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Interpreting sumissions for NLP project.
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| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| # test_set and answer_key path | |
| test_set_path = 'test.csv' | |
| answer_key_path = 'key.csv' | |
| # 4 submission files from 30 submissions | |
| submission_files = ['submit_20141118_a9c3331.csv', | |
| 'submit_20141203_702eb0a.csv', | |
| 'submit_20141211_ec6ccf6.csv', | |
| 'submit_20141220_06c89ea.csv'] | |
| # read base date | |
| test_set = pd.read_csv(test_set_path, index_col='Question ID', | |
| usecols=[0, 3, 5], header=0) | |
| answer_key = pd.read_csv(answer_key_path, index_col='Question ID', header=0) | |
| master_set = answer_key.join(test_set) | |
| master_set.sort_index(inplace=True) | |
| submissions = {} | |
| for ii in submission_files: | |
| submission_date = ii.split('_')[1] | |
| print ">>> ", submission_date | |
| submission = pd.read_csv(ii, names=['Question ID', 'Estimation'], | |
| index_col='Question ID', header=0) | |
| submission.sort_index(inplace=True) | |
| # accuracy of each category per submission | |
| num_items_cat = master_set.groupby('category').size() | |
| is_correct = master_set['Answer'] == submission['Estimation'] | |
| num_correct_items_cat = master_set[is_correct].groupby('category').size() | |
| category_accuracy = num_correct_items_cat / num_items_cat | |
| # accuracy of submission | |
| num_questions = len(master_set) | |
| num_correct = len(master_set[is_correct]) | |
| submission_accuracy = float(num_correct) / float(num_questions) | |
| # accuracy of submission with accuracy of each category | |
| accu = category_accuracy.append(pd.Series(submission_accuracy, | |
| index=['accuracy'])) | |
| print accu, "\n" | |
| # collect accuracy information | |
| submissions.update({pd.to_datetime(submission_date): accu}) | |
| # making dataframe | |
| df = pd.DataFrame(submissions).T | |
| # with from 0.0 to 1.0 | |
| #ax = df.plot(ylim=(0,1), marker='x') | |
| ax = df.plot(figsize=(12, 10), marker='o') | |
| plt.show() |
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| { | |
| "metadata": { | |
| "name": "", | |
| "signature": "sha256:7407e6df92a9050721aef6fd434ab0b4446ae5dfe14d961b385487f30fd6b8ec" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "%matplotlib inline" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 1 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import os\n", | |
| "\n", | |
| "os.getcwd()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 2, | |
| "text": [ | |
| "'/home/sanghee/dev/nlp2014/results'" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 2 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import pandas as pd\n", | |
| "import matplotlib.pyplot as plt" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 3 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# test_set and answer_key path\n", | |
| "test_set_path = 'test.csv'\n", | |
| "answer_key_path = 'key.csv'\n", | |
| "\n", | |
| "# 4 submission files from 30 submissions\n", | |
| "submission_files = ['submit_20141118_a9c3331.csv',\n", | |
| " 'submit_20141203_702eb0a.csv',\n", | |
| " 'submit_20141211_ec6ccf6.csv',\n", | |
| " 'submit_20141220_06c89ea.csv']" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 4 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# read base date\n", | |
| "test_set = pd.read_csv(test_set_path, index_col='Question ID',\n", | |
| " usecols=[0, 3, 5], header=0)\n", | |
| "answer_key = pd.read_csv(answer_key_path, index_col='Question ID', header=0)\n", | |
| "master_set = answer_key.join(test_set)\n", | |
| "master_set.sort_index(inplace=True)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 5 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "submissions = {}\n", | |
| "for ii in submission_files:\n", | |
| " submission_date = ii.split('_')[1]\n", | |
| " print \">>> \", submission_date\n", | |
| " submission = pd.read_csv(ii, names=['Question ID', 'Estimation'],\n", | |
| " index_col='Question ID', header=0)\n", | |
| " submission.sort_index(inplace=True)\n", | |
| "\n", | |
| " # accuracy of each category per submission\n", | |
| " num_items_cat = master_set.groupby('category').size()\n", | |
| " is_correct = master_set['Answer'] == submission['Estimation']\n", | |
| " num_correct_items_cat = master_set[is_correct].groupby('category').size()\n", | |
| " category_accuracy = num_correct_items_cat / num_items_cat\n", | |
| "\n", | |
| " # accuracy of submission\n", | |
| " num_questions = len(master_set)\n", | |
| " num_correct = len(master_set[is_correct])\n", | |
| " submission_accuracy = float(num_correct) / float(num_questions)\n", | |
| "\n", | |
| " # accuracy of submission with accuracy of each category\n", | |
| " accu = category_accuracy.append(pd.Series(submission_accuracy,\n", | |
| " index=['accuracy']))\n", | |
| " print accu, \"\\n\"\n", | |
| "\n", | |
| " # collect accuracy information\n", | |
| " submissions.update({pd.to_datetime(submission_date): accu})" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| ">>> 20141118\n", | |
| "history 0.680851\n", | |
| "lit 0.634146\n", | |
| "science 0.452381\n", | |
