phy-4660/lag/data/clag_analysis-ZoghbiCopy.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 0x7f8268013a10>"
      ]
     },
     "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/1367A_shifted.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",
    "                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 0x7f82810f4090>"
      ]
     },
     "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": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OqPSFfbBdpS/WSfpCiByRpxaEGLDZbOTlraC6+kcoqeEEVEXafnJyntC928Jms5G3JE9t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\nubqlE3riVNKXQvzh0l1FiG47pue75spd6Pi5XW2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fPj2CVBQuWV776YzTMS8UbYOuoL5+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\nQkpg7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f825d37ca50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "errorbar(t1, l1, yerr=l1e, fmt='o')\n",
    "t2, l2, l2e = np.loadtxt(echo_file).T\n",
    "errorbar(t2, l2, yerr=l2e, fmt='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 4.337e-01 6.129e+01 inf -- -4.045e+02 -- 1 1 1 1 1 1 1\n",
      "   2 7.642e-01 6.012e+01 6.908e+01 -- -3.354e+02 -- 0.659275 0.584349 0.573488 0.567303 0.567058 0.566313 0.572424\n",
      "   3 3.228e+00 5.916e+01 6.581e+01 -- -2.696e+02 -- 0.42785 0.202009 0.156574 0.137043 0.135869 0.133522 0.149486\n",
      "   4 1.558e+00 5.870e+01 6.191e+01 -- -2.077e+02 -- 0.331792 -0.095026 -0.241091 -0.287087 -0.291742 -0.297484 -0.270575\n",
      "   5 6.161e-01 5.838e+01 5.801e+01 -- -1.497e+02 -- 0.307892 -0.233206 -0.602158 -0.695208 -0.712571 -0.725065 -0.692226\n",
      "   6 3.835e-01 5.717e+01 5.345e+01 -- -9.623e+01 -- 0.307174 -0.236649 -0.896104 -1.06223 -1.12044 -1.1459 -1.11873\n",
      "   7 2.763e-01 5.434e+01 4.635e+01 -- -4.988e+01 -- 0.329917 -0.225069 -1.07001 -1.34027 -1.50052 -1.55267 -1.54775\n",
      "   8 2.125e-01 4.842e+01 3.677e+01 -- -1.311e+01 -- 0.365734 -0.216758 -1.11613 -1.48335 -1.81897 -1.92787 -1.97538\n",
      "   9 1.662e-01 3.735e+01 2.505e+01 -- 1.194e+01 -- 0.396836 -0.209034 -1.12234 -1.51466 -2.02433 -2.23196 -2.39516\n",
      "  10 1.251e-01 2.200e+01 1.359e+01 -- 2.553e+01 -- 0.414786 -0.203235 -1.12662 -1.51315 -2.10323 -2.41253 -2.79331\n",
      "  11 8.212e-02 8.898e+00 5.471e+00 -- 3.100e+01 -- 0.420985 -0.199803 -1.12907 -1.51521 -2.11742 -2.47391 -3.14266\n",
      "  12 4.003e-02 2.505e+00 1.412e+00 -- 3.241e+01 -- 0.421103 -0.198221 -1.13105 -1.52048 -2.11896 -2.4902 -3.40075\n",
      "  13 1.174e-02 5.152e-01 1.981e-01 -- 3.261e+01 -- 0.419844 -0.197745 -1.1323 -1.52425 -2.11903 -2.49687 -3.53687\n",
