{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# O'Connor 2013 Models\n",
    "\n",
    "Data from O'Connor & Ott 2013, 32 progenitors (Woosley and Heger 2007) and 2 EOS (LS220 and HShen) for 500 ms post bounce in spherical symmetry (no explosions)\n",
    "  \n",
    "Reference: O'Connor and Ott ApJ 762 126 2013\n",
    "- [doi:10.1088/0004-637X/762/2/126](https://doi.org/10.1088/0004-637X/762/2/126)\n",
    "- [arXiv:1207.1100](https://arxiv.org/abs/1207.1100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from astropy import units as u \n",
    "\n",
    "from snewpy.neutrino import Flavor, MassHierarchy\n",
    "from snewpy.models.ccsn import OConnor_2013\n",
    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, ThreeFlavorDecoherence\n",
    "\n",
    "mpl.rc('font', size=16)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Models\n",
    "\n",
    "To start, let’s see what progenitors are available for the `OConnor_2013` model. We can use the `param` property to view all physics parameters and their possible values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "OConnor_2013.param"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quite a lot of choice there! Let’s initialise all of these progenitors and compare their $\\nu_e$ luminosities. If this is the first time you’re using a progenitor, snewpy will automatically download the required data files for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {}\n",
    "for mass in OConnor_2013.param['progenitor_mass']:\n",
    "    models[int(mass.value)] = OConnor_2013(progenitor_mass=mass, eos='LS220')\n",
    "\n",
    "for model in models.values():\n",
    "    plt.plot(model.time, model.luminosity[Flavor.NU_E]/1e51, 'C0', lw=1)\n",
    "\n",
    "plt.xlabel(r'$t$ [s]')\n",
    "plt.ylabel(r'luminosity [foe s$^{-1}$]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let’s plot the luminosity of different neutrino flavors for two of these progenitors. (Note that the `OConnor_2013` simulations didn’t distinguish between $\\nu_x$ and $\\bar{\\nu}_x$, so both flavors have the same luminosity.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharex=True, sharey=True, tight_layout=True)\n",
    "\n",
    "for i, model in enumerate([models[12], models[20]]):\n",
    "    ax = axes[i]\n",
    "    for flavor in Flavor:\n",
    "        ax.plot(model.time, model.luminosity[flavor]/1e51,  # Report luminosity in units foe/s\n",
    "                label=flavor.to_tex(),\n",
    "                color='C0' if flavor.is_electron else 'C1',\n",
    "                ls='-' if flavor.is_neutrino else ':',\n",
    "                lw=2)\n",
    "    ax.set(xlim=(-0.05, 0.51),\n",
    "           xlabel=r'$t-t_{\\rm bounce}$ [s]',\n",
    "           title=r'{}: {} $M_\\odot$'.format(model.metadata['EOS'], model.metadata['Progenitor mass'].value))\n",
    "    ax.grid()\n",
    "    ax.legend(loc='upper right', ncol=2, fontsize=18)\n",
    "\n",
    "axes[0].set(ylabel=r'luminosity [foe s$^{-1}$]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial and Oscillated Spectra\n",
    "\n",
    "Plot the neutrino spectra at the source and after the requested flavor transformation has been applied.\n",
    "\n",
    "### Adiabatic MSW Flavor Transformation: Normal mass ordering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adiabatic MSW effect. NMO is used by default.\n",
    "xform_nmo = AdiabaticMSW()\n",
    "\n",
    "# Energy array and time to compute spectra.\n",
    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
    "E = np.linspace(0,100,201) * u.MeV\n",
    "t = 400*u.ms\n",
    "\n",
    "ispec = model.get_initial_spectra(t, E)\n",
    "ospec_nmo = model.get_transformed_spectra(t, E, xform_nmo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(1,2, figsize=(12,5), sharex=True, sharey=True, tight_layout=True)\n",
    "\n",
    "for i, spec in enumerate([ispec, ospec_nmo]):\n",
    "    ax = axes[i]\n",
    "    for flavor in Flavor:\n",
    "        ax.plot(E, spec[flavor],\n",
    "                label=flavor.to_tex(),\n",
    "                color='C0' if flavor.is_electron else 'C1',\n",
    "                ls='-' if flavor.is_neutrino else ':', lw=2,\n",
    "                alpha=0.7)\n",
    "\n",
    "    ax.set(xlabel=r'$E$ [{}]'.format(E.unit),\n",
    "           title='Initial Spectra: $t = ${:.1f}'.format(t) if i==0 else 'Oscillated Spectra: $t = ${:.1f}'.format(t))\n",
    "    ax.grid()\n",
    "    ax.legend(loc='upper right', ncol=2, fontsize=16)\n",
    "\n",
    "ax = axes[0]\n",
    "ax.set(ylabel=r'flux [erg$^{-1}$ s$^{-1}$]')\n",
    "\n",
    "fig.tight_layout();"
   ]
  }
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