LabelingWithoutMatching.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "suspended-purple",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Cannot import ROOT\n",
      "using device: cpu\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import os.path as osp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec\n",
    "import torch\n",
    "from torch_geometric.data import Dataset, Data, DataLoader\n",
    "from torch.utils.data import ConcatDataset, ChainDataset\n",
    "from pathlib import Path as Path\n",
    "from dataset import *\n",
    "from utils import *\n",
    "from cpt import *\n",
    "from functools import partial\n",
    "from tqdm import tqdm\n",
    "tqdm = partial(tqdm, position=0, leave=True)\n",
    "import matplotlib.pylab as pylab\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "USE_GPU = True\n",
    "dtype = torch.float32 # we will be using float\n",
    "if USE_GPU and torch.cuda.is_available():\n",
    "    device = torch.device('cuda')\n",
    "else:\n",
    "    device = torch.device('cpu')\n",
    "print('using device:', device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "grave-parks",
   "metadata": {},
   "source": [
    "## Load dataset (ttH, ttW, or ttbar)\n",
    "(the model is trained on ttH, so the last two test generalization to different processes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fifth-arrow",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Padding up to 2 outputs\n",
      "X: (pT, y, phi, m)\n",
      "Y: (pT, y, phi, m)\n"
     ]
    }
   ],
   "source": [
    "### DON'T CHANGE ###\n",
    "max_num_output = 2 # assert that there are at most two tops\n",
    "detector_x = True # use cartesian coordinates for input\n",
    "detector_y = True # use cartesian coordinates for output\n",
    "to_torch_test = get_to_torch(max_num_output, detector_x=detector_x, detector_y=detector_y, test=True) # reformat the data for torch\n",
    "### DON'T CHANGE ###\n",
    "\n",
    "# Pick a dataset by uncommenting it\n",
    "\n",
    "# ttH\n",
    "dataset_names = ['data/final/ttH']\n",
    "Ds = [ConcatDataset([LMDBDataset(f'{dataset_name}/data_{i}', transform=to_torch_test, use_cache=False, readahead=False) for i in range(0, 1)]) for dataset_name in dataset_names]\n",
    "Ds = [split_dataset(D, name=ds_name, max_train_event=None, max_test_event=None) for D, ds_name in zip(Ds, dataset_names)]\n",
    "D_train = ConcatDataset([D[0] for D in Ds])\n",
    "D_val = ConcatDataset([D[1] for D in Ds])\n",
    "D_test = ConcatDataset([D[2] for D in Ds])\n",
    "\n",
    "# # ttW\n",
    "# dataset_names = ['data/final/ttW']\n",
    "# D_test = LMDBDataset(f'{dataset_names[0]}/data_0', transform=to_torch_test, use_cache=True, readahead=False)\n",
    "\n",
    "# # ttbar\n",
    "# dataset_names = ['data/final/ttbar']\n",
    "# D_test = LMDBDataset(f'{dataset_names[0]}/data_0', transform=to_torch_test, use_cache=True, readahead=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "whole-tower",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1152124\n",
      "1152135\n",
      "6912425\n"
     ]
    }
   ],
   "source": [
    "print(len(D_test))\n",
    "print(len(D_val))\n",
    "print(len(D_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "normal-raise",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'x': {'num_target': 2, 'graph': Data(x=[8, 9], edge_index=[2, 56])},\n",
       " 'y': {'momenta': tensor([[ 7.7298e+01,  9.6422e-03, -1.7443e+00,  1.7100e+02],\n",
       "          [ 5.6345e+01,  4.8867e-01,  3.0083e-01,  1.7151e+02]]),\n",
       "  'num_target': 2},\n",
       " 'num_target': 2,\n",
       " 'truth_matched': tensor([0., 0.]),\n",
       " 'identified': tensor([0., 0.]),\n",
       " 'reco_top': tensor([[ 1.0000,    -inf, -1.0000,  0.0000],\n",
       "         [ 1.0000,    -inf, -1.0000,  0.0000]]),\n",
       " 'gnn_predicted': tensor([0., 0.]),\n",
       " 'gnn_reco_top': tensor([[0., nan, 0., 0.],\n",
       "         [0., nan, 0., 0.]]),\n",
       " 'info': tensor([5, 0, 7, 0, 2, 2, 3]),\n",
       " 'W_decay_pid': tensor([2., 3.]),\n",
       " 'reco_triplet_indices': tensor([[-1, -1, -1],\n",
       "         [-1, -1, -1]])}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "normal-protocol",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_result_attn_j_only = torch.load('test_result_attn_j_only.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "attached-ethics",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_dir = ''\n",
    "version_name = 'ttH_final' # Choose from {ttH_final, ttW_final, ttbar_final} to use precomputed test results\n",
    "test_result = torch.load(f'{output_dir}test_result_{version_name}.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ultimate-highway",
