243 1.188e+00 3.697e+04 1.653e-02 -- 4.240e+02 -- 0.103284 -0.65547 -1.90755 -2.25358 -3.02048 -3.07631 -4.66474 -3.90961 -0.420093 0.395593 0.802519 0.612832 0.328377 0.282136 -1.6771 -0.32474\n",
      " 245 1.358e+00 2.720e+04 1.127e-02 -- 4.240e+02 -- 0.105274 -0.655324 -1.9075 -2.25386 -3.02173 -3.07647 -4.6432 -3.90956 -0.370176 0.401032 0.801865 0.617094 0.33017 0.279051 -1.68397 -0.324968\n",
      " 247 1.201e+00 3.825e+04 1.163e-02 -- 4.240e+02 -- 0.10316 -0.655475 -1.90755 -2.25357 -3.02047 -3.0763 -4.6639 -3.9096 -0.420433 0.395542 0.802489 0.612697 0.328368 0.282145 -1.67673 -0.324687\n",
      " 249 1.369e+00 2.772e+04 4.985e-03 -- 4.240e+02 -- 0.105187 -0.655326 -1.9075 -2.25385 -3.02174 -3.07647 -4.64206 -3.90955 -0.369933 0.401066 0.801825 0.617023 0.330189 0.279013 -1.68371 -0.324919\n",
      " 251 1.204e+00 3.800e+04 4.642e-03 -- 4.240e+02 -- 0.103037 -0.655479 -1.90755 -2.25356 -3.02046 -3.0763 -4.66298 -3.9096 -0.42056 0.395511 0.802457 0.612574 0.328364 0.282145 -1.67639 -0.324635\n",
      " 253 1.367e+00 2.692e+04 2.208e-03 -- 4.240e+02 -- 0.105097 -0.655329 -1.9075 -2.25385 -3.02174 -3.07646 -4.641 -3.90955 -0.369915 0.401078 0.801787 0.616934 0.330199 0.278988 -1.68342 -0.324869\n",
      " 255 1.198e+00 3.649e+04 2.379e-03 -- 4.240e+02 -- 0.102935 -0.655483 -1.90755 -2.25355 -3.02045 -3.0763 -4.66196 -3.9096 -0.420493 0.395502 0.802421 0.612468 0.328367 0.282132 -1.67608 -0.324583\n",
      " 257 1.355e+00 2.519e+04 8.313e-03 -- 4.240e+02 -- 0.105017 -0.655333 -1.9075 -2.25384 -3.02173 -3.07646 -4.64003 -3.90955 -0.370125 0.401067 0.801751 0.616826 0.330203 0.278975 -1.6831 -0.324816\n",
      " 259 1.184e+00 3.425e+04 7.673e-03 -- 4.240e+02 -- 0.102867 -0.655486 -1.90755 -2.25354 -3.02045 -3.07629 -4.66087 -3.9096 -0.420294 0.395514 0.802383 0.612379 0.328378 0.282107 -1.67578 -0.324532\n",
      " 261 1.337e+00 2.308e+04 1.201e-02 -- 4.240e+02 -- 0.104954 -0.655337 -1.9075 -2.25383 -3.02172 -3.07646 -4.63915 -3.90955 -0.370512 0.401035 0.801718 0.616703 0.3302 0.278973 -1.68273 -0.324763\n",
      " 263 1.168e+00 3.192e+04 1.022e-02 -- 4.240e+02 -- 0.102836 -0.655489 -1.90755 -2.25353 -3.02045 -3.07629 -4.65973 -3.9096 -0.420052 0.395538 0.802343 0.612303 0.328395 0.282072 -1.67548 -0.324481\n",
      " 265 1.317e+00 2.111e+04 1.292e-02 -- 4.241e+02 -- 0.104913 -0.655342 -1.9075 -2.25381 -3.02171 -3.07645 -4.63833 -3.90955 -0.370989 0.40099 0.801685 0.616573 0.330194 0.278977 -1.68232 -0.324708\n",
      " 267 1.153e+00 3.000e+04 9.954e-03 -- 4.241e+02 -- 0.102837 -0.655491 -1.90754 -2.25352 -3.02046 -3.07629 -4.65859 -3.90959 -0.419854 0.395565 0.802301 0.612233 0.328414 0.282033 -1.67517 -0.32443\n",
