phy-4660/lag/data/clag_analysis-origbins-6439A.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 0x7fb933276c10>"
      ]
     },
     "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",
    "\n",
    "ref_file=\"lc/1367A.lc\"\n",
    "echo_file=\"lc/6439A.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([ 0.00964867,  0.02886003,  0.0556922 ,  0.08632291,  0.13380051,\n",
       "        0.20739079,  0.32145572,  0.49825637])"
      ]
     },
     "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",
    "fqd\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 0x7fb93329dbd0>"
      ]
     },
     "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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cLTya9zlWuU7BQx4I3FG5GL3y3zAU9hZS/9/jq4Q3nu8IrADKsyTopP1m2AXM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb93281d590>"
      ]
     },
     "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 4.345e-01 5.367e+01 inf -- -2.331e+02 -- 1 1 1 1 1 1 1 1\n",
      "   2 7.681e-01 5.318e+01 6.465e+01 -- -1.684e+02 -- 0.582708 0.565919 0.567071 0.565455 0.565472 0.567054 0.565985 0.567541\n",
      "   3 3.306e+00 5.243e+01 6.380e+01 -- -1.046e+02 -- 0.185981 0.132028 0.135218 0.13138 0.131144 0.134919 0.132269 0.135939\n",
      "   4 1.864e+00 5.146e+01 6.266e+01 -- -4.198e+01 -- -0.181134 -0.300836 -0.294737 -0.302717 -0.302476 -0.296396 -0.30108 -0.294221\n",
      "   5 5.957e-01 5.034e+01 6.140e+01 -- 1.942e+01 -- -0.518821 -0.728805 -0.721808 -0.738858 -0.734893 -0.72747 -0.734418 -0.722812\n",
      "   6 3.763e-01 4.873e+01 5.988e+01 -- 7.930e+01 -- -0.826205 -1.14111 -1.14324 -1.17897 -1.16599 -1.15914 -1.16891 -1.15053\n",
      "   7 2.737e-01 4.601e+01 5.715e+01 -- 1.365e+02 -- -1.07976 -1.51457 -1.54719 -1.6226 -1.59467 -1.59132 -1.60536 -1.57616\n",
      "   8 2.126e-01 4.210e+01 5.222e+01 -- 1.887e+02 -- -1.22751 -1.79949 -1.90925 -2.06669 -2.0187 -2.02254 -2.04251 -1.99341\n",
      "   9 1.708e-01 3.838e+01 4.572e+01 -- 2.344e+02 -- -1.23813 -1.90084 -2.20766 -2.50544 -2.43783 -2.45004 -2.47673 -2.39386\n",
      "  10 1.443e-01 3.612e+01 4.007e+01 -- 2.745e+02 -- -1.15982 -1.77137 -2.45761 -2.92172 -2.8541 -2.86716 -2.89804 -2.78318\n",
      "  11 1.465e-01 3.205e+01 3.355e+01 -- 3.080e+02 -- -1.00642 -1.66795 -2.68038 -3.28735 -3.25239 -3.25214 -3.28683 -3.18475\n",
      "  12 1.147e-01 2.460e+01 2.338e+01 -- 3.314e+02 -- -0.859013 -1.64077 -2.83751 -3.57526 -3.57815 -3.56594 -3.6097 -3.59927\n",
      "  13 9.727e-02 1.530e+01 1.253e+01 -- 3.439e+02 -- -0.770014 -1.63264 -2.92007 -3.72033 -3.73522 -3.78014 -3.82976 -4.01212\n",
      "  14 7.615e-02 7.436e+00 5.601e+00 -- 3.495e+02 -- -0.717971 -1.62697 -2.94479 -3.70604 -3.70977 -3.91796 -3.95008 -4.4024\n",
