GiBUU_PairNtuple.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.22/06\n"
     ]
    }
   ],
   "source": [
    "import ROOT\n",
    "from ROOT import TFile\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import copy\n",
    "import pandas as pd \n",
    "import time\n",
    "from os import listdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class particle:\n",
    "    def __init__(self, pid, fourvector, Q2, Nu):\n",
    "        #In the lab frame, but with the photon is aligned with z-direction, the photon has 4-momentum q= (0,0,sqrt(Nu2+Q2),Nu) (note q2 = -Q2)        \n",
    "        self.virtual_photon = ROOT.TLorentzVector()\n",
    "        self.virtual_photon.SetPxPyPzE(0,0,np.sqrt(Nu*Nu+Q2),Nu)\n",
    "        self.virtual_photon_CM = self.virtual_photon\n",
    "        self.proton = ROOT.TLorentzVector()\n",
    "        self.proton.SetPxPyPzE(0,0,0, 0.938)\n",
    "        self.proton_CM = self.proton\n",
    "        \n",
    "        self.W = (self.virtual_photon + self.proton).M()\n",
    "        incoming_e = ROOT.TLorentzVector()\n",
    "        incoming_e.SetPxPyPzE(0,0,5.0,5.0)####INCONSISTENT\n",
    "        part1 = self.virtual_photon.Vect().Cross(incoming_e.Vect()).Unit()\n",
    "        part2 = self.virtual_photon.Vect().Cross(fourvector.Vect()).Unit()\n",
    "        sign  = np.sign(part1.Dot(fourvector.Vect()))\n",
    "        self.PhiPQ = sign*np.arccos(part1.Dot(part2))\n",
    "        \n",
    "        photon_pz = np.sqrt(Nu*Nu+Q2) #direction is positive by definition\n",
    "        self.bcm = photon_pz/(Nu + 0.938)#photon-nucleon center-of-mass velocity \n",
    "        self.ycm = 0.5*np.log(( 1+self.bcm)/(1-self.bcm)) #photon-nucleon center-of-mass rapidity\n",
    "        self.virtual_photon_CM.Boost(0.0,0.0,-self.bcm)\n",
    "        self.proton_CM.Boost(0.0,0.0,-self.bcm)\n",
    "        \n",
    "        self.LorentzVector = fourvector #hadron four-vector\n",
    "        self.PhiLab = self.LorentzVector.Phi()\n",
    "        self.E = self.LorentzVector.E() #energy in lab frame\n",
    "        self.vector = self.LorentzVector.Vect()\n",
    "        self.Pt = self.vector.Perp(self.virtual_photon.Vect().Unit()) #pT with respect to photon direction\n",
    "        self.Pl  = self.vector.Dot(self.virtual_photon.Vect().Unit()) #pL with respect to photon direction (in lab frame)\n",
    "        self.y =  0.5*np.log( (self.E+self.Pl)/(self.E-self.Pl)) #rapidity in lab frame\n",
    "        self.mT = np.sqrt(self.LorentzVector.M2() + self.Pt*self.Pt)\n",
    "        self.mass = self.LorentzVector.M() \n",
    "        self.y_star = self.y - self.ycm\n",
    "        self.Pl_star = self.mT*np.sinh(self.y_star)\n",
    "        self.Pstar = np.sqrt(self.Pl_star*self.Pl_star + self.Pt*self.Pt)\n",
    "        self.eta = np.arctanh(self.Pl_star/self.Pstar)\n",
    "        #self.eta =  0.5*np.log( (self.Pstar+self.Pl_star)/(self.Pstar-self.Pl_star)) \n",
    "        \n",
    "        self.Xf = 2.0*self.Pl_star/self.W \n",
    "        self.pid = pid\n",
    "        self.Zh = self.E/Nu\n",
    "        self.ThetaPQ = np.arctan(self.Pt/self.Pl)\n",
    "        self.Xb = Q2/(2*0.938*Nu)\n",
    "\n",
    "        #'phi =%2.2f'%self.PhiLab,\n",
    "    def print_properties(self):\n",
    "        #print ('Hello, let me introduce myself, i am particle pid = ' , self.pid)\n",
    "        #print ('PID', self.pid, ' zh = %2.2f'%self.Zh,  'E = %2.2f'%self.E, 'phi =%2.2f'%self.PhiLab,'theta=%2.2f'%self.LorentzVector.Theta(),'pt %2.2f'%self.Pt)\n",
    "        print ('PID', self.pid, ' pT = %2.2f'%self.Pt,  'Pl= %2.2f'%self.Pl_star, 'Xf=%2.2f'%self.Xf, 'Zh=%2.2f'%self.Zh, 'phi =%2.2f'%self.PhiLab)\n",
    "\n",
    "        \n",
    "        #print ('%2.3f,'%self.LorentzVector.Px(),'%2.3f,'%self.LorentzVector.Py(),'%2.3f,'%self.LorentzVector.Pz(), '%2.23f,'%self.LorentzVector.E())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class mytupla:\n",
    "    def __init__(self):\n",
    "        self.tupla = {}\n",
    "        variables = ['dphi','dphi_lab','qt','drap','h1_z','h2_z','z_tot','h1_xf','h2_xf',\n",
    "                     'h1_rap', 'ycm','h2_rap','h1_pid','h2_pid','h1_cm_ph','h2_cm_ph',\n",
    "                      'h1_cm_th','h2_cm_th','pair_mass','pair_pt','mx_eh1h2',\n",
    "                      'Q2','x','nu','W','h1_ph','h1_th','h2_ph','h2_th',\n",
    "                       'pos_x', 'pos_y','pos_z','pos_x_asso', 'pos_y_asso','pos_z_asso']\n",
    "        \n",
    "        for var in variables:\n",
    "            self.tupla[var] = []\n",
    "        \n",
    "        trigger_variables = ['h1_pid','h1_xf','h1_z','h1_rap','ycm','h1_pt','Q2','x','nu','W','h1_cm_ph','h1_cm_th','mass', 'pstar','plstar','eta',\n",
    "                             'TargType','h1_ph','h1_th','pos_x','pos_y','pos_z']\n",
    "        \n",
    "        self.tupla_hadron = {}\n",
    "        for var in trigger_variables:\n",
    "            self.tupla_hadron[var] = []    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def getDataframes(filename, Target=1,maxevents=1e9,beamenergy=5.0):\n",
    "    dphi = np.array([])\n",
    "    ParticlesFromPrevious = []\n",
    "    try:\n",
    "        myfile = TFile.Open('%s'%filename,'READ')\n",
    "    except:\n",
    "        print(\"could not open file\")\n",
    "\n",
    "        \n",
    "    myfile.Print()\n",
    "    mytree = myfile.Get('RootTuple')\n",
