phy-4660/lag/data/clag_analysis-origbins-3471A.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": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3cb1b15d50>"
      ]
     },
     "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/3471A.lc\"\n",
    "\n",
    "\n",
    "dt = 0.01\n",
    "t1, l1, l1e = np.loadtxt(ref_file).T\n",
    "errorbar(t1, l1, yerr=l1e, fmt='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.005     ,  0.01861938,  0.04473305,  0.06933623,  0.10747115,\n",
       "        0.16658029,  0.25819945,  0.40020915,  0.62032418])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n",
    "                0.25819945, 0.40020915, 0.62032418])\n",
    "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n",
    "nfq = len(fqL) - 1\n",
    "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n",
    "\n",
    "\n",
    "fqL\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 4.342e-01 5.077e+01 inf -- -5.530e+02 -- 1 1 1 1 1 1 1 1\n",
      "   2 7.674e-01 5.065e+01 8.300e+01 -- -4.700e+02 -- 0.653018 0.587019 0.568277 0.567457 0.566281 0.566085 0.565773 0.566163\n",
      "   3 3.298e+00 5.043e+01 8.075e+01 -- -3.893e+02 -- 0.414806 0.209135 0.141159 0.13784 0.133393 0.132728 0.131612 0.132761\n",
      "   4 1.572e+00 5.010e+01 7.754e+01 -- -3.117e+02 -- 0.322539 -0.0834456 -0.273066 -0.284295 -0.297612 -0.299435 -0.302479 -0.300412\n",
      "   5 5.908e-01 4.964e+01 7.386e+01 -- -2.379e+02 -- 0.302357 -0.214604 -0.654658 -0.688754 -0.723838 -0.729279 -0.736418 -0.733888\n",
      "   6 3.713e-01 4.877e+01 6.953e+01 -- -1.683e+02 -- 0.284419 -0.200357 -0.96379 -1.05472 -1.13798 -1.15477 -1.17031 -1.16748\n",
      "   7 2.709e-01 4.671e+01 6.269e+01 -- -1.056e+02 -- 0.277768 -0.185001 -1.13047 -1.34026 -1.52128 -1.56845 -1.6043 -1.60101\n",
      "   8 2.135e-01 4.361e+01 5.281e+01 -- -5.282e+01 -- 0.277012 -0.185189 -1.16375 -1.49463 -1.83211 -1.9477 -2.03737 -2.03476\n",
      "   9 1.764e-01 3.767e+01 4.019e+01 -- -1.264e+01 -- 0.282161 -0.184207 -1.17891 -1.53049 -2.01851 -2.24569 -2.46562 -2.46922\n",
      "  10 1.508e-01 2.738e+01 2.645e+01 -- 1.382e+01 -- 0.289545 -0.182463 -1.18795 -1.52893 -2.08812 -2.41103 -2.87498 -2.90486\n",
      "  11 1.349e-01 1.468e+01 1.390e+01 -- 2.772e+01 -- 0.293547 -0.180898 -1.1897 -1.52803 -2.10944 -2.46526 -3.22702 -3.34288\n",
      "  12 1.358e-01 5.365e+00 5.378e+00 -- 3.309e+01 -- 0.295456 -0.179941 -1.19052 -1.52801 -2.11928 -2.48294 -3.46233 -3.79368\n",
      "  13 1.868e-01 1.338e+00 1.633e+00 -- 3.473e+01 -- 0.297315 -0.17923 -1.19104 -1.52666 -2.12491 -2.48975 -3.55567 -4.30889\n",
      "  14 6.248e-01 2.604e-01 4.517e-01 -- 3.518e+01 -- 0.299091 -0.178645 -1.191 -1.52463 -2.12805 -2.4915 -3.5672 -5.11363\n",
      "  15 2.744e+02 2.611e-01 7.022e-02 -- 3.525e+01 -- 0.300248 -0.178286 -1.19075 -1.52307 -2.12961 -2.49161 -3.56337 -8\n",
      "  16 2.745e+02 2.782e-01 4.368e-04 -- 3.525e+01 -- 0.300566 -0.178158 -1.19062 -1.52252 -2.13009 -2.49151 -3.5617 -8\n",
      "  17 2.745e+02 2.805e-01 6.410e-05 -- 3.525e+01 -- 0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n",
      "********************\n",
      "0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n",
      "0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n",
      "-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n",
    "p1 = np.ones(nfq)\n",
    "p1, p1e = clag.optimize(P1, p1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t### errors for param 0 ###\n",
      "+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n",
      "+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n",
      "+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n",
      "+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n",
      "+++ 3.525e+01 3.475e+01 3.006e-01 5.545e-01 0.997 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n",
      "+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n",
      "+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n",
      "+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n",
      "+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n",
      "+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02   1 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n",
      "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n",