      " 269 1.300e+00 1.967e+04 1.134e-02 -- 4.241e+02 -- 0.104892 -0.655346 -1.9075 -2.2538 -3.0217 -3.07645 -4.63755 -3.90954 -0.371464 0.400942 0.801653 0.616443 0.330188 0.278982 -1.6819 -0.324654\n",
      " 271 1.141e+00 2.883e+04 7.453e-03 -- 4.241e+02 -- 0.102859 -0.655493 -1.90754 -2.25352 -3.02046 -3.07629 -4.6575 -3.90959 -0.419761 0.395587 0.80226 0.612163 0.328433 0.281995 -1.67484 -0.32438\n",
      " 273 1.290e+00 1.899e+04 7.993e-03 -- 4.241e+02 -- 0.104887 -0.65535 -1.90749 -2.25379 -3.02169 -3.07645 -4.63677 -3.90954 -0.371856 0.400899 0.801621 0.61632 0.330185 0.278983 -1.68147 -0.324601\n",
      " 275 1.136e+00 2.857e+04 3.456e-03 -- 4.241e+02 -- 0.102888 -0.655496 -1.90754 -2.25351 -3.02047 -3.07629 -4.65646 -3.90959 -0.419808 0.395598 0.802221 0.612086 0.328449 0.281962 -1.67448 -0.324329\n",
      " 277 1.287e+00 1.915e+04 3.678e-03 -- 4.241e+02 -- 0.10489 -0.655354 -1.90749 -2.25378 -3.02168 -3.07644 -4.63596 -3.90954 -0.372112 0.400869 0.801587 0.616209 0.330186 0.278977 -1.68105 -0.324549\n",
      " 279 1.138e+00 2.921e+04 1.107e-03 -- 4.241e+02 -- 0.102911 -0.655499 -1.90754 -2.2535 -3.02047 -3.07628 -4.6555 -3.90959 -0.419993 0.395594 0.802183 0.612 0.32846 0.281938 -1.67411 -0.324279\n",
      " 281 1.292e+00 2.009e+04 7.159e-04 -- 4.241e+02 -- 0.104892 -0.655357 -1.90749 -2.25377 -3.02167 -3.07644 -4.63511 -3.90954 -0.372211 0.400854 0.801552 0.616112 0.330192 0.278962 -1.68066 -0.324498\n",
      " 283 1.145e+00 3.061e+04 5.484e-03 -- 4.241e+02 -- 0.102917 -0.655502 -1.90754 -2.25349 -3.02046 -3.07628 -4.65461 -3.90958 -0.420293 0.395575 0.802148 0.611903 0.328466 0.281923 -1.67373 -0.324229\n",
      " 285 1.303e+00 2.160e+04 4.414e-03 -- 4.241e+02 -- 0.104887 -0.65536 -1.90749 -2.25376 -3.02167 -3.07644 -4.63421 -3.90954 -0.372166 0.400855 0.801516 0.616028 0.330201 0.27894 -1.6803 -0.324448\n",
      " 287 1.157e+00 3.247e+04 8.809e-03 -- 4.241e+02 -- 0.102901 -0.655505 -1.90754 -2.25348 -3.02046 -3.07628 -4.65378 -3.90958 -0.420664 0.395545 0.802115 0.611796 0.328466 0.281916 -1.67334 -0.324178\n",
      " 289 1.318e+00 2.338e+04 6.750e-03 -- 4.241e+02 -- 0.104869 -0.655362 -1.90749 -2.25376 -3.02167 -3.07644 -4.63327 -3.90953 -0.372009 0.400869 0.801478 0.615952 0.330214 0.278912 -1.67997 -0.3244\n",
      " 291 1.170e+00 3.441e+04 1.052e-02 -- 4.241e+02 -- 0.102861 -0.655509 -1.90754 -2.25347 -3.02045 -3.07628 -4.65299 -3.90958 -0.421049 0.395508 0.802083 0.611682 0.328462 0.281915 -1.67297 -0.324127\n",
      " 293 1.334e+00 2.506e+04 7.174e-03 -- 4.241e+02 -- 0.104837 -0.655364 -1.90749 -2.25375 -3.02168 -3.07643 -4.6323 -3.90953 -0.371803 0.40089 0.801441 0.615882 0.330228 0.278881 -1.67967 -0.324351\n",
