{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 3: Tidal disruption event (TDE) simulation \n",
    "\n",
    "### Simplified example in Python for modeling isotropic electromagnetic cascade and neutrino emission from tidal disruption events (TDEs).\n",
    "\n",
    "### Model based on [Yuan, Winter and Lunardini, ApJ 969 (2024)](<https://arxiv.org/pdf/2401.09320>).\n",
    "\n",
    "### If using this TDE model, please cite the above paper additionally to AM3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import packages and define the physical constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append('/path to AM3 library/')\n",
    "import am3\n",
    "\n",
    "c0 = 3e10 # speed of light\n",
    "Msun = 2e33 #solar mass in grams\n",
    "pc2cm = 3.08e18 # pc to cm\n",
    "k_Boltzmann = 8.6e-5 # eV/K, convert K to eV\n",
    "eV2Hz = 2.4e14 # convert eV to Hz\n",
    "Jy2CGS = 1e-23 # 1 Jy = 1e-23 erg/s/cm^2/Hz\n",
    "protonmass = 1.67e-24 # proton mass in g\n",
    "elecmass = 9.1e-28 # electron mass in g\n",
    "d2s = 24*3600.0 # day to s \n",
    "erg2GeV = 624.15 # erg to GeV\n",
    "ElecStatic = 4.8e-10 # electron charge "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Define a class for generating external photon spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ExtPH:\n",
    "    '''\n",
    "    set_external_G_spec(Obs_time, radius, Elist, corrected_lu, timelist, Ebb)\n",
    "    input: time in observer's frame, radius of the radiaton zone, energy array for external photon spectra, \n",
    "           bolometric luminosity array, time array for luminosity, black body energy\n",
    "    output: external photon rate spectra dN/dlogE/dt[cm^-3 s^-1]\n",
    "    '''          \n",
    "    @classmethod\n",
    "    def BlackBodySpec(cls, Energy, Temp0): #in eV, return in arbitrary units, dn/dlnE\n",
    "        theta = Energy / Temp0\n",
    "        tt = np.array(np.exp(theta), dtype = float)\n",
    "        np.clip(tt, 1e-50, 1e100)\n",
    "        spec = 1 / (tt - 1)\n",
    "        if hasattr(Energy, \"__len__\"):\n",
    "            spec[spec < 1e-20] = 0\n",
    "        return spec * Energy**3\n",
    "\n",
    "    \n",
    "           \n",
    "    @classmethod\n",
    "    def set_external_G_spec(cls, Obs_time, radius, Elist, corrected_lu, timelist, Ebb):\n",
    "        IntegralFlux = linear_interpolation(Obs_time, timelist, corrected_lu) \n",
    "        EnergyUp = Ebb * 1e6\n",
    "        EnergyLow = Ebb / 1e6\n",
    "        dLogE = np.log(EnergyUp / EnergyLow) / 200\n",
    "        EnergyList = np.exp(np.arange(np.log(EnergyLow), np.log(EnergyUp), dLogE))\n",
    "        dEnergy = EnergyList * np.exp(dLogE) - EnergyList\n",
    "        integral = sum(dEnergy * cls.BlackBodySpec(EnergyList, Ebb))\n",
    "        return  IntegralFlux / integral * cls.BlackBodySpec(Elist/(1+redshift), Ebb) / (4*np.pi/3*radius**3) * erg2GeV * 1e9/3\n",
    "\n",
    "def linear_interpolation(x, index_array, interp_array):\n",
    "    '''\n",
    "    1D linear interpolation\n",
    "    input: x, x-array, y-array\n",
    "    '''  \n",
    "    length = len(index_array)\n",
    "    if len(index_array) != len(interp_array):\n",
    "        print (\"Interpolation error!\")\n",
    "        return 0\n",
    "  \n",
    "    elif (x < index_array[0]) or x > (index_array[length-1]):\n",
