phy-4660/lag/data/clag_analysis-origbins-9157A.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 0x7f5265f94c10>"
      ]
     },
     "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/9157A.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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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 0x7f528c4d9050>"
      ]
     },
     "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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Fd+kb0Rc/WOok/nOTMEGRFRRYP0v4qqtSzKhkgqux7GKVzG9+uTn4/twZ58VR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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5265afdd10>"
      ]
     },
     "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 4.359e-01 7.161e+01 inf -- -2.181e+02 -- 1 1 1 1 1 1 1 1\n",
      "   2 7.742e-01 7.111e+01 8.740e+01 -- -1.307e+02 -- 0.564074 0.564103 0.564133 0.564559 0.565233 0.56464 0.564848 0.564132\n",
      "   3 3.436e+00 7.050e+01 8.697e+01 -- -4.372e+01 -- 0.13087 0.128441 0.128217 0.128912 0.13058 0.129543 0.129728 0.127363\n",
      "   4 1.429e+00 6.964e+01 8.635e+01 -- 4.262e+01 -- -0.295217 -0.304667 -0.307069 -0.306748 -0.303835 -0.30536 -0.305448 -0.310246\n",
      "   5 5.887e-01 6.841e+01 8.530e+01 -- 1.279e+02 -- -0.704126 -0.731704 -0.741301 -0.742618 -0.738088 -0.740228 -0.740922 -0.748518\n",
      "   6 3.715e-01 6.674e+01 8.354e+01 -- 2.115e+02 -- -1.07182 -1.14488 -1.17363 -1.17921 -1.17251 -1.17519 -1.17707 -1.18728\n",
      "   7 2.720e-01 6.454e+01 8.085e+01 -- 2.923e+02 -- -1.35117 -1.52737 -1.60252 -1.61695 -1.60778 -1.61012 -1.61437 -1.62665\n",
      "   8 2.151e-01 6.151e+01 7.746e+01 -- 3.698e+02 -- -1.49897 -1.84925 -2.02742 -2.05591 -2.04475 -2.04475 -2.05353 -2.06755\n",
      "   9 1.795e-01 5.724e+01 7.348e+01 -- 4.433e+02 -- -1.55318 -2.06643 -2.44779 -2.49458 -2.48325 -2.47853 -2.49523 -2.5119\n",
      "  10 1.552e-01 5.129e+01 6.861e+01 -- 5.119e+02 -- -1.57472 -2.15815 -2.85964 -2.92873 -2.92176 -2.90848 -2.9396 -2.96289\n",
      "  11 1.358e-01 4.343e+01 6.173e+01 -- 5.736e+02 -- -1.57452 -2.1858 -3.24732 -3.34792 -3.35575 -3.328 -3.38557 -3.42282\n",
      "  12 1.168e-01 3.415e+01 5.061e+01 -- 6.242e+02 -- -1.56805 -2.20666 -3.56401 -3.72207 -3.774 -3.72255 -3.82733 -3.8877\n",
      "  13 9.496e-02 2.420e+01 3.428e+01 -- 6.585e+02 -- -1.56623 -2.22075 -3.74568 -3.98216 -4.14916 -4.05587 -4.24428 -4.34169\n",
      "  14 6.665e-02 1.351e+01 1.640e+01 -- 6.749e+02 -- -1.5696 -2.22354 -3.78328 -4.0829 -4.4261 -4.2599 -4.58517 -4.75399\n",
      "  15 2.964e-02 4.358e+00 4.427e+00 -- 6.793e+02 -- -1.57425 -2.21978 -3.77127 -4.13056 -4.55417 -4.30434 -4.77899 -5.07083\n",
