phy-4660/lag/data/clag_analysis-origbins-noLFerrors-4775A.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 0x7f64bc2a5d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import sys\n",
    "import getopt\n",
    "sys.path.insert(1,\"/usr/local/science/clag/\")\n",
    "import clag\n",
    "%pylab inline\n",
    "\n",
    "from scipy.stats import norm\n",
    "from scipy.stats import lognorm\n",
    "from scipy.optimize import curve_fit\n",
    "import numpy.fft\n",
    "\n",
    "ref_file=\"lc/1367A.lc\"\n",
    "echo_file=\"lc/4775A.lc\"\n",
    "\n",
    "dt = 0.01\n",
    "t1, l1, l1e = np.loadtxt(ref_file).T\n",
    "errorbar(t1, l1, yerr=l1e, fmt='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.005     ,  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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OIZVKcf36darVKrlcjhs3bpDNZrtdlhQp9yRIkqQg9yRI0gnivkW11CsMCZJ0AkOAhpWH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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64cbfba0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xscale('log'); ylim(-4,2)\n",
    "errorbar(fqd, p1, yerr=p1e, fmt='o', ms=10, color=\"black\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WvXCs6FjO51hlOwUPOSB0R+Vh+Mp/g1DUXUTd/0isEl4+3xFYAZRvSdhJe2/U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\nIiIiIiIiIiKO+v8Bl+sGsAOf55gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64bbe07d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t2, l2, l2e = np.loadtxt(echo_file).T\n",
    "errorbar(t1, l1, yerr=l1e, fmt='o', color=\"green\")\n",
    "errorbar(t2, l2, yerr=l2e, fmt='o', color=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 4.348e-01 6.523e+01 inf -- -3.028e+02 -- 1 1 1 1 1 1 1 1\n",
      "   2 7.699e-01 6.427e+01 8.100e+01 -- -2.218e+02 -- 0.580653 0.565804 0.565201 0.565214 0.565618 0.565947 0.565564 0.568603\n",
      "   3 3.354e+00 6.351e+01 8.006e+01 -- -1.417e+02 -- 0.182695 0.132486 0.130058 0.130216 0.131744 0.132399 0.131339 0.138486\n",
      "   4 1.556e+00 6.272e+01 7.903e+01 -- -6.268e+01 -- -0.16907 -0.296462 -0.306097 -0.304877 -0.301753 -0.300835 -0.302915 -0.292641\n",
      "   5 5.969e-01 6.132e+01 7.726e+01 -- 1.458e+01 -- -0.432204 -0.709149 -0.744405 -0.7396 -0.734995 -0.733546 -0.736839 -0.725614\n",
      "   6 3.744e-01 5.885e+01 7.418e+01 -- 8.877e+01 -- -0.574318 -1.06985 -1.18591 -1.17296 -1.16819 -1.16502 -1.16974 -1.15872\n",
      "   7 2.704e-01 5.526e+01 6.977e+01 -- 1.585e+02 -- -0.632315 -1.30174 -1.62986 -1.60311 -1.60167 -1.59376 -1.60104 -1.58978\n",
      "   8 2.118e-01 5.083e+01 6.476e+01 -- 2.233e+02 -- -0.652594 -1.36692 -2.06821 -2.02717 -2.03482 -2.01697 -2.03052 -2.01815\n",
      "   9 1.754e-01 4.545e+01 5.869e+01 -- 2.820e+02 -- -0.636309 -1.38302 -2.45398 -2.43781 -2.46343 -2.42984 -2.45896 -2.44563\n",
