From 0a27b1b8c0209462f969c9934faa05d6392b13b7 Mon Sep 17 00:00:00 2001 From: Michael Pradel Date: Fri, 3 Feb 2023 09:58:56 +0100 Subject: [PATCH] Tighter figures --- .../analyze_code_coverage_effectiveness.ipynb | 6334 +++++++++++++---- 1 file changed, 4901 insertions(+), 1433 deletions(-) diff --git a/src/notebooks/analyze_code_coverage_effectiveness.ipynb b/src/notebooks/analyze_code_coverage_effectiveness.ipynb index ff82161..6583011 100644 --- a/src/notebooks/analyze_code_coverage_effectiveness.ipynb +++ b/src/notebooks/analyze_code_coverage_effectiveness.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 311, "metadata": { "id": "NWC_ZXbMFRVF" }, @@ -42,31 +42,346 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 312, "metadata": { "id": "PMOQ6MjVCDFe" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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filepredictorcovered_iidstotal_usesguided_usesexecuted_linescovered_linesexecution_time
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1./popular_projects_snippets_dataset/black/bodi...FrequencyValuePredictor443000.001013
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pd.read_csv(home_dir+'/total_lines_function_bodies_dataset.csv')\n", "random_functions_total_lines_df" ] }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 314, "metadata": { "id": "9hofjFYjrTXn" }, @@ -108,11 +423,354 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 315, "metadata": { "id": "eaXw9WBHuiha" }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_49244/1503667806.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_As_Is['predictor'] = 'AsIs'\n", + "/tmp/ipykernel_49244/1503667806.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_As_Is['covered_iids'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_As_Is['total_uses'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:5: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_As_Is['guided_uses'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_As_Is['executed_lines'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:7: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_As_Is['covered_lines'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:8: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_As_Is['execution_time'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:11: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Naive['predictor'] = 'NaiveValuePredictor'\n", + "/tmp/ipykernel_49244/1503667806.py:12: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Naive['covered_iids'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:13: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Naive['total_uses'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:14: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Naive['guided_uses'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:15: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Naive['executed_lines'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:16: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Naive['covered_lines'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Naive['execution_time'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:20: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Random['predictor'] = 'RandomPredictor'\n", + "/tmp/ipykernel_49244/1503667806.py:21: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Random['covered_iids'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:22: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Random['total_uses'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:23: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Random['guided_uses'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:24: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Random['executed_lines'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:25: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Random['covered_lines'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:26: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Random['execution_time'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:29: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Frequency['predictor'] = 'FrequencyValuePredictor'\n", + "/tmp/ipykernel_49244/1503667806.py:30: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Frequency['covered_iids'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:31: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Frequency['total_uses'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:32: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Frequency['guided_uses'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:33: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Frequency['executed_lines'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:34: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Frequency['covered_lines'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:35: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Frequency['execution_time'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:38: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Fine_Grained['predictor'] = 'CodeT5ValuePredictor_fine_grained'\n", + "/tmp/ipykernel_49244/1503667806.py:39: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Fine_Grained['covered_iids'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:40: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Fine_Grained['total_uses'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:41: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Fine_Grained['guided_uses'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:42: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Fine_Grained['executed_lines'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:43: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Fine_Grained['covered_lines'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:44: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Fine_Grained['execution_time'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:47: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Deterministic['predictor'] = 'CodeT5ValuePredictor_coarse_grained_deterministic'\n", + "/tmp/ipykernel_49244/1503667806.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Deterministic['covered_iids'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:49: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Deterministic['total_uses'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:50: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Deterministic['guided_uses'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:51: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Deterministic['executed_lines'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:52: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Deterministic['covered_lines'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:53: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Deterministic['execution_time'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:56: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Randomized['predictor'] = 'CodeT5ValuePredictor_coarse_grained_randomized'\n", + "/tmp/ipykernel_49244/1503667806.py:57: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Randomized['covered_iids'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:58: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Randomized['total_uses'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:59: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Randomized['guided_uses'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:60: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Randomized['executed_lines'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:61: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Randomized['covered_lines'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:62: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Randomized['execution_time'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:65: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Pynguin['predictor'] = 'PynguinTests'\n", + "/tmp/ipykernel_49244/1503667806.py:66: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Pynguin['covered_iids'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:67: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Pynguin['total_uses'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:68: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Pynguin['guided_uses'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:69: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Pynguin['executed_lines'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:70: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Pynguin['covered_lines'] = 0\n", + "/tmp/ipykernel_49244/1503667806.py:71: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Pynguin['execution_time'] = 0\n" + ] + } + ], "source": [ "files_without_results_As_Is = files_without_results_As_Is\n", "files_without_results_As_Is['predictor'] = 'AsIs'\n", @@ -189,79 +847,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 316, "metadata": { "id": "4d1mP4_ToXjb" }, - "outputs": [], - "source": [ - "random_functions_df = random_functions_df.merge(random_functions_total_lines_df, how='left', on='file')\n", - "random_functions_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "8nTLxCuLJRTC" - }, - "outputs": [], - "source": [ - "random_functions_df = pd.concat([random_functions_df, files_without_results_As_Is])\n", - "random_functions_df = pd.concat([random_functions_df, files_without_results_Naive])\n", - "random_functions_df = pd.concat([random_functions_df, files_without_results_Random])\n", - "random_functions_df = pd.concat([random_functions_df, files_without_results_Frequency])\n", - "random_functions_df = pd.concat([random_functions_df, files_without_results_Fine_Grained])\n", - "random_functions_df = pd.concat([random_functions_df, files_without_results_Deterministic])\n", - "random_functions_df = pd.concat([random_functions_df, files_without_results_Randomized])\n", - "random_functions_df = pd.concat([random_functions_df, files_without_results_Pynguin])\n", - "random_functions_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "PD6bw03MJ-wN" - }, - "outputs": [], - "source": [ - "random_functions_df['covered_lines_percentage'] = random_functions_df['covered_lines'] / random_functions_df['total_lines']\n", - "random_functions_df['line_execution_time'] = random_functions_df['execution_time'] / random_functions_df['executed_lines']\n", - "random_functions_df['completely_executed'] = random_functions_df['total_lines'] - random_functions_df['covered_lines']\n", - "random_functions_df['completely_executed'] = [1 if value == 0 else 0 for value in random_functions_df['completely_executed']]\n", - "random_functions_df.replace([np.inf, -np.inf], 0, inplace=True)\n", - "random_functions_df" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7l4cf3NbK3jB" - }, - "source": [ - "Overall Comparisson" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 430 - }, - "id": "QOvO3nmmJmYV", - "outputId": "bc6cebd1-2bcc-4ff7-d2f4-1087b6ba11ed" - }, "outputs": [ { "data": { "text/html": [ - "\n", - "