      "  15 4.898e-02 2.805e+00 2.111e+00 -- 3.516e+02 -- -0.694246 -1.6192 -2.93083 -3.65405 -3.66478 -4.01526 -4.01935 -4.73764\n",
      "  16 2.092e-02 1.546e+00 5.495e-01 -- 3.522e+02 -- -0.686453 -1.6148 -2.9107 -3.61859 -3.64327 -4.06623 -4.07053 -4.96968\n",
      "  17 6.268e-03 6.434e-01 9.603e-02 -- 3.523e+02 -- -0.684867 -1.61298 -2.89653 -3.5983 -3.63262 -4.0834 -4.10603 -5.07364\n",
      "  18 2.301e-03 2.165e-01 1.331e-02 -- 3.523e+02 -- -0.685248 -1.61215 -2.88778 -3.5897 -3.62755 -4.08853 -4.12306 -5.10544\n",
      "  19 7.739e-04 7.392e-02 1.675e-03 -- 3.523e+02 -- -0.685882 -1.61169 -2.88397 -3.58619 -3.6255 -4.0902 -4.1289 -5.11719\n",
      "  20 2.809e-04 2.462e-02 2.072e-04 -- 3.523e+02 -- -0.686276 -1.61151 -2.88251 -3.58466 -3.62482 -4.09073 -4.13094 -5.12115\n",
      "  21 1.264e-04 8.449e-03 2.600e-05 -- 3.523e+02 -- -0.686469 -1.61146 -2.88191 -3.58405 -3.62462 -4.0909 -4.13161 -5.12257\n",
      "********************\n",
      "-0.686469 -1.61146 -2.88191 -3.58405 -3.62462 -4.0909 -4.13161 -5.12257\n",
      "0.231147 0.207462 0.274193 0.294642 0.222424 0.244627 0.185969 0.531275\n",
      "-0.00158514 0.000476266 0.00320976 0.00304574 0.000752612 -0.00194507 -0.00844944 -0.00236823\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",
      "+++ 3.523e+02 3.519e+02 -6.866e-01 -4.554e-01 0.735 +++\n",
      "+++ 3.523e+02 3.515e+02 -6.866e-01 -3.398e-01 1.52 +++\n",
      "+++ 3.523e+02 3.517e+02 -6.866e-01 -3.976e-01 1.11 +++\n",
      "+++ 3.523e+02 3.518e+02 -6.866e-01 -4.265e-01 0.915 +++\n",
      "+++ 3.523e+02 3.518e+02 -6.866e-01 -4.121e-01 1.01 +++\n",
      "+++ 3.523e+02 3.518e+02 -6.866e-01 -4.193e-01 0.962 +++\n",
      "+++ 3.523e+02 3.518e+02 -6.866e-01 -4.157e-01 0.986 +++\n",
      "+++ 3.523e+02 3.518e+02 -6.866e-01 -4.139e-01 0.999 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 3.523e+02 3.518e+02 -1.611e+00 -1.404e+00 0.91 +++\n",
      "+++ 3.523e+02 3.513e+02 -1.611e+00 -1.300e+00 1.93 +++\n",
      "+++ 3.523e+02 3.516e+02 -1.611e+00 -1.352e+00 1.38 +++\n",
      "+++ 3.523e+02 3.517e+02 -1.611e+00 -1.378e+00 1.13 +++\n",
      "+++ 3.523e+02 3.518e+02 -1.611e+00 -1.391e+00 1.02 +++\n",
      "+++ 3.523e+02 3.518e+02 -1.611e+00 -1.397e+00 0.964 +++\n",
      "+++ 3.523e+02 3.518e+02 -1.611e+00 -1.394e+00 0.992 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 3.523e+02 3.518e+02 -2.882e+00 -2.608e+00 0.933 +++\n",
      "+++ 3.523e+02 3.513e+02 -2.882e+00 -2.470e+00 2.04 +++\n",
      "+++ 3.523e+02 3.516e+02 -2.882e+00 -2.539e+00 1.44 +++\n",
      "+++ 3.523e+02 3.517e+02 -2.882e+00 -2.573e+00 1.17 +++\n",
      "+++ 3.523e+02 3.518e+02 -2.882e+00 -2.590e+00 1.05 +++\n",
      "+++ 3.523e+02 3.518e+02 -2.882e+00 -2.599e+00 0.991 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 3.523e+02 3.518e+02 -3.584e+00 -3.289e+00 0.911 +++\n",
