phy-4660/lag/data/clag_analysis-origbins-6175A.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 0x7f60c13cfc10>"
      ]
     },
     "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",
    "\n",
    "ref_file=\"lc/1367A.lc\"\n",
    "echo_file=\"lc/6175A.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([ 0.00964867,  0.02886003,  0.0556922 ,  0.08632291,  0.13380051,\n",
       "        0.20739079,  0.32145572,  0.49825637])"
      ]
     },
     "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",
    "fqd\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 0x7f60e50f8b90>"
      ]
     },
     "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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jlekUPGSBwB2Vi5Gd/4agoKeA6v8RWyW8XL4jsAIo99KgD+3Xwi5gNlcUTIVR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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60c0f38e50>"
      ]
     },
     "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 4.356e-01 6.706e+01 inf -- -2.964e+02 -- 1 1 1 1 1 1 1 1\n",
      "   2 7.719e-01 6.609e+01 8.091e+01 -- -2.155e+02 -- 0.566297 0.565079 0.565579 0.565108 0.564503 0.565385 0.564367 0.564961\n",
      "   3 3.379e+00 6.484e+01 7.996e+01 -- -1.355e+02 -- 0.138579 0.130954 0.131695 0.130103 0.128857 0.130335 0.128712 0.130511\n",
      "   4 1.429e+00 6.354e+01 7.865e+01 -- -5.686e+01 -- -0.27205 -0.301257 -0.300578 -0.304918 -0.306545 -0.305024 -0.306246 -0.301896\n",
      "   5 5.891e-01 6.225e+01 7.700e+01 -- 2.014e+01 -- -0.639865 -0.728428 -0.729483 -0.7395 -0.741278 -0.740758 -0.740477 -0.732461\n",
      "   6 3.717e-01 6.064e+01 7.473e+01 -- 9.487e+01 -- -0.916158 -1.1392 -1.15122 -1.17266 -1.17468 -1.17713 -1.17495 -1.16261\n",
      "   7 2.723e-01 5.813e+01 7.142e+01 -- 1.663e+02 -- -1.05542 -1.49675 -1.55907 -1.60318 -1.60661 -1.61473 -1.61106 -1.59295\n",
      "   8 2.151e-01 5.427e+01 6.693e+01 -- 2.332e+02 -- -1.09159 -1.71699 -1.94737 -2.02949 -2.03872 -2.0544 -2.04928 -2.02094\n",
      "   9 1.766e-01 4.902e+01 6.164e+01 -- 2.949e+02 -- -1.0864 -1.76184 -2.31512 -2.44735 -2.4733 -2.49635 -2.4886 -2.44039\n",
      "  10 1.488e-01 4.321e+01 5.498e+01 -- 3.498e+02 -- -1.06321 -1.76835 -2.64945 -2.8386 -2.90987 -2.9371 -2.92734 -2.84392\n",
      "  11 1.266e-01 3.774e+01 4.629e+01 -- 3.961e+02 -- -1.05206 -1.78791 -2.90485 -3.15492 -3.34002 -3.36876 -3.363 -3.22984\n",
      "  12 1.020e-01 3.183e+01 3.581e+01 -- 4.319e+02 -- -1.04328 -1.80162 -3.05493 -3.33738 -3.73969 -3.77247 -3.78883 -3.60537\n",
      "  13 8.417e-02 2.370e+01 2.339e+01 -- 4.553e+02 -- -1.03318 -1.80414 -3.12825 -3.41545 -4.06016 -4.10442 -4.17529 -3.97261\n",
      "  14 5.584e-02 1.304e+01 1.058e+01 -- 4.659e+02 -- -1.02449 -1.80162 -3.1555 -3.47184 -4.25708 -4.30813 -4.44309 -4.307\n",
      "  15 2.209e-02 4.062e+00 2.538e+00 -- 4.684e+02 -- -1.02032 -1.79854 -3.15803 -3.51038 -4.34263 -4.3778 -4.52295 -4.54752\n",
      "  16 5.247e-03 5.023e-01 2.477e-01 -- 4.687e+02 -- -1.02062 -1.79556 -3.15642 -3.51858 -4.37903 -4.39008 -4.51121 -4.64796\n",
