examples/emplot/emplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "94b7676e-436d-4fc7-aa7a-8c5f587a05af",
   "metadata": {},
   "source": [
    "# EM Plots using EM Calculator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "386915b9-1875-4d36-99ff-98be90c14faf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "#####################################################################\n",
      "!!! Warning !!!\n",
      "A non-system python (e.g., Anaconda version of Python) is detected.\n",
      "If you have a problem with Qt5 library version,\n",
      "set the environment variables, \"G4PY_QT5_PRELOAD = 1\"\n",
      "to preload the system Qt5 library as a temporal solution.\n",
      "Please consider installing a Geant4 library for Geant4Py\n",
      "without the Qt feature.\n",
      "#####################################################################\n",
      "\n",
      "=============================================================\n",
      "  _____              __  ____ ___\n",
      " / ___/__ ___ ____  / /_/ / // _ \\__ __  Geant4-Python Interface\n",
      "/ (_ / -_) _ `/ _ \\/ __/_  _/ ___/ // /  Version: 1100\n",
      "\\___/\\__/\\_,_/_//_/\\__/ /_//_/   \\_, /   Date: (31-October-2021)\n",
      "                                /___/\n",
      "=============================================================\n",
      "\n",
      "Environment variable \"G4FORCE_RUN_MANAGER_TYPE\" enabled with value == Serial. Forcing G4RunManager type...\n",
      "\n",
      "**************************************************************\n",
      " Geant4 version Name: geant4-10-07-ref-09 [MT]   (31-October-2021)\n",
      "                       Copyright : Geant4 Collaboration\n",
      "                      References : NIM A 506 (2003), 250-303\n",
      "                                 : IEEE-TNS 53 (2006), 270-278\n",
      "                                 : NIM A 835 (2016), 186-225\n",
      "                             WWW : http://geant4.org/\n",
      "**************************************************************\n",
      "\n",
      "Visualization Manager instantiating with verbosity \"warnings (3)\"...\n"
     ]
    }
   ],
   "source": [
    "from geant4 import *\n",
    "from geant4.utils import SimpleBox\n",
    "from geant4.utils import ParticleGun"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae4a5f8d-d019-4773-a250-d3283ae6fabc",
   "metadata": {},
   "source": [
    "----\n",
    "## Building up Geant4 Mockup\n",
    "* Geometry : Simple Box\n",
    "* Physics List : FTFP_BERT\n",
    "* Primary Generator : Particle Gun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c540c8c9-15c2-459c-84f1-6d950d1263aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<<< Geant4 Physics List simulation engine: FTFP_BERT\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# set detectot construction\n",
    "box = SimpleBox()\n",
    "gRunManager.SetUserInitialization(box)\n",
    "\n",
    "# set physics list\n",
    "physics_list = FTFP_BERT()\n",
    "gRunManager.SetUserInitialization(physics_list)\n",
    "\n",
    "# ParticleGun as PGA\n",
    "particle_gun = ParticleGun()\n",
    "\n",
    "# User Action Initialization\n",
    "class AppBuilder(G4VUserActionInitialization):\n",
    "    def Build(self):\n",
    "        # setup PGA\n",
    "        self.SetUserAction(particle_gun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f2834a64-be96-49d0-b830-d2f9043ce750",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- G4CoupledTransportation is used \n",
      "\n",
      " hInelastic FTFP_BERT : threshold between BERT and FTFP is over the interval \n",
      " for pions :   3 to 6 GeV\n",
      " for kaons :   3 to 6 GeV\n",
      " for proton :  3 to 6 GeV\n",
      " for neutron : 3 to 6 GeV\n",
      "\n",
      "### Adding tracking cuts for neutron  TimeCut(ns)= 10000  KinEnergyCut(MeV)= 0\n",
      "=======================================================================\n",
      "======                 Electromagnetic Physics Parameters      ========\n",
      "=======================================================================\n",
      "LPM effect enabled                                 1\n",
      "Enable creation and use of sampling tables         0\n",
      "Apply cuts on all EM processes                     0\n",
      "Use general process                                0\n",
      "Enable linear polarisation for gamma               0\n",
      "Enable sampling of quantum entanglement            0\n",
      "X-section factor for integral approach             0.8\n",
      "Min kinetic energy for tables                      100 eV \n",
      "Max kinetic energy for tables                      100 TeV\n",
      "Number of bins per decade of a table               7\n",
      "Verbose level                                      1\n",
      "Verbose level for worker thread                    0\n",
      "Bremsstrahlung energy threshold above which \n",
      "  primary e+- is added to the list of secondary    100 TeV\n",
      "Bremsstrahlung energy threshold above which primary\n",
      "  muon/hadron is added to the list of secondary    100 TeV\n",
      "Lowest triplet kinetic energy                      1 MeV\n",
      "Enable sampling of gamma linear polarisation       0\n",
      "5D gamma conversion model type                     0\n",
      "5D gamma conversion model on isolated ion          0\n",
      "Livermore data directory                           livermore\n",
      "=======================================================================\n",
      "======                 Ionisation Parameters                   ========\n",
      "=======================================================================\n",
      "Step function for e+-                              (0.2, 1 mm)\n",
      "Step function for muons/hadrons                    (0.2, 0.1 mm)\n",
      "Step function for light ions                       (0.2, 0.1 mm)\n",
      "Step function for general ions                     (0.2, 0.1 mm)\n",
      "Lowest e+e- kinetic energy                         1 keV\n",
      "Lowest muon/hadron kinetic energy                  1 keV\n",
      "Fluctuations of dE/dx are enabled                  1\n",
      "Use ICRU90 data                                    0\n",