      "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n",
      "+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n",
      "+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 3.525e+01 3.482e+01 -1.522e+00 -1.345e+00 0.865 +++\n",
      "+++ 3.525e+01 3.432e+01 -1.522e+00 -1.257e+00 1.86 +++\n",
      "+++ 3.525e+01 3.459e+01 -1.522e+00 -1.301e+00 1.32 +++\n",
      "+++ 3.525e+01 3.471e+01 -1.522e+00 -1.323e+00 1.08 +++\n",
      "+++ 3.525e+01 3.476e+01 -1.522e+00 -1.334e+00 0.97 +++\n",
      "+++ 3.525e+01 3.473e+01 -1.522e+00 -1.329e+00 1.03 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.522e+00 -1.331e+00 0.998 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 3.525e+01 3.481e+01 -2.130e+00 -1.977e+00 0.874 +++\n",
      "+++ 3.525e+01 3.429e+01 -2.130e+00 -1.901e+00 1.91 +++\n",
      "+++ 3.525e+01 3.457e+01 -2.130e+00 -1.939e+00 1.35 +++\n",
      "+++ 3.525e+01 3.470e+01 -2.130e+00 -1.958e+00 1.1 +++\n",
      "+++ 3.525e+01 3.476e+01 -2.130e+00 -1.968e+00 0.983 +++\n",
      "+++ 3.525e+01 3.473e+01 -2.130e+00 -1.963e+00 1.04 +++\n",
      "+++ 3.525e+01 3.474e+01 -2.130e+00 -1.965e+00 1.01 +++\n",
      "+++ 3.525e+01 3.475e+01 -2.130e+00 -1.966e+00 0.997 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 3.525e+01 3.474e+01 -2.491e+00 -2.358e+00 1.01 +++\n",
      "+++ 3.525e+01 3.512e+01 -2.491e+00 -2.425e+00 0.263 +++\n",
      "+++ 3.525e+01 3.496e+01 -2.491e+00 -2.392e+00 0.579 +++\n",
      "+++ 3.525e+01 3.486e+01 -2.491e+00 -2.375e+00 0.781 +++\n",
      "+++ 3.525e+01 3.480e+01 -2.491e+00 -2.367e+00 0.893 +++\n",
      "+++ 3.525e+01 3.477e+01 -2.491e+00 -2.363e+00 0.952 +++\n",
      "+++ 3.525e+01 3.476e+01 -2.491e+00 -2.361e+00 0.982 +++\n",
      "+++ 3.525e+01 3.475e+01 -2.491e+00 -2.360e+00 0.997 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 3.525e+01 3.507e+01 -3.561e+00 -3.413e+00 0.363 +++\n",
      "+++ 3.525e+01 3.484e+01 -3.561e+00 -3.339e+00 0.814 +++\n",
      "+++ 3.525e+01 3.468e+01 -3.561e+00 -3.301e+00 1.13 +++\n",
      "+++ 3.525e+01 3.477e+01 -3.561e+00 -3.320e+00 0.962 +++\n",
      "+++ 3.525e+01 3.473e+01 -3.561e+00 -3.311e+00 1.04 +++\n",
      "+++ 3.525e+01 3.475e+01 -3.561e+00 -3.315e+00   1 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 3.525e+01 3.524e+01 -8.000e+00 -6.000e+00 0.0178 +++\n",
      "+++ 3.525e+01 3.515e+01 -8.000e+00 -5.000e+00 0.187 +++\n",
      "+++ 3.525e+01 3.494e+01 -8.000e+00 -4.500e+00 0.618 +++\n",
      "+++ 3.525e+01 3.466e+01 -8.000e+00 -4.250e+00 1.18 +++\n",
      "+++ 3.525e+01 3.482e+01 -8.000e+00 -4.375e+00 0.851 +++\n",
      "+++ 3.525e+01 3.475e+01 -8.000e+00 -4.312e+00   1 +++\n",
      "********************\n",
      "0.300605 -0.178131 -1.19059 -1.52241 -2.13019 -2.49148 -3.56144 -8\n",
      "0.253864 0.205597 0.228999 0.191096 0.1638 0.131949 0.246154 3.6875\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "p1, p1e = clag.errors(P1, p1, p1e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFrCAYAAABbtho0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1Wpt9anVaq5RkDrAkCC
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3cd80692d0>"
      ]
     },
     "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\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3X98lfV9///HCfkBSQhBDT9MgEAglESckjZCUKnjl67T\ntk4qsbYNY5Ntrq1bN+jW/aCf2z77brltbee6VfnMmar1KLKtte2KmHUqCJoaysqIFYkBSQBzQH4m\nQH6d7x9XrpNzTs7vc13n5/N+u52bkpyc68qV65zrdb3fr/frBSIiIiIiIiIiIiIiIiIiIiIiIiIi\nIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIjE7CowEeHw7ifskIiIiKexaYJrXYyVG8HB7MndKRERE\n0se3gMPJ3gkRERFJD/nAaeCryd4RERERsU6uja/9KWAK0BLiOTNHHyIiIhKdk6OPhHPY+NovAVeA\nTwb5/szrr7/+xIkTJ2zcBRERkYzVA3yMJAQQdo08zMFIlvx0iOfMPHHiBM888wyLFi2yaTfE3yOP\nPMK3vvWtZO9GVtExTzwd88TTMU+st99+mwcffLAcY/Q+Y4KHDcAHwI/DPXHRokUsWbLEpt0Qf6Wl\npTreCaZjnng65omnY55dcmx6zQ3AdzGWaYqIiEgGsSN4WAVUAP9qw2uLiIhIktkxbbELmGDD64qI\niEgKsGPkQVJYY2Njsnch6+iYJ56OeeLpmGcXO5dqhrMEaG9vb1eSjYiISBT2799PXV0dQB2wP9Hb\n18iDiIiIREXBg4iIiERFwYOIiIhERcGDiIiIREXBg4iIiERFwYOIiIhERcGDiIiIREXBg4iIiERF\nwYOIiIhERcGDiIiIREXBg4iIiERFwYOIiIhERcGDiIiIREXBg4iIiERFwYOIiIhEJTfZOyAikkmc\nB504/9dJz4Ue3r/wPpcHL1OQW8DVoatMypvE7JLZlJeU03hDI42LG5O9uyIxUfAgImKhxsWNrJqx\nis1bN3N632lOnzzNVa4ylDvEddOv48ZbbqR5azNlZWXJ3lWRmCl4EBGxUG9vLw13NdD5kU44C3wC\nhiqGwAHHRo7R0tPC7jt3s2/nPgUQkraU8yAiYqEtX99C582dcBRYCcwCHKPfzDH+3XlzJ5u3bk7W\nLorETcGDiIiF2g60QQXgwvhvIOWjzxNJUwoeREQsNIQxReF5BJIz+jyRNKXgQUTEQrnkgpuxRyAj\no88TSVMKHkRELFR/Uz10A2UY/w2kZ/R5ImlKwYOIiIWatzZT9fMqqAT+CzgOjIx+c8T4d9XPq2je\n2pysXRSJm4IHERELlZWVsW/nPppKm5g9dTb8GHK35cK/wJwfz6GpoEnLNCXtadJNRMRCngqTN/bQ\nX9lP0WCRp8JkX14fvyj5BRtbN6rCpKQ1BQ8iIhZqXKygQDKfpi1EREQkKgoeREREJCoKHkREbOQ8\n6GTt42uZddcsimuLya/Jp7i2mFl3zWLt42txHnQmexdFoqacBxERi5jJkgBXhq5w7PwxZjpm8rNH\nf0b/bf1wC+CAwZFB+nr6KNhWwKp7VyV3p0VioJEHERGLNC5u5IlVT3Dtnms58u0jHP7Hw7Q3txuB\ngxpkSQbRyIOIiEU87bhv7oS7AAdceuZS6AZZrWqQJelHwYOIiEU87bhneX0xBzXIkoyjaQsREYt4\n2nF7U4MsyUAKHkRELOJpx+1NDbIkAyl4EBGxiKcdt7flGA2y3kcNsiRjKHgQEbGIpx23tyJgHdAO\nxd8thmdh7s65apAlaU3Bg4iIRTztuP3bcH8IhZcLqfvjOqq/WM3835/PmVvPsLF1o4pESVqyI1On\nHPhb4E5gEnAY2Ajst2FbIiIpo/VUK1UPVXH1+1c5u+8sA+4B8h35TJ09lZo/rKGpoUlNsyQjWB08\nTAVex5jhuxPoBaqAcxZvR0Qk5Xg6am5K9p6I2Mvq4GELcAxjpMH0vsXbEBERkSSyOufhHqAdeAH4\nAGOq4rcs3oaIiIgkkdXBwzzgd4F3gDXAd4BHgc9bvB0RERFJEqunLXKANuDPRv/9P8ANwO8ATwX6\ngUceeYTS0lKfrzU2NtLYqKQiERERp9OJ0+m7KufcueSmEgaruB6ro8Au4CGvr/0u8DXGF21dArS3\nt7ezZMkSi3dDRCS1BGrXPWfKHCbmTgSg8YZGrcSQiO3fv5+6ujqAOpKwmtHqkYfXgY/4fa0aI6gQ\nEclajYsbWTVjFZu3bubVt16l61wXg6WDrPjoCpq3NqtYlKQVq4OHbwJ7gT/BSJqsB3579CEikrUC\ntevuGumiq6eL3XfuVrVJSStWJ0y+BXwaaAQOYkxXfBlQCTURyWo+7brNCeMcYBZ03tzJ5q2bk7h3\nItGxo8Lkj0cfIiIyqu1AG6wO8s1yaGttS+j+iMRDvS1ERBIgYLtuU87o90XShIIHEZEECNiu2zQy\n+n2RNKHgQUQkAQK26zb1jH5fJE0oeBARSYCg7bqPw6TXJnHixhPc47xHLbolLSh4EBFJgLKyMvbt\n3EdTQROzfzgbHoPcbbnwEkwrmcb1v7ieJ1Y9oUJRkhY0ySYikgBmhcmri6/ietkFn4ChCiOJ8tjI\nMVp6WlTvQdKGRh5ERBKgcXEjLza+yPUHr+fy7ZdV70HSmkYeROKgfgUSLdV7kEyg4EEkDupXINFS\nvQfJBAoeROKgfgUSLU+9h0ABhOo9SJpQzoNIHNSvQKKleg+SCRQ8iMSh7UAbVAT5Zvno90W8hKr3\nUPXzKpq3Nidx70Qio+BBJA6avxYwEmfXPr6WWXfNori2mPyafIpri5l11yzWPr7Wp/BT66lWqh6q\noqKngqIdReQ9n0fRjiIqeiqoeqiK1lOtSfxNRCKjyTWROGj+WgBWTl/Jn2/7c7pv7oZbAAcMjgzS\n19NHwbYCVt27yvPcxsXGChxng5OWvS10fL+Ds++f5YNjH3D20bN0fL+Dlk+10NTQpJU6krI08iAS\nB81fC8SW+7Jy+ko6t3XSXd5N37o+Bu8fpO++PrrLu+nc1smqGavG/YxIqlDwIBIHzV8LxJb7omRb\nSWcKHkTiYM5fX3P0GnKeyYHHMB4vQdeFLj7S+JFxc96SOZwHndzjvIdjF45FnfuiZFtJZ5qQFYmC\n86DvPPWAe4B8Rz4l00soyCng8icuGxcEB4yMjPBhz4fGEPS9GoLORGaRsAVfWxB17ouSbSWdaeRB\nJArB5qlPfniSyyvUryDb9Pb2suzOZZwvOR917osn2TYQJdtKilPwIBIh50EnNz1wU+B56n40BJ2F\nPHkLa4D/Ynzuy/vBc1/CJdteKLmg6S5JWQptRSK0cvpKTh86DbcG+KYDDUFnIU+TKwewDngdeG30\n3yMwZXAK+34WuER589Zmdt+5m87LndAFnB79uQEoHipm165dLFq0KHG/jEgUNPIgEqEtX9/CYOFg\n4CDBjYags5BP3kIRxgjEZ4EHgAdhaPIQG1s3BhxBKCsr48WnX2Ty7slQM/ozDwCfh0trL3H3g3fj\ncrkS9JuIREfBg0iE2g60wQQCBwllqN5DFgqXtzCnZA4vNr4YsNiT86CTtV9Zy8U1F5UrI2lHwYNI\nhIYYCh4kLAdeAt5H9R68RFO2OR3FUySscXEjJRdKlCsjaUnBg0iEcsmFBgInxp2B3Ku5rGc9c3fO\nhWdh7s65NBU0ZXVb7kyvohhvkTAt15R0pYlYkQjV31RPx9mO8YlxbqAQJlRN4HD9YeZ/fD555/OY\nM2UOZ3LPsLF1I403NGZlnwKfKoomc1geY1j+yX96Mlm7FzOz3sdbL7zFuXPn4McYQUM+5EzMoXRO\nqafJVWNZ8L+7eqNIutKZKRIhT3b8zZ2wCuMiOAL0GHeZ+17M3hGGYDyrEQIph7bW9ByWNxthfXjz\nh8bqm9HVFfTA3J/PZZ8zsnOh/qZ6Oro7fIMrk3JlJIVp2kIkQmVlZezbuY+mgqagUxOZPscfrUwd\nlreqL4V6o0i60siDSIS
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3cb1682110>"
      ]
     },
     "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 4.362e-01 5.425e+01 inf -- -3.468e+02 -- 1 1 1 1 1 1 1 1\n",
      "   2 7.747e-01 5.347e+01 7.019e+01 -- -2.766e+02 -- 0.58045 0.569962 0.564602 0.563764 0.567626 0.565054 0.565279 0.564008\n",
      "   3 3.447e+00 5.213e+01 6.905e+01 -- -2.076e+02 -- 0.198681 0.140203 0.130333 0.127063 0.134237 0.12998 0.131082 0.127044\n",
      "   4 1.448e+00 4.998e+01 6.682e+01 -- -1.408e+02 -- -0.0825582 -0.283347 -0.300715 -0.310826 -0.299703 -0.304649 -0.30132 -0.310849\n",
      "   5 5.884e-01 4.684e+01 6.330e+01 -- -7.746e+01 -- -0.194194 -0.676998 -0.726162 -0.750752 -0.733583 -0.738758 -0.729946 -0.74976\n",
      "   6 3.739e-01 4.258e+01 5.850e+01 -- -1.895e+01 -- -0.203226 -0.953186 -1.14229 -1.19152 -1.16459 -1.17342 -1.15279 -1.19094\n",
      "   7 2.741e-01 3.740e+01 5.292e+01 -- 3.397e+01 -- -0.205901 -0.9956 -1.54459 -1.63073 -1.58521 -1.61127 -1.5669 -1.63629\n",
      "   8 2.128e-01 3.180e+01 4.679e+01 -- 8.076e+01 -- -0.180502 -0.943953 -1.92935 -2.06126 -1.98071 -2.05288 -1.97074 -2.08385\n",
      "   9 1.675e-01 2.595e+01 3.791e+01 -- 1.187e+02 -- -0.15428 -0.934563 -2.26437 -2.45352 -2.31988 -2.48374 -2.35707 -2.52729\n",
      "  10 1.286e-01 2.002e+01 2.537e+01 -- 1.440e+02 -- -0.13956 -0.939381 -2.49352 -2.71726 -2.56371 -2.84902 -2.70919 -2.95055\n",