      "+++ 3.523e+02 3.512e+02 -3.584e+00 -3.142e+00 2.13 +++\n",
      "+++ 3.523e+02 3.516e+02 -3.584e+00 -3.216e+00 1.45 +++\n",
      "+++ 3.523e+02 3.517e+02 -3.584e+00 -3.252e+00 1.17 +++\n",
      "+++ 3.523e+02 3.518e+02 -3.584e+00 -3.271e+00 1.03 +++\n",
      "+++ 3.523e+02 3.518e+02 -3.584e+00 -3.280e+00 0.971 +++\n",
      "+++ 3.523e+02 3.518e+02 -3.584e+00 -3.275e+00   1 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 3.523e+02 3.519e+02 -3.625e+00 -3.402e+00 0.838 +++\n",
      "+++ 3.523e+02 3.513e+02 -3.625e+00 -3.291e+00 1.88 +++\n",
      "+++ 3.523e+02 3.516e+02 -3.625e+00 -3.347e+00 1.31 +++\n",
      "+++ 3.523e+02 3.518e+02 -3.625e+00 -3.374e+00 1.06 +++\n",
      "+++ 3.523e+02 3.518e+02 -3.625e+00 -3.388e+00 0.946 +++\n",
      "+++ 3.523e+02 3.518e+02 -3.625e+00 -3.381e+00   1 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 3.523e+02 3.521e+02 -4.091e+00 -3.969e+00 0.288 +++\n",
      "+++ 3.523e+02 3.520e+02 -4.091e+00 -3.907e+00 0.659 +++\n",
      "+++ 3.523e+02 3.518e+02 -4.091e+00 -3.877e+00 0.904 +++\n",
      "+++ 3.523e+02 3.518e+02 -4.091e+00 -3.862e+00 1.04 +++\n",
      "+++ 3.523e+02 3.518e+02 -4.091e+00 -3.869e+00 0.972 +++\n",
      "+++ 3.523e+02 3.518e+02 -4.091e+00 -3.865e+00 1.01 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 3.523e+02 3.518e+02 -4.132e+00 -3.946e+00 0.976 +++\n",
      "+++ 3.523e+02 3.512e+02 -4.132e+00 -3.853e+00 2.17 +++\n",
      "+++ 3.523e+02 3.515e+02 -4.132e+00 -3.899e+00 1.49 +++\n",
      "+++ 3.523e+02 3.517e+02 -4.132e+00 -3.923e+00 1.24 +++\n",
      "+++ 3.523e+02 3.517e+02 -4.132e+00 -3.934e+00 1.1 +++\n",
      "+++ 3.523e+02 3.518e+02 -4.132e+00 -3.940e+00 1.04 +++\n",
      "+++ 3.523e+02 3.518e+02 -4.132e+00 -3.943e+00 1.01 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 3.523e+02 3.521e+02 -5.123e+00 -4.857e+00 0.383 +++\n",
      "+++ 3.523e+02 3.518e+02 -5.123e+00 -4.724e+00   1 +++\n",
      "********************\n",
      "-0.686556 -1.61144 -2.88168 -3.58378 -3.62457 -4.09096 -4.13184 -5.12305\n",
      "0.272685 0.217188 0.282737 0.308392 0.243262 0.225511 0.188902 0.398741\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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Az1zx2LLrH+655x42btx42X3tdphPkuohlUpdNSAsls/nSaVSZLPZNc1Zr2DS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb932dc0bd0>"
      ]
     },
     "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 9.080e+02 8.628e+00 inf -- 3.946e+02 -- -0.492976 -1.19479 -2.33614 -2.85309 -3.17738 -3.59122 -4.14664 -6.86153 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
      "   3 2.942e+01 1.064e+01 2.632e+00 -- 3.972e+02 -- -0.481238 -1.16249 -2.32998 -2.86236 -3.17204 -3.5971 -4.23481 -6.56153 0.196538 0.194241 0.217236 0.210439 0.208644 0.19838 0.076294 2.89715\n",