      "  10 1.490e-01 3.871e+01 4.984e+01 -- 3.318e+02 -- -0.605808 -1.40775 -2.67391 -2.81453 -2.87421 -2.81781 -2.88685 -2.87465\n",
      "  11 1.236e-01 3.039e+01 3.772e+01 -- 3.696e+02 -- -0.57763 -1.42753 -2.69296 -3.11171 -3.23381 -3.14653 -3.31141 -3.30288\n",
      "  12 9.229e-02 2.069e+01 2.286e+01 -- 3.924e+02 -- -0.556681 -1.44088 -2.69001 -3.27817 -3.48572 -3.34948 -3.71554 -3.7112\n",
      "  13 5.640e-02 1.099e+01 9.621e+00 -- 4.020e+02 -- -0.542063 -1.44901 -2.6978 -3.33497 -3.57555 -3.3977 -4.04347 -4.0537\n",
      "  14 2.620e-02 4.040e+00 2.310e+00 -- 4.043e+02 -- -0.533703 -1.45096 -2.69258 -3.36521 -3.54261 -3.39935 -4.19655 -4.28234\n",
      "  15 9.422e-03 1.233e+00 3.575e-01 -- 4.047e+02 -- -0.531223 -1.44814 -2.69847 -3.37914 -3.48828 -3.41313 -4.19651 -4.39454\n",
      "  16 5.169e-03 4.540e-01 5.788e-02 -- 4.048e+02 -- -0.532521 -1.44581 -2.70748 -3.37398 -3.45542 -3.42428 -4.19558 -4.43406\n",
      "  17 2.718e-03 2.442e-01 1.172e-02 -- 4.048e+02 -- -0.533779 -1.44399 -2.71046 -3.36898 -3.43756 -3.43204 -4.19666 -4.4455\n",
      "  18 1.439e-03 1.307e-01 3.039e-03 -- 4.048e+02 -- -0.534412 -1.44278 -2.71234 -3.36531 -3.42821 -3.43643 -4.19795 -4.44912\n",
      "  19 7.592e-04 6.935e-02 8.433e-04 -- 4.048e+02 -- -0.534752 -1.44211 -2.71311 -3.36323 -3.42328 -3.43886 -4.19871 -4.45056\n",
      "  20 4.030e-04 3.692e-02 2.371e-04 -- 4.048e+02 -- -0.534926 -1.44173 -2.71356 -3.36205 -3.42068 -3.44015 -4.19914 -4.45124\n",
      "  21 2.137e-04 1.961e-02 6.686e-05 -- 4.048e+02 -- -0.53502 -1.44153 -2.71378 -3.36142 -3.4193 -3.44084 -4.19936 -4.45158\n",
      "********************\n",
      "-0.53502 -1.44153 -2.71378 -3.36142 -3.4193 -3.44084 -4.19936 -4.45158\n",
      "0.23132 0.206392 0.257293 0.247733 0.190033 0.14318 0.192361 0.189515\n",
      "-0.000780421 0.00249097 -0.00128998 0.00612834 0.0196141 -0.0166488 -0.00464909 -0.00601261\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",
      "+++ 4.048e+02 4.043e+02 -5.351e-01 -3.037e-01 0.864 +++\n",
      "+++ 4.048e+02 4.039e+02 -5.351e-01 -1.881e-01 1.82 +++\n",
      "+++ 4.048e+02 4.041e+02 -5.351e-01 -2.459e-01 1.3 +++\n",
      "+++ 4.048e+02 4.042e+02 -5.351e-01 -2.748e-01 1.07 +++\n",
      "+++ 4.048e+02 4.043e+02 -5.351e-01 -2.893e-01 0.967 +++\n",
      "+++ 4.048e+02 4.043e+02 -5.351e-01 -2.821e-01 1.02 +++\n",
      "+++ 4.048e+02 4.043e+02 -5.351e-01 -2.857e-01 0.993 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 4.048e+02 4.043e+02 -1.441e+00 -1.235e+00 0.928 +++\n",
      "+++ 4.048e+02 4.038e+02 -1.441e+00 -1.132e+00 1.97 +++\n",
      "+++ 4.048e+02 4.041e+02 -1.441e+00 -1.183e+00 1.41 +++\n",
      "+++ 4.048e+02 4.042e+02 -1.441e+00 -1.209e+00 1.16 +++\n",
      "+++ 4.048e+02 4.043e+02 -1.441e+00 -1.222e+00 1.04 +++\n",
      "+++ 4.048e+02 4.043e+02 -1.441e+00 -1.229e+00 0.983 +++\n",
