tutorials/T02-DataLoading.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "011bd23a-b89e-4a15-a604-680d00c7b867",
   "metadata": {},
   "source": [
    "# Tutorial 02 - Data Loading\n",
    "\n",
    "This tutorial demonstrates how to load training data for supervised and weakly trainings from the prepared datasets using the paws API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "114f28d3-8557-42c8-ba58-fc7695b365c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from paws.components import DataLoader\n",
    "\n",
    "datadir = \"datasets\"\n",
    "\n",
    "# here we select variables with the index 3, 5 and 6 from the feature array in the dataset by specifying variables=\"3,3,6\";\n",
    "# they correspond to the jet mass, tau12 and tau23 variables respectively\n",
    "# for more details of the parameters, use help(DataLoader.__init__)\n",
    "data_loader = DataLoader(datadir=datadir, feature_level=\"high_level\", decay_modes='qq,qqq',\n",
    "                         variables=\"3,5,6\", noise_dimension=0, seed=2023)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99191fa8-d81a-4b7f-a9a6-ee8e59d2854c",
   "metadata": {},
   "source": [
    "### Fully supervised datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a9b09ab-ab06-40ac-9fce-8c44be16a68d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-01 20:27:21.403402: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 38385 MB memory:  -> device: 0, name: NVIDIA A100-PCIE-40GB, pci bus id: 0000:c3:00.0, compute capability: 8.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Number of events in each dataset split:\n",
      "            train     val    test\n",
      "QCD         60700   30327   30325\n",
      "extra_QCD  306450  153208  153200\n",
      "W_qq        29100   14550   14526\n",
      "W_qqq       29688   14825   14825\n",
      "total      425938  212910  212876\n",
      "num_batch     416     208     208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-01 20:27:34.068578: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] ShuffleDatasetV3:24: Filling up shuffle buffer (this may take a while): 389245 of 425938\n",
      "2024-06-01 20:27:34.937544: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:452] Shuffle buffer filled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading dedicated dataset (cold run) : 11.632s\n",
      "Loading dedicated dataset (hot run) : 1.064s\n",
      "m1 : 0.477, m2 : 0.123\n"
     ]
    }
   ],
   "source": [
    "# supervised dataset\n",
    "datasets = data_loader.get_datasets(mass_point=[500, 100])\n",
    "\n",
    "# cache dataset\n",
    "from quickstats import timer\n",
    "with timer() as t:\n",
    "    events = list(datasets['train'])\n",
    "print(f\"Loading dedicated dataset (cold run) : {t.interval:.3f}s\")\n",
    "with timer() as t:\n",
    "    events = list(datasets['train'])\n",
    "print(f\"Loading dedicated dataset (hot run) : {t.interval:.3f}s\")\n",
    "\n",
    "event = list(datasets['train'].take(1))[0]\n",
    "assert event[0][0].shape == (1024, 2, 3)\n",
    "assert event[-1].shape == (1024, 1)\n",
    "m1, m2 = tf.reduce_mean(event[0][0][tf.reshape(event[-1] == 1, [-1])], axis=0)[:, 0]\n",
    "\n",
    "# note the mass is in unit of TeV\n",
    "print(f\"m1 : {m1:.3f}, m2 : {m2:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd6ccca1-1efb-4511-b4b4-33df526d7c85",
   "metadata": {},
   "source": [
    "More advanced usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "187a21a6-1bc5-4c80-92dd-684c415fbde5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Number of events in each dataset split:\n",
      "            train    val   test\n",
      "QCD         60700  30327  30325\n",
      "W_qq        29100  14550  14526\n",
      "W_qqq       29688  14825  14825\n",
      "total      119488  59702  59676\n",
      "num_batch      59     30     30\n",
      "Loading dedicated dataset (cold run) : 3.558s\n",
      "Loading dedicated dataset (hot run) : 3.413s\n"
     ]
    }
   ],
   "source": [
    "# custom batchsize, cache_dataset, cache_test\n",
    "datasets = data_loader.get_datasets(mass_point=[500, 100], mu=None, alpha=None,\n",
    "                                    samples=['QCD', 'W_qq', 'W_qqq'], custom_masses=None, split_index=0,\n",
    "                                    batchsize=2048, cache_dataset=False, cache_test=False)\n",
    "\n",
    "from quickstats import timer\n",
    "with timer() as t:\n",
    "    events = list(datasets['train'])\n",
    "print(f\"Loading dedicated dataset (cold run) : {t.interval:.3f}s\")\n",
    "with timer() as t:\n",
    "    events = list(datasets['train'])\n",
    "print(f\"Loading dedicated dataset (hot run) : {t.interval:.3f}s\")\n",
    "\n",
    "event = list(datasets['train'].take(1))[0]\n",
    "assert event[0][0].shape == (2048, 2, 3)\n",
    "assert event[-1].shape == (2048, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69acd281-7b1a-4c95-bc3d-a3c24ec71676",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Number of events in each dataset split:\n",
      "            train     val    test\n",
      "QCD         60700   30327   30325\n",
      "extra_QCD  306450  153208  153200\n",
      "W_qq        29100   14550   14526\n",
      "W_qqq       29688   14825   14825\n",
      "total      425938  212910  212876\n",
      "num_batch     416     208     208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-01 20:30:06.077058: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] ShuffleDatasetV3:874: Filling up shuffle buffer (this may take a while): 394570 of 425938\n",
      "2024-06-01 20:30:06.908309: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:452] Shuffle buffer filled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Number of events in each dataset split:\n",
      "            train     val    test\n",
      "QCD         60700   30327   30325\n",
      "extra_QCD  306450  153208  153200\n",
      "W_qq        29100   14550   14526\n",
      "W_qqq       29688   14825   14825\n",
      "total      425938  212910  212876\n",
      "num_batch     416     208     208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-01 20:30:17.984194: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] ShuffleDatasetV3:1154: Filling up shuffle buffer (this may take a while): 375889 of 425938\n",
      "2024-06-01 20:30:19.296040: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:452] Shuffle buffer filled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Number of events in each dataset split:\n",
      "            train     val    test\n",
      "QCD         60700   30327   30325\n",
      "extra_QCD  306450  153208  153200\n",
      "W_qq        29100   14550   14526\n",
      "W_qqq       29688   14825   14825\n",
      "total      425938  212910  212876\n",
      "num_batch     416     208     208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-01 20:30:30.306607: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] ShuffleDatasetV3:1434: Filling up shuffle buffer (this may take a while): 380669 of 425938\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seed 1 vs Seed 2 :  False\n",
      "Seed 2 vs Seed 3 :  True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-01 20:30:31.545027: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:452] Shuffle buffer filled.\n"
     ]
    }
   ],
   "source": [
    "# seed test\n",
    "data_loader.seed = 2022\n",
    "datasets = data_loader.get_datasets(mass_point=[500, 100])\n",
    "event_1 = list(datasets['train'].take(1))[0]\n",
    "data_loader.seed = 2023\n",
    "datasets = data_loader.get_datasets(mass_point=[500, 100])\n",
    "event_2 = list(datasets['train'].take(1))[0]\n",
    "data_loader.seed = 2023\n",
