analysis/DNN/train_T5_DNN.ipynb

0001 {
0002  "cells": [
0003   {
0004    "cell_type": "code",
0005    "execution_count": 1,
0006    "metadata": {},
0007    "outputs": [],
0008    "source": [
0009     "import os\n",
0010     "import uproot\n",
0011     "import numpy as np\n",
0012     "\n",
0013     "def load_root_file(file_path, branches=None, print_branches=False):\n",
0014     "    all_branches = {}\n",
0015     "    with uproot.open(file_path) as file:\n",
0016     "        tree = file[\"tree\"]\n",
0017     "        # Load all ROOT branches into array if not specified\n",
0018     "        if branches is None:\n",
0019     "            branches = tree.keys()\n",
0020     "        # Option to print the branch names\n",
0021     "        if print_branches:\n",
0022     "            print(\"Branches:\", tree.keys())\n",
0023     "        # Each branch is added to the dictionary\n",
0024     "        for branch in branches:\n",
0025     "            try:\n",
0026     "                all_branches[branch] = (tree[branch].array(library=\"np\"))\n",
0027     "            except uproot.KeyInFileError as e:\n",
0028     "                print(f\"KeyInFileError: {e}\")\n",
0029     "        # Number of events in file\n",
0030     "        all_branches['event'] = tree.num_entries\n",
0031     "    return all_branches\n",
0032     "\n",
0033     "branches_list = [\n",
0034     "    't5_innerRadius',\n",
0035     "    't5_bridgeRadius',\n",
0036     "    't5_outerRadius',\n",
0037     "    't5_pt',\n",
0038     "    't5_eta',\n",
0039     "    't5_phi',\n",
0040     "    't5_isFake',\n",
0041     "    't5_t3_idx0',\n",
0042     "    't5_t3_idx1',\n",
0043     "    't5_pMatched',\n",
0044     "    't5_sim_vxy',\n",
0045     "    't5_sim_vz'\n",
0046     "]\n",
0047     "\n",
0048     "# Hit-dependent branches\n",
0049     "suffixes = ['r', 'z', 'eta', 'phi', 'layer']\n",
0050     "branches_list += [f't5_t3_{i}_{suffix}' for i in [0, 2, 4] for suffix in suffixes]\n",
0051     "\n",
0052     "file_path = \"1000_no_dnn_for_phi.root\"\n",
0053     "branches = load_root_file(file_path, branches_list)"
0054    ]
0055   },
0056   {
0057    "cell_type": "code",
0058    "execution_count": 2,
0059    "metadata": {},
0060    "outputs": [
0061     {
0062      "name": "stdout",
0063      "output_type": "stream",
0064      "text": [
0065       "Z max: 267.2349853515625, R max: 110.10993957519531, Eta max: 2.5\n"
0066      ]
0067     }
0068    ],
0069    "source": [
0070     "z_max = np.max([np.max(event) for event in branches[f't5_t3_4_z']])\n",
0071     "r_max = np.max([np.max(event) for event in branches[f't5_t3_4_r']])\n",
0072     "eta_max = 2.5\n",
0073     "phi_max = np.pi\n",
0074     "\n",
0075     "print(f'Z max: {z_max}, R max: {r_max}, Eta max: {eta_max}')\n",
0076     "\n",
0077     "def delta_phi(phi1, phi2):\n",
0078     "    delta = phi1 - phi2\n",
0079     "    # Adjust delta to be within the range [-pi, pi]\n",
0080     "    if delta > np.pi:\n",
0081     "        delta -= 2 * np.pi\n",
0082     "    elif delta < -np.pi:\n",
0083     "        delta += 2 * np.pi\n",
0084     "    return delta"
0085    ]
0086   },
0087   {
0088    "cell_type": "code",
0089    "execution_count": 3,
0090    "metadata": {},
0091    "outputs": [],
0092    "source": [
0093     "features_list = []\n",
0094     "eta_list = [] # Used for DNN cut values\n",
0095     "\n",
0096     "for event in range(branches['event']):\n",
0097     "    # Determine the number of elements in this event\n",
0098     "    num_elements = len(branches['t5_t3_idx0'][event])\n",
0099     "\n",
0100     "    for i in range(num_elements):\n",
0101     "        features_iter = []\n",
0102     "        eta_iter = []\n",
0103     "        \n",
0104     "        idx0 = branches['t5_t3_idx0'][event][i]\n",
0105     "        idx1 = branches['t5_t3_idx1'][event][i]\n",
0106     "\n",
0107     "        eta1 = np.abs(branches['t5_t3_0_eta'][event][idx0])\n",
0108     "        eta2 = np.abs(branches['t5_t3_2_eta'][event][idx0])\n",
0109     "        eta3 = np.abs(branches['t5_t3_4_eta'][event][idx0])\n",
0110     "        eta4 = np.abs(branches['t5_t3_2_eta'][event][idx1])\n",
0111     "        eta5 = np.abs(branches['t5_t3_4_eta'][event][idx1])\n",
0112     "\n",
0113     "        phi1 = (branches['t5_t3_0_phi'][event][idx0])\n",
0114     "        phi2 = (branches['t5_t3_2_phi'][event][idx0])\n",
0115     "        phi3 = (branches['t5_t3_4_phi'][event][idx0])\n",
0116     "        phi4 = (branches['t5_t3_2_phi'][event][idx1])\n",
0117     "        phi5 = (branches['t5_t3_4_phi'][event][idx1])\n",
0118     "\n",
0119     "        z1 = np.abs(branches['t5_t3_0_z'][event][idx0])\n",
0120     "        z2 = np.abs(branches['t5_t3_2_z'][event][idx0])\n",
0121     "        z3 = np.abs(branches['t5_t3_4_z'][event][idx0])\n",
0122     "        z4 = np.abs(branches['t5_t3_2_z'][event][idx1])\n",
0123     "        z5 = np.abs(branches['t5_t3_4_z'][event][idx1])\n",
0124     "\n",
0125     "        r1 = branches['t5_t3_0_r'][event][idx0]\n",
0126     "        r2 = branches['t5_t3_2_r'][event][idx0]\n",
0127     "        r3 = branches['t5_t3_4_r'][event][idx0]\n",
0128     "        r4 = branches['t5_t3_2_r'][event][idx1]\n",
0129     "        r5 = branches['t5_t3_4_r'][event][idx1]\n",
0130     "\n",
0131     "        innerRad = branches['t5_innerRadius'][event][i]\n",
0132     "        bridgeRad = branches['t5_bridgeRadius'][event][i]\n",
0133     "        outerRad = branches['t5_outerRadius'][event][i]\n",
0134     "\n",
0135     "        # Construct the input feature vector using pairwise differences\n",
0136     "        features_iter = [\n",
0137     "            eta1 / eta_max,                      # First hit eta, normalized\n",
0138     "            np.abs(phi1) / phi_max,              # First hit phi, normalized\n",
0139     "            z1 / z_max,                          # First hit z, normalized\n",
0140     "            r1 / r_max,                          # First hit r, normalized\n",
0141     "\n",
0142     "            eta2 - eta1,                         # Difference in eta between hit 2 and 1\n",
0143     "            delta_phi(phi2, phi1) / phi_max,     # Difference in phi between hit 2 and 1\n",
0144     "            (z2 - z1) / z_max,                   # Difference in z between hit 2 and 1, normalized\n",
0145     "            (r2 - r1) / r_max,                   # Difference in r between hit 2 and 1, normalized\n",
0146     "\n",
0147     "            eta3 - eta2,                         # Difference in eta between hit 3 and 2\n",
0148     "            delta_phi(phi3, phi2) / phi_max,     # Difference in phi between hit 3 and 2\n",
0149     "            (z3 - z2) / z_max,                   # Difference in z between hit 3 and 2, normalized\n",
0150     "            (r3 - r2) / r_max,                   # Difference in r between hit 3 and 2, normalized\n",
0151     "\n",
0152     "            eta4 - eta3,                         # Difference in eta between hit 4 and 3\n",
0153     "            delta_phi(phi4, phi3) / phi_max,     # Difference in phi between hit 4 and 3\n",
0154     "            (z4 - z3) / z_max,                   # Difference in z between hit 4 and 3, normalized\n",
0155     "            (r4 - r3) / r_max,                   # Difference in r between hit 4 and 3, normalized\n",
0156     "\n",
0157     "            eta5 - eta4,                         # Difference in eta between hit 5 and 4\n",
0158     "            delta_phi(phi5, phi4) / phi_max,     # Difference in phi between hit 5 and 4\n",
0159     "            (z5 - z4) / z_max,                   # Difference in z between hit 5 and 4, normalized\n",
0160     "            (r5 - r4) / r_max,                   # Difference in r between hit 5 and 4, normalized\n",
0161     "\n",
0162     "            np.log10(innerRad),\n",
0163     "            np.log10(bridgeRad),\n",
0164     "            np.log10(outerRad)\n",
0165     "        ]\n",
0166     "\n",
0167     "        # Use the abs eta value of first hit to select cut thresholds\n",
0168     "        eta_iter.extend([np.abs(branches['t5_t3_0_eta'][event][idx0])])\n",
0169     "        \n",
0170     "        # Append the feature vector to the list\n",
0171     "        features_list.append(features_iter)\n",
0172     "        eta_list.append(eta_iter)\n",
0173     "\n",
0174     "# Convert the list of features to a NumPy array\n",
0175     "features = np.array(features_list).T\n",
0176     "eta_list = np.array(eta_list).T"
0177    ]
0178   },
0179   {
0180    "cell_type": "code",
0181    "execution_count": 4,
0182    "metadata": {},
0183    "outputs": [],
0184    "source": [
0185     "import torch\n",
0186     "\n",
0187     "# Stack features along a new axis to form a single array suitable for NN input\n",
0188     "input_features_numpy = np.stack(features, axis=-1)\n",
0189     "\n",
0190     "# Identify rows with NaN or Inf values\n",
0191     "mask = ~np.isnan(input_features_numpy) & ~np.isinf(input_features_numpy)\n",
0192     "\n",
0193     "# Apply mask across all columns: retain a row only if all its entries are neither NaN nor Inf\n",
0194     "filtered_input_features_numpy = input_features_numpy[np.all(mask, axis=1)]\n",
0195     "t5_isFake_filtered = np.concatenate(branches['t5_isFake'])[np.all(mask, axis=1)]\n",
0196     "\n",
0197     "# Convert to PyTorch tensor when ready to use with NN\n",
0198     "input_features_tensor = torch.tensor(filtered_input_features_numpy, dtype=torch.float32)"
0199    ]
0200   },
0201   {
0202    "cell_type": "code",
0203    "execution_count": 5,
0204    "metadata": {},
0205    "outputs": [
0206     {
0207      "name": "stdout",
0208      "output_type": "stream",
0209      "text": [
0210       "Using device: cuda\n",
0211       "Initial dataset size: 10921577\n",
0212       "Dataset size after initial 100.0% downsampling: 10921577\n",
0213       "Class distribution after initial downsampling - Class 0: 2985946, Class 1: 7935631\n",
0214       "Final class distribution after balancing - Class 0: 2985946, Class 1: 2985946\n",
0215       "Epoch [1/150], Loss: 0.4802, Test Acc: 81.32%\n",
0216       "Epoch [2/150], Loss: 0.4255, Test Acc: 81.45%\n",
0217       "Epoch [3/150], Loss: 0.4602, Test Acc: 82.69%\n",
0218       "Epoch [4/150], Loss: 0.4475, Test Acc: 83.97%\n",
0219       "Epoch [5/150], Loss: 0.4417, Test Acc: 82.76%\n",
0220       "Epoch [6/150], Loss: 0.4643, Test Acc: 83.01%\n",
0221       "Epoch [7/150], Loss: 0.4474, Test Acc: 81.68%\n",
0222       "Epoch [8/150], Loss: 0.4050, Test Acc: 84.32%\n",
0223       "Epoch [9/150], Loss: 0.3563, Test Acc: 83.51%\n",
0224       "Epoch [10/150], Loss: 0.4774, Test Acc: 83.24%\n",
0225       "Epoch [11/150], Loss: 0.4846, Test Acc: 84.06%\n",
0226       "Epoch [12/150], Loss: 0.5055, Test Acc: 83.28%\n",
0227       "Epoch [13/150], Loss: 0.4461, Test Acc: 84.35%\n",
0228       "Epoch [14/150], Loss: 0.4607, Test Acc: 82.75%\n",
0229       "Epoch [15/150], Loss: 0.4795, Test Acc: 83.22%\n",
0230       "Epoch [16/150], Loss: 0.4414, Test Acc: 83.83%\n",
0231       "Epoch [17/150], Loss: 0.4357, Test Acc: 82.26%\n",
0232       "Epoch [18/150], Loss: 0.4498, Test Acc: 83.85%\n",
0233       "Epoch [19/150], Loss: 0.4779, Test Acc: 84.34%\n",
0234       "Epoch [20/150], Loss: 0.4248, Test Acc: 83.84%\n",
0235       "Epoch [21/150], Loss: 0.4538, Test Acc: 83.97%\n",
0236       "Epoch [22/150], Loss: 0.4335, Test Acc: 84.28%\n",
0237       "Epoch [23/150], Loss: 0.4248, Test Acc: 84.43%\n",
0238       "Epoch [24/150], Loss: 0.4072, Test Acc: 83.57%\n",
0239       "Epoch [25/150], Loss: 0.3732, Test Acc: 83.51%\n",
0240       "Epoch [26/150], Loss: 0.4559, Test Acc: 83.91%\n",
0241       "Epoch [27/150], Loss: 0.4071, Test Acc: 83.04%\n",
0242       "Epoch [28/150], Loss: 0.4709, Test Acc: 84.22%\n",
0243       "Epoch [29/150], Loss: 0.4079, Test Acc: 83.83%\n",
0244       "Epoch [30/150], Loss: 0.3676, Test Acc: 83.50%\n",
0245       "Epoch [31/150], Loss: 0.4944, Test Acc: 83.84%\n",
0246       "Epoch [32/150], Loss: 0.4316, Test Acc: 84.63%\n",
0247       "Epoch [33/150], Loss: 0.4229, Test Acc: 83.43%\n",
0248       "Epoch [34/150], Loss: 0.3955, Test Acc: 83.82%\n",
0249       "Epoch [35/150], Loss: 0.4467, Test Acc: 83.64%\n",
0250       "Epoch [36/150], Loss: 0.4164, Test Acc: 84.49%\n",
0251       "Epoch [37/150], Loss: 0.4656, Test Acc: 83.64%\n",
0252       "Epoch [38/150], Loss: 0.4274, Test Acc: 84.10%\n",
0253       "Epoch [39/150], Loss: 0.3943, Test Acc: 84.63%\n",
0254       "Epoch [40/150], Loss: 0.4476, Test Acc: 84.55%\n",
0255       "Epoch [41/150], Loss: 0.4765, Test Acc: 84.51%\n",
0256       "Epoch [42/150], Loss: 0.4007, Test Acc: 84.32%\n",
0257       "Epoch [43/150], Loss: 0.4716, Test Acc: 84.01%\n",
0258       "Epoch [44/150], Loss: 0.4063, Test Acc: 83.81%\n",
0259       "Epoch [45/150], Loss: 0.3712, Test Acc: 84.63%\n",
0260       "Epoch [46/150], Loss: 0.3657, Test Acc: 84.43%\n",
0261       "Epoch [47/150], Loss: 0.4514, Test Acc: 84.26%\n",
0262       "Epoch [48/150], Loss: 0.3817, Test Acc: 84.30%\n",
0263       "Epoch [49/150], Loss: 0.3744, Test Acc: 83.23%\n",
0264       "Epoch [50/150], Loss: 0.4377, Test Acc: 84.43%\n",
0265       "Epoch [51/150], Loss: 0.4331, Test Acc: 84.19%\n",
0266       "Epoch [52/150], Loss: 0.4022, Test Acc: 84.39%\n",
0267       "Epoch [53/150], Loss: 0.4272, Test Acc: 84.67%\n",
0268       "Epoch [54/150], Loss: 0.4146, Test Acc: 84.06%\n",
0269       "Epoch [55/150], Loss: 0.3798, Test Acc: 84.52%\n",
0270       "Epoch [56/150], Loss: 0.4070, Test Acc: 83.82%\n",
0271       "Epoch [57/150], Loss: 0.5018, Test Acc: 84.64%\n",
0272       "Epoch [58/150], Loss: 0.5112, Test Acc: 84.71%\n",
0273       "Epoch [59/150], Loss: 0.4554, Test Acc: 84.41%\n",
0274       "Epoch [60/150], Loss: 0.4313, Test Acc: 84.78%\n",
0275       "Epoch [61/150], Loss: 0.4101, Test Acc: 83.46%\n",
0276       "Epoch [62/150], Loss: 0.4139, Test Acc: 84.60%\n",
0277       "Epoch [63/150], Loss: 0.3841, Test Acc: 84.47%\n",
0278       "Epoch [64/150], Loss: 0.4931, Test Acc: 83.95%\n",
0279       "Epoch [65/150], Loss: 0.3589, Test Acc: 84.59%\n",
0280       "Epoch [66/150], Loss: 0.4328, Test Acc: 84.87%\n",
0281       "Epoch [67/150], Loss: 0.4525, Test Acc: 84.00%\n",
0282       "Epoch [68/150], Loss: 0.4745, Test Acc: 84.31%\n",
0283       "Epoch [69/150], Loss: 0.4585, Test Acc: 84.07%\n",
0284       "Epoch [70/150], Loss: 0.4348, Test Acc: 84.95%\n",
0285       "Epoch [71/150], Loss: 0.4101, Test Acc: 85.12%\n",
0286       "Epoch [72/150], Loss: 0.4001, Test Acc: 84.77%\n",
0287       "Epoch [73/150], Loss: 0.4505, Test Acc: 84.56%\n",
0288       "Epoch [74/150], Loss: 0.3493, Test Acc: 84.78%\n",
0289       "Epoch [75/150], Loss: 0.4316, Test Acc: 83.27%\n",
0290       "Epoch [76/150], Loss: 0.4963, Test Acc: 84.52%\n",
0291       "Epoch [77/150], Loss: 0.4214, Test Acc: 84.66%\n",
0292       "Epoch [78/150], Loss: 0.5551, Test Acc: 84.71%\n",
0293       "Epoch [79/150], Loss: 0.3809, Test Acc: 84.12%\n",
0294       "Epoch [80/150], Loss: 0.3979, Test Acc: 84.31%\n",
0295       "Epoch [81/150], Loss: 0.3920, Test Acc: 84.49%\n",
0296       "Epoch [82/150], Loss: 0.4278, Test Acc: 84.71%\n",
0297       "Epoch [83/150], Loss: 0.3696, Test Acc: 84.69%\n",
0298       "Epoch [84/150], Loss: 0.3483, Test Acc: 84.02%\n",
0299       "Epoch [85/150], Loss: 0.3976, Test Acc: 84.13%\n",
0300       "Epoch [86/150], Loss: 0.3335, Test Acc: 84.92%\n",
0301       "Epoch [87/150], Loss: 0.3972, Test Acc: 84.58%\n",
0302       "Epoch [88/150], Loss: 0.4135, Test Acc: 84.32%\n",
0303       "Epoch [89/150], Loss: 0.4556, Test Acc: 84.21%\n",
0304       "Epoch [90/150], Loss: 0.4180, Test Acc: 84.28%\n",
0305       "Epoch [91/150], Loss: 0.3586, Test Acc: 84.78%\n",
0306       "Epoch [92/150], Loss: 0.4388, Test Acc: 84.43%\n",
0307       "Epoch [93/150], Loss: 0.4243, Test Acc: 84.12%\n",
0308       "Epoch [94/150], Loss: 0.4133, Test Acc: 84.55%\n",
0309       "Epoch [95/150], Loss: 0.4201, Test Acc: 84.86%\n",
0310       "Epoch [96/150], Loss: 0.4670, Test Acc: 84.50%\n",
0311       "Epoch [97/150], Loss: 0.4199, Test Acc: 84.89%\n",
0312       "Epoch [98/150], Loss: 0.4076, Test Acc: 84.35%\n",
0313       "Epoch [99/150], Loss: 0.3696, Test Acc: 84.98%\n",
0314       "Epoch [100/150], Loss: 0.3553, Test Acc: 84.70%\n",
0315       "Epoch [101/150], Loss: 0.4054, Test Acc: 84.40%\n",
0316       "Epoch [102/150], Loss: 0.4168, Test Acc: 84.65%\n",
0317       "Epoch [103/150], Loss: 0.3675, Test Acc: 84.69%\n",
0318       "Epoch [104/150], Loss: 0.4107, Test Acc: 84.10%\n",
0319       "Epoch [105/150], Loss: 0.4310, Test Acc: 84.55%\n",
0320       "Epoch [106/150], Loss: 0.4340, Test Acc: 83.76%\n",
0321       "Epoch [107/150], Loss: 0.4691, Test Acc: 84.93%\n",
0322       "Epoch [108/150], Loss: 0.4838, Test Acc: 84.37%\n",
0323       "Epoch [109/150], Loss: 0.4057, Test Acc: 84.73%\n",
0324       "Epoch [110/150], Loss: 0.4900, Test Acc: 84.75%\n",
0325       "Epoch [111/150], Loss: 0.4501, Test Acc: 84.18%\n",
0326       "Epoch [112/150], Loss: 0.4528, Test Acc: 84.51%\n",
0327       "Epoch [113/150], Loss: 0.4126, Test Acc: 84.74%\n",
0328       "Epoch [114/150], Loss: 0.4142, Test Acc: 84.99%\n",
0329       "Epoch [115/150], Loss: 0.3894, Test Acc: 84.30%\n",
0330       "Epoch [116/150], Loss: 0.4518, Test Acc: 84.30%\n",
0331       "Epoch [117/150], Loss: 0.4568, Test Acc: 84.43%\n",
0332       "Epoch [118/150], Loss: 0.3588, Test Acc: 84.32%\n",
0333       "Epoch [119/150], Loss: 0.3891, Test Acc: 84.38%\n",
0334       "Epoch [120/150], Loss: 0.4558, Test Acc: 84.62%\n",
0335       "Epoch [121/150], Loss: 0.4732, Test Acc: 84.41%\n",
0336       "Epoch [122/150], Loss: 0.4008, Test Acc: 84.54%\n",
0337       "Epoch [123/150], Loss: 0.4279, Test Acc: 84.21%\n",
0338       "Epoch [124/150], Loss: 0.4658, Test Acc: 84.58%\n",
0339       "Epoch [125/150], Loss: 0.4696, Test Acc: 84.59%\n",
0340       "Epoch [126/150], Loss: 0.4663, Test Acc: 84.00%\n",
0341       "Epoch [127/150], Loss: 0.3993, Test Acc: 84.53%\n",
0342       "Epoch [128/150], Loss: 0.4316, Test Acc: 84.31%\n",
0343       "Epoch [129/150], Loss: 0.4189, Test Acc: 84.63%\n",
0344       "Epoch [130/150], Loss: 0.3826, Test Acc: 83.96%\n",
0345       "Epoch [131/150], Loss: 0.3437, Test Acc: 84.45%\n",
0346       "Epoch [132/150], Loss: 0.4950, Test Acc: 85.07%\n",
0347       "Epoch [133/150], Loss: 0.4394, Test Acc: 84.15%\n",
0348       "Epoch [134/150], Loss: 0.3998, Test Acc: 84.38%\n",
0349       "Epoch [135/150], Loss: 0.3154, Test Acc: 84.99%\n",
0350       "Epoch [136/150], Loss: 0.4408, Test Acc: 84.83%\n",
0351       "Epoch [137/150], Loss: 0.4970, Test Acc: 84.38%\n",
0352       "Epoch [138/150], Loss: 0.4473, Test Acc: 84.13%\n",
0353       "Epoch [139/150], Loss: 0.4615, Test Acc: 84.66%\n",
0354       "Epoch [140/150], Loss: 0.4316, Test Acc: 84.38%\n",
0355       "Epoch [141/150], Loss: 0.5141, Test Acc: 84.62%\n",
0356       "Epoch [142/150], Loss: 0.4030, Test Acc: 84.03%\n",
