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": "iVBORw0KGgoAAAANSUhEUgAAAg8AAAFkCAYAAACn/timAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzJw5yObNyX/esix+/et
      "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": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFrCAYAAABbtho0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1Wpt9anVaq5RkDrAkCC
      "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": "iVBORw0KGgoAAAANSUhEUgAAAg8AAAFkCAYAAACn/timAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nz/HHK1uH5e3qS6HeKJK
      "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": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFrCAYAAABbtho0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQvSFKIk1CQYe9q4VNZa
      "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": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAFrCAYAAACE+GArAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKwaZer2eEydOZHh4OPP
      "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": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGYCAYAAACQz+KaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7WglqTx1RTKyMqFlzb2
      "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": {
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      "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
}