{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "13f819cb",
   "metadata": {},
   "source": [
    "Side note: we are evaluating particle level information\n",
    "maybe we should also do the event level information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba38b25a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/billyli/miniforge_x86_new/lib/python3.9/site-packages/coffea/util.py:154: FutureWarning: In coffea version v0.8.0 (target date: 31 Dec 2022), this will be an error.\n",
      "(Set coffea.deprecations_as_errors = True to get a stack trace now.)\n",
      "ImportError: coffea.hist is deprecated\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "import logging\n",
    "from pathlib import Path\n",
    "import numba as nb\n",
    "\n",
    "import awkward as ak\n",
    "import click\n",
    "import h5py as h5\n",
    "import numpy as np\n",
    "import vector\n",
    "\n",
    "from coffea.hist.plot import clopper_pearson_interval\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# from src.data.cms.convert_to_h5 import MIN_JETS, N_JETS, N_FJETS\n",
    "\n",
    "vector.register_awkward()\n",
    "\n",
    "logging.basicConfig(level=logging.INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "11586b69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read test target file\n",
    "test_file = \"//Users/billyli/UCSD/hhh/reports/bv2/hhh_test.h5\"\n",
    "test_h5 = h5.File(test_file)\n",
    "\n",
    "# read spanet prediction\n",
    "spanet_file = \"//Users/billyli/UCSD/hhh/reports/bv2/dp_on/pred_v53.h5\"\n",
    "s_h5 = h5.File(spanet_file)\n",
    "\n",
    "# read baseline prediction\n",
    "baseline_file = \"//Users/billyli/UCSD/hhh/reports/bv2/pred_baseline.h5\"\n",
    "b_h5 = h5.File(baseline_file)\n",
    "\n",
    "# read spanet prediction\n",
    "pb_off_file = \"//Users/billyli/UCSD/hhh/reports/bv2/pb_off_pred_v61.h5\"\n",
    "pb_h5 = h5.File(pb_off_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed6167b0",
   "metadata": {},
   "source": [
    "### Reco Boosted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f57ba12",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reset_collision_dp(dps, aps):\n",
    "    ap_filter = aps < 1/(13*13)\n",
    "    dps_reset = dps\n",
    "    dps_reset[ap_filter] = 0\n",
    "    return dps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "29011e52",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dp_to_HiggsNumProb(dps):\n",
    "    # get maximum number of targets\n",
    "    Nmax = dps.shape[-1]\n",
    "    \n",
    "    # prepare a list for constructing [P_0H, P_1H, P_2H, ...]\n",
    "    probs = []\n",
    "    \n",
    "    # loop through all possible number of existing targets\n",
    "    for N in range(Nmax+1):\n",
    "        # get all combinations of targets\n",
    "        combs = list(itertools.combinations(range(Nmax),N))\n",
    "    \n",
    "        # calculate the probability of N particles existing for each combination\n",
    "        P_exist_per_comb = [np.prod(dps[:,list(comb)], axis=-1) for comb in combs]\n",
    "        \n",
    "        # calculate the probability fo Nmax-N particles not existing for each  combination\n",
    "        P_noexist_per_comb = [np.prod(1- dps[:, list(set(range(Nmax))-set(comb))], axis=-1) for comb in combs]\n",
    "        \n",
    "        # concatenate each combination to array for further calculation   \n",
    "        P_exist_per_comb = [np.reshape(P_comb_e, newshape=(-1,1)) for P_comb_e in P_exist_per_comb]\n",
    "        P_exist_per_comb = np.concatenate(P_exist_per_comb, axis=1)\n",
    "        P_noexist_per_comb = [np.reshape(P_comb_e, newshape=(-1,1)) for P_comb_e in P_noexist_per_comb]\n",
    "        P_noexist_per_comb = np.concatenate(P_noexist_per_comb, axis=1)\n",
    "        \n",
    "        # for each combination, calculate the joint probability \n",
    "        # of N particles existing and Nmax-N not existing\n",
    "        P_per_comb = P_exist_per_comb * P_noexist_per_comb\n",
    "        \n",
    "        # sum over all possible configurations of N existing and Nmax-N not existing\n",
    "        P = np.sum(P_per_comb, axis=-1)\n",
    "        \n",
    "        # reshape and add to the prob list\n",
    "        probs.append(np.reshape(P, newshape=(-1,1)))\n",
    "\n",
    "    # convert the probs list to arr\n",
    "    probs_arr = np.concatenate(probs, axis=1)\n",
    "    \n",
    "    return probs_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3dee6df4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sel_pred_bH_by_dp_ap(dps, aps, bb_ps):\n",
    "    # get most possible number of H_reco by dps\n",
    "    HiggsNumProb = dp_to_HiggsNumProb(dps)\n",
    "    HiggsNum = np.argmax(HiggsNumProb, axis=-1)\n",
    "    \n",
    "    # get the top N (dp x ap) jet assignment indices\n",
    "    ps = dps*aps\n",