      "  17 2.880e-03 1.411e-01 1.010e-02 -- 4.687e+02 -- -1.02146 -1.79354 -3.15518 -3.51435 -4.40201 -4.39592 -4.50461 -4.66221\n",
      "  18 1.495e-03 7.076e-02 1.728e-03 -- 4.687e+02 -- -1.02146 -1.79301 -3.15424 -3.51272 -4.41468 -4.39804 -4.50083 -4.6632\n",
      "  19 7.709e-04 3.584e-02 4.325e-04 -- 4.687e+02 -- -1.02146 -1.79285 -3.15372 -3.51155 -4.42128 -4.39875 -4.49916 -4.66361\n",
      "  20 3.958e-04 1.822e-02 1.104e-04 -- 4.687e+02 -- -1.02145 -1.79279 -3.15346 -3.51104 -4.42469 -4.39897 -4.4983 -4.66381\n",
      "  21 2.024e-04 9.268e-03 2.840e-05 -- 4.687e+02 -- -1.02145 -1.79276 -3.15333 -3.51076 -4.42644 -4.39904 -4.4979 -4.6639\n",
      "********************\n",
      "-1.02145 -1.79276 -3.15333 -3.51076 -4.42644 -4.39904 -4.4979 -4.6639\n",
      "0.234388 0.20444 0.264527 0.210547 0.308149 0.209858 0.179189 0.173278\n",
      "1.75973e-05 0.000353327 0.000863132 0.0021508 -0.00926831 -0.00174289 0.00467078 -0.00131932\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",
      "+++ 4.687e+02 4.637e+02 -1.021e+00 -2.145e-02 9.96 +++\n",
      "+++ 4.687e+02 4.671e+02 -1.021e+00 -5.214e-01 3.22 +++\n",
      "+++ 4.687e+02 4.682e+02 -1.021e+00 -7.714e-01 0.935 +++\n",
      "+++ 4.687e+02 4.677e+02 -1.021e+00 -6.464e-01 1.95 +++\n",
      "+++ 4.687e+02 4.680e+02 -1.021e+00 -7.089e-01 1.41 +++\n",
      "+++ 4.687e+02 4.681e+02 -1.021e+00 -7.402e-01 1.16 +++\n",
      "+++ 4.687e+02 4.682e+02 -1.021e+00 -7.558e-01 1.05 +++\n",
      "+++ 4.687e+02 4.682e+02 -1.021e+00 -7.636e-01 0.99 +++\n",
      "+++ 4.687e+02 4.682e+02 -1.021e+00 -7.597e-01 1.02 +++\n",
      "+++ 4.687e+02 4.682e+02 -1.021e+00 -7.617e-01   1 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 4.687e+02 4.683e+02 -1.793e+00 -1.589e+00 0.875 +++\n",
      "+++ 4.687e+02 4.678e+02 -1.793e+00 -1.487e+00 1.84 +++\n",
      "+++ 4.687e+02 4.680e+02 -1.793e+00 -1.538e+00 1.33 +++\n",
      "+++ 4.687e+02 4.681e+02 -1.793e+00 -1.563e+00 1.09 +++\n",
      "+++ 4.687e+02 4.682e+02 -1.793e+00 -1.576e+00 0.981 +++\n",
      "+++ 4.687e+02 4.682e+02 -1.793e+00 -1.569e+00 1.04 +++\n",
      "+++ 4.687e+02 4.682e+02 -1.793e+00 -1.573e+00 1.01 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 4.687e+02 4.683e+02 -3.153e+00 -2.889e+00 0.804 +++\n",
      "+++ 4.687e+02 4.678e+02 -3.153e+00 -2.756e+00 1.76 +++\n",
      "+++ 4.687e+02 4.681e+02 -3.153e+00 -2.823e+00 1.24 +++\n",
      "+++ 4.687e+02 4.682e+02 -3.153e+00 -2.856e+00 1.01 +++\n",
      "+++ 4.687e+02 4.682e+02 -3.153e+00 -2.872e+00 0.904 +++\n",
      "+++ 4.687e+02 4.682e+02 -3.153e+00 -2.864e+00 0.957 +++\n",
      "+++ 4.687e+02 4.682e+02 -3.153e+00 -2.860e+00 0.983 +++\n",
      "+++ 4.687e+02 4.682e+02 -3.153e+00 -2.858e+00 0.997 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 4.687e+02 4.686e+02 -3.511e+00 -3.405e+00 0.282 +++\n",
      "+++ 4.687e+02 4.684e+02 -3.511e+00 -3.353e+00 0.62 +++\n",
      "+++ 4.687e+02 4.683e+02 -3.511e+00 -3.326e+00 0.835 +++\n",
