{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine learning style preprocessing with lightkurve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Machine learning offers interesting avenues for automated lightcurve classification, and complex regression tasks.  The key idea is to hand a pile of data to a black box algorithm that learns labels on training data through gradient descent optimization, while mitigating overfitting by checking classification performance against held-out test data.  \n",
    "\n",
    "Machine learning algorithms work best when data are \"clean\", or devoid of missing values and spurious noise.  Data scientists and machine learning practitioners often quip that data preparation, cleaning, and preprocessing can occupy a large fraction of a machine learning project, so making this step easier will help faciliate machine learning projects.\n",
    "\n",
    "In this tutorial we replicate the machine learning *Input Representations* of Kepler lightcurves described by [Shallue & Vanderburg 2018](https://ui.adsabs.harvard.edu/abs/2018AJ....155...94S/abstract):\n",
    "\n",
    "> We generate a **global view** of the light curve [...]  All light curves are binned to the same length\n",
    "\n",
    "> We generate a **local view** of the transit [...] so that the transit occupies a fixed fraction of the resulting vector. \n",
    "\n",
    "The section concludes with a normalization procedure to make the transits all the same depth:\n",
    "\n",
    "> ...we normalize all light curves to have median 0 and minimum value –1 so that all TCEs have a fixed transit depth.\n",
    "\n",
    "The global view has a fixed length of 2001 bins, while the local view has a fixed length of 201 bins."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial requires version 1.5 or greater of `lightkurve`, which added new binning features that offer more flexibility and user-control of where and how to assign bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.4.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import lightkurve as lk\n",
    "import numpy as np\n",
    "lk.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at exoplanet host star KIC 757450, which has a period of $P=8.88492\\;d$, $T_0=134.452\\;d$, and transit duration $2.078$ hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "lcfs = lk.search_lightcurvefile('KIC 757450', mission='Kepler').download_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "period, t0, duration_hours = 8.88492, 134.452, 2.078"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can stitch all of the quarters together in one fell swoop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65031,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc_raw = lcfs.PDCSAP_FLUX.stitch()\n",
    "lc_raw.flux.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean outliers, but only those that are above the mean level (e.g. attributable to stellar flares or cosmic rays)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "lc_clean = lc_raw.remove_outliers(sigma=20, sigma_upper=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to mask the transit to avoid self-subtraction the genuine planet signal when we flatten the lightcurve.  We have to do a hack to find where the time series should be masked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_fold = lc_clean.fold(period, t0=t0)\n",
    "fractional_duration = (duration_hours / 24.0) / period\n",
    "phase_mask = np.abs(temp_fold.phase) < (fractional_duration * 1.5)\n",
    "transit_mask = np.in1d(lc_clean.time, temp_fold.time_original[phase_mask])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now flatten the mask, but interpolate the trend over the points possessing the transit signal of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "lc_flat, trend_lc = lc_clean.flatten(return_trend=True, mask=transit_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now fold the cleaned, flattened lightcurve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lc_fold = lc_flat.fold(period, t0=t0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's generate the **global** input representation by binning into a lightcurve with length 2001."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2001,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc_global = lc_fold.bin(bins=2001, method='median').normalize() - 1\n",
    "lc_global = (lc_global / np.abs(lc_global.flux.min()) ) * 2.0 + 1\n",
    "lc_global.flux.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 848.5x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 368,
       "width": 748
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lc_global.scatter();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent!  The pattern matches those seen the original publication figures.  What about the **local** view?  We need to *zoom in* on the transit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "phase_mask = (lc_fold.phase > -4*fractional_duration) & (lc_fold.phase < 4.0*fractional_duration)\n",
    "lc_zoom = lc_fold[phase_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(201,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc_local = lc_zoom.bin(bins=201, method='median').normalize() -1\n",
    "lc_local = (lc_local / np.abs(lc_local.flux.min()) ) * 2.0 + 1\n",
    "lc_local.flux.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 848.5x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 368,
       "width": 748
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lc_local.scatter();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voilà!  We've condensed the raw signal of over 65,000 time series data points into two cleaned, simplified input representation using `lightkurve`'s built-in functions.  These views could be passed into a neural network classifier to establish whether the signal is likely to be an exoplanet or not.  "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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