   "metadata": {},
   "outputs": [],
   "source": [
    "#test_loader_batch1 = DataLoader(D_test, 1, num_workers=2, prefetch_factor=1, follow_batch=['x_in']) if D_test else None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "rapid-relevance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\njacs = []\\nwith torch.no_grad():\\n    for data in tqdm(test_loader_batch1):\\n        myin = data[\\'x\\']\\n        myin = {k: v.to(device) for k, v in myin.items()}\\n        y_pred, count_logits = model(myin, inference=True, force_correct_num_pred=True)\\n        #print(y_pred)\\n        \\n        input = data[\\'x\\']  \\n        input = {k: v.to(device) for k, v in input.items()}\\n        graph = input[\\'graph\\']  \\n        self_attn_edges, cross_attn_edges = model.get_decoder_edges(graph, data[\\'num_target\\'], inference=True)\\n        model.my_forward(graph.x.cuda(), graph.edge_index.cuda(), graph.batch.cuda(), data[\\'num_target\\'].cuda(), self_attn_edges.cuda(), cross_attn_edges.cuda())\\n        my_fwd = lambda x: model.my_forward(x, graph.edge_index.cuda(), graph.batch.cuda(), data[\\'num_target\\'].cuda(), self_attn_edges.cuda(), cross_attn_edges.cuda())\\n        myjac = torch.autograd.functional.jacobian(my_fwd, graph.x.cuda())\\n        jacs+=[myjac[0].cpu()]\\n        if (len(jacs)%100==0):\\n            print(\"on \",len(jacs))\\n            np.save(\"jacs\",np.array(jacs))\\n        #break\\n'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "jacs = []\n",
    "with torch.no_grad():\n",
    "    for data in tqdm(test_loader_batch1):\n",
    "        myin = data['x']\n",
    "        myin = {k: v.to(device) for k, v in myin.items()}\n",
    "        y_pred, count_logits = model(myin, inference=True, force_correct_num_pred=True)\n",
    "        #print(y_pred)\n",
    "        \n",
    "        input = data['x']  \n",
    "        input = {k: v.to(device) for k, v in input.items()}\n",
    "        graph = input['graph']  \n",
    "        self_attn_edges, cross_attn_edges = model.get_decoder_edges(graph, data['num_target'], inference=True)\n",
    "        model.my_forward(graph.x.cuda(), graph.edge_index.cuda(), graph.batch.cuda(), data['num_target'].cuda(), self_attn_edges.cuda(), cross_attn_edges.cuda())\n",
    "        my_fwd = lambda x: model.my_forward(x, graph.edge_index.cuda(), graph.batch.cuda(), data['num_target'].cuda(), self_attn_edges.cuda(), cross_attn_edges.cuda())\n",
    "        myjac = torch.autograd.functional.jacobian(my_fwd, graph.x.cuda())\n",
    "        jacs+=[myjac[0].cpu()]\n",
    "        if (len(jacs)%100==0):\n",
    "            print(\"on \",len(jacs))\n",
    "            np.save(\"jacs\",np.array(jacs))\n",
    "        #break\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "parliamentary-delicious",
   "metadata": {},
   "outputs": [],
   "source": [
    "jacs = np.load(\"jacs.npy\",allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "external-prompt",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 2, 5, 8, 9])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(jacs[0])\n",
    "#2 top quarks / event, 5 attributes / top quark, 8 reconstructed objects / 9 attributes per object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "decreased-referral",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18500"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(jacs)\n",
    "#can run over more events, but this is enough for our purposes here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "vulnerable-plastic",
   "metadata": {},
   "outputs": [],
   "source": [
    "def myid(myin):\n",
    "    if myin[0]==1 and myin[1]==0 and myin[2]==0 and myin[3]==0 and myin[4]==0 :\n",
    "        return \"q/g\"\n",
    "    elif myin[0]==0 and myin[1]==1 and myin[2]==0 and myin[3]==0 and myin[4]==0 :\n",
    "        return \"b\"\n",
    "    elif myin[0]==0 and myin[1]==0 and myin[2]==1 and myin[3]==0 and myin[4]==0 :\n",
    "        return \"gamma\"\n",
    "    elif myin[0]==0 and myin[1]==0 and myin[2]==0 and myin[3]==1 and myin[4]==0 :\n",
    "        return \"lepton\"\n",
    "    elif myin[0]==0 and myin[1]==0 and myin[2]==0 and myin[3]==0 and myin[4]==1 :\n",
    "        return \"met\"\n",
    "    else:\n",
    "        return \"N/A\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "commercial-degree",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mym(fvecs):\n",
    "    px = 0.\n",
    "    py = 0.\n",
    "    pz = 0.\n",
    "    E = 0.\n",
    "    for i in range(len(fvecs)):\n",
    "        px+=fvecs[i][0]*np.cos(fvecs[i][2])\n",
    "        py+=fvecs[i][0]*np.sin(fvecs[i][2])\n",
    "        pz+=fvecs[i][0]*np.sinh(fvecs[i][1])\n",
    "        E+=fvecs[i][0]*np.cosh(fvecs[i][1])\n",
    "        pass\n",
