phy-4660/lag/data/clag_analysis-7bins-3465A.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": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfac014a10>"
      ]
     },
     "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",
    "ref_file=\"lc/1367A.lc\"\n",
    "echo_file=\"lc/3465A.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])"
      ]
     },
     "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.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n",
    "                0.25819945, 0.40020915])\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 4.337e-01 6.112e+01 inf -- -4.041e+02 -- 1 1 1 1 1 1 1\n",
      "   2 7.647e-01 6.015e+01 6.901e+01 -- -3.351e+02 -- 0.65784 0.58285 0.5699 0.567505 0.56704 0.566344 0.573791\n",
      "   3 3.242e+00 5.939e+01 6.602e+01 -- -2.691e+02 -- 0.42512 0.198095 0.146253 0.137492 0.135449 0.133237 0.150987\n",
      "   4 1.563e+00 5.894e+01 6.233e+01 -- -2.067e+02 -- 0.328446 -0.100568 -0.261985 -0.286046 -0.293214 -0.298752 -0.270142\n",
      "   5 6.151e-01 5.858e+01 5.845e+01 -- -1.483e+02 -- 0.300114 -0.231637 -0.637422 -0.692493 -0.715789 -0.728243 -0.692465\n",
      "   6 3.834e-01 5.758e+01 5.397e+01 -- -9.432e+01 -- 0.288119 -0.218711 -0.948517 -1.05695 -1.12601 -1.152 -1.11841\n",
      "   7 2.764e-01 5.488e+01 4.687e+01 -- -4.745e+01 -- 0.288807 -0.20359 -1.129 -1.33421 -1.50858 -1.56203 -1.54725\n",
      "   8 2.123e-01 4.884e+01 3.703e+01 -- -1.042e+01 -- 0.290741 -0.199424 -1.16697 -1.47951 -1.82761 -1.93862 -1.97495\n",
      "   9 1.660e-01 3.761e+01 2.500e+01 -- 1.458e+01 -- 0.297078 -0.192793 -1.17471 -1.51254 -2.03046 -2.24008 -2.39415\n",
      "  10 1.251e-01 2.218e+01 1.352e+01 -- 2.810e+01 -- 0.304071 -0.185609 -1.18142 -1.51049 -2.10848 -2.41366 -2.79148\n",
      "  11 8.256e-02 9.018e+00 5.492e+00 -- 3.359e+01 -- 0.305677 -0.180534 -1.18507 -1.51162 -2.12584 -2.46833 -3.14066\n",
      "  12 4.067e-02 2.560e+00 1.439e+00 -- 3.503e+01 -- 0.30378 -0.178308 -1.18784 -1.51647 -2.12974 -2.48272 -3.39994\n",
      "  13 1.216e-02 5.340e-01 2.063e-01 -- 3.524e+01 -- 0.301773 -0.177939 -1.18966 -1.52028 -2.13067 -2.48953 -3.5382\n",
      "  14 2.104e-03 8.200e-02 1.337e-02 -- 3.525e+01 -- 0.30075 -0.178052 -1.19052 -1.52208 -2.13075 -2.49214 -3.58123\n",
      "  15 2.833e-04 1.082e-02 3.730e-04 -- 3.525e+01 -- 0.300445 -0.178142 -1.19077 -1.52264 -2.13068 -2.4928 -3.58876\n",
      "  16 3.684e-05 1.402e-03 6.992e-06 -- 3.525e+01 -- 0.300387 -0.178169 -1.19082 -1.52276 -2.13064 -2.4929 -3.58978\n",
      "********************\n",
      "0.300387 -0.178169 -1.19082 -1.52276 -2.13064 -2.4929 -3.58978\n",
      "0.23893 0.202426 0.232625 0.177239 0.153017 0.132987 0.308424\n",
      "-0.000180614 -0.000143998 -0.000174906 -0.000705814 0.000447101 -0.000873593 -0.00140183\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 3.481e+01 3.004e-01 5.393e-01 0.89 +++\n",
      "+++ 3.525e+01 3.432e+01 3.004e-01 6.588e-01 1.87 +++\n",
      "+++ 3.525e+01 3.458e+01 3.004e-01 5.990e-01 1.34 +++\n",
      "+++ 3.525e+01 3.470e+01 3.004e-01 5.692e-01 1.11 +++\n",
      "+++ 3.525e+01 3.475e+01 3.004e-01 5.543e-01 0.996 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 3.525e+01 3.476e+01 -1.782e-01 2.426e-02 0.974 +++\n",
      "+++ 3.525e+01 3.422e+01 -1.782e-01 1.255e-01 2.07 +++\n",
      "+++ 3.525e+01 3.451e+01 -1.782e-01 7.486e-02 1.48 +++\n",
      "+++ 3.525e+01 3.464e+01 -1.782e-01 4.956e-02 1.21 +++\n",
      "+++ 3.525e+01 3.471e+01 -1.782e-01 3.691e-02 1.09 +++\n",
      "+++ 3.525e+01 3.474e+01 -1.782e-01 3.058e-02 1.03 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.782e-01 2.742e-02   1 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.075e+00 0.275 +++\n",
      "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.597 +++\n",