      "   5 7.015e+01 1.272e+01 2.261e+00 -- 3.995e+02 -- -0.466455 -1.13277 -2.31839 -2.86564 -3.1624 -3.59969 -4.33223 -6.86153 0.280457 0.266919 0.318442 0.315419 0.302037 0.289194 0.0452378 0.657906\n",
      "   7 1.553e+02 1.490e+01 1.934e+00 -- 4.014e+02 -- -0.450147 -1.10622 -2.30366 -2.86384 -3.15036 -3.59968 -4.44186 -6.56153 0.351939 0.32391 0.403816 0.412369 0.381156 0.372195 0.00381962 2.32595\n",
      "   9 7.337e+01 1.721e+01 1.703e+00 -- 4.031e+02 -- -0.433442 -1.08282 -2.28754 -2.85817 -3.13723 -3.59775 -4.56867 -6.86153 0.41207 0.369439 0.474927 0.499989 0.447819 0.447139 -0.0555143 2.71453\n",
      "  11 2.642e+02 1.966e+01 1.517e+00 -- 4.046e+02 -- -0.417104 -1.0623 -2.27119 -2.84975 -3.12388 -3.59444 -4.72052 -7.16153 0.462324 0.406445 0.533924 0.577792 0.503961 0.514362 -0.147032 1.64655\n",
      "  13 1.134e+02 2.224e+01 1.353e+00 -- 4.060e+02 -- -0.401589 -1.04435 -2.25531 -2.83959 -3.11085 -3.59024 -4.9108 -6.86153 0.504215 0.43699 0.582926 0.646084 0.551409 0.574431 -0.303917 2.12243\n",
      "  15 4.422e+01 2.495e+01 1.214e+00 -- 4.072e+02 -- -0.387134 -1.02863 -2.24025 -2.8285 -3.09839 -3.58552 -5.16145 -6.92565 0.539131 0.46254 0.623794 0.705676 0.59172 0.627943 -0.619596 -3.09158\n",
      "  17 2.645e+02 2.779e+01 1.104e+00 -- 4.083e+02 -- -0.373836 -1.01484 -2.22621 -2.81703 -3.08666 -3.58053 -5.46145 -7.22565 0.568271 0.484154 0.658085 0.757538 0.626161 0.675551 -1.4082 2.0864\n",
      "  19 2.867e+01 3.074e+01 9.808e-01 -- 4.093e+02 -- -0.361705 -1.00274 -2.21326 -2.80561 -3.07569 -3.57544 -5.41113 -6.92565 0.592636 0.502612 0.687049 0.802652 0.655755 0.718052 2.88736 -2.83886\n",
      "  21 2.872e+02 3.381e+01 9.306e-01 -- 4.102e+02 -- -0.350685 -0.992095 -2.20141 -2.79451 -3.06542 -3.57021 -5.11113 -7.22565 0.612993 0.518498 0.711608 0.842016 0.681241 0.756352 -2.50407 1.58824\n",
      "  23 4.288e+03 3.693e+01 9.343e-01 -- 4.111e+02 -- -0.340752 -0.982709 -2.19051 -2.78383 -3.05601 -3.56539 -4.81113 -6.92565 0.630223 0.532271 0.732844 0.876431 0.703498 0.789821 -2.82897 -0.0394334\n",
      "  25 3.415e+01 4.012e+01 8.120e-01 -- 4.119e+02 -- -0.331787 -0.974425 -2.18054 -2.7737 -3.04741 -3.56096 -4.66441 -6.62565 0.644673 0.544307 0.750981 0.90648 0.723128 0.819985 -2.85376 -1.97808\n",
      "  26 9.994e+02 4.117e+02 2.909e+00 -- 4.148e+02 -- -0.251075 -0.9012 -2.08945 -2.67816 -2.96888 -3.52102 -3.7456 -8 0.766488 0.649993 0.907611 1.17077 0.897255 1.08952 -2.95842 2.74048\n",
      "  27 6.433e+02 1.002e+01 3.929e+00 -- 4.188e+02 -- -0.248558 -0.908886 -2.07891 -2.65067 -2.96976 -3.54662 -3.96146 -8 0.701601 0.621254 0.876275 1.16829 1.0404 1.20006 -2.9623 -2.8748\n",
      "  28 8.040e+02 4.356e+00 3.810e-01 -- 4.192e+02 -- -0.24941 -0.908256 -2.07352 -2.6406 -2.96684 -3.53274 -4.09371 -8 0.719662 0.643488 0.90068 1.14499 0.973393 1.15172 -2.91207 1.1996\n",
