phy-4660/lag/data/complete_clag_analysis-3471_origbins.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 0x7fa848d60a10>"
      ]
     },
     "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/3471A.lc\"\n",
    "\n",
    "\n",
    "dt = 0.01\n",
    "t1, l1, l1e = np.loadtxt(ref_file).T\n",
    "errorbar(t1, l1, yerr=l1e, fmt='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.005     ,  0.01861938,  0.04473305,  0.06933623,  0.10747115,\n",
       "        0.16658029,  0.25819945,  0.40020915,  0.62032418])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n",
    "                0.25819945, 0.40020915, 0.62032418])\n",
    "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n",
    "nfq = len(fqL) - 1\n",
    "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n",
    "\n",
    "\n",
    "fqL\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 4.342e-01 5.077e+01 inf -- -5.530e+02 -- 1 1 1 1 1 1 1 1\n",
      "   2 7.674e-01 5.065e+01 8.300e+01 -- -4.700e+02 -- 0.653018 0.587019 0.568277 0.567457 0.566281 0.566085 0.565773 0.566163\n",
      "   3 3.298e+00 5.043e+01 8.075e+01 -- -3.893e+02 -- 0.414806 0.209135 0.141159 0.13784 0.133393 0.132728 0.131612 0.132761\n",
      "   4 1.572e+00 5.010e+01 7.754e+01 -- -3.117e+02 -- 0.322539 -0.0834456 -0.273066 -0.284295 -0.297612 -0.299435 -0.302479 -0.300412\n",
      "   5 5.908e-01 4.964e+01 7.386e+01 -- -2.379e+02 -- 0.302357 -0.214604 -0.654658 -0.688754 -0.723838 -0.729279 -0.736418 -0.733888\n",
      "   6 3.713e-01 4.877e+01 6.953e+01 -- -1.683e+02 -- 0.284419 -0.200357 -0.96379 -1.05472 -1.13798 -1.15477 -1.17031 -1.16748\n",
      "   7 2.709e-01 4.671e+01 6.269e+01 -- -1.056e+02 -- 0.277768 -0.185001 -1.13047 -1.34026 -1.52128 -1.56845 -1.6043 -1.60101\n",
      "   8 2.135e-01 4.361e+01 5.281e+01 -- -5.282e+01 -- 0.277012 -0.185189 -1.16375 -1.49463 -1.83211 -1.9477 -2.03737 -2.03476\n",
      "   9 1.764e-01 3.767e+01 4.019e+01 -- -1.264e+01 -- 0.282161 -0.184207 -1.17891 -1.53049 -2.01851 -2.24569 -2.46562 -2.46922\n",
      "  10 1.508e-01 2.738e+01 2.645e+01 -- 1.382e+01 -- 0.289545 -0.182463 -1.18795 -1.52893 -2.08812 -2.41103 -2.87498 -2.90486\n",
      "  11 1.349e-01 1.468e+01 1.390e+01 -- 2.772e+01 -- 0.293547 -0.180898 -1.1897 -1.52803 -2.10944 -2.46526 -3.22702 -3.34288\n",
      "  12 1.358e-01 5.365e+00 5.378e+00 -- 3.309e+01 -- 0.295456 -0.179941 -1.19052 -1.52801 -2.11928 -2.48294 -3.46233 -3.79368\n",
      "  13 1.868e-01 1.338e+00 1.633e+00 -- 3.473e+01 -- 0.297315 -0.17923 -1.19104 -1.52666 -2.12491 -2.48975 -3.55567 -4.30889\n",
      "  14 6.248e-01 2.604e-01 4.517e-01 -- 3.518e+01 -- 0.299091 -0.178645 -1.191 -1.52463 -2.12805 -2.4915 -3.5672 -5.11363\n",
      "  15 2.744e+02 2.611e-01 7.022e-02 -- 3.525e+01 -- 0.300248 -0.178286 -1.19075 -1.52307 -2.12961 -2.49161 -3.56337 -8\n",
      "  16 2.745e+02 2.782e-01 4.368e-04 -- 3.525e+01 -- 0.300566 -0.178158 -1.19062 -1.52252 -2.13009 -2.49151 -3.5617 -8\n",
      "  17 2.745e+02 2.805e-01 6.410e-05 -- 3.525e+01 -- 0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n",
      "********************\n",
      "0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n",
      "0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n",
      "-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n",
    "p1 = np.ones(nfq)\n",
    "p1, p1e = clag.optimize(P1, p1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t### errors for param 0 ###\n",
      "+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n",
      "+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n",
      "+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n",
      "+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n",
      "+++ 3.525e+01 3.475e+01 3.006e-01 5.545e-01 0.997 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n",
