{
 "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/envs/phys239/lib/python3.7/site-packages/coffea/util.py:154: FutureWarning: In coffea version v2023.3.0 (target date: 31 Mar 2023), 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",
    "    \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",
    "    \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 corresponding boosted H target\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": [],
   "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": [],
   "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": [],
   "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7756, 17578, 18600, 14751, 9706, 5847, 3794, ..., 213, 116, 87, 58, 37, 19, 12]\n"
     ]
    }
   ],
   "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7756, 17578, 18600, 14751, 9706, 5847, 3794, ..., 213, 116, 87, 58, 37, 19, 12]\n"
     ]
    }
   ],
   "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7756, 17578, 18600, 14751, 9706, 5847, 3794, ..., 213, 116, 87, 58, 37, 19, 12]\n"
     ]
    }
   ],
   "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": [
       "(-0.1, 1.1)"
      ]
     },
     "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()\n",
    "\n",
    "ax[0].set_ylim([-0.1, 1.1])\n",
    "ax[1].set_ylim([-0.1, 1.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2214532e-40ab-4bb6-badc-366907be5b75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 2, 49, 350, 1180, 2154, 1731, ..., 280, 201, 109, 84, 56, 37, 19, 12]\n"
     ]
    }
   ],
   "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 2, 49, 350, 1180, 2154, 1731, ..., 280, 201, 109, 84, 56, 37, 19, 12]\n"
     ]
    }
   ],
   "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 2, 49, 350, 1180, 2154, 1731, ..., 280, 201, 109, 84, 56, 37, 19, 12]\n"
     ]
    }
   ],
   "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": [
       "(-0.1, 1.1)"
      ]
     },
     "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()\n",
    "\n",
    "ax[0].set_ylim([-0.1, 1.1])\n",
    "ax[1].set_ylim([-0.1, 1.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3d858d9c-4423-4402-9344-b68dbfb67ca0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7756, 17578, 18598, 14702, 9356, 4667, 1640, 556, ..., 12, 7, 3, 2, 0, 0, 0]\n"
     ]
    }
   ],
   "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7756, 17578, 18598, 14702, 9356, 4667, 1640, 556, ..., 12, 7, 3, 2, 0, 0, 0]\n"
     ]
    }
   ],
   "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7756, 17578, 18598, 14702, 9356, 4667, 1640, 556, ..., 12, 7, 3, 2, 0, 0, 0]\n"
     ]
    }
   ],
   "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": [
       "(-0.1, 1.1)"
      ]
     },
     "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()\n",
    "\n",
    "ax[0].set_ylim([-0.1, 1.1])\n",
    "ax[1].set_ylim([-0.1, 1.1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30989ff4",
   "metadata": {},
   "source": [
    "## Code below shows the envelop effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9dff16bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_eff_envelop(LUT_boosted_pred, LUT_resolved_pred, bins):\n",
    "\n",
    "    predHs = []\n",
    "\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",
    "    # 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_resolved = [predH[0:2] for event in LUT_resolved_pred for predH in event]\n",
    "    predHs += predHs_resolved\n",
    "\n",
    "    # then merge into the list with their pT\n",
    "    predHs = np.array(predHs)\n",
    "    predHs_inds = np.digitize(predHs[:,1], bins)\n",
    "    \n",
    "    match_per_bin = []\n",
    "    for bin_i in range(1, len(bins)):\n",
    "        match_per_bin.append(predHs[:,0][predHs_inds==bin_i])\n",