| "social 0.595745\n", | |
| "accuracy 0.617647\n", | |
| "dtype: float64 \n", | |
| "\n", | |
| ">>> 20141203\n", | |
| "history 0.819149\n", | |
| "lit 0.796748\n", | |
| "science 0.761905\n", | |
| "social 0.808511\n", | |
| "accuracy 0.800654\n", | |
| "dtype: float64 \n", | |
| "\n", | |
| ">>> 20141211\n", | |
| "history 0.840426\n", | |
| "lit 0.804878\n", | |
| "science 0.761905\n", | |
| "social 0.829787\n", | |
| "accuracy 0.813725\n", | |
| "dtype: float64 \n", | |
| "\n", | |
| ">>> 20141220\n", | |
| "history 0.829787\n", | |
| "lit 0.796748\n", | |
| "science 0.761905\n", | |
| "social 0.808511\n", | |
| "accuracy 0.803922\n", | |
| "dtype: float64 \n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 6 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# making dataframe\n", | |
| "df = pd.DataFrame(submissions).T" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 7 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "df.plot(figsize=(12, 10), marker='o')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 9, | |
| "text": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f3fa27d3950>" | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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hPmiufB11JVf68G3ki49/6f9b7vyeZKrkp94eF4p9LRan2w2rV1fqslJl/cAG\ns7nSbTAYDFgsFvbs2UPjxo1p3bo1AAsXLuSNN96gffv2AHR18ttWKcWHH37Izp07CQ0NBWDatGmM\nGjWqMBT7+fnxwgsv4OPjw//8z//QoEED9u3bR+/evfnkk0/4/fffad68OQDXXnstAIsXL+a2225j\nyJAhAAwaNIjevXvz/fffM3bs2Er/bEJ4q82b9fKIjAyYP7/6k+jKcio3lzt276bDDTcQtHgxB0eP\nLtwXtXgxk0aOrJ0ndlPWLGulwm7BSgwlw27I9SHFShpqeiWG+ihuTRyTF0wmpUfRAFHKAv17Ccae\nw8fXB/9wf/zD/at8rtM66hLh+dKxS5Wqo67KSh++ob4uqaOOHT6clC++KDbHwxmPC8VWo/O/pmwx\nMfDDDxWfHxMD8fGlzzdVfvwmOjqauXPnMmPGDPbs2UNMTAxz5swhPT2dqKiocs89ffo02dnZ9OrV\nq3CbUgq73V54v3Hjxvg4TK4JDAwkKyuLM2fOYDabnT7HoUOHWLp0Kd9++23Rz2q1MtDJTHfhWq6u\nu6pv0tJg2jRYvx7++U8YN676k+jKsvfSJW7ftYsxTZsyY9w4vm/dmvnLlmFGHyGeNHKk09UnLocr\n+5FSCmuG1WnQLVm/q2zKaZ1u0FVBhSUNshJD3Zr3n3lFgTgVaAspPVKYv2S+hOJ6QvPR9NKLEF+I\nrNq5peqo88P0+o3r6dOsD9bzVnJSckoHaid11FVd6eNyJ7EOHTiQ3Zt2snLcK2ws5ziPC8WDY2OZ\nnpJSrATi2agohkyaVCfnFxgxYgQjRozg4sWLPProo0ydOpVWrVqRnJxM586dyzwvPDycgIAA9u7d\nWzjaW1nh4eGYTCaSk5NLjUK3bt2aMWPG8MEHH1TpMYXwVufP65PoPv5Yn0T34Ye1eyW6NefOMSox\nkdlRUYxt1gzQ34hrKgTXBaVKrMRQTtjV/LTSYTfCn+BewcXCrqzEUDssVgsZlgwumC9wwXyBDLPD\n95Yyvs8/5mjaUadBKD41nmazm9HQ2LDMW7B/cLn7GxobYvI1yWvuxcqqow5rGEarAa0qPL+wjrqM\nSYiWYxYu7bnkNFAX1lFXcaWPX//4leSP9/Dy4We5mTVlts3jQnFB3e/z8+djMJuxmUwMmTSp0vXA\n1T0fYP/+/Rw5coT+/ftjNBoxmUwopXjooYd4/vnn6dy5M1FRUezatYuWLVsS5rBotY+PDw8//DCP\nP/4477yR4/QvAAAgAElEQVTzDk2aNOHo0aPs2bOHwYMHl/u8Pj4+PPjggzz55JMsWrSIK664gs2b\nN9OrVy9Gjx5Nnz59iI+P55ZbbiEvL49NmzbRvn17WrjLZbgE4N51V94gNxfeew9efhnuuAN274Yq\n/v1ZZR8eO8bzqal8fdVV3JhfFlXbqtKPlE2Rezq3zHV1C8PvyVwMQYZSo7qmdiZC+ocUu6CEN67E\nUFeUUlzKu1R+mC3YbnF+jM1uI8QUQqgplFBTKCHG0t93aNzB6TEPbn+QBBL0xrQtatctbW7h80c/\nJ9OSycXci2RaMkvdMiwZpGemO91XcI7Vbq1ykHYWvhv4N8DgI/3MU1T2Pamu6qgdty06vIgHrQ9W\n+PgVhmJN04YAcwED8JFS6vUS+8OBxUCz/MebrZT6tDLnXq4bhw6t1hJq1T3fYrEwbdo0EhMT8fPz\no3///nzwwQdcccUVWCwWBg8ezJkzZ+jUqRPLli0DKPZX8+uvv87MmTO59tprOXPmDC1atODvf/97\nYSgu7y/s2bNnM23aNPr06UNWVhbdu3fnhx9+oGXLlqxYsYIpU6YwYsQIDAYDffv25d13373sn1MI\nT6IULFumT6KLjoaffoIuXWr3Oe1K8czBgyw/c4YNPXrQPjCwdp+w5PPn2ck9WXHYzTudh28j31IX\nlAjsHEijWxoVhl1ZiaFybHYbmZbM8sNsOSO2mZZM/A3+5QbasIAw2jZq6/wYUwgBvgGXPRr75Ogn\nObTgULGa4qitUTw+8XGaBzeneXD1/orMteVy0eI8VDveUs+nlhm+My2ZXMq7RKBfYNnh2d8hRBvL\nD97+hqrX3Qr3c7l11EsGLIF1FR9X7jrFmqYZgH3AIOAo8AcwQimV6HDMDMColJqWH5D3AU0BVdG5\n+efLxTvclLwGtUNqimve77/rk+guXoQ33oAKPnSpEdk2G6MTEzmbl8c3V1/t9PLKCXEJLJ+3HM2i\noYyK4bHDK3UpXZvZpi875mRd3YKgu+nQJrpe6opfE78Kr54mKzEUZ7FaLivMFhxzKe8SDY0NSwXV\nUFMoocbQskdw87eHGEPwM7j2ctxxa+KYv2Q+J46doFlEMyaNmOR29cR2ZScrN6v0iHRZgTu37ABu\n0AylR6MLgrR/JUevjcEE+QVJaUgZ3Pl3W2xMLHfF6+vG38zlr1N8DZCslEoD0DTtS+AOwDHYHgcK\nClwbAmeVUlZN066rxLlCCHHZUlP1SXS//KJPohs7tvYm0Tk6brHwl9276RQYyJLOnTE6uepcQlwC\nSyYvYVRK0WznxcmLsRyzcF2H68oMu7nHc7FdKrESQ364DelftBLD+dTz3HjHjfVuJQalFFm5WVUK\ntCX32+y20mG2RIDt0LhDmccEG4Px0Tz7j4yhtw5l6K1D3TrI+Gg+hYG0OpRSWGyWCkeuMy2ZHM86\nXqocxPFmtpqrVA5S1rHBxmB8fTyugtVjDY8dzhcpXxR7P3amopHie4AYpdTD+fdHA32VUpMcjvEB\nEoArgWDgPqXUqsqcm79dRordlLwGwl05TqKbPFkfJQ4Kqpvn3pWVxe27dvFw8+ZMb9OmzFEjx5EJ\nR58GfEpsz9hyr57mF+bnVleqqklWu7Ww9KDcelqL88CbYc7A5GsqN9A6HcF12C4TwcTlstqtFZaG\nlFcS4niMydd0WRMZS96MhqpfEbM+SohLYMX8FcxbPe+yR4ork4ieBbYrpQZomhYFrNE0rVtVGyuE\nEBVxnEQ3fHjdTKJztOrsWcYlJfF2dDQjmjYt91iV6fztM+SaEHqu7VkbzasTZqu54slh5ZQg5OTl\nEGwMLjfMtg5pTRdTF6fHNDQ2dHnpgai/fH18aRTQiEYBjSo+uBwFky0rKgk5n3OeQxcOOS0NKTjH\npmyVqrt2WjricGvg38DjPwEpz8ChAxk4dCDztHllHlNRKD4KOK6v0Qo4UuKYfsDLAEqpFE3TUoEO\n+cdVdC4A48ePJzIyEoDQ0FC6d+9eQbNEXVm7di1QNKtU7lf//vbt23n88cfdpj2ecP+mmwbwzTcQ\nG7uWli0hIWEAV1+t79+3r27as+DoUZ5ftoyZkZGFgbis46++eDUZf2awne0AdEd/T9vOdo5kF70N\nVqc9Bd9X5fyff/6ZnLwcuvTtQoYlg59//pms3Cxad2vNBfMFtv62Va+X7dCQC+YLpG5PJSs3C3sb\nOxnmDM4lnkOhCOsURogxBJ9DPjTwb0DbHm0JNYaSuS+TBv4N6HFdD0JNoRzefpgGgQ24eejNhJhC\n2PX7LgL8Ahh488Dy23td8fu9BvSq9r+X3Jf3I3e6r2kaWzZuKbW/EY24c8CdRcf7wYBby3+8666/\njou5F4n/KZ7svGyu7HUlmZZMNv2yiey8bJpc3YRMSybr1q0jOy+boCuDyLRkcmTnES7lXsLWRp84\nemn/JUy+JsI6heklK2kQ6BdIZPdIgo3BZCZlEugXSNe+XWlobMiRnUcI9Avk+huvp6GxIV9//DW9\ne/bmf279H/wN/m7z713wfVpaGhWpqHzCF32y3C3AMWAzpSfavQlkKKVe0jStKfAneo1xZkXn5p8v\n5RNuSl6D2rHWjWv43NGmTXp5RFYWzJ4Nt95