    "    \n",
    "    print (filename, ' has ', mytree.GetEntries(), ' entries')\n",
    "    print ('The max number of events to be analyzes is ', maxevents)\n",
    "    df = mytupla()    \n",
    "    \n",
    "    start = time.time()\n",
    "    for ievt  in range(mytree.GetEntries()):\n",
    "        if(ievt%1e5==0):\n",
    "            print ('Event # ', ievt)\n",
    "            end = time.time()\n",
    "            print ('Processed in',  end-start, 'seconds')\n",
    "            start = time.time()\n",
    "        mytree.GetEntry(ievt)   \n",
    "        if mytree.Q2<1.0: continue\n",
    "        if ievt>maxevents: break\n",
    "        Nu = mytree.nu\n",
    "        Q2 = mytree.Q2\n",
    "        phi_e = mytree.phiL \n",
    "        E    = beamenergy\n",
    "        Eprime = E-Nu\n",
    "        \n",
    "        incoming_e = ROOT.TLorentzVector()\n",
    "        incoming_e.SetPxPyPzE(0,0,E,E)\n",
    "        \n",
    "        #scattered electron\n",
    "        scattered_e = ROOT.TLorentzVector()\n",
    "        cos_thetae = 1-Q2/(2*E*Eprime)\n",
    "        sin_thetae = np.sqrt(1-cos_thetae*cos_thetae)\n",
    "        scattered_e.SetPxPyPzE(Eprime*sin_thetae*np.cos(phi_e),\n",
    "                               Eprime*sin_thetae*np.sin(phi_e),\n",
    "                               Eprime*cos_thetae,\n",
    "                               Eprime)\n",
    "        virtual_photon  = incoming_e - scattered_e\n",
    "        virtual_photon_unitvector = virtual_photon.Vect().Unit()\n",
    "\n",
    "        proton = ROOT.TLorentzVector()\n",
    "        proton.SetPxPyPzE(0,0,0, 0.938)\n",
    "        #print virtual_photon.M2(), ' ' , Q2\n",
    "        #print virtual_photon.Vect().Mag(), ' ' , np.sqrt(Nu*Nu+Q2*Q2)\n",
    "        \n",
    "        #virtual_photon = ROOT.TLorentzVector()\n",
    "        #virtual_photon.SetPxPyPzE( 0, 0, np.sqrt(Nu*Nu+Q2*Q2),Nu)  #lab frame but with z in photon direction \n",
    "        #cos_thetaphoton = E -Eprime\n",
    "        \n",
    "        #need to rotate to lab frame with z aligned with beam frame\n",
    "        #virtual_photon.Rotate()\n",
    "        #### virtual photon \n",
    "        #electron in lab frame = (0,0,E,E)\n",
    "        #scattered electron in lab frame = (E'sintheta*cosphi,E'sintheta'sinphi,E'costheta,E')\n",
    "        \n",
    "        #Q2= 2EE'(1-cos_thetae)\n",
    "        # cos_thetae =1- Q2/2EE'\n",
    "\n",
    "        #photon in lab frame is e-e'\n",
    "        # (-E'sintheta*cosphi, -E'sintheta*sinphi, E-E'costheta, E-E')\n",
    "        \n",
    "        #cos_thetaphoton = (E- E'*costheta)/(E-E')\n",
    "        #cos_thetaphoton = (E- E'*(1- Q2/2EE'))/(E-E')\n",
    "        #sin_thetaphoton = sqrt(1-cos_thetaphoton*cos_thetaphoton)\n",
    "        #Thus, photon pT in lab frame with z-lepton direction is:\n",
    "        #pT = E'sqrt(1-cos_thetaphoton^{2})\n",
    "        #pT = E'sqrt(1- (E- E'*(1- Q2/2EE'))/(E-E')^{2} )\n",
    "        #and finally, photon phi angle is:\n",
    "        # phi_photon = -phi_electron (given)\n",
    "      \n",
    "        for i in range(len(mytree.Px)):\n",
    "            if abs(mytree.barcode[i]) !=211: continue\n",
    "            i_lv = ROOT.TLorentzVector()    \n",
    "            i_lv.SetPxPyPzE(mytree.Px[i],mytree.Py[i],mytree.Pz[i],mytree.E[i]) #with respect to photon direction\n",
    "            i_part = particle(mytree.barcode[i], i_lv, Q2, Nu)\n",
    "            if i_part.Zh > 0.5:\n",
    "                df.tupla_hadron['TargType'].append(999)\n",
    "                df.tupla_hadron['h1_pid'].append(i_part.pid)\n",
    "                df.tupla_hadron['h1_xf'].append(i_part.Xf)\n",
    "                df.tupla_hadron['h1_z'].append(i_part.Zh)\n",
    "                df.tupla_hadron['h1_rap'].append(i_part.y_star)\n",
    "                df.tupla_hadron['ycm'].append(i_part.ycm)\n",
    "                df.tupla_hadron['h1_pt'].append(i_part.LorentzVector.Pt())\n",
    "                df.tupla_hadron['h1_ph'].append(i_part.PhiPQ)\n",
    "                df.tupla_hadron['h1_th'].append(i_part.ThetaPQ)\n",
    "                df.tupla_hadron['Q2'].append(Q2)\n",
    "                df.tupla_hadron['x'].append(i_part.Xb)\n",
    "                df.tupla_hadron['nu'].append(Nu)\n",
    "                df.tupla_hadron['W'].append(i_part.W)\n",
    "                df.tupla_hadron['h1_cm_ph'].append(i_part.LorentzVector.Phi())\n",
    "            \n",
    "                df.tupla_hadron['h1_cm_th'].append(i_part.LorentzVector.Theta())\n",
    "                df.tupla_hadron['mass'].append(i_part.mass)\n",
    "                df.tupla_hadron['eta'].append(i_part.eta)\n",
    "                df.tupla_hadron['pstar'].append(i_part.Pstar)\n",
    "                df.tupla_hadron['plstar'].append(i_part.Pl_star)\n",
    "\n",
    "                df.tupla_hadron['pos_x'].append(mytree.x[i])\n",
    "                df.tupla_hadron['pos_y'].append(mytree.y[i])\n",
    "                df.tupla_hadron['pos_z'].append(mytree.z[i])\n",
    "                \n",
    "                for j in range(len(mytree.Px)): \n",
    "                    if i==j: continue\n",
    "                    if abs(mytree.barcode[j]) !=211 and mytree.barcode[j]!=2212: continue\n",
    "                    j_lv = ROOT.TLorentzVector()    \n",
    "                    j_lv.SetPxPyPzE(mytree.Px[j],mytree.Py[j],mytree.Pz[j],mytree.E[j])\n",
