{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Color-magnitude diagram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tutorial shows how to create a color-magnitude diagram which combines the photometry from field and young/low-gravity objects, synthetic photometry computed from isochrones and model spectra, and photometry from directly imaged objects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initiating *species*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import species\n",
    "import wget\n",
    "import numpy as np"
   ]
  },
  {
   "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"
     ]
    }
   ],
   "source": [
    "species.SpeciesInit()\n",
    "database = species.Database()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding data to the database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All available photometric data of directly imaged planets and brown dwarfs is added with `add_companion` by setting `name=None`. These data are extracted from the [dictionary with apparent magnitudes](https://github.com/tomasstolker/species/blob/master/species/data/companions.py) in the `data.companions` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding filter: LCO/VisAO.Ys... [DONE]\n",
      "Downloading Vega spectrum (270 kB)... [DONE]\n",
      "Adding Vega spectrum... [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.IRDIS_D_H23_2... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_D_H23_3... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_D_K12_1... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_D_K12_2... [DONE]\n",
      "Adding object: HIP 65426 b... [DONE]\n",
      "Adding filter: MKO/NSFCam.J... [DONE]\n",
      "Adding filter: MKO/NSFCam.H... [DONE]\n",
      "Adding filter: MKO/NSFCam.K... [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: Subaru/CIAO.z... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_B_J... [DONE]\n",
      "Adding filter: Keck/NIRC2.H... [DONE]\n",
      "Adding filter: Keck/NIRC2.Ks... [DONE]\n",
      "Adding object: HR 8799 b... [DONE]\n",
      "Adding object: HR 8799 c... [DONE]\n",
      "Adding object: HR 8799 d... [DONE]\n",
      "Adding object: HR 8799 e... [DONE]\n",
      "Adding filter: Gemini/GPI.H... [DONE]\n",
      "Adding filter: Gemini/GPI.K1... [DONE]\n",
      "Adding object: HD 95086 b... [DONE]\n",
      "Adding object: PDS 70 b... [DONE]\n",
      "Adding object: PDS 70 c... [DONE]\n",
      "Adding filter: HST/NICMOS1.F090M... [DONE]\n",
      "Adding filter: HST/NICMOS1.F110M... [DONE]\n",
      "Adding filter: HST/NICMOS1.F145M... [DONE]\n",
      "Adding filter: HST/NICMOS1.F160W... [DONE]\n",
      "Adding object: 2M1207 b... [DONE]\n",
      "Adding object: AB Pic B... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_B_H... [DONE]\n",
      "Adding object: HD 206893 B... [DONE]\n",
      "Adding filter: HST/WFPC2.f606w... [DONE]\n",
      "Adding filter: HST/WFPC2.f814w... [DONE]\n",
      "Adding filter: HST/NICMOS2.F171M... [DONE]\n",
      "Adding filter: HST/NICMOS2.F190N... [DONE]\n",
      "Adding filter: HST/NICMOS2.F215N... [DONE]\n",
      "Adding filter: Subaru/CIAO.CH4s... [DONE]\n",
      "Adding filter: Subaru/CIAO.K... [DONE]\n",
      "Adding filter: Subaru/CIAO.Lp... [DONE]\n",
      "Adding object: GQ Lup B... [DONE]\n",
      "Adding filter: Paranal/SPHERE.ZIMPOL_R_PRIM... [DONE]\n",
      "Adding filter: Paranal/SPHERE.ZIMPOL_I_PRIM... [DONE]\n",
      "Adding filter: Gemini/NIRI.H2S1v2-1-G0220... [DONE]\n",
      "Adding object: PZ Tel B... [DONE]\n",
      "Adding filter: Subaru/CIAO.J... [DONE]\n",
      "Adding filter: Subaru/CIAO.H... [DONE]\n",
      "Adding filter: Subaru/CIAO.Ks... [DONE]\n",
      "Adding filter: LBT/LMIRCam.M_77K... [DONE]\n",
      "Adding object: kappa And b... [DONE]\n",
      "Adding filter: Keck/NIRC2.J... [DONE]\n",
      "Adding object: ROXs 42 Bb... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_D_Y23_2... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_D_Y23_3... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_D_J23_3... [DONE]\n",
      "Adding filter: Subaru/IRCS.Lp... [DONE]\n",
      "Adding object: GJ 504 b... [DONE]\n",
      "Adding filter: Gemini/GMOS-S.z... [DONE]\n",
      "Adding filter: CFHT/Wircam.Y... [DONE]\n",
      "Adding filter: CFHT/Wircam.J... [DONE]\n",
      "Adding filter: CFHT/Wircam.H... [DONE]\n",
      "Adding filter: CFHT/Wircam.Ks... [DONE]\n",
      "Adding filter: WISE/WISE.W1... [DONE]\n",
      "Adding filter: WISE/WISE.W2... [DONE]\n",
      "Adding object: GU Psc b... [DONE]\n",
      "Adding object: 2M0103 ABb... [DONE]\n",
      "Adding filter: Gemini/NIRI.J-G0202w... [DONE]\n",
      "Adding filter: Gemini/NIRI.H-G0203w... [DONE]\n",
      "Adding filter: Gemini/NIRI.K-G0204w... [DONE]\n",
      "Adding filter: Gemini/NIRI.Lprime-G0207w... [DONE]\n",
      "Adding object: 1RXS 1609 B... [DONE]\n",
      "Adding filter: MKO/NSFCam.Kp... [DONE]\n",
      "Adding filter: MKO/NSFCam.Lp... [DONE]\n",
      "Adding object: GSC 06214 B... [DONE]\n"
     ]
    }
   ],
   "source": [
    "database.add_companion(name=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The broadband photometry and parallaxes of the [Database of Ultracool Parallaxes](http://www.as.utexas.edu/~tdupuy/plx/Database_of_Ultracool_Parallaxes.html) is added."
   ]
  },
  {
   "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(phot_library='vlm-plx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The isochrones from the AMES-Cond and AMES-Dusty evolutionary models are downloaded with `wget`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data/model.AMES-dusty.M-0.0.NaCo.Vega'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wget.download('https://phoenix.ens-lyon.fr/Grids/AMES-Cond/ISOCHRONES/model.AMES-Cond-2000.M-0.0.NaCo.Vega',\n",
    "              out='data/model.AMES-Cond-2000.M-0.0.NaCo.Vega')\n",
    "\n",
    "wget.download('https://phoenix.ens-lyon.fr/Grids/AMES-Dusty/ISOCHRONES/model.AMES-dusty.M-0.0.NaCo.Vega',\n",
    "              out='data/model.AMES-dusty.M-0.0.NaCo.Vega')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And added to the HDF5 database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding isochrones: iso_cond... [DONE]\n",
      "Adding isochrones: iso_dusty... [DONE]\n"
     ]
    }
   ],
   "source": [
    "database.add_isochrones(filename='data/model.AMES-Cond-2000.M-0.0.NaCo.Vega',\n",
    "                        isochrone_tag='iso_cond',\n",
    "                        model='baraffe')\n",
    "\n",
    "database.add_isochrones(filename='data/model.AMES-dusty.M-0.0.NaCo.Vega',\n",
