phy-4660/lag/data/clag_analysis-7bins-3465A.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tvhMr3fCZ8Lp89zzkk4IHhw3vbrVPgqN3hXp1RoCTIhcg+4IZ3239oquHGZIZ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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfac014a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import sys\n",
    "import getopt\n",
    "sys.path.insert(1,\"/usr/local/science/clag/\")\n",
    "import clag\n",
    "%pylab inline\n",
    "\n",
    "ref_file=\"lc/1367A.lc\"\n",
    "echo_file=\"lc/3465A.lc\"\n",
    "\n",
    "\n",
    "dt = 0.01\n",
    "t1, l1, l1e = np.loadtxt(ref_file).T\n",
    "errorbar(t1, l1, yerr=l1e, fmt='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.005     ,  0.01861938,  0.04473305,  0.06933623,  0.10747115,\n",
       "        0.16658029,  0.25819945,  0.40020915])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n",
    "                0.25819945, 0.40020915, 0.62032418])\n",
    "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n",
    "                0.25819945, 0.40020915])\n",
    "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n",
    "nfq = len(fqL) - 1\n",
    "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n",
    "\n",
    "\n",
    "fqL\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 4.337e-01 6.112e+01 inf -- -4.041e+02 -- 1 1 1 1 1 1 1\n",
      "   2 7.647e-01 6.015e+01 6.901e+01 -- -3.351e+02 -- 0.65784 0.58285 0.5699 0.567505 0.56704 0.566344 0.573791\n",
      "   3 3.242e+00 5.939e+01 6.602e+01 -- -2.691e+02 -- 0.42512 0.198095 0.146253 0.137492 0.135449 0.133237 0.150987\n",
      "   4 1.563e+00 5.894e+01 6.233e+01 -- -2.067e+02 -- 0.328446 -0.100568 -0.261985 -0.286046 -0.293214 -0.298752 -0.270142\n",
      "   5 6.151e-01 5.858e+01 5.845e+01 -- -1.483e+02 -- 0.300114 -0.231637 -0.637422 -0.692493 -0.715789 -0.728243 -0.692465\n",
      "   6 3.834e-01 5.758e+01 5.397e+01 -- -9.432e+01 -- 0.288119 -0.218711 -0.948517 -1.05695 -1.12601 -1.152 -1.11841\n",
      "   7 2.764e-01 5.488e+01 4.687e+01 -- -4.745e+01 -- 0.288807 -0.20359 -1.129 -1.33421 -1.50858 -1.56203 -1.54725\n",
      "   8 2.123e-01 4.884e+01 3.703e+01 -- -1.042e+01 -- 0.290741 -0.199424 -1.16697 -1.47951 -1.82761 -1.93862 -1.97495\n",
      "   9 1.660e-01 3.761e+01 2.500e+01 -- 1.458e+01 -- 0.297078 -0.192793 -1.17471 -1.51254 -2.03046 -2.24008 -2.39415\n",
      "  10 1.251e-01 2.218e+01 1.352e+01 -- 2.810e+01 -- 0.304071 -0.185609 -1.18142 -1.51049 -2.10848 -2.41366 -2.79148\n",
      "  11 8.256e-02 9.018e+00 5.492e+00 -- 3.359e+01 -- 0.305677 -0.180534 -1.18507 -1.51162 -2.12584 -2.46833 -3.14066\n",
      "  12 4.067e-02 2.560e+00 1.439e+00 -- 3.503e+01 -- 0.30378 -0.178308 -1.18784 -1.51647 -2.12974 -2.48272 -3.39994\n",
      "  13 1.216e-02 5.340e-01 2.063e-01 -- 3.524e+01 -- 0.301773 -0.177939 -1.18966 -1.52028 -2.13067 -2.48953 -3.5382\n",
      "  14 2.104e-03 8.200e-02 1.337e-02 -- 3.525e+01 -- 0.30075 -0.178052 -1.19052 -1.52208 -2.13075 -2.49214 -3.58123\n",
      "  15 2.833e-04 1.082e-02 3.730e-04 -- 3.525e+01 -- 0.300445 -0.178142 -1.19077 -1.52264 -2.13068 -2.4928 -3.58876\n",
      "  16 3.684e-05 1.402e-03 6.992e-06 -- 3.525e+01 -- 0.300387 -0.178169 -1.19082 -1.52276 -2.13064 -2.4929 -3.58978\n",
      "********************\n",
      "0.300387 -0.178169 -1.19082 -1.52276 -2.13064 -2.4929 -3.58978\n",
      "0.23893 0.202426 0.232625 0.177239 0.153017 0.132987 0.308424\n",
      "-0.000180614 -0.000143998 -0.000174906 -0.000705814 0.000447101 -0.000873593 -0.00140183\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n",
    "p1 = np.ones(nfq)\n",
    "p1, p1e = clag.optimize(P1, p1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t### errors for param 0 ###\n",
      "+++ 3.525e+01 3.481e+01 3.004e-01 5.393e-01 0.89 +++\n",
      "+++ 3.525e+01 3.432e+01 3.004e-01 6.588e-01 1.87 +++\n",
      "+++ 3.525e+01 3.458e+01 3.004e-01 5.990e-01 1.34 +++\n",
      "+++ 3.525e+01 3.470e+01 3.004e-01 5.692e-01 1.11 +++\n",
      "+++ 3.525e+01 3.475e+01 3.004e-01 5.543e-01 0.996 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 3.525e+01 3.476e+01 -1.782e-01 2.426e-02 0.974 +++\n",
      "+++ 3.525e+01 3.422e+01 -1.782e-01 1.255e-01 2.07 +++\n",
      "+++ 3.525e+01 3.451e+01 -1.782e-01 7.486e-02 1.48 +++\n",
      "+++ 3.525e+01 3.464e+01 -1.782e-01 4.956e-02 1.21 +++\n",
      "+++ 3.525e+01 3.471e+01 -1.782e-01 3.691e-02 1.09 +++\n",
      "+++ 3.525e+01 3.474e+01 -1.782e-01 3.058e-02 1.03 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.782e-01 2.742e-02   1 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.075e+00 0.275 +++\n",
      "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.597 +++\n",
      "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.873e-01 0.798 +++\n",
      "+++ 3.525e+01 3.480e+01 -1.191e+00 -9.727e-01 0.909 +++\n",
      "+++ 3.525e+01 3.477e+01 -1.191e+00 -9.655e-01 0.966 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.618e-01 0.995 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 3.525e+01 3.482e+01 -1.523e+00 -1.346e+00 0.861 +++\n",
      "+++ 3.525e+01 3.433e+01 -1.523e+00 -1.257e+00 1.85 +++\n",
      "+++ 3.525e+01 3.459e+01 -1.523e+00 -1.301e+00 1.32 +++\n",
      "+++ 3.525e+01 3.471e+01 -1.523e+00 -1.323e+00 1.08 +++\n",
      "+++ 3.525e+01 3.477e+01 -1.523e+00 -1.334e+00 0.967 +++\n",
      "+++ 3.525e+01 3.474e+01 -1.523e+00 -1.329e+00 1.02 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.523e+00 -1.332e+00 0.994 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 3.525e+01 3.482e+01 -2.131e+00 -1.978e+00 0.868 +++\n",
      "+++ 3.525e+01 3.430e+01 -2.131e+00 -1.901e+00 1.9 +++\n",
      "+++ 3.525e+01 3.458e+01 -2.131e+00 -1.939e+00 1.34 +++\n",
      "+++ 3.525e+01 3.471e+01 -2.131e+00 -1.958e+00 1.09 +++\n",
      "+++ 3.525e+01 3.476e+01 -2.131e+00 -1.968e+00 0.977 +++\n",
      "+++ 3.525e+01 3.473e+01 -2.131e+00 -1.963e+00 1.03 +++\n",
      "+++ 3.525e+01 3.475e+01 -2.131e+00 -1.966e+00 1.01 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 3.525e+01 3.476e+01 -2.493e+00 -2.360e+00 0.992 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 3.525e+01 3.511e+01 -3.590e+00 -3.436e+00 0.274 +++\n",
      "+++ 3.525e+01 3.491e+01 -3.590e+00 -3.358e+00 0.68 +++\n",
      "+++ 3.525e+01 3.477e+01 -3.590e+00 -3.320e+00 0.971 +++\n",
      "+++ 3.525e+01 3.468e+01 -3.590e+00 -3.301e+00 1.14 +++\n",
      "+++ 3.525e+01 3.472e+01 -3.590e+00 -3.310e+00 1.05 +++\n",
      "+++ 3.525e+01 3.475e+01 -3.590e+00 -3.315e+00 1.01 +++\n",
      "+++ 3.525e+01 3.476e+01 -3.590e+00 -3.317e+00 0.991 +++\n",
      "********************\n",
      "0.300387 -0.178169 -1.19082 -1.52276 -2.13064 -2.4929 -3.58978\n",
      "0.253863 0.205589 0.22899 0.191086 0.164971 0.132987 0.27228\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "p1, p1e = clag.errors(P1, p1, p1e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ExBjJ5NmG+iSTZzl9+qmYKpJaz5AgSQGpVIpMZgW4ts0eS2Qyb8T2JEqpHQwJknQXs7PT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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfc553d0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xscale('log'); ylim(-4,2)\n",
