phy-4660/lag/data/clag_analysis-origbins-3471A.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 0x7f3cb1b15d50>"
      ]
     },
     "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",
    "from scipy.optimize import curve_fit\n",
    "import numpy.fft\n",
    "\n",
    "ref_file=\"lc/1367A.lc\"\n",
    "echo_file=\"lc/3471A.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,  0.62032418])"
      ]
     },
     "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",
    "fqL\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\nAAAPYQAAD2EBqD+naQAAGp1JREFUeJzt3X9s4/d93/HnxZaj5dxOi12RZ+98bLhdaBhJDbLSHzrb\n5TIn2IY26dBOIbFsiLTCQdoOuHUrcMMgzZCAYS2G9uqua3FbD9kQjNQNaDYH2LXFUKW5UVqnimm7\nbmadUT+W5kxmTXvr6kSpVnt/UHeR1I9+8I7fL389HwAhivx8vp+3cJ+TXvx+P9/vFyRJkiRJkiRJ\nkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkvSA/iGwBvwh0AQ+A1zsakWSJKkn3AT+NvA08H7gs8AW\n8K4u1iRJknrQ48BbwHPdLkSSJJ3sHTGONbb39fdjHFOSJPW4M7QON/xqtwuRJEmn83BM4/xz4BmO\nP9Rwbu8hSZLa88beo6PiCAk/DXw38AJw+4g255544onbt28f9bYkSTrGl4EJOhwUogwJZ2gFhI8A\neWD7mLbnbt++zac//WmefvrpCEvqvMuXL3P16tW+HO9BttVu33ban6btSW2Oez/uf7NOca51vr1z\nLcy51vn2Uc611157jY997GNP0tob3zch4WeAIq2Q8CaQ3Hv9DrAT6vD000+TzWYjLKnzxsbGYq25\nk+M9yLba7dtO+9O0PanNce/H/W/WKc61zrd3roU51zrfPuq5FpWHItz2Z4F3AjPA39/3+CLwm4fa\nngM+8YlPfIJz5/pvWcL73ve+vh3vQbbVbt922p+m7Ultjnq/VCpRLBZPXUsvca51vr1zLcy51vn2\nUc21N954g2vXrgFco8N7Es50cmMPIAusr6+v92XqVn/58Ic/zKuvvtrtMjQEnGuKQ7VaJZfLAeSA\naie3Hed1EiRJUh8xJGjo9OvuX/Uf55r6nSFBQ8df3IqLc039zpAgSZKCDAmSJCnIkCBJkoIMCZIk\nKciQIEmSggwJkiQpyJAgSZKCDAmSJCnIkCBJkoIMCZIkKciQIEmSggwJkiQpyJAgSZKCDAmSJCnI\nkCBJkoIMCZIkKciQIEmSggwJkiQpyJAgSZKCDAmSJCnIkCBJkoIMCZIkKciQIEmSggwJkiQpyJAg\nSZKCDAmSJCnIkCBJkoIMCZIkKciQIEmSggwJkiQpyJAgSZKCDAmSJCnIkCBJkoIMCZIkKciQIEmS\nggwJkiQpyJAgSZKCDAmSJCkoypDwAvBZ4MvAW8BHIhxLkiR1WJQh4V3AF4Af2vv+7QjHkiRJHfZw\nhNv+xb2HJEnqQ65JkCRJQYYESZIUZEiQJElBUa5JaNvly5cZGxs78FqxWKRYLHapIkmSekepVKJU\nKh147c6dO5GNdyayLR/0FvC9wKtHvJ8F1tfX18lmszGVJElS/6tWq+RyOYAcUO3ktqPck3AW+Iv7\nvn8P8CzwVeBLEY4rSZI6IMqQMAH8yt7zt4Gf2Hv+KWA2wnElSVIHRBkSPocLIyVJ6lv+EZckSUGG\nBEmSFGRIkCRJQYYESZIUZEiQJElBhgRJkhRkSJAkSUGGBEmSFGRIkCRJQYYESZIUZEiQJElBhgRJ\nkhRkSJAkSUGGBEmSFGRIkCRJQYYESZIUZEiQJElBhgRJkhT0cLcLkKJQKpUolUoA7OzssL29zYUL\nFxgdHQWgWCxSLBa7WaIk9TxDggbS/hBQrVbJ5XKUSiWy2WyXK5Ok/uHhBkmSFGRIkCRJQYYESZIU\nZEiQJElBhgQNrK2tLWZnZ5mengZgenqa2dlZtra2uluYJPUJz27QwGk2mxQKBWq1Go1G497r9Xqd\ner3OzZs3yWQylMtlEolEFyuVpN5mSNBAaTabTE1NsbGxcWSbRqNBo9Hg0qVLVCoVg4IkHcHDDRoo\nhULh2ICwX71ep1AoRFyRJPUvQ4IGxubmJrVara0+tVrNNQqSdARDggbG4uLigTUIp9FoNFhYWIio\nIknqb4YEDYy1tbVY+0nSoDMkaGDs7u7G2k+SBp0hQQNjZGQk1n6SNOgMCRoYExMT99VvcnKyw5VI\n0mAwJGhgzM/Pk0wm2+qTTCaZm5uLqCJJ6m+GBA2MVCpFJpNpq08mkyGVSkVTkCT1OUOCBkq5XCad\nTp+qbTqdZmlpKeKKJKl/GRI0UBKJBJVKhXw+f+Shh2QyST6fZ2VlhfHx8ZgrlKT+YUjQwEkkEiwv\nL7O6usrMzMy9PQvpdJqZmRlWV1dZXl42IEjSCbzBkwZWKpXi+vXrVKtVcrkcN27cIJvNdrssSeob\nUe9J+EFgE/g68OvAcxGPJ0mSOiTKkPBR4CeBReBZ4BZwEzgf4ZiSJKlDogwJPwL8K+A68DvA3wO+\nBHwywjElSVKHRBUSHgGywC8fev2XgamIxpQkSR0U1cLFx4GHgOah178CtHdJPOk+lEolSqUSADs7\nO1y8eJErV64wOjoKQLFYpFgsdrNESep5nt2ggWQIOJ3DYWp7e5sLFy4YpiQBcCai7T4CvAl8P/Af\n9r3+U8D7gb90qH0WWH/++ecZGxs78Ia/pKR43D1VdH193VNFpR61P9jfdefOHW7dugWQA6qdHC+q\nPQl/DKwDH+JgSPgg8JmjOl29etVfTpIkHSH0wfluwI9ClGc3/ATwA8AM8DSt0yH/PPBzEY4pqU1b\nW1vMzs4yPT0NwPT0NLOzs2xtbXW3MEldF+WahBvAY8A8cA74b8Bfo3UapKQuazabFAoFarUajUbj\n3uv1ep16vc7NmzfJZDKUy2USiUQXK5XULVEvXPzZvYekHtJsNpmammJjY+PINo1Gg0ajwaVLl6hU\nKgYFaQh5gydpCBUKhWMDwn71ep1CoRBxRZJ6kSFBGjKbm5vUarW2+tRqNdcoSEPIkCANmcXFxQNr\nEE6j0WiwsLAQUUWSepUhQRoya2trsfaT1L8MCdKQ2d3djbWfpP5lSJCGzMjISKz9JPUvQ4I0ZCYm\nJu6r3+TkZIcrkdTrDAnSkJmfnyeZbO9mrMlkkrm5uYgqktSrDAnSkEmlUmQymbb6ZDIZUqlUNAVJ\n6lmGBGkIlctl0un0qdqm02mWlpYirkhSLzIkSEMokUhQqVTI5/NHHnpIJpPk83lWVlYYHx+PuUJJ\nvcCQIA2pRCLB8vIyq6urzMzM3NuzkE6nmZmZYXV1leXlZQOCNMSivsGTpB6XSqW4fv36vXvS37hx\ng2w22+2yJPUA9yRIkqQgQ4IkSQrycIM0xEqlEqVSCYCdnR0uXrzIlStXGB0dBaBYLFIsFrtZoqQu\nMiRIQ8wQIOk4Hm6QJElBhgRJkhRkSJAkSUGGBEmSFGRIkCRJQYYESZIUZEiQJElBhgRJkhRkSJAk\nSUGGBEmSFGRIkCRJQYYESZIUZEiQJElBhgRJkhRkSJAkSUGGBEmSFGRIkCRJQYYESZIUZEiQJElB\nhgRJkhRkSJAkSUGGBEmSFGRIkCRJQYYESZIUFFVI+EfACvA14A8iGkOSJEUoqpAwAiwB/yKi7UuS\npIg9HNF2X977+vGIti9JkiLmmgRJkhQU1Z4ESeq4UqlEqVQCYGdnh+3tbS5cuMDo6CgAxWKRYrHY\nzRKlgdJOSHgZmD+hzXcC1fuuRpKOsT8EVKtVcrkcpVKJbDbb5cqkwdROSPhp4N+e0Gb7AWrh8uXL\njI2NHXjNTwaSJLXs35t21507dyIbr52Q8NW9R2SuXr3qJwJJko4Q+uB8d69aFKJak/AU8O69rw8B\n3wGcAb4IvBnRmJIkqYOiOrthgdbahJeBs8AXgHUgmqgjaWhsbW0xOzvL9PQ0ANPT08zOzrK1tdXd\nwqQBFNWehI/jNRIkdVCz2aRQKFCr1Wg0Gvder9fr1Ot1bt68SSaToVwuk0gkulipNDg8BVJSz2s2\nm0xNTbGxsXFkm0ajQaPR4NKlS1QqFYOC1AFeTElSzysUCscGhP3q9TqFQiHiiqThYEiQ1NM2Nzep\n1Wpt9anVaq5RkDrAkCCppy0uLh5Yg3AajUaDhYWFiCqShochQVJPW1tbi7WfpG8yJEjqabu7u7H2\nk/RNhgRJPW1kZCTWfpK+yZAgqadNTEzcV7/JyckOVyINH0OCpJ42Pz9PMplsq08ymWRubi6iiqTh\nYUiQ1NNSqRSZTKatPplMhlQqFU1B0hAxJEjqeeVymXQ6faq26XSapaWliCuShoMhQVLPSyQSVCoV\n8vn8kYcekskk+XyelZUVxsfHY65QGkzeu0FSX0gkEiwvL7O1tcXCwgKf//znqdfrpNNpXnjhBebn\n5yM7xFAqlSiVSgDs7Oywvb3NhQsXGB0dBaBYLFIsFiMZW+qmM90uYE8WWF9fXyebzXa7Fkl9oFqt\nksvliPv3RrfGlY5yd04COaDayW17uEGSJAUZEiTpFLa2tpidnWV6ehqA6elpZmdnvZGUBpprEiTp\nGM1mk0KhQK1WO3CjqXq9Tr1e5+bNm2QyGcrlMolEoouVSp1nSJDUNw4vILx48SJXrlyJbAFhs9lk\namqKjY2NI9s0Gg0ajQaXLl2iUqkYFDRQDAmS+kbcZxEUCoVjA8J+9XqdQqHA8vJyxFVJ8XFNgiQF\nbG5uUqvV2upTq9Vco6CBYkiQpIDFxcUDaxBOo9FosLCwEFFFUvwMCZIUsLa2Fms/qRcZEiQpYHd3\nN9Z+Ui8yJEhSwMjISKz9pF5kSJCkgImJifvqNzk52eFKpO4xJEhSwPz8/JF3nDxKMplkbm4uooqk\n+BkSJCkglUqRyWTa6pPJZCK7E6XUDYYESTpCuVwmnU6fqm06nWZpaSniiqR4GRIk6QiJRIJKpUI+\nnz/y0EMymSSfz7OyssL4+HjMFUrRMiRI0jESiQTLy8usrq4yMzNzb89COp1mZmaG1dVVlpeXDQga\nSN67QZJOIZVKcf36darVKrlcjhs3bpDNZrtdlhQp9yRIkqQg9yRI0gnivkW11CsMCZJ0AkOAhpWH\nGyRJUpAhQZIkBRkSJElSkCFBkiQFGRIkSVKQIUGSJAUZEiRJUlBUISEF/DywAXwN+J/Ay8BIRONJ\nkqQOi+piSu8FzgAv0QoI7wP+JXAW+NGIxpQkSR0UVUj4pb3HXVvAPwM+