lightkurve-to-timeseries-demo.ipynb

lightkurve-to-timeseries-demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using AstroPy TimeSeries with Lightkurve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1: Creating a `TimeSeries` object from a `LightCurve`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>TimeSeries masked=True length=3</i>\n",
       "<table id=\"table139661901245856\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>time</th><th>flux</th><th>flux_err</th></tr></thead>\n",
       "<thead><tr><th>object</th><th>int64</th><th>float64</th></tr></thead>\n",
       "<tr><td>50001.0</td><td>1</td><td>--</td></tr>\n",
       "<tr><td>50002.0</td><td>2</td><td>--</td></tr>\n",
       "<tr><td>50003.0</td><td>3</td><td>--</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<TimeSeries masked=True length=3>\n",
       "  time   flux flux_err\n",
       " object int64 float64 \n",
       "------- ----- --------\n",
       "50001.0     1       --\n",
       "50002.0     2       --\n",
       "50003.0     3       --"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import lightkurve as lk\n",
    "lc = lk.LightCurve(time=[50001, 50002, 50003], flux=[1, 2, 3], time_format='mjd')\n",
    "lc.to_timeseries()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: Creating a `LightCurve` from a `TimeSeries` object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([50001., 50002., 50003.]), array([1, 2, 3]))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc2 = lk.LightCurve.from_timeseries(lc.to_timeseries())\n",
    "lc2.time, lc2.flux"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 3: Creating a `TimeSeries` object from Kepler pixel data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>TimeSeries length=3</i>\n",
       "<table id=\"table139661878172920\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>time</th><th>flux</th><th>flux_err</th><th>quality</th><th>centroid_col</th><th>centroid_row</th></tr></thead>\n",
       "<thead><tr><th>object</th><th>float64</th><th>float64</th><th>int32</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>2455093.2242107596</td><td>0.9996814460550739</td><td>3.805234567764771e-05</td><td>0</td><td>655.6720992281885</td><td>246.83730620073658</td></tr>\n",
       "<tr><td>2455093.2446439834</td><td>0.999765992390867</td><td>3.805352769153062e-05</td><td>0</td><td>655.6723609340896</td><td>246.83726195077818</td></tr>\n",
       "<tr><td>2455093.2650771076</td><td>0.9997966460141161</td><td>3.8053968026149504e-05</td><td>0</td><td>655.6722715551962</td><td>246.83746779140847</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<TimeSeries length=3>\n",
       "       time               flux        ...    centroid_col      centroid_row   \n",
       "      object            float64       ...      float64           float64      \n",
       "------------------ ------------------ ... ----------------- ------------------\n",
       "2455093.2242107596 0.9996814460550739 ... 655.6720992281885 246.83730620073658\n",
       "2455093.2446439834  0.999765992390867 ... 655.6723609340896 246.83726195077818\n",
       "2455093.2650771076 0.9997966460141161 ... 655.6722715551962 246.83746779140847"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc = lk.search_targetpixelfile(\"Kepler-10\", quarter=3).download().to_lightcurve().flatten(window_length=301)\n",
    "ts = lc.to_timeseries()\n",
    "ts[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's plot the phase-foled planet using the TimeSeries object\n",
    "import matplotlib.pyplot as pl\n",
    "from astropy import units as u\n",
    "from astropy.time import Time\n",
    "folded_ts = ts.fold(period=0.837*u.day, midpoint_epoch=Time(2454953.6874, format='jd'))\n",
    "pl.plot(folded_ts.time.jd, folded_ts['flux'], 'k.', markersize=1);"
   ]
  }
 ],
 "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}