{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Effective Volume ##\n",
    "\n",
    "Here, we compute the effective volume and basically re-derive the w2 weight that\n",
    "enters in the computation of the effective area.\n",
    "\n",
    "The Monte Carlo file contains reconstructed events, which are a small subset of all the neutrino interactions that were originally generated by the event generator. These a large number ($N_{gen}$) of events was generated in a (huge) volume ($V_{gen}$), where the interacting neutrinos have a power-law spectrum with index $\\gamma$. We need to get all these numbers from teh event file's header.\n",
    "\n",
    "The effective volume is than given by\n",
    "$$\n",
    "V_{eff}(E) = V_{gen} \\frac{N_{det}(E)}{N_{gen}(E)},\n",
    "$$\n",
    "\n",
    "The tricky part is to know $N_{gen}(E)$: the number of events in a given energy bin that was originally generated by genhen. We can compute this number from the information in the header using the nnu function defined below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ttt 0xa4641a0\r\n",
      " EventFile io / wall time = 0.00576591 / 100.515 (0.00573636 % spent on io.)\r\n"
     ]
    }
   ],
   "source": [
    "ROOT.gStyle.SetOptStat(0)\n",
    "\n",
    "f = EventFile(\"../evtfiles/numu_jgandalf.root\")\n",
    "Ngen = float( f.header.get_field(\"genvol\",\"numberOfEvents\") )\n",
    "Vgen = float( f.header.get_field(\"genvol\",\"volume\") )\n",
    "gamma= float( f.header.get_line(\"spectrum\"))\n",
    "Emin, Emax, theta_min, theta_max = (float(x) for x in f.header.get_line(\"cut_nu\").split() )\n",
    "\n",
    "def nnu(E1, E2):\n",
    "    \"How many neutrinos where generated between E1 and E2?\"\n",
    "    return Ngen *( E2**(1+gamma) - E1**(1+gamma) ) / ( Emax**(1+gamma) - Emin**(1+gamma) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TFile::Append:0: RuntimeWarning: Replacing existing TH1: hvol (Potential memory leak).\n",
      "TFile::Append:0: RuntimeWarning: Replacing existing TH1: hxsec (Potential memory leak).\n",
      "TCanvas::Constructor:0: RuntimeWarning: Deleting canvas with same name: c\n"
     ]
    },
    {
     "data": {
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lw3/STnfvVhwNgCZp7qLXh0oFdMXUg31uotKmUr6zJyev1kUvP5uesAmEbWqcKpQqdsmp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\nAACAB4QqAAAAD/4fin6C0j0i8HQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hvol  = TH1D(\"hvol\",\"effective volume (m^{3});log(E/GeV);V(m^{3})\", 24, 1,7 )\n",
    "hxsec = TH1D(\"hxsec\",\"cross section;log(E/GeV);xsec (m^{2})\", 24, 1,7 )\n",
    "\n",
    "f.roottree().Draw(\"log10(mc_trks[0].E)>>hvol\" )\n",
    "f.roottree().Draw(\"log10(mc_trks[0].E)>>hxsec\",\"w2list[2]\") # [2] is the cross-section(Enu)\n",
    "hxsec.Divide( hvol )\n",
    "\n",
    "for b in range(1, hvol.GetNbinsX()+1):\n",
    "    \n",
    "    \"how many neutrinos generated in this bin?\"\n",
    "    e1 = 10**hvol.GetXaxis().GetBinLowEdge(b)\n",
    "    e2 = 10**hvol.GetXaxis().GetBinUpEdge(b)   \n",
    "    hvol.SetBinContent( b, hvol.GetBinContent(b) * Vgen / nnu( e1,e2) )\n",
    "\n",
    "c = ROOT.TCanvas('c','c',800,400)\n",
    "c.Divide(2,1)\n",
    "c.cd(1); ROOT.gPad.SetLogy(); hvol.Draw(\"Lhist\")\n",
    "c.cd(2); ROOT.gPad.SetLogy(); hxsec.Draw(\"Lhist\")\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TFile::Append:0: RuntimeWarning: Replacing existing TH1: haeff2 (Potential memory leak).\n"
     ]
    },
    {
     "data": {
      "image/png": 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UmLgG0qkTZ9632a1URSpd038h0fdYAIyNJFLWWu/9Dpf8i4hUmCZr72yXUVm7/L/1hrxb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\nywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c.cd(1)\n",
    "hmass = hvol.Clone(\"hmass\")\n",
    "hmass.SetTitle(\"effective number of nuclei\")\n",
    "hmass.SetYTitle(\"\")\n",
    "hmass.Scale(6.022e23 * 1e6 ) # the 10^6 is for gr -> m3 of water\n",
    "hmass.Draw()\n",
    "\n",
    "c.cd(2)\n",
    "haeff = hmass.Clone(\"haeff\")\n",
    "haeff.SetTitle(\"effective Area\")\n",
    "haeff.SetYTitle(\"area (m^{2})\")\n",
    "haeff.Multiply( hxsec )\n",
    "haeff.Draw(\"Lhist\")\n",
    "\n",
    "# -- draw the effective area directly from the w2 for comparison --\n",
    "haeff2  = TH1D(\"haeff2\",\"effective area\", 24, 1,7 )\n",
    "f.roottree().Draw( \"log10(mc_trks[0].E)>>+haeff2\",\"w[1]/mc_trks[0].E\" ,\"goff\")\n",
    "haeff2.Scale ( 1 / ( ngen * log(10) * haeff.GetBinWidth(1) * 3600.0 * 24 * 365 * 4*pi ))\n",
    "haeff2.SetLineColor(2)\n",
    "haeff2.Draw(\"Lhist same\")\n",
    "\n",
    "c.Draw()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare this to the effective area computed directly from the w2 weight: (but note the Earth-survival probability is at this stage not yet included) in the effective volume.\n",
    "\n",
    "Okay, so the effective volume is basically just the effective area, devided by the cross section and\n",
    "(depending on how you want to define it) the Earth absorption propbability. So we can plot it in one go like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TFile::Append:0: RuntimeWarning: Replacing existing TH1: hv (Potential memory leak).\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hv  = TH1D(\"hv\",\"effective volume\", 24, 1,7 )\n",
    "f.roottree().Draw( \"log10(mc_trks[0].E)>>hv\",\"w[1]/mc_trks[0].E/w2list[2]/w2list[4]\")\n",
    "hv.Scale( 1 / ( 6.022e23 * 1e6 * ngen * log(10) * hv.GetBinWidth(1) * 3600.0*24*365 * 4*pi ) )\n",
    "\n",
    "hv.Draw()\n",
    "hvol.SetLineColor(2)\n",
    "hvol.Draw(\"Lhist same\")\n",
    "ROOT.gPad.Draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}