{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Library with empirical spectra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, we will have a look at some brown dwarf spectra from the [IRTF library](http://irtfweb.ifa.hawaii.edu/~spex/IRTF_Spectral_Library/). We will also calculate synthetic fluxes and combine the spectra and photometric fluxes in a plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by importing [species](https://species.readthedocs.io/en/latest/species.html) and initiating the database with the [SpeciesInit](https://species.readthedocs.io/en/latest/species.core.html#species.core.init.SpeciesInit) class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============\n",
      "species v0.5.5\n",
      "==============\n",
      "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n",
      "Creating species_config.ini... [DONE]\n",
      "Configuration settings:\n",
      "   - Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n",
      "   - Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n",
      "   - Interpolation method: linear\n",
      "   - Magnitude of Vega: 0.03\n",
      "Creating species_database.hdf5... [DONE]\n",
      "Creating data folder... [DONE]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<species.core.init.SpeciesInit at 0x110d7f4c0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import species\n",
    "species.SpeciesInit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding a spectral library to the database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create an instance of  [Database](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.Database), which is used for adding data to the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "database = species.Database()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now download and add the spectral library. For [IRTF](http://irtfweb.ifa.hawaii.edu/~spex/IRTF_Spectral_Library/) spectra, we can set the spectral types that we want to use. The parallax will be queried from [SIMBAD](http://simbad.u-strasbg.fr/simbad/) and several [VizieR](https://vizier.u-strasbg.fr/viz-bin/VizieR) catalogs. A warning will be printed in case no parallax can be retrieved and a `NaN` is stored. Therefore, computing an absolute, synthetic magnitude from these spectra will not be possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading IRTF Spectral Library - L dwarfs (850 kB)... [DONE]\n",
      "Downloading IRTF Spectral Library - T dwarfs (100 kB)... [DONE]\n",
      "Unpacking IRTF Spectral Library... [DONE]\n",
      "Adding spectra... [DONE]                   \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tomasstolker/.pyenv/versions/3.10.0/envs/species3.10/lib/python3.10/site-packages/astroquery/simbad/core.py:135: UserWarning: Warning: The script line number 3 raised an error (recorded in the `errors` attribute of the result table): 'DENIS-P 025503.3-470049.0' this identifier has an incorrect format for catalog: \tDENIS : Deep Near-Infrared Survey\n",
      "  warnings.warn(\"Warning: The script line number %i raised \"\n",
      "/Users/tomasstolker/.pyenv/versions/3.10.0/envs/species3.10/lib/python3.10/site-packages/astroquery/simbad/core.py:135: UserWarning: Warning: The script line number 3 raised an error (recorded in the `errors` attribute of the result table): Identifier not found in the database : 2MASS J05591915-1404489\n",
      "  warnings.warn(\"Warning: The script line number %i raised \"\n"
     ]
    }
   ],
   "source": [
    "database.add_spectra(spec_library='irtf', sptypes=['L', 'T'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading a spectral library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To read the spectra from the database, we first create an instance of [ReadSpectrum](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_spectrum.ReadSpectrum). The full spectra are read if the argument of `filter_name` is set to `None`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "read_spectrum = species.ReadSpectrum(spec_library='irtf', filter_name=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spectra are extracted with the [get_spectrum](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_spectrum.ReadSpectrum.get_spectrum) method and by providing a list of spectral types as argument for `sptypes`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "specbox = read_spectrum.get_spectrum(sptypes=['L0', 'L1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method returns a [SpectrumBox](https://species.readthedocs.io/en/latest/species.core.html#species.core.box.SpectrumBox) with the requested data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Opening a SpectrumBox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the attributes that are stored in the [SpectrumBox](https://species.readthedocs.io/en/latest/species.core.html#species.core.box.SpectrumBox) by using the [open_box](https://species.readthedocs.io/en/latest/species.core.html#species.core.box.Box.open_box) method, which can be used on any of the [Box](https://species.readthedocs.io/en/latest/species.core.html#species.core.box.Box) objects. Among others, it contains the spectrum, the original names (from the IRTF library) and SIMBAD names, and the distances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening SpectrumBox...\n",
      "spectrum = None\n",
      "wavelength = [array([0.8125008, 0.8125008, 0.8127338, ..., 4.126095 , 4.1269007,\n",
      "       4.127706 ], dtype=float32), array([0.93558633, 0.93558633, 0.93585825, ..., 2.416591  , 2.4171278 ,\n",
      "       2.417665  ], dtype=float32), array([0.8106775 , 0.8106775 , 0.81091094, ..., 4.113586  , 4.1143913 ,\n",
      "       4.1151967 ], dtype=float32)]\n",
      "flux = [array([1.8895840e-14, 1.8895840e-14, 2.1758479e-14, ..., 3.7088523e-15,\n",
      "       4.1113438e-15, 4.4284915e-15], dtype=float32), array([-1.0867816e-15, -1.0867816e-15,  2.5373139e-15, ...,\n",