at89vU4qnkpNZff4833XpQpSTS7cXsFvspExN4cyy\nM5z5xxmWzF3CI8cfKdz/fvP3efjDhys12c4Zx9KDHxN+pH3P9pWeHFaw6kGAX0CVJoiV3CalB95F\n3o9EAZvd5nxiYyXKQTItmZzac4rcVrlkWjLxM/hd9lJ8jvcD/QJr7P2m4NLm8Z/GX175RP6EuYnA\navRl1RYqpRI1TXs0f//7wCvAJ5qm7QB8gClKqXMAzs6tkZ9MCA8mv4Aq5+BBfRLdr7/W7SQ6R1lW\nKyMTE7lks7GxRw8aOVlhokD2gWz23r8XU6SJ3tt6E/9nPL+3/p0/A/8kwBpAjm8OeY3zuM12G0ln\nkiqsp3W2Pycvh4bGhkVhdX3p8oI2IW3KrKcNMYXI5B5RjLwfiQIGHwMhJv19ojqUUpit5koF6aOZ\nRwtLQ5yVkOTacstebs+/4qX4Cm7r163nyfeeLLYMoTPljhTXBRkpdl/yGghXOH8eZs2CTz+Fxx+H\nJ5+su0l0jo6YzQzbvZteDRrw3pVX4udTdq3dicUnSHkihciXIon4WwSaphHz1xjiI0tfUt5vnR9t\n72pb5clhoaZQGvg3kFFaIUS9kWfLq3CkuliYLmtZvu8z9boFgBlc9kQ7IUQNk48rncvNhXff1VeV\nuPNO2LMH8q+WXOe2XbzIX3bvZmKLFkxp1arMIGrNsnJg4gEyN2XS7cduNOhWdB3po5eOOj2nX+t+\nrJ24ttptlH4kaoL0I1FTaqMv+Rn8CAsIIywgrOKDy3FT4k2sZ32Fx3nvNEMPctttt7Fo0aJKHRsZ\nGclPP/1Uyy0Sou4oBV9/DZ07w5o18PPP8P77rgvE3545w+CdO3krKoqprVuXGYgvbr/In73+RPPR\n6P1n78JAfCb7DKO+GcXBswednmfyMdVa24UQQpRW2fddCcVu4Pvvv2fMmDGVOlbTNPn41MPJqEyR\n336D/v31col//xvi4uCqq1zTFqUUc9PTeWz/fuK6dOGeK64o87gj7xxh5607iXwhko4fd8QQZEAp\nxZe7v+Tqd6+meYPmLHp6EVHbooqdG7U1ikkjJjl93KqSfiRqgvQjUVPcuS/Fjowt9X7sjEeWT8Ql\nJDBv+XIsmoZRKWKHD2fowMrP5q7u+UKI6nGcRDdrFowZU/eT6BxZ7XYmJyez7sIFNvbsSRuT81GF\nvHN5JD2YhCXdQo+NPQhsHwjAsYvH+Fvc30g+l8yKB1bQt2VfAEy+JuYvmY/ZbsbkY2LSRPe7lK4Q\nQni7gvfd+Uvms5rVZR7ncSPFcQkJTF6yhPi77mLdnXcSf9ddTF6yhLiEhDo5v8Drr79Oy5Ytadiw\nIR07diQhIYHc3Fwef/xxWrRoQYsWLXjiiSfIzc0tPGfFihV0796dkJAQoqOjiY/XJ+EMGDCAhQsX\nApCSksLAgQMJDw+nSZMmjB49moyMjCq1Tbg3x7UT65tz5/Tl1fr0gS5dYP9+GD/etYE402pl2O7d\nJOfk8Gs5gfjCLxfY0mMLAe0C6LmxJ4HtA1FK8fG2j+n+7+50a9qNrY9sLQzEoL8R//DxD6z9dC0/\nfPxDjQbi+tyPRM2RfiRqirv3pYL34/J4XCiet3w5KaOKX7s6ZdQo5q9YUSfnA+zbt48FCxawZcsW\nMjMziY+PJzIyklmzZrF582Z27NjBjh072Lx5M7NmzQJg8+bNjBs3jjlz5pCRkcH69etp06YNULok\nYvr06Rw/fpzExETS09OZMWNGpdsmhDuyWOCtt6BjR7h0SZ9E99xzEBjo2nYdNpu5fts2Ik0m4rp0\nIcS39IdnyqZIm5XGnnv20H5Be6LfjMbH6EPahTRiFsfw7h/vsmbMGmbePBOjr9EFP4UQQoia4HHl\nE5Yy6mlXZ2SgVeavlMxMp5vNVWiDwWDAYrGwZ88eGjduTOvWrQH4z3/+wzvvvEN4eDgAL774Io8+\n+igzZ85k4cKFTJgwgVtu0dcEiYiIcPrYUVFRREXpdS/h4eE88cQTzJw5swqtE+7OneuualrBJLpn\nnoFOnfRJdK6qGS7pj8xMhu/ezdOtWvF4y5ZOa/Utxywkjk5E2RW9/+yNsYURu7KzYPMCZq6fydPX\nPc1T/Z5yydq/9akfidoj/UjUFG/oSx4Xio1lrJsbExLCD5V4QWK++YbSK4dCVeaDR0dHM3fuXGbM\nmMGePXuIiYlhzpw5HDt2rHD0F6B169YcO3YMgCNHjjB0aMUfnZ48eZLJkyfzyy+/cPHiRex2O2Fh\n1VuKRAhX2LgRnn4acnLggw/gllsqPqeufHP6NI/u389HHTpwR/4fsSWdXXWWfQ/uI+KxCNo81wbN\noLHvzD4mrJwAwC9//YUO4R3qstlCCCFqkceVT8QOH07UF18U2xa1eDGT7rijTs4vMGLECDZs2MCh\nQ4fQNI2pU6cSERFR7Nrahw8fpkWLFgC0atWK5OTkCh/32WefxWAwsHv3bjIyMli0aBF2u71KbRPu\nzd3rrqorJQXuvRfuvx8efRT+/NN9ArFSijcOHyb2wAF+6NrVaSC259pJfjqZ/Y/sp/OXnYl8MRKb\nZuO1X16j/8f9eeDqB1j/1/UuD8Te3o9E3ZB+JGqKN/QljxspLlglYv6yZZjRR3gnjRxZ6dUjqns+\nwP79+zly5Aj9+/fHaDRiMplQSjFixAhmzZpFnz59AJg5cyajR48GYMKECQwePJjbb7+dAQMGcPz4\ncbKysujQofgv1qysLEJCQmjYsCFHjx7ljTfeqHS7hHClc+f0lSQ++0y/Ct1nn7m+ZthRnt3OPw4c\nYHNmJpt69qSlkwl1OQdz2PvAXvyb+tNrWy/8w/3ZcWIHD658kLCAMLY8soXI0Mi6b7wQQojap5Ry\n6U1vQmllbXcHO3fuVNdcc40KDg5WYWFhatiwYer48ePKbDar2NhY1bx5c9W8eXM1efJkZbFYCs9b\ntmyZ6tq1qwoODlbR0dEqPj5eKaXUgAED1MKFC5VSSu3Zs0f16tVLNWjQQPXo0UPNmTNHtWrVqvAx\nIiMj1U8//VQnP6c7vwbCfZjNSs2Zo1R4uFKPPabUiROublFp53Nz1aDt29XQHTtUZl6e02NOfnlS\n/RL+i0qfm67sdrsy55nV8wnPqyb/aqIWbl2o7HZ7HbdaCCFETcvPNk4zqabKqNGtK5qmKWdt0DQN\nV7etvpPXQJRHKVi6VJ9E17kz/Otf+ld3k5qTw9Bdu7i1USPejI7GUGJCnS3bRvLkZC6svUDnLzsT\n3CuY34/8zoMrH6R9WHveHfouEcHOJ8YKIYTwLPnZxumqDR5XUyyEp/OGuquNG6FfP3j1VfjwQ/ju\nO/cMxL9lZNB/2zb+FhHB2+3blwrEWbuy+LPPn9hybPTa2gtDVwNPrX6K4V8N54UbX2DZ/cvcNhB7\nQz8Srif9SNQUb+hLHldTLIRwnZQUfWR40yZ4+WUYPRp83PRP669OnWLSgQN80rEjQxs3LrZPKcXx\nD46T+lwqUbOjaDq2KesOreOhlQ/Rt2Vfdv1tF+GBzlelEEII4Z2kfEKUSV4DUeDsWX0S3aJF+iS6\nxx93r0l0jpRSvHL4MO8fO8a3XbrQrUGDYvvzLuSx/+H9ZB/IpvOXnbG1tTFlzRS+2/8d7w19j2Ed\nhrmo5UIIIWqblE8IIS6LxQJz5uhXorNY9CvRPfus+wbiXLudvyYl8c3p02zq2bNUIM7YlMGfPf7E\nr6kfPTf1ZJ1hHVe/ezV2ZWf333dLIBZCiHpMQrEQdcwT6q6Ugq++0q9Ct24drF8P774LTZu6umVl\nO5eXx+AdO7hgtbK+Rw8ijEWXXFZ2xeHXD7P7jt1EvRlF43815q+r/srE7yfy6fBP+WDYB4SaQl3Y\n+qrzhH4k3J/0I1FTvKEvSU2xEKKYX3+Fp56C3Fz46COowhLeLpOcnc3QXbsY1rgxr0dFFZtQl3sy\nl8Sxidgu2ej1Ry++y/qO2Pdiue+q+9j1t10E+Qe5sOVCCCHchdQUizLJa1C/JCfrk+g2b9Yn0Y0a\n5b6T6BxtuHCBe/fs4aW2bXk0ovhKEefWnCNpfBLN/tqMgKcDmBg/kb2n97LwLwvp16qfi1oshBDC\nVaSmWAhRprNn9Ylz114LvXrBvn0wZoxnBOLFJ05w9549fN6pU7FAbM+zc3DaQZLGJ9Hxs45suGcD\n3T7sRsfGHdn26DYJxEIIIUrxgF97QngXd6m7slhg9mx9El1uLuzdC9OmQUCAq1tWMaUUM1JTeT4t\njYRu3RgcFla4Lycth+03bSdrexbN1zVn1MlRzP19LqtHr+blW17G5Fv68s6eyF36kfBs0o9ETfGG\nviShWIh6xnES3fr1RZPorrjC1S2rHLPNxujERFadO8emnj252mGFidP/Pc3Wa7bS+M7G/DrjV65Z\ndg03tL6BzQ9tpkfzHi5stRBCCHfnkTXFCXEJLJ+3HM2ioYyK4bHDGTi08rOBqnu+KxX8m2ia03KY\nGiU1xd7nl1/g6achL08fJb75Zle3qGrO5OYyfPdumhuNfNaxI4EGAwC2HBspT6Vw7odzBL8fzN+P\n/p08Wx4L/7KQTk06ubjVQggh3IVX1RQnxCWwZPIS7oq/izvX3cld8XexZPISEuIS6uT8Aq+99hrR\n0dE0bNiQq666iuXLlxfu+/DDD+ncuXPhvm3btgGQnp7OXXfdxRVXXEF4eDiTJk0CYMaMGYwZM6bw\n/LS0NHx8fLDb7QAMGDCA5557jv79+xMUFMTBgwf55JNPCp8jKiqKDz74oFj7VqxYQffu3QkJCSE6\nOprVq1ezdOlSevfuXey4N998k+HDh1fpZxee58ABuPtuGDkSJk6EP/7wvEC8Lzuba7du5YbQUL7q\n3LkwEF9KvMTWvlvJPZPLb//+jZu23cSdHe9kw183SCAWQghRaR4XipfPW86olFHFto1KGcWK+Svq\n5PwC0dHR/PLLL2RmZvLiiy8yevRoTpw4wdKlS3nppZdYtGgRmZmZrFy5ksaNG2Oz2bj99ttp27Yt\nhw4d4ujRo4wYMQKo3Kjv4sWL+eijj8jKyqJNmzY0bdqUuLg4MjMz+eSTT3jiiScKw/fmzZsZN24c\nc+bMISMjg/Xr1xMZGckdd9xBamoqSUlJhY