    "    idx_descend = np.flip(np.argsort(ps, axis=-1), axis=-1)\n",
    "    idx_sel = [idx_e[:N_e] for idx_e, N_e in zip(idx_descend, HiggsNum)]\n",
    "    \n",
    "    # select the predicted bb assignment via the indices\n",
    "    bb_ps_sel = bb_ps[idx_sel]\n",
    "    \n",
    "    # require bb assignment is a fatjet\n",
    "    ak8Filter = bb_ps_sel>9\n",
    "    bb_ps_passed = bb_ps_sel.mask[ak8Filter]\n",
    "    bb_ps_passed = ak.drop_none(bb_ps_passed)\n",
    "    \n",
    "    return bb_ps_passed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "51a6c71b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sel_target_bH_by_mask(bb_ts, bh_pts, bh_masks):\n",
    "    bb_ts_selected = bb_ts.mask[bh_masks]\n",
    "    bb_ts_selected = ak.drop_none(bb_ts_selected)\n",
    "    \n",
    "    bh_selected_pts = bh_pts.mask[bh_masks]\n",
    "    bh_selected_pts = ak.drop_none(bh_selected_pts)\n",
    "    \n",
    "    return bb_ts_selected, bh_selected_pts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8e1c2469",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A pred look up table is in shape\n",
    "# [event,\n",
    "#    pred_H, \n",
    "#       [correct, pred_H_pt]]\n",
    "def gen_pred_bH_LUT(bb_ps_passed, bb_ts_selected, fj_pts):\n",
    "    LUT = []\n",
    "    # for each event\n",
    "    for bb_t_event, bb_p_event, fj_pt_event in zip(bb_ts_selected, bb_ps_passed, fj_pts):\n",
    "        # for each predicted bb assignment, check if any target H have a same bb assignment\n",
    "        LUT_event = []\n",
    "        for i, bb_p in enumerate(bb_p_event):\n",
    "            correct = 0\n",
    "            predH_pt = fj_pt_event[bb_p-10]\n",
    "            for bb_t in bb_t_event:\n",
    "                if bb_p == bb_t+10:\n",
    "                    correct = 1\n",
    "            LUT_event.append([correct, predH_pt])\n",
    "        LUT.append(LUT_event)\n",
    "    return LUT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f497215f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A target look up table is in shape\n",
    "# [event,\n",
    "#    target_H, \n",
    "#        target_bb_assign,\n",
    "#           [retrieved, targetH_pt]]\n",
    "def gen_target_bH_LUT(bb_ps_passed, bb_ts_selected, targetH_pts):\n",
    "    LUT = []\n",
    "    # for each event\n",
    "    for bb_t_event, bb_p_event, targetH_pts_event in zip(bb_ts_selected, bb_ps_passed, targetH_pts):\n",
    "        # for each target fatjet, check if the predictions have a p fatject same with the t fatjet\n",
    "        LUT_event = []\n",
    "        for i, bb_t in enumerate(bb_t_event):\n",
    "            retrieved = 0\n",
    "            targetH_pt = targetH_pts_event[i]\n",
    "            for bb_p in bb_p_event:\n",
    "                if bb_p == bb_t+10:\n",
    "                    retrieved = 1\n",
    "            LUT_event.append([retrieved, targetH_pt])\n",
    "        LUT.append(LUT_event)\n",
    "    return LUT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3d4c78fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate pred/target LUT\n",
    "# each entry corresponds to [recoH correct or not, reco H pt]\n",
    "# or \n",
    "# [targetH retrieved or not, target H pt]\n",
    "def parse_boosted_w_target(testfile, predfile):\n",
    "    # Collect H pt, mask, target and predicted jet and fjets for 3 Hs in each event\n",
    "    # h pt\n",
    "    bh1_pt = np.array(testfile['TARGETS']['bh1']['pt'])\n",
    "    bh2_pt = np.array(testfile['TARGETS']['bh2']['pt'])\n",
    "    bh3_pt = np.array(testfile['TARGETS']['bh3']['pt'])\n",
    "\n",
    "    # mask\n",
    "    bh1_mask = np.array(testfile['TARGETS']['bh1']['mask'])\n",
    "    bh2_mask = np.array(testfile['TARGETS']['bh2']['mask'])\n",
    "    bh3_mask = np.array(testfile['TARGETS']['bh3']['mask'])\n",
    "\n",
    "    # target assignment\n",
    "    bb_bh1_t = np.array(testfile[\"TARGETS\"][\"bh1\"]['bb'])\n",
    "    bb_bh2_t = np.array(testfile[\"TARGETS\"][\"bh2\"]['bb'])\n",
    "    bb_bh3_t = np.array(testfile[\"TARGETS\"][\"bh3\"]['bb'])\n",
    "\n",
    "    try:\n",
    "        # pred assignment\n",
    "        bb_bh1_p = np.array(predfile[\"TARGETS\"][\"bh1\"]['bb'])\n",
    "        bb_bh2_p = np.array(predfile[\"TARGETS\"][\"bh2\"]['bb'])\n",
    "        bb_bh3_p = np.array(predfile[\"TARGETS\"][\"bh3\"]['bb'])\n",
    "    \n",
    "        # boosted Higgs detection probability\n",
    "        dp_bh1 = np.array(predfile[\"TARGETS\"][\"bh1\"]['detection_probability'])\n",
    "        dp_bh2 = np.array(predfile[\"TARGETS\"][\"bh2\"]['detection_probability'])\n",
    "        dp_bh3 = np.array(predfile[\"TARGETS\"][\"bh3\"]['detection_probability'])\n",
    "\n",
    "        # fatjet assignment probability\n",
    "        ap_bh1 = np.array(predfile[\"TARGETS\"][\"bh1\"]['assignment_probability'])\n",
