{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to perform aperture photometry with custom apertures?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have discussed in [previous tutorials](http://lightkurve.keplerscience.org/tutorials/1.03-what-are-lightcurves.html) how Simple Aperture Photometry works. We choose a set of pixels in the image and sum those to produce a single flux value. We do this for every image as a function of time to produce a light curve.\n",
    "\n",
    "The [Kepler Data Pipeline](https://github.com/nasa/kepler-pipeline) produces an aperture, which is used by default by lightkurve. However, there are some cases where you might want to produce your own aperture. The field may be crowded, or you may wish to change the aperture size to change the relative contribution of the background. We can do this simply with lightkurve.\n",
    "\n",
    "First, let's load a target pixel file. Let's choose [KIC 6679295](https://exoplanetarchive.ipac.caltech.edu/cgi-bin/DisplayOverview/nph-DisplayOverview?objname=KOI-2862.01&type=KEPLER_CANDIDATE). This is a Kepler planet canidate. We'll use the `from_archive` function to download every target pixel file available for each quarter of this data set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "from lightkurve import KeplerTargetPixelFile\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "kic = '6679295'\n",
    "\n",
    "#List to hold our TPFs\n",
    "tpfs = [] \n",
    "for q in range(1,18):\n",
    "    #Note some quarters are missing, so we'll use Python's try/except to avoid crashing\n",
    "    try:\n",
    "        tpfs.append(KeplerTargetPixelFile.from_archive(kic, quarter=q))\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've now created a list of `KeplerTargetPixelFiles`, where each item is a different quarter. We're going to be able to combine these just like in the [stitching tutorial](). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[KeplerTargetPixelFile Object (ID: 6679295),\n",
       " KeplerTargetPixelFile Object (ID: 6679295),\n",
       " KeplerTargetPixelFile Object (ID: 6679295),\n",
       " KeplerTargetPixelFile Object (ID: 6679295),\n",
       " KeplerTargetPixelFile Object (ID: 6679295),\n",
       " KeplerTargetPixelFile Object (ID: 6679295),\n",
       " KeplerTargetPixelFile Object (ID: 6679295),\n",
       " KeplerTargetPixelFile Object (ID: 6679295),\n",
       " KeplerTargetPixelFile Object (ID: 6679295),\n",
       " KeplerTargetPixelFile Object (ID: 6679295),\n",
       " KeplerTargetPixelFile Object (ID: 6679295),\n",
       " KeplerTargetPixelFile Object (ID: 6679295),\n",
       " KeplerTargetPixelFile Object (ID: 6679295),\n",
       " KeplerTargetPixelFile Object (ID: 6679295)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tpfs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at just one of those target pixel files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build the light curve\n",
    "pipeline_lc = tpfs[0].to_lightcurve().flatten()\n",
    "for tpf in tpfs:\n",
    "    pipeline_lc = pipeline_lc.append(tpf.to_lightcurve().flatten())\n",
    "    \n",
    "#Clean the light curve\n",
    "pipeline_lc = pipeline_lc.remove_nans().remove_outliers()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we have created the light curve from the target pixel files, stitched them all together in the same way as in the [stitching tutorial] using lightkurves `append` function. To recap the steps we:\n",
    "\n",
    "* Convert to a `KeplerLightCurve` object with `to_lightcurve()`\n",
    "* Remove NaNs with `remove_nans()`\n",
    "* Remove long term trends with `flatten()`\n",
    "* Remove outliers with simple sigma clipping using `remove_outliers()`\n",
    "\n",
    "The period for this planet candidate is 24.57537 days. Let's plot it up and take a look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.998, 1.0015)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1923f518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pipeline_lc.fold(period=24.57537, phase=-0.133).bin().plot();\n",
    "plt.xlim(-0.015,0.015)\n",
    "plt.ylim(0.998,1.0015)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like a great candidate. However, we might just want to check on the pixels. Let's plot one of the target pixel files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c19156a90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102444080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tpf.plot(frame=100, aperture_mask=tpf.pipeline_mask, mask_color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Kepler Pipeline aperture is in red. It looks like there is a nearby contaminate star! We might want to check that the signal is not really coming from the bright, nearby contaminant, rather than our target star. Let's use the top right corner four pixels as our new mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1931f898>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c193786a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "aper = np.zeros(tpf.shape[1:])\n",
    "aper[-2:, 0:2] = 1\n",
    "tpf.plot(aperture_mask=aper, mask_color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new mask covers the bright star. Now we can iterate through the target pixel files and build the light curve in the same way as before, but this time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build the NEW aperture, and the light curve\n",
    "aper = np.zeros(tpfs[0].shape[1:])\n",
    "aper[-2:, 0:2] = 1\n",
    "user_lc = tpfs[0].to_lightcurve(aperture_mask=aper.astype(bool)).flatten()\n",
    "for tpf in tpfs:\n",
    "    aper = np.zeros(tpf.shape[1:])\n",
    "    aper[-2:, 0:2]=1\n",
    "    user_lc = user_lc.append(tpf.to_lightcurve(aperture_mask=aper.astype(bool)).flatten())\n",
    "\n",
    "#Clean the light curve\n",
    "user_lc = user_lc.remove_nans().remove_outliers()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have our new light curve we can plot it up again and find out if there is still a planet signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.998, 1.0015)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c20e92dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_lc.fold(period=24.57537, phase=-0.133).bin().plot();\n",
    "plt.xlim(-0.015,0.015)\n",
    "plt.ylim(0.998,1.0015)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the planet signal is only in the target star and doesn't belong to the contaminant. This is just one of many checks you might want to perform to validate your planet candidates!"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}