{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to recover a known planet in Kepler data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial will demonstrate the basic steps required to recover the signal of [Kepler-10b](https://en.wikipedia.org/wiki/Kepler-10b), the first rocky planet that was discovered by Kepler!\n",
    "\n",
    "Let's start by downloading the pixel data for this target for one of Kepler's observing quarters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightkurve as lk\n",
    "tpf = lk.search_targetpixelfile(\"Kepler-10\", author=\"Kepler\", quarter=3, cadence=\"long\").download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the `plot` method to show the pixel data at one point in time (frame index 100). We'll also pass along a few plotting arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tpf.plot(frame=100, scale='log', show_colorbar=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target pixel file appears to show one bright star with a core brightness of approximately 50,000 electrons/seconds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will use the [to_lightcurve](https://docs.lightkurve.org/reference/api/lightkurve.KeplerTargetPixelFile.to_lightcurve.html?highlight=to_lightcurve) method to create a simple aperture photometry lightcurve using the\n",
    "mask defined by the pipeline which is stored in [tpf.pipeline_mask](https://docs.lightkurve.org/reference/api/lightkurve.KeplerTargetPixelFile.pipeline_mask.html?highlight=pipeline_mask)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lc = tpf.to_lightcurve(aperture_mask=tpf.pipeline_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the output lightcurve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lc.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use the [.flatten()](https://docs.lightkurve.org/reference/api/lightkurve.LightCurve.flatten.html?highlight=flatten#lightkurve.LightCurve.flatten) method, which removes long-term variability that we are not interested in using a high-pass filter called *Savitzky-Golay*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flat, trend = lc.flatten(window_length=301, return_trend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the trend estimated in red:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = lc.errorbar(label=\"Kepler-10\")                   # plot() returns a matplotlib axes ...\n",
    "trend.plot(ax=ax, color='red', lw=2, label='Trend');  # which we can pass to the next plot() to use the same axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and the flat lightcurve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flat.errorbar(label=\"Kepler-10\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's run a period search function using the well-known Box-Least Squares algorithm (BLS), which was added to the [AstroPy package](http://docs.astropy.org) in version 3.1.\n",
    "\n",
    "We will use the BLS algorithm to search a pre-defined grid of transit periods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "periodogram = flat.to_periodogram(method=\"bls\", period=np.arange(0.5, 1.5, 0.001))\n",
    "periodogram.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like we found a strong signal with a periodicity near 0.8 days!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_fit_period = periodogram.period_at_max_power\n",
    "print('Best fit period: {:.3f}'.format(best_fit_period))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flat.fold(period=best_fit_period, epoch_time=periodogram.transit_time_at_max_power).errorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We successfully recovered the planet!"
   ]
  }
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