      "Use built-in Birks satuaration                     0\n",
      "Build CSDA range enabled                           0\n",
      "Use cut as a final range enabled                   0\n",
      "Enable angular generator interface                 0\n",
      "Max kinetic energy for CSDA tables                 1 GeV\n",
      "Max kinetic energy for NIEL computation            0 eV \n",
      "Linear loss limit                                  0.01\n",
      "Read data from file for e+e- pair production by mu 0\n",
      "=======================================================================\n",
      "======                 Multiple Scattering Parameters          ========\n",
      "=======================================================================\n",
      "Type of msc step limit algorithm for e+-           1\n",
      "Type of msc step limit algorithm for muons/hadrons 0\n",
      "Msc lateral displacement for e+- enabled           1\n",
      "Msc lateral displacement for muons and hadrons     0\n",
      "Urban msc model lateral displacement alg96         1\n",
      "Range factor for msc step limit for e+-            0.04\n",
      "Range factor for msc step limit for muons/hadrons  0.2\n",
      "Geometry factor for msc step limitation of e+-     2.5\n",
      "Safety factor for msc step limit for e+-           0.6\n",
      "Skin parameter for msc step limitation of e+-      1\n",
      "Lambda limit for msc step limit for e+-            1 mm\n",
      "Use Mott correction for e- scattering              0\n",
      "Factor used for dynamic computation of angular \n",
      "  limit between single and multiple scattering     1\n",
      "Fixed angular limit between single \n",
      "  and multiple scattering                          3.1416 rad\n",
      "Upper energy limit for e+- multiple scattering     100 MeV\n",
      "Type of electron single scattering model           0\n",
      "Type of nuclear form-factor                        1\n",
      "Screening factor                                   1\n",
      "=======================================================================\n",
      "\n",
      "phot:  for gamma SubType=12 BuildTable=0\n",
      "      LambdaPrime table from 200 keV to 100 TeV in 61 bins \n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      " LivermorePhElectric : Emin=    0 eV  Emax=  100 TeV  SauterGavrila Fluo\n",
      "\n",
      "compt:  for gamma SubType=13 BuildTable=1\n",
      "      Lambda table from 100 eV  to 1 MeV, 7 bins/decade, spline: 1\n",
      "      LambdaPrime table from 1 MeV to 100 TeV in 56 bins \n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "       Klein-Nishina : Emin=    0 eV  Emax=  100 TeV\n",
      "\n",
      "conv:  for gamma SubType=14 BuildTable=1\n",
      "      Lambda table from 1.022 MeV to 100 TeV, 18 bins/decade, spline: 1\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "     BetheHeitlerLPM : Emin=    0 eV  Emax=  100 TeV  ModifiedTsai\n",
      "\n",
      "Rayl:  for gamma SubType=11 BuildTable=1\n",
      "      Lambda table from 100 eV  to 100 keV, 7 bins/decade, spline: 0\n",
      "      LambdaPrime table from 100 keV to 100 TeV in 63 bins \n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "   LivermoreRayleigh : Emin=    0 eV  Emax=  100 TeV  CullenGenerator\n",
      "\n",
      "msc:  for e-  SubType= 10\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "            UrbanMsc : Emin=    0 eV  Emax=  100 MeV Nbins=42 100 eV  - 100 MeV\n",
      "          StepLim=UseSafety Rfact=0.04 Gfact=2.5 Sfact=0.6 DispFlag:1 Skin=1 Llim=1 mm\n",
      "        WentzelVIUni : Emin=  100 MeV Emax=  100 TeV Nbins=42 100 MeV - 100 TeV\n",
      "          StepLim=UseSafety Rfact=0.04 Gfact=2.5 Sfact=0.6 DispFlag:1 Skin=1 Llim=1 mm\n",
      "\n",
      "eIoni:  for e-  XStype:1  SubType=2\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 1\n",
      "      StepFunction=(0.2, 1 mm), integ: 1, fluct: 1, linLossLim= 0.01\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "        MollerBhabha : Emin=    0 eV  Emax=  100 TeV\n",
      "\n",
      "eBrem:  for e-  XStype:4  SubType=3\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 1\n",
      "      LPM flag: 1 for E > 1 GeV,  VertexHighEnergyTh(GeV)= 100000\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "             eBremSB : Emin=    0 eV  Emax=    1 GeV  ModifiedTsai\n",
      "            eBremLPM : Emin=    1 GeV Emax=  100 TeV  ModifiedTsai\n",
      "\n",
      "CoulombScat:  for e- XStype:3 SubType=1 BuildTable=1\n",
      "      Lambda table from 100 MeV to 100 TeV, 7 bins/decade, spline: 0\n",
      "      ThetaMin(p) < Theta(degree) < 180; pLimit(GeV^1)= 0.139531\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "  eCoulombScattering : Emin=  100 MeV Emax=  100 TeV\n",
      "\n",
      "msc:  for e+  SubType= 10\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "            UrbanMsc : Emin=    0 eV  Emax=  100 MeV Nbins=42 100 eV  - 100 MeV\n",
      "          StepLim=UseSafety Rfact=0.04 Gfact=2.5 Sfact=0.6 DispFlag:1 Skin=1 Llim=1 mm\n",
      "        WentzelVIUni : Emin=  100 MeV Emax=  100 TeV Nbins=42 100 MeV - 100 TeV\n",
      "          StepLim=UseSafety Rfact=0.04 Gfact=2.5 Sfact=0.6 DispFlag:1 Skin=1 Llim=1 mm\n",
      "\n",
      "eIoni:  for e+  XStype:1  SubType=2\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 1\n",
      "      StepFunction=(0.2, 1 mm), integ: 1, fluct: 1, linLossLim= 0.01\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "        MollerBhabha : Emin=    0 eV  Emax=  100 TeV\n",
      "\n",
      "eBrem:  for e+  XStype:4  SubType=3\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 1\n",
      "      LPM flag: 1 for E > 1 GeV,  VertexHighEnergyTh(GeV)= 100000\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "             eBremSB : Emin=    0 eV  Emax=    1 GeV  ModifiedTsai\n",
      "            eBremLPM : Emin=    1 GeV Emax=  100 TeV  ModifiedTsai\n",
      "\n",
      "annihil:  for e+ XStype:2 SubType=5 BuildTable=0\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "            eplus2gg : Emin=    0 eV  Emax=  100 TeV\n",
      "\n",
      "CoulombScat:  for e+ XStype:3 SubType=1 BuildTable=1\n",
      "      Lambda table from 100 MeV to 100 TeV, 7 bins/decade, spline: 0\n",
