{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np \nimport pandas as pd\nimport os\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\nfrom matplotlib import style\nstyle.use('ggplot')\n\nimport seaborn as sns\nsns.set()\n\nfrom IPython.display import HTML\ndisplay(HTML(\"<style>.container { width:98% !important; }</style>\"))\n\nimport timeit\nfrom tqdm import tqdm\n\nfrom ipywidgets import interact\nimport ipywidgets as widgets\n\nfrom scipy import fftpack\n\nfrom os import listdir\nprint(listdir(\"../input\"))","execution_count":1,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<style>.container { width:98% !important; }</style>"},"metadata":{}},{"output_type":"stream","text":"['test', 'train.csv', 'sample_submission.csv']\n","name":"stdout"}]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"train_nrows = !wc -l ../input/train.csv\ntrain_nrows_val = int(train_nrows[0].split()[0])\nprint('train.csv contains {:,} rows'.format(train_nrows_val))","execution_count":2,"outputs":[{"output_type":"stream","text":"train.csv contains 629,145,481 rows\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"!head ../input/train.csv","execution_count":3,"outputs":[{"output_type":"stream","text":"acoustic_data,time_to_failure\r\n12,1.4690999832\r\n6,1.4690999821\r\n8,1.469099981\r\n5,1.4690999799\r\n8,1.4690999788\r\n8,1.4690999777\r\n9,1.4690999766\r\n7,1.4690999755\r\n-5,1.4690999744\r\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### TTF Precision - Check MAX precision"},{"metadata":{"trusted":true},"cell_type":"code","source":"start_time = timeit.default_timer()\nmax_precision = 0\ncount = 0\nwith open('../input/train.csv', 'r') as f:\n    while count<10: #True: #count <10:\n        line = f.readline()\n        if not line: \n            break\n        else: \n            print(line.rstrip())\n            if count > 0:\n                print(line[:-1].split('.')[1])\n                if '.' in line: \n                    str_len = len(line[:-1].split('.')[1])\n                    print(str_len)\n                    if max_precision < str_len:\n                        print(line)\n                    max_precision = max_precision if max_precision > str_len else str_len\n                print(line)\n                print(line.split('.')[1])\n        count +=1\nprint (max_precision)\nprint('elapsed time: {:.2f} sec'.format(timeit.default_timer()-start_time))","execution_count":7,"outputs":[{"output_type":"stream","text":"acoustic_data,time_to_failure\n12,1.4690999832\n4690999832\n10\n12,1.4690999832\n\n12,1.4690999832\n\n4690999832\n\n6,1.4690999821\n4690999821\n10\n6,1.4690999821\n\n4690999821\n\n8,1.469099981\n469099981\n9\n8,1.469099981\n\n469099981\n\n5,1.4690999799\n4690999799\n10\n5,1.4690999799\n\n4690999799\n\n8,1.4690999788\n4690999788\n10\n8,1.4690999788\n\n4690999788\n\n8,1.4690999777\n4690999777\n10\n8,1.4690999777\n\n4690999777\n\n9,1.4690999766\n4690999766\n10\n9,1.4690999766\n\n4690999766\n\n7,1.4690999755\n4690999755\n10\n7,1.4690999755\n\n4690999755\n\n-5,1.4690999744\n4690999744\n10\n-5,1.4690999744\n\n4690999744\n\n10\nelapsed time: 0.06 sec\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"column_names = !head -n1 ../input/train.csv\nprint(column_names[0].split(','))","execution_count":8,"outputs":[{"output_type":"stream","text":"['acoustic_data', 'time_to_failure']\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train_sample = pd.read_csv('../input/train.csv', skiprows = 0, nrows=100,\n                       dtype={'acoustic_data': np.int16, 'time_to_failure': np.float64}) ","execution_count":9,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def display_df_with_preset_precision(df, precision):\n    curr_precision = pd.get_option(\"display.precision\")\n    pd.set_option(\"display.precision\", precision)\n    display(df)\n    pd.set_option(\"display.precision\", curr_precision)\n    \ndisplay_df_with_preset_precision(df_train_sample.head(9), max_precision)","execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"   acoustic_data  time_to_failure\n0             12     1.4690999832\n1              6     1.4690999821\n2              8     1.4690999810\n3              5     1.4690999799\n4              8     1.4690999788\n5              8     1.4690999777\n6              9     1.4690999766\n7              7     1.4690999755\n8             -5     1.4690999744","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>acoustic_data</th>\n      <th>time_to_failure</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>12</td>\n      <td>1.4690999832</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>6</td>\n      <td>1.4690999821</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>1.4690999810</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5</td>\n      <td>1.4690999799</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>8</td>\n      <td>1.4690999788</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>8</td>\n      <td>1.4690999777</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>9</td>\n      <td>1.4690999766</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>7</td>\n      <td>1.4690999755</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>-5</td>\n      <td>1.4690999744</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"try:\n    del(df_train_sample)    \nexcept NameError:\n    pass","execution_count":11,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"start_time = timeit.default_timer()\nfrom collections import Counter\ndiff_ttf_2_counter_dict = Counter()\ntry:\n    del(df_train_iter)    \nexcept NameError:\n    pass\ndf_train_iter = pd.read_csv('../input/train.csv', chunksize=train_nrows_val//100,\n                       dtype={'acoustic_data': np.int16, 'time_to_failure': np.float64},iterator=True)\ntime_to_failure_diffs_set = set()\ndf_after_jumping_up_points = pd.DataFrame()\ndf_after_long_jumps_down_points=pd.DataFrame()\nfor df in df_train_iter:\n    df['diff_in_time_to_failure']=df['time_to_failure'].diff()\n    nparr_ = df['diff_in_time_to_failure'].values\n    nparr_ = nparr_[~np.isnan(nparr_)]\n    diff_ttf_2_counter_dict += Counter(nparr_)\n\n    df_jumps_up = df.loc[(df['diff_in_time_to_failure'] > 0)]\n    df_long_jumps_down = df.loc[(df['diff_in_time_to_failure'] < -0.0001)]\n    df_after_jumping_up_points=df_after_jumping_up_points.append(df_jumps_up)\n    df_after_long_jumps_down_points=df_after_long_jumps_down_points.append(df_long_jumps_down)\nprint('elapsed time: {:.2f} sec'.format(timeit.default_timer()-start_time))","execution_count":12,"outputs":[{"output_type":"stream","text":"elapsed time: 307.33 sec\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(list(diff_ttf_2_counter_dict.keys()))","execution_count":13,"outputs":[{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"451"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"diff_ttf_2_counter_dict","execution_count":14,"outputs":[{"output_type":"execute_result","execution_count":14,"data":{"text/plain":"Counter({-1.0999998689698032e-09: 25318543,\n         -1.100000313059013e-09: 4893808,\n         -1.0999996469251982e-09: 26651657,\n         -1.100000091014408e-09: 363912910,\n         -0.0009954954999999988: 5893,\n         -0.0010954954999997657: 1169,\n         -0.0010954954999999877: 22199,\n         -0.0009954954999997767: 17243,\n         -0.001095495479999875: 2,\n         -1.0999999799921056e-09: 36789889,\n         -1.1000002020367106e-09: 363297,\n         -0.0009954954999998877: 545,\n         -0.0010954954999998767: 571,\n         -0.0010954955000000988: 517,\n         -0.0009954955000001098: 263,\n         -1.1000000355032569e-09: 5120202,\n         -0.0009954955000000543: 328,\n         -0.0010954955000000433: 753,\n         -0.0010954955000000155: 617,\n         -1.1000000077476813e-09: 5024997,\n         -0.0009954955000000265: 129,\n         -0.00109549549999996: 164,\n         -1.0999999938698934e-09: 2781206,\n         -1.100000021625469e-09: 266318,\n         -0.0010954955000000016: 439,\n         -0.0009954955000000126: 98,\n         -0.0010954966000000094: 1,\n         -0.0009954954970000096: 1,\n         -1.0990000159916136e-09: 18062,\n         -1.1009999856259611e-09: 23609,\n         -1.0990000021138258e-09: 122792,\n         -1.1010000133815367e-09: 21593,\n         -1.098999988236038e-09: 29055,\n         -1.100999999503749e-09: 146757,\n         -0.0010954954990000099: 7,\n         -0.001095495498999996: 17,\n         -0.0009954954999999849: 74,\n         -0.0009954954989999931: 9,\n         -0.0010954954999999947: 99,\n         -1.1000000008087873e-09: 1491709,\n         -1.100999992564855e-09: 19730,\n         -1.1010000064426428e-09: 18950,\n         -1.0990000090527197e-09: 15535,\n         -1.0989999951749319e-09: 36956,\n         -0.0009954954999999918: 10,\n         -0.001095495499000003: 14,\n         -0.0009954955000000057: 34,\n         -1.0989999986443788e-09: 25447,\n         -1.1010000029731959e-09: 16406,\n         -1.0999999973393404e-09: 355357,\n         -1.1000000042782343e-09: 127073,\n         -1.100999996034302e-09: 8986,\n         -0.0009954954999999953: 11,\n         -0.0010954954999999982: 98,\n         -0.0009954955000000022: 22,\n         -0.0009954955000000005: 25,\n         -1.0990000003791023e-09: 11548,\n         -1.1009999977690255e-09: 949,\n         -1.1000000025435108e-09: 21462,\n         -1.0999999990740639e-09: 151440,\n         -1.1010000012384724e-09: 5205,\n         -1.0989999969096553e-09: 1411,\n         -0.0010954954999999999: 32,\n         -0.0010954954998000001: 3,\n         -1.0996000013308027e-09: 7992,\n         -1.1004999984215447e-09: 2596,\n         -1.099499999726583e-09: 10723,\n         -1.1005000001562681e-09: 47198,\n         -1.1001000006782835e-09: 43421,\n         -1.10009999894356e-09: 19078,\n         -1.0995999995960792e-09: 30274,\n         -1.1003999985520485e-09: 549,\n         -1.0995000014613066e-09: 1653,\n         -1.1000999972088366e-09: 1164,\n         -1.100400000286772e-09: 3057,\n         -1.0994999979918596e-09: 372,\n         -1.1004000020214955e-09: 323,\n         -1.1005000018909916e-09: 139,\n         -0.000995495499799999: 3,\n         -1.0995999978613558e-09: 2510,\n         -1.0996000030655262e-09: 64,\n         -0.0010954954995999995: 9,\n         -1.0999999999414256e-09: 127650,\n         -1.1000999998109218e-09: 34360,\n         -1.100000001676149e-09: 2053,\n         -1.0995999987287175e-09: 4789,\n         -1.099600000463441e-09: 25386,\n         -1.1003999994194102e-09: 2021,\n         -1.1004999992889064e-09: 4265,\n         -1.0995000005939448e-09: 3492,\n         -1.1005000010236299e-09: 451,\n         -1.0994999988592213e-09: 1009,\n         -1.1004000011541337e-09: 510,\n         -1.1000999980761983e-09: 342,\n         -1.0996000021981645e-09: 118,\n         -0.0010954955000999989: 4,\n         -0.0009954954997999999: 5,\n         -0.0009954954999000006: 9,\n         -1.1001000002446026e-09: 12413,\n         -1.1000000003751065e-09: 10990,\n         -1.0999999995077447e-09: 6734,\n         -1.099500000160264e-09: 4789,\n         -1.1004000007204529e-09: 137,\n         -1.100099999377241e-09: 3416,\n         -1.100500000589949e-09: 413,\n         -1.1003999998530911e-09: 1089,\n         -1.0996000000297601e-09: 13967,\n         -1.0994999992929022e-09: 418,\n         -0.0010954955001000004: 1,\n         -1.0994999999434235e-09: 1254,\n         -1.1004999997225873e-09: 4433,\n         -1.1004999999394277e-09: 4495,\n         -1.100100000461443e-09: 485,\n         -1.0999999997245852e-09: 3717,\n         -1.100000000158266e-09: 11178,\n         -1.1004000000699315e-09: 469,\n         -1.1001000000277622e-09: 7559,\n         -1.0996000002466005e-09: 1665,\n         -1.1000999995940813e-09: 754,\n         -1.0995999998129197e-09: 2267,\n         -0.00109549550011: 1,\n         -1.1000300000323787e-09: 7412,\n         -1.0995799999691247e-09: 3779,\n         -1.10048999990911e-09: 1295,\n         -1.099580000077545e-09: 2589,\n         -1.1004900000175302e-09: 2711,\n         -1.0995799998607045e-09: 1255,\n         -1.100030000140799e-09: 2505,\n         -1.1000399999542762e-09: 4481,\n         -1.1000400000626964e-09: 3849,\n         -1.1000299999239585e-09: 7521,\n         -1.1000400001711166e-09: 897,\n         -1.1004800000956327e-09: 188,\n         -1.100039999845856e-09: 1220,\n         -1.1004900001259504e-09: 731,\n         -1.099569999938807e-09: 380,\n         -1.0995800001859651e-09: 442,\n         -1.1004799999872125e-09: 543,\n         -1.0995700001556474e-09: 75,\n         -1.0995700000472272e-09: 308,\n         -1.1004899998006898e-09: 103,\n         -1.1004799998787923e-09: 106,\n         -1.1000300002492192e-09: 20,\n         -1.0995699998303868e-09: 23,\n         11.54000450717705: 1,\n         -1.000000082740371e-09: 37590721,\n         -1.9999983891239026e-09: 1578413,\n         -1.0000018590972104e-09: 19731179,\n         -9.999983063835316e-10: 23384023,\n         -2.000000165480742e-09: 5978104,\n         -2.0000019418375814e-09: 1196222,\n         -1.999996612767063e-09: 227223,\n         -0.0010954949999995023: 3629,\n         -9.999965300266922e-10: 115031,\n         -0.0009954949999997353: 2423,\n         -0.0010954950000012786: 1425,\n         -0.0009954950000015117: 425,\n         -0.0010954960000013614: 1407,\n         -0.0010954953000013035: 1,\n         -1.0999983146575687e-09: 10879553,\n         -1.1000018673712475e-09: 1970691,\n         -0.00109549550000132: 7302,\n         -0.0010954954999995437: 25766,\n         -0.000995495500001553: 2056,\n         -0.001095496600001411: 7,\n         -1.0999992028359884e-09: 28510663,\n         -1.1000009791928278e-09: 11215056,\n         -0.0009954955000006649: 8396,\n         -0.0010954955000004318: 19699,\n         -0.0010954966000005228: 9,\n         -0.000995496600000756: 9,\n         -0.0009954955000002208: 8588,\n         -1.100000535103618e-09: 9197177,\n         -0.0009954966000003118: 8,\n         -0.0010954966000000788: 16,\n         -0.0010954955000002098: 3097,\n         -0.0009954955200000004: 1,\n         -1.1099998697972069e-09: 214708,\n         -1.0900003122316093e-09: 10979,\n         -1.0900000901870044e-09: 232816,\n         -1.0900002012093069e-09: 60811,\n         -1.1099997587749044e-09: 13678,\n         -1.109999647752602e-09: 4106,\n         -0.001095495490000098: 51,\n         -0.0010954955099997665: 6,\n         -0.0010954955099998775: 20,\n         -0.000995495490000109: 19,\n         -0.0009954955099998886: 7,\n         -0.000995495489999998: 100,\n         -1.089999979164702e-09: 793433,\n         -1.1099999808195093e-09: 612912,\n         -1.1100000918418118e-09: 235252,\n         -1.0899998681423995e-09: 59461,\n         -1.1100002028641143e-09: 27682,\n         -1.089999757120097e-09: 7848,\n         -0.0010954955099999886: 79,\n         -0.000995495489999887: 37,\n         -0.001095495489999876: 78,\n         -0.0009954955099999996: 7,\n         -0.001095495489999987: 236,\n         -0.0010954966099999686: 1,\n         -0.0010954955100000996: 15,\n         -1.1100000363306606e-09: 192757,\n         -1.0899999236535507e-09: 52435,\n         -0.0009954954899999424: 32,\n         -0.0010954954899999314: 48,\n         -0.001095495510000044: 18,\n         -0.000995495510000055: 3,\n         -0.0009954954999999432: 345,\n         -0.0010954954999999322: 367,\n         -1.0899999514091263e-09: 14940,\n         -1.1100000640862362e-09: 10005,\n         -1.110000008575085e-09: 110666,\n         -0.0010954955100000163: 10,\n         -0.0009954955100000273: 1,\n         -0.0010954954899999592: 7,\n         -0.000995495499999971: 172,\n         -1.0900000069202775e-09: 130740,\n         -1.0900000346758532e-09: 128710,\n         -1.1099999530639337e-09: 7853,\n         -0.0009954954900000257: 16,\n         -0.0010954954900000424: 54,\n         -0.0010954954900000147: 29,\n         -0.0010954955099999608: 1,\n         -0.0010954955100000024: 3,\n         -1.0900000207980653e-09: 6443,\n         -1.1099999669417215e-09: 1574,\n         -1.1099999946972972e-09: 18393,\n         -1.0899999930424897e-09: 14719,\n         -0.0010954954900000008: 8,\n         -0.0009954954900000118: 2,\n         -0.0010954954950000012: 1,\n         -0.0010954954996000004: 12,\n         -0.0009954954998999998: 12,\n         -0.0010954955001000006: 7,\n         -0.0009954954998000008: 2,\n         -1.0996000008971218e-09: 369,\n         -0.0010954955001000002: 3,\n         -1.1003999989857294e-09: 8,\n         -0.0009954954999: 1,\n         -1.1005000003731086e-09: 96,\n         -1.1003999996362507e-09: 82,\n         -0.0010954954996600002: 1,\n         -1.100479999770372e-09: 4,\n         14.17990450782529: 1,\n         -2.000003718194421e-09: 3042,\n         -0.0010954969999996678: 4,\n         -0.0010954970000014441: 4,\n         -0.0010954953999995354: 1,\n         -0.0009954954999980004: 468,\n         -0.0010954965999996347: 21,\n         -0.0010954954999977673: 79,\n         -0.0009954965999998677: 17,\n         -0.0009954954999988885: 2080,\n         -0.0010954954999986555: 265,\n         -0.0009954965999989795: 2,\n         -0.000995495540000002: 1,\n         -0.0010954954960000068: 1,\n         -0.0010954955000000086: 54,\n         -0.0010954954997999984: 1,\n         -0.0010954954999999995: 2,\n         -0.0010954955000999998: 9,\n         -0.0009954954999000002: 3,\n         8.85590450633608: 1,\n         -0.0010954955299999902: 1,\n         -0.0010954955020000129: 1,\n         -0.0010954955002000014: 1,\n         -0.0010954954997000003: 2,\n         -0.0010954954996: 3,\n         -0.0010954955001: 2,\n         12.6929045071319: 1,\n         -0.001095495999999585: 5637,\n         -0.000995495550000003: 1,\n         -0.001095495495999993: 1,\n         -0.001095495500000005: 4,\n         -0.0010954954996999998: 1,\n         -0.0010954955001199998: 1,\n         8.0549045061459: 1,\n         -0.0009954965999999787: 3,\n         -1.0999999244809544e-09: 142,\n         -1.1099999253083581e-09: 61962,\n         -0.0009954954900000534: 12,\n         -1.09999995223653e-09: 4,\n         -0.0010954954999999739: 1,\n         -1.1100000224528728e-09: 6510,\n         -0.000995495489999984: 2,\n         -0.0010954955049999882: 1,\n         -0.000995495499: 9,\n         -0.001095495500099998: 1,\n         -0.001095495499999999: 2,\n         -0.0010954955: 1,\n         7.057904505904601: 1,\n         -0.0010954954700000963: 1,\n         -0.0009954954969999819: 1,\n         -0.0010954955001999996: 1,\n         -0.0010954955000000003: 2,\n         -0.0010954955000200002: 1,\n         16.106904507467007: 1,\n         -1.0000036354540498e-09: 56705,\n         -0.0009954959999980417: 1526,\n         -0.0010954959999978087: 1897,\n         -0.0009954960000015944: 600,\n         -0.000995495999999818: 2922,\n         -0.0009954969999981245: 3,\n         -0.0010954969999978914: 4,\n         -0.0010954956999995602: 1,\n         -0.001095495479999986: 1,\n         -1.0900001456981556e-09: 2553,\n         -0.001095495509999933: 5,\n         -0.000995495509999944: 1,\n         -0.0009954954899999702: 3,\n         -0.001095495489999973: 1,\n         -1.0899999652869141e-09: 1612,\n         -0.0009954955100000135: 1,\n         -0.0009954954949999983: 1,\n         -0.0010954954999: 2,\n         -0.0009954954999000015: 1,\n         -0.0009954955003: 1,\n         -0.0009954954998999995: 2,\n         -0.0010954954996099998: 1,\n         7.904904506085151: 1,\n         -0.0009954954700001073: 1,\n         -0.0010954955039999825: 1,\n         -1.0991000002485984e-09: 7,\n         -0.0010954954995999987: 1,\n         -0.0009954954997999995: 1,\n         -0.00099549549984: 1,\n         -1.1000399999813813e-09: 1069,\n         -1.1000300000052737e-09: 1221,\n         -1.1000299999781686e-09: 1361,\n         -1.0995799999962298e-09: 643,\n         -1.1004899999904252e-09: 556,\n         -1.0995800000233348e-09: 499,\n         -1.1000400000355914e-09: 366,\n         -1.1000299999510636e-09: 16,\n         -1.1000400000084863e-09: 611,\n         -1.0995800000504399e-09: 73,\n         -1.0995700000201222e-09: 36,\n         -1.1004800000414226e-09: 15,\n         -1.100489999936215e-09: 24,\n         -1.1004899999633201e-09: 55,\n         -1.099569999965912e-09: 49,\n         -1.1004900000446353e-09: 100,\n         -1.1004800000143176e-09: 40,\n         -1.0995699999930171e-09: 48,\n         -1.1004799999601075e-09: 2,\n         9.63690450647408: 1,\n         -0.0010954965999999677: 3,\n         -0.0009954955009999905: 2,\n         -0.0010954954989999821: 4,\n         -0.000995495499000007: 3,\n         -0.0010954954989999995: 7,\n         -0.0009954954989999983: 2,\n         -0.0010954955003999994: 1,\n         -0.0009954954999000024: 1,\n         -0.0010954954996000013: 2,\n         -1.0990999998149176e-09: 1,\n         -0.0009954954999000004: 1,\n         -0.0010954955000500003: 1,\n         11.42590450661124: 1,\n         -0.0010954960000031377: 31,\n         -0.0010954958000013448: 1,\n         -0.0009954954999995547: 1,\n         -0.0010954965999998567: 1,\n         -0.0009954955030000018: 1,\n         -1.099100000031758e-09: 2,\n         -0.00109549549964: 1,\n         -1.0991200000110782e-09: 1,\n         11.02400450628308: 1,\n         -0.0009954954999993326: 252,\n         -0.0010954954999990996: 48,\n         -0.001095495540000102: 1,\n         -0.0010954965999999955: 1,\n         -0.0009954954969999957: 1,\n         -0.0010954955000000008: 1,\n         -0.0009954954998: 1,\n         -0.00109549549959: 1,\n         -1.1000299999917211e-09: 3,\n         -1.1000399999949338e-09: 1,\n         -1.1000300000188262e-09: 2,\n         -1.1000359999800962e-09: 1,\n         -1.1000330000062375e-09: 275,\n         -1.1000339999930062e-09: 2543,\n         -1.0995789999959085e-09: 598,\n         -1.100489000017209e-09: 167,\n         -1.100488999990104e-09: 137,\n         -1.099579000009461e-09: 427,\n         -1.1000340000065587e-09: 1162,\n         -1.1000329999791324e-09: 191,\n         -1.1000340000201113e-09: 497,\n         -1.100032999992685e-09: 197,\n         -1.1000340000336638e-09: 126,\n         -1.099578999982356e-09: 355,\n         -1.10003300001979e-09: 169,\n         -1.1004879999897826e-09: 176,\n         -1.1004889999765514e-09: 105,\n         -1.0995790000230135e-09: 144,\n         -1.0995800000097823e-09: 61,\n         -1.1000339999794537e-09: 226,\n         -1.1004880000033352e-09: 108,\n         -1.1000330000333425e-09: 18,\n         -1.1004890000036564e-09: 200,\n         -1.1004880000168877e-09: 83,\n         -1.1004880000304402e-09: 45,\n         -1.0995799999826772e-09: 21,\n         -1.1004879999762301e-09: 30,\n         -1.1004890000307615e-09: 21,\n         -1.100489000044314e-09: 5,\n         -1.099579000036566e-09: 1,\n         -1.0995800000368873e-09: 3,\n         -1.100033000046895e-09: 7,\n         8.828004506336834: 1,\n         -0.0009954965999994236: 1,\n         -0.001095495539999991: 1,\n         -0.0010954954979999904: 1,\n         -0.0009954955001000004: 1,\n         -0.0010954955001000015: 2,\n         -0.0010954954996000008: 1,\n         -0.0009954954998000003: 1,\n         -0.0010954955000699999: 1,\n         -1.1004899999768726e-09: 1,\n         -1.1004879999626776e-09: 1,\n         -1.099124999999132e-09: 1,\n         8.565904506238308: 1,\n         -0.0009954966000000898: 1,\n         -0.0010954955029999908: 1,\n         -0.0010954954995000005: 1,\n         -0.0010954955000999993: 2,\n         -0.0010954954999999997: 1,\n         -0.00099549549985: 1,\n         14.75090450749273: 1,\n         -1.4000001158365194e-09: 1,\n         -0.0009954954970000235: 1,\n         -0.0009954954998999989: 1,\n         -0.0009954955004: 1,\n         -0.0010954954995699999: 1,\n         9.45890450638701: 1,\n         -0.0010954954599999844: 1,\n         -0.0010954955009999934: 3,\n         -0.0010954955009999795: 1,\n         -0.0010954955010000073: 1,\n         -0.0010954955010000003: 4,\n         -1.0990000055832727e-09: 1317,\n         -0.0010954955010000038: 1,\n         -0.0010954954989999977: 1,\n         -0.0009954954997000009: 1,\n         -1.0992000001180946e-09: 1,\n         -0.0010954954995999998: 1,\n         -0.00109549549966: 1,\n         11.6180045065607: 1,\n         -0.0009954956999997933: 1})"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# positive jumps\n{k: v for k, v in diff_ttf_2_counter_dict.items() if k>0}","execution_count":15,"outputs":[{"output_type":"execute_result","execution_count":15,"data":{"text/plain":"{11.54000450717705: 1,\n 14.17990450782529: 1,\n 8.85590450633608: 1,\n 12.6929045071319: 1,\n 8.0549045061459: 1,\n 7.057904505904601: 1,\n 16.106904507467007: 1,\n 7.904904506085151: 1,\n 9.63690450647408: 1,\n 11.42590450661124: 1,\n 11.02400450628308: 1,\n 8.828004506336834: 1,\n 8.565904506238308: 1,\n 14.75090450749273: 1,\n 9.45890450638701: 1,\n 11.6180045065607: 1}"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"10.