iSFBkqS+EOeahDHgqzGO\nJ0mSHkBc925IAz8M/EhM40lSXzt8U6nt7W0uXLjgTaUUq3b3JLwMvHXC4/AN1p8AfhG4AVx/gFol\naWgUi0VeeeUVHn/8cTY2Nnj99dfZ2Njg8ccf55VXXjEgKBZn2mz/2N7jONvAN/aePwEsA6vAx4/p\nkwXWn3/+ecbGxg68YVqWNGyazSaFQoFarUaj0fhT7yeTSTKZDOVymUQi0YUK1S379zDddefOHW7d\nugWQA6qdHK/dkNCOJ2kFhDXgY8Dbx7TNAuvr6+tks4d3REjS8Gg2m0xNTbGxsXFi23Q6TaVSMSgM\nuWq1Si6XgwhCQlQLF58EPkdrr8KPAgkgufeQJB2hUCicKiAA1Ot1CoVCxBVpmEW1cPGDtBYrvgf4\n3X2vvw08FNGYktTXNjc3qdVqbfWp1WpsbW2RSqWiKUpDLao9CZ/a2/ZDe1/fse97SVLA4uJicA3C\ncRqNBgsLCxFVpGHnvRskqUesra3F2k86iSFBknrE7u5urP2kkxgSJKlHjIzc3z3w7refdBJDgiT1\niImJifvqNzk52eFKpBZDgiT1iPn5eZLJ9s4UTyaTzM3NRVSRhp0hQZJ6RCqVIpPJtNUnk8l4+qMi\nY0iQpB5SLpdJp9OnaptOp1laWoq4Ig0zQ4Ik9ZBEIkGlUiGfzx956CGZTJLP51lZWWF8fDzmCjVM\nDAmS1GMSiQTLy8usrq4yMzNzb89COp1mZmaG1dVVlpeXDQiKXFSXZZYkPaBUKsX169fv3cDnxo0b\n3gRPsTIkSFIP2n9L4J2dHS5evMiVK1cYHR0FoFgsUiwWu1mihoAhQZJ6kCFAvcA1CZIkKciQIEm6\np1Qq8eKLL/LUU0/x6KOP8sgjj/Doo4/y1FNP8eKLL947BKLh4OEGSRIAzWaTa9euUavVDtyyend3\nlzfffJPd3V2uXbvGBz7wARKJRBcrVVwMCZIkms0mU1NTbGxsHNmm0WjQaDS4dOkSlUrFoDAEPNwg\nSaJQKBwbEPar1+sUCoWIK1IvMCRI0pDb3NykVqu11adWq7G1tRVNQeoZhgRJGnKLi4sH1iCcRqPR\nYGFhIaKK1CsMCZI05NbW1mLtp/5hSJCkIbe7uxtrP/UPQ4IkDbmRkZFY+6l/GBIkachNTEzcV7/J\nyckOV6JeY0iQpCE3Pz9PMplsq08ymWRubi6iitQrDAmSNORSqRSZTKatPplMhlQqFU1B6hmGBEkS\n5XKZdDp9qrbpdJqlpaWIK1IvMCRIkkgkElQqFfL5/JGHHpLJJPl8npWVFcbHx2OuUN1gSJAkAa2g\n8NJLL/HMM89w/vx5zp49y8jICGfPnuX8+fM888wzvPTSSwaEIeINniRJ9xSLRYrFYrfLUI9wT4Ik\nSQoyJEiSpCBDgiRJCjIkSJKkIEOCJEkKMiRIkqQgQ4IkSQoyJEiSpCBDgiRJCjIkSJKkIEOCJEkK\niiokvApsA18HbgP/BjgX0ViSJCkCUYWEXwH+BnAR+D4gDfxCRGNJkqQIRHUXyKv7nn8J+DHgM8BD\nwJ9ENKYkSeqgONYkvBv4m8AyBgRJkvpGlCHhx4A/An4P+HbgoxGOJUmSOqydkPAy8NYJj+y+9j8O\nPAt8CPgG8O+BMw9csSRJikU7f7Qf23scZ5tWIDjsSVprE54DVgLvZ4H1559/nrGxsQNvFItFisVi\nG2VKkjSYSqUSpVLpwGt37tzh1q1bADmg2snx4vpkf55WgPgu4Fbg/Sywvr6+TjabDbwtSZJCqtUq\nuVwOIggJUZzdMLn3+M/AHwDvARaALwKrEYwnSZIiEMXCxa8Bfx34T0AN+Hngt2jtRfh/EYwnSZIi\nEMWehN8G/nIE25UkSTHy3g2SJCnIkCBJkoIMCZIkKciQIEmSggwJkiQpyJAgSZKCDAmSJCnIkCBJ\nkoIMCZIkKciQIEmSggwJkiQpKIp7N0iSdGqlUolSqQTAzs4O29vbXLhwgdHRUQCKxSLFYrGbJQ4t\nQ4Ikqav2h4BqtUoul6NUKpHNZrtcmTzcIEmSggwJkiQpyJAgSZKCDAmSJCnIkCBJ6rqtrS1mZ2eZ\nnp4GYHp6mtnZWba2trpb2JDz7AZJUtc0m00KhQK1Wo1Go3Hv9Xq9Tr1e5+bNm2QyGcrlMolEoouV\nDidDgiSpK5rNJlNTU2xsbBzZptFo0Gg0uHTpEpVKxaAQMw83SJK6olAoHBsQ9qvX6xQKhYgr0mGG\nBElS7DY3N6nVam31qdVqrlGImSFBkhS7xcXFA2sQTqPRaLCwsBBRRQoxJEiSYre2thZrP90fQ4Ik\nKXa7u7ux9tP9MSRIkmI3MjISaz/dH0OCJCl2ExMT99VvcnKyw5XoOIYESVLs5ufnSSaTbfVJJpPM\nzc1FVJFCDAmSpNilUikymUxbfTKZDKlUKpqCFGRIkCR1RblcJp1On6ptOp1maWkp4op0mCFBktQV\niUSCSqVCPp8/8tBDMpkkn8+zsrLC+Ph4zBXKkCBJ6ppEIsHy8jKrq6vMzMzc27OQTqeZmZlhdXWV\n5eVlA0KXeIMnSVLXpVIprl+/TrVaJZfLcePGDbLZbLfLGnruSZAkSUGGBEmSFGRIkCRJQYYESZIU\n5MJFSVJXlUolSqUSADs7O1y8eJErV64wOjoKQLFYpFgsdrPEoWVIkCR1lSGgd3m4QZIkBRkSJElS\nUNQh4Z3AbwBvAe+PeCzpVO4e+5Si5lxTv4s6JPw48OWIx5Da4i9uxcW5pn4XZUj4q8CLwD+IcAxJ\nkhSRqEJCArgG/C3g6xGN0RPi/qTQyfEeZFvt9m2n/WnantRmED/BOdc63965FuZc63z7fp1rUYSE\nM8CngJ8FqhFsv6f4n6nz7fv1P1PUnGudb+9cC3Oudb59v861dq6T8DIwf0KbCeAS8CjwTw+9d+ak\nAV577bU2yukNd+7coVqNLwt1crwH2Va7fdtpf5q2J7U57v24/806xbnW+fbOtTDnWufbRznXovzb\neeIf7n0e23scZxsoA98DvL3v9YeAPwE+DcwE+p0D1oAn26hHkiS1fJnWB/U3OrnRdkLCaZ0HvmXf\n908CvwR8H/BrwO0j+p3be0iSpPa8QYcDQlxSeJ0ESZL6TlxXXHz75CaSJEmSJEmSJEmSJEmx+xbg\nvwJfAH4b+OHulqMBdh74HPDfgd8Evr+r1WjQfQb4feDfdbsQDazvBmrA68Df6XItkXkHMLr3/M8A\nG8C3da8cDbAk3zwT59uAL9Gac1IUvovWL3FDgqLwMPA7tC4v8CitoPDudjYQ19kND+otYGfv+buA\n3X3fS53UAH5r7/n/pvUpr63/VFIbfhX4o24XoYE1SWuv6Bu05tl/BD7Uzgb6JSQA/Flau3//F/BT\nwP/tbjkaAt9J64Jj3u5cUj96goO/v36XNq9s3E8h4f8A3wF8O/BDwF/objkacI8B/xp4qduFSNJ9\neuBrFEUVEl4APksrwbwFfCTQ5geBTVq3kv514Ll97/1dWosUq8DIoX5fobWw7NmOVqx+FcVceyfw\nC8A/Af5LJFWrH0X1e82LzekoDzrnbnNwz8F5emTP6F8BFoDvpfWDffjQ+x8FvgHMAu8FfpLW4YPz\nR2xvHPjWveffSuuY8Xs7W7L6VKfn2hmgBPzjKIpVX+v0XLsrjwsXFfagc+5hWosVn6B1luDrwJ+L\nvOo2hX6wXwN+5tBr/4PWJ7eQLK0E/ht7j9CdJKVOzLXnaN2xtEprzn0BeKaDNWowdGKuQevmd18B\n3qR1Jk2uUwVq4NzvnPseWmc4fBH4gciqewCHf7BHaJ2dcHi3yVVahxGk++VcU1yca4pbV+ZcNxYu\nPg48BDQPvf4VWueoS53iXFNcnGuKWyxzrp/ObpAkSTHqRkj4PVrHfBOHXk/QuuCD1CnONcXFuaa4\nxTLnuhES/hhY509f9emDwEr85WiAOdcUF+ea4tbXc+4sresYPEtrscXlved3T8uYpnXaxgzwNK3T\nNv6Qk08Vkg5zrikuzjXFbWDnXJ7WD/QWrd0hd59f39fmk7QuALEDrHHwAhDSaeVxrikeeZxrilce\n55wkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSVIf+P9cYZ1EAfTlhQAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3cd80692d0>"
      ]
     },
     "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 0x7f3cb1682110>"
      ]
     },
     "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 4.362e-01 5.425e+01 inf -- -3.468e+02 -- 1 1 1 1 1 1 1 1\n",
      "   2 7.747e-01 5.347e+01 7.019e+01 -- -2.766e+02 -- 0.58045 0.569962 0.564602 0.563764 0.567626 0.565054 0.565279 0.564008\n",
      "   3 3.447e+00 5.213e+01 6.905e+01 -- -2.076e+02 -- 0.198681 0.140203 0.130333 0.127063 0.134237 0.12998 0.131082 0.127044\n",
      "   4 1.448e+00 4.998e+01 6.682e+01 -- -1.408e+02 -- -0.0825582 -0.283347 -0.300715 -0.310826 -0.299703 -0.304649 -0.30132 -0.310849\n",
      "   5 5.884e-01 4.684e+01 6.330e+01 -- -7.746e+01 -- -0.194194 -0.676998 -0.726162 -0.750752 -0.733583 -0.738758 -0.729946 -0.74976\n",
      "   6 3.739e-01 4.258e+01 5.850e+01 -- -1.895e+01 -- -0.203226 -0.953186 -1.14229 -1.19152 -1.16459 -1.17342 -1.15279 -1.19094\n",
      "   7 2.741e-01 3.740e+01 5.292e+01 -- 3.397e+01 -- -0.205901 -0.9956 -1.54459 -1.63073 -1.58521 -1.61127 -1.5669 -1.63629\n",
      "   8 2.128e-01 3.180e+01 4.679e+01 -- 8.076e+01 -- -0.180502 -0.943953 -1.92935 -2.06126 -1.98071 -2.05288 -1.97074 -2.08385\n",
      "   9 1.675e-01 2.595e+01 3.791e+01 -- 1.187e+02 -- -0.15428 -0.934563 -2.26437 -2.45352 -2.31988 -2.48374 -2.35707 -2.52729\n",
      "  10 1.286e-01 2.002e+01 2.537e+01 -- 1.440e+02 -- -0.13956 -0.939381 -2.49352 -2.71726 -2.56371 -2.84902 -2.70919 -2.95055\n",
