{
 "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": "3dee6df4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sel_pred_bH_by_dp(dps, aps, bb_ps, dp_cut, ap_cut=1/13):\n",
    "    # parse predicted bb assignment by DP\n",
    "    dp_filter = dps>dp_cut\n",
    "    ap_filter = aps>ap_cut\n",
    "    ak8_filter = bb_ps>9\n",
    "    filter = dp_filter&ak8_filter\n",
    "    \n",
    "    bb_ps_passed = bb_ps.mask[filter]\n",
    "    bb_ps_passed = ak.drop_none(bb_ps_passed)\n",
    "    \n",
    "    return bb_ps_passed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "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": 6,
   "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": 7,
   "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, dp_cut=0.8):\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",
    "    # convert some arrays to ak array\n",
    "    dps = np.concatenate((dp_bh1.reshape(-1, 1), dp_bh2.reshape(-1, 1), dp_bh3.reshape(-1, 1)), axis=1)\n",
    "    dps = ak.Array(dps)\n",
    "    aps = np.concatenate((ap_bh1.reshape(-1, 1), ap_bh2.reshape(-1, 1), ap_bh3.reshape(-1, 1)), axis=1)\n",
    "    aps = ak.Array(aps)\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_ts_selected, targetH_selected_pts = sel_target_bH_by_mask(bb_ts, bh_pts, bh_masks)\n",
    "    bb_ps_selected = sel_pred_bH_by_dp(dps, aps, bb_ps, dp_cut)\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": 8,
   "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": 9,
   "id": "e3896b0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sel_pred_h_by_dp(dps, aps, b1_ps, b2_ps, dp_cut=0.0, ap_cut=0):\n",
    "    # parse predicted bb assignment by DP\n",
    "    dp_filter = dps > dp_cut\n",
    "    ap_filter = aps > ap_cut\n",
    "    b1_ak4_filter = b1_ps<10\n",
    "    b2_ak4_filter = b2_ps<10\n",
    "    filter = dp_filter & ap_filter & b1_ak4_filter & b2_ak4_filter\n",
    "    \n",
    "    b1_ps_passed = b1_ps.mask[filter]\n",
    "    b1_ps_passed = ak.drop_none(b1_ps_passed)\n",
    "    \n",
    "    b2_ps_passed = b2_ps.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": 10,
   "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": 11,
   "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",
    "            \n",
    "\n",
    "            \n",
    "            \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": 12,
   "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": 13,
   "id": "135e1e4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_resolved_w_target(testfile, predfile, dp_cut=0.5, 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",
    "    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(dps, aps, b1_ps, b2_ps, dp_cut=dp_cut)\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": 14,
   "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": 15,
   "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": 16,
   "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": 17,
   "id": "25ac25cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "dp_cut=0.5\n",
    "# dp_cut\n",
    "# dR_min\n",
    "# bin_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3b613116",
   "metadata": {},
   "outputs": [],
   "source": [
    "LUT_boosted_pred_spanet, LUT_boosted_target_spanet, fjs_reco_spanet = parse_boosted_w_target(test_h5, s_h5, dp_cut=dp_cut)\n",
    "LUT_resolved_pred_spanet, LUT_resolved_target_spanet, _ = parse_resolved_w_target(test_h5, s_h5, dp_cut=dp_cut, fjs_reco=None)\n",
    "LUT_resolved_wOR_pred_spanet, LUT_resolved_wOR_target_spanet, _ = parse_resolved_w_target(test_h5, s_h5, dp_cut=dp_cut, fjs_reco=fjs_reco_spanet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a25e8585",
   "metadata": {},
   "outputs": [],
   "source": [
    "LUT_boosted_pred_pb, LUT_boosted_target_pb, fjs_reco_pb = parse_boosted_w_target(test_h5, pb_h5, dp_cut=dp_cut)\n",
    "LUT_resolved_pred_pb, LUT_resolved_target_pb, _ = parse_resolved_w_target(test_h5, pb_h5, dp_cut=dp_cut, fjs_reco=None)\n",
    "LUT_resolved_wOR_pred_pb, LUT_resolved_wOR_target_pb, _ = parse_resolved_w_target(test_h5, pb_h5, dp_cut=dp_cut, fjs_reco=fjs_reco_pb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3aa74f8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "LUT_boosted_pred_base, LUT_boosted_target_base, fjs_reco_base = parse_boosted_w_target(test_h5, b_h5, dp_cut=dp_cut)\n",
    "LUT_resolved_pred_base, LUT_resolved_target_base, _ = parse_resolved_w_target(test_h5, b_h5, dp_cut=dp_cut, fjs_reco=None)\n",
    "LUT_resolved_wOR_pred_base, LUT_resolved_wOR_target_base, _ = parse_resolved_w_target(test_h5, b_h5, dp_cut=dp_cut, fjs_reco=fjs_reco_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 22,
   "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": 23,
   "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": 24,
   "id": "be384f5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x150564580>"
      ]
     },
     "execution_count": 24,
     "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 DP>{dp_cut}, AP>1/13\")\n",
    "ax[1].set(xlabel=r\"Gen H pT (GeV)\", ylabel=r\"Matching purity\", title=f\"Merged DP>{dp_cut}, AP>1/13\")\n",
    "ax[0].legend()\n",
    "ax[1].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 26,
   "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": 27,
   "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": 28,
   "id": "58eeb79e-9238-42d5-8a23-b1413112724b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1501c7100>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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99913AgCba8xeeuklQS6XG3rG2uqdas1f/vIXw9+qb9++wuuvvy5cunTJofHZSoGrra0VwsLChLfffrvJ45tKgTP+t2Zs7NixgkwmE06fPu3QWImIxMZrPa/1erzW28ZrfevGmXTySF988YXhLnl5eTk++OADnD59GpmZmYbUq7Fjx2LkyJF49tln8euvv6J///746quv8K9//QtPPvkkevToAUDX63LatGl45513cOXKFdx5550oKirCxo0bMX78eEOq0caNG7F27Vrcf//96NGjB6qqqvDuu+8iLCzM0B4jODgYCQkJ2Lp1K3r16oXw8HD07dsXffv2xdtvv43bb78d/fr1w2OPPYbu3bujrKwMe/fuxW+//YYjR44AAJ555hn84x//QFpaGubPn482bdrgnXfeQZcuXXD06FG7f0fbt29H27ZtUVtbiwsXLuDLL7/EDz/8gP79+2Pbtm0t+r0fPHgQv/32G1auXIlp06YhKirKruMmTJiA2267DTNmzEBxcTEiIiKwdu1aaLVaLFu2zGTfUaNGAWjs11paWoqBAwdi8uTJ6NOnDwDgyy+/xOeff460tDT88Y9/NDlef3dff7w1mzdvhp+fn0nvUWPjxo3Ds88+iy1btli08GnOBx98gEmTJuHRRx9FcnKy3cd99tlnhn8DdXV1OHr0KP7v//7PMJ5bb70V33//PVQqldVxHz161NCH98yZM7h69arh+P79+2Ps2LEAgJdeegk//PAD0tLS0LlzZ1RWVuKjjz7C/v378cQTT7gsFZOIqDm81jeP1/pfbY6H13pe632au+8SEBmz1pZFoVAIAwYMEHJycizufldVVQkLFiwQYmNjhYCAAKFnz57CK6+8YrFfXV2dsGzZMqFbt25CQECAEBcXJyxevNikUMyhQ4eEyZMnC507dxaCgoKEqKgoYcyYMcKBAwdMzrVnzx4hMTFRCAwMtCgs8/PPPwtTp04VoqOjhYCAAKFTp07CmDFjhO3bt5uc4+jRo8Kdd94pKBQKoVOnTsKLL74o/P3vf3fo7rrx7+emm24SxowZY1H8Rs/eu+vXrl1rdh9bKisrhVmzZgkdO3YUQkJChDvvvNNqO5MuXbqY3Bn+/fffhYcffliIj48XQkJChKCgIOGWW24Rli9fbrUAUEREhNWKuXq1tbVCx44dheHDhzc53m7dugkDBw4UBMGxu+st/R3ZqroLQNiwYYMgCLpWQQkJCVaPt9WyCIDJnfqvvvpKGDNmjOH/RGhoqDBs2DBhw4YNFv8viIjcgdd6Xut5ree1npomEwRBkCb8JyISV3FxMW655Rb8+9//tnnn3JslJCRgzJgxePnll909FCIiIrfgtZ6IheOIyIt8/fXXGDp0qE9etGtrazFp0iQ8+OCD7h4KERGR2/BaTwRwJp2IiIiIiIjIQ8ib34WIiIiIiIiIXIFBOhEREREREZGHYJBORERERERE5CHcGqR/++23GDt2LGJjYyGTyfDpp582e0xhYSEGDRqEoKAgxMfHIzc3V/JxEhEREREREbmCW6u7V1dXo3///pg5cyYeeOCBZvc/e/Ys7rvvPsyePRubN29GQUEBHn30UcTExGD06NF2fc+GhgZcvHgRoaGhkMlkzv4IREREThMEAVVVVYiNjYVcziQ3MfB6T0REnsSRa73HVHeXyWT45JNPMH78eJv7LFq0CDt27MCxY8cM2/785z/jypUryM/Pt+v7/Pbbb4iLi3N2uERERKL73//+h5tuusndw/AJvN4TEZEnsuda71V90vfu3YvU1FSTbaNHj8aTTz5p85iamhrU1NQYHuvvSfzvf/9DWFiYJOMkIiJyhEqlQlxcHEJDQ909FJ+h/13yek9ERJ7AkWu9VwXppaWlUCqVJtuUSiVUKhWuX7+O4OBgi2Oys7OxbNkyi+1hYWG8aBMRkUdhWrZ49L9LXu+JiMiT2HOt9/mFb4sXL8bVq1cNH//73//cPSQiIiIiIiIiq7xqJj06OhplZWUm28rKyhAWFmZ1Fh0AgoKCEBQU5IrhERERERERETnFq2bShw4dioKCApNtO3fuxNChQ900IiIiIiIiIiLxuDVIv3btGg4fPozDhw8D0LVYO3z4MM6fPw9Al6o+depUw/6zZ8/GL7/8gmeeeQYnTpzA2rVr8c9//hMLFixwx/CJiIiIiIiIROXWIP3AgQMYOHAgBg4cCADIyMjAwIED8fzzzwMASkpKDAE7AHTr1g07duzAzp070b9/f7z22mt477337O6RTkREREREROTJPKZPuquoVCq0a9cOV69eZbVXIiLyCLw2iY+/UyIi8iSOXJe8ak06ERERERERkS9jkE5ERETkzapKga+zdZ898XxE5BukeG3g641VDNKJiIiIvFlVKfDNCnGDdDHPR0S+QYrXBr7eWOVVfdKJiIiIqAUatMC5PcC1MqCtEuiSAsj93D0qIvI1fK0RBWfSiYiISDLffvstxo4di9jYWMhkMnz66afNHlNYWIhBgwYhKCgI8fHxyM3NlXycPq04D1jdF9g4Bvholu7z6r667UREYuFrjWgYpBMREZFkqqur0b9/f7z99tt27X/27Fncd999GDlyJA4fPownn3wSjz76KL788kuJR+qjivOAf04FVBdNt6tKdNv55pmIxMDXGlEx3Z2IiIgkc++99+Lee++1e/9169ahW7dueO211wAAN998M77//nu8/vrrGD16tFTD9A3114Ha6sbHDVrgi2cAWOu2KwCQAfmLgO4jTNNR669LO04i8m5ivdboz0UWGKQTEZHHq1BXoOJ6hcPHRQZHIjIkUoIRkVT27t2L1NRUk22jR4/Gk08+2eRxNTU1qKmpMTxWqVRSDM+zrU9z8ABBN+u1Ik6S4RCRm1WVAgc2AINnAKHRzp9PfVn3ma81kmOQTkREHm/bqW3IOZLj8HHp/dMxZ8Acq88x8PdMpaWlUCqVJtuUSiVUKhWuX7+O4OBgq8dlZ2dj2bJlrhgiEZF30FdO732vLkivKm1ZFfXQaN2HPkgnyTFIJyIijzex10SMiBthsk1Tr8G0/GkAgI1pG6HwV1gcFxlsO5iWIvAn91m8eDEyMjIMj1UqFeLiWtmszcx8IPpWXVXla2VAyRHgi0XNH3fvSiCmv64Sc1slUHq0BTNlROTxDmzQBe2OujMTGLm48fG4N4G+f3L+tQbg640NDNKJiMjjRYZYzl6r69SGr/uE90FIQIhD55Qi8CfnRUdHo6yszGRbWVkZwsLCbM6iA0BQUBCCgoKkHp5n8w8GAtsA4d11HzclAT+s0RVusrpWVAaExQJDHjNdJ+pv+/dMRF5s8AzdrLre2W91rxHqS43b2kQCKfOAbnc0bjNPlfdXiPNaA1i+3jg72+8jGKQTEVGrJEXgT84bOnQoPv/8c5NtO3fuxNChQ900Ii8m9wPSVuoqK0MG0zfPMt2ntBXsYUzUWhgHssV5wM7nYRFUV1/SbX/wfSBhnH3nFfO1RqzZfi/HIJ2IiIgkc+3aNZw5c8bw+OzZszh8+DDCw8PRuXNnLF68GBcuXMD7778PAJg9ezbeeustPPPMM5g5cyZ2796Nf/7zn9ixY4e7fgTvljBO92b7i2eAqpLG7WGxujfN9r4JJyLvJVrnB43t7yHWa435bL9+/PqU+Jn51rN9fGgWHWCQTkRERBI6cOAARo4caXisXzc+bdo05ObmoqSkBOfPnzc8361bN+zYsQMLFizAmjVrcNNNN+G9995j+zVnJIzTvdnWV1aesh3ocRdn0IlaC1dVYxfjtcZa2rrxDYboW3Wp9j6OQToRERFJZsSIERAEa7M1Orm5uVaP+e9//yvhqFoh4zfJXVIYoBORNPhaIwoG6URE5HEq1BXYdmobJvaaKFq7MynOSeQRQqN16zHFSvcU+3zkmcTuoS32+Ug8+s4Peuf2AJsnNH/clO26QFvvl0Jgy0NASEfxxsbXG6vk7h4AERGRuYrrFcg5ktOiPuauPCeRRwiN1hVMEjNIF/N85Jn0PbRbUknbFecj8eg7P+g/etylWyuuL+pmQQaEddLtZ3xcWCfd02IH6Xy9scAgnYiIvJK2QWv4+mDZQZPHREQ+p6oU+DpbvCBYfdn0szUNWuDsd8CP23Wf+TrrG/TV2AFYBurs/OAJmO5OREReZ9e5XcguyjY8nlMwB8oQJTKTMpHaJdWNIyMikoh+prr3veLMOpoH6eb9qc9+C+x5A6g2yj6y1UObs6Deh50fPBqDdCIi8iq7zu1CRmEGBLPWMeXqcmQUZmDViFUM1ImodWrQ6tYbXysD2iodK9xlT3/q6gpg5xLTbT7Wn7pVYecHj8UgnYiIPJamXgN1ndrwWNugRXZRtkWADsCwbUXRCiRHJ8PP7E2Gpqn+rkRE3q44T9fbWnWxcVtYrC6tualZ0XqNrsVV/0lAjxG6QP+f0wD1JdvHtIkEJubqgrm2St3x9dfF+knIldxdjZ0FDK1ikE5ERB5rWv40h48pU5chZUtK8zsSEXkz4/T0s99aznADuoD9n48Ad7/YmKJunp6e94TuwxHVFUDufS0bN7mON3R+EHsZh9jncxMG6URE1Kr8rvldlPOwpRsRuUX9dd3M9b6/Ad+vsu8Y4wD+9gzgjoW6GXTybfrK6Z56PnsZFyw8t6dVpOQzSCciIo+1MW0j+oT3MTw+WHYQcwrmNHvc2lFrkahMNNmWfzYfWXuz8HuNSEH6jZZuI+JGMEgnItdZn+bc8d+vMg3ux70J9P1T4+OW9tAuPer82IjMFefpitvpbZ5g3zIOL8cgnYiIPJbCX4GQgBDD45TYFChDlChXl1tdly6DDMoQJVJiUyzWpAf6Bzb7/czbulk7DxGRT/FX6Hpg69Png8N1a86Nq7qbaxOl2+/S6cb0ef9g142ZPIN5RwDAtDZB6VHr/y7s7QhQnAf8cypgfr1Xlei2P/i+zwbqDNKJiMhr+Mn9kJmUiYzCDIvnZDd6uy5KWtSiwJpt3YhINFIUr9K3SvvzZqD7SF0F92tlQMkR4ItFzR9/70ogpr+u0FtbJXDsI9O16PZUd9erLgfeu0v3tTdXd7cWZNqDbed0mvs3Yyuzwtq/Gf0yDr0G7Y0ZdMsb8rptMl2hxO4jTFPffaSAIYN0IiLyKqldUrFqxCpkF2WjXF1u2K4MUWJR0qIWBdRs60ZEopKieJU+SK9V62a+j2y1P6gGGgN5fYDkrzB9fvAM3Xj1rPZJjwJSnrDsky4FsW90lBwF8jN1PcBjbtVtc+TGhDH979BHKom3mPm/GXtZ+105vFRC0BVG1LeP8zEM0omIvFCFugIV15tIRbQhMjjSJ9ZPp3ZJRXJ0sqGK+9pRa+1OTa+trxWlrRtbuhGRU5zpaQ6YBki2qrvrmVd3t8Z8djh2ADD0/zk3RmeY3+hwdta74gRw7gfdZ32Qbi3IrL/eGDDOzLedrm1tjJ5O7PR0ZhRIhkE6EZEX2nZqG3KO5Dh8XHr/dMwZ0HzhNW9gHDAnKhPtTnHP2puFrL1ZDn0vtnUjIlG1tKe5MeMAKXYA0KGrlXN20s0ct3TdrtwP6Da8ZceKzdlZb2usBZkaVePXNdeAm5J8p5K4mOnpYpuZD0Tf2vhYrAKGXrqkgUE6EZEXmthrIkbEjTDZpqnXGPqKb0zbCIV5KiN0M+neIDI4Eun900Udb4egDqKdi4jIIWL1NG9Kwjigz332z3yHdDT97Cwpemgbs5aO/8MaQH2pcVubSCBlXsvT8T2tkrjYAaaY6eli8w/WLePQ63GX7nevKoH1deky3fPm7djMMwGkuLnjAgzSiYi8UGSIZdq6cQp3n/A+JlXRvU1kSKToM/4dFLogXay2bicqTxhuihCRlxN7bbF+/bjqNyCip/M9zQH7+po7MvMtdpAuNv3vUP/ZOPAszgN2Pg+L4K36km57S6p+i1FJXOygWuwA05vS0+V+upsj/5wKQAbTv4uuUCzSVjSf5eDskgY3YZBOREStilht3axlKhCRlxJ7bbE+sNwyxbnzmPc0Bzx35lvsNeSGGx0XRKr6rWn8bHw+p85pVklc7KDaSwNM0SSM090c+eIZoKqkcXtYrP3LOKzdmDD++0ffajqD7yEYpBMRUasmZVs3IvIhzhZ6E4uYQbqU6bxiBax5T5i2imtWM1W/HT6fHefUEzuo9tIAU1QJ43Q3R/S/+ynbLVPcfRCDdCIiavWkaOtGRD6kpYXexr0J9P2T8z3NAcuCWJ7OFWvIPYW1lHw9R4Lq1t7SzRbjgNxdN8dcjEE6EZEXqFBXYNupbZjYa6IoLdTEPp+183tbizhn2roRkY8Rq9Cbv0KcnuaA9RlYTybWGnL9jQ69llb9PvaRbgbd/HxinFMfpDvL21q6iUHsZRdSFzB0EQbpREReoOJ6BXKO5GBE3AhxgnSRz2fOW1vEtbStGxH5iPrruplPZwu9mRd5E7unuSfT/w71nF1Drr/Rodfiqt8K6+cT45xNadA2fn1uT9Op2ubF8pzlDTPzYi+7kHoZh4swSCci8hFaozcCB8sOunUW2BNbxInd1k2KNnFE5GbOppNbK/QGuKanuadw+Hdo53pvPbGqfkt9TsDxlm5SBOmtbWbeRzBIJyLyAbvO7UJ2Ubbh8ZyCOVCGKJGZlOmW9dSe2CJO7LZuUrSJI6JWwtGe5mRKjKrfUp9TjJZu1jgyM09ei0E6EZGX23VuFzIKMyzah5Wry5FRmIFVI1ax8BkRkT1m5uuKe+k5u1a5KY70NPcmrvodSlH1u6XnNG/r5myKvy2OzsyT12KQTkTkRTT1GpMZaW2DFtlF2Vb7e+u3rShageToZJPUd01zbwSIiFoj/2DdemV94bjgcF0F8uomCmG2idLtd+l0Y1p7WCfdc2K1SwO8pyCW/neoJ9Z6b2t91437lAe1BUp/tDy9tYrrTWlJJXGx28RZI9XMPHkkBulERF5Ev6bbEWXqMkPFciIisoMjPb6ry4H37tJ9ra/Grg/OxQ7SvbEgVkvXe5v/Dpv7m9haC6//m0T2AboM033Way7wLz1qu695aLS4f1+TMYg1M3/dyv7kDRikExEREREZs9bje88bpjPqbaKAlCe8v8e3VIwD4PadgbtfsNEn/Qnd8xcP67aZB8D6z+Z/E3vp/yYxtwIzPjd9ztnAXz82sVP8XTEzTx6NQToRkRfZmLYRfcIbZwEOlh3EnILmi5etHbUWicpEw+MTlSdaNCtPRNQqmKdIxw4Ahv4/FnpzhD3ZCNXllu3ojHvDG3M0bd0ezgb+elKl+FOrxSCdiMiLKPwVJlXRU2JToAxRolxdbnVdugwyKEOUFu3YrLU/82QV6gpUXDddE2q8rv5E5QmbLd2k6ANPRK2QrxZ6k4pYAbCUpAj8Aedbuo17E+j7p8bHLZ2ZLz3qfFtBcgsG6UREXsxP7ofMpExkFGZYPCe78UZgUdIit/VLF8u2U9uQcyTH5vO2sgLS+6ezTRoRNU/somzeUuRNTOY/s7MBsLf/Dp1p6eavEGlm3sp6evIKDNKJiFzA2kywPeyZCU7tkopVI1Yhuygb5epyw3ZliBKLkhb5RPu1ib0mYkTcCIePiwzmLDoR2UHsomzeWuTNGfwdWhKrTZyzM/PkdRikExG5QHMzwbbYOxOc2iUVydHJhirua0ettUhx92aRIUxbJyIiL9SSlm7WODMzT16HQToRkQtYmwnW1GsMadob0zbaXFNtL+OAPFGZ6NIAvUJdgW2ntmFir4miBNNin4+IiEh0YqfkN9e6T6yZefJ4DNKJiFzA2kywuk5t+LpPeB+TgnAWxwdHIr1/umjp22Kfr+J6BXKO5GBE3AhxgnSRz0dERCQ6sVPymwvSAfFm5smjyd09ACIial5kSCTmDJgjWsAq9vnsoW3QGr4+WHbQ5DEREVGrxwKGdAODdCIiktyuc7swPm+84fGcgjkY/dFo7Dq3y32DIiIi8iT6mXkxg3Qxz0cuw3R3IiIzzlZil2I9tTev0d51bhcyCjMs+riXq8uRUZiBVSNW+UQFeiIiIiIxMEgnIjLjbCV2KdZTe8sabU29xmStvbZBi+yibIsAHYBh24qiFUiOTjYpdKep10g/WCIiIjFVleo+jNVfb/y69Kj13uXO9pQnn8MgnYjIjCsqsQOWa7R9oWWa/nfkiDJ1maF1HBERkdc6sAH4ZoXt59enWd9+Z6b394QnUTFIJyIy42wldnvsOrcL2UXZhsdzCuZAGaJEZlImU7+JiIi80eAZQO97HT+Os+hkhkE6EbV6Yq/3/l3zu8lnc2Ks0ba2bt44RfxE5Qmbs/1SpstvTNuIPuF9DI8Plh3EnII5zR63dtRaJCoTDY9PVJ5o0aw8ERGR2zBtnUTCIJ2IWj2x13v/XqMLzsuqy0xm4AHx1mg3t27eVoCrXzcvFYW/wiTLICU2BcoQJcrV5VZ/ZhlkUIYoLVL9rd1gICIialW4xr3VYpBORGSHlqwfz9qbhay9WQ5/L3vWaFtbN28PR9fNO8tP7ofMpExkFGZYPCeDDACwKGmR16/FJyIiEp3Ia9yd7V5DrsMgnYioGZ64fjxSq0VkTa3jBwZqm99HZKldUrFqxCpkF2WjXF1u2K4MUWJR0iKuwSfyBtZm9OzBGT1qRUQPgkVe4+5s9xpyHQbpROT1pLwz7Mz68WVDlyGtm+ldbtHWaDd3d90WN1WQTe2SiuToZEOGwNpRa91ezd5T1/UTeSQve80hcgfRg2CRb3K5qnsNOY9BOhF5PbEuimL1+K6t181wB/oHWlSBF22NtrW76/XXG1PfZubbXqfmJsY/T6Iy0e0p7p66rp/II3naa05Vqe7GweAZ4nwPsc9HrZKnB8Gu6F7jdj7y2sAgnYi8nlgXRVf0+BZtjba1u+u11Y1fR98KBLaxe1zOigyORHr/dNHeaIh9Pmu8ZV0/kUfwsNccVJXqZvZ73yveG3Exz0etUqsIgj2dj7w2MEgnIq9j3jLN2YuirVZpUvHFNdqRIZGizi6LfT5b34Np60Q+wtqa+Qatrvq1+jIQ0lF3I8H8BijXzBO1PkbFgHFuD9DjLsvXBjdjkE5EXkeqlmnma8hbun48/2x+s1XdPXGNNhGR1+KaeSKyR3Ee8MUzjY83TwDCYoG0lUDCOPeNywyDdCLySS1pmWa+hryl68cD/QPtGqOnrdEmIvJaxmvmz34L7Fxie9+7XwS63aH7mrPoRJ5Fyt7wxXnAP6cC5u/pVCW67Q++7zGButzdA3j77bfRtWtXKBQKJCcno6ioqMn9V69ejd69eyM4OBhxcXFYsGABNBpNk8cQUeuy69wujM8bb3g8p2AORn80GrvO7XLoPPr149Y0tX68Q1AHk89iiNQKSA/ri0it5c2CFqkqBb7ObllLJSIiT1F/Xbc2PigUiOgJhHcH/rO2iQNkwL4c3X4RPXXH1VabBgEeqkJdgbWH16JC7Xg3E1ecrzXi30QCBzYA79xp+mHcD359muXz79ypO86Y/rVB/6FR3ZhBt/Y+6sa2/EW6/YyPc9Nrg1tn0rdu3YqMjAysW7cOycnJWL16NUaPHo2TJ08iKirKYv8PPvgAmZmZWL9+PVJSUnDq1ClMnz4dMpkMq1atcsNPQESexpmWada0ZP14B0UHk89iiKyvw5wjnwPJi8Q5IYskEZEvMH7zbhcBUF0EVsRJMhwpib3US+zztUZS/02kbDHrscTqDe/lrw1uDdJXrVqFxx57DDNmzAAArFu3Djt27MD69euRmWk5e7Vnzx4MGzYMDz30EACga9eumDx5Mvbt2+fScRM5w7zombNOXD6BlftXYtGQRejTsY8IIxR/jFIRu2WaLV6zftwLCqEQEZE4rAVwDUIDfrr8E67UXEH7oPa4uePNkMtME2e9OoDzQi1Zfqcnet91b8BijgDcGKTX1tbi4MGDWLy4sViHXC5Hamoq9u7da/WYlJQUbNq0CUVFRUhKSsIvv/yCzz//HI888ojN71NTU4OamhrDY5VKJd4PQdQCYt91PXP1DA6UHcCZq2fQp2MfUe66Sn13Xaw7w65omabn8evHvaQQChGRqGbm66q2653bo3v9a86U7UAXo2tB6dEWzLy5V6sM4LzMrnO7kF2UbXg8p2AOlCFKZCZl2pXV5+l91z2al782uC1Iv3TpErRaLZRKpcl2pVKJEydOWD3moYcewqVLl3D77bdDEATU19dj9uzZ+Otf/2rz+2RnZ2PZsmWijp1ITGLfCffEi7b5zLyzY5SqZZpYa8hd0ePbhAiFUFplSh0ReT//YNP+7D3u0t2gVJXA+tpTme5580wja4WoPJxxALevZB9WHbS99DMjMQPJMckAGMC5ihjL79h33Qle/trgVdXdCwsLsXz5cqxduxbJyck4c+YM5s+fjxdffBFLlliv4rl48WJkZGQYHqtUKsTFecZaAyJA/KDa/K7rvpJ92HBsg6HNGACEK8Ix/Zbphgs2IO1F23xm3tk7w2K3TDtReQLT8qeJtobcWo9va0Gwpr6x6OWJyhM2f2aTC7S+EIpeg7aZQigyXSGU7iNMLzpmhVA88eYOEZHdjCtCJ6c3Ud1dAJJnA6U/6h56YWqtfqlXm4A2aBPQBtoGLZ746Ykmj9n802ZM6DnBkAWmrlObXIPIOWItvxP7byL28kVvWQ5pldxPl134z6kAZDB936QrBoy0FR6zTNBtQXpERAT8/PxQVlZmsr2srAzR0dZfLJcsWYJHHnkEjz76KACgX79+qK6uxuOPP45nn30WcrllsfqgoCAEBQWJ/wMQiUTsO+HGd113nduF1w++bnGR+F3zO14/+LrDRdTEYu3OcFVNleHr6rpq9I/s77KWadaCY7E1FwTbSt23CIIlKoTClDoi8mqO9Ek3DuC9sE+6K5d6kX089W/CYoNmkyTKeITeuxzR365CQHXjxEld20iUDl+AKmU8cLkYgPszBd0WpAcGBiIxMREFBQUYP348AKChoQEFBQWYO3eu1WPUarVFIO7np3ujLQgitSUichGx7oTri57V1teKchdXPzZXcnbNlp6+ZVpGYYbFc021TDMn2qy3EWtBsD1cFQQzpY6IvJq1itANWt16UvVlIKSjbn2q+eu/l82ik7i41Mu5wnbewNokiTxSgUGhUYjUalHh54dDiiA0nMgBTjTu5+5MQbemu2dkZGDatGkYPHgwkpKSsHr1alRXVxuqvU+dOhWdOnVCdrbuzfvYsWOxatUqDBw40JDuvmTJEowdO9YQrBN5C7HvumbtzULW3izRzmdMyouYJ7RMMyfarLcRa0Fwi3h5IRQi8gJVpbqZ6cEzxAtipTinMVtp6zclWm7zchvTNqJPeGM3F2eXenkLKd6LiLXUy1v/JmJNkngyq5mCmiuYtusvAICNqX+DQtHe4jh3Zwq6NUifNGkSKioq8Pzzz6O0tBQDBgxAfn6+oZjc+fPnTWbOn3vuOchkMjz33HO4cOECIiMjMXbsWLz00kvu+hGIfJq+QJtYhd5Kr5Wia1hXw/Oe2jLNo2e9vbwQSosZrzd1hBeuN/VVb7/9Nl555RWUlpaif//+ePPNN5GUlGRz/9WrVyMnJwfnz59HREQEJkyYgOzsbCgU0i9PafWqSnWp473vFTdIF/OcodG6VHWxxif2+SSk8Fd4zVIvMUlRO0WspV7e+DcRY5LEIzMRzP4vW80UVF8yfN2nQy+EhETYfT5XcXvhuLlz59pMby8sLDR57O/vj6ysLGRlOTZbSOSJxLrrmn82H1l7s0QromZ8Tn2BNrEKvc0vnN/seMy5o2WaaLPeruBlhVBazJH1psaaWm/KwN9ltm7dioyMDKxbtw7JyclYvXo1Ro8ejZMnTyIqKspi/w8++ACZmZlYv349UlJScOrUKUyfPh0ymQyrVtmu20EuZpQmi3N7LG8GSik0Wty15GKfzwWMA6QpN0+xWdNGgICHbn4IJ38/CcB7U7WlqJ0i1VIvsZbfiUWqwnYeWXTWR14b3B6kE7VWYt11DfQPNHwW43zG59QTq9Cb2Ly2ZZrYEsbp2qx98QxQVdK4PSxWF6D7Qp90a+tN6683puzPzLeeHdBUMC1F4E9WrVq1Co899phhOdu6deuwY8cOrF+/HpmZmRb779mzB8OGDcNDDz0EAOjatSsmT56Mffv2uXTc1ITivBudJW7YPOHGa85K33jN8QKOBEjGAby719ray7ySuLMB9YnLJ7By/0osGrIIfTr2sbmfI/SZgtZaw7Zk+V1T53NmfFIVtmPRWekwSCdyM6nuhEt5F7ela5i8qWWa10kYp2uzpq/iPmW7a2e1pGZt9tq4FV30rabLAOwhReBPFmpra3Hw4EEsXtx4Y0MulyM1NRV79+61ekxKSgo2bdqEoqIiJCUl4ZdffsHnn3+ORx55xOb3qampQU1NjeGxSqUS74cgU8V5N7J3zG4Aq0p02x98n4G6C1gLkBqEBvx0+SdcqbmC9kHtcXPHmyGXmRZd9pYASexK4meunsGBsgM4c/WMeEH6jUxB4za3xhxdftfc+Vo6PqlIkYngkSn0bsAgnagZYveENL9LKvadcOMXt9i2sViQuMBqn/Rpt0xDbNtYFDvYasKZNUxStUyTohq7VzK+6HdJcWuA7hV/EykCf7Jw6dIlaLVaQ70ZPaVSiRMnTlg95qGHHsKlS5dw++23QxAE1NfXY/bs2fjrX/9q8/tkZ2dj2bJloo691au/bvp/AtCluH/xDKzXwBAAyID8RbqbhsavQfXXpRtnK2VraVbfiL52HW/+fsTZ4MgdPbSlqEwu9jmdWX4n1vjEniSRkkem0LsBg3SiZoh9J9f8LqnYd8LteXG7rLlsMWNv7cVNrLZutgq9iTXbL0U1do/jZUWSWsXfhCRTWFiI5cuXY+3atYZuLvPnz8eLL76IJUuWWD1m8eLFyMhofC1RqVSIi4tz1ZB9U4