    "errorbar(fqd, p1, yerr=p1e, fmt='o', ms=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YhoPT6WTq1HJqaxeyb986YC379r1Cbe1Cpk4tx+VK7oZYVpOqPUCE1EXCFoLQCy4W8x43\nLrnKMgerW7FvXyNbtpSHvfL1ngwN+m6xrIuRqj1AhNRFPA+CEAWSpVui0YtjzJi7aG5ejlUrXzUZ\nJo/nJd6kag8QIXWx2nh4BBWo83wctHgfgpDwJEsRobIyJ83N5bS3pwFTPf7jApYC84Cf8swzr4fV\n3Mt/MvTc3m189NHnfbZZWCCSxdgUBINohC3+H6pGr4Gc9UKfI1mKCJnhhccwJ3snsEh/zswUaW0N\nvbmXdypr5NtLFKKVRZKqPUCE1CUaYYsu1N3CeByLwj4EIaG5mKBy48bEmCzN8IIx2buAu4DIQhje\nnpcVKMMh+cWAVgtLDaQ1t5BsRMPzcBVwADiPunv8CNgXhf0IQsKSqEWEjHoC779fw8mTDrq6DqMm\n9GLgNVRRK98Qhiehife8PS97SBUxoNXCUgOpWCkkG1YbD9uBvwGagEuBHwPbgAnAFxbvSxCEMCkr\nc/Lww4v44ovHUBqEW1Eeh2rgZuAp4KdEKt7znAwPHDiMplkvBoxHYaVoZZEkqrEpCMGw2nh43eP3\nPSgfXDNwD/DvFu9LEBKaRKwaaE5+V6J0CBNQNv9UYCRqUoy8uZfnZDhhwjwcDmN7LtSK3QGkA518\n+unBsEp2G8d19+5/4fDhd7yqeLrd3bS1RU9LISmVgqCIdp2HdmA3qsh/QB544AEGDRrk9VxFRQUV\n4qMTkhxjld/aarq4oz25XQxz8qtG6RAKUb04HgWyMUMY1on3TDGgYbCYxwO66ezcTnNz6MfDOK6H\nDo0CfkMsa0lISqUQD+rq6qjzSUs6ceJEnEYTG7KBVlT4wpdJgLZz505NEFKRysolGjRooAV4bNMq\nK5fEfExFRfP1/d+qQbf+u1ODJRpcoz/n1OAmDbZp0KW/pkuD97TCwps0p9MZ1j6dTqdWWHiTBnfr\n24zseJjH1fMz+D66tOLiW3tziHqkuDj2+xSEQOzcuVNDuQjj0t/B6myLXwBfAcagJNwvAXnAMxbv\nRxASnkQslGSmUHquoI1Y+1dRHgc7UA+sAeYDC4AycnIe6lWmiKF/yMjYg/ISeNZ8WKD//AMvvviX\nkGo+mMc19l6AZKnfIQjRxmrj4TKgDvgf4A/AOdTdYr/F+xGEhCfRXNx1dXD8+JUoKVKgokTVqOSo\nbcAlKIPiFeCHFBbaaGl5kQ0bwtdpVFTAhg12rrxyJMpwKAcWAqrnhdrHN+joOMCsWRfveWEe19gX\nVpKUSkFQWG08VKAMiGygALgTZUgIQp8j0aoGlpU5yc7+C7AEGIoSSnpieBxWkZFRYnltCvV5awhW\n88Ht/nVINR/M4xp7L0Cy1O8QhGgjjbFSkERU+fdFEq1q4LJlK2hp+QVKJPkI8LfAr1GZFkYFzL0U\nFu6noeE1y1tAq+Oxk+DpiFNpbHwsxO3sQHlKDLGnZxXPBgoLH45KFU9JqRQEhTTGSkGMfgWtrQtp\na1uH272WtrZXaG1dqKvapR1yLEg0F7epFbADTwDvAn/E0DXYbNdHdQVdU1NNZuZxIg3lmMd1LypK\nugb4GjCbtLRrGTZsNe3tExgzppKMjAWkp88iPX0m6elzychYQF7evLD7ahgNxEaNWkpe3jyysnq3\nHUFIFcTzkIJcrJDN9dc/Qnp6jnglokyiVQ3012B4r6CvumoBH38cvRX1xo12srKG4XZHVkPCOK7H\nj6sqmd3d6dhsNtLSJpKX9384ceLv6Oh4DGUouVDpoT/T/7bR1hZ+umwipt0KQl9FUjWjRM/pZIe0\nzMyr9VS3I3qK3i0azNJstmJtxIi/0+bMcWrPPRfvTyFYTSKkGUY7fdV/+9bsLxHTbg2ee07T5sxx\nagUFS7Tc3Fu1zMz5Wm7urVpBwRK5llOYVEvVFBKAnlX+j+J2P41ZsGchsB54E03bzaFDFRLaSFES\nIc0w1FBOb8ME/umx1qTLJmLarYF3mHI1bvc42to0Wls/4M03Z/Lgg38voRXBcsR4SEGCq/ydwNso\ngVywTofTkq7ToRAaiaDBCDVbobe6HX/DOfJ02bo6aGkh4u1EC/+S47IgEKKPaB5SkOAq/xWo/gU2\n1IpMavT3Jex2Ow0N9VRX17B583L27UtnzJguZswopqamd90gwyXUbIXeNqAyDWdjog+9T0ewLKX8\n/Cs4e7Yl5O3EGv+S477HbFrUynXHA8kmSwzE85CCBF9hvg9k4V9h0Bep0Z+K1NXBvffaOXZsBWPH\nrqeoaC1jx67n2LEV3HuvPaHc2uGECTxDHE1NB1DnvUHooZpg3o5Dh9rQtBt62E5DXCtLmt6WxA2t\nWIlkkwkimIwSwQRU6emlHgKynsVzl112q4iwLEIEbeFj9uAI/Cgqmn/htUeOHNF7Zxgi4Js02Kr3\n43D6/G306djm16cjuCjyVo/t+vb72KplZl4ddr8PKzGFsKEfs2QmkcWrsSTegkkJW6QgwVzDqjXy\nUlRcdBRmK2ZfdnDjjVfw/vvlkppmAZLmFz5m+OEo3i28u4DxHDjQTl2dOtf9Qxz1+nseBTpITz9G\ndvaP0LRszp7NDhqqCd5uOx0Y5rHd5R5jKSYz8/KYp916YoYpI2+lngxIW/TEQMIWfQjlWv0UdRPM\nQ1UY3Eog8ZzNlu5xQ/YuIyyCyvDwntyS61jGqziSOldfI3AfjIWkpbkuuKfNEIfRcKsS+Bg4D7jp\n6uqkvT2f8+czyMgYx8mTtXz4oX+oJniWkjEpGwb5en0s64GfM3p0Rlxj7GaYMlDJcYPUadqVaD1j\n+ipiPPQRXC4X5861k5m5GGgC/gNVYfBloAz4Ejk5t1xQvW/Z8il9IX4aCxI5ze9iRCu+fDGjpLS0\nmry8ZSjvga/RNZXTp5+8YHSpycK34davUcbwz4D/B6ylu/sVOjsXMnhwOa+/7mLtWrwm/eBZSsUk\n8qRsZLCMGJGHzRZ8QZAqTbsSrWdMX0WMhxTH5XIxder3GD78Kzz//N/gdm9GlST+GvDXwGsMGnQN\nc+a8wa9/vYH9+1ewYYOd3Fyx7q0iWVdKdXUwceJPaG5ejtVek4sZJQCaNpLAfUEASi8YXYEbbgVL\nRQ4+7uB1MKqBB4nFpNwbT4/RtfTf/u0JZs9+l4KCP6Z0065EqFcixBcRTEaZJ588ouXk3KTB3brQ\nK7jAyFfUB9fEvRphqpAIlR17w5EjR7TMzOKojD0U0Vuookm1rZt8xhn+MXc6nbrwcpsGhzXP6qtQ\npKWl3ailpc3R0tOjJ3j1Fn92XxgvNPgJPPsq3t9TzyLYVCbegknxPKQwO3asoL39MZToLNgKTrnN\nfVeC8FUS2VUbDaIV30/WldKyZStwu426IGBqCuYBC4A5OByf9KrhVM+hnCt59tk3+eSTfYTing7c\ncCt8b4/p/v8dNttMPIstwUd0d69gzBg3hw49zZkz6y946azUOySzPiZWSFt0QTwPFhPce3DxFZz/\nStBIces71r1/yp81fT/ivVLqbaqo8pgYK3gjVbEhyN/GZ3pVS0+/RsvJmdPjfoJ7FTy3++BFvRPG\n58vNnROx58EgnqmAyeqlEmJPvD0P8USMB4vxd3nOD/lGGvim5fSYQL+U8rUJzEkj2MS4tVeTfbzr\nPPTWFa4meOOYhNJwKthx899P8ElyiWaG2EI3YK1siBXPCTyc+ha9Jd7nY6KMIdkR40GMB8vwv4Ea\nN0Hr4supjDlppFYRmt6upNXxMAyCmdrFV/ah76fngkye2zUM2Fs1mK/ZbNcEnGD8vTuhF4fyJZ7X\nQiwMl0TQVSTCGJKdeBsPonlIIfzjyEasvRoIVK566wW1uHf6k29s+1Y+/fTzlO/Ml6plfnubKupd\nF+QcF9cUhL6f4CXUO3y2611b4aqrxgTUGvjGwdPT7yUtLY20tB+RlnYL6ekLyM6eT3b2s7S2TmDU\nqMoLmpZLLlnK0KEfccklSuvS1LQPdS34XgfzgCVAe5DPGDmx0McE1lUcA/5Ac7PG8OHRr+Uh2g4h\nEsTzYDH+KyZPt6+3etw3hh8tl30ykaplfnu7kvZezd8Sguch9P0Ec1tnZEyO2so7+Gp3vZaRMc7j\n+SUarAtyHWzT0tOv01aujM51YIU+xvfYpqfP19LSyjSbbYamPEhX+xzj0MNNViHajsgRz4NgCS6X\niyNHDoKXOt2OWjWuARYDb5Gba6OgYCKzZ2/i3/7tPy6s4MyV4FICF+ZJ/Vbd5qovtYrQ9Laojudq\nPiPjIGbDKRfQ5vG3Qej7MWoT7N+/gjNn1tPRsZYzZ9bzrW/dRLRW3sFXu+/Q2bna4/lq4J8JVqCq\nq+tJtm+PznVgRSZBWZmTDz4op7X1ZtraxtPVdYLu7s/QtGr9sxTgnUFzF6rkduy8AMla+0RIDMTz\nYBHh1HMIhrFaiebKL9ExV329P46JiBXZA+axMVbkr2r+YsYHIz5uPa28hw27SZs509FrkV3w1W6g\n532zN5LnOlDf93r9+1mvqayrbZp/UzzD4+BbIyP6n1U8D5ETb89DPBHjwSLMySGYOv29kN2PfVk4\naRhQI0Z8T7PZrtbgPb8JLBlDN1a6wnNyZmhKhOgvZoRpms1WpPVGpOi7n0AGwowZe7Rhw3rvXg9+\nbgd6Pv7XQWQptkaaq2cBLV9BsPEz9p/V26D1PY9majk5MyTr4iKI8SDGQ8R4W/G+F+ItWkbG9SFf\niLIiSL00Mis/z8XOj9zcsqgdt0g9KOF5HuJ7HTz3nKbNmLFHS0+/LoCxtE3Lzw/uhcnJ8azPYdwH\nPA0iY5ExU/M2KmL3Wf09Wf6fMRkN9VgixoMYDxFjpbcgngVyhMQnsCj34qmUkWBVSC34ue1ZW8Lz\nufhdB0eOHNHy8q4JMC7jsU4bMCCQYdGglxT3NBg8DQnPRUZp3D5rYE+W3G/CQYwHMR4ixkpvQbyr\nIQqJjfe5FrlKPxSviJklMSsiIzlw74qbNRivQaHmHaY6rMFULV6hq8D9OjwfD/Yw6d6teXsVPEMY\ngerARB7uDAen06lVVi7Riotv1bKywuuh4/neoqL5WnHxrVpl5ZI+eV+Kt/Eg2RYpgJW54f758gtI\nS5tDWtoP2bcvkxEj7o16DriQuHifa+F3rvQllJbfZpZEFkSQBePfu+ImfXu/AbZhdpudDdxMVlYx\n2dnPk50d+/4JqiZGHsEzEj4Cpgb5Xw02235Ub5pioAxV5+UmvOu9GK3GPbOy5qNqWpSRk/OQ5Z91\n5Uono0eXU1u7EIdjHR0dYwg168L3vU1Na3E4XqG2diGjR5ezalXv2sMLyYd4Hiwimt4CqQQneNJz\n7YfwPV6hhMmsrvxp7jNxQ3QqPNSTR7HnUGX//jN1vcQrmpl18aAGZRpM1KBY97iMC+hdycm5KSq1\nLIJXwb34OSQhVW/E8yBETDS7zEklOMETz3PNZjtMpLn63tUvfSs6/oRnn32TlhYw6y8ErpSak/MQ\npaXVIX0Gc5+JW0lUeVHGE9yjeJqevDBDhuRQVraRIUM2k5aWBjwEbADaSEsbxpAhtzJnzmZWrtxC\nZeUfKS6eT1HRAoqL51NZuYaWlnqqqqz3rgSvghsIb69pbyulCtEhI94DECKnogIqKowyvtaiLshg\nBsIUGhuXW75PIXHxPNcmTJiHw6ER2IAIrZiWWSzICSxChUFq9Oe66ezcjs32bdREabjXa1BFjdKB\nTgYOPMgnn7yJ3R7aZGfuM3ELFZWUFONw3Iwylh5FTZppKGNpOxkZB+js3E7g0MUOZs8uZvXq0O4J\nVVXW3zeC4V8cqhoox/8z7tBL59f38F5PpLBUrBHPg9AjfeWCrauDuXNdjBql+hsYfQ9E2xEcK7Q2\nZvXLYPqJabjdN2BWs/TudQEPc/vts0M2HLz3mbiVRGtqqhk2bAWql8Yf8NQipKdXUVKymmHDHsLf\nC9NwoV9NIuJf7dRTb/E1YFJQr2lvK6UK0UGMB6FH+soFG4pwT/AmeHOr0Ccw0wDpySVdg812b8D9\nhBOu8N9n9JtQ9ZaNG+1MnFhPQcE75OZ+RGZmOrm5XRQU3EBZ2dtMmjRd/781ocpYGc+BDU7DIHyY\nysoyzpxZH7D5WSyahgnJgQgmk4C+IlLqK5/TSqwoPmUKMHtOw7zyyjmWpeitXOnUy7kbJZx9hcZb\noyYY7A1biKp+AAAgAElEQVSxKFoWK2F0JOJuSSP3Jt6CyXgixkMS0FcuWKmsGR/i1VPFqBdQVFSm\n5edP1LKyrtHy80u1oqI5CVc3IBYTe6yM50gMoVSr/Bop8TYeRDAp9Iihrj9/vobjx5fT0ZFOVlYX\ngwcXX3CPeroWk5W+ou1INAwB5uLFN1FbuwOlefDFepe03W5n9erYCQUjwTvjyeAY8AeamzWGD19A\nTs4QBg8upri4msrK8K/JWAmjIxF3R1MYLoSPaB6EHqmogMpKO8XF1QweXExWVhcdHekcP+7A4aih\ntjY1xISpqO2IpQjU5XKxePFSJkyYx7hxC5gwYR6LFy/F5QpNK1JaWk1OTmD9RG90DamEf4qiE5Wh\nsBB4G01riFifI8azkExI2CJJ6AuFolJR8xCr781sCe+/n3C0A1J6ODD+/USsP1clbJd8xDtsEU/E\neEgSUnFi9SUVtR2x+t76wvkRT/y75lqvDwn8HRpNz2ZqNltp3PQFonUITLyNBwlbJDGxckv3hcpu\n0azSaRCr78vYz7PPvkMsvre+cH7EEzNF0QhXDMbqEIN32OgI8D3gK1gZGrkYwUJf11/vkDRqwQvx\nPERIrNzSVrb87svE6vuyqgtlqMj5EV1Mr9jdumcsOiEGp9OplZber9lsV3vsKzbepJ5CX+npPbUm\n77ueLfE8CL0mVn0nUlFMGA+i9X35rtiKiubQ3LycSLtQhoqcH9HF8IplZOxBnTvRKZZkt9u5+upc\nNG01cJTAmS8QDW/Sjh0raG83ro2jqB4n84HH6OrqjulYhNCIpvHwzyi59L9HcR99mli5i6WymzVE\n4/sK1Kb45MmRqJ4Hsfne5PyILhUVsGGDnSuvHImVDcICYZ6jntkXvg3L5tPUdDBKoVHPTJJ1qBLk\nhUgmSOIRLePhy8B3gA8JviQRIiRW6VVWlCEWovN9ea/YjG1ncLFJxsrvTc6P2GB6eDz7QRg9L77G\nwIHfj7gbpnmOGvsKNJm/QmfnE5bqDcz9BupxIp6tRCQaxkMe8CxwH3A8CtsXdGLlLo6FmDBexLIW\nQjS+r8DejJ4nmYyM71v6vaXy+ZFIeHt4Im8QFgjzHDX2FbxhWXRCo8b57OntOIjZGM0X8WylEs8A\n/6b/vgl4PMjrRDAZIZIiFzmxrGERje8rsFhRzotUxOzJ4Z9ObFUvDvMcdWqq78fMqIgzg+93vgZH\n9H0b16Qxlq1+nztZ06itIN6CSatZBPw3SqkF8A5iPESNWNxMUp1YGmDRqCXhX9zHqcH3NLhag/fk\nZptiRLuQlvc5eliD0iDXhrWZNOZ+Z2rwYIBrMjFqTiQS8TYerOxtMQr4P8AsoEN/zkbwIC8ADzzw\nAIMGDfJ6rqKigopUaJgQZaqq7NxxRz3V1TU0Ni6nszOdjIwuSkqKqampj9iF2ReIVU1/sK5PSF0d\n1Na6cDhqOHz4AMqlOw0Vn16EcjP/C8rl/CjgBg4yaNDNKdWPpC8S7Z4c3ueog7a2M6j5KdBt3PrQ\n6KFDd9LevhP//hVGmKab8ePns2fPekv2myzU1dVR5xNDPXHiRJxGYz23oVRSbo9HNyoA24H/2See\nByHuJFuNguee07QZM/Zo6enX6aszw8W7NciKTcIVQu+JdWjU6XRqmZkTk+qajBfx9jxYKZjcCFwD\nXK8/JgLvo8STE5GsCyEBSaYaBS6Xiz/96Xu8++4CurpWokRsw1CiyJeBDUilR8FK/CtPLgVuBWZj\ns93Hxx+fC7n5WShs3GgnK2sYyXJN9mWsNB7OoKSyxmMP0A58of8tCAlHstQoMOo51Ne3oWmj8C6a\nY7h0xyD58IKVVFXZaWmpp7T0d9hsM1Epm+uBN9G03TQ0fJPRo8tZtcoaA6KiAu688zqS4Zrs60S7\nwqThVhGEhCRZahSY9RyOAgMIbCQkjxdFSB68K0/6V0dtb3+U7dutSdmE5Lkm+zrRNh5uAn4Q5X0I\nQq9JlhoF3pX/AhkJLqANyYcXokEsm58lyzXZ17Ey20KIES6XS8+wcPhkWFRLhkWYVFRARYXh9k9c\nvCv/jUe5dY3QhZFlsQz4MSrDYgpqbdANbKew8MfU1NTHethCihCraraQPNdkX0eMhyRj5UonDz64\nSHdh16Au6G4cjkZeeKGcxx+PrDxtXyKZjDDvyn83o8pOG0aCkZI5FbgBdV4sR3kpTpOTo1FY+KKk\naAq9xjz/jqLOLwemF2w8Bw60U1dHj+eXZ4rx8eMOr1Tl4uJqKivl/BRCQ1I1w8TpdGpjx87Q0/Li\nk44X7SI1saKnFsCJWGDLv/Lfej0181YNrtFiUQVQ6Luo82+95l350Sw+NmDAdRe9B8SymmtfIN6p\nmvFEjIcwMCe7m+I2USTbhNsTyVba27/y3xINbtFglm48SF68ED2cTqeWl3eNfv4FOs+2XvSaSbZr\nLtGJt/EQbcGkYBGm2j6PeKXjBe7gGB3FdbSJpQDMCrxFZH9LZubH5ObaKCiYSG7uSCTLQogmGzfa\n0bSReKcIe1J60Wsm3Gsulk3rhPAR4yFJMC+8+KXjJduE2xOxFICFwsVulAAbNtjZv38FZ86sp6Nj\nLWfOrGf//hWSFy9EnYoKuOyybMxrxrPr5QJgPq2tB3ssGBXuNVdW5qS5uZzW1oW0ta3D7V5LW9sr\ntLYutLQduNA7xHhIEswLL35FjRJtwo2ERKssGcmNUvLihVhgXjNOoBxVMGodqiX4K5w69USPBaPC\nveaWLVtBc3NgT6eV7cCF3iHGQ5JgXnjVKKW970SxNeoTRaJNuJGQaJUlI7lRSl68EAvMa2YFqvma\n77k6rcfwZbjXXCp5OlMRMR6SBPPCs6N6GawB5qNchmXk5DwU9Yki0SbcSEi01XokN8qKiuAhjQ0b\nJP1NsAazz8X79OZc9e6T4X3N5eQ8RGmp9zWXSp7OVESMhyTBe7K7BGX9vwL8kMJCGy0tL0Z9oki0\nCTcSEm21LjdKIdEx+lzk55+jN+eq8f7KyjUUF8+nqGgBxcXzqaxcQ0uLf32aVPJ0piJSJCpJMCa7\n8+drOH58uVeBFWOyi/YKMxHGYBXxrmLnW6CqpWUf6kYZ6KYsN0ohMbDb7RQUDMHh6N25arfbWb06\ntGuupKQYh8OzkqonyeXpTEXEeEgS4j3ZJcoYUoHAVUKXANtRVSJ9kRulkDjEalIvLa3mhRfKaW/3\nLbe+Qw9zSLn1eBLM9xQLJgE7d+7cyaRJfa5AlhBH4l2WevHipdTWLsT75utCKdiX68+nAUdQAtn3\nSU8fTb9+SClfIe64XC6mTi2nudl/Ui8sfIiGhnrLrqN4X6uJzK5du5g8eTLAZGBXrPcvxoPQp/Be\n9U/B6A0CjeTk/CgmvUEmTJiHw7EO/8vPBfycrKwNFBSMZP/+z3G7VwNXorw9ewA3NttBLr30Zq69\n9n+JESHEHM8eFV988RfOnj0GdJKWlke/fvlMnnwdL70kk3u0ibfxIILJJMDlcrF48VImTJjHuHEL\nmDBhHosXL+2xIIsQmESokhlYHOlChTA+QtOyOHbsCG730yjDYREqp3498CaatptDhyqkUI4QF4zs\nnoceWgp0o2lPomkf0tXVQFvba7z77sIe6z0IqYEYDwnOypVORo8up7Z2IQ7HOpqa1uJwvEJtbXwv\nUF+DZty4WVx11UzGjZub0AZOIuSO+6vIvYvuuN07OXlyJEr/EDynXgrlCPEkEQxxIX6I8ZDgJOIF\n6m/Q/Jqmpm727v0ZTU2vJ4yBE4hESIn0r5cRyEDI0H+Pv7EjCIFIBENciB9iPCQ4iXiB+hs0wVbH\nibcCSYTccf96GYG+Y2Oc8Td2BCEQiWCIC/FDjIcEJxEvUG+DxgW8Q6IZOMFIhCqZvgWq4DD+37Ex\nzvgbO4IQiHAMcdFtpR5iPCQ4ibBS9sU0aIxY/WAi6bYXSxKhSqZnOel9+2oZONCG/3ds9DAZiqr/\nEAip/yDEj1AN8UTVbQmRIcZDgpMIK2VfTIPGCFdkEUm3vViSSGWpjZvqyZMT8DcQjB4m54B7gK2E\n0g9AEGKFtyF+BLVouBWYTUZGJZs2bWfcuLksWzaX9najfklihzWF0JEKkwlOIlZZMyvMOVDphYaB\n8wdM7YOBd7e9qqr4VqdMpCqZpnakEGV0+X7HTeTkuPjXf/0Te/b8hsbGx3wK5VhXjEcQwsUwxI8d\n+xdOnHgHWI26H7jo7FxES4tRS+VrBK6cCiqsuTxGIxasRIyHBKeqys4dd9TrVdaWJ8TkYRo0Rvii\nGjX5aaibRyDkJuGL0oIY5anr9d+Xo0SSnQwceJBPPnlT/47jb+wIgieGIb54cS61tasxFw2eAmoQ\n0W9qIsZDEhBOM5lYYBg0V111CydPapgu9gXITSJ0vMWw/gbC8OELxLMgJDymEQymgNpzEWGEOaXp\nWyohmgehV9jtdm6//SZMPYYdGEKiiTsTmUQUwwpCuPQsoAYzrBkIEf0mK2I8CL2mtLSanBzPzIVi\nJDMgdBJRDCsI4RJcQA3KE9EOLCaQ6DdWGU6C9YjxIPSaqio7LS31VFauobh4PsOGfYjN9rdIZkBo\n+BtfIMdLSDZMI9io/2L8bXgi/gbYDPwRJZ6cDVzLoEHPxTzDSbAO6aopWIq00A0POV5CsrNqlYsf\n/MAQUL+J2V5+FPBdAmdaNFBZuSahtFzJRry7aorxIAiCIESEy+XSBdR/Rk0rLuAWwPjbl26Ki+ez\nZ8/6WA4zpYi38SBhC0EQBCEiAguoRyLZV6mLGA9Cn6GuDubOdTFq1FLy8uaRlbWAvLx5jBq1lLlz\nXdTVxXuEgpC8+Gt4JJsolRHjQegzlJU5aW4up7V1IW1t63C719LW9gqtrQtpbi5n1qz4l88WhGTF\nV0Cdn38QZUgEQrKJkh0xHoQ+w7JlK2huDtw6vLn5Uaqrpca+IESCUdBuz5717N37BoWFPyaeTeiE\n6CHGg9Bn8G4l7ktitQ4XhGQnkZrQCdYj5amFPoN3OWhfRMAlCFaSSE3oBOsRz4NgKYksSpRy0IIg\nCNYgxoNgKYksSpRy0IIgCNYgxoNgKYksSpRy0IIgCNYgmgfBUrzb8/oyhcbG5bEcjhdGK3FVDnq5\nTznoeikHLQiCECJiPAiW4i1KdKEMCQeQDnTR2noQl8sVt4naSCUTBEEQeo+ELQRLMUWJRke9hcA6\nYC3wCqdOPcHo0eWsWiUFmQRBEJIVq42H+4H/Bk7qj23AVy3eh5DAmKLEFUAg7cM02tsfZft2Kcgk\nCIKQrFhtPOwHlqE6Zk4G3kYtOSdYvB8hQTFFie8jBZkEQRBSE6uNh3XA60AzsBf4MXAaKLF4P0KC\nYtS3z88/hxRkEgRBSE2iKZhMB+4EsoEtUdyPkGDY7XYKCobgcGjAUXxFkzAeaI/jCAVBEIRIiIZg\n8lrgDHAOeAq4C+WFEPoQSvvwGoFEk7CQ5maXiCYFQRCSlGh4Hv4HuA4YiPI8PA/MBHYFevEDDzzA\noEGDvJ6rqKigoqIiCkMTYkVpaTW/+93NdHU9hRJNGqQBU+nqepLt22uoqpK0SUEQhJ6oq6ujzqe2\n/4kTJ+I0GkWwoLSVvAm0AN/2eX4SsHPnzp1MmjQpBsMQYs24cXNpanqdwKdZN8XF89mzZ32shyUI\ngpD07Nq1i8mTJ4NKTgi4OI8msajzkBaj/QgJRzYimhQEQUg9rA5b/BR4FZWyOQBYBMwAHrV4P0IS\nYBaMCux5kC6WgiAIyYnVHgE78FuU7mEj8GVgLqreg9DHkC6WgiAIqYnVxsN9wBigHzAcmAO8ZfE+\nhCRBulgKgiCkJtIYS4ga0sVSEAQhNRHjQYgq0sVSEAQh9RDjQegTuFwu3QPi8PGAVIsHRBAEIUzE\neBBSnpUrnTz44CLa2x9Dlcq2Ad04HI288EI5jz9eT1WVGBCCIAihIvUXhJRnx44VuuHg2x68VNqD\nC4Ig9AIxHoSUR7X/lvbggiAIViHGg5DyqEqWUulSEATBKkTzIMSUeAgXpdKlIAiCtYjxIMSMeAkX\nS0qKcTh24N3d00AqXQrW8vev/j1179Rx/A/H6W7rhnSgC9Jy0xi8cDCTr5sM7eD4o4Pjnx+nQ+sg\ny5bF4MsHU3xbMZXTKqm4VroKC4mNGA9CzPAWLhp4Cxej0aK7tLSaF14op739UZT2IQ1V6XKHXumy\n3vJ9Cn2X717xXVY+s5LuBd1QALQD70H3oW6O/fYYb2S/oZ67DXU62sDd7abtQBsd/9nBrDtmxXX8\nghAKonkQYka8hItVVXZaWuqprFxDcfF8iooWUFw8n8rKNbS0SJqmYC23/c1tdC7ohFGAC9Xtpxi4\nB7gbOIcyHEbhnfwzCpxTnFQ/ImXbhcRHPA9CzPAXLrpQ4QsHkM7evZ+zePHSqOgfpNKlECs+P/y5\n8jicAV4EFqC6/dQCp4F81P8DcRk0bmyMxTAFISLE8yDEDFO4COAEyoGFwDpgLR0d/01t7UJGjy5n\n1SpXvIYpCBGhpWvKRt4G5AJZwPPALGCw/nfw5B866YzJOAUhEsR4EGKGd4vuFYAUbhJSD1uXTdnI\nLpSh8CoqTDEYaEP97wzwBvB74Dn95xvAaWg71xaPYQtCWIjxIMQM7xbde5DCTUIqUbe7jrmr5tLV\n1QWtKJtYQ4kjC1CeiH7AQJQnYjzw1/qjAhgNPAOnvzjNuOnjmDB9Aou/vxiXS7xwQuIhxoMQMzyF\ni1lZh5HCTUIqUTa8jF2/3EUXXfAy0IEyFGz6wwWMAM4CczEFk20o78QG4Otw6p5TNM1pwjHLQe35\nWq6YdgWrNq+Kx0cShKCI8SDEFEO4OHbsCEz9gy/WFW5yuVwsXryUCRPmMW7cAiZMmMfixUtlNSdY\nhsvlYvH3F1NUUsTR7KMwH+VJaEMZChpmjbIbUXIfQzBpiCrPEDQD4+xXzvLMr56J2ecRhFAQ40GI\nC976B1+sKdy0cqWT0aPLqa1diMOxjqamtTgcr0QkyjQmioLJBeRclUPa0DRsdhu24TZsg23YBtmw\n5es/B9uwXaI/RtjILspmRsUMMVxSiJWbVjL6xtHUnq/lZNZJM0RhBypRhkIOKoyh6b8PwjQQtgHT\nUF6JYBkYBeD4i4TxhMRCjAchLnjrH7r1Z7uBBr1wU+i57r7ehXHjZnHVVTNZtmw27e3LsUqU6TlR\nHJhygLPHz6LN1VSsuhPoD2QDmcA81CRxO/D3wHeho6KDd3PfZfSNo8UNnSLseHEH7X/VbnoMjAeo\nTItBwNeAP6LOj1b9f4bTzQXsQ50zPWRgnHWfjcLoBaH3SJ0HIS5UVdm54456vc/Fcp8+F/Uh13lw\nOp1Mm7aI5maj5LULWAQsAx4GpgZ55xQaG5eHNWaviWI1Km69DRXXHqW/6CjKbf0RUKY/3wZs1Ydm\ng/aOdn5w3w9Ys2SNlCJOct7c/qb6vsE0CM6gzgsXcBIYijol/wSsQZ0TrZgGx1GU3Ry8/QpI+xUh\nwRDjQYgbVhRuuvPOFbrhUIq6W98FLEWlgg7GSlFm4weNMFv/ox21YiwD3kXd4NMAN8r9/C7KHb0O\naAG+rv+9DXBCe3c7b/6vN9l97W5q76wVIyJJ6ZfRzzzF7MAJVCbFXNS58iamoXAfypDchDIkFmCe\nNzaP13kam13Aaejo6iCtKI2s9Czso+3SA0OIO2I8CEnN0aMOlMfBiVrepQHvoGpI/AQru2l20qk2\ndQZzxThY/1tDVQ40imh2AS+hvBJf11/3EsrYmK1eo3VrHDpwiJyncqSfQZJy1HXUPMWmozxSX8f0\nRE1HCSLLgMtQoYxbgL2Q/mo62KAru0u9fgNQAryH8mZMQ50zC4AC0Gwa57vP03qgleynsuWcEeKK\naB6EuBJJNoTL5aK19QvUndsoOpWHihlMQTUUsE6UmUGGmig2oYwDw0DoROXva/pzGmr1WIZ3jr8R\nxvBR0zd/qVn6GSQARp2GUbeMIm9CHlnFWeRNyGPULaOYu2oudbvr/F538uRJU8eQizIWPYWPucCd\nqFPyOWAV5L6US8EXBZQ9UsYTdU+Q15kHRSgvxDsow2EUcs4ICY0YD0LccDqdTJ0aOBti6tTyHg0I\nI5Pi1Kn+qNnaaLrVhbn8rwYCiTK3hi3KBCiZWKImiv0oY+E06uaegcrfz0EJ31pRV1YB3jn+PfUz\n+CBx+hkYGSUTpk/oU8WKyoaX0fxUM62XtdJ2Zxvucjdt32ij9bJWmp9qZtals7xfN6RVffdvoc6J\nbsxTz5NcYA7wLSgaU8SZPWfY/9p+NlRtIH9IPjf84w2kvZ2mvFOexscR1HOBKlEOSqxzRuh7SNhC\niBveegUDlQ3R3Pwo3/hGDZs3B9ZEmO29/4DyLhh37WJgJ8qgsAP1qLDGcv01nQwceJBPPnkzZFFm\n3e46arfVsvt/dmP70IZm05Rr+nnUjT4Plb//PErz8DIqhGFUGDSEdEZBIA/xpDHMli9aqNtdF/cY\n9spNK/nHb/8jHSUdqsjRfjVGx6cOautrGVg0kJ//9OdUzagKaXsul4vqR6pp/KCRTjppO9dG96Bu\nsMGpQ6fo0DrIsmUx+PLBcY/jL/vXZTR/qTmgyLW5o5nR00Zz45IbObfpnHrdRyiD8RsoL8G7KIFk\nD8LHDJ9brvFZswZk8fbyt+nM0UNjTn1bL6HCFxoqTGYDDgK/hU8GfJIQ54zQNxHjQYgbpl4hEFM4\nejR4NoQqX10DFKIabBmzdDVwM7AdlWlhR4U0DLZx++0vh9W18+Sxk2yp2cLZfmfV6jIDGIZ3vn4O\nSnKxCfgUOIVpv3yCEtKdwU/3QDdwANzr3BdWtvGibncd/3v5/1aGwxbU+MtQwlB94jp56CTfL/8+\np1edZsnXl/S4vZWbVvJg1YMqQ8X4vKdR9txclMFlA3e3m7YDbRHF8X2NlAwyKJlYQs0jNSF/1xcE\nsUG+p/YD7TQ/1Ux6RrrSLbyL8jgdR3kWQHkFDOGjLwd075UPFddWUHFtBeN+O44mV5PyONSjbF1D\nZFuG6cnSz5nOlzvjfs4IfRcxHoS44d+i2xPvbAiXy6WndTo4d66TlpZD+nsN78KdqPDENOAF1Ez+\nXygDIg11x91OTs6PKS2tD2ucm57ZxNmOszAAVbfhVdTEmo5pIBgTxjz9TcYkMh3VinkkSnk/DfgQ\nlYWhcUFp7x7qpmhhEf/1xH/FbSVZNryMo3uOKp3GEGACASeurgNd/PN3/5kBgwb06IHwSm01VvJ7\nMWP6BkYcHxXHX/3EasD0+Dj+6OD458f9vBRFo4po2t9E4+8bOfHRCeUN8pjsHQccvHDjCzz+1OMh\neUouCGKNzzwE9Z0ZWQ9taoyAOkZnUQaQpyDSEEjerB8z49Q7AIV/KaTm9eC1RTLIUGGLF1Geq3OY\nGT0BvCFavsa0udPYtmGb5S3sBeFiiOZBiBveLbp9MbMhvLURv+HTT910dw/1eK8ddcf9McqAGAds\nRMUPyrDZvsSIEbdQWfkyLS31VFWFd6N9/Z3X1UrZED8WoAwDw2iYjnfcG5TNsgE4hpoIZuuv3QIc\nQhUOuh/4LvBtoBTaPm6L60py2b8uw53jVl4Gz1TUAIK9rlu72P7C9h631/hBozpWRgnm0aivzIjp\nt+Edz98ML69/+YK24mIahPtH30/Tk02cOHXCzHDwGWf7X7UHHaen8LHf+H40fdJkdsMcjApDnUR5\njY4Bl6KWW+f1z5OB8jgZgsg6VAqmDXgLbCttZNZnKoHkgQIKv1PIxsMbgx6vkoklyiDJQ3XjTEd9\nF77H0Dj1s2Dv8b0Mu2YYxY8UXxB0CkIsEM9DH8UKN2+klJQU43DswFvzYGBmQ5jaiELUnfoxlGHg\n+V5PfcOP6N//HGPGDKGk5AZqaqoj+kzt59u9xY8zUc6NaSijoQxYiLJbNgPdkNmeybyb5tGPfqw5\nv4aOvA51tQ0BbsB7VatrH9yXuvmHZf9A3W+iOwkE++63vb/NlI4YMXZPkadP/YHaE7U8+6dnIQ+y\ntCzllUjvQtM0uk9309ndqdz5xkr+Q5T3xkh3DRAaOHngJKNvHM3jTz3O9he2mxoEA8NLcbaZSWWT\nOD/vvAof9CRG3RhYWHh9+vU0PNrA6TmnYQzq9DG6YW5CZdGcRH1XbajT7zOUALIMZTAYHqc5Phvf\nD/dk33PBixIKNY/U8Pvrfo87362OvxvznDPKWAcJYbT+ppVZ35cQhhA7xHjogwSMRffCzRsppaXV\nvPBCOe3tj6IyJQwf7w49G0KFF5Q2YglK25CGWtZfpf/t+d5LgNsoLNxJQ8NaS4yglZtW4u52e4sf\nc1G1qLainl+PcmunQc7gHIaMHkLxbcXcNe0uKq6tYML0CTg0h9meeTCmq9tH+1D/p3pmbp4ZteN/\n4bu/od1LhOd41aFWvYUoQd55zIm+DZVC2IJa4V+P0ql+FTr3dcJh6Dzdqf6Xhaqi+DXUpG6s5Gej\nwj05+nO+oYFDqP13Q3tGO9WV1dhsNrjb5wN4jOV8+nlvo874v48gtamtibmr5nqJMVduWsnfVfwd\nXfO61PfxjPo8vKVvZz/K03ADqrT0bShnlqFzKdDH7hmyME7fVij8oOcQRSA2Ht5I5pBM3G638i6c\nRIlWjWNoaFACGFOnZ5/2CvkIQrQR46EP4hWLNvBx88bCePAsUb1t28McPnyMc+c66dcvj0svzWf7\n9hruuKNa1z78AuVxeAxvrYNnJkUX+fkHaWh4IyLD4UJ2xQu7Ofzfh9VV4qttMNLvDPbDV9q+wua6\nzX7bK5lYgqPVoSZWG8o7EWQS0GZr/OKffsGv+v/KEo+Qr5fhyIEjtE9vD7yC/S1qdWs0cOzA9BB4\nFruqxdujACrG341y3S/UP9sgzJW8YXx16s+5MOUp01EGyzx9PO1w6r1TKmziKYnxHct6vI26Nvy9\nGaehc2Mn7zzyDs2XN7M8YznXjbuObY3b6OrXpfb3Kur7vAplBDyvfxYjTIU5LgbrP22YNRy2ogwl\nY2vpDrQAAB/OSURBVCzHoXC5ClFU2EPXr1RcW8HyQcuVoTkGaNI/k3EMfT1BnhQE97AIQjQQ4yHF\nCeSiPnDogP+KzqAHN280sNvt/PznS5k2bRGnTj0JTKGjw8apU900NTWyZUs56emZqDoONZhVmAwD\nwjOTopuCgvkRexzKhpfxD7/8B46eOapW357ahh5Wmi+9/lLA7dU8UsOWr26hOa1ZuaJbgBmoeL9n\nyuZAwAl7Z+/1mtR76xEK6GF6Fm8tg0EaStR5HOVVqUdNXG9iluAu0P9O99nGuyjDqA5lMBgx+sMo\nj4JhfBmlmN/Sf24GZuHdB8QwEKbpx8nw1niKLTfp+zD6QeShvp8PfT6XE+UhKQN3jpvmo83QBY6P\nHOozGOGIFrwrmRunl/FI11+Xpv+vw+M1vkZkN2T93yw2VG0I9rX0SMnEEhwnHMowuxQl31mHaqpl\nGJ+BSNMFn4IQI0QwmaK4XC6m3j6V4cXDqT1fi2O2g6Y5TThmOThpO9njTeic+9yFIkGFJYUMGj2I\n3CtyyRmXQ8bwDNLsadiG20gbmUbmqExGzhoZUZdI73oPR1G9KeYDj9HcrPH55wdQd2yjjkN0W3kv\n+9dlHM0+qkSSWSiNw0aUaG4hZrXA3wJPwNQTU2l4vSGo0bLx8EYKv1NITlaO+hg5qAlyPPDX+qMC\nZVjMRk1qrwJPAiuB9dB+sp2f/cPPwirU5NfxEdQVH2wFawg/z6IMmTtRk/Jg/Tmj2JXvKtimj3cQ\nZnfIbahJtQI10Xp6IO7Ut9eqb8OzgJZnbH8IKs3VU2w5GDWRG9v6BPhc3/8+j+39GvUdlenbKkZ5\nK7qAy1EiVg1llAzQfzdEiRmYhoJRNXQrylgZqo/TqCrpywHol9UvyD8vTuldpeS8n6OOQX+UYXYz\n3kZLIALUkBCEaCLGQwpitI7evm872tc1/8nDSDEMxGnY//l+ZXBMc/Cp61NOzjpJ+13tnD17lq6s\nLrXN+0Gr0uj8204OjT7EP933T702IJSmYQpqqViOmqHXAWuBtzh37ueoGcKo42Bd1chANH7QaLqs\n3ajJ/i7gf/QhGcV6hsOwS4ex7Q89p8pVXFvBhqoNPP7rx9UEcI7AWQxnMFX+vhkZVdDy5RYKSgu8\njnNP1SAvZDuAmdlwAm+NgCe6Gz7/rXwGdg5Uuod8lKFjeA9scGGB67lSP4sytIw+H4ZBYOhD3tCP\n5Sn9eI7Bf4UPZovqMv11b+i/t6Am+W2YpcAv1/8/QN9XPuoUeh4lfxnksa1s4Hf6ftpR18BQ/Tgb\nvxteliz9u8lBGQk5+uuMcXfhn13Trf/9Flw67NIABzc0qmZU0fJeC5WDKinOKubKMVcycNtABmgD\nsJ209Wi0BKohIQjRQoyHFOTCitMzZuuJ4YYPxEZwf83tX1vfWAnOJWBK3NmvnL1o6l4wzHoPRn+K\nUp8dzAMmoQwGQ+uwBuWdWACUMXz4Q71Kw/SlbncdLSdaTKHgCdSxMtzT30R5Cr4JjIf8rPyQt101\no4rCMYVm6WpfzuN9nA0x4e9RE+K70NGvg+9+87vYhtuwDbYxbNww07M0vQmH5qD21VqGXz+cj1s+\nNjMbXlTjpZCeV7D9oeCyAj758ycU/qVQvbcMFdJoxQw/nPLYhh1zpa6HcbwMglyUC/6r+t9GCMiN\nubr3rMJpeDVOYJZrdqEmeReqMFMryiMEarI/of//VZS4sV1/3mhe9iLKGDNc/3aUIXDO4/eD+r40\nVFjpGCrd9gZ9e1/T/74U5Rkw0jOf039+pJ6/tLD3xgPo3WafWM2erXtobmzmRMsJ7vzpnWg5mjJe\nP8fbaPkc0t9Op/SuQFlLghAdxHhIQS6sOIOtMA33tO9NaD/YDtnMic3TnexCTSQ9CLY2bO1dnNes\n92B4IALxSzIz70UZEJegDI1XgB9SWGhj9+4XI9Y61O2u4yd/+AntR9pNl3YewVeZb0NaRniXUHZG\nttqmZ3bAGyiRYjvmcTYmPM/QxgKUJqEbNQleAdyBWUDoRdQkOAK0PI2uzi7vzIZRqKJGhggvEPoK\n1gi12NDPB+OcyUHdNTo8tjFR/3soyivwFv4GygnMBJl1qIk5Xd/GII9tBdIbGL/bUav+G/V9fKG/\n11MCYxjMNo/nt6EMGMMw0DBDI1kev+d5bOc4KuQyQv9fp/75FqGyIF4DrtZfY4SdxkPh/xTy0q8C\na18ioXtrtzK+KlEeME+jZSfcOfPOmIicBcFAgmQpyIVKecaN0teAyAUWwsCXBnLZZZd5qfq3jN5C\ns02voudpfNhQK2NjRR6gP8ORo0d6VWvfrPfQU8XJ4YwadTlf+coaGhuX09mZTkZGFyUlxdTU1Edk\nOBii0m2N22ja22QW/zmEijt/HX9FvR24E069fSqsfZVMLFFpkUZ2QD3wV8AB1H67UBOvEfv/yGO/\nJ1GrXlCrYc/6BpvxrwPwJmZmw2yPQVyGKpNxG95VEFsh690sSn9deqFk8vCVw3HanGZmwSZ9jEZD\nqDKUzdcfZbjsBe5BGTKeZZo9DYC7UemW51Er+YH6z7mYoQRPb4TnhP8MZmGmrSgPSKH+nmlAM+Z3\nZGzLhbfHoQ1lHNyJyvYwfn9Wf5+nMPYW/fhs8Pg892FeA1vU8cg4n8Gl1156oRBUOFkWoXChdLYN\n/5oS3fDhxg8t3Z8gXAwxHlKQC62jPVMLfTkBt8+73S8vfML0CabB4Wl8GIKtYP0Z9kK3o5v77rqP\ne2z3hNXsyKz3EMzaAeimX78MVq8O3Cirt3hlJHShBH+zUROzW/87B/8bthoSQ7KHhLW/0rtK+d2a\n39HV2gW79O1+iMo6eBvV+8GGmvQO4n2cn0WtrMFbrOhE9dPIwLuUsRtV8dBo0uX53c3CbOakF7Va\neOtCfrXtV16G2NCcoTg1p5lZMA+VInkEsyFUs/57Pcq4MTI2PDNTPL/aXNRkPg81+R4F/kbf1gGU\nQWBU8Gzz+P04SjjpW5hpNMpoMbwyxrlvPD8AU+czHRUC2qC//xuojIybfbbtm4J5FjL2ZtC5oFN9\nnlz9GOplp3sSzFrBhQVBICTTQogDErZIQS60jg5UNlmPkeZsyQkYI73wXvDWRhj3RUNUZuge2lBx\n5jeAhdBe3h60lXEwqqrstLTUM3ashgpLBMKaTAq/rRr6kMGoMI4NFd83btRDCe7ibw1fpFY1o4rd\nb+9mwBsD1PdilLouQLnIh+kvbCOwqNJYwRt6jDOoSTsDUyvgRGWCXIdyc5/FP3yRx4U20dwN7nlu\n+vXr5zcBep0PBjP1bRoNoQbp4zZc+mvwz0w5ifd2XKgwxiKUgZaH8hzYUBP5K6iwzBeoif4K1Ll8\nFd7n9HT9c92ovycL89zfhvLqnMT8HnP1fY5AGUG/QxlkL6OMoD+hzoP+mNkifwVZ2Vlc9b2rKDhQ\nQO5LuWGVnbaCCwuCQEimhRAHxHhIQUrvKiVnS4668fqkFtpW2ph6Yiot77UEjJFeeO9+VCFHQxsx\nFbUyN0RlYArx3ATtLdD8JdXs6GLY7XaWLHmRnByjP4WntdNgWSaFLxf0IdtQk1gaZpphIcoVH8QA\nS3+1dyK1Dzo/YOpDU01jwFiRT0etujtRx7oAZSSsR6VtHsNcwW/GbLaVhprsDKPCEAcaRsIYzPBF\nT2WcP/Cv7+F1Phifvz9qUl6DqZsxVvv36Q+PzBTbaRuDxg0i7a00czuGEZSrb8/4TLNQRs89qFLQ\nGSiP1+soI+B1/Rj9CVgJtudt9M/oT87uHHIG5pDuTvc2Xg6jvA6e32N/lNejSh2nAdkDcH7sRDui\n4XQ4qexXSfHGYoreKKJ4YzGV2ZW0bmvF8YiD/a/t58yeM3Q4Ojiz5wz7X9vPhqoNUW9mFtCIM5BM\nCyEOBHOE9ZYfoiRc41Brk23AMlStNF8mATt37tzJpEmTLB6GEEnvCs/3nnOf45jzGG7NzfmO83T1\n71I39jZUvHgWyrX716gV3CbMrEp9cszPyGfvzr0huXU9u2d66xoi608RjHHTx9E0p0mtQE+jJpo8\nj8/zImpF7NGWmg5IP5fOz1b+7KJtqXvCZrfB91GGwf2Yk//zKAPimyivgobSA3yoj0lDeRcqURoA\nG2bc344yEu7BW5T5Iur7CFYcDCh6o4iPt37s93ywc6n676qp+c8aXl7/MidnnQwcHtsPldmVrH5i\ntdd29u7bS8d3OtQY30AJQ18FvkPgu1I3DPzdQE58eiL4B0CJXp/a8BSNqxo5e/osWppmZnXMQaV8\nenyPtMP4+8fz8MKH49bNNBRWbV7FD77zA+Ul8yxQdkB5EWNVUl5IHHbt2sXkyZMBJqOCoDHFauPh\nNZT+98+oddyjwLWoEi3tPq8V4yHJWLV5FfffdT/a3ZqajGyoSaoOlQngOdH5NO7JeieLX/3mVwl3\ng5swfQKOaQ5TLDcUZfYak2wAcWh+Rz57/xyaMdQTaUPT0G7TVPaB4Skw9rka5aJvR4kjB2NWfbSh\nRIYLUR6lLpRmoBY1qQxEGT+etKE+Yw+Tc/HGYvZs3RP25+jNxLb4+4upPV/rnSlyDlXTIghZv87i\n/P7zYY8PEqMRXKSkwmcQrCPexoPVgbJbfP5ejFojTQLes3hfQoypmlHFpq9t4vnXnlcehy2Ybvet\nmB0jA/Rs6JjZEbOeGeFQMrEEx2sOlWI4FCXYM1aqQcoPF2wssORmPfSSobg2uNR+jWwDQ4w3BuVJ\nyMTMnJiD8uzo4+BF/f/dmIWsXiRwlo2xzWAC2ghc31UzqrjjvTvUxLbRZ2J7L/DEVnpXKS985wXT\n4LgTZTAF18sqI6mXGLUTkplU+AxC6hBtlc0g/ecXUd6PECNm3j2T5//0vHfOvB2lhchErZDX4xe6\noAC2sS0+g+6B0rtKqa2vhSLU5HpE/0cUJllfZv9gNs/98DmVDdGO8tdtRh0zt/6c0cXRSLdM19/c\nidkXwhAjGuMdSuDxT0d5iebgnaKpewhKn+p9kaFwJ7ZABscn2idorVrg494K/bP693p8giBYSzQF\nkzbg31HrU0cU9yPEkKoZVWT2yzRz5g1l+znUyrAe/9LK3waug6Z9TRH1wIgGdxTfgS3TptT6hjr/\nMkzVvU9hqGBZKr3h91W/x7HVwdjssWRmZGI7aIMTkHkmk8IhheTn5Ht7ETzTZzMwizedQgkJjWyR\nYCLPY8AZ1YvDVxAYTEAbTTwrKX689WOm/NUU5YEJVJDrDRj/pfExHZ8gCMGJpufhP4EJ/P/27j84\nivO+4/j7JCQhEZD4IYMNuAER3MNxSwIRCBuZDMFAndZxcBxIMomI28Ez7thOmqlnktSj/pg40zZx\nQ5pAXJfBJBPhmNpDnBCIIRYBg7AxNqYVCRRDiDAUQSyBED8k3fWP7y67tzpkTvdTp89rRoO0t7d7\n952H2+89+32exz6WJY+EIqHek+mUYveux2G3Ltyplf0TSd0Ejc805syti9WNq/nyX32ZaE80duKh\ndqw34OdYet0D06ZM67Mbvr/C4TCH9x2O+9jyh5azdvtab1pot5fnAt58EO7Qw0bn9V7GClhrsZTd\nnafAmaNjRs0Mdv1X7vUAAdQ9Wsf+L+7n4t6LXg9MBBhmvQ51j9Zl9wWKyFWpLph0fRcroavFBlzF\n82Hg9blz51JRURHzwLJly1i2LHcrnwe7ivdXeNX1F7DZAo9gY+xHYMM23cmIAoWToY0hVm1YlRMJ\nRM2SGppGNvUuWPQ7DuXbymk71neVfzr8YPsPePBTD9qiTqPxkrL1WFHnQ/T+H+yO1hiJ3fbwX4DP\nl/LkmidzIvbXoqJAkd4aGhpoaGiI2dbW1saOHTsgT0ZbhLDE4R5sKpkjfeyr0RYD1LIvLmP9S+u9\nAr+t2Ox827BK/zHY0Dv/6AF3xEIPlEfLuXfxvVm/IFRMrqD98+02RPAk3vvx1QKwBWbPnM3uF641\neVV63TLrFg7VHrJv4kexlHwkNkz2LuInPIdgysEpFJcW6wIskqfybbTF97A52e7B68QGmwvvUorP\nJVkybMEwCn5VQGRvxC5q57Bivj14yzG7aynEmc66PdLO2hNr2bFoR9qn9e3Lxa6Llu7Ow76t7yW2\nu7wsB7rLh2DzTtxNbBJWhNVl/AW9ix/3lvGVp76S0z0MIjKwpTp5eBC7fDQGttdhE+ZKHnh62dM8\n8bEnrnYvH750mK5QF4wl9n48xE6J7HJnn+QI9z18H9sbtmf2DWDd492Xu621unUDrxAzG0noRIjf\nHfxdVr+tX52WON6w0fPxFzdLdV2GiEhQqpMHTXc9SPiH5t16+600R5u9RYfctRT8QwzjGQ9ntp7J\nxMuN4S6GFSHiDWkMXpiPw4htI7J+Ea6eXk1zS3NCi5uJiKSbLvaStKvz7rvf4Evx5uH3L+sdlKXV\nALf/cLs3OVGODw2Mu7ZEGoaNiogkQsmDJC3mAleKJRDuglruoknxZGE1wNWNq3n2xWet6PAsNjfF\na9g0z+7Pa1BUWJQTQwNX3LmCYzuPUVfSe7GmbMzNICIC6RuqeT002iKPBIfY0QWR7ginz57m3IJz\n3rBO36gL2iEUDTGkeAihISFKi0pZ/NHFrPzmyrTdLqhZUkPTm01WyhvGhj4G1q6gFJbOWErDmoY+\njiQikj35NtpCBqlrTU98ddGkmZ1e8eQcbCbKUogujNI1oQtCcCVyhfUn1rNxzkaefDq18xG0trby\n8GMP07S9yRIGtxYjRGytA0AE3tr6VsrOLSKSb3TbQtLK7XYf++ZYSxxGYbNSjsLmVZiI1//ljMK4\nWHuRpp80pew1rG5czfjq8TY3RRE2S2MPOVeLISIyUCh5kLSrrKxkdMVoqzNwl/LuxOYnuIBNq7wK\n+D6wGvgFPP/i87S2tqbk/Hue20NXeZclKxGs5+M8OVWLISIykCh5kIzoptu7bVGMJRDuVMotWA1C\nOTa99TA4N+oc4dvDKUkgdu3d5SUrIeBdbEbMlms84QSMuXlM0ucVEclX+nolGTGEIV6dgfuN/xVg\nODZD5Ux6rYNxdstZ5nxuDoe3xF84Kijeugi3Tb2Nt1vetlkaO7HbFluwVVe2YcmMf0rqFqh6s4oN\nmzek6J2LiOQfJQ+SEdXTq2ne3Owt5d2OrSdxkd6LUjm1D9wFrdtar2uxJHfip86ZnbaC5O+BKDT/\nptl6M7qwWya12CLx/4O9lp9j9Q9FUNRTxJK7l7Byc/pGe4iI5AMlD5IRs++fzTMbniEajdpMlGvw\nvu1PcHbyD+V0eiDa29qZ/OHJdNR2WI/FGXuseVMzG366gVd/+SrhcNgmfprZaUtQu8uFv46trVEJ\nvAMswpKS8XhTUY8ArkDRpSJOHDihpEFE5DooeZCMWHHnCho/3sj6lvV2Aa/ALuyFWKIQXECrE1vq\n+wyWOLj1EiOxxauOQ0d3B9PumEYoFLKkpAqrm7jF2WchdmviFHAZL0kJTkUdgUmbJylxEBG5TiqY\nlIyZ9/l53kyUhVjrcydm8i+gdQG7xdCBJRlHncdKgLXY7Y75WL3EIoiWRq2WoQX4A9bjsBBbnu0y\nliiMpM+hmUqjRUSun5IHyRj/VMvF54utiHECdtFvxRu6+Rw2/+hpbGTGGezi/xPslsNC4DA25HIX\ntth7KZaERLE6iqNYslCC1+OgoZkiIimh5EEyyp2J8jP3fsaKGO8EtmJFi27iEMJ6DyrwVufchfVW\ndGKJxDEsQZjj7HOjc4yIs98Z7Pgj8Io0+xiaWT29OtVvVUQkbyl5kKyYff9sQhdDNufC/VhvwStY\nMtDl/F0IjMFGT7hFlG4iMRxLEI5ityzuALqxRKLb2a/QOZlbQLmN3qtTHtfqlCIiiVJfrWTFijtX\nUPtyLbMWzuL8gvPepE0tWJ3CEKznYRJ2i2IE3gqdrXiFls7oC8qAP8JW8oxiCUcPlny0YLUUn8IS\nlF87z7kCY8vGcmDnARVLiogkQD0PkjXhcJgjrx+hbmgd4zrG2W2LUVhdQxeWBPwaGI1NJOUmCT3Y\nbYgrzja3bmIBMBRLMtqwHolJeD0OpVjx5DLgDijsKORAoxIHEZFEKXmQrHJrIE6+dZLi4mKraViA\nJQhngKVY4jAJ60U47fw9B0s2ruDVTZzFEoOpWFJxDtgM1ADNQAPwY2Ad8AJ8/VtfV+IgItIPum0h\nOaNwaKFd9N+H3abodH4fDszD1sEA61FwayUafL/7J5gaAxTDTV030bq1la5oFxRCQaSAm2+8mU2b\nNhEOhzP47kRE8od6HiRnTLxhojfcsgSbzMn9uwzrhZiAFUQ+jxVVfhr4Gdbr8DHgs85+tVDWVcbj\nTz3OldNXiLZGiZ6K0nO6h6P7jypxEBFJgnoeJGeMmzyOQwcPWf1CJXZbwv3dLXq829nZncr6HRha\nPJSSbSVQAGMqx1AypMTWvtj5z7otISKSBkoeJGdsWLmB6vnVHNtyDOYCO7EVMOfSewXMUiAMVZeq\n2L15t5IEEZEM0m0LyRmVlZW8uu1Vli5YSvmucoouF1ndw2YInQ8R2hiiYFUBw9cNZ+rmqdSV1Clx\nEBHJAvU8SE6prKykYU1Dtl+GiIj0QT0PIiIikhAlDyIiIpIQJQ8iIiKSECUPIiIikhAlDyIiIpIQ\nJQ8iIiKSECUPIiIikhAlDyIiIpIQJQ8iIiKSECUPIiIikhAlDyIiIpIQJQ8iIiKSECUPIiIikhAl\nDyIiIpIQJQ8iIiKSECUPIiIikhAlD4NMQ0NDtl/CoKOYZ55innmK+eCSjuShFngROAFEgHvScA7p\nJ/0HzzzFPPMU88xTzAeXdCQPZcAbwEPO39E0nENERESyZEgajrnZ+REREZE8pJoHERERSUg6eh4S\ncvDgwWy/hEGlra2Nffv2ZftlDCqKeeYp5pmnmGdWtq+doTQfPwJ8AvhpnMduBF4Dxqf5NYiIiOSj\nE8BHgJOZPnE2ex5OYm/6xiy+BhERkYHqJFlIHCD7ty2y9sZFRESkf9KRPAwDPuD7ezIwHTgL/D4N\n5xMREZEBbh5W6xABeny/r8niaxIREREREREREREREREREZHBqx6vfsH9eSewTxib06ENOAfsBiYG\n9qkBfgV0AO8CLwNDfY8fi3OebwSOcTO2+FYH0Ap8Byjq5/vKZfUkF/P3x3m++7PEd4yRwA+dY7QB\n64DywHkUc08qYn4szuNq5/3/bLkJ+DFwCovXPmLjDWrnfvVkJubH4pxH7bz/Ma8CXgBOA+3As8AN\ngWPkXDuvB95yXqj7M9r3eBU2ouKbwJ9iH6KLgUrfPjXYm/lbLEhVwCeBYt8+R4GvBc4zzPd4IXAA\n2OqcZz7QAqxM9g3moHqSi3lB4Lk3AH+HNboy33F+AewHZgGznXP6J/ZSzD2pirnauaee5D9bXgaa\ngJnO418DurGRXi61c089mYm52rmnnuRiPgw4AmwAbgU+iCUSe4id8DHn2nk9tlrmtawHnnmPYzQB\nf/8e+xwFHunj8cVYAx3n2/Zp4CLwvvc49kBTT/IxD3oD+A/f32EsA/6Ib9ssZ5s75FYx96Qi5qB2\n7ldP8jE/D3w2sO0MsNz5Xe08Vj3pjzmonfvVk1zM78Ji5Y9LBdaG5zt/Z6ydJ7ow1gew6TDfBhqA\nSb7j/BlwGNgC/B+WKNzje+4NQDXWRbIL6+pqBG6Pc57HsEb4BvBVYrtTarCs6ZRv2y+BEmBGgu9n\nIEgm5kEzsEzzP33barBvxa/5tu1xts3x7aOYpy7mLrVzT7Ix/xmwFOuyLXB+L8Y+Y0DtPJ50x9yl\ndu5JJuYlQBS44tt2GUsM3OtoTrbzRcC9WHfJfKzL6iQwCstgItj9k0eAP8EaTA9Q6zx/trPPGeAL\n2Afqt4FLwBTfeR4F5mJdMg9g93b839qeIv6S35ew7CmfJBvzoO8D/x3Y9lXgt3H2/a1zPFDMUx1z\nUDv3S0XMS7Fu2Aj24dqG920M1M6DMhFzUDv3SzbmY7AYP4nFfhjw787zVjn7DIh2Xoa98S9h61NE\ngB8F9tmIFdSAZT0R4J8C++yndwGN3yed5410/n4Ky8yC8rGxBSUac79SrOF9KbD9ehubYp66mMej\ndu7pT8yfx4rLPgrcBjyOFWR/0Hlc7bxv6Yh5PGrnnv7EfAHwv1hS0YXd5tgLfM95PGPtPNHbFn6d\nWNfHFKw3oRtoDuzzG6yqE7w1LIL7HPTtE88e51+3d+IUMDawz0isu+wU+S3RmPvdh13M1gW2n6J3\ntS7OtlO+fRTz1MU8HrVzT6IxD2Or9z6AfZs7APwD9qH6kLOP2nnf0hHzeNTOPf35bHnJ2b8SK7b8\nAjABuw0CGWznySQPJcA0LCnowu6x/HFgn6nYUB2cf9+Js88tvn3i+ZDzr5t87MIyW/+bvwu79/P6\ndb72gSrRmPs9gGWxZwPbd2PDeIIFNuVYrEExT3XM41E79yQac/dzrCewTwSvCl3tvG/piHk8auee\nZD5b/oAN5ZyPJRLuaIqcbOf/it17meS8mBexLll3DOonnJP/JZYZ/TUWkDm+YzziPGeJs88/Ahfw\nikZmY104051t92NDSF7wHaMAG3rykrPffOA4Nk4136Qi5jiP9WANJJ5NwJvEDu3Z6HtcMU9tzNXO\nYyUb80LsG9t27EOzCvgbLP6LfOdRO/dkIuY1qJ37peKzZTnWdquAz2E9Fv8SOE/OtfMGrEr0MtYA\nnqN3lrQcOIR1x+wD/jzOcR5zXmgHsJPYwHwIy5zedY5xELuPNjRwjIlY4C9gwfs38nNSkVTF/Bv0\n3btTgU0q0u78rANGBPZRzD3JxlztPFYqYj7Zed5J7LPlDXoPI1Q792Qi5mrnsVIR8yeweF/Gbmk8\nGuc8auciIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKSVf8P\ncfEwB7+DSYoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfa181b850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t2, l2, l2e = np.loadtxt(echo_file).T\n",
    "errorbar(t1, l1, yerr=l1e, fmt='o')\n",
