phy-4660/lag/data/clag_analysis-origbins-noLFerrors-5404A.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 0x7f736504bd10>"
      ]
     },
     "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/5404A.lc\"\n",
    "\n",
    "dt = 0.01\n",
    "t1, l1, l1e = np.loadtxt(ref_file).T\n",
    "errorbar(t1, l1, yerr=l1e, fmt='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.005     ,  0.01861938,  0.04473305,  0.06933623,  0.10747115,\n",
       "        0.16658029,  0.25819945,  0.40020915,  0.62032418])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n",
    "                0.25819945, 0.40020915, 0.62032418])\n",
    "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n",
    "nfq = len(fqL) - 1\n",
    "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n",
    "\n",
    "\n",
    "fqL\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 4.342e-01 5.077e+01 inf -- -5.530e+02 -- 1 1 1 1 1 1 1 1\n",
      "   2 7.674e-01 5.065e+01 8.300e+01 -- -4.700e+02 -- 0.653018 0.587019 0.568277 0.567457 0.566281 0.566085 0.565773 0.566163\n",
      "   3 3.298e+00 5.043e+01 8.075e+01 -- -3.893e+02 -- 0.414806 0.209135 0.141159 0.13784 0.133393 0.132728 0.131612 0.132761\n",
      "   4 1.572e+00 5.010e+01 7.754e+01 -- -3.117e+02 -- 0.322539 -0.0834456 -0.273066 -0.284295 -0.297612 -0.299435 -0.302479 -0.300412\n",
      "   5 5.908e-01 4.964e+01 7.386e+01 -- -2.379e+02 -- 0.302357 -0.214604 -0.654658 -0.688754 -0.723838 -0.729279 -0.736418 -0.733888\n",
      "   6 3.713e-01 4.877e+01 6.953e+01 -- -1.683e+02 -- 0.284419 -0.200357 -0.96379 -1.05472 -1.13798 -1.15477 -1.17031 -1.16748\n",
      "   7 2.709e-01 4.671e+01 6.269e+01 -- -1.056e+02 -- 0.277768 -0.185001 -1.13047 -1.34026 -1.52128 -1.56845 -1.6043 -1.60101\n",
      "   8 2.135e-01 4.361e+01 5.281e+01 -- -5.282e+01 -- 0.277012 -0.185189 -1.16375 -1.49463 -1.83211 -1.9477 -2.03737 -2.03476\n",
      "   9 1.764e-01 3.767e+01 4.019e+01 -- -1.264e+01 -- 0.282161 -0.184207 -1.17891 -1.53049 -2.01851 -2.24569 -2.46562 -2.46922\n",
      "  10 1.508e-01 2.738e+01 2.645e+01 -- 1.382e+01 -- 0.289545 -0.182463 -1.18795 -1.52893 -2.08812 -2.41103 -2.87498 -2.90486\n",
      "  11 1.349e-01 1.468e+01 1.390e+01 -- 2.772e+01 -- 0.293547 -0.180898 -1.1897 -1.52803 -2.10944 -2.46526 -3.22702 -3.34288\n",
      "  12 1.358e-01 5.365e+00 5.378e+00 -- 3.309e+01 -- 0.295456 -0.179941 -1.19052 -1.52801 -2.11928 -2.48294 -3.46233 -3.79368\n",
      "  13 1.868e-01 1.338e+00 1.633e+00 -- 3.473e+01 -- 0.297315 -0.17923 -1.19104 -1.52666 -2.12491 -2.48975 -3.55567 -4.30889\n",
      "  14 6.248e-01 2.604e-01 4.517e-01 -- 3.518e+01 -- 0.299091 -0.178645 -1.191 -1.52463 -2.12805 -2.4915 -3.5672 -5.11363\n",
      "  15 2.744e+02 2.611e-01 7.022e-02 -- 3.525e+01 -- 0.300248 -0.178286 -1.19075 -1.52307 -2.12961 -2.49161 -3.56337 -8\n",
      "  16 2.745e+02 2.782e-01 4.368e-04 -- 3.525e+01 -- 0.300566 -0.178158 -1.19062 -1.52252 -2.13009 -2.49151 -3.5617 -8\n",
      "  17 2.745e+02 2.805e-01 6.410e-05 -- 3.525e+01 -- 0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n",
      "********************\n",
      "0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n",
      "0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n",
      "-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n",
    "p1 = np.ones(nfq)\n",
