phy-4660/lag/data/clag_analysis-origbins-7647A.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": "iVBORw0KGgoAAAANSUhEUgAAAg8AAAFkCAYAAACn/timAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11415a1c10>"
      ]
     },
     "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/7647A.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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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11653021d0>"
      ]
     },
     "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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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f114110a090>"
      ]
     },
     "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 4.352e-01 7.004e+01 inf -- -2.548e+02 -- 1 1 1 1 1 1 1 1\n",
      "   2 7.709e-01 6.945e+01 8.689e+01 -- -1.680e+02 -- 0.568202 0.566496 0.565546 0.565389 0.565371 0.564829 0.565108 0.56515\n",
      "   3 3.365e+00 6.850e+01 8.615e+01 -- -8.181e+01 -- 0.138452 0.134629 0.131417 0.131051 0.130581 0.129376 0.130493 0.130733\n",
      "   4 1.432e+00 6.742e+01 8.494e+01 -- 3.128e+00 -- -0.281314 -0.293481 -0.30187 -0.302706 -0.304046 -0.305979 -0.303068 -0.302158\n",
      "   5 5.888e-01 6.660e+01 8.345e+01 -- 8.658e+01 -- -0.670439 -0.713504 -0.734283 -0.735609 -0.738394 -0.740978 -0.735503 -0.733408\n",
      "   6 3.712e-01 6.580e+01 8.170e+01 -- 1.683e+02 -- -0.98186 -1.11559 -1.16661 -1.16796 -1.17265 -1.17538 -1.16791 -1.16475\n",
      "   7 2.710e-01 6.441e+01 7.924e+01 -- 2.475e+02 -- -1.15319 -1.47413 -1.59924 -1.60004 -1.60675 -1.60901 -1.601 -1.59713\n",
      "   8 2.138e-01 6.207e+01 7.606e+01 -- 3.236e+02 -- -1.2017 -1.73389 -2.0317 -2.03093 -2.04033 -2.04197 -2.03495 -2.02955\n",
      "   9 1.767e-01 5.874e+01 7.252e+01 -- 3.961e+02 -- -1.21944 -1.84738 -2.46191 -2.45696 -2.47336 -2.47447 -2.47007 -2.4612\n",
      "  10 1.505e-01 5.422e+01 6.836e+01 -- 4.645e+02 -- -1.22867 -1.87558 -2.87636 -2.86554 -2.90591 -2.90606 -2.90644 -2.89281\n",
      "  11 1.304e-01 4.826e+01 6.187e+01 -- 5.263e+02 -- -1.23499 -1.89484 -3.22854 -3.22689 -3.33472 -3.3325 -3.34386 -3.32489\n",
      "  12 1.163e-01 4.093e+01 5.179e+01 -- 5.781e+02 -- -1.23852 -1.90721 -3.44389 -3.49376 -3.74893 -3.73895 -3.77938 -3.75848\n",
      "  13 9.980e-02 3.115e+01 3.779e+01 -- 6.159e+02 -- -1.23702 -1.90846 -3.49545 -3.63342 -4.12276 -4.08853 -4.19527 -4.19544\n",
      "  14 7.004e-02 1.776e+01 2.014e+01 -- 6.360e+02 -- -1.23105 -1.90503 -3.4814 -3.68042 -4.41637 -4.31891 -4.53403 -4.61415\n",
      "  15 2.773e-02 5.191e+00 5.391e+00 -- 6.414e+02 -- -1.22657 -1.90214 -3.47116 -3.69656 -4.59718 -4.39786 -4.69506 -4.93731\n",
      "  16 1.057e-02 6.378e-01 4.477e-01 -- 6.419e+02 -- -1.22775 -1.90089 -3.46879 -3.70567 -4.68256 -4.39619 -4.69519 -5.07423\n",
      "  17 7.888e-03 4.329e-01 3.041e-02 -- 6.419e+02 -- -1.22951 -1.90056 -3.46786 -3.71222 -4.73204 -4.38418 -4.69485 -5.07494\n",