      "+++ 4.687e+02 4.682e+02 -3.511e+00 -3.313e+00 0.953 +++\n",
      "+++ 4.687e+02 4.682e+02 -3.511e+00 -3.307e+00 1.01 +++\n",
      "+++ 4.687e+02 4.682e+02 -3.511e+00 -3.310e+00 0.984 +++\n",
      "+++ 4.687e+02 4.682e+02 -3.511e+00 -3.308e+00 0.999 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 4.687e+02 4.683e+02 -4.427e+00 -4.119e+00 0.792 +++\n",
      "+++ 4.687e+02 4.678e+02 -4.427e+00 -3.965e+00 1.84 +++\n",
      "+++ 4.687e+02 4.680e+02 -4.427e+00 -4.042e+00 1.31 +++\n",
      "+++ 4.687e+02 4.682e+02 -4.427e+00 -4.080e+00 1.03 +++\n",
      "+++ 4.687e+02 4.682e+02 -4.427e+00 -4.100e+00 0.907 +++\n",
      "+++ 4.687e+02 4.682e+02 -4.427e+00 -4.090e+00 0.968 +++\n",
      "+++ 4.687e+02 4.682e+02 -4.427e+00 -4.085e+00   1 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 4.687e+02 4.682e+02 -4.399e+00 -4.189e+00 0.899 +++\n",
      "+++ 4.687e+02 4.677e+02 -4.399e+00 -4.084e+00 1.98 +++\n",
      "+++ 4.687e+02 4.680e+02 -4.399e+00 -4.137e+00 1.33 +++\n",
      "+++ 4.687e+02 4.682e+02 -4.399e+00 -4.163e+00 1.06 +++\n",
      "+++ 4.687e+02 4.682e+02 -4.399e+00 -4.176e+00 0.941 +++\n",
      "+++ 4.687e+02 4.682e+02 -4.399e+00 -4.170e+00   1 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 4.687e+02 4.682e+02 -4.498e+00 -4.319e+00 1.01 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 4.687e+02 4.686e+02 -4.664e+00 -4.577e+00 0.267 +++\n",
      "+++ 4.687e+02 4.684e+02 -4.664e+00 -4.534e+00 0.602 +++\n",
      "+++ 4.687e+02 4.683e+02 -4.664e+00 -4.512e+00 0.82 +++\n",
      "+++ 4.687e+02 4.682e+02 -4.664e+00 -4.501e+00 0.942 +++\n",
      "+++ 4.687e+02 4.682e+02 -4.664e+00 -4.496e+00 1.01 +++\n",
      "********************\n",
      "-1.02145 -1.79274 -3.15326 -3.51062 -4.42734 -4.39906 -4.49769 -4.66395\n",
      "0.259766 0.220088 0.295514 0.2023 0.342171 0.229522 0.179146 0.167871\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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NayxEPp+nr69vSf1C8crJ3r17+c53vsOJEyd4/fXXmZqa4vXXX+fEiRN85zvf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\nvogAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60c06ee2d0>"
      ]
     },
     "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 1.071e+02 1.046e+01 inf -- 5.152e+02 -- -0.660422 -1.28544 -2.47192 -2.81652 -3.57876 -3.74527 -4.32957 -6.63198 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
      "   3 2.672e+01 1.099e+01 2.000e+00 -- 5.172e+02 -- -0.619685 -1.25308 -2.4665 -2.85762 -3.55873 -3.77022 -4.28599 -6.33198 0.133144 0.179124 0.1802 0.166312 0.0659412 0.140772 0.112456 -0.971453\n",
      "   5 9.529e+00 1.283e+01 1.721e+00 -- 5.189e+02 -- -0.585859 -1.22408 -2.4589 -2.89696 -3.54076 -3.79301 -4.25035 -6.03198 0.157464 0.240277 0.249232 0.240742 0.0381923 0.179734 0.122603 1.62466\n",
      "   7 2.140e+01 1.475e+01 1.536e+00 -- 5.204e+02 -- -0.557456 -1.19853 -2.45014 -2.93384 -3.52464 -3.81372 -4.22037 -6.12619 0.175865 0.28851 0.308387 0.323331 0.0151409 0.217034 0.131397 0.0766008\n",