    "        ap_bh2 = np.array(predfile[\"TARGETS\"][\"bh2\"]['assignment_probability'])\n",
    "        ap_bh3 = np.array(predfile[\"TARGETS\"][\"bh3\"]['assignment_probability'])\n",
    "    except:\n",
    "        # pred assignment\n",
    "        bb_bh1_p = np.array(predfile[\"TARGETS\"][\"bh1\"]['bb'])+10\n",
    "        bb_bh2_p = np.array(predfile[\"TARGETS\"][\"bh2\"]['bb'])+10\n",
    "        bb_bh3_p = np.array(predfile[\"TARGETS\"][\"bh3\"]['bb'])+10\n",
    "    \n",
    "         # boosted Higgs detection probability\n",
    "        dp_bh1 = np.array(predfile[\"TARGETS\"][\"bh1\"]['mask']).astype('float')\n",
    "        dp_bh2 = np.array(predfile[\"TARGETS\"][\"bh2\"]['mask']).astype('float')\n",
    "        dp_bh3 = np.array(predfile[\"TARGETS\"][\"bh3\"]['mask']).astype('float')\n",
    "\n",
    "        # fatjet assignment probability\n",
    "        ap_bh1 = np.array(predfile[\"TARGETS\"][\"bh1\"]['mask']).astype('float')\n",
    "        ap_bh2 = np.array(predfile[\"TARGETS\"][\"bh2\"]['mask']).astype('float')\n",
    "        ap_bh3 = np.array(predfile[\"TARGETS\"][\"bh3\"]['mask']).astype('float')\n",
    "    \n",
    "    # collect fatjet pt\n",
    "    fj_pt = np.array(testfile['INPUTS']['BoostedJets']['fj_pt'])\n",
    "    \n",
    "    dps = np.concatenate((dp_bh1.reshape(-1, 1), dp_bh2.reshape(-1, 1), dp_bh3.reshape(-1, 1)), axis=1)\n",
    "    aps = np.concatenate((ap_bh1.reshape(-1, 1), ap_bh2.reshape(-1, 1), ap_bh3.reshape(-1, 1)), axis=1)\n",
    "\n",
    "    # convert some arrays to ak array\n",
    "    bb_ps = np.concatenate((bb_bh1_p.reshape(-1, 1), bb_bh2_p.reshape(-1, 1), bb_bh3_p.reshape(-1, 1)), axis=1)\n",
    "    bb_ps = ak.Array(bb_ps)\n",
    "    bb_ts = np.concatenate((bb_bh1_t.reshape(-1, 1), bb_bh2_t.reshape(-1, 1), bb_bh3_t.reshape(-1, 1)), axis=1)\n",
    "    bb_ts = ak.Array(bb_ts)\n",
    "    fj_pt = ak.Array(fj_pt)\n",
    "    bh_masks = np.concatenate((bh1_mask.reshape(-1, 1), bh2_mask.reshape(-1, 1), bh3_mask.reshape(-1, 1)), axis=1)\n",
    "    bh_masks = ak.Array(bh_masks)\n",
    "    bh_pts = np.concatenate((bh1_pt.reshape(-1, 1), bh2_pt.reshape(-1, 1), bh3_pt.reshape(-1, 1)), axis=1)\n",
    "    bh_pts = ak.Array(bh_pts)\n",
    "    \n",
    "    # select predictions and targets\n",
    "    bb_ps_selected = sel_pred_bH_by_dp_ap(dps, aps, bb_ps)\n",
    "    bb_ts_selected, targetH_selected_pts = sel_target_bH_by_mask(bb_ts, bh_pts, bh_masks)\n",
    "    \n",
    "    # generate correct/retrieved LUT for pred/target respectively\n",
    "    LUT_pred = gen_pred_bH_LUT(bb_ps_selected, bb_ts_selected, fj_pt)\n",
    "    LUT_target = gen_target_bH_LUT(bb_ps_selected, bb_ts_selected, targetH_selected_pts)\n",
    "    \n",
    "    # reconstruct bH to remove overlapped ak4 jets\n",
    "    fj_eta = np.array(testfile['INPUTS']['BoostedJets']['fj_eta'])\n",
    "    fj_phi = np.array(testfile['INPUTS']['BoostedJets']['fj_phi'])\n",
    "    fj_mass = np.array(testfile['INPUTS']['BoostedJets']['fj_mass'])\n",
    "    \n",
    "    fjs = ak.zip(\n",
    "        {\n",
    "            \"pt\": fj_pt,\n",
    "            \"eta\": fj_eta,\n",
    "            \"phi\": fj_phi,\n",
    "            \"mass\": fj_mass,\n",
    "        },\n",
    "        with_name=\"Momentum4D\"\n",
    "    )\n",
    "    fj_reco = fjs[bb_ps_selected-10]\n",
    "    \n",
    "    return LUT_pred, LUT_target, fj_reco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "52eadffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_unoverlapped_jet_index(fjs, js, dR_min=0.8):\n",
    "    overlapped = ak.sum(js[:, np.newaxis].deltaR(fjs)<dR_min, axis=-2)>0\n",
    "    jet_index_passed = ak.local_index(js).mask[~overlapped]\n",
    "    jet_index_passed = ak.drop_none(jet_index_passed)\n",
    "    return jet_index_passed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e3896b0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sel_pred_h_by_dp_ap(dps, aps, b1_ps, b2_ps):\n",
    "    # get most possible number of H_reco by dps\n",
    "    HiggsNumProb = dp_to_HiggsNumProb(dps)\n",
    "    HiggsNum = np.argmax(HiggsNumProb, axis=-1)\n",
    "    \n",
    "    # get the top N (dp x ap) jet assignment indices\n",
    "    ps = dps*aps\n",
    "    idx_descend = np.flip(np.argsort(ps, axis=-1), axis=-1)\n",
    "    print('ap', aps[0])\n",
    "    print('dp', dps[0])\n",
    "    print('HiggsNumProb', HiggsNumProb[0])\n",
    "    print('HiggsNum', HiggsNum[0])\n",
    "    print('b1', b1_ps[0])\n",
    "    print('b2', b2_ps[0])\n",
    "    \n",
    "    idx_sel = [idx_e[:N_e] for idx_e, N_e in zip(idx_descend, HiggsNum)]\n",
    "    \n",
    "    # select the predicted b assignment via the indices\n",
    "    b1_ps_sel = b1_ps[idx_sel]\n",
    "    b2_ps_sel = b2_ps[idx_sel]\n",
    "\n",
    "    # require b1 b2 assignment are AK4 jet\n",