0357       "Epoch [143/150], Loss: 0.4777, Test Acc: 84.15%\n",
0358       "Epoch [144/150], Loss: 0.4286, Test Acc: 84.78%\n",
0359       "Epoch [145/150], Loss: 0.4194, Test Acc: 84.73%\n",
0360       "Epoch [146/150], Loss: 0.3649, Test Acc: 84.84%\n",
0361       "Epoch [147/150], Loss: 0.4346, Test Acc: 84.00%\n",
0362       "Epoch [148/150], Loss: 0.4373, Test Acc: 84.60%\n",
0363       "Epoch [149/150], Loss: 0.4238, Test Acc: 84.78%\n",
0364       "Epoch [150/150], Loss: 0.4499, Test Acc: 84.80%\n"
0365      ]
0366     }
0367    ],
0368    "source": [
0369     "from torch import nn\n",
0370     "from torch.optim import Adam\n",
0371     "from torch.utils.data import DataLoader, TensorDataset, random_split\n",
0372     "import torch\n",
0373     "\n",
0374     "# Set device\n",
0375     "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
0376     "print(f\"Using device: {device}\")\n",
0377     "\n",
0378     "# Create labels tensor\n",
0379     "labels_tensor = 1 - torch.tensor(t5_isFake_filtered, dtype=torch.float32)\n",
0380     "\n",
0381     "# Set initial downsample fraction\n",
0382     "initial_downsample_fraction = 1.0  # Adjust this value as needed\n",
0383     "\n",
0384     "class MyNeuralNetwork(nn.Module):\n",
0385     "    def __init__(self):\n",
0386     "        super(MyNeuralNetwork, self).__init__()\n",
0387     "        self.layer1 = nn.Linear(input_features_numpy.shape[1], 32)\n",
0388     "        self.layer2 = nn.Linear(32, 32)\n",
0389     "        self.output_layer = nn.Linear(32, 1)\n",
0390     "\n",
0391     "    def forward(self, x):\n",
0392     "        x = self.layer1(x)\n",
0393     "        x = nn.ReLU()(x)\n",
0394     "        x = self.layer2(x)\n",
0395     "        x = nn.ReLU()(x)\n",
0396     "        x = self.output_layer(x)\n",
0397     "        x = torch.sigmoid(x)\n",
0398     "        return x\n",
0399     "\n",
0400     "class WeightedBCELoss(nn.Module):\n",
0401     "    def __init__(self):\n",
0402     "        super(WeightedBCELoss, self).__init__()\n",
0403     "        \n",
0404     "    def forward(self, outputs, targets, weights):\n",
0405     "        eps = 1e-7\n",
0406     "        losses = -(weights * (targets * torch.log(outputs + eps) + \n",
0407     "                            (1 - targets) * torch.log(1 - outputs + eps)))\n",
0408     "        return losses.mean()\n",
0409     "\n",
0410     "def calculate_sample_weights(t5_sim_vxy, weight_factor=6.0):\n",
0411     "    \"\"\"\n",
0412     "    Calculate sample weights giving higher importance to displaced t5's\n",
0413     "    \n",
0414     "    Args:\n",
0415     "        t5_sim_vxy: Array of t5 simulation values\n",
0416     "        weight_factor: How much more weight to give to displaced samples\n",
0417     "    \n",
0418     "    Returns:\n",
0419     "        Tensor of sample weights\n",
0420     "    \"\"\"\n",
0421     "    weights = torch.ones(len(t5_sim_vxy))\n",
0422     "    displaced_mask = t5_sim_vxy > 0.1\n",
0423     "    weights[displaced_mask] = weight_factor\n",
0424     "    return weights\n",
0425     "\n",
0426     "# Print initial dataset size\n",
0427     "print(f\"Initial dataset size: {len(labels_tensor)}\")\n",
0428     "\n",
0429     "# Calculate sample weights\n",
0430     "sample_weights = calculate_sample_weights(torch.tensor(np.concatenate(branches['t5_sim_vxy'])))\n",
0431     "\n",
0432     "# Remove rows with NaN and update weights accordingly\n",
0433     "nan_mask = torch.isnan(input_features_tensor).any(dim=1) | torch.isnan(labels_tensor)\n",
0434     "filtered_inputs = input_features_tensor[~nan_mask]\n",
0435     "filtered_labels = labels_tensor[~nan_mask]\n",
0436     "filtered_weights = sample_weights[~nan_mask]\n",
0437     "\n",
0438     "# Initial downsampling of entire dataset\n",
0439     "if initial_downsample_fraction < 1.0:\n",
0440     "    total_samples = len(filtered_labels)\n",
0441     "    samples_to_keep = int(total_samples * initial_downsample_fraction)\n",
0442     "    indices = torch.randperm(total_samples)[:samples_to_keep]\n",
0443     "    filtered_inputs = filtered_inputs[indices]\n",
0444     "    filtered_labels = filtered_labels[indices]\n",
0445     "    filtered_weights = filtered_weights[indices]\n",
0446     "\n",
0447     "print(f\"Dataset size after initial {initial_downsample_fraction*100}% downsampling: {len(filtered_labels)}\")\n",
0448     "\n",
0449     "# Count samples in each class after initial downsampling\n",
0450     "class_counts = torch.bincount(filtered_labels.int())\n",
0451     "print(f\"Class distribution after initial downsampling - Class 0: {class_counts[0]}, Class 1: {class_counts[1]}\")\n",
0452     "\n",
0453     "# Balance classes while maintaining weights\n",
0454     "minority_class = 0 if class_counts[0] < class_counts[1] else 1\n",
0455     "minority_indices = (filtered_labels == minority_class).nonzero(as_tuple=True)[0]\n",
0456     "majority_indices = (filtered_labels == (1 - minority_class)).nonzero(as_tuple=True)[0]\n",
0457     "downsampled_majority_indices = majority_indices[torch.randperm(len(majority_indices))[:len(minority_indices)]]\n",
0458     "balanced_indices = torch.cat((minority_indices, downsampled_majority_indices))\n",
0459     "\n",
0460     "# Create balanced dataset with weights\n",
0461     "balanced_inputs = filtered_inputs[balanced_indices]\n",
0462     "balanced_labels = filtered_labels[balanced_indices]\n",
0463     "balanced_weights = filtered_weights[balanced_indices]\n",
0464     "\n",
0465     "# Verify balanced distribution\n",
0466     "balanced_counts = torch.bincount(balanced_labels.int())\n",
0467     "print(f\"Final class distribution after balancing - Class 0: {balanced_counts[0]}, Class 1: {balanced_counts[1]}\")\n",
0468     "\n",
0469     "# Create dataset with weights\n",
0470     "dataset = TensorDataset(balanced_inputs, balanced_labels, balanced_weights)\n",
0471     "\n",
0472     "# Split into train and test sets\n",
0473     "train_size = int(0.8 * len(dataset))\n",
0474     "test_size = len(dataset) - train_size\n",
0475     "train_dataset, test_dataset = random_split(dataset, [train_size, test_size])\n",
0476     "\n",
0477     "# Create data loaders\n",
0478     "train_loader = DataLoader(train_dataset, batch_size=1024, shuffle=True, num_workers=10, pin_memory=True)\n",
0479     "test_loader = DataLoader(test_dataset, batch_size=1024, shuffle=False, num_workers=10, pin_memory=True)\n",
0480     "\n",
0481     "# Initialize model and optimizer\n",
0482     "model = MyNeuralNetwork().to(device)\n",
0483     "loss_function = WeightedBCELoss()\n",
0484     "optimizer = Adam(model.parameters(), lr=0.0025)\n",
0485     "\n",
0486     "def evaluate_model(loader):\n",
0487     "    model.eval()\n",
0488     "    total = 0\n",
0489     "    correct = 0\n",
0490     "    with torch.no_grad():\n",
0491     "        for inputs, targets, weights in loader:\n",
0492     "            inputs, targets = inputs.to(device), targets.to(device)\n",
0493     "            outputs = model(inputs)\n",
0494     "            predicted = outputs.squeeze() > 0.5\n",
0495     "            total += targets.size(0)\n",
0496     "            correct += (predicted == targets.bool()).sum().item()\n",
0497     "    model.train()\n",
0498     "    return 100 * correct / total\n",
0499     "\n",
0500     "# Training loop\n",
0501     "num_epochs = 150\n",
0502     "loss_log = []\n",
0503     "\n",
0504     "for epoch in range(num_epochs):\n",
0505     "    for inputs, targets, weights in train_loader:\n",
0506     "        inputs, targets, weights = inputs.to(device), targets.to(device), weights.to(device)\n",
0507     "    \n",
0508     "        # Forward pass\n",
0509     "        outputs = model(inputs)\n",
0510     "        loss = loss_function(outputs.squeeze(), targets, weights)\n",
0511     "        \n",
0512     "        loss_log.append(loss.item())\n",
0513     "\n",
0514     "        # Backward and optimize\n",
0515     "        optimizer.zero_grad()\n",
0516     "        loss.backward()\n",
0517     "        optimizer.step()\n",
0518     "\n",
0519     "    test_accuracy = evaluate_model(test_loader)\n",
0520     "    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}, Test Acc: {test_accuracy:.2f}%')"
0521    ]
0522   },
0523   {
0524    "cell_type": "code",
0525    "execution_count": 6,
0526    "metadata": {},
0527    "outputs": [],
0528    "source": [
0529     "torch.save(model.state_dict(), \"model.pth\")"
0530    ]
0531   },
0532   {
0533    "cell_type": "code",
0534    "execution_count": 7,
0535    "metadata": {},
0536    "outputs": [
0537     {
0538      "name": "stdout",
0539      "output_type": "stream",
0540      "text": [
0541       "Baseline accuracy: 0.8745944499969482\n",
0542       "Feature importances:\n",
0543       "Feature 21 importance: 0.3800\n",
0544       "Feature 20 importance: 0.2052\n",
0545       "Feature 0 importance: 0.2036\n",
0546       "Feature 22 importance: 0.1572\n",
0547       "Feature 17 importance: 0.1333\n",
0548       "Feature 12 importance: 0.1323\n",
0549       "Feature 13 importance: 0.1207\n",
0550       "Feature 5 importance: 0.1142\n",
0551       "Feature 2 importance: 0.0741\n",
0552       "Feature 16 importance: 0.0638\n",
0553       "Feature 15 importance: 0.0420\n",
0554       "Feature 8 importance: 0.0402\n",
0555       "Feature 9 importance: 0.0399\n",
0556       "Feature 6 importance: 0.0305\n",
0557       "Feature 7 importance: 0.0274\n",
0558       "Feature 4 importance: 0.0269\n",
0559       "Feature 3 importance: 0.0247\n",
0560       "Feature 14 importance: 0.0162\n",
0561       "Feature 10 importance: 0.0128\n",
0562       "Feature 19 importance: 0.0117\n",
0563       "Feature 11 importance: 0.0106\n",
0564       "Feature 18 importance: 0.0089\n",
0565       "Feature 1 importance: 0.0001\n"
0566      ]
0567     }
0568    ],
0569    "source": [
0570     "from sklearn.metrics import accuracy_score\n",
0571     "\n",
0572     "# Convert tensors to numpy for simplicity in permutation\n",
0573     "input_features_np = input_features_tensor.numpy()\n",
0574     "labels_np = labels_tensor.numpy()\n",
0575     "\n",
0576     "def model_accuracy(features, labels, model):\n",
0577     "    model.eval()  # Set the model to evaluation mode\n",
0578     "    inputs = features.to(device)\n",
0579     "    labels = labels.to(device)\n",
0580     "    with torch.no_grad():\n",
0581     "        outputs = model(inputs)\n",
0582     "        predicted = (outputs.squeeze() > 0.5).float()  # Update threshold as necessary\n",
0583     "        accuracy = (predicted == labels).float().mean().item()\n",
0584     "    return accuracy\n",
0585     "\n",
0586     "# Use the original input_features_tensor and labels_tensor directly\n",
0587     "baseline_accuracy = model_accuracy(input_features_tensor, labels_tensor, model)\n",
0588     "print(f\"Baseline accuracy: {baseline_accuracy}\")\n",
0589     "\n",
0590     "# Initialize an array to store feature importances\n",
0591     "feature_importances = np.zeros(input_features_tensor.shape[1])\n",
0592     "\n",
0593     "# Permute each feature and calculate the drop in accuracy\n",
0594     "for i in range(input_features_tensor.shape[1]):\n",
0595     "    permuted_features = input_features_tensor.clone()\n",
0596     "    permuted_features[:, i] = permuted_features[torch.randperm(permuted_features.size(0)), i]  # Permute feature\n",
0597     "\n",
0598     "    permuted_accuracy = model_accuracy(permuted_features, labels_tensor, model)\n",
0599     "    feature_importances[i] = baseline_accuracy - permuted_accuracy\n",
0600     "\n",
0601     "# Ranking features by importance\n",
0602     "important_features_indices = np.argsort(feature_importances)[::-1]  # Indices of features in descending importance\n",
0603     "important_features_scores = np.sort(feature_importances)[::-1]  # Importance scores in descending order\n",
0604     "\n",
0605     "print(\"Feature importances:\")\n",
0606     "for idx, score in zip(important_features_indices, important_features_scores):\n",
0607     "    print(f\"Feature {idx} importance: {score:.4f}\")"
0608    ]
0609   },
0610   {
0611    "cell_type": "code",
0612    "execution_count": 8,
0613    "metadata": {},
0614    "outputs": [
0615     {
0616      "name": "stderr",
0617      "output_type": "stream",
0618      "text": [
0619       "/tmp/ipykernel_909590/52354147.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
0620       "  inputs = torch.tensor(features, dtype=torch.float32).to(device)\n"
0621      ]
0622     },
0623     {
0624      "data": {
0625       "image/png": 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",
0626       "text/plain": [
0627        "<Figure size 640x480 with 1 Axes>"
0628       ]
0629      },
0630      "metadata": {},
0631      "output_type": "display_data"
0632     }
0633    ],
0634    "source": [
0635     "from sklearn.metrics import roc_curve, auc\n",
0636     "import matplotlib.pyplot as plt\n",
0637     "\n",
0638     "def model_outputs(features, model):\n",
0639     "    model.eval()  # Set the model to evaluation mode\n",
0640     "    with torch.no_grad():\n",
0641     "        inputs = torch.tensor(features, dtype=torch.float32).to(device)\n",
0642     "        outputs = model(inputs).squeeze().cpu().numpy()\n",
0643     "    return outputs\n",
0644     "\n",
0645     "# Calculate model outputs\n",
0646     "probabilities = model_outputs(filtered_inputs, model)\n",
0647     "\n",
0648     "# Calculate ROC curve and AUC\n",
0649     "fpr, tpr, thresholds = roc_curve(filtered_labels, probabilities)\n",
0650     "roc_auc = auc(fpr, tpr)\n",
0651     "\n",
0652     "# Plot ROC curve\n",
0653     "plt.figure()\n",
0654     "lw = 2  # Line width\n",
0655     "plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.3f)' % roc_auc)\n",
0656     "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
0657     "plt.xlim([0.0, 1.0])\n",
0658     "plt.ylim([0.0, 1.05])\n",
0659     "plt.xlabel('False Positive Rate')\n",
0660     "plt.ylabel('True Positive Rate')\n",
0661     "plt.title('Receiver Operating Characteristic')\n",
0662     "plt.legend(loc=\"lower right\")\n",
0663     "plt.show()"
0664    ]
0665   },
0666   {
0667    "cell_type": "code",
0668    "execution_count": 9,
0669    "metadata": {},
0670    "outputs": [
0671     {
0672      "name": "stderr",
0673      "output_type": "stream",
0674      "text": [
0675       "/tmp/ipykernel_909590/52354147.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
0676       "  inputs = torch.tensor(features, dtype=torch.float32).to(device)\n"
0677      ]
0678     },
0679     {
0680      "name": "stdout",
0681      "output_type": "stream",
0682      "text": [
0683       "Prediction scores for displaced tracks (t5_sim_vxy > 0.1):\n",
0684       "Mean score: 0.8229\n",
0685       "Median score: 0.9340\n"
0686      ]
0687     },
0688     {
0689      "data": {
0690       "image/png": 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",
0691       "text/plain": [
0692        "<Figure size 640x480 with 1 Axes>"
0693       ]
0694      },
0695      "metadata": {},
0696      "output_type": "display_data"
0697     }
0698    ],
0699    "source": [
0700     "# Get model predictions\n",
0701     "probabilities = model_outputs(filtered_inputs, model)\n",
0702     "\n",
0703     "# Get displaced track mask\n",
0704     "displaced_mask = np.concatenate(branches['t5_sim_vxy'])[~nan_mask] > 0.1\n",
0705     "\n",
0706     "# Calculate statistics for displaced tracks\n",
0707     "displaced_predictions = probabilities[displaced_mask]\n",
0708     "mean_score = np.mean(displaced_predictions)\n",
0709     "median_score = np.median(displaced_predictions)\n",
0710     "\n",
0711     "print(f\"Prediction scores for displaced tracks (t5_sim_vxy > 0.1):\")\n",
0712     "print(f\"Mean score: {mean_score:.4f}\")\n",
0713     "print(f\"Median score: {median_score:.4f}\")\n",
0714     "\n",
0715     "plt.hist(displaced_predictions, bins=100, histtype='step', linewidth=1.5)  # Outline only, no fill\n",
0716     "plt.yscale('log')\n",
0717     "plt.xlabel(\"DNN Score\", fontsize=12)\n",
0718     "plt.ylabel(\"Frequency (log scale)\", fontsize=12)\n",
0719     "plt.title(\"DNN Score for Displaced T5s\", fontsize=14, weight='bold')\n",
0720     "\n",
0721     "plt.grid(visible=True, which='both', linestyle='--', linewidth=0.5)\n",
0722     "plt.tight_layout()\n",
0723     "plt.show()"
0724    ]
0725   },
0726   {
0727    "cell_type": "code",
0728    "execution_count": 10,
0729    "metadata": {},
0730    "outputs": [
0731     {
0732      "name": "stdout",
0733      "output_type": "stream",
0734      "text": [
0735       "HOST_DEVICE_CONSTANT const float bias_layer1[32] = {\n",
0736       "-1.3837075f, -0.0653152f, -0.7900129f, 0.0714758f, -1.1574365f, -1.4634879f, -0.9317133f, -0.1455518f, -0.0459635f, -0.2055620f, 0.0586231f, -0.8943899f, -0.1009487f, 0.0166031f, -0.5451909f, -0.1384538f, 1.2664700f, -1.8996916f, -0.0025585f, -0.1647783f, -1.9019107f, 0.0707104f, -0.2373025f, 0.0357050f, -0.0048417f, 2.3127339f, -0.0508943f, -0.1116435f, -0.1610904f, -1.6463890f, -1.0739423f, -0.0962902f };\n",
0737       "\n",
0738       "HOST_DEVICE_CONSTANT const float wgtT_layer1[23][32] = {\n",
0739       "{ -0.1881404f, -0.0534256f, 1.6563641f, 0.0401664f, 2.8318353f, 1.5049738f, 1.4111555f, -0.2339872f, 0.0431970f, 0.1220361f, -0.0450153f, -1.6025578f, 0.0394025f, -0.3051167f, 1.9442217f, 0.1599094f, 0.1376955f, 2.4181051f, -0.0226484f, -0.1801709f, -0.4861264f, -0.0268545f, 0.5463807f, 0.2420150f, -0.1238829f, 0.2916382f, 0.1507791f, 0.7952659f, 0.2736979f, 3.2790639f, 1.2062043f, -0.0884467f },\n",
0740       "{ -0.0469924f, 0.2013927f, 0.0307775f, -0.1241788f, -0.0100412f, 0.0422375f, 0.0211071f, -0.0359304f, 0.0451861f, 0.0291862f, -0.2094866f, -0.0013007f, 0.1191471f, 0.0750159f, 0.0184378f, 0.0419437f, -0.0207304f, -0.0444109f, 0.0013400f, -0.0699210f, -0.0668742f, -0.0880825f, -0.0107244f, 0.0363424f, 0.1391699f, -0.0112885f, -0.0060098f, -0.0073863f, -0.0566143f, -0.0224207f, 0.0103718f, -0.0015193f },\n",
0741       "{ 0.4520382f, 0.1227609f, -1.3887709f, -0.0542129f, -3.2003114f, -0.8354173f, -1.3173198f, 0.3292131f, -0.1657729f, -0.1982902f, 0.1599589f, -0.0417666f, -0.1461042f, -1.3237997f, -5.3609071f, -0.0981676f, 0.2922535f, -1.8692241f, -0.0345302f, 0.1810613f, 0.4473544f, -0.0159401f, -0.7293931f, -1.4816793f, -0.1431545f, -0.0955672f, -0.2370718f, -0.7204540f, 0.8451244f, -3.4310548f, -1.3518151f, 0.1551731f },\n",
0742       "{ 0.2670300f, 0.1343590f, 3.0347505f, -0.1783503f, 2.1586559f, 2.4137778f, 2.0080864f, -0.2545274f, -0.1985905f, 0.1653812f, -0.1714860f, 4.1022782f, -0.1045471f, 4.4776497f, 3.3737848f, -0.0849546f, -6.1899095f, 3.6970129f, 0.0007382f, 0.1675882f, 0.6014717f, -0.0287709f, 0.0495882f, 2.2192705f, -0.1043157f, -4.7508621f, -0.0022774f, 0.3766513f, -0.7505829f, 1.9759512f, 1.6747239f, -0.1004091f },\n",