      "   9 4.054e+01 1.673e+01 1.375e+00 -- 5.218e+02 -- -0.53338 -1.17617 -2.44091 -2.96736 -3.51007 -3.8326 -4.19508 -5.82619 0.19012 0.327294 0.359004 0.414056 -0.00394175 0.251863 0.139262 -0.0873399\n",
      "  11 1.539e+02 1.878e+01 1.257e+00 -- 5.231e+02 -- -0.512807 -1.15664 -2.43168 -2.9966 -3.49691 -3.84978 -4.17339 -5.52619 0.201361 0.359027 0.402429 0.511897 -0.0199226 0.284261 0.146507 0.000430987\n",
      "  13 6.674e+00 2.124e+01 1.152e+00 -- 5.242e+02 -- -0.495111 -1.13956 -2.42273 -3.02071 -3.48496 -3.86545 -4.15466 -5.35324 0.210341 0.385373 0.439852 0.614993 -0.0333288 0.314186 0.153466 0.00706348\n",
      "  15 3.252e+00 2.410e+01 1.060e+00 -- 5.253e+02 -- -0.479803 -1.1246 -2.41423 -3.03903 -3.47409 -3.87976 -4.13835 -5.24709 0.217599 0.407526 0.472271 0.72086 -0.0446408 0.341603 0.160157 0.0117776\n",
      "  17 2.016e+00 2.719e+01 9.825e-01 -- 5.263e+02 -- -0.466499 -1.11148 -2.40628 -3.05126 -3.46421 -3.89282 -4.124 -5.17073 0.223523 0.426356 0.500528 0.826462 -0.0542649 0.366566 0.166562 0.0156079\n",
      "  19 1.362e+00 3.054e+01 9.145e-01 -- 5.272e+02 -- -0.454889 -1.09993 -2.3989 -3.05757 -3.45521 -3.90473 -4.1113 -5.1118 0.228399 0.442508 0.525313 0.928622 -0.0625105 0.389157 0.172682 0.0187539\n",
      "  21 9.515e-01 3.414e+01 8.538e-01 -- 5.280e+02 -- -0.444723 -1.08976 -2.3921 -3.05859 -3.44701 -3.9156 -4.09998 -5.06443 0.232442 0.456468 0.547196 1.02454 -0.0696201 0.409485 0.178522 0.0213081\n",
      "  23 7.093e-01 3.801e+01 7.988e-01 -- 5.288e+02 -- -0.435796 -1.08077 -2.38588 -3.05525 -3.43953 -3.92551 -4.08984 -5.0253 0.235816 0.468611 0.566644 1.11219 -0.0757858 0.427675 0.18409 0.0233356\n",
      "  25 5.803e-01 4.215e+01 7.484e-01 -- 5.296e+02 -- -0.427935 -1.07281 -2.38021 -3.04863 -3.43269 -3.93455 -4.0807 -4.99237 0.23865 0.479229 0.584038 1.19049 -0.0811613 0.443868 0.189393 0.0248916\n",
      "  27 4.826e-01 4.655e+01 7.016e-01 -- 5.303e+02 -- -0.421 -1.06576 -2.37506 -3.03976 -3.42645 -3.94279 -4.07243 -4.96428 0.241046 0.488555 0.599693 1.25918 -0.085871 0.458207 0.194441 0.0260258\n",
      "  29 4.068e-01 5.122e+01 6.579e-01 -- 5.309e+02 -- -0.414869 -1.0595 -2.3704 -3.02952 -3.42074 -3.95028 -4.06492 -4.94006 0.243083 0.496775 0.613868 1.31864 -0.0900155 0.470841 0.199241 0.0267841\n",
      "  30 3.534e+01 8.279e+04 1.751e+01 -- 5.134e+02 -- -0.360586 -1.00374 -2.32839 -2.92007 -3.3685 -4.01834 -3.99643 -4.7296 0.260518 0.56944 0.742943 1.82845 -0.126634 0.581593 0.244852 0.0310258\n",
      "  31 2.628e+00 5.230e+02 1.385e+01 -- 5.273e+02 -- -0.367347 -0.995704 -2.41817 -2.93715 -3.22222 -3.67509 -3.96857 -4.92098 0.252274 0.493499 0.92368 1.27613 -0.141219 0.150824 0.421865 -1.06553\n",
      "  33 1.245e+00 1.834e+02 5.511e+00 -- 5.328e+02 -- -0.366953 -0.996565 -2.41698 -2.92546 -3.2361 -3.70408 -3.95122 -4.90366 0.254103 0.498794 0.945127 1.30107 -0.17833 0.138325 0.416295 -0.885392\n",
      "  34 2.632e+00 2.549e+02 4.231e+00 -- 5.370e+02 -- -0.363913 -1.00399 -2.39017 -2.80507 -3.34439 -3.93632 -3.87676 -4.84813 0.268355 0.551262 1.00931 1.53983 -0.400317 0.167888 0.35166 0.103037\n",
      "  35 2.312e-01 2.769e+01 1.185e+00 -- 5.382e+02 -- -0.36551 -1.00259 -2.35867 -2.82081 -3.32433 -3.99735 -3.92146 -5.15833 0.258537 0.557539 0.798409 1.50393 -0.115974 0.467843 0.342653 -0.168121\n",
      "  36 1.282e-01 1.344e+01 1.168e-01 -- 5.383e+02 -- -0.365155 -1.00375 -2.35465 -2.81558 -3.34281 -3.98947 -3.93824 -5.07587 0.261859 0.557604 0.867168 1.51669 -0.142791 0.48333 0.289755 -0.174609\n",
      "  37 2.322e-01 2.131e+00 2.015e-02 -- 5.383e+02 -- -0.365299 -1.00349 -2.35109 -2.82381 -3.34204 -3.98257 -3.95106 -5.06742 0.260301 0.559365 0.842097 1.51206 -0.130381 0.490731 0.287031 -0.152216\n",
      "  38 6.590e-02 2.211e+00 4.939e-03 -- 5.383e+02 -- -0.365272 -1.00361 -2.3515 -2.82539 -3.34697 -3.98003 -3.95473 -5.01474 0.260526 0.558922 0.844539 1.51206 -0.138355 0.502691 0.282545 -0.116878\n",
      "  39 5.891e-02 3.773e-01 1.420e-03 -- 5.383e+02 -- -0.365297 -1.00355 -2.3514 -2.82741 -3.34661 -3.9783 -3.95801 -4.99087 0.260204 0.559082 0.839989 1.51118 -0.135923 0.505973 0.282768 -0.109176\n",
      "  40 2.572e-02 4.637e-01 4.968e-04 -- 5.383e+02 -- -0.365292 -1.00357 -2.35165 -2.82796 -3.34757 -3.97813 -3.95907 -4.96797 0.260227 0.558996 0.840181 1.51095 -0.137625 0.508445 0.282456 -0.102744\n",
      "  41 1.649e-02 5.031e-02 1.977e-04 -- 5.383e+02 -- -0.365297 -1.00356 -2.35169 -2.8285 -3.34746 -3.97798 -3.95998 -4.95472 0.260155 0.559001 0.83919 1.51074 -0.137022 0.509266 0.282795 -0.100101\n",
      "  42 9.246e-03 1.178e-01 8.567e-05 -- 5.383e+02 -- -0.365297 -1.00356 -2.35177 -2.82872 -3.34765 -3.9781 -3.96035 -4.94463 0.260153 0.558976 0.839165 1.51071 -0.137277 0.509832 0.282928 -0.0984504\n",
      "********************\n",
      "-0.365297 -1.00356 -2.35177 -2.82872 -3.34765 -3.9781 -3.96035 -4.94463 0.260153 0.558976 0.839165 1.51071 -0.137277 0.509832 0.282928 -0.0984504\n",
      "0.0033599 0.00994879 0.12815 0.199699 0.0939759 0.373185 0.0923458 1.11837 0.0669538 0.104719 0.458098 0.560874 0.328106 0.903943 0.28723 1.91846\n",
      "-0.117785 0.0115573 -0.00195453 -0.00472824 0.000416438 -0.000751097 -0.0358018 0.00519553 -0.00437069 -0.000802906 -0.00125322 -8.26251e-05 0.00185064 0.000334232 0.00218703 -0.00017406\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "Cx = clag.clag('cxd10r', [[t1,t2]], [[l1,l2]], [[l1e,l2e]], dt, fqL, p1, p2)\n",
    "p  = np.concatenate( ((p1+p2)*0.5-0.3,p1*0+0.1) ) # a  good starting point generally\n",
    "p, pe = clag.optimize(Cx, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:root:Line magic function `%autoreload` not found.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t### errors for param 0 ###\n",
      "+++ 5.384e+02 5.381e+02 -3.653e-01 -3.636e-01 0.473 +++\n",
      "+++ 5.384e+02 5.376e+02 -3.653e-01 -3.628e-01 1.49 +++\n",
      "+++ 5.384e+02 5.379e+02 -3.653e-01 -3.632e-01 0.867 +++\n",
      "+++ 5.384e+02 5.378e+02 -3.653e-01 -3.630e-01 1.14 +++\n",
