Workspace-Background-Mixing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot4\n",
    "import awkward1 as ak\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import mplhep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fileName = \"/eos/cms/store/group/phys_pps/dilepton/DoubleMuon/UL-MiniAOD-2017/DoubleMuon_UL2017B.root\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#proton_selection = \"SingleRP\"\n",
    "proton_selection = \"MultiRP\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "root = uproot4.open( fileName )\n",
    "tree = root[\"ggll_miniaod/ntp1\"]\n",
    "\n",
    "#keys = [\"Run\", \"LumiSection\", \"BX\", \"EventNum\", \"CrossingAngle\",\"nHLT\", \"HLT_Accept\", \"HLT_Prescl\", \"HLT_Name\",\n",
    "#        \"nMuonCand\", \"MuonCand_pt\", \"MuonCand_eta\", \"MuonCand_phi\", \"MuonCand_e\", \"MuonCand_charge\", \"MuonCand_vtxz\", \"MuonCand_istight\",\n",
    "#        \"nPrimVertexCand\", \"PrimVertexCand_z\", \"PrimVertexCand_chi2\", \"PrimVertexCand_ndof\", \"PrimVertexCand_tracks\",\n",
    "#        \"Weight\", \"PUWeightTrue\"]\n",
    "#keys.append( \"nPfCand\" )\n",
    "#keys.extend( tree.keys( filter_name=\"PfCand*\" ) ) \n",
    "#keys.append( \"nRecoProtCand\" )\n",
    "#keys.extend( tree.keys( filter_name=\"ProtCand*\" ) )\n",
    "keys = [\"Run\", \"LumiSection\", \"BX\", \"EventNum\", \"CrossingAngle\",\"nHLT\", \"HLT_Accept\", \"HLT_Prescl\", \"HLT_Name\",\n",
    "        \"nMuonCand\", \"MuonCand_pt\", \"MuonCand_eta\", \"MuonCand_phi\", \"MuonCand_e\", \"MuonCand_charge\", \"MuonCand_vtxz\", \"MuonCand_istight\",\n",
    "        \"nPrimVertexCand\", \"Weight\", \"PUWeightTrue\"]\n",
    "keys.append( \"nRecoProtCand\" )\n",
    "keys.extend( tree.keys( filter_name=\"ProtCand*\" ) )\n",
    "\n",
    "print ( keys )\n",
    "\n",
    "events = tree.arrays( keys, library=\"ak\", how=\"zip\", entry_stop=10000 )\n",
    "#events = tree.arrays( keys, library=\"ak\", how=\"zip\", entry_stop=None )\n",
    "ak.to_list( events[:10] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "events = ak.concatenate( [events]*10, axis=0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print ( len(events) )\n",
    "ak.to_list( events[10000:10010] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed( 42 )\n",
    "index_rnd = np.random.permutation( len( events ) )\n",
    "#arrays_rnd = arrays[ index_rnd ]\n",
    "print ( len( index_rnd ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "protons = events.ProtCand\n",
    "ak.to_list( protons[:10] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ak.num( protons[:10] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "protons_rnd = protons[ index_rnd ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ak.to_list( protons_rnd[:10] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ak.num( protons_rnd[:10] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "events[ \"ProtCandRnd\" ] = protons_rnd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ak.to_list( events.ProtCand[:10] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ak.to_list( events.ProtCandRnd[:10] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ak.num( events.ProtCand[:10] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ak.num( events.ProtCandRnd[:10] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from select_events import *\n",
    "\n",
    "maxEvents_ = 500000\n",
    "#maxEvents_ = None\n",
    "\n",
    "apply_exclusive_ = False\n",
    "random_protons_ = True\n",
    "resample_factor_ = 20\n",
    "resample_ = False\n",
    "if resample_factor_ > 1: resample_ = True\n",
    "\n",
    "np.random.seed( 42 )\n",
    "\n",
    "columns = ( \"Run\", \"LumiSection\", \"BX\", \"EventNum\", \"CrossingAngle\",\n",
    "            \"MultiRP\", \"Arm\", \"RPId1\", \"RPId2\", \"TrackX1\", \"TrackY1\", \"TrackX2\", \"TrackY2\",\n",
    "            \"Xi\", \"T\", \"ThX\", \"ThY\", \"Time\",\n",
    "            \"TrackThX_SingleRP\", \"TrackThY_SingleRP\",\n",
    "            \"Track1ThX_MultiRP\", \"Track1ThY_MultiRP\", \"Track2ThX_MultiRP\", \"Track2ThY_MultiRP\",\n",
    "            \"Muon0Pt\", \"Muon0Eta\", \"Muon0Phi\", \"Muon0VtxZ\", \"Muon1Pt\", \"Muon1Eta\", \"Muon1Phi\", \"Muon1VtxZ\",\n",
    "            \"PrimVertexZ\", \"InvMass\", \"ExtraPfCands\", \"Acopl\", \"XiMuMuPlus\", \"XiMuMuMinus\" )\n",
    "protons_keys = {}\n",
    "for col_ in columns:\n",
    "    protons_keys[ col_ ] = col_\n",
    "protons_keys[ \"MultiRP\" ] = \"ismultirp\"\n",
    "protons_keys[ \"Arm\" ] = \"arm\"\n",
    "protons_keys[ \"RPId1\" ] = \"rpid1\"\n",
    "protons_keys[ \"RPId2\" ] = \"rpid2\"\n",
    "protons_keys[ \"TrackX1\" ] = \"trackx1\"\n",
    "protons_keys[ \"TrackY1\" ] = \"tracky1\"\n",
    "protons_keys[ \"TrackX2\" ] = \"trackx2\"\n",
    "protons_keys[ \"TrackY2\" ] = \"tracky2\"\n",
    "protons_keys[ \"Xi\" ] = \"xi\"\n",
    "protons_keys[ \"T\" ] = \"t\"\n",
    "protons_keys[ \"Time\" ] = \"time\"\n",
    "protons_keys[ \"TrackThX_SingleRP\" ] = \"trackthx_single\"\n",
    "protons_keys[ \"TrackThY_SingleRP\" ] = \"trackthy_single\"\n",
    "protons_keys[ \"Track1ThX_MultiRP\" ] = \"trackthx_multi1\"\n",
    "protons_keys[ \"Track1ThY_MultiRP\" ] = \"trackthy_multi1\"\n",
    "protons_keys[ \"Track2ThX_MultiRP\" ] = \"trackthx_multi2\"\n",
    "protons_keys[ \"Track2ThY_MultiRP\" ] = \"trackthy_multi2\"\n",
    "protons_keys[ \"ExtraPfCands\" ] = \"nExtraPfCandPV3\"\n",
    "\n",
    "protons_list = {}\n",
    "for col_ in columns:\n",
    "    protons_list[ col_ ] = []\n",
    "\n",
    "selections = None\n",
    "counts = None\n",
    "\n",
    "counts_label_protons_ = \"Protons\" if not random_protons_ else \"ProtonsRnd\"\n",
    "\n",
    "root_ = uproot4.open( fileName )\n",
    "tree_ = root_[\"ggll_miniaod/ntp1\"]\n",
    "\n",
    "keys = [\"Run\", \"LumiSection\", \"BX\", \"EventNum\", \"CrossingAngle\",\"nHLT\", \"HLT_Accept\", \"HLT_Prescl\", \"HLT_Name\",\n",
    "        \"nMuonCand\", \"MuonCand_pt\", \"MuonCand_eta\", \"MuonCand_phi\", \"MuonCand_e\", \"MuonCand_charge\", \"MuonCand_vtxz\", \"MuonCand_istight\",\n",
    "        \"nPrimVertexCand\", \"PrimVertexCand_z\", \"PrimVertexCand_chi2\", \"PrimVertexCand_ndof\", \"PrimVertexCand_tracks\",\n",
    "        \"Weight\", \"PUWeightTrue\"]\n",
    "keys.append( \"nPfCand\" )\n",
    "keys.extend( tree_.keys( filter_name=\"PfCand*\" ) ) \n",
    "keys.append( \"nRecoProtCand\" )\n",
    "keys.extend( tree_.keys( filter_name=\"ProtCand*\" ) )\n",
    "print ( keys )\n",
    "\n",
    "for events_ in tree_.iterate( keys , library=\"ak\", how=\"zip\", step_size=\"100 MB\", entry_stop=maxEvents_ ):\n",
    "\n",
    "    events_sel_, selections_, counts_ = select_events( events_, apply_exclusive=apply_exclusive_ )\n",
    "    \n",
    "    # Repeat events by resample factor\n",
    "    if resample_:\n",
    "        counts_ = counts_ * resample_factor_\n",
    "        \n",
    "    if selections is None:\n",
    "        selections = selections_\n",
    "        counts = counts_\n",
    "    else:\n",
    "        msk_selections = np.full_like( selections, False, dtype='bool' )\n",
