example_extinction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example Extinction Calculations\n",
    "\n",
    "Show example of using `extinction` package to\n",
    "1. Calculate extinction as a function of $\\lambda$ for a given $R_V$, $E(B-V)$, and extinction law\n",
    "2. Calculate color excess relationship for a given extinction law.  Specifically,  \n",
    "  1. How to convert $E(g-r)$ to $E(B-V)$\n",
    "  2. How to convert $E(g-r)$ to $A_V$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import extinction\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import astropy.units as u\n",
    "import astropy.constants as const"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a spectrum over some set of optical wavelengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "wavelength = np.linspace(3000, 10000, 701) * u.angstrom\n",
    "frequency = const.c / wavelength\n",
    "frequency = frequency.to(u.Hz)\n",
    "# Constant AB erg/s/cm^2/Hz\n",
    "norm = 1000 * u.erg / u.s / u.cm ** 2\n",
    "flux_density_frequency = norm / frequency\n",
    "flux_density_wavelength = flux_density_frequency * const.c / wavelength ** 2\n",
    "flux_density_wavelength = flux_density_wavelength.to(\n",
    "    u.erg / u.s / u.cm ** 2 / u.angstrom\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the spectral flux densities vs. wavelength and vs. frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.gca()\n",
    "ax1.plot(\n",
    "    wavelength,\n",
    "    flux_density_wavelength,\n",
    "    color=\"blue\",\n",
    "    ls=\"-\",\n",
    "    label=\"spectral flux density ($d\\lambda$)\",\n",
    ")\n",
    "ax1.set_xlabel(r\"$\\lambda [\\AA]$\")\n",
    "ax1.set_ylabel(r\"$\\frac{dF}{d\\lambda}$ [erg/s/cm$^2$/$\\AA$]\")\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(\n",
    "    wavelength,\n",
    "    flux_density_frequency,\n",
    "    color=\"blue\",\n",
    "    ls=\"--\",\n",
    "    label=r\"spectral flux density ($d\\nu$)\",\n",
    ")\n",
    "ax2.set_xlabel(r\"$\\nu$ [Hz]$\")\n",
    "ax2.set_ylabel(r\"$\\frac{dF}{d\\nu}$ [erg/s/cm$^2$/$\\nu$]\")\n",
    "\n",
    "lines1, labels1 = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines1 + lines2, labels1 + labels2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Calculating Extinction\n",
    "\n",
    "We can use the `extinction` package to calculate a sample wavelength-dependent extinction.\n",
    "\n",
    "```\n",
    "pip install extinction\n",
    "```\n",
    "\n",
    "We need to choose a dust extinction law.  This is what translates a given color excess to the extinction per wavelength.  The dust law takes $A_V$ as input, with $R_V$ as optional.\n",
    "\n",
    "We here choose a Fitzpatrick99 dust extinction law."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function fitzpatrick99 in module extinction:\n",
      "\n",
      "fitzpatrick99(...)\n",
      "    fitzpatrick99(wave, a_v, r_v=3.1, unit='aa')\n",
      "    \n",
      "    Fitzpatrick (1999) dust extinction function.\n",
      "    \n",
      "    Fitzpatrick (1999) [1]_ model which relies on the parametrization\n",
      "    of Fitzpatrick & Massa (1990) [2]_ in the UV (below 2700 A) and\n",
      "    spline fitting in the optical and IR. This function is defined\n",
      "    from 910 A to 6 microns, but note the claimed validity goes down\n",
      "    only to 1150 A. The optical spline points are not taken from F99\n",
      "    Table 4, but rather updated versions from E. Fitzpatrick (this\n",
      "    matches the Goddard IDL astrolib routine FM_UNRED).\n",
      "    \n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    wave : numpy.ndarray (1-d)\n",
      "        Input wavelengths or wavenumbers (see units).\n",
      "    a_v : float\n",
      "        Total V-band extinction in magnitudes.\n",
      "    r_v : float\n",
      "        Ratio of total to selective extinction, A_V / E(B-V).\n",
      "    unit : {'aa', 'invum'}, optional\n",
