Hermes_HadronicGammaFlux_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter Example for HERMES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('/home/tospines/Hermes/build/')\n",
    "from pyhermes import *\n",
    "from pyhermes.units import *\n",
    "\n",
    "import astropy.units as u\n",
    "\n",
    "import numpy as np\n",
    "import healpy\n",
    "\n",
    "import astropy.io.fits as pyfits\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rc, rcParams\n",
    "import matplotlib.ticker as tick\n",
    "import matplotlib.colors as colors\n",
    "from matplotlib import cm\n",
    "\n",
    "###############\n",
    "# LaTeX block #\n",
    "###############\n",
    "\n",
    "rc('text', usetex=True)\n",
    "rc('font',**{'family':'serif','serif':['Palatino']})\n",
    "rc('xtick', labelsize=18)\n",
    "rc('ytick', labelsize=18)\n",
    "rcParams['legend.numpoints'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjusting the input parameters for our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "nside = 8\n",
    "sun_pos = Vector3QLength(8.*kpc, 0*pc, 0*pc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "hermes: info: reading species with Z = 1 A = 1 at HDU = 6\n",
      "hermes: info: reading species with Z = 1 A = 1 at HDU = 7\n",
      "hermes: info: reading species with Z = 2 A = 4 at HDU = 11\n",
      "Number of rings: 12\n",
      "Number of rings: 12\n"
     ]
    }
   ],
   "source": [
    "DragonRuns = '/home/tospines/Hermes/DragonRuns/'\n",
    "filename = DragonRuns + \"Basemodel.fits.gz\"\n",
    "\n",
    "dragon2D_proton = cosmicrays.Dragon2D(filename, [Proton])\n",
    "dragon2D_helium = cosmicrays.Dragon2D(filename, [Helium])\n",
    "cr_list = [dragon2D_proton, dragon2D_helium]\n",
    "\n",
    "XCOvalues = [1.8, 1.8, 1.8, 3.5, 3.5, 3.5, 3.5, 7., 7., 7., 7., 7.]\n",
    "#Correspond to the bins: {0_kpc, 0.86_kpc, 2_kpc, 3_kpc,  4_kpc,  5_kpc, 6_kpc, 7_kpc, 9_kpc, 12_kpc, 15_kpc, 18_kpc}\n",
    "\n",
    "neutral_gas_HI = neutralgas.RingModel(neutralgas.GasType.HI)\n",
    "neutral_gas_H2 = neutralgas.RingModel(neutralgas.GasType.H2, XCOvalues)\n",
    "\n",
    "kelner_crossections = interactions.KelnerAharonianGamma()\n",
    "#kamae_crosssection = interactions.Kamae06Gamma()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "integratorHI = PiZeroIntegrator(cr_list, neutral_gas_HI, kelner_crossections)\n",
    "integratorH2 = PiZeroIntegrator(cr_list, neutral_gas_H2, kelner_crossections)\n",
    "\n",
    "integratorHI.setupCacheTable(60, 60, 24) # nx,ny,nz\n",
    "integratorHI.setObsPosition(sun_pos)\n",
    "integratorH2.setupCacheTable(60, 60, 24)\n",
    "integratorH2.setObsPosition(sun_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building SkyMapRange object for pi0\n"
     ]
    }
   ],
   "source": [
    "print(\"building SkyMapRange object for pi0\")\n",
    "skymapHI_range = GammaSkymapRange(nside, 0.1*GeV, 10.*TeV, 40)\n",
    "skymapH2_range = GammaSkymapRange(nside, 0.1*GeV, 10.*TeV, 40)\n",
    "\n",
    "mask_edges = ([-90.*deg, 90.*deg], [360.*deg, 0*deg])\n",
    "mask = RectangularWindow(*mask_edges)\n",
    "\n",
    "skymapHI_range.setMask(mask)\n",
    "skymapH2_range.setMask(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"starting calculation of skymaps... \")\n",
    "\n",
    "# Calculation of HI skymap\n",
    "skymapHI_range.setIntegrator(integratorHI)\n",
    "skymapHI_range.compute()\n",
    "nameHI = \"Test_BaseRunHI_nside{}\".format(nside)\n",
    "skymapHI_range.save(outputs.HEALPixFormat(\"!{}.fits.gz\".format(nameHI)))\n",
    "print(\"pi0 HI done\")\n",
    "\n",
    "# Calculation of H2 skymap\n",
