{
 "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 v0.1.0... [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 0x12b8385f8>"
      ]
     },
     "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(colorbox=[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
}