    "                        isochrone_tag='iso_dusty',\n",
    "                        model='baraffe')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also the synthetic spectra from the AMES-Cond and AMES-Dusty atmospheric models are dowloaded and added to the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading AMES-Cond model spectra (823 MB)... [DONE]\n",
      "Unpacking AMES-Cond model spectra (823 MB)... [DONE]\n",
      "Adding AMES-Cond model spectra... [DONE]                                        \n",
      "Downloading AMES-Dusty model spectra [Fe/H]=0.0 (106 MB)... [DONE]\n",
      "Unpacking AMES-Dusty model spectra [Fe/H]=0.0 (106 MB)... [DONE]\n",
      "Adding AMES-Dusty model spectra... [DONE]                                       \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tomasstolker/applications/species/species/util/data_util.py:268: UserWarning: Interpolation is not possible at the edge of the parameter grid. A NaN value is stored for Teff = 4000.0 K.\n",
      "  warnings.warn(f'Interpolation is not possible at the edge of the '\n"
     ]
    }
   ],
   "source": [
    "database.add_model(model='ames-cond',\n",
    "                   wavel_range=(0.5, 10.),\n",
    "                   teff_range=(100., 4000.),\n",
    "                   spec_res=1000.)\n",
    "\n",
    "database.add_model(model='ames-dusty',\n",
    "                   wavel_range=(0.5, 10.),\n",
    "                   teff_range=(100., 4000.),\n",
    "                   spec_res=1000.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Database content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at all the data that is stored in the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database content:\n",
      "- filters: <HDF5 group \"/filters\" (10 members)>\n",
      "\t- CFHT: <HDF5 group \"/filters/CFHT\" (4 members)>\n",
      "\t\t- Wircam.H: <HDF5 dataset \"Wircam.H\": shape (2, 747), type \"<f4\">\n",
      "\t\t- Wircam.J: <HDF5 dataset \"Wircam.J\": shape (2, 1077), type \"<f4\">\n",
      "\t\t- Wircam.Ks: <HDF5 dataset \"Wircam.Ks\": shape (2, 791), type \"<f4\">\n",
      "\t\t- Wircam.Y: <HDF5 dataset \"Wircam.Y\": shape (2, 875), type \"<f4\">\n",
      "\t- Gemini: <HDF5 group \"/filters/Gemini\" (9 members)>\n",
      "\t\t- GMOS-S.z: <HDF5 dataset \"GMOS-S.z\": shape (2, 928), type \"<f4\">\n",
      "\t\t- GPI.H: <HDF5 dataset \"GPI.H\": shape (2, 1895), type \"<f4\">\n",
      "\t\t- GPI.K1: <HDF5 dataset \"GPI.K1\": shape (2, 2128), type \"<f4\">\n",
      "\t\t- NICI.ED286: <HDF5 dataset \"NICI.ED286\": shape (2, 387), type \"<f4\">\n",
      "\t\t- NIRI.H-G0203w: <HDF5 dataset \"NIRI.H-G0203w\": shape (2, 368), type \"<f4\">\n",
      "\t\t- NIRI.H2S1v2-1-G0220: <HDF5 dataset \"NIRI.H2S1v2-1-G0220\": shape (2, 129), type \"<f4\">\n",
      "\t\t- NIRI.J-G0202w: <HDF5 dataset \"NIRI.J-G0202w\": shape (2, 213), type \"<f4\">\n",
      "\t\t- NIRI.K-G0204w: <HDF5 dataset \"NIRI.K-G0204w\": shape (2, 423), type \"<f4\">\n",
      "\t\t- NIRI.Lprime-G0207w: <HDF5 dataset \"NIRI.Lprime-G0207w\": shape (2, 933), type \"<f4\">\n",
      "\t- HST: <HDF5 group \"/filters/HST\" (9 members)>\n",
      "\t\t- NICMOS1.F090M: <HDF5 dataset \"NICMOS1.F090M\": shape (2, 633), type \"<f4\">\n",
      "\t\t- NICMOS1.F110M: <HDF5 dataset \"NICMOS1.F110M\": shape (2, 768), type \"<f4\">\n",
      "\t\t- NICMOS1.F145M: <HDF5 dataset \"NICMOS1.F145M\": shape (2, 829), type \"<f4\">\n",
      "\t\t- NICMOS1.F160W: <HDF5 dataset \"NICMOS1.F160W\": shape (2, 1319), type \"<f4\">\n",
      "\t\t- NICMOS2.F171M: <HDF5 dataset \"NICMOS2.F171M\": shape (2, 1226), type \"<f4\">\n",
      "\t\t- NICMOS2.F190N: <HDF5 dataset \"NICMOS2.F190N\": shape (2, 375), type \"<f4\">\n",
      "\t\t- NICMOS2.F215N: <HDF5 dataset \"NICMOS2.F215N\": shape (2, 506), type \"<f4\">\n",
      "\t\t- WFPC2.f606w: <HDF5 dataset \"WFPC2.f606w\": shape (2, 1300), type \"<f4\">\n",
      "\t\t- WFPC2.f814w: <HDF5 dataset \"WFPC2.f814w\": shape (2, 1513), type \"<f4\">\n",
      "\t- Keck: <HDF5 group \"/filters/Keck\" (5 members)>\n",
      "\t\t- NIRC2.H: <HDF5 dataset \"NIRC2.H\": shape (2, 1506), type \"<f4\">\n",
      "\t\t- NIRC2.J: <HDF5 dataset \"NIRC2.J\": shape (2, 1149), type \"<f4\">\n",
      "\t\t- NIRC2.Ks: <HDF5 dataset \"NIRC2.Ks\": shape (2, 1399), type \"<f4\">\n",
      "\t\t- NIRC2.Lp: <HDF5 dataset \"NIRC2.Lp\": shape (2, 1694), type \"<f4\">\n",
      "\t\t- NIRC2.Ms: <HDF5 dataset \"NIRC2.Ms\": shape (2, 627), type \"<f4\">\n",
      "\t- LBT: <HDF5 group \"/filters/LBT\" (1 members)>\n",
      "\t\t- LMIRCam.M_77K: <HDF5 dataset \"LMIRCam.M_77K\": shape (2, 626), type \"<f4\">\n",
      "\t- LCO: <HDF5 group \"/filters/LCO\" (1 members)>\n",
      "\t\t- VisAO.Ys: <HDF5 dataset \"VisAO.Ys\": shape (2, 27), type \"<f4\">\n",
      "\t- MKO: <HDF5 group \"/filters/MKO\" (5 members)>\n",
      "\t\t- NSFCam.H: <HDF5 dataset \"NSFCam.H\": shape (2, 970), type \"<f4\">\n",
      "\t\t- NSFCam.J: <HDF5 dataset \"NSFCam.J\": shape (2, 1253), type \"<f4\">\n",
      "\t\t- NSFCam.K: <HDF5 dataset \"NSFCam.K\": shape (2, 971), type \"<f4\">\n",
      "\t\t- NSFCam.Kp: <HDF5 dataset \"NSFCam.Kp\": shape (2, 998), type \"<f4\">\n",
      "\t\t- NSFCam.Lp: <HDF5 dataset \"NSFCam.Lp\": shape (2, 1242), type \"<f4\">\n",
      "\t- Paranal: <HDF5 group \"/filters/Paranal\" (18 members)>\n",
      "\t\t- NACO.H: <HDF5 dataset \"NACO.H\": shape (2, 23), type \"<f4\">\n",
      "\t\t- NACO.J: <HDF5 dataset \"NACO.J\": shape (2, 20), type \"<f4\">\n",
      "\t\t- NACO.Ks: <HDF5 dataset \"NACO.Ks\": shape (2, 27), type \"<f4\">\n",
      "\t\t- NACO.Lp: <HDF5 dataset \"NACO.Lp\": shape (2, 31), type \"<f4\">\n",
      "\t\t- NACO.Mp: <HDF5 dataset \"NACO.Mp\": shape (2, 18), type \"<f4\">\n",
      "\t\t- NACO.NB374: <HDF5 dataset \"NACO.NB374\": shape (2, 46), type \"<f4\">\n",
      "\t\t- NACO.NB405: <HDF5 dataset \"NACO.NB405\": shape (2, 67), type \"<f4\">\n",
      "\t\t- SPHERE.IRDIS_B_H: <HDF5 dataset \"SPHERE.IRDIS_B_H\": shape (2, 481), type \"<f4\">\n",
      "\t\t- SPHERE.IRDIS_B_J: <HDF5 dataset \"SPHERE.IRDIS_B_J\": shape (2, 393), type \"<f4\">\n",
      "\t\t- SPHERE.IRDIS_D_H23_2: <HDF5 dataset \"SPHERE.IRDIS_D_H23_2\": shape (2, 113), type \"<f4\">\n",
      "\t\t- SPHERE.IRDIS_D_H23_3: <HDF5 dataset \"SPHERE.IRDIS_D_H23_3\": shape (2, 180), type \"<f4\">\n",
      "\t\t- SPHERE.IRDIS_D_J23_3: <HDF5 dataset \"SPHERE.IRDIS_D_J23_3\": shape (2, 129), type \"<f4\">\n",
      "\t\t- SPHERE.IRDIS_D_K12_1: <HDF5 dataset \"SPHERE.IRDIS_D_K12_1\": shape (2, 175), type \"<f4\">\n",