      "+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n",
      "+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n",
      "+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n",
      "+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n",
      "+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02   1 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n",
      "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n",
      "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n",
      "+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n",
      "+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 3.525e+01 3.482e+01 -1.522e+00 -1.345e+00 0.865 +++\n",
      "+++ 3.525e+01 3.432e+01 -1.522e+00 -1.257e+00 1.86 +++\n",
      "+++ 3.525e+01 3.459e+01 -1.522e+00 -1.301e+00 1.32 +++\n",
      "+++ 3.525e+01 3.471e+01 -1.522e+00 -1.323e+00 1.08 +++\n",
      "+++ 3.525e+01 3.476e+01 -1.522e+00 -1.334e+00 0.97 +++\n",
      "+++ 3.525e+01 3.473e+01 -1.522e+00 -1.329e+00 1.03 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.522e+00 -1.331e+00 0.998 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 3.525e+01 3.481e+01 -2.130e+00 -1.977e+00 0.874 +++\n",
      "+++ 3.525e+01 3.429e+01 -2.130e+00 -1.901e+00 1.91 +++\n",
      "+++ 3.525e+01 3.457e+01 -2.130e+00 -1.939e+00 1.35 +++\n",
      "+++ 3.525e+01 3.470e+01 -2.130e+00 -1.958e+00 1.1 +++\n",
      "+++ 3.525e+01 3.476e+01 -2.130e+00 -1.968e+00 0.983 +++\n",
      "+++ 3.525e+01 3.473e+01 -2.130e+00 -1.963e+00 1.04 +++\n",
      "+++ 3.525e+01 3.474e+01 -2.130e+00 -1.965e+00 1.01 +++\n",
      "+++ 3.525e+01 3.475e+01 -2.130e+00 -1.966e+00 0.997 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 3.525e+01 3.474e+01 -2.491e+00 -2.358e+00 1.01 +++\n",
      "+++ 3.525e+01 3.512e+01 -2.491e+00 -2.425e+00 0.263 +++\n",
      "+++ 3.525e+01 3.496e+01 -2.491e+00 -2.392e+00 0.579 +++\n",
      "+++ 3.525e+01 3.486e+01 -2.491e+00 -2.375e+00 0.781 +++\n",
      "+++ 3.525e+01 3.480e+01 -2.491e+00 -2.367e+00 0.893 +++\n",
      "+++ 3.525e+01 3.477e+01 -2.491e+00 -2.363e+00 0.952 +++\n",
      "+++ 3.525e+01 3.476e+01 -2.491e+00 -2.361e+00 0.982 +++\n",
      "+++ 3.525e+01 3.475e+01 -2.491e+00 -2.360e+00 0.997 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 3.525e+01 3.507e+01 -3.561e+00 -3.413e+00 0.363 +++\n",
      "+++ 3.525e+01 3.484e+01 -3.561e+00 -3.339e+00 0.814 +++\n",
      "+++ 3.525e+01 3.468e+01 -3.561e+00 -3.301e+00 1.13 +++\n",
      "+++ 3.525e+01 3.477e+01 -3.561e+00 -3.320e+00 0.962 +++\n",
      "+++ 3.525e+01 3.473e+01 -3.561e+00 -3.311e+00 1.04 +++\n",
      "+++ 3.525e+01 3.475e+01 -3.561e+00 -3.315e+00   1 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 3.525e+01 3.524e+01 -8.000e+00 -6.000e+00 0.0178 +++\n",
      "+++ 3.525e+01 3.515e+01 -8.000e+00 -5.000e+00 0.187 +++\n",
      "+++ 3.525e+01 3.494e+01 -8.000e+00 -4.500e+00 0.618 +++\n",
      "+++ 3.525e+01 3.466e+01 -8.000e+00 -4.250e+00 1.18 +++\n",
      "+++ 3.525e+01 3.482e+01 -8.000e+00 -4.375e+00 0.851 +++\n",
      "+++ 3.525e+01 3.475e+01 -8.000e+00 -4.312e+00   1 +++\n",
      "********************\n",
      "0.300605 -0.178131 -1.19059 -1.52241 -2.13019 -2.49148 -3.56144 -8\n",
      "0.253864 0.205597 0.228999 0.191096 0.1638 0.131949 0.246154 3.6875\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "p1, p1e = clag.errors(P1, p1, p1e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ExBjJ5NmG+iSTZzl9+qmYKpJaz5AgSQGpVIpMZgW4ts0eS2Qyb8T2JEqpHQwJknQXs7PT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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa86c4d3810>"
      ]
     },