    "datasets = data_loader.get_datasets(mass_point=[500, 100])\n",
    "event_3 = list(datasets['train'].take(1))[0]\n",
    "\n",
    "print(\"Seed 1 vs Seed 2 : \", np.array_equal(event_1[0][0].numpy(), event_2[0][0].numpy()))\n",
    "print(\"Seed 2 vs Seed 3 : \", np.array_equal(event_2[0][0].numpy(), event_3[0][0].numpy()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "177b39ed-6ca1-4cd5-8bd8-91cb508d7424",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Number of events in each dataset split:\n",
      "            train     val    test\n",
      "QCD         60700   30327   30325\n",
      "extra_QCD  306450  153208  153200\n",
      "W_qq        29100   14550   14526\n",
      "W_qqq       29688   14825   14825\n",
      "total      425938  212910  212876\n",
      "num_batch     416     208     208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-01 20:31:20.294339: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] ShuffleDatasetV3:1714: Filling up shuffle buffer (this may take a while): 399833 of 425938\n",
      "2024-06-01 20:31:20.992963: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:452] Shuffle buffer filled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Number of events in each dataset split:\n",
      "            train     val    test\n",
      "QCD         60700   30327   30325\n",
      "extra_QCD  306450  153208  153200\n",
      "W_qq        29100   14550   14526\n",
      "W_qqq       29688   14825   14825\n",
      "total      425938  212910  212876\n",
      "num_batch     416     208     208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-01 20:31:32.106901: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] ShuffleDatasetV3:1998: Filling up shuffle buffer (this may take a while): 366416 of 425938\n",
      "2024-06-01 20:31:33.846354: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:452] Shuffle buffer filled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Number of events in each dataset split:\n",
      "            train     val    test\n",
      "QCD         60700   30327   30325\n",
      "extra_QCD  306450  153208  153200\n",
      "W_qq        29100   14550   14526\n",
      "W_qqq       29688   14825   14825\n",
      "total      425938  212910  212876\n",
      "num_batch     416     208     208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-01 20:31:44.964228: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] ShuffleDatasetV3:2290: Filling up shuffle buffer (this may take a while): 363420 of 425938\n",
      "2024-06-01 20:31:46.757118: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:452] Shuffle buffer filled.\n"
     ]
    }
   ],
   "source": [
    "# noise dimension test\n",
    "data_loader.noise_dimension = 0\n",
    "datasets = data_loader.get_datasets(mass_point=[500, 100])\n",
    "event = list(datasets['train'].take(1))[0]\n",
    "assert event[0][0].shape == (1024, 2, 3)\n",
    "data_loader.noise_dimension = 2\n",
    "datasets = data_loader.get_datasets(mass_point=[500, 100])\n",
    "event = list(datasets['train'].take(1))[0]\n",
    "assert event[0][0].shape == (1024, 2, 4)\n",
    "data_loader.noise_dimension = 10\n",
    "datasets = data_loader.get_datasets(mass_point=[500, 100])\n",
    "event = list(datasets['train'].take(1))[0]\n",
    "assert event[0][0].shape == (1024, 2, 8)\n",
    "data_loader.noise_dimension = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84dbb4d1-39c6-424e-bb5d-725ec5f13418",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Number of events in each dataset split:\n",
      "            train     val    test\n",
      "QCD         60700   30327   30325\n",
      "extra_QCD  306450  153208  153200\n",
      "W_qq        29100   14550   14526\n",
      "W_qqq       29688   14825   14825\n",
      "total      425938  212910  212876\n",
      "num_batch     416     208     208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-01 20:32:05.357237: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] ShuffleDatasetV3:2578: Filling up shuffle buffer (this may take a while): 383183 of 425938\n",
      "2024-06-01 20:32:06.567621: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:452] Shuffle buffer filled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Number of events in each dataset split:\n",
      "            train     val    test\n",
      "QCD         60700   30327   30325\n",
      "extra_QCD  306450  153208  153200\n",
      "W_qq        29100   14550   14526\n",
      "W_qqq       29688   14825   14825\n",
      "total      425938  212910  212876\n",
      "num_batch     416     208     208\n"
     ]
    }
   ],
   "source": [
    "# variables test\n",
    "data_loader.variables = \"1,2,3,4\"\n",
    "datasets = data_loader.get_datasets(mass_point=[500, 100])\n",
    "event = list(datasets['train'].take(1))[0]\n",
    "assert event[0][0].shape == (1024, 2, 4)\n",
    "# using all variables\n",
    "data_loader.variables = None\n",
    "datasets = data_loader.get_datasets(mass_point=[500, 100])\n",
    "event = list(datasets['train'].take(1))[0]\n",
    "assert event[0][0].shape == (1024, 2, 7)\n",
    "data_loader.variables = \"3,5,6\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ce1b8866-6488-4392-81b4-4e83dfaea0ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Number of events in each dataset split:\n",
      "            train     val    test\n",
      "QCD         60700   30327   30325\n",
      "extra_QCD  306450  153208  153200\n",
      "W_qq        29100   14550   14526\n",
      "W_qqq       29688   14825   14825\n",
      "total      425938  212910  212876\n",
      "num_batch     416     208     208\n",
      "[INFO] Number of events in each dataset split:\n",
      "           train    val   test\n",
      "QCD        60700  30327  30325\n",
      "total      60700  30327  30325\n",
      "num_batch     60     30     30\n",
      "[INFO] Number of events in each dataset split:\n",
      "            train     val    test\n",
      "QCD         60700   30327   30325\n",
      "extra_QCD  306450  153208  153200\n",
      "total      367150  183535  183525\n",
      "num_batch     359     180     180\n",
      "[INFO] Number of events in each dataset split:\n",
      "            train     val    test\n",
      "extra_QCD  306450  153208  153200\n",
      "W_qqq       29688   14825   14825\n",
      "total      336138  168033  168025\n",
      "num_batch     329     165     165\n"
     ]
    }
   ],
   "source": [
    "# select samples\n",
    "datasets = data_loader.get_datasets(mass_point=[500, 100], samples=None)\n",
    "datasets = data_loader.get_datasets(mass_point=[500, 100], samples=['QCD'])\n",
    "datasets = data_loader.get_datasets(mass_point=[500, 100], samples=['QCD', 'extra_QCD'])\n",
    "datasets = data_loader.get_datasets(mass_point=[500, 100], samples=['extra_QCD', 'W_qqq'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3d1691f4-8963-48c9-b9c7-18d42960daab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Number of events in each dataset split:\n",
      "              train       val      test\n",
      "total      57758533  28872135  28869739\n",
      "num_batch     56405     28196     28194\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-01 20:36:03.354106: W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.\n"
     ]
    }
   ],
   "source": [
    "# parameterised dataset, two-prong decay\n",
    "data_loader = DataLoader(datadir=datadir, feature_level=\"high_level\", decay_modes='qq',\n",
    "                         variables=\"3,5,6\", noise_dimension=0, seed=2023)\n",
    "datasets = data_loader.get_datasets(mass_point=None)\n",