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7412 rows × 9 columns

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" ], "text/plain": [ - " covered_iids total_uses \\\n", - "predictor \n", - "AsIs 1218 1218 \n", - "CodeT5ValuePredictor_coarse_grained_deterministic 9690 18731 \n", - "CodeT5ValuePredictor_coarse_grained_randomized 9351 11317 \n", - "CodeT5ValuePredictor_fine_grained 9591 16859 \n", - "FrequencyValuePredictor 4229 4231 \n", - "NaiveValuePredictor 4229 4231 \n", - "PynguinTests 0 0 \n", - "RandomPredictor 4407 4409 \n", - "\n", - " guided_uses covered_lines \\\n", - "predictor \n", - "AsIs 0 154 \n", - "CodeT5ValuePredictor_coarse_grained_deterministic 10894 2034 \n", - "CodeT5ValuePredictor_coarse_grained_randomized 6730 1987 \n", - "CodeT5ValuePredictor_fine_grained 9781 2019 \n", - "FrequencyValuePredictor 2976 428 \n", - "NaiveValuePredictor 2976 426 \n", - "PynguinTests 0 233 \n", - "RandomPredictor 3060 477 \n", + " file \\\n", + "0 ./popular_projects_snippets_dataset/black/bodi... \n", + "1 ./popular_projects_snippets_dataset/black/bodi... \n", + "2 ./popular_projects_snippets_dataset/black/bodi... \n", + "3 ./popular_projects_snippets_dataset/black/bodi... \n", + "4 ./popular_projects_snippets_dataset/black/bodi... \n", + "... ... \n", + "7407 ./popular_projects_snippets_dataset/scrapy/bod... \n", + "7408 ./popular_projects_snippets_dataset/pandas/bod... \n", + "7409 ./popular_projects_snippets_dataset/scrapy/bod... \n", + "7410 ./popular_projects_snippets_dataset/flask/bodi... \n", + "7411 ./popular_projects_snippets_dataset/scrapy/bod... \n", "\n", - " completely_executed \\\n", - "predictor \n", - "AsIs 19 \n", - "CodeT5ValuePredictor_coarse_grained_deterministic 342 \n", - "CodeT5ValuePredictor_coarse_grained_randomized 338 \n", - "CodeT5ValuePredictor_fine_grained 340 \n", - "FrequencyValuePredictor 76 \n", - "NaiveValuePredictor 74 \n", - "PynguinTests 18 \n", - "RandomPredictor 80 \n", + " predictor covered_iids total_uses guided_uses \\\n", + "0 FrequencyValuePredictor 5 5 4 \n", + "1 FrequencyValuePredictor 4 4 3 \n", + "2 FrequencyValuePredictor 3 3 3 \n", + "3 FrequencyValuePredictor 3 3 2 \n", + "4 FrequencyValuePredictor 2 2 2 \n", + "... ... ... ... ... \n", + "7407 PynguinTests 0 0 0 \n", + "7408 PynguinTests 0 0 0 \n", + "7409 PynguinTests 0 0 0 \n", + "7410 PynguinTests 0 0 0 \n", + "7411 PynguinTests 0 0 0 \n", "\n", - " execution_time \\\n", - "predictor \n", - "AsIs 2.414686 \n", - "CodeT5ValuePredictor_coarse_grained_deterministic 469.546773 \n", - "CodeT5ValuePredictor_coarse_grained_randomized 371.841410 \n", - "CodeT5ValuePredictor_fine_grained 439.997213 \n", - "FrequencyValuePredictor 5.285355 \n", - "NaiveValuePredictor 5.088755 \n", - "PynguinTests 67.778833 \n", - "RandomPredictor 5.626200 \n", + " executed_lines covered_lines execution_time total_lines \n", + "0 1 1 0.007880 17 \n", + "1 0 0 0.001013 2 \n", + "2 0 0 0.000731 8 \n", + "3 0 0 0.000933 2 \n", + "4 1 1 0.006371 8 \n", + "... ... ... ... ... \n", + "7407 1 1 0.154378 4 \n", + "7408 2 1 0.142572 1 \n", + "7409 0 0 0.131013 10 \n", + "7410 1 1 0.153872 1 \n", + "7411 0 0 0.140629 3 \n", "\n", - " Approach \n", - "predictor \n", - "AsIs Pynguin Tests \n", - "CodeT5ValuePredictor_coarse_grained_deterministic Fine-grained \n", - "CodeT5ValuePredictor_coarse_grained_randomized Coarse-grained \\n(randomized) \n", - "CodeT5ValuePredictor_fine_grained Coarse-grained \\n(deterministic) \n", - "FrequencyValuePredictor Frequency \n", - "NaiveValuePredictor Naive \n", - "PynguinTests As Is \n", - "RandomPredictor Random " - ] - }, - "execution_count": 152, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "random_functions_df_grouped = random_functions_df.groupby(by=\"predictor\")[[\"covered_iids\",\"total_uses\",\"guided_uses\",\"covered_lines\",\"completely_executed\", \"execution_time\"]].sum()\n", - "random_functions_df_grouped['Approach'] = ['Pynguin Tests', 'Fine-grained', 'Coarse-grained \\n(randomized)', 'Coarse-grained \\n(deterministic)', 'Frequency', 'Naive', 'As Is', 'Random']\n", - "random_functions_df_grouped" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 942 - }, - "id": "jdC6tHCpcIAe", - "outputId": "b1960172-2730-4970-8e85-b2e9155592cc" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0, 'Fully executed functions (%)')" + "[7412 rows x 9 columns]" ] }, - "execution_count": 153, + "execution_count": 316, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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SnFpVC3qPOxVOdxntf/5/0253SUl2ZDJUugP49Vxer6pOA06by2vMl6q6Azi53cbV56+Z4+9EkiRJkjTJqXC620uyyMLQfdqtztT1jI6vqqvnpipJkiRJku76DJYk2DzJL5K8OMk6vW8mWTHJ02lGwjyoPXwH8O4lWaQkSZIkSXc1ToWTGju2G0kuonni2i3A6sA2wKo97d/SLvgtSZIkSdLdlsGSBL0LO2/SbtO5Fnh9VX1mbkuSJEmSJOmuz2BJd3tVdV6SBwJPA3aleUrZ+jSjlG4BrgJOB04Ajqqq6+arVkmSJEmS7koMliSgqv4I/BE4dL5rkSRJkiRpaeHi3ZIkSZIkSRqJwZIkSZIkSZJGYrAkSZIkSZKkkaSq5rsGSbrLW7BgQZ1yyinzXYYkSZIkzYskp1bVgt7jjliSJEmSJEnSSAyWJEmSJEmSNBKDJUmSJEmSJI3EYEmSJEmSJEkjMViSJEmSJEnSSAyWJEmSJEmSNBKDJUmSJEmSJI3EYEmSJEmSJEkjMViSJEmSJEnSSAyWJEmSJEmSNBKDJUmSJEmSJI3EYEmSJEmSJEkjMViSJEmSJEnSSAyWJEmSJEmSNBKDJUmSJEmSJI3EYEmSJEmSJEkjMViSJEmSJEnSSAyWJEmSJEmSNBKDJUmSJEmSJI3EYEmSJEmSJEkjMViSJEmSJEnSSAyWJEmSJEmSNBKDJUmSJEmSJI3EYEmSJEmSJEkjMViSJEmSJEnSSAyWJEmSJEmSNBKDJUmSJEmSJI3EYEmSJEmSJEkjMViSJEmSJEnSSAyWJEmSJEmSNBKDJUmSJEmSJI3EYEmSJEmSJEkjMViSJEmSJEnSSAyWJEmSJEmSNBKDJUmSJEmSJI3EYEmSJEmSJEkjMViSJEmSJEnSSAyWJEmSJEmSNBKDJUmSJEmSJI3EYEmSJEmSJEkjMViSJEmSJEnSSAyWJEmSJEmSNBKDJUmSJEmSJI3EYEmSJEmSJEkjMViSJEmSJEnSSAyWJEmSJEmSNBKDJWkpluTEJNVu+893PfMlyQWd+7DnfNcjSZIkSXcXK8x3AXMlyTbA44G9gPsBawNrANcDVwGnA78AvlpV581XnZIkSZIkSUurZS5YSvJQ4F3AE/s0WbPdtgSeCbwvyUnAwqo6cYkUKUmSJEmStAxYpqbCJXkDcAqLhkpX04xQ+jHwG+Di7mnA7sCPk7xpSdQpSZIkSZK0LFhmRiwl+Qjwys6h24AjgS8Av6mq6ml/P+ApwKuBzdvDGy6BUqWxqao957uGu4uLL76YQw45ZL7LkCRJkrQMWLhw4XyXMDbLxIilJC9laqh0NrBtVb2mqk7tDZUAqur8qvow8MD23JuWTLWSJEmSJEnLhqU+WEqyOfDhzqHzgF2r6pxBzq+q26vqCGBn4Nw5KFGSJEmSJGmZtNQHS8AbgVXa/TuB/avqimE7qarTq+ojs7VL8oQkn07yxyTXJrk5yYVJvpnk5UlWHfSaSe6dZJ8kRyb5eZLLk9yW5Ia2z/9O8ookqw3Y31GdR64f3B5LkicnOTbJ2Un+1r5/+DTn3zfJvyX5cZJLk9zS1nN1kt+1fbwmyf0HrGeFJPsmOaa99nWd+/XVJC9MMufTMZMsSPLxJH9KcmOSq5KcluRdSTbttKvOtlmfvsZxjw9IcnR7T69Jcnv77/8m+XySf0iSAT/biZ169u/TZs9Omws6xzdt78Fv2+vflOScJJ9Mst0g1++5zqpJXprka0nObX/HN7T7Ryd5xqCfq6fG9yY5M8n17W/o90k+lGTbYWuUJEmSJI3XUr3GUpK1gRd1Dn2nqk6ao2ttCnwO2GOatzdptycBb01yQFV9Z5b+3gC8E1h5mrdXBFZr+3wqcHCSF87W5zTXWBf4PPC4Adq+CjiUyZCu6z7t9mDgOcBhSdarqstn6O/RNGtcbTXN2xP36xnAQUn2rarfzlbjsNoQ4/3Aa5kaoq5K82TA7YHXJHlZVX1pxGsMc4+Po/nM04Ura7TbVsB+wO+SPGvQkXfDSrIfzffTG1pu3m4vTvL6qvrQgP09GziM6dcpu3+7PQ/4ZZLnVNWFA/S5P/AR4J49b23Tbv+c5E1VddggNUqSJEmSxm+pDpaAvZkazHxyLi6SZAuaJ8pt3Dl8M/B7mrWZtgLWb49vDByfZL+qOnaGbh/A1NovBv4M3EATfGxNE34ArA18I8lTq+rbA5a9EvAtYEH7+nLgTzQBy5Y9n+8lTJ1OCHBhu90O3IsmGFi7837f0W5taPEZpv6+LqOZanh729d92+MPAH6S5LFV9asBP9ugPga8vOfYuTT3eXVgO5pg5egk143Q/8D3uPUQJkOlO2mmbV4G3EIT3D2QyWDvIcDPkzy0qv4yQm19JXkBTRgGzSL3ZwDX04R9EwvZLw8cnuS8qvrGLP0dBLy75/CfgQva/a2A9dr9HYGTk+xWVefN0Of+NL+hbgh3MXAOzXe2HXAP4INJbp6pPkmSJEnS3Fnap8Lt1tkvmvBnrJKsCHyZyVDpDmAhsF5VPbyq9gA2Ap4GXNK2WQH4TJKtZ+j6TuC7wAuBdapqo6raqar2qqpdqmotmtFRp7TtlweOStI7eqOfV9AEHucCTwDWr6pdq2oXmhDssM7nO7Rz3jeALatqs6rao61nx6paB7gf8C/AWf0ummRnpoZKPwZ2rqr1q+qRVbVnVW0C7AL8rm1zb+DYJPce8LPNKskzmRoqnQLsUFVbVNWjqmoHYAPg4zThxedGuMxA97jjZpp780TgnlW1Zdt+r6p6GE24tB9wadt+beATI9Q1k7VpAtjbgYOAtapqQVU9uqq2AHalCXAmHD7T9LUkz2JqqHQc8KCq2qSqdq+q3Wnu8xNpgkpoRjUd028aZBvkHslkqPRXmtGAG7e/yQU093di6uphwFqDfXxJkiRJ0jgt7cHSgs7+2VV1/Rxc40Bgh87rA6rqHVX1t4kDVXVnVR0P7Alc3R5ehUVHAXW9tqqeUFVfqKorp2tQVT+lCc9+2R5ahyaIGsS9aEaN7FpV3+0+Ga+q/t6ZirQTk/8pPx/oO/2qqi5on6S3Lc3onCmSLE8zEmYiMPgcsFdV/WKavn5OE2Kc0R66H/CqAT/bjJIsB/xH59DpwKOr6rSeGq6sqpcDhzN1NNagBr3HE3aqqpdU1XeqapFRNlV1a1V9kSZ0u7Y9/IQk24xQWz+r0Yy0emZVvbeqbuip4X9oputNfJb7M/30T5KswdRRgu+sqmdV1e97+qx2GufOTIZWO9JMq5zO+5kczXcN8Kiq+nbP/b2mql5NE4quzKLT5SRJkiRJS8DSHiyt09m/aI6u8YrO/req6rP9GlbV2cDbOoce22/UUlXdOMjFq+oW4K2dQ08b5LzW66rq0lnadKf3/aqqbhugpqqqO6d565nAFu3++cCBfdpN9HMDcEDn0D8Pu7hzH48HNu28fnk3CJzGQTQB0SgGucfAUN/5+cBHO4eG+c4H8bmZpre1UxJ/1jn0yD5ND6BZFwrg5Kp6+0wXrapLgNd3Dr2it02SjYCndA4trKo/zdDtv9FMP5QkSZIkzYOlPVjqTn8ZZY2cGSV5IM0aQBMGWcj4s51aAjx9DKV0R/w8fMBzrgC+PkC7Wzr7D2pH+4xqv87+x6rq1tlOaEczndu+3JBmbanF9aTO/unt6KiZargFOGqE6wx6j0fR/c4fMea+Pz5Am26w9MA+bbrf96ALaH+VZl0ygEdk0ScePpVm2idtu8/M1FlV3c5gn0eSJEmSNAeW9sW7V+rszxpijGDHzv7NDLCGU1XdkuQEmtE7vX1MK8kDaKbRPYhmSta9mPzPda/7JFm1qm7q8/6EX1TVHbNdGzi1s78t8Nkkb2lHlwysHWnUHdlywhCnn8HkotE7AH8c5trT6IZvPxrwnB/RjH4ZxqD3eIrOvdqRJrRZg2aKWne01pqd/Y2GvcYMbmNy3a6ZdBcMv0/vm0nWpPm9TPjhIBevqtuSnE3zRL7laRYpP7nTpBuinTTgKK/vAB8Y5PqSJEmSpPFa2oOla5mcDje2hZ87tujsnzVEiHAGk8HSFv0aJXk4zfo+uwxZ1+pMjvro59xZ3gegqi5KchyT9b4QeH6Sn9GEBT+jCVBme/LWxkwNID6YZNZpda3tOvvr9G01uE06+30XGu8xaLuuge5xV5LnA//O1Kl6s1l92OvM4KoBf8fd39eq07y/LVOfcPelIWYxdj977/fd/Xs5c8D+/pdmMfIVBy1AkiRJkjQeS3uwdDWT/zFdc6aGI1qjs3/VEOd1F+NeZLQHQJJ9gC/Rf2TSTFaavQkzrSnU62U093H39vXyNAs2TyzafGuSk4BjgGP6THHrfSrXo4a4fteUECXJ55h8VH0//1FVP+i87n5v1/Y27mOUqZTD3GOSHE7zVL1hDfJ9D2rQsK9rusSo+30vBzxutHIWCc26fy8D/c1V1d+TXMdoC7BLkiRJkhbD0h4sncfkmjzbJkn3yVFj0P0P/TD/Ie+2XSQUSLIpzdPTJkKlK4FP0UzHOodm7Z6bq+rvnXOG/Vx9F83uVVXXJHkU8Fzg5TTTtLprLa0E7NVuByfZv6p6