     "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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s1gYNsgosKj2W4uaAwRxImCcZM2P2Wa0zWXK/CQUJHiR4CBs7swWxdkMU4hvv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa84670c610>"
      ]
     },
     "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 4.362e-01 5.425e+01 inf -- -3.468e+02 -- 1 1 1 1 1 1 1 1\n",
      "   2 7.747e-01 5.347e+01 7.019e+01 -- -2.766e+02 -- 0.58045 0.569962 0.564602 0.563764 0.567626 0.565054 0.565279 0.564008\n",
      "   3 3.447e+00 5.213e+01 6.905e+01 -- -2.076e+02 -- 0.198681 0.140203 0.130333 0.127063 0.134237 0.12998 0.131082 0.127044\n",
      "   4 1.448e+00 4.998e+01 6.682e+01 -- -1.408e+02 -- -0.0825582 -0.283347 -0.300715 -0.310826 -0.299703 -0.304649 -0.30132 -0.310849\n",
      "   5 5.884e-01 4.684e+01 6.330e+01 -- -7.746e+01 -- -0.194194 -0.676998 -0.726162 -0.750752 -0.733583 -0.738758 -0.729946 -0.74976\n",
      "   6 3.739e-01 4.258e+01 5.850e+01 -- -1.895e+01 -- -0.203226 -0.953186 -1.14229 -1.19152 -1.16459 -1.17342 -1.15279 -1.19094\n",
      "   7 2.741e-01 3.740e+01 5.292e+01 -- 3.397e+01 -- -0.205901 -0.9956 -1.54459 -1.63073 -1.58521 -1.61127 -1.5669 -1.63629\n",
      "   8 2.128e-01 3.180e+01 4.679e+01 -- 8.076e+01 -- -0.180502 -0.943953 -1.92935 -2.06126 -1.98071 -2.05288 -1.97074 -2.08385\n",
      "   9 1.675e-01 2.595e+01 3.791e+01 -- 1.187e+02 -- -0.15428 -0.934563 -2.26437 -2.45352 -2.31988 -2.48374 -2.35707 -2.52729\n",
      "  10 1.286e-01 2.002e+01 2.537e+01 -- 1.440e+02 -- -0.13956 -0.939381 -2.49352 -2.71726 -2.56371 -2.84902 -2.70919 -2.95055\n",
      "  11 9.197e-02 1.277e+01 1.299e+01 -- 1.570e+02 -- -0.130332 -0.946187 -2.57446 -2.74449 -2.69402 -3.05697 -3.00449 -3.33007\n",
      "  12 5.076e-02 6.047e+00 5.021e+00 -- 1.621e+02 -- -0.124515 -0.945944 -2.55165 -2.71422 -2.73704 -3.10737 -3.209 -3.63633\n",
      "  13 1.434e-02 1.485e+00 1.060e+00 -- 1.631e+02 -- -0.12047 -0.945369 -2.54157 -2.70197 -2.76441 -3.10602 -3.30159 -3.8209\n",
      "  14 4.769e-03 3.557e-01 7.280e-02 -- 1.632e+02 -- -0.119516 -0.946002 -2.53635 -2.69518 -2.78858 -3.10381 -3.32152 -3.8757\n",
      "  15 2.150e-03 1.530e-01 4.222e-03 -- 1.632e+02 -- -0.119506 -0.945491 -2.53265 -2.69429 -2.80187 -3.10094 -3.32488 -3.87969\n",
      "  16 1.017e-03 7.109e-02 8.430e-04 -- 1.632e+02 -- -0.119394 -0.945124 -2.53033 -2.69448 -2.8079 -3.09858 -3.32603 -3.87983\n",
      "  17 4.817e-04 3.356e-02 1.918e-04 -- 1.632e+02 -- -0.119311 -0.944963 -2.52941 -2.69447 -2.81076 -3.09727 -3.32654 -3.87984\n",
      "  18 2.335e-04 1.618e-02 4.417e-05 -- 1.632e+02 -- -0.119282 -0.944891 -2.52886 -2.69454 -2.81211 -3.09664 -3.32676 -3.87987\n",
      "********************\n",
      "-0.119282 -0.944891 -2.52886 -2.69454 -2.81211 -3.09664 -3.32676 -3.87987\n",
      "0.233661 0.204163 0.319993 0.254151 0.198248 0.179386 0.161786 0.221522\n",
      "0.000372164 0.000983332 0.00164575 -0.000696472 -0.0161795 0.00744155 -0.00415795 -0.000828998\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)\n",
    "p2 = np.ones(nfq)\n",
    "p2, p2e = clag.optimize(P2, p2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WM3L1CFwVUmAx5BfnyR/J035DOwNPDjRsUPB2gySpoXTe0hkEhKnuXiyC7NVZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8466ecf90>"
      ]
     },
     "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": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t### errors for param 0 ###\n",
      "+++ 1.632e+02 1.627e+02 -1.193e-01 1.144e-01 0.905 +++\n",
      "+++ 1.632e+02 1.622e+02 -1.193e-01 2.312e-01 1.9 +++\n",
      "+++ 1.632e+02 1.625e+02 -1.193e-01 1.728e-01 1.37 +++\n",
      "+++ 1.632e+02 1.626e+02 -1.193e-01 1.436e-01 1.13 +++\n",
      "+++ 1.632e+02 1.627e+02 -1.193e-01 1.290e-01 1.01 +++\n",
      "+++ 1.632e+02 1.627e+02 -1.193e-01 1.217e-01 0.958 +++\n",
      "+++ 1.632e+02 1.627e+02 -1.193e-01 1.254e-01 0.985 +++\n",