    "events = list(datasets['train'].take(5000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "affc28c7-6084-446e-87bd-a430bcc3e859",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = np.concatenate([d[0][0] for d in events])\n",
    "params = np.concatenate([d[0][-1] for d in events])\n",
    "labels = np.concatenate([d[-1] for d in events])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4c055bec-f6a8-477e-b8c5-112a7c62d833",
   "metadata": {},
   "outputs": [],
   "source": [
    "bkg_indices = (labels == 0).flatten()\n",
    "sig_indices = (labels == 1).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "54a49c1a-4770-435d-9b5a-b6e1566262f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mass points: 144\n",
      "Number of events for each mass point: [35909 35925 35751 35900 35582 35629 35906 35879 35406 35399 35524 35312\n",
      " 35931 35933 35909 35780 36062 36234 35804 35582 35659 35925 36143 35182\n",
      " 35703 36039 35949 35871 35958 35834 35668 35770 35440 35551 35538 35417\n",
      " 35495 36069 35897 35818 36090 36056 35842 35965 35617 35839 35923 35453\n",
      " 35027 35796 36014 35980 35670 35869 35971 35651 35738 35360 35723 35415\n",
      " 35187 35568 35810 35636 35859 35785 35460 35811 35561 35763 35759 35102\n",
      " 34595 35600 35995 35940 35810 35605 35608 35649 35958 35940 34974 35349\n",
      " 34787 35374 35729 35972 35907 35931 35644 35819 35727 35745 35792 35435\n",
      " 34401 34934 35559 35794 35446 35539 35189 35784 35595 35366 35185 34783\n",
      " 34262 35048 35505 35696 35641 35838 35856 35823 35414 35600 35640 35198\n",
      " 33982 35018 35209 35533 35489 35290 35599 35442 35584 35357 35419 35087\n",
      " 33739 34934 35099 35327 35128 35045 35183 35319 35433 35295 34934 34991]\n"
     ]
    }
   ],
   "source": [
    "unique_param, counts = np.unique(params, return_counts=True, axis=0)\n",
    "print(f\"Number of mass points: {len(unique_param)}\")\n",
    "print(f\"Number of events for each mass point: {counts}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2fb93cea-6b4f-4736-9f83-c3d8a362143b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(433887, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params[sig_indices].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9cd5456-721d-45e9-9dcd-4929b8b37153",
   "metadata": {},
   "source": [
    "### Visualize kinematic distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f749dc90-ff34-473b-92ea-2fc05facf362",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from quickstats.plots import VariableDistributionPlot\n",
    "import pandas as pd\n",
    "m1, m2 = (500, 100)\n",
    "dfs = {\n",
    "    'signal': pd.DataFrame({\n",
    "        'mj1': features[sig_indices][:, 0, 0],\n",
    "        'mj2': features[sig_indices][:, 1, 0],\n",
    "        'm1': params[sig_indices][:, 0],\n",
    "        'm2': params[sig_indices][:, 1],\n",
    "    }).query(f'(m1 == {m1}) & (m2 == {m2})'),\n",
    "    'background': pd.DataFrame({\n",
    "        'mj1': features[bkg_indices][:, 0, 0],\n",
    "        'mj2': features[bkg_indices][:, 1, 0],\n",
    "        'm1': params[bkg_indices][:, 0],\n",
    "        'm2': params[bkg_indices][:, 1],\n",
    "    }).query(f'(m1 == {m1}) & (m2 == {m2})')\n",
    "}\n",
    "plotter = VariableDistributionPlot(dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "30fbd47a-3fb1-457f-bca2-7eb0f5c49a3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Using deduced bin range (0.025, 1.105)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='$m_{j1}$ [TeV]', ylabel='Fraction of Events / 0.02'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 799.992x599.976 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter.draw(\"mj1\", xlabel='$m_{j1}$ [TeV]', bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2a24c20a-d3ad-42c5-b023-3a319aa7dca5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Using deduced bin range (-0.000, 0.774)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='$m_{j2}$ [TeV]', ylabel='Fraction of Events / 0.02'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 799.992x599.976 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter.draw(\"mj2\", xlabel='$m_{j2}$ [TeV]', bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b9a01f4-aeb0-4ed1-9017-239c0d58910a",
   "metadata": {},
   "source": [
    "### Weakly-supervised datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "01bb18b1-8f20-4c69-b9db-20cff4677cdf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(m1, m2) = (500, 100) GeV, mu = 0.1, alpha = 0 (all W->qq)\n",
      "[INFO] Number of events in each dataset split:\n",
      "                              train     val    test\n",
      "QCD + extra_QCD (label = 0)  183530   91787       0\n",
      "QCD + extra_QCD (label = 1)  183530   91787       0\n",
      "W_qq                          20392   10198   14550\n",
      "W_qqq                             0       0   14838\n",
      "QCD                               0       0   30350\n",
      "extra_QCD                         0       0  153225\n",
      "total                        387452  193772  212963\n",
      "num_batch                       379     190     208\n",
      "\n",
      "(m1, m2) = (500, 100) GeV, mu = 0.1, alpha = 1 (all W->qqq)\n",
      "[INFO] Number of events in each dataset split:\n",
      "                              train     val    test\n",
      "QCD + extra_QCD (label = 0)  183530   91787       0\n",
      "QCD + extra_QCD (label = 1)  183530   91787       0\n",
      "W_qq                              0       0   14550\n",
      "W_qqq                         20392   10198   14838\n",
      "QCD                               0       0   30350\n",
      "extra_QCD                         0       0  153225\n",
      "total                        387452  193772  212963\n",
      "num_batch                       379     190     208\n",
      "\n",
      "(m1, m2) = (500, 100) GeV, mu = 0 (all QCD), alpha = 0 \n",
      "[INFO] Number of events in each dataset split:\n",
      "                              train     val    test\n",
      "QCD + extra_QCD (label = 0)  183530   91787       0\n",
      "QCD + extra_QCD (label = 1)  183530   91787       0\n",
      "W_qq                              0       0   14550\n",
      "W_qqq                             0       0   14838\n",
      "QCD                               0       0   30350\n",
      "extra_QCD                         0       0  153225\n",
      "total                        367060  183574  212963\n",
      "num_batch                       359     180     208\n",
      "\n",
      "(m1, m2) = (500, 100) GeV, mu = 0.0005, alpha = 0.5\n",
      "[INFO] Number of events in each dataset split:\n",
      "                              train     val    test\n",
      "QCD + extra_QCD (label = 0)  183530   91787       0\n",
      "QCD + extra_QCD (label = 1)  183530   91787       0\n",
      "W_qq                             45      22   14550\n",
      "W_qqq                            45      22   14838\n",
      "QCD                               0       0   30350\n",
      "extra_QCD                         0       0  153225\n",
      "total                        367150  183618  212963\n",
      "num_batch                       359     180     208\n"
     ]
    }
   ],
   "source": [
    "# weakly dataset\n",
    "data_loader = DataLoader(datadir=datadir, feature_level=\"high_level\", decay_modes='qq,qqq',\n",
    "                         variables=\"3,5,6\", noise_dimension=0, seed=2023)\n",
    "print('\\n(m1, m2) = (500, 100) GeV, mu = 0.1, alpha = 0 (all W->qq)')\n",
    "all_ds = data_loader.get_datasets(mass_point=[500, 100], mu=0.1, alpha=0)\n",
    "print('\\n(m1, m2) = (500, 100) GeV, mu = 0.1, alpha = 1 (all W->qqq)')\n",