    "                    j_part = particle(mytree.barcode[j], j_lv, Q2, Nu)\n",
    "                    \n",
    "                    dphi = abs(ROOT.TVector2.Phi_mpi_pi(i_part.PhiPQ-j_part.PhiPQ))\n",
    "                    dphi_lab = abs(ROOT.TVector2.Phi_mpi_pi(i_part.PhiLab-j_part.PhiLab))\n",
    "                    dipion = i_part.LorentzVector+j_part.LorentzVector\n",
    "                    qt = dipion.Pt()\n",
    "                    X = (virtual_photon + proton - dipion) #unobserved hadronic system\n",
    "\n",
    "                    \n",
    "            \n",
    "                    \n",
    "                    df.tupla['dphi_lab'].append(dphi)\n",
    "                    df.tupla['dphi'].append(dphi_lab)\n",
    "                    df.tupla['qt'].append(qt)\n",
    "                    df.tupla['drap'].append(i_part.y_star-j_part.y_star)\n",
    "                    df.tupla['h1_z'].append(i_part.Zh)\n",
    "                    df.tupla['h2_z'].append(j_part.Zh)\n",
    "                    df.tupla['z_tot'].append(i_part.Zh+j_part.Zh)\n",
    "                    df.tupla['h1_xf'].append(i_part.Xf)\n",
    "                    df.tupla['h2_xf'].append(j_part.Xf)\n",
    "                    df.tupla['h1_rap'].append(i_part.y_star)\n",
    "                    df.tupla['ycm'].append(i_part.ycm)\n",
    "                    df.tupla['h2_rap'].append(j_part.y_star)\n",
    "                    df.tupla['h1_pid'].append(i_part.pid)\n",
    "                    df.tupla['h2_pid'].append(j_part.pid)\n",
    "                    df.tupla['h1_ph'].append(i_part.PhiPQ)\n",
    "                    df.tupla['h2_ph'].append(j_part.PhiPQ)\n",
    "                    df.tupla['h1_th'].append(i_part.ThetaPQ)\n",
    "                    df.tupla['h2_th'].append(j_part.ThetaPQ)\n",
    "                    df.tupla['pair_mass'].append(dipion.M())\n",
    "                    df.tupla['pair_pt'].append( dipion.Pt())\n",
    "                    df.tupla['mx_eh1h2'].append(X.M())\n",
    "                    df.tupla['Q2'].append(Q2)\n",
    "                    df.tupla['x'].append(i_part.Xb)\n",
    "                    df.tupla['nu'].append(Nu)\n",
    "                    df.tupla['W'].append(i_part.W)\n",
    "                    df.tupla['h1_cm_ph'].append(i_part.LorentzVector.Phi())\n",
    "                    df.tupla['h1_cm_th'].append(i_part.LorentzVector.Theta())\n",
    "                    df.tupla['h2_cm_ph'].append(j_part.LorentzVector.Phi())\n",
    "                    df.tupla['h2_cm_th'].append(j_part.LorentzVector.Theta())\n",
    "                    df.tupla['pos_x'].append(mytree.x[i])\n",
    "                    df.tupla['pos_y'].append(mytree.y[i])\n",
    "                    df.tupla['pos_z'].append(mytree.z[i])\n",
    "                    df.tupla['pos_x_asso'].append(mytree.x[j])\n",
    "                    df.tupla['pos_y_asso'].append(mytree.y[j])\n",
    "                    df.tupla['pos_z_asso'].append(mytree.z[j])\n",
    "        \n",
    "        \n",
    "    end = time.time()\n",
    "    print ('Processed in',  end-start, 'seconds')\n",
    "    df_pair = pd.DataFrame(df.tupla)\n",
    "    df_hadron = pd.DataFrame(df.tupla_hadron)\n",
    "    return df_pair, df_hadron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration for CLAS12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/home/miguel/GiBUU/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/miguel/GiBUU/GiBUU_D.root  has  8363332  entries\n",
      "The max number of events to be analyzes is  10000000.0\n",
      "Event #  0\n",
      "Processed in 3.4332275390625e-05 seconds\n",
      "Event #  100000\n",
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      "Event #  5500000\n",
      "Processed in 11.714881658554077 seconds\n",
      "Event #  5600000\n",
      "Processed in 11.71602201461792 seconds\n",
      "Event #  5700000\n",
      "Processed in 11.725102424621582 seconds\n",
      "Event #  5800000\n",
      "Processed in 11.809822797775269 seconds\n",
      "Event #  5900000\n",
      "Processed in 11.600075006484985 seconds\n",
      "Event #  6000000\n",
      "Processed in 11.77761697769165 seconds\n",
      "Event #  6100000\n",
      "Processed in 11.6822829246521 seconds\n",
      "Event #  6200000\n",
      "Processed in 11.762516498565674 seconds\n",
      "Event #  6300000\n",
      "Processed in 11.72368335723877 seconds\n",
      "Event #  6400000\n",
      "Processed in 11.75396203994751 seconds\n",
      "Event #  6500000\n",
      "Processed in 11.711896657943726 seconds\n",
      "Event #  6600000\n",
      "Processed in 11.810887813568115 seconds\n",
      "Event #  6700000\n",
      "Processed in 11.758881092071533 seconds\n",
      "Event #  6800000\n",
      "Processed in 11.622071504592896 seconds\n",
      "Event #  6900000\n",
      "Processed in 11.761296272277832 seconds\n",
      "Event #  7000000\n",
      "Processed in 11.603450536727905 seconds\n",
      "Event #  7100000\n",
      "Processed in 11.622036933898926 seconds\n",
      "Event #  7200000\n",
      "Processed in 11.940702676773071 seconds\n",
      "Event #  7300000\n",
      "Processed in 11.520340919494629 seconds\n",
      "Event #  7400000\n",
      "Processed in 11.825332164764404 seconds\n",
      "Event #  7500000\n",
      "Processed in 11.821866989135742 seconds\n",
      "Event #  7600000\n",