    "    b1_ak4_filter = b1_ps_sel<10\n",
    "    b2_ak4_filter = b2_ps_sel<10\n",
    "    filter = b1_ak4_filter & b2_ak4_filter\n",
    "    \n",
    "    b1_ps_passed = b1_ps_sel.mask[filter]\n",
    "    b1_ps_passed = ak.drop_none(b1_ps_passed)\n",
    "    \n",
    "    b2_ps_passed = b2_ps_sel.mask[filter]\n",
    "    b2_ps_passed = ak.drop_none(b2_ps_passed)\n",
    "    \n",
    "    return b1_ps_passed, b2_ps_passed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4bcac1c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sel_target_h_by_mask(b1_ts, b2_ts, h_pts, bi_cat_H, h_masks):\n",
    "    b1_ts_selected = b1_ts.mask[h_masks]\n",
    "    b1_ts_selected = ak.drop_none(b1_ts_selected)\n",
    "    \n",
    "    b2_ts_selected = b2_ts.mask[h_masks]\n",
    "    b2_ts_selected = ak.drop_none(b2_ts_selected)\n",
    "    \n",
    "    h_selected_pts = h_pts.mask[h_masks]\n",
    "    h_selected_pts = ak.drop_none(h_selected_pts)\n",
    "    \n",
    "    bi_cat_H_passed = bi_cat_H.mask[h_masks]\n",
    "    bi_cat_H_passed = ak.drop_none(bi_cat_H_passed)\n",
    "    \n",
    "    return b1_ts_selected, b2_ts_selected, h_selected_pts, bi_cat_H_passed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6b54c1ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A pred look up table is in shape\n",
    "# [event,\n",
    "#    pred_H, \n",
    "#       [correct_or_not, pt, overlap_w_H_reco, has_boost_H_target, which_H_target]]\n",
    "@nb.njit\n",
    "def gen_pred_h_LUT(b1_ps_passed, b2_ps_passed, b1_ts_selected, b2_ts_selected, js, goodJetIdx, bi_cat_H_selected, builder):\n",
    "    # for each event\n",
    "    for b1_ps_e, b2_ps_e, b1_ts_e, b2_ts_e, jets_e, goodJetIdx_e, bi_cat_H_e in zip(b1_ps_passed, b2_ps_passed, b1_ts_selected, b2_ts_selected, js, goodJetIdx, bi_cat_H_selected):\n",
    "        # for each predicted bb assignment, check if any target H have a same bb assignment\n",
    "        builder.begin_list()\n",
    "        for b1_p, b2_p in zip(b1_ps_e, b2_ps_e):\n",
    "            if (b1_p in goodJetIdx_e) and (b2_p in goodJetIdx_e):\n",
    "                overlap = 0\n",
    "            else:\n",
    "                overlap = 1\n",
    "            correct = 0\n",
    "            has_t_bH = -1\n",
    "            bH = -1\n",
    "            \n",
    "            predH_pt = (jets_e[b1_p]+jets_e[b2_p]).pt\n",
    "#             try:\n",
    "#                 predH_pt = (jets_e[b1_p]+jets_e[b2_p]).pt\n",
    "#             except:\n",
    "# #                 print(jets_e[b1_p])\n",
    "# #                 print(jets_e[b2_p])\n",
    "#                   pass\n",
    "            \n",
    "            for i, (b1_t, b2_t, bi_cat_H) in enumerate(zip(b1_ts_e, b2_ts_e, bi_cat_H_e)):\n",
    "                if set((b1_p, b2_p)) == set((b1_t, b2_t)):\n",
    "                    correct = 1\n",
    "                    has_t_bH = bi_cat_H\n",
    "                    bH = i\n",
    "                    \n",
    "            builder.begin_list()\n",
    "            builder.append(correct)\n",
    "            builder.append(predH_pt)\n",
    "            builder.append(overlap)\n",
    "            builder.append(has_t_bH)\n",
    "            builder.append(bH)\n",
    "            builder.append(b1_p)\n",
    "            builder.append(b2_p)\n",
    "            builder.end_list()\n",
    "        \n",
    "        builder.end_list()\n",
    "    return builder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "38a62113",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A target look up table is in shape\n",
    "# [event,\n",
    "#    target_H, \n",
    "#        target_bb_assign,\n",
    "#           [retrieved, targetH_pt, can_boost_reco]]\n",
    "@nb.njit\n",
    "def gen_target_h_LUT(b1_ps_passed, b2_ps_passed, b1_ts_selected, b2_ts_selected, targetH_pts, bi_cat_H_selected, builder):\n",
    "    # for each event\n",
    "    for b1_ps_e, b2_ps_e, b1_ts_e, b2_ts_e, tH_pts_e, bi_cat_H_e in zip(b1_ps_passed, b2_ps_passed, b1_ts_selected, b2_ts_selected, targetH_pts, bi_cat_H_selected):\n",
    "        # for each target fatjet, check if the predictions have a p fatject same with the t fatjet\n",
    "        builder.begin_list()\n",
    "        for b1_t, b2_t, tH_pt, bi_cat_H in zip(b1_ts_e, b2_ts_e, tH_pts_e, bi_cat_H_e):\n",
    "            retrieved = 0\n",
    "            can_boost_reco = bi_cat_H\n",
    "            for b1_p, b2_p in zip(b1_ps_e, b2_ps_e):\n",
    "                if set((b1_p, b2_p)) == set((b1_t, b2_t)):\n",
    "                    retrieved = 1\n",
    "            builder.begin_list()\n",
    "            builder.append(retrieved)\n",
    "            builder.append(tH_pt)\n",
    "            builder.append(can_boost_reco)\n",
    "            builder.end_list()\n",
    "        \n",
    "        builder.end_list()\n",