      "  16 3.872e-03 6.183e-01 4.700e-01 -- 6.798e+02 -- -1.57487 -2.21895 -3.77396 -4.16245 -4.5755 -4.2897 -4.83862 -5.22114\n",
      "  17 1.291e-03 1.659e-01 1.113e-02 -- 6.798e+02 -- -1.57331 -2.22027 -3.78214 -4.16768 -4.57631 -4.28246 -4.85735 -5.23199\n",
      "  18 5.326e-04 6.959e-02 1.219e-03 -- 6.798e+02 -- -1.57311 -2.22061 -3.78349 -4.168 -4.57225 -4.27943 -4.86362 -5.23031\n",
      "  19 2.494e-04 2.982e-02 2.268e-04 -- 6.798e+02 -- -1.57309 -2.22069 -3.78429 -4.1678 -4.56992 -4.2784 -4.86621 -5.23009\n",
      "  20 1.140e-04 1.317e-02 4.436e-05 -- 6.798e+02 -- -1.57306 -2.22073 -3.78469 -4.16784 -4.56878 -4.27802 -4.86734 -5.22989\n",
      "********************\n",
      "-1.57306 -2.22073 -3.78469 -4.16784 -4.56878 -4.27802 -4.86734 -5.22989\n",
      "0.237078 0.201135 0.284718 0.248327 0.252841 0.150914 0.188371 0.222107\n",
      "0.000184037 -0.000586923 -0.00230695 0.00112283 0.00799663 0.00635492 -0.0131691 -0.000270457\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",
      "+++ 6.798e+02 6.794e+02 -1.573e+00 -1.336e+00 0.88 +++\n",
      "+++ 6.798e+02 6.789e+02 -1.573e+00 -1.217e+00 1.85 +++\n",
      "+++ 6.798e+02 6.791e+02 -1.573e+00 -1.277e+00 1.33 +++\n",
      "+++ 6.798e+02 6.793e+02 -1.573e+00 -1.306e+00 1.09 +++\n",
      "+++ 6.798e+02 6.793e+02 -1.573e+00 -1.321e+00 0.984 +++\n",
      "+++ 6.798e+02 6.793e+02 -1.573e+00 -1.314e+00 1.04 +++\n",
      "+++ 6.798e+02 6.793e+02 -1.573e+00 -1.317e+00 1.01 +++\n",
      "+++ 6.798e+02 6.793e+02 -1.573e+00 -1.319e+00 0.998 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 6.798e+02 6.794e+02 -2.221e+00 -2.020e+00 0.889 +++\n",
      "+++ 6.798e+02 6.789e+02 -2.221e+00 -1.919e+00 1.88 +++\n",
      "+++ 6.798e+02 6.791e+02 -2.221e+00 -1.969e+00 1.35 +++\n",
      "+++ 6.798e+02 6.792e+02 -2.221e+00 -1.994e+00 1.11 +++\n",
      "+++ 6.798e+02 6.793e+02 -2.221e+00 -2.007e+00 0.996 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 6.798e+02 6.793e+02 -3.785e+00 -3.500e+00 0.976 +++\n",
      "+++ 6.798e+02 6.787e+02 -3.785e+00 -3.358e+00 2.11 +++\n",
      "+++ 6.798e+02 6.791e+02 -3.785e+00 -3.429e+00 1.49 +++\n",
      "+++ 6.798e+02 6.792e+02 -3.785e+00 -3.465e+00 1.22 +++\n",
      "+++ 6.798e+02 6.793e+02 -3.785e+00 -3.482e+00 1.1 +++\n",
      "+++ 6.798e+02 6.793e+02 -3.785e+00 -3.491e+00 1.04 +++\n",
      "+++ 6.798e+02 6.793e+02 -3.785e+00 -3.496e+00 1.01 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 6.798e+02 6.796e+02 -4.168e+00 -4.044e+00 0.34 +++\n",
      "+++ 6.798e+02 6.794e+02 -4.168e+00 -3.982e+00 0.75 +++\n",