      "Processed in 11.807842016220093 seconds\n",
      "Event #  7700000\n",
      "Processed in 11.816753625869751 seconds\n",
      "Event #  7800000\n",
      "Processed in 11.879093647003174 seconds\n",
      "Event #  7900000\n",
      "Processed in 11.859401941299438 seconds\n",
      "Event #  8000000\n",
      "Processed in 11.85285234451294 seconds\n",
      "Event #  8100000\n",
      "Processed in 12.052199125289917 seconds\n",
      "Event #  8200000\n",
      "Processed in 11.831558465957642 seconds\n",
      "Event #  8300000\n",
      "Processed in 11.876708984375 seconds\n",
      "Processed in 7.49612283706665 seconds\n",
      "CPU times: user 16min 43s, sys: 15.2 s, total: 16min 58s\n",
      "Wall time: 16min 57s\n",
      "TFile: name=/home/miguel/GiBUU/GiBUU_D.root, title=, option=READ\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df['D'], df['D_trigger'] = getDataframes(path+'GiBUU_D.root',maxevents=1e7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/miguel/GiBUU/GiBUU_C.root  has  21776066  entries\n",
      "The max number of events to be analyzes is  10000000.0\n",
      "Event #  0\n",
      "Processed in 2.3126602172851562e-05 seconds\n",
      "Event #  100000\n",
      "Processed in 12.258294820785522 seconds\n",
      "Event #  200000\n",
      "Processed in 12.278035163879395 seconds\n",
      "Event #  300000\n",
      "Processed in 12.187198162078857 seconds\n",
      "Event #  400000\n",
      "Processed in 12.232116460800171 seconds\n",
      "Event #  500000\n",
      "Processed in 12.24931001663208 seconds\n",
      "Event #  600000\n",
      "Processed in 12.133278846740723 seconds\n",
      "Event #  700000\n",
      "Processed in 12.280840396881104 seconds\n",
      "Event #  800000\n",
      "Processed in 12.20078992843628 seconds\n",
      "Event #  900000\n",
      "Processed in 12.163358688354492 seconds\n",
      "Event #  1000000\n",
      "Processed in 12.144698143005371 seconds\n",
      "Event #  1100000\n",
      "Processed in 12.262501239776611 seconds\n",
      "Event #  1200000\n",
      "Processed in 12.278451919555664 seconds\n",
      "Event #  1300000\n",
      "Processed in 12.27940034866333 seconds\n",
      "Event #  1400000\n",
      "Processed in 12.23959755897522 seconds\n",
      "Event #  1500000\n",
      "Processed in 12.260415315628052 seconds\n",
      "Event #  1600000\n",
      "Processed in 12.39736533164978 seconds\n",
      "Event #  1700000\n",
      "Processed in 12.25579047203064 seconds\n",
      "Event #  1800000\n",
      "Processed in 12.242683410644531 seconds\n",
      "Event #  1900000\n",
      "Processed in 12.329729318618774 seconds\n",
      "Event #  2000000\n",
      "Processed in 12.224028587341309 seconds\n",
      "Event #  2100000\n",
      "Processed in 12.283827543258667 seconds\n",
      "Event #  2200000\n",
      "Processed in 12.324070930480957 seconds\n",
      "Event #  2300000\n",
      "Processed in 12.212036848068237 seconds\n",
      "Event #  2400000\n",
      "Processed in 12.150533676147461 seconds\n",
      "Event #  2500000\n",
      "Processed in 12.227105379104614 seconds\n",
      "Event #  2600000\n",
      "Processed in 12.053323984146118 seconds\n",
      "Event #  2700000\n",
      "Processed in 12.235792398452759 seconds\n",
      "Event #  2800000\n",
      "Processed in 12.191451072692871 seconds\n",
      "Event #  2900000\n",
      "Processed in 12.326781988143921 seconds\n",
      "Event #  3000000\n",
      "Processed in 12.16546630859375 seconds\n",
      "Event #  3100000\n",
      "Processed in 12.224681377410889 seconds\n",
      "Event #  3200000\n",
      "Processed in 12.211862087249756 seconds\n",
      "Event #  3300000\n",
      "Processed in 12.553918838500977 seconds\n",
      "Event #  3400000\n",
      "Processed in 12.478393077850342 seconds\n",
      "Event #  3500000\n",
      "Processed in 12.115615367889404 seconds\n",
      "Event #  3600000\n",
      "Processed in 12.039143323898315 seconds\n",
      "Event #  3700000\n",
      "Processed in 12.08693242073059 seconds\n",
      "Event #  3800000\n",
      "Processed in 12.047743082046509 seconds\n",
      "Event #  3900000\n",
      "Processed in 12.130867719650269 seconds\n",
      "Event #  4000000\n",
      "Processed in 12.078051090240479 seconds\n",
      "Event #  4100000\n",
      "Processed in 12.133219480514526 seconds\n",
      "Event #  4200000\n",
      "Processed in 12.081933975219727 seconds\n",
      "Event #  4300000\n",
      "Processed in 12.10460877418518 seconds\n",
      "Event #  4400000\n",
      "Processed in 11.787297010421753 seconds\n",
      "Event #  4500000\n",
      "Processed in 11.835587978363037 seconds\n",
      "Event #  4600000\n",
      "Processed in 11.777840375900269 seconds\n",
      "Event #  4700000\n",
      "Processed in 11.840535402297974 seconds\n",
      "Event #  4800000\n",
      "Processed in 11.809929609298706 seconds\n",
      "Event #  4900000\n",
      "Processed in 11.879269361495972 seconds\n",
      "Event #  5000000\n",
      "Processed in 11.8298180103302 seconds\n",
      "Event #  5100000\n",
      "Processed in 11.835338830947876 seconds\n",
      "Event #  5200000\n",
      "Processed in 11.783244848251343 seconds\n",
      "Event #  5300000\n",
      "Processed in 11.872411012649536 seconds\n",
      "Event #  5400000\n",
      "Processed in 11.780450582504272 seconds\n",
      "Event #  5500000\n",