    "    return builder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "135e1e4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_resolved_w_target(testfile, predfile, fjs_reco=None):\n",
    "    # h pt\n",
    "    h1_pt = np.array(testfile['TARGETS']['h1']['pt'])\n",
    "    h2_pt = np.array(testfile['TARGETS']['h2']['pt'])\n",
    "    h3_pt = np.array(testfile['TARGETS']['h3']['pt'])\n",
    "    \n",
    "    # resolved mask\n",
    "    h1_mask = np.array(testfile['TARGETS']['h1']['mask'])\n",
    "    h2_mask = np.array(testfile['TARGETS']['h2']['mask'])\n",
    "    h3_mask = np.array(testfile['TARGETS']['h3']['mask'])\n",
    "    \n",
    "    h_masks = np.concatenate((h1_mask.reshape(-1, 1), h2_mask.reshape(-1, 1), h3_mask.reshape(-1, 1)), axis=1)\n",
    "    # h_masks = h_masks.astype(float)\n",
    "    # h_masks = ak.Array(h_masks)\n",
    "    \n",
    "    # boosted mask\n",
    "    bh1_mask = np.array(testfile['TARGETS']['bh1']['mask'])\n",
    "    bh2_mask = np.array(testfile['TARGETS']['bh2']['mask'])\n",
    "    bh3_mask = np.array(testfile['TARGETS']['bh3']['mask'])\n",
    "    \n",
    "    bh_masks = np.concatenate((bh1_mask.reshape(-1, 1), bh2_mask.reshape(-1, 1), bh3_mask.reshape(-1, 1)), axis=1)\n",
    "    # bh_masks = bh_masks.astype(float)\n",
    "    # bh_masks = ak.Array(bh_masks)\n",
    "    \n",
    "    # findout which resolved higgs also have boosted reco\n",
    "    bi_cat_H = h_masks & bh_masks\n",
    "    bi_cat_H = bi_cat_H.astype(float)\n",
    "    bi_cat_H = ak.Array(bi_cat_H)\n",
    "    \n",
    "    \n",
    "    # target assignments\n",
    "    b1_h1_t = np.array(testfile[\"TARGETS\"][\"h1\"]['b1']).astype('int')\n",
    "    b1_h2_t = np.array(testfile[\"TARGETS\"][\"h2\"]['b1']).astype('int')\n",
    "    b1_h3_t = np.array(testfile[\"TARGETS\"][\"h3\"]['b1']).astype('int')\n",
    "\n",
    "    b2_h1_t = np.array(testfile[\"TARGETS\"][\"h1\"]['b2']).astype('int')\n",
    "    b2_h2_t = np.array(testfile[\"TARGETS\"][\"h2\"]['b2']).astype('int')\n",
    "    b2_h3_t = np.array(testfile[\"TARGETS\"][\"h3\"]['b2']).astype('int')\n",
    "    \n",
    "    # predict assignments\n",
    "    b1_h1_p = np.array(predfile[\"TARGETS\"][\"h1\"]['b1']).astype('int')\n",
    "    b1_h2_p = np.array(predfile[\"TARGETS\"][\"h2\"]['b1']).astype('int')\n",
    "    b1_h3_p = np.array(predfile[\"TARGETS\"][\"h3\"]['b1']).astype('int')\n",
    "\n",
    "    b2_h1_p = np.array(predfile[\"TARGETS\"][\"h1\"]['b2']).astype('int')\n",
    "    b2_h2_p = np.array(predfile[\"TARGETS\"][\"h2\"]['b2']).astype('int')\n",
    "    b2_h3_p = np.array(predfile[\"TARGETS\"][\"h3\"]['b2']).astype('int')\n",
    "    \n",
    "    # resolved Higgs detection probability\n",
    "    dp_h1 = np.array(predfile[\"TARGETS\"][\"h1\"]['detection_probability'])\n",
    "    dp_h2 = np.array(predfile[\"TARGETS\"][\"h2\"]['detection_probability'])\n",
    "    dp_h3 = np.array(predfile[\"TARGETS\"][\"h3\"]['detection_probability'])\n",
    "    \n",
    "    # ak4 jets assignment probability\n",
    "    ap_h1 = np.array(predfile[\"TARGETS\"][\"h1\"]['assignment_probability'])\n",
    "    ap_h2 = np.array(predfile[\"TARGETS\"][\"h2\"]['assignment_probability'])\n",
    "    ap_h3 = np.array(predfile[\"TARGETS\"][\"h3\"]['assignment_probability'])\n",
    "    \n",
    "    # reconstruct jet 4-momentum objects\n",
    "    j_pt = np.array(testfile['INPUTS']['Jets']['pt'])\n",
    "    j_eta = np.array(testfile['INPUTS']['Jets']['eta'])\n",
    "    j_phi = np.array(testfile['INPUTS']['Jets']['phi'])\n",
    "    j_mass = np.array(testfile['INPUTS']['Jets']['mass'])\n",
    "    js = ak.zip(\n",
    "        {\n",
    "            \"pt\": j_pt,\n",
    "            \"eta\": j_eta,\n",
    "            \"phi\": j_phi,\n",
    "            \"mass\": j_mass,\n",
    "        },\n",
    "        with_name=\"Momentum4D\"\n",
    "    )\n",
    "    \n",
    "    # convert some numpy arrays to ak arrays\n",
    "    dps = np.concatenate((dp_h1.reshape(-1, 1), dp_h2.reshape(-1, 1), dp_h3.reshape(-1, 1)), axis=1)\n",
    "    # dps = ak.Array(dps)\n",
    "    aps = np.concatenate((ap_h1.reshape(-1, 1), ap_h2.reshape(-1, 1), ap_h3.reshape(-1, 1)), axis=1)\n",
    "    # aps = ak.Array(aps)\n",
    "    \n",
    "    dps = reset_collision_dp(dps, aps)\n",
    "    \n",
    "    b1_ps = np.concatenate((b1_h1_p.reshape(-1, 1), b1_h2_p.reshape(-1, 1), b1_h3_p.reshape(-1, 1)), axis=1)\n",
    "    b1_ps = ak.Array(b1_ps)\n",
    "    b1_ts = np.concatenate((b1_h1_t.reshape(-1, 1), b1_h2_t.reshape(-1, 1), b1_h3_t.reshape(-1, 1)), axis=1)\n",
    "    b1_ts = ak.Array(b1_ts)\n",
    "    b2_ps = np.concatenate((b2_h1_p.reshape(-1, 1), b2_h2_p.reshape(-1, 1), b2_h3_p.reshape(-1, 1)), axis=1)\n",
    "    b2_ps = ak.Array(b2_ps)\n",
    "    b2_ts = np.concatenate((b2_h1_t.reshape(-1, 1), b2_h2_t.reshape(-1, 1), b2_h3_t.reshape(-1, 1)), axis=1)\n",