    "all_ds = data_loader.get_datasets(mass_point=[500, 100], mu=0.1, alpha=1)\n",
    "print('\\n(m1, m2) = (500, 100) GeV, mu = 0 (all QCD), alpha = 0 ')\n",
    "all_ds = data_loader.get_datasets(mass_point=[500, 100], mu=0, alpha=0)\n",
    "print('\\n(m1, m2) = (500, 100) GeV, mu = 0.0005, alpha = 0.5')\n",
    "all_ds = data_loader.get_datasets(mass_point=[500, 100], mu=0.0005, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "448d9c50-2d8c-4504-96e4-50f20e620a24",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Number of events in each dataset split:\n",
      "                              train     val    test\n",
      "QCD + extra_QCD (label = 0)  183530   91787       0\n",
      "QCD + extra_QCD (label = 1)  183530   91787       0\n",
      "W_qq                              0       0   14550\n",
      "W_qqq                             0       0   14838\n",
      "QCD                               0       0   30350\n",
      "extra_QCD                         0       0  153225\n",
      "total                        367060  183574  212963\n",
      "num_batch                       359     180     208\n",
      "[INFO] Number of events in each dataset split:\n",
      "                              train     val    test\n",
      "QCD + extra_QCD (label = 0)  183530   91787       0\n",
      "QCD + extra_QCD (label = 1)  183530   91787       0\n",
      "W_qq                            461     230   14550\n",
      "W_qqq                           461     230   14838\n",
      "QCD                               0       0   30350\n",
      "extra_QCD                         0       0  153225\n",
      "total                        367982  184034  212963\n",
      "num_batch                       360     180     208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-01 20:40:16.598239: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] ShuffleDatasetV3:3984: Filling up shuffle buffer (this may take a while): 361962 of 367982\n",
      "2024-06-01 20:40:16.776504: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:452] Shuffle buffer filled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Number of events in each dataset split:\n",
      "                              train     val    test\n",
      "QCD + extra_QCD (label = 0)  183530   91787       0\n",
      "QCD + extra_QCD (label = 1)  183530   91787       0\n",
      "W_qq                          10196    5099   14550\n",
      "W_qqq                         10196    5099   14838\n",
      "QCD                               0       0   30350\n",
      "extra_QCD                         0       0  153225\n",
      "total                        387452  193772  212963\n",
      "num_batch                       379     190     208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-01 20:40:27.863352: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] ShuffleDatasetV3:4375: Filling up shuffle buffer (this may take a while): 362352 of 387452\n",
      "2024-06-01 20:40:28.432261: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:452] Shuffle buffer filled.\n"
     ]
    }
   ],
   "source": [
    "# check distributions at various signal injections\n",
    "def get_features_df(ds):\n",
    "    X = np.concatenate([d[0][0] for d in ds])\n",
    "    Y = np.concatenate([d[-1] for d in ds]).flatten()\n",
    "    X_0 = X[(Y == 0)]\n",
    "    X_1 = X[(Y == 1)]\n",
    "    df_0 = pd.DataFrame({\n",
    "        'mj1': X_0[:, 0, 0],\n",
    "        'mj2': X_0[:, 1, 0]\n",
    "    })\n",
    "    df_1 = pd.DataFrame({\n",
    "        'mj1': X_1[:, 0, 0],\n",
    "        'mj2': X_1[:, 1, 0]\n",
    "    })\n",
    "    return df_0, df_1\n",
    "dfs_0 = {}\n",
    "dfs_1 = {}\n",
    "all_ds = data_loader.get_datasets(mass_point=[500, 100], mu=0, alpha=0.5)\n",
    "dfs_0['mu_0'], dfs_1['mu_0'] = get_features_df(list(all_ds['train']))\n",
    "all_ds = data_loader.get_datasets(mass_point=[500, 100], mu=0.005, alpha=0.5)\n",
    "dfs_0['mu_0p005'], dfs_1['mu_0p005'] = get_features_df(list(all_ds['train']))\n",
    "all_ds = data_loader.get_datasets(mass_point=[500, 100], mu=0.1, alpha=0.5)\n",
    "dfs_0['mu_0p1'], dfs_1['mu_0p1'] = get_features_df(list(all_ds['train']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9349d5ed-2918-493a-9d5b-e19e1e82e791",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from quickstats.plots import VariableDistributionPlot\n",
    "label_map = {\n",
    "    'mu_0': '$\\mu = 0$',\n",
    "    'mu_0p005': '$\\mu = 0.005$',\n",
    "    'mu_0p1': '$\\mu = 0.1$'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ae3898f5-5d9d-4b45-b3eb-795c8aaebbc0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Events with label 0:\n",
      "[INFO] Using deduced bin range (0.017, 1.173)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 799.992x599.976 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Using deduced bin range (-0.000, 0.816)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BAAAA3qTI8Ny4cWPt2rVLly5dcuqkv/zyi0wmk+rUqeNScQAAAIA3KXLO89ixY/Xmm2/qzjvv1KJFixxalnHhwoV67LHHJEkpKSmqVauW24ot65jzDAAA4N1cemAwMzNT0dHR2rFjhypVqqROnTqpY8eOlrdtVKxYUWfOnFFaWpoSExO1evVqHT58WCaTSR988IEGDx7smbsqowjPAAAA3s2l8CxdXgrypZde0pw5c3T69OkCJ83391PcfvvtmjJlijp06OBS4eUR4RkAAMC7uRye82VnZys+Pl47duzQkSNHlJmZqZycHNWuXVt16tRR3bp1FRUVpXr16rmv+nLGkZV/Jk6caHO5SAAAALjObDZr0qRJRR7jcniG6xh5BgAA8G4uvecZAAAAwP8QngEAAAAHEZ4BAAAABxGeAQAAAAcRngEAAAAHFRmet23bVlJ1AAAAAF6vyFfVBQYG6tprr1W/fv3Uv39/RUVFlWRt5Q6vqgMAAPBuLr2q7s8//9Sbb76pEydOqFu3bqpfv75Gjx6tH374gQAIAACAq47Di6RcunRJa9as0bJly7R8+XL5+fnp3nvvVf/+/dW5c2dVqMD0aXsYeQYAAPBublskpWLFiurVq5fmzZunkydP6uOPP1ZeXp4efvhh1a5dW8OHD9fXX3+t7Oxs91QOAACAEpObm6spU6aoVatWqly5ssLCwhQXF+exczhzbEREhEwmk9XPhAkTnL5XV7i8PHdeXp42bNigZcuWadmyZTp//rx69+6t/v37q0+fPu6qs1xg5BkAAHirYcOGad68eQoNDVW7du20ZcsWpaSkaPLkyYqJiXH7OZw5NiAgQPXr19ddd91V6Jp33nmnWzOn3bxmuNmmTZuMp59+2ggNDXX3qcs8SXY/EydOLO0yAQDAVWbjxo2GJCM6Otq4dOmSYRiGcenSJaNjx46GJGPbtm1uPYczxx4/ftyQZIwdO9Zt9ztx4kS7mcwWt09Uvv322zVt2jQlJye7+9TlhmEYNj9ms7m0ywMAAFeZ+fPnS5KmTZsmPz8/SZKfn5+mT58uSVqyZIlbz+HMsQcOHJAkNWrUqDi3ZpXZbLaZxezhKT8AAAAvMWnSJJlMJn377beF9jVs2FDXXXedR667efNmBQcHKzIyssD2yMhIBQcHa8WKFW49hzPH5ofnxo0bO31fnkB4BgAAuIrl5OQoKSlJzZo1s7o/PDxcKSkpbjuHs9fLD8/btm1T27ZtFRgYqGbNmumxxx7Tn3/+WfTNeYBviV8RAADAAxoOn1vaJTgsee7w0i7B4uzZs8rLy1ONGjWs7g8KCtK5c+eUlZUlf39/l8+RlZXl1PXypwLHxsaqXbt2uu+++7Rz507NnTtXK1as0NatWxUSElKMOy8eRp4BAACuYllZWZKkKlWqWN2fvz09Pd0t53D2emlpaapTp44WL16sTZs26aOPPtL27dsVFxenkydPavTo0UXen7sx8gwAAOBlrD24Zu9htvxXrBWlU6dOWr9+fYFtQUFBkqTMzEyrbTIyMiRJ1atXt3leZ86RX6ej11u+fLnV45599lktWLBAK1eu1MWLF1WpUiWb9bmTW8NzamqqgoKCLE9NAgAAlBRvmgrhKmuLzuXk5BTZZtSoUXbPGxYWVmibv7+/AgMDlZaWZrVNenq6qlatqsqVK9s8r7PncPV6+dq1a6ft27crKSlJLVq0sHu8OzgdntPS0jR//nwFBwfroYcekiTt3r1b999/v/bu3auKFStqwIABeu+992zOiwEAAIBt58+fL7TtwoULRQ5Qzpw5s9jXCw4OVmJiogzDKDCCbRiG9u7dq+DgYLeew5ljc3NzZTKZVKFC4dnGvr6Xo2y1atWcu2EXOBWeDx48qNtvv13Hjx/XP//5T0t4fuihh5SYmKjatWvL19dXCxYs0KVLl/T55597pGgAAIDy7Mr1MtLT03X69GnVrVvXZpsnn3zS7nnDwsL01FNPFdreu3dvxcXFKSEhQTfffLNle0JCglJTUzVo0CC753bmHI4em5ycrEaNGql3795WX5cXHx+vqlWr6vrrr7dbn9s4sxrLkCFDDJPJZAwePNjYuXOnYRiXVxQ0mUxGly5djOzsbOPChQtG+/btDR8fH+P333935vTlnuysWAMAAK5uZrPZkGQ0aNDAyMzMtGyfNGmSIcmoW7euzbays2KeJKNTp05W2+7atcuQZHTo0KHAin8dOnQwJBl79uyxW7sz53Dm2Ntvv92oUKGCsWLFigLXmzVrliHJmDBhgt3anGEvrzmV5Bo3bmyEhIQYeXl5lm3PP/+8UaFCBePzzz+3bFu4cKFhMpmMzz77rBgll1+EZwAAUJT88FyhQgWjQYMGxsiRI42ePXsaJpPJqF69epHh2VXjx483JBkNGzY0HnroISM0NNSQZMTExBQ69vTp08aoUaOMUaNGFfscjh77yy+/GDVr1jRMJpPRuXNnY9CgQUZkZKQhyWjTpo1x7tw5t/aDvbzm1Kvqjh8/rgYNGhSYm/LDDz9Iku644w7Ltvx5KocPH3bm9AAAAJA0a9YsNWnSRJ9++qkOHz6suXPnKioqyqPX/Pe//60ZM2aofv36Wr16tUJCQjR79mxNnjy50LGZmZmaNWuWZs2aVexzOHrszTffrJ9//ln9+vXTnj17tHTpUvn5+emll17Sjz/+6NCDhe5k+v8J2yHh4eE6duyY0tPT5evra5l7ExERoR07dliOmz9/voYPH645c+Zo6NChnqi7THLkFTITJ06U2Wz2fDEAAMDrTJo