      "+++ 1.632e+02 1.627e+02 -1.193e-01 1.272e-01 0.999 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 1.632e+02 1.627e+02 -9.449e-01 -7.407e-01 0.961 +++\n",
      "+++ 1.632e+02 1.622e+02 -9.449e-01 -6.386e-01 2.04 +++\n",
      "+++ 1.632e+02 1.625e+02 -9.449e-01 -6.897e-01 1.46 +++\n",
      "+++ 1.632e+02 1.626e+02 -9.449e-01 -7.152e-01 1.2 +++\n",
      "+++ 1.632e+02 1.627e+02 -9.449e-01 -7.279e-01 1.08 +++\n",
      "+++ 1.632e+02 1.627e+02 -9.449e-01 -7.343e-01 1.02 +++\n",
      "+++ 1.632e+02 1.627e+02 -9.449e-01 -7.375e-01 0.989 +++\n",
      "+++ 1.632e+02 1.627e+02 -9.449e-01 -7.359e-01   1 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 1.632e+02 1.630e+02 -2.529e+00 -2.369e+00 0.307 +++\n",
      "+++ 1.632e+02 1.629e+02 -2.529e+00 -2.289e+00 0.681 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.529e+00 -2.249e+00 0.919 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.529e+00 -2.229e+00 1.05 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.529e+00 -2.239e+00 0.984 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.529e+00 -2.234e+00 1.02 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.529e+00 -2.236e+00   1 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 1.632e+02 1.630e+02 -2.695e+00 -2.567e+00 0.306 +++\n",
      "+++ 1.632e+02 1.629e+02 -2.695e+00 -2.504e+00 0.681 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.695e+00 -2.472e+00 0.922 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.695e+00 -2.456e+00 1.05 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.695e+00 -2.464e+00 0.988 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.695e+00 -2.460e+00 1.02 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.695e+00 -2.462e+00   1 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 1.632e+02 1.629e+02 -2.813e+00 -2.614e+00 0.665 +++\n",
      "+++ 1.632e+02 1.624e+02 -2.813e+00 -2.515e+00 1.57 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.813e+00 -2.565e+00 1.07 +++\n",
      "+++ 1.632e+02 1.628e+02 -2.813e+00 -2.590e+00 0.853 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.813e+00 -2.577e+00 0.956 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.813e+00 -2.571e+00 1.01 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.813e+00 -2.574e+00 0.983 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.813e+00 -2.573e+00 0.997 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 1.632e+02 1.628e+02 -3.096e+00 -2.917e+00 0.837 +++\n",
      "+++ 1.632e+02 1.623e+02 -3.096e+00 -2.827e+00 1.87 +++\n",
      "+++ 1.632e+02 1.625e+02 -3.096e+00 -2.872e+00 1.29 +++\n",
      "+++ 1.632e+02 1.627e+02 -3.096e+00 -2.895e+00 1.06 +++\n",
      "+++ 1.632e+02 1.627e+02 -3.096e+00 -2.906e+00 0.947 +++\n",
      "+++ 1.632e+02 1.627e+02 -3.096e+00 -2.900e+00   1 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 1.632e+02 1.627e+02 -3.327e+00 -3.165e+00 0.992 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 1.632e+02 1.631e+02 -3.880e+00 -3.769e+00 0.278 +++\n",
      "+++ 1.632e+02 1.629e+02 -3.880e+00 -3.714e+00 0.631 +++\n",
      "+++ 1.632e+02 1.628e+02 -3.880e+00 -3.686e+00 0.862 +++\n",
      "+++ 1.632e+02 1.627e+02 -3.880e+00 -3.672e+00 0.991 +++\n",
      "********************\n",
      "-0.119263 -0.944854 -2.52866 -2.69453 -2.81277 -3.09632 -3.32687 -3.87987\n",
      "0.246439 0.208948 0.29245 0.232296 0.24017 0.196142 0.161799 0.207668\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "p2, p2e = clag.errors(P2, p2, p2e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 2.664e+02 1.060e+01 inf -- 2.187e+02 -- -0.209329 -0.861493 -2.15962 -2.40847 -2.77148 -3.0939 -3.74416 -6.23993 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