      "  18 5.819e-03 2.998e-01 1.477e-02 -- 6.419e+02 -- -1.2294 -1.90021 -3.46615 -3.71187 -4.76937 -4.37843 -4.69171 -5.07347\n",
      "  19 4.291e-03 2.100e-01 7.619e-03 -- 6.419e+02 -- -1.22925 -1.89995 -3.46439 -3.71159 -4.79712 -4.37418 -4.69047 -5.07198\n",
      "  20 3.153e-03 1.486e-01 3.972e-03 -- 6.419e+02 -- -1.22914 -1.89977 -3.46327 -3.71125 -4.8177 -4.3712 -4.68937 -5.07092\n",
      "  21 2.314e-03 1.059e-01 2.083e-03 -- 6.419e+02 -- -1.22907 -1.89964 -3.46247 -3.71101 -4.83289 -4.36903 -4.68864 -5.07007\n",
      "  22 1.695e-03 7.595e-02 1.096e-03 -- 6.419e+02 -- -1.22901 -1.89955 -3.46191 -3.71083 -4.84408 -4.36746 -4.68811 -5.06945\n",
      "  23 1.239e-03 5.467e-02 5.783e-04 -- 6.419e+02 -- -1.22898 -1.89949 -3.46151 -3.7107 -4.85229 -4.36633 -4.68772 -5.06898\n",
      "  24 9.051e-04 3.947e-02 3.054e-04 -- 6.419e+02 -- -1.22895 -1.89945 -3.46122 -3.7106 -4.8583 -4.3655 -4.68745 -5.06863\n",
      "  25 6.605e-04 2.855e-02 1.614e-04 -- 6.419e+02 -- -1.22893 -1.89941 -3.46102 -3.71052 -4.8627 -4.3649 -4.68725 -5.06838\n",
      "  26 4.816e-04 2.069e-02 8.536e-05 -- 6.419e+02 -- -1.22891 -1.89939 -3.46087 -3.71047 -4.86591 -4.36446 -4.68711 -5.06819\n",
      "********************\n",
      "-1.22891 -1.89939 -3.46087 -3.71047 -4.86591 -4.36446 -4.68711 -5.06819\n",
      "0.234933 0.200019 0.259825 0.196536 0.332059 0.153872 0.153084 0.180761\n",
      "0.000177336 0.000464495 0.00122062 -0.000734339 -0.0206875 0.00948273 0.00295267 0.00321028\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",
      "+++ 6.419e+02 6.415e+02 -1.229e+00 -9.940e-01 0.854 +++\n",
      "+++ 6.419e+02 6.411e+02 -1.229e+00 -8.765e-01 1.78 +++\n",
      "+++ 6.419e+02 6.413e+02 -1.229e+00 -9.352e-01 1.28 +++\n",
      "+++ 6.419e+02 6.414e+02 -1.229e+00 -9.646e-01 1.06 +++\n",
      "+++ 6.419e+02 6.415e+02 -1.229e+00 -9.793e-01 0.954 +++\n",
      "+++ 6.419e+02 6.414e+02 -1.229e+00 -9.719e-01 1.01 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 6.419e+02 6.415e+02 -1.899e+00 -1.699e+00 0.892 +++\n",
      "+++ 6.419e+02 6.410e+02 -1.899e+00 -1.599e+00 1.89 +++\n",
      "+++ 6.419e+02 6.413e+02 -1.899e+00 -1.649e+00 1.35 +++\n",
      "+++ 6.419e+02 6.414e+02 -1.899e+00 -1.674e+00 1.11 +++\n",
      "+++ 6.419e+02 6.414e+02 -1.899e+00 -1.687e+00   1 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 6.419e+02 6.414e+02 -3.461e+00 -3.201e+00 1.01 +++\n",
      "+++ 6.419e+02 6.418e+02 -3.461e+00 -3.331e+00 0.268 +++\n",
      "+++ 6.419e+02 6.417e+02 -3.461e+00 -3.266e+00 0.586 +++\n",
      "+++ 6.419e+02 6.416e+02 -3.461e+00 -3.233e+00 0.788 +++\n",
      "+++ 6.419e+02 6.415e+02 -3.461e+00 -3.217e+00 0.898 +++\n",
      "+++ 6.419e+02 6.415e+02 -3.461e+00 -3.209e+00 0.956 +++\n",
      "+++ 6.419e+02 6.415e+02 -3.461e+00 -3.205e+00 0.985 +++\n",
      "+++ 6.419e+02 6.414e+02 -3.461e+00 -3.203e+00   1 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 6.419e+02 6.415e+02 -3.710e+00 -3.514e+00 0.934 +++\n",