      "  11 9.197e-02 1.277e+01 1.299e+01 -- 1.570e+02 -- -0.130332 -0.946187 -2.57446 -2.74449 -2.69402 -3.05697 -3.00449 -3.33007\n",
      "  12 5.076e-02 6.047e+00 5.021e+00 -- 1.621e+02 -- -0.124515 -0.945944 -2.55165 -2.71422 -2.73704 -3.10737 -3.209 -3.63633\n",
      "  13 1.434e-02 1.485e+00 1.060e+00 -- 1.631e+02 -- -0.12047 -0.945369 -2.54157 -2.70197 -2.76441 -3.10602 -3.30159 -3.8209\n",
      "  14 4.769e-03 3.557e-01 7.280e-02 -- 1.632e+02 -- -0.119516 -0.946002 -2.53635 -2.69518 -2.78858 -3.10381 -3.32152 -3.8757\n",
      "  15 2.150e-03 1.530e-01 4.222e-03 -- 1.632e+02 -- -0.119506 -0.945491 -2.53265 -2.69429 -2.80187 -3.10094 -3.32488 -3.87969\n",
      "  16 1.017e-03 7.109e-02 8.430e-04 -- 1.632e+02 -- -0.119394 -0.945124 -2.53033 -2.69448 -2.8079 -3.09858 -3.32603 -3.87983\n",
      "  17 4.817e-04 3.356e-02 1.918e-04 -- 1.632e+02 -- -0.119311 -0.944963 -2.52941 -2.69447 -2.81076 -3.09727 -3.32654 -3.87984\n",
      "  18 2.335e-04 1.618e-02 4.417e-05 -- 1.632e+02 -- -0.119282 -0.944891 -2.52886 -2.69454 -2.81211 -3.09664 -3.32676 -3.87987\n",
      "********************\n",
      "-0.119282 -0.944891 -2.52886 -2.69454 -2.81211 -3.09664 -3.32676 -3.87987\n",
      "0.233661 0.204163 0.319993 0.254151 0.198248 0.179386 0.161786 0.221522\n",
      "0.000372164 0.000983332 0.00164575 -0.000696472 -0.0161795 0.00744155 -0.00415795 -0.000828998\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",
      "+++ 1.632e+02 1.627e+02 -1.193e-01 1.144e-01 0.905 +++\n",
      "+++ 1.632e+02 1.622e+02 -1.193e-01 2.312e-01 1.9 +++\n",
      "+++ 1.632e+02 1.625e+02 -1.193e-01 1.728e-01 1.37 +++\n",
      "+++ 1.632e+02 1.626e+02 -1.193e-01 1.436e-01 1.13 +++\n",
      "+++ 1.632e+02 1.627e+02 -1.193e-01 1.290e-01 1.01 +++\n",
      "+++ 1.632e+02 1.627e+02 -1.193e-01 1.217e-01 0.958 +++\n",
      "+++ 1.632e+02 1.627e+02 -1.193e-01 1.254e-01 0.985 +++\n",
      "+++ 1.632e+02 1.627e+02 -1.193e-01 1.272e-01 0.999 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 1.632e+02 1.627e+02 -9.449e-01 -7.407e-01 0.961 +++\n",
      "+++ 1.632e+02 1.622e+02 -9.449e-01 -6.386e-01 2.04 +++\n",
      "+++ 1.632e+02 1.625e+02 -9.449e-01 -6.897e-01 1.46 +++\n",
      "+++ 1.632e+02 1.626e+02 -9.449e-01 -7.152e-01 1.2 +++\n",
      "+++ 1.632e+02 1.627e+02 -9.449e-01 -7.279e-01 1.08 +++\n",
      "+++ 1.632e+02 1.627e+02 -9.449e-01 -7.343e-01 1.02 +++\n",
      "+++ 1.632e+02 1.627e+02 -9.449e-01 -7.375e-01 0.989 +++\n",
      "+++ 1.632e+02 1.627e+02 -9.449e-01 -7.359e-01   1 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 1.632e+02 1.630e+02 -2.529e+00 -2.369e+00 0.307 +++\n",
      "+++ 1.632e+02 1.629e+02 -2.529e+00 -2.289e+00 0.681 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.529e+00 -2.249e+00 0.919 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.529e+00 -2.229e+00 1.05 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.529e+00 -2.239e+00 0.984 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.529e+00 -2.234e+00 1.02 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.529e+00 -2.236e+00   1 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 1.632e+02 1.630e+02 -2.695e+00 -2.567e+00 0.306 +++\n",
      "+++ 1.632e+02 1.629e+02 -2.695e+00 -2.504e+00 0.681 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.695e+00 -2.472e+00 0.922 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.695e+00 -2.456e+00 1.05 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.695e+00 -2.464e+00 0.988 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.695e+00 -2.460e+00 1.02 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.695e+00 -2.462e+00   1 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 1.632e+02 1.629e+02 -2.813e+00 -2.614e+00 0.665 +++\n",
      "+++ 1.632e+02 1.624e+02 -2.813e+00 -2.515e+00 1.57 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.813e+00 -2.565e+00 1.07 +++\n",
      "+++ 1.632e+02 1.628e+02 -2.813e+00 -2.590e+00 0.853 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.813e+00 -2.577e+00 0.956 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.813e+00 -2.571e+00 1.01 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.813e+00 -2.574e+00 0.983 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.813e+00 -2.573e+00 0.997 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 1.632e+02 1.628e+02 -3.096e+00 -2.917e+00 0.837 +++\n",
      "+++ 1.632e+02 1.623e+02 -3.096e+00 -2.827e+00 1.87 +++\n",
      "+++ 1.632e+02 1.625e+02 -3.096e+00 -2.872e+00 1.29 +++\n",
      "+++ 1.632e+02 1.627e+02 -3.096e+00 -2.895e+00 1.06 +++\n",
      "+++ 1.632e+02 1.627e+02 -3.096e+00 -2.906e+00 0.947 +++\n",
      "+++ 1.632e+02 1.627e+02 -3.096e+00 -2.900e+00   1 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 1.632e+02 1.627e+02 -3.327e+00 -3.165e+00 0.992 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 1.632e+02 1.631e+02 -3.880e+00 -3.769e+00 0.278 +++\n",
      "+++ 1.632e+02 1.629e+02 -3.880e+00 -3.714e+00 0.631 +++\n",
      "+++ 1.632e+02 1.628e+02 -3.880e+00 -3.686e+00 0.862 +++\n",
      "+++ 1.632e+02 1.627e+02 -3.880e+00 -3.672e+00 0.991 +++\n",
      "********************\n",
      "-0.119263 -0.944854 -2.52866 -2.69453 -2.81277 -3.09632 -3.32687 -3.87987\n",