0SWazWd98843uvvvu0i6n3DKbzZo0aVKRx9iKyE6NPN9+++06f/684uLidOrUKT333HPKy8tT3759LcccP35cr7/+uiTZXHbxamdcni5j9UNwBgAA8Cyz2Wwzi9njVHh+8cUXVaVKFcXExKhOnTqaP3++qlWrpieeeEKS9P7776tRo0bau3evoqKidNtttxXvjgAAAAAv5FR4btiwobZu3aoHH3xQERER6tatm9asWaNatWpJkk6dOqUKFSqoX79++uKLLzxSMAAAAFBanJrzbE9WVhYLoxQhf86zG7scAAAAbmQvrzk18jxs2DBNnz7d5v784HzkyBENGzZMP/74ozOnBwAAALyaU+F5/vz5Wrt2rd3jUlNTNX/+fG3ZsqXYhf1dbm6upkyZolatWqly5coKCwtTXFxcsc7Vp08fPfbYYzb3V6lSRSaTyernvffe81hdAAAA8H52l+fu0qVLga/j4+MLbfu7vLw8JSYmymQyKSAgwPUKJY0YMULz5s1TaGio+vbtqy1btmjcuHG6ePGiYmJiHD5PcnKy1qxZY3OJyZMnT+rcuXNq06aN1XcpRkREeKQuAAAAlA125zxXqPC/wWmTyeTwfN1GjRrp559/Vs2aNV0qcNOmTerQoYOio6O1du1a+fn5KTs7W127dtUPP/ygbdu2qXXr1jbbG4ah5ORkbdq0SZMnT1ZSUpLlHdRX+umnn9SuXTvNmjXL8gYRd9bFnGcAAADvZi+v2R15TklJsZygYcOG6tSpkz788MMi2/j4+Cg4ONihRUHsmT9/viRp2rRp8vPzkyT5+flp+vTpat26tZYsWVJkeP7uu+/UtWtXh6514MABSZeDv6frAgAAQNnj1Ns2hg4dqpYtW+rpp5/2YEkFNWvWTJmZmTp69GihffXq1VONGjW0a9cum+1PnjypjRs3Wv7/yJEjbY48T548WS+++KL27dunpk2bur0uRp4BAAC8m8sjz383b9481ytyQk5OjpKSktSpUyer+8PDwxUfH1/kOWrXrq1+/fpJkg4ePFjksfkjz19//bX69++vAwcOqFGjRrrrrrs0adIkyxxuV+tyZUSe4A0AAFA0d8x+sMWp8CxJGRkZWrVqlfbv31/kcSaTSbGxscUuTJLOnj2rvLw81ahRw+r+oKAgnTt3zm3vl05OTpYkPffcc7r99tt10003KT4+Xm+88YZWrFihX375RVWrVi3xugAAAOAdnArP+/fv1x133KEjR47YHQF1R3jOysqSdPn1cdbkb09PT3dLSM3IyFBISIgWLFig9u3bS7r8Orpnn31Wb731liZOnKhp06a5XBejxwAAAJ7jStayN2rtVHiOiYnR4cOH1ahRIz3++OOqU6dOsQtzRFBQkCQpMzPT6v6MjAxJUvXq1d1yvYSEhELbfHx89Oqrr2rRokVasmSJpk2bVuJ1AQAAwDs4FZ43bdqkKlWq6Mcff1Tt2rU9VZOFv7+/AgMDlZaWZnV/enq6qlatqsqVK3u0jkqVKql169ZavXq1MjMzFRgY6BV1AQAAoGQ5tcJgRkaGWrVqVSLBOV9wcLASExMLDb8bhqG9e/cqODjYLdcxDEO5ubm2n6z09VXFihUt0zBKqi4AAAB4D6fCc0REhA4dOuSpWqzq3bu3UlNTC02pSEhIUGpqqnr16uWW63z//ffy9fXV6NGjC+3Lzs5WQkKCwsPD5evrW6J1AQAAwHs4FZ5ffPFFHTlyxLJASEkYMmSIJGnMmDHKzs6WdDnMjhkzRtLld0+7Q/v27RUaGqoPPvhAW7dutWzPy8tTbGysjhw5olGjRpV4XQAAACUhNzdXU6ZMUatWrVS5cmWFhYUpLi6u2Od78803FRYW5sYKvYNTc5579Oihjz/+WE8++aQ2bNig/v37KzQ0VJUqVbJ6vDs6LCIiQuPHj9fUqVMVHh6uqKgobdmyRSkpKYqJiVHz5s0txz755JOSpEmTJlke6nOUn5+f3n77bfXr10+33Xab7rrrLtWoUUO//PKL9u3bp549e2r48OHFqgsAAMDbjRgxQvPmzVNoaKj69u2rLVu2aNy4cbp48aJiYmKcOtelS5dKdLC1RBlOMJlMRoUKFSz/W9THx8fHmVPbNWPGDKNDhw5GtWrVjI4dOxqzZ88udIwkQ5KRkpJi9RwpKSmGJGP48OE2r7N161aje/fuRs2aNY3AwECjffv2xsyZM428vLxi13VlfQAAAN5k48aNhiQjOjrauHTpkmEYhnHp0iWjY8eOhiRj27ZtDp0nNTXVWLVqldG1a1dDktGkSRNPlu0R9vKaU8tzd+7c2akVW7777jsnYnz5x/LcAADAGw0bNkzz5s1TQkKCIiMjLdu3b9+u1q1ba8KECZoyZUqR58jNzbU8G5avSZMmSkpK8kjNnmIvrzkVnuEawjMAACjKpEmTZDab9c033+juu+8usK9hw4a6cOGCjh8/7vbrNmvWTJmZmTp69GihffXq1VONGjW0a9euIs9hGIaWLVtm+fpf//qXqlWrVu7Cs9PLcwMAAKD8yMnJUVJSkjp16mR1f3h4uOLj4+2ex2QyqV+/fpavx44d664SvUqxwnNeXp6++OILffPNNzp27JiOHTum7du3a+PGjTp79qy6desmHx8fd9cKAABgU4cvPi3tEhy28d6BpV2CxdmzZ5WXl6caNWpY3R8UFKRz584pKyvLst7F1cypV9VJ0tq1axUSEqL7779fH3zwgb7++mvt3LlT0uV5MX369FGnTp106tQptxdbXphMJpsfs9lc2uUBAICrSFZWliSpSpUqVvfnb09PTy+xmjzNbDbbzGL2OBWed+7cqf79+ys9PV1ms1m7d+9Wly5dLPv79u2r3r17a/PmzRo8eLDzd3KVMAzD5ofwDAAArM23tffMVFGDc/mfzp07F2qX/3rfzMxMq+fNyMiQJFWvXt3Ju/BeZrPZZhazx6lpG2+++ab++usvLVmyRPfee6+ky+9HzhcSEqIvv/xS3bt31zfffKMdO3bopptucu5uAAAAisGbpkK4Kn8Btr/Lyckpss3fF3OzxdoaHP7+/goMDFRaWprVNunp6apataoqV65s9/xXA6fCc3x8vEJCQizB2ZYRI0bo22+/JTwDAAAUw/nz5wttu3DhQoFByyvNnDmz2NcLDg5WYmKiDMMoMHXBMAzt3btXwcHBxT53eeNUeE5NTVWzZs3sHhcQECDpf8P8AAAAcFxycnKBr9PT03X69GnVrVvXZpv8lZaLEhYWpqeeeqrQ9t69eysuLk4JCQm6+eabLdsTEhKUmpqqQYMGOVF9+eZUeG7durV+/vlnnT17VlWrVrV53M8//yyTyaRWrVq5XCAAAMDV5t1339WoUaMsD+s5Mqo8a9Ysu8d06tTJangeMmSI4uLiNGbMGK1bt05+fn7Kzs7WmDFjJElDhw518g7KL6fCc+/evbVu3ToNGTJEn376qdW5Lzt27NBrr72mmjVrqk2bNm4rFAAA4Gpx+PBhtWjRQj179tTBgwe1evVquw/subIIW0REhMaPH6+pU6cqPDxcUVFR2rJli1JSUhQTE6PmzZsXOD4tLU0TJ06U5Np0kbLIqbdtPPXUU+rRo4e+/PJLNW3aVP/617904MABSdKrr76qhx56SG3bttX58+f10Ucf2XzlCQAAAGybNWuWmjRpok8//VSHDx/W3LlzFRUV5dFr/vvf/9aMGTNUv359rV69WiEhIZo9e7YmT55c6NjMzEzNmjXLodHu8sbp5bmzsrL05ptvKi4uzuqc5ptuukmvvvqq7rzzTrcVWV6wPDcAAChKUctzo2S4fXluf39/xcTE6KmnntKvv/6qpKQknTp1Sg0bNlSTJk14uwYAAADKLafC8549e3TDDTdIkgIDA9W+fXu1b9/eI4UBAAAA3sapOc8tWrRQ27ZtNWvWLJbfBgAAwFXHqTnPjRo1UkpKikwmk3x9fdWjRw/94x//UK9evYp8aTcuY84zAACAd7OX15waeT5w4IC2bt2qsWPHqn79+lq+fLn69++vunXratSoUfr5559dr/gqUNSa82azubTLAwAAKNfMZrPNLGaP02/b+Ltt27Zp0aJFWrp0qZKTk2UymRQWFqbBgwfrkUceUUhISHFPXS4x8gwAAODd7OU1l8Lz3/3yyy9avHixFi1apMOHD6tChQrKzs52x6nLDcIzAACAd3PrtA1bzp8/r4MHD+ro0aPKyMiQYRjKy8tzx6kBAAAAr+H0e57zpaWlaeXKlfriiy+0Zs0aXbx4UYZhKDg4WIMHD9b999/vzjoBAACAUudUeD569Ki+/PJLffHFF/rhhx+Um5srwzBUp04d9e/fX/fff7/at2/v0GRrAAAAoKxxas6zj4+PpMtzQGrVqqV+/frp/vvvV3R0NIHZAcx5BgAA8G5uXZ67Ro0alsDcuXNnVajglinTAAAAQJng1Mhzbm6uZfQZzmPkGQAAwLu59LaNjRs3avfu3ZavHQ3O06ZN07BhwxytEQAAACgTigzP0dHRev75563ua9y4sc2AvHbtWn344YcuFwcAAAB4E7uTlm0NWScnJ+vEiRNuLwgAAADwVjzxVwpsraVuMplkNptLuzwAAIByzWw228xi9hR7kRQUHw8MAgAAlB6z2WxzwNJegGbkGQAAAHAQ4RkAAABwEOEZAAAAcBDhGQAAAHCQ3QcGs7KydPjwYaf2ZWVluV4ZAAAA4GWKXJ67QoUKDr2yw5bc3Nxity2PWJ4bAADAu9nLa0WOPDdo0MCl8AwAAACUJ0WOPMO9GHkGAADwbvbyGg8MAgAAAA4iPAMAAAAOIjwDAAAADiI8lwKTyWTzY2uddQAAALiH2Wy2mcXs4YHBEsQDgwAAAN6NBwYBAAAANyE8AwAAAA4qMjy/8cYb+uyzzyxfv/zyywW+BgAAAK4mRc559vf31w033KBt27ZJurxcd48ePfTVV1+VWIHlCXOeAQAAvJvLy3Pv2LFDvXv3Vu3atSVJO3fu1LBhwxy68Ny5c52t16rc3Fy9+uqrWrRokZKSklSvXj09/vjjGjt2rNPn6tOnj+rUqaM5c+ZY3Z+cnKzJkydr7dq1On36tJo2baoePXooNjZW/v7+BY6NiIjQnj17rJ5n/Pjx+ve//+10fQAAAPBeRY48L1q0SP/4xz+UnZ19+WCTyeFRU5PJpNzcXLcUOWzYMM2bN0+hoaFq166dtmzZopSUFE2ePFkxMTEOnyc5OVnNmzfXoEGDrIbnpKQktW3bVhkZGWrVqpVuvPFGbd++Xbt27VJERIS2bt1aIEAHBASofv36uuuuuwqd684771SfPn0KbGPkGQAAwLu5NPL8wAMPqHPnzvr999+Vm5urzp0769Zbb9Wrr77q/kpt2LRpk+bNm6fo6GitXbtWfn5+ys7OVteuXRUbG6vu3burdevWNtsbhqHk5GRt2rRJkydP1sWLF20eO3HiRJ05c0bTp0/X6NGjLe1feOEFTZ06VS+//LKmTJkiSTpx4oTOnz+vPn366PXXX3fvTQMAAMArFRmeJal27dqWKRvR0dGKiopSx44dPV5Yvvnz50uSpk2bJj8/P0mSn5+fpk+frtatW2vJkiVFhufvvvtOXbt2tXsdwzC0evVqhYSEWIKzdPm3j8mTJ2vevHlavXq1JTwfOHBAktSoUaNi3xsAAADKFrvh+e/Wr1/vqTps2rx5s4KDgxUZGVlge2RkpIKDg7VixQpLoLXmxhtv1JIlSyRJJ0+e1MiRI60el56eroyMDKtTMHx8fFS/fn3t3bvXsi0/PDdu3NjpewIAAEDZ5FR4znf+/Hm9++672rp1qw4cOKC0tDSFhoaqSZMmGjRokG677Ta3FJeTk6OkpCR16tTJ6v7w8HDFx8cXeY7atWurX79+kqSDBw/aPC4gIEDff/+96tatW2jf6dOn9euvvxYYZc4Pz9u2bVNsbKwSExNVr149dejQQVOmTFGtWrXs3R4AAADKGKfD85dffqnHH39cp06dKjCRev/+/Vq3bp3ef/999enTR59//rkqVarkUnFnz55VXl6eatSoYXV/UFCQzp07p6ysrEJvwnBWpUqVFB0dXWj7xYsX9dhjjyk3N1cPP/ywZXtycrIkKTY2Vu3atdN9992nnTt3au7cuVqxYoW2bt2qkJAQq9dyZN10W3jYEAAAoGiuZC17nFphcO/evRo0aJDS09M1ZMgQbdy4UcePH9f58+e1b98+vffee2rUqJFWrFihJ5980uXisrKyJElVqlSxuj9/e3p6usvXsmbfvn2688479cUXX6hly5YFXo2XlpamOnXqaPHixdq0aZM++ugjbd++XXFxcTp58mSBedMAAAAoH5waeZ48ebLOnz+vOXPmaOjQoQX2hYWFKSwsTPfff7+ioqL0wQcf6Pnnn3dpTnBQUJAkKTMz0+r+jIwMSVL16tWLfQ1rLl26JLPZrLi4OGVnZ6tLly5asGBBgRC/fPlyq22fffZZLViwQCtXrtTFixetjr4zegwAAOA5rmQte6PWTo08JyQk6LrrrisUnP+uWrVqGjNmjAzDsKxMWFz+/v4KDAxUWlqa1f3p6emqWrWqKleu7NJ1/i4pKUlRUVH697//rcDAQL3zzjtas2aN6tSp4/A52rVrp9zcXCUlJbmtLgAAAJQ+p0aejx07pubNm9s9rkGDBpKk48ePF6+qvwkODlZiYqIMwyjwm4BhGNq7d6+Cg4Ndvka+48ePq2vXrjp06JAGDhyo6dOnq2bNmlaPzc3NlclkUoUKhX//8PW93K3VqlVzW20AAAAofU6NPLds2VK//vqr/vrrryKP27hxo0wmk1q2bOlScZLUu3dvpaamKiEhocD2hIQEpaamqlevXi5fI9/IkSN16NAhTZs2TZ988onN4JycnCxfX1/17dvX6v74+HhVrVpV119/vdtqAwAAQOlzKjzfeeedunDhgvr166czZ85YPWb58uV6/fXXVb16dbVp08blAocMGSJJGjNmjGWZ8OzsbI0ZM0aSipxC4oxjx45p5cqV6tixo55++ukij23YsKFuv/12rVq1SitXriywb/bs2frpp5/0xBNPuKUuAAAAeA+npm1MmDBBq1ev1tq1a3X99dfrvvvuU1hYmAIDA3XkyBH98MMP+uWXX2QYhubPn6/AwECXC4yIiND48eM1depUhYeHKyoqSlu2bFFKSopiYmIKTCPJf8PHpEmTLA8bOio+Pl65ubk6d+6czTeFVK9eXS+//LIk6a233tLdd9+te+65R506dVL9+vW1a9cubd++XW3atFFsbGwx7xgAAADeymQ4+Thienq6Jk+erHfeeUcXLly4fBKTyfJUY8uWLTVlyhT16NHDrYXOnDlTixcv1s6dO9WqVSs98MADhVYLzJ8TnZKSYpl3/XcHDx5UaGiohg8frjlz5hTYN336dMtoti3169fX4cOHLV/v379f48eP18aNG5WZmakWLVqoV69eGj9+vGUpcWv18bYNAAAA72QvrzkdnvOdOnVKO3fu1IEDB3TmzBmFhoaqcePGhZbRxv8Qnu3r8MWnTrfZeO9AD1QCAACuRvbyWrGW55aka6+9VnfccYfuuOOO4p4CAAAAKFOKHZ4BT3JkNLk4o9QAAACucOptGwAAAMDVjPBcCkwmk82P2Wwu7fIAAADKNbPZbDOL2cO0jVLAA4MAAAClx2w22xywtBegGXkGAAAAHER4BgAAABxEeAYAAAAc5HR4fuONN9S0aVP5+PgU+fH1ZTo1AAAAyhenEu57772