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   "source": [
    "# Observation format\n",
    "\n",
    "This section will help you convert your observationnal data into the ForMoSA format\n",
    "\n",
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.io import fits\n",
    "from astropy.table import Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data ``.fits``\n",
    "\n",
    "Your observed data (spectroscopy and/or photometry) should be formated in a ``.fits`` file with the following extensions :\n",
    "\n",
    "- **'WAV'** : (array) wavelength grid.\n",
    "- **'FLX'** : (array) flux.\n",
    "- **'ERR'** or **'COV'** : (array or 2D-array) errors or covariance matrix. The covariance matrix should have ``diag(COV)=ERR²``.\n",
    "- **'RES'** : (array) resolution. <u>Note :</u> You must put res = 0.0 where you have photometric points.\n",
    "- **'INS'** : (array) instrument name. <u>Note :</u> You must use the right filter name when you have photometric points. Check the phototeque for more information.\n",
    "\n",
    "exemple :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ColDefs(\n",
      "    name = 'WAV'; format = 'D'\n",
      "    name = 'FLX'; format = 'D'\n",
      "    name = 'ERR'; format = 'D'\n",
      "    name = 'RES'; format = 'D'\n",
      "    name = 'INS'; format = '3A'\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# CHECKUP FORMAT\n",
    "hdul = fits.open('~/YOUR/PATH/formosa_desk/inversion_targetname/inputs/data.fits')\n",
    "print(hdul[1].columns)\n",
    "wav = hdul[1].data['WAV']\n",
    "flx = hdul[1].data['FLX']\n",
    "err = hdul[1].data['ERR']\n",
    "res = hdul[1].data['RES']\n",
    "ins = hdul[1].data['INS']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "optional extensions can also be used when dealing with stellar-contaminated high-resolution spectroscopy:\n",
    "- **'TRANSM'** : (array) transmission (atmospheric + instrumental)\n",
    "- **'STAR_FLX'** or **'STAR_FLXi'** : (array or i arrays) star flux or shifted star flux (to account for LSF changes)\n",
    "- **'SYSTEM'** or **'SYSTEMj'** : (array or j arrays) systematic model(s) (usually computed from PCA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Format your data\n",
    "\n",
    "To format your data, you can use the simple Python routine below :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correction successful\n"
     ]
    }
   ],
   "source": [
    "# FITS converter :\n",
    "table = Table([wav, flx, res, res, ins], names=('WAV', 'FLX', 'ERR', 'RES', 'INS'))\n",
    "hdul = fits.HDUList()\n",
    "hdu = fits.BinTableHDU(table)\n",
    "hdul.append(hdu)\n",
    "hdul.writeto('~/YOUR/PATH/formosa_desk/inversion_targetname/inputs/data.fits')\n",
    "print('correction successful')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have multiple observations, we recommand that you create separated ``.fits`` files (e.g ``data_1.fits``, ``data_2.fits``, ...)"
   ]
  }
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