    "    return np.sqrt(E**2-px**2-py**2-pz**2)\n",
    "\n",
    "def mypT(fvecs):\n",
    "    px = 0.\n",
    "    py = 0.\n",
    "    pz = 0.\n",
    "    E = 0.\n",
    "    for i in range(len(fvecs)):\n",
    "        px+=fvecs[i][0]*np.cos(fvecs[i][2])\n",
    "        py+=fvecs[i][0]*np.sin(fvecs[i][2])\n",
    "        pz+=fvecs[i][0]*np.sinh(fvecs[i][1])\n",
    "        E+=fvecs[i][0]*np.cosh(fvecs[i][1])\n",
    "        pass\n",
    "    return np.sqrt(px**2+py**2)\n",
    "\n",
    "def myeta(fvecs):\n",
    "    px = 0.\n",
    "    py = 0.\n",
    "    pz = 0.\n",
    "    E = 0.\n",
    "    for i in range(len(fvecs)):\n",
    "        px+=fvecs[i][0]*np.cos(fvecs[i][2])\n",
    "        py+=fvecs[i][0]*np.sin(fvecs[i][2])\n",
    "        pz+=fvecs[i][0]*np.sinh(fvecs[i][1])\n",
    "        E+=fvecs[i][0]*np.cosh(fvecs[i][1])\n",
    "        pass\n",
    "    return np.arcsinh(pz/np.sqrt(px**2+py**2))\n",
    "\n",
    "def mycosphi(fvecs):\n",
    "    px = 0.\n",
    "    py = 0.\n",
    "    pz = 0.\n",
    "    E = 0.\n",
    "    for i in range(len(fvecs)):\n",
    "        px+=fvecs[i][0]*np.cos(fvecs[i][2])\n",
    "        py+=fvecs[i][0]*np.sin(fvecs[i][2])\n",
    "        pz+=fvecs[i][0]*np.sinh(fvecs[i][1])\n",
    "        E+=fvecs[i][0]*np.cosh(fvecs[i][1])\n",
    "        pass\n",
    "    return np.arccos(px/np.sqrt(px**2+py**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "marked-peace",
   "metadata": {},
   "outputs": [],
   "source": [
    "#test_result['top_gradient2_indices'] = []\n",
    "#for i in range(len(jacs)):\n",
    "#    test_result['top_gradient2_indices']+=[np.array([np.argsort([abs(jacs[i][0][0][0][j][0].numpy()) for j in range(np.shape(jacs[i])[3])])[::-1][0:3],\n",
    "# np.argsort([abs(jacs[i][0][1][0][j][0].numpy()) for j in range(np.shape(jacs[i])[3])])[::-1][0:3]])]\n",
    "#    #break\n",
    "    \n",
    "test_result['top_gradient2_indices'] = []\n",
    "for i in range(len(jacs)):\n",
    "    absjacs0 = [np.sqrt(jacs[i][0][0][0][j][0]**2+jacs[i][0][0][1][j][1]**2+jacs[i][0][0][2][j][2]**2) for j in range(np.shape(jacs[i])[3])]\n",
    "    absjacs1 = [np.sqrt(jacs[i][0][1][0][j][0]**2+jacs[i][0][1][1][j][1]**2+jacs[i][0][1][2][j][2]**2) for j in range(np.shape(jacs[i])[3])]\n",
    "    pids = [myid(D_test[i]['x']['graph'].x.numpy()[j][4:9]) for j in range(np.shape(jacs[i])[3])]\n",
    "    absjacs0 = absjacs0*(1.-np.array([p=='gamma' for p in pids]))\n",
    "    absjacs1 = absjacs1*(1.-np.array([p=='gamma' for p in pids]))\n",
    "    test_result['top_gradient2_indices']+=[np.array([np.argsort([absjacs0[j] for j in range(np.shape(jacs[i])[3])])[::-1][0:3],\n",
    " np.argsort([absjacs1[j] for j in range(np.shape(jacs[i])[3])])[::-1][0:3]])]\n",
    "    #break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "loaded-oracle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "5000\n",
      "6000\n",
      "7000\n",
      "8000\n",
      "9000\n",
      "10000\n",
      "11000\n",
      "12000\n",
      "13000\n",
      "14000\n",
      "15000\n",
      "16000\n",
      "17000\n",
      "18000\n",
      "19000\n"
     ]
    }
   ],
   "source": [
    "#First, compute the number of (b)jets for each event.\n",
    "njets = []\n",
    "for eve in range(20000):\n",
    "    if (eve%1000==0):\n",
    "        print(eve)\n",
    "    njets+= [sum([myid(D_test[eve]['x']['graph'].x.numpy()[i][4:9])=='q/g' for i in range(len(D_test[eve]['x']['graph'].x.numpy()))])+sum([myid(D_test[eve]['x']['graph'].x.numpy()[i][4:9])=='b' for i in range(len(D_test[eve]['x']['graph'].x.numpy()))])]\n",
    "    \n",
    "nbjets = []\n",
    "for i in range(20000):\n",
    "    bs = 0\n",
    "    for k in range(len(D_test[i]['x']['graph'].x.numpy())):\n",
    "        if (myid(D_test[i]['x']['graph'].x.numpy()[k][4:9])=='b'):\n",
    "            bs+=1\n",
    "    nbjets += [bs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "portuguese-cambridge",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.0\n",
      "1000 0.05\n",
      "2000 0.1\n",
      "3000 0.15\n",
      "4000 0.2\n",
      "5000 0.25\n",
      "6000 0.3\n",
      "7000 0.35\n",
      "8000 0.4\n",
      "9000 0.45\n",
      "10000 0.5\n",
      "11000 0.55\n",
      "12000 0.6\n",
      "13000 0.65\n",
      "14000 0.7\n",
      "15000 0.75\n",
      "16000 0.8\n",
      "17000 0.85\n",
      "18000 0.9\n",
      "19000 0.95\n"
     ]
    }
   ],
   "source": [
    "mbjj = []\n",
    "mWjj = []\n",
    "for i in range(20000):\n",
    "    if (i%1000==0):\n",
    "        print(i,1.*i/20000)\n",
    "    myevent = D_test[i]['x']['graph'].x.numpy()\n",
    "    if (njets[i] >= 6 and nbjets[i] == 2 and int(test_result['reco_triplet_indices'][i][0][0]) > 0 and int(test_result['reco_triplet_indices'][i][1][0]) > 0):\n",