      "+++ 6.798e+02 6.793e+02 -4.168e+00 -3.951e+00 1.01 +++\n",
      "+++ 6.798e+02 6.794e+02 -4.168e+00 -3.966e+00 0.876 +++\n",
      "+++ 6.798e+02 6.793e+02 -4.168e+00 -3.958e+00 0.942 +++\n",
      "+++ 6.798e+02 6.793e+02 -4.168e+00 -3.954e+00 0.976 +++\n",
      "+++ 6.798e+02 6.793e+02 -4.168e+00 -3.952e+00 0.993 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 6.798e+02 6.794e+02 -4.568e+00 -4.316e+00 0.875 +++\n",
      "+++ 6.798e+02 6.788e+02 -4.568e+00 -4.189e+00 2.1 +++\n",
      "+++ 6.798e+02 6.791e+02 -4.568e+00 -4.252e+00 1.41 +++\n",
      "+++ 6.798e+02 6.792e+02 -4.568e+00 -4.284e+00 1.13 +++\n",
      "+++ 6.798e+02 6.793e+02 -4.568e+00 -4.300e+00 0.996 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 6.798e+02 6.794e+02 -4.278e+00 -4.127e+00 0.875 +++\n",
      "+++ 6.798e+02 6.789e+02 -4.278e+00 -4.052e+00 1.83 +++\n",
      "+++ 6.798e+02 6.791e+02 -4.278e+00 -4.089e+00 1.36 +++\n",
      "+++ 6.798e+02 6.792e+02 -4.278e+00 -4.108e+00 1.1 +++\n",
      "+++ 6.798e+02 6.793e+02 -4.278e+00 -4.118e+00 0.986 +++\n",
      "+++ 6.798e+02 6.793e+02 -4.278e+00 -4.113e+00 1.04 +++\n",
      "+++ 6.798e+02 6.793e+02 -4.278e+00 -4.115e+00 1.02 +++\n",
      "+++ 6.798e+02 6.793e+02 -4.278e+00 -4.116e+00   1 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 6.798e+02 6.794e+02 -4.868e+00 -4.679e+00 0.799 +++\n",
      "+++ 6.798e+02 6.788e+02 -4.868e+00 -4.585e+00 1.9 +++\n",
      "+++ 6.798e+02 6.792e+02 -4.868e+00 -4.632e+00 1.28 +++\n",
      "+++ 6.798e+02 6.793e+02 -4.868e+00 -4.656e+00 1.03 +++\n",
      "+++ 6.798e+02 6.793e+02 -4.868e+00 -4.668e+00 0.908 +++\n",
      "+++ 6.798e+02 6.793e+02 -4.868e+00 -4.662e+00 0.966 +++\n",
      "+++ 6.798e+02 6.793e+02 -4.868e+00 -4.659e+00 0.996 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 6.798e+02 6.796e+02 -5.230e+00 -5.119e+00 0.305 +++\n",
      "+++ 6.798e+02 6.795e+02 -5.230e+00 -5.063e+00 0.696 +++\n",
      "+++ 6.798e+02 6.793e+02 -5.230e+00 -5.036e+00 0.953 +++\n",
      "+++ 6.798e+02 6.793e+02 -5.230e+00 -5.022e+00 1.1 +++\n",
      "+++ 6.798e+02 6.793e+02 -5.230e+00 -5.029e+00 1.02 +++\n",
      "+++ 6.798e+02 6.793e+02 -5.230e+00 -5.032e+00 0.988 +++\n",
      "+++ 6.798e+02 6.793e+02 -5.230e+00 -5.030e+00 1.01 +++\n",
      "********************\n",
      "-1.57305 -2.22075 -3.78488 -4.16779 -4.56826 -4.27788 -4.86784 -5.22982\n",
      "0.253747 0.213705 0.289194 0.215344 0.268522 0.161511 0.209074 0.199524\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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b30fymeT8QWG6sNQjFpZSfTEkqG4kEglSqVRRfVIpP/lpbrFYjIEnBuh8sZP4U3E4Q1BQ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\nJEmSJEmSJEmSJEmSJEmSpBrw/wHPHATciaTfPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5265b07dd0>"