    "    b2_ts = ak.Array(b2_ts)\n",
    "\n",
    "    \n",
    "    \n",
    "    h_pts = np.concatenate((h1_pt.reshape(-1, 1), h2_pt.reshape(-1, 1), h3_pt.reshape(-1, 1)), axis=1)\n",
    "    h_pts = ak.Array(h_pts)\n",
    "    \n",
    "    # select predictions and targets\n",
    "    b1_ts_selected, b2_ts_selected, targetH_selected_pts, bi_cat_H_selected = sel_target_h_by_mask(b1_ts, b2_ts, h_pts, bi_cat_H, h_masks)\n",
    "    b1_ps_selected, b2_ps_selected = sel_pred_h_by_dp_ap(dps, aps, b1_ps, b2_ps)\n",
    "    print(b1_ps_selected)\n",
    "    print(b2_ps_selected)\n",
    "    \n",
    "    # find jets that are overlapped with reco boosted Higgs\n",
    "    if fjs_reco is None:\n",
    "        goodJetIdx =  ak.local_index(js)\n",
    "    else:\n",
    "        goodJetIdx = get_unoverlapped_jet_index(fjs_reco, js, dR_min=0.4)\n",
    "    \n",
    "    # generate look up tables\n",
    "    LUT_pred = gen_pred_h_LUT(b1_ps_selected, b2_ps_selected, b1_ts_selected, b2_ts_selected, js, goodJetIdx, bi_cat_H_selected, ak.ArrayBuilder()).snapshot()\n",
    "    LUT_target = gen_target_h_LUT(b1_ps_selected, b2_ps_selected, b1_ts_selected, b2_ts_selected, targetH_selected_pts, bi_cat_H_selected, ak.ArrayBuilder()).snapshot()\n",
    "    \n",
    "    \n",
    "    return LUT_pred, LUT_target, goodJetIdx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4bef1a35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate efficiency\n",
    "# if bins=None, put all data in a single bin\n",
    "def calc_eff(LUT_boosted_pred, LUT_resolved_pred, bins):\n",
    "\n",
    "    predHs = []\n",
    "    \n",
    "    if LUT_boosted_pred is not None:\n",
    "        # boosted H don't need post processing\n",
    "        predHs_boosted = [predH for event in LUT_boosted_pred for predH in event]\n",
    "        predHs += predHs_boosted\n",
    "\n",
    "    if LUT_resolved_pred is not None:\n",
    "        # Remove overlapped resolved H_reco \n",
    "        predHs_resolved = [predH[0:2] for event in LUT_resolved_pred for predH in event if predH[2]==0]\n",
    "        predHs += predHs_resolved\n",
    "        \n",
    "    # then merge into the list with their pT\n",
    "    predHs = np.array(predHs)\n",
    "    \n",
    "    predHs_inds = np.digitize(predHs[:,1], bins)\n",
    "    \n",
    "    correctTruth_per_bin = []\n",
    "    for bin_i in range(1, len(bins)):\n",
    "        correctTruth_per_bin.append(predHs[:,0][predHs_inds==bin_i])\n",
    "    correctTruth_per_bin = ak.Array(correctTruth_per_bin)\n",
    "    \n",
    "    means = ak.mean(correctTruth_per_bin, axis=-1)\n",
    "    \n",
    "    errs = np.abs(\n",
    "    clopper_pearson_interval(num=ak.sum(correctTruth_per_bin, axis=-1),\\\n",
    "                             denom=ak.num(correctTruth_per_bin, axis=-1)) - means\n",
    "    )\n",
    "    \n",
    "    return means, errs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f8b011c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate purity\n",
    "def calc_pur(LUT_boosted_target, LUT_resolved_target, bins):\n",
    "\n",
    "    targetHs = []\n",
    "\n",
    "    if LUT_boosted_target is not None:\n",
    "        # boosted H don't need post processing\n",
    "        targetHs_boosted = [targetH for event in LUT_boosted_target for targetH in event]\n",
    "        targetHs += targetHs_boosted\n",
    "\n",
    "    if LUT_resolved_target is not None:\n",
    "        # only consider resolved target H that doesn't have a boosted reco\n",
    "        targetHs_resolved = [targetH[0:2] for event in LUT_resolved_target for targetH in event if targetH[2]==0]\n",
    "        targetHs += targetHs_resolved\n",
    "\n",
    "    targetHs = np.array(targetHs)\n",
    "\n",
    "    targetHs_inds = np.digitize(targetHs[:,1], bins)\n",
    "    \n",
    "    correctTruth_per_bin = []\n",
    "    for bin_i in range(1, len(bins)):\n",
    "        correctTruth_per_bin.append(targetHs[:,0][targetHs_inds==bin_i])\n",
    "    correctTruth_per_bin = ak.Array(correctTruth_per_bin)\n",
    "    \n",
    "    means = ak.mean(correctTruth_per_bin, axis=-1)\n",
    "    \n",
    "    errs = np.abs(\n",
    "    clopper_pearson_interval(num=ak.sum(correctTruth_per_bin, axis=-1),\\\n",
    "                             denom=ak.num(correctTruth_per_bin, axis=-1)) - means\n",
    "    )\n",
    "    \n",
    "    return means, errs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c7ddc22c",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = np.arange(0, 1000, 50)\n",
    "bin_centers = [(bins[i]+bins[i+1])/2 for i in range(bins.size-1)]\n",
    "xerr=(bins[1]-bins[0])/2*np.ones(bins.shape[0]-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3b613116",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ap [0.         0.         0.20228793]\n",