    "            #print(i)\n",
    "            for j in range(2):\n",
    "                q = 0\n",
    "                myevent_copy = list(test_result['reco_triplet_indices'][i][j].copy())\n",
    "                for qq in range(3):\n",
    "                    #print(\"    \",qq,myid(D_test[i]['x']['graph'].x.numpy()[test_result['reco_triplet_indices'][i][j][qq]][4:9]))\n",
    "                    if (myid(D_test[i]['x']['graph'].x.numpy()[test_result['reco_triplet_indices'][i][j][qq]][4:9])=='b'):\n",
    "                        q = test_result['reco_triplet_indices'][i][j][qq]\n",
    "                        myevent_copy.remove(q)\n",
    "                        break\n",
    "                    pass\n",
    "                b0 = myevent[q]\n",
    "                #print(\"  \",myevent_copy,q,test_result['reco_triplet_indices'][i][j])\n",
    "                j0 = myevent[myevent_copy[0]]\n",
    "                j1 = myevent[myevent_copy[1]]\n",
    "                mbjj+=[mym([b0,j0,j1])]\n",
    "                mWjj+=[mym([j0,j1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "blind-crossing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 159.83267407949447 46.41634079720836\n",
      "1 159.58888964363234 22.83862343676412\n",
      "2 159.50672209644532 21.085119176194304\n",
      "3 159.4900143574469 20.554823747704102\n",
      "4 159.43668275698712 19.638430816428755\n",
      "4 159.43668275698712 19.638430816428755\n",
      "0 75.58729655234454 20.558807329540308\n",
      "1 75.58144545649452 15.255540021325837\n",
      "2 75.50917323342615 14.491000147377418\n",
      "3 75.49681563760834 14.162434687067474\n",
      "4 75.49681563760834 14.162434687067474\n",
      "4 75.49681563760834 14.162434687067474\n"
     ]
    }
   ],
   "source": [
    "mbjj = np.array(mbjj)\n",
    "mWjj = np.array(mWjj)\n",
    "\n",
    "mycenter = np.median(mbjj)\n",
    "mywidtht = np.std(mbjj)\n",
    "for it in range(5):\n",
    "    print(it,mycenter,mywidtht)\n",
    "    mycenter = np.median(mbjj[(mbjj > 0)*(mbjj<mycenter + 5*mywidtht)])\n",
    "    mywidtht = np.std(mbjj[(mbjj > 0)*(mbjj<mycenter + 5*mywidtht)])\n",
    "print(it,mycenter,mywidtht)\n",
    "\n",
    "mycenter = np.median(mWjj)\n",
    "mywidthw = np.std(mWjj)\n",
    "for it in range(5):\n",
    "    print(it,mycenter,mywidthw)\n",
    "    mycenter = np.median(mWjj[(mWjj > 0)*(mWjj<mycenter + 5*mywidthw)])\n",
    "    mywidthw = np.std(mWjj[(mWjj > 0)*(mWjj<mycenter + 5*mywidthw)])\n",
    "print(it,mycenter,mywidthw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "exclusive-definition",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.0\n",
      "1000 0.05\n",
      "2000 0.1\n",
      "3000 0.15\n",
      "4000 0.2\n",
      "5000 0.25\n",
      "6000 0.3\n",
      "7000 0.35\n",
      "8000 0.4\n",
      "9000 0.45\n",
      "10000 0.5\n",
      "11000 0.55\n",
      "12000 0.6\n",
      "13000 0.65\n",
      "14000 0.7\n",
      "15000 0.75\n",
      "16000 0.8\n",
      "17000 0.85\n",
      "18000 0.9\n",
      "19000 0.95\n"
     ]
    }
   ],
   "source": [
    "#let's do chi^2\n",
    "test_result['top_chi2_indices'] = []\n",
    "for i in range(20000):\n",
    "    if (i%1000==0):\n",
    "        print(i,1.*i/20000)\n",
    "    bs = []\n",
    "    bs_lab = []\n",
    "    lights = []\n",
    "    lights_lab = []\n",
    "    if (njets[i] < 6):\n",
    "        test_result['top_chi2_indices'] += [[[-1,-1,-1],[-1,-1,-1]]]\n",
    "        continue\n",
    "    for k in range(len(D_test[i]['x']['graph'].x.numpy())):\n",
    "        if (myid(D_test[i]['x']['graph'].x.numpy()[k][4:9])=='b'):\n",
    "            bs+=[D_test[i]['x']['graph'].x.numpy()[k]]\n",
    "            bs_lab+=[k]\n",
    "        elif (myid(D_test[i]['x']['graph'].x.numpy()[k][4:9])=='q/g'):\n",
    "            lights+=[D_test[i]['x']['graph'].x.numpy()[k]]\n",
    "            lights_lab+=[k]\n",
    "    if (len(bs)!=2):\n",
    "        test_result['top_chi2_indices'] += [[[-1,-1,-1],[-1,-1,-1]]]\n",
    "        continue\n",
    "    chi2 = 99999999.\n",
    "    top_lab1 = []\n",
    "    top_lab2 = []\n",
    "    #it is late and I don't want to think too hard ... \n",
    "    for i0 in range(len(lights)):\n",
    "        for i1 in range(0,len(lights)):\n",
    "            if (i0==i1):\n",
    "                continue\n",
    "            for i2 in range(len(lights)):\n",
    "                if (i2==i1 or i2==i0):\n",
    "                    continue\n",
    "                for i3 in range(len(lights)):\n",
    "                    if (i3==i2 or i3==i1 or i3==i0):\n",
    "                        continue\n",
    "                    #print(i,[i0,i1],[i2,i3])\n",