      "0.246439 0.208948 0.29245 0.232296 0.24017 0.196142 0.161799 0.207668\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\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzt3X9w1Pd95/EnBtk0ITHBlF071Gy1LV3SyM5JFjUo5kTO\n7TS+JO01V1c7yd0U1ec0dY/h7uwp1w46j7hpk4ZpXJpeO9SWe3eJV3DT5s6+MY3bVBRXyKkiObY5\n2Dq30mITs0sxwU2dwsnA/bESFvirHyvtd38+HzM7knY/3/18iD/RvvT9fr7vD0iSJEmSJEmSJEmS\nJEmSJEmSJEmSJEmSJEmSJEmSJEmSJGmR/iMwDPw9kAe+Cqyv6IgkSVJVOAj8a2ADcBvwFJAF3lXB\nMUmSpCq0GrgEfLjSA5EkSXO7rox9rZz8eraMfUqSpCq3hMLlhr+q9EAkSdL8LCtTP18CfpzZLzXc\nPPmQJEnFOTX5KKlyhITfAz4GbAFem6HNzbfccstrr70208uSJGkW3wHaKXFQCDMkLKEQEH4G6ARO\nzNL25tdee40vf/nLbNiwIcQhld6OHTt45JFHarK/xbxXsccW034+bedqM9vr5f5vVirOtdK3d64F\nc66Vvn2Yc+348eN8+tOffj+Fs/E1ExJ+H0hSCAlvAtHJ588B54MO2LBhA62trSEOqfRWrlxZ1jGX\nsr/FvFexxxbTfj5t52oz2+vl/m9WKs610rd3rgVzrpW+fdhzLSxLQ3zvp4AbgG3Af5j2+DbwwjVt\nbwY+85nPfIabb669ZQktLS01299i3qvYY4tpP5+2c7WZ6fVUKkUymZz3WKqJc6307Z1rwZxrpW8f\n1lw7deoU+/btA9hHic8kLCnlmy1CKzAyMjJSk6lbteUTn/gETz75ZKWHoQbgXFM5jI6O0tbWBtAG\njJbyvctZJ0GSJNUQQ4IaTq2e/lXtca6p1hkS1HD8xa1yca6p1hkSJElSIEOCJEkKZEiQJEmBDAmS\nJCmQIUGSJAUyJEiSpECGBEmSFMiQIEmSAhkSJElSIEOCJEkKZEiQJEmBDAmSJCmQIUGSJAUyJEiS\npECGBEmSFMiQIEmSAhkSJElSIEOCJEkKZEiQJEmBDAmSJCmQIUGSJAUyJEiSpECGBEmSFMiQIEmS\nAhkSJElSoDBDwhbgKeA7wCXgZ0LsS5IklViYIeFdwPPAA5M/Xw6xL0mSVGLLQnzvP5t8SJKkGuSa\nBEmSFMiQIEmSAhkSJElSoDDXJBRtx44drFy58qrnkskkyWSyQiOSJKl6pFIpUqnUVc+dO3cutP6W\nhPbOV7sE/Czw5AyvtwIjIyMjtLa2lmlIkiTVvtHRUdra2gDagNFSvneYZxLeDfzotJ+bgQ8BrwOv\nhtivJEkqgTBDQjvwl5PfXwZ+Z/L7Pwa6Q+xXkiSVQJgh4RAujJQkqWb5IS5JkgIZEiRJUiBDgiRJ\nCmRIkCRJgQwJkiQpkCFBkiQFMiRIkqRAhgRJkhTIkCBJkgIZEiRJUiBDgiRJCmRIkCRJgcLc4Emq\nmNRLKVJHUwCcf+s8J944wbob17F82XIAkh9MkmxJVnKIklT1DAmqS8mWt0PA6KlR2va1kfpkitab\nWys8MkmqHV5ukCRJgQwJqlvZbJbuB7q59+fuhSfg3p+7l+4Huslms5UemiTVBC83qO7k83m67usi\nfTZN7gM5+OnC8xkyZE5mOPipgyRWJeh/tJ9IJFLZwUpSFTMkqK7k83k237OZsTvH4I6ABmshtzZH\n7nSOjns6GHx60KAgSTPwcoPqStd9XYWAsGaOhmsgc2eGrvu6yjIuSapFhgTVjfHxcdJn03MHhClr\nIH027RoFSZqBIUF1Y/ee3YU1CEXIbcjRu6c3pBFJUm0zJKhuDL84DGuLPGgtDL8wHMp4JKnWGRJU\nNyYuThR/0BKYuLSA4ySpARgSVDealjYVf9BlaLpuAcdJUgMwJKhutN/WDieLPOgkbLx9YyjjkaRa\nZ0hQ3eh5qIfosWhRx0SPR9n14K6QRiRJtc2QoLoRi8VIrErA6XkecBoSqxLEYrEwhyVJNSvskPAr\nwDjwj8A3gQ+H3J8aXP+j/cSfi88dFE5D/Lk4+x/bX5ZxSVItCrMs8y8AXwQ+CwwCvwwcBD4AvBpi\nv2pgkUiEwacH2frRrYxlx7jwnguwfFqD83DD926gOdbMoT87xJo18628JEmNJ8wzCf8eeBToA/4W\n+HcUwsFnQ+xTIhKJcGz0GOnRNNs+vo21P7AWzsDaH1jLto9vIz2a5tjoMQOCJM0hrJBwPdAKPHPN\n888Am0PqU7pKLBaj70t9fO7zn4Oz8LnPf46+L/W5BkGS5imskLAaWArkr3n+NFDc8nNpgbLZLN3d\n3ez8zE4Adn5mJ93d3e7VIEnz5FbRqjv5fJ6tH9vK2LfHuPDGhSvPnzxxkscff5wn/vQJmn+0mYH/\nPdDw20SnXkqROpoC4Pxb5znxxgnW3biO5csKCzmSH0ySbElWcoiSKiiskHAGuAhc+xs4Apya6aAd\nO3awcuXKq55LJpMkk/6S0vzk83k2b97M2NjYjG0uvHGB4988TkdHB4ODgw0dFJItSTa9ZxO9X+jl\n8OhhMmczXFx1kS2tW+h5qMdLM1KVSaVSpFKpq547d+5caP0tCe2d4TlgBHhg2nPHgK8Cv3FN21Zg\nZGRkhNbW1hCHpHq3detWDh06NO/2nZ2dDAwMhDegKpbP5+m6r4v02XRh98zpm2OdhOixKIlVCfof\n7W/oICVVu9HRUdra2gDagNFSvneYlxt+B/jvFOojPAfcT+HX0B+G2Kca2Pj4OOl0uqhj0uk02Wy2\n4f5izufzbL5nM2N3jsEdAQ3WQm5tjtzpHB33dDD4dGOfcZEaVZi3QB4AdgA9wPMUCindgzUSFJLd\nu3eTy+WKOiaXy9Hb2xvSiKpX131dhYAw112gayBzZ4au+7rKMi5J1SXsiot/APwwhXI27cBfh9yf\nGtjw8HBZj6tV4+PjpM+m5w4IU9ZA+mzau0KkBuTeDaobExMTZT2uVu3es7uwBqEIuQ05evc03hkX\nqdEZElQ3mpqaynpcrRp+cfjqRYrzsRaGX2isMy6SDAmqI+3t7Qs6buPGjSUeSXWbuLiAMydLYOJS\nY51xkWRIUB3p6ekhGi2uoGc0GmXXrl0hjag6NS1dwJmTy9B0XWOdcZFkSFAdicViJBKJoo5JJBIN\nd/tj+23tcLLIg07Cxtsb64yLJEOC6kx/fz/xeHxebePxOPv37w95RNWn56EeoseKPONyPMquBxvr\njIskQ4LqTCQSYXBwkM7OzhkvPUSjUTo7Ozly5EhDbhcdi8VIrEoUtlubj9OQWNV4Z1wkGRJUhyKR\nCAMDAwwNDbFt27YrZxbi8Tjbtm1jaGiIgYGBhgwIU/of7Sf+XHzuoHAa4s/F2f9Y451xkeQukKpj\nsViMvr6+K3XNDxw44N4gkyKRCINPDxb2bvhWmtyGyb0blgCXKezdcLywd8P+g/sbOlBJjcyQIDWo\nSCTCwFMDZLNZevf0cvhrhV0g46vibGnbQs9X3AVSanSGBNWl6dupnj9/nvXr17Nz506WL18OuAX5\nlNRLKVJHU9ABzT/RzNI3lrLuxnWcWXaG7UPbSX4vSbLF/52kRhXmVtHFcKtoSZIWIMytol24KEmS\nAhkSJElSIEOCJEkKZEiQJEmBDAmSJCmQIUGSJAUyJEiSpECGBEmSFMiQIEmSAhkSJElSIEOCJEkK\nZEiQJEmB3AVSamDX7pZ54sQJ1q1b526ZkgDPJEgNLZlMsnfvXlavXs3Y2Bgvv/wyY2NjrF69mr17\n9xoQpAbnmQSpQeXzebq6ukin0+RyuSvPZzIZMpkMBw8eJJFI0N/fTyQSqeBIJVWKIUFqQPl8ns2b\nNzM2NjZjm1wuRy6Xo6Ojg8HBQYOC1IDCutzwG8AR4PvAd0PqQ9ICdXV1zRoQpstkMnR1dYU8IknV\nKKyQ0ATsB/5LSO8vaYHGx8dJp9NFHZNOp8lms+EMSFLVCiskPAz8LnA0pPeXtEC7d+++ag3CfORy\nOXp7e0MakaRq5d0NUoMZHh4u63GSapchQWowExMTZT1OUu0q5u6Gh4GeOdrcAYwueDSSQtfU1FTW\n40op9VKK1NHJ4k9vnefEGydYd+M6li+bLP70wSTJFms7SKVSTEj4PeCJOdqcWMRY2LFjBytXrrzq\nOSu+SaXV3t7O0aPFLxfauHFjCKMpTrIlyab3bKL3C70cHj1M5myGi6susqV1Cz0P9RCLxSo9RClU\n06ukTjl37lxo/S0J7Z0LfhH4IvC+Odq1AiMjIyO0traGPCSpsWWzWTZt2lTU4sVoNMrQ0FBFP4Tz\n+Txd93WRPpsm94EcrJ324kmIHouSWJWg/1GLP6mxjI6O0tbWBtBGic/mh1VM6VZg1eTXpcDtFALJ\nt4E3Q+pT0jzEYjESiURRISGRSFQ8IGy+ZzNjd44VLmpeay3k1ubInc7RcU8Hg09b/EkqhbAWLvZS\nSDMPA+8GngdGKKQcSRXW399PPB6fV9t4PM7+/ftDHtHsuu7rKgSENXM0XAOZOzN03WfxJ6kUwgoJ\nvzj53tdROJMw9fVwSP1JKkIkEmFwcJDOzk6i0Whgm2g0SmdnJ0eOHGHNmrk+ncMzPj5O+mx67oAw\nZQ2kz1r8SSoFb4GUGlQkEmFgYIChoSG2bdt25cxCPB5n27ZtDA0NMTAwUNGAALB7z+7CGoQi5Dbk\n6N1j8SdpsQwJUgNLpVJs376dM2fO0NzczPr162lububMmTNs3779HauoK2H4xeGrFynOx1oYfsHi\nT9JiuQuk1MBq4RbjiYsLKOK0BCYuWfxJWizPJEiqak1LF1DE6TI0XVf54k9SrTMkSKpq7be1w8ki\nDzoJG2+vfPEnqdYZEiRVtZ6HeogeC74DYybR41F2PbgrpBFJjcOQIKmqxWIxEqsScHqeB5yGxKrK\nFn+S6oUhQVLV63+0n/hz8bmDwmmIPxdn/2OVLf4k1QtDgqSqF4lEGHx6kM5XOok+E4VXgcuTL14G\nXoXoM1E6X+nkyMHSF39KvZTi7r13c+tHb2VFywqu/8D1rGhZwa0fvZW7995N6qXK3yoqhcFbICXV\nhEgkwsBTA2SzWXr39HL4a4VdIOOr4mxp20LPV8LZBTKfz7Pv1/e9vbHUnYXnJ5jgzZNvMrF/gn1/\nvo+PPPoR94tQ3TEkSKopsViMvi/1MXpqlLZ9bRy4/wCtN4eze6wbS6nRGRIk1YzUSylSRwun9s+/\ndZ71N61n51/sZPmy5QAkP5gk2VK64lAL2Vhq4KmBkvUvVZohQVLNSLaUNgTM5srGUkFnEIKsgfS3\nChtLeWeF6oULFyUpgBtLSYYESQrkxlKSIUGSArmxlGRIkKRAbiwlGRIkKZAbS0mGBEkK5MZSkiFB\nkgK5sZRkSJCkGbmxlBqdIUGSZlDpjaWkSrPioiTNolIbS0nVwJAgSXO4smdEBzT/RDNL31jKuhvX\ncWbZGbYPbSf5vfKVi5bKyZAgSXMo554RUjVxTYIkSQpkSJAkSYEMCZIkKVBYISEGPAaMAd8H/i/w\nMGBRc0mSakRYCxd/DFgC3E8hILQAfwS8G3gopD4lSVIJhRUSvjb5mJIF9gCfxZAgSVJNKOeahJXA\n62XsT5IkLUK5QkIc+FXgD8vUnyTVvGw2S/cD3bTc1UJic4KWu1rofqCbbDZb6aGpQRR7ueFhoGeO\nNncAo9N+vgX4M+AA0Fdkf5LUcPL5PFu7tjL292NcaL0Ad7/92tGTR3nik0/Q/N5mBvoHiEQilRuo\n6t6SItvfNPmYzQngwuT3twADwBDwi7Mc0wqM3HXXXaxcufKqF5LJJMmklc4kNYZ8Ps/mezYzducY\nzLZf1OTOk4NPDxoUGkgqlSKVSl313