      "  11 9.197e-02 1.277e+01 1.299e+01 -- 1.570e+02 -- -0.130332 -0.946187 -2.57446 -2.74449 -2.69402 -3.05697 -3.00449 -3.33007\n",
      "  12 5.076e-02 6.047e+00 5.021e+00 -- 1.621e+02 -- -0.124515 -0.945944 -2.55165 -2.71422 -2.73704 -3.10737 -3.209 -3.63633\n",
      "  13 1.434e-02 1.485e+00 1.060e+00 -- 1.631e+02 -- -0.12047 -0.945369 -2.54157 -2.70197 -2.76441 -3.10602 -3.30159 -3.8209\n",
      "  14 4.769e-03 3.557e-01 7.280e-02 -- 1.632e+02 -- -0.119516 -0.946002 -2.53635 -2.69518 -2.78858 -3.10381 -3.32152 -3.8757\n",
      "  15 2.150e-03 1.530e-01 4.222e-03 -- 1.632e+02 -- -0.119506 -0.945491 -2.53265 -2.69429 -2.80187 -3.10094 -3.32488 -3.87969\n",
      "  16 1.017e-03 7.109e-02 8.430e-04 -- 1.632e+02 -- -0.119394 -0.945124 -2.53033 -2.69448 -2.8079 -3.09858 -3.32603 -3.87983\n",
      "  17 4.817e-04 3.356e-02 1.918e-04 -- 1.632e+02 -- -0.119311 -0.944963 -2.52941 -2.69447 -2.81076 -3.09727 -3.32654 -3.87984\n",
      "  18 2.335e-04 1.618e-02 4.417e-05 -- 1.632e+02 -- -0.119282 -0.944891 -2.52886 -2.69454 -2.81211 -3.09664 -3.32676 -3.87987\n",
      "********************\n",
      "-0.119282 -0.944891 -2.52886 -2.69454 -2.81211 -3.09664 -3.32676 -3.87987\n",
      "0.233661 0.204163 0.319993 0.254151 0.198248 0.179386 0.161786 0.221522\n",
      "0.000372164 0.000983332 0.00164575 -0.000696472 -0.0161795 0.00744155 -0.00415795 -0.000828998\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)\n",
    "p2 = np.ones(nfq)\n",
    "p2, p2e = clag.optimize(P2, p2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t### errors for param 0 ###\n",
      "+++ 1.632e+02 1.627e+02 -1.193e-01 1.144e-01 0.905 +++\n",
      "+++ 1.632e+02 1.622e+02 -1.193e-01 2.312e-01 1.9 +++\n",
      "+++ 1.632e+02 1.625e+02 -1.193e-01 1.728e-01 1.37 +++\n",
      "+++ 1.632e+02 1.626e+02 -1.193e-01 1.436e-01 1.13 +++\n",
      "+++ 1.632e+02 1.627e+02 -1.193e-01 1.290e-01 1.01 +++\n",
      "+++ 1.632e+02 1.627e+02 -1.193e-01 1.217e-01 0.958 +++\n",
      "+++ 1.632e+02 1.627e+02 -1.193e-01 1.254e-01 0.985 +++\n",
      "+++ 1.632e+02 1.627e+02 -1.193e-01 1.272e-01 0.999 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 1.632e+02 1.627e+02 -9.449e-01 -7.407e-01 0.961 +++\n",
      "+++ 1.632e+02 1.622e+02 -9.449e-01 -6.386e-01 2.04 +++\n",
      "+++ 1.632e+02 1.625e+02 -9.449e-01 -6.897e-01 1.46 +++\n",
      "+++ 1.632e+02 1.626e+02 -9.449e-01 -7.152e-01 1.2 +++\n",
      "+++ 1.632e+02 1.627e+02 -9.449e-01 -7.279e-01 1.08 +++\n",
      "+++ 1.632e+02 1.627e+02 -9.449e-01 -7.343e-01 1.02 +++\n",
      "+++ 1.632e+02 1.627e+02 -9.449e-01 -7.375e-01 0.989 +++\n",
      "+++ 1.632e+02 1.627e+02 -9.449e-01 -7.359e-01   1 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 1.632e+02 1.630e+02 -2.529e+00 -2.369e+00 0.307 +++\n",
      "+++ 1.632e+02 1.629e+02 -2.529e+00 -2.289e+00 0.681 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.529e+00 -2.249e+00 0.919 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.529e+00 -2.229e+00 1.05 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.529e+00 -2.239e+00 0.984 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.529e+00 -2.234e+00 1.02 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.529e+00 -2.236e+00   1 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 1.632e+02 1.630e+02 -2.695e+00 -2.567e+00 0.306 +++\n",
      "+++ 1.632e+02 1.629e+02 -2.695e+00 -2.504e+00 0.681 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.695e+00 -2.472e+00 0.922 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.695e+00 -2.456e+00 1.05 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.695e+00 -2.464e+00 0.988 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.695e+00 -2.460e+00 1.02 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.695e+00 -2.462e+00   1 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 1.632e+02 1.629e+02 -2.813e+00 -2.614e+00 0.665 +++\n",
      "+++ 1.632e+02 1.624e+02 -2.813e+00 -2.515e+00 1.57 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.813e+00 -2.565e+00 1.07 +++\n",
      "+++ 1.632e+02 1.628e+02 -2.813e+00 -2.590e+00 0.853 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.813e+00 -2.577e+00 0.956 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.813e+00 -2.571e+00 1.01 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.813e+00 -2.574e+00 0.983 +++\n",