    "                    sig0 = [i0,i1]\n",
    "                    sig1 = [i2,i3]\n",
    "                    \n",
    "                    #print(sig0,sig1,lights)\n",
    "    \n",
    "                    #combo1\n",
    "                    mt1 = mym([bs[0],lights[sig0[0]],lights[sig0[1]]])\n",
    "                    mt2 = mym([bs[1],lights[sig1[0]],lights[sig1[1]]])\n",
    "                    mW1 = mym([lights[sig0[0]],lights[sig0[1]]])\n",
    "                    mW2 = mym([lights[sig1[0]],lights[sig1[1]]])\n",
    "                    chi2a = (mt1-175.)**2/mywidtht**2 + (mt2-175.)**2/mywidtht**2 + (mW1-80.)**2/mywidthw**2 + (mt2-80.)**2/mywidthw**2\n",
    "\n",
    "                    #combo2\n",
    "                    mt1 = mym([bs[1],lights[sig0[0]],lights[sig0[1]]])\n",
    "                    mt2 = mym([bs[0],lights[sig1[0]],lights[sig1[1]]])\n",
    "                    mW2 = mym([lights[sig0[0]],lights[sig0[1]]])\n",
    "                    mW1 = mym([lights[sig1[0]],lights[sig1[1]]])\n",
    "                    chi2b = (mt1-175.)**2/mywidtht**2 + (mt2-175.)**2/mywidtht**2 + (mW1-80.)**2/mywidthw**2 + (mt2-80.)**2/mywidthw**2\n",
    "\n",
    "                    if (chi2a < chi2):\n",
    "                        top_lab1 = [bs_lab[0],lights_lab[sig0[0]],lights_lab[sig0[1]]]\n",
    "                        top_lab2 = [bs_lab[1],lights_lab[sig1[0]],lights_lab[sig1[1]]]\n",
    "                        chi2 = chi2a\n",
    "                    if (chi2b < chi2):\n",
    "                        top_lab1 = [bs_lab[1],lights_lab[sig0[0]],lights_lab[sig0[1]]]\n",
    "                        top_lab2 = [bs_lab[0],lights_lab[sig1[0]],lights_lab[sig1[1]]]\n",
    "                        chi2 = chi2b\n",
    "    test_result['top_chi2_indices'] += [[top_lab1,top_lab2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "engaged-fellowship",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12800"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_result_attn_j_only['top_attention_indices'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "higher-share",
   "metadata": {},
   "outputs": [],
   "source": [
    "mycounter = [[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0]]\n",
    "denom = [[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0]]\n",
    "for i in range(12800): #18500):\n",
    "    if (abs(test_result['W_decay_pid'][2*i]) > 10) or (abs(test_result['W_decay_pid'][2*i+1]) > 10):\n",
    "        continue\n",
    "    for k in range(5):\n",
    "        if (k == 0):\n",
    "            #take all events\n",
    "            qq=0\n",
    "        elif (k==4 and njets[i] < 6):\n",
    "            continue\n",
    "        elif (k==1 and njets[i] != 6):\n",
    "            continue\n",
    "        elif (k==2 and njets[i] != 7):\n",
    "            continue\n",
    "        elif (k==3 and njets[i] < 8):\n",
    "            continue\n",
    "        for q in range(3):\n",
    "            if (q==0):\n",
    "                qq=0\n",
    "            if (q==1 and nbjets[i]<2):\n",
    "                continue\n",
    "            if (q==2 and (test_result['reco_triplet_indices'][i][0][0]==-1 or test_result['reco_triplet_indices'][i][1][0]==-1)):\n",
    "                continue\n",
    "            if (test_result['reco_triplet_indices'][i][0][0]!=-1):\n",
    "                mycounter[q][k] += set(test_result['reco_triplet_indices'][i][0]) == set(test_result_attn_j_only['top_attention_indices'][i][0])\n",
    "                denom[q][k] += 1.\n",
    "            if (test_result['reco_triplet_indices'][i][1][0]!=-1):\n",
    "                mycounter[q][k] += set(test_result['reco_triplet_indices'][i][1]) == set(test_result_attn_j_only['top_attention_indices'][i][1])\n",
    "                denom[q][k] += 1.\n",
    "    ###\n",
    "            if (test_result['reco_triplet_indices'][i][0][0]!=-1):\n",
    "                mycounter[q][k] += set(test_result['reco_triplet_indices'][i][0]) == set(test_result_attn_j_only['top_attention_indices'][i][1])\n",
    "            if (test_result['reco_triplet_indices'][i][1][0]!=-1):\n",
    "                mycounter[q][k] += set(test_result['reco_triplet_indices'][i][1]) == set(test_result_attn_j_only['top_attention_indices'][i][0])\n",
    "\n",
    "mycounter2 = [[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0]]\n",
    "denom2 = [[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0]]\n",
    "for i in range(18500):\n",
    "    if (abs(test_result['W_decay_pid'][2*i]) > 10) or (abs(test_result['W_decay_pid'][2*i+1]) > 10):\n",
    "        continue\n",
    "    for k in range(5):\n",
    "        if (k == 0):\n",
    "            #take all events\n",
    "            qq=0\n",
