1.04-lightcurve-files.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are Light Curve Files?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous tutorial we looked at `LightCurve` objects, which contain time and flux points. Now we will look at `LightCurveFiles`. Rather than being generated by you using a [Target Pixel File](http://lightkurve.keplerscience.org/tutorials/1.02-target-pixel-files.html), these files have been pregenerated using NASA's [Kepler Data Processing Pipeline](https://github.com/nasa/kepler-pipeline/).\n",
    "Usually, you will access these files through the [MAST archive](https://archive.stsci.edu/kepler/data_search/search.php). \n",
    "\n",
    "We will demonstrate the difference between a `LightCurve` and a `LightCurveFile` using data from Kepler."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kepler light curves from MAST have some level of processing (more details [here](https://arxiv.org/pdf/1207.3093.pdf)) and allow you to access the two kinds of flux; the **SAP** flux and the **PDCSAP** flux. SAP flux is Simple Aperture Photometry flux, the same as we made in the [previous tutorial](http://lightkurve.keplerscience.org/tutorials/1.03-what-are-lightcurves.html). PDCSAP is the Pre-search Data Conditioning SAP flux. Long term trends have been removed from this data using so-called Cotrending Basis Vectors (CBVs). PDCSAP flux is usually slightly cleaner data than the SAP flux and will have fewer long term trends.\n",
    "\n",
    "We can read in a light curve file from the Kepler mission using `KeplerLightCurveFile`. We can use the same `from_archive` function as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading URL https://mast.stsci.edu/api/v0/download/file?uri=mast:Kepler/url/missions/kepler/lightcurves/0069/006922244/kplr006922244-2010078095331_llc.fits to ./mastDownload/Kepler/kplr006922244_lc_Q111111111111111111/kplr006922244-2010078095331_llc.fits ... [Done]\n"
     ]
    }
   ],
   "source": [
    "from lightkurve import KeplerLightCurveFile\n",
    "lcf = KeplerLightCurveFile.from_archive(6922244, quarter=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`lcf` is now a `KeplerLightCurveFile` object. In this case it contains two `KeplerLightCurve` objects, one for the SAP flux and one for the PDCSAP flux. The `plot` method on a `KeplerLightCurveFile` object will plot up both of these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fea1e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lcf.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the PDCSAP flux is flatter. To work more with this data we must choose which type of flux we want to work with. Let's choose PDCSAP flux:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdcsap = lcf.PDCSAP_FLUX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can choose SAP flux in a similar way using\n",
    "  \n",
    "    sapflux = lcf.SAP_FLUX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KeplerLightCurveFile(KIC: 6922244)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lcf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KeplerLightCurve(KIC: 6922244)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdcsap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This has created a `KeplerLightCurve` object. The only flux it contains is the PDCSAP flux. This has the same methods we used in the [previous tutorial](http://lightkurve.keplerscience.org/tutorials/what-are-lightcurves.html). For example you can check the meta data and the CDPP noise metric:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Kepler'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdcsap.mission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdcsap.quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "407.9620564266182"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdcsap.cdpp()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}