      "+++ 1.632e+02 1.627e+02 -2.813e+00 -2.573e+00 0.997 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 1.632e+02 1.628e+02 -3.096e+00 -2.917e+00 0.837 +++\n",
      "+++ 1.632e+02 1.623e+02 -3.096e+00 -2.827e+00 1.87 +++\n",
      "+++ 1.632e+02 1.625e+02 -3.096e+00 -2.872e+00 1.29 +++\n",
      "+++ 1.632e+02 1.627e+02 -3.096e+00 -2.895e+00 1.06 +++\n",
      "+++ 1.632e+02 1.627e+02 -3.096e+00 -2.906e+00 0.947 +++\n",
      "+++ 1.632e+02 1.627e+02 -3.096e+00 -2.900e+00   1 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 1.632e+02 1.627e+02 -3.327e+00 -3.165e+00 0.992 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 1.632e+02 1.631e+02 -3.880e+00 -3.769e+00 0.278 +++\n",
      "+++ 1.632e+02 1.629e+02 -3.880e+00 -3.714e+00 0.631 +++\n",
      "+++ 1.632e+02 1.628e+02 -3.880e+00 -3.686e+00 0.862 +++\n",
      "+++ 1.632e+02 1.627e+02 -3.880e+00 -3.672e+00 0.991 +++\n",
      "********************\n",
      "-0.119263 -0.944854 -2.52866 -2.69453 -2.81277 -3.09632 -3.32687 -3.87987\n",
      "0.246439 0.208948 0.29245 0.232296 0.24017 0.196142 0.161799 0.207668\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "p2, p2e = clag.errors(P2, p2, p2e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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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\nKKyQ0ATsB/5LSO8vaYH
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3cb164f6d0>"
      ]
     },
     "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 2.664e+02 1.060e+01 inf -- 2.187e+02 -- -0.209329 -0.861493 -2.15962 -2.40847 -2.77148 -3.0939 -3.74416 -6.23993 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
      "   3 2.987e+01 1.264e+01 2.257e+00 -- 2.210e+02 -- -0.16831 -0.824919 -2.12464 -2.39488 -2.7608 -3.0706 -3.74724 -5.93993 0.0804747 0.16472 0.155918 0.19816 0.150032 0.148466 -0.0426696 2.76354\n",
      "   5 3.330e+01 1.479e+01 2.062e+00 -- 2.230e+02 -- -0.134691 -0.793404 -2.09481 -2.37915 -2.74942 -3.04926 -3.74185 -6.23993 0.0666414 0.2131 0.199386 0.281712 0.193626 0.185727 -0.170109 -2.41599\n",
      "   7 4.032e+02 1.707e+01 1.889e+00 -- 2.249e+02 -- -0.106698 -0.766368 -2.06929 -2.36269 -2.73774 -3.02999 -3.73105 -6.53993 0.0565128 0.250258 0.234434 0.352126 0.231411 0.214451 -0.279352 -0.654819\n",
      "   9 1.006e+02 1.948e+01 1.734e+00 -- 2.267e+02 -- -0.0831017 -0.743148 -2.04735 -2.34643 -2.7261 -3.0127 -3.71739 -6.23993 0.0489265 0.27947 0.263536 0.411371 0.264011 0.236678 -0.370557 0.617417\n",
      "  11 5.264e+01 2.201e+01 1.603e+00 -- 2.283e+02 -- -0.0630157 -0.723142 -2.02838 -2.33091 -2.71472 -2.99725 -3.70277 -5.93993 0.0431576 0.302888 0.288287 0.46139 0.292059 0.253914 -0.445773 0.691506\n",
      "  13 3.251e+00 2.467e+01 1.448e+00 -- 2.297e+02 -- -0.0457823 -0.705838 -2.0119 -2.31642 -2.70374 -2.98344 -3.68838 -5.63993 0.0387303 0.321962 0.309779 0.503862 0.316158 0.267284 -0.507655 -2.9489\n",
      "  15 5.697e+00 2.744e+01 1.389e+00 -- 2.311e+02 -- -0.0308979 -0.690819 -1.99751 -2.30307 -2.6933 -2.97112 -3.67473 -5.93993 0.0353185 0.337702 0.32866 0.540271 0.336854 0.27759 -0.559235 -3.12456\n",
      "  17 9.962e+01 3.030e+01 1.295e+00 -- 2.324e+02 -- -0.0179753 -0.677732 -1.9849 -2.29088 -2.68339 -2.9601 -3.66228 -6.23993 0.0326949 0.350836 0.34562 0.571642 0.354613 0.285527 -0.602072 1.49229\n",
      "  19 8.259e+01 3.325e+01 1.196e+00 -- 2.336e+02 -- -0.00670527 -0.666291 -1.97381 -2.2798 -2.67406 -2.95025 -3.6511 -5.93993 0.0306947 0.361894 0.361049 0.598897 0.369841 0.291591 -0.63793 -0.807155\n",
      "  21 5.466e+01 3.625e+01 1.126e+00 -- 2.347e+02 -- 0.00316205 -0.65626 -1.96403 -2.26977 -2.6653 -2.94143 -3.64118 -5.63993 0.0291917 0.371274 0.375204 0.622756 0.382913 0.296138 -0.668602 -0.4239\n",
      "  23 9.811e+00 3.926e+01 1.030e+00 -- 2.358e+02 -- 0.0118288 -0.647441 -1.95537 -2.26071 -2.65711 -2.93352 -3.63242 -5.33993 0.0280965 0.379279 0.388328 0.643807 0.394112 0.29949 -0.69488 1.89304\n",
      "  24 1.540e+02 1.799e+03 6.966e+00 -- 2.427e+02 -- 0.0881649 -0.569745 -1.87911 -2.17878 -2.58105 -2.86227 -3.55241 -6.66644 0.0206511 0.447973 0.510379 0.832388 0.489337 0.32364 -0.914155 2.17049\n",
      "  25 6.954e+03 5.225e+01 3.722e+00 -- 2.464e+02 -- 0.0820074 -0.577455 -1.8985 -2.17524 -2.54532 -2.85023 -3.57378 -8 0.0966827 0.412543 0.647274 0.885367 0.477195 0.280838 -0.833819 0.980251\n",