      "+++ 4.048e+02 4.043e+02 -1.441e+00 -1.225e+00 1.01 +++\n",
      "+++ 4.048e+02 4.043e+02 -1.441e+00 -1.227e+00 0.997 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 4.048e+02 4.046e+02 -2.714e+00 -2.585e+00 0.296 +++\n",
      "+++ 4.048e+02 4.045e+02 -2.714e+00 -2.521e+00 0.647 +++\n",
      "+++ 4.048e+02 4.043e+02 -2.714e+00 -2.489e+00 0.868 +++\n",
      "+++ 4.048e+02 4.043e+02 -2.714e+00 -2.473e+00 0.989 +++\n",
      "+++ 4.048e+02 4.042e+02 -2.714e+00 -2.465e+00 1.05 +++\n",
      "+++ 4.048e+02 4.043e+02 -2.714e+00 -2.469e+00 1.02 +++\n",
      "+++ 4.048e+02 4.043e+02 -2.714e+00 -2.471e+00 1.01 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 4.048e+02 4.043e+02 -3.361e+00 -3.113e+00 0.873 +++\n",
      "+++ 4.048e+02 4.038e+02 -3.361e+00 -2.990e+00 1.95 +++\n",
      "+++ 4.048e+02 4.041e+02 -3.361e+00 -3.051e+00 1.36 +++\n",
      "+++ 4.048e+02 4.042e+02 -3.361e+00 -3.082e+00 1.1 +++\n",
      "+++ 4.048e+02 4.043e+02 -3.361e+00 -3.098e+00 0.985 +++\n",
      "+++ 4.048e+02 4.043e+02 -3.361e+00 -3.090e+00 1.04 +++\n",
      "+++ 4.048e+02 4.043e+02 -3.361e+00 -3.094e+00 1.01 +++\n",
      "+++ 4.048e+02 4.043e+02 -3.361e+00 -3.096e+00   1 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 4.048e+02 4.045e+02 -3.419e+00 -3.229e+00 0.635 +++\n",
      "+++ 4.048e+02 4.040e+02 -3.419e+00 -3.134e+00 1.47 +++\n",
      "+++ 4.048e+02 4.043e+02 -3.419e+00 -3.181e+00 1.01 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 4.048e+02 4.044e+02 -3.441e+00 -3.298e+00 0.824 +++\n",
      "+++ 4.048e+02 4.039e+02 -3.441e+00 -3.226e+00 1.85 +++\n",
      "+++ 4.048e+02 4.041e+02 -3.441e+00 -3.262e+00 1.29 +++\n",
      "+++ 4.048e+02 4.043e+02 -3.441e+00 -3.280e+00 1.04 +++\n",
      "+++ 4.048e+02 4.043e+02 -3.441e+00 -3.289e+00 0.93 +++\n",
      "+++ 4.048e+02 4.043e+02 -3.441e+00 -3.285e+00 0.985 +++\n",
      "+++ 4.048e+02 4.043e+02 -3.441e+00 -3.282e+00 1.01 +++\n",
      "+++ 4.048e+02 4.043e+02 -3.441e+00 -3.283e+00 0.999 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 4.048e+02 4.046e+02 -4.199e+00 -4.103e+00 0.303 +++\n",
      "+++ 4.048e+02 4.044e+02 -4.199e+00 -4.055e+00 0.684 +++\n",
      "+++ 4.048e+02 4.043e+02 -4.199e+00 -4.031e+00 0.933 +++\n",
      "+++ 4.048e+02 4.043e+02 -4.199e+00 -4.019e+00 0.966 +++\n",
      "+++ 4.048e+02 4.043e+02 -4.199e+00 -4.013e+00 1.03 +++\n",
      "+++ 4.048e+02 4.043e+02 -4.199e+00 -4.016e+00 0.999 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 4.048e+02 4.043e+02 -4.452e+00 -4.262e+00 0.879 +++\n",
      "+++ 4.048e+02 4.038e+02 -4.452e+00 -4.167e+00 2.03 +++\n",
      "+++ 4.048e+02 4.041e+02 -4.452e+00 -4.215e+00 1.39 +++\n",
      "+++ 4.048e+02 4.042e+02 -4.452e+00 -4.239e+00 1.12 +++\n",
      "+++ 4.048e+02 4.043e+02 -4.452e+00 -4.250e+00 0.996 +++\n",
      "********************\n",