0743       "{ 0.6639504f, -0.0384022f, -10.0415087f, -0.0032648f, 0.3049855f, -2.0427964f, -1.1522077f, 0.0935732f, 0.1232134f, 0.0868663f, -0.0230848f, -1.8257296f, -0.0799238f, 6.8892417f, -1.3941933f, 0.0445172f, 0.9485117f, -2.5238073f, -0.0148513f, 0.2256772f, 0.5914315f, -0.1278037f, 0.1609928f, 11.3438406f, -0.0831544f, 0.1928522f, 0.0361467f, 0.0137040f, 4.9549832f, 2.3954937f, 0.3917757f, 0.1206975f },\n",
0744       "{ 29.6590214f, -0.0836848f, -1.3028307f, -0.1391431f, -0.3703596f, 5.3762760f, 1.8429571f, 21.0697041f, -0.1232606f, 0.0066067f, -0.0308768f, -0.9960231f, 0.1865301f, -1.2142091f, 0.9273136f, 0.0974103f, 1.4067870f, 0.7268439f, 0.0035755f, 0.0619486f, -32.8901024f, -0.1950644f, -0.3978897f, -3.1790049f, -0.1371673f, 0.1569460f, 0.0268667f, -0.4512640f, 0.3055371f, -0.2241473f, -0.6455348f, 0.1178979f },\n",
0745       "{ -2.9178317f, -0.2023720f, -0.2946439f, -0.1851392f, -0.3493766f, -1.5397958f, -1.5902523f, 1.0981250f, -0.1796725f, -0.0540953f, 0.0926500f, 2.0021629f, -0.1277778f, 3.3643394f, -7.5327554f, -0.0084912f, 2.7298651f, 0.2535582f, 0.0474618f, -0.1377846f, -2.2746830f, -0.2016302f, -0.7150622f, 4.4011140f, -0.1688751f, -1.2160714f, -0.0055839f, -1.1319760f, -2.2543004f, 0.6365916f, -1.4942099f, -0.0992425f },\n",
0746       "{ -5.9751196f, -0.1597221f, -3.8946304f, 0.0537821f, 0.4741110f, 3.6895070f, 2.5116272f, 1.7058172f, -0.0860321f, -0.1519644f, 0.1465356f, 1.4165760f, -0.0984433f, 1.6990343f, 4.0953226f, 0.1742475f, -3.2570388f, 3.1653547f, 0.0135764f, 0.0092055f, -5.0966530f, -0.0542810f, 0.4907863f, 0.5900084f, -0.1736992f, -4.9153452f, 0.2017547f, 0.2854181f, 3.1490057f, 0.2885774f, 0.9775900f, -0.2207156f },\n",
0747       "{ 0.3805595f, 0.0308984f, -9.5846119f, -0.0547350f, 1.9641919f, 2.0823991f, 9.9298115f, 0.0344243f, -0.1557834f, -0.1847700f, -0.1195207f, 4.4698248f, 0.1492174f, 0.4272707f, 4.7265644f, 0.0200772f, -14.3444443f, 4.9532328f, 0.0319610f, -0.0645846f, -0.6238102f, 0.1038110f, 0.2483765f, -5.1799927f, 0.0782294f, 16.8777409f, 0.0196593f, 0.8423936f, -8.5921221f, -0.0184179f, -5.7857180f, -0.0551181f },\n",
0748       "{ 17.1570740f, 0.0265437f, -1.4766232f, -0.0528512f, 1.0128449f, 3.1529653f, -0.6560294f, 8.7189465f, -0.1728377f, 0.1245629f, 0.1072764f, 0.2649773f, 0.0254132f, -0.8094708f, 1.8371828f, 0.1586192f, 1.9410020f, 0.9662392f, -0.0839922f, -0.2894930f, -16.5091496f, -0.1079556f, -0.1204132f, -0.9694697f, 0.0537786f, 0.2476868f, 0.0076408f, 0.1025890f, 0.1267423f, 0.4956081f, 0.1457323f, 0.1342634f },\n",
0749       "{ -0.5389574f, 0.1333421f, -4.6338782f, -0.0645123f, -0.6526322f, -3.2958410f, -1.2309581f, -1.0803053f, -0.1170542f, -0.0169311f, 0.1147491f, 2.9890807f, -0.1234096f, 0.6792320f, -3.9311285f, -0.0678321f, -2.7922039f, 4.9413238f, 0.1060735f, -0.1114068f, -2.2443752f, -0.1649915f, -0.3656403f, 2.5320942f, -0.0249616f, -4.5098810f, -0.1773834f, -1.9516623f, -1.6839710f, -0.1365123f, 1.0296160f, -0.0419825f },\n",
0750       "{ -2.4413636f, 0.1075683f, -1.4518708f, 0.0537449f, 0.1154493f, -0.5463845f, 1.3964951f, 2.6729572f, -0.0206257f, 0.1435281f, -0.1819518f, 0.4540120f, -0.1910136f, 1.7696143f, 2.3670278f, 0.1324464f, -0.5837788f, -2.2784615f, 0.0345478f, -0.0980538f, -0.4999657f, 0.1178097f, 0.5756868f, -0.1058674f, 0.1920418f, -3.5473657f, 0.2146371f, 0.2557987f, 1.3935618f, 0.3242345f, 0.2029733f, -0.1844350f },\n",
0751       "{ -0.9069599f, -0.2032758f, -0.5786582f, 0.1395915f, 3.9338124f, -1.6806563f, 0.4269728f, -0.3697720f, -0.0306356f, -0.0341866f, -0.0635755f, 1.8898975f, 0.1968578f, -17.2182655f, 1.4839698f, -0.0541308f, 15.9838457f, 18.5951862f, 0.0078872f, -0.1186571f, -2.4982276f, 0.0033835f, 0.3749593f, -15.0238085f, 0.0595601f, -16.8588371f, 0.1146287f, 0.1274172f, 19.3332062f, -7.0513921f, -5.4852023f, 0.1681230f },\n",
0752       "{ -5.1457887f, 0.0335570f, 1.8620163f, 0.0560381f, -0.6397949f, -4.0867515f, 1.3578068f, -23.9992580f, -0.1034287f, 0.1437906f, 0.1076568f, -0.6930848f, -0.1176134f, 2.2855785f, -0.8021089f, 0.0424611f, -0.6139123f, -3.1381547f, 0.0188163f, -0.1728741f, 0.6676420f, -0.1124282f, 0.1077818f, 2.3839712f, 0.1340676f, 1.3538554f, 0.0421035f, 0.4513423f, -0.1543196f, 0.5120541f, -0.8940096f, -0.1175765f },\n",
0753       "{ 2.1656792f, 0.1638565f, 4.5302448f, 0.0741160f, 3.3850696f, -4.8867540f, 2.8059542f, -0.0023008f, -0.1248942f, -0.0075225f, -0.0082212f, -1.0955724f, -0.1462416f, -1.7098176f, -4.1775723f, 0.1950609f, 3.6847639f, 1.6520064f, 0.0310502f, -0.0430167f, 3.4527576f, 0.1453262f, -1.0126116f, 1.8785841f, -0.0615105f, 1.0451943f, -0.2653875f, -1.2223006f, -1.0100641f, 1.2076828f, 0.4882897f, -0.0618375f },\n",
0754       "{ 2.4578559f, -0.1464199f, -1.3086185f, 0.1208716f, -0.2079897f, -2.7138259f, -1.4107026f, -0.4483974f, -0.1599056f, 0.0242936f, 0.1326804f, 0.8664415f, 0.0588684f, 0.7366717f, 2.3159802f, -0.1917707f, -2.0800066f, -7.5100355f, 0.0585225f, 0.1582773f, 1.8128076f, -0.0756957f, 0.8521049f, 0.5539182f, -0.1738797f, -0.2020151f, 0.2219591f, 0.1088298f, -1.9535940f, 2.4130275f, -0.0741222f, 0.1156681f },\n",
0755       "{ -0.4152933f, -0.0679605f, -0.5760314f, -0.0201883f, -14.1784763f, 0.7755737f, -19.5469246f, 0.0381304f, 0.0160074f, 0.1124380f, -0.0478151f, -2.3719466f, 0.0819727f, -12.5069208f, 2.0468810f, 0.0964909f, 7.8784809f, -6.3555703f, -0.0429914f, -0.0162720f, -0.9493829f, 0.0296786f, -0.0244959f, -12.6325788f, -0.1871653f, -9.8338795f, 0.0391840f, -0.1199073f, -11.7859421f, 8.7398720f, 19.4971046f, -0.1954873f },\n",
0756       "{ -4.8962007f, -0.1695992f, 0.7760146f, -0.0199836f, -0.0576061f, -6.0196476f, -2.3023551f, -20.0125084f, -0.1957836f, -0.0993785f, 0.1109372f, -0.0710161f, -0.0553650f, 0.2546394f, -1.7578228f, 0.1498791f, -2.6269529f, 1.3973731f, 0.0464059f, -0.2307575f, 1.6730053f, -0.0038867f, 0.1040150f, 2.6721606f, 0.2027777f, -1.2358316f, -0.0587254f, 0.0610504f, -0.1700777f, -0.4323797f, 1.0359807f, -0.0127435f },\n",
0757       "{ 1.1245984f, -0.1806923f, -1.5868790f, 0.1536594f, 1.6837788f, -1.6474472f, -3.9225550f, 0.4506312f, 0.1854908f, -0.1023232f, -0.0306957f, -0.8615071f, 0.0945480f, 2.0585704f, 0.6044773f, 0.1269336f, 2.4720187f, -4.5123949f, -0.0657749f, 0.1738364f, 2.4188614f, 0.0038840f, -0.2019601f, -0.3842189f, -0.0493631f, 3.6777370f, -0.1003436f, 0.6174496f, 1.0476112f, 2.7601521f, 0.9059890f, -0.1691816f },\n",
0758       "{ 1.9658293f, 0.2083382f, 1.7833723f, 0.0662620f, -0.3932888f, -1.0642430f, 0.1807114f, -1.1486723f, -0.0177136f, -0.1706942f, 0.1730027f, 0.6712329f, 0.0485299f, 0.6379296f, -0.2880911f, -0.1993632f, -0.9471832f, 1.9425983f, 0.0328524f, 0.0777725f, 0.6454380f, 0.0143852f, 0.0192997f, 1.6793132f, -0.1872064f, -1.5757623f, 0.0242778f, -0.5992475f, 2.2148299f, -3.5215647f, -2.9748621f, 0.0112703f },\n",
0759       "{ 0.3737165f, 0.0361593f, -0.1075856f, -0.0312021f, -0.0786010f, 1.3149793f, 0.0237401f, -0.0819654f, -0.1388431f, -0.0306386f, -0.0704427f, -2.3997226f, -0.1392045f, 0.7729424f, 0.1253861f, -0.0819755f, -0.7590774f, -0.3295609f, -0.0172208f, -0.0551179f, 0.4599459f, -0.1143881f, 2.7430685f, 0.3621114f, -0.1475701f, 0.2296079f, -2.2224922f, -0.9080986f, 0.2101683f, 0.1190262f, -2.2205217f, -0.0811555f },\n",
0760       "{ 0.3946800f, -0.1204188f, 0.0543225f, -0.0392627f, 1.9454094f, 0.1865290f, 1.5276426f, -0.0342965f, 0.0117116f, -0.1873923f, -0.1045035f, 1.8535231f, -0.0207077f, 0.0981549f, -0.0327459f, -0.1486938f, 0.6359531f, -0.1314566f, -2.1469448f, -0.1665767f, 0.5134121f, -0.0341647f, -2.1786075f, -0.5976576f, 0.0111857f, 0.3272055f, 2.1917374f, -1.6247722f, 1.6025572f, -1.9965295f, 0.3347488f, 0.1113990f },\n",
0761       "{ 0.0340557f, -0.1659652f, -0.0042457f, 0.0010229f, -2.1550148f, -0.4728722f, -1.3667214f, 0.2625635f, -0.0302200f, -0.0322885f, 0.0227866f, 0.6977839f, 0.0050141f, -1.6183628f, 0.0869662f, -0.0775411f, 0.4754244f, 0.4596581f, 2.1509945f, -0.0313832f, 0.0336208f, -0.1547154f, -0.6017126f, 0.0369996f, -0.1102583f, -0.5788267f, 0.0017006f, 2.6352038f, -1.7847317f, 1.7510574f, 2.1478791f, -0.2251654f },\n",
0762       "};\n",
0763       "\n",
0764       "HOST_DEVICE_CONSTANT const float bias_layer2[32] = {\n",
0765       "-0.2689391f, 1.5461178f, -0.2424639f, 0.4424149f, -0.0411816f, -4.1070848f, 1.4709516f, -0.2439820f, -0.1750926f, 2.8802166f, -0.1573734f, -1.3724055f, 0.3671952f, 1.8267332f, 1.5655776f, -0.7323843f, 1.6318209f, 2.2198663f, -1.5951139f, -0.0870247f, 0.2806863f, -0.2407108f, 0.1310665f, -0.5246177f, 0.1914421f, -0.3386542f, -0.6310596f, 3.2995102f, 0.7519229f, -0.1565450f, -0.1496341f, 1.0073272f };\n",
0766       "\n",
0767       "HOST_DEVICE_CONSTANT const float wgtT_layer2[32][32] = {\n",
0768       "{ -0.1731049f, 1.7775618f, -0.2532010f, -0.2902778f, -0.1392802f, 4.2428946f, -0.1866968f, -0.1800365f, -0.0634398f, 0.0763313f, 0.0472901f, -0.8030146f, 0.3161853f, -1.0713238f, -4.6514492f, -0.3908085f, 1.1607268f, 0.8834935f, -0.1194544f, -0.0785166f, 0.4967587f, -0.0558136f, -0.9601135f, -0.1001592f, 3.4427991f, -0.2144053f, -0.3632556f, 0.0117088f, 0.1742481f, -0.2540179f, -0.1705156f, -0.2627344f },\n",
0769       "{ -0.1478276f, -0.1659575f, 0.1602777f, -0.0758106f, 0.1067696f, -0.0247068f, -0.1123443f, -0.1724832f, -0.0013103f, -0.0685904f, 0.1537329f, 0.1042632f, -0.0360880f, -0.0679077f, 0.0672719f, 0.1597116f, -0.0150259f, 0.0367102f, -0.0545881f, -0.0693004f, -0.1008447f, -0.0672846f, -0.1395939f, -0.0324785f, -0.1051702f, -0.0530534f, -0.1019061f, -0.0921245f, 0.1195077f, 0.0453448f, 0.0257045f, -0.0622537f },\n",
0770       "{ -0.0363173f, -0.1990481f, -0.0452148f, 0.4074381f, -0.0731660f, -0.0823270f, 0.3154473f, -0.1909118f, -0.0165690f, 0.1325824f, -0.0760181f, 0.7768906f, -0.2702211f, -0.6023573f, 1.5904741f, 0.2384946f, 0.7610655f, -2.8705251f, 0.5754877f, -0.1587478f, -0.5708794f, -0.3421216f, 0.5023443f, 1.2806857f, 0.2158970f, -0.1364033f, -0.3398291f, 0.9066412f, -1.2935438f, 0.0273695f, -0.1850613f, -0.9301611f },\n",
0771       "{ -0.1281746f, 0.1695392f, 0.0805936f, -0.0598281f, 0.1266985f, -0.1697189f, -0.1091505f, -0.1569477f, 0.0363969f, -0.0628394f, 0.0107523f, 0.0659535f, -0.0568244f, -0.1299786f, 0.0005438f, -0.0806242f, -0.0806848f, -0.0919798f, -0.0748445f, 0.0792912f, 0.0022868f, 0.0211520f, -0.0183716f, 0.1279848f, -0.1518286f, -0.0113527f, 0.0824359f, -0.0178597f, 0.0272009f, 0.0288935f, 0.0123459f, 0.1685353f },\n",
0772       "{ 0.1099675f, -0.3914332f, -0.0647218f, -0.8259028f, -0.0283726f, -0.0860217f, -2.0489185f, 0.1042144f, 0.1024824f, 0.0735443f, -0.1235109f, -3.3674469f, -0.1799957f, -7.1867313f, 1.6053666f, -0.5203959f, 0.8686391f, -0.0675404f, -2.8893898f, -0.0796400f, 1.2672142f, -0.0371844f, -1.8065344f, -2.2551982f, 0.0355568f, 0.0672171f, 0.7150316f, 1.3620002f, -0.4106106f, 0.0126076f, 0.0408083f, 1.5958146f },\n",
0773       "{ 0.0525989f, 1.8947815f, -0.2513640f, -0.3715420f, -0.1752283f, 1.3911799f, -0.7633898f, -0.1716654f, -0.0145629f, -1.7601604f, -0.1943324f, -0.5716376f, -0.8281464f, -0.0308049f, -1.4709659f, -0.4294116f, -0.1030817f, -0.1823493f, 0.7561242f, -0.1608112f, 0.3980689f, -0.2464017f, -1.3065518f, 0.0875702f, -0.1504322f, -0.0352198f, -0.4051513f, 0.7010455f, -0.2363433f, -0.1118084f, -0.1329087f, -0.3257700f },\n",
0774       "{ -0.1209070f, 0.1677164f, -0.1353413f, -0.0410048f, -0.1432644f, 0.2649301f, 0.2247741f, -0.0425357f, -0.2644008f, 1.4204332f, -0.2540753f, 0.2481354f, 1.9494507f, -0.2003033f, -0.5938342f, -0.3314930f, 1.5038266f, -2.4000788f, -1.6202501f, -0.0256936f, -0.2890913f, -0.2113032f, 0.9030544f, 1.1483711f, 0.0545346f, -0.1961582f, -0.2267976f, 0.2372836f, 2.5995049f, -0.1469661f, -0.1017130f, 1.6176132f },\n",
0775       "{ 0.0542207f, 2.7658713f, -0.1700335f, -0.3357265f, -0.1097085f, 1.6508883f, 0.0132292f, 0.1211861f, -0.0852982f, 0.9232512f, 0.0202751f, 0.3138782f, 0.2674713f, 0.1247260f, 0.3859081f, 0.3961721f, 1.0556988f, 0.8574673f, -0.1462571f, -0.1600272f, 0.4117427f, -0.1561815f, 0.0553897f, -0.2753994f, 5.8420453f, 0.0883128f, 0.3594444f, -0.7174141f, 0.5683901f, 0.0096710f, -0.0957449f, -0.0195320f },\n",
0776       "{ 0.1561092f, -0.0417566f, -0.1044470f, 0.1186895f, -0.1195878f, 0.0446987f, -0.1386125f, -0.0103878f, 0.1173026f, 0.1349312f, -0.0676422f, -0.1452308f, 0.0093872f, 0.0069650f, 0.1739093f, -0.1592752f, -0.1329019f, -0.0459163f, -0.1511888f, -0.0040456f, 0.0065862f, 0.0106182f, 0.0318060f, 0.1003269f, 0.0249398f, 0.1661194f, -0.0286407f, -0.1062361f, 0.0026465f, -0.0091479f, -0.1493473f, 0.0519762f },\n",
0777       "{ -0.0702637f, 0.1154817f, -0.0680643f, 0.1447217f, 0.1394082f, -0.0691432f, 0.0939426f, 0.0483852f, 0.1437123f, -0.1085759f, 0.0333924f, -0.0683726f, 0.0707103f, -0.0723069f, 0.0124601f, -0.0309495f, -0.0308395f, -0.0695953f, -0.1078720f, 0.0858701f, -0.0773453f, 0.0477413f, 0.0615588f, 0.1656474f, 0.1718751f, -0.1125762f, 0.1753366f, -0.0557704f, 0.0921221f, 0.0372290f, -0.1084552f, -0.0438967f },\n",
0778       "{ -0.0557757f, 0.0694144f, 0.1150911f, -0.0202319f, 0.0661389f, -0.0928373f, 0.0441888f, -0.0028318f, -0.0039446f, 0.0294675f, 0.1353384f, 0.0427515f, 0.0695194f, 0.1329748f, 0.1339706f, 0.0713900f, -0.1384726f, 0.0925476f, 0.1581103f, 0.0100842f, -0.1248652f, -0.0173615f, 0.1637451f, -0.0025173f, -0.0331219f, -0.0335269f, 0.0949441f, 0.0538645f, 0.0834281f, 0.0137191f, -0.1360130f, 0.0074489f },\n",
0779       "{ -0.0949665f, -0.2181539f, 0.0871969f, 3.0772011f, -0.1152011f, -0.0022047f, 1.2700632f, -0.1173392f, -0.1678371f, -1.3448639f, -0.2893313f, 1.5105180f, -0.6029126f, -1.1568675f, 1.4823192f, 0.1635401f, -2.2136483f, -1.4164798f, -0.4795305f, -0.0807557f, -1.6675406f, -0.0992591f, 2.1212378f, -0.9400231f, -0.5339298f, -0.0342672f, -2.3564072f, 1.3407421f, -3.8635128f, -0.1171367f, -0.0364181f, -3.2491686f },\n",
0780       "{ -0.1047117f, -0.0540412f, -0.1137928f, 0.1582367f, -0.0982449f, 0.0511854f, -0.0805884f, -0.1141258f, 0.0931992f, -0.0227052f, 0.0780590f, -0.1288135f, -0.1186576f, -0.0754066f, -0.1234059f, -0.0091936f, 0.0205475f, 0.1640417f, -0.1527465f, 0.0068472f, -0.1239804f, -0.0448335f, -0.0061169f, -0.0078998f, 0.0253047f, 0.0712901f, 0.0024753f, -0.0259875f, -0.1238613f, 0.1096537f, -0.0953007f, 0.1385384f },\n",
0781       "{ 0.0521762f, 1.4885306f, -0.1298001f, 2.3033395f, -0.1589162f, -0.8458843f, 0.0631668f, -0.1424429f, -0.0384785f, 0.5599840f, 0.0008631f, -1.5839294f, 1.9202064f, 0.6930331f, 0.4948464f, -0.6195241f, -3.0526664f, 3.1423819f, -1.3433597f, -0.1167206f, -1.3491610f, -0.0901343f, -1.2291449f, 3.5039587f, 0.4674770f, -0.3027362f, 0.8279622f, 0.3417586f, 0.1367343f, -0.1085793f, -0.1048759f, 1.2729272f },\n",
0782       "{ -0.0029521f, 0.2439991f, -0.0858953f, -2.7804739f, -0.0220416f, 0.0256599f, -0.3304259f, -0.0586597f, -0.0459698f, 0.1670698f, -0.1359344f, -0.3957845f, -1.6954739f, 0.3318155f, 0.9375985f, 0.5211958f, 0.6071047f, -3.4249072f, 1.3199407f, 0.0136374f, 1.2692807f, 0.0233104f, -0.0731508f, 2.2171400f, -0.6052189f, -0.0698463f, 1.6376522f, -1.1908000f, -0.1706121f, -0.0380146f, 0.0144418f, 1.5177792f },\n",
0783       "{ -0.0314772f, 0.0523589f, -0.0517322f, -0.0100344f, 0.0714635f, -0.1646974f, 0.0800682f, 0.1132821f, -0.0028872f, -0.1239987f, -0.1322138f, -0.1059789f, 0.1752418f, 0.0475279f, -0.0046871f, 0.1574167f, -0.0231106f, -0.0261228f, 0.0236005f, 0.1663371f, 0.1059707f, 0.1229704f, 0.1427562f, -0.1648343f, 0.0992667f, -0.0631751f, -0.1411413f, -0.0999486f, -0.0972435f, -0.1422556f, 0.0973614f, -0.0156000f },\n",
0784       "{ -0.1309903f, -0.5060971f, -0.1911870f, 2.2349114f, 0.1010354f, 0.5538697f, 1.8757060f, -0.1538645f, -0.2073075f, -1.8350753f, 0.0532570f, 1.8151909f, -0.6800886f, 0.2615838f, -0.6204563f, -0.1238837f, -0.4772464f, -2.4070835f, -0.2783994f, -0.0211087f, -4.4925098f, -0.0790045f, 1.3566529f, -0.3650998f, -0.4658130f, -0.0479139f, -1.9361999f, 2.1485121f, -3.1108823f, -0.0020647f, -0.0489678f, -0.4781263f },\n",
0785       "{ -0.0099352f, -1.9572417f, 0.0918592f, 0.7327217f, -0.0609625f, -0.1969659f, 0.1922992f, -0.1091586f, -0.2125459f, -1.9542989f, -0.1648019f, -0.9355955f, 0.9144324f, -5.0530005f, -0.2265045f, -0.5638458f, 4.4370432f, -2.0318019f, -1.5679311f, 0.0221776f, -0.4063498f, -0.1160609f, 0.9651156f, -0.2401051f, 0.1903293f, -0.2355373f, 0.2334733f, 0.1025979f, 0.7150746f, 0.0315593f, -0.0001765f, 0.0137871f },\n",
0786       "{ 0.0320691f, -1.8876421f, -0.1241799f, -3.1652985f, -0.1528286f, 2.1882250f, -2.5907574f, 0.0210803f, -0.1545521f, 0.7706368f, -0.1652040f, -4.1518817f, 4.2974262f, 0.3074523f, 3.3711803f, -37.9055862f, 1.0623894f, 0.4360786f, -2.6417589f, 0.1113010f, 3.8902094f, -0.1616735f, 0.5595753f, 1.5364015f, -2.4740698f, -0.0240434f, -28.0232792f, 0.6092473f, 1.6978041f, -0.0458809f, 0.0664777f, 0.2603019f },\n",
0787       "{ 0.1044999f, 0.0054908f, 0.1407564f, -0.1701076f, -0.1274551f, 0.0443607f, 0.1182709f, -0.1103420f, -0.1343671f, -0.0042888f, -0.1611361f, 0.0154269f, 0.2285106f, 0.0870507f, 0.0914433f, 0.0657276f, -0.1664300f, -0.0342912f, 0.1037545f, -0.1175308f, 0.1135652f, 0.1325845f, -0.1459545f, -0.2156865f, -0.1673723f, -0.1156510f, 0.0179541f, 0.0541515f, 0.0957617f, -0.1297485f, 0.1045326f, 0.2950188f },\n",
0788       "{ -0.1401742f, -2.8181052f, -0.0588381f, -0.1517100f, -0.0608850f, -3.5837226f, -0.1528927f, -0.0211265f, 0.0881796f, -0.4448619f, -0.1457623f, -0.8828475f, 0.1261238f, -1.0495204f, -3.7918513f, -0.4645159f, -0.0800092f, 0.0624971f, 0.1528609f, -0.1069645f, 0.4319421f, 0.0651448f, -0.6571375f, -0.0323338f, -4.6534319f, -0.0538999f, -0.2221518f, 0.0972160f, 0.1496329f, 0.0570569f, -0.1125795f, -0.0153687f },\n",
0789       "{ -0.1065502f, 0.0606179f, -0.1400291f, -0.0220975f, -0.0613350f, -0.0038843f, -0.0132201f, 0.1678067f, 0.1008587f, -0.1255144f, -0.0675021f, -0.0475353f, 0.0278098f, 0.0527470f, -0.0089845f, -0.0622052f, 0.1088723f, 0.0053812f, 0.0627310f, -0.0226460f, -0.1096366f, -0.0505830f, -0.0301058f, -0.0775778f, -0.0008928f, -0.1157909f, 0.0544982f, 0.0430219f, -0.0134386f, -0.1095094f, 0.1215172f, 0.0081556f },\n",
0790       "{ -0.1747307f, -0.7465636f, -0.0497346f, -2.0686443f, 0.0190713f, -2.9156351f, -5.4731860f, -0.0728399f, -0.0845178f, -14.8429976f, -0.1068359f, 1.8549156f, -3.1135283f, -0.0907917f, -0.0262453f, -8.8010912f, -4.3007965f, -1.6772208f, -0.2576891f, -0.0163111f, -7.8583646f, 0.0697906f, -0.0943863f, -0.7450574f, 1.1493169f, 0.0921000f, -0.2395420f, 0.5794312f, -4.2405462f, -0.0910322f, -0.1381017f, -1.0270567f },\n",
0791       "{ -0.0446755f, -0.8131990f, -0.1741483f, -1.7555307f, 0.0153283f, 0.0734032f, -0.5930048f, -0.0398877f, -0.0215982f, 0.0497884f, -0.0504920f, 0.0942539f, -1.1370168f, -0.8821361f, -0.0879569f, 0.3811991f, 1.2224945f, 0.3782545f, 1.4800016f, 0.0494110f, 1.7101970f, -0.2885793f, -0.1778114f, -1.3913733f, -0.0944610f, -0.3578439f, 0.3491475f, -3.0349872f, 0.8044587f, 0.0928676f, -0.0395946f, 0.2008810f },\n",
0792       "{ 0.0721043f, -0.1181163f, 0.0108281f, -0.1215726f, 0.1285277f, 0.0851443f, 0.0791321f, 0.1765833f, -0.0324889f, -0.0150838f, -0.0051942f, 0.1685798f, 0.1521861f, 0.0283858f, 0.0326072f, 0.0346215f, -0.1081120f, -0.0745824f, -0.1762613f, 0.0901582f, 0.1335704f, 0.1599123f, -0.0097813f, 0.0364541f, -0.0391450f, -0.0079635f, 0.1014886f, 0.0130333f, 0.0438304f, -0.0074333f, 0.0845035f, -0.0471010f },\n",