      " 295 1.181e+00 3.602e+04 1.026e-02 -- 4.240e+02 -- 0.102801 -0.655513 -1.90754 -2.25346 -3.02043 -3.07627 -4.65219 -3.90958 -0.421397 0.39547 0.802052 0.611566 0.328457 0.281916 -1.6726 -0.324076\n",
      " 297 1.347e+00 2.628e+04 5.695e-03 -- 4.240e+02 -- 0.104792 -0.655367 -1.90749 -2.25374 -3.02168 -3.07643 -4.63133 -3.90953 -0.37161 0.400913 0.801403 0.615811 0.330241 0.27885 -1.67939 -0.324303\n",
      " 299 1.190e+00 3.698e+04 8.084e-03 -- 4.240e+02 -- 0.102728 -0.655517 -1.90754 -2.25345 -3.02042 -3.07627 -4.65139 -3.90958 -0.421657 0.395437 0.802021 0.611451 0.328451 0.281917 -1.67226 -0.324025\n",
      " 301 1.355e+00 2.683e+04 2.723e-03 -- 4.240e+02 -- 0.104738 -0.655369 -1.90749 -2.25373 -3.02168 -3.07643 -4.63037 -3.90953 -0.371488 0.400931 0.801366 0.615734 0.330251 0.278823 -1.6791 -0.324254\n",
      " 303 1.193e+00 3.715e+04 4.763e-03 -- 4.240e+02 -- 0.102653 -0.655521 -1.90754 -2.25344 -3.02041 -3.07626 -4.65054 -3.90957 -0.421811 0.395414 0.801989 0.611342 0.328448 0.281913 -1.67193 -0.323974\n",
      " 305 1.356e+00 2.668e+04 9.591e-04 -- 4.240e+02 -- 0.104681 -0.655372 -1.90749 -2.25373 -3.02168 -3.07643 -4.62944 -3.90952 -0.371477 0.40094 0.80133 0.615649 0.330258 0.278801 -1.67881 -0.324204\n",
      " 307 1.192e+00 3.660e+04 1.062e-03 -- 4.240e+02 -- 0.102586 -0.655524 -1.90753 -2.25343 -3.02041 -3.07626 -4.64965 -3.90957 -0.421857 0.395402 0.801955 0.611242 0.328448 0.281903 -1.67162 -0.323923\n",
      " 309 1.352e+00 2.595e+04 4.356e-03 -- 4.240e+02 -- 0.104627 -0.655375 -1.90748 -2.25372 -3.02167 -3.07643 -4.62856 -3.90952 -0.371584 0.400936 0.801295 0.615554 0.330261 0.278787 -1.67851 -0.324154\n",
      " 311 1.186e+00 3.557e+04 2.052e-03 -- 4.240e+02 -- 0.102535 -0.655528 -1.90753 -2.25342 -3.0204 -3.07626 -4.64873 -3.90957 -0.42182 0.395401 0.80192 0.611151 0.328452 0.281886 -1.67132 -0.323873\n",
      " 313 1.344e+00 2.491e+04 6.784e-03 -- 4.240e+02 -- 0.104581 -0.655379 -1.90748 -2.25371 -3.02167 -3.07642 -4.62773 -3.90952 -0.371792 0.40092 0.801262 0.615451 0.33026 0.278779 -1.67818 -0.324102\n",
      " 315 1.178e+00 3.435e+04 3.937e-03 -- 4.240e+02 -- 0.102503 -0.655531 -1.90753 -2.25341 -3.0204 -3.07626 -4.64777 -3.90957 -0.421744 0.395407 0.801883 0.611068 0.328459 0.281864 -1.67102 -0.323822\n",
      " 317 1.333e+00 2.384e+04 7.835e-03 -- 4.241e+02 -- 0.104546 -0.655382 -1.90748 -2.2537 -3.02166 -3.07642 -4.62694 -3.90952 -0.372063 0.400896 0.801229 0.615343 0.330256 0.278774 -1.67783 -0.32405\n",
      " 319 1.170e+00 3.326e+04 4.332e-03 -- 4.241e+02 -- 0.102489 -0.655533 -1.90753 -2.25341 -3.0204 -3.07625 -4.64682 -3.90957 -0.421675 0.395418 0.801846 0.610989 0.328467 0.281839 -1.67072 -0.323772\n",