ncePGSZICAgJUq1YtjxQFAAAAeCOnwvM777wjk8mk1157Tc8884xDr/MAAAAAygunwvMff/yhsLAwPfvss56qBwAAAPBaTs15rlatmqpXr+6hUgAAAADv5tTIc9++fTV//nz9+eefzHeGwzp88WlplwAAAOAWTo08T506VTfccIPuuece/fHHH56qCQAAAPBKTo08x8XF6Y477tC7776rG264QS1btlRoaKgqVapU6FiTyaRPP2XEEf+z8d6BpV0CAACAS5wKzy+//LJMJpMMw5AkJSQkKCEhweqxhGfbinpLycSJE22utQ4AAADXmc1mTZo0qVhtnQrP33//fbEugoLyf/kAAABAyTObzTYHK+29itmp8NyxY0dnDgcAAADKFZfW0L5w4YKSk5OVlpam0NBQ1atXz111AQAAAF7Hqbdt5Fu8eLFatmypgIAAtWjRQh07dlRISIgqV66sf/7znzp27Ji76wQAAABKndMjz4MGDdLChQtlGIYaN26sJk2aqFq1ajp8+LB27typ999/XwsXLtSWLVsUERHhiZoBAACAUuFUeP7www+1YMECNWnSRB988IE6dOhQYH9GRoZefvllvfnmm7r//vu1Y8cO+fn5ubVgAAAAoLQ4NW1j0aJF8vHx0ddff10oOEuXl++Oi4vTww8/rL1799p8jR0AAABQFjkVnrdv364WLVqocePGRR7Xv39/GYZBeAYAAEC54lR4rlSpkrKysuwed/HiRZlMJvn7+xe7MAAAAMDbOBWeIyMjlZSUpM2bN9s8xjAMffjhh5bjAQAAgPLCqfD8r3/9S5J0zz33aO7cubp06VKB/YmJiRowYIDWrFmj22+/XS1btnRfpQAAAEApcyo833333Zo0aZLS09P1+OOPq2rVqmrSpIkiIyNVq1YttWjRQsuWLdP111+vzz//3O7yhgAAAEBZ4vQiKbGxsUpISNC9996r2rVrKzk5Wb/++quysrLUokULxcXFad++fQoODvZEveWCyWSy+bG1zjoAAADcw2w228xi9pgMwzBcufilS5d09uxZXXvtta6c5qqQ/wfiYpeXOR2++FSStPHegWXivAAA4OplL68Va3nuv6tYsSLBGQAAAFeFIlcY/OSTTyRJ9957r6pUqWL52lGDBg0qfmUAAACAlyly2kaFChVkMpm0d+9ehYWFWb62xzAMmUwm5ebmurXYso5pG56ZtuEspnkAAABb7OW1IkeeJ06cKJPJZJmWkf81AAAAcDVy+YFBOI6R59Id8fWWOgAAgPdy6wODw4YN0/Tp0+0ed+TIEQ0bNkw//vijM6cHAAAAvJpT4Xn+/Plau3at3eNSU1M1f/58bdmypdiFAQAAAN6myDnPktSlS5cCX8fHxxfa9nd5eXlKTEyUyWRSQECA6xUCAAAAXsJueF6/fr3l/5tMJp0+fbrANlsaNWqkAQMGuFYdAAAA4EXshueUlBRJlydNN2zYUJ06ddKHH35YZBsfHx8FBwfzZg4AAACUK3bDc0hIiOX/DxkyRC1btiywDQAAALhaOPXA4Lx58/T0009Lkg4ePKiMjAzLvry8PK1YsUKnTp1ya4Hlkclksvkxm82lXR4AAEC5ZjabbWYxe5wKz5K0efNmNWvWTI0aNdLOnTst23NyctS3b1/VrVtXo0ePVlZWlrOnvmoYhmHzQ3gGAADwLLPZbDOL2eNUeP7tt9/UqVMn/f7772rfvr2Cg4Mt+3x9fTV69GjVrl1bs2bN0r/+9S/n7wQAAADwYk6F5//7v/9TXl6ePvjgA23YsEGNGzf+34kqVNC0adO0Z88e3Xjjjfrkk0+0e/dutxcMAAAAlBanwnNCQoLCwsL06KOP2jymevXqGj9+vAzD0LZt21wuEAAAAPAWToXnzMxM1ahRw+5x1atXlySdOXOmODUBAAAAXsmp8NymTRvt2LFDaWlpRR63bt06mUwmRUZGulRcvtzcXE2ZMkWtWrVS5cqVFRYWpri4uGKdq0+fPnrsscds7s/KytK4cePUvHlzVa5cWTfeeKM+/vhjj9cFAAAA7+dUeB4wYIAuXryoHj16aP/+/YX2G4ahDz74QNOmTVNwcLDatGnjliJHjBihmJgYnT17Vn379lVOTo7GjRunV155xanzJCcna82aNTb3G4ahPn36KC4uThUqVNA999yjU6dOafDgwfr00089VhcAAADKBruLpPzdY489pg0bNmjhwoUKDw/XbbfdpiZNmqhGjRo6duyYfv75Zx08eFA+Pj76z3/+I39/f5cL3LRpk+bNm6fo6GitXbtWfn5+ys7OVteuXRUbG6vu3burdevWNtsbhqHk5GRt2rRJkydP1sWLF20eu2DBAv33v//Vww8/rAULFkiSMjIyFBUVpWHDhunOO+9UnTp13FIXAAAAyh6n3/P88ccfa/78+QoJCdGmTZv04Ycfatq0aVq0aJFSUlLUs2dPbdu2Te3atXNLgfPnz5ckTZs2TX5+fpIkPz8/TZ8+XZK0ZMmSItt/9913aty4sQYPHqykpCS716pQoYLeeusty7Zq1appypQpunTpkpYvX+62ugAAAFD2mAxH3gZthWEYOnLkiPbv368///xTDRo0UJMmTRQUFOTWAps1a6bMzEwdPXq00L569eqpRo0a2rVrl832J0+e1MaNGy3/f+TIkRo+fLjmzJlT4Li8vDwFBgbqpptu0ubNmwvs++uvv1SzZk3deeed+uqrr4pdV/6qNcXs8jKrwxeXp7xsvHcgdQAAAK9mL685NW3jyhNff/31uv766wvtS01N1erVq3XHHXeoQYMGxb2EcnJylJSUpE6dOlndHx4ervj4+CLPUbt2bfXr10/S5SXFbUlJSdH58+fVrFmzQvuqVKmievXqKSUlxS11ObL0oy1XW/AGAABwlitZyx6np204YuHChRo+fLjLUxfOnj2rvLw8m6/HCwoK0rlz59yyFHj+a/WKutapU6dKvC4AAAB4D6dHnqdNm6Z33nnH6ts2rtS8efNiFZUvP3xWqVLF6v787enp6S4/nOjItfJf0edqXYweAwAAeI4rWcveqLVT4fmzzz7Ts88+K0m67rrrlJqaqry8PAUHB6tixYpKS0vT2bNnFRQUpMGDB+uuu+4qduGSLPOnMzMzre7PyMiQ9L9FWTx9rfyR5pKsCwAAAN7DqWkb7733nkwmk1atWqWjR48qISFBPj4+mjVrlg4cOKC0tDS9+eabOnfunO655x75+hZ7SrUkyd/fX4GBgTYXZUlPT1fVqlVVuXJll64jyfIKuqKuVbdu3RKvCwAAAN7DqfCclJSkZs2aqXv37pKkli1bKiIiQuvXr798sgoV9PTTT6tXr14aMmSIsrOzXS4wODhYiYmJhYbfDcPQ3r17FRwc7PI1pMujyddcc4327NlTaN/Zs2d19OjRAtcqqboAAADgPZwKz6dPn7aMvua74YYb9OuvvxbY1q9fPx08eFC//PKLywX27t1bqampSkhIKLA9ISFBqamp6tWrl8vXyNerVy/98ssvOnnyZIHta9euVU5OToFrlWRdAAAA8A5Ohedrr71Whw8fLrCtadOm2rlzp3JycizbqlWrJsMwtHXrVpcLHDJkiCRpzJgxlpHs7OxsjRkzRpI0dOhQl6/x92vl5eVp9OjRlm0ZGRmKiYnRNddco4cffrhU6gIAAIB3cCo8d+zYUfv37y+wwEjbtm2VkZGhZcuWWbatXbtWJpNJVatWdbnAiIgIjR8/Xhs3blR4eLgefvhhNW3aVBs3blRMTEyBN3o8+eSTevLJJ23ORbane/fuevjhh/X555+rRYsWeuihh9SsWTP9/vvveuuttwq8ms6ZugAAAFA+OLXC4C+//KJOnTopKytLPXr00MqVK3Xp0iXVqlVLFStW1OjRo/XXX39pxowZys3NVWJioho1auSWQmfOnKnFixdr586datWqlR544AGNHDmy4M38/1eLpKSkWF2c5eDBgwoNDbW6wmC+3NxcTZ48WatWrVJSUpJuueUWDR8+XPfff3+x67qyvqvtVXXesrKft9QBAAC8l7285vTy3Fu2bNH06dNVsWJFffLJJ5KkpUuX6oEHHlBeXp6kyw8Ovvrqq5bX2uEywjPhGQAAeDe3L8/drl07tWvXrsC2fv366ffff9fGjRt1/vx5dejQQS1atChGuQAAAID3cio8r1y5UrVq1VJUVFShfY0aNXLbFA0AAADAGzkVnu+//361aNHCLW/RAAAAAMoap9620aFDByUmJur06dOequeqYDKZbH7MZnNplwcAAFCumc1mm1nMHqfC85w5c3Tttddq8ODBSk1NLXbBVzvDMGx+CM8AAACeZTabbWYxe5yatjFv3jz16NFDc+bMUaNGjRQeHq4GDRqocuXKhY41mUz69NNPnTk9AAAA4NWcCs8vv/yyTCaTDMNQVlaWduzYoR07dlg9lvAMAACA8sap8Pz99997qAwAAADA+zkVnjt27OipOgAAAACvV+QDg8OGDdP06dNLqBQAAADAuxUZnufPn6+1a9da3ffss89qzpw5HikKAAAA8EZOvaru76ZNm6Yvv/zSjaUAAAAA3q3Y4RkAAAC42jj1wCBQHnT4wvFXKG68d6AHKwEAAGUNI88AAACAgxh5xlXDmVFkZ0anAQDA1YOR51JgMplsfsxmc2mXBwAAUK6ZzWabWcweRp5LgWEYpV0CAADAVctsNtscsLQXoO2G5w0bNqhx48ZWT2xrX/7+P/74w97pAQAAgDLDbng+f/68kpOTnd7nyLA3AAAAUJYUGZ5TUlJKqAwAAADA+xUZnkNCQkqqDgAAAMDr8bYNAAAAwEGEZwAAAMBBhGcAAADAQYRnAAAAwEGEZwAAAMBBhGcAAADAQYTnUmBrLXWTyWRzqUgAAAC4h9lstpnF7LG7wiDczzCM0i4BAADgqmU2m20OWNoL0Iw8AwAAAA4iPAMAAAAOIjwDAAAADiI8AwAAAA4iPAMAAAAOIjwDAAAADiI8AwAAAA4iPAMAAAAOIjwDAAAADiI8AwAAAA4iPAMAAAAOIjyXApPJZPNja511AAAAuIfZbLaZxezxLYH6cAXDMEq7BAAAgKuW2Wy2OWBpL0Az8gwAAAA4iPAMAAAAOIjwDAAAADiI8AwAAAA4qEyE59zcXE2ZMkWtWrVS5cqVFRYWpri4OIfbnz59Wo8//riaNGmigIAAtW3bVqtWrSpwzIYNG4p8C0b+Z8OGDZY2ERERNo+bMGGC2+4fAAAA3qFMvG1jxIgRmjdvnkJDQ9W3b19t2bJF48aN08WLFxUTE1Nk26ysLEVHR2v37t1q06aNIiMjtW7dOt1333365ptv1LlzZ0lSvXr1NGrUKJvnWbVqlY4cOaLrr7/esi0lJUVNmzbVXXfdVej4du3aFfNuAQAA4K28Pjxv2rRJ8+bNU3R0tNauXSs/Pz9lZ2era9euio2NVffu3dW6dWub7d944w3t3r1bL7zwgl555RVJ0qFDh9S2bVsNGDBAx48fl5+fn5o0aaKZM2daPce+ffv0/vvv6/nnn1ejRo0kSSdOnND58+fVp08fvf766+6/cQAAAHgdrw/P8+fPlyRNmzZNfn5+kiQ/Pz9Nnz5drVu31pIlS4oMz/Pnz1dQUJBeeukly7aQkBA9//zzeuaZZ7R+/XqrI8d/98QTTyg0NFSxsbGWbQcOHJAkS5i+2nT44tPSLgEAAKDEeX143rx5s4KDgxUZGVlge2RkpIKDg7VixQpNmTLFatsTJ07owIEDevjhh+Xj41NgX8+ePfXMM89oxYoVRYbnhQsXat26dVq/fr0qVapk2Z4fnhs3blzcW0MZ4MwvCRvvHejBSgAAgDfw6vCck5OjpKQkderUyer+8PBwxcfH22y/Z88eSVKzZs0K7QsLC1OFChWUkpJis/3Fixf13HPPqVu3boVqyA/P27ZtU2xsrBITE1WvXj116NBBU6ZMUa1atYq+uXKCwAgAAK4mXh2ez549q7y8PNWoUcPq/qCgIJ07d05ZWVny9/cvtP/MmTOSZLW9yWRSjRo1dOrUKZvXnzNnjo4ePar//Oc/hfYlJydLkmJjY9WuXTvdd9992rlzp+bOnasVK1Zo69atCgkJsXpeR9ZNt4WlvUuGM78UMIUFAADv4krWsserX1WXlZUlSapSpYrV/fnb09PTi90+LS3N6r6LFy9q6tSpuvPOO3XbbbcV2p+WlqY6depo8eLF2rRpkz766CNt375dcXFxOnnypEaPHl30zQEAAKDM8eqR56CgIElSZmam1f0ZGRmSpOrVqxe7fdOmTa3u+/jjj3X06FG9++67VvcvX77c6vZnn31WCxYs0MqVK3Xx4sUC86TzMXoMAADgOa5kLXuj1l498uzv76/AwECbo8Pp6emqWrWqKleubHV/nTp1JMlq+7y8PGVkZKhu3bpW286fP181a9bU3Xff7XTd7dq1U25urpKSkpxuCwAAAO/l1eFZkoKDg5WYmFjoNwjDMLR3714FBwcX2Vb634ODf7dv3z4ZhmG1fVJSkrZs2aIBAwZYXo93pdzcXOXl5Vnd5+t7eUC/WrVqNmsDAABA2eP14bl3795KTU1VQkJCge0JCQlKTU1Vr169bLatW7eu2rRpozVr1ignJ6fAvtWrV0uS1fYLFy6UJPXv39/qeZOTk+Xr66u+ffta3R8fH6+qVasWWI0QAAAAZZ/Xh+chQ4ZIksaMGaPs7GxJUnZ2tsaMGSNJGjp0qN32Z86c0YsvvmjZdujQIb322msKDg5Wt27dCrVZu3atfHx8FBUVZfWcDRs21O23365Vq1Zp5cqVBfbNnj1bP/30k5544gmH7xEAAABlg1c/MChJERERGj9+vKZOnarw8HBFRUVpy5YtSklJUUxMjJo3b2459sknn5QkTZo0yfKw4NChQ7V06VJNnTpV3333nUJCQrRu3TqdP39eS5YsKbR4yrlz5xQfH68bb7xRAQEBNut66623dPfdd+uee+5Rp06dVL9+fe3atUvbt29XmzZtCqxGCAAAgPLB60eeJenf//63ZsyYofr162v16tUKCQnR7NmzNXny5ALHzZo1S7NmzSrwdg1/f3+tWrVKTz/9tLKysrR+/Xq1b99eq1atsjplY9OmTcrOzrY56pzv5ptv1s8//6x+/fppz549Wrp0qfz8/PTSSy/pxx9/tPkQIwAAAMouk8F700pM/qtPykOX5y8MwgqD9AUAAOWJvbxWJkaeAQAAAG9AeAYAAAAcRHgGAAAAHER4LgUmk8nmx2w2l3Z5AAAA5ZrZbLaZxezx+lfVlUfl4YFBAACAsspsNtscsLQXoBl5BgAAABxEeAYAAAAcRHgGAAAAHER4BgAAABxEeAYAAAAcRHgGAAAAHER4BgAAABxEeAYAAAAcRHgGAAAAHER4BgAAABxEeAYAAAAcRHguBSaTyebH1jrrAAAAcA+z2Wwzi9njWwL14QqGYZR2CQAAAFcts9lsc8DSXoBm5BkAAABwEOEZAAAAcBDhGQAAAHAQc54BN+nwxacOH7vx3oEerAQAAHgKI88AAACAgxh5BlzkzCiyM6PTAADA+zDyDAAAADiI8AwAAAA4iPAMAAAAOIjwDAAAADiI8AwAAAA4iPBcCkwmk82PrXXWAQAA4B5ms9lmFrOHV9WVAsMwSrsEAACAq5bZbLY5YGkvQDPyDAAAADiI8AwAAAA4iPAMAAAAOIjwDAAAADiI8AwAAAA4iPAMAAAAOIjwDAAAADiI8AwAAAA4iPAMAAAAOIjwDAAAADiI8AwAAAA4iPBcCkwmk82PrXXWAQAA4B5ms9lmFrPHtwTqwxUMwyjtEgAAAK5aZrPZ5oClvQDNyDMAAADgIEaegVLQ4YtPnTp+470DPVQJAABwBiPPAAAAgIMYeQZKkLMjyM6OUAMAAM8qEyPPubm5mjJlilq1aqXKlSsrLCxMcXFxDrc/ffq0Hn/8cTVp0kQBAQFq27atVq1aZfXYKlWq2Hz68r333nNrXQAAAChbysTI84gRIzRv3jyFhoaqb9++2rJli8aNG6eLFy8qJiamyLZZWVmKjo7W7t271aZNG0VGRmrdunW677779M0336hz586WY0+ePKlz586pTZs2ioqKKnSuiIgIt9UFAACAssfrw/OmTZs0b948RUdHa+3atfLz81N2dra6du2q2NhYde/eXa1bt7bZ/o033tDu3bv1wgsv6JVXXpEkHTp0SG3bttWAAQN0/Phx+fn5SZIOHDggSRoyZIieeOIJj9YFAACAssfrp23Mnz9fkjRt2jRLyPXz89P06dMlSUuWLLHbPigoSC+99JJlW0hIiJ5//nmdPn1a69evt2zPD8+NGjXyeF0AAAAoe7w+PG/evFnBwcGKjIwssD0yMlLBwcFasWKFzbYnTpzQgQMH1K1bN/n4+BTY17NnT0kq0D4/PDdu3NijdQEAAKBs8urwnJOTo6SkJDVr1szq/vDwcKWkpNhsv2fPHkmy2j4sLEwVKlQo0D4/PH/99ddq2bKlqlSpopYtW2rs2LE6d+6c2+oqanluex8AAAAUzZNZy6vD89mzZ5WXl6caNWpY3R8UFKRz584pKyvL6v4zZ85IktX2JpNJNWrU0KlTpyzbkpOTJUnPPfecrr32Wt133326dOmS3njjDUVGRurs2bNuqQsAAABlk1eH5/zwWaVKFav787enp6cXu31aWprl64yMDIWEhGjdunX67rvv9PHHH2v37t0aPXq0kpKSNHHiRLfUZRhGsT8AAAAomiezlleH56CgIElSZmam1f0ZGRmSpOrVqxe7/d9HjxMSEnTw4EG1b9/ess3Hx0evvvqq6tata3kI0NW6AAAAUDZ5dXj29/dXYGBggdHhv0tPT1fVqlVVuXJlq/vr1KkjSVbb5+XlKSMjQ3Xr1rVbR6VKldS6dWsdOXJEmZmZLtcFAACAssmrw7MkBQcHKzExsdAwumEY2rt3r4KDg4tsK/3vwcG/27dvnwzDsBxjGIZyc3NtDtf7+vqqYsWK8vf3d7kuAAAAlE1eH5579+6t1NRUJSQkFNiekJCg1NRU9erVy2bbunXrqk2bNlqzZo1ycnIK7Fu9erUkWdp///338vX11ejRowudJzs7WwkJCQoPD5evr6/LdQEAAKBs8vrwPGTIEEnSmDFjlJ2dLelymB0zZowkaejQoXbbnzlzRi+++KJl26FDh/Taa68pODhY3bp1kyS1b99eoaGh+uCDD7R161bLsXl5eYqNjdWRI0c0atQot9UFAACAssfrl+eOiIjQ+PHjNXXqVIWHhysqKkpbtmxRSkqKYmJi1Lx5c8uxTz75pCRp0qRJlof6hg4dqqVLl2rq1Kn67rvvLG/TOH/+vJYsWWJZPMXPz09vv/22+vXrp9tuu0133XWXatSooV9++UX79u1Tz549NXz48GLVBQAAgPLBZJSR95/NnDlTixcv1s6dO9WqVSs98MADGjlyZIFj8l9snZKSogYNGli2Z2Vl6YUXXtC6det07Ngx3XbbbRo9erS6dOlS6Dq//PKL/u///k/x8fG6dOmS5VpPPPGE1RdnO1LXlfWVkS4vUocvPpUkbbx3YClXUr7RzwAAlCx7ea3MhOfygPAMZ9HPAACULHt5zevnPAMAAADegvAMAAAAOIjwDAAAADiI8FwKTCaTzY/ZbC7t8gAAAMo1s9lsM4vZ4/WvqiuPysMDgwAAAGWV2Wy2OWBpL0ATnoEyIP+tG47gzRwAAHgO0zYAAAAABzHyDHgxZ0aRnRmdBgAAxcPIMwAAAOAgwjMAAADgIMIzAAAA4CDCMwAAAOAgwjMAAADgIMIzAAAA4CDCMwAAAOAgwjMAAADgIMJzKTCZTDY/ttZZBwAAgHuYzWabWcweVhgsBYZhlHYJAAAAVy2z2WxzwNJegGbkGQAAAHAQ4RkAAABwEOEZAAAAcBDhGQAAAHAQ4RkAAABwEOEZAAAAcBCvqgPKmQ5ffOrwsRvvHejBSgAAKH8YeQYAAAAcxMgzUE44M4rszOg0AAD4H0aeAQAAAAcRnkuBrbXUTSaTzaUiAQAA4B5ms9lmFrOHaRulwDCM0i4BAADgqmU2m20OWNoL0Iw8AwAAAA4iPAMAAAAOIjwDAAAADiI8AwAAAA4iPAMAAAAOIjwDAAAADiI8AwAAAA4iPAMAAAAOIjwDAAAADiI8AwAAAA5ieW7gKtbhi0+dOn7jvQM9VAkAAGUDI8+lwGQy2fzYWmcdAAAA7mE2m21mMXtMhmEYJVAjJMsfSHno8vwRS0Yirw78eQMArhb28hojzwAAAICDCM8AAACAgwjPAAAAgIMIzwAAAICDykR4zs3N1ZQpU9SqVStVrlxZYWFhiouLc7j96dOn9fjjj6tJkyYKCAhQ27ZttWrVKqvHJicna9iwYQoJCVFAQIAiIyMVExOjrKysQsdGRETYfFJzwoQJxb5fAAAAeKcy8Z7nESNGaN68eQoNDVXfvn21ZcsWjRs3ThcvXlRMTEyRbbOyshQdHa3du3erTZs2ioyM1Lp163Tffffpm2++UefOnS3HJiUlqW3btsrIyFCrVq3UqVMnbd++XVOmTNHy5cu1detW+fv7W45PSUlR06ZNdddddxW6brt27dzXAQAAAPAKXh+eN23apHnz5ik6Olpr166Vn5+fsrOz1bVrV8XGxqp79+5q3bq1zfZvvPGGdu/erRdeeEGvvPKKJOnQoUNq27atBgwYoOPHj8vPz0+SNHHiRJ05c0bTp0/X6NGjJV1+TckLL7ygqVOn6uWXX9aUKVMkSSdOnND58+fVp08fvf766x7uBQAAAHgDr5+2MX/+fEnStGnTLCHXz89P06dPlyQtWbLEbvugoCC99NJLlm0hISF6/vnndfr0aa1fv17S5ZC8evVqhYSEWIKzdPldf5MnT1bt2rW1evVqy/YDBw5Ikho1auT6TQIAAKBM8PrwvHnzZgUHBysyMrLA9sjISAUHB2vFihU22544cUIHDhxQt27d5OPjU2Bfz549JcnSPj09XRkZGbr11lsLncfHx0f169dXUlKSZVt+eG7cuHHxbgwAAABljldP28jJyVFSUpI6depkdX94eLji4+Nttt+zZ48kqVmzZoX2hYWFqUKFCkpJSZEkBQQE6Pvvv1fdunULHXv69Gn9+uuvBUaZ88Pztm3bFBsbq8TERNWrV08dOnTQlClTVKtWLUdvEygz8lcadASrEQIAyiOvHnk+e/as8vLyVKNGDav7g4KCdO7cOatvwpCkM2fOSJLV9iaTSTVq1NCpU6ckSZUqVVJ0dLTCw8MLHHfx4kU99thjys3N1cMPP2zZnpycLEmKjY1VxYoVdd9998nf319z585VixYtdOjQIZv3ZesNHY58AAAAUDRPZi2vHnnOD8VVqlSxuj9/e3p6eoG3YDjTPi0tzeb19+3bp8cee0wbN25Uy5YtNXbsWMu+tLQ01alTR2+//bbuu+8+y/Y33nhDY8eO1ejRo/XFF1/YuUOgbHBmFNmZ0WkAAMoarw7PQUFBkqTMzEyr+zMyMiRJ1atXL3b7pk2bFtp+6dIlmc1mxcXFKTs7W126dNGCBQsKhPDly5dbPeezzz6rBQsWaOXKlbp48aIqVapU6BjDMKy2BQAAgOtcyVr2Rp+9etqGv7+/AgMDbY4Op6enq2rVqqpcubLV/XXq1JEkq+3z8vKUkZFRaI5zUlKSoqKi9O9//1uBgYF65513tGbNGsu5HNGuXTvl5uYWeMAQAAAAZZ9XjzxLUnBwsBITE2UYRoHfBAzD0N69exUcHFxkW+l/Dw7+3b59+2QYRoH2x48fV9euXXXo0CENHDhQ06dPV82aNa2eOzc3VyaTSRUqFP79w9f3crdWq1bNsZsEAABAmeDVI8+S1Lt3b6WmpiohIaHA9oSEBKWmpqpXr14229atW1dt2rTRmjVrlJOTU2Bf/jub/95+5MiROnTokKZNm6ZPPvnEZnBOTk6Wr6+v+vbta3V/fHy8qlatquuvv96RWwQAAEAZ4fXheciQIZKkMWPGKDs7W5KUnZ2tMWPGSJKGDh1qt/2ZM2f04osvWrYdOnRIr732moKDg9WtWzdJ0rFjx7Ry5Up17NhRTz/9dJHnbNiwoW6//XatWrVKK1euLLBv9uzZ+umnn/TEE084c5sAAAAoA7x+2kZERITGjx+vqVOnKjw8XFFRUdqyZYtSUlIUExOj5s2bW4598sknJUmTJk2yPCw4dOhQLV26VFOnTtV3332nkJAQrVu3TufPn9eSJUssi6fEx8crNzdX586ds5znStWrV9fLL78sSXrrrbd0991365577lGnTp1Uv3597dq1S9u3b1ebNm0UGxvryW5xO96QAAAAYJ/JKCOvfpg5c6YWL16snTt3qlWrVnrggQc0cuTIAsfkz4lOSUlRgwYNLNuzsrL0wgsvaN26dTp27Jhuu+02jR49Wl26dLEcM336dMtoti3169fX4cOHLV/v379f48eP18aNG5WZmakWLVqoV69eGj9+vGUpcWv1eWOXFzc8sxAGrpT/vcT3BgCgLLKX18pMeC4PykJ4JvDAVcX5RYzvOwCAt7CX17x+zjMAAADgLbx+zjOAsoXVCAEA5RkjzwAAAICDCM+lwGQy2fyYzebSLg8AAKBcM5vNNrOYPUzbKAXe+MAgAADA1cJsNtscsLQXoBl5BgAAABxEeAYAAAAcRHgGAAAAHER4BgAAABzEA4MASp0z73tmNUIAQGli5BkAAABwECPPAEoNqxECAMoaRp4BAAAABxGeAQAAAAcRngEAAAAHMecZQJni7Nxn3s4BAHAnRp5Lgclksvmxtc46AAAA3MNsNtvMYvaYDMMwSqBGSJY/EG/s8vzRPEbpUF7wPQ0AKA57eY2RZwAAAMBBhGcAAADAQTwwCKBcY+lvAIA7MfIMAAAAOIiRZwDlEkt/AwA8gZFnAAAAwEGEZwAAAMBBhGcAAADAQcx5BoD/jzdzAADsYeQZAAAAcBAjzwCueryZAwDgKMJzKchfM92aiRMnymw2l1wxAIqFKR4AUHaZzWZNmjSpWG0Jz6XAMIzSLgEAAOCqZTabbQ5WFjXIKRGeAcApTPEAgKsbDwwCAAAADmLkGQA8zNkRaOZIA4D3YuQZAAAAcBAjzwDgIc6OIDNHGgC8H+EZALwMr8EDAO/FtA0AAADAQYw8l2P57ynkvdL/Q58URp8UVlp94s2vweP7pDD6pDD6pDD6xLqy3C+EZwAow5jiAQAli2kbAAAAgIMYeQaAMqg4Uzx43zQAuI6R51JgMplsfmytsw4AAAD3MJvNNrOYPSajLM7ULqNKenK8M9fLH5Eq7yNNZfkBBU+hTwq72vukOA8jlvefHdZc7d8n1tAnhdEn1nlzv9irjWkbAACX8eAigKsF4RkAUIC1cGtrJKY4o9QEbQBlGXOey4iSngtd3OsVp11JXssVJV1nSfZlcdEn7rteWe2TjfcOtPvp8usfJRqCy8LPlLLwfeJKu5K8VllpV5LXKivtSvJa7qyROc8lyJX5PSaTyel2rsx5Ls71ituupK8lldyfQUm3o0/c10aiT65sI5VcnxR3EZiN9w4ssb4s798nxWlHn1g/XqJPrLWRnO+XkqiROc8elpubq1dffVWLFi1SUlKS6tWrp8cff1xjx44t7dIA4KrT4YtP1X7ZJw6Fb6aEACgORp5dNGzYMM2bN0+hoaFq166dtmzZopSUFE2ePFkxMTEFjmXk2X1tXLmWVH5HAOgT97WR6JMr20je2yclvVx5URwJ5WXh+6Q47bz9+6Q02tEntttIZXPkmfDsgk2bNqlDhw6Kjo7W2rVr5efnp+zsbHXt2lU//PCDtm3bptatW1uOLwv/7El4du/1SrodfeK+NhJ9cmUbqXz0iTcFbcm5EXBv60trx0vl4/vEXe3oE9ttJMLzVSd/1DkhIUGRkZGW7du3b1fr1q01YcIETZkyxbKd8Oy+Nq5cSyq/P8ToE/e1keiTK9tI5bdPitPuyj7xtlDuLFtvWSEoutaOPrHdRiI8X3WaNWumzMxMHT16tNC+evXqqUaNGtq1a5dlW2mF5+LM6/P2v8hduZZUfn+I0SfuayPRJ1e2kcpvnxSnnat90n7ZJ063Q/Fd+XdhWfk+Ka/tCM9XoZycHFWqVEmdOnXSunXrCu2/4447FB8fr7/++suyLf8PAwAAAN7NVkTmPc/FdPbsWeXl5alGjRpW9wcFBencuXPKysoq4coAAADgKbyqrpjyQ3GVKlWs7s/fnp6eLn9/f0neuX47AAAAHMfIczEFBQVJkjIzM63uz8jIkCRVr169pEoCAACAhxGei8nf31+BgYFKS0uzuj89PV1Vq1ZV5cqVS7gyAAAAeArh2QXBwcFKTEwsNB3DMAzt3btXwcHBpVQZAAAAPIHw7ILevXsrNTVVCQkJBbYnJCQoNTVVvXr1KpW6cnNzNWXKFLVq1UqVK1dWWFiY4uLiSqWWkuLOe+7Tp48ee+wxN1dY8lztk507d2rAgAEKDg5WYGCgbr31VsXFxSk3N9eDVXuWq30SHx+v7t27q379+qpWrZpuueUWvf3221d1n1xp8ODB8vPzc2OFJc+VPvnzzz9lMplsfr799lsPV+8Zrn6fGIahGTNmqG3btgoMDFRYWJief/55nTt3zoNVe1Zx++Sjjz4q8nsk/3Pw4MESuAv3cvX7ZP/+/Xr44YfVsGFDVa1aVW3bttWcOXOUl5fnwaqLwUCx7dq1y5BkdOjQwbh06ZJhGIZx6dIlo0OHDoYkY8+ePaVS19ChQw1JRmhoqPHQQw8ZoaGhhiRj8uTJpVJPSXDXPR84cMCoVKmSMXz4cA9VWnJc6ZNNmzYZFStWNHx8fIyoqChj4MCBRuPGjQ1JRpcuXYy8vLwSuAP3c6VPvv76a8PHx8cICAgwunXrZgwaNMho0KCBIckYMmRICVTvGe78ebFy5UpDkuHr6+uBSkuOK33y888/G5KMzp07G6NGjSr0Ka2/F1zl6vfJkCFDDElGw4YNjYEDBxo333yzIcm44447jNzcXA9X7xnF7ZOffvrJ6vdG/qdmzZpGjRo1jIyMjBK6E/dx5ftkz549RkBAgOHr62v06NHDGDx4sOXvnREjRpRA9Y4jPLto/Pjxlh8If/9GiYmJKZV6Nm7caEgyoqOjCwT6jh07GpKMbdu2lUpdnuTqPefl5Rn79+83PvroIyMsLMyQVObDs6t90q5dO8NkMhlLly61bLt06ZIxaNAgQ5Lx3nvvebR+T3C1T5o3b24EBAQYO3futGzLysqy/LKckJDg0fo9wZ0/L9LT043g4OAyH55d7ZPPPvvMkGR89dVXJVFuiXC1T7788ktDkvHAAw8UCMr9+/c3JBlLlizxaP2e4Km/a7/77jtDkjF37lx3llsiXO2Tnj17Gj4+PsaGDRss27Kzs42ePXsakozt27d7snynEJ7dYMaMGUaHDh2MatWqGR07djRmz55darXk/9Z35V/kCQkJhiRjwoQJpVSZ57h6z//9738NSQU+ZT08u9Inp