diff --git a/HEROS_Demo_Notebook.ipynb b/HEROS_Demo_Notebook.ipynb index ab2a3d8..64227bc 100644 --- a/HEROS_Demo_Notebook.ipynb +++ b/HEROS_Demo_Notebook.ipynb @@ -58,7 +58,7 @@ "outputs": [], "source": [ "# Notebook Operation Parameters ---------------------------------------------------------------------------------\n", - "load_from_cloned_repo = True # Leave True if running from cloned repository, and change to False if relying on 'pip' installation of HEROS\n", + "load_from_cloned_repo = False # Leave True if running from cloned repository, and change to False if relying on 'pip' installation of HEROS\n", "local_save = True # If True, saves output in pre-designated local folder within cloned repository folder\n", "folder_path = './output' # Specify new path for output files generated by this notebook (if local_save = False)\n", "run_all_cells = True # If True, run all cells of notebook (i.e. generate all example outputs and visualizations). False will only run key cells\n", @@ -116,15 +116,19 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "f931305f", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "c:\\Users\\ryanu\\Documents\\GitHub\\heros\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'skheros'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[3], line 5\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mskheros\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mheros\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HEROS\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# Load HEROS Notebook via pip installation\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mskheros\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mheros\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HEROS\n\u001b[0;32m 7\u001b[0m \u001b[38;5;66;03m# Load all other packages used in this notebook --------------------------------\u001b[39;00m\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'skheros'" ] } ], @@ -171,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "6ebd4719", "metadata": {}, "outputs": [], @@ -228,23 +232,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "8ffac0c7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " A_0 A_1 R_0 R_1 R_2 R_3 Class Group InstanceID\n", - "0 1 0 1 0 1 1 1 2 1\n", - "1 1 0 0 0 0 1 0 2 2\n", - "2 0 1 1 0 0 1 0 1 3\n", - "3 1 1 1 1 1 1 1 3 4\n", - "4 0 0 0 1 1 0 0 0 5\n" - ] - } - ], + "outputs": [], "source": [ "# Load training dataset ------------------------\n", "train_df = pd.read_csv(train_data_path, sep=\"\\t\")\n", @@ -295,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "3bd1b3c0", "metadata": {}, "outputs": [], @@ -322,20 +313,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "0f7d39ba", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading MultiSURF Scores\n", - "['A_0', 'A_1', 'R_0', 'R_1', 'R_2', 'R_3']\n", - "[0.0653628803320886, 0.0917736536553188, 0.069767762335029, 0.0134458939320383, 0.0229747192062444, 0.0148895858857669]\n" - ] - } - ], + "outputs": [], "source": [ "# Further data preparation for expert knowldge generation (i.e. balanced subsampling of training instances for faster run times)\n", "fs_train_df = balanced_sampling(train_df, outcome_label, max_instances)\n", @@ -398,351 +379,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "42047be2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data Manage Summary: ------------------------------------------------\n", - "Number of quantitative features: 0\n", - "Number of categorical features: 6\n", - "Total Features: 6\n", - "Total Instances: 450\n", - "Feature Types: [1, 1, 1, 1, 1, 1]\n", - "Missing Values: 0\n", - "Quantiative Feature Range: [[inf, -inf], [inf, -inf], [inf, -inf], [inf, -inf], [inf, -inf], [inf, -inf]]\n", - "Categorical Feature Values: [[np.int64(1), np.int64(0)], [np.int64(0), np.int64(1)], [np.int64(1), np.int64(0)], [np.int64(0), np.int64(1)], [np.int64(1), np.int64(0)], [np.int64(1), np.int64(0)]]\n", - "Average States: 2.0\n", - "Rule Specificity Limit: 6\n", - "Classes: [np.int64(1), np.int64(0)]\n", - "Class Counts: {np.int64(1): 225, np.int64(0): 225}\n", - "Class Weights: {np.int64(1): 0.5, np.int64(0): 0.5}\n", - "Majority Class: 1\n", - "Expert Knowledge Weights Used: True\n", - "--------------------------------------------------------------------\n", - "Archiving: 500\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 1000 0.813 176 500 0.609\n", - "Archiving: 1000\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 2000 0.895 200 500 1.36\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 3000 0.905 207 500 2.148\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 4000 0.898 219 500 2.89\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 5000 0.896 213 500 3.632\n", - "Archiving: 5000\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 6000 0.904 220 500 4.394\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 7000 0.903 211 500 5.156\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 8000 0.893 225 500 5.881\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 9000 0.894 210 500 6.655\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 10000 0.91 210 500 7.384\n", - "Archiving: 10000\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 11000 0.877 206 500 8.147\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 12000 0.895 203 500 8.914\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 13000 0.904 208 500 9.647\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 14000 0.901 213 500 10.384\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 15000 0.902 208 500 11.098\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 16000 0.9 209 500 11.799\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 17000 0.9 222 500 12.55\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 18000 0.892 207 500 13.259\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 19000 0.903 200 500 13.955\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 20000 0.897 217 500 14.676\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 21000 0.909 211 500 15.372\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 22000 0.896 202 500 16.082\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 23000 0.893 214 500 16.783\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 24000 0.905 214 500 17.484\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 25000 0.891 204 500 18.187\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 26000 0.896 206 500 18.875\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 27000 0.903 207 500 19.565\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 28000 0.898 206 500 20.266\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 29000 0.894 213 500 20.958\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 30000 0.907 209 500 21.666\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 31000 0.907 209 500 22.363\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 32000 0.899 221 500 23.075\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 33000 0.909 209 500 23.774\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 34000 0.888 213 500 24.462\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 35000 0.902 206 500 25.161\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 36000 0.902 209 500 25.855\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 37000 0.899 214 500 26.559\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 38000 0.896 212 500 27.239\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 39000 0.902 218 500 27.937\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 40000 0.894 217 500 28.656\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 41000 0.902 209 500 29.35\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 42000 0.909 213 500 30.036\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 43000 0.898 227 500 30.738\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 44000 0.9 218 500 31.434\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 45000 0.906 222 500 32.141\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 46000 0.909 216 500 32.831\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 47000 0.895 218 500 33.539\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 48000 0.899 223 500 34.241\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 49000 0.904 218 500 34.941\n", - " Iteration Pred.Acc.Est. Unique Rule Count Rule Pop Size Total Time\n", - "0 50000 0.906 214 500 35.633\n", - "Archiving: 50000\n", - "--------------------------------------------------------------------\n", - "Original Population Size: 214\n", - "Post-Cleaning Population Size: 214\n", - "16 rules subsumed with a specificity of 2\n", - "55 rules subsumed with a specificity of 3\n", - "50 rules subsumed with a specificity of 4\n", - "3 rules subsumed with a specificity of 5\n", - "Post-Subsumption Compaction Population Size: 90\n", - "HEROS (Phase 1) run complete!\n", - "Number of Unique Rules Identified: 0\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 1 0.973 0.973 16 1.023\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 2 0.973 0.973 16 1.491\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 3 0.973 0.973 16 1.941\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 4 0.973 0.973 16 2.397\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 5 0.973 0.973 16 2.835\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 6 0.973 0.973 16 3.235\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 7 0.973 0.973 16 3.55\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 8 0.973 0.973 16 3.982\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 9 0.973 0.973 16 4.315\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 10 0.973 0.973 16 4.625\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 11 0.973 0.973 14 4.972\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 12 0.991 0.991 16 5.315\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 13 1.0 1.0 17 5.645\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 14 1.0 1.0 16 6.056\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 15 1.0 1.0 13 6.439\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 16 1.0 1.0 13 6.714\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 17 1.0 1.0 11 7.196\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 18 1.0 1.0 11 7.567\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 19 1.0 1.0 11 8.005\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 20 1.0 1.0 10 8.411\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 21 1.0 1.0 10 8.679\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 22 1.0 1.0 10 9.065\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 23 1.0 1.0 10 9.42\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 24 1.0 1.0 10 9.754\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 25 1.0 1.0 10 10.128\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 26 1.0 1.0 9 10.434\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 27 1.0 1.0 9 10.773\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 28 1.0 1.0 9 11.138\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 29 1.0 1.0 9 11.561\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 30 1.0 1.0 9 11.976\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 31 1.0 1.0 9 12.282\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 32 1.0 1.0 9 12.734\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 33 1.0 1.0 9 13.1\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 34 1.0 1.0 9 13.468\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 35 1.0 1.0 8 13.79\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 36 1.0 1.0 8 14.168\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 37 1.0 1.0 8 14.519\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 38 1.0 1.0 8 14.923\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 39 1.0 1.0 8 15.331\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 40 1.0 1.0 8 15.716\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 41 1.0 1.0 8 16.077\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 42 1.0 1.0 8 16.432\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 43 1.0 1.0 8 16.866\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 44 1.0 1.0 8 17.274\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 45 1.0 1.0 8 17.61\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 46 1.0 1.0 8 17.99\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 47 1.0 1.0 8 18.412\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 48 1.0 1.0 8 18.813\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 49 1.0 1.0 8 19.262\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 50 1.0 1.0 8 19.662\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 51 1.0 1.0 8 19.996\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 52 1.0 1.0 8 20.389\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 53 1.0 1.0 8 20.754\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 54 1.0 1.0 8 21.167\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 55 1.0 1.0 8 21.559\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 56 1.0 1.0 8 21.89\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 57 1.0 1.0 8 22.264\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 58 1.0 1.0 8 22.627\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 59 1.0 1.0 8 22.984\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 60 1.0 1.0 8 23.338\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 61 1.0 1.0 8 23.672\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 62 1.0 1.0 8 24.057\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 63 1.0 1.0 8 24.537\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 64 1.0 1.0 8 24.952\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 65 1.0 1.0 8 25.3\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 66 1.0 1.0 8 25.617\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 67 1.0 1.0 8 25.968\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 68 1.0 1.0 8 26.463\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 69 1.0 1.0 8 26.919\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 70 1.0 1.0 8 27.367\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 71 1.0 1.0 8 27.785\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 72 1.0 1.0 8 28.204\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 73 1.0 1.0 8 28.601\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 74 1.0 1.0 8 28.993\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 75 1.0 1.0 8 29.401\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 76 1.0 1.0 8 29.731\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 77 1.0 1.0 8 30.15\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 78 1.0 1.0 8 30.568\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 79 1.0 1.0 8 30.967\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 80 1.0 1.0 8 31.404\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 81 1.0 1.0 8 31.723\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 82 1.0 1.0 8 32.161\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 83 1.0 1.0 8 32.537\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 84 1.0 1.0 8 32.88\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 85 1.0 1.0 8 33.349\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 86 1.0 1.0 8 33.802\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 87 1.0 1.0 8 34.213\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 88 1.0 1.0 8 34.716\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 89 1.0 1.0 8 35.11\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 90 1.0 1.0 8 35.599\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 91 1.0 1.0 8 36.043\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 92 1.0 1.0 8 36.484\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 93 1.0 1.0 8 36.886\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 94 1.0 1.0 8 37.219\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 95 1.0 1.0 8 37.591\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 96 1.0 1.0 8 37.94\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 97 1.0 1.0 8 38.38\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 98 1.0 1.0 8 38.738\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 99 1.0 1.0 8 39.147\n", - " Iteration Training Accuracy Coverage Rules in Model Total Time\n", - "0 100 1.0 1.0 8 39.543\n", - "HEROS (Phase 2) run complete!\n", - "Random Seed Check - End: 0.37661169893523605\n" - ] - } - ], + "outputs": [], "source": [ "# Initialize HEROS algorithm with run parameters\n", "heros = HEROS(outcome_type=outcome_type,iterations=iterations,pop_size=pop_size,cross_prob=cross_prob,mut_prob=mut_prob,nu=nu,beta=beta,theta_sel=theta_sel,\n", @@ -765,23 +405,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "d4a8ff12", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " A_0 A_1 R_0 R_1 R_2 R_3 Class Group InstanceID\n", - "0 0 0 0 1 0 0 0 0 8\n", - "1 1 0 0 1 1 0 1 2 14\n", - "2 0 0 1 1 1 1 1 0 29\n", - "3 1 1 0 0 1 0 0 3 44\n", - "4 0 0 1 1 0 0 1 0 58\n" - ] - } - ], + "outputs": [], "source": [ "# Load testing dataset ---------------------------\n", "test_df = pd.read_csv(test_data_path, sep=\"\\t\")\n", @@ -812,67 +439,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "47927583", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "11 non-dominated models on Pareto-front.\n", - "----------------------------------------\n", - "Model testing accuracies: [1.0, 0.94, 0.96, 0.9, 0.76, 0.6200000000000001, 0.5800000000000001, 0.5800000000000001, 0.6799999999999999, 0.54, 0.5]\n", - "Model testing coverages: [np.float64(1.0), np.float64(0.98), np.float64(0.94), np.float64(0.88), np.float64(1.0), np.float64(1.0), np.float64(1.0), np.float64(1.0), np.float64(1.0), np.float64(0.48), np.float64(0.48)]\n", - "Model rule counts: [8, 7, 7, 6, 5, 4, 3, 3, 2, 1, 1]\n", - "----------------------------------------\n", - "Best model testing accuracy: 1.0\n", - "Best model testing coverage: 1.0\n", - "Best rule count: 8\n", - "Best model index: 0\n", - "----------------------------------------\n", - " Condition Indexes Condition Values Action Numerosity Fitness \\\n", - "0 [0, 4, 1] [1, 0, 0] 0 3 0.997857 \n", - "1 [1, 5, 0] [1, 1, 1] 1 5 0.997429 \n", - "2 [2, 1, 0] [0, 0, 0] 0 8 1.000000 \n", - "3 [1, 3, 0] [1, 1, 0] 1 7 0.998143 \n", - "4 [2, 1, 0] [1, 0, 0] 1 3 0.998714 \n", - "5 [4, 1, 0] [1, 0, 1] 1 7 0.997857 \n", - "6 [3, 1, 0] [0, 1, 0] 0 7 0.997714 \n", - "7 [0, 5, 1] [1, 0, 1] 0 5 0.996857 \n", - "\n", - " Useful Accuracy Useful Coverage Accuracy Match Cover Correct Cover \\\n", - "0 1.0 27.5 1.0 55 55 \n", - "1 1.0 26.0 1.0 52 52 \n", - "2 1.0 34.0 1.0 68 68 \n", - "3 1.0 28.5 1.0 57 57 \n", - "4 1.0 30.5 1.0 61 61 \n", - "5 1.0 27.5 1.0 55 55 \n", - "6 1.0 27.0 1.0 54 54 \n", - "7 1.0 24.0 1.0 48 48 \n", - "\n", - " Mean Absolute Error Prediction Outcome Range Probability Birth Iteration \\\n", - "0 None None None 538 \n", - "1 None None None 938 \n", - "2 None None None 147 \n", - "3 None None None 459 \n", - "4 None None None 182 \n", - "5 None None None 248 \n", - "6 None None None 181 \n", - "7 None None None 477 \n", - "\n", - " Specified Count Average Match Set Size Deletion Probabiilty \n", - "0 3 87.337309 0.002396 \n", - "1 3 96.095497 0.007325 \n", - "2 3 99.853940 0.019436 \n", - "3 3 92.333984 0.013786 \n", - "4 3 99.660789 0.002731 \n", - "5 3 95.647239 0.014285 \n", - "6 3 100.720970 0.015045 \n", - "7 3 93.752155 0.007151 \n" - ] - } - ], + "outputs": [], "source": [ "best_model_index = heros.auto_select_top_model(X_test,y_test,verbose=True)\n", "set_df = heros.get_model_rules(best_model_index)\n", @@ -889,27 +459,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "0485d99a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "HEROS Top Model Testing Data Performance Report:\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Report performance results for the top model\n", "predictions = heros.predict(X_test,whole_rule_pop=False, target_model=best_model_index)\n", @@ -927,72 +480,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "b9e0ce2c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Prediction Probabilities for all Testing Instances:\n", - "[[1. 0.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [1. 0.]\n", - " [1. 0.]\n", - " [0. 1.]\n", - " [0. 1.]]