      "+++ 6.419e+02 6.409e+02 -3.710e+00 -3.416e+00 2.02 +++\n",
      "+++ 6.419e+02 6.412e+02 -3.710e+00 -3.465e+00 1.43 +++\n",
      "+++ 6.419e+02 6.414e+02 -3.710e+00 -3.489e+00 1.17 +++\n",
      "+++ 6.419e+02 6.414e+02 -3.710e+00 -3.502e+00 1.05 +++\n",
      "+++ 6.419e+02 6.415e+02 -3.710e+00 -3.508e+00 0.991 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 6.419e+02 6.417e+02 -4.868e+00 -4.535e+00 0.545 +++\n",
      "+++ 6.419e+02 6.414e+02 -4.868e+00 -4.369e+00 1.17 +++\n",
      "+++ 6.419e+02 6.416e+02 -4.868e+00 -4.452e+00 0.745 +++\n",
      "+++ 6.419e+02 6.415e+02 -4.868e+00 -4.411e+00 0.943 +++\n",
      "+++ 6.419e+02 6.414e+02 -4.868e+00 -4.390e+00 1.05 +++\n",
      "+++ 6.419e+02 6.414e+02 -4.868e+00 -4.400e+00 0.998 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 6.419e+02 6.415e+02 -4.364e+00 -4.210e+00 0.842 +++\n",
      "+++ 6.419e+02 6.410e+02 -4.364e+00 -4.133e+00 1.87 +++\n",
      "+++ 6.419e+02 6.413e+02 -4.364e+00 -4.172e+00 1.32 +++\n",
      "+++ 6.419e+02 6.414e+02 -4.364e+00 -4.191e+00 1.07 +++\n",
      "+++ 6.419e+02 6.415e+02 -4.364e+00 -4.201e+00 0.951 +++\n",
      "+++ 6.419e+02 6.414e+02 -4.364e+00 -4.196e+00 1.01 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 6.419e+02 6.418e+02 -4.687e+00 -4.610e+00 0.306 +++\n",
      "+++ 6.419e+02 6.416e+02 -4.687e+00 -4.572e+00 0.686 +++\n",
      "+++ 6.419e+02 6.415e+02 -4.687e+00 -4.553e+00 0.933 +++\n",
      "+++ 6.419e+02 6.414e+02 -4.687e+00 -4.544e+00 1.07 +++\n",
      "+++ 6.419e+02 6.414e+02 -4.687e+00 -4.548e+00   1 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 6.419e+02 6.418e+02 -5.068e+00 -4.978e+00 0.293 +++\n",
      "+++ 6.419e+02 6.416e+02 -5.068e+00 -4.932e+00 0.662 +++\n",
      "+++ 6.419e+02 6.415e+02 -5.068e+00 -4.910e+00 0.903 +++\n",
      "+++ 6.419e+02 6.414e+02 -5.068e+00 -4.899e+00 1.04 +++\n",
      "+++ 6.419e+02 6.415e+02 -5.068e+00 -4.904e+00 0.969 +++\n",
      "+++ 6.419e+02 6.414e+02 -5.068e+00 -4.901e+00   1 +++\n",
      "********************\n",
      "-1.2289 -1.89937 -3.46076 -3.71043 -4.86825 -4.36415 -4.687 -5.06805\n",
      "0.256958 0.212521 0.257777 0.202656 0.468138 0.168247 0.138715 0.166617\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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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1140fb3850>"
      ]
     },
     "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 1.149e+03 8.511e+00 inf -- 6.871e+02 -- -0.76415 -1.33875 -2.62568 -2.91642 -3.79922 -3.72781 -4.42422 -6.83403 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
      "   3 3.083e+01 1.011e+01 2.462e+00 -- 6.895e+02 -- -0.722947 -1.31306 -2.64209 -2.92939 -3.79892 -3.74348 -4.4401 -6.53403 0.112591 0.202668 0.237888 0.184561 0.247154 0.169837 0.149615 -0.977256\n",
      "   5 2.313e+01 1.184e+01 2.138e+00 -- 6.917e+02 -- -0.689217 -1.28746 -2.64829 -2.93823 -3.79051 -3.75521 -4.45318 -6.83403 0.122108 0.284555 0.367997 0.266931 0.379308 0.236851 0.196816 2.03535\n",