      "+++ 5.384e+02 5.379e+02 -3.653e-01 -3.631e-01 0.998 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 5.384e+02 5.382e+02 -1.004e+00 -9.986e-01 0.343 +++\n",
      "+++ 5.384e+02 5.379e+02 -1.004e+00 -9.961e-01 0.962 +++\n",
      "+++ 5.384e+02 5.376e+02 -1.004e+00 -9.949e-01 1.48 +++\n",
      "+++ 5.384e+02 5.378e+02 -1.004e+00 -9.955e-01 1.2 +++\n",
      "+++ 5.384e+02 5.378e+02 -1.004e+00 -9.958e-01 1.08 +++\n",
      "+++ 5.384e+02 5.378e+02 -1.004e+00 -9.959e-01 1.02 +++\n",
      "+++ 5.384e+02 5.379e+02 -1.004e+00 -9.960e-01 0.989 +++\n",
      "+++ 5.384e+02 5.378e+02 -1.004e+00 -9.960e-01   1 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 5.384e+02 5.382e+02 -2.352e+00 -2.288e+00 0.363 +++\n",
      "+++ 5.384e+02 5.378e+02 -2.352e+00 -2.256e+00 1.15 +++\n",
      "+++ 5.384e+02 5.380e+02 -2.352e+00 -2.272e+00 0.665 +++\n",
      "+++ 5.384e+02 5.379e+02 -2.352e+00 -2.264e+00 0.878 +++\n",
      "+++ 5.384e+02 5.378e+02 -2.352e+00 -2.260e+00 1.01 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 5.384e+02 5.382e+02 -2.829e+00 -2.729e+00 0.379 +++\n",
      "+++ 5.384e+02 5.378e+02 -2.829e+00 -2.679e+00 1.17 +++\n",
      "+++ 5.384e+02 5.380e+02 -2.829e+00 -2.704e+00 0.689 +++\n",
      "+++ 5.384e+02 5.379e+02 -2.829e+00 -2.692e+00 0.902 +++\n",
      "+++ 5.384e+02 5.378e+02 -2.829e+00 -2.685e+00 1.03 +++\n",
      "+++ 5.384e+02 5.379e+02 -2.829e+00 -2.688e+00 0.964 +++\n",
      "+++ 5.384e+02 5.379e+02 -2.829e+00 -2.687e+00 0.996 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 5.384e+02 5.382e+02 -3.348e+00 -3.301e+00 0.337 +++\n",
      "+++ 5.384e+02 5.379e+02 -3.348e+00 -3.277e+00 0.983 +++\n",
      "+++ 5.384e+02 5.376e+02 -3.348e+00 -3.265e+00 1.56 +++\n",
      "+++ 5.384e+02 5.377e+02 -3.348e+00 -3.271e+00 1.24 +++\n",
      "+++ 5.384e+02 5.378e+02 -3.348e+00 -3.274e+00 1.11 +++\n",
      "+++ 5.384e+02 5.378e+02 -3.348e+00 -3.276e+00 1.04 +++\n",
      "+++ 5.384e+02 5.378e+02 -3.348e+00 -3.276e+00 1.01 +++\n",
      "+++ 5.384e+02 5.379e+02 -3.348e+00 -3.277e+00 0.997 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 5.384e+02 5.381e+02 -3.978e+00 -3.792e+00 0.447 +++\n",
      "+++ 5.384e+02 5.376e+02 -3.978e+00 -3.698e+00 1.41 +++\n",
      "+++ 5.384e+02 5.379e+02 -3.978e+00 -3.745e+00 0.822 +++\n",
      "+++ 5.384e+02 5.378e+02 -3.978e+00 -3.722e+00 1.08 +++\n",
      "+++ 5.384e+02 5.379e+02 -3.978e+00 -3.733e+00 0.946 +++\n",
      "+++ 5.384e+02 5.378e+02 -3.978e+00 -3.727e+00 1.01 +++\n",
      "+++ 5.384e+02 5.379e+02 -3.978e+00 -3.730e+00 0.979 +++\n",
      "+++ 5.384e+02 5.379e+02 -3.978e+00 -3.729e+00 0.996 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 5.384e+02 5.382e+02 -3.961e+00 -3.914e+00 0.206 +++\n",
      "+++ 5.384e+02 5.381e+02 -3.961e+00 -3.891e+00 0.527 +++\n",
      "+++ 5.384e+02 5.380e+02 -3.961e+00 -3.880e+00 0.771 +++\n",
      "+++ 5.384e+02 5.379e+02 -3.961e+00 -3.874e+00 0.919 +++\n",