\n", - "Coverage for all Testing Instances:\n", - "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1]\n", - "50 instances covered out of 50\n" - ] - } - ], + "outputs": [], "source": [ "predict_prob = heros.predict_proba(X_test,whole_rule_pop=False, target_model=best_model_index)\n", "print(\"Prediction Probabilities for all Testing Instances:\")\n", @@ -1015,21 +506,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "f79beb2d", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Get false positive rate, true positive rate, and thresholds\n", "fpr, tpr, thresholds = roc_curve(y_test, predict_prob[:, 1]) #based on class 1\n", @@ -1058,7 +538,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "d18f5118", "metadata": {}, "outputs": [], @@ -1079,21 +559,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "1583cf95", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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AAbHFFltUj2HDhq319QAAANSHru0d6a6Hx7qaMWNGjBo1KsaNGxd33XVXDBo0KIYPHx4LFy5s9fVz5syJY489Nm6++eaYO3dubLfddnHIIYfEE0880Z4vPwAAAHUd3U3NdfFYV5MmTYqTTz45Ro4cGbvttltMmzYtNt1007jyyitbff03v/nN+NjHPhaDBw+OXXfdNa644opoamqK2bNnt+fLDwAAQH2vXh51YenSpdVjVT169Kger7Rs2bK48847Y8yYMSu3de3atZoyXkaxX40XXnghXnrppdhyyy074OgBAACo0+nlzXXxmDBhQvTq1avFo2xrzaJFi2L58uXRr1+/FtvL8/nz57+qr9vo0aNjwIABVagDAABQv9q3enkbFyHrbMqodblGe1WtjXJ3hIsvvjiuu+666jrvsggbAAAA9cv08rVMJW9Nnz59olu3brFgwYIW28vz/v37r/W9//Vf/1VF989+9rPYc88923XMAAAAdH4WUlvHhdQ23njjGDJkSItF0FYsirbffvut8X2XXHJJXHDBBTFr1qzYZ5992vNlBwAAoDFGuutkqHsdlanoI0aMqOJ53333jSlTpsSSJUuq1cyLE044IbbZZpuV14V//vOfj7Fjx8b06dOre3uvuPZ7s802qx4AAADUp/Zd090UDemYY46JJ598sgrpEtDlVmBlBHvF4mrz5s2rVjRf4atf/Wq16vl73vOeFvsp9/k+77zz1vvxAwAAsCGMdLfhHtf14rTTTqserSmLpK3qscceW09HBQAAQP2MdDfo9HIAAADIH+muk1uGAQAAQAYj3QAAANA5r+lu0JXUAAAAID+62/VuAAAAqGumlwMAAEAS08sBAACgU0a3kW4AAABIml7ulmEAAACwRka6AQAAoFNG93LXdAMAAMCaGOkGAACAJKIbAAAAOuf08uUddiAAAABQb4x0AwAAQBILqQEAAEASI90AAACQxDXdAAAAkMRINwAAACQR3QAAANAZo7vZ9HIAAABIGuluMtINAAAAa2IhNQAAAEjimm4AAADolNd0NzV13JEAAABAnenarncvb6qPRxtMnTo1Bg4cGD179oyhQ4fG7bffvtbXX3/99bHrrrtWr99jjz1i5syZbfyiAwAA0BjR3dxUH491NGPGjBg1alSMGzcu7rrrrhg0aFAMHz48Fi5c2Orrb7311jj22GPjwx/+cNx9991x1FFHVY/f//737fryAwAAUMfRXW4ZVg+PdTVp0qQ4+eSTY+TIkbHbbrvFtGnTYtNNN40rr7yy1dd/8YtfjHe+853xmc98Jt74xjfGBRdcEHvvvXdceuml7fnyAwAAUNfR3dRcF4+lS5fG4sWLWzzKttYsW7Ys7rzzzhg2bNg/vohdu1bP586d2+p7yvZVX1+UkfE1vR4AAID60K6F1G5aPiPqwXnnnRfjx49vsa1MHS/bX2nRokWxfPny6NevX4vt5fl9993X6v7nz5/f6uvLdgAAAOpX+24ZVifGjBlTXaO9qh49etTseAAAAKgPovv/B/arjew+ffpEt27dYsGCBS22l+f9+/dv9T1l+7q8HgAAgPrQvtXLG9DGG28cQ4YMidmzZ6/c1tTUVD3fb7/9Wn1P2b7q64ubbrppja8HAACgPhjpboMyFX3EiBGxzz77xL777htTpkyJJUuWVKuZFyeccEJss802MWHChOr56aefHgceeGBMnDgxDj/88LjuuuvijjvuiMsuu6zGfxIAAAAydWlubm5O/Qx1qtzu6wtf+EK1GNrgwYPjS1/6UgwdOrT62EEHHRQDBw6Mq6++euXrr7/++jjnnHPisccei5122ikuueSSOOyww2r4J+CfaZq/c60PAYBkwwcMqvUhAJDopqbro9ZEN6yB6Aaof6IboL7d1Ami2zXdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQpHtH7mzMmDHx4osvduQuoWYmjq71EQAAABu6Do3uEtyTJ0/uyF1CzTTNv6HWhwAAAGzgTC8HAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACBJ96wdA8CGZPiAQbU+BGrgxr/cU+tDoAb8fQfWJyPdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAku4dubN77703zjjjjI7cJdTMxNG1PgIAAGBD16HR3dTUFJMnT+7IXULNNM2/odaHAAAAbOBMLwcAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAABasWzZsliyZEm0h+gGAACgoV133XVxxhlntNg2fvz42GyzzaJ3797x7ne/O55//vk27Vt0AwAA0NAmTpzYYkT71ltvraJ7+PDhVYzPmjUrPve5z7Vp39078DgBAABgg/Pwww/HiBEjVj6fPn169O/fP77//e9H9+7do6mpKb773e/GhAkT1nnfRroBAABoaEuXLo2ePXuufP7Tn/40Dj300Cq4i9122y3+/Oc/t2nfohsAAICGtv3228fPfvaz6vd33HFHPPTQQ/HOd75z5ccXLFhQXd/dFqaXAwAA0NA+8pGPxOmnnx5//OMfqxHtbbfdNv793/995cd//etfx5ve9KY27Vt0AwAA0NA+/vGPV9PLZ86cGUOGDInRo0fHJptsUn3s6aefjvnz58epp57apn2LbgAAABreySefXD1eacstt6ymnLeV6AYAAID4vwXV7rrrrli4cGHsv//+0adPn3bv00JqAAAANLwvfelLsfXWW1exffTRR8fvfve7avuiRYuq+L7yyivbtF/RDQAAQEO76qqr4pOf/GS1YnmJ6+bm5pUfK8F98MEHx3XXXdemfYtuAAAAGtrEiRPjXe96V0yfPj2OOOKI1T5eFlf7wx/+0KZ9i24AAAAa2kMPPRSHHnroGj9eFlN76qmn2rRv0Q0AAEBD6927d3Xt9pqU+3f379+/TfsW3QAAADS0ww47LC677LJ45plnVvtYmVZ++eWXx5FHHtmmfYtuAAAAGtqFF14Yy5cvj9133z3OOeec6NKlS1xzzTVx/PHHxz777BN9+/aNsWPHtmnfohsAAICGNmDAgLjzzjur1ctnzJhRrV5+7bXXxo9//OM49thj47bbbmvzPbu7d/jRAgAAwAamb9++ccUVV1SPJ598MpqammKrrbaKrl3bN1bdodG9dOnSjtwdAECq4QMG1foQqIEb/3JPrQ+BGvD3nXVRYrujdGh0L1u2rCN3BwAAAB3u/PPPX+f3lOu8zz333HV+n+nlAAAANJTzzjtvnd8jugEAAOBVKNdrry9WLwcAAIAkohsAAACSmF4OAABAQ9t+++2ra7bXpnz84YcfXud9i24AAAAa2oEHHrhadC9fvjwef/zx+PWvfx2777577LXXXm3at+gGAACgoV199dVr/Ng999wTw4cPjw984ANt2rdrugEAAGANBg0aFB/5yEdi9OjR0RaiGwAAANaiX79+8cc//jHaQnQDAADAGjz11FPxta99LbbddttoC9d0AwAA0NAOPvjgVrc/88wzcd9998WyZcvi61//epv2LboBAABoaE1NTautXl6el1uJDRs2LD70oQ/FLrvs0qZ9i24AAAAa2pw5c9b4sTLKXVY3f9e73hX333//Ou9bdAMAANCQli1bFj/60Y/i4Ycfji233DIOP/zwGDBgQPWxF154IS699NKYMmVKzJ8/P3bcccc2fQ7RDQAAQMP5y1/+EgcddFAV3M3NzdW2nj17xo9//OPYeOON47jjjosnnngi9t133/jyl78cRx99dJs+j+gGAACg4Zx99tnx6KOPxmc/+9k44IADqt+ff/75ccopp8SiRYviTW96U3zjG9+IAw88sF2fR3QDAADQcG666aYYOXJkTJgwYeW2/v37x3vf+95qmvkPf/jD6Nq1/XfZdp9uAAAAGs6CBQviLW95S4ttK56X1co7IrgL0Q0AAEDDWb58eXUN96pWPO/Vq1eHfR7TywEAAGhIjz32WNx1110rnz/77LPVrw8++GD07t17tdfvvffe6/w5ujSvWKatA+y3334xd+7cjtod1FTT/J1rfQjAejR8wKBaHwKwntz4l3tqfQjUgH/nG9NNTdev8WNl+niXLl1W214S+ZXbV2wro+Prykg3AAAADeeqq65aL59HdAMAANBwRowYsV4+j4XUAAAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJN07cmddunSJM844oyN3CTUzcXStjwAAANjQdWh0b7bZZjF58uSO3CXUTNP8G2p9CAAAwAbO9HIAAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAANgQonurrbbqyN0BAADABq1Do7tv374duTsAAADYoJleDgAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJOmetWMAAOiMhg8YVOtDoAZu/Ms9tT4EGpSRbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASNK9I3fWs2fPOOOMMzpyl1AzE0fX+ggAAIANXZfm5ubmWh8EdEZN83eu9SEA69HwAYNqfQgAJLrxL/fU+hCoga79H6j9MdT6AAAAAKBeiW4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJJ0aW5ubs7aObBhWrp0aUyYMCHGjBkTPXr0qPXhsJ44743JeW9Mzntjct4bk/Nee6IbWM3ixYujV69e8eyzz8bmm29e68NhPXHeG5Pz3pic98bkvDcm5732TC8HAACAJKIbAAAAkohuAAAASCK6gdWURTbGjRtnsY0G47w3Jue9MTnvjcl5b0zOe+1ZSA0AAACSGOkGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCTds3YMdH7z58+Pn/70p3HffffF008/XW3bcsstY9ddd41DDjkk+vfvX+tDpAaeeuqp+MMf/hBvf/vba30oAAAbPNENDeill16KT33qUzFt2rRYvnx5bL311rHFFltUH/vb3/4Wf/3rX6Nbt27x0Y9+NCZOnBjdu/unopHMmTMn3ve+91X/bVA/yg/Wyg/Zli1bFu9+97vjNa95Tfz5z3+OSy65JB566KHYcccd4/TTT483vOENtT5UEtx9993xl7/8pfqhajnXr7Ro0aKYOXNmnHDCCTU5PjpeU1NTfO9736vOa2s/XD/88MOrfwu6djXxtdHce++9cf3118fYsWNrfSgNw326oQGNHj06Lrvssuqb7RJXvXr1avHxxYsXx7e//e347Gc/G6ecckpcfPHFNTtW1r/vfve7orvOPPjgg3HwwQfHE088UT1//etfHzfddFP867/+a3We3/jGN8bvfve7KshLnA0cOLDWh0wHeeGFF+LII4+Mm2++Ocq3fF26dImjjjoqvvKVr0S/fv1Wvu43v/lNvPWtb/X3vo5msh122GFxzz33xODBg6u/46v+cL1EePm7Xj52ww03mNnWYPx/fv0zfAUN6Otf/3pMnjw5TjzxxFY/vvnmm8dJJ51UjXafddZZortO7Lnnnq/qdeWHLtSXs88+uxrdKuFVfj3ttNOqb8hLXM+aNSs22WST6ryXML/wwgvjiiuuqPUh00HK+fztb38b3/jGN2KvvfaKX/7ylzF+/PjYZ5994ic/+UnsvvvutT5EEnz84x+Pl19+uRrR3HnnnVt9zQMPPBDvfe974xOf+ET1g3Y2fCtmM/wzzz33XPqx0JLohgZU/rHddttt/+nrymv8w1w/yjdfb3rTm6pvvNfm8ccfjz/96U/r7bjI9+tf/zomTZq0cur4RRddFDvssEN8/vOfr4J7xQ/byjfqJdKoH9/5znfiggsuiGOPPbZ6XqYVH3300XHMMcfE2972tvjBD34QBx10UK0Pkw524403xvTp09cY3EX5WPn7fvzxx6/XYyNPnz59qtks/8yKWS+sP6IbGtB+++1XfdP95je/ebWp5SuUUa/ymjLdkPpQRrR22mmnuOqqq/7ptLNf/OIX6+24WD+jH2XthhVW/NDtda97XYvXbb/99iunoFMfyvksP2xb1Wtf+9pqhsPIkSPj0EMPjWuuuaa65ID6sdFGG8XSpUv/6evKa6zbUj823XTTahHU8kO1tfmf//mf+OpXv7rejgvRDQ3p0ksvraaRbrfddjFs2LBq5KN3797Vx5599tnqWq+f/exn1UJLs2fPrvXh0kGGDh1aTSd9NSz3UX+jH6vOXiiXjnzgAx+otq/qySefjM0226wGR0iWAQMGVNOIX3k3ghJa1157bfTt2zeOO+646r8H6ke5bn/UqFHV/9vL2g2t+fnPfx6f/vSnq5kP1IchQ4ZUC+iNGDFira8r/86L7vXLQmrQoJ555pnqH9wy2lEiuyysUpSFVkqEl9GPU089dWWMs+F7+OGHq1uBlUWV1ubvf/97LFy40MhXHTniiCOqke6ygOLalG/Sy/W/5Ztx6sOHP/zhanX6tc1eKet2lPU7ynRTCyvVhzJbrSyUVe5YUP6/vssuu7T44fr9999f/X+/3B60XM9dfsjOhq8sgPu1r32tuvXn2pTv/codah599NH1dmyNTnQD62TevHnVyInpaI3Fed+wlW+snn/++dhjjz3W+rqywNbee+9dRTr14Y477ogZM2bEmWeeWU0rX5Ny/W9Z0f6fXX7ChmXu3Llr/eH6W97yllofIh18S9hyx4I1XTpI7Yhu4FUrIyAbb7xxdS1Q+cacxuC8N7Zyt4MS4StuN0TjcO4bk/PemJz3XF2T9w/UGT+na0zOe+P+wKUstmUKYuNx7huT896YnPd8ohsAWCM/cGlczn1jct4bk/OeS3QDAABAEtENAAAASUQ3AAAAJBHdwFpZVKMxOe8AAB1DdAOrWbRoUUydOjX233//eMMb3rBye5cuXeLAAw+M17zmNTU9PnI47xTPPfdcrQ+BGnHuG5Pz3pic9/VLdAOVF154Ib75zW/G4YcfHttss0184hOfiBdffDEmT5688jVdu3aNm2++OXbaaaeaHisdx3lnhYULF8ZZZ50Vr3vd61qc+3HjxsWAAQNqemzkcu4bk/PemJz32ujSbH14aOj7Ms6aNSumT58eP/rRj6oA69+/f8yfPz++9a1vxfve975aHyIJnPfGdNttt8U111wT8+bNix122KH6AUv5QcqCBQvi/PPPj6uuuipeeumleP/73x/XXnttrQ+XDuTcNybnvTE5751UiW6gsfzqV79q/tjHPta81VZbNXfp0qW5T58+zaeeemrzLbfc0vz0009X237xi1/U+jDpYM5745o5c2Zzt27dmrt27drcr1+/5o022qj5ta99bbW9/HdQPnb88cc333///bU+VDqYc9+YnPfG5Lx3Xka6oQGVaUTlOt13vOMdMWrUqDjkkEOie/fu1ceeffbZ2GKLLWLOnDnx9re/vdaHSgdy3hvXAQccUF028MMf/rCaPvj888/HSSedFN/73vdi6623rn4dMmRIrQ+TBM59Y3LeG5Pz3nm5phsa0B577FFmucQvfvGL+OIXv1hNM7agRv1z3hvXvffeG2efffbK6/U222yzuOSSS+Lll1+Oiy++2Ddhdcy5b0zOe2Ny3jsv0Q0N6J577onf//738ZnPfCYefPDBOPHEE6tresu1vOWno2U0lPrjvDeup59+erUFcsrCeYUF8uqbc9+YnPfG5Lx3XqIbGtRuu+0WF110UTzyyCPxy1/+sgqwMgJafi3KSOgtt9xS68OkgznvjWtNP1Tp1q3bej8W1i/nvjE5743Jee+cXNMNtFjV+sYbb6xWsC4jn0uWLInXv/71VaBRv5z3xrief9NNN61+XVW53u+V28s3bOUaf+qDc9+YnPfG5Lx3Xv+3gg7A//8p6GGHHVY9/v73v8cPfvCDKsSob857/Sv3X6UxOfeNyXlvTM5752WkGwAAAJK4phsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAGC9u/rqq6v7xLb2OPPMMzv88916661x3nnnxTPPPNPh+waAtXGfbgCgZs4///zYfvvtW2zbfffdU6J7/PjxceKJJ0bv3r07fP8AsCaiGwComUMPPTT22Wef2FAtWbIk/uVf/qXWhwFAJ2Z6OQDQKf3kJz+JAw44oIra17zmNXH44YfHH/7whxav+d3vfleNXu+www7Rs2fP6N+/f3zoQx+Kp556auVryrTyz3zmM9Xvy6j6imnsjz32WPUovy/T3V+pbC/vXXU/Zdsf//jHOO6442KLLbaIt73tbSs//o1vfCOGDBkSm2yySWy55Zbx/ve/P/70pz+12OeDDz4Y//Ef/1EdZznebbfdtnrds88+26FfOwA6DyPdAEDNlNhctGhRi219+vSJa6+9NkaMGBHDhw+Pz3/+8/HCCy/EV7/61Spy77777hg4cGD12ptuuikeeeSRGDlyZBWyJcovu+yy6tfbbrutiuSjjz46HnjggfjWt74VkydPrvZfbLXVVvHkk0+u8zG/973vjZ122ikuuuiiaG5urrZ97nOfi3PPPTfe9773xUknnVTt98tf/nK8/e1vr463TGlftmxZ9edZunRpfPzjH6+O94knnoj//u//rq4179WrV4d8TQHoXEQ3AFAzw4YNW23bc889F5/4xCeqeC0BvUKJ8F122aWK3RXbP/axj8WnPvWpFu9/y1veEscee2z86le/qkbK99xzz9h7772r6D7qqKNWBnvRlugeNGhQTJ8+feXzxx9/PMaNGxcXXnhhnHXWWSu3l9jfa6+94itf+Uq1vYyQP/roo3H99dfHe97znpWvGzt27DofAwAbDtENANTM1KlTY+edd26xrYxel5HfEs6rjoJ369Ythg4dGjfffPPKbWUq9wovvvhiPP/881V0F3fddVcV3R3t1FNPbfH8e9/7XjQ1NVWj3KsebxnJLiPi5XhLdK8Yyb7xxhvjsMMOi0033bTDjw2Azkd0AwA1s++++662kNoll1xS/XrwwQe3+p7NN9985e+ffvrpalXy6667LhYuXNjidVnXSb9ytfVynXaZZl4CuzUbbbTRyveNGjUqJk2aFN/85jerHwgceeSRcfzxx5taDlDHRDcA0KmUUeOiXNddRotfqXv3f3z7UkaXy+3AykJpgwcPjs0226x6/zvf+c6V+1mbcs13a5YvX77G96w6ur7ieMt+ysJvZTT+lcoxrTBx4sRq4bcf/vCH8dOf/rSaRj9hwoTq+vOyqBoA9Ud0AwCdyo477lj92rdv31av+V7hb3/7W8yePbsa6V71uugy8vxq47qsQF6U6eyrKtdpr8vxlpHuMpL9yqnyrdljjz2qxznnnFP9wGD//fePadOmVdeEA1B/3DIMAOhUygrfZQp5WTDtpZdeWu3jKxY/WzGqvGIF8RWmTJmy2ntW3Ev7lXFdPk9ZzfyWW25psb0sfvZqlQXTyrGU+H/lsZTnK25ftnjx4nj55ZdbfLzEd9euXasVzQGoT0a6AYBOpYRwuT3YBz/4wWrV8XIf63J7r3nz5sUNN9xQjQxfeuml1evKLbnKNeAlzrfZZptqynZZIfyVyv2zi7PPPrvaX7nO+ogjjqhivKySfvHFF1e/luvLS4CXW4yty0h3GaUeM2ZMdd/vskJ6ua94OY7vf//7ccopp8SnP/3p+PnPfx6nnXZadcuxMiJeArxMoS/BXu7dDUB9Et0AQKdz3HHHxYABA6oY/sIXvlCNBJeoLouPlXtyr1Bu3VXueV1WQS+jyoccckh1bXV576re/OY3xwUXXFBN4541a1Z1HXaJ4hLdZWp6GT3/zne+E9/+9rfj0EMPrfZRpre/WmeeeWYV0uU+4GXEu9huu+2q4ymLpa241VgZxf/xj39c3Z+7rF5etpXPtWLFdQDqT5fmV86DAgAAADqEa7oBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgcvw/s1xGy3yo3uAAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "if run_all_cells:\n", " heros.get_rule_set_heatmap(feature_names, best_model_index, weighting='useful_accuracy', specified_filter=1, display_micro=False, show=True, save=True, output_path=output_path)" @@ -1109,21 +578,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "f49f7597", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "if run_all_cells:\n", " node_size = 1000\n", @@ -1141,21 +599,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "f3950089", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "if run_all_cells and feat_track != None:\n", " heros.run_model_feature_tracking(best_model_index)\n", @@ -1179,21 +626,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "07a336b2", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Run permutation importance\n", "result = permutation_importance(heros, X_test, y_test, n_repeats=100, random_state=random_state, scoring='balanced_accuracy')\n", @@ -1224,29 +660,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "66567604", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total Number of Available Testing Instances: 50\n", - "Making Prediction on Testing Instance Index: 0\n", - "PREDICTION REPORT ------------------------------------------------------------------\n", - "Outcome Prediction: 0\n", - "Model Prediction Probabilities: {np.int64(0): 1.0, np.int64(1): 0.0}\n", - "Instance Covered by Model: Yes\n", - "Number of Matching Rules: 1\n", - "PREDICTION EXPLANATION -------------------------------------------------------------\n", - "Supporting Rules: --------------------\n", - "8 rule copies assert that IF: (A_0 = 0) AND (A_1 = 0) AND (R_0 = 0) THEN: predict outcome '0' with 100.0% confidence based on 68 matching training instances (15.11% of training instances).\n", - "Contradictory Rules: -----------------\n", - "No contradictory rules matched.\n" - ] - } - ], + "outputs": [], "source": [ "# Get example testing instance (with no label) --------------------------------\n", "print(\"Total Number of Available Testing Instances: \"+str(len(X_test)))\n", @@ -1269,21 +686,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "fb557984", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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2YcGoXWLBcH777uNjEZYGhq9RvlYdqV8v3sGCkenCghFfYsEom02jTesGJRaMoy4sGLUdLBinXFgwkkosGJerpurZyskWjAZ+99jfsDPsxrP2wAzxttvVuv9ue4lubtHRwcp5+RyyRaF3m+dd7sPIWWe3YCQ4WDAOXbZguNpv+u6HaXTjdy9bBxiJ6KsjU8kvs0aZfXwlH9qWPU8c19W6fZcWCAvGvU2mlTnm+ktzii0YLqycvsk+1KDG3WX6t3Tby8KCcXWNx8sc78O/n6Vba3fSlJUz+8xZ6nnTmLIWjMMOFgyGH6H8OHB+1NLJ9P961YIBFymgdxcpo5xT3QuMa6+9VmSQsgXAMBxBzy5S/ICpTgxG1okvDReDIfloLo7fFGz7KJ2+G3GApCIiiy/RLbPrU5thsWp3y9jgWq82H/p8T4/5zqQii1UMpt8peorut95c6Tqljyfnsyra737f6fSpz6rL8Q3WG+nDorFu9aOiY8o5H3KO5wQHd3krBqPpveIFhNNw4f4St6gaJeKC65AhBgNoCAgM5TGVwOAgb7ZY7Nixw76M03hxQZLqBnkbUWAA9cg9doGy9l+gqAYBFJ4YIJZB8AGtwdmN9vscpwbWBEqiWlVeV6Xj+cSWXWc5Tg2kcj6rnHWVfp6L/Tgz03qfv+lqa3O6kpoo0o+Kjlnd/sk9nhrMevtrGj1mbhkLsrBksLFxX1nLBf9+vzHjcXriib5e7SsEBrABgaE8uhYYZ8+epX37ioMor7jiCpoxY4ZIQcvpuOrWrStcobhi4SeffCK24S/KFREff/xxuv/+++mnn36iJ598UmSW4mDvagmMY0soPDzYo99PF2AQDEwGhB8wDTKudXbvS6l/t0hsUVkdjOKPsAjX1UOHlwh3Rm8CgQFsQGAoj66zSP35559CUNgYNWqUmA4dOlQU0Dtx4oQolW6DvySLiaeffpreeustSkpKEnl8qyougAtKxYoAlcHg1+NYcM1rCgg+DyLjWo8MD6bFX0yk2++cKB5HFYkMFhdsvViyZKrXxQUAQDtozoKhBnYLxpFFsGDIBQMCYDZwzQOTCb4fftxMdw2cKoroieUOwwdbnEVwcIAQFzf2uFKVvsKCAWzAgqE8urZgAJ2CN8DaAwNgz4JrXpvguvcYPXpcSYcOfEGffv4jzZ79jaj7YoMDukeM6EP33tuTIiJguQDA7EBgOMKFwmzFwny8l/UCAI9QJGMA7KXMNABoTvhBmFTpvLDb0xMj+tCIx++klHoD6fjx01SrVoTXs0UBoGfWrVtHY8eOpX///Zdq1KhBrVu3pnnz5gnLgLfhsg2PPvoo/fjjj5SdnU3NmjWjN998kzp16uTWcSEwyqMKgWxABSD8PEupStJAQ0D8eRZYpKoluFhM1KodKQTGmTPOtV8AAOXD7nt33HGHEBScJZVrtq1atUrUsFGDS5cuUUpKCv36668ijnnx4sV022230aFDhyg0VL41Er9YpQdXVjca8I7wQ9NeA955PqFpr5k4CQIXHWW4yCYXNAXASJzIO0Eb0jaIqZLs2bOHAgICqE+fPqLwKw/i77zzTpEplRk2bBg98cQT1LVrVxFHxIN9Lr1g4+eff6Z27dpRZGQkXXfddbR//+Xir9u2bRP78To+3pIlSyrtT0hICE2aNElsz/0ZOHAg+fv70+7du936nhAYSuKOOEHzXAOeR22BgwbxpxYmFkzR0ZeDqDMzcz1wcgFQh8U7FtO1/7mWBi8eLKY8rxSNGjWiwsJCevDBB4XlghMNlebTTz+l6dOnU3p6OoWFhdHIkSPF8iNHjlC/fv1EcenMzEzq27evEAQ2y0iPHj1oyJAhlJGRQZs3b6amTZuKdQsWLKBWrVpVqX979+4VgqZhQy5yIx8IDEcw+DUmagscNFz7aqK2wEEzjvgrdd9GQWAAA8IWi+d/eJ6sJYKbpzyvlCUjIiJCxGBw7AOLgVq1atHgwYMpLy/Pvg27TnXs2JGCgoJo6tSpwhLBWds+//xzYe3o0qWLcKliSwe7MnFbtmwZpaamCuHi5+cnjst14mwFqLdv315p386dOyf6wjXnuJ/ugBgMtcFAS5sg4NPz4NrXNrgHPI+WRIaM+LboqMsDkKzMHIU7BIA6HDpzyC4ubPD84TOHKT4sXpHPaNGihbBSMGxp6N+/P7388sv0yiuviGV16tSxb8v/ZjHCVgWuA8f7cZyEDbaGcAHqo0ePuhUkfvHiRdEPtlywy5S7QGA4opc3q/jh9zx6uA7MCq5/74B7QLuofQ/w4Mvi42zByIIFAxiDlJop5GPxcRIZPJ9cM9kjn9euXTsRj7Fz5077MnaFcvw3x2xERUVRYmIiDR8+nGbNmlXmOIcPH6Zvv/1WVh+sVquwpnDyho8//liRjHD4pdYjaru2oMHtR03U/huj4R5QG438zRCDAYwIWyle7vGyEBUMT3leKevFrl27RBrY48eP24O+WRh06NDBvs3SpUvpjz/+EC5LU6ZMEXEXPOhnVyd2l/rll1+EKGC3qi+//FLs06tXL3Gs+fPnC2sEx2E4ipaKePjhh+nEiRPi2Jw2VwlgwXDEyD+Qar/xMgtGvX6MAO4B74B7QJ/3AbtrVdNNKrpmmP3fWRAYwEDc1fIu6pLSRbhFseVCKXHBcND2+vXrRRA3B3hHR0cLATFu3Diycc8999Do0aNp69atIt6CrQoMu0AtXLiQxowZI4QKZ4Dq1q2b2J9jJlasWEFPPvkkPfXUUyKT1IwZM4Q7Fsdu/N///R/9/fff5Mry8cEHH4jK3DExMfbl33//vfhsuUBgmAX86GsXDHy9A+4B7YN7wbv3gZvnGy5SwMiwqFBSWNhgN6fK0sfGxcXR7NmzXa7jNLQbN250ua5t27ainkVpWLBwc0VycrIIIFcaCAxHTJTXvEqgsJd3wMBX+2Dg6x1wL6h/PZfEV1QFWx0MBkHeAABHIDBA+UBsaRsIQO+Bga+2gQBUBcRgAADKAwLDEb3mJtcSMlIdAplAAGofiEDvAAGovBDjc1rJ9hERIaLyLwebwkVKXbiuwmuvvSZSnnKw7jfffCNqKVTE2rVradSoUcIvn1OhTpgwQVSRBury0UcfkRGAwHDAYpXIgh8q95B5+iS8gQSGpEjebhDqQCtitwKhweIiKiqMTp/OQZC3yuTn51Pr1q3p/vvvFylPK+PgwYMi69AjjzwiAoBXr14tCrTFx8dTz549vdJnYGwgMMrLIoUBr1eBsNM2EIBeBpZU7aNHEVgFq4QcNykWGHCRUpebb75ZtKoyd+5ckZHojTfeEPNNmzYVwcGcPhUCAygBBEZ5YMCrfTDo9RoQgPoBYtBL6EUEKiSE+Bng6tqKKgn0zssroMLCi+Tv70dm5/z586K6srtwZp/SBc+44Bo3d9mwYQN1797daRkLC05vCoASQGAoWQcDP+zeBYNe7YN7wutADGof1UWggpaM6OgI+7+zsvIoLi6KzC4u6tVLpJMns9w+VmhoKJ09e9Zp2eTJk0XhNXc5efIkxcbGOi3jea7LwMXdgoKC3P4MYG4gMJQEP+z6QO0fdzOBe0I/4L4wlAj0mIgpJU6camFk5pheYLDlgsXFoYMLKTw8WPZxcnMLKKXeQDpy5AiFh18+x0pYLwDwBhAYWqiDgUwz3gWDXu2Dwa73wX1hvntDAUuGcy0MxGHYYHERHh6iwHHCnQSGUnAht/T0dKdlPM+fBesFUAL8imtJ2KBptwF13BXR9NGA99DK36LkuehUCwOpai+j8fuqU6dOInOUI6tWrRLLgXfSCl911VUUERFB0dHR1K1bN5HZSy0eeughkUGMBWbLli3p22+/dfuYsGA4wqXSTRa4B6oIRIZ+gEXQ+0BkaAO2RsixSsjZx6WLFASGHXdFQjX35ViNffv22ed5sLpt2zaKioqiunXr0vjx4+nYsWP0ySefiPWcnnb27Nk0duxYkdr2p59+osWLF9Py5cvl9xlUiZycHLrjjjto3rx5olZJQUGBEHe+vr6q9Ynrobz99tvCBe+PP/4QCQAOHDggxI9c8Eus96KAaNpuwPuobe1Cg2XQRETXDLP/Gy5S6vHnn3/SFVdcIZptwMj/njRpkpjn4ntpaWn27TlFLYsJHthy/QxOV/vBBx8gRa0DR+koraE1Yqoke/bsEQN5rlfCtWQ4mP/OO+8UQpDhYodPPPEEde3aVVgUbrvtNsrKupw04Oeff6Z27dpRZGQkXXfddbR//377OhaVvB+v4+MtWbKkSn1q0qSJPb6HM5dxLBELUneABcMRo5v74dfufSAy9AUsg94FIsNjVrvy0spW+ryq5j3g5CKVmVO9zzO6RwQ3d/avBjzQ5LS21akOzfts3bpVVveMzjyaRw/RQ2QlK/mQD/2H/kMP0AOKHLtRo0ZiAM+FDQcMGEAdO3YsE2fz6aef0sqVK6lVq1b0wAMP0MiRI8UyDvrv168fff3113T11VfTO++8QwMHDhRWB7aM9OjRg/7v//6PfvzxR8rOzrbH2SxYsIBeeeUV2r59e7n9euyxx2j+/PkiE9ott9wiXKXcASNOM6G2nzaaJnxvQQWobfFCg3VQZ9jqYDCIwXBAXKfu/A7gGlcLtljYxAXD04fpYcUsGRERESIG48KFCzRkyBCqVasWDR48mPLy8uzbsOsUCw8OuJ86daqwRLCA5KrrbO3o0qWLcKliS8ehQ4dEW7ZsGaWmpgrh4ufnJ47bokULcbxBgwZVKC4YFivsasfihIVK6Ros1QUWDEcwuHMNLB/eB9eh/sB94l0wAKuatUGGVUJYlqoYy+RowYCLFDACe2mvXVzYKKIi2kf7KImSFPmMFi1aCIsEs3nzZurfvz+9/PLLwsrA1KlTx74t/5vFCLtJsZsb78fxMjZs7kxHjx4Vrm/uwKLlhhtuoJkzZwqxwpYMuUBgOII3Y66xFsnbD+4mwEzIFYUQJsAb10w1RIPTNV3JZzm7SEFgKPbCEi+ZVCOVUoVblKPI8CVfakgNPfJ57dq1E/EYO3futC9jVyjHf3N8BAfsJyYm0vDhw2nWrFlljnP48GFFsj8xly5dckoaIAf8sgHPobYLBRpcTvSA2u54aNVvaqOFPpQQEOBPISGB4t9ZcJG6jBGuM5PCVgqOuWBRwfD0PXpPMevFrl276M0336Tjx4/bg75ZGHTo0MG+zdKlS0VcBVdV58rtHHfBLkvs6sTuUr/88gtZrVbhVvXll1+KfXr16iWOxXEUFy9epIyMDCfRUh4cu8ExGuwexcKCj79mzRq69tpr3fqeEBiOqP2jhYaHqpqoLXDQIAr1gp6fj3KOUck+0dERYgoLhgNa+FsD2XBA9yE6JLJI8VSpAG8mLCyM1q9fLywXISEhIiUsi4Nx48aRjXvuuYdGjx5NsbGxdObMGeGyxLAL1MKFC2nMmDHCosHZn1iM2GI7VqxYIdLfxsTEUPv27Wn37t1iHcduNG/enFzBwuX999+npKQkkZaW3bRYcLRp04bcAS5SjjimTkQuffXAg1VfwMVHPSAy9IeSrqP899eAKyrXwkhLSxcWDA5EdTc4FAAtwBYLpawWjrCbU2XpY7nSOtcpcQWnod24caPLdW3btqVff/21zHIWLNxcwRms2GKhNBAY5YH0ifoDolAdIAj1CYShvkSh0kKC79tqXgPlpb6NLskkdelSEeXm5lNERKhi3dT1GMKdZyPGIEDnQGA4ooTbgQbeJpkWPJD1B0ShekAY6ofKhICcAG4FKR3oDYFBZJGsormzPwB6BgJDaeCyoD8gCtUDP6L6BMLQOJYmGZaM0vuwi5SjwKhfP0HJHgJgKj5yURRRj0BglLFgqDTggbuCekAU6g+IQnWBMPQOHkgr6wlsLlIMamGU4G6gNiyMQOdAYGgFPEz0B0ShekAU6heIQ20iR5yUuGY5WTCycpTvmxldrvGMAzoHAqNUABs3veAq2A54ER1dK8AB3DfqgoFT9USYAi5MnsaWppZBqloAAAOBoWP0JIbAZSAMVQb3jb7B/eNZZKS+ja55OagbLlIlwEUKmBwIDEfMUrwKLgqqAmGobyAQVcbI909F15aMgX95aWWVJsrRgoFq3sVAYACTA4FhRswgoowKxKHqQCDqF4jDalCN1LcI8gYAlAYCQ8nCOAB4GncuTwyugMmxWIs0KfCrJXxkxWS4UfG7Cp9Xug4G4D+q5F62Nd4fGJZ169bR2LFj6d9//6UaNWpQ69atad68eVSvXj1V+7Vhwwa65ppraOrUqTRhwgS3jgWB4Qg/DJB+0TMgb776QDzrGwhEY1p/fSzyXJncyPqkNBERIeTr60NFRVZkkbIBFyndk3s0lzL3ZlJ0ajSFJ10W0e6Sk5NDd9xxhxAUvXv3poKCAlq1ahX5+vqSmlitVnr66afpyiuvVOR4EBjAO0C46ReIQ22AAYe+0btArEDQWCwWiooKp4yMbLhI2UCaWl2zZd4WWvbQMpKsEll8LHTrf26ltg+0VeTYe/bsoYCAAOrTp4+YDw0NpTvvvNO+ftiwYRQWFkbbt2+nrVu3UteuXenjjz+mqKgosf7nn3+mUaNG0f79+6lNmzZCqDRo0ECs27ZtG40cOZL++usvCg8PpzfeeIP69+9fpX795z//oY4dOwoBpAQ6f+J56IGApp0GtGPZQ9N3A9p4o+3um+0yx5U0IVZtblJwkQJGsFzYxAXD02UPLxPLlaBRo0ZUWFhIDz74oLBc5OaWPe6nn35K06dPp/T0dCE2WDQwR44coX79+tHMmTMpMzOT+vbtSwMHDhTrWBj06NGDhgwZQhkZGbR582Zq2rSpWLdgwQJq1apVuX3iY/ExX3jhBVIKCAxHPPHwB+6htsBBg0A0CmoLHLTKxZ4OfnvKS3LAFgzm7NlzVFh40cu90oGglNOAKrBblE1c2JCKJMral6XI8SMiIkQMxoULF4QYqFWrFg0ePJjy8vLs27DrFFsTgoKCRDzEkiVLSJIk+vzzz4W1o0uXLsKl6oknnqBDhw6JtmzZMkpNTRXCxc/PTxy3RYsW4niDBg0SFpHyeP755+mpp56iyMhIUgq4SJUHbm79o3eXBCMAkWEMkL1M+y6HLFws6hXkKx3oHR8fTaaG/x7ujCNgdVQNjrlgtyhHkWHxtVBUw2IXJSVo0aKFsFIwbGlgN6aXX36ZXnnlFbGsTp069m353yxGsrKyKC0tTey3ePFi+3q2hhw7doyOHj0qK0ic3bD++OMPmjNnDikJBIYjSrx1xQ+xdoBI1DcQiNoBQlEZqvr7wOdb678lpcRJFAQGMAgc0M0xF+wWxZYLFhe3vnerooHejrRr107EY+zcudO+jF2hHP/NMRscg5GYmEjDhw+nWbNmUWkOHz5M3377LVUXjunYvXu3OLbN1YozW3GMx/z580kuEBhKgx9i/aP1H3azAIFoHCAWvfdckZXG1n1LRnTU5WJ7WZnIJMWuZO7UzEG9HXXhgO6GPRsKtyi2XCgpLnbt2kXff/89DRgwgBISEkTQNwuDe+65x77N0qVLhVWBLR1TpkwRcRecTIFdna6++mph8eB0svn5+bRy5UqxvlevXsLNiUUBu1xlZ2eLGA6bm1R5PPTQQ/Y4DobjPdgSMm7cOLe+J576SvtMwt9S/6gdt4CG+A+jofazVe0mzoFkTDeZkj46uUihmndJHQw3G1AVFhUp16UobrkICwuj9evXC8tFSEgIde/eXYgDxwE9i43Ro0dTbGwsnTlzRgRgMzzwX7hwIY0ZM0ZYNJo0aSLEiC22Y8WKFSKrVExMDLVv315YJhiO3WjevLnL/gQHB1NcXJy9cdwHZ7ZyNx4DFgwtAZGhf/CmVjtAZBgHWBU1b8ko7SIFAHANuyJx0HZF8EB/9uzZLtdx2tqNGze6XNe2bVv69ddfyyxnweJoIamIjz76iJQAAsMRvb75xI+vdoBINA4Qi9pBj8/l8p7NKgdjewpHCwZqYaDQHgAQGEZArz++wBkIRW2BH3jjoPHBuSeRVyW8+kHm0TXD7P+GBQMCAwAIDEdQkAqoSZEb+6LaNgDlYy3yrvivyoBelguTG9ml5OxbDWuLrQ4Gk5mFIG8A5KKUi5LaQGA4wtoC1oCqgbft2gLC2FhAMGqL6vwuVPfZKGfgr0GXKbhIKexyjbEI0DkQGEAeePgZB4hF7QHBqD/0Igo9FANSutCe6YGLFDA5EBiOIF2sd9DYmzfTA7FoPCAa1UWGhUFerIR2LBn+/n4UGhpEZ8+eoyykqS2xYLgjMPBcBvoGAgN4H4g4Y6GRAQ5wAIMT76FXMeeBNLZsxWCBAQsGAAACwxEubIMfZm2j1x9zIwPBaDwgGpXBW/EV7gR/KwjXwjh8OF1YMCRJEpWHTYu7xfJQaA/oHAiM8oKyNPCwBi6AADQWuM+0CUSjeYSYgu5c0VERYlpUZKWcnHyKjAwl04IYDGByDPB09LDYQDNOA9pD7WsCDfeaXKozKJczWJRzHlVODuAc6I1UtQCUx7p16+iqq66iiIgIio6Opm7dutHBgwdJLa677joKDAyk0NBQ0W6++Wa3jwkLhieCvI3wVsuImGXgYyZgAdEmRr/XjHjdKRCTwS5SNjgOo0GDRDIt7rpcw0VKdXLoImVRIUWRP0WQn3LHzcmhO+64g+bNm0e9e/emgoICWrVqFfn6+pKafPDBBzR48GDFjgeB4Qlg2jQeEI3axOgDWbNixAG8xtLKeoJoh2J7pq+FARcpXbOFztC3dJz4F4afRrdRArWlmooce8+ePRQQEEB9+vQR82wxuPPOO+3rhw0bRmFhYbR9+3baunUrde3alT7++GOKiooS63/++WcaNWoU7d+/n9q0aSOESoMGDcS6bdu20ciRI+mvv/6i8PBweuONN6h///6kBhg1OaK2WwEGS9r/sUAzXgPaQ+3ncFWez3KuHT084+V8rxLXLCcLBlLVAh1bLmziguEpz/NyJWjUqBEVFhbSgw8+KCwXubll75VPP/2Upk+fTunp6UJssGhgjhw5Qv369aOZM2dSZmYm9e3blwYOHFjc75wc6tGjBw0ZMoQyMjJo8+bN1LRpU7FuwYIF1KpVqwr79fTTT1OtWrXoxhtvFOLGXSAwtIbaP6Bonh2UAO2htsBBg3CsCrIEjXfPA2IwHMA1rFvYLar0L7dUslwJIiIiRAzGhQsXhBjgQT27JuXl5dm3Ydepjh07UlBQEE2dOpWWLFkiMrN9/vnnwtrRpUsX4VL1xBNP0KFDh0RbtmwZpaamCuHi5+cnjtuiRQtxvEGDBlUoGljMcAxIWlqaEBgcg+HYHzlAYJR+C6PXBrSL2gIHDQLSjHhLrMi5BvTwzJbxvaJrhtn/DRcpPFv0CsdclHbStJQsV4oWLVoIK8XJkydp/fr1or388sv29XXq1HH6N4uRrKwsIQB4v8jISHvLz8+nY8eO0dGjR6levXqy+tOhQwfhqsWCZuzYscJqsnHjRre+I2IwHNH1TV3k3u7weQZAlVuv2v71wHtU57norVgJ/o2q5vNaVpVwGcBFChgBDujmmIvSMRhKBno70q5dOxGPsXPnTvsydoVy/DfHbHAMRmJiIg0fPpxmzZpFpTl8+DB9++23pAQ+Pj7CYuLWMRTpCdA/ar8FRvNcA9pGbesnmj6twrJcpjxrbUGQtwNmuAYNDAd0P0WNaCiliKlSAd7Mrl276M0336Tjx4/bg75ZGLAVwcbSpUvpjz/+oHPnztGUKVNE3AUXrmRXJ3aX+uWXX8hqtQo3pi+//FLs06tXL3Gs+fPn08WLF0UchqNoKY/s7GwRC8JWEo4N4b6xtYRdtNwBAsMRtQeCem1A26h9faDh/tMrcs6vt/bRIM4xGCYXGLi3dQ9bLOpRiOKWi7CwMOESxZaLkJAQ6t69uxAH48aNs29zzz330OjRoyk2NpbOnDkjgroZdoFauHAhjRkzRlg0mjRpIsSI6G9EBK1YsUJklYqJiaH27dvT7t27xTqO3WjevLnL/rAYGT9+vNgnLi5OiJ3vvvtOHM8d4CIF3AcPQuMC1zntg/vP3Ne8HDcrD7lzhYeHUI0avnTpUhFcpNwN1EaQt2FJTEwUVoiK4IH+7NmzXa7jtLXlxUe0bduWfv311zLLWbBwcwUHg//555+kNBAYjuCtgffR24+52cD9YA5wH3ogVqL6+3grVsJTsAtHVFQ4nTp1Bi5SAJgcCAygLhjAGhsMXPUB7kP9X6v8N6zud/CAcGI3KRYYSFPr5gtL3JNA50BgOILc0/pDx2/7TAF+JM2B3gfnHh3Ee2ngrxHYgsHk55+nCxcKKSBAudSe+hMY7rhI4dlpVj766CMyAhAYDkhWSTTGYpYfTL0DQWhsdDrIMh16HwwZ6XnvpXS55blzlQ70TkiIqV5fAACGAAKjHGxCAxgXiEgdAAFpHtQSk3KeA15zR5LxOSrjVAvDzAIDLlLA5EBgOMJFRZS4qXX2g2BWICLNAYSkTlBDTPKAX4eDeC0FppfeB7UwbLhbywIvV4C+gcDwBBi4mgMjDkoMCISkefC6mPSWVUKO25NKREdfzp2fmWXyQG8ATAwERmkLhpul0RXBgoGrLsDA1TxATBpKTLoUIrBkuLdPiQgq7SJlWuAiBUwOBIaSWR+0AIJiAVCeIgV+7PHiQH2qIiC8lSnKS8HYbtXYkPG9omuG2f9tahcpCAxQAevWraOxY8fSv//+SzVq1KDWrVuLCtxcqVstpk+fTm+//TZlZ2dTw4YNRR+56rhcNDkanTNnDqWkpFBgYCB17NiRNm3aVOH2XEK9cePGFBQURHXq1KGnn36azp8/T6ZOtYtm/Ab0aSFFU6dpFTn3soYHn6WzSAEAnMnJyaE77rhDCIwzZ87Q4cOH6YknniBfX19Sc9y9YsUK+u233yg3N5c+/vhj8vd3L8W05gTGokWLaNSoUTR58mTasmWLUHU9e/akU6dOudx+wYIFNG7cOLE9K0FWgHyM5557Tv4bBzM3oB/UFjhohheVR4PzaE3sETE1HXICdN0K6vUScp7z1fhetjoYTGaWiQWGDu934MzRo0Rr1hRPlWTPnj0UEBBAffr0IR8fHwoNDaU777yT6tatK9YPGzZMCI6uXbtSeHg43XbbbZSVlWXf/+eff6Z27dpRZGQkXXfddbR//377um3bton9eB0fb8mSJZX2p6ioiF5++WV6//33xT4Wi4VatWol+mgogTFjxgwaPnw43XfffdSsWTOaO3cuBQcH04cffuhy+/Xr19M111xDgwYNElaPHj160N13312p1QOUg9oCBw2CEpSPF8XMvAY7qV6fj6h7z2/ElOdVF1hGHqTJuR81+P0cLRhwkcLzWa/Mm0eUnEzUrVvxlOeVolGjRlRYWEgPPvggrVq1SlgMSvPpp58Kl6X09HThpjRy5Eix/MiRI9SvXz/huZOZmUl9+/algQMH2i0jPAYeMmQIZWRk0ObNm6lp06b2l/EsGlxx9OhRKigooC+//JJiY2OFRxCLDXfRlMDgE84npHv37vZlrO54fsOGDS73ufrqq8U+NkFx4MAB+u677+iWW24p93MuXLgg/qCOTaD2YM8IDegLta8XNE3eo0eDz9IjndaQ1ad4e54+2mmNWK5r5DyjvLWPt5FjbamCoImKuuyznZlp4ixSXrpXgfKwxeKhhy5f7jx9+GHlLBkREREivoHHoiwGatWqRYMHD6a8vMuW4t69e4sQAXb9nzp1qrBESJJEn3/+ubB2dOnSRbhUsaXj0KFDoi1btoxSU1OFcPHz8xPHbdGihTgev4Tfvn27y/4cO3ZMiBO2rPBx+LPYC+iXX34xjsA4ffq0MNWwgnKE50+ePOlyHz5pfPI7d+4sTmiDBg2EyagiF6lp06aJP7CtcdwGUAi1B09o3m1Af1Th77o39IxdXNgo8pFoX2i2+tecwa9NDsbWsyXD39+PwsKCxb+zzOwiBXTL3r1lb6miIqJ9+5T7jBYtWggrBY9t2ROHG7sp2XAcl/K/WYywm1RaWprYj12gbC0/P1+IBLZEyAkSZxHDTJo0SfybLR1sFeGX9YYRGHJYu3Yt/d///R+98847Imbj66+/puXLl9OLL75Y7j7jx48Xas3W2OQkUPsHEc17Fw5QBrWvFzSPtNTsCPKxOmcP8rVaqGF2uCHuU1m1UXQ+8FeUSr6XzU3K1EHeZnLvMxipqWWTtXH8dcOGnvm8du3aiXiMnTt32pfZx6Ul/+Z4iKioKEpMTBRhBJzpydbYvYlDBViIsAVCjssWB3Rz7IUNx38bQmDExMQIkw/7nDnC83FxcS73mThxojAxsUmoZcuWwnTEgoOtFNZyblD+Q3HgjGMDGkEDgys0LzegOZIKQmnur12FqGB4+u6vXcVygdrXjBmuJ1mCRhtxHLZaGFlZeeX+DhseI2c+MzhJSUT/+U+xqGB4+t57xcuVYNeuXfTmm2/S8ePHxTy7Jn377bfUoUMH+zZLly6lP/74g86dO0dTpkwRcRc86GevHXZhYvclvrfYrYpjJ5hevXqJY82fP58uXrwo4jAcRUt5hISEiOOzBYUtJZwwiZMlVRRqoDuBwQqKldzq1avty/gE8nynTp1c7sPKjeM0HLGl+mJ/tWqh9g8jGgYXZkTtawbNZXtgV1M68MVgWv3t7WLK84a7V702iNfI9/WSO1d0SSYp/v3Oycn3cq8AcJ8HHiBiYwBnkeIpzytFWFiYcIni8S4P7jnOmMUBZ0S1cc8999Do0aNFiACnsuWgboZdoBYuXEhjxowRFo0mTZoIMcKwyz+nmuVsqvzCvn379rR7926xjmM3mjdvXmGaWg5T4P1YWLAXEMd5uIPmCu1xitqhQ4eKE8Nqjk8q+5dxVinm3nvvFSYitlAwnL6LM09dccUVIiBm3759wqrBy6udU9jxh8+IFV3NgEF/yEEl4H71CEn5oaLp5l414nWggyJ+ldXCqOlQfM80uCuk8VumOmyxUMpq4QiPYStLH8teO7Nnz3a5jtPQbty40eW6tm3b0q+//lpmOQsWbuXBsRxfffUVKYnmBMaAAQOEWYeDTTj4pU2bNkKR2QK/OcDF0WIxYcIEYTbiKQe5cNQ8iwvHYBlZ4OY2J0YcoJgB3K/mRYl7Vs4gXtY+yg3iNUWp7xUVHeGUSaphw0QyHRAYwORoTmAwI0aMEK28oG5HuMQ6F9njpkTgn6zgPxdYMFDVJ3iomxvct/qj9EBfzsBfD3jLKqGACLK5SJHZa2EAYGI0KTCMgFJCBegPiEvvcDTkLO2NyKHUnAjl3Hhw3+oPJe83b1klDC6CbEHepq7mzefCnQB3PVSFBx7ho48+IiMAgeEIMjcABZCK3LyGFEgPZ3Q+bLqLHrn+F1GrgdOpzl3The7/t4na3QLeFvIVDdLdGcR7y2VKy1YJNywZ0Q4xF6ZNVQsXKWByIDAcYXFhlJvaiG/HzAJEbqWWC5u4sFeZvv4X6nE40XMByd4GIlM5K6Fh4x6qL2g465PkBWuLY5A3XKSA2lQ7oyhQ5FxCYBgVowglIA8DC8y9kbmuq0xH5hpHYOAHURviS46FQeUMTlrA2UUqh0wJezi5ZcFQsjPmxJZJtLCw0F6tGrgHn0umKllaITAc0VJ+drUx8ADVFBj4Ok49Ey7cohxFhqgyfcYYVaYVxaD3Mce4VTvWiUWbES1D3ooBqYZwQpA3XKS0ACcBCg4OFplJ/fz8ytRMA9WD69rwueRzyue2MiAwgGvwcAMaHZwmnQ2huT91pke7/SosF6LK9E+dxXJg4PtYo9ej6nEcGqR0HQwz4m5WSiSKcR8uYRAfH08HDx6kw4cPq90dQ8AirW7duuLcVgYERhmTptqdMBB4WaBvNPwDd/+ORtTjYKJwi2qYHV4iLrTbX29wNDSf9kbmUGp2hPJiS6uDXq/Vr9DBwF+OJcND7lxhYfyG05cuXSoybxYpoAn8/f0pNTXV7toD3D+fVbUEQWAAzwGxBjwoMnkQDatFMR82302PdP/tclatH6+h+/9ubAyxqfWBvQcG/rKCsTUEv91kK0Z6+hnzuki5m5UScViKwQPiwMBAtbthOiAwPFRoD7gP6kkYAIhMr1gubOLCnlWr+2/U42CS9gSYp8bMMiwMsuI49BCM7bVaHhXvYxMYZnWRQgwGMDsQGECzQOwBGxCb5bO3Zk75WbW0JjCqIzg1Po7X7MBfI0RFRYhpQcF5On++kAID/dXuEgDAi0BgOIIsUsYDA1NDALFZPg0zXWfVapAVptvzJltQohK3Zty5nAO9cygxsRaZClgwgMmBwCjvgWDEHw8zgoc0sGHQe5qtFO/+cA091uM3e1atd364RnvWCznWDn2+vNddWlmP18LIzIXAkLM/ADoGAqM8cHMDgw9MTYeB7+n7/0qlHgcSaH9kLjXgrFp5Os6qpcT9huxS7qFATAZqYQBgbiAwHEGaWuDJgakR38YCzZCUEyxaMToUFzxQdzVYd2cQ7zWhoWGrhDtxHG6kvnVykTJjqlpYMIDJgcDQcBYpBLYaDIhXUBqIzmKq8qwzqrXAW6LBywHjzi5SOWQ2JMnNQntIUwt0DgSGhtGS2AHaAKLTYEB0ekdkecvCYFQRJON7RdcMs/8bLlIAmA8IDKNnkTLij52JgegEZHbhyW92LQb8vl4SQd4q4uecRcqEAgMuUsDkwEBvloccGppjA4Z079RTU+dESfJcmLyxjx6oxveKcgjyNnUMhpef03PmzKGUlBRRubpjx460adOmcre97rrrRNX10q1Xr172bYYNG1Zm/U033SSrb8BcwIJRGoy9ymLAl4WmByIDlIca1hBvVeLWAwap5eFowTCli5S7L3Nk7Lto0SIaNWoUzZ07V4iLmTNnUs+ePWn37t1Uu3btMtt//fXXVFhYaJ/PzMyk1q1bU//+/Z22Y0Exf/58+3xAQEC1+wbMBwQGqByMRYErDDi20wNHw/JpX3QeNcwMK0lHqzPxqQVRIGsQj5iM6ggaJwuGGQWGCsyYMYOGDx9O9913n5hnobF8+XL68MMPady4cWW2j4qKcppfuHAhBQcHlxEYLCji4uI83HtgNCAwNJxFykgY8k2j2cGt4nXmX7GXHr1to6jazdW73/32KrpvayppEjm3PAbx7sVKuJFWVmn8/GpQeHgI5ebmU5YZXaTYHc+dTFAl++bm5pYZ7LuyILAlYvPmzTR+/Hj7Mh8fH+revTtt2LChSh85b948GjhwIIWEOL+4WLt2rbCA1KxZk7p160YvvfQSRUdHy/xiwCxAYACvAOEGygPis2ocDc+3iwuGp4/dtpFu3J9ASbkarNpd+pY3yp9ZBylivUYl34vdpFhgmDJNrdW9UBzbvnXq1HFaPnnyZJoyZUqZ7U+fPk1FRUUUGxvrtJznd+3aVenncazGzp07hcgo7R7Vp08fqlevHu3fv5+ee+45uvnmm4Vo8fX1lfflgCmAwHAEhfa0jwF/o80OxGfV2Fsz1y4ubBT5SLQvMpcSs20F9owhImXFVxg1jsNrKXaVF0FcC+PgwRN05sxZslqt4o06qB5Hjhyh8PBwj8c/sLBo2bIldejQwWk5WzRs8PpWrVpRgwYNhFXjhhtu8EhfgDGAwAD6AgIQVISBxy8cc+FjZcvF5WW+Vgs1yLpcbwB4EbhzVerOFV0Sh8HiIjv7rFNchuFRKMibxYWjwCiPmJgYYVFIT093Ws7zlcVP5Ofni/iLqVOnVvo59evXF5+1b98+CAxg1p9jGaidOhTNOw0Y3wppwJaUHULvLL1KiAqGp3OWdhTL1e5buU2Jv2e195Fxj/Pbe60j53vJ8dFR8Fw4V/M2WRyGl3+r/P39qV27drR69erLXbBaxXynTp0q3HfJkiV04cIFGjx4cKWfc/ToUZFtKj4+vlr9A+YDFoxyfCY9EPMGtAJEBqgKGnzTfN/mVLpxbwLtj86jBpxFSouxF+XBt532TqkyzxMNuCNpglLfq3SxvVSN5iMwCpyidujQodS+fXvh6sRpatk6Ycsqde+991JiYiJNmzatjHtU7969ywRunz17ll544QXq27evsIJwDMbYsWOpYcOGIv0tABUBgeGIw1s3ReokGfD3AwDToKAQVfKFRVJ2sGjFaFQsVzTgljUg1/A+3o6VkJP1yduB6SV9jI6KsC/KMlmgt1JB3tVhwIABlJGRQZMmTaKTJ09SmzZtaMWKFfbA77S0tDJxMFwj49dff6UffvihzPHY5Wr79u308ccfU3Z2NiUkJFCPHj3oxRdfRC0MUCkQGJ5EB1Z3oAEgRHWf3WlfTB41PF2+RcGohZ1d4VHrrx6EhpbxstBwcpEyW6paTjPrzksKmSluR4wYIZorODC7NI0bNyapnM8KCgqilStXyuoHABAYCr5x8BRw1zI4GrzmQNWYf+U+erwvp44lEYA956ur6L4/GnruAzX+LKjOs0oXGZzkoGWrhJeJrhlm3hgMAEwOBIYO0KLoAdoDQtS7HI3It4sLhqcj+v5ON+6Jp6QcD8VGaPlZ4M3rj9+4WrwQ92BQS4a8In7VPxeOMRhZZhMY7iY60PK9DkAVgMAo80DQqE+zuxjwRxI4AyHqXfZF5TmljLXVpdgflUeJZ0JMKUplFYXm69aI4thrsRLaFUFmdpFiC507NX5QHwjoHQgMs4CHFagOGh2waIkGp13Xpah/OsxUolRNy5ksNys51g9ZykkHeNidy1YHw4xB3rBgALMDgVHqd0dmXJVpqO7vMtApEKSVknQmmOYs6Ugj+m8SlgsWF7OXdBDLNZvdSWnKG5wisFqFwHTtxWSUTlMLADAPEBigWkCAgepgdEE67PeG1H1XAh2IyROWi6QcW/pYE6AlMYDsUpp05woNDSI/vxp08eIl07lIiXcM7vxe4rcW6BwIDEeUqj4LKkdbL9qAhzCDIE3MDhaNMcHXrZ5wlBNfYdRBvAwLg7xgbO1YMiwWi7BinDyZZbogb8RgALMDgQHUAUIOVBdtjJlMj6atUjLEiaw4Dg0N4rVeJ8MmMOAiBYC5gMAoHYOBga+uMGLcJSgH3JvqI7uwc/UH8aiTYYzsUlElgd7nzl0QLSjIJBWgEeQNTI4sgXHzzTfTQw89RLfffrsoJQ+AWkAQArlAnMpDThImr2OyQbyW3bmioyPs/2YrRlJSLTID7hbuxW8bMKXA4NLxP/zwA9WuXZuGDRtGDzzwADVs6MHqtV5CukQklTwfMfgAwNgo/gNu4GeGy+ehrBSnfDCN18nQctYnb1UJV9BlyqkWRmaOaQQGAGZH1lNn3759NHbsWPLx8aFXX32VGjduTDfccAMtXLiQCgsLyUhvH9DQqtuASbEatHnkXEna3cfbyOmjnAcND/xVwLkWhoniMLR67wGgZYFRv359mjZtGqWlpdE333xDt9xyC61bt47uueceSkhIoFGjRtE///xDeq2DoWQD5kNtgYOm/6anXwhN9VdJZAkag54MOd+r5MJwqoVholS1hnoOACADtwzPHH9xxx130LfffivExtSpUykyMpLeeustatmyJXXu3Jk+/vhjOn/+PJkVT4gWNPM0YE7UFjjeGtzISsVpVEuGnJMuZ+DvZRHk7CJlHoEBgNlRzLM1Pj6enn32WWHZ4H9LkkTr16+n+++/n5KSkui1114jq9bf7gizpEWbDZgStQUOmjGaV9D44102RhU0cpDxvaJrhpnTRUpy0z3KoJcQMA+KpKnds2cPffDBB/TJJ59QRkYGBQQE0JAhQ2jo0KG0ZcsWmj17No0bN45OnTolhAaQAUQGcBcf/GKZleqIDFUyRMkJ5OYvVd3OGrZ+RfWDv2UV8ZNB6SBvs+CuuPfaiwEAtCYw2O1pyZIlQlj8+uuvwmLRpEkTISRYWNSsWVNs161bN3riiSfoxhtvFAJEywLDLH6PyJBlUiBSQXVFqIyBPz9DDfmMkfPF5OyjBxFUje/lGORtxhgMd/YHwHQCY8SIEbRgwQLKyckhPz8/GjBgAD388MPUtWtXl9uzRaNnz57022+/udtfoAB4cAElMOQg0sxowcLlrUrccqwfekCDtTwcg7xN5SIFgMmRJTDeeecdatCgAY0fP57uu+8+iomJqXSf6667jiZNmkRaxiwWDKXAANPc4F4xz70syyph2CJ03qqToYPzVwVBY6vkbbogb1TyBiZHlsBYtWqVqHtRHa655hrRgHHAABMoBcSqemjy3GMQ731Lhod822rU8KWIiBDKycmnLLhIVWt/AEwnMKorLvSCJFlEA97HYtGAewZQDfyYqkP1rRIygrG9XYnbWyhQ5VpLwdhuUcn3io6OEALDVBYMAEyOrKcW17Zo164dHT9+3OV6Xs7rOU4DgOqIOzQ0JRowBt6qkyHrc/SARooF2tykzpzJo6KiIjIDukkvDYCWLBgfffQR+fv7i6rdruDlQUFBNG/ePBo0aBDpBcRgGANNunwArwKR4VmLoZwYaVnB2HrAa+5cOsguVUmgN2ebzM4+KywahsfdGlbI+gfMKDD++ecf6tu3b4XbtGnThr766ivSE3hrYAwkhV+QGTHZDDAnrsS3W673cgbK/Iy1eMM1y8uDeG99nqxz7p10ueW5c5Wu5m0KgQGAyZElMDg9ra3ORXmEh4fTmTNnSFfwW0/bWwMtpGwEmgCiE3gCzQlXfvZp/bmH4G9dxI2U3scxVa1Z4jAQ5A3MjiyBwS5Q27Ztq3Cbv/76i2JjY0m3wDwJPIHWB3DAsMLVk4IGaWx1YpVQCcdie2apheFuPBjcPIEpBUb37t3pww8/FOlquUJ3aX744QdasWIFPfDAA6QnJKtFNKWxYFAJbEC4Ak9SzrOmOuJCFzXoYMnQhyWjRAQ5uUhl5VTvGAAA8wgMLrC3aNEiuuWWW2jIkCFCZCQmJtKxY8eEuPjss8+EixRvB4qFCwCeAOJV2xyLyqcDsXlUPz2MErNCPP+Brp413rxGZMRKIPjb+7ES3sYx5gIuUlXfHwDTCYx69erR8uXLaeDAgSKjFKettcFZIpKSkmjx4sViO12hgyBvzb9ZBF4F4lW7fHbdXnr6wQ1k9ZHIx2qhNz/oRIPXpqoiQOVlfdKNB442XZh0gKwaGzLORXTNUBO6SLkpMDQ+FgHAIwKD6dy5Mx04cICWLl1KmzZtEoHfkZGR1KFDB7r99ttFGlugPHjoAE8CAauc5cImLhiejnpgA12/PcE7lgyVrVvyYjJkZIrSgz+Xt6wSGhZBzi5SZhEYiMEA5ka2wGBYRPTv3180I+CpGAytAvcaUBoIWGXYH5trFxc2inwlOlA7jxIyPS8wyhtze23grwdg1vGaO1d0VITpLBgAmB23BAbQN2YSU0AdzCpiG5wMF25RjiLDt8giYjE8jaZe6HspsFoXcRwmLshnxjS1HA/l1m8sfp+BmQXG0aNHac2aNXT8+HG6cOFCmfUWi4UmTpxIegGF9gw0sAGawKwiNv50KL3xn040evgGYblgcfH6+53EckkDgo7dL6pbzduoL++RXcrz7lwhIYHk7+9HhYUXTZNFyt3xBMYiwLQCY8yYMfTWW29RUVGRU4A3iwrHf+tJYAD3wAMReAO9CNnBaxpRt78S6WBcLtU7GU4JHo690LS1yGuVuI058JcXjK0dSwaPBdiKceJEJlykADAJsgTG+++/T2+88YZIT/vII49Q3759adiwYdSzZ09at24dffDBB9S7d2967LHHSE9YJYtoQBv4VPMNKzAHehKy8ZkhojEetVxYiq1F1RUZiMlw083KW/UkvI0HvldUicAwi4sUgryB2ZElMP7zn/9QSkoKff/99+RT8kDh+QEDBoh21113CfGht+BvswV5a50ispjnDS8AColwb8Uuy/uc6g/i5Q383bBkeCuDk7fiK7x8LsqzttiqeZ8/X0gFBecpODiQjIy74wmMRYApBcauXbtEgT2buGAuXbpk/3fXrl2pV69e9Prrr1O/fv2U6SkAboIHNvA2ELXlwKfFolE3Kz3grSJ+Hgz0NrrAAMDsyI7B4JoXNkJCQigzM9NpfePGjenHH38kPYEgb2DEWABgPFFbFeEix2VKZK6p9j4YxLu3jzlcppxqYWTmUp06tcnIIMgbmB1ZAiMxMVFkkLLRoEED+v3335222blzpxAeevWZrG6GFWA+8AOgT45H59PB+FyqdyLcKzUplMZQwtZrg3iDiiBvFfFTAOdaGMbPJIUYDGB2ZAmMa665hn755Rf7/B133EEvvfQSPfzww6KK96+//iriMzj4W6/g5gZqAGHrWRbcsIfGPvqbqE/BdSqmv3sNDVrdSO1uaQ7UoNNZrISWKbkwok1YzRsAMyNLYHD8Bde+OHz4MCUnJ4uUtcuWLRPZpTiDFKeo5aDv1157jfSEJ4O84YsNqgKErWctFzZxwfD02Ud+o65bkzRnySi2suRQvRMRTn2TJUDluD15Ga8Fcht1EO+tbFZuWD9Ku0gZHSsX2nRjPOHOvgDoVmBcd911otkIDQ2ljRs30tKlS2n//v1CdNx22226c5HyJAgwBmoBcVsMD9gdK2szXATvUFyupgTGFzfsprGPOVhZ3rmG7l7dWBXrFtLYqiBoDGoKiq55uYq9GWphIAYDmB1ZAoNrXYSHh1ObNm3sy/z8/HSfMUpPdTBQIwJ4WtyKt+gJOVTvuPNbdL2ScixSDNgdRQZX2E4+HqGZFwDCylIiLuxWlkd/o67bkigxK9i+nayaFzLcnryOHHEi54vpIbBaDjIEjbwiftX/HLNZMBCDUQy/dJ4/f76YcnHm2rVrCxf6unXrUvPmzdXuHvAgsp6w119/vaiFAdQXQ2honmgLuu+mqz5YSANe+k5Med5xvR5hkfTqnM5CVDA8feWdzpoSTyzoyrOyqD7wp+q/iDckcr6YnH1YBBkIWx0MJjPL+EHegOjnn3+mli1biiRAX3/9NZ09e1Ys/+uvv2jy5Mlqdw9o0YLBCjQw0Hg5rN1946BnEFwMbJyIPkvjHv/V6S36+Md+pa5bkig+M7R4mU7vkwGrmtC1W5LoUHwupZwIF99HS8O4lGMRLq0sKScvD84c4b9Dda2ZBvXAkRfHoQuzjgy8FWRejYvJMcjbHC5SsGCMGzdOJAAaNWoUhYVddpHr1q0bzZ49W9W+AY0KDK7SvXbtWhHMbTHiw9mEGOFhBpThQHyey7foB+PyKO705R8JvYpcFhU2oaQ1uF+vzOksBB2fcxYXr757TbGVRQMvAeTFZBg0GBspdqtFTYcYDDO4SLlr7dXrSxxHduzYQQsWLHD5kvr06dOq9AloXGC88sor1KlTJ3rooYfo1VdfpaioKDICKLSnLNCe+qTe8XDXb9FPuH6L7shfDU/RpuYnqcPfcdR6X/UKaUHkFjPgh6bCWnQ4IYdSTkRQ4pnLsReKppWVLMa0XJp4EK+IJcNDtTVq1PClyMhQys4+S1lIU2sKuCDziRMnqF69ek7Lt27dKuqpAWMjS2AMHjxYXDgffvghffbZZ+LiiY2NLWPN4PnVq1cr1VegMyDW9Enc6VCa9nYXem7EL/a36P83u4tYXtGf9Jmn1tBXN+wl4seARNR3dSq9MfN6UpvSA292ATuYkCuElJYtGYlZJbEhWswCJicYW9Y+Ohj4eyndq6xgbI3BblIsMMxgwXA37b1WEk+4w8CBA+nZZ5+lJUuWiPGg1Wql3377jUaPHk333nuv2t0DWhQY7B5l48KFC7Rr1y7RSqM39yk9B7ACoCT9f2hKnTfXocMJuZR8vPJYBbZc2MUFYyExP/jbFtW2ZCiOw/h8UY9d9PwT6+wpYF9++1oa8EMTzWRzc3xkyouvkJFdyo2YDHkF+aofK+GtOhmyPseNz9N0ET8PCKeoqHDav/+4EBlFRUXk6+tLRgVpaon+7//+jx5//HGqU6eO+Hs3a9ZMTAcNGkQTJkxQu3vAw8j6WWEVWpXGF5Ic5syZIwr1cSB5x44dadOmTRVun52dLS7i+Ph4CggIoEaNGtF3330nOygLzVwNuIZFxVU7Eqr0lv+PFicuiwsbFqLNzU+SVmDLhU1cMDydMGKdWC4XpbJ2Kf0mU87gxLBZn3hwXe19DHoyvJTNiq0troiOjijuhiTRmTPy7zugD/z9/UUBZk5Ry8WY2eOFX0Z/+umnhhaXwA0LhidZtGiRyDgwd+5cIS5mzpxJPXv2pN27d4vAoNIUFhaKoHNe9+WXXwq/Pq4wzi5cAFQFiAz3ab8jvthS4HgqJaK2O+OqdH7/aphOf7Y4Se13cuxGrNv9YdFwKDFHZGWyCSR2i3KZAjY+t0rB666OqQSGiIOQlYGo1PVi6iJ+XrJKqIxzLYwciokpFhxGxEpuBnlX++bQLlzzghswF5oTGDNmzKDhw4fTfffdJ+ZZaCxfvlzEe3DKs9Lw8qysLFq/fr0o9sew9UMOEuGNNlAGQwwaqwGLgj4/NqKvu++xx2DwfFXEwpinfyqz32tvdpPdl8U9/qXnn3Rwg5p1Ld31Q1OXKWB9SgrtyT2mN68TfjZZvOAyRWwx0WLch7sg65PX40ZK7+OYqtbocRjuWsiNMBa5//77K1zP4zdgXGQJjKlTp1ZpO47BmDhxYpWPy9aIzZs30/jx4+3LfHx8qHv37rRhwwaX+/zvf/8TGa3YRWrp0qVUq1Yt4d/HgUXlmeA4boSbjdzcXLtbgc2arvXfB6BtjPDjUF2mz7iB7vm2BW1udpLa/RNHrffGVhgUzvyVmn5ZXDAWEvN8HN6/upyIOWsXAgxP2S2q8+a6FH86jF56qytNePJnsvpKQly8NKursF5Iso/pniXDaEIUaWzdxMO1KNQmyiFV7aGDJ6hTp+a6i9UEVefMmTNO8xcvXqSdO3cKt3auhQGMjSyBMWXKlArX8wPDViOjOgKD8yJz3AZnpHKE510FkTMHDhygn376ie655x4Rd7Fv3z567LHHxIVcXqXIadOm0QsvvKC42y4AnkbrYyoWBdURBn9yjIar2I1mJ2UJjEMuKmGzmDgcnyPEAFsdumypI+aTT0RUSSBUdkxviwtZteFkWCWMWoNO08HYCnDiXB4dzs+i5LBoig8K14QlI/tMLn3y2SqaM+cb+7KhQ/+PXpj6ET3++J107703iRS2RnvJ5I6LlBFeUn3zzeW/tw2Oz3300UepQYMGqvQJaFxgrFmzxuXynJwc2rJlC82aNUtYHdiq4Gn4YuX4i//85z/CYtGuXTs6duwYvfbaa+UKDLaQcJyHowWDsxx4OujXaG8rgfcxmvBt93ec69iNf+JkfddkdoMqsggBYIPn6x6PsB8vNiNUNMaq0DGri22sKsftSRfIiJXwVqYoXSCzFsWSYztpws4fyEoS+ZCFXmp9E/VPuYLU5Icf/qC7Bk6lggL2GnC+1g8eOEGjn3mHJk2cR4sXv0A9enYgowAXKdewVwqPv6677joaO3as2t0BWhMYXbt2LXfd7bffLqwJbdu2pb59+1bruDExMUIkpKenOy3n+bi4OJf7cOYojr1wdIdq2rQpnTx5UrhccRaD0nCmKW7exqgPDKB/1BrkttoTS3euakzf3LjbHoPB87xcDlyr48VZXWmigxsUz/NyGydjzgqrRMrxCKfl7hyzOqg5HpaXVlY3HjgaTffqHRF04nyeXVyIjyWJJmxfQZ1jG1RuyZBDFb7XD6v+pNvvnCg8GriVxrbs3LkLdPvt4+l//5tmGJHBGtudXGQGzWMm4KxSly5dUrsbQI9B3qmpqXTnnXeKit8DBgyo8n4sBtgCwcX5evfubbdQ8PyIESNc7nPNNdeIUvS8HStjZs+ePUJ4uBIXRquDoXQefmBO1BS/r7xxAw36tgVtaXZSWC5YXLhzVfdb0Yw6/1lXVMLmAG7HAoFLev5Dk0ZeFgpT3+pK/Vc2c+uYSgg5b9W80EdMhoxMUUb156qGCDqcf8YuLuy7SxKl5Z8RAuPEuVw6fDaLkkNjKD443ONF/LjWxV13vyhEhLUSUx+v54+/667JdOjwEsO5S5kVR08Rhq8FruzNiXuGDh2qWr+AzrNIsdsSp5aVc0Hyhde+fXvq0KGDSFObn59vzyrF1R85FS3HUTDsyzd79mwaOXIkPfHEE7R3715R3OXJJ58kM6A3QQTMRVUHzSwq5FotXMECoLSFgS0XNnHB8JTnu2yuW2VLhlyrBaMlVyivDfz1gLfMOh42BSWH1BRuUY4iw8diobohNWnJ4W00Ydv3l12n2vai/vXbKfPB5Xwvjrlgt6jSlgvfCF/yi/Gji6cvUlFOkZPI4O0//XQlPfFE9bwftAhcpIi2bt3qNM8vgTkRzxtvvFFphimgfzwiMDhD04oVK2TVomCLR0ZGBk2aNEm4ObVp00Ycyxb4nZaWZrdUMBw7sXLlSnr66aepVatWQnyw2OAsUtUFhde0ORgC+kVLAvggB2s7xFEw1pI6GLUzKq+D4Q2hJWesK8f64XW85FrkViVuLVOFcxEfGEYvtehxOQbDYqGXWt0k1tnEhd11auty6hyXWsaSoZQ7F4uKOe8uLRNzEXZlGMX0ixF/I/5bnf7yNOX9kee0zezZX9OIEX10n12KT6VbdTA0fku7E68LzIEsgfHJJ5+4XM4+dRxgvXDhQpH1Sa4Vgd2hynOJWrt2bZllnKZ248aNsj4LuAZCC3gTti4cTsym5GORVbYSVFcE87HLC9b2JFoe/MurrWHUmAz918noX6cVdY6pR2nns6lucE2KD4mgjZlprl2nzmaVFRgKuXNxjYsDB06UsVzYxAXDU54v2FNgt2SwMDmw/zhlZeXaq34DAEwkMIYNG+by7YLNFMrr7r77bhGDoSf0GIMBgN756qa/6YWn19jjIia/eT31XdG88h2rea/WOhUujl36s3j5ZUcNZYUBD95lxVfIGfi7EYbgNZcpWft4dxAvL5uVd9K9ViVWIj4ojOJDLguH5OBI165TwZHFfbAdu2TKTlTEdwSfg6LKLoySdT5Wh8hmK53NKyizJbtFlT6vPO8X7efkKsXk5Z3TvcAwq4vUFVdcUWXrE2cdBcZFlsCYP3++y+XsulSzZk0RqM1B1nqDf6BdJLoAGkfnlnQyu+XCNuBneMrz1/yZ7Fa8Q3mwcOFjpyVmU91KrCXuvGxwJSjkiAajBn/LwVBpbO3+L46Ky1r+oN1heSmpUOG2vCA+IFSkq+WMUmy5EK5TbW5R1npRitCQwDLLOOai9N+Q5y9mXiyzbVhYEOmdYhcp9/bXI7YEPQDIEhiI/gdaAqJQv7BblKu4iLSEbHutCqVxrIPhiUtHz4JXVuyyrBhpDcVKOFoSxHi/OD6AB/LFlHNC7IN6n2IRI6wBpbctRzR42cesf93WIl0tZ5SqGxblmbS1DkRHh1OD+vF04OBJu2cDWyk45qJ0DIaj9YLffNerH09RUZ7tH/Ac5dUfA+bDY1mk9Ju3WiM/ekAT+HhkCAps1D3qOi6izrHqJ4jQAlUZaMt5qS7HKqG7zK1WyX63WRwcdooH75LDwN9hnSM+l0VS8TFKio5U+CrYyNUGnGFRIYSFF4QdC4XHH7mdnnn2P07LOaCbYy7YLYotF6VdoxgjBHib2UUKABuyXqN8/PHHwg3q+PHjLtfzcl7P9SkA0DMsONE812qfDqOJb3YTooLhKc/zcrX7Vt1mKKw8MOcBUonrqLVk4G51HsTb56XLPiFO25b8u3i+ZKBvPwYfuEQn6NUfBJTLvfd0p+DgAPIpJWhYVJw/cL6MuODtePshQ3qSEcAzhaioqIhef/11UXKAiyVHRUU5NWBsZFkwPvroI1HELiEhweV6Xh4UFETz5s2jQYMGkV5Amlqg1zTAOxudpC0tj1PbHQnUYo/rqvdapc/3zemaP+pSWmIO1T0WQbGnPZsyViuWrqrGZNjiMNiKcfmVkG2wbnHw0pHsVgubxaN44F+Stce2p80SUuIOZeuHzT2qUjcpo9bDMDpyAtHdgIvlLf58At3ed5L42IqK7bG4YKvFkiVTUWTPQLzwwgv0wQcf0DPPPEMTJkyg559/ng4dOkT//e9/RSkCYGxkCYx//vmH+vatuBAO16/46quvSK9ZpLScWhLoHyWF7MSxP9C3Pf8tHkFKRLetbEovTu9BeqJ2RrhojB7vvJIxPPmU+Jvz28fiAX/x4N0uEuzzxZ4qjtdBsfu/g0iQERQOgJboeWM7+t83L4qK3lxEj3EsvGdzhQoKChDi4sYeV5JRcDdpjBFiCz///HN6//33qVevXjRlyhSRXbRBgwaiZhmXFjBLQWSzIktg5OTkiGxRFREeHk5nzpwhvYJ0tUAPsOXCLi4YC4n5Af9tpTtLBgC6RavZqjRAjxvb06F9n9OnC1bT7Dn/pQMHLrtWc0A3x1zce29PiogwluXC3bT3RhiDcLHkli1bin+HhoaKsSNz66230sSJE1XuHdCkwGAXqG3btlW4zV9//WWvvq0XvJGm1gCxa0BDbGt17LK4sGEh2triBDXbrb9U0XpDaUunLqpyA1BN2O3piRF30ojHe9Pan/+iHj3H0A+r3qDrrqt6zQS9UZyHzI0gbwPEYCQlJdGJEyeobt26wnLxww8/UNu2bemPP/6ggIAAtbsHPIwsh8zu3bvTypUradWqVS7X80W0YsUK6tnTGMFaSmIPmkRDU6C12Z5Y1qdIImq9I8Fjn5kek0d/tDkipgAA4BKHIn42WExElMRYsMXCqOICFHPnnXfS6tWrxb+feOIJYbVITU2le++9l+6//361uwe0aMEYP348LVq0iG655RYaMmQI3XjjjZSYmEjHjh0T4uKzzz4TLlK8nZ7QcyVvvPU0J813x9GtK5rSspsux2DwPC/3BP+9ZSe9/MxqeyXs59+4gXp/14LMBsZFXgTuR0CH2F7IuLO/Xpk9ezYNHjyYXnnlFfuyAQMGCEvGhg0bhMi47bbbVO0j0KjAqFevHi1fvpwGDhwoMkpx2lobHMDFZrHFixeL7YB30KswAu4z+ZWbqN83beivlseo9Y5EIS4c3x3+3fikcKVia4c7wiO9Vp5dXDA85fmOm1IoNqPyzE9V6QeEMgCVY7FaSfJiRihQfcwcg8HZosaOHSssGA888AB169ZNLO/UqZNowBzILrTXuXNnOnDgAC1dupQ2bdokgnciIyNFvuPbb79dpLHVG+zzaAS/R6XhswK0DQ/YXQ3ap4xbSctv+sdu3ei1ohlNeUWe6+KRJNdVt48mZlcqMKraD63/qEIAAQBA5cHdS5Ysofnz5wsPF7ZcsEvUsGHDqE6dOmp3D+ihkjeLiP79+4tmBNgkiXpPrvDcoA+eD57j78YnLg/qGQuJ+X7ftKbmMgLAk47WdFl1O/FYzQqFudL9UAOjiuxKa15ohJN0gQ7ROUqhIIqnIM9+GFyyPFpDo+0Vjehi4Wqv1uTQ4wtLPb/s5DpoHGfBjV9Es6cL10Xjuhgcw8tWjd69e5Ofn5/aXQUexEdudcbc3FyyugjiclzPUwAqoqT4L5oH2raWrjNMbWtxXNbxap0Ko3Gvd3equs3zvNyb/fB2A+qyxHKCrvfZSEMtf9H1tJGWSJfTnAKgVfDsKaZ+/fo0depUOnjwIH3//fcUHR0tLBkctwuMjSyBwSq0du3alJmZ6XJ9VlaWSFH78ssvkx59JtGM0cxO6woyTMnl9uUt6b93PUhznuwvpjyvRj+AOWDLxSTfvfZChjydSLvppHTeY595ougcbbxwWkyB8uzefYQ6dxlBu3enqd0V4EU4Y1iNGjXElGN1L168qHaXgBYFxrJly+iGG26gWrVquVzPy9kMxvEZAKiF2gJH7dZkVwLd8n2zy4N7icQ8L3fnuDGnwumKrXXFVM1+aF2gQuS6oJqvZQ9ZztnFhf0QRHSYLg/+WWxstJ5RRHQsOZdG153+kYZkraeuJ3+gxfmHyeOU4wlgVPILztHvv/9L+fmeE4laQAvPIC1w5MgRYcFgSwbHYxw/flxU9+b6GMDYyIrBYJ+666+/vsJtGjduTL/99hvpCQR5A6P55U+YdjP1+aYNbW9xnFrtTKBmu5xjHk7VyqMjSWeoztGaVLsKmaA81Q8AXJEiBZEPu4tYnN+KJZfEYSyxHqeJ0m4hOnyKiF6s0ZT615DnesEWiwm5f9kzsPF0QvZf1CUojuJreDjuAxgOM8dgFBYW0tdff00ffvgh/fTTTxQfH09Dhw4Vgd4sNIA5kCUw2LTlU0mAFpvBzp839hsKYG708gPQdFeCaIyjJPq21w6aPuYHe02Lsa/1oNuq4PKkdD+0hh6Eo1mIowCaWpRqd5NisfGipTHFWQKFxcImLhieTrz0L3XxjRbrq8vhonyn9M7Fx5To8KWzEBgAVIO4uDgqKCigW2+9lb799ltRdLmyMSMwHrIERsOGDYUqrQher7c6GEYKrAL6x5OJbNhyYRMXDE95nmtaeNKSAUB16S/FUxdrNB32OScsF/GW4sE+Z5UqKwiIDlsLKM63RGDwA72KN1Kyb4iwjjge04cslFyjuPK0YajGOQHqjSf0PBaZMGGCKMJcnhs9MAeyJGWfPn1o27ZtNGnSpDKZoniey8Hzer2lr5Uki70BoDaezIyUlnjGZU2LI7xcA5mbkC2q+hjFZ7s8S0ZHqklxdNkywSlrS/+ACfcpn2BZnxHvG0QvhbcWoqL4WBZ6KbI1rBcKk5IcRx/NH0cpKfKLfurJRcqdpldGjRoFcQHkWTCeeeYZWrhwocgSxVOOx+CUY8eOHaM1a9bQ/v37qWnTpjR69GjSKxAZwCiwteJonTOUdORynEXikSjXNS2O1lSxpwBUHXaDYncpewwGFcdgyHGPstE/qC519q9FadYCSvYPFaJDi5w4f5YOXcih5JAoig8M01XV8aiocLrnnhsNXwfDzBYMAGQLjNDQUFq3bh09+uij9M0339C+ffvs69jPrl+/fvTOO++I7fQE/0h5K5+HsR+tQCssu3U7vT72cpzF6Ok96NZlrYTQ4H+XXlfrVLjpIhAsqM6tW/r7JFAXSzQdls5Rsm+wW+LCBouKeL9gsrhwIzpxqYAOFeVTil8oxfuHVKkg3V8FmfTnuQxqH1yLWofVdrt/i0/8QxN2rxHxIcLK0vQG6l/Hc7FTSpORkU1LvlxL/e/qRrVqRardHcMxZ84ceu2110Q17datW9Pbb79NHTp0cLktF8C77777nJYFBAQgfhaoW8mbzV9ffvklpaen059//kk5OTkUGRlJ7du3FzUyQMWYKzEhUMtyYRMQDE95vv3v9YTAuGVZK/HvY0lnhOWClyl9XUJIA0/DokIIC4tnrc6L8w7RhKwtdmvJS7Xa0V3hFccZjj2ynr7OOWif71OzPk1P7iy7DycunLWLC4anE/5dTZ1rpWjekmHjyNFTNHLk23RVpxaGFhjuppqVs++iRYuEe9LcuXOpY8eONHPmTBFgvXv37nLHZeHh4WK9Y4IeAFQVGDa4oF6vXr3KLGf1zOp43LhxpBeMknvaB29kAREdq+M6zoIFhc1ViqeeDOrWspCG+HEPyUpkMclJPHHpnF1c2FPYZmyhLsGxFO/n2lLPlgtHccF8feYA3RPTmFqHxsrqx6Fz2XZxYYPn0wqydSMwzAL/ldz5JZaz74wZM2j48OF2qwQLjeXLl4t0seWNxVhQcNYnb8Axujt27KDk5GSqWRPuuEbHR+mLh4vr3X777VS3bl16/vnnlTw8qCJqFyhD00ZLSCuOs3CE5xOORKneN6UbAJ7k0KWzrlPYXjxb7j5/njvlcvnm/AzZ/UgJirQHodvg+brBxrUEmJ3c3FynduHChXJrT2zevFkUOXZ0Wef5DRs2lHv8s2fPigF/nTp16I477qC///5bsb4/9dRTNG/ePPv4sGvXrtS2bVvxWWvXrlXsc4CBBQab15599llKSkoSGaa40jcr4ueee47MlvUBDU0rLSYjnJ5+taddZPCU53m52n1TuhVJPpU2d4QIfwZQGRWjXlNqhLrIWGWh5HKsF0z7INcuKe1C5GfXiQ8IpZcaX++c6arpDd6zXpis6rg78DPDnZcmtmcOD8YjIiLsbdq0aS4/7/Tp02IQz14ljvA8e5SUVxCZrRv8Yvizzz4jq9VKV199NR09elSRc8Bu9BwHwnA9jIMHD9KuXbvo6aefxgtoEyDbRYqLqLC/H6tTVseSVPzw54tp+vTpQjXrzZfPKOkqjQDStCvDLcta05UiziKbEo9GUq2McLW7BIDu4FS1L0W1pQlZWy8HV9dqS/E1yk+J2zo4mvpE1CsTg9HaDYHB3BXfjLrUrEuHL+RS3aAIig+OID0RFhpMN97YnsLC5KUTNkvSGNu+R44cEXESjkHYStGpUyfRbLC44Ayg7733Hr344otuH59Fj8396rvvvhOlCxo1aiQqer/11ltuHx8YTGBs3LhRiIrFixcL0xoLC74o7733XnrkkUfoyiuvpBtvvNEzvQWmAUJPOaJPhYvGmPH9ox7FKr+v0dn7GcMhWSWnTFJ3haVQl6DadLiogJL9QlxnkSrF9DpX0z3RjWnzuQxqp1AWKSY+MJTigvX5siA1NYm+W/6q4dPUKgWLC0eBUR4xMTHk6+srEu84wvNVjbHw8/OjK664wikzqDuw9eSff/6h+Ph4WrFiBb377rv2F9TcV2Bsqiww3njjDWFKY/MWi4r69esLMxcLC/43wwJDz8Cf2zwgEB4AUF3YYlEVYVHaktE6FEXHbLAbT37+eQoJCzb0INPdor3V3dff35/atWtHq1evpt69e4tl7PLE8yNGjKhWEPYtt9xCSsDB5nfddZcQGOzRYosP+f3336lJkyaKfAYwgMAYM2aMCBgaNmyYMG9dc801nu0ZAB7ETEIyo1auyCiVeKSmx12kvPlZlQERCXRntlXA3HbifB4dPpdDycE1KT5Ee1aOv7bvp44dH6XfN71Hbds2IqOilItUdeAUtUOHDhXlArj2Baepzc/Pt2eV4hfCXBTZFscxdepUuuqqq6hhw4aUnZ0t6mccPnyYHnzwQVKCKVOmUIsWLYSbF7tH2dy7WFjqKcMo8LDAYPXJapgDuMPCwigoKEhkAzAS7qaVA8ARLUiYFbf9RW+NW2EvpjfylZvopm9b6/6zgLLw21IUHNQ/S47uoAl/r7ocJ9KyJ/Wv20rtbpkSNSp5DxgwgDIyMmjSpEkisLtNmzbCNckW+J2WliZeFNs4c+aMSGvL23LaWLaArF+/npo1a0ZKwYWXHWEhwyIIGJ8qO0Fy9P/EiROFsJg1a5aItWjevDm9+uqrdOzYMc/2EgAdC1a12qlauTSzZMDP8JQFAC/X82dV1oDxOEkXaCOdoZOECsMVWS5s4sJehG/nSjpxLq/8QibAcLA7FFshOJ0tuyJxwT0bnBqW65PZePPNN+3bssjgmhkcg6EUPD7kZEA22F0qOjpaZBzdvn27Yp8DdC4wuK7FCy+8IITG999/T3379qX9+/fT+PHjRQ5lPWaNKo27aeXQ0NRo5XG8zhmSXBTa4+VK483PAgagGmPbJZYTdL3PRhrqu52up420RDruyZ7plsMFLorwSVyED/egGiiRflvvcKE/TrPLrFq1SjQeP9500000evRotbsHtJZFikUEl57nlpWVRZ988onIKvXTTz+J9V988QVdunRJxGpce+21pCcc34Dq/9YGZoFFxunal2MfYkoyRsUfiSJLkcVp4M+uS7y8ImEiB29+Vnkg5sKYlotJvnvJWnIJ8XQi7aYuUhTFUaDa3dMUycHFRfgcRYaPhYvwaaRiMtfQMFHmKDVcpLQGW0VsAoPd69mC0aNHD0pJSXGyrABj4tbdHhUVJSo1ctYBTl/LgUHs38cmuOuvv55SU1NJr6jt3oKGVtXGsQ9D//sOjX9ngZjyPC/n1LRPTLvZqdDeiGk3i+VyPqciWNQ8Weqz+LNtYkcreEvsAGU4ZDlnFxeOxo/DdM58o7VK4GJ7LzW/0bkIX4ueFB/kpSJ8VaRli/p0/NhX1LJlcfZJYFw4roMDvBmOBbFlkeJMpJyxChgb2YX2SsMZC2xZC7hGBls1fvvtN9ITlbmcKA3euAJ3YcvF2+O/t1sOeDp7/PfU7vd6YnDf89vW4t/Hk85QwtHL1g05VHa19vi2NbUt9VneuMIhGYxLihREPvwm2OL8ViyZgkhrnLhYQIcu5lFKQATFB1Qvla1S9E9qSZ1jUijtXI6wXGgxi5SfXw2qVSvS8NYMd92cjOAi1adPHxo0aJB42ZyZmUk333yzWL5161aRuQoYG8UEho3g4GDhHsVtz549Sh/eUOBtKnCXo0muYx+OJWZTVHpxhV+e2v7t6bBOpT4L4hswcRRAU4tS7W5SLDZetDSmOIs67lEnLhXQoaJ8SvELdaqHsTj3AE049ae45nnY/FJcB7orqqFqlgwtV/fev/84jR79Dr0+43Fq0CCRjApcpIqDyNkdiq0Y06dPp9DQULH8xIkT9Nhjj6ndPaA3geEIl4Q3U95qALxNXDmxD7FHa+r6Wq5MfPvoSISYLQXsSZ8LdLjGeUqxBlKctTjvvTv0l+KpizVauEUl+wRRvEUd68XivEM0IWvLZRFRqx3dFV5PiA6buGB4OuHkH9QlLMF7lgyFamh4g5zcs7Rs+QaaOHmY2l0BHoYrg7sK5uYizcD4GNtGKbPyJlr1G1AHdkN6vFTsw2MajH0A3kNSUct8FZxON9baTPdH/U3dozfTV4HpilkyOlKkVwK7TxSdo40XToupfdmlc3ZxYRcRGVuKLRqFZ8uIeQ60PlxYTnpYI8AB26BKFgx3mhH49NNPqXPnzpSQkCBS4jLsSr906VK1uwb0bMEA5gEiQz26L72C2mxoQCeSzlC8F2Mf1MBMlgA9Wi6mRBxwyvg0OWw/XXMmUhFLhjdYci6NJuT+ddlKEdmG7gqrR4culSMiLp6lFP9wsa3jeg6wTvbXVnA18C6IwSB69913RdE/Tgb08ssv2wO7IyMjhci444471O4i8CCwYLhwkULTbgOuYVHRcksyLBfA+5TcmOwWVSbjk4UozVcfxfHYYmETF3YrRfZfwnqRUiO0zI+lEBEci1EjmF6q3d45e1PclRTvF+z17wCAlnj77bfp/fffp+eff558fX3ty9u3by+yjwJjAwsG0BUQGeYEb0K0T/KlwLIZnySiukVVc2s6ablAh3w4diOI4itwhZKsElk8EG9wuCjftZXi0lm6KrAWvRTVliZkbRXLhIio1VaIC+au8PrUJSReWDSSA8IhLiogMSGGpk9/hBITY8jororuuDmp6eqoFFyY2VVl8ICAAMrPz1elT8B7QGA4YCS/R+AeOomXBEAzsBvUlJz6djcpFhcv5DWoknvUlzVO0iT/ffb9pl5Kpf7WePImyb4hrl2dahRnvrkrLIW6BNWmw0UFlOwX4pRFimFRIYSFwdOvuktsbBQ9/VR/w58nd63uRniZVq9ePdq2bRslJyc7LeeaGE2bNlWtX8A7QGAA4AIITW0Aoacv+hbE0jWFNSmtxnlKtmWR8qnccmETFwxPJ9XYS10KuVq392I34n2D6KXw1jQhd/tlK0Vka4r3udwHtliUFhagepw5k0erV2+hG25sTzVrGjdOxd0EKEaIaxw1ahQ9/vjjdP78eVFcb9OmTfTFF1/QtGnT6IMPPlC7e0ALAuOTTz6R/QH33nsvmSUoCwA58FUHgFFgURFXGECWKr6gPuTjolq3heiw5ZxXBQbTP6gudfavRWnWAqpbI4QS1HB14gxNBn67f/DQCbp70FT6fdN7hhYYgOjBBx+koKAgmjBhAhUUFIiie5xN6q233qKBAweq3T2gBYHBRfMsluoNvFmt8j56EhgAqAFErTzRxbUy9FILA5QPx1y4it1IloJUKdPOlgxTxVB4W9AYIbigCsBFqph77rlHNBYYZ8+epdq1a6vdJaAlgTF//nwyA4jBAEYHLkdAa8RJATS1sGGZGAxvWy8AUBJU8nYmODhYNGAeqiQwhg4dSmaA34jaKgjjzSgwIlr/0dK6AILVxDP0uxRHnYtq0mFfjt0IongfzxfUk4MorFeUTymcnhaxGABUSHp6uqjkvXr1ajp16pTwbHHEVhcDGBMEeZeDTWgAAOSDwbh7cKCnWYoLsiUjzqpNYcEszjtkr+YtivDVakd3hddTu1u6IyjQn9q0aUhBQca2UPFd686da4S7nt3r09LSaOLEiRQfH19tV3ugbyAwFHwgyAW3HDC7UIcQcZ9T/hfoWGAB1TkfRLGX5A3eJCtVOTjbTHCxPZu4sBfhy9hCXYJjKd6vOI0tqBpNmybTH5veM3Qg+2UXKYthrc1V4ddff6VffvmF2rRpo3ZXgF4Eho+PT5WUKG9z6dIl0gtGj8HQuvsJMCe4LN3n21on6NV6e+wxDOMPpdIdWXGkdTxVNK9c+AEv4/MOXTrrugjfxbMVCwyZnycbKESgIerUqVPGLQqYB1kC49prr3UpMHJycmjv3r2iQmPr1q0pMjJSiT4ChTCyeAL6A4JXGU75n7eLC4an01L20lV5NSn2orHdULxFSo1Q10X4zG69kJGBauu2fdS5y5P0629z6IorUsmowEWKaObMmTRu3Dh67733KCUlRe3uAD0IjLVr15a7jlOR8QXFlRpXrVpF+gJ1MJQE9R0A8DxHAl3XkTgacA4CQyHiawTRS1FtaULW1stF+Gq1FYX3QPXgN9qFhRcN/2YbWaSIBgwYIMaEDRo0EBmk/Pz8nNZnZWWp1jegwxgMvohmzZpFV155JY0ZM8Y0KW5BWSDWgCsgPJWFYy5c1ZFIuhCkSn9O+lygwzXOU4qtkrdBuCsshboE1abDRQWU7BeCLFIAVMKbb76JwG4T47Eg7y5dutBnn31GesLoMRhGBy43wIzULgykZw82KhODoYb14qvgdJoSccDejxfyGlDfwlgyCmyxgLAAVQGF9oqzSAHz4jGBkZGRIao2AuAtIA61DQSg57gtI5465kQ5Z5HiEb6XLRc2ccHwdHLYfrrmTKShLBkAVAX2AHPHC8wIHmS+vr504sSJMtW7MzMzxTLUwTA2igsMq9VKn3/+OS1atIjat29PZnrjAMwFcrUALVG7MIDiLvoXz6gg5tgtylUsSJrveQgMYKdpk7q0bes8qt8wkYzuImx140Y0gotxeXE2Fy5cIH//kmcVMCyyBEb9+vVdLueUtFyt8eLFiyKYZ9q0ae72DwDNAjFaNSDEzEHypUCXsSB1i7RbPA94Hy6w17x5iuHrYJgZjsNlOP7igw8+oNDQy9nW2Gqxbt06atKkiYo9BJoVGGylcBW4w6KiRYsWIsB7xIgR1Lx5czKTSRMAT2PUeDkuSOXtYnu2isxG/Y6S1UIWL7pJxRYF0JSc+mViMDxmvfD+H9C7fn5e/7zqp5yVw+HD6fTytM/p+Qn3UnKy9mu1yMXMLlIc3G2zYMydO1e4StlgywWnrOXlwNjIEhiHDh1SvicAAEP96BhVDIHy6VsQS9cU1qS0Gucp2ZZFqopj1pOWC3TIh7NPBVE8wephVDKzcmn+/O/pkUd7G1pgmDnI++DBg2J6/fXX09dff001a9ZUu0tAqwKjbdu29Mgjj9BDDz0k5j/55BNR+r1Vq1ZkJKxu+kwCYER8kFZWF5zyv+Ac5K0SLCriCgOqVVD6yxonaZL/PrvlY+qlVOpvjfdkNwEAHmbNmjVqdwFoXWBs27aNTp486ZR6bMqUKcYTGA5papHxBoBiqiu6IUi8z7e1TpRJU3v7aX0M0NlyYRMXDE8n1dhLXQqjKI4QHA70iVkL7Y0aNYpefPFFCgkJEf+uiBkzZnitX0CjAiMmJoZOnz5NRofvZ9s9XaTCzQ1NA/QOhLn3OeV/3i4uGJ5OS9kr0tbGFWk/U8shH9eVyA9bzkFgAEOMJ+Tur0e2bt0qEv3Y/l0eKMBnfKokMFq3bk2ffvopJSYmUnx8vN2qwa5SlXHvvfe630uToNcHCgD4qVCPI4GuB+hHA89RXL6/5it5c8yFq+xTyVIQLiy9UI2A9NjaNWnsmLspNhZ++UZ1izpw4ABFRETARcrkVElgvPLKK3TLLbfQ+PHj7apz6dKlopUHZw/gbfUkMMxWyRtvmwHQPxxz4WqAnnQ+SBeVvOOkAJpa2LBMDIahrBdeytCk2udVg8TEGHr55Qc12z+lMKuLFJOamupUYG/AgAEidW1sbMXPAmBCgdGuXTvat28fbdq0iY4dOyZiMO644w7RgH7R8wMMaAOIVPWpXRhIzx5sVCYGI/aid6t5u1PJu9+lOOpcVJMO+3L2qSCK9wk0VgpYYCcvr4C2bN1Hbds3obCwYDIqZk5TW7rA3nfffYe6aCakymlqw8LC6IYbbhD/ZoHBWaSGDh1Kxksrhx8dT4MgYACU5baMeBFzoVQWKclK1coCxaS5U8lbImGxiLMiPa3R2bvvGHW/8Rn6fdN71LZtI7W7AwDQWqE9AOQCEad/IBK9S1WK9NUuDKC4iyUxFyrcYnWrUMmbM0ZxfEZyUZCxXKAAKIWZ62Cwe3zpIG4EdZsPWQLDRmFhIf3444+0a9cuys/Pp4kTJ4rl58+fp9zcXJF9ykdHfpao5G1s8HwDSsHPCVxPzrCVoqJK3l/6p9PkkP2X151vSP0uwicbGBMzx2CwixR7ugQEBNjHhFxLjVPXOsJF+IBxkS0w/ve//4nCexkZGfaAbpvA2L59O3Xq1Elknho0aJCS/QVANhCP7oNBNZBTyZvjM2ziwh6fEbiPOl+KRNVuYEjMmqaWKe0+P3jwYNX6AnQmMH777Tfq16+fSFn71ltv0caNG+mLL76wr+/QoQM1bNiQvvrqK10JDHdNmgC4Qj82PH3CP8TQPdrBVSVvDt52GZ/hc57iJQgMM+HnV0NkkuIpMCbz589XuwtAA8i6w7lKY2RkJG3evFm4QWVmZpbZpn379vT7778r0UcAdI0RRCtEEnCH5KJy4jM4qBvqUFNYrFaSPOja3LJFPTp0cBHS1FZhfwBMJzBYOLAFg8VFedSpU6fCOhmaBDEYwIB4262Jg/gRBA5KWzVeyG9QJgaDa2BAYHggpRdQHYksormzPwB6RtZT68KFCxQeHl7hNtnZ2boK8AbAqNiSF8hpAChFv8JYWp3djj4+24JW57bXb4A3Xi27xY6dByml3gDaseOA2l0BAGjNglG/fn36448/Ktxmw4YN1KRJE9ITiMEA4DJ4PQCUhi0WcZdKYi6UekGLonm64uLFS3Ts2GkxNTKSm1oUMhaYUmD07duXXnrpJRHIc99995VZ//rrr9POnTtp+vTpZCafSQCMAMZqwC0w4AcAMRjA9Mh6STlmzBhq2rQpPfjgg3TjjTfS6tWrxfKxY8dSly5d6NlnnxWVvkeMGCG7Y3PmzKGUlBQKDAykjh070qZNm6q038KFC0XK3N69e8tOK6d2AwAApYCrG/BqvAgAAMi1YISGhtIvv/wiBMTixYupqKjIbrngwf1dd91F77zzjr3ISnVZtGgRjRo1iubOnSvExcyZM6lnz560e/duql27drn7HTp0iEaPHi1Ejp7BeACoAd45A2AwYE1SDTPXwQCAkZ2IumbNmvT555/TrFmzRDxGVlaWCPy+8sorKTbWveC9GTNm0PDhw+3uVyw0li9fTh9++CGNGzfO5T4scu655x564YUXhPjhIPPqYpUsopkZHwseawB4EkmykAX3GTApqQ0T6cdVb1BqahIZGbhIAbPjdqWb6Ohouummm5TpDREVFhaK+hrjx4+3L+NsVN27dxeB4+UxdepUYd144IEHhMCoLAsWNxu5ubkK9V7/mF1gmRGISgC8BCwKFBYWTF27tjF8HQwAzI5sgWG1WsukoWUBsGzZMhE3wdaHpKTqv6E4ffq0sEaUtoLw/K5du1zu8+uvv9K8efNo27ZtVfqMadOmCUtHaRADoT7m/ukFagCLQvmk+16gw/7nKfliIMUVyXN51TQY8HsdziD1zrv/o8dG3EmJibXIqEgl/7mzPwB6RtYrhKeffpqCg4Od3JC+/PJLEfvAg/fJkydT27Zt6ejRo+Rp8vLyaMiQIfT+++9XWPjPEbaO5OTk2NuRI0c83k9QNdQOsDdTA6Aivg5Pp5tSttDwxH/opuQt9HVYutpdAgYg/dQZmv7aF5SefobM4CLlTgPAdBaMNWvWULdu3SgyMtK+bNKkSRQREUFvvfUWnTx5UgziOeibA7SrA4sEX19fSk93/jHj+bi4uDLb79+/XwR333bbbU7WFaZGjRoiMLxBgwZO+3DwuasAdNzUAC8zASBKr3GBXowtrrrN8HRqrQN0zflIz1oyYFEABgFB3sDsyBIY/Ma/a9eu9vmDBw8K9yW2XAwePFgs4ziIFStWVPvY/v7+1K5dO5H61pZqlgUDz7tKe8vF/Hbs2OG0bMKECcKywWKnTp06Mr4hMCtmEJgYv4HKSPM/bxcXNnj+iN95Y7pKAQAAUF9g5OfnU0hIiH3+559/Fulpb775ZvuyZs2a2etjVBdOUTt06FBq3749dejQQVhB+DNtWaXuvfdeSkxMFO5YHO/RokULp/1tlpXSyysDFgygBcwsAIz2Avt0wDk6HlJAiQVBFHMhiPRC3cJA8uHnocPfgufrXCypwg2Mj9FuRi+DLFLA7MgSGAkJCcL1yAZbKrg2BlseHDMzya2DMWDAAMrIyBBuV+xuxUX7+DNsgd9paWllAswBMAqe+mHBWMG7/JCYRnNa7CBOzMYx5E/804JuPq6P1JyxlwJoYnoDu5sUi4tJGfVhvVATdv01wO9edFQ43XffzRQdHU5GL3DpTpFLFMgEphQY7B71xRdf0OzZs4UF4euvvxbuTBw74RgbISeLlA12hyqvEvjatWsr3Pejjz6S9ZkIfgVGBdrC+5YLm7hgeDq72U5qnxlDtS7owwrQJzeWrimIpLSAc1TXlkVK/+NboDLJybH0n/dGG0IsAQAUFhjPP/88/fe//6WRI0eSJEnCXWrKlCn29Rz/sG7dOho2bBjpCbhIAaPhrtWC36JZoE6qzfGQfLu4sMGWgBPB+W4JDMlqIQubE7xoyYi1+uMacAVciGRx7twFOnAwneo3TKSgIONaxDjVjNXN/QEwncBo2LAh/fPPP/TVV1+Jec7glJycbF+/d+9eevjhh2nQoEGkJxwtGPjZAADIJSE/RLhFOYoM1gXxBZdj1wAwozvXv7vSqONVj9Hvm96jtm0bkVFBDAYwO7IL7cXHx5frwsQ1MLjpGdzbQM9AIKsLB3Q/vrMlvdNihz2GYcQ/LRRxjzrlf56OBRVQ0vkgikXQ9WVgUQAAAP0LDCMiaeytAX4rAdA2VslCPuVUAe9xrK6IuTgeXECJ50qySLlZMXxZ7HF6PXWXXbSMPdCYbjsV79YxAQAewM0gb7zlBKYRGPfff3+F6zmrExfaa9WqFd15550UHm7sDBHeQEtiB+gDiFJtwaKCm8VNYWGzXNjEBcPT6fV3U8fsmiJWAgCjZ6DSE4jBAGanygKjqpmZuB7GU089RfPmzaM+ffqQnkAWKddgzAqA+hwNOuey+N3RwHMUe1Z7AkOyElkwpgUuxgj+/n5iCgAwLlUWGGvWrKlwPVfbPn36NK1fv16ICw7w/v3336l169ZK9BOoCESX9sFPtfFJOhfksvgdx2IAnWFi9XVFm4aUf3aF4S0qqIMBzE6N6tS+qAr9+/cX7lRXXnklvfHGG/TJJ5+QXkCaWu0DFyAgJx7CCNQuDKTRe5uUicHg5WIGAKAZ4CIFzI5HgrxbtmxJt99+u6iFAYCSQAA6A8FlLm5NT6COOTVlZZHydg0NAFzx77+H6d5hr9Ann06gpk0vp7c3GlwjjJs7+wOgZzyWRapRo0b0v//9j/QELBigOmBwD9SALRaxF0tiLnANKgOCoL3mznXufCFt27ZPFNwDABgXjwmM8+fPk7+/v6cOD4DqqClGIW4AAEC7oNAeMDseExg///yzqPitJ5BFCugBaAsAADD2eAJjEaB3FLcJczapF154gbZs2UK9e/dW+vAAAJ0AF+KKwRvK8pFwcsoH5wYAYKZCeywsMjMz6Y8//qCMjAxKTU2lp59+mvQEYjCAloFbFABA79RLiaMvFkyievWMXYEeLlLA7CheaI/jLu655x6RojYsLIz0BFykgBaBrgBAw5i4poWc4PmaNcOoX7+uhg+qh8AAZkexQns+Pj4UERFBjRs3poAA7VWVre4DAW+LAQDgsrsbCi8DJUhPP0MLFv5Eg+65kWJjo9TuDgBAL4X2jALeHgC1gcj1/j2Pcw6AZzl2/DSNHTuXul53haEFRrFHhBt1MBTtDQAGyiKl17d0Wg1MxdtDAAAAboF6H14DLlLA7EBg6AStCh+gHzGJN/TAFMCfCwAAVAcCwwFrSQNVA+/BAFAHSbKQxYK3DgAY1SMCLxWB3oHAALKBGFMWCDYATIYJzYoR4SF0a69OFBERQkaG4y+sbsVgQGEAfQOB4QDqYBgDk/1eAwCAbmjQIIG++eYlw8eCwIIBzA4EBjAcehOJEERlkchCFrzBM46p09hjSXOgUID4xYuXKDungCKjwsnPD0MQAIwKHvsuCu2hoXmqAQCAmdmx8yAlJPalHTsOkBliOt1pAOgZvD4AwIs4igwYLozB6cBzdDLkLMUXhFDt84Fqdwe4AzJQAYWQJEk0d/YHQM9AYJS6oa24qYEX8MEgxhCsrnOY5rb8iyQLESd1enxnS7rxaF21uwVMgMVqJcngcQwAAP2Cp5OLoCw0NE81p+tNrQsdKEJm4Dm7uGB4+k6LncKioWmsELfA+2LIrElj3GlymDNnDqWkpFBgYCB17NiRNm3aVO6277//PnXp0oVq1qwpWvfu3ctsP2zYMLJYLE7tpptuktc5YCogMAAAQAYnQvLt4sKG1SLRieACtbpkPMw3LgUGgVPUutuqy6JFi2jUqFE0efJk2rJlC7Vu3Zp69uxJp06dcrn92rVr6e6776Y1a9bQhg0bqE6dOtSjRw86duyY03YsKE6cOGFvX3zxhezzAswDXKQcQGAV8BRQ8sYjPj9EuEU5igwfyULxBcFqdgsATdO6VX3KPP0/CgnDfaI0M2bMoOHDh9N9990n5ufOnUvLly+nDz/8kMaNG1dm+88//9xp/oMPPqCvvvqKVq9eTffee699eUBAAMXFxXnhGwAjgXGPA+W5sgBgdiC8yxJ9Poge2dGafEpcjlhcPLazBcWcD1K7a0AN9JYfWyV8fX0pPDxETI2MyBzojkttyXFyc3Od2oULF1x+XmFhIW3evFm4Odnw8fER82ydqAoFBQV08eJFioqKKmPpqF27NjVu3JgeffRRyszMdOPMALMAC0Y5QGQAJUAst7G54UgyXXG6Fp0Mzqf4c8HIIgX0gYoVxPfuPUojn55Db80aSampSWRU5Lo5Oe7PsNuSI+z+NGXKlDLbnz59moqKiig2NtZpOc/v2rWrSp/57LPPUkJCgpNIYfeoPn36UL169Wj//v303HPP0c033yxEi9FFInAPCAydVvJGcTYAXMO3sDdvD7ZYcLOwvxRC9wGokLyz52jVqj8pLw+xSlXhyJEjFB4e7uSu5AleeeUVWrhwobBWcIC4jYEDB9r/3bJlS2rVqhU1aNBAbHfDDTd4pC/AGEBg6BS9CCGzAgEIqookcWYW3NAAGAlHNye5+zMsLhwFRnnExMQIi0J6errTcp6vLH7i9ddfFwLjxx9/FAKiIurXry8+a9++fRAYoEIQg+GA2lWejdYAAMZC0lEwjoS3MMBEWaT8/f2pXbt2IkDb3gerVcx36tSp3P2mT59OL774Iq1YsYLat29f6eccPXpUxGDEx8dXq3/AfMCCATyGGX/eYbgAAFQbrhOhl6J5euqrinDRXrdiMGQEgnKK2qFDhwqh0KFDB5o5cybl5+fbs0pxZqjExESaNm2amH/11Vdp0qRJtGDBAlE74+TJk2J5aGioaGfPnqUXXniB+vbtK6wgHIMxduxYatiwoUh/C0BFQGCUicEw47DY3KCqNgAAeIc6SbXorbeeoDp1aqvdFcMxYMAAysjIEKKBxUKbNm2EZcIW+J2WliYyS9l49913Rfapfv36uQwkZ5er7du308cff0zZ2dkiAJzrZLDFw1OxIMA4QGAA06OEqIRIAQCAyqlVK5Iee7S34a0gUsl/7uwvhxEjRojmCg7MduTQoUMVHisoKIhWrlwpqx8AQGDoNIsU0A4I6AYAaIbAQ0Qh+4nOpRKdTyGtkZWVS9+v+INu7tWJoqIqD17WKzyUcCdkCUMRoHcgMBxAcDKoDhZzprGvNvwjq6d3lRJZyKKTJwEb3/RiPOMAcYteLgRd3WAOMRFJn5Gl5u/imhBZjLKvIjo5jDSD1UqHDqfTsPteod83vWdogQGA2dHL4x4AAAAA5RF0yC4uGJ5aIjcWWzSA4bNIAaA1YMFwAC5SoCro5cUmAMBEBB8oY80SloygfZp0lTI6kuRmDAYSzgCdAwsGAAAAoHcK6tuLs9kQ8+caqtUjAICJgQVDwawPwNiwZz4AAGiScykknelIVDoGQ2PWi5DgQOrYsSmFhASSkXHXzQkuUkDvQGA4IDm4SMENBgAAgK4K0R0dTFLmtSSFHCi2XLC48NFW5H3jxnXo119mGz5NLQQGMDsQGOWAWAxgA2ITGBE9ZaDSVVYntbkUQXQ+qXgKAAAqAYHhgDAp61RY6GagAICBsZKFfPDmEahFzQ1kSfqCLBaJJMlC0vF7iHI7k5bYsnUvdbzqMZGmtm3bRmRUbLmg3NkfAD0DgWEQ9CqMtAxEGwBAN/idsYsLRkwTFpBU0JzoUk21e2c64CIFzA4EhgP8vgDvDLyLsb1wgbvAMwaAKuKfYRcXNiwWK0n+pzwjMNSMNdEBEBjA7EBgAFXRoqDDTyYAoNoE5hKFZRPl1yQqjPS+oi6sJdyiHEWGJPkQFdaWdzwAAHADCIzShXHga2RqLCbwi4JVAJgKb1zwyX8RtVlhj32g7bcQHbuCvMrFmiQdvZsoaWGx5ULyIen4ILhHqYS15D939gdAz0BgOIBK3uYFA24AqgGPfWDqKyYwzy4uGCEyWn5HlNmA6Hy4d/typhNJZ5uRFJBBdDG2WFxo7O/UrGky/fv3R5SUHE9GRuLrwOJOkDcGI0DfQGAAoABWSSIfE1g/gOco7d4CdEJoVtnYBx+JpOAs7woMm6WGRYUQFhpTFiUEBvpTw4aJRL4YfgBgZHCHKxiUBfSHD6pzAwDc4WxU2dgHq4WoIErVbmmVgwdP0OQXPqYXpj5A9eoZ14ohuTmegAUD6B1tvuIAAAAAVEaqis/s+TCitOb2VOFieqyF992jdMKZ7LP0xRer6cyZPDJDDIY7/wGgZ2DBcIB/FxDjbQ7gzWR8rJKFfOByBLwRg1H3b/szhadS4k6ivddDZAAATAsEBgAAAO9htDRmWonBcAfUtFAcVPIGZgcCwwHEYBgfxFwAABQFMRjABVaLVaQLlr0/BAbQOXhl4QC7R9kaAKAsuDe0iRjQAnXgGIxtN9n/BmK64xaigjC1e6ZJ4uOiaOKEIRQfH612VwAAHgQWjHLAQMpYIOYCAOAxDrcmSq9HkruVvE0AC4tJE+81fJpatkBYUGgPmBhj3+HVxIguUnAJAgAAL8DxFoUR5vMNqGb8Rm5uPm3c+A9ddU0rCg8PIaMCgQHMjpkeg6YWTWZtAAADUZW0sUDT7Nt/nHrd9hzt23eMjIwSv2AA6BlYMEpVY8agVNugWjbwFkhzC1QDWZ0AADoHAgPoTgRWBQgRAAAAamGlIrJQkVv7A6BnIDBKF9pTuxPAbfQmLYxWFgDoC8nKdRvI+OBGA15EEv+5UwcDoxGgbyAwHCh+HOCm1isIaNc2GN8B4AU0rhgDAvyoQf0EMQUAGBcIDAAAAN4hPJ8sMXkkZYUTnTVuBiFQPs2bpdCufz82fppaFNoDJsfYd3g1kaRioybQFxbEWwCNwaFCuCyd8Wm/j2r02ShervNL9qL/XUXWLalqdwsAD8ZgyLckIQYD6B3t2lEBAABoGhYKVSI83y4uGJ763v67WK6brE5AEbbvOEDxif1o+/b9ancFAOBBYMFwALUT9AViLgDQB+wWVToswOIjkSUqj+hsqFrdAiqkx710qYhOn84RU2Pjbi0LiFqgbyAwAAAAeBTpQo0ybmM8LxX64jUBMCRWiQWUj5v7A6BfIDAcgAVDH8ByAYC+sARcKhOTwvMWfwyiAADAiEBgOFAc4l0sMCwYxOq+IJ8Ziu2ZJfWr1UQBY0asIC6dDiuTPVWyWkjKCjP+k9Y0N6lJvmcVKX5d6U4dDLhIAX0DgVEOyCalPSD6ANApuSF06eurqEaf30XsBYuLoqUdxXLTKEcgaJSaROt+fosaNapDRkaiIpLcuLh5fwD0DASGiV2k4GoEAPAW1j8b0sV98WSJziPpTDjEhUkJDQ2iTlc1M34dDGGBQB0MYF7weDcxNkGl9QYAMAi5ISQdjCsWF8CUHD2aQaPHzBVTAIBxMfYrhGqCAa32gJUFAHPHb7hCskpkMYO/vwHjGk5lZNNbs76iQYN7UFJSLTIqxRGd7sRgGP8+BsYGAsMBCAztAGEBlMKAYzQAgMaRJI7BsLi1PwB6RrMuUnPmzKGUlBQKDAykjh070qZNm8rd9v3336cuXbpQzZo1RevevXuF2wMAAACGBtXHAQAqokmBsWjRIho1ahRNnjyZtmzZQq1bt6aePXvSqVOnXG6/du1auvvuu2nNmjW0YcMGqlOnDvXo0YOOHTtWrc9VP9oADXgP2OqAaU1aAHgYqwL/AaBnNCkwZsyYQcOHD6f77ruPmjVrRnPnzqXg4GD68MMPXW7/+eef02OPPUZt2rShJk2a0AcffEBWq5VWr17t9b4DAAAAwDUx0RH0yMO3UUxMBBk/Ta17DQA9o7kYjMLCQtq8eTONHz/evszHx0e4PbF1oioUFBTQxYsXKSoqyuX6CxcuiGYjNzdXTBGDoR6IuQBAXSTJQhYTBIiDyrFYrST5eOb9Y926tentWU8aPk0tAGZHcxaM06dPU1FREcXGxjot5/mTJ09W6RjPPvssJSQkCFHiimnTplFERIS9sUsVAAAAADxLQcF52rJ1r5gaGUmyut0A0DOaExju8sorr9DChQvpm2++EQHirmDrSE5Ojr0dOXLEwYKB/7z5nxbRZq+AWsBlH9jBxeA2u3YfoY5XPUa7dqWRkTHq7yMAVUVzNsqYmBjy9fWl9PR0p+U8HxcXV+G+r7/+uhAYP/74I7Vq1arc7QICAkQDAAAAgILwm3eL4d5dAgD0LjD8/f2pXbt2IkC7d+/eYpktYHvEiBHl7jd9+nR6+eWXaeXKldS+fXtZn22zXwDP42M84xkA5oUfm7ilAbCDOhjA7GhOYDCconbo0KFCKHTo0IFmzpxJ+fn5IqsUc++991JiYqKIpWBeffVVmjRpEi1YsEDUzrDFaoSGhopWVSSLVTTGIuHXEoDysJKFfJAQAYDq1aXwUOA00B6o5A3MjiYFxoABAygjI0OIBhYLnH52xYoV9sDvtLQ0kVnKxrvvviuyT/Xr18/pOFxHY8qUKbL6YBMaQFkg3AAAwLz4+FgoLCzY6TfciIhAbbcsGBiDAH2jSYHBsDtUeS5RXFjPkUOHDinymcWPA7w1KA1SyAJA5FcrjwKTsqnwWARdzAir0j5WyUI+SP0KgJ02rRtS1umlSFMLgMHBHe4Ax19YEINRBqsC8RYQKUDPxNyyg1JG/0gWX4mkIgulvXEDZX3fQu1uGQLJKpHFB8+HCkHgtA7hGAz39gdAz+CJBQAAFeBfK88uLhie1n1mNfnF5KndNQB0xz//HqbWbR6kf/5RxvNAq6AOBjA7sGA4IIk3DnhroBQW8iWzwUNQvIutGHZE1JMrYmDSGbu4sMHzAYnZdPF01Vyl5CBJRBZcTBWD7FVVq92hIQvR+fOFQmTwFABgXCAwAACgAs4frSncohxFBs9fOBapar8AANql2AKBIG9gXiAwHEAMhjKgxgUwEoUZYXT6h6YUc9M/wqLAloWsH5p41HoBADDCeMINgYGxCNA5GAkCAEAlMRgxPf61uyvxNKrHLsRgGMF1CFQMzhEAQCawYDhgtVjJgvoXsvFBjQtgQNSKwXAHxG8ArVK/Xjx9/eULVL9+AhkZuEgBswOBAQDQDZJkIYuX60ogBgMA5SqXR0aG0m23XW34OhiSVKTq/gCoDV45l/J5RKt+A8DoMRiHXu8uRAVjq4OhVesF8AJwHZLNyZNZ9Or0L8QUAGBcjP0KAQAAFOD0dy0p949kCkzMpsLjxZW84YIEQPU5fiKTJkz8kG7s2ZHi4qLIqEgiFbfVzf0B0C8QGGWyPsAsWVV8TFjnwpMgpb+2YVHBzcfLLlreBkWjAXAfd2MoEIMB9A4ERjlpapFqFQAAgNEL3wHPAIEBzA4EhgOOMQVFiC1wiUVIL4gvoD/0VkFcjyB7FQAAAAYCAwAAANBxZiY9ERkRSn37dBHZpIyMuwlQkEAF6B0IDAckYbfQ98PbUyDeAgAAgLvUrx9PC7+YZII0tXCRAuYGo2ngFdj5DHgOnF3vuFgBD4KL2PPWDw1QWHiRjh7NEFMAgHGBwHBA7XoSWmwAAACAUuz8+xDVazCIdu48SEaGLRDuNgD0jLFtlAAAAABwD+QuloG7AgECA+gbCAwHrKIGBh6iDGIuAFAOSbKQxeD1M1QFqV8BAEBTQGAAAAAAwDMYIPOVHBDkDcwOBIYDiDsornMBzIVVshi+OjUA5QLrB/AASFMLzA4EBgAAAAC8QpvWDehs7nLyCwxUuysAAA8CgeGAyNxg0rcGFgTwAaBJEL+hYWD9qDY+Pj4UEOBveLcpicvauzGeKN4fAP1i7DscAAAAAJphz56jdMONz9CePUfI2BQp0ADQL7BgOGAVbxvMdVMjWxQAAABvcTb/HK1bt53Onj1HRqY4SFu+dQsWDKB3IDBMGuStp2Bu/qv4oIqyJoGHiLaRrBay+GCgoklQWwIAYGAgMByQqIgkEwxkLbBaAAAqAeNfDQNlrwPcs2DwiAQAPQOBAQAAAACgJG66SBFcpIDOgcBwwCoeCMZ1kfLB60igIfASFgDzFb6rW6c2zX33aapbN1a1PgAAPA8EBgAAAGWBejQmCoiTmJgIeuD+W4h8jT38kNx0cXJ3fwDUBq+0y8RgGK8BAAAAWuD06Rya9+F3YmpsrAq06jNnzhxKSUmhwMBA6tixI23atEnxbwZAVYDAAIaGM1AB42FcR0YgrB/AcFjY+kFEaUdO0SOPvklpaelqd8lwLFq0iEaNGkWTJ0+mLVu2UOvWralnz5506tQptbsGTAgERulK3gZqAAAAAFADqThQW26T8XJsxowZNHz4cLrvvvuoWbNmNHfuXAoODqYPP/zQI98QgIqAwAAAAGAMYP0AmkFy67/qCozCwkLavHkzde/e3b7Mx8dHzG/YsMED3w+AijF2lJVJC+3pqYgeAN7GKhUHH/tYMBgFAHgS958xubm5TvMBAQGileb06dNUVFREsbHO2bl4fteuXW73A4DqgpEoAAAAUBIjADxLaEgQXXttKwoNDSIj4u/vT3FxcUQiwYp7LTQ0lOrUqUMRERH2Nm3aNLW/IgBVAhYMB0Tsgo6zLlksqNANAABAuzRqlESrV71h2DS1nL3p4MGDwmXJXSRJIovFOd2zK+sFExMTQ76+vpSe7hw8z/PFggcA72LMO1wm7B5ldafypkr4wBDlETjOrtSzHQDhYgX3KuAEJ9WQW8jUZDVDrFYrXbx4ifwCfUSMgFFFBjdvW07atWtHq1evpt69e9vPNc+PGDHCq30BgDHm3S0TZGACAHgj/gMAs7Ltr/0UGt6Ltm3bp3ZXDAenqH3//ffp448/pn///ZceffRRys/PF1mlAPA2sGCUgx5EhkXuGzMAAACGqC0hGdQKAKrPgAEDKCMjgyZNmkQnT56kNm3a0IoVK8oEfgPgDSAwHCjOIKWPN4zIFKVtTOb1ALwAXLOAouAhZUjYHQouUUALYJQKAAAAgKqDjFsAgEqABcMBSdJ+BilkigIAVBckLAAAAOBNIDAAAAAA4BVaNK1LB/cvoNrxMWp3BQDgQSAwygR2a/M1HwK6zQPeNgNNYbUQ+SD2AyiDv78fJSXVMmwdDABAMRi1AgAA8Ag6SMYHvMyBgydo4N1T6cCB42p3BQDgQSAwSmWR0loDAAAAjEJ29ln66utfxBQAYFwgMAAwWOZJAIyAhIsZAAB0C5wgNRqDgZgLAACQCQKZAABAVTCKBQAI8L4YgBJxAgAAwC1gwXBAEj8s6sY9wHIB9AjfOXhfDEyPO9WxuXidj/Gf/wnx0fTSi/dTQgLS1AJgZCAwyrhIqQOEBQAAAKOLk7i4KHp27N1IUwuAwdHHE8lLSFRkbwAA7wCHFABUxMsv1jh71LffrkcWKQAMDgRGFcSGJxsAAACgW8uJjDoYffpNRh0MAAwObJQqukgp5RZlCSsin2grSVlEUp6vIscEQA9YJQv5WGADAQAAALQEBIbO8W9zgYJuLSDWKqyPLiwPo0vbQtTuFgC6gF8pwIwLAAAAKAsEhgoWDOUsF1a7uBDzPkQBvfKoaH8gLBkAAAAAAEAV8PJOx/hGF9nFhQ2e94m6pFaXADANKJcAQPUJDPSnZk2TxRQAYFxgwXBA8nANDIvCeq4o01e4RTmKDJ63ZuHPCoCWkSQLWRA7AkwIi4u/tn2ANLUAGBxYMHSMlOdD55YF27MM2mIw4B4FgHGRrChpCAAAQNvgFYIXYjAcYy58won8Yix0KVMiKc/9YxduC6Ci/f7kE2Ul6YyfEBcYfgAAyhMnFh9YToB6bPtrP3W7cTT9tOYtatOmodrdAQB4CAgMLxLczoci76xBFh8LSVaJcpZKdH6z+8dlUVHEwsICywUAwHMxJxa8vQBuYrVaKS+vQEwBAMYFAsODMRiOMRe+4WQXF2Kdj4Ui7iAq3EtkzVX0YwEAwDj5gN3Yl1/k2J65AAAAvAcEhiNKuUi5SENbI8anzA8dz9eIJip0U2DEJxRS/Qbn6eCBYDpxPMC9gwEAAOAqjkRyxQm/nfdBiCMAwLzgCeglLp22irdpjvD8pUz3jnvPkNO09e+/6L/Ld9OWnVtp0JBT7h0QAACAepT6nfDavgAAoCAQGA5I4j+r7FYRRblE2d9csosMWwyGO+5RCQmF9Oasw+RbEnrB09dnHqD4hAvyDwoAAMB8uBMTUY19mzSuQ79vfIeaNKkr//MAAJoHLlJlskhZFK227UjBZiud31tIfjG+JVmkfNzK+FS/wQW7uLBRowZRvfrn4SoFAABAcwQHB1LbK1JRBwMAgwMLRgVio6qtOrDFovCge5YLG/lnfcpUE+b5/Hz8WQEAAGiPtLRT9MSTsygtLV3trgAAPAhGok5YZTT1CAm1lkkbyfMhIUj/BwAAQHuczsyhue99S6dP56jdFQCAB4HA8DKJiUV0bdfzlJh4ye1jHdgfQEVFzssuXSI6eCDQ7WMDAAAAAAAgBwgML7hF2Rg2LJ/27DtJK3/IoN17T9DQYe6V8j5+3J+efjJZiAqGp6Ofqo/4CwAAAAAAoBqIsvKi5eKdudn21OgcnD17Thb9uCqUjh+T/2f4/NMYWrs6UgR2HzpYXAcDZaUAAAAAAIBawILhhOdiLq66qrBM3SUWGR07nne71yeO+9P6X8NhuQAAAKBpateKpJFP9qXatWuq3RUAgAeBBUNlUBYJAACAWUhKqkWvv/YI0tQCYHBgwXDCyhXwXDc32bjRv0wtIg7Q3vQ7ArIBAACYg7Nnz9GGjf+IKQDAuEBguKjl7fifUhw75kuPPRJpD8guukQ04vEot+IvAAAAAD2xZ+9RurbrSNqz54jaXQEAeBCMbr3IRx+F0KpVgdSgYREd2F9DZIFCQDYAAAAAADASmrVgzJkzh1JSUigwMJA6duxImzZtqnD7JUuWUJMmTcT2LVu2pO+++07Gp8oL5q5O2lq2ZPyyLpCOlVguJCpVyMJDn1t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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "if run_all_cells:\n", " resolution = 500\n", @@ -1302,21 +708,10 