      "  26 6.093e+00 2.044e+01 2.548e-01 -- 2.467e+02 -- 0.0836413 -0.576897 -1.8864 -2.18141 -2.55069 -2.85268 -3.54966 -5 0.0710854 0.434328 0.591545 0.904499 0.417485 0.266475 -0.938934 1.34469\n",
      "  27 1.995e+00 5.009e+00 1.592e-01 -- 2.469e+02 -- 0.0831948 -0.576805 -1.88848 -2.17663 -2.54734 -2.85305 -3.5567 -4.22205 0.0761925 0.426631 0.625953 0.910649 0.424127 0.266244 -0.891405 -0.565813\n",
      "  28 1.248e+00 1.375e+01 4.599e-02 -- 2.469e+02 -- 0.0833105 -0.576655 -1.88396 -2.17799 -2.5475 -2.85314 -3.57374 -4.03476 0.0730522 0.429262 0.625062 0.905512 0.418149 0.264942 -0.967314 0.563133\n",
      "  29 2.171e+00 9.396e+00 3.382e-01 -- 2.472e+02 -- 0.0829995 -0.576375 -1.88776 -2.17599 -2.54493 -2.85329 -3.58608 -4.02931 0.0763743 0.428086 0.638377 0.920493 0.411594 0.274496 -0.815157 -0.139608\n",
      "  30 9.841e-01 1.149e+01 1.388e-01 -- 2.474e+02 -- 0.0832295 -0.57636 -1.88467 -2.17976 -2.54698 -2.8543 -3.58978 -3.89975 0.0738415 0.429686 0.629413 0.910992 0.412336 0.271501 -0.957932 0.163421\n",
      "  31 2.338e+01 3.752e+00 6.030e-02 -- 2.474e+02 -- 0.0829583 -0.576172 -1.88634 -2.17783 -2.54432 -2.85466 -3.60447 -3.874 0.0761271 0.428633 0.633503 0.910755 0.410289 0.277401 -0.858061 0.00260138\n",
      "  32 3.920e-01 3.182e+00 1.581e-02 -- 2.475e+02 -- 0.0830523 -0.576117 -1.88559 -2.18042 -2.54532 -2.85537 -3.60659 -3.85372 0.0749087 0.429912 0.628364 0.908088 0.410987 0.278401 -0.917876 0.0634286\n",
      "  33 3.181e-01 1.292e+00 4.081e-03 -- 2.475e+02 -- 0.0829733 -0.576083 -1.88614 -2.17987 -2.54423 -2.85554 -3.60968 -3.84594 0.0757541 0.429324 0.627903 0.906455 0.410964 0.280705 -0.882158 0.0385634\n",
      "  34 9.740e-02 4.768e-01 1.118e-03 -- 2.475e+02 -- 0.0830058 -0.576059 -1.88614 -2.18078 -2.54437 -2.85582 -3.61078 -3.84115 0.0755814 0.429797 0.625777 0.906066 0.411713 0.281667 -0.897167 0.0508303\n",
      "  35 6.139e-02 4.667e-01 3.378e-04 -- 2.475e+02 -- 0.0829936 -0.576051 -1.88632 -2.18077 -2.54398 -2.85585 -3.61168 -3.83901 0.0759106 0.429622 0.624827 0.905205 0.411841 0.282606 -0.888007 0.0458794\n",
      "  36 2.074e-02 6.100e-02 1.146e-04 -- 2.475e+02 -- 0.0830052 -0.576043 -1.8864 -2.18106 -2.54393 -2.85595 -3.61204 -3.83759 0.0759487 0.42976 0.623936 0.905027 0.412248 0.283153 -0.891435 0.0486959\n",
      "  37 1.454e-02 1.834e-01 4.331e-05 -- 2.475e+02 -- 0.0830055 -0.57604 -1.88647 -2.18112 -2.54378 -2.85597 -3.61236 -3.83689 0.0760799 0.429719 0.623352 0.904662 0.412343 0.283555 -0.889115 0.047686\n",
      "********************\n",
      "0.0830055 -0.57604 -1.88647 -2.18112 -2.54378 -2.85597 -3.61236 -3.83689 0.0760799 0.429719 0.623352 0.904662 0.412343 0.283555 -0.889115 0.047686\n",
      "0.00496524 0.0080639 0.0332817 0.053869 0.0497847 0.0510814 0.188809 0.22006 0.0806462 0.0938084 0.215752 0.243886 0.211727 0.201233 0.481882 0.380625\n",
      "0.183432 0.0383088 -0.0465819 -0.037514 0.0169821 -0.0145299 -0.00365015 0.00975964 0.0073746 0.00371696 -0.00866648 -0.0017458 0.00395749 0.0070437 -0.00320102 0.00463012\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "Cx = clag.clag('cxd10r', [[t1,t2]], [[l1,l2]], [[l1e,l2e]], dt, fqL, p1, p2)\n",
    "p  = np.concatenate( ((p1+p2)*0.5-0.3,p1*0+0.1) ) # a  good starting point generally\n",
    "p, pe = clag.optimize(Cx, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "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",
      "+++ 2.475e+02 2.473e+02 8.301e-02 8.549e-02 0.306 +++\n",
      "+++ 2.475e+02 2.470e+02 8.301e-02 8.673e-02 0.912 +++\n",
      "+++ 2.475e+02 2.467e+02 8.301e-02 8.735e-02 1.46 +++\n",
      "+++ 2.475e+02 2.469e+02 8.301e-02 8.704e-02 1.16 +++\n",
      "+++ 2.475e+02 2.469e+02 8.301e-02 8.689e-02 1.03 +++\n",
      "+++ 2.475e+02 2.470e+02 8.301e-02 8.681e-02 0.969 +++\n",
      "+++ 2.475e+02 2.470e+02 8.301e-02 8.685e-02 0.999 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 2.475e+02 2.473e+02 -5.760e-01 -5.720e-01 0.399 +++\n",
      "+++ 2.475e+02 2.469e+02 -5.760e-01 -5.700e-01 1.13 +++\n",
      "+++ 2.475e+02 2.471e+02 -5.760e-01 -5.710e-01 0.698 +++\n",
      "+++ 2.475e+02 2.470e+02 -5.760e-01 -5.705e-01 0.897 +++\n",
      "+++ 2.475e+02 2.470e+02 -5.760e-01 -5.702e-01 1.01 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 2.475e+02 2.473e+02 -1.887e+00 -1.870e+00 0.291 +++\n",