      "        Wavelength units: Angstroms or inverse microns.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    Extinction in magnitudes at each input wavelength.\n",
      "    \n",
      "    References\n",
      "    ----------\n",
      "    .. [1] Fitzpatrick, E. L. 1999, PASP, 111, 63\n",
      "    .. [2] Fitzpatrick, E. L. & Massa, D. 1990, ApJS, 72, 163\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(extinction.fitzpatrick99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rv = 3.1\n",
    "e_bv = 0.2\n",
    "av = rv * e_bv\n",
    "\n",
    "mag_ext = extinction.fitzpatrick99(wavelength.to(u.angstrom), av, rv)\n",
    "extincted_flux_density_wavelength = flux_density_wavelength * 10 ** (-0.4 * mag_ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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/TteuXWX58uWyefNm6d69u5w7d05ERO6991754IMPRETkiiuukCNHjmR7DCBz584VEZHHHntMnnvuOfe/wSeffCIiIm+99VaGn6Fu3bq543jvvfckMjJSjh8/LmfOnJHatWvLnj173M/Xq1fvou9ExPPvFbBGvPg/Vmt5+UD37rYS8dNPQ//+OtmxKMmqOGSnTp2YPXs2w4cPz7EsikubNm2oWbMmgHuxrnLlylG9enX3LPmyZct6PHfRokXMnTvXXfY9OTmZPXv2sGzZMkaMGAFATEwMMTExuf6MWRk6dCizZ8/m7bff9vgdrFq1ig4dOrgLSN5+++389ttvgJ3n4ur7APjrr7/c5VM8FWSMjo7mkUce4YknnqB79+7u2fdZqVOnDpUqVWLdunXuApSVKlXKcMzRo0cpX768+3F8fDwTJkxg8+bNNGnShD///JPExERWrFjhbilNnDjRXVF67969bNu2jbi4OOrWrcvKlSupX78+W7du5ZprrmHSpEkkJCS4/+3OnDlD1apVM8TwzTffZHlMWFiYe/Gyli1b8vXXXwOwYsUK99IC/fv359FHH83ye+jYsaN7HlGTJk3YvXs3tWrVAqBq1aocOHDgou/lUmhC8QFjbHHC6GgYPRqclrAqwtLS0tiyZQslS5bkzz//dCeK7GRXiDAnIsKnn35Kw4YN8xRvVrIrbnn69Gn27dsH2Nnnuam6m5aWxsqVKz0WV/T0PTRo0IC1a9cyf/58nnzySTp27MiYMWOyfY+hQ4fy/vvvc/DgQfflq/QyF7esUaMGx48fZ8GCBbRr144//viDWbNmUbp0acqUKcPSpUtZvHgxK1asoGTJknTo0MF9ft++fZk1axaNGjWid+/eGGMQEQYOHMi//vWvLGPM7pjQ0FD3kry5/Xlwye5nKjk52ef9ZdqH4iONGsGIEXa9lIQEf0ej/O3VV1+lcePGfPLJJwwePJjzTgdbaGio+743GjZsSGJionvtjaSkJFJSUi4qZNilSxdef/11d7/LunXrAFtg0tVRu3HjRvcCWN6qU6cO69evJy0tjb179/LTTz+5n3viiScYMGAAY8eO9bhq41VXXcV3333HsWPHOH/+vLuYJdgaXK+//rr7cU5LABw4cICSJUtyxx138Nhjj11U3BIu/m579+7NggULWL16NV26dLno+AoVKpCampohqcTFxTFhwgTatWvnLm7pag2dOHGCChUqULJkSbZu3crKlSszvNcXX3zB9OnT6du3L2BbB3PmzHEXq/zjjz8yFMT09pjM4uLi+PTTT4G8F7cUEQ4ePOjzKgeaUHxozBh7uWvECB1GXFS4OoJd28iRI/n111+ZMmUK48ePJz4+nnbt2rlrXQ0bNoyYmJiLOr+zEhYWxsyZM3nggQdo1qwZnTp1Ijk5+aJChk899RTnz58nJiaGqKgonnrqKQDuvfdeTp48SePGjRkzZoy7lLy3rrnmGiIjI2nSpAkjRoygRYsWgF2Aa/Xq1e6kEhYWdlGJ++rVq/PMM8/Qtm1brrnmmgyzrydOnMiaNWuIiYmhSZMmvP3229nGsWHDBnfH9bPPPuuxXErm7zYsLIzrrruOv/3tb1mO4urcuTPff/+9+3F8fDwpKSnUq1ePFi1a8Mcff7gTSteuXUlJSaFx48aMHDmSuLg493kVKlSgcePG7N69mzZt2gD2EtPzzz9P586diYmJoVOnTiQmJmZ4f2+OyWzChAm88sorxMTEsH37dvclrZiYGEJCQmjWrJm7Uz4rCQkJxMXFXbTGzSXzpqOlsGz51Smf3pQptoP+44/z/a2KvEDolFeBKzU1VZo1aya//fZblsckJCTIHXfcUYBRXbpTp05JWlqaiNiBBD169Mj1a4wYMUIWL17s8blL6ZTXFoqPDR4MrVrB44/rmilK+cvmzZupV68eHTt2pH79+lke16JFC6677roCn9h4KRISEoiNjSUmJoY333yT8ePH5/o1mjZtSseOHX0em1+LQxpjugKvASHAFBEZl+n5/wOGA6nASWCYiGx2nhsF/N15boSILMzp/XxZHDI7K1bA1VfbGfQvvpjvb1dkaXFIpXwvKItDGmNCgEnAjUAToJ8xJvOycJ+ISLSIxAIvAq845zYB+gJRQFfgTef1AkLbtvD3v8Mrr8CGDf6ORimlCoY/hw23AbaLyO8AxpgZQE/APThdRP5Kd3wpwNWc6gnMEJGzwE5jzHbn9VYURODe+Pe/4YsvbJn777+HYnpxMX8lPAR/Zj9SKNcqxELL7BerDwkJITo6mpSUFCIjI/noo48yzG1Ir27dunz11VcZhvY+9NBDVK9e3eMSwJl17dqVxMREUlJSiI+PZ9KkSRd1Nm/dupXBgwezdu1aXnjhhWznKLh06NCBxMRE9xDSevXqMWfOHMB2AFesWJG77rqLQYMG8d1331GuXDmSk5Pp168fTz/9dK4+50033cT48eN5//33c4zLZcGCBTz44IOkpqYydOhQj+VtXnnlFaZMmULx4sWpUqUKU6dO5YorrvD6PZRv+PO/uRrA3nSP9zn7MjDGDDfG7MC2UEbk5lx/qlQJxo+3l7/efdff0aj84prYuHHjRipWrMikSZOyPLZv374ZhnmmpaUxZ84c9zDTnMyaNYuff/6ZjRs3cuTIkQzDcF0qVqzIxIkTvUok6U2bNs1dj8yVTFJSUpg6dSr9+/d3H/fSSy+5j/vggw/YuXNnrj5ndHQ0+/btY8+ePV7FlZqayvDhw/nqq6/YvHkz06dPzzAh0qV58+asWbOGX375hT59+mQoM6IKTsBPbBSRScAkY0x/4ElgYG7ON8YMA4YB1K5d2/cBZuPOO+G992ydr549IdMkWeVLObQkCkLbtm2znefRr18/br/9dvdf9cuWLeOKK67w+i9p1yz5lJQUzp075570ll7VqlWpWrUq8+bNy8MnyOjbb7+lRYsWHoeWuuZulCpV6qLncvqcN998MzNmzPDqP/2ffvqJevXqUbduXcAmqy+++MJdENLluuuuc9+Pi4vj448/9vJTKl/yZwtlP1Ar3eOazr6szAB65fZcEXlHRFqJSKsqBVwTxRh46y072uuRRwr0rVUBS01N5ZtvvqFHjx5ZHhMdHU2xYsXcpVhmzJhBv379APj1118zzGdJvx0/ftz9Gl26dKFq1aqUKVOGPn36+Cz+AQMGuN/PVejwhx9+uGjeymOPPUZsbCw1a9akb9++F5USyelzArRq1Yrly5cDsGTJEo+