      "      ThetaMin(p) < Theta(degree) < 180; pLimit(GeV^1)= 0.139531\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "  eCoulombScattering : Emin=  100 MeV Emax=  100 TeV\n",
      "\n",
      "msc:  for proton  SubType= 10\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "        WentzelVIUni : Emin=    0 eV  Emax=  100 TeV Nbins=84 100 eV  - 100 TeV\n",
      "          StepLim=Minimal Rfact=0.2 Gfact=2.5 Sfact=0.6 DispFlag:0 Skin=1 Llim=1 mm\n",
      "\n",
      "hIoni:  for proton  XStype:1  SubType=2\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 1\n",
      "      StepFunction=(0.2, 0.1 mm), integ: 1, fluct: 1, linLossLim= 0.01\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "               Bragg : Emin=    0 eV  Emax=    2 MeV\n",
      "          BetheBloch : Emin=    2 MeV Emax=  100 TeV\n",
      "\n",
      "hBrems:  for proton  XStype:1  SubType=3\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 0\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "               hBrem : Emin=    0 eV  Emax=  100 TeV  ModifiedMephi\n",
      "\n",
      "hPairProd:  for proton  XStype:1  SubType=4\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 0\n",
      "      Sampling table 17x1001; from 7.50618 GeV to 100 TeV \n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "           hPairProd : Emin=    0 eV  Emax=  100 TeV  ModifiedMephi\n",
      "\n",
      "CoulombScat:  for proton XStype:3 SubType=1 BuildTable=1\n",
      "      Lambda table from threshold  to 100 TeV, 7 bins/decade, spline: 0\n",
      "      ThetaMin(p) < Theta(degree) < 180; pLimit(GeV^1)= 0.139531\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "  eCoulombScattering : Emin=    0 eV  Emax=  100 TeV\n",
      "\n",
      "msc:  for GenericIon  SubType= 10\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "            UrbanMsc : Emin=    0 eV  Emax=  100 TeV\n",
      "          StepLim=Minimal Rfact=0.2 Gfact=2.5 Sfact=0.6 DispFlag:0 Skin=1 Llim=1 mm\n",
      "\n",
      "ionIoni:  for GenericIon  XStype:1  SubType=2\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 1\n",
      "      StepFunction=(0.2, 0.1 mm), integ: 1, fluct: 1, linLossLim= 0.02\n",
      "      Stopping Power data for 17 ion/material pairs\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "            BraggIon : Emin=    0 eV  Emax=    2 MeV\n",
      "          BetheBloch : Emin=    2 MeV Emax=  100 TeV\n",
      "\n",
      "msc:  for alpha  SubType= 10\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "            UrbanMsc : Emin=    0 eV  Emax=  100 TeV\n",
      "          StepLim=Minimal Rfact=0.2 Gfact=2.5 Sfact=0.6 DispFlag:0 Skin=1 Llim=1 mm\n",
      "\n",
      "ionIoni:  for alpha  XStype:1  SubType=2\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 1\n",
      "      StepFunction=(0.2, 0.1 mm), integ: 1, fluct: 1, linLossLim= 0.02\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "            BraggIon : Emin=    0 eV  Emax=7.9452 MeV\n",
      "          BetheBloch : Emin=7.9452 MeV Emax=  100 TeV\n",
      "\n",
      "msc:  for anti_proton  SubType= 10\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "        WentzelVIUni : Emin=    0 eV  Emax=  100 TeV Nbins=84 100 eV  - 100 TeV\n",
      "          StepLim=Minimal Rfact=0.2 Gfact=2.5 Sfact=0.6 DispFlag:0 Skin=1 Llim=1 mm\n",
      "\n",
      "hIoni:  for anti_proton  XStype:1  SubType=2\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 1\n",
      "      StepFunction=(0.2, 0.1 mm), integ: 1, fluct: 1, linLossLim= 0.01\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "            ICRU73QO : Emin=    0 eV  Emax=    2 MeV\n",
      "          BetheBloch : Emin=    2 MeV Emax=  100 TeV\n",
      "\n",
      "hBrems:  for anti_proton  XStype:1  SubType=3\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 0\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "               hBrem : Emin=    0 eV  Emax=  100 TeV  ModifiedMephi\n",
      "\n",
      "hPairProd:  for anti_proton  XStype:1  SubType=4\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 0\n",
      "      Sampling table 17x1001; from 7.50618 GeV to 100 TeV \n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "           hPairProd : Emin=    0 eV  Emax=  100 TeV  ModifiedMephi\n",
      "\n",
      "CoulombScat:  for anti_proton XStype:3 SubType=1 BuildTable=1\n",
      "      Lambda table from threshold  to 100 TeV, 7 bins/decade, spline: 0\n",
      "      ThetaMin(p) < Theta(degree) < 180; pLimit(GeV^1)= 0.139531\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "  eCoulombScattering : Emin=    0 eV  Emax=  100 TeV\n",
      "\n",
      "msc:  for kaon+  SubType= 10\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "        WentzelVIUni : Emin=    0 eV  Emax=  100 TeV Nbins=84 100 eV  - 100 TeV\n",
      "          StepLim=Minimal Rfact=0.2 Gfact=2.5 Sfact=0.6 DispFlag:0 Skin=1 Llim=1 mm\n",
      "\n",
      "hIoni:  for kaon+  XStype:1  SubType=2\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 1\n",
      "      StepFunction=(0.2, 0.1 mm), integ: 1, fluct: 1, linLossLim= 0.01\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "               Bragg : Emin=    0 eV  Emax=1.05231 MeV\n",
      "          BetheBloch : Emin=1.05231 MeV Emax=  100 TeV\n",
      "\n",
      "hBrems:  for kaon+  XStype:1  SubType=3\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 0\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "               hBrem : Emin=    0 eV  Emax=  100 TeV  ModifiedMephi\n",
      "\n",
      "hPairProd:  for kaon+  XStype:1  SubType=4\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 0\n",
      "      Sampling table 18x1001; from 3.94942 GeV to 100 TeV \n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "           hPairProd : Emin=    0 eV  Emax=  100 TeV  ModifiedMephi\n",
      "\n",
      "CoulombScat:  for kaon+ XStype:3 SubType=1 BuildTable=1\n",
      "      Lambda table from threshold  to 100 TeV, 7 bins/decade, spline: 0\n",
      "      ThetaMin(p) < Theta(degree) < 180; pLimit(GeV^1)= 0.139531\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "  eCoulombScattering : Emin=    0 eV  Emax=  100 TeV\n",
      "\n",
      "msc:  for kaon-  SubType= 10\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "        WentzelVIUni : Emin=    0 eV  Emax=  100 TeV Nbins=84 100 eV  - 100 TeV\n",
      "          StepLim=Minimal Rfact=0.2 Gfact=2.5 Sfact=0.6 DispFlag:0 Skin=1 Llim=1 mm\n",