    "        for key in selections_:\n",
    "            #print ( key )\n",
    "            #print ( selections == key )\n",
    "            msk_selections |= ( selections == key )\n",
    "        counts[ msk_selections ] += counts_\n",
    "\n",
    "    # Repeat events by resample factor\n",
    "    if resample_:\n",
    "        events_sel_ = ak.concatenate( ( [events_sel_] * resample_factor_ ), axis=0 )\n",
    "        \n",
    "    # Randomize proton arrays\n",
    "    if random_protons_:\n",
    "        protons_sel_ = events_sel_.ProtCand\n",
    "    \n",
    "        index_rnd_ = np.random.permutation( len( events_sel_ ) )\n",
    "    \n",
    "        protons_rnd_ = protons_sel_[ index_rnd_ ]\n",
    "    \n",
    "        events_sel_[ \"ProtCandRnd\" ] = protons_rnd_    \n",
    "\n",
    "        print ( ak.num( events_sel_.ProtCand ) )\n",
    "        print ( ak.num( events_sel_.ProtCandRnd ) )    \n",
    "        \n",
    "    protons_ = None\n",
    "    if not random_protons_: protons_ = select_protons( events_sel_, \"ProtCand\" )\n",
    "    else:                   protons_ = select_protons( events_sel_, \"ProtCandRnd\" )    \n",
    "        \n",
    "    counts_protons_ = len( protons_ )\n",
    "    if not counts_label_protons_ in selections:\n",
    "        selections = np.concatenate( ( selections, np.array( [ counts_label_protons_ ] ) ) )\n",
    "        counts = np.concatenate( ( counts, np.array( [counts_protons_] ) ) )\n",
    "    else:    \n",
    "        counts[ selections == counts_label_protons_ ] += counts_protons_ \n",
    "    print ( selections )\n",
    "    print ( counts )\n",
    "    \n",
    "    protons_flatten_ = {}\n",
    "    for col_ in columns:\n",
    "        protons_list[ col_ ].append( ak.flatten( protons_[ protons_keys[ col_ ] ] ) )\n",
    "        \n",
    "    #print ( \"Protons (flattened): {}\".format( len( protons_list[ \"Xi\" ][-1] ) ) )\n",
    "        \n",
    "#print ( selections )\n",
    "#print ( counts )\n",
    "root_.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "protons_flatten_all = {}\n",
    "for col_ in columns:\n",
    "    protons_flatten_all[ col_ ] = np.array( np.concatenate( protons_list[ col_ ] ) )\n",
    "    \n",
    "print ( \"Protons (flattened): {}\".format( len( protons_flatten_all[\"Xi\"] ) ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ = np.stack( list( protons_flatten_all.values() ), axis=1 )\n",
    "print ( data_ )\n",
    "print ( data_.shape )\n",
    "\n",
    "columns_ = np.array( columns, dtype='S' )\n",
    "print ( columns_ )\n",
    "\n",
    "event_counts_ = counts\n",
    "print ( event_counts_ )\n",
    "selections_ = np.array( selections, dtype='S' )\n",
    "print ( selections_ )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File( 'output-test-mixing.h5', 'w') as f:\n",
    "    dset = f.create_dataset( 'protons', data=data_ )\n",
    "    dset_columns = f.create_dataset( 'columns', data=columns_ )\n",
    "    dset_counts = f.create_dataset( 'event_counts', data=event_counts_ )\n",
    "    dset_selections = f.create_dataset( 'selections', data=selections_ )\n",
    "\n",
    "    print ( dset )\n",
    "    print ( dset[-1] )\n",
    "    print ( dset_columns )\n",
    "    print ( list( dset_columns ) )\n",
    "    print ( dset_counts )\n",
    "    print ( list( dset_counts ) )\n",
    "    print ( dset_selections )\n",
    "    print ( list( dset_selections ) )\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['columns', 'event_counts', 'protons', 'selections']\n",
      "(2141500, 38)\n",
      "[[2.97227000e+05 5.30000000e+01 2.75900000e+03 ... 5.92592856e-01\n",
      "  1.09871883e-02 1.22591492e-02]\n",
      " [2.97227000e+05 5.30000000e+01 2.75900000e+03 ... 5.92592856e-01\n",
      "  1.09871883e-02 1.22591492e-02]\n",
      " [2.97227000e+05 5.30000000e+01 2.75900000e+03 ... 5.92592856e-01\n",
      "  1.09871883e-02 1.22591492e-02]\n",
      " ...\n",
      " [2.99062000e+05 1.33000000e+02 1.72100000e+03 ... 6.64818845e-01\n",
      "  3.52177158e-03 7.56784159e-02]\n",
      " [2.99062000e+05 1.33000000e+02 1.72100000e+03 ... 6.64818845e-01\n",
      "  3.52177158e-03 7.56784159e-02]\n",
      " [2.99062000e+05 1.33000000e+02 1.72100000e+03 ... 6.64818845e-01\n",
      "  3.52177158e-03 7.56784159e-02]]\n",
      "(38,)\n",
      "[b'Run', b'LumiSection', b'BX', b'EventNum', b'CrossingAngle', b'MultiRP', b'Arm', b'RPId1', b'RPId2', b'TrackX1', b'TrackY1', b'TrackX2', b'TrackY2', b'Xi', b'T', b'ThX', b'ThY', b'Time', b'TrackThX_SingleRP', b'TrackThY_SingleRP', b'Track1ThX_MultiRP', b'Track1ThY_MultiRP', b'Track2ThX_MultiRP', b'Track2ThY_MultiRP', b'Muon0Pt', b'Muon0Eta', b'Muon0Phi', b'Muon0VtxZ', b'Muon1Pt', b'Muon1Eta', b'Muon1Phi', b'Muon1VtxZ', b'PrimVertexZ', b'InvMass', b'ExtraPfCands', b'Acopl', b'XiMuMuPlus', b'XiMuMuMinus']\n",
      "['Run', 'LumiSection', 'BX', 'EventNum', 'CrossingAngle', 'MultiRP', 'Arm', 'RPId1', 'RPId2', 'TrackX1', 'TrackY1', 'TrackX2', 'TrackY2', 'Xi', 'T', 'ThX', 'ThY', 'Time', 'TrackThX_SingleRP', 'TrackThY_SingleRP', 'Track1ThX_MultiRP', 'Track1ThY_MultiRP', 'Track2ThX_MultiRP', 'Track2ThY_MultiRP', 'Muon0Pt', 'Muon0Eta', 'Muon0Phi', 'Muon0VtxZ', 'Muon1Pt', 'Muon1Eta', 'Muon1Phi', 'Muon1VtxZ', 'PrimVertexZ', 'InvMass', 'ExtraPfCands', 'Acopl', 'XiMuMuPlus', 'XiMuMuMinus']\n",
      "[63278080, 943020, 881140, 640180]\n",
      "[b'All', b'Muon', b'Vertex', b'ProtonsRnd']\n",
      "['All', 'Muon', 'Vertex', 'ProtonsRnd']\n",
      "              Run  LumiSection      BX     EventNum  CrossingAngle  MultiRP  \\\n",
      "0        297227.0         53.0  2759.0   36430301.0          120.0      0.0   \n",
      "1        297227.0         53.0  2759.0   36430301.0          120.0      0.0   \n",
      "2        297227.0         53.0  2759.0   36430301.0          120.0      0.0   \n",
      "3        297227.0         53.0  2759.0   36430301.0          120.0      0.0   \n",
      "4        297227.0         53.0  2759.0   36430301.0          120.0      1.0   \n",
      "5        297227.0         53.0  2297.0   36577201.0          120.0      0.0   \n",
      "6        297227.0         53.0  2297.0   36577201.0          120.0      0.0   \n",
      "7        297227.0         53.0  2297.0   36577201.0          120.0      0.0   \n",
      "8        297227.0         53.0  2297.0   36577201.0          120.0      1.0   \n",
      "9        297227.0         55.0   867.0   38079658.0          120.0      0.0   \n",
      "10       297227.0         55.0   867.0   38079658.0          120.0      0.0   \n",
      "11       297227.0         55.0   867.0   38079658.0          120.0      0.0   \n",
      "12       297227.0         55.0   867.0   38079658.0          120.0      0.0   \n",
      "13       297227.0         56.0  1102.0   38791639.0          120.0      0.0   \n",
      "14       297227.0         56.0  1102.0   38791639.0          120.0      0.0   \n",
      "15       297227.0         56.0  1102.0   38791639.0          120.0      0.0   \n",
      "16       297227.0         56.0  1102.0   38791639.0          120.0      0.0   \n",
      "17       297227.0         56.0   378.0   38604580.0          120.0      0.0   \n",