      "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.873e-01 0.798 +++\n",
      "+++ 3.525e+01 3.480e+01 -1.191e+00 -9.727e-01 0.909 +++\n",
      "+++ 3.525e+01 3.477e+01 -1.191e+00 -9.655e-01 0.966 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.618e-01 0.995 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 3.525e+01 3.482e+01 -1.523e+00 -1.346e+00 0.861 +++\n",
      "+++ 3.525e+01 3.433e+01 -1.523e+00 -1.257e+00 1.85 +++\n",
      "+++ 3.525e+01 3.459e+01 -1.523e+00 -1.301e+00 1.32 +++\n",
      "+++ 3.525e+01 3.471e+01 -1.523e+00 -1.323e+00 1.08 +++\n",
      "+++ 3.525e+01 3.477e+01 -1.523e+00 -1.334e+00 0.967 +++\n",
      "+++ 3.525e+01 3.474e+01 -1.523e+00 -1.329e+00 1.02 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.523e+00 -1.332e+00 0.994 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 3.525e+01 3.482e+01 -2.131e+00 -1.978e+00 0.868 +++\n",
      "+++ 3.525e+01 3.430e+01 -2.131e+00 -1.901e+00 1.9 +++\n",
      "+++ 3.525e+01 3.458e+01 -2.131e+00 -1.939e+00 1.34 +++\n",
      "+++ 3.525e+01 3.471e+01 -2.131e+00 -1.958e+00 1.09 +++\n",
      "+++ 3.525e+01 3.476e+01 -2.131e+00 -1.968e+00 0.977 +++\n",
      "+++ 3.525e+01 3.473e+01 -2.131e+00 -1.963e+00 1.03 +++\n",
      "+++ 3.525e+01 3.475e+01 -2.131e+00 -1.966e+00 1.01 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 3.525e+01 3.476e+01 -2.493e+00 -2.360e+00 0.992 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 3.525e+01 3.511e+01 -3.590e+00 -3.436e+00 0.274 +++\n",
      "+++ 3.525e+01 3.491e+01 -3.590e+00 -3.358e+00 0.68 +++\n",
      "+++ 3.525e+01 3.477e+01 -3.590e+00 -3.320e+00 0.971 +++\n",
      "+++ 3.525e+01 3.468e+01 -3.590e+00 -3.301e+00 1.14 +++\n",
      "+++ 3.525e+01 3.472e+01 -3.590e+00 -3.310e+00 1.05 +++\n",
      "+++ 3.525e+01 3.475e+01 -3.590e+00 -3.315e+00 1.01 +++\n",
      "+++ 3.525e+01 3.476e+01 -3.590e+00 -3.317e+00 0.991 +++\n",
      "********************\n",
      "0.300387 -0.178169 -1.19082 -1.52276 -2.13064 -2.4929 -3.58978\n",
      "0.253863 0.205589 0.22899 0.191086 0.164971 0.132987 0.27228\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": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFrCAYAAABbtho0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nW6zUI7wEUlLHq9VqHD6
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfc553d0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xscale('log'); ylim(-4,2)\n",
    "errorbar(fqd, p1, yerr=p1e, fmt='o', ms=10)\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": "iVBORw0KGgoAAAANSUhEUgAAAg8AAAFkCAYAAACn/timAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/KPAz1GTYDewg8LCh3Q
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfa181b850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t2, l2, l2e = np.loadtxt(echo_file).T\n",
    "errorbar(t1, l1, yerr=l1e, fmt='o')\n",
    "errorbar(t2, l2, yerr=l2e, fmt='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 4.360e-01 4.662e+01 inf -- -2.840e+01 -- 1 1 1 1 1 1 1\n",
      "   2 7.723e-01 4.603e+01 5.319e+01 -- 2.478e+01 -- 0.595569 0.573902 0.564885 0.564933 0.564693 0.564817 0.564018\n",
      "   3 3.411e+00 4.536e+01 5.196e+01 -- 7.675e+01 -- 0.241284 0.160851 0.128635 0.129632 0.129254 0.129464 0.128634\n",
      "   4 1.484e+01 4.466e+01 5.009e+01 -- 1.268e+02 -- -0.00703234 -0.219623 -0.310096 -0.306017 -0.306362 -0.306197 -0.306021\n",
      "   5 5.921e-01 4.382e+01 4.797e+01 -- 1.748e+02 -- -0.111363 -0.525952 -0.75296 -0.741885 -0.742361 -0.742634 -0.74129\n",
      "   6 3.733e-01 4.246e+01 4.584e+01 -- 2.207e+02 -- -0.140827 -0.700007 -1.19878 -1.1757 -1.17861 -1.18043 -1.17869\n",
      "   7 2.744e-01 4.002e+01 4.318e+01 -- 2.638e+02 -- -0.163132 -0.756421 -1.63306 -1.59991 -1.61462 -1.6204 -1.61864\n",