      "\t\t- SPHERE.IRDIS_D_K12_2: <HDF5 dataset \"SPHERE.IRDIS_D_K12_2\": shape (2, 191), type \"<f4\">\n",
      "\t\t- SPHERE.IRDIS_D_Y23_2: <HDF5 dataset \"SPHERE.IRDIS_D_Y23_2\": shape (2, 94), type \"<f4\">\n",
      "\t\t- SPHERE.IRDIS_D_Y23_3: <HDF5 dataset \"SPHERE.IRDIS_D_Y23_3\": shape (2, 89), type \"<f4\">\n",
      "\t\t- SPHERE.ZIMPOL_I_PRIM: <HDF5 dataset \"SPHERE.ZIMPOL_I_PRIM\": shape (2, 189), type \"<f4\">\n",
      "\t\t- SPHERE.ZIMPOL_R_PRIM: <HDF5 dataset \"SPHERE.ZIMPOL_R_PRIM\": shape (2, 169), type \"<f4\">\n",
      "\t- Subaru: <HDF5 group \"/filters/Subaru\" (8 members)>\n",
      "\t\t- CIAO.CH4s: <HDF5 dataset \"CIAO.CH4s\": shape (2, 150), type \"<f4\">\n",
      "\t\t- CIAO.H: <HDF5 dataset \"CIAO.H\": shape (2, 967), type \"<f4\">\n",
      "\t\t- CIAO.J: <HDF5 dataset \"CIAO.J\": shape (2, 1253), type \"<f4\">\n",
      "\t\t- CIAO.K: <HDF5 dataset \"CIAO.K\": shape (2, 958), type \"<f4\">\n",
      "\t\t- CIAO.Ks: <HDF5 dataset \"CIAO.Ks\": shape (2, 200), type \"<f4\">\n",
      "\t\t- CIAO.Lp: <HDF5 dataset \"CIAO.Lp\": shape (2, 1242), type \"<f4\">\n",
      "\t\t- CIAO.z: <HDF5 dataset \"CIAO.z\": shape (2, 200), type \"<f4\">\n",
      "\t\t- IRCS.Lp: <HDF5 dataset \"IRCS.Lp\": shape (2, 1224), type \"<f4\">\n",
      "\t- WISE: <HDF5 group \"/filters/WISE\" (2 members)>\n",
      "\t\t- WISE.W1: <HDF5 dataset \"WISE.W1\": shape (2, 141), type \"<f4\">\n",
      "\t\t- WISE.W2: <HDF5 dataset \"WISE.W2\": shape (2, 168), type \"<f4\">\n",
      "- isochrones: <HDF5 group \"/isochrones\" (2 members)>\n",
      "\t- iso_cond: <HDF5 group \"/isochrones/iso_cond\" (3 members)>\n",
      "\t\t- evolution: <HDF5 dataset \"evolution\": shape (1940, 8), type \"<f4\">\n",
      "\t\t\t- model: baraffe\n",
      "\t\t- filters: <HDF5 dataset \"filters\": shape (38,), type \"|O\">\n",
      "\t\t- magnitudes: <HDF5 dataset \"magnitudes\": shape (1940, 38), type \"<f4\">\n",
      "\t- iso_dusty: <HDF5 group \"/isochrones/iso_dusty\" (3 members)>\n",
      "\t\t- evolution: <HDF5 dataset \"evolution\": shape (1459, 8), type \"<f4\">\n",
      "\t\t\t- model: baraffe\n",
      "\t\t- filters: <HDF5 dataset \"filters\": shape (38,), type \"|O\">\n",
      "\t\t- magnitudes: <HDF5 dataset \"magnitudes\": shape (1459, 38), type \"<f4\">\n",
      "- models: <HDF5 group \"/models\" (2 members)>\n",
      "\t- ames-cond: <HDF5 group \"/models/ames-cond\" (4 members)>\n",
      "\t\t- flux: <HDF5 dataset \"flux\": shape (40, 13, 2998), type \"<f4\">\n",
      "\t\t- logg: <HDF5 dataset \"logg\": shape (13,), type \"<f4\">\n",
      "\t\t- teff: <HDF5 dataset \"teff\": shape (40,), type \"<f4\">\n",
      "\t\t- wavelength: <HDF5 dataset \"wavelength\": shape (2998,), type \"<f4\">\n",
      "\t- ames-dusty: <HDF5 group \"/models/ames-dusty\" (4 members)>\n",
      "\t\t- flux: <HDF5 dataset \"flux\": shape (36, 6, 2998), type \"<f4\">\n",
      "\t\t- logg: <HDF5 dataset \"logg\": shape (6,), type \"<f4\">\n",
      "\t\t- teff: <HDF5 dataset \"teff\": shape (36,), type \"<f4\">\n",
      "\t\t- wavelength: <HDF5 dataset \"wavelength\": shape (2998,), type \"<f4\">\n",
      "- objects: <HDF5 group \"/objects\" (22 members)>\n",
      "\t- 1RXS 1609 B: <HDF5 group \"/objects/1RXS 1609 B\" (2 members)>\n",
      "\t\t- Gemini: <HDF5 group \"/objects/1RXS 1609 B/Gemini\" (4 members)>\n",
      "\t\t\t- NIRI.H-G0203w: <HDF5 dataset \"NIRI.H-G0203w\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NIRI.J-G0202w: <HDF5 dataset \"NIRI.J-G0202w\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NIRI.K-G0204w: <HDF5 dataset \"NIRI.K-G0204w\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NIRI.Lprime-G0207w: <HDF5 dataset \"NIRI.Lprime-G0207w\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- 2M0103 ABb: <HDF5 group \"/objects/2M0103 ABb\" (2 members)>\n",
      "\t\t- Paranal: <HDF5 group \"/objects/2M0103 ABb/Paranal\" (4 members)>\n",
      "\t\t\t- NACO.H: <HDF5 dataset \"NACO.H\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.J: <HDF5 dataset \"NACO.J\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.Ks: <HDF5 dataset \"NACO.Ks\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.Lp: <HDF5 dataset \"NACO.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- 2M1207 b: <HDF5 group \"/objects/2M1207 b\" (3 members)>\n",
      "\t\t- HST: <HDF5 group \"/objects/2M1207 b/HST\" (4 members)>\n",
      "\t\t\t- NICMOS1.F090M: <HDF5 dataset \"NICMOS1.F090M\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NICMOS1.F110M: <HDF5 dataset \"NICMOS1.F110M\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NICMOS1.F145M: <HDF5 dataset \"NICMOS1.F145M\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NICMOS1.F160W: <HDF5 dataset \"NICMOS1.F160W\": shape (4,), type \"<f4\">\n",
      "\t\t- Paranal: <HDF5 group \"/objects/2M1207 b/Paranal\" (4 members)>\n",
      "\t\t\t- NACO.H: <HDF5 dataset \"NACO.H\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.J: <HDF5 dataset \"NACO.J\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.Ks: <HDF5 dataset \"NACO.Ks\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.Lp: <HDF5 dataset \"NACO.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- 51 Eri b: <HDF5 group \"/objects/51 Eri b\" (4 members)>\n",
      "\t\t- Keck: <HDF5 group \"/objects/51 Eri b/Keck\" (2 members)>\n",
      "\t\t\t- NIRC2.Lp: <HDF5 dataset \"NIRC2.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NIRC2.Ms: <HDF5 dataset \"NIRC2.Ms\": shape (4,), type \"<f4\">\n",
      "\t\t- MKO: <HDF5 group \"/objects/51 Eri b/MKO\" (3 members)>\n",
      "\t\t\t- NSFCam.H: <HDF5 dataset \"NSFCam.H\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NSFCam.J: <HDF5 dataset \"NSFCam.J\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NSFCam.K: <HDF5 dataset \"NSFCam.K\": shape (4,), type \"<f4\">\n",
      "\t\t- Paranal: <HDF5 group \"/objects/51 Eri b/Paranal\" (2 members)>\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_2: <HDF5 dataset \"SPHERE.IRDIS_D_H23_2\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_1: <HDF5 dataset \"SPHERE.IRDIS_D_K12_1\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- AB Pic B: <HDF5 group \"/objects/AB Pic B\" (2 members)>\n",
      "\t\t- Paranal: <HDF5 group \"/objects/AB Pic B/Paranal\" (3 members)>\n",
      "\t\t\t- NACO.H: <HDF5 dataset \"NACO.H\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.J: <HDF5 dataset \"NACO.J\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.Ks: <HDF5 dataset \"NACO.Ks\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- GJ 504 b: <HDF5 group \"/objects/GJ 504 b\" (3 members)>\n",