    "p1, p1e = clag.optimize(P1, p1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t### errors for param 0 ###\n",
      "+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n",
      "+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n",
      "+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n",
      "+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n",
      "+++ 3.525e+01 3.475e+01 3.006e-01 5.545e-01 0.997 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n",
      "+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n",
      "+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n",
      "+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n",
      "+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n",
      "+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02   1 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n",
      "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n",
      "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n",
      "+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n",
      "+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 3.525e+01 3.482e+01 -1.522e+00 -1.345e+00 0.865 +++\n",
      "+++ 3.525e+01 3.432e+01 -1.522e+00 -1.257e+00 1.86 +++\n",
      "+++ 3.525e+01 3.459e+01 -1.522e+00 -1.301e+00 1.32 +++\n",
      "+++ 3.525e+01 3.471e+01 -1.522e+00 -1.323e+00 1.08 +++\n",
      "+++ 3.525e+01 3.476e+01 -1.522e+00 -1.334e+00 0.97 +++\n",
      "+++ 3.525e+01 3.473e+01 -1.522e+00 -1.329e+00 1.03 +++\n",
      "+++ 3.525e+01 3.475e+01 -1.522e+00 -1.331e+00 0.998 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 3.525e+01 3.481e+01 -2.130e+00 -1.977e+00 0.874 +++\n",
      "+++ 3.525e+01 3.429e+01 -2.130e+00 -1.901e+00 1.91 +++\n",
      "+++ 3.525e+01 3.457e+01 -2.130e+00 -1.939e+00 1.35 +++\n",
      "+++ 3.525e+01 3.470e+01 -2.130e+00 -1.958e+00 1.1 +++\n",
      "+++ 3.525e+01 3.476e+01 -2.130e+00 -1.968e+00 0.983 +++\n",
      "+++ 3.525e+01 3.473e+01 -2.130e+00 -1.963e+00 1.04 +++\n",
      "+++ 3.525e+01 3.474e+01 -2.130e+00 -1.965e+00 1.01 +++\n",
      "+++ 3.525e+01 3.475e+01 -2.130e+00 -1.966e+00 0.997 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 3.525e+01 3.474e+01 -2.491e+00 -2.358e+00 1.01 +++\n",
      "+++ 3.525e+01 3.512e+01 -2.491e+00 -2.425e+00 0.263 +++\n",
      "+++ 3.525e+01 3.496e+01 -2.491e+00 -2.392e+00 0.579 +++\n",
      "+++ 3.525e+01 3.486e+01 -2.491e+00 -2.375e+00 0.781 +++\n",
      "+++ 3.525e+01 3.480e+01 -2.491e+00 -2.367e+00 0.893 +++\n",
      "+++ 3.525e+01 3.477e+01 -2.491e+00 -2.363e+00 0.952 +++\n",
      "+++ 3.525e+01 3.476e+01 -2.491e+00 -2.361e+00 0.982 +++\n",
      "+++ 3.525e+01 3.475e+01 -2.491e+00 -2.360e+00 0.997 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 3.525e+01 3.507e+01 -3.561e+00 -3.413e+00 0.363 +++\n",
      "+++ 3.525e+01 3.484e+01 -3.561e+00 -3.339e+00 0.814 +++\n",
      "+++ 3.525e+01 3.468e+01 -3.561e+00 -3.301e+00 1.13 +++\n",
      "+++ 3.525e+01 3.477e+01 -3.561e+00 -3.320e+00 0.962 +++\n",
      "+++ 3.525e+01 3.473e+01 -3.561e+00 -3.311e+00 1.04 +++\n",
      "+++ 3.525e+01 3.475e+01 -3.561e+00 -3.315e+00   1 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 3.525e+01 3.524e+01 -8.000e+00 -6.000e+00 0.0178 +++\n",
      "+++ 3.525e+01 3.515e+01 -8.000e+00 -5.000e+00 0.187 +++\n",
      "+++ 3.525e+01 3.494e+01 -8.000e+00 -4.500e+00 0.618 +++\n",
