{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are LightCurve objects?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`LightCurve` objects are data objects which encapsulate the brightness of a star over time. They provide a series of common operations, for example folding, binning, plotting, etc. There are a range of subclasses of `LightCurve` objects specific to telescopes, including `KeplerLightCurve` for Kepler and K2 data and `TessLightCurve` for TESS data.\n",
    "\n",
    "Although `lightkurve` was designed with Kepler, K2 and TESS in mind, these objects can be used for a range of astronomy data.\n",
    "\n",
    "You can create a `LightCurve` object from a `TargetPixelFile` object using Simple Aperture Photometry (see our tutorial for more information on Target Pixel Files [here](http://lightkurve.keplerscience.org/tutorials/1.02-target-pixel-files.html). Aperture Photometry is the simple act of summing up the values of all the pixels in a pre-defined aperture, as a function of time. By carefully choosing the shape of the aperture mask, you can avoid nearby contaminants or improve the strength of the specific signal you are trying to measure relative to the background.\n",
    "\n",
    "To demonstrate, lets create a `KeplerLightCurve` from a `KeplerTargetPixelFile`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Found cached file ./mastDownload/Kepler/kplr006922244_lc_Q111111111111111111/kplr006922244-2010078095331_lpd-targ.fits.gz with expected size 2009425. [astroquery.query]\n"
     ]
    }
   ],
   "source": [
    "from lightkurve import KeplerTargetPixelFile, KeplerLightCurve\n",
    "#First we open a Target Pixel File from MAST, this one is already cached from our previous tutorial!\n",
    "tpf = KeplerTargetPixelFile.from_archive(6922244, quarter=4)\n",
    "\n",
    "#Then we convert the target pixel file into a light curve using the pipeline-defined aperture mask.\n",
    "lc = tpf.to_lightcurve(aperture_mask=tpf.pipeline_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've built a new `KeplerLightCurve` object called `lc`. Note in this case we've passed an **aperture_mask** to the `to_lightcurve` method. The default is to use the *Kepler* pipeline aperture. (You can pass your own aperture, which is a boolean `numpy` array.) By summing all the pixels in the aperture we have created a Simple Aperture Photometry (SAP) lightcurve.\n",
    "\n",
    "`KeplerLightCurve` has many useful functions that you can use. As with Target Pixel Files you can access the meta data very simply:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Kepler'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc.mission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc.quarter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And you still have access to time and flux attributes. In a light curve, there is only one flux point for every time stamp:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([352.37632485, 352.39675805, 352.43762445, ..., 442.16263546,\n",
       "        442.18306983, 442.2035041 ]),\n",
       " array([43689.15 , 43698.08 , 43694.105, ..., 43155.8  , 43148.465,\n",
       "        43151.562], dtype=float32))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc.time, lc.flux"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also check the \"CDPP\" noise metric of the lightcurve using the built in method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "407.35519723966718"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc.cdpp()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the built in `plot` function on the `KeplerLightCurve` object to plot the time series. You can pass `plot` any keywords you would normally pass to `matplotlib.pyplot.plot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Mz8/XfKx8Pg80l8FYsqtWIPkuMAFRN5e7n9OZIkmvUkukv5XP/KrW1RWnXonXrVTbWKtQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efbfa46a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "lc.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a set of useful functions in `LightCurve` objects which you can use to work with the data. These include:\n",