    "        return 0\n",
    "    else:\n",
    "        for i in range(length-1):\n",
    "            if x <= index_array[i+1] and x >= index_array[i]:\n",
    "                return interp_array[i] + (interp_array[i+1] - interp_array[i])/(index_array[i+1] - index_array[i]) * (x - index_array[i])\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Initialize AM3 and set the switches\n",
    "\n",
    "See https://am3.readthedocs.io/en/latest/examples/blazar_detailed_example.html for a more complete usage of the switches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "am3 = am3.AM3()\n",
    "\n",
    "Eg_eV = am3.get_egrid_photons() #energy grid for photons\n",
    "En_eV = am3.get_egrid_neutrinos() #energy grid for neutrinos\n",
    "\n",
    "am3.set_estimate_max_energies(1)\n",
    "am3.set_process_parse_sed(1)\n",
    "am3.set_process_hadronic(1)\n",
    "am3.set_process_merge_positrons_into_electrons(0)\n",
    "am3.set_process_escape(1)\n",
    "am3.set_process_expansion(0)\n",
    "am3.set_process_adiabatic_cooling(0)\n",
    "am3.set_process_electron_syn(1)\n",
    "am3.set_process_ssa(1)\n",
    "am3.set_process_proton_syn(1)\n",
    "am3.set_process_quantum_syn(0)\n",
    "am3.set_process_electron_compton(1)\n",
    "am3.set_process_proton_compton(1)\n",
    "am3.set_process_compton_photon_energy_loss(0)\n",
    "\n",
    "am3.set_process_muon_syn(1)\n",
    "am3.set_process_pion_syn(1)\n",
    "am3.set_process_muon_compton(1)\n",
    "am3.set_process_pion_compton(1)\n",
    "am3.set_process_pion_decay(1)\n",
    "am3.set_process_muon_decay(1)\n",
    "\n",
    "am3.set_process_annihilation(1)\n",
    "am3.set_optimize_annihilation_pair_emission(1)\n",
    "\n",
    "am3.set_process_bethe_heitler(1)\n",
    "am3.set_optimize_bethe_heitler_outgoing_pairs_grid(1)\n",
    "am3.set_optimize_bethe_heitler_incoming_protons_min(1e12)\n",
    "am3.set_optimize_bethe_heitler_target_photon_max(1e6)\n",
    "\n",
    "am3.set_process_photopion(1)\n",
    "am3.set_optimize_photopion_target_photon_grid(1)\n",
    "am3.set_optimize_photopion_target_photon_max(1e6)\n",
    "\n",
    "am3.init_kernels()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Define simulation parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_start = -250 # start time t-t_pk in observer's frame\n",
    "t_obs_nu = 370 # neutrino detection time in observer's frame\n",
    "t_stop = t_obs_nu + 20 # time to stop the simulation\n",
    "redshift = 0.995 # redshift\n",
    "LuminDistance = 8.45e9 * pc2cm # luminosity distance in cm \n",
    "\n",
    "E_OUV = 1.38 # peak energy of OUV black body spectra [eV]\n",
    "L_OUV = np.array([2.85e44, 3.47e45, 6.51e45, 1.80e46, 1.37e46, 8.43e45, 2.08e45]) #bolometric OUV luminosities\n",
    "T_OUV = np.array([-280, -183, -148, 0.0, 172, 350, 1197]) #time list for the bolometric OUV luminosities\n",
    "\n",
    "E_X = 72 # peak energy of X-ray black body spectra [eV]\n",
    "L_X = np.array([3.40e45, 3.40e45]) #bolometric X-ray luminosities\n",
    "T_X = np.array([-200, 600]) #time list for the bolometric X-ray luminosities\n",
    "\n",
    "E_IR = 0.16 # peak energy of IR black body spectra [eV]\n",
    "L_IR = np.array([1.85e44, 1.76e45, 2.93e45, 3.87e45, 3.88e45, 3.87e45, 3.71e45, 3.45e45, 3.12e45, 2.38e45]) #bolometric IR luminosities\n",
    "T_IR = np.array([-250, -122, -56, 0, 58, 142, 250, 470, 630, 810]) #time list for the bolometric IR luminosities\n",