      "\n",
      "hIoni:  for kaon-  XStype:1  SubType=2\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 1\n",
      "      StepFunction=(0.2, 0.1 mm), integ: 1, fluct: 1, linLossLim= 0.01\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "            ICRU73QO : Emin=    0 eV  Emax=1.05231 MeV\n",
      "          BetheBloch : Emin=1.05231 MeV Emax=  100 TeV\n",
      "\n",
      "hBrems:  for kaon-  XStype:1  SubType=3\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 0\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "               hBrem : Emin=    0 eV  Emax=  100 TeV  ModifiedMephi\n",
      "\n",
      "hPairProd:  for kaon-  XStype:1  SubType=4\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 0\n",
      "      Sampling table 18x1001; from 3.94942 GeV to 100 TeV \n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "           hPairProd : Emin=    0 eV  Emax=  100 TeV  ModifiedMephi\n",
      "\n",
      "CoulombScat:  for kaon- XStype:3 SubType=1 BuildTable=1\n",
      "      Used Lambda table of kaon+\n",
      "      ThetaMin(p) < Theta(degree) < 180; pLimit(GeV^1)= 0.139531\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "  eCoulombScattering : Emin=    0 eV  Emax=  100 TeV\n",
      "\n",
      "msc:  for mu+  SubType= 10\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "        WentzelVIUni : Emin=    0 eV  Emax=  100 TeV Nbins=84 100 eV  - 100 TeV\n",
      "          StepLim=Minimal Rfact=0.2 Gfact=2.5 Sfact=0.6 DispFlag:0 Skin=1 Llim=1 mm\n",
      "\n",
      "muIoni:  for mu+  XStype:1  SubType=2\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 1\n",
      "      StepFunction=(0.2, 0.1 mm), integ: 1, fluct: 1, linLossLim= 0.01\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "               Bragg : Emin=    0 eV  Emax=  200 keV\n",
      "          BetheBloch : Emin=  200 keV Emax=    1 GeV\n",
      "        MuBetheBloch : Emin=    1 GeV Emax=  100 TeV\n",
      "\n",
      "muBrems:  for mu+  XStype:1  SubType=3\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 0\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "              MuBrem : Emin=    0 eV  Emax=  100 TeV  ModifiedMephi\n",
      "\n",
      "muPairProd:  for mu+  XStype:1  SubType=4\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 0\n",
      "      Sampling table 21x1001; from 1 GeV to 100 TeV \n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "          muPairProd : Emin=    0 eV  Emax=  100 TeV  ModifiedMephi\n",
      "\n",
      "CoulombScat:  for mu+ XStype:3 SubType=1 BuildTable=1\n",
      "      Lambda table from threshold  to 100 TeV, 7 bins/decade, spline: 0\n",
      "      ThetaMin(p) < Theta(degree) < 180; pLimit(GeV^1)= 0.139531\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "  eCoulombScattering : Emin=    0 eV  Emax=  100 TeV\n",
      "\n",
      "msc:  for mu-  SubType= 10\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "        WentzelVIUni : Emin=    0 eV  Emax=  100 TeV Nbins=84 100 eV  - 100 TeV\n",
      "          StepLim=Minimal Rfact=0.2 Gfact=2.5 Sfact=0.6 DispFlag:0 Skin=1 Llim=1 mm\n",
      "\n",
      "muIoni:  for mu-  XStype:1  SubType=2\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 1\n",
      "      StepFunction=(0.2, 0.1 mm), integ: 1, fluct: 1, linLossLim= 0.01\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "            ICRU73QO : Emin=    0 eV  Emax=  200 keV\n",
      "          BetheBloch : Emin=  200 keV Emax=    1 GeV\n",
      "        MuBetheBloch : Emin=    1 GeV Emax=  100 TeV\n",
      "\n",
      "muBrems:  for mu-  XStype:1  SubType=3\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 0\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "              MuBrem : Emin=    0 eV  Emax=  100 TeV  ModifiedMephi\n",
      "\n",
      "muPairProd:  for mu-  XStype:1  SubType=4\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 0\n",
      "      Sampling table 21x1001; from 1 GeV to 100 TeV \n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "          muPairProd : Emin=    0 eV  Emax=  100 TeV  ModifiedMephi\n",
      "\n",
      "CoulombScat:  for mu- XStype:3 SubType=1 BuildTable=1\n",
      "      Used Lambda table of mu+\n",
      "      ThetaMin(p) < Theta(degree) < 180; pLimit(GeV^1)= 0.139531\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "  eCoulombScattering : Emin=    0 eV  Emax=  100 TeV\n",
      "\n",
      "msc:  for pi+  SubType= 10\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "        WentzelVIUni : Emin=    0 eV  Emax=  100 TeV Nbins=84 100 eV  - 100 TeV\n",
      "          StepLim=Minimal Rfact=0.2 Gfact=2.5 Sfact=0.6 DispFlag:0 Skin=1 Llim=1 mm\n",
      "\n",
      "hIoni:  for pi+  XStype:1  SubType=2\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 1\n",
      "      StepFunction=(0.2, 0.1 mm), integ: 1, fluct: 1, linLossLim= 0.01\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "               Bragg : Emin=    0 eV  Emax=297.505 keV\n",
      "          BetheBloch : Emin=297.505 keV Emax=  100 TeV\n",
      "\n",
      "hBrems:  for pi+  XStype:1  SubType=3\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 0\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "               hBrem : Emin=    0 eV  Emax=  100 TeV  ModifiedMephi\n",
      "\n",
      "hPairProd:  for pi+  XStype:1  SubType=4\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 0\n",
      "      Sampling table 20x1001; from 1.11656 GeV to 100 TeV \n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "           hPairProd : Emin=    0 eV  Emax=  100 TeV  ModifiedMephi\n",
      "\n",
      "CoulombScat:  for pi+ XStype:3 SubType=1 BuildTable=1\n",
      "      Lambda table from threshold  to 100 TeV, 7 bins/decade, spline: 0\n",
      "      ThetaMin(p) < Theta(degree) < 180; pLimit(GeV^1)= 0.139531\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "  eCoulombScattering : Emin=    0 eV  Emax=  100 TeV\n",
      "\n",
      "msc:  for pi-  SubType= 10\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "        WentzelVIUni : Emin=    0 eV  Emax=  100 TeV Nbins=84 100 eV  - 100 TeV\n",
      "          StepLim=Minimal Rfact=0.2 Gfact=2.5 Sfact=0.6 DispFlag:0 Skin=1 Llim=1 mm\n",
      "\n",
      "hIoni:  for pi-  XStype:1  SubType=2\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 1\n",