      "  14 1.974e-03 7.723e-02 1.236e-02 -- 3.262e+01 -- 0.418984 -0.197677 -1.1328 -1.52593 -2.11879 -2.49921 -3.57839\n",
      "  15 2.574e-04 9.878e-03 3.258e-04 -- 3.262e+01 -- 0.418666 -0.197682 -1.13291 -1.52643 -2.11863 -2.49979 -3.58545\n",
      "  16 4.291e-05 1.242e-03 5.812e-06 -- 3.262e+01 -- 0.418586 -0.197687 -1.13292 -1.52653 -2.11856 -2.49988 -3.58637\n",
      "********************\n",
      "0.418586 -0.197687 -1.13292 -1.52653 -2.11856 -2.49988 -3.58637\n",
      "0.230614 0.201096 0.230036 0.178274 0.152947 0.133797 0.307332\n",
      "-0.000341872 -4.95209e-05 -1.00476e-05 -0.000577377 0.000648932 -0.000804247 -0.00124186\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": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i4+oNBgUVFEOCpLyRSPDnU/s9PTBtGnzlK1BeHuyLx4MtXRoun3PsgHC0Sjh0bQ8Nl8+h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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f825d339950>"
      ]
     },
     "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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t### errors for param 0 ###\n",
      "+++ 3.262e+01 3.224e+01 4.186e-01 6.492e-01 0.774 +++\n",
      "+++ 3.262e+01 3.180e+01 4.186e-01 7.645e-01 1.66 +++\n",
      "+++ 3.262e+01 3.203e+01 4.186e-01 7.069e-01 1.18 +++\n",
      "+++ 3.262e+01 3.214e+01 4.186e-01 6.780e-01 0.967 +++\n",
      "+++ 3.262e+01 3.209e+01 4.186e-01 6.924e-01 1.07 +++\n",
      "+++ 3.262e+01 3.212e+01 4.186e-01 6.852e-01 1.02 +++\n",
      "+++ 3.262e+01 3.213e+01 4.186e-01 6.816e-01 0.993 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 3.262e+01 3.217e+01 -1.977e-01 3.409e-03 0.913 +++\n",
      "+++ 3.262e+01 3.166e+01 -1.977e-01 1.040e-01 1.94 +++\n",
      "+++ 3.262e+01 3.193e+01 -1.977e-01 5.368e-02 1.38 +++\n",
      "+++ 3.262e+01 3.205e+01 -1.977e-01 2.855e-02 1.14 +++\n",
      "+++ 3.262e+01 3.211e+01 -1.977e-01 1.598e-02 1.02 +++\n",
      "+++ 3.262e+01 3.214e+01 -1.977e-01 9.693e-03 0.968 +++\n",
      "+++ 3.262e+01 3.213e+01 -1.977e-01 1.284e-02 0.995 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 3.262e+01 3.219e+01 -1.133e+00 -9.029e-01 0.868 +++\n",
      "+++ 3.262e+01 3.170e+01 -1.133e+00 -7.879e-01 1.84 +++\n",
      "+++ 3.262e+01 3.197e+01 -1.133e+00 -8.454e-01 1.32 +++\n",
      "+++ 3.262e+01 3.208e+01 -1.133e+00 -8.741e-01 1.08 +++\n",
      "+++ 3.262e+01 3.214e+01 -1.133e+00 -8.885e-01 0.973 +++\n",
      "+++ 3.262e+01 3.211e+01 -1.133e+00 -8.813e-01 1.03 +++\n",
      "+++ 3.262e+01 3.212e+01 -1.133e+00 -8.849e-01   1 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 3.262e+01 3.219e+01 -1.527e+00 -1.348e+00 0.872 +++\n",
      "+++ 3.262e+01 3.169e+01 -1.527e+00 -1.259e+00 1.88 +++\n",
