{
 "metadata": {
  "name": "",
  "signature": "sha256:5bfb01edbec4ab885ccc4b29ba6631fa121abb1ca17e285db69ecd3fc873def1"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#SIP demo."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This demo shows how to compute an SIP for your favourite K2 target."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import wget\n",
      "import h5py\n",
      "import fitsio\n",
      "from SIP import SIP, eval_freq\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Download the Campaign 1 Eigen Light Curves (ELCs) and a light curve for example star, EPIC 201133037."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wget.download(\"http://bbq.dfm.io/ketu/elcs/c1.h5\")\n",
      "wget.download(\"http://bbq.dfm.io/ketu/lightcurves/c1/201100000/33000/ktwo201133037-c01_lpd-lc.fits\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "'ktwo201133037-c01_lpd-lc.fits'"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load the light curve."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = fitsio.read(\"ktwo201133037-c01_lpd-lc.fits\")\n",
      "aps = fitsio.read(\"ktwo201133037-c01_lpd-lc.fits\", 2)\n",
      "y = data[\"flux\"][:, np.argmin(aps[\"cdpp6\"])]\n",
      "x = data[\"time\"]\n",
      "q = data[\"quality\"]\n",
      "m = np.isfinite(y) * np.isfinite(x) * (q==0)  # remove nans and bad data points\n",
      "y, x = y[m], x[m]\n",
      "y = y / np.median(y) - 1 # median normalise"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load the top 150 ELCs."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nELC = 150\n",
      "with h5py.File(\"c1.h5\", \"r\") as f:\n",
      "    basis = f[\"basis\"][:nELC, m]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the raw light curve."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(x, y, \"k\")\n",
      "plt.xlabel(\"BJD - 2454833 (days)\")\n",
      "plt.ylabel(\"Normalised Flux\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "<matplotlib.text.Text at 0x10a58fed0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZ8AAAEPCAYAAACdhMnXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aa8c550>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Compute the SIP."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "periods = np.arange(1., 70., .1)  # test periods ranging from 1 to 70 days\n",
      "freqs = 1./periods\n",
      "s2n, amp2s, w = SIP(x, y, basis, freqs)\n",
      "\n",
      "plt.plot(periods, s2n, \"k\")\n",
      "plt.xlabel(\"Period (days)\")\n",
      "plt.ylabel(\"Relative (S/N)^2\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<matplotlib.text.Text at 0x10aad3690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZ4AAAEPCAYAAAByRqLpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108d462d0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now to compute the conditioned light curve. Firstly, locate the highest peak in the SIP."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "peaks = np.array([i for i in range(1, len(periods)-1) if s2n[i-1] < s2n[i] and s2n[i+1] < s2n[i]])\n",
      "l = s2n[peaks] == max(s2n[peaks])\n",
      "Pmax = periods[peaks][l][0] \n",
      "print \"Pmax = \", Pmax, \"days\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Pmax =  24.8 days\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Construct arrays."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "AT = np.concatenate((basis, np.ones((3, len(y)))), axis=0)\n",
      "ATA = np.dot(AT, AT.T)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Compute trends."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_, _, trends = eval_freq(x, y, 1./Pmax, AT, ATA, compute_trends=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(x, y, \".5\", label=\"raw\")\n",
      "plt.plot(x, y-trends, \"k\", label=\"conditioned\")\n",
      "plt.xlabel(\"BJD - 2454833 (days)\")\n",
      "plt.ylabel(\"Normalised Flux\")\n",
      "plt.legend(loc=\"best\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "<matplotlib.legend.Legend at 0x10aa142d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZ8AAAEPCAYAAACdhMnXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZKWk8YnSZIkaTn/A3+5qZXBD1TUAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a689650>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}