      "   3 2.987e+01 1.264e+01 2.257e+00 -- 2.210e+02 -- -0.16831 -0.824919 -2.12464 -2.39488 -2.7608 -3.0706 -3.74724 -5.93993 0.0804747 0.16472 0.155918 0.19816 0.150032 0.148466 -0.0426696 2.76354\n",
      "   5 3.330e+01 1.479e+01 2.062e+00 -- 2.230e+02 -- -0.134691 -0.793404 -2.09481 -2.37915 -2.74942 -3.04926 -3.74185 -6.23993 0.0666414 0.2131 0.199386 0.281712 0.193626 0.185727 -0.170109 -2.41599\n",
      "   7 4.032e+02 1.707e+01 1.889e+00 -- 2.249e+02 -- -0.106698 -0.766368 -2.06929 -2.36269 -2.73774 -3.02999 -3.73105 -6.53993 0.0565128 0.250258 0.234434 0.352126 0.231411 0.214451 -0.279352 -0.654819\n",
      "   9 1.006e+02 1.948e+01 1.734e+00 -- 2.267e+02 -- -0.0831017 -0.743148 -2.04735 -2.34643 -2.7261 -3.0127 -3.71739 -6.23993 0.0489265 0.27947 0.263536 0.411371 0.264011 0.236678 -0.370557 0.617417\n",
      "  11 5.264e+01 2.201e+01 1.603e+00 -- 2.283e+02 -- -0.0630157 -0.723142 -2.02838 -2.33091 -2.71472 -2.99725 -3.70277 -5.93993 0.0431576 0.302888 0.288287 0.46139 0.292059 0.253914 -0.445773 0.691506\n",
      "  13 3.251e+00 2.467e+01 1.448e+00 -- 2.297e+02 -- -0.0457823 -0.705838 -2.0119 -2.31642 -2.70374 -2.98344 -3.68838 -5.63993 0.0387303 0.321962 0.309779 0.503862 0.316158 0.267284 -0.507655 -2.9489\n",
      "  15 5.697e+00 2.744e+01 1.389e+00 -- 2.311e+02 -- -0.0308979 -0.690819 -1.99751 -2.30307 -2.6933 -2.97112 -3.67473 -5.93993 0.0353185 0.337702 0.32866 0.540271 0.336854 0.27759 -0.559235 -3.12456\n",
      "  17 9.962e+01 3.030e+01 1.295e+00 -- 2.324e+02 -- -0.0179753 -0.677732 -1.9849 -2.29088 -2.68339 -2.9601 -3.66228 -6.23993 0.0326949 0.350836 0.34562 0.571642 0.354613 0.285527 -0.602072 1.49229\n",
      "  19 8.259e+01 3.325e+01 1.196e+00 -- 2.336e+02 -- -0.00670527 -0.666291 -1.97381 -2.2798 -2.67406 -2.95025 -3.6511 -5.93993 0.0306947 0.361894 0.361049 0.598897 0.369841 0.291591 -0.63793 -0.807155\n",
      "  21 5.466e+01 3.625e+01 1.126e+00 -- 2.347e+02 -- 0.00316205 -0.65626 -1.96403 -2.26977 -2.6653 -2.94143 -3.64118 -5.63993 0.0291917 0.371274 0.375204 0.622756 0.382913 0.296138 -0.668602 -0.4239\n",
      "  23 9.811e+00 3.926e+01 1.030e+00 -- 2.358e+02 -- 0.0118288 -0.647441 -1.95537 -2.26071 -2.65711 -2.93352 -3.63242 -5.33993 0.0280965 0.379279 0.388328 0.643807 0.394112 0.29949 -0.69488 1.89304\n",
      "  24 1.540e+02 1.799e+03 6.966e+00 -- 2.427e+02 -- 0.0881649 -0.569745 -1.87911 -2.17878 -2.58105 -2.86227 -3.55241 -6.66644 0.0206511 0.447973 0.510379 0.832388 0.489337 0.32364 -0.914155 2.17049\n",
      "  25 6.954e+03 5.225e+01 3.722e+00 -- 2.464e+02 -- 0.0820074 -0.577455 -1.8985 -2.17524 -2.54532 -2.85023 -3.57378 -8 0.0966827 0.412543 0.647274 0.885367 0.477195 0.280838 -0.833819 0.980251\n",
      "  26 6.093e+00 2.044e+01 2.548e-01 -- 2.467e+02 -- 0.0836413 -0.576897 -1.8864 -2.18141 -2.55069 -2.85268 -3.54966 -5 0.0710854 0.434328 0.591545 0.904499 0.417485 0.266475 -0.938934 1.34469\n",
      "  27 1.995e+00 5.009e+00 1.592e-01 -- 2.469e+02 -- 0.0831948 -0.576805 -1.88848 -2.17663 -2.54734 -2.85305 -3.5567 -4.22205 0.0761925 0.426631 0.625953 0.910649 0.424127 0.266244 -0.891405 -0.565813\n",
      "  28 1.248e+00 1.375e+01 4.599e-02 -- 2.469e+02 -- 0.0833105 -0.576655 -1.88396 -2.17799 -2.5475 -2.85314 -3.57374 -4.03476 0.0730522 0.429262 0.625062 0.905512 0.418149 0.264942 -0.967314 0.563133\n",
      "  29 2.171e+00 9.396e+00 3.382e-01 -- 2.472e+02 -- 0.0829995 -0.576375 -1.88776 -2.17599 -2.54493 -2.85329 -3.58608 -4.02931 0.0763743 0.428086 0.638377 0.920493 0.411594 0.274496 -0.815157 -0.139608\n",
      "  30 9.841e-01 1.149e+01 1.388e-01 -- 2.474e+02 -- 0.0832295 -0.57636 -1.88467 -2.17976 -2.54698 -2.8543 -3.58978 -3.89975 0.0738415 0.429686 0.629413 0.910992 0.412336 0.271501 -0.957932 0.163421\n",