      "-0.535069 -1.44142 -2.7139 -3.36108 -3.41857 -3.44121 -4.19949 -4.45176\n",
      "0.249396 0.214451 0.243242 0.265109 0.23744 0.157753 0.183354 0.201379\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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ESVJNpTYbAparsC83XA18A3gTuC3ktiRJUgWFHRJ+A/hWyG1IkqQQhBkSPgi8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64bb850750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xscale('log'); ylim(-6,2)\n",
    "errorbar(fqd, p1, yerr=p1e, fmt='o', ms=10, color=\"green\")\n",
    "errorbar(fqd, p2, yerr=p2e, fmt='o', ms=10, color=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 1.165e+02 1.119e+01 inf -- 4.615e+02 -- -0.417232 -1.10978 -2.25224 -2.74174 -3.07438 -3.26634 -4.18046 -6.52588 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
      "   3 7.488e+01 1.326e+01 2.469e+00 -- 4.640e+02 -- -0.379546 -1.07024 -2.2349 -2.70308 -3.05386 -3.26052 -4.19422 -6.22588 0.0594438 0.143922 0.211494 0.183563 0.124903 0.187977 0.0382351 -1.06488\n",
      "   5 3.958e+01 1.546e+01 2.253e+00 -- 4.663e+02 -- -0.347917 -1.03742 -2.21505 -2.66882 -3.03593 -3.25222 -4.20507 -5.92588 0.0292929 0.176748 0.302165 0.24639 0.145443 0.263839 -0.0201518 0.625425\n",
      "   7 2.952e+01 1.780e+01 1.997e+00 -- 4.683e+02 -- -0.321166 -1.00987 -2.19485 -2.63901 -3.02019 -3.2426 -4.21334 -5.62588 0.00616037 0.202118 0.375333 0.294616 0.162597 0.328567 -0.0750795 -1.84997\n",
      "   9 3.498e+01 2.030e+01 1.927e+00 -- 4.702e+02 -- -0.298358 -0.986546 -2.1755 -2.61326 -3.00636 -3.23255 -4.21943 -5.92588 -0.0120242 0.222305 0.43445 0.33256 0.177047 0.383459 -0.125124 0.240961\n",
      "  11 2.323e+01 2.295e+01 1.750e+00 -- 4.719e+02 -- -0.278794 -0.966621 -2.15757 -2.59104 -2.99413 -3.22256 -4.22358 -5.62588 -0.0266225 0.238634 0.482619 0.362829 0.189318 0.429938 -0.171714 -0.602008\n",
      "  13 9.908e+00 2.576e+01 1.642e+00 -- 4.736e+02 -- -0.261904 -0.949487 -2.14126 -2.57185 -2.98327 -3.21301 -4.22619 -5.32588 -0.0385241 0.252101 0.522228 0.387431 0.199792 0.469237 -0.213944 0.796178\n",
      "  15 5.329e+01 2.871e+01 1.637e+00 -- 4.752e+02 -- -0.247262 -0.934659 -2.12658 -2.55525 -2.97361 -3.20401 -4.22742 -5.02588 -0.0483821 0.263296 0.555178 0.407636 0.208732 0.502656 -0.25301 0.00733246\n",
      "  17 2.560e+00 3.181e+01 1.529e+00 -- 4.767e+02 -- -0.234505 -0.921766 -2.11346 -2.54086 -2.96485 -3.19577 -4.22761 -4.804 -0.056605 0.27274 0.582856 0.424353 0.216437 0.5308 -0.288552 0.0464091\n",
      "  19 1.172e+00 3.504e+01 1.400e+00 -- 4.781e+02 -- -0.223355 -0.910503 -2.10178 -2.52834 -2.9569 -3.18826 -4.2269 -4.68017 -0.0635434 0.28074 0.606367 0.438267 0.223049 0.554643 -0.321322 0.0582901\n",
      "  21 7.724e-01 3.838e+01 1.293e+00 -- 4.794e+02 -- -0.21358 -0.900628 -2.09139 -2.51744 -2.94968 -3.18148 -4.22551 -4.59311 -0.0694486 0.287569 0.626536 0.449937 0.228692 0.574921 -0.351228 0.0651221\n",