      "+++ 5.384e+02 5.378e+02 -3.961e+00 -3.871e+00   1 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 5.384e+02 5.382e+02 -4.938e+00 -4.387e+00 0.366 +++\n",
      "+++ 5.384e+02 5.370e+02 -4.938e+00 -4.112e+00 2.72 +++\n",
      "+++ 5.384e+02 5.379e+02 -4.938e+00 -4.250e+00 0.924 +++\n",
      "+++ 5.384e+02 5.376e+02 -4.938e+00 -4.181e+00 1.5 +++\n",
      "+++ 5.384e+02 5.378e+02 -4.938e+00 -4.215e+00 1.17 +++\n",
      "+++ 5.384e+02 5.378e+02 -4.938e+00 -4.232e+00 1.04 +++\n",
      "+++ 5.384e+02 5.379e+02 -4.938e+00 -4.241e+00 0.98 +++\n",
      "+++ 5.384e+02 5.378e+02 -4.938e+00 -4.237e+00 1.01 +++\n",
      "\t### errors for param 8 ###\n",
      "+++ 5.384e+02 5.379e+02 2.601e-01 3.271e-01 0.994 +++\n",
      "\t### errors for param 9 ###\n",
      "+++ 5.384e+02 5.382e+02 5.590e-01 6.113e-01 0.275 +++\n",
      "+++ 5.384e+02 5.380e+02 5.590e-01 6.375e-01 0.603 +++\n",
      "+++ 5.384e+02 5.379e+02 5.590e-01 6.506e-01 0.808 +++\n",
      "+++ 5.384e+02 5.379e+02 5.590e-01 6.571e-01 0.921 +++\n",
      "+++ 5.384e+02 5.379e+02 5.590e-01 6.604e-01 0.979 +++\n",
      "+++ 5.384e+02 5.378e+02 5.590e-01 6.620e-01 1.01 +++\n",
      "\t### errors for param 10 ###\n",
      "+++ 5.384e+02 5.379e+02 8.389e-01 1.297e+00 0.964 +++\n",
      "+++ 5.384e+02 5.374e+02 8.389e-01 1.526e+00 1.86 +++\n",
      "+++ 5.384e+02 5.376e+02 8.389e-01 1.412e+00 1.4 +++\n",
      "+++ 5.384e+02 5.378e+02 8.389e-01 1.354e+00 1.18 +++\n",
      "+++ 5.384e+02 5.378e+02 8.389e-01 1.326e+00 1.07 +++\n",
      "+++ 5.384e+02 5.378e+02 8.389e-01 1.311e+00 1.02 +++\n",
      "+++ 5.384e+02 5.379e+02 8.389e-01 1.304e+00 0.991 +++\n",
      "\t### errors for param 11 ###\n",
      "+++ 5.384e+02 5.380e+02 1.511e+00 2.072e+00 0.717 +++\n",
      "+++ 5.384e+02 5.377e+02 1.511e+00 2.352e+00 1.37 +++\n",
      "+++ 5.384e+02 5.378e+02 1.511e+00 2.212e+00 1.04 +++\n",
      "+++ 5.384e+02 5.379e+02 1.511e+00 2.142e+00 0.876 +++\n",
      "+++ 5.384e+02 5.379e+02 1.511e+00 2.177e+00 0.957 +++\n",
      "+++ 5.384e+02 5.379e+02 1.511e+00 2.195e+00 0.999 +++\n",
      "\t### errors for param 12 ###\n",
      "+++ 5.384e+02 5.380e+02 -1.371e-01 1.910e-01 0.629 +++\n",
      "+++ 5.384e+02 5.377e+02 -1.371e-01 3.551e-01 1.31 +++\n",
      "+++ 5.384e+02 5.379e+02 -1.371e-01 2.730e-01 0.947 +++\n",
      "+++ 5.384e+02 5.378e+02 -1.371e-01 3.141e-01 1.13 +++\n",
      "+++ 5.384e+02 5.378e+02 -1.371e-01 2.935e-01 1.03 +++\n",
      "+++ 5.384e+02 5.379e+02 -1.371e-01 2.833e-01 0.991 +++\n",
      "\t### errors for param 13 ###\n",
      "+++ 5.384e+02 5.380e+02 5.100e-01 1.414e+00 0.77 +++\n",
      "+++ 5.384e+02 5.378e+02 5.100e-01 1.866e+00 1.16 +++\n",
      "+++ 5.384e+02 5.378e+02 5.100e-01 1.640e+00   1 +++\n",
      "\t### errors for param 14 ###\n",
      "+++ 5.384e+02 5.379e+02 2.831e-01 5.706e-01 0.999 +++\n",
      "\t### errors for param 15 ###\n",
      "********************\n",
      "-0.365298 -1.00356 -2.3518 -2.8289 -3.34762 -3.97818 -3.96065 -4.9382 0.260133 0.55897 0.838914 1.51069 -0.137073 0.510048 0.283115 -0.0975401\n",