      "+++ 3.525e+01 3.466e+01 -8.000e+00 -4.250e+00 1.18 +++\n",
      "+++ 3.525e+01 3.482e+01 -8.000e+00 -4.375e+00 0.851 +++\n",
      "+++ 3.525e+01 3.475e+01 -8.000e+00 -4.312e+00   1 +++\n",
      "********************\n",
      "0.300605 -0.178131 -1.19059 -1.52241 -2.13019 -2.49148 -3.56144 -8\n",
      "0.253864 0.205597 0.228999 0.191096 0.1638 0.131949 0.246154 3.6875\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "p1, p1e = clag.errors(P1, p1, p1e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFrCAYAAABbtho0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1Wpt9anVaq5RkDrAkCC
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7388dbb2d0>"
      ]
     },
     "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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\nODSR72P8qcrXbnm71qX
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7364bb1710>"
      ]
     },
     "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 4.610e-01 9.098e+01 inf -- 1.744e+02 -- 1 1 1 1 1 1 1 1\n",
      "   2 8.066e-01 8.980e+01 1.014e+02 -- 2.758e+02 -- 0.764458 0.592778 0.539049 0.562235 0.572289 0.566582 0.574488 0.570186\n",
      "   3 4.133e+00 8.890e+01 1.012e+02 -- 3.771e+02 -- 0.390803 0.167503 0.104265 0.129187 0.143015 0.13475 0.14757 0.142553\n",
      "   4 7.028e+01 8.821e+01 1.004e+02 -- 4.775e+02 -- 0.00503816 -0.253372 -0.326662 -0.304159 -0.285875 -0.29734 -0.280456 -0.284431\n",
      "   5 8.140e-01 8.698e+01 9.903e+01 -- 5.765e+02 -- -0.34905 -0.657519 -0.755903 -0.73766 -0.713908 -0.729735 -0.710276 -0.71228\n",
      "   6 3.770e-01 8.522e+01 9.618e+01 -- 6.727e+02 -- -0.633175 -1.02654 -1.18245 -1.1696 -1.13984 -1.16159 -1.14079 -1.13949\n",
      "   7 2.735e-01 8.339e+01 9.226e+01 -- 7.650e+02 -- -0.806027 -1.33498 -1.60319 -1.59717 -1.56166 -1.59163 -1.57085 -1.56469\n",
      "   8 2.138e-01 8.120e+01 8.777e+01 -- 8.527e+02 -- -0.862131 -1.54146 -2.00828 -2.01427 -1.9772 -2.01825 -2.00048 -1.98894\n",
      "   9 1.754e-01 7.801e+01 8.241e+01 -- 9.351e+02 -- -0.842095 -1.59954 -2.37394 -2.40556 -2.38506 -2.43954 -2.42825 -2.41302\n",
      "  10 1.483e-01 7.298e+01 7.502e+01 -- 1.010e+03 -- -0.783896 -1.56622 -2.64497 -2.73499 -2.78365 -2.85462 -2.85094 -2.83628\n",
      "  11 1.272e-01 6.498e+01 6.421e+01 -- 1.074e+03 -- -0.728704 -1.54164 -2.75253 -2.94491 -3.15964 -3.25681 -3.26391 -3.25681\n",
      "  12 1.111e-01 5.364e+01 4.985e+01 -- 1.124e+03 -- -0.697013 -1.52135 -2.76748 -3.01573 -3.47965 -3.61977 -3.65793 -3.67111\n",
      "  13 9.921e-02 3.928e+01 3.311e+01 -- 1.157e+03 -- -0.68286 -1.50549 -2.78198 -3.01943 -3.7022 -3.88826 -4.01576 -4.07908\n",
      "  14 8.742e-02 2.316e+01 1.815e+01 -- 1.175e+03 -- -0.67696 -1.49524 -2.79447 -3.00982 -3.82195 -4.00921 -4.30702 -4.48377\n",
      "  15 6.880e-02 9.460e+00 7.667e+00 -- 1.183e+03 -- -0.675273 -1.48938 -2.80142 -2.99712 -3.87606 -4.02132 -4.49151 -4.87573\n",
      "  16 3.697e-02 2.241e+00 2.003e+00 -- 1.185e+03 -- -0.675444 -1.48628 -2.80573 -2.98594 -3.90153 -4.01347 -4.55974 -5.21119\n",
      "  17 6.073e-03 2.318e-01 2.232e-01 -- 1.185e+03 -- -0.675725 -1.48454 -2.80827 -2.9785 -3.91588 -4.0101 -4.56645 -5.40386\n",
      "  18 8.426e-04 9.306e-02 4.878e-03 -- 1.185e+03 -- -0.675636 -1.48368 -2.80908 -2.97402 -3.92351 -4.00944 -4.56338 -5.43668\n",
      "  19 4.159e-04 3.811e-02 3.790e-04 -- 1.185e+03 -- -0.675401 -1.48333 -2.809 -2.97151 -3.92669 -4.00953 -4.56255 -5.4346\n",
