Lomb-Scargle quick scaling test

LombScargle.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:c7b70fa7df855d4e227979ec9175cb22f5e23fd1c7ca2cbd7e67f9e222e84c94"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from astroML.time_series import lomb_scargle\n",
      "from time import time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = 1000 * np.random.random(100000)\n",
      "dy = 0.1 + 0.1 * np.random.random(t.shape)\n",
      "y = np.random.normal(np.sin(t / 100), dy)\n",
      "omega = 10 ** np.linspace(-2, 2, 50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Ns = 10 ** np.linspace(2, 5, 20)\n",
      "times = []\n",
      "for N in Ns:\n",
      "    t0 = time()\n",
      "    power = lomb_scargle(t[:N], y[:N], dy[:N], omega)\n",
      "    t1 = time()\n",
      "    times.append(t1 - t0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.loglog(Ns, times)\n",
      "plt.grid(True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OZufOndTU1FBXV0dFRQXDhw+PZWwiIhInYRX/MWPGMGjQIHbs2EF6ejplZWW0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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106603910>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}