**max_precision","execution_count":16,"outputs":[{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"10000000000.0"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"negative_diff_tff_2_counter_dict = Counter()","execution_count":17,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for k, v in diff_ttf_2_counter_dict.items():\n    if k<=0:\n        negative_diff_tff_2_counter_dict[int(-k*(10.**max_precision))] += v \nnegative_diff_tff_2_counter_dict","execution_count":18,"outputs":[{"output_type":"execute_result","execution_count":18,"data":{"text/plain":"Counter({10: 190859716,\n         11: 405650006,\n         9954954: 27393,\n         10954954: 51479,\n         10954955: 32699,\n         9954955: 19968,\n         10954966: 34,\n         19: 1805636,\n         9: 23499054,\n         20: 7177368,\n         10954949: 3629,\n         9954949: 2423,\n         10954950: 1425,\n         9954950: 425,\n         10954960: 1438,\n         10954953: 2,\n         9954966: 18,\n         10954969: 8,\n         10954970: 4,\n         10954965: 26,\n         9954965: 23,\n         10954959: 7534,\n         9954959: 4448,\n         9954960: 600,\n         9954969: 3,\n         10954956: 1,\n         10954958: 1,\n         14: 1,\n         9954956: 1})"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#time diff within sampling frames\ntime_diff_within_sampling_frames_2_count_dict = {k: v for k, v in negative_diff_tff_2_counter_dict.items() if k<100}\ntime_diff_within_sampling_frames_2_count_dict","execution_count":19,"outputs":[{"output_type":"execute_result","execution_count":19,"data":{"text/plain":"{10: 190859716, 11: 405650006, 19: 1805636, 9: 23499054, 20: 7177368, 14: 1}"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#time diff between sampling frames\ntime_diff_between_sampling_frames_2_count_dict = {round(k/(10.**max_precision),6): v for k, v in negative_diff_tff_2_counter_dict.items() if k>=100}\ntime_diff_between_sampling_frames_2_count_dict","execution_count":20,"outputs":[{"output_type":"execute_result","execution_count":20,"data":{"text/plain":"{0.000995: 1, 0.001095: 1}"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(df_after_jumping_up_points.shape)\ndisplay_df_with_preset_precision(df_after_jumping_up_points, max_precision)","execution_count":21,"outputs":[{"output_type":"stream","text":"(16, 3)\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"           acoustic_data           ...             diff_in_time_to_failure\n5656574                4           ...                       11.5400045072\n50085878               1           ...                       14.1799045078\n104677356              4           ...                        8.8559045063\n138772453             -4           ...                       12.6929045071\n187641820              2           ...                        8.0549045061\n218652630              4           ...                        7.0579045059\n245829585              2           ...                       16.1069045075\n307838917              2           ...                        7.9049045061\n338276287              3           ...                        9.6369045065\n375377848              0           ...                       11.4259045066\n419368880             -3           ...                       11.0240045063\n461811623              3           ...                        8.8280045063\n495800225              2           ...                        8.5659045062\n528777115              2           ...                       14.7509045075\n585568144              7           ...                        9.4589045064\n621985673              2           ...                       11.6180045066\n\n[16 rows x 3 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>acoustic_data</th>\n      <th>time_to_failure</th>\n      <th>diff_in_time_to_failure</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5656574</th>\n      <td>4</td>\n      <td>11.5407999870</td>\n      <td>11.5400045072</td>\n    </tr>\n    <tr>\n      <th>50085878</th>\n      <td>1</td>\n      <td>14.1805999900</td>\n      <td>14.1799045078</td>\n    </tr>\n    <tr>\n      <th>104677356</th>\n      <td>4</td>\n      <td>8.8566999914</td>\n      <td>8.8559045063</td>\n    </tr>\n    <tr>\n      <th>138772453</th>\n      <td>-4</td>\n      <td>12.6939999940</td>\n      <td>12.6929045071</td>\n    </tr>\n    <tr>\n      <th>187641820</th>\n      <td>2</td>\n      <td>8.0554999956</td>\n      <td>8.0549045061</td>\n    </tr>\n    <tr>\n      <th>218652630</th>\n      <td>4</td>\n      <td>7.0589999970</td>\n      <td>7.0579045059</td>\n    </tr>\n    <tr>\n      <th>245829585</th>\n      <td>2</td>\n      <td>16.1074000000</td>\n      <td>16.1069045075</td>\n    </tr>\n    <tr>\n      <th>307838917</th>\n      <td>2</td>\n      <td>7.9056000019</td>\n      <td>7.9049045061</td>\n    </tr>\n    <tr>\n      <th>338276287</th>\n      <td>3</td>\n      <td>9.6371000039</td>\n      <td>9.6369045065</td>\n    </tr>\n    <tr>\n      <th>375377848</th>\n      <td>0</td>\n      <td>11.4264000060</td>\n      <td>11.4259045066</td>\n    </tr>\n    <tr>\n      <th>419368880</th>\n      <td>-3</td>\n      <td>11.0242000080</td>\n      <td>11.0240045063</td>\n    </tr>\n    <tr>\n      <th>461811623</th>\n      <td>3</td>\n      <td>8.8281000103</td>\n      <td>8.8280045063</td>\n    </tr>\n    <tr>\n      <th>495800225</th>\n      <td>2</td>\n      <td>8.5660000120</td>\n      <td>8.5659045062</td>\n    </tr>\n    <tr>\n      <th>528777115</th>\n      <td>2</td>\n      <td>14.7518000150</td>\n      <td>14.7509045075</td>\n    </tr>\n    <tr>\n      <th>585568144</th>\n      <td>7</td>\n      <td>9.4595000169</td>\n      <td>9.4589045064</td>\n    </tr>\n    <tr>\n      <th>621985673</th>\n      <td>2</td>\n      <td>11.6186000190</td>\n      <td>11.6180045066</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"display_df_with_preset_precision(df_after_long_jumps_down_points.head(),max_precision)","execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"       acoustic_data           ...             diff_in_time_to_failure\n4095             -13           ...                       -0.0009954955\n8191              -2           ...                       -0.0010954955\n12287             -4           ...                       -0.0010954955\n16383              0           ...                       -0.0009954955\n20479              3           ...                       -0.0010954955\n\n[5 rows x 3 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>acoustic_data</th>\n      <th>time_to_failure</th>\n      <th>diff_in_time_to_failure</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4095</th>\n      <td>-13</td>\n      <td>1.4680999843</td>\n      <td>-0.0009954955</td>\n    </tr>\n    <tr>\n      <th>8191</th>\n      <td>-2</td>\n      <td>1.4669999843</td>\n      <td>-0.0010954955</td>\n    </tr>\n    <tr>\n      <th>12287</th>\n      <td>-4</td>\n      <td>1.4658999843</td>\n      <td>-0.0010954955</td>\n    </tr>\n    <tr>\n      <th>16383</th>\n      <td>0</td>\n      <td>1.4648999843</td>\n      <td>-0.0009954955</td>\n    </tr>\n    <tr>\n      <th>20479</th>\n      <td>3</td>\n      <td>1.4637999843</td>\n      <td>-0.0010954955</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.unique(np.diff(df_after_long_jumps_down_points.index))","execution_count":23,"outputs":[{"output_type":"execute_result","execution_count":23,"data":{"text/plain":"array([4095, 4096, 8192])"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(np.where(np.diff(df_after_long_jumps_down_points.index)==8192)[0])","execution_count":24,"outputs":[{"output_type":"execute_result","execution_count":24,"data":{"text/plain":"17"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(np.where(np.diff(df_after_long_jumps_down_points.index)==4095)[0])","execution_count":25,"outputs":[{"output_type":"execute_result","execution_count":25,"data":{"text/plain":"119"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(np.where(np.diff(df_after_long_jumps_down_points.index)==4096)[0])","execution_count":26,"outputs":[{"output_type":"execute_result","execution_count":26,"data":{"text/plain":"153445"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"max(time_diff_within_sampling_frames_2_count_dict.keys())/(10.**max_precision)*8192","execution_count":27,"outputs":[{"output_type":"execute_result","execution_count":27,"data":{"text/plain":"1.6384e-05"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.mean(list(time_diff_between_sampling_frames_2_count_dict.keys()))","execution_count":28,"outputs":[{"output_type":"execute_result","execution_count":28,"data":{"text/plain":"0.001045"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"(max(time_diff_within_sampling_frames_2_count_dict.keys())/(10.**max_precision)*8192)/np.mean(list(time_diff_between_sampling_frames_2_count_dict.keys()))","execution_count":29,"outputs":[{"output_type":"execute_result","execution_count":29,"data":{"text/plain":"0.015678468899521535"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"start_time = timeit.default_timer()\ntry:\n    del(df_train_iter)    \nexcept NameError:\n    pass\n\ndf_train_iter = pd.read_csv('../input/train.csv', chunksize=train_nrows_val//100,\n                       dtype={'acoustic_data': np.int16, 'time_to_failure': np.float64},iterator=True) #use chunksize to iterate\ndf_before_jumping_up_points = pd.DataFrame()\nfor df in df_train_iter:\n    if len(df.index.intersection(df_after_jumping_up_points.index-1)) > 0:\n        try:\n            df_before_jumping_up_points=df_before_jumping_up_points.append(df.loc[df.index.intersection(df_after_jumping_up_points.index-1),:])\n        except KeyError:\n            print('KeyError')\n            pass\nprint('elapsed time: {:.2f} sec'.format(timeit.default_timer()-start_time))","execution_count":30,"outputs":[{"output_type":"stream","text":"elapsed time: 156.71 sec\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(df_before_jumping_up_points.shape)\ndf_before_jumping_up_points","execution_count":31,"outputs":[{"output_type":"stream","text":"(16, 2)\n","name":"stdout"},{"output_type":"execute_result","execution_count":31,"data":{"text/plain":"           acoustic_data  time_to_failure\n5656573                4         0.000795\n50085877               8         0.000695\n104677355              6         0.000795\n138772452              3         0.001095\n187641819              7         0.000595\n218652629              5         0.001095\n245829584              1         0.000495\n307838916              7         0.000695\n338276286              4         0.000195\n375377847              0         0.000495\n419368879              9         0.000196\n461811622              5         0.000096\n495800224              1         0.000096\n528777114              5         0.000896\n585568143              6         0.000596\n621985672             10         0.000596","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>acoustic_data</th>\n      <th>time_to_failure</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5656573</th>\n      <td>4</td>\n      <td>0.000795</td>\n    </tr>\n    <tr>\n      <th>50085877</th>\n      <td>8</td>\n      <td>0.000695</td>\n    </tr>\n    <tr>\n      <th>104677355</th>\n      <td>6</td>\n      <td>0.000795</td>\n    </tr>\n    <tr>\n      <th>138772452</th>\n      <td>3</td>\n      <td>0.001095</td>\n    </tr>\n    <tr>\n      <th>187641819</th>\n      <td>7</td>\n      <td>0.000595</td>\n    </tr>\n    <tr>\n      <th>218652629</th>\n      <td>5</td>\n      <td>0.001095</td>\n    </tr>\n    <tr>\n      <th>245829584</th>\n      <td>1</td>\n      <td>0.000495</td>\n    </tr>\n    <tr>\n      <th>307838916</th>\n      <td>7</td>\n      <td>0.000695</td>\n    </tr>\n    <tr>\n      <th>338276286</th>\n      <td>4</td>\n      <td>0.000195</td>\n    </tr>\n    <tr>\n      <th>375377847</th>\n      <td>0</td>\n      <td>0.000495</td>\n    </tr>\n    <tr>\n      <th>419368879</th>\n      <td>9</td>\n      <td>0.000196</td>\n    </tr>\n    <tr>\n      <th>461811622</th>\n      <td>5</td>\n      <td>0.000096</td>\n    </tr>\n    <tr>\n      <th>495800224</th>\n      <td>1</td>\n      <td>0.000096</td>\n    </tr>\n    <tr>\n      <th>528777114</th>\n      <td>5</td>\n      <td>0.000896</td>\n    </tr>\n    <tr>\n      <th>585568143</th>\n      <td>6</td>\n      <td>0.000596</td>\n    </tr>\n    <tr>\n      <th>621985672</th>\n      <td>10</td>\n      <td>0.000596</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"start_time = timeit.default_timer()\n\ntry:\n    del(df_train_tail)    \nexcept NameError:\n    pass\ndf_train_tail = pd.read_csv('../input/train.csv', skiprows = train_nrows_val-100000, iterator=False, names=column_names[0].split(','))\ndf_train_tail['acoustic_data'] = df_train_tail['acoustic_data'].astype(np.int16)\ndf_train_tail['time_to_failure'] = df_train_tail['time_to_failure'].astype(np.float64)\nprint('elapsed time: {:.2f} sec'.format(timeit.default_timer()-start_time))","execution_count":33,"outputs":[{"output_type":"stream","text":"elapsed time: 35.37 sec\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train_tail.tail()","execution_count":34,"outputs":[{"output_type":"execute_result","execution_count":34,"data":{"text/plain":"       acoustic_data  time_to_failure\n99995              7         9.759796\n99996              9         9.759796\n99997             10         9.759796\n99998              6         9.759796\n99999              5         9.759796","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>acoustic_data</th>\n      <th>time_to_failure</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>99995</th>\n      <td>7</td>\n      <td>9.759796</td>\n    </tr>\n    <tr>\n      <th>99996</th>\n      <td>9</td>\n      <td>9.759796</td>\n    </tr>\n    <tr>\n      <th>99997</th>\n      <td>10</td>\n      <td>9.759796</td>\n    </tr>\n    <tr>\n      <th>99998</th>\n      <td>6</td>\n      <td>9.759796</td>\n    </tr>\n    <tr>\n      <th>99999</th>\n      <td>5</td>\n      <td>9.759796</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# TTF steps\ndf_train_tail.tail(20000)['time_to_failure'].plot();","execution_count":35,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"start_time = timeit.default_timer()\n\ntry:\n    del(df_train_head)    \nexcept NameError:\n    pass\ndf_train_head = pd.read_csv('../input/train.csv', skiprows = 0, nrows = 100000, iterator=False)\n\nprint('elapsed time: {:.2f} sec'.format(timeit.default_timer()-start_time))","execution_count":36,"outputs":[{"output_type":"stream","text":"elapsed time: 0.03 sec\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# TTF step in the first section\ndf_train_head.head(20000)[8192:8192+4096]['time_to_failure'].plot();","execution_count":37,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"set(df_train_head.head(20000)[8192:8192+4095]['time_to_failure'].diff())","execution_count":38,"outputs":[{"output_type":"execute_result","execution_count":38,"data":{"text/plain":"{nan,\n -1.100000313059013e-09,\n -1.100000091014408e-09,\n -1.0999998689698032e-09,\n -1.0999996469251982e-09}"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"n=0\nset(df_train_head.head(2000000)['time_to_failure'].diff())","execution_count":39,"outputs":[{"output_type":"execute_result","execution_count":39,"data":{"text/plain":"{nan,\n -0.0010954954999999877,\n -0.0010954954999997657,\n -0.0009954954999999988,\n -0.0009954954999997767,\n -1.100000313059013e-09,\n -1.100000091014408e-09,\n -1.0999998689698032e-09,\n -1.0999996469251982e-09}"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"index_ranges = [(ent[0],ent[1]) for ent in zip([0]+list(df_after_jumping_up_points.index)[:-1],list(df_before_jumping_up_points.index))]\nindex_ranges","execution_count":40,"outputs":[{"output_type":"execute_result","execution_count":40,"data":{"text/plain":"[(0, 5656573),\n (5656574, 50085877),\n (50085878, 104677355),\n (104677356, 138772452),\n (138772453, 187641819),\n (187641820, 218652629),\n (218652630, 245829584),\n (245829585, 307838916),\n (307838917, 338276286),\n (338276287, 375377847),\n (375377848, 419368879),\n (419368880, 461811622),\n (461811623, 495800224),\n (495800225, 528777114),\n (528777115, 585568143),\n (585568144, 621985672)]"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_set_lengths =np.array([ent[1]-ent[0] for ent in zip([0]+list(df_before_jumping_up_points.index)[:-1],list(df_before_jumping_up_points.index))])\ntrain_set_lengths","execution_count":41,"outputs":[{"output_type":"execute_result","execution_count":41,"data":{"text/plain":"array([ 5656573, 44429304, 54591478, 34095097, 48869367, 31010810,\n       27176955, 62009332, 30437370, 37101561, 43991032, 42442743,\n       33988602, 32976890, 56791029, 36417529])"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_set_lengths.mean(), train_set_lengths.std()","execution_count":42,"outputs":[{"output_type":"execute_result","execution_count":42,"data":{"text/plain":"(38874104.5, 13078782.58174961)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"range_index = 3\nwindow_size = 15000\nwindow_offset = -window_size\nstart_time = timeit.default_timer()\ntry:\n    del(df_sample)    \nexcept NameError:\n    pass\ndf_sample = pd.read_csv('../input/train.csv', skiprows = index_ranges[range_index][0], nrows= index_ranges[range_index][1]-index_ranges[range_index][0],\n                       dtype={'acoustic_data': np.int16, 'time_to_failure': np.float64})\ndf_sample.columns=['acoustic_data','time_to_failure']\nprint(df_sample.index)\n\nfig, axs = plt.subplots(nrows=1, ncols=2, sharex=False)\nfig.set_size_inches(32,4)\ndf_sample['acoustic_data'].plot(ax=axs[0]);\nplt.show()\ndf_sample['time_to_failure'].plot(ax=axs[1]);\nplt.show()\nprint('elapsed time: {:.2f} sec'.format(timeit.default_timer()-start_time))","execution_count":43,"outputs":[{"output_type":"stream","text":"RangeIndex(start=0, stop=34095096, step=1)\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 2304x288 with 2 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time: 29.00 sec\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"max_time_to_failure_points = pd.read_csv('../input/train.csv', skiprows = 0, nrows= 1, dtype={'acoustic_data': np.int16, 'time_to_failure': np.float64})['time_to_failure'].append(df_after_jumping_up_points['time_to_failure'])\nmax_time_to_failure_points","execution_count":44,"outputs":[{"output_type":"execute_result","execution_count":44,"data":{"text/plain":"0             1.4691\n5656574      11.5408\n50085878     14.1806\n104677356     8.8567\n138772453    12.6940\n187641820     8.0555\n218652630     7.0590\n245829585    16.1074\n307838917     7.9056\n338276287     9.6371\n375377848    11.4264\n419368880    11.0242\n461811623     8.8281\n495800225     8.5660\n528777115    14.7518\n585568144     9.4595\n621985673    11.6186\nName: time_to_failure, dtype: float64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"max_time_to_failure_points.values[:-1]","execution_count":45,"outputs":[{"output_type":"execute_result","execution_count":45,"data":{"text/plain":"array([ 1.46909998, 11.54079999, 14.18059999,  8.85669999, 12.69399999,\n        8.0555    ,  7.059     , 16.1074    ,  7.9056    ,  9.6371    ,\n       11.42640001, 11.02420001,  8.82810001,  8.56600001, 14.75180002,\n        9.45950002])"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"decline_angle_tangents = np.array([ent[0]/ent[1] for ent in zip(max_time_to_failure_points.values[:-1], max_time_to_failure_points.index[1:])])\nprint(decline_angle_tangents.mean())\nprint(decline_angle_tangents.std())","execution_count":46,"outputs":[{"output_type":"stream","text":"6.559536110169193e-08\n7.398732224660528e-08\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"try:\n    del(df_sample)    \nexcept NameError:\n    pass","execution_count":47,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## EDA"},{"metadata":{"trusted":true},"cell_type":"code","source":"test_seg_files = listdir(\"../input/test\")\ntest_seg_files[:5]","execution_count":48,"outputs":[{"output_type":"execute_result","execution_count":48,"data":{"text/plain":"['seg_0b082e.csv',\n 'seg_9e7dff.csv',\n 'seg_b6c10d.csv',\n 'seg_4435bd.csv',\n 'seg_c09a41.csv']"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(test_seg_files)","execution_count":49,"outputs":[{"output_type":"execute_result","execution_count":49,"data":{"text/plain":"2624"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"os.path.join(\"../input/test\",test_seg_files[0])","execution_count":50,"outputs":[{"output_type":"execute_result","execution_count":50,"data":{"text/plain":"'../input/test/seg_0b082e.csv'"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"!wc -l {os.path.join(\"../input/test\",test_seg_files[0])}","execution_count":51,"outputs":[{"output_type":"stream","text":"150001 ../input/test/seg_0b082e.csv\r\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"!head {os.path.join(\"../input/test\",test_seg_files[0])}","execution_count":52,"outputs":[{"output_type":"stream","text":"acoustic_data\r\n3\r\n10\r\n4\r\n4\r\n1\r\n3\r\n7\r\n6\r\n-1\r\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"\ndef plot_test_seg_by_index(idx):\n    df_test_seg = pd.read_csv(os.path.join(\"../input/test\",test_seg_files[idx]), dtype={'acoustic_data': np.int16})\n    (df_test_seg['acoustic_data']-df_test_seg['acoustic_data'].mean()).plot();","execution_count":53,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"interact(plot_test_seg_by_index, idx=widgets.IntSlider(min=0,max=len(test_seg_files)-1,step=1,value=0));","execution_count":54,"outputs":[{"output_type":"display_data","data":{"text/plain":"interactive(children=(IntSlider(value=0, description='idx', max=2623), Output()), _dom_classes=('widget-intera…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2c5b22b79f754b59b9a1fb2381719535"}},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# make sure that all seg files have the same length: 150,000 samples:\nseg_files_lengths = !for filename in ../input/test/*; do wc -l $filename; done\n{ent.split(' ')[0] for ent in seg_files_lengths}","execution_count":55,"outputs":[{"output_type":"execute_result","execution_count":55,"data":{"text/plain":"{'150001'}"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"(max(time_diff_within_sampling_frames_2_count_dict.keys())/(10.