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   "metadata": {},
   "source": [
    "# Detector Hits in IceCube-86\n",
    "\n",
    "Demonstrate detector hits in the IC86 configuration of IceCube."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2eb397f-92c2-40d2-932f-5816b85cf46b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy import units as u\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from snewpy.neutrino import Flavor\n",
    "\n",
    "from asteria.simulation import Simulation\n",
    "from asteria import set_rcparams\n",
    "from asteria import interactions\n",
    "\n",
    "set_rcparams(verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7af47266-c61c-45a7-b572-b865a1092864",
   "metadata": {},
   "source": [
    "## Load a SNEWPY Model and Configure the Simulation\n",
    "\n",
    "Set up a progenitor 10 kpc from Earth and generate simulated hits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dff7931-b55f-4563-ab3b-12cf5e78e2bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = {'name': 'Nakazato_2013',\n",
    "         'param':{\n",
    "             'progenitor_mass': 13 * u.Msun,\n",
    "             'revival_time': 300 * u.ms,\n",
    "             'metallicity': 0.004,\n",
    "             'eos': 'shen'}\n",
    "         }\n",
    "\n",
    "sim = Simulation(model=model,\n",
    "                 distance=10 * u.kpc, \n",
    "                 Emin=0*u.MeV, Emax=100*u.MeV, dE=1*u.MeV,\n",
    "                 tmin=-1*u.s, tmax=1*u.s, dt=1*u.ms)\n",
    "sim.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3052237c-8160-44b7-ac0e-bb2c4e986ee2",
   "metadata": {},
   "source": [
    "## Plot the Energy Deposit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8252ebd0-52b8-4504-915e-3f36189f5419",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(8,6), tight_layout=True)\n",
    "\n",
    "for flavor in sim.flavors:\n",
    "    ax.plot(sim.time, sim.E_per_V[flavor], label=flavor.to_tex())\n",
    "ax.legend()\n",
    "ax.set(xlabel=r'$t-t_\\mathrm{bounce}$ [s]',\n",
    "       ylabel='energy deposit [Mev m$^{-3}$])',\n",
    "       xlim=(-0.15, 0.75));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe116039-a071-467a-8bd6-06aa2a812468",
   "metadata": {},
   "source": [
    "## Plot Detector Response\n",
    "\n",
    "### Expected Signal from Each Subdetector\n",
    "\n",
    "Set a time resolution `dt`. Using the `sim.detector_signal()` function we can read out the detector signal for each subdetector class. Separately plot hits from the main IceCube strings and DeepCore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d90d09cf-5ecc-475c-bbc5-178c95e4e2c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = 2 * u.ms\n",
    "t, sim_i3 = sim.detector_signal(subdetector='i3', dt=dt)\n",
    "t, sim_dc = sim.detector_signal(subdetector='dc', dt=dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52d3c595-17e0-4df0-9746-af9d47e77a35",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(8,6), tight_layout=True)\n",
    "ax.plot(t, sim_i3, label='IceCube DOM')\n",
    "ax.plot(t, sim_dc, label='HQE DOM (DeepCore)')\n",
    "ax.legend()\n",
    "ax.set(xlabel=r'$t-t_\\mathrm{bounce}$ [s]',\n",
    "       ylabel=f'detector hits',\n",
    "       xlim=(-0.15, 0.75),\n",
    "       ylim=(0,4000));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5854b46f-9889-48fd-bdef-d69678b5c147",
   "metadata": {},
   "source": [
    "### Generated Hits from Signal Only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c29ae3a0-b978-4ada-8580-a0a167d89dba",
   "metadata": {},
   "outputs": [],
   "source": [
    "t, hits_i3 = sim.detector_hits(subdetector='i3', dt=dt)\n",
    "t, hits_dc = sim.detector_hits(subdetector='dc', dt=dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb6a1409-3da3-4071-ab48-e9a8d5bfdfef",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(8,6), tight_layout=True)\n",
    "ax.plot(t, hits_i3, label='IceCube DOM')\n",
    "ax.plot(t, hits_dc, label='HQE DOM (DeepCore)')\n",
    "ax.legend()\n",
    "ax.set(xlabel=r'$t-t_\\mathrm{bounce}$ [s]',\n",
    "       ylabel=f'detector hits',\n",
    "       xlim=(-0.15, 0.75),\n",
    "       ylim=(0,2000));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f3f7030-fb90-4a89-b98f-d0d0e9787446",
   "metadata": {},
   "source": [
    "### Generated Hits from Signal + Background\n",
    "\n",
    "Separately compute the background hits and signal from each subdetector and add them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2f39f58-504e-42ca-8f0f-8a72f7dd8eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "bkg_i3 = sim.detector.i3_bg(dt, size=hits_i3.size)\n",
    "bkg_dc = sim.detector.dc_bg(dt, size=hits_dc.size)\n",
    "bkg = bkg_i3 + bkg_dc\n",
    "hits = hits_i3 + hits_dc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4df03ef-e5f6-40a4-a2cd-1f0c94c51d46",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(8,6), tight_layout=True)\n",
    "ax.plot(t, hits_i3 + bkg_i3, label='IceCube DOM')\n",
    "ax.plot(t, hits_dc + bkg_dc, label='HQE DOM (DeepCore)')\n",
    "ax.plot(t, hits + bkg, label='Total hits')\n",
    "ax.legend(loc='upper right', fontsize=14)\n",
    "ax.set(xlabel=r'$t-t_\\mathrm{bounce}$ [s]',\n",
    "       ylabel=f'detector hits',\n",
    "       xlim=(-0.15, 0.75),\n",
    "       ylim=(0,6000));"
   ]
  }
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