      "   8 2.214e-01 3.558e+01 3.862e+01 -- 3.024e+02 -- -0.180826 -0.786047 -2.01272 -1.99632 -2.05034 -2.06438 -2.06287\n",
      "   9 1.964e-01 2.792e+01 3.104e+01 -- 3.335e+02 -- -0.198109 -0.808078 -2.25955 -2.32516 -2.48547 -2.51643 -2.51957\n",
      "  10 2.013e-01 1.674e+01 2.083e+01 -- 3.543e+02 -- -0.213273 -0.817818 -2.33481 -2.52428 -2.9116 -2.9802 -3.01438\n",
      "  11 2.834e-01 5.753e+00 1.015e+01 -- 3.645e+02 -- -0.221915 -0.819446 -2.34863 -2.58195 -3.28722 -3.43843 -3.62111\n",
      "  12 1.282e+00 5.594e-01 2.962e+00 -- 3.674e+02 -- -0.22692 -0.818447 -2.35934 -2.59 -3.51729 -3.8012 -4.6475\n",
      "  13 7.073e+02 9.360e-02 2.736e-01 -- 3.677e+02 -- -0.229484 -0.817489 -2.36471 -2.59372 -3.56061 -3.9224 -7.6475\n",
      "  14 1.523e+03 1.144e-01 1.474e-03 -- 3.677e+02 -- -0.229711 -0.817671 -2.36494 -2.59367 -3.55253 -3.91079 -8\n",
      "  15 1.523e+03 1.092e-01 3.831e-04 -- 3.677e+02 -- -0.229667 -0.817642 -2.365 -2.59369 -3.55271 -3.9141 -8\n",
      "  16 1.523e+03 1.099e-01 6.214e-05 -- 3.677e+02 -- -0.229676 -0.817649 -2.36499 -2.59368 -3.55237 -3.91373 -8\n",
      "********************\n",
      "-0.229676 -0.817649 -2.36499 -2.59368 -3.55237 -3.91373 -8\n",
      "0.26864 0.229014 0.349667 0.273159 0.62553 0.906575 7060.11\n",
      "-0.00293561 -0.00849564 -0.0205528 -0.0525552 -0.0608705 -0.109908 -0.000245353\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",
      "+++ 3.677e+02 3.672e+02 -2.297e-01 3.897e-02 0.967 +++\n",
      "+++ 3.677e+02 3.667e+02 -2.297e-01 1.733e-01 2.01 +++\n",
      "+++ 3.677e+02 3.670e+02 -2.297e-01 1.061e-01 1.45 +++\n",
      "+++ 3.677e+02 3.671e+02 -2.297e-01 7.255e-02 1.2 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.297e-01 5.576e-02 1.08 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.297e-01 4.736e-02 1.02 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.297e-01 4.316e-02 0.995 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 3.677e+02 3.672e+02 -8.176e-01 -5.886e-01 0.936 +++\n",
      "+++ 3.677e+02 3.667e+02 -8.176e-01 -4.741e-01 1.97 +++\n",
      "+++ 3.677e+02 3.670e+02 -8.176e-01 -5.314e-01 1.41 +++\n",
      "+++ 3.677e+02 3.671e+02 -8.176e-01 -5.600e-01 1.16 +++\n",
      "+++ 3.677e+02 3.672e+02 -8.176e-01 -5.743e-01 1.05 +++\n",
      "+++ 3.677e+02 3.672e+02 -8.176e-01 -5.815e-01 0.991 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 3.677e+02 3.675e+02 -2.365e+00 -2.190e+00 0.303 +++\n",
      "+++ 3.677e+02 3.674e+02 -2.365e+00 -2.103e+00 0.671 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.365e+00 -2.059e+00 0.905 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.365e+00 -2.037e+00 1.03 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.365e+00 -2.048e+00 0.968 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.365e+00 -2.043e+00   1 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 3.677e+02 3.675e+02 -2.594e+00 -2.457e+00 0.301 +++\n",
      "+++ 3.677e+02 3.674e+02 -2.594e+00 -2.389e+00 0.667 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.594e+00 -2.355e+00 0.902 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.594e+00 -2.338e+00 1.03 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.594e+00 -2.346e+00 0.966 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.594e+00 -2.342e+00 0.999 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 3.677e+02 3.675e+02 -3.552e+00 -3.240e+00 0.35 +++\n",
      "+++ 3.677e+02 3.672e+02 -3.552e+00 -3.083e+00 0.938 +++\n",
      "+++ 3.677e+02 3.670e+02 -3.552e+00 -3.005e+00 1.39 +++\n",
      "+++ 3.677e+02 3.671e+02 -3.552e+00 -3.044e+00 1.15 +++\n",
      "+++ 3.677e+02 3.672e+02 -3.552e+00 -3.064e+00 1.04 +++\n",
      "+++ 3.677e+02 3.672e+02 -3.552e+00 -3.073e+00 0.988 +++\n",
      "+++ 3.677e+02 3.672e+02 -3.552e+00 -3.069e+00 1.01 +++\n",