      "  31 2.338e+01 3.752e+00 6.030e-02 -- 2.474e+02 -- 0.0829583 -0.576172 -1.88634 -2.17783 -2.54432 -2.85466 -3.60447 -3.874 0.0761271 0.428633 0.633503 0.910755 0.410289 0.277401 -0.858061 0.00260138\n",
      "  32 3.920e-01 3.182e+00 1.581e-02 -- 2.475e+02 -- 0.0830523 -0.576117 -1.88559 -2.18042 -2.54532 -2.85537 -3.60659 -3.85372 0.0749087 0.429912 0.628364 0.908088 0.410987 0.278401 -0.917876 0.0634286\n",
      "  33 3.181e-01 1.292e+00 4.081e-03 -- 2.475e+02 -- 0.0829733 -0.576083 -1.88614 -2.17987 -2.54423 -2.85554 -3.60968 -3.84594 0.0757541 0.429324 0.627903 0.906455 0.410964 0.280705 -0.882158 0.0385634\n",
      "  34 9.740e-02 4.768e-01 1.118e-03 -- 2.475e+02 -- 0.0830058 -0.576059 -1.88614 -2.18078 -2.54437 -2.85582 -3.61078 -3.84115 0.0755814 0.429797 0.625777 0.906066 0.411713 0.281667 -0.897167 0.0508303\n",
      "  35 6.139e-02 4.667e-01 3.378e-04 -- 2.475e+02 -- 0.0829936 -0.576051 -1.88632 -2.18077 -2.54398 -2.85585 -3.61168 -3.83901 0.0759106 0.429622 0.624827 0.905205 0.411841 0.282606 -0.888007 0.0458794\n",
      "  36 2.074e-02 6.100e-02 1.146e-04 -- 2.475e+02 -- 0.0830052 -0.576043 -1.8864 -2.18106 -2.54393 -2.85595 -3.61204 -3.83759 0.0759487 0.42976 0.623936 0.905027 0.412248 0.283153 -0.891435 0.0486959\n",
      "  37 1.454e-02 1.834e-01 4.331e-05 -- 2.475e+02 -- 0.0830055 -0.57604 -1.88647 -2.18112 -2.54378 -2.85597 -3.61236 -3.83689 0.0760799 0.429719 0.623352 0.904662 0.412343 0.283555 -0.889115 0.047686\n",
      "********************\n",
      "0.0830055 -0.57604 -1.88647 -2.18112 -2.54378 -2.85597 -3.61236 -3.83689 0.0760799 0.429719 0.623352 0.904662 0.412343 0.283555 -0.889115 0.047686\n",
      "0.00496524 0.0080639 0.0332817 0.053869 0.0497847 0.0510814 0.188809 0.22006 0.0806462 0.0938084 0.215752 0.243886 0.211727 0.201233 0.481882 0.380625\n",
      "0.183432 0.0383088 -0.0465819 -0.037514 0.0169821 -0.0145299 -0.00365015 0.00975964 0.0073746 0.00371696 -0.00866648 -0.0017458 0.00395749 0.0070437 -0.00320102 0.00463012\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "Cx = clag.clag('cxd10r', [[t1,t2]], [[l1,l2]], [[l1e,l2e]], dt, fqL, p1, p2)\n",
    "p  = np.concatenate( ((p1+p2)*0.5-0.3,p1*0+0.1) ) # a  good starting point generally\n",
    "p, pe = clag.optimize(Cx, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t### errors for param 0 ###\n",
      "+++ 2.475e+02 2.473e+02 8.301e-02 8.549e-02 0.306 +++\n",
      "+++ 2.475e+02 2.470e+02 8.301e-02 8.673e-02 0.912 +++\n",
      "+++ 2.475e+02 2.467e+02 8.301e-02 8.735e-02 1.46 +++\n",
      "+++ 2.475e+02 2.469e+02 8.301e-02 8.704e-02 1.16 +++\n",
      "+++ 2.475e+02 2.469e+02 8.301e-02 8.689e-02 1.03 +++\n",
      "+++ 2.475e+02 2.470e+02 8.301e-02 8.681e-02 0.969 +++\n",
      "+++ 2.475e+02 2.470e+02 8.301e-02 8.685e-02 0.999 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 2.475e+02 2.473e+02 -5.760e-01 -5.720e-01 0.399 +++\n",
      "+++ 2.475e+02 2.469e+02 -5.760e-01 -5.700e-01 1.13 +++\n",
      "+++ 2.475e+02 2.471e+02 -5.760e-01 -5.710e-01 0.698 +++\n",
      "+++ 2.475e+02 2.470e+02 -5.760e-01 -5.705e-01 0.897 +++\n",
      "+++ 2.475e+02 2.470e+02 -5.760e-01 -5.702e-01 1.01 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 2.475e+02 2.473e+02 -1.887e+00 -1.870e+00 0.291 +++\n",
      "+++ 2.475e+02 2.470e+02 -1.887e+00 -1.862e+00 0.915 +++\n",
      "+++ 2.475e+02 2.467e+02 -1.887e+00 -1.857e+00 1.53 +++\n",
      "+++ 2.475e+02 2.469e+02 -1.887e+00 -1.859e+00 1.18 +++\n",
      "+++ 2.475e+02 2.469e+02 -1.887e+00 -1.860e+00 1.04 +++\n",
      "+++ 2.475e+02 2.470e+02 -1.887e+00 -1.861e+00 0.971 +++\n",
      "+++ 2.475e+02 2.470e+02 -1.887e+00 -1.861e+00   1 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 2.475e+02 2.473e+02 -2.181e+00 -2.154e+00 0.29 +++\n",
      "+++ 2.475e+02 2.471e+02 -2.181e+00 -2.141e+00 0.808 +++\n",
