{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Astrophysical source population modeling with Gammapy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "The [gammapy.astro.population](http://docs.gammapy.org/0.7/astro/population/index.html) package contains some simple Galactic source population models.\n",
    "\n",
    "Here we provide some Python code to compute observable parameter distributions for Galactic gamma-ray source populations.\n",
    "\n",
    "* Observables: Flux, GLON, GLAT\n",
    "* Source classes: Pulsar (PSR), Supernova remnant (SNR), pulsar wind nebula (PWN)\n",
    "\n",
    "References:\n",
    "\n",
    "* Section 6.2 in the Fermi-LAT collaboration paper [\"The First Fermi-LAT Catalog of Sources Above 10 GeV\"](http://adsabs.harvard.edu/abs/2013arXiv1306.6772T)\n",
    "* Axel Donath's bachelor thesis [\"Modelling Galactic gamma-ray source populations\"](http://pubman.mpdl.mpg.de/pubman/item/escidoc:912132:1/component/escidoc:912131/BScThesis_ddonath.pdf), specifically Chapter 4.\n",
    "* Casanova & Dingus (2008), [\"Constraints on the TeV source population and its contribution to the galactic diffuse TeV emission\"](http://adsabs.harvard.edu/abs/2008APh....29...63C)\n",
    "* Strong (2007), [\"Source population synthesis and the Galactic diffuse gamma-ray emission\"](http://adsabs.harvard.edu/abs/2007Ap%26SS.309...35S)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import astropy.units as u\n",
    "from gammapy.utils.random import sample_powerlaw\n",
    "from gammapy.astro import population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulate positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spatial distribution using Lorimer (2006) model\n",
    "n_sources = int(1e5)\n",
    "\n",
    "table = population.make_base_catalog_galactic(\n",
    "    n_sources=n_sources,\n",
    "    rad_dis='L06',\n",
    "    vel_dis='F06B',\n",
    "    max_age=1e6 * u.yr,\n",
    "    spiralarms=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulate luminosities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several source population models, e.g. the 1FHL paper or Strong (2007), use power-law luminosity functions.\n",
    "\n",
    "Here we implement the \"reference model\" from the 1FHL catalog paper section 6.2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Source luminosity (ph s^-1)\n",
    "\n",
    "luminosity = sample_powerlaw(\n",
    "    x_min=1e34,\n",
    "    x_max=1e37,\n",
    "    gamma=1.5,\n",
    "    size=n_sources,\n",
    ")\n",
    "table['luminosity'] = luminosity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute observable parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Table length=100000>\n",
      "   name     dtype     unit                description              \n",
      "---------- ------- --------- --------------------------------------\n",
      "       age float64        yr                      Age of the source\n",
      "     n_ISM float64   1 / cm3            Interstellar medium density\n",
      " spiralarm   str18                                 Which spiralarm?\n",
      "   x_birth float64       kpc   Galactocentric x coordinate at birth\n",
      "   y_birth float64       kpc   Galactocentric y coordinate at birth\n",
      "   z_birth float64       kpc   Galactocentric z coordinate at birth\n",
      "         x float64       kpc            Galactocentric x coordinate\n",
      "         y float64       kpc            Galactocentric y coordinate\n",
      "         z float64       kpc            Galactocentric z coordinate\n",
      "        vx float64    km / s Galactocentric velocity in x direction\n",
      "        vy float64    km / s Galactocentric velocity in y direction\n",
      "        vz float64    km / s Galactocentric velocity in z direction\n",
      "     v_abs float64    km / s     Galactocentric velocity (absolute)\n",
      "luminosity float64                                                 \n",
      "  distance float64        pc     Distance observer to source center\n",
      "      GLON float64       deg                     Galactic longitude\n",
      "      GLAT float64       deg                      Galactic latitude\n",
      "     VGLON float64 deg / Myr         Velocity in Galactic longitude\n",
      "     VGLAT float64 deg / Myr          Velocity in Galactic latitude\n",
      "        RA float64       deg                        Right ascension\n",
      "       DEC float64       deg                            Declination\n",
      "      flux float64  1 / kpc2                            Source flux\n"
     ]
    }
   ],
   "source": [
    "table = population.add_observed_parameters(table)\n",
    "table.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulation is done, you could save the simulated catalog to a file.\n",
    "\n",
    "Here we just plot a few distributions to check if the results look OK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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UJZdUrEjr9gS1HQ8PVTGx5UYc33YTjm+7CeNfuBHz+tyT8YKMIyxRzYG06yXlEdHwEyZL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108a706d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(table['x'][:1000], table['y'][:1000]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iq4Hn+tpNtdhM8RMk2ZpkIsnE4cOH55meJGkui30UUKaJ1SzxE4NV26tqvKrGx8bGFjU5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\nJKmjLACS1FH/H4alT9yfhGuDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1038c22e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(table['GLON'], bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x104710198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(table['GLAT'], bins=100, log=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/x8JpN27bE7rlLwg/kYuKz2+BNFM9Mq3t7wR6v3kfUVI77ruL/nllzJ3Thzdq7Qv+AfmO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\nAZKNwQzpPXgDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c1a630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(table['GLON'][:1000], table['GLAT'][:1000]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k5nPVH+j/ATfRXy4w/jxeoN8SOWXFeCMi4lT6wfjFzPy7ariTy2S1uXR1uVS1/wi4l36P\nfthzv1Jv9fMZ+m3Fjfnbr3sjRdNf9M+KdZT+houXN1K8ve26VtT4BuBNA5f/iX5v/S84cePZZ6rL\nl3PixrMHq/Ezge/T33B2RnX5zA2aww5O3IhZW+30zzf8bl7d8HfZBs7j7IHLf0S/Pwrwdk7cKHaU\n/gaxoa834MucuOHtIw3NIej3zT+7Yrxzy2SNuXRquQBnAW+uLv8U8E3gimHPDXyUEzfG3r7e+a2r\n3qb+wJr8or9XwXfp98Q+3nY9q9R3frVgHgEef7lG+j25bwBPVd9f/iML4IZqPt8BegOP9Xv0N9As\nAh/aoPpvo//v8//SX7O4ss7agR7wWHWf66k+uLdB8/jbqs5H6Z/MfjBgPl7V9CQDe50Me71Vy/nB\nan5fBk5vaB6/Sv/f9keBh6uvyzq6TIbNpVPLBfgF4F+qeh8D/nSt5wZeX11frH5+/nrnt54vPxkr\nSYXrYo9ekjQGg16SCmfQS1LhDHpJKpxBL0mFM+glqXAGvSQVzqCXpML9P80/Wdph2KIcAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c96470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(table['distance'], bins=100, log=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BPVX1fOA3gHcm+Y7tHe3WBsTzA8Abge8Dfpi1PzHfMKDrQh4f6BQTrB2jHvBq4I+TPHcW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\nASZ7SWqAyV6SGmCyl6QGmOwlqQH/Byv3EEbzF2t+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109336668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.log10(table['luminosity']), bins=100, log=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Bvprkzazv4PgGcGt3JU7Mlp9rxzVNw6C/339Icinrn+ljwC1dFnmBZ8ZKUuPmonUjSdo9\ng16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMb9P4AHwTHqTGVlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109329f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.log10(table['flux']), bins=100, log=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: plot GLON, GLAT, FLUX distribution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Start exercises here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What next?\n",
    "\n",
    "TODO: summarise what was done here briefly.\n",
    "\n",
    "TODO: add some pointers to other documentation."
   ]
  }
 ],
 "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": "3.5.3"
  }
 },
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