      "      StepFunction=(0.2, 0.1 mm), integ: 1, fluct: 1, linLossLim= 0.01\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "            ICRU73QO : Emin=    0 eV  Emax=297.505 keV\n",
      "          BetheBloch : Emin=297.505 keV Emax=  100 TeV\n",
      "\n",
      "hBrems:  for pi-  XStype:1  SubType=3\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 0\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "               hBrem : Emin=    0 eV  Emax=  100 TeV  ModifiedMephi\n",
      "\n",
      "hPairProd:  for pi-  XStype:1  SubType=4\n",
      "      dE/dx and range tables from 100 eV  to 100 TeV in 84 bins\n",
      "      Lambda tables from threshold to 100 TeV, 7 bins/decade, spline: 0\n",
      "      Sampling table 20x1001; from 1.11656 GeV to 100 TeV \n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "           hPairProd : Emin=    0 eV  Emax=  100 TeV  ModifiedMephi\n",
      "\n",
      "CoulombScat:  for pi- XStype:3 SubType=1 BuildTable=1\n",
      "      Used Lambda table of pi+\n",
      "      ThetaMin(p) < Theta(degree) < 180; pLimit(GeV^1)= 0.139531\n",
      "      ===== EM models for the G4Region  DefaultRegionForTheWorld ======\n",
      "  eCoulombScattering : Emin=    0 eV  Emax=  100 TeV\n",
      "\n",
      "====================================================================\n",
      "                  HADRONIC PROCESSES SUMMARY (verbose level 1)\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for neutron\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:             hElasticCHIPS: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:        G4NeutronElasticXS: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: neutronInelastic\n",
      "        Model:                      FTFP: 3 GeV ---> 100 TeV\n",
      "        Model:            BertiniCascade: 0 eV  ---> 6 GeV\n",
      "     Cr_sctns:      G4NeutronInelasticXS: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: nCapture\n",
      "        Model:               nRadCapture: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:        G4NeutronCaptureXS: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: nKiller\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for B-\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:            Glauber-Gribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: B-Inelastic\n",
      "        Model:                      FTFP: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:            Glauber-Gribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for D-\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:            Glauber-Gribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: D-Inelastic\n",
      "        Model:                      FTFP: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:            Glauber-Gribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for GenericIon\n",
      "\n",
      "  Process: ionInelastic\n",
      "        Model:  Binary Light Ion Cascade: 0 eV /n ---> 6 GeV/n\n",
      "        Model:                      FTFP: 3 GeV/n ---> 100 TeV/n\n",
      "     Cr_sctns:  Glauber-Gribov Nucl-nucl: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for He3\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV /n ---> 100 TeV/n\n",
      "     Cr_sctns:  Glauber-Gribov Nucl-nucl: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "  Process: He3Inelastic\n",
      "        Model:  Binary Light Ion Cascade: 0 eV /n ---> 6 GeV/n\n",
      "        Model:                      FTFP: 3 GeV/n ---> 100 TeV/n\n",
      "     Cr_sctns:  Glauber-Gribov Nucl-nucl: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for alpha\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV /n ---> 100 TeV/n\n",
      "     Cr_sctns:  Glauber-Gribov Nucl-nucl: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "  Process: alphaInelastic\n",
      "        Model:  Binary Light Ion Cascade: 0 eV /n ---> 6 GeV/n\n",
      "        Model:                      FTFP: 3 GeV/n ---> 100 TeV/n\n",
      "     Cr_sctns:  Glauber-Gribov Nucl-nucl: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for anti_He3\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV /n ---> 100.1 MeV/n\n",
      "        Model:              AntiAElastic: 100 MeV/n ---> 100 TeV/n\n",
      "     Cr_sctns:              AntiAGlauber: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "  Process: anti_He3Inelastic\n",
      "        Model:                      FTFP: 0 eV /n ---> 100 TeV/n\n",
      "     Cr_sctns:              AntiAGlauber: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "  Process: hFritiofCaptureAtRest\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for anti_alpha\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV /n ---> 100.1 MeV/n\n",
      "        Model:              AntiAElastic: 100 MeV/n ---> 100 TeV/n\n",
      "     Cr_sctns:              AntiAGlauber: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "  Process: anti_alphaInelastic\n",
      "        Model:                      FTFP: 0 eV /n ---> 100 TeV/n\n",
      "     Cr_sctns:              AntiAGlauber: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "  Process: hFritiofCaptureAtRest\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for anti_deuteron\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV /n ---> 100.1 MeV/n\n",
      "        Model:              AntiAElastic: 100 MeV/n ---> 100 TeV/n\n",
      "     Cr_sctns:              AntiAGlauber: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "  Process: anti_deuteronInelastic\n",
      "        Model:                      FTFP: 0 eV /n ---> 100 TeV/n\n",
      "     Cr_sctns:              AntiAGlauber: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "  Process: hFritiofCaptureAtRest\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for anti_lambda\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:            Glauber-Gribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: anti_lambdaInelastic\n",
      "        Model:                      FTFP: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:            Glauber-Gribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: hFritiofCaptureAtRest\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for anti_neutron\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV  ---> 100.1 MeV\n",