      ]
     },
     "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 4.263e+02 8.743e+00 inf -- 7.200e+02 -- -0.936223 -1.49944 -2.78774 -3.1451 -3.64922 -3.68468 -4.51464 -6.91491 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
      "   3 4.860e+01 9.447e+00 3.012e+00 -- 7.230e+02 -- -0.89632 -1.47767 -2.84644 -3.16314 -3.6728 -3.70288 -4.53842 -6.61491 0.0812657 0.222203 0.312409 0.206336 0.095992 0.137977 -0.0249432 2.12014\n",
      "   5 1.834e+02 1.086e+01 2.400e+00 -- 7.254e+02 -- -0.863738 -1.45362 -2.88196 -3.17521 -3.69408 -3.71906 -4.5538 -6.91491 0.0679893 0.320406 0.548492 0.312861 0.089923 0.174248 -0.146165 0.867841\n",
      "   7 2.704e+01 1.233e+01 1.965e+00 -- 7.273e+02 -- -0.83671 -1.42964 -2.88961 -3.18149 -3.71318 -3.73336 -4.56179 -6.61491 0.0582761 0.398604 0.784091 0.415604 0.0825065 0.208572 -0.259049 -2.48409\n",
      "   9 8.381e+01 1.445e+01 1.699e+00 -- 7.290e+02 -- -0.814012 -1.40696 -2.87289 -3.18272 -3.73036 -3.74595 -4.56372 -6.91491 0.0510133 0.460993 0.990591 0.511606 0.0740674 0.240773 -0.360623 -2.05029\n",
      "  11 5.382e+01 1.673e+01 1.479e+00 -- 7.305e+02 -- -0.794769 -1.38613 -2.84208 -3.17997 -3.74567 -3.75695 -4.56136 -7.21491 0.0454691 0.511132 1.15265 0.598735 0.0649178 0.270666 -0.449551 2.56611\n",
      "  13 5.234e+02 1.913e+01 1.324e+00 -- 7.318e+02 -- -0.778329 -1.36729 -2.80694 -3.1744 -3.75929 -3.7665 -4.55618 -7.51491 0.041178 0.551805 1.27263 0.676139 0.0552576 0.298158 -0.525871 1.32105\n",
      "  15 2.636e+02 2.168e+01 1.189e+00 -- 7.330e+02 -- -0.764197 -1.35038 -2.77293 -3.16703 -3.77138 -3.7748 -4.54947 -7.21491 0.0378314 0.585121 1.36044 0.743875 0.0453504 0.323244 -0.590433 1.29064\n",
      "  17 1.149e+02 2.437e+01 1.088e+00 -- 7.341e+02 -- -0.751986 -1.33529 -2.74211 -3.15871 -3.78208 -3.78199 -4.54208 -6.91491 0.03521 0.612663 1.4256 0.802584 0.0353532 0.345957 -0.644618 -1.31496\n",
      "  19 6.722e+01 2.722e+01 9.849e-01 -- 7.351e+02 -- -0.741387 -1.32184 -2.71491 -3.15002 -3.79154 -3.78825 -4.53464 -6.61491 0.0331641 0.635624 1.47504 0.853226 0.0254728 0.36641 -0.689866 1.22418\n",
      "  21 2.720e+01 3.023e+01 9.444e-01 -- 7.360e+02 -- -0.732157 -1.30986 -2.69111 -3.14143 -3.79983 -3.79362 -4.52741 -6.31491 0.0315615 0.654913 1.51347 0.896759 0.0156921 0.384625 -0.727661 -0.72188\n",
      "  23 1.618e+01 3.340e+01 8.070e-01 -- 7.368e+02 -- -0.724091 -1.29919 -2.67035 -3.13314 -3.80709 -3.79838 -4.52086 -6.01491 0.0303421 0.671224 1.54392 0.93425 0.00644024 0.400889 -0.759178 1.24133\n",