s6QAiA6mJjVg+JRuqlWc4GR67uoS1FZXJHz6lfdifW8rvmztfSn1mqSRIpMIVeh0E6kYOs3Wm2N6i2dtFy9k64OTErk0vZ1k1PjJZpHl2NXSxeVgilVfxNyC4RERHw8/NDWVmZyfaysjJER1u/SbRkyRI88sgjePTRRwEA/fr1Q3V1NR5//HE8++yzJp1f9IKCghAUFCT+D0DkIcRemqVfMqb/bO11W12nxowvdbUkModkon9Uf49Inxe7fWtLz2n+O3RWU+cT82f2tMJ2xqQq5udtGKQTOcjZO81i33WNbxePwcrBiG8XD0CcyuRijc1ezrZM86pq7K0E/yakFxgYiMTERBQUFGD8+PEAgIaGBhQUFNjs7qJWqy0CcT8/3euBIFhLsyZJzMzXLfswdm6Prkhcc6Zs1y270Ss9ajozz+4KHsf8ddt81nbF/hVu6aEtVmVyfVafeZagM+c0r3YuFrF/ZmvEmCQh6TBIJ3KQ8Z3mfSX7bBZ6A4CMxAwkxyQDaLzTLPZd1z4d+2BD2gZRzqWnH5vYbeKaIvaaLSLyHBkZGZg2bRoGDx6MpKQkrF69GtXV1YZq71OnTkWnTp2Qna0LCMaOHYtVq1Zh4MCBhnT3JUuWYOzYsYZgnVzAP9iyLkOPu3RV3FUlsL4uXaZ73rxwpXkRRh/vriDF+mxXrvmWYqa6pcSuTN6SLMHmzik2qaqxm3N2koSkwyCdyE76u5ptAtqgTUAbaBu0eOKnJ5o8ZvNPmzGh5wTDi526Ti3ZXVcpiNUmTtlGVzBKP0PvbFExr2+Z5gHEfrPnjoJB5D0mTZqEiooKPP/88ygtLcWAAQOQn59vKCZ3/vx5k5nz5557DjKZDM899xwuXLiAyMhIjB07Fi+99JK7fgTSk/vp2qz9cyoAGUwDdV2aLNJWWBauVF82/exsd4WqUl2gP3iGR86sS7E+W6o131L10GaVbu/ASRLPxCCdyE6uuqvpyVq6hsk8e8DZomI+0TLNzcR+s+fqgkHkfebOnWszvb2wsNDksb+/P7KyspCV5fhsF7lAwjhdm7UvngGqShq3h8XqAnRr7dfMg3RnuytUlepm4nvf65FBujmx69mISez3Ny3pXmNMf63Xn2fNiDW4LfY2w/POZvWZVzp35pxSVTsXK5PxPyX/wfyv54u2jJGTJK7DIJ2IHMJCb62HFK1tiMhHJIzTtVnTV3Gfst0yxd1RRq85OLfH+fN5kNbUVqqp7jWOVOnWn0etVYuS1RfoH2j4bF54zNOqnYuVyRjdRncDS6wllpwkcR0G6UR2EuuuptQ9JstVGpRX1Th8XFRoEKLCrF9szGcAYtvGYuXwlVYrvur7rgOOV7QnB1grumTch7j0qO1UUTtmnaRobUNEPsY4gO6S4lxAXZx3o//6DZsn3JiZX2l9Zt7LOFvPRkpS1Z+xdq2vqqkyfF1dV43+kf0dvvkrRWVyT652Dnj++Eh8DNKJ7CTWXU2x77qWqzTYvO88piR3RlSYApv3nceagtMOn2f+qJ5YcHcvAEBldW3j547NzwCs2G+9+I83zgB4jeaKLtnqb2xH0SVPKhhERG4QGq17rRAzjTyko+lnY8V5N9a4m11LVSW67Q++77WBulj1bPTnkoJY72/0M9W2iHnzV4rK5GKc09maO1KPj7wHg3QiBxm/AE+5eYrNu+ECBDx080M4+ftJANKtKyuvqsGagtO4O0GJqDAFpiR3xt0JSpN9NHVaTFi3FwCwffZQKAIs77RGhTb2FxbqQ1FTMQpCfSgA59PTuYZJAtaKLtnD7E23VAWDiMiLhUaLX0FdH5wHhpiuPW/Q3phBt1YpXgAgA/IX6VLrjWcJjTOHPJg31rORYtZWipu/UlQmd/acztbckXp85D0YpBM5yJF1ZcYBvKtmlaPCFBZp61WaOsPX12rqMbBzB/jJZTbP0SEoArWX7kaHoAgAzqencw2TBETqFeyxbyClqNzs4dWgiVoFW1k+NgmA6mLj2neSjPEkRGzbWCxIXIANxzYY1oYDQLgiHNNumYbYtrGG5W3mkxDmfcil6PGtJ0VlcmfO6YqaO6zG3jowSCdykLUXYHsrtLpD/rESZOUdNzyevmE/YtopkDU2AWl9Y9wyJqJmSVG52cuqQROR9xNrvTcgfU0beyYhLmsuW2QQmk9CtKQPeVM3f8WqTB7fLh6DlYMR3y5elPMBlpmCTk9qiJx5yExG78UgnchBtl6A+0b0dcNompZ/rATpmw5Z3LsuvapB+qZDyHl4EAP1Vs5bCiJa5cOVoIl81sx8XXs1vXN7dEXimjNlu644nV7p0RbMyruefr23fpa6g6IDwhXhqNRU2jymo6IjOig64FfVryaz1FJVEtfz1M4rYlUm79OxDzakbRDlXHpiZwp6+vnIdRikEzXDW+5qauq0UNfWGx5rGwRk5R1vapUfluYVY1h8hEXqu6ZOa/JYiorx5Bk8tSBis3y8EjSRz/IPNu1/3uMu3f9dVQmsr0uX6Z43vwlnrXuFB3NkqdxlzWVM3jEZgLRL5cSeBdbPeIvV19z85q+URdmIPA2DdKJmiH0XUleULRVC91BRzqevxK4vDGf3OACUqjTot/SrZvcVo2I8eQevaPPiw5WgiVoduZ/u5to/p0J3+9j4//WNG8hpK7w+S8Z8lnpfyT7kHs81mVHvqOiIabdMM7RfA6SdpRb7/Y1+xju6bbQkN3+lLspG5EkYpBO5mHk1dmdnqfVBupTEqBhP3sOj2rzUXxenErT+XETkeRLG6W6uffEMUFXSuD0sVheg+8BNN/NZ6oSOCZiaMBWHyg+hQl2ByJBIDIoa5JNFwMS6+StFOj5n58lTMUgncjOxZqlf/lM/jOkfa3hcdLYS0zfsb/Y8uTOGIKlbuMm2H85cwmPvH0Rlte7mgbWK8cap9QmxYQgJtP1yYt7LnTyfx7R5YSVootYhYZzu5pr+/+6U7T5fZ8JP7och0UPcPQyXEOPmr7Pp+NZwdp48FYN0Ijczn6Xec+YS/vbtL7hsNEMe0TYQjw/vjpT4CMM281nqQH8/k0B5eM9IxLRToPSqxtYqP0S3U2B4z0iLNenVNbo16ZXVdVaOdJx59gB5B59t86K+LM552NKNSFzGrzFdUrw6QJei/oy3V+r2mJu/Rjy1WJ6nqlBXYNupbZjYa6JoN0ykOKcvYJBO5GbGs9T5x0qQ/cUJi6D68rVaZH9xwqFq7H5yGbLGJiB90yFbq/yQNTahyX7pTdE2NJ6x6Gyl1WCfPJe3FEQUrRI0ABz7CMh7QtwgnS3diOwTGg3cmSne/xWxzycyKapqe1ul7uZSydsEtMHJ309aHOfKVHIpZud9WcX1CuQcycGIuBHiBekSnNMXMEgnchOxqrHX1mutHKGT1jcGOQ8PQlbecZSpGte9RzvZJ93Z3uusFu9+XtPmRaxK0ABgTwV6tnUjkkZoNDBycePjqlLdhzHjuhGlR61XcA+NbvwwPh+5nfnNWmdTyb09c6C10hpdRw+WHXR7toS3YpBOPkfstBmp0nCkrMZuLK1vDIbFRxiOy50xxKlZbzF6r7NaPLWYlJWg2daNyHUObNBlothiqx7FnZkMzj2U+c1aZ1PJvS1zgIBd53Yhuyjb8HhOwRwoQ5TITMp0bdFZH8AgnXyOedqMtXQre+jTrbwtDcfaLLVx3/O2Qf74qURlcZy1WeraenFm+837rrNaPDlFikrQbOtG5FqDZ+iWijjKQ9PbyRJTyT2PlNXsd53bhYzCDIs2e+XqcmQUZmDViFUM1B3AIJ18XnPpVrZIXblz++yhSIgNMzxuaTV2fSX28DYBAJqfpbY1g288Sx3eJhAA8MxHP+KZj35s/oe5wd7ZfmerxRM5VQm6XiNOWze2dCNqOX3Kuofwliw8al3EDqrFqmavqddAXac2PNY2aJFdlG0RoAMwbFtRtALJ0ckWqe/GPw814rtg8nnm6Vb7SvZhw7EN+L3md8O2cEU4pt8yHckxyYZtUq+BUgSIU409pp1uzV54G90ss7VZansYz1Lrg3Qij9bSStB5T+g+7Ma2bkS+TuysOW/LwiPPJHaLOLGq2dv6vk0pU5cZKvtT8xikk88zTrfadW4XXj/4usWdvt81v+P1g6+7NRVHrGrs1mapW0rs2X5933Vnse96KyBF5eaQjuKdi4haHW2DFofKD6FCXYHIkEgMihrEglgkKbFbxHEJgvdgkE4+S6xUHFem4UhVjb2lxJrtZ991cpgUlZv1QbpYbd1Kj9oubkVEXs08zXhfyT7kHs9FpabSsM1WFh6DIBKLpwbVG9M2ok94H8Pjg2UHMaeg+SWia0etRaIy0WTbicoTLZqZ93UM0slniZ2KU1ldC4gwEVdZXWvy2bzQ200dQvDGnwdi0jv/AQAsHZuAgZ07wE8uw7ELVw37uaMdmZS919l3nVxGrLZu1tpDEfkqZ1umeRl76tlUaiqx6uAqk21S17Mh8gQKfwVCAkIMj1NiU6AMUaJcXW51MkwGGZQhSqvt2KytqScG6UR2+91GUG0vfVBtHqQ3V+ht6WfFVre7qx2ZFLP9zvZdJ3KKlG3diHxFK2mZps/CG9N9DG6LuQ0NQgMyCjNM6tiYC1eE47U7X4NcJkdEcATUdWoWw6JWxU/uh8ykTGQUZlg8J7txHV2UtIjLQxzAIJ18llipOJ+fOohlBxqPc7bHt74Ku/6zs4XeokKDMH9UT5e2JxOz97oYfdedvXHiEtZmoezhpbNQXkeKtm5EvqSVtExrSRZepaYSM76cIcFoiLxHapdUrBqxCtlF2ShXlxu2K0OUWJS0iO3XHMQgnXyWWKk4QXLT4Nc8qN5z5hL+9u0vuHxjZhwAItoG4vHh3ZESH2HYpg+i9VXY9Z+dLfQWFaZwy4y6cUCe1C3crgBdqr7rzt44cYnmZqFs8bJZKK/mTFs3Il/HG4ZE1IzULqlIjk42LB1dO2qt1RR3ah6DdGo1xErFMQ6q84+VIPuLExZB5uVrtcj+4oRds8CeSOzZean7rlvLRtDUaQ094bfPHgpFgOXf1ZXZB1ZnoeqvN6aIzsy3vZ6TXKelbd2IyCeIlYXHYljUWhm/j05UJjJAbyEG6dSqOJOKU1PfIMkssCcyn523lk5u/HMUX1TZDIKjwhSS9123lo1g/LdKiA0zqVLvFtZmoWqrG7+OvtW0mJmLmVcyBkw7G5yoPGG1uIvNSsaemN4vdls3KdrEEZFbiZWFx2JY5IsigyOR3j/dZos3TzmnL2CQTj7PPPiIbRuLlcNXGtaPZQ7JRP+o/pDL5Ci+3FikzTz4eOajo1iouWz397V3FtgbNJdOrp+xNmeeTi5W3/Xiiyqb37O1EHsdfnOVjG3NCNmsZOyJ6f1it3WTok0cEXkE4/cOU26eYlHFXU+AgIdufggnfz8JgC3YyLdFhkSK3r1AinP6Agbp5POaCz5W7LceSLCNSiNni9vpidV33dqsfWsj9jr8ib0mYkTcCIfPZ/PON9P7iciL2dOCTc84gOd7ByLHVKgrsO3UNkzsNVGUG1xin89dGKSTzzFPm3E2+Ggf1BE1FaPwzuSRSOnWzfB8a5oFdra4nS1S9l13q6pS3Uzy4BniBZ1m5xR7HX5kiMizPx6e3k9E1BRr7x0ahAb8dPknXKm5gvZB7XFzx5shl8lN9mHKLpFjKq5XIOdIDkbEjRAnSBf5fO7CIJ18jnnajLPBR4egCNReuhvRbaM4CywBKfquO83Z9dRVpbpU7973ihukG53TK9bhExF5KVvvHfpG9HXDaIhaN21DYx2kg2UHW0XFeL6DI3KQ8VrgGSldsfyLE1b3E248/1OJCoCLe3J7GTH7rovCVeupjS46OLeH7b6IiNxE7OJVLIZFJI5d53Yhuyjb8HhOwRwoQ5TITMr06d7rDNKJHOTIWmDjAN6lPbm9kCN91yura0w+O6tcpcHmfecxJbmz7kaKK9ZTF+cBXzzT+HjzBCAsFkhbqevXTURELiN28SoWwyJy3q5zu5BRmGHRWaFcXY6MwgysGrHKZwN1BulEDrK2FljbIOD4xav4XV2HDiEBuCW2nUWQ6dKe3B5G7L7rjSvWxZlpL6+qwZqC07g7QakL0qVeT12cB/xzKmC+UEJVotv+4PsM1ImIiKjV0NRroK5TGx5rG7TILsq22vpQv21F0QokRyebpL4bt4/1Zg4H6XfeeSdmzZqFiRMnIjjYykwSkY+zVUStf1x71w/GS5j3XXeWvu96U/3XtQ2NL+pFZyvdkz5ff900uAd0Ke5fPAOLAB24sU0G5C8Cuo8wTX2vvy7dOImIiIjcyFar16aUqcuQsiXF6nO/a353dkgA3Fct3uEgfeDAgVi4cCGeeOIJPPjgg5g1axZuu+02KcZG5BHEngUWf1aZzOUfK0FW3nHD4+kb9iOmiUJ0/uoyPOm/Hf7qngDaOT8A9WXdZ316vEMEQHURWBHn/Dh8ibVifsY3LkqP2l6CwLZuRERErcrvNSIF6W6qFu9wkL569Wq8+uqryMvLw8aNG3HHHXcgPj4eM2fOxCOPPAKl0vFeykSeTOxZYLHP542Mi+/paeoai6gVX1TZbB/WXPG9/GMlSN90yGKeuvSqBumbDiHn4UEWgbq/uhxP+n+MM+pZAET42+iDdBJPc8X8bN0QcbSYHxEREbncxrSN6BPex/D4YNlBzClovq7D2lFrkahMNDzOP5uPrL1ZTR6jFYyqxZcfRkrnkR5XLb5Fa9L9/f3xwAMP4IEHHkB5eTneeecdLFmyBH/961/xhz/8AfPmzcNdd90l9liJyEc0V3zPVh958+J7mjqtSdsxbYOArLzjTSWSY2leMYbFR5ikvtfWNzQ/6JZUYh/3JtD3T6bbzu3RFYlrzpTtQBejFK7Soy2cmfcR1or52YOz6ERERB5P4a9ASECI4XFKbAqUIUqUq8utrkuXQQZliNKiHVugv+2lkMCNavH7lhsez/lmgUdWi3eqcFxRURE2bNiALVu2ICoqCtOnT8eFCxcwZswYzJkzB6+++qpY4yQiV6oq1c1cDp7R2Pfb2b7hRuezVnzPHuZLBGwF87YIAEpVGkOrN71bZGexw+jU5jP9YWe/QMyeLAToN2yegNo2MSgduhSqbo2Bo8VMv7/CssBcj7t0VdxVJbC+Ll2me978JoC1VO7WhGnrRERErYaf3A+ZSZnIKMyweE52o3DwoqRFDs2Ae1O1eIeD9PLycvzjH//Ahg0bcPr0aYwdOxYffvghRo8eDZlM9wubPn060tLSGKRTi1SoK1BxvcLh4yKDI126VsSnVZXqUot736sLjJztG252PlvF9zyF8Uz/aHkRcgJW654wqjvnf60EN+38C9LrnsSXDUkA7GyzJ/fTtVn759QbJzS+UNz4Bmkr2C+diIiIWrXULqlYNWIVsouyUa4uN2xXhiixKGlRkwF1bX2tV1eLdzhIv+mmm9CjRw/MnDkT06dPR2SkZVB06623YsiQIaIMkFqfbae2IedIjsPHpfdPZ09SqVhLNa69BuTep/v63pVApyGWgaXEM5/bZw9FQmyY4XHR2UpM37C/2eNyZwxBUrdww+Nff9wDfAbI6jVAbTWmDOqIe3qGAg1a9NqeAZnastmbXAYIkOHN9ltwesJjgNwPkaGBumruzb2gJ4zTtVn74hmgqqRxe1isLkBn+zUiIiIipHZJRXJ0sqGK+9pRay1S3K3J2pvV7Np0c01Vi3c1h4P0goICDB8+vMl9wsLC8PXXX7d4UNS6Tew1ESPiRphs09RrDK0ZNqZthMLfchY2MriVzqKbp6aLQV/4TP/ZPNW4OO9GG7Ebvlh0I8Bc6dIAUxHgh5DAxpex4T0jEdNOgdKrGluJ5Ihup7BoxxboLwcA9PjsT8BnQBR0H82RQUCguhS3vH+L44NPGKdrs6av4j5lu33r3KUi8pIGUcYj9r9rIiIi8jrGAXmiMtHjirxJoUUz6adPn0bPnj1Ntp8+fRoBAQHo2rWrWGOjVioyxDJt3ThdpU94H5PCEq2eeWq6s8EWYBmkGyvOu5GqbRYGq0p02x98v/lAXYwxWuEnlyFrbALSNx2ylUiOrLEJru+XDusV7WV1aujD++MBfSGUXLM4zp6K9qIQeUmD08Q+HxEREXmcyOBIpPdPF22yrUNQBwDAmhFrcFtsY5vwllaLP1F5okU93J3lcJA+ffp0zJw50yJI37dvH9577z0UFhaKNTYiaglngy1jN9K/DRq0N2bQm6ifnr9IN0NsfJdTdUH3WR/0OznGpvqap/WNQc7Dg5CVdxxlqsagOLqJPul6P4/9CD36DW3c0NJK7L8UAlseAkI6GjZZq2gfDA1+uhF/T1i3B9dhGYwb1rmHRut+fqkCVmtLGuqvN1aUn5lvuw+5vVpSIZ+IiIh8VmRIpKjLVTsodEF6dNtoUarFW8vedQWHg/T//ve/GDZsmMX22267DXPnzhVlUETkBPNg6+y3wA9rAPWlxm1tIoGUeUC3Oxq3WQu28p7QfdhNAFQXG1O4zemDdCcDwub6mqf1jcGw+AhDFffcGUMsUtytjt68GntLK7GHddJ9NgrSrVW0r1VXAZt1X2+elYzAkFCL72CoaB8aLW2/b2tZCsY3aKJvtaxU7wjzJRKbJ7hliQQRERG1IjeyN/0AZMZPQsbRNyx20b07FLAo/kH4lf6o2+jmrjIOB+kymQxVVVUW269evQqtVmvlCCJyKeMXleI8YOfzsAgwqy/pttuTmi4Fay98GlXj1zXXgJuSnJplNQ7Ik7qFtyzF3agSuwAZZE5UYrdW0V5tlN3eJyYUIW3bwSeJsUSCiIiIyFFG2ZupAFaFBCO7YweU+zeGwcr6eiy6/DtS/7Ww8ThrGaYu5HCQfscddyA7Oxsffvgh/Px0b0y1Wi2ys7Nx++23iz5AIrJT/XVxUtOBxurk494E+v6pcXtL07+PfdT0jLyDs6zhIYEmn62tcZfVa3GL7Kzu69IjgL+VQPrGzQKL8xm7UYld+PwZyK6xEnuzxPp3WH9d2nESERGR7zPL3kwFkFyvRsrXfwEArB24ECkd+8FPJjc9zs31cBwO0leuXIk77rgDvXv3NlR5/+6776BSqbB7927RB0hEdtKnitutmdR0ABAr/bup9TwtmGUNbxNo8tnaGvdgADtuZIpjvY3vfeMuqcX5zCWMg+am2xGyqhsAQPPgVij63M311NZI8e+QXO7OO+/ErFmzMHHiRAQHW1l6QkRE5A2sZG/6GRWkTrx5Ivw8sCC1w0F6QkICjh49irfeegtHjhxBcHAwpk6dirlz5yI8PLz5ExC1dmK3lrJWgV0qRunfsFU/van0b7EK0ZnPslpZ4379+jUE/0PXx/36IzsQHNzW8ls48vs3+v4NnYcyQCefNnDgQCxcuBBPPPEEHnzwQcyaNQu33XZb8wcSERG5kdjV4sU+n70cDtIBIDY2FsuXLxd7LNQKVagrsO3UNkzsNdGi7ZonnVNUYreW0gfpYqWmA02np99I/8YXzwBVDqZ/S1WIzspdUuHa1cavlf2AptZ7y+RAl2G6z2KwVondWkr+9cZF6bKyHwGVjRsJodEoV2mwed95TEnuLEpLNrHPZzAzX1dkTq+l/w5Lj7ZgVp7Esnr1arz66qvIy8vDxo0bcccddyA+Ph4zZ87EI488AqVS2fxJiIiIXEzsavFin89eLQrSr1y5gqKiIpSXl6OhocHkualTp4oyMGodKq5XIOdIDkbEjRAvSJfgnJKy1jO8QasLUtSXdRXCo2+1nLk1D0zFSk3Xn6upMbbvDPzpPSBXN1ONe1cCnYboznPxsO0xik2sLAKhATj3g+6zGKxVYreRkm/4+sasv4UbKfnlVTVYU3AadycoxQnSRT6fgX+wSEskXJBibe3/nnGWRulR210GWkHvdn9/fzzwwAN44IEHUF5ejnfeeQdLlizBX//6V/zhD3/AvHnzcNddd7l7mERE5CYV6gpUXK8w2abR1zWCrse4tRZmkcGR3vEe3Y0cDtI/++wzTJkyBdeuXUNYWBhkssaKyTKZjEE6SUJr1F/5YNlBix6GXk3MvuaAaeCRnA7sXGLjBAKQPBuwp9VEc2P8YpF9Y3RVITrApCe3/PxeoIk15JXVtQg3+uwsq7PU1lLy67WYkLMXALA9fSiCbRW3s5O2oTEILjpbaVfbOck5u0RCSs39u7Y1k+/miq+uVlRUhA0bNmDLli2IiorC9OnTceHCBYwZMwZz5szBq6++6u4hEhGRG2w7tQ05R3JsPj8tf5rV7en9090yO+1NHA7Sn3rqKcycORPLly9HSIjnLbIn37Pr3C5kF2UbHs8pmANliBKZSZlI7ZLqxpGJxDh4O/ttE0E1gLtfbOxt3tKA2pjx9zIOPPT9vfWfrfU1t4d+jPrzhHWSvhAdABTnQfF5Y7V4xT8nNVktvlJ9I0i/8dnp1HRrs9TWUvJr63Fc0H0fIbo/ENii5CYAQP6xEmTlHTc8nr5hP2LaKZA1NgFpfWNafF5ROLNEQkrO/rv2YeXl5fjHP/6BDRs24PTp0xg7diw+/PBDjB492nBzfvr06UhLS2OQTkTUSk3sNREj4kY4fJyr13d7I4ffEV64cAHz5s1jgE4usevcLmQUZkAwC+DK1eXIKMzAqhGrvDdQ17eqCgrVfTRogQ//3MQBMmBfDpA4vTFgra1ubJemZy3wsDd9Xs88SHc2vdf8fHpSzLLeqBYvc6Ynt5UbHYGCDHsbbkY52iMqdyGS5CfgJzP7Hm6aYc0/VoL0TYcsbnOUXtUgfdMh5Dw8yCJQ91eX4Un/7fBX9wQgQn92/dIDW0sQEsbpCgDq6wtM2W59qYUrtZK09Za46aab0KNHD8ycORPTp09HZKTlG6pbb70VQ4YMccPoiIjIE0SGMG1dKg4H6aNHj8aBAwfQvXt3KcZDrZSmXgO1UTsEQJfinl2UbRGgAzBsW1G0AsnRySap7xrzoNVTSdWqylbgcVOig9/PRZyZZb1RLb68SoOKqlqgQYte/34a/hBgmeQtQIAMdf9+GqcVgwC5HyJDAxEVqoCsmRsd+WeuY+k3V1Ba3bhmPaatHFl3tEdavNGaZRcEfJo6LdS19YbH2gYBWXnHm6qPj6V5xRgWH2GS+t6gKsWT/h/jjHoWgF7OD6y5IB0wDci7pLBCvgcrKCgwtFm1JSwsDF9//bWLRkRERNR6OByk33fffXj66adRXFyMfv36ISAgwOT5cePclLZIXs3WmpXmlKnLkLIlpfkdWwPzWWpnWKtOLqWWzrLeqBYfBSDKjm8jg4BAdSluef8Wk+09zHc0utGRf6wE6Z9bmaW+1oD0zyutzlJLacK6vQ7tLwAoVWnQb+lXJttvkZ1t7CNvi9G6fpzb0/TfxFa2REu5+t8gmcjKysLHH3+M9u3bm2xXqVQYP348du/e7Z6BERERtQIOB+mPPfYYAOCFF16weE4mk0Gr1VpsJyIrxG5VJXaQ7uq0bQ+YZa2tbxBlllpT5wOvg8V5N3rY37B5QpPr+iUJ0ltRcTZP880336C2ttZiu0ajwXfffeeGEREREbUeDgfp5i3XiMSwMW0j+oT3Mdl2sOwg5hQ0X/lx7ai1SFQ2pnKfqDzR4pl5l/KmVlXOMp8Vdbb1lT4Q/PNmoPvIxudbeKPj5x/3osdnf8LT24/i+LYqO38o27PUUto+eygSYsMMj4vOVmL6hv3NHpc7YwiSujXWrv/1xz3AZzZ2vrGu3+LfoSPr+skrHT16FAAgCAKKi4tRWtr4/1Sr1SI/Px+dOnVy1/CIiIhahZaXEobujrpC4Vx/3bfffhuvvPIKSktL0b9/f7z55ptISkqyuf+VK1fw7LPP4uOPP0ZlZSW6dOmC1atX4w9/+INT4yD3UvgrEBJgWowwJTYFyhAlytXlVtelyyCDMkRp0Y7NWj9GjyZFyzRPYz4r6mzrK0O1+JtEudEheNG/GUWAH0KMqsAP7xmJmHYKlF7V2PqJEd1OYdGOLdBfrnv+xrp+gwbtjRn0JvIH8hfplicY3ywyvslCXmvAgAGQyWSQyWRWe6AHBwfjzTffdMPIiIiIWg+Hg3StVovly5dj3bp1KCsrw6lTp9C9e3csWbIEXbt2xaxZs+w+19atW5GRkYF169YhOTkZq1evxujRo3Hy5ElERVmuMK2trcXdd9+NqKgobN++HZ06dcK5c+cs1syRb/CT+yEzKRMZhRkWz8lulAVblLTI+/uli9EyzdtI1frKyWrxr0y4FV37Nc6wt3SWuviiyuG14y3lJ5cha2wC0jcdsvUTI2tsgs1+6T0++5PtGXWr7CxgSF7p7NmzEAQB3bt3R1FRkUlV98DAQERFRcHPz8tfc4mIiDycw0H6Sy+9hI0bN+Lll182rE8HgL59+2L16tUOBemrVq3CY489hhkzZgAA1q1bhx07dmD9+vXIzMy02H/9+vWorKzEnj17DAXrunbt6uiPQF4ktUsqVo1YheyibJSryw3blSFKLEpa5L3t14yJ0TLN20iZBeBEtfhAf7kos9T6de2V1TXO/jQGldW1Jp+NpfWNQc7Dg5CVdxxlqsbvGe0pfdLJa3Tp0gWANEvbmDlHRERkH4eD9Pfffx/vvPMORo0ahdmzZxu29+/fHydOnLD7PLW1tTh48CAWL26cCZTL5UhNTcXevdZnoPLy8jB06FD8v//3//Cvf/0LkZGReOihh7Bo0SKbd/ZrampQU9P4plWlUtk9RvIMqV1SkRydbKjivnbUWosUd6/mbS3TvIFIPblbOktdWV1n8lkMTQXpgC5QHxYfYVgfnztjiMXNA2t+HvsRevQb2rjB2QKG5LXy8vJw7733IiAgAHl5eU3u62gnF2bOERER2c/hIP3ChQuIj4+32N7Q0IC6OvvfkF66dAlarRZKpdJku1KptBns//LLL9i9ezemTJmCzz//HGfOnMGcOXNQV1eHrKwsq8dkZ2dj2bJldo+LPJNxQJ6oTPTuAF3s1lJsVWWdSNXipZyl1jY0hv1FZyvtCqqbYnxsUrdwu84l+CtaTwFDatL48eNRWlqKqKgojB8/3uZ+Lenkwsw5IiIi+zkcpCckJOC7774zpMTpbd++HQMHDhRtYNY0NDQgKioK77zzDvz8/JCYmIgLFy7glVdesRmkL168GBkZjWuaVSoV4uK4ltJTRAZHIr1/OiKDI5vf2Y3nFJXYraVaY6sqkW9M1IdEYXX9A0gLsd5tvaWz1E3JP1aCrLzjhsfTN+xHTBOBf3ibAJPPzgoPCTT5bODkun7yXsYp7mKmuzNzjoiI3KVcpUF5lenyQ422sdhtcYkKCj/Lieao0CBEhbmvsLDDQfrzzz+PadOm4cKFC2hoaMDHH3+MkydP4v3338e///1vu88TEREBPz8/lJWVmWwvKytDdLT1N94xMTEICAgwuUDffPPNKC0tRW1tLQIDAy2OCQoKQlBQkN3jIteKDInEnAHNt1lz9znJw5jfmHCypVt9iBKr6ycgNURpuc8NLZmltiX/WAnSNx2ymKcuvapB+qZDyHl4kEWgHt4myOSzs8LbBJp8NuHEun7yfnV1dUhLS8O6devQs2dPp8/HzDkiInKXzfvOY03BadONslqE3uj8PCFnLyBYvheaP6onFtzdywUjtM7hIP2Pf/wjPvvsM7zwwgto06YNnn/+eQwaNAifffYZ7r77brvPExgYiMTERBQUFBjS6hoaGlBQUIC5c+daPWbYsGH44IMP0NDQALlc1z7o1KlTiImJsRqgE1Er4WRLt6jQIMwf1RNRoeIGwG0C/QxF5PS0DQKy8o431eAMS/OKMSw+wuRGgKbOsfRip4m0rp+8T0BAgKFfurswc46IiMQwJbkz7k4wvUl8VXMNs7/Xfb350SS0U7S1OE6s94Qt1aI+6cOHD8fOnTud/uYZGRmYNm0aBg8ejKSkJKxevRrV1dWGNWtTp05Fp06dkJ2dDQBIT0/HW2+9hfnz5+OJJ57A6dOnsXz5csybN8/psRCRF3OypVtUmELUu6X6IP2xfxx0+FgBQKlKY0itdyuR1vWT93n44Yfx97//HStW2NkesgnMnCMiIneJClNYpK1fVssNX/eJCUPHkFBXD6tZLQrSxTJp0iRUVFTg+eefR2lpKQYMGID8/HxDStz58+cNM+YAEBcXhy+//BILFizArbfeik6dOmH+/PlYtGiRu34EIvIEUrZ081UsYEhNqK+vx/r167Fr1y4kJiaiTZs2Js+vWrXK7nMxc46IiMgxdgXp4eHhOHXqFCIiItChQwfIZLbXYlZWVjo0gLlz59q8SBcWFlpsGzp0KP7zn/849D2IiNxh++yhSIgNM9lWdLYS0zfsb/bY3BlDkNQt3PC4+KIKE9ZZL7LVIixgSE04duwYBg0aBEAXHBtr6j2ALcycIyIisp9dQfrrr7+O0NBQw9ctuUATEbU2igA/hASavswO7xmJmHYKlF7V2Gpwhuh2Covq8YoAppqT63z99deino+Zc0RERPazK0ifNm2a4evp06dLNRYiIp/nJ5cha2wC0jcdstXgDFljE5yqHu8RnKy4T76HmXNERET2cXhN+ueffw4/Pz+MHj3aZPtXX30FrVaLe+9tQfEmIiMV6gpUXK8w2aap1xi+PlF5Agp/y76FkcGRiAzx0N7oREbS+sYg5+FByMo7jjJVY+/O6Cb6pHsdJyvuk3uNHDmyyay53bt3u3A0RERErYvDQXpmZqbVaq8NDQ3IzMxkkE5O23ZqG3KO5Nh8flr+NKvb0/unsz86eY20vjEYFh9hqOKeO2OIRYq7V3Oy4j6514ABA0we19XV4fDhwzh27JhJdh0RERGJz+Eg/fTp00hISLDY3qdPH5w5c0aUQVHrNrHXRIyIG+HwcZHBEs2iV5XqZgUHzxAngBD7fOS1jAPypG7hvhOgA0xb93Kvv/661e1Lly7FtWvXXDwaIiKi1sXhIL1du3b45Zdf0LVrV5PtZ86csWjRQtQSkSEelrZeVapL2+19r3hBupjnI48TFRqE+aN6IipUvJ7NUpyTyFEPP/wwkpKS8Oqrr7p7KERERD7L4SD9j3/8I5588kl88skn6NGjBwBdgP7UU09h3Lhxog+QyONYK4jVoNUVwlJfBkI6AtG3AnKzatycWWw1osIUWHB3L48/J5Gj9u7dC4XCsiYIERERicfhIP3ll19GWloa+vTpg5tuugkA8Ntvv2H48OG8s06tQ3MFsWxhQSyvUa7SoLyqxmSbpk5r+Lr4ospqS7So0CBEhTGAIe/3wAMPmDwWBAElJSU4cOAAlixZ4qZRERERtQ4tSnffs2cPdu7ciSNHjiA4OBi33nor7rjjDinGR+R5jAtinf0W2NnEG9a7XwS63fi/wVl0r7F533msKTht8/kJ6/Za3T5/VE/OdpNPaNeuncljuVyO3r1744UXXsA999zjplERERG1Dg4H6QAgk8lwzz338EJNrUv9daC2GggK1X00aIEP/9zEATJgXw6QOL0x9b222rRXNHmkKcmdcXeC0uHjvHq9OPuak5ENGza4ewhEREStll1B+htvvIHHH38cCoUCb7zxRpP7zps3T5SBEXkcW32dbRIA1UVgRZwkwyHpRIUpWl/aOvuakxUHDhzATz/9BABISEhAYmKim0dERETk++wK0l9//XVMmTIFCoXCZlsWQDfDziCdiMgLsa85Gfntt98wefJk/PDDD2jfvj0A4MqVK0hJScGWLVsMNWmIiIhIfHYF6YcPHzasTzt79qykAyLyWDPzdVXb9c7tATZPaP64KduBLimNj0uPtmBWnkhiTFsnI48++ijq6urw008/oXfv3gCAkydPYsaMGXj00UeRn5/v5hFSa1ehrsC2U9swsddERIZEokJdgYrrFQ6fJzI40nC88fmIiNzJriA9PDwcJSUliIqKwl133YWPP/7YcGedqNXwDwYC2zSu3Q0OB9pEAtVNvCloE6Xb79LpxiDI2rpeIiIP8s0332DPnj2GAB0AevfujTfffBPDhw9348iIdCquVyDnSA5GxI1AZEgktp3ahpwjOQ6fJ71/OuYMmGNxPiIid7IrSG/bti0uX76MqKgoFBYWoq6uTupxEXkuR1qwVZcD792l+5prd4nIS8TFxVm91mu1WsTGxrphRERNm9hrIkbEjTDZpq5TY8aXMwAAmUMy0T+qP+Qyuck+kcEMyIk8ibWsGE29xvD1icoTUPhb1g3SZ8X4CruC9NTUVIwcORI333wzAOD+++9HYGCg1X13794t3uiIPJH52t2z3wJ73jCdUW8TBaQ80dh+DWAqMRF5jVdeeQVPPPEE3n77bQwePBiArojc/Pnz8eqrr7p5dESWIkNM36DvOrcL2UXZhscr9q+AMkSJzKRMpHZJdccQicgOzWXFTMufZnW7PivGV9gVpG/atAkbN27Ezz//jG+++Qa33HILQkJCpB4bkWcyX7sbOwAY+v90a9SvlQFtlbo16Pq2a0QSKFdpUF5VY7JNU6c1fF18UQVFgOW/wajQoNZXuZ4cNn36dKjVaiQnJ8PfX/dWob6+Hv7+/pg5cyZmzpxp2LeystJdwySyate5XcgozIAAwWR7ubocGYUZWDViFQN1Ig9lLSvGHr6WFWNXkF5XV4fZs2cD0N1JX7lyJdekExmT+wHduE6TXGfzvvNYU3Da5vMT1u21un3+qJ5YcHcvqYZFPmL16tXuHgKRXTT1Gqjr1IbH2gYtsouyLQJ0AIZtK4pWIDk6GX5GN9ON02mJyH3Ms2JaK7uC9A4dOhgKx8lkMqnHRORZQqN168nFSlcX+3zUKk1J7oy7E5QOHxcVGiTBaMjXTJtmPZ2QyNPYSn1tSpm6DClbUprfkYjITRwuHPfNN9+wcBy1LqHR4hZ8E/t81CpFhSmYtk5ERETkgxwuHCcIAgvHEREREZHbbUzbiD7hfQyPD5YdxJyC5otHrR21FonKRMPjE5UnWjQrT0QkBRaOIyIiIiKvpPBXICSg8T1pSmwKlCFKlKvLra5Ll0EGZYgSKbEpJmvSrbV0IiJyF7uC9ODgYBaOIyIiIiKP5if3Q2ZSJjIKMyyek0FXV2lR0iKTAJ2IyNPYFaQb+/rrrwEAtbW1OHv2LHr06GFoz0JERNaxZRoRkXQq1BWouF4BAIhtG4sFiQuw4dgG/F7zu2GfcEU4pt0yDbFtY1F8uRiArm0TK0kTkadxOLq+fv065s6di40bNwIATp06he7du+OJJ55Ap06dkJmZKfogiYi8nae3TLN2E8EevIngm+6//36r3VxkMhkUCgXi4+Px0EMPoXfv3m4YHZGlbae2IedITpP7XNZcxqqDq0y2pfdPx5wBza9h9wTlKg027zuPKcmdERWmcPp12/x8ROQ5HA7SMzMzceTIERQWFiItLc2wPTU1FUuXLmWQTkRkhae3TGvuJoIt7Lvum9q1a4dPP/0U7du3R2KirrjWoUOHcOXKFdxzzz3YunUrVq5ciYKCAgwbNszNoyUCJvaaiBFxIxw+LjLYe2bRy6tqsKbgNO5OUCIqTOH067b5+YjIczgcpH/66afYunUrbrvtNpO77Lfccgt+/vlnUQdHROQrPL1lmrWbCJo6rWGGf/vsoTbT8cn3REdH46GHHsJbb70FuVwOAGhoaMD8+fMRGhqKLVu2YPbs2Vi0aBG+//57N4+WWqPI4Eik9083BNmRIc6lrZufzxtYe92urqnHpHf+AwBYOjYBAzt3gJ/cNCuGr9tEns/hIL2iogJRUVEW26urq62mxhERkeezdhNBXVtv+DohNgwhgaw/0lr8/e9/xw8//GAI0AFALpfjiSeeQEpKCpYvX465c+di+PDhbhwltWaRIZGipqmLfT5XMH/dzj9Wgqy844bHSz8rRkw7BbLGJiCtb4w7hkhELSRvfhdTgwcPxo4dOwyP9YH5e++9h6FDh4o3MiIiInKL+vp6nDhxwmL7iRMnoNXqCh4qFArenCfyEPnHSpC+6RDKVKZr1EuvapC+6RDyj5W4aWRE1BIOT4ssX74c9957L4qLi1FfX481a9aguLgYe/bswTfffCPFGImIiETHYnm2PfLII5g1axb++te/YsiQIQCA/fv3Y/ny5Zg6dSoA4JtvvsEtt9zizmEStUqaOq1JppO2QUBW3nErXeEBAYAMwNK8YgyLjzBJfS+5eh0AUFmtex1sjYXoeB1wDn9/0nE4SL/99ttx+PBhrFixAv369cNXX32FQYMGYe/evejXr58UYyQiIhIdi+XZ9vrrr0OpVOLll19GWVkZAECpVGLBggVYtGgRAOCee+4xKSBLRK5hqxuILQKAUpUG/ZZ+ZfX5yuo6AM6/JpoXovOGAI7XAefw9yedFi0w7NGjB959912xx0JEROQyLJZnm5+fH5599lk8++yzUKlUAICwsDCTfTp37uyOoRGRRMQuROcNARyvA87h7086rAJEREStEovl2cc8OCci99o+eygSYhv/XxadrcT0DfubPS53xhAkdQs3PP73kYt45qMfDY/FLkTnDQEcrwPO4e9POvytERERkYmysjIsXLgQBQUFKC8vhyCYrnbVF48