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": {
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      "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": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFrCAYAAABbtho0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1Wpt9anVaq5RkDrAkCC
      "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": "iVBORw0KGgoAAAANSUhEUgAAAg8AAAFkCAYAAACn/timAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nke9j/InK1255u9al0No
      "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": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFrCAYAAABbtho0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfz+dnZ0ApNNp0ul0PUv
      "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": "iVBORw0KGgoAAAANSUhEUgAAAggAAAFkCAYAAABFIsPfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nycWLFzMzM9N14WB5eTl
      "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": "iVBORw0KGgoAAAANSUhEUgAAAiIAAAGYCAYAAAB/DYmkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzt3Xl4U2Xax/FvoSAgaxFXFCmbCCq2grjXHRdccenojFh1\n1HEZZhBlRkfcZ5zKDI7buFBxrYqKGyoqiruiLQgiKmIBFVzGggrI2rx/3Cdv0pC0Sc5JTpbf57py\nJTk5y92kSe4853nuB0RERERERERERERERERERERERERERERERERERERERERERERERERERERERCS9\nDgbuAz4HVgFfA08BJVHWLQFeAX4BlgNPAD3TE6aIiIjkoseA14Dzgf2BE4F3gHXAgWHr7QT8DMwA\nhgHHA3OxxGWL9IUrIiIiuWTLKMs2B5YBL4ctewz4DmgftmwHYC3wj5RFJyIiInnpVWC+c7sQWA3c\nHmW9F4HP0hWUiIiINNbC7wBSoBPWH2Sec78X0AaYE2XduUBvoHV6QhMREZFwuZiI3Aa0Ba537nd1\nruujrFsPFABd0hCXiIiIRCj0OwCPXQv8BrgQmOXRPrdxLiIiIpKYZc4lplxKRMYBlwN/pXF/kB+d\n66Io2xQBAWw4bzTbbLvttkuXLl3qWZAiIiJ5ZD5WaiNmMpIrici4sEvkKJiFwK/ArlG22wVYgA33\njWabpUuX8uCDD9K/f/9mgxg1ahQTJkyIO2jJzufM75jTcXyvj+HF/tzsI5ltE9nG7/+JbJSNz5nf\nMWfbe3/+/Pmcfvrp/bGzCjmdiPwNS0CudS6RNgDPAicAlwIrneU7YLVGxjd3gP79+1NSEq1GWmOd\nO3eOaz0JycbnzO+Y03F8r4/hxf7c7COZbRPZxu//iWyUjc+Z3zFn43s/HtmeiIwGrsaG4T4PDI14\n/D3nehzwAfAc1mLSFrgG+J44EhFJnfLycr9DSJjfMafj+F4fw4v9udlHMtv6/Trnumx8fv2OORvf\n+/EoSPsRvfUaVlE12t8RAFqG3S8BbgT2wlpJpgOXAHVN7L8EqKmpqYkrQzzmmGN45pln4otcRHKG\n3vsim6qtraW0tBSgFKiNtV62t4gc2Pwq/68WODRVgYiIiEjicrGOiG/8brYTEX/ovS+SPCUiHtKH\nkUh+0ntfJHlKRERERMQ3SkRERETEN0pERERExDdKRERERMQ3SkRERETEN0pERERExDdKRERERMQ3\nSkRERETEN0pERERExDdKRERERMQ3SkRERETEN0pERERExDdKRERERMQ3SkRERETEN0pERERExDdK\nRERERMQ3SkRERETEN0pERERExDdKRERERMQ3SkRERETEN0pERERExDdKRERERMQ3SkRERETEN0pE\nRERExDdKRERERMQ3SkRERETEN0pERERExDdKRERERMQ3SkRERETEN0pERERExDdKRERERMQ3SkRE\nRETEN0pERERExDdKRERERMQ3SkRERETEN0pERERExDdKRERERMQ3SkRERETEN4Ue7GNzYB9gT2Ar\noBvQCVgB/AB8C7wPvAOs9uB4IiIikiOSTUS6AacDJwMlzn4KmtlmPVADPAY8hCUpIiIikscSPTXT\nC6gClgDjsVaQVjROQlYCS4FVEdu2AoYC/wIWAxOd/YmIiEieirdFpCtwHXBW2DZrgVeB97BTLx8B\n9VjLR1ArYAtgEDAES1wOAtoAZ2KtKlXA5c62IiIikkfiTUQ+B7o4t18HHgQmAz83s916YJlzecFZ\n1gk4CTgNOAA417m/RdxRi4iISE6I99RMF2AqMBg4EDut0lwSEstPwD3OfgY7+y1Kcl8iIiKSxeJt\nERkCfJiC49cAw4E9UrBvERERyXDxtoikIglJ5/5FREQkA6mgmYiIiPgmFxKR9sA/gZew2iQNwLgo\n601yHou8fJKWKEVERGQTbhOR1sDOzqVNlMfbYnVDvgZ+xb70L3J5zEhbAOdgQ4WnOMsCMdb9Fatl\nEn45xeN4REREJE5uS7wfBzyCtURsH+XxJ4HDw+7vBNwM9AEudnnsoEWEhhZ3Bc5uYt2NwEyPjisi\nIiIuuW0RCSYZU4B1EY8dFfb418BTWMVVgAuAvVweO5rmysw397iIiIikkdtEpNS5fiPKY2c6158D\nA4ATnOtPsYSgqZaLVGmLFVfbAHwF3EKoNUVERETSzO2pmS2x/hgLI5a3AA51bt8K/OLc/sm5fyuw\nt8tjJ2o2MAv42LlfBvwJOBgrrBY5N05U1dXVVFdXA7BmzRoWL15Mjx49aNPGusiUl5dTXl7ubeQi\nIiI5ym0iEizLviZi+SCgA5akTI14LJgIROtTkkoTIu5PxxKTx7HWmZvj2Ul4olFbW0tpaSnV1dWU\nlJR4GauIiEhecJuIrMNOd0TOE7O/c/01UBfxWLB1pKXLY3thCtYSsmdTK40aNYrOnTs3WlZeXk6/\nfv1SGJqIiEh2CD9bELRixYq4tnWbiCzChu4OxVoYgoY7129G2SY4r8wPLo/thQLi6CczYcKEqC0e\ntbW1qYhJREQkq0TrlhA8a9Act51VX3OuL8QSEoBjsP4XAM9H2WaAc73M5bG9MAJr0XnX70BERETy\nkdsWkVuA3wNbAXOB5YRaPL4BnoiyzWHO9VyXxw53BLA51i8FLNkZ4dyeinWqfRB4GPgSawk5APgj\n1mflHg9jERERkTi5TUQ+B04H7gXaEUpCVgDlwNqI9bcmlIi86vLY4W4Heji3A8BJziUA9MRG63wP\njMGSppbYaaWbgRuwiqsiIiKSZm4TEYDJWB2Ro7BEYynwDFAfZd1dsVaJANFP2ySrZxzrnOjh8URE\nRMQDXiQiAN8BVXGs95JzEREREcmJ2XdFREQkS7lNRD4FLsX6XYiIiIgkxG0i0hf4BzZvy9PAsWRG\noTIRERHJAm4TkVnOdSFWxGwKVk21EtjJ5b5FREQkx3kx++4gbBjsj86yrYDRwDzgHWwel/YujyMi\nIiI5yIvOqnOwWWy3xYqITQU2YkXDhgJ3YVVU7wX28+B4IiIikiO8HDWzHngSO0WzPTAW+Mx5bHPg\nDOB1rAjaWGAbD48tIiIiWShVw3e/Bf4J9Af2xkqoB2fd7Y1VM10MPAccjzq4ioiI5KV01BF5D5uP\n5jQsQQkqBI7E5qNZjJ3e8arAmoiIiGSBVCciPYBxwEJseO/WzvINwIvYsF+w/iXjgfeBLimOSURE\nRDJEKhKRtthEeNOxBGQcNhdMAbAA6x/SHWsN6QkcDrzibLs7cFUKYhIREZEM5GUishehETL3Awc6\n+18DPASUAf2wviPfO9s0AC9jM/Le4iwb7mFMIiIiksHc9snYFvgtMBJLMsJ9hHVSfRD4KY593Qdc\nhI24ERERkTzgNhFZQuNWlV+AaiwB+TDBff3sXGsEjYiISJ5wm4gEk5B3gbuBx4DVSe7rW6ACCLiM\nSURERLKE20RkApaAzPcglpXAJA/2IyIiIlnCbSLyZ0+iEBERkbyUjoJmIiIiIlEpERERERHfeFlS\n/SDgOGBXYAussFlBM9sUe3h8ERERyTJeJCJbAY8AB3iwLxEREckjbhORVsDzWGl2gNnAUqx8O8AD\nQBFQAmzjLKsFPkbDdEVERPKe2z4iIwklIRVYwjHWuR8AzsBKtncHjsfKv/cHngXOdHlsERERyXJu\nE5ETnesXaboGSACbfXd/YD1Wzr2vy2OLiIhIlnObiAxyrh+M8XhkZ9WFWBG0dsAfXR5bREREspzb\nRKQIa+34MmzZurDb7aJs86pzfYjLY4uIiEiWc5uIrIu4htDkdQDbRdlmTROPiYiISB5xm4gswU6/\nbBW27Dts3pgCYM8o2+zsXGvUjIiISJ5zm4jUOte7hy0LAG84t0cBm4U91hm41LntxUR5IiIiksXc\nJiLTneujI5bf4VzvDswFKoHbnds7OY/d7/LYvqqqqmLEiBEAjBgxgqqqKp8jEhERyT5uE5GnsNMz\n3YFeYcunAsFv5t7AaOA