    "* `flatten()`: Remove long term trends using a [Savitzky–Golay filter](https://en.wikipedia.org/wiki/Savitzky%E2%80%93Golay_filter)\n",
    "* `remove_outliers()`: Remove outliers using simple sigma clipping\n",
    "* `remove_nans()`: Remove infinite or NaN values (these can occur during thruster firings)\n",
    "* `fold()`: Fold the data at a particular period\n",
    "* `bin()`: Reduce the time resolution of the array, taking the average value in each bin.\n",
    "\n",
    "We can use these simply on a light curve object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YmVnjzvaI+J3sH7AN+AmSvpGrgacX2biksyXdI2mPpM115h8n6cuS7pD0bUmn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\nIw4kZmbWEQcSMzPriAOJmZl15P8DpQvenX75aXgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efbfad41668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flat_lc = lc.flatten(window_length=401)\n",
    "flat_lc.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1WnnBZe50WffwjY4ve/fuZd++fR15DCqrzA8SZ+sh0lpEajCdNi0ifkL640ZJ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\ns5b8f4M3fCAqTad3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efbfa466c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "folded_lc = flat_lc.fold(period=3.5225)\n",
    "folded_lc.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Jj0j6am7Z1yWtzMU8u26b9UaAlfS3aZTjpZK+KWmLtPz8dOwhSTdK2jktn5vb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efbfadc3160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "binned_lc = folded_lc.bin(binsize=10)\n",
    "binned_lc.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or we can do these all in a single (long) line!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Jj0j6am7Z1yWtzMU8u26b9UaAlfS3aZTjpZK+KWmLtPz8dOwhSTdK2jktn5vb\n/z2S3tuiDt+RNKvB8r/Jx12WpNdJ+vp47c8mPicSm7Ai4g8RMTsiZgOXAF+qzUfEm9sczt3AnIjY\nh+yLZBfUrT8fuLnBdn+fi3motrDRCLBpHKaPpuPsDUwj+1IbwIURsU96La4n+w4GwNJUfjZwGPA1\nSeuNWCFpL2BaRKzY0IpvqIhYAvSmLxzaFOBEYl1J0tPp99sk3STp+5JWSBpQ9pyL25U95+KVqdz2\nkq6VdEf6OXBDjhcRP42IkTS7zki1kt5INuDkhgwL3mwE2E2BLVMy6AF+l46fH1/sZUCk5SPpi3CQ\nDZvT7Ithx5EbOUDSSZKGJd1O9qXP2vL3SLpN2fNufixpR0mbKHtmzPapzCbKnrexvaT3pdbTPZLy\nifRfeSkJ2iTnRGKTwb7AacBryb4F3xcRc8nGcPpIKnMRWYvmTcB/p9ww/KMj1UraBPgCcE6Tsp9L\nXVJfkvQXaZuGI8BGxKPAPwIPAY+RDZOSb7F8RtLDZEnhvNzy/SQtA5YAp+USS96BwF2p/E7Ap9Oy\nt5A9O6PmF8D+EfF6smHQPxYRLwL/ko4L2SgE90TEEymOd0bEvsCRuf3cCRzU5DWxScaJxCaDO9Jz\nS54D/o2XWgZLgBlp+u3AVyUNkY1/9AplIwpvEEnHkw28d2Fa9GFgUUQ80qD4uUAf8CZgW+Djafno\nCLB1+96G7PkVM4GdgZel4wEQEZ+MiF2Bq8iG4qgtvy0i9krHObd2XaXOTsATaXo/4GcR8UR6bsa3\ncuV6gRskLQH+HtgrLb8cOCFNfxC4Ik3/Evi6pFPIuuJqHk91sCnAo//aZPBcbvrF3PyLvPQZ34Ts\nP+0/N9uJpCvInnfyu4hYbzwvSW8nG/7+rSlpARwAHCTpw8BfAptLejoi5ucGXXwu7bvWamk4Aiyw\nGbAy/aePpO8CbyZrDeRdRTY21z/kF0bEfanLb2+yFkHes2RdX2P5CvDFiFio7FEFn0r7fljZaM2H\nkD3A6bi0/DRJ+5E9bO0uSW9Mo/hukY5pU4BbJDZV3MhL3VzU30EFEBEnpYvijZLI64GvAUdGxOO5\nbY6LiN3SaK7nkD3lcH7aZqf0W2RPf1yatmk2AuxDwP6SetI280iPVJa0Ry6co4D70/KZtYvr6U6y\n1wD/3qD+9wGvStO3AW9VdlfcZsD7cuW24qWhz09kXQvIktq3I+KFdMxXphbReWQtntow6n21+trk\n5xaJTRXcNqu0AAAA8klEQVQfBS6WdC/Z5/5msusqRV1I1uL4dmpJPBQRR7behKvSBWoBQ2MdLyJu\nk/Qd4Ndkz6q/m5eeYz4g6dVkrawHc/t6CzBf0pq07sMRsarB7v8f8Dbgx+n5L58CbgGeTLHVfCrV\n8Y/AT8i62WoWknVpXZFbdmFKcgIWA/ek5QenY9oU4NF/zaYASVsCPwUOrLUmNmIfc8huWGh5ET3d\nVHAT8JYmF/5tknHXltkUEBHPkl1T2ajnwEuaT3a78rkFiu8GzHcSmTrcIjEzs1LcIjEzs1KcSMzM\nrBQnEjMzK8WJxMzMSnEiMTOzUpxIzMyslP8PnBK2S4SKLBgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efbfa167358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lc.remove_nans().flatten(window_length=401).fold(period=3.5225).bin(binsize=10).plot();"
   ]
  }
 ],
 "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
}