      "+++ 3.262e+01 3.196e+01 -1.527e+00 -1.304e+00 1.33 +++\n",
      "+++ 3.262e+01 3.208e+01 -1.527e+00 -1.326e+00 1.09 +++\n",
      "+++ 3.262e+01 3.213e+01 -1.527e+00 -1.337e+00 0.979 +++\n",
      "+++ 3.262e+01 3.211e+01 -1.527e+00 -1.332e+00 1.03 +++\n",
      "+++ 3.262e+01 3.212e+01 -1.527e+00 -1.334e+00 1.01 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 3.262e+01 3.220e+01 -2.119e+00 -1.966e+00 0.845 +++\n",
      "+++ 3.262e+01 3.170e+01 -2.119e+00 -1.889e+00 1.85 +++\n",
      "+++ 3.262e+01 3.197e+01 -2.119e+00 -1.927e+00 1.3 +++\n",
      "+++ 3.262e+01 3.209e+01 -2.119e+00 -1.946e+00 1.06 +++\n",
      "+++ 3.262e+01 3.215e+01 -2.119e+00 -1.956e+00 0.951 +++\n",
      "+++ 3.262e+01 3.212e+01 -2.119e+00 -1.951e+00 1.01 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 3.262e+01 3.214e+01 -2.500e+00 -2.366e+00 0.978 +++\n",
      "+++ 3.262e+01 3.155e+01 -2.500e+00 -2.299e+00 2.16 +++\n",
      "+++ 3.262e+01 3.187e+01 -2.500e+00 -2.333e+00 1.51 +++\n",
      "+++ 3.262e+01 3.201e+01 -2.500e+00 -2.349e+00 1.23 +++\n",
      "+++ 3.262e+01 3.207e+01 -2.500e+00 -2.358e+00 1.1 +++\n",
      "+++ 3.262e+01 3.211e+01 -2.500e+00 -2.362e+00 1.04 +++\n",
      "+++ 3.262e+01 3.212e+01 -2.500e+00 -2.364e+00 1.01 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 3.262e+01 3.249e+01 -3.586e+00 -3.433e+00 0.274 +++\n",
      "+++ 3.262e+01 3.228e+01 -3.586e+00 -3.356e+00 0.68 +++\n",
      "+++ 3.262e+01 3.214e+01 -3.586e+00 -3.317e+00 0.97 +++\n",
      "+++ 3.262e+01 3.205e+01 -3.586e+00 -3.298e+00 1.14 +++\n",
      "+++ 3.262e+01 3.210e+01 -3.586e+00 -3.308e+00 1.05 +++\n",
      "+++ 3.262e+01 3.212e+01 -3.586e+00 -3.313e+00 1.01 +++\n",
      "+++ 3.262e+01 3.213e+01 -3.586e+00 -3.315e+00 0.99 +++\n",
      "********************\n",
      "0.418586 -0.197687 -1.13292 -1.52653 -2.11856 -2.49988 -3.58637\n",
      "0.263044 0.210523 0.248008 0.192202 0.167286 0.135887 0.271316\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "p2, p2e = clag.errors(P2, p2, p2e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 1.751e+01 2.876e+01 inf -- 5.733e+01 -- 0.0594864 -0.487928 -1.46187 -1.82465 -2.4246 -2.79639 -3.88808 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
      "   2 1.970e+01 3.013e+01 1.798e+01 -- 7.532e+01 -- 0.29683 -1.52364 -1.4649 -2.25166 -2.5651 -3.43969 -4.03355 1.21455 0.923908 -1.61801 1.85071 -0.285296 -0.531157 0.012722\n",
      "   4 6.537e+00 1.384e+01 2.528e+00 -- 7.784e+01 -- 0.291378 -1.68551 -1.44919 -2.09362 -2.55998 -3.48384 -4.03518 1.14909 2.74383 -1.47933 1.80908 -0.301606 -0.875227 0.0310364\n",