**max_precision)*150000)","execution_count":56,"outputs":[{"output_type":"execute_result","execution_count":56,"data":{"text/plain":"0.00030000000000000003"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.mean(list(time_diff_between_sampling_frames_2_count_dict.keys()))*(150000/4096)","execution_count":57,"outputs":[{"output_type":"execute_result","execution_count":57,"data":{"text/plain":"0.03826904296875"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Correlations"},{"metadata":{"trusted":true},"cell_type":"code","source":"start_time = timeit.default_timer()\ntry:\n    del(df_sample)    \nexcept NameError:\n    pass\n\nrange_index=0 #first training sequence is the shortest one\ndf_sample = pd.read_csv('../input/train.csv', skiprows = index_ranges[range_index][0], nrows= index_ranges[range_index][1]-index_ranges[range_index][0],\n                       dtype={'acoustic_data': np.int16, 'time_to_failure': np.float64})\ndf_sample.columns=['acoustic_data','time_to_failure']\n\nfig, axs = plt.subplots(nrows=1, ncols=2, sharex=False)\nfig.set_size_inches(32,4)\ndf_sample['acoustic_data'].plot(ax=axs[0]);\nplt.show()\nprint ('time_to_failure decline rate = {:.16f}'.format(df_sample['time_to_failure'][0]/df_sample['time_to_failure'].shape[0]))\ndf_sample['time_to_failure'].plot(ax=axs[1]);\nplt.show()\nprint('elapsed time: {:.2f} sec'.format(timeit.default_timer()-start_time))","execution_count":58,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2304x288 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"stream","text":"time_to_failure decline rate = 0.0000002597155527\nelapsed time: 4.18 sec\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_sample['time_to_failure'][0]/df_sample['time_to_failure'].shape[0]","execution_count":59,"outputs":[{"output_type":"execute_result","execution_count":59,"data":{"text/plain":"2.5971555272070207e-07"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_sample['time_to_failure'].shape[0]","execution_count":60,"outputs":[{"output_type":"execute_result","execution_count":60,"data":{"text/plain":"5656573"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_sample['acoustic_data'].head()\n","execution_count":61,"outputs":[{"output_type":"execute_result","execution_count":61,"data":{"text/plain":"0    12\n1     6\n2     8\n3     5\n4     8\nName: acoustic_data, dtype: int16"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_sample['acoustic_data'].mean()","execution_count":62,"outputs":[{"output_type":"execute_result","execution_count":62,"data":{"text/plain":"4.560983125295121"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_values = (df_sample['acoustic_data']-df_sample['acoustic_data'].mean()).values\ntrain_values","execution_count":63,"outputs":[{"output_type":"execute_result","execution_count":63,"data":{"text/plain":"array([ 7.43901687,  1.43901687,  3.43901687, ..., -2.56098313,\n       -0.56098313,  0.43901687])"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_test_seg = pd.read_csv(os.path.join(\"../input/test\",test_seg_files[0]), dtype={'acoustic_data': np.int16})\ndf_test_seg.head()","execution_count":64,"outputs":[{"output_type":"execute_result","execution_count":64,"data":{"text/plain":"   acoustic_data\n0              3\n1             10\n2              4\n3              4\n4              1","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>acoustic_data</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_test_seg['acoustic_data'].mean()","execution_count":65,"outputs":[{"output_type":"execute_result","execution_count":65,"data":{"text/plain":"3.68208"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_values = (df_test_seg['acoustic_data']-df_test_seg['acoustic_data'].mean()).values","execution_count":66,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(train_values.shape, test_values.shape)","execution_count":67,"outputs":[{"output_type":"stream","text":"(5656573,) (150000,)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from scipy import signal\nsignal_corr = signal.correlate(np.square(train_values), np.square(test_values),mode='valid', method='fft')","execution_count":68,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.6/site-packages/scipy/signal/signaltools.py:491: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n  return x[reverse].conj()\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"signal_corr.shape","execution_count":69,"outputs":[{"output_type":"execute_result","execution_count":69,"data":{"text/plain":"(5506574,)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"pd.DataFrame(signal_corr).plot()","execution_count":70,"outputs":[{"output_type":"execute_result","execution_count":70,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f26509b4208>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def correlation_with_test_seg_idx(idx):\n    df_test_seg = pd.read_csv(os.path.join(\"../input/test\",test_seg_files[idx]), dtype={'acoustic_data': np.int16})\n    test_values = (df_test_seg['acoustic_data']-df_test_seg['acoustic_data'].mean()).values\n    signal_corr = signal.correlate(np.square(train_values), np.square(test_values),mode='same', method='fft')\n    pd.DataFrame(signal_corr).plot();","execution_count":72,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"interact(correlation_with_test_seg_idx, idx=widgets.IntSlider(min=0,max=len(test_seg_files)-1,step=1,value=0));","execution_count":73,"outputs":[{"output_type":"display_data","data":{"text/plain":"interactive(children=(IntSlider(value=0, description='idx', max=2623), Output()), _dom_classes=('widget-intera…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ebd654ebc08840cdab09845332759289"}},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Spectrogram"},{"metadata":{"trusted":true},"cell_type":"code","source":"from scipy.signal import spectrogram\n\nM = 1024\nN = 1024\nfreqs, times, Sx = signal.spectrogram(df_test_seg['acoustic_data'].values, fs=1, window='hanning',\n                                      nperseg=N, noverlap=M - 100,\n                                      detrend=False, scaling='spectrum')\n\nf, ax = plt.subplots(figsize=(4.8, 2.4))\nax.pcolormesh(times, freqs / 1000, 10 * np.log10(Sx), cmap='viridis')\nax.set_ylabel('Frequency [kHz]')\nax.set_xlabel('Time [s]');","execution_count":74,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 345.6x172.8 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"f, t, Sxx = spectrogram(df_test_seg['acoustic_data'].values)\nplt.pcolormesh(t, f, 10 * np.log10(Sxx))\nplt.show()\nplt.plot(Sxx)\nplt.ylabel('Frequency [Hz]')\nplt.xlabel('Time [sec]')\nplt.show()","execution_count":76,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from skimage import util\n\nM = 1024\n\nslices = util.view_as_windows(df_test_seg['acoustic_data'].values, window_shape=(M,), step=100)\nprint(f'data shape: {df_test_seg[\"acoustic_data\"].values.shape}, Sliced data shape: {slices.shape}')","execution_count":77,"outputs":[{"output_type":"stream","text":"data shape: (150000,), Sliced data shape: (1490, 1024)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"win = np.hanning(M + 1)[:-1]\nslices = slices * win\nslices = slices.T\nprint('Shape of `slices`:', slices.shape)\nspectrum = np.fft.fft(slices, axis=0)[:M // 2 + 1:-1]\nspectrum = np.abs(spectrum)","execution_count":79,"outputs":[{"output_type":"stream","text":"Shape of `slices`: (1024, 1490)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"L=df_test_seg['acoustic_data'].values.shape[0]","execution_count":80,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"rate = 10\nf, ax = plt.subplots(figsize=(4.8, 2.4))\n\nS = np.abs(spectrum)\nS = 20 * np.log10(S / np.max(S))\n\nax.imshow(S, origin='lower', cmap='viridis',\n          extent=(0, L, 0, rate / 2 / 1000))\nax.axis('tight')\nax.set_ylabel('Frequency [kHz]')\nax.set_xlabel('Time [s]');","execution_count":81,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 345.6x172.8 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAVoAAAC7CAYAAAAkPlblAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvemvZmt63vV7pjW8055r13TqVJ3p7XZ76O64MbY7CYIYkighkQAZK8LwwZYIEfAfBIME4gOfkKzYAQk5JDIBmYCQ+IAlhOOJeMBu2217nz51hpprz3u/wxqeiQ/3qn0ay3ZOH9ONFfYtlarqHde71vPcw3Vd971Uzplru7Zru7Zr++aZ/v/6AK7t2q7t2v5Zt2tHe23Xdm3X9k22a0d7bdd2bdf2TbZrR3tt13Zt1/ZNtmtHe23Xdm3X9k22a0d7bdd2bdf2TbZrR3tt13Zt1/ZNtmtHe23Xdm3X9k02+636ovl8/g7wU8AOcAL88MHBwdf+wGsM8F8CfxHIwH9+cHDwX3+C534M+PeAZ8NH/eLBwcHf+mb/pmu7tmu7tk9i38qM9ieAHz84OHgH+HHgJ/+Q1/wN4C3gbeB7gR+bz+f3P8FzAH/v4ODg88Ofayd7bdd2bX9q7FviaOfz+Q3gi8BPDw/9NPDF+Xy+9wde+oPAf3VwcJAODg6OgP8J+Dc+wXPXdm3Xdm1/au1bBR28Bjw9ODiIAAcHB3E+nz8bHj/6utfdAz76uv8/Gl7zT3sO4N+cz+f/MvAC+I8ODg5++RMeWwl8CXgOxE/4nmu7tmv7/6cZ4Bbwq0D3Sd/0LcNov8n2E8B/enBw4Ofz+Q8A//N8Pv/swcHBySd475eAn//mHt61Xdu1/TNmfxb4hU/64m+Vo30M3JnP52bIZg1we3j86+0R8DoSLeD/mcX+kc8dHBy8ePUBBwcHPzufzx8D3w783Cc4tucA/9r/8g84fLkmbQboNXrqSQtHeWjpbvcQNKrX5CKBzhAVutGkUQIlE9BUY0BDeaLp32lIl47i1DB5BKdf8qBAFQl1WGIbRX5zRWgdo4OC7jtXxJMKNj2/8Nf+Ft//D38C1WkBdyIQFbmOTL7mWN/KqARp10Nj5HkN7szQ7wRUVmSTUI0hVwmKhFJQPSxo7nmwGffC4W96lILcaVQd5XctHIwCnDvMjYaf+yv/AV/+mb8DJsGFozw2dLsRdyHfNXpkaW4lOZMJ2PRUXyvxs0y605HPHGx4ctAonWFhyS5DBtVp3FLT7wSKU4PfTKhOYW411L86Zn0zE3Y99Bo1DmSv+cV//W/y/f/dT6B6LZTohofzQs75LJNdRu105LMCogIF7lKTdcbf8BAVswPH6mZGZeQ8WojTiKoi+qggTqKcQzucwzJ9vFp0xk56wqqgnHX873/pP+TLP/N3yEGhVgaQ6x+mmTCLqFZjekXYCqjGYNcKvxGhTKi1kfUUFcWJxc8SJMhVQrUalSCXGYKSc+g1ymv0WpPKBAbK54YwyYStgF5Yfv5Hf4Qv//2/S7YJrKxT1cm5yi6j6ijHajO51+ilIY2jPL6yYBOqN2Qnx8AswJkj1xE1irJW1laOs0yoKsHCorwimwwbAfvCke81/Nxf/ff5/n/wk6hOo3sl59hrMBl7qQl1BgXFpSa5LOfFJfAaNax3kpLrrDPKZsxhQQZco+in6Wq/YhPKZbhw5DLK+phE8lCj6kvLz//Ij/Lln/q7MA1Mv1Jy+bke1RqyybK+VvbqGrtZT340ImxEVFTgFXkSuLtd8w//wr9z5Tc+qX1LHO3BwcHhfD7/TeCHgL8//P0bA9b69fY/AD86n8//R0Sd8NeRyPHHPjefz+8cHBw8Hf79eeA+cPAJDy8CvGguOTxq8GUHFw77SONvLXFdQeg7cmPAZGgU5XNLv5NgHCBlyvcruu1IHkfIYLMjfphRqSNtBPj2jHrp0AHMWuG3G3Cgzns4LsmbKzgN6FOPOlIAHD5c4zcS2WR0q3ELLf/fzqi1Im0E9GNL2hJH5M4s8W7L6Cs1q7tJHMg4onNAeYidQReWfCqbD5PhCFSvSOOIeWmImwFzZomThJn1cFDDX4Fn/RmxNVSPCrLJhKLFndeEPhPvdrgnJf3tHnpD/RVLu7eGBGrZk1cFGI+2ieQ1OWogY44LigtFKjKxh3SrRR2WxGlk9EsO/53PiS9reArFhcZPEjrIuXlyscCdGbIByobQF6hCo9aQ6iTaE72WzTYN5MJAmSgeOvqtCDe5cqRkcUT5MqHOFco3ZJ/JNqM7jYqIc5xlxo81i7mHLqHWPXklu/hpd0b11Zr1Gx7lEozEEakjhV0r+u0I54ACXMYaT3xRwqZHpUxaOdRWIPcGgkK1imyzOJ1WSaBcZXJr0KNA9aim28oUDxaE1zV+XUADaDk/j5cXUCWUzugiErNBLS0kRXaJ0WNLM+/ISQ0JREQ/d8RtDx5xXC2gM+YDi713THdao7MnXToJEp1Bn2nQmVRk9HbP6Ndrmu9eE1xB8dsG/io8PV1Snmj8Z9Ykr1FFJvUGpQ32SONv9rChZE02wEphVpr6uWb5RkANx6yqSF5ppg8L+hmk+RK/KOEc9NqQqoTqFRhQricbBYcO3coaQ04NjznFvldw6jtWCw824WzAnztc7fEXJWZhMO9Dv3dG9a6lvR0oTgx9iHIev85vfFL7VkIH/y7wU/P5/G8DZ8APA8zn8/8V+NsHBwe/Bvy3wPcAr2Rf/8nBwcEHw7//uOf+s/l8/meQH98D/9bXZ7mfyDSE11uJoArCJEEvC8HYhDorCRuR8sjQ3QyQFO5FQVbQvdHCwqFswj0q6fciKijZ6L1GdRrTyuf613pyUNgTR3AW2yuilUgcxx9nTv0tT/Hc0d/pyV4T3mrgsIQNLwtWZ9J0uNY247cDdIbVa1E2qdeoKpKWDndhUAqSyzDzVO9WtDeiZJYOikNLf1OcRKoyqlfExqL2evl8lTHnlvZWAJVRJwXtvR6lM8pk+lseWsnmmte8vKWMmCcVyWViZ8gnBTpCqjO5SMRtTzMWLtasNfp5JZmLyXTbGdLwXKNob3vIEM1QOQSF3wtM3nU0poJRwt5o8I2jfr/AzzKxSpSnhnYk1yJrjZ9kdK/J2z15aSWLXziyS1Amss6gs2Q2GtIkolyiv5CMbvFWwp5asoHyVLG+J8eTVpb1A4+uAsYmfGtQo0C+dMRCUb209JuJVCX02mCfOdJGgpMCFRRMI2TJmooTQ78XJUNrDUw89nlJGMkxppWj2U/kItGuCnJSuFrOec7iTcyGx7xfEd9oSUcVCsgzj6sC6ckI00NuDWalieOEfVGQ7zXoFxXFuabbG9ZQhjiL8HhMfa7pP+Mlq41KrqFNmEtLHsu6WN1NpEZcSrjfyrVK0D7o4LwQpznzaBdJrSbdazHPS7IB3SniKJGLTNwKLCcKO/WEi4Li2KKCo7vXsXwQQUFelFJJeg07HfqklKx5v5PzMIzZjrNI9cQR6+EBr4nTRKc1JJj9ZsnyvsP2Cr8xaANut/SNQTWG9kGHvnD025HJQ4u9p74ht/LKvmWO9uDg4PcRR/kHH//LX/fvCPzNP+L9f9xz//af9Phmv13wdL/ALDX5XkM6LTFTT1xb4mWBA+ylQSVF+UIyWr/fiyNNCrPUVB9WrO8kykND/3pHUg5URnsIGxF7Ls4IDfm1BnUqEML4iSHWsHwzoht5jWqMlJJeLn5cW9ytNfpgQnevg0sLLlO8LOi3Iyp9DGXYpSGWmby2qKhQAfxeQAUNC4dbQReUZOA24ZVDrQ2sDeWtNbs/PeLpv2Qk4wPyi4pcZuysJx5V5Glg8nsFq7tJNqTKqEkg9xpVJMyzErt2dPtRykkGp9VqskuYsWf6iyMWDxJxFklFpjzVhHfWKK9JRaZ4WKG9orktQQ0AL39nlyHB6n7ELDTuwuBNAUDzuscdW8pjQ3u/R19Yssm4U4PfiqhGkzsDNqNtwr009FtJ/Hoa9qeRDMiOxYHpRUFfI+cSCOPIeisxeVe+00wCsTOgID6vYRqvviO7TPuah05jLw1hM9Dt9OS1xSw15ZliPYHqYUlzOxAmGXtuCDP5ycpk/E7AnVj8KOIOrVQBpThL5TX6cSHB38q51h9V+NeHKqxMYBPlo5LuNYUaJdpdgRLiVCowP4uosxJspn3Q4V4WhEkiu4Q7doSbPaEriEuLHgVYSGWjp55UR1SGeFFge0UCgTaUuJZUJUZfE2fabSeUycS1RY0iqEx5omnmHaE1ElwaA53FdIpgMvUTS3M3SNLSGIETQC6UksokNRamAV0HyoMR7W2PbgxmLQE2jDPF2bCGgqK8saZ7NgZg/T1r0mVBmkil584N9rGlvR3RvVQTsUpUzyyre5Gtja+Dkb4Bu+4MG2z5mmBlYSegHtW4C028KDDnFjKYVhH2PH6S6O4OWdvCStZxaeG1RsqT3Y5YAEvJlJTXkt16RTayOfSlJbYWu9R025HLtyPr72hQZSKNZSHp7W5wKIrqWFO8sISTWjLbIJ9ZnAo+h5asMN3oMAtN2PWSQbuE22tw33aJXg9YblD0G+AWQ7Z9WpCLRJ4GinNNe1rx5C9HOfYgyyMXmawz8awkTwK0mtV92WDFiYY6wrlDuYT7qCTWmX47Ueyt5eSajOrkmN20J7+oOP9iT5xGdB1kQQP5WUVuLLmOdHe8ZOA2QxkxCyOlNIBLzH7foTpFHCf8nhfnqMGeWMKtnm43QgLdK/I0kDWYmUf3CtVoCIq4cPRvN7hLcYIqS0aJydSPHRmIZyX9XkCvzJVTwmbU2hBGcjgpKNwLRz4pyS6jq4BeSMDUncIdOcmQC7meXDr0WuCg9QOPWWuaBz2602QNYSdIMDaZ4r0aTCbc7iArisvBYdgkwWCpiRWgoHokjt/ve3JnMKPA3v8p663fFC6hem7wex7dCj5LVNilwWx15CKjLxx2KRmrPXXEKqNMojxTKK/J54VgveOA0mCflqiTQoLMNFG8cHCjY/quA2D02LJ+s4csUFbyGuUS6syRX1S0n2uEG7AJXQ2ZtJXkhKBpXgty3Tdkz9kjhz1x6DJSPBM8FgUbXymIR9VQ/Qgc5rcjYc+Tdaa5O+yrmcd/OIHNHhSUvzVCrzXFoYUiSVD5jiV5FMgKYmsws55uP2JXGqb+U/mXa0c7WHaZPAnUH8kCSRZUr0k3ekaPLN0bLfbQEbeC4G9ekeskuGujCQvH6rWIelliOkVxotEricDhhidPAqlKuDODvbVGmSSZRFCycA9LIRUKWRBx5TBLA2WUTGeaqZ4ZzIeVHN9moL/pyTajgiLWQlbFOoNXhF2PWlr6y5LVRSW/bxRRCfq3Grr9wPgjSxpF7Nij1pb+fkdxbAUvy2CWw/JQSNk3CrgjR3EmZEmaBMFJsyKXiRw1/c6QcSToTmv5nDKSy0QaRfzKoaJAJ2hIa0scJUwrZV6x1crG6zX+QYs9tZjj4iroAKi1Yfm6ECF2qXHHcs1Upwkbkew1ppENHW4IxJHutsSlfBcadKshKdLK4d9sSEOp7LcTemlp7vfEpcNeanZ+1ZI14gQAjGSq4ydDBmkzySGl7DTASUkuMqoOhGnEb0bspYGdjnK7gakn1UmcX4Y4EDppHMmjiJv01E8kwLevd9Bpqq9VYDLr1yJxnCSonVj8ZiTs9ZCgvTNk4BfDOsqKs89CNqC2O9yLguZOwJw5Rs+0kIFZEWaRcF5QvrSkOtJvJTk3VrDqtLboIHg+M49Zarb/cUk6K7ArRaoSOIFe+p1IunSsb8q5ae5EiIrmTmB64MitAZVJowgaindr1GkBvaY8qHHnhp1f0/i7HapX2Iuhwjtz6Kkn7PfEcSJfFvQ3AyjQdWDxRqI80+jGUBwZVBExU9kDdq0wC1nLaemIO0JM61Fg9YanOhYYQbsIK0t6MqJ4VoCGyfaauHDUTwzhVkdefjoQ4NrRvjIlf9obkVRmuTidIntNczeSW8P0Q1CtwZw5cY5rg4pKon4VMWtNnES6u73gXNs9ygyl9dIy+cAweqaIwQgmp+U73WZHKjNqU0pKAIIi1Qn3XLKFvN3TvO7xs0RxaNFFxJzJZiQhrPRQxpMVymT0bodemeEYxEGlKpHWVkqhd3pU0KhHtbDfC8Gi3ImVDGUiGaRZatyFRruE34z4aYKgMOeWWGbBCLPCnDjsQoLLq81HmcgJyufyu9zYo+40FOdKFvc4YBrN+jMdqtX0aydZ1sKgn1UU54q4IUFKDTBKdlmClZay2r61AC3Bz6xEHRI2IpwXkBQb/6Si/L1aNlcv2Vrels1mFgZXBOxKgoVuFXmrRxfCXPvdwOl3ZHKZMDMPlahO9MRz+mXBsNNZQdwM5FGEpcXsN7gzLaX9SAJP2Aik1hK8RR+WwnK7LOqClUE3WrL7pcFfFjT3e/REiE5MprnrMacWtdGjG33128tDw+SrJXocrnBJNzgVnlXoHvI4kJMiTBK6k8pn+dmefKMjm4xZaoozQ78VqbZb3KVGjz3sdRSnGtUaLj/XY2416KOCVGZO/rlANpn2nXaoJjJmo0evteyD8deV+EkxeWhpdzK60ZjnJSqIEkEAZLALQ7eTCLPE6b/YkjuD2uoJmwHVGXH4K4c9FNhC+YFASwId5K0eP0nYW2tSCealBIJcRfp7PfleI2t5OpzTDPm0QBWJ9tsa+j3hMwiK+lDT3/KkIrF8PoEisX5D1riqvyEO7MquHe1g2WbUylDcXFNcaLrdAV/sNKpTqFZz/n0deSSb/hVxQga918pC3vPokTD3upGNpl0SCdY0sHgncPmllvyyhKVFj8U5hlPJUvNFgR1KTmzGnWnCxiAfS3IMarOn3/ek1oi8S4Pe6dEmY5dmcN6Z4qOS4vdrzFpRPqxwZxo2vOCMvSZ8Zk35xJFNJt1rsGeWYm+NmXnCVKRBt35OMsiwGUgPGmGpbaK8tb7CA9OuJxxVKK9kc2VQrcZtdSibUCaRgqZ7rQeXSVkR1kIeqTIJsXKjo5p0sDk47KmXrMVm1m94zDiIAx0wWlRGHYyp3y0pNjqak1qkVQridoBxwMx61HaHPXJcfl9DrITFd0txXDkJHmc6mPxvE2IFqojEbSm783khzq8KjO9doqoo57OMVB+VGBcxL6RUZyL4oK6irAvAv96By+QM7lSOTZWRdF4IhLBwH2fIWkg//aySAJVFDZB6I9DUQNDqu2vS0qES2A8rCUwbmW43k1ojciygu9tTPqzIVjJMe+xE0TAdAkpnIEHqDKoOpCrTb4mMqb0oafcDqbGkpSNZRFrlEuGoIk4SuUroRoJDjkrIqpWl+sqI4lxTnOkrz5Jtxl4YVvcj/oYnb/kr0jdWiW43kuoka2wa0FsdOWpGHzpS0NSPpFqZfGCgSOheuBIhFjVmFGQfXjpMq0gfjAl7PWFbCFRVRcpxTzyXa5WiEhmfS7DpGc1azOMKe2nQE08eB0KFwGpBoSYBtba4I4t9XlB+WH4q/3LtaAdzGx15EvCdJdRZMM9CSB49wATmWYlaSLanW015YgQPfVgLy98Y8mUBtzrytjiS5LVgTkUUPeNCSmcmAf28Er1gKSUjILpRJPvs98TZ67VBGcFRU2fQwzHk1pBVxrxfkU4L4s1OMois6Hcj7Zsdscp0exF/ryOvLLpVoio4Eueux550UhLLTHdesfl/VILPVYFnf2nAozSEhRMdrAL/4QTVa3a+ooZSUOATANMpVFL41qKPCnm+15gTB63GvTuS4LAwEoiOCpI3tC/GVzBDPC+kpN72qEYTF042TZDDsWcW3SvWDzz+xUj0zXUUXDqDeVFQ/vaIHDVhv2fjFytso6A1FJ8/E4Kx1/hZZO83E2efy6S9HveklIDWabLNxN2efF7QvD9j5+ek7OZ5Sbcd8auCdEeYdfu8QD+r4KjEbvaE44ocBAd2LwvR79pMDprquUENWPkrzWwuE1kjkIBXjD4SEpNXahWXmP2uo/y1CZhMdaTp7/aYRshFALxm4/cHXLiI4sCqhJ55wkw00rk19Ld7iiMLYcDozSBlm3q5djZDHSk2OgnKb65kbV46ci3ZvD2x5O2eNAuYMpIeNFQvDKsHAT/NNK8F9PoV7JTJJpOriC5FscA0oFuF22+onwv2bqZepIUXBe5xIYF4YWlek8x5+WaEVn532AioURCd8WGJLiLMPP2uwBF0gj2jIa8s7bJg9EQqKvO8JLeGuHTktWV1NMLve+J+J9XloZC4AHpXpJ7ZZMI04RaK/u3mU/mXa0c7mG/EkaSVI2xEtBciwz4rRQJzYgk3xPGMHhvs7TXt3Z7ihaPfi5SbrZBNCdJJQe40uojoYwc2ExtLeSoNDyqLk4z7HbqTEkifW+ylRl/IgsiTQPlyYG7HkeJhzey3Sqa/W8Buh760TB5aNn/X0t8Igg1eOuxui15rciWsbtoIqI0e86wUlthB3pWSt7vjJUtN6qoJ4/TzQoLF0/JKsqWXBt0YrIsom0Qsr+D4CxlMFmnOzY7i1JDKTJqK1CxuRFSZwGTcUlOcGPwgm0tVwqw0qcqUjyXbsE9Lyt+pZYOMImplr5QRyX0MZWSXae4G8ArTKqbvCYGlHqyE1b7V0e4lclDQa86+K9K800EVWVzU2FnP6COLbjVP/2KEvY4cFH6SJQvfEvKG1lw1Vxz/8wG32QpGv+lRjSENMI9KUJ4oTKNIgyOh19iJR/egF1YCdIZu3pBmgeLWCpXlvOsqCLZciLxpfd+TR0KIqSTvX3yxZfmOB51Z342iDNgYgvNUzsvqzy1lvTSWPA0SxIOGMlF9VGAWoqft973AD0FJYweQWovfiKhzh7aJ4A12p6VfCn5ql1qc1+DAzLMSU0fiZUEMGj/N6LVm8liJHLCT6sNtdKiguPWzltRLUM5rQ5xEfGtZv9VTv1+QeoO71JiNXjBlJwGofmoxU8/kocFdGgmSNjOadMRppH4h2mx1VmBn/ZXKBUA3St7TGpr5x3IzBjWNCorqqQTx7DVpbfE3PaNHBntpiGsLMy9SvSrR7aSra/6N2rWjHUytDeUHFSQYPbL4jUScRpFJlYniQmHKiNruWb3pyR+MZdEBahTon4tcJNss3VgmU3x1RCoHjNYlujdbVKMJ04huNcYlyUJtJpcZvxlJk4+xrX47Snk0Dvi3GxZvRJZvRsGq1ormC2suvrtDjwK6kKzYN070tV6gC1MHzOOKVAipoSKU71fc/nnJssnSgWNXWjaBypJhF4ndXxFHm2aBVCTi45FkqaOI8gq1Lxl0HCWUyvR3eqpDRfmkEHw2KHKnqT8q6O539He8wB1DEha3ghAsQHaJOMqsXx+yIS+Suc1fL6QJYMMzeX8IPBZUHSRb2wxcflcPrcG/GFG/W0rpv9NLlVEK9JIbyazts5LqN0as73spwUeBdCksdp4Gio9KKc/H0q2GysSpqB7yh2PU0pIbg9trrjBjMqw/2+Fv9aLhVBk1CoTLgv5+R6oTeq+FTuM+qKTiOBxRHBvBH88HPe3gI1SnoTOYqcfvBNIswIVDVcN1rSN+J8jrXEbNhEEPxzUA9tSiFDBI7lDQ3vXEiTTVqM7gFoo8iuhtCbrmwlyRfNNfrUkLh78owWv0xBOGcl+NAskKhxE787Fedb8n1YnFGwJ9xGrQPCvgXsPxFxSmDuz8spOg2GtYOtyoJ5WZHBT9ViKu7dX/9TjQ7kXyYcni23vCJKFKCfar8xqyYjXv5TdmCJcCy5ilXPdXXXiq11TvSgXn9wSeKjdb1EZPrDL1+8MaM5JsrOY9sZKEwLgkUIuWqqd66j6Vf7l2tIPlUaS9GTCbPX4qi8ReWNR+izm3rF8PxAsnC0eB7gGXCfda7NPyKoNQ44A9l9KsuT+03epM9hp9WEi30SjgLjRh5aQ1tZGOILMyFLNhTkWS7qC0EUgnBfHCCSGzEqLCv9aRekP1sCQFTTwvhBxrNfYVSVYk0lGF347kbY+5lJK03Q88/7ISDNAKw5wR58XQErr965bT75TzYM4HAX+diaNEMe1RN1rU40okQgqUBqJi8RmPf7NBLyRjREG7H7HPCmg1xaMCNQkUZwZzakl1orsli784H5jhQRkQdj3nX/Ai+WoM7efXH1+vtUXrjF4Z6oeFwAcuSTZUiFNw5wJN6FHArLVk6nVmfUucQdrrib3hzf9eiCpXe8EkAX1U4CdCLFIk9J2GVGZMIzKn4M0VZtxvC1utjDQ75LXFPSolc7ocHAsiLcpaMOxcJvoHrciFlLSzmoVoSbMTLW1sRYtLBrPS5MagW43qDObcUt5eoeuA+6BCVfHqeOpDhXaJyW+Vgq+uBb/VnZagY5Pobk0mnQwZbZ0F+lKZy7ckEZi8J9lkagYooxRFB0CYSCWiokK/LCk/LDEbPex0mFlP2pTqL/SGFBV+K+Bc5OTPiFY8jyJESI/HdHe9VC9FFkgDsKeO6qs1dq1Jo4g+t6heSbebk1Zlhs43XQ+Y0tAKz+0WLi1hLDI0u/64yUCtpGuvfzkiNfKa9paQ3YBI8y4tzAJqHAiDkzWnDvu0pL/W0f7JTNlEtdNAVvgtwXxSkdEf1qT9wflVSaK4V4Q3W4l4TyrCLWlcSBsBzgpilbCznuJQxPI5ipSIW/I5uTWEt9eyCVZGGOoqEjcD/kiyEmxm+jVL+aTA7rWoq441gRE4K+DC0d6MFE8LMJm4GaifW8K2x50b6Ax2JVige1rw9k+dYe6tUEmRpkHUCABlZOe3M/q4QLUGFTTn83y1WV51izG0wPZnFfGyIOwEigtNeWhJUaGXIg1TClIpZbC5NNilJt9vMBue7lbAfVSKDOyVbjUqyQDfWaO8ErbdK1RnmBw46sfSxRWPhYjIM4/Z6AmNvXLkbPXStWRAnziRDGXIN1s4KaXrqBayKo0S1XMrDrzXPPwhIYf4YER9KJjoK5hC1QMM8rIm7/SE7YA7lzIzzYYNXiSpAqLCnQ2t2plBnSKZfVg60mqAeRQiETwphF0PUpLHUaKcSaltG8Fo1UrOT6wzqkyCUyeRhMUgZFR3c8h4h2Jo8VlPOi0INdhjR/XC4PYbcVgXTppikoLOiPM1+arkrg6tKFi2e9Z3JMNUrSZNA7abneNQAAAgAElEQVSMKJtJ40iYRfoHLdVLI1K1NxvU45q0cuRDydoBzJMK90HFaHdNtypwZ0bW3tCYEzfD1cwH1SvMTPTVKkJ7I8Hra8wkkOpEeW8pa/ZZJZ4rg3u/kmtRJ0YfOulYW4l00N1bYZ+Irru5M1yrzV400uMIQWH31xQ31gLTtAbjhNBlKeujeCbZbtrv8Hte9vinsGtHO1j2mvIfT0mDM8mNlRkA/UD4DJkevRZs7XmJqoOUSAsreM+0E9KhyOSntchXMlQfFcIIPy9JdaI4ssS1xR458ijS78SPy9DZQIbpLOXSWw3+okRt9BSzDrPZi0OKiBDfCX6JFhKkuR0+DhY24bdF0N3vBg5+dJN+WeDONO7IoU8c+aKArDj8AS9NECcyZCMX+aorjamHWRD500xeN/7AinMvM91+wDwvSRsee2GIlw7dSonLrQ6/GYlnJebDCnti6W973KlsUD/NVxIfbRJ5Eqi+Wgt+WEZW9yPtvIWFpToaFBmNwXxQUz0e8OqdHhYO7RLtzYC+syaViXivHVqQNW6zw5w4CVgm097rxYF3knUrrwl3OvxUuvJUlNI6ez3gjFwRZX4jXUmagCtiiazw2xFVRvyWtBnb86E779W51Jn6qcj70jSgiqEVWknnWXcmTqTfkGFAeZBJ5TKRkyLueOxChqikpzW5sZQvLGav/XgxJ8h1pL0tXXftfqRfFJhxII0iaq/DXgjLvnmA/JYk8E13vyP3BuMS9vaaeqsBDdXjgngqRBJa3uMel7Rvt3JslwWz95EsflfUHgD+hqe741mf17AQOSBBYZcyOMaeWZgEVFCkSSQndXUtTKvw5yLT0hNP92Qi7dw3uyuVSX+vl+MHgYNeDUdS0HcWvxUl2Xi1z1eW8O3LK6zZX5b0jfu4IeWiJE2FgDMrTawEdpr+XxWqGyrAT2HXjvaVec36ztC2Wg2dSAncAsnkXun2QDKaQsrEVMuJ162iPallolFUxN2e9rZHeU3/VoPe7jGdkr7++RKClolOnZH2VSsLUB9KKZdbg1obgStqURv0ywKtE+mwYuNrStpmTZZmhYHIUyPpYlKdRlcRt9miJ6KAMHsttpaMym9G9J01xbFglzkqIbJKaYDIJl/JhfRRIcGkDqgXJdlm0eCuDKMX0vobdjzuhRA0atAAK50pf6fGnRmKvTX9TakSqseFNHostXToDIx4fDyCXtPcTORRxJ6IBKoed2Sb6d4RxlclRX5jLef3sJSsq1OopwMOd1FKOX5SoupItx8IRxXcaTA316hWM3qvoH1HnIReGph5ctS0NyJqs8edDhOhigQfjkh7vWwyhZBdSWGPBnlXBrs02GMnDmvoblONkcx4WDeYjDlztHviqFWR5Dp3QyY885iJyNOyzVRPChkyU0X02KNMQp87eLCmfljKZxcSrKLX6AE6MJOAWlncbkOaRMxOh7mwqKcVug7ES0eYRvJZwemXe6pHBWokbdy5GdZd0KiDMfm3ZnLu32xF7hQUeinQRbjXoo4Lca4Rzv9sK51hRyX6/kr2yrineDYQTkWSTD0rwjSRtzzm/hL3tJAJaSZfzUowL0rCHUlcmHlS0Ki9Dn3mKN6rr4JTjkoaVhJgM8UHlUzkqqQLTVpoM9P3XmFC4BeldCp6+S3aZtztlVyzVwHRZpKVbj4zCSzekmooD5n4N2rXjvaVZWFvVS1gu7m3wm10tLsDqD+MbjNLTb8VyXWSzDaD3WuoX8qFe4W3MrD5tlHki4K4cJi1THNqL6UE1uOAmvUfZ01RkYf1YC8HiUqVBJNSoFaGFGWs3dl3ReylBq8xu510WrmEHrSZKgoREXpLPi0xhwWhtYTGDhm0wq8LupsevdaUTx2pSvjdIWMq05XOMzlpb829Id3oUb2ifOqk0eCWyIPsicPvD8NNJlJeqTPH+g3PxrvQt04c+o40c3S3gsiOFo77PzN8TyXj6cxeK40N99ZgE+vDMaqOpG4YQ3hoCIc1xaGVrHHsyTs96XYrk668lk6lSrSuxVYrePdpSfEbE7LNNJ9pMS9LcIk0C+JgmoHcOZbBQHQGpTNhlmDphCTzirw2MjPh7iD1sZk4lu62V9mVObfkKgpcUcix6IUlTqJg0EDuNfbCkkZJplidFSKyX4tYv70ZMBfD1K2LQqqGTdEt199zLKThoPfkvJDjBOx7Aj/1K6mk9MOafKMjljI560pxUEk3oZ8mITlLaWjQAfSA47YPOib7Szh3pNkAm4wjk8eKtHRorxi9W6J3eum6eqMRFcpj6U9Oj8eydluDPbf4uwMMF0Rx4TtLvytzGlztBU+eCRlV1l6kdoOWOJ1It1Z3vwOXGD90qNaQ7jVQR8HIP7MkF4nJ78mYTH1hSbs9/nsX8r0Jxg+dBNZRhBsdsTN0lxKcAZTNuGNLLhN2ryGdFjz4R5KgFMfXqoM/kYl2UjLDfF7Qn5f4RUl+vUFNguhet0RSQh2xp4O0p9f484rlZ3pxRq9mlyZF9cLi3r7ELoQAa29G4nwFXjP6yJIuHfm0RF/aj7OwgQUOez25jjIX4bwAr3GXmnRcopcG1SvcQhZrPC2xD6QciguHaUT2FC8d6lg2ULrdSgCpg2SYpwZz4jCXstGloUAcehhJKWkvxbHlWgblqLWBc0eeil4ybAbi3ZZcDeqJqCh3Gsm6bBKJT1Cc/AudYJFJYZ+X6EYxfs9dna+P/lUDLuMuZDmqj2opGZ/V2BNHtduQg8INQ178Z9YiIWpkglrojMxqfVGiVoIJlzNRc4TNSOgN5bGBSaC5KVpV81y0w3QGOxpaOdeaPImw08nEtYUhnxYCnYwD1bOhu22rI5fpSgSvikhxouluBQnUoyAjBwF9aUXz2QxzT0fhqlrSl5as5TH3ohD1SSFEoP2wgjKiB0gwlwnzxlLgi62e8/MxaZQotlrijhdlzImcv/71DrcUKCtPokzSUgIn2BPpatS7HdVjR3miyQpGL5Qw77sd5u6auHSSLEQl3VEb0pnmBzLo4tsClNJoEMaZeOmonlnMBzXpziCDA+IkyqyI7Q7TIeugDrDZkxsjRNuga07PaiFEj2VKXXc4Eu3u0NXo9sWJ5yjrfnU/CKF2JvrnnGH6s6L+6b57iT11pFEiN6KlBTArw+qBx7woISo2fqFCrQzK5oEYFgguvd5AAr8uyHXkgx+WeQyviPJv1P5I9zyfz//eJ/yM7uDg4Ec/1bf/KTLtFWoURRngFX4vYgaJjvJSMiUtkZCgRd5SKcFKbcLWgTBkvSpDzjKtKB+PqDpFuHTSUthZ6qeW5maSkXg3hoHQ54aw36MH3E8NbDUZef1dmezEVi8yraBob4iQffGFlhg05QtLnq/wvhYnvbDYlSIoTbICEZRPrGBZZaT8oMK/3YhetjMydWkkDtLc6uhejSXsNe0djyoS5Ycl7aAtJSo4KWWIdBQSqzutsV7BwpKs4FxpBO7Y4vc93sgMgNV4GBY9YJb+OxtMU9CsHO7+GnVYSwdRFtmSOzeEYTBzbC3VsaH7bCPazF4PQ1hEY+t3PVxUV40HnJR0tz3mSFpllUtk77D7a9LTEfqoRr3eoc4t9/6R5tFfRzDP55UETq9xI097TwbExOc1jJJkkogCorvjRU42jD7MN1tSNugbLbEXJ1ueGrqxJm553KQnXY6J44g+KQi3enJU0gk2DvT7+aqSycPYxe68kpGWe4IVq9lAno6iDJ4ZyfXKrcG0iua8ksx7M5EbQ/XMEQu5JmFsia/3V850fRtJMpIi9Aa9Mrg7K8KjMeWpZv1GYvNAcfmGIm33gsO7iKoyfSEwEVj6vSBDjwYss9pt6J+OSYPWlqDQgwzLrTR+JwiWH2RAUNwKmBNHaqzokoPM1WAaCL20k6t2GJA0C9jzgjCSkZLVc8PlX1iiTmr8eQUz4SnoNWZQopgW0l4i7HjMqePs81ECmpERj3atiWcl5UtDvB2wx46w12OflbLX7/+/j9H+IPDwE/z5wU/1zX/KLO54WDp0UPgbXiYXLZ1IWpwwre7mGjpD9djR7UVu/IoIoE0dr/ST7lyjgrT+mU6kQd22EBrp0snshNc8bPX0d3uKY0uaBsKeXHj14uMWv6wzWQ/sK8BOB2eFyKYm0rK4ei3BeYFfFHS7Ef9ScGKy4KTxjZa85VGtodhpad7ppLlCIUzx00o6u6YedbOVQeBFplsV3PsZyWjdmWb0gZPp+gFQMtTmFYnFJMhg5tMCvZYZp9lktBcmnaXDb0RGX5OglQfhuopKes33Er5xV85bvzsWWZNNgmk3Istic5iP2w93sFgKHGGWRgYAzYZ2zKEBQ28Lxpemg5TpRocqRcqTb3SEo5q4GUhvNuQgOPPTPy9OVSvB6LDShu0vB62l4ooMYlBl6ImXzr9ayCZ9d41+Xom2+Vkljm+jJxaibjGjQPSGOI7YpZCCaCEfzUrDpcWeWxHQ35DyWR2LimL0QjF+32FXgqtnmxlvNpgLIfBAuhbb/WF62W4ncrNT6XiUEZ0tainEjj2xTN+1TN/XV3rUfFnItLPfnBI3It1WRleR02/P8GBFbEXQr4zMGtYuoU4Lup0kmtWPPl7D/eMxdiHzJdJU5INxI8rMhXutYKRDk44aSaNHthm30VG/1KIwaTQ5KVLQ2JVGb3W4haaadPj9HrUt1V/zoCe0TirIKGqb6rFUCmnAXpPjSlscN4ZWbQXqRYVZGOLtDt0qmf5WDgN7vCaWMpryFTT0jdofBzg8Pjg4+I//aR8wn89/6FN9858ys5WnemHoNyU6xpsdzkX4aEQYJXKdxRmYTPt6jz1ynP+1JfGswj2u8DuyiNTbS/KzMXltpT33uJAp/Y2ie6MdpF6SBeEkKxq9V7B+q5dyc8Ah9W5HPipJRZJBJkjHD1WETsNSxrqlvZ7ig4quTJTHBt0rNt9TPP/zQsolr8XpNBp/WMsUsYcTxqeK5vON4KReoY4qkSHea4gDIfHsz8mx+HsdfRwG1XxxjXk4QyXJ0uI4oS4c9kZD0FYGj7w7ptsLhFnCbHhRcgRNKri6PcmNXzIcfl+k/KikuxFQlyJVww4qhoUhgRB9JstowlfksZPWZrPSxI0AeWhFzYKlEwGD9P8n5C4Lvab+wNF+rpERkEaCZznp6I5rqr2GblWg7zSo90YSfAoIY4ZNK3Kr5MRxq5VBLYeGDi/Yri4jVBJ0JQuqCNtesFnlMPca6A3xokBPpS031nKdOHfopMh3WvKlI4VM/cjRPJDgYhtFuuk5/5zMqijPNKExZJ1p35+KFHFH8M9UJBljeOFIlRbS8fU1oZVGBnVU4m6t6c8qwmaEQ8vFlzqKxwXZQSwzkw81y+9uYOWIuz0sHQrw55VMixsnWcNBk7JCKyG7dKslgA/Xyq4VflsIQVVH9GFJ3PYimTwvwGV2f11z/q/0+LMSNQkkZUgXJf4dj9ID2Zth9LWS9esBLgu439IfjySQtga70aNUxi+