      "18       297227.0         56.0   378.0   38604580.0          120.0      0.0   \n",
      "19       297227.0         56.0   378.0   38604580.0          120.0      1.0   \n",
      "20       297227.0         56.0  1182.0   38257820.0          120.0      0.0   \n",
      "21       297227.0         56.0  1182.0   38257820.0          120.0      0.0   \n",
      "22       297227.0         56.0  1182.0   38257820.0          120.0      0.0   \n",
      "23       297227.0         56.0  1182.0   38257820.0          120.0      0.0   \n",
      "24       297227.0         56.0  1182.0   38257820.0          120.0      0.0   \n",
      "25       297227.0         56.0  1182.0   38257820.0          120.0      1.0   \n",
      "26       297227.0         57.0   385.0   39720740.0          120.0      0.0   \n",
      "27       297227.0         57.0   385.0   39720740.0          120.0      0.0   \n",
      "28       297227.0         57.0   385.0   39720740.0          120.0      0.0   \n",
      "29       297227.0         57.0   385.0   39720740.0          120.0      0.0   \n",
      "...           ...          ...     ...          ...            ...      ...   \n",
      "2141470  299062.0        131.0  2946.0  224920334.0          140.0      0.0   \n",
      "2141471  299062.0        131.0  2946.0  224920334.0          140.0      0.0   \n",
      "2141472  299062.0        131.0  2946.0  224920334.0          140.0      0.0   \n",
      "2141473  299062.0        131.0  2946.0  224920334.0          140.0      0.0   \n",
      "2141474  299062.0        131.0  2946.0  224920334.0          140.0      0.0   \n",
      "2141475  299062.0        131.0  2946.0  224920334.0          140.0      0.0   \n",
      "2141476  299062.0        131.0   960.0  224908076.0          140.0      0.0   \n",
      "2141477  299062.0        131.0   960.0  224908076.0          140.0      0.0   \n",
      "2141478  299062.0        131.0   960.0  224908076.0          140.0      0.0   \n",
      "2141479  299062.0        131.0   960.0  224908076.0          140.0      0.0   \n",
      "2141480  299062.0        131.0   960.0  224908076.0          140.0      1.0   \n",
      "2141481  299062.0        131.0   960.0  224908076.0          140.0      1.0   \n",
      "2141482  299062.0        133.0  2008.0  227787107.0          140.0      0.0   \n",
      "2141483  299062.0        133.0  2008.0  227787107.0          140.0      0.0   \n",
      "2141484  299062.0        133.0  2008.0  227787107.0          140.0      0.0   \n",
      "2141485  299062.0        133.0  2008.0  227787107.0          140.0      0.0   \n",
      "2141486  299062.0        133.0  2008.0  227787107.0          140.0      0.0   \n",
      "2141487  299062.0        133.0  2008.0  227787107.0          140.0      1.0   \n",
      "2141488  299062.0        133.0  2008.0  227787107.0          140.0      1.0   \n",
      "2141489  299062.0        133.0  1412.0  227575948.0          140.0      0.0   \n",
      "2141490  299062.0        133.0  1412.0  227575948.0          140.0      0.0   \n",
      "2141491  299062.0        133.0  1412.0  227575948.0          140.0      0.0   \n",
      "2141492  299062.0        133.0  1412.0  227575948.0          140.0      0.0   \n",
      "2141493  299062.0        133.0  1412.0  227575948.0          140.0      1.0   \n",
      "2141494  299062.0        133.0  1721.0  226402907.0          140.0      0.0   \n",
      "2141495  299062.0        133.0  1721.0  226402907.0          140.0      0.0   \n",
      "2141496  299062.0        133.0  1721.0  226402907.0          140.0      0.0   \n",
      "2141497  299062.0        133.0  1721.0  226402907.0          140.0      0.0   \n",
      "2141498  299062.0        133.0  1721.0  226402907.0          140.0      0.0   \n",
      "2141499  299062.0        133.0  1721.0  226402907.0          140.0      1.0   \n",
      "\n",
      "           Arm  RPId1  RPId2    TrackX1  ...    Muon1Pt  Muon1Eta  Muon1Phi  \\\n",
      "0       -999.0   23.0   -1.0   9.954590  ...  54.994999  0.252747 -2.969282   \n",
      "1       -999.0   23.0   -1.0  10.817871  ...  54.994999  0.252747 -2.969282   \n",
      "2       -999.0  103.0   -1.0   5.083252  ...  54.994999  0.252747 -2.969282   \n",
      "3       -999.0  123.0   -1.0   5.223572  ...  54.994999  0.252747 -2.969282   \n",
      "4          1.0  103.0  123.0   5.083252  ...  54.994999  0.252747 -2.969282   \n",
      "5       -999.0    3.0   -1.0   5.887939  ...  63.807861  1.228711 -2.968801   \n",
      "6       -999.0   23.0   -1.0   4.089600  ...  63.807861  1.228711 -2.968801   \n",
      "7       -999.0   23.0   -1.0   5.682251  ...  63.807861  1.228711 -2.968801   \n",
      "8          0.0    3.0   23.0   5.887939  ...  63.807861  1.228711 -2.968801   \n",
      "9       -999.0    3.0   -1.0   3.419312  ...  62.493637 -1.693658 -0.273957   \n",
      "10      -999.0   23.0   -1.0   3.547302  ...  62.493637 -1.693658 -0.273957   \n",
      "11      -999.0   23.0   -1.0   3.592834  ...  62.493637 -1.693658 -0.273957   \n",
      "12      -999.0  103.0   -1.0   9.713867  ...  62.493637 -1.693658 -0.273957   \n",
      "13      -999.0   23.0   -1.0   4.049805  ...  65.022888 -1.087328 -0.051032   \n",
      "14      -999.0   23.0   -1.0   4.946960  ...  65.022888 -1.087328 -0.051032   \n",
      "15      -999.0  123.0   -1.0   6.663513  ...  65.022888 -1.087328 -0.051032   \n",
      "16      -999.0  123.0   -1.0   9.748047  ...  65.022888 -1.087328 -0.051032   \n",
      "17      -999.0    3.0   -1.0  12.928223  ...  70.969543 -1.393078 -2.552279   \n",
      "18      -999.0   23.0   -1.0  13.111084  ...  70.969543 -1.393078 -2.552279   \n",
      "19         0.0    3.0   23.0  12.928223  ...  70.969543 -1.393078 -2.552279   \n",
      "20      -999.0    3.0   -1.0   5.074707  ...  82.668289  1.051386  1.033899   \n",
      "21      -999.0   23.0   -1.0   5.225037  ...  82.668289  1.051386  1.033899   \n",
      "22      -999.0  123.0   -1.0   4.517944  ...  82.668289  1.051386  1.033899   \n",
      "23      -999.0  123.0   -1.0   7.212769  ...  82.668289  1.051386  1.033899   \n",
      "24      -999.0  123.0   -1.0  10.018433  ...  82.668289  1.051386  1.033899   \n",
      "25         0.0    3.0   23.0   5.074707  ...  82.668289  1.051386  1.033899   \n",
      "26      -999.0    3.0   -1.0  13.495605  ...  64.020714  0.272010 -0.568019   \n",
      "27      -999.0   23.0   -1.0  13.702759  ...  64.020714  0.272010 -0.568019   \n",
      "28      -999.0  103.0   -1.0   7.404785  ...  64.020714  0.272010 -0.568019   \n",
      "29      -999.0  123.0   -1.0   7.258057  ...  64.020714  0.272010 -0.568019   \n",
      "...        ...    ...    ...        ...  ...        ...       ...       ...   \n",
      "2141470 -999.0   23.0   -1.0   4.320984  ...  54.683372 -0.390408 -0.294749   \n",
      "2141471 -999.0   23.0   -1.0   4.507141  ...  54.683372 -0.390408 -0.294749   \n",
      "2141472 -999.0   23.0   -1.0   8.053467  ...  54.683372 -0.390408 -0.294749   \n",
      "2141473 -999.0  123.0   -1.0   2.907257  ...  54.683372 -0.390408 -0.294749   \n",