      "  24 8.891e+01 4.172e+02 8.637e+00 -- 7.455e+02 -- -0.65346 -1.20411 -2.48868 -3.0558 -3.86972 -3.83787 -4.45774 -5.36047 0.0208476 0.809948 1.7913 1.25651 -0.0861695 0.542223 -1.02272 0.0117388\n",
      "  25 3.521e+01 9.060e+00 9.410e-01 -- 7.464e+02 -- -0.661625 -1.20837 -2.49819 -3.05016 -3.85526 -3.84511 -4.44606 -4.62172 0.0636491 0.75819 1.74668 1.16355 0.00391259 0.465285 -0.857723 -1.03192\n",
      "  26 2.800e-01 2.694e+00 4.013e-01 -- 7.468e+02 -- -0.659925 -1.20808 -2.49692 -3.04136 -3.86296 -3.86865 -4.52444 -4.7803 0.0440885 0.773556 1.74836 1.18011 0.141663 0.501264 -1.01577 -0.968135\n",
      "  27 1.118e-01 1.396e+00 6.852e-02 -- 7.469e+02 -- -0.660557 -1.20802 -2.49682 -3.04782 -3.86407 -3.85635 -4.50129 -4.79491 0.0494721 0.769251 1.75312 1.18102 0.159998 0.494797 -0.942213 -0.697092\n",
      "  28 6.066e-02 5.475e-01 7.813e-03 -- 7.469e+02 -- -0.660375 -1.20798 -2.49707 -3.04899 -3.85699 -3.85319 -4.501 -4.80785 0.0481904 0.770454 1.74927 1.18272 0.163201 0.485505 -0.957511 -0.619169\n",
      "  29 2.205e-02 2.173e-01 1.373e-03 -- 7.469e+02 -- -0.660499 -1.20797 -2.4971 -3.05002 -3.85533 -3.85154 -4.49936 -4.80815 0.048302 0.77034 1.75136 1.18473 0.158919 0.484615 -0.950396 -0.581613\n",
      "  30 2.005e-02 7.267e-02 2.580e-04 -- 7.469e+02 -- -0.660478 -1.20797 -2.49717 -3.05009 -3.85287 -3.85128 -4.49884 -4.81171 0.0482529 0.770456 1.75045 1.18445 0.155414 0.48385 -0.951932 -0.569748\n",
      "  31 1.192e-02 5.990e-02 5.760e-05 -- 7.469e+02 -- -0.660494 -1.20797 -2.49717 -3.05021 -3.85235 -3.85102 -4.49839 -4.81176 0.0482126 0.770461 1.75114 1.18511 0.152299 0.483729 -0.950569 -0.563021\n",
      "********************\n",
      "-0.660494 -1.20797 -2.49717 -3.05021 -3.85235 -3.85102 -4.49839 -4.81176 0.0482126 0.770461 1.75114 1.18511 0.152299 0.483729 -0.950569 -0.563021\n",
      "0.015038 0.00527965 0.0219253 0.14394 0.404753 0.263604 0.317912 0.460201 0.139209 0.0769038 0.175352 0.449778 0.985963 0.654083 0.765981 0.99603\n",
      "0.0218905 0.0599 -0.0352934 0.00081409 0.00482364 -0.00014268 0.0010011 -0.00637734 0.00136757 0.00106954 -0.00732663 -0.00116 -0.00186662 -0.000327004 -0.000366653 0.00318593\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",
      "+++ 7.469e+02 7.467e+02 -6.605e-01 -6.530e-01 0.383 +++\n",
      "+++ 7.469e+02 7.463e+02 -6.605e-01 -6.492e-01 1.15 +++\n",
      "+++ 7.469e+02 7.466e+02 -6.605e-01 -6.511e-01 0.685 +++\n",
      "+++ 7.469e+02 7.465e+02 -6.605e-01 -6.502e-01 0.892 +++\n",
      "+++ 7.469e+02 7.464e+02 -6.605e-01 -6.497e-01 1.01 +++\n",
      "+++ 7.469e+02 7.464e+02 -6.605e-01 -6.499e-01 0.95 +++\n",