+7aNEixo0bV6WfXVRPXdZdnTkDkyfDdddBnz76JDp3\nvjRzWX4+f54bt23j2TZteLVdO3zyP8U4/vFxtt+4HW28xoODHuS7E9+x+eHNPH7t4xh8DK5udq3y\nhvo94XrSj0RN8Ya+5HHrFGsW5wEyY3UGa7W1FZ6fSabzHeaqteOee+4p/P6+++7j1VdfZfPmzSxc\nuJCpU6fSq1cvAKKiogD47bffOH78OG+88QY++YmkXz99BnxFJQqapjF+/Hg6ddJHvXx8fLjtttsK\n9994440MHjyYDRs20KNHDxYuXMiECRO45ZZbAIhwmJV/3333sXjxYmbNmsWePXs4dOgQt99+e9V+\neOH2zGaYPx/+9S+4/359Ep2n1AyX9Mnx4zxz8CBLOndmYKNGAFgzrex/bD8Xd15k47828q8z/+KV\nXq/wUM+H6qS0SAghhPfxuFCsjM4DZEhMCAN+GFDh+d/EfAPxTnZUcUL6559/zltvvUVaWhoAWVlZ\nnDlzhvT09MIg7Cg9PZ02bdoUBuKqatWqVbH7q1at4qWXXuLAgQPY7Xays7Pp2rUrAEeOHGHo0KFO\nH2fcuHGMHDmSWbNmsWjRIu6//378/Pwuq03i8gwYMKDWHrtgEt20adC1K2zYoK8u4YnsSvFcaipf\nnTrFuu7d6RikX2Qjc0smex/Yi/U6K5MfnkyEKYJtj26jZcOWLm5x3arNfiTqD+lHoqZ4Q1/ysA9R\nYXjscL6I+qLYtsVRi7lj0h11cj7AoUOHeOSRR1iwYAHnzp3j/PnzXH311SilaNWqFcnJyaXOadWq\nFYcPH8Zms5Xa16BBA7KzswvvnzhxotQxjqNfFouFu+++mylTpnDq1CnOnz/PbbfdVjjiXFYbAK69\n9lr8/f1Zv349S5YsKVbLLDzbhg36WsOzZ8Mnn8CKFZ4biHNsNkbs3cu6CxfY1LMnHYOCUHZF+pvp\n7LxtJxtHbOSebvfw1MCnWPnAynoXiIUQQtQ8jwvFA4cOZMTbI1gWs4xlNy1jWcwyRr49stKrR1T3\nfIBLly6haRrh4eHY7XY++eQTdu/ejaZpPPTQQ8yePZutW7eilCI5OZnDhw/Tt29fmjdvzjPPPEN2\ndjZms5mNGzcC0L17d9avX096ejoZGRm8+uqrpZ7TscQiNzeX3NxcwsPD8fHxYdWqVcTHFw1/T5gw\ngU8++YSEhATsdjtHjx5l3759hfvHjBnDxIkT8ff3LyzhEHWnpuuu9u+Hu+7Sa4VjY/Ur0nnyH+wn\nc3O5eft2DJrGT9260cTfn9zTuewatouUz1P437//L5u7bWbn33YyssvIelsu4Q31e8L1pB+JmuIN\nfcnjyidAD7bVWUKtuud37tyZp556iuuuuw4fHx/Gjh3L9ddfD+i1xmfPnmXkyJEcPXqUtm3bsmjR\nIlq3bs23335LbGwsrVu3RtM0Ro0aRb9+/Rg0aBD3338/Xbt2pUmTJkyZMoXvvvuu2HM6/uIPDg5m\n3rx53HfffVgsFoYNG8YddxSNdPfp06dw8l1qairNmjVjwYIFdOjQAdBD8QsvvMALL7xw2f8GwvXO\nnIGZM+E//9GXWfviC8+48EZ59l66xNBduxjbtCkzIiPRNI3za8+zd/Redl+7m5dHvcy8YfMY3lFW\nTBFCCFGzPHKdYlE9OTk5NG3alG3btjmtfy4gr4F7KjmJ7sUXoUkTV7eq+tacO8eoxETmREUxplkz\n7FY7h/55iLT30phz1xyu+J8rmDN4Do0CGrm6qUIIITxUeesUe+RIsaie9957j2uuuabcQCzcj91e\nNImue3f9Qhz5g/8e78Njx3g+NZWvr7qKG0NDMaeb2TViFwcuHuD1f7zOG6PfYHDUYFc3UwghhBeT\nUFzPROZ/JO14sRFRt9auXVvlWbrr1+slEkrBZ5/BTTfVTtvqml0pph48yIozZ9jQowftAwM5s/IM\nOx/cydK+S/F9xpdfB/9KA/8GFT9YPXM5/UiIkqQfiZriDX1JQnE9U7CEnPAM+/fD1KmwdSu8+io8\n8IDnXXijLNk2G6MTEzmbl8dvPXvSyG5gx993cOjrQ7w39j2mPz6d61tf7+pmCiGEqCekpliUSV4D\n1zlzBl56CZYsgSlT9FUlTFVcS9udHbdY+Mvu3XQODOSDDh2wHsjh1+G/st13O5kzM5l++3QC/Dx8\n1qAQQgi3U15NsZeMOQnhHcxmfQJdwfrCiYl6KPamQLwzK4trt25leHg4n3bsSMpHSazrs47l3Zdz\n8w83M+vOWRKIhRBC1DkJxULUMWdrOdrt+tJqHTvCb7/Br7/qK0x4w6oSjr4/e5Zbduzg9XbtmBrW\ngpXDVrL1+a0kzk1k7uK59GnRx9VN9BjesCaocD3pR6KmeENfcuua4vq6KL+oX9avh6eeAk2Dzz+H\nG290dYtqx4KjR5l16BArrr6aRtuOsXzEBlKjUhmyaQhd2nZxdfOEEELUc25bUyyEt9u/Xy+N2L5d\nn0R3//3eM4nOkU0pnkpOZvX586zo3InfX1hOww8acvbps4x7YRwGH4OrmyiEEKKekHWKhXAjp0/r\nV6L78ks9FH/5pXfVDDvKsloZkZhIjs3Gxw1MrLnhcxpdaESHtR3o2Lujq5snhBBCFPLCcana4Q21\nMsK1cnLg9dchOnotmqZPovvf//XeQHzEbOaG7dtp4mvg9g0bONp/P807N+e+pPskENcAeU8SNUH6\nkagp3tCXZKRYiFpmt+tLqz37LPTqBe+8A2PGuLpVtWvbxYv8Zfdu7goE/2c+JXrdjUR/EE3H++pX\nGF4fF0f8vHn4WixYjUYGx8Zy49Chrm6WEIV988jJk/zYtKn0TeH1Cvp8eaSmWIhatG6dPonOxwfm\nzIEbbnB1i2rfyjNnmJCUxJCj2+k7HaJDoxmwfACmll46JF6G9XFxrJ48mZdTUgq3TY+KIubttyV8\nCJeSvinqG8c+r0GZNcUSioWoBfv26fXCO3fqk+juu887J9E5Ukox98gRXk5L4br//h+PfTaUNv9o\nw1UvXYVmqH8ryTwXE8Os+PhS258H/unnV/cNEiLfc3l5zHKyXfqm8FaOfb68UCzlE5XkDdf0FrXv\n9Gn9SnRffaWH4q++Kl0z7I19yWq387d9iaw8nMK4f/3BbXvvpteKXoTeGOrqprmMr8XidLvhhhvg\nxx+r/fhr161jwE03VftxRP3jO2gQbNgAwFpgQP72muqbon5y5/ckxz5f7nF10BYhvF5ODrz9Nsye\nDYhqE3wAACAASURBVKNG6ZPowsNd3aq6kWm1MmjLes7sOMD82eFEXTmcrru74te4fo84WcsIxbbA\nQPD3r/4T+PnVzOOIesca4PyKkTXWN0X95MbvSWX1+ZK8/APdmuNtI3uiZtjtsHixfiW6P/7Qr0b3\n9tvlB2Jv6ks7M04Rue5bwr/ay8JpV9JvUl96ftez3gdiTp1icEoK05s2Lbb52agobp00qUaewpv6\nkahbg2NjmR4VBRSNEtdk3xT1kzu/Jzn2+fJITbEQl2ntWnj6aTAY9El011/v6hbVrTd3xfF8qoWX\n3lX0T4ng6q+uJrhnsKub5Xp5eTBoENx4I+uvvZY18+djMJuxmUzcOmmSTGQSbmF9XJz0TVGvFPT5\nWatXy0S76vLGOlBxeZKSYOpUfRLda6/pk+iqckVyT+9Lpy+d5q6EuRw/0Y95rwXRtl9T2r/bHt9g\nqcYCYNIkSEuDFStqdXalp/cj4R6kH4ma4il9qbwr2kn5hBCVdOoU/OMf+rJqN9ygh+P7769aIPZk\nSimW7PqSdt88S7OEm/jomYb0fKEjnRZ1kkBc4OOPYc0avabG25cbEUIILyMjxUJUICcH5s7VSyRG\nj4bnn4fGjV3dqrp17OIxHo37B1utfXly4bVcdy6Q7v93NYEdAl3dNPfx++8wbBisX68XmQshhHA7\nMlIsxGWw22HRIujQAf78U59EN3du/QrESikWbl1I1w/7kXN2PAum9mNI5+b0/b2XBGJHx4/DPffA\nwoUSiIUQwkNJKK4kb7imt6i8n3+GPn1gwQL9Es1ffw3t29fMY3tKX0o9n8rgxYN5e+tXPJD8GU+/\n2Ijr3unEVQs6YDAZXN0892GxwN13wyOP6CPFdcRT+pFwb9KPRE3xhr4khYBCOEhM1CfR7d5ddCW6\n+lIzXMCu7Lyz+R1mrpvJ2A4vM+zljkRhZMDW7pha169LNVdKbCw0awbTp7u6JUIIIaqhwppiTdOG\nAHMBA/CRUur1EvufBkbl3/UFOgHhSqkLmqalAZmADchTSl3j5PGlpli43KlTMGMGLF0KzzwDEyeC\n0ejqVtW9pDNJTFg5AR/Nh3E+bxH2VBbB45twy2ud8PGVD5ZKef99mDcPNm2CYFmOTggh3F15NcXl\nhmJN0wzAPmAQcBT4AxihlEos4/jbgceVUoPy76cCvZRS58p5DgnFwmVycuCtt+DNN2HMGHjuufpV\nM1zAarcye+NsZm+czYv9ZxD6RX+ClmbQ+tMr6T00wtXNc0+//KKXTfz6K0RHu7o1QgghKqE6E+2u\nAZKVUmlKqTzgS+COco4fCSwp+fyVbqkb84ZaGVHEbofPP9cn0W3bpg/0vfVW3QRid+tLO07soO9H\nfUlITWD94N9o+o8eZO3Ios/W3hKIy3LkiL4e32efuSwQu1s/Ep5J+pGoKd7QlyoKxS2AdIf7R/K3\nlaJpWiAQA/zXYbMCftQ0bYumaQ9Xp6FC1JSEBOjdG957D778Ui+ZqI8DfRarhecTnufWRbcysc9E\n/u3/GakDj5F0qx9j1l5Hq1YNXN1E92Q2w1136bXEQ4a4ujVCCCFqSEXlE3cDQ5RSD+ffHw30VUqV\nukC6pmn3AyOVUnc4bGuulDquaVoTYA0wSSm1ocR5Uj4h6kRiIkyZAnv26Feiu/fe+jeJrsCmI5uY\nsHICVza+kndufodj0zNIW3WaffOa8OydnfCpr/8wFVEK/vpXve7myy/rbwcSQggPVV75REWrTxwF\nWjncb4U+WuzMA5QonVBKHc//elrTtGXo5RgbSp44fvx4IiMjAQgNDaV79+6FlwosGI6X+3L/cu+f\nOwdr1gzg66/h3nvXMmkSDB7sPu2ry/s//