Llz53j22WcB2rj6j/RFKzYkFOP9FALCMPBp3t47LUgrMDIy\nMkJra2uIQ5Kk6rb141s5dOuh2QPClNPQ+UonA08NhDwqVbPR0VHa2toghJAQ1pqE9wOHKJxVeAiI\nANHJhyQpwPj4OOmz6fkFBIA1kD6bdo2CQhNWSPhJCosVPwKcBF6bfHwnpP4kqebt3rOb3AdyRR2T\n25Cjd09vSCNSowsrJPzx5Hsvnfx63bSfJUkBhl8chrVFHrQWhl8YDmU8kns3SFKVmLg4UfxBS2Di\n0gKOk+bBkCBJVaJp6QK2t7kMTde5LY7CYUiQpCrRflt7YRVXMU7Cxts3hjIeyZAgSVWi56EeoseK\nuwksejzKrgd3hTQiNbqwNniSpJKbXkjm/PnznDhxgnXr1rF8+XKg9ouvxWIxEqsS5E7n5l0nIbEq\nQSwWC3toalBhFlMqhsWUJBVlqoBMvf3eyOfzdNzTQebOzLwqLh45eIQ1a+ZbWEH1qBaLKUmSFiAS\niTD49CAbjm3ghidvgFd5u17tZeBVuOHJG9hwbIMBQaHzcoMkVZlIJMKxgWNks1l69/Qy/PVhJi5N\n0HRdE+23t9PzJz2hXmLIZrP0fqGX4ReHmbg4QdPSJtpva6fnoXD7VfUxJEiqKdlslt7eXg4fPgzA\nvffey5YtW+jpqb8PsFgsRt+Xyrd5bj6fp+u+LtJn04XKj9fsPnnwUwdJrErQ/2i/m0o1CEOCpJqQ\nz+fp6uoinU6Ty71dujiTyZDJZDh48CCJRIL+fj/AFuKq3SfvCGiwFnJrc+RO5+i4p8PdJxuEaxIk\nVb18Ps/mzZs5dOjQVQFhulwux6FDh+jo6CCfz5d5hLWv676uubenBlgDmTszdN3XVZZxqbIMCZKq\nXldXF2NjY/Nqm8lk6OryA6wY7j6pmRgSJFW18fFx0ul0Ucek036AFcPdJzUT1yRIqmq7d++e8RLD\nTHK5HL29vfT1lWbRX70XcRp+cfiqRYrzshaGv+7uk/XOkCCpqg0PL+yDaKHHBZkeAqYK16RSqbop\n4uTuk5qJlxskVbWJiYV9EC30uJlks1m6u7u59957gcKtl93d3XVxWcPdJzUTzyRIqmpNTQv7IFro\ncddqhFsv229r5+jJo7C2iIPcfbIhGBIkVbX29naOHj1a9HEbNy7+A2zq1svZ7qzI5XLkcjk6OjoY\nHCxd7YByroPoeaiHg586SG7t/Nd+RI9H2fUVd5+sd27wJKmqZbNZNm3aVNTixWg0ytDQ0KIrMG7d\nupVDhw7Nu31nZycDAwOL6jNIOTaz2vrxrRy69dC8d5/sfKWTgadK/29V8dzgSVLDisViJBKJoo5J\nJBa/fXI13HpZznUQ/Y/2E38uDqfnaDi5++T+x/aXfAyqPoYESVWvv7+feDw+r7bxeJz9+xf/AbaY\nWy8XK5/Ps3XrVjZt2sTjjz9OJpMBCusgHn/8cTZt2sTWrVtLWllyavfJzlc6iT4TDdx9MvpMlM5X\nOt19soG4JkFS1YtEIgwODgYuIJwSjUZJJBLs37+/JB9glbr1spLrICKRCANPDcy8++RX6m8TLc3O\nkCCpJkQiEQYGBq7aBTKTyRCPx0PZBbJSt14upAR1qddBlHv3SVUvQ4KkmhKLxejr67uyWOvAgQOh\nLOarxK2Xi1kH4V/4CoNrEiQpQHt7+4KOW8ytl5VcByEFMSRIUoCenh6i0WhRx0SjUXbtWnjtgGoo\nQS1NZ0iQpACVuPWyWkpQS1NckyCpZlxbhXD9+vXs3LkztN0Y+/v76ejouHIL4mxKcetlpUtQS9cK\nKyQ8CdxOoXbXd4G/AH4NOBVSf5IaQLm3ZC73rZeVLEEtBQnrcsNfAj8PrAc+CcSBPw2pL0kKzdSt\nl0NDQ2zbtu1KUad4PM62bdsYGhpiYGCgJLUZKrEOQppNWCHhEeBvKNTsGgI+D2wElobUnySFaurW\nywMHDgBw4MAB+vr6SnrrYaVKUE+XSqW4++67ufXWW1mxYgXXX389K1as4NZbb+Xuu+++crlHjaEc\naxJWAZ8CBoCLZehPkkqqnGshyr0OYrp8Ps++ffvecWllYmKCN998k4mJCfbt28dHPvKRmt0WW8UJ\ncxfIzwMPAO8Cvgl8FHh9hrbuAilJk/L5fFlLUE/1OVc56CnxeLyk5aC1ONWyC+TDwKU5HtM/4X8b\n+BDwU8AF4H9SPVtTS1LVKuc6iCkLKQet+lfMh/ZNk4/ZnKAQCK71fgrrEz4MHAl4vRUYueuuu1i5\ncuVVL5R7NbMkVZupvxTDOts6Pj7O5s2bi6r2GI1GGRoashx0mU2/9DXl3LlzPPvssxDCmYRi1iS8\nzsyXC+YydcZi1oWLjzzyiJcbJKnMFlMOuq/PjaDKKegP52mXG0oujIWLGycff02hRkIz0At8m8Kd\nDpKkOZRzsaTloDWTMELC94F/QWENw7spFFA6COwG3gqhP0mqO+W81Go5aM0kjJBwFPhnIbyvJCkE\nloPWTNzgSZIaXCW2xb5WNpulu7ublpYWEokELS0tdHd3k81mS9aHilcttyRaJ0GSKiSbzbJp06aK\n3N2Qz+fZunUrY2NjXLjwzpvjbrjhBpqbmxkYGLAuwwyqpU6CJKkOVaoc9FQBp+PHjwcGBIALFy5w\n/PhxOjo6yOfzi+pPxTMkSJLo7++/UrRpLqUqB20Bp+pnSJAkXdkWu7Ozc8adKKPRKJ2dnRw5cmTR\n1R7Hx8dJp9NFHZNOp12jUGaGBEkSUN5y0Isp4KTyMSRIkq5IpVJs376dM2fO0NzczPr162lububM\nmTNs3769ZFtFW8CpNpRjq2hJUo0oVxEnCzjVBs8kSJLKzgJOtcGQIEkqu2oo4KS5GRIkSWXX09Mz\n410UM4lGo+zatSukESmIIUGSVHaxWIzm5uaijmlubl50AScVx5AgSaqMdwPvnWfb9062V1kZEiRJ\nZTc+Ps7Ym2NwP7AOWDFDwxWTr98PY2+OWUypzAwJkqSy271nN7kP5Aoh4A7gBymcLbgeaJr8+t7J\n5+8AVkBuQ47ePRZTKifrJEiSym74xWG4e/KHlsnHXNbC8NctplROnkmQJJXdxMUFFEVaAhOXLKZU\nToYESVLZNS1dQFGky9B0ncWUysmQIEkqu/bb2uFkkQedhI23W0ypnAwJkqSy63moh+ixIospHY+y\n60GLKZWTIUGSVHaxWIzEqgScnucBpyGxKmExpTLz7gZJUkX0P9pPxz0dZO7MwJpZGp6G+HNx9h/c\nX9L+U6nUla2vz58/z4kTJ1i3bh3Lly8HyrcjZjXzTIIkqSIikQiDTw/S+Uon0Wei8CpwefLFy8Cr\nEH0mSucrnRw5eIQ1a2ZLEsVLJpPs3buX1atXMzY2xssvv8zY2BirV69m7969DR8QwDMJkqQKikQi\nDDw1QDabpXdPL4e/dpjM2QzxVXG2tG2h5ys9oVxiyOfzdHV1kU6nyeVyV57PZDJkMhkOHjxIIpGg\nv7+fSCRS8v5rhSFBklRxsViMvi/1MXpqlLZ9bRy4/wCtN7eG0lc+n2fz5s2MjY3N2CaXy5HL5ejo\n6GBwcLBhg4KXGyRJDaWrq2vWgDBdJpOhq6sr5BFVL88kSJIqKvVSitTRyQWEb51n/U3r2fkXO1m+\nbHIB4QeTJFtKsz5gfHycdDpd1DHpdJpsNtuQd1YYEiRJFZVsKV0ImMvu3buvWoMwH7lcjt7eXvr6\n+kIaVfUK+3LDDcC3gEvAbSH3JUnSrIaHF7ZB1EKPq3Vhh4TfBr4Tch+SJM3LxMTCNoha6HG1LsyQ\n8FEKG4E+GGIfkiTNW1PTwjaIWuhxtS6skBAB9gH/CvjHkPqQJKko7e3tCzpu48bG3FgqjJCwBPhj\n4A+A0RDeX5KkBenp6SEaLXJjqWiUXbsac2OpYu5ueBjomaNNO9ABrAA+d81rS+bqYMeOHaxcufKq\n56ydLUkqlVgsRiKRKOoOh0SiejaWmr7fxJRz586F1t+cH9zT3DT5mM0JoB/4OG9X4AZYClwEvgxs\nCziuFRgZGRmhtTWcCluSJEGh4mJHRweZTGbOtvF4nCNHSr9vRCmNjo7S1tYG0EaJz+AXcybh9cnH\nXLYDvzHt5/cDXwPuBb5RRH+SJJVcJBJhcHAwcO+GKdFolEQiwf79+6s6IIQtjGJKr17z8/cnv2aA\n10LoT5KkokQiEQYGJjeW6u3l8OHDZDIZ4vE4W7ZsoacnnI2lak259m64PHcTSZLKJ5VKsX37ds6c\nOUNzczPr16+nubmZM2fOsH379ndc+29E5SjLnKWwJkGSpKrhwvi5uQukJEkKZEiQJEmBDAmSJCmQ\nIUGSJAUyJEiSpECGBEmSFKgct0BKklR1Ui+lSB0t1EI4/9Z5TrxxgnU3rmP5suUAJD+YJNnS2LdI\nGhIkSQ0p2fJ2CBg9NUrbvjZSn0zRerN7CE3xcoMkSQpkSJAkSYEMCZIkKZAhQZIkBTIkSJKkQIYE\nSZIUyJAgSZICGRIkSVIgQ4IkSQpkSJAkSYEMCZIkKZAhQZIkBTIkSJKkQIYESZIUyJAgSZICGRIk\nSVIgQ4IkSQpkSJAkSYEMCZIkKVBYISELXLrm8Zsh9SVJkkKwLKT3vQzsAv5o2nNvhtSXJEkKQVgh\nAeAfgNMhvr8kSQpRmGsSfg04AzwP/DrQFGJfkiSpxMI6k/C7wAjwXeAngN8Cfhj4NyH1J0mSSqyY\nMwkP887FiNc+WifbPgI8CxwFHgN+Gfgl4H2lGLQkSQpfMWcSfg94Yo42J2Z4/huTX38EGJ7p4B07\ndrBy5cqrnksmkySTyfmOUZKkupVKpUilUlc9d+7cudD6KyYkvD75WIh/Mvn11GyNHnnkEVpbW2dr\nIklSwwr6w3l0dJS2trZQ+gtjTcKdwCZgAHgDaAd+B/hfwMkQ+pMkSSEIIyRcAO4FeoAbKFyC2Af8\ndgh9SZKkkIQREp6ncCZBkiTVMPdukCRJgQwJkiQpkCFBkiQFMiRIkqRAhgRJkhTIkCBJkgIZEiRJ\nUiBDgiRJCmRIkCRJgQwJkiQpkCFBkiQFMiRIkqRAhgRJkhTIkCBJkgIZEiRJUiBDgiRJCmRIkCRJ\ngQwJkiQpkCFBkiQFMiRIkqRAhgRJkhTIkCBJkgIZEiRJUiBDgiRJCmRIkCRJgQwJkiQpkCFBkiQF\nMiRIkqRsfGo2AAAEgklEQVRAYYaEfw58A/g+8HfAn4TYlzRvqVSq0kNQg3CuqdaFFRI+Cfw34DHg\nNmAz8JWQ+pKK4i9ulYtzTbVuWUjv+bvAg8Dj057/dgh9SZKkkIRxJqEVuAW4DDwPvAY8Dfx4CH1V\nXLn/Uihlf4t5r2KPLab9fNrO1aYe/4JzrpW+vXMtWKPONV4Kr69anWthhITmya8PA73Ax4DvAoeA\n94XQX0U16v+Z/MVdfs610rd3rgVr1LlmSHinYi43PAz0zNGmnbeDx38Gvjr5/TbgJPDzwL6ZDj5+\n/HgRw6kO586dY3R0tCb7W8x7FXtsMe3n03auNrO9Xu7/ZqXiXCt9e+dasEaca8f/7jich+MvHodT\npe8rzLkW5mfnkiLa3jT5mM0JCosUvw