      "        Model:              AntiAElastic: 100 MeV ---> 100 TeV\n",
      "     Cr_sctns:              AntiAGlauber: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "  Process: anti_neutronInelastic\n",
      "        Model:                      FTFP: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:              AntiAGlauber: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "  Process: hFritiofCaptureAtRest\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for anti_proton\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV  ---> 100.1 MeV\n",
      "        Model:              AntiAElastic: 100 MeV ---> 100 TeV\n",
      "     Cr_sctns:              AntiAGlauber: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "  Process: anti_protonInelastic\n",
      "        Model:                      FTFP: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:              AntiAGlauber: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "  Process: hFritiofCaptureAtRest\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for anti_triton\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV /n ---> 100.1 MeV/n\n",
      "        Model:              AntiAElastic: 100 MeV/n ---> 100 TeV/n\n",
      "     Cr_sctns:              AntiAGlauber: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "  Process: anti_tritonInelastic\n",
      "        Model:                      FTFP: 0 eV /n ---> 100 TeV/n\n",
      "     Cr_sctns:              AntiAGlauber: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "  Process: hFritiofCaptureAtRest\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for deuteron\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV /n ---> 100 TeV/n\n",
      "     Cr_sctns:  Glauber-Gribov Nucl-nucl: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "  Process: dInelastic\n",
      "        Model:  Binary Light Ion Cascade: 0 eV /n ---> 6 GeV/n\n",
      "        Model:                      FTFP: 3 GeV/n ---> 100 TeV/n\n",
      "     Cr_sctns:  Glauber-Gribov Nucl-nucl: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for e+\n",
      "\n",
      "  Process: positronNuclear\n",
      "        Model:   G4ElectroVDNuclearModel: 0 eV  ---> 1 PeV\n",
      "     Cr_sctns:          ElectroNuclearXS: 0 eV  ---> 100 TeV\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for e-\n",
      "\n",
      "  Process: electronNuclear\n",
      "        Model:   G4ElectroVDNuclearModel: 0 eV  ---> 1 PeV\n",
      "     Cr_sctns:          ElectroNuclearXS: 0 eV  ---> 100 TeV\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for gamma\n",
      "\n",
      "  Process: photonNuclear\n",
      "        Model:               GammaNPreco: 0 eV  ---> 200 MeV\n",
      "        Model:            BertiniCascade: 199 MeV ---> 6 GeV\n",
      "        Model:           TheoFSGenerator: 3 GeV ---> 100 TeV\n",
      "     Cr_sctns:            GammaNuclearXS: 0 eV  ---> 100 TeV\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for kaon+\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:            Glauber-Gribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: kaon+Inelastic\n",
      "        Model:                      FTFP: 3 GeV ---> 100 TeV\n",
      "        Model:            BertiniCascade: 0 eV  ---> 6 GeV\n",
      "     Cr_sctns:            Glauber-Gribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for kaon-\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:            Glauber-Gribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: kaon-Inelastic\n",
      "        Model:                      FTFP: 3 GeV ---> 100 TeV\n",
      "        Model:            BertiniCascade: 0 eV  ---> 6 GeV\n",
      "     Cr_sctns:            Glauber-Gribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: hBertiniCaptureAtRest\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for lambda\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:            Glauber-Gribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: lambdaInelastic\n",
      "        Model:                      FTFP: 3 GeV ---> 100 TeV\n",
      "        Model:            BertiniCascade: 0 eV  ---> 6 GeV\n",
      "     Cr_sctns:            Glauber-Gribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for mu+\n",
      "\n",
      "  Process: muonNuclear\n",
      "        Model:      G4MuonVDNuclearModel: 0 eV  ---> 1 PeV\n",
      "     Cr_sctns:     KokoulinMuonNuclearXS: 0 eV  ---> 100 TeV\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for mu-\n",
      "\n",
      "  Process: muonNuclear\n",
      "        Model:      G4MuonVDNuclearModel: 0 eV  ---> 1 PeV\n",
      "     Cr_sctns:     KokoulinMuonNuclearXS: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: muMinusCaptureAtRest\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for pi+\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:           hElasticGlauber: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:  BarashenkovGlauberGribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: pi+Inelastic\n",
      "        Model:                      FTFP: 3 GeV ---> 100 TeV\n",
      "        Model:            BertiniCascade: 0 eV  ---> 6 GeV\n",
      "     Cr_sctns:  BarashenkovGlauberGribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for pi-\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:           hElasticGlauber: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:  BarashenkovGlauberGribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: pi-Inelastic\n",
      "        Model:                      FTFP: 3 GeV ---> 100 TeV\n",
      "        Model:            BertiniCascade: 0 eV  ---> 6 GeV\n",
      "     Cr_sctns:  BarashenkovGlauberGribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: hBertiniCaptureAtRest\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for proton\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:             hElasticCHIPS: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:  BarashenkovGlauberGribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: protonInelastic\n",
      "        Model:                      FTFP: 3 GeV ---> 100 TeV\n",
      "        Model:            BertiniCascade: 0 eV  ---> 6 GeV\n",
      "     Cr_sctns:  BarashenkovGlauberGribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for sigma-\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV  ---> 100 TeV\n",