      "   7 1.836e+01 1.371e+01 1.807e+00 -- 6.935e+02 -- -0.66115 -1.2633 -2.64623 -2.94323 -3.77682 -3.76343 -4.4638 -6.53403 0.129539 0.350049 0.485494 0.345407 0.493126 0.299979 0.241096 -2.67313\n",
      "   9 2.046e+01 1.572e+01 1.610e+00 -- 6.951e+02 -- -0.637504 -1.24117 -2.63838 -2.94489 -3.76048 -3.76869 -4.47217 -6.83403 0.135502 0.402878 0.588 0.418868 0.588605 0.358702 0.28198 -1.29684\n",
      "  11 3.541e+01 1.788e+01 1.456e+00 -- 6.966e+02 -- -0.617387 -1.22121 -2.62698 -2.94377 -3.7434 -3.77151 -4.47878 -7.13403 0.140359 0.445949 0.67527 0.486465 0.667539 0.412608 0.319287 1.3562\n",
      "  13 8.288e+02 2.020e+01 1.291e+00 -- 6.978e+02 -- -0.60014 -1.20337 -2.61379 -2.94045 -3.72685 -3.77239 -4.48389 -6.83403 0.144381 0.48146 0.748601 0.54777 0.732508 0.461524 0.352994 -0.12492\n",
      "  15 6.267e+01 2.266e+01 1.179e+00 -- 6.990e+02 -- -0.585259 -1.18747 -2.60004 -2.9355 -3.71148 -3.77179 -4.48775 -7.13403 0.147758 0.511044 0.809901 0.602807 0.786107 0.50562 0.383157 -2.33832\n",
      "  17 5.670e+01 2.527e+01 1.086e+00 -- 7.001e+02 -- -0.572351 -1.17335 -2.58649 -2.92939 -3.6976 -3.77007 -4.49056 -7.43403 0.150636 0.53593 0.861183 0.651797 0.830596 0.545107 0.409928 1.85585\n",
      "  19 6.344e+01 2.802e+01 9.867e-01 -- 7.011e+02 -- -0.561107 -1.1608 -2.57356 -2.92251 -3.68525 -3.76757 -4.49259 -7.13403 0.153107 0.557037 0.9042 0.695116 0.867766 0.580281 0.433567 -2.38363\n",
      "  21 7.571e+01 3.089e+01 9.220e-01 -- 7.020e+02 -- -0.551274 -1.14965 -2.56148 -2.91518 -3.6744 -3.76453 -4.49398 -7.43403 0.155257 0.575074 0.940495 0.733284 0.89908 0.611551 0.454317 1.34492\n",
      "  22 4.003e-01 2.319e+04 1.251e+01 -- 6.895e+02 -- -0.46504 -1.05058 -2.44998 -2.83968 -3.57966 -3.73073 -4.50329 -4.43403 0.174105 0.730184 1.24835 1.06835 1.1647 0.888615 0.636009 2.63401\n",
      "  23 3.602e-01 6.251e+01 2.137e+01 -- 7.109e+02 -- -0.479175 -1.05499 -2.40759 -2.71044 -3.63212 -3.67843 -4.55868 -4.7139 0.243794 0.662729 1.29622 1.06228 1.10731 0.828249 0.484699 2.85439\n",
      "  24 1.324e-01 3.847e+01 7.851e-01 -- 7.117e+02 -- -0.473576 -1.05695 -2.43181 -2.73925 -3.6054 -3.70634 -4.40662 -4.87766 0.183146 0.709437 1.27054 0.992921 1.08464 0.839337 0.659286 2.73599\n",
      "  25 1.821e-01 8.521e+00 1.578e-01 -- 7.118e+02 -- -0.475023 -1.05607 -2.43744 -2.74267 -3.61814 -3.70001 -4.50477 -4.97221 0.199534 0.699185 1.24387 0.993811 1.07721 0.861017 0.572018 2.42745\n",
      "  26 2.014e-01 5.280e+00 6.998e-02 -- 7.119e+02 -- -0.474604 -1.05634 -2.44031 -2.7453 -3.60481 -3.70497 -4.4716 -5.01385 0.194772 0.702203 1.23578 0.990364 1.07625 0.849061 0.623206 1.98547\n",
      "  27 1.559e-01 8.470e-01 5.248e-02 -- 7.119e+02 -- -0.474838 -1.05633 -2.44133 -2.7456 -3.61013 -3.70353 -4.4991 -4.96485 0.195398 0.70079 1.23042 0.988953 1.0694 0.8531 0.602048 1.5856\n",
      "  28 1.154e-01 1.799e+00 3.734e-02 -- 7.120e+02 -- -0.474876 -1.05636 -2.44221 -2.74624 -3.6043 -3.70638 -4.49561 -4.88587 0.194273 0.700465 1.22605 0.986774 1.06378 0.847619 0.626675 1.33843\n",