      "  23 6.475e-01 4.182e+01 1.196e+00 -- 4.806e+02 -- -0.204987 -0.891941 -2.08217 -2.5079 -2.94312 -3.17538 -4.22361 -4.5264 -0.0745103 0.293433 0.644002 0.459774 0.233469 0.59222 -0.378357 0.0697841\n",
      "  24 6.033e-01 4.904e+02 9.259e+00 -- 4.899e+02 -- -0.129265 -0.815314 -2.00033 -2.42441 -2.88331 -3.12077 -4.20108 -3.99042 -0.118156 0.344039 0.796617 0.542976 0.273449 0.74018 -0.62334 0.104006\n",
      "  25 1.633e+00 2.416e+01 2.127e+00 -- 4.920e+02 -- -0.136332 -0.820492 -2.00326 -2.42953 -2.86836 -3.13063 -4.19803 -4.0221 -0.107855 0.34592 0.942222 0.436475 0.11856 0.656515 -0.24726 0.0760793\n",
      "  26 2.297e-01 1.685e+01 3.223e-01 -- 4.923e+02 -- -0.136021 -0.81967 -2.01733 -2.4291 -2.86411 -3.12575 -4.28732 -4.0298 -0.106863 0.340823 0.802305 0.502205 0.115029 0.705918 -0.651119 0.0904933\n",
      "  27 2.114e-01 1.002e+01 6.323e-02 -- 4.924e+02 -- -0.136189 -0.820198 -2.00806 -2.43011 -2.86374 -3.13176 -4.15965 -4.03428 -0.10583 0.344597 0.86383 0.461694 0.102618 0.695825 -0.501555 0.0943452\n",
      "  28 7.282e-02 6.910e+00 4.695e-02 -- 4.925e+02 -- -0.136116 -0.819859 -2.01221 -2.42953 -2.86299 -3.12599 -4.24062 -4.03499 -0.106121 0.343076 0.828348 0.490418 0.10121 0.697346 -0.60756 0.0921811\n",
      "  29 6.355e-02 4.447e+00 1.251e-02 -- 4.925e+02 -- -0.136164 -0.820085 -2.00914 -2.42964 -2.86356 -3.12997 -4.19174 -4.03653 -0.106059 0.34408 0.850084 0.472121 0.0987163 0.695127 -0.563315 0.0960744\n",
      "  30 3.208e-02 2.630e+00 6.361e-03 -- 4.925e+02 -- -0.136149 -0.819937 -2.01076 -2.4294 -2.86326 -3.12717 -4.2269 -4.03604 -0.106248 0.343798 0.836239 0.482367 0.0971931 0.696096 -0.599112 0.0951975\n",
      "  31 2.383e-02 1.888e+00 2.157e-03 -- 4.925e+02 -- -0.136169 -0.820025 -2.00964 -2.42939 -2.86343 -3.12896 -4.20665 -4.03645 -0.106249 0.344186 0.84463 0.475064 0.0961482 0.695566 -0.579893 0.0964022\n",
      "  32 1.323e-02 1.058e+00 9.738e-04 -- 4.925e+02 -- -0.136163 -0.819962 -2.01029 -2.42931 -2.86326 -3.12779 -4.22134 -4.03608 -0.106327 0.344123 0.839179 0.479109 0.0951898 0.696016 -0.593714 0.0963018\n",
      "  33 9.243e-03 7.911e-01 3.707e-04 -- 4.925e+02 -- -0.136171 -0.819998 -2.00986 -2.4293 -2.86331 -3.12857 -4.21272 -4.03621 -0.106324 0.344261 0.842547 0.476174 0.0946849 0.695851 -0.58586 0.0965931\n",
      "  34 5.341e-03 4.358e-01 1.598e-04 -- 4.925e+02 -- -0.136168 -0.819972 -2.01013 -2.42927 -2.86323 -3.12808 -4.21887 -4.03601 -0.106356 0.344247 0.840355 0.477825 0.0941922 0.69603 -0.591275 0.0966528\n",
      "  35 3.654e-03 3.303e-01 6.407e-05 -- 4.925e+02 -- -0.136172 -0.819986 -2.00996 -2.42926 -2.86324 -3.12842 -4.2152 -4.03606 -0.106352 0.344296 0.84173 0.476631 0.0939684 0.695975 -0.588117 0.0967043\n",
      "********************\n",