      "2141474 -999.0  123.0   -1.0   5.195251  ...  54.683372 -0.390408 -0.294749   \n",
      "2141475 -999.0  123.0   -1.0   9.468384  ...  54.683372 -0.390408 -0.294749   \n",
      "2141476 -999.0    3.0   -1.0   4.358398  ...  70.602394  1.388745 -1.295744   \n",
      "2141477 -999.0   23.0   -1.0   4.388367  ...  70.602394  1.388745 -1.295744   \n",
      "2141478 -999.0  103.0   -1.0   2.815308  ...  70.602394  1.388745 -1.295744   \n",
      "2141479 -999.0  123.0   -1.0   2.635010  ...  70.602394  1.388745 -1.295744   \n",
      "2141480    0.0    3.0   23.0   4.358398  ...  70.602394  1.388745 -1.295744   \n",
      "2141481    1.0  103.0  123.0   2.815308  ...  70.602394  1.388745 -1.295744   \n",
      "2141482 -999.0    3.0   -1.0   8.796387  ...  78.674065  1.664394 -2.532995   \n",
      "2141483 -999.0   23.0   -1.0   8.771118  ...  78.674065  1.664394 -2.532995   \n",
      "2141484 -999.0  103.0   -1.0   9.555664  ...  78.674065  1.664394 -2.532995   \n",
      "2141485 -999.0  123.0   -1.0   2.611084  ...  78.674065  1.664394 -2.532995   \n",
      "2141486 -999.0  123.0   -1.0   9.523682  ...  78.674065  1.664394 -2.532995   \n",
      "2141487    0.0    3.0   23.0   8.796387  ...  78.674065  1.664394 -2.532995   \n",
      "2141488    1.0  103.0  123.0   9.555664  ...  78.674065  1.664394 -2.532995   \n",
      "2141489 -999.0    3.0   -1.0   3.514893  ...  56.450363  2.362192  1.253203   \n",
      "2141490 -999.0   23.0   -1.0   3.363617  ...  56.450363  2.362192  1.253203   \n",
      "2141491 -999.0   23.0   -1.0  21.239746  ...  56.450363  2.362192  1.253203   \n",
      "2141492 -999.0  123.0   -1.0   3.584137  ...  56.450363  2.362192  1.253203   \n",
      "2141493    0.0    3.0   23.0   3.514893  ...  56.450363  2.362192  1.253203   \n",
      "2141494 -999.0    3.0   -1.0   3.010132  ...  53.149601 -1.497319  1.987280   \n",
      "2141495 -999.0   23.0   -1.0   2.945831  ...  53.149601 -1.497319  1.987280   \n",
      "2141496 -999.0   23.0   -1.0   3.207184  ...  53.149601 -1.497319  1.987280   \n",
      "2141497 -999.0  103.0   -1.0   4.512695  ...  53.149601 -1.497319  1.987280   \n",
      "2141498 -999.0  123.0   -1.0   4.497681  ...  53.149601 -1.497319  1.987280   \n",
      "2141499    1.0  103.0  123.0   4.512695  ...  53.149601 -1.497319  1.987280   \n",
      "\n",
      "         Muon1VtxZ  PrimVertexZ     InvMass  ExtraPfCands     Acopl  \\\n",
      "0        -4.387092    -4.388010   91.897397          26.0  0.592593   \n",
      "1        -4.387092    -4.388010   91.897397          26.0  0.592593   \n",
      "2        -4.387092    -4.388010   91.897397          26.0  0.592593   \n",
      "3        -4.387092    -4.388010   91.897397          26.0  0.592593   \n",
      "4        -4.387092    -4.388010   91.897397          26.0  0.592593   \n",
      "5         7.642523     7.640311   12.129827          48.0  0.983012   \n",
      "6         7.642523     7.640311   12.129827          48.0  0.983012   \n",
      "7         7.642523     7.640311   12.129827          48.0  0.983012   \n",
      "8         7.642523     7.640311   12.129827          48.0  0.983012   \n",
      "9        -0.406477    -0.412115  123.616515          27.0  0.132612   \n",
      "10       -0.406477    -0.412115  123.616515          27.0  0.132612   \n",
      "11       -0.406477    -0.412115  123.616515          27.0  0.132612   \n",
      "12       -0.406477    -0.412115  123.616515          27.0  0.132612   \n",
      "13       -1.935442    -1.928177  152.786010          29.0  0.058782   \n",
      "14       -1.935442    -1.928177  152.786010          29.0  0.058782   \n",
      "15       -1.935442    -1.928177  152.786010          29.0  0.058782   \n",
      "16       -1.935442    -1.928177  152.786010          29.0  0.058782   \n",
      "17        1.891896     1.899735   90.916580          28.0  0.588286   \n",
      "18        1.891896     1.899735   90.916580          28.0  0.588286   \n",
      "19        1.891896     1.899735   90.916580          28.0  0.588286   \n",
      "20        2.199047     2.202265  181.937078          16.0  0.006314   \n",
      "21        2.199047     2.202265  181.937078          16.0  0.006314   \n",
      "22        2.199047     2.202265  181.937078          16.0  0.006314   \n",
      "23        2.199047     2.202265  181.937078          16.0  0.006314   \n",
      "24        2.199047     2.202265  181.937078          16.0  0.006314   \n",
      "25        2.199047     2.202265  181.937078          16.0  0.006314   \n",
      "26       -4.808328    -4.800040   90.311864          48.0  0.719986   \n",
      "27       -4.808328    -4.800040   90.311864          48.0  0.719986   \n",
      "28       -4.808328    -4.800040   90.311864          48.0  0.719986   \n",
      "29       -4.808328    -4.800040   90.311864          48.0  0.719986   \n",
      "...            ...          ...         ...           ...       ...   \n",
      "2141470  -5.183831    -5.182962  140.142973          17.0  0.190766   \n",
      "2141471  -5.183831    -5.182962  140.142973          17.0  0.190766   \n",
      "2141472  -5.183831    -5.182962  140.142973          17.0  0.190766   \n",
      "2141473  -5.183831    -5.182962  140.142973          17.0  0.190766   \n",
      "2141474  -5.183831    -5.182962  140.142973          17.0  0.190766   \n",
      "2141475  -5.183831    -5.182962  140.142973          17.0  0.190766   \n",
      "2141476  -8.591934    -8.594353  163.795522          71.0  0.117016   \n",
      "2141477  -8.591934    -8.594353  163.795522          71.0  0.117016   \n",
      "2141478  -8.591934    -8.594353  163.795522          71.0  0.117016   \n",
      "2141479  -8.591934    -8.594353  163.795522          71.0  0.117016   \n",
      "2141480  -8.591934    -8.594353  163.795522          71.0  0.117016   \n",
      "2141481  -8.591934    -8.594353  163.795522          71.0  0.117016   \n",
      "2141482  -2.850110    -2.848965  171.503505          44.0  0.017278   \n",
      "2141483  -2.850110    -2.848965  171.503505          44.0  0.017278   \n",
      "2141484  -2.850110    -2.848965  171.503505          44.0  0.017278   \n",
      "2141485  -2.850110    -2.848965  171.503505          44.0  0.017278   \n",
      "2141486  -2.850110    -2.848965  171.503505          44.0  0.017278   \n",
      "2141487  -2.850110    -2.848965  171.503505          44.0  0.017278   \n",
      "2141488  -2.850110    -2.848965  171.503505          44.0  0.017278   \n",
      "2141489  -2.640838    -2.621965  104.313161          38.0  0.316116   \n",
      "2141490  -2.640838    -2.621965  104.313161          38.0  0.316116   \n",
      "2141491  -2.640838    -2.621965  104.313161          38.0  0.316116   \n",
      "2141492  -2.640838    -2.621965  104.313161          38.0  0.316116   \n",
      "2141493  -2.640838    -2.621965  104.313161          38.0  0.316116   \n",
      "2141494  -3.900965    -3.901474   92.508173          19.0  0.664819   \n",
      "2141495  -3.900965    -3.901474   92.508173          19.0  0.664819   \n",
      "2141496  -3.900965    -3.901474   92.508173          19.0  0.664819   \n",