      "  20 1.919e-04 1.760e-02 7.212e-05 -- 1.185e+03 -- -0.675286 -1.48317 -2.80889 -2.97028 -3.92799 -4.00961 -4.56253 -5.43514\n",
      "********************\n",
      "-0.675286 -1.48317 -2.80889 -2.97028 -3.92799 -4.00961 -4.56253 -5.43514\n",
      "0.231904 0.203388 0.246283 0.177512 0.182405 0.144335 0.166268 0.423476\n",
      "0.000864464 0.00208153 0.00125836 0.0175976 -0.0171085 -0.00187558 -1.56774e-05 2.12549e-05\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",
      "+++ 1.185e+03 1.185e+03 -6.752e-01 -4.433e-01 0.84 +++\n",
      "+++ 1.185e+03 1.184e+03 -6.752e-01 -3.274e-01 1.76 +++\n",
      "+++ 1.185e+03 1.185e+03 -6.752e-01 -3.854e-01 1.27 +++\n",
      "+++ 1.185e+03 1.185e+03 -6.752e-01 -4.144e-01 1.04 +++\n",
      "+++ 1.185e+03 1.185e+03 -6.752e-01 -4.288e-01 0.94 +++\n",
      "+++ 1.185e+03 1.185e+03 -6.752e-01 -4.216e-01 0.992 +++\n",
      "\t### errors for param 1 ###\n",
      "+++ 1.185e+03 1.185e+03 -1.483e+00 -1.280e+00 0.943 +++\n",
      "+++ 1.185e+03 1.184e+03 -1.483e+00 -1.178e+00   2 +++\n",
      "+++ 1.185e+03 1.185e+03 -1.483e+00 -1.229e+00 1.43 +++\n",
      "+++ 1.185e+03 1.185e+03 -1.483e+00 -1.254e+00 1.18 +++\n",
      "+++ 1.185e+03 1.185e+03 -1.483e+00 -1.267e+00 1.06 +++\n",
      "+++ 1.185e+03 1.185e+03 -1.483e+00 -1.273e+00 0.999 +++\n",
      "\t### errors for param 2 ###\n",
      "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.563e+00 0.987 +++\n",
      "+++ 1.185e+03 1.184e+03 -2.809e+00 -2.439e+00 2.1 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.501e+00 1.5 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.532e+00 1.23 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.547e+00 1.11 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.555e+00 1.05 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.559e+00 1.02 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.561e+00   1 +++\n",
      "\t### errors for param 3 ###\n",
      "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.792e+00 0.612 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.703e+00 1.31 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.748e+00 0.934 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.726e+00 1.12 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.737e+00 1.02 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.742e+00 0.978 +++\n",
      "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.740e+00   1 +++\n",
      "\t### errors for param 4 ###\n",
      "+++ 1.185e+03 1.185e+03 -3.929e+00 -3.746e+00 0.715 +++\n",
      "+++ 1.185e+03 1.185e+03 -3.929e+00 -3.655e+00 1.61 +++\n",
      "+++ 1.185e+03 1.185e+03 -3.929e+00 -3.701e+00 1.12 +++\n",
      "+++ 1.185e+03 1.185e+03 -3.929e+00 -3.723e+00 0.906 +++\n",
      "+++ 1.185e+03 1.185e+03 -3.929e+00 -3.712e+00 1.01 +++\n",
      "\t### errors for param 5 ###\n",
      "+++ 1.185e+03 1.185e+03 -4.010e+00 -3.865e+00 0.991 +++\n",
      "\t### errors for param 6 ###\n",
      "+++ 1.185e+03 1.185e+03 -4.563e+00 -4.396e+00 1.01 +++\n",
      "\t### errors for param 7 ###\n",
      "+++ 1.185e+03 1.185e+03 -5.435e+00 -5.223e+00 0.386 +++\n",
      "+++ 1.185e+03 1.185e+03 -5.435e+00 -5.118e+00 0.993 +++\n",
      "********************\n",
      "-0.675245 -1.48309 -2.80886 -2.96971 -3.92857 -4.00963 -4.56252 -5.43512\n",