      "dp [0.        0.        0.7409017]\n",
      "HiggsNumProb [0.2590983 0.7409017 0.        0.       ]\n",
      "HiggsNum 1\n",
      "b1 [5, 6, 0]\n",
      "b2 [5, 6, 4]\n",
      "[[0], [0, 1], [2], [0], [1, 0], [0], ..., [...], [3], [], [0], [1, 2], [1, 4]]\n",
      "[[4], [2, 5], [4], [1], [2, 6], [2], ..., [...], [4], [], [4], [3, 4], [2, 6]]\n",
      "ap [0.         0.         0.20228793]\n",
      "dp [0.        0.        0.7409017]\n",
      "HiggsNumProb [0.2590983 0.7409017 0.        0.       ]\n",
      "HiggsNum 1\n",
      "b1 [5, 6, 0]\n",
      "b2 [5, 6, 4]\n",
      "[[0], [0, 1], [2], [0], [1, 0], [0], ..., [...], [3], [], [0], [1, 2], [1, 4]]\n",
      "[[4], [2, 5], [4], [1], [2, 6], [2], ..., [...], [4], [], [4], [3, 4], [2, 6]]\n"
     ]
    }
   ],
   "source": [
    "LUT_boosted_pred_spanet, LUT_boosted_target_spanet, fjs_reco_spanet = parse_boosted_w_target(test_h5, s_h5)\n",
    "LUT_resolved_pred_spanet, LUT_resolved_target_spanet, _ = parse_resolved_w_target(test_h5, s_h5, fjs_reco=None)\n",
    "LUT_resolved_wOR_pred_spanet, LUT_resolved_wOR_target_spanet, _ = parse_resolved_w_target(test_h5, s_h5, fjs_reco=fjs_reco_spanet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a25e8585",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ap [0.         0.45723268 0.        ]\n",
      "dp [0.         0.85143286 0.        ]\n",
      "HiggsNumProb [0.14856714 0.85143286 0.         0.        ]\n",
      "HiggsNum 1\n",
      "b1 [1, 0, 6]\n",
      "b2 [1, 3, 6]\n",
      "[[0], [1, 2], [1], [0], [1, 0, 3], [0], ..., [1], [], [0, 2], [1, 0], [0, 2]]\n",
      "[[3], [3, 4], [2], [5], [2, 4, 6], [2], ..., [2], [], [4, 5], [3, 4], [1, 6]]\n",
      "ap [0.         0.45723268 0.        ]\n",
      "dp [0.         0.85143286 0.        ]\n",
      "HiggsNumProb [0.14856714 0.85143286 0.         0.        ]\n",
      "HiggsNum 1\n",
      "b1 [1, 0, 6]\n",
      "b2 [1, 3, 6]\n",
      "[[0], [1, 2], [1], [0], [1, 0, 3], [0], ..., [1], [], [0, 2], [1, 0], [0, 2]]\n",
      "[[3], [3, 4], [2], [5], [2, 4, 6], [2], ..., [2], [], [4, 5], [3, 4], [1, 6]]\n"
     ]
    }
   ],
   "source": [
    "LUT_boosted_pred_pb, LUT_boosted_target_pb, fjs_reco_pb = parse_boosted_w_target(test_h5, pb_h5)\n",
    "LUT_resolved_pred_pb, LUT_resolved_target_pb, _ = parse_resolved_w_target(test_h5, pb_h5, fjs_reco=None)\n",
    "LUT_resolved_wOR_pred_pb, LUT_resolved_wOR_target_pb, _ = parse_resolved_w_target(test_h5, pb_h5, fjs_reco=fjs_reco_pb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3aa74f8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ap [1. 1. 1.]\n",
      "dp [1. 1. 1.]\n",
      "HiggsNumProb [0. 0. 0. 1.]\n",
      "HiggsNum 3\n",
      "b1 [0, 1, 4]\n",
      "b2 [3, 2, 5]\n",
      "[[4, 1, 0], [4, 1, 0], [2, 1, 0], [3, ...], ..., [2, 1, 0], [1, 0], [3, 1, 0]]\n",
      "[[5, 2, 3], [5, 3, 2], [4, 5, 3], [5, ...], ..., [3, 4, 5], [3, 2], [5, 2, 4]]\n",
      "ap [1. 1. 1.]\n",
      "dp [1. 1. 1.]\n",
      "HiggsNumProb [0. 0. 0. 1.]\n",
      "HiggsNum 3\n",
      "b1 [0, 1, 4]\n",
      "b2 [3, 2, 5]\n",
      "[[4, 1, 0], [4, 1, 0], [2, 1, 0], [3, ...], ..., [2, 1, 0], [1, 0], [3, 1, 0]]\n",
      "[[5, 2, 3], [5, 3, 2], [4, 5, 3], [5, ...], ..., [3, 4, 5], [3, 2], [5, 2, 4]]\n"
     ]
    }
   ],
   "source": [
    "LUT_boosted_pred_base, LUT_boosted_target_base, fjs_reco_base = parse_boosted_w_target(test_h5, b_h5)\n",
    "LUT_resolved_pred_base, LUT_resolved_target_base, _ = parse_resolved_w_target(test_h5, b_h5, fjs_reco=None)\n",
    "LUT_resolved_wOR_pred_base, LUT_resolved_wOR_target_base, _ = parse_resolved_w_target(test_h5, b_h5, fjs_reco=fjs_reco_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8af09055",
   "metadata": {},
   "outputs": [],
   "source": [
    "eff_s, efferr_s = calc_eff(LUT_boosted_pred_spanet, LUT_resolved_wOR_pred_spanet, bins)\n",
    "pur_s, purerr_s = calc_pur(LUT_boosted_target_spanet, LUT_resolved_wOR_target_spanet, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b54aaa9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "eff_b, efferr_b = calc_eff(LUT_boosted_pred_base, LUT_resolved_wOR_pred_base, bins)\n",
    "pur_b, purerr_b = calc_pur(LUT_boosted_target_base, LUT_resolved_wOR_target_base, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7a900c1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "eff_pb, efferr_pb = calc_eff(LUT_boosted_pred_pb, LUT_resolved_wOR_pred_pb, bins)\n",
    "pur_pb, purerr_pb = calc_pur(LUT_boosted_target_pb, LUT_resolved_wOR_target_pb, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "be384f5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x151e8a1c0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "ax[0].errorbar(x=bin_centers, y=eff_b, xerr=xerr, yerr=efferr_b, fmt='o', capsize=5, label='Baseline')\n",
    "ax[0].errorbar(x=bin_centers, y=eff_s, xerr=xerr, yerr=efferr_s, fmt='o', capsize=5, label='SPANet')\n",