58GDgy7bXngD8HdgUcdzMwDLy/iPFIkqSC71D4Q32eEWd+\nijmT8PrkYy4jwAUgwdshoQmIUQgRQU5R+MfdXMR4JElSwSlKHBDC9EXgVeAngR8DHqUw+BsrOShJ\nklR5y4AvADngDeBrwIaKjkiSJEmSJEmSJEmSJOmd3gP8DYUKjkeBX63scFTHfohC4a//A7wA/MuK\njkb17qvAWeB/VHogqlsfA9LAy8AvVXgsobkOWD75/Q8AY8APVm44qmNRCpuSQWGOvUphzklh+KcU\nfokbEhSGZcDfUigvsIJCUFhVzBuEuVV0KV0Czk9+/y5gYtrPUinlgBcnv/87Cn/lFfV/KqkIfwX8\nQ6UHobq1kcJZ0VMU5tnTwE8V8wa1EhKgUGPhBeAVCrtMfq+yw1EDuINCVdLvVHogkrQAt3D176+T\nFFnZuJZCwhvA7cAPAw8AP1LZ4ajO3QT8V+D+Sg9Ekhbo8mLfIKyQsAV4ikKCuQT8TECbXwHGgX8E\nvklhr4cp/5bCIsVRCiWdpztNYWHZh0o6YtWqMObaDcCfAr9JYc8RCcL7vbboX+SqW4udc69x9ZmD\nH6JKzoz+NIVton+Wwj/sE9e8/gsU9nfoplC2+YsULh/80AzvtwZ47+T376VwzfjHSjtk1ahSz7Ul\nQAr4T2EMVjWt1HNtSicuXFSwxc65ZRQWK95C4S7Bl4H3hT7qIgX9w74B/P41zx2j8JdbkFYKCfxb\nk49tpRyg6kYp5tqHgYsU/tp7fvLx4yUco+pDKeYaFErWnwbepHAnTVupBqi6s9A593EKdzh8G7gv\ntNEtwrX/sOsp3J1w7WmTRyhcRpAWyrmmcnGuqdwqMucqsXBxNbAUyF/z/GkK96hLpeJcU7k411Ru\nZZlztXR3gyRJKqNKhIQzFK75Rq55PkKh4INUKs41lYtzTeVWljlXiZDw/4AR3ln16SeBI+UfjuqY\nc03l4lxTudX0nHs3hToGH6Kw2GLH5PdTt2XcS+G2jW3ABgq3bfw9c98qJF3LuaZyca6p3Op2znVS\n+AddonA6ZOr7vmltPkuhAMR5YJirC0BI89WJc03l0YlzTeXViXNOkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiSpBvx/61GKXj4iGQUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3cb164f6d0>"
      ]
     },
     "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 2.664e+02 1.060e+01 inf -- 2.187e+02 -- -0.209329 -0.861493 -2.15962 -2.40847 -2.77148 -3.0939 -3.74416 -6.23993 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
      "   3 2.987e+01 1.264e+01 2.257e+00 -- 2.210e+02 -- -0.16831 -0.824919 -2.12464 -2.39488 -2.7608 -3.0706 -3.74724 -5.93993 0.0804747 0.16472 0.155918 0.19816 0.150032 0.148466 -0.0426696 2.76354\n",
      "   5 3.330e+01 1.479e+01 2.062e+00 -- 2.230e+02 -- -0.134691 -0.793404 -2.09481 -2.37915 -2.74942 -3.04926 -3.74185 -6.23993 0.0666414 0.2131 0.199386 0.281712 0.193626 0.185727 -0.170109 -2.41599\n",
      "   7 4.032e+02 1.707e+01 1.889e+00 -- 2.249e+02 -- -0.106698 -0.766368 -2.06929 -2.36269 -2.73774 -3.02999 -3.73105 -6.53993 0.0565128 0.250258 0.234434 0.352126 0.231411 0.214451 -0.279352 -0.654819\n",
      "   9 1.006e+02 1.948e+01 1.734e+00 -- 2.267e+02 -- -0.0831017 -0.743148 -2.04735 -2.34643 -2.7261 -3.0127 -3.71739 -6.23993 0.0489265 0.27947 0.263536 0.411371 0.264011 0.236678 -0.370557 0.617417\n",
      "  11 5.264e+01 2.201e+01 1.603e+00 -- 2.283e+02 -- -0.0630157 -0.723142 -2.02838 -2.33091 -2.71472 -2.99725 -3.70277 -5.93993 0.0431576 0.302888 0.288287 0.46139 0.292059 0.253914 -0.445773 0.691506\n",
      "  13 3.251e+00 2.467e+01 1.448e+00 -- 2.297e+02 -- -0.0457823 -0.705838 -2.0119 -2.31642 -2.70374 -2.98344 -3.68838 -5.63993 0.0387303 0.321962 0.309779 0.503862 0.316158 0.267284 -0.507655 -2.9489\n",
      "  15 5.697e+00 2.744e+01 1.389e+00 -- 2.311e+02 -- -0.0308979 -0.690819 -1.99751 -2.30307 -2.6933 -2.97112 -3.67473 -5.93993 0.0353185 0.337702 0.32866 0.540271 0.336854 0.27759 -0.559235 -3.12456\n",
      "  17 9.962e+01 3.030e+01 1.295e+00 -- 2.324e+02 -- -0.0179753 -0.677732 -1.9849 -2.29088 -2.68339 -2.9601 -3.66228 -6.23993 0.0326949 0.350836 0.34562 0.571642 0.354613 0.285527 -0.602072 1.49229\n",
      "  19 8.259e+01 3.325e+01 1.196e+00 -- 2.336e+02 -- -0.00670527 -0.666291 -1.97381 -2.2798 -2.67406 -2.95025 -3.6511 -5.93993 0.0306947 0.361894 0.361049 0.598897 0.369841 0.291591 -0.63793 -0.807155\n",
      "  21 5.466e+01 3.625e+01 1.126e+00 -- 2.347e+02 -- 0.00316205 -0.65626 -1.96403 -2.26977 -2.6653 -2.94143 -3.64118 -5.63993 0.0291917 0.371274 0.375204 0.622756 0.382913 0.296138 -0.668602 -0.4239\n",
      "  23 9.811e+00 3.926e+01 1.030e+00 -- 2.358e+02 -- 0.0118288 -0.647441 -1.95537 -2.26071 -2.65711 -2.93352 -3.63242 -5.33993 0.0280965 0.379279 0.388328 0.643807 0.394112 0.29949 -0.69488 1.89304\n",
      "  24 1.540e+02 1.799e+03 6.966e+00 -- 2.427e+02 -- 0.0881649 -0.569745 -1.87911 -2.17878 -2.58105 -2.86227 -3.55241 -6.66644 0.0206511 0.447973 0.510379 0.832388 0.489337 0.32364 -0.914155 2.17049\n",
      "  25 6.954e+03 5.225e+01 3.722e+00 -- 2.464e+02 -- 0.0820074 -0.577455 -1.8985 -2.17524 -2.54532 -2.85023 -3.57378 -8 0.0966827 0.412543 0.647274 0.885367 0.477195 0.280838 -0.833819 0.980251\n",
      "  26 6.093e+00 2.044e+01 2.548e-01 -- 2.467e+02 -- 0.0836413 -0.576897 -1.8864 -2.18141 -2.55069 -2.85268 -3.54966 -5 0.0710854 0.434328 0.591545 0.904499 0.417485 0.266475 -0.938934 1.34469\n",
      "  27 1.995e+00 5.009e+00 1.592e-01 -- 2.469e+02 -- 0.0831948 -0.576805 -1.88848 -2.17663 -2.54734 -2.85305 -3.5567 -4.22205 0.0761925 0.426631 0.625953 0.910649 0.424127 0.266244 -0.891405 -0.565813\n",
      "  28 1.248e+00 1.375e+01 4.599e-02 -- 2.469e+02 -- 0.0833105 -0.576655 -1.88396 -2.17799 -2.5475 -2.85314 -3.57374 -4.03476 0.0730522 0.429262 0.625062 0.905512 0.418149 0.264942 -0.967314 0.563133\n",
      "  29 2.171e+00 9.396e+00 3.382e-01 -- 2.472e+02 -- 0.0829995 -0.576375 -1.88776 -2.17599 -2.54493 -2.85329 -3.58608 -4.02931 0.0763743 0.428086 0.638377 0.920493 0.411594 0.274496 -0.815157 -0.139608\n",
      "  30 9.841e-01 1.149e+01 1.388e-01 -- 2.474e+02 -- 0.0832295 -0.57636 -1.88467 -2.17976 -2.54698 -2.8543 -3.58978 -3.89975 0.0738415 0.429686 0.629413 0.910992 0.412336 0.271501 -0.957932 0.163421\n",
      "  31 2.338e+01 3.752e+00 6.030e-02 -- 2.474e+02 -- 0.0829583 -0.576172 -1.88634 -2.17783 -2.54432 -2.85466 -3.60447 -3.874 0.0761271 0.428633 0.633503 0.910755 0.410289 0.277401 -0.858061 0.00260138\n",
      "  32 3.920e-01 3.182e+00 1.581e-02 -- 2.475e+02 -- 0.0830523 -0.576117 -1.88559 -2.18042 -2.54532 -2.85537 -3.60659 -3.85372 0.0749087 0.429912 0.628364 0.908088 0.410987 0.278401 -0.917876 0.0634286\n",