    "    match_per_bin = ak.Array(match_per_bin)\n",
    "    \n",
    "    merge_means = ak.mean(match_per_bin, axis=-1)\n",
    "    \n",
    "    merge_errs = np.abs(\n",
    "        clopper_pearson_interval(num=ak.sum(match_per_bin, axis=-1),\\\n",
    "                             denom=ak.num(match_per_bin, axis=-1)) - merge_means\n",
    "    )\n",
    "    \n",
    "    merge_effs = [merge_means, merge_errs]\n",
    "    \n",
    "    # calculate boosted part: \n",
    "    # numerator: number of correctly matched reco boosted higgs\n",
    "    # denom: number of all reco higgs\n",
    "    predHs_boosted = np.array(predHs_boosted)\n",
    "    predHs_boosted_inds = np.digitize(predHs_boosted[:,1], bins)\n",
    "    \n",
    "    boosted_match_per_bin = []\n",
    "    for bin_i in range(1, len(bins)):\n",
    "        boosted_match_per_bin.append(predHs_boosted[:,0][predHs_boosted_inds==bin_i])\n",
    "    boosted_match_per_bin = ak.Array(boosted_match_per_bin)\n",
    "    \n",
    "    boosted_part_means = ak.sum(boosted_match_per_bin, axis=-1)/ak.num(match_per_bin, axis=-1)\n",
    "    \n",
    "    boosted_errs = np.abs(\n",
    "        clopper_pearson_interval(num=ak.sum(boosted_match_per_bin, axis=-1),\\\n",
    "                             denom=ak.num(match_per_bin, axis=-1)) - boosted_part_means\n",
    "    )\n",
    "    \n",
    "    boosted_effs = [boosted_part_means, boosted_errs]\n",
    "    \n",
    "    # calcualte resolved part:\n",
    "    # numerator: number of correctly matched reco resolved higgs (after overlap removal)\n",
    "    # denom: number of all reco higgs\n",
    "    predHs_resolved = np.array(predHs_resolved)\n",
    "    predHs_resolved_inds = np.digitize(predHs_resolved[:,1], bins)\n",
    "    \n",
    "    resolved_match_per_bin = []\n",
    "    for bin_i in range(1, len(bins)):\n",
    "        resolved_match_per_bin.append(predHs_resolved[:,0][predHs_resolved_inds==bin_i])\n",
    "    resolved_match_per_bin = ak.Array(resolved_match_per_bin)\n",
    "    \n",
    "    resolved_part_means = ak.sum(resolved_match_per_bin, axis=-1)/ak.num(match_per_bin, axis=-1)\n",
    "    \n",
    "    resolved_errs = np.abs(\n",
    "        clopper_pearson_interval(num=ak.sum(resolved_match_per_bin, axis=-1),\\\n",
    "                             denom=ak.num(match_per_bin, axis=-1)) - resolved_part_means\n",
    "    )\n",
    "    \n",
    "    resolved_effs = [resolved_part_means, resolved_errs]\n",
    "    \n",
    "    return merge_effs, boosted_effs, resolved_effs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ae913c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_pur_envelop(LUT_boosted_target, LUT_resolved_target, bins):\n",
    "\n",
    "    targetHs = []\n",
    "\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",
    "    # only consider resolved target H that doesn't have a corresponding boosted H target\n",
    "    # targetHs_resolved = [targetH[0:2] for event in LUT_resolved_target for targetH in event if targetH[2]==0]\n",
    "    targetHs_resolved = [targetH[0:2] for event in LUT_resolved_target for targetH in event]\n",
    "    targetHs += targetHs_resolved\n",
    "\n",
    "    # then merge into the list with their pT\n",
    "    targetHs = np.array(targetHs)\n",
    "    targetHs_inds = np.digitize(targetHs[:,1], bins)\n",
    "    \n",
    "    match_per_bin = []\n",
    "    for bin_i in range(1, len(bins)):\n",
    "        match_per_bin.append(targetHs[:,0][targetHs_inds==bin_i])\n",
    "    match_per_bin = ak.Array(match_per_bin)\n",
    "    \n",
    "    merge_means = ak.mean(match_per_bin, axis=-1)\n",
    "    \n",
    "    merge_errs = np.abs(\n",
    "        clopper_pearson_interval(num=ak.sum(match_per_bin, axis=-1),\\\n",