      " 321 1.324e+00 2.298e+04 7.501e-03 -- 4.241e+02 -- 0.104523 -0.655386 -1.90748 -2.25369 -3.02165 -3.07642 -4.62618 -3.90952 -0.372353 0.400869 0.801196 0.615234 0.330252 0.278772 -1.67746 -0.323999\n",
      " 323 1.163e+00 3.251e+04 3.402e-03 -- 4.241e+02 -- 0.102489 -0.655536 -1.90753 -2.2534 -3.0204 -3.07625 -4.64588 -3.90956 -0.421652 0.395427 0.801809 0.610912 0.328476 0.281813 -1.67041 -0.323723\n",
      " 325 1.317e+00 2.251e+04 5.976e-03 -- 4.241e+02 -- 0.104509 -0.65539 -1.90748 -2.25368 -3.02164 -3.07641 -4.62542 -3.90951 -0.372611 0.400843 0.801164 0.615127 0.330249 0.278768 -1.6771 -0.323947\n",
      " 327 1.159e+00 3.225e+04 1.455e-03 -- 4.241e+02 -- 0.102495 -0.655539 -1.90753 -2.25339 -3.0204 -3.07625 -4.64497 -3.90956 -0.421696 0.395431 0.801772 0.610833 0.328484 0.28179 -1.67009 -0.323673\n",
      " 329 1.315e+00 2.251e+04 3.759e-03 -- 4.241e+02 -- 0.1045 -0.655393 -1.90748 -2.25367 -3.02163 -3.07641 -4.62466 -3.90951 -0.372805 0.400822 0.801131 0.615027 0.330248 0.278761 -1.67674 -0.323896\n",
      " 331 1.159e+00 3.251e+04 1.007e-03 -- 4.241e+02 -- 0.102501 -0.655541 -1.90753 -2.25338 -3.0204 -3.07625 -4.6441 -3.90956 -0.421818 0.395428 0.801737 0.610749 0.32849 0.281771 -1.66976 -0.323623\n",
      " 333 1.316e+00 2.296e+04 1.324e-03 -- 4.241e+02 -- 0.104493 -0.655396 -1.90748 -2.25366 -3.02163 -3.07641 -4.62388 -3.90951 -0.372917 0.400809 0.801097 0.614933 0.330248 0.278749 -1.67639 -0.323846\n",
      " 335 1.163e+00 3.322e+04 3.487e-03 -- 4.241e+02 -- 0.102499 -0.655544 -1.90753 -2.25337 -3.02039 -3.07624 -4.64326 -3.90956 -0.422006 0.395417 0.801702 0.610659 0.328492 0.281756 -1.66942 -0.323574\n",
      " 337 1.322e+00 2.376e+04 8.469e-04 -- 4.241e+02 -- 0.104483 -0.655399 -1.90748 -2.25365 -3.02162 -3.0764 -4.62308 -3.90951 -0.372946 0.400805 0.801063 0.614847 0.330252 0.278734 -1.67606 -0.323797\n",
      " 339 1.168e+00 3.423e+04 5.485e-03 -- 4.241e+02 -- 0.102486 -0.655547 -1.90753 -2.25336 -3.02039 -3.07624 -4.64247 -3.90956 -0.422237 0.395399 0.801669 0.610564 0.328492 0.281747 -1.66908 -0.323524\n",
      " 341 1.330e+00 2.475e+04 2.283e-03 -- 4.241e+02 -- 0.104467 -0.655402 -1.90748 -2.25364 -3.02162 -3.0764 -4.62225 -3.90951 -0.372907 0.400809 0.801028 0.614767 0.330257 0.278714 -1.67574 -0.323748\n",
      " 343 1.175e+00 3.533e+04 6.691e-03 -- 4.241e+02 -- 0.10246 -0.655551 -1.90752 -2.25335 -3.02038 -3.07624 -4.6417 -3.90955 -0.422488 0.395377 0.801637 0.610465 0.328489 0.28174 -1.66875 -0.323474\n",
      " 345 1.338e+00 2.574e+04 2.776e-03 -- 4.241e+02 -- 0.104443 -0.655404 -1.90748 -2.25364 -3.02162 -3.0764 -4.62141 -3.90951 -0.372833 0.400817 0.800993 0.61469 0.330263 0.278693 -1.67544 -0.323699\n",
