{
 "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": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:24:56.439442Z",
     "iopub.status.busy": "2023-11-03T14:24:56.438807Z",
     "iopub.status.idle": "2023-11-03T14:25:04.221607Z",
     "shell.execute_reply": "2023-11-03T14:25:04.221271Z"
    }
   },
   "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": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:25:04.223651Z",
     "iopub.status.busy": "2023-11-03T14:25:04.223519Z",
     "iopub.status.idle": "2023-11-03T14:25:04.459374Z",
     "shell.execute_reply": "2023-11-03T14:25:04.458997Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 848.5x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:25:04.461333Z",
     "iopub.status.busy": "2023-11-03T14:25:04.461192Z",
     "iopub.status.idle": "2023-11-03T14:25:04.478638Z",
     "shell.execute_reply": "2023-11-03T14:25:04.478332Z"
    }
   },
   "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": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:25:04.481333Z",
     "iopub.status.busy": "2023-11-03T14:25:04.481212Z",
     "iopub.status.idle": "2023-11-03T14:25:04.490536Z",
     "shell.execute_reply": "2023-11-03T14:25:04.490266Z"
    }
   },
   "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": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:25:04.492269Z",
     "iopub.status.busy": "2023-11-03T14:25:04.492171Z",
     "iopub.status.idle": "2023-11-03T14:25:04.505921Z",
     "shell.execute_reply": "2023-11-03T14:25:04.505441Z"
    }
   },
   "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": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:25:04.507721Z",
     "iopub.status.busy": "2023-11-03T14:25:04.507602Z",
     "iopub.status.idle": "2023-11-03T14:25:04.607205Z",
     "shell.execute_reply": "2023-11-03T14:25:04.606817Z"
    }
   },
   "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": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:25:04.609111Z",
     "iopub.status.busy": "2023-11-03T14:25:04.608986Z",
     "iopub.status.idle": "2023-11-03T14:25:04.659225Z",
     "shell.execute_reply": "2023-11-03T14:25:04.658927Z"
    }
   },
   "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": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:25:04.660942Z",
     "iopub.status.busy": "2023-11-03T14:25:04.660851Z",
     "iopub.status.idle": "2023-11-03T14:25:04.702643Z",
     "shell.execute_reply": "2023-11-03T14:25:04.702340Z"
    }
   },
   "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": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:25:04.704774Z",
     "iopub.status.busy": "2023-11-03T14:25:04.704656Z",
     "iopub.status.idle": "2023-11-03T14:25:04.707043Z",
     "shell.execute_reply": "2023-11-03T14:25:04.706750Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best fit period: 0.838 d\n"
     ]
    }
   ],
   "source": [
    "best_fit_period = periodogram.period_at_max_power\n",
    "print('Best fit period: {:.3f}'.format(best_fit_period))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:25:04.708530Z",
     "iopub.status.busy": "2023-11-03T14:25:04.708410Z",
     "iopub.status.idle": "2023-11-03T14:25:04.816584Z",
     "shell.execute_reply": "2023-11-03T14:25:04.816255Z"
    }
   },
   "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!"
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}