      "2141497  -3.900965    -3.901474   92.508173          19.0  0.664819   \n",
      "2141498  -3.900965    -3.901474   92.508173          19.0  0.664819   \n",
      "2141499  -3.900965    -3.901474   92.508173          19.0  0.664819   \n",
      "\n",
      "         XiMuMuPlus  XiMuMuMinus  \n",
      "0          0.010987     0.012259  \n",
      "1          0.010987     0.012259  \n",
      "2          0.010987     0.012259  \n",
      "3          0.010987     0.012259  \n",
      "4          0.010987     0.012259  \n",
      "5          0.056195     0.004091  \n",
      "6          0.056195     0.004091  \n",
      "7          0.056195     0.004091  \n",
      "8          0.056195     0.004091  \n",
      "9          0.001953     0.048346  \n",
      "10         0.001953     0.048346  \n",
      "11         0.001953     0.048346  \n",
      "12         0.001953     0.048346  \n",
      "13         0.002211     0.062855  \n",
      "14         0.002211     0.062855  \n",
      "15         0.002211     0.062855  \n",
      "16         0.002211     0.062855  \n",
      "17         0.003354     0.038210  \n",
      "18         0.003354     0.038210  \n",
      "19         0.003354     0.038210  \n",
      "20         0.061436     0.003189  \n",
      "21         0.061436     0.003189  \n",
      "22         0.061436     0.003189  \n",
      "23         0.061436     0.003189  \n",
      "24         0.061436     0.003189  \n",
      "25         0.061436     0.003189  \n",
      "26         0.025574     0.013122  \n",
      "27         0.025574     0.013122  \n",
      "28         0.025574     0.013122  \n",
      "29         0.025574     0.013122  \n",
      "...             ...          ...  \n",
      "2141470    0.004553     0.028325  \n",
      "2141471    0.004553     0.028325  \n",
      "2141472    0.004553     0.028325  \n",
      "2141473    0.004553     0.028325  \n",
      "2141474    0.004553     0.028325  \n",
      "2141475    0.004553     0.028325  \n",
      "2141476    0.087380     0.001866  \n",
      "2141477    0.087380     0.001866  \n",
      "2141478    0.087380     0.001866  \n",
      "2141479    0.087380     0.001866  \n",
      "2141480    0.087380     0.001866  \n",
      "2141481    0.087380     0.001866  \n",
      "2141482    0.050741     0.003437  \n",
      "2141483    0.050741     0.003437  \n",
      "2141484    0.050741     0.003437  \n",
      "2141485    0.050741     0.003437  \n",
      "2141486    0.050741     0.003437  \n",
      "2141487    0.050741     0.003437  \n",
      "2141488    0.050741     0.003437  \n",
      "2141489    0.098440     0.000848  \n",
      "2141490    0.098440     0.000848  \n",
      "2141491    0.098440     0.000848  \n",
      "2141492    0.098440     0.000848  \n",
      "2141493    0.098440     0.000848  \n",
      "2141494    0.003522     0.075678  \n",
      "2141495    0.003522     0.075678  \n",
      "2141496    0.003522     0.075678  \n",
      "2141497    0.003522     0.075678  \n",
      "2141498    0.003522     0.075678  \n",
      "2141499    0.003522     0.075678  \n",
      "\n",
      "[2141500 rows x 38 columns]\n"
     ]
    }
   ],
   "source": [
    "df = None\n",
    "#with h5py.File('output-test-mixing.h5', 'r') as f:\n",
    "with h5py.File('output/output-UL2017B-PreSel-Rnd-Res20.h5', 'r') as f:    \n",
    "    print ( list( f.keys() ) )\n",
    "    dset = f['protons']\n",
    "    print ( dset.shape )\n",
    "    print ( dset[:,:] )\n",
    "    dset_columns = f['columns']\n",
    "    print ( dset_columns.shape )\n",
    "    columns_ = list( dset_columns )\n",
    "    print ( columns_ )\n",
    "    columns_str = [ item.decode(\"utf-8\") for item in columns_ ]\n",
    "    print ( columns_str )\n",
    "    dset_counts = f['event_counts']\n",
    "    event_counts_ = list( dset_counts )\n",
    "    print ( event_counts_ )\n",
    "    dset_selections = f['selections']\n",
    "    selections_ = list( dset_selections )\n",
    "    print ( selections_ )\n",
    "    selections_str = [ item.decode(\"utf-8\") for item in selections_ ]\n",
    "    print ( selections_str )\n",
    "\n",
    "    df = pd.DataFrame( dset, columns=columns_str )\n",
    "    print ( df )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Run</th>\n",
       "      <th>LumiSection</th>\n",
       "      <th>EventNum</th>\n",
       "      <th>CrossingAngle</th>\n",
       "      <th>MultiRP</th>\n",
       "      <th>Arm</th>\n",
       "      <th>RPId1</th>\n",
       "      <th>RPId2</th>\n",
       "      <th>TrackX1</th>\n",
       "      <th>TrackY1</th>\n",
       "      <th>...</th>\n",
       "      <th>Xi</th>\n",
       "      <th>T</th>\n",
       "      <th>ThX</th>\n",
       "      <th>ThY</th>\n",
       "      <th>Time</th>\n",
       "      <th>InvMass</th>\n",
       "      <th>ExtraPfCands</th>\n",
       "      <th>Acopl</th>\n",
       "      <th>XiMuMuPlus</th>\n",
       "      <th>XiMuMuMinus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>53</td>\n",
       "      <td>36430301</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>23</td>\n",
       "      <td>-1</td>\n",
       "      <td>9.954590</td>\n",
       "      <td>1.930664</td>\n",
       "      <td>...</td>\n",
       "      <td>0.095958</td>\n",
       "      <td>-0.097366</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000048</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.897397</td>\n",
       "      <td>26</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.010987</td>\n",
       "      <td>0.012259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>297227</td>\n",
       "      <td>53</td>\n",
       "      <td>36430301</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>23</td>\n",
       "      <td>-1</td>\n",
       "      <td>10.817871</td>\n",
       "      <td>3.526855</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103006</td>\n",
       "      <td>-0.249191</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000079</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.897397</td>\n",
       "      <td>26</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.010987</td>\n",
       "      <td>0.012259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>297227</td>\n",
       "      <td>53</td>\n",
       "      <td>36430301</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>103</td>\n",
       "      <td>-1</td>\n",
       "      <td>5.083252</td>\n",
       "      <td>0.758118</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075516</td>\n",
       "      <td>-0.038826</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.897397</td>\n",
       "      <td>26</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.010987</td>\n",
       "      <td>0.012259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>297227</td>\n",
       "      <td>53</td>\n",
       "      <td>36430301</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>123</td>\n",
       "      <td>-1</td>\n",
       "      <td>5.223572</td>\n",
       "      <td>0.882141</td>\n",
       "      <td>...</td>\n",
       "      <td>0.078583</td>\n",