      "     Cr_sctns:            Glauber-Gribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: sigma-Inelastic\n",
      "        Model:                      FTFP: 3 GeV ---> 100 TeV\n",
      "        Model:            BertiniCascade: 0 eV  ---> 6 GeV\n",
      "     Cr_sctns:            Glauber-Gribov: 0 eV  ---> 100 TeV\n",
      "\n",
      "  Process: hBertiniCaptureAtRest\n",
      "\n",
      "---------------------------------------------------\n",
      "                           Hadronic Processes for triton\n",
      "\n",
      "  Process: hadElastic\n",
      "        Model:              hElasticLHEP: 0 eV /n ---> 100 TeV/n\n",
      "     Cr_sctns:  Glauber-Gribov Nucl-nucl: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "  Process: tInelastic\n",
      "        Model:  Binary Light Ion Cascade: 0 eV /n ---> 6 GeV/n\n",
      "        Model:                      FTFP: 3 GeV/n ---> 100 TeV/n\n",
      "     Cr_sctns:  Glauber-Gribov Nucl-nucl: 0 eV  ---> 25.6 PeV\n",
      "\n",
      "================================================================\n",
      "=======================================================================\n",
      "======       Pre-compound/De-excitation Physics Parameters     ========\n",
      "=======================================================================\n",
      "Type of pre-compound inverse x-section              3\n",
      "Pre-compound model active                           1\n",
      "Pre-compound excitation low energy (MeV)            0.1\n",
      "Pre-compound excitation high energy (MeV)           30\n",
      "Type of de-excitation inverse x-section             3\n",
      "Type of de-excitation factory                       Evaporation+GEM\n",
      "Number of de-excitation channels                    68\n",
      "Min excitation energy (keV)                         0.01\n",
      "Min energy per nucleon for multifragmentation (MeV) 2e+05\n",
      "Limit excitation energy for Fermi BreakUp (MeV)     20\n",
      "Level density (1/MeV)                               0.075\n",
      "Use simple level density model                      1\n",
      "Use discrete excitation energy of the residual      0\n",
      "Time limit for long lived isomeres (ns)             1\n",
      "Isomer production flag                              1\n",
      "Internal e- conversion flag                         1\n",
      "Store e- internal conversion data                   0\n",
      "Correlated gamma emission flag                      0\n",
      "Max 2J for sampling of angular correlations         10\n",
      "=======================================================================\n"
     ]
    }
   ],
   "source": [
    "# initialization\n",
    "app_builder = AppBuilder()\n",
    "gRunManager.SetUserInitialization(app_builder)\n",
    "\n",
    "gRunManager.Initialize()\n",
    "gRunManager.BeamOn(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "053bd041-f0bf-4ef4-804f-86c2e7564ce0",
   "metadata": {},
   "source": [
    "-----\n",
    "## EM Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3261d813-4aad-4dd7-9350-9b5aa8fff40b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "be65862c-4743-4227-bb30-73f5e3cc8706",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_photon_xsec(xsec_data, title_=\"\") :\n",
    "    xlist_tot = []\n",
    "    xlist_comp = []\n",
    "    xlist_pe = []\n",
    "    xlist_conv = []\n",
    "    for x in xsec_data :\n",
    "        xlist_tot.append((x[0]/MeV, x[1][\"tot\"]/(cm2/g)))\n",
    "        xlist_comp.append((x[0]/MeV, x[1][\"compt\"]/(cm2/g)))\n",
    "        xlist_pe.append((x[0]/MeV, x[1][\"phot\"]/(cm2/g)))\n",
    "        xlist_conv.append((x[0]/MeV, x[1][\"conv\"]/(cm2/g)))\n",
    "        \n",
    "    nbin = len(xlist_tot)\n",
    "    ebin = np.zeros(nbin)\n",
    "    tot_vec = np.zeros(nbin)\n",
    "    comp_vec = np.zeros(nbin)\n",
    "    pe_vec = np.zeros(nbin)\n",
    "    conv_vec = np.zeros(nbin)\n",
    "    \n",
    "    for i in range(0, nbin) :\n",
    "        ebin[i] = xlist_tot[i][0]\n",
    "        tot_vec[i] = xlist_tot[i][1]\n",
    "        comp_vec[i] = xlist_comp[i][1]\n",
    "        pe_vec[i] = xlist_pe[i][1]\n",
    "        conv_vec[i] = xlist_conv[i][1]\n",
    "    \n",
    "    xlabel = \"kE (MeV)\"\n",
    "    ylabel =\"Sigma(cm2/g)\"\n",
    "    \n",
    "    # make plots\n",
    "    plt.rcdefaults()\n",
    "    fig = plt.figure(figsize=(12,8))\n",
    "    plt.axis('off')\n",
    "    plt.title(title_)\n",
    "    \n",
    "    #\n",
    "    plot = fig.add_subplot(2,2,1)\n",
    "    plt.plot(ebin, tot_vec, '-', markersize=8, color=\"tab:red\", label='Total')\n",
    "    plt.xscale(\"log\")\n",
    "    plt.yscale(\"log\")\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.grid(which=\"both\", color='#cccccc')\n",
    "    plt.legend()\n",
    "    #\n",
    "    plot = fig.add_subplot(2,2,2)\n",
    "    plt.plot(ebin, comp_vec, '-', markersize=8, label='Compton')\n",
    "    plt.xscale(\"log\")\n",
    "    plt.yscale(\"log\")\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.grid(which=\"both\", color='#cccccc')\n",
    "    plt.legend()\n",
    "    #\n",
    "    plot = fig.add_subplot(2,2,3)\n",
    "    plt.xscale(\"log\")\n",
    "    plt.yscale(\"log\")\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.grid(which=\"both\", color='#cccccc')\n",
    "    plt.plot(ebin, pe_vec, '-', markersize=8, label='P.E.')\n",
    "    plt.legend()\n",
    "    #\n",
    "    plot = fig.add_subplot(2,2,4)\n",
    "    plt.plot(ebin, conv_vec, '-', markersize=8, label=\"Conv.\")\n",
    "    plt.xscale(\"log\")\n",
    "    plt.yscale(\"log\")\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.grid(which=\"both\", color='#cccccc')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "da646167-0773-4c7d-8ee7-8c6713e71243",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_dedx(data, title_=\"\") :\n",
    "    xlist_tot = []\n",
    "    xlist_ioni = []\n",
    "    xlist_brems = []\n",
    "\n",
    "    for x in data :\n",
    "        xlist_tot.append((x[0], x[1][\"tot\"]/(MeV*cm2/g)))\n",
    "        xlist_ioni.append((x[0], x[1][\"ioni\"]/(MeV*cm2/g)))\n",
    "        xlist_brems.append((x[0], x[1][\"brems\"]/(MeV*cm2/g)))\n",