0793       "{ 0.0360538f, -0.9701002f, -0.2217611f, -1.1626705f, 0.0548465f, 0.6605385f, -0.6693703f, -0.1432099f, -0.0754442f, -0.2380328f, -0.0754142f, -2.3242903f, 3.5773275f, 0.0707042f, 0.2052065f, -1.3753067f, -0.8530636f, 3.1850073f, -0.2901604f, -0.1291050f, -4.4672642f, -0.2425279f, 0.1252670f, 0.4261391f, -0.8620862f, 0.1153403f, -0.1999598f, -4.7756801f, 2.8851914f, -0.1340472f, 0.0482952f, 1.7996837f },\n",
0794       "{ -0.1654812f, 0.9604513f, 0.1770310f, -16.5736618f, -0.0350192f, -0.5557595f, -35.3047371f, -0.1299658f, 0.0065243f, -3.0823336f, 0.0351931f, 4.9456911f, -1.4382623f, -1.6900688f, -1.9084880f, -3.1811504f, -8.0212736f, -7.3994560f, 4.9219728f, 0.0433824f, 0.6197430f, 0.0308996f, 5.2004323f, 0.5327767f, 1.0885966f, 0.1487215f, -21.4211712f, -1.8733859f, 1.9195696f, -0.0539309f, -0.0795544f, -3.1121061f },\n",
0795       "{ -0.0058153f, 1.7521383f, -0.2205407f, 2.6318321f, -0.0038140f, -1.4131194f, 3.0181022f, 0.0373498f, -0.1246315f, -1.8323456f, -0.1470954f, 2.9131169f, 1.1522563f, 0.6036215f, -3.3962972f, 7.0906253f, -1.5353408f, -0.2648884f, 0.5501783f, -0.2262681f, -2.4874980f, -0.0533402f, 3.0222948f, 0.3296265f, 1.4057258f, 0.0185255f, 6.1208682f, 0.7210779f, -0.3055671f, -0.2595702f, -0.1286864f, 0.6510819f },\n",
0796       "{ -0.2145578f, 0.4758183f, -0.1186396f, -0.6096930f, -0.1574199f, -0.1929667f, -0.6877209f, -0.2098342f, 0.0726678f, 0.1379885f, 0.0710437f, -1.1860796f, 0.6582619f, 0.2388466f, 0.0458675f, -0.0634391f, -0.1678368f, -8.2454395f, -0.6461441f, -0.2063597f, 0.0304686f, 0.0319904f, -1.0730971f, 1.1281222f, 0.1292592f, -0.3054110f, 0.7732272f, -1.0069786f, -0.0847367f, -0.2342585f, -0.1553642f, 1.5100089f },\n",
0797       "{ -0.1022291f, 2.7367072f, -0.1738961f, -1.0328600f, -0.0864617f, -0.3224345f, -2.6092832f, -0.2382921f, 0.0578183f, 0.4115438f, 0.0121692f, -1.0689495f, 0.5158959f, 2.9600139f, 0.8839240f, -0.7147520f, -2.7168157f, 1.2148006f, 1.5884653f, -0.1227511f, 1.3176637f, -0.1335970f, -1.4691980f, 1.1131358f, -0.1302031f, 0.0779746f, 0.2622980f, 0.0837635f, 2.7756395f, -0.0315265f, 0.0868374f, -4.2980185f },\n",
0798       "{ 0.0228074f, 2.1787968f, -0.1889012f, -0.8560471f, -0.1063542f, -0.2869910f, 0.2767612f, -0.1183861f, -0.0992468f, 2.1517978f, -0.0428540f, 1.0697522f, 1.9683092f, 2.1042306f, -0.0426359f, -0.3499008f, -0.9989156f, 0.0880459f, 2.9753070f, -0.1941337f, -3.1616704f, -0.0093505f, 1.4922180f, 2.8480091f, 0.2656264f, -0.1299839f, -1.0458518f, -1.6748481f, -3.1420829f, -0.1360553f, -0.1117443f, -1.3989290f },\n",
0799       "{ -0.0246332f, 0.1165779f, 0.0255498f, -0.0601489f, 0.1545041f, -0.0977981f, 0.1242626f, -0.1533627f, -0.1294386f, -0.0231293f, -0.1460808f, 0.1763088f, 0.0953614f, -0.0716483f, -0.1003436f, 0.0804519f, 0.1373295f, -0.0686773f, 0.1198382f, 0.1519430f, 0.1640775f, -0.1675753f, 0.0790529f, -0.1521838f, 0.0378523f, 0.1039687f, -0.0701027f, 0.0509319f, 0.1355647f, 0.0978021f, 0.0391430f, 0.0241266f },\n",
0800       "};\n",
0801       "\n",
0802       "HOST_DEVICE_CONSTANT const float bias_output_layer[1] = {\n",
0803       "-0.7420582f };\n",
0804       "\n",
0805       "HOST_DEVICE_CONSTANT const float wgtT_output_layer[32][1] = {\n",
0806       "{ 0.0381968f },\n",
0807       "{ 1.0667214f },\n",
0808       "{ 0.0505496f },\n",
0809       "{ -1.5677565f },\n",
0810       "{ 0.0066824f },\n",
0811       "{ -0.9951485f },\n",
0812       "{ 0.9438043f },\n",
0813       "{ 0.0068631f },\n",
0814       "{ -0.0216870f },\n",
0815       "{ 0.6560486f },\n",
0816       "{ -0.0235629f },\n",
0817       "{ 0.9653404f },\n",
0818       "{ 0.6641668f },\n",
0819       "{ -0.5351945f },\n",
0820       "{ -0.5303048f },\n",
0821       "{ 1.9339687f },\n",
0822       "{ 0.4359012f },\n",
0823       "{ -0.7492802f },\n",
0824       "{ -0.5728400f },\n",
0825       "{ 0.0473893f },\n",
0826       "{ -0.5091293f },\n",
0827       "{ -0.1926489f },\n",
0828       "{ -0.6562935f },\n",
0829       "{ -0.5583456f },\n",
0830       "{ -0.7618014f },\n",
0831       "{ -0.0316967f },\n",
0832       "{ 1.1637378f },\n",
0833       "{ -0.5158406f },\n",
0834       "{ -0.5268564f },\n",
0835       "{ 0.0735416f },\n",
0836       "{ 0.0270067f },\n",
0837       "{ -0.5614370f },\n",
0838       "};\n",
0839       "\n"
0840      ]
0841     }
0842    ],
0843    "source": [
0844     "def print_formatted_weights_biases(weights, biases, layer_name):\n",
0845     "    # Print biases\n",
0846     "    print(f\"HOST_DEVICE_CONSTANT const float bias_{layer_name}[{len(biases)}] = {{\")\n",
0847     "    print(\", \".join(f\"{b:.7f}f\" for b in biases) + \" };\")\n",
0848     "    print()\n",
0849     "\n",
0850     "    # Print weights\n",
0851     "    print(f\"HOST_DEVICE_CONSTANT const float wgtT_{layer_name}[{len(weights[0])}][{len(weights)}] = {{\")\n",
0852     "    for row in weights.T:\n",
0853     "        formatted_row = \", \".join(f\"{w:.7f}f\" for w in row)\n",
0854     "        print(f\"{{ {formatted_row} }},\")\n",
0855     "    print(\"};\")\n",
0856     "    print()\n",
0857     "\n",
0858     "def print_model_weights_biases(model):\n",
0859     "    # Make sure the model is in evaluation mode\n",
0860     "    model.eval()\n",
0861     "\n",
0862     "    # Iterate through all named modules in the model\n",
0863     "    for name, module in model.named_modules():\n",
0864     "        # Check if the module is a linear layer\n",
0865     "        if isinstance(module, nn.Linear):\n",
0866     "            # Get weights and biases\n",
0867     "            weights = module.weight.data.cpu().numpy()\n",
0868     "            biases = module.bias.data.cpu().numpy()\n",
0869     "\n",
0870     "            # Print formatted weights and biases\n",
0871     "            print_formatted_weights_biases(weights, biases, name.replace('.', '_'))\n",
0872     "\n",
0873     "print_model_weights_biases(model)\n"
0874    ]
0875   },
0876   {
0877    "cell_type": "code",
0878    "execution_count": 11,
0879    "metadata": {},
0880    "outputs": [],
0881    "source": [
0882     "# Ensure input_features_tensor is moved to the appropriate device\n",
0883     "input_features_tensor = input_features_tensor.to(device)\n",
0884     "\n",
0885     "# Make predictions\n",
0886     "with torch.no_grad():\n",
0887     "    model.eval()\n",
0888     "    outputs = model(input_features_tensor)\n",
0889     "    predictions = outputs.squeeze().cpu().numpy()\n",
0890     "\n",
0891     "full_tracks = (np.concatenate(branches['t5_isFake']) == 0) * (np.concatenate(branches['t5_pMatched']) > 0.95)"
0892    ]
0893   },
0894   {
0895    "cell_type": "code",
0896    "execution_count": 12,
0897    "metadata": {},
0898    "outputs": [
0899     {
0900      "data": {
0901       "image/png": 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fgxCXgxMhCl/PgIAAYTAYREpKinB0dBSLFy+2qXtO8XvXt29fMXToUEv61fbt06eP8PDwsPoOuFLxd8yV7+fs2bMFYOmaGh0dLTQajXj66aet8mVmZoqAgADLRXbx90e7du2sPjPnzp0TWq220oKTiIgISxe88pR3vhd/zl977bWr7l/8fZ2TkyPuuece4e7uLjZu3GiV56mnnhIeHh7XdExCCJGQkCAA8e6775baVvy5KT7/ij322GNCpVKJ8+fPCyEq7talVqtFnz59KqxLo0aNxIABA0qlx8bGCqBUl8grldetqzh4mj17tlV6cRCxcOHCq5a7Zs0a8dprr4nff/9dbN26VXzyySciJCRE6PV6sX///lL5T548KQCxYMGCq5YrSXWN7NYlWRFXaT729vbm5Zdf5pdffrlqs3hJL730El5eXrz88svXVA9HR0eWL1/O0aNH+eCDD3jggQdISkri7bffpmnTppauJP/++y8ZGRk8+eST5Q7oPXHiBLGxsYwYMcKqm42Liwv33HMPO3bsICcnx2qfe+65x+rxqlWrUBSFRx55BKPRaPkJCAigdevWFQ7gHDt2LLm5uVYDiBctWoSjoyMPPfQQAF5eXkRERDBnzhzmzp3Lf//9h9lstvk1K0vxQPjirkfF3STOnz9fZjezvn374u/vb3msVqu5//77OXXqlKWLU6dOnVizZg2TJ09my5Yt5ObmXlOdZs2axe7du0v9lHzeq/n555/p3r07Li4uaDQatFotX331FceOHbumehTbvHkzQKnuI506daJp06alXidPT0/69OlzXc9VkfLO4SvTrzZ4veS2CRMmcOnSJY4dO0ZCQgJ33XUX3377LQcPHmTOnDlcunSJhx9+GF9fXyIiIvjss8+uqb5jxowhISGBNWvWWAb+X9mVpqTPPvuMdu3a4eTkZHnvNm3aZNN7l5OTw9atW7nvvvss3X+u5q677rJ63KpVKwBLl8Z169ZhNBoZOXKk1WfaycmJnj17Wj7Txd8fDz30kNVrGx4eTrdu3Sqsh63uuusuSxe8kmw539esWQMUzrxWkZSUFPr06cOuXbv4+++/6du3r9X2Tp06kZaWxoMPPsjKlStJTk62qf7FXeb8/PzK3O7q6lrqPXnooYcwm81s27bNpucwGo1lfm+VxdbPyLX4888/gdLfFffeey96vb7Cug0cOJAZM2YwePBgbr31ViZOnMhff/2FoihMmTKlVP7i1/LixYvXVV9Jqq1kcCJZZGdnk5KSQlBQULl5iqdmfOmll2wq083Njddff521a9daLgKvRdOmTXn22WdZsmQJ0dHRzJ07l5SUFN544w2gsB87QEhISLllFM92EhgYWGpbUFAQZrOZ1NRUq/Qr8yYkJCCEwN/fH61Wa/WzY8eOCv+BN2/enI4dO7Jo0SIATCYTS5Ys4e6778bLywvAMi5lwIABzJ49m3bt2uHr68szzzxT5jTOFcnMzOTnn3+mU6dO+Pr6kpaWZpnBSVEUS+BSUkBAQLlpxa/jRx99xMsvv8yKFSvo3bs3Xl5eDBkyhJMnT9pUrwYNGtChQ4dSP2VdmF3p119/5b777iM4OJglS5awfft2du/ezdixYy1jKa5VRefHlbPllJXvRhXP1FPWzDyXLl2ynCPFecvKl52dTUFBgVVeAL1ebxlLk5KSwvPPP8+8efPw9PTkf//7H5cuXeLUqVP8+OOPvPDCC9f0OQ0PD6dv3758/fXXfP311zzwwAPodLoy886dO5cJEybQuXNnfvnlF3bs2MHu3bsZOHCgTQFuamoqJpPpqp/1kkrOfgSFNzwAy3MlJCQA0LFjx1Kf6aVLl1o+08Wv9dU+G5WhrPPK1vM9KSkJtVptU32ioqLYuXMngwYNokWLFqW2jxgxgq+//prz589zzz334OfnR+fOndmwYcNVyy1+XZ2cnMrcXtbNhyu/WypLeZ+R4lker/yM2ColJQWNRlMqOFYUhYCAgOs6jnr16nHLLbewY8eOUtuKX8trvQEkSbWdpuIskr34448/MJlM9OrVq9w8zs7OTJs2jccff5w//vjDpnInTJjAhx9+yMsvv8yECROuu36KovDcc8/x5ptvcvjwYQDLP4krBy6XVHyREhcXV2pbbGwsKpUKT0/PUs9Vko+PD4qi8Ndff1kuckoqK+1KY8aM4cknn+TYsWOcOXOGuLi4UgOHw8PDLUFDVFQUP/30E9OmTaOgoOCa72r/8MMP5OTksGvXrlLHB4WDRlNTU622lZxr/8q04tdRr9czffp0pk+fbrlrPnnyZO68806OHz9+TXW8VkuWLKF+/fosXbrU6j0qb+0WW5Q8P6688I2NjcXHx8cq7Xrvul5N8UXioUOHSk05fOjQIauLyJYtW/Ljjz8SHx9vdTF66NAhq7LK8vzzz9O+fXsefPBBoPCO+6JFi3B3d6djx47079+f1atX07t3b5vrPnbsWB555BHMZjMLFiwoN9+SJUvo1atXqTy2Bt5eXl6o1eqrftavRfH7umzZMsLDw8vNV3x+XO2zURnKOq9sPd99fX0xmUzEx8dXGDx37dqVe++9l3HjxgGFg9ivnLhhzJgxjBkzhuzsbLZt28bUqVMZPHgwUVFR5b5Wxa9nedO8FweDJV353VJZWrZsyQ8//IDRaLSaYMKWz8jVeHt7YzQaSUpKsgpQhBDEx8fTsWPH6ypXCFHm5BnFr+WV30GSVNfJlhMJgOjoaF544QXc3d0ZP378VfOOHTuWpk2bMnnyZJu6HTk4ODBjxgx2797Nzz//bFN9ygokoPBiMSMjw9K6061bN9zd3fnss8/K7ZLWuHFjgoOD+f77763yZGdn88svv1hm8LqawYMHI4Tg4sWLZd71b9myZYXH9OCDD+Lk5MTixYtZvHgxwcHB9O/fv9z8kZGRvP7667Rs2ZJ9+/ZVWP6VvvrqK1xdXdm0aRObN2+2+pkzZw75+fl89913Vvts2rTJ6iLCZDKxdOlSIiIiyrxj7e/vz+jRo3nwwQc5ceJEqe5x18vR0bHMu4WKouDg4GB1oRYfH2/TbF3lKe6itWTJEqv03bt3c+zYsVLdXqpCcHAwnTp1YsmSJaXWITlx4gTDhg2zpN19990oisI333xjVcbixYtxdnZm4MCBZT7H5s2b+fnnn5k/f74lTQhBdna25XFWVtY1zww0dOhQhg4dytixY+nSpUu5+RRFKRXEHzx4sNSMeVe2cBRzdnamZ8+e/PzzzzZ3NbqaAQMGoNFoOH36dJmf6Q4dOgCF3x+BgYH88MMPVq/N+fPn+ffff2+4Hldj6/k+aNAggKsGhyWNGjWKH3/8kUWLFjFy5Eirc64kvV7PoEGDeO211ygoKODIkSPllhkeHo6zszOnT58uc3tmZmapmcO+//57VCoVt956K1D+e3+thg4dSlZWFr/88otV+jfffENQUBCdO3e+6v7l1aP4u+DK74pffvmF7Ozs6/quOHv2LP/880+Zn53imSCbNWt2zeVKUm0mW07s0OHDhy39qxMTE/nrr79YtGgRarWa5cuXV9ifW61W88477zB06FDgcl/uq3nwwQd57733LH2jK/L444+TlpbGPffcQ4sWLVCr1Rw/fpwPPvgAlUplGcPi4uLC+++/z6OPPsptt93GY489hr+/P6dOneLAgQN88sknqFQqZs+ezcMPP8zgwYMZP348+fn5zJkzh7S0tDJXsr9S9+7defzxxxkzZgx79uzh1ltvRa/XExcXx99//03Lli0rbBXy8PBg6NChLF68mLS0NF544QWru2UHDx7kqaee4t5776VRo0Y4ODjw559/cvDgQSZPnmzJN27cOL755htOnz5d7l3Mw4cPs2vXLiZMmFDm+Iju3bvz/vvv89VXX1mtCu/j40OfPn1444030Ov1zJ8/n+PHj1tNJ9y5c2cGDx5Mq1at8PT05NixY3z77bc2BXkAJ0+eLLMLQ0hIiCUAKm4dWLp0KQ0aNMDJyYmWLVsyePBgfv31V5588kmGDx9OTEwMb731FoGBgTZ3K7tS48aNefzxx/n4449RqVQMGjSIc+fO8cYbbxAaGspzzz13XeUWW7ZsGXD5QmPPnj2WaX2HDx9uyTdr1iz69evHvffey5NPPkliYiKTJ0+mRYsWVi1szZs3Z9y4cUydOhW1Wk3Hjh1Zv349CxcuZMaMGWV2WcnPz2f8+PFMmzaN+vXrW9IHDBjAm2++iZubGydPnmTTpk02d9ks5uTkZDnGqxk8eDBvvfUWU6dOpWfPnpw4cYI333yT+vXrYzQaLflcXV0JDw9n5cqV9O3bFy8vL3x8fKhXrx5z587llltuoXPnzkyePJmGDRuSkJDAb7/9xueff35Niz/Wq1ePN998k9dee40zZ84wcOBAPD09SUhIYNeuXZYWQpVKxVtvvcWjjz7K0KFDeeyxx0hLS2PatGmV2q2rLLae7z169GDEiBHMmDGDhIQEBg8ejKOjI//99x86nY6nn366VNnDhw9Hp9MxfPhwcnNz+eGHH3BwcOCxxx7D2dmZ7t27ExgYSHx8PDNnzrS0rpXHwcGBrl27lvnZhsJWhwkTJhAdHU1kZCSrV6/miy++YMKECYSFhQFXf+8BNBoNPXv2rHBsx6BBg+jXrx8TJkwgIyODhg0b8sMPP7B27VqWLFmCWq2+6v7FN5tmzZrFoEGDUKvVtGrVin79+jFgwABefvllMjIy6N69OwcPHmTq1Km0bduWESNGXLXc2267jVtvvZVWrVrh5ubGoUOHmD17Noqi8NZbb5XKv2PHDtRqtSV4kyS7UVMj8aXqVzyLTfGPg4OD8PPzEz179hTvvPNOmTPglDW7UbFu3boJ4KqzdZW0fv16y3NXNFvXunXrxNixY0WzZs2Eu7u70Gg0IjAwUAwbNqzMGY1Wr14tevbsKfR6vdDpdKJZs2Zi1qxZVnlWrFghOnfuLJycnIRerxd9+/YV//zzj83HK4QQX3/9tejcubPQ6/XC2dlZREREiJEjR4o9e/Zc9XjKeg2ioqKstiUkJIjRo0eLJk2aCL1eL1xcXESrVq3EBx98YLVo2KhRowQgzp49W+7zPPvsswIocwaYYpMnTxaA2Lt3rxDi8vs2f/58ERERIbRarWjSpIn47rvvSu3XoUMH4enpKRwdHUWDBg3Ec889J5KTk6967BXN1lVypqFz586J/v37C1dXV8vUzsXeffddUa9ePeHo6CiaNm0qvvjiC8v7VpHy3l+TySRmzZolIiMjhVarFT4+PuKRRx6xmkFMiMJZh5o3b17h85R0tWO+0vr160WXLl2Ek5OT8PLyEiNHjixzwciCggIxdepUERYWJhwcHERkZKT46KOPyq3D66+/Llq3bm01ZbMQQiQmJorhw4cLd3d3ERoaatNMcSVn6ypPWbMu5efnixdeeEEEBwcLJycn0a5dO7FixYoyFzLcuHGjaNu2rXB0dBSAGDVqlGXb0aNHxb333iu8vb2Fg4ODCAsLE6NHj7ZM41rejIDF59+V03KvWLFC9O7dW7i5uQlHR0cRHh4uhg8fXmoWqy+//FI0atTI8np//fXXlbIIY/FsXXPmzCkzv63nu8lkEh988IFo0aKFcHBwEO7u7qJr167i999/t+Qpa3bFzZs3CxcXFzFw4ECRk5MjvvnmG9G7d2/h7+8vHBwcRFBQkLjvvvtsms72q6++Emq1WsTGxlqlF39utmzZIjp06CAcHR1FYGCgePXVV0udk1d777FxKmEhCmdde+aZZ0RAQIBwcHAQrVq1Ej/88INN++bn54tHH31U+Pr6CkVRrL5vc3NzxcsvvyzCw8OFVqsVgYGBYsKECSI1NbXCcp999lnRrFkz4erqKjQajQgKChKPPPKI1fT8JfXo0UPceeedNtVZkuoSRQi5uo8kSZIkSTcmLy+PsLAwnn/+easZGnv16kVycrJlrKBUsdOnT9OoUSPWrVtXalFLSarr5JgTSZIkSZJumJOTE9OnT2fu3LlWY5mkazdjxgz69u0rAxPJLskxJ5IkSZIkVYri8YJnzpyxaaIQqTSj0UhERASvvPJKTVdFkmqE7NYlSZIkSZIkSdJNQXbrkiRJkiRJkiTppiCDE0mSJEmSJEmSbgoyOJEkSZIkSZIk6aZgdwPizWYzsbGxuLq6Wq26K0mSJEmSJN0chBBkZmYSFBRktWDxzSIvL4+CgoIqKdvBwQEnJ6cqKbs2sLvgJDY2ltDQ0JquhiRJkiRJklSBmJgYQkJCaroaVvLy8qgf7kJ8oqlKyg8ICODs2bN2G6DYXXDi6uoKwOK/G6N3ta+WE2+1/c07L4QK95yGeLgdQ1HMNV2danXeqK3pKlQ7IVT8mzCADOdYUOxrIsKoLP+arkK1U4TCIMdsDC7n7O79vsX5bE1XodoJocI5uy2+7idR2d33eVZNV6HaZWZCj86Jluu2m0lBQQHxiSbO762Hm2vltupkZJoJb3+OgoICGZzYi+KuXDoXNTo7C05c1Ddfs2hVE0KFi0qDq6sKe+vF52K0z/fbKcuBfJ3W7i5WtcKhpqtQ7RSh4OxkQOOisbv321Vnn59vZ0WLm6salZ19obva5fd54e+buQu+i6uCSyVfS5q5eY+3uthdcCJJkiRJkiRJN8okzJgq+b6ISdhXq2BZ7C8UlyRJkiRJkiTppiSDE0mSJEmSJEm6RmZElfwAdOzYkWbNmvHpp5/W8FFWP9mtqyxCQREuqISOOhW/iZyarkH1Ewpmsw8GY7CdDYgXYM4HJdPu+uJLkiRJUm23e/du3NzcaroaNUIGJ1dQmT1xyb0HZ3NTFKVuvTwq7OnivJiCQWhIzTBgX2PMBBphxqw6iFn3I6gv1XSFJEmSJKlOMWOu9Curyi+x9qlbV983SqjxzP4fLo4heHrr0GiVOjXDk9ouT3gFtVmLSlUA2FcLQk4BXEpxJzezHib3V0Ex1nSVJEmSJEmSrspugxNHxYDTFZGHyuyNg+KOn78OR+c61J3LQl3TFagRKpMGRW3AzppOcHJU4a3RcyHGizRDCGZ1Yk1XqcoJoUKvzgdNjt11Zxvut6emq1D9hEJEnicuzmftrNsmpf5/2QOBwmmjkQsFBXb3fu/ObVLTVah2uTkmIL6mq3FVJiEwicr9X1PZ5dVGdfEK/AaoAAVFvipSHVF4LisodhqYSpIkSZJUu9hty4kkSZIkSZIkXa+Ss2tVZpn2TgYnkiRJkiRJknSNzAhMMjipdDI4qSJmE1z8W092vAZ9gJHgW7JRyZ41N7Xxj75BenomP/48r6arIkmSJEmSZJfk6IoqcHKFG19FRrJsQH3WjApl2YD6fBUZyckVVTdfdWZmNpNfmEWLRv3x9+hAv16PsHfPYas8M9+aT4dWdxLo1YmwgG7cNehR9uw6aJXn1ZdmEx7YneYN+7HspzVW235dtpb7hz1VYV2++78VuDu1tPw0DOvJ/cOe4tjRU9d0TC0jBzD/42+vaR9bnD93ERen1hw8cNwqffb7L/HZF29W+vOVZeuWXQy7eyJhQbfi69mZ9m2G8srL7xF7McHmMsY/+gYP3Pts1VVSkiRJkqRyVeUijPZMBieV7OQKN1Y9GErWRa1VelasllUPhlZZgPL0hKls3rSdz79+h3/3/kqfvt0YcvtjVhe7DRuFM+eDV/l3zy+s+/P/CAsPZujg8SQnFa6BseaPLfy8dDXLVy1k+tvPMfHxN7iUkgZAWloGb039mPfmvWZTfdzcXIg6t5kTZ//kp+Xzyc7O5b4hEykoMFT6sVcWd3dXPDyqfsGjr774mTtvH4+/vzdLfnifPf/9yocfv05GehYfffh/Vf78kiRJkiRJNysZnFRACDBkKzb95GcobJkUWLScxhXTPIrCx1ueDyA/w7bybJ1NLjc3j9+Wb+TNdybRvUcHIiLCeOWNJwmvF8xXC5da8t37wB307tuV+g1CadqsIe/MfpGMjCwOH4oC4MTxM9xya0fatW/O8Ptvx9VNz9mzFwCY8upcHh1/P6FhgTbVSVEU/AN8CAj0pV375kx8ZgTR0bGcjDprybNz+34G9R2Fv0cHmkXcxkuTZpKdXbiK/R39xhAdHcsrL862tMDYsh8Utri8N+sLJj7+BsE+nWnauCdff7nMsr15k9sB6Nb5flycWjOw3zigdEtEfn4BL0x6l3qhvfB270i/3qOsWqO2bd2Ni1NrNv+5kx7dHsTXszN9e40kKupcua/LxQsJvPj8LCZMfIgFC9/k1p4dCa8XzC092vPpZ9OY/Op4AN5+awFdO91nte+nHy+hWeQgy/bvlvzGqt834+LUGhen1mzbutum90aSJEmSpBtXPJVwZf/YOznmpALGHIVPvJtXTmFCIeuiA/P9bCvvqZQjaPUVn6RGowmTyYSjo4NVupOzIzv+/a/MfQoKDCz+ahnu7q60bNUYgBatGrP4q2WkpqZz7uwF8nLzaRARyvZ/9nFg/zE++PgNm+p9pbS0DH7+cTUAWm3hKXfkcBTD7hzPa1Of4pPP3yQ5KZUXnnuHF599h/lfzODbpfO4peM9jB43nFFjh1vKqmi/Yp98+A2vTX2KSS8/xm/L/uTZZ96me4/2NG5cn61/f0fPWx7m99ULadYsAq2DdStXsddf/YCVKzay8MsZhIYFMu/9xQy5cwIHjqzCy8vdku/NaR/zzrvP4+Pryf+emsGTj09l45Zvyixz+a/rKSgw8Nyk0WVut7Xl5n/PjeLEiTNkZmTz2cLCrmieJeokSZIkSZJUG9ltcHIwLwwnrfUIdZ3Zl/ZCQ65Zi9FcuM1grrmFrnLMDmjNFQcnar0DHTq35d2ZXxAW2Rhffx+W/7SKPbsO0aBhODnmwqBFoLBh9WYmjHqO3Jxc/AN8+eH3RTh5BZBthq59+zD0gcP06v4QTs5OzFs4C5w9ePaZt5n3+bvM/+wXvv5sCV7ensz55C0aN2tUZn3yhZb09EwCvTsjhCA3JxeA/nf0JbhRU7LNMHfu/zHkvjsZNfFRAAIawPT33uCe/o/w1oczcPLwQ1FrcHBxx8UvGMC2/ZwcMaPQZ0BPHnpsFABPTGrKJ58uZsOW/QQ1aoqzdwAATp5+6PxCAMg0gwE1BqEi0+xETnYOXy78mXkLZ9K1320AzPz0HTZt6sMXi37nyeceJVcUvq4vTn2eNrd0BeDJ55/gkWGPk5Sj4OTkWOq1OXbyIq5uLuj9Q8m8yhpiBUKDWShkmp0saXlCg5miNJ0TGic9qjyT5RjygfwSZZqECqPJRL5Zw96c+mQpVd9lrcYJBT+hxiDUYGf9dlNMLjVdheonFFzNepKNHna3KJ9WMdV0FaqdECqO54aSrRJ2t8jqfxmhNV2FamfMMgB/1XQ1rspc9FPZZdo7uw1ObKXRCcYmnbQpb9w/zqwZElJhvkErLhDYPdem57bVx1/N5rknXqVtw1tRq9W0bNOMofcP5tD+o1b5uvfszIYdK7mUksp3X//E+BHP8sfWn/Hx8wbghdef4YXXn7Hkf2/GR/To3Q2NVsOHsxbw5+5VbFizmWcefYl1/y4vtz4urnrW/bsCo9HIjr92s2Del8z6aLpl+8H/jnDu9Hl+Xfq7JU0IgdlsJuZcDI2aNCyzXFv3a9ri8mq6iqLg5+9DSlKKLS8lAOfORGMwGOjYtZ0lTavV0qZDK04eP22Vt2mLxpa//QJ8AUhOSiEkNKhUuUIIFDtc2VmSJEmSJMkWMjipgKJgU9cqgJC+OeiDDWTHaixjTKwLE+iDjYT0zan0aYXrNQhj+fol5GTnkJmRhX+gH+NHPEtYuHWwpNPrqB8RTv2IcNp3akP3lv344ZufefrFJ0qVefLEaZYv/Z3121fww//9QpdbOuLt68Vd9wxi0hOvkJmRhatb2XdrVSoV9SPCAWjUOILEhCSeGPksyzd8D4DZbOaRcQ8w7smRpfYNDi1/XIut+xV3HyumoGC2oRWqWHGXzysDibKCi5LPVbxNmMu+99GgUX0y0jNJiEvEP9Cv3OdXqRTEFXf+jQajzfWXJEmSJKlqmapgnZPKLq82kgPiK5FKDd3mJBU+uLLJuehxt9lJVbreiU6vwz/Qj7TUdLZs/JsBg/teNb8Qgvz8gjLTX3rqDabMnIzeRY/ZZMJgKJxpy1B0kWwu5wK8LI8/PYajh46zZuV6AFq2aU7UsZOWQKnkj4NDYXcpBwctJpN11wVb9quIVls4xuTKskuqHxGGg4OWXf/utaQZDAYO7jtMoyYRNh/3lQYPHYCDg5ZPP/iyzO3paRkAePt4kZiQjCgxMO7wwWOljsNskg3AkiRJklQTTKJqfgA6duxIs2bN+PTTT2v2IGuADE4qWYMhWfT7Lg59kPVdbn2wkX7fxdFgSFaVPO/mDX/x5/ptRJ+LYeumfxg+cCQRjerzwMhhAORk5zBzyvvs3bWfC9EXOfjfEZ6f8CpxF+O5c9igUuV99/VSfHy9LcFNxy7t+GfrDvbu2s/CjxcR2bQh7tcw7a6rmwsPjb6X997+GCEEEyc9xp6d+3nl2WkcPnCUM6fOsW7VJl6bdHmdkZDwYHb8vYe4i/GkJBdOd2zLfhXx8fPGydmJzRv+IikhmYz0zFJ5dHodIx97kLdenc2f67dx4tgpXpj4Brm5eTw4angZpdomOCSQ6bNe4ctP/49JT7zKv3/tIib6Iru27+XFp6bwwbvzAeh6a2dSki7x6dwvOHcmmkWffcfm9dZ9b0PDgzl6+ASnos6QknzJEjxKkiRJklS77d69m6NHjzJx4sSarkq1k926qkCDIVnUuzOL+H+cyYnXoAswEtA9t0pbTDIzMnlnylziLsbj4enBHUP6M3nac5ZWApVazamoM/z84HIupaTi6eVJ6/YtWb7h+1ID25MSkvlozuf89uePlrS2HVsz/pmxjBz2ON6+Xny4cNY113HcxFF8Nf9bfv91DXfdczu/rlvCu9M/YGi/hxFCUK9BKHfdc7sl/4tv/I+Xn36Dbi1uIz+/gNicKJq1bFLhfhXRaDTMeO915s78lDlvfUTn7h34dV3pxR5fe+sFhFnw9KMvkZ2ZTat2Lfhh5Zd4eN7YrFijxz9Mg0b1WTDvK8Y98BR5uXmEhAfTb1Avxj89BoDIJhHMnDeVj+Z8zgfvLuCOIf154n9jWfL1T5ZyHh5zH//+tYuBt9xDdlYOv6z9P7rd2vmG6iZJkiRJAJgUdAcaoU52x+STTk7rk6CWXY5KkgPiq4YihH1NqJyRkYG7uztv7eyLk2sZs3WZHiUozB+tYxVGEjVEXLn2ip1Qm7WYVaW7rtV1JqHCmG8iNjqebcq3ZCm2TwhQawkFvzx/svTRdjebj7u64kk26hyhUC/fA5VrlJytyw4IoeJ4Uk+y7fDzXd2zdblsaYv/B/ejTfSypBn8LpHw3FKyepW9REFlM2YZWNZvMenp6bi53VyzTRZfS+4/6oera+V2QsrMNNOmWeJNedzVRXbrkiRJkiRJkoDCwCT4lSfQJHpapWsSPQl+5QlctrStoZrdfMwomCr5x2ynN5JLksGJJEmSJEmSBCYF/w/uBwpnuSyp+LH/B/eDSV5AS1XHbsecnM3xQau2XhncHU9aadQUmDWYzXWvW5fZHmNRAQ5Cg7HUxLx1n0DBJBSMQk1cvjupdvAKKEIhRGXCSTHaXbePVKO+pqtQ/YSCk8GDvAI/u3u/HRQ7nFpcKFzI9ySBAoSdvd+nUn2qpFxthjMuMX64RvvhEuOL55Fwq65cV1JQ0CZ6kfp3B1Jana2SOhUzZ+dXafmVwSwKfyq7THtnt8GJJEmSJElSnSfA8ZJrUQBSGIQU/+2YVvZaZRVxvORayZWUpMtkcCJJkiRJklTbmRWcEz0srSAu0X64FgUj2mzncnfL8U0lKyyRrNAkhMpMxK+3VvhU+V6lp+C3R8XjRCq7THsngxNJkiRJkqRaQjGq0MV5W4IQSzBywRd1ftmLEQuVmeyAS0VBSCKZRcFIVmgSJucSs1maFIK2tcIp2b3UmBMAgSDPJ52U5ueq6OhqFxmcVA0ZnEiSJEmSJN1klDwt2ugAtGeDiDwaaQlC9LHeqIxlX76ZNEayg5MtLSGZRcFIdkgyZq0N00+rBUfG/0H7tx9CIKwClOKRm0fG/yHXO5GqlAxOJEmSJEmSaoiS5YT2XBAO5wLRnglCey4I7dkgNLE+KKJwIhvfK/YxOuUXtXyUbAVJJCcwFaG+sTV/4rsfYe9r39P88ztwTvawpOf5pHNk/B/Edz9yQ+XXJWahYBaV29JR2eXVRjI4kSRJkiRJKo9JwWl/JOpkD0w+aeS1ibqulgNVmosl+HA4G4T2XGBhEHKV2bFMblkY6seSGJBuaQXJDEskzycDVFXXehHf/QjxXY7ifaQejpdcyffKLOzKJVtMpGogg5MqYkaQqMohRzGiExr8zDpUsh/hTe2F8S+SkZ7Bwh8/r+mqSJIkSTcB3eb2eL3/kFUAYfS7xKXnvyen997SOwhQJ3oWBR7BOJwNtPytTit/hiujTyqGenEY6sdhqH8RQ704CurHYvbKAAUOJwdWxeFdnVpU+XTBtZ0cc1I1ZHBSBc6rM9jlkECO6vI89Dqzhk4F/oSb3KrkObMys3j/rQ9Y//t6kpNSaN66GVNnT6F1+1aWPNlZ2cyeMosNqzaQeimVkLAQRk0YzSOPPWLJM2PyDH75bhk6vZ7Jb03mznvvtGz745dVLP9xOV/+/NVV67JsyTJeeuJFy2NvX29ad2jNS9NfJrJZpM3H1KPZLYyZOJaxE8favI8tLpy/QI/mt/LHv6to1qqZJX3K7DeorqVAtm/dzsIPF7J/zwHycvMICQuhZ/+ePPr0OAKCAmwqQwZTkiRJVUe3uT2+L08sla5O9MD35YmkvPQtJv9LaItaQQpbQ4JQXWVmLENgUlEAEouhXmzR7zjMbjlVeSiSVKvI4KSSnVdnsMXxYqn0HMXIFseL9MqnSgKUlye+QtTRk8z94n38A/1Y/uNKHrlzBBv2rLNc7M6Y/BY7tu1g7pcfEBIewl+btjHluSn4B/rRb3B/Nq3eyG8/reSblf/HuVPneGnCi9zS5xY8vT3JSMvgvTffZ8mqJTbVx8XNlU3/bUIIQXxsPLNef5dxw8eyaf+fODiUPZtITXNzr5rA8Urff/U9bzw3hXseHsaCJfMJCQ/hYkwsv/7wK19+9CWvv/t6tdRDkiRJKodJwev9h4CyVkpXIRD4zB5Z5q5CbcIYkkhBcfBRHIyExyFKzowl1XomVJgqeYFrG6YtqPPsNjiJz3NHo7FeIb5AccWkU2EUaoS58KURCEw23k43I9jpnFD44MpWOQUQsNMhAZ9sd5u6eKlRypzK70p5uXmsXbmOBT98Qbtu3QB46pXnWbdqI98s/IFJUwpbMfbt/I+7HxxO+1u6AzB89Ai+//oH9u89Qq/bB3Hi+Bk63dKVpm3a0rRNW956+S3OnLlIK08f3nn9XR4aNxK/4DAMFYy1MwkViqLg4VsYFHn6BTJy4qNMuH8cUSfO07h5k6L67OH9qe9yaN8BPL296Dd4AJOmTUan1zHi9vu4GH2RGS+/xYyX3wLgREZ0hfsB9GnRjftGP8T5M+dYu+IPPDzceeLFp3lgTOE/mh7NC+dwv6PbYAA63dKFJauX8vITz5ORnsGCH74AoCA/n1mvv8Mfv/xOVmYWLdq25NWZU2jVvjUAO//azog7HmDxb9/x3tR3OXX8JE1bNmPmgvdo0CiizNcm/mIc0158k5FPjOHVd6dY0v1Dw2nXrSsZaekYzGo+eucDNv6xnt/+WWPJs/jTr/hmwddsPvwPH73zAb989wsA9V0aAPDtHz/SuUfXK94LM2ahkF7gzCVR9/8pKkJB0eWgUsx2t2J4urH8u7V1llDQGN1Izfe2u/dbzY0Neq6VhEJKvo5Yk3uVrxDv9l/Dq44FKf7fnBuURFaTGHLDE8gNjycnPIG8kCREWTNjCWfIub7PaVqW7rr2q81ETuVe9Eu1h90GJ7YyIfjJtZJmplAgVzGyzPWoTdnvy2yOxobgxGg0YjKZcHRytEp3cnJk7449lsftunbkz9UbGD7ifvwC/dn513bOnjrLq7MKL9abtGjGT4u+Jz01jZhz0eTl5RHeIJw923dx5MBhpn3wzjUc7GUZaems+mklABpt4Sl34shxxg0dwf9ee563P5nDpZQU3nphCm+98AYzF7zPx0sWcnf3Adw3+iHuG/2gpayK9iu26OOFPPP6Czwx6Sk2rlzDtOdeo2P3TkRENmTZ5t8Y3vsuFv/2HY2aRqLVlt2SM/uNmaz7bQ2zPnuf4LBgvpj3OeOGjmDD/m14eHlY8n3w5ntMfvt1vHy8mPLsa7z65Iv8uOHXMstcs/wPDAUFPPrs+DK3u3m42/SajnvmcU5HnSIrI5N3F7wHgLunx9V3kiRJkiqkOxNI8Hd9bcobM24NyX33VXGNpJuVqILZuoScrUsGJ3WBi6sLbTu149PZHxPRuBE+fj6s+nklB/bsp15EfUu+12dP542nX+bWJp3QaDQoKhUzPp5Fh66dAOhxW0/uun8ow3vdiZOzE7M+m4uzXsf0515j5oL3+eHLb/n288V4envy1kfv0qhp43LrlJmeQdvAJgghyM3JBaDP7f2IiGwIwFcffsad997N6ImPAlCvYX1emz2NEYPuY9oHb+Ph5YFarUbvosfX389SbkX7OTo5AXBr/948/NhIEDDhuYl8Nf8Ldv21g4jIhnj5FN4N8/DytCq7pJzsHH74agnvLniPnv17AzDj43fpvfkvln37I4/+7wlL3uemvECnW7oA8PhzE3j83jHk5+VZ6lLS+dNncXFzxS/Av9zXzhZ6Fz1OTk4U5BeUewySJEmSbVR5Wrw3tyXg9264Hq1n834FXhlVVynppicHxFcNGZxUQI3CfZnNbcqbqM5mi+5chfl65dTDz6S36bltNWfhPF6Z+CK3NO6EWq2meesW3Hnv3Rw5cNiS59vPFrF/938sWPoVQaEh7PlnJ9Offx2/AD+69e4BwNOvTuLpVydZ9vn4nbl07XULGq2WBXM+5vcd69m8dhMvj3+OX7etLrc+elcXlm9bjdFkZPffO/jqw8+ZPu9yy8uR/Yc4f+Y8v/+0wpImhMBsNnPhfAwRjRuVWa6t+zVu0dSyXVEUfPx9SUlKse3FBKLPnsdgMNCuSwdLmlarpVX7Npw+ccoqb5MSz+UXUBgopCSlEBQaXKpcUVQfSZIkqebpzgTi/1s3fDd0QFM0kN2sNnGp+yHcDzREk64vd6X0Ar80Mlqdru4qS1KdJ4OTCigoNnWtAggwuaAza8lRDKXHnAAI0AktASaXSp9WOLxBON+v+Ymc7ByyMjPxC/Dnf6MnEhIeChSOS/lg+mw++W4hvQYWNlc3adGUY4eO8tVHCy3BSUmno07x+08rWP73Gn75dikdunfCy8ebQUMH8+qTL5CVkYmLW9lTI6pUKsIj6gEQEdmQ5IQknhs9ke/WLgPAbBY8MOYhRjxReiauwNCgco/T1v00GutTW1EUzGbb+2gLISz7XZl+ZZrVcxVtK++56kXUJzM9g8T4hKu2nqhUKksdihmNBpvrL0mSJJVNlafFZ3Nb/K9oJckLSibhju0kDtyFwTsTr22taDxlTLkrpZ99arlc98POmYQKk6jkAfHylKrkKQbsnAqF9vlFc5FfeXIVPW6fH1il653o9Dr8AvxJT03nr03buO2O/gAYDAYMBgOKyvotV6tViDIupIUQTHlmMi+/8zp6Fz1mkxmjoXBqZKOh8CL5Wi72R098lOOHj7Hh97UANGvdgpPHowiPqFfqp3g2L61WW+o5bNmvItqifGZT+fUPb1APrYMDe7fvtqQZDAYO/3eQiMYNbT7uKw0ccjtaBwe+nFf29L8ZaekAePl4kZyQZBWgHDtoPVZJ66DFbJLzekiSJNlCdyaQ+vPuocM9b9Jw1kO4Hq2HWW0iued+jrw3n31L3ubiw5sweGcCcOnWg5x4cxEFvmlW5RT4pXHizUVcuvVgDRyFJNV9suWkkoUa3emRF8Zex7jCFpQiOqGlfX4goUbbBjxfq782bkUIQf1GDTh/5jyz3niH+g0bcM8j9wLg6uZKp1u6MOeNt3FydiIoNJjd/+xkxQ+/MPmdKaXK+2nx93j7etP39sLgpl2XDnz87gfs37WPbRs207BJI5sHb0Ph1ML3jnyAj96Zy22DB/DYcxO4v+/dTJ/0OveNfhBnnY7TUSf598+/eeO9NwEIDgth9z87ueOeu9A6OuDl7WXTfhXx9vXGydmJvzZuISA4AEdHR1yvmEZYp9fx0LhHmPXGO7h7ehAUGsQX8z4nLyeX4SMesPm4rxQYEsSrM9/gzRemkJWZyZAH7yE4LIT42DhW/PArOr2OV955g049unDp+RS+mPcZA+++nW0bt7BtwxarlqrgsBD+3rSNMydP4+HliaubK1qt9irPLkmSZF8qbCUZtBODV1a5+1+69SCXuh/C7WAEDpfcKPDKKOzKJVtMJMCMgrmS7/Obq2vBtStoNBpatGgBQIcOHfjyyy9rpB4gg5MqEWp0J9joRpI6m1zFiLPQ4GvSV2mLSWZGJu9Nm0V8bDwenu4MuGsQk6a8aHWxOnfRJ8ydNosXHn2G9NQ0gkJDeG7KSzw47hGrspITk/j8/U/5ocSMU606tGHMU48x/t7RePn6MOuzuddcx5ETxvLtZ4tYs3wVtw+7k29X/8y8N2fz0MDhIASh9cO5fdjlRR+fee15pjz7Cre17kFBfj4nMqJp0qJphftVRKPR8PrsaXw66yM+fHsuHbp1YsnqpaXyvTD9ZcxmMy8+/hzZWdm0aNuSr5Z/i7vnjQWYDz82knoNG/D1RwuZ+NDj5OXlERwWQu+BfRnzVOFA/4aNGzFt7gw+e/9T5s/+iP53DWLsM4/z0+IfLOXcP/pBdv21g3t63kl2VnaZUwlLkiTZI93pQPx/L2MsyS2HSLjzX9LbnQSVjReBakFG21MV55OkWszDw4P9+/fXdDUAUMSVHdvruIyMDNzd3bl9zeNoXKzvMnsr7ozS3U5AWCBqh7oXt5ntcQYIAY6oKVCMNV2TGmEqMJIQHcfi7DUki/Sark6VU4TCYF0OufrzdrfuxcV8z5quQvUTCt55QaQ6X7S799te1zlJSmpDtOZSmeucqHId8NnS5rpbSW5mZxJ9aroK1U7k5HF69Nukp6fj5lY9iyTbqvha8reDEehd1ZVadnamibtana724/bx8SE5Obnanu9q6t4VuI3S8h1Ra63XBdGqHDHrFExCqZPzTNfFY7KFuQqm+qsNVErhsE0BGMwqCkTlfoHejBShYDBryDdr7e5iNcNYeurquk4RClqDEylqlypflO9mo6qhrh81SREKOQUOpBudKPnvzPVMAKFrOhG0qS3anMLPgVltIqHrUWJu30lKmzOXW0nyaudipYYcO+yymyvHVNpq27ZtzJkzh7179xIXF8fy5csZMmSIVZ758+czZ84c4uLiaN68OfPmzaNHj8uTIWVkZNC+fXucnZ15++236dmzZzUfxWV2G5xIkiRJklQ7qfO0BGxrRejqTngeD7Ok5wSmEDNoFxf67aPAs3a2kki1R9XM1lUYSGdkWK+h4+joiKOjY1m7kJ2dTevWrRkzZgz33HNPqe1Lly7l2WefZf78+XTv3p3PP/+cQYMGcfToUcLCCj8/586dIygoiMOHD3PHHXdw6NChGmuxksGJJEmSJEm1gsNZX5quu9W2VhJJqmKFA+Irt2dGcXmhoaFW6VOnTmXatGll7jNo0CAGDRpUbplz585l3LhxPPpo4bjWefPmsW7dOhYsWMDMmTMBCAoqXI6hRYsWNGvWjKioKDp06FBumVVJBieSJEmSJN20lFwHnDd0Rr+8Fw6HL0/lLltJpLosJibGquWivFaTihQUFLB3714mT55sld6/f3/+/fdfAFJTU9HpdDg6OnLhwgWOHj1KgwYNrr/yN0gGJ5IkSZIk3XQ0J0PRL++FbnU3VNk6AITaRHy3I8QM2iVbSaQaZ0aFqYqmEnZzc6uUblXJycmYTCb8/a0Xfvb39yc+Ph6AY8eOMX78eFQqFYqi8OGHH+Ll5XXDz329ZHAiSZIkSdJNobxWEmNwIjlDtpDcNYEo71jsdH4XSbpuimL9oRFCWNK6devGoUOHaqJaZZLBiSRJkiRJNUpzMhT9r73QrSnZSmIkt/c+coZsJr/jMRQFTElNa7imknRZVQ6Iryw+Pj6o1WpLK0mxxMTEUq0pNwsZnEiSJEmSVO2u1kqSPXQzOYP/xuxdYsYi2Vwi2ZGOHTuiVquZOHEiEydOvO5yHBwcaN++PRs2bGDo0KGW9A0bNnD33XdXRlUrnQxOJEmSJEmqNra0ksixJFJtYEaFuYrGnOzevdvmMSdZWVmcOnXK8vjs2bPs378fLy8vwsLCmDRpEiNGjKBDhw507dqVhQsXEh0dzRNPPFGpda8sdhuc5BgcUBU4WKXlqrUIoWAWKrjRZjqTCd2/O9EkJGL09yOnW2dQ1+wieJXcUlhrCBSbvjxenfAcmekZfPz9V9VQq+ogEEKFQCHHqCXb7FDxLrWcIiDXbCTT5GR3izBm2ukijA5GR1ILdHa3CGNto+Q64LGpPV4rb0F/tL4lPT84kUt3/UPq7TswemUWJhp1ZZchFMwFWjJxxFwdlb6Z5Nb9RXRLscdjvk579uyhd+/elseTJk0CYNSoUSxevJj777+flJQU3nzzTeLi4mjRogWrV68mPDy8pqp8VXYbnFQl199WEzB5KtrYOEuaISiQ+Henk3nX7VXynNmZWXz09hw2rVrLpeRkmrZqweR3p9OyXRtLnuTEJD6Y9g7/bt5GZno67bt15tVZbxEecXm6uNmvTWfF9z+h0+uZNP01br/ncpPf2uW/8/vSX/j0x8VXrcuK73/i9YmTLI+9fH1o2a4Nz019hYZNG9t8TP1bdWHEhEcZMeFRm/exxcXzMfRv3ZVl29bRtFVzS/or705HVFMEt3PbPyz6+DMO7vmP/Lw8gsJC6XFbb0ZNfAz/oECbyqh7wZQkSXWN08lgvH67Bc91nVBnF67OLtQm0nvu59Ldf5PVLkq2kki1lkkomCq5u+H1lNerV68Kr1+efPJJnnzyyeutVrWSwUklc/1tNSGjxpdqptDExRMyajwXvvm8SgKUKc+8yMljJ3j38w/xDfRn1dJfeXTIg/y240/8gwIRQvC/R8ah0Wj56LuvcHF15f8+XcijQx5k5Y7N6PQ6tqzZwB/LVrDw1++JPn2WN56aRLfet+Lh5UlGejofzZjFVyuW2lQfF1dXVu3eihCChLh45k59myfvH8Ufe7ahdbg57+C7ulfPSqg/LVrCW8+/yt0P3su8/1tIcFgocRcu8tuPy1j8yUJefmdqtdRDkiTpmpgU9Acaok1xx+CdTnbrU6AufUF0Ta0kklSLmapgKmFTUbeuyhpzUhvJ4KQiQqDk5NqW12Qi4OUphftcsUkRAqEoBEyeSlavHjZ18RI6Z1AqjqDzcnPZ8NtqPv7+azp07wLAxFeeZ9Mf6/jx62/53+svcf70WQ7s3seKfzdZWi9ef/8dbm3UmtW/rGD4yIc4E3WSjt270qJta1q0bc27r04j5tx5PLw8mTv1bR4YN4rA0GCbXgpFUfDx9wPAN8CfkRMe46mHxnD25GkimxfOtvLfzj3Mmz6Tw//tx8PLi76DB/LslFfQ6XWMHjyc2JgLzHp1GrNenQbA4dQLFe4HhS0uw0c9TPTZc6xfuQp3d3cef/EZ7hv9SOH21l0BGH7rAAA6du/C4j+WlWqJKMjP5703ZrDm19/IysyiedtWvPzOVEtr1K6//mXMnffx1cofmTv1HU6fiKJJy+bM+HQu9RtFlPm6xF+M5Z2Xp/Dw+LFMnjnNkh4cHkqH7l3ISEsH4NOZ77Ppj3X8+vd6S57/m/8l3y74kg2HdvDpzPdZ+cPPADT3CAFg0e8/0alHN5veH0mSpGvhtqUNQfPuxSHJ05JW4JtK7LM/k9FrPyBbSSSpMl3LmJO6RgYnFVBycmkaHFk5ZQmBNjaOpmG2TYV47GIUQl9239uSTEYTJpMJRyfr1UOdnJ34b/suoPBCG8ChRB61Wo3WwYH/duxm+MiHaNyiGT9/8x3paWlcOBdNfl4eYQ3qsW/7Lo4eOMwb78+09VCtZKSn88eyFQBotFoAoo4cY/zwh3n61Rd58+M5XEq+xDsvvc47L73OjE/n8uG3XzDslv7cO/phho98yFJWRfsV++bTz3n61Rd5fNJTbF65hrcmvUqHbl1oENmQH/9cxQN9BvPVyh+JaBKJ1kFbZr3fn/I2G35fzTsLPiAwNISvP1zA48MeZs1/f+Phefkf9IdvzeLFGW/g6ePNm8+9wutPPc9361aUWea6FX9gKChg3P8mlLndzcPdptd09NNPcCbqFFmZmZbjdvf0sGlfSZKka+G2pQ3hrz1WKl2b5EH4a4+RMmwrzsfDZStJHaMo4B+oQ6fTkJNjJCEux27HrpbHLFSF45QrtUz5IsvgpA7Qu7rQplN7Pps9jwaRDfH282X1shUc3PMf4RGF/yzqRzYkKDSED998lykfvItOp+ObTxeSnJBIUkIiAN379mLwfcN4oM8dODk58fb8D9DpdLz1/CvMmP8BS7/6P77/YhEeXl5MmzfrquNHMjMy6BgSCUKQW9Ty1HtQfxpEFk4Xuejjz7jjniGW8SThEQ145d03GT14OG+8/w7unp6o1Wr0Li6WFhhb9nN0KhwU3KNfHx54dBQA4599iq8XLGT339tpENkQL29vANw9PfEtUXZJOdk5/Pj1t7w9fy49+vUBYPpHs9neahu/fvsjY5+5HFz8742X6XhLYWvMo889yYT7RpGfl2epS0nnz5zFxc0V34Abm1tc76LH0cmJgvyCco9BkiTphpkUgubdC4ByRZ8ABQWBwOfXXgCYNUYybj0gW0nqgPD6rnTu7o+Ly+Wbd1lZBnb+k8D5szLYlKqWDE4qIHTOHLsYZVNe3b87Cb93RIX5zv/8beHsXTY8t61mfv4hb0x8nt5NO6BWq2naugV33DuEowcOA6DVavng/xYy5ekX6F6/BWq1mi69bqHHbb2typk4+XkmTn7e8vjTd9+nS68eaDQaPn//I5b/s5Gt6zby6oRn+WnLmnLro3d14ectazAaTez5ZweLPl7AlLmXW16OHjhE9JlzrFq2vMQBC8xmMxfOxxDRuFGZ5dq6X3HXMSjsYubt50tKUnLFL2SRmLPnMBoMtOvc0ZKm1Wpp2a4NZ06csspb8rl8ixY0SklKIaisLnBClPoHL0mSdLPSH2ho1ZXrSsXfZyl3/0X8Y79j8syqrqpJVSS8vit9+pf+/6XXa+jTP5g/11+UAUqRqhxzYs9kcFIRRbGpaxVAdp9bMQQFoomLRymjWU4oCsagQLL73Frp0wqH1a/HN6t/ISc7h+zMTHwD/Hl+zARCwkMteZq3acUvf60nMz0Dg8GAl483D942mOZtWpdZ5pmoU/zx83KWbV3Hr9/9SIdunfHy8WbAkDt546nnycrIxMXNtcx9VYqKsAaFrTYNIhuSnJjIC2Of5JvVvwBgNpu5d/TDPDJ+bKl9A0PKH9di635arXVXLUVREGbbJ5+0vH1XjPkRovQwII2mxMeoaGN5zxUe0YDMjAyS4hOu2nqiqFSlZt4wGg22VV6SJOk6qTKdcT4djNOpwh+XvbbNsJjd9qQMTOoARYHO3f2L/r6ipUxREELQuZs/0ecyZRcvqcrI4KQyqdXEvzudkFHjEYpiFaCIog95/MxpVbreiU6vQ6fXkZ6Wxj+btjLpzVdL5Smeler86TMc+e8gT736Yqk8QgimP/syL741BZ2LHrPJjMFQeHFcfJFsFrZf7I+c8Bj/9+kXbFy1htsGD6JZqxacPh5lCWDKonXQYjKZrNJs2a8ixWNMzGZTuXnCGtRD6+DAvh27CAotXFHVYDBwZP+BG5rauP/dd/DB9Jl89eECqwHxxTLS0nHzcMfLx5uUxCSEEJZ/EMcPHSl1HFe+PpIkSTYxKzhc9MH5VAhOJ4MLf58OxiHe+7qKM3inV3IFpZrgH6iz6sp1JUVRcHHV4h+oIz42pxprdnMyc31T/1ZUJsjZuuxSvkGLUmD9AczXaBECzEKB6zzZ0u+8A/HNQgImT8HhynVOZk4n487bqYoWu382bUEIQb1GEUSfOcfcKW9Rr1EEdz/0QOHxAOuW/46XjzcBIcGcPHqcWa9Moc/tA+nWu1epOyA/L/4OLx9veg0agBDQplNH5r87l/279vH3xs1ENI7E1c2jzDsnxWmixGuod3Vj2IgH+XTmXPrcPogx/5vII/3v5K0XXmP4yIdw1uk4E3WK7Vu28eqsGQAEhYay59+dDBx6Nw6Ojnh6e9m0X3Edip+/uBqiKN3TxwcnZyf+2rAFv8BAHB0draYRFgKcdTruHzuC96fMwN3Dg8CQYL7+aAG5ObkMfeSBwvKxLtfqNSgjDSAgOIiX357K2y+9TlZmJnc9MJzg0FDiY+P4/cdl6Fz0vDhjCh26d+VScgpfzZtPv7vv4J+NW/hrw2ZcXF0t5QaFhvLPpq2ciTqNh5cnLm6uVi1GxXUQAgwmDQWmuv9xVwTkmBzINDrZ3aJ8WQbHijPVMYpQcDI4kCYcr/cr2y6os5zQnw1EfzoIl9NB6M8EoT8bgDqv7HMmz+8S2RGxZEXEkl0vnohPh+CQ6lpml1SBIN83jYtNLkB+1S4EqgjQm1QYhdruFmFU5VZu16HyuGjLD0xK0mu0VV+nvOo55puVnK1LqlQZd95Oxu0D0G/fiSY+EWOAH9ldq3aF+MyMDD58810SYuNw9/Tgtjtv55nXX7a6WE1OSGTO69NJSUrG19+PO+8fzhMvPluqrOTEJL784GO+XbvSktayfVtGThzPxAdG4uXjw9vz511zHR954lG+X/g161b8zsChd7Ho91/4aMYsRt0xDCEEofXCGTj0Lkv+ia+8wJuTXub29t0pyM/n0KWLNG7erML9KqLRaJj87pt8Nnsen858j3ZdO7F41bJS+Z6b+gpms5lXnvgf2VnZNG/Tis9/+Q53D49rPvaSHnh0FOENG7D4k8/43yOPWhZh7Nm/L6MmPg5ARONGvP7eO3wx92M+e+9D+t15O6OfeoJl33xnKWf4qIfY/c927u9zOzlZ2Xz9+090ukVOJSxJdsus4BTnXRiAnA5CfyYQl9NBOJXTGmJyLCC7XjzZEbFkN4i1BCQmlzyrfEJjotm00Qisx8yJols0pyeuKHO9E6n28AlwIqKpGxFNbbsYzs02VnGNagczKsyVPOakssurjRRRXUti3yQyMjJwd3en6feTUXTWd40CNS68EtAd39BgVA51MG6zq3f6MmdFRR721/1JpQjMBUYSYmL5IGUrCaa6P4BREXCHWz6XnOPsruXkXPb1dcepzRSh4JMbwDlNit21nKhzHNGfCSwKQopbRAKv0hqSahWAZDeIJTc42eagwntbSxp+OhTHJI/LZfqmcnriClJuPVQZh1QhRYA+NYwTItPuWk6MMfpKL9PFTUODJm5ENHXH3evy4shms0BRSo85gcIu39mZRn75+kzVjznJy+Psq6+Rnp5+07UgFF9LfrK3M84ulXu9mJtl5Kn2O2/K464udfAKXJIkSZLqCLOCU5zX5QCkKAhxjvMpO7vWQHb9eKsgJLtBHEa3GxsfkHLrIVK6H8b9UAMcUtwo8M4gveUZ2WJSy2gdVNRr5EpEMzcCQi5P9mMwmIk+lcXpo+loHVT0GhxkNeYRsEzSsmtrohwML1UpGZxIkiRJ0k1AneOI7mxAiSCkMBDR5JY9liPfJ60oAIkjO+IiWRGx5IYkg7qK2hXUgvQ2p6umbKnKKCoIDtcT0dSN0AgXNJrCbkNCCOJicjh9LIPzJzMxGi5HHJtXxdK5lx9618tdw7Mzjezamkj0KTkrWzEzCuZKXh6gssurjWRwIkmSJEnXwqTcWAuCAKd4r8IApES3LOfYq7SG1IsvCkIut4gY3eVsSVL5vPwciWjqRoPGbjjrL1/upaXkc/pYBqePZZCTVfbYkehTWcSczsI/2BlnvYbcbCMJF3Nli0k1krN1SZIkSZJUobLGXuT7pnFq4vIyx16och3Qnw20jAkpDkY0OeW0hninFwYgDWLJjogju8FFckOTEBp7G2UhXQ+dXkODooHtnj6Xxx/l5hg5e7wwIElJzLepLCEg/kJuVVW1TjAJFSZRyYswFpUnZ+uSgMKZRwSUPQesJNVGReeysNfZECSpEnlva0mzaaNLpTskudNs2mhOPrsMg3dGYXesoml7nWK9Ucq4eDFrjeSEx5follXYGmLwyK6GI5HqEo1WIbyhKxFN3QgM01nGiZiMZqJPZ3H6WAYXz2dzDUuTSVKNksFJCWmmfAxmE6LAAI62zfUtSWUSoMlzRGVUYdaYMTrlUxPdSM35RgxmE+nmvIozS5JUPpNCw08LF2S9cr0PBQWBIHLevWXumu+VYZkpq7BVJI7csATZGiJdN0WBgFAdDZu5EdbQFa32cgAcf+HyOJKCfHmOVSUTKkyVPPVvZZdXG8ngpIQ8YWRb5nkGahzwABQHbeE3QF1hpzfPTYqCuRonntTkOOCc6oqqxKKHZrUTuZ6ZGHUF1VMJITAXGEhNvsS/OWfJF3JOekm6Ee6HGlh15bpSccCSG5hMRouzZDeIK2wViYjF4CkHEEuVw8PbgYhm7jRo4oq+xEru6akFnD6WwZljGWRlGGqwhpJ04+w2ODGaFBRT6eh0edppzGaFXsYwtKqqWzSxRtjbQgBFtIqCsZqCE3W+FscMPWmkld6YAPlu2Zgcq/4fhwCMZhN/ZZ1jVdYphJ181FUCsk2QZtDZ3TonGQX2uEI8uBg1ZJurfoV4twTb1pE5MXIjcb0PWCdWwXuj2Nn5DYXvt1OBGoNJY1frnDg7aWjc2pOI5m54+V8eq5Sfa+Lc8UxOH84gOe5y67iqjsz2JAw3/3GYhYK5kr98Kru82sg+rliugQBWZJxibeZZPNROdapxzWiwv7dbBTTSOnGWrCr/Z6aYFXpPH4Fzqgtl9+ES5HpmsXnqtwhV1V5YCCBHlU2ebDGRpBtnVnA/EWJT1nyvjCqujGQP1GqFsFB3Gtb3ICjQFZWqaByJSXDhdBZnjmRy8Uw2ZpP9BalS3Wd/V6s2yhMm4o11a2CivQYnXoogjuwqDU5UBWpCtzfDvN+Xq541553x/7IblxrFUqDLw6DPo0B/+bfJwVhpY1MctDUQmJgUfI6G43TJlTyvTJKbnZeLtEm1mj7alxbzhuF1tB5QOLnElWNOitPzfNO51OJc9VZQqlMC/PVE1PekXpg7Dg6Xe28kXczl9JEMzh/PJD/PntqNbm7mKhhzYi4qT04lLElS+QQ4ZDmhS3ZHn+SOPsmjxN/u6JLdcU5zsbm4Jn90KXebSWPCoLscsBh0+RQUBzA660DGoC/apivOm4fRyVAjA+8Bgv5tSusvbkeX4m5Jy/FO58Bjq4ntdqxmKiVJ10kxqGnwU08a/tgblUGD0SmfuJ4HCVnXoVSAUjwb3rHxq2QwLl0zdzdHIup7EFHfExcXB0t6ZlYBp8+kcvpsKtkn6lI/jrrDLFSYK3kqYbOcSlgGJ5KkmBSc0lzQJ3kUBhtJ1oGHPskdbV7FfcaNWgMaQ8WzvCU2PY9ZY8Yh2xFtthMO2U5oc5xQmVWojWrUGXqcMvTXdSxmldkSvBhdcq1aZsr6KdyWa3lsdC64ruAm6N+mdHn3gVLpziludHn3AXZM/lEGKFKt4XEslBbzhuF6PgCAxI4nOPL0cvL80knqeIKmnw3GOdnDkj/PN51j41eRcMuRGqqxVNs4OqppEO5BRANPfH10lvSCAhNnz6dx+kwqCUmXF9lUU/a6OJJUF8ngRKrzlHwNrsneOF8RcBS2gLihS3FDZap48oM89yyyfdPJ9kknp+h3tm/R377pGJzzuPPJZ9CluJXb7SPHO4M/p/8f4sq7qwI0eVq0OUXBSlHQYvk7pzjNsUQeR0tg45DthMqkRmVW4ZipwzFTV+r5bSFU5hKtNlcEMy6lgxmDPg+Dcz5tPh9c+FqXM8Vq6y8HEdv5uLyrLN3U1LkORC7uT/hvXVGEigL3LI4+sYq4XgcsQXvCLUdI6HoUr8P1cLzkRr5XRmFXLnluSxVQqRRCg11p2MCTkGA3yzgSs1lwITaT02dSibmYgUmOI6k1TCiYKrm7QmWXVxvJ4ESqUopZwe94KM6pLuR6ZpHYJKZyB4MLcMx0Rp/ihj7ZHX2yOy7JxX+74ZLsjlOGntYVFGNWm8jxzrgcbFgFHmnkeGdgcqx4DMe+seu4Zc695Xb72Dd2XenABEABo7MBo7OBXO/Ma3kFip8Adb62RDDjiC5fi7bosTbL+XIrTcmfHCe0WUXBjVGDcoPBTVkUFHTJHvgcDSe55blKK1eSKpPP7khafDwE50RPAC723cexx//A4J5TOrNacKn12WquoVRb+fnqiKjvSf1wdxwdL192JafkcPpMKmfOpZGXb6rBGkrSzUUGJ1KVCdvVmI7f9EN/6XKfyWyvDHaP2kB0pxM2laGYFHSproXBRpIb+hR3XEr8rU92Q5vvUGE5Bud8sn3TyPbJIMc3rTDwKApAsn3TyfPIKjtouEYXuhzn7xd/pt3XA9Bbjb3IYN/YdVzocvyGn6NMCpicDOQ6XQ5urmlAvABVgaZE8OJcFNQUBi6anCsCm6zLrTuOqS445DhX+BROl1yv9+gkqco4pOlp8vkdBG9uC0COXypHnllOcoeTNVwzqTZzdXEgooEnEfU9cHO93C04O7uA02fTOHU2lfT0/BqsoVQZqnLMiT2TwYlUJcJ2NabnB8NKpesuudLzg2Fsfe5XojudQJOnLWz1SHK3tH64lPhbd8kVlbniD2qOR1Zha0dR60dW0d85vumEBxo4qk/CXE0tpRe6HOdixxP4HguztBglNY2ulOCnyihgdjSS55hFnte1LRjnc6gePV8bW2E+o1M1LUApSbYQEPRnG5p+PhiHDD1CZebc3f9ycuQGTM7yXJUuUxTw99Pj7KwlN9dAQmI2ooyvcwcHNfXD3Ymo74m/3+VxgwaDiXPR6Zw+m0Z8QlaZ+0qSdJndBifGfA2o7evwhal6rs4Vs0LHb/oV/l3OGIQeHw3B4JSPU3bF3YdMalNh0OGdTpZPBtk+6WR5F/32ySDHMwOTQ9lN4iogyEmHyayq9kW74prGlHikQDW9/sXyq+njHdvoItne6eWOtSnW/sOhHBixidN9/qOqFhBSATlGFRkqJ7tbhDHPWPFkDHWNIiDfqCEX7TXdfNAleNB2/p0E/NcIgPTwePY+vZLUyIuFGQpu/tfSHhdhVAkQZhXCpKK6jj48zI3OnQJw0V9uoc/KLmDnrnjOR2egUikEB7vQsIEHYaGuqNWFX25msyA2LotTp9OIjsnAaCyu8fV9+antsJFF1IJjNlH5Y0RkBz87Dk6kKiKgwT/NrLpyXUlBQW1Soy4KTAp0eWR5F7Z0ZPlkWFo9CoOPdHLdsqvsYla6cUIt2Dt2HT2uMtYmxycdfbIHnRfcScTGtux+bA2pEXE1VWXJXpkUGq7qQvMlfdHkO2DSGjh2/1aihv2N0Mi1IyRr4WFu9OkVWipdr9PSp1coFy5m4evjjJPT5UupS5fyOHUmldNn0snNlYvgStL1kMGJdEMUk4JXtD/+J0LxPxGK34kQnDNtmwZ39/1/EtVnPwZdLbg9Il3VhS7H+evFn2lfxlibvWPXcbFDFJFrOtJqaS98ToYw8OVHOdVvLwce+pMC17warLlkL9zO+dH+kyF4RRWu9J7U7Bz7nlpJVkhKDddMuhkpCnTuFFD09xU9AIoeh4YUjqPLyTFw+mw6p0+ncSlVfp/Zk6occyIXYZQkG6kL1HifDcT/RCgBx8PwPRmMwxVrgJjURtSmik+tlIg4GZjUIRWNtTlx507O33KEtv93G/W3taLR+g6Ebm/GgUeqtquXZN9UBRqa/HQrjX/pgcqkxqDL49Do9Zztv7ewn5AklcHfT2/Vlas8u/bEc+RoshxHYqdMQoWpkoMTk1yEUQYn0tVpch3wOxlsaRnxPROE2mB92hTo8khodIGEJjEkRMZwKTyBYS+NR3fJtdz1PrK9MkhoHFNqm1S7CbUgscX5crfneWax/X8rOH3bf3T4ciAe0f6yq5dUZbyPhNP+k7twvegLQGznY/z3xCryrme6bsmueHpWvPAuFLaayMBEkipXjQcn8+fPZ86cOcTFxdG8eXPmzZtHjx49ys3/3XffMXv2bE6ePIm7uzsDBw7kvffew9vbuxprXXc5ZjjjHxVqCUa8zvmjuuKuQK57FgmRMcQ3iSGxcQypoUml1i7Z+cgGen80rNwxCLse2Vi5651ItUpi8/OsmfOF7OolVQlNtiMtv+lHg7WdAMj1zGT/+D+I7XoUub6ZVB5FgbBQNxpHehIc5GLTPjk5clyJPRMomCv5S0XIL6maDU6WLl3Ks88+y/z58+nevTuff/45gwYN4ujRo4SFhZXK//fffzNy5Eg++OAD7rzzTi5evMgTTzzBo48+yvLly2vgCGo/fbKbJRDxPxGKR6xPqTyZvqkkNI4hoXFhQJLpn1rhP/jojlFsfuZXOi8pvc7Jrkc2Et0xqrIPRaplhMYsu3pJlS5wZ2PafDYYXdHYp7P99nJozDoMLjLglcrmotcSGelJZENPdLrLM7UZTWbUKqXUmBMAIQTZOYXTCkuSVLlqNDiZO3cu48aN49FHHwVg3rx5rFu3jgULFjBz5sxS+Xfs2EG9evV45plnAKhfvz7jx49n9uzZ1VrvWkuAe5wXfidCCTgRhv+JEFySPUplSw1OKuyiVRSQ5HhdXxeI6I5RxLQ/if+JUJzTXMj1yCKhcSWvEC/VerKrl1QZHFNdaLPwdkL+aQFAVmAK+yb+RlIruZK7VJqiFA5obxLpRXCwiyUAyc01EnUqlaioS3h5OdOnVyhCCKsARRT149q5K1526bJzVTnmxJ7VWHBSUFDA3r17mTx5slV6//79+ffff8vcp1u3brz22musXr2aQYMGkZiYyLJly7jjjjvKfZ78/Hzy8y8Pus7IyAAKb/zX9YYzxazged4Pv+Nh+B0Pxe94aKmZtMwqM5fqxZPQuLCLVmLkBfJdc63y3NDHRCVIbBp9uU5U7+uuKno+e/yo17ZjTm5+nnVzvqDRmo60vKKr1yEbu3qpKFz7QhF1/dNdmh0ue1H4Xpuh3qa2NF80EIdsZ8wqE6eG/MuxBzZjdjTW2THv9neGF85fcKPf53q9lkaNPGnUyBN9iVaS2NgsTkRdIiYmE7O58KTJzjKweUsMnTsFoC8xOD47x8CuXfHERGdU2/dsbfs+rwx19KMr2aDGgpPk5GRMJhP+/v5W6f7+/sTHx5e5T7du3fjuu++4//77ycvLw2g0ctddd/Hxxx+X+zwzZ85k+vTppdIbCVfUwvnGDuImoxhU6M/443o8qPAnKhB1nvVsI2atkayIBDKbxJLZOJasRgmYnQwA6IH6aIGbfwEyW6mAYBwwC8X+vuhqaVdo86ATHO0SQ8iSW/D+uwmN1neg3vbmXHzwX5J7H7nqf2kFcC0AT1PdOYdtpSmwbQBvXeIY60HYp4NwOBQMQE6DBKInbCS3QRJhuNXaz4BUNhXgjzOKUK5tUV0FPEKc8Yt0wSPE2dIKYsg1kXQqi8SoLPIzjeiAxlwx1uS84HB0HK7+jjg4qynINZGZkI9eQFNcK+nIKubopK6257pZmFA4UdOVqIBZKJgr+WZYZZdXG9X4gPgr+3Je2Xxa0tGjR3nmmWeYMmUKAwYMIC4ujhdffJEnnniCr776qsx9XnnlFSZNmmR5nJGRQWhoKFGmbDDW7nU4NbkO+J0KKlxfJKqcmbSc80hsdLGwi1bkBVLqxWPWXnHcdfgfuApQtM4cN2VV+wrx0g1wy+Lgk7/i1zuMTov74xnjR72FfdH/2YRdo9dxqUHZNzBUgL9KcF6dgr19v18qsG19obpAMamIWNmV+t/1RV2gxehg4NADW4gavMMydbVU96iAAoMDxwuybfo+1+k1RDbxpFFjD/Qul29YxF3M5sSxVKLPXW4lqVB0zY4t0aXX+OVatRP5cpyYvaqxs93Hxwe1Wl2qlSQxMbFUa0qxmTNn0r17d1588UUAWrVqhV6vp0ePHsyYMYPAwMBS+zg6OuLoWPqOYm28UHXMdMY/KqRw8HpUKN7n/VGZr5hJyy2b+MaFU/omNL5Aamji5TEe9na1VkRQ+H7Xxvfc3sU3jeb3t7+m6fr2tP7lVnxPBXP7G2OI6vMf++7fQkEZg5yFcvnHnpjt5HjdTwXS7uMheJwOAiCzZTRbn1hJRkBqDddMqg4VfZ8rCoSEutC4qSchoS6oVJfHkpyKSuPE8TQy0guqq7qVxh7/f9WG2wwmVJgqudNdZZdXG9VYcOLg4ED79u3ZsGEDQ4cOtaRv2LCBu+++u8x9cnJy0Gisq6xWFzZ1ipt8VJpiVooGhuvJ9ci2aWC4PsXVEogElDeTlk9a4SxakTEkNIkhw4aZtCSpNhEaM0dv383Zrkdp/31fIv5pQeNN7Qjf2YR9D27mZM8D9tkh286o87Q0+aE3DZd3Q2VWU+CSw5Fxa6FHNFlKRk1XT6phOp2GyCYeRDbxxKVkK0lsYSvJubPX0EoiSTaS3bqqRo22E06aNIkRI0bQoUMHunbtysKFC4mOjuaJJ54ACrtkXbx4kf/7v/8D4M477+Sxxx5jwYIFlm5dzz77LJ06dSIoKKgmD+WqwvdE0nnJbehTS0yp65nBzkc2cr5D0ZS6AtzivQgontY3KhTXZPdSZaUGJRXNonWBhMgYsuViYpKdyPXM5u+Jv3Gyz346F3X16vbFHTTa3IYdV+nqJdV+Pgfq0/aTu3GJK1zP6kKPQxx8fDUGjyzCCuQaV/ZKUSA4pLCVJDTscitJXp6RU1HpHD+WWitbSSQJoGPHjqjVaiZOnMjEiRNrujrVqkaDk/vvv5+UlBTefPNN4uLiaNGiBatXryY8PByAuLg4oqMvz/Q0evRoMjMz+eSTT3j++efx8PCgT58+zJo1q6YOoULheyLp/fHQUum6VFd6fzyUkz0O4pDriH9UKM4ZpWfSSglPKBovUjhm5MqZtCTJ3iSU0dVrcFFXr/33bwFv+RmpK7RZTrT4aiD1NrQHINc7nf1PriK+83GAOjsTl3R1zjoNDRt7ENnEA1fXy5O+xMdlc/xYKufPZmIyyZNDqnpmVJgruem+uLzdu3fj5uZWQe66SRE3e3+oSpaRkYG7uzvhn00FZ6cqfS7FrHDvpAnoUl2tVkkvj1FrJLlBrGXMSGLDWIzOlXjXxw6bClVAc2dnjskB8XWSc6re0tULIM8lh5xHN3Pkjj/tbmB0Sk4dGhAvIOif5rT+