      "+++ 2.475e+02 2.470e+02 -1.887e+00 -1.862e+00 0.915 +++\n",
      "+++ 2.475e+02 2.467e+02 -1.887e+00 -1.857e+00 1.53 +++\n",
      "+++ 2.475e+02 2.469e+02 -1.887e+00 -1.859e+00 1.18 +++\n",
      "+++ 2.475e+02 2.469e+02 -1.887e+00 -1.860e+00 1.04 +++\n",
      "+++ 2.475e+02 2.470e+02 -1.887e+00 -1.861e+00 0.971 +++\n",
      "+++ 2.475e+02 2.470e+02 -1.887e+00 -1.861e+00   1 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 2.475e+02 2.473e+02 -2.181e+00 -2.154e+00 0.29 +++\n",
      "+++ 2.475e+02 2.471e+02 -2.181e+00 -2.141e+00 0.808 +++\n",
      "+++ 2.475e+02 2.468e+02 -2.181e+00 -2.134e+00 1.25 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.181e+00 -2.137e+00 1.01 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.181e+00 -2.139e+00 0.905 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.181e+00 -2.138e+00 0.957 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.181e+00 -2.138e+00 0.984 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.181e+00 -2.138e+00 0.997 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 2.475e+02 2.470e+02 -2.544e+00 -2.494e+00 0.973 +++\n",
      "+++ 2.475e+02 2.459e+02 -2.544e+00 -2.469e+00 3.08 +++\n",
      "+++ 2.475e+02 2.466e+02 -2.544e+00 -2.482e+00 1.78 +++\n",
      "+++ 2.475e+02 2.468e+02 -2.544e+00 -2.488e+00 1.32 +++\n",
      "+++ 2.475e+02 2.469e+02 -2.544e+00 -2.491e+00 1.14 +++\n",
      "+++ 2.475e+02 2.469e+02 -2.544e+00 -2.492e+00 1.05 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.544e+00 -2.493e+00 1.01 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.544e+00 -2.494e+00 0.993 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 2.475e+02 2.473e+02 -2.856e+00 -2.830e+00 0.277 +++\n",
      "+++ 2.475e+02 2.471e+02 -2.856e+00 -2.818e+00 0.713 +++\n",
      "+++ 2.475e+02 2.469e+02 -2.856e+00 -2.811e+00 1.04 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.856e+00 -2.814e+00 0.867 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.856e+00 -2.813e+00 0.953 +++\n",
      "+++ 2.475e+02 2.470e+02 -2.856e+00 -2.812e+00 0.993 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 2.475e+02 2.473e+02 -3.612e+00 -3.518e+00 0.392 +++\n",
      "+++ 2.475e+02 2.469e+02 -3.612e+00 -3.471e+00 1.06 +++\n",
      "+++ 2.475e+02 2.471e+02 -3.612e+00 -3.494e+00 0.67 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.612e+00 -3.483e+00 0.845 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.612e+00 -3.477e+00 0.947 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.612e+00 -3.474e+00   1 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 2.475e+02 2.474e+02 -3.836e+00 -3.727e+00 0.218 +++\n",
      "+++ 2.475e+02 2.471e+02 -3.836e+00 -3.672e+00 0.684 +++\n",
      "+++ 2.475e+02 2.469e+02 -3.836e+00 -3.644e+00 1.16 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.836e+00 -3.658e+00 0.894 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.836e+00 -3.651e+00 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.836e+00 -3.654e+00 0.955 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.836e+00 -3.653e+00 0.988 +++\n",
      "+++ 2.475e+02 2.470e+02 -3.836e+00 -3.652e+00   1 +++\n",
      "\t### errors for param 8 ###\n",
      "+++ 2.475e+02 2.470e+02 7.613e-02 1.568e-01 0.869 +++\n",
      "+++ 2.475e+02 2.465e+02 7.613e-02 1.971e-01 1.9 +++\n",
      "+++ 2.475e+02 2.468e+02 7.613e-02 1.769e-01 1.35 +++\n",
      "+++ 2.475e+02 2.469e+02 7.613e-02 1.668e-01 1.1 +++\n",
      "+++ 2.475e+02 2.470e+02 7.613e-02 1.618e-01 0.98 +++\n",
      "+++ 2.475e+02 2.469e+02 7.613e-02 1.643e-01 1.04 +++\n",
      "+++ 2.475e+02 2.470e+02 7.613e-02 1.630e-01 1.01 +++\n",
      "\t### errors for param 9 ###\n",
      "+++ 2.475e+02 2.473e+02 4.298e-01 4.767e-01 0.29 +++\n",
      "+++ 2.475e+02 2.471e+02 4.298e-01 5.001e-01 0.641 +++\n",
      "+++ 2.475e+02 2.470e+02 4.298e-01 5.118e-01 0.863 +++\n",
      "+++ 2.475e+02 2.470e+02 4.298e-01 5.177e-01 0.985 +++\n",
      "+++ 2.475e+02 2.469e+02 4.298e-01 5.206e-01 1.05 +++\n",
      "+++ 2.475e+02 2.470e+02 4.298e-01 5.192e-01 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 4.298e-01 5.184e-01   1 +++\n",
      "\t### errors for param 10 ###\n",
      "+++ 2.475e+02 2.471e+02 6.230e-01 8.388e-01 0.802 +++\n",
      "+++ 2.475e+02 2.466e+02 6.230e-01 9.467e-01 1.77 +++\n",