       "      <td>-0.038955</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.897397</td>\n",
       "      <td>26</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.010987</td>\n",
       "      <td>0.012259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>297227</td>\n",
       "      <td>53</td>\n",
       "      <td>36430301</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>103</td>\n",
       "      <td>123</td>\n",
       "      <td>5.083252</td>\n",
       "      <td>0.758118</td>\n",
       "      <td>...</td>\n",
       "      <td>0.057598</td>\n",
       "      <td>-0.383160</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>-0.000035</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.897397</td>\n",
       "      <td>26</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.010987</td>\n",
       "      <td>0.012259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>297227</td>\n",
       "      <td>53</td>\n",
       "      <td>36577201</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>5.887939</td>\n",
       "      <td>-1.379761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.058167</td>\n",
       "      <td>-0.281994</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.129827</td>\n",
       "      <td>48</td>\n",
       "      <td>0.983012</td>\n",
       "      <td>0.056195</td>\n",
       "      <td>0.004091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>297227</td>\n",
       "      <td>53</td>\n",
       "      <td>36577201</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>23</td>\n",
       "      <td>-1</td>\n",
       "      <td>4.089600</td>\n",
       "      <td>0.040321</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041184</td>\n",
       "      <td>-0.002246</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.129827</td>\n",
       "      <td>48</td>\n",
       "      <td>0.983012</td>\n",
       "      <td>0.056195</td>\n",
       "      <td>0.004091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>297227</td>\n",
       "      <td>53</td>\n",
       "      <td>36577201</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>23</td>\n",
       "      <td>-1</td>\n",
       "      <td>5.682251</td>\n",
       "      <td>-1.620361</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055893</td>\n",
       "      <td>-0.347741</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.129827</td>\n",
       "      <td>48</td>\n",
       "      <td>0.983012</td>\n",
       "      <td>0.056195</td>\n",
       "      <td>0.004091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>297227</td>\n",
       "      <td>53</td>\n",
       "      <td>36577201</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>5.887939</td>\n",
       "      <td>-1.379761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.077986</td>\n",
       "      <td>-0.813752</td>\n",
       "      <td>-0.000107</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.129827</td>\n",
       "      <td>48</td>\n",
       "      <td>0.983012</td>\n",
       "      <td>0.056195</td>\n",
       "      <td>0.004091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>297227</td>\n",
       "      <td>55</td>\n",
       "      <td>38079658</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>3.419312</td>\n",
       "      <td>0.312195</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036575</td>\n",
       "      <td>-0.125956</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000055</td>\n",
       "      <td>0.0</td>\n",
       "      <td>123.616515</td>\n",
       "      <td>27</td>\n",
       "      <td>0.132612</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.048346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>297227</td>\n",
       "      <td>55</td>\n",
       "      <td>38079658</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>23</td>\n",
       "      <td>-1</td>\n",
       "      <td>3.547302</td>\n",
       "      <td>0.134613</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037652</td>\n",
       "      <td>-0.012823</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>123.616515</td>\n",
       "      <td>27</td>\n",
       "      <td>0.132612</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.048346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>297227</td>\n",
       "      <td>55</td>\n",
       "      <td>38079658</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>23</td>\n",
       "      <td>-1</td>\n",
       "      <td>3.592834</td>\n",
       "      <td>0.440948</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038108</td>\n",
       "      <td>-0.118145</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000054</td>\n",
       "      <td>0.0</td>\n",
       "      <td>123.616515</td>\n",
       "      <td>27</td>\n",
       "      <td>0.132612</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.048346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>297227</td>\n",
       "      <td>55</td>\n",
       "      <td>38079658</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>103</td>\n",
       "      <td>-1</td>\n",
       "      <td>9.713867</td>\n",
       "      <td>5.133301</td>\n",
       "      <td>...</td>\n",
       "      <td>0.133569</td>\n",
       "      <td>-0.276776</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000084</td>\n",
       "      <td>0.0</td>\n",
       "      <td>123.616515</td>\n",
       "      <td>27</td>\n",
       "      <td>0.132612</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.048346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>297227</td>\n",
       "      <td>56</td>\n",
       "      <td>38791639</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>23</td>\n",
       "      <td>-1</td>\n",
       "      <td>4.049805</td>\n",
       "      <td>0.465942</td>\n",
       "      <td>...</td>\n",
       "      <td>0.039120</td>\n",
       "      <td>-0.117024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>152.786010</td>\n",
       "      <td>29</td>\n",
       "      <td>0.058782</td>\n",
       "      <td>0.002211</td>\n",
       "      <td>0.062855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>297227</td>\n",
       "      <td>56</td>\n",
       "      <td>38791639</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>23</td>\n",
       "      <td>-1</td>\n",
       "      <td>4.946960</td>\n",
       "      <td>2.633789</td>\n",
       "      <td>...</td>\n",
       "      <td>0.047181</td>\n",
       "      <td>-1.692289</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000205</td>\n",
       "      <td>0.0</td>\n",
       "      <td>152.786010</td>\n",
       "      <td>29</td>\n",
       "      <td>0.058782</td>\n",
       "      <td>0.002211</td>\n",
       "      <td>0.062855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>297227</td>\n",
       "      <td>56</td>\n",
       "      <td>38791639</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>123</td>\n",
       "      <td>-1</td>\n",
       "      <td>6.663513</td>\n",
       "      <td>-1.212891</td>\n",
       "      <td>...</td>\n",
       "      <td>0.086289</td>\n",
       "      <td>-0.054168</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.0</td>\n",