    "        elif (k==4 and njets[i] < 6):\n",
    "            continue\n",
    "        elif (k==1 and njets[i] != 6):\n",
    "            continue\n",
    "        elif (k==2 and njets[i] != 7):\n",
    "            continue\n",
    "        elif (k==3 and njets[i] < 8):\n",
    "            continue\n",
    "        for q in range(3):\n",
    "            if (q==0):\n",
    "                qq=0\n",
    "            if (q==1 and nbjets[i]<2):\n",
    "                continue\n",
    "            if (q==2 and (test_result['reco_triplet_indices'][i][0][0]==-1 or test_result['reco_triplet_indices'][i][1][0]==-1)):\n",
    "                continue\n",
    "            if (test_result['reco_triplet_indices'][i][0][0]!=-1):\n",
    "                mycounter2[q][k] += set(test_result['reco_triplet_indices'][i][0]) == set(test_result['top_gradient2_indices'][i][0])\n",
    "                denom2[q][k] += 1.\n",
    "            if (test_result['reco_triplet_indices'][i][1][0]!=-1):\n",
    "                mycounter2[q][k] += set(test_result['reco_triplet_indices'][i][1]) == set(test_result['top_gradient2_indices'][i][1])\n",
    "                denom2[q][k] += 1.\n",
    "    ###\n",
    "            if (test_result['reco_triplet_indices'][i][0][0]!=-1):\n",
    "                mycounter2[q][k] += set(test_result['reco_triplet_indices'][i][0]) == set(test_result['top_gradient2_indices'][i][1])\n",
    "            if (test_result['reco_triplet_indices'][i][1][0]!=-1):\n",
    "                mycounter2[q][k] += set(test_result['reco_triplet_indices'][i][1]) == set(test_result['top_gradient2_indices'][i][0])\n",
    "\n",
    "                \n",
    "mycounter3 = [[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0]]\n",
    "denom3 = [[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0]]\n",
    "for i in range(18500):\n",
    "    if (abs(test_result['W_decay_pid'][2*i]) > 10) or (abs(test_result['W_decay_pid'][2*i+1]) > 10):\n",
    "        continue\n",
    "    for k in range(5):\n",
    "        if (k == 0):\n",
    "            #take all events\n",
    "            qq=0\n",
    "        elif (k==4 and njets[i] < 6):\n",
    "            continue\n",
    "        elif (k==1 and njets[i] != 6):\n",
    "            continue\n",
    "        elif (k==2 and njets[i] != 7):\n",
    "            continue\n",
    "        elif (k==3 and njets[i] < 8):\n",
    "            continue\n",
    "        for q in range(3):\n",
    "            if (q==0):\n",
    "                qq=0\n",
    "            if (q==1 and nbjets[i]<2):\n",
    "                continue\n",
    "            if (q==2 and (test_result['reco_triplet_indices'][i][0][0]==-1 or test_result['reco_triplet_indices'][i][1][0]==-1)):\n",
    "                continue\n",
    "            if (test_result['reco_triplet_indices'][i][0][0]!=-1):\n",
    "                mycounter3[q][k] += set(test_result['reco_triplet_indices'][i][0]) == set(test_result['top_chi2_indices'][i][0])\n",
    "                denom3[q][k] += 1.\n",
    "            if (test_result['reco_triplet_indices'][i][1][0]!=-1):\n",
    "                mycounter3[q][k] += set(test_result['reco_triplet_indices'][i][1]) == set(test_result['top_chi2_indices'][i][1])\n",
    "                denom3[q][k] += 1.\n",
    "    ###\n",
    "            if (test_result['reco_triplet_indices'][i][0][0]!=-1):\n",
    "                mycounter3[q][k] += set(test_result['reco_triplet_indices'][i][0]) == set(test_result['top_chi2_indices'][i][1])\n",
    "            if (test_result['reco_triplet_indices'][i][1][0]!=-1):\n",
    "                mycounter3[q][k] += set(test_result['reco_triplet_indices'][i][1]) == set(test_result['top_chi2_indices'][i][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "appointed-family",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for q in range(3):\n",
    "\n",
    "    barWidth = 0.25\n",
    "\n",
    "    fig = plt.figure(figsize=(8, 6))\n",
    "    gs = gridspec.GridSpec(1, 1) \n",
    "    ax0 = plt.subplot(gs[0])\n",
    "    ax0.yaxis.set_ticks_position('both')\n",
    "    ax0.xaxis.set_ticks_position('both')\n",
    "    ax0.tick_params(direction=\"in\",which=\"both\")\n",
    "    plt.xticks(fontsize=20)\n",
    "    plt.yticks(fontsize=20)\n",
    "    ax0.minorticks_on()\n",
    "\n",
    "    # set height of bar\n",
    "    Att = [mycounter[q][0] / denom[q][0],mycounter[q][4] / denom[q][4],mycounter[q][1] / denom[q][1],mycounter[q][2] / denom[q][2],mycounter[q][3] / denom[q][3]]\n",
    "    Gra = [mycounter2[q][0] / denom2[q][0],mycounter2[q][4] / denom2[q][4],mycounter2[q][1] / denom2[q][1],mycounter2[q][2] / denom2[q][2],mycounter2[q][3] / denom2[q][3]]\n",