l5bl+WNBvJdpbgeqpI35WuuYUCPxio9yN4UmNvtOSbnTkoHEu4sdpuEPIUHVte1S0kox9Oj/7R2e0BwcHb32SDzg4OPjMp/vqP10WFgXtm51MNCpFSxdOKvx+jxmmYG38yoCPWhHG69+aSmQcet61S8T3J6RpoH5sKcY9G1+Tcra9GajerdAXFnf8cZdPdyPIdP4yYl8Wgl8ByYtQ22wOdzJ4dZ+yUcDeaGASKF9YXBnodyNuo6O/31Gew/F3SuNENhlWQqakG8LcF8+ENFndj8RBxkRU6B7CrY54XqAvLNWH5VUHl3tcyt0kWsP66YS4EQSfHQ/42bZMtVebvUi0bIZScN38siQ3VtQOCNGgOs3xD7TgEv3rnbDd0yD4JAjuvO1Rs16Y7lI26itRuRt5ykeijTVnjuL+EndiBRLQWWaXRqkImHnMy2Eo+J0o0EyGdFxSPHWkJLMZ4nsT8tqQntX4Waa/7Qk7HtUZiv21YKDnWtQHJqOHWwABqIXFnjiBMbIinBeyFvY7dClDU9yZleBwKFIlrTNu1omErIrkIsvYyZelOOttLzIkr6lfaPxrHeGigCriFor2lgxAKY8N+k4Ds4DWr84f0vE3irjHJcWFRj0cUYw89UFJGgluDWBmvcwRWFr8vQ4/i+SdnsvPd9hHFfbCCElVB/ReK1knorJRTia0qaWRhKBMpEmk/p2aNMA8yUm3IXYgFG/I2EcuHGarozwyHH1PJCWRTU6+Uglev9lKdt1Y1CRg6sD6vtx6xs4EujCzHjvxjB5bwmWB++0xaLkfHl+4JFvQjabfGiomIAzzJPLQORmmkbiyQoz2WsZ4joLMOBn4En3i4HaLPXZX+t9v1D4RGTafz394Pp/f+UMe/8TZ7Hw+f2c+n//yfD5/d/j77T/kNWY+n//4fD5/OJ/P35vP5z/ySZ77utfM5/P5ej6f/xef9LhemTuxV9INe+pkutI4oBaWcF5g9hvOvzTcA+y8IO31NPe8lKwrGe2nnlfEWx3KJZp7Hv3VCau7kPc7KBLNfRk8nQfNq70Q7E15jTHSEZSHjJaFaDy1EtwMwF0YUi+Me24NfprpL0pyFWWC1rHcT8kPUIUaD/jXwsoAl6nHbyaYevRmj1pZzFaH7jT+NWnjtJs9uciCyb4unVjpwTD9KiPdRwoZJF5FIYiOSvT7Nak1dGfVlVbST2XjFSciF/Lz9dWQEUDudfakQM16TBUwU497KSSMagzTX63FCa+FddbDzQ/DSSXkHRB3e5rLCr8dsKXcY8o+LVFbPfUzEZ2HjShSqmpQ9A+tpv0dL4RnmYT0TDKgvX6hMVXAjXtUpwhBhmh3u5G8tuSgKC6GwdnD58X9DnMma0hPvXTtfa0SYqeQ4eR5bQk7nnxaoj6qCSf1oOfM3PglQznrZHpX0OgTJ+M0y0hzS773FSac3mhEn7uQGcLm98dUkw71vjD9MuNA9ML9fqC568WJtpb1vSBTxLw0SsROMFDdinIBI3i7e1nQ3/ToAKEVyCsuHa6UGcAqKWwxMP0uf9x40mra71oz+nCYEZylI46gyWVE24zd7LErTfWbI/q3G8zSYN+rySaz/mIDKqPfHeOm/dW8Bq0lqNtTR1gNOu2XFeGioNlP2EtDdSyEVrbQLkvR867U1aAlOSBklsUrCAiERL7Ry/znoREiey2ywSyT1WJjZBj5Vv+NuhbgEzpa4L8B/sl8Pv/eP/D4T34D3/UTwI8fHBy8A/z4H/HevwG8BbwNfC/wY/P5/P4neI7hzro/yf/N3pvF2JamaXnPP6xxDzHHiTPnySmyKqu6m6ahDVhiugAs2cJCCIFMS92NkblBshuDjSyEWpaFZAESvmOwhY2FEBJGNmojYwkZgxtMT1U9VEWOZz4n4sS8917TP/niWxGZVU1VZ6VpdwFeUipPTHvv2LHWv77/+973eeHvfgev6frwI4Q5nRZMH0lPSa3EeaSikqDAq0XQCnVejZVv+UImyzGXhTJ1huKlQLjjWw3pIkcXImRPd1vCnlSOxalsx81Gj7soCFecA4CZp3hlCC8rSeXNA24eMJWHuafabjB7rVQUVtB11ZGmvSlpruokF/L9MOIRRzq+WhtEs7qQdNpwLCYK+yIXsEaQwZ/qNP5EdKnhpJD+51VvduwNqpUl1EnQe0YkPPmReNVVFTB3G/TS4KuEzT1hmaHOcvKtDnVYUL60uLu9LNQqURRObhJaJGPLH2yJhbRXzOyTbVsyCZwwa82pMETtmSVFLaSlNRlstPcHweRttmIddRo1IifNSJcq1zvRlp4W8rPrgebtXqKDvjIlFdLfMzYwvSnbT3uc0d721z3RfLeh+LDELoULMP/JCqUT/eYI4Ok11VonPFvEaOL2BkmgMGI7PX0X4WWUkWw6EDa8TNcHTZoIxDrZJOfChxXrX5f2knlRyA3gF+ZyE0XUE2tfzcFLckB2YtFHuVSHE7khZUdjIXEpssBQC8RF1zLMchseXQTc7UFQjxOHWRj8ixq3FgQK/ri+1hYPt9w1RU09rSRVAcneS6Pzz55kqCclfpnhtjzd9zXEVcbaOydiY76y0LaG/pbD9xa9PpAdZrhVjq488WZH9TBD7Y4ysTxeGy7OvieK2aJVAvDuDcNmoH7t8trVmK5aDaXHTJzEUM0dycuuj6DQIzIVIO32DLccxbOc/EwgU5/n+KwLbQP8EeDv7u/v/+inPq++xfd/w7G/v78LfD/wN8dP/U3g+/f393e+6Vv/APBXDg4O4piQ+3eB3/8ZvgbwnwF/D3jvM/5Ov+zILsRbPvyOCznh5g7WHNVzQ1p3InHSCbvZSaUC6K0eN5N+VZp/wsDsd+Vict0oHO8M9mlBPCuk0gFWbzrSWS7WUp2ERXBVdSmZIsdKFpqicOi5I1zmpATmn80p//kEe5hj80B+ZFk9kNebWhmwZLdXsjhdZFTPhMakn5aYiReC/0UGBvHXG6RvuRI9LUq2XfBJj1LlkulkR4iJ7sbobAW81sjgyinKQ0MKiuGiEM3oeFGoTsN2T/pwgr7TMLzZMvmFEtsofG9xg7jNsnkvNtMxj23zK5qUuNafqoks9ioANzvMpREZ16VUZPlOg24M2VFG9jSnXxbgFfnm2OJZWNEGJ9A/M8MYqfKvXGMk2RYPX24k0sRp/POa5UvBFPr1gJ0PcmECQ5vR3RMXX1E5zr/PkQbN7KG+7q+35yVxxD/qC4vJI/2iEP2zl6Gh3ukkjeJJjc4C5Xsl2Vzs3VeHmXqGXc/itVHK92DJ5JHBvd1IywuxHV++GSjXO4ZdT7ZUhDVRJqRBSx/zxiAOu51OCohc4DvaJLLpIMPUywwW0of1g5F+/G5HcSRqFnWvEXmWFxlfyoSbYTqumQVXWuw0xnrHO9IywkaCEwnd6bN1UJDNB+JpLjlnYdzeJ3BrATP2vvMPKlGbHBeQoHgqcO9QR9JEuMDDRhDL+KBRU0/fWymKAJUUugyEk4IwGkvyrwsUPis92StL9l4l8PPKo5+V6Exg/ss3vZgoPsfxWRfadHBw8PeReO8f29/f/2/GCvKztobvAs/GgMWroMXn4+c/fdwDHn3q48ef+p5v+bX9/f3vBX4X8Bc/4+v55ceghNa/0HSPZnBUkAZNcpKempyEvKXO4Dor4I9nBepxJSJ+r8if55I9tJCkAX2WiU/+SY3qDX4mW7TuLRHWE2WAIMkIECZBKi6kN+XnAT115Kea5rwinedQyHZt9W7PsJ7w2474tMbd7aX94cRsQEIWLpuonxmat3vyQnB85uPymkqWxorKT8O11Vh3WpQWn3K96aVoVfX6INWfTaS9HoqIuzOQ/fxE/O9vt3S7ATqDbgzVM4t6WeDOSlhzTH62EqvrWUlKiuU7A+nNFTqLxCigZ39UjWYQhW4Vp7+1l+HK2MNVOgmUplfEk0IIaVZ+53ze01+UqCAtlFBJFYhCtNEXcoGqTXG2de+2hCA9w42vjECeXqNtEqCIk4SBlCWyc4GzmIXBX+bXLR07UtDCIqNdFKhOpHYXXxCUY7bRCaWrCPJYW4MskuXoq1fSx49nBfpJid8eXWpXaD+vSK+kdxu9wiwkDUGVgeHFhPamwNWvt8djlE63KJgeiBPNnlmKtU5oXEVg+rUC02rCsews9KVFN4YwGMzXJ1JdltLOKp9mpJWVVteTin5XopVgZAO3krisay834xFAI98QKScD+VovCpSjAnMsZgIuMjnH8ni9klzhGPXEYydj4rPT0uLwWoqPZTYaQZKAeUySWCkbBQs5FzaCqgL1L5XiBBtlkymPKAXFXsPkvVwil95uhf371em4Q4P6uSKd5/gbA/n7lVTENl5Ddb7T47PaHBTAwcHBe2P74H8E/sF38PO/asf+/n4G/GXghw8ODsL+/v7nepx//CPfXVLgR3/0P/21fgnXx4d/8j/5tX4J33B8/If+9K/1S/iG44M/8F/8Wr+Ebzg+/uM/9mv9Eq6PRz/yp36tX8I3HI9+9E9+8sHv+//ueT/rQvkPr/5xcHBwsb+//+8C/xXw4DP+/BPg9v7+vhkXQwPcGj//6eMxcB/45+PHn65iv9XXbgJvAD8xLrLrgNrf358fHBz80c/4+vi3/7u/wtPuQrZAZUBPPFonSRboZUscc5lia5tIRwWTBxc0TUE6KlG7HSSFsYHweEJY82RzqdyqZ5ZhLWHurRiaTETXKqEOP/GrExT1xxn9uy0f/cE/zet/4c+TrKTvut2R4TpaS00lPu40CAQ5f56RNLg9ce/EndHW+0pSTNnuRYIEUjk4LT3Pl1LdhQ1Bw6kbHdl7Nd2eHzF0suC/9t//ORHNlwF1lsFOT/ZRiZsmintLwsEMFRDcnZWeHEBeO/yzmlSIKaHbjqKtreJ1qGS20KTXG4LXmCyQHk0kZSKJQkGfZCJ78yJTe/TDf4rX/8Kfx7SK4aaDoDCXlnRTptSxtejKY/OAeyWaYr0SitmwJX1Ae27xmx5zYcTpZxOmDBgbiA8nqHsN7qyQ7Kut8Tlqz/wfVZx9j7RKzNpAOir5+I//GG/+rf8S9WFNeqMhRk1ohXvg50F2F6OzKRVREnjnHvsyl0TdTNQkqteoCNVLTfulVvr6rSbd7OT9TIo0aHQjFmy9EOla3BnDJ51YiB/+sT/BO3/mLwr6bzSHmCziO7nU8xcZw0bArg/EqK4rahKkKP3JsJIWllLSSgKEzZAJ2EYZcVsVmy39WUm+LoM2fZwTqki22eHOy+tzJ3uW4+9LdZ6ioA6TFx1s/3QqbshFRioD+XQgPK1RN7vrl+Y7i7IRbRJaR5kdKIQJ0RomDy2r/YFi2jM0At0nj5g8kuWe/qim2lvytX//z7L/438R08HqdfcNdmk9KNy2x55Y4u2OdJkLOPz+iu6slOFvgtsbM/7xD33mZeX6+EwL7cHBwb/3TR8n4D8f//ssP3+0v7//c8AfBP7G+P+fHSkIOysAACAASURBVHutnz7+NvAf7u/v/x1gC/i9SLviW37t4ODgMbB99QD7+/t/FpgeHBz8ic/y2q6PJEOWcrehb8TXztTJIlgGQmMxjSb7qEQ7aB44Fme1LFq7HfG0EH3mmDibH1mu5pPdWx3Ja0IjPNvUjyCXfBTln1lCFWne6q8HLGEqTf60EVhfb3A/ucnqNY9eWoKSfqrwFBLDTUc+HVAva+xKM5QZ+YXG3+tIaaTsDxozHzAfVoQ3WhGaG/mdTeUJOqGPStFABumNXQ3/bOlQz3J4s8Ofi4wpvNkSL3Pay5IigJsmssPsOhaboPDPatjt0Qr6VtoNfkMiSgCBUJ8Z+KAm7nlJW73docf+W5wK+CZcFrIlPZeLPmnhoeaHmfyOrRE5URSDyOTnKvrNRNoSd599njFsizVW20ioAlyR8rOEKWVQF3ONNolwVMHEQysRPGHTE08Kzn5AFt3szOJ0hrqyS78qSdsBzgppxmWRcEt02O6iQE8cetMTzgrU5iAX/6YWiLVOkt3VSOrE6m3BMlIGQiaSKLPUxCKh1weiV+TznsGV0m98lQvGcupFRgWEKmEXGrfpRemQR+lLrzmGG2LGyZ/UdO+2RGfQWUCbJJFGUwGKmzNJDFZRyaR9kUFjMI5rWZ8ezSHusMI0WmLSg8ItiutoeHOUE17rSCc5akO0ruk0J00D3SpH7/QoIBSBer2l7zPKV5rVLMNMncjvFlYMD0ERr4aCEdnOKyhPE+FRTv+6sBuuWkzxPMdtSC++fTGV92Z/RXcpJgb7KsPvDoR5IAQxHMU7HbETWJMeNP2LGrXmSN3o+hw+01jqlx3fdqHd39//kV/pAQ4ODv7bz/hc/xHw1/f39/8McAb80PgcPwH8mYODg58C/gfgB4H3x5/58YODg4/Hf3+7r/2/PtLMowdHf1hf8wqSF+te6IyI4zc80VqBUXiFOc/hdivT9LnDHBYCGjksGbYl3mZ4WVNOBtqjmvzMCFTDJIojQ7fnUZ2Rqu5Gj31YMtyWizc/Nri7ElR3cVGTf/8CFgXxyvo6ngyqFZbn0IjFt8+tWHu3A3QWeyaSIlV5wnlOuO2ovlbjJmOU9CQSLnMBPK87qnqg73JCJxg9AH9SoSYJXtSkjYGydrirChlEauU0qdDi+hpdOXEiC5rqNdndhviqopr1dCs7wnKkh1odKoY9ZACzzKAWlkTKNa7NZNrcWbLleJLv9hSlYPPSxXixeiXV0tKy/HKPMiKTCouMzY8Tp28OxLHXp7NIfmiJb6+Y/ZMJiwcWteWov16w2u/Ja4f5+SntbS/V0lR4wvY4w7y+ZOgmgjAcF2s9KOKaiOtja+Vv+jLDrUfsShPKgHqSw61B4DB7A3ppiF6A6/lWx5CUDF90wneWyXpL/94cv+sItQwkY2vJTw2ulgSI7EkhaMWtnmra0w4jTGbbk6/35MBwUmLKcSFpZdjr5wG/GWElYG/9uCLeE4VHdpyhB4k5Uq1U4Qxaeq5GUxxa2h05R7tlgV0Y8jMlseJKkhNMa/Aj6yDdkZs6WcI8l8BIlSAuZH4Q8ih970HR1xnqaUX7biuzjVZg6myM51dUqEWGXhOATbnWEz6YcvLrBZKUWouOyOD2TJJGtI6EK0IbkH11QtyKAgF6sCQ+FzaC2umJXqpt1RphOzy3xFzJbmu7JzrN5ONfnR7tH/6mj38L8E8+9XECPtNCe3Bw8HVkofzmz/87n/p3AP7Yt/j5b/m1b/q+P/tZXs83H7ZypBFRqCsvKagnGXHLkdUO14s98Sr9FZ0I89GlUojbKe11pLOCNJVKaugymHsJHswi9p1LhsMJZn2gu6WwMyeT8ncXpCYTn/g4YHHrkexpMU5QLcMm13dwVQTRPtpIChp9JtuuNLYWhltRhjDHFrftJRa8/oQA1r8tjAC9sKz/bMbFfoS5xIe0ixL7MiesB8xELqikE2prQL8oiSbhP54StkU8bkov2UpZvEY9yhuaZJFfl7wlYyL25pLhwzlMRpF7qeBWR6MLmXi3huLukvaiJNzqSYOmfq+geV1sqMPeKI+6yBlGa60px4upz0diVxSWaubhoxluFnn1gwGrZJBJFokLqXDto5rLNwWmbQ5zVg9ksRxOSvIijYtYkptQyIm3e+KzCdmthuANupLXEzY8058vab63FTBM5hnykRk7j6RVRrjbiX3aKfqHM9LWWNnPnaQ1jBVgAspZz+qsgk1p4RTbLcOLiUzUb0BRDwRv8NOECnI++g9mqDE7TE88wzj0o5S496vzSi8NcRauEY1hzHbLTURvdMSmxk/GLXUjTjjTGvytAbXQtG8Lrxav0DOH3/CEXVEsqJHnoZbldepBWIibMjs3JA3lgwXNs+l1IGT5tQo/SbibA1zm2DstvCwp7qzoTyuSE3u4zpyYDmZOcrvyiHMG8/oKzsrrhIq0KzuqNJFK2LkS3Qs1DqD/ckN8JaGo7tmE/M6K7qRCHxWkuRdXZCmtvGFztJJfyC4SlWh+3ecLZ/y2C+3BwcFv//TH+/v7Z9/8uX9djjBYUAPZTstwVqKmnlBoaA0uCnAmdXKCZQuLn0bJZEqQP8ulgt0acBqpBh+XsljNHaES/WtzNEEPQuCyjSZdWth0QhYaJ63X09E64IJifmC5fMfDypKdG9zeQD3raR/PxOBQBNiQajreFIaB0omgDOFOJzbQdS/Smae1JIf2BWw52O5Z7CZJcH1ewjSSdCQUQpQKXqoAVQWqyUB83dE9m8rj1eIR9y9q8oX0S1UWKecD/Oyc9sEgcqAiwtISDmZSoc9FZpQfWoY6iMlje0BfZMJkUIl6vWV4f062UjT3PLr0RJ9hzsbWgUqEyzFnqwyiNZ552X0kCUjsLyrYldwvlyniUSmeeD9mo5lEvNsRBy0urtaiF4Zyu8E/KQi12JaTSTKBj2CelbhtJ3CTlyV+lJsRFM33jeyFTPTXqYjkz3LsFy7pjwpiyKmfGbrtRNhwVB+M0Oyg4dxKDE8F+jzD3RhdSPca3EVB/Q+n6N+5kK126VG/MCPsBtgc0M+FXGXfXOBXV0GLEr9CUqIH3QjXShI9iAEkJYS1e2kpb61ozwQ7Ge8KG0F1IudKKpFdWoqHBW4WyR8WdLckficuhdJmS9G8qvWBOBjiDYe6IptVAX1Y4G8OKBNZnVVS5c8iVT3QvK2uterm3ODJyW62eD9GBWlE6nhUYXc6wkmBXh8EZnSSMcyv5JAQtwfSKsPOxaHoQFp0TtyNAPFEDD7YBNNEd1FIr3Zsx9UbK9pHM5LR6F5hekV4s8V+XDFsipvy8xzfaR38OZ2+3/1HVoqlcTiX7VhWSbNcTfwYyxwxS43e7iUYb32g/rrEjw970su1/3xGdBp7kkkyaYJ4mktyrJfMJrXTY1daknbvis5VKQHT6GelVIdANe/Ib664/J5BqPGFAIxxmtXRhFhGsahmwqv1W450mUush9fYVznRj/SqJESmWEbCzV4WwIXIruLxGPOy16On4syKtTiRrrXCmYTh9U+noinXifhkQgiC2BtuOYGVeM3QZ7R3Pdl0EAmY06Q6EF7rRPY2HyifZsLEzSUAs/qwQO30cGkJv7BG10hMTnt/IN/sZHjoJV0AYO3rkvt0lZHFdi8XVOI6IibbFFC1mybUxkCceoGqRySyaFxAzWg6UVmU/LI+Y9gMpBs95t5KcsUuc9IkkL11KXCazoo5ZdQ8q05fL26mGq3POuHuDLRPZ7IAFIHuexvizkAxl5TeetZTTHuyvYbqmZX34Yb0++OtTipdrzj/wYFuKekGcZXRv9GhW032uCDc6qk+yumaHHPVFx2xjLqS8zSuJEMuZVHcfiZhp46UROcavjaTm9mDVjK5Jg7dKexmh+41/U1HvxUIc4FfX1Wr+UaHyqK464Km/vmK/LkwYlkbgTuHBXG3F/leZwUYvivYyXYlphptI9V7ch6q2uOPS8zHFXanlZvqKhMTSxpz9FrpN/tNJxwHEEC6SUw+toSzAndciRxxKS02d3fUv84d+ZEM11hZMSWN51BK0HUiOSuOhXfS7wZ8Zxk2gyAwP986+//zaK8O90qcX7o1MHO4Q3H3qGPJOeK4IGw5wuqTXKx+XS5ilUWKJzmrewH7MhcdZBGpn0hAYQriq45zCVqMuTin3DK/5miGlUw7r4Te7UUpTqFRtJ0dZZh+JMKPyQcpKVhmUo06zfrdc7mIcrnYrxYoMmkxpFoWlWqnkdywDEwrmVFxJXxcNw40VK+vM7FSRBKAdzvi3AvZbMxOShMhZqmFZf2nc4nbNmKhJI+UzzJZvM9ymDr8IqPflQrr5t/LqZ5Yuhuy/Vebg0zSG4vKJaGg/Mnp9d/oCr138T2DWIW3BlksbUTVAfNEsqriusMfV+Q3Guxq7PsmiQNHQ3dZMNlqCK0l9EYm9ucZphEiFlkiXmaSMDBuUwmKdlmgGisX6mZ/vR1P1SeVbbjMBejSGGzppKUTRTwfnQQsDq9q7I2GdpVjf2aGO6po73r6d1r04+raKJJWFrvVYQsvj5HH65iWeKPHzSPqOKe947G5x4836exArLjGyOJmLyxxKn1vf1qiNwYxyXQGNfGEN1pSVBSlI50IFzm/vyQ+E+E+UbgJRHAPOmE6FIFhmUv122vKRznDPOHu9ZKkfNW/vruCy4ywyFCFLNY4hT7OJFTyRk/2Xk3z+iDDUZXQmwPDtsddFjLwW8mMxHxcik0W4UuYC0u6MQ6Qy4DNAqsHXjL4ikC1IdHuycg5AqM+/bWO1Bnqm0uBw08cxgbMSYZ+WFG+sNI28JpsvSOrHNnG+Hf4lw2V+TftUJsD9Y7Eb6TWYLZ7iskAuz1+GomTwOSgENeSjeLy0VxHb/S7kqnlbgzYo4zslaW9FQhzqS5UISdqKqURj05kE0f1KENfCKxCHX4yrb3O6yqE9O/Wg1DEtgf0bjdWwolUBcIkkJ0bzh+vU8574pMJcUvcSemOVISm9NQf5qTB0B7XEp5YBZJN1NuNxD+P/V/lBE93RacyWSAs5fe0pReW58ST/2KNGqsCFeDiXWmvZCdWBmRe0T/o8CN6TlsJGCx2G0jw/Hd72tcHkkm4VYbNAt1NL1IawGz1XH7RjX8gZPINomgYE4Tt+zXqo1qq+rn0pnUW0Zs92U/NcLuO/MSgS3FUXVG2ulaCKPFaxOp7DUkjwv+IJBy/2VJtNxAUk60GLqUXPmwFTBYkHZgxqtvJ+0cmvU/mkt2GRvjF6yOMpteYhZakh8uc1X2xNtv5QDoTyVfa66i/UqGqQPbVCW4lUS3b/ygnX5MF3rwsSFUkrnmqZ1bwgCPwZViL4lg8l9532B2wJxYF1I8NeSGFQLbek5WeeFrAMqNZFEIJW2Z0hxPUrY7+9iBGDZNkh3dUiNHiKo1iNNwM61HaRyaNQaKyIrkzUeGoMkhM+3RADZpQR/pbjrC0uLdaiXTKokQTrSzFoYRCptGIEm72DLcH7P0leEV1e0ncGYidhZljstYxnJUCnHkgUrLu+QS9ENuxX8q5o1tN9Jpio2N1OJHXsspw5yXaKaovntM/6FE7Pao14lhsM4xJBK8l0upzHL+S6uAJ39guWNvf33/86e85ODi497me+bvsSCcFw/GcOPIn85+vafd7ktPYVqN2WlZvJszTmrxTaCe5WqEQ90n8DZd0TQ5nOX5DeqLmcU3QYDZ6yXRaWJKGjV9UnP5AIprE8G4jHIskgI8rYhZFQM8HeFZh7y/xXmytcZmRPbWEt3rS2KdMZZAMsE4zdJasUfSDlmHYjmhAQ2eFErYwxDoye9+yeMdJxMkvzmFLtm02D9AoUieVOUBZOlLUUjGN0/EYFN2e5FHFOCbffqp/ZY5zwtyP2syIuduIpnbN0zcZ9maL/VAcbQkgwXBaSmTN6FaLpzlUkfwXa/qdSBy3o6oKpF4SIvwkEracOIkvNW4jSDU69SzfGcYIbUatqAIdJRInquub2uShZVXklOearjLkx+KRD5c57rwiu9/QPJ/KlTBoiq2WGITkBaBudajTguJGI5bR5xOyjQ7/aAqbjuwowyWEd7rhSZuRZiaLtq08vrMYG/AjtnF4VbN8Z8DkgeHLDeqVuOoWv2dJ+nBK2ghwp8UA6WVJe8eTV45hTBGAcQiVR7IjeY/8eiDf6Fht55hHU7QBl2UUTzPYGKEyXQ73G7gCFS0y9MSTVCaVvtdkF5pwOxAKQ/koJ31pQX8qQaRx0OTzHr+ZZMgL5JsdKSoZZNmEOy9QUaG9EuKXhtCK8sYVolfXs4GhlbZJysb8OaSK7UfIe/d4Rpp5qo2W/umU3kbMzEkychJtbCwSce5RiWvqVtJgi4D9mRn9eJNTuew60v2BxZM5dlsW6hjFOpyt9XSnpewo6s9X0v5KqoP/4HM96r+Ch9ocCAvBESadaL8g+kzdGLjTUv3kFPdFJxrO+530XC8yWGa0X27Rg4XL7Prx/HEJVYSJp6p7VovyGqt38Vs9s5+uWHxBTggJrktQj6GQIIOdxqJ25cJLtZcJ/BsD3a0RwDGTKbl9XOJ2HbEEfVLg32zJdCReiO1yaIWGb+qAM3IBhN9ygX48Ew3qW41M5aMW3OGDFSkYrqh7zYdrsNdL1VaK9jMFdd0jjZfZJynBNhHKJBeuSeizDHOrkYWplu8xJwVRJ+zbSziqsZs9fpWhgqL8oCBZGOZSranW0D4YmP9izuU745Z6RDvKFQQMmtAZ4q2eajLgBslGU51BrQ24wmBelKgiknIl0fLvlbQPBnQRWD1Q5JOBftuQnRmGrRFMXY3wdhPRvSK7t6K7KBiOaoHzXF1zTyrSlmPoLHGZYXc7hlc1KpNerX1rgXtVw80OC6IH1aLQ8J3FnGS4S0t+s6E/K9FrA9UvVbS35PWbnQ7fG4Ymp7pUxPsDPK4ErbgubFx3XEE1AubX/XUmV7jTSZ7kT1cs3qikAtVgbshC3d/4ZAirdCQGqVRNNbYrjouRWSyKlsnzxOnrGabyqO/p6M6qkf8gPOburESNNm5AZgNeU7+f09zz7Nw7Y/WPd2juerKJDBZTa3HrgfqjnFAm+vMC5g57mMOuk/73RYbZ7knPKjFx1Ap1ntGXGfm5pt/Q6KOC6kLhv7QkLC2bP6c5+Y0RtcivI6IwCd9a9CSJfPFVgSk8/rgSi/T6gD8tIciAjkGTopbI86uk689x/Eqqg/8DYH9//9bBwcHzb/76/v7+932uZ/0uPPTzAkwLSZGP5K0wDeRnmq7M6XZki90vZ+inJdkbS/qopCJpLeTjUGWrlyF1yKCQiI/F4fQT+pRJxNOcxb6DsSouDzVJK9z3NjglA6hrSU6S2G2TR/pNubKL3Yb+RS0AmbMctzeG2q0PRGNRZ7loN3d7ia/mE7gHTh6z/3hGKhJp6mVrqoS+pdcHQm9F/XAir0XdlDgWtMC3wzKj3OjoeiND4aDQ55aw6SXeZFe2sJlKmKc5LtboO41Ujq957IMl/SrH9ZbiRkP3ShaJZCPtgyD4xHNxogUvEJDLfS8KBpDQRK8Im55i2qOjwp2X2MIz9BnRK6qHOf6LK4yNdE5j7y/pjyvBNDaW9jUR7vuNMXDT5pitHldZ4QH0Ct1Z+ns9XBbo3Z743hRuOPRmj35YoV8TqY9fFxqW25E4H3+ZU+w0uGcTskpE9/XuivblVEwFZSTb6MQFt+MIM4GguF76/7GxuO9Zks5LCBo/KHQeKH+pEmTiKkPnCXVj1A9nWqrwcThXzTtiVAxHNXanxQ+G5Q+0lAcV3bbC3hLod7bWYxaG+c1Lzp6vyW7KJNZ+ybL8TeI4Y6cXyPzYjz75QXGahcucllxg90uD3elwF8V1zpxfH7nKXkNnaO558s2O4482YS9i1gfcMhdH3MyRWkvz+oCpPbO6p+8zwt0oMKbjjFgmwZUqoW/pMhBHrGi350VXrhL9fkdscpRJnPxAZLLTsDqur8FgSSeKWY/uczxgbrakhxOYREIvLZI0MiiSl8BVfzEqKLzCr//qqg7+t/39/c1Pf2J/f/83AP/r53rW78LDb7trdquvJULGLA3dLY9dG/B1wntD9folbtPTLwuyw4z0vJQFduRy1l+t4FjsmwSFsRHdyl292mopn+bUT8dK2UhFEgpo7wT4eIIZT1T7qBQJik6oxmCs9O5Iiv75hGyhpYKeeWabKxiTZ83Eke3IVD15sSJm80EsoBc5090V9tIQtx1qfZDIbJMw55bp7Ut4XpKc2GF1N/ajHlfYQpJKr6y4Q29Rg2b6f9Wy/dv0oq7oDemolFSaoHFvtbJdfjyhfbeV7KsRwhy9OG/II+Ykg3bs03kFrzWEQazC2YfVNV0KhI5mtzqmGw3GRBkqaqHrh0vp3XY7AX9ckhLos4zhUF6nUlzbSUmgSy+Ki0IGcqYK2LWB/Fzi18vpIJZnr3GzOEbA57gtj3l/HDpNvdikx8j37NwwdBn5nRUxavrjinYpIHXlZdH0R5VAgy5FQZHvNaSV5cHfETOMP6wx80HA7UkGhN27LXq7J3tlZQEEUtBMP7KkN1fX2+P2pCJ+JK0Oa4OoJBqLHpAb60t5z/1xSdyRoEfVSzJuvt7T3E7oh5XsWpTEyjCiA1V/xYZN4jQsgjBjk7pGhYY6SnovkB3K38Y0muFSQDbVnTGRNiip2LMokTI2Yd+vWbyaEj+eoJ+U0Gv8tsPebDDrgwy/bCI2luq5wN7NzKGKSFj3MiD2CrMlnOV2lY8ytpGPO/H0Z6WAnV4V+F4UJMWJtKKUSmRHGZPNVs7LrR7KIIPSSYCn1edaXz7rQvuXkcV2CrC/v/+bgf8Z+NFv+1P/Ch15PTD/qsh4kk3kFxpudlRPLOGkgJ1eKoPDKdmayHL8VAZbqhMgNyax/ELP5MEFYdMz2W4ITpPdWmFeFiKP+tIl0YK91ZDNB0nAfUdwfG7bo/vRsHBvvJCWVrLoR686KpHfXDFse27+nwp1mrFalrC0Emu+ynCt8BQmG63wBpxMbYsbDcvTWhB3JqKfldcQ7jCJLC8qspUCp3GXxbW7J97uJDPLRrI1SbRN55IOev79AymPFFOJAlErS5yOg6CjgqIcKB+JB14dFdcpCSwFXK17jeoNYUswkJxLC8EPEhgZLnL6e71c0CejoeQkI7ysaD9Yo10VAspupPIhl6DM6vaSVEZi0GQLjV1K1YdOcpNbWrjbYh+VxMaSl54YDFnuCU7TvDGgx9ZIWGToLEqyxbqjfGHRK0O/M74/x4UkMbyUOHK/N5AawT4qJMPLvCzQm/KYcZBhmb2QKBo18XhvoIg8/l2CCUx5pP7pWoJBs8j6V8a2VFL4K4eZDZSznn49icTqateiwG140tTTPZ6Rz3vMxKP+rXP0hUVvS8pBMok0CJWtPNKozQHvjCSGvL5CafDLDN/b0amXMEuNPy7Rtb+ml8V1qdpTZ+T8nbtrKV7SiD53y4mS5N4K5wzRabLTEWTfWZkDBEXxvWfyO9xrcWuyiOsiMJyWkhvWWLl5NoZuR4xCYdAUH4seNi2F0+AbS5wE4kr+dldyM1vIzij1MqtInSFtOro9L73+LBDvdQwHc3EXPq6gMwLv7zRh71cR/H1wcPCXkIX1J/b3938P8D8Bf/jg4OAnPtezfhceQ5PT/5aF6CqR7PjQivQoZaKdBJi9Zwkva9L7U2wjoJM0DlWqjZb8ec7y6Zx6raV5MpOE1JMKv+mIeaTvMoYviikCINYydKnnHSoPUhkhigJl4vVFh5KqRi8sQ5uRr/c8/x2RtOlYX1uhNwbKGysxPmSRsh4oMkd4PGFjY0mYRobegk50N+XOH+90zN9XqEZQd9nTgv5BJ1zZlcGMVbIaBd2pN+LdLz12q6OYCShZFYGhzaQiiGBrL1E8ZaRrctw8Yrc78vtLcSSt7Kg1RaAuSeRjsbGkcnTjWHFmqVqMCDwviRvjcO7BgjgJ0qvzWrbAN1piKa/TX+ZiETYjnPqLC3itQQ+KonJi9LCJ6aRj2JFgyb7JKMqBobPoI2Er+C3H8GSCmQ/EzhCjyL+61wZiHZk8HpkNM48aRsiNEjYEIziIxxXZQkuLYJERtwfotbjvJhG7M3IwekNWDxIhHxTTDy2Ltx1p3ZF6w+VvbtE6EVojSSBz+ds6Z5g+GbWz440hmw0UGx157Yhzj2szQmNZHooOWilREGQbUh2rxtDtym5EvShJQROjJp7lmIknrSx+d0A3mmylsAsNrwriMiMsha0bLzOyeY/fcqIOuZrOR1D7S/JHBeZFgVvlxGc1tvC4DYG8KxNZnVbUH+a0bS4VfWPRUzcaRqRfWv5ixezGEqUQ5c2ao3qvoJr1pHcXqJUl3xIITzZxmFqkh/bDEpvJdb37tytYWszEoxuNqrwUHVMnUTZnsgtyewP6OMe+sURPnYSmZkl2GJ/j+MzyroODgx9HyFl/C/j9BwcH//vnesbv0sOWToLrnoocheWol90c5AQ2IphevhZkcXzQYlolYYBZIrWGblng73XYrY7mpIb1ATURd9KV1Ced5jLkqj0hCFiEPMJPr0mFMEJl4iqTj5VcOMkLBzVtSFpvfDhh/nVLGjQXlxPqn67wTiymYZHRP51ydjKD2y2nL9ZIVcA+LFHL8QQee4Fnv14WCX2jY7ghHNR0oydWo4QN5LWPr0OVIm1yrWzH1dib5lIoWyqCsYHZuoDAYyO9W+8M7VlFaoy8Hwrs9mgcmI7VURwpVkG0xakKpNYK0OfeSkwkQLuSdod6OeY8XRS4y3Hg4RV64sQVtZAYm+6iwJ+UdA96+qOa7Ezs1BfnNcVGR3IaZRLNqwkpaNTtViRUlWf2sSyuxbwnnhaoywz7KkN5xertMVPq3F4/t9ns8YMVfu+Lkni7E7H7KJVLY5BguN2hNwf8q5K1r+RkpewC+td7ULB8ZwAtFdiVQcC3lvJpLqqLRohlcTCsfpsk01a3JAEiGXbJAQAAIABJREFU++oE/dUZ1gZJoNBJHGWDojjR+MscO3X44xI/ES343k/Knzqs+evYmlRJnpaaCown1pH2psdtBuI0SGtDjaacicf3VhKhVaJ8JhV4qBLZ/z3Dvd4S73So1gi9LcpglaAoJwPm3NJvCm2LBHjNZNZR7gjJLNWSGba8rIRjGxTZ44J2L9Cel/RnpQyynKF8WLD2D2rRl5tEzEF9KG2ew9/XS4L0eX49nDbPpP1npv7a8abPMvL7S9zDqdx4ihGIPkrovuP15Vt94V8g7QJZmDXwN664r/+6yLusjQwfTFB3W8os0J2WsoiCRMFEJWSnUijyKUqqLYtMtKVdLrrVaIhZRPUCwSi3WroNgbuEKlLfWdK1Yy8RyDYEKUcaJVHjEEFPHeqFVL1uojHrA+buQGwzUhHwtae97+CikOHal3tM1DI4yBNqt8foKD2rqJjtLumnGbTZ6BSSqTRBoXZ7fG+kl5WAowLWPOlKtjr61VMS6n04q6EOVLeWNJelOOcUMhj0inBc4dcHibQpJCaHBH6SyPYa8odT3B2PW439u1zE5l1vUJMgBoSPaoY9h1lpsnsrvDPyuwCxE5F83B2gsZiVONRSFUSydVyglgo1TVSPLe2NiN7uRQvsFf5+x2Qy0HWZ8Cic6DWVU6hppKwGOnLCq5Llb2qIFzn9MsNs9uIS85qkE/mL7BvOITPx1xdMnAT0ypB9VNHviXPOThz+PCcelqTNgRQUdqfjos7QLyr0Xkf2LGfY9pCBzuVmnM5zUh7JTq2ccyCQI2vIZ5LiofNwnTzbvtMxW2tZLsrrCJToDHa7p9tQ0Fr0RxX+hkcNmvorFS9+mxdlQR4pZz3GRFYnteiCG0vx0uLfbvCtpV5v6R7PJG23FBwjrR3Tikcjy9gh0oOi+/6G7b9fcfRbHWomBU04LahvLvG/NKdNCqYRe2HQ/3QNdV8058vzGnWeYW+0mIMJ3Z2Bsh5oG4t9IjeJ8pWh2wO1Jpbw0Bm6247uhoIqkNcONesZjmSh9ec52UaPB7mOg8bNA/njkmHXY1YadbNDrUfakwrWPUU90AeFOyvI77vPt758m6/9GyPtAuiPamJ5QfW1muELDXqMco6DuY4SiUD9UJixVd2zuqgwm6Pcwwoa0c0j070VS1uhLjPcwymZlwhntTGwOhdau3IKvS3VD1nEzRMxT7LdQYY2+u4Kf1yJe+ciF1TbTk/yQqDygxWK/McZqweRdJYRb/aQ4MFfVTz8Ixr1tIQtx/LxnFRG6q0G/2TOcFsMCcXNBqUS7UUti0QVSBvuGxZ9bETZURt5lXLgNbkNNAjEJPaGeFRiO4XfcaSosNsd7rwg3usIg0bbyLDKcfekkijnPUNviYclvRUMIAq0ifTbXnqeQba6/iKXqCG41gJX9cBgErFS4kQaua1xzTOsiZOsvxGZ1j3hn23QvO4kEltD32WEzsoASSWxgu51BK/p3l8T7W0t0/Wr3zn7ek16u4V5IjcRPx/jxuuIfVFIdLeN5LMBr5KkIXxpAa8mECR7jSwJb+I8hzVHfFahcpnuKx1xs0i93TD0GSkppv+0YvWbZIHz8yhtAzWiCr0mBI05LAhlZBi3x7bwXL6cSRpHPrq0dCKOPc5qq6UNshtRpWf5pkU5TX1rifea7rKQfvwri9uTnnT84hJ/UqLnjua8Qm04yvcLho1Idqnp3+gwhwXeS+Zcf2M8j2+38LTi6LeLPl2XiRQhzRzN4QTzegetYfpRRvPlluX2OAx+lWFOc/o9j3lYi6nhPJdBqo3wekdhI+2sEquxkuijfDownBfXih/XWcGSjtK3bKOXGUZjYOKZ/XzB4guO+KDFPqnIloohVnC7lTSMmWd4WWO2BnTtcK7+XOvLt1xor6Rd/6YcyikoI90bPUUW6NYctgiU1UDfSwXonWH4UkPoLOFr63AjUD0saPYSNoGbJdLMszidgFPku62E2c0ldiRdiuSLLF5XPkondBnwN6VXFUYGbHRawNw7wgKtbqxoj2qU1+LL7+WCK6Y9q7ckeDBOJEJG9ZoP/1BkUjW0hfQbsQnGPqO736OPc9SNXgYTJ5LUqkpJKTU24G/Ga3eaag16q0eXDh5OiPdaFHB+PGW+teLyZILOA3rX4y8lrjyuQVrlmK1BMJMj01e1Gr01EFrD0FnswxJ3rxejBODajP4ygyoQiiRZV8e1JPWO/WvtFPlWS7soKCYDLlnseo/6sMbd68kKL5bqecTaQPveOv51h5k4wjJDa1Eq6NJLJQsw5PjeYIqAvr+SjLJXlbxvY6Jsd9NTV4MoCQ5rsaUiri6fWcwIyx4uC+rNhmaQKtwsNWHdU+20NGcVsVQioxvBLViRzbnzErXmcO/P8TsOtTJcvOvRh+V1xHmYQbHe0V+U4vq6zFFVFA//+B7GpzV5q/CvOexSEbYd4SITa3MZaC9KiQ1PCo5z9HaP0tB9NMM2CsZEZLfnmH4tp/11DfFY7Lg+KdTMQVS0t8etdik8jTCJnwRQHoo0MAwGrRCmwNRLxL1Nn2iggWw6oH/zingyEcTkdks4zehveLLZQLw0TCYdK2D7JwoOf6eXv0FnmO0sWbyYkZ0buN8ynJaQRYpZT386qlXGvDsA34+cjLWB1Fma26KgsO/VdDc86l5POqzkJrwxMPu5ktWdCC8K3DSiJ58KUP0Ojm/Zo/0XxXl/i+/710J5EOvAxvaCataJzKcIhOOC1eM57rSU/tPzAt8IkKJ7q0MPisU7jrDpCHu9+P5bqYB1Y8iyQH3/UgThI6Amqx1qJSkA8bQQQMaYllBU7nqIkKIky1ZfK8l3GrrnE4l4fi5hjPZVRr0mOk5TBjZuXYiff+Ip9hryeU+zLK7dVKjEbG9Bd1KRPSrI7qwIjRWW7riQ2OcFWkf0SMDX45Avv9GIS6rNcDuOsMqof6qGXnP5YibaT68pq0FylXKRpIFM5FEQvZItwbqjKKW6MTaKz368AfhhrGaW0tMO03idBRan/lreFW9L5alMYmgF0u6aDLcm02T3spbhYD2gfmlG8ealDCpzz+TDjCz3VI8z6Uc3FvOyQO122EKSK4azEt9loKF4mUnMudeULy3tqqBb5lQ3l9eDU99ZkT49LUVvbCPNWYU5t3SHE2FK1J7hgznKJtR8kJuOHRfZQQuPwSRSa7FvLqg/yGHumd5YEstImnnCWkAVkf6kGi3dip27Z1wFC3btqPe81cnNqPQMe44YFMV2K4OhwlM9zEU54pWwIBbCorD3VvS7gXI6wEWGWhpWX5Y+cDIJt+OkIInCKlBRybC2Eut1KsWqbWwgPx+XlktLrCNqXfrZk82W5IUdS0IGVVYKD1t5YSsvc8KGtFu8M8QqsXi0RrjIuPi90qu3NmAelyyfzuX3f/uS6LWAX1YWrZMM7+pB4DdGzuViImyS6DXruwvBoE4c/YMOuzYQk5KCxStIsPjSIOfzrU5kZI//JVe0wF/Y39//a1zfd/6FhwL+a+Cvfa5n/y469NRz9nIu0JWnGeG2p35paB7I9DIvHO266DyzwjNcFiTFdZWElYspKEv5JGdYjzSL4lrrWnwseLk4+vBDa6lvLmmfTyEPZPOe9lygKIAsLlWge7cnLgvMoIhVwt8aMEkRqkT7fEr13NC/NbB8tAnbYo3VPzNj+GKLsZH8azXtFzqBjzcFZj7ghgJe1VAEstlAaGpRCtwLmI9quh2pJPJCtn/9WYk9N6RME7cHsicFiy9JuCFr7jryZHk4FefRCD9RjUFt9aRlJv3eTFJYm6HCzB3qg5qwI9V+9IrpWsvq4ZpYf4OS0L/5APkoJr8aZK+kgrW5x52W5FsdfDDBrUXsqZU4987gdYQyMTyckTYdOg9UEbouh3WpZPTU4U1CB0UMGhaWbKVxWwndK9zrLcVBRbvh8PsDVeFoXk1wgywoADoLxEKR3VqR+kzkR0Wi2GxZnVbo3gr/905Hngl+sthoaU8rAZAXEG91Ut3fGXAfzQi3g0zjL6pPeulFoJr0DEdz4tShas/xq/m1Sy2OLOBwVuBvetRhjdkYMB+VDHcVeiFpC/7NDjPG0gx7XLNphy5DVZ5umZPvtgxNhrVR4pwqL0OhqaeedXQmkjo7hhbKomZu9rJdP6xga4S4jFW/flkwe6xpbuZww1HvrISU5gvSkWiczXyQm/EoU6s+yvHv9vgZsJCCYHg6IdYBNWiKt5b4VS583jFgM+XCefBeU722oHkxJdVS6AB0yxylE/V7Bf43jjf5TGLKk03i9gtiPCGBzjzRRvwIx093Px+P9tupDqaAB9yv8F/5uZ75u+yIl5nkJPWa9pan2m4ESxiEQTD0GeVW+4lvO0IqJMNI1yKBCeN2vt8MxDUn2+G5w5aO7qbHXlhpO1zmqJWhOanRIx3fD1Z0oGOVpPPAZKchL8RiGaYie0q9ll4f4shq3u7JKofbcxTrHdWkp70dsHnAt5aYgX1e0L6q8SuR+aSZZ/rh6OA5qkgbA0UpGWLhQUux3uEHw+pwpM8PWmAjRRJ2awHKRNTGIP2vEYqiBiXZY61BdUa0rKdCP6vWOpSNTLcasuOM4hcqwSuORoTUWPK/t04sZTeRH1ryOytIit3/pYBOX9O7SBKf4lbSPw1PaolLySPZ4pNqyZ+UmDeWYklVibDIpEILSizFC1F2FGsd6SyHkSPrNoWBa262koT6paVYok8KmrOK6qnFdVbaE8jUOpsP9E+n8KJAvRA50eqipFrvSNsD5bQnLxxDl4mT8OfmmInHdErA4zYInrET3oXd6jA2Yl7mn2x/daJ7MkMPUH+lIp3lVDOxg7tVfr0DAaRFNXPE85ziy+ekQRNnoijQr3LUUzHWoMTSnB9ZzJhYO9toSB9PpAd8XFBuCrkKp9BLK67CIInB9VZD8VRs1u6sYLgoKI/MdR4XJlFvtKS9nosvety62MfbZSGuMQ2UwtmIr0aegBcOtP/iCvO1qahC5kL3ipXwEZg7+ucT0qDpl4VAwrOEcpq4sriLQga1AVE2HMgypXRCneV0O5HmkRRWYZzBhN7Aq0Jy1kwkLjN5jQAR2QF+KlnkOzm+XUX74DM+xmeiLOzv778N/HUk7+sE+KGDg4P3v+l7DPCXgN89Pu6fOzg4+Kuf4Ws/DPzHjCZC4K+M2t/PfOi5Q68i9JqoI92LCfatBhNHvN1RAXc8pgiYj4UKn9YdMYj4mjxiC1nc0iSgmv+HuzePtTVLz7t+a61vfdMez3jPne+tqq5d1Z2enLbbQxSBQZEcJ4RBwQRiJCSCAsIKIQYpQUgkIBKwEULBURAhIGErsiDCiiBABImVGIjb7rjc7nbXqa6uO09n3sM3r28t/ni/e6ptxe2qktpue0lXumfvc87eZ+9vr+F9n+f3RHSdZv6W5eJzLelORTc1UEvzS1WiJbW2pzzPULEnKhVu2NGGs4Ri2DGkJ2IzHF0pKI5Gsos8jQeIc0TXi/yqWQ0T8KSjO87QncK9UUq3vjYSfNdr9MOM9ZsdehXhJ3IE9V5qbKrTdEcp4ztLunzQiSae5tUOHXm0CnCjRN/L6fbf3+33vUYvtZQCnCayvTR0Mo9TYse1iXTR3UErNWmFyJSQkMnT73Rkjyz1az39vsMMF/WLH2yITBC0H6BHHR5L/Mxi3lhTRwlcyOtRv14TDzg7ZyO6zqBXg/zKDoyGMoJyUDO0gi3UnSK+tqE6ziHxmMcp4XYlOWe1xWYd/diRPIqpbneYpMfeG55PC80qxl6pcGcpQXuMV9AYiZdR0D+YCIPXK5L9ku7jAvqpFzWhiFC1xW7XuOMMd6WFykrd+1YlnIZeSXhF6mmnnv4VhwFxnHVSqupffhJHIscKyxjVKtZDY4zGyIS232KeDU41I7s488YaV1nh2kaeeKnornjQWuDYemh6GqnHZuOGNu7pvjZl9Fyx3Mow2y3+LMZ/ak04k0UoSvpLIFJ8bDBvrqnWCaE1JLOa5iXr+WmC23GYk1jIcnkv5aSdHlUafKvlaB8rxpOazSbFj5zogbMOpy2hNOitluCR4FMTIJaJvjsShUicdfhkQDA2GjNr0SpIqObaEhUKp2L63UZSUWInDeusJ0p6uPi1SpMPOr5ZM+zBR/qNv/H4q8BPHB4e/uRisfjjwH8DfP+v+55/BXgN+BgyIf/SYrH4vw4PD+//Jvf9TeB/ODw8DIvFYgJ8ebFY/Ozh4eGXPuiTC6cJfRvD9sAk0Ih3+8JA7jEHFV0bETy0V2SCGf98zvoVz/jWiqqKUXo4Mq8sYSxc19X3VtAaof8MNkV1EhN2WkaThuIsE+uh7XFvlPCSTB+QN3rs0PsFvBhRvBgxPtjQdYY2svi9Bpxm/oWEeg/qOw3KBMbTivIoIRzUTMYVF+cjkokkhKrISzqER3YHw1EpBCVOpWnL9FDR3NREvzCBfwHSrVp2Dx763UZqfzdrIhPwRylVasl2S6qrnVxQQf4APyTXhthTPR+ja0W320rn3IqdVfeKsNUJGLrTYo28iKW88rLj32i89TAZVBCnCcoGukmg2yTMvpDQ/dNL2lbKC/2jnH7ao2pNP+5Jrxe0j0Zkt9fUldRXy3WCbyLCJpIMqonYTdFBXpvbFb7T0jQC4RC0mvaWGA78eUJ9VxQnfliQu9KKXfPM0k8NxJ5sWsMvT+HTK6JWXIbNWYbqFMmZwb1WEcYdvjO400yyqkygXwmzwQdgKlrl9LHFLUr0vYzuhoelJblaYiYNxdFI7LwwpCtAvCcwn3CaQipSwtm4ou4i1sWw4KsAXoDtwu2FfuTw37FGPc9JTg39QpyB3huJRZo77M9NqT7Vws2a1XXk5KWgtpaujaS2Duj3MgHjm4C7W9M3VhaBob5uR500DF/ZoIOiHXjGdtziasv41orNgxnpC0OT9cy2Ci5OxuKW29tw8XSKP7OEmWP31gWnZ2NCrzBXGyajms1FTrlKMUMKb1tZ1GlM1Cn6Ky1p2lHdn5CcasJn1ug9j6tiYfMmjubpiGi/otskxNMac/7NKqm/8fgt4dEuFot94DuAvzHc9DeA71gsFnu/7lt/CNmN+iEh92eAP/qb3Xd4eLgaknkBcsDyIdMgwuCZ1mtRGNi8JdmuiJcKDHSFFVnKMiaZNiRfzqm+Z4Pabmi+OsN8PZOVz2kh2W8i7LTBVxE2b4kmQ8fcBPRBTZx1FKc5e1eXhNLgn2SCIRyOXGG7JdQG1xnqRxOphkeBzfMx3dORGBCs1PEuvrMRhqYOcB6LTncs8TPnRxNYWlwXDd51qZul2+IAw8pRraksbq/DH6ecf2dH/+UZmzdlIqlPMuKrBcnNDb41olRojdCeUo/KHNVAcYpsL66fIsYXlr2DJcoNds8rDcZ6JlfXcuXNO5KbG5mMjxJM3JPkHWq7IQTF/OpKjnojx2S3YDQbIqu3WoIOwsw1nvWrnuLFCN8b2tIKrNp6susi4G+bSML/GotfieogNIbQakliGD479YMJZmMw55bwImU8E8iQTZ1YaHNHPq2lVJT05NP68vrReqCXdZp+0qMfpkSpo7pIqV6R+rt+L0PNW0kFmDja21JL1i+P2ZMO8ywhvEjQk478nkV1WvgXOlBf6+g7LanHDxIJ43yRU24kFcRcHeqH5