      "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": "iVBORw0KGgoAAAANSUhEUgAAAjYAAAGJCAYAAACZwnkIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nL7SyrXJlKCiAdu1gyBB
      "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": "iVBORw0KGgoAAAANSUhEUgAAAjYAAAGJCAYAAACZwnkIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3Xd4VNXWx/HvBATpSIcAKoIKKGKC4EUFRPTSRLrEgqBY\nEcSOFXsvXBWxXBQVjYAae0NFxY4EpVtRBKSIIldB+vvHOvPOECaTmZwzcyYzv8/znOckp66MYBZ7\nr703iIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiURccATwDfAn8D\ny4EXgZwY768HTAbWOvd/AnT1PEoRERGRGEwDZgLnAp2AAVhysgU4uoR7KwLzgZ+BPCxJKnDu7ZSg\neEVERESKVS/CsSrAr8CMEu49D9gBdAg7Vg5YAHzmSXQiIiIiHngPWFzCNTOARRGOj8USnoZeByUi\nIiLxy/I7AJ/VwGpsFpZw3UHAvAjH5zv71l4GJSIiIqWT6YnNBKAScHMJ19UCfo9wPHistpdBiYiI\nSOmU9zsAH90InAScD8z1ORYRERHxQKYmNuOAq4ArgQdjuH4d1mpTVK2w88VpiGpwRERESuNXZ4tZ\nJiY248K222K8Zz7QJsLxg539gmLua9ioUaOVK1eujC9CERERAVgBHEYcyU0gcbGkpGuA67FuqHFx\n3HcO1rJzOPCFc6w88BWwAehYzH05wJwpU6bQsmXLUgWcLsaMGcP48eP9DiMl6LMwpf0cunfvztq1\na6lbty5vvvlmAiJLLv15CNFnYfQ5mMWLF3PKKacA5AKFsd6XSS02F2NJzZvA61iSEi44H80kYCjQ\nDPjFOfYYMBKYjg3xXovNbdMC6FbSi1u2bElOTqwTHKenmjVrZvxnEKTPwpT2c6hQocL/79Phc9Sf\nhxB9FkafgzuZlNj0BnYC3Z0t3E5swj2wkWJZ7NqatQWbbfgO4H6gMlZw3AOYlbiQRUREJB6ZlNiU\ntGxC0HBnK2oNMMyzaERERMRzmT6PjYiIiKQRJTaSFHl5eX6HkDL0WRh9DkafQ4g+C6PPwZ1MGxWV\nbDnAnDlz5qgQTMQjjRs3ZsWKFWRnZ7N8+XK/wxGRBCksLCQ3NxfiHBWlFhsRERFJG0psREREJG0o\nsREREZG0ocRGRERE0oYSGxEREUkbSmxEREQkbSixERERkbShxEZERETShhIbERERSRtKbERERCRt\nKLERERGRtKHERkRERNKGEhsRERFJG0psREREJG2U9+g5+wMdgPpAXaAGsB5YC6wCPge+9+hdIiIi\nIhGVNrHZA+gNDAY6AQ2AQJTrd2IJzgfANOBVYFsp3y0iIiISUbxdUTWAa4FlwPPAiUBDoic1OOcb\nAkOAF4BfgGuc56W9hx6CxYv9jkJERCT9xZrYVAAuB5YC12FdTgCLgceBc4B/YV1SdZzr6wIHAEcA\nI4EngCXOffWB653nXY61AKWtZ56BVq3gkEPg1lvhxx/9jkhERCQ9xdoVtQBo7ny9FHgGmAJ8E+We\ndc72HfApMNE5fiBwCnASsA9wK3AGlhSlpRkzYPVqmDoVbroJrrwS2reHIUNg8GDIzvY7QhERkfQQ\na4tNc2A+MBDYD+tGipbURLMEuNp5zkDnuc2j3lHGVawIfftCfj6sWQPPPguNGsHYsdCkCXTuDBMn\n2jkREREpvVgTm8HAIVh9jFd2Os9r6zw/I1SpAieeCAUFlsg8/jhUrgyjRlmyc9xx8Nhj8Mcffkcq\nIiJS9sSa2DyXwBh2Jvj5KatGDTjtNHjjDVi1CiZMgK1bYcQIqF8f+vSx+py//vI7UhERkbJBE/Sl\niDp14OyzYeZMWL4c7roLfvsNTj4Z6tWzWpwXXoBNm/yOVEREJHUpsUlBjRrB6NHwySewdClcdx18\n/z0MGGAtOUOHwuuvw5YtfkcqIiKSWrxMbKpjo5sexSbgexfYu8g12UAroJmH701r++wDl10GhYWw\nZAlccgnMng29ekGDBnDmmfDuu7B9u9+RioiI+M+rxOZcbNK+R7HkpifQBahS5LqjsaHjC4FaHr07\nYxxwAFx7LSxaBF9/DeecA++9B9262ZDxUaPg449hxw6/IxUREfGHF4nN1cAErMVmM1AY5dp8YDVQ\nERjgwbszUiAAbdrALbdYF9UXX1gtTkEBHHmktfJceinMmQM7d/odrYiISPK4TWwOwWYQBktaGgLt\noly/ndCQ8W4u3y1YknPYYXD33bBsGXz4IRx/PDzxBLRrB/vvD9dcAwsX+h2piIhI4rlNbEZh60B9\nAZyKrehdkk+cfRuX75YisrLgqKNs2PjKlfD229CpEzzwABx0kG033QTffed3pCIiIonhNrHp4uwf\nAGKt7Fjq7Bu5fLdEUb48HHssTJpkyzm88gq0bQu33WatOO3a2ZDyZcv8jlRERMQ7bhObRtgEe/F0\ndGx09nu6fLfEqEIF6N0bpkyx2Y6nT7c6nKuvhr33trqcBx6wSQJFRETKMreJzTZnXy6Oe2o7+z9d\nvltKoXJlGDgQnnvOkpynnoKaNeHCC21k1THHwKOPwrp1fkcqIiISP7eJzXKsxubAOO45ytn/4PLd\n4lL16nDKKfDqq9Zd9fDDVox8zjk2R06vXpb4bNjgd6QiIiKxcZvYzHT2p8Z4fU3gbOfrd12+WzxU\nq5atUfXOO1Z4PH68JTRDh9qSDgMGWBfWxo0lP0tERMQvbhObh7Aam27YJH3R1AFeAuoDW4CHXb5b\nEqR+fRg5EmbNsuLim2+2/eDBluScfLIVI2/e7HekIiIiu3Kb2MwH7sS6ox4ACoAhzrkA0BE4GXgQ\n+J5QN9R1wC8u3y1J0KQJXHyxLePw3XdwxRUwb56tPF6/Ppx+ug0r37at5GeJiIgkmhczD1+BJTUB\n4ATgmbBzjwBPAedgMxMD3A3c5sF7JcmaN4erroL5820bNcpadf79b1u487zzbIJALekgIiJ+8SKx\n2QmMBo4D3qP4+Ww+BroDl3rwTvHZQQfBjTfCt9/Cl1/CaadZEXLnztC0KVx0kS31oCUdREQkmcp7\n+Kx3nK06cChQDxsGvhb4GvjNw3dJiggEIDfXtttvh88+g2efhWeegXvvhX33hRNPtC6rFi38jlZE\nRNKdV6t7h9sAfABMB57FRj8pqckAWVnQsSPcdx+sWAHvvmsrjz/yiK1npaUcREQk0dwmNnt5EoWk\nnXLloGtXS2p+/NHmxTnhBPjf//yOTERE0pnbxGYVNoR7MFoiQYpRowa89JK14gwdquJiERFJHLeJ\nzR7A8ViX02pgMnAsNkJK5P8dcAA8/bQlODfe6Hc0IiKSrtwmNhOB4KpC1YChwJvACuBeoJ3L50sa\n6d0bbrgBrrvOEhwRERGvuU1sRgINsVabfGzl7gDQALgA+Bz4BrgW2M/luyQNXHkl9O9va1QtWuR3\nNCIikm68GBW1DXgNm2G4PnAK8AawHUtyWmAzDX8LfAaMAup68F4pg7KyYPJk2Htv6NsX1q/3OyIR\nEUknXg/3/hubebgX1pJzPvCpcy4AtAf+g3VVveHxu2NRFbgDeBubX2cHMC7Ge4c510fa6nkdaDqr\nVg1efBHWroWTToLt2/2OSERE0kUi5rEJ+g1bI+oIoBlwNRDsfCiPzVScbHWAM7Gi5wLnWLxz4w4D\nDi+y/e5RfBmjeXObyO+tt+Daa/2ORkRE0oWXMw9H8xPwAlAJaATUTNJ7I8URnHunNjCiFM9YABR6\nFVAm+/e/4dZb4fLLoW1bGDTI74hERKSsS3Ri0wjIA04C2rLrMPDNCX53SUo7JF1D2T106aUwdy4M\nG2ZDwtu08TsiEREpyxLRFVUDOANbEPNn4E5s7agA1u3zLnA6VmhcFr2KFUyvA54HWvsbTtkWCMCk\nSbD//lZMvG5dyfeIiIgUx6sWm4pAb2xkVA/n+3BzgaexIeG/evTOZPsVuAkb2bUBaAOMdb7vCMz3\nL7SyrXJlKCiAdu1gyBB
      "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
}