      "  33 3.181e-01 1.292e+00 4.081e-03 -- 2.475e+02 -- 0.0829733 -0.576083 -1.88614 -2.17987 -2.54423 -2.85554 -3.60968 -3.84594 0.0757541 0.429324 0.627903 0.906455 0.410964 0.280705 -0.882158 0.0385634\n",
      "  34 9.740e-02 4.768e-01 1.118e-03 -- 2.475e+02 -- 0.0830058 -0.576059 -1.88614 -2.18078 -2.54437 -2.85582 -3.61078 -3.84115 0.0755814 0.429797 0.625777 0.906066 0.411713 0.281667 -0.897167 0.0508303\n",
      "  35 6.139e-02 4.667e-01 3.378e-04 -- 2.475e+02 -- 0.0829936 -0.576051 -1.88632 -2.18077 -2.54398 -2.85585 -3.61168 -3.83901 0.0759106 0.429622 0.624827 0.905205 0.411841 0.282606 -0.888007 0.0458794\n",
      "  36 2.074e-02 6.100e-02 1.146e-04 -- 2.475e+02 -- 0.0830052 -0.576043 -1.8864 -2.18106 -2.54393 -2.85595 -3.61204 -3.83759 0.0759487 0.42976 0.623936 0.905027 0.412248 0.283153 -0.891435 0.0486959\n",
      "  37 1.454e-02 1.834e-01 4.331e-05 -- 2.475e+02 -- 0.0830055 -0.57604 -1.88647 -2.18112 -2.54378 -2.85597 -3.61236 -3.83689 0.0760799 0.429719 0.623352 0.904662 0.412343 0.283555 -0.889115 0.047686\n",
      "********************\n",
      "0.0830055 -0.57604 -1.88647 -2.18112 -2.54378 -2.85597 -3.61236 -3.83689 0.0760799 0.429719 0.623352 0.904662 0.412343 0.283555 -0.889115 0.047686\n",
      "0.00496524 0.0080639 0.0332817 0.053869 0.0497847 0.0510814 0.188809 0.22006 0.0806462 0.0938084 0.215752 0.243886 0.211727 0.201233 0.481882 0.380625\n",
      "0.183432 0.0383088 -0.0465819 -0.037514 0.0169821 -0.0145299 -0.00365015 0.00975964 0.0073746 0.00371696 -0.00866648 -0.0017458 0.00395749 0.0070437 -0.00320102 0.00463012\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",
      "+++ 2.475e+02 2.473e+02 8.301e-02 8.549e-02 0.306 +++\n",
      "+++ 2.475e+02 2.470e+02 8.301e-02 8.673e-02 0.912 +++\n",
      "+++ 2.475e+02 2.467e+02 8.301e-02 8.735e-02 1.46 +++\n",
      "+++ 2.475e+02 2.469e+02 8.301e-02 8.704e-02 1.16 +++\n",
      "+++ 2.475e+02 2.469e+02 8.301e-02 8.689e-02 1.03 +++\n",
      "+++ 2.475e+02 2.470e+02 8.301e-02 8.681e-02 0.969 +++\n",
      "+++ 2.475e+02 2.470e+02 8.301e-02 8.685e-02 0.999 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 2.475e+02 2.473e+02 -5.760e-01 -5.720e-01 0.399 +++\n",
      "+++ 2.475e+02 2.469e+02 -5.760e-01 -5.700e-01 1.13 +++\n",
      "+++ 2.475e+02 2.471e+02 -5.760e-01 -5.710e-01 0.698 +++\n",
      "+++ 2.475e+02 2.470e+02 -5.760e-01 -5.705e-01 0.897 +++\n",
      "+++ 2.475e+02 2.470e+02 -5.760e-01 -5.702e-01 1.01 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 2.475e+02 2.473e+02 -1.887e+00 -1.870e+00 0.291 +++\n",
      "+++ 2.475e+02 2.470e+02 -1.887e+00 -1.862e+00 0.915 +++\n",
      "+++ 2.475e+02 2.467e+02 -1.887e+00 -1.857e+00 1.53 +++\n",
      "+++ 2.475e+02 2.469e+02 -1.887e+00 -1.859e+00 1.18 +++\n",
      "+++ 2.475e+02 2.469e+02 -1.887e+00 -1.860e+00 1.04 +++\n",
      "+++ 2.475e+02 2.470e+02 -1.887e+00 -1.861e+00 0.971 +++\n",
      "+++ 2.475e+02 2.470e+02 -1.887e+00 -1.861e+00   1 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 2.475e+02 2.473e+02 -2.181e+00 -2.154e+00 0.29 +++\n",
      "+++ 2.475e+02 2.471e+02 -2.181e+00 -2.141e+00 0.808 +++\n",
      "+++ 2.475e+02 2.468e+02 -2.181e+00 -2.134e+00 1.25 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.181e+00 -2.137e+00 1.01 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.181e+00 -2.139e+00 0.905 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.181e+00 -2.138e+00 0.957 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.181e+00 -2.138e+00 0.984 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.181e+00 -2.138e+00 0.997 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 2.475e+02 2.470e+02 -2.544e+00 -2.494e+00 0.973 +++\n",
      "+++ 2.475e+02 2.459e+02 -2.544e+00 -2.469e+00 3.08 +++\n",
      "+++ 2.475e+02 2.466e+02 -2.544e+00 -2.482e+00 1.78 +++\n",
      "+++ 2.475e+02 2.468e+02 -2.544e+00 -2.488e+00 1.32 +++\n",
      "+++ 2.475e+02 2.469e+02 -2.544e+00 -2.491e+00 1.14 +++\n",
      "+++ 2.475e+02 2.469e+02 -2.544e+00 -2.492e+00 1.05 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.544e+00 -2.493e+00 1.01 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.544e+00 -2.494e+00 0.993 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 2.475e+02 2.473e+02 -2.856e+00 -2.830e+00 0.277 +++\n",
      "+++ 2.475e+02 2.471e+02 -2.856e+00 -2.818e+00 0.713 +++\n",
      "+++ 2.475e+02 2.469e+02 -2.856e+00 -2.811e+00 1.04 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.856e+00 -2.814e+00 0.867 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.856e+00 -2.813e+00 0.953 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.856e+00 -2.812e+00 0.993 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 2.475e+02 2.473e+02 -3.612e+00 -3.518e+00 0.392 +++\n",
      "+++ 2.475e+02 2.469e+02 -3.612e+00 -3.471e+00 1.06 +++\n",
      "+++ 2.475e+02 2.471e+02 -3.612e+00 -3.494e+00 0.67 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.612e+00 -3.483e+00 0.845 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.612e+00 -3.477e+00 0.947 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.612e+00 -3.474e+00   1 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 2.475e+02 2.474e+02 -3.836e+00 -3.727e+00 0.218 +++\n",
      "+++ 2.475e+02 2.471e+02 -3.836e+00 -3.672e+00 0.684 +++\n",
      "+++ 2.475e+02 2.469e+02 -3.836e+00 -3.644e+00 1.16 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.836e+00 -3.658e+00 0.894 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.836e+00 -3.651e+00 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.836e+00 -3.654e+00 0.955 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.836e+00 -3.653e+00 0.988 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.836e+00 -3.652e+00   1 +++\n",