f2VXWf//+/e5SIQA1a9Zk//7s/puAd999lxtvvDEP34K6VP5soawG6htjIrHJoC/QP/0Bxpj6IrLNedgNcN2fC3xijHkFuByoD/xEAGrUCEaOhOees0OKr7/e3xEpX3JNbNy/fz+NGzemU6dO2R7fr18/ZsyYQVRUFJ9//jnPPvssYGfE5zRbHGDhwoUkJyczYMAAvv322xzfz1vTpk2jVauMg3gSExMvGu3z0ksv0adPH06ePEnHjh358ccfPa7pktXnhAs1pMC2LLz53N76+OOPWbNmDd99953PXlN5z28tFBFJAe4HFgJbgFkisskYM9YY4/oz735jzCZjzHrgYZzLXSKyCZiF7cBfAAwXkYAdSD5qlF13/p574PRpf0ejfMnVh7J7925EJNs+FLhQ82nx4sXExMRQrVo1wPsWCkBERAQ9e/Z0l6jPL5lrXaVXunRpOnTowPfff8+qVavcsbrKv2f1OSFjDamcWig1atRg794L3aX79u2jRg3P3aWLFy/mhRdeYO7cuRlqWKmC49c+FBGZD8zPtG9MuvsPZnPuC8AL+Red75QoAZMnw3XXwVNP2c56VbiULFmSiRMn0qtXL+67774sS1pceeWVVK5cmZEjR/Lggxd+vHNqoZw8eZKkpCSqV69OSkoK8+bNy7HibmYdO3bkww8/zPI/5MwaN27M9u3bPT6XkpLCqlWreOCBB7jqqqsuij2rzwnw22+/uVeEzKmF0rp1a7Zt28bOnTupUaMGM2bM8LiI1Lp167jnnntYsGCBx8twqoB4M52+sGwFUXolO/feK2KMyIoVfg2j0AiE0iuZ1zfp3r27fPjhh9me8+qrr0p4eLh7TQ9vHDx4UFq1aiXR0dESFRUl999/v5w/f15E7JoYb731loiIJCYmSo0aNaRMmTJSrlw5qVGjhpw4cUJSU1Oldu3acvr06Yteu3379tKgQQP3mi4dO3YUEZFdu3ZJfHy8+7iBAwdKnTp1pFmzZtK4cWO5//773SVAcvM5hw8f7l7nwxvz5s2T+vXrS926deX5559373/qqafkiy++EBGRjh07StWqVd2f4eabb/b69VVGl1J6xa8z5QtaQc2Uz0pSEjRtCqVKwdq14KFyt8oFnSnvvY0bNzJ16lReeeWVXJ3Xu3dvXnzxxWzLl+TG2bNnad++Pd9//73vCxMqnwjKmfJFUZky9tLXli0wdqy/o1FFSdOmTXOdTADGjRuXY/Xb3NizZw/jxo3TZFJI6b9qAevcGYYMsTW+br0VcllNXAWBDRs2cOedd2bYFx4ezqpVq/wUUd41bNjQp4t21a9f32etHRV4NKH4wfjxsGCBHUa8ejXogJTCJTo62qdDYZUKFnrJyw/Kl7fLBG/YYNehV0qpwkATip906wbDhtlLX8uW+Tua4FWUBpUold8u9fdJE4ofjR8PV14Jd90FJ074O5rgExERwbFjxzSpKOUDIsKxY8eIuIThp9qH4kelS8NHH8G119p16D/4wN8RBZeaNWuyb98+jhw54u9QlCoUIiIiqFmzZp7P14TiZ3FxMHq0HUZ8883gw3p/hV5oaCiRkZH+DkMp5dBLXgHgySehdWtb68upmaeUUkFHE0oACA21l77OnIFBgyAtzd8RKaVU7mlCCRANG8Krr8LXX9uRX0opFWw0oQSQYcPgb3+zl8B++MHf0SilVO5oQgkgxtgJj1dcAf36wbFj/o5IKaW8pwklwJQrBzNnwsGDtjSLTrFQSgULTSgBqFUreOkl+N//4LXX/B2NUkp5RxNKgBoxAnr2hMcftwUklVIq0GlCCVDGwNSpUL26nex49Ki/I1JKqexpQglgFSvCp5/CoUO2kz411d8RKaVU1jShBLhWreDNN2HxYjucWCmlApUmlCAwZIidozJuHPz3v/6ORimlPNOEEiQmToQ2bWDgQNi61d/RKKXUxTShBInwcNufUqIE9O4Nf/3l74iUUiojTShBpGZNmDULtm2DAQO0k14pFVg0oQSZDh3s5a8vv4RRo/wdjVJKXaALbAWh++6DTZvsbPomTWzJe6WU8jdtoQSpCROgY0c7+uv77/0djVJKaUIJWqGhMHs2REbaTvqdO/0dkVKqqPM6oRhjKnqxlc/PYFVGFSrYApIpKdCjh478Ukr5V276UA44m8nmmBCgtrcvaIzpCrzmnDdFRMZlev5hYCiQAhwBhojIbue5VGCDc+geEenh7fsWJg0a2JbKjTfCLbfA/PkQFubvqJRSRVFuLnltEZG6IhKZ1QZ4vSSUMSYEmATcCDQB+hljmmQ6bB3QSkRigDlA+sVxz4hIrLMVyWTicsMNMHkyfPMN/P3vuoaKUso/cpNQ2vroGJc2wHYR+V1EzgEzgJ7pDxCRJSJy2nm4EqiZi9cvUgYNgueeg48/htGj/R2NUqoo8jqhiEhyVs+5+k6yO8aDGsDedI/3Ofuy8nfgq3SPI4wxa4wxK40xvXLxvoXW6NF21Ne//gVvveXvaJRSRU2OfSjGmJZAd2Aiti8jKtPWFCgF5FuHvDHmDqAV0D7d7itEZL8xpi7wrTFmg4js8HDuMGAYQO3aXnfvBCVjYNIkOHAA7r8fLr/cLtKllFIFwZsWyn+AL4E9wK/Ac0AssB2IBpqLSF6SyX6gVrrHNZ19GRhjbgBGAz1E5Kxrv4jsd25/B5YCzT29iYi8IyKtRKRVlSpV8hBmcCleHGbMgJYt7RoqP/7o74iUUkWFNwnlR+AxYC32P/zJIvKAiLwJnBWRw3l879VAfWNMpDEmDOgLzE1/gDGmOTah9Uj/PsaYCsaYcOd+ZeAaYHMe4yh0SpWypVlq1IBu3eDnn/0dkVKqKMgxoYjICOxw3fa/f3YFAAAgAElEQVRAFyDOGLPCGHMjkOfxRCKSAtwPLAS2ALNEZJMxZqwxxjVq6yWgNDDbGLPeGONKOI2BNcaYn4ElwDgR0YSSTtWqdlGu0qWhc2f47Td/R6SUKuyM5GGMqTGmDvbS1y1AdxFZ4tuw8kerVq1kzZo1/g6jQG3dCu3aQUSELdFSyLuRlFL5wBiTICKtcjouNzPl2xpjDICI7BKRO7GXmp4wxnyX91BVfmrUCBYuhBMnoFMnOJzXC5RKKZWD3MxDuQtIMMbMMMYMMsZcJiLrRaQr8Ez+hKd8oXlzmDcP9u6FLl3g+HF/R6SUKoxyMw/lXhFpgU0eFYD3nb6UfwKpzsx3FaCuvdauR79pE3TtqnW/lFK+l+tqwyKyVURedVom1wPfA7cBq3wdnPKtrl3tio8JCbaloklFKeVLl1S+XkTOiMh8Zxhxjh02yv969YKZM2HNGptgkpL8HZFSqrDwKqEYYzoZYyYbY2Kdx8PyNyyVn265xU5+/OknW6VYk4pSyhe8baEMwU5uvMMYcz12prwKYrfeapPKypVw002aVJRSl87bhJIkIsdF5FGgM9A6H2NSBaRPH5g+HVassC2VEyf8HZFSKph5m1Dmue6IyEjgw/wJRxW0226zLZVVq+wa9ce8XtFGKaUy8iqhiMgXmR6/nj/hKH/o0wc+/xw2boT27SEx0d8RKaWCUa5HeRljWhljPjPGrDXG/GKM2WCM+SU/glMFp1s3+Oor2LUL4uNh925/R6SUCja5WVPeZRq2g34DkObbcJQ/XXedLSh54412IuQ339g165VSyht5mYdyRETmishOEdnt2nwemfKLuDhYsgTOnrUtFS19r5TyVl4SytPGmCnGmH7GmFtcm88jU34TGwvLlkFYmK1UvCQoakkrpfwtLwllMHYeSlfgZmfr7suglP81amRXe6xZ086onznT3xEppQJdXvpQWotIQ59HogJOrVp2DZWePaFvXzv666GH/B2VUipQ5aWF8qMxponPI1EBqUIFWLTIlmv5f/8PHn0U0nQohlLKg7y0UOKA9caYncBZwAAiIjE+jUwFjIgIW6X4wQdh/HjbUpk6FcLD/R2ZUiqQ5CWhdPV5FCrghYTA669DjRrwj3/Anj3w2WdQubK/I1NKBYq8XPIaC5xIN1z4L+Bp34alApExMGqUrf+1ejW0aQObN/s7KqVUoMhLQokREfcisiLyJ9DcdyGpQNe3LyxdCqdPQ9u2to9FKaXyklCKGWMquB4YYyqSt0tnKojFxdn1VOrUseXv33zT3xEppfwtL4lgPLDSGDPLeXwb8ILvQlLBonZtO6y4f38YPhy2bIFXX4Xi+ueFUkWS1y0UY0xbY4wRkQ+B3sAhZ7tFRD7KrwBVYCtTxlYqfvhheOMNu1b9kSP+jkop5Q+5ueR1F5BgjJkBtAHmiMgbIqLdskVcSIgdTvz++/DDD9CqFSQk+DsqpVRB8zqhiMi9ItICeAaoALxvjFlhjPmnMaadMSYkv4JUwWHgQJtQAK65Bj74wL/xKKUKVq475UVkq4i8KiJdgeuB77H9KKt8HZwKPi1bwpo1NqEMGgT33w/nzvk7KqVUQcjLKC83ETkjIvNF5AERaeWroFRwq1IFFi6ERx6BSZPs0sK6CqRShV9eVmy8zRhTxrn/lDHmv8YYnYeiMiheHF5+2U6CXLvWlsT/+mt/R6WUyk95aaE8JSJJxphrgY7Au8Dbvg1LFRZ9+9pZ9ZUr2xFgTz0FKSn+jkoplR/yklBSndtuwDsiMg8Iy8ubG2O6GmN+NcZsN8aM9PD8w8aYzc7a9d8YY65I99xAY8w2ZxuYl/dXBaNJEzsJctAgeP55uOEGOHDA31EppXwtLwllvzHmP8DtwHxjTHheXscZFTYJuBFoAvTzUBZ/HdDKqWQ8B3jRObcitn7YVdghzE+nn72vAk+pUrZC8Qcf2BZLbKyWbFGqsMn1xEbgb8BCoItT06si8Fge3rsNsF1EfheRc8AMoGf6A0RkiYicdh6uBGo697sAX4vIH04tsa/RKshB4a67bEKpWtWuBDlypI4CU6qwyPXERmAqUBZIAhCRRBHJy9+aNYC96R7vc/Zl5e/AV3k8VwUQ1yWwoUPh3/+2BSa3bvV3VEqpSxUUExuNMXcArYCX8nDuMGPMGmPMmiNaEyRglCwJ77xj11TZvRtatLAFJkX8HZlSKq/8ObFxP1Ar3eOazr4MjDE3AKOBHiJyNjfnOvG+IyKtRKRVlSpVchmiym+9esGGDdCunS0wefPNcOiQv6NSSuWFkVz+SWiMedjD7hNAgoisz8XrFAd+ww493g+sBvqLyKZ0xzTHdsZ3FZFt6fZXxF5+a+HsWgu0FJE/snvPVq1ayZo1a7wNURWgtDRbXPLxx6FcOXj3Xeje3d9RKaUAjDEJ3kxez8sor1bA/2H7LGoA92A7xN8xxjzu7YuISApwP7aDfwswS0Q2GWPGGmN6OIe9BJQGZhtj1htj5jrn/gE8h01Cq4GxOSUTFdiKFYMRI2zZlssusy2VwYPh+PGcz1VKBYa8tFCWATeJyEnncWlgHjapJIhI5qG/AUNbKMHh7FkYO9Z22F92me1ruekmf0elVNGVny2UqsDZdI/PA9VE5Eym/UrlSXg4vPACrFwJ5ctDt27aWlEqGOQloUwDVhljnjbGPA38AHxijCkF6Nooymdc66r84x/w0UfQtCnMn+/vqJRSWclVQnEmNr4PDAOOO9v/ichYETklIgN8H6Iqyjy1Vu68U1eFVCoQ5SqhiO1wmS8ia0TkNWfTTgmV71ytlSefhJkzoVEjeO89nbeiVCDJyyWvtcaY1j6PRKkchIfDc8/B+vXQuDEMGQLXXw+//urvyJRSkLeEchWwwhizw6kCvMEY84uvA1MqK02awLJl8J//wLp1EBNjR4Wd1SEhSvlVXhJKF+BK7Cz5m4Huzq1SBaZYMRg2zNYA690bnn7aVjBessTfkSlVdOWl9MpuT1t+BKdUTi67DGbMsKO/kpPtJbDbb4e9e3M+VynlW3lZx8QYY+4wxoxxHtc2xrTxfWhKee/GG2HzZnj2WZg713ba//OfehlMqYKUl0tebwJtgX7O4yTsQllK+VWJEjBmDGzZYpcbHj3azl2ZN8/fkSlVNOSpU15EhgPJAM4CV3laAlip/FCnDvz3v7BwIYSE2CKTN98M27f7OzKlCre8JJTzztonAmCMqQKk+TQqpXygc2f45Rd46SVYutSODnv4YfhDy4gqlS/yklAmAp8BVY0xL2DXQ/mnT6NSykfCwuDRR+G33+zywxMmQL168OqruvSwUr6Wl1Fe04DHgX8BiUAvEZnt68CU8qXq1WHKFDspsnVr21Jp0gTmzNHZ9kr5itcJxRiz1nXfWbVxkoi8ISJbPB2jVCCKibF9KwsW2E78226Da6+1tcKUUpcmNy2Uxs7M+Ky2DUDl/ApUKV/q0sW2ViZPht9/h7Zt4ZZbYNOmnM9VSnnm9QJbxpgrvDgsVUT2XVpI+UcX2FKenDwJr7wC48dDUhLccYedzxIZ6e/IlAoM3i6wlesVG4OZJhSVnWPH7CqRr78Oqalw9922unH16v6OTCn/ys8VG5UqlCpVghdfhB07YOhQu/TwlVfCE0/YZKOUyp4mFKUyufxyePNNWxa/Tx87jyUy0rZWNLEolbVLSijGmFRfBaJUoKlbFz780E6O7NrV1garUwdGjtQVI5XyJMeEYozJrqyK8WEsSgWkpk1h1izYsMGWcHnxRZtYHn0UDh70d3RKBQ5vWigTAIwx7T08d1GPvjGmjjGm96UGplSgiYqCTz6xVY1vvdXOto+MhIceggMH/B2dUv6Xm0tet2f1hDHmCWPM18aYlcC9QPglR6ZUgGrUyF4K27oV+vaFN96wl8fuuQe2bfN3dEr5jzcJpY0x5k2giTGmmTHG0zmp2FUblwE7RWSGL4NUKhDVrw/vvWfrhA0cCB98AA0b2o781av9HZ1SBc/bS17TnNtbgJkejjkI1BaRx4Edxpj/+C5EpQJb3bp2fftdu2yH/eLF0KYNXHcdfPWV1gpTRYc3CeUw8CzwCBAD7MjimDeNMcuA+3wXnlLB47LL7EiwvXvh5Zft5a+bboJmzeDjj+H8eX9HqFT+yjGhiMgiYJWIxAN3AaWzOaadc4z+6qgiq0wZeOQRWyPsvffsrPs777Qd+OPG6VwWVXh52ylf1hjTAjgLlLqEY5QqMsLCYNAgO9z4f/+znfmjRkGtWrYDXwtRqsLG24TyMHAt8DawMN1+k8UxC3wSnVKFQLFidhnixYvtJMkBA+wosaZN7aqS8+ZBmq55qgoBvxaHNMZ0BV4DQoApIjIu0/PtsIMBYoC+IjIn3XOpwAbn4R4R6ZHT+2lxSBUojh61tcImTbJzWOrXhxEj7KqSZcv6OzqlMgr44pDOuvSTgBuBJkA/Y0yTTIftAQYBn3h4iTMiEutsOSYTpQJJ5crwj3/YkWHTp9vClA88YOuI3XOPXatFqWDjz+KQbYDtIvK7iJwDZgA90x8gIrtE5BdALwioQik01E6OXLECfvoJ/vY3+OgjaN7cLvr1wQdw5oy/o1TKO/5MKDWAveke73P2eSvCGLPGGLPSGNPLt6EpVfBat4apU2H/fpgwAY4ft536NWrAww/bCZRKBbJgLl9/hXNNrz8wwRhzpaeDjDHDnMSz5oiWiFVBoEIFePBBWzNsyRLo1Mku+tWwIdxwA8ycCcnJ/o5SqYv5M6HsB2qle1zT2ecVEdnv3P4OLAWaZ3HcOyLSSkRaValSJe/RKlXAjIEOHWwC2bsXXngBtm+3l8guv9z2uaxb5+8olbrAnwllNVDfGBPplMjvC8z15kRjTAVjTLhzvzJwDbA53yJVys8uu8x24v/+O3z9tV2fZfJkaNHC9re8/rpOmFT+57eEIiIpwP3YeS1bgFkisskYM9YY0wPAGNPaGLMPuA34jzHGNRWsMbDGGPMzsAQYJyKaUFShV6yYvez1ySeQmGiHHRcrZoccX365bb0sWmRn5ytV0Pw6D6Wg6TwUVVitX2/LvHz8MfzxB9SsCf3725IvTZv6OzoV7AJ+HopSyndiY+G11+wkyZkzbUHK8eMhOto+9/LLdvSYUvlJE4pShUh4uJ3L8uWX9pLY669DRAQ89pitIXbDDfD++/DXX/6OVBVGmlCUKqSqVIH774eVK+0cljFj7Mz8wYOhWjXb3zJ3Lpw96+9IVWGhCUWpIqB+fXjmGbtGy4oV8Pe/22KVPXva5DJoEMyfD+fO+TtSFcw0oShVhBgDcXHwxhu2v2X+fOjdGz7/HLp1s8llyBBYuFAXBFO5pwlFqSIqLAxuvNGODjt0yK7ZcvPNMGeOnedy2WVw9922JZOS4u9oVTDQhKKUIjzcrtny4Ydw+DB88YVNKjNm2NIv1avD0KF27RYt+6KyoglFKZVBRAT06AHTptnk8t//2tFhs2bZpFOlih1JNn06nDjh72hVINGJjUopr5w9a4tVfvaZbcEcOmTL73fsCL162Q7+yy7zd5QqP3g7sVETilIq19LS7HDkzz6z244dFzr8e/e2fTENG9p9KvhpQvFAE4pSvicCmzbZxPL557B2rd1/5ZV25Fj37tCune2nUcFJE4oHmlCUyn979tjhyF9+Cd98YzvxS5WCzp1tgrnpJtvJr4KHJhQPNKEoVbBOn7b9LvPm2QSz11mjtWVL23Lp1s3eL6bDgwKaJhQPNKEo5T8isHGjTSxffmn7YNLS7KixTp1sC6ZzZ229BCJNKB5oQlEqcBw9CgsW2Fn5ixbZIcpgKyR36WKTS3y8Hcas/EsTigeaUJQKTGlp8MsvNrEsXAjff2/ripUoAe3b2+TSpQs0bqwjx/xBE4oHmlCUCg6nTsF3311ovWzdavfXrGknWV5/PVx3nX2s8p8mFA/ynFBSzkCxUChW3PdBKaVytHs3fP21TTDffmtXpQRo0OBCcunQAapW9WuYhZYmFA/ynFAWd4DD39mkElISipeC8CpQ4jKIqAYRl0Gp2lD6SihdF0rVgZAwX4evlOLC5bFvv7UjyL77DpKS7HPR0TbBXH+9nftSvrx/Yy0sNKF4kOeEsnManPwdUk9DymlIOQnJhyH50IUtLd0qRaYYlKwFZepDuaZQLgrKN4VyTSC0rO8+kFKKlBRISLAJ5ttvbf9LcrIdityixYXkcs01mmDyShOKB/nWhyICyQchaYdNPCd32O2vrXBiM6SeuXBsydoXEkz5aCgfA2UbQYhOI1bKF86ehVWrLiSYlSvt2i7GQEyMTS7x8XbT2mPe0YTigV865SUNTu6EE5vsdnyjvf1rC6Q5y+OZ4lC2oU0uriRTPgZK1tQhLUpdotOnbYJZvhyWLbMrVp4+bZ+rX98mFleSiYzUXzlPNKF4EFCjvNJSIOk3OL4Bjv9y4fbU7gvHhJa7OMmUbwqhZfwXt1JB7vx5WLfOJpfly+3255/2ucsvv5Bcrr0WoqIgJMS/8QYCTSgeBFRCycq5E3Bio00uf/4CJzbY25SkC8eUiryQZCrEQLloKFNPR6EplQdpabB5c8YEs3+/fa5MGWjTBq6+Gtq2tdWUK1Twb7z+oAnFg6BIKJ6I2JaLuzXjtGiSfrWX1ABCIpy+meiMrZoIHUepVG6IwM6d8OOPdluxwo4qS3N+1Ro1ssnFtTVpUvhrkWlC8SBoE0pWUs7YvpjMiSb50IVjIqplSjLRUKaBXjZTKhdOnoTVq21ycW3HjtnnypaFq66yyeXqq22LprC1YjSheFDoEkpWkg9fnGRObILUdIuBR1S1w5rL1IfS9Zz79eymQ5uVypYIbN+eMcFs2HChFVOvnk0srVvbrXlzKFnSvzFfCk0oHhSZhOJJWgokbbf9M0nb4eR2SNpm7585kPHYsAp2hFnJWs5W88JticvtpM6wClBMeyuVcklKgp9+stvq1Xbbt88+FxJiO/hdCaZNG2ja1C6hHAw0oXhQpBNKdlJO2Tk0Sdtsojm1B07vg9N77e3ZIx5OMhBeEcIrO1sVCCsPxctA8dL2klr62+JlILS0rTIQUsreujYdTKAKqcTEC8nFtbnKxkREQGzshSTTurUtJROI/TGaUDzQhJJHqclOgtkHZxLh7FEP2xE4dxzOJ9kRaZLq/esXC8+YYFxb5sRTvHTWx4WWgbCKEF7JaT1pklKBx9Xhn74Vk5BwYV5M6dI2yTRvbrcWLWynv79bMkGRUIwxXYHXgBBgioiMy/R8O2ACEAP0FZE56Z4bCDzpPHxeRD7I6f00oRQQEVuK5vxJm1zOJ9lyNeeTIPWUbRF52rJ87uSF++lL3GQntJyTXCo5t06yyVCDLd1WvET+fidKZSE1FbZssUlm3TpYuxZ+/tlWXAYIC7M1ylwJpnlzO+O/IPtkAj6hGGNCgN+ATsA+YDXQT0Q2pzumDlAWeBSY60ooxpiKwBqgFSBAAtBSRP7M7j01oRQCaSlOTbVMSed8Epw7Bmf/cG6dzXX/3B/29vwJz68bWjZTknGSTolMjzX5qAKQmmo7/deutUnGlWhcl8uKFbPDl9MnmdjY/Btd5m1C8ed1gTbAdhH5HcAYMwPoCbgTiojscp5Ly3RuF+BrEfnDef5roCswPf/DVn5VrDgUK5v3kWipZ9MV9jx4objnmUMX9p3YBIe+hXNZ/H0SWs5p5ThbieqZHju34ZV14ILKk5AQaNjQbv362X0isHfvheSybh0sXQrTpl04r3ZtaNbswhYTY0ecFVS/jD8TSg1gb7rH+4CrLuHcGj6KSxVmIeFQqpbdcpJ6Ds4ehjMHM1aWPnMQkhPt7Z9r4cDBjJUMXEyIHZ6dOdGUSJeIXI+Ll9YiUipbxtiEUbs29Ox5Yf+RIxdaMT//bLf5820rB+ylsehomDXLnpufCn3PpTFmGDAMoHZ+f5uqcAkJc4ZMe7EsYMopJ9k4iSb54MW3JzbY+5Li4b1KZp9wXLfhVXWtHZVBlSp2ieTOnS/sS0625WRcCeaXX+xx+c2fCWU/kP7PxJrOPm/P7ZDp3KWeDhSRd4B3wPah5DZIpbxSvJRdXK103eyPkzTbz5PsIemcSbT3/9oKh5bafh9PwitBRPWLE5DrvisRhVXQVk8RFRFh+1ZatCjY9/VnQlkN1DfGRGITRF+gv5fnLgT+aYxxdUF1Bkb5PkSlfMwUg4jKdqNp9se6+3vSJZvMrZ4jP9jLb+mrILgUC/VwuS2L/h4daKB8wG8JRURSjDH3Y5NDCDBVRDYZY8YCa0RkrjGmNfAZUAG42RjzrIhEicgfxpjnsEkJYKyrg16pQsPb/h4ROP+X54TjSkan9sCxn2yCwkNDPbTshZaNx/6ey2yrSAcaqGzoxEalipK0FDsRNfNltgwJyLk9/9fF55tith/Hm/6e4mX0klshEQzDhpVSBa1Ycaef5TLb7s+Oe6BBNv09JzZmM9CgRPaX2Vz9PTrQoNDQhKKU8iw3Aw3O/Zku4Xjo7/nrVzj8nZ1c6kl4pSwut2UafBBWUVs9AUwTilLq0phiTlmbSkBU9semnrswgTSr/p4jP+Yw0KBa1pfZ0u/XgQYFThOKUqrghIR5P9AgJcnzZTbX41N74NhqO/lUMhfTwCmnk0VLJ33i0YEGPqMJRSkVeIyxCSG0LJRtkP2xaam22nWGVk9ixpbPn+vgQGIWFQ3SDzTIJvFoRYMcaUJRSgW3YiF5G2jgcW5Pol3lNPlQ9hUNPA6xTj/4oKq9PFfEaEJRShUdealokFXiObE5+yKi4ZWzntuTvtUTWr7QtHo0oSilVGbpKxqU96aigYdBBukTUdJv9tbTej7FwrIeXp1h4EE1CInIn8/rI5pQlFLqUoSEQ6kr7JYdETh/3HPicd0/+bsd5eZx2W1sa8bjvB7XyLdq6QYaFPx/75pQlFKqIBhjC3aGVYByjbM/Nu18ujpuWQw0OLbK7ks94+nNnEturmRTDVq8Yvt28pEmFKWUCjTFQqFkDbtlR8QukZ1hrZ50i8W5Fo9L2gEm//+714SilFLByhgILWO3MvX8HQ0FtDCkUkqpwk4TilJKKZ/QhKKUUsonNKEopZTyCU0oSimlfEITilJKKZ/QhKKUUsonNKEopZTyCSMi/o6hwBhjjgC783h6ZeCoD8PJT8EUKwRXvMEUKwRXvMEUKwRXvJca6xUiUiWng4pUQrkUxpg1ItLK33F4I5hiheCKN5hiheCKN5hiheCKt6Bi1UteSimlfEITilJKKZ/QhOK9d/wdQC4EU6wQXPEGU6wQXPEGU6wQXPEWSKzah6KUUsontIWilFLKJ4psQjHGRBhjfjLG/GyM2WSMedbZH2mMWWWM2W6MmWmMCXP2hzuPtzvP10n3WqOc/b8aY7rkY8whxph1xpgvgyDWXcaYDcaY9caYNc6+isaYr40x25zbCs5+Y4yZ6MT1izGmRbrXGegcv80YMzAf4y1vjJljjNlqjNlijGkbiPEaYxo636lr+8sY81Agxpruff6f8zu20Rgz3fndC8ifXWPMg06cm4wxDzn7Aua7NcZMNcYcNsZsTLfPZ/EZY1o6v7fbnXNNrgIUkSK5AQYo7dwPBVYBccAsoK+z/23gXuf+fcDbzv2+wEznfhPgZyAciAR2ACH5FPPDwCfAl87jQI51F1A5074XgZHO/ZHAv537NwFfOf8mccAqZ39F4HfntoJzv0I+xfsBMNS5HwaUD+R4nfcLAQ4CVwRqrEANYCdQIt3P7KBA/NkFmgIbgZLYxQcXA/UC6bsF2gEtgI358XsF/OQca5xzb8xVfPn1wx5Mm/MDtBa4Cjv5p7izvy2w0Lm/EGjr3C/uHGeAUcCodK/lPs7HMdYEvgGuB7503jsgY3VeexcXJ5RfgerO/erAr879/wD9Mh8H9AP+k25/huN8GGs57H96JhjiTff6nYEfAjlWbELZ6/znVdz52e0SiD+7wG3Au+kePwU8HmjfLVCHjAnFJ/E5z21Ntz/Dcd5sRfaSF7gvIa0HDgNfY//qOS4iKc4h+7C/EHDhFwPn+RNApfT7PZzjSxOwP9xpzuNKARwrgACLjDEJxphhzr5qIpLo3D8IVMscb6a4CireSOAI8J6xlxSnGGNKBXC8Ln2B6c79gIxVRPYDLwN7gETsz2ICgfmzuxGIN8ZUMsaUxP6FX4sA/W7T8VV8NZz7mfd7rUgnFBFJFZFY7F//bYBGfg7JI2NMd+CwiCT4O5ZcuFZEWgA3AsONMe3SPyn2T6BAGWJYHHsZ4S0RaQ6cwl46cAuweHH6HHoAszM/F0ixOtfze2KT9uVAKaCrX4PKgohsAf4NLAIWAOuB1EzHBMx364m/4yvSCcVFRI4DS7BN7/LGmOLOUzWB/c79/di/VnCeLwccS7/fwzm+cg3QwxizC5iBvez1WoDGCrj/MkVEDgOfYRP2IWNMdSeu6tiWYYZ4M8VVUPHuA/aJyCrn8RxsggnUeMEm6rUicsh5HKix3gDsFJEjInIe+C/25zkgf3ZF5F0RaSki7YA/gd8I3O/WxVfx7XfuZ97vtSKbUIwxVYwx5Z37JYBOwBZsYunjHDYQ+MK5P9d5jPP8t85fA3OBvs7olEigPrZjy2dEZJSI1BSROtjLHN+KyIBAjBXAGFPKGFPGdR97rX9jprgyx3uXMyolDjjhNOEXAp2NMRWcv3Q7O/t8SkQOAnuNMQ2dXR2BzYEar6MfFy53uWIKxFj3AHHGmJLOiCHXdxuoP7tVndvawC3YQTCB+t26+CQ+57m/jDFxzr/VXeleyzu+7NQKpg2IAdYBv2D/sxvj7K+L/UHdjr2cEO7sj3Aeb3eer5vutUZj+19+JZejIvIQdwcujPIKyFiduH52tk3AaGd/JezAgm3YETQVnf0GmOTEtQFole61hjifYzswOB+/11hgjfPz8Dl29EtAxou9bHQMKJduXyFkeLUAAAIVSURBVEDG6rzPs8BW5/fsI+xIrUD92V2OTXg/Ax0D7bvF/hGRCJzHtqz/7sv4gFbOv9MO4A0yDVTJadOZ8koppXyiyF7yUkop5VuaUJRSSvmEJhSllFI+oQlFKaWUT2hCUUop5ROaUJRSSvmEJhSllFI+oQlFqQBjjHndGLPWGNPa37EolRuaUJQKIE6pmqrAPUB3P4ejVK5oQlEqnxljoo0xu40x96bbV8cYc8ZZPsFNRE5h16VYCkx0ji1h7OqM54wxlQsydqVyQxOKUvlMRDZgi3relempHWKXT3AzxlTCLviWBKQ4559xjjtQAOEqlWeaUJQqGIeBKC+OexK7INUmL49XKmBoQlGqYIwDwo0xV2R1gDGmDnA1MBO7lIImFBVUNKEolc+MMTdiS87PI/sk8TwwVmwJcE0oKuhoQlEqHxljIrDLyt6HXZOiaRbHxWIXdJrkrMz5FJpQVJDRhKJU/noS+FBEdpFNQsEmnR4iUkfsypzN0ISigowmFKXyibOkcCdggrPLY0IxxlwPlBSRxa59YteKL22MqVgQsSrlC7pio1J+4HTAfykiWbVYPJ2zC7uM69F8CkupS6ItFKX8IxUol3lioyeuiY1AKJCW75EplUfaQlFKKeUT2kJRSinlE5pQlFJK+YQmFKWUUj6hCUUppZRPaEJRSinlE5pQlFJK+YQmFKWUUj6hCUUppZRP/H9LzkO9k5xzpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.gca()\n",
    "\n",
    "ax1.plot(\n",
    "    wavelength, flux_density_wavelength, color=\"blue\", label=\"Flux density (wavelength)\"\n",
    ")\n",
    "ax1.plot(\n",
    "    wavelength,\n",
    "    extincted_flux_density_wavelength,\n",
    "    color=\"orange\",\n",
    "    label=\"Extincted flux density (wavelength)\\nR_V=3.1, E(B-V)=0.2\",\n",
    ")\n",
    "ax1.set_xlabel(r\"$\\lambda [\\AA]$\")\n",
    "ax1.set_ylabel(r\"$\\frac{dF}{d\\lambda}$ [erg/s/cm$^2$/$\\AA$]\")\n",
    "ax1.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Translating color excess to $A_V$\n",
    "\n",
    "$A_V = R_V \\times E(B-V)$\n",
    "are the standard reference bands for extinction calculations.  This is an arbitrary choice.  We can compute the relationship between the excess in other colors and the extinction in a given band.\n",
    "\n",
    "We will first need some filters to define our bandpasses.  We will use the data associated with the `stsynphot` package as one convenient way of grabbing some standard filters.  We're not actually using the `stsynphot` package itself because it's a little awkward to use to do something as simple as we're doing here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a bit more work to grab the actual data files.  We only need `synphot1.tar.gz`, which has the filters.\n",
    "\n",
    "ftp://archive.stsci.edu/pub/hst/pysynphot/synphot1.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 90.0M  100 90.0M    0     0  16.3M      0  0:00:05  0:00:05 --:--:-- 18.5MM      0  0:00:05  0:00:04  0:00:01 15.6M\n",
      "x grp/hst/cdbs/mtab/3241637sm_tmt.fits\n",