      "  29 6.002e-01 1.402e+00 4.879e-01 -- 4.187e+02 -- -0.249394 -0.90855 -2.07276 -2.63793 -2.96077 -3.52618 -4.14701 -5 0.719126 0.6433 0.900826 1.17205 0.953098 1.1439 -2.86205 -1.93902\n",
      "  30 1.613e+02 1.454e+00 5.129e-01 -- 4.192e+02 -- -0.249496 -0.908472 -2.07365 -2.63477 -2.95891 -3.5366 -4.14887 -6.64352 0.723003 0.644781 0.906634 1.17771 0.938562 1.11686 -2.77459 -0.775149\n",
      "  31 1.624e+03 1.161e+00 1.714e-02 -- 4.192e+02 -- -0.249667 -0.908595 -2.07175 -2.63868 -2.95842 -3.52179 -4.1679 -8 0.72384 0.64458 0.904311 1.17024 0.944786 1.13843 -2.857 -1.41511\n",
      "  32 1.970e+02 3.152e-01 7.158e-04 -- 4.192e+02 -- -0.249609 -0.908562 -2.07305 -2.63658 -2.95704 -3.52378 -4.16495 -8 0.721446 0.644368 0.906308 1.1724 0.941102 1.13792 -2.85898 -2.42836\n",
      "  33 8.595e+02 3.798e-01 6.275e-04 -- 4.192e+02 -- -0.24955 -0.908572 -2.07254 -2.6381 -2.95677 -3.52227 -4.16647 -8 0.722642 0.644061 0.90574 1.17222 0.940858 1.13781 -2.8621 2.99735\n",
      "  34 9.794e+02 1.202e-01 9.051e-05 -- 4.192e+02 -- -0.249592 -0.908557 -2.07291 -2.63765 -2.95644 -3.52276 -4.16658 -8 0.721993 0.644122 0.906381 1.1716 0.940534 1.13825 -2.86125 2.8441\n",
      "********************\n",
      "-0.249592 -0.908557 -2.07291 -2.63765 -2.95644 -3.52276 -4.16658 -8 0.721993 0.644122 0.906381 1.1716 0.940534 1.13825 -2.86125 2.8441\n",
      "0.0338278 0.0081193 0.0342279 0.0767303 0.0666745 0.170245 0.340225 1391.22 0.211725 0.0953128 0.214457 0.311582 0.260149 0.464275 0.812667 2572.32\n",
      "0.0179335 -0.0240087 0.0250454 -0.120246 -0.0820404 -0.0104754 -0.0627678 -0.000341413 0.00675955 -0.0176544 -0.00416412 0.00520845 -0.00874943 0.000211486 -0.0321303 -0.000438528\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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:root:Line magic function `%autoreload` not found.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t### errors for param 0 ###\n",
      "+++ 4.199e+02 4.196e+02 -2.495e-01 -2.326e-01 0.408 +++\n",
      "+++ 4.199e+02 4.192e+02 -2.495e-01 -2.242e-01 1.23 +++\n",
      "+++ 4.199e+02 4.195e+02 -2.495e-01 -2.284e-01 0.733 +++\n",
      "+++ 4.199e+02 4.194e+02 -2.495e-01 -2.263e-01 0.956 +++\n",
      "+++ 4.199e+02 4.193e+02 -2.495e-01 -2.252e-01 1.09 +++\n",
      "+++ 4.199e+02 4.193e+02 -2.495e-01 -2.257e-01 1.02 +++\n",
      "+++ 4.199e+02 4.194e+02 -2.495e-01 -2.260e-01 0.988 +++\n",
      "+++ 4.199e+02 4.194e+02 -2.495e-01 -2.259e-01   1 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 4.199e+02 4.197e+02 -9.086e-01 -9.045e-01 0.351 +++\n",
      "+++ 4.199e+02 4.194e+02 -9.086e-01 -9.025e-01   1 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 4.199e+02 4.197e+02 -2.073e+00 -2.056e+00 0.357 +++\n",
      "+++ 4.199e+02 4.192e+02 -2.073e+00 -2.047e+00 1.24 +++\n",