      "   6 3.785e+00 7.896e+00 1.984e+00 -- 7.983e+01 -- 0.288282 -1.38551 -1.42819 -1.9952 -2.55472 -3.47151 -4.03617 1.0893 2.48495 -1.36763 1.78981 -0.314059 -1.2346 0.0473648\n",
      "   8 2.247e+00 7.953e+00 1.903e+00 -- 8.173e+01 -- 0.287062 -1.08551 -1.40581 -1.9248 -2.54944 -3.41037 -4.03676 1.03541 2.70301 -1.2801 1.77686 -0.323434 -1.51696 0.0617974\n",
      "  10 1.524e+00 9.195e+00 1.868e+00 -- 8.360e+01 -- 0.287304 -0.84158 -1.38402 -1.87105 -2.54419 -3.33668 -4.03696 0.987277 2.6684 -1.21194 1.76655 -0.330285 -1.69315 0.0746248\n",
      "  12 1.161e+00 1.058e+01 1.526e+00 -- 8.513e+01 -- 0.288653 -0.721287 -1.36379 -1.8284 -2.53901 -3.27143 -4.03682 0.944635 2.66148 -1.15906 1.75754 -0.335021 -1.79864 0.0859985\n",
      "  14 9.236e-01 1.358e+01 1.320e+00 -- 8.645e+01 -- 0.29076 -0.640904 -1.34555 -1.79371 -2.53397 -3.21778 -4.03644 0.907049 2.65848 -1.11758 1.74949 -0.338064 -1.86544 0.0959851\n",
      "  16 7.887e-01 1.616e+01 1.163e+00 -- 8.761e+01 -- 0.293363 -0.581712 -1.32932 -1.76496 -2.5291 -3.17401 -4.03591 0.874019 2.65704 -1.08472 1.74218 -0.339743 -1.91057 0.104678\n",
      "  18 6.852e-01 1.848e+01 1.035e+00 -- 8.864e+01 -- 0.296262 -0.535833 -1.31497 -1.7408 -2.52442 -3.13802 -4.03528 0.84503 2.65643 -1.05844 1.73544 -0.340323 -1.94268 0.112177\n",
      "  20 6.016e-01 2.063e+01 9.268e-01 -- 8.957e+01 -- 0.299311 -0.499119 -1.30233 -1.72029 -2.51992 -3.1081 -4.03459 0.819584 2.65634 -1.03727 1.7292 -0.340017 -1.96645 0.11858\n",
      "  21 1.026e+00 1.793e+03 1.113e+01 -- 7.844e+01 -- 0.330255 -0.19883 -1.191 -1.54462 -2.4769 -2.85684 -4.0275 0.595995 2.65885 -0.865635 1.67089 -0.329856 -2.14791 0.172608\n",
      "  22 2.203e+01 4.653e+01 1.881e+01 -- 9.725e+01 -- 0.346046 -0.283463 -1.21608 -1.56969 -2.45327 -2.92337 -4.05674 0.633366 2.66967 -1.02345 1.65017 -0.216181 -2.10193 -0.00456476\n",
      "  23 9.017e-02 4.157e+01 4.743e-01 -- 9.772e+01 -- 0.350061 -0.2856 -1.19812 -1.56937 -2.44621 -2.91187 -4.02571 0.593047 2.67704 -0.939304 1.64631 -0.234834 -2.06914 0.0960162\n",
      "  24 1.011e-01 1.709e+01 7.447e-02 -- 9.780e+01 -- 0.351939 -0.286126 -1.19593 -1.56919 -2.44211 -2.91054 -4.02815 0.594891 2.68864 -0.964614 1.63615 -0.233118 -2.07263 0.0873586\n",
      "  25 7.198e-03 7.315e+00 9.879e-03 -- 9.781e+01 -- 0.352451 -0.285708 -1.19443 -1.5689 -2.44029 -2.90957 -4.02701 0.591849 2.68648 -0.958907 1.63497 -0.229941 -2.07127 0.0961923\n",
      "  26 7.573e-03 2.607e+00 1.318e-03 -- 9.781e+01 -- 0.352645 -0.285889 -1.19403 -1.56883 -2.43938 -2.90916 -4.02687 0.591744 2.6883 -0.960874 1.63369 -0.228302 -2.0711 0.0968847\n",
      "  27 1.407e-03 1.002e+00 1.762e-04 -- 9.781e+01 -- 0.35271 -0.285841 -1.19384 -1.5688 -2.43903 -2.909 -4.02679 0.59146 2.68804 -0.960478 1.63339 -0.22751 -2.07101 0.0976184\n",