      "        2.0060095e-15,  2.1450817e-15,  2.2615936e-15], dtype=float32), array([3.8497135e-15, 3.8497135e-15, 2.5441878e-15, ..., 1.3200795e-15,\n",
      "       1.2305833e-15, 1.0569799e-15], dtype=float32)]\n",
      "error = [array([2.5401353e-15, 2.5401353e-15, 2.8612678e-15, ..., 4.1515258e-16,\n",
      "       4.3247181e-16, 4.4612945e-16], dtype=float32), array([2.2947999e-15, 2.2947999e-15, 1.3184451e-15, ..., 2.5160439e-16,\n",
      "       1.7856300e-16, 9.8540571e-17], dtype=float32), array([1.9933140e-15, 1.9933140e-15, 2.2894352e-15, ..., 3.6852164e-16,\n",
      "       3.4700822e-16, 3.5634480e-16], dtype=float32)]\n",
      "name = ['2MASS J07464256+2000321', '2MASS J02081833+2542533', '2MASS J14392836+1929149']\n",
      "simbad = ['LSPM J0746+2000', '2MASS J02081833+2542533', '2MASS J14392836+1929149']\n",
      "sptype = ['L0', 'L1', 'L1']\n",
      "distance = None\n",
      "spec_res = [nan, nan, nan]\n",
      "spec_library = irtf\n",
      "parallax = [(array([81.1]), array([0.9])), (43.0992, 0.3249), (array([69.6]), array([0.5]))]\n"
     ]
    }
   ],
   "source": [
    "specbox.open_box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthetic photometry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To calculate synthetic fluxes, we use again the [ReadSpectrum](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_spectrum.ReadSpectrum) class but we now also set the argument of `filter_name` to a filter as listed by the [SVO Filter Profile Service](http://svo2.cab.inta-csic.es/svo/theory/fps/). In this example, use an H-band filter from the MKO system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding filter: MKO/NSFCam.H... [DONE]\n"
     ]
    }
   ],
   "source": [
    "read_spectrum = species.ReadSpectrum(spec_library='irtf', filter_name='MKO/NSFCam.H')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The flux is now calculated with the [get_flux](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_spectrum.ReadSpectrum.get_flux) method by again selecting the L0- and L1-type objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data from 'https://archive.stsci.edu/hlsps/reference-atlases/cdbs/current_calspec/alpha_lyr_stis_011.fits' to file '/Users/tomasstolker/applications/species/docs/tutorials/data/alpha_lyr_stis_011.fits'.\n",
      "100%|████████████████████████████████████████| 288k/288k [00:00<00:00, 156MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding Vega spectrum..."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [DONE]\n",
      "Reference: Bohlin et al. 2014, PASP, 126\n",
      "URL: https://ui.adsabs.harvard.edu/abs/2014PASP..126..711B/abstract\n"
     ]
    }
   ],
   "source": [
    "photbox = read_spectrum.get_flux(sptypes=['L0', 'L1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the content of the returned [PhotometryBox](https://species.readthedocs.io/en/latest/species.core.html#species.core.box.SpectrumBox)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening PhotometryBox...\n",
      "name = ['2MASS J07464256+2000321', '2MASS J02081833+2542533', '2MASS J14392836+1929149']\n",
      "sptype = ['L0', 'L1', 'L1']\n",
      "wavelength = [1.62982596 1.62982596 1.62982596]\n",
      "flux = [[4.58844233e-14 8.37910450e-18]\n",
      " [6.09467685e-15 5.98150313e-18]\n",
      " [1.75018643e-14 5.29699886e-18]]\n",
      "app_mag = None\n",
      "abs_mag = None\n",
      "filter_name = ['MKO/NSFCam.H' 'MKO/NSFCam.H' 'MKO/NSFCam.H']\n"
     ]
    }
   ],
   "source": [
    "photbox.open_box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting spectra and synthetic fluxes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have created a [SpectrumBox](https://species.readthedocs.io/en/latest/species.core.html#species.core.box.SpectrumBox) and [PhotometryBox](https://species.readthedocs.io/en/latest/species.core.html#species.core.box.PhotometryBox), we pass them to the [plot_spectrum](https://species.readthedocs.io/en/latest/species.plot.html#species.plot.plot_spectrum.plot_spectrum) function. The uncertainties on the spectral fluxes have been propagated into the synthetic photometric flux, but the uncertainties are smaller than the plotted markers. The horizontal \"error bars\" of the fluxes indicate the FWHM of the filter profile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding filter: MKO/NSFCam.J... [DONE]\n",
      "Adding filter: MKO/NSFCam.Ks... [DONE]\n",
      "Plotting spectrum..."
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [DONE]\n"
     ]
    }
   ],
   "source": [
    "fig = species.plot_spectrum(boxes=[specbox, photbox],\n",
    "                            filters=['MKO/NSFCam.J', 'MKO/NSFCam.H', 'MKO/NSFCam.Ks'],\n",
    "                            xlim=(0.9, 2.5),\n",
    "                            ylim=(-6e-15, 8.5e-14),\n",
    "                            offset=(-0.14, -0.03),\n",
    "                            legend={'loc':'upper right', 'frameon': False, 'fontsize': 11.},\n",
    "                            figsize=(7., 3.),\n",
    "                            output=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [plot_spectrum](https://species.readthedocs.io/en/latest/species.plot.html#species.plot.plot_spectrum.plot_spectrum) function returned the [Figure](https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure) object of the plot. The functionalities of [Matplotlib](https://matplotlib.org/stable/index.html) can be used for further customization of the plot. For example, the axes of the plot are stored at the `axes` attribute of `Figure`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Axes: xlabel='Wavelength (μm)', ylabel='$F_\\\\lambda$ (10$^{-14}$ W m$^{-2}$ μm$^{-1}$)'>,\n",
       " <Axes: ylabel='$T_\\\\lambda$'>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.axes"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}