06dMkwmk9GxY8dC+86cOWNUqFDBuOeee9xdsse50id//vmnIcl45JFHCu1btGiRIcl4//333V6zp7nz58U//vEPo3LlykajRo3KdHh2tU9eeeWVUv2XR09wtU/uuOMOo3LlykZmZmaB7fv27TM6depkvPnmm26v2dM88XftxYsXjWbNmhnR0dFl8l/3XO2TWrVqGa1atSq0fdmyZYYk491333VnuS4hPJcz4eHhRnBwsNV9wcHBRkRERAlX5Hmu3nNqaqqxZMkSY8mSJcbbb79dLsKzK32ybds2Q5Ixbtw4q/uDgoKMG264wS11liRX+mTv3r1G+/btrQbk/FG1sjhS5K6fF1999ZUhyXjrrbeMTp06lenw7GqfDBs2zDCZTEZWVpYnyisVrvTJhQsXDD8/P+PBBx/0VHmlwhN/177yyitGxYoVjX379rlaXqlwtU9uuukmIzg42MjJySmw/Z133vG6f6HggcFyJCcnR0lJSWrWrJnV/eHh4UpJSSnZojzMHfdcu3Zt9evXT/369VOPHj08UGXJcrVPGjdurO+//15PPPFEoX2//vqr0tLSVK9ePXeVWyJc7ZPw8HBt3Lix0IOkhmHok08+kSR17tzZbfWWBHf9vDhz5oxGjBih9u3ba9SoUW6usmS5o08OHDigmjVravr06QoPD1eVKlXUpk0bTZ48WdnZ2R6o2rNc7ZPffvtN2dnZat68uc6cOaOlS5cqJiZG7733npKSkjxUtWd54u/a1NRUvfLKKxo6dKiaNm3qhipLljv65KGHHtKxY8c0fPhwHTx4UOfOndPq1as1ceJEVatWTZ06dXJ/4cXECoPliDNLhueveljWXY33bI+rfVKtWjVFR0cX2n7mzBmNHDlSkvTwww+7t2gPc/f3yeuvv66EhARt3rxZJ0+e1Pz589WoUSN3l+1R7uqTp59+Wunp6Zo3b54qVCjb4zHu6JPk5GSdOnVKr7zyitq3b682bdpo06ZNevHFF/XNN99ow4YN8vHx8eRtuJWrfZKamipJOn36tG6++WYdOHDAsq9ixYqaOnWqxowZ45niPcQTf++8/vrrys7O1vjx491ZaolxR5+MGzdOf/75p+Li4vThhx9atterV08rV65UzZo1PVF6sZTtn3QowJklw8uLq/Ge7fFEn2zdulXt27fXjz/+qLvuuksDBw50vdAS5O4+WbFihT7//HMdPnxY1apVk8lkck+hJcgdfbJq1Sp99NFHmjx5ssLCwtxfZAlztU9yc3OVlZWl5s2bKz4+Xl9//bUWLFigffv2qX///tq8ebNmzZrlmeI9xNU+yV9td8aMGWrYsKG2bdumzMxMbdiwQU2aNNEzzzyj77//3v2Fe5C7f56kpqbqnXfe0ZAhQ9SgQQP3FFnC3NEnGzdu1IcffqhrrrlGPXv21KBBgxQeHq6TJ09qzZo17i/aBYTncuRqXDL8arxne9zZJ5mZmRoxYoRuvfVW7d69Ww8++KCWLl0qX9+y9Y9W7v4+2bhxo/766y9t3rxZoaGhGjJkiBYuXOiWWkuKq32SP10jKipKTz/9tCdKLHGu9omPj49OnDihPXv2qHnz5pbt11xzjWbPnq2KFStqyZIl7i3aw1ztk0qVKkmSQkNDtXLlSrVu3VpVqlRRx44dtWTJEvn4+Gjq1KnuL9yD3P3zZMaMGTp//ryeeuopt9RXGlztk4yMDPXt21cVKlTQzp079dVXX+njjz/W7t27NXLkSL344ot6//33PVJ7cRCey5Grccnwq/Ge7XFXn2zdulU33XST5syZo/r16+s///mPPvvsM5sjC97ME98nAQEBuu2227Rs2TKZTCbNmTPHXeWWCFf7ZMKECTp9+rTmz59f5qdr5PPkz5PatWurSZMmSkxMdLXMEuVqn9SuXVvS5cWnrvzn+ubNmyssLEw7d+50b9Ee5s7vk7y8PH388cdq2bKlWrRo4e5SS4yrffLll18qPT1dL7zwQoF/xfLx8dHrr7+u6tWra+bMmR6pvTjKx088WFyNS4Zfjfdsj6t9smfPHt15551KSUnRM888o71792rAgAGeLNnjXOmTL7/8Uo899pj27NlTaF+9evVUp04dy9zOssSVPtm7d68uXryo5s2bF1gV7fvvv1dOTk6ZXSXNlT4xDEO5ubmF2ubz9fVVtWrV3FpvSXClT+rWrStJNue5BgYGlskHKd319853332nI0eOlLnnSKxxpU9OnjwpSVYfOPTz81OTJk106NAh9xbsAsJzOeOtS4Z70tV4z/a42icDBw5UZmam/vOf/+iNN94oFyP3rvTJ+fPnNXfuXP3444+F9p07d05//vlnmZzz60qf3HvvvRo1alShT7169WQymSxfBwYGevo23MqVPvnwww/l6+uradOmFdp35swZ/f7774qIiHB7zZ7mSp+EhYWpcePG2rRpU6F9mZmZ2rNnj2688Ua31+xp7vp7Z8GCBZKk/v37u73GkuZKn+RPc9q7d2+hfZcuXdIff/yh8PBw9xbsihJ9MR48zluXDPckd95zSkpKuXjPsyt98tNPPxmSjMGDB5dQtSXDlT45fPiwUalSJeOGG24w0tLSLNtzcnKMkSNHGpKMt99+2+P34G6e+HlR1t/z7EqfnD592ggMDDTq1KljHDhwwLL9woULxiOPPGJIMr799luP34O7ufp9MnHiRENSgQXEsrOzjUcffdSQZHz44Ycerd8T3PXfTr169Yy6det6stQS40qfpKWlGddee61Rp04dIykpybI9JyfHeOqppwxJxqRJkzx+D44iPJdD3rZkeElw9J5HjRpljBo1yjh9+rTV85SX8GwYxe+TadOmGZKMzp07W/Zd+SmLK4IZhmvfJ/krx1177bXGgAEDjEGDBhlNmzY1JBldunQpsOxwWeKu/3bylfXwbBiu9cncuXMNHx8fw9/f3+jbt6/x4IMPGg0aNDAkGf/6179K+lbcxpU+OXv2rNGuXTtDknHrrbca//jHPyz/7ZTF1Urzufrfzr59+wxJRt++fUuybI9ypU+WLVtm+Pn5Gf7+/kavXr0K/IyNioqyBHJvQHgup7xpyfCS4sg96/8vv52SkmL1HOUpPBtG8fpk9OjRhZYrv/LTvn37kr4Vt3Hl+2TZsmVGhw4djNq1axvVq1c32rVrZ8ycObPQilhljTv+28lXHsKzYbjWJ99++60RHR1tVK9e3ahRo4Zxxx13GJ999llJle4xrvRJRkaGMXz4cOO6664zqlSpYvlvpywuQ/13rvTJu+++a0gypk6dWlLllghX+uS3334z7rvvPiM0NNQIDAw02rZta7z22mteFZwNwzBMhmHjyQYAAAAABfDAIAAAAOAgwjMAAADgIMIzAAAA4CDCMwAAAOAgwjMAAADgIMIzAAAA4CDCMwAAAOAgwjMAAADgIMIzAAAA4CDCMwAAAOAgwjMAAADgIMIzAAAA4CDCMwAAAOAgwjMAAADgIMIzAAAAnPboo4/KZDJZPm+99VahYy5evKhPP/1UPXr0UOPGjeXv76969eqpffv2ev7553Xs2DGX6/jhhx9kMplUs2ZNZWdnO3Rs7dq1lZOTo0mTJhW4h0cffdTu9QjPAAAAKLa2bduqW7duatCgQYHtv/32m26++WYNGjRIX3/9tf7880+1bt1a1157rbZv367XXntNDRs21IwZM1y6focOHdSgQQOlpaXp22+/LfLYpUuXSpIGDBggX19fNWnSRN26dVPbtm0dvh7hGQAAAMX22muv6euvv1bfvn0t2w4cOKAuXbpo9+7duuWWW7R9+3adOXNGmzdv1q+//qqTJ0/qrbfeUoUKFTR69Gh99NFHxb6+yWTSI488Ikn67LPPbB5nGIYlPD/00EOSpEceeURff/21XnvtNYevR3gGAACAWw0dOlR//vmnBg4cqM2bN+umm25ShQr/i50BAQF66qmnLGF2woQJys3NLfb1Bg4cKElavny5zp8/b/WYn376SUePHtX111+v22+/vdjXIjwDAAB4ufy5uevWrdO3336rW2+9VQEBAbrpppv0wgsvKDs7W3/99ZfGjRun1q1bq0qVKrrxxhv14Ycflnit69ev14YNGxQUFKQZM2bIz8/P5rE9evTQQw89pHr16mnHjh2F9sfHx+uhhx5S48aNVatWLfXs2VPvv/9+oeOaN2+u1q1b69y5c1q5cqXVay1ZskSS9OCDD8pkMhXv5iT5FrslAAAAStTKlSs1c+ZMhYWFqVmzZkpISLBMg/jtt9/066+/6qabblLNmjW1a9cuPfroo5bQWVIWLVokSRo3bpxq1Khh9/iFCxda3T5nzhyNGjVK2dnZCg8PV0hIiNauXavVq1dr+/btmj17doHR7EGDBikhIUGfffaZHnjggULnu3LKRnEx8gwAAFBGzJgxQ/Pnz9fevXu1bds2zZ8/X5L0wQcf6NSpU9q1a5d++uknHThwQPfff78kafHixSVa42+//SZJuvXWW4t9juTkZI0aNUpBQUHatGmTEhMTtW3bNv3xxx9q06aN3n33XX3zzTcF2jz44IPy8fHR119/rTNnzhTYt3XrVh08eFDh4eGKjIwsdl0S4RkAAKDM6NOnj/7xj39Yvh4yZIiqVKkiSYqLi1OTJk0kST4+Pho1apQk6dChQyVaY3JysiQpPDy80L4LFy6oe/fuVj9vvvmm5biXXnpJly5d0qxZs3TbbbdZtoeEhOizzz6Tn5+fZs+eXeDcdevWVdeuXXXp0iUtW7aswD53jTpLhGcAAIAyo3379oW21axZU9LlV7b9Xa1atSRdfstEvs6dO+tf//qXJCk7O1vjx49X/fr1FRAQoDZt2hQKncVx+vRpSVL16tUL7cvNzdU333xj9ZM/Yi1dfh+zv7+/7rnnnkLnaNKkiW666SatWbNGeXl5BfblPzj4+eefF9j+9/nOrmLOMwAAQBkRGBhYaFv+w29X7rP2UNyCBQssD/DFxsbq3Xff1YwZM3TjjTdq1apVGjBggJYvX65evXoVu8agoCCdOHFCR44cUdOmTQvsCwgIKBDmJen3338vMEqdnZ2tgwcP6pprrlHv3r2tXuPgwYPKycnRn3/+qTp16li233vvvQoICNB3332n1NRU1alTRzt27ND+/fvVunVrq6PhziI8AwAAXCWCg4Mt//+TTz7RxIkTNXjwYElS69atlZiYqNmzZ7sUnps3b64TJ04oISGhUHi2Zu/evQW+Tk9PV25urs6dO1doXvOVjh49WiA8BwQE6N5779Wnn36qxYsXa9SoUZZRZ3dM2ZCYtgEAAHDVyJ+2ce7cOVWrVs3qVI/Dhw+7dI27775bkvTOO+84dPzPP/9c4OvatWsrICBATZs2lWEYRX5at25d6Hz5UzfyF0xZsmSJTCaT1TdwFAfhGQAA4CoTEBCgxMRE3XLLLZZtR44c0ZIlSxQVFeXSuZ944gkFBwfrhx9+sDtyfPjwYavLczdu3FjJycnKyMiw2m7OnDl69913re7r2rWr6tatqy1btuirr77Svn371L59e11//fXO34wVhGcAAICr3FdffWUJzWaz2aVzValSRa+++qokqW/fvlq+fLnV4w4ePKgBAwbor7/+KrRvyJAhys7OtlrLsmXLNGLECK1du9bqeX18fPTQQw/JMAyNGDFCkvumbEiEZwAAgKvW4cOH1b17d/Xu3VstWrTQzz//rPr167t83kceeUTDhg3TxYsX1a9fP91///36+OOPtX79ei1cuFDPPPOMbrzxRiUkJOjDDz9UpUqVCrQfOXKkGjZsqOnTp6t///5atGiR/vvf/yo2NlaPPPKIrrnmGk2YMMHm9fOnbhw/fly+vr7q37+/y/eUjwcGAQAArkLbt29X586dFRQUpJUrV7r0kOCVTCaT5s6dq6ioKL344otavHhxocVabr31Vs2cOVO33HKLPvjggwL7KlWqpM2bN+vhhx/W0qVLLe9plqT69evr3XffVZs2bWxev3Xr1mrevLkSExPVtWtXy2v73IHwDAAA4OUmTpyoiRMnWt2XvyjJlcLDwwu9Fi6fYRgaMGCAIiIitGbNGgUEBLit1r8bPny4Hn30UW3atEm7d+9WRkaGatWqpXbt2ikiIsJy3A8//FCo7XXXXad169bpt99+U0JCgs6cOaOmTZuqS5cuuuaaa+xee8+ePW69l3yEZwAAgKtMfHy89u/frwkTJujEiRMF9vn6+qpBgwZuu5aPj4+io6MVHR3tdNsKFSqoVatWatWqldvqcRXhGQAA4CqT/zq64cOHF9oXGhpqczQbhGcAAICrRv40jv79+9uc0uGs5557TkFBQXr88cfVt29ft5yzpCxYsECffvqp0tLSHG5DeAYAACjn8vLytH//fiUmJurmm29267nj4+MlSd26dXPreUvCH3/8Yfdd1FcyGe76tQMAAABe6ejRo2rUqJFuvPFGLViwQOHh4aVdUplFeAYAAAAcxCIpAAAAgIMIzwAAAICDCM8AAACAgwjPAAAAgIMIzwAAAICDCM8AAACAgwjPAAAAgIMIzwAAAICD/h+mOvjG529/iQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 799.992x599.976 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter = VariableDistributionPlot(dfs_0, label_map=label_map)\n",
    "print(\"Events with label 0:\")\n",
    "plotter.draw(\"mj1\", xlabel=\"$m_{j1}$ [GeV]\", bins=50)\n",
    "plt.show()\n",
    "plotter.draw(\"mj2\", xlabel=\"$m_{j2}$ [GeV]\", bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e9594480-3dcb-49d1-a5d2-81847764d8a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Events with label 1:\n",
      "[INFO] Using deduced bin range (0.021, 1.282)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 799.992x599.976 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Using deduced bin range (-0.000, 0.819)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 799.992x599.976 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter = VariableDistributionPlot(dfs_1, label_map=label_map)\n",
    "print(\"Events with label 1:\")\n",
    "plotter.draw(\"mj1\", xlabel=\"$m_{j1}$ [GeV]\", bins=50)\n",
    "plt.show()\n",
    "plotter.draw(\"mj2\", xlabel=\"$m_{j2}$ [GeV]\", bins=50)\n",
    "plt.show()"
   ]
  }
 ],
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