      "\t\t- Paranal: <HDF5 group \"/objects/GJ 504 b/Paranal\" (6 members)>\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_2: <HDF5 dataset \"SPHERE.IRDIS_D_H23_2\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_3: <HDF5 dataset \"SPHERE.IRDIS_D_H23_3\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_J23_3: <HDF5 dataset \"SPHERE.IRDIS_D_J23_3\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_1: <HDF5 dataset \"SPHERE.IRDIS_D_K12_1\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_Y23_2: <HDF5 dataset \"SPHERE.IRDIS_D_Y23_2\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_Y23_3: <HDF5 dataset \"SPHERE.IRDIS_D_Y23_3\": shape (4,), type \"<f4\">\n",
      "\t\t- Subaru: <HDF5 group \"/objects/GJ 504 b/Subaru\" (5 members)>\n",
      "\t\t\t- CIAO.CH4s: <HDF5 dataset \"CIAO.CH4s\": shape (4,), type \"<f4\">\n",
      "\t\t\t- CIAO.H: <HDF5 dataset \"CIAO.H\": shape (4,), type \"<f4\">\n",
      "\t\t\t- CIAO.J: <HDF5 dataset \"CIAO.J\": shape (4,), type \"<f4\">\n",
      "\t\t\t- CIAO.Ks: <HDF5 dataset \"CIAO.Ks\": shape (4,), type \"<f4\">\n",
      "\t\t\t- IRCS.Lp: <HDF5 dataset \"IRCS.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- GQ Lup B: <HDF5 group \"/objects/GQ Lup B\" (3 members)>\n",
      "\t\t- HST: <HDF5 group \"/objects/GQ Lup B/HST\" (5 members)>\n",
      "\t\t\t- NICMOS2.F171M: <HDF5 dataset \"NICMOS2.F171M\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NICMOS2.F190N: <HDF5 dataset \"NICMOS2.F190N\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NICMOS2.F215N: <HDF5 dataset \"NICMOS2.F215N\": shape (4,), type \"<f4\">\n",
      "\t\t\t- WFPC2.f606w: <HDF5 dataset \"WFPC2.f606w\": shape (4,), type \"<f4\">\n",
      "\t\t\t- WFPC2.f814w: <HDF5 dataset \"WFPC2.f814w\": shape (4,), type \"<f4\">\n",
      "\t\t- Subaru: <HDF5 group \"/objects/GQ Lup B/Subaru\" (3 members)>\n",
      "\t\t\t- CIAO.CH4s: <HDF5 dataset \"CIAO.CH4s\": shape (4,), type \"<f4\">\n",
      "\t\t\t- CIAO.K: <HDF5 dataset \"CIAO.K\": shape (4,), type \"<f4\">\n",
      "\t\t\t- CIAO.Lp: <HDF5 dataset \"CIAO.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- GSC 06214 B: <HDF5 group \"/objects/GSC 06214 B\" (3 members)>\n",
      "\t\t- LBT: <HDF5 group \"/objects/GSC 06214 B/LBT\" (1 members)>\n",
      "\t\t\t- LMIRCam.M_77K: <HDF5 dataset \"LMIRCam.M_77K\": shape (4,), type \"<f4\">\n",
      "\t\t- MKO: <HDF5 group \"/objects/GSC 06214 B/MKO\" (4 members)>\n",
      "\t\t\t- NSFCam.H: <HDF5 dataset \"NSFCam.H\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NSFCam.J: <HDF5 dataset \"NSFCam.J\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NSFCam.Kp: <HDF5 dataset \"NSFCam.Kp\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NSFCam.Lp: <HDF5 dataset \"NSFCam.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- GU Psc b: <HDF5 group \"/objects/GU Psc b\" (4 members)>\n",
      "\t\t- CFHT: <HDF5 group \"/objects/GU Psc b/CFHT\" (4 members)>\n",
      "\t\t\t- Wircam.H: <HDF5 dataset \"Wircam.H\": shape (4,), type \"<f4\">\n",
      "\t\t\t- Wircam.J: <HDF5 dataset \"Wircam.J\": shape (4,), type \"<f4\">\n",
      "\t\t\t- Wircam.Ks: <HDF5 dataset \"Wircam.Ks\": shape (4,), type \"<f4\">\n",
      "\t\t\t- Wircam.Y: <HDF5 dataset \"Wircam.Y\": shape (4,), type \"<f4\">\n",
      "\t\t- Gemini: <HDF5 group \"/objects/GU Psc b/Gemini\" (1 members)>\n",
      "\t\t\t- GMOS-S.z: <HDF5 dataset \"GMOS-S.z\": shape (4,), type \"<f4\">\n",
      "\t\t- WISE: <HDF5 group \"/objects/GU Psc b/WISE\" (2 members)>\n",
      "\t\t\t- WISE.W1: <HDF5 dataset \"WISE.W1\": shape (4,), type \"<f4\">\n",
      "\t\t\t- WISE.W2: <HDF5 dataset \"WISE.W2\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- HD 206893 B: <HDF5 group \"/objects/HD 206893 B\" (2 members)>\n",
      "\t\t- Paranal: <HDF5 group \"/objects/HD 206893 B/Paranal\" (6 members)>\n",
      "\t\t\t- NACO.Lp: <HDF5 dataset \"NACO.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.Mp: <HDF5 dataset \"NACO.Mp\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.NB405: <HDF5 dataset \"NACO.NB405\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_B_H: <HDF5 dataset \"SPHERE.IRDIS_B_H\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_1: <HDF5 dataset \"SPHERE.IRDIS_D_K12_1\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_2: <HDF5 dataset \"SPHERE.IRDIS_D_K12_2\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- HD 95086 b: <HDF5 group \"/objects/HD 95086 b\" (3 members)>\n",
      "\t\t- Gemini: <HDF5 group \"/objects/HD 95086 b/Gemini\" (2 members)>\n",
      "\t\t\t- GPI.H: <HDF5 dataset \"GPI.H\": shape (4,), type \"<f4\">\n",
      "\t\t\t- GPI.K1: <HDF5 dataset \"GPI.K1\": shape (4,), type \"<f4\">\n",
      "\t\t- Paranal: <HDF5 group \"/objects/HD 95086 b/Paranal\" (1 members)>\n",
      "\t\t\t- NACO.Lp: <HDF5 dataset \"NACO.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- HIP 65426 b: <HDF5 group \"/objects/HIP 65426 b\" (2 members)>\n",
      "\t\t- Paranal: <HDF5 group \"/objects/HIP 65426 b/Paranal\" (7 members)>\n",
      "\t\t\t- NACO.Lp: <HDF5 dataset \"NACO.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.Mp: <HDF5 dataset \"NACO.Mp\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.NB405: <HDF5 dataset \"NACO.NB405\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_2: <HDF5 dataset \"SPHERE.IRDIS_D_H23_2\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_3: <HDF5 dataset \"SPHERE.IRDIS_D_H23_3\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_1: <HDF5 dataset \"SPHERE.IRDIS_D_K12_1\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_2: <HDF5 dataset \"SPHERE.IRDIS_D_K12_2\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- HR 8799 b: <HDF5 group \"/objects/HR 8799 b\" (4 members)>\n",
      "\t\t- Keck: <HDF5 group \"/objects/HR 8799 b/Keck\" (3 members)>\n",
      "\t\t\t- NIRC2.H: <HDF5 dataset \"NIRC2.H\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NIRC2.Ks: <HDF5 dataset \"NIRC2.Ks\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NIRC2.Ms: <HDF5 dataset \"NIRC2.Ms\": shape (4,), type \"<f4\">\n",
      "\t\t- Paranal: <HDF5 group \"/objects/HR 8799 b/Paranal\" (7 members)>\n",
      "\t\t\t- NACO.Lp: <HDF5 dataset \"NACO.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.NB405: <HDF5 dataset \"NACO.NB405\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_B_J: <HDF5 dataset \"SPHERE.IRDIS_B_J\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_2: <HDF5 dataset \"SPHERE.IRDIS_D_H23_2\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_3: <HDF5 dataset \"SPHERE.IRDIS_D_H23_3\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_1: <HDF5 dataset \"SPHERE.IRDIS_D_K12_1\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_2: <HDF5 dataset \"SPHERE.IRDIS_D_K12_2\": shape (4,), type \"<f4\">\n",
