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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kuroda 2020 Models\n",
    "\n",
    "Models from Takami Kuroda. The paper describing the simulations is \"Impact of magnetic field on neutrino-matter interactions in core-collapse supernova\" arXiv:2009.07733.\n",
    "Included with SNEWPY with permission of the authors."
   ]
  },
  {
   "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 Kuroda_2020\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 `Kuroda_2020` 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": [
    "Kuroda_2020.param"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quite a lot of choice there! However, for this model, not all combinations of these parameters are valid. We can use the `get_param_combinations` function to get a list of all valid combinations or to filter it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will print a tuple of all combinations:\n",
    "Kuroda_2020.get_param_combinations()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We’ll pick one of these progenitors and initialise it. 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": [
    "model = Kuroda_2020(**Kuroda_2020.get_param_combinations()[1])\n",
    "# This is equivalent to:\n",
    "# model = Kuroda_2020(rotational_velocity=1*u.rad/u.s, magnetic_field_exponent=12)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let’s plot the luminosity of different neutrino flavors for this model. (Note that the `Kuroda_2020` simulations didn’t distinguish between $\\nu_x$ and $\\bar{\\nu}_x$, so both have the same luminosity.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(8,6), tight_layout=False)\n",
    "\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",
    "\n",
    "ax.set(xlim=(0.0, 0.35),\n",
    "       xlabel=r'$t-t_{\\rm bounce}$ [s]',\n",
    "       ylabel=r'luminosity [foe s$^{-1}$]')\n",
    "ax.grid()\n",
    "ax.legend(loc='upper right', ncol=2, fontsize=18);"
   ]
  },
  {
   "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 = 50*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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