PgDC7cu5BfDL7w95G2C/whm6TUryI6+iiY/tuP6Eyms\nX3fKbdrrdvdjY2HDBgbs3Ama5vr2yH25L/flvtwv937B92lpaVSkopFiX/SJdrcAx4DNOJlop2la\nCHAQaKmUysnfFggYlFIXNU0LAuKBl5RS8SXO9YiR4rVr1xb+QwvPkJ2t1wm/9RaMHatPogsLc3Wr\nXNeXfk79mYe+fYjrWl7HWzFvYf3Kyt7/TeaDR2DC050ZXB9nGFbFzz/DiBH6VVzatnV1a+Q9SdQI\n6UeipnhKX7rskWKllFXTtInAavQl2RYqpRI1TXs0f//7+YcOB1YXBOJ8TYFlmv7xoi/wRclALERt\nsNth8WJ92djrrtOvvhsV5epWuU6mJZMpa6YQdyCO94a+x5BmQ9j/8H4O/nmeV98x8MFfunFVUJCr\nm+ne0tL0QPzFF24RiIUQQtS8CtcprvUGeMhIsfAMCQnw1FMQEACzZ0O/fq5ukWt9f+B7HvvuMYZE\nD+GNW99A262x54G9bO8FX8T68t8+XWnq7+/qZrq37Gzo31//uOGJJ1zdGiGEENVw2esU1wUJxaIm\n7N2rT6JLTNQn0d1zT/2eA3U2+yyPr36cjekb+XDYh9zc5maOzD1C2muHWPyUH+dvb8BnHTsSYJDL\nNZdLKRg1CgwGfQ2/+typhBDCC1RnnWKRz7FgW7iPkyfhscdgwAC45RY9HLv7qhK12ZeUUiz9f/bu\nOzyqamvg8O8kQAg19N5JICGBFOCKDawoKE2UIiC9iFiuIOhFREEFVBAsdBBRRBHpVUpAOqRAOoQS\nSCABAiQhPTPn++OIHyqknsmZmaz3ee4jJ3PKIlnkrtmz9t5ha/Cc70l15+qcGn2Khys8TMjzIVxc\nHc+EBQ5Ue7EGqz08pCDOjy++gKgoWLTI6pJKficJPUgeCb3YQy7ltfqEEFYpLU3bhW7OHBg8GCIj\nrWMSnZGupFxh7NaxRF6P5LeXfqNDgw7c3HuTkwNPktm7Mi+/k8nHbs0YUqeO0aHahp07taL46FGt\nH0cIIYRdk/YJYVNMJli5UltJ4sEH4dNPS/YkOtBGh1ecXME7v7/DSL+RTH50MmUoQ8xHMVxZcoWL\nX9TkjYYJrPbw4LEqVYwO1zacPasl2Jo18OijRkcjhBBCJ0VZp1gIq7F7N4wfrw3arVmjrSxR0l1M\nusjITSNJSE1gx4Ad+NTxIeNSBif7n0Qpq7BzXQ1+UK+zz8ublrLCRP7cvg09esCUKVIQCyFECSI9\nxflkD70ytiosDLp2hZEjtWXWDh607YJYj1wyq2a+Pf4tfov8eLTRoxwbfgyfOj5c33CdgLYBVHq2\nCp/MLsWu0ikc8fWVgji/VFXrx2nfHl591ehociW/k4QeJI+EXuwhl2SkWFit+Hj44ANYtw7eew9+\n+w2cnIyOyninE08zfONwcsw57B+8H/ca7pgyTJx56wzXN16n7i8t6FchhmaOzuzyaENZmVCXf59+\nCrGx2kLXVjaxTgghhGVJT7GwOncm0X35pTZo97//gbTCQo45hzmH5zDz4Ezef/R9Xmv/Go4OjqSd\nTiO8Tzhlm5ZFnduAbrERDKpVi6mNG6NIYZd/W7ZoH0ccOwb16hkdjRBCCAuQnmJhE+6eRPfww1pt\n0rSp0VFZh5CEEIZuHEolp0ocG3GMplW0b0z8ynjO/vcsjT9qTPhLZXk5MpTZzZoxoHZtgyO2MVFR\nMGQIrF8vBbEQQpRQ0lOcT/bQK2PNdu0CPz9YvBh+/RVWr7bfgrgguZRlymKq/1Qe//5xRvmNYtfA\nXTSt0pSc2zlEvBLBxU8u0mZ3G7Z0VxgYGcnaVq2kIC6o5GRtYt3HH9vUFojyO0noQfJI6MUecklG\nioWhQkO1nehOn4aZM6FXL2nlvON43HGGbhxKY5fGBI0Kon6l+gCkBKUQ3jecyg9Xxue4L+/GX2Dj\npUT+8PHBtVw5g6O2MWYzDBwIjz0GI0YYHY0QQggDSU+xMER8vLbi1fr1Ws/wmDFQpozRUVmH9Ox0\npuydwspTK5nTeQ59Pfve6YEi7us4Yj6Kofm85lR4qToDIiK4mZ3Nb56eVC1d2ujQbc/Uqdpaf7t3\nSwIKIUQJID3FwmqkpmqbhM2dq7VwRkXJJLq77Y/Zz7CNw/Cr48epMaeoWb4mANmJ2UQOjSQzLhOf\nwz7cauBIx6AgPMuX52cPD8o4SCdUga1fD8uWwfHjUhALIYSQnuL8sodeGSOZTLB8ObRoAeHhWh3y\n+eclsyC+Vy6lZKYwdstY+q3tx+dPfc7q3qv/Kohv/XGLEz4ncG7ujO8hX6Jrm+kQGEjPGjVY3rKl\nFMSFER6urTSxdi3UqmV0NIUiv5OEHiSPhF7sIZdkpFhY3O+/azvRVayoTaJ74AGjI7IuO6J3MHLz\nSJ5s8iShY0Kp4qy9U1BNKjGfxBD3TRwtl7akWtdqbE1MZHBkJPOaN6evjRZzhrt1S5tY9/nn0K6d\n0dEIIYSwEtJTLCwmNBQmTIDoaG0SXc+eMonubjfSb/DfHf9lX8w+Fj23iKeaPfXXa5mXM4l4OQIA\n9x/ccarnxDdxcUyPieG3Vq3oULmyUWHbNpMJnn8e3Ny0hbCFEEKUKLn1FMvnrkJ3V65on0w/8QQ8\n+6y2TbOsKvF3v0X8hue3nlRyqkTImJC/FcSJWxM54XsCl8ddaLOrDaXqluGNM2f4Oi6Ogz4+UhAX\nxfvvQ0YGfPaZ0ZEIIYSwMlIU55M99MpYWmoqfPQReHqCi4s2ie7112UO090SbifQcWpH3t39Lr+8\n+Avznp1HhTIVADBnmYl+O5rTo0/T6pdWNH6/MamqiR6hoYSlpnLYx4emzs4G/w1s2C+/wKpV8PPP\nYAcrdcjvJKEHySOhF3vIJSmKRZGZTNokfjc3iIiAEydg1iytMBYaVVX54dQPtF7QmnoV6xE8KpiH\nGz781+vpZ9MJeiiI9Oh02ga1xeVRF2IzMngkOJhapUuzrXVrXOygkDPMqVMwdiysWwc1ahgdjRBC\nCCskPcWiSHbu1CbRVa6szVv6z3+Mjsj6XEq6xOgto4lNjmVZt2X41fX72+sJPyUQ/Xo0jd5vRL1x\n9VAUhcCUFLqHhvJ6vXqMb9AARXpPCi8xUZtQ9/HH0K+f0dEIIYQwUG49xVIUi0IJCdEm0Z09q40K\n9+ghPcP/ZFbNLA5YzOS9k3m9/etMfHgiZRz/v5fElGrizOtnSPojCY/VHlT0rQjAhuvXGR4VxQI3\nN16QUc2iycnRGtt9fLREFUIIUaLJRDsd2EOvjB6uXIHhw7VJdF26aJPoZFWJfzt74yxPfP8Ey4KX\n4f+KP+93fP+vgtjf35/bIbcJaBeAmq3iF+BHRd+KqKrKnEuXePX0abZ4eUlBrIdJk8DBAT791OhI\ndCe/k4QeJI+EXuwhl6QoFvmSmgoffqhNoqtaFU6flkl092Iym5h9eDb/WfIfnnd7nkNDD9GqZqu/\nXldVlWsbr3Hy8ZM0nNQQ9+/dKVWxFDlmM2PPnGFZfDyHfH1pX6mSgX8LO/Hjj9qudT/9BI6ORkcj\nhBDCykn7hMiVyQTffQdTpkDHjvDJJ9C4sdFRWaewq2EM2ziMsqXKsqTbEppXbf6317NvZRM1PIqM\nsxl4rPagXItyACTn5PBSWBiKovCzhweVSsmeOkUWGAidO8OePeDlZXQ0QgghrIS0T4hC2bFDa8Vc\nsUKbtL9qlRTE95Jtymb6/ul0WtGJwd6D2fPKnn8VxEmHkwjwCcCprhM+h33+KohjMjJ4KCiIps7O\nbPL0lIJYD1evagtjL1ggBbEQQoh8k6I4n+yhVya/QkLgmWdg3Dht3eF9+6B9e6Ojsk6BVwJpt7gd\nhy4dInBkIKPbjsZB+f9/VqpZJWZGDKE9Qmn+ZXNc57nyx5E/ADienMyDgYEMq12bb1xdKeUg/xyL\nLDsbXnoJBgyAF14wOhqLKkm/k4TlSB4JvdhDLsmwlPjL5ctam8SmTdrGX6NG2cUeBxaRkZPBh/4f\nsix4GZ8/9TkDWg/417JpmfGZRA6KxJxuxu+EH2UblP3rtbXXrjH69GmWtmhBt+rVizt8+/X221Ch\ngtYAL4QQQhSA9BQLbt/W1hj+6ittZYl335WNN3Jz8OJBhm0chlctL75+9mtqVaj1r3Nu7LxB5OBI\n6gyvQ6MpjXAopY0Cq6rKZ5cuMS82lo1eXvhWrFjc4duv5cthxgw4elQSWAghxD3l1lMsI8UlmMmk\n1RFTpsBjj0FAgPQM5+Z21m3e2/0ev4b/ytddvqaXe69/nWPONnP+/fMk/JCA+4/uVHmsyl+vZZvN\njDl9mhMpKRzx9aV+2bL/ul4U0tGj8M47sH+/FMRCCCEKRZoY88keemXutn07eHvD99/Dhg3a6lVS\nEN/frnO78JrvRVJmEqGvht6zIE6/kE7wo8GknkqlbVDbvxXEt7KzefbUKRKys/k0OVkKYj1duaL1\nDy9dCu7uRkdTbOztd5IwhuSR0Is95JKMFJcwp05pO9FduKBt8NWtm2y8kZtbGbd4e8fb7Dq/iwVd\nF/Cs67P3PO/qr1c58+oZGk5qSP0366M4/P839Vx6Ol1DQuhcpQpfNG/OH/v2FVf49i8zUyuIR47U\nklkIIYQoJOkpLiEuX9Ymz23erLVLjBwpk+jysiFyA2O3jqVbi27MeHIGlZz+vaGGKd3E2f+e5cbO\nG3is9qBSu7+fcygpiRfCwpjcqBFj69UrrtBLjlGjtCXY1q7Vdq4TQgghciE9xSXY7dvw2Wfw9dcw\nYoS2E13lykZHZd2upV7j9e2vc+LyCX7s9SMdG3e853mp4amE9wmnXKtytA1sS6nKf//ntDohgXHR\n0axo2ZIu1aoVR+gly8KFcOAAHDkiBbEQQogik/8nySdb65UxmWDxYnBzg7NntQ2+ZsyQgjg3qqry\nU8hPeM33on7F+pwcffKeBbGqqlxZeoXgjsHUf7M+Hj95/K0gVlWV6RcuMPHcOXa3afOvgtjWcskq\nHTigffSxfj2U0BU8JI+EHiSPhF7sIZdkpNjOqKq2E92ECVC1KmzcCG3bGh2V9YtLjmPMljGcu3mO\njf020r7evXcryUnK4fTo06SGpuK9z5vyHuX/9nqm2czIqCjCUlM54utLHSen4gi/ZImN1TboWLEC\nXF2NjkYIIYSdkJ5iO3LypFYMx8RoLRPPPy+T6PKiqipLg5by7u53ebXtq7z3yHs4lbp3IZt8PJnw\nvuFUfboqzWY3w9HZ8W+v38jOpmdoKNVKl2aluzvlHR3veR9RBBkZ8Oij2jbOkyYZHY0QQggbk1tP\nsRTFdiAuTvskecsWmURXEOdvnmfEphHcyrjFsu7LaF2r9T3PU80qsXNiuTjzIq7fulKzd81/nXMm\nLY2uISF0r16dmU2b4iDvRvSnqjBkCKSlwc8/yzs+IYQQBZZbUSw9xflkjb0yKSlaEdy6NdSqpU2i\nGztWCuK8mMwm5h6ZS7vF7ejcrDNHhh+5b0GcdS2LkOdCuPbrNXyP+d6zIP7j1i0eCQpifIMGfNas\nWZ4FsTXmkk34+mutOX75cimIkTwS+pA8Enqxh1ySnmIblJMDy5bB1KnwxBNandCokdFR2YbI65EM\n2zgMB8WBQ8MO4VbN7b7n3tx7k4iBEdQeWJvGHzXGofS/30OujI/n7bNn+dHdnaeqVrVk6CWbvz9M\nnw6HD0P58nmeLoQQQhSUtE/YEFXVdqKbMAGqV4cvvgA/P6Ojsg3Zpmw+P/Q5s4/MZmrHqYxpNwYH\n5d4flJhzzMR8FMOVJVdo+V1Lqj7972JXVVWmXrjA9wkJbPbyopUUapYTEwMPPAArV8KTTxodjRBC\nCBsm6xTbgeBgrRi+dEmbRPfcc/IJcn4FxwczdMNQapSvwYkRJ2jkcv9h9YxLGUT0j8DB2QG/QD+c\nav970l2GycTQqCjOpadzxNeXWmXKWDL8ki0tDXr21JJfCmIhhBAWJD3F+WRUr0xcnDa36JlntNog\nJERWlcivzJxMJu+ZzNMrn2Zc+3Fsf3l7rgXx9Q3XCWgbQNWuVWm9vfU9C+JrWVk8efIkOarKXm/v\nQhXE9tB3VSxUVdtxxsMD3nrL6GisjuSR0IPkkdCLPeSSjBRbqZQUmDULvv1W28k2Kko23iiII7FH\nGLphKC2qt+Dk6JPUqVjnvueaMkyce+cciZsS8VzvSeUO9/5GR6am8lxICH1q1mRakyaywoSlzZ4N\nkZHaRh3yvRZCCGFh0lNsZXJyYOlS+PBD7dPi6dOhYUOjo7IdqVmpTN4zmdVhq5n3zDx6e/RGyaWg\nSotKI7xvOGWblaXFkhaUdrn30h17bt6kX3g4M5o2ZUid+xfYQie//w6DBsHRo/IPQAghhG6kp9gG\nqCps26a1TtasCZs2ySS6gtp7fi/DNw2nQ/0OhIwJoXq56rmeH/99PGffPkvjaY2pO6rufYvn5Veu\nMOncOVZ7ePBYlSqWCF3c7dw5GDAAfvlFCmIhhBDFRnqK88mSvTLBwfDUU/D22zBjBuzZIwVxQSRl\nJDFq0ygGrR/EvGfm8UOvH3ItiHNScogYFMHFGRdps6cN9UbXu2dBbFZV3j13jo9jYtjv46NbQWwP\nfVcWc/s29OihLcDdsaPR0Vg1ySOhB8kjoRd7yCUpig0UGwuDB2uT6F54QSbRFcbm05vxnO8JQOiY\nULq6dc31/JSgFAL8AlDKKPgd96OCV4V7npduMtE3PJw/bt3iiK8vLcqV0z128Q93dqxr1w5efdXo\naIQQQpQw0lNsgJQUmDkT5s+H0aNh4kSoVMnoqGzL9bTrvLn9TQ7HHmbx84t5vMnjuZ6vqipxX8UR\nMy2G5vOaU6tfrfuem5CVRfeQEJo5O7O0RQvKOjrqHb64l08/hQ0btI06ypY1OhohhBB2SHqKrcSd\nSXRTp8LTT2ttEw0aGB2VbVFVlTXha3hj+xv08+zHqdGnKF8m940zshOziRwSSdaVLHyP+OLczPm+\n54b9ucLEK7Vq8UHjxrlO0hM62rpV28b52DEpiIUQQhhC2ifyqSi9MqoKW7ZA69bw88/an1eskIK4\noK6kXKHXL72Y6j+V3176jdmdZ+dZEN/af4sTPidwdnPG56BPrgXxzhs3eCw4mGmNGzO1SROLFcT2\n0Helq9OntT6iNWugXj2jo7EZkkdCD5JHQi/2kEsyUmxhQUEwfjxcvqztRNe1q/QMF5SqqnwX/B0T\nd01kpN9IVr+wGqdS/95Y42/XmFRiPo4h7ts4Wi5rSbUu1XI9f+Hly3xw/jxrW7XiERcXPcMXuUlO\n1ibWffwxPPig0dEIIYQowaSn2EJiY+F//4OdO+GDD2D4cCglb0EKLOZWDCM3j+Ra6jWWdV+Gd23v\nPK/JjMskYkAEAO4/uONU7/4FtElVmXj2LJsSE9ni5UVzmVBXfMxm6NUL6tTRGuyFEEIIC8utp1ja\nJ3SWnAyTJ0ObNlp7RFSUNplOCuKCMatmvjn2DX6L/OjUqBNHhx/NV0GcuCWRE34ncHnchTa72uRa\nEKeaTPQOCyPg9m0O+/pKQVzcpk2D69dh7lyjIxFCCCGkKM6vvHplcnJgwQJo0UIbJQ4O1najk1Ul\nCu504mk6fdeJVaGrODD0AO8+8i6lHe+909wd5iwz0W9Hc3rMaVqtaUXj9xujON6/T+VyZiYdg4Ko\n7OjIjtatqVo69/vryR76ropswwZt1umvv0KZMkZHY5Mkj4QeJI+EXuwhl/IsihVFeUZRlEhFUc4o\nijLxHq+PVxQl6M//hSiKkqMoikt+rrUHqgqbN4OXlzZPaOtW+O47mURXGDnmHGYdnMWDSx+kt0dv\n9g/eT8vqLfO8Li06jaCHgkiPTqdtUFtcHsm9J/jk7dt0CAykZ40aLG/ZkjIO8t6wWIWHw4gRsHYt\n1K5tdDRCCCEEkEdPsaIojkAU8CQQBxwH+qmqGnGf858D3lRV9cn8XmvLPcWBgdokuvh4bRJdly4y\nia6wQhJCGLpxKJWdKrPo+UU0rdI0X9cl/JRA9OvRNJrSiHqv3XtnurttTUxkcGQkX7m60qdmTT1C\nFwVx6xa0bw/vvaetOCGEEEIUo6KsU9weiFZV9cKfN1oNdAfuWRQD/YGfCnmtzbh0SZtE9/vv2prD\nw4ZJz3BhZZmy+Hj/x3x74ls+feJThvkMy9dSaKZUE2deP0PSH0m03tmaij4V87zm69hYPr54kQ2e\nnnSoXFmP8EVBmEzQvz88+6wUxEIIIaxOXp8b1wMu3XUc++fX/kVRlHJAZ2BtQa+1Bf7+/iQna8Ww\ntzc0aqQtrzpqlBTEhXUs7hi+C30Jig8ieFQww32H56sgvn3qNgFtA1BzVPwC/PIsiE2qyutnzvDt\n5csc9PExvCC2h76rQpkyBdLT4fPPjY7ELpTYPBK6kjwSerGHXMqrnCtIX8PzwAFVVW8V4lqrlpOj\nzQvq2xeeeQZOnoT69Y2OynalZafxwd4PWHlqJXM6z6GvZ998FcOqqnJ5wWUuTLlAsy+aUXtQ3v2o\nKTk59AsPJ8Ns5pCPDy7FOKFO3GXNGvjxRzh+HORnIIQQwgrlVRTHAXdPGWuANuJ7L335/9aJAl07\nePBgGjduDICLiwve3t506tQJ+P93HkYcqyp8+qk/CxaAq2sntm2DpCR/oqOhfn3j47PF47mr5zLr\n0Cwe7fgoIWNCCDsexr59+/K8/qE2DxE1IoqDwQdpNLsRtQfWzvN5sRkZdPruO1qUK8e2AQMo7eBg\n+N//n++krSUeix6fPUunSZNg5078w8KMj8dOjjt16mRV8cix7R7fYS3xyLFtHt/5mrXEc3d++/v7\nc+HCBfKS10S7UmiT5Z4ALgPHuPdkucrAOaC+qqrpBbzWKifaBQRok+iuXtUm0T37rEyiK4qUzBQm\n7prIxqiNfNv1W7q16Jbva5MOJxHeL5zq3avTbFYzHJwc8rwmMCWFbiEhvFG/PuMbNLDYls0iD4mJ\n0K6dtj5h//5GRyOEEKKEK/TmHaqq5gCvATuAcOBnVVUjFEUZpSjKqLtO7QHsuFMQ53Zt0f4qlnfx\nIgwcCM89B/36aa0SXbrAvn3+Rodms7ZHb8dzvieZOZmEvhqa74JYNavEfBpDaI9QXOe64jrXUtsc\n3wAAIABJREFUNV8F8Ybr1+l86hTzXF2Z0LCh1RXE/xydsVs5OVrP0QsvSEFsASUmj4RFSR4JvdhD\nLuU5RUxV1W3Atn98beE/jlcAK/JzrbVKToYZM2DhQhg7VptEVzHvBQ1ELm6k3+CtHW+xP2Y/S55f\nwlPNnsr3tZnxmUQOjMScYcbvhB9lG5TN8xpVVfkyNpbPL11iq5cX7WTnFGNNmqR9vPLpp0ZHIoQQ\nQuQp1/aJYgnA4PaJ7GxYvBg++kgbEZ42DerZ7BoZ1mNt+FrGbRtHb4/efPLEJ1QoUyHf197YeYPI\nwZHUGV6HRlMa4VAq79HhHLOZcdHRHExKYrOXFw3L5l1ECwv68UdttYnjx6FqVaOjEUIIIYCirVNs\nt1QVNm2Cd97RVpLYvl1bak0UTcLtBF7b9hohCSGseXENDzV8KN/XmrPNnH//PAk/JOD+oztVHquS\nr+uSc3J4KSwMRVE44ONDJVkjz1iBgfDmm7BnjxTEQgghbEbeQ3B2KCAAHn8c3n0X5szRNuHIqyC2\nh14ZS1JVlZUnV9J6QWuaV2lO8OjgAhXE6efTCXokiNRTqbQNapvvgjgmI4OHgoJo6uzMJk9PmyiI\n7TqXrl6Fnj1h/nxt73NhMXadR6LYSB4JvdhDLll/BaGjixe1zTd274YPP4QhQ2TjDT1cSrrEqM2j\niEuJY2v/rfjV9SvQ9VfXXOXM2DM0nNSQ+m/WR3HI38S4Y8nJ9AwNZUKDBrxRv77VTagrcbKz4aWX\nYMAA6N3b6GiEEEKIAikRPcVJSdokukWLtEl0EybIJDo9mFUziwIW8f7e93m9/etMfHgiZRzL5Pt6\nU7qJ6Leiufn7TTxWe1CpXf4nxv169SpjzpxhaYsWdKtevTDhC729/jqcPQsbN4Kjo9HRCCGEEP9S\nYnuKs7O1QnjaNG0S3alTMolOL9E3ohm+cTgZORn4v+JPq5qtCnR9angq4X3CKe9ZnrZBbSlVKX+p\nqKoqsy5d4uu4OHa0bo2vvLuxDsuXa435x45JQSyEEMIm2WVPsapq2zJ7esL69bBjByxbVrSC2B56\nZfRgMpv44tAXPLDkAbq16MbBoQcLVBCrqsrlJZcJ7hhM/bfq477KPd8FcbbZzIioKFZfvcphHx+b\nLYjtLpeOHtVmrG7YAC4uRkdTYthdHglDSB4JvdhDLtndSPGJE/D229pGWnPnQufOshOdXsKuhjFs\n4zCcSztzZPgRmldtXqDrc5JyiBoVRVp4Gt77vCnvUT7f197MzqZ3WBjlHR35w9ubCtIMbh3i47X+\n4SVLwN3d6GiEEEKIQrObnuKYGG0S3Z492prDgwfLJDq9ZJmymHlgJvOOzWP6Y9MZ4TcCB6VgHzIk\nH08mvG84VTtXpdkXzXB0zv9H7OfS0+kaEkLnKlX4onlzHOVdjnXIyoLHHoOnn4YPPjA6GiGEECJP\ndt1TnJSkbZi1eDGMGwcLFkCF/O8TIfIQcDmAoRuHUr9SfQJHBtKgcoMCXa+aVWLnxHJx5kXc5rtR\n44UaBbr+UFISL4SFMblRI8ZKQ7h1ef11qFED3n/f6EiEEEKIIrPZnuLsbPj6a3Bzg2vXtEl0U6da\nriC2h16ZgkjPTmfSrkl0WdWFCQ9OYHO/zQUuiLOuZhHyXAjXfr2G7zHfAhfEqxMS6BEayrIWLeyq\nILaLXFq4EPbvh++/Bweb/TVi0+wij4ThJI+EXuwhl2xupFhVtRWf3nkHGjXSNt5o3droqOzLgYsH\nGLZxGG1qteHU6FPUqlCrwPe4uecmEYMiqD2wNo0/aoxD6fwXTqqq8nFMDIuvXGFXmza0lqF/63Lw\noDY6fOAAVMr/MnpCCCGENbOpnuLjx2H8eLhxAz7/XJtEJ/RzO+s27+56l7URa/m6y9f0cu9V4HuY\nc8zEfBjDlaVXaLmiJVWfKtg2v5lmMyOjoghLTWWTlxd1nJwKHIOwoLg4aN9e61fq0sXoaIQQQogC\nsfme4pgYeO892LtXm0Q3ZIgshaq338/+zsjNI+nYqCOhr4ZS1blgxSxAxsUMwvuH41jOEb9AP5xq\nF6ygTczOpldoKNVKl2afjw/l5YdsXTIyoFcveO01KYiFEELYHatuBrx1CyZOBF9fcHWF06dh+HBj\nCmJ76JW5l5vpNxm6YSjDNw1nftf5fNfju0IVxNfWXyOgXQDVn69O6+2tC1wQn0lLo0NgIP+pVIlf\nW7Wy64LYJnNJVWHMGGjYECZNMjoagY3mkbA6kkdCL/aQS1Y5Upydra0iMX06PP88hIRA3bpGR2V/\nNkRuYOzWsXRv0Z3QMaFUdCr4ZhimDBPnJpwjcXMinus9qdyhcoHvsf/WLV4KC2NakyaMkB+0dfrm\nGwgIgEOHZOFvIYQQdsmqeopVVduBbuJEaNoUZs2SSXSWcC31GuO2jSPwSiBLui3h0UaPFuo+aVFp\nhPUJw7m5My2WtKC0S+kC32NlfDxvnz3Lj+7uPFW14CPUohj4+0PfvlpB3LSp0dEIIYQQhWb1PcWd\nO0+mc+enWb/+UW7dgq++kkl0lqCqKj+F/sR/d/yXQW0Gsbz7cpxLOxfqXvEr4jk7/ixNpjehzsg6\nKAUcPVRVlQ8uXOCHhAT8vb3xKJ//3e1EMbp4Efr1gx9+kIJYCCGEXbOKnuKdO6fzzjs78PHZT1CQ\ndRbEtt4rE5ccR7fV3fj0wKds6reJWU/NKlRBnJOSQ8TACC7OvEibPW2oO6pugQviDJOJlyMi+P3m\nTQ77+pa4gthmciktDXr0gAkT4MknjY5G/IPN5JGwapJHQi/2kEtWURQDmEwfExX1u6wqoTNVVVkc\nsBjvhd741fEjYGQA7eq1K9S9UgJTCPANQHFS8DvuRwWvgq8ffC0riydOnsSkquxp04ZaZcoUKhZh\nYaoKI0eChwe89ZbR0QghhBAWZxU9xaDF0LHjVPz9pxoajz05d/McIzaNIDkzmWXdluFVy6tQ91FV\nlbiv4oiZHkPzec2p1bfgm3kARKam0jUkhL41azKtSRMcZMKW9Zo9G378Udugw7lwLTZCCCGEtbH6\nnuI7ypY1GR2CXTCZTXx97Gum7Z/GxIcm8laHtyjlULgfdXZiNpFDIsm6koXvYV+cmxWuQNpz8yb9\nwsOZ2bQpg+vUKdQ9RDHZtQs++wyOHpWCWAghRIlhNe0TzZq9x7hxTxkdxn3ZSq9MxLUIHln+CGsj\n1nJo2CEmPDSh0AXxrf23OOFzAmc3Z3wO+hS6IF525Qr9wsP52cNDCmKsPJfOnYMBA2D1am1NYmG1\nrDqPhM2QPBJ6sYdcsoqR4s6d32fcuGfo2rVwS4MJyDZl89mhz5h9eDYfPfYRo9uOxkEp3Hse1aQS\n83EMcd/G0XJZS6p1qVao+5hVlf+dP8+aq1fZ7+NDi3LlCnUfUUxSU7WJdZMnQ8eORkcjhBBCFCur\n6Ck2OgZbF3QliKEbh1KzfE0WPbeIRi6NCn2vzLhMwl8OR3FQcP/BHae6BduZ7o50k4lBkZFcycxk\nvacn1WVCnXVTVejTBypUgKVLZYMOIYQQdslmeopFwWTkZDBt3zQWBy5m1lOzeKXNKwVeHu1uiVsS\niRwWSb3X6tHo3UYojoW7V0JWFt1CQnB1dma3tzdODlbTpSPuZ+ZMiImBffukIBZCCFEiSbWST9bW\nK3P40mF8FvoQcT2Ck6NPMth7cKELYnOWmej/RnN6zGlarWlF48mNC10Qh6Wm8kBgIF2qVWOlu7sU\nxPdgbbnEtm3ajjm//QZlyxodjcgnq8sjYZMkj4Re7CGXZKTYxqRmpTJ5z2RWh61m3jPz6O3Ru0ij\nw2nRaYT3DcepvhNtg9tSumrBt2q+Y+eNGwyIiGBO8+a8XKtwy7aJYnbmDAwerBXE9eoZHY0QQghh\nGOkptiF7zu9hxKYRPNjgQb7s/CXVyhVuAtwdCasSiH4jmkYfNKLe2HpFKq4XXr7MB+fPs6ZVKx5x\ncSlSXKKYJCfDAw/Am29qG3UIIYQQdi63nmIpim1AUkYSE36fwLbobSzouoCubl2LdD9Tqokz486Q\ndDAJj589qOhdsfD3UlXeOXuWzYmJbPHyormsMGEbzGbo1Qtq14YFC4yORgghhCgWuRXF0vCZT0b1\nymw+vRnP+Z4oKISOCS1yQXz71G0C2gagmlX8AvyKVBCnmky8EBpK4O3bHPb1lYI4n6yi72raNLh+\nHebNMzoSUUhWkUfC5kkeCb3YQy5JT7GVup52nTe2v8GR2CN83+N7HmvyWJHup6oql+df5sIHF2g2\nuxm1B9Yu0v0uZ2bSLSQEz/Ll+aVVK8rIhDrbsWEDLFkCx4+DLJUnhBBCANI+YXVUVWVN+Bre2P4G\n/Tz7Me2xaZQvU75I98y+mU3U8CgyzmXg8bMH5dyKNqJ78vZtng8JYXTdurzbsGGRepFFMQsP1zbm\n2LIF2rc3OhohhBCiWMk6xTbicsplxm4dS9T1KNb1WccD9R8o8j2TDiUR3j+c6t2r47HKAwenoo3o\nbklMZHBkJF+7utKnZs0ixyeK0a1b2o51s2ZJQSyEEEL8g3zmnU+W7JVRVZVlQcvwXuCNZw1PgkYF\nFbkgVs0qMZ/GENozFNd5rrjOdS1yQfxVbCzDo6LY6OkpBXERGNJ3ZTJB//7wzDMwZEjxP1/ozh76\n94TxJI+EXuwhl2Sk2GAXbl1g5KaRXE+7zs6BO/Gu7V3ke2bGZxI5MBJzphm/E36UbVC0DRlMqspb\n0dHsunmTQz4+NHF2LnKMophNmQJpafDFF0ZHIoQQQlgl6Sk2iFk18+3xb5nqP5W3O7zN+AfHU9qx\n8Btn3HFjxw0ih0RSZ0QdGr3fCIdSRRsdTsnJoV94OJmqyhoPD1xKFz1GUczWrIEJE7SJdTVqGB2N\nEEIIYRjpKbYypxNPM2zjMMyqmQNDD9Cyessi39Ocbeb85PMk/JiA+4/uVHmsSpHvGZuRwXMhIbSv\nVIlvXF0pLStM2J5Tp+DVV2HHDimIhRBCiFxIlZNPevTK5JhzmHlgJg8ufZAXPV5k/+D9uhTE6efT\nCXokiNTQVNoGtdWlIA5ISeGBwEBerlWLhW5uUhDrqNj6rm7cgJ494csvwde3eJ4pio099O8J40ke\nCb3YQy7JSHExOZVwiqEbhuJS1oXjI47TpEoTXe57dc1Vzow9Q8N3G1L/jfooDkVfHm3D9esMj4pi\noZsbvWR00Tbl5EDfvlpR/PLLRkcjhBBCWD3pKbawzJxMPv7jY+afmM+MJ2Yw1GeoLuv6mtJNRL8V\nzc1dN/FY7UGltpWKfE9VVZkTG8sXly6x3tOTdpWKfk9hkAkTIDgYtm2DUvLeVwghhADpKTbMsbhj\nDN0wlKZVmhI8Kph6lerpct/UsFTC+oRRoXUF2ga2pVSlov8Yc8xmXjtzhkPJyRz29aVh2aKtWCEM\ntGoVrF2rTayTglgIIYTIF2kUzaeC9MqkZacxfud4uv3UjcmPTmZD3w26FMSqqnJ5yWWCOwXT4L8N\ncP/RXZeCOCknh64hIcRkZnLAx0cKYguzaN9VYCC88QasXw/VqlnuOcJw9tC/J4wneST0Yg+5JMNI\nOtt3YR/DNw2nXd12hIwJoUZ5fXpyc5JyiBoVRVp4Gt77vSnvXrStn++4kJ7OcyEhdHRxYW7z5pSS\nCXW269o16NULvv0WWrc2OhohhBDCpkhPsU6SM5OZ+PtENp3exLddv6Vbi2763ftYMuH9wqnauSrN\nvmiGo7OjLvc9lpxMj9BQJjZsyOv16unS6ywMkp0NTz8NHTrAJ58YHY0QQghhlaSn2MK2ndnGqM2j\neLrZ04S+GopLWRdd7quaVS7NvsSlWZdwm+9GjRf0Wwni16tXGXPmDMtatOD56tV1u68wyPjx4OwM\n06YZHYkQQghhk+Sz8ny6V6/MjfQbDFo3iFe3vsqy7stY0m2JbgVx1tUsQrqGcP236/ge89WtIFZV\nlZkXL/LW2bPsbN1aCmID6N539d132ioTq1aBoz6fIgjrZw/9e8J4kkdCL/aQS1IUF9La8LV4futJ\nlbJVCBkTwpNNn9Tt3jd33+SEzwkq+FTAe583zo2ddblvltnMiKgofr56lSO+vvhUrKjLfYWBjh3T\nll9bvx5c9HlDJoQQQpRE0lNcQPG343lt62uEXg1labelPNTwId3ubc4xc2HqBeKXxdNyRUuqPlVV\nt3vfzM6md1gY5R0dWeXuTgVZqsv2xcdDu3bw1VfQo4fR0QghhBBWL7eeYhkpzidVVfn+5Pe0nt8a\nt2puBI8O1rUgzriYQXCnYFKOp9A2qK2uBfG59HQeDAqidYUKrPP0lILYHmRlQe/eMGyYFMRCCCGE\nDqQozoeLSRf5z+T/MPvwbLYP2M4nT3xC2VL6reV7bf01AtoFUL1bdVpva02ZWmV0u/ehpCQeCgpi\nXL16zGneHEdZYcJwuvRdvfGGtg7xlClFv5ewSfbQvyeMJ3kk9GIPuSRDhrkwq2YWnljIFP8pdKvZ\njQUjFlDasbRu9zdlmDg34RyJmxPx3OBJ5Qcq63ZvgJ8SEng9OprvW7bkWdnIwX4sWgT79sGRIyDr\nSgshhBC6kJ7i+4i+Ec3wjcPJyMlgWfdleNTw0PX+aVFphPUJo5xrOdwWu1HaRb9iW1VVpsfEsOTK\nFTZ5edG6QgXd7i0MduiQ1i5x4AC4uRkdjRBCCGFTpKe4AExmE58f+pwHljxA9xbdOTj0oK4Fsaqq\nxK+IJ+jhIOqNqYfHLx66FsSZZjOvREayMTGRI76+UhDbk7g4ePFFbQk2KYiFEEIIXeVZFCuK8oyi\nKJGKopxRFGXifc7ppChKkKIooYqi+N/19QuKopz687VjOsZtEaFXQ+mwtANbz2zl6PCjvNXhLRwd\ntHVf9eiVyUnJIXJQJBdnXqTNnjbUHVVX113kErOzefrkSVJNJvZ5e1PHyUm3ewv9FCqXMjK0LZxf\new26dNE9JmF77KF/TxhP8kjoxR5yKdeeYkVRHIGvgSeBOOC4oigbVVWNuOscF+AboLOqqrGKoty9\nG4QKdFJV9Yb+oesny5TFjAMz+OrYV3z8+McM9x2Og6LvIHpKYArhfcNx6eiC3wk/HMvpu8nCmbQ0\nuoaE0KN6dWY0bYqDTKizH6oKY8dCw4YwaZLR0QghhBB2KdeeYkVROgAfqKr6zJ/HkwBUVZ1x1zmv\nArVVVf3XNHhFUc4DbVVVTczlGYb2FJ+4fIKhG4bSoHIDFnRdQIPKDXS9v6qqxM2LI2Z6DM2/ak6t\nvrV0vT/A/lu3eCksjGlNmjCibl3d7y8M9s03sHCh1k8s7TBCCCFEoeXWU5zX6hP1gEt3HccC//nH\nOa5AaUVR9gIVgbmqqq788zUV2KUoiglYqKrq4gJHbyHp2elM9Z/Kdye/44unv+Blr5d1bWUAyLqe\nRdTQKLLis/A96otzU312prvb9/HxjD97llXu7jxZVb+1jYWV2LcPpk2TglgIIYSwsLx6BPIzhFsa\n8AW6AJ2B9xVFcf3ztYdVVfUBngXGKorySKEj1dGBiwfwXujN+VvnOTX6FANaD8izIC5or8yt/bcI\n8AmgXIty+Bzw0b0gVlWVKefP88GFC/h7e0tBbEPynUsXL0LfvrByJTRtatGYhO2xh/49YTzJI6EX\ne8ilvEaK44C7+wkaoI0W3+0ScF1V1XQgXVGU/UAb4IyqqpcBVFW9pijKOqA98Mc/HzJ48GAaN24M\ngIuLC97e3nTq1An4/2+yHscpmSkMmjOIPy7+weJxi+np3hN/f38iiMjz+jvyet7e3XtJWJlAwx0N\nabGsBSHOIVw6dEnXv0+WycTyOnW4kJHBFykpXD1+HA8LfL/k2DLHwcHBeZ//n/9Az5749+gBpUuj\nvWod8cuxHMux/Rzn6/eRHMtxPo6Dg4OtKp47x3f+fOHCBfKSV09xKSAKeAK4DBwD+v1jol1LtMl4\nnQEn4CjQB7gAOKqqmqIoSnlgJ/Chqqo7//GMYukp3nl2JyM3jeSxJo8x++nZVHGuovszMuMyCX85\nHMVRwX2lO0519V/94VpWFj1CQ6nv5MR3LVvi7KjvhD1hBVQVBg7U/vvDDyCTJoUQQghdFLqnWFXV\nHEVRXgN2AI7AUlVVIxRFGfXn6wtVVY1UFGU7cAowA4tVVQ1XFKUp8NufbQmlgB//WRAXh5vpN3l7\n59vsPr+bRc8tonPzzhZ5zvXN14kaHkX9cfVpOKkhiqP+hUxEairPhYTQr2ZNPmrSRFaYsFdz5kB4\nuLZBh/yMhRBCiGJh1zvarY9cz9itY+nRogcznpxBRaeKhb6Xv7//X0PydzNnmjk36RzXfruG+4/u\nuDzsUoSI72/3zZv0Dw9nZtOmDK5TxyLPEMXjfrkEwK5d2ijxkSPQqFGxxiVsS655JEQ+SR4JvdhK\nLhVl9QmbdDX1KuO2jSPoShA/vfATjzZ61CLPSYtOI7xvOGUblKVtUFtKV9VvZ7q7Lb1yhffOneNn\nDw86VdG/7UNYiXPn4OWX4eefpSAWQgghipldjRSrqsqqkFX8d+d/eaXNK3zY6UOcS+u/DBpAwqoE\not+IpvHUxtR9Vd+d6e4wqyrvnTvH2uvX2ezlRYty5XR/hrASqanQoQOMGAHjxhkdjRBCCGGXchsp\ntpuiODY5ltGbRxOTFMOybstoV6+dDtH9mynVxJlxZ0g6mITHzx5U9C58S0Zu0kwmBkVEkJCdzbpW\nrahepoxFniOsgKpCnz5QvjwsWyZ9xEIIIYSF5FYUOxR3MHpTVZXFAYvxWehDu7rtCBgZYJGC2N/f\nn9snbxPQNgDVrOIX4Gexgjg+M5PHgoMp6+DArjZtpCC2M3cvEwPAzJlw4QLMny8Fsci3f+WREIUg\neST0Yg+5ZNM9xedunmPEphEkZyazZ9AevGp5WeQ5qqpybf01Tv54kmazm1F7YG2LPAcg9PZtngsJ\nYUidOkxp1MgibRnCimzbBl99BUePQtmyRkcjhBBClFg22T5hMpv46thXTN8/nUkPT+LNB96klINl\n6vvsm9lEDYsi40IGHqs9KOdmub7eHTduMDAigjnNm/NyrVoWe46wEmfOwEMPwbp12n+FEEIIYVF2\ntfpExLUIhm0cRimHUhwedhjXaq55X1RISYeSCO8fTvUe1fH4yQMHJ8t1myyIi2PqhQv81qoVD7tY\nZlk3YUVSUqB7d5g2TQpiIYQQwgrYTE9xtimbj/d/zCPLH2FA6wH4D/a3WEGsmlRiPokhtGcorvNc\ncf3Slf2H91vkWSZV5e3oaObExnLAx0cK4hLAf88eGDQIHnkERo0yOhxho+yhf08YT/JI6MUecskm\nRoqDrgQxdONQapWvRcDIABq5WG4N18z4TCIGRKBmaZPpyta3XJ9nqsnEy+HhJJlMHPb1pWppy6xz\nLKzMypVw9SqsXm10JEIIIYT4k1X3FGfkZDBt3zQWBy7ms6c+Y1CbQRadeHZjxw0ih0RSZ2QdGk1u\nhEMpyw2kX87M5PmQEFpXqMBCNzfKONjMoL0oio0bYexYOHYMZGdCIYQQoljZZE/x4UuHGbpxKO7V\n3Tk5+iR1KlqugDBnmzk/+TxXV13FfZU7VTpZdte4k7dv83xICGPq1mVSw4aywkRJEREBw4bB5s1S\nEAshhBBWxuqGJ1OzUnlz+5v0+qUXH3X6iLUvrbVoQZx+Pp2gR4JIDUvFL8jvvgWxXr0yWxITeerk\nST5v1ox3Zcm1kuPWLejRA2bNwj893ehohB2wh/49YTzJI6EXe8glqyiKOw/pzJbft7D73G685nuR\nmJ5I6JhQXmz1okWLxqtrrhL4n0Bq9qmJ1yYvylS37CYZX8XGMiIqio2enrxUs6ZFnyWsiMkEL78M\nTz8NQ4YYHY0QQggh7sEqeoqZChUPVMTJzYkVb66gi2sXiz7TlGYi+q1obu6+icdqDyq1rWTR5+WY\nzbx19ix7bt5ks5cXTZydLfo8YWUmT4Y//oBdu0AmUwohhBCGsYme4pSHU2h/vr3FC+LUsFTC+oRR\noU0F2ga2pVQly34LUnJy6BseTpaqctDHBxcpikqWX3/VVps4flwKYiGEEMKKWUX7xB055Fjs3qqq\ncnnxZYI7BdPg7Qa4/+BeoIK4ML0ylzIyeCQoiHpOTmz18pKCuKQJCYExY+C33+Cudhl76LsSxpM8\nEnqQPBJ6sYdcspqRYoCyDpZZEzgnKYeokVGkRabhvd+b8u7lLfKcuwWkpNA9JIQ369fn7QYNZEJd\nSXPjhjaxbs4c8PMzOhohhBBC5MFqeoqbBTZj7mtz6fpUV13vn3wsmfC+4VR9tirNPm+Go7Ojrve/\nl/XXrjHi9GkWubnRs0YNiz9PWJmcHOjSBby84IsvjI5GCCGEEH+y+p7izjGdGffaOF0LYtWscumL\nS1z67BJuC9yo0cvyxamqqsyOjWX2pUts9fKiXSXLTuATVuq998BshpkzjY5ECCGEEPlkFT3F25dt\n17UgzrqaRUjXEK6vu47fcT9dCuK8emWyzWbGnD7Nivh4Dvv6SkFcUv30kza57uefodS933PaQ9+V\nMJ7kkdCD5JHQiz3kklUUxXq6ufsmJ3xOUMGnAt77vCnbyDJ9yndLysnhuZAQLmZmcsDHh4ZlLf9M\nYYWCguD112HdOqhWzehohBBCCFEAVtFTrEcM5hwzF6ZeIH55PC1XtKTqk1V1iC5vF9LTeS4khE4u\nLnzZvDmlHOzufYbIj2vXoF07mDULXnrJ6GiEEEIIcQ9W31NcVBkXMwjvH45jeUfaBralTC3L7kx3\nx9HkZHqGhjKxYUNer1dPVpgoqbKzoU8f6NdPCmIhhBDCRtn8sOa1ddcIaBdA9W7Vab2ttcUK4n/2\nyvx69SrPhYSw0M2NN+rXl4K4JJswAcqWhenT83W6PfRdCeNJHgk9SB4JvdhDLtnsSLEpw8TZ8We5\nseUGnhs8qfxA5WJ5rqqqzLx4kW8uX2Zn69b4VKxYLM8VVmrFCti6FY4dA0fLL/cnhBCO9jW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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x7f3fa9ce1690>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 9 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [] | |
| } | |
| ], | |
| "metadata": {} | |
| } | |
| ] | |
| } |
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