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "eb49bc82", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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W5UHQCAie1AO6iAA5QDnKzChZewzmtEIEJvSG01CI+b8ugsWJqnBUJBVjcLkuWIxBWjpXfSndqdkj6UOrVl14VkjaK3SO6nFnjV3CXiEf/8plcWfMCiWfTMZDjz0Co6UMXrEd4R2XCIVWB7fDBktBFkqO74SgUOCrLz7D7Nmz0VaUlZXhpRdfwkcffgyFU4kgSwS08JJyJWywwuCVD7Nowi23zMXTzzx92fs2MThdAIMTERERiaINMH0B0bFbPha0YwD9tRCEmm/Ms7Ky8NFHH2L3rs0wGErh5eWF6Jj2uOWWOzBy5Mhmu1xKenuXaS6WCz3sKTkJi8shj0tnm+gXKS/F6xkQC7Wi9vuVpGakgYGB6DHneah0XihLP4Ty7KNyE1eV3gcab3/o/COhDQiDQqmEOe8kenbvIu93qU4pFWPw9RRhkANStXAkFWlQK5W1mhUynlFB7mKzQueiUCqq9gOdDkFnBqPKUtu1mBWSipZNnDgJAQiBQqWCU3BBKSohuEWUuvPx3PPP4ZlnnkFbZLPZ8PPPP2P+9z+iIL8AbrcbIaHBuHrG1bjhhhvg41O3IhINhcHpAhiciIiI2jbRVQzR9B7gypLexkPwuhGCdhhaA6PDip0lJ7C9+DjyLGVV44EabwwK6YgBQQkI0tbvDaq07zyxS3f0uOVftQqMpswjGDWwJ6ZOGFMVjqRldjpBgKXcWm32xwxjabWvK/cOyX2F6jErpPPWViulfUahhDMqyjXGXqFPP/0U9/z1HrhdIrzhBytMcAoO3HPfPXjrrbca9LXo8maDFlWOnIiIiOhSiI5kiKYPAdEICL4QfO6CoOqIlswlunGsPEeuineoLBPuyr+JqxVK9AqIQ1JIR3T0iahXQJAq2aXkFCE5qxAHUrMRN+YGiG4XBGkPzynS6zlcEKQPqYqcxQGFzQlxxx6EBsagzH0I2ZUhyVx+abNCp0ton91P6NRtqgss57scbr/9dtx66614++23sW/fPrmYmVQRuqlmVKjhMDgRERFRmyDa1kM0fy/VNQOUcRB87oagCEJLVWAtw/bKQg8VDkvVeJx3CJKCE9A3sD30qroVTHA4XTiRWywHpePZRcgoMNTogRQQGAhTSR68vUKhLDVDlV8BVYkFwhlhyOWwwqdMB2chkFJ28tyzQv7n7yckfUhFE6TPOu/mX0HuTNKetwceeKCpT4MaGIMTERERtWqi6AQsP0K0rZOPBc1AwGsOBKH5V8E7k9XlwD6p51JxCtKMhVXjUiW8/sEd5MAUqQ+s9fO53G45HJ0KSifzSs4q3hAi7UdSqaEtt0GwarH2t/XwjkyEoDi9FwkKAW6Np3qcqFGi6OQxdB8Ug7/cOanGPqFToaipZ4WI6oP/1xIREVGrJborPEvznMc9Ndv0VwLayS1qBkPajn7CWCAXethXmg6721OyW7qCrv7RSApORDf/aKiqB5kLPFduSTmSszzL71JzimA9owS4r06DEKUUlKxwpJeg5EQycipnlHxEqSeSD8ryU6Hv3h1ieBBcQV5w+2oBQYDb6UDWpoUwmvdizc8pZxWGIGrJGJyIiIioVRKdmRBN7wPuYkDQQfC+HYK6F1oKg92MnSWp2F6UgiJbRdV4qM4Pg4I7on9QB/hrvC4alKR+SVJQOp7tmVWSmsxWp1MqEahQQV9hgzOzBIaThSh216wd5hvkg/jusWjXPRZ3/O9GdOrZGY69GxAY3xO+8d2gKFDBXJCJ4iNb4Xa7sOS3RQxN1OowOBEREVGrI9p3QzR9DsAOKEIh+NwLQRmJ5s7pduFQWRa2FR2XCz6cii8ahQp9A+PlQg/x3qEXnDErM1nlkHRq+V1JhbnG7QoRCBQU0FU45KBkziyGSQROt8MFAsL80b5HHNp1j0F8jzgESb2aqr3m/v170K93f5Se3A9jZoo8++V02wG3C++9/y6mTJnS4N8boqbG4EREREStrKntbxCtSzwDqq4QvOdBUHijOcsxl8pL8aQZJrPTXjXewSdMDku9A9pBq1Sft/KdFJCSpRmlrELklZ6enZKITjf8RAE6ox3OzFI4cgyQujp5Ojt5lvyFRAfJAUmaVYrvEQv/kAuXZe7SpQtKy0vk8tpfffmV3KNn+IjheOWVVxAeHt4A3xGi5od9nIiIiKgVNbX9DKJjr3wsaMcB+hkQhIvv/WkKZqcNe6RCD0UpyDIXV437qfVyg9qBwQnysrwz2R1OuYiDNKOUnF2EzOqV76TK4HYnvF0i9EY7XFmlEAuMqPEdEIDwuFC07ynNKMXKYUkq3EDUFpWzjxMRERG1JaKrqLKpbXZlU9ubIGiHormR/l59vCJXrop3wJABp9tTdEEpKNAjIFYOTJ39IqEQFFWPcbqkynelOC4VdMguRJpU+a7ycVJQslvt0Djc0JsccGeXwqvYDJUo3wTpWQSFgKgO4Z4ZpR6xiOsaAy9ffVN9C4haLAYnIiIiatFEx1GIpo8A0QQIfp7+TKoODfb8TqcTO3bsQHFxMfR6PXr37o2QkJA6PUexrULutyR9lNpP7yaK1AdgUEgi+gW1l0uKy9cjisguKqvaoyQ1oLWdqnwnrUQ02+QGs15SUMoxwK/MCk21NkpKlQLRiVFySJL2KcV2iYJWr22g7wZR28XgRERERC2SvNvAthai5cfKprbtKpva1r6P0YWUlpbi3XffxdsfvAej2QhdsB9cVgfMBQZcc+0M/OOhh9G/f//zPt7hdmJ/aYY8u5RSkVc1rleq0S+oAwaFdES03tOAV6p8ty8rTd6jJFe+s9qqrtFmssFltsszSmJuGfyMdmjdnr1JEpVGhbgu0VUzSjGdIqHWnHs/FBHVH4MTERERtcymtubvIdo3yseCZhAgLc9roKa2J0+exOgJ42DyERB4+1DEJXWEoPQsn7NkFGH90n1YMGI4Pnr/A8ydO7faeYnIMBdje9Fx7C1Ng8XlKcEghZxEv0h5KV7PgFiYLU4kZxZiXVa6HJRKjZ7Kd6JbhMVkhcNog85kB/LL4WN2Qu86HZS0Xhq06xpbVfEuumMElKrmuY+LqDVhcCIiIqIW2NT2A8CZUtnU9mpAO7HBmtoaDAaMmTAO9q7BaH/3eHmPUHX6uBDE3DUOfoMTcOc9dyE4OBijJ43HzpIT2F58HHmWsqr7Bmq85Zml7t6xKCqy4vjBQizPXldV+c7tEmExWmAzWqEx2qEoqIC3zQ1vpyjvT5Jfz1eH+O6nK95FtA+DQnF6DxQRXR4MTkRERNRiiM4MTxEIdykg6CF43wFB3aNBX+P999+H0QvnDE3V+fWJR+xDU/HYHx9iSlQp3JWFitUKJbr7xSDCHQJLoYgDx4qxtCBNrnzndrphNlpgqbBCZbRBWWSCr92NMIdYVflOajbbrpsnJLXvEYvQ2JAGC4VEVH8MTkRERNQiiPYdEE1fejoQKcIh+NwDQRnRoK/hcrnw1vvvImDuoAuGplMChySiYk86CgoLkRDSDsHWQNgLFdiXV4Zd7lK4HC6YKzxBSTDaoCoxwc8hykFJqnwnCQj1q9qfdK5ms0TUPDA4ERERUQtoarsIonWZZ0DV3TPTpPBq8Nfau3evvFQvelDHi5yTlLKkpkoqKO2ByFpegcCOShTYS6qCkqvCCq3BCl+HG6EOEZrKoBQcJTWb9fRPkj4HhPo3+HUQUcNjcCIiIqJmSxQtlU1t98vHgnYioL8aQrU+Rw1JLjke5AvFOYotiFJQcgqAQ4BYpoJYqgJKFVBmOmG1O5FhSoe+wi4HpRCHWFX5LrxdqGdGqXuM3HDWN9CnUc6diBoXgxMRERE1S6KrAKJR2s+UK79lEbxuhqAd3KivKfVpclhsNc9DaqFkVUA0KyEWKSHkKoByEUqzE8oKC5RZBfA3utG9kzcU1ZrNSiGpXTc2myVqLRiciIiIqNkRHYchmj4GRDMg+Hv2M6niG/c1RRF+HSMRMKknnCYrlBqdJzBVqIB0AcJJEaoKM5QWKxQWCxQ2G6SKDoXJRzHxuqmYe891iOsazWazRK0UgxMRERE1s6a2qyFafq5satsegs9dEBQBjfaaLtGNA4YMrM0/jAxTIdqPGoDyEhEKtxZIE6DIdEFZZoTaYIBCdMIVq4G7rxaOOB+UleXjxJ59ePKt1fDyavg9V0TUfDA4ERERUbMgig7A/B1E+2b5WNAMAbxmN1hT2zPZXA5sL06RA1Op3YgKsw3FZRb4O2NRvPIkVHBAY7FAZSgDAgHHFT5wddcDSk/FO2e5GSee/x1XTp/O0ETUBjA4ERERUZMT3WUQjR8ArhOVTW2vBbTjGqUsd5ndjA2FR7Gl8BgsLjvKzTYYDFaoCnVQpephyzUiJNOBsv2bgQRvuGa3g9jHF6g8F2lWrOJABk6+ugSC0Y6vv/yqwc+RiJofBiciIiJqUqIzDaLxfUA0VDa1vROCuluDv06OuRTrCg5jd8lJON0ulJutqCh1Qp2vgzJVh4qCCoRaXOhgcSOkd3f8b99iFB8rgus5F4JGdIE2IgBumwMlG47CUWyEEgq8//570Gq5p4moLWBwIiIioiYj2rdDNH1V2dQ2EoLP3RCU4Q33/KKI4xW58nK8o+U58nGZyQpriQhVng44AZQVGuTA1N7qRkLnaIy+fhg69m2PifeOwLgx42A3umBdlQ6zMg2CW4BeoYZLFPDSKy/ijjvuaLBzJaLmjcGJiIiILjtRdAOWhRBty+VjQd0T8L4dgtAwpbulGaW9pWlYk38IuRaDHJgMRgtcRSooc/VwpltQXliEcCksWd1I7BGHMdcPk8uIn1oe2KtXL5xMP4lvv/0W77/zAXJyc6DX6XHl1dNx7333onPnzg1yrkTUMgiiXL6m7SgvL4e/vz/Kysrg5+fX1KdDRETU5ohuM2D+FKLjoHws6CYDuisbpKmtxWnHlqJkrC84gnKHxbMfyWiDq0ANRbYG5ZkVsBQbEWFxI8zqRpd+HTBq1lC06xrTAFdGRK05G3DGiYiIiC4b0ZXn2c/kzgOghuA9F4Jm4CU/b4nNKIelrUXHYXc74RbdsBrdcOaqgBwfVGSVwVZSjEiLG4lWN7oPSsToWUMR3TGyQa6LiFo/BiciIiK6LETHocqmthZAEQjB+24IqnaX9JwZpiK54MO+0nS4RREutxtukwBHlgaubAGlOQY4Sk1yYAqxu9FrWFeMum4IIuLDGuy6iKhtYHAiIiKiy9DUdgVEy6/SEaBKgOAtNbX1q/fzHSnPxpq8Q0g15stjUmBSm7SwpStgy3WhJKcULoMFURYXgh1A31Hd5cAUEh3cwFdHRG0FgxMRERE1GlG0A+ZvINq3yceCZhjgdQMEQV3n53K4ndhZfEKeYSqwlstjbrcIL5M3ylMBU74dxTmFEMuscmAKdQvoO7YXRl47GEERgQ1+bUTUtjA4ERERUaMQ3QbPfiZXGgAFBP1MQDumzk1tjU4rNhcew8aCY/LXEqWogE+FL0pSXMgvsKA4pxSKcmmGyY0QUYGBE/th+DVJCAj1b6SrI6K2hsGJiIiIGpzoPAHR+AEglgGCNwTveRDUXev0HIXWcnl2aUdxKhxulzzmJWjhZfBF/nEb0gsr5MCkKrciVgpMCiUGTR2IoVcNhF+QbyNdGRG1VQxORERE1KBE2xaI5m+kbkqAMhKC9z0QlGG13r+UZiqU+y8dMmRKO6JkwSpfaAq9kX3cjOzCYhTnlkJbYUM7KTCp1BhyZRKGXjkA3v7ejXptRNR2MTgRERFRgxBFF2BZANG2Qj4W1L0B79sgCLqLPlYqH37AkIm1+YeQbiqqGo/ThAJ5WpxILkN5UZ48w6Q32tHB4kaoVoMh1w7EkCv6Q+/TMI1ziYjOh8GJiIiILpnoNkE0fQI4D8vHgm4aoLviovuZbC4HthenYn3BYRTbjPKYSqFAR00UbJkKpKSWwlBYgOIcA3xMdnSUGtd66TBsdhKSpvaDzkt7Wa6PiIjBiYiIiBqgqe27gLsAgKayqe2ACz6mzG7GpsJjctEHs8suj3mpNOiijUXZCREHUgtRVliGkjwDfI0OdLK4EOHvg+HXDcKASb2h0Wku09UREXkwOBEREVG9iY4Dnpkm0Qoogiqb2sad9/55FoO8HG9XyUm4RLc8FqL1RWd1DHKTrdiWlg9DQRlK8wzwMznRWQpMgb4YedMQ9BvfE2pN3cuYExE1BAYnIiIiqmdT2+UQLQsrm9p2rGxq63vO+x6vyJMD09HynKrxeO9QJCiikHq4HGszMlGSb4AhvwyBJie6WlyIDA3AyFuGoO/YHlCqlJf5ComIamJwIiIiono0tf0aon27fCxoRgBe10MQar6tcLpd2FeajrUFh5BtLvXcF0CPgDjEusJw4EARlmWloLQyMAWZnehmcSM6Igij7xyKniO7QqlkYCKi5oHBiYiIiGpNdJd4+jO50j1Nbb1mQdCOrnEfi9OOrcXHsaHgCAx2szymUSgxMLgjQqxB2L4rGztzDqM0rwxlBWUIMTvRw+pGbGwoRs8aim5DO0OhUDTRFRIRnRuDExEREdWK6EypbGpbUdnU9i4I6k5VtxvsJqwvOIItRcmwuZzymK9ah2EhXeBT4YP1m9OwOi8TxbkGVBSUIdTiQk+LG+06RGD09cPQJanjRavwERG12eD07rvv4r///S/y8vLQu3dvvP3220hKSjrnfR0OB1555RV8+eWXyM7ORufOnfHvf/8bkydPvuznTURE1JokJyfjg48+xK59e2C1WRERFoGbZt2Aq666Cmq1GqJtE0TztwBcgDIagve9EJTB8mOzzMVYm38Ye0vT4Jb2PgEI1/ljZFhXoEiN1WtTkZ1/TO7BZCqqQKjZhXirGwmdozFq1lAk9uvAwEREzZ4gyrs7m8b8+fMxZ84cfPDBBxg0aBDeeOMN/PTTTzh27BjCws7uMP7YY4/hm2++wccff4wuXbpg+fLleOihh7B582b07du3Vq9ZXl4Of39/lJWVwc/PrxGuioiIqOWQ/hB50y1zsGHdegQN7gT4agG3G4JKCevuDGhFAb98eT2GDbDJ9xfUfQHvW+Wy40fKs+XAlFKRV/V8HX0jMDK0K8pynFi1OwW5hQY5MJmLyhEu9WCyupHYIw6jZw1D+55xDExE1KTqkg2aNDhJYWngwIF455135GO3W1rfHIu//e1vePzxx8+6f1RUFJ566ince++9VWMzZsyAXq+XA1VtMDgRERF5pKWlYdCwIRC6h0Phq0bx0t2ICFEgKECJw8l2hHcOxsPPdECCIxeJCR3RqedDcGomYXdpmhyY8q1l8vMoBAF9A+MxLLgLMtIrsGrPceQXlMmByVJcgYjKwNSlXwd5hqld15imvnQiojpngyZbqme327Fr1y488cQTVWPSRtDx48djy5Yt53yMzWaDTqerMSaFpo0bN573daTHSB/VvzlERERtncvlwqRpU6AaEAt4KeFYsQMrfojCsCSdPAtkKHNh/2EbTLYcKDpE4e6n9mPSg0Uwxy5AhcMqP4dWqcKQkE5ICkzE4eOF+HTdLhQUSoGpBLYSkxyYEq1udE9KlANTTGJkU182EVG9NVlwKioqkn9oh4eH1xiXjo8ePXrOx0yaNAmvvfYaRo4ciYSEBKxatQq//vqr/DznI+2Jev755xv8/ImIiFqyZcuWIbswD4nPTMKhOW9j/cJoJPU9/cfJAH8lRgzW4/e1dry+KQIl1/fFL6mbMTxiOAI0XhgR1hW9/eKx63A23v5zMwqLPDNMjhITIi1uhNjd6Dm0C0ZdNwSR7Wv+riciaomavDhEXbz55puYN2+evL9J+muYFJ5uvfVWfPbZZ+d9jDSjJe2Dqj7jJC0HJCIiastef/tNBEzqifI9JxEfq6wRmiRmlxIpVn8s0LdHgQPwCvdH4R/7MHRoNCYmDMWmg+n4z/61KC4qlwOTy2BGpMWFYLuIvqO6Y+TMIQiLDWmy6yMiajXBKSQkRG5ql5+fX2NcOo6IiDjnY0JDQ7Fw4UJYrVYUFxfLe56kvVAdOnQ47+totVr5g4iIiE7buH4DOr1xMyoOZSE+4nSTWaNLhUKHHrsqQrGtIhxulRuW1Cz49G4P654crA8/iO17LCipDEyiwYIoiwshLgF9x/SUA1NwZGCTXhsRUasKThqNBv3795eX20mlTk8Vh5CO77vvvgs+VtrnFB0dLZcn/+WXX3DdddddprMmIiJq+ZxOJ+xWG7R+WowYpsSI7gGwO4FClzdOWn2x0xiKXLsPBvgWYudvB2HerYG7/xj4x4/G0RwrvPLSoCiXApMbIW4BAyb0wfAZgxEY5t/Ul0ZE1DqX6klL6ObOnYsBAwbIvZukcuQmk0lefieRSpVLAUnapyTZtm2bXDa1T58+8ufnnntODluPPvpoU14GERFRi6JUApPH+eLOhH0I8hdgTfXFmpwgpKgicdLqh+F+ebgt/ChOpIr4x/I4hI8aAySroDPq4CqvQIwGCBGUSJo8AMOvSYJfkG9TXxIRUesOTrNmzUJhYSGeeeYZuQGuFIj++OOPqoIRGRkZcqW9U6Qlev/85z9x4sQJ+Pj4YOrUqfj6668REBDQhFdBRETUMsgdSBw7IJoX4Z7b/KE1l6HMOxx/aAdh3QobggQTbmu/HV5a4IH17bA1JRqBXYKgLVVBYSiHc8thhIVHYPoj4zHsqoHw9vdu6ksiIrpsmrSPU1NgHyciImqbgWk/ROtCwJUjL3VfsHoD1lg6YH9EH7gEJZzlFuR+vwnKLC304V2g1XpDq/GF2mqHurQMtswc5OQl48a7rsK/X/2/pr4kIqK208eJiIiILkNgch6BaFkEuNLkMatbhT+LI/BiSi84lXqIOenw69ceSi89okZOh5ClgFDkgKrMDFVOARRKB0wdndi3dRW8IuLhEpxNfVlERE2CwYmIiKgVEp0pnsDkTPYcQ41D1k6Yn+cDs0sBm/UgVKWBOPHN1wgc1BlRw6ZCWQgoimzQFBRCKVjhGOGFfFcRTry1FOF9x8GSe+Ks/otERG0FgxMREVErIjozAOtiiI4DlSNKlKA/vsvzQZrZJo90C4jGyTQ1Nu/bh85X/R02QwEc20qhcCuhyMxBeYwZxUElKPrwIESbG9FJV8I3pjPytv+BmTNnNun1ERE1FQYnIiKiVkB05QEWKTDtqhxRwKUehBWlsVhVkA0RNnirtLg6Ngl9A+PR5a8arLvlPqjUemh1UVCajXDvPYYsVTrKjOVQaX0Q3e9K+LfrAUGpRPaW3zB8xEjEx8c38ZUSETUNBiciIqIWTHQVA9bfIdq3yAvyJIJmIFIdSfj+5HGU2rPlsf5BHXBl7AD4qHRwud3IF/2QMHAyXHnFUBkBdW45rOP6IyxwBMLOeI3SlD0oOrQBr23d2gRXSETUPDA4ERERtUCiuwywLoNoWw/AJY8J6t6wqCdjYW4OdhbvlccCNd64Nm4wuvpHy8cWmwNf/LkD2/eeQIg+FCX79qHiRCrsQxPh46+FUO01bGVFKDywAUWHN+PLLz6XG9cTEbVVDE5EREQtiOg2ArY/IVpXA3B4BlVdAN107KlQYGHqDhidVjkAjQjriilRfaBVquW7FZYZ8dHvW3H4UAZKc0rQ0ejCwOv/gu82fo6Va7+HYusv8AnvAEGlgr2iBKbCDAQGBmPpkt8xefLkpr1wIqImxuBERETUAoiiFbCuhGhbAUhfS5TtIeivQpkYi5/Tt+JwmWdZXoTeH7PihqKdT2jV45OzCvHp0m04cSQLtmIjulY4MemqwZgwZxTmCrNgMBjw5JNPYuOGTbDbzYjo2h5Pfv0RJk6c2FSXTETUrLABLhERUTMminbAtg6i9Q9ANHoGldFyYBKVPbCl+Dh+z94Fm8sJpaDAhMieGBveAyqFsuo5Nh1Kww8rdyHjWA5U5VZ0toiYee8U9B3bs+kujIioGWADXCIiohZOFJ2AfTNEy++AWOYZVIRB0E8H1ANQYCvH/BPLkWYslG+K9w7FrHZDEK4PqHoOqQjEwk0H8eeWI8hOyYW/0YFuSg1uenEG2nWNaapLIyJqkRiciIiImhFRdAP27RCtvwHuIs+gIhCC7gpAMxguEViTdwB/5u6HS3RDo1BhWnQ/DA/tDEE4XdqhehGIvJMFiDI50Sc0CDc9MxOBYf5Nd4FERC0UgxMREVEzIK+cd+yBaFkMuHM9g4IvBN1UQDsCgqBGuqkIP6ZvRq7FIN/cxS8KM9sNRqDGp8ZzyUUglmzF4YOeIhAdjC4M6Z2AmY9cAa1e2xSXR0TU9oLTs88+i9tuuw3t2rVrnDMiIiJqa4HJeRiiZRHgSvcMCnoIusmAdgwEQQuby4FlOTuwoeCI3KnJ08h2IPoGtq8xy3SqCMRny7Yh9fDpIhATrxwkF4FQKBRNc5FERG0xOC1atAgvvfQSRo0ahdtvvx0zZsyAVsu/XhEREdWV6EyBaFkIOI9Xjmgh6MYB2gkQFF7yyLHyHPyYvgWldtNZjWzPtPlwGr5fsQsZyTlQlVnRwyLi2vumod84FoEgImqSqnp79uzB559/ju+//x5OpxPXX3+9PAs1cOBANHesqkdERE1NdGZUBqZDlSMqCNpRgG4KBIWvPGJy2rA4ayd2FKees5FtdVIRiMWbD+GPLYeRfTwXfkYHuivUuOnJGWjXLfayXhsRUUtSl2xwSeXIHQ4HfvvtNzlELV++HF26dJFnoW655Rb5BJojBiciImoqoisXsCyG6NhdOaKAoBkG6KdCUAR57iOK2FuahgWZpxvZDg/rgqlRfasa2VYnFYH4csVObN+TitzKIhC9QwJx87PXsQgEEVFzKUcu/XCXwpPdbpe/DgwMxDvvvIOnn34aH3/8MWbNmnUpT09ERNQqiK4iwPo7RPtW6Uj6uyUEzUBAdwUEZVjV/Qx2E37J2IZDZVnnbWR7ZhGIj5dsw6FD6SjN9hSBGNyrA2Y+Mh06Ly6jJyJqSPUKTrt27apaqiftb5ozZw7effdddOzYUb797bffxv3338/gREREbZroNgDWpRBtG6UFdfKYoO4D6KdDUJ5ecif98XFzUXKNRrbjI3piXETNRrbVpWQX4ZOlW5F6JAu2IiO6SEUgrkjCxFtGswgEEVEjqPNSvZ49e+Lo0aOYOHEi5s2bhyuuuAJKZc0f6kVFRQgLC4Pb7UZzw6V6RETU2ES3EbAuh2hbIy1s9wyqukLQXwlB1b7GffOtZXLxh5PGgvM2sj3TlsPp+G7lLmQey4Gy3ILOZhHX3j0Z/cb3atwLIyJqZRp1qd51110nF4KIjj57c+opISEhzTI0ERERNSZRtADWVRBtKwDR6hlUJUDQXQlB3bnGfZ1uF9bkHzqjkW1fDAvtDIVw7hkjt1vEos0HzyoCceMLMxDfnUUgiIga0yUVh2iJOONEREQNTRTtgG0NROtyQPSUDYcyVp5hgqrHWb2WMkxFmF+LRrbVWewOfPnnTmzfm4rcE54iEL2CAzHn2ZkIDD//7BQRETXRjJPUtykpKQmPPfZYjfH//Oc/2LFjB3766ae6PiUREVGLJIpOwL4RomUpIJZ5BhXhEPTTAXX/swKTp5Ht3qpGtl4qDa6OSUK/oLMb2VZXVGbCR0u2sggEEVETqnNwWr9+PZ577rmzxqdMmYJXX321oc6LiIio2RJFN2DfCtH6O+Au9gwqgiHo/gJoBkEQzi7okCw1ss3YihKbUT7uH9QeV8YMhI/67Ea21bEIBBFRCw1ORqMRGo3mrHG1Wi1PdREREbVW8up2x26Ilt8Ad65nUPCDoJsKaIdDEM7us3RmI9sAjRdmxg05ZyPbM209ko7vVuxCRmURiB5mETPumYL+E3o3/MUREVHDBiepqt78+fPxzDPP1Bj/4Ycf0K1bt7o+HRERUcsITM5DEC2LAFeGZ1DwgqCbDGjHQBA053zMPkM6fs3YXqOR7ZSovtCdo5HtmUUgFm85hGWbD1UVgegmqHDj8zPQvkdcY10mERE1ZHCSmttec801SE1NxdixY+WxVatWyT2duL+JiIhaG9GRDNG6CHCmVI5oIejGA9rxEBRe53zMmY1sw3X+conxeJ/TzW4vVATiqz93YltlEYhIkxO9gwJw87MzERQR2KDXRkREjRicpL5NCxcuxMsvv4yff/4Zer0evXr1wsqVKzFq1Ki6Ph0REdFl43A4sGjRInz26Xs4eTIVOp0Oo0ZPwj333IdOnTrVuK/oTPPMMDkPV46oIWhHA7pJEBS+53z++jSyPbMIxMdLt+LQwQyUZBfLRSAG9WiP6x69kkUgiIiaGMuRExFRm5Ceno6pU8bBbMrHnTfp0KeHBiaziF+W2PDrkjL84x+P4l//esmzd8myGKJjT+UjFRC0wwHdVAiK88/4nNnItp13CGa1G4qICzSyrS41RyoCsQ0pR7JgLapAYrkL46cNwOTbxrAIBBFRSyxHTkRE1NIYDAaMHzcCY4eZ8M7LEVAqT5f+vmaaD44k++Kme9/GqIFpGDdS+sUp/U1RgKBJAnRXQFCGnve569PI9kzbjqTj2+pFIEwirr1nMotAEBE1I3UOTi6XC6+//jp+/PFHZGRkwG6317i9pKSkIc+PiIjokn344YcID67Au6+EQKE4u19S104arPgpDNt2bYDTMQFqryRAPx2CMuqCz3uuRrbXxg1GkPb8jWzPKgKx9RCWbTpdBKKroMJNL7AIBBFRc1Pnuf/nn38er732GmbNmiVPaT300ENysQhpGcG5+jsRERE1JWlF+ocfvIn75+nOGZpOCQpQIrfAjflLO0DwueuCoUlqZLsoayfePLpUDk1SI9sb44djXsdxtQ5NUhGIT5Ztw+LVe5FxJBthZXYM8vfHPf+7haGJiKg1zDh9++23+PjjjzFt2jQ5KN1www1ISEiQC0Rs3boV999/f+OcKRERUT3Xr59My8X4ER0uet/0TCtyDh7Hzbec/z71bWRbXUm5GR8t3YqDB9JrFIGY+Y/p0HvX/nmIiKgZB6e8vDy5l5PEx8dHnnWS/OUvf5FLlRMRETVHyosXtYNw/gmpczaylZbldfOPqdN5nMgtxsdLtlYVgehSWQRi0q2joazNSRIRUcsITjExMcjNzUVcXJw80/Tnn3+iX79+2LFjB7RalkolIqLmtUzPV5+Gd/4vDkaTCN+LrKJbtcGNWTf1b7BGtmfadjQD3/65Uy4CoagsAnHNXZMwcFKf+lweERE15+B09dVXyw1vBw0ahL/97W+46aab8Omnn8qFIv7+9783zlkSERHVgSjaAftWiNaVgDsfE8Z0RGpaCsJDlefd57R9jxV7D5mxZM6cqjGD3YxfMrfikOF0I9vr2g1B+1o0sj2zCMRvWw9j2eaDyErOha/RgW5SEYjnr0H7nu0u8WqJiKhF9HGS9jVt3rwZiYmJcnPc5o59nIiIWi/RXQHY1kG0rQXECs+goIfZ2R9JIx7EiIEuvPNKaI1y5JJjKXaMmZGDm26+D//732vyLNMWuZHtblhdDrmRrdTEVmpmW5tGttVZ7U58vXIntuxORe7JfEQanegV5I+bn7kOwZHn7wtFRETNKxvUKThJHdf/+te/ynuZ2rdvj5aIwYmIqPURXXmAdSVE+1bpt5VnUBEEQTsO0A7Hs8+/gv+89j9I9RsC9G7cf4sP+vTQwmR244ffzPjltwroY0KgMbrx2+rl2KUqwIlqjWylWaZIfd1DjlQE4uOlW3HgYDpKsooRLxWB6B6P6x69kkUgiIhac3CSSE+8d+9eBiciImpS8q8vVypg/ROiY39l01qpCkQ7CLoJgLofBEGJL774Avc8+Dck/OcGaCMDUbr5GIwrdsNeUAaFWgltj/YIntYfuthglK49An2AD8aMHQMvjb7OjWzPLALxSWURCEtRBTpWuDB+cj9Mvn0si0AQEbWF4DR37lz06dOnxe5nYnAiImrZRNEFOPZAtK4AXGlV44K6J6CdCKgSIVSWx5Oatsd2aAf97AEIHtWtFs8twnw4B4MjOuG/1z5Q655M5ywCsaKyCESZBZ1MImbcOQEDJ/et1/MREVHTZ4M6F4eQ9jK98MIL2LRpE/r37w9vb+8at7OPExERNQZRtAG2TRBtUsGH4spRFQTNYEA3AYIy4qzH/PHHH6iwmBE1rHOtXkMKXE6bHaue+wyBs5+s8zlKRSB+33YYSzedLgLRFSrc9Nw16NCLRSCIiFqyOs84XWiJnvQL58SJE2jOOONERNSyiG4DYFsD0bYOEC2eQcEbgnY0oB0NQXH+n+U3zZ2DNdYURN86utav57Y7ceD6t7Fz2/aqvoW1LQLxzcpd2Lw7RS4CESEVgQiUikDMREhUUK2fh4iIWsmM08mTJy/l3IiIiGpFdGUD1hUQ7dulRXeeQUUoBN14QDMUgqC56HPkFuRBneBbp9dVaFTQB/misLCwXkUgirOK0d7oQlK3dpj12FUsAkFE1ErUOTgRERE1FnkRhPOIZ/+S8/DpG1QJEKT9S+peEOpQqEGr0cLtqJylqgO301Xrpu4n80rw8e9bqopAdGERCCKiVqnOwem222674O2fffbZpZwPERG1QaLoBOw7Idqkgg+eZrOAAEHd17N/SdWhXs/bs2s37N66tE6PseWXwVxSjoSEhIved8exTHz9546qIhDdjW5cc+dEJE1hEQgiotamzvVVS0tLa3wUFBRg9erV+PXXX2EwGOp8Au+++y7i4+Oh0+kwaNAgbN8uLck4vzfeeAOdO3eGXq9HbGysXN3ParXW+XWJiKjpiW4zROtyiGVPQTR/XhmaNBC0YyD4/QuCz1/rHZokd867E8b0Arislb2daqH4j32YPGUyIiIizn/eoojfthzCp79txokDGdCXWNDHpcK852YxNBERtVJ1nnFasGDBWWNutxt33313rf46V938+fPx0EMP4YMPPpBDkxSKJk2ahGPHjiEsLOys+3/33Xd4/PHH5VmtoUOHIjk5GbfccotclOK1116r66UQEVETEV3FgG0VRNtGaY7HMyj4yYEJ2pEQFPUrA16d0+3CEV0Zej5yDYx5BujjQy/6GHtRBUqX78eDP/563vvYHE58vWIXtuxJQc4JFoEgImor6lxV73yksDN69Gjk5ubW+jFSWBo4cCDeeeedqgAmzSL97W9/kwPSme677z4cOXIEq1atqhp7+OGHsW3bNmzcKP3yvThW1SMiajqiMx2wSQUfdkk/9T2DikhPw1pNEgRB3SCvk2Uuxvdpm5BrMcBkMmH5Rz9A1CkRNmNQVY+nc4Wm9Gd/wRUjJ+LLzz4/5/1KK8z4aOk2HDiQdroIRFepCMSV0PvoG+TciYiolVTVO5/U1FQ4nc5a399ut2PXrl144oknqsYUCgXGjx+PLVu2nPMx0izTN998Iy/nS0pKkkufL126FDfffPN5X8dms8kf1b85RER0uQs+HKgs+JB8+gZVF09gUnU/b5ipzyzT6vyD+DN3P9yiCB+VDrf0HI27HxmCCZMnIm1XBvz/0huBgxMhKD2r1e2F5Sj6Yx9Kl+3HNdOvwqcffXzO86kqAnE0C5ZCTxGIcZP6YcodLAJBRNQW1Dk4SUvrzvyFKM0yLVmyBHPnzq318xQVFckd3cPDw2uMS8dHjx4952Nmz54tP2748OHy60pB7a677sKTT56/SeErr7yC559/vtbnRUREDUMU7YB9uycwufMqRxUQNAMB7XgIqrgGfb08iwHfpW2SZ5skvQLicG3cYPiodUBgHI4cPIyPP/4Yb73/LrLfWg6vYH+4HE4Y80swdsI4/P3bHzB58uRzhiapCMQ3f+5E+rFsuQhEN5Mb19wxAYOm9mvQayAiola0VG/MmDE1jqVZotDQUIwdO1auuKdS1S6L5eTkIDo6Gps3b8aQIUOqxh999FGsW7dOXn53prVr1+L666/Hiy++KC/zS0lJwQMPPIB58+bh6aefrvWMk7QckEv1iIgah+g2Ara1EG1rAbHCMyjoIGhGALqxEBQNuw/ILbqxNv8wluXshUt0w0upwTVxg9A3MP6cIUj6o93u3btRXFwslxzv2LGj/HvhnNciiliy/Qh+33AAWcm58DE60E1UYfZjV6Fjn/M3hCciopahUZfqrVmzBg0hJCREXtqQn59fY1w6Pl8lIykcScvy7rjjDvlY6ugurV2/88478dRTT8kh7kzSL8Xa9uIgIqL6E10FgG0lRNtmAJVV7BSBELTjAO1wCELD7wEqtJbLe5nSTJ5mtd38ozEzbgj8NV7nfYz0u0faX3sxUhGIb1buxubdx08XgQjw8xSBiA5u0OsgIqLmr87B6eTJk/ISucTExBrjx48fh1qtlkuL14ZGo0H//v3lQg9XXXVVVXEI6VgqAnEuZrP5rHB0al15A9W4ICKiOhKdKYB1BUTHPunIM6iM8+xfUveDIDR8r3XpZ/7GwqP4PXs3HG4XtEoVro5JwsDghIvul5JmmqTl5dLSb+kPa127dpWLG1X//VJqtODjJVtx4GAaijKLES8VgegSh+sfv4pFIIiI2qg6/zaTyn9LS/LODE7S0rpPPvlEXk5Xl/1S0r6oAQMGyMUepHLk0gzSrbfeKt8+Z84ceTmftE9JcsUVV8hlx/v27Vu1VE+ahZLGuTGXiOjyEUU34Njr2b/kOlE1Lqh7AFqp4EPnBiv4cKYSmxE/pG9GSoVn31SibwSujx+KQM2FS5gfPnwYL738Cn766Sf4hUVD5R0AuF2oyEtDSHAQ/v7A/bj33nuRXVwhh6bjR7JgLiyXi0CMndAHU+eNh1LF3zVERG1VnYPTnj17MGzYsLPGBw8efN6ZovOZNWsWCgsL8cwzzyAvLw99+vTBH3/8UVUwIiMjo8ZfAP/5z3/Kv4ilz9nZ2fLeKik0vfTSS3W9DCIiqgdRtAG2zRBtKwF3UeWoEoJmMKCbAEEZ2YivLWJbcQoWZu6A3e2ERqHEFTEDMDSk00VD2ooVK3DlVVfDr0MfJFz1APRBp5eEu11OGE7sx3P/9yp+/nMT+kycheyUPKDMiu4mF665fTwGTevfaNdFRESttDiEtHlKmlWSZn2qk0qLS0sdKioqNwI3U+zjRERUd6K7DLCtgWhbD4gmz6DgDUE7CtCOhqDwb9TXN9jN+DF9M46W58jH7X3CcEO7oQjRXfznuFQIYtjwEQgfchWCOg047/3cTjvsRXnwhTfa6/3QTVSzCAQRUStXXodsUOfgJM3w6PV6fP/991XL46QKRdLskbTMbtmyZWjOGJyIiGpPdOUA1pUQ7Vuln/aeQUVoZcGHoRCExi2+I/2K2lVyAgsyt8PickClUGBqVD+MDOsChXB2QaBzGT1mHI6VKxCZNPWC9xNsTrnUuOvIcYxuH48nP3qQRSCIiFq58sasqvfvf/8bI0eOROfOnTFixAh5bMOGDfKLrl69uv5nTUREzahh7TGI1j8B56HTN6gSIGjHA+o+EGoZWi6F0WHFTxlbccCQIR/HegVjdvwwhOsDav0cUuGijRs3oOvsp85/J1GEYHZAabRBnVYCU6kBh2P3MDQREdGlBadu3bph//79eOedd7Bv3z559kkq4iDtbwoKatjeHEREdPmIohOw74Jokwo+ZFaOChDUfQCd1LC242U7l32l6fg5YytMThuUggITI3thbEQP+eu6+PbbbxHUoSfUXuf5K6JbhMJog6LCBk1qEVzBXnCO6YM/57+MkpIS/l4jIqIq9aoRGxUVhZdffrk+DyUiostA6on38ccfY8nvP8nLqGNi4nDrbXfL7R+k1hHViaIFsG2AaFsNuEsrR9UQtMMA7TgIyrDLdt5SUPo1cxv2lKTJx5H6AMyOH45or/oFmPSMTCh8zjNz5HJDUW6FsswKzYki2BNC4IgLhEYQoFKp5e8hgxMREdU7OH3++efw8fHBzJkza4xL5V2lPktSeXEiImo60h7U226bixGDffHX2WoEBSpw+NgBPPaP2/DsM49j6bJVcs890V0CWFdDtG8ARKvnwYIvBO0YQDsKguLC5b0b2uGyLPyYvgXlDgsUgoCx4T3kmSaVov4lwD2VWcWzl+bZXfIsk7LYBHW2AdZeUXCF+NRYrniupupERNR21Tk4ST2VPvzww7PGw8LCcOeddzI4ERE1oeXLl2PevFvwy6ehmDzWu2r8LxOAh+8W8bcnS3DrnFFYuuAxaBUHpLVqnjsoIj0NazUDIQiay3rOFqcdi7N2yqXGJWE6P9wQPxztvEMu+bk7JnSAbcXG0wNOFxRGuzzLpM4yAE43LEnt4PY5XeTCaiiA6HYjMrLxSqsTEVEbCE5Sb6X27c8uzdquXTv5NiIiajrPPP0PvPAPvxqh6RSlUsC7/xeETdvtyElf6PlZLjWqlQKTqkejNay9kOTyXPyQvkkuNy69+qjwbpgS1QdqRb1Wkp9l3LSr8M6izXDbbVDZRHmWSZ1dBmWJGY74INg6BAPqmjNahQc3YvpVV7HyKhER1VDn30zSzJJUHEJa5lGdVCgiOJgViIiImor0c/jQ4aO47dvY895HCkftYhT4cfEJPPLPTyCo4tAUbC4Hfs/ejU2Fx+TjYK0Prm83DAm+ngbol6qk3Iwl249gx7EMxMT3hCOvDJpSB1R5FXCG+cA0ogNEr7Nn1hymMhiSd+Ch91c2yHkQEVEbDk433HAD7r//fvj6+splySXr1q3DAw88gOuvv74xzpGIiGrh2LFj6NHFF36+F94T5HSJePKlNPzjuaYJTSeM+fghbTOKbJ6G6UNDO+GK6P7QKmsWragPi82BlbuTsXb/CZQVV6AgowhxDi+krlkLq58PFCP6w11tL1N1DnM5ji9+DxMnTMCwYcMu+VyIiKiNB6d//etfSEtLw7hx46BSeR7udrvlkuQvvfRSY5wjERHVgq+XBWOGXTx8mC0i1GcsT7scHG4nluXsxbr8w3K5hgCNF65vNxSd/KIu+bmdLje2HE7DHzuOoaRUCkzFUBQb0dHkQpiPH6Y8Pwd/fXIe9FsOI6zPGPjGdKrqReW0GFF0dBsK9q5B757dsHDBr02ybJGIiFpZcNJoNJg/fz5efPFF7N27V+7j1LNnT3mPExERXV6i2wg4dkK0bcXoAUfhNKhgMrvh7XX+inALlpgwcsTQy3qeGaYifJ+2CfnWMvk4KTgBV8YMhF51aYUopOp3B9PysHjLIeQWlaM4uwTWXANiTC4EiQKGXJGEUbOGQu+tw/ArB+Paa2fi4B+fQanWQq33g+h2wVZRBK3OCw/cexf+97//MTQREdE5CaLcIv7SlJeXy00GP/30U+zcuRPNmXSu/v7+KCsr48ZfImqRRNEBOA4C9q0QHVJlPFflLQJ+WXgAx44dxxMP+J8zABQUOdF3fD4++vgHXHHFFY1+rk63C3/m7sfq/INwiyJ81XrMajcE3fxjLvm5MwpKsWjzIRzPLoShoBzlWSUIL3cg1OZG16RETLptDEKizu7DlJubi3//+99yQSOdToeJEyfKFWEZmIiI2p7yOmSDSypbtGbNGnz22Wf49ddf5Re8+uqrL+XpiIjoPOS/cblOAPZtEO07ANF8+kZlLATNYLmU+KBxZXjwn/2RnVeKFx71R3DQ6SV5ew5YccsDBowZMwXTpk1r9HPONpfIs0w5Fk9T3b5B8bgmdhC8VadLf9dHaYUZS7YdwY7kTJjKzCjOKEJAiRXdrG5ExIRg6rxx6Njn7Oqvp0hlxt94441LOgciImp76hycsrOz8cUXX8iNcA0GA0pLS/Hdd9/huuuu41/riIgamOgqrAxLWwF34ekbBH9PWNIOgqCMrhqOi/PHxo3bcdutsxHXfycmj/VDUABw6JgbB46Ycffd9+D//u+/jdrc1SW6sTrvoDzTJH0tBaVr4wajd+ClLem22B1YuctT+MFsssqFH3QFRnQyu+DvrcO4v47EgEm9oVRe/v1bRETU+tU6OP3yyy/yUrz169djypQpePXVV+XP3t7e8h4nhiYiooYhuk2AYxdE+zbA6WkK66GFoOkDSIFJ1aWquMGZpP5Ma9ZuwaFDh/D777/DZDJh0KgY+Q9cAQEBjXru+RYDvkvbhExzsXzcIyAWM+MGy0v0LqXww9Yj6Vi24yjKKywoyimBK9uAdkYnfKHAoL8MxJjrh0LvU//XICIiarDgNGvWLDz22GNyYQipFDkRETUcUXQCjkOV+5b2S3Gh8hbBE5LkpXh9IQi1X+bWvXt3+eNycIturC84iqU5u+F0u6FXqnF1bBL6B3Wo9x/WpOWJh9LzsXjzIeSXVsBQWA5TZjEiDXb4O0R07p+AybePRWgMewgSEVEzCk6333473n33XaxduxY333yzHKQCAwMb9+yIiFr9vqV0T1iS9y0ZT9+ojKoMS0kQFM37Z22RtRzfp2/GSWOBfNzFLwrXtRsqlxuvr4wCAxZtPoiUnCKYyy0wZBQjqNCMGJsbYdFBmHLHOHTqn9CAV0FERNRAwenDDz+UN9P++OOPckGIBx98EJMmTZJ/8Ut9nIiIqHZEV3G1fUv5p28QfCFoBgHSh1TwoZkvgZZ+/m8uSsZvWTthd7ugUahwZcwADA5JrPe5lxotWLL1sFz4wWF1oCizGN455ehodcPHW4exc4YjaUpfKFXcx0RERC2kHPnx48flAhFffvkljEajXKHp2muvxTXXXIPmjOXIiagpiKIFsO/2hCVncrVb1NX2LXWFILSMQFBqN2J++hYkl+fKxx19w3F9u2EI0vrUu/DDqj3HsXZvKmx2B4pzSqHMLEWk0QkdBCRN6Yexs4fDy5f7mIiIqGmywSX3cZJmm5YsWSIXjli2bBlsNhuaMwYnIrpcRNEFOA9Xzi7tBeCotm+pU7V9Sy0nDEi/MnYUp2Jh1g5YXQ6oFUr8Jbofhod2qdcsk8vtxpbDnsIPRrMNZUXlsGeUIKLECm8XkNAnHlPvGIewuNBGuR4iImrbyi9ncKquoKAAYWFhaM4YnIio8fctZVbuW9oOiBWnb1REQtB6luIJirMbszZ3ZXYzfsrYgsNl2fJxvHcoro8fijCdf72+T4fT8+UGtvmGCpgrLDBllCAoz4gAhyg3rp1y+1h0GpDQ7JcsEhFRy3XZGuCeqbmHJiKixiK6SwH7doj2LYDLs3zt9L6lAZ6leMp2LTIESCFnT2kafs3YBrPLDqWgwJSoPhgd3g2K85REv5CsQgMWbj6E49mFcNicKMsqgXeGAe1tbnh5azFmznAMmtaP+5iIiKhZadDgRETUloiirdq+pWPSSOUtKgjq3oBW2rfUDYLQcn/UGh1W/JK5DftK0+XjGK8g3BA/DJH6wPoVfth2BDuPZcLlcsGQZ4A6rQRxRqf8y2jg5L4Yd+NwePt7N8KVEBERXZqW+9uciKgJiKIbcB6tXIq3B4D99I2qjhA0QwB1PwiK+pfibi4OGDLwU/pWGJ1WKAQBEyN7YWx4D6gUdZsJstqdWLknWS784HC6UFZcASG9BJHFFmjdQPuecZg6bzwi4rlqgYiImi8GJyKiWhCdWdX2LZWdvkERVllCfDAEZQhaA7PThgWZO7Cr5IR8HKH3x+z44YjxCq5z4YetRzKwdPsRGC02WCqscGSWICi7At4uEUERAZh8+zh0SerYIpcwEhFR28LgRER0HqLbULlvaRvgyjp9g+Bdbd9S+1b1pv9IWTbmp29GucMi1f7DmIgemBzZu06zTKcKPyzecgh5pRXyPiZLrgHeqcUIc4jQ6jUYPWsYhlzRHyo1fw0REVHLUKvfWIGBgbV+Y1BSUnKp50RE1GRE0Q449kK0bQGcR6rtW1JCUPfyhCV1jxa9b+lcpNLii7N2YmvRcfk4VOeH2e2GoZ1PaJ0LP0iV8pKzC+F2iTAVlkObUogooxMKAeg/sTfG3zQSPgHcx0RERC1LrX7zv/HGG1VfFxcX48UXX8SkSZMwZMgQeWzLli1Yvnw5nn766cY7UyKixiwhLhV3kJfi7QZQrR+dKsGzFE/dH4Kifs1dm7uUijx8n7YJpXaTfDwyrCumRveFRqGqU+GHpduOYMexTPn7aSw1yoUfwgvNUIlAfPdYeR9TZIfwRrwSIiKixlPnPk4zZszAmDFjcN9999UYf+edd7By5UosXLgQzRn7OBG1PkePHkVycjJUKhX69++P8PDavTkXpbLh9i2efUtSOfFTFMGeIg9SvyVlyy1YkJ+fjzVr1sBkMiE6Ohrjxo2DWq2uut3udmJJ9m5sKDgqHwdqvOWKeR19I+pU+GHVnuNYszcFDpcLVqMViqwy+GaUyoUfAkL95H1M3YZ0alVLGomIqHVo1Aa4Pj4+2Lt3Lzp27FhjPCUlBX369IHRaERzxuBE1Hr8+eef+NcLT2Hnrr3o3NEXdrsbqWkmzLjmSjz/witITEw86zGiu6LaviVPiW2ZoIegHuApIa5s2U1Xs7Ky8ODDD2HhggXQxwRD6aWFLc8AvUKNhx98CI8/9hgyLMX4IW0TCm2eBr1DQhJxRcwA6JSng1VtCj8s234EFRYbnHYnxPwKaI8VwMcpQq1TY9TMoRh65QCoNbV7TiIiolbVADc4OBiLFi3Cww8/XGNcGpNuIyK6HD777DPcf//deOYhPyz4OA5BgZ7iBcmpdvzvvdUYPLg/1qzZiF69elXuW9oH2LdBdBwC4K58FgUEdc9q+5Y0aOnS0tIwYHASxM4h6PbebdDFeH4uiy43ynadwEsfvI4NxhR0mzFaHvdXe2FWuyHo4h9dq+eX/tZ2JCNf3sckFX5wu0W4SkzQHMmDj8kJKW/2HdcT428eCb8g30a9ViIiosupzjNOX3zxBe644w5MmTIFgwYNkse2bduGP/74Ax9//DFuueUWNGeccSJq+Q4fPowBA/rg92/CMHroufslvfhaKTbt0mLJwn9B4doLiNbTNyrjIUhhSTMAgqJ1vbnvmzQA2cEutLt/8jlnzVw2B0xHstEtsQuu7T8WV8ckQa+qXWDMLiqTA9OxrAK5ZoajwgJNShG8C0xQAIjrEo2pd45HdMfIRrgyIiKiFrZU71RQeuutt3DkiFRxCujatSvuv//+qiDVnDE4EbV89957F4xFP+PzN4POex9pJmTNJhu69+iLiIiIyn1LSZX9lmq/h6cl2blzJ4aMGIZe39wHpbf2vPez5hlg+no7UlbuqNWSRINU+GH7EWw/mgkRIhxmO/TZZdCcKJYLP/iH+GHSrWPQY3iXFr3EkYiI2p7yxlyqJ5EC0rffflvf8yMiuiQ//PAdFnzmf8H7KBQCIsIE/LGqBLf+9b+AKrHVv6n//ocfEDCs8wVDk0QT5IMjWw5i37598t7U87E5PIUfVu/xFH6Q9jF5lVig3Z8tF35Qa1UYMWMIhl+TxH1MRETU6tUrOKWmpuLzzz/HiRMn5FLlYWFhWLZsGeLi4tC9e/eGP0siokputxslJRVoV7l350J27rPimwUO3HZfJ7RG0oIBg8OEAms5CqxlOOZvQsjkXhd9nEKjgsbfC0VFReedrdt6NF0uLy4VfhDdItTlNmj2Z0Nndsj36T26OybOHQ2/4Na11JGIiKjBgtO6devk/U3Dhg3D+vXr5Z5OUnCS/nL56aef4ueff67rUxIR1ZpCoYCvrx4FRS7ERl94liMv34WAgPMv52spHG4nCq0VKLCVyQEp3+r5XGgth93tqrqfvZ0vlCZPsLkQqVCEtD9JWppwduGHAizecgi5JeXyPial1QHNkXyoCoyQ5utiOkVh6rxxiO1cu2ISREREbTY4Pf7443JYeuihh+Dre/ovjWPHjpV7ORERNbaH7x8Pp2vfBe8jhYCvfnLgqWdmoyWQztfktFWFolOfC2zlKLUZpQxzTgpBQKjWD2E6f0RHq/DyP59D+yeuhOICS+cM21IQ4OePfv36nbvwg/S8Tje80kuB44Vy4QffYB9MmjsGvUZ1a/VLHomIiBokOB04cADffffdWePSrNP5ln0QETUE0V0CmH/A327XY8sWF0pKXVVlyM/06XflKDYocO2116I5cYlulNiMnlAkf5Qjv3Imyey0n/dxeqUa4boAhOk8Icnz4YcgjQ9UCs/3QOwg4r2SZ5E7fyuibx5x7te32pH79QY8ed8DUCqVKDNZ5SV5245myIUfpNmowFIb7LvToXADKo0KI64ZhOHXDIJG1/LLtRMREV224BQQEIDc3Fy0b9++xviePXvkzvRERA1NFF2AbQ1Ey2KpZIG8/M6hGI+eY+bjtecDcM1UH6jVnlmQUoML735Whv+8Z8Rvvy2DTqdrknO2uuxVe49OfZZmkYpsFXJ4OhfpCgI03givDEaez56g5KPSXXSmR7r9l/k/YcjwoXDbHIicNQQqX33V7eaTBUh7fSl6RCfgvvsfkCvlrdmbArvTJe9jCrS54dyRDrfZLv9y6Dmiq1wtT6qaR0RE1NbVOThdf/31eOyxx/DTTz/Jv6SljdqbNm3CI488gjlz5jTOWRJRmyU60yGavwFcGZ4BVQIEr5tw1fVR2LInDPc8/g7ufawAfXpoYbOL2LHXhqjIMHz++dcYM2ZM456bKKLMYa4KRfnVQlK5w3Lex6kVytMzR9rT4ShU5weNol41e6pIpVSVCjWKVxxF/uJd8OsTD5WvFpb0YljSC6HU6qHvkYD//LReLvwgrQH0EwUo9uXAnV8uL8uL7hiBKfPGo13XmEs6FyIiotakzn2c7HY77r33XrkRrsvlgkqlkj/Pnj1bHpOWfjRn7ONE1DKIUsNay2KIttXSESDoIehnAJrhcmC5/+8P4tPPP0PgpJ7QxgXDbbFLlSOg0Kph3ZuB0i3J+PrLrzBz5swGKc4gzRSdnkGSQpLna7vbed7H+ar1CK++tK5yL1KgxrvR9gldMf0qbE7OQbsxN8BSnIPyjKNwO+1Qe/sjIKEPFEoVTHkn0K9PHyRER8P7RDEsR/Pk2S7fIB9MmDMKfcb04D4mIiJqE8obuwGuJDMzU97vZDQa0bdvXyQmJqIlYHAiav5E+z6Ilu8Bd6l8LDeu1c+EoPD8m33iqSfx7ucfIf7FmdBFBZ7zOQzbjiP937/h90W/YcKECbV6XaPTWmNp3amvi20VFyzOEKL1rbHvKFyeRfKHXnV59wRlZ2cjrl07dJn1OLR+5y/Xbi3KQ0BWDsaEdYdCqpynVmLYVUkYNXMI9zEREVGbUn45glNLxeBE1NyLP8yH6NjrGVAEQ/C6EYK6e41wEN++PTq/NRde8aEXfL78RTuhXZ+JYwcPV82gSPuLpCp1UrU6eWmdpazWxRmqh6NTXwdXK85wOUiNaM1WByw2B8w2O0xWO8zS11Y71m3cjI++/A4RAyad+8GiCGWRCerdJ2FJO46//GUaug/rIu9jCgy7cENhIiKitp4N6ryYfsaMGUhKSpL3OVX3n//8Bzt27JD3PhER1YUoFUuQiz8skos/AAoIuomAbhoEoeYMyIcffYTAfh0uGpokIZN7I21vOt5c+yMC4sLlPkhSP6TzFWeQBFYrzlA9IPnWojhD7a9XhNXhhMXqCT9S8DkVgCxyGPIEITkQ2ap9ttrlQg7nk5lVBu/IhHO9IBQmO7QHc6EqtcDtdMMMI2596QZ06NmuQa6JiIiotatzcJKa3j733HNnjUtNcV999dV6ncS7776L//73v8jLy0Pv3r3x9ttvy+HsXEaPHi034T3T1KlTsWTJknq9PhE1HdGZUVn8Ib1a8YcbISjPXaXzxwU/w3dal1o9t1KrRuTNI/BH1m508+teozjDqd5Hp8KRFJakJXda5YWb6lbncrsrZ35OBZ3K0GOzy+NSGDr1uXoIksakx9aXAAFeWjX0OjW8tRp46TTycUXGEZQc2wGvsFi5QIRgcUBhtkOdXQZ1XoX8WFGrQlFAOVLyDiEi4eLhk4iIiOoZnKQ9TRrN2Wvg1Wq1PNVVV/Pnz5eb6X7wwQcYNGgQ3njjDUyaNAnHjh2Te0Od6ddff5ULVJxSXFwsh62G2ABORJePKNoAy6Izij9cA2hGXHBmp6SkBMFBPrV+HZfJCo3BiCsnDqgKSGcWZ3A4XXK4KTaY5WVwpsrgcyoQnQpAp2eGPAHIandc0vdApVTCWwpAWg28dWp4ySHojM9aNbx1Gui1nmPpa51GJZ+/NHNVUWpEUVYJCrOKsfN4MYJTHNAYtsNLH1LzxRQC7PFBsLYPRPqChXC7HDAYDPDy8rqkayAiImor6hycevbsKYedZ555psb4Dz/8gG7dutX5BF577TXMmzcPt956q3wsBShp5uizzz7D448/ftb9g4KCznpd6Rc/gxNRSy7+MLCy+MPF99lILRCc5rP3Ism7NaVJHGklm0MBOATApYBpezncXv4oTXEjy5oPiy2ramZIXhZnc8DpOv/yt9rQaaRQczrYyLNBlYHn1GzQqRB0KhBJt6lVtdsb5XK6UJxbioLULBRmlaAoq1gOSkXZxbBV+14Y0k0IEALgKCyEI1QLlX8A3N4auPx0sLcLgluvRMbaHyGWe2affH19L+m6iYiI2pI6B6enn34a11xzDVJTUzF27Fh5bNWqVfj+++/rvL9JmjnatWsXnnjiiaoxhUKB8ePHY8uWLbV6jk8//VTuLeXt7X3O2202m/xxSn1mxYioYYhSUJKLP+ypVvxhNgR1j1o93uK0I2JUT7h8dDUCk1iqgnhS5wlM1V9PFKGxRcIWHo01+1Iu+NxSdbzqIefMAFQVhE6FoGq3KxU1X7e+LEZLzWBUOZNUklcqN6g9JwEICg9ASEwwuo/uhD/+sQhhYgwOFi6AFhEIjhoEpdYLliP7UXxwE9QOBcJdUWg3PJLBiYiIqDGD0xVXXIGFCxfi5Zdfxs8//wy9Xo9evXph5cqVGDVqVJ2eq6ioSO4BFR4eXmNcOj569OhFH799+3YcPHhQDk/n88orr+D555+v03kRUWMUf1gL0boIkPozycUfJgC6v5xV/OFcTE4bNhQckT8CR3VFfkE+XBY7BKse4kk9YK8WXKQvVaL8YcsthCkvDT2H98eoPonycrgay+KqhSCtWnlZehdJYc5QWI7CzKKqYHQqKJnKzOd9nFqnRlhMsByQQqs+ByEoMhAq9ekf5fPXfYPtf+zGEPsE5Bako7BkLZxwQi/q0dXVAz7wwz7tJrz1j/82+rUSERG1JvVqUT9t2jT5o6lJgUlaOni+QhISaTZL2kNVfcYpNjb2Mp0hEZ1V/EHZHoLXTRBUMRd9rNFhxdr8Q9hYeKyq0WykVyBObDkI5w4vBPTpBEGpADQihBgrhBAHhMoMZT5ZgGPPfwvB6cYN776IgQNPF4e4HOw2B4qzqwcjz0xSUU4JnPYLNM0N9kFYTEhVMDoVlKTmtLUJdh998iEGJw1GSs4+xNk7I97VWR53i24UIgf7tVtwy+1z5T+CERERUSMHp1PL7AoKCuT9BtXFxcXV+jlCQkKgVCqRn59fY1w6joiIuOBjTSaTvL/phRdeuOD9tFqt/EFETVH8YTFE26pqxR+uBjQjLxoADHazHJi2FCXD4fbsP4rSByIB0djtDMBuIRcKtR6GPSlQJbjhMyoUSi9PNTx7iRGFy/Yi7+ft8I3shAidGwMGDGikaxRhNJhqBKNTQUmaVTofpUqBoMigypmjU589QUmrv7SfV6Ghodi+czse/vvD+P6HH+Cn8YdK0MDoLIOfvy9e+edLuPfeey/L7BoREVGbDk7Hjx/Hbbfdhs2bN5/1BkL6RSwtvastqTpf//795T1SV111lTwmBTHp+L777rvgY6X9VNLepZtuuqmul0BEjUx07Idoloo/lMjHgnoA4CUVfwi44ONKbEaszj+IbUUpVb2W4ryC0VkZi8P7S7C2IEMei46MwIlDuyGqdcj9bhPsHxmgDQ+E6HLDllsMn9iOiB06A0W7luGJN1+/5JAgFWcozTfI4ajGErvsYlhNp/dQnknvq0NodGUoij29xC4gzE/+o1FjCQ4OxhdffYHX33wda9eulf/QFBMTgxEjRjTq6xIREbVmdQ5Ot9xyC1QqFX7//XdERkZe8hsSaRnd3Llz5b8IS0vupHLk0i/5U1X25syZg+joaHmv0pnL9KSwJb1BIKKWXfyhyFqOlXkHsLPkBNxyeTygg08Yuqnb4eD+IvyRc0Ie06iUGNU7AU9eNxITJ4xHZo4JcaNuhAARdqMBgkIJ7fBQ2AwFyN+yADffcL38M6S2LCbrOWePSvIMcLvcFy3OUH3mSPrs7de0pb4DAwNx9dVXN+k5EBERtdngtHfvXrkSXpcutWtAeTGzZs1CYWGhXN5caoDbp08f/PHHH1UFIzIyMuRKe9VJPZ42btyIP//8s0HOgYgaovjDOojWhaeLP2gnAPoLF3/ItxjkwLS75KS0mE/WyS8SPbXxOHSgGIvSj8ljKoUCw3q0x4R+ifD18lTU++KzTzFwYBJKf30DXoGR0IfGQHS7UJF1DE67BYF+fnjttVfP+uPOqeIMZ1auk2aPjKWm856rWqtCSLQnHEmzR6eCUlBkANSa2jfNJSIiopZJEKV3EXUwcOBAvP766xg+fDhaIqk4hL+/P8rKyuDn59fUp0PUJos/ZJtL5MC0vzS9KjB1849GP++O2L+/AHtSs6tKhA/qEodJAzoj0Lfm7M24MeOwdu06DMBoVKAUFpghQIAvAuCGC0ewC/958X+YMHJSjcp1RdkXKc4Q5IOQ6Gr7jiqX2PkF+3JfEBERUStTl2xQ5+C0evVq/POf/5TLkUsV7dTqmn9pbe5hhMGJqCGLP/xWWfzBDQi6asUfzt3XKN1UhJW5+3GoLKtqrFdAHAb6JmLfwXxsP5oJsTJK9esYg6lJXRAa4HPOf8eB/kGIQjy6CH3PcW4iSlEIrV6DcePHnbs4Q0QgQmOl6nWnQ5IUmHReLCZDRETUVpTXIRvUeame1JxWMm7cuEsuDkFEraX4Q3/A67rzFn84YczHitz9OFae67k/gD5B8Rjs1wn7Dhfgs0O74Kys0NkzPhJTB3VBVLD/eV9fqrwpzS75Ieict0s/i1SiGhaHBXFdoj2hqNreo8BwfxZJICIiojqpc3Bas2ZNXR9CRK2E6DZUFn/YXa34ww0Q1D3Pvq8o4nhFnhyYUo35VUvv+gd1wJDAzjhwOB8f/rkDjso/tnSKDsW0QV0RH3HuMFRdQEAAfOAPb5w9G3XKceyHJkCBef+5uf4XTERERFTf4DRq1Ki6PoSIWkPxB/t6iJYF1Yo/jK8s/qA9KzAdKc+WA5O0NE+iFBRICk7AsOCuOHAkH++v3Aar3SHfFh8eJAemTjGhtTqXE/vTseKrdejpMwAmo0lu7Ko4Y2lgmVgCA4rwz7v/2WDfAyIiImrb6rzH6RSz2SxXvJMa4VbXq1cvNGfc40RUN6IzE6L5W8B10jOgjK8s/hBb836iiAOGTKzM248ss2cJn1qhxOCQRAwP7oKDyQVYufs4jFZP3yNpKd5fBnVFt3bhtSq6kHksWw5MJw94ejmVlpVi4YafYBIrkIie8rI9aX9UIXJwFLuh8VIjPSOdLQuIiIioafY4SaXDpR5Ly5YtO+ft3ONE1JqKP/wO0bbydPEH3VWAdlSN4g/SjM/e0nSsyjuAXItBHtMoVBga2gnDQ7riSGoh3lm9BQaTRb4t1N9HLvrQt2N0rQJTXloBVn69Hsd2pFQVdhg4uS9GzhyCYX/0wZyb52CXax0UUMINt7z3yT/AH5u2bGRoIiIiogZT5+D04IMPwmAwYNu2bRg9ejQWLFggb9R+8cUX8eqrrzbcmRFRkxEdByqLPxTLx4K6H+A1q0bxB6fbhd2lJ7Eq7yAKreXymE6pxvDQLhgR2gXH0orw7i+bUVTm6Y0U4KPHlIFdMLBzLJRn9GY7F6mv0urvNuLAhiOeAQHoN64XRl8/DIFhnsIRN9xwA6ZNmyY3xF69eg10Oi1mz56N6dOns/gDERERNe1SvcjISCxatAhJSUnydNbOnTvRqVMnLF68GP/5z3/kxrTNGZfqEV2s+MOPEB27PAOKoMriD71qBKYdxalYlX8QJTajPOal0mBkWFcMD+mC45nFWLrtKPJKPWHKR6/FxP6dMLR7PNS1CDOGwjKs+X4T9qw+ANHt+fHUY3hXjLtxuNyAloiIiKhFLNUzmUwICwuTvw4MDJSX7knBSerptHt3ZaUtImpR5L+f2NedUfxhHKC/oqr4g8PtxNaiFKzJPwiD3SyP+ah0GB3eDUNCOiE914D3F21FRkGpfJteo8b4fokY0bMDtOqL/6gxGkxY9+Nm7PhjD1xOT2nyTgMSMP6mkYjsEN6o109ERER0MXUOTp07d8axY8cQHx+P3r1748MPP5S//uCDD+TZKCJqWURnFkTzN9WKP7SrLP4QJx/aXA5sLkrG2vzDqHB49in5qfUYG9FDLvyQlV+GT37fjpQcTwU9KSSN6pWAsX06Qq+t2SD7XCxGCzb+uh1bftsBh80pj8X3iMOEOaPkHkxERERELTI4PfDAA8jN9TSxfPbZZzF58mR8++230Gg0+OKLLxrjHImoEYiiHbD+DtG64pzFH6wuOzYUHMW6gsMwOz3VMwM13hgX0QMDgxOQX2zE50t34HCGp0eTSqnE8O7xGN+/E3z1NUuUn4vdaseWxTuxccE2WE2eSnvRiZEYf/NIJPSOr1XhCCIiIqJmX468elnyo0ePIi4uDiEhIWjuuMeJSCr+cBCi+btqxR/6VhZ/CITJacOGgiPyh8Xl6bUUovWVA5PUvLa4zIxl249iT2p2VVPbwV3bYeKAzgj00V/0tR12B3Ys24v1P2+Bqcyz5C8sLgTjbx6FLkkdGZiIiIiodexxOpOXlxf69et3qU9DRJet+MNPEB07PQOKQAj6GyBoesPosGJt7m5sLDgKu9uzZC5c54/xET3RJygeZRVWzF+zDzuOZcr9kqSy3/07xWDywM5yifGLcTld2L3qANbO34Tyogp5LCgiAONuGomeI7oyMBEREVGzVqvg9NBDD9X6CV977bVLOR8iarTiD+sriz9I+5SEyuIP01HucGFN5g5sKUqGw+3pwxalD8SEyF7oFRCHcrMNCzYcwJbD6XC6PUUberaPxNSkrogK9qvVa+9ffxirv92AkjxPnye/EF+MuX44+o7tAaWKZcOJiIiolQSnPXv21OrJ+BdjouZa/OFbwHWiRvGHUlcQVmfuwbaiFLhETyCK9QrGxMhe6OYfA7PNgd+2Hsb6/SfgqGxs3TkmDNMGdUW78MCLv64o4uj2FKz6Zj3y0wvlMW9/L7lx7cDJfaDWXLxwBBEREVGLCk5r1qxp/DMhosYt/gAtBP1VKBb7YVXWYewoWQd35RbH9j5hcmDq5BsJq8OJP3cmY/W+FFjtnj1O7SOCMG1QNyRGh9QqMKXuS8PKr9cj+7inkIzOW4vhVw/CkOkDoNFpGvnKiYiIiBreJe9xIqLmR3Qc8swyVRV/6INCYQpW5GZhd8linKoIk+gbIS/JS/AJh9Plxpp9qVi5Kxkmm6eKXkyIvzzD1DUuvFYzyulHsuTAlHYwQz5Wa1UYMn0ghl+dBH0tCkcQERERtargtHPnTvz444/IyMiA3e55g3XKr7/+2lDnRkR1JLrLAMtPEO07PAOKQBQJU7GsQMT+0vVVgamrfzQmRPREvE+YHJg2HUrDnzuPocwsNb8FwgJ85D1MfRKiahWYck/kY+U365G8M1U+VqoUSJrST16W5xPg3XgXTERERNRcg9MPP/yAOXPmYNKkSfjzzz8xceJEJCcnIz8/H1dffXXjnCURoaCgAKtWrYLRaERwcLD8b8/Hx6da8YcNEC2/VhV/KEESfi+Kwn5DWtVz9AyIw4TInojxCobbLWL70Qz8seMYiitM8u1Bvl6YPLALBnSKgVKhuOg5FWUXY+U3G3Bo01H5WFAI6DeuF0ZfPxQBof6N9r0gIiIiavbB6eWXX8brr7+Oe++9F76+vnjzzTfRvn17/PWvf0VkZGTjnCVRG5aWloYHHv47flu4GGqdAmqFCJtTgOAWcOvtt+O/Lz0AH9ViwOmZ7Sl3h2JZaXfsKJP2JxVAmi+SyomPC++JKK9AOWTtS83B0u1HkFfqKQvu56XDxP6dMLhbO6iVF69yV1pQhjXfb8Se1QdwahpLKik+9sYRCIkKatxvCBEREVFLCE6pqamYNm2a/LVGo4HJZJKX8vz973/H2LFj8fzzzzfGeRK1SVJz6QFJA2GzmqDVAHOv9UL3zlqs32rBkpVGWEu/x+YVKzB61AjYFSpsqOiM1aWhEOGQG9NKDWulxrVhOn85MB1Oz5cDU2ahpyy4l1aD8X0TMaJne2jUF/9xUFFqxLoft2Dn8j1wOT2V+LokJWLcTSMQER/W6N8PIiIiohYTnAIDA1FR4fkrdXR0NA4ePIiePXvCYDDAbDY3xjkStUlOpxOjx42FyWxGkJ8CO5bHIj7WU8L73tsCkF/oxM59NrjUIj7fnoHsmJkwu72gFBQYGJyAsRE9EKL1le+fmlOEJduOIDXXUyxCq1ZhdO8EjOndEXrtxcuCmyss2PjrNmz5bSecdk9z3A692mH8zSMR2zm6Ub8PRERERC0yOI0cORIrVqyQw9LMmTPxwAMPYPXq1fLYuHHjGucsidqgxYsXo6S8FH5hejx9t74qNEmkLU36QD3U7QPx2dEo7M3WY7w/MD6+sxyYAjSeggwZBQZ5hulIRr58LC3DG96zPcb1TYSvXnvRc7BZbNi8aCc2LdwGm7my0l6nKEyYM0oOTkRERERtRZ2D0zvvvAOr1VN566mnnoJarcbmzZsxY8YM/POf/2yMcyRqk95853Ukxoro21OFmdN9qwJTuUuNQoceu42hWJwfiIMFbhi2J0NljsU1/3eXfL+8kgo5MO07kSMfS4UeBndtJ+9jCqhFWXCH3YHtS/dg/c9bYC6Xik0A4e1C5cDUaUACm10TERFRm1Pn4BQUdHrjt0KhwOOPP97Q50TUJomizVPgwZkC0ZmMR+7IApThUCo8gcktAmlWPxy1BGCfMRhGtwa6lGSUrCxH4eqjOOIKRVGZCX/sPIadxzIhQoQAQa6QJ1XKC/G/eFlwl9OF3Sv3Y838TagoNspjwZGBGHfTSPQY3oWBiYiIiNqsS2qAK202X7NmDSwWC4YOHSrvfyKi2hHdZsCVAjg8QQmudACeggueSnVOlJWJSNeEY+n7aej71yE4aA2GWhAxNiAb3VxZ6PvvEwi4cyqUmzKQbtbi5e9XweX2PEfvDlGYktQFkUF+Fz0Xt9uN/esOY/V3G1Ga7ykc4Rfii7E3DEefsT2grEWlPSIiIqLWrNbBSSr+IO1n2r17NwYPHoxXX30VU6dOlZfpScLCwuS+Tr169WrM8yVqsUR3BeA8Ln+I0mdX1qmEdJoiEIKqE/YeNOHeJ75GVq4dnf9vDFyB0di2zgvdO9hxc9BhZK/Kx13/LYG6ZyeoTGFoN+56uEQBJrMFfRJjMW1QV8SFBdbqjx9HtibLvZgKM4vkMW9/L4y6bigGTu4DVS0q7RERERG1BbV+V/TII49gy5YtmDt3Ln777TdMnjxZftMljUlL9h599FF5z5N0GxFJQalUXnYHZzJEx3HAnXv2nRRhEFSJgPzRCYIyWB5+9uWrYNb3gCowGW6LA37Du8KSUYTFTy7BN8kF8IsNRvDU2QgKS0TFgWxAdEBwOeBVcgx3XzHr4ucmikjdm4aVX69DdkqePKbz1mLEjMEY/Jf+0Og0Df8NISIiImrBBFF6B1ULUunx7777DqNGjUJ2djZiY2PlanqjR4+Wb9++fTumT5+OvDzPm7Dmqry8HP7+/igrK4Of38WXMBHVhvzPyF0sh6SqGSV34dl3VEZVC0qJEBQBZ93F5XJBq9Wh8y0PwxGdCaicUOjU8IoPgdLbC2KeBmKOFm6TC+YT+TDn5UJ0OaH28ocqYzuOHz1ywXNNP5yJlV+vR9qhTPlYrVNj6PSBGHZ1EvTeuob7phARERE1c3XJBrWeccrPz0enTp2qQpROp5PD0ylxcXEoLDzHG0WiVhuU8s8ISqVn3EsAlLGVQakToOoIQeFz0eeW/uGqo/yhS9JAp+oAw+HDSH93OVzlNrSbOgsa32C4rSUw5+TCkLYXGq8AxI26Hub8DBSXlJz3eXNS8+TAdHz3CflYqVYiaUo/jJo5GN61KBxBRERE1JbVOjhJm8erbxCXvq5eYYvVtqjVByVpT1L1PUqipxH0aQpAGQ9BXRmUlB0gKLzq/Fpp9mJEzxkJKN0QTFr4mgYhortUQtwNmFSwlhWi6MhWQHAjrOdo+MV1k//9uV1OaDRn92YqyCzC6m834NDmY/KxoBDQf0JvjJ41FP4hnHUlIiIiqo067fz+5JNP4OPj+Yu50+nEF198gZCQEPm4ouLMN5FELZcoOgFX5hlBydPP6DQ1oGpfbUapAwTh0vYG7So+ge9ztkLv4w1bZhk0he2hgIDgTv3l26Vw5LQY4RfXFUpNzWV15ryT6Nq1a9WxVB1vzfebsGfNAU8NCgHoNbI7xs4eLpcYJyIiIqJG2OMUHx9fq1mlkydPojnjHic6F1F0AM600yFJ6qcE2xn30gKqBE9QUkszSu0gCOoGO4cNBUewIHOH/HXp7hP4/d8/I3bC7bU7f7cLx3/8P3z/1ecYNWw01s3fjF0r9sHl9JQm7zIoEeNvGik3sSUiIiKiRtzjlJaWVtu7EtWLlOG3bt2Kr776HDnZaXK1xti4jrjttjvQp0+fRm02C6cU+J017yR4Vc4mdaxcehcLQVA2ynUvz92HP3P3y8cjwrpgyBUTMf+Rd2A3lUPjffGAX3xkCwJ03lAVeOH1Oz+E0+65loTe8Rh/80jEdIpq8PMmIiIiakvYpIWahRUrVuDRf9yPEydP4MZrfDBukBLSXOi+wzswbNhH6N2rB157/T25h1j9m82mAo7jZzebPUXwlXsonap4B2V0o+/dk0LTL5nbsLkwWT6eHNUbEyJ6ocBgxPT7nsXOvQekFXZQXyA8lR7dCdfm3ZgxcjY2L/LMWMV1iZYDU/ue7Rr1/ImIiIjaCgYnanJSmft5827By08E4LbZsfD2UtS4/X/PBuC9zzMwfvxo/Pjjr3Lj5YsR3cZq+5OSL9Bs9tT+pERAEX5Zi5w43S58n74Je0rS5HB0TdwgDAvtjOyiMrz322bovP1gryhBxqof4BvbCSE9h8MrLE4+R1F0ozz9KFzb98G3VAkVYuCl9UJE+zCMv3kUOvWX9luxYAsRERFRQ2Fwoia1efNmzJt3K379LBQTRp27JHaAvxJPPhiEjh3UmDVrBrZu3Ynu3bvXuI/oNpwOSo7k8zSbDa2cUZKW3nUGFEFNFi5sLge+PLEOR8tzoBAEzI4fjn5B7ZGWV4IPft8Ci92BnBPHIOw6jP62Icg6cQKpJ96DoFRBqdTBz+GDcHc0fMUg6OAFg6oQmkQR97xxKwMTERERUSNgcKIm9dKLT+Ohv/qeNzRVd910X6zZZMf//vsyPvv0zcoeSpV7lM7ZbDayxtK7czWbbQompw2fpKxCuqkIGoUSczuMRlf/aKRkF+GjpVthczjRPiIIPy/7DuHmOPgIfugi9kFHsQesbhPUDg0UkGaZ1FUhyeSswNbDmxiaiIiIiBoJgxM1GakC44qV6/DBS3G1fswzD/tj5brVsBf9A2qN5oxmszHVgpLUbNYXzY3BbsZHKSuQZymDl1KDOzqORbxPGI5k5OPTZdvhcLmQGB2KeVMH4YXbKuRwdIpKUMEH/ud8XilGmYymy3glRERERG2LqrZl+mqLJb6pthYsWIBxI/0RHVn7/B4ZpkJ8nBZ5+UWIjR9e2WxWKuSQUK9ms5dTobUcHxxfgVK7CX5qPe5KnIAIfQD2n8jFl3/ugNPtRvd2Ebh10kCoVUqEBIfAUmaCP4Iu+txWwYzomA6X5TqIiIiI2qJavWMNCAio9RIgl8t1qedEbURRURHi6lEle+EyKzp0G4W/3f8IWopscwk+PL4SRqcVIVpfOTQFaX2w63gWvlm5C25RRJ+EKNw8fgBUSk9xjFvvuAVvv/A+ImyxF3xut+hGkS4bc2/972W6GiIiIqK2p1bBac2aNTX6OT3++OO45ZZbMGTIEHlsy5Yt+PLLL/HKK6803plSq6PVamF31H1PTvIJFzr1vvieqOYipSIPn6auhs3lRLRXIO7sOB6+aj22HknHD2v2QoSIgZ3jcMOYPlAqTlcUvO6qWfj6nz/DLlqhEXTnff4sIQV+gX6YPHnyZboiIiIioranVsFp1KhRVV+/8MILeO2113DDDTdUjU2fPh09e/bERx99hLlz5zbOmVKr06VLF3z3tV3uZVTbGU2bzY2de014+PHOaAkOGjLx1cl18jK8BJ9w3JYwBnqVBuv2p+LXjQfk+wzr3h4zR/aq8T3ISc3DL/9eisF9hmLX3p2AW4FQRNa4j0t0IVM4jmxNKtYuXAulsuGb8xIRERGRR82GObUgzS4NGDDgrHFpbPv27XV9OmrDrrrqKpSUCVi90VLrx/y61AR//xCMHj0azd32ohR8nrrGs3cpIAZ3Jo6TQ9PK3clVoWlMn45nhaYj247jk8e/QUWJEX0G98Y/PvkbcoNTsEO3CiniAaSJx3BcsQ9bNcshxluxbsM6JCUlNeGVEhEREbV+dQ5OsbGx+Pjjj88a/+STT+Tb6urdd99FfHw8dDodBg0adNHwZTAYcO+99yIyMlJe6tWpUycsXbq0zq9LTU/673f77X/Fi68b4XCc0Zz2HExmN/7zrhl33/N3KKotaWuO1uQfwg/pm+WWuwODE3BLh9FQCUr8vu0wftt6WL7P5AFdcOWQ7lWhSZp527RwO757+Rc4bE4k9InHvP/chJtunY2cvGx88d1nGD13CHpdlYjJd4zFsj+X4nhqMgYOHNjEV0tERETU+gmi9G6tDqSQMmPGDHTs2FEOOhIp7Bw/fhy//PILpk6dWuvnmj9/PubMmYMPPvhAfq433ngDP/30E44dO4awsLCz7m+32zFs2DD5tieffBLR0dFIT0+Xi1f07t0bta0Q6O/vj7KyMlYAbAYqKiowcsQgJLbLw5dvBUGrPXcgMprcmDmvGFZnZ/y5Yp0cupoj6Z/Tkpw9WJ13UD4eHd4NV0T3l79euOkg1u5Plb+ePrg7xvVLrHqcy+nC7x+uwM7le+XjgZP7Ytpfx3P5HREREVEjqks2qHNwkmRmZuL999/H0aNH5eOuXbvirrvuqvOMkxSWpL+Wv/POO/Kx2+2Wn+Nvf/ubXIDiTFLA+u9//yu/rlp9ur9NXTA4NT8FBQX4y7QJmD4+H7Ov1iMiXAldZYAqLnHhyx/L8d4XVnRI6INffv1d/u/XHEnV7X7K2IptRSny8V+i+2FsRA85TP24bh82H06Tx68d0Qsjep4uHW4xWTH//xYidV+a3I5qym3jMGT6ADazJSIiImrpwakhSLNHXl5e+Pnnn+W9LqdIxSWk5XiLFi066zHSbFZQUJD8OOn20NBQzJ49G4899th5/zJvs9nkj+rfHCmcMTg1L1ZLMXKTb0Fm+nHc/2QGKkxqOXDk5FkweFB/3HPvQ7jmmmugUjXPns0OtxPfntyI/YYMKftgZrshGBySCJfbje9X78GO5EwIEHDD2L4Y1OV0w9/SfAO+fv4nFGYVQ61TY+bD09F10OmZKCIiIiJqHsGpXu9CN2zYgA8//BAnTpyQl9ZJS+a+/vprtG/fHsOHD691Dx+p51N4eHiNcen41EzWmaTXW716NW688UZ5yWBKSgruueceOBwOPPvss+d8jFQi/fnnn6/HVdLlpBV2oF1cFNq1H4T5v1yLnJxceR+TFHI7dGjejV2tLgc+S10jlx1XCgrc3H4EegW2g9PlxlcrdmLfiRy5zPhN4/qhX2JM1eMyjmbju5d+ganMDN9gH9z89ExEdqj574GIiIiImoc677CX9jFNmjQJer0eu3fvrprNkVLayy+/jMYkLeWT9jdJZc/79++PWbNm4amnnpKX8J3PE088IZ/bqQ9pmSE1L6LogmhbK38taMegc+cuGDNmjFwGv7mHJqPDiveT/5RDk0ahkivnSaHJ4XThk2Xb5NCkUihw2+SkGqHpwIYj+Oyp7+TQFNE+DHf9by5DExEREVFrCk4vvviiHFSkynrV9xlJRRukIFVbISEh8vK6/Pz8GuPScURExDkfI1XSk6roVV+WJ+2vysvLk5f+nYtURECadqv+Qc2MYx/gLgUEb0DTcirEldqNeDv5D2Sai+Gt0uKeThOR6BsJq92JD37fgiMZ+VArlbhz2mD0iPf8Py0tP1w7fxN+/O8iuBwudElKxLx/3wS/YN+mvhwiIiIiasjgJFW8Gzly5Fnj0tpAaW9SbWk0GnnWaNWqVTVmlKTjIUOGnPMxUjiTludJ9zslOTlZDlTS81HLJNrWyJ8F7XAIQsv475hvMeDtY3+g0FqOAI0X7us8GXHeIbDYHHj/t81IySmCTq3C3VcMQedYT4VIp8OJX17/Hau+3SAfD71yIG548mpodC3jmomIiIjasjoHJ2k2SAovZ9q4cWOdl1U99NBD8szVl19+iSNHjuDuu++GyWTCrbfeKt8ulSqXltqdIt1eUlKCBx54QA5MS5YskZcHSn2dqGUSnVmAM9nzv6K2+Te1laSbiuSZJoPdjDCdH+7vPAXhOn8YLTa8s2gj0vJL4KXV4N4rhyEhKkR+jLnCgi+e/gH71h6CoBAw/Z5JmHL7uGbfj4qIiIiI6lkcYt68eXJw+eyzz+RyyTk5OdiyZQseeeQRPP3003V6LmmPUmFhIZ555hl5uV2fPn3wxx9/VBWMyMjIqPHGUioUsHz5cvz9739Hr1695KIU0rlIVfWohTq1t0ndG4IiCM1dcnkOPktdC7vbiVivYMxLHAcflQ5lJiveW7wJeaUV8NFrce/0oYgK9pRNL8oulivnleQZoPXS4PrHr0bHPu2b+lKIiIiIqA7qXI5curs0yyNVqzObzVX7iKTg9K9//QvNHfs4NR+i2wSxTOrXZYfg8zAEdSc0Z/tK0/HNyQ1wiW508ovELR1GQ6dUo6TcjHd/24SiMhMCvPXyTFNYgI/8mJMH0vHdKwtgNVoREOqHm5+7DmGxnlkoIiIiImoDfZykYgzSkj2j0Yhu3brBx8fzRrG5Y3BqPkTrnxAtvwDKaAi+Tzfrhq9bCpPxc8ZWSP9Yege2w43xw6FSKFFoMOKdxZtgMFoQ7OuNe68cimA/b/kxu1fux+L3/oDL6UZMpyjc9PQMePt7biMiIiKiVt7H6auvvsLAgQPlanZSYDrFarXixx9/lPclEV2MKLprlCBvrqFJ+rvCyrwDWJazVz4eEpKIGXGDoBAUyC0px3uLN6PcbJVnmO6ZPgyBPnrPY75ej/U/b5Ef02N4V1zz4FSoNaerUBIRERFRy1LnGSdpz5G3tze++OILzJgxo0YZ8aioKLmpbXPGGafmQbTvg2h6DxC8IPj/u1lW05P+aSzK2on1BUfk4/ERPTElqo8c8jILDXh/8WaYbHZ5L9M9VwyBr5cODrsDv7z2Ow5tPiY/ZvSsYRg7e3izDYZEREREbVl5Y844SZ5//nncfPPNOHDgAJ577rn6nie1YVUlyDXDmmVocrpd+DFjC3YWn5CPr4wZgFHhnhnWk3kl+PD3LbDYHYgLC8RdfxkCb50GRoMJ3/zrZ2Qfz4VSpcCV901B37E9m/hKiIiIiKgh1Cs43XTTTRg6dCiuvvpqHDx4EF9//XWDnAy1DaIrF3BKszhCsyxB7nA78dWJ9ThUlgWFIGBWu6EYGJwg35acVYhPlm2DzeFEQmQw5k0bDL1Gjfz0Qnzzwk8wFJZD76vDDU9cg/Y94pr6UoiIiIiogdS5icypJUeDBw/Gtm3b5AIRUohKS0trqHOi1u7UbJO6FwRl86owZ3Ha8eHxlXJoUiuUuLXD6KrQdDg9Dx8t2SqHps4xYfjrX4bIoSl5Vyo+evRrOTQFRwXhzv/OYWgiIiIiauszTtW3RMXFxWHz5s248cYbMWHChIY+N2qFRLcZom2r50A7Bs1JucOCj46vRI6lVC4zfnvCGCT4Rsi37UvNwVcrdsLpdqNnfCTmThwAtUqJbUt34/cP/4RUbi++RxxueOJqePnqm/pSiIiIiKipg9Ozzz5bo/S4l5cXFixYII+vX7++oc+PWhu7VGnOBigiAVUXNBfFtgp5pqnIViE3tP1r4nhEe3ka8u44lolvV+2GCBF9O0bjpnH9oRCApR+vxJbfdsr3kfYyXXnfZChVyia+EiIiIiJqDPXu49RSsape05H+VxPLnwbchRC8boDQTPY35ZhL8VHKSnnGKVDjjbsTJyBE5/l/Y9OhNPy0bp8cmgZ1icP1o/vCaXdg/n8WIXlnqnyfCXNGYcSMwaycR0RERNTWq+otXrwYU6ZMgVqtlr8+H+mN4xVXXFH3M6a2wXlIDk0Q9IBmCJqDk8YCfJKyChaXA5H6ANzZcTz8NV7ybWv3pWLBpgPy1yN6dsCM4T1RXlwhV87LO1kAlUaFGQ9Ok/s0EREREVHrVqvgdNVVVyEvLw9hYWHy1xcKTs29jxM1HdG6Wv4saIZCELRNfTo4XJaFL0+sg8PtQrxPKO5IGAsvlVaeGftzVzKWbq/s39Q3EX8Z3A05qXn49sVfUFFihHeAF258agZiO0c39WUQERERUXMJTm63+5xfE9WW6Mr3zDg1kxLku0tO4Lu0TXCLIrr4RWFuh1HQKtVyaPp962Gs3HNcvt/UgV0xcUAnHNl2HD+/uhgOmxNhcSG46ZmZCAzzb+rLICIiIqLm3MeJqP4lyLtDUIY16alsKDiKBZnb5a/7BbXH9e2GQqVQyqHp140HsP6Ap+ntVUN7YHTvBGxauB3Lv1gjV85L7NcB1z16JXReTT9jRkRERESXT62C01tvvVXrJ7z//vsv5XyoFRJFK0S5ml7TliCXgtHy3H34M3e/fDw8tDOujk2Sl5i63SLmr92LrUfTIUDAzFG9MbhzLBa/txw7l++V7580pS+m3jkeSiUr5xERERG1NbUKTq+//nqtnkx6A8rgRGexbZE2OAGKMEDVvUlOQZ5NytyOTYXH5OPJUb0xIaKX/P+s0+WWy43vTsmSQ9ON4/qhR0wovn7+J6TuS5NXF069YzwG/6U/K+cRERERtVG1Ck4nT55s/DOh1luC3LZW/lrQjmmS4OF0u/B9+ibsKUmTMhCuiRuEYaGd5dscLhe+XL4TB9JyoVQoMGdCf8R5e+Hjf3yNwqxiqHVqzPrHleg8sONlP28iIiIiaj64x4kal/MI4M6T1ugB2stfgtzmcsiV846W50AhCJgdP1ze1ySxO5z49I/tOJpZAJVSidsmDYSPxYmPnvsapjIz/EJ8cdM/r0Vkh/DLft5ERERE1AqCU1ZWltzPKSMjA3a7vcZtr732WkOdG7WmohBaqQS5/rK+tMlpw6cpq5FmKoRGocTcDqPR1d9TPtxid+DjJVuRmlsMrVqFO6YMgvVEIT5/cwlcDpcclm565lr4Bfle1nMmIiIiolYSnFatWoXp06ejQ4cOOHr0KHr06IG0tDR5SVa/fv0a5yypRRJdhRAdngayl7sEucFuxkcpK5BnKYOXUoM7Oo5FvI+nmp/JaseHv29BekEpdBo1/jp1MNLXH8Xq7zbIt3cZlIiZD18BjU5zWc+ZiIiIiJovRV0f8MQTT+CRRx7BgQMHoNPp8MsvvyAzMxOjRo3CzJkzG+csqWWyrZPiE6DqBkEZcdlettBajrePLZNDk59aj3s7T6oKTRUWG95ZtEkOTd5aDe6eOhi7f9hcFZqGXT0Is5+8hqGJiIiIiC5txunIkSP4/vvvPQ9WqWCxWODj44MXXngBV155Je6+++66PiW1QqJog2jfKH8t6C5fCfJscwk+PL4SRqcVIVpf/DVxPIK1nuV2BqMF7y7ehAKDEb56LW4d2w8r3lyGjCNZEBQCrrh7EgZO6nPZzpWIiIiIWnFw8vb2rtrXFBkZidTUVHTv7ikxXVRU1PBnSC2TfRsgWgBFCKDqcVleMrUiD5+mroHV5UC0VyDu7DgevmrPvqrichPeXbQZxRUmBPjoMTupGxa/tAAleQbovLWY9dhV6NjHUzSCiIiIiOiSg9PgwYOxceNGdO3aFVOnTsXDDz8sL9v79ddf5duIPCXIV8tfC9rREIQ6rwits4OGTHx1ch2cbjcSfMJxW8IY6FWe5XbSDJM00yTNOIX4e+MvCXH4+fmfYTXZEBDmj5ufnYmw2JBGP0ciIiIiakPBSaqaZzQa5a+ff/55+ev58+cjMTGRFfXIw3kMcOUC0ACaYY3+ctuLU/Bj+ha4RRHd/7+9+wCPqsz6AP6fmSST3kiBkNBrqNJ7F5QiiBQLgqzl0xV7WRGpIpa1oCssLqIouIuoqIgC0ruE3ntLAqSRXmcyM99z3jgxgbQJM2SA/+955sudO/feeW+Y9ZuTc97z+odjbN0ecNUWfLQvXUnDnOXbkZmTh+oBPujq54+f3v0ZZpMZEY3D8NAb98HLz8vhYyQiIiKi2yxwkm56Rcv25s2bZ+8x0S3TgrwTNFpPh77VxvijWB67W223r1Yfo2p3hu7PDFd0Qgr+/csOZOcZULOaH5pkmrHmf2vUay26N8W9zw2Eq5urQ8dHRERERLf5ArgyzykhIQFms7nY/lq1atljXHSTspiuwGI8UPBE39uh5YC/XdqHdXGH1fNeoZEYUrMtNBqNen728hXVcjzXmI+IID8En0rGrp2nCo4d3RV9HuxWeCwRERERkd0Dp5MnT+LRRx/F9u3br/kiK19ETSaTrZekW4nB2oK8MTS6MIe8hdlixvfRO/FHUkEgNKjmHegT2rwwEDoRk4D5v+2E0WRCrUBf6Hecx+kz8dC5aDHsmYFo3fvGNKsgIiIiots4cBo/frxqQ75ixQrVVY9/tScri8UAS96fLcj1fSp1DQm8N27ciOjoaPXZktLQbt26QastKL/LN5uw+NwWHEyNhnzyRtTqhM7BjQrPP3w+Dl+uilJNIiJ8vZH/+zGkJmXAw8cdD026D7UjI+x0t0RERER0O7E5cNq/fz/27NmDJk2aOGZEdPMy7AIsWYC2GuDa0qZTpcnIxx9/jE/+PQdp6Wlw8faAREbGtGyEhoTghWeew6P/9zi+id2OUxlxah7Tw3W7o2VA7cJr7Dt9EYvW7oHJbEaYXo+sXw4iP8eAamGBqnNetRoBDrhpIiIiIrod2Bw4RUZGcr0mKqUFubUpRE+bWpAnJiai1519cS4uBoasXLj4esC9aQ25KIyHonE5ORGTP5qFJen70PGunvB088CjDXqjoU+NwmvsPB6N/63fBwssCMnXIGP1AWgsQN0WtfDAxHvhIYEYEREREdGNCpzeffddvPrqq5g1axZatGgBV9fiXcl8fX0rOxa6mZnOAKYYAK42tSDPy8vDnXffhbOJsbBogPqvDYXvHXWh0RaUgFpMZqTtOYf89Gyk6fOxe0cU/vvom6jjE1J4ja2Hz+G7zQfU1Crf1Fzk7Dyvyvja9GuJe/4+ADoXnUNumYiIiIhuHzYHTv369VM/+/btW2w/m0Pc5nL/XPDWrQM0Wu8Kn7Z06VKcPHcaZh3QdPY4uAUWP1ej08K/Q32Ycg1I3XEKh75dhxNNh6POgAHq9XX7TmH5jiNqXSaPi2mwHI1XQdOdY3ui+32dOAePiIiIiKomcNqwoaAci8jKYk6GxbivUi3IP/hkNkwuQNiD3a4JmorSubup+U5GoxGzP/0E/fv3x8pdx7F69wnkG/LheioRbjFpcHVzwYgXh6BZl8bXe1tERERERJUPnHr27GnrKXSry9ssTcIBl4bQuFS8a11sbCwO7NkHrbsrAntGlnt8tV6RiPl8PVb9uhLfbdyHbceikZuVB5djcfBJyoF3gBceemMEwhv+NfeJiIiIiMgeKj6Dv4gtW7ZgzJgx6NKlCy5evKj2LVq0CFu3FrSiptu1Bblt2SZpCuHmoYc+1A869+Jz5Uri6u8FV18PBDXtjHV7TyIzJQuafTEITMpBaO1g/N/7Yxk0EREREZFzBE4//PADBgwYAA8PD+zdu1dN7hdpaWmqYQTdZgx7AEsGoA0AXFvbdKper4c53wSzIb9Cx5vNFgTW7wjfWk2RkZQJ1wOxCM7MR8M29fDYu2PgH+xXyZsgIiIiIrJz4DRz5kzMmzcP8+fPL9ZRr2vXriqQotu0BblbD2g0tnWvq1OnDlxcXWFITEf2+cSy38sM5G41wDu0Prx0PvA7mYTgXAs6DmqLhybfB3dP/XXdCxERERGRXQOnEydOoEePHtfs9/PzQ2pqqq2Xo5uZ6RxgulAwVU7f3ebTPT09MW7cWOhD/BD/Y1SZQZP5pAfyz+XD0+KN2glGBOUDAx/vh8H/dyd0OrYbJyIiIiInC5yqV6+O06dPX7Nf5jfVq1fPXuOim0Fhtqk9NFqfSl3igVH3I/dyCpK3HkdK1LWfK4sJMB/3RN6BDOhTddDuOonIoGCMeWMEOg9pd923QERERETkkK56jz/+OJ577jl88cUXao2cS5cuYceOHXj55ZcxefJkWy9HNymLORUWmd8kbGwKUdQ777wLrc6toDmE2YzMo7Fq3pN7jQBYTBoY9+pguZgJfZYLsOs4jKlXEOd9Ho3bN7DfzRARERER2Ttweu2112A2m9UCuNnZ2apsTyb5S+D0zDPP2Ho5ulnlbZFaPUBXDxqX2pW6hCyWvGbdeljMJtQdNxoWbS6yziTg8pJtMGUYUaP9XfCpFgH3PDdg9zHAzRWndcdw9tejWGgpCNyJiIiIiJyyVE++rE6aNAnJyck4fPgw/vjjD9VW+s0333TMCMnpWCz5sKi1mwCNe59KX+eXX36ByZgHr0bh8GlZFz7hjaCJDoCreyBqdh0Kv4Da8MjVQ388Ae71GgGDu8NoykZubg6iokqfE0VEREREVGWBU1ZWFp566inUrFkTwcHBGDt2rPrZoUMHeHt7231g5MSMewFLOqDxA1zvqPRl9u3bB2g1COrfvGBHogcCwzug8ZDnEBLWGl7aAHhFZ0NTPxx5bWtB46KTyB0ay5/nEhERERE5W+Ak85dkkdvBgwfjwQcfxPr16/HEE084dnTklCy569VPjV5akNtc7VnIYDAgoFNjuAZ4wJxrhCX2z5biBhM0uUa4xabC0CAYeY1DVMCUk3xZ0l3Q6dyQk5Njr9shIiIiIrJf4PTjjz/iyy+/xGeffYaPP/4YK1euxIoVK5CfX7HFS8syZ84ctaaPu7s7OnbsWGYZ1sKFC1W5YNGHnEc3hiX/fEEbcugq1YK8KK8gP4T0agGNzgUpG08C+QUfR222Abq0XOT7usMY4V94fNLhbXDRe8FD4wp//7/2ExERERE5TeAUGxurFrm1atu2rVoAV7rqXY9vv/0WL774IqZOnaoW0G3VqhUGDBiAhISEUs/x9fXF5cuXCx8XLshaQnRjW5C3hUbrd12X8uzSABY3ICc6AdHLliMnOQ4wmqDNy4drbCqMdQILj02POYErJ3bBmJOJbFOW+owQERERETld4CSd9CRQKsrFxUV1RrseH374oWpxPn78eERGRmLevHlqYVRpd14ayTLJelLWR2ho6HWNgSrGYs6AxbDruluQi3OZCYh1y4afnz+Mv0VD6+KO3OTLsCSnwuViCkxBXrB4uMKYlYbLu1bh7Kov1NymappgDBo4GGFhYfa5KSIiIiKiCqjwBBWLxaJakEuwZCXtyIcMGQI3N7fCfZI1smWOy549ezBx4sTCfVqtFv369VNrQ5UmMzMTtWvXVsFcmzZtMGvWLDRr1qzEY/Py8tTDKj09vcLjo9JakNeGxqXyix2bLWYsi9mptu9q1AHvxn+N2k3ugsbFA5qkdGTv2YdzvjEwHM1DXloiXDx8pHc5wlAXCa6xmDjpNTveFBERERGRHQMnKaW72tChQ3E9kpKSVMbq6oyRPD9+/HiJ5zRu3Fhlo1q2bIm0tDS8//776NKlC44cOYLw8PBrjn/77bcxffr06xonWVuQb1LbGn3lW5CLnUmncTE7BR46V7x65yiYpiZj6Y4z8DH7w+XIKSRZriA9taD8UqdxQ/WsYOgRgWjXk/j3vLno1KmTXe6JiIiIiOiGBE5VoXPnzuphJUFT06ZNVdOKktaSkmyWzKEqmnGKiIi4YeO9ZRj3A5ZUQOMDuLWt9GWy8vPw66WCrORdYa3hrnWDT4N2aJbmjpg1+2FJS4GbRY9ItFPH5Fqykeh+EV6+Hvj2syUYNmyY3W6JiIiIiKiiKt9L2g6CgoKg0+kQHx9fbL88l7lLFSHzru644w6cPn26xNf1er160PWxWJtC6LtDoyk+180WKy/tQ3a+ATU8/NEluDE27j+Dy8np8HPxRuOIBrDUD8f+tJ2IiY5RZZtN69bF7L+/g4EDB6rPChERERHRbRc4ydwo6c63bt26wkyCzFuS5xMmTKjQNaTU79ChQ+qLNTmGJT8ayJfAVAvoe1T6OrHZV7Aj8aTaHh7RAWkZuVi16zhMRhN8zifDTaPF+KmPol7LGXYcPRERERHRTR44CSmjGzduHNq1a4cOHTpg9uzZyMrKUl32xNixY1GzZk01V0nMmDFDzXFp0KABUlNT8c9//lO1I3/ssceq+E5uYXkb1Q+NaxtotAGVuoQ0F1kWHQULgDsC66Cedyjm/7YThnwT3DLy4JmVj5oNa6Bui1p2HjwRERER0S0QOI0ePRqJiYmYMmUK4uLi0Lp1a6xataqwYUR0dLQq2bJKSUlR7cvl2ICAAJWx2r59u2plTvZnMWfCYvhzQWL3yrcg35V8BuezEuGmdcGQmu1w6FwcjlyIUy3GPY7GQwOgx4hOqtU8EREREZGz0VgkFVBJubm5cHd3x81EmkP4+fmpjnyykC6VzZK7CpacHwFdBDQ+kyoV2OTkG/DO0Z+QYczF4Jpt0CWwCd5esg6pmTmoZdIidc1RBIdXwzNzHmPgREREREROGRtUeAFcK5mDJN3rpHzO29sbZ8+eVfsnT56MBQsWVH7U5HQsFlORFuS9Kx3UrL58QAVNwe6+6BHSVM1rkqAp0NsDubvOqWO63cdsExERERE5L5sDp5kzZ2LhwoV47733ii1827x5c3z++ef2Hh9VJeNBwJwMaLwAt/aVukRcTiq2Jh4vbAgRdyUDmw6eUc8jXT2Qm5oD3yAftOrJUksiIiIiuoUCp6+//hr/+c9/8NBDDxVrD92qVatSF62lm70FeTdoNG6VawgREwWzxYLm/hFo6F0DSzcdUM9b1auB6HVH1HHd7u0InQtbjRMRERHRLRQ4Xbx4UXW0K6mEz2g02mtcVMUs+bFA/gkJmwB9r0pd40DqBZzOiIOrVoeh4e2w4+h5XEhIgburC5ro9EhNSIOnrwfa9m9l9/ETEREREVVp4CTd67Zs2XLN/u+//14tREu3Wgvy1tBoA20/3WTE8tjdartPaHO45rvilz+OqucDOzTF3uV71HbnIe3gpq/8grpERERERE7Zjlzahsu6S5J5kizTsmXLcOLECVXCt2LFCseMkm4oizkLFsPOgif6PpW6xtq4Q0g1ZCPAzQt9qjfD/9btR47BiIhgf4TkmRF/IRGu7q7oOKiNfQdPREREROQMGaehQ4fil19+wdq1a+Hl5aUCqWPHjql9d955pyPGSDeaYZv8H0BXE3BpaPPpibnp2BhfkF26N6IDzl5Mxp5TsdBAg1E9W2Pr93+o1zrcdQc8vD3sPnwiIiIiIqdYALd79+5Ys2aN3QdDVc9iMcNiLdOrRAtyaQjxY0wUTBYzmviGoZFXDbz7a0GTie4t6sKSlIHo4xehc9Giy7DKdeojIiIiInL6jBPd4oyHAPMVQOMJuHW0+fSjabE4nn4JOo0WwyLaY92+00hKy4KfpzsGdmyKzd8VZJvu6NsSvoE+DrgBIiIiIiInyDgFBASUmIWQfe7u7qrj3iOPPILx48fba4xUFS3I3bra3ILcaM7Hj7G71HbP0EggV4e1e0+q5/d2a4HUi8k4ueeMatTXbbjtQRkRERER0U2TcZI5TVqtFoMGDcL06dPVQ7Zl39NPP41GjRrhqaeewvz58x0zYnIYiykOyD9W6RbkG+KPIDkvE76uHugX2lyt2ZRvNqNprVC0rh+GLX/ObWretSmq1QhwwB0QERERETlJxmnr1q2YOXMmnnzyyWL7P/vsM/z+++/44Ycf0LJlS3zyySd4/PHH7TlWcjRrtsm1JTS6IJtOlYBpXdxhtX1PeDscOhOPUxcT4arTYUT3lkiOS8WhrRKUAT1GdHLA4ImIiIiInCjjtHr1avTr1++a/X379lWviYEDB+Ls2bP2GSHdEBZzNix5OwqeVCLbJGs2Gc0m1PcORWOPMPy0vSCI6t+uEYL8vLB12U7AAjRsUw816oXae/hERERERM4VOAUGBqrW41eTffKayMrKgo8PJ/7fVAwSNOUB2hqAS1ObTj2ZfgkHU6Oh1WhU+/EVO48hMycP1QN80Kd1Q6QnZ2DfuoPq2O7MNhERERHR7VCqN3nyZDWHacOGDejQoYPat2vXLvz222+YN2+eei6tynv27Gn/0ZJDSAvxwqYQ7r1sakGebzZhWUyU2u4W3AR56RZsP3pePR/ZoxVcdFps/3k3TPlm1GpSE3WaRTjoLoiIiIiInChwknlLkZGR+PTTT7Fs2TK1r3Hjxti0aRO6dOminr/00kv2Hyk5Tv4RwJwIaDwAt842nbol8TgSctPh7eKOvqEtMPfHgnK/Do1roUHNIORk5iBq5V61r8fIzjavC0VEREREdNMugNu1a1f1oFuDJXe9+qlx6wKNRl/h89IM2Vh96YDaHlyzDXYdjcWlK2nw0rthaJdman/Ub/tgzDUipFYQGrWr76A7ICIiIiJywsDJKjc3FwaDodg+X1/f6x0T3UAWU3xBxqkSLchXXNwDgzkftb2C0MCtBt6OKgjA7unSDN4eehjyjNi+vGBdpx4jmG0iIiIiotuoOUR2djYmTJiAkJAQeHl5qQVxiz7oJpO3Uf3QuDaDRhdS4dPOZMRjT/I5CbdUQ4hlWw/DkG9CvRrV0LFJLXXMnt8PIDs9B/4hfmjevYnDboGIiIiIyOkCp1deeQXr16/Hv//9b+j1enz++edqEdywsDB8/fXXjhklOYTFkgeLYXvBE33vCp9nspixLGan2u4U1BBpCUYcOn8ZOq0Wo3q2UpklU74J2378s2nE8I7Q6XSOuQkiIiIiImcs1ZO24xIg9erVC+PHj0f37t3RoEED1K5dG9988w0eeughx4yUHNOC3JILaEMAl4I5SRWxI/EkLuekwlPnhr4hLfDxd9vU/t6t6qNGYEGp5sHNR5GWlA4vP0+06dfCYbdAREREROSUGafk5GTUq1evcD6TPBfdunXD5s2b7T9CclwL8tw/W5Dre1d4/lGmMRcrL+1T23fXvAOb951HamYOAn08MaBd48Jrb/n+D7XdZWh7uLq5Ouw+iIiIiIicMnCSoOncuXNqu0mTJli6dGlhJsrf39/+IyTHyD8GmOOkRg/QV7wF+a+X9iLHZERNzwDUQig2HTij9o/s0RJurgUJzONRp5EYewV6Tze0v/sOh90CEREREZHTBk5SnnfgQEEL6tdeew1z5syBu7s7XnjhBTX/iW4S1gVv9dKC3KNCp1zISkJU0mm1fW94B3y/+SDMFgta1QtDZO3qhdmmzd8VrOXUcWBbeHi5O+wWiIiIiIicdo6TBEhW/fr1w/Hjx7Fnzx41z6lly5b2Hh85gMWUCIvxUMGTCrYgl4BoWfROWAC0DayHy9E5OB+fDL2rC4Z3+2sO07lD0Yg9eQkubi7ofE87R90CEREREZHzZpyMRiP69u2LU6dOFe6TphDDhw9n0HQzydskoRDgEgmNriBTVJ6oK6cRk30Fep0LegU2wy87ZO0nYFCHpvD3/itjZc02tenbAt7+Xg66ASIiIiIiJw6cXF1dcfDgQceNhm5QC/KtalvjXrEW5Nn5efj1YkFDiAE1WmPDrrPIMRgRHuSPbi3qFh538fRlnDlwHhqtBt3u6+SgOyAiIiIiugnmOI0ZMwYLFixwzGjI8Qw7AUsOoA0CXJpX6JTVlw8gMz8Xoe5+CDVUw66TMdBAg9G9Wqm1m6ysnfRa9ohEQIifw26BiIiIiMjp5zjl5+fjiy++wNq1a9G2bVt4eRUvx/rwww/tOT6ydwvywqYQvaDRlB83X8pOwdaE42p7SFhb/PBbwdyobs3roFZIQOFxSRev4MiOE2q7O7NNRERERHS7B06HDx9GmzZt1PbJkyeLvVbRtYCoiuSfBEyXALgBbl0r1hAipqAhRKuA2og+nYnEtEz4erpjUMfIYsdu+UEyWUDj9g0QWjvYgTdBRERERHQTBE4bNhRkLOgmZM02uXWERutZ7uH7Us7hbGYCXLU6dPVpgn//vlPtv7drc3jo/1rUNi0pHQc2HlbbPUZWfE0oIiIiIqJbdo6T1enTp7F69Wrk5OQUZifIeVlMV2Ax7i94UoGmELkmI5bH7lHb/aq3wO87ziDfbEaTiBDc0aBmsWO3/bQLpnwz6jSLQK0mxV8jIiIiIrotA6crV66oluSNGjXCwIEDcfnyZbX/0UcfxUsvveSIMZI9GKwtyBtDoys/uFl7+SDSjTkI0vvAN90PJ2IT4KLTYUSPlsVKMrMzcrD794KArPsIzm0iIiIioluTtjIL4Epb8ujoaHh6/lXuNXr0aKxatcre4yM7sFgMsOT92YJc36fc4+Nz07Ap4ZjaviukNZZvP6q2+7dthGA/72LH/vHLbhhzjaheNwQN29RzyPiJiIiIiG66OU6///67KtELDw8vtr9hw4a4cOGCPcdG9mLYBViyAG01wLXshYql5PLHmCiYLGZE+tXEmWPpyMzJQ6i/D/rc0aD4ZXMN+OPXgnK+niM7szkIEREREd2ybM44ZWVlFcs0WSUnJ0Ov19trXOSQFuQ9y21Bfig1BifTL0On0aKtviG2HTmv9o/s2QquOl2xY3evPoCcjFwEVvdHZJfGDrwLIiIiIqKbLHDq3r07vv7668LnkmUwm81477330Lt3+U0H6AYznQFMMQBcy21BbjDn4+fYXWq7Z0hTrNl+BhZY0L5xLTSsGVTs2HxjPrb+VNBlr9t9naAtshAuERERERFu91I9CZCkOcTu3bthMBjw6quv4siRIyrjtG3bNseMkiovd736oXHrAI22+Pykq62PO4wUQxb83TzhluiFi1di4Kl3w9Auza459sDGI8i4kgmfQG/c0ae5w4ZPREREROQMbE4TNG/eXC18261bNwwdOlSV7g0fPhz79u1D/fr1HTNKqhSLOQUW476CJ/qys4FJeRlYH1+wFlOfai2xdvdptX1P52bw8ShegikZRrXgLYAuQ9vDxdXm+JuIiIiI6KZSqW+8fn5+mDRpkv1HQ/aVt1nCHMClATQuEWUeKiV6sk5TQ5/qOH4gBXnGfNStHohOTWtdc+zRHSdx5VIy3L3d0f6u1g68ASIiIiKimzTj1KBBA0ybNg2nTp1yzIjIji3It6htTTnZpmNpF3EkNRZajQbNNHVx6Nxl6LRajOrZ6ppOedJsYvN3O9R2p0Ftob8qG0VEREREdCuyOXB6+umn8euvv6Jx48Zo3749Pv74Y8TFxTlmdFR5hj2AJQPQBgCupWeF8s0m/BQTpba7VGuM9TvOqe1ereojrJrfNcefOXAel8/Gw1Xvgk5D2jrwBoiIiIiIbvIFcHft2oXjx49j4MCBmDNnDiIiItC/f/9i3fbISVqQu/WARlN6ReamhKNIzMuAj6sHzLF6pGRmI9DHEwPaldxe3Jptate/Nbx8r21LT0RERER0K6p0D+lGjRph+vTpqlHEli1bkJiYiPHjx9t3dFQ5pnOASRYjdgH03Us9LNWQhTWXD6rtLj6Nse1gwZpNI7q3hL6Ehg8xJy7i3KFoaHVadBnW3oE3QERERETkXK5r8Z2oqCg8//zzuPfee1UANXLkyEpdR7JWderUgbu7Ozp27KiuWxFLlixRc3CGDRtWqfe9ZRVmm9pDo/Up9bDlsXtgMJtQxysYR/elwGyxoGXdMDSrU73E4zd/94f62apnM/gHX1vGR0RERER0q7I5cJIAaerUqSrj1LVrVxw7dgzvvvsu4uPjVSBjq2+//RYvvviiuubevXvRqlUrDBgwAAkJCWWed/78ebz88stqQV76i8WcBovMbxJlNIU4lXEZ+1POQ1o/1DHUxPm4FJVlGt69RYnHJ8Qk4XjUKcgJ3e7r6KjhExERERHdGoFTkyZNsGrVKtUkIjY2FqtXr8bYsWPh7V324qql+fDDD/H444+rMr/IyEjMmzcPnp6e+OKLL0o9x2Qy4aGHHlKlgvXq1avU+96yVCc9E6CrB41L7TIaQuxS223862HHrli1PbBDUwR4e5R4zpbvC7JNTTs2QkhEkMOGT0RERER0S6zjdOLECTRs2NAub24wGLBnzx5MnDixcJ9Wq0W/fv2wY0dBE4KSzJgxAyEhIXj00UfV/Kqy5OXlqYdVeno6blUWSz4seZvUtsa9T6nHbU86ics5qfB0cUPeWRdk5xkQHuSH7i3qlnh8SkIaDm4+qrZ7jOzsoNETEREREd1CgZM1aJKAR8r0hGSK2rRpY/ObJyUlqexRaGhosf3yXLr2lWTr1q1YsGAB9u/fX6H3ePvtt1Vm6rZg3AdY0gGNL+B6R4mHZBhzsOpSwe+ulVt9bD11ERpoMLJHK7V2U0m2/RgFs8mMei1rI7xhDYfeAhERERHRLRE4ydyj0aNHY9OmTfD391f7UlNT0bt3bzXHKTg4GI6SkZGBhx9+GPPnz0dQUMXKxSSbJXOoimacpH36rciSu0791Oh7ltqCfMXFvcg1GRHmEYAT+1LVvq7N6qBO9cASj89Ky8KeNQfUNrNNRERERHS7snmO0zPPPIPMzEwcOXIEycnJ6nH48GEVkDz77LM2XUuCH51OpxpLFCXPq1e/trPbmTNnVFOIIUOGwMXFRT1k7ajly5erbXn9anq9Hr6+vsUetyJL/vmCNuTQldqC/HxmAnZdKfgdVU8PRWJqFnw89BjcKbLU625fvhv5hnzUbFBdZZyIiIiIiG5HNgdO0hhi7ty5aNq0aeE+KdWTluIrV6606Vpubm5o27Yt1q0ryJQIs9msnnfu3LnExhSHDh1SZXrWxz333KOyXbJ9q2aSbGtB3hYa7bWtws0WM5bFFLR5j/QKx74DBcHqvd1awEPvWuIlc7PzEPXbXrXdfURn1fqdiIiIiOh2ZHOpngQ2rq7XftGWffKaraSMbty4cWjXrh06dOiA2bNnIysrq3AxXenYV7NmTTVXSdZ5at68ebHzreWCV++/nVjMGbAYdpXZgnxn0mnEZifDXeeKzFM65JtMaBwegjYNapZ63V0r9yE3Kw9BNQMR2bmRo4ZPRERERHTrBU59+vTBc889h//9738ICwtT+y5evIgXXngBffv2tXkAMl8qMTERU6ZMQVxcHFq3bq2yWtaGEdHR0arTHlWkBXltaFyubc+elZ+HXy8VZI4aIgL7Y5LgotNhZI+WpWaRjAYjti8vCMaYbSIiIiKi253GYrFYbDkhJiZGlcfJHCdraZzsk4yPzDUKDw+HM5O5WH5+fkhLS7sl5jupFuRpkwBLKjSe46HRd7rmmO+j/8D2xJMIdvNF1h49MrLzMLB9Uwxo37jU6+5atQ/L566Gb5APXvzPk9C56Bx8J0REREREzhsb2JxxkmBp7969WLt2bWHLcJnvJGsvURUw7ldBEzQ+gFvba16Ozb6CHYkn1bZfUgDislMQ4u+NPm0alHpJaRG/5YedarvrsA4MmoiIiIjotmdz4CSkbOvOO+9UD6paFmtTCH13aDTF555JMnFZdBQkpVjHNRQnjxW0H5c1m1x1pQdDR7adQEp8Kjx83NGufysH3wERERERkfOr8OSh9evXq+55ks66mqS2mjVrhi1bZK4N3SiW/Bgg/3TBP6O+xzWv704+i/NZiXDV6pB+ArDAgvaNItAovPS1tiTY2vL9H2q785D2cHN3c+g9EBERERHdUoGTdLt7/PHHS6z9k7rA//u//8OHH35o7/FRWazZJtc7oNEGFHspJ9+AFRf3qO2aeaFITMyBh5srhnYtu/vgyT1nEXc+Aa7urug4qI0DB09EREREdAsGTgcOHMBdd91V6uv9+/fHnj0FX9TJ8SzmTFgMBesywb3PNa//HncQGcZc+Oo8EX0oW+0b0rmZWvC2LJu/26F+drjrDnj6eDhi6EREREREt27gFB8fX+L6TVYuLi6qrTjdIIat0hkC0EUAuvrFXorLScWWhGNq2yPOF0ajGXVCA9ElsnaZl7xwNAbRx2Khc9Giy7D2Dh0+EREREdEtGTjJIrSHDx8u9fWDBw+iRo0a9hoXlcFiMcGSt0lta/S9i62xpBpCxETBbLEgxBKAS2ezodVoMLpXq3LXYtr859ym1r1bwDfQx8F3QURERER0CwZOAwcOxOTJk5Gbm3vNazk5OZg6dSoGDx5s7/FRSYwHAXMyoPEC3Ipnhg6kXsDpjDjooEX68YJAqWer+gir5lfmJWVe08ndZwAN0O2+jg4dPhERERHRLduO/I033sCyZcvQqFEjTJgwAY0bFyyeKms5zZkzR639M2nSJEeOla5pQd4NGs1fXe/yTEYsj92ttgMy/RGfno8Ab0/c3b5Jude0zm1q1qUJgsICHTZ2IiIiIqJbOnAKDQ3F9u3b8dRTT2HixImqJExI+deAAQNU8CTHkGNZTBeB/BPymwf0vYq9ti7uMFIN2dBb3JBwLF/tu697C+hdy/5nTo5LwaGtBXOieo7s7MDRExERERHdBgvg1q5dG7/99htSUlJw+vRpFTw1bNgQAQHFW2GTA+VaW5C3hkb7V2YoKTcdG+KPSGgFXawnLGYzWtStoR7l2bosSk5DgzvqokY9Br9ERERERNcVOFlJoNS+Pbuu3WgWcxYshp0FT/S9i732U+wumCxmeBu8kHrJBHdXV9zXrUW518xIycTedQfVdg9mm4iIiIiIrq85BDkBw3b5P4AuDHBpVLj7aFosjqZdhMUMZB7XQQMN7mrfBAE+nuVecvvPu2AymhDROAx1mkU4+AaIiIiIiG5ODJxuEhaLuUhTiD6FrcWN5nz8GFOwEK5nqjdMORrUrOaHni3rlXvNnKxcRK3cp7a7j+hcbrtyIiIiIqLbFQOnm4XxEGC+Amg8Abe/2oVvjD+KK3mZ0Bi1SDstLSM0GNWzFXTa8v9po37dC0OOAcERQWjSoYGDb4CIiIiI6Dab40Q3XmG2ya1rYQvy5LxMrI07VNDhMNodOosWnSNro0718tuJG/KM2PFLQevyHiM6MdtERERERFQGZpxuAhZTHJB/7JoW5LJmk9Fsgi7LFflJOnh76DGkU2SFrrl3zUFkpWXDP9gXLXo0deDoiYiIiIhufsw43Qys2SbXltDogtT2yfRLOJgaDWO+CZZTHpCWEPd2bQ5P978WxC2NKd+EbT8WdOfrNrwjdDqdg2+AiIiIiOjmxsDJyVksObDk7Sh48me2Kd9swjLVEMICXaI7LAZXNKoZjLYNwyt0zUNbjiE1MR1efp5oc2dLRw6fiIiIblNmsxkGg6Gqh0GkuLm5QVuBHgBlYeDk7FTQlAdoawAuBSV1WxKPIyE3HYYcCxDjBr1Wi5E9W1VonpLMh9r8XUEg1vme9nB1c3X4LRAREdHtRQKmc+fOqeCJyBlI0FS3bl0VQFUWAycnJkFOYVMI914qMEozZGP1pQMwmc0wR+vhatGhX9tGCPH3rtA1j0edRmLsFeg93dBh4B0OvgMiIiK6Hb+/XL58WU0FiIiIuO6/8hNdLwngL126pD6XtWrVqnRTNAZOziz/CGBOkKgJcOusdq24uBcGcz6MaRq4pLoj2M8b/do0rNDlimabOtzdBh5e7g4dPhEREd1+8vPzkZ2djbCwMHh6elb1cIiU4OBgFTzJ59PVtXIVV/wTgBOz5K4v0oJcjzMZ8diTfBa5BiNMZ93Vmk0je7SEq0vFmjucPxyN2JOXoHPVofM97Rw8eiIiIrodmUwm9fN6SqKI7M36ebR+PiuDgZOTspjiCzJOf7YgN1vM+DEmSmWNTHGucM/Xq2YQjSNCKnzNTX9mm9r2awmfgIqV9hERERFVBteIpFvt88hSPWeVt1H90Lg2g0YXgu2JJ3ApJwVZWUa4xgXAw81VtR+vqEtn4nBm/3lotBrVgpyIiIjI2UhDiXXr1iE9PR2+vr7o27evmtBPZYuOjkbLli2RmppapeNYuHAhFi9ejLVr1+JWxIyTE7JY8mAxbC94ou+NTGMufru4V63ZJA0hXCw6tdCtj2fF5yht/v4P9bNFt6YICPV31NCJiIiIbLZjxw4MHnQnGjduiG8W/gPbNsxSP+W57JfX7aFOnTqoXbs2jEZj4b4nn3wS06ZNg73JNWUujY+Pj3o0b94cU6dOVfO/7E0aHtgjaNq4cSMaNGgARwVVLi4u8Pb2Lnx88skndn2PRx55BDNnzoSjMHByRoYdMsEJ0IYALs3w26V9yDEZkZ1igmeGN+qEBqJLszoVvlzSxSs4sv242u4+opMDB05ERERkm6VLl6Jfv15o1XAfzuyshXXfV8N38wPVT3ku++V1Oc4eMjIy8OWXX+JGGDdunHq/xMREzJ8/H6tWrcKdd955XfNsbma9evVCZmZm4ePZZ58t9ro0bnBmDJycsQV57p9levreiMm+gp1Jp5CRkwttjBd0Gi1GVXDNJqstP+yUtXLRqF19VK9T8TlRRERERI4kmaTx4x/Gt/8JwpuvBaJmjeKzSOS57JfX5Th7ZJ5eeOEFzJo1q1jWqai5c+eiXr16qgvbmDFjkJaWViwbM2PGDAQGBqrs1erVqyv0nu7u7ujcuTN++uknHDhwACtWrFD7U1JS8MADDyAoKAj169fHZ599Vix7IoFF7969VXZmyJAhKgAbMWKEKmOU/VeuXFHHnj9/XmVzrGRsH3zwAZo2bQp/f39MmDCh8LUzZ86gR48ear90Pnz99dfVfgnm7r77bpw9e7YwIyRycnLU+WFhYQgPD8c777xTeK2srCw89NBD6lpt2rTBqVOnUJlgavLkyWjXrh28vLzUv8uyZcvU2AMCAjB48GBcvHix2H1KEFqjRg1Ur14dX331lXpNfn7zzTd488031dglk2hvDJycTf4xwHxZavRgceuEZTFRMFnMMMTp4Gn0QM+W9VEzyK/Cl0u/koEDGw+r7R4jC1qaExERETmDt2ZOwfOP+2BgX68yj5PX5bhZb0297veUgENK26R07Gpr1qxRX7wlsJEv6RI0PPfcc4Wvyz69Xo+EhAQVcDzxxBM2vbd82ZcAYdu2beq5NaCROUo//vgj3njjDWzatKnw+O+++w6ffvqpWn9I3rtbt25qPElJSaoMsKxSNwnStmzZgsOHD6ts3YYNBWuDCrlHuYa8l8xJkmNl3a2VK1eqoNGaERIvv/wykpOTcfLkSURFRWHRokWFgd/06dMRHx+vxv/f//4XX3/9NSrjf//7H5YsWaKC1NOnT2P8+PEqOIqLi1PjkQDWSgI8uacLFy6o93v66adVVk+yexLESRAmY583bx7sjYGTszaF0HfGruSLiM5KQkpaLryS/ODv7YG7OzSx6XLbfoqCKd+M2pHhqN003EGDJiIiIrK9EcSatRvw5DjfCh3/f2N9sPr39SqAuF4y16ikrJN8eZdgKDIyUmU/5Jhvv/1WVQQJ2ffKK6+orId8mZeAwda5RZIlkUyTBAASGMl7yHpX0tzhscceUwGI1ciRI9GsWTM1R2rgwIFo3Lgxunfvrlpr33vvvSp7VZrnn39eZbIkSyRZHeuxktnq2bOnuoeGDRuqYGPr1q0lXkPuW8oa33//fZXFkazTU089he+//169LuOXYE8yYE2aNFHBS1kkUJPslPUhAah49NFHVTZPMnNy7WHDhqkgUYJU+f3I+CR4tJoyZYr6HfTv31/97iSLdiMwcHIiFlMSLMaDajtX1xW/XtyHPKMRlovucDW74L5uLaF3rXgjxOyMHOxavV9tM9tEREREzkS653Vp73dNeV5pwsNc0bWDn106tkm3vpo1axaWeVnJAqmSjbKSRhK5ubkq4yKkfE+rLfj6bF3cV7IbktmxlrdJuVtZJACQEjTJ+EjgdvX7yRisQkL+mmLh4eFxzXNrVqgkoaGhhdsyVuuxUvYmQZcEcH5+fpg9e3Zhyd/VpDRQsm4SSPr/GexIpk2yTNZ7iYiIKDy+6HZJJGCTQNP6sN6PBHel/RvI77RatWqFvxfJjMnzku7N0Rg4OV22yQK4RGJVQhwy83OQlmSEf6YfWtSpgZb1ath0uT9W7IEx16jmNTVsU89hwyYiIiKylbQcDwywbW2dAH+tKsuyh5KyTpJRkSySlWxLFkTmNJVFskDW8jYpdyuNlJ7t2bMHXbt2VdkgKbe7+v1kDI4kGSIJ3KT0TkrjJDNlzahdPYdexihZH5n3lPpnsCP/btZ7lNLDmJiYwuOLbtui6Pte/W8g86gksKvI78XRa4cxcLrB5K8WUkvao3t7hIYGwMvLHaGh/ujXpx3OnVwMs8mEJLTD1oTjSM3MgXucD/QuLhjerYVN72PINeCPFbsLO+lxEToiIiJyJlLelZxS8IW9olJSzapszR6ku51kXWR+j9Xo0aPV3Jpjx46pL+yTJk3CqFGjrvt7VF5eHnbu3KkyPS1atFANDyRzIo0eJJCRFuUyb2fBggW4//774UgSeMrvUDI58p7yvdRKMkCSZZJ7F5Jdk/K7l156SQVNZrNZ/W5krpOQ8UvwKcHUiRMnKj3HqSi5pvybbN++HQaDQf1+unTpooK08sj47VHKWRoGTjeIfNBmzpyBiIhQzJz+dwzvfx7Lv/LBrlXVsfwrXzz98EWcO7MX3yxZhee/+w1Gkwk5cRp4GbxwV/smCPQtSAdX1O7VB5CTkYvA6v5o1rWxw+6LiIiIqLLlctui0nDxcsVaUMdeMqrj+/XrZ7cxSNbJWoYnZM7MxIkT1XwiKZuTjJCUslWWlAJKkCIZK5nHI/cspYYSNAlp/CAtuKXE7Z577lFrP0nzCkeS+UHSKEICV+nad9999xW+Jp3shg4dqsYjZXnio48+UiV9LVq0UPcxduxYNUfL+vuTsjk5XroDPvzww9c9PhnD559/rhpESLmhBGRFg7uy/O1vf1MBqoz973//O+xNY7Hm5m4TEhHLP76kJuUDcyNICvjBB0Zg/971mPuuH/p080B8ogm/b8xGWroZvr5ajBjsDU8PDdae8MBru5rBw68mWpo6oG5gMF4a0RMuuorHuKZ8Ez58Yh7SkzJwz98HoP1ddzj0/oiIiIiKVtdI44e6deuqMreyyOK2sk6TtBwvz+R3knHwdBv8suJ3O46WbvfPZboNsUHFOw1QpUhc+sQTf8PpExux7ZcQpKab8OBTcfhpZRbattQjuJoOPt5aBAXqUC1Qh/X61rijlTcOrDfg2JUjeHX6szYFTWL/hsMqaPIJ9MYdfW0r8SMiIiK6USa9MUMtbtu5vb7MluS/rs3C7PkZWLduxg0dH1FRDJwcTCbP/briB+xdUx0xl4zoP/oihg/0xsENtdCwnlvhcVnZZizZ7YUV+zWoV9sTbQIsWLdpC47t64B6YYNsKglUC94C6HxPe7jY0IWPiIiI6EaSRWG//HIRRo9/GM8/nqdajkv3vKLleZ99naGCpoULF6NTp05VOl66vXGOk4PNnfMR/u9hT5VVGvLwJbz0ZAA+ez+0WNAkXNxdkFC3KcIiAnBuowEB7gY80OU45s750Kb3O/bHKVy5lAx3Lz063N3azndDREREZF/SfGHt2o04ePoONOgUg74jrmDEYynqpzyX8rx16zapNY2IqhLTEQ4kdZS/r9mAuW9GYPH36QgLdcE/ngm45jiZZXY0OxCXcrzgp/FCckYmXJP34qmH3VG/4ybVAlJWTa5IWeDm73ao7Y6D2kLvoXfIfRERERHZO/P0y4o16ruTrO9k7fz25eJ+qFOnTlUPj0hh4ORAP//8M7p38lQp5/8sSsOzj/uX2M4yw+SKLWk1EJ/qjeAUMwIapGD50kN486na6NHZU7VkfPHFF8t9vzMHzuPSmTi4uLmg85C2DrorIiIiIseQifuPPfZYVQ+DqEQs1XMgWeSsdrgGZrMFh48b0LvrtS3FzRbgbK4vDqcGwS3RBR5mE/6v3zEcOWFQ59UJ16hVmSvCmm1q178VvPxKn2BJRERERES2YeDkQMePH4eL7q9yvJKa4yUZ3bEvIxhJyd6olpGPvk3PIsQ3Sx1vNgMuLhrVv748sScv4dyhaGh1WnS9t4MD7oaIiIiI6PbFwMmBZBXo2DgTtFoNaoe7YO+hvGKvXzG64euEJtiaUBMBV8wI8cpGv2ZnsO9wHmrVdFFB08V4U+HqzWXZ9Ge2qVXPZvAP9nPYPRERERER3Y4YODmQh4cH1m2RRW5NGH+/L+Z9lVbs9RXJdXEu0w+piV7wyTFjRPvDcNWZ1XF/e9BXnff7hix1nbIkxCTh+M5TarvbfR0dek9EREREjiLNIT7//HN8+OGH6qc8dwYbN25EgwYNqnoYVMWcInCaM2eO6pgiq/h27NgRUVFRpR67bNkytGvXDv7+/vDy8kLr1q2xaNEiOKOcnBzofdyx6LsMPDbGD9uicvDjb5mFr/fzj0F2kjtCU/LRptZlNK5+BT+vysSWP3Lw6IN+WPx9Btx83NVKx2XZ+ue6TU06NkRIRJDD74uIiIjInnbs2IE77x6Aho0bYdLcd/HPn79UP+W57JfX7WHz5s1qLSg/Pz9Uq1YNffr0cUhwNm3aNLi6usLb27vwIc2+7KlXr15YvHhxqa//+uuv6NKli7rXsLAw1WjMaDTadQy3myoPnL799lv1Dzl16lTs3bsXrVq1woABA5CQkFDi8YGBgZg0aZL6H9DBgwcxfvx49Vi9ejWcjQSCOUYN3p+fodZxWjSnOsZOiMOcL1KRk2PG2QvB8I/Rwk9jwF3NjmDul6kY8/c4fP1pdfj7avH+5xnINWnVdUqTmpiGA5uOqO2eIzvfwLsjIiIiun5Lly5Fr759cMw/C82/eBK1Z41ExMQh6qc8l/3yuhx3PdLS0jB06FC8+uqrSElJwYULF/DMM89Ap/tzQrqdjRs3DpmZmYWPYcOGFXs9Pz8fjpSenq4COGlWduDAAezatQv//Oc/Hfqet7oqD5wkFfv444+r4CcyMhLz5s2Dp6cnvvjii1Kj63vvvRdNmzZF/fr18dxzz6Fly5bYunUrnE2jRo1gyjYgw80bo55MwN19vPDjwjBViler7Tl8/KUBJ04bcH5PFJp1OY5/L0zDsi9rYFA/L4x6KgHpOm/kZ+ap65Rm249RMJvMqNuiFsIbhd3Q+yMiIiK6HvKH8IcfGYfarw1B2LgecAvyKfa6PJf98rocdz2Zp5MnT0Kv12P48OHQarUqCyTfKWvVqqVef+SRRzBz5szC4xcuXIh+/foVu8bkyZMREBCgvputWbPG5jFIhdV7772nvsdaS//mzp2r1usMDg7GmDFjVIBXtDxwxowZKnEg51oTBW+++Sa2bNmiWrfLfcyaNeua93rggQfQv39/NeVDrv3www/bLXN3u6rSwMlgMGDPnj3FPpTyQZbnFfmHlQVfZZE06TrXo0ePEo/Jy8tTEXfRx41So0YNuPsEwhIYiD/O69H/oThENnLDwY218MOCMNR1P4jgtJWo6X4G331eQ+1v1rjguB1n9UBQNXV+9erVS7x+VloWdv9+QG33YLaJiIiIbjJTZkxD8NC28O9Q9vwheV2OmzpzeqXfS4Id+e4pwYYEPbZ+Jzx//jxMJhPi4+NV8DNy5EiVubKVTDuRoOjYsWNqHBIErVixQl1fpnlIUqDoe0qwJ5VYr7/+Op544onCAK579+5qHphks+S1ipQpNmvWzObxkpMETklJSeoDGBoaWmy/PJe0YmkkEpfo2s3NDYMGDcK//vUv3HnnnSUe+/bbb6vaTusjIiICN8rgwYNhSE9Bxr7zCBzWGadcwlC3wwXc+1gC8k0WvPmPavjqfTfM/Ec11X5c9svrJzQ1EDi8C9L3noUhPRn33HNPidff8cse5BvyEVa/Ouq34qraREREdPOQuUUb121A0KA7KnR80MDWWL9mnQomKkO+B0rwIH9Ul+yLNcOTkZFRofNdXFwwZcoU9f1Tyu6aN2+OlStXlnq8zMGXOfnyKPpHcAmM5LuuZIKWLFmigiGpupK5+5I5kmkskhwQsu+VV15R7y1jjY6ORmpqqs33/sMPP6hkg0yPoZu4VK8yfHx8sH//flWr+dZbb6kPgUTuJZk4caIKtKyPmJiYGzbOxo0bo3PnLvCv0xLnP14N7+4t0PSzJ7DHqynueSwJXnXPwC3itPo5+G+J2OPZBE3nPQGfXi1xfvYq+NVpiY6duqBJkybXXDs3Ow87f91TmG3SaDQ37L6IiIiIrpd8kQ9sXvua8rzSuAX7olrzOli7dm2l31OCHQlo5A/027dvVw/5LlkREmgVnXcuf4y/fPmyKpmzNoC4++67C1+X4EyCHHkUTQiEh4cXbl+6dKmwVFDUrl1bNQVLTk4ufE+pxhIylUVIhskWGzZswFNPPYVffvkFISEhNp1LxbmgCgUFBakJeZLyLEqel1aeJuQDZK0Lla56kuqUzJLMf7qapDflUVVeeP5ZjHv0CYR3vQ9n3/0B/l0aIfSeNqgxtidMmXkwZedB56mHzluP7FNxuLRoK1K2n1THJ+/7HS+8Na/E6+5atR+5WXmoFhaIpp0a3vD7IiIiIroeUiqn9Sm9AVZJtN76CmeIytO2bVs13+nw4cOF2R0plbO6+vupVEpJUGMNnuSP8UOGDFElc7YEM0X/2C3d7iSLZCXbcn2Z02TLdUqzc+dOjBo1Ct99953qSk03ccZJUp3yoZW/OFiZzWb1vHPnis/ZkXMk7eqMJJXbq3tXZJ7ejYZDJkB3xR8nJn6LoxO+wsXFW5C4ar/6eeyZr9V+zRU/NBgyQR3fvWtnNWnxakaDEdt/LmjZ3v2+joV/iSAiIiK6Wfj6+sKcUfaSK1czZ+apyqPKOH78OD766COV5bE2i5AsTIcOHdRz6ewsLbwloDt79iwWLFhQ7Hxp5S3NI+Tn8uXLVcBVNMNUGaNHj8b8+fNVEiArK0t1jpZApyJBkWSPyipbPHTokArs5D5KSi6Q7ar8G7eU2ckH5quvvlIfGkklygdHuuyJsWPHqnI7K8ksyUQ6+UDL8R988IFKuUrdpzOSoGbpt0vQvF444rf/gMDGHdF8zDQE1+0FzUV/5B/Vqp9BtXuo/dUad0LC9h8QWTcM3y/9tsQWmfvXH0ZmShZ8g3zQunfzKrkvIiIiouvRt29fXDl8HoakimWQDInp6virO91VlARcUponf7SX7JJcR+bKv/baa4WlddKxWUrppCOdPIqSrnYS0EjA8tJLL6m5SNJh73pI1zv5njtw4EBVpidrP82ePbtC50orden8J3Oo3nnnnRI7V1+5cgUPPvhgiaWEZDuNxTr7rAp9+umnqq+81H9K6d0nn3yiFsIVEiHLB1U+GOKNN95QH9TY2Fg1qU7m/8gkO4nYK0L+iiCTA2W+k/yl40aRLi4vv/IqPvvsM/iEhMO7YQd4Va8LraseZmMesuLOI/N0FDLiY1S3l48+/EBl5K4mzTQ+eWo+kuNScfejfdFlaPsbdg9ERERE5ZFyNmn8ULdu3TLXohSyuK2s0yQtx8tz6avNiEz3xu+/rrLjaOl2/1ym2xAbOEXgdCNVVeBkJRMEJbv2yadzceHcWZhM+dDpXFCrTl08O+HvarG0sv56cWjLMSz958/w8HHHywv+Djf3a4MrIiIiopshcJLlZ2RxW1mnqayW5KlRp3HhnV+waf0GdOrUyQGjpltdrh0CpyptDnE7knSqZMjkITGrdZJhRWpZ5fjN3xWsb9VpcDsGTURERHRTkzntixZ+pRa3zR7aVrUcl+55Rcvzkn7bj8Sf92DxV18zaKIqxcCpCkmwJOWGFXVq71nEnU+Aq7srOg1u69CxEREREd0I0gxBWntPfXM61j/6mWo5Lt3zpBGEzGnqc2dfzGCmiZwAA6ebiDXb1H5Aa3j6VDzgIiIiInL2zNPvv61SpVTSXVlajkszB2ngIHPdiZwBA6ebxIVjsbhwNBY6Fy0bQhAREdEtSeafSJMsImdU5e3IybZsU6tezeEXdOObWhARERER3c4YON0EZF7Tyd1nAE3BgrdERERERHRjsVTvJrDl+z/Uz2ZdmiCoZrWqHg4RERGRQ1jnOEmLaGkNLYvkSvkekTNgxsnJJcel4OCWo2q7xwh2kyEiIqJbj6zndOeAu9CwUSO8/tYHeO8/36if8lz2y+tVaePGjWjQoPR1puj2wMDJyW37MQqwAPVb10FY/epVPRwiIiIiu1q6dCl69e6DI1fMaHL/6wi/6wmE9R6jfspz2S+vy3HXa/PmzaqtuSx4Wq1aNfTp00dluext2rRpcHV1hbe3d+Hjp59+sut79OrVC4sXLy719T/++AMtW7ZUa4iGhIRg3LhxyMzMtOsYbjcMnJxYRkom9qw9qLZ7juxc1cMhIiIisivJJD08dhzC+4xB9fZ3w83bv9jr8lz2y+ty3PVkntLS0jB06FC8+uqrSElJwYULF/DMM89Ap9PBEayBivUxbNiwYq/n5+fDkSRDtnLlSqSmpuL8+fMwm82YPn26Q9/zVsfAyYntWL4bJqMJ4Y3CUKd5raoeDhEREZFdTZk2HdWad4df7cgyj5PX5bip02dU+r1OnjwJvV6P4cOHQ6vVqizQvffei1q1Cr5jPfLII5g5c2bh8QsXLlTrSBU1efJkBAQEoFGjRlizZo3NY5A1qd577z00bdq0sPRv7ty5qFevHoKDgzFmzBgV4BUtD5wxYwYCAwPVuatXr1avvfnmm9iyZYtq3S73MWvWrGveKygoCDVr1lTbFotF3fOZM2dsHjP9hYGTk8rJykXUyr1qu8fIztBoNFU9JCIiIiK7kRK5jevXI7Bpxapq5Lj169aq7EllSLBjMBhUsCFBjzSgsIW8r8lkQnx8vAp+Ro4cqTJXtlq2bJkKio4dO6bGIUHQihUr1PVzcnLw3HPPFXtPCfYSEhLw+uuv44knnigM4Lp3747PP/9cZbPktZJER0erUj0Jrn744QdMmDDB5vHSXxg4Oamo3/YhL9uA4PBqaNKBkxGJiIjo1iLd8wLC619TnlcaOS4wvAHWrl1bqfeTeU0yxykvLw8PP/xwYYYnIyOjQue7uLhgypQpcHNzU2V3zZs3V6VwpVm0aJEKWuRRvfpf89QlMAoNDYWHhweWLFmigqHIyEh4eXmpzNG3336rMkRC9r3yyivqvWWsEghJ6V1FSTZNjpdgb+LEiYXZNaocBk5OyGgwYsfyXWqb2SYiIiK6FUnGR6v3tOkcOb6igU5JJNiRgCYuLg7bt29Xj7feeqtC50qg5e7uXvg8IiICly9fViVz1gYQd999d+HrEpxJ0CIPeT+r8PDwwu1Lly4VC2Zq166N3NxcJCcnF76nlNgJT8+C31VlGjxIc4i77roLDz74oM3n0l+4jpMT2rvmILLSsuEf7IsW3ZtW9XCIiIiI7E7WaTLnZdt0jhzv4+Njl/dv27atmu90+PDhwuyOlMpZSZamqKSkJBXUWIOnmJgYDBkyRJXM2RLMFP2DeFhYmMoiWcm2XF/mNNlynYqQZhSnT5+26RwqjhknJ2PKN2Hrsp1qu+u9HaFzcUynFyIiIqKqJIvbJseehiGzYqVncpwcf3XDhoo6fvw4PvroI5XlsTaL+OWXX9ChQwf1vFWrVvj1119VJuzs2bNYsGBBsfONRqNqHiE/ly9frgKuohmmyhg9ejTmz5+v5jtlZWVh0qRJGDVqVIWCIskilTXfS+ZNnThxQpX9yT3LtaX9OlUeAycnc2jLMaQmpsPLzxNt7mxZ1cMhIiIicoi6deuid5++SD5WsRbjclyfvv1Ud7nKkEyVlOZJpkmySxKADRo0CK+99lphaV39+vVVKd0DDzygHkXJ+0pAIwHLSy+9pOYiSYe969G/f38192jgwIGqTE/Wfpo9e3aFzpVW6tL5T+ZQvfPOO9e8LuWBEthJCWG7du3U9T/77LPrGu/tTmOxzj67TchfEWRyoLR6lBSxM5F/in9NWIDEmCT0G9MDPUd1qeohEREREdlEytmkY54ERkXnBJVE1mWSxW1lnaayWpKnXTiK2PWLsWnjBrWALZG9Ppe2xAac41RF5B9OUsRSEyt/CZAWmXmJ+SpocvNwQ4dBbap6iEREREQO1blzZyz6+iu1uG1O8+6q5XjRLnuqPO/YDlw5vAWLF33NoImqFAOnG0gm5Um96UcfzMb2Hdvg5x4AF40L8i1GpOWkoluNO9EgrBGGPzkEHl5l/4WGiIiI6FYgc3qkQ50sbrt+ySzVcly650kjCJnTJOV5M5hpIifAwOkGkUmG/fv1R+LlJITkRaCz5S7os/8KjgyWPBguGbE3bi82TluJVT1/U3W2RERERLdD5un3VStVRY6s7yQtx2VOksxDquycJiJ7Y+B0A0hJXqeOneGXGYx2+X2h1WiBq5qluGn0cIMe7iZPpFxOQLu27fHHzh1o3LhxVQ2biIiI6IaS+SePPfZYVQ+DqETsqudgMtGsb+9+8M8IQQNTi4KgqQzyen1TCwRmVlfn2bI6NBEREREROQYDJwf74osvkJdiRD1TM5vOq2uKhCHVpM4nIiIiIqKqxcDJgcxmM2Z/+DFCc2rZvLqzHC/nyflyHSIiIiIiqjqc4+RA69evx5XEK2iAyrUWD0FNnEs6grVr16oF0oiIiIhuZdbmELK2jqyp07dvXzXvicgZMOPkQFFRUQhECLQaXaXOl/lOgZYQ7Ny50+5jIyIiInIWshDunX37o2GDhpjy/HTMfuNT9VOey355vSpt3LgRDRo0qNIxUNVj4ORAqrGD8fp+xRqjlg0iiIiI6Ja1dOlS9OrZG2c2xKKz+S40y+6Exrlt1U95LvvldTnuem3evFmtB+Xn54dq1aqhT58+Kstlb9OmTYOrqyu8vb0LHz/99JNd36NXr15YvHhxhY69++674eLCQrPrxd+gA7m7u0OjswD513ERHeDh4WHHURERERE5B8kkPTxmLCKN7RCkqXHNci3uGg/URzP4GQMxdsxYtVCurPlU2U7HQ4cOxYIFCzBs2DBkZ2djzZo10OkqVxlUnnHjxuHzzz8v9fX8/PwbEsxIwCbrYtH1Y8bJgWrWrIk815zruobBLUddh4iIiOhWM+WNqaiZX68gaCqDvB6WXw/Tpky7rnU19Xo9hg8fDq1Wq7JA9957L2rVqqVef+SRRzBz5szC4xcuXKgW4C1q8uTJCAgIQKNGjVTQZStZzPe9995D06ZNC0v/5s6di3r16iE4OBhjxoxRAV7R8sAZM2YgMDBQnbt69Wr12ptvvoktW7aoNa/kPmbNmlXi++Xm5uKNN97AO++8Y/NY6VoMnBxoxIgRSDEmIdNS8D8AW2Va0pFsSMTIkSPtPjYiIiKiqiQlchs3bUBNS70KHV/TUhfr1q/D+fPnK/V+EuwYDAYVbEjQIw0obCHvazKZEB8fr4If+X6WkpJi8ziWLVumgqJjx46pcUgQtGLFCnX9nJwcPPfcc8XeU4K9hIQEvP7663jiiScKA7ju3burjFZmZqZ6rSQSMN1///0IDw+3eZx0LQZODiS1syNHjMRllwuVOj/O5TzuGzECQUFBdh8bERERUVWS7nnB7tVVOV5FuGs8EeJRQ3UbrgyZ1yRznPLy8vDwww8XZngqWsYmZXVTpkyBm5ubKvVr3rw5Vq5cWerxixYtgr+/v3pUr169cL8ERqGhoWoqxpIlS1QwFBkZCS8vL5U5+vbbb2GxWNSxsu+VV15R7y1jjY6OrvDcdwm6ZF7Yyy+/XKHjqXwMnBzs+RefR5zmAtIttv1FQo6/rLmAF1583mFjIyIiIqoqkvFxMbvadI7O7Hpd83Uk2JGAJi4uDtu3b1ePt956q0LnSqAl89etZL7V5cuXVcmctQGENGGwkuBMghx5yPtZFc3+XLp0qbBUUNSuXVuV1yUnJxe+p5QVCk9PT/VTMkwV8cILL6hsVtEx0/Vh4ORg7dq1w/Q3p+OoPqrCJXtynBw/dfpUtG/f3uFjJCIiIrrRZJ2mfK3RpnNMWiN8fHzs8v5t27ZV850OHz5cmN2RUjkrKckrKikpSQU1VjExMahRo4YqmZNgRh5lZaCsNJq/OmCEhYWpLJKVbEugI3OabLlOSaQc8Omnn1bZLvk+KWWGsn3kyJFyr00lY+B0A7z66qt45fWXsd91Cy5YTsBgySvxOKPFgAuWk9jvthUv/eNFvPbaazd8rEREREQ3gixum5BzGbmWijXSyrVkq+OvbthQUcePH8dHH32ksjzWZhG//PILOnTooJ63atUKv/76q8qEnT17VnXfK8poNKrmEfJz+fLlKuAqmmGqjNGjR2P+/PlqvlNWVhYmTZqEUaNGlRsUiZCQkDLne504cQL79+9Xj99++011D5Ttxo0bX9eYb2cMnG4A+fBLTex3y75DYDtP7NCtwgmXfYi1nEWcJVr9POG6D9t1KxHQ1gNLv/8W06ZPq9D/aIiIiIhuRnXr1kXvXn1wUXO2Qsdf1JxD3z59VXe5ypBMlZTmSaZJsksSgA0aNKjwD9VSWle/fn1VSvfAAw+oR1HyvvLdTAKWl156Sc1Fkg5716N///6YOHEiBg4cqMr0ZO2n2bNnV+jcZ555RnX+kzlUJXXNk3FKhkkeUvInZJvrOVWexmKdfXabkL8iyORAafUoKeKqIH+hmPfveTh86AjS09LVOCJbROKpp55EixYtqmRMRERERPYg5WzSMU8Co/Lm18g6TrK4beE6TqVIslzGMdfd2Lh5o1rAlshen0tbYgOGnFVAJiZ+OufTqh4GERERUZWSxWwXLf5aLW4r6zRJy3Hpnle0PE8yTZdcz2LRN4sYNFGVYqkeEREREVUZmdOzYdMGNOgTgR3a1Tji+QeOu+9RP+V5w74RKtPEdS2pqjHjRERERERVnnn6fe1qVUol6ztJy3GZkyTzkCo7p4nI3hg4EREREZFTkPknjz32WFUPg6hELNUjIiIiIru7zfqP0W3weWTGiYiIiIjsRlpqS9vuxMRE1Qaby6uQMwRN8nmUz6J8PiuLgRMRERER2Y0stCprIcXGxpa5QCvRjSRBk3wu5fN5UwdOc+bMwT//+U/ExcWpVZv/9a9/Fa7ifDVZXfnrr79WayEJWcRs1qxZpR5PRERERDeWt7c3GjZsCKPRWNVDIVIk03Q9QZNTBE6y6vKLL76IefPmoWPHjmq15AEDBuDEiRNqxeOrbdy4Ua3k3KVLF7V41bvvvqtWXT5y5Ahq1qxZJfdARERERMXJl9Tr/aJK5Ew0liqeuSfBUvv27fHppwULwprNZkREROCZZ57Ba6+9Vu75JpMJAQEB6vyxY8eWe7wtqwMTEREREdGty5bYoEq76hkMBuzZs0f16C8ckFarnu/YsaNC18jOzlZp4MDAQAeOlIiIiIiIbmdVWqqXlJSkMkahoaHF9svz48ePV+ga//jHPxAWFlYs+CoqLy9PPawkmrRGl0REREREdPtK/zMmqEgRXpXPcboe77zzDpYsWaLmPcl8p5K8/fbbmD59+jX7pRyQiIiIiIgoIyNDlew5beAUFBSkJg3Gx8cX2y/Pq1evXua577//vgqc1q5di5YtW5Z63MSJE1XzCSuZQ5WcnIxq1apxXQEnj/4luI2JieFcNKoQfmbIVvzMkK34mSFb8TPj/CTTJEGTVLCVp0oDJzc3N9VOfN26dRg2bFhhYCPPJ0yYUOp57733Ht566y2sXr0a7dq1K/M99Hq9ehTl7+9vpzsgR5P/yPA/NGQLfmbIVvzMkK34mSFb8TPj3MrLNDlNqZ5kg8aNG6cCIFmLSdqRZ2VlYfz48ep16ZQnbcal5E5I+/EpU6bgv//9L+rUqaPWfrKuFyAPIiIiIiIie6vywGn06NFITExUwZAEQa1bt8aqVasKG0ZER0erTntW//73v1U3vhEjRhS7ztSpUzFt2rQbPn4iIiIiIrr1VXngJKQsr7TSPGn8UNT58+dv0KioKkl5pQTDV5dZEpWGnxmyFT8zZCt+ZshW/MzcWqp8AVwiIiIiIiJnV6UL4BIREREREd0MGDgRERERERGVg4ETERERERFRORg4ERERERERlYOBEzkVWa+rffv28PHxQUhIiFoY+cSJE1U9LLpJvPPOO9BoNHj++eereijkxC5evIgxY8agWrVq8PDwQIsWLbB79+6qHhY5KZPJhMmTJ6Nu3brq81K/fn28+eabYG8tstq8eTOGDBmCsLAw9f+Dfvrpp2Kvy2dFlt2pUaOG+gz169cPp06dqrLxUuUxcCKnsmnTJjz99NP4448/sGbNGhiNRvTv318tikxUll27duGzzz5Dy5Ytq3oo5MRSUlLQtWtXuLq6YuXKlTh69Cg++OADBAQEVPXQyEm9++67ag3JTz/9FMeOHVPP33vvPfzrX/+q6qGRk5DvKK1atcKcOXNKfF0+L5988gnmzZuHnTt3wsvLCwMGDEBubu4NHytdH7YjJ6cmiyNL5kkCqh49elT1cMhJZWZmok2bNpg7dy5mzpypFtKePXt2VQ+LnNBrr72Gbdu2YcuWLVU9FLpJDB48GKGhoViwYEHhvvvuu09lDhYvXlylYyPnIxmnH3/8UVXMCPmaLZmol156CS+//LLal5aWpj5TCxcuxP3331/FIyZbMONETk3+4yICAwOreijkxCRLOWjQIFX+QFSW5cuXo127dhg5cqT6o8wdd9yB+fPnV/WwyIl16dIF69atw8mTJ9XzAwcOYOvWrbj77ruremh0Ezh37hzi4uKK/f8nPz8/dOzYETt27KjSsZHtXCpxDtENYTab1VwVKatp3rx5VQ+HnNSSJUuwd+9eVapHVJ6zZ8+qsqsXX3wRr7/+uvrcPPvss3Bzc8O4ceOqenjkpFnK9PR0NGnSBDqdTs15euutt/DQQw9V9dDoJiBBk5AMU1Hy3Poa3TwYOJFTZxEOHz6s/rJHVJKYmBg899xzaj6cu7t7VQ+HbpI/yEjGadasWeq5ZJzkvzMy94CBE5Vk6dKl+Oabb/Df//4XzZo1w/79+9Uf9aT8ip8ZotsLS/XIKU2YMAErVqzAhg0bEB4eXtXDISe1Z88eJCQkqPlNLi4u6iHz4WQSrmzLX4aJipKuVpGRkcX2NW3aFNHR0VU2JnJur7zyiso6yVwU6cD48MMP44UXXlBdYInKU716dfUzPj6+2H55bn2Nbh4MnMipyCRKCZpkYuX69etV+1ei0vTt2xeHDh1SfwG2PiSbICU0si1lNURFSenv1UscyNyV2rVrV9mYyLllZ2dDqy3+dUn+2yLZS6LyyPcYCZBknpyVlH5Kd73OnTtX6djIdizVI6crz5NyiJ9//lmt5WSt/5WJlNLBiKgo+YxcPf9N2rzK+jycF0clkUyBTPaXUr1Ro0YhKioK//nPf9SDqCSyPo/MaapVq5Yq1du3bx8+/PBD/O1vf6vqoZETdXY9ffp0sYYQ8sc7aWwlnxsp7ZSOrw0bNlSBlKwLJqWe1s57dPNgO3JyujaeJfnyyy/xyCOP3PDx0M2nV69ebEdOZZIy4IkTJ6oFKOVLjDSKePzxx6t6WOSkMjIy1BddqYSQ0mD5wvvAAw+oBU2lqQjRxo0b0bt372v2yxw4aTkuX7WnTp2q/kCTmpqKbt26qeUzGjVqVCXjpcpj4ERERERERFQOznEiIiIiIiIqBwMnIiIiIiKicjBwIiIiIiIiKgcDJyIiIiIionIwcCIiIiIiIioHAyciIiIiIqJyMHAiIiIiIiIqBwMnIiJyuoWwf/rpJ9xK6tSpw0WZiYhucgyciIjILh555BEV9MjD1dUVdevWxauvvorc3NwbPpbExEQ89dRTqFWrFvR6PapXr44BAwZg27ZtFb7GtGnT0Lp163KPy87OxsSJE1G/fn24u7sjODgYPXv2xM8//1x4zK5du/DEE09U+n6IiKjquVT1AIiI6NZx11134csvv4TRaMSePXswbtw4FUi9++67N3Qc9913HwwGA7766ivUq1cP8fHxWLduHa5cuWL393ryySexc+dO/Otf/0JkZKR6j+3btxd7LwmmiIjo5saMExER2Y01uxMREYFhw4ahX79+WLNmTZkla5LVkexOaWJiYjBq1Cj4+/sjMDAQQ4cOxfnz50s9PjU1FVu2bFHBWu/evVG7dm106NBBZYXuueeeYsc99thjKqjx9fVFnz59cODAAfXawoULMX36dPXcmkWTfSVZvnw5Xn/9dQwcOFDdX9u2bfHMM8/gb3/7W4n3LdexXrPoo+jv4PPPP0fTpk1VBqtJkyaYO3duOb95IiJyNAZORETkEIcPH1aZFzc3t0pfQzJXUmLn4+OjgiEptfP29laZLckolURel4fMk8rLyyv12iNHjkRCQgJWrlypsmNt2rRB3759kZycjNGjR+Oll15Cs2bNcPnyZfWQfSWRQPG3335DRkZGhe5JrmO9pjz+97//wcXFBV27dlWvf/PNN5gyZQreeustHDt2DLNmzcLkyZNV9oyIiKoOS/WIiMhuVqxYoYKW/Px8FbRotVp8+umnlb7et99+C7PZrDIwkpURUgoo2aeNGzeif//+15wjQYhkdR5//HHMmzdPBUQy5+j+++9Hy5Yt1TFbt25FVFSUCpwkSybef/99FWx9//33aj6S3IdcSwKjsvznP//BQw89hGrVqqFVq1bo1q0bRowYURgIXc3Dw0M9xJkzZ/D000+r4OjOO+9U+6ZOnYoPPvgAw4cPV89lrtjRo0fx2WefqdJHIiKqGsw4ERGR3Uhp3P79+9WcH/mSP378eDXfqLKkVO706dMq42TNJEm5njSckKBDslDW/fKQbI2Q97x06ZIqo5PslARZEkBZy+3kupmZmSrYKXr+uXPn1HVt0aNHD5w9e1bNoZKA6ciRI+jevTvefPPNMs9LS0vD4MGDMWjQILzyyitqX1ZWlnr/Rx99tNi4Zs6cafO4iIjIvphxIiIiu/Hy8kKDBg3U9hdffKEyMAsWLFCBgJAMlMViuaYcrzQS3MicIWtAVJTMTZIyQAnUrEJDQwu3ZX6QZHHkIaVuMp9JsjnS/U+uW6NGDRVQXU2yWbaSLoISLMnjH//4hwp0ZsyYobZLKlU0mUyqZE/mVknGquj9ivnz56Njx47FztHpdDaPi4iI7IeBExEROYQESdI04cUXX8SDDz6oytMk2JF5PVbp6ekqy1MayRJJuV5ISIgKMkpiDdTKIx3vrOtDyXXj4uJUKZ40biiJBDwS4FSGvJeUK0pmrKTA6YUXXsChQ4ewe/duFeAVDfzCwsJUBkvK/4iIyHmwVI+IiBxGGjBIpmTOnDnquXSuW7RokSqxk8BByvnKyqRI8BAUFKQ66ck5EmRJlujZZ59FbGxsiedIG3B5n8WLF+PgwYPqnO+++w7vvfeeuo6Qbn+dO3dWnf9+//131aVPGllMmjRJBTNCAio5VzJaSUlJpTaa6NWrl5p/JA0m5DrSKEICRilbLCnYkzla0iVP5l/JvC0J4ORhzTZJN7+3334bn3zyCU6ePKl+T3LOhx9+WIl/ASIishcGTkRE5DCS0ZkwYYIKWmT+jrQEl0YN1rk9ErjIwrGl8fT0xObNm9VCttIsQVp0S9mfZHJKy0DJnCApc/voo4/U/KPmzZurUj1pFmFtVCEBiwQ48rrMw2rUqJFqHnHhwoXCcj+ZJyXzoyQAkkyZdL8riXT9k4530qhCxietyGXf0qVLSzx+06ZNKpMlrdGlXND6kOYUQkoKpRmGBEstWrRQvy+ZmyVNIoiIqOpoLFcXmxMREREREVExzDgRERERERGVg4ETERERERFRORg4ERERERERlYOBExERERERUTkYOBEREREREZWDgRMREREREVE5GDgRERERERGVg4ETERERERFRORg4ERERERERlYOBExERERERUTkYOBEREREREZWDgRMRERERERHK9v9PzDLa3ziEzAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "if run_all_cells:\n", " resolution = 500\n", @@ -1335,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "2cf56653", "metadata": {}, "outputs": [], @@ -1360,21 +755,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "1fb01410", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Save Phase 1 Rule Training Performance Estimates to .csv\n", "rule_tracking_df = heros.get_performance_tracking()\n", @@ -1393,21 +777,10 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "ce8af5c4", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "model_tracking_df = heros.get_model_performance_tracking()\n", "model_tracking_df.to_csv(output_path+'/model_tracking.csv', index=False)\n", @@ -1427,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "3f39c1c1", "metadata": {}, "outputs": [], @@ -1450,7 +823,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "75c2551d", "metadata": {}, "outputs": [], @@ -1468,28 +841,10 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "9a6f9391", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Global Phase 1 Phase 2 Rule Initialization Rule Covering \\\n", - "0 75.350175 35.665026 39.558793 0.003188 0.003982 \n", - "\n", - " Rule Equality Rule Matching Rule Evaluation Feature Tracking \\\n", - "0 1.912445 3.157274 33.191789 0.0 \n", - "\n", - " Rule Subsumption Rule Selection Rule Mating Rule Deletion \\\n", - "0 0.54021 1.156045 5.114025 5.235724 \n", - "\n", - " Rule Compaction Rule Prediction \n", - "0 0.003994 1.010038 \n" - ] - } - ], + "outputs": [], "source": [ "time_df = heros.get_runtimes()\n", "time_df.to_csv(output_path+'/runtimes.csv', index=False)\n", @@ -1517,21 +872,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "708cf494", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "if run_all_cells:\n", " heros.get_rule_pop_heatmap(feature_names, weighting='useful_accuracy', specified_filter=1, display_micro=True, show=True, save=True, output_path=output_path)" @@ -1547,21 +891,10 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "892ea1a5", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "if run_all_cells:\n", " node_size = 1000\n", @@ -1581,21 +914,10 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "4454a331", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Save Feature Tracking Scores to .csv\n", "if heros.feat_track != None:\n", @@ -1617,27 +939,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "48a269b0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "HEROS Whole Rule Population Testing Data Performance Report:\n", - " precision recall f1-score support\n", - "\n", - " 0 0.92000000 0.85185185 0.88461538 27\n", - " 1 0.84000000 0.91304348 0.87500000 23\n", - "\n", - " accuracy 0.88000000 50\n", - " macro avg 0.88000000 0.88244767 0.87980769 50\n", - "weighted avg 0.88320000 0.88000000 0.88019231 50\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "predictions = heros.predict(X_test,whole_rule_pop=True)\n", "print(\"HEROS Whole Rule Population Testing Data Performance Report:\")\n", @@ -1657,66 +962,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "d4390561", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Condition Indexes Condition Values Action Numerosity Fitness \\\n", - "0 [0, 1, 4] [1, 0, 0] 0 3 0.997857 \n", - "1 [0, 1, 5] [1, 1, 1] 1 5 0.997429 \n", - "2 [0, 1, 2] [0, 0, 0] 0 8 1.000000 \n", - "3 [0, 1, 3] [0, 1, 1] 1 7 0.998143 \n", - "4 [0, 1, 2] [0, 0, 1] 1 3 0.998714 \n", - "5 [0, 1, 4] [1, 0, 1] 1 7 0.997857 \n", - "6 [0, 1, 3] [0, 1, 0] 0 7 0.997714 \n", - "7 [0, 1, 5] [1, 1, 0] 0 5 0.996857 \n", - "\n", - " Useful Accuracy Useful Coverage Accuracy Match Cover Correct Cover \\\n", - "0 1.0 27.5 1.0 55 55 \n", - "1 1.0 26.0 1.0 52 52 \n", - "2 1.0 34.0 1.0 68 68 \n", - "3 1.0 28.5 1.0 57 57 \n", - "4 1.0 30.5 1.0 61 61 \n", - "5 1.0 27.5 1.0 55 55 \n", - "6 1.0 27.0 1.0 54 54 \n", - "7 1.0 24.0 1.0 48 48 \n", - "\n", - " Mean Absolute Error Prediction Outcome Range Probability Birth Iteration \\\n", - "0 None None None 538 \n", - "1 None None None 938 \n", - "2 None None None 147 \n", - "3 None None None 459 \n", - "4 None None None 182 \n", - "5 None None None 248 \n", - "6 None None None 181 \n", - "7 None None None 477 \n", - "\n", - " Specified Count Average Match Set Size Deletion Probabiilty \n", - "0 3 87.337309 0.002396 \n", - "1 3 96.095497 0.007325 \n", - "2 3 99.853940 0.019436 \n", - "3 3 92.333984 0.013786 \n", - "4 3 99.660789 0.002731 \n", - "5 3 95.647239 0.014285 \n", - "6 3 100.720970 0.015045 \n", - "7 3 93.752155 0.007151 \n", - "HEROS Top 'Default' Model Testing Data Performance Report:\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Save Top Model selected by Default from the Front (Model on front with highest training accuracy)\n", "set_df = heros.get_model_rules() #returns top training model by default based on balanced accuracy, then covering, then rule-set size.\n", @@ -1739,70 +988,10 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "ddeab87c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Condition Indexes Condition Values Action Numerosity Fitness \\\n", - "0 [0, 1, 5] [1, 1, 1] 1 5 0.997429 \n", - "1 [0, 1, 2] [0, 0, 0] 0 8 1.000000 \n", - "2 [0, 1, 3] [0, 1, 0] 0 7 0.997714 \n", - "3 [0, 1, 4] [1, 0, 1] 1 7 0.997857 \n", - "4 [0, 1, 3] [0, 1, 1] 1 7 0.998143 \n", - "5 [0, 1, 2] [0, 0, 1] 1 3 0.998714 \n", - "6 [0, 4, 5] [1, 0, 0] 0 8 0.998000 \n", - "7 [0, 1, 5] [1, 1, 0] 0 5 0.996857 \n", - "8 [0, 1, 4] [1, 0, 0] 0 3 0.997857 \n", - "\n", - " Useful Accuracy Useful Coverage Accuracy Match Cover Correct Cover \\\n", - "0 1.0 26.0 1.0 52 52 \n", - "1 1.0 34.0 1.0 68 68 \n", - "2 1.0 27.0 1.0 54 54 \n", - "3 1.0 27.5 1.0 55 55 \n", - "4 1.0 28.5 1.0 57 57 \n", - "5 1.0 30.5 1.0 61 61 \n", - "6 1.0 28.0 1.0 56 56 \n", - "7 1.0 24.0 1.0 48 48 \n", - "8 1.0 27.5 1.0 55 55 \n", - "\n", - " Mean Absolute Error Prediction Outcome Range Probability Birth Iteration \\\n", - "0 None None None 938 \n", - "1 None None None 147 \n", - "2 None None None 181 \n", - "3 None None None 248 \n", - "4 None None None 459 \n", - "5 None None None 182 \n", - "6 None None None 450 \n", - "7 None None None 477 \n", - "8 None None None 538 \n", - "\n", - " Specified Count Average Match Set Size Deletion Probabiilty \n", - "0 3 96.095497 0.007325 \n", - "1 3 99.853940 0.019436 \n", - "2 3 100.720970 0.015045 \n", - "3 3 95.647239 0.014285 \n", - "4 3 92.333984 0.013786 \n", - "5 3 99.660789 0.002731 \n", - "6 3 98.934327 0.010854 \n", - "7 3 93.752155 0.007151 \n", - "8 3 87.337309 0.002396 \n", - "HEROS Top 'Default' Model Testing Data Performance Report:\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Save Top Model selected by Default from the Front (Model on front with highest training accuracy)\n", "desired_model_index = 1\n", @@ -1826,72 +1015,10 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "ceac3c53", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rule population evaluation at iteration 500\n", - "Run Time: 0.27272582054138184\n", - " precision recall f1-score support\n", - "\n", - " 0 0.80000000 0.76923077 0.78431373 26\n", - " 1 0.76000000 0.79166667 0.77551020 24\n", - "\n", - " accuracy 0.78000000 50\n", - " macro avg 0.78000000 0.78044872 0.77991196 50\n", - "weighted avg 0.78080000 0.78000000 0.78008804 50\n", - "\n", - "Rule population evaluation at iteration 1000\n", - "Run Time: 0.6125838756561279\n", - " precision recall f1-score support\n", - "\n", - " 0 0.92000000 0.79310345 0.85185185 29\n", - " 1 0.76000000 0.90476190 0.82608696 21\n", - "\n", - " accuracy 0.84000000 50\n", - " macro avg 0.84000000 0.84893268 0.83896940 50\n", - "weighted avg 0.85280000 0.84000000 0.84103060 50\n", - "\n", - "Rule population evaluation at iteration 5000\n", - "Run Time: 3.640860080718994\n", - " precision recall f1-score support\n", - "\n", - " 0 0.92000000 0.82142857 0.86792453 28\n", - " 1 0.80000000 0.90909091 0.85106383 22\n", - "\n", - " accuracy 0.86000000 50\n", - " macro avg 0.86000000 0.86525974 0.85949418 50\n", - "weighted avg 0.86720000 0.86000000 0.86050582 50\n", - "\n", - "Rule population evaluation at iteration 10000\n", - "Run Time: 7.391563892364502\n", - " precision recall f1-score support\n", - "\n", - " 0 0.92000000 0.82142857 0.86792453 28\n", - " 1 0.80000000 0.90909091 0.85106383 22\n", - "\n", - " accuracy 0.86000000 50\n", - " macro avg 0.86000000 0.86525974 0.85949418 50\n", - "weighted avg 0.86720000 0.86000000 0.86050582 50\n", - "\n", - "Rule population evaluation at iteration 50000\n", - "Run Time: 35.637484073638916\n", - " precision recall f1-score support\n", - "\n", - " 0 0.92000000 0.85185185 0.88461538 27\n", - " 1 0.84000000 0.91304348 0.87500000 23\n", - "\n", - " accuracy 0.88000000 50\n", - " macro avg 0.88000000 0.88244767 0.87980769 50\n", - "weighted avg 0.88320000 0.88000000 0.88019231 50\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "if stored_rule_iterations != None:\n", " rule_iteration_list = [int(x) for x in stored_rule_iterations.split(',')]\n", @@ -1915,50 +1042,10 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "4ddb1cb2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Top default model evaluation at iteration 10\n", - "Run Time: 4.625010967254639\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n", - "Top default model evaluation at iteration 50\n", - "Run Time: 19.661582708358765\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n", - "Top default model evaluation at iteration 100\n", - "Run Time: 39.54275989532471\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "if stored_model_iterations != None:\n", " model_iteration_list = [int(x) for x in stored_model_iterations.split(',')]\n", @@ -1979,86 +1066,10 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "cf9e32c0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---------------------------------------------------------------------------------------------\n", - "Top model evaluation at iteration 10\n", - "Run Time: 4.625010967254639\n", - "11 non-dominated models on Pareto-front.\n", - "----------------------------------------\n", - "Model testing accuracies: [0.98, 0.8600000000000001, 0.8400000000000001, 0.8, 0.72, 0.8200000000000001, 0.78, 0.52, 0.5800000000000001, 0.45999999999999996, 0.56]\n", - "Model testing coverages: [np.float64(0.96), np.float64(0.76), np.float64(0.84), np.float64(0.74), np.float64(0.88), np.float64(0.56), np.float64(0.54), np.float64(0.78), np.float64(0.56), np.float64(0.48), np.float64(0.36)]\n", - "Model rule counts: [8, 7, 7, 6, 5, 4, 3, 3, 2, 1, 1]\n", - "----------------------------------------\n", - "Best model testing accuracy: 0.98\n", - "Best model testing coverage: 0.96\n", - "Best rule count: 8\n", - "Best model index: 0\n", - "----------------------------------------\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n", - "---------------------------------------------------------------------------------------------\n", - "Top model evaluation at iteration 50\n", - "Run Time: 19.661582708358765\n", - "11 non-dominated models on Pareto-front.\n", - "----------------------------------------\n", - "Model testing accuracies: [1.0, 0.94, 0.9199999999999999, 0.6200000000000001, 0.62, 0.64, 0.6799999999999999, 0.6000000000000001, 0.6, 0.6599999999999999, 0.52]\n", - "Model testing coverages: [np.float64(1.0), np.float64(0.88), np.float64(0.8), np.float64(1.0), np.float64(0.92), np.float64(0.92), np.float64(1.0), np.float64(0.82), np.float64(0.78), np.float64(0.54), np.float64(0.54)]\n", - "Model rule counts: [8, 7, 7, 6, 5, 4, 3, 3, 2, 1, 1]\n", - "----------------------------------------\n", - "Best model testing accuracy: 1.0\n", - "Best model testing coverage: 1.0\n", - "Best rule count: 8\n", - "Best model index: 0\n", - "----------------------------------------\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n", - "---------------------------------------------------------------------------------------------\n", - "Top model evaluation at iteration 100\n", - "Run Time: 39.54275989532471\n", - "11 non-dominated models on Pareto-front.\n", - "----------------------------------------\n", - "Model testing accuracies: [1.0, 0.96, 0.96, 0.88, 0.76, 0.6200000000000001, 0.5800000000000001, 0.5800000000000001, 0.6799999999999999, 0.44000000000000006, 0.5800000000000001]\n", - "Model testing coverages: [np.float64(1.0), np.float64(0.98), np.float64(0.94), np.float64(0.88), np.float64(1.0), np.float64(1.0), np.float64(1.0), np.float64(1.0), np.float64(1.0), np.float64(0.48), np.float64(0.48)]\n", - "Model rule counts: [8, 7, 7, 6, 5, 4, 3, 3, 2, 1, 1]\n", - "----------------------------------------\n", - "Best model testing accuracy: 1.0\n", - "Best model testing coverage: 1.0\n", - "Best rule count: 8\n", - "Best model index: 0\n", - "----------------------------------------\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00000000 1.00000000 1.00000000 25\n", - " 1 1.00000000 1.00000000 1.00000000 25\n", - "\n", - " accuracy 1.00000000 50\n", - " macro avg 1.00000000 1.00000000 1.00000000 50\n", - "weighted avg 1.00000000 1.00000000 1.00000000 50\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "if stored_model_iterations != None:\n", " model_iteration_list = [int(x) for x in stored_model_iterations.split(',')]\n", @@ -2074,7 +1085,7 @@ ], "metadata": { "kernelspec": { - "display_name": "heros_env", + "display_name": "base", "language": "python", "name": "python3" }, @@ -2088,7 +1099,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.11" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/pyproject.toml b/pyproject.toml index b0d6ad6..60eb6ad 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -49,10 +49,11 @@ test = [ [project.urls] "Source Code" = "https://github.com/UrbsLab/heros" -[tool.setuptools] -package-dir = {"" = "src"} +#[tool.setuptools] +#package-dir = {"" = "src"} +#packages = ["skheros"] # Explicitly includes your package [tool.setuptools.packages.find] where = ["src"] -include = ["heros*"] -exclude = ["tests*"] \ No newline at end of file +#include = ["heros*"] +#exclude = ["tests*"] \ No newline at end of file