      "+++ 7.469e+02 7.464e+02 -6.605e-01 -6.498e-01 0.981 +++\n",
      "+++ 7.469e+02 7.464e+02 -6.605e-01 -6.497e-01 0.997 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 7.469e+02 7.467e+02 -1.208e+00 -1.205e+00 0.378 +++\n",
      "+++ 7.469e+02 7.464e+02 -1.208e+00 -1.204e+00 1.1 +++\n",
      "+++ 7.469e+02 7.466e+02 -1.208e+00 -1.205e+00 0.668 +++\n",
      "+++ 7.469e+02 7.465e+02 -1.208e+00 -1.204e+00 0.864 +++\n",
      "+++ 7.469e+02 7.464e+02 -1.208e+00 -1.204e+00 0.977 +++\n",
      "+++ 7.469e+02 7.464e+02 -1.208e+00 -1.204e+00 1.04 +++\n",
      "+++ 7.469e+02 7.464e+02 -1.208e+00 -1.204e+00 1.01 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 7.469e+02 7.467e+02 -2.497e+00 -2.486e+00 0.424 +++\n",
      "+++ 7.469e+02 7.461e+02 -2.497e+00 -2.481e+00 1.65 +++\n",
      "+++ 7.469e+02 7.465e+02 -2.497e+00 -2.483e+00 0.847 +++\n",
      "+++ 7.469e+02 7.463e+02 -2.497e+00 -2.482e+00 1.18 +++\n",
      "+++ 7.469e+02 7.464e+02 -2.497e+00 -2.483e+00   1 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 7.469e+02 7.467e+02 -3.050e+00 -2.978e+00 0.391 +++\n",
      "+++ 7.469e+02 7.463e+02 -3.050e+00 -2.942e+00 1.19 +++\n",
      "+++ 7.469e+02 7.466e+02 -3.050e+00 -2.960e+00 0.703 +++\n",
      "+++ 7.469e+02 7.464e+02 -3.050e+00 -2.951e+00 0.918 +++\n",
      "+++ 7.469e+02 7.464e+02 -3.050e+00 -2.947e+00 1.04 +++\n",
      "+++ 7.469e+02 7.464e+02 -3.050e+00 -2.949e+00 0.98 +++\n",
      "+++ 7.469e+02 7.464e+02 -3.050e+00 -2.948e+00 1.01 +++\n",
      "+++ 7.469e+02 7.464e+02 -3.050e+00 -2.948e+00 0.996 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 7.469e+02 7.468e+02 -3.852e+00 -3.650e+00 0.275 +++\n",
      "+++ 7.469e+02 7.465e+02 -3.852e+00 -3.549e+00 0.847 +++\n",
      "+++ 7.469e+02 7.462e+02 -3.852e+00 -3.498e+00 1.35 +++\n",
      "+++ 7.469e+02 7.464e+02 -3.852e+00 -3.523e+00 1.07 +++\n",
      "+++ 7.469e+02 7.464e+02 -3.852e+00 -3.536e+00 0.948 +++\n",
      "+++ 7.469e+02 7.464e+02 -3.852e+00 -3.530e+00 1.01 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 7.469e+02 7.467e+02 -3.851e+00 -3.719e+00 0.406 +++\n",
      "+++ 7.469e+02 7.463e+02 -3.851e+00 -3.653e+00 1.18 +++\n",
      "+++ 7.469e+02 7.466e+02 -3.851e+00 -3.686e+00 0.714 +++\n",
      "+++ 7.469e+02 7.464e+02 -3.851e+00 -3.670e+00 0.922 +++\n",
      "+++ 7.469e+02 7.464e+02 -3.851e+00 -3.662e+00 1.04 +++\n",
      "+++ 7.469e+02 7.464e+02 -3.851e+00 -3.666e+00 0.981 +++\n",
      "+++ 7.469e+02 7.464e+02 -3.851e+00 -3.664e+00 1.01 +++\n",
      "+++ 7.469e+02 7.464e+02 -3.851e+00 -3.665e+00 0.996 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 7.469e+02 7.467e+02 -4.498e+00 -4.339e+00 0.402 +++\n",
