{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using MEarth SpeX data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.io import fits\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in the fits data\n",
    "\n",
    "Exract data and header from the fits file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6, 3, 1014)\n"
     ]
    }
   ],
   "source": [
    "hdu_list = fits.open('2MASSJ03480588+4032226_tc.fits')\n",
    "data = hdu_list[0].data\n",
    "header = hdu_list[0].header\n",
    "\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the spectrum\n",
    "\n",
    "The first axis contains the order--the orders have not been merged to produce a single spectrum. The second axis contains wavelength, flux, and error in that order. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "l = data.shape[0]\n",
    "for i in range(l):\n",
    "    plt.plot(data[i,0,:], data[i,1,:], c='C0', linewidth=2, alpha=0.7)\n",
    "\n",
    "plt.xlabel('Wavelength in '+header['XUNITS'])\n",
    "plt.ylabel('Flux in '+header['YUNITS'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