    "\n",
    "SMBHmass = 1e8 * Msun # SMBH mass in g\n",
    "Starmass = 1.0 * Msun # mass of disrupted star in g\n",
    "L_Edd = 1.3e45 * SMBHmass / (1e7 * Msun) # Eddington Luminosity\n",
    "SuperEddingtonParam = 100 *7.0/18 # M_acc / L_Edd at t_peak\n",
    "eta_p = 0.2 # proton efficiency \n",
    "SpecIndex = 2.0 # proton spectral index\n",
    "\n",
    "L_p = SuperEddingtonParam * eta_p * L_Edd * L_OUV/max(L_OUV) # proton luminosity \n",
    "\n",
    "runtime = t_start # record current run time\n",
    "\n",
    "radius = 5e17 # radius of the dust torus\n",
    "fracdt = 0.01 # time step = fracdt * R / c\n",
    "Epmax = 1.5e9 * 1e9 # proton maximum energy in SMBH rest frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Run the simulation from `t_start` to `t_stop` in a loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/25/d230kxr90hj414fnx8hg1f_4000956/T/ipykernel_82300/1259833014.py:11: RuntimeWarning: overflow encountered in exp\n",
      "  tt = np.array(np.exp(theta), dtype = float)\n"
     ]
    }
   ],
   "source": [
    "while runtime < t_stop:    \n",
    "    '''\n",
    "    set the runtimes and time steps\n",
    "    '''    \n",
    "    T_rest = runtime / (1+redshift) # SMBH rest frame time\n",
    "    mag_B = 0.1 # set magnetic field to 0.1 Gauss\n",
    "    \n",
    "    time0 = runtime\n",
    "    t_esc = radius / c0\n",
    "    am3.set_escape_timescale(radius / c0) #free streaming time\n",
    "    delta_runtime = min(fracdt * t_esc/d2s,  1) * (1 + redshift) # control the time step to not exceed 1 day\n",
    "    \n",
    "    \n",
    "    am3.set_solver_time_step(min(fracdt * t_esc, 24*3600)) # solver time step in seconds\n",
    "    runtime += delta_runtime\n",
    "    \n",
    "\n",
    "    \n",
    "\n",
    "    '''\n",
    "    set external photon spectra\n",
    "    '''\n",
    "    spec_extG = ExtPH.set_external_G_spec(runtime, radius, Eg_eV, L_OUV, T_OUV, E_OUV) +  ExtPH.set_external_G_spec(runtime, radius, Eg_eV, L_X, T_X, E_X) + ExtPH.set_external_G_spec(runtime, radius, Eg_eV, L_IR, T_IR, E_IR)\n",
    "    \n",
    "    am3.set_injection_rate_photons(spec_extG)\n",
    "\n",
    "    '''\n",
    "    set magnetic field and inject protons\n",
    "    '''\n",
    "    am3.set_mag_field(mag_B)\n",
    "    \n",
    "    volume = (4*3.14/3*radius**3)\n",
    "    pro_luminosity = linear_interpolation(runtime, T_OUV, L_p) \n",
    "    p_inj_Emin_eV = 1e9 \n",
    "    p_inj_Emax_eV = Epmax \n",
    "    p_inj_index = SpecIndex\n",
    "    \n",
    "    am3.set_powerlaw_injection_parameters_protons(volume, pro_luminosity, p_inj_Emin_eV, p_inj_Emin_eV, p_inj_Emax_eV, p_inj_index, p_inj_index, 1.0)    \n",
    "    \n",
    "    '''\n",
    "    evolve the system by one time step\n",
    "    '''    \n",
    "    am3.evolve_step()\n",
    "\n",
    "    \n",
    "    \n",
    "    '''\n",
    "    convert the spectra for photon components and neutrinos to the observer's frame and obtain the SEDs at neutrino detection time t_obs_nu\n",
    "    '''\n",
    "    \n",
    "    if t_obs_nu < runtime and t_obs_nu >= time0:\n",
    "        \n",
    "        '''\n",
    "        cascade photon spectra\n",
    "        '''\n",
    "        spec_const = Eg_eV/1e9/erg2GeV  * c0 * radius**2/(LuminDistance**2)  #\n",
    "        energy = Eg_eV  / (1 + redshift) # eV\n",