      "  29 7.922e-02 1.175e+00 2.365e-02 -- 7.120e+02 -- -0.474959 -1.05637 -2.44284 -2.74635 -3.60609 -3.70691 -4.50684 -4.82345 0.193792 0.699958 1.22274 0.984729 1.05776 0.847888 0.625222 1.18395\n",
      "  30 5.978e-02 9.905e-01 1.545e-02 -- 7.120e+02 -- -0.475014 -1.05638 -2.44337 -2.74658 -3.60376 -3.70854 -4.50832 -4.7745 0.193235 0.699587 1.22004 0.983324 1.05292 0.845739 0.637938 1.09015\n",
      "  31 4.332e-02 8.004e-01 9.908e-03 -- 7.120e+02 -- -0.475061 -1.05639 -2.4438 -2.74665 -3.60426 -3.70921 -4.5135 -4.73769 0.192849 0.699306 1.21807 0.982147 1.04865 0.845744 0.640636 1.02498\n",
      "  32 3.306e-02 6.233e-01 6.372e-03 -- 7.120e+02 -- -0.475099 -1.05639 -2.44413 -2.74676 -3.60331 -3.71016 -4.51536 -4.70979 0.19251 0.699065 1.21644 0.981352 1.04524 0.844821 0.647708 0.980583\n",
      "  33 2.501e-02 5.124e-01 4.073e-03 -- 7.120e+02 -- -0.475129 -1.0564 -2.4444 -2.74681 -3.6034 -3.71069 -4.51806 -4.68842 0.192252 0.698889 1.21522 0.980704 1.04238 0.844743 0.650651 0.948169\n",
      "  34 1.931e-02 3.988e-01 2.597e-03 -- 7.120e+02 -- -0.475154 -1.0564 -2.44461 -2.74688 -3.603 -3.71126 -4.51949 -4.67205 0.192037 0.69874 1.21423 0.980247 1.04011 0.844313 0.654788 0.924451\n",
      "  35 1.493e-02 3.244e-01 1.651e-03 -- 7.120e+02 -- -0.475173 -1.0564 -2.44478 -2.74692 -3.60297 -3.71164 -4.52101 -4.65936 0.19187 0.698628 1.21347 0.97988 1.03824 0.844226 0.657098 0.906602\n",
      "  36 1.163e-02 2.542e-01 1.047e-03 -- 7.120e+02 -- -0.475189 -1.0564 -2.44491 -2.74696 -3.60279 -3.71199 -4.52198 -4.64952 0.191733 0.698535 1.21286 0.979613 1.03676 0.844016 0.659591 0.893069\n",
      "  37 9.093e-03 2.048e-01 6.628e-04 -- 7.120e+02 -- -0.475201 -1.0564 -2.44501 -2.74699 -3.60275 -3.71224 -4.52287 -4.64183 0.191626 0.698464 1.21238 0.9794 1.03556 0.843944 0.661216 0.882678\n",
      "  38 7.134e-03 1.613e-01 4.191e-04 -- 7.120e+02 -- -0.475212 -1.0564 -2.4451 -2.74701 -3.60265 -3.71246 -4.52351 -4.63582 0.191539 0.698406 1.212 0.979241 1.03461 0.843836 0.662747 0.874652\n",
      "  39 5.608e-03 1.291e-01 2.647e-04 -- 7.120e+02 -- -0.475219 -1.05641 -2.44516 -2.74703 -3.60262 -3.71262 -4.52405 -4.6311 0.191471 0.698362 1.21171 0.979114 1.03385 0.843785 0.663834 0.868413\n",
      "  40 4.418e-03 1.020e-01 1.670e-04 -- 7.120e+02 -- -0.475226 -1.05641 -2.44521 -2.74705 -3.60257 -3.71276 -4.52445 -4.62739 0.191416 0.698326 1.21147 0.979017 1.03325 0.843726 0.664783 0.863542\n",
      "  41 3.485e-03 8.130e-02 1.053e-04 -- 7.120e+02 -- -0.475231 -1.05641 -2.44525 -2.74706 -3.60255 -3.71286 -4.52479 -4.62447 0.191373 0.698297 1.21128 0.978941 1.03276 0.843692 0.665491 0.859727\n",
      "  42 2.753e-03 6.436e-02 6.636e-05 -- 7.120e+02 -- -0.475235 -1.05641 -2.44529 -2.74707 -3.60252 -3.71295 -4.52504 -4.62216 0.191338 0.698275 1.21113 0.978881 1.03238 0.843658 0.666084 0.856731\n",
      "********************\n",