      "+++ 2.475e+02 2.468e+02 -2.181e+00 -2.134e+00 1.25 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.181e+00 -2.137e+00 1.01 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.181e+00 -2.139e+00 0.905 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.181e+00 -2.138e+00 0.957 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.181e+00 -2.138e+00 0.984 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.181e+00 -2.138e+00 0.997 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 2.475e+02 2.470e+02 -2.544e+00 -2.494e+00 0.973 +++\n",
      "+++ 2.475e+02 2.459e+02 -2.544e+00 -2.469e+00 3.08 +++\n",
      "+++ 2.475e+02 2.466e+02 -2.544e+00 -2.482e+00 1.78 +++\n",
      "+++ 2.475e+02 2.468e+02 -2.544e+00 -2.488e+00 1.32 +++\n",
      "+++ 2.475e+02 2.469e+02 -2.544e+00 -2.491e+00 1.14 +++\n",
      "+++ 2.475e+02 2.469e+02 -2.544e+00 -2.492e+00 1.05 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.544e+00 -2.493e+00 1.01 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.544e+00 -2.494e+00 0.993 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 2.475e+02 2.473e+02 -2.856e+00 -2.830e+00 0.277 +++\n",
      "+++ 2.475e+02 2.471e+02 -2.856e+00 -2.818e+00 0.713 +++\n",
      "+++ 2.475e+02 2.469e+02 -2.856e+00 -2.811e+00 1.04 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.856e+00 -2.814e+00 0.867 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.856e+00 -2.813e+00 0.953 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.856e+00 -2.812e+00 0.993 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 2.475e+02 2.473e+02 -3.612e+00 -3.518e+00 0.392 +++\n",
      "+++ 2.475e+02 2.469e+02 -3.612e+00 -3.471e+00 1.06 +++\n",
      "+++ 2.475e+02 2.471e+02 -3.612e+00 -3.494e+00 0.67 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.612e+00 -3.483e+00 0.845 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.612e+00 -3.477e+00 0.947 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.612e+00 -3.474e+00   1 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 2.475e+02 2.474e+02 -3.836e+00 -3.727e+00 0.218 +++\n",
      "+++ 2.475e+02 2.471e+02 -3.836e+00 -3.672e+00 0.684 +++\n",
      "+++ 2.475e+02 2.469e+02 -3.836e+00 -3.644e+00 1.16 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.836e+00 -3.658e+00 0.894 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.836e+00 -3.651e+00 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.836e+00 -3.654e+00 0.955 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.836e+00 -3.653e+00 0.988 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.836e+00 -3.652e+00   1 +++\n",
      "\t### errors for param 8 ###\n",
      "+++ 2.475e+02 2.470e+02 7.613e-02 1.568e-01 0.869 +++\n",
      "+++ 2.475e+02 2.465e+02 7.613e-02 1.971e-01 1.9 +++\n",
      "+++ 2.475e+02 2.468e+02 7.613e-02 1.769e-01 1.35 +++\n",
      "+++ 2.475e+02 2.469e+02 7.613e-02 1.668e-01 1.1 +++\n",
      "+++ 2.475e+02 2.470e+02 7.613e-02 1.618e-01 0.98 +++\n",
      "+++ 2.475e+02 2.469e+02 7.613e-02 1.643e-01 1.04 +++\n",
      "+++ 2.475e+02 2.470e+02 7.613e-02 1.630e-01 1.01 +++\n",
      "\t### errors for param 9 ###\n",
      "+++ 2.475e+02 2.473e+02 4.298e-01 4.767e-01 0.29 +++\n",
      "+++ 2.475e+02 2.471e+02 4.298e-01 5.001e-01 0.641 +++\n",
      "+++ 2.475e+02 2.470e+02 4.298e-01 5.118e-01 0.863 +++\n",
      "+++ 2.475e+02 2.470e+02 4.298e-01 5.177e-01 0.985 +++\n",
      "+++ 2.475e+02 2.469e+02 4.298e-01 5.206e-01 1.05 +++\n",
      "+++ 2.475e+02 2.470e+02 4.298e-01 5.192e-01 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 4.298e-01 5.184e-01   1 +++\n",
      "\t### errors for param 10 ###\n",
      "+++ 2.475e+02 2.471e+02 6.230e-01 8.388e-01 0.802 +++\n",
      "+++ 2.475e+02 2.466e+02 6.230e-01 9.467e-01 1.77 +++\n",
