{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running *species*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial shows what a typical workflow with *species* looks like. The colors and magnitudes of a few directly imaged objects are compared with the field dwarfs from the [Database of Ultracool Parallaxes](http://www.as.utexas.edu/~tdupuy/plx/Database_of_Ultracool_Parallaxes.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initiating *species*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by importing the *species* toolkit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import species"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, the configuration file (*species_config.ini*) is read from the working folder. Since the file does not exist, it is created with default values for the path of the HDF5 database and the location where the external data is stored. The database and data folder are also created since this is the first time that we run *species* with the current configuration file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initiating species v0.1.4... [DONE]\n",
      "Creating species_config.ini... [DONE]\n",
      "Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n",
      "Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n",
      "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n",
      "Creating species_database.hdf5... [DONE]\n",
      "Creating data folder... [DONE]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<species.core.setup.SpeciesInit at 0x12d606550>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species.SpeciesInit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding a photometric library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `Database` object is now created which can be used for importing data from the chosen data folder into the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "database = species.Database()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The photometry and parallaxes from the Database of Ultracool Parallaxes are downloaded and added to the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading Database of Ultracool Parallaxes (307 kB)... [DONE]\n",
      "Adding Database of Ultracool Parallaxes... [DONE]\n"
     ]
    }
   ],
   "source": [
    "database.add_photometry('vlm-plx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding companion data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distance and magnitudes of 51 Eri b, beta Pic b, and PZ Tel B are added to the database. This will automatically download the filter transmission profiles and a flux calibrated spectrum of Vega. These are required for the flux calibration of the magnitudes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding filter: MKO/NSFCam.J... [DONE]\n",
      "Downloading Vega spectrum (270 kB)... [DONE]\n",
      "Adding Vega spectrum... [DONE]\n",
      "Adding filter: MKO/NSFCam.H... [DONE]\n",
      "Adding filter: MKO/NSFCam.K... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_D_H23_2... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_D_K12_1... [DONE]\n",
      "Adding filter: Keck/NIRC2.Lp... [DONE]\n",
      "Adding filter: Keck/NIRC2.Ms... [DONE]\n",
      "Adding object: 51 Eri b... [DONE]\n",
      "Adding filter: LCO/VisAO.Ys... [DONE]\n",
      "Adding filter: Paranal/NACO.J... [DONE]\n",
      "Adding filter: Gemini/NICI.ED286... [DONE]\n",
      "Adding filter: Paranal/NACO.H... [DONE]\n",
      "Adding filter: Paranal/NACO.Ks... [DONE]\n",
      "Adding filter: Paranal/NACO.NB374... [DONE]\n",
      "Adding filter: Paranal/NACO.Lp... [DONE]\n",
      "Adding filter: Paranal/NACO.NB405... [DONE]\n",
      "Adding filter: Paranal/NACO.Mp... [DONE]\n",
      "Adding object: beta Pic b... [DONE]\n",
      "Adding filter: Paranal/SPHERE.ZIMPOL_R_PRIM... [DONE]\n",
      "Adding filter: Paranal/SPHERE.ZIMPOL_I_PRIM... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_D_H23_3... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_D_K12_2... [DONE]\n",
      "Adding filter: Gemini/NIRI.H2S1v2-1-G0220... [DONE]\n",
      "Adding object: PZ Tel B... [DONE]\n"
     ]
    }
   ],
   "source": [
    "database.add_companion('51 Eri b')\n",
    "database.add_companion('beta Pic b')\n",
    "database.add_companion('PZ Tel B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading color-magnitude data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The colors and magnitudes are read from the database by first creating an object of `ReadColorMagnitude`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "colormag = species.ReadColorMagnitude(library='vlm-plx',\n",
    "                                      filters_color=('MKO/NSFCam.J', 'MKO/NSFCam.H'),\n",
    "                                      filter_mag='MKO/NSFCam.J')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is read by selecting the object type of the isolated brown dwarfs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "colorbox = colormag.get_color_magnitude(object_type='field')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list of directly imaged objects is created with the filter names for the colors and magnitudes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "objects = [('51 Eri b', 'MKO/NSFCam.J', 'MKO/NSFCam.H', 'MKO/NSFCam.J'),\n",
    "           ('beta Pic b', 'Paranal/NACO.J', 'Paranal/NACO.H', 'Paranal/NACO.J'),\n",
    "           ('PZ Tel B', 'Paranal/NACO.J', 'Paranal/NACO.H', 'Paranal/NACO.J')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting a color-magnitude diagram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `ColorMagBox` object is provided in a list to `plot_color_magnitude`. The list with directly imaged objects are also included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting color-magnitude diagram: color_mag.png... [DONE]\n"
     ]
    }
   ],
   "source": [
    "species.plot_color_magnitude(boxes=[colorbox, ],\n",
    "                             objects=objects,\n",
    "                             label_x='J - H [mag]',\n",
    "                             label_y='M$_\\mathregular{J}$ [mag]',\n",
    "                             companion_labels=True,\n",
    "                             field_range=('late M', 'late T'),\n",
    "                             legend=None,\n",
    "                             output='color_mag.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the result. The plot is stored in the working folder. The discrete colorbar shows the spectral types of the field dwarfs and the directly imaged objects are labeled individually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('color_mag.png')"
   ]
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}