      "x grp/hst/cdbs/mtab/41g1452dm_tmc.fits\n",
      "x grp/hst/cdbs/mtab/41g16069m_tmg.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_block1_002_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_block2_002_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_block3_002_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_block4_002_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_blocking1_001_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_blocking2_001_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_blocking3_001_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_blocking4_001_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_cor_aper_001_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_cor_aper_002_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f115lp_001.tab\n",
      "x grp/hst/cdbs/comp/acs/acs_f115lp_002.tab\n",
      "x grp/hst/cdbs/comp/acs/acs_f115lp_003.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f115lp_004_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f115lp_005_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f115lp_006_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f115lp_007_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f115lp_sbc_001.tab\n",
      "x grp/hst/cdbs/comp/acs/acs_f115lp_sbc_002.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f115lp_sbc_003_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f115lp_sbc_004_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f115lp_tds_def_001_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f115lp_tds_mjd_001_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f122m_001.tab\n",
      "x grp/hst/cdbs/comp/acs/acs_f122m_002.tab\n",
      "x grp/hst/cdbs/comp/acs/acs_f122m_003.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f122m_004_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f122m_005_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f122m_006_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f122m_007_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f122m_sbc_001.tab\n",
      "x grp/hst/cdbs/comp/acs/acs_f122m_sbc_002.tab\n",
      "x grp/hst/cdbs/comp/acs/acs_f122m_sbc_003.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f122m_sbc_004_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f122m_sbc_005_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f122m_tds_def_001_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f122m_tds_mjd_001_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f125lp_001.tab\n",
      "x grp/hst/cdbs/comp/acs/acs_f125lp_002.tab\n",
      "x grp/hst/cdbs/comp/acs/acs_f125lp_003.tab\n",
      "x grp/hst/cdbs/comp/acs/acs_f125lp_004.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f125lp_005_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f125lp_006_syn.fits\n",
      "x grp/hst/cdbs/comp/acs/acs_f125lp_007_syn.fits\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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    {
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    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "x grp/hst/cdbs/comp/wfpc2/wfpc2wf3.txt\n",
      "x grp/hst/cdbs/comp/wfpc2/wfpc2wf4.coldef\n",
      "x grp/hst/cdbs/comp/wfpc2/wfpc2wf4.headpar\n",
      "x grp/hst/cdbs/comp/wfpc2/wfpc2wf4.tab\n",
      "x grp/hst/cdbs/comp/wfpc2/wfpc2wf4.txt\n"
     ]
    }
   ],
   "source": [
    "! curl -O ftp://archive.stsci.edu/pub/hst/pysynphot/synphot1.tar.gz\n",
    "! tar xvzf synphot1.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "rootdir = './synphot_data/grp/hst/cdbs'\n",
    "basedir = os.path.join(rootdir, 'comp', 'nonhst')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "bandpass = {} \n",
    "johnson_filters = [\"B\", \"V\"]\n",
    "sdss_filters = [\"g\", \"r\"]\n",
    "\n",
    "for f in sdss_filters:\n",
    "    file = os.path.join(basedir, f\"sdss_{f}_005_syn.fits\")\n",
    "    bandpass[f] = Table.read(file)\n",
    "    \n",
    "for f in johnson_filters:\n",
    "    file = os.path.join(basedir, f\"landolt_{f}_004_syn.fits\")\n",