      "+++ 2.475e+02 2.468e+02 6.230e-01 8.928e-01 1.25 +++\n",
      "+++ 2.475e+02 2.470e+02 6.230e-01 8.658e-01 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 6.230e-01 8.523e-01 0.906 +++\n",
      "+++ 2.475e+02 2.470e+02 6.230e-01 8.591e-01 0.96 +++\n",
      "+++ 2.475e+02 2.470e+02 6.230e-01 8.624e-01 0.988 +++\n",
      "+++ 2.475e+02 2.470e+02 6.230e-01 8.641e-01   1 +++\n",
      "\t### errors for param 11 ###\n",
      "+++ 2.475e+02 2.470e+02 9.046e-01 1.149e+00 0.88 +++\n",
      "+++ 2.475e+02 2.465e+02 9.046e-01 1.271e+00 1.88 +++\n",
      "+++ 2.475e+02 2.468e+02 9.046e-01 1.210e+00 1.35 +++\n",
      "+++ 2.475e+02 2.469e+02 9.046e-01 1.179e+00 1.1 +++\n",
      "+++ 2.475e+02 2.470e+02 9.046e-01 1.164e+00 0.989 +++\n",
      "+++ 2.475e+02 2.469e+02 9.046e-01 1.171e+00 1.05 +++\n",
      "+++ 2.475e+02 2.470e+02 9.046e-01 1.168e+00 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 9.046e-01 1.166e+00   1 +++\n",
      "\t### errors for param 12 ###\n",
      "+++ 2.475e+02 2.471e+02 4.125e-01 6.242e-01 0.737 +++\n",
      "+++ 2.475e+02 2.467e+02 4.125e-01 7.300e-01 1.59 +++\n",
      "+++ 2.475e+02 2.469e+02 4.125e-01 6.771e-01 1.13 +++\n",
      "+++ 2.475e+02 2.470e+02 4.125e-01 6.507e-01 0.924 +++\n",
      "+++ 2.475e+02 2.469e+02 4.125e-01 6.639e-01 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 4.125e-01 6.573e-01 0.974 +++\n",
      "+++ 2.475e+02 2.470e+02 4.125e-01 6.606e-01 0.999 +++\n",
      "\t### errors for param 13 ###\n",
      "+++ 2.475e+02 2.470e+02 2.838e-01 4.851e-01 0.854 +++\n",
      "+++ 2.475e+02 2.465e+02 2.838e-01 5.857e-01 1.84 +++\n",
      "+++ 2.475e+02 2.468e+02 2.838e-01 5.354e-01 1.31 +++\n",
      "+++ 2.475e+02 2.469e+02 2.838e-01 5.103e-01 1.07 +++\n",
      "+++ 2.475e+02 2.470e+02 2.838e-01 4.977e-01 0.96 +++\n",
      "+++ 2.475e+02 2.470e+02 2.838e-01 5.040e-01 1.01 +++\n",
      "+++ 2.475e+02 2.470e+02 2.838e-01 5.008e-01 0.987 +++\n",
      "+++ 2.475e+02 2.470e+02 2.838e-01 5.024e-01   1 +++\n",
      "\t### errors for param 14 ###\n",
      "+++ 2.475e+02 2.470e+02 -8.899e-01 -4.078e-01 0.957 +++\n",
      "+++ 2.475e+02 2.465e+02 -8.899e-01 -1.668e-01 1.91 +++\n",
      "+++ 2.475e+02 2.468e+02 -8.899e-01 -2.873e-01 1.41 +++\n",
      "+++ 2.475e+02 2.469e+02 -8.899e-01 -3.476e-01 1.18 +++\n",
      "+++ 2.475e+02 2.469e+02 -8.899e-01 -3.777e-01 1.07 +++\n",
      "+++ 2.475e+02 2.470e+02 -8.899e-01 -3.928e-01 1.01 +++\n",
      "+++ 2.475e+02 2.470e+02 -8.899e-01 -4.003e-01 0.984 +++\n",
      "+++ 2.475e+02 2.470e+02 -8.899e-01 -3.965e-01 0.998 +++\n",
      "\t### errors for param 15 ###\n",
      "+++ 2.475e+02 2.471e+02 4.838e-02 4.285e-01 0.661 +++\n",
      "+++ 2.475e+02 2.470e+02 4.838e-02 6.186e-01 0.961 +++\n",
      "+++ 2.475e+02 2.469e+02 4.838e-02 7.136e-01 1.07 +++\n",
      "+++ 2.475e+02 2.470e+02 4.838e-02 6.661e-01 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 4.838e-02 6.424e-01 0.991 +++\n",
      "********************\n",
      "0.0830101 -0.576037 -1.88651 -2.18123 -2.54372 -2.856 -3.61248 -3.8364 0.0761257 0.429759 0.622953 0.90456 0.412522 0.283831 -0.889856 0.0483793\n",
      "0.00383825 0.00579502 0.025767 0.0436043 0.050153 0.0439107 0.138708 0.1845 0.086924 0.0886716 0.241158 0.261188 0.248061 0.218563 0.493323 0.593987\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "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 0x7fa846cbd410>"
      ]
     },
     "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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.43381244,  0.4889989 ,  0.68917289,  0.48155621,  0.29506678,\n",
       "        0.16772896,  0.24424756,  0.18973346])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lage"
   ]
  }
 ],
 "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
}