      "\t\t- Subaru: <HDF5 group \"/objects/HR 8799 b/Subaru\" (1 members)>\n",
      "\t\t\t- CIAO.z: <HDF5 dataset \"CIAO.z\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- HR 8799 c: <HDF5 group \"/objects/HR 8799 c\" (3 members)>\n",
      "\t\t- Keck: <HDF5 group \"/objects/HR 8799 c/Keck\" (3 members)>\n",
      "\t\t\t- NIRC2.H: <HDF5 dataset \"NIRC2.H\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NIRC2.Ks: <HDF5 dataset \"NIRC2.Ks\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NIRC2.Ms: <HDF5 dataset \"NIRC2.Ms\": shape (4,), type \"<f4\">\n",
      "\t\t- Paranal: <HDF5 group \"/objects/HR 8799 c/Paranal\" (7 members)>\n",
      "\t\t\t- NACO.Lp: <HDF5 dataset \"NACO.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.NB405: <HDF5 dataset \"NACO.NB405\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_B_J: <HDF5 dataset \"SPHERE.IRDIS_B_J\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_2: <HDF5 dataset \"SPHERE.IRDIS_D_H23_2\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_3: <HDF5 dataset \"SPHERE.IRDIS_D_H23_3\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_1: <HDF5 dataset \"SPHERE.IRDIS_D_K12_1\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_2: <HDF5 dataset \"SPHERE.IRDIS_D_K12_2\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- HR 8799 d: <HDF5 group \"/objects/HR 8799 d\" (3 members)>\n",
      "\t\t- Keck: <HDF5 group \"/objects/HR 8799 d/Keck\" (3 members)>\n",
      "\t\t\t- NIRC2.H: <HDF5 dataset \"NIRC2.H\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NIRC2.Ks: <HDF5 dataset \"NIRC2.Ks\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NIRC2.Ms: <HDF5 dataset \"NIRC2.Ms\": shape (4,), type \"<f4\">\n",
      "\t\t- Paranal: <HDF5 group \"/objects/HR 8799 d/Paranal\" (7 members)>\n",
      "\t\t\t- NACO.Lp: <HDF5 dataset \"NACO.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.NB405: <HDF5 dataset \"NACO.NB405\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_B_J: <HDF5 dataset \"SPHERE.IRDIS_B_J\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_2: <HDF5 dataset \"SPHERE.IRDIS_D_H23_2\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_3: <HDF5 dataset \"SPHERE.IRDIS_D_H23_3\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_1: <HDF5 dataset \"SPHERE.IRDIS_D_K12_1\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_2: <HDF5 dataset \"SPHERE.IRDIS_D_K12_2\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- HR 8799 e: <HDF5 group \"/objects/HR 8799 e\" (3 members)>\n",
      "\t\t- Keck: <HDF5 group \"/objects/HR 8799 e/Keck\" (1 members)>\n",
      "\t\t\t- NIRC2.Ks: <HDF5 dataset \"NIRC2.Ks\": shape (4,), type \"<f4\">\n",
      "\t\t- Paranal: <HDF5 group \"/objects/HR 8799 e/Paranal\" (7 members)>\n",
      "\t\t\t- NACO.Lp: <HDF5 dataset \"NACO.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.NB405: <HDF5 dataset \"NACO.NB405\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_B_J: <HDF5 dataset \"SPHERE.IRDIS_B_J\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_2: <HDF5 dataset \"SPHERE.IRDIS_D_H23_2\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_3: <HDF5 dataset \"SPHERE.IRDIS_D_H23_3\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_1: <HDF5 dataset \"SPHERE.IRDIS_D_K12_1\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_2: <HDF5 dataset \"SPHERE.IRDIS_D_K12_2\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- PDS 70 b: <HDF5 group \"/objects/PDS 70 b\" (2 members)>\n",
      "\t\t- Paranal: <HDF5 group \"/objects/PDS 70 b/Paranal\" (4 members)>\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_2: <HDF5 dataset \"SPHERE.IRDIS_D_H23_2\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_3: <HDF5 dataset \"SPHERE.IRDIS_D_H23_3\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_1: <HDF5 dataset \"SPHERE.IRDIS_D_K12_1\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_2: <HDF5 dataset \"SPHERE.IRDIS_D_K12_2\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- PDS 70 c: <HDF5 group \"/objects/PDS 70 c\" (2 members)>\n",
      "\t\t- Paranal: <HDF5 group \"/objects/PDS 70 c/Paranal\" (1 members)>\n",
      "\t\t\t- NACO.NB405: <HDF5 dataset \"NACO.NB405\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- PZ Tel B: <HDF5 group \"/objects/PZ Tel B\" (3 members)>\n",
      "\t\t- Gemini: <HDF5 group \"/objects/PZ Tel B/Gemini\" (2 members)>\n",
      "\t\t\t- NICI.ED286: <HDF5 dataset \"NICI.ED286\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NIRI.H2S1v2-1-G0220: <HDF5 dataset \"NIRI.H2S1v2-1-G0220\": shape (4,), type \"<f4\">\n",
      "\t\t- Paranal: <HDF5 group \"/objects/PZ Tel B/Paranal\" (12 members)>\n",
      "\t\t\t- NACO.H: <HDF5 dataset \"NACO.H\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.J: <HDF5 dataset \"NACO.J\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.Ks: <HDF5 dataset \"NACO.Ks\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.Lp: <HDF5 dataset \"NACO.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.Mp: <HDF5 dataset \"NACO.Mp\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.NB405: <HDF5 dataset \"NACO.NB405\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_2: <HDF5 dataset \"SPHERE.IRDIS_D_H23_2\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_H23_3: <HDF5 dataset \"SPHERE.IRDIS_D_H23_3\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_1: <HDF5 dataset \"SPHERE.IRDIS_D_K12_1\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.IRDIS_D_K12_2: <HDF5 dataset \"SPHERE.IRDIS_D_K12_2\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.ZIMPOL_I_PRIM: <HDF5 dataset \"SPHERE.ZIMPOL_I_PRIM\": shape (4,), type \"<f4\">\n",
      "\t\t\t- SPHERE.ZIMPOL_R_PRIM: <HDF5 dataset \"SPHERE.ZIMPOL_R_PRIM\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- ROXs 42 Bb: <HDF5 group \"/objects/ROXs 42 Bb\" (2 members)>\n",
      "\t\t- Keck: <HDF5 group \"/objects/ROXs 42 Bb/Keck\" (5 members)>\n",
      "\t\t\t- NIRC2.H: <HDF5 dataset \"NIRC2.H\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NIRC2.J: <HDF5 dataset \"NIRC2.J\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NIRC2.Ks: <HDF5 dataset \"NIRC2.Ks\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NIRC2.Lp: <HDF5 dataset \"NIRC2.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NIRC2.Ms: <HDF5 dataset \"NIRC2.Ms\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- beta Pic b: <HDF5 group \"/objects/beta Pic b\" (4 members)>\n",
      "\t\t- Gemini: <HDF5 group \"/objects/beta Pic b/Gemini\" (1 members)>\n",
      "\t\t\t- NICI.ED286: <HDF5 dataset \"NICI.ED286\": shape (4,), type \"<f4\">\n",