       "      <td>152.786010</td>\n",
       "      <td>29</td>\n",
       "      <td>0.058782</td>\n",
       "      <td>0.002211</td>\n",
       "      <td>0.062855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>297227</td>\n",
       "      <td>56</td>\n",
       "      <td>38791639</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>123</td>\n",
       "      <td>-1</td>\n",
       "      <td>9.748047</td>\n",
       "      <td>1.336060</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120796</td>\n",
       "      <td>-0.035551</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>152.786010</td>\n",
       "      <td>29</td>\n",
       "      <td>0.058782</td>\n",
       "      <td>0.002211</td>\n",
       "      <td>0.062855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>297227</td>\n",
       "      <td>56</td>\n",
       "      <td>38604580</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>12.928223</td>\n",
       "      <td>2.162598</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111797</td>\n",
       "      <td>-0.092316</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000046</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.916580</td>\n",
       "      <td>28</td>\n",
       "      <td>0.588286</td>\n",
       "      <td>0.003354</td>\n",
       "      <td>0.038210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>297227</td>\n",
       "      <td>56</td>\n",
       "      <td>38604580</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>23</td>\n",
       "      <td>-1</td>\n",
       "      <td>13.111084</td>\n",
       "      <td>2.165527</td>\n",
       "      <td>...</td>\n",
       "      <td>0.112146</td>\n",
       "      <td>-0.082408</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.916580</td>\n",
       "      <td>28</td>\n",
       "      <td>0.588286</td>\n",
       "      <td>0.003354</td>\n",
       "      <td>0.038210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>297227</td>\n",
       "      <td>56</td>\n",
       "      <td>38604580</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>12.928223</td>\n",
       "      <td>2.162598</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107395</td>\n",
       "      <td>-0.029823</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.916580</td>\n",
       "      <td>28</td>\n",
       "      <td>0.588286</td>\n",
       "      <td>0.003354</td>\n",
       "      <td>0.038210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Run  LumiSection  EventNum  CrossingAngle  MultiRP  Arm  RPId1  RPId2  \\\n",
       "0   297227           53  36430301          120.0        0 -999     23     -1   \n",
       "1   297227           53  36430301          120.0        0 -999     23     -1   \n",
       "2   297227           53  36430301          120.0        0 -999    103     -1   \n",
       "3   297227           53  36430301          120.0        0 -999    123     -1   \n",
       "4   297227           53  36430301          120.0        1    1    103    123   \n",
       "5   297227           53  36577201          120.0        0 -999      3     -1   \n",
       "6   297227           53  36577201          120.0        0 -999     23     -1   \n",
       "7   297227           53  36577201          120.0        0 -999     23     -1   \n",
       "8   297227           53  36577201          120.0        1    0      3     23   \n",
       "9   297227           55  38079658          120.0        0 -999      3     -1   \n",
       "10  297227           55  38079658          120.0        0 -999     23     -1   \n",
       "11  297227           55  38079658          120.0        0 -999     23     -1   \n",
       "12  297227           55  38079658          120.0        0 -999    103     -1   \n",
       "13  297227           56  38791639          120.0        0 -999     23     -1   \n",
       "14  297227           56  38791639          120.0        0 -999     23     -1   \n",
       "15  297227           56  38791639          120.0        0 -999    123     -1   \n",
       "16  297227           56  38791639          120.0        0 -999    123     -1   \n",
       "17  297227           56  38604580          120.0        0 -999      3     -1   \n",
       "18  297227           56  38604580          120.0        0 -999     23     -1   \n",
       "19  297227           56  38604580          120.0        1    0      3     23   \n",
       "\n",
       "      TrackX1   TrackY1  ...        Xi         T       ThX       ThY  Time  \\\n",
       "0    9.954590  1.930664  ...  0.095958 -0.097366  0.000000 -0.000048   0.0   \n",
       "1   10.817871  3.526855  ...  0.103006 -0.249191  0.000000 -0.000079   0.0   \n",
       "2    5.083252  0.758118  ...  0.075516 -0.038826  0.000000 -0.000029   0.0   \n",
       "3    5.223572  0.882141  ...  0.078583 -0.038955  0.000000 -0.000029   0.0   \n",
       "4    5.083252  0.758118  ...  0.057598 -0.383160  0.000091 -0.000035   0.0   \n",
       "5    5.887939 -1.379761  ...  0.058167 -0.281994  0.000000  0.000084   0.0   \n",
       "6    4.089600  0.040321  ...  0.041184 -0.002246  0.000000 -0.000004   0.0   \n",
       "7    5.682251 -1.620361  ...  0.055893 -0.347741  0.000000  0.000093   0.0   \n",
       "8    5.887939 -1.379761  ...  0.077986 -0.813752 -0.000107  0.000097   0.0   \n",
       "9    3.419312  0.312195  ...  0.036575 -0.125956  0.000000 -0.000055   0.0   \n",
       "10   3.547302  0.134613  ...  0.037652 -0.012823  0.000000 -0.000017   0.0   \n",
       "11   3.592834  0.440948  ...  0.038108 -0.118145  0.000000 -0.000054   0.0   \n",
       "12   9.713867  5.133301  ...  0.133569 -0.276776  0.000000 -0.000084   0.0   \n",
       "13   4.049805  0.465942  ...  0.039120 -0.117024  0.000000 -0.000053   0.0   \n",
       "14   4.946960  2.633789  ...  0.047181 -1.692289  0.000000 -0.000205   0.0   \n",
       "15   6.663513 -1.212891  ...  0.086289 -0.054168  0.000000  0.000035   0.0   \n",
       "16   9.748047  1.336060  ...  0.120796 -0.035551  0.000000 -0.000024   0.0   \n",
       "17  12.928223  2.162598  ...  0.111797 -0.092316  0.000000 -0.000046   0.0   \n",
       "18  13.111084  2.165527  ...  0.112146 -0.082408  0.000000 -0.000043   0.0   \n",
       "19  12.928223  2.162598  ...  0.107395 -0.029823  0.000019  0.000011   0.0   \n",
       "\n",
       "       InvMass  ExtraPfCands     Acopl  XiMuMuPlus  XiMuMuMinus  \n",
       "0    91.897397            26  0.592593    0.010987     0.012259  \n",
       "1    91.897397            26  0.592593    0.010987     0.012259  \n",
       "2    91.897397            26  0.592593    0.010987     0.012259  \n",
       "3    91.897397            26  0.592593    0.010987     0.012259  \n",
       "4    91.897397            26  0.592593    0.010987     0.012259  \n",
       "5    12.129827            48  0.983012    0.056195     0.004091  \n",
       "6    12.129827            48  0.983012    0.056195     0.004091  \n",
       "7    12.129827            48  0.983012    0.056195     0.004091  \n",
       "8    12.129827            48  0.983012    0.056195     0.004091  \n",