    "errorbar(t2, l2, yerr=l2e, fmt='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 4.360e-01 4.662e+01 inf -- -2.840e+01 -- 1 1 1 1 1 1 1\n",
      "   2 7.723e-01 4.603e+01 5.319e+01 -- 2.478e+01 -- 0.595569 0.573902 0.564885 0.564933 0.564693 0.564817 0.564018\n",
      "   3 3.411e+00 4.536e+01 5.196e+01 -- 7.675e+01 -- 0.241284 0.160851 0.128635 0.129632 0.129254 0.129464 0.128634\n",
      "   4 1.484e+01 4.466e+01 5.009e+01 -- 1.268e+02 -- -0.00703234 -0.219623 -0.310096 -0.306017 -0.306362 -0.306197 -0.306021\n",
      "   5 5.921e-01 4.382e+01 4.797e+01 -- 1.748e+02 -- -0.111363 -0.525952 -0.75296 -0.741885 -0.742361 -0.742634 -0.74129\n",
      "   6 3.733e-01 4.246e+01 4.584e+01 -- 2.207e+02 -- -0.140827 -0.700007 -1.19878 -1.1757 -1.17861 -1.18043 -1.17869\n",
      "   7 2.744e-01 4.002e+01 4.318e+01 -- 2.638e+02 -- -0.163132 -0.756421 -1.63306 -1.59991 -1.61462 -1.6204 -1.61864\n",
      "   8 2.214e-01 3.558e+01 3.862e+01 -- 3.024e+02 -- -0.180826 -0.786047 -2.01272 -1.99632 -2.05034 -2.06438 -2.06287\n",
      "   9 1.964e-01 2.792e+01 3.104e+01 -- 3.335e+02 -- -0.198109 -0.808078 -2.25955 -2.32516 -2.48547 -2.51643 -2.51957\n",
      "  10 2.013e-01 1.674e+01 2.083e+01 -- 3.543e+02 -- -0.213273 -0.817818 -2.33481 -2.52428 -2.9116 -2.9802 -3.01438\n",
      "  11 2.834e-01 5.753e+00 1.015e+01 -- 3.645e+02 -- -0.221915 -0.819446 -2.34863 -2.58195 -3.28722 -3.43843 -3.62111\n",
      "  12 1.282e+00 5.594e-01 2.962e+00 -- 3.674e+02 -- -0.22692 -0.818447 -2.35934 -2.59 -3.51729 -3.8012 -4.6475\n",
      "  13 7.073e+02 9.360e-02 2.736e-01 -- 3.677e+02 -- -0.229484 -0.817489 -2.36471 -2.59372 -3.56061 -3.9224 -7.6475\n",
      "  14 1.523e+03 1.144e-01 1.474e-03 -- 3.677e+02 -- -0.229711 -0.817671 -2.36494 -2.59367 -3.55253 -3.91079 -8\n",
      "  15 1.523e+03 1.092e-01 3.831e-04 -- 3.677e+02 -- -0.229667 -0.817642 -2.365 -2.59369 -3.55271 -3.9141 -8\n",
      "  16 1.523e+03 1.099e-01 6.214e-05 -- 3.677e+02 -- -0.229676 -0.817649 -2.36499 -2.59368 -3.55237 -3.91373 -8\n",
      "********************\n",
      "-0.229676 -0.817649 -2.36499 -2.59368 -3.55237 -3.91373 -8\n",
      "0.26864 0.229014 0.349667 0.273159 0.62553 0.906575 7060.11\n",
      "-0.00293561 -0.00849564 -0.0205528 -0.0525552 -0.0608705 -0.109908 -0.000245353\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)\n",
    "p2 = np.ones(nfq)\n",
    "p2, p2e = clag.optimize(P2, p2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t### errors for param 0 ###\n",
      "+++ 3.677e+02 3.672e+02 -2.297e-01 3.897e-02 0.967 +++\n",
      "+++ 3.677e+02 3.667e+02 -2.297e-01 1.733e-01 2.01 +++\n",
      "+++ 3.677e+02 3.670e+02 -2.297e-01 1.061e-01 1.45 +++\n",
      "+++ 3.677e+02 3.671e+02 -2.297e-01 7.255e-02 1.2 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.297e-01 5.576e-02 1.08 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.297e-01 4.736e-02 1.02 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.297e-01 4.316e-02 0.995 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 3.677e+02 3.672e+02 -8.176e-01 -5.886e-01 0.936 +++\n",
      "+++ 3.677e+02 3.667e+02 -8.176e-01 -4.741e-01 1.97 +++\n",
      "+++ 3.677e+02 3.670e+02 -8.176e-01 -5.314e-01 1.41 +++\n",
      "+++ 3.677e+02 3.671e+02 -8.176e-01 -5.600e-01 1.16 +++\n",
      "+++ 3.677e+02 3.672e+02 -8.176e-01 -5.743e-01 1.05 +++\n",
      "+++ 3.677e+02 3.672e+02 -8.176e-01 -5.815e-01 0.991 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 3.677e+02 3.675e+02 -2.365e+00 -2.190e+00 0.303 +++\n",
      "+++ 3.677e+02 3.674e+02 -2.365e+00 -2.103e+00 0.671 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.365e+00 -2.059e+00 0.905 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.365e+00 -2.037e+00 1.03 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.365e+00 -2.048e+00 0.968 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.365e+00 -2.043e+00   1 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 3.677e+02 3.675e+02 -2.594e+00 -2.457e+00 0.301 +++\n",
      "+++ 3.677e+02 3.674e+02 -2.594e+00 -2.389e+00 0.667 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.594e+00 -2.355e+00 0.902 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.594e+00 -2.338e+00 1.03 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.594e+00 -2.346e+00 0.966 +++\n",
      "+++ 3.677e+02 3.672e+02 -2.594e+00 -2.342e+00 0.999 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 3.677e+02 3.675e+02 -3.552e+00 -3.240e+00 0.35 +++\n",
      "+++ 3.677e+02 3.672e+02 -3.552e+00 -3.083e+00 0.938 +++\n",
      "+++ 3.677e+02 3.670e+02 -3.552e+00 -3.005e+00 1.39 +++\n",
      "+++ 3.677e+02 3.671e+02 -3.552e+00 -3.044e+00 1.15 +++\n",
      "+++ 3.677e+02 3.672e+02 -3.552e+00 -3.064e+00 1.04 +++\n",
      "+++ 3.677e+02 3.672e+02 -3.552e+00 -3.073e+00 0.988 +++\n",
      "+++ 3.677e+02 3.672e+02 -3.552e+00 -3.069e+00 1.01 +++\n",
      "+++ 3.677e+02 3.672e+02 -3.552e+00 -3.071e+00   1 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 3.677e+02 3.673e+02 -3.914e+00 -3.460e+00 0.7 +++\n",
      "+++ 3.677e+02 3.667e+02 -3.914e+00 -3.234e+00 2.01 +++\n",
      "+++ 3.677e+02 3.671e+02 -3.914e+00 -3.347e+00 1.23 +++\n",
      "+++ 3.677e+02 3.672e+02 -3.914e+00 -3.404e+00 0.939 +++\n",
      "+++ 3.677e+02 3.672e+02 -3.914e+00 -3.375e+00 1.08 +++\n",
      "+++ 3.677e+02 3.672e+02 -3.914e+00 -3.390e+00 1.01 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 3.677e+02 3.677e+02 -8.000e+00 -6.000e+00 0.0117 +++\n",
      "+++ 3.677e+02 3.676e+02 -8.000e+00 -5.000e+00 0.203 +++\n",
      "+++ 3.677e+02 3.674e+02 -8.000e+00 -4.500e+00 0.665 +++\n",
      "+++ 3.677e+02 3.671e+02 -8.000e+00 -4.250e+00 1.19 +++\n",
      "+++ 3.677e+02 3.672e+02 -8.000e+00 -4.375e+00 0.891 +++\n",
      "+++ 3.677e+02 3.672e+02 -8.000e+00 -4.312e+00 1.03 +++\n",
      "+++ 3.677e+02 3.672e+02 -8.000e+00 -4.344e+00 0.958 +++\n",
      "+++ 3.677e+02 3.672e+02 -8.000e+00 -4.328e+00 0.994 +++\n",
      "********************\n",
      "-0.229674 -0.817648 -2.365 -2.59368 -3.55236 -3.91386 -8\n",
      "0.272837 0.236171 0.32235 0.251819 0.481357 0.524238 3.67188\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "p2, p2e = clag.errors(P2, p2, p2e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ThMmvBgRJFRkfHyeX62D6gDDpWnK5BVatlKrAWyAlNbTNm3eU1yDMXLHYx8CAG75Kc2VI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\nMZ1FSscAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfa18073d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xscale('log'); ylim(-6,2)\n",
    "errorbar(fqd, p1, yerr=p1e, fmt='o', ms=10)\n",
    "errorbar(fqd, p2, yerr=p2e, fmt='o', ms=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 6.628e+02 9.007e+00 inf -- 4.160e+02 -- -0.264643 -0.797908 -2.07791 -2.35822 -3.1415 -3.50338 -6.09489 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
      "   3 6.842e+02 1.078e+01 2.116e+00 -- 4.181e+02 -- -0.226169 -0.759199 -2.03908 -2.32032 -3.11387 -3.43598 -6.39489 0.0502949 0.152916 0.212494 0.121125 0.216976 0.336684 -0.244596\n",
      "   5 1.559e+02 1.270e+01 1.915e+00 -- 4.201e+02 -- -0.193741 -0.726441 -2.00316 -2.28893 -3.08464 -3.36731 -6.69489 0.0145685 0.191324 0.293499 0.136888 0.312024 0.482796 1.86942\n",