      "\t### errors for param 8 ###\n",
      "+++ 2.475e+02 2.470e+02 7.613e-02 1.568e-01 0.869 +++\n",
      "+++ 2.475e+02 2.465e+02 7.613e-02 1.971e-01 1.9 +++\n",
      "+++ 2.475e+02 2.468e+02 7.613e-02 1.769e-01 1.35 +++\n",
      "+++ 2.475e+02 2.469e+02 7.613e-02 1.668e-01 1.1 +++\n",
      "+++ 2.475e+02 2.470e+02 7.613e-02 1.618e-01 0.98 +++\n",
      "+++ 2.475e+02 2.469e+02 7.613e-02 1.643e-01 1.04 +++\n",
      "+++ 2.475e+02 2.470e+02 7.613e-02 1.630e-01 1.01 +++\n",
      "\t### errors for param 9 ###\n",
      "+++ 2.475e+02 2.473e+02 4.298e-01 4.767e-01 0.29 +++\n",
      "+++ 2.475e+02 2.471e+02 4.298e-01 5.001e-01 0.641 +++\n",
      "+++ 2.475e+02 2.470e+02 4.298e-01 5.118e-01 0.863 +++\n",
      "+++ 2.475e+02 2.470e+02 4.298e-01 5.177e-01 0.985 +++\n",
      "+++ 2.475e+02 2.469e+02 4.298e-01 5.206e-01 1.05 +++\n",
      "+++ 2.475e+02 2.470e+02 4.298e-01 5.192e-01 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 4.298e-01 5.184e-01   1 +++\n",
      "\t### errors for param 10 ###\n",
      "+++ 2.475e+02 2.471e+02 6.230e-01 8.388e-01 0.802 +++\n",
      "+++ 2.475e+02 2.466e+02 6.230e-01 9.467e-01 1.77 +++\n",
      "+++ 2.475e+02 2.468e+02 6.230e-01 8.928e-01 1.25 +++\n",
      "+++ 2.475e+02 2.470e+02 6.230e-01 8.658e-01 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 6.230e-01 8.523e-01 0.906 +++\n",
      "+++ 2.475e+02 2.470e+02 6.230e-01 8.591e-01 0.96 +++\n",
      "+++ 2.475e+02 2.470e+02 6.230e-01 8.624e-01 0.988 +++\n",
      "+++ 2.475e+02 2.470e+02 6.230e-01 8.641e-01   1 +++\n",
      "\t### errors for param 11 ###\n",
      "+++ 2.475e+02 2.470e+02 9.046e-01 1.149e+00 0.88 +++\n",
      "+++ 2.475e+02 2.465e+02 9.046e-01 1.271e+00 1.88 +++\n",
      "+++ 2.475e+02 2.468e+02 9.046e-01 1.210e+00 1.35 +++\n",
      "+++ 2.475e+02 2.469e+02 9.046e-01 1.179e+00 1.1 +++\n",
      "+++ 2.475e+02 2.470e+02 9.046e-01 1.164e+00 0.989 +++\n",
      "+++ 2.475e+02 2.469e+02 9.046e-01 1.171e+00 1.05 +++\n",
      "+++ 2.475e+02 2.470e+02 9.046e-01 1.168e+00 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 9.046e-01 1.166e+00   1 +++\n",
      "\t### errors for param 12 ###\n",
      "+++ 2.475e+02 2.471e+02 4.125e-01 6.242e-01 0.737 +++\n",
      "+++ 2.475e+02 2.467e+02 4.125e-01 7.300e-01 1.59 +++\n",
      "+++ 2.475e+02 2.469e+02 4.125e-01 6.771e-01 1.13 +++\n",
      "+++ 2.475e+02 2.470e+02 4.125e-01 6.507e-01 0.924 +++\n",
      "+++ 2.475e+02 2.469e+02 4.125e-01 6.639e-01 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 4.125e-01 6.573e-01 0.974 +++\n",
      "+++ 2.475e+02 2.470e+02 4.125e-01 6.606e-01 0.999 +++\n",
      "\t### errors for param 13 ###\n",
      "+++ 2.475e+02 2.470e+02 2.838e-01 4.851e-01 0.854 +++\n",
      "+++ 2.475e+02 2.465e+02 2.838e-01 5.857e-01 1.84 +++\n",
      "+++ 2.475e+02 2.468e+02 2.838e-01 5.354e-01 1.31 +++\n",
      "+++ 2.475e+02 2.469e+02 2.838e-01 5.103e-01 1.07 +++\n",
      "+++ 2.475e+02 2.470e+02 2.838e-01 4.977e-01 0.96 +++\n",
      "+++ 2.475e+02 2.470e+02 2.838e-01 5.040e-01 1.01 +++\n",
      "+++ 2.475e+02 2.470e+02 2.838e-01 5.008e-01 0.987 +++\n",
      "+++ 2.475e+02 2.470e+02 2.838e-01 5.024e-01   1 +++\n",
      "\t### errors for param 14 ###\n",
      "+++ 2.475e+02 2.470e+02 -8.899e-01 -4.078e-01 0.957 +++\n",
      "+++ 2.475e+02 2.465e+02 -8.899e-01 -1.668e-01 1.91 +++\n",
      "+++ 2.475e+02 2.468e+02 -8.899e-01 -2.873e-01 1.41 +++\n",
      "+++ 2.475e+02 2.469e+02 -8.899e-01 -3.476e-01 1.18 +++\n",
      "+++ 2.475e+02 2.469e+02 -8.899e-01 -3.777e-01 1.07 +++\n",
      "+++ 2.475e+02 2.470e+02 -8.899e-01 -3.928e-01 1.01 +++\n",
      "+++ 2.475e+02 2.470e+02 -8.899e-01 -4.003e-01 0.984 +++\n",
      "+++ 2.475e+02 2.470e+02 -8.899e-01 -3.965e-01 0.998 +++\n",
      "\t### errors for param 15 ###\n",
      "+++ 2.475e+02 2.471e+02 4.838e-02 4.285e-01 0.661 +++\n",
      "+++ 2.475e+02 2.470e+02 4.838e-02 6.186e-01 0.961 +++\n",
      "+++ 2.475e+02 2.469e+02 4.838e-02 7.136e-01 1.07 +++\n",
      "+++ 2.475e+02 2.470e+02 4.838e-02 6.661e-01 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 4.838e-02 6.424e-01 0.991 +++\n",
      "********************\n",
      "0.0830101 -0.576037 -1.88651 -2.18123 -2.54372 -2.856 -3.61248 -3.8364 0.0761257 0.429759 0.622953 0.90456 0.412522 0.283831 -0.889856 0.0483793\n",
      "0.00383825 0.00579502 0.025767 0.0436043 0.050153 0.0439107 0.138708 0.1845 0.086924 0.0886716 0.241158 0.261188 0.248061 0.218563 0.493323 0.593987\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([ 1.25569486,  2.37000041,  1.7802513 ,  1.66775218,  0.49069246,\n",
       "        0.21781609, -0.44057362,  0.01545348])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAFrCAYAAACE+GArAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nj/r/GjGpMEGJgxMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3cb164fd90>"
      ]
     },
     "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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3cb0edb550>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGYCAYAAAC3YWNyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIYQQQgghhLik+B/9AXC8Mr0B8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3cb0b294d0>"
      ]
     },
     "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": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3cb0f130d0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjYAAAGJCAYAAACZwnkIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSJEmSJEmSJEmSpBr0f5d9jqaM3twBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3cb0e25410>"
      ]
     },
     "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([2.02,2.02], [-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
}