    "skymapH2_range.setIntegrator(integratorH2)\n",
    "skymapH2_range.compute()\n",
    "nameH2 = \"Test_BaseRunH2_nside{}\".format(nside)\n",
    "skymapH2_range.save(outputs.HEALPixFormat(\"!{}.fits.gz\".format(nameH2)))\n",
    "print(\"pi0 H2 done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting the maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We are considering the map at E =  11.25335582600766\n"
     ]
    }
   ],
   "source": [
    "reference_energy = 10.*GeV\n",
    "energy_vector = np.asarray([s.getEnergy() for s in skymapHI_range])\n",
    "index = np.argmin(np.abs(energy_vector - reference_energy))\n",
    "print(\"We are considering the map at E = \", energy_vector[index]/GeV)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Opening saved file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Runpath = '/home/tospines/Hermes/Work/HermesRuns/'\n",
    "H2_hdulist = pyfits.open(Runpath + nameH2 + \".fits.gz\")  # = skymapH2_range\n",
    "header = H2_hdulist[index+1].header\n",
    "HI_hdulist = pyfits.open(Runpath + nameHI + \".fits.gz\")  # = skymapHI_range\n",
    "\n",
    "nside = header['NSIDE']\n",
    "npix = healpy.nside2npix(nside)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Displaying the spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "GeV_units = 1/(1.6021766339999998e-10)  # From Joules to GeV\n",
    "\n",
    "def calc_mean_flux(hdulist, nside):\n",
    "    energies = np.asarray([float(hdulist[index+1].header['ENERGY'])*GeV_units for index in range(len(hdulist)-1)])\n",
    "    fluxesMatrix = np.zeros(( healpy.nside2npix(nside), len(hdulist)-1))\n",
    "    for index in range(len(hdulist)-1):\n",
    "        fluxesMatrix[:,index] = np.asarray([hdulist[index+1].data])\n",
    "    fluxes = np.asarray([ np.sum(fluxesMatrix[:,index])/healpy.nside2npix(nside) for index in range(len(hdulist)-1)])\n",
    "    return energies, fluxes\n",
    "\n",
    "\n",
    "def plot_spectrum(hdulists, power, label, nside, color, marker='-'):\n",
    "    fluxes = [(calc_mean_flux(hdul, nside)[1]) for hdul in hdulists]\n",
    "    energies = calc_mean_flux(hdulists[0], nside)[0]\n",
    "    plt.plot(energies, np.sum(fluxes, axis=0)*energies**power , marker, label=label, color=color) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ei = 2.\n",
    "\n",
    "fig = plt.figure(figsize=[10,6])\n",
    "ax = fig.add_subplot(111)\n",
    "rc('font', size=18)\n",
    "\n",
    "plot_spectrum([HI_hdulist], ei, r'Pion decay HI', nside=64, color = \"red\")\n",
    "plot_spectrum([H2_hdulist], ei, r'Pion decay H2', nside=64, color = \"orange\")\n",
    "plot_spectrum([HI_hdulist, H2_hdulist], ei, r'Pion decay total', nside=64, color = \"black\")\n",
    "\n",
    "\n",
    "plt.legend(frameon = False)\n",
    "\n",
    "plt.title(\"Full sky\", fontsize=18)\n",
    "plt.xlabel(\"$E$  [GeV]\", fontsize=18)\n",
    "plt.ylabel(\"$E^{\" + str(int(ei)) + \"}\\, \\mathrm{d}\\Phi_\\gamma / \\mathrm{d} E_\\gamma \\,\\,[\\mathrm{GeV} \\, \\mathrm{m}^{-2} \\, \\mathrm{s}^{-1} \\, \\mathrm{sr}^{-1}$]\", fontsize=18)\n",
    "\n",
    "ax.tick_params(which='minor', direction='in', axis='both', bottom=True, top=True, left=True, right=True, length=3)\n",
    "ax.tick_params(which='major', direction='in', axis='both', bottom=True, top=True, left=True, right=True, length=5)\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "\n",
    "plt.ylim(top = 1, bottom=1e-5)\n",
    "plt.xlim(left = 0.1, right=1e3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"/mnt/c/Users/pedro/OneDrive/Escritorio/diffuse-gamma-emission.png\", dpi=150)    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}