      "+++ 4.199e+02 4.195e+02 -2.073e+00 -2.051e+00 0.685 +++\n",
      "+++ 4.199e+02 4.194e+02 -2.073e+00 -2.049e+00 0.93 +++\n",
      "+++ 4.199e+02 4.193e+02 -2.073e+00 -2.048e+00 1.08 +++\n",
      "+++ 4.199e+02 4.194e+02 -2.073e+00 -2.049e+00   1 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 4.199e+02 4.197e+02 -2.645e+00 -2.605e+00 0.304 +++\n",
      "+++ 4.199e+02 4.195e+02 -2.645e+00 -2.585e+00 0.798 +++\n",
      "+++ 4.199e+02 4.192e+02 -2.645e+00 -2.574e+00 1.23 +++\n",
      "+++ 4.199e+02 4.194e+02 -2.645e+00 -2.579e+00 0.995 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 4.199e+02 4.198e+02 -2.954e+00 -2.922e+00 0.194 +++\n",
      "+++ 4.199e+02 4.196e+02 -2.954e+00 -2.905e+00 0.544 +++\n",
      "+++ 4.199e+02 4.194e+02 -2.954e+00 -2.897e+00 0.845 +++\n",
      "+++ 4.199e+02 4.193e+02 -2.954e+00 -2.893e+00 1.04 +++\n",
      "+++ 4.199e+02 4.194e+02 -2.954e+00 -2.895e+00 0.94 +++\n",
      "+++ 4.199e+02 4.194e+02 -2.954e+00 -2.894e+00 0.991 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 4.199e+02 4.197e+02 -3.498e+00 -3.421e+00 0.356 +++\n",
      "+++ 4.199e+02 4.193e+02 -3.498e+00 -3.382e+00 1.02 +++\n",
      "+++ 4.199e+02 4.195e+02 -3.498e+00 -3.401e+00 0.628 +++\n",
      "+++ 4.199e+02 4.194e+02 -3.498e+00 -3.392e+00 0.807 +++\n",
      "+++ 4.199e+02 4.194e+02 -3.498e+00 -3.387e+00 0.909 +++\n",
      "+++ 4.199e+02 4.194e+02 -3.498e+00 -3.385e+00 0.964 +++\n",
      "+++ 4.199e+02 4.194e+02 -3.498e+00 -3.383e+00 0.992 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 4.199e+02 4.197e+02 -4.180e+00 -4.005e+00 0.338 +++\n",
      "+++ 4.199e+02 4.193e+02 -4.180e+00 -3.918e+00 1.02 +++\n",
      "+++ 4.199e+02 4.195e+02 -4.180e+00 -3.962e+00 0.609 +++\n",
      "+++ 4.199e+02 4.195e+02 -4.180e+00 -3.940e+00 0.793 +++\n",
      "+++ 4.199e+02 4.194e+02 -4.180e+00 -3.929e+00 0.899 +++\n",
      "+++ 4.199e+02 4.194e+02 -4.180e+00 -3.924e+00 0.956 +++\n",
      "+++ 4.199e+02 4.194e+02 -4.180e+00 -3.921e+00 0.986 +++\n",
      "+++ 4.199e+02 4.194e+02 -4.180e+00 -3.920e+00   1 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 4.199e+02 4.195e+02 -4.560e+00 -4.379e+00 0.634 +++\n",
      "+++ 4.199e+02 4.190e+02 -4.560e+00 -4.288e+00 1.79 +++\n",
      "+++ 4.199e+02 4.193e+02 -4.560e+00 -4.333e+00 1.09 +++\n",
      "+++ 4.199e+02 4.194e+02 -4.560e+00 -4.356e+00 0.841 +++\n",
      "+++ 4.199e+02 4.194e+02 -4.560e+00 -4.345e+00 0.961 +++\n",
      "+++ 4.199e+02 4.193e+02 -4.560e+00 -4.339e+00 1.03 +++\n",
      "+++ 4.199e+02 4.194e+02 -4.560e+00 -4.342e+00 0.993 +++\n",
      "\t### errors for param 8 ###\n",
      "+++ 4.199e+02 4.197e+02 7.176e-01 8.234e-01 0.288 +++\n",
      "+++ 4.199e+02 4.195e+02 7.176e-01 8.762e-01 0.636 +++\n",
      "+++ 4.199e+02 4.194e+02 7.176e-01 9.027e-01 0.85 +++\n",