    "                             denom=ak.num(match_per_bin, axis=-1)) - merge_means\n",
    "    )\n",
    "    \n",
    "    merge_purs = [merge_means, merge_errs]\n",
    "    \n",
    "    # calculate boosted part: \n",
    "    # numerator: number of correctly matched target boosted higgs\n",
    "    # denom: number of all target higgs\n",
    "    targetHs_boosted = np.array(targetHs_boosted)\n",
    "    targetHs_boosted_inds = np.digitize(targetHs_boosted[:,1], bins)\n",
    "    \n",
    "    boosted_match_per_bin = []\n",
    "    for bin_i in range(1, len(bins)):\n",
    "        boosted_match_per_bin.append(targetHs_boosted[:,0][targetHs_boosted_inds==bin_i])\n",
    "    boosted_match_per_bin = ak.Array(boosted_match_per_bin)\n",
    "    \n",
    "    boosted_part_means = ak.sum(boosted_match_per_bin, axis=-1)/ak.num(match_per_bin, axis=-1)\n",
    "    \n",
    "    boosted_errs = np.abs(\n",
    "        clopper_pearson_interval(num=ak.sum(boosted_match_per_bin, axis=-1),\\\n",
    "                             denom=ak.num(match_per_bin, axis=-1)) - boosted_part_means\n",
    "    )\n",
    "    \n",
    "    boosted_purs = [boosted_part_means, boosted_errs]\n",
    "    \n",
    "    # calcualte resolved part:\n",
    "    # numerator: number of correctly matched target resolved higgs\n",
    "    # denom: number of all target higgs\n",
    "    targetHs_resolved = np.array(targetHs_resolved)\n",
    "    targetHs_resolved_inds = np.digitize(targetHs_resolved[:,1], bins)\n",
    "    \n",
    "    resolved_match_per_bin = []\n",
    "    for bin_i in range(1, len(bins)):\n",
    "        resolved_match_per_bin.append(targetHs_resolved[:,0][targetHs_resolved_inds==bin_i])\n",
    "    resolved_match_per_bin = ak.Array(resolved_match_per_bin)\n",
    "    \n",
    "    resolved_part_means = ak.sum(resolved_match_per_bin, axis=-1)/ak.num(match_per_bin, axis=-1)\n",
    "    \n",
    "    resolved_errs = np.abs(\n",
    "        clopper_pearson_interval(num=ak.sum(resolved_match_per_bin, axis=-1),\\\n",
    "                             denom=ak.num(match_per_bin, axis=-1)) - resolved_part_means\n",
    "    )\n",
    "    \n",
    "    resolved_purs = [resolved_part_means, resolved_errs]\n",
    "    \n",
    "    return merge_purs, boosted_purs, resolved_purs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f6c86c9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_eff_s, boosted_eff_s, resolved_eff_s = calc_eff_envelop(LUT_boosted_pred_spanet, LUT_resolved_wOR_pred_spanet, bins)\n",
    "merge_pur_s, boosted_pur_s, resolved_pur_s = calc_pur_envelop(LUT_boosted_target_spanet, LUT_resolved_wOR_target_spanet, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ee8712e6",
   "metadata": {},
   "outputs": [
    {
     "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=merge_eff_s[0], c='r', xerr=xerr, yerr=merge_eff_s[1], fmt='o', capsize=5, label='Merge', alpha=0.8)\n",
    "ax[0].errorbar(x=bin_centers, y=boosted_eff_s[0], c='y', xerr=xerr, yerr=boosted_eff_s[1], fmt='o', capsize=5, label='Boosted', alpha=0.5)\n",
    "ax[0].errorbar(x=bin_centers, y=resolved_eff_s[0], c='b', xerr=xerr, yerr=resolved_eff_s[1], fmt='o', capsize=5, label='Resolved', alpha=0.2)\n",
    "\n",
    "ax[1].errorbar(x=bin_centers, y=merge_pur_s[0], c='r', xerr=xerr, yerr=merge_pur_s[1], fmt='o', capsize=5, label='Merge', alpha=0.8)\n",
    "ax[1].errorbar(x=bin_centers, y=boosted_pur_s[0], c='y', xerr=xerr, yerr=boosted_pur_s[1], fmt='o', capsize=5, label='Boosted', alpha=0.5)\n",
    "ax[1].errorbar(x=bin_centers, y=resolved_pur_s[0], c='b', xerr=xerr, yerr=resolved_pur_s[1], fmt='o', capsize=5, label='Resolved', alpha=0.2)\n",
    "\n",
    "\n",
    "ax[0].set(xlabel=r\"Reco H pT (GeV)\", ylabel=r\"Matching efficiency\", title=f\"SPANet\")\n",