    "    Chi = [mycounter3[q][0] / denom3[q][0],mycounter3[q][4] / denom3[q][4],mycounter3[q][1] / denom3[q][1],mycounter3[q][2] / denom3[q][2],mycounter3[q][3] / denom3[q][3]]\n",
    "\n",
    "    # Set position of bar on X axis\n",
    "    br1 = np.arange(len(Att))\n",
    "    br2 = [x + barWidth for x in br1]\n",
    "    br3 = [x + barWidth for x in br2]\n",
    "\n",
    "    # Make the plot\n",
    "    plt.bar(br1, Att, color ='darkred', width = barWidth,\n",
    "            edgecolor ='darkred', label ='Attention')\n",
    "    plt.bar(br2, Gra, color ='crimson', width = barWidth,\n",
    "            edgecolor ='crimson', label ='Gradient')\n",
    "    plt.bar(br3, Chi, color ='lightcoral', width = barWidth,\n",
    "            edgecolor ='lightcoral', label ='$\\chi^2$')\n",
    "\n",
    "    # Adding Xticks\n",
    "    #plt.xlabel('Branch', fontweight ='bold', fontsize = 15)\n",
    "    plt.ylabel('Frequency', fontsize = 20)\n",
    "    plt.xticks([r + barWidth for r in range(len(Att))],\n",
    "            ['Inclusive','$\\geq 6$ jets', '6 jets', '7 jets','$\\geq 8$ jets'], fontsize = 20)\n",
    "\n",
    "    plt.ylim([0,1])\n",
    "\n",
    "    plt.legend(fontsize=20,frameon=False)\n",
    "\n",
    "    if (q==0):\n",
    "        plt.title(\"All events\",loc=\"right\",fontsize=20)\n",
    "        #plt.show()\n",
    "        plt.savefig(\"inclusive.pdf\",bbox_inches='tight')\n",
    "    if (q==1):\n",
    "        plt.title(\"Two $b$-jets\",loc=\"right\",fontsize=20)\n",
    "        #plt.show()\n",
    "        plt.savefig(\"twobs.pdf\",bbox_inches='tight')\n",
    "    if (q==2):\n",
    "        plt.title(\"Two reconstructable tops\",loc=\"right\",fontsize=20)\n",
    "        #plt.show()\n",
    "        plt.savefig(\"reconstructable.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "metropolitan-design",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-13-7da05e9185c5>:12: RuntimeWarning: invalid value encountered in sqrt\n",
      "  return np.sqrt(E**2-px**2-py**2-pz**2)\n"
     ]
    }
   ],
   "source": [
    "ms = []\n",
    "m12s = []\n",
    "m23s = []\n",
    "\n",
    "mbjj = []\n",
    "mjj = []\n",
    "for i in range(10000):\n",
    "    if (abs(test_result['W_decay_pid'][2*i]) > 10) or (abs(test_result['W_decay_pid'][2*i+1]) > 10):\n",
    "        continue\n",
    "    myevent = D_test[i]['x']['graph'].x.numpy()\n",
    "\n",
    "    for j in range(2):\n",
    "        if (int(test_result['reco_triplet_indices'][i][j][0]) < 0):\n",
    "            ms+=[-1]\n",
    "            m12s+=[-1]\n",
    "            m23s+=[-1]\n",
    "            continue\n",
    "        t1a = myevent[int(test_result['reco_triplet_indices'][i][j][0])]\n",
    "        t1b = myevent[int(test_result['reco_triplet_indices'][i][j][1])]\n",
    "        t1c = myevent[int(test_result['reco_triplet_indices'][i][j][2])]\n",
    "        ms+=[mym([t1a,t1b,t1c])]\n",
    "        m12s+=[mym([t1a,t1b])]\n",
    "        m23s+=[mym([t1b,t1c])]\n",
    "            \n",
    "ms_att = []\n",
    "m12s_att = []\n",
    "m23s_att = []\n",
    "ms_att_miss = []\n",
    "pT_att_miss = []\n",
    "eta_att_miss = []\n",
    "pT_true_miss = []\n",
    "eta_true_miss = []\n",
    "for i in range(10000):\n",
    "    if (abs(test_result['W_decay_pid'][2*i]) > 10) or (abs(test_result['W_decay_pid'][2*i+1]) > 10):\n",
    "        continue\n",
    "    myevent = D_test[i]['x']['graph'].x.numpy()\n",
    "\n",
    "    for j in range(2):\n",
    "        if (int(test_result_attn_j_only['top_attention_indices'][i][j][0]) < 0):\n",
    "            ms_att+=[-1]\n",
    "            m12s_att+=[-1]\n",
    "            m23s_att+=[-1]\n",
    "            continue\n",
    "        t1a = myevent[int(test_result_attn_j_only['top_attention_indices'][i][j][0])]\n",
    "        t1b = myevent[int(test_result_attn_j_only['top_attention_indices'][i][j][1])]\n",
    "        t1c = myevent[int(test_result_attn_j_only['top_attention_indices'][i][j][2])]\n",
    "        ms_att+=[mym([t1a,t1b,t1c])]\n",
    "        m12s_att+=[mym([t1a,t1b])]\n",
    "        m23s_att+=[mym([t1b,t1c])]\n",
    "        \n",
    "        if (int(test_result['reco_triplet_indices'][i][j][0]) < 0):\n",
    "            ms_att_miss+=[mym([t1a,t1b,t1c])]\n",
    "            pT_att_miss+=[mypT([t1a,t1b,t1c])]\n",
    "            eta_att_miss+=[myeta([t1a,t1b,t1c])]            \n",
    "            pT_true_miss+=[test_result['y_target'][2*i+j][0]]\n",
    "            eta_true_miss+=[test_result['y_target'][2*i+j][1]]\n",
    "        \n",
    "ms_grad2 = []\n",
    "m12s_grad2 = []\n",
    "m23s_grad2 = []\n",
    "ms_grad2_miss = []\n",
    "pT_grad2_miss = []\n",
    "eta_grad2_miss = []\n",
    "for i in range(10000):\n",