      "  28 7.648e-04 3.587e-01 2.373e-05 -- 9.781e+01 -- 0.352735 -0.28586 -1.19379 -1.56879 -2.43888 -2.90894 -4.02677 0.59142 2.68826 -0.960644 1.63322 -0.22719 -2.07099 0.0977376\n",
      "********************\n",
      "0.352735 -0.28586 -1.19379 -1.56879 -2.43888 -2.90894 -4.02677 0.59142 2.68826 -0.960644 1.63322 -0.22719 -2.07099 0.0977376\n",
      "0.00496596 0.0557416 0.0244243 0.0261901 0.169649 0.216583 0.627326 0.0836975 0.256304 0.179223 0.163052 0.470721 0.550365 1.46594\n",
      "0.358674 0.00129977 0.0426552 0.00780676 0.00197808 0.000483372 2.94351e-05 -0.00466008 -0.000401306 0.00086537 -0.00189251 0.000598726 2.9782e-05 3.40563e-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": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t### errors for param 0 ###\n",
      "+++ 9.781e+01 9.767e+01 3.527e-01 3.552e-01 0.284 +++\n",
      "+++ 9.781e+01 9.736e+01 3.527e-01 3.565e-01 0.898 +++\n",
      "+++ 9.781e+01 9.705e+01 3.527e-01 3.571e-01 1.51 +++\n",
      "+++ 9.781e+01 9.723e+01 3.527e-01 3.568e-01 1.17 +++\n",
      "+++ 9.781e+01 9.730e+01 3.527e-01 3.566e-01 1.02 +++\n",
      "+++ 9.781e+01 9.733e+01 3.527e-01 3.565e-01 0.959 +++\n",
      "+++ 9.781e+01 9.731e+01 3.527e-01 3.566e-01 0.991 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 9.781e+01 9.757e+01 -2.859e-01 -2.580e-01 0.487 +++\n",
      "+++ 9.781e+01 9.709e+01 -2.859e-01 -2.441e-01 1.43 +++\n",
      "+++ 9.781e+01 9.738e+01 -2.859e-01 -2.510e-01 0.864 +++\n",
      "+++ 9.781e+01 9.725e+01 -2.859e-01 -2.475e-01 1.12 +++\n",
      "+++ 9.781e+01 9.732e+01 -2.859e-01 -2.493e-01 0.985 +++\n",
      "+++ 9.781e+01 9.728e+01 -2.859e-01 -2.484e-01 1.05 +++\n",
      "+++ 9.781e+01 9.730e+01 -2.859e-01 -2.488e-01 1.02 +++\n",
      "+++ 9.781e+01 9.731e+01 -2.859e-01 -2.491e-01   1 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 9.781e+01 9.763e+01 -1.194e+00 -1.182e+00 0.354 +++\n",
      "+++ 9.781e+01 9.727e+01 -1.194e+00 -1.175e+00 1.09 +++\n",
      "+++ 9.781e+01 9.749e+01 -1.194e+00 -1.179e+00 0.638 +++\n",
      "+++ 9.781e+01 9.739e+01 -1.194e+00 -1.177e+00 0.837 +++\n",
      "+++ 9.781e+01 9.733e+01 -1.194e+00 -1.176e+00 0.954 +++\n",
      "+++ 9.781e+01 9.730e+01 -1.194e+00 -1.176e+00 1.02 +++\n",
      "+++ 9.781e+01 9.732e+01 -1.194e+00 -1.176e+00 0.986 +++\n",
      "+++ 9.781e+01 9.731e+01 -1.194e+00 -1.176e+00   1 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 9.781e+01 9.765e+01 -1.569e+00 -1.556e+00 0.327 +++\n",
      "+++ 9.781e+01 9.736e+01 -1.569e+00 -1.549e+00 0.907 +++\n",
      "+++ 9.781e+01 9.712e+01 -1.569e+00 -1.546e+00 1.39 +++\n",