      "0.00220546 0.00757768 0.0921231 0.142074 0.0708427 0.249334 0.0895957 0.701524 0.0669616 0.10308 0.465309 0.683925 0.420368 1.13017 0.287504 9.44994\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "%autoreload\n",
    "p, pe = clag.errors(Cx, p, pe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phi, phie = p[nfq:], pe[nfq:]\n",
    "lag, lage = phi/(2*np.pi*fqd), phie/(2*np.pi*fqd)    \n",
    "cx, cxe   = p[:nfq], pe[:nfq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.29089818,  3.08255955,  2.39741626,  2.78529154, -0.1630473 ,\n",
       "        0.39141847,  0.14017202, -0.03115664])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60c090e390>"
      ]
     },
     "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": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f60c02c2a90>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hL5JTTgmdo/32t6FOiVTNkkxGci0C3uyifUtSxaqpgdNPD721HnccvPkmXHst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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60c04bb4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "\n",
    "# Define model function to be used to fit to the data above:\n",
    "def tophat_time(x, *p):\n",
    "    mean, width = p\n",
    "    if x>(mean+width): y=0\n",
    "    if x<(mean-width): y=0\n",
    "    if x==(mean+width) | x==(mean-width): y=5\n",
    "    return y\n",
    "\n",
    "def tophat_freq(f, *pars):\n",
    "    A,T,t0 = pars\n",
    "    #return A*T*sinc(pi*f*T)*exp(-i*2*pi*f*t0)\n",
    "    return A*T*sinc(pi*f*T)*cos(2*pi*f*t0)\n",
    "\n",
    "x=np.logspace(fqd[0],fqd[-1],200)\n",
    "\n",
    "# p0 is the initial guess for the fitting coefficients\n",
    "p0 = [3, 3, 3]\n",
    "coeff, var_matrix = curve_fit(tophat_freq, fqd, lag, p0)\n",
    "fit = tophat_freq(fqd, *coeff)\n",
    "\n",
    "\n",
    "mpl.rcParams['xtick.labelsize']=12\n",
    "mpl.rcParams['ytick.labelsize']=12\n",
    "xscale('log'); xlim(.009,.6)\n",
    "xlabel(\"Fourier Frequency (days$^{-1}$)\",fontsize=20)\n",
    "ylabel(\"Time Lag (days)\",fontsize=20)\n",
    "\n",
    "\n",
    "errorbar(fqd, lag, yerr=lage, fmt='o', ms=5,color=\"black\")\n",
    "plot(fqd,fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f60c07f9cd0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GjLHkZtQoqFbNanJERERygdvE5j/Y7MG9seao4PKL9wEXA/sDfwP+6hwPjpj6DbjdZdni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\nvTxuKkoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60c07f9e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_fit = irfft(fit)\n",
    "\n",
    "mpl.rcParams['xtick.labelsize']=12\n",
    "mpl.rcParams['ytick.labelsize']=12\n",
    "ylabel(\"Response (relative)\",fontsize=20)\n",
    "xlabel(\"Time (days)\",fontsize=20) \n",
    "\n",
    "ylim(-0.5,2)\n",
    "xlim(0,7)\n",
    "\n",
    "plot(time_fit)\n",
    "plot([3.13,3.13], [-50, 50], color='k', linestyle='-', linewidth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}