      "Processed in 11.791391611099243 seconds\n",
      "Event #  5600000\n",
      "Processed in 11.764832973480225 seconds\n",
      "Event #  5700000\n",
      "Processed in 11.729100704193115 seconds\n",
      "Event #  5800000\n",
      "Processed in 11.821215152740479 seconds\n",
      "Event #  5900000\n",
      "Processed in 11.74751591682434 seconds\n",
      "Event #  6000000\n",
      "Processed in 11.798069953918457 seconds\n",
      "Event #  6100000\n",
      "Processed in 11.797969341278076 seconds\n",
      "Event #  6200000\n",
      "Processed in 11.75513744354248 seconds\n",
      "Event #  6300000\n",
      "Processed in 11.865456104278564 seconds\n",
      "Event #  6400000\n",
      "Processed in 11.808537483215332 seconds\n",
      "Event #  6500000\n",
      "Processed in 11.784433126449585 seconds\n",
      "Event #  6600000\n",
      "Processed in 11.781094074249268 seconds\n",
      "Event #  6700000\n",
      "Processed in 11.824466466903687 seconds\n",
      "Event #  6800000\n",
      "Processed in 11.733608484268188 seconds\n",
      "Event #  6900000\n",
      "Processed in 11.847438097000122 seconds\n",
      "Event #  7000000\n",
      "Processed in 11.899449825286865 seconds\n",
      "Event #  7100000\n",
      "Processed in 11.800605058670044 seconds\n",
      "Event #  7200000\n",
      "Processed in 11.821359157562256 seconds\n",
      "Event #  7300000\n",
      "Processed in 11.761766195297241 seconds\n",
      "Event #  7400000\n",
      "Processed in 11.761291742324829 seconds\n",
      "Event #  7500000\n",
      "Processed in 11.857356071472168 seconds\n",
      "Event #  7600000\n",
      "Processed in 11.808155536651611 seconds\n",
      "Event #  7700000\n",
      "Processed in 11.802304744720459 seconds\n",
      "Event #  7800000\n",
      "Processed in 11.862969636917114 seconds\n",
      "Event #  7900000\n",
      "Processed in 11.788493156433105 seconds\n",
      "Event #  8000000\n",
      "Processed in 11.79090166091919 seconds\n",
      "Event #  8100000\n",
      "Processed in 11.829086780548096 seconds\n",
      "Event #  8200000\n",
      "Processed in 11.841280937194824 seconds\n",
      "Event #  8300000\n",
      "Processed in 12.061887741088867 seconds\n",
      "Event #  8400000\n",
      "Processed in 11.969030380249023 seconds\n",
      "Event #  8500000\n",
      "Processed in 11.848598957061768 seconds\n",
      "Event #  8600000\n",
      "Processed in 11.788425922393799 seconds\n",
      "Event #  8700000\n",
      "Processed in 11.943000793457031 seconds\n",
      "Event #  8800000\n",
      "Processed in 11.849244594573975 seconds\n",
      "Event #  8900000\n",
      "Processed in 11.860206365585327 seconds\n",
      "Event #  9000000\n",
      "Processed in 11.76530933380127 seconds\n",
      "Event #  9100000\n",
      "Processed in 11.969897270202637 seconds\n",
      "Event #  9200000\n",
      "Processed in 11.83586049079895 seconds\n",
      "Event #  9300000\n",
      "Processed in 11.83381962776184 seconds\n",
      "Event #  9400000\n",
      "Processed in 12.053618907928467 seconds\n",
      "Event #  9500000\n",
      "Processed in 11.949392080307007 seconds\n",
      "Event #  9600000\n",
      "Processed in 11.977443218231201 seconds\n",
      "Event #  9700000\n",
      "Processed in 12.021684885025024 seconds\n",
      "Event #  9800000\n",
      "Processed in 11.788514137268066 seconds\n",
      "Event #  9900000\n",
      "Processed in 11.739378690719604 seconds\n",
      "Event #  10000000\n",
      "Processed in 11.811563730239868 seconds\n",
      "Processed in 0.0001723766326904297 seconds\n",
      "CPU times: user 20min 21s, sys: 10 s, total: 20min 31s\n",
      "Wall time: 20min 30s\n",
      "TFile: name=/home/miguel/GiBUU/GiBUU_C.root, title=, option=READ\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df['C'], df['C_trigger'] = getDataframes(path+'GiBUU_C.root',maxevents=1e7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/miguel/GiBUU/GiBUU_Fe.root  has  5596315  entries\n",
      "The max number of events to be analyzes is  10000000.0\n",
      "Event #  0\n",
      "Processed in 0.0001361370086669922 seconds\n",
      "Event #  100000\n",
      "Processed in 12.065704822540283 seconds\n",
      "Event #  200000\n",
      "Processed in 11.886005640029907 seconds\n",
      "Event #  300000\n",
      "Processed in 12.018973350524902 seconds\n",
      "Event #  400000\n",
      "Processed in 11.917577028274536 seconds\n",
      "Event #  500000\n",
      "Processed in 11.957167625427246 seconds\n",
      "Event #  600000\n",
      "Processed in 12.011845588684082 seconds\n",
      "Event #  700000\n",
      "Processed in 12.173636436462402 seconds\n",
      "Event #  800000\n",
      "Processed in 12.114562034606934 seconds\n",
      "Event #  900000\n",
      "Processed in 12.084140062332153 seconds\n",
      "Event #  1000000\n",
      "Processed in 12.043122053146362 seconds\n",
      "Event #  1100000\n",
      "Processed in 12.057468175888062 seconds\n",
      "Event #  1200000\n",
      "Processed in 11.996628284454346 seconds\n",
      "Event #  1300000\n",
      "Processed in 12.116889476776123 seconds\n",
      "Event #  1400000\n",
      "Processed in 12.137232303619385 seconds\n",
      "Event #  1500000\n",
      "Processed in 12.075749397277832 seconds\n",
      "Event #  1600000\n",
      "Processed in 11.98284363746643 seconds\n",