      "0.253646 0.209743 0.248217 0.230185 0.21667 0.144334 0.166265 0.317595\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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\nS5SkmmdIUEMyBEjS2pU
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7365078710>"
      ]
     },
     "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 3.996e+02 1.150e+01 inf -- 1.244e+03 -- -0.48732 -1.13061 -2.29972 -2.54606 -3.32938 -3.55056 -4.36198 -7.01756 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
      "   3 1.146e+02 1.274e+01 2.644e+00 -- 1.247e+03 -- -0.46592 -1.09839 -2.27929 -2.51606 -3.2945 -3.53263 -4.40205 -7.31756 0.173375 0.179509 0.201787 0.171316 0.150055 0.152867 0.0990853 -2.1868\n",
      "   5 6.107e+01 1.422e+01 2.363e+00 -- 1.249e+03 -- -0.445096 -1.06944 -2.25767 -2.48924 -3.26504 -3.51627 -4.44162 -7.01756 0.23367 0.240665 0.283299 0.22705 0.187337 0.196036 0.0982347 -2.11553\n",
      "   7 2.105e+01 1.585e+01 2.131e+00 -- 1.251e+03 -- -0.425484 -1.04389 -2.23659 -2.46561 -3.24012 -3.50157 -4.48059 -6.71756 0.283283 0.288529 0.348404 0.27129 0.21583 0.231595 0.0974289 -2.46919\n",
      "   9 2.358e+01 1.743e+01 1.909e+00 -- 1.253e+03 -- -0.40737 -1.02151 -2.21687 -2.44489 -3.21894 -3.48846 -4.51886 -6.41756 0.324291 0.326635 0.400676 0.306939 0.238059 0.261125 0.0966177 -1.38312\n",
      "  11 4.074e+01 1.911e+01 1.751e+00 -- 1.255e+03 -- -0.390833 -1.00195 -2.19886 -2.42677 -3.20086 -3.47683 -4.55632 -6.71756 0.358383 0.357437 0.443033 0.336042 0.255704 0.285884 0.0957404 1.63909\n",
      "  13 1.370e+01 2.129e+01 1.587e+00 -- 1.256e+03 -- -0.375838 -0.984839 -2.18261 -2.41092 -3.18537 -3.46656 -4.59286 -6.41756 0.386914 0.382651 0.477698 0.360091 0.269847 0.306761 0.095098 2.03338\n",
      "  15 1.158e+01 2.351e+01 1.408e+00 -- 1.258e+03 -- -0.362294 -0.96986 -2.16805 -2.39704 -3.17203 -3.45749 -4.62837 -6.11756 0.410934 0.403523 0.506367 0.380155 0.281317 0.324474 0.0944395 -1.46371\n",
      "  16 1.607e+03 7.266e+02 8.010e+00 -- 1.266e+03 -- -0.240266 -0.83846 -2.03814 -2.27523 -3.05681 -3.37736 -4.97188 -8 0.614202 0.577977 0.745974 0.548634 0.37576 0.476151 0.0813258 0.437418\n",
      "  17 9.038e+02 4.128e+00 3.834e+00 -- 1.270e+03 -- -0.232783 -0.852575 -2.04678 -2.29283 -3.08252 -3.39513 -5.04543 -8 0.548929 0.498486 0.710668 0.487552 0.327531 0.450717 -0.157389 -0.565487\n",
      "  18 2.647e+02 5.497e-01 2.932e-02 -- 1.270e+03 -- -0.23322 -0.852233 -2.04339 -2.2905 -3.07964 -3.39057 -5.07095 -8 0.552004 0.51698 0.698282 0.491552 0.331349 0.451833 -0.156166 -2.69909\n",
      "  19 9.652e-01 2.219e+00 6.531e-01 -- 1.269e+03 -- -0.233246 -0.852217 -2.0438 -2.29075 -3.08035 -3.39107 -5.06163 -5 0.551423 0.515009 0.700327 0.490665 0.330402 0.452209 -0.176384 -0.814953\n",
      "  20 7.789e+00 7.955e-01 6.402e-01 -- 1.270e+03 -- -0.233412 -0.852282 -2.04423 -2.29033 -3.07948 -3.38989 -5.09639 -5.98364 0.551849 0.515428 0.700779 0.489124 0.333636 0.45985 -0.205833 -1.60153\n",
      "  21 8.557e+01 4.082e-02 1.163e-02 -- 1.270e+03 -- -0.233294 -0.852207 -2.04379 -2.29075 -3.08047 -3.39073 -5.05794 -7.0122 0.551775 0.515347 0.699486 0.490646 0.330835 0.451633 -0.186288 -1.50956\n",
      "  22 1.000e+03 9.633e-02 1.325e-03 -- 1.270e+03 -- -0.233263 -0.852211 -2.04374 -2.29072 -3.08027 -3.39085 -5.06491 -8 0.551627 0.515234 0.700132 0.49056 0.330468 0.452479 -0.17545 1.26429\n",