       "9   123.616515            27  0.132612    0.001953     0.048346  \n",
       "10  123.616515            27  0.132612    0.001953     0.048346  \n",
       "11  123.616515            27  0.132612    0.001953     0.048346  \n",
       "12  123.616515            27  0.132612    0.001953     0.048346  \n",
       "13  152.786010            29  0.058782    0.002211     0.062855  \n",
       "14  152.786010            29  0.058782    0.002211     0.062855  \n",
       "15  152.786010            29  0.058782    0.002211     0.062855  \n",
       "16  152.786010            29  0.058782    0.002211     0.062855  \n",
       "17   90.916580            28  0.588286    0.003354     0.038210  \n",
       "18   90.916580            28  0.588286    0.003354     0.038210  \n",
       "19   90.916580            28  0.588286    0.003354     0.038210  \n",
       "\n",
       "[20 rows x 22 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df = df[ ['Run', 'LumiSection', 'EventNum', 'CrossingAngle', \n",
    "#          'MultiRP', 'Arm', 'RPId1', 'RPId2', 'TrackX1', 'TrackY1', 'TrackX2', 'TrackY2',\n",
    "#          'Xi', 'T', 'ThX', 'ThY', 'Time',\n",
    "#          'TrackThX_SingleRP', 'TrackThY_SingleRP',\n",
    "#          'Track1ThX_MultiRP', 'Track1ThY_MultiRP', 'Track2ThX_MultiRP', 'Track2ThY_MultiRP',          \n",
    "#          'Muon0Pt', 'Muon1Pt', 'InvMass', 'ExtraPfCands', 'Acopl', 'XiMuMuPlus', 'XiMuMuMinus'] ].astype( { \"Run\": \"int64\", \"LumiSection\": \"int64\", \"EventNum\": \"int64\", \"MultiRP\": \"int32\", \"Arm\": \"int32\", \"RPId1\": \"int32\", \"RPId2\": \"int32\", \"ExtraPfCands\": \"int32\" } )\n",
    "df = df[ ['Run', 'LumiSection', 'EventNum', 'CrossingAngle', \n",
    "          'MultiRP', 'Arm', 'RPId1', 'RPId2', 'TrackX1', 'TrackY1', 'TrackX2', 'TrackY2',\n",
    "          'Xi', 'T', 'ThX', 'ThY', 'Time',\n",
    "          'InvMass', 'ExtraPfCands', 'Acopl', 'XiMuMuPlus', 'XiMuMuMinus'] ].astype( { \"Run\": \"int64\", \"LumiSection\": \"int64\", \"EventNum\": \"int64\", \"MultiRP\": \"int32\", \"Arm\": \"int32\", \"RPId1\": \"int32\", \"RPId2\": \"int32\", \"ExtraPfCands\": \"int32\" } )\n",
    "df[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proton selection: MultiRP\n",
      "Number of protons: 234, 221\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\xi_{\\\\mu\\\\mu}$')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import LogNorm\n",
    "\n",
    "msk_excl = ( df[\"InvMass\"] >= 110. ) & ( df[\"Acopl\"] <= 0.009 ) & ( df[\"ExtraPfCands\"] <= 1 )\n",
    "\n",
    "msk_protons_1 = None\n",
    "msk_protons_2 = None\n",
    "if proton_selection == \"SingleRP\":\n",
    "    # Single-RP in pixel stations\n",
    "    msk_protons_1 = msk_excl & ( df[\"MultiRP\"] == 0) & ( df[\"RPId1\"] == 23 )\n",
    "    msk_protons_2 = msk_excl & ( df[\"MultiRP\"] == 0) & ( df[\"RPId1\"] == 123 )\n",
    "elif proton_selection == \"MultiRP\":\n",
    "    # Multi-RP\n",
    "    msk_protons_1 = msk_excl & ( df[\"MultiRP\"] == 1 ) & ( df[\"Arm\"] == 0 )\n",
    "    msk_protons_2 = msk_excl & ( df[\"MultiRP\"] == 1 ) & ( df[\"Arm\"] == 1 )\n",
    "\n",
    "print ( \"Proton selection: {}\".format( proton_selection ) )\n",
    "print ( \"Number of protons: {}, {}\".format( len( df[ \"Xi\" ][ msk_protons_1 ] ), len( df[ \"Xi\" ][ msk_protons_2 ] ) ) )\n",
    "\n",
    "fig, axes = plt.subplots( 2, 2, figsize=(20,20) )\n",
    "axes[0,0].plot( df[ \"Xi\" ][ msk_protons_1 ], df[ \"XiMuMuPlus\" ][ msk_protons_1 ], 'ko' )\n",
    "axes[0,0].plot( (0.,0.15), (0.,0.15), 'k--', linewidth=1 )\n",
    "axes[0,0].plot( (0.,0.15), (0.,0.90*0.15), 'k:', linewidth=1 )\n",
    "axes[0,0].plot( (0.,0.15), (0.,1.10*0.15), 'k:', linewidth=1 )\n",
    "axes[0,0].set_xlim(0.,0.15)\n",
    "axes[0,0].set_ylim(0.,0.15)\n",
    "axes[0,0].set_xlabel( r\"$\\xi_p$\", fontsize=22 )\n",
    "axes[0,0].set_ylabel( r\"$\\xi_{\\mu\\mu}$\", fontsize=22 )\n",
    "#axes[0,0].set_xlabel( r\"$\\xi_p$\", fontsize=22, ha='right', x=1.0 )\n",
    "#axes[0,0].set_ylabel( r\"$\\xi_{\\mu\\mu}$\", fontsize=22, ha='right', y=1.0 )\n",
    "axes[0,1].plot( df[ \"Xi\" ][ msk_protons_2 ], df[ \"XiMuMuMinus\" ][ msk_protons_2 ], 'ko' )\n",
    "axes[0,1].plot( (0.,0.15), (0.,0.15), 'k--', linewidth=1 )\n",
    "axes[0,1].plot( (0.,0.15), (0.,0.90*0.15), 'k:', linewidth=1 )\n",
    "axes[0,1].plot( (0.,0.15), (0.,1.10*0.15), 'k:', linewidth=1 )\n",
    "axes[0,1].set_xlim(0.,0.15)\n",
    "axes[0,1].set_ylim(0.,0.15)\n",
    "axes[0,1].set_xlabel( r\"$\\xi_p$\", fontsize=22 )\n",
    "axes[0,1].set_ylabel( r\"$\\xi_{\\mu\\mu}$\", fontsize=22 )\n",
    "counts, xedges, yedges, im = axes[1,0].hist2d( df[ \"Xi\" ][ msk_protons_1 ], df[ \"XiMuMuPlus\" ][ msk_protons_1 ], bins=(20,20), norm=LogNorm(), cmap='viridis' )\n",
    "fig.colorbar(im, ax=axes[1,0])\n",
    "axes[1,0].plot( (0.,0.15), (0.,0.15), 'k--', linewidth=1 )\n",
    "axes[1,0].plot( (0.,0.15), (0.,0.90*0.15), 'k:', linewidth=1 )\n",
    "axes[1,0].plot( (0.,0.15), (0.,1.10*0.15), 'k:', linewidth=1 )\n",
    "axes[1,0].set_xlim(0.,0.15)\n",
    "axes[1,0].set_ylim(0.,0.15)\n",
    "axes[1,0].set_xlabel( r\"$\\xi_p$\", fontsize=22 )\n",
    "axes[1,0].set_ylabel( r\"$\\xi_{\\mu\\mu}$\", fontsize=22 )\n",
    "counts, xedges, yedges, im = axes[1,1].hist2d( df[ \"Xi\" ][ msk_protons_2 ], df[ \"XiMuMuMinus\" ][ msk_protons_2 ], bins=(20,20), norm=LogNorm(), cmap='viridis' )\n",
    "fig.colorbar(im, ax=axes[1,1])\n",
    "axes[1,1].plot( (0.,0.15), (0.,0.15), 'k--', linewidth=1 )\n",
    "axes[1,1].plot( (0.,0.15), (0.,0.90*0.15), 'k:', linewidth=1 )\n",
    "axes[1,1].plot( (0.,0.15), (0.,1.10*0.15), 'k:', linewidth=1 )\n",
    "axes[1,1].set_xlim(0.,0.15)\n",
    "axes[1,1].set_ylim(0.,0.15)\n",
    "axes[1,1].set_xlabel( r\"$\\xi_p$\", fontsize=22 )\n",
    "axes[1,1].set_ylabel( r\"$\\xi_{\\mu\\mu}$\", fontsize=22 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.2 , 0.15, 0.45, 0.35, 0.75, 0.55, 0.75, 0.85, 0.8 , 0.8 , 0.4 ,\n",
       "        0.  , 0.  , 0.  , 0.  ]),\n",
       " array([-5.        , -4.46666667, -3.93333333, -3.4       , -2.86666667,\n",
       "        -2.33333333, -1.8       , -1.26666667, -0.73333333, -0.2       ,\n",
       "         0.33333333,  0.86666667,  1.4       ,  1.93333333,  2.46666667,\n",
       "         3.        ]),\n",
       " <a list of 15 Patch objects>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots( 1, 2, figsize=(20,10) )\n",
    "\n",
    "resample_factor = 20\n",
    "weights_1 = None\n",
    "if resample_factor > 1:\n",
    "    weights_1 = np.full_like( df[ \"Xi\" ][ msk_protons_1 ], ( 1./resample_factor ) )\n",
    "vals_1 = 1. - df[ \"Xi\" ][ msk_protons_1 ] / df[ \"XiMuMuPlus\" ][ msk_protons_1 ]\n",
    "axes[0].hist( vals_1, bins=15, range=(-5.,3.), weights=weights_1 )\n",
    "\n",
    "weights_2 = None\n",
    "if resample_factor > 1:\n",
    "    weights_2 = np.full_like( df[ \"Xi\" ][ msk_protons_2 ], ( 1./resample_factor ) )\n",
    "vals_2 = 1. - df[ \"Xi\" ][ msk_protons_2 ] / df[ \"XiMuMuMinus\" ][ msk_protons_2 ]\n",
    "axes[1].hist( vals_2, bins=15, range=(-5.,3.), weights=weights_2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}