    "    if (abs(test_result['W_decay_pid'][2*i]) > 10) or (abs(test_result['W_decay_pid'][2*i+1]) > 10):\n",
    "        continue\n",
    "    myevent = D_test[i]['x']['graph'].x.numpy()\n",
    "\n",
    "    for j in range(2):\n",
    "        if (len(test_result['top_gradient2_indices'][i][j]) < 3):\n",
    "            ms_grad2+=[-1]\n",
    "            m12s_grad2+=[-1]\n",
    "            m23s_grad2+=[-1]\n",
    "            continue\n",
    "        t1a = myevent[int(test_result['top_gradient2_indices'][i][j][0])]\n",
    "        t1b = myevent[int(test_result['top_gradient2_indices'][i][j][1])]\n",
    "        t1c = myevent[int(test_result['top_gradient2_indices'][i][j][2])]\n",
    "        ms_grad2+=[mym([t1a,t1b,t1c])]\n",
    "        m12s_grad2+=[mym([t1a,t1b])]\n",
    "        m23s_grad2+=[mym([t1b,t1c])]\n",
    "        \n",
    "        if (int(test_result['reco_triplet_indices'][i][j][0]) < 0):\n",
    "            ms_grad2_miss+=[mym([t1a,t1b,t1c])]\n",
    "            pT_grad2_miss+=[mypT([t1a,t1b,t1c])]\n",
    "            eta_grad2_miss+=[myeta([t1a,t1b,t1c])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "floral-retreat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "gs = gridspec.GridSpec(1, 1) \n",
    "ax0 = plt.subplot(gs[0])\n",
    "ax0.yaxis.set_ticks_position('both')\n",
    "ax0.xaxis.set_ticks_position('both')\n",
    "ax0.tick_params(direction=\"in\",which=\"both\")\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "ax0.minorticks_on()\n",
    "\n",
    "plt.scatter(eta_att_miss[0:1000],eta_true_miss[0:1000])\n",
    "\n",
    "plt.xlim([-3,3])\n",
    "plt.ylim([-3,3])\n",
    "plt.plot([-3,3],[-3,3],color=\"black\",ls=\":\",lw=3)\n",
    "plt.xlabel(\"Attention-selected trijet $\\eta$\",fontsize=20)\n",
    "plt.ylabel(\"Truth top $\\eta$\",fontsize=20)\n",
    "plt.title(\"No Truth Match\",fontsize=20,loc=\"right\")\n",
    "plt.savefig(\"eta_scatter_attention.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "overhead-boost",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "gs = gridspec.GridSpec(1, 1) \n",
    "ax0 = plt.subplot(gs[0])\n",
    "ax0.yaxis.set_ticks_position('both')\n",
    "ax0.xaxis.set_ticks_position('both')\n",
    "ax0.tick_params(direction=\"in\",which=\"both\")\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "ax0.minorticks_on()\n",
    "\n",
    "plt.scatter(pT_att_miss[0:1000],pT_true_miss[0:1000])\n",
    "\n",
    "plt.xlim([0,500])\n",
    "plt.ylim([0,500])\n",
    "plt.plot([0,500],[0,500],color=\"black\",ls=\":\",lw=3)\n",
    "plt.xlabel(\"Attention-selected trijet $p_T$ [GeV]\",fontsize=20)\n",
    "plt.ylabel(\"Truth $p_T$ [GeV]\",fontsize=20)\n",
    "plt.title(\"No Truth Match\",fontsize=20,loc=\"right\")\n",
    "plt.savefig(\"pT_scatter_attention.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "medium-dryer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "gs = gridspec.GridSpec(1, 1) \n",
    "ax0 = plt.subplot(gs[0])\n",
    "ax0.yaxis.set_ticks_position('both')\n",
    "ax0.xaxis.set_ticks_position('both')\n",
    "ax0.tick_params(direction=\"in\",which=\"both\")\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "ax0.minorticks_on()\n",
    "\n",
    "plt.hist(ms,bins=np.linspace(0,500,20),histtype=\"step\",color=\"black\",label=\"Truth-matched\")\n",
    "plt.hist(ms_att,bins=np.linspace(0,500,20),alpha=0.5,label=\"Attention-based\")\n",
    "plt.hist(ms_grad2,bins=np.linspace(0,500,20),alpha=0.5,label=\"Gradient-based\")\n",
    "#plt.hist(ms_grad2,bins=np.linspace(0,500,20),histtype=\"step\",color=\"black\",ls=\":\",label=\"Gradient-based 2\")\n",
    "plt.legend(fontsize=20)\n",
    "plt.xlabel(\"$m_{jjj}$ [GeV]\",fontsize=20)\n",
    "plt.ylabel(\"counts\",fontsize=20)\n",
    "#plt.title(\"No Truth Match\",fontsize=20,loc=\"right\")\n",
    "plt.savefig(\"mjjj_all.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "electoral-clark",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "gs = gridspec.GridSpec(1, 1) \n",
    "ax0 = plt.subplot(gs[0])\n",
    "ax0.yaxis.set_ticks_position('both')\n",
    "ax0.xaxis.set_ticks_position('both')\n",
    "ax0.tick_params(direction=\"in\",which=\"both\")\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "ax0.minorticks_on()\n",
    "\n",
    "plt.hist(ms_att_miss,bins=np.linspace(0,500,20),alpha=0.5,label=\"Attention-based\")\n",
    "plt.hist(ms_grad2_miss,bins=np.linspace(0,500,20),alpha=0.5,label=\"Gradient-based\")\n",
    "\n",
    "plt.legend(fontsize=20)\n",
    "plt.xlabel(\"$m_{jjj}$ [GeV]\",fontsize=20)\n",
    "plt.ylabel(\"counts\",fontsize=20)\n",
    "plt.title(\"No Truth Match\",fontsize=20,loc=\"right\")\n",
    "plt.savefig(\"mjjj_nomatch.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "seasonal-january",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}