      "-0.136172 -0.819986 -2.00996 -2.42926 -2.86324 -3.12842 -4.2152 -4.03606 -0.106352 0.344296 0.84173 0.476631 0.0939684 0.695975 -0.588117 0.0967043\n",
      "0.0115263 0.0058258 0.0387362 0.00846868 0.0539018 0.0780724 0.334539 0.108003 0.121876 0.0805411 0.223567 0.102776 0.222423 0.256036 0.800481 0.273811\n",
      "0.0127444 0.33028 -0.0607609 0.127486 0.0143628 0.0331918 -0.0227012 0.00709893 -0.000657376 -0.00220005 -0.0168474 0.0629554 -0.0038883 0.00145515 -0.00332533 0.000655605\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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phi, phie = p[nfq:], pe[nfq:]\n",
    "lag, lage = phi/(2*np.pi*fqd), phie/(2*np.pi*fqd)    \n",
    "cx, cxe   = p[:nfq], pe[:nfq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.75428512,  1.89869772,  2.40546134,  0.87877231,  0.11177487,\n",
       "        0.53410247, -0.29118093,  0.03088965])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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auh9AtxMk6Ck3b95s634A3U6QoKf09fW1dT+AbidI0FPGxsaK9tu7d2+TKwHoDoIEPeXo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64bb3b7550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "xscale('log'); ylim(-10,10)\n",
    "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n",
    "\n",
    "lag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f64bbde9d50>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PvIOoTuJ+Rv6pWLRoUazXU3UySEhSQE7MpBgsdhw/SED8mRSdnZ3U19eP8Wwf8HagFfjr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\nSZIkSZIkSZIkSZIkSZIkSZIC9f8BHaAKSNiHdccAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64bb1a8b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s, loc, scale = lognorm.fit(lag,loc=.008)\n",
    "\n",
    "xscale('log'); ylim(-10,15)\n",
    "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n",
    "plot(np.logspace(np.log(fqd[0]),np.log(fqd[-1])),lognorm.pdf(np.logspace(np.log(fqd[0]),np.log(fqd[-1])),s,loc,scale))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.47677903915219444, 1.214804919002201)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64b94d0850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loc, scale = norm.fit(lag,loc=.01)\n",
    "\n",
    "xscale('log'); ylim(-10,10)\n",
    "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n",
    "plot(np.logspace(np.log(fqd[3]),np.log(fqd[-1])),norm.pdf(np.logspace(np.log(fqd[0]),np.log(fqd[-1])),loc,scale))\n",
    "\n",
    "norm.fit(lag,loc=.01,scale=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f64bb3ec6d0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H9eqpBLaIiN/k58Muu8Bhh7mOxHteJxGDgQeA24AuwGzgLaBdJccfBeQDxwNZwHTg9ci5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64b95531d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(irfft(lag))"
   ]
  },
  {
   "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
}