      "-0.475235 -1.05641 -2.44529 -2.74707 -3.60252 -3.71295 -4.52504 -4.62216 0.191338 0.698275 1.21113 0.978881 1.03238 0.843658 0.666084 0.856731\n",
      "0.00699188 0.00954622 0.0841246 0.0745967 0.124782 0.148601 0.368254 0.278607 0.0954532 0.101842 0.352028 0.291475 0.397613 0.407863 0.879459 0.681533\n",
      "-0.064356 -0.00407965 -0.00327632 -0.000885349 0.00210339 -0.00248053 -0.0014596 0.0217369 -0.00315135 -0.00176922 -0.000952405 -0.000682482 -0.00194583 -0.000139415 0.000655392 -0.00209613\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",
      "+++ 7.120e+02 7.118e+02 -4.752e-01 -4.717e-01 0.444 +++\n",
      "+++ 7.120e+02 7.114e+02 -4.752e-01 -4.700e-01 1.35 +++\n",
      "+++ 7.120e+02 7.116e+02 -4.752e-01 -4.709e-01 0.796 +++\n",
      "+++ 7.120e+02 7.115e+02 -4.752e-01 -4.704e-01 1.04 +++\n",
      "+++ 7.120e+02 7.116e+02 -4.752e-01 -4.706e-01 0.912 +++\n",
      "+++ 7.120e+02 7.116e+02 -4.752e-01 -4.705e-01 0.975 +++\n",
      "+++ 7.120e+02 7.115e+02 -4.752e-01 -4.705e-01 1.01 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 7.120e+02 7.118e+02 -1.056e+00 -1.052e+00 0.401 +++\n",
      "+++ 7.120e+02 7.115e+02 -1.056e+00 -1.049e+00 1.14 +++\n",
      "+++ 7.120e+02 7.117e+02 -1.056e+00 -1.050e+00 0.702 +++\n",
      "+++ 7.120e+02 7.116e+02 -1.056e+00 -1.050e+00 0.903 +++\n",
      "+++ 7.120e+02 7.115e+02 -1.056e+00 -1.050e+00 1.02 +++\n",
      "+++ 7.120e+02 7.116e+02 -1.056e+00 -1.050e+00 0.959 +++\n",
      "+++ 7.120e+02 7.116e+02 -1.056e+00 -1.050e+00 0.988 +++\n",
      "+++ 7.120e+02 7.115e+02 -1.056e+00 -1.050e+00   1 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 7.120e+02 7.119e+02 -2.445e+00 -2.403e+00 0.347 +++\n",
      "+++ 7.120e+02 7.115e+02 -2.445e+00 -2.382e+00 1.08 +++\n",
      "+++ 7.120e+02 7.117e+02 -2.445e+00 -2.393e+00 0.631 +++\n",
      "+++ 7.120e+02 7.116e+02 -2.445e+00 -2.387e+00 0.829 +++\n",
      "+++ 7.120e+02 7.116e+02 -2.445e+00 -2.385e+00 0.946 +++\n",
      "+++ 7.120e+02 7.115e+02 -2.445e+00 -2.384e+00 1.01 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 7.120e+02 7.119e+02 -2.747e+00 -2.710e+00 0.245 +++\n",
      "+++ 7.120e+02 7.117e+02 -2.747e+00 -2.691e+00 0.695 +++\n",
      "+++ 7.120e+02 7.115e+02 -2.747e+00 -2.682e+00 1.08 +++\n",
      "+++ 7.120e+02 7.116e+02 -2.747e+00 -2.686e+00 0.87 +++\n",
      "+++ 7.120e+02 7.116e+02 -2.747e+00 -2.684e+00 0.97 +++\n",
      "+++ 7.120e+02 7.115e+02 -2.747e+00 -2.683e+00 1.02 +++\n",
      "+++ 7.120e+02 7.115e+02 -2.747e+00 -2.684e+00 0.996 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 7.120e+02 7.117e+02 -3.603e+00 -3.540e+00 0.596 +++\n",
      "+++ 7.120e+02 7.111e+02 -3.603e+00 -3.509e+00 1.86 +++\n",
      "+++ 7.120e+02 7.115e+02 -3.603e+00 -3.525e+00 1.09 +++\n",
      "+++ 7.120e+02 7.116e+02 -3.603e+00 -3.532e+00 0.813 +++\n",
      "+++ 7.120e+02 7.116e+02 -3.603e+00 -3.528e+00 0.942 +++\n",
      "+++ 7.120e+02 7.115e+02 -3.603e+00 -3.526e+00 1.01 +++\n",