    "\n",
    "    nbin = len(xlist_tot)\n",
    "    ebin = np.zeros(nbin)\n",
    "    tot_vec = np.zeros(nbin)\n",
    "    ioni_vec = np.zeros(nbin)\n",
    "    brems_vec = np.zeros(nbin)\n",
    "    \n",
    "    for i in range(0, nbin) :\n",
    "        ebin[i] = xlist_tot[i][0]\n",
    "        tot_vec[i] = xlist_tot[i][1]\n",
    "        ioni_vec[i] = xlist_ioni[i][1]\n",
    "        brems_vec[i] = xlist_brems[i][1]\n",
    "    \n",
    "    title = \"Stopping Power (\" + title_ + \")\"\n",
    "    xlabel = \"kE (MeV)\"\n",
    "    ylabel =\"dE/dX (MeV*cm2/g)\"\n",
    "       \n",
    "    # make plots\n",
    "    plt.rcdefaults()\n",
    "    fig = plt.figure(figsize=(8,5))\n",
    "    plt.title(title)\n",
    "    plt.xscale(\"log\")\n",
    "    plt.yscale(\"log\")\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.grid(which=\"both\", color='#cccccc')\n",
    "    plt.plot(ebin, tot_vec, '-', markersize=8, color=\"tab:red\", label='Total')\n",
    "    plt.plot(ebin, ioni_vec, '-', markersize=8, color=\"tab:blue\", label='Ionization')\n",
    "    plt.plot(ebin, brems_vec, '-', markersize=8, color=\"tab:orange\", label='Brems.') \n",
    "    plt.legend()\n",
    "    #\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b3baa2b-8b30-4195-b906-195201a2db92",
   "metadata": {},
   "source": [
    "----\n",
    "### Phton Processes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "242a09ab-c5d5-4361-ba79-3be15e230fb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function CalculatePhotonCrossSection in module geant4.utils.emcalculator:\n",
      "\n",
      "CalculatePhotonCrossSection(material_name=None, Elist=None, verbose=0)\n",
      "    Calculate photon cross section for a given material and\n",
      "    a list of energy, returing a list of cross sections for\n",
      "    the components of \"Copmton scattering\", \"rayleigh scattering\",\n",
      "    \"photoelectric effect\", \"pair creation\" and total one.\n",
      "    \n",
      "    Arguments:\n",
      "      material_name:  material name (String)\n",
      "      Elist:          energy list [None]\n",
      "      verbose:        verbose level [0]\n",
      "    \n",
      "    Keys of index:\n",
      "      \"compt\":     Compton Scattering\n",
      "      \"rayleigh\":  Rayleigh Scattering\n",
      "      \"phot\" :     photoelectric effect\n",
      "      \"conv\" :     pair Creation\n",
      "      \"tot\"  :     total\n",
      "    \n",
      "    Example:\n",
      "      xsec_list = CalculatePhotonCrossSection(...)\n",
      "      value = xsec_list[energy_index][\"compt\"]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from geant4.utils import emcalculator\n",
    "\n",
    "help(emcalculator.CalculatePhotonCrossSection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0f57a06d-d783-4048-9836-b663037a2636",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup\n",
    "material_name = \"G4_Au\"\n",
    "box.material = gNistManager.FindOrBuildMaterial(material_name)\n",
    "gRunManager.GeometryHasBeenModified()\n",
    "gRunManager.BeamOn(0)\n",
    "\n",
    "# Calculate Phton cross sections\n",
    "# energy list\n",
    "elist = []\n",
    "for n in range(-3, 4):\n",
    "    for i in range(10,99):\n",
    "        elist.append(i/10.*10.**n *MeV)\n",
    "\n",
    "# calculate cross sections\n",
    "xsection_list = emcalculator.CalculatePhotonCrossSection(material_name, Elist=elist, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "785e6c0e-2287-4bb0-8855-3a3bbe22a329",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_photon_xsec(xsection_list, title_=material_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac718de5-725d-4d7b-ba3d-b1eb504425af",
   "metadata": {},
   "source": [
    "----\n",
    "### Electron Processes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "da7f6607-ce60-418e-8848-4e20daa48955",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function CalculateDEDX in module geant4.utils.emcalculator:\n",
      "\n",
      "CalculateDEDX(particle_name=None, material_name=None, Elist=None, verbose=0)\n",
      "    Calculate stopping powers for a give particle, material and\n",
      "    a list of energy, returing stopping power for the components of\n",
      "    \"Ionization\", \"Radiation\" and total one.\n",
      "    \n",
      "    Arguments:\n",
      "      particle_name:   particle name\n",
      "      materia_name:    material name\n",
      "      Elist:           list of energy\n",
      "      verbose:         verbose level [0]\n",
      "    \n",
      "    Keys of index:\n",
      "      \"ioni\":   ionization\n",
      "      \"brems\":  Bremsstrahlung\n",
      "      \"tot\":    total\n",
      "    \n",
      "    Example:\n",
      "      dedx_list = CalculateDEDX(...)\n",
      "      value = dedx_list[energy_index][\"ioni\"]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(emcalculator.CalculateDEDX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "fba83a57-3f96-41e0-a708-cdee841c6c55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup\n",
    "material_name = \"G4_WATER\"\n",
    "box.material = gNistManager.FindOrBuildMaterial(material_name)\n",
    "gRunManager.GeometryHasBeenModified()\n",
    "gRunManager.BeamOn(0)\n",
    "\n",
    "# energy\n",
    "elist= []\n",
    "for n in range(-3, 3) :\n",
    "  for i in range(10,99) :\n",
    "    elist.append(i/10.*10.**n *MeV)\n",
    "\n",
    "# calculate stopping power\n",
    "pname = \"e-\"\n",
    "dedx_list_e = emcalculator.CalculateDEDX(pname, material_name, Elist=elist, verbose=0)\n",
    "#\n",
    "pname = \"proton\"\n",
    "dedx_list_p = emcalculator.CalculateDEDX(pname, material_name, Elist=elist, verbose=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "871fe7b6-7ad1-4fe7-9888-05c85a404893",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"e-,\" + material_name\n",
    "plot_dedx(dedx_list_e, title_=title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6d35f7e9-2576-4c31-9ff2-7b61dbba77b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"p,\" + material_name\n",
    "plot_dedx(dedx_list_p, title_=title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b2141e1-9f4c-4946-bd9c-20a8900fdcd9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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