      "+++ 9.781e+01 9.725e+01 -1.569e+00 -1.548e+00 1.13 +++\n",
      "+++ 9.781e+01 9.730e+01 -1.569e+00 -1.548e+00 1.01 +++\n",
      "+++ 9.781e+01 9.733e+01 -1.569e+00 -1.549e+00 0.958 +++\n",
      "+++ 9.781e+01 9.732e+01 -1.569e+00 -1.549e+00 0.985 +++\n",
      "+++ 9.781e+01 9.731e+01 -1.569e+00 -1.548e+00 0.998 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 9.781e+01 9.766e+01 -2.439e+00 -2.354e+00 0.291 +++\n",
      "+++ 9.781e+01 9.739e+01 -2.439e+00 -2.312e+00 0.833 +++\n",
      "+++ 9.781e+01 9.716e+01 -2.439e+00 -2.290e+00 1.3 +++\n",
      "+++ 9.781e+01 9.729e+01 -2.439e+00 -2.301e+00 1.04 +++\n",
      "+++ 9.781e+01 9.734e+01 -2.439e+00 -2.306e+00 0.934 +++\n",
      "+++ 9.781e+01 9.732e+01 -2.439e+00 -2.304e+00 0.988 +++\n",
      "+++ 9.781e+01 9.730e+01 -2.439e+00 -2.302e+00 1.02 +++\n",
      "+++ 9.781e+01 9.731e+01 -2.439e+00 -2.303e+00   1 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 9.781e+01 9.760e+01 -2.909e+00 -2.801e+00 0.412 +++\n",
      "+++ 9.781e+01 9.723e+01 -2.909e+00 -2.746e+00 1.17 +++\n",
      "+++ 9.781e+01 9.745e+01 -2.909e+00 -2.774e+00 0.72 +++\n",
      "+++ 9.781e+01 9.735e+01 -2.909e+00 -2.760e+00 0.923 +++\n",
      "+++ 9.781e+01 9.729e+01 -2.909e+00 -2.753e+00 1.04 +++\n",
      "+++ 9.781e+01 9.732e+01 -2.909e+00 -2.757e+00 0.98 +++\n",
      "+++ 9.781e+01 9.730e+01 -2.909e+00 -2.755e+00 1.01 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 9.781e+01 -inf -4.027e+00 -3.027e+00 inf +++\n",
      "+++ 9.781e+01 9.626e+01 -4.027e+00 -3.527e+00 3.1 +++\n",
      "+++ 9.781e+01 9.763e+01 -4.027e+00 -3.777e+00 0.349 +++\n",
      "+++ 9.781e+01 9.724e+01 -4.027e+00 -3.652e+00 1.14 +++\n",
      "+++ 9.781e+01 9.748e+01 -4.027e+00 -3.714e+00 0.653 +++\n",
      "+++ 9.781e+01 9.737e+01 -4.027e+00 -3.683e+00 0.869 +++\n",
      "+++ 9.781e+01 9.731e+01 -4.027e+00 -3.667e+00 0.996 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 9.781e+01 9.733e+01 5.914e-01 6.750e-01 0.966 +++\n",
      "+++ 9.781e+01 9.688e+01 5.914e-01 7.169e-01 1.87 +++\n",
      "+++ 9.781e+01 9.711e+01 5.914e-01 6.960e-01 1.4 +++\n",
      "+++ 9.781e+01 9.722e+01 5.914e-01 6.855e-01 1.18 +++\n",
      "+++ 9.781e+01 9.727e+01 5.914e-01 6.803e-01 1.07 +++\n",
      "+++ 9.781e+01 9.730e+01 5.914e-01 6.777e-01 1.02 +++\n",
      "+++ 9.781e+01 9.731e+01 5.914e-01 6.763e-01 0.992 +++\n",
      "\t### errors for param 8 ###\n",
      "+++ 9.781e+01 9.765e+01 2.688e+00 2.816e+00 0.315 +++\n",
      "+++ 9.781e+01 9.747e+01 2.688e+00 2.880e+00 0.685 +++\n",
      "+++ 9.781e+01 9.735e+01 2.688e+00 2.912e+00 0.915 +++\n",
      "+++ 9.781e+01 9.729e+01 2.688e+00 2.929e+00 1.04 +++\n",