      "\t\t- LCO: <HDF5 group \"/objects/beta Pic b/LCO\" (1 members)>\n",
      "\t\t\t- VisAO.Ys: <HDF5 dataset \"VisAO.Ys\": shape (4,), type \"<f4\">\n",
      "\t\t- Paranal: <HDF5 group \"/objects/beta Pic b/Paranal\" (7 members)>\n",
      "\t\t\t- NACO.H: <HDF5 dataset \"NACO.H\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.J: <HDF5 dataset \"NACO.J\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.Ks: <HDF5 dataset \"NACO.Ks\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.Lp: <HDF5 dataset \"NACO.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.Mp: <HDF5 dataset \"NACO.Mp\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.NB374: <HDF5 dataset \"NACO.NB374\": shape (4,), type \"<f4\">\n",
      "\t\t\t- NACO.NB405: <HDF5 dataset \"NACO.NB405\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "\t- kappa And b: <HDF5 group \"/objects/kappa And b\" (4 members)>\n",
      "\t\t- Keck: <HDF5 group \"/objects/kappa And b/Keck\" (1 members)>\n",
      "\t\t\t- NIRC2.Lp: <HDF5 dataset \"NIRC2.Lp\": shape (4,), type \"<f4\">\n",
      "\t\t- LBT: <HDF5 group \"/objects/kappa And b/LBT\" (1 members)>\n",
      "\t\t\t- LMIRCam.M_77K: <HDF5 dataset \"LMIRCam.M_77K\": shape (4,), type \"<f4\">\n",
      "\t\t- Subaru: <HDF5 group \"/objects/kappa And b/Subaru\" (3 members)>\n",
      "\t\t\t- CIAO.H: <HDF5 dataset \"CIAO.H\": shape (4,), type \"<f4\">\n",
      "\t\t\t- CIAO.J: <HDF5 dataset \"CIAO.J\": shape (4,), type \"<f4\">\n",
      "\t\t\t- CIAO.Ks: <HDF5 dataset \"CIAO.Ks\": shape (4,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (2,), type \"<f4\">\n",
      "- photometry: <HDF5 group \"/photometry\" (1 members)>\n",
      "\t- vlm-plx: <HDF5 group \"/photometry/vlm-plx\" (6 members)>\n",
      "\t\t- 2MASS: <HDF5 group \"/photometry/vlm-plx/2MASS\" (3 members)>\n",
      "\t\t\t- 2MASS.H: <HDF5 dataset \"2MASS.H\": shape (618,), type \"<f4\">\n",
      "\t\t\t- 2MASS.J: <HDF5 dataset \"2MASS.J\": shape (618,), type \"<f4\">\n",
      "\t\t\t- 2MASS.Ks: <HDF5 dataset \"2MASS.Ks\": shape (618,), type \"<f4\">\n",
      "\t\t- MKO: <HDF5 group \"/photometry/vlm-plx/MKO\" (6 members)>\n",
      "\t\t\t- NSFCam.H: <HDF5 dataset \"NSFCam.H\": shape (618,), type \"<f4\">\n",
      "\t\t\t- NSFCam.J: <HDF5 dataset \"NSFCam.J\": shape (618,), type \"<f4\">\n",
      "\t\t\t- NSFCam.K: <HDF5 dataset \"NSFCam.K\": shape (618,), type \"<f4\">\n",
      "\t\t\t- NSFCam.Lp: <HDF5 dataset \"NSFCam.Lp\": shape (618,), type \"<f4\">\n",
      "\t\t\t- NSFCam.Mp: <HDF5 dataset \"NSFCam.Mp\": shape (618,), type \"<f4\">\n",
      "\t\t\t- NSFCam.Y: <HDF5 dataset \"NSFCam.Y\": shape (618,), type \"<f4\">\n",
      "\t\t- distance: <HDF5 dataset \"distance\": shape (618,), type \"<f4\">\n",
      "\t\t- flag: <HDF5 dataset \"flag\": shape (618,), type \"|O\">\n",
      "\t\t- name: <HDF5 dataset \"name\": shape (618,), type \"|O\">\n",
      "\t\t- sptype: <HDF5 dataset \"sptype\": shape (618,), type \"|O\">\n",
      "- spectra: <HDF5 group \"/spectra\" (1 members)>\n",
      "\t- calibration: <HDF5 group \"/spectra/calibration\" (1 members)>\n",
      "\t\t- vega: <HDF5 dataset \"vega\": shape (3, 8827), type \"<f4\">\n"
     ]
    }
   ],
   "source": [
    "database.list_content()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthetic photometry from isochrones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Magnitudes are available in the isochrone data which can be extracted with the `get_isochrone` function of `ReadIsochrone`. However, in this example, we consistently recompute the synthetic photometry by making use of both the isochrones and the model spectra."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The isochrones will be iterpolated for three different ages and the synthetic photometry is computed for 100 logarithmically spaced masses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ages = [20., 100., 1000.]  # [Myr]\n",
    "masses = np.logspace(-1., 4., 100)  # [Mjup]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Object of `ReadIsochones` are initiated for both the AMES-Cond and AMES-Dusty isochrones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "read_iso_cond = species.ReadIsochrone(isochrone_tag='iso_cond')\n",
    "read_iso_dusty = species.ReadIsochrone(isochrone_tag='iso_dusty')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The colors and magnitudes are computed by chosing the corresponding model spectra that are stored in the database. The results of two models are stored in `ColorMagBox` objects for the three different ages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "boxes = []\n",
    "\n",
    "for item in ages:\n",
    "\n",
    "    modelcolor1 = read_iso_cond.get_color_magnitude(age=item,\n",
    "                                                    masses=masses,\n",
    "                                                    model='ames-cond',\n",
    "                                                    filters_color=('MKO/NSFCam.H', 'MKO/NSFCam.Lp'),\n",
    "                                                    filter_mag='MKO/NSFCam.Lp')\n",
    "\n",
    "    modelcolor2 = read_iso_dusty.get_color_magnitude(age=item,\n",
    "                                                     masses=masses,\n",
    "                                                     model='ames-dusty',\n",
    "                                                     filters_color=('MKO/NSFCam.H', 'MKO/NSFCam.Lp'),\n",
    "                                                     filter_mag='MKO/NSFCam.Lp')\n",
    "\n",
    "    boxes.append(modelcolor1)\n",
    "    boxes.append(modelcolor2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Photometry of directly imaged objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before selecting the photometric data of the directly imaged planets and brown dwarfs, let's see which objects and magnitudes are stored in the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object name = beta Pic b\n",
      "Distance [pc] = 19.75 +/- 0.13\n",
      "LCO/VisAO.Ys [mag] = 15.53 +/- 0.34\n",
      "Paranal/NACO.J [mag] = 14.11 +/- 0.21\n",
      "Gemini/NICI.ED286 [mag] = 13.18 +/- 0.15\n",
      "Paranal/NACO.H [mag] = 13.32 +/- 0.14\n",
      "Paranal/NACO.Ks [mag] = 12.64 +/- 0.11\n",
      "Paranal/NACO.NB374 [mag] = 11.25 +/- 0.23\n",
      "Paranal/NACO.Lp [mag] = 11.3 +/- 0.06\n",
      "Paranal/NACO.NB405 [mag] = 10.98 +/- 0.05\n",
      "Paranal/NACO.Mp [mag] = 11.1 +/- 0.12\n",
      "\n",
      "Object name = HIP 65426 b\n",
      "Distance [pc] = 109.21 +/- 0.75\n",