7A6c0gpnRDpzx06OjNqAUXd5aniVgLACb06QIT/fdiDY341WIcF4hDpbWkny80ycPJlG1LFU0tLqbiuJLto+B8Qff+9V0tPTb7qL9OJryaf+HoqjS+XODpmfZeCTW5bflMddXezvbK9G/idCrbpylefErfs51eMwyfXjSs+kJUlSucrq6uU07w5ar2vBqf/9QlbkhZquonSNnJJdaf3ZnQTtaApAZkgS/z29gpTm0RXsKdVFTo4aIur50LCeDy76y5PbJMTncPxYKufOZMhWEqnGmISCqZJv/FZ2ebWRDE6qkHOabXcy45qdJ1FeREnSdSvu6tVsfXva/XorbsfCaTvhWeIGb+fcuDUY3XJquopSRcwK9dZ2oMXi/mhznDCrTUTdu40T923D7FCH5zuXyhTg50qj+r6EBHpYWkkKCoxcOpXDjiNJXErNr6AESZJqK/sNTsxKlc+9metu27zoue7Z1TIPqDDZXzQuKDxuYbK/RRiFHd59OdJ/L14DTuD0dXeCNrcl6Pfu+Gxtw4kxa7kwYG+dHqRQYKi9i7S5XvSmw/y78DtaON4wJfICu5/8jfTwxMIM5RybCjCZVBjNatmtqw5wctTQsL4XkRHeuLpcbiVJTM4m6nQy0TFpRGR4kpZfgN1NS2mPJ3gtOGY5W1fVsN/gpBokRF4g2zOj3DEnAkG2VyYJstVEkiqNySubgy/9RMyg3TT79C5czwfQ8sN7CF3XkSMTV5LRKLamqygVURnUNF7RjeY/9URt1GBwKuDQw5s4NWiX3Y0ZsmcBfi40jvAmNNgdtbpwMHBBgYnT5y8RdTqFtPTC9YzkjOHVS2U20z7mDL5ZGSS5uLE3tAFmlXwXpKong5MqJFSCnQ9tovenQxAIqwBFFN3H3/XgpgrXO5Ek6dpdanWWfz79mPDfutJoyW14HA+j2zMTibl9F1Gj12OQM9/VKK+oYDrOvwuP84WL7sa1PcmeJ1aR45dewzWTqoOTo4aI+l5ENvDGzfVyK0lSSjZRp1M4F52G0VQLbp3XUbedOMgrG5YTmHn58xjn6s7MfkPZ2LhVDdbs5iKECrOo3IBNVHJ5tZEMTqrY+Q5RbJ64gs7f97Ve58Qrk10Pbrq8zokkSZVOaMycG/YPcT0P0vjLQQRvbkvYH10I+KulXXT1uhlpch1o8UNvIld1QREKeW7Z/DduLdE9DtldTx17FODnQmSEN2ElW0kMJs6cu0TUmRRS0/JquIbSbScOMu/XxaXS/TPTmffrYp4dNloGKEVMKJgq+YurssurjWRwUg3Od4giut1J/KNCcE5zIdcji4TIC7LFRJKqSb53Jgdf/okLsqtXjQrY15AOnw1Gn+QBwLmeB/hv7DoK5IQFdZqjg5qG9b1oFOGNu+vlKfyTU7I5IVtJbioqs5lXNiwv/PvKbRQOA3llwwr+bNRCdvGSqowMTqqJUAnim8TUdDUkya7Jrl41wyFDR9uvB1Bva2sAsn3T2PPEKuLbnarhmklVyd9XT2SED+Ehl1tJDAYTZ86nEnU6hUtp8vN2s2kfc8aqK9eVVEBgZhrtY86wO7xh9VXsJmUWlT+A3SzvW8vgRJIk+yK7elUjAWF/taTtVwNxytAjFEHU4B0cfnBz5S4wK1ULRQE/Hxd0zhpyco0kJmdx5TLOjg5qIuoVzrjl7laileRSDlGnkzkbnYbRKFtJblYhaSk25fPNyqjimkj2TAYnkiTZJdnV68YpJgWfY+E4p7qQ65lFctPzllm2dInutP98MEH7GgGQFpbA7om/cSnyYk1WWbpOYcHudGoXjF7nYEnLzilg176LRF9Mx89HT2SEN/VCPaxbSaKLWklSZSvJzco9N5veJ4/Q78RBbjlz3KZ9klzsc+XyK5mrYEB8ZZdXG8ngRJIkuya7el2f4O1NaffVQHQp7pa0HO90/huzFudUN1p+1xdtngMmjZEj923lxJB/MWtNNVhj6XqFBbvTq3u9Uuk6Zy29utcjJ8eAXn85aElJzSHqdApnzqfKVpKblFd2Jn2jDtPvxEE6nz+J1nz5fTIqKtTCXOawbDOQ4OrB3tAG1VZXyf7I4ESSJLsnu3pdm+DtTek++75S6c4pbnR77z7LtOlJTc+z+8nfyQxJru4qSpVEUaBTu+Civ5UrthU+1usdMBhNnI1OI+p0MimXZEB/M/LNTKffiYP0O3GQDjFnUJfok3fCN5D1TVqxoXFr6qUkMG/5NwisB8UXhy8z+w2Rg+GLmFEoO4y7sTLtnd0GJ8KoAqN9fbgUO7yBpVA4Z7gwq+xuhXhM9vkFV2BSkWfSYL6Ow891y2XnpF/x6beP1p8Nxj3an5Yf3kPw2k7sf2IVaQ1v3q5eRlP1rBCvmBTafjWw8O8r/okWPxYIdo9bzckB+4qWca+auqkAs0GFyWiHK8RX0yrSAf56q65c5dm8JYYLF7OKHlXNpYUAVCZQGezvu01lvL79AtMv0S/qIP2iDtI29pzVtiP+Iaxv3JoNka2I9vS1pJ/xDGDS3aOZvGk5AVmXB8cnuHowq88Q/oxodd31uRaiGp5DujnZbXAiSZJUnuSW5/jzw/lErOpM0+/74HUilN6TxnN24B6OjNhof129BGhzHHHM0BF4oAH6El25yqKgkB6aIpf0rsWcnTWEhrjSONLTpvwODtUTHEsVC0tNol/UQfqfOEDzhAtW2/YH1WN9ZCs2RrYi1t2r3DI2Rrbiz4YtaH/hDD7ZGSTr3dgbIleIv5JJKJgq+UZBcXkdO3ZErVYzceJEJk6cWKnPcbOTwYkkSVIZhMbMqSHbuXDrIVosGkjYltY0WNOJ4L+bc2TUBs7123e5q5dJwedoOE6XXMnzyiS52XlQ37xtdSqDGscMHY6ZOpwydEV/O+OYoccpwxnHzKK0jKLtmTpU19j64ZzmUkW1l6qKt5cToaGuhIW44ePjfE375uTI29w1RggiUhLof+IA/aIOEpkcZ9lkUhT2hjRgQ2QrNjVqSaKrh83FmlUqdofJ6YKvpioHxO/evRs3N/uceEAGJ5IkSVeR55XFnueXca7/HktXr3afDKHehvbsf2IVukQPWi28vdTA8IOPrya229Gqr6BZQZvthCbJA6dMZ0tQURxgOJX42zFDh1OmDm2u43U9lcGpAKNjAc7pFQceuR5ZFeaRapZarRAU6EJoiCuhoa7odVqr7UlJOURfyKRZEy+cnDSlxpwACCHIzjGQkJhdXdWWAISgaeJF+kUd5LaogzS4lGjZZFCp2BXWiA1FrR+X9K41WFFJunYyOJEkSbJBmV29nhsPZQxedE5xo/PMB9j5yo/XHKCo87Q4FAUSDhn6ot86HNP11r8zin5n6lDM137nzqwyk++WQ75r4U+eW07R41zy3bLJd8slz7UorSifydGIYlK4+8mn0aW4lRpzAoXjTXK8M0hqGn3NdZKqnk5X2F0rNMSVoEAXNJrL547BYOJibBYxFzK5cCGL3LzC1pC0tHz69ApFCGEVoIiiAdU7d8WXWu9EqnyKMNMyLtoSkISmX7JsK1Cr+bdeYzZEtmJzRHMynPU1WFP7YUap/EUY5YB4GZxIkiTZyqqr19cDCSta8fxKCgoCQasvBpHc9DyOmTpLQOGYXjLoKPqdfjnY0ORXPPi4LAW6POtgw73471zyioKNfNcc8ovSC3R51zUmRKgFe8esp8d7wxEIqwBFFE07sXfMest6J1LN8/F2JjS0MCDx8bburpWVVUD0hUxiYjKJj8/GVMby1OejM/hzSwydOwXgoi+5zomBnbviOR8tF+SrKiqzmfZnznLnP4e5Leqg1QD1XI2Wv+s3YUNkK7ZGNCfb0ekqJUlS7SGDE0mSpGuU55XFuQF7yg1OoDBA0SV7MHjk5Gsu36wxWlosCtyzC3+7ZZPvXvTbrcRv92zyXXPJMVffgOSYLsf564VltF/U32pwfI53BnvHrCemi20LuUlVQ6Mp0V0rxBVdie5aQggSk3KJuZBJTEwGqWn5NpV5PjqD6JgM/P306HQacnKMJCRmyxaTKqAxmeh86jQDDxyi/8HD+GZmWrZlax3ZGtGM9ZGt+Kd+E3Idrq+LplQ5RBVMJSxky4kMTiRJkq6H0yXb+3Hnu+ZQ4FY6sMh3zy5KL/w7v+hvo3N+Wb3FKniS6p0tKabLcS50PIHvsTCc01zI9cgiqWm0bDGpIXq9ltAQV8JCXAkI1KNRW3fXunCxqLvWxUzy8q5vMUwhID5Bji2pCg5GI91OnGTQgYPcdugInjk5lm3pzs5sadCc9ZGt2V4vkgKN9iolSVLtJ4MTSZKk65DnlVlxJuCvtxaR1OZMFdemZgi1ILHF+Zquhl1SlMvdtcJCXPHysu6ulZlZQHRMBjEXMolPyMFcRnctqWY5Fhjoeew4Aw8cou+Ro7jm5Vm2pej1rG/VgrWtW7GjUQTa+Ovr7ilVLbOogjEn1bSG0c3MboMTxaCAxs5OAHv93ySwz2MvsM/56A0GDfloq3xRvtiGsWR7p1c4MPxi4wuIgqq/02ky2N86EwLApCAM9rHIqlarIihIT1hY4exazs6X/4WbzYLEpBxiorOIjskkrVR3rdr//674/VaMZX3iagddfj69jx5lwKFD9Dp2DH1BgWVbgpsb61q2ZF2rVuyuXx+T+vJn2sHuVhkF+1tZVSpmt8GJJEnSjRBqwb6x67hlzr3lDgzfN3ad7OYk3RAXF60lGAkM1KEu0V2roMDEhQtZxMRkEnMhi/z86+uuJVUt19xc+h45wsCDB+lx4gROxstrwlz09GRtUUCyLzwcIRc5rFWqcp0TeyaDE0mSpOt0octx/n7xZ9p9PaDUwPB9Y9dxQQ4Ml66RooCvrzNhYa6Ehbni6Wk9A1NGRj7RRa0jCbK71k3LMyuL24oCkm4nT+Jguhw4nvPxYW3Llqxt1YpDoaGFb7pUK8luXVVDBieSJEk34EKX41wsHhie6kKupxwYLl0brVZFcLBLUQuJC05O1t21EhJyiI4unO43Pb2oG5C8fqk2KrOZjmfO4JeRQaKbG7sbNMBcRguHT0YG/Q8fZtCBA3Q6cwaN+XK/pCh/f9a1asWaVq04ERgoAxJJugoZnEiSJN0gOTBculauriW7a+lRqS5frObnm7hwIZPo6CwuXMiioEB216op/Q8eZMqKFQSmX15fJM7dnTeHDGF9q1YEpqYy4NAhBh48SPtz51CVmFv5SFAQa1u3Zm3Llpzx96+J6ktVzFwFUwnLRRhlcCJJkiRJ10RRwN9fd3m9j4ScCtf7UBTw89NZWkeu7K6VlpZPTEwm0dGZNpUnVb3+Bw/y6TfflEr3T09n/jffcM7Hh/rJyVbb9oeFsbZVK9a1bEm0j091VVWS6hQZnEiSJEmSjcLDXenSJRAXl8szsGVlGdixI47z562nl3ZwUBES4kJoqCshIaW7a8XHZxMTk0V0dCYZGQVINw+V2cyUFSsK/75yW9Hv+snJmIE9DRoUzrLVsiVxnp7VWEuppskxJ1VDBieSJEmSZIPwcFf69g0tla7Xa+jbN5RNm2JITc23dNcKCNBd0V3LaAlGLl7MoqBAzpV6s+p45oxVV67yTBw1ivWtWlVDjSTJfsjgRJIkSZIqoCjQpUtg0d/KFdsUhBD06RNqFYwApKbmWQKSxETZXetmpzKb6XryJM+sW2dTfscS0wJL9ke2nFQN+w1O7HFhPns94c0gjIrdvd2KnY6hNZsVTEJld+t3mQz2Nze+ADArKOaqX5QvIFBn1ZXrSoqioChgMgni47KJic4iJjqTzExDyVyVV0+T/X2fK4BSRYswNkhMYNje3QzZt5eAjIpbTIol6d1RjFX/Xtjjv297PGapkP0GJ5IkSZJkI53Otn+X//4dy8ko2y9upZrjlpPD4AP/MWzvbtrERFvS05ydWd2qDf2PHMIrK6vUmBMoXLw83t2D3fUbVFt9pZuPbDmpGjI4kSRJkqQK5OTY1n3HuqVEutmoTSZ6RJ1g2N7d3Hb0sGVxRKNKxbbIJvzaviN/NmtOgUbD35GN+eTbxZixHhRf3Co7464hZa53ItkPGZxUDRmcSJIkSVIFEuJzyM8z4eCoKjXmBEAIQXa2kYT4nBqonVSRyLhYhu3dw1379+KXeXlWteMBgfzaviO/tW1Hsqub1T7rW7TiqRGjeeO35VaD4+PdPZhx1xDWt5AD4SWpKsjgRJIkSZIq0KKlN45OaqAwECkZoIiiUe47t8fLAe83Ea+sLAYf2Mc9e3bTPPaiJf2SXs/KNu1Y3r4jR4OCr7pa+/oWrdjYrAUdz57BNzODJFc3dtcve4V4yf4IKn/RRPkVIoMTSZIkSbqqjp39adnKG4Bz5zLw8XG2GhyfnW1k5/Z4zp/LLK8IqZpojUZ6njjGPXt20+v4UbTmwk5YBWo1m5s0Y3n7jmxt3ASDxvbLH7NKxc6IhlVVZUm6aeTk5NC0aVPuvfde3nvvvRqrhwxOJEmSJKkMigK33BpEo0gPAHbtSODwoZTCFeIDSqwQHy+nCK5RQtA89iLD9uzizgP/4ZWdbdl0KDiEX9t3ZFWbtqTqXWqwklJdVNfGnLz99tt07ty5xp6/mAxOJEmSJOkKarVC79tCCAtzxWwW/L0tllMnC8cdCAHxcXJsSU3zyczg7n17GbZvN43j4y3pia6urGzbnl/bd+RkQGAN1lCSao+TJ09y/Phx7rzzTg4fPlyjdZHBiSRJkiSV4OCgot+AMPwDdBiNZjZvukBMdFZNV0sCtAYDgw7sZ8i+3fSIOoGmqNtWvkbDxmYt+KV9R/5pFIlJra7hmkr24GZpOdm2bRtz5sxh7969xMXFsXz5coYMGWKVZ/78+cyZM4e4uDiaN2/OvHnz6NGjh2X7Cy+8wJw5c/j3339v9BBumP0GJ0KxuxV+FHtblY7CRbvs8b0GUFXDwmA3GxVgNimYTPa3CKM9LsoHgFkBQ+Udu7NOw4DBoXh5O5GfZ2Lj6hgS4nOhypd5vEb29J0mBG3PRTN85x7u3nsAl9zLrVb/hYbza9uOrGnZhgxnXWGiGVR17AtAbYczVAvbZu+WgOzsbFq3bs2YMWO45557Sm1funQpzz77LPPnz6d79+58/vnnDBo0iKNHjxIWFsbKlSuJjIwkMjJSBieSJEmSdLNwc3dgwJ2huLo5kJNtYN3vMaReyq/patmtgNQ0hu3ayz279hCRkGRJj3PzYEXbDqxo04Gzvn41WEPJ3lVly0lGRoZVuqOjI46OjmXuM2jQIAYNGlRumXPnzmXcuHE8+uijAMybN49169axYMECZs6cyY4dO/jxxx/5+eefycrKwmAw4ObmxpQpUyrpqK6NDE4kSZIku+ft60T/O0Jx1mlIT8tn3e8xZMkFFaudU0EBAw4cZviO3XSPOoWqaKaBXK2WtW1a8l/HHiwLCMUop/KVbgJVGZyEhoZapU+dOpVp06Zdc3kFBQXs3buXyZMnW6X379/f0koyc+ZMZs6cCcDixYs5fPhwjQUmIIMTSZIkyc4FBuu4bVAIWgc1yYm5rP8jhrxcU01Xy34IQcfTZxm+cw+3/3cA17zLrVU7GzZgWecOrGnTihxnJ1qaXDBnyskIpLovJiYGN7fLC4OW12pSkeTkZEwmE/7+/lbp/v7+xJeYSOJmIoMTSZIkyW7Va+BKz35BqNUqYi9ks2nNBQyGOjZg4SYVkpzCPbv2MmzXXsKTUyzp0d5e/NqpPb907kCMj7clXbaVSDcbIRREJbecFJfn5uZmFZzcKOWKxUavXEy22OjRoyvtOa+XDE4kSZIku9S4mQfdegagKApnT2ewbWMsJpNcsKQq6fPyuP2/g9yzcw9dTp2xpGc5OrK6bSuWdenA7gb1EbLbliRVCh8fH9RqdalWksTExFKtKTcLGZxIkiRJdqd1e2/ady4cTH38SCrbt8XLhRSriGI20/Xkae7ZuYdB+w+iKygcy2NWFP6NbMiyzh1Y17oFudfZbUWSaooZBXMlz+RXXF7Hjh1Rq9VMnDiRiRMnXnd5Dg4OtG/fng0bNjB06FBL+oYNG7j77rtvuL5VQQYnkiRJkl3pfIs/zVt5AfDf7iT+251cwzWqm+olJnHPzj0M3bWXkNQ0S/oZXx+WdenIio7tiPXyrLkKStJNbPfu3TZ368rKyuLUqVOWx2fPnmX//v14eXkRFhbGpEmTGDFiBB06dKBr164sXLiQ6Ohonnjiiaqq/g2RwYkkSZJkF1Qq6NEniIhIdwC2/xXPsUOpNVyr2kNlNtPp1Bn8MjJJdHNlV8MGmK/ofuWWk8sd+w5wz649dDhzzpKe4ezE7+3a8EvnDuyrHw5l9HWXpNrmZlmEcc+ePfTu3dvyeNKkSQCMGjWKxYsXc//995OSksKbb75JXFwcLVq0YPXq1YSHh1davSuTDE4kSZKkOk+jUegzMISQMBfMJsG2TbGcOZVR8Y4SAAP2H2LqshUEpaVb0mI93Jk+fAgbWjWnx/Eohu/YQ/+Dh3E0Fq6eZ1IUtjVtzC+dO7ChZXPyHbQ1VX1JqtN69eqFqKBf6pNPPsmTTz5ZTTW6MXYbnChmsLclpBWz/d2pUgBMCopRudnWd656dvh+A5hNasxGtb19vBFG+xtALCj8jCumq3++HZ3U9LszBN8AZwwGM5tXX+RidDa1+VuhOr/PBxw4yKeLvimVHpCWzmdffkO6szMeubmW9BMBAfzaqQMr27cj0d3dkq66wRW/VYAiQDFRi9+566O2w7VAzbVgmaGqnK3LntltcCJJkiTVfXoXDf3vDsXDy5G8XBMbfo8hOSGvpqtVa6jMZqYsX1H495Xbin575OaS6uzMbx3a8WvHjhwKDZHdtiTpBlXWgPjaSAYnkiRJUp3k7unAgLtD0btqyc40sG5lDOmpBTVdrVql06nTBJboylWeZ0aN4J8mjauhRpJ086jKMSfXMiC+rpHBiSRJklTn+Pg70e/OUJyc1aRdymf9yhiys26wX5GdcM3N5ZYTUfQ+cpT+Bw/ZtI9XdnYV10qSbj6yW1fVkMGJJEmSVKcEhenpc3swWq2KpPhcNvx+gfw8U01X6+YlBBEJifQ+epTeR4/R4cxZtOZrG7WVaKd3eCVJqnwyOJEkSZLqjPqNXOnRLwi1WuHi+Sz+XHMRo0GurnglB4OBLqdOWwKSsJRLVttP+/myuVkztjRpzJwfluKfnl5qzAkUzisT7+HB7ogG1VJvSbqZiCro1iVbTmRwIkmSJNURTVt50vlWPxRF4UxUBn9tiOUaGwDqtIC0NHodPUafI0fpdvIUuoLL42/y1Wp2Noxgc7NmbG7elGgfH8u2N4cN4dNF32DGelB88Uv71tC7S613IknSjZED4iVJkiSpFmvb2Yc2nQovqI8eSGXntoQarlHNU5nNtDl/nt5Hj9H7yDGaxcZabY93dysMRpo15d/IRuQ4OpZZzrrWrZg4ZhRTlq+wGhwf7+HBW0PvZl3rVlV6HJJ0sxJABcuLXFeZIAfES5IkSVLtpECXXv40bukJwL4dSRzYnVLDlao57tk53Hr8OL2PHuPW48fxys6xbDMrCv+Fh1kCkmPBQTZP+buudSs2tGxBx9Nn8MvIINHNjd0RpVeIlyRJulF2G5wocDk8tROKHY4HVaCw74HJDvtw1sD5rSjgH+yMs15DbraRhIu5lX5XqUICu1tgFQoXIrQ3apVCw94+eEXoEEKw488EThxMr9WLK9rK8n0uBJHx8fQ+dpTex4/R7tw51CU+dOnOzvwV2ZjNTZuyrXETLrm4XC7jGicvE6jYVa/h5QRT9f5fUSjsVqaqBYvzVTa7HIZQC47ZjFLp3zfm2nDgVcxugxNJkipXWEMXOvfyQ++qtaRlZxrYuSWR6FNZNVgzqS7SaFXcNiAEr2AdJqOZbWvjOH/SPs4zp4ICukadovexY/Q+dozgtFSr7VH+AWxu2pTNTZuyL7weJrW6hmoqSZJ07a4rODl9+jSLFi3i9OnTfPjhh/j5+bF27VpCQ0Np3rx5ZddRkqSbXFhDF3oPDiqVrnPR0HtwEJtXxcoARao0Tk5q+t8eio+vM6YCM5t+u0hsTE7FO9ZiQZcu0efYMXofPUbXkydxMl5u9sjTaNjesBGbmzZlS5OmXPTyqsGaSpL9kOucVI1rDk62bt3KoEGD6N69O9u2bePtt9/Gz8+PgwcP8uWXX7Js2bKqqKckSTcpRYHO/9/encdFVe99AP+cmWGGfV9EZHUBcUsBE3dMMcy1vNnjo6npLdMyL7fFbre9m61mPanVzTS7Zd5KbdFU3E1TEXEFQUUFFWSRfZ+Z8/wxiCKgDMzK+bxfrynmzJkz3+OPM8x3fst3uHfdz8JtjwkQRRH9h3kj63yZ6Yd4Ubvj6GiD0Q/4w8VVhcpKNTI25yOnHSYmco0G/S5exIiUVAxPTUVoTk6Dx6+6umJXWHfs6h6OP7t0QZVSaaZIiaRLKwoQjFQhnqt16WHRokV46623EB8fDycnp/rtMTEx+Pjjjw0aHBFZPh8/uwZDuW4nCAIcnW3g42eHnMuVJoyM2htXNxXuf8Af9g42KC2tQcKmLPgXt58P5W5lZRh6Jg0jUlIwNC0NLpU3rxeNIOBoUBB2hesSknTvDi2ezE5E1oerdenh5MmT+O677xpt9/LyQkGBdFdIIZIqO4eWvY20dD+ipnj72GFUnD9UKjkKr1dh66YsVFWoAVhxciKK6H71qm6p39QU9L2UCdkt3YuF9vbYExaGXeG6yezFDvb1j0lxgRMiSyOKRlhKmCMM9E9OXF1dkZ2djeDg4Abbk5OT4efnZ7DAiMjyOTgp0K2nS4v27RTsgKyMMlbrJr11CnDEiJF+UNjIcC2nAglbslBTrW2yYrmls6uuxqCzZxFTN1zLt7i4weOpvr7YFR6OneHdcSwwkEv1EpHk6J2cTJ06FS+88AJ++OEHCIIArVaL/fv349lnn8Wjjz5qjBiJyMIoFAJ6RrmjZ6Q7FAoZxLqvem6fcwIAoihCEAR07u6CDv72SNqXj4wzJaYOmaxUl64uGDzcFzKZgMxLpdi1/Qo0avMmuDKtFlEZt9T7CLlzvQ//ggLEpKRgREoK7j13HirNzW6PCqUSB7p2wa7u4djdPQzZbm6mOAUiMgBOiDcOvZOTf/3rX5g5cyb8/PwgiiLCw8Oh0WgwdepU/POf/zRGjERkQYJDnRA5xKt+nkl2VgWyzpchaphXfSJyw42k5cThAgR1c4KzqxJD43wR1scVh3ZfQ8G1arOcA1mHHr3cce9AHwDAufQi7NuTDdHMNWxGnzihq5R+S49HtosL3pg0EVt76yqlKzQaRGZc0NUeSUlFl9zcBsfIdHevnztysEtn1Ng0P2eLiEhq9EpORFHE1atX8e9//xtvvvkmjh49Cq1Wi759+6Jr167GitE4NII0C/NJkCDW3cwdiInJag17xh4dVIga4Q3vTnYAgNKiWiTtzkNmum6J4IpCNaLu84KD880PWhWlaiTuyEPm2TKc2Hcd4ZGu6BXtAe+Odhj7P4E4d7IEyXvzUVVhmAH0MgBQC0Ct9IbCCAZub3OLiPZC7wgPAMCp5OtI3J8L3FbuTICut87Qv+vNiT11Ap9+83Wj7T7FxVi++mt8Ez0IXmVlGJyeBqfqqvrH1TIZjgQFY1dYOHaHheO8t3eDyeytKSoo07OAYnsgAwAbqb6fmzsC09NawTmz58Q49E5OunbtitOnT6Nr164ICQkxVlxEZCHsHOToO9QTXXrp5pbU1mhx8uB1pCQWQqu5Obwm82wZss6VwbuTHewcFagsUyP38s0K8VqNiFOHCnH+dAn6DfVC557O6NrbBYGhjjhx4DrOJBVCK8HK7tSQIAADYzqgW7grACBxfy5OJV83b1DQDeV6+ZcNup9vfwyACODRP/fXbytwcMSe0DDsDgvHvm6hKLWzM1msRGT9uJRwC8lkMnTt2hUFBQXW11NCRHqRyYX6ng4bpe7j2PlTJTi6Nx+VZU1/bSuKwLWsOy8XXFmmwf7NOUhLLkL/kd7w9LVFZIwXuvZxwZGdebiSUW7wcyHrIJcLGDa6IwJDnKDVijiwKwdnU4vv/kQTiLqQ0Wjy+q1ufNe5vl8Evo0ejBOd/CFyMjtRu2bMOidcSlgP7733Hp577jmsWLECPXv2NEZMRGRm/l0dETncE05uumVa865WInFHHvKzq+7yzJbLz67C5m8y0bmnM/oN9YSLuxL3TfbD5fNlSNyZh9JCK+jTJ4OxUcow8oFO6OBnD7Vaiz1bryLzQpm5w6rnVdqyRRz2hnbH8YBAI0dDRJaASwkbh97JybRp01BRUYE+ffpAqVTC7rau6uvXzd/9TkSt4+qpRNR93vAN1NVTqChV4+iePGSklBrtNc+fKkFmehl6Rbuje6QbOnV2hG+QA84kFeLEgeuoreFYr/bOzl6OUeP84eFli5pqDbZvuoxrVy2rYGeeU8u+wWzpfkRE1DS9k5OlS5caIQwiMieVnQz3DPZE1z4ukMkEaNRanD5ciFOHrpukLkltjRZH9+Tj7IliRI3wQqfOjujR3x0h4c44ujcf509x6eH2ysnZBrET/OHsokRFuRoJv2bher7lreKWGBSMMqUSjjU1TT6uBZDj4orEYM7FJJIKXc+JoSfEG/RwVknv5GTGjBnGiIOIzECQAaH3uKLPYA+obOUAgItnSnF0Tx7Kik2/HFBpYS12/nQVHYPtEXWfN1zclRg0pgNC+7ri8PZcgw4rI/Nz91Rh1Dh/2DsoUFJcg20/Z6G0xAKH84ki4rf9Xp+YiGi4WtSNvr23xk9k0UQiojbSOzkBAI1Gg40bNyI1NRWCICA8PBzjx4+HXC43dHxEZCQdg+wROcILrp4qAMD1a1VI3Jl31wntpnD1QgV+/eoiwiLc0HugOzx9bTFmegDOny7B0T15qCwzzNLDZD4+He0w8oFOUKrkKMirQsKvWag00JLShvbUjm2Yu3snAOD7/gMwLK1hZfccF1e8NX4itvXsba4QicgMuJSwceidnJw7dw5jxozBlStXEBoaClEUkZ6eDn9/f2zatAmdO3c2RpxEZCBObjaIjPGCfxdHAEBVhRrJ+wpw7kSxRXUna7VASmIhMk6X1C1l7IzOPZwR0NURJ/8sQMqRogZLGZP1CAh2xLDRHaFQyJBzpQLbN1222LlFj+/egYUJWwEA/xo7AauGDNNViL+QAa/SEuQ5OSMx+M4V4omIqOX0Tk4WLFiAzp074+DBg3B3dwcAFBQUYNq0aViwYAE2bdpk8CCNQdDiZl88tWuCVoCgESRXtEu47UtoG5UMvQa5IyzSDXK5AK1GxJkjhTix/zpqq3UXgyX+G1WXanBw0zWkJxUhapSuCGS/YV7o2tsFR3bk4fLZm0sPC4CuuKraEs/EuAQreT/rEu6CgSM6QCYTkHm+FHu2XIVGI7bqd0+AriCfrOlpIG326J978fzvur9pH44cg6/vHVb3WjIk+ne5uaO6ce0To5NyLiTB7yRUJVZygRuQRm355yzC8L+OEvz1bkTv5GTPnj0NEhMA8PDwwDvvvINBgwYZNDgiajtBADr3dkHfYR6wddBd8pfPlSFpRx5Krlvg+P5mXM+pxtZvshDcwwn9Yrzg5KZEzGQ/XL1QjiMJeSguMNInVDKYnhHuiBzkDQBIP12EP3fmWFRv3a0eOXwA/9y8EQDw6fBYfD5spHkDIiJJYRFGPahUKpSWNl5WtKysDEql0iBBEZFhePvbIWqkF9w72AIAigtqcGR7Lq5mVJg5sta7cLoUWell6BntjvB73dAx2AFj59gjLakIJ/cVmDs8akbkYG/07Kf7UuvEkQIcPZBn5oia9+DRw3jj1x8BAF8MjsEnI0abOSIiskTGnHPCIox6GDt2LB5//HGsXLkS/fv3BwAcOnQIc+fOxfjx4w0eIBHpz9FJgchYbwR1dwIA1FRpcPyPAqQlFUG0/J7yu1LXiji2twDnTpQgYoQXAkId0T3KDcE9nHA1uRhnTpazb9xCCDJg0H2+6NLdBQBweN81pCQXmjmq5o09noS3N64DAKyOHoIPYsfquh+JiG7HcV1GoXdy8sknn2DGjBmIjo6GjY0NAECtVmP8+PH4+OOPDR4gEbWcQiGgV6Q7eka4Q66QQasVcfZYMY7vLUB1pWWuhNQWZUW12LP+KjoE2SNqpBdcvVQIGeQBl24OOLg/B9eyzb/ymJTJFQKGx/nBP9gRWq2IP7ZnI+OM5dasGX3qON5bvxYyUcR3UQPxdtxEJiZERCam95Q6V1dX/Pzzz0hPT8ePP/6IH374AWlpadiwYQNcXFz0DmD58uUIDg6Gra0tIiIisG/fvjvuX11djZdeegmBgYFQqVTo3LkzvvrqK71fl6i9CQl1wqRHg9HnXk/IFTLkXKzApq8u4fDW3HaZmNwq52IFfvvqEo4k5EJdrYWHly0emBiE4SM7wsGhVSumUxspVTLETvSHf7Aj1LVa7PztskUnJjFnTmPJD99AodXip75ReH3sg0xMiOjO6oZ1GfIGLiXcujonANClSxd06dLl7jvewbp167Bw4UIsX74cgwYNwueff464uDikpKQgICCgyec8/PDDuHbtGlauXIkuXbogNzcXarXpi8URWQpPH1v0H+YNb187AEBpcQ0S9+XhysnyuzyzfRG1QNqRIgh5NXDs44Bu4a4I6eqCgCAnnEguwMljBdBw6WGTsHdQYNSETnDztEV1lQY7fr2MXAvuxRp89gz+7/vVsNFq8Wuvvnhp4hSIXBqYiMgs9E5OJk+ejMjISCxatKjB9vfffx+HDx/GDz/80OJjLVmyBLNnz8acOXMAAEuXLsXWrVuxYsUKLF68uNH+W7ZswZ49e5CRkVG/WlhQUJC+p0DULtg5yBEx0AtdwnU9lrU1WpxILEBKciE0GhFyi1wY2PjUVVr8uTcHqacLMWBwB3ToaI9+/b3QNcwFiX/m4mJG4wU9qHUEAfDpaA87BzkqyzW4drUCTi42iJ0YAEdnG5SX1SJhYxaKrlvuSmr3ZpzF8u++glKjwZbw3nj+oamsWUJELSKKMPiKg5a6gqEptWop4VdffbXR9vvvvx8ffPBBi49TU1ODpKSkRklObGwsDhw40ORzfvnlF0RGRuK9997DN998AwcHB4wfPx5vvvkm7OzsmnxOdXU1qqur6++XlOiGFQiwzJoOZFgy6Nq5PX3UkMkFhPd1Q68oD9godWd2PqUYRw/kobJcN3xLhvZ1zi11a3sXFVRjy8+XENTZCZHRPnByVmLE6E7IvlKOw39cQ+H16rsczbqYur0DOjsiaqgPHJxs6rdVVqghlwtQquQoKaxBwsYslJfWGi22tl7f/S5l4PP/rIStWo1doeF47i/TIMrlkrx2rEF7fD9vKSmOMJTiOZOO3slJc0sG29jY1H/wb4n8/HxoNBr4+Pg02O7j44OcnJwmn5ORkYE//vgDtra22LBhA/Lz8zFv3jxcv3692Xknixcvxuuvv95oezc7O8ibSWio/ZAB6KRUAWgfC2C4Bdmh0wBX2DrrLt2ya9XIPFCE8rwaBEEF3PIrLZNJ751dAOBrYwMBt9RYvaxF6voc+PZ2RsdeTvD1c8D4vwQjN60Ml48WQ13dDpYvAyAz4dQatyA7dB7p2Wi7rZ0cgiCgqrgW53/LRYDaBrCzaeIIhiEA6CSqGrZ3C3W7eAGvf/Ml7GtrkBwajmVznkSIjfFiNTjpXd6QAfBTtK69rZ1DR6mdMVBbLeCQuYO4C2MuJSxlev8569mzJ9atW4dXXnmlwfbvv/8e4eHhegcg3JYai6LYaNsNWq0WgiDg22+/rZ98v2TJEkyePBnLli1rsvfkxRdfRHx8fP39kpIS+Pv742x5JQSJjT8XNNL7hZcBEGXAmYoKq/5j5uqpQtQwL/gGOAAAKspqkbQvDxfOND9EyaZUmu2tsbPFmcrG7Z3yRzkcjuchcpA3gro6w6e7E1yD7XHsUB7SThZZfVe6oDZNewsC8OAA37qfhdseEyCKIjQy4FRBudH/TWUA5IIM6aX6Xd/hVy/j5dXLYV9dhYPBXfDkXx5FVWUtUGk9RUmF9r3GRZNkAOS2wNlC634/bw3HPCt/g2oFTW2VuUMgM9E7OXn55Zfx0EMP4fz58xgxYgQAYMeOHVi7dq1e8008PT0hl8sb9ZLk5uY26k25wdfXF35+fg1WBevevTtEUcTly5fRtWvXRs9RqVRQqVSNtmshvS+epHa+N4jQtbc1/jFT2cnRN9oT3Xq5QCYToFZrcfrIdZw8ch3q2jv/sbLG8zWEO7V3aakau7ZcRYeTRbh3qDfcPW1x77AO6NbDFYf25SL7svUWpzTV9e0XYN9gKFejOAQBDk428PKzQ85l40+C1/f67nbtKr5c8zmcq6qQFBCMJ6fORoUVFhDm+7m0WPuXJ61hFedsjNW12HOif3Iyfvx4bNy4EW+//TZ+/PFH2NnZoXfv3ti+fTuGDRvW4uMolUpEREQgISEBkyZNqt+ekJCACRMmNPmcQYMG4YcffkBZWRkcHR0BAOnp6ZDJZOjUqZO+p0JksQQZ0L2PG+4Z4AGlrRwAcCG9BEn78lBWwtXp2irnSgV++f4iuvVwRb8BXnDztMX9kwJw8VwpEvfnoqzEer5BNxaZXICLmxKuHkq4earg6qGCq4cSTi4tG/pkZ4FLOIfkXcOq1Z/BraIcx/0C8Ndpf0VFE19eERG1BCfEG0er/no88MADeOCBB9r84vHx8Zg+fToiIyMRHR2NL774ApmZmZg7dy4A3ZCsK1euYM2aNQCAqVOn4s0338SsWbPw+uuvIz8/H8899xwee+yxZifEE1kbv0B7RA3zhquH7kNTQW4VDu/OxbUrlrsUqzUSRSDtVBEunC1B33u9ENbLFUFdnNApyAGnjl7HyaQCqNXt/6+EIADOrkq4eirh6qGCW10S4uymbNO8pcpyy0qiAwry8PXqFfAsL0NKBz/MefRxlNvamjssIqImRUVFQS6XY/78+Zg/f765wzGpNn21VVVVhXXr1qG8vByjRo1qcljVnUyZMgUFBQV44403kJ2djZ49e2Lz5s0IDAwEAGRnZyMzM7N+f0dHRyQkJODpp59GZGQkPDw88PDDD+Ott95qy2kQWQRnNxtEDfWGf4iuV7CyQo2j+/Nx7nQxv0kxoppqLQ7tvYa0U4W4d6gPOvo74J7+nuja3QWJ+3Nx4Wz7WXrY0dkGrp7KugREBTcPJVzclZArml7/qLpKg6KCahTm16CooFp3u16DcVMDYe+oaHJ+oCiKKC9VW1Qy7Vd4HV+vXgHv0hKke3fAYzOeQImdvbnDIiJrJ8Lwq+3UHS8xMRHOzs4GPrh1aHFy8txzz6GmpgYff/wxAN1SwAMGDEBKSgrs7e3x/PPPIyEhAdHR0XoFMG/ePMybN6/Jx1avXt1oW1hYGBISEvR6DSJLplTJ0PteD4Tf4waZXIBWIyIluRDHDxWgtkZqI6vNp+h6DbZuzEJAiCP6D/aGk4sSw+/3Q/feFTi09xoK8qxn6WF7B0XDnhBPJVzdVfVLT9+utkZbl3zUoLCgGkX51SgsqK5fmvp2h3bnImZsx0YLmIh1WfThPbkWk1D7FBfh69XL0bG4CBme3pg580kUOjiaOywiImpGi5OT33//HW+//Xb9/W+//RaZmZk4e/YsAgIC8Nhjj+Gtt97Cpk2bjBIoUXsjCEDXni7oN9ATtva6SzErowyJe3NRUsg5D+aSmVGGK5fK0aOvO3pHesCnoz3GTQlCekoxjv6Zh6pKy1kmSWUrh1tdD4ibmy1cPXQJiapuntLtNGotiq/XoLBA1xOiS0Rq9J5jk3muDLt+u4p7h3s3mBxfXqrG4T25yDxX1qbzMhSv0hJ8vXoF/Auv45K7B2bMnIsCRydzh0VE7QSXEjaOFicnmZmZDZYK3rZtGyZPnlw/BOuZZ57BmDFjDB8hkRUSBMDHzw52DgpUluuGuNz6TXKHTnboP8wb7t66Me9FBdVI3JOLK5esd7Wo9kSjEXHiSAHOpRYjcpAXOoe6ILSHbk7KscP5SD1RCNGEnVo2Shnc3HVzQXT/1/WI2Nk3/Rau1YooKaxp1BNSWlRrsB6NzHNlyDpfdsffc3NyKy/DqtUrEFyQh8uubpgxcx5ynV3NHRYREd1Fi5MTmUxW32UPAAcPHsTLL79cf9/V1RWFhYWGjY7ICgV0cWziG+VaHNqdi+u5VYgc4o2gbrpvb6urNDj2Zz7OnCgy6YddapmKcjX2bsvGmZNFuHeoDzy9bXHvEB+E1i09fDWzHEBdMtrRHnb2clRWaHDtakWrPqTLFQJc3W6ZE1KXiDjeYenekuK6XpC8mvqhWcWFNdCaoI6TKMIkywXry6WiHKu+/gxd864hx9kFM2c+iWxXN3OHRUTtkYV8IdOetDg5CQsLw6+//or4+HicPn0amZmZiImJqX/80qVLzdYnsUjGWJuaLJMICKJp6gIEdHHE8LEdG223d1QgZmxHaLUi5HIZtFoR6SeKcOxAAaqrdMOEDB2fIME3TGO1cW52JX7770V06e6CiGgvuLqrMHqCPzIzSnE5sxx9IjwaJ6P7ruHS+aaHN8lkgIubCq7uDRMRJxebZovQlpfV6npBCqpRWFCDwuvVKL5eXb+imKzmtoKIBjp3SyZAlxgK6pvn61hViZX/+QLdc64iz8EJs6Y9icvOnhAsa/Gwtmt6+lC7J2gBmeWMrCQiI9BrQvz//M//YNOmTTh9+jTGjBmD4ODg+sc3b96M/v37GyVIImsgCED/GO+6nxtXzgYAuVxAdmY5Du/ORVF+jcljpNYTReBsSjEunivFPf09Ed7bDQEhTvAPbjy52t5RgZg4P+z6/QoKC6p1CYi7qv7/Lq5KyORNpw9VlWoU5lej8Hpdj8h1XUJSw8UR7sqhugr//u7f6HU1C9ftHTBr+pO44Olt7rCIqJ3inBPjaHFy8tBDD2Hz5s3YtGkTYmNj8fTTTzd43N7evtlVt4ikwNvP7o6Vs284frCAiYkVq63RIvGPXKSnFGH8lCAomliGVxAEiKKImDi/ZntCaqo1up6Q67qeEN3/qy1qwr01sa2twYrvV6Lv5YsosrXDY9Pm4px3B3OHRUTtmRGXEpYyveqcjBw5EiNHjmzysVdffdUgARFZK/sWVsRu6X5k2ezsFE0mJjfcSEo0ai2u19UHubFCVmFBNSosrEihNVOqa7Hs+6/Q/9J5lKpsMed/n8CZDn7mDouIiFqBn5KIDKSl33jzQ2n7YGff9HK9t/tjRzYy0ttPIUdLo1Cr8fF/V2PQhXSU2yjx+NS/4pRfgLnDIiJJEGCEGaMGPp71YXJCZAD+nR0RNczrjvuIooiKUjVyLahyNrVeZUVLk1EO0zIWhUaDv//nSww4l4pKhQ3m/s8cJPsH3/2JRERksZicELWBq4cSUcO90THQAQBQXamGsq4AXpOVs3dbTuVsaptrVytQXloLe0dFk/NKRFFEeZka166ydo0xyLRavL/2Oww4eQzVcgXmT3kMiUFdzB0WEUkJ55wYBZMTolZQ2spwT7QnQvu4QiYToFFrcTqpECcPF6BjoAP6xzSsc1JRqsbh3ZZTOZvaThSBQ/uuISbOD6IoNp2M7rvGZNQIBK0W765bh7HHjqFWLsfCv8zAgc6h5g6LiIgMgMkJkR4EAejW2xX3DPSErZ2uh+TS2VIc2ZuHsuJaADcrZ3v72cHeQYGKct1QLn5IbX8unS/Drt+v4N4hPg3rnJSpcfgOdU6o9QStFm/99BMeTDoCtUyGJdPmYE8gExMiMgP2nBiF3snJtWvX8Oyzz2LHjh3Izc1tUDUeADQaKxlfbYxfKAsnWEnTGJIAXdEuQdP2KWYdAuwRNcIbbl4qAEBhXjUSd+YiJ7Oi/rVulXup4dwSk09xk9jv9w2CWoCs1nT/2llnynE5LaPJZFRmwlY35TmbjSji5V9/xiOHDkIjCHju4f/FpfC+kBVJb+icIMGyNzIAMgGQqSG59zdBI7ETBiBopXfOpKN3cjJz5kxkZmbi5Zdfhq+vb7Nr+BO1F44uNogY7oXAbk4AgOpKDY79kY/040XsDSEAuiFe1y5zoQOjEkW88PuvePTPP6AVBCya/Ag29+mLHuaOi4ikSxR0N0MfE0BUVBTkcjnmz5+P+fPnG/Y1LJzeyckff/yBffv24Z577jFCOESWQ2EjoOe9HugR5Qa5QgatVkRachGOH8hHTZUEv7YkMqOFCVswZ99uAMArEydjY78oNF9lhojI+EQRBv+S8sbxEhMT4ezsbNiDWwm9kxN/f/9GQ7mI2puQcGf0G+YFe0fdJXL1YjkSd+aiuICV3YlMbd7OBMzflQAAeGPcJKzrH23miIiIyFj0Tk6WLl2KRYsW4fPPP0dQUJARQiIyH48Otuh/nze8OtoBAEqLanBkVx6yuMoWkVnM3rsLf0v4HQDwTtw4fDNwiJkjIiKqwwnxRqF3cjJlyhRUVFSgc+fOsLe3h42NTYPHr1+/brDgiEzFzkGOvkO90KWnCwCgtkaLk38WICWpEFoJTkQksgTTD+zDot9/BQB8NCoOK4fGmDkiIiIytlb1nBC1FzK5gPAIN/SK9oCNUjeC/dypYiTvzUMlK3sTmc2Uw3/ilV83AACWx4zE8hGjzBwREdFtjDghXsr0Tk5mzJhhjDiITM6/iyMiY7zg5KoEAORdrcThHbkoyKkyc2RE0jYpKRFvbPwRAPDlkOH4aFScmSMiIqLmHD16FDY2NujVqxcA4Oeff8aqVasQHh6O1157DUqlUq/jtaoIo0ajwcaNG5GamgpBEBAeHo7x48dDLpe35nBEJuXioUTUCG90DHIAAFSUqXF0Tx4yUkrMHBkRPXA8GYt/+h4yUcSa6MF4N26crvopEZGFEUTdzdDHtDZPPPEEFi1ahF69eiEjIwOPPPIIJk2ahB9++AEVFRV6j7rSOzk5d+4cxowZgytXriA0NBSiKCI9PR3+/v7YtGkTOnfurO8hzcIYv1CWTopFuwRA10WqFaC0leGewR7o1tcVMpkAjVqL04cLcergdahrRZihTKJRSbW9ZWoBspr21ZYtIas1dwRtNzLlBD7477eQiyLWRQzA26MnQqZuvi1lAARlXaFV04VpEaRaVBdiXXvz73e7J8Vztlbp6en1JUZ++OEHDB06FN999x3279+PRx55xPjJyYIFC9C5c2ccPHgQ7u7uAICCggJMmzYNCxYswKZNm/Q9JJFxCUC3vi7oM8QTKjtd796ltFIk7cpHWXE7+ERH1A4MS0vBRz98A4VWi419IvHquMkQZaxkQkQWjKt1AQBEUYRWq8smt2/fjrFjxwLQlR/Jz8/X+3h6Jyd79uxpkJgAgIeHB9555x0MGjRI7wCIjMknyA7hY3xg76FbVa4wrxqJO3KRc4nVvIksxaBzafi/dauh1Giwqec9+MfEKUxMiMjycUI8ACAyMhJvvfUWRo4ciT179mDFihUAgAsXLsDHx0fv4+mdnKhUKpSWljbaXlZWpveEFyJjcXBVoN9ILwR0dwIAVFdqcGxfPtKPFRu8misRtV7/C+ewbO1XUKnV2Na9F55/6H+h4fxFIiKr8dFHH2HatGnYuHEjXnrpJXTp0gUA8OOPP2LgwIF6H0/v5GTs2LF4/PHHsXLlSvTv3x8AcOjQIcydOxfjx4/XOwAiQ1LYCOgx2B3dB7hBrpBBqxWRd6oce3bloKqKA1iNTabVIiIzA15lJchzdEZSQAi0/AacmtE38wI++/ZL2NXWYle3cMT/ZTrUTEyIyFpwWBcAoE+fPjh58mSj7e+//z4UCv3X3tL7GZ988glmzJiB6Ojo+gKMarUa48ePx8cff6x3AESGEtTLCX1HeMHeWfdrnZ1RjuRtefDWKFDDxMToRqWewItbN8C3tLh+W7aTCxaPnoSE7r3NGBlZol5XMvHvb/4Nh5oa7A/phgVTZqC2FX/EiIjIvEJCQpCYmAgPD48G26uqqtCvXz9kZGTodTy9/xK4urri559/xtmzZ3HmzBmIoojw8PD6LhwiU/PoaIuI0V7w6mQHACi9XoOj2/NwOa0cMgDe7vzAY2yjUk9g6Y+rG233KS3G0h9XY+HkmUxQqF5Y9hV8ueZzOFVX4XBgCOZNfQw1dV92ERFZDfacAAAuXrwIjabxEoLV1dW4fPmy3sdr9ae2rl27omvXrq19OlGb2TrKcU+MJzrf4wIAqK3R4tS+Apw5VAStxgqvbisl02rx4lZdJe/bB3DJAGgBvLhtI3aE9uQQL0KX3Bys+vozuFZW4qh/EOZOm4MqzlckIrI6v/zyS/3PW7duhYuLS/19jUaDHTt2IDg4WO/jtig5iY+Px5tvvgkHBwfEx8ffcd8lS5boHQSRPmRyAWH3uqLnYA/YqHQfdjOOF+PYznxUlklw8X8zi8jMaDCU63YyAL4lRYjIzEBiEHtYpSwoPxerV6+Ae0U5Tnb0x1+n/xXlKltzh0VE1DoS7zmZOHEiAEAQBMyYMaPBYzY2NggKCsKHH36o93FblJwkJyejtra2/ud2wRi/UGR0nbo5oN8oLzi5675pzb9SiSNb8lBwtarZ59wouGl9i/O1jazaNK/jU1jSwv2KIfM1biwyQLLXtqUXLPO/no+vV62AV1kpzvj4Ys60x1GutGtT3AJ0xQhlaoOFaTWscLVRgxAkWoRRlOA6EVxZ0/LdqG0SHByMxMREeHp6GuS4LUpOdu3a1eTPRKbi4qVExCgv+HZ2AABUlKpxbEceLpxsvKw1mVaeo3OL9nty/zZct3fCwaBuRo6ILE3HoutY/fUKdCgtxlkvH8x69EkU2zuYOywiorZhnRMAunomhqT3nJPHHnsMH3/8MZycnBpsLy8vx9NPP42vvvrKYMERKW1l6DXMA90iXSGTCdCotUg9WIjT+69DXcOvVcxNELUYeDHtjvvc6MgIKczDyv9+hoMBXfHx0Dic6BhkihDJzLxLirDq6xXwKy7EBQ8vzHr0SRQ6OJo7LCIiMqAdO3Zgx44dyM3Nre9RuUHf3EDv2alff/01KisbV9eurKzEmjVr9D0cUZMEAega4YJx84MR1t8NMpmArDNl+G3FJRzfVcDExALY1tbgw1++weMHdwDQJSC3j9DR1m1/5f4p+CZiCGrkcgzIPIu1//kEn6z/Cl3ysk0cNZmSR1kpVn/9GQILC5Dl6o6Zjz6JfKeW9bQREVm6+mHjBr5Zm9dffx2xsbHYsWMH8vPzUVhY2OCmrxb3nJSUlEAURYiiiNLSUtja3pzEqNFosHnzZnh7e+sdANHtfALtEDHaG24+KgBAUW41krblIedChZkjoxs8y0qwbP1K9MzJQq1MjlfvfxjlShVe3LEBHW6ZHH/NyRXv3DcR27vplhH+OnIY5h3YhgmnEnHfuVOIOXcav4X3w7LB9+Oyq0dzL0dWyLW8DKvWrEBIQS6uOrti5ox5uObiau6wiIgMR+IT4m/47LPPsHr1akyfPt0gx2txcuLq6gpBECAIArp1azxmXBAEvP766wYJiqTJwVWBfiO9ENBdN2SwulKDE7vzcTapmBPjLEjYtSv4dP1K+JYWocjWHgsmzUKSf2cAwM4uPRFx+ZYK8Z0aVojPdnHHy3GPYFX/GDz1xxaMTjuO8SlJiDuTjJ96D8Bn0aOQ5+TS3EuTlXCurMDKbz5Ht9wc5Do6Y+aMJ3HFzd3cYRERURNKS0sxYsQI1NbWQqPRYMGCBfjrX//a4ufX1NRg4MCBBounxcnJrl27IIoiRowYgZ9++gnu7jf/0CiVSgQGBqJjx44GC4ykQ2EjoMcgd3SPdoNcIYNWK+LskSKc2FuAmkoLX4JIYmLOnsJ7v/0H9rU1yHD3xvyHZiPTzav+ca1MhsSAuy8XnOHhg/gJMxCek4Vn9m3G4AtpeOTYAUw4lYjv+g3GyntHoNiOE6atkUNVFf79ny/QI+cK8h0cMWPGk8j08Lr7E4mIyCzs7e2xZ88e2Nvbo6KiAj179sSDDz7YqOJ7c+bMmYPvvvsOL7/8skHiaXFyMmzYMAC6GfkBAQEQBOtbTYBMTxAArwA72DkqUFmmRl5mZYNekKBeTug7wgv2zrpfxZwLFTiyNRfFeTVmipiaJIqYmbgbf9/9G2QQ8WdgV8RPmIESW/s2HTalgz+e+MsTiMw8h4X7NqPvlYuYfXgXHj72J1b1j8E3kUNRoVQZ6CTI0GRaLSIv3ewpS+ngh8+/+xJ9rmSi0M4Bjz06Fxe8fMwdJhER3YFcLoe9ve7veVVVFTQaDUQ9hqxUVVXhiy++wPbt29G7d2/Y2Ng0eFzfGoh6r9a1c+dOODo64i9/+UuD7T/88AMqKioaFWEh6fIPc0RErBccXG7+kpYX1yJpWx7Ki2sROdobXv52AIDSwhocTcjD5bRyc4VLzbDRqPHPhJ8w+cQhAMC6PtF4e+SDUMsNt/D+kYAumDb1aQzNSMXCvZsQmpeNBX/8jv89ug9fDBiJ/94TjRqFzd0PRCYzKuUE/rFlA3xLbs4xqpbLodJoUGxrh9nTn0C6D3vTiaj9EmD4Ceyt+ep/7969eP/995GUlITs7Gxs2LChvkDiDcuXL8f777+P7Oxs9OjRA0uXLsWQIUPqHy8qKsKwYcNw9uxZvP/++3rVLDlx4gTuueceAMCpU6cank8rOjP0Tk7eeecdfPbZZ422e3t74/HHH2dyQgB0icmQyY0r7tk7KzBksm/9L2ttjRan/yhA6sEiaDWcWGJpXCrL8dHPX+PezHPQCALejxmPbyKG6rrEDE0QsLdzOPaFhOH+M8fx9L7fEViUjxd3bsTMxN1YPigWP/eMgkYmwWpkFmZUygl8/N/VjbarNBqIAP49aARSOnYyeVxERO1FSUnDAscqlQoqVdMjCcrLy9GnTx/MmjULDz30UKPH161bh4ULF2L58uUYNGgQPv/8c8TFxSElJQUBAQEAdHPLjx8/jmvXruHBBx/E5MmT4ePTsp5vQ9dA1Ds5uXTpEoKDgxttDwwMRGZmpkGCMgWZGhAkVlXYVHV9BAGIGO1V97Nw22M3758/UYxjO/NRWaape9DwsYgAoNVVkZbaQMS2fpsTUJiHZT99ieDCPJTbqPDcuOnY2zlc9+9o1DxShi1hfbG9a29MOHUYTx7YBt/SIry55b+YdXg3Ph18PxK69YYoNF4JXQCk294mej+TabX4x+8bdD838bgI4H8T92PVvTENFkMwBgHQ/RWT4NQ0KY6sFgDIakTIqkSrXG61LWS15o7A9ERr+IxmxCKM/v7+DTa/+uqreO2115p8SlxcHOLi4po95JIlSzB79mzMmTMHALB06VJs3boVK1aswOLFixvs6+Pjg969e2Pv3r2NRkmZit7Jibe3N06cOIGgoKAG248fP97iiTPUvnkH2MHB+e5DcDKOl9xMTMiiRGWew9KfV8GlqhJXnd3w1KTZSPc27RAdtVyOn/pE47fwCEw5dgB/PbQDIddzseSXNUjx9sMnQ8bgj+AwaX5KM6OIzAz43rJc9O1kAHxLihCRmYHEoLsvjkBERI1lZWXB2flmXajmek3upqamBklJSVi0aFGD7bGxsThw4AAA4Nq1a7Czs4OzszNKSkqwd+9ePPnkky1+jZiYmDsO39q5c6deMeudnDzyyCNYsGABnJycMHToUADAnj178Mwzz+CRRx7R93DUDjl7Klu0n52j3r9+ZAKTTh7CK9t+gI1Wi+O+AVgw8TEUOJqvcF61jRJroobjp94D8OiRPZhxZDfCc6/gs5/+jSOdQvDxkDFI7hRitvikQqHRYMDFdMw50LI/Ml5lJXffiYjImhmxzomzs3OD5KS18vPzodFoGg3R8vHxQU5ODgDg8uXLmD17dn09w6eeegq9e/du8WvcmG9yQ21tLY4dO4ZTp061arqH3p8O33rrLVy6dAn33XcfFArd07VaLR599FG8/fbbegdA7YOdoxwBYU4ICHeEd90k97upLLOGPlvpkGm1WLh3Ex5L1I0d/T3sHvzz/kdQbdOyZNPYylW2WDFoNNb2HYQ5h3bif5L/QOTlDHyz9lPsDemOjwePwVkfP3OH2a7YqNUYeCEdsanHMSL9FFyrKlv83DwzJrRERCZhRUUYb+/ZEEWxfltERASOHTvW6mN/9NFHTW5/7bXXUFZWpvfx9E5OlEol1q1bhzfffBPHjx+HnZ0devXqhcDAQL1fnKzb7QnJrb/4GrUWMrnQZDefKIqoKFEjN7PlH3TIuOxqqvHOpm9x3zndKhsromOxfFBsk/M6zK3I3hEfxIzHN5FD8cSBbXjw5GEMzUjF0IxU/B52D355cBLS7JzMHabVUqprMSgjDfenHEfM2dNwqq6qfyzPwQk7Qnti1JkTcKsob3LOiRbANWdXJAWwN4uIqLWioqIgl8sxf/58zJ8/v9XH8fT0hFwur+8luSE3N7fFE95ba9q0aejfvz8++OADvZ7X6nE13bp1a7JSPLVvdk4KBIQ5IrC7I7xuS0jysipxKbUUmWfK4OFri6GTfRtk5gDq180+si2PVd8thE9pET5dvxLdc6+gRi7Hy/c/gk3hEeYO666uObnijdEPY3X/GMzfvwUPpCYj7swxxL5zAj/1jcKnMbHIcXEzd5hWQVVbg6HnzyA29ThizqbAoaa6/rFrTs7YFtYb28L64Kh/MLQyGQ6EhGLpj6uhRcNJ8TfmpS+OnWj0yfBEROYmiEZYSrjueImJiQYZ1qVUKhEREYGEhARMmjSpfntCQgImTJjQ5uPfyZ9//glbW1u9n9ei5CQ+Ph5vvvkmHBwcEB8ff8d99S20QpavpQlJRcnNYVoVJWXY+2M2Ikd7NZgcX1GixpFtechK07+bjwyvR04W/m/9SniXl6DA3hHPTJyFY36NV+OzZJluXnhh7HR81X8EFvzxO4adT8HDSYcw4XgSvus/CJ8NvQ+FDo7mDtPi2NVUY+i5VIw+cwJDz6bAofZm4dNsJ1ds694bW7v3wbFOgY160BLCemPh5Jl4ceuGBpPjrzm7YnHsRCSEtXysMhERtU1ZWRnOnTtXf//ChQs4duwY3N3dERAQgPj4eEyfPh2RkZGIjo7GF198gczMTMydO9cgr//ggw82uC+KIrKzs3HkyJFWVY1vUXKSnJyM2tra+p+bw6rx7Ud9QhLu1GgOSW5WJTKbSEhul5VWhsvpZfC+pUJ87m0V4sl8RqYdx+LN38FOXYuznh0w/8E5uOribu6wWi3N2w9PPzgHcdVXMfmX9Yi6mIFZB/bgL0cOYvXAYfhq0DCU2bZsPlR7ZV9dheHnUhGbehxDz6XCTn1zfdIrLm7Y2r0PtoX1xgm/gLsO6UsI640d3XoiIvNmhfikgBD2mBCRdFjInJMjR44gJiam/v6NjoQZM2Zg9erVmDJlCgoKCvDGG28gOzsbPXv2xObNmw02JcPFxaXBfZlMhtDQULzxxhuIjY3V+3iCqE99+nagpKQELi4u6PqPtyG0oqvJmt1tKW57JwX825iQWBoZgFBne6SVVkiuFIKyudVeRRFzDu3Awn2bAQD7gsPw7LhHUa6y/utBBiDYzx5pJeWIPp+O+IRN6HX1MgCg0M4enw+9D9/eO8hiJvkbkry66e2OVZUYfjYFo1OPY8j5M1Bpbl6/ma4e2Na9N7Z0vwenfTtZ3bLMMgDdbe1xtkB617fkCvlA197hKjucy62Q3JdcqhKJnTAAjboKhzb+E8XFxQYZ3mRINz5LBr31L8gM/FlSW1WFi/98ySLP21Qku5arKEByb+6yJkqK2DkpEBDuiIBwJ3gF3JaQZFYiK6UUmSllqCy9+YHGmr4XrY9VYm3dHBu1Gq9u+wETTycCAL7tNxjvxUxoV1XXBREQIOBA51AcCOmG2JQTWLjjd3TOz8Wirb9i1oE9WDY8Fj/1uxdqeTs671s+nTtVVSLm7CmMTj2OQRfSoNTcvPgvuntha5iuhyTVx68+IRGM8Q2gkQnQFaeT1xp+3LelEySXjQEyAYCtAFEmSC450SokdsKwktqqRuw5MdSEeFNKSkpCamoqBEFAeHg4+vbt26rjtCg5uX0s2Z2sX7++VYGQ6bQmISHr51pRhqU/r0bk5QyoBRneGTER3/cbbO6wjEsQsK1HH+wI64kJx5Pw1K6t8CsuxBu//ojZ+3fhkxH3Y1PPvhDbwVAkl8py3Jd+CrFnjiP6wlnYaG8mJOc9vHUJSfc+SPfytboeEiIiqTHUhHhTyM3NxSOPPILdu3fD1dUVoiiiuLgYMTEx+P777+Hl5aXX8VqUnNw6lkwURWzYsAEuLi6IjIwEoMuUioqK9EpiyLTsnRUIDK1LSG4fsnWpApkpZchKZULSXoUUXMOyn76Ef3EBSpW2+Pv4R3EgOMzcYZmMRi7H+n798Wvvfphy5E88uScBgdcL8OGP3+LxfTux9L447AztYXUf2t3KyzAy9SRGnz6BARfOwkZ787vGdK8O2BbWB9vC+uCcVwczRklE1D4Zc7Uua/L000+jpKQEp0+fRvfu3QEAKSkpmDFjBhYsWIC1a9fqdbwWJSerVq2q//mFF17Aww8/jM8++wzyuiERGo0G8+bNs5oMTyrsnW+sstUwIRFFEXmZlUxIJCL6Yho+/OVrOFdX4bKLO+Y/OAfnPaX5YbVWocB/BgzB+r79Mf3gPszZvxOh17Kx4ruvkNwpEB+NHINDIV3NHeYdeZSVYlTqSdx/6jj6XzwH+S3jW854d6wfsnXB07jr1xMRSZ4o3H1Cb2uOaWW2bNmC7du31ycmABAeHo5ly5a1akK83nNOvvrqK/zxxx/1iQkAyOVyxMfHY+DAgXj//ff1DoIMx95ZgcDujgjo7gSvTs0kJCmlqCxrYgIKtTsPHzuAf2xfD4WoxVG/YDwzcRYK7bmsboVKhc+HjcT3UdGYvX8XHj24D30vX8Ka1SvwR+duWDpyDE76BZg7zHreJcUYlXoCo0+fQNSlDMhuSUhO+XbClh59sKNLb1xy16/rnIiIqK20Wi1sbGwabbexsYFWq//sIb2TE7VajdTUVISGhjbYnpqa2qoAqO3ulJDkZukSkswzpaguZkIiFTKtFs/v/AWPJu0FAPwaHoFXRk9BrUKya2A0qdjeAUtGjcU3A4Zi7p4EPJx0EIPPp2Pw+XRsDe+Fj0fE4by3eXqZfIqLMDrlBEafPo5+WRcbJCTH/QKwtUdvbA3vg8vuHgAAeVVzRyIiIqPghHgAwIgRI/DMM89g7dq16NixIwDgypUr+Nvf/ob77rtP7+Pp/Ull1qxZeOyxx3Du3DkMGDAAAHDw4EG88847mDVrlt4BUOs4uCgQEOaEgO6OjROSzEpcSi1D1pmGPSTWP+WXWsKhugpL1v0HMWkpAIBPBsfhiwEjrW4+hSnlOTnjzbEPYdWg4Xhq11ZMOJ6E0SknMTL1FH7uE4lPY0bjipvxa8B0LLqO2NMnMDrlBPplXWzw2FH/IGwN741tPXrjqqv11qMhIqK7s6YJ8Z9++ikmTJiAoKAg+Pv7QxAEZGZmolevXvjPf/6j9/H0Tk4++OADdOjQAR999BGys7MBAL6+vnj++efx97//Xe8AqOVak5CQtHQsvI7PvlmJsGvZqFIo8I8xU7Et9B5zh2U1Lrt5YNGDU/Hl4BF4ZsfviE09iQePJWLsyaNYFxmNz4aORL6TYf9YdLpegNEpx3H/6ePofSWrfrtWEHQJSY8+2BreG9dcXA36ukRE1DacEK/j7++Po0ePIiEhAWfOnIEoiggPD8fIkSNbdTy9kxOZTIbnn38ezz//PEpKSgDAajI7a+TgokBAdycEdneEp18TCUlKKbLSypiQEHpnXcKK/3wFr7JS5Dk64emJj+GUr2Gqv0rNOe8OePp/ZqHX5Uv42/bfMSgjHdMP/YGHjh7GmgFDsHJwDErs7Ov3l2m1iLyUAa/SEuQ5OeNI4J0rpQcW5GH06eMYffoEemZfrt+uEQQcCQzBlh59sL17L+Q6uzR7DCIiInPauXMnnnrqKRw8eBDOzs4YNWoURo0aBQAoLi5Gjx498Nlnn2HIkCF6HbdVA9DVajV2796N8+fPY+rUqQCAq1evwtnZGY6O1jHZ1hjZ7h1fTwC8Auxg56hAZZkaeZmVzRaRupGQBIQ3kZBcqktIzpShqvxmQtKSATuCBPMXAbpiZYKmfddhjDuZjHc3rIVKrcYZH1/M/d85KKx1k9w8BJlQ195qw7T3qQ6BmD1tLu69cBZ/27kJ91zJxNx9O/A/iQewcmAMvuk/BIPPp+EfWzfAt6S4/nnZzi54e/QkJHTvXb8tOP+abg5J6nF0v3a1frtGEHAoqAu2du+D7WG9UODoVP+YoMdCerLatp2rNZIBEO0BrY2VFGwzICm2t24RIxGCKFpdwdC2UlRK7TccENRWcM5GnHNiDZYuXYq//vWvTXZSuLi44IknnsCSJUuMn5xcunQJ999/PzIzM1FdXY1Ro0bByckJ7733HqqqqvDZZ5/pe8h2zz/MERGxXnBwubmSQXlxLZK25SHrTBmAuoQk3AkB3Z3g6Wdbv59Wq+shyWwiISGCKGL+7m1YsGsrAGBnt3A8+5dpKFfZwjbPzLG1I4eCu+KRx57BiPTTWLhzM7rl5SB+52bM3r8TztVVjf6W+JQU4+MfVmNx7EQ4V1didMpxdMvLqX9cLchwMLgrtob3xvbQXih0sI4vdYiIiG44fvw43n333WYfj42NxQcffKD3cfVOTp555hlERkbi+PHj8PDwqN8+adIkzJkzR+8A2jv/MEcMmezbaLu9swJDJvviwokSOHuqmJCQ3pS1tXh74/cYdzIZALBy4HB8EDv2jsOJqA0EATtDe2J313CMOZ2MBbt+R0DRdd1Dt+0qg+7Lr5e2bazfViOT48+Qrtgafg92duuBInsHU0VORETGYIxROFa0Wte1a9eaXEL4BoVCgbw8/b8p1Ts5+eOPP7B//34olcoG2wMDA3HlyhW9A2jPBAGIiPWq+1m47THd/ZA+ujHlWq1uyFZmKhMSujuPslJ8unYV+mVdRK1MhtfHPoQfIqPNHZYkaGUy/NYrAgUOjlj1n8+b3e/GFX+0UxDWRURjV7ceDeapEBGRlTPisC5rWK3Lz88PJ0+eRJcuXZp8/MSJE/D1bfwF/d3onZxotVpoNI0/OF++fBlOTk5NPEO6vALsGgzlak7qn9eR8mchExJqka7XsvHZt1+iU1Ehim3tsOCRmTho4VXN2yP3ivIW7fdt/8HY1LOfkaMhIiIyrTFjxuCVV15BXFwcbG1tGzxWWVmJV199FWPHjtX7uHonJ6NGjcLSpUvxxRdfAND1AJSVleHVV1/FmDFj9A6gPbNzbNk/b0F2NRMTapGh6an46Ic1cKyuxkV3T8ydNgcXPL3NHZYk5Tm27Butlu5HRERWRuIT4v/5z39i/fr16NatG5566imEhoZCEASkpqZi2bJl0Gg0eOmll/Q+rt7JyZIlSzBixAiEh4ejqqoKU6dOxdmzZ+Hp6Ym1a9fqHUB7VlnWsqV2WrofSdu0g/vwj983Qi6KOBTUGQsemcl5C2Z0JCAE2c4u8CkpbrLAqRZAjrMrjgSEmDo0IiIio/Px8cGBAwfw5JNP4sUXX4RYtwytIAgYPXo0li9fDh8fH72Pq3dy4ufnh2PHjuH7779HUlIStFotZs+ejf/93/+FnZ3d3Q8gIXmZlSgvroW9s6LRnBNAtzRwRYluWWGi5sg1Gvzj942Ydng/AOCnvv3x6rjJqFW0aiVwMhCtTIa3R0/Cxz+shhZokKDcWABz8eiJXKCAiKidYhFG3ZzzzZs3o7CwEOfOnYMoiujatSvc3NxafUy9Pt3U1tYiNDQUv/32G2bNmoVZs2a1+oWlQBSBpG15GDLZF6IoNkhQbmSXSdvymq13QuRYVYml69ZgyPk0aAUBH458AF8OjtGttkBml9C9N575y8xGdU5ynF2xePTEBnVOiIiI2is3NzdERUUZ5Fh6JSc2Njaorq5ushfA6hhjnGATslLLsO+HbESMbljnpKJEjaStN+ucmILYDppNX2KjH6xHp+sF+Py7L9El7xoqbJR47qGp2H7jw24LzkeUYMdK/T+LCX/XE8J7Y0dYT0Rm3lIhPuDOFeLJcORVgLwCkFnhNd4WUry+28NHj9aS1UjsFxyAqJbeOZOO3m9vTz/9NN599118+eWXUHBYSYtknSnD5bSyFleIJ4q4lIFPv18F94pyXHNyxtypc5DSsZO5w6JmaGUyHA5qeilFIiIifVlDnRNj0Tu7OHToEHbs2IFt27ahV69ecHBoOCF3/fr1BguuPRFFIPcS55bQ3U04fgRv/bwOSo0Gp3w74cmpjyHX2dXcYREREdGtJF7nxFj0Tk5cXV3x0EMPGSMWIkkTtFo8s2sLnty7HQCwtXsvvPDgVFQqVWaOjIiIiG7HCfHGoXdysmrVKmPEQSRptjU1eGfjWsSdPg4A+HzwCHx03xiInLdAREREEtLi5ESr1eLDDz/Exo0bUVtbi5EjR+KVV15pVBGSiPTjVVqC5WtXoveVLNTI5Xhl3F+woW9/c4dFREREd8OeDoNr8dey7777LhYtWgQHBwf4+vpiyZIlWLBggTFjI2r3QnOu4L//XoreV7JQaGePWY/OZWJCREREktXi5GT16tX4v//7P2zbtg0///wzNm7ciDVr1tTX6yAi/cSkncbalf+HjsVFyPD0xsN/fQZHgjqbOywiIiJqCdFIN4lr8bCuS5cuYezYsfX3R48eDVEUcfXqVfj5+RklOKJ2SRQx68AePJ/wK2SiiAMhXfHMwzNQYmdv7siIiIiIzKrFyUlNTQ3s7Ozq7wuCAKVSierqaqMERtQeKTQavLLpJ0xJOggA+D4yGm+OeRBqudzMkREREZE+uFqXcei1WtfLL78Me/ub3+7W1NTgX//6F1xcXOq3LVmyxHDRGZFMDQi15o6CjE1o9INpyLRaRF66pWJ4YAgcq6vwyfdfI/rCWWgFAe+MHo+vo4careyxoDbKYS2aIACCRnd9S428xtwRmJ5MACDVlbYl+wFGgCgIkjt9ja30Vm7UqKV3zrdiEcYWGDp0KNLS0hpsGzhwIDIyMurvC0b6kEVkTUalnMBLmzfAt6S4flueoxO0EOBTVoJypQrxf5mG3aE9zBglERERtQmLMBpFi5OT3bt3GzEMovZhVMoJfPL96kbbPctKIQC4bm+PmTPnIa1DR5PHRkRERIbDYV3GIe0+MyIDkmm1eGnzBt3Ptz0mQPdlSK1cgbPeHUwdGhEREZFVYHJCZCCRlzLgW1Lc7EUlAPApLUHkpYxm9iAiIiKrwaWEjcLsycny5csRHBwMW1tbREREYN++fS163v79+6FQKHDPPfcYN0CiFvIqLTHofkRERERSY9bkZN26dVi4cCFeeuklJCcnY8iQIYiLi0NmZuYdn1dcXIxHH30U9913n4kiJboLUURgQV6Lds1zkuYENyIionaFPSdGYdbkZMmSJZg9ezbmzJmD7t27Y+nSpfD398eKFSvu+LwnnngCU6dORXR0tIkiJWpeWPYVrFm1HM/s2gqg+fcVLYCrzq44EhhistiIiIiIrEmLV+u6W2/GDQEBAS3ar6amBklJSVi0aFGD7bGxsThw4ECzz1u1ahXOnz+P//znP3jrrbfu+jrV1dUNCkWWlOiG1Mhg8tIXZAY32tkYWbh7WSme2fE7Jh89BJkookqhwO5u4YhNOQHxttfU1v3/nTETAZnMJN8KyCT4Cy4TjNfelk7K7S3FVeyles5sb+mwhnPmal3G0eLkJCgoqMk6JqIo1m8XBAFqdcuqn+Xn50Oj0cDHx6fBdh8fH+Tk5DT5nLNnz2LRokXYt28fFIqWhb548WK8/vrrjbZ3dbKD3NauiWe0X6IEi5ALAPzsVBBwM0FoK4VajQf27cTD2zbDoaoKALCvbyTWjJ2EPHcPHDuRjDnr18GzuKj+OQWublg56WFk9u6LUAPFcTdKQ52wFZEJgI+dCoJGej3jcgkWIxQEoKNKJcmhEBqluSMwPZkAdFQpIYgiRIm1t71GelWja2q1+NPcQZBZtDg5SU5ObnK7KIr4/vvv8cknn8DR0VHvAG5PeG5Ndm6l0WgwdepUvP766+jWrVuLj//iiy8iPj6+/n5JSQn8/f1xrrgSQpW03t20NuaOwPRkAEQZkF5a0fbkRBQRk3YaL2z5BYHX8wEApzp2wuK4iTh6Y6hWaQXSgkPxzd/+iYhbKsQnBYZAK5MBpRVtjaLF7ApM9lIWQxAAtRI4W2iA9rYyColWiJfbAefyKyT3YVVra+4ITE8QALm9iHN50mtv54vVd9+pndFoqswdwt0ZsQijlLU4OenTp0+jbdu3b8eiRYuQnp6O559/Hs8++2yLX9jT0xNyubxRL0lubm6j3hQAKC0txZEjR5CcnIynnnoKAKDVaiGKIhQKBbZt24YRI0Y0ep5KpYJK1fgrRS2kN6xLah/WbhChO/e2nH/Xa9l48fefMSgjHQCQ6+iEj0Y+gA33REKUNR5EpJXJcCi4Sxtese20EnyDk8Ew7W2NpNjeQN1nAxGS+7Aqxfa+cX1Lsb2ldr6AlZyzEZOTqKgoyOVyzJ8/H/Pnzzfwi1i2Ficnt7oxV2Tfvn2YM2cONm/eDG9vb72OoVQqERERgYSEBEyaNKl+e0JCAiZMmNBof2dnZ5w8ebLBtuXLl2Pnzp348ccfERwc3JpTIbojt/IyPL1rKx5JPAC5KKJGLseqgcPw+dCRKFdJ8KtLIiIiMrrExEQ4O0tzdU+9kpNz587hpZdewk8//YSHH34YKSkpCAlp/cpD8fHxmD59OiIjIxEdHY0vvvgCmZmZmDt3LgDdkKwrV65gzZo1kMlk6NmzZ4Pne3t7w9bWttF2orZSaDSYeng/ntq1FS5VlQCAreG98F7seFx29zBzdERERGRunBBvHC1OTubNm4eVK1ciJiYGR44cMUjxwylTpqCgoABvvPEGsrOz0bNnT2zevBmBgYEAgOzs7BavEkZkKEPTU7Foy8/onJ8LADjj44u34ybiUEhXM0dGRERE1L4JotiyUX0ymQy2trYICwu7435Hjx41SGDGUlJSAhcXF4T9/W0IEhuWI9UJ8d1c7ZHWggnxIXnXsGjLzxh29gwAoMDBEUvvi8OP/e7VTWa3Mna55o7A9GQCENzRHulSnBAvvfmykAlAmL09zkpwgrRGWn++ANS1t4MdzuVKr72lOiF+z943UFxcbHHDm+o/Sz79NuQG/iypqa7Cmf/7h0Wet6m0uOfk1VdfNWYcRGbjXFmBp3ZtxdTD+2Gj1aJGLsc39w7B8uGjUCax5aaJiIiIzInJCUmWXKPBlCN/YsHOLXCr1C3xuzO0B965fzwueXiZOToiIiKyZJxzYhytWq2rPdAoAUGCRcukRgQgaAFB3XDp6IHn07Bo28/olqdbyjrdqwPeiZ2AA511JRKFltUStWiC1MY1oa6CtFjX5uYOxtQk+gdNlNXdJHb+NuUSO2Horm+ZCpDXSK+9ax2l93FNo5beOZNOi1s+JiamyeKItxIEATt27GhzUETGEliQhxcSfsGI9NMAgCI7e3wy/H6si4iGRiY3c3RERERkNViE0ShanJzcaXWukpISrF27FtXV0puwRdbBsaoSc/cmYNrhfVBqNVALMnwXNQjLhsWi2M7B3OERERGRtWFyYhQtTk4++uijRtvUajWWLVuGf/3rX/Dz88Obb75p0OCI2kqm1WLUn/swZfPP8KgoAwDs7RyGd2InIMPLx8zREREREdGtWj2g79tvv8Urr7yCyspKvPbaa3j88cehUHB8IFmO/hfO4R9bNqJ7zlUAQIaHNxbHTsC+rt3NHBkRERFZOwGGn98oufmSTdA7m9iyZQsWLVqECxcu4Nlnn0V8fDwcHDgshixHp+sFeH7bLxidehIAUGZnj0+GxuK7yEFQyzmvhIiIiMhStTg5OXz4MF544QUcPHgQc+fOxfbt2+Hp6WnM2Ij04lBdhSf2bsesP/dAqdFAIwhYFzkQW8ZOwhGNTHJF+YiIiMiIOOfEKFqcnAwYMAB2dnZ48sknERQUhO+++67J/RYsWGCw4IhaQtBqMen4Efxt+yZ4l5UCAPaHdMPi+yfgvI8vwhztgeIKM0dJRERERHfT4uQkICAAgiBgw4YNze4jCAKTEzKpiEsZ+MfvG9Ez+zIA4KK7J94dPR47Q3sAggCZmeMjIiKi9olFGI2jxcnJxYsXjRiGGRhjFpOFk9WYOwLD6Vh0Hc/u/A1xKccAAKUqW6wYPAr/iRqCWoUCsroiivXJicTaGoBku4ZFGSDKpXf68nZ0fbeUIAAytQh5jSi5onwapfTe1AQB0NgAapX0ijAqzR0AmVxUVBTkcjnmz5+P+fPnmzsck+LyWmRV7Gqq8dcDOzHr4C7YqtXQQsCPfe/Fx8PjcN3BydzhERERkVQYcc5JYmIinJ2dDXxw66BXcqLVarF69WqsX78eFy9ehCAICA4OxuTJkzF9+vS7VpAnai1B1GLcyaOI3/UbfEpLAACHAzpjcexEnOngZ+boiIiISJIk1otnCi1OTkRRxPjx47F582b06dMHvXr1giiKSE1NxcyZM7F+/Xps3LjRiKGSVPW5fBEvJmxEnyuZAIDLLu54b+Q4JIT11vXzExEREVG70OLkZPXq1di7dy927NiBmJiYBo/t3LkTEydOxJo1a/Doo48aPEiSJp+SIsTv/A3jTx0FAJQrVfh80H34+t5hqFHYmDk6IiIikjJOiDeOFicna9euxT/+8Y9GiQkAjBgxAosWLcK3337L5ITazLa2Bo/9uQuz/9wF+9oaaCFgQ58ofDx8DPKcpDn+koiIiEgKWpycnDhxAu+9916zj8fFxeGTTz4xSFAkUaKIMSnH8Pcdv6JjSREAIKlTMN4ePREpvv7mjY2IiIjoVizCaBQtTk6uX78OHx+fZh/38fFBYWGhQYIi6elxNQsvbtuIiMsXAABXnd3wwX1j8Xv4PZxXQkRERCQRLU5ONBoNFIrmd5fL5VCr1QYJiqTDq7QEC3dtwoMnEgEAFTZKfDlwBL4aMBzVNlzZnYiIiCwT55wYh16rdc2cORMqlarJx6urqw0WFLUfMq0WEZkZ8CorQZ6jM5ICQqCVyaBU12LmwT14fP92ONTqqsf90jMCS0Y8gGvOruYNmoiIiIjMosXJyYwZM+66jzVNhhc0gCCxjh5TZ+Mjz5zAi9s2wLe0uH5btpMLfg/vi9gzJ9Cp+DoA4HjHACyOnYgTfkEGj1OArp1lEqyeLcrNHYHpiQIgU+uqpUtuMKDW3AGYgQDIa0UoqiVYIV4lud9wQABkGt01LrX2timT2AcWADKNFZwz55wYRYuTk1WrVhkzDmpnRp45gaU/rW60vUNpMR47tBsAkOPkgo9iHsBvPftBFGSmDZCIiIiILI5eFeKJWkKm1eLFbRt0P9/2mADdlwJlShXGPf48ym3tTB0eERERUZtxzolx8OtqMrgR6SfhW1rc7C+XAMCpphrh166YMiwiIiIiwxGNdJM49pxQm6lqa9Av6wIGXkjHoIw0hOVebdHzvMpKjBwZEREREVkTJiekP1FE17xsDMpIx8ALaYjMPA/bViwjnefIau9ERERkpTgh3iiYnFCLeJSVIvpiOgZmpGHghXR439brcc3RGQdCQrE/JBSHAzpj3aql8GlmaJcWwDVnVyT5h5gkdiIiIiKyDkxOqElKdW3dUK00DMpIR/fb5odUKmyQGNgZB4JDcSAkFOc8fRpUcl8cOwlLf1oNLRpObLqx2uniUROhlXHKExEREVknTog3DiYnpCOK6JKXg0EX0jAwIw2RmRmwU9c22CXFxw/7Q3TJSHKnINQobJo93Paw3lj40MxGdU6uObti8aiJ2B7W22inQkRERETWSbLJiaDV3aTk9qKT7uWliL6UjoEX0jHwYlqjoVq5js44EBSKA8Hd8GdgN1x3cLrj8W63o0tv7ArpiYjLt1SI76SrEG+qApjCjf+wk0YyRBmglUuvJqHU3s8AXWetCF3xTal92SivltoZ69pboxKgthckV4RRlEuv6KZoDaV0OefEKCSbnEiRUl2LvpcvYOBFXTJy+1K+lQobHPGvG6oV1A3nPDs0GKrVGlqZDIkBXdp0DCIiIiKSBiYn7Zkookv+NQzK0A3Virp0vtFQrVRvPxwI7ob9QaFI7hR8x6FaRERERKQjiCIEA3fjGfp41ojJSTvjVl6G6Lp6I4MupMGntPmhWgcDu6HgtqFaRERERNQC7WRYV1ZWFqZPn47c3FwoFAq8/PLL+Mtf/mL6QOowObFyNmo1+mVdwKALaRiUkYbwnIZDtaoUCiQGdMb+kFD8GRBqkKFaRERERNQ+KBQKLF26FPfccw9yc3PRr18/jBkzBg4ODuaJxyyvSq0niuhcN1RrUN2qWva1NQ12SfHxw58h3bA/OBRJATeHaslqmzogEREREemrvSwl7OvrC19fXwCAt7c33N3dcf36dSYn1Dy38jJEXzyLgXUJSYdbluYFgDxHJ+wP1hVA/DO4GwocOVSLiIiISAr27t2L999/H0lJScjOzsaGDRswceLEBvssX74c77//PrKzs9GjRw8sXboUQ4YMaXSsI0eOQKvVwt/f30TRN8bkxERkWi0iMm9ZUjcgpNkihDZqNfpevlDXO5KOHjmXGzxepVDgSN1Qrf0h3XDWy5dDtYiIiIhMyULmnJSXl6NPnz6YNWsWHnrooUaPr1u3DgsXLsTy5csxaNAgfP7554iLi0NKSgoCAgLq9ysoKMCjjz6KL7/8si1n0GZMTkxg1JkTeHFrw2KE2U4uWDx6EhLCegOiiJCC3PqhWlGXzjcaqpXq0xEHQkIbDdUiIiIiovalpKThgkYqlQoqlarJfePi4hAXF9fssZYsWYLZs2djzpw5AIClS5di69atWLFiBRYvXgwAqK6uxqRJk/Diiy9i4MCBBjqL1pFscmKqIowjz5zA0vWrG233KS3Gxz+uxsHALgi6ng/f0qIGj+c76IZqHQjphj+DuiHf0bnB41IsuNZaMjUgqzZ3FKYnl+A5CwKAujHAUutL1Erw+wqZUNfWWkiucJkU/wYIAmBToYWqWCu9IowSHB1hDedszDkntw+revXVV/Haa6/pfbyamhokJSVh0aJFDbbHxsbiwIEDAABRFDFz5kyMGDEC06dPb1XchiTZ5MQUZFotXkzYoPv59sfq/h996RyAuqFa/p3xZ3A37A8JRTqHahERERFJUlZWFpydb34x3Vyvyd3k5+dDo9HAx8enwXYfHx/k5OQAAPbv349169ahd+/e2LhxIwDgm2++Qa9evVoXfBsxOTGiiKyMBkO5mvPeiHFYGzEI1TZKE0RFRERERG1mxDknzs7ODZKTthJu+8JbFMX6bYMHD4ZWazndsUxOjMirrOTuOwHIdXJhYkJERERkRaxhKWFPT0/I5fL6XpIbcnNzG/WmWIqml4sig8hzbFnG29L9iIiIiKj9i4qKQnh4OJYtW9am4yiVSkRERCAhIaHB9oSEBLNPfG8Oe06MKMk/BNlOLvApLW4yC9QCuObsiiT/EFOHRkRERERtYcRhXYmJiS0e1lVWVoZz587V379w4QKOHTsGd3d3BAQEID4+HtOnT0dkZCSio6PxxRdfIDMzE3PnzjVw8IbB5MSItDIZFo+ahKXrV0OLht1UN0b2LR45sdl6J0REREREd3LkyBHExMTU34+PjwcAzJgxA6tXr8aUKVNQUFCAN954A9nZ2ejZsyc2b96MwMBAc4V8R0xOjGx7WG8sfHAmXkxoWOfkmrMrFo+ciO1hvc0YHRERERG1lqHniLTG8OHDId5lfe158+Zh3rx5JoqobZicmMD2sN7Y2a0nIrJuqRDv33yFeCIiIiKSrqioKMjlcsyfPx/z5883dzgmJdnkRFADgtx0rydChiN+XW5uMFERyAakXDZFgucuU1vA1zkmJgiATKO7Sa0on7xaYieMulJQdpDk9S3VIowiAFGQ3OVNlkoUYfCKoHXH02fOSXvDr+6JiIiIiMgiSLbnhIiIiIiotayhzok1YnJCRERERKQvIy4lLGUc1kVERERERBaByQkRERERkZ4ErXFugOEqxFsjDusiIiIiIrIgUl6ti8kJEREREZG+OOfEKDisi4iIiIiILAJ7ToiIiIiI9MSlhI1DusmJDJLrNxI05o7A9CRYOLqeVCtIQwSgheS6xqX4B03S17dE21tRJUJZpjV4UW5LZ1NSbe4QTE6mkd45k47EPp4TERERERmAKBrnBq7WRUREREREejDmsC4pr9bFnhMiIiIiIrII7DkhIiIiItIXlxI2CvacEBERERGRRWDPCRERERGRnriUsHGw54SIiIiIiCwCkxMiIiIiIn1xKWGjkOywLlGA9Cp4Se18UTdXTVZ3M3cwJqZRSq/BBQGQqQFZjfS6xmW15o7A9AQBkFWLUFRIryhfhbf0/nwLAiDKBWgVguTaW9BI7IQBCFrpnfOtpLyUsPTe3YiIiIiI2ohzToyDyQkRERERkb64lLBRcM4JERERERFZBPacEBERERHpicO6jIM9J0REREREZBHYc0JEREREpC+tqLsZ+pgSx54TIiIiIiKyCExOiIiIiIj0JRrpBhZhlCRZLSAwNWv3BOiK8slrpFeDUpTaCQOAoCu4CTkkV6RNUak1dwgmJwiAzEGErEaUXHtLsUCdIKDRBzipqHVVmTsEk9NIsPDkrViEkYiIiIiIWkyAEVbrMuzhrBKTEyIiIiIifYmi4bvppdYN3AQObCIiIiIiIovAnhMiIiIiIj2xCKNxmL3nZPny5QgODoatrS0iIiKwb9++Zvddv349Ro0aBS8vLzg7OyM6Ohpbt241YbRERERERGQsZk1O1q1bh4ULF+Kll15CcnIyhgwZgri4OGRmZja5/969ezFq1Chs3rwZSUlJiImJwbhx45CcnGziyImIiIhI0oy4lLCUmTU5WbJkCWbPno05c+age/fuWLp0Kfz9/bFixYom91+6dCmef/55REVFoWvXrnj77bfRtWtX/PrrryaOnIiIiIiIDM1sc05qamqQlJSERYsWNdgeGxuLAwcOtOgYWq0WpaWlcHd3b3af6upqVFdX198vKSkBoMvKuFxb+3ejnc0+ftEMBAn+ggtC3dKOgvTaXLLtLUj33KWG7S0t1nDOgihCMPDqWoY+njUyW3KSn58PjUYDHx+fBtt9fHyQk5PTomN8+OGHKC8vx8MPP9zsPosXL8brr7/eaHuIux0Utnb6BW3lpDjJSgDgZ6eCoJFeT6ncVmpnrPtj5qe0gY2treRWYxT8zR2B6QkAvJ1VEOWC5Nq71kFq6TcAAfBxUEIUpPd+blOuMXcIJldbKzd3CGQmZl+tS7gtNRZFsdG2pqxduxavvfYafv75Z3h7eze734svvoj4+Pj6+yUlJfD398f5okoIKmm9vQnSKyANGQCZE3DuegWkVlBZUS6xE4YuOZG7iDh/rUJyH1ZlanNHYHqCAMhrRVy4XC659q5yld4HN0EAFK4iMq5Kr71VxdK7wDWaKnOHcHfaupuhjwkgKioKcrkc8+fPx/z58w38IpbNbMmJp6cn5HJ5o16S3NzcRr0pt1u3bh1mz56NH374ASNHjrzjviqVCiqVqtF2LaQ3rEtq53uDCEArQnLJidT+eN8gwjh1sSyd1M73hhttLbXzl9r53sD2lg5rOGdjDutKTEyEs7OzQY9tLczWL6xUKhEREYGEhIQG2xMSEjBw4MBmn7d27VrMnDkT3333HR544AFjh0lERERERCZi1mFd8fHxmD59OiIjIxEdHY0vvvgCmZmZmDt3LgDdkKwrV65gzZo1AHSJyaOPPoqPP/4YAwYMqO91sbOzg4uLi9nOg4iIiIgkxhhL/1pBj5GxmTU5mTJlCgoKCvDGG28gOzsbPXv2xObNmxEYGAgAyM7OblDz5PPPP4darW40/m7GjBlYvXq1qcMnIiIiIiIDMvuE+Hnz5mHevHlNPnZ7wrF7927jB0REREREdDfGmABlDZNtjEyCaxESEREREZElMnvPCRERERGRtRFEw9eQk2JNutux54SIiIiIiCyCZHtOZLWAwNSs3RMAaOWAVmH4OkmWTorfvggAtDYCNCrpVQyX10jshKEryierESGv1Eqvve2l9wdMEABRBohy6V3fshqp/QUDRI0VnDPnnBiF9N7diIiIiIjIIkm254SIiIiIqLUEre5m6GNKHZMTIiIiIiJ9cViXUXBYFxERERERWQT2nBARERER6Uusuxn6mBLHnhMiIiIiIrIITE6IiIiIiPQkiKJRbgAQFRWF8PBwLFu2zMxnaXoc1kVEREREZEESExPh7Oxs7jDMQrrJiVB3kxCpFuUTxJs3KZHXmDsC0xOEuqUdNZDcuF21rcTe0KBrb62NAI2tTHIL3CiqJHbC0LW3qrAW9jnVkmvvWgfpfVzTqK3gnLlal1FwWBcREREREVkEK0hLiYiIiIgsjAjA0EUT2XHC5ISIiIiISF+3TmA35DGljsO6iIiIiIjIIrDnhIiIiIhIXyKMMCHesIezRuw5ISIiIiIii8CeEyIiIiIifXEpYaNgzwkREREREVkE6faciJDcuD5Bbe4ITE+4tdimxGrUaeXmjsD0BAGAqCvEKLXrW6aW2Amjrugm6gqsSuz0bYpqzR2CyQkCAGdl3bfV5o7GtFT5VeYOweQ0Wis4Zy0M/9nC0EsTWyH2nBARERERkUWQbs8JEREREVErsc6JcTA5ISIiIiLSFyfEGwWHdRERERERkUVgzwkRERERkb7Yc2IU7DkhIiIiIiKLwJ4TIiIiIiJ9sefEKNhzQkREREREFkGyPSdaG0CwMXcUpmVTae4ITE8QAHkVIK8AZBL7MsLuusbcIZicIAAydxHyGlFyXz4pKqVXuUsQAMFJhFCrlVxRvmo3if0Bg6691Q4K1LgqJXd92+ZZQUFCg7OCyskswmgU7DkhIiIiIiKLwOSEiIiIiEhPN4owGvpmDpMmTYKbmxsmT55slte/FZMTIiIiIiJ93ZgQb+ibGSxYsABr1qwxy2vfjskJEREREZGExcTEwMnJydxhAGByQkRERESkP61onJue9u7di3HjxqFjx44QBAEbN25stM/y5csRHBwMW1tbREREYN++fQb4BzAOJidERERERFaqvLwcffr0waefftrk4+vWrcPChQvx0ksvITk5GUOGDEFcXBwyMzNNHGnLSHYpYSIiIiKiVjNiEcaSkpIGm1UqFVQqVZNPiYuLQ1xcXLOHXLJkCWbPno05c+YAAJYuXYqtW7dixYoVWLx4sYECNxz2nBARERERWRB/f3+4uLjU31qbRNTU1CApKQmxsbENtsfGxuLAgQOGCNXg2HNCRERERKQ3Y6yupTteVlYWnJ2d67c212tyN/n5+dBoNPDx8Wmw3cfHBzk5OfX3R48ejaNHj6K8vBydOnXChg0bEBUV1arXbCvJJieyWkCQWL+RKLHzBXQVhSFA10cosYrCtfbSa3Chrq1FudlWYyQTk6m1kNdoJdfeigq1uUMwOUEAZJ52kEmwvWXl1eYOweREscbcIZiVs7Nzg+SkrQShYSl7URQbbNu6davBXqutJJucEBERERG1mhHnnBiKp6cn5HJ5g14SAMjNzW3Um2IppPfVKhERERFRWxlxKeGoqCiEh4dj2bJlbQpRqVQiIiICCQkJDbYnJCRg4MCBbTq2sbDnhIiIiIjIgiQmJrZ4WFdZWRnOnTtXf//ChQs4duwY3N3dERAQgPj4eEyfPh2RkZGIjo7GF198gczMTMydO9dY4bcJkxMiIiIiIn2JWt3N0MfU05EjRxATE1N/Pz4+HgAwY8YMrF69GlOmTEFBQQHeeOMNZGdno2fPnti8eTMCAwMNFrYhMTkhIiIiIrJSw4cPh3iXuSrz5s3DvHnzTBRR2zA5ISIiIiLSlxVMiLdGnBBPRERERGRBDDUh3hqx54SIiIiISF9aEQYvola3Wpc+E+LbG8kmJ4oaQJBYz5nWxtwRmIFw913aK2WxNIu0aQMEaOWC5HrGFZUac4dgcoIACGpRkkX51PbS+/MtCLpiwqJMete3UFJu7hDMQNpFGKVMeu9uRERERERtxTknRsHkhIiIiIhIXyKMkJwY9nDWiBPiiYiIiIgsCCfEExERERFRyxlxWJeUJ8Sz54SIiIiIiCwCe06IiIiIiPSl1QLQGuGY0saeEyIiIiIisgjsOSEiIiIi0heXEjYKySYnigoRMhtp/QJoJdjaggDIVSJsykXJXe9alfQ6RgUJF90UpXjyAlDjbIMqL5Xkrm/bvGpzh2ByggAoytRQFtVIrr2hll6RVUCK53xTVFQU5HI55s+fj/nz55s7HJOS4MdVIiIiIqI24mpdRsHkhIiIiIhIX1oRBq+aqJVat2Bj0hv3QUREREREFok9J0REREREehJFLUTRsEv/Gvp41og9J0REREREZBHYc0JEREREpC9RNPwcEcktRdcYe06IiIiIiCxIVFQUwsPDsWzZMnOHYnLsOSEiIiIi0pdohNW6uJSwdJMTrRyA3NxRmJa8xtwRmJ4U69LdYFNca+4QTE4QALu8GjhlVkmuZ9wmt9TcIZicIAOELk4QNKLBPx9YPKn9gt+QdgE4elVyy61Wjo4ydwgmp1FXAVfMHQWZg2STEyIiIiKiVtNqAcHAq2txtS4mJ0REREREejPisC4p44R4IiIiIiKyCOw5ISIiIiLSk6jVQjTwsC4WYWTPCRERERERWQj2nBARERER6YtzToyCPSdERERERBaERRiJiIiIiKjltCIgsAijoUk2OXHIroVcLq0qjFqV9DrKBAGwVarhkF0juZ5S5ZnL5g7B5ASZALlXMBTFlZJbKl5rqzR3CCYnyABVbjnsLxVLrr2FknJzh2BygkyA4KeCzM4OosSKMNqdlN77uRoSrBxNACScnBARERERtZooAjB0EUZpJd5Nkd5X6UREREREZJHYc0JEREREpCdRK0I08JwTkT0nTE6IiIiIiPQmamH4YV0Sm0DXBA7rIiIiIiIii8CeEyIiIiIiPXFYl3Gw54SIiIiIiCwCe06IiIiIiPTFOSdGIbnk5EZ3mVpdJbmlpLUy6XWUCQJQUyPN9pZrpVjASkBNbRXUmmrJvb+LogSvbxGoqVVIsr0FbbW5QzADATVqLdRijeSGvsgk+H5+owijJbe1GrWAgcNTo9awB7RCgmjJrW4EGRkZ6Ny5s7nDICIiIqK7OH/+PEJCQswdRgNVVVUIDg5GTk6OUY7v7OwMX19fyGQyzJ8/H/PnzzfK61gqySUnRUVFcHNzQ2ZmJlxcXMwdDhlZSUkJ/P39kZWVBWdnZ3OHQ0bG9pYWtre0sL2lpbi4GAEBASgsLISrq6u5w2mkqqoKNTXG6dFSKpWwtbU1yrGtgeSGdcnqhja5uLjwzU1CnJ2d2d4SwvaWFra3tLC9pUVmoUPSbW1tJZ1AGJNltjgREREREUkOkxMiIiIiIrIIkktOVCoVXn31VahUKnOHQibA9pYWtre0sL2lhe0tLWxv6ZLchHgiIiIiIrJMkus5ISIiIiIiy8TkhIiIiIiILAKTEyIiIiIisghMToiIiIiIyCK0y+Rk+fLlCA4Ohq2tLSIiIrBv37477r9nzx5ERETA1tYWISEh+Oyzz0wUKRmCPu29e/duCILQ6HbmzBkTRkyttXfvXowbNw4dO3aEIAjYuHHjXZ/D69t66dvevL6t1+LFixEVFQUnJyd4e3tj4sSJSEtLu+vzeH1bp9a0N69v6Wh3ycm6deuwcOFCvPTSS0hOTsaQIUMQFxeHzMzMJve/cOECxowZgyFDhiA5ORn/+Mc/sGDBAvz0008mjpxaQ9/2viEtLQ3Z2dn1t65du5ooYmqL8vJy9OnTB59++mmL9uf1bd30be8beH1bnz179mD+/Pk4ePAgEhISoFarERsbi/Ly8mafw+vberWmvW/g9S0BYjvTv39/ce7cuQ22hYWFiYsWLWpy/+eff14MCwtrsO2JJ54QBwwYYLQYyXD0be9du3aJAMTCwkITREfGBEDcsGHDHffh9d1+tKS9eX23H7m5uSIAcc+ePc3uw+u7/WhJe/P6lo521XNSU1ODpKQkxMbGNtgeGxuLAwcONPmcP//8s9H+o0ePxpEjR1BbW2u0WKntWtPeN/Tt2xe+vr647777sGvXLmOGSWbE61uaeH1bv+LiYgCAu7t7s/vw+m4/WtLeN/D6bv/aVXKSn58PjUYDHx+fBtt9fHyQk5PT5HNycnKa3F+tViM/P99osVLbtaa9fX198cUXX+Cnn37C+vXrERoaivvuuw979+41RchkYry+pYXXd/sgiiLi4+MxePBg9OzZs9n9eH23Dy1tb17f0qEwdwDGIAhCg/uiKDbadrf9m9pOlkmf9g4NDUVoaGj9/ejoaGRlZeGDDz7A0KFDjRonmQevb+ng9d0+PPXUUzhx4gT++OOPu+7L69v6tbS9eX1LR7vqOfH09IRcLm/0rXlubm6jb1du6NChQ5P7KxQKeHh4GC1WarvWtHdTBgwYgLNnzxo6PLIAvL6J17d1efrpp/HLL79g165d6NSp0x335fVt/fRp76bw+m6f2lVyolQqERERgYSEhAbbExISMHDgwCafEx0d3Wj/bdu2ITIyEjY2NkaLldquNe3dlOTkZPj6+ho6PLIAvL6J17d1EEURTz31FNavX4+dO3ciODj4rs/h9W29WtPeTeH13U6ZbSq+kXz//feijY2NuHLlSjElJUVcuHCh6ODgIF68eFEURVFctGiROH369Pr9MzIyRHt7e/Fvf/ubmJKSIq5cuVK0sbERf/zxR3OdAulB3/b+6KOPxA0bNojp6eniqVOnxEWLFokAxJ9++slcp0B6KC0tFZOTk8Xk5GQRgLhkyRIxOTlZvHTpkiiKvL7bG33bm9e39XryySdFFxcXcffu3WJ2dnb9raKion4fXt/tR2vam9e3dLS75EQURXHZsmViYGCgqFQqxX79+jVYmm7GjBnisGHDGuy/e/dusW/fvqJSqRSDgoLEFStWmDhiagt92vvdd98VO3fuLNra2opubm7i4MGDxU2bNpkhamqNG0tJ3n6bMWOGKIq8vtsbfdub17f1aqqdAYirVq2q34fXd/vRmvbm9S0dgijWzR4jIiIiIiIyo3Y154SIiIiIiKwXkxMiIiIiIrIITE6IiIiIiMgiMDkhIiIiIiKLwOSEiIiIiIgsApMTIiIiIiKyCExOiIiIiIjIIjA5ISIiIiIii8DkhIioDXbv3g1BEFBUVGS01xg+fDgWLlxotOMTERFZCiYnRER3ceDAAcjlctx///3mDqVFLl68CEEQcOzYsTYfa+bMmRAEodHt1n8LQRCwcePGNr8WERGRwtwBEBFZuq+++gpPP/00vvzyS2RmZiIgIMDcIZnU/fffj1WrVjXYplKpzBQNERG1Z+w5ISK6g/Lycvz3v//Fk08+ibFjx2L16tVN7rd//3706dMHtra2uPfee3Hy5Mn6xy5duoRx48bBzc0NDg4O6NGjBzZv3lz/+J49e9C/f3+oVCr4+vpi0aJFUKvVzcbUVE+Fq6trfWzBwcEAgL59+0IQBAwfPrx+v1WrVqF79+6wtbVFWFgYli9fftd/A5VKhQ4dOjS4ubm5AQCCgoIAAJMmTYIgCPX3z58/jwkTJsDHxweOjo6IiorC9u3b7/paREQkbUxOiIjuYN26dQgNDUVoaCimTZuGVatWQRTFRvs999xz+OCDD5CYmAhvb2+MHz8etbW1AID58+ejuroae/fuxcmTJ/Huu+/C0dERAHDlyhWMGTMGUVFROH78OFasWIGVK1firbfeanXMhw8fBgBs374d2dnZWL9+PQDg3//+N1566SX861//QmpqKt5++228/PLL+Prrr1v9WomJiQB0SU92dnb9/bKyMowZMwbbt29HcnIyRo8ejXHjxiEzM7PVr0VERO0fh3UREd3BypUrMW3aNAC64U1lZWXYsWMHRo4c2WC/V199FaNGjQIAfP311+jUqRM2bNiAhx9+GJmZmXjooYfQq1cvAEBISEj985YvXw5/f398+umnEAQBYWFhuHr1Kl544QW88sorkMn0/w7Jy8sLAODh4YEOHTrUb3/zzTfx4Ycf4sEHHwSg62FJSUnB559/jhkzZjR7vN9++60+mbrhhRdewMsvv1z/Wq6urg1eq0+fPujTp0/9/bfeegsbNmzAL7/8gqeeekrvcyIiImlgckJE1Iy0tDQcPny4vudBoVBgypQp+OqrrxolJ9HR0fU/u7u7IzQ0FKmpqQCABQsW4Mknn8S2bdswcuRIPPTQQ+jduzcAIDU1FdHR0RAEof75gwYNQllZGS5fvmyw+S15eXnIysrC7Nmz8de//rV+u1qthouLyx2fGxMTgxUrVjTY5u7ufsfnlJeX4/XXX8dvv/2Gq1evQq1Wo7Kykj0nRER0R0xOiIiasXLlSqjVavj5+dVvE0URNjY2KCwsrJ930ZwbCcecOXMwevRobNq0Cdu2bcPixYvx4Ycf4umnn4Yoig0Skxuvcevzmzru7UPLbgwha45WqwWgG9p17733NnhMLpff8bkODg7o0qXLHfe53XPPPYetW7figw8+QJcuXWBnZ4fJkyejpqZGr+MQEZG0cM4JEVET1Go11qxZgw8//BDHjh2rvx0/fhyBgYH49ttvG+x/8ODB+p8LCwuRnp6OsLCw+m3+/v6YO3cu1q9fj7///e/497//DQAIDw/HgQMHGiQbBw4cgJOTU4Ok6FZeXl7Izs6uv3/27FlUVFTU31cqlQAAjUZTv83Hxwd+fn7IyMhAly5dGtxuTKBvLRsbmwavBQD79u3DzJkzMWnSJPTq1QsdOnTAxYsX2/Q6RETU/rHnhIioCb/99hsKCwsxe/bsRsOeJk+ejJUrVzaYO/HGG2/Aw8MDPj4+eOmll+Dp6YmJEycCABYuXIi4uDh069YNhYWF2LlzJ7p37w4AmDdvHpYuXYqnn34aTz31FNLS0vDqq68iPj6+2fkmI0aMwKeffooBAwZAq9XihRdegI2NTf3j3t7esLOzw5YtW9CpUyfY2trCxcUFr732GhYsWABnZ2fExcWhuroaR44cQWFhIeLj45v9t6iurkZOTk6DbQqFAp6engB0K3bt2LEDgwYNgkqlgpubG7p06YL169dj3LhxEAQBL7/8cn3vDRERUXPYc0JE1ISVK1di5MiRTc7HeOihh3Ds2DEcPXq0fts777yDZ555BhEREcjOzsYvv/zSoAdj/vz56N69O+6//36EhobWL+Hr5+eHzZs34/Dhw+jTpw/mzp2L2bNn45///GezsX344Yfw9/fH0KFDMXXqVDz77LOwt7evf1yhUOCTTz7B559/jo4dO2LChAkAdMPLvvzyS6xevRq9evXCsGHDsHr16rv2nGzZsgW+vr4NboMHD24QT0JCAvz9/dG3b18AwEcffQQ3NzcMHDgQ48aNw+jRo9GvX7+7/bMTEZHECWJTa2ISERERERGZGHtOiIiIiIjIIjA5ISIiIiIii8DkhIiIiIiILAKTEyIiIiIisghMToiIiIiIyCIwOSEiIiIiIovA5ISIiIiIiCwCkxMiIiIiIrIITE6IiIiIiMgiMDkhIiIiIiKLwOSEiIiIiIgswv8DcbcEUVu4YGwAAAAASUVORK5CYII=",
0902       "text/plain": [
0903        "<Figure size 1000x600 with 2 Axes>"
0904       ]
0905      },
0906      "metadata": {},
0907      "output_type": "display_data"
0908     },
0909     {
0910      "name": "stdout",
0911      "output_type": "stream",
0912      "text": [
0913       "\n",
0914       "pt: 0 to 5\n",
0915       "93% Retention Cut: {0.7831, 0.8153, 0.8313, 0.823, 0.7426, 0.7532, 0.8392, 0.8636, 0.9172, 0.9389} Mean: 0.8307\n",
0916       "98% Retention Cut: {0.4493, 0.4939, 0.5715, 0.6488, 0.5709, 0.5938, 0.7164, 0.7565, 0.8103, 0.8593} Mean: 0.6471\n",
0917       "99% Retention Cut: {0.2946, 0.3312, 0.4081, 0.5213, 0.4509, 0.495, 0.6333, 0.6726, 0.7225, 0.7661} Mean: 0.5295\n"
0918      ]
0919     },
0920     {
0921      "data": {
0922       "image/png": 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",
0923       "text/plain": [
0924        "<Figure size 1000x600 with 2 Axes>"
0925       ]
0926      },
0927      "metadata": {},
0928      "output_type": "display_data"
0929     },
0930     {
0931      "name": "stdout",
0932      "output_type": "stream",
0933      "text": [
0934       "\n",
0935       "pt: 5 to inf\n",
0936       "93% Retention Cut: {0.6982, 0.7335, 0.7395, 0.8015, 0.7356, 0.6149, 0.6848, 0.6468, 0.7187, 0.7079} Mean: 0.7081\n",
0937       "98% Retention Cut: {0.4488, 0.4448, 0.5067, 0.5929, 0.4836, 0.4112, 0.4968, 0.4403, 0.5597, 0.5067} Mean: 0.4891\n",
0938       "99% Retention Cut: {0.3302, 0.3319, 0.3761, 0.4848, 0.3578, 0.2981, 0.3546, 0.3146, 0.4669, 0.4086} Mean: 0.3724\n"
0939      ]
0940     }
0941    ],
0942    "source": [
0943     "import numpy as np\n",
0944     "from matplotlib import pyplot as plt\n",
0945     "from matplotlib.colors import LogNorm\n",
0946     "\n",
0947     "def plot_for_pt_bin(pt_min, pt_max, percentiles, eta_bin_edges, eta_list, predictions, full_tracks, branches):\n",
0948     "    \"\"\"\n",
0949     "    Calculate and plot cut values for specified percentiles in a given pt bin\n",
0950     "    \n",
0951     "    Parameters:\n",
0952     "    -----------\n",
0953     "    pt_min : float\n",
0954     "        Minimum pt value for the bin\n",
0955     "    pt_max : float\n",
0956     "        Maximum pt value for the bin\n",
0957     "    percentiles : list\n",
0958     "        List of percentiles to calculate (e.g., [92.5, 96.7, 99])\n",
0959     "    eta_bin_edges : array\n",
0960     "        Edges of the eta bins\n",
0961     "    eta_list : list\n",
0962     "        List of eta values\n",
0963     "    predictions : array\n",
0964     "        Array of DNN predictions\n",
0965     "    full_tracks : array\n",
0966     "        Boolean array for track selection\n",
0967     "    branches : dict\n",
0968     "        Dictionary containing branch data\n",
0969     "    \"\"\"\n",
0970     "    # Filter data based on pt bin\n",
0971     "    abs_eta = eta_list[0][full_tracks & (np.concatenate(branches['t5_pt']) > pt_min) & \n",
0972     "                         (np.concatenate(branches['t5_pt']) <= pt_max)]\n",
0973     "    predictions_filtered = predictions[full_tracks & (np.concatenate(branches['t5_pt']) > pt_min) & \n",
0974     "                                    (np.concatenate(branches['t5_pt']) <= pt_max)]\n",
0975     "    \n",
0976     "    # Dictionary to store cut values for different percentiles\n",
0977     "    cut_values = {p: [] for p in percentiles}\n",
0978     "\n",
0979     "    # Loop through each eta bin\n",
0980     "    for i in range(len(eta_bin_edges) - 1):\n",
0981     "        # Get indices of tracks within the current eta bin\n",
0982     "        bin_indices = (abs_eta >= eta_bin_edges[i]) & (abs_eta < eta_bin_edges[i + 1])\n",
0983     "        \n",
0984     "        # Get the corresponding DNN prediction scores\n",
0985     "        bin_predictions = predictions_filtered[bin_indices]\n",
0986     "        \n",
0987     "        # Calculate the percentile cut values for the current bin\n",
0988     "        for percentile in percentiles:\n",
0989     "            cut_value = np.percentile(bin_predictions, 100 - percentile)  # Convert retention to percentile\n",
0990     "            cut_values[percentile].append(cut_value)\n",
0991     "\n",
0992     "    # Plot 2D histogram\n",
0993     "    plt.figure(figsize=(10, 6))\n",
0994     "    plt.hist2d(abs_eta, predictions_filtered, bins=[eta_bin_edges, 50], norm=LogNorm())\n",
0995     "    plt.colorbar(label='Counts')\n",
0996     "    plt.xlabel(\"Absolute Eta\")\n",
0997     "    plt.ylabel(\"DNN Prediction Score\")\n",
0998     "    plt.title(f\"DNN Score vs. Abs Eta for 100% Matched Tracks (pt: {pt_min} to {pt_max})\")\n",
0999     "\n",
1000     "    # Plot the cut values with different colors\n",
1001     "    cut_x = eta_bin_edges[:-1] + (eta_bin_edges[1] - eta_bin_edges[0]) / 2  # Mid-points of the bins\n",
1002     "    colors = plt.cm.rainbow(np.linspace(0, 1, len(percentiles)))  # Generate distinct colors\n",
1003     "    \n",
1004     "    for percentile, color in zip(percentiles, colors):\n",
1005     "        plt.plot(cut_x, cut_values[percentile], '-', color=color, marker='o', \n",
1006     "                label=f'{percentile}% Retention Cut')\n",
1007     "    \n",
1008     "    plt.legend()\n",
1009     "    plt.grid(True, alpha=0.3)\n",
1010     "    plt.show()\n",
1011     "    \n",
1012     "    # Print the cut values\n",
1013     "    print(f\"\\npt: {pt_min} to {pt_max}\")\n",
1014     "    for percentile in percentiles:\n",
1015     "        values = cut_values[percentile]\n",
1016     "        print(f\"{percentile}% Retention Cut:\", \n",
1017     "              '{' + ', '.join(str(x) for x in np.round(values, 4)) + '}',\n",
1018     "              \"Mean:\", np.round(np.mean(values), 4))\n",
1019     "\n",
1020     "# Example usage:\n",
1021     "def analyze_pt_bins(pt_bins, percentiles, eta_bin_edges, eta_list, predictions, full_tracks, branches):\n",
1022     "    \"\"\"\n",
1023     "    Analyze and plot for multiple pt bins and percentiles\n",
1024     "    \n",
1025     "    Parameters:\n",
1026     "    -----------\n",
1027     "    pt_bins : list\n",
1028     "        List of pt bin edges\n",
1029     "    percentiles : list\n",
1030     "        List of percentiles to calculate\n",
1031     "    Other parameters same as plot_for_pt_bin function\n",
1032     "    \"\"\"\n",
1033     "    for i in range(len(pt_bins) - 1):\n",
1034     "        plot_for_pt_bin(pt_bins[i], pt_bins[i + 1], percentiles, eta_bin_edges, \n",
1035     "                       eta_list, predictions, full_tracks, branches)\n",
1036     "\n",
1037     "# Example call:\n",
1038     "percentiles = [93, 98, 99]\n",
1039     "pt_bins = [0, 5, np.inf]\n",
1040     "eta_bin_edges = np.arange(0, 2.75, 0.25)\n",
1041     "analyze_pt_bins(pt_bins, percentiles, eta_bin_edges, eta_list, predictions, full_tracks, branches)"
1042    ]
1043   },
1044   {
1045    "cell_type": "code",
1046    "execution_count": null,
1047    "metadata": {},
1048    "outputs": [],
1049    "source": []
1050   }
1051  ],
1052  "metadata": {
1053   "kernelspec": {
1054    "display_name": "analysisenv",
1055    "language": "python",
1056    "name": "python3"
1057   },
1058   "language_info": {
1059    "codemirror_mode": {
1060     "name": "ipython",
1061     "version": 3
1062    },
1063    "file_extension": ".py",
1064    "mimetype": "text/x-python",
1065    "name": "python",
1066    "nbconvert_exporter": "python",
1067    "pygments_lexer": "ipython3",
1068    "version": "3.11.7"
1069   }
1070  },
1071  "nbformat": 4,
1072  "nbformat_minor": 2
1073 }