      "Event #  1700000\n",
      "Processed in 11.900533437728882 seconds\n",
      "Event #  1800000\n",
      "Processed in 11.853337526321411 seconds\n",
      "Event #  1900000\n",
      "Processed in 11.926622867584229 seconds\n",
      "Event #  2000000\n",
      "Processed in 11.895166873931885 seconds\n",
      "Event #  2100000\n",
      "Processed in 11.93445348739624 seconds\n",
      "Event #  2200000\n",
      "Processed in 11.883372783660889 seconds\n",
      "Event #  2300000\n",
      "Processed in 11.931948900222778 seconds\n",
      "Event #  2400000\n",
      "Processed in 11.914846658706665 seconds\n",
      "Event #  2500000\n",
      "Processed in 11.953429698944092 seconds\n",
      "Event #  2600000\n",
      "Processed in 11.95327353477478 seconds\n",
      "Event #  2700000\n",
      "Processed in 11.974931478500366 seconds\n",
      "Event #  2800000\n",
      "Processed in 11.869654178619385 seconds\n",
      "Event #  2900000\n",
      "Processed in 11.909219026565552 seconds\n",
      "Event #  3000000\n",
      "Processed in 11.909501552581787 seconds\n",
      "Event #  3100000\n",
      "Processed in 11.894086599349976 seconds\n",
      "Event #  3200000\n",
      "Processed in 11.894286394119263 seconds\n",
      "Event #  3300000\n",
      "Processed in 11.962424993515015 seconds\n",
      "Event #  3400000\n",
      "Processed in 11.971068382263184 seconds\n",
      "Event #  3500000\n",
      "Processed in 11.94174861907959 seconds\n",
      "Event #  3600000\n",
      "Processed in 11.850257873535156 seconds\n",
      "Event #  3700000\n",
      "Processed in 11.8999183177948 seconds\n",
      "Event #  3800000\n",
      "Processed in 11.967703104019165 seconds\n",
      "Event #  3900000\n",
      "Processed in 11.944498538970947 seconds\n",
      "Event #  4000000\n",
      "Processed in 11.969223976135254 seconds\n",
      "Event #  4100000\n",
      "Processed in 11.851470708847046 seconds\n",
      "Event #  4200000\n",
      "Processed in 11.889395713806152 seconds\n",
      "Event #  4300000\n",
      "Processed in 11.991541862487793 seconds\n",
      "Event #  4400000\n",
      "Processed in 11.98237657546997 seconds\n",
      "Event #  4500000\n",
      "Processed in 11.917160511016846 seconds\n",
      "Event #  4600000\n",
      "Processed in 11.865257263183594 seconds\n",
      "Event #  4700000\n",
      "Processed in 12.003043174743652 seconds\n",
      "Event #  4800000\n",
      "Processed in 11.919552564620972 seconds\n",
      "Event #  4900000\n",
      "Processed in 11.995782136917114 seconds\n",
      "Event #  5000000\n",
      "Processed in 11.929548501968384 seconds\n",
      "Event #  5100000\n",
      "Processed in 12.058419227600098 seconds\n",
      "Event #  5200000\n",
      "Processed in 12.012263298034668 seconds\n",
      "Event #  5300000\n",
      "Processed in 12.04872751235962 seconds\n",
      "Event #  5400000\n",
      "Processed in 12.017796516418457 seconds\n",
      "Event #  5500000\n",
      "Processed in 11.940354347229004 seconds\n",
      "Processed in 11.616015195846558 seconds\n",
      "CPU times: user 11min 21s, sys: 2.07 s, total: 11min 23s\n",
      "Wall time: 11min 23s\n",
      "TFile: name=/home/miguel/GiBUU/GiBUU_Fe.root, title=, option=READ\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df['Fe'], df['Fe_trigger'] = getDataframes(path+'GiBUU_Fe.root',maxevents=1e7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/miguel/GiBUU/GiBUU_Pb.root  has  8315826  entries\n",
      "The max number of events to be analyzes is  10000000.0\n",
      "Event #  0\n",
      "Processed in 2.7894973754882812e-05 seconds\n",
      "Event #  100000\n",
      "Processed in 12.081351280212402 seconds\n",
      "Event #  200000\n",
      "Processed in 12.027133703231812 seconds\n",
      "Event #  300000\n",
      "Processed in 12.071229934692383 seconds\n",
      "Event #  400000\n",
      "Processed in 11.97878885269165 seconds\n",
      "Event #  500000\n",
      "Processed in 11.979308605194092 seconds\n",
      "Event #  600000\n",
      "Processed in 12.208984136581421 seconds\n",
      "Event #  700000\n",
      "Processed in 12.238576173782349 seconds\n",
      "Event #  800000\n",
      "Processed in 11.911232709884644 seconds\n",
      "Event #  900000\n",
      "Processed in 12.11084794998169 seconds\n",
      "Event #  1000000\n",
      "Processed in 12.17910361289978 seconds\n",
      "Event #  1100000\n",
      "Processed in 12.134846448898315 seconds\n",
      "Event #  1200000\n",
      "Processed in 12.071356058120728 seconds\n",
      "Event #  1300000\n",
      "Processed in 12.006340026855469 seconds\n",
      "Event #  1400000\n",
      "Processed in 12.075510025024414 seconds\n",
      "Event #  1500000\n",
      "Processed in 12.14522123336792 seconds\n",
      "Event #  1600000\n",
      "Processed in 12.122291326522827 seconds\n",
      "Event #  1700000\n",
      "Processed in 11.963065147399902 seconds\n",
      "Event #  1800000\n",
      "Processed in 12.020586252212524 seconds\n",
      "Event #  1900000\n",
      "Processed in 12.203827619552612 seconds\n",
      "Event #  2000000\n",
      "Processed in 12.03182864189148 seconds\n",
      "Event #  2100000\n",
      "Processed in 12.031517505645752 seconds\n",
      "Event #  2200000\n",
      "Processed in 12.083537340164185 seconds\n",
      "Event #  2300000\n",
      "Processed in 11.983155727386475 seconds\n",
