bmorris3/gaia_synphot_helper.ipynb

## gaia_synphot_helper.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sketch for a new Gaia-based synphot helper function, which: \n",
    "\n",
    "1. Identifies a target by its name\n",
    "2. Queries Gaia for the target radius, effective temp, parallax (distance)\n",
    "3. Constructs a scaled `SourceSpectrum` object based on the above props"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query finished.\n"
     ]
    }
   ],
   "source": [
    "import astropy.units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.constants import R_sun\n",
    "\n",
    "from astroquery.gaia import Gaia\n",
    "\n",
    "from synphot import SourceSpectrum\n",
    "from synphot.models import BlackBody1D\n",
    "\n",
    "identifier = 'HAT-P-11'\n",
    "\n",
    "coord = SkyCoord.from_name(identifier)\n",
    "width = u.Quantity(1, u.arcmin)\n",
    "height = u.Quantity(1, u.arcmin)\n",
    "r = Gaia.query_object_async(coordinate=coord, width=width, height=height)\n",
    "\n",
    "# Here I take the first result as the \"true\" result -- we'll need to do better than this, \n",
    "# for example, we could extract the nearest object with a T_eff and Radius measurement, \n",
    "# but for now -- the first item is actually the one we want: \n",
    "result = r[0]\n",
    "\n",
    "# Distance in parsecs is approximately the inverse of the parallax in arcseconds:\n",
    "distance = 1 * u.pc / (result['parallax'] * u.mas).to(u.arcsec).value\n",
    "radius = result['radius_val'] * R_sun\n",
    "T_eff = result['teff_val']\n",
    "\n",
    "photlam = SourceSpectrum(BlackBody1D, temperature=T_eff) * float(radius / distance) ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "photlam.plot(left=3000, right=10000)"
   ]
  }
 ],
 "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"Sketch for a new Gaia-based synphot helper function, which: \n",
	"\n",
	"1. Identifies a target by its name\n",
	"2. Queries Gaia for the target radius, effective temp, parallax (distance)\n",
	"3. Constructs a scaled `SourceSpectrum` object based on the above props"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 27,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"Query finished.\n"
	]
	}
	],
	"source": [
	"import astropy.units as u\n",
	"from astropy.coordinates import SkyCoord\n",
	"from astropy.constants import R_sun\n",
	"\n",
	"from astroquery.gaia import Gaia\n",
	"\n",
	"from synphot import SourceSpectrum\n",
	"from synphot.models import BlackBody1D\n",
	"\n",
	"identifier = 'HAT-P-11'\n",
	"\n",
	"coord = SkyCoord.from_name(identifier)\n",
	"width = u.Quantity(1, u.arcmin)\n",
	"height = u.Quantity(1, u.arcmin)\n",
	"r = Gaia.query_object_async(coordinate=coord, width=width, height=height)\n",
	"\n",
	"# Here I take the first result as the \"true\" result -- we'll need to do better than this, \n",
	"# for example, we could extract the nearest object with a T_eff and Radius measurement, \n",
	"# but for now -- the first item is actually the one we want: \n",
	"result = r[0]\n",
	"\n",
	"# Distance in parsecs is approximately the inverse of the parallax in arcseconds:\n",
	"distance = 1 * u.pc / (result['parallax'] * u.mas).to(u.arcsec).value\n",
	"radius = result['radius_val'] * R_sun\n",
	"T_eff = result['teff_val']\n",
	"\n",
	"photlam = SourceSpectrum(BlackBody1D, temperature=T_eff) * float(radius / distance) ** 2"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 29,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"photlam.plot(left=3000, right=10000)"
	]
	}
	],
	"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": "3.5.4"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}