      " 347 1.182e+00 3.631e+04 6.809e-03 -- 4.241e+02 -- 0.102422 -0.655554 -1.90752 -2.25335 -3.02037 -3.07623 -4.64094 -3.90955 -0.422723 0.395353 0.801606 0.610364 0.328485 0.281736 -1.66842 -0.323424\n",
      " 349 1.346e+00 2.653e+04 2.229e-03 -- 4.240e+02 -- 0.104411 -0.655407 -1.90747 -2.25363 -3.02162 -3.0764 -4.62057 -3.9095 -0.372755 0.400828 0.800958 0.614613 0.330268 0.278672 -1.67515 -0.323651\n",
      " 351 1.187e+00 3.698e+04 5.968e-03 -- 4.240e+02 -- 0.102376 -0.655558 -1.90752 -2.25334 -3.02036 -3.07623 -4.64017 -3.90955 -0.422919 0.395331 0.801574 0.610264 0.32848 0.281732 -1.6681 -0.323374\n",
      " 353 1.351e+00 2.699e+04 8.519e-04 -- 4.240e+02 -- 0.104374 -0.655409 -1.90747 -2.25362 -3.02161 -3.07639 -4.61973 -3.9095 -0.372705 0.400836 0.800923 0.614535 0.330273 0.278652 -1.67487 -0.323602\n",
      " 355 1.190e+00 3.726e+04 4.385e-03 -- 4.240e+02 -- 0.102327 -0.655561 -1.90752 -2.25333 -3.02035 -3.07623 -4.63939 -3.90955 -0.423061 0.395314 0.801542 0.610166 0.328476 0.281726 -1.66779 -0.323324\n",
      " 357 1.353e+00 2.708e+04 9.742e-04 -- 4.240e+02 -- 0.104335 -0.655412 -1.90747 -2.25361 -3.02161 -3.07639 -4.61891 -3.9095 -0.372705 0.400841 0.800888 0.614453 0.330275 0.278635 -1.67458 -0.323553\n",
      " 359 1.191e+00 3.715e+04 2.518e-03 -- 4.240e+02 -- 0.102281 -0.655564 -1.90752 -2.25332 -3.02035 -3.07622 -4.6386 -3.90955 -0.423146 0.395302 0.801509 0.610073 0.328474 0.281717 -1.66749 -0.323274\n",
      " 361 1.352e+00 2.684e+04 2.848e-03 -- 4.240e+02 -- 0.104296 -0.655415 -1.90747 -2.2536 -3.02161 -3.07639 -4.61812 -3.9095 -0.372767 0.400839 0.800855 0.614366 0.330276 0.278621 -1.67429 -0.323503\n",
      " 363 1.189e+00 3.672e+04 7.728e-04 -- 4.240e+02 -- 0.102241 -0.655568 -1.90752 -2.25331 -3.02034 -3.07622 -4.63778 -3.90955 -0.423181 0.395296 0.801476 0.609984 0.328473 0.281705 -1.6672 -0.323225\n",
      " 365 1.349e+00 2.638e+04 4.327e-03 -- 4.240e+02 -- 0.10426 -0.655418 -1.90747 -2.2536 -3.0216 -3.07639 -4.61735 -3.9095 -0.372885 0.400831 0.800822 0.614275 0.330274 0.278611 -1.67399 -0.323453\n",
      " 367 1.185e+00 3.614e+04 4.647e-04 -- 4.240e+02 -- 0.102211 -0.65557 -1.90752 -2.2533 -3.02034 -3.07622 -4.63696 -3.90954 -0.423187 0.395295 0.801442 0.609901 0.328474 0.28169 -1.66691 -0.323175\n",
      " 369 1.344e+00 2.584e+04 5.155e-03 -- 4.240e+02 -- 0.10423 -0.655421 -1.90747 -2.25359 -3.02159 -3.07638 -4.61661 -3.90949 -0.373044 0.400818 0.800789 0.614181 0.33027 0.278603 -1.67367 -0.323403\n",
      " 371 1.181e+00 3.557e+04 9.155e-04 -- 4.240e+02 -- 0.102191 -0.655573 -1.90752 -2.25329 -3.02033 -3.07622 -4.63612 -3.90954 -0.42319 0.395297 0.801407 0.60982 0.328476 0.281673 -1.66662 -0.323126\n",
      " 373 1.339e+00 2.538e+04 5.246e-03 -- 4.241e+02 -- 0.104207 -0.655425 -1.90747 -2.25358 -3.02158 -3.07638 -4.61588 -3.90949 -0.373222 0.400803 0.800757 0.614086 0.330266 0.278597 -1.67335 -0.323353\n",