      "Event #  2400000\n",
      "Processed in 11.920783758163452 seconds\n",
      "Event #  2500000\n",
      "Processed in 12.058390617370605 seconds\n",
      "Event #  2600000\n",
      "Processed in 12.226970195770264 seconds\n",
      "Event #  2700000\n",
      "Processed in 12.061786651611328 seconds\n",
      "Event #  2800000\n",
      "Processed in 12.34624195098877 seconds\n",
      "Event #  2900000\n",
      "Processed in 11.898306369781494 seconds\n",
      "Event #  3000000\n",
      "Processed in 11.98176908493042 seconds\n",
      "Event #  3100000\n",
      "Processed in 12.384451150894165 seconds\n",
      "Event #  3200000\n",
      "Processed in 12.060682535171509 seconds\n",
      "Event #  3300000\n",
      "Processed in 12.001295804977417 seconds\n",
      "Event #  3400000\n",
      "Processed in 12.250942468643188 seconds\n",
      "Event #  3500000\n",
      "Processed in 11.957308530807495 seconds\n",
      "Event #  3600000\n",
      "Processed in 12.069040775299072 seconds\n",
      "Event #  3700000\n",
      "Processed in 11.971104621887207 seconds\n",
      "Event #  3800000\n",
      "Processed in 12.110379934310913 seconds\n",
      "Event #  3900000\n",
      "Processed in 12.054636240005493 seconds\n",
      "Event #  4000000\n",
      "Processed in 12.379897594451904 seconds\n",
      "Event #  4100000\n",
      "Processed in 11.933329820632935 seconds\n",
      "Event #  4200000\n",
      "Processed in 11.967482328414917 seconds\n",
      "Event #  4300000\n",
      "Processed in 12.142145156860352 seconds\n",
      "Event #  4400000\n",
      "Processed in 12.1020188331604 seconds\n",
      "Event #  4500000\n",
      "Processed in 11.963763236999512 seconds\n",
      "Event #  4600000\n",
      "Processed in 12.026733636856079 seconds\n",
      "Event #  4700000\n",
      "Processed in 11.988625049591064 seconds\n",
      "Event #  4800000\n",
      "Processed in 12.294074773788452 seconds\n",
      "Event #  4900000\n",
      "Processed in 11.890476703643799 seconds\n",
      "Event #  5000000\n",
      "Processed in 11.925487518310547 seconds\n",
      "Event #  5100000\n",
      "Processed in 11.935492992401123 seconds\n",
      "Event #  5200000\n",
      "Processed in 11.92970609664917 seconds\n",
      "Event #  5300000\n",
      "Processed in 12.120105981826782 seconds\n",
      "Event #  5400000\n",
      "Processed in 12.1006019115448 seconds\n",
      "Event #  5500000\n",
      "Processed in 12.106819152832031 seconds\n",
      "Event #  5600000\n",
      "Processed in 12.07930040359497 seconds\n",
      "Event #  5700000\n",
      "Processed in 12.154765605926514 seconds\n",
      "Event #  5800000\n",
      "Processed in 12.076568126678467 seconds\n",
      "Event #  5900000\n",
      "Processed in 12.098220825195312 seconds\n",
      "Event #  6000000\n",
      "Processed in 12.035748720169067 seconds\n",
      "Event #  6100000\n",
      "Processed in 12.19303584098816 seconds\n",
      "Event #  6200000\n",
      "Processed in 12.2343909740448 seconds\n",
      "Event #  6300000\n",
      "Processed in 12.037294387817383 seconds\n",
      "Event #  6400000\n",
      "Processed in 12.21239185333252 seconds\n",
      "Event #  6500000\n",
      "Processed in 11.972564697265625 seconds\n",
      "Event #  6600000\n",
      "Processed in 11.924792766571045 seconds\n",
      "Event #  6700000\n",
      "Processed in 12.213578939437866 seconds\n",
      "Event #  6800000\n",
      "Processed in 11.94530200958252 seconds\n",
      "Event #  6900000\n",
      "Processed in 11.961965560913086 seconds\n",
      "Event #  7000000\n",
      "Processed in 11.965133428573608 seconds\n",
      "Event #  7100000\n",
      "Processed in 12.155780792236328 seconds\n",
      "Event #  7200000\n",
      "Processed in 12.203144788742065 seconds\n",
      "Event #  7300000\n",
      "Processed in 12.06519603729248 seconds\n",
      "Event #  7400000\n",
      "Processed in 12.149724960327148 seconds\n",
      "Event #  7500000\n",
      "Processed in 12.027476072311401 seconds\n",
      "Event #  7600000\n",
      "Processed in 12.116267919540405 seconds\n",
      "Event #  7700000\n",
      "Processed in 12.20483684539795 seconds\n",
      "Event #  7800000\n",
      "Processed in 12.602330207824707 seconds\n",
      "Event #  7900000\n",
      "Processed in 12.599526405334473 seconds\n",
      "Event #  8000000\n",
      "Processed in 12.039162158966064 seconds\n",
      "Event #  8100000\n",
      "Processed in 12.028613805770874 seconds\n",
      "Event #  8200000\n",
      "Processed in 12.09328579902649 seconds\n",
      "Event #  8300000\n",
      "Processed in 12.013994216918945 seconds\n",
      "Processed in 1.8967933654785156 seconds\n",
      "CPU times: user 16min 58s, sys: 2.75 s, total: 17min 1s\n",
      "Wall time: 17min\n",
      "TFile: name=/home/miguel/GiBUU/GiBUU_Pb.root, title=, option=READ\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df['Pb'], df['Pb_trigger'] = getDataframes(path+'GiBUU_Pb.root',maxevents=1e7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import root_pandas\n",
    "from root_pandas import read_root\n",
    "from root_pandas import to_root "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1148422\n",
      "999262\n"
     ]
    }
   ],
   "source": [
    "print (df['Pb'].shape[0])\n",
    "print (df['Pb_trigger'].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2099630\n",