      "+++ 7.120e+02 7.116e+02 -3.603e+00 -3.527e+00 0.976 +++\n",
      "+++ 7.120e+02 7.115e+02 -3.603e+00 -3.527e+00 0.994 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 7.120e+02 7.119e+02 -3.713e+00 -3.639e+00 0.357 +++\n",
      "+++ 7.120e+02 7.116e+02 -3.713e+00 -3.602e+00 0.966 +++\n",
      "+++ 7.120e+02 7.113e+02 -3.713e+00 -3.583e+00 1.46 +++\n",
      "+++ 7.120e+02 7.114e+02 -3.713e+00 -3.592e+00 1.19 +++\n",
      "+++ 7.120e+02 7.115e+02 -3.713e+00 -3.597e+00 1.08 +++\n",
      "+++ 7.120e+02 7.115e+02 -3.713e+00 -3.599e+00 1.02 +++\n",
      "+++ 7.120e+02 7.116e+02 -3.713e+00 -3.600e+00 0.992 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 7.120e+02 7.117e+02 -4.525e+00 -4.341e+00 0.622 +++\n",
      "+++ 7.120e+02 7.111e+02 -4.525e+00 -4.249e+00 1.92 +++\n",
      "+++ 7.120e+02 7.115e+02 -4.525e+00 -4.295e+00 1.13 +++\n",
      "+++ 7.120e+02 7.116e+02 -4.525e+00 -4.318e+00 0.843 +++\n",
      "+++ 7.120e+02 7.116e+02 -4.525e+00 -4.306e+00 0.978 +++\n",
      "+++ 7.120e+02 7.115e+02 -4.525e+00 -4.301e+00 1.05 +++\n",
      "+++ 7.120e+02 7.115e+02 -4.525e+00 -4.304e+00 1.01 +++\n",
      "+++ 7.120e+02 7.115e+02 -4.525e+00 -4.305e+00 0.996 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 7.120e+02 7.118e+02 -4.620e+00 -4.343e+00 0.54 +++\n",
      "+++ 7.120e+02 -inf -4.620e+00 -4.204e+00 inf +++\n",
      "+++ 7.120e+02 7.115e+02 -4.620e+00 -4.274e+00 1.18 +++\n",
      "+++ 7.120e+02 7.116e+02 -4.620e+00 -4.308e+00 0.803 +++\n",
      "+++ 7.120e+02 7.116e+02 -4.620e+00 -4.291e+00 0.974 +++\n",
      "+++ 7.120e+02 7.115e+02 -4.620e+00 -4.282e+00 1.07 +++\n",
      "+++ 7.120e+02 7.115e+02 -4.620e+00 -4.287e+00 1.02 +++\n",
      "+++ 7.120e+02 7.115e+02 -4.620e+00 -4.289e+00 0.998 +++\n",
      "\t### errors for param 8 ###\n",
      "+++ 7.120e+02 7.119e+02 1.913e-01 2.390e-01 0.269 +++\n",
      "+++ 7.120e+02 7.117e+02 1.913e-01 2.629e-01 0.595 +++\n",
      "+++ 7.120e+02 7.116e+02 1.913e-01 2.748e-01 0.803 +++\n",
      "+++ 7.120e+02 7.116e+02 1.913e-01 2.808e-01 0.917 +++\n",
      "+++ 7.120e+02 7.116e+02 1.913e-01 2.838e-01 0.977 +++\n",
      "+++ 7.120e+02 7.115e+02 1.913e-01 2.853e-01 1.01 +++\n",
      "\t### errors for param 9 ###\n",
      "+++ 7.120e+02 7.119e+02 6.983e-01 7.492e-01 0.276 +++\n",
      "+++ 7.120e+02 7.117e+02 6.983e-01 7.746e-01 0.62 +++\n",
      "+++ 7.120e+02 7.116e+02 6.983e-01 7.874e-01 0.82 +++\n",
      "+++ 7.120e+02 7.116e+02 6.983e-01 7.937e-01 0.936 +++\n",
      "+++ 7.120e+02 7.115e+02 6.983e-01 7.969e-01 0.997 +++\n",
      "\t### errors for param 10 ###\n",
      "+++ 7.120e+02 7.116e+02 1.211e+00 1.563e+00 0.938 +++\n",
      "+++ 7.120e+02 7.111e+02 1.211e+00 1.739e+00 1.91 +++\n",
      "+++ 7.120e+02 7.113e+02 1.211e+00 1.651e+00 1.4 +++\n",
      "+++ 7.120e+02 7.115e+02 1.211e+00 1.607e+00 1.16 +++\n",
      "+++ 7.120e+02 7.115e+02 1.211e+00 1.585e+00 1.05 +++\n",
      "+++ 7.120e+02 7.116e+02 1.211e+00 1.574e+00 0.993 +++\n",