      "  23 6.947e+01 7.583e-02 5.012e-04 -- 1.270e+03 -- -0.23325 -0.852208 -2.04374 -2.29072 -3.08029 -3.39091 -5.06372 -8 0.551664 0.515214 0.700074 0.490679 0.330402 0.452326 -0.175132 2.98034\n",
      "  24 1.062e+00 2.147e+00 6.150e-01 -- 1.269e+03 -- -0.23325 -0.852208 -2.04374 -2.29072 -3.08028 -3.39091 -5.06387 -5 0.551636 0.515215 0.70009 0.490675 0.330405 0.452326 -0.175244 -0.97755\n",
      "  25 5.159e+00 7.693e-01 6.227e-01 -- 1.270e+03 -- -0.233412 -0.852294 -2.04429 -2.29036 -3.07951 -3.38938 -5.09276 -5.9203 0.551804 0.515509 0.700612 0.48922 0.334134 0.458766 -0.237317 -2.01536\n",
      "  26 3.256e+00 2.577e+00 8.259e-01 -- 1.269e+03 -- -0.233292 -0.852208 -2.04377 -2.29076 -3.08053 -3.3906 -5.0573 -4.88481 0.551755 0.515396 0.699418 0.490641 0.330803 0.451361 -0.185258 0.154178\n",
      "  27 8.306e+00 8.513e-01 7.419e-01 -- 1.269e+03 -- -0.2334 -0.85217 -2.04371 -2.2903 -3.07979 -3.394 -5.12588 -5.6438 0.551608 0.514436 0.70228 0.489167 0.329463 0.464863 0.0259555 0.65621\n",
      "  28 3.018e+02 2.512e-01 7.557e-02 -- 1.270e+03 -- -0.23325 -0.852177 -2.04375 -2.29075 -3.08018 -3.3918 -5.06768 -7.58414 0.552082 0.51496 0.699848 0.491275 0.330113 0.452176 -0.189638 -1.62114\n",
      "  29 9.648e+02 1.054e-01 5.781e-05 -- 1.270e+03 -- -0.233258 -0.852207 -2.04374 -2.29072 -3.08024 -3.39094 -5.06451 -8 0.551479 0.515219 0.700113 0.49073 0.330409 0.452292 -0.172337 -0.754914\n",
      "********************\n",
      "-0.233258 -0.852207 -2.04374 -2.29072 -3.08024 -3.39094 -5.06451 -8 0.551479 0.515219 0.700113 0.49073 0.330409 0.452292 -0.172337 -0.754914\n",
      "0.0271527 0.0112859 0.0294371 0.0238447 0.0419821 0.0761252 1.60955 678.342 0.189103 0.111853 0.196777 0.152506 0.193957 0.252076 3.72644 1583.62\n",
      "0.0121717 -0.0302322 -0.0279866 -0.0393548 -0.105374 -0.0168605 -0.00293106 -0.000917936 0.00679344 -3.13658e-05 -4.94438e-05 -0.00117381 0.00319937 0.00794062 0.00314398 -0.000234878\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "Cx = clag.clag('cxd10r', [[t1,t2]], [[l1,l2]], [[l1e,l2e]], dt, fqL, p1, p2)\n",
    "p  = np.concatenate( ((p1+p2)*0.5-0.3,p1*0+0.1) ) # a  good starting point generally\n",
    "p, pe = clag.optimize(Cx, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phi, phie = p[nfq:], pe[nfq:]\n",
    "lag, lage = phi/(2*np.pi*fqd), phie/(2*np.pi*fqd)    \n",
    "cx, cxe   = p[:nfq], pe[:nfq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.0966594 ,  2.8412865 ,  2.00075511,  0.90476609,  0.39301911,\n",
       "        0.34709626, -0.08532525, -0.24113739])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhkAAAFtCAYAAACqQXjBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTE1N5dChQ9m6daugAW0
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f736317bf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "xscale('log'); ylim(-10,15)\n",
    "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n",
    "\n",
    "lag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7363203250>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAGYCAYAAABPgZiFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmwhs/thgXs8lS0dUSZK
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7362fc36d0>"
      ]
     },
     "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": 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
}