test_PyKE_detrending.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 166
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 167
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import kepfit\n",
      "import kepmsg\n",
      "\n",
      "length = 100000\n",
      "time1 = np.arange(length, dtype=np.float) / 100.\n",
      "true_p = [-1e-6, 1e-3, .2, 1., 3.]\n",
      "data1 = np.polyval(true_p, time1)\n",
      "#data1 += np.random.normal(length)\n",
      "err1 = 1. * np.ones_like(data1)\n",
      "nsig1 = 3.\n",
      "niter1 = 3.\n",
      "npoly1 = 4\n",
      "pinit = np.zeros(npoly1 + 1, dtype=np.float)\n",
      "pinit[0] = data1.mean()\n",
      "logfile = \"kepler.log\"\n",
      "verbose = True\n",
      "kepmsg.log(logfile, \"logging\",verbose)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "logging\n"
       ]
      }
     ],
     "prompt_number": 168
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(time1, data1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 169,
       "text": [
        "[<matplotlib.lines.Line2D at 0xd864f90>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wEhiU6/WYSiLHj9vkvxUrYNEiSxwHD8Jvf2vHTTfZfuXJyV5HKiJ+5pckkgz8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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xfe44e90>"
       ]
      }
     ],
     "prompt_number": 169
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "functype = 'poly' + str(npoly1)\n",
      "coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx1, ploty1, status = \\\n",
      "    kepfit.lsqclip(functype,pinit,time1,data1,err1,nsig1,nsig1,niter1,\n",
      "                   logfile,verbose)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 170
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print true_p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[-1e-06, 0.001, 0.2, 1.0, 3.0]\n"
       ]
      }
     ],
     "prompt_number": 172
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print coeffs[::-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ -1.00000000e-06   1.00000000e-03   2.00000000e-01   1.00000000e+00\n",
        "   3.00000000e+00]\n"
       ]
      }
     ],
     "prompt_number": 175
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}