      "+++ 9.781e+01 9.732e+01 2.688e+00 2.921e+00 0.977 +++\n",
      "+++ 9.781e+01 9.731e+01 2.688e+00 2.925e+00 1.01 +++\n",
      "\t### errors for param 9 ###\n",
      "+++ 9.781e+01 9.767e+01 -9.606e-01 -8.710e-01 0.287 +++\n",
      "+++ 9.781e+01 9.750e+01 -9.606e-01 -8.262e-01 0.615 +++\n",
      "+++ 9.781e+01 9.740e+01 -9.606e-01 -8.038e-01 0.814 +++\n",
      "+++ 9.781e+01 9.735e+01 -9.606e-01 -7.927e-01 0.921 +++\n",
      "+++ 9.781e+01 9.732e+01 -9.606e-01 -7.871e-01 0.976 +++\n",
      "+++ 9.781e+01 9.731e+01 -9.606e-01 -7.843e-01   1 +++\n",
      "\t### errors for param 10 ###\n",
      "+++ 9.781e+01 9.737e+01 1.633e+00 1.796e+00 0.887 +++\n",
      "+++ 9.781e+01 9.687e+01 1.633e+00 1.878e+00 1.89 +++\n",
      "+++ 9.781e+01 9.713e+01 1.633e+00 1.837e+00 1.35 +++\n",
      "+++ 9.781e+01 9.725e+01 1.633e+00 1.817e+00 1.11 +++\n",
      "+++ 9.781e+01 9.731e+01 1.633e+00 1.806e+00 0.995 +++\n",
      "\t### errors for param 11 ###\n",
      "+++ 9.781e+01 9.744e+01 -2.271e-01 2.436e-01 0.733 +++\n",
      "+++ 9.781e+01 9.707e+01 -2.271e-01 4.789e-01 1.47 +++\n",
      "+++ 9.781e+01 9.726e+01 -2.271e-01 3.612e-01 1.09 +++\n",
      "+++ 9.781e+01 9.736e+01 -2.271e-01 3.024e-01 0.906 +++\n",
      "+++ 9.781e+01 9.731e+01 -2.271e-01 3.318e-01 0.996 +++\n",
      "\t### errors for param 12 ###\n",
      "+++ 9.781e+01 9.655e+01 -2.071e+00 -1.071e+00 2.53 +++\n",
      "+++ 9.781e+01 9.739e+01 -2.071e+00 -1.571e+00 0.834 +++\n",
      "+++ 9.781e+01 9.697e+01 -2.071e+00 -1.321e+00 1.68 +++\n",
      "+++ 9.781e+01 9.719e+01 -2.071e+00 -1.446e+00 1.24 +++\n",
      "+++ 9.781e+01 9.729e+01 -2.071e+00 -1.508e+00 1.03 +++\n",
      "+++ 9.781e+01 9.734e+01 -2.071e+00 -1.540e+00 0.932 +++\n",
      "+++ 9.781e+01 9.732e+01 -2.071e+00 -1.524e+00 0.981 +++\n",
      "+++ 9.781e+01 9.731e+01 -2.071e+00 -1.516e+00 1.01 +++\n",
      "\t### errors for param 13 ###\n",
      "********************\n",
      "0.352744 -0.285856 -1.19376 -1.56878 -2.43883 -2.90891 -4.02676 0.591388 2.68824 -0.960618 1.63318 -0.22706 -2.07098 0.0978123\n",
      "0.00383633 0.0367967 0.0178294 0.0203568 0.135817 0.153961 0.359375 0.0849581 0.236274 0.176365 0.173234 0.558872 0.554688 10\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "p, pe = clag.errors(Cx, p, pe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f825d256650>"
      ]
     },
     "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": [
       "array([ 1.40138477,  1.30298225,  0.50400939,  0.31939362,  0.66477489,\n",
       "        0.42567588,  4.95106888])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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
}