      "+++ 7.469e+02 7.463e+02 -4.498e+00 -4.260e+00 1.2 +++\n",
      "+++ 7.469e+02 7.465e+02 -4.498e+00 -4.300e+00 0.719 +++\n",
      "+++ 7.469e+02 7.464e+02 -4.498e+00 -4.280e+00 0.934 +++\n",
      "+++ 7.469e+02 7.464e+02 -4.498e+00 -4.270e+00 1.06 +++\n",
      "+++ 7.469e+02 7.464e+02 -4.498e+00 -4.275e+00 0.995 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 7.469e+02 7.466e+02 -4.813e+00 -4.582e+00 0.567 +++\n",
      "+++ 7.469e+02 7.458e+02 -4.813e+00 -4.466e+00 2.24 +++\n",
      "+++ 7.469e+02 7.463e+02 -4.813e+00 -4.524e+00 1.15 +++\n",
      "+++ 7.469e+02 7.465e+02 -4.813e+00 -4.553e+00 0.813 +++\n",
      "+++ 7.469e+02 7.464e+02 -4.813e+00 -4.539e+00 0.968 +++\n",
      "+++ 7.469e+02 7.464e+02 -4.813e+00 -4.531e+00 1.05 +++\n",
      "+++ 7.469e+02 7.464e+02 -4.813e+00 -4.535e+00 1.01 +++\n",
      "+++ 7.469e+02 7.464e+02 -4.813e+00 -4.537e+00 0.989 +++\n",
      "+++ 7.469e+02 7.464e+02 -4.813e+00 -4.536e+00   1 +++\n",
      "\t### errors for param 8 ###\n",
      "+++ 7.469e+02 7.468e+02 4.824e-02 1.178e-01 0.265 +++\n",
      "+++ 7.469e+02 7.466e+02 4.824e-02 1.526e-01 0.589 +++\n",
      "+++ 7.469e+02 7.465e+02 4.824e-02 1.700e-01 0.794 +++\n",
      "+++ 7.469e+02 7.465e+02 4.824e-02 1.787e-01 0.907 +++\n",
      "+++ 7.469e+02 7.464e+02 4.824e-02 1.831e-01 0.963 +++\n",
      "+++ 7.469e+02 7.464e+02 4.824e-02 1.853e-01 0.993 +++\n",
      "\t### errors for param 9 ###\n",
      "+++ 7.469e+02 7.464e+02 7.705e-01 8.474e-01 1.02 +++\n",
      "+++ 7.469e+02 7.468e+02 7.705e-01 8.089e-01 0.265 +++\n",
      "+++ 7.469e+02 7.466e+02 7.705e-01 8.281e-01 0.586 +++\n",
      "+++ 7.469e+02 7.465e+02 7.705e-01 8.378e-01 0.788 +++\n",
      "+++ 7.469e+02 7.465e+02 7.705e-01 8.426e-01 0.899 +++\n",
      "+++ 7.469e+02 7.464e+02 7.705e-01 8.450e-01 0.957 +++\n",
      "+++ 7.469e+02 7.464e+02 7.705e-01 8.462e-01 0.986 +++\n",
      "+++ 7.469e+02 7.464e+02 7.705e-01 8.468e-01   1 +++\n",
      "\t### errors for param 10 ###\n",
      "+++ 7.469e+02 7.464e+02 1.751e+00 1.926e+00 0.955 +++\n",
      "+++ 7.469e+02 7.460e+02 1.751e+00 2.014e+00 1.89 +++\n",
      "+++ 7.469e+02 7.462e+02 1.751e+00 1.970e+00 1.4 +++\n",
      "+++ 7.469e+02 7.463e+02 1.751e+00 1.948e+00 1.17 +++\n",
      "+++ 7.469e+02 7.464e+02 1.751e+00 1.937e+00 1.06 +++\n",
      "+++ 7.469e+02 7.464e+02 1.751e+00 1.932e+00 1.01 +++\n",
      "\t### errors for param 11 ###\n",
      "+++ 7.469e+02 7.465e+02 1.185e+00 1.635e+00 0.84 +++\n",
      "+++ 7.469e+02 7.461e+02 1.185e+00 1.860e+00 1.62 +++\n",
      "+++ 7.469e+02 7.463e+02 1.185e+00 1.747e+00 1.22 +++\n",
      "+++ 7.469e+02 7.464e+02 1.185e+00 1.691e+00 1.03 +++\n",