      "Paranal/SPHERE.IRDIS_D_H23_2 [mag] = 17.94 +/- 0.05\n",
      "Paranal/SPHERE.IRDIS_D_H23_3 [mag] = 17.58 +/- 0.06\n",
      "Paranal/SPHERE.IRDIS_D_K12_1 [mag] = 17.01 +/- 0.09\n",
      "Paranal/SPHERE.IRDIS_D_K12_2 [mag] = 16.79 +/- 0.09\n",
      "Paranal/NACO.Lp [mag] = 15.33 +/- 0.12\n",
      "Paranal/NACO.NB405 [mag] = 15.23 +/- 0.22\n",
      "Paranal/NACO.Mp [mag] = 14.65 +/- 0.29\n",
      "\n",
      "Object name = 51 Eri b\n",
      "Distance [pc] = 29.78 +/- 0.12\n",
      "MKO/NSFCam.J [mag] = 19.04 +/- 0.4\n",
      "MKO/NSFCam.H [mag] = 18.99 +/- 0.21\n",
      "MKO/NSFCam.K [mag] = 18.67 +/- 0.19\n",
      "Paranal/SPHERE.IRDIS_D_H23_2 [mag] = 18.41 +/- 0.26\n",
      "Paranal/SPHERE.IRDIS_D_K12_1 [mag] = 17.55 +/- 0.14\n",
      "Keck/NIRC2.Lp [mag] = 16.2 +/- 0.11\n",
      "Keck/NIRC2.Ms [mag] = 16.1 +/- 0.5\n",
      "\n",
      "Object name = HR 8799 b\n",
      "Distance [pc] = 41.29 +/- 0.15\n",
      "Subaru/CIAO.z [mag] = 21.22 +/- 0.29\n",
      "Paranal/SPHERE.IRDIS_B_J [mag] = 19.78 +/- 0.09\n",
      "Keck/NIRC2.H [mag] = 18.05 +/- 0.09\n",
      "Paranal/SPHERE.IRDIS_D_H23_2 [mag] = 18.08 +/- 0.14\n",
      "Paranal/SPHERE.IRDIS_D_H23_3 [mag] = 17.78 +/- 0.1\n",
      "Keck/NIRC2.Ks [mag] = 17.03 +/- 0.08\n",
      "Paranal/SPHERE.IRDIS_D_K12_1 [mag] = 17.15 +/- 0.06\n",
      "Paranal/SPHERE.IRDIS_D_K12_2 [mag] = 16.97 +/- 0.09\n",
      "Paranal/NACO.Lp [mag] = 15.52 +/- 0.1\n",
      "Paranal/NACO.NB405 [mag] = 14.82 +/- 0.18\n",
      "Keck/NIRC2.Ms [mag] = 16.05 +/- 0.3\n",
      "\n",
      "Object name = HR 8799 c\n",
      "Distance [pc] = 41.29 +/- 0.15\n",
      "Paranal/SPHERE.IRDIS_B_J [mag] = 18.6 +/- 0.13\n",
      "Keck/NIRC2.H [mag] = 17.06 +/- 0.13\n",
      "Paranal/SPHERE.IRDIS_D_H23_2 [mag] = 17.09 +/- 0.12\n",
      "Paranal/SPHERE.IRDIS_D_H23_3 [mag] = 16.78 +/- 0.1\n",
      "Keck/NIRC2.Ks [mag] = 16.11 +/- 0.08\n",
      "Paranal/SPHERE.IRDIS_D_K12_1 [mag] = 16.19 +/- 0.05\n",
      "Paranal/SPHERE.IRDIS_D_K12_2 [mag] = 15.86 +/- 0.07\n",
      "Paranal/NACO.Lp [mag] = 14.65 +/- 0.11\n",
      "Paranal/NACO.NB405 [mag] = 13.97 +/- 0.11\n",
      "Keck/NIRC2.Ms [mag] = 15.03 +/- 0.14\n",
      "\n",
      "Object name = HR 8799 d\n",
      "Distance [pc] = 41.29 +/- 0.15\n",
      "Paranal/SPHERE.IRDIS_B_J [mag] = 18.59 +/- 0.37\n",
      "Keck/NIRC2.H [mag] = 16.71 +/- 0.24\n",
      "Paranal/SPHERE.IRDIS_D_H23_2 [mag] = 17.02 +/- 0.17\n",
      "Paranal/SPHERE.IRDIS_D_H23_3 [mag] = 16.85 +/- 0.16\n",
      "Keck/NIRC2.Ks [mag] = 16.09 +/- 0.12\n",
      "Paranal/SPHERE.IRDIS_D_K12_1 [mag] = 16.2 +/- 0.07\n",
      "Paranal/SPHERE.IRDIS_D_K12_2 [mag] = 15.84 +/- 0.1\n",
      "Paranal/NACO.Lp [mag] = 14.55 +/- 0.14\n",
      "Paranal/NACO.NB405 [mag] = 13.87 +/- 0.15\n",
      "Keck/NIRC2.Ms [mag] = 14.65 +/- 0.35\n",
      "\n",
      "Object name = HR 8799 e\n",
      "Distance [pc] = 41.29 +/- 0.15\n",
      "Paranal/SPHERE.IRDIS_B_J [mag] = 18.4 +/- 0.21\n",
      "Paranal/SPHERE.IRDIS_D_H23_2 [mag] = 16.91 +/- 0.2\n",
      "Paranal/SPHERE.IRDIS_D_H23_3 [mag] = 16.68 +/- 0.21\n",
      "Keck/NIRC2.Ks [mag] = 15.91 +/- 0.22\n",
      "Paranal/SPHERE.IRDIS_D_K12_1 [mag] = 16.12 +/- 0.1\n",
      "Paranal/SPHERE.IRDIS_D_K12_2 [mag] = 15.82 +/- 0.11\n",
      "Paranal/NACO.Lp [mag] = 14.49 +/- 0.21\n",
      "Paranal/NACO.NB405 [mag] = 13.72 +/- 0.2\n",
      "\n",
      "Object name = HD 95086 b\n",
      "Distance [pc] = 86.44 +/- 0.24\n",
      "Gemini/GPI.H [mag] = 20.51 +/- 0.25\n",
      "Gemini/GPI.K1 [mag] = 18.99 +/- 0.2\n",
      "Paranal/NACO.Lp [mag] = 16.27 +/- 0.19\n",
      "\n",
      "Object name = PDS 70 b\n",
      "Distance [pc] = 113.43 +/- 0.52\n",
      "Paranal/SPHERE.IRDIS_D_H23_2 [mag] = 17.94 +/- 0.24\n",
      "Paranal/SPHERE.IRDIS_D_H23_3 [mag] = 17.95 +/- 0.17\n",
      "Paranal/SPHERE.IRDIS_D_K12_1 [mag] = 16.65 +/- 0.06\n",
      "Paranal/SPHERE.IRDIS_D_K12_2 [mag] = 16.44 +/- 0.05\n",
      "\n",
      "Object name = PDS 70 c\n",
      "Distance [pc] = 113.43 +/- 0.52\n",
      "Paranal/NACO.NB405 [mag] = 15.05 +/- 0.59\n",
      "\n",
      "Object name = 2M1207 b\n",
      "Distance [pc] = 64.42 +/- 0.65\n",
      "HST/NICMOS1.F090M [mag] = 22.58 +/- 0.35\n",
      "HST/NICMOS1.F110M [mag] = 20.61 +/- 0.15\n",
      "HST/NICMOS1.F145M [mag] = 19.05 +/- 0.03\n",
      "HST/NICMOS1.F160W [mag] = 18.27 +/- 0.02\n",
      "Paranal/NACO.J [mag] = 20.0 +/- 0.2\n",
      "Paranal/NACO.H [mag] = 18.09 +/- 0.21\n",
      "Paranal/NACO.Ks [mag] = 16.93 +/- 0.11\n",
      "Paranal/NACO.Lp [mag] = 15.28 +/- 0.14\n",
      "\n",
      "Object name = AB Pic B\n",
      "Distance [pc] = 50.12 +/- 0.07\n",
      "Paranal/NACO.J [mag] = 16.18 +/- 0.1\n",
      "Paranal/NACO.H [mag] = 14.69 +/- 0.1\n",
      "Paranal/NACO.Ks [mag] = 14.14 +/- 0.08\n",
      "\n",
      "Object name = HD 206893 B\n",
      "Distance [pc] = 40.81 +/- 0.11\n",
      "Paranal/SPHERE.IRDIS_B_H [mag] = 16.79 +/- 0.06\n",
      "Paranal/SPHERE.IRDIS_D_K12_1 [mag] = 15.2 +/- 0.1\n",
      "Paranal/SPHERE.IRDIS_D_K12_2 [mag] = 14.88 +/- 0.09\n",
      "Paranal/NACO.Lp [mag] = 13.79 +/- 0.31\n",
      "Paranal/NACO.NB405 [mag] = 13.16 +/- 0.34\n",
      "Paranal/NACO.Mp [mag] = 12.77 +/- 0.27\n",
      "\n",
      "Object name = GQ Lup B\n",
      "Distance [pc] = 151.82 +/- 1.1\n",
      "HST/WFPC2.f606w [mag] = 19.19 +/- 0.07\n",
      "HST/WFPC2.f814w [mag] = 17.67 +/- 0.05\n",
      "HST/NICMOS2.F171M [mag] = 13.84 +/- 0.13\n",
      "HST/NICMOS2.F190N [mag] = 14.08 +/- 0.2\n",
      "HST/NICMOS2.F215N [mag] = 13.4 +/- 0.15\n",
      "Subaru/CIAO.CH4s [mag] = 13.76 +/- 0.26\n",
      "Subaru/CIAO.K [mag] = 13.37 +/- 0.12\n",
      "Subaru/CIAO.Lp [mag] = 12.44 +/- 0.22\n",
      "\n",
      "Object name = PZ Tel B\n",
      "Distance [pc] = 47.13 +/- 0.13\n",
      "Paranal/SPHERE.ZIMPOL_R_PRIM [mag] = 17.84 +/- 0.31\n",
      "Paranal/SPHERE.ZIMPOL_I_PRIM [mag] = 15.16 +/- 0.12\n",
      "Paranal/SPHERE.IRDIS_D_H23_2 [mag] = 11.78 +/- 0.19\n",
      "Paranal/SPHERE.IRDIS_D_H23_3 [mag] = 11.65 +/- 0.19\n",
      "Paranal/SPHERE.IRDIS_D_K12_1 [mag] = 11.56 +/- 0.09\n",
      "Paranal/SPHERE.IRDIS_D_K12_2 [mag] = 11.29 +/- 0.1\n",
      "Paranal/NACO.J [mag] = 12.47 +/- 0.2\n",
      "Paranal/NACO.H [mag] = 11.93 +/- 0.14\n",
      "Paranal/NACO.Ks [mag] = 11.53 +/- 0.07\n",
      "Paranal/NACO.Lp [mag] = 11.04 +/- 0.22\n",
      "Paranal/NACO.NB405 [mag] = 10.94 +/- 0.07\n",
      "Paranal/NACO.Mp [mag] = 10.93 +/- 0.03\n",
      "Gemini/NICI.ED286 [mag] = 11.68 +/- 0.14\n",
      "Gemini/NIRI.H2S1v2-1-G0220 [mag] = 11.39 +/- 0.14\n",
      "\n",
      "Object name = kappa And b\n",