      " 375 1.177e+00 3.514e+04 6.996e-04 -- 4.241e+02 -- 0.10218 -0.655576 -1.90752 -2.25329 -3.02033 -3.07621 -4.6353 -3.90954 -0.423208 0.395299 0.801372 0.60974 0.328479 0.281655 -1.66632 -0.323077\n",
      " 377 1.336e+00 2.510e+04 4.657e-03 -- 4.241e+02 -- 0.104188 -0.655428 -1.90747 -2.25357 -3.02158 -3.07638 -4.61517 -3.90949 -0.37339 0.400787 0.800725 0.613991 0.330262 0.278591 -1.67303 -0.323303\n",
      " 379 1.175e+00 3.496e+04 1.708e-04 -- 4.241e+02 -- 0.102173 -0.655579 -1.90752 -2.25328 -3.02033 -3.07621 -4.63449 -3.90954 -0.423257 0.395299 0.801338 0.609661 0.328481 0.281638 -1.66603 -0.323028\n",
      " 381 1.334e+00 2.506e+04 3.566e-03 -- 4.241e+02 -- 0.104174 -0.655431 -1.90747 -2.25356 -3.02157 -3.07637 -4.61446 -3.90949 -0.373529 0.400773 0.800693 0.6139 0.330259 0.278583 -1.67271 -0.323253\n",
      " 383 1.174e+00 3.505e+04 1.439e-03 -- 4.241e+02 -- 0.102167 -0.655581 -1.90752 -2.25327 -3.02032 -3.07621 -4.6337 -3.90954 -0.423345 0.395295 0.801304 0.60958 0.328481 0.281623 -1.66572 -0.322979\n",
      " 385 1.334e+00 2.528e+04 2.296e-03 -- 4.241e+02 -- 0.104161 -0.655434 -1.90747 -2.25355 -3.02156 -3.07637 -4.61374 -3.90949 -0.373628 0.400763 0.80066 0.613812 0.330257 0.278573 -1.6724 -0.323204\n",
      " 387 1.176e+00 3.541e+04 2.759e-03 -- 4.241e+02 -- 0.102158 -0.655584 -1.90751 -2.25326 -3.02032 -3.0762 -4.63294 -3.90953 -0.423468 0.395287 0.801271 0.609496 0.328481 0.28161 -1.66542 -0.32293\n",
      " 389 1.337e+00 2.570e+04 1.086e-03 -- 4.241e+02 -- 0.104146 -0.655437 -1.90747 -2.25354 -3.02156 -3.07637 -4.61301 -3.90949 -0.373679 0.400758 0.800627 0.613728 0.330255 0.278562 -1.67209 -0.323155\n",
      " 391 1.179e+00 3.596e+04 3.999e-03 -- 4.241e+02 -- 0.102145 -0.655587 -1.90751 -2.25325 -3.02031 -3.0762 -4.63219 -3.90953 -0.423623 0.395276 0.801239 0.609409 0.328478 0.2816 -1.66511 -0.322881\n",
      " 393 1.341e+00 2.626e+04 1.867e-04 -- 4.241e+02 -- 0.104129 -0.655439 -1.90746 -2.25353 -3.02155 -3.07636 -4.61227 -3.90948 -0.373694 0.400757 0.800594 0.613647 0.330255 0.278548 -1.67179 -0.323106\n",
      " 395 1.182e+00 3.660e+04 4.819e-03 -- 4.241e+02 -- 0.102124 -0.65559 -1.90751 -2.25324 -3.0203 -3.0762 -4.63147 -3.90953 -0.423794 0.395262 0.801207 0.60932 0.328475 0.281592 -1.66481 -0.322832\n",
      " 397 1.345e+00 2.685e+04 2.326e-04 -- 4.241e+02 -- 0.104109 -0.655442 -1.90746 -2.25353 -3.02155 -3.07636 -4.61153 -3.90948 -0.373684 0.400759 0.800561 0.613569 0.330256 0.278532 -1.6715 -0.323058\n",
      " 399 1.186e+00 3.720e+04 5.033e-03 -- 4.240e+02 -- 0.102097 -0.655593 -1.90751 -2.25324 -3.0203 -3.07619 -4.63075 -3.90953 -0.423959 0.395246 0.801175 0.609229 0.32847 0.281586 -1.66451 -0.322783\n",
