{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running *species*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial shows what a typical workflow with [species](https://species.readthedocs.io/en/latest/species.html) looks like. We will compare the colors and magnitudes of a few directly imaged companions with those of 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](https://species.readthedocs.io/en/latest/species.html) toolkit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import species"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, the [configuration file](https://species.readthedocs.io/en/latest/configuration.html) (*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](https://species.readthedocs.io/en/latest/species.html) with the current configuration file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initiating species v0.5.1... [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.init.SpeciesInit at 0x14bbaf2e0>"
      ]
     },
     "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](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.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 photometric data and parallaxes from the [Database of Ultracool Parallaxes](http://www.as.utexas.edu/~tdupuy/plx/Database_of_Ultracool_Parallaxes.html) are downloaded and added to the database with the [add_photometry](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.Database.add_photometry) method of [Database](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.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": [
    "A library of distances and magnitudes of directly imaged companions are available in the [species](https://species.readthedocs.io/en/latest/species.html) package (see [species.data.companions](https://github.com/tomasstolker/species/blob/master/species/data/companions.py)), which can be added to the [Database](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.Database) with the [add_companion](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.Database.add_companion) method (alternatively, photometric data of individual objects can be manually imported with [add_object](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.Database.add_object)). In this example, we add the distance and magnitudes of 51 Eri b, beta Pic b, and PZ Tel B to the database. This will automatically download the filter profiles and a flux calibrated spectrum of Vega. These are required for the flux calibration of the companion magnitudes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading Vega spectrum (270 kB)... [DONE]\n",
      "Adding Vega spectrum... [DONE]\n",
      "Adding object: 51 Eri b [DONE]\n",
      "Adding object: beta Pic b"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tomasstolker/applications/species/species/data/database.py:1058: UserWarning: Found 33 fluxes with NaN in the data of GPI_YJHK. Removing the spectral fluxes that contain a NaN.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [DONE]\n",
      "Adding object: PZ Tel B [DONE]\n"
     ]
    }
   ],
   "source": [
    "database.add_companion('51 Eri b', verbose=False)\n",
    "database.add_companion('beta Pic b', verbose=False)\n",
    "database.add_companion('PZ Tel B', verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading color-magnitude data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The colors and magnitudes from the [Database of Ultracool Parallaxes](http://www.as.utexas.edu/~tdupuy/plx/Database_of_Ultracool_Parallaxes.html) are read from the database in the working folder by first creating an instance of [ReadColorMagnitude](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_color.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 of the field dwarfs are now read with the [get_color_magnitude](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_color.ReadColorMagnitude.get_color_magnitude) method. This returns a [ColorMagBox](https://species.readthedocs.io/en/latest/species.core.html#species.core.box.ColorMagBox) with the requested data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "colorbox = colormag.get_color_magnitude(object_type='field')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also create a list with the color and magnitude filter names that we want to extract for the directly imaged companions."
   ]
  },
  {
   "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": [
    "We will now combine the selected data in a color-magnitude diagram. The [ColorMagBox](https://species.readthedocs.io/en/latest/species.core.html#species.core.box.ColorMagBox) is provided in a list to the [plot_color_magnitude](https://species.readthedocs.io/en/latest/species.plot.html#species.plot.plot_color.plot_color_magnitude) function. The list with directly imaged objects is provided as argument of `objects`. We also add a reddening vector to show how the color changes for a size distribution of enstatite grains with a geometric mean radius of 0.1 $\\mu$m and an extinction in the $J$ band of 1 mag. The discrete colorbar shows the spectral types of the field dwarfs and the directly imaged objects are labeled individually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading optical constants (87 kB)... [DONE]\n",
      "Unpacking optical constants... [DONE]\n",
      "Adding optical constants of MgSiO3... [DONE]\n",
      "Adding optical constants of Fe... [DONE]\n",
      "Downloading log-normal dust cross sections (231 kB)... [DONE]\n",
      "Adding log-normal dust cross sections:\n",
      "   - Data shape (n_wavelength, n_radius, n_sigma): (67, 20, 20)\n",
      "   - Wavelength range: 0.4 - 10.0 um\n",
      "   - Mean geometric radius range: 0.001 - 10.0 um\n",
      "   - Geometric standard deviation range: 1.0 - 10.0\n",
      "Downloading power-law dust cross sections (231 kB)... [DONE]\n",
      "Adding power-law dust cross sections\n",
      "   - Data shape (n_wavelength, n_radius, n_exponent): (132, 50, 50)\n",
      "   - Wavelength range: 0.4 - 10.0 um\n",
      "   - Maximum grain radius range: 0.01 - 100.0 um\n",
      "   - Power-law exponent range: -10.0 - 10.0\n",
      "Plotting color-magnitude diagram... [DONE]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x345.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "species.plot_color_magnitude(boxes=[colorbox, ],\n",
    "                             objects=objects,\n",
    "                             reddening=[(('MKO/NSFCam.J', 'MKO/NSFCam.H'), ('MKO/NSFCam.J', 1.), 'MgSiO3', 0.1, (-0.8, 10.))],\n",
    "                             label_x=r'$J - H$',\n",
    "                             label_y=r'M$_J$',\n",
    "                             xlim=(-1.2, 1.5),\n",
    "                             ylim=(21., 8.),\n",
    "                             offset=(-0.08, -0.08),\n",
    "                             companion_labels=True,\n",
    "                             field_range=('late M', 'late T'),\n",
    "                             legend=None,\n",
    "                             output=None)"
   ]
  }
 ],
 "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}