jItdTBPiZBD7De0Yipp0CpVc1VtelywBEt1NgeM9Ik1nwQH/LAFlPX4jO/Hz6QnQ5Dw+yCNT1a9v1n50N4LxxjTuRWBikExERkYnp06fj/PnzWLJkCWJiYljFnciD+cllyBqbgPRNhyADTAJr/f/crLEJFmnqerX14hSiK72qAdC4xt1Z5mvciVoTBulERERk4vvvv8d3332HAQMGuHsoRGSDcWG2mzqEYPG9ffC3b3/B5epawz4d2wbi8eHdcVOHEBy7cBWAZWG2Zz760STtvTnNFaJrirahMfQvOltpMbvv7byhWB55BwbpREREZCIuLs4ixZ2I7CdFqnbljeBb/9mewmyXrtVi+RcnTLa5q7K2+Rr36Rv2O7TG3VlS/E3Mz+kNxfJamwp1BSquV5hsu6pRG74+9ftJtLseYnFcZHAkIkMiJR+fLQ4H6ffff7/VtDeZTAaFQoH4+Hg89NBD6N27tygDJCIiItdavXo1MjMz8be//Q1du3Z193CIvI4UqdrmQbq1wmz2MC/M9vKf+mFM/1jDY7EK0RlryRp3sUnxNzE/pzcUy2tttp3ahpwjOTaff3zXDKvb0/unY86AOVINq1kOB+nt2rXDp59+ivbt2yMxMREAcOjQIVy5cgX33HMPtm7dipUrV6KgoADDhg0TfcBEREQkrUmTJkGtVqNHjx4ICQlBQECAyfOVlZVuGhmRd7KWBq1tEHD84lX8rq5Dh5AA3BLbzmp7M31AGd4mwOSztcJsjghvEwgAiG4XLGohOrHWuGvq3FOg0pmUfFY79zwTe03EiLgRJtvsuXESGey+WXSgBUF6dHQ0HnroIbz11luQy+UAgIaGBsyfPx+hoaHYsmULZs+ejUWLFuH7778XfcBEREQkrdWrV7t7CEQ+RYw06PA2QSafnaUP0vWf9ZwtROfKNe5ic3dKPokvMsQybV1dW48GzXkAQJ/wmz3yxonDI/r73/+OH374wRCgA4BcLscTTzyBlJQULF++HHPnzsXw4cNFHSgRERG5xrRp09w9BCKfYpwGvefMJYt14sb+em8fpMRHAHBfGnRa3xjkPDwIWXnHUaZqzACI9uGA1RNS8on0HA7S6+vrceLECfTqZVrc4MSJE4a+qQqFgu1aiIiIvIhKpUJYWJjh66bo9yOipmnqdKnfbRX+aKvwh7ZBwKyNZ23uLwOw/odfMTm5s2GmWp8u7er077S+MRgWH2GY4c6dMcSu1G+x1rgXX1QZUpLFpP+bGPO2lHzyfQ4H6Y888ghmzZqFv/71rxgyZAgAYP/+/Vi+fDmmTp0KAPjmm29wyy23iDtSIiIikkyHDh1QUlKCqKgotG/f3urNdkEQIJPJDDfliahpjgaZ7kz9trZu3jgIbRvkj59KLG/gmbcPC/T3E2WNu7V1wmJoSeDvSSn51Do4HKS//vrrUCqVePnll1FWVgYAUCqVWLBgARYtWgQAuOeee5CWlibuSImIiEgyu3fvRni4bhbr66+/dvNoiEhqUaFBmD+qpyGlvrl187aCW/26eanWuBO1Rg4H6X5+fnj22Wfx7LPPGtLhzNPeOnfuLM7oiIiIyCXuvPNOq18TUcttnz0UCbGN75NbmvoNiJ/+HRWmMOnN7WxLN1tBOiDOGndrM/32js94pt/8bwJ4Xko+kVOl7LgmjYiIyDdduXIFRUVFKC8vR0NDg8lz+uVtRNQ0RYA4qd/6c0nJ2ZZuzWnpGnc9MSrkA5Z/E8DzUvKJHA7Sy8rKsHDhQhQUFKC8vByCYPpPmevUiIiIvNtnn32GKVOm4Nq1awgLCzNZny6TyRikEznIeBZ4RkpXm9XdhRvP69d+m88CezvjADepW7hDKe7WZvrt6XdtT4V8puSTp3E4SJ8+fTrOnz+PJUuWICYmhlXciYiIfMxTTz2FmTNnYvny5QgJCXH3cIi8niOzwMYBvPkssCczX+Mu9vmszfQbV2lPiA1zqt91S1LyK6trTD47q1ylweZ95zElubNP3Zwhxzn8L/n777/Hd999hwEDBkgwHCIiInK3CxcuYN68eQzQiURibRZY2yDg+MWr+F1dhw4hAbgltp3FTK27+qS3hPka9+aqxRdfVNmc+dYH5K6+QeFoSn5ldZ3JZ2eVV9VgTcFp3J2gZJDeyjkcpMfFxVmkuBMREZHvGD16NA4cOIDu3bu7eyhEPsHWeu/+ce1dPxgXcbZavNizypXVtSafbXEmJd8abUNj3FR0ttKhdfjUejkcpK9evRqZmZn429/+hq5du0owJCIiInK1vLw8w9f33Xcfnn76aRQXF6Nfv34ICAgw2XfcuHGuHh6RVxE79Vuqc0rJ2Wrx4s8qC2afnddURXsAyD9Wgqy844bH0zfsR4wbKtqT93E4SJ80aRLUajV69OiBkJAQiwt3ZWWlaIMjIiIi1xg/frzFthdeeMFim0wmY5FYomZIkartjvRvZ0hdLR5wbJY6vE2QyWcxNBWk5x8rQfqmQxa3BEqvapC+6RByHh7UbKAuVkV78j4tmkknIiIi32LeZo2IyJM5O0stJk2d1qSInbZBQFbecatz9gJ0FeOX5hVjWHyEyU0F4zX7gLQV7X2V2Msk3FXMz+Egfdq0aVKMg4iIiIiIqFlizFKLydb6elsEAKUqjaFAnS1SV7T3RWIvk3BXMT+5PTupVCqTr5v6ICIiz1eu0uD1nadQrtJ45PnIvebNm4c33njDYvtbb72FJ5980vUDIqJWSz9Lrf+o0tQ1OUsN6GapqzR1JseZz1JT62W+TML4saew69ZLhw4dUFJSgqioKLRv395qb3RBELhOjYjIS0h9p5nFbrzbRx99ZFJITi8lJQUrVqzg0jcichmpZqkB59vE6W2fPRQJsWGGx0VnKzF9w/5mv3/ujCFI6hZu8v0c/XnJMZ60TKIpdgXpu3fvRni47h/Q119/LemAiIjIMzjTNobFbrzb5cuX0a5dO4vtYWFhuHTpkhtGREQkPmfbxOkpAvxM0s6H94xETDsFSq9qrM74ywBEt1NYXFet3RDwJFKsz3blmm9PWybRFLuC9DvvvNPq10RE5JucvdPMYjfeLT4+Hvn5+Zg7d67J9i+++IK904nIpaScpXa2TZwtfnIZssYmIH3TIchg2vRNH5JnjU3wun7pUqzPlmrNt1TF/FylRZUGrly5gqKiIpSXl1tUg506daooAyMiIvcQ404zi914t4yMDMydOxcVFRW46667AAAFBQV47bXXmOpORC4l5Sy1lG3i0vrGIOfhQcjKO44yVWNKfbQHplaLyZksPDFJuUzCFRx+h/TZZ59hypQpuHbtGsLCwkzWp8tkMgbpRERexNvvNJM0Zs6ciZqaGrz00kt48cUXAQBdu3ZFTk4Or/NE5FbeNEud1jcGw+IjDIFf7owhbgtaXcFb1nt7A4eD9KeeegozZ87E8uXLERISIsWYiIjIRbz9TjNJJz09Henp6aioqEBwcDDatm3r7iEREQHwrllq44A8qVu4TwfonrTe29uL+dnVgs3YhQsXMG/ePAboREREPuquu+7ClStXAACRkZGGAF2lUhnS3x319ttvo2vXrlAoFEhOTkZRUZFdx23ZsgUymQzjx49v0fclIt+U1jcGuzIaa2XlzhiC7xfd5dJAMCo0CPNH9RStnorY55OKWG3xpGyNp18mof/QL5OwdYtEBiDmxjIJ4+PcVczP4Zn00aNH48CBAywcQ56rqhQ4sAEYPAMIjfa88xF5EG+/00zSKCwsRG1trcV2jUaD7777zuHzbd26FRkZGVi3bh2Sk5OxevVqjB49GidPnkRUVJTN43799VcsXLgQw4cPd/h7EpHvc/csdVSYQtSOJGKfTyremIXnTcskADuDdONeqffddx+efvppFBcXo1+/fggICDDZd9y4ceKOkMhRVaXANyuA3veKF6SLeT4iD9Ja2saQfY4ePWr4uri4GKWlpYbHWq0W+fn56NSpk8PnXbVqFR577DHMmDEDALBu3Trs2LED69evR2ZmptVjtFotpkyZgmXLluG7774zzOwTUevQWmepSTretEzCriDdWorZCy+8YLFNJpNBq2XxIPIwVaW6D2MNWqD0KKC+DIR0BKJvBeRmQUZoNINyanW87U4ziWvAgAGQyWSQyWRW09qDg4Px5ptvOnTO2tpaHDx4EIsXLzZsk8vlSE1Nxd69tmdjXnjhBURFRWHWrFl2zd7X1NSgpqbxTZdKpXJonETkWVrrLLU3ECsLD3B9Jp63FPOzK0g3b7MmtrfffhuvvPIKSktL0b9/f7z55ptISkpq9rgtW7Zg8uTJ+OMf/4hPP/1U0jGSFzuwQTcT7qg7M4GRi5vfj8jHeNOdZhLX2bNnIQgCunfvjqKiIkRGRhqeCwwMRFRUFPz8HMuauHTpErRaLZRK017ESqUSJ06csHrM999/j7///e84fPiw3d8nOzsby5Ytc2hsPsvazen6641flx4F/IMtj+PNaSK3KldpsHnfeUxJ7ixKazixz6cnVhae/lyu5u5lEvZwe5NarlMjyQ2eoUtVB4Cz3wI7l9je9+4XgW536L7mGxVqxbzlTjOJq0uXLgCkvznflKqqKjzyyCN49913ERERYfdxixcvRkZGhuGxSqVCXFycFEP0fM3dnF6fZn07b06TBytXaVBeVWOyzbjoWPFFlfVe6KFBkvVCF1t5VQ3WFJzG3QlKcYJ0s/NZ+x3ao7nfoSdl4fnKMgmHg/R58+YhPj4e8+bNM9n+1ltv4cyZM1i9erVD5+M6NZJM/XWgthoICtV9NGiBD//cxAEyYF8OkDi9MfW9ttp09oGoFfGGO80kreLiYpw/f96iiJwj9WciIiLg5+eHsrIyk+1lZWWIjra8Gfrzzz/j119/xdixYw3b9DcN/P39cfLkSfTo0cPiuKCgIAQFca0pANOb045oxTenvWUGszXbvO881hSctvm8rZTp+aN6+lSau7ahMQQuOlvp0A305n6HttjzO/SULDxfWSbhcJD+0UcfmRSS00tJScGKFSscCtJdsU6Na9RaMVszBTYJgOoisKKVzrxQq+Ird5pJGr/88gvuv/9+/Pjjj5DJZBAE3ZtCmUz3RtCR+jOBgYFITExEQUGBocZNQ0MDCgoKMHfuXIv9+/Tpgx9//NFk23PPPYeqqiqsWbOm9c6OO4Jp6w6TegaTnDcluTPuTlA2v6MZX7ou5R8rQVbeccPj6Rv2I8aBINja71BTpzXc4Ng+e6jNbAR7MAtPPA4H6ZcvX0a7du0stoeFheHSpUsOncsV69S4Ro2IyJKv3GkmacyfPx/dunVDQUEBunXrhqKiIly+fBlPPfUUXn31VYfPl5GRgWnTpmHw4MFISkrC6tWrUV1dbciimzp1Kjp16oTs7GwoFAr07dvX5Pj27dsDgMV2IlfSNggoOluJ8ioNokIVzDBysagwRau+4ZF/rATpmw5ZrPkuvapB+qZDyHl4ULOBurXfobq23vB1QmyYyVrzlmAWnjgc/ivEx8cjPz/f4u73F198IXnv9JasU+MatVZsZr6uarveuT3A5gnNHzdlO9AlpfFx6dEWzMoTEXmvvXv3Yvfu3YiIiIBcLodcLsftt9+O7OxszJs3D//9738dOt+kSZNQUVGB559/HqWlpRgwYADy8/MNN+nPnz8PuVwuxY9C1CLma3f3nLmEd777BZeuNS79iGgbiMeHd0dKfON7Um9a/0yeS1OnNQmetQ0CsvKOWy3KJkC37ntpXjGGxUeYBMXGa/bFIEXWHDPxrHM4SM/IyMDcuXNRUVFhaM9SUFCA1157zeH16K5Yp8Y1aq2YfzAQ2KbxcY+7gLBYQFUC2Ko9GRar28+4HZu1CrhERD5Mq9UiNDQUgO5affHiRfTu3RtdunTByZMnW3TOuXPnWk1vB4DCwsImj83NzW3R9yRqKXvW7l66VovlX5hmfvra+mdyjFjF7RxtSSYAKFVpDGnmUpEia46ZeNY5HKTPnDkTNTU1eOmll/Diiy8CALp27YqcnBxMnTrVoXNxnRq5hHErmuT0Jqq7C0DybKD0xr8xrukjolaqb9++OHLkCLp164bk5GS8/PLLCAwMxDvvvCN51hyRO+lnMB8Y1AnDe0ZA2yBgzuZDuFxda/OYjm0CsXbKIPjJZYgMDYK6tl70GUzyDixuR2Jp0aKD9PR0pKeno6KiAsHBwWjbtm2LB8B1aiQ5R/qkGwfwbEVDRK3Uc889h+rqagC6Yq1jxozB8OHD0bFjR2zdutXNoyOSjqMzmABwuboWk975jwSjIW8jVnG77bOHIiE2zPC46Gwlpm/Y3+x5cmcMQVK3cMPj4ouqFv2bJvdzOEi/66678PHHH6N9+/aIjIw0bFepVBg/fjx2797t0Pm4To0kZ60VTYNWt9ZcfRkI6ahbuy43Sz/iLDoRtVKjR482fB0fH48TJ06gsrISHTp0MFR4JyIiU2IVt1ME+JkUcBveMxIx7RQovaqxtWAT0e0UFpXUraXWk3dwOEgvLCy06JcKABqNptl2aLZwnRpJylba+k2Jrh8LEZGXCg8Pb34nIi/HGUzyRH5yGbLGJiB90yHIYFpZSR+SZ41NYCV1H2J3kH706FHD18XFxSgtLTU81mq1yM/PR6dOncQdHVFLhEbrUtXFmgkX+3xERB5q5syZdu23fv16iUdC5B6cwSRPldY3BjkPD0JW3nGUqRqL00U70CedvIfdQfqAAQMgk8kgk8kMVd2NBQcH48033xR1cEQtEhot7lpysc9HROShcnNz0aVLFwwcOBCCYC0kodaqQl2BiusVDh8XGRyJyJDI5nf0MMZVumekdLWo4q4n3Hj+pxIVALZgI2ml9Y3BsPgIQxX33BlDLG4QkW+wO0g/e/YsBEFA9+7dUVRUZLIePTAwEFFRUfDz411DIiIib5Weno4PP/wQZ8+exYwZM/Dwww8zzZ0AANtObUPOkRyHj0vvn445A+ZIMCJp2dOCTc84gGeVbpKacUCe1C2cAbqPsjtI79KlC4DGvuRERETkW95++22sWrUKH3/8MdavX4/Fixfjvvvuw6xZs3DPPfewaFwrNrHXRIyIG2GyTVOvwbT8aQCAjWkbofC3nEGODPa+WXTAepVubYOA4xev4nd1HTqEBOCW2HYWAZJ5lW4iR0SFBmH+qJ6i/TsS+3zkOi1qwQbo1qWfP3/eoojcuHHjnB4UERGR2MpVGmzedx5TkjuLko4q9vk8RVBQECZPnozJkyfj3LlzyM3NxZw5c1BfX4/jx4871XaVvFdkiGXaurpObfi6T3gfhASEuHpYkrFVpbt/XHvXD4ZajagwhaiZGGKfj1zH4SD9l19+wf33348ff/wRMpnMsGZNf3ddq9WKO0IiIiIRlFfVYE3BadydoBQnSBf5fJ5ILpcbrvW8vpOYPG2NO2cwiciTOBykz58/H926dUNBQQG6deuGoqIiXL58GU899RReffVVKcZIRETkEtqGxmJpRWcrW2VBnpqaGkO6+/fff48xY8bgrbfeQlpaGuRyubuHRz7C09a4cwaTiDyJw0H63r17sXv3bkREREAul0Mul+P2229HdnY25s2bh//+979SjJOIiEhS+cdKkJV33PB4+ob9iGllrW3mzJmDLVu2IC4uDjNnzsSHH36IiIgIdw+LfFBrW+NORPYx7qzgCF/rrOBwkK7VahEaGgoAiIiIwMWLF9G7d2906dIFJ0+eFH2AREREUss/VoL0TYcs+iCXXtUgfdMh5Dw8qFUE6uvWrUPnzp3RvXt3fPPNN/jmm2+s7vfxxx+7eGTk7SrUFdh2ahsm9ppoWN/uzBp38/MRkW9wpLOCMV/rrOBwkN63b18cOXIE3bp1Q3JyMl5++WUEBgbinXfeQffu3aUYIxERkWg0dVqoa+sNj7UNArLyjlsE6ICuB7IMwNK8YgyLjzBJfdfU+d4a7alTp7KCO0mi4noFco7kYETcCFGCarHPR0SewVpnBU2dFhPW7QUAbJ89FIoAy7bfvlb/weEg/bnnnkN1dTUA4IUXXsCYMWMwfPhwdOzYEVu3bhV9gERERGLSX+jtJQAoVWnQb+lX0gzIg+Tm5rp7CNSKaRsab3wdLDuIlNgU+Mkt34wTkX28sauJtc4KxjfWE2LDEBLY4gZlXsPhn3D06NGGr+Pj43HixAlUVlaiQ4cOvPtORERERA7bdW4XsouyDY/nFMyBMkSJzKRMpHZJdePIiLwXu5p4L1FuQ4SHh4txGiIiIsltnz0UCbFhhsdFZysxfcP+Zo/LnTEESd0ar3fFF1UOz8oTkaVd53YhozADgtmik3J1OTIKM7BqxCoG6kQSYVcTz2R3kD5z5ky79lu/fn2LB0NERCQ1RYCfSarc8J6RiGmnQOlVjdV16TIA0e0UFm9crK2JI6Kmaeo1JgXitA1aZBdlWwToAAzbVhStQHJ0sknqu6ZeI/1giXwcu5p4LruD9NzcXHTp0gUDBw6EIFh7G0NEROR9/OQyZI1NQPqmQ5ABJqGCPiTPGpvAmQUiEehbrDmiTF2GlC0pEoyGqPViVxPPZneQnp6ejg8//BBnz57FjBkz8PDDDzPNnYiIfEJa3xjkPDwIWXnHUaZq7M8azRkFIiJyE2s9w407ixRfVNmsdG68ZpxdTbyP3UH622+/jVWrVuHjjz/G+vXrsXjxYtx3332YNWsW7rnnHhaNIyIir5bWNwbD4iMMVdxzZwzh2jwikW1M24g+4X0Mjw+WHcScgjnNHrd21FokKhMNj09UnmjRrDyRN2muZ7ituijmPcPZ1cT7OFQ4LigoCJMnT8bkyZNx7tw55ObmYs6cOaivr8fx48fRtm1bqcZJREQkOeOAPKlbOAN0IpEp/BUICQgxPE6JTYEyRIlydbnVdekyyKAMUVq0Y1P4s7I0+T5rPcPt4Ws9w1ujFld3l8vlkMlkEAQBWi1TH4iIvJlYKXWeLCo0CPNH9RTtzYvY5yNqjfzkfshMykRGYYbFc7IbVSEWJS1iv3Rqlaz1DG8JqbqatIb3Du7iUJBeU1NjSHf//vvvMWbMGLz11ltIS0uDXC6XaoxERCQxsVLqPFlUmELUsYp9PqLWKrVLKlaNWIXsomyUq8sN25UhSixKWsT2a0ROkqqrSWt47+Audgfpc+bMwZYtWxAXF4eZM2fiww8/REREhJRjIyIiF2FKHRG5U2qXVCRHJxuquK8dtdYixZ2IxCFWVxO+d5CO3UH6unXr0LlzZ3Tv3h3ffPMNvvnmG6v7ffzxx6INjoiIXEOslDoiopYyDsgTlYkM0IkkJEZXE753kI7dQfrUqVNZwZ2IiIjIx1WoK7Dt1DZM7DURkSGRopxTDjkGKwdDDnGWR0YGRyK9fzoig8UZH1FrxK4mnsvuID03N1fCYRARERGRJ6i4XoGcIzkYETdCtCC9AQ04UHYADWjQfQ91BSquV5jso6nXGL4+UXnCagX3yOBIRIboPuYMaL51GxE1jV1NPFOLq7sTERERUeulbWis4nyw7KBDa8i3ndqGnCM5Np+31QM9vX86g3MiO7GrifdikE5EREREDtl1bheyi7INj+cUzIEyRInMpEy7qrFP7DURI+JGOPx9md5OZD92NfFeDNKJiIiIyG67zu1CRmEGBLPmTeXqcmQUZmDViFXNBur6lHUiIrLEIJ2IiIiILGjqNVDXqU22aRu0yC7KtgjQARi2rShageToZJPUd+P15kRE1DQG6URERERkwda68OaUqcsM/c6JiMhx4vTBICIiIiIiIiKncSadiIiIiCxsTNuIPuF9TLYdLDuIOQXNV1dfO2otEpWJhscnKk+0eGaeiKi1YZBORERERBYU/gqEBISYbEuJTYEyRIlydbnVdekyyKAMUVq0Y7PW85yIiKxjujsRERER2cVP7ofMpEyrz8kgAwAsSlpkd790IiKyxCCdiIiIiOyW2iUVq0asQlRIlMl2ZYjSrvZrRETUNKa7ExEREZFDUrukIjk62VDFfe2otRYp7kRE1DKcSSciIiIihxkH5InKRAboREQiYZBORERERAaRwZFI75+OyOBIjz4nEZGvYro7ERERERlEhkRizoDm26y5+5xERL6KM+lEREREREREHoIz6URERERERD6uXKVBeVWNyTZNndbwdfFFFRQBlrUlokKDEBWmkHx81IhBOhERERERkY/bvO881hSctvn8hHV7rW6fP6onFtzdS6phkRUM0omIiIiIiHzclOTOuDtB6fBxUaFBEozGNbw1e4BBOhERERERkY+LClO0urR1b80eYJBOREQez9qdcHu4+044ERERuY+3Zg8wSCciIqeVqzTYvO88piR3FiUoNj9fc3fCbXH3nXAiIiJyH2/NHmCQTkRETiuvqsGagtO4O0EpTpBudj5rd8I1dVpDmtr22UNtrikjIiIi8iYM0omIyCW0DYLh66KzlRjeMxJ+cpldx1q7E66urTd8nRAbhpBAXtKIiIjI+8ndPQAiIvJ9+cdKkLrqG8Pj6Rv24/aVu5F/rMSNoyIiIiLyPAzSiYhIUvnHSpC+6RDKVKaF30qvapC+6RADdSIiIiIjDNKJiEg0mjot1LX1ho8qTR2y8o5DsLKvftvSvGJUaepMjjPuYUpERES+qVylwes7T6FcpfHI87kLF/AREZFobPUbtUUAUKrSoN/Sr6QZEBEREXksqQvPeivOpBMREREREZFHMi88a/zYV3EmndyrqhQ4sAEYPAMIjfbccxKRXbbPHoqE2DDD46KzlZi+YX+zx+XOGIKkbuGGx8UXVQ7PyhORdCrUFai4XmGyTVPfmE56ovIEFP6Ws1aRwZGIDImUfHxE5Jvyj5UgK++44fH0DfsR006BrLEJSOsb48aRSYtBOrlXVSnwzQqg973iBulin5OI7KII8DNphTa8ZyRi2ilQelVjdV26DEB0O4VFOzZrPc+JyH22ndqGnCM5Np+flj/N6vb0/umYM2COVMMiIh+mLzxr/v5BX3g25+FBPhuoM0gn79CgBc7tAa6VAW2VQJcUQM438USezk8uQ9bYBKRvOgQZYHKh1YfkWWMT7O6XTkTuMbHXRIyIG+HwcZHBnEUnoubpC8/qaRuEJgvPyqArPDssPsLkPYSvFJ5lkE6ep6pU96F39ltgzxtAtVGaXZtIIGUe0O2Oxm2h0Zw5J/JAaX1jkPPwIGTlHTdpwxbdCtLVqNHbb7+NV155BaWlpejfvz/efPNNJCUlWd333Xffxfvvv49jx44BABITE7F8+XKb+5P0IkOYtk5E0mHhWVMM0snzHNigS1dvSnUFsHOJ6bY7M4GRi6UbFxG1WFrfGAyLjzBcTHNnDLFIcSfftXXrVmRkZGDdunVITk7G6tWrMXr0aJw8eRJRUVEW+xcWFmLy5MlISUmBQqHAypUrcc899+D48ePo1KmTG34CIiIi12GQTp6h/jpQW637uv8koMcIXYr7P6cB6ku2j2sTCUzM1aW+t1XqzlF/3RUjJiIHGQfkSd3CGaC3IqtWrcJjjz2GGTNmAADWrVuHHTt2YP369cjMzLTYf/PmzSaP33vvPXz00UcoKCjA1KlTXTJmIiJyHRaeNcUgnTzD+rSWHVddAeTeJ+5YiMhhUaFBmD+qJ6JCgzzyfOQ+tbW1OHjwIBYvbsx0ksvlSE1Nxd699r2RUqvVqKurQ3h4uM19ampqUFPTuJxCpVK1fNBERORSLDxrin3SiYjIaVFhCiy4uxeiwixbMHnC+ch9Ll26BK1WC6VSabJdqVSitLTUxlGmFi1ahNjYWKSmptrcJzs7G+3atTN8xMXFOTVuIiJyH33hWaCx0Kxeayg8y5l08gwz84HoW023ndsDbJ7Q/LFTtuuqveuVHm35zDwRtRrlKg3Kq2pMthlXhS2+qLJ6Rz4qNIg3D1xoxYoV2LJlCwoLC6FQ2P69L168GBkZGYbHKpWKgToRkRdrzYVnGaSTZ/APBgLbmG7rcRcQFguoSgBbiS5hsbr9jNux+QdLOVIi8hGb953HmoLTNp+3taZt/qieWHB3L6mG5XMiIiLg5+eHsrIyk+1lZWWIjm66I8err76KFStWYNeuXbj11lub3DcoKAhBQVweQUTkS1pr4VkG6eR5jFuwJadbVnE3EIDk2UDpj7qHbMFGRA6YktwZdycom9/RDNfJOyYwMBCJiYkoKCjA+PHjAQANDQ0oKCjA3LlzbR738ssv46WXXsKXX36JwYMHu2i0RETkaVpj4VkG6eR57GnBpmccwLMFGxE5ICpMwbR1F8nIyMC0adMwePBgJCUlYfXq1aiurjZUe586dSo6deqE7OxsAMDKlSvx/PPP44MPPkDXrl0Na9fbtm2Ltm3buu3nICIicbHwrHUM0snzDJ4B9L7XdFuDVrfWXH0ZCOmoW78uN1sryll0IiKPNGnSJFRUVOD5559HaWkpBgwYgPz8fEMxufPnz0Mub6xlm5OTg9raWkyYYFqXJCsrC0uXLnXl0ImISEL6QrGeej53YZBOnsdW2vpNia4fCxERiWLu3Lk209sLCwtNHv/666/SD4iIiMhDeUQLtrfffhtdu3aFQqFAcnIyioqKbO777rvvYvjw4ejQoQM6dOiA1NTUJvcnDxcarUtTF3MWXIpzEhERERERuYDbg/StW7ciIyMDWVlZOHToEPr374/Ro0ejvLzc6v6FhYWYPHkyvv76a+zduxdxcXG45557cOHCBRePnEQRGq1bRy52kC72OYmIiIiIiFzA7UH6qlWr8Nhjj2HGjBlISEjAunXrEBISgvXr11vdf/PmzZgzZw4GDBiAPn364L333jNUiSUiIiIiIiLyZm4N0mtra3Hw4EGkpqYatsnlcqSmpmLvXuv9ac2p1WrU1dUhPDzc6vM1NTVQqVQmH0RERERERESeyK1B+qVLl6DVag3VXfWUSqWh3UpzFi1ahNjYWJNA31h2djbatWtn+IiLi3N63ERERERERERScHu6uzNWrFiBLVu24JNPPoFCYb3X7eLFi3H16lXDx//+9z8Xj5KIiIiIiIjIPm5twRYREQE/Pz+UlZWZbC8rK0N0dNNFv1599VWsWLECu3btwq233mpzv6CgIAQFOd7MXqvVoq6uzuHjiMh9AgMDTXotExEReSu+FyXSqamtR6dQP93XGg3kDZ7bRVys96Ju/QkDAwORmJiIgoICjB8/HgAMReBs9VIFgJdffhkvvfQSvvzySwwePFjUMQmCgNLSUly5ckXU8xKR9ORyObp164bAwEB3D4WIiKhF+F6UyFSDIGDpyCgAwMXfzkMuk7l5RLaJ9V7U7bchMjIyMG3aNAwePBhJSUlYvXo1qqurMWPGDADA1KlT0alTJ2RnZwMAVq5cieeffx4ffPABunbtali73rZtW7Rt29bp8ehfFKOiohASEgKZB/8jIKJGDQ0NuHjxIkpKStC5c2f+3yUiIq/E96JEprQNAurLqwAAXaNC4Sf3zP8TYr4XdXuQPmnSJFRUVOD5559HaWkpBgwYgPz8fEMxufPnz5ukDOTk5KC2thYTJkwwOU9WVhaWLl3q1Fi0Wq3hRbFjx45OnYuIXC8yMhIXL15EfX09AgIC3D0cIiIih/C9KJElbYMAmX8NAEChUHhskA6I917U7UE6AMydO9dmenthYaHJ419//VWycejX/YSEhEj2PYhIOvrUIq1WyyCdiIi8Dt+LEnk3sd6LekSQ7mkcSU0oV2nwzre/4I5eEQhvY3+BusrqGnx76hIev6M7osKsV6YnIscwJZCIiHyB1O9FK6tr8MWPpXhkaBckxLZr6TCJyIxY70UZpDupvKoG731/Fu99f7ZFx48f2IlBOhERERG1iFPvRWVA9gO2uyQRkXswSBfJ6kkDEB/VWLiuuqYek975DwBg6dgEDOzcwWT9xJnya3hy62FXD1NSXbt2xZNPPoknn3wSgO5O0ieffGKo3E9ERETeqVylQXlVjcPHRYUGcTLCRRx5L6p/H3pv3xi3jFUqfC9KvoJBukjio9qibyddulD+sRJk5R03PLf0s2LEtFMga2wC0iR6MZw+fTo2btxoeBweHo4hQ4bg5ZdfbrKPvJRKSkrQoUMHt3xvIiIiEs/mfeexpuC0w8fNH9UTC+7uJcGIyFxL3ouGtxGvZSnfixKJh0G6yPKPlSB90yEIZttLr2qQvukQch4eJFmgnpaWhg0bNui+X2kpnnvuOYwZMwbnz5+X5Ps1Jzo62i3fl4iIiMQ1Jbkz7k5QmmzT1GkxYd1eAMD22UOhCPCzOC4q1P56PSQOe96L3tRBmsJ0fC9KJA5587uQPTR1WlRp6pCVd9ziRRGAYdvSvGJUaeqgqdOKPoagoCBER0cjOjoaAwYMQGZmJv73v/+hoqICALBo0SL06tULISEh6N69O5YsWWKoIgoAR44cwciRIxEaGoqwsDAkJibiwIEDhue///57DB8+HMHBwYiLi8O8efNQXV1tczwymQyffvopAF1VfplMho8//hgjR45ESEgI+vfvj71795oc4+j3ICIiIulFhSnQt1M7k4+E2DDD8wmxYRbP9+3UjqnuLuTIe9HqmnpJxsD3okTiYJAukgnr9qLf0q9QprK9XksAUKrSoN/Srwx3nqVy7do1bNq0CfHx8YY+m6GhocjNzUVxcTHWrFmDd999F6+//rrhmClTpuCmm27C/v37cfDgQWRmZhpaB/z8889IS0vDn/70Jxw9ehRbt27F999/b7N1ni3PPvssFi5ciMOHD6NXr16YPHky6uvrRf0eRERERK2NI+9F9WvVpcT3okQtx3R3H/Lvf/8bbdvqCoZUV1cjJiYG//73vyGX6+7FPPfcc4Z9u3btioULF2LLli145plnAADnz5/H008/jT59+gAAevbsadg/OzsbU6ZMMRTi6NmzJ9544w3ceeedyMnJgUJh353yhQsX4r777gMALFu2DLfccgvOnDmDPn36iPY9iIiIiMj1+F6USBycSRfJ9tlDkTtjiF375s4Ygu2zh4o+hpEjR+Lw4cM4fPgwioqKMHr0aNx77704d+4cAGDr1q0YNmwYoqOj0bZtWzz33HMma4QyMjLw6KOPIjU1FStWrMDPP/9seO7IkSPIzc1F27ZtDR+jR49GQ0MDzp61v+WHceGQmBjd2vzy8nJRvwcRERFRa+PIe9GlYxMkGQPfixKJg0G6SBQBfhjeMxIx7RSw1cJeBiCmnQLDe0ZaLa7irDZt2iA+Ph7x8fEYMmQI3nvvPVRXV+Pdd9/F3r17MWXKFPzhD3/Av//9b/z3v//Fs88+i9raWsPxS5cuxfHjx3Hfffdh9+7dSEhIwCeffAJAl7L0l7/8xfDCe/jwYRw5cgSnT59Gjx497B6jPmUJ0K0TAoCGhgZRvwcRERFRa+PIe9GBnaWpeM73okTiYLq7iPzkMmSNTUD6pkOQASZFO/QvllljE0z6pUtJJpNBLpfj+vXr2LNnD7p06YJnn33W8Lz+rqaxXr16oVevXliwYAEmT56MDRs24P7778egQYNQXFyM+Ph4ycbriu9BRERE5Kv4XtQ5fC9KnoIz6SJL6xuDnIcHISrMtOVIdDuFpO3XAKCmpgalpaUoLS3FTz/9hCeeeALXrl3D2LFj0bNnT5w/fx5btmzBzz//jDfeeMNwZxIArl+/jrlz56KwsBDnzp3DDz/8gP379+Pmm28GoKvGuWfPHsydOxeHDx/G6dOn8a9//UvUQhqu+B5EREREvozvRVuO70XJU3AmXQJpfWMwLD4C/ZZ+BUC3Bn14z0jJ71rm5+cb1taEhoaiT58+2LZtG0aMGAEAWLBgAebOnYuamhrcd999WLJkCZYuXQoA8PPzw+XLlzF16lSUlZUhIiICDzzwAJYtWwZAt37nm2++wbPPPovhw4dDEAT06NEDkyZNEm38rvgeRERERL6O70Vbhu9F3a9O24B6bYPJtgajlBBNnRbW/hn7+8kR4Oc7888M0kVypvyayWPjPuhtg/zxU4mqyf2dlZubi9zc3Cb3efnll/Hyyy+bbNNXrwwMDMSHH37Y5PFDhgzBV199ZfP5X3/91eSxIDT+j+ratavJYwBo3769xbbmvgcRERERWXLkvah+38rqWoiF70VJDJXVtShTaWw+/3OF9RhKGaaAMsx3qu8zSHdSVGgQkruF48mth23uY6snenK3cESFBll9joioNStXabB533lMSe6MKJEuulKck4jcQ+z/z978+uDMe9FvT1Xgjl6REo2MyHHhbQIRpnA8RPX3oVl0gEG606LCFHhz8kCUV9U4fmxokNddCIiIXKG8qgZrCk7j7gSleEG6BOckIvcQ+/+zN78+OPtelMiTBPhY2npLMUgXQVSYwute0ImIvJ3WaJFa0dlKl6y3JCLvoW0QUHS2EuVVGkSFKpDULdxnXyP4XpTItzBIJyIir5N/rARZeccNj6dv2I+YdgpkjU2QtHIxUWvgjctNylUak5nkPWcu4Z3vfsGla41rriPaBuLx4d2REh9h2MasRiLyRAzSiYjIq+QfK0H6pkMQzLaXXtUgfdMhyVsMEfk6b1xusnnfeawpON3kPpeu1WL5FydMts0f1RML7u4l+niIiJzBIJ2IiDyWpk4LdW294bG2QUBW3nGLAB0ABAAyAEvzijEsPsIirdW40jEROc8TlpzoXyMeGNQJw3tGQNsgYM7mQ7jcRNXyjm0CsXbKIPjJZYgMDYK6tp6vD0TkURiki6GqVPfhqNBo3QcREVllqyKxLQKAUpXG0BuYiKThKUtOHH2NAIDL1bWY9M5/JBiNG/G9KJFPYZDurKpSYPtM4NwPjh/bZRgwYT1fHImIiMhrcMmJh3HmvejQucDol8QfExE5hUG6s6pKdS+KD7wLRBitaaq/DqxP0309Mx/wDzY97tIp4OPHdMczSCcisur/t3ffYVEd+//A30tdYClKRwVWISKgqEEIGIOJKKjXHoJejKiIxkhEiQ0jEjWKJcaKXQETK4nxqj+jURQvNggGTBSCigWvSrHSQWB+f/DlyHGXvrArfF4++zzunJk5c+Zhz8ycMvPzFy6wNdPhvifce46JEX/UmS5yUh84idvzwlIe5zbqrhshbdXbr5sAjX/lpLkeJ5fVOeKdPj80pi9a1Q/t8knLlpUQUi80SJcVg/cAs55vvhfnvvl/ST7Q0QlQUm7xYhFCiDy8PdMywO+kpzzOhVBV8pz49kzLQlVlaKq9aar6WRvCVFeIzFfFUgcJAgAmukKp78ZK2x8hpGaNGbS29CsnVeeIqnNOO001GIjUeLO6v81ApIZ2mmq4m1PAnXNaxfmhMX1RTf0WKRohpGFopfjmkHIMCHd6833fp8B6+8rwZpSTk4Pp06fD3Nwc6urqMDExgYeHBy5dqnz8ydLSEgKBAAKBAFpaWujduzeio6N5eRQVFaF9+/YwMDBASUmJxD6q8rh6lf8u16xZs9C/f/96l/X+/fsQCARITk5u8HESQhTfvvgM/GvTRd6neof/021XJLb/a9NF7IvPqDVfZSUBQofZAqgckFdX9T10mG2rXQuZECJd1TlnRPilWgfoQOUs7yPCL9XrnPPOkkNflPqhhMgO3UmXtZRjwOEJwNv3eHKfVIZ/thewHd4sux4zZgxKS0sRFRWFzp07IysrCzExMXj27BkXZ+nSpfD390dubi7Wrl0Lb29vdOjQAa6urgCAX375BXZ2dmCM4ejRo/D29pbYj1AoxPz583HhwoVmOQ5CyLvPx9kcA22NG5zOSFu9zjie9qbYOr43Qo/dRFbum06cCa2TTohMvf0oOaC4j5O/fc5pyDrprU59+qJ65jLfLfVDCZEdGqTLSllR5WNFv82DxEkRAPem1qn5QOf+lfFl6OXLl4iLi0NsbCzc3NwAABYWFnBycuLF09bWhomJCUxMTBAeHo6ffvoJx48f506Ou3fvxvjx48EYw+7du6WeHKdOnYpt27bh5MmTGDJkSI1l2rVrF9auXYt79+7B0tISM2fOxJdffgkAEIvFAIBevXoBANzc3BAbG9vkeiCEKAYjHWGzrIVcxdPeFH2tDLhHaiMn9ZHL8k+EtGZvv24CNP6Vk+Z+nPztc459B1349euMhHvPkZ1XDCNtIZzE7Vv3OaIhfdHRO2W6a+qHEiJbNEiXlaqJOWrFgNzHwMpOMt+9SCSCSCTC0aNH8cEHH0Bdve4rwyoqKlBVVUVpaeVV5vT0dFy5cgVHjhwBYwyzZ8/GgwcPYGFhwUsnFovxxRdfIDg4GJ6enlBSknxrYt++fVi8eDE2b96MXr16ISkpCf7+/tDS0oKvry8SEhLg5OSEs2fPws7ODmpqarKpCEJIm1G9s93qO9+EKIiqV06m//QnBOAPBRXtlRNlJQFcurShd64b0heNHCrTXVM/lBDZonfSWwkVFRVERkYiKioKenp66Nu3LxYuXIi//vpLavzS0lKEhYXh1atX+OSTypk99+zZg8GDB6Ndu3Zo3749PDw8EBERITX9okWLcO/ePezbt0/q9tDQUKxduxajR4+GWCzG6NGjMXv2bGzfvh0AYGhoCADQ19eHiYkJ2rdvLzUfQkjbZKStjsAB1jJ9FLU58iSkLap65cRIh/9bMtEVttjya7L+PdP5oWmoH0qIbNEgXVYmnwJ8fq5fXJ+fK+PL2JgxY/D48WMcO3YMnp6eiI2NRe/evREZGcnFmT9/PkQiETQ1NbFq1SqsXLkSQ4cORXl5OaKiojB+/Hgu7vjx4xEZGYmKigqJfRkaGmLOnDlYvHgxdwW0SkFBAdLT0+Hn58ddWRWJRPjuu++Qnp4u8+MmhLQ+RjpCzB74nkwfmW+OPAlpqzztTXE2yI37HjmpDy7O/6TF5oSQ9e+5VZwfGtIXHbxK5runfighskOPu8uKikbl0hY6ZpUTc9T0ppaOWeWalJl/N0sxhEIhBg4ciIEDByIkJARTpkxBaGgoJk6cCACYO3cuJk6cCJFIBGNjYwgElY+jnT59Go8ePZJ496e8vBwxMTEYOHCgxL6CgoKwZcsWbNmyhReen58PANi5cyecnZ1525SVW8ESJ4QQQgihV04UTUP6oh36NEsRqB9KiGzQnXRZUlIGPKuuTNawOJDnyhZdL93W1hYFBQXcdwMDA1hZWcHExIQ7MQKVE3WMHTsWycnJvM/YsWOxe/duqXmLRCKEhIRg+fLlyMvL48KNjY1hZmaGu3fvwsrKivepmqij6t2f8vJyqXkTQgghRD7odZN3mIL1RakfSkjj0J10WbMdXrm0xW/zgLwnb8J1zCpPis20/NqzZ8/g5eWFyZMno0ePHtDW1kZiYiJWr16NESNG1Jo2JycHx48fx7Fjx2Bvb8/bNmHCBIwaNQrPnz+X+r7O1KlTsW7dOuzfv593tXLJkiWYOXMmdHV14enpiZKSEiQmJuLFixcICgqCkZERNDQ0cOrUKXTs2BFCoRC6urqyqQxCCCGENFrVo9+KniepQX36oo+TZbpL6ocSIlt0J7052A4HZiS8+e7zMzDr72YboAOVVxOdnZ2xbt06fPTRR7C3t0dISAj8/f2xefPmWtPu3bsXWlpaGDBggMS2AQMGQENDAz/99JPUtKqqqli2bBmKi4t54VOmTMGuXbsQERGB7t27w83NDZGRkdwVTBUVFWzcuBHbt2+HmZlZnSdwQgghhBBSTy3cF6V+KCGyJWCMSXthpdXKzc2Frq4uXr16BR0dHd624uJi3Lt3D2KxGEJhPScOeZwM7HCrXG/SoNoV4rKiN0thTD5V+Z5QdU9vAUf8gakXALOejT4eQsgbjfoNk3dWYWkZbBefBgCkLPWQWM/5XVJb20Qah+q0+TXkN5idW4x98RnwcTaXyeRsss5PUbRYX7SqHzr+CGAlOTgmhDRObb/hhrRL726PRlFomwAWfStPdDWpad1Ki76V6QkhhBBCWrHsvBJsiLmNgbbGshmkyzi/d1pT+qLp52iQTogCokF6U2mbAJ/uAfIyG5eWBumEEEIIUXDZucXIzivhhRW/fjPpVsrjXAhVJScjM9JWlzqIlpZfeQXDzcev8KLwNdppqsLOTFdixvia8mvTmtoXJYQoHBqkywINtgkhhBDSiu2Lz8CGmNs1bv902xWp4YEDrKVOGFdXfjWpKb82j/qihLQqNEgnhBBCCCG18nE2x0Bb4wanq2nJter5Xb7zFCt++6fGPBYOtoGrlUGt+RFCSGtCg3RCCCGEEFIrIx2hTB4zL35djsLSMoiEKhAJVVBeweAXda/G+AIAey7dxzhnc+7R98LSMt6j9oQQ0trQIJ0QQgghhLSImh6LrwkDkJlbjO7f/t48BSKEEAVEg3QZyCnMQU5RToPTGWoYwlDTsBlKRAghhBBC2grqixLSutAgvYlyCnMw77/zkJiV2OC0jsaOWP3Rajo5EkIIIaRN+PkLF9iavVkfOOHec0yM+KPOdJGT+sBJ3J77nvI4t8F35VurpvRFJ9hOwNw+c5uhVISQpqBBehPlFOUgMSsRYf3C0Fm3MxdeXFYM31O+AIAozygIVfjvcd19dRfBccHIKcqhQTohhBBC2gShqjI01VS4JdjaaarBQKSGp/mlNaYxEKmhnaYa7uYUcEuwSVvura1qTF+0qh/qauba4uUlhNRNSd4FaC0663aGrb4t97HWs+a2FbwuQNd2XXnbq59EieKYOHEiRo4cWa+49+/fh0AgQHJycrOW6V0riyzt2LEDnTp1gpKSEtavX19jGCGEkHfLvvgM/GvTRYwIv1TrAB0AnuaXYkT4Jfxr00Xsi89ooRK+exrSF63qh7YTtpNXcUkNqC+qWOTVF6VBejM4++AsRh4byX3/MuZLePzigbMPzjbbPnNycjB9+nSYm5tDXV0dJiYm8PDwwKVLl7g4lpaWEAgEEAgE0NLSQu/evREdHc3Lp6ioCO3bt4eBgQFKSkok9lOVx9WrV3nhs2bNQv/+/etd3vr+kKviVX309fUxaNAgJCUl1XtfDdn/hg0bEBkZ2aS8GyI2NpZ3fBoaGrCzs8OOHTtarAwtrby8HOvWrUP37t0hFArRrl07DB48mPe3CgC5ubkICAjA/Pnz8ejRI0ydOlVqGCGEtHWp2f/D578sQ2r2/xQyP2l8nM1x4qsPuc/CwTYwEKnx4hiI1LBwsA0vno+zebOVqTWhvmjdqC9aifqiitkXpUG6jJ19cBZBsUHILszmhWcXZiMoNqjZTo5jxoxBUlISoqKicOvWLRw7dgz9+/fHs2fPePGWLl2KJ0+eICkpCX369IG3tzcuX77Mbf/ll19gZ2cHGxsbHD16VOq+hEIh5s+f3yzHUZOzZ8/iyZMnOH36NPLz8zF48GC8fPmyUXmVltZ8xV5XVxd6enqNK2QTpKWl4cmTJ0hJScG0adMwffp0xMTEtHg5mhtjDGPHjsXSpUsRGBiI1NRUxMbGolOnTujfvz/vby4jIwOvX7/G0KFDYWpqCk1NTalhhBDS1t159hjJ+Ydx59ljhcxPGiMdIew76HKfqW5dEL/QHQf8P8CGsT1xwP8DxC90x1S3Lrx4slgGrrWjvmjzoL5o6/Cu9EVpkC4jxWXFyCvJQ1hCGBiYxHb2f/9WJqxEXkkeisuKZbbvly9fIi4uDqtWrcLHH38MCwsLODk5ITg4GMOHD+fF1dbWhomJCd577z2Eh4dDQ0MDx48f57bv3r0b48ePx/jx47F7926p+5s6dSquXr2KkydP1lquXbt2oVu3bhAKhbCxscGWLVu4bWKxGADQq1cvCASCOq986uvrw8TEBI6Ojvj++++RlZWF+Ph4pKenY8SIETA2NoZIJEKfPn1w9iy/8bG0tMSyZcswYcIE6OjoYOrUqTXu/+1HjCoqKrB69WpYWVlBXV0d5ubmWL58eY3lvHHjBgYPHgyRSARjY2N8/vnnePr0aa3HBgBGRkYwMTGBWCzGzJkzIRaL8eeff3LbT506hQ8//BB6enrQ19fHv/71L6Snp9eYX3l5Ofz8/CAWi6GhoYGuXbtiw4YNvDhVx/r999/D1NQU+vr6mDFjBl6/fs3FKSkpwfz589GpUyeoq6vDysqK93fR0OM9fPgwfv75Z+zduxdTpkyBWCyGg4MDduzYgeHDh2PKlCkoKChAZGQkunfvDgDo3LkzBAKB1LD79+/XWbeEEEKA0rIyRF47i6Xnf0LktbMoLSuTd5EkKCsJ4NJFHyN6doBLF31uXXRSPw3pixa+LpTpvqkvSn3Rt1FftGlokC4jvqd84XrQVeKq5duyCrPgetCVm8hDFkQiEUQiEY4ePSr1saCaqKioQFVVlbual56ejitXruCzzz7DZ599hri4ODx48EAinVgsxhdffIHg4GBUVFRIzXvfvn1YvHgxli9fjtTUVKxYsQIhISGIiooCACQkJAB4c1XyyJEj9S63hoYGgMqrkPn5+RgyZAhiYmKQlJQET09PDBs2DBkZ/HfWvv/+ezg4OCApKQkhISH13n9wcDBWrlyJkJAQpKSkYP/+/TA2NpYa9+XLl/jkk0/Qq1cvJCYm4tSpU8jKysJnn31W72NjjOHUqVPIyMiAs7MzF15QUICgoCAkJiYiJiYGSkpKGDVqVI31X1FRgY4dOyI6OhopKSlYvHgxFi5ciMOHD/PinT9/Hunp6Th//jyioqIQGRnJe8RqwoQJOHDgADZu3IjU1FRs374dIpGo0ce7f/9+vPfeexg2bJjEtq+//hrPnj3DmTNn4O3tzTVwCQkJePLkCby8vCTCOnXqVL+KJYTIXXh4OCwtLSEUCuHs7Mydh2sSHR0NGxsbCIVCdO/evc7OOKnZmrhoOO79GGtvzEZ0xiqsvTEbjns/xpq46LoTy4iRtjoCB1jDSFtdIfNrDRrSF510epJM9019UeqLvo36ok3E2phXr14xAOzVq1cS24qKilhKSgorKiqqd343n95k9pH2jf7cfHpTJsf1888/s3bt2jGhUMhcXV1ZcHAwu379Oi+OhYUFW7duHWOMsZKSErZixQoGgJ04cYIxxtjChQvZyJEjufgjRoxgoaGhUvPIzs5m2trabO/evYwxxgIDA5mbmxsXr0uXLmz//v28tMuWLWMuLi6MMcbu3bvHALCkpKRaj+vteC9evGCjRo1iIpGIZWZmSk1jZ2fHNm3axCtz9eOqbf++vr5sxIgRjDHGcnNzmbq6Otu5c2e9yrZs2TI2aNAgXpyHDx8yACwtLU1qHufPn2cAmJaWFtPS0mIqKipMSUmJfffdd1LjV8nJyWEA2N9//13r8VQ3Y8YMNmbMGN6xWlhYsLKyMi7My8uLeXt7M8YYS0tLYwDYmTNnpObXmOO1sbHh6vdtz58/ZwDYqlWrGGOMJSUlMQDs3r17XBxpYdU15jdM3l0FJa+ZxfwTzGL+CVZQ8lrexWmS2tqm1uDgwYNMTU2N7dmzh928eZP5+/szPT09lpWVJTX+pUuXmLKyMlu9ejVLSUlhixYtYqqqqtw5rz5ae51WdywlntlH2rNjKfES21b/9zCzi7BndhH8/kdV2Or/Hm5QfqR5tXRfVFb9UMaoL1od9UWla+t90Ya0S3QnXUaiPKOwZcCWuiMC2DJgC6I8o2S6/zFjxuDx48c4duwYPD09ERsbi969e0tMPDF//nyIRCJoampi1apVWLlyJYYOHYry8nJERUVh/PjxXNzx48cjMjJS6hUyQ0NDzJkzB4sXL5Z4r6agoADp6enw8/PjrqyKRCJ89913tT4WUxtXV1eIRCK0a9cO169fx6FDh2BsbIz8/HzMmTMH3bp1g56eHkQiEVJTUyWuXjo6OjZ4n6mpqSgpKcGAAQPqFf/69es4f/4875htbGwAoM7jjouLQ3JyMpKTk7Fr1y6sWLECW7du5bbfvn0b48aNQ+fOnaGjowNLS0sAkDjO6sLDw/H+++/D0NAQIpEIO3bskIhvZ2cHZeU3y9iYmpoiO7vyCnxycjKUlZXh5uYm0+NlTPIRPEJI6/bDDz/A398fkyZNgq2tLbZt2wZNTU3s2bNHavwNGzbA09MTc+fORbdu3bBs2TL07t0bmzdvbuGSv1vySwvxrDCP+zzJe4G9tyofLxW89eR41fcfb23Ek7wXvHT5pbJ9FJq0jIb0RRf0WSDz/VNflPqib6O+aOPROukyIlQRwsHQAcaaxsguzJb6LpAAAhhrGsPVzBVpL9JkXwahEAMHDsTAgQMREhKCKVOmIDQ0FBMnTuTizJ07FxMnTuTe2xD8Xyt9+vRpPHr0CN7e3rw8y8vLERMTg4EDB0rsLygoCFu2bOG93wMA+fn5AICdO3fyHpMBwPsRNsShQ4dga2sLfX193mQac+bMwZkzZ/D999/DysoKGhoa+PTTTyVO1lpaWg3eZ9WjTPWVn5+PYcOGYdWqVRLbTE1Na00rFou547Kzs0N8fDyWL1+O6dOnAwCGDRsGCwsL7Ny5E2ZmZqioqIC9vX2NE48cPHgQc+bMwdq1a+Hi4gJtbW2sWbMG8fHxvHiqqqq87wKBgGsI6zr+xhzve++9h9TUVKnbqsLfe++9WvdL2qaqNZWrK35dzv0/5XGu1HWTq9ZUJvJTWlqKa9euITg4mAtTUlKCu7s7rly5IjXNlStXEBQUxAvz8PCocRIpoPK9xeqP2ebm5jat4O+gFclfYUXym+/lRaZQ1niFmt7sFggApvISA/Z5Q1njSUsUkTSjhvRFHYwcmqcM1Belvuj/ob5o09AgXYaUlZSxwGkBgmKDJLYJ/q+JnO80H8pKjTs5NJStra1Eh8bAwABWVlYScXfv3o2xY8fim2++4YUvX74cu3fvlnpiFIlECAkJwbfffsubFMTY2BhmZma4e/cufHx8pJZNTa1ymZXy8nKp29/WqVMndOnSRSL80qVLmDhxIkaNGgWg8sdanwkc6rN/a2traGhoICYmBlOmTKkzz969e+OXX36BpaUlVFSa9tNSVlZGUVERAODZs2dIS0vDzp070a9fPwDAxYsXa01/6dIluLq64ssvv+TCGnrluHv37qioqMCFCxfg7u4usb0xxzt27Fj8+9//xvHjxyXeBVq7di309fWl/q0Rsi8+Axtibte4/dNt0gd7gQOsMXug/Bvbtuzp06coLy+XeIfS2NgY//zzj9Q0mZmZUuNnZmbWuJ+wsDAsWbKk6QVuRRhTrTtSA+IRxVffvqiSoGUepqW+aM2oL1q3ttwXpUG6jLlbuOOH/j8gLCGMN3GHsaYx5jvNh7uF5B9YUz179gxeXl6YPHkyevToAW1tbSQmJmL16tUYMWJEnelzcnJw/PhxHDt2DPb29rxtEyZMwKhRo/D8+XO0b99eIu3UqVOxbt067N+/n3elcsmSJZg5cyZ0dXXh6emJkpISJCYm4sWLFwgKCoKRkRE0NDRw6tQpdOzYEUKhELq6ug0+dmtraxw5cgTDhg2DQCBASEhIjRNYVFef/Vct7zFv3jyoqamhb9++yMnJwc2bN+Hn5yeR54wZM7Bz506MGzcO8+bNQ/v27XHnzh0cPHgQu3btqvXKbXZ2NoqLi1FSUoKEhAT8+OOP+PTTTwEA7dq1g76+Pnbs2AFTU1NkZGRgwYLaH1OztrbG3r17cfr0aYjFYvz444/4448/uJlE68PS0hK+vr6YPHkyNm7cCAcHBzx48ADZ2dn47LPPGnW8Y8eORXR0NHx9fbFmzRoMGDAAubm5CA8Px7FjxxAdHd2oK82k9fNxNsdAW+kT5dSGJpVqO4KDg3l333Nzc9vc5JILe27CIOv3cfbOn0jOvI3bTzOR9rrmR1Gr2Om6wNpgFHqaWMPdqjd+v30NK5K/aoESk+ZQn75oyrMUme6T+qLUF5VWL9QXbTx6J70ZuFu44+jwo9z3LQO24NSYU80yQAcqryI6Oztj3bp1+Oijj2Bvb4+QkBD4+/vX6/29vXv3QktLS+r7LgMGDICGhgZ++uknqWlVVVWxbNkyFBfzl5SbMmUKdu3ahYiICHTv3h1ubm6IjIzkfpgqKirYuHEjtm/fDjMzs3qdwKX54Ycf0K5dO7i6umLYsGHw8PBA796960xX3/2HhITg66+/xuLFi9GtWzd4e3tz78m8zczMDJcuXUJ5eTkGDRqE7t27Y9asWdDT04OSUu0/ta5du8LU1BRWVlaYP38+pk2bhk2bNgGofCz04MGDuHbtGuzt7TF79mysWbOm1vymTZuG0aNHw9vbG87Oznj27BnvSmZ9bd26FZ9++im+/PJL2NjYwN/fHwUFBY0+XoFAgMOHD2PhwoVYt24dunbtin79+uHBgweIjY3lLTlCSHVvr6lc3w896i5/BgYGUFZWRlZWFi88KysLJiYmUtOYmJg0KD4AqKurQ0dHh/dpa0RqmtDX1IZ3DzeEDZqC/Z8tgKBcDzW9fskYICjTw/7PFiBs0BR493CDvqY2RGrNs+4vaTnUF6W+KPVF3+2+qIC9C2/Oy1Bubi50dXXx6tUriQa8uLgY9+7dg1gshlBYv45dyrMUeJ/wRli/MHTW7fwmr7Jibpm1KM8oCFX4+d19dRfBccE49K9DsNW3beJREUKAxv2GCVEEtbVNrYGzszOcnJy4Dl9FRQXMzc0REBAg9W6Mt7c3CgsLeWsnu7q6okePHti2bVu99tna67S646kJWJjghxVOuzGsmxNv25q4aESlLwXAnzyuqvfn22Ux5vbzqnd+pHm1VF+0qh+6zX0b+nboK9uDIKQNq+033JB2iR53byJDDUM4GjsiOC64xjg1rYnuaOwIQw3D5ioaIYQQohCCgoLg6+sLR0dHODk5Yf369SgoKMCkSZVrNU+YMAEdOnRAWFgYACAwMBBubm5Yu3Ythg4dioMHDyIxMRE7duyQ52G8k6oG4D/e3gim/JILVyrXw+fvzZQYoJN3T1P6opcfX6ZBOiEKiAbpTWSoaYjVH61GTlFOw9NqGMJQkwbphBBCWjdvb2/k5ORg8eLFyMzMRM+ePXHq1ClucriMjAzeo4murq7Yv38/Fi1ahIULF8La2hpHjx6VeFeV1M/cfl4IdBmF/ddjkZGbCXMdE/zboT/UmjixFFEMTe2LEkIUD52dZcBQkwbbhBBCSG0CAgIQEBAgdVtsbKxEmJeXF7y86C6vrKipqGDi+83zPjKRP+qLEtK60MRxhBBCCCHvMCt9M/QUfQYrfTOFzI8QQkjD0J10KdrYXHqEtBr02yWEtEXdjDrixzEhCpsfaThqzwh5N8nqt0t30qtRVVUFABQWFsq5JISQxigtLQWAWtcBJYQQQhQV9UUJebfJqi9Kd9KrUVZWhp6eHrf2oKamJgTV1yshhCisiooK5OTkQFNTEyo0GRIhhJB3EPVFCXl3ybIvSj3Zt5iYmAAAd3IkhLw7lJSUYG5uTh0aQggh7yzqixLy7pJVX5QG6W8RCAQwNTWFkZERXr9+Le/iEEIaQE1NjbeMEyGEEPKuob4oIe8uWfVFFWKQHh4ejjVr1iAzMxMODg7YtGkTnJycaowfHR2NkJAQ3L9/H9bW1li1ahWGDBki0zIpKyvTe62EEEIIIUQuqC9KSNsl91tOhw4dQlBQEEJDQ/Hnn3/CwcEBHh4eNT7ic/nyZYwbNw5+fn5ISkrCyJEjMXLkSNy4caOFS04IIYQQQgghhMiWgMl5jQdnZ2f06dMHmzdvBlD5wn2nTp3w1VdfYcGCBRLxvb29UVBQgBMnTnBhH3zwAXr27Ilt27bVub/c3Fzo6uri1atX0NHRkd2BEEIIIY1EbZPsUZ0SQghRJA1pl+R6J720tBTXrl2Du7s7F6akpAR3d3dcuXJFaporV67w4gOAh4dHjfFLSkqQm5vL+xBCCCGEEEIIIYpIru+kP336FOXl5TA2NuaFGxsb459//pGaJjMzU2r8zMxMqfHDwsKwZMkSiXAarBNCCFEUVW2SnB9ua1Wq6pLae0IIIYqgIW29Qkwc15yCg4MRFBTEfX/06BFsbW3RqVMnOZaKEEIIkZSXlwddXV15F6NVyMvLAwBq7wkhhCiU+rT1ch2kGxgYQFlZGVlZWbzwrKwsbo3It5mYmDQovrq6OtTV1bnvIpEIDx8+hLa2dpPXr8vNzUWnTp3w8OFDet+tkagOm47qsOmoDpuO6rBpGGPIy8uDmZmZvIvSapiZmVF7r0CoDpuO6rBpqP6ajuqwaRrS1st1kK6mpob3338fMTExGDlyJIDKieNiYmIQEBAgNY2LiwtiYmIwa9YsLuzMmTNwcXGp1z6VlJTQsWPHphadR0dHh/5Qm4jqsOmoDpuO6rDpqA4bj+6gyxa194qJ6rDpqA6bhuqv6agOG6++bb3cH3cPCgqCr68vHB0d4eTkhPXr16OgoACTJk0CAEyYMAEdOnRAWFgYACAwMBBubm5Yu3Ythg4dioMHDyIxMRE7duyQ52EQQgghhBBCCCFNJvdBure3N3JycrB48WJkZmaiZ8+eOHXqFDc5XEZGBpSU3kxC7+rqiv3792PRokVYuHAhrK2tcfToUdjb28vrEAghhBBCCCGEEJmQ+yAdAAICAmp8vD02NlYizMvLC15eXs1cqrqpq6sjNDSU9847aRiqw6ajOmw6qsOmozokrRn9fTcd1WHTUR02DdVf01EdthwBo/VeCCGEEEIIIYQQhaBUdxRCCCGEEEIIIYS0BBqkE0IIIYQQQgghCoIG6YQQQgghhBBCiIKgQTohhBBCCCGEEKIgaJDeBOHh4bC0tIRQKISzszMSEhLkXSSFEBYWhj59+kBbWxtGRkYYOXIk0tLSeHGKi4sxY8YM6OvrQyQSYcyYMcjKyuLFycjIwNChQ6GpqQkjIyPMnTsXZWVlLXkoCmPlypUQCASYNWsWF0Z1WLdHjx5h/Pjx0NfXh4aGBrp3747ExERuO2MMixcvhqmpKTQ0NODu7o7bt2/z8nj+/Dl8fHygo6MDPT09+Pn5IT8/v6UPRS7Ky8sREhICsVgMDQ0NdOnSBcuWLUP1+UapDklrR219zai9ly1q6xuH2vqmobZeQTHSKAcPHmRqampsz5497ObNm8zf35/p6emxrKwseRdN7jw8PFhERAS7ceMGS05OZkOGDGHm5uYsPz+fi/PFF1+wTp06sZiYGJaYmMg++OAD5urqym0vKytj9vb2zN3dnSUlJbGTJ08yAwMDFhwcLI9DkquEhARmaWnJevTowQIDA7lwqsPaPX/+nFlYWLCJEyey+Ph4dvfuXXb69Gl2584dLs7KlSuZrq4uO3r0KLt+/TobPnw4E4vFrKioiIvj6enJHBwc2NWrV1lcXByzsrJi48aNk8chtbjly5czfX19duLECXbv3j0WHR3NRCIR27BhAxeH6pC0ZtTW147ae9mhtr5xqK1vOmrrFRMN0hvJycmJzZgxg/teXl7OzMzMWFhYmBxLpZiys7MZAHbhwgXGGGMvX75kqqqqLDo6mouTmprKALArV64wxhg7efIkU1JSYpmZmVycrVu3Mh0dHVZSUtKyByBHeXl5zNramp05c4a5ublxDTfVYd3mz5/PPvzwwxq3V1RUMBMTE7ZmzRou7OXLl0xdXZ0dOHCAMcZYSkoKA8D++OMPLs5vv/3GBAIBe/ToUfMVXkEMHTqUTZ48mRc2evRo5uPjwxijOiStH7X1DUPtfeNQW9941NY3HbX1ioked2+E0tJSXLt2De7u7lyYkpIS3N3dceXKFTmWTDG9evUKANC+fXsAwLVr1/D69Wte/dnY2MDc3JyrvytXrqB79+4wNjbm4nh4eCA3Nxc3b95swdLL14wZMzB06FBeXQFUh/Vx7NgxODo6wsvLC0ZGRujVqxd27tzJbb937x4yMzN5dairqwtnZ2deHerp6cHR0ZGL4+7uDiUlJcTHx7fcwciJq6srYmJicOvWLQDA9evXcfHiRQwePBgA1SFp3aitbzhq7xuH2vrGo7a+6aitV0wq8i7Au+jp06coLy/nnRABwNjYGP/884+cSqWYKioqMGvWLPTt2xf29vYAgMzMTKipqUFPT48X19jYGJmZmVwcafVbta0tOHjwIP7880/88ccfEtuoDut29+5dbN26FUFBQVi4cCH++OMPzJw5E2pqavD19eXqQFodVa9DIyMj3nYVFRW0b9++TdThggULkJubCxsbGygrK6O8vBzLly+Hj48PAFAdklaN2vqGofa+caitbxpq65uO2nrFRIN00qxmzJiBGzdu4OLFi/Iuyjvl4cOHCAwMxJkzZyAUCuVdnHdSRUUFHB0dsWLFCgBAr169cOPGDWzbtg2+vr5yLt274fDhw9i3bx/2798POzs7JCcnY9asWTAzM6M6JITwUHvfcNTWNx219U1Hbb1iosfdG8HAwADKysoSs2tmZWXBxMRETqVSPAEBAThx4gTOnz+Pjh07cuEmJiYoLS3Fy5cvefGr15+JiYnU+q3a1tpdu3YN2dnZ6N27N1RUVKCiooILFy5g48aNUFFRgbGxMdVhHUxNTWFra8sL69atGzIyMgC8qYPafscmJibIzs7mbS8rK8Pz58/bRB3OnTsXCxYswNixY9G9e3d8/vnnmD17NsLCwgBQHZLWjdr6+qP2vnGorW86auubjtp6xUSD9EZQU1PD+++/j5iYGC6soqICMTExcHFxkWPJFANjDAEBAfj1119x7tw5iMVi3vb3338fqqqqvPpLS0tDRkYGV38uLi74+++/eT/4M2fOQEdHR+Jk3BoNGDAAf//9N5KTk7mPo6MjfHx8uP9THdaub9++EksB3bp1CxYWFgAAsVgMExMTXh3m5uYiPj6eV4cvX77EtWvXuDjnzp1DRUUFnJ2dW+Ao5KuwsBBKSvxmQllZGRUVFQCoDknrRm193ai9bxpq65uO2vqmo7ZeQcl75rp31cGDB5m6ujqLjIxkKSkpbOrUqUxPT483u2ZbNX36dKarq8tiY2PZkydPuE9hYSEX54svvmDm5ubs3LlzLDExkbm4uDAXFxdue9WSIoMGDWLJycns1KlTzNDQsM0sKSJN9RlfGaM6rEtCQgJTUVFhy5cvZ7dv32b79u1jmpqa7KeffuLirFy5kunp6bH//Oc/7K+//mIjRoyQuqRIr169WHx8PLt48SKztrZuM0uK+Pr6sg4dOnDLshw5coQZGBiwefPmcXGoDklrRm197ai9lz1q6xuG2vqmo7ZeMdEgvQk2bdrEzM3NmZqaGnNycmJXr16Vd5EUAgCpn4iICC5OUVER+/LLL1m7du2YpqYmGzVqFHvy5Akvn/v377PBgwczDQ0NZmBgwL7++mv2+vXrFj4axfF2w011WLfjx48ze3t7pq6uzmxsbNiOHTt42ysqKlhISAgzNjZm6urqbMCAASwtLY0X59mzZ2zcuHFMJBIxHR0dNmnSJJaXl9eShyE3ubm5LDAwkJmbmzOhUMg6d+7MvvnmG96yPlSHpLWjtr5m1N7LHrX1DUdtfdNQW6+YBIwxJp97+IQQQgghhBBCCKmO3kknhBBCCCGEEEIUBA3SCSGEEEIIIYQQBUGDdEIIIYQQQgghREHQIJ0QQgghhBBCCFEQNEgnhBBCCCGEEEIUBA3SCSGEEEIIIYQQBUGDdEIIIYQQQgghREHQIJ0QQgghhBBCCFEQNEgnhCiUkJAQTJ06VW77Ly0thaWlJRITE+VWBkIIIaS1SUtLg4mJCfLy8uRWhgULFuCrr76S2/4JqS8apBPSzCZOnAiBQACBQABVVVWIxWLMmzcPxcXFci/XyJEjJcJjY2MhEAjw8uXLRuUbGRnJHW9Nn/v370tNm5mZiQ0bNuCbb76RCA8MDISVlRWEQiGMjY3Rt29fbN26FYWFhfUq19q1a9GuXTup9V5YWAgdHR1s3LgRampqmDNnDubPn9/gYyeEEEIA2bRbjVVbO25paYn169c3Ou+62vdvv/22xrTBwcH46quvoK2tzYUxxrBz5064uLhAR0cHIpEIdnZ2CAwMxJ07d+pVpqysLKiqquLgwYNSt/v5+aF3794AgDlz5iAqKgp3796t/0ETIgc0SCekBXh6euLJkye4e/cu1q1bh+3btyM0NFTexWoW3t7eePLkCfdxcXGBv78/L6xTp05S0+7atQuurq6wsLDgwu7evYtevXrh999/x4oVK5CUlIQrV65g3rx5OHHiBM6ePVuvcn3++ecoKCjAkSNHJLb9/PPPKC0txfjx4wEAPj4+uHjxIm7evNmIGiCEENKWyardUkTV2/L169dDR0eHFzZnzhyp6TIyMnDixAlMnDiRC2OM4d///jdmzpyJIUOG4Pfff0dKSgp2794NoVCI7777rl5lMjY2xtChQ7Fnzx6JbQUFBTh8+DD8/PwAAAYGBvDw8MDWrVsbfvCEtCRGCGlWvr6+bMSIEbyw0aNHs169enHfy8vL2YoVK5ilpSUTCoWsR48eLDo6mpfmxo0bbOjQoUxbW5uJRCL24Ycfsjt37nDplyxZwjp06MDU1NSYg4MD++233xpcLsYYO3/+PAPAXrx4UWNaAGzLli3M09OTCYVCJhaLJcpbxc3NjQUGBtZalip2dnZs8+bNvDAPDw/WsWNHlp+fLzVNRUUF9/8XL14wPz8/ZmBgwLS1tdnHH3/MkpOTue2jR49mAwYMkFpGb29vXtjHH3/MFi1aVK9yE0IIIVVk2W6FhoYyBwcHtnfvXmZhYcF0dHSYt7c3y83NrXH/tbXjFhYWbN26dTWmreobfPvtt1yZpk2bxkpKSiTiRkREMF1d3Rrzqm7NmjXM0dGRF3bgwAEGgP3nP/+RmqZ6PTHG2M6dO5mNjQ1TV1dnXbt2ZeHh4dy2Y8eOMSUlJfbgwQOJMgqFQl5dREVFsY4dO9ar3ITIC91JJ6SF3bhxA5cvX4aamhoXFhYWhr1792Lbtm24efMmZs+ejfHjx+PChQsAgEePHuGjjz6Curo6zp07h2vXrmHy5MkoKysDAGzYsAFr167F999/j7/++gseHh4YPnw4bt++3WzHERISgjFjxuD69evw8fHB2LFjkZqa2uj8nj9/jpSUFDg6OnJhz549w++//44ZM2ZAS0tLajqBQMD938vLC9nZ2fjtt99w7do19O7dGwMGDMDz588BVD7ydu7cOTx48IBLc/fuXfz3v//lrrJXcXJyQlxcXKOPhxBCSNsj63YLANLT03H06FGcOHECJ06cwIULF7By5cpmO4aYmBikpqYiNjYWBw4cwJEjR7BkyZIm5RkXF8dr3wHgwIED6Nq1K4YPHy41TfV62rdvHxYvXozly5cjNTUVK1asQEhICKKiogAAQ4YMgbGxMSIjI3l5REREYPTo0dDT0+PCnJyc8L///a/GV+8IUQjyvkpASGvn6+vLlJWVmZaWFlNXV2cAmJKSEvv5558ZY4wVFxczTU1NdvnyZV46Pz8/Nm7cOMYYY8HBwUwsFrPS0lKp+zAzM2PLly/nhfXp04d9+eWX9SpX9Y9QKKzXnfQvvviCF+bs7MymT58uEbe+d9KTkpIYAJaRkcGFXb16lQFgR44c4cXV19fnyjtv3jzGGGNxcXFMR0eHFRcX8+J26dKFbd++nTHGWFlZGevQoQMLDQ3ltoeEhDBzc3NWXl7OS7dhwwZmaWlZZ7kJIYSQKrJut0JDQ5mmpibvzvncuXOZs7NzjWWoupP+dvuupaXFBAJBnXfS27dvzwoKCriwrVu3MpFIJNFONuROuoODA1u6dCkvzMbGhg0fPpwXFhgYyJW1Q4cOXHiXLl3Y/v37eXGXLVvGXFxcuO8LFixgYrGYuwN/584dJhAI2NmzZ3npXr16xQCw2NjYepWdEHlQkd/lAULajo8//hhbt25FQUEB1q1bBxUVFYwZMwYAcOfOHRQWFmLgwIG8NKWlpejVqxcAIDk5Gf369YOqqqpE3rm5uXj8+DH69u3LC+/bty+uX79er3JVFx8fz72bXRsXFxeJ78nJyXWmq0lRUREAQCgU1hk3ISEBFRUV8PHxQUlJCQDg+vXryM/Ph76+vkS+6enpAABlZWX4+voiMjISoaGhYIwhKioKkyZNgpIS/8EiDQ2NZp/chxBCSNvQ2HYLqJzsrfpka6ampsjOzq5zn3Fxcbx0ANC/f/860zk4OEBTU5P77uLigvz8fDx8+JA3Z0xDFBUV1at9/+abbxAQEIAjR45gxYoVACrfK09PT4efnx/8/f25uGVlZdDV1eW+T548GStXrsT58+fxySefICIiApaWlvjkk094+9DQ0AAAauOJQqNBOiEtQEtLC1ZWVgCAPXv2wMHBAbt374afnx/y8/MBAP/v//0/dOjQgZdOXV0dwJsGpTnLVeV///tfs+yrLgYGBgCAFy9ewNDQEABgZWUFgUCAtLQ0XtzOnTsD4NdLfn4+TE1NERsbK5F39cfcJk+ejLCwMJw7dw4VFRV4+PAhJk2aJJHm+fPnXDkIIYSQ+miOduvtC/QCgQAVFRV1lkUsFvPyAQAVFfl0/Q0MDPDixQtemLW1tUQ9GRoawtDQEEZGRlxYVT9p586dcHZ25sVXVlbm5devXz9ERESgf//+2Lt3L/z9/XmPzQPgXiWgNp4oMnonnZAWpqSkhIULF2LRokUoKiqCra0t1NXVkZGRASsrK96nahb0Hj16IC4uDq9fv5bIT0dHB2ZmZrh06RIv/NKlS7C1tW2247h69arE927dujU6vy5dukBHRwcpKSlcmL6+PgYOHIjNmzejoKCg1vS9e/dGZmYmVFRUJOqx6gJA1X7c3NywZ88eREREwN3dXeqdgRs3bnBPMhBCCCH10RztVku7fv0693QbUNm+i0SiGldmqY9evXrx2ncAGDduHNLS0vCf//yn1rTGxsYwMzPD3bt3JepJLBbz4vr5+eGXX37BL7/8gkePHvFmk69y48YNqKqqws7OrtHHQ0hzo0E6IXLg5eUFZWVlhIeHQ1tbG3PmzMHs2bMRFRWF9PR0/Pnnn9i0aRM3IUpAQAByc3MxduxYJCYm4vbt2/jxxx+5K9Bz587FqlWrcOjQIaSlpWHBggVITk5GYGBgsx1DdHQ09uzZg1u3biE0NBQJCQkICAhodH5KSkpwd3fHxYsXeeFbtmxBWVkZHB0dcejQIaSmpiItLQ0//fQT/vnnH+4quru7O1xcXDBy5Ej8/vvvuH//Pi5fvoxvvvkGiYmJvDz9/Pxw5MgR/PrrrxITxlWJi4vDoEGDGn08hBBC2qbmaLdaUmlpKfz8/JCSkoKTJ08iNDQUAQEBEq+FNYSHhweuXLmC8vJyLmzs2LH49NNPMXbsWCxduhTx8fG4f/8+Lly4gEOHDvHuki9ZsgRhYWHYuHEjbt26hb///hsRERH44YcfePvx8vKCqqoqpk2bhkGDBkm9sBAXF4d+/fo121OKhMiEvF+KJ6S1q2mps7CwMGZoaMjy8/NZRUUFW79+PevatStTVVVlhoaGzMPDg124cIGLf/36dTZo0CCmqanJtLW1Wb9+/Vh6ejpjrHIJtm+//ZZ16NCBqaqqtsgSbOHh4WzgwIFMXV2dWVpaskOHDkmN25Al2E6ePMk6dOggMTnN48ePWUBAABOLxUxVVZWJRCLm5OTE1qxZw5vcJjc3l3311VfMzMyMqaqqsk6dOjEfHx/eZHSMMVZYWMh0dXVZ+/btJSbsYYyxy5cvMz09PVZYWFivchNCCCHVyardqlqCrbp169YxCwuLGvctiyXYFi9ezPT19ZlIJGL+/v5S28qGTBz3+vVrZmZmxk6dOsULLy8vZ9u2bWPOzs5MS0uLqampsc6dOzN/f3+WkpLCi7tv3z7Ws2dPpqamxtq1a8c++ugjiQn6GGNs6tSpDAA7fPiw1LJ07dqVHThwoF7lJkReBIwxJt/LBISQd41AIMCvv/6KkSNHyjRfxhicnZ0xe/ZsjBs3TqZ5N4S3tzccHBywcOFCuZWBEEIIaWkTJ07Ey5cvcfToUZnnHR4ejmPHjuH06dMyz7u+fvvtN3z99df466+/5PZ+PiH1QY+7E0IUhkAgwI4dO7j13+WhtLQU3bt3x+zZs+VWBkIIIaS1mTZtGj766CPk5eXJrQwFBQWIiIigATpReHQnnRDSYM11J50QQggh8tOcd9IJIfVHg3RCCCGEEEIIIURB0OPuhBBCCCGEEEKIgqBBOiGEEEIIIYQQoiBokE4IIYQQQgghhCgIGqQTQgghhBBCCCEKggbphBBCCCGEEEKIgqBBOiGEEEIIIYQQoiBokE4IIYQQQgghhCgIGqQTQgghhBBCCCEK4v8D13zTcdcpTTQAAAAASUVORK5CYII=\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 DP>{dp_cut}, AP>1/13\")\n",
    "ax[1].set(xlabel=r\"Gen H pT (GeV)\", ylabel=r\"Matching purity\", title=f\"Boosted DP>{dp_cut}, AP>1/13\")\n",
    "ax[0].legend()\n",
    "ax[1].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "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": 31,
   "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": 32,
   "id": "65661bca-ad89-47c1-84f5-91b60ad4a8c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14fb53700>"
      ]
     },
     "execution_count": 32,
     "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 DP>{dp_cut}, AP>1/13\")\n",
    "ax[1].set(xlabel=r\"Gen H pT (GeV)\", ylabel=r\"Matching purity\", title=f\"Resolved DP>{dp_cut}, AP>1/13\")\n",
    "ax[0].legend()\n",
    "ax[1].legend()"
   ]
  },
  {
   "cell_type": "code",
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