    "ax[1].set(xlabel=r\"Gen H pT (GeV)\", ylabel=r\"Matching purity\", title=f\"SPANet\")\n",
    "ax[0].legend()\n",
    "ax[1].legend()\n",
    "\n",
    "ax[0].set_ylim([-0.1, 1.1])\n",
    "ax[1].set_ylim([-0.1, 1.1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8314d606",
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_eff_b, boosted_eff_b, resolved_eff_b = calc_eff_envelop(LUT_boosted_pred_base, LUT_resolved_wOR_pred_base, bins)\n",
    "merge_pur_b, boosted_pur_b, resolved_pur_b = calc_pur_envelop(LUT_boosted_target_base, LUT_resolved_wOR_target_base, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d05d55ee",
   "metadata": {},
   "outputs": [
    {
     "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=merge_eff_b[0], c='r', xerr=xerr, yerr=merge_eff_b[1], fmt='o', capsize=5, label='Merge', alpha=0.8)\n",
    "ax[0].errorbar(x=bin_centers, y=boosted_eff_b[0], c='y', xerr=xerr, yerr=boosted_eff_b[1], fmt='o', capsize=5, label='Boosted', alpha=0.5)\n",
    "ax[0].errorbar(x=bin_centers, y=resolved_eff_b[0], c='b', xerr=xerr, yerr=resolved_eff_b[1], fmt='o', capsize=5, label='Resolved', alpha=0.2)\n",
    "\n",
    "ax[1].errorbar(x=bin_centers, y=merge_pur_b[0], c='r', xerr=xerr, yerr=merge_pur_b[1], fmt='o', capsize=5, label='Merge', alpha=0.8)\n",
    "ax[1].errorbar(x=bin_centers, y=boosted_pur_b[0], c='y', xerr=xerr, yerr=boosted_pur_b[1], fmt='o', capsize=5, label='Boosted', alpha=0.5)\n",
    "ax[1].errorbar(x=bin_centers, y=resolved_pur_b[0], c='b', xerr=xerr, yerr=resolved_pur_b[1], fmt='o', capsize=5, label='Resolved', alpha=0.2)\n",
    "\n",
    "\n",
    "ax[0].set(xlabel=r\"Reco H pT (GeV)\", ylabel=r\"Matching efficiency\", title=f\"Baseline\")\n",
    "ax[1].set(xlabel=r\"Gen H pT (GeV)\", ylabel=r\"Matching purity\", title=f\"Baseline\")\n",
    "ax[0].legend()\n",
    "ax[1].legend()\n",
    "\n",
    "ax[0].set_ylim([-0.1, 1.1])\n",
    "ax[1].set_ylim([-0.1, 1.1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "944db286",
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_eff_pb, boosted_eff_pb, resolved_eff_pb = calc_eff_envelop(LUT_boosted_pred_pb, LUT_resolved_wOR_pred_pb, bins)\n",
    "merge_pur_pb, boosted_pur_pb, resolved_pur_pb = calc_pur_envelop(LUT_boosted_target_pb, LUT_resolved_wOR_target_pb, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "45aaee2c",
   "metadata": {},
   "outputs": [
    {
     "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=merge_eff_pb[0], c='r', xerr=xerr, yerr=merge_eff_pb[1], fmt='o', capsize=5, label='Merge', alpha=1)\n",
    "ax[0].errorbar(x=bin_centers, y=boosted_eff_pb[0], c='y', xerr=xerr, yerr=boosted_eff_pb[1], fmt='o', capsize=5, label='Boosted', alpha=0.5)\n",
    "ax[0].errorbar(x=bin_centers, y=resolved_eff_pb[0], c='b', xerr=xerr, yerr=resolved_eff_pb[1], fmt='o', capsize=5, label='Resolved', alpha=0.2)\n",
    "\n",
    "ax[1].errorbar(x=bin_centers, y=merge_pur_pb[0], c='r', xerr=xerr, yerr=merge_pur_pb[1], fmt='o', capsize=5, label='Merge', alpha=0.8)\n",
    "ax[1].errorbar(x=bin_centers, y=boosted_pur_pb[0], c='y', xerr=xerr, yerr=boosted_pur_pb[1], fmt='o', capsize=5, label='Boosted', alpha=0.5)\n",
    "ax[1].errorbar(x=bin_centers, y=resolved_pur_pb[0], c='b', xerr=xerr, yerr=resolved_pur_pb[1], fmt='o', capsize=5, label='Resolved', alpha=0.2)\n",
    "\n",
    "\n",
    "ax[0].set(xlabel=r\"Reco H pT (GeV)\", ylabel=r\"Matching efficiency\", title=f\"SPANet Particle Balance Off\")\n",
    "ax[1].set(xlabel=r\"Gen H pT (GeV)\", ylabel=r\"Matching purity\", title=f\"SPANet Particle Balance Off\")\n",
    "ax[0].legend()\n",
    "ax[1].legend()\n",
    "\n",
    "ax[0].set_ylim([-0.1, 1.1])\n",
    "ax[1].set_ylim([-0.1, 1.1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a81db5b1",
   "metadata": {},