      "\t### errors for param 11 ###\n",
      "+++ 7.120e+02 7.117e+02 9.788e-01 1.270e+00 0.74 +++\n",
      "+++ 7.120e+02 7.113e+02 9.788e-01 1.416e+00 1.49 +++\n",
      "+++ 7.120e+02 7.115e+02 9.788e-01 1.343e+00 1.1 +++\n",
      "+++ 7.120e+02 7.116e+02 9.788e-01 1.307e+00 0.912 +++\n",
      "+++ 7.120e+02 7.115e+02 9.788e-01 1.325e+00   1 +++\n",
      "\t### errors for param 12 ###\n",
      "+++ 7.120e+02 7.116e+02 1.032e+00 1.430e+00 0.828 +++\n",
      "+++ 7.120e+02 7.111e+02 1.032e+00 1.628e+00 1.84 +++\n",
      "+++ 7.120e+02 7.114e+02 1.032e+00 1.529e+00 1.29 +++\n",
      "+++ 7.120e+02 7.115e+02 1.032e+00 1.479e+00 1.05 +++\n",
      "+++ 7.120e+02 7.116e+02 1.032e+00 1.455e+00 0.935 +++\n",
      "+++ 7.120e+02 7.116e+02 1.032e+00 1.467e+00 0.99 +++\n",
      "\t### errors for param 13 ###\n",
      "+++ 7.120e+02 7.119e+02 8.436e-01 1.048e+00 0.276 +++\n",
      "+++ 7.120e+02 7.117e+02 8.436e-01 1.150e+00 0.604 +++\n",
      "+++ 7.120e+02 7.116e+02 8.436e-01 1.201e+00 0.808 +++\n",
      "+++ 7.120e+02 7.116e+02 8.436e-01 1.226e+00 0.92 +++\n",
      "+++ 7.120e+02 7.116e+02 8.436e-01 1.239e+00 0.977 +++\n",
      "+++ 7.120e+02 7.115e+02 8.436e-01 1.245e+00 1.01 +++\n",
      "\t### errors for param 14 ###\n",
      "+++ 7.120e+02 7.115e+02 6.665e-01 1.546e+00   1 +++\n",
      "\t### errors for param 15 ###\n",
      "********************\n",
      "-0.475238 -1.05641 -2.44531 -2.74708 -3.6025 -3.71301 -4.52525 -4.62034 0.191311 0.698257 1.21102 0.978834 1.03208 0.843636 0.666538 0.854372\n",
      "0.00475354 0.00682415 0.0617908 0.0635266 0.0755408 0.112636 0.220195 0.331549 0.0939739 0.0986604 0.363072 0.346135 0.434851 0.401558 0.879854 3.38957\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
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.15567684,  3.85069114,  3.46079358,  1.80469275,  1.2276475 ,\n",
       "        0.64741975,  0.33000748,  0.27290686])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAFrCAYAAACE+GArAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqPGoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f114076d690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "xscale('log'); ylim(-10,10)\n",
    "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n",
    "\n",
    "lag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f11404bf410>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiIAAAGYCAYAAAB/DYmkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNygXy0FjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f113e81f150>"
      ]
     },
     "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": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f113e6b9290>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjYAAAGJCAYAAACZwnkIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f113e6b9f90>"
      ]
     },
     "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([3.99,3.99], [-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
}