      "+++ 3.677e+02 3.672e+02 -3.552e+00 -3.071e+00   1 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 3.677e+02 3.673e+02 -3.914e+00 -3.460e+00 0.7 +++\n",
      "+++ 3.677e+02 3.667e+02 -3.914e+00 -3.234e+00 2.01 +++\n",
      "+++ 3.677e+02 3.671e+02 -3.914e+00 -3.347e+00 1.23 +++\n",
      "+++ 3.677e+02 3.672e+02 -3.914e+00 -3.404e+00 0.939 +++\n",
      "+++ 3.677e+02 3.672e+02 -3.914e+00 -3.375e+00 1.08 +++\n",
      "+++ 3.677e+02 3.672e+02 -3.914e+00 -3.390e+00 1.01 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 3.677e+02 3.677e+02 -8.000e+00 -6.000e+00 0.0117 +++\n",
      "+++ 3.677e+02 3.676e+02 -8.000e+00 -5.000e+00 0.203 +++\n",
      "+++ 3.677e+02 3.674e+02 -8.000e+00 -4.500e+00 0.665 +++\n",
      "+++ 3.677e+02 3.671e+02 -8.000e+00 -4.250e+00 1.19 +++\n",
      "+++ 3.677e+02 3.672e+02 -8.000e+00 -4.375e+00 0.891 +++\n",
      "+++ 3.677e+02 3.672e+02 -8.000e+00 -4.312e+00 1.03 +++\n",
      "+++ 3.677e+02 3.672e+02 -8.000e+00 -4.344e+00 0.958 +++\n",
      "+++ 3.677e+02 3.672e+02 -8.000e+00 -4.328e+00 0.994 +++\n",
      "********************\n",
      "-0.229674 -0.817648 -2.365 -2.59368 -3.55236 -3.91386 -8\n",
      "0.272837 0.236171 0.32235 0.251819 0.481357 0.524238 3.67188\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": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFrCAYAAABbtho0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\netigILUhLzdIbah3Y28
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfa18073d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xscale('log'); ylim(-6,2)\n",
    "errorbar(fqd, p1, yerr=p1e, fmt='o', ms=10)\n",
    "errorbar(fqd, p2, yerr=p2e, fmt='o', ms=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 6.628e+02 9.007e+00 inf -- 4.160e+02 -- -0.264643 -0.797908 -2.07791 -2.35822 -3.1415 -3.50338 -6.09489 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
      "   3 6.842e+02 1.078e+01 2.116e+00 -- 4.181e+02 -- -0.226169 -0.759199 -2.03908 -2.32032 -3.11387 -3.43598 -6.39489 0.0502949 0.152916 0.212494 0.121125 0.216976 0.336684 -0.244596\n",
      "   5 1.559e+02 1.270e+01 1.915e+00 -- 4.201e+02 -- -0.193741 -0.726441 -2.00316 -2.28893 -3.08464 -3.36731 -6.69489 0.0145685 0.191324 0.293499 0.136888 0.312024 0.482796 1.86942\n",
      "   7 1.618e+03 1.477e+01 1.743e+00 -- 4.218e+02 -- -0.166298 -0.698635 -1.97153 -2.26256 -3.05584 -3.30885 -6.99489 -0.0118999 0.220073 0.352843 0.149084 0.389017 0.574531 -0.403772\n",
      "   9 2.935e+02 1.701e+01 1.600e+00 -- 4.234e+02 -- -0.142929 -0.674898 -1.94419 -2.24018 -3.02852 -3.26097 -7.29489 -0.0319925 0.242138 0.397314 0.158774 0.451737 0.636061 -2.89416\n",
      "  11 9.523e+02 1.944e+01 1.478e+00 -- 4.249e+02 -- -0.122901 -0.654512 -1.92069 -2.22102 -3.00314 -3.22181 -6.99489 -0.0475429 0.25942 0.431356 0.166636 0.503311 0.679979 0.381182\n",
      "  13 4.366e+02 2.207e+01 1.373e+00 -- 4.262e+02 -- -0.105633 -0.636902 -1.9005 -2.20452 -2.97982 -3.18951 -7.29489 -0.0597637 0.273183 0.4579 0.173119 0.546186 0.712957 1.78135\n",
      "  15 1.284e+02 2.490e+01 1.279e+00 -- 4.275e+02 -- -0.0906648 -0.62161 -1.8831 -2.19023 -2.95852 -3.16261 -7.59489 -0.0694848 0.284294 0.478919 0.178532 0.582205 0.73873 -2.12851\n",
      "  17 6.684e+01 2.796e+01 1.196e+00 -- 4.287e+02 -- -0.0776289 -0.608271 -1.86808 -2.17781 -2.93913 -3.14003 -7.29489 -0.0772912 0.293368 0.495775 0.183096 0.612767 0.759519 -2.12851\n",
      "  19 1.131e+03 3.125e+01 1.119e+00 -- 4.298e+02 -- -0.0662284 -0.596588 -1.85505 -2.16698 -2.92148 -3.12092 -6.99489 -0.0836055 0.300851 0.509435 0.186975 0.638935 0.776775 -0.468363\n",
      "  21 7.866e+01 3.476e+01 1.049e+00 -- 4.309e+02 -- -0.0562219 -0.586319 -1.84372 -2.1575 -2.90544 -3.10467 -6.69489 -0.0887399 0.307075 0.5206 0.190292 0.661523 0.791406 3.12663\n",