      "Distance [pc] = 50.06 +/- 0.87\n",
      "Subaru/CIAO.J [mag] = 15.86 +/- 0.21\n",
      "Subaru/CIAO.H [mag] = 14.95 +/- 0.13\n",
      "Subaru/CIAO.Ks [mag] = 14.32 +/- 0.09\n",
      "Keck/NIRC2.Lp [mag] = 13.12 +/- 0.1\n",
      "LBT/LMIRCam.M_77K [mag] = 13.3 +/- 0.3\n",
      "\n",
      "Object name = ROXs 42 Bb\n",
      "Distance [pc] = 144.16 +/- 1.54\n",
      "Keck/NIRC2.J [mag] = 16.91 +/- 0.11\n",
      "Keck/NIRC2.H [mag] = 15.88 +/- 0.05\n",
      "Keck/NIRC2.Ks [mag] = 15.01 +/- 0.06\n",
      "Keck/NIRC2.Lp [mag] = 13.97 +/- 0.06\n",
      "Keck/NIRC2.Ms [mag] = 14.01 +/- 0.23\n",
      "\n",
      "Object name = GJ 504 b\n",
      "Distance [pc] = 17.54 +/- 0.08\n",
      "Paranal/SPHERE.IRDIS_D_Y23_2 [mag] = 20.98 +/- 0.2\n",
      "Paranal/SPHERE.IRDIS_D_Y23_3 [mag] = 20.14 +/- 0.09\n",
      "Paranal/SPHERE.IRDIS_D_J23_3 [mag] = 19.01 +/- 0.17\n",
      "Paranal/SPHERE.IRDIS_D_H23_2 [mag] = 18.95 +/- 0.3\n",
      "Paranal/SPHERE.IRDIS_D_H23_3 [mag] = 21.81 +/- 0.35\n",
      "Paranal/SPHERE.IRDIS_D_K12_1 [mag] = 18.77 +/- 0.2\n",
      "Subaru/CIAO.J [mag] = 19.78 +/- 0.1\n",
      "Subaru/CIAO.H [mag] = 20.01 +/- 0.14\n",
      "Subaru/CIAO.Ks [mag] = 19.38 +/- 0.11\n",
      "Subaru/CIAO.CH4s [mag] = 19.58 +/- 0.13\n",
      "Subaru/IRCS.Lp [mag] = 16.7 +/- 0.17\n",
      "\n",
      "Object name = GU Psc b\n",
      "Distance [pc] = 47.61 +/- 0.16\n",
      "Gemini/GMOS-S.z [mag] = 21.75 +/- 0.07\n",
      "CFHT/Wircam.Y [mag] = 19.4 +/- 0.05\n",
      "CFHT/Wircam.J [mag] = 18.12 +/- 0.03\n",
      "CFHT/Wircam.H [mag] = 17.7 +/- 0.03\n",
      "CFHT/Wircam.Ks [mag] = 17.4 +/- 0.03\n",
      "WISE/WISE.W1 [mag] = 17.17 +/- 0.33\n",
      "WISE/WISE.W2 [mag] = 15.41 +/- 0.22\n",
      "\n",
      "Object name = 2M0103 ABb\n",
      "Distance [pc] = 47.2 +/- 3.1\n",
      "Paranal/NACO.J [mag] = 15.47 +/- 0.3\n",
      "Paranal/NACO.H [mag] = 14.27 +/- 0.2\n",
      "Paranal/NACO.Ks [mag] = 13.67 +/- 0.2\n",
      "Paranal/NACO.Lp [mag] = 12.67 +/- 0.1\n",
      "\n",
      "Object name = 1RXS 1609 B\n",
      "Distance [pc] = 139.67 +/- 1.33\n",
      "Gemini/NIRI.J-G0202w [mag] = 17.9 +/- 0.12\n",
      "Gemini/NIRI.H-G0203w [mag] = 16.87 +/- 0.07\n",
      "Gemini/NIRI.K-G0204w [mag] = 16.17 +/- 0.18\n",
      "Gemini/NIRI.Lprime-G0207w [mag] = 14.8 +/- 0.3\n",
      "\n",
      "Object name = GSC 06214 B\n",
      "Distance [pc] = 108.84 +/- 0.51\n",
      "MKO/NSFCam.J [mag] = 16.24 +/- 0.04\n",
      "MKO/NSFCam.H [mag] = 15.55 +/- 0.04\n",
      "MKO/NSFCam.Kp [mag] = 14.95 +/- 0.05\n",
      "MKO/NSFCam.Lp [mag] = 13.75 +/- 0.07\n",
      "LBT/LMIRCam.M_77K [mag] = 13.75 +/- 0.3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "database.list_companions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list with object names and filters for the colors and magnitudes is created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "objects = [('HR 8799 b', 'Keck/NIRC2.H', 'Paranal/NACO.Lp', 'Paranal/NACO.Lp'),\n",
    "           ('HR 8799 c', 'Keck/NIRC2.H', 'Paranal/NACO.Lp', 'Paranal/NACO.Lp'),\n",
    "           ('HR 8799 d', 'Keck/NIRC2.H', 'Paranal/NACO.Lp', 'Paranal/NACO.Lp'),\n",
    "           ('HR 8799 e', 'Paranal/SPHERE.IRDIS_D_H23_2', 'Paranal/NACO.Lp', 'Paranal/NACO.Lp'),\n",
    "           ('kappa And b', 'Subaru/CIAO.H', 'Keck/NIRC2.Lp', 'Keck/NIRC2.Lp'),\n",
    "           ('GSC 06214 B', 'MKO/NSFCam.H', 'MKO/NSFCam.Lp', 'MKO/NSFCam.Lp'),\n",
    "           ('ROXs 42 Bb', 'Keck/NIRC2.H', 'Keck/NIRC2.Lp', 'Keck/NIRC2.Lp'),\n",
    "           ('51 Eri b', 'MKO/NSFCam.H', 'Keck/NIRC2.Lp', 'Keck/NIRC2.Lp'),\n",
    "           ('2M1207 b', 'Paranal/NACO.H', 'Paranal/NACO.Lp', 'Paranal/NACO.Lp'),\n",
    "           ('2M0103 ABb', 'Paranal/NACO.H', 'Paranal/NACO.Lp', 'Paranal/NACO.Lp'),\n",
    "           ('1RXS 1609 B', 'Gemini/NIRI.H-G0203w', 'Gemini/NIRI.Lprime-G0207w', 'Gemini/NIRI.Lprime-G0207w'),\n",
    "           ('beta Pic b', 'Paranal/NACO.H', 'Paranal/NACO.Lp', 'Paranal/NACO.Lp'),\n",
    "           ('HIP 65426 b', 'Paranal/SPHERE.IRDIS_D_H23_2', 'Paranal/NACO.Lp', 'Paranal/NACO.Lp'),\n",
    "           ('PZ Tel B', 'Paranal/NACO.H', 'Paranal/NACO.Lp', 'Paranal/NACO.Lp'),\n",
    "           ('HD 206893 B', 'Paranal/SPHERE.IRDIS_B_H', 'Paranal/NACO.Lp', 'Paranal/NACO.Lp')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading color-magnitude data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The colors and magnitude of the Database of Ultracool Parallaxes are read from the HDF5 database by first creating an object of `ReadColorMagnitude`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "colormag = species.ReadColorMagnitude(phot_library=['vlm-plx', ],\n",
    "                                      filters_color=('MKO/NSFCam.H', 'MKO/NSFCam.Lp'),\n",
    "                                      filter_mag='MKO/NSFCam.Lp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then extracting the `ColorMagBox` objects for field and young/low-gravity objects separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_field = colormag.get_color_magnitude(object_type='field')\n",
    "color_young = colormag.get_color_magnitude(object_type='young')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting a color-magnitude diagram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The color-magnitude diagram is plotted with the `plot_color_magnitude` function. The boxes with photometric data are provided as list to the `colorbox` parameter. The boxes with synthetic photometry are provided to the `models` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting color-magnitude diagram: color_mag.png... [DONE]\n"
     ]
    }
   ],
   "source": [
    "species.plot_color_magnitude(colorbox=[color_field, color_young],\n",
    "                             objects=objects,\n",
    "                             models=boxes,\n",
    "                             mass_labels=[1., 3., 5., 10., 20., 50., 100., 200.],\n",
    "                             companion_labels=False,\n",
    "                             field_range=('late M', 'late T'),\n",
    "                             label_x='H - L$^\\prime$ [mag]',\n",
    "                             label_y='M$_\\mathregular{L\\prime}$ [mag]',\n",
    "                             xlim=(0.3, 4.),\n",
    "                             ylim=(15., 7.1),\n",
    "                             offset=(-0.08, -0.09),\n",
    "                             legend=(0.04, 0.04),\n",
    "                             output='color_mag.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "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
}