      "+++ 4.199e+02 4.194e+02 7.176e-01 9.159e-01 0.967 +++\n",
      "+++ 4.199e+02 4.193e+02 7.176e-01 9.225e-01 1.03 +++\n",
      "+++ 4.199e+02 4.194e+02 7.176e-01 9.192e-01 0.997 +++\n",
      "\t### errors for param 9 ###\n",
      "+++ 4.199e+02 4.194e+02 6.417e-01 7.370e-01 0.917 +++\n",
      "+++ 4.199e+02 4.189e+02 6.417e-01 7.847e-01 1.94 +++\n",
      "+++ 4.199e+02 4.192e+02 6.417e-01 7.609e-01 1.4 +++\n",
      "+++ 4.199e+02 4.193e+02 6.417e-01 7.490e-01 1.15 +++\n",
      "+++ 4.199e+02 4.193e+02 6.417e-01 7.430e-01 1.03 +++\n",
      "+++ 4.199e+02 4.194e+02 6.417e-01 7.400e-01 0.972 +++\n",
      "+++ 4.199e+02 4.194e+02 6.417e-01 7.415e-01   1 +++\n",
      "\t### errors for param 10 ###\n",
      "+++ 4.199e+02 4.194e+02 9.056e-01 1.120e+00 0.922 +++\n",
      "+++ 4.199e+02 4.189e+02 9.056e-01 1.227e+00 1.85 +++\n",
      "+++ 4.199e+02 4.192e+02 9.056e-01 1.173e+00 1.37 +++\n",
      "+++ 4.199e+02 4.193e+02 9.056e-01 1.147e+00 1.14 +++\n",
      "+++ 4.199e+02 4.193e+02 9.056e-01 1.133e+00 1.03 +++\n",
      "+++ 4.199e+02 4.194e+02 9.056e-01 1.127e+00 0.975 +++\n",
      "+++ 4.199e+02 4.194e+02 9.056e-01 1.130e+00   1 +++\n",
      "\t### errors for param 11 ###\n",
      "+++ 4.199e+02 4.193e+02 1.164e+00 1.485e+00 1.01 +++\n",
      "\t### errors for param 12 ###\n",
      "+++ 4.199e+02 4.195e+02 9.406e-01 1.198e+00 0.789 +++\n",
      "+++ 4.199e+02 4.191e+02 9.406e-01 1.326e+00 1.57 +++\n",
      "+++ 4.199e+02 4.193e+02 9.406e-01 1.262e+00 1.16 +++\n",
      "+++ 4.199e+02 4.194e+02 9.406e-01 1.230e+00 0.972 +++\n",
      "+++ 4.199e+02 4.193e+02 9.406e-01 1.246e+00 1.07 +++\n",
      "+++ 4.199e+02 4.193e+02 9.406e-01 1.238e+00 1.02 +++\n",
      "+++ 4.199e+02 4.194e+02 9.406e-01 1.234e+00 0.995 +++\n",
      "\t### errors for param 13 ###\n",
      "+++ 4.199e+02 4.197e+02 1.187e+00 1.403e+00 0.29 +++\n",
      "+++ 4.199e+02 4.195e+02 1.187e+00 1.511e+00 0.642 +++\n",
      "+++ 4.199e+02 4.194e+02 1.187e+00 1.565e+00 0.862 +++\n",
      "+++ 4.199e+02 4.194e+02 1.187e+00 1.592e+00 0.982 +++\n",
      "+++ 4.199e+02 4.193e+02 1.187e+00 1.606e+00 1.04 +++\n",
      "+++ 4.199e+02 4.193e+02 1.187e+00 1.599e+00 1.01 +++\n",
      "+++ 4.199e+02 4.194e+02 1.187e+00 1.596e+00 0.997 +++\n",
      "\t### errors for param 14 ###\n",
      "+++ 4.199e+02 4.195e+02 -3.113e+00 -2.282e+00 0.756 +++\n",
      "+++ 4.199e+02 4.193e+02 -3.113e+00 -1.866e+00 1.19 +++\n",
      "+++ 4.199e+02 4.194e+02 -3.113e+00 -2.074e+00 0.996 +++\n",
      "\t### errors for param 15 ###\n",
      "+++ 4.199e+02 4.197e+02 1.032e+00 1.598e+00 0.239 +++\n",
      "+++ 4.199e+02 4.195e+02 1.032e+00 1.881e+00 0.622 +++\n",
      "+++ 4.199e+02 4.194e+02 1.032e+00 2.023e+00 0.848 +++\n",
      "+++ 4.199e+02 4.194e+02 1.032e+00 2.094e+00 0.957 +++\n",