      " 401 1.350e+00 2.736e+04 5.035e-05 -- 4.240e+02 -- 0.104084 -0.655444 -1.90746 -2.25352 -3.02155 -3.07636 -4.61078 -3.90948 -0.373668 0.400763 0.800528 0.613492 0.330256 0.278517 -1.67122 -0.323009\n",
      "********************\n",
      "0.104084 -0.655444 -1.90746 -2.25352 -3.02155 -3.07636 -4.61078 -3.90948 -0.373668 0.400763 0.800528 0.613492 0.330256 0.278517 -1.67122 -0.323009\n",
      "0.0351976 0.000839204 0.000349775 0.00402504 0.00707581 0.00275589 0.124412 0.000905571 0.189505 0.0199362 0.00701796 0.0326727 0.0352226 0.0205451 0.268968 0.0111152\n",
      "-77.2828 -5289.45 -27360.2 -1043.89 -821.646 -3179.45 28.7066 -13573.9 -24.1291 -159.085 170.979 -62.8103 -40.6872 59.9863 5.53304 592.733\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": 13,
   "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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-6.16365918,  2.21009502,  2.28771584,  1.13110535,  0.3928378 ,\n",
       "        0.21373824, -0.82743402, -0.10317688])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9759b06d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "xscale('log'); ylim(-10,10)\n",
    "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n",
    "\n",
    "lag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9759d39910>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9759d39f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s, loc, scale = lognorm.fit(lag,loc=.008)\n",
    "\n",
    "xscale('log'); ylim(-10,15)\n",
    "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n",
    "plot(np.logspace(np.log(fqd[0]),np.log(fqd[-1])),lognorm.pdf(np.logspace(np.log(fqd[0]),np.log(fqd[-1])),s,loc,scale))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.10734722921479302, 2.5047796344038056)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97583fcc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loc, scale = norm.fit(lag,loc=.01)\n",
    "\n",
    "xscale('log'); ylim(-10,10)\n",
    "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n",
    "plot(np.logspace(np.log(fqd[3]),np.log(fqd[-1])),norm.pdf(np.logspace(np.log(fqd[0]),np.log(fqd[-1])),loc,scale))\n",
    "\n",
    "norm.fit(lag,loc=.01,scale=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9759af8890>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vvXbalagnDBDlaWqKs2sDB8YATSnLzJ2SJKkshghJklQWQ4QkSSqL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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9759ce0590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(irfft(lag))"
   ]
  },
  {
   "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
}