zFhFdMUMa41xPslwSn6RznHj+cUm1SSQ0xgd3eNvohIvpyR7srPKxOoTzLy6xuijXTqp1c2l4kYqjFUB4Fk3IjbrY7ono5EeqWChCgOXf78k2KpjY5ifG3oK4PfDEyBVKhh/WmCtT3tKiHeqiWd4+EIcyTXMirQ3JHAzeWjmez0I8/ywUxCKyOpM5+eTC65I31thDkcSbbeSzUPFzHqoKYfe4lN/8UZPvNUr4kCp1yJ1ZdfmtI+Gsnn8X6OnnRURYK/8f57/mHGbxX4+ybwZAhYfBm0+HS4/RvHLeAbd9IPv+F7vtl9LBaLf2axWHxl+J4fOzw8/JUP8wTjrRoT9fjES1fSa7Hl7njJm5+0AgzZq2jLmOqNWjSuGvRrG9ydGn0cY08jsv0SvScAGFVL/aw/l4uIZwlx0tFsEpJnEasiFdjJtRoVe9JBBB82kUw0Z+Jg0WPZFdhzg24Vs1+ORczeD+qApSUbNyTXCpK0E+fSMpUO7G4jWlHr8acx1DKxqqu1oOgAziXmJUw7aRJ9YiXdZ0T61j4bSQlCB/qNZfROTN8Z9Eg0s/G0QUee+jTD1QIyJ/KsilQo9lP5Xf1JgveaZCwLQ3WRSnrsvGMyrmhKS1jFsLasi5R03BCGHXexHEhkzbAjXlkpR2w59LgT1YPT4q5qDFWRkD6M8Z1EbxMUxJ7NgxnJfIhhWSZCSgPMtRJTKqLbG3zi2TyZoiJPd5LhtkQrXG4StAlk81oaMEB3ntCdJ/I3Wy9yr6n45HUqNLJ01tBtedRzAbmYuMdmAsruOy07X4CbFfFK41tDOw/k1zZSTx466gTF7K2YZr8XNYD1aBOYXF/RnQ/kt7QnO9igygibOpLYCYglDaTbNeE0IZQR0VHMuhRrc/l6Q1NKw2k0rjGFplyl+O9ZMpmXFGUitvCBkdBfr6VJupTU4/TmmigWVOJoUl8mLDivmewWpK8vxVmlxIwTgph+ukFGtTnPsZMG1xncWSpkLiP82mhPmLa+sDAVYp3WnmAD6TOD6rk80hvr2d9dEWVCpTORWLtfKkSYtxLAOO5EBvipjSgpnKavIsa/Kq9N94kSdVAzur7GjT3KBOyDhMnoWxDO+DtpHB4e/i3gby0Wi1vAzywWi799eHh4+EF//u/9wT/1rXtyH2E8+JP/3m/3U7gc9//tH/3tfgq/Zjz41//93+6n8GvGb/l79a9+87sf/Ilvn9fnK3/kz/92P4VfM+7/8J/9bXncDzTRLhaLPwX81OHh4clHfJxHwPXFYmEODw/7obF1bbj9G8dD4DbCVIBfu4v9ZvddjsPDw4eLxeILwB8CPvBE+30//Vd50p2TjRqaOpad39QxmlX0vaZaJ+8n1NbSaR/tltRVjI0d9XpoLhQR49tLjApcPBWNn5nILtFaEbRX60SUAhfpZQ0wip0knj6wfPU/+dPc/u/+86FeKDEqxdGIZLsiijx9r6nXww7KQ3JQorWXpkOArhQwh6r1pRusPJdaMJuIaLfC90astSpg1ga/36KPYsKB7DT7xhAdx3z9R/9d7v7Ufyq7iaF2Zr46or7byG55u6G/iDGFkbj1s0RISVqUBLrWhK1WygkXqSRHWGnU4EFttWz/3ZST73FCuh8stepaLU6pZSJH8sSjKsP9f+tHufOX/wt53pXGTXrQciLpnSEcJ5grlXAfAiR5h7s3Jrq7ESh1rUnODN3rpUS4BC6J+t2QUJBtVVSrVNQT01biVcY9dtbgjjNMqTF3N3RtxL1/+c9x56/8OCHvGW+VaBWoqhgf1CVPwk6HbDMTZNdoAr4xlzXV3V9SnP9AiWsiglPkWxXVszG60vQ7Hfa5JUSgbpZCcRtq36KL7UVTfBITDNz/kT/DnZ/8i3JKaA2T7YKui2he5MzeNlz8XiGh+U6LMuU4JUSBdK+i64zUxSMxq9h5gztJidaabt5jlwaXB5h1xHlL10gQaJxL0KPWga6JUFqkZvd/+M/y5v/yH1FeZKTTBndvjL8mEJ1QGbG1bzWoR3JiiMYd/UkiOMIB7p4/iugmAe4WYupookt5pcmHqHsvqRvqLEbt1/TL+H1RqvWSqtxYvv4v/Qcs/vx/ednLCJuI0YOI4mOCEU2ulqi3JlS3hJURb9WkSUc9SPZcG3F3O/9Im7IPWjr4fuD+YrH4XxeLxQ8tFovkwzzI4eHhEfAW8MeGm/4Y8EtDrfUbx/8E/InFYqGH+u0/C/zPv9l9i8XizZe/YLFY7AL/JPChSgcqCiRZR3may5unJDus3CRURzk2FfBJqI1QfEygWKb0a4t7b8x8Z0N0ZmHW4X5xi+XjGaaQWmFfRPQXMVHUU78YYVMnnexZg4k8k2lFt0xQiUd9bgkgtsLaoAtDcSbA6uYso3l3Ks0GkADGrZb6NCP6+wKl6daJKCOWEXqnJd8pJVSwiJjMS5IrpdRpXyRSjlCQvrJGGxFl+7VIWpQOhOtDTbQX0Mvu/xehzZDG2xjx/r+cgO9s0CaQXCsElxeJ2SDYgLFezBt7BQw1uvH1FWEsDZST7+swSyPR2wq42uDKSFw8QaLeVSTuJ0Am8qkjemUjoGkT0F+VEEc/dTLJOU06ktemzz2909iJlC+auzVJ0hFttIjtH+bD/ZIHF0XyeEHJhJi9smLr6oq+MwQraQK9M8KMGN6HeNRSrFKaVqRFya/kaBOIJi+PyZqdn84lFdiJk89UirDTUu0pulXMeF4CUK5S4v0Sn3q07el2HW7aS2lgWHj1YFjwRylmMImEAYweuiFkEqjemePfGRPynuL7CvSFlSafCYQHOfM7F6hcWLzBK6n1XkggpWvkc6B6QT12By16t2E0q2SSreVI3i4TuvNUmsVBwbOE2cu/5UjcXvUqod9vBzPM0BSetfjTBDfrMSNHmrXYK9XlImJmLc2Op5v1WNvTnWSMJjVaB1Slyb+YEZ1HmFWEOpUSW/rLOWrk0KNOylWNprzIiBO5dtyilGZcp4lPhVo22q4IqadeJdSv15iNId8TvkZRJqKUaSLRGp9/NDXrB8Uk/hFkN/m/A/8O8HyxWPy1xWLx+z/EY/1J4EcWi8U7wI8MX7NYLP72YrH43PA9/yPwHvA14B8Cf+Hw8PDeB7jv31gsFl9ZLBZvAf838F8fHh7+nQ/x3NBxT32RYsbCzUyvFpIU2wrN3zURyagVvWHqiJ9acZk4BTcrLo7HhBsVrCPqg54QC8DaZI541pA/iiiLhHivpKus6BqDyKrqr8zJdipM3F/WIX0hhgZ2BEpN7BntF7j5kMTZaonNDgLCbr5vLR/wWS1M1d2WfmXlQteB5FzTtBFXDAbkAAAgAElEQVSxFWxedmfNaFyDgvLpmDRrBXIzb2QxCUocXCC7UxXY/MAG10WiWrCe/jiFXmGnLfUqYTIWZUa6U9GXEcmTmGi7xq0t3dem1FUsDiVg/WIsTSgVsC8s+npFlDriY0OStui0F5nYtJVdeOLeZ9mqQPQspnmR47aF9tROPSHAeKskinrscytx3WlPelDQrYdd5kQ0z01jMa9uhGV7p8BXEb0zbP/DmPXRmDjtZBcdBYp1ysWDuUxEL00BTr+PkeyURMkAvTOYSUfzyRIT9dIw9IreaV7884KVNLHU3LurLdHThO671+jcCUQl9sR5R+8GM4gGlcgiZeYtZoCuJ2mHHbWCiwT5vUPDdbRVSfS4Egi3u1WjCkOWtfipE5v1Q6HUlXUssHsnGtJoJVloqjKy2IwcPpH69XRLuBjFMhONb+5Iku4SUB+cJOb2057lg9nwXklmHk6RTyWSx2Q9rKV+Gu1VAlBSgXKTDJHxwuANRyn9zJHsCiKSSUdZJLSPR4xvrVi/2eF2OuEp7DWoWqO/51zMPk4IZOleRfLECrcYJN9Oc6ksim4WtE3EaLcEr4izjuyVFeVFhjFe6vovTUS9ej889UOOD1yjPTw8PAV+AviJxWLxKWTi+9cGytd/C/xXh4eHm2/y828Dn//H3P4Hv+H/PfBv/gY//83u+9Mf9O/4jYa5nxLtLXEbi1kbOuvJr24on4zJb2yEI3ucE32sgN7QbXnCJpFYmWGV0ypg9ivadSwX/isbmnVC7xXtKx1UEU0VYU8jui1HX0WYzGHfXF02WWw60LtyR3GcS1bR0M1uW0O+I+WKdKeiqSQ5tj9OCE9issUa/YtTmk+VwjzIRJXgy4j4M+esTkc0PgUdKJ+MCVkvSQOtpqmlGx8P9H+VDzBqQL9I0DdKmqOckPYkByXd0xF+IuCXNggrYb3J8E7RBjFfuI+V7P5vGUffFVB3C6J3R/R3K2mgLWPMRUS4WXHw8z3P7shkFL2xEWiJEcZACIoQBXpn0EOZxViPjwPxfkl7nDO5vqIYpfiThFqL5Cx7YyWC9XVE1SvstBHN8sZit6Q52NaCq1RK5FB+bVn/EyWsYtmZBQHCty9ymTCsx7eKkA4lpKOh+RQH0anWhp4BbagDyf87IV4Fyh9cUT0b4+ct6XZNfZqhekW01YCX+PPsfkx1V5pk3YuM9FqBK1NCIkGUFQmhipgerFk9mwi8Zd5KA6mNJFdsSGQuBvsvW06IZr2kA6xORqSzhqay9Fdb4lyasuQ9rjXMXj3n/PEMnCY7KKiWKdGpJX/9gqaNWD2bDKegcNkIrJ6NUdOO0BlCbRjtF9RVjDkedn46CBmu0xQXGdGRxV+vMdsN5u0x/aKQMocXKzBTwCmSBynmM0vKTSKEtbfHsO3Jr69pop7i3gxlAvFBKbvrjSW/tpHFysiOVecSBBqudZcc2XzUyLU+7umzHl9boqcJ/rW1gHd6Tff2GLXlcZGXMpdX6KMEn3vi8bcQ/P1yLBaLf2qxWPz3wM8CL5Cy/A8Dn+V3QaxN7zTT/Q3qao0rLOWTMWre0jZyZNaVwj/J6WvD+MYK+8JKTSoTG6lrInh3RDxpMRtDc5K9D8eOh0nNBPzNmvy+JZ9XciTqJJVVP0zxw+qZjVomVzZo7YmsIPe6TUz1dIx6nhD9/ES0t414ydudHve1CcWrHW5jsVu1WBJXMdkjy3qZQaeZ7G2Ixy0h9ZhzS/52QrRT4b1ie1pSPhnDvMWXkVhSAa5XUhsci43VOUOYd9i8I7xayCqvIDxNRTZ1FuOv1/jzhOaPXqC8JCbEH1/iViI9ivdLsTz2ikc/GEizliTrqNcJ9mig4psgiwxgjH+f6N8r+rlMZsEENuuUfmlJT7QEH456nBPqfrCBKHNwb0R5PBLgs/HM/s4ImzhCKSB1O20ZHwihyk5b/D15H7smkvyw7QZXR9gbhUj4hnh24LLOPtorBT7URrLA/v4l9R9eUh6PJGooQH2aYaYCae9bg79do5NeXFBJjz+VLrvWARWLO2m8V6BOY1Tm6JwRIPrWAH7ZxNhE/iY17LZM5vC7Lcp4pjsF6VikUShk4nFayHIv8YrWw3HC+dEEFRTxuEV9cSrv9bSnc0Ym5IHVynrQ3J4kAgH3CmwgfxDhnCYEUB+TPZfOHeZM2LZR6lB3Crm2vKLZ7fFPMyGubSyTa2uZ1EaO+o6Ya5QOjH9ZkIjZ1Q3tV2a40wx9pWZ+50JCP+uIretLyrOc0aySfoLTTCcVoRP3WT5A2pvaYiIv8sNeERqDO2hpKksyaejWMd3d+hK2H45SjPHC6c16uuajGRY+0ES7WCx+fLFYPEacWW8Dnzw8PPwDh4eHP3V4ePgPkJrrZz/SM/g2Gf2dmvFbUmNzFzGq1pgdifSObE9XR/SzHn2two5ayq/P6G/Wlx9WfSKNFH+3Iv7iWKhMJsDbY+GlOn1pr5xNC8qPtZSrlP5rY9rzlOAU7lpzWVurq5j1yQgfFN1mgBQrBEp+taH9zo1g9OalTHRJT/LGUib1tMedZpi9WoA0ex7zLCF9GlGWCb0zZFsV+kZJOwt0FynhIubFg21QkE8aOQIOTiyC6G+19aS/KkcqZYR12z/PpZEwboS0ZT1qu5XXJXdcHE3wuTz3Yp2S7xWESGq2qhNHGzqweTGmOcqlafTahr4yTHYL4nspZtIJ2PulFhLQSU/33kTqeUD2JCJ8ai1mhlrLcf3NDSpzuMYIzT/29JuItracfFcvC4n1l0624umEaEfq0sqDvy8g8H7s6TdW3GC2Z7Qvk216U7KvzLMEvbQUz0fEucTYpI9iyidj6iommraMf0VOLNFUOLRumKz7xuAbI1FBAzSnf55RlfK1mcpxOb5ZSMmjsqQHBb7T5KPmEqs5G1cSewRE78rkleQdRZnIIuU0dtTK7eMGtd2gh4Xu5eMm0wa7W9FuYrpPb/CDrjsEaUylzyLy/YLR9bX8HTpgVwpWkbizxnLy8Bv7Pjt4Y0XOlkrTzjXyO3e2NuKYmzl4nhBNW5rGwmqoIesgDOLThPpzBf20F1DMYkPIHfGXc86fzIgTx3i3YP3VbVTcszkeXUoJl/fnwlBIeqpFc/naNkWMHYl9XMW9NC0Li/3ChHSrln5FbbCJI725xsaOdKsWXKr+UPL896/XD/h9KfDPHR4efuLw8PA/Ozw8fPKNdx4eHnbA5/7xP/o7YyRJR/EdFdNRLUT5HYFh213pQL/Uivqg6J/mBA2+ii4vKALywXGa7nNrRvsFZuzoU9DXKkEODjEv5+djbNaRPIrpb9VgvdQkO0kkAPG+xy+sxHJMGoGQB8Tqa50cl7wiGoTyUdJTrFLmV1fS0W0UbhXTVuJS6681uDdK+jIiso5qmaK/luNvV0wP1qh5S7pbXTZUst1S9LcgOtN1hD9J6D+7ZpyLuqA/TwhGuv/tixydORH3G080OIOiUYdKeowRvWe1Fup9XcVwqxIwS69kAtYBVUQy4TrNZpnRXO1QTwTCHj8fJpK4x6+sxGAPEPbwmTX8ygSfSmyOqy3+SU7yXnrZpR/NK8x6aNRZj2ul82/zjnqToKYtrolwrUG9WqDvFLCOJCBy0sLzhOZwJsdmHajLYXG9VssHclvSZ1Xa075WSaxOYyS/6vuWEhywtmQjgVSrRLS0NhfsYPpWTsh6zEFF/LWMsBQNMIjpQk8lAaM6yaHTVO/O6FcxbRFz/GgLNdSMm2sdodESf+40m1MpfbjjTIhdkUc/S/FOYR8lcCITeddGtKeCZIy+PEbnjmQmf5MuDM1Oj/rCTL420oCrbvSERJIR3C35XlS45ECozMF5jHdKMt82FjtuObm3TbRToZMee6ugP0twT3PCQLqzT6R8o3dll2nHLebvzemdhJ5WbwhtqypjinWKT4YasQ6kaSfqlt0Gkzr8WczsF4Yyz+BCDA+F4xHKiHTaEE1byk9XjLPhmk97uiairmLK84y+V2xfv7hMA/6w44NOtH8RePcbb1gsFluLxeLay6+HGuzv2BFQhPOY4ydzOI8FH6i4jJcxieyAwlGKz6Sbnm1VtIU4V67/rCfbFmKW6yK09qhHKW6nE2smEM1byZSPPD4omquOJJXjuDGSwPqyodE/koSBcBHL7nSrQTUGZTxtZRlNJMWz+kc7jPcKIisT9aYQq29IAnZeX3byQ2PonRah/XtjaV4sCvplzOrZRFCDreAb6yqmOslJfkWQcNoEoo2GeUdTxBRVQhgL0i57ZtB3CvT2EOtivLwGTkvW16MM1pbeCTPCZh04jVtb9L3sUn0RgiJaC44vmMDB39eSPTWg/EIU6F+VRlr3PGd8dSP11dwRWnHQ9R/fEG/VUtdtNP2Wg9+zFgKGkkZIPxWhP06JGWTi6NaxHDGVNEWTvCO8N5LJQstC41pDMGCXivosZXJoGX9RJpOwjGkaS3eRYgdnnBqaN/rCYs4txUs1C9C/NcMYL0qWoHBHGWbWUtzsSaYN2njqKw67VxFajXqRENkeGzt0LDKz8V5BP3eXhgFdiWRK3gQuY8t1JEdgOkV8pcTkjqqM4UYl8q5RwM+cpBEjYOz4YUz/yY00x/7RhLa0pDfXIrv6/JLyNBewkgqXdLXyIhPXow7E80auF4QtECYOfRzj5x1q1jKfVKh5K7wBE8jThmA9YacVNURlaPecwOJNL7E7TYT//nNQgfFOSSgjlPayW99E+ImTGKNVRHGeQWPI8kY+vxPH6rvl2skOE9nEvLpi8lWLnkiwZJJ2qKOE09MxSnspB57EhIsYVQhMpmpiaZJ+hPFBJ9qfAW78uttuICGNvytG/XQk9KdpI2mnXkIOzeEInTmyrMWtYtSexA9PbqxkYrI9ujQ8/qFO8HatwdxL6boIf72Weldp0GNxPk2+EhO9m8lEXmmqixTvRIHQN+aSFqRvlIy2KsLYkR1s5Bg/rPZx1rE5GUnO12uif3SdId+qpJk3bcUxNOziUAFVy8RVnWW4/Q4Te9qzlOnVtSSNjjv6WlgBcdKRP4xodt7vsJrXNkLYehrTHuXMd6UGV+97eGckaQJRj/cCSG+WKf0mQt0qBV1nAnqIc062K3RpZCHxCt8bqmUqmlgTGO0XFP/iEjVvGV/dSAmgU0QDlpBZR/FIrMXBaYg8/VlCHEuJJ0k7oomAs7vOkGzVqKQninq2DlaEVqLfR3eXJNOG+ZU10WkkbGCnaUqLfX2FmrXkVzf4whLFPcpBfcWjS8PmlZ71Z2T3s3X7HN9JNHpTy8KrgHTUSr7c1KEacYeZSUf7MdF1dkcyUc/uXOBbSSpuTjJp/phAe54y2d/Qzx1taXGPRvSrWFIgqhgV97idjvFuQXprLc4xwGYdZtYN+MnhJBQJW2AyqqX0owKzSYW9XmBfWAiKNJMdfXO9o13HhCRQ3hC+a1PHqI1MYir2qMhL3tqww1OlTGwmGk4vr8j10ZxkwnbOhUDmq4jT8zHhLCbdkudy9nQmybRlBF4x3i/AK9GalwJk13F/uZh1X5pLFLiRBAU1clJn7hWmVkx2CtKdiqqKCacJWztr9BALVX9cNkflSc560bGztaF/nFPXFn2tknLKwxGR7fFTJxzdeStOvUY2DB9lfNCfWvx6S+vw9Rsf6VG/DUcYvNnhwUiiLtIefxpLwX5tKdYp04O1yE8aw+rpRFbMXuOnQppPXlsROk27K4GAfmMxXxqTnBjUi4TzF1Oaz29ob7WkT6zEQ7eaKOnRKhCdCHADRPxd3Z8QvYipH00kHXaoXekvSW3SVRHhIqZ7PBLdqTMk8xqtAtl2RbMSkHc6bol2a+xuhc4cJnUiPcp6Vs8nAiwPSHPLQxT1+M+u8buyevsXKeFXJ+zdPEe/vkHNWi5eTBjtlIzuLOleEvkrK8dTQMU9u9eXeC+hkv55Sr/lSP6fCW0ZS86UV+Sz6tICS+wxI0dxNBJ78XlMscykhmykiw8SUWN2G6lXB4jzjni/pHlnCkFRnGe4VUxyIMBy/+6YUERsnkwpa/mA15uEskwIXxtTt1aIWZHQmthYyosMX1iKiww7r+nOE/pZT3pzjZ8LeOgl6+D8XAL87GEmabZWFpzqXAj/qpW48/okI44dad5i7qVS9giKqrGMZhXBQPo8gpUl2arRo45inRJlssD2cyfX4FmCqyN0JHruzclISGkvJwEV8CeJ7KxVACcRLzZ1rIsU9V6OD4rTB1v0ztDfrGGrpXtnSugV2ayWUlbWE+1WRBstLId5OzBaPb6IaE4kKNEkPUwc66Mxd/9CR/1iRFNIWcXMW2zsMPMWtYlIZjXqWcrW3XMxSDiREuoT2SAkgx4ZM1DTXljJ/QLa0kqo6cfKyxDTOO8Elh47VN5jFys2y0wA97bH7NWcHU3lVINowrNpLQvtXsnx4zn9zNEXVhrRKsjnOAivwVcR2oraYDKuLtN0P+z4oBPt0WKxeO0bbxi+Pv1Ij/rtOJT4wM3djXA3TyzRXg0TASzjFetHUzhKUK00cdZPJ9JhrQ1hGdO+PJLmjq39NclORfNGRXO3IbpZoErD9rQglIb6dovqFeOrG1wZER7mcKOiH2LN28oK/OJAtI9m3Als43kqk/qkgUYTYo+fO/HSH2ciT6ks1VK+rx5kW11ppel2HjMZ1YQAo1kFkRc03cbSrWKM7VmfCCDkZeqszzzt7YaLVU59kWITR3wcUSxTIS/FPcl7KaNZzejGGp5kaOs5vbclqombBfFNIeOXny+JEkd8UDLeLYQaVVjmuxuiE0v6pQw7a6jLGL3TyJE+6TGzju73rQDhpyrtJe22V+z/tDQ4olcFUBNljtF+QVPEotF8tbikiCkFTWXRJ1LOsW++PJl4VBkJGGgkDODdGxeo0sgRdyRM27qMBRN4dSOLLpCPG0bzivqqQ0VCKvONIX1iUTqQXi3wt6Rm2zweSzf99c0l4Nrans2LMeGgxuVBgh9jqbemv5qRpB3J11OyezHF1yXR2OatpFe0GozED0WDNJCgUDsNxgSRyQ316HYTC+x7V+Lg1VS00yYSXau/VaM2EdVJTpaLm83Vlv668IlR0oT0J4lAZjqN3hXZnI57VOx5/B9rQtqjhsZc8Ir6WCY9BiyovbNhuRoRvJziVDrseIOkH2vrsScRUdrhrUiutPFExzHmeULfaVTcU2/iS91yV8SEeoDClKLl9m+LiUUVhv5ASluTeYn64pT66YjiLCPZqolOLemwQenLSPLkOk36TkoyE6eZe5pzcTKmKT6UV+tyfNCJ9q8Df3OxWPyhxWLx8cVi8YcRV9Zf+0iP+m04khdW6EOAnbaoazUoOaazsiKPmTjCTovZbcR90shuFCdWwigSk4JNHOePZ7hHI+y9VHYAvSa+WrCuUnZvXRCdCGWqrmLyrYr+SotrzGU8DUHhO00yatn+eYtvDf2zDPYa2jcqxv/nmHinRmeOeNTStRHj90SUjlNCOEokFlwpmOwUUgudOJYPZ3SrRGRlwHRxxvzaCpWI5VWnPbNZKfVnQDlFcFLHwgSaTUJ4VSyRm9McdxHTvlZRnOZUZYLfb6RGvSUXd3hnLEeu+7nIuFKpi9Vfm8mHPPYUVUx/0FC+2Ug912lZdIKQn/q1pVqll6+NOxXwtZ02PPl+6Sw3lRXiWRVR35ugIo9fW9plwnhfFrqX7/FL4LX3ShiqnYZJJ5EsnYagOD2ZoOYt0biT3aKSBN541FJtEsojaagUz0eUz8boRgs5K+2xeYf9jMTMVKuUJOnQ447s1pqujUQWOG0vJ2s7a6R2fKuSmmeRDtCTguI8o77d0n28pJ85tg5WJInQt1TimWwXNBcpbjlYiLNWJrjzFH0/kwbOSYKqDSbqsVNRRoQzOfH0T3NUq+g3EfnNNXbWUDwfoZdSh+VUaqmzWSmkMRNYnYwwtSLPpXyiVZAwzFpQi2ZnQEi2hmjeUm9i9FZLVQkQpt9E6McDJMgpxu9F8FRq3N5pwVQ+yUUauBQKWZ95vBX4PBtL8jDBviOuyZfGnSjpJRzyKKfdd3LdTd2lmsf1mvrjFWHUYzJJDnaznvo8xXVipgjPUnQUqK+K0gEjdWx0IHy0De0Hnmj/EvCTwI8jrIEfG77+Sx/tYb/9RjvzjL4kmrluJRq77iKVY2fsSbKOKHHYh4kcpTuNTzy+V6ht+ZA0tUW3SmpUk46tryh0owhHCV1h0TpQrlJaZ3BbjvaaNMe6NkIfx+D0pYPFxD1KB6x11H9gJRSrRD6VadZS/oBIi3xhJWOpNhSfrai/PEdnYhfe216TjlvKRxOKdYo7TVErsQmr2F86X0JQrFaZdMCnMhHaqCedDB3Y2Is7DWQhCFImiFLxhNttOUJHZ6JoiO+njLJWJG1BEV4tca3Bvr5ic5bT91qA6XEgiiTe2nuNfZzAKpJ67tISHVv0xojVdvq+UDxUhtF9Q3xQyoQ7cvSNwKXF8RNI74osJ5q12GlD8WQi+MXaCE/hRkn0MKVaJ6RXC/JpTRQ7dO4Y7ZREV0qSewm+NbhVLFrcTqNafSlgz/bEZppfKQixJ725Rg2pyN15IieeoUZanuYoHSiOc5QCY3u0CYzHNeXLXVKj6TtNMm1QSha0bhVL3lxlBC046lgu88v3JbSa6p050bi7DEYsD+dSAtEBuxHims88ZqshSzv804xoaTB7Nf3THJ95omulnOi0F01up8mfaDFf7Nf4TrN+Z4v4yzlBB+g0btazPhrLzrLX+Npchi6OR3JN2LwVuaQJJF/OMPcyyqOR8B80ArafiVwxur1BKYk76lcx+lolgY+zFt8YMV/sDdek9TQ3W9qPVfQby2S3kFOJCpidBrPVoEvRp1NIcCaAUjL5j7YkybpeJ2xdW0oPA2E02FsFfauJVpq2jSAKmNSR3hcr+0cZH+inDg8PPTK5/thHepTfCSPAZtERNVaixjPRhHbbQK+on4zRrSJ+c033fIh46WQi2d1Zc/xihlYwee2Ci+cT7LTl5Ps6kXUp+aBUXqKRAzDZ27B5NJXolGVCUoGPJTAPwJURuwcrzlc53mm0CYRI9JDOaVwXkb2V0X68IZ/WdG0k4u5Pn3J+OiFKO87e2oNXCinmFxY17NjixNGepjSxRcU960L88u1pSrZf4jrD8dlcAgABXRrSnZLNyYhsqxpSYYH7I9RBC/dH6FsV0StynDYNrNYZykjWUjSVJN/mOMbeKLE/N8V/75p2rinXCaGIwAb6g8GPvorJbmyksXaWob40QX1ic1nK0OOO8qrGr5L3ITXnMSH10lTxog3VOtC2htBYzFaLNj1Ki6OrVQnsOUzsqc4yzMgRvBhFiucjoo2hvtUSpQ6nzNCJdmTjhvJ4RHRdUniBS5lX97Upft5fkpBV3sM6YnzfMHnY8+z3J4yui9uwj6Wz3f3iFtEnN1J/dIrQS0Ns9DBCfWZDt07Iv5pQ3HWifPBKjvcaKRlkjq3rF6x/YY/6+vD6bTkir7GjDvfpFs7EDRieZGwOFNGNknaVoL0aXG1SWlAmYL44Qf3eNWy11Hsed54Snxm41uKvNJRzI5OSV4LUHKSF3guwez6pWL47YpMNVnKvSXYrmvOU8tUWVRspqS1TohsV2Vdy6uE6U0Z0uDj5zPigCFbYE3okulq/kiSKkDtJ8SgtWwcrLu7PYd7Rr4XhkeUtxdgSvYhxe92l4qMqYkZbFdXDCeZKxfbeiot3t4mulqRpJzlmdcToMEF9/gJrejYqJUk7iu0UNUgeP+z4wNPzYrFYAJ9GssQux+Hh4V//SI/8bTZC4rGPJRZG7zZwFjO+uaaqYvo6Qs86/GAJVbNWVspZhzqLWd3bgxstfRFRWenguiMxDPRBXmI1F7E4CrouEr/6QUl9mpFsVzQ2Ybxdov7BXL4/9lyscnGlVBG9g/nBmj4omq/MCbdrvIWDgwteHM/ENFFqzsYJ2XZF+2BMv+ewQTGa1lRRgjY9ndO0Q6BhV0ciyO808bymy8RRZWOHi6UpA7DzsVMu1hnKeuoiFqiOUxKjvrSEWxWuinAbS7ZdUb1RM5+WnL+YojJHf5wSXSnpBwlR871rAZE3Gjtr6AYCE2oIuAwRSgVcZ1G9IvncGW2Ritzsh8THPr67oixE9tReyATW1Fbg6a1mcyxqkShxhFjRnyf4SYc2AXO1JDQRodOMRjWr9QTfK7KRJAejwM3dpcD/ZbDfy0kWFYgTUZGAyLuIPdwuyWMn35N41LlFX6lZJ5byIAI8xfMR5KIdDk4L7/i5ZMep3UbIU1mP+vwGt04xo47yTY85sfR6YDe8MOjrLf7tGd2bjqKOae40lxpPvTG4AOkzSzcKwjY4qEj2C4qHU9qshyhgY0cTS1xSqGK89RR3HCkwfivF5eBud/R3atRpLCcIE+h7iz3TqINOpHZ7G5YPZxB7LtYZo0+eX5og4tjRvTth9PqK4jhncmNF+fYcdhx9p3GvNShEz6omHSbqyQ/WrJ5PMMYzubKmbi1J2l1CbvpO1Dm9kobk+u1t4hraOZDISbB+b0J8s6QF1MYIrAa5dtrW4POecJpy3hnCuMdv4svfS1AUr3TsWsfJiyk0ms7E2P0KwrcQKrNYLP4c8MvAn0Esty///fGP9KjfjiPxuDyIDvM0Qe/Kh64fEhK0Fj1ifZZibD9IixR+1tEnAb2OsCcR7bC7mdxe0hcWnfSoypBknciKcjkCp5nQ8fW4o302QhWGYpmx/oTcn2Qdbm1pT1PhIWQdZR1T15b2aocxnvLVlpPziezean1ZRzJfmNJvObL7lq6wInPxcoGqTSSOLJAj3lYpR7fGYk4tXWWJI2EkvDQvnJ6NGf3cmNBqQicRzvZUZDXJtYLppGR7f4UZd1QnudDvlzkHN85EFzl2BK8J261ogo2/TGw1JmAKTTwXeE6WtULy7zWRdSTHhtXX5/RVxOj3CezNxJ6uM9j35KLfurqiOM0JT8KktvkAACAASURBVDKBS6ee+FjkWmnW4o9S9LwV55QKEhkD4DTrVQaJhCKWxyOBW4+cxME/FqdaZHuytKM8yzFTScuono9ZrmThiPelC94/y4SsZiXCZnx3SZq1xLMGv98Qxg671aCsv0wgyG6vUTsNfmMlBcCEy2y2KHZ4p4nSjuzVFdl2JQjLT26oa0v9sZq+0zR1zGReXmIX87srRnsl9e0GrkvumX+a4b8wJ3uhpWFbSMc/HaRvupTbx+9G1JuY5vMbmm15jt4pov1K3rdOC5LyZk1bxMzvnnNxNpLoJiU72ItnU1aH2wC098dwq6J/awY2EEc95pWNNHa9MDxG01psxQqaIma9lIaoMZ6LkzFaezYXGem4gYGypwdoTnmaM3njjORTF5K9phAJ3MhLiu+oQ223jAYjgk0dcSxORbxoxIm8ROqcJQK7d5I4fHI8EdlYLOkro7zBr7+FpQOE2PVdHyaD63faUDqQXh9qRFcGNqhXkrVViXQmOrFEr2wkrC4OjLYrnNO0I0lP9dNA9Dymv9awXskOMMk66rOEupCmV9tEeC+TSHZ1Q3mSEx8IFck9GOOvDDWoAMlzS3Ozpb+IJWhu4LJOdwpW57kwCwLYKNAklihzhKcpykF6P8Z9csP2qGa9yXBBX/6NzmnytGVZR1TvziQMzwaiazUUltU6wztNMpc6m68iVt9dYZQkhvreMP3EGZsyob5IadpcOvqlwew2EBSjSc3zh9uXIJgAsJT6cHmWk21VdDaieZ6jrza4Zzlqv8Y5IxI2HaifjNn9zmMuVjmhMxg9SPCOE9R1R/dahfaK88czzLylB3xtRTs8r2hfjFg/n2AcbM83nD3bwfsExo74QUL/WoXSHtdGhBaS7Qr3cCS1cKfIXxNkpQJWJyPhngL+KIXtITkYiWPHerwJqMc5KvN0WUR1mqESAZNEcU9XWroLAfBEmTRqlBI76mkYi0c/gO8M60dT2UGtLWz3lIXIBXWtaWcd2aSBr04x33HB5mRE+WR+KcfbXGQkoxYaQ4g8ppR6apl7iQYf1AneK0Eo7jv6rQ5VRHTftYbzlE5Z1F4jOzGFpIQsI8K4p1tlhMn7i7/SUpcNLfTLGF1r9M0CQEwVQH1NmrarIqU7ydBbjRhakl7SaE2gLyLMuJNm2NMMd10WsLqKSR4mVDcU8/01y4cz8usbOX0MfIeiTNi6vmS1zrFDc9hEAsvxheXiQk6K3SaW0+ZugzpNiZ9lJN9zSu819UGgbSJU7NnZ2XB6f4v0oKDa5PQqoraW6fRba1ioEMbB795RRqJFfGs2RNSIzrMZ0jzTaYPbb6kvUnTSMz1Y0x5O6Z6OxD65JSoFf6OWpszSgg7yO6cdoZF4camzwc60oDzNRSiedCLwn7nL/CitA81VqYGhABsk4yvA6vlEar8Ilq+tpb7nqojoVsH6Ey3ujZJunbBcjcQzXhuCkxC+bpWw3ogEK3ttyeTWCpX19K0R4X6niY5iCSQEzHJwNNVGPhxA/3d30DpgVoaQO0zu0Hs1favpNxFlkUIU8HWEXkckSYfZFX89yE7He0W0V2Fjh9qTHUxTWsZjqTkzE5arO0uJ046zldTGfdZjrdDP9NdyiMQgICGTUJzkVA8mqF6Bh/jOhuPnM+I7G/S2AFbaWy39eUJXxkPjCNrjnH7bEVLP+IGmWKesn0+ITC9pv/MG/yLFXhPuQGIHTGLWY0cdujD0Ox3RWuMa0YcGp8hyoYWZVI7sUSb61jjtKJYZRR0LAzh3ktvVK/R2S1dH2HORNvkhQn10Z3mpv9WfFbpVPG3oc4+OpdYZ5x3tUS6yxOOEcKMWpGbaY5YGThJh2DqNWayJpw3ZvEbtNLTHOcRemA2rWDChCsbbpTi3ao3uxMzTnSeXOli8YBpNqTEHFd4PU0urcXXE5GCN6wzdcUZ6pUAhxhjfacK8k+BNr+hXMcnXU1EYPMlJnll8EWE/dYHNOy5eTLD7lbAgTmNM0nPxcE6SdOKKVIHu/hjfabFYm4A9M1LuA+gU6fWNuP12HOVrLRdnY6ra4u+NCBcxrCLOzkeEKAjBbCPSNvfeWBp+H2F80In2PwT+8mKxuDqAty//faRH/TYcQQf8RUz/ezYwlVRSOs386gqbOtGPpqKl9AMQWHeIU2ze0jstwX06DAGN4RIawuDP1oVMeH5jefp0G0xg/G7EZp2Kc2ncXR6pm8oSzxo51o07dvdXRLcKVG2It+rLnaJ+nIoEaq9i/HaM6yJU5EVrmPRMJyXqJL6MZe4rQzxrsLHDt4bi0YSiSElGLeN5iU8DyagVGPMgzg4miAIi7wjHCX1tWH2qpVknxLc3xI9jmWBXMdmkId2u8ecxu1dWmNzhZ50gCxm4CSYwff0cY2Q36TqD7xXuSU6UONYXOQEIpZFUUxNo1gJHAZFC1YOMqL3dMNotyfMGlTnixzHjvQJ2G8FA5j3VSU781NK24vySTCtDfnVD8thKIkWQa0BF4jAqrgdM5FG5o/m5XWEKlBZ1pSYcjonWhuXFYFGOBLDDbkM8avE2iIg/FoaFcwbXyA4rnjb0raErJDDTHMWkcSeyJBXoneb27WNs7AhVRHitoHeGeNpgt2rWTyfE4xbvFcWpPP70/xgxur4WzCCy+E7eNcKMrcXx1T2UDUF0qxBIeFCi+T3OaVcJ1TIVdsegJ/ZeEa00+f/P3ZvG2pat51nPmH232r12d5o659Q5VbvKt/XlWrFjUIiRwRApQYhgKyGWQBgRfvAjEBrHjhBgQ5AIAgFKRJASEmFFgGgUEOZHJLCS2HHia9/re127qk5Xp93t6mY/x5iDH988+9a17JtTZRvfeEhb5+y99mr2WnOOOcb3ve/zTitBX7YerH2ia4Xwah0pV9khSQKEdKXMt7IA4r0SVblsn4/kYqEQlc39mLbxBTyfNeBYRgdbop0K9wtr5jdXmLin2ZcLXPF0hONKWkhbSs22D3usUSTXcsp1LFD6yqPfb3BfBkRJK5yJW7U8N2KgsF+T5BN/1BBPagGZl4FobTONjWXrEs4rsnmJ3m1xlx59YK/UC590vO5E+ZeBHwOeAt3wpYd/f08MFQ4RyMuIMO7EGz6rWZ1lMnkaJaYC10Lj4Cgr2/rSQ7lW5E6uwLBn+xtZQfQKZyX8ADxLH/aM9nOp+Sw9VOXS/CMF6jREXQToyiPOpHTg+oa2CFDnAX3lsc4jdOdBpjFGGlbtZYRJJThO1z72+9Yk6auoEA9beixfjGGvwU9b2m0ggYSNR/M8ZTIrCA8FNNNUPmUZ4h6UNOcxwayW6BnAv1HQL0O6bQALiQFxQ4MqXOoyoL3WQe0SziusVQJAmbV0P7uQuJxeQS2TaTaqme7krB5NZTIxAr9JRzVqv6Eb2BEAeFZcVIO0qx0Qdd0qoivkb3H9nuI0ZXORCvJw15CfpvJ4tYtaDav9kRXZndeTrxLcaStln2mPKlxU4xDM64F5YDGzjt3ZlsmkxLpIs2vQ93Y3W/RMC0cC5GL6RII628sI92aJycXwEUYdbS10fmMc2osILxrMJxaSt1esPpgT/GpCX3ro05jnl2O5sE/k/XA9Q/84pSsDnFFHN4BvommNXQWsjpA6/cDJiJOG/LYci/2tWpx2aU82F37vzq2lTECV8HhxLNNFLrXjzhFplGNx7uZEgTiv2gsxwChl2VsMxpG5XLjdSOA4fWzoPUv/LBY9N1CtI2zQ4+WuwJEmLbrwaW+09BdCxqtzCX4s8oh6FVGcJ2gjSSZOJQYcZ97SXMYC5A4NbevhTlv6rU+5DSWBZBsy3ikk9fZAtM7RRJJ647k0w0ZpjXmnoG/dq1W3oyyjuRhqwlQYv96jiKYIKIsQP9KYkaHPDGbzO4hJBO4MX29+7OvV978nhvJ7gllN/NSj3oSkuyVR2OHGhlFS441b1K0CrMKdtLiDVXZxuBZerG/YnGR4exXrBzNxHyUa56CW3KiRmByMcfCTTmhDsdTtOKxRe9IkuYqp6R3ihwH9VEvjxCrpjI9q0ewWAfFuSXZzA+eh0KSqAK1dnLWHasWh9irm3HSuUOsrD+9piH9QUrc+zYsEP2tlNZUPB5FnRZkwkxqt7jzcWYN/4eE9joZUWggPy6E51jE63GK0MBf6QvS0m3uSCItRpPvCId0+npD/2oxgNRzkXk84ryjWMWbtC76u8ORE9QcUY+kRjhpMK/fxxi37hyu8UMMT4bt65z5EhminIpg2tK2HM21l1eNZ+klHW/oEgUa5gmn0vB6mHd5uxeSNNb1xSGfi4ArHDefrjPUqoft8DlrobWx9bONcAdPlDXIwBw32JMSbCMNWRfLZtg9HKAfinQp7EuHPa/yvZuiBbhX6Gv9GQXVUDxHsLVHUEc8qkVz5PeaRZG2hLNNpQRB1OM8j7LCT0VMjK8ZhW1s+HDO+s8KOOzxfwDv4PXUVML25omp93ESLG8rv8dOW9TrBaoU/JGDYDzK0dinrUEoCk47oUOquRROgPpLX74QCuzFPE5xE048Ms68rYVEA00WOE2tG71ySxi2ch2SLAlV4pDe38CjBfxKgTkLRF29cvKyjqgKSeSmwGGQn5KQSvWRWAf1lSBR1RLsCx1GDpXtzKY6zIJFmW/C3R/jTWtQyQKsHU08rkKM6D7FA23q0G5nwuzygf7NiPCux2mE6LonnFdG0Fg7Dpxivq6N9DDCUCvaPj49ffNInOjo6ehv4K8AOYt390ePj4w9+3e+4CPP2h5D+yX9yfHz8l17jtp8EfgQwyCr7x4+Pj3/2k7w+2zn0xkG/I02fYhWTzUrCqOX8YsRoXKGNgwkMUdRRPsuIzlzygbjeFz6qVSKNmnYCyIgMunPEPusbQff1Dl3pk10X6dgora9qS0oJ/QpA1x5k32RfBqHGPkmowkA859qhOk1QWjF66NBe7zBGUZ0nJLe2lJtI4NFOTz+sBP1IYxyLtygFtTd41PVFBJlGhQISd1ON5+srBKSpBSfY73c4a08mzqwm38Q4W494pyDfRuJsGiyh/f0M51aFaV2cWItjyIHFvQvOz8aoSIAhdh3QuL4AUSIB4RD2lA/H+LVCVSHuO5VYbM/kZNGbgDwKsb0jMiynp5tq1Naj7hXpTFxq0bSGsVhM23KIHlKW+azgcpUKqlDJbqSPOvQqwF1IflV7khAdFrSlh4rEyNAWgZgzhvuEwyrJz1oct6eZKTxXQO39cYa+VdPvWuzWp6pd3P2abhOijySOW52GLJ9HqJtifHAKFxsYticZi+trKXW0Lt6dnLb2mP1CwPJ7R6STimamcd/LUEcFwbkAel6xad3DSoJBIyHORUlLU/v0pxHbJzF6rnFyF3UzJwg01SZid39NXoV0H47Qi46oU1inFyedhXRck5+nqNDIuTCVRAlV+6gbHWbe4boWJ3fZ/tM5zpDOsc1lQu6MS36R4O3V5MsEZ9pSlSFmV9yGWIVaBigXTO0xW2xZLVOc0GCNg10F2ETcjsFIotHLbYjj95J3ZhRu2tFfhvRA24u+d/NuxzRuaYcyWNP4lI3A073nISaytIlMg/ODNeXfX5B+fk1VBFS19FnOXk7kmDpJaKJPpzp4XXnX9Ojo6H8AagZc4tHR0R8+Ojr6jz7Bc/0F4L8+Pj5+G4nE+Yu/we/8ceAe8BbwfcC/f3R0dPs1bvu7wPccHx9/HviXgb9+dHQUf4LXJrKfxmU8qjCtgxsa8heSa2WNEsORI57yqgywqcH77hX1ZURb+tJJ3q3pOhc3kJWVG4g9F6to6gBrBLHnrjzyZYKpPIoyxA80s0kh2+4TqbtFowbntmxn/GcB5TbEvZ3j7tb4kcaJDAxqgc1nJJmgW4eoTsmWqJImRv8kZbG7hcbFnMT0nYv+YCSA7qEEYCPxqqdflzp0fyGErlf2UCcwZHfWeJHGzkWuZK1ib7EhfkOiVfp2cOEoCM48uoNWVg6ORTlyAUnmJWcfzcCxxFGH+yzC26lRvZIt/oCYjMaNbFPvFjRvS7PM8XtMLBchidmRNAUn0ejWQ2mHnTeXOKGhbV1UKxQu9SRinNZgFF3jURUhy/fmQ16aFQyea9k+HRMuKrFNB1rs1lZUHkkkLjflCTNCKaB1iIfAvyDUNBcxQdLh/UpG8zyFtwridJAwKSuaXIbQzctAQhCvlag3ClGhBAZ3X1QGyaJknYtL0TYO9SbEWkW9I821/DTFyzp4J8f2Cp1KGoM9kVKP6/ViBwZJaM5D+trFPSgJ39rgpR39SNP3SjLeRg0XS2lO6rkmHDeYo4JuEwpsfeNT5iGqkvp28eFEzBLjln5ojrqBZHz1mZEd0MNhxesa1IkYO7xEC0R71NBvfOxZSDqvZCUbGtx5Qx8b5rsblhcZ6jLA9Xo5B9YOox2JvamXEaNJJW6zUDgHzmBsUK1iPC+ww/ERnHqsXoyv6vv2NBIYUKrpdjusP8DKH2T0vYN5R6RzdgDuO14PWlG+yKQE9TsZzohMkmskoPGVF/LvAD/8Onc+OjraA74E/Mzwo58BvjSk2X58/DDw3x4fH/dDQu7/CvzRf9Btx8fHP3t8fFwOv/dVpE+/85p/GwDRhwMlvwpwg16Qgb1skcezkrKIRBDfekIjCg1N7QsFaFIz393IatQqTDt0Jp/Eom9dCfg4nVXgWex+A7XDfG+D7lyqVcT2F3ZFBziVt7e+jDDGoVuH9Hcq1EVwlTIqzieulAfRrGY8L0gWJThDs2Hc4TyJmB7D+ms7uKOO7PYaagdzoyaeyKrqVcdYAdUXSwkZHGus30tHGUhHNZvzVKyZS/GdF5uIi6/tSkNmmLRVYIgXJe1MPORd90qq1UsUDgx+ccXmPEUftNiPEqxrSW5tUJeDD/6DjOBGQVv6V1ASd2BNgGSYNauIKGnpaw9WsupabsTeqs9iIU8FBj0xXA4rIz/U2Nxj9s4l2xcjzNYnfeJic4/sxoa29rEK9P0M5fYYo5ju5KyXQ3KCdvBiibp2KpfVpaggqo9GhKce9kFK9bboZXXn0rYubqQJZ7XUWt0e90mEeyiHqutauqXgJLtlKPSoTKPfG0tEjW/EFhtrHK+nujVYnsdS5+yephgt+mTnJCS7I3K0tvHwr4kltb0/ZjrP8RJ9xbewVvSrfa+EU/Espd/6hIHwE3QnkCMVSDzO7PYS/2GEmrfk5ylmpkkeiO7XLR3JINv6uPsVwbjBWmh3ZAWpW49+pyM/yUR9AdTbAdKzaPBdQzqpCKMW5Qh68fJ8JEjFWJJ4rQX3nS3bs4zxYBLRRhQ4beNLGWBIdVAHNZ12rxQY3Vh0sK/y79yDkvIyIXwQMloUxM9dorglfmfF5uGU3rgEgSYd1cz2tqKf9yxuPiSuqG/uMj/JeN2J9p8A/o2hZGABhslu7zXvfxN4NgQsvgpafD78/OPjDeDxx77/6GO/8+1u+/j4UeD+8fHx09d8bQBE33OJfaUXrKU2trhzSfVSpB9JKgQjNcR+7++sSRMJeitWMasP5zSDJXO2sxVd7KLDPSwJ90q5ElcB2bwUIPNQOw3jTppibzYCsV7J9tgfGkBOKm4Z97DCGgF/OK4wFpysw5821OuQ8r0p5SqGrJMmS9qgD1ouvq9DHwriraoCSfYd+ArhvCIIO9ysk5DHykNNWoFLj6WzDJCvYzlRz2PGd1b02sEWHt6bOQxgaWfQM1braOhGS223b+RAtoVgHMf7OfGHIcmswvF79I7Q8H3X0Ic9+jSmm0sHXuWerCQTgz6LrmJEzESkQH3vyHZZgWolsTgIO5x5i8ql2RHMa4JIX3F+g52a1Td2SPYKVONQf7HEnba0rSeg50U7wL4lQ636lZnU+gINnUjo4lkFiwaGmmgfG5qDjm5mBNQ97CawwtBtywAcKTd0ex3O+ylKS9NQxYZ4p2L8gYfZBNjSpZsKW6LchiRRg6lE0+2PWuiht5JB1481XEhoY3R3w2aZXB3P4d8ZESctwd0NqydT2ZHlLt1p/M1FxOMUf+Xi7NVEJ57c30gvIHxLkJ9OYNjmMerdnL52iT/ySe5L1I18FkYMBFknTOVhmx/tyITonAa4gcEdomqsVSi3JxgahasXko5cbiJh4W5DVOmhCvcKm4hV1C9T3LSjMy4qkHpz37joVUAwr/GnNb0RlU+1eqVwUai5vGevwiRHaY0KDN1EwD3O96zITzLyBxOsksZmFjdUVcDybCQw97TFezNHVx6qfd0p81vH6xYc1sACuKrNHh0dvfHx778TxtHR0R8A/kPgBz/pff/GH/zT8Ad/+1/Tpx0f/vBP/G6/hKvx6E/8e7+9D/iHf2t3f/yv/Nu/Pa/jdcYP/YN/5fGP/Ta8nn/ut/4Qr8bDP/bj8Md++x7vtzIe/Kl/83f7JXzL+Mof+qnfled93Yn2LyGYxD8DOEdHR98H/DRSUnid8QS4fnR05B4fH5uhsXVt+PnHx0dIeeIXh+8/vor9drcxvKa/BvyR4+Pj49d8XVfjH/8//kteNkuqyxj8/gqU4fmGdh2Kxzxpqc4Sptc2oqFcBTiLRlaXrsVsfNyJ2HV1LQ2oZFxT175YKX1xqjh+L0T90wA9NowOttRVgD/45B//q3+a23/tPxbzwvMIMx/gNJ5YN5NM7MHOWcDud51xejFGORZ7GmFnQxzIMhAdadCLh38Vk84qlLK0rUcUSnCf2QSoyEDh4c1rYdKOOkZpzeo84/G/9O9w6y//OZTX4z8JUW/lgoNUVqhTVjK82u2wEs8EMhMerfFdw/J8BFoJf6H1MJUoCNra58b+kucXE0zpMdvbsnl/hkl7kj2pWzavpF6KK7fY/R/5M7z5Mz+NqV28WF+lOnRlgMrFt+5GYmZ4lVKbTCpBExY+KpBmlX2SyGpaAVrhpkOs+8pHzYWoP5oXVGWIehwTvrMWCdLDCc6+RHv7acuH/8JPcvc//fOoHuztijDqqMqANKvpvjJDfW4j9uYHEe1CYOv+kwB9u0a5UlaxvYj6aR3iZx7hly/Jj2fo3Ra18a8suuleQbGKcT4Gn+4LX46LykVNWx7+8R/ni3/jJ9j+2hw91/iZGB9edeXV0ie4Mbi2tEO3DXAzsXSr9zKCLyyphr5Bd3+EnhictKOvPUly8DXnzyZitHnpUb/RorYediTNSOv3wmpQ8OhH/13e/M/+PM6NwWlZCzvWWuFDWE/kZGrSko1qqlrq310tn506F9WNGxrhdGxD4tkgIXyS4hxIbFGa1pTloBioPdTWI76eX8kBp+OSi8czHv3Jf4t3f+I/F6j9cYZ5s2KU1rTavVrxNhshdHl7YnfuTmKUVnjXSzzPcDMb83//4J/6pNPLa5cO/hzw15Emlo/waf834L94nTsfHx+fAr+MpOUy/PuVofzw8fE/Aj82mCF2gX8W4d5+29uOjo6+Z3h9//zx8fEvvebf9C3DWokijucVzsqX9IJ1gPkoxR+1uM9D+q+PiRYVq8sUL9RE13OCsMMaxWxS4E8biaMxQlmKsobmgzEAcdrIZOH3VyQhe70mXpS4yjLKKkkBHYYfauKkwYw1bmTw1tL5twOmLYw70rtrtlWEH2iCQJM9Ft94EEq45HQ3JxsPWUkPA6rHI5rGozuN2T4bM0ply4WykHVSKjAKxxFWwccz7K124F4hQvl1RD5g//wHseRHuYLw042HuV7TfW1CWQfSDPQsxjhEUUeQtQSBJow7njyfYzZipnAdi3Ug3KkolzFNKZHjtnOkIWOlsQNiA8YodOFjHmV06xD3UuRCftKSJA3hM18wj62wALpNIO6nYQtvr9fSvGscVCCday/QMNZwGjLdySkeTQCJ8SmejWgaH69U9CcRKvcEZg3oRQdvFvIZ1oLDrMoQ+5mt/N1fjenvVvizGts4dLcGrbTbC8jkJCJIW9zCoXqzoShD7PUa98LH2y8Zjyrm11cCahm2rsoBa0XrjXbYubNEDWdz3fpws8JLO7pliHcqWVfZtKRPDW3jEQUdUdzij1ocZUmiFn2voixD4fnm4igLZpL0rFphJ5eNj5u7klv25XP8uEPtNHjnPuG1AmckHIIrsPrt/OpzIxQdqvMsEqeWA/5uhfssoqoCujyQifIikON8T+D2jtPTGxdvKcd+3yvYawijDpNLQ1kXPl3pk05qnEVD9SJDrwLsi4jzsxEMpbryToe+n2HvFcwnBa12aR6MaWvJzEMBkw7zIpbFgwNqv6G9iCg3kYShforxuvIui0yqrzWx/ibjXwP+ytHR0Z8FlkgtlaOjo/8T+LPHx8d/D/irwO8DXsm+/oPj4+OHw/+/3W3/DRADf1EgYwD8iV8fv/Pthvdein97Rdt6eNdK6Raf+ZgbNV0ewH6Lbh1YRUTTmlFSs/zVBXqnwwkNlx/OUXvS3TaNixsa0qilvLehykNqE8iqt/RwY0MYtZQXCS1QXcTS9HB7bDagAB1LdX/MzrHi8h8z2KlmerAlLyKK01RgNLVHlLS0lS9ZR79/i+1cdOdhWofNyRT3oBKi/7sVjrK4rkUrCHZL8iKSgxYhU1UvM5xJS987OGcB4yOpwy32NlStj9YOnteLqUA7jNKazTi6CiNsLyLG17aUZUh3p8YHZgcbOuMOYBuFvogIb3TCVo07ol8NKb7Ycf5sgnKFF+qd+6hbpbjtZpKJ1m1C+lBOWD/uUM9T2v0OPdGySipcIl90yq6yNLuGsa9ph/fTW3q02XAhax1GOzlrk2AdiYHvenWVUNCPNVXj0481/danTxThfikgm7dzVOdiW5d2uIhGk0ZALL4Rg0IjyMog0FTHU4q7HY5R2FYkbsqx8nutB2ch7rWSNG5YRWL9Vo6lvwzwG0UQ6iuJVPCNmBio7vYis5uLXE1FhstVeoVn7FpPUJOLEvc8prnZYjuP9iRBjTvUi4j1KsCdN4xHJfXfXtB+WUtSrctVTE/XuXSNXNxH1ze0X53CZzpMKk3fdS74wHwV048MkWcgZKEW2AAAIABJREFUkmZXdldMDfVlBEYRLiralwk27AnuyW1uJjrt8maNA6hGbOLOQU0Wt+QvM6bXNsJv1i7cKqhfpkzfWLFqUqqPRjg9BE987Be21NuQ4jRlfLAlN7Ib7VaRrIiHCVKVwoJWtc957eOFmtHbSzYPpjj7NYfXL3l+vId/oxDNNVAUESrTxGmD6cevO6V86/zyOr90dHT0A7/ZbcfHx3/zdR5jSMn9fb/Bz/+Zj/3fAH/yN7n/t7vte17nNXzb8bktzeNBkjJpBMmWiMDfiSSzya596MHd6Tl7MiN9a4PZRPSlh5q1BL4hnHVULzKiacX50ynZfk6Utldbk1crxnId4w1NKFtJ02P50UxSRRF95+ydS86zKbQOTqJZnWdk85K8Fh6BG/SM4obyMsG6WrSmoSZNa/KTCW4tJ2oStqw2Cf3TBHOzYHHnkuXXFyRvrwh9TdkEEmK4V9KcJPSphrRnU0hTIa9CAZAYmWidtYd7rRSFxmGJfZyy+4UTni8j2s5DD3bHTsEyl9Whoyz6MsKGgwnBOOjGozyUMkq2VxAHHVXrU+92pKHG/fkJm8+5zPc3VF/LGLInRFVw0LF/uOL0gwWz62vav5uQ3wypL2Iqv2d8uCXwjJxspzHBm7moCoYGVn48g4MGN9a4Wc/if4pZ/UiFlzRsno8kILFHVBlnIc2OwjkL6A7FaeRkHfE7KwCRkSmoq+Aqt8uPO/JNTPLWBvXeGHu7I0iE4dD3ApRWT2JxDBqX1TIV+6uCdhkR7ZXUQUR7kuGknWhhvwvGoxJdRLijhvoiprUijYtubum/Kivw/iQiulFQbUPcu0LG6i8DosNCFAIjKQfoysNmCr68pjxJGV/bst3E7B6uubjMhIdQCKWtbT30vYrmIpEIcSCOOklWcC0kmnwVQ+fgFC55KzRVfyJRN+V5gjMT5UZ5loqJgiGWyLWYdSiJEa3wDvJB0bBeyu7RDzRVEWLDnqIKidOGzhfdd+36BBaU2zM5zFmvEmyvSLKGbiDCNUMpz8ZG4O2uWOW7xmG1DIlv5pjjEadOf0UqK4tIjBLPIsK7OVq7V0aMTzpet3Tw3/26r/8d+L/4PRRl0zZy8iit6Cqf6TwnulbgpZ24oXqFs1eL7dBIfnzfK7JpiZNqMTy8n1EVIcFeKVfQ0FA8G1E/y/B9yYn3Jw3Br6Qot7+iP7HToI2LGn/TcVbnA3XfsYwXBX3hM9vdEnia8bzg+uGS3ijWRYwKpCZmGwdjHOrGx71W4tzL6Tr3asK88YUXhKHmcp3i3cnJ84jLdSps3FAMDzbVXD9cwsfkXfUyol2FV4xRO+/QJ4nUoa1i9O4lZRMQvZSYdTfWRB8F9LVE8+RFhLqfYBONqmULaq2UI/xbBX7SURYhmyLCdw3upU/1eMTm8y3ZTsnl2Zhmt7+yUQK4seb0dILaaTg/G9H/gRW+L/IkHEtRhqzzSGpss5ZmGUmwnoVsP8feqK4mxTDsuPijJU0dUL4/Jd4r8UNNMqtwC4fkzgbbS8KqqSVLLMtq8oF1EMSdxOZUniRjeD1tIbrp4jzB3KyxLyKqIkR3LqOswr0fYw4a+p0OUwgb1x83OK6sULvOJZ5VwrVwLVURYtY+y2cT0dSuQ0YHW4KkxT0o0dqlvj2gAK8XtLVI9yb/r4RAqqmsfq1xCE88XK8nntSsH8yoPxqRHhT0VjEaVzSd1HStVUQLCSRs1hGO0xNMhM42GZWUx1Na7eKHmsVii7P0hb4V91L3R+Rdwd+cMD3Y0muH6FFIdt/DDn0NWgf3eYi/GRCRrSK77wmdbJg87eOEwDMkWTMc65YqD6XfceGDJ7uHIO6ovyJ4RsfvyS8SAex8Y3KVXhtMGtGNhwL2j3cq/Hkt9d2bssAyJ5ICEUYtfelhDhtR6YTdFTr0k47XmmiPj4/vfPwLmAA/BfxXn+pZvwNH3zoiuh93BGnL8tmEahOhGxf1do73KCJLawmhU5bxXk51mlA8nDCdFCjP0u5pbOXStZ7UWxsX1SlsYiifZrgvxWZYvNWiLgKqIhAamGupB+nLld507UvGfNBjgenhhryIcB3LdhPz8mLCdFpgjCKIpcEQzoRzoI5TiSzp5fEcx+K8n7KuIvKTjCDQmA/EjGFqD30eobVLlweo0uP56fRKYA8w2ssJpg36QmyftnHwDwYt6Hup4BJbn/pAtu7pLybUhyK7qkvxswefWeP4PeFBKbW1zmW2uyUOW6bjErsMBFLjSTx39IZE9RTrmOwbAdO7l9KoBJLruUzogcH2itlOTtN4FI8mjHfz4SRVsuLaaTlcrJlfW0uNPNE0xxOh9gcGU/gUDyc0m5D+IsDeqHAcqSmXm4jgllysgqSjXRjmexvyKhQw9aDV9LyeINS4aUfyC1K7VrkrFywl9XSTShMufBDRdB7dGw3hgwjlSkpuNGnEoWYUXtS96uNJgOFZhNUO8V5J+sjDAuNFQVmGNNuQrvIxT5MrQHn7MmFnnuMnLcvP9dKEehZJ4KXf439+RZcHtI1PcmtDP9F0nUvfK9rOY3OSEQ5mjK4Vpu9sf4PvG9Rxihd1lHWIeqOkeTDGaLmY95nh2s5aShbDe+N4PasvSuPVjzT2M1uK2+YK0KTCnv56LVUPrye9t6b8fEV1muBcr3BckUluHkwpLoU416xl4ZCMGuxeA6OOshSSWDM3jCYVcdIyWhR0tUd3s7l6b7ozkSr27RBh/jRDD+hSfyiBqd0Gp3aIgo54p4JcPpfti9FVg/WTjk8lChu28T8F/P+os/mdHV4oHvggEmiHv3almWIVzSoi+MxamjsgtdcyHArlNdtC6pRe2okhwSgcxxIvSvqJWFuzNzYCGnYs2bwkfXNNlLTgWuKkwf9KJrrF5dBEOBCftXJ7tsuE9SrBaEfsq5cB6mnE8iIT+pWW0L1mHRGPasy9iuK+bCMdz9I+yrDv5Fe0qSxuMJG43OKHsqU0xsGJDN5cmkT6LMZx5WQpBpcSFpo6wB8PpRBl6Y5KXNfSlD7BuUtzGWP/wJJsPyee1PSFL1yGQJpt9hsjtHZwfcPy+YTl8wkXH+zg7VZMFznnyxE4QzyMgnhUk/7AKZenY8IX8t4Uq5gw1ARhRxh3rC6lIdanhu2zMc2TDNO4bDcxpnI5XWU0nS8XgVFN+u6S3jhwFgodbb9mvr8hfuHSdw5VIakabmgIfU3XeiRRi4qMkLy0g+qUoBCB4jRlPiowq4DNu1p2M2PN89MpeP03o27WIc1twUGqi4Dp954ITez6lvoixgsNi52BGVFIWKg+iwgOC7xTnzhsKd5p4FlM6Osrt1MyrknvrqWZB5IQUAd0m5D0uoQtjj5zIavYRFx9GIVZ+xSbCMc3WCsGE/0gwxt1dO+PieIWs/XJVwmbrZhB+rdKmaSfpHSrkOjuhr5z6BoPb+Xx5OEu1eabtf8o6kjmJfaxXID6XhHtF6SDecG2AurpQ8t8saXTLkGkIerpKp84afBeaaU9i9kEpDslbH2KSwH5vAKk602At1uzfTKmrgWSFH0YCWRm+KzcuaiE/LQVOt1OIyvhbUhbijrIlB7Ofs1qnUr5ZSZpzOFORbr76VgHvxXM4Q9ylZb+D/+wKOlID42MbrcTn7dWeCtxZLVrQfUVPz+cbJGRGtNwlZuOS9xIABe+r6lfplA7xGlD6Bn0fku9DSkfjym2EV3n4viGsogo7nSMdgr66dDw8Q37hyvsKsBZ+XiBoc99yD1YNMRHK8JUSg196zJKawkBPB+C+/YklddxDWauWUxywqTDv3Q5ezrFLlrJ1Jr21M+FWdvXLt06lIPqoLyid7mu4PTUrMVULs4HCepJTDJqpOSgHZzTEOuBijX5KsFzJBFVGRGon5+PMKVHN+lpNkMA5bglnFf0qUHdl3QD/4OYcFLDech4XtA0Pk3n4cUa//NSE3V8Q/F0hOf2NC8SJrNCVh5eD1kHi4bJV2QljWvpyoBqmOz0359R1gFx3LL3zplIiIxim8dUNwxh0uEFhtFcmiGrkxGchqwuU9zA0H63dNHTG1sR0wOza2vOVhlO7QjBrHfENjqskIJI3nd33GKNEnfWvOPsckz8YEgjdiw705zLdYrr9ty6dQYKkhs5zWVM+PaG1SolylqUhfOLEW0R4MaaKg/ZXKTCiQDcRtE8GuEkmvwkQzmWqgnon8Xwt6YUL1PSvQJ3IoGj1jjok0Tiyd8o0aWHvtZgjMPBrQtREbiGYi27OjfRhG/kstV3xCLLRUjvWQEZnfpX50T5QqytwVsb2jygqyTNuOk8oqgTuHztYndaLk/GGONgtEM0avAifSV7VHu1rJJ74Q3Ti6U3SWv6XqD2bu5IXTmRBULfuqgvrrGnkaBKkX5hGHd4noCFlCPnWpg1pJNaGmeR5JHZy4DpvECvAgmj7D4d5wBevxn2hKueJgAJEAH/+qd+5u+wkSU1/bkwaOmRg9DXtIBeWKhl0vV9Q/mWZChFb2ypilAcKdphvY1FuqUs0UzT7FbYXlGepbgHW+aLLX3vsHYTglBWSmHcUZcB8U7F9jRjvCed/uIiofQjZreXLB/MCULNtbtrnl+OMS8TVFajvj7C++wW+zxiHSSkC+mMt6Uv6MROuKfRpOH5o4W4d6bCSQ3jjuY0IbmzFYjyi0hQkamBzpEa37Dd6q26ihpxIoN6t2EUtiyfT/CnNfoiYvL2kqrxUYNlcpvH9IVHsKjoH6boRSe+9h1LvkxEEtYrTCd5W87budQGP7uhyaXEsl3HeIFhdToCo8iHVUkQaZLbBZfnI+JrBaGvKaqAKGsJfE3+aEL5/bmc4ElHu4zojWK8m9PXCbg9RRGRbyPsoiUddMkAga/xXMNmm6Bzqf+5hxX++ynNriY9kNW/Hmr5AOv7M/qxRi1a2toXupZjqc4TpocbVs/HqKzHDhplz+vpo44gMDTvWvrGw0k0JycTUUQcbNnU8nocp5ea82mKUzrUiY9zrcZ1LONZzuXpGGfrCXBlmGiDuxtZGDwcwVjjeYa+d7D7DUUgYJzqoxF9arCxOBnrkcibdOfirj2caxXNk4zTXR+7DnDmNSr32J7OMRMDC022W1Den8j2fQgtHGU1m8bllQoqu74h8jWrTYKqXGyq8SJNdZpA1JPNStSioql8sp2SsgglH+yDEcHbG4wRi69yLbrwcUYdyoHxQUn+jTnbHZ90p5QJ+sYAhE8cUQp1ijbwBe35iplrlLgXQ0PfOYxmUt/WjzM6zzK9d8lmm5DFDdGtjm0eS5NwHYtSqJx8qvnldVe0/yLfmhX2Q8C14+Pj//5TPet34NisUrJ5KdEYkegq07jBGsVoXtC/FOlScZ4wmlQ4jRoiaYxk2299YR1oxXhRcHYyoat8dOcS7VTkT8as39vB96Q+1dY+fesShy1h3FFtZRtbDI0rN5UGy+XZGDswaC/LmG4b0o81m+cjmh1DW3uYmebGYkVxmtKdSR3Llp5oEodYcacWLJw3bYWO30nzra4C+s6h329Y3L0UcEenBAQzyJ3sywg1lW3kqwbeK+KY41jUtGWbx1I2WAWDnVeCBM0QrY5rKZ9nRL7G8Y3wbAdEIlpRr6KrqPUgaVGd1J57q0h3StSQmwbSnLs8HxGmLXURcHIywXUtWdwI4CY2+L5Iel6pPbxUHqv9ck61iYjilihpiVPx21vj4JQOm7NMLhJGMT9c40aarvYwb5XCj3B6wp8b0RvnKrG4n2jx1q99HK+/gpS74/aKomWjXowwvqbcyoWkKmQ1LZxXoBb1xepkRBx0YhroPEZ7OW7u0GfSwOkrD731WS4z4kmN0yjJaxtq2Fq7ku+V9PgTiXL3PIP/MJKAxdqX6KJUVC/LixF1GaA7uZCFt7foRrbP6kVEfJDj+QZ/r2L07iW4lmobyuSW9kwmUndXjiV/MJHEhmG1X344YblOiX4xxdupmc4LaUw5sjMpH4/RDzKipKUshvdlK1D9qghp8pB0VMOLiOSBz95ig+sZNu/PCI/Wgnp0erK4ETKadnHcXpCWCuzz6FuWiFHSCqa0c0gntUjsALNo6acdly+lcVbUAVUjPIeyDImzhroK5Fz/FON1m2H/z6/7+nvHx8ebT/WM36EjGjXkJxnJtMIZZCPr9+c4Xs/25YidowvyPGI0rDh3Pn+G0Q6TUUVV+9hhUnEnLW3nDVufTraNvsaZt5jMcLHMcH4tQylL/DDg8sWEpvSZ7QiJ6VXCgtkEJGmD4xth5fqafC3bamfjoYzC36+wq4B4UvPRiznJbkF0UMiWeUgDfcVY6DONKjx0Iyeh8zjGGocobnG8nuBhRKddml+dgiNoweDVRBtavMcR08MN9QvZ2urGI1mUNHkor/sVo2GvIopb3NhwuL8ijCTM0A16bNCTVyGOa+kvQtJxLVFBrpz0waNQ2AC1j9npRA+8kvpuMmqYjkR1EJx6gnzU4rJTrqVeRZw/nYKF+bW1dPhb6W674xbXk3JDGHaowsVakWOVZynL0xHxqCZcKkE8WoUfany3Z5TWpJMazxep2PYyZf1ZmQT9dFCNGEWvHQ7fOsP5KGb/cEXTSJqD0wgJbrSXC2A7q6Rungf0tcd0WmAvA6K4Bc+itUuyU7IqYtraZ5JWdJ1Hem9N8kjCPrNFMYSXKaqLWOK0a4/4iayw23UoPN5ZQ/8kJQo6ik1Ec6PD+6URjt8T7JboVSCLiV5ARa9ilNr7Y+FdBJrp+1CuY+oXKb5vyEtxAwaJrDoBiq/NJZTzcYx3vWQ+Lq4SNTgUra75/jXWKvJCaqb7b0hwZz/pUG+UWKvoO4fFbEt6HNLsGWzuET0OqL8xpd9t0V/Mefl8hu5cFt91DsB8VJCvYy6XoiNOo/aqCaxiKZsFkcZ7IRdxaxXeN1Jx4QVDVJJ2BPm49QSmbpSUN4qAg8MleutTPx5JMKX36RIWXrd08Ff51tLBbziOj49/9FO9iu+AUW9DVKcpT1LUECk+ureSbrp2OHs+hR62jZCgCiekX4asniTELxXu920x2kGvQvpEg1XsLLZsfmWH7u1cQB5a0W98zDs59jSm/9wWtxNN7GopfFQ7NBHGB6Jp9ENZzWrjiCnBKoKVQ/ylC1arFGengV8ew71GGLR+z3xacL4KsUYR70vx3vnlCc73L4Ub+yxm9F1LijKkfDLC3yi6cY+rXfStGtuKJVE/k5NldH3Dxh0RWkV4UNI2HulErJD0guXLbqxZrxOMdqmMw3hUcvK1fcxOB61DslvQB5r2aUp46WDektVEslvIKsSx1Nc7mvOUeCq3OcoS7hcs1yn9KqAboNTdzYZJ0rDZxBIf7vXwIsQcNix2tpy9nDAeGLl2E0gIZi7Ny3wdg5KmHhYIesbzgu0qgbelMWKeJOhrNacf7mAzQ5C2sjP5eoz5bAWXIfFhS2kHCZ4SnuqL0ymTd5ec3t+BIbywHTtQemw3AbNray43Cb2W0E/9OMPuK2zUo7WDE2up7dc+nteTjmpOXk7JZiVVFaCPpGSQn6Vk7/vk3yUXsb5XjEcVa0/q826q2Z3krKuIYs/BWIXr9+jGpf/yhgCoiwBv2rI8HTFeFBRlCC8i7EyjM8M0rVlkBY9/wCUNNaUXSp3bInEyjQ+3Kmhc+jst+9Oc9U5MvQk5Nw7BkPZsapfci+jOYqxjr5IqLp7s0u+3YlEvBYc4nResi5j6TodTudh5i3tQMIkazlcZ3UmMoxWTe1tOnk/xLnzyRUL6QUD7xRyqkFZ/M9Pr2sGSdRVJvtpbsi7U2qF/p8J3elabBL31cVJpttm4x3yYkV0qipuG2Z0lF+uUdLfEPzCsH00pqt9BHi2wQiyvLhJn4wB/ZPj5/Y99/cM71h7+XoU7bSWa2ijy92Y4Ts/BwYpg3JAsSmG4uj1m65Nd32Cv1+jfv6FtPLrSJ1pUhEkHuUejXcydivaJ5DUx0uBLc2b0xobmPGY+lRWyH2mScS3SJCTVU4r2cgWtywDTuLRFgHknZ/lsgjM0wvRnc+KR3NdehFx+OBeh+FzcPcY4qO9fkueR2DQVFFWAfZzgLBq6aS91LCWTZpC1wiR4Q15b/XVJEK3KkDgU51hVBWjtoDx7hb9jGTDKKvQmYL1JSN5akUwqCA2u21NvQqI3tpjPStqwM6y27YCjdAID2pHHRWrD5TIWHkBo6B8JllA5suoE0I2L3vrYG+IyeqUZrn5tSjaqGV3fUG4iAW1PWqK0lcTiToDYbqTprcILNW4goOzk3lpoXWMt8PewY29nQ3m3FdffzZJWe1duo93DNaN5Ab1iW0h0SzqpBHP4KEB5Emm9uszoyoBk1NAsI8yiZXmRMTvYSN0296kHK6rnGfKTjOlOLrbeAcFprVC88rdkq663PkGo2WzjK3u27WFbhxRPR8LaXUq9OZg0pFF7leIRJw1v3j6lLOWibCYa5fVkhzmr52M+Op/RrUKKVcxosHLbVYC1SuqojYR1RnHLyUdz2saDzsHCVbz3K5Sn9Sz+rCGaNJKCcW9zhTJ0vZ4o6MiPZ7TPUkkTmXSChvxoxHKbYBpXLNY3Crqf28Fdyd/gpy3tFwpp5O2+sjZbbOPy/OUM/StTOZ6G5lxvXEzhYV7EUgbzLdE3ZFeVzEv09YbmSwWMNWUd4nk9ZR5KAkUk3JNPM153en4b+EPHx8c/9+oHR0dH/yjwk8fHx//Up3rm77Dh7DaoxjIbl1yuUvZ2N5xUc+yDMRc3JemzaXyScU1VBMwONxRViO1hlpWcackWc5xecHCeJX86xiZGTjTAizrSWcv2wymbeoTSiqbzrrrXxTrCC+XgM7V0ZZvav/o3TiuSsOXsZII7FhBIPGlFveBY2tbFaRV9JCdltwrxZzXNJmR6raL25LGKfTng+kSCCKe3VpR1gO8aCMVLrrxvCkp0bPFXLjp22Lw/Y/7uBVXrk/wvE8oDRftlyXNKb25ZP5riNQrdg00byosEN+vYvhxJXPeQatBrMXioOwLvsO9n9AtDsisAdGstcdiyeTFDe1Z0poP0LUpa6q9N6RcGd9wRTWqshWob0WxD/KQj+8yW9SaR7ejulrwK8X3N9uUIFRnCtKV7f4yZGfJGdLJx2KKNQ137dFtJly22EWx91PUNyazCfmWCCX3qu9XVBebsyQxv3OIGBl15hC996lGAcnqiLy2plwnhuGE2Knn5YjasDMURZWuXTR6zmOYUfiwyu9DQdR67N5csf3VB9Paa/DIhGjdii12HhAMwu3Wl+RiEmm5ohinXSjx6bLCdw3hWoo0EKZaNf5XisT3JqN+bCquhVwSThrb0pX6farrKFweXguKDKcm9NXkjetvqNCHZL7C/NKH/Uku2n1NsI8J5RVv7XD6Vi7OKNfZ+iudCF3q4WUv7NBXZY+5SegGEPblVRG+tyc9TskVBsRnA57UiDDvSuBFEooLqu0vMJiA4c/EDI3HkXkf7NGU7c2DrkV7PKZZSs1YKistX/7fMD9esVinFSSq9g3utRKdvIsn/ywNUYKgvI5xEi7LHyEUO/3eWR/u9wM//up/9ApJ08HtiRFFLb0Q50JceZxcjnLTD7HQyKfUO80kh7phIY3pHGi6u5cXJVBwl2qFpfBF7W9i5vYRa6nTj3RzfNxRVQHjucO3Nc8lMQjrdXevhhUZqdYAXaUK/I04a8XS7PeUm4uzpVFxI5+KMKYuQtgwIPM0kq0jfWuHsNNLYcKzkT01rTl5Mh8nYI80kpdbZabGdg+7lJCwqseJW5wnpuL6K0/ZzhYkstvAwac/Zsynl4zHn/2RN+fmKINBEOwJU8fZL9Ewzvb6h61zSRQnPI3ZvLkn2C4rTlFHScP1wiU4kJly/SLB3SxjcSO4A3V49nNHvttjalZMzkIM8CjrafcnCStOa4mU6mApagqwVW2brizPtg5DzsxH1OpSygWNFIgd0Cy1JBxc+7ToUmDSgL2SFE4ZSY7d+z3YTi5nicznmbkV/Ki42AEIjCgUQGPXcoFeBxBZ1HmiH9iThYpXhRZoobkn3CrxA48Siuz19uDPYs8W9lUQN2zIiuLdBaxfvzKdeRphaAhXbyhdA+YmoXpoXyVXZyXF6RosCHCTO5TxFa1eis90eO1DpwlmNTnpU6eIkWnLUTgPak0Ti6WuX4FEkYJ8DibJxAkOzisgOc6ptRPVGxyyVMpJSsttLR/WVsD+IO/T1BnuzknRoC+5hKcf3YUn20GO8U+AOK/hsUdDUvphweoX/ligPNnksippnUit20g6dWslN8wfDxUSLXM0Xw4kba9p7FY7Tk8xkRW5al6aTz8qdtEIuizR7/3NE8kEoMHyjsJVHOBPCWpw0kjCsLIvdT9eaet2J9ivAT7+Khxn+/SmEyPV7YgSDBMYYh/0bS3GJKaBHOtRxS915uEpkU9tVguv00jSKO8Kkw3EtdogHObh9wTqPuPbmOSY1aOPQPBoJUf+7t5wtRwIcziPK96cC9x4ONgC9DsjLiPKDKcmtjSSBngVcv3UhpP/9UhIGKg8cy+WTKVXrs3kxktWSsoSzmqoU26e7FNpUe5aQr2OcWSthf4ucrvOkK/3VTJoimVhii2oQ2r9Z4x6UJPsFqldkuwV9bLAXIepliP1bM9qnKUHYoVtJ/ZXOvUPxMsXsdCzXqWxZ/Z6Ly4zTVcb8rUsR0M8GF5qW5k7+IiMIDPtvn4kWtZH49ldw+802YXawkTIAEO0K7q57nnJtvubOjTORzo0bqkNJaI0mQk9TQY8zb0SrOZVIk/juBhWK7bW8SCDVXLt2OTRKXPB7ptNCutDalWgjBevNN0sZ/qhF155oO6/nRLuV7Co6V/S9DhKXtAmuWA+OIzjLNG5xZxKbDrCz2BL5mqbyqZ6OaGuP/npNOK0Fh3geMJ0VmIOW+bsX6LMIf1+ssgDmRULXiR48iltU0KNbl92D9RWcu+9FqmhHGutZ0qyW9NlFR3Zzg7fyCE9dmj1dZom1AAAgAElEQVThfIzHFdmiwH8gK0PfNUzn/x97bxprW5red/3WPK89nvmOdW/VrrnbPTi07dhO23IA4URCikJkRBAKYpDChyQMEsIy5iNG8MUyEUgMEcoHDEL54ACOSWyI4ybuubu6Tt2qO595z2ue+fCse6pinO7qslpqg1/p6N6zh7P32Wetd73v8/z/v3+MFlRcfGOP5MKjjQ1MvSE/HlyDv9uHPl2j0mxMlFyV2JhWpZlb1I98kjdzibs3a9ydhDSxriOdtO94WL8ZUuSSTmGZNcqOrOq9IGf2S+9gmxXViScW3BMxeOih1NTbKwkSzTOTtF/dOkEukPn8I5v5TuH0X6ipPyW/D0aLOe+TKPpjwJpKOkrVfDLrwcd91r8K/Diwmc1mFwgI/CeAv/yJXvWHcKzXnsicco2LiwG6UzMeJmhuTf3NAWls0bYqcWLTLCwsT9xhqiqdYkWRgzoMMoGlFCZ1bnA+H+DuJaRXHtadCHOco/Y1Rj0sBSF3Q/S2gVsIthDQwkrMAEFDvHakhnkj5XwZiuRoa5GuHJGxAGoo5QWMFu2RLbzP93w5+ZQO805MvHSlrlzJysKyKqL3RjSNQrp2KCZSb/P9HNsprw0Lbanh2BVZZKONRHNqjQRHZ92NiF8vaIOadO2gG01f622oNpZIonQBydSXjqTclirV2mabSFyPqktDyxjl2GP5/YvcYLH2Ra5WKmjjAqXqtZCdwur5gKrUiU9C8q3FeuOhH6RklcHDJ7s0q94UMRHZT5GYdMu+89wqKO/3xo5SRf2dIeqlRbWWrWKXa+SVTrx0aRKJShm5GU2tMQgTdqdb9FTB+aqcvF2jStqD1YhNW22x/4FE5ahqi3Eu6bPtcxejb/R1ndQxO6ul6RTCICVZO3TAcuWxThwsp8K/vUFRpRxQZgb2XkI7qNlsXUaTiKtnI/xbW+pSE7cbYK5UHKvEdEvqWusRnbDceCSnAW2iUxU6+YMBSqyjDSriZ6HIyNa6TEQHOVXQYS5kZVvWmsRyf3bO+GBDUemsnw5l97FfYKw0zHEuaQy30+veQncrg1wFq0UZlnSdwvD/cNB3chqnvV4x5oVBXQtetFlYKI3C4J+5ZPuTGa6XSyqz3tBdSj05uvI5+1uHbLYend5jKe+ndGYnx6DaCiNX7XDcknAqTWHtHw5IMpOg32G6biEpy57Ev7eVihUU2G+uJY0ik1JJ3cfjbOb+J5pfPq686/Hx8fGPAfcQPv794+PjH/sIpvCP/TCfWAzHsXwipYCY5+chozAl+Owc9dKUK/7GZHJ3RfO+T9soFLnBIEixDAkMVNWWrpRm0WgSMR4mkuHlV+haS/PMxbNLmsSQg6Hrk3MLneWD8TXjtEl1srSPeu4D8Ayzpsl1TLPGGWdYoUhzppOoj3ERyZH2aoRSKVhvipOq2loUz33CSULyaCBZUBcW1YOQdqck8HKJ8w6l3tW+aLr0k77hluSFwXRnSxikmHaNY1U0pUr9bijBek4jTFJbpE9N0zvn+sDG8TBmen+BZdWyKmsUykTi0ZtEpzh3UY99tN8PMMY5da5TxaIYaAa1AFH25f00kYE2lIuUUomdVFFl2xplForRYu2KZKjOdJxxhjfIUMYFxLrApYet4Aythu3rFUoD7k6CZVfoa4260dDshsObCwCefO2QutCIYofLRYiWKZQDWWK7g4y2UbCdkvXcpyx1ij+zxRjIRbC5mbO6CDHvxGjf8ekahSIxWa89MDqS2CawSrRlL0fTW4pcsrzy3EDVpJlo2DWOVaLZgmMsKqmhRnNPMtteQH/ejGRH0UpcTrqUWnWzNVEKBXNYSE7abIUyLNGNGgYVoV3g3ttIE8hsMG8kqLOYYD+iLAzSd4esNh7LkyHF4wB9kuO7wqutpsJE7loJ4rT7spOidGhDiZw3bfk7RnegWlkwqGgTXd7nysZ3c5SlSWe1aPspV0u5WFWVTngQUdQa5o2ENjIY7kas5z6GWTO4tZG4pE5BqRTKXMexK4Jxgqp1JEuHaCsXxejNkvrSkZJVpZFENsZCp3rqibsy1inmDttLn+DmFm+cUaYCCBqPEpT8Q1XD9zM+9jp4NptNgJ8Gfur4+PjpbDY7nM1mNz7Rq/4QjvJujq61KHqL4ogFc7gjXfflysO4naAvhYGw2rqgwHCYyMkdO2SFQVXobCMX1A7fyykqAcNoektXq5SVjnqUsU1sFKvh5nQt/upWEeDKzZi83647Izl5ORVxvelWZLGFE+YS6LhwKDPx788/GGO4kuxgWjXZxsY4EtlU9dTDGuZ4dzYidxqXKHOT8OUV7U0BUW8jV5JL3RpVa0k2DnWt0vQC9rrQ8ZyCq+dDlvOAIjXYbh2CUSo5S1tJQvBfXVHkBvnCIfBydEuyvUyrZrH0uXo6ojgeQFihjgqMvg6n98zY4rCi/JEYx65A6fAmKXlqopithBD2SgN7kl03CFunIdyNmQ5jylJSbv1Q4smrXGc4lb9h+iRE1TrcQ7HQDm+vaXNpcipmw+ituRCwTj2U2ynR0xBNb5hvfDBbjNuJJCJorVwYX01x314Bwq/oXgC5U43yzCPb2FSZgdarVNyHBrZZob69kUlR7VC1DicUje7ZKqQJG3GP6S3jYcz4/hLDaGgqTZJ8gbLW8bycsjDIEgF6q1ZDd2GLGwoor1xMqxI3WCPaYNsv0BIV82Yiq02zZX0SXufHdbXK6fkIXW2pH/l0Dz0Mo8Z3CuK1Kxl44xr1iY2WqHQagqBsNDynwJ2IBM20JV03SmX3UEUm7ZUtKRBKRwd096QM5foFSq4JgElv2USuqA0cWXi8AIYXmUH8eED8NGTgZQSHEWWtoTkycdetKuUKp5TJO9fJCwNdFSCTFRaMRnIc+KMUdVKS5YYYJxRobuQ0Q9FNK7WCt59ghAXVPx6RrKWu35Yaq3fHKMUPsHTQZ3EdI5Hf/1F/88vAr32iV/0hHJZVCT7QrOlSnSS2KWuNTWxzsLNB01pGb83xXlvJKu12yvJkKKumK1siqTuJJR/uxKzOQpJLjy7TKXMd3a6wXwikT12UpUlUWChqR7G1JDm01K/rbOUTH81oaEYSjVKXGprRYvS1ZMVp6AoN3WhQKwXTFGZCWegoukRkF5mBe29DVepE5wG6KV3oblwRp5YIsIcFTanhebkkOjz0r/kNVu/EMp5ZrK4CVE/qdYomB16eGwRvLVD9Cs7s699vdLiRmJxaxR+m5JGF/tjGmmQ0R7kgJUsN7dgDpaOZWyi9UcM0G6LnIfv7a5KVOM1erCbomz2+U5BcuTC3mH5Jp6x0ksJEVTq6ShqSykrYs1FioyiSsmuY0mCsKo28NDCDEvVK4DWLpS9ksr7hpox7HWijQqWSzx0m45i2UUX6BcJBAEl4HeYM3Ax9mtP5NaMdSXswjBrLrqjeTlidhbiWlIRUvaOpVSyjputXdOaVzvDmmsPRhqvzAZrairxN65hfBdSnLvHcI8tERqebstJtE4P91y5xx30QtF+J8qUV2Zr1zERROhq3pcgM0VvbDYpfC2LwgYNm1wxGCWWt47yypr2doQDLtUeXabSdwvRwQ7Uv5axuVFI+CqSclooTbmcUoXwtwLarawmZ6tYwlIBFTRNZXdv1Tb8ORi8tJemjN3+MJrGcR6lO26jY45zJOEbbT1FLhatFQJoIJwTkItd8eYhyOyVLLdRLEyMoKDcW8bfGeG5BU2uMXakZZ+8PaBuFOjdw/QLPz6VOPsrISoNuVOKYFU2tUQ06RtOIrpTGWrtTolWfbH75uNPzfwH8xePj438W6BFBfAn40U/2sj98Q+2vWjz2wGxpc438SUBd6mwym7pWWW08ikrH93Msu0LrY587syNObLredbXZuGB0qH6F6lV4QU61ko5vM7foRhXKTsH8gzG2J/UjTZcU2rJflaiHGY5dMd3fSlmgd2kliaAKWRv4D/ru6Y0UU5foG1WT+JeyFqZo/EyI8N5uQrmx8CepJK2mJm6YU65sgmGKqnSijZ3U6HND0Ht9za8atVCqondNJeKHQqVa2aw3Ho5boh5m1I0c/LrWUm0sUEBXW4aTmPqOQK81vcUZ5nSlSu2KltY6SBneX+I+NEhPfSZ3Vhhqy3Aa05ktmi3NxBdJu5vIwZ2mOLciNj+XkK9tko1DtbF45c45B6OtAGN66E+emrStlEOWVyHmOy7al0LU7/hSOgkzNKNBeSCwFd2Q542ClLZSJU69UShqDd/LiTcOmt6g9gBsLdKoa/nM60IXQ0smNd/4aUi8cgUJWKk0rYLx25KTNv5ti6LSqRc2/l6M/uqWNDdZZzY/++Z3uFpI7LZl9+UjRazE44EYDAZ+Rnbmo4UlF4vBtYW5KzWJf0l0qsyg2Knl4twoOF5BOErlc3cqlFSjmsnff73wqSqN6MpnOoypao220Di8O6drFVYbcVQZQYnlVrS7BQeDLY5dkTwcoCkd+SsCeXE9KZvYTklXaCh6S1EII9cwGopC8J1RYpPmspAA2GxdlLnJ+MZaIpqMmrQQA4fSCMzbsktsW2y0ZaFTvxmjamJv7vbEDUerYL66FXxiqXG+lYti8MpK/r61aLfrWsPcSxm4Gfm3hhAbzJ8OaVOd7ihnvfYY721pIgNlYVIHP0AeLXDn+Pj4t/r/vxCSlXx8He4P/cguPCkb3E0kR8po2X/9Ej/MqL8qmkDDFDRdHMmJ3WQ6ZlgIbd8pGe5GqGqHYdUMJzGDMKUtNKLTADWomF+GKGNZRVl2ib4j8TVtp4id1GyuM6HaE4emVUkLg+FXTJpGGiaGKfEqzlFM8kZBXWpiN1Q60liaPtcymkcOk7srocWf+EwPN5Slzs17V+ztr0kuhHRflDq61qLrjVgQb+TyWfQ1P+8wwr6QLrYWVHRaR3gY4faTUlXJynp+OsC0KtaRg1KKWykvDcpao61VJqOYamORx5ZMOrslhtGQr8W9k7+RoU9y5pchp/MhSWqh+RLAuL7yaSK5sFRbsc7mPTXfnyZMd7bs3FxRNDpVq5KXBl6Y0zUq+zsbVLWjyAys5wb5/YL49QL9U2upl/arr+q2xMfnsQWdQpxbmE6FN8jZuzcnXrtE747pMqnzvrj4cZSh661slyMd/YnUG41BgVoqWH5BtbJRc4XVyYD0CwmoHek/F2HojWiuVy7pVnZG67WHo5XcOVig+RXpmQ9KRxvUNAuLbWpT5Tqb2ME5iEWTXKnX8fA0Ck2lMdyPmO5EePsJ+dLGOYjJnwbXK2JF6bD3E5qs52LorUjawoJ17FBXGt4oY5NKfVN7bKPYDYGfibFga/DBs12yzEQ7yDg5GYOCrJj7Y6csDA5vLdDNmmppY3kCIG9blXjp0rWivlDUjibTmYxinDsRm8hFf3FuNKowoF9fEC9cVLUjfTCkq1XRD6e9JK1Pw2h3CzBbylKUA06QX5/n6yufam2hmKJ3f7HyzkoDdRZj7qSEhxHeNKUpNCbjmCSzCPZi7NuRuNs+wfi4E+07s9nsDxoTfhb42Jlcs9nsldls9o9ms9l7/b8v/yGP0Waz2a/OZrMPZrPZ+7PZ7K98zPt+bjab/f5sNitms9mvfNz39E8MTybRptZQA8HHnb2/w/3xnPrVlKbWhBK1cRiPEuHX5pqsQDuoao0kM0Xr2KhkhcHqPMTwS/yDWBCHhSqcgRspWWRTbWVLF1/KqtAPcrEoAupRJlZXuyT7yYj6ypEO63d8FK+mKAxp8KiI3lEXTS+VKhzcVkV9JWYTOVK7G1S0nSDhnn2ww8X5ENWvuLG3ujYRqGqH/qVAgDBqd10T7TqFxhJVhfbIRnFqsl4yU13KCWkZEuCXbhxsu0JPVZLMJJu7FI8D/GHGYun3Pw86txbTQKXhTVJWZyHDgYCaw3Ei7p1alS2y3jDciaVEAYwORMvYNQrDICNeuKy+NcXQGopGQ1M6otghfRKiXZooQLxwYW1gvr3GG2QEI9kFWEGB5xQ0Zy66WdNUKl2hMhzHZIkp2EylY8+NMZyKZrcEtaP7jk/TE6EGgVhSXbuks1uqo5K8NGTltC+OO9SOdlqhhZLQoWgd+YUnXIxTYd+Go1Rq/Ar8vcczHj3fEQmWW2NZEt2j72SEbo7pimGlqjT8MMMfpteryPHRmvEoIS8Nlg/Gwv/1a9mu90GZqzNBEmaRJQkMmnTr21alLqXuajslmtqKweLKQptFqHOD1dKHaYG5J7sjw6wZDxK0pYFuirvshSa56xTOLoYMgwzFbagf+VSlzs4owhtlhEFK2yqyutY6lhuRzDWZTvruENuo2R9GjLxM9K+VSDCbUUU4Ec2tbtdiDY9MrFsxjl9g+ZKWYFpSskv71b7hVWBJMnTXKLx8cHk9BeQbgdqkqUWycvBHKcsHY6rnHlUltWx98AOEygB/HfgfZrPZfwc4s9nsbwL/LfDvfh+v9V8Cv3p8fPwKkqb7N/+Qx/wCcB+p/34B+KXZbHbnY9z3EPgrwH/6fbyf/9fQjj3auUVXq2LjUzs2pSNQjFrF0BucICdKhVykTzJ0R5wzValLbMegoK3EzGCEheDyMlOE8nqH95Ht/XA/Ipp7WKOc9MynKHWUfntcRSbtqcOmD+ab3F3Rrk30N7foRkPoZ70jrEV1a94cn4ulUO8onvkC27ZKlA9c3EdSj1rOA/LcQEtVgrE4sJ492gFgEzsUuUH8RoEzzLGsinItB6drlfhvLBl5GdWNEsOuqUuN4U5M59e0hcb6LMS/sYVKwTZq2luibdXDEg5yfLugTXW0oJIAxispkRQrqbf5u8n1iZQkNrrf81yVDlNvWC98Ke0ATav2oBjR5I73tphrBUNt+bnDd3n2dMr+ZEOndqi3E9apg+bWaNOC0BYdpqULCrE6lRNb3cvpWhU3KNAieZ2uU66jhcpWo7p0xFoblBQ7zXWzcHkyxO2bVf5ULsJFIWGWt/aW4rSqVVSzYTqK6BpFAibDEt8p0D7dR5knNvf2rziYbrgzWbK3t8Y0akxXPrPlxqOpNbGk1hrZ0mEcpuSZia62143UqpGGrWsXeHc3lGcegd+Hc2YqbaKDKQoYdWOwc7hG1Vs0vSVZOWjPbJbnA6JLn7wwsN6VY7B6PxDlxLlFm+qUV1Iiqx6ERJnFjU+doaodd/YXhK58HpYthpMotbl1uKAeV0yGUofNEpPlRdhLJFWxQFcqmtriDjNau2P53pi81olyi6mfcPveJYrSobs1vl2IXtyuCFyp8xZPfdIrj7a/mNUPApaXIZO+GVaXGkraN+AUOZaaWmMbiXvPsUqaSsU8N4jnHt2ootNkEtaNBk37Aa5oj4+Pfw94G/g2EjX+CPjR4+Pjf/xxnj+bzXaBzwB/u7/pbwOf6WPDPzr+IvBfHR8ft30U+f8C/IXvdd/x8fH7x8fHX+PD+vH3PbpKJb8lXcsu1agrDXcvYWLLyoC1QZKL26frFLpapTmT7u54mEgcS6VSXTg4vqD3dL0VElUhyEDDL8kKQ5wtdk2c2OhujaKAsx9L578/sXeO1qiHGc1T78Nu+15CGgmvs2kFRGzZkjLwfz55CdcqsZ4btAMBmqS5STlt4LMbOQkLDV2XVVa09JiM5eArEllZG2aNcW5SPPfJYuva3bPaeESxw8mzCaZbUl06DEcJ69MQNxRpmLebYOkN48MNVa3RbEyixKZOxUp8fjZCCyrGw1hqY4OanTDGmYizLH0UkmxsfuTVx0xHEXUhkUCK0rGNnGsQNEDywYBBkMqB339exZsZs+EFhtLw+dceklU6zlFMVegkawddlwiiy7WPprXcDFfi3nJ79mxiiJZ1Y9NOK4pKOvHVxqLIDR6c7YIK6aNQgjs76LYysfl7MXlusI1c4gsfzy0YDxI8p+Txsx0JeAwkyPDidMhoHEuZxi1lIsnMng5W82Qx4tOTE4ZmRtnvokyzxtCaawNCdeXQrk38nYTk7+9iWjWr8w/TWYtCrNam3hAtPOyjmDQ3iZ6HYrdVkBDFGyn6gdhzDaNB+/0A1Wrw3ljhPjRQ7IYyNcheKkXFMqrJIokht0c55k6K4ZeM375C01oWiYthNDy9HF/X6/MnAdYoZxIkbHML1Wooa43l1sW0azSnQflGgPN7ovlWjZapnxA4Ba3doh1IE0vXGnyz4Om7e5SFQVOpXK0ClIcueWbStAradzzaoTgGu07hYLqBuwmjnYiBLRO/GxSo4wLPFQPLxE5QVMGilucu67WHGxR4by3R3BpvkKFnCv4kJVuKnv2TDKX7Hs+czWYa8FvAnz0+Pv5E6+bZbPZZ4L8/Pj5+4yO3vQP8y8fHx1/5yG3fBP61FxP4bDb794Abx8fH/853u+8jz/8lwD8+Pv4b38fbu4NcOP5k/Mn4k/En4+OOu8Djj/vg79nMOj4+bmaz2V3+aLE3P/Tjx3/913gebTFcoSKVscRXYLYoWivg36cBbVBjnZiYn1qRRAKh6FoFOtCe2+gvR0Jb6slKelAR+BlR7PRbj5ZkY+OGOcXDEI7Ei63rrThQnno8/Gt/nTu/+is4h/E1lMVxSuJLKTMofalC1RosS7ZQf2r3Cb9/dYuTs5GEQtaKRCp3EBxGUurQWrZL8atrmmhuVbUlfTDEvieUq9tHc+axx24Q8/DZDk/+8n/A7H/6ZUyjZnvpM9yLrq2oo0HC6ptTzJe3eHbJ/CpkOI7ZbN3eIdbhWBWblYftF+QXHvpGZfD2gvW3Jhj3I7IrF38/ls58rnLn5QssrebhxVR0qFZDk+gotqxeP/iX/kPe+ju/SAfk7wzpNKgnFeE04d54zj1/zq9/+XMc3lww3/hUuc6N/RWr1KEo+vgUvSG0c9aZzfIyRMk1Oq3DHPXNyd5GrfZ66Cixcd0C26jFMbZx8MIcXW35+s//J7z1d36ROLbx/Jx46+CHGfHW4WB3zenZCCcoKEsdxylF3ZEbYkPdmni7ktiQLxxJwMh0fuHzv8fDZMo3Lw9InoZM7y9Yb12q2MQdS/29zHW6RiJzXLskSS1sp+Rbf+6XefUX/3PsH12QFSb5wpEVd2bS1JKZlmxtIaBVGuaZgffWktVFyPRgI00htZVG4yOP9pYkxLaXtqTt5gZ3b1zx9CtHklJ7O5Fj/ZmHepRSzx30aUY9d3j8b/8NXvn1X0bTWu5NF2wLm2cXI9rIwJpmtI1KlRooupxfZWEwHUXiCIxNtI2Gfivh9mTF8/UA16pIvjSlmLS0QY1mNzS5hmoJK7gutWumrqK2TAYJq8ilbRVuTtf8/S/+Z9z51V+h8xqUWEOdFvzM/WPOsgHvnOyL883P2ESu8BF+1ye50dEe5LSFlMqOvJDf+OL3UzGV8XEnz/8Y+LXZbHa7b0qpL74+5vOfAUf96vjFKvmwv/2j4ylw+yPf3/rIY77bfX/k4YY5411hClS5juFWqH7FcBJj902bbiLx192rsSRiIuFzu5MtbWTQmhKEaNkyuSqOWPxMXSAjZaGTxhZOUJCuHbr9XE58SxpxjlWh3hAtpDIsSbc21plBk4tLDLOlbcR9FgYpdanj2wWXi5Dfu7jD+TLE9ktRBtgNalDJhQKwDGlg+SNpmrStSpaaRAsP/XbM2EvpOjhfS5MkrQzCkbyXvUHEdu7hTlK2WwfTrGkvbeaLgCZoaVuV5cYjGKas1540VRqVwC0EL9cJB9TaTenuyJZYvyfbZy1VKQod/coAo+Px0x3e+9YNqtjs68jgPDU+7KgDRakTXfrUR5Kd5o0z2k7B1Bp+9/Iun3rlKadnI9mee6UoGDYO9aVD83+PWL8zIflvDlmeDAkmCd5hJA3NC+eaQeD54k5reiB1mlokuUmemZhORbJxiN4bAdI80Y2GeO1ysCtuPNstuVoFqGuDPDUxzVrgMFpDtbJxHGmqqWqLYdQ4PedAX+msK5d16bAbxKjTgm1ii8RrLeqQIjVoUx3bL4QnkZk0547UHYH8oJHHZQbmKL9OHZiMJS14twejqFsd47Utq/MQf5owvwwx9Vpq7K2K/nIk+ttaxelTib1BxpPLMc4ra4x7EZZV0zYq+k2ZcP0bWw7HW7wjeXxTa6Qbh4eLCU8e74AC/kFM6OWYVo03ygTerogB4moVSBTUMMe4LaYbED3z3eGC6U+cMb6/RN3qNJGBHRb4fk7o5bh+geMW1GsT266kfPGdgGGQYajN9Xml2zXDO2tx2ykt33x4hNpb6E1d+h90Ct0XV7QHEve+f7Ri82xAWRufaH75uBPlfw38K0jTqQQqpB76seS7x8fHlwiA5i/1N/0l4Kt9rfWj438E/vV+Et9BGLi//jHu+yOPLBG/PB34gwzLrpiMYrLCID/zqBLzOsXTsmQC057b4hwBjFGBd2+D/VJEXWmsH40IwoymVVnHDu5ewksHc0m+BeywEFBJahIvXJKtTdsp18V2RRXxdrFfSRc+17B9eW5V6pLIuzWk9qk3DKycttSwzUroUOOMtlIJpwlFKTQrgPRxSFlKN1bTRKvqWBUX6wAyDd8psIyai6sBek+nutgEqLbIuLpej6ruConemGY0jYLTH6R7OxvaRmVvvCXJ5TNzPrAY+Dn52qaODao+aiVeuzReS50bhK8v8KcJ490t+k7OaG9LEtu0hUb7tsjmzD4CW1FgtL+FtYltSMOsqjTqVuXA23ISDfCHGdmJT55IeoHllQzvrEnvl9SDmsVbiqD9YhtdbfH2Eoy9DK2XuRXfHKLZNdGljxsUDAKZCF8I4AE4kvohTx3KjVi451/eI0st8hOfemELqcpoyOYu3TsBWWEKehDR30ZnAfHalXq02mG8FPE0HTG1Y5LSpNn0f7cnIfa9LVluEAwyxociWeuQi33rtPh9A0odlizXPoNBSl1pNLVKGZnMzwZUsXmdINGNS4rcYP/mkuRpiKK3lLV+HRDp2QLteRH3Q6eQpZbQ2oya8olPlsnnWyQmRWyRZybPr0bXZHDpZBkAACAASURBVLOuU9DsGlOvuX3nCjoYuVmPwuxB3LVK+tzncLTBcUqOJhsCV9CX7cKiaHSGfsazaEjZaGIu0Tr8vVjMK6nFJraJz8T+bE5yorlH0yoURyVXT0bX57kf5ChqS/KNMeMw4X9973Xo1SNlqXN+OkLvd3vbucfudEu9NblaBXhHEaryg9XR3u2/XvrI14vvP+74N4G/OpvN3gP+av89s9nsN2az2ef6x/wtZDJ/gGAZf/kjPIV/6n2z2ewnZrPZc+CvAf/GbDZ7/ofI0b7r0M0a1ysw3ApNEWnT6ltTaVyNS6hU2q3QlHS1RUk0Wh20lS66xo1FlgvL0zBrnBtyRc8zE8NoyCKbk9WAbO6SRZZkZBUm42HC4Y0lw1EiDNF+KEpHmptYo5zt0kMxWly7wPdyqlynzA06R9I7h0GGo1f4w5QodoRX0CqMpxFFKQ6bLLKEDbAnW8HsUuKfk8xkfR7QdaB0CpvYpm5VaBVWK5Fj+T3E2bZF9mZoDc2VDb3+dzIQfuji8YjF13ZpKpVl7FLXIlyv3Y7k96aEOzHjgw11pVGWmkjk7AbNbKT7rHTEX5vQ1CqhLYqNYJJgmWINLh5+2PAx9YbOavFMiT4v5g5PNmPS2qSsNcpvD1ALldE4Jlk7aFrL6irAHWS40xR7tiFNZCW6nvsUuUG5lsZXWeror2/pGgV7JCd81YgmM0kt2lbBdMtrLGHjtmg9y8J8XRQEgztrjB0xnVS5DlZD+DlZVwSDjOQkoNMAuyH8iiW800ahbRWWmctXz2+w+soO01trirlD69doqkQINa1KFDtU3w7RH7hEK4HVVH0Dqq0VlGdiBFDUjp1RRDBN8CYp3jgjSiWmxXZLujNxQCrjAi/IZcfllJQLm00s9Kt0K4+pSw3TknRozyxppyU7o4g0EY7y4eESx5Gk5BfvRVE6HEe0s6fLkMk45tmjHepGpXovpEylETt5acXDB/t0ncJV5HN1MpTEEbeRxrLask1tHEOav+pUYtMXC18g7naFlmjoulxwzAuDHS/BDEoUv+ZsK8dO/aWRrHZv51zOQ/YmG166c8l4kFDEFqid5P1VGnduX1FUOmqu9uhO9Tpj7Psd3/VZs9ls//j4+Pz4+PjJJ/rpHxnHx8fvAn/qD7n9n//I/xvg3/qnPP+73fd/AX8k7kK9sklWIca+TFbN1oBhzdFgw/HVIegtZlBeS0Gm95bMH44x1irl+yH6zYydYczpoymVW6PqHfVjG/3lGN+WPDI17MShVegcTjacrwPmZwOsQY6ut+huLUJwoG0UmsrAfGzDzZLBQN5XtTUZ7kds1i7mqUH9UkOU2jzIBb/XLkx27y+4vAopn/h49zakqYVmyXZSc0VxoA3l4Nf1FnsvRgHM2zlJbpJENrb/Yd8z+vKUyefkoKtWNp1TYuyLu6gq5ULT1SruQUxquNhuRfeNkPpejmE0OK/Ldnq79NDtmt3xltOTsaxudiM0tSMxTbYLjzf+9GMu4oBF4uK4BWkq5gHDrKmcXmFw6TJPfUazJZvMZuSnKErHjhdz4GxZZi6J3aEeZZLx1CgkSwdnKO40s48HanONvZ01S8OlaVTCw5Q4tSm2FrpbszvdcnExFOxioxHlluhOC40m7DCtPlNNl8lkPg8EM6m3rM8DkT4VOqreEoYZTQ/MLsKKTu0wD1MCt6D+mYTuyQBjL+NgtOXZ1YifuX/Mb25fY/lgDMOK4TihajTcoCC58HB3E9KdRnZgwwxdbdmseyJZIlhF3y6pGo1N6pAsHZRcQxsXAicPCszfCck/l1PWOrvT7T+x6zEOUmy7Is4NdKshO/HprJZi4WPfjjhfhxh2zcXlAH8guWZpYYqUsf5w/aYbDaGTcztY8bXiiKvnQ+7cu+BkPqS9lcPSpDU6ikBDCyvKQuf+/hXPFDFx2GGBpdVcRD43R2uO3ztCS1Smr82JMjk2vEFGEttoNyRmKZm76Hcz3j/d4c7BgodPdtleyaKheCuFPr0kW9sceFvOk5CrRYCSafhHW6JLyfR7cjqhKzXMgxTDaDB1Kal8kvG9nvXeR7+ZzWb/8yd6lT8GY/fWktFLS/HFezmK06BYLbZWM9rfsn+0kvqo1jAextSNijYpKHcamoMCy644fTYRvKFbYVoVzd0MzymI/sEe+7cXZIlJ1m9l81pQdS+A4GMvxfymQDVAOAOq3lHdyyQx9u+OMPsVYPydEV2josxidsZbAjcnO/XpOoXX337KxdkQw65RDzPywiDwP5TIZKmFHRRy4Jii94x6Bu4mtkV726/oy77m94U/+02uno2oag3sRmyjpUa+tTAtkRGFk4Ts1EdxGkyjpnwloys0ssgm2ojLjUqhWVqsE4dgkgicGlhtXQy9QY10jtw1UWoRzz2aRmU6jGkrleqpB6F8NvZeQmu3ZIVJ3aqczwc0jcrQzHC0irGTsvfmJdXKYpM4oHTXXvquUzD1viY+zFmlDumzgIGXsbwMKVMDbSXyoctFCGp3PckmmYmyNVAc4RcYPaFqeLSlKHQst7r26ns7KYre4n3DxnZLmk4RhGCr4Ps5Ry/NaVsFTW1Jvj1C38mpS43HH+xxb2/OxEh49dY5w/tLqFU8qyTPBSRzeHdOnpoYwxxvL7muF3f9BGeMczSjYbORXYWp1yhGi39jS53r183Q9osrbFf6D6vIJS8NDKtmZzanqUVrajhCY7MP5W81mS0krDE2GfgZw1FCdOmjqq3UihuV/dsLPEvKXI5VMt/47NoRbx+covk1T84mjAdiSqED1a8EkI4kRfhGgW8XDCcxpiHf17VG1Wq88eoz7n76hLZTGHqZyA0rTRpzakd9ZeNNxUHpeCVPL8fcvjnHGkhZZW+85fbukmxjM9yJue0uGTspbaXhHUZEC4EwlamJ5VbXOMX0xL8+Lz7J+F4T7R/8qT/9iV7lj8FoO4Xl6UAYoZ1CMEzRzIbQFKti06qyqkpN6kb4pHWuo4elxGt/fYC+6q2clcbAkxopQPv5LYu1j+VUdCuTJLZZbjxUreNosGE/jLjc+GQ3mutYm6ZPqNU0idHIfzYSLawC4RsL7KDAMms2iUPdqPzU599hHCScRQHDaUxTa+yOomuiWFupbN+ZUPdaWgBLbzC/46L5db8l7t9vqZHH5rVmcFW4KIV6TRnrOoXhMCGYJPhOQdspxLGNOi5RFibb0wDfzxntb3HDHGVhUrwfYo1yRrdX0mV/d0i8diXh1i3oAKUBR6t4de+ScCcWan6n0JUqjdNe51bliYl/c8veIGLsZmKv3dj86PARl4WPrrZEucXoxgZdbxjvbWlPHRSlQ1Fb8tLgrd0z9gaREJ78muXGww6FKNa67XVZoMs0pk7MbHoppZO9lC7VyTOT6OpDNmkVC5w6zU0sp6KuVXFJ/cSKu5Ml8ZMBnltg7Ehq8tXGl0QDtWXyI5dYdsV4lLB3a0lWG1yVAavcIbBKgp2YReTxysGlAGhqSc+1rFrSCNySdrfAuJQL484wRlFgb2dDMXcEXFNo1yezZjR0nUJ0HpCtHEZBSvXcQ/1KQJXr6Gp7rZAxrtUjJUqusYkdtIWBN8iJM4tt5KKvdPLUZHcYSz1cazD7Gu02EvKXpdY8j4bsjLdMxjGryEWphL/QNQr5wkE3aqZ+Qt2qXC1DDL0hjm2eR0MMveFfPPwqO3bM2SakqHSuVgHTMEHXWzSjwbVLvJsRpl5j6A1TP+HO3oK0ErIeSJba6WogJDa1ZVs7HDgb9g9WJBsHZ5DTPPbxhymmUWP158rhy1doaoeqfrIU3O810X5Cee4fv6EqHYe3F7IasXqCT6XyD9+/R2gXeGaJrjfs7Els8vPFEOPCoE4Mwbm9GaHcSnC9nCbXiTKbWwdL4q9PsM0+SK/SYFAxCFNJNwhTqlbjg2e7FLGFexBfk6GqXKeuVW7vyio7jy0su8QPMzS1YxIkdEC2tXHMCpWOq1XAy+M5yTsjdKNmk9ncPlhIcyzMaY9yDg9WFO8OKDKDTeSQ3axoMg2nn9TXaw8qldEkvt4aP1mPePXtpxSxhf7I5uXdK6LEJt46LDdiI6WD7kqAOfqwJNo4QvxSW/yXNgSvLqX++GxI26jUTscrt87ZZjZtq8pFZL/gp8N30dWGNLUYBlJPPLy1APXDg1FZmsTPQp483MU3pW59+/YVO3rE2+EJd7wFe0FMVph4Volt1Og3+yyypx6hmxPXFidfPmR9EqKkGu1zlzIXKAye/N5hkGGNcqLKZteKsHSJoTfHQjqbHmwAyAoDza3JM5NpmPDy7hXFxpb4eaXjg6spnVdTNZqoWjYWhtGwEyS8NJiziqQZWtQaf3r/A37+6Bv8wvQfMXFSQitnx0+4O13w4GyX6TSSv1shpSLbKYUVoHVUE3nfaWFyMNqyjl2UVuHicoAzztBUSfxVFGEQGMMc1alZfH2X8StLus9uUbQOz5D6c11p0iwbRGy/PcG/LRS7u595TvvlAXli0mQa7myN6xdsc4siN5nHHudLqYm2fTzPa84pt4IVtl7jmSWfvfFMUiXubphOI4xBITH2rcrzaMirR+esty508NJgztRP2Nc3PFjvkKUmliFZXienYyyjYhhkNJ1C8+Uhdauiaw2a2vJSMKduVH78vmTH7vgJgZtLvlsjF/JDS87pLtfw7BLnlTVJbJPlBtuvTdgJYqZOQmAVjMP0k80v3+N+fTab/ZnZbPbF2Wz2xT/4fX/b/ydGlFucno7RtZZPTU94abCgKzReu3lO1ylscwvXrFisfKpGVoXtrZy9I2GS5j11K4ltdLvGMSuenk4wXt9yZ7i8TmCYTgXKXCUmutbw+fETDvdXjHe2BE5+fQVVVia63rLJbdQz+3q755oVm9ghKUzy3MAdZPhGyZ615e0bJ3z99IjP/OQx+8OIm8M169QhWciqQlFgGXk4r63RTnsbsV+huzWqIuBow5JIkawwsU3Zbv/UjfdJK1MaSW+veLYeUucGliMkqSIxUVRwb29Rr0zphL9vM93bymd3FlwDagACP8O9KavJLLUInJzDyQbPy3nZuMLWat66ccJiKVvSq6/sEexH1zBptVIwdjOC/Yh3T/do+uieHW3LQMuIaptNbjPbvWRg5WSlwc3JGscqsV/esPryDrPggmpasX93gXsU00wqjnbXDIYpk2mEsjVYn4SEXs7jiwlFq7OJbSaTmBuTNaGTM3Y+POlct7i24X7zwQ3MsMAwa9ZLj+LCxR4UJJce1iMLIywpS42i0ThJhrQ9zlABqk7j0FiTdwa3vBWXiayaq1YkWuvIwbTqni8hHn5db/D8HPQPM9VeyLLcw5h7N67E6qt2xJFNubXoLmWybxOd9mbO/HRA+cTn3q+2bEuLQSChmU2lik11v6QoDByr5P0HB2ifW6MaLardkOUGycYhXrscTtdYRnXt2JuMRRnwvBxzYG9w9Iqi0ShbUUO0rUpgFVRbkyyxOHk64aXhgrZTqBKDNtf585OvceBu+d/XbzAbXjIIU6LUpm0VXrp1SZpbbGIHzyrxv3BFEtmsFgEH7pa2U3HMirgSO3laGSxWvuzOlI43/FOe5yOqRmV0uMEzSwK7oM01SSN+bcs89jiJBsxj77qO/f2O79VCu0Qsty/G4g983/H9KQ9+aMdL4zknqUHTKgyNjIfRFLWfgIq+g3q1DLCdkrGXcnI5RNE6slL4nooCVWSB3uKPUsmXVzvKQudbpwd4jthvk8ima1RoFLapzVUZsIzdPoFVY7mWZlg3LjG0hiQ30e7EWGZNlksc9XiQcHExQFE7Pn3jhJN4wMN0ytPtiDf2z/j25T7xlcdLL11Q1pp0+72UwC5QgNOLIRwWmGZNmRvs7WyIMpsylxpdeuWhhSV8K4Cfh4Gecfk7hxifW1E1GqZeM5n28GW1vW6cta1CM6og1anfiNG1hrYw8PYSNlsP9akN01qytpSOotGZjiKSwuT8ysca5FSdypGzJqktLnc26GrL6vVWVs390O4IFza68JkebYi+MmHxNpzXA87KAaZaX8NJklxWP8+XMqGFXs7uF074e89mHN5YcrkMmY4iqkrjYh1ITT21MPZTmucu87lwfC21xrJqotRivXWpIwOnz8XSdTF97L91wfliwGg3QtcErKKqHbnaycWoUchvlyhzGwYV69jl9uEJSWhyOQ8ZjbeUrc5pNeTH7Cfcda5Qdzq+ujhiz414bf+C9xdThm5G58rKVdMlFaMuNeijfs7PRlCo2FMxw7z/eI9gkpCllmyZnRrNL0k3gpssHgWE9zcMbuc88HY4VCIW8wB/KDClq8jDdEsURSKfgv2IPDdoK1Vi3BML525EFtksE5f8YUAzkovi2Emx9JrzYsC89Hi2HjL1E+l9jGKSr044eVVl/9aSTeKgeB1pLWzh8d6W0C5oUJhaMY/jCaGZSfp0JyqYdWbj2gWrRyNKV5QXt/aXnC4HfP3ikLrW+PyNJ3z94uj6+OkahYO9NdvMxlULbtgr3renPH64i3NbEp4PbyyvqWXxlcfR/VPOtiHby08WZfNdJ9rj4+M7n+in/jEcF0lInjVkqcmz8YiLyMe0aly9xFAdXKciyfqcLr1BPbFprQ5nFpNYDU1kYI1kRVPVmshAWoXmwsG6EbNZu3SpjrMjsTf5V8e0I5Uvnd0S1m3ic7C3vhbmW07F+jzAHud4TkGS9cT6jYUyjAlGKZbesCpcArPgIg2w9ZpH6zFNo/LKvTPee7yPN8ooSp3HqymjSSxEsaC8/r11s5Z6mSLNhKbWsMYZhtEQfkGE7af5ED69pe1XjqvLAPqUgG0SgEoff61ghYWkG2QGF/Mp+DWqIXVmdDDDAssSQPj7j/YIpok0XfyS3UHMLz39czzbDkgyi2JrEUwTFGDop5yfC65S11uSxOSV+2escof25ZSmUfmNxduMzRRHqxiFKXmlC+lJk1RZgLQwiFKLzxw95/31lGZuoY23MiHOHcx9SQiYDBJWNzs8p8Axav63D14TPm+nMBokpJZxDZ/OEhP/5TUXy5AbOys0teXRyVRCO80aLZTjIlFs2eZP5AKeL22eBUPi3GI4TFimDr85f5W/8FrKv//sz5M3Bt96csjB7ppV4fLsakTXKrhWSVr0nAW3YL32uLG/4vm56EVVs8EfJ1hGzdXpEPSW6Mon2JEUClXtMPqdU1XqOPe2ZJmoPl4YXLpKFeBRKPSxps/FGwxE4RElwnw1hgXqpBVjQaLjTCvc1xYs350AME9dVosA+34lW3q1xdEr1qVDnNo09zOodK6WIU1kEOxHHDgbfuu3P023n9MNFL6W3OYr85vMhpe8s9q71gsvT4YYS41qp0bPVdLcknqwJonQu17MRRzwuw/vcXN3CcD5+RBF7YhyC1OveZDt0aBiqg23715xtgrZGcSkpUHg5MS5BR08WYxpGoX96Q+mdPD/m9F0Cl0Lk1HM0MxwzYqmUSgbjVXqcL4JRHztSNe9OSiwb0XEudDlQYT0linZYe1CNHnGYUK2selyDWOUk6empA/MEgHXmOIM082GshZ8HEi3VnEaisRkeSpBhE2tCeT5coBrVmwTG08viUuLHScmr3X2/JjPHD7n0dUYZ5CLnbTQ8QY5RSWTf9cpVCur/9emKiW3SdNaOLPwnAJdbeUgA4pWY+SnxGc+8VqiuE23gk7BP4gxhjmWWWMYtUijrAYKjb17c7pCpYl1vCBHvSm/c9UHH+quyKzKhVhCTy6HvDk4ZXkV9uCdhqGTk6YCyLae9u8nl274s9WQOLPYGUVkiYVKx7p0+Lvvv05Ra9dgH8esUBUEAVj/P+29adBm+Vne9zv79uzv3vtMz/QZaZDQSMJA4hgcE8ckJsZJMHYZ88FlG1NOVfIlTmyXDYGYKI5TSRxj49hJlSukSGEXxiQhwTEBBBghBAIhoTmz9TK9vNuzn33Nh/u8b7dGo5meGb3dkvpcVV3V7/N/lvs553/u5z73cl0afTcjrXRmix7O+ZC8TQUZS40ktqgrjf17Y4pMZ3Y0IMpM/N1DBr2E8TBien2Mpsk0E4g44+qoR7k0ufXiDo4unSeekxG9Ij2ZTaPQHyboN2wUYDKMGOyE1I1CdORK0Qh4du+QVemgKg37YZ/Le6JZllcafS+lKVXKSiU6kpa0+XEfVa/Zn/flTgmko8IUXmCjl6NEOhcvHbOeu1hmSf17faLYQm0b85tG5MeNI4PRRoihCS/xxYtTHKugaUSmW1EbFjdEzkhdit4awO5IRry9c2vmL07ICh336vL02vKGEvkPTRGuBFikDlvDUIZeWq2x3ctT+u0PYrUhtIlNozAtPO4cjrjsTLH1ktXaZbnyMMcp3vNzjKlOtSusbMnKZpmJ8Gdwd4f5ixOGg4i0bGPKWmF7a0Uc2axDh2C9w91kyN3VgJu3NykOHOaRg6Y27F/fIJy5uJsxG/1IGNNC6135l87RtohTk8EgoSg1PnHvMkWlous1n7u3R5qYp5LHIE3YvWFCPHckT2pLhFjXUn0vEgOGBbpZkh07mL0c1SvpuZlMmsykAKTpFYtPbVHXIi64bh0KQJRY6GaJbheMdkV8sY51NK1me2uFpgpJ81HiUTUKfT3j6PUx++s+nznc4/zG8pSqzuunTLyY5G6P3k7I3saS/rk1RWLg3BGWqqaBYmHBXsbs9kh4EdrbpOd795itPXauzNCsivN7c2l1yUSEssx0VnNXRkzdEtMqUJzyNOoypjrqxyUarTNNSMmdktFAeFyxa/KFJXI1wPlzMzZ7ETQwi6Q31NQrnA/NTo+z2vLlKkrDbC1MZF8/uE3ZqORzm0ujhURMlco6sbD0kqpRMM2SKDX53C89g+elFIXGbNEjjiyq8+mptLzdz4Qw3KzwrJyRGTPfH0gF/lxIHFv3xRDb9PPmxQXmbsyrR5ukK0tGWbdysqXkE+PYIr+QMxpIVKipNfs3JfIb9GPWN4eEucVvHl/gafeYS4M5s8jl3v6YnpGzWjkMxxFZoeNtxRhGieG2nSm1itl2rJhWSVa0vA52gbUTc+fzO2xurbGMkuLpFMfJcaxC2stmDsnCpjZEnXmVWvTdlHVqYegVSWTx7MUD2f+jAjKNZlPaADW95mjtUR/ZMkodqazu9WVkHERjLjW41jvkl4JnWa5dilpDV2uma4+tDUnb1JFBmFpUtcrPv+LjDhMcJ8ezcqaZR12qfGZ1nrEV47oZOxtLSfNENspTEcNhzOYoZLK1YrbysMyS8TDi2kduMX9tQtTuRWeYcnB7TFNLj++WHTI0UnYHa8aba5RxjmsVVLWCsx2DIjnvODfQtPvdKO8UnaNtkeeGEHfnBj0rZ3ZXqqp74xVm+6tumwWqVkmjfhsdVokuSgD9gjw0hWqw1doq1hb9czI+2pQqZa2yim04pQtU0L9uRXwszmTUSxi0v+hFpuM5cuGs1g7aXCIIyyo4PB6Q5AZZZpAWOj0zx9EKBrtr8lInz3XSUtp0VFWIng8WcosfHnrcPhiLtMnCoHw+YjQJhYt1O2I4iLA3E3StwmpVcH91dpUiF10uy5JbwHzf5dzlKdXaENo/q6Is5PZS12rUY9nY1jhFuRph/qFjylzHvmWSx5KDS3IDVavRrAp3M2Y67ZHVOs9P7rGIRStLU2tGg5jZyr0/+ZRrbeVcZv8dK8fUSw7zPnWjSEP8cii6T4WGrtXklXDQpolJGpkMXpjiWjnboxBVq2hKVUaiI5Oi0Oi7KYvIoQ4NlomNoxU4k4S00MkzA8sqGLdjuef35kx2l4SJhWvnkqaoFZaxg2bU0PZfOm6G05fzu/jEDmFkc+Xqwangn7Ebc2Uwpaw0Xos3+ey9PfJCZ3t7yWsHm0zGEauVQ/bagGjmkOciVTPsJVQL81R6KF5b6Fp9yr2rqjW9K0vyUmM679HMhLPBNgu2+yFGL6e/GdF/ZsF6IdHifOmhKA3TeQ9Vr3nptT2y1KA3SEQiKBe+Vz7bJ0tNxk/PaGqV/keOMcYprivfU7cqymObD7q3+JMf/BRb4zWvfvY8rpHz1OaUKDM5t7HEeV2Xsd5S4+rOMSNP2irvHQ8x1Yr+OOZa7z5J9+FswDp0cN2M+oYUqba9kPnNMU0jPeOXBnN0tebCcwecG0garOdknL80pS6FQPyKM+VPbn6CoZlwabhgYxJy/PqIDU9SfFs7SxyjlDtPuziNzt8pOkfbYmu0ZnsUUtz05MLaSNjZXjKPHXStptcTldwikRFNRWmgVTSoKpUq0zC8HF2vKRY25g0LbyNmfbdPUWiiQqo0pLGJ109P84fZywNQEdE/rZLxV0DVa6pGQddrYeB6ZoHbz6Sh2inIS5298QpDk86ErNZJEhNDE0IXTWk4/PQOZamSxiZ5bHD52j79nZCdrSVxaMFmRhGK7LWi1vQdKSqU13vs355gta1mwf625CWvC0nzfO2ibmYczAaoscZy5WE7Iv9dzCWS0S9GlO1orGmWVLXcnqZXMokAW34Cz8no9xIhczFqvnXwecpanMOl7ZkoV6QmnpOf3nYabs6oJzJBaWaw/vyEu69tElYWh7GkeAxdJFc0rWZ1t8/xtE+SSzteE+nkpcbRbMDxJ3dE34x2zHYlx6pphMFLcUrCY49P3rtMGplM3ER6cRPzNEranw6Z3RmRzmzmBwOSzKS3GZGmBhe25iixhmGUxJGNZ+fMVy71+0MUteFo3aYv2lvbT9x4ivP9JQdxXzTLauWUgaqqFZR9m8qrcMeSR6dWOD4a0NsLT++4WBrEmcEidkQxoK0rAGxN1igT4TAoK43Xf/UCqtqQ/86YxXEP3S5w3Iy9jSXr0GE4kKkoxazYmqwlkl9ITr2pFfQPLdBaEvUGRBLcrAhvDIFWM8yq+VfrZ/lnr3w9+/sjnAtrDsIeq8wmPPS487kdlA8vOb49oqpVwsI8HXn2eim/e7hHVan8yuFVxGW+UQAAIABJREFUPnPnHEWpsTESLuVw5dB/34yhl/D6YoR7LhSSo+M+F9wF99Yy2n1nKfakhc69I+HCAFiVNj85/X28Nt/gIO5xdDBEqRWuH2wQxRZxZnLnzoTD397BMQsc892pM3aOtsU8dDhc9KiG1WmrU15qbPYi1guX9c2hECQ7BernepSlij3IUHLpZaRQceyC6jND1FRl8JFjylIFW5q+61phOZcCQjh1CW9Jn+FHfn/A7oUZplVwvPbY9eSXt1qZxLHcuhlWyeKgTxKZIlTYRil3ZzI6mBY6N8IJo0FM3ShsjdfMI4diN6euFbY3V+iHJkfrHsmLI+ZrV0hFZib6VEc3SzSt4eDOGGsvxrm2QLXL00r/CxduS4dBrpClhkQMeiXk1k8tJSJsFFyrQHGlBzZbW3JxOxXRrQFhJHlY1ajRzBpNa5gMIhYLT1iRjlx2Jytu5Fv8wos+aW5wezpi3IvZGa5FM6yN2AyjYp3YOMOUq9vHlBsF2lA4KkZ2wiwWZYrlwiWPDZztmKZUiQ49JrtLdq7M2BusRIfr2RilTZ3kuY4yFGn3KDUZDWJ0q8QepULEYpfcvLOBqorsz8nce7U2sDcSetuiBZbMHMKFw+Yo5MbLOzRGQ5oa2E7OdCZKEXlk0tSihyXHLief22yN1+xHfd43OuBo3pe+20NHBl0SC/VCzMWrR5h6RTh3JY9ZKsShxWQgqRf3QiiyS4cePSfD6OVCurK2iTKTemVQlyrTVycUT6VkSxv1+RVmL8exC8KFwzSUEejF3BNdraVov7luhnEuolhb7G4uCecu+Uo4I1xXWOHqWoFNiWjDhYO7EfOpo0tkicHl81MujJbs9dfcPRyBAnW/VXZ2hc/hoO3B9aycKLQZOCnxkce3730OGoXNfsQ6kdTN5saaslY5uDsizQ2iuYPj5Gz8K5P9dEBearx+MMbUJWioKhVFhZ6dUVUqR3mPXWvFuYEMFTnXTUYXFyi3HIb9hOLzAxStYfh1U5ahQ5h0Odr3BFVrmAxiqYobJfFcqqJxYaBbJdqWFJMMoyK9kmNZUvxpnJp45jLeXbFeuNgvzGgmOavIxrEK+pOIjX5EkenSKzkz8CYJTV+Y8++GQ6ZLjzyTKOozd84BovukveYQRTbZ2kKxK9EUA8rQOG2ev7Q7oyw19twlx69N6NsZ05WHqVfs7i7QtIb9mxu4vvANlBMpWNlWS8xhNJRTYY43ejnp1CFa26haQxZLz+Briw3WC5dyL0M3Koa9lCLTOXp9zHrqUVca0aHH8cEAIh1Nr3BumESJKeqtw0LUYdWG7Q2R/VbbYpum12iTDGc3ZBa6/Pzxc/zxD3wa2xRZ7qN5n/3FAMWsT6v8cD8v+tK9bQxPaCkP0x5RYYroXqqzsRHSG8nkmN3PmJxfMJ/3WMU2t2ZjHCcXob5BfqrB1VQK+coiWdpC4nLscG68PJU8oVCpX+0x6MdstI5NH0ieVFdrNndWmEOp1B9N+yi9EnszoWjJUxS1gUg/lRJKpyIhdHx9wvjckkXkkJcav3V0gZ3JCruX4ZwLaSwRy1S1mtuvbMs4cKSJ2KFR43i5tHUB0dRlfnfIYHfN8VFfFDzsgtEoElkdV9Qa+peXaEaF5hUkM4ci02kAzZSIPU0NmkTOp7GTsLgxIlw5UmAcJ6xTC0WrMfoZut4OmbgJWWLQzCXap1LIMoML/QXPXdzn7mzAMpNClW5UaF6JYlXizNcGq5fGvP/8PrNFj4NbE7xeyrXREfpS40424v3n9oly4fM1TOmq0NUa1apI5zbeOMHQKpZX4eXZJmlmMBlHqG1qNV440ps971PPLfbsFXEltupGhfrCEk1tKEcVulahXgvZ3l5St8FF3zlbzbCveVh6SZQJb2hW6Jh9mQTbvzOmWFpsT1bigKcuhpsTzlzqWhWNqLnOYuHJ+GajYLs5+T2P5dwjujEkKXTh4fRStt93hGvljDdCkmOXZWJTrCxGg/iUABxgPutRXMyoK4XeJKY3SBg6KdyzcCaJSDCbFTdubqFpNb/8O89Jb2bscHljfqo+O/QS3K2I+KUR0dKGUnZceGMo1e/nptAvxPk6Oc5mTK+fohsVrO83Z18+f4ztSYQ8f3FySveo6DWOm8lAwSCjcYVHQPvwQgQib9qYbo5qysU0/e1t0qlz2lSvGxVlaBAvJef4Bzde4rfnF0hzg/T3RtSNQnbgYr1iM+zJD028cHCtgmRpYxgS4S+mPV6abqEqDa6dMxpL6iJ5ZUi8tlDVmtmRREqGXpHMHGyjxNpIaBaSe3atAmVuQqWgmhXLSFRm7y0G7A1Fchq1QXlaCF7u3pJCVl0L61ZWyFCDptU0qUZdiIpqupDjLpLwOeaW9DSXKxPFKWkqBXolmtpwbrTihe07TJyYohI6ySSysEYp2cyhKjWMzQRNa7B2YxldtWQq7UTqZ3NviRapqEpzX3W2UZjdG56qLdepRlUL61WV6nibMdubK2kD6yUM+gllruNsxuQzW8hxNkQ6PE1FD20989jcXLM7lrHXulS5c3uCYZWYO5K/NttWwqGRcHs5pMx0skJnYxSeksOzNKhylWefu8P4uRmf+/QVNL3CmiSsjz1+/fZlNj94yC/fucq6sIhTizQ0pU2tlzM/6mO7OWosnSZRYsGlhIuDJcWBw/HtkUilA944Ee4Lo2LzyoykMng13GSV2ei6EMeEn9wEYLr0SFcWYWpRVhpZagqRzbtA52hbWIbQ/8WRRRRbDLyUeOmg6M0pBd18f8BgO6QITRS9Jl5bhAsH8+oKTa/JY0Mmb3IdfTthvLHGPB+x/uwGplWS5gZHs4Hc1i5dMGtWU4/BdkhWSk6qPqlqNgpuP8PtCaHG+rDH/mxA5UmBKwktirXJ5UvHxJHF88+9DmuDaN/j+tEEXa+Y3xwzX7vkr4hW1ObWGm2UM95eU5vS23p0e0STCUv9+rBHsrZEW6rQsE4uFq3i1v5EeF9zHS1RhBh6kGO5BeHcJVw5wuA/TCX3nBqc21hS7BZkkUmV6uhGiXFtJQ3vuUF4e8CoF2P0c6ye6Di9nGxzqTcXlq3nVlShgTbJKPz7KrqqVbGOLc6dn5Hd9WhiHXKVc4MVd+ZDVmuHslZZHPdQzsuEUxJZUpWvFcK1zWhnzfH+gKrUaOyaJBhR1QqNAlu/JucijWTM1TJKrh9sMDm/wOznlHdcwqVDb0si2rpQSVbSIzvqJ9IqN5DOhXVsCd+pLgWxaOZQ1wp3b8n7oYA3SCHSmb4mEek8dzDVinO9FXFk0UQ62coCRRQf8qU07Ne1gjVJJH+tVacFqLTQ8W6phJFN/WpP6gaFhuaVGKYo6m6148PrqYdml0Rzh8OjASgNZa2yvDWkP0hIYxN9mOP1U6pUw+jl92kJ7ZLFymWdWqS5CIC644RsblO3aR5dlwJuUhkSIbdE5boqtIOaXtFoMkJ843jCOrZwLq1x7Zzitoe20Lm2dcT+nTHPb+0D0vq4syN1hnJqi2adneNdWVI30pFSRgaf/eTTNF5Ff3eN0x6bJDap77hUlcLR/pCoFMd593iEa0rtI72U8/Qz+zh2AWpDdOyyOvaoYv3+9fkO0TnaFmM7YbV20drc4/J3N1BXOnu7c7ZHIa5RiJx2bGENMppcw3jdwu7lxEeezOHnKnWusTOR5v7FiXTyhVQq5G1BYthLMO2S3jhmtBmymnkUvzUmWcjFCqDfNYlDizzTMY2SzXNLVK1GzVSa3xiim0KRt/g/z1Gncku9eWWG4lYUkUme62w/PWXgpdjXliixRvmzmzSVwip08HYjmrmJ1i+k11JrsMepyOAUCmWsU9wU+5+f3GMyjohnLnWqUfablm6wwLUz7Jsmpl2IakNktiTdJbcPxtKm088wXCkUVpUqMinAztVjskKXcctcp6xULLUkq3Su7R2KEOUoRdVqjJed07HOEyn0o3mf7WePUUoFY5jx0u0dPnr+Fue3FhhahdXP5EKuVGGpOnAYTULqyID/d4LhFQz6IjBYOTWr0KFxK47/TfmBs9p0Qt0o9L1UcqRqQ2M1OC/ahEdyfAynwBslZK8MMLSK/J6H40jBbtRLUJwSRauZ3x1iDzPKQweMmiixcHuZ5L3VBkYFh+seN5dj3j+4R15rEu2aNWgNqlcKA5gl/cGq2pDNbdaRTX3HZTVrp+HWNsk3h6haTbFdkAcDmkZhOIhIDl10rWYd28ShhWpWNLXCYCOCRkFJNMK5y8ZTc2Gouyv9tkkikX6/l9BUEs1WiY6qSdG2LDWiI5d46uJsJNIqCMQHHmls8vJii6e3p2hznbrSWMQOliXqvs52zMYkJF9Z1JWQgC/2+/SeXqKeT0grHcWs+ejwBltOeNpBsDEJYVBgGBXTmZB+J6nB1iBEyVSMKyH2QIrQJ2mnOtapjQY16KE5JTvWimXucHF7xmzlcn64RLUrjkOPJBVVZqMvNRjFrE6lct4p3h2L7dcgXl+MqNKc4XbE4eGQZjfH7uXcOxyhmyVFZDK6uGQd2eT7LtZuTKZZKJHQ1RWheTrOeOjIlAtmTalqWC86WEvI/2BOfWyReSlZYpAr7eFXGrIN4aM9qRyXvRpVE2Xa1bGHutJhK8M8H1HuKdIBoTesPpqitPaHoY1q1Bie5EAXoUN+4KKMc+zdiGXfxPVyokOPaG2gjnP6XkpZq9hGyfH+gK2Lc45vTFD05nSM8uM3nyFv2Y8UvaF2KwZeynrpoA1r0p2SvlmSzW20nnwHQ6uIG0u0xG5MaNySplZFZmRLeGL3r28wvrDEtiWNkCYmv3TvGUytOiUlOemVVQrQ2h+q+tiiMhu0UGV608V8OiSbCR3iy4stjud9VK1m4KXMFh6KVpNnBupGznLpYiw0Fl9XYr3ssn4W6tsuODXDfswsHkiPdGSj3rLJzmeMxyHLlYemV9L50C+oXohRT7iDa5VoaWBejlinFoxzwiOPaqJSvtzHeDqSabiBIgWsUYF500J9X9vKtzKZXFgwvz5mFemYxxo/lXw9npMxHMWUtUr60hDj6loiTLMiz3RpGbtlkFxt0PZSlJYmsYl1al36rCunpLpQsTcMuXN9E3srOSWYN3o5hlHJLfGdAeooZ/L0jOm8x/HhAEoF7ZK0WWlajT5KiVMLbabL3miHXMI7A5RBjj1JSdfCoeG+LvtF7RcoKhwcDjlUB/SuLlnNXaLMgVLBGqW4thQJKaWwnCQuzcZ9FrtZIsXb/+PeB6kbhVv7E6wXHZJnMrSpQZhoqG5JNnVQvJK70yHKMCddy11M9skJ2UXZy/ZYgh7jYkZ+awDvg3VmcbQ/ZLAR8fLtbZpcY5V5KJGGuZMIcbveYLkFNM678i9dRNuibhTMXs583sPppxhOIWxb7Uy1EurCt5nojK4syCITFKmim6bIfOSZQW03lCsTjAb9yKRMdZILJcvnKil4DYrTQQCvn6LpNYZdYrV8n3EkkYBSKVSpRnzkYd8yMS9G1KVKupLbxvxGD8WoYWkwHMboWkVTyXSbbRYYvzgki0wap2L8CzbpPQ9FE+6FCz+noPVKDKukrFWSVgrE8AqODoYSXakNZiud03clZ2sOMuxehuoIrWITS8+utZmgKQ39HelJjUNLUiMKHN0b0lgVlCpVqaLmUuFf3BlgTFLS3CAOLYmutJpNNxIWplSnqRXq6yKrUn4gpH5Njtvw8hJqqIYV9YX0lCPU3YyZLj2qUqXMNZZrh7pQMaxSbvsWJoZVUmwVTM4vyDdqzN/x8J5d0NgVy5XH7vk5yUsjaWh/dk1TqCzXLoZZSpHs0JWe6FJDD1uFhVRSF1UldzyK2qAkmrSstdpoaWoQ3uthOAVNopFvVThWTnKrj2K3ChObGdowJ9+RMeDZ7RG2Kfpk5kIhWdmwMKgqlY1RKFLu3zhFXclwgtcWS82x/Pimt/rUxxa2m7NMbLAr0tDE8ApUq6JMDeG1uGGjprJ3Zoseml7hjRIwatQbDtnMkbbGA7nlrjcLlFhDW0qfsrGZnKa91JVOPrNJfLGlmd0f7NjeXLHa79Mfx8KxbNYUuS790Q2Yk5Q0MXH8BU0NeaEzGYkAaJHqvG94wI2XdzDtkvIDId5QdMW0lY72uo23E6FMTYb9mDoUwiWA9FqKNZH/nwwHhQuHpl9SoXK0P0RzStYrBxoFzS2lm8OVtIfTy9C9gvTIIX2Xk2GPLKL1ff8a8I+BDYSc5nuDIHj5Dc/RgL8D/BGEsOZjQRD8o/ey9rAwtYp8KWOzce6gWpXwii5cYsOisWrKXMcbJ8SpiX5gUp9PqdYG6b7FxucVph9VwK1QFzpNodCcTyHRMJYaxbCS5vyZTeXVwmlqSA5Q06WgU1cK9UlUMsnx+hnJjT7p5Rw109GsCu7aFAqomkyo1UZDVkhfr26VQrRx2Mf410OUXKrI8bevURIDyy5I1ha3v6MSjti+Su0U1AsTfUuKcUVioA9zylSnaLfHbNGjSjUm2ysWC496baCUCupYqu1ZZrA4dtBGOY6TE6UGTaqJ4KCL5JRjkzrV0HdjIbMpVEyzovzdIcqwRtsRVdQXb+6h6DXqQkfdTVGfikhjk2Zu0uy0mmmVBnqDOchQf69H9VwkBb+VjdPP0LSCdGVR5xraQkd91YJzJY1dkR+4OHsRi1cn6KlC8w1Lykq01VZTj7zUqHYzmrVBriJdBoZKAehecTqqXKQ6zVPiTJRIo7FqYfv3UtYLl97lJXFk09SQZRra3MC7ump1ssSxLdeuSNQcmTQ7GbZdEK9sIRtPDJwt4XfQrYryQyGOVlPYOuXK5KAeMvwdk9U3OzSTnOY3hsQXJOLPIxMyVbpKIoPkXk+Ypa0KFCiPbaG19CrcrYi0Z1LnGq6by4ipopKmavvdEUXlWgGvkjFws6KZSI5fve5RuA2jp+YsVy7qdkqdSocNgLIhYonebsQqtlGckvWxhz41aMYllpcTRyKLVGSimxbH0o1Sv9Rj/Sw0F1PUfYv/6zMfwNmWqbq61ohDiybVMC9FlLlGdOxi7IkSCXZFGpvUiY7WK8jmtuydYwdjrlJtlWA03ElGGF5+qphbtnp2qlZRhCbp50dwJUZRa4Yv6nj/7lf+wMKPAT8aBME14EeBf/Amz/nTwDPAs8A3Az/o+/6V97j2UFivHVSnxJskoIqIWxpaqP1CCjL9gqZQKUtp8yk3C+pMA6XBurJm/v4GY66haDX2sYq6kaFqFZpbUpkN+lqjKjXsp9ZC3FFJAz9AecujXLVOdyr5y6ZUZfjArYXs+9ekP9C4HDHZWqHtxdSRcIrmmUGeGWhaQ7S2mewupchTKXjDlPSuh2ZIRK2ZteQ4vUoUc7UGvJL80CW900M1K8pYNvxweJ9AQ7MrZq+PaFYmxiSl8SrUezbxQqIAdyeizjXCY5Ez1wc5ycomDS3q3xrSLE2MY4PyyGG936cZFGSpQfF0Qm3XKK+4GJ9zGfymkHjUbo2q1dhWQVOJ0zjhFshzDb1XUBw4pBdz6ttygSiRTnLkimrrMMU4NKicmuSy9Pc6w5TGaEQocZxjzqX1KLnXk2gmV5ndG8LawN5KaNr+/82ft1BeF+Jw0yolYl3r95V5awW9V9AcWqzu9iGUSL+eCwk4lUI1KomWNuqvDXFvadSbkkaiVOWHd2USzR2aUoFSxXSFPNwbihpvz01FaPFIHAZLg9VHhftYN0uqF9ao47b1qJF/zcxCX2rQK7F3IpxRKoW5YUFjNKhmRXK7T51qWL2MeGWj3bOoFia6XkmeM1TQNxMpJObiLsqVKTLfWkNzOYFxzvzOUDoF7AJyVXh9AQ4tjElKEgsNoukILaf17ArVKUkXNo6boe2bsDaoc40qNCRvfyEjTwzpErgUoVqyhxVFJidpQO8V1Nc99Js2aA31654wmTUKdaHRe8UQwvP2rkcbZyjXQmHmW+mEhcX4Zz1WLe2i62YiR6Q2oID13JJiYdHUKuE3xSSp+U7cyikeSUTr+/428GHg32of+gng7/q+v/UGJdzvBv5hEAQ1cOT7/k8D3wX8N+9h7e2gAXzTlZykCbHVgmVeo6kV5oUlNQpZqeMabTRVa6hKg6GU1KinFIrVeXluUWtsf2BGXMmYqaPmrJ+xhNSkkZyjfXl++jpTTVH8NXEhJ1D1JYXwRz6wYl3YbF6YUjUq6geb1pYG0Cg2anrPzClbkhhLqygblayq8PSacjdilrrYmsLg0pS8Vk97CYtKQ1NlyEBTG3QlIau0VklC7OoZKatC8lHf9kyM0RaF1PY28CjW2PjgiroBVTl5zzXrwsZQanpmhqGUaEpD+H6Tnp6T1zplo2KoFWFhUTcKZa0ytoRIJ69VTFX6RbNKQ1cbslIn285RabA0iSa+9UqBpZfML9ds2kIYoigNzZUMV8/JavmxMp46ZJk5GGp1ql5qXRFu4LCwUP0ESy/RlDVZO4mUVCYjMyEqTQxV1DbsbyhZ5TZNozC2IopGp376vhjit33jEk2t4JmYuLRwNFEZKJ/KqBtwnpJzWjQ69dcp0pOpyh4KC1vIb5SaGgWttbNqVFTknNcXC4pKo3duxeJKycBMhS9AqdGU6nQfnpzff6c9L7raEBVf6BisS9JtEJcWPSMjqzJ0pSKrDHrmiuU1G01pZL8/lZGUBgNT0jN5LSTorpaKQGetEJcmE1uOn6VVFLWGd+WAshGn/B3fckTVqGSlztBMWOYOhpbJOSEmLk1svYRLh6SlTtlo6EqFpVXUKBhKef98qjEqdfv3/Qkt97nFaS8sCIl/3Sisc4v+B1aoSkNRyd75zucWzHMXT09QlBW7Vs1L37/Ca69vlYa8rqhqDUeX/HRzMSevVfp6hqGcpg7uN3U/BB5V6uAicKcVWCQIgsr3/bvt4w862kvAg0KQt9rnvJe1t8MewN9+4T95yKc/GvyDj/7Nx23CKf6nb/gvH7cJX4B/9Pt+6HGb8AX4SrPn73/kK2fv/J0XPva4TfgC/LcfepjY66GwB7z6sE/uug7gN4B/A7gHvDtBoA4dOjwp0BAn+xvv5EWPytG+Dpz3fV9ro1kNONc+/iBuAZe5/yUejFTf7drbIQN+5eG/SocOHZ5wPHQke4JHUgwLguAQ+G3gT7UP/Sng02/IzwL8E+DP+76v+r6/BXwn8E/f41qHDh06PFY8ytTBXwT+se/7fwOYA98L4Pv+zwJ/IwiCTwH/K/CNwEnb1w8FQXC9/f+7XevQoUOHxwqlOaFB6tChQ4cOZ4JuMqxDhw4dzhido+3QoUOHM0bnaDt06NDhjNE52g4dOnQ4YzzRAwsPQ3TzLt5zA+mCuArkSCfE9wVBcOT7/jchHA8OcAP4nrb1jbNYexPbfgD4QeADQRB89nHY4/u+Dfx3wLcBKfBrQRD8hbc6F2ex9oA9fxT4YYR2RQH+iyAIfupR2QN8H/AfAFdOzstZfeeHXPMBF7C4v0++5J5+L/vhIdZ+BhgjfupbgiD4+BvO3Rfs5zO25aGusS+FJz2ifRiim3eKBvhbQRD4QRB8AGlu/pjv+yrw48Bfaj/v48DHAM5i7Y3wff/DwDfRDnI8Rnv+FuJgr7XH56+3j7/VuTiLNXzfVxAH8meCIPgQ8GeQFkT1Edrz08Af4IsHbB718fix9rF/D/jLyDk6wZvu6fYYnuU++gHgaWABfMGM/Bv38yOw5W2vsbfCE+toHyC6+Yn2oZ8APtwOPLxrBEEwC4LgFx946BPI1NpHgDQIgpMptB8D/kT7/7NYO4Xv+xZyEX3/Aw8/cnt83+8hUdxfD4KgAQiC4OCtzsVZrL3h8NTAsP3/CBnF3nxU9sghCL5gQvJRH48H19pz96NIRDuGt9zTcLb76O+3x2aNUKCeHJ83289nbctbXmNvhyfW0fImRDfACdHNlwXtr+H3I7dAXzAWHATBMaD6vj85o7UH8UPAjwdBcOOBxx6HPVeRW9Yf8H3/U77v/6Lv+7+ftz4XZ7F2Yl+DXDT/3Pf9m0h0+b2Py54H8Kg//83WSmD3jYa9YU/DI9hHyI+h8sA+erP9/EhseYtr7C3xJDvaR4H/EQiBv/u4DPB9/5uBjwJ/73HZ8AA05Fbw00EQfBT4z4CfAnqPwxjf93XgrwB/LAiCy8B3AD/5uOz5KsFj3dNfYfv5ofEkO9pTohs4VWl4M6KbdwXf9/82QkT+3YHw5J4Q35ysbwJ1EASzM1o7wbcA7wOu+75/A7gA/BxClP6o7bmFREo/ARAEwa8Dx0DClz4Xb3We3u3aCT4EnAuC4Fdbe34ViJD85OOw5wRn8RnvdE0H9h806k32NDyCfY34qaZde9P97Pv+H34UtnyJa+xt8cQ62uDhiW7eMXzf/xEkt/OdQRC0tPf8JuC0t8og3A//5AzXAAiC4GNBEJwLguBKEARXgNvAv42Qoj9Se9rbrl+gJYD3pdK9DbzElzgXb3We3u3aA4fnNnDB932/ted9wA5SVX8c9pwcpy/7Z7yLtQzhJKE9Nm+2p+HR7Os+Ehx8yf0cBMG/eES2fNE19jB4orkOfN9/DmlpGdMS3QRBELzH93we+CziPE4Ehq4HQfDHfd//15BKr839NpGD9nVf9rUvYd8N4I8G0rbzyO3xff9p4H9BWowK4K8FQfB/v9W5OIu1B+z508B/juQBAX4gCIKfflT2AH8J+PeRfOgxMA2C4PlHfTweWLuGtHdpJ/Ygeew33dNvd87f49rPIIXKE1uOgiB4/g3n7wbtfj5jWx76GnszPNGOtkOHDh0eBZ7Y1EGHDh06PCp0jrZDhw4dzhido+3QoUOHM0bnaDt06NDhjNE52g4dOnQ4Y3SOtkOHDh3OGE80TWKHr134vv9XgaeDIPhzZ/w53wr8f0AM/IdBEPw/b/P8VxFugZ8MguB7ztK2Dl856Bxth69K+L4fPvCni0wyVe3f3xcEwY88QnPuBkFw4WGeGATBVd/3fxAZge7whKCFkBTWAAACBklEQVRztB2+KhEEwSnxSzsd9OeCIPiXj82gDh3eAp2j7fA1iZOoMQiC7/F9/wpwHfizCMVeD2Ht+k3gf0ao8H48CIL/6IHX/1ngP0VGYz8J/IUgCN5Izv2lPvuZ9n0/hIwZ/3wQBN/95flmHb4a0RXDOjxJ+EZa9ingvwf+GiKp8zzwJ3zf/xYA3/f/GPBXEQ6CLeCXuU+Y/TD4YeBfIJwCFxBqwQ5PMDpH2+FJwg8HQZC2TE8RoihwGATBHcSZvtA+7y8C/1UQBJ8PgqAEfgT4kO/7l9/8bb8IBUKtd679vF95uxd0+NpG52g7PEl4kHEpeZO/T/K+l4H/wff9he/7C2CGCDeef8jP+cvt8z/p+/7n2jREhycYXY62Q4cvxuvA3wyC4H97Ny8OgmAf+PMALY/pv/R9/+NBELzyZbSxw1cRuoi2Q4cvxo8Bf6XlFsb3/aHv+9/1sC/2ff+7fN8/afeaIyqy9Vu8pMPXODpH26HDGxAEwT8D/mvgf/d9f4WQXn/7O3iLbwB+ve31/RngPw6C4LUvv6UdvlrQEX936PAe4Pv+H0BkVjJES+vn3ub5AZLr/ckgCLrc7ROCztF26NChwxmjSx106NChwxmjc7QdOnTocMboHG2HDh06nDE6R9uhQ4cOZ4zO0Xbo0KHDGaNztB06dOhwxugcbYcOHTqcMf5/QkrBQimKe1AAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"yf = fftpack.fft(df_test_seg['acoustic_data'].values)\nplt.plot(np.abs(yf))\nplt.grid()\nplt.show()","execution_count":82,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAZYAAAEBCAYAAABbm4NtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAFexJREFUeJzt3X2wJNV9n/Gne2bu6y4LexeEFgGLQHusQhgFiTLERDgvsuOKVbEtBZsKoApxWShEcVxJbCkpvdiqqDYSVU7JIMCWKW+JREmociTZjotUjAheLBkLgzCRdbSodoEFBMvdBfbu3peZ6c4f3btc2Jc7u5x5ubrPp+rWzJwzfefX3dPznT4905OVZYkkSankwy5AkvTDxWCRJCVlsEiSkjJYJElJGSySpKQMFklSUgaLJCkpg0WSlJTBIklKymCRJCVlsEiSkmoOu4ABGgcuB54DukOuRZJWi0Z9+STQ6WWCtRQslwN/NuwiJGmVugDY3csd11KwPAewf/9BisIzOktSL/I844wzpk9qmrUULF2AoigNFknqIw/eS5KSMlgkSUkZLJKkpAwWSVJSBoskKSmDRZKUlMHSg9/9w+/whw/uGnYZkrQqrKXvsZyyJ58/QLvjWWAkqRfusUiSkjJYJElJGSySpKQMFklSUgaLJCkpg0WSlJTBIklKymCRJCVlsEiSkjJYJElJGSySpKQMFklSUgZLj8phFyBJq4TB0oNs2AVI0irS02nzQwi7gYX6D+DXY4z3hhCuAO4EJoHdwHUxxhfqaQbaJ0kaDSezx/KBGOM76797Qwg5cDdwc4xxK/AAsA1g0H2SpNHxRobC3gUsxBh31LfvAK4ZUp8kaUScTLD8lxDCYyGEL4QQTgfOA5483BljfBHIQwgbh9AnSRoRvQbL34kxXgpcTnUs+9b+lSRJWs16CpYY49P15SLwBeDHgaeA8w/fJ4SwCShijPuG0CdJGhErBksIYTqEsKG+ngG/CDwKPAxMhhCuqu96E3BPfX3QfZKkEdHLHsubgPtDCI8BjwNbgX8RYyyA64HbQwg7gauBjwIMuk+SNDqyslwz3ynfAuyanZ2jKE5unj/+xb/g7Jkpbv65S/pSmCSNqjzPmJlZB3AB1fcHV56mnwVJktYeg0WSlJTBIklKymDp1Zo5FCVJb4zB0gtPbyxJPTNYJElJGSySpKQMFklSUgaLJCkpg0WSlJTBIklKymCRJCVlsEiSkjJYJElJGSySpKQMFklSUgaLJCkpg0WSlJTB0iPPmi9JvTFYeuBZ8yWpdwaLJCkpg0WSlJTBIklKymCRJCVlsEiSkjJYJElJGSySpKSaJ3PnEMIngU8Bl8QYHw8hXAHcCUwCu4HrYowv1PcdaJ8kaTT0vMcSQrgMuAJ4sr6dA3cDN8cYtwIPANuG0SdJGh09BUsIYRy4DfjwsuZ3AQsxxh317TuAa4bUJ0kaEb3usfwmcHeMcfeytvOo914AYowvAnkIYeMQ+iRJI2LFYAkhXAm8G/hC/8uRJK12veyxXA28HdgVQtgNvAW4F7gIOP/wnUIIm4AixrgPeGrAfX1Xlp7fWJJ6sWKwxBi3xRg3xxi3xBi3AHuAnwI+B0yGEK6q73oTcE99/eEB9/WZ5zeWpF6d8vdYYowFcD1wewhhJ9WezUeH0SdJGh3ZGhri2QLsmp2doyhObp4/8XsPcebpE3zk/T/al8IkaVTlecbMzDqAC6i+P7jyNP0sSJK09hgskqSkDBZJUlIGiyQpKYNFkpSUwSJJSspgkSQlZbBIkpIyWCRJSRkskqSkDBZJUlIGSw8yT24sST0zWCRJSRkskqSkDBZJUlIGiyQpKYNFkpSUwSJJSspgkSQlZbBIkpIyWCRJSRkskqSkDBZJUlIGiyQpKYOlR2U57AokaXUwWHrgyY0lqXfNXu4UQvgKcAFQAHPAR2KMj4YQtgLbgRlgFrghxriznmagfZKk0dDrHssHY4yXxhj/FnALcFfdfgdwW4xxK3AbcOeyaQbdJ0kaAT3tscQYX152cwNQhBDOAi4D3lu3fxm4NYRwJtXo0cD6Yox7e59lSVI/9XyMJYTwxRDCU8B/BD4InAs8E2PsAtSXz9btg+6TJI2InoMlxvhLMcbzgH8PfK5/JUmSVrOT/lRYjPFLwN8F9gDnhBAaAPXlZuDp+m+QfZKkEbFisIQQ1oUQzl12+33APuAF4FHg2rrrWuCRGOPeGONA+05lxiVJ/dHLwftp4J4QwjTQpQqV98UYyxDCTcD2EMIngP3ADcumG3SfJGkEZOXa+Ur5FmDX7OwcRXFy8/ypux5i42kT/KsP/GhfCpOkUZXnGTMz66D6LuPunqbpZ0GSpLXHYJEkJWWwSJKSMlgkSUkZLL3w9MaS1DODRZKUlMEiSUrKYJEkJWWwSJKSMlgkSUkZLJKkpAwWSVJSBoskKSmDRZKUlMEiSUrKYJEkJWWwSJKSMlh6tIZ+aVOS3hCDpQeZpzeWpJ4ZLJKkpAwWSVJSBoskKSmDRZKUlMEiSUrKYJEkJWWwSJKSMlgkSUk1V7pDCGEG+BJwIbAE7AQ+FGPcG0K4ArgTmAR2A9fFGF+opxtonyRpNPSyx1ICn40xhhjjJcD3gW0hhBy4G7g5xrgVeADYBjDoPknS6FgxWGKM+2KM9y9r+iZwPvAuYCHGuKNuvwO4pr4+6D5J0og4qWMs9V7Dh4GvAecBTx7uizG+COQhhI1D6JMkjYiTPXj/28AccGsfahlpnttYknrTc7CEEG4B3gb8QoyxAJ6iGhI73L8JKGKM+4bQ11+e3FiSetZTsIQQPkN1jONnY4yLdfPDwGQI4ar69k3APUPqkySNiBWDJYRwMfAxYDPw5yGER0MI/7Pea7keuD2EsBO4GvgowKD7JEmjI1tDv4y4Bdg1OztHUZzcPP/G7/8lG6bH+Nf/5NK+FCZJoyrPM2Zm1gFcQPX9wZWn6WdBkqS1x2CRJCVlsEiSkjJYJElJGSySpKQMFklSUgaLJCkpg0WSlJTBIklKymCRJCVlsPTAkxtLUu8MFklSUgaLJCkpg0WSlJTBIklKymCRJCVlsEiSkjJYJElJGSySpKQMFklSUgaLJCkpg0WSlJTBIklKymDpUVkOuwJJWh0Mlh5knt5YknpmsEiSkjJYJElJNVe6QwjhFuD9wBbgkhjj43X7VmA7MAPMAjfEGHcOo0+SNDp62WP5CvAe4MnXtd8B3BZj3ArcBtw5xD5J0ohYcY8lxrgDIIRwpC2EcBZwGfDeuunLwK0hhDOpfsl3YH0xxr0nOc+SpD461WMs5wLPxBi7APXls3X7oPskSSPEg/eSpKRONVieBs4JITQA6svNdfug+yRJI+SUgiXG+ALwKHBt3XQt8EiMce+g+06lfklS//TycePPAz8PnA38nxDCbIzxYuAmYHsI4RPAfuCGZZMNuk+SNCKycu2cBGsLsGt2do6iOLl5/vT2v2Td5Bi/es2lfSlMkkZVnmfMzKwDuADY3dM0/SxIkrT2GCySpKQMlh6VrJkhQ0l6QwyWnnjefEnqlcEiSUrKYJEkJWWwSJKSMlgkSUkZLJKkpAwWSVJSBoskKSmDRZKUlMEiSUrKYJEkJWWwSJKSMlgkSUkZLL3y5MaS1BODpQeZJzeWpJ4ZLJKkpAwWSVJSBoskKSmDRZKUlMEyYO1OMewSJK0Cq/m1wmAZoOf3HeJDt9zPXX/8N8MuRdIIe2LPy3zolvu596Gnhl3KKTFYBuhjv/NNAHb89XP8ztf+35CrkTSKHvj2s3zm7ocB+O/3PTHkak6NwTIk3/zO88MuQdII+v0/+e5rbne6q29IzGDpk8/+17/ixm33cdf/qoa9vvf0S0fd58Zt9w26LEkj7It/9J2j2j52ZzXS8dDfPM+N2+7jxm33UZa9nwrk9/7oO9y47b6BBlRzYI+USAhhK7AdmAFmgRtijDuHUcvXH3mGex96in/20z/CWWdMsXPPS9zx1dcOce147Dl2PPbccf/Hjdvu485/+xO0mq9mfLvT5cG//gFf3bGLz/zyFUyMNTi40GHdZKtv8yKpPw4tdBgfy8mzjE9v/xZXvuNsrrrkzUyOv/bl9yP/+QEOLnSOmn72lYWj3oT+8//0dQCmJ5p8+pd+jP0HFvn09m/xk5efyy/+/be95r4PPv4DABaWuqybHMy+RHYyyTcKQgj3AXfFGO8OIVwH3Bhj/Hs9TLoF2DU7O0dRnNw8/8rn/4wDh9r81r/8cTrdkn93+5+ffOEj4KJzNjA33+YH+w4x1spZahdcefGbeHb2EHkGTz0/x09fcR7ffmKWCzefxtREi42njXPWGZP830ee5eHv7eWGfxg4Y904+w4s8sC3n2W8mXPJhTOsnxrjgjefxh9/Yzf/6MotNPKMxXaXPXvnyLOMs2emWDfRYrHd5cB8m6nxJjv3vMw7LthIo5Fx2tQYS+0uL80tMbNhgqeeP8BZp09y2vQYzWZOt1uy67lX2LxpmkaeUZQleZYxPtYgX3bOnaIsOXBwifXTYxRFSbPx6obULQoyMvK8uv9iu0ueQavZOGpZHf7/xzO/2DnqhWFUzC92XrNcDm/j2bLb2THmrVsULLWLI/NVliXd1y3DTrcgzzLmFqp1uLzv8GOXZbX8xls5u547wDlnTjM13qRblLxycIlnZw9y0TkbePqFOc46fZKxVoOFpS6L7S5P7HmZN89MUQKNPGN6ssXcoTYvzS1ycKHNpg2TnL1xir0vzfOt777ApRdtYvaVBRYWO/zpX+1h04ZJ3vHWjayfHGNyvMF/+9OdLCx1+ZUPXMrTLxyg0y35wb5DPPb9Wd7x1o3MvrzA8/sPMTneZKzZ4IlnXj4yLxNjDa5+52bufejppOtnUD7+wXezfrLFr93xDQBu+9X3nNJzNs8zZmbWAVwA7O5lmlUVLCGEs4DvATMxxm4IoUG11/K2GOPeFSbfwikGi0NWkla72//N1Yy3jn4TtZJTCZbVdozlXOCZGGMXoL58tm6XJB3H8uH2fhvNffkR87u/9hN8/5lXaHcLWo2c6ckWE3XyT000WWx3WTfZYt+BRcZbDdZPtcizargmA7pFWQ8xVNN0i5I8zyjLksWlgsV2l7IsaXcKpiaajLcazC92mBhr0qmHbw4ttGk2c5baXdqdgmYzZ26+TVlAo5ExNdHkpQOLtLsF6yfHWFiqpl9sd1k/1WJhqUuzkVeP2e4CUJaw1OkyNd5irJlzYL4NVF/MGm/ljLUaHFrsMNasxofJoNMpmJvvsH6qGtaammgyd6jNpg0THFzo0O4UTIw1KIFWI2ex3aWRZywsdekW1TBKo5FzaKHDWCuvhlryaniq2cjpdgva3YJ2pxqSmZtvMzXRrGtqUBQlS52Coig5bboaPusUJdTzsrBUzW+rkdMpSoqiWgeH/2cjz8iy6vEyqo2tBOYOtSnKkomxxpEvpo3V63isXtbV8E6DRp5xaLFDnlU1jzVzJsebvDS3SJ5nRw6Szh1qMz3ZOrKH3KyXY5bB4lL1nDm02GFhqcvEWINOtziyzg6PJIyPNVhqV/+v1czJMuh2qyGqRp4x1mrQbGTVOH6rqn1+qUOWZUxPNI8MTW08bYKXDy4yOdakKEsOzrc58/RJ9s8tkmfZkWXcyDOW2gXj9XJoNfNlj5VTltUQ1dx8m4PzbZqNnI0bJlhc6jI90aTZzJlf7LC41GW81SDLMrpFSauRMbfQYbyVk+cZRVG/0JUljUbO/gOLTE80q2FOqne880tdmo2MPMs4bXqMTrekLEs63YIsq5bzeKtxZGgzzzJePrhII88pypJ1k61qnhoZjTzjlYNtTptuMb9QLZ8sq5ZvUUBJyeJS9VydHG/S6dbLv5XT6ZZkWTXfnW5ZbdcZHFzo0Mwz1k9Vw67d+q/VzBlv5ZBltBo5C0sd1k+NUZblq8/p8SbzS12KoqTdLWhk1fKdmnj1JTnLqufo4W2o2aieq4cHMQ8cah95zegW1dDt4efO/GKHufk2Wd229S2nn3BoNzWHwiRJx/VDPxQWY3wBeBS4tm66Fnikh1CRJA3IahwKuwnYHkL4BLAfuGHI9UiSlll1wRJj/C7wY8OuQ5J0bKtqKEySNPoMFklSUgaLJCmpVXeM5Q1oAEc+8y5JWtmpvGaupWB5M8AZZ0wPuw5J+qG2qr4g+QaNA5cDzwHdIdciSavF4ROMPQkcffrlY1hLwSJJGgAP3kuSkjJYJElJGSySpKQMFklSUgaLJCkpg0WSlJTBIklKai198/6UhBC2AtuBGapfq7whxrizD48zA3wJuBBYAnYCH4ox7g0hXAHcCUxS/YLbdfWPntGPvh5q/STwKeCSGOPjo1RfCGEC+C3gHwALwDdijL98ovXYj74T1PczwKepfmE2A34jxvgHw6wvhHAL8H6qX1m9JMb4eL8e9w30HVXjibaZepqBPS+PtwyXLePXbDODro8Bc49lZXcAt8UYtwK3Ua20fiiBz8YYQ4zxEuD7wLYQQg7cDdxc1/AAsA2gH30rCSFcBlxB9S3cvtTwRuoDPksVKFvr5fjxuv1E67EffUcJIWRUL4TXxxjfCVxP9aN1+ZDr+wrwHup12ufHPdW+Y9V4zG0GhvK8PN4yPGqbGVJ9A2WwnEAI4SzgMuDLddOXgctCCGemfqwY474Y4/3Lmr4JnA+8C1iIMe6o2+8Arqmv96PvuEII41Qb/IeXNY9SfeuoflH04zHGEiDG+PyJ1mM/+lYoswA21NdPpzrF0KZh1hdj3BFjfHp526CX2Ur1HqvGE2wzMODn5bHqq5fjsbaZgdc3aAbLiZ0LPBNj7ALUl8/W7X1Tv/P4MPA14DyWvdOJMb4I5CGEjX3qO5HfBO6OMe5e1jZK9V1INYTyyRDCt0II94cQruLE67EffcdUh901wFdDCE9Svcu9YVTqe51B1/SG6n3dNgOj87w81jYzSvX1hcEymn4bmANuHXYhh4UQrgTeDXxh2LWcQAN4K/BIjPHdwK8DfwCsG2pVtRBCE/gY8I9jjOcD7wP+ByNS3yrnNjNCDJYTexo4J4TQAKgvN9ftfVEfBHwb8AsxxgJ4ild37wkhbAKKGOO+PvUdz9XA24FdIYTdwFuAe4GLRqQ+6uk61MMpMca/AF4E5jn+ejzROj7VvuN5J7A5xvhgXd+DwEGqY0KjUN9y/XjcvtR7jG0GRmO7OeY2E0L4yRGpr28MlhOI1acpHgWurZuupXo3vLcfjxdC+AzVOOnPxhgX6+aHgcl6SAfgJuCePvYdU4xxW4xxc4xxS4xxC7AH+Cngc6NQX13ji8DXgffCkU8ZnQV8j+OsxxOt41PtO0GJe4C3hBBCXd/bgTdRfZppFOo7oh+P2496j7PNwAhsN8fbZmKM/3sU6usnT5u/ghDCj1B9BPIMYD/VRyBjHx7nYuBxqhfB+bp5V4zx50IIf5vqEzITvPoRwufr6ZL39VjvbuBnYvWxz5GpL4TwVuAuqo+stoH/EGP8kxOtx370naC+fwp8lOogPsAnY4xfGWZ9IYTPAz8PnE21hzcbY7x40DWt0HdUjVTHq465zdTTDOx5ebxl+LrlvJt6mxl0fQyYwSJJSsqhMElSUgaLJCkpg0WSlJTBIklKymCRJCVlsEiSkjJYJElJGSySpKT+P73d497Eo/OZAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Fourier transform per sampling frame"},{"metadata":{"trusted":true},"cell_type":"code","source":"display_df_with_preset_precision(df_after_long_jumps_down_points.head(),max_precision)","execution_count":83,"outputs":[{"output_type":"display_data","data":{"text/plain":"       acoustic_data           ...             diff_in_time_to_failure\n4095             -13           ...                       -0.0009954955\n8191              -2           ...                       -0.0010954955\n12287             -4           ...                       -0.0010954955\n16383              0           ...                       -0.0009954955\n20479              3           ...                       -0.0010954955\n\n[5 rows x 3 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>acoustic_data</th>\n      <th>time_to_failure</th>\n      <th>diff_in_time_to_failure</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4095</th>\n      <td>-13</td>\n      <td>1.4680999843</td>\n      <td>-0.0009954955</td>\n    </tr>\n    <tr>\n      <th>8191</th>\n      <td>-2</td>\n      <td>1.4669999843</td>\n      <td>-0.0010954955</td>\n    </tr>\n    <tr>\n      <th>12287</th>\n      <td>-4</td>\n      <td>1.4658999843</td>\n      <td>-0.0010954955</td>\n    </tr>\n    <tr>\n      <th>16383</th>\n      <td>0</td>\n      <td>1.4648999843</td>\n      <td>-0.0009954955</td>\n    </tr>\n    <tr>\n      <th>20479</th>\n      <td>3</td>\n      <td>1.4637999843</td>\n      <td>-0.0010954955</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"range_index=15 #first training sequence is the shortest one\nstart_time = timeit.default_timer()\ntry:\n    del(df_sample)    \nexcept NameError:\n    pass\ndf_sample = pd.read_csv('../input/train.csv', skiprows = index_ranges[range_index][0], nrows= index_ranges[range_index][1]-index_ranges[range_index][0],\n                       dtype={'acoustic_data': np.int16, 'time_to_failure': np.float64})\ndf_sample.columns=['acoustic_data','time_to_failure']\nprint('elapsed time: {:.2f} sec'.format(timeit.default_timer()-start_time))","execution_count":84,"outputs":[{"output_type":"stream","text":"elapsed time: 47.67 sec\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_sample.index\nindex_ranges[range_index]\nixs_of_indexes = np.where(np.all([df_after_long_jumps_down_points.index <= index_ranges[range_index][1],df_after_long_jumps_down_points.index >= index_ranges[range_index][0]-1],axis=0))[0]\nixs_of_indexes1 = np.where(np.logical_and(df_after_long_jumps_down_points.index <= index_ranges[range_index][1],\n                        df_after_long_jumps_down_points.index >= index_ranges[range_index][0]))[0]\nixs_of_indexes,ixs_of_indexes1","execution_count":85,"outputs":[{"output_type":"execute_result","execution_count":85,"data":{"text/plain":"(array([142945, 142946, 142947, ..., 151832, 151833, 151834]),\n array([142945, 142946, 142947, ..., 151832, 151833, 151834]))"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.array_equal(ixs_of_indexes1,ixs_of_indexes)","execution_count":86,"outputs":[{"output_type":"execute_result","execution_count":86,"data":{"text/plain":"True"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"indexes = df_after_long_jumps_down_points.index[ixs_of_indexes].union(index_ranges[range_index])","execution_count":87,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.unique(np.diff(indexes))","execution_count":88,"outputs":[{"output_type":"execute_result","execution_count":88,"data":{"text/plain":"array([4095, 4096])"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"frame_indexes = list(zip(indexes[:-1],indexes[1:]))","execution_count":89,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(list(frame_indexes))","execution_count":90,"outputs":[{"output_type":"execute_result","execution_count":90,"data":{"text/plain":"8891"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#MEAN (Bias) is removed \nlow_path_filter_n_freqs = 2048\navg_len=1\nframe_sequence_offset = -frame_indexes[0][0]\ndef show_frame_and_fft(sequence_idx):\n    fig, axs = plt.subplots(nrows=1, ncols=3, sharex=False)\n    the_df = df_sample[frame_sequence_offset+frame_indexes[sequence_idx][0]:frame_sequence_offset+frame_indexes[sequence_idx][1]]\n    print(the_df.shape)\n    diff_ttf = the_df['time_to_failure'].diff()[1:]    \n    acustic_data_series = the_df['acoustic_data']\n    \n    print('ttf = {:.9f}'.format(the_df['time_to_failure'].mean()))\n    print('diff_ttf.mean = {:.9f}, diff_ttf.std = {:.9f}'.format(diff_ttf.mean(), diff_ttf.std()))\n    print('acustic.mean = {:.9f}, acustic.std = {:.9f}'.format(acustic_data_series.mean(), acustic_data_series.std()))\n    \n    (acustic_data_series - acustic_data_series.mean()).plot(ax=axs[0]);\n    fig.set_size_inches(32,4)\n    \n    acustic_data_rft = fftpack.rfft(acustic_data_series-acustic_data_series.mean())\n    acustic_data_ft = fftpack.fft(acustic_data_series-acustic_data_series.mean())\n    \n    rfreqs = fftpack.rfftfreq(acustic_data_rft.size,diff_ttf.mean())\n    freqs = fftpack.fftfreq(acustic_data_ft.size, diff_ttf.mean())    \n    rfreqs = -rfreqs\n    \n    the_dict = {}\n    for idx in range(len(acustic_data_rft)):\n        if rfreqs[idx] in the_dict:\n            the_dict[rfreqs[idx]] = (the_dict[rfreqs[idx]]+np.abs(acustic_data_rft[idx]))/2.\n        else: \n            the_dict[rfreqs[idx]] = np.abs(acustic_data_rft[idx])\n    unique_rfreqs = np.unique(rfreqs)\n    print(\"arrays are equal is {}\".format(np.array_equal(sorted(unique_rfreqs),unique_rfreqs)))\n    \n    pd.DataFrame.from_dict({'acustic_data_rft_amp': [the_dict[ent] for ent in unique_rfreqs][:low_path_filter_n_freqs], \n                            'rfreqs':unique_rfreqs[:low_path_filter_n_freqs]}).set_index('rfreqs').plot(ax=axs[1])\n    pd.DataFrame.from_dict({'acustic_data_ft_amp': (np.abs(acustic_data_ft)[len(freqs)//2:]), \n                            'freqs':freqs[len(freqs)//2:]}).set_index('freqs').plot(ax=axs[2])","execution_count":91,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('range_index = {}'.format(range_index))\ninteract(show_frame_and_fft, sequence_idx=widgets.IntSlider(min=0,max=len(list(frame_indexes))-1,step=1,value=0));","execution_count":92,"outputs":[{"output_type":"stream","text":"range_index = 15\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"interactive(children=(IntSlider(value=0, description='sequence_idx', max=8890), Output()), _dom_classes=('widg…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3c6d03aca7d84f7f920cb1db69b2bd04"}},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def calc_fft_amp_per_sequence_index(sequence_idx):\n    the_df = df_sample[frame_sequence_offset+frame_indexes[sequence_idx][0]:frame_sequence_offset+frame_indexes[sequence_idx][1]]\n\n    diff_ttf = the_df['time_to_failure'].diff()[1:]   \n    acustic_data_series = the_df['acoustic_data']\n    \n    print('ttf = {:.9f}'.format(the_df['time_to_failure'].mean()))\n    print('diff_ttf.mean = {:.9f}, diff_ttf.std = {:.9f}'.format(diff_ttf.mean(), diff_ttf.std()))\n    print('acustic.mean = {:.9f}, acustic.std = {:.9f}'.format(acustic_data_series.mean(), acustic_data_series.std()))    \n    \n    acustic_data_rft = fftpack.rfft(acustic_data_series-acustic_data_series.mean())\n       \n    rfreqs = -fftpack.rfftfreq(len(acustic_data_rft),diff_ttf.mean())   \n    \n    the_dict = {}\n    for idx in range(len(acustic_data_rft)):\n        if rfreqs[idx] in the_dict:\n            the_dict[rfreqs[idx]] = (the_dict[rfreqs[idx]]+np.abs(acustic_data_rft[idx]))/2.\n        else: \n            the_dict[rfreqs[idx]] = np.abs(acustic_data_rft[idx])\n    \n    unique_rfreqs = np.unique(rfreqs)\n    \n    return pd.DataFrame.from_dict({'acustic_data_rft_amp': [the_dict[ent] for ent in unique_rfreqs][:low_path_filter_n_freqs], \n                                   'rfreqs':unique_rfreqs[:low_path_filter_n_freqs]}).set_index('rfreqs')","execution_count":93,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"(calc_fft_amp_per_sequence_index(0)['acustic_data_rft_amp']).values[:low_path_filter_n_freqs]","execution_count":94,"outputs":[{"output_type":"stream","text":"ttf = 9.459497765\ndiff_ttf.mean = -0.000000001, diff_ttf.std = 0.000000000\nacustic.mean = 4.055419922, acustic.std = 2.667235273\n","name":"stdout"},{"output_type":"execute_result","execution_count":94,"data":{"text/plain":"array([  0.        , 390.57631821, 242.90575096, ...,  80.7214865 ,\n        57.91800415,  16.89011352])"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def features_row_per_sequence_index(sequence_idx):\n    the_df = df_sample[frame_sequence_offset+frame_indexes[sequence_idx][0]:frame_sequence_offset+frame_indexes[sequence_idx][1]]\n\n    diff_ttf = the_df['time_to_failure'].diff()[1:]   \n    acustic_data_series = the_df['acoustic_data']   \n    \n    acustic_data_rft = fftpack.rfft(acustic_data_series-acustic_data_series.mean())\n       \n    rfreqs = -fftpack.rfftfreq(len(acustic_data_rft),diff_ttf.mean())   \n    \n    the_dict = {}\n    for idx in range(len(acustic_data_rft)):\n        if rfreqs[idx] in the_dict:\n            the_dict[rfreqs[idx]] = (the_dict[rfreqs[idx]]+np.abs(acustic_data_rft[idx]))/2.\n        else: \n            the_dict[rfreqs[idx]] = np.abs(acustic_data_rft[idx])\n    \n    unique_rfreqs = np.unique(rfreqs)\n    \n    return [acustic_data_series.mean()]+[the_dict[ent] for ent in unique_rfreqs][:low_path_filter_n_freqs][1:]+[the_df['time_to_failure'].mean()]","execution_count":95,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"start_time = timeit.default_timer()\npd.DataFrame(np.array([features_row_per_sequence_index(idx) for idx in range(len(list(frame_indexes)))])).to_csv('../working/feat_rfft_data_for_range_index_{}_nfreqs_{}.csv'.format(range_index, low_path_filter_n_freqs))\nprint('elapsed time: {:.2f} sec'.format(timeit.default_timer()-start_time))","execution_count":96,"outputs":[{"output_type":"stream","text":"elapsed time: 125.05 sec\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### Test frames"},{"metadata":{"trusted":true},"cell_type":"code","source":"def plot_test_seg_by_index(idx):\n    df_test_seg = pd.read_csv(os.path.join(\"../input/test\",test_seg_files[idx]), dtype={'acoustic_data': np.int16})\n    (df_test_seg['acoustic_data']-df_test_seg['acoustic_data'].mean()).plot();","execution_count":97,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#MEAN (Bias) is removed \ndiff_ttf_mean = -0.000000001\navg_len=1\nframe_sequence_offset = -frame_indexes[0][0]\ndef show_test_frame_and_fft_by_idx(idx):\n    df_test_seg = pd.read_csv(os.path.join(\"../input/test\",test_seg_files[idx]), dtype={'acoustic_data': np.int16})\n    fig, axs = plt.subplots(nrows=1, ncols=3, sharex=False)\n\n    print(df_test_seg.shape)\n    \n    acustic_data_series = df_test_seg['acoustic_data']\n   \n    print('acustic.mean = {:.9f}, acustic.std = {:.9f}'.format(acustic_data_series.mean(), acustic_data_series.std()))\n    \n    (acustic_data_series - acustic_data_series.mean()).plot(ax=axs[0]);\n\n    fig.set_size_inches(32,4)\n    \n    acustic_data_rft = fftpack.rfft(acustic_data_series-acustic_data_series.mean())\n    acustic_data_ft = fftpack.fft(acustic_data_series-acustic_data_series.mean())\n    \n    rfreqs = fftpack.rfftfreq(acustic_data_rft.size,diff_ttf_mean)\n    freqs = fftpack.fftfreq(acustic_data_ft.size, diff_ttf_mean)\n        \n    rfreqs = -rfreqs\n    \n    the_dict = {}\n    for idx in range(len(acustic_data_rft)):\n        if rfreqs[idx] in the_dict:\n            the_dict[rfreqs[idx]] = (the_dict[rfreqs[idx]]+np.abs(acustic_data_rft[idx]))/2.\n        else: \n            the_dict[rfreqs[idx]] = np.abs(acustic_data_rft[idx])\n    unique_rfreqs = np.unique(rfreqs)\n    print(\"arrays are equal is {}\".format(np.array_equal(sorted(unique_rfreqs),unique_rfreqs)))\n    \n    pd.DataFrame.from_dict({'acustic_data_rft_amp': [the_dict[ent] for ent in unique_rfreqs][:low_path_filter_n_freqs], \n                            'rfreqs':unique_rfreqs[:low_path_filter_n_freqs]}).set_index('rfreqs').plot(ax=axs[1])\n    pd.DataFrame.from_dict({'acustic_data_ft_amp': (np.abs(acustic_data_ft)[len(freqs)//2:]), \n                            'freqs':freqs[len(freqs)//2:]}).set_index('freqs').plot(ax=axs[2])","execution_count":98,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"interact(show_test_frame_and_fft_by_idx, idx=widgets.IntSlider(min=0,max=len(test_seg_files)-1,step=1,value=0));","execution_count":99,"outputs":[{"output_type":"display_data","data":{"text/plain":"interactive(children=(IntSlider(value=0, description='idx', max=2623), Output()), _dom_classes=('widget-intera…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5e2757114e1c43899ffb166dbf87aae2"}},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def test_features_row_per_sequence_index(idx):\n    df_test_seg = pd.read_csv(os.path.join(\"../input/test\",test_seg_files[idx]), dtype={'acoustic_data': np.int16}) \n    acustic_data_series = df_test_seg['acoustic_data'] \n    \n    acustic_data_rft = fftpack.rfft(acustic_data_series-acustic_data_series.mean())\n       \n    rfreqs = -fftpack.rfftfreq(len(acustic_data_rft),diff_ttf_mean)   \n    \n    the_dict = {}\n    for idx in range(len(acustic_data_rft)):\n        if rfreqs[idx] in the_dict:\n            the_dict[rfreqs[idx]] = (the_dict[rfreqs[idx]]+np.abs(acustic_data_rft[idx]))/2.\n        else: \n            the_dict[rfreqs[idx]] = np.abs(acustic_data_rft[idx])\n    \n    unique_rfreqs = np.unique(rfreqs)\n    \n    return [acustic_data_series.mean()]+[the_dict[ent] for ent in unique_rfreqs][:low_path_filter_n_freqs][1:]#+[the_df['time_to_failure'].mean()","execution_count":100,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(test_features_row_per_sequence_index(0))","execution_count":101,"outputs":[{"output_type":"execute_result","execution_count":101,"data":{"text/plain":"2048"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"start_time = timeit.default_timer()\npd.DataFrame(np.array([test_features_row_per_sequence_index(idx) for idx in range(len(test_seg_files))])).to_csv('../working/feat_test_rfft_data_nfeatures_{}.csv'.format(low_path_filter_n_freqs))\nprint('elapsed time: {:.2f} sec'.format(timeit.default_timer()-start_time))","execution_count":102,"outputs":[{"output_type":"stream","text":"elapsed time: 1019.63 sec\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Wavelets transform per sampling sequence"},{"metadata":{"trusted":true},"cell_type":"code","source":"range_index=12 \nsequence_length = 4096\nstart_time = timeit.default_timer()\ntry:\n    del(df_sample)    \nexcept NameError:\n    pass\ndf_sample = pd.read_csv('../input/train.csv', skiprows = index_ranges[range_index][0], nrows= index_ranges[range_index][1]-index_ranges[range_index][0],\n                       dtype={'acoustic_data': np.int16, 'time_to_failure': np.float64})\ndf_sample.columns=['acoustic_data','time_to_failure']\nprint(df_sample.shape[0]/sequence_length)\nprint('elapsed time: {:.2f} sec'.format(timeit.default_timer()-start_time))","execution_count":103,"outputs":[{"output_type":"stream","text":"8297.998291015625\nelapsed time: 37.34 sec\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#MEAN (Bias) is removed \navg_len=2\nsequence_offset = 0\ndef show_frame_and_cwt(sequence_idx):\n    fig, axs = plt.subplots(nrows=1, ncols=3, sharex=False)\n\n    print(df_sample[sequence_offset+sequence_length*sequence_idx:sequence_offset+sequence_length*sequence_idx+sequence_length].shape)\n    df_sample[sequence_offset+sequence_length*sequence_idx:sequence_offset+sequence_length*sequence_idx+sequence_length]['time_to_failure'].plot(ax=axs[0]);\n    \n    acustic_data_series = df_sample[sequence_offset+sequence_length*sequence_idx:sequence_offset+sequence_length*sequence_idx+sequence_length]['acoustic_data']\n    acustic_data_series_zero_mean = (acustic_data_series - acustic_data_series.mean())\n    acustic_data_series_zero_mean.plot(ax=axs[1])\n    fig.set_size_inches(32,4)\n    \n    acustic_data_cwt = signal.cwt(acustic_data_series_zero_mean, signal.morlet, #signal.morlet, signal.ricker\n                                  np.arange(1,31))\n    axs[2].imshow(acustic_data_cwt, cmap='PRGn', aspect='auto',)\n    print(len(acustic_data_cwt))\n    print(acustic_data_cwt.shape)","execution_count":104,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"interact(show_frame_and_cwt, sequence_idx=widgets.IntSlider(min=0,max=df_sample.shape[0]/sequence_length,step=1,value=0));","execution_count":105,"outputs":[{"output_type":"display_data","data":{"text/plain":"interactive(children=(IntSlider(value=0, description='sequence_idx', max=8297), Output()), _dom_classes=('widg…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5ae7d86472fb49368189b6607d771fcf"}},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Discrete wavelet transform"},{"metadata":{"trusted":true},"cell_type":"code","source":"import pywt \nfrom pywt import wavedec\n#MEAN (Bias) is removed \navg_len=2\nsequence_offset = 0\ndef show_frame_and_dwt(sequence_idx):\n    fig, axs = plt.subplots(nrows=1, ncols=2, sharex=False)\n\n    print(df_sample[sequence_offset+sequence_length*sequence_idx:sequence_offset+sequence_length*sequence_idx+sequence_length].shape)\n    df_sample[sequence_offset+sequence_length*sequence_idx:sequence_offset+sequence_length*sequence_idx+sequence_length]['time_to_failure'].plot(ax=axs[0]);\n    \n    acustic_data_series = df_sample[sequence_offset+sequence_length*sequence_idx:sequence_offset+sequence_length*sequence_idx+sequence_length]['acoustic_data']\n    acustic_data_series_zero_mean = (acustic_data_series - acustic_data_series.mean())\n    acustic_data_series_zero_mean.plot(ax=axs[1])\n\n    fig.set_size_inches(32,4)\n    \n    acustic_data_dwt_coeffs = wavedec(acustic_data_series_zero_mean,'db1', level=1)\n    print(len(acustic_data_dwt_coeffs))\n    print((acustic_data_dwt_coeffs[0]).shape)\n    print(acustic_data_dwt_coeffs)","execution_count":106,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"interact(show_frame_and_dwt, sequence_idx=widgets.IntSlider(min=0,max=df_sample.shape[0]/sequence_length,step=1,value=0));","execution_count":107,"outputs":[{"output_type":"display_data","data":{"text/plain":"interactive(children=(IntSlider(value=0, description='sequence_idx', max=8297), Output()), _dom_classes=('widg…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cd10e36573da4a53a94e7e2fbba105a2"}},"metadata":{}}]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}