      "  23 4.749e+01 3.851e+01 9.840e-01 -- 4.319e+02 -- -0.0474108 -0.577266 -1.83385 -2.14919 -2.89085 -3.09077 -6.39489 -0.0929293 0.312289 0.529789 0.19314 0.681153 0.80404 2.58773\n",
      "  25 9.016e+01 4.250e+01 9.239e-01 -- 4.328e+02 -- -0.0396304 -0.569264 -1.82521 -2.14191 -2.87758 -3.07884 -6.69489 -0.0963531 0.316687 0.537395 0.195593 0.698325 0.815127 2.31075\n",
      "  26 2.291e+03 4.856e+03 4.196e+00 -- 4.370e+02 -- 0.0292432 -0.498358 -1.74955 -2.0779 -2.75662 -2.9761 -8 -0.124329 0.354004 0.600746 0.216726 0.849429 0.913805 -2.9851\n",
      "  27 3.278e+00 8.497e+01 6.218e+00 -- 4.432e+02 -- 0.0254995 -0.502475 -1.77412 -2.11199 -2.72719 -3.04402 -5 -0.0720726 0.308662 0.702517 0.165496 0.94859 0.890579 1.11967\n",
      "  28 3.369e+02 3.689e+01 7.032e-01 -- 4.439e+02 -- 0.02803 -0.501445 -1.76227 -2.08919 -2.72868 -3.00045 -7.18712 -0.103292 0.35578 0.648429 0.1705 0.951458 0.974389 -2.55109\n",
      "  29 1.859e-01 1.619e+01 2.026e-01 -- 4.437e+02 -- 0.0284028 -0.501795 -1.76339 -2.09611 -2.722 -3.01268 -4.18712 -0.0900085 0.341162 0.653855 0.173506 0.94415 0.962545 2.3652\n",
      "  30 9.710e-01 1.003e+01 2.272e-01 -- 4.439e+02 -- 0.0287103 -0.501625 -1.76349 -2.09457 -2.72212 -3.0105 -4.78581 -0.0929545 0.347387 0.644721 0.172745 0.942888 0.964175 2.80497\n",
      "  31 3.231e+01 4.382e+00 4.330e-02 -- 4.440e+02 -- 0.0288105 -0.50173 -1.76305 -2.0945 -2.72206 -3.00972 -5.95917 -0.0912361 0.344716 0.65491 0.172811 0.94407 0.967832 -0.754455\n",
      "  33 7.887e+00 3.999e+00 5.730e-03 -- 4.440e+02 -- 0.0288182 -0.501725 -1.76307 -2.09454 -2.72196 -3.00967 -5.65917 -0.0912811 0.344831 0.654757 0.172631 0.944361 0.968167 3.09091\n",
      "  34 1.021e+03 1.567e+00 3.306e-03 -- 4.440e+02 -- 0.0288906 -0.501682 -1.76326 -2.09497 -2.72112 -3.00942 -8 -0.0916682 0.345845 0.653313 0.170964 0.94684 0.970952 2.33492\n",
      "  35 1.958e+03 8.857e-01 2.455e-04 -- 4.440e+02 -- 0.0289281 -0.501698 -1.76317 -2.09482 -2.72066 -3.00928 -8 -0.0913907 0.345397 0.655119 0.171217 0.948376 0.971555 -1.95846\n",
      "  36 2.231e+01 1.235e+00 1.134e-03 -- 4.440e+02 -- 0.0289502 -0.501688 -1.76324 -2.09495 -2.7204 -3.00936 -5 -0.0914472 0.345652 0.654699 0.170847 0.949278 0.972212 0.258881\n",
      "  37 8.016e+00 5.056e-01 1.360e-02 -- 4.440e+02 -- 0.0289689 -0.501701 -1.76321 -2.09488 -2.71981 -3.00893 -5.06546 -0.0913367 0.345498 0.655382 0.171555 0.950824 0.972854 0.766704\n",
      "  38 1.884e+02 2.121e-01 1.200e-02 -- 4.440e+02 -- 0.0289819 -0.501697 -1.76328 -2.09496 -2.71958 -3.00916 -6.54275 -0.0913617 0.345681 0.654623 0.17157 0.951297 0.972966 0.903638\n",
      "  39 1.947e+04 3.463e-01 5.080e-04 -- 4.440e+02 -- 0.0289824 -0.501696 -1.76321 -2.0949 -2.71995 -3.00932 -8 -0.0913416 0.345612 0.655284 0.170969 0.950797 0.973096 0.295641\n",
      "  40 3.049e+03 1.048e-01 3.268e-05 -- 4.440e+02 -- 0.0289805 -0.501691 -1.76324 -2.09495 -2.72006 -3.00933 -8 -0.0913641 0.345627 0.655123 0.170779 0.950573 0.972811 -0.79378\n",
      "********************\n",
      "0.0289805 -0.501691 -1.76324 -2.09495 -2.72006 -3.00933 -8 -0.0913641 0.345627 0.655123 0.170779 0.950573 0.972811 -0.79378\n",
      "0.00488089 0.00320555 0.0166349 0.0498124 0.0675131 0.0930602 5801.93 0.082796 0.0625792 0.157936 0.242192 0.285454 0.316561 13418.5\n",
      "-0.0761655 -0.104783 0.024591 0.0175272 0.0313802 0.0122834 6.72756e-05 0.000572629 -0.00671213 0.00501444 0.000107792 -0.0065959 -0.0147919 -1.32979e-05\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": "stdout",
     "output_type": "stream",
     "text": [
      "\t### errors for param 0 ###\n",
      "+++ 4.441e+02 4.440e+02 2.894e-02 3.139e-02 0.184 +++\n",
      "+++ 4.441e+02 4.439e+02 2.894e-02 3.262e-02 0.538 +++\n",
      "+++ 4.441e+02 4.437e+02 2.894e-02 3.323e-02 0.868 +++\n",
      "+++ 4.441e+02 4.436e+02 2.894e-02 3.354e-02 1.1 +++\n",