    "    bandpass[f] = Table.read(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = {\"B\": \"cyan\", \"V\": \"blue\", \"g\": \"green\", \"r\": \"red\"}\n",
    "for f, data in bandpass.items():\n",
    "    plt.plot(data[\"WAVELENGTH\"], data[\"THROUGHPUT\"], color=colors[f], label=f)\n",
    "\n",
    "plt.xlabel(\"Wavelength [$\\AA$]\")\n",
    "plt.ylabel(\"Throughput\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the Johnson B, V filters are normalized to a max transmission of while the SDSS g, r filters are likely in some absolute transmission.\n",
    "\n",
    "This doesn't matter for what we're doing below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.interpolate import interp1d\n",
    "flux_density_wavelength_interp = interp1d(wavelength, flux_density_wavelength)\n",
    "mag_ext_interp = interp1d(wavelength, mag_ext)\n",
    "\n",
    "source = {}\n",
    "extincted_source = {}\n",
    "for f, data in bandpass.items():\n",
    "    source[f] = flux_density_wavelength_interp(bandpass[f][\"WAVELENGTH\"]) * bandpass[f][\"THROUGHPUT\"]\n",
    "    extincted_source[f] = 10 ** (-0.4 * mag_ext_interp(bandpass[f][\"WAVELENGTH\"])) * source[f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13962d940>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.gca()\n",
    "ax1.plot(wavelength, flux_density_wavelength, color=\"black\", label=\"Flux Density\")\n",
    "ax1.plot(wavelength, extincted_flux_density_wavelength, color=\"black\", ls=\":\", label=\"Flux Density extincted\")\n",
    "\n",
    "for f, data in bandpass.items():\n",
    "    ax1.plot(data[\"WAVELENGTH\"], source[f], color=colors[f], label=f)\n",
    "    ax1.plot(data[\"WAVELENGTH\"], extincted_source[f], ls=\":\", color=colors[f], label=f\"{f} extincted\")\n",
    "\n",
    "ax1.set_xlabel(\"Wavelength [$\\AA$]\")\n",
    "ax1.set_ylabel(\"Throughput\")\n",
    "\n",
    "ax1.set_ylabel(r\"$\\frac{dF}{d\\lambda}$ [erg/s/cm$^2$/$\\AA$]\");\n",
    "\n",
    "ax1.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.integrate import trapz\n",
    "\n",
    "base_flux = {}\n",
    "extincted_flux = {}\n",
    "\n",
    "for f, data in bandpass.items():\n",
    "    base_flux[f] = trapz(bandpass[f][\"WAVELENGTH\"], source[f])\n",
    "    extincted_flux[f] = trapz(bandpass[f][\"WAVELENGTH\"], extincted_source[f])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_color = {}\n",
    "extincted_color = {}\n",
    "base_color['B-V'] = -2.5 * np.log10(base_flux['B'] / base_flux['V'])\n",
    "base_color['g-r'] = -2.5 * np.log10(base_flux['g'] / base_flux['r'])\n",
    "extincted_color['B-V'] = -2.5 * np.log10(extincted_flux['B'] / extincted_flux['V'])\n",
    "extincted_color['g-r'] = -2.5 * np.log10(extincted_flux['g'] / extincted_flux['r'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "excess_color = {}\n",
    "\n",
    "excess_color['B-V'] = extincted_color['B-V'] - base_color['B-V']\n",
    "excess_color['g-r'] = extincted_color['g-r'] - base_color['g-r']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'B-V': 0.20078651730637842, 'g-r': 0.2352584800391574}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excess_color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, that's reassuring.  We asked for extinction for $E(B-V)$ = 0.2, and that's the excess color we got.\n",
    "\n",
    "The $g-r$ color is similar, but slightly different.\n",
    "\n",
    "So we can now calculate a ratio between g-r color and A_V for our constant flux density source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_gr = av / excess_color['g-r']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So if we wanted to estimate some equivalent $A_V$ based on $E(g-r)$ we have\n",
    "\n",
    "$A_V = R_V E(B-V) = R_gr E(g-r)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If R_V = 3.1 then R_gr = 2.635\n"
     ]
    }
   ],
   "source": [
    "print(f\"If R_V = {rv} then R_gr = {r_gr:.03f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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