      "2047709\n"
     ]
    }
   ],
   "source": [
    "print (df['D'].shape[0])\n",
    "print (df['D_trigger'].shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save dataframes to ROOT files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_root(df['D'],'GiBUU_Pairs_D.root', key='D')\n",
    "to_root(df['D_trigger'],'GiBUU_Pairs_D.root', key='D_trigger', mode='a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_root(df['C'],'GiBUU_Pairs_C.root', key='C')\n",
    "to_root(df['C_trigger'],'GiBUU_Pairs_C.root', key='C_trigger', mode='a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_root(df['Fe'],'GiBUU_Pairs_Fe.root', key='Fe')\n",
    "to_root(df['Fe_trigger'],'GiBUU_Pairs_Fe.root', key='Fe_trigger', mode='a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_root(df['Pb'],'GiBUU_Pairs_Pb.root', key='Pb')\n",
    "to_root(df['Pb_trigger'],'GiBUU_Pairs_Pb.root', key='Pb_trigger', mode='a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## HERMES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to_root(df['D'],'GiBUU_Pairs_HERMES_D.root', key='D')\n",
    "# to_root(df['D_trigger'],'GiBUU_Pairs_HERMES_D.root', key='D_trigger', mode='a')\n",
    "# to_root(df['Kr'],'GiBUU_Pairs_HERMES_Kr.root', key='Kr')\n",
    "# to_root(df['Kr_trigger'],'GiBUU_Pairs_HERMES_Kr.root', key='Kr_trigger', mode='a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to_root(df['Xe'],'GiBUU_Pairs_HERMES_Xe.root', key='Xe')\n",
    "# to_root(df['Xe_trigger'],'GiBUU_Pairs_HERMES_Xe.root', key='Xe_trigger', mode='a')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CLAS12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to_root(df['D'],'GiBUU_Pairs_CLAS12_D.root', key='D')\n",
    "# to_root(df['D_trigger'],'GiBUU_Pairs_CLAS12_D.root', key='D_trigger', mode='a')\n",
    "# to_root(df['Pb'],'GiBUU_Pairs_CLAS12_Pb.root', key='Pb')\n",
    "# to_root(df['Pb_trigger'],'GiBUU_Pairs_CLAS12_Pb.root', key='Pb_trigger', mode='a')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check dataframes variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          2.439464\n",
       "1          2.782521\n",
       "2          2.729391\n",
       "3          1.480757\n",
       "4          2.673237\n",
       "             ...   \n",
       "2099625    3.123719\n",
       "2099626    3.139406\n",
       "2099627    3.091658\n",
       "2099628    3.130992\n",
       "2099629    3.048682\n",
       "Name: dphi, Length: 2099630, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['D']['dphi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['dphi', 'dphi_lab', 'qt', 'drap', 'h1_z', 'h2_z', 'z_tot', 'h1_xf',\n",
       "       'h2_xf', 'h1_rap', 'ycm', 'h2_rap', 'h1_pid', 'h2_pid', 'h1_cm_ph',\n",
       "       'h2_cm_ph', 'h1_cm_th', 'h2_cm_th', 'pair_mass', 'pair_pt', 'mx_eh1h2',\n",
       "       'Q2', 'x', 'nu', 'W', 'h1_ph', 'h1_th', 'h2_ph', 'h2_th', 'pos_x',\n",
       "       'pos_y', 'pos_z', 'pos_x_asso', 'pos_y_asso', 'pos_z_asso'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['D'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['D'].query('h1_z>0.5').hist(figsize=(15, 15),alpha=0.5,bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['D_trigger'].hist(figsize=(15, 15),alpha=0.5,bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/hist.py:412: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  ax.hist(data[col].dropna().values, bins=bins, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['D'].hist(figsize=(15, 15),normed=True,alpha=0.5,bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          -211\n",
       "1          2212\n",
       "2           211\n",
       "3           211\n",
       "4          -211\n",
       "           ... \n",
       "2099625    2212\n",
       "2099626    2212\n",
       "2099627    2212\n",
       "2099628    2212\n",
       "2099629    -211\n",
       "Name: h2_pid, Length: 2099630, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['D']['h2_pid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-24-3bb06a02a3ba>:1: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  y_D, x , _  = plt.hist(df['D']['h2_z'],bins=100,normed=True,alpha=0.7,label='D')\n",
      "<ipython-input-24-3bb06a02a3ba>:2: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  y_C, x , _  = plt.hist(df['Pb']['h2_z'],bins=100,normed=True,alpha=0.7,label='C')\n"
     ]
    },
    {
     "data": {
      "image/png": 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urpPyCuosp5n32qnomf69tn9O0t2S3hcRCx2qrUxZxmVU0v31kN4i6TrbJyPi7zpTYsdl/f/n1Yj4oaQf2n5M0hWSqhzUWcblI5L2Ra1J/YLt/5D0M5Ke6kyJHZJT03+DpKOStuts03/Hsm3er7ceTHyq7AZ9kbeMY7JN0guS3l12vSmNy7LtD6r6BxOz/K28U9I/1rf9EUmHJV1Wdu0JjMtfSfqT+v0LJP23pC1l1573LZcZdaxymrntvfXnP6Pa0fvr6sH0v6q9E1ZWxjH5I0mbJf1lffZ4Mip+RbCM49JTsoxJRDxv+6uSnpF0WrVvWjpcXtXFy/i38qeSDtp+VrVJ4G0RUblLwnIKOQAkjjMTASBxBDUAJI6gBoDEEdQAkDiCGgASR1ADQOIIagBI3P8DRUweUEEFiqMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_D, x , _  = plt.hist(df['D']['h2_z'],bins=100,normed=True,alpha=0.7,label='D')\n",
    "y_C, x , _  = plt.hist(df['Pb']['h2_z'],bins=100,normed=True,alpha=0.7,label='C')\n",
    "x = (x[1:] + x[:-1])/2.0\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratio = np.divide(y_C, y_D)\n",
    "plt.errorbar(x, ratio, marker='o')\n",
    "\n",
    "plt.ylim([0.0,2.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Broadening"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}