    "        dataG = np.transpose([energy, spec_const * am3.get_photons(),\n",
    "                            spec_const * (am3.get_photons_injected_electrons_syn()),\n",
    "                            spec_const * (am3.get_photons_injected_electrons_compton()),\n",
    "                            spec_const * am3.get_photons_bethe_heitler_pairs_syn_compton(),\n",
    "                            spec_const * am3.get_photons_annihilation_pairs_syn_compton(),\n",
    "                            spec_const * am3.get_photons_photo_pion_pairs_syn_compton(),\n",
    "                            spec_const * am3.get_photons_protons_syn_compton(),\n",
    "                            spec_const * am3.get_photons_pi0_decay()\n",
    "                            ])\n",
    "        \n",
    "        \n",
    "        '''\n",
    "        external photon spectrum\n",
    "        '''\n",
    "        dataext = np.transpose([energy, spec_const*spec_extG*4*3.14/3 * radius/c0]) \n",
    "        \n",
    "        \n",
    "        '''\n",
    "        neutrino spectrum\n",
    "        '''\n",
    "        spec_const = En_eV/1e9/erg2GeV * c0 * radius**2/(LuminDistance**2)  #\n",
    "        energy = En_eV / (1 + redshift) # eV\n",
    "        dataNu = np.transpose([energy, spec_const * am3.get_neutrinos_photopion()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.  Plot the multi-wavelength and neutrino SEDs\n",
    "\n",
    "Cf. Fig 3 of Yuan, Winter and Lunardini 2024 (<https://arxiv.org/pdf/2401.09320>)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "plt.fill_between([300,1e4], [1e-20,1e-20], [1,1], color = \"C1\", alpha = 0.15)\n",
    "plt.fill_between([1e8,8e11], [1e-20,1e-20], [1,1], color = \"cyan\", alpha = 0.15)\n",
    "\n",
    "data=dataG\n",
    "datacas = data[:,4] + data[:,5] + data[:,6] + data[:,7] + data[:,8]\n",
    "plt.plot(data[:,0], datacas, lw=2, color = \"k\", label = \"All cascade\")\n",
    "plt.loglog(data[:,0], data[:,4], lw=2,color = \"blue\", alpha=0.6, label = r\"bh-syic\")\n",
    "plt.loglog(data[:,0], data[:,5], lw=2,color = \"C1\",alpha=0.6, label = r\"pair-syic\")\n",
    "plt.loglog(data[:,0], data[:,6], lw=2,color = \"green\",alpha=0.6, label = r\"pg-syic\")\n",
    "plt.ylim(1e-15,1e-10)\n",
    "plt.xlim(1e-2,1e18)\n",
    "\n",
    "data = dataext\n",
    "plt.loglog(data[:,0], data[:,1], lw=2, color = \"magenta\", alpha=0.4,label = r\"B.B.\")\n",
    "\n",
    "data=dataNu\n",
    "plt.loglog(data[:,0], data[:,1], color = \"red\", lw=2, label = r\"Neutrinos\")\n",
    "\n",
    "plt.legend(ncol=2)\n",
    "\n",
    "plt.grid(color=\"gray\", alpha = 0.1, which=\"minor\")\n",
    "plt.grid(color=\"gray\", alpha = 0.2, which=\"major\")\n",
    "\n",
    "plt.xlabel(r\"$E_{\\rm obs}$ [eV]\", fontsize=12)\n",
    "plt.ylabel(r\"$\\nu F_\\nu~[\\rm erg~s^{-1}~cm^{-2}]$\", fontsize=12)\n",
    "\n",
    "plt.xticks(10.0**(1+2*np.arange(-1,9)), fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "plt.plot([1e8, 8e11], [1.25e-12, 1.25e-12], lw=1.5, color = \"cyan\")\n",
    "\n",
    "plt.text(3e-2, 5e-11, r\"$\\Delta T_{\\rm IR}=180$ d\") \n",
    "\n",
    "plt.errorbar([1e10],[1.25e-12], xerr=[[1e10-1e8], [8e11-1e10]], yerr=[[5e-13], [7e-12]], uplims=True, color = \"cyan\")\n",
    "plt.arrow(1.06e14, 3e-13, 0, -2e13, color=\"red\")\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}