      "+++ 2.475e+02 2.468e+02 6.230e-01 8.928e-01 1.25 +++\n",
      "+++ 2.475e+02 2.470e+02 6.230e-01 8.658e-01 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 6.230e-01 8.523e-01 0.906 +++\n",
      "+++ 2.475e+02 2.470e+02 6.230e-01 8.591e-01 0.96 +++\n",
      "+++ 2.475e+02 2.470e+02 6.230e-01 8.624e-01 0.988 +++\n",
      "+++ 2.475e+02 2.470e+02 6.230e-01 8.641e-01   1 +++\n",
      "\t### errors for param 11 ###\n",
      "+++ 2.475e+02 2.470e+02 9.046e-01 1.149e+00 0.88 +++\n",
      "+++ 2.475e+02 2.465e+02 9.046e-01 1.271e+00 1.88 +++\n",
      "+++ 2.475e+02 2.468e+02 9.046e-01 1.210e+00 1.35 +++\n",
      "+++ 2.475e+02 2.469e+02 9.046e-01 1.179e+00 1.1 +++\n",
      "+++ 2.475e+02 2.470e+02 9.046e-01 1.164e+00 0.989 +++\n",
      "+++ 2.475e+02 2.469e+02 9.046e-01 1.171e+00 1.05 +++\n",
      "+++ 2.475e+02 2.470e+02 9.046e-01 1.168e+00 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 9.046e-01 1.166e+00   1 +++\n",
      "\t### errors for param 12 ###\n",
      "+++ 2.475e+02 2.471e+02 4.125e-01 6.242e-01 0.737 +++\n",
      "+++ 2.475e+02 2.467e+02 4.125e-01 7.300e-01 1.59 +++\n",
      "+++ 2.475e+02 2.469e+02 4.125e-01 6.771e-01 1.13 +++\n",
      "+++ 2.475e+02 2.470e+02 4.125e-01 6.507e-01 0.924 +++\n",
      "+++ 2.475e+02 2.469e+02 4.125e-01 6.639e-01 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 4.125e-01 6.573e-01 0.974 +++\n",
      "+++ 2.475e+02 2.470e+02 4.125e-01 6.606e-01 0.999 +++\n",
      "\t### errors for param 13 ###\n",
      "+++ 2.475e+02 2.470e+02 2.838e-01 4.851e-01 0.854 +++\n",
      "+++ 2.475e+02 2.465e+02 2.838e-01 5.857e-01 1.84 +++\n",
      "+++ 2.475e+02 2.468e+02 2.838e-01 5.354e-01 1.31 +++\n",
      "+++ 2.475e+02 2.469e+02 2.838e-01 5.103e-01 1.07 +++\n",
      "+++ 2.475e+02 2.470e+02 2.838e-01 4.977e-01 0.96 +++\n",
      "+++ 2.475e+02 2.470e+02 2.838e-01 5.040e-01 1.01 +++\n",
      "+++ 2.475e+02 2.470e+02 2.838e-01 5.008e-01 0.987 +++\n",
      "+++ 2.475e+02 2.470e+02 2.838e-01 5.024e-01   1 +++\n",
      "\t### errors for param 14 ###\n",
      "+++ 2.475e+02 2.470e+02 -8.899e-01 -4.078e-01 0.957 +++\n",
      "+++ 2.475e+02 2.465e+02 -8.899e-01 -1.668e-01 1.91 +++\n",
      "+++ 2.475e+02 2.468e+02 -8.899e-01 -2.873e-01 1.41 +++\n",
      "+++ 2.475e+02 2.469e+02 -8.899e-01 -3.476e-01 1.18 +++\n",
      "+++ 2.475e+02 2.469e+02 -8.899e-01 -3.777e-01 1.07 +++\n",
      "+++ 2.475e+02 2.470e+02 -8.899e-01 -3.928e-01 1.01 +++\n",
      "+++ 2.475e+02 2.470e+02 -8.899e-01 -4.003e-01 0.984 +++\n",
      "+++ 2.475e+02 2.470e+02 -8.899e-01 -3.965e-01 0.998 +++\n",
      "\t### errors for param 15 ###\n",
      "+++ 2.475e+02 2.471e+02 4.838e-02 4.285e-01 0.661 +++\n",
      "+++ 2.475e+02 2.470e+02 4.838e-02 6.186e-01 0.961 +++\n",
      "+++ 2.475e+02 2.469e+02 4.838e-02 7.136e-01 1.07 +++\n",
      "+++ 2.475e+02 2.470e+02 4.838e-02 6.661e-01 1.02 +++\n",
      "+++ 2.475e+02 2.470e+02 4.838e-02 6.424e-01 0.991 +++\n",
      "********************\n",
      "0.0830101 -0.576037 -1.88651 -2.18123 -2.54372 -2.856 -3.61248 -3.8364 0.0761257 0.429759 0.622953 0.90456 0.412522 0.283831 -0.889856 0.0483793\n",
      "0.00383825 0.00579502 0.025767 0.0436043 0.050153 0.0439107 0.138708 0.1845 0.086924 0.0886716 0.241158 0.261188 0.248061 0.218563 0.493323 0.593987\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "%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([ 1.25569486,  2.37000041,  1.7802513 ,  1.66775218,  0.49069246,\n",
       "        0.21781609, -0.44057362,  0.01545348])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAFrCAYAAACE+GArAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbf2CrWNuRhMGbT7IajU
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3cb164fd90>"
      ]
     },
     "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 0x7f3cb0edb550>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGYCAYAAAC3YWNyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYYc5r66ujuLWIiIimS3
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3cb0b294d0>"
      ]
     },
     "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 0x7f3cb0f130d0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3cb0e25410>"
      ]
     },
     "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([2.02,2.02], [-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
}