      "+++ 4.199e+02 4.193e+02 1.032e+00 2.129e+00 1.01 +++\n",
      "********************\n",
      "-0.249474 -0.908595 -2.07282 -2.6453 -2.95426 -3.49832 -4.17972 -4.56002 0.717619 0.641666 0.90563 1.164 0.940625 1.18717 -3.11332 1.03245\n",
      "0.0235989 0.00609752 0.0240131 0.0658289 0.0603077 0.115006 0.260075 0.218293 0.201566 0.0998359 0.224231 0.321435 0.293375 0.408381 1.03966 1.09658\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "%autoreload\n",
    "p, pe = clag.errors(Cx, p, pe)"
   ]
  },
  {
   "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": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 11.83713905,   3.53860689,   2.58807242,   2.14608694,\n",
       "         1.11886842,   0.91105232,  -1.54142564,   0.32978838])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9325403d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "xscale('log'); ylim(-5,16)\n",
    "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n",
    "\n",
    "lag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb92cdfda10>]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SaRGZ5DKmTNQFGA7MYtMkRCRuLVvCpEmWkJSX2/3jj/c7KhGRzJdPw3cfAuqw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\niIiI+Oz/ALKB7rJ0agN2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb92d76b810>"
      ]
     },
     "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": 97,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb92d28e290>]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Nfkcrkl6U2IiIJFBWVmj5hiVLYOFCK0T+4QfIy4NGjWD6dL+jFEkfSmxERJIk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb92d548290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_fit = irfft(fit)\n",
    "\n",
    "mpl.rcParams['xtick.labelsize']=12\n",
    "mpl.rcParams['ytick.labelsize']=12\n",
    "ylabel(\"Response (relative)\",fontsize=20)\n",
    "xlabel(\"Time (days)\",fontsize=20) \n",
    "\n",
    "ylim(-0.5,2)\n",
    "xlim(0,7)\n",
    "\n",
    "plot(time_fit)\n",
    "plot([3.22,3.22], [-50, 50], color='k', linestyle='-', linewidth=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb92cf6c890>]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Nfkcrkl6U2IiIJFBWVmj5hiVLYOFCK0T+4QfIy4NGjWD6dL+jFEkfSmxERJIk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb92cd7de10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_fit = irfft(fit)\n",
    "\n",
    "mpl.rcParams['xtick.labelsize']=12\n",
    "mpl.rcParams['ytick.labelsize']=12\n",
    "ylabel(\"Response (relative)\",fontsize=20)\n",
    "xlabel(\"Time (days)\",fontsize=20) \n",
    "\n",
    "ylim(-0.5,2)\n",
    "xlim(0,7)\n",
    "\n",
    "plot(time_fit)\n",
    "plot([3.22,3.22], [-50, 50], color='k', linestyle='-', linewidth=2)"
   ]
  },
  {
   "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
}