      "   7 1.618e+03 1.477e+01 1.743e+00 -- 4.218e+02 -- -0.166298 -0.698635 -1.97153 -2.26256 -3.05584 -3.30885 -6.99489 -0.0118999 0.220073 0.352843 0.149084 0.389017 0.574531 -0.403772\n",
      "   9 2.935e+02 1.701e+01 1.600e+00 -- 4.234e+02 -- -0.142929 -0.674898 -1.94419 -2.24018 -3.02852 -3.26097 -7.29489 -0.0319925 0.242138 0.397314 0.158774 0.451737 0.636061 -2.89416\n",
      "  11 9.523e+02 1.944e+01 1.478e+00 -- 4.249e+02 -- -0.122901 -0.654512 -1.92069 -2.22102 -3.00314 -3.22181 -6.99489 -0.0475429 0.25942 0.431356 0.166636 0.503311 0.679979 0.381182\n",
      "  13 4.366e+02 2.207e+01 1.373e+00 -- 4.262e+02 -- -0.105633 -0.636902 -1.9005 -2.20452 -2.97982 -3.18951 -7.29489 -0.0597637 0.273183 0.4579 0.173119 0.546186 0.712957 1.78135\n",
      "  15 1.284e+02 2.490e+01 1.279e+00 -- 4.275e+02 -- -0.0906648 -0.62161 -1.8831 -2.19023 -2.95852 -3.16261 -7.59489 -0.0694848 0.284294 0.478919 0.178532 0.582205 0.73873 -2.12851\n",
      "  17 6.684e+01 2.796e+01 1.196e+00 -- 4.287e+02 -- -0.0776289 -0.608271 -1.86808 -2.17781 -2.93913 -3.14003 -7.29489 -0.0772912 0.293368 0.495775 0.183096 0.612767 0.759519 -2.12851\n",
      "  19 1.131e+03 3.125e+01 1.119e+00 -- 4.298e+02 -- -0.0662284 -0.596588 -1.85505 -2.16698 -2.92148 -3.12092 -6.99489 -0.0836055 0.300851 0.509435 0.186975 0.638935 0.776775 -0.468363\n",
      "  21 7.866e+01 3.476e+01 1.049e+00 -- 4.309e+02 -- -0.0562219 -0.586319 -1.84372 -2.1575 -2.90544 -3.10467 -6.69489 -0.0887399 0.307075 0.5206 0.190292 0.661523 0.791406 3.12663\n",
      "  23 4.749e+01 3.851e+01 9.840e-01 -- 4.319e+02 -- -0.0474108 -0.577266 -1.83385 -2.14919 -2.89085 -3.09077 -6.39489 -0.0929293 0.312289 0.529789 0.19314 0.681153 0.80404 2.58773\n",
      "  25 9.016e+01 4.250e+01 9.239e-01 -- 4.328e+02 -- -0.0396304 -0.569264 -1.82521 -2.14191 -2.87758 -3.07884 -6.69489 -0.0963531 0.316687 0.537395 0.195593 0.698325 0.815127 2.31075\n",
      "  26 2.291e+03 4.856e+03 4.196e+00 -- 4.370e+02 -- 0.0292432 -0.498358 -1.74955 -2.0779 -2.75662 -2.9761 -8 -0.124329 0.354004 0.600746 0.216726 0.849429 0.913805 -2.9851\n",
      "  27 3.278e+00 8.497e+01 6.218e+00 -- 4.432e+02 -- 0.0254995 -0.502475 -1.77412 -2.11199 -2.72719 -3.04402 -5 -0.0720726 0.308662 0.702517 0.165496 0.94859 0.890579 1.11967\n",
      "  28 3.369e+02 3.689e+01 7.032e-01 -- 4.439e+02 -- 0.02803 -0.501445 -1.76227 -2.08919 -2.72868 -3.00045 -7.18712 -0.103292 0.35578 0.648429 0.1705 0.951458 0.974389 -2.55109\n",
      "  29 1.859e-01 1.619e+01 2.026e-01 -- 4.437e+02 -- 0.0284028 -0.501795 -1.76339 -2.09611 -2.722 -3.01268 -4.18712 -0.0900085 0.341162 0.653855 0.173506 0.94415 0.962545 2.3652\n",
      "  30 9.710e-01 1.003e+01 2.272e-01 -- 4.439e+02 -- 0.0287103 -0.501625 -1.76349 -2.09457 -2.72212 -3.0105 -4.78581 -0.0929545 0.347387 0.644721 0.172745 0.942888 0.964175 2.80497\n",
      "  31 3.231e+01 4.382e+00 4.330e-02 -- 4.440e+02 -- 0.0288105 -0.50173 -1.76305 -2.0945 -2.72206 -3.00972 -5.95917 -0.0912361 0.344716 0.65491 0.172811 0.94407 0.967832 -0.754455\n",
      "  33 7.887e+00 3.999e+00 5.730e-03 -- 4.440e+02 -- 0.0288182 -0.501725 -1.76307 -2.09454 -2.72196 -3.00967 -5.65917 -0.0912811 0.344831 0.654757 0.172631 0.944361 0.968167 3.09091\n",
      "  34 1.021e+03 1.567e+00 3.306e-03 -- 4.440e+02 -- 0.0288906 -0.501682 -1.76326 -2.09497 -2.72112 -3.00942 -8 -0.0916682 0.345845 0.653313 0.170964 0.94684 0.970952 2.33492\n",
      "  35 1.958e+03 8.857e-01 2.455e-04 -- 4.440e+02 -- 0.0289281 -0.501698 -1.76317 -2.09482 -2.72066 -3.00928 -8 -0.0913907 0.345397 0.655119 0.171217 0.948376 0.971555 -1.95846\n",
      "  36 2.231e+01 1.235e+00 1.134e-03 -- 4.440e+02 -- 0.0289502 -0.501688 -1.76324 -2.09495 -2.7204 -3.00936 -5 -0.0914472 0.345652 0.654699 0.170847 0.949278 0.972212 0.258881\n",
      "  37 8.016e+00 5.056e-01 1.360e-02 -- 4.440e+02 -- 0.0289689 -0.501701 -1.76321 -2.09488 -2.71981 -3.00893 -5.06546 -0.0913367 0.345498 0.655382 0.171555 0.950824 0.972854 0.766704\n",
      "  38 1.884e+02 2.121e-01 1.200e-02 -- 4.440e+02 -- 0.0289819 -0.501697 -1.76328 -2.09496 -2.71958 -3.00916 -6.54275 -0.0913617 0.345681 0.654623 0.17157 0.951297 0.972966 0.903638\n",
      "  39 1.947e+04 3.463e-01 5.080e-04 -- 4.440e+02 -- 0.0289824 -0.501696 -1.76321 -2.0949 -2.71995 -3.00932 -8 -0.0913416 0.345612 0.655284 0.170969 0.950797 0.973096 0.295641\n",
      "  40 3.049e+03 1.048e-01 3.268e-05 -- 4.440e+02 -- 0.0289805 -0.501691 -1.76324 -2.09495 -2.72006 -3.00933 -8 -0.0913641 0.345627 0.655123 0.170779 0.950573 0.972811 -0.79378\n",
      "********************\n",
      "0.0289805 -0.501691 -1.76324 -2.09495 -2.72006 -3.00933 -8 -0.0913641 0.345627 0.655123 0.170779 0.950573 0.972811 -0.79378\n",
      "0.00488089 0.00320555 0.0166349 0.0498124 0.0675131 0.0930602 5801.93 0.082796 0.0625792 0.157936 0.242192 0.285454 0.316561 13418.5\n",
      "-0.0761655 -0.104783 0.024591 0.0175272 0.0313802 0.0122834 6.72756e-05 0.000572629 -0.00671213 0.00501444 0.000107792 -0.0065959 -0.0147919 -1.32979e-05\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "Cx = clag.clag('cxd10r', [[t1,t2]], [[l1,l2]], [[l1e,l2e]], dt, fqL, p1, p2)\n",
    "p  = np.concatenate( ((p1+p2)*0.5-0.3,p1*0+0.1) ) # a  good starting point generally\n",
    "p, pe = clag.optimize(Cx, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t### errors for param 0 ###\n",
      "+++ 4.441e+02 4.440e+02 2.894e-02 3.139e-02 0.184 +++\n",
      "+++ 4.441e+02 4.439e+02 2.894e-02 3.262e-02 0.538 +++\n",
      "+++ 4.441e+02 4.437e+02 2.894e-02 3.323e-02 0.868 +++\n",
      "+++ 4.441e+02 4.436e+02 2.894e-02 3.354e-02 1.1 +++\n",
      "+++ 4.441e+02 4.436e+02 2.894e-02 3.339e-02 0.977 +++\n",
      "+++ 4.441e+02 4.436e+02 2.894e-02 3.346e-02 1.04 +++\n",
      "+++ 4.441e+02 4.436e+02 2.894e-02 3.342e-02 1.01 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 4.441e+02 4.440e+02 -5.016e-01 -5.000e-01 0.272 +++\n",
      "+++ 4.441e+02 4.437e+02 -5.016e-01 -4.993e-01 0.765 +++\n",
      "+++ 4.441e+02 4.435e+02 -5.016e-01 -4.989e-01 1.18 +++\n",
      "+++ 4.441e+02 4.437e+02 -5.016e-01 -4.991e-01 0.954 +++\n",
      "+++ 4.441e+02 4.436e+02 -5.016e-01 -4.990e-01 1.06 +++\n",
      "+++ 4.441e+02 4.436e+02 -5.016e-01 -4.990e-01 1.01 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 4.441e+02 4.439e+02 -1.763e+00 -1.754e+00 0.496 +++\n",
      "+++ 4.441e+02 4.433e+02 -1.763e+00 -1.750e+00 1.76 +++\n",
      "+++ 4.441e+02 4.437e+02 -1.763e+00 -1.752e+00 0.96 +++\n",
      "+++ 4.441e+02 4.435e+02 -1.763e+00 -1.751e+00 1.3 +++\n",
      "+++ 4.441e+02 4.436e+02 -1.763e+00 -1.752e+00 1.12 +++\n",
      "+++ 4.441e+02 4.436e+02 -1.763e+00 -1.752e+00 1.03 +++\n",
      "+++ 4.441e+02 4.436e+02 -1.763e+00 -1.752e+00 0.994 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 4.441e+02 4.439e+02 -2.096e+00 -2.071e+00 0.418 +++\n",
      "+++ 4.441e+02 4.435e+02 -2.096e+00 -2.058e+00 1.22 +++\n",
      "+++ 4.441e+02 4.438e+02 -2.096e+00 -2.064e+00 0.741 +++\n",
      "+++ 4.441e+02 4.437e+02 -2.096e+00 -2.061e+00 0.959 +++\n",
      "+++ 4.441e+02 4.436e+02 -2.096e+00 -2.060e+00 1.08 +++\n",
      "+++ 4.441e+02 4.436e+02 -2.096e+00 -2.060e+00 1.02 +++\n",
      "+++ 4.441e+02 4.436e+02 -2.096e+00 -2.061e+00 0.989 +++\n",