   "outputs": [
    {
     "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=boosted_eff_pb[0], c='y', xerr=xerr, yerr=boosted_eff_pb[1], fmt='o', capsize=5, label='Boosted')\n",
    "ax[0].errorbar(x=bin_centers, y=resolved_eff_pb[0], c='slateblue', xerr=xerr, yerr=resolved_eff_pb[1], fmt='o', capsize=5, label='Resolved')\n",
    "ax[0].errorbar(x=bin_centers, y=merge_eff_pb[0], c='r', xerr=xerr, yerr=merge_eff_pb[1], fmt='o', capsize=5, label='Merge')\n",
    "\n",
    "ax[1].errorbar(x=bin_centers, y=boosted_pur_pb[0], c='y', xerr=xerr, yerr=boosted_pur_pb[1], fmt='o', capsize=5, label='Boosted')\n",
    "ax[1].errorbar(x=bin_centers, y=resolved_pur_pb[0], c='slateblue', xerr=xerr, yerr=resolved_pur_pb[1], fmt='o', capsize=5, label='Resolved')\n",
    "ax[1].errorbar(x=bin_centers, y=merge_pur_pb[0], c='r', xerr=xerr, yerr=merge_pur_pb[1], fmt='o', capsize=5, label='Merge')\n",
    "\n",
    "\n",
    "ax[0].set(xlabel=r\"Reco H pT (GeV)\", ylabel=r\"Matching efficiency\", title=f\"SPANet Particle Balance Off\")\n",
    "ax[1].set(xlabel=r\"Gen H pT (GeV)\", ylabel=r\"Matching purity\", title=f\"SPANet Particle Balance Off\")\n",
    "ax[0].legend()\n",
    "ax[1].legend()\n",
    "\n",
    "ax[0].set_ylim([-0.1, 1.1])\n",
    "ax[1].set_ylim([-0.1, 1.1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "81893038",
   "metadata": {},
   "outputs": [],
   "source": [
    "LUT_boosted_target = LUT_boosted_target_pb\n",
    "LUT_resolved_target = LUT_resolved_wOR_target_pb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "116dd2b8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "42d9610b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tbH: [   0    0    2   49  350 1180 2154 1731 1138  711  441  280  201  109\n",
      "   84   56   37   19   12]\n",
      "tbH wo/ OR: [ 7756 17578 18599 14734  9618  5668  3554  2051  1133   587   256   123\n",
      "    72    31    18    10     9     6     2]\n",
      "tbH w/ OR: [ 7756 17578 18598 14702  9356  4667  1640   556   219    97    49    24\n",
      "    12     7     3     2     0     0     0]\n"
     ]
    }
   ],
   "source": [
    "targetHs = []\n",
    "\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",
    "num_tbH, _ = np.histogram(np.array(targetHs_boosted)[:,1], bins)\n",
    "print(\"tbH:\", num_tbH)\n",
    "\n",
    "# only consider resolved target H that doesn't have a corresponding boosted H target\n",
    "targetHs_resolved_raw =  [targetH[0:2] for event in LUT_resolved_target for targetH in event]\n",
    "num_trH_raw, _ = np.histogram(np.array(targetHs_resolved_raw)[:,1], bins)\n",
    "print(\"tbH wo/ OR:\", num_trH_raw)\n",
    "\n",
    "targetHs_resolved = [targetH[0:2] for event in LUT_resolved_target for targetH in event if targetH[2]==0]\n",
    "num_trH, _ = np.histogram(np.array(targetHs_resolved)[:,1], bins)\n",
    "print(\"tbH w/ OR:\", num_trH)\n",
    "targetHs += targetHs_resolved\n",
    "\n",
    "# then merge into the list with their pT\n",
    "targetHs = np.array(targetHs)\n",
    "targetHs_inds = np.digitize(targetHs[:,1], bins)\n",
    "\n",
    "match_per_bin = []\n",
    "for bin_i in range(1, len(bins)):\n",
    "    match_per_bin.append(targetHs[:,0][targetHs_inds==bin_i])\n",
    "match_per_bin = ak.Array(match_per_bin)\n",
    "\n",
    "merge_means = ak.mean(match_per_bin, axis=-1)\n",
    "\n",
    "merge_errs = np.abs(\n",
    "    clopper_pearson_interval(num=ak.sum(match_per_bin, axis=-1),\\\n",
    "                         denom=ak.num(match_per_bin, axis=-1)) - merge_means\n",
    ")\n",
    "\n",
    "merge_purs = [merge_means, merge_errs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6175a15b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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