      "+++ 4.441e+02 4.436e+02 2.894e-02 3.339e-02 0.977 +++\n",
      "+++ 4.441e+02 4.436e+02 2.894e-02 3.346e-02 1.04 +++\n",
      "+++ 4.441e+02 4.436e+02 2.894e-02 3.342e-02 1.01 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 4.441e+02 4.440e+02 -5.016e-01 -5.000e-01 0.272 +++\n",
      "+++ 4.441e+02 4.437e+02 -5.016e-01 -4.993e-01 0.765 +++\n",
      "+++ 4.441e+02 4.435e+02 -5.016e-01 -4.989e-01 1.18 +++\n",
      "+++ 4.441e+02 4.437e+02 -5.016e-01 -4.991e-01 0.954 +++\n",
      "+++ 4.441e+02 4.436e+02 -5.016e-01 -4.990e-01 1.06 +++\n",
      "+++ 4.441e+02 4.436e+02 -5.016e-01 -4.990e-01 1.01 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 4.441e+02 4.439e+02 -1.763e+00 -1.754e+00 0.496 +++\n",
      "+++ 4.441e+02 4.433e+02 -1.763e+00 -1.750e+00 1.76 +++\n",
      "+++ 4.441e+02 4.437e+02 -1.763e+00 -1.752e+00 0.96 +++\n",
      "+++ 4.441e+02 4.435e+02 -1.763e+00 -1.751e+00 1.3 +++\n",
      "+++ 4.441e+02 4.436e+02 -1.763e+00 -1.752e+00 1.12 +++\n",
      "+++ 4.441e+02 4.436e+02 -1.763e+00 -1.752e+00 1.03 +++\n",
      "+++ 4.441e+02 4.436e+02 -1.763e+00 -1.752e+00 0.994 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 4.441e+02 4.439e+02 -2.096e+00 -2.071e+00 0.418 +++\n",
      "+++ 4.441e+02 4.435e+02 -2.096e+00 -2.058e+00 1.22 +++\n",
      "+++ 4.441e+02 4.438e+02 -2.096e+00 -2.064e+00 0.741 +++\n",
      "+++ 4.441e+02 4.437e+02 -2.096e+00 -2.061e+00 0.959 +++\n",
      "+++ 4.441e+02 4.436e+02 -2.096e+00 -2.060e+00 1.08 +++\n",
      "+++ 4.441e+02 4.436e+02 -2.096e+00 -2.060e+00 1.02 +++\n",
      "+++ 4.441e+02 4.436e+02 -2.096e+00 -2.061e+00 0.989 +++\n",
      "+++ 4.441e+02 4.436e+02 -2.096e+00 -2.061e+00   1 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 4.441e+02 4.440e+02 -2.720e+00 -2.686e+00 0.217 +++\n",
      "+++ 4.441e+02 4.438e+02 -2.720e+00 -2.670e+00 0.62 +++\n",
      "+++ 4.441e+02 4.436e+02 -2.720e+00 -2.661e+00 0.996 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 4.441e+02 4.439e+02 -3.008e+00 -2.962e+00 0.426 +++\n",
      "+++ 4.441e+02 4.435e+02 -3.008e+00 -2.939e+00 1.27 +++\n",
      "+++ 4.441e+02 4.438e+02 -3.008e+00 -2.951e+00 0.758 +++\n",
      "+++ 4.441e+02 4.436e+02 -3.008e+00 -2.945e+00 0.986 +++\n",
      "+++ 4.441e+02 4.436e+02 -3.008e+00 -2.942e+00 1.12 +++\n",
      "+++ 4.441e+02 4.436e+02 -3.008e+00 -2.944e+00 1.05 +++\n",
      "+++ 4.441e+02 4.436e+02 -3.008e+00 -2.944e+00 1.02 +++\n",
      "+++ 4.441e+02 4.436e+02 -3.008e+00 -2.945e+00   1 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 4.441e+02 4.440e+02 -4.031e+00 -3.728e+00 0.351 +++\n",
      "+++ 4.441e+02 4.435e+02 -4.031e+00 -3.577e+00 1.31 +++\n",
      "+++ 4.441e+02 4.438e+02 -4.031e+00 -3.653e+00 0.699 +++\n",
      "+++ 4.441e+02 4.437e+02 -4.031e+00 -3.615e+00 0.961 +++\n",
      "+++ 4.441e+02 4.436e+02 -4.031e+00 -3.596e+00 1.12 +++\n",
      "+++ 4.441e+02 4.436e+02 -4.031e+00 -3.605e+00 1.04 +++\n",
      "+++ 4.441e+02 4.436e+02 -4.031e+00 -3.610e+00 0.999 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 4.441e+02 4.440e+02 -9.183e-02 -5.033e-02 0.277 +++\n",
      "+++ 4.441e+02 4.438e+02 -9.183e-02 -2.959e-02 0.608 +++\n",
      "+++ 4.441e+02 4.437e+02 -9.183e-02 -1.921e-02 0.805 +++\n",
      "+++ 4.441e+02 4.437e+02 -9.183e-02 -1.403e-02 0.914 +++\n",
      "+++ 4.441e+02 4.436e+02 -9.183e-02 -1.143e-02 0.97 +++\n",
      "+++ 4.441e+02 4.436e+02 -9.183e-02 -1.014e-02 0.998 +++\n",
      "\t### errors for param 8 ###\n",
      "+++ 4.441e+02 4.440e+02 3.447e-01 3.758e-01 0.288 +++\n",
      "+++ 4.441e+02 4.438e+02 3.447e-01 3.913e-01 0.618 +++\n",
      "+++ 4.441e+02 4.437e+02 3.447e-01 3.991e-01 0.821 +++\n",
      "+++ 4.441e+02 4.437e+02 3.447e-01 4.030e-01 0.931 +++\n",
      "+++ 4.441e+02 4.436e+02 3.447e-01 4.049e-01 0.987 +++\n",