      "+++ 7.469e+02 7.464e+02 1.185e+00 1.663e+00 0.932 +++\n",
      "+++ 7.469e+02 7.464e+02 1.185e+00 1.677e+00 0.979 +++\n",
      "+++ 7.469e+02 7.464e+02 1.185e+00 1.684e+00   1 +++\n",
      "\t### errors for param 12 ###\n",
      "\t### errors for param 13 ###\n",
      "+++ 7.469e+02 7.465e+02 4.836e-01 1.138e+00 0.903 +++\n",
      "+++ 7.469e+02 7.461e+02 4.836e-01 1.465e+00 1.67 +++\n",
      "+++ 7.469e+02 7.463e+02 4.836e-01 1.301e+00 1.29 +++\n",
      "+++ 7.469e+02 7.464e+02 4.836e-01 1.219e+00 1.1 +++\n",
      "+++ 7.469e+02 7.464e+02 4.836e-01 1.179e+00 0.999 +++\n",
      "\t### errors for param 14 ###\n",
      "+++ 7.469e+02 7.465e+02 -9.510e-01 -1.852e-01 0.856 +++\n",
      "+++ 7.469e+02 7.462e+02 -9.510e-01 1.976e-01 1.43 +++\n",
      "+++ 7.469e+02 7.463e+02 -9.510e-01 6.198e-03 1.17 +++\n",
      "+++ 7.469e+02 7.464e+02 -9.510e-01 -8.952e-02 1.02 +++\n",
      "+++ 7.469e+02 7.464e+02 -9.510e-01 -1.374e-01 0.937 +++\n",
      "+++ 7.469e+02 7.464e+02 -9.510e-01 -1.134e-01 0.978 +++\n",
      "+++ 7.469e+02 7.464e+02 -9.510e-01 -1.015e-01 0.998 +++\n",
      "\t### errors for param 15 ###\n",
      "********************\n",
      "-0.66049 -1.20797 -2.49719 -3.05019 -3.85156 -3.85104 -4.49824 -4.81261 0.048236 0.77047 1.75092 1.18493 0.150483 0.48362 -0.95096 -0.560876\n",
      "0.0107478 0.0038354 0.014397 0.101761 0.321854 0.186386 0.223452 0.276776 0.137021 0.0762971 0.180879 0.498941 2 0.694997 0.849478 4.9864\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": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.79565312,  4.2489229 ,  5.00370082,  2.18467546,  0.17899829,\n",
       "        0.37113758, -0.47082688, -0.17915708])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5265077890>"
      ]
     },
     "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": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5261e9ce90>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jU+nu4KwFuJTQJfxBhA7MjomeqwM+AZwL7AD8ELg65rElqSi9K+6NGzeOGTNm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\nSctlMDAAAAAKSURBVJIkqQr9f23jV5XuCWdGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f526218fb50>"
      ]
     },
     "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": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5261ef0f90>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZGHY+lrXYOljnYEnbeGxNrAVAG+9CFK/ccAOsWgUPPeR3JCIikk5uE5svgcHA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5261f2d750>"
      ]
     },
     "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([3.93,3.93], [-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
}