      "+++ 4.441e+02 4.436e+02 -2.096e+00 -2.061e+00   1 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 4.441e+02 4.440e+02 -2.720e+00 -2.686e+00 0.217 +++\n",
      "+++ 4.441e+02 4.438e+02 -2.720e+00 -2.670e+00 0.62 +++\n",
      "+++ 4.441e+02 4.436e+02 -2.720e+00 -2.661e+00 0.996 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 4.441e+02 4.439e+02 -3.008e+00 -2.962e+00 0.426 +++\n",
      "+++ 4.441e+02 4.435e+02 -3.008e+00 -2.939e+00 1.27 +++\n",
      "+++ 4.441e+02 4.438e+02 -3.008e+00 -2.951e+00 0.758 +++\n",
      "+++ 4.441e+02 4.436e+02 -3.008e+00 -2.945e+00 0.986 +++\n",
      "+++ 4.441e+02 4.436e+02 -3.008e+00 -2.942e+00 1.12 +++\n",
      "+++ 4.441e+02 4.436e+02 -3.008e+00 -2.944e+00 1.05 +++\n",
      "+++ 4.441e+02 4.436e+02 -3.008e+00 -2.944e+00 1.02 +++\n",
      "+++ 4.441e+02 4.436e+02 -3.008e+00 -2.945e+00   1 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 4.441e+02 4.440e+02 -4.031e+00 -3.728e+00 0.351 +++\n",
      "+++ 4.441e+02 4.435e+02 -4.031e+00 -3.577e+00 1.31 +++\n",
      "+++ 4.441e+02 4.438e+02 -4.031e+00 -3.653e+00 0.699 +++\n",
      "+++ 4.441e+02 4.437e+02 -4.031e+00 -3.615e+00 0.961 +++\n",
      "+++ 4.441e+02 4.436e+02 -4.031e+00 -3.596e+00 1.12 +++\n",
      "+++ 4.441e+02 4.436e+02 -4.031e+00 -3.605e+00 1.04 +++\n",
      "+++ 4.441e+02 4.436e+02 -4.031e+00 -3.610e+00 0.999 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 4.441e+02 4.440e+02 -9.183e-02 -5.033e-02 0.277 +++\n",
      "+++ 4.441e+02 4.438e+02 -9.183e-02 -2.959e-02 0.608 +++\n",
      "+++ 4.441e+02 4.437e+02 -9.183e-02 -1.921e-02 0.805 +++\n",
      "+++ 4.441e+02 4.437e+02 -9.183e-02 -1.403e-02 0.914 +++\n",
      "+++ 4.441e+02 4.436e+02 -9.183e-02 -1.143e-02 0.97 +++\n",
      "+++ 4.441e+02 4.436e+02 -9.183e-02 -1.014e-02 0.998 +++\n",
      "\t### errors for param 8 ###\n",
      "+++ 4.441e+02 4.440e+02 3.447e-01 3.758e-01 0.288 +++\n",
      "+++ 4.441e+02 4.438e+02 3.447e-01 3.913e-01 0.618 +++\n",
      "+++ 4.441e+02 4.437e+02 3.447e-01 3.991e-01 0.821 +++\n",
      "+++ 4.441e+02 4.437e+02 3.447e-01 4.030e-01 0.931 +++\n",
      "+++ 4.441e+02 4.436e+02 3.447e-01 4.049e-01 0.987 +++\n",
      "+++ 4.441e+02 4.436e+02 3.447e-01 4.059e-01 1.02 +++\n",
      "+++ 4.441e+02 4.436e+02 3.447e-01 4.054e-01   1 +++\n",
      "\t### errors for param 9 ###\n",
      "+++ 4.441e+02 4.438e+02 6.661e-01 8.218e-01 0.763 +++\n",
      "+++ 4.441e+02 4.434e+02 6.661e-01 8.997e-01 1.49 +++\n",
      "+++ 4.441e+02 4.436e+02 6.661e-01 8.607e-01 1.11 +++\n",
      "+++ 4.441e+02 4.437e+02 6.661e-01 8.413e-01 0.932 +++\n",
      "+++ 4.441e+02 4.436e+02 6.661e-01 8.510e-01 1.02 +++\n",
      "+++ 4.441e+02 4.436e+02 6.661e-01 8.461e-01 0.976 +++\n",
      "+++ 4.441e+02 4.436e+02 6.661e-01 8.486e-01 0.999 +++\n",
      "\t### errors for param 10 ###\n",
      "+++ 4.441e+02 4.437e+02 1.618e-01 4.046e-01 0.907 +++\n",
      "+++ 4.441e+02 4.432e+02 1.618e-01 5.261e-01 1.91 +++\n",
      "+++ 4.441e+02 4.434e+02 1.618e-01 4.654e-01 1.38 +++\n",
      "+++ 4.441e+02 4.436e+02 1.618e-01 4.350e-01 1.13 +++\n",
      "+++ 4.441e+02 4.436e+02 1.618e-01 4.198e-01 1.02 +++\n",
      "+++ 4.441e+02 4.437e+02 1.618e-01 4.122e-01 0.962 +++\n",
      "+++ 4.441e+02 4.436e+02 1.618e-01 4.160e-01 0.99 +++\n",
      "+++ 4.441e+02 4.436e+02 1.618e-01 4.179e-01   1 +++\n",
      "\t### errors for param 11 ###\n",
      "+++ 4.441e+02 4.438e+02 9.572e-01 1.242e+00 0.573 +++\n",
      "+++ 4.441e+02 4.435e+02 9.572e-01 1.384e+00 1.26 +++\n",
      "+++ 4.441e+02 4.437e+02 9.572e-01 1.313e+00 0.888 +++\n",
      "+++ 4.441e+02 4.436e+02 9.572e-01 1.348e+00 1.07 +++\n",
      "+++ 4.441e+02 4.436e+02 9.572e-01 1.330e+00 0.976 +++\n",
      "+++ 4.441e+02 4.436e+02 9.572e-01 1.339e+00 1.02 +++\n",
      "+++ 4.441e+02 4.436e+02 9.572e-01 1.335e+00 0.999 +++\n",
      "\t### errors for param 12 ###\n",
      "+++ 4.441e+02 4.437e+02 9.835e-01 1.298e+00 0.846 +++\n",
      "+++ 4.441e+02 4.432e+02 9.835e-01 1.456e+00 1.85 +++\n",
      "+++ 4.441e+02 4.435e+02 9.835e-01 1.377e+00 1.3 +++\n",
      "+++ 4.441e+02 4.436e+02 9.835e-01 1.338e+00 1.06 +++\n",
      "+++ 4.441e+02 4.437e+02 9.835e-01 1.318e+00 0.952 +++\n",
      "+++ 4.441e+02 4.436e+02 9.835e-01 1.328e+00 1.01 +++\n",
      "\t### errors for param 13 ###\n",
      "********************\n",
      "0.028939 -0.501626 -1.76254 -2.09557 -2.71971 -3.00832 -4.0306 -0.0918281 0.344656 0.666137 0.16175 0.957202 0.983537 -1.17645\n",
      "0.0044848 0.00262318 0.0102132 0.0350358 0.0585583 0.0635997 0.42048 0.0816919 0.0607682 0.182442 0.256176 0.377633 0.344174 7.10562\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "p, pe = clag.errors(Cx, p, pe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phi, phie = p[nfq:], pe[nfq:]\n",
    "lag, lage = phi/(2*np.pi*fqd), phie/(2*np.pi*fqd)    \n",
    "cx, cxe   = p[:nfq], pe[:nfq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfa18251d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "xscale('log'); ylim(-10,10)\n",
    "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdf9ea293d0>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e4H0U64n8hGLAIkmSWljaYORB4CDgQKLZbqn3JNM1wGPAE0TOyDIiJ2VrynPn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\nRxjcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfa0ca5dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "\n",
    "# Define model function to be used to fit to the data above:\n",
    "def tophat_time(x, *p):\n",
    "    mean, width = p\n",
    "    if x>(mean+width): y=0\n",
    "    if x<(mean-width): y=0\n",
    "    if x==(mean+width) | x==(mean-width): y=5\n",
    "    return y\n",
    "\n",
    "def tophat_freq(f, *pars):\n",
    "    A,T,t0 = pars\n",
    "    #return A*T*sinc(pi*f*T)*exp(-i*2*pi*f*t0)\n",
    "    return A*T*sinc(pi*f*T)*cos(2*pi*f*t0)\n",
    "\n",
    "x=np.logspace(fqd[0],fqd[-1],200)\n",
    "\n",
    "# p0 is the initial guess for the fitting coefficients\n",
    "p0 = [3, 3, 3]\n",
    "coeff, var_matrix = curve_fit(tophat_freq, fqd, lag, p0)\n",
    "fit = tophat_freq(fqd, *coeff)\n",
    "\n",
    "\n",
    "mpl.rcParams['xtick.labelsize']=12\n",
    "mpl.rcParams['ytick.labelsize']=12\n",
    "xscale('log'); xlim(.009,.4)\n",
    "xlabel(\"Fourier Frequency (days$^{-1}$)\",fontsize=20)\n",
    "ylabel(\"Time Lag (days)\",fontsize=20)\n",
    "\n",
    "\n",
    "errorbar(fqd, lag, yerr=lage, fmt='o', ms=5,color=\"black\")\n",
    "plot(fqd,fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdf9eb16f90>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dvPVW5Lr55z/rXSNJUrPLMrDpA+wNXEQM6342WR4k+tbsRbaPvtQDbLhhtNos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\nSZIkSZIkSQ3o/wHwh9tnGRaNgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdf9eb16750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_fit = irfft(fit)\n",
    "\n",
    "mpl.rcParams['xtick.labelsize']=12\n",
    "mpl.rcParams['ytick.labelsize']=12\n",
    "ylabel(\"Response (relative)\",fontsize=20)\n",
    "xlabel(\"Time (days)\",fontsize=20) \n",
    "\n",
    "ylim(-0.5,2)\n",
    "xlim(0,7)\n",
    "\n",
    "plot(time_fit)\n",
    "plot([1.80,1.80], [-50, 50], color='k', linestyle='-', linewidth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}