    "ax[0].errorbar(x=bin_centers, y=eff_pb, xerr=xerr, yerr=efferr_pb, fmt='o', capsize=5, label='SPANet Particle Balance Off')\n",
    "\n",
    "ax[1].errorbar(x=bin_centers, y=pur_b, xerr=xerr, yerr=purerr_b, fmt='o', capsize=5, label='Baseline')\n",
    "ax[1].errorbar(x=bin_centers, y=pur_s, xerr=xerr, yerr=purerr_s, fmt='o', capsize=5, label='SPANet')\n",
    "ax[1].errorbar(x=bin_centers, y=pur_pb, xerr=xerr, yerr=purerr_pb, fmt='o', capsize=5, label='SPANet Particle Balance Off')\n",
    "\n",
    "ax[0].set(xlabel=r\"Reco H pT (GeV)\", ylabel=r\"Matching efficiency\", title=f\"Merged\")\n",
    "ax[1].set(xlabel=r\"Gen H pT (GeV)\", ylabel=r\"Matching purity\", title=f\"Merged\")\n",
    "ax[0].legend()\n",
    "ax[1].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2214532e-40ab-4bb6-badc-366907be5b75",
   "metadata": {},
   "outputs": [],
   "source": [
    "eff_s, efferr_s = calc_eff(LUT_boosted_pred_spanet, None, bins)\n",
    "pur_s, purerr_s = calc_pur(LUT_boosted_target_spanet, None, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5b5c67f0-96be-4f8c-bf8f-3679f9b1fe33",
   "metadata": {},
   "outputs": [],
   "source": [
    "eff_b, efferr_b = calc_eff(LUT_boosted_pred_base, None, bins)\n",
    "pur_b, purerr_b = calc_pur(LUT_boosted_target_base, None, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e4492689-18e9-4851-8d60-329b2895833e",
   "metadata": {},
   "outputs": [],
   "source": [
    "eff_pb, efferr_pb = calc_eff(LUT_boosted_pred_pb, None, bins)\n",
    "pur_pb, purerr_pb = calc_pur(LUT_boosted_target_pb, None, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "58eeb79e-9238-42d5-8a23-b1413112724b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1520a7550>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "ax[0].errorbar(x=bin_centers, y=eff_b, xerr=xerr, yerr=efferr_b, fmt='o', capsize=5, label='Baseline')\n",
    "ax[0].errorbar(x=bin_centers, y=eff_s, xerr=xerr, yerr=efferr_s, fmt='o', capsize=5, label='SPANet')\n",
    "ax[0].errorbar(x=bin_centers, y=eff_pb, xerr=xerr, yerr=efferr_pb, fmt='o', capsize=5, label='SPANet Particle Balance Off')\n",
    "\n",
    "ax[1].errorbar(x=bin_centers, y=pur_b, xerr=xerr, yerr=purerr_b, fmt='o', capsize=5, label='Baseline')\n",
    "ax[1].errorbar(x=bin_centers, y=pur_s, xerr=xerr, yerr=purerr_s, fmt='o', capsize=5, label='SPANet')\n",
    "ax[1].errorbar(x=bin_centers, y=pur_pb, xerr=xerr, yerr=purerr_pb, fmt='o', capsize=5, label='SPANet Particle Balance Off')\n",
    "\n",
    "ax[0].set(xlabel=r\"Reco H pT (GeV)\", ylabel=r\"Matching efficiency\", title=f\"Boosted\")\n",
    "ax[1].set(xlabel=r\"Gen H pT (GeV)\", ylabel=r\"Matching purity\", title=f\"Boosted\")\n",
    "ax[0].legend()\n",
    "ax[1].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3d858d9c-4423-4402-9344-b68dbfb67ca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "eff_s, efferr_s = calc_eff(None, LUT_resolved_pred_spanet, bins)\n",
    "pur_s, purerr_s = calc_pur(None, LUT_resolved_target_spanet, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bf8c9a14-b0ac-48e4-a0df-68597915e5fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "eff_b, efferr_b = calc_eff(None, LUT_resolved_pred_base, bins)\n",
    "pur_b, purerr_b = calc_pur(None, LUT_resolved_target_base, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c8a359a6-de1e-48f7-8138-05108961187e",
   "metadata": {},
   "outputs": [],
   "source": [
    "eff_pb, efferr_pb = calc_eff(None, LUT_resolved_pred_pb, bins)\n",
    "pur_pb, purerr_pb = calc_pur(None, LUT_resolved_target_pb, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "65661bca-ad89-47c1-84f5-91b60ad4a8c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x151c27ca0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "ax[0].errorbar(x=bin_centers, y=eff_b, xerr=xerr, yerr=efferr_b, fmt='o', capsize=5, label='Baseline')\n",
    "ax[0].errorbar(x=bin_centers, y=eff_s, xerr=xerr, yerr=efferr_s, fmt='o', capsize=5, label='SPANet')\n",
    "ax[0].errorbar(x=bin_centers, y=eff_pb, xerr=xerr, yerr=efferr_pb, fmt='o', capsize=5, label='SPANet Particle Balance Off')\n",
    "\n",
    "ax[1].errorbar(x=bin_centers, y=pur_b, xerr=xerr, yerr=purerr_b, fmt='o', capsize=5, label='Baseline')\n",
    "ax[1].errorbar(x=bin_centers, y=pur_s, xerr=xerr, yerr=purerr_s, fmt='o', capsize=5, label='SPANet')\n",
    "ax[1].errorbar(x=bin_centers, y=pur_pb, xerr=xerr, yerr=purerr_pb, fmt='o', capsize=5, label='SPANet Particle Balance Off')\n",
    "\n",
    "ax[0].set(xlabel=r\"Reco H pT (GeV)\", ylabel=r\"Matching efficiency\", title=f\"Resolved\")\n",
    "ax[1].set(xlabel=r\"Gen H pT (GeV)\", ylabel=r\"Matching purity\", title=f\"Resolved\")\n",
    "ax[0].legend()\n",
    "ax[1].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fff82094-85ee-4241-ac4e-8a41580d01a0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}