      "+++ 4.441e+02 4.436e+02 3.447e-01 4.059e-01 1.02 +++\n",
      "+++ 4.441e+02 4.436e+02 3.447e-01 4.054e-01   1 +++\n",
      "\t### errors for param 9 ###\n",
      "+++ 4.441e+02 4.438e+02 6.661e-01 8.218e-01 0.763 +++\n",
      "+++ 4.441e+02 4.434e+02 6.661e-01 8.997e-01 1.49 +++\n",
      "+++ 4.441e+02 4.436e+02 6.661e-01 8.607e-01 1.11 +++\n",
      "+++ 4.441e+02 4.437e+02 6.661e-01 8.413e-01 0.932 +++\n",
      "+++ 4.441e+02 4.436e+02 6.661e-01 8.510e-01 1.02 +++\n",
      "+++ 4.441e+02 4.436e+02 6.661e-01 8.461e-01 0.976 +++\n",
      "+++ 4.441e+02 4.436e+02 6.661e-01 8.486e-01 0.999 +++\n",
      "\t### errors for param 10 ###\n",
      "+++ 4.441e+02 4.437e+02 1.618e-01 4.046e-01 0.907 +++\n",
      "+++ 4.441e+02 4.432e+02 1.618e-01 5.261e-01 1.91 +++\n",
      "+++ 4.441e+02 4.434e+02 1.618e-01 4.654e-01 1.38 +++\n",
      "+++ 4.441e+02 4.436e+02 1.618e-01 4.350e-01 1.13 +++\n",
      "+++ 4.441e+02 4.436e+02 1.618e-01 4.198e-01 1.02 +++\n",
      "+++ 4.441e+02 4.437e+02 1.618e-01 4.122e-01 0.962 +++\n",
      "+++ 4.441e+02 4.436e+02 1.618e-01 4.160e-01 0.99 +++\n",
      "+++ 4.441e+02 4.436e+02 1.618e-01 4.179e-01   1 +++\n",
      "\t### errors for param 11 ###\n",
      "+++ 4.441e+02 4.438e+02 9.572e-01 1.242e+00 0.573 +++\n",
      "+++ 4.441e+02 4.435e+02 9.572e-01 1.384e+00 1.26 +++\n",
      "+++ 4.441e+02 4.437e+02 9.572e-01 1.313e+00 0.888 +++\n",
      "+++ 4.441e+02 4.436e+02 9.572e-01 1.348e+00 1.07 +++\n",
      "+++ 4.441e+02 4.436e+02 9.572e-01 1.330e+00 0.976 +++\n",
      "+++ 4.441e+02 4.436e+02 9.572e-01 1.339e+00 1.02 +++\n",
      "+++ 4.441e+02 4.436e+02 9.572e-01 1.335e+00 0.999 +++\n",
      "\t### errors for param 12 ###\n",
      "+++ 4.441e+02 4.437e+02 9.835e-01 1.298e+00 0.846 +++\n",
      "+++ 4.441e+02 4.432e+02 9.835e-01 1.456e+00 1.85 +++\n",
      "+++ 4.441e+02 4.435e+02 9.835e-01 1.377e+00 1.3 +++\n",
      "+++ 4.441e+02 4.436e+02 9.835e-01 1.338e+00 1.06 +++\n",
      "+++ 4.441e+02 4.437e+02 9.835e-01 1.318e+00 0.952 +++\n",
      "+++ 4.441e+02 4.436e+02 9.835e-01 1.328e+00 1.01 +++\n",
      "\t### errors for param 13 ###\n",
      "********************\n",
      "0.028939 -0.501626 -1.76254 -2.09557 -2.71971 -3.00832 -4.0306 -0.0918281 0.344656 0.666137 0.16175 0.957202 0.983537 -1.17645\n",
      "0.0044848 0.00262318 0.0102132 0.0350358 0.0585583 0.0635997 0.42048 0.0816919 0.0607682 0.182442 0.256176 0.377633 0.344174 7.10562\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "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
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAFrCAYAAACE+GArAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nr9r4lST/O9X1Ix5MckO
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfa18251d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "xscale('log'); ylim(-10,10)\n",
    "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdf9ea293d0>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGYCAYAAACQz+KaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzt3XmYHHWd+PH35IBwJgxHuANRowHUOKOIskpEV8X7iK6j\nrMKIP93VXaNj3OyCgit4ZUfjqrArMiKgg2YFZUXx4lpRATOggEGUhCCEewinISQzvz8+1XbPpHum\ne6p6qqf7/Xqeeqqnzm9X9VR/+nuCJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJKk5LAIu\nBtYDjwMPAL8E3pFnoiRJamUz8k7AJJsN3A58E7gT2JkIRM4FDgJOyy1lkiSppf2KyC2RJEmTbFre\nCWgQDwBb8k6EJElqHW1EEdWewD8CTwL/kGuKJElSS/kvYCiZngQ+mG9yJElqXW15JyAnBxC5InsB\nrwPeC/wb8Nky2+6TTJIkqTZ3JdOYWjUYGe104ARgP+C+kuX77Lvvvhs2bNiQT6okSZra7gSexzgB\nSas17a3kWuB9wMGMCkY2bNjAeeedx8KFC6s+2NKlS1m5cmXGSWxtzXJNG+l9THZa6nW+LI+b9lhp\n9p/Ivo30eWoGzXI9G+V9rFmzhmOPPXY/onTBYKQKLwG2AreWW7lw4UI6OjqqPticOXNq2l7ja5Zr\n2kjvY7LTUq/zZXnctMdKs/9E9m2kz1MzaJbrWe599Pf309/fD8CmTZtYv3498+bNY9asWQB0dXXR\n1dU16WktaLVg5KvAQ0ROyD3AHsBbgLcCnyOa+KoB5flPkqVGeh+TnZZ6nS/L46Y9Vpr9G+mz0aqa\n5R6Uex+lwcbAwACdnZ309/c3TPDVanVGjgOOBxYCc4BHgeuBrwHfKrN9B7B69erVNd2w173udVx0\n0UWpEytJY/FZo4koBCO1frdN9DxAJzAw1ratljNydjJJkqQGYQ+sddAsWX2SGpvPGjULg5E68AEh\naTL4rFGzMBiRJEm5MhiRJEm5MhiRJEm5MhiRJEm5MhiRJEm5MhiRJEm5MhiRJEm5MhiRJEm5MhiR\nJEm5MhiRJEm5MhiRJEm5MhiRJEm5MhiRJEm5MhiRJEm5mpF3AiSpoL+/n/7+fgA2bdrE+vXrmTdv\nHrNmzQKgq6uLrq6uPJMoqQ4MRiQ1jNJgY2BggM7OTvr7++no6Mg5ZZLqyWIaSZKUK4MRSZKUK4MR\nSZKUK4MRSZKUK4MRSZKUK1vTSBmxWaokTYzBiJQRm6VK0sRYTCNJknLVasHIS4FvALcAjwF3AN8D\n/OkqSVJOWi0YeS9wIPAF4Bjgg8BewK+Bl+SYLkmSWlar1Rn5AHDvqGWXAH8C/g24bNJTJElSi2u1\nnJHRgQhEcc0aYP9JToskSaL1gpFyZhN1Rm7KOyGSJLUigxH4CrADcFreCZEkqRW1Wp2R0T4JvJ2o\nS3JdzmmRJKkltXIwcjJwIlFx9fSxNly6dClz5swZsczeNFVJX18fp556KgBLlizhpJNOoru7O+dU\nTS1eQ2lqKe2BumDjxo05pWbqOBkYAj42znYdwPDq1auHpWqcddZZw+3t7cPAX6f29vbhs846K++k\nTRleQ6m+Vq9ePTwZ322F81BFX16tWGfkY0Qw8slkkjLT29vL4ODgiGWDg4P09vbmlKKpx2sotZ5W\nK6bpAT5B9C3yQ+CIUet/PekpUlPZsmVLTcu1La+h1HpaLRh5DZFl9MpkKjUMTJ/0FKmpzJhR/l+q\n0nJty2sotZ5WK6Z5CRFwTCszGYgotZ6eHtrb20csa29vp6enJ6cUTT1eQ6n1tFowItVVd3c3K1as\nYP78+QDMnz+fFStW2BKkBl5DqfVkke+5E3Ak8HxgLrAn0avpRuA+4G7gauCXwOMZnE9qaN3d3Sxa\ntIjOzk5WrVpFR4eDQtfKayi1lokGI3sCxwJvJZrszADaxtnnSWA18B3gm0SgIkmSWlytxTRPAfqA\n24FeIjdkJiMDkUeBDcQAdKVmEq1XPg+sB85KjidJklpYtTkjuwOnAu8u2ecJ4FKiOezVwG+BQSIH\npGAmsAewCDicCF6OBmYBxxO5K31ET6gjOxaQJEktodpg5BZgt+T1FcB5wCrg4XH2exK4K5l+lCyb\nDbwFeAdwFPDe5O89qk61JElqGtUW0+wGXAw8j2geexbjByKVPAR8LTnO85Ljto+5hyRJalrV5owc\nDvymDudfDbwWeG4dji1JkqaAanNG6hGITObxJUlSg7LTM0mSlCsHe5Ay0t/fT39/PwCbNm1iwYIF\nLF++nFmzZgHQ1dVFV1dXnkmUMjP6875+/XrmzZvn510TkjYY2Q54avJ6LbBp1PodgNOIztF2B9YB\nZwBfSnleqeH48FUrKf28DwwM0NnZSX9/v73lakLSBiNvAM4nelM9oMz6C4BXlPz9DOCLwNOAf055\nbkmS1ATS1hkpBBoXAptHrXt1yfo7gO8RPbMCvB94QcpzS5KkJpA2GOlM5leWWXd8Mr8FOBR4UzK/\nmeg+/oSU55YkSU0gbTCyFzAM3FrmuH+bvP4y8Ejy+qHkb4AXpjy3JElqAmmDkUIX7qMrri4CdiEC\nlYtHrbsxmZerYyJJklpM2gqsm4kWM6PHlXlxMr+DaEFTqpBLMj3luSU1GZtHS60pbTByG3AIcATw\n85Llr03m/1dmn8I4NPelPLekJmOwIbWmtMU0lyXzDxBBCcDrgMXJ6x+W2efQZH5XynNLkqQmkDYY\n+RLwJDAXuAG4n2jC2wbcCXy3zD4vT+Y3pDy3JElqAmmDkVuAY4HHiQCkUASzEegCnhi1/d4Ug5FL\nU55bkiQ1gSzGpllF9DPyaiLY2ABcBAyW2fZZwLeIVjblinAkSVKLyWqgvHuAviq2+0kySZIkAemL\naSRJLayvr48lS5YAsGTJEvr6qvldKo2UNhi5GfgoUYFVktRC+vr6WLZsGevWRXdS69atY9myZQYk\nqlnaYGQB8Bngz8D3gdfT+J2Z7Qx8jiguug8YAk7ONUWSNAX19vYyODiyeuDg4CC9vb05pUhTVdpg\n5LpkPoPo6OxCotfVFcAzUh67XvYA3gPMJNILUaFWklSDLVu21LRcqiSLUXsXAV8EHkiWzQV6gJuA\nXxKj8+6c8jxZug3YDXgJ8K/5JkWSpq4ZM8q3gai0XKokiwqsvwM+BOwLLCEGxttK9DtyBPBVorfV\nrwMvyuB8WWrLOwGSNFX19PTQ3t4+Yll7ezs9PT05pUhTVZataZ4ELiCKaw4AlgN/SNbtBLwLuILo\nKG05sE+G55YkTbLu7m5WrFjB/PnzAZg/fz4rVqygu7s755RpqqlX0967iUqiC4EXAl+jOFrvU4FP\nAeuBHwBvpPErvUqSyuju7mbVqlUArFq1ykBEEzIZBXu/TqaLiCKbvUvO/apk2gD0EmPdNFzNp6VL\nlzJnzpwRyxxdVJKaT39/P/39/QBs2rSJ9evXM2/ePGbNmgX47K+k9LoVbNy4ser96x2MzAOOA94J\nHESxjsYW4GfECL4HEPVNeolxbl4GPFjndNVk5cqVdHR05J0MSVKdlQYbAwMDdHZ20t/f73fAOMoF\naYXrV416FNPsQAQVPwduJfrwOJgIRP5I1BfZn8gRORh4BRGYADwHOKUOaZIkSQ0qy2DkBRRbzpxD\nNJ2dBmwCvgksBp5O1CW5N9lnCPgpMZLvl5Jlr80wTZIkqcGlLabZF/h7oijm6aPW/ZaouHoe8FAV\nx/oG8E9EsU29HUO08Nkl+ftQolkyRNPkv0xCGiRJEumDkdsZmbvyCNBPBCG/qfFYDyfzyWhZczpR\nnwWi99W3JNMwUXR0+ySkQZIkkT4YKQQivwLOBL4DPD7BY90NdDM5XbMfnNWBrrkG7r8f2pKquW1t\n274ea101r/PYv9nPKUlqHGmDkZVEELImg7Q8CpydwXEm1Yknws9+Nv52ajz1DqDa2mDGDJg+PabC\n69HzsdY12/7TphkQamr
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfa0ca5dd0>"
      ]
     },
     "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,.4)\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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdf9eb16f90>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjYAAAGJCAYAAACZwnkIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8Epy/e8Tw8AfIT88/F3
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdf9eb16750>"
      ]
     },
     "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([1.80,1.80], [-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
}