pwX2rpUzqt41nvZg9tE9x47hugPfr1HOSfJspoZKZwGfppkGdiFNAHbzxO83yZ4MMPVyHs3V9z3q39xcTIWVJEmSJM1iaQ+WTgKe0O6vQRMyLe76PF3dUSz3GuK8btvpRsy8hsnHqZ8HPHKmJ4slGebaI2mf3nYMcEz7GPnd221PmnWPJkatbAJ8J8nu7dPDJvSO+Nlg0KelzYHrgHXb/UGnkU07smyMDurs/zewT7vwdD9z/p0vpu73fWtVrdy35XCu7+yP+jcnSZIkSVpClvanwp3Y8/q5Y+7/is7+/Yc4b/PO/hXTvL93Z/+dAwQwGw9x7cVWVddW1fFV9YaqWkATJr0XmBhBtRLw7p7TLut5vS5jUFWbVVVm2Y7qOe2izn6/J5r1esDsTUaTZF2axaonvGaWUAmW8Hc+gu73vVKSca1x1u33foOckGQt5maNNUmSJEnSLJbqYKl9jHx3gd+XJllljJf4TWd/4ySDPp2r+yS430zzfndx6V8P0N+wi3uPVVX9paoOAt7VObxHkpU6ba6gGX01YeclVd80uvd00LWeRl0TahD37exfWVUXDHDOvH7nA/gdzZMSJ4zr+z6ts/+Ivq2mGrSdJEmSJGnMlupgqfX+zv5GLDqSZmBJtuw59EumrvPyvAH6eADwsM6hn07TbNinV714yPZz5eud/RVYdMHu73b257Pmb3f2t0+yY9+WQBuQ7T+H9Qz1fSdZHXjGHNUyFlV1G1PXgBrX9939e3lAkgcPcM6+Y7q2JEmSJGlIy0KwdDTwk87rf0nyimE6SLJKks8DU86rquuB/9c59KZ22s1M3tfZvwI4fpo2l3T2d52ltufSLKY9JzLEM+JZdLrRNT2vP8TkgtY7Jjlg5MIWz3eAP3de/2eSe87Q/t+ZfYHwxdH9vtdOsnXflo1DGd/i2HPpsM7+Pkme0Lfl4E4A/tp5PWNQnORBDBD4SpIkSZLmxlIfLLVPTtsXuLw9FOCjST6ZZP2Zzk2yfJIX0yz4vV+fZocCE+vhrA18I8ma0/S1XJJDgad0Dr+/qqZ7WtWJnf23J7nvNG1I8iTgMzN9hjF4U5Ijk8y4FlGS1YB3dA79qqq6U6GoqrOBIzuHjkjy2iTLM4Mkayb5lyRfHrb46bS/iTd0Dm0P/DBJd50jkqyV5GPA62mezDcnqupC4ILOoY92pxF26lkuyULgwLmqZZyq6gTgW+3L5YCvJHn+bOcl2SDJ25N8ZJo+/87UcPZJSd49XQDaPl3x60w+XVGSJEmStIQt7U+FA6CqLmkfz/4tJhf8fSnw/CQ/oBkFcRFwNc1TwjagGQX0RGCdWfo+PcnBNKNaoFlL5g9JPg78AriF5ml0LwEWdE79GfDBPt1+GHgRTQi2IXBakiOA/6F5bPqmwLOYDKk+CbxspjoXwyrAy4GXJ/kd8EOadW4uA24C1qSZ2vdipq4N9S6m9zqaIOeRNL+vDwKvaEOjU2gCnHvQTKPbluZ+Pqpt+8txfaiq+q8kj6X5HUCzDs9pSc6hGc20OlrABH8AACAASURBVPBgJv8GXgx8o9PFMI+6H8Th7QawV1vLx2jWCFsB2IZmOt5D2zZz+Z2P0340fwdb0Yyy+mKSNwLHAb+lGdW2Ck0o+xCaEXo70wRR/YLEI2jC4p3a1wcBj0ryGeBP7XV2p/nd3pvm72ZT7voLnkuSJEnSMmeZCJYAquqsJDsBnwCe1h5eBXhqu83mB+250/X97nbEzr+2h9YD3j5DXz8DntyOvpiuv9OS/CvwnvbQWjP0dyLwKpZMyPCQdpvNW6rqG9O9UVW3JdmbZqTVc9rDmzN575akA4EbgH+hCfEAtmi3CTe17U7oOfe6MdfyUZqnAT6xff1AYJERO633At9jKQiWquqaJI8EvgLs2R4e9HfUr8+/J3kKzRTXbdrDOzEZNHVdRDMVbrq1zCRJkiRJc2ypnwrXVVWXV9XTaf4D+hVmDweuAz4L7FpVe1fVH2bo+600I01OmaG/S4HXAI+uqhmvXVXvpRm1dEmfJlcBBwN79ZlONy5foQk4zpmlXdEs1rxbVR06Y8Oqm6rqucATgJOYXHepX7+/Ad4G7DNo0YOoqjur6rU0T+n7FM1T626mGUXzO5oA50FV9UVg3c6pN1fVjWOu5e/A02mmed3cp9k5wD7tE/iWGlV1JfAYmlFGp83S/A6aEUavpQlMZ+pzJ+BjTD967A6a9c8eVlUXjVC2JEmSJGkMUlXzXcOcadf2WUAzPW4dmmkzf6OZjvU74Kyqmin06NfvpjRTvdanmdZ1BXAG8Osa8oYmuQewG820sHvSrBV1AfCTqrp9hlPHLsl6NNPD7kczBW554Pq2nl9X1aUj9rsmzRSojYD70KxZdS3NtKbTq+rqxS5+MSV5Fk3IBvCLqtp5Dq91H5rpf/ejuceXAn+oqplCy6VGkg2AXWj+Ptagmd55FXA2zff9tyH7WwN4LM1UzAL+Avx01N/jqDbccMM68MClYvkrSZIkSXdxCxcunO8Shpbk1Kpa0Ht8mZkKN512lMgvGePaPW2/FwIXjqmv22jWNfrhOPpbzFouo5kSOO5+r2b6p+Pdlbyks3/yXF6oqq4BvjqX15hPVXUJzRpL4+rvWiZDP0mSJEnSXcgyNRVO6pruSWJ92v0j8PjOoaPmpCAtdZIckOSUJKfcdNNN812OJEmSJN3lGCxpWXZEkg8l2TnJIr/1JJsm+TDN+ksTvlZVZyy5EnVXVlWfqKoFVbVg1VVXne9yJEmSJOkuZ5meCqe7vXsDzwdeDdyU5GzgamBFmkfT36+n/fk0T4iTJEmSJEkDMFjSsqy7MPuqwPYztD0BeH5VXTG3JUmSJEmStOwwWNKy7EDgv4HHAA8DNqV5StnywDXAX4GfAV+pqp/OV5GSJEmSJC2tDJa0zKqqm2meTja2J5RJkiRJkqRJLt4tSZIkSZKkkRgsSZIkSZIkaSQGS5IkSZIkSRqJwZIkSZIkSZJGYrA0oCQLktyZpJIcPsZ+D277rCRHjatfLVlJ9u98jyfOw/WP6lz/4CV9/UEluaBT555L4Hp7dq73jrm+niRJkiTd3RgsDSBJgI8AAf4G/Pv8ViRpEFV1IvD99uUbkmw6j+VIkiRJ0jLHYGkw+wA7tftHVtUV81nMuMz3KBstvZaWEVKtQ9p/VwHeNZ+FSJIkSdKyxmBpFkmWAw5uX94GjG0anKS5V1UnAye3L/dNsvV81iNJkiRJyxKDpdk9A3hgu39cVV0yn8XorqmqjqqqtNue83D9/TvXP3hJX39QVbVZp84Tl+ClP9L+uzzwliV4XUmSJElaphksze41nf1PzlsVkhbH14Cr2/19k6w7n8VIkiRJ0rLCYGkGSbYFdm1f/hU4cf6qkTSqqroVOK59uRKw//xVI0mSJEnLDoOlmb2gs//1qqpBT0yyYpKXJvlhkkuT3JLk/CTfSPLMdu2mkSRZte37a0nOTXJDu52b5Ogkz2ifZNfv/BOTFPDZzuE9Oosx9257zlLPnkk+kuT0JFckuTXJxe113pTkPgN8pu5j4S/oHN8qybuT/CbJ5UnuTHJt5/2DO+cd1Tn+mCTHJDknyU1Jrkvy03bB8kXufZL7Jnlf+xlubM85K8kHkqwzQP0DLYSe5ILe+5pkpSQvSnJCkr+09+/SJN9J8oKZvstOvwMvpp1k7SSvS/K9JH9NcnOS25Ncm+T37e/qzUm2m+bcan87L+ocXtjvtzPI5x/gs22Q5PVtvRe0388tbe0nJFmY5MEDdPW1zv4L+raSJEmSJA1shfku4C7umZ397w56UpItgK8A2/e8tVm7PRn4QZJ9hy0oybOBw4ANp3n7/u32POCXSZ5TVRcOe40harkf8HHgsdO8vUG77QG8OcmrquqYIft/A/BuYMUhzlkZOAL4x563VgF2a7enJnl2Vd3RnvOC9nOs2nPOA9pt/ySPrarThql/wHo3p/mtPLTnrfWAx7fbPyZ5alXdMIbr7UPzWacL+1Zvt22ApwPvTbJjVf1qca87iiTL0yyc/zoW/W6g+RvYEHgMcHCS11bVTIvr/xi4lWbE0nZJtqqqs8dbtSRJkiTdvRgs9dGGJlu2L+8EThrwvPsCPwQ26Ry+Gfh9++/WwLo0Ycx/0/xnd9CaDqIJWrr+DFzQ7m9FE0gA7AicnGS3qjqv55xfAbcAGwEPao9d0x6fztW9B5JsTxO2rdc5fCPwB+BvwPo0i54HWBP4YpLVq+rIGT5it//XA+9vX94GnAlcTxMkzLQ+zqdpgjWAS4A/AfegCflWbo8/g+bpfq9sw73Pt3XeCpwB3EDz3W/Utl8L+HaSB1TVdYPUP6D1gS8AG7evzwH+AtwTeAiTgdqjgM8xNegcWpK9gWOZOlLxYuB8mt/mPYFNaQLBCb2ju77X/rsdk+HmuW3tY5NkVZqpa4/veevStt5baX4HW9MsyA2wxkx9VtUtSX5BE3YCPA4wWJIkSZKkxWCw1N8enf2zhwgUPsNkqFQ0QdChVfU3gHYa1jOBI4FH0vzHeFZJnsXUUOk4YGFV/b7TJjT/ET+SJiDYEDgmya4To3MAqupNbfv9mZwOd3pV9f4nvl8t9wGOZzJUugB4I810wTs67TYFPgA8iya4+VCSX1bVb2a5xLrAe4E7gHcAh0/cv7bfzfuc92SaEOh84OXADyamLyZZA/gEsE/b9sAkXwU+RRMcvgP44MSooPZevrB9fwWaEOj1wNtnqX0YH23r/Q7wuqr6Y+czrg38J5Nh0j8k2aOqfrIY1/sAk0HRz4FXTDcKK8n6NPfygN73Jn4j7bTDielwX5yDJ9F9jKmh0g+Bf+0dPdUGUE8EXkbz9zabXzH5t70nk0+LkyRJkiSNwDWW+ntYZ//MQU5I8jRgr86hN1bV27qhSFXdWVVfAfamGSWy9gD9rsHUJ9K9s6qe1Q2V2r6rqr4D7EwzEgWakUvPGaT+IXwQuG+7fzqwQ1X9v26o1NZzYVXtQxO2QTMC59AB+l+FJszZv6re2b1/bb/n9jlvLZpRSo+squ9318SqqmuB5zM5smYFmkBnVeAlVfWO7lSz9l5+jibgmvDCAWofxlrAfwFP7oZK7fWvBJ5Lc38nvHjUC7Uj6SbWTLoBeFK/qX1VdWlVfaqqHgH8etRrjirJE5m6htMngcdONyWvqm5qf3uPA/5jgO7P6OwvWLxKJUmSJEkGS/1t09nvF2T0+qfO/ik0Acy02lE77xuw3wOYnOZzclXNOGqmqi6hGV0z4RUDXmdWSTaiCWigGVG0b1VdM8tprwauavf3SrLVAJf6VlUdPUKJr28//yKq6nYmQy5opsh9vw2Q+vnPzv6mSTbu23J41wEHVNWd073ZBnUf6hx65GJcq1v37wf4ziZq+PtiXHNUb+nsn04zsmrW0UgDrkHV/VveJMk9hy1OkiRJkjTJYKm/TTv7f52tcfsf1O4i1kcM8J/hI4FB/uO+X2f/sAHaA3wVuKndf0SS1QY8bzbPZXLtn+9V1R9mO6GqbgS+3jn06AGu84kRarsO+H+ztOkd9fKpmRpX1V9p1j2a8MAR6urn2AGmWP6ss795knuMeK1bOvtbJlllxH7mVBtc7tY59O42EByX3r/lTaZtJUmSJEkaiMFSf90paoOM7ngYU+/nrE+Rq6rLgBnXG0qyJrBt59APB6iFqrqNyYWJl6dZDHocuv/pP2GI87pTkHYYoP3PZm+yiFMHCCF6RzP9YoB+u+fMuED0kE4eoE031ArNU9tGcRaTQeOawFdnWKtqPnV/X3fQrOU1Tr0L0a8z5v4lSZIk6W7Fxbv7647wuXmA9lt09q+qqksHvM6ZwMNneH9bmkABmkWmv9SsKz2Q7qircf0HervO/v5JBlrwm8knrA1Sy7VVtciT6AZw2QBtbup5Pew50z32flSz/kaq6qae73uk67dPRDuSySmSjwf+lOTXNAHhSTTTLK8fpf8x6o4I+0NVDfK3N4ze/sY1kk+SJEmS7pYMlgYzSJJzn87+VX1bLWq2tmt19pejeUT6KEYd6dKrW89DGG0k1Gy1/G2W9/u5bdgT2pFdwxg41RvA0PUu5vX/lSZsfFanr0e0G8Dfk/wSOBY4qnfR9CVkzc7+5XPQ/8j3b8MNN2ThwoXjrEWSJEmSlnpOhevvxs7+IOvRrNTZHyYwuHWW98c1omJc3/U46pmtlmkXs9biqarb2qf0PYnmiXi90waXB3YBPgycn2TcTxMcRPfvaLa/jVH0/i3fOG0rSZIkSdJAHLHU3xVMjqy5z0wNW90pRPca4jqzte0u7nxrVa08RN9z4TomRy3tW1XHzmcxGl5VfRv4drug+yOB3YE9gJ1pwiVovuMvJbm1qr4+fU9z4trO/jjXs5rQ+7d8xRxcQ5IkSZLuNhyx1N+Fnf2N+raa1F2rZ8Mhnt51/yH6XSnJvQfsd65061l33qrQYquqG6vq+1X1tqraDVgPeDOTa0oF+MASLqu77tQWfVuNrvdv+aI5uIYkSZIk3W0YLPX3h87+IP/BPa2zvyLw0NlOSLMq84JZmv2OqQsO7zxALYPqTjkbdO2Z7lPUxlmL5llVXVVV7wNe0Tm8eZ+nx43y2xlE9/e1XpKtx9g3TP1bvqiqbhhz/5IkSZJ0t2Kw1N+pnf3t+raadA5TH0s/yPo0jwLWn6lBu7j0jzuHXjxAv4Madh0pgO929p+SZK2+LbW06p36tt40bUb57Qzi18A1ndcHjrFvmPq3fGrfVpIkSZKkgRgs9ffTzv4WSWZcZ6mqCvh859CBSTbp1z7JcsC7BqzlsM7+PkmeMOB5s+lOO7p/ep5r38fXgAva/dWAj4ypFs2hAb/bCb3TLa+eps2cTFmrqtuBj3cOvTLJrKP/hvDwzv5PxtivJEmSJN0tGSz1UVXnA2e3L0OzwPFsPgJMPKJ9VeD4JIuM9kiyAvAxBpxKVlUnAN9qXy4HfCXJ82c7L8kGSd6epF/4cwZwR7u/FvDCAWq5A3hT59C+ST6fZMZFyJPcI8kzk/wiyXwvQH539PwkxyR5+EyN2t/moZ1DlzD5d9D1m87+3km2GUONEz7IZHC1IvC9JLvOdEKSXZI8eZY2KwM7dQ59b7GqlCRJkiT5VLhZHAcc1O4/HvjvmRpX1V+TvAU4oj30EODMJP8JnAzcAjwQeBmwPc3j1L8DPH2AWvajWX9mK5qRQl9M8sa2xt/STB9aBVi7ve6uNMHVcsCX+9R7fZJvdq5/VJJ/Bc4Fbus0fVtVndk57ytJ3g+8sVPbU5J8CfgZTRhxJ81TvbaiGSXyOBYdCaMlZwVgX5og8ByaUOVU4GLgBprv5sHAi2h+oxPeU1V3sqgfApfTLOC+GnB6ktNoAqG/TzSqqkF+21NU1RVtcPpdmmBpHeCkJN8GjgfOo/nbWRfYAXgqsC1wCPDNGbreA1ip3f9DVf1x2NokSZIkSVMZLM3sC0wGS09L8s/tlLe+qupjSTZlclTP2sDbpmn6d+CfgU0YIFiqqmuSPBL4CrBne/gh7bY4XkuzgPjG7eut2q3r8GnqeVOSK4H30IRXawD/1G66a9uCwaavHQF8dLo3quq2JC+h+T2uDCzP7AvRD6yqftRO+TwOWL09/MR2G9UzOvtfXIx+JEmSJEktp8LNoKrOAk5qX24APHrA895MMzrkL32anAU8rqo+M2Q9VwKPafs+bZbmdwD/QxMcvWqGPi+gCacOollX6nKmjlaaqZ730SyG/CWa0VgzOZ9mquDDq2q2thq/HwPvA84EZgxHaRbQflpVvXKmILWqvknz2zmcZmrctXRGKy2uqvoh8ADgSJpRVf3cSDMq77h+DZLcA3hm+/I24LNjKlOSJEmS7tYyywCcu70kzwC+2r48tqr2HeLc5YDdgG1oRl1cBpxRVaeMqbYNgF1oniy3Bs30oKto1sQ5var+NsPpY9WuX7MLcH+a9ZqWA66jWej7zKq6aEnVopm1C9Fvz+R3dQ+a4OYi4NSqunAey5tWGww9kmak1do0Uy2vBP4InFJVt85y/j7Af7Uvv1BVs64n1mvBggV1yilj+dOVJEmSpKVOklOrapGZKgZLs2jDodNp1nC5Ddisqi6Z36okDSPJSTTrjt0JbFdVfxi2D4MlSZIkSXdn/YIlp8LNol24+OD25T2A18xfNZKGlWQnmlAJmlGHQ4dKkiRJkqTpGSwN5jiaJ7IB/FOSdeazGElDObj99xbgrfNYhyRJkiQtcwyWBtAuYPxqmkWP74X/OZWWCkn2AB7Xvnx/u1i9JEmSJGlMVpjvApYWVfVrDOKkpUpV/QTIfNchSZIkScsqgxJJkiRJkiSNxGBJkiRJkiRJIzFYkiRJkiRJ0kgMliRJkiRJkjQSgyVJkiRJkiSNxGBJkiRJkiRJIzFYkiRJkiRJ0kgMliRJkiRJkjQSgyVJkiRJkiSNxGBJkiRJkiRJIzFYkiRJkiRJ0kgMliRJkiRJkjQSgyVJkiRJkiSNxGBJkiRJkiRJIzFYkiRJkiRJ0kgMliRJkiRJkjQSgyVJkiRJkiSNxGBJkiRJkiRJIzFYkiRJkiRJ0kgMliRJkiRJkjQSgyVJkiRJkiSNxGBJkiRJkiRJIzFYkiRJkiRJ0kgMliRJkiRJkjQSgyVJkiRJkiSNxGBJkiRJkiRJIzFYkiRJkiRJ0kgMliRJkiRJkjQSgyVJkiRJkiSNxGBJkiRJkiRJIzFYkpZiSU5MUu22/3zXM1+SXNC5D3vOdz2SJEmSdHexwnwXMFeSbAM8HtgLuB+wNrAGcD1wFXA68Avgq1V13nzVKUmSJEmStLRa5oKlJA8F3gU8sU+TNdttS+CZwPuSnAQsrKoTl0iRkiRJkiRJy4BlaipckjcAp7BoqHQ1zQilHwO/AS7ungbsDvw4yZuWRJ2SJEmSJEnLgmVmxFKSjwCv7By6DTgS+ALwm6qqnvb3A54CvBrYvD284RIoVRqbqtpzvmu4u7j44os55JBD5rsMSZIkSUuhhQsXzncJc2aZGLGU5KVMDZXOBratqtdU1am9oRJAVZ1fVR8GHtiee9OSqVaSJEmSJGnZsNQHS0k2Bz7cOXQesGtVnTPI+VV1e1UdAewMnDsHJUqSJEmSJC2TlvpgCXgjsEq7fyewf1VdMWwnVXV6VX1ktnZJnpDk00n+mOTaJDcnuTDJN5O8PMmqg14zyb2T7JPkyCQ/T3J5ktuS3ND2+d9JXpFktQH7O6rzyPWD22NJ8uQkxyY5O8nf2vcPn+b8+yb5tyQ/TnJpklvaeq5O8ru2j9ckuf+A9ayQZN8kx7TXvq5zv76a5IVJ5nw6ZpIFST6e5E9JbkxyVZLTkrwryaaddtXZNuvT1zju8QFJjm7v6TVJbm///d8kn0/yD0ky4Gc7sVPP/n3a7Nlpc0Hn+KbtPfhte/2bkpyT5JNJthvk+j3XWTXJS5N8Lcm57e/4hnb/6CTPGPRz9dT43iRnJrm+/Q39PsmHkmw7bI2SJEmSpPFaqtdYSrI28KLOoe9U1UlzdK1Ngc8Be0zz9ibt9iTgrUkOqKrvzNLfG4B3AitP8/aKwGptn08FDk7ywtn6nOYa6wKfBx43QNtXAYcyGdJ13afdHgw8BzgsyXpVdfkM/T2aZo2rraZ5e+J+PQM4KMm+VfXb2WocVhtivB94LVND1FVpngy4PfCaJC+rqi+NeI1h7vFxNJ95unBljXbbCtgP+F2SZw068m5YSfaj+X56Q8vN2+3FSV5fVR8asL9nA4cx/Tpl92+35wG/TPKcqrpwgD73Bz4C3LPnrW3a7Z+TvKmqDhukRkmSJEnS+C3VwRKwN1ODmU/OxUWSbEHzRLmNO4dvBn5PszbTVsD67fGNgeOT7FdVx87Q7QOYWvvFwJ+BG2iCj61pwg+AtYFvJHlqVX17wLJXAr4FLGhfXw78iSZg2bLn872EqdMJAS5st9uBe9EEA2t33u872q0NLT7D1N/XZTRTDW9v+7pve/wBwE+SPLaqfjXgZxvUx4CX9xw7l+Y+rw5sRxOsHJ3kuhH6H/getx7CZKh0J820zcuAW2iCuwcyGew9BPh5kodW1V9GqK2vJC+gCcOgWeT+DOB6mrBvYiH75YHDk5xXVd+Ypb+DgHf3HP4zcEG7vxWwXru/I3Bykt2q6rwZ+tyf5jfUDeEuBs6h+c62A+4BfDDJzTPVJ0mSJEmaO0v7VLjdOvtFE/6MVZIVgS8zGSrdASwE1quqh1fVHsBGwNOAS9o2KwCfSbL1DF3fCXwXeCGwTlVtVFU7VdVeVbVLVa1FMzrqlLb98sBRSXpHb/TzCprA41zgCcD6VbVrVe1CE4Id1vl8h3bO+wawZVVtVlV7tPXsWFXrAPcD/gU4q99Fk+zM1FDpx8DOVbV+VT2yqvasqk2AXYDftW3uDRyb5N4DfrZZJXkmU0OlU4AdqmqLqnpUVe0AbAB8nCa8+NwIlxnoHnfcTHNvngjcs6q2bNvvVVUPowmX9gMubduvDXxihLpmsjZNAHs7cBCwVlUtqKpHV9UWwK40Ac6Ew2eavpbkWUwNlY4DHlRVm1TV7lW1O819fiJNUAnNqKZj+k2DbIPcI5kMlf5KMxpw4/Y3uYDm/k5MXT0MWGuwjy9JkiRJGqelPVha0Nk/u6qun4NrHAjs0Hl9QFW9o6r+NnGgqu6squOBPYGr28OrsOgooK7XVtUTquoLVXXldA2q6qc04dkv20Pr0ARRg7gXzaiRXf8/e/cdLUlVLmz8eRniIJIEZEAQQRRBQSVjIBtRuQqioBdMoJiumWsYMCBes9cEooBixiyKgoqAXCSKIAomQILk6AAj8H5/7DrfqenpUF2nz/Sc4fmt1at37dq1a3f1ObPWeWfvd2fmSfWd8TLzvtpSpG2Z/KP870DP5VeZeXm1k96mlNk5C4iIWZSZMBMBg+OAXTPzrC59/R8liHFRVbUB8LqGn62viFgK+Git6vfAzpl5QccYbszMg4BPsOBsrKaaPuMJ22bmyzPzp5m50CybzLwnM4+nBN1uraqfERGPaTG2XlakzLR6fmYekZl3dozhN5TlehOf5RF0X/5JRKzCgrME35eZL8jMP3T0mdUyzu2YDFptQ1lW2c2HmZzNdwuwU2b+pOP53pKZr6cERZdn4eVykiRJkqRFYKYHltaola+cpnscXCufmJnH9GqYmZcB76pV7dZr1lJm/qvJzTPzbuCdtarnNrmu8qbM/OeANvXlfWdn5vwGY8rMvL/LqecDG1XlvwMH9mg30c+dwKtqVa8ZNrlzD08H1q8dH1QPBHZxCCVA1EaTZwwM9Z3/Hfh0rWqY77yJ4/otb6uWJJ5Rq9qhR9NXUfJCAZyZme/pd9PMvBZ4c63q4M42EbEOsEetam5m/rlPt++mLD+UJEmSJI3BTA8s1Ze/tMmR01dEbELJATShSSLjY2pjCeB5IxhKfcbPVg2vuQH4foN2d9fKm1Wzfdp6Sa382cy8Z9AF1Wymv1aHcyi5pabqWbXy76vZUf3GcDdwbIv7NH3GbdS/861H3PeRDdrUA0ub9GhT/76bJtD+LiUvGcDWsfCOh8+hLPukavelfp1l5r9p9nkkSZIkSdNgpifvXq5WHhjEaGGbWvkuGuRwysy7I+IUyuydzj66iohHU5bRbUZZkrUSk39cd1o1ImZn5rwe5yeclZn3Dro3cF6tvClwTES8o5pd0lg106g+s+WUIS6/iMmk0U8A/jTMvbuoB99+2fCaX1Jmvwyj6TNeQO1ZbUMJ2qxCWaJWn621Wq28zrD36GM+k3m7+qknDF+182RErEb5eZnwiyY3z8z5EXEZZUe+WZQk5WfWmtSDaKc3nOX1U+AjTe4vSZIkSRqtmR5YupXJ5XAjS/xcs1Gt/MchgggXMRlY2qhXo4jYipLfZ/shx7Uyk7M+evnrgPMAZOaVEfEdJsf7UmDfiDiDEiw4gxJAGbTz1rosGID4WEQMXFZXeWytvEbPVs2tVyv3TDTeoWm7ukbPuC4i9gU+wIJL9QZZedj79HFTw5/j+s/X7C7nN2XBHe6+PsQqxvpn7/y+678vFzfs71JKMvJlmg5AkiRJkjQaMz2wdDOTf5iu1q9hS6vUyjcNcV09GfdCsz0AImIv4Ov0npnUz3KDm9Avp1CnV1Ke41Oq41mUhM0TSZvviYjTga8BX+uxxK1zV66dhrh/3QJBlIg4jsmt6nv5aGaeXDuuf2+3djbuoc1SymGeMRHxCcquesNq8n031TTYV9ctYlT/vpcCntZuOAsFzeq/L41+5zLzvoi4jXYJ2CVJkiRJUzDTA0t/YzInz6YREfWdo0ag/gf9MH+Q19suFBSIiPUpu6dNBJVuBI6mLMf6CyV3z12ZeV/tmmE/V8+k2Z0y85aI2AnYBziIskyrnmtpOWDX6nVoROyfmZ3LAjtz5bTVmePpqQye3fONEdy38fNqc01E7M2CQaU/Al+kLAO7ghIAu2vi5zcidqTB0ssxmq7vu+3v3HQsqc9FOAAAIABJREFUhZUkSZIkDTDTA0unA8+oyqtQgkxTzc9TV5/FstIQ19Xbdpsx80Ymt1P/G7BDv53FImKYe7dS7d72NeBr1TbyT6leO1LyHk3MWlkP+GlEPKXaPWxC54yftZvuljYNbgPWrMpNl5F1nVk2QofUyj8A9qoST/cy7d/5FNW/73syc/meLYdze63c9ndOkiRJkrSIzPRd4U7tON5nxP3fUCs/YojrNqyVb+hyfvda+X0NAjDrDnHvKcvMWzPzh5n5lszckhJMOgKYmEG1HHB4x2XXdRyvyQhk5sMzMwa8ju247MpaudeOZp0ePbhJOxGxJiVZ9YQ3DggqwSL+zluof9/LRcSocpzV+92gyQURsTrTk2NNkiRJkjTAjA4sVdvI1xP8viIiVhjhLc6vldeNiKa7c9V3gju/y/l6culzGvQ3bHLvkcrMqzLzEOD9teqnRsRytTY3UGZfTdhuUY2vi/ozbZrrqW1OqCYeVivfmJmXN7hmrN95AxdSdkqcMKrv+4JaeeuerRbUtJ0kSZIkacRmdGCp8uFaeR0WnknTWEQ8sqPqtyyY5+XFDfp4NPDEWtVpXZoNu3vVAUO2ny7fr5WXZuGE3SfVyuMc809q5S0iYpueLYEqQLb/NI5nqO87IlYG9pymsYxEZs5nwRxQo/q+678vj46IxzW45kUjurckSZIkaUhLQmDpq8Cva8dviIiDh+kgIlaIiC8DC1yXmbcDJ9Sq3lYtu+nnf2rlG4Afdmlzba38pAFj24eSTHtaxBB7xLPwcqNbOo4/yWRC620i4lWtBzY1PwX+UTv+fEQ8qE/7DzA4QfhU1L/vh0TEo3q2LD7E6JJjT6eP18p7RcQzerZs7hTg6tpx30BxRGxGg4CvJEmSJGl6zPjAUrVz2ouA66uqAD4dEV+IiIf2uzYiZkXEAZSE3y/p0exDwEQ+nIcAP4qI1br0tVREfAjYo1b94czstlvVqbXyeyLiYV3aEBHPAr7U7zOMwNsi4nMR0TcXUUSsCLy3VnV2ZtaXQpGZlwGfq1V9JiL+KyJm0UdErBYRb4iIbw47+G6qn4m31Kq2AH4REfU8R0TE6hHxWeDNlJ35pkVmXgFcXqv6dH0ZYW08S0XEXODA6RrLKGXmKcCJ1eFSwLcjYt9B10XE2hHxnoj43y593seCwdlnRcTh3QKg1e6K32dyd0VJkiRJ0iI203eFAyAzr622Zz+RyYS/rwD2jYiTKbMgrgRupuwStjZlFtAzgTUG9P37iDiUMqsFSi6ZSyLiSOAs4G7KbnQvB7asXXoG8LEe3X4K+E9KEGwOcEFEfAb4DWXb9PWBFzAZpPoC8Mp+45yCFYCDgIMi4kLgF5Q8N9cB84DVKEv7DmDB3FDvp7s3UQI5O1B+vj4GHFwFjc6lBHCWpSyj25TyPHeq2v52VB8qM78VEbtRfg6g5OG5ICL+QpnNtDLwOCZ/Bw4AflTrYpit7pv4RPUC2LUay2cpOcKWBh5DWY73+KrNdH7no/QSyu/BxpRZVsdHxFuB7wC/o8xqW4ESlN2cMkNvO0ogqlcg8TOUYPG21fEhwE4R8SXgz9V9nkL5uX0w5fdmfRb/hOeSJEmStMRZIgJLAJn5x4jYFjgKeG5VvQLwnOo1yMnVtd36PryasfPfVdVawHv69HUG8Oxq9kW3/i6IiP8GPlhVrd6nv1OB17FoggybV69B3pGZP+p2IjPnR8TulJlWL6yqN2Ty2S1KBwJ3Am+gBPEANqpeE+ZV7U7puPa2EY/l05TdAJ9ZHW8CLDRjp3IE8DNmQGApM2+JiB2AbwM7VtVNf4569XlfROxBWeL6mKp6WyYDTXVXUpbCdctlJkmSJEmaZjN+KVxdZl6fmc+j/AH6bQYHB24DjgGelJm7Z+Ylffp+J2Wmybl9+vsn8EZg58zse+/MPIIya+naHk1uAg4Fdu2xnG5Uvk0JcPxlQLukJGt+cmZ+qG/DzHmZuQ/wDOB0JvMu9er3fOBdwF5NB91EZt6fmf9F2aXvaMqudXdRZtFcSAngbJaZxwNr1i69KzP/NeKx3Ac8j7LM664ezf4C7FXtwDdjZOaNwC6UWUYXDGh+L2WG0X9RAqb9+twW+CzdZ4/dS8l/9sTMvLLFsCVJkiRJIxCZOe4xTJsqt8+WlOVxa1CWzdxBWY51IfDHzOwX9OjV7/qUpV4PpSzrugG4CDgnh3ygEbEs8GTKsrAHUXJFXQ78OjP/3efSkYuItSjLwzagLIGbBdxejeeczPxny35XoyyBWgdYlZKz6lbKsqbfZ+bNUx78FEXECyhBNoCzMnO7abzXqpTlfxtQnvE/gUsys1/QcsaIiLWB7Sm/H6tQlnfeBFxG+b7vGLK/VYDdKEsxE7gKOK3tz2Nbc+bMyQMPnBHpryRJkiQtZubOnTvuIUxZRJyXmVt21i8xS+G6qWaJ/JYR5u6p+r0CuGJEfc2n5DX6xSj6m+JYrqMsCRx1vzfTfXe8xcnLa+Uzp/NGmXkL8N3pvMc4Zea1lBxLo+rvViaDfpIkSZKkxcgStRROquu2k1iPdi8Dnl6rOnZaBqQZJyJeFRHnRsS58+bNG/dwJEmSJGmxY2BJS7LPRMQnI2K7iFjoZz0i1o+IT1HyL034XmZetOiGqMVZZh6VmVtm5pazZ88e93AkSZIkabGzRC+F0wPeg4F9gdcD8yLiMuBmYBnK1vQbdLT/O2WHOEmSJEmS1ICBJS3J6onZZwNb9Gl7CrBvZt4wvUOSJEmSJGnJYWBJS7IDgR8AuwBPBNan7FI2C7gFuBo4A/h2Zp42rkFKkiRJkjRTGVjSEisz76LsTjayHcokSZIkSdIkk3dLkiRJkiSpFQNLkiRJkiRJasXAkiRJkiRJkloxsCRJkiRJkqRWDCyNWURsGRH3R0RGxCfGPZ6ZJiKOrZ5dRsSh4x7POETEqbVnsP+4x7OoNPnuI2LHWpv3LuIhSpIkSdISz8DSGEVEAP8LBHAH8IHxjkhasmTmqcDPq8O3RMT6YxyOJEmSJC1xDCyN117AtlX5c5l5wzgHIy2hDqveVwDeP86BSJIkSdKSxsDSmETEUsCh1eF8wGVw0jTIzDOBM6vDF0XEo8Y5HkmSJElakhhYGp89gU2q8ncy89pxDkYzV2bumJlRvY4d93gWU/9bvc8C3jHOgUiSJEnSksTA0vi8sVb+wthGIT0wfA+4uSq/KCLWHOdgJEmSJGlJYWBpDCJiU+BJ1eHVwKnjG4205MvMe4DvVIfLAfuPbzSSJEmStOQwsDQe+9XK38/M7Ne4Y8v0y2v1G0fE4RFxfkRcHxH3R8StXa5fPiKeHREfj4jTIuLaiLg7IuZFxFUR8bOIeFtErN5k8BFxaG08x9bqt46IL0bEpRHxr4i4NSIuiIgPRMRDmvRd62vFiPiviDgzIm6oxvqXiPhmROw+TF9d+t4hIj4dERdHxM3Vs7gqIn4REW+KiFUb9nN57TnsWNU9KCJeXT3nayJifkRcHRHHR8QWPfrZPSK+FRH/qNrfFBGnRsQB1c6Bg8Zxam0c+3c5v3/t/LCvQwfcOyJij4g4OiIu6XieJ0XEayJihSbPs9bndH3336uV9+vZSpIkSZLU2NLjHsAD1PNr5ZPadBARbwEOB5YZ0O5FwJHASj2arFO9dgfeHRGvz8xjhhzLMsARwJu6nN6ier0mIvbIzDMa9LcV8E1gg45TG1avvSPieODAIce5KvBFSn6rThPPYWfgnRHxpsw8bsj+N6PMitm449QcYF9gn4h4cWZ+q2q/PHAcsHdH+9WAp1avvSJiz2rGzWKlCpQdBWzV5fTE83wacEhEHJCZpzToc1q++8qvgHsoM5YeGxEbZ+ZlLfqRJEmSJFUMLC1iEbEB8Mjq8H7g9BZ9vBn4cHU4H7gYuJ0SwOjMHbMhCwaVbgAur9ovX51/aHXuQcCXImLZzDxyiCF9Dnh5Vb4F+BPwb0py8jWq+lWAEyPiMZl5dZ/Ptjnw86r9hNuBSyjP6zHVuf0oQbW7mwwwItYAfgE8tlb9byaf3QbAelX9asCxEbFGZn6kSf+UIMq3KJ83gT9QnvXawKOrNrOA4yPiH8BvKUGoZ1bn/gn8mfI7+XjKdwPwDODjwGsajqObq4GfNWy7AvCUQY0iYlfKDKAH1apvAS4F7gIeBmxU1a9L+e73yswf9ulzWr77CZl5d0ScRQnYQQl6GViSJEmSpCkwsLToPbVWviwzbxvy+jUps4PuBd4LfCIz75g4GREbdrRPylbrXwZOzMyrOjuMiMdXfU4sM/p4RJyUmVc0GM+zgdWBa4E3AN/NzPuqfpcCXgZ8lhIIeDDwfuCAbh1FxLLA15gMLNxD2cHr85l5d63Ny4CPAi8EbmwwRigzlepBpc8AczPzptr9n0xJpD6xHf3/RMQ5mfnrBv1/kvIcvgW8uf6cq1k436cE/pahPIOTKEGlvwOvBn4+sSQyIlYBjmZyZtuBEfHxzPxzw8+6gMw8GTh5ULtq2d03a1V3AQsFgiLiEZSg2ERQ6XfA24BfZOb9tXabAp8GdgSWBY6LiMdl5j+69Dmd333d2Uz+Du7I5G5xkiRJkqQWzLG06D2xVr64xfUrUAKC+2fm++pBJYDM/GtH+09k5g6ZeWS3oFJ1zQWUmTETOWhWAA5uOJ7VKX/gb5+Z354IKlX93p+ZRwPvrLXfOyJm9+jr1ZRZKRP2y8xPTAQWqj7nZ+bngRdQgmYDczdFxLOBPWpV783M19aDSlXfp1Nm6/x94lLgyCZ5jijP4ZjMfGHnc87McyhL4SbsTAkuXQvskJk/q+fZysxbgRcDE9/lUh3XT5fDgL0mhgH8Z2ae36XdFylBQigBq20z8+R6UAkgM/8A7EaZhQQlaPSeHveelu++i4tq5S1bXC9JkiRJqjGwtOjV/3juDAI1dWJmfrVJw8z8V8N29wNvrVU9d4jxvCUzL+9z/nOU2S8AsylLvbo5qFb+bmae0KvDzPwpZRZWE6+tlX9PmenVq9/rWTCo9ihKcGSQm4HX9+n3VBZcdrUsZWbTtT3azwe+VKt6Urd2oxIR+wDvrlW9NzO/3aXdVpSZPgC3Afv2y/+UmfcCr6QsOwTYLyIe3KXpdH33neq/c+tFxIN6tpQkSZIkDWRgadFbv1bumWtogKNGMZBO1WynieVFj6yWZA1yB/D1Af3eSVkuNWGTzjYRsQmTuYig2RKlgW2qwMGutapP12dVdVMFLi6tVXVL9t3pG9Xn7OfsWvk2YKHATYff1soLPbNRiYitgXrC9m9TZi9189Ja+fjMvGFQ/5l5JXBadbg8sH3H/aflu++h83duva6tJEmSJEmNmGNp0asv37mlZR8Dd1brJiLWowRZHkfJ1fRgFv4ZmJjBEZScQLcO6PbcanbNIPXlYat2Ob91rTyPBknNM/O8iLiByQTh3WxJSZo94cRB/VZ+zGSupW0atD+rQZv67KTzqtk8Tds3CfINLSLWBX7AZLLw8ynLLLPHJU+ulQfu8lZzEbBLVX4CC+6GOF3ffTc3dxwPe70kSZIkqcbA0qK3Yq18V89Wvd2amZ1/HPcVERsDnwCeTgkYNbVygzb/bNjXvFq5W46ljWrlPw6aVVRzMbBTn/P1fm/OzGsa9lvPxbNRz1aTrmvQpv4Mhm3fKy9VaxGxIiU598SugNcCz83MeT3aB7BZrertEXFQt7Zd1J9hZzBnur77bjp/51bs2kqSJEmS1IiBpfEaJsgz4Y7BTWo3iHgSZXZImz+gl2vQpslspU7dPnd9FtNNXc73MqhtfabPMP3WdxxbKSJmDQh4DPsc2jy3kamCRF9hMt/V3cDzeiV4r6zMgrO/tm15+86A5XR99920+Z0DYM6cOcydO7ft5ZIkSZK0RDLH0qJXT6a9Qovr7x/cpIiIlYATmAwq3UnJTbMHsDFlKdwymRkTL+CKFmMahXoQa5igS8/E0VPst7NtkyDbTPIBFswd9bLMPLtX48qoZvd0/rszXd99N52/c42S20uSJEmSunPG0qJ3A5MzNrrlGhqllwFrVeVbKdvCX9qnPcBK0zuknm5vOYZBbW8bQb//7rU8bCaKiP2AQ2pVH8jMvgnYK7d1HG+XmU1ySw0yXd99N52/cwOTj0uSJEmSenPG0qJXnxG0zjTfa/da+ZODgkpVzp1pSRLdQD3n0AZDXPeIAefrgYM5EdF05tGGPfqY0SJiO+DoWtX3gHc3ubba9a4eYFtzRMOaru++m87fuStb9CFJkiRJqhhYWvQuqZWbJIWeivpW6uc0aL8t4/uZuKBWXi8iHtqzZSUiHszkzm29nF8rL03ZJa6J+k5w5/dsNYNUuwJ+j8mlZxcCL+mzA1w39RlK241oaNP13XdT/527sgqWSZIkSZJaMrC06J1XKz92mu+1zJDtD5iWUTRzNgvmzNm7wTUvYMBnzMw/U3Y7m/DiQZ1GxKrAs2pVpzUYy2ItIh4E/IjJpZHXAc/JzGFzDJ1UK+8bEcP+jHUzLd99D/XfufN6tpIkSZIkNWJgadGrByk2qoIY06UeUHlSv4YRsQPwomkcS1+ZeRvw/VrVO6pZKV1FxAo0XMIFHFMrvzwiNh7Q/r3A8lV5PvDlhvdZLEXEUsBXgcdVVfcAe2Zmm2VgRzO5M+HDgMOmOr5p/u47bVUr/7plH5IkSZKkioGlRSwz/w5cVh0G8JRpvN2ptfJrI2Lzbo0iYmvKH/bj/nn4EHBfVV4bOKHa2W4BETEb+Abw8Ib9fpqSvBzKMrAfV8vCFhIRrwVeW6s6OjOv69Z2BjkceE7t+FWZ+X9tOsrMWyiBtwmHRMSHBuWuiogVI+KAiPhljybT9d3Xr12estxzws+G7UOSJEmStCB3hRuP7zC5K9fTgR9M032OAt5O2WL9QcCZEfF54JeUnbjWAZ4N7APMovyh/RjKTJRFLjMviIiPAW+tqnYDLqrGfB6QwObAqynJtW+i5OfZdUC/11YBo+OrqkcCF0fEUZTg2x2UpNH7AbvULv0z8Lapf7Kx26dW/hfw4ogYuCSwcnxmHl+vyMyPRMRWTC5Zexvwkoj4OiUH0/WUIOWqwCaUfFW7Un4Ouwbppuu77/BUJvNLXZKZfxriWkmSJElSFwaWxuMrTAaWnhsRrxkygXIjVUDlFdX9lgJmA2+qXp0uBvZl/Hln3gGsy+SyvPWBD3ZpdxclELRPl3MLycyvVjNgPkN5FisBb65e3fwR2K1FDqLF3YrA04Zof1aP+hdTllq+oTpem+4/V8OYlu++Zs9a+fierSRJkiRJjY176dMDUmb+ETi9Olwb2Hka7/U1yqykv/Zocifwv8DWmXnTdI2jqcy8nxLgej1wc49m5wDbZ+ZJPc736vvzwNbALygzYLq5FXgfsFVmXj1M/w8kmXlfZr6RsjPcT4B7B1xyCXAEZdZQrz6n7buPiGWB51eH81kw75YkSZIkqaWYhokyaiAi9gS+Wx1+IzOnNXF2RMyiLEl6PLAy5Q/3K4FTM3PedN67rWrHsZ2BjSmzra4BzsvMS0bQ91qU/FZzqr5vBC4FzszMQUESdah2nXsSsB6wOnA/cBsloHlRZv5zyP5G+t1HxF7At6rDr2TmS4ftY8stt8xzzz23ze0lSZIkacaLiPMyc8uF6g0sjUe1U9fvgU0pMygenpnX9r9KUhsRcTol8HU/8Ng2ASoDS5IkSZIeyHoFllwKNybVsp9Dq8NlgTeObzTSkisitqUElaDMDpzyjDdJkiRJUmFgaby+w2Ry5FdHxBrjHIy0hDq0er8beOcYxyFJkiRJSxwDS2NU7QT3ekoi6ZXwj15ppCLiqUzugvfhzLx8jMORJEmSpCXO0uMewANdZp6DAT5pWmTmr4EY9zgkSZIkaUllQEOSJEmSJEmtGFiSJEmSJElSKwaWJEmSJEmS1IqBJUmSJEmSJLViYEmSJEmSJEmtGFiSJEmSJElSKwaWJEmSJEmS1IqBJUmSJEmSJLViYEmSJEmSJEmtGFiSJEmSJElSKwaWJEmSJEmS1IqBJUmSJEmSJLViYEmSJEmSJEmtGFiSJEmSJElSKwaWJEmSJEmS1IqBJUmSJEmSJLViYEmSJEmSJEmtGFiSJEmSJElSKwaWJEmSJEmS1IqBJUmSJEmSJLViYEmSJEmSJEmtGFiSJEmSJElSKwaWJEmSJEmS1IqBJUmSJEmSJLViYEmSJEmSJEmtGFiSJEmSJElSKwaWJEmSJEmS1IqBJUmSJEmSJLViYEmSJEmSJEmtGFiSJEmSJElSKwaWJEmSJEmS1IqBJUmSJEmSJLViYEmSJEmSJEmtLD3VDiJiNvACYAdgE2BVYDYQDbvIzNxwquOQJEmSJEnSojWlwFJEHAx8AFipbRdATmUMkrQoXHPNNRx22GHjHoY0rebOnTvuIUiSJGmGaR1YiogPAW+h+cwkSZIkSZIkLUFaBZYiYgfgrUzONroLOAE4A7gKmDeS0UmSJEmSJGmx1XbG0qtr5T8Az8rMK0cwHkmSJEmSJM0QbXeFe1Kt/EKDSpIkSZIkSQ88bQNLa1GWwf0xMy8Z4XgkSZIkSZI0Q7QNLN1Rvf9zVAORJEmSJEnSzNI2sHQ5ZTe41UY3FEmSJEmSJM0kbQNL363eN4uIVUc1GEmSJEmSJM0cbQNLRwM3ArOAQ0Y3HEmSJEmSJM0UrQJLmXkjsB9wP/CmiDhopKOSJEmSJEnSYq/tjCUy8+fAbsAtwGci4pSI2Dsi1o2IpUc2QkmSJEmSJC2WegaAIuK+IfoJYKfqNXF902szMw1ESZIkSZIkzTD9ZixF7b3fCyA7rhv2JWkxEREPj4iceI17PJIkSZKkxdegpXBNgj4GibRYiohj6wGSLq9/R8SNEXFRRBxXLeVcdtzjliRJkiRppui3BG2DRTYKaTyWBlavXpsBLwX+EREvy8xTxjoySZIkSZJmgJ6Bpcy8YlEORJpmdwO/7qhbBngo8GgmZ+89DPhpRDw3M3+yCMcnSZIkSdKMY9JsPVBcl5lP73YiItYC5gKvrqqWBo6JiA0z885FNUBJkiRJkmaaQTmWpCVeZl6Xma8BPl2rXhPYb0xDkiRJkiRpRjCwJE06DLi/drzLuAYiSZIkSdJM0DqwFBEfi4gvRcQXImL2kNe+qLr2SxGxfdsxSKOUmTcCf6pV9U1gHxEbR8TrI+KEiLgkIm6rdpq7KSIujoijImLXpvfv2LHu4VXdShFxcEScERHXRsTdEXFVRHw3IvYY9jNGxLbV793fIuKuiLg+Is6OiHdExJrD9lfr98ER8bqI+FlEXFmN85bquXw+InZq2M/+tWdwaq1+q+rfmj9FxJ3V65yIeGO3nfwiYrWIeE9EnBsRt1fj+Ws1FjcmkCRJkqQRaZVjKSK2A94IJPDtzJw3ZBe/B75aXb868Nw245CmwS218sq9GkXEucATe5xerXptCrwyIn4F7JOZ1w8zkIh4IvAt4BEdp9YB9gT2jIivAy/NzHsH9DUL+DjwWiBqp5YH1gC2At4UEfsAfxtynHsD/0tZPli3HLAKsAlwYET8FNh/mOcQEUsB7wMO6Rg3wJbVa++I2H0iH1ZE7AZ8nfJvS90jgAOBl0TE8zPzpKbjkCRJkiR113bG0nNq5WOGvTgz/wD8lvKH4u4RsULLcUijVg9G3NGn3Ra18r3AH4EzgF9SAqf/rp3fCTgjIlYaYhyPAX5FCYYkcEl1fCELLtd7EfChBv19AXgdCwZnLgVOrcYOJcD0E2DzpoOMiFcD32DBoNI1wGnAucA9tfpnAKdHxJym/QPvB/67GvdNwG8oz7n+3WxXjYGIeArwI8r3eC/wO8pzqwfLZgPfmZgVJkmSJElqr21gaWL52nzgFy37+Gn1vixltoQ0VtXucBvXqi7q0/xW4FPAjsCKmfmYzHxyZu6SmZtTZiy9Dri9av9ImgWAJnwZWAn4CrBeZm6amTtn5haUYNPptbZviIjOWU31z7UfcECt6kxg08x8dGbulJmPoXzuX1FmGR3dZIARsTVlptJEsOpy4OnAupn51MzcCliL8rmzarMx8OWI6Jx91M3jgHcANwL7AGtl5pMy88mUQNanam2fFRHPpgSYlqMkYn9oZj6+em4bAs8EJnb5mw28t8nnlCRJkiT11jaw9CjKH4p/GrQEp48LO/qTxu0wFvydOKFP2/Uz8w2Z+evMnN95MjPvzMxPA7tRZs4AHBARncuzelkd+EhmvjQzr+ro+wrg2cA/q6pZwEu7dRIRywEfqVWdA+yamZd09PlnSlDoNOAhDcf42ereUGYpPTkzf5aZE0EkMvO2zHwH8LbadbtQAkWDrArMA3bMzG9m5n21fu/OzDcAv661/w6wNnBoZr4uM2/q+Iw/pSzhnfD8iFixwTgkSZIkST20DSytWr3f1LdVf/VrV+3ZSppmEfHQiPg8Jf/OhNOAH/e6JjP/1aTvzDybapkWJZ/R7g2H9WdKXqFe/d5OWd42YYceTfekzBqCEgx+ZWbe1aPP+cArWXAZX1cRsS0L5ph6Q2cArMNHgbNqx68bdI/KB6qls70cWSsvS1nW974+7Y9nchndbODx/W4eEa+qEoCfO2/esKnkJEmSJGnJ1zawdHf1PpX/7a/vJJc9W0mjsVZEnNTxOiUiLgauZsGg0nnAXvWZN1NUD6hs3fCaLzWYDXhGrbxJjzZ71sqnZeaFPdoBkJmXAT9rML56v1dRZgv16zeBT9aqtquWHg7yxQHnz+44PiYz7+/asozjHhacLdnruU20Pyozt8zMLWfPHmrzS0mSJEl6QGi1Kxwl58lKwEZTuHd9+dsNU+hHamJ54GkD2lwNHA58ITMHztoBiIilKcm5t6TkD3owJWhazyG0To9yP2c2aFOfIdRr1l89kNV0F7SfUpba9bNNvX3DINxPKEHkiWezDfDDPu3/1mAHuWs7js/q2qr3Nas0aC9JkiRJ6qHGVIq8AAAgAElEQVRtYOkiYANgtYjYKTN/1aKPF9TKf+zZSlp05lASRg8MkkTELOC1lB3L1hzQvG7lhu3+ObgJ9bVZC02nqYJeD69VXdzw3k3a1YPK/ZKc/3+ZeXtEXFEb06DA9HUN+pzXkQd84DUMeG6SJEmSpObaLoWrL5X5UEQsM8zFEbEn8OTq8GYWXs4ijdoVmRkTL0rS6XUpOY9+VLUJypK4r/TrqArYfBP4BMMFlaDsWNbEQgnBW+icjdM0J1qTdvW+h8m1dmOtPCi3WptnMOw1TXankyRJkiT10Daw9FXglqr8ROCEiHhwkwsj4hlM/uGewJEjzGUjNZKZ92fm1Zl5cmY+B3hX7fQ+EfGqPpe/CXh+7fhs4GBgK0qi7NnAUrUg1gEjHn5TnUGspkGXe4bse5hgTr1t0yCbJEmSJGkx1SqwVO1IdRiT/9v/bOCSiHhLRDyis31ErBARu0XECZSdtmZTgkpXAx9qNXJphDLzA5TcQhP+JyJW62xXLYF7W63qs8C2mfnZzDw3M6/PzLs6gqUrTc+oB7q947jpOJq0u61Fv51tbx3iOkmSJEnSYqjtjCUy81PAcUwGl+ZQgkR/joibI+LSiPh9RFxJ+SP0JMpOUhPt7wCelZl3IC0eXg9M7MS2MnBIlzZPAFavyvOAtzaYcbfuaIY3nOp3665a1QYNL10oONxFPeF+k/ZESYZUH4NJ+yVJkiRphmsdWKq8DHgvCyY7Dkr+lY2ATSl/VC9dOwdwCbB9ZjZK+istCpn5F+DYWtXBEbFWR7P1auVLMnMeg20/1bFNwQW18tY9Wy2oSbvza+VtG/a7KfCgHn1IkiRJkmagKQWWsjgU2AI4ngVnRwQLJsYN4A/AQcATM/OSqdxbmiYfZHLW0gosuOwNYNhE9Y8EdhjBuNo6rVbeMyKW7de4Wuq395D97hgRaze4Zr9a+TbgwgbXSJIkSZIWY1OdsQRAZl6cmS+lzFTajjKT6S3AOylJjZ8PrJOZj83MozKzSXJgaZHLzL8BX6tVHRQR9Z3frq2VN4uIlXv1VS39+hTj3XnsuFp5Lcpyv35eCTy8Qb/foCwFhDIj8fB+jSNiPeC19XFl5r292kuSJEmSZoaRBJYmZOa9mfnbzDw2Mz+WmR/MzM9l5vcy89rBPUiLhcOB+6vybOCttXNnMzkzb3ngkxGx0O9RNTPoSODp0zjOgTLzT8D3alWHR8Qe3dpGxK7Axxv2exvw6VrV/hHxjh79rk1J2r9iVfWvpveRJEmSJC3eRhpYkpYEmXkp8O1a1asjYo3q3F2UgNGE/wTOjIhXRMRTIuJpEfF2yrLPV1KW1R2ziIbey+uBW6ryMsAPIuKbEfHCaswviIivAD+jBMu+0bDfucDFteMPRsTpEfHKiNgxIp4ZEYdXbR5ba/emzLx8Sp9IkiRJkrRYWHpwE+kB6QOUXENBmWnzFuDt1bl3AU+h7BAHsE316nQf8AbKkrEDpnOw/WTmVRHxDOBkYCXKZ9qb7rmUTgbeDezToN+7I2IXSkBqi6r6SdWrl7dk5lFDDF+SJEmStBhzxpLURbVj4fdrVQdHxEOqc/8Cngp8kclE351+B+yamZ+d1oE2lJm/BR4P/LxHk9spSwCfQe/P1K3f6yl51d4J3NSn6RmUnSA/2rRvSZIkSdLiLzJzNB2Vbdm3BtYBVgaWa3ptZr53JIOQFrHq534nYD1KXqZrgQsW510PI2IDyoyrtYE7gCuAX2bmvL4XDu53FmXm1mOAhwD3UJ7HaZl5zZQGvRiYM2dOHnjggeMehjSt5s6dO+4hSJIkaTEVEedl5pad9VNeChcRL6AsE9pqCt0YWNKMlJnX0Twn0WIhM/8O/H0a+r0POLN6SZIkSZIeAFoHlqrZCV9mMhfLoC3VJ6ZGdbYbzZQpSZIkSZIkLVJTmbH0MeBFteMrKVuxbw/MoQSMvkxJFrwusDlledxEIOknwI1TuL8kSZIkSZLGqFVgKSIeBRxcHd5P2enpE9W5n1ICS2TmAbVrVgD2BQ6j5HbZHHhBZp7devSSJEmSJEkam7a7wr2sujaBT00ElfrJzLsy82hgM+AcyiymEyNinZZjkCRJkiRJ0hi1DSw9pXpP4CPDXJiZtwDPAW4DVgMWi+3YJUmSJEmSNJy2gaWHU4JKf+23jXhELNOtvtpJ62hKIu9nRMSaLcchSZIkSZKkMWkbWFqter+6y7l7auXZffo4rXqfBTyp5TgkSZIkSZI0Jm0DS/+u3rPLudtr5X75k26ulee0HIckSZIkSZLGpG1g6frqfZUu566slTfv08fatfKKLcchSZIkSZKkMVm65XV/ouRZemSXc7+rlZ8HfL1HH8+vla/v0UaSFgtz5sxh7ty54x6GJEmSJC1W2s5YOqt6XzEiHtNx7mfAXVX5PyLi+R3niYgDgBfWqn7TchySJEmSJEkak7aBpZNr5T3qJzLzDuAYyo5vSwHfiohfRsSHqtdvKDvCQcnR9OvMvKzlOCRJkiRJkjQmkdkt/3aDCyP+QUnO/bfM3Kjj3IOBs4GN6Z7gG0rg6WZgm8z8a6tBSNIisuWWW+a555477mFIkiRJ0lhExHmZuWVnfdscSwC7Ag+pOl8uM++ZOJGZt0fEUykzl57e4/oLgH0NKkmSJEmSJM1MrQNLmXkpcGmf89cBz4yIxwG7A+sBywDXAqdm5mlt7y1JkiRJkqTxm8qMpUYy8/fA76f7PpIkSZIkSVq02ibvliRJkiRJ0gOcgSVJkiRJkiS1MvKlcNWOcI8AVgWWA24FbjBJtyRJkiRJ0pJlJIGliFgXOAjYE3gUEF3a3A6cAXwB+FFm5ijuLUmSJEmSpPGY0lK4iFgqIt4F/AU4BHh01Wc9sBTVa2XgmcD3gPMiYrOp3FuSJEmSJEnj1TqwFBGzgBOAw4BlJ6rrTVh45tJE3RbAbyPiyW3vL0mSJEmSpPGayoylDwHPqx3fCRwJ7AE8HHgQJeC0OrA18Drgt1XbBFYAflQto5MkSZIkSdIM0yqwFBEbA6+nBIgAfgg8MjNfnZknZuaVmTkvM+/NzFsy89zM/ExmbgfsBdxRXbsS8IERfA5JkiRJkiQtYm1nLL2UycTfPwH2zMzrm1yYmd+h5Fq6n7Isbu+IWLHlOCRJkiRJkjQmbQNLu1fvCbx22B3eMvNM4MvV4bLAji3HIUmSJEmSpDFpG1hajxJUuiQzr2jZx4kd/UmSJEmSJGkGaRtYWqV6v2EK965fu/IU+pEkSZIkSdIYtA0s3VK9rz2Fe9evvXUK/UiSJEmSJGkM2gaWLqck3n5URDyyZR/P7ehPkiRJkiRJM0jbwNLPauXPRcSsYS6OiJ2BfarDu4FTW45DkiRJkiRJY9I2sHQcML8q7wScGBHrNLkwIl4C/IAy4ymBr2Xm3S3HIUmSJEmSpDFZus1Fmfn3iPgI8N+U4NBuwKUR8T3Kbm8XATdRgk8rAY8AtgVeBGxKCSpBydX0rql8AEmSJEmSJI1Hq8BS5d2UgNE+lODSbODF1auJO4FnZuZ1UxiDJEmSJEmSxqTtUjgyM4F9gbcA91TVUXvv9zoL2DIzz257f0mSJEmSJI1X68ASlOBSZn4MWI+yLO5s4N89ml8DfAPYPTO3z8zLpnJvSZIkSZIkjddUlsL9f5l5I3AEcERELAs8DFgFWA64DbghM68fxb0kSZIkSZK0eGgVWIqIj9UOP56Z/5g4yMz5wF+nOjBJkiRJkiQt3trOWHojJWH3jcCbRzccSZIkSZIkzRRtcyzdWb3/uUriLUmSJEmSpAeYtoGla6v3+0c1EEmSJEmSJM0sbQNL5wMBPHKEY5EkSZIkSdIM0jawdHz1vmZE7DaqwUiSJEmSJGnmaBVYyswTgZ9TZi19OiLWGOmoJEmSJEmStNhrO2MJ4CXAuZTlcOdExJ4RMZX+JEmSJEmSNIMs3eaiiHhPVTwFeBSwHnACcENEnAX8FbiDhsm9M/O9bcYhSZIkSZKk8WkVWAIOBbJ2nJRlcWsCe7Toz8CSJEmSJEnSDNM2sAQlkDRMfS85uIkkSZIkSZIWN20DS8eNdBSSJEmSJEmacVoFljLzgFEPRJIkSZIkSTOLu7hJkiRJkiSpFQNLkiRJkiRJasXAkiRJkiRJklqZyq5wXUXEusAjgVWA5YDbgeuBizLznlHfT5IkSZIkSeMxksBSRDwOOBDYE1irR7N7I+Jc4Bjgq5l51yjuLUmSJEmSpPGY0lK4iFgpIo4EzgcOAh4KRPWi430ZYFvgSOCPEfH0qdxbkiRJkiRJ49U6sBQRDwFOBV5R9RMdTe4F/tVRP1FeD/hxRLyq7f0lSZIkSZI0XlOZsfQN4PG142uA9wM7ACtn5nKZuRIwC9gIeDHwo6ptVvf+bEQ8dQpjkCRJkiRJ0pi0yrEUEXsBO1MCRFCWt705M+d1ts3MBP5Wvb4REdsB3wbWpgSXPgNs1mYckiRJkiRJGp+2M5ZeUisfk5mv7hZU6iYz/48SlJpI3r1JRDyh5TgkSZIkSZI0Jm13hZtYAncv8PZhL87MyyLiC8Abav2d33IskjTtrrnmGg477LBxD0Nqbe7cueMegiRJkpZAbWcsPYSyDO6izLypZR+/6OhPkiRJkiRJM0jbwNIN1fvtU7h3/doberaSJEmSJEnSYqltYOlSIIBHTOHeG9bKl02hH0mSJEmSJI1B28DSV6v3h0XEji37eGn1fmVmntGyD0mSJEmSJI1J28DS14ALKLOWjo6IOcNcHBFvA55KydP0jpZjkCRJkiRJ0hi1Cixl5nzgP4C/UJbDnRMRL46Ivv1FxHoR8WXgg5Sg0mGZ+c02Y5AkSZIkSdJ4Ld3mooiYWMb2KeA9wNrAV4CPR8QvgIuBm4D5wErABsDWwDaUWU4A3wIur/XVVWZ+uc0YJUmSJEmSNL1aBZaAYykzjiYkJWC0BvDC6tVN1NrvXb36ScDAkiRJkiRJ0mKobWAJJoNETeuHbSNJkiRJkqTFWNvA0mksOGNJkiRJkiRJDzCtAkuZueOIxyFJkiRJkqQZptWucJIkSZIkSZKBJY1MRBwbEdnytcq4xy9JkiRJkoZjYEmSJEmSJEmtTGVXuK4iYhawHrAKsBxwO3B9Zt446ntpsXY38Osh2v97ugYiSZIkSZKmx0gCSxGxBrA/8B/A5pSAUmebq4HTgWMz8+RR3FeLtesy8+njHoQkSZIkSZo+U1oKFxGzIuIQ4HLgCGBrYHkgurzWBfYBToqIsyLicVO5tyRJkiRJksardWApIpYHfgS8H1hhonrQZdVra+CsiHhW2/tLkiRJkiRpvKYyY+kYoL7U6S7geGBfynK4dYE1gEcAuwL/Dfy+apuUmU3fceaSJkTEjrVd4i6v1W8cEYdHxPkRcX1E3B8Rt/bp54kR8T8RcW5E/DMi5kfEdRHxfxExNyLWGXJcERF7R8SPI+KqiLg7Iv4REadExH9GxHJVu/1r4z+1R18Pr++GN5Xn0mDMe0TE0RFxSUTcXI37qog4KSJeExErNOin62eKiMdExCcj4g8RcXtE3FHd55MR8fAmY+y4z8rVmH4QEX+r+pz43k6LiCMiYvsu1/yrNr7nDHG/Daufo4lrtxp2zJIkSZKkljmWImJ34IWUAFEAJwIHZuY1XZrfRFkq90vgiIjYBzgSeBCwLPA5YIc249CSLyLeAhwOLNOg7erAZyg/m53WrF7bAm+LiHdl5scb9LkG8E1gp45T61avXYDXR8TzB/W1qETEFsBRQLdgyTrV62nAIRFxQGaeMmT/b6V8J53/fmxSvV4VES/JzBMa9vd64FBg1S6nJ763JwNvj4hPZuYbATLztoj4JnBA1fYVwA8bfoyXMznD8sLMPKfhdZIkSZKkmrYzll5WK/8YeG6PoNJCMvMblJlO91VV20bEo1uOQ0uwiHgz8GFKUGk+cD5wKnAZJahZb7s+cCYLBpXuqa75JXAhkz9zs4GPRcQRA+6/MvBzFgwq3QtcQNnx7qqq7gnAycBKw3y+6RARu1KS5NeDSrcAZwG/Av5Sq18XOHHImT7vAv6HElT6F3A25Tu5qtZseeDrEfH4AX0tFRHHAJ9kwaDSzVW/v6LMcpxfO7dKRzdH1srPjIi1G3yGWcB/1qq+MOgaSZIkSVJ3bQNL21bvCRycmfcPc3Fm/h/w5S79SRPWpCSEvxd4D/CQzHxiZu6UmY8CtpxoGBHLAt8DNq6qbqTMXlmlumaXzNwCeCjw2do93h4Re/QZw0eBLWrHRwNrZ+YTMnPHzHwYsBtlRt5G1TjHJiIeAXyHMhsQ4HfA7pRnt11m7pyZjwQ2owSDoMwaPC4iHtbgFo8F3gvcDrwKWD0zt6m+k4cBzwXuqNouDXxsQH/voewmOeF8yvNco+p358zcnBKweybwbSaDgwBk5m8pQUOAWUzOXurnWcCcqnwX8NUG10iSJEmSumgbWFqLElT6Q2b+o2UfJ3b0J9WtQAlO7J+Z78vMO+onM/OvtcNDgInZMVcBT8zML2bm3R3X3JiZBwNza9UfiYiFks5Xs23qM/M+k5mvzMwbO/o8BdgRuB54yDAfcBp8EXhwVT4Z2DYzT+4M/GbmHygBnJ9XVavQLCi2GmUW2C6Z+YXMvKej3x9SlphNeGpEbNCto4h4LPDuWtWJwPaZeUqX8c7PzJ9m5t7AG7p0V5+19LJu32eHV9TKJ2Rmz3xdkiRJkqT+2gaWbq7eb+zbqr/6tbdMoR8tntavJ6nu8+r3R/2Jmdl3NkmVgPp1taqXZ+aVA8b2PuCPVXljSpCl04FM5uC5Cnhrr84y8wrg7QPuOa2q5NM7Voe3Aft2Bn7qMvNe4JXAv6uq/SLiwb3a1xyRmef2OX8CMPH8A9i+R7u3Mvnvz7XAfv3GOyEz7+xS/VXKsjyADVk4H9b/Vy2Ve2atymVwkiRJkjQFbQNLf6P80dhk+Uwv9Wv/2rOVHsiOatDmmcDqVfkPmfnzfo0BMjOBr9Wqdu7SbM9a+YuZedeAbr/KZMB1HF5aKx+fmTcMuqAKwJ1WHS5P7yBQ3ZH9TlbP9je1qk0620TEMkA92fknpzJrKDNvB75eq3pFr7aUpXKzqvKlmXl6v74j4lVRdhc8d968eW2HKEmSJElLrFa7wlF2ydoB2DAiNs/MCwdd0MXe1fsNTOZ70ZLjbkqC60G6zUCZcEaD659cKw+zu9lFtfIT6ieqROBr1qpOGtRZZv47In4B7DXEGEZpKs9hl6r8BPp/1r9n5j8b9FlP5N1tp7cnUhKoT/hOgz4HOYrJgNJ/RMRqmblAoK9aIldf3nj0oE4z86iqb+bMmZMDmkuSJEnSA07bwNJxwH8BDwe+GBE7debA6SciXgQ8h5Kn6fDMvG/AJZp5rsvMp0/h+ls7AwM9PLZWfsYQOwyuViuv0XFuo47jixv2eTFjCCxVAZPNalVvj4iDGl5e/6ydz6FTk6ASQH1qz+wu5+uzmG7LzL90aTOUzDwnIi6g5NpaDngJZbe5up0oS+Wg7DR33FTvK0mSJEkPdK0CS5l5R0Q8n5Ig+PHAbyLioMw8s991ETEbeAcl2TLAsZn5qTZj0BKvaaBy9Vp5YyZ3hhvGyh3H9Vk283vk9enmphb3HoWVmVzeBe13Wex8Dp3mt+izWyLtelBv4JK9IRwJfL4qv5yFA0v1xOI/bLJcUJIkSZLUX6vAUkQ8pSq+Efg4ZbbE6RFxIWUpzcWUP7LnU7YK3wDYmrLN90rVtd+gbHP+FPrIzNP6ndcS6/7BTQBYcQT36sw1tlytPEwwZWDy6WkyimcA7XOuDav+fEf5zL4GfAR4EPDYiNgmM38LEBGrAv9Ra2vSbkmSJEkagbZL4U6lLGP7f+zdd5zlV10//tebhAQSeg0hdEMHQZamCKEpSheQDqFIESv6BQE1RAELws+GKCIgvQYEwRABE0Dq0ns1QAwEEAiQhASS9++Pzx3ms8OUO5+Z3dmdfT4fj/vYc889n/M5d3buZuaVUxZ0hpkJN0jy06tctzB7oZPce/ZYTWf6GNk/nD4qP6G7/3wT+vzuqHxoVdVsU+q1XHjtJpMcsMbrpy95fvPufs9uGstmGG/UfbHN6nQ2k/KlSR4xq3p4kvfOyg/IsEF5kpycYbYlAAAAG7TRGQrjZS6dxYBppcdCm6zRbvyA1Zw2Kl9mxVbT+6wMe4nN46pztNllBtTshLS1rBq+zJbqjfc12qyvw+4y3qvpsKrarBlXya6n1t171Pd4Gdzz5gwKAQAAWMPU2UBfzq4zlmCrvCfJL8/KN9+kPj+W5EdZ/HzcJMn/zHHdTeZos3TvqEtk1yBrOddb4/Vk+DrcZla+eZLXz3HNVhnPpjogyS2SvHkzOu7uD1bVziQ7Mswgu09VfTSLMynPTfK8zbgXAAAA0zfvvvImjwOmOj7Jn8zKN62qa3f3JzfSYXefNQsnFjbBvneSV6x2TVVdLXMES7PlWt/O4gbh18/ay7Lutla/Gb4OC8HS/avqj7v7h3Nct8d196lV9Ykk15lVPTKbFCzNPCdDsJQkv5bkI6PX/qO7/3cT7wUAALBf21Ob9cJu0d3vT/Lfs6eV5NlzLi9by/go+rtV1Vqh0VPX0feHRuVV9xmrql/N6vuWLXhuFmdDXSHJsesYz1b4u1H57lV1x03s+2VZ/FrcNMmDRq89dxPvAwAAsN/b0mCpqi5XVY/byjGwLfy/DEvXkuSWSf69qi672gVVdUBV/UJVvbmqrrFMkxcl+cpC8ySvqaqfWqGvJ2XtjejHjhuVH1xVt1qh31tnztPLuvvbWZy5lSRPqKq/qKqDV7pmdo9Dq+ohVfW2ee6zif41yXhm2Sur6ldWapwkVXXdqrrfWh3P9px6yahqYdPuryZ543oHCgAAwMr2+IlrVXVQhqU9Rye5fYZw6y/39DjYPrr73VX1O0n+flb1C0n+p6pemeEEw1MyHGt/sQwbbN8oyR2SXHrW/ic2ie/uM6rq0UneMHv9iCQfrqrnJPmvDCfHXS3JgzOEWZ3kVUl+dY4hvzjJHyY5LMNn8ISqenaSE5J8P8OMo7smuefs3i/MrrNuVvo6/FVV3Xg0hscleWBVvSzDvkZfz/B5u3iSa2WYzXO7JBfM2vs8baru/sFsNta7M+yFdEiG8O4dSV6d5DMZNiS/ZIYZW780G++/JnnpHLf4pySPWlL3/O7+0XKNAQAAmGaPBUuzpURHZ5jZsXDK1cJJcbAh3f2sqvpOhqVOF8gQljx49pja5xur6jeSPGtWdWiS3509ljo2yZcyR7DU3adX1cOSvC7J+ZMclOS3Z4+l/jzD/kNrBksz98swM2ehr8sleeyc1+5R3f2Jqvr5DLOILj+r/vnZY6N9f7iq3pfFfa86yb9stF8AAAB2tVuXws2Wuj2+qj6ZYWbCI7O4afHCLBHBEpuiu1+S5BpJ/jE/efraUqdmOB3s1hlmx6zU5z9kmNXz6RWafDnJ/bp7XXsadfebMsysWqnfLyW5b3c/YZ39ntvdv5PhZLg3ZXGJ4Eo+mSG8WnY53u7W3R/JsIn3nyX51ipNz8kQQK1nj6SPjspv6+4vrn+EAAAArKa6NzfXmS11u3uG2Um3yxBeLV1q1BmCplclebVTmthsVXVghtkq10hyqQwzg76XIQj6ZHd/bkKfN05ywwzLs76RIZD67+4+b/b60UmeP2t+UncfNUefleTGSX4mySWSfDND2PTOhX43oqoulOQWSa44G/d5SU5P8oUkH+vur230Hpulqg7I8Hd2rQzLFCvJtzN8nd/f3Weso68LZAgPF4Ls+3T3qif7reXwww/vRz7ykRvpArbUMcccs9VDAABgH1ZVH+juHUvrN20pXFXdLEOY9KtJLrpQPWrSSd6VIUx6jTCJ3Wm2l867Zo/N6vP9Sd6/Wf3N+uwk75s9Nt1sI+vjd0ffm627z80QOL97E7q7VxZDpW8mee0m9AkAAMASGwqWqurwDHu/PDjJ1ReqR006i/soXaG7T93I/QDWUlXnS/L7o6rndvc5WzUeAACA7WzdwdLs+PKFpW63zeI+TeNA6asZTm56dIbTniJUAna32XK6P0ly/VnVWUn+dutGBAAAsL3NHSxV1c2zuNTtIgvVoyZnJDkuw1Hqb+3u86rq4Zs0ToBlVdV9ktwnycFJrp1hP6kFz+jur27JwAAAAPYDqwZLVXX5LC51O3KhetTk3CRvSfKiJK/r7jN3xyABVnHNJHddpv4dSZ6yh8cCAACwX1lrxtKXMgRJS091+1CGMOll3X3a7hgYwARnJ/lckpdlmK109haPBwAAYFtbK1g6X4aNtzvJV5K8JMlLuvuTu3tgsK/p7hckecEWD2O/091PTvLkLR4GAADAful8azf5se8m+XaS7+ymsQAAAACwD5knWFpYBnedJH+R5EtV9Z9V9aCqOnT3DQ0AAACAvdlawdL1kjwzyWlZ3GvpgCS3SfL8JKdV1Uuq6g5VtZ7ZTwAAAADs41YNg7r7E939+0mukOTOSV6T5JwshkyHZDjm+41J/reqnllVN9q9QwYAAABgb1Ddvb4Lqi6e5H5JHpxkx5KXFzr7dJIXJ3likkOTdHcfsLGhAmydHTt29M6dO7d6GAAAAFuiqj7Q3UtzoHVt3p0k6e5vd/ezuvsmSa6b5BlJvrZwn9njmkmekmFG08IALjFl4AAAAADsnTa0L1J3f7K7/1+SI5LcKcmrkpydxYApGWYxVZKvVdUJVfWIqrrURu4LAAAAwNbblA23u/u87n5Td987yeWS/EaS92cxYOokBya5bZJnJzm1qt5aVY+qqstsxhgAAAAA2LM2/SS37v5Od/9Dd980yXWS/FV2XSqXDCHTUUmeleSUzR4DAAAAALvfpgdLY939qe5+XIZT5X45ySsznCqXLM5msqk3AAAAwD5otwZLC2ZL5Y7v7vskOSzJY5K8b0/cGwAAAIDdY7zbmAYAACAASURBVI8ES2PdfXp3P7u7b5Zhqdxf7ukxAAAAALBxB27lzbv7U0mesJVjAAAAAGCaPT5jCQAAAIDtQbAEAAAAwCSCJQAAAAAmESwBAAAAMIlgCQAAAIBJBEsAAAAATCJYAgAAAGASwRIAAAAAkwiWAAAAAJhEsAQAAADAJIIlAAAAACYRLAEAAAAwiWAJAAAAgEkESwAAAABMIlgCAAAAYBLBEgAAAACTCJYAAAAAmESwBAAAAMAkgiUAAAAAJhEsAQAAADCJYAkAAACASQRLAAAAAEwiWAIAAABgEsESAAAAAJMIlgAAAACYRLAEAAAAwCSCJQAAAAAmESwBAAAAMIlgCQAAAIBJBEsAAAAATCJYAgAAAGASwRIAAAAAkwiWAAAAAJhEsAQAAADAJIIlAAAAACYRLAEAAAAwiWAJAAAAgEkESwAAAABMIlgCAAAAYBLBEgAAAACTCJYAAAAAmESwBAAAAMAkB271AAD2BaeeemqOPfbYrR4GzOWYY47Z6iEAALCfMGMJAAAAgEkESwAAAABMIlgCAAAAYBLBEgAAAACTCJYAAAAAmESwBAAAAMAkgiUAAAAAJhEsAQAAADCJYAkAAACASQRLAAAAAEwiWAIAAABgEsESAAAAAJMIloANq6qjqqpnj5O3ejwAAADsGYIl2ItU1QtGAc3C4zYTr3/d7hwrAAAACJZg7/fUrR4AAAAALEewBHu/m1XVXbZ6EAAAALCUYAn2DX9aVbXVg1hJd5/Y3TV7XHmrxwMAAMCeIViCvdcXkvSsfP0k993CsQAAAMBPECzB3uvjSV41en5sVR24VYMBAACApQRLsHf7oyTnzso/leShm9FpVR1YVbetqj+rqrdU1Veq6syq+kFVnVpVJ1XVsVV1xJz9HTU6je7kZV5/6ej116xzrG8aXfu8Od7XfWf3+2xVnV5VZ1XVl6rquKp6kHAOAABg8wiWYC/W3Z9N8q+jqj+qqgtspM+qOirJV5O8JckfJLltkiOSXDDJwUkul+SWSf44yReq6okbud/MS0blO1bVRecc66WT3H5U9eJV2t4mySeSvDTDssEjk1wkyQWSXDHJ3TN8LT9WVTdY1+gBAABYlmAJ9n7HJjlnVj4iyaM32N8RSS41ev6dJB9I8rYk70jy5dFrByV5alX92Qbv+eYk35iVD05yzzmvu3eShRlGpyQ5cblGVfXA2T2uPqo+Lcm7kpyU5Cuj+msmOamqbjLnGAAAAFiBYAn2ct395ST/NKp6QlVdaIPdfizJ7yY5srsv3t07uvu23X3L7r5ShoBmPMvo8VV1s6k36+4fJXnFqOoBc146bvey7j5vaYOqunmS52UxgPqvJDfv7sO6++e6+6juvmKSn03ykVmbiyR5eVVdZD3vAwAAgF0JlmDf8NQkZ8zKl84QCk312u6+fnf/dXd/frkG3f257n5Akr+dVVWSx27gnsmuy9huudb+TVV1tSQ3XeH6hTYHJHlhFkOlf01yu+5+z9K23f3uJLfIEKolyVWS/ObcowcAAOAnCJZgH9Ddp2Ux5EmS36uqS0zs64y1W/3Yk5L8YFa+4yzImaS735tkIcg6X4Z9kFZz/1H549390WXa3CPDpuZJ8j9JHrncrKbRGL6f5BGjql+vqlqpfVU9oqp2VtXOM888c43hAgAA7H8ES7Dv+MsM+yElyUWTPH5333AWxHx89vSQJNfdYJfj5XVrLYcbB0srbdr9wFH5H7r77LUGMJvN9IXZ08OTXGOVts+ZLRPcccghh6zVNQAAwH5HsAT7iO7+TpKnj6p+o6oO20ifVXXpqnpAVT29ql5SVa+vquPHjyRXG11y+Y3cL7sGRNevqmWDqqq6cRY34u4MJ70tbVNJfm5U9ZZ1jONjo/LPrOM6AAAARg5cuwmwF/mbJL+V5LIZZhD9UZLHrLeTqrpckmckuVfW9+/ARdd7r7Hu/nxVvTeLeyfdP8kTlmk6nq10Und/ZZk2RyS5+Oj5M6vqnGXaLed6o/Kl57wGAACAJQRLsA/p7jOq6mkZAqYkeXhVPb27T563j6q6ZpITM4RT63XwhGuWenEWg6X7VdUTu7sXXpzt43SfJe2Xc8klz289cTwbCssAAAD2Z5bCwb7nH5N8eVY+KMmx8144C21emcVQ6Zwkz09yzyTXTnKxJAd3dy08kpy0WQOfeUWSH83KV0zy80tev91ofGcnefUK/Ry6SePx7yAAAMBEfqGCfUx3n5Ndw6QHVNW15rz8TllcBvbDJLft7od292u6+1Pdffqs/7ELb3DIu+jubyQ5YVR1/yVNxs//vbtPX6GrpfWXGwdi63g8eYNvCQAAYL8lWIJ9078m+eysfL4kfzrndb8wKr+0u985xzVHrGdgcxovb7tXVR2UJFV1SJK7r9BuqdOWPL/MJo0NAACAOQmWYB/U3ecm+eNR1a9U1Tynm11xVH7/Wo2r6mrZPYHNvyX53qx88SS/PCvfNcmFZuVvJXnTSh3MZj59cVR1800eIwAAAGsQLMG+65VJPjwrV5KnzXHN+dd5j4ess/1cuvvMJK8dVd1/yZ9J8qplluUtdfyovFvGCgAAwMoES7CPmp2k9oejql9M8rNrXPbVUfkWqzWczVb63Wmjm8t4mdudZvf7xVHdS+bo42+SnDcr37SqHrFZgwMAAGBtgiXYh3X3G5P896jqyDUuOXFUvldV/cJyjarqp5K8OckhGxrg6t6WxaDrAhmCpANnz09Osub+T9392STPHlU9q6p+d3b63Yqq6hJV9dtV9Yp1jxoAAIAfO3DtJsBe7knZNTBazSuT/FmSyyU5IMkbq+r5GfYy+r8kl86wwfeDM4Q9H0lydpKbbO6Qh32iqurlWZwVddPRyy+dzciax2OT3CDJz2X4N+2ZSR4zC412JvlmkoOSXDLJdTLsxXTrWdv3bvR9AAAA7M8ES7CP6+6TquqE7Hri20ptz6qq+2XYm+jgDP8G/NrssdRXktwzyXM3cbhLvTjLL7db7TS4XXT3ObOZV89Lcu9Z9dWSPHHjwwMAAGA1lsLB9vCkeRt294lJbpnFjb+XOjvDsrQbdPfnNz60VcfywSSfWlL9we5eWrdWP2d2932S/FKSd2Rx36Vlmyf5YIb9qe61nvsAAACwKzOWYC/S3UcnOXrCdTsznAw3b/v3JblhVd0wwxK0SyQ5PckpSU7q7u+M2h41R38nruf+S6699pTrVujr+CTHV9UlMmxOfvkkF0/ywyTfSfK5JB/t7m9t1j0BAAD2Z4Il2I9194eSfGirx7HZZsHR67d6HAAAANudpXAAAAAATCJYAgAAAGASwRIAAAAAkwiWAAAAAJhEsAQAAADAJIIlAAAAACYRLAEAAAAwiWAJAAAAgEmqu7d6DAB7vR07dvTOnTu3ehgAAABboqo+0N07ltabsQQAAADAJIIlAAAAACYRLAEAAAAwiWAJAAAAgEkESwAAAABMIlgCAAAAYBLBEgAAAACTCJYAAAAAmESwBAAAAMAkgiUAAAAAJhEsAQAAADCJYAkAAACASQRLAAAAAEwiWAIAAABgEsESAAAAAJMIlgAAAACYRLAEAAAAwCSCJQAAAAAmESwBAAAAMIlgCQAAAIBJBEsAAAAATCJYAgAAAGASwRIAAAAAkwiWAAAAAJhEsAQAAADAJIIlAAAAACYRLAEAAAAwiWAJAAAAgEkESwAAAABMIlgCAAAAYBLBEgAAAACTCJYAAAAAmESwBAAAAMAkgiUAAAAAJhEsAQAAADCJYAkAAACASQRLAAAAAEwiWAIAAABgEsESAAAAAJMIlgAAAACYRLAEAAAAwCSCJQAAAAAmESwBAAAAMIlgCQAAAIBJBEsAAAAATCJYAgAAAGASwRIAAAAAkwiWAAAAAJhEsAQAAADAJIIlAAAAACY5cKsHALAvOPXUU3Psscdu9TDmdswxx2z1EAAAgP2AGUsAAAAATCJYAgAAAGASwRIAAAAAkwiWAAAAAJhEsAQAAADAJIIlAAAAACYRLAEAAAAwiWAJAAAAgEkESwAAAABMIlgCAAAAYBLBEgAAAACTCJYAAAAAmESwBCNV9eSq6tnjBVs9HgAAANibCZZ2s6p6wSioWO5xTlV9o6o+WFX/WFW3r6ra6nGzd1rje2nq4wVb/b4AAADYNwmWtt75k1wqyQ2TPDLJCUl2VtU1t3RUsIWq6uhR8HXiVo8HAACA5R241QPYz/wgyUlL6g5KckSSI0d1P5Pk3VX1s939qT01OPYJb56jzS+Oyl9I8vk12n9s+nAAAADYnwmW9qzTuvsOy71QVUcm+eskvzyruliSl1fVDbq799QA93fd/eQkT97iYaxope+fsaoaf7+8ePaeAAAAYNNZCreX6O7PJblzdp2Rcv1ZHQAAAMBeR7C0F+nu85L8/pLqX9qKsQAAAACsRbC0l+nujyc5ZVR1/YVCVb1ktKHxcevpt6rePrr2Wcu8Pj4l7MqzugtX1WOq6p1V9dWq+kFVnVJVx1XVumdSVdVtqurFVfU/s75Oq6r3VtXjq+rSszZXHo9llb5OHrU7as77/8R7XKbNk+c5LW1PfL32pKo6Yvb38F9V9ZXZ2L9dVR+vqr+tqh3r6OugqrpfVb2qqj5fVd+rqh/N/vxiVZ1QVU+bfT8csOTaE2d/788fVd9qlRPtjlrm/uerqrtU1Yuq6lNVdfrs/t+vqi/P3uMzqupOVXXQ5C8aAAAA9ljaS30lw4beSXLJUf0/JbnfrHynqrpsd5+2VmdVdfUkPz+q+uc5rrlRklcmueqSly6f5O5J7l5VL0vyoO7+0Rp9HZzkX5Lcf8lLl5k9bpLk96rqvhk2m97nbObXa0+aBTvHZJgpd8ElLx+cYa+v6yT5jap6YZJHdfcPVunvehm+Dsudanih2eMqSW6f5AlJHp/kLzf4Nsb3PyLJq5PcdJmXD509rpDkqCSPTfLsJL++WfcHAADY3wiW9k7jWRTnLBS6++1V9ekMv7SfP8mDM98v5Q8flXd294fXaH/tJC9PcuEkneRTSU5Lcokk18viTLf7Jvlqkt9bqaNZcPGaJHdc8tKnknwtyaUzBBeXTvLGWZ/7mk37eu1Js8DvFUnuOqruJJ/O8HdzSJLrZghjKsP321Wq6vbdfc6S7lJVhyV5W5JLjapPT/LZ2Z8XTHLZDOHbwtdk6azJ92U4PfHys3snybdn9cv51uj+F5zdf3zC4pmz9/PtDJ+rS81eX/i3z6xNAACADfBL1V6mqs6X5KdGVV9f0uSfRuWHzdHf+ZM8aFT13DmG8cIMIcmLklyxu6/T3bfp7htkCAXeMWr721W1dJbO2O9k11DphCQ/1d3XnvV5vSRXTvK6DDNknjPH+PY2m/n12pOemcVQ6UdJnpbksNHfzc0yzJj7zSRnzdrdMsmfrdDfE7MYKn01yV2SXLK7b9Ldt+/uW3T3kUkukmEW13FJzh130N2Pm51894xR9Ue7+w4rPD46avfoLIZK38sQhF28u2/U3bfr7lt297UzzJr6xSQvSHL2fF8qAAAAliNY2vvcIclFR8/fu+T1F2aY0ZEkV6+qW67R350zzBJJkjOSvHSOMVwyyV9194O6e7zfU7r7S0nulGFGS5IckF2Dqx+rqosmOXZUdUKSO3b3LsvduvvLSe6RIWgYz3bZV2zK12tPmu1NtLAE7IdJ7tzdT+ruXYLM7j67u/8+w3tYCIF+u6qutEy34wDxQd39hu4+d2mj7j6ju1/X3ffIEG5tlvH9H9vdL1xuZtXsPZ3Q3Q/JsBwOAACAiQRLe5GqumySv1tS/Zrxk+7+VpJXjaoentWNX39Fd39vjqF8LsP+N8vq7u9m132afm6FpvfPsIwqGZb0PWKl/YVmJ+L9epLvzzG+vc1mfb32pMeNyn/V3cev1ri735bF2W4HJHnkMs2OGJXfOc8glgueNmCr7w8AALDfESxtsao6f1Vdpap+PckHs+vmz6/u7g8uc9l4Odw9q+piK/R9RIYlPwvmWQaXJM+bY4Pp8S/u11qhzXgGyfGz2Tsrmm1E/to5xre32ayv1x5RVZfKMDMuGWYh/c2cl754VL7NMq+PN/X+6QlD26hNv39VPaKqdlbVzjPPPHMzugQAANhWBEt71pWWHpeeYSbPF5M8K8nho7YfTPLQ5Trp7v9O8onZ0wvmJ09bW/DQLP4df6K73z3nON81R5vxkq+Lr9DmxqPy2+a897zt9iab9fXaU26RYTPuJPn4PCcLznxsVL5hVdWS13eOyi+ZY5nmZhvf/++r6i7LjHFduvs53b2ju3cccsghGxweAADA9uNUuL3P9zIcgX7Mase6Z9jkemGmycMyBFM/NvuF+iGjqvFSrLV8be0mGU/f+InfuKvqAhlOelvwqTnvPW+7vcmGv1572PVG5ctX1arL4FZwUIZNuE8f1f11FmcyXS3JSVX1hSTHZ9jA/J3d/b8T7jWvv8+wf9WBGfbq+rckp87e30mz+39xN94fAABgvyNY2rN+kOEX3LEfZvjl/JQMR6r/55z7IL0wyZ9nmLF0w6q6UXd/YPT67TKctpYMJ1+9aB3j/IkNjydYujzvO3Ned/raTfY6m/H12pMuOSpfKrsul1yPi2b099Xdb6iqx2X4vlyYKXe1JI+ZPVJVn86wSfs/d/fJE++7rO7+UFU9OMm/JLnArPrwDDP3Hjq7/5cynED43O7++GbeHwAAYH9kKdyeddoyx6Xfubsf0N1/0N3HzRkqpbu/k+SVo6qlm3iPnx832/R7X3DeVg9gP3Do2k3m8hP/fnT305NcP8nzs3xIeM0kT0zy2ap6alUdsEljWbj/S2f3+LskX1+myZWS/HaSj1bVP1fVBTfz/gAAAPsbwdK+bbyJ932r6pAkqapLJrnb6LX1LIPbLEtDhYvOed1u2X9oswOMfdz47+bN3V0THycv13l3f6K7H5phZtRNM5xA9+/Z9cS/82cImP5qs99cd3+pu38ryWEZNvH+rQynK47D1coQvr50s+8PAACwPxEs7cNmm3EvbKh80ST3mpUflGEPnCT5fJIT9+zIku4+K8k3R1XznoR2zTnbjZefnX+O9suenLefGm/WfZnddZPuPre739fdT+/uO2dYdnffJCePmv1mVV15N92/u/uj3f133X3PDO/1jkk+Omp2t6q6xe64PwAAwP5AsLTvG89aWlj+9rBR3b90d+/B8Yy9f1S+9ZzXzNtuvGTwEnO0v97aTfYb7xmVr1dVF9oTN+3us7v75Rn2dPrhrPqAJLddpvl4SeSGTnYb3f/c7n7T7H7j0HPqHlMAAAD7PcHSvu/FWTxx7BZV9ZAk15k9/1GSF2zFoGbeNCr/UlVdYbXGVXXpJL8yZ99fGpWvP0f7u63dZL/xnixupn5gkgfsyZt392eTfHJUddllmp0xKm/qPkjd/c0k/73G/QEAAJiDYGkf192nJ3nFqOofRuV/7+6v7eEhjY1Dr4OTPKeqlj2JsKoqybOSXHjOvj80Kt+zqlb8Xq6qq2bXWVz7te7+YYav9YJj1wr91jL7+1uP8d/zchvLj79vrzpP/+scw0XWuD8AAABzECxtD+PlcBcYlbdi0+4fm51cd+yo6g5J3jALen5sFmq8KsMeUeMlSqs5blS+epInLNdo1vcbkuyR5V77kGcm+cqsfJkk/1VVN1rroqq6XlU9dzYzbuxKVfXOqrrLSuHhqI/HJBl/D5y0TLOPZZhxlwybgD9orbEl+UhVPWCtk96q6i5JbrXG/QEAAJjDqr8Asm/o7vdW1UcynIC14CtJjt+iIY09I8O+SXeYPb9Dki9U1SczbCJ9qSTXzbCPzjlJHpHF0OiHWUF3f6Kq/iPJL82qnjILRl6e5KsZwohbZ5ipdGiSF2a+cGK/0N3fqqp7ZNjY/ZAkV0vy/qo6Psl/JPlchlPcLpzk8CQ3THK7JNeYdbFzmW5/Lsm/Jfm/qnpThj22vpjhFLqDkxyZYanj7UfXHNfdn1pmfN+tqn/P4hLGF1TVE5N8Ibtu3P6H3f3xWfl6SV6U5Nmz7433Jflskm9n2MvpKknulOTuWdy36X1JTljxCwUAAMCqBEvbxz9l12Vwz+/u81ZqvKd097lVdfckz09yn9FL1549Fvxfkvsl+fKo7vQ1un90hr1yLj97fvfZY6kTkzwygqVddPf7ZyeivS7JFTOELb+UxbBuqksmeeDssZp3Z/Ulir+bZEeSI2bPrz57jP31MtddKMPst3st89rYZ5Lcs7vPXaMdAAAAK7AUbvsYH6F+XpLnbdVAluruH3T3fTPMVHlZho23z86w7G1nkicluU53n5BhWdaCVZfFdfeXktwiK8/M+m6SpyS5fXf/YENvYpvq7g9l2Oz9CUn+d43m30/y+iT3zU9uCn9akscleXt2nVG0nC8l+b0kt5otl1xpbCdnmIX3hFm/X1+j799I8ubsuvH3cr6e4fviRt39lTXaAgAAsIraupPo2UxV9ewkj5o9Pb67NzrrZEtU1e8nefrs6ctngdQ81101yS2THJYhWDg5yVu7+8zVrmNXVXXtJD+TYYnioRm+lqcl+XSSj882/l6rj4MzBEJHZjhx7ZAkZ2UIdD4862e3/cMz2+PpehlmNx2e4X0sBJkfS/Lh7v7Ryj0s7/DDD+9HPvKRmznU3eqYY47Z6iEAAADbSFV9oLt3LK23FG4bqKpDMywjW7Clm3ZPNTvVa7wp9Lvmvba7v5hhPx82oLs/meSTG+zj7Ax7F71vUwa1/vv/KMOpgR9aqy0AAAAbYync9vDoLB6ffkqGDZT3Gus4Bv7YLO67dHaGZXMAAADAXsqMpX1cVd0kyR+Nqp6xF25G/JrZKXCv6u6PLH2xqq6V4T2Ml739Q3evuscSAAAAsLUES/uYqrpMkudkOMHr8CQ3yuLR6Z9P8o9bNLTVXCbDaW1PqqrvZjgC/vQkF0hy5Sye6rZgZ5In7skBAgAAAOsnWNr3HJLkrsvUn5Hkfnvp6WfnjcoXyXCE/HI6ySuSPHwvfR8AAADAiGBp33ZuhpO23prkT7v7s1s8npXcKcldktwmw2lhV8wQMFWSb2U4fv7tSV7S3R/eqkECAAAA6yNY2sd098lZXPq2T+ju7yZ58ewBAAAAbBNOhQMAAABgEsESAAAAAJMIlgAAAACYpLp7q8cAsNfbsWNH79y5c6uHAQAAsCWq6gPd/ROnvJuxBAAAAMAkgiUAAAAAJhEsAQAAADCJYAkAAACASQRLAAAAAEwiWAIAAABgEsESAAAAAJMIlgAAAACYRLAEAAAAwCSCJQAAAAAmESwBAAAAMIlgCQAAAIBJBEsAAAAATCJYAgAAAGASwRIAAAAAkwiWAAAAAJhEsAQAAADAJIIlAAAAACYRLAEAAAAwiWAJAAAAgEkESwAAAABMIlgCAAAAYBLBEgAAAACTCJYAAAAAmESwBAAAAMAkgiUAAAAAJhEsAQAAADCJYAkAAACASQRLAAAAAEwiWAIAAABgEsESAAAAAJMIlgAAAACYRLAEAAAAwCSCJQAAAAAmESwBAAAAMIlgCQAAAIBJBEsAAAAATCJYAgAAAGASwRIAAAAAkwiWAAAAAJhEsAQAAADAJIIlAAAAACYRLAEAAAAwiWAJAAAAgEkESwAAAABMIlgCAAAAYBLBEgAAAACTCJYAAAAAmESwBAAAAMAkgiUAAAAAJjlwqwcAsC849dRTc+yxx271MNZ0zDHHbPUQAACA/YgZSwAAAABMIlgCAAAAYBLBEgAAAACTCJYAAAAAmESwBAAAAMAkgiUAAAAAJhEsAQAAADCJYAkAAACASQRLAAAAAEwiWAIAAABgEsESAAAAAJMIlgAAAACYRLAEAAAAwCSCJQAAAAAmESzBfqKqrltVPXqcW1VX2OpxzaOqjloy9qO2ekwAAAAIlmB/cvSS5+dL8qAtGAcAAADbhGAJ9gNVdWCS+y/z0oP39FgAAADYPgRLsH+4Q5LDZuVPJPnKrHxkVf3c1gwJAACAfZ1gCfYPR4/KL0zykhVeAwAAgLkJlmCbq6pLJLnz7Ol5SV6a5MWjJr9aVRfc4wMDAABgn3fgVg8A2O3um+SgWfnE7j4lySlV9aEkN0xykSR3zxA4zaWqDkpyz9l1N0xy2SQXTHJWkm8k+XySnUnekuSk7j53c97K3OO7QoaZWLdJcq0kF8sQpH8/wzLATyV5T5LXd/cX9+TYAAAAthPBEmx/R4/KL1pSvuGozVzBUlVdL8krk1xzmZcvNHtcJcntkzwhyeOT/OV6BrwRVfWbSf4iQ9C11MVnj+snuXeS/6+qLtvdX99T4wMAANhOBEuwjVXVdZLsmD09K8lrRi+/LMnTkxyQ5LZVdcRsNtNq/R2W5G1JLjWqPj3JZ2d/XjDD7KWrZnGp7R5bcltVD0vyt0uqvzR7/DDJhWdjG4/fkmAAAICJBEuwvR09Kv9bd39v4Ul3f62q3pLkFzOEKw9K8rQ1+ntiFkOZryZ5ZJI3LV3qVlWHZpix9MAke2QZXFWdP8NMpQVvSPLY7v78Mm2vnOQuSR61J8YGAACwXfk/9bBNVdUBSR4wqnrRMs3GdQ+eo9s7jsoP6u43LLd/Unef0d2v6+57JHnmXAPeuJslueSs/D9J7rlcqDQb38nd/bdJrpPEMjgAAICJBEuwfd0hyWGz8teTnLBMm9clOWNWvnpV/ewafR4xKr9znkHswY27x2N7X3efs9YFPThvpder6hFVtbOqdp555pmbMkgAAIDtRLAE29d4BtLLu/tHSxt09xlJjhtVHb1Gnz8YlX96+tB2i/HYrltVG/73rbuf0907unvHIYccstHuAAAAth3BEmxDVXXxDHsILXjxKs3Hr/1qVV1glbY7R+WXVNUtp4xvN/nAqHydJM+vqstt1WAAAAD2B4Il2J7um+TgWfkz3f3+Vdq+NcNG3Ely0SR3X6Xtvv+rDgAAIABJREFUX4/KV0tyUlV9vqr+vqruXVWXnzziDeruL2fXU+8elOQrVXViVf1RVd26qi64RcMDAADYlgRLsD0dPSovt2n3j832QHrpCtcubfuGJI9LMt6X6GpJHpPk5UlOqapPVdVTZyev7Wm/luTto+cHJLlVkj9J8rYk366q/6yqh1TVwct1AAAAwPwES7DNVNW1k9x4VPWEqvrOao8kjx61v91qM4+6++lJrp/k+UlOX6bJNZM8MclnZwHTARt/V/Pp7m8nuXWS+yd5R3YNwJJhFtftkjxvNr5b76mxAQAAbEeCJdh+jl7y/NAMS9xWe4x3pj5fhmVkK+ruT3T3Q5NcMslNM8xi+vck3x81O3+GgOmvJr6PSbr7vO5+aXffcja+uyZ5RoY9mHrU9IpJ/qOqbrInxwcAALCdCJZgG5nNDnrAJnT14LWbDMvouvt93f307r5zkktl2N/p5FGz39yiZXHp7u909+u7+/e7e0eGMOnPk5w7a3JwkqdtxdgAAAC2A8ESbC+/kGThJLRzk1ymu2ueR5Ijsjij5xpVdbP13ry7z+7ulyf5xSQ/nFUfkOS2G3tbm6O7T+nuJyR5yqj6VvZbAgAAmEawBNvL0aPyid39jXkv7O7/TfKuFfpal+7+bJJPjqouO7Wv3eR1o/KBGZbMAQAAsE6CJdgmqupiGfYTWvDqCd28alS+d1VdYNR/rbOvC4/K35owlnVZ5/gusuT5tzdzLAAAAPsLwRJsH/fNsGdQMiyDO25CH6/J4nK4iyW52+i1K1XVO6vqLlV14GqdVNVjklx1VHXShLGs1+Oq6tlVda3VGlXVoUn+ZFT1vu4+a/cODQAAYHta9ZdDYJ9y9Kj8ju7++no76O5TqurdSX521OfLR01+Lsm/Jfm/qnpTkvcn+WKS0zOEWkcm+ZUktx9dc1x3f2q9Y5nggkkeleRRVfWRJG9N8qEkpyU5M8klktwoyUMybOK94CkBAABgEsESbAOzWTo3GVW9aqW2c3hVFoOl21fV5Wf7L41dMskDZ4/VvDvJwzYwlql+evZYyx909xt292AAAAC2K0vhYHt48Kh8XqYtg1vw6iwuhztfFsOj05I8Lsnbk5yzRh9fSvJ7SW7V3d/ZwFjW41VJ/i7J59do10n+K8nPd/df7PZRAQAAbGNmLME20N1/kOQPNqmvU7JM6Dzbh+jpSZ5eVQdnmBF0ZIYT3w5JclaSryf5cJKPd3cv7WMDYzoxyaqbc3f3J5L8VpJU1WWTXD/JVTIsgTsgyXeTnJzk/d39tc0aGwAAwP5MsASsW3efneR9s8dep7tPS/KfWz0OAACA7c5SOAAAAAAmESwBAAAAMIlgCQAAAIBJBEsAAAAATCJYAgAAAGASwRIAAAAAkwiWAAAAAJhEsAQAAADAJNXdWz0GgL3ejh07eufOnVs9DAAAgC1RVR/o7h1L681YAgAAAGASwRIAAAAAkwiWAAAAAJhEsAQAAADAJIIlAAAAACYRLAEAAAAwiWAJAAAAgEkESwAAAABMIlgCAAAAYBLBEgAAAACTCJYAAAAAmESwBAAAAMAkgiUAAAAAJhEsAQAAADCJYAkAAACASQRLAAAAAEwiWAIAAABgEsESAAAAAJMIlgAAAACYRLAEAAAAwCSCJQAAAAAmESwBAAAAMIlgCQAAAIBJBEsAAAAATCJYAgAAAGCS6u6tHgPAXq+qvpfkM1s9DmBFl0ryza0eBLAin1HYu/mMMo8rdfell1YeuBUjAdgHfaa7d2z1IIDlVdVOn1HYe/mMwt7NZ5SNsBQOAAAAgEkESwAAAABMIlgCmM9ztnoAwKp8RmHv5jMKezefUSazeTcAAAAAk5ixBAAAAMAkgiUAAAAAJhEsAQAAADDJgVs9AIC9UVXdJMnRSW6V5PJJDkjyv0nem+SF3f3WrRsdbC9VdekkO5LcePTnYaMmD+nuF0zs+zJJHpjk7kmumuQSSb6e5HNJXpnkZd393cmDh22uqg5J8vNJbpPkZ5JcM8mlZi9/O8mnk7wjyQu6+38m9O8zChNV1UEZ/pt5syQ/neQaSa6Y5GIZftc/PclXkrw/yauSvKXXuclyVV07w8/Ev5DkiCSHZPiZ+MNJXpLk9d193ia8HfZhNu8GGJn9AP2MJI9ao+lxSX6tu7+1+0cF21NVHZbkPUmutEbTScFSVd0jwyk3l1il2ZeTPLC7377e/mE7q6rLJvmbJHdKcugcl5yX5J+T/H53f3/Oe/iMwgZU1T8nefg6LvlAkod190fm6PuAJH+c5EkZ/gfrSt6e4TP65XWMg21GsAQwM/sP6OuT/PKo+swkn0zyoyTXTnKR0WsfSHLL7j5zjw0StpGqunKSeWY4rDtYqqr7JnnpkurPJPlakitn1zDrnCS36+53rOcesJ1V1Y4MsxyWOjnJVzN8bq6SYXbE2M4kt11rlpHPKGxcVT03ycNGVd9L8sUMswmT5HJJjsyuW+B8L8kduvtda/T9j0keOar6YZJPJDkjw8zFS45eOznJTbv76+t/F2wH9lgCWHRMdg2VnpPkiO6+cXffPMnhSZ4yev1GSZ69B8cH29k3khyf4TN21410NJu2/7xR1WeS7Ojua3b3Ud195QxT+k+bvX5QktfOluQBu+okJyZ5cJLDuvsq3f2zs8/SlTIsw3nPqP2ODP/9XJHPKGyaM5K8NkO4dGR3X6S7b9Ddt549rpkhXHpaknNn11w4yUurasWZiFX10OwaKv1bkqt09w27+xYZfib+rQz/4zUZwuBXbuL7Yh9jxhJAkqq6XJLPZ1g3niQv6u4HrdD2T5P84expJ7nhPFOKgV1V1UUy/PL4/u7+0pLXxj+grGvGUlW9NsndZk+/meS63X3aMu2uk2Hm4cGzqv+vux87/zuA7auqfibDEphjuvvja7Q9KMkJGfYlXHDD7v7wCu19RmEPq6qHZ1iuumDZ/7ZW1QUz/Ex8+KzqxAwzBs9dpu3Dkjx3VHXX7n79Zo2ZfYcZSwCD385iqHRmkt9Zpe2fZtgIMUkqyeN347hg2+ru73b3q5eGShtRVdfM4i+sSfKHy/3COrv/J5L89ajq0VV10c0aC+zLuvuD3X2PtUKlWdtzsuvshiT5leXa+ozC1uju5yb5wqjq1is0fXAWQ6VO8ujlQqVZn/+S4WCbBX+w0XGybxIsAQzGPwC/crVNuWc/QD9/VHWn2f+tBbbe+LP8/Qwn1qxmvGTnAtl1OSwwp+7+TIaT3BZca4WmPqOwdT44Kh+2QpvxZ/Sk7v70Gn3+06h8s6o6fMWWbFuCJWC/V1VXz7Cx4YLj57jsP0blC2fX6f/A1rnTqPzOtU6n6u4vZtjfZbnrgfX5v1H5Iiu08RmFrXPgqPwTG+zP9l06alS13p+JK8kdJ42MfZpgCSD56SXP3z3HNR/McErNSn0Ae1hVVZLrj6rm+SwvbeezDNONT3L7idOhfEZh61TV+ZPcfFS13Klw105y/tHzNT+j3f21DKfCLfAZ3Q8JlgB2na5/Thb3T1rRbDncuN1KU/6BPecKScan3HxhpYZLjNsdWVUHbN6QYP9QVTfLcPrUgvcs08xnFLbOU7O4/O2bSf51mTZLf56d8hn1M/F+SLAEMByRuuCUnv+4zC+v0AewNa685PmXl2u0jHG7g7K4aSkwvyeMyj9Ictwyba685LnPKOwmVXVgVV2uqu5WVSck+X+zl85Kct8V9hO98qj8oyRfnfN2fibezx24dhOAbW+8D8Tp67huvDb9wps0FmC6pXu6zPt5XrrPhM8zrENV3T/JXUZVf9fdy/1C6jMKu1FV/SDJwSu83ElOSPJ7sxMXlzP+jH6vu8+b89Z+Jt7PmbEEsOu0/B+s47qzRuULbdJYgOkOXfJ83s/zWUue+zzDnKrqetn1VKjPJDl2heY+o7B1Tkzy90k+uUobPxMziRlLALtuUvijdVw3bnvQJo0FmO78S57P+3le2s7nGeZQVUckeWMWfxk9K8l9uvv/b+++wyWpyn2Pf38MDDCAIDkMMAQlyEiSKOoAEgwg4lEJEiUdQEygICojR7hyAEWQg+RwSYogikiGEbg8CAKKZAWHnBniMJxheO8fq9peu6eru7p379lhfp/nqWeqq1etWt1d1bPr7bXe9VbJLr5GzQbWtdSvj7lJOZU+SOpQsmmx3C5ph4h4vMn+/pvYuuLAkpkZTM3W5+lgv7xsy+mSzWyWmNrwuOr13FjO17NZG5IWJd3ELltsehf4ckT8tcVuvkbNBlBEbNu4TdIiwN7AD4AxwIbAzZLWi4jG2Rv9N7F1xUPhzMz6/gc4bwf7jSmpw8wGR+N1WPV6HtPw2NezWQuSFiIFlWqzP70H7BIRV7TZ1deo2SwWES9HxE+Aj1O/dpYDjm9S3H8TW1ccWDIzS1Ou1ixVWmpmS2brL/eoLWbWvZcaHle9npdseOzr2ayEpAWAq4G1i00BfDUiLq6wu69Rs0ESEXcBR2ebdpC0cEOx/BqdX1LVfEn+m3g258CSmVlKNFqziKTGX0bLLJutP9TD9phZdx4h3eTWLFdxv/xafiEipvSuSWYjh6T5SDmVNsg27x8R51Sswteo2eD6dbY+J/CRhucfbnjczTXqv4lnQw4smZnNPDvGWu12kLQMsFi26cGetsjMOhYRbwJPZpvaXsuFtbN1X8tmTUiaB/g98LFs8zcj4pdV6/A1ajbonmx4vGjD427+Jp4LWCPb5Gt0NuTAkpkZ3EHfKVU3qbDPxxoe39y75phZP+TXYttrufiDOO994WvZrIGk0cBvgc2yzYdGxAldVOdr1GzwLNjw+NX8QUT8i77Bpyp/E69L33xMvkZnQw4smdlsr5gW+YZs084VdsvL/D0iHuttq8ysS7/L1leTtHZpyWRbYIHs8eW9b5LZ8CVpTuBXwNbZ5iMi4pguq/Q1ajZ4Gn8YfbRJmd9n618sAsut5H8Tv4IDS7MlB5bMzJKzs/UPS9qmrKCkdYBPlexrZoPrj0A+ffL3ywpKGgUcmm26NyLuHqiGmQ03xTVyPrBdtvnoiDiyH9X6GjUbBEWAKL/eHo2IxpxKAOdk64sC+7aocyywW7bp/Ih4tz/ttOHJgSUzs+QyIP9j9VRJqzYWkrQ0cAEwqtj0NHDKwDfPzKqIiKnAUdmm7SV9vbGcJJGmWs4Tl5be4JrNbopr5Azgy9nm4yLi8P7U62vUrDckbS/pvyQtXqHs0sAV9M1X9n+alY2Iv5CGvtYcLemjTep8H3AR9R6FU+k765zNRhQR7UuZmc0Giu74twK1WeFeB/4HuAWYAawHHAgsUTw/A9gmIq6axU01GzEknQ7s0uSpubP1d0nXW6NVIuLxJnXOBVwPfDzbfAVwIfAcMA7YC8j/UL4wIqoMgzWbLUj6EmkIXM3/Ajd1UMXzEbFbsyd8jZr1n6TdSb3mZ5D+Vv1/wH3AS8DbpIDPiqThb5+jbx6ky4HtoyQYIGk5Ug7S2t+875ACzdcCbwFrkv4mXiHbbd+IOK0HL82GIQeWzMwyxRC4i4D52hR9FzjA/4Ga9Y+kc+jbjb4TK0TE5JJ6FyHduFaZdepaYNuIeKfLdpiNONlNa7cej4hxLer3NWrWD11eo0EKEB0QEdPb1L8+cCUzzxzXrM4fR8QPO2yLjSAeCmdmlomIWjfhq4H3SordBmzsoJLZ0BURL5NmkjoamFJS7FngW8DWvmE1m7V8jZr12y3AiUCzPEmN3gEuAT4eEfu0CyoBRMQdwHhSz8X/LSl2L/ApB5XMPZbMzEoU49E3AZYh5VR6GrgjIprNoGFmQ1SRsHQCqcv+wsCLwD+AWyOi2RA7M5uFfI2a9Y+khYEPk4a+LQqMBt4kBW0fJCW+n9bP+icAY0lD6p4B7omI+/rXchspHFgyMzMzMzMzM7OueCicmZmZmZmZmZl1xYElMzMzMzMzMzPrigNLZmZmZmZmZmbWFQeWzMzMzMzMzMysKw4smZmZmZmZmZlZVxxYMjMzMzMzMzOzrjiwZGZmZmZmZmZmXXFgyczMzMzMzMzMuuLAkpmZmZmZmZmZdcWBJTMzM5vlJO0uKYplUotyk7Jyu8+6FpoNDdn5H5LG9bju9SWdJekhSW9Iei871na9PNbsRtLk7L2cMNjtGSkkzSHpnuJ9fUbSvIPdpjLZ/1+vSFpksNtjNpAcWDIzMxuBJJ3TcEPaybLQYLffzAaWpEOA24E9gFWA+QENaqPM2tsXWKtYPzIi3i4rKGkJSSdI+qekaZJelnRNp0FTSctLeqv4//GsDnb9XvHv+4H/6uSYZsONA0tmZmZmNmK558jMJK0LHEM9kPQWcBtwTbY8PzitG1oagvQTB7s9szNJ8wE/Kh4+BZzZouw6wP3A14GVgLmBhYEtgd9KOkVS1UDqz4ExwBTgu1XbGxG3AdcVD/eR9MGq+5oNN3MOdgPMzMxswE0D/tRB+ekD1RAzGxL2pB5UuhvYNCJeH8T2mFVxELBYsX5CRDT9v0rSgsDvgNrwszeBu4AlgFWLbfsBDwIntjqgpE8DnyseHh4RL3bY5mOBLYBRwBHAzh3ubzYsOLBkZmY28j0fEVsPdiPMbMhYN1s/yUGl3ouIcYPdhpFE0jzAt4qHbwNntCi+PzC2WL8F+FxETCnq+QpwLmnkzhGSTouIaS2OeVLx8C7g1E7bHRHXSXqIFNDaQdIPIuKxTusxG+o8FM7MzMzMbPaSJxJ+ctBaYVbdzsCixfolEfFai7JfLv4NYN9aUAkgIs4HLi8e1obGlTkUWBF4D9g/It7rpuFALS/THMCBXdZhNqQ5sGRmZmZmNnuZK1ufMWitMKtu32z9grJCkkYDaxQP/xoRDzYp9n+z9XWbPI+kFannUzojIu7ooK2NLiQFuQD2kDR3P+oyG5IcWDIzM7NS2XTJIWn3ivvMkmTJkj7QMJvd+A723Szbb7qkpXrYrrGSvivpJklPFrMRTZF0n6QTJX2kzf7nZm17TdIKFY55eLbPu5I2aVFWkraRdIakB4qpsKdJekrS1ZL272YKb0lLSfp2MevS5GIWpWmSnpZ0vaQjJH24ZN/ds/ZPqni8idk+5zQ8N6H2HLB89tRNJTMhtjympIUlHSTpquK1TZX0uqSHi/dx8yptbqjzQ5JOKup4q5ix6q+SfixpXKf1VTje5A7ek4nZfj39bLIy4/JjZtsXk3SopDslvVicQ49LuqDVed2iLaMkfVHS2ZIeLN7n6cU1eZekUyVtJ2muhv1q7dot23xEyfkTNFAX34OSPiXpTEkPSXpV0tvFa/+DpP0kjalYT962ccW2BSQdIOlWSc9m1/xlkrapUm9Rz2hJO0m6RGm2tTeUvnPekPSYpGslHa30HTuqar1tjrkKsF7x8FXgphbFFyblMwKYXFLmX9n64iVlTgTmAV4GDqvU0BIR8TRwZ/FwIeAz/anPbChyjiUzMzMbliLiH5JuAjYtNu1NSu5axV7Z+pUR8Wx/21PcRB0BHAw0BmbmJt1QfAg4UNJ5wH4luT0OBDYhDcF4H3C+pI9HRNOeJZLWByZmm46OiFtLyq4FnEb9Ji23TLFsBRwmaY+IuL5ZPQ11jiqO/y3SzEmNli6WzYGJkr4ZESe0q3eokHQgcCRpyvBGCwAfBL4q6Y/ALhHxSoU6DyPNbpUHM8aQborXBL4haa+IuLi/7R9OJG0JnE89QXPNcsBOwE6SjouIQyrW90ngF8AqTZ5eCFinWPYB/kZ9GvtZStLypLw/n2jy9HLF8hngcEn7RMRVHda/LvBr0ndKbhng88DnJV0E7BoR77aoZ3xRz6pNnp6/WFYgJas+jNTj5787aWuJL2TrN5Ql7e5Ay9ngJH2OevDn0CrXdAVXAesX6/8BXNaDOs2GDAeWzMzMbDg7lXpgaWdJh0TEO612kPR+0s1Uzen9bYTS0IZfUZ89CNLQh4eA50hBgzWA+Ug3NbsBK0jaIiL+N68rIt6QtDMp6eycwMbA96lPs50fd37SsJDa33S3k4Igzdr4SeC3pJu/minAw6RkuMsCKxfbxwJXSvpiRPy+xeseA1wKNCaHf47UK+AdUo+AVaj3IliorL4eegW4plj/BKnnAaReA81uEu9t3CBJwP+QZo/KPUqa6nwuYHXqr+fTwC2SPtbqRlTSEfQNBELqWTGZFLwaTzpPLpTUKo9Mp/5EmhUL2r8n/+zhcSuRtClwJelcngHcV7RrSWC1rOjBkp6IiJNmrqVPfXsDp1A/7yDNkPkg6byvBQUXLJ5rPC9r5894UmAU0mff0/dG0sqkHjhjs81vA/cDU4s2LllsHwv8XtIuHQQdVwcuJr3eIL3+50lBzPHUR7DsCDwLfLuknUsCN1LPcwTwGvBI8e+8pPNrxazOXo2O2Spbn9Sm7CuknEhz0LdnXi7f3meWt+I77efFw9uBMyu3srVJpB8eALaQNEc/cjaZDT0R4cWLFy9evHgZYQtwDukmIoDJ/ahnUlbP7hX3mZztM6GkzO5ZmUndHh8YDbyQldmpQvsOyso/CYzqwft9clbndOAoYPGGMnOTeiNNzcoe36LOH2Tl3gU2alLm7KzM68BKJXWtSLr5q5W9h9SrYI6Gch8i3eTWyk0Blq14ngVwPbB+k3JjSL/SXwMc0e350LDPxGyfc/pzPpbsd0jDa/slsHxDmVGkG/KXs3KXtqjzY6Sb3lrZB4CNG8osTeoVEqSb3rwN4/p7rnb6ngzUZwOMa3htLxXvzfHAIg1l1yAFWvJzff4Wx9+MFJyqlX8W2BMY01BOpBw7PwXuq3COT+zle0wKTt6VlZsO/BBYICszB7At8ExWbiqwSotjN76vAZwHjG0otzxwc1b2XWDFkjpPzMo9A2xDk+9OUlB0O1LA+ZAenKujSQHq2rE3qLDP34qy7wGrNnn+11l92zU8d1SxfQawTi+ut6Le+el77Y/vVd1evAyFxTmWzMzMbNiK1Nvn7GzTXmVlM1/N1s+OkiFmVRX5U/YvHk4HtomIwyPihYa2vhMRvwA+Sz1h8teLYTDNHA3UhrSNAi6Q9L7suF8k3fTXHBQRj5bUdSZpWB3AdcCGEXFdNPxiHhH3kwJO1xabFiLd6M5E0qfpm3/mdGCLaJLkNiKmRsRvImIrUuBgSJP0AdINZs1eEbFfRDyel4uIGRFxEfBx4M1i8/aSPlpS9YnUh+H8C/hERNzWUOczpFmtLqZv75CRbhHgaxHx7Yh4OX8iIu4j9Qh7u9i0AClQOROl5M216eQBHiMFI86KiKkN9UZE3BUR3wI27N1LqWxf0lC8mn0i4siIeKO2ISLei9RrcAL1nmXzks6lKhYBjouIXSPiqfyJ4nz+LKmHIaTvmV1L6snzAu0aEVc0++6MiLci4vKI+AIpYNdf40nBJUgBmfsr7HNJ8a+AUyT9uzeapC9RH1r3KvXvOiR9kDSUGeCUiLi7H+3uIyLepG9up5a59syGGweWzMzMRr7lyxLONiyvDnZDu3Qa9Rl3JkhaqaxgkY+olkD6PXozzOE72fpxEXF1q8IRcSNwRvFwFH1nO8rLzQB2IfU0gpS75GRICcJJwwBrLomIc5rVI2k90k0pRV07R4vhgpFyrOxNCpIBfCUPaGUOzdbvBQ6IiGhSrrH+N9uVGQK+ST3/0UUR0fI8KQJyeSDqgMYykjakbw6fr0fEi43livqiqGNKs+dHqEkRcXLZk0UQ5DfZprLg3VeoDysL0vn+RLuDD9J5mZ8nV0bE2WUFI+IR0pDYmi2KpNbt/IMWyacj4nX6Dgcue1/zoXpNc7g1qbsXMw6unq2/UPFzOpnUSw3Sd98TShNR3E8asly7B/5xQ7DxJOq9YPP3ulfywP/qpaXMhiEHlszMzGxYK3rp3FA8FH17JDXKezRd39gDpVOSFqWeX2gG9dwc7ZyfrW9WVigiJlPvDQUpyLMzabrsWjLppygJThXyHgjnlwUzGo77BGmIDKRcPBvnz0tahjSsq+bo6H9C3SFB0hykRNE1VXtd5J/ppk2ez/N6TY6IK1pVFilPU+m06iPQqe2L9AlorFZSZsds/ZqIuL37Jg0cSavRNwl2le+Os6kHmkUactbOWdEiIXehyvuaTzSwZoXj9kreo/PpKjtExBTS9Vb7sWQBUl6xPJhzDvCz2gNJ/wFsWTz8TkS8mj23saRLJT0v6Z1ixr9TJS3X4WvJ21/WU9VsWHJgyczMbOSbRspt025pOwPYEHZatr67pJkmKJE0H7BDtqnfSbtJs7fVhjbdFxHPV9zv79n62kWi6KYi4kL6Bi3Oo94D6T3STGSterbkAaBOPuO8jes0PJfX+S5QmuB7GPow9YTOr0XEX6rsVAwzqt2MLilp6YYi62fr11BNR7N/DXO3tS9CPpRrpln6iut+o2zTpf1t1ADaIFt/m5TbrKVIs0jm1/AGZWUz/X5fC/l1cIGkj1eotxfy4aCVe/BFxJ9JOeN+QRoO+U6x/w3AFyNij9pQ4OL/hlqQ6VbSdyzFc18vtm1PmohgNCkotA9wn6RNOngteZL8xpkPzYY1zwpnZmY28j0fEY2zdo00l5NmOloCWIqUD+R3DWW+TPrlGtJQh8bnuzE+W19GUsthcCVGk/IftZoB7ADSEJUV6PvD4LERMalspyJgtUa26buSGmc5K7Nytt54E5T3anggIt5m5Mg/0zk7/EznydYXIyU5rsnfz/sq1le13EjwXPsi5MOWxjR5fllS8uiau/rVooGVnw8PVuhVVPN36jmCVm5VsNCL9xXgBOq9K1cC/iTpUeBq0gyWt0ZEpR5FHco/z46+Z4p8ZV8rllZ+SBrqN4NsSK+kzUivG1LwbSJpONvHgMNJ/5/8VtJqEfFShSbl7Z+vtJTZMOTAkpmZmQ17ETFd0tnU8/7sxcyBo3yI3Hk9Grq1SLa+KH2nxe7EgrQILEXE68U09edlmx8hzRzXrt58uvVuExQv2PAXa9djAAANMklEQVR44Wz9BUaW/DOdj/59prm8J8jLVFO13LBXJOLvRLNefgs3PB7K5+ZC2Xonn3MewCjrXZTr9H1tKiKukPQd4CfUg9srkYLeBwBIegi4DDi9GMbba6U9O7uuMA1J/Gbx8KSIuDd7+pji3+nAphHxz+LxJEnPknq9LkqaQfK7VQ7XgyabDUkeCmdmZmYjxenUk3h/Kh+KVNw85HmCzqA3evWrc8u/ySTNy8wJeFcE1m5T70C1b+5svTQR+DA1K96zqjf7I+29HWhzNzweyu9fN+dDY9nG1zugIuJY0lDRPNdTblXge8Ajko6SNKpJmU69la3P24P6Gp1MStT/LHBEbaOklanP3HZZFlSqOZd6b7CdqCZv/1ulpcyGIQeWzMzMrNd6cTPRsYh4DLgua8Me2dN50u6bI+LhHh02v7m6JiLU5TK5zXGOZ+akunOScp3MX7F9ABt12b7dG+rJZxBciMExUOdZ/p493I/PdFJDva9n6wtQTdVyQ82gfAfQ97yEwTs3q8jPs04+57zsLJ/JMyLuj4g9ST37NiDNivkHIJ+tbS5SgOm4Hhwyn2ygSg+tyiTtSD3R/sHFDHk1ef6qmfJfFT1eaxMcjJW0VIVD5u1vO4mC2XDiwJKZmZm1kv86Pldpqb4G82Yun1lqTyWjgV2y7b3qrQQpr1PN4j2s998kfQb4z2zTMcAbxfrKtJhNqpiaO8+f0qs25nlbquR5aWconWcD9Znm9a5QcZ8Ve3j8bg2lz6adxnxCHxiUVlSTBxY6+ZxXKqljloqIGRFxR0QcGxHbkIaE7QhMzop9TdK4fh4qn7lzmX7W9W+SFiAF7AEmFZMk5PJA0TM0l+eUakzW30ze/n7NSGo21DiwZGZmZq28ka035i+ZSXET0aoHzUD7PWlIA6Sbtc2Az1FPPv0q8JseHi+fynx8m95DHZO0BHBWtunKiDgUODDbtqek7Su2caPSUp3J61xC0ir9rK+j86wwvn0RIM2cV1Mlx0n+2t7fg9dWc0+2vn5pqb6qlhtIA/nZ9FREvAL8I9vUi5nLOj1/qro7Wx8rqWrQJO9Jc3dpqVksIt6JiItJOclq+etGAZv3s+oHsvXFJb2vn/XV/IgUPJpOkSOqQafDDKuUz4PwD5SWMhuGHFgyMzOzVvJfVT9cofx2A9WQKoqZlc7ONu1N32Fw5/d4BrPbqQ9HmRP4Sg/rhhRUqvWaeR7YEyAizgN+nZU7vcWNaT6r2c6SqvY6aeVO+k79vW8/68vPs5Ullc1MBYCkxakeJOsoR0tEPEnfm749ysp26OZsfXNJi5aWrNuxR8fuj4H8bAbCtdn6bpL6m4dooHL8/Jm+vcHa5umRtCqwbrbp5rKygyUiHqHv9bNEP6u8j77v0xplBauSNJ76THE/j4hmQZ58qGJZb6T8O7flsMTiR4dx2aahPGOhWcccWDIzM7NW8l4Wn27VI0fS+4GDB75JbZ1OvZfB54FPZs/1chhcLc/GydmmH0lathd1SzoQ+HS2aY+IyGe52hd4slhfGDhXUrMeFWdQ73WyLOmX+n4pXnc+7PBASe0SibfyIDCtWJ8TaNUDC9K036Mr1t3NsL2fZetfk7RWxf1a+RX11zgX6TWUkrQ1aVrzwTaQn81A+AX1JP5LAT/uZ329HvYJpJke6dt78juSFikrX/jvbP1FUg/NAVfyvdJKngfqlf4cu5gtMO9FuF5/6iucTDqXn6b8+/ChbH2mXldFgL7WI2468GibY36Eeo+3V0gBM7MRw4ElMzMza+VK6r8WLwT8tFmhIqh0OT3MgdGtIhF2rdfCaOp/79wZEX8bgEP+lHqAZ3HgJknrtigPpF/NJZ0haaYeMZJWp+9N5C8i4qq8TES8CuxKPYi2OfDtxroiYgpwZLbpMEnHtOvJIWk+SXtIurGkyE+p33TPBVwjaZM2dW4s6bNN2jidlAC45qiyZLiSvk3fnFPt5MOF9pC0YIV9zqUeVB0DXCtpy3Y7SVpJ0vGSDm98rvi8Tsk2HSBpv5J61gTOr9DOATfAn03PRcRD9B0+erCkIyXNWbaPpMUkfaPk6fz82bK4NnvlGOrDxhYFrpA003BDSXNIOgbYJtt8bETMqlnvlpd0q6RtW72PAJIOoG/OqD/14Ph5r8sJ/alI0q7UA7bfLPLQNXM79d5q20lqzNe1G7BksX5rhc9iQrZ+XUS8V1bQbDhq+cVgZmZms7eIeEXSmdRvFvcu/sA+i5SkdUFgY9KQs0WBG4FVGPwA06nA1g3bTh+IAxXv0ReASaQgxErAnZKuBq4i5Xx5k/Qr/tLA2qReVLXcPX/J6yuSjV9IfdjNA6SZl5ode5Kk47Lnj5J0XWMALSKOk7Qe8KVi03eAXSRdRLqBeoEUgHs/afa5DYo2zkvfpNN5nS9K2pl00zcXKY/VLZL+SOpJ8RhpuvfFgXWAbYEPkXoI/KFJlccDXyD9qr8ccI+kn2XvzyqkJOwbkm74rqF97xmAi4BDinrXAp6WdDdpKF+tZ8t9EfH97LVNL/JW3VG8rsVIgbNbi9d2P2mWt/lIQ33WIt041nptHVPSlh+ShovWknefImk70uf9OCl4uyXwVVLOlouBHSq8xoE2UJ/NQDmIlJ+qluvpB8COki4gDUGaQroeVyPNCrYVKUHzCU3quoF0fSxO+rzvlXQPKag6o1YoIjoeBhwR90qaCBxVbNoIeEDSqaTrchrpvf0qqcdLza2UBPkH0EeB3wEvF9f4naRr/DXSufoB0me+RbbPZRHxYA+OfSlwdLG+qaTRRU+mjhRB5WOLh9dFxCVlZSPiTUlnk/LZzQXcWHxW/yQFpr6fFS+dQCGzVbZ+aSftNhsWIsKLFy9evHjxMsIW4BzSTXMAk/tZ10KkbvvRZvk7aQrqydm2CSV17p6VmdTi2JOycrt30ObaMIfavm8CCwzwe742KTjQ7n1qXPZrqOe47LlpwJptjjua1Kuits/9wLxNyo0i3Th32r7n2hx/c1J+kar1TWxR148r7P82KSH7xGzbOW3a2K7epucgKQD0ty7es5+0aMtKDedm2fJX4H0N28b16FydnNXZ9Bod6M+GlG/m3/tWbMOEbJ/JbcouTMpBVPUzK60P+Gzx2kr37897TAosVW3nLcCCberr6Jxp91k0Pl9xuQ1YqBfna9GGP2d1b91lHScV+78DrFKh/IKkHwZavc4LK9SzNKlnaZACcfP06n3x4mWoLB4KZ2ZmZi1FGsLzCVKvimhSZBrwS2CDiHh5VratTKQk3nlC1osj4o2y8j065j2kHjmH0Xca6mbeJPV82ZEUBARA0ubAt7Jy34s2w/ci/XK/E+nGF2B1UnCqsdyMiPgGqVfEH4F327TxAeAnpM++1fFvAFYlDfMqG1YCqSfLr2jxa32kXkP70zcxeO4OYKOI+F2rNpXUuxlpeNnDRTubncuN+/2LlNPlP+k721gz04DrSL33jiorFBGPkoKQF5P1eGmo5zRg40h5eIaEgfpsBkqkGeI2JSXvb5X/Jkg9rya2qOsPwJqkwOzdpEBqs8+u27YeTuoh+JcWxZ4DvgFsFhGvtSg3EJ4n9XK8mb6JtJt5nDQk9xPF/x29kud027nTnYsccLWet8dHxMPt9ine5wmkXmuN3iMFqnatcPgdqOdXOjciprUqbDYcKaLt/6lmZmZmAEhamnSztjQpN8gTwI09voHot6KdT5B66QBsGBF/nsVtWJ00BGxR0hCat0g3aA+Rhl5Nb7H7gCsSsW9CGtq0COlG6TXSTfjfI+K5FruX1TmaNGRmZdLrfg94ifSa/xIVc8IU+Z8mkIYBjSENU7orIu7vtE29JGkF0jDBxUm9iaaSkig/DNzb6Q2jpCVJAa+xpIDSk8BNQ+16yg3Vz6adYka1j5A+u3lJQxkfI52XTYd7DgZJy5OuoSVJvRFfJPUGvTOGwI1b8fmvSRr6tgTpHHibNFzwr6Tvtp63szjuE6TP721g6U6uk2LI6Vqk76TjImJqh8dfizTse0FSkO+6iHiq4r73kwL+AXwwIv7ZybHNhgMHlszMzGzEkfQD6gmr742INQezPWZm1j+SvkvqRQlwcEQcP5jtqaLohXp98fBXETEUcqaZ9ZwDS2ZmZjaiSJqP1OtmiWLTfhFxaotdzMxsiCu+2x8j9Vp6ClhxsHt+tlNM4rAVqaf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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "random_functions_df_grouped = random_functions_df_grouped.sort_values(by=['completely_executed'])\n", - "random_functions_df_grouped['completely_executed'] = [x * 100 for x in random_functions_df_grouped['completely_executed'] / 1000]\n", - "\n", - "#plt.figure(figsize=(60,6))\n", - "random_functions_df_grouped.plot.barh(x='Approach', y=['completely_executed'], color='gray', legend=None)\n", - "plt.rcParams.update({'font.size': 35})\n", - "#plt.rcParams[\"figure.figsize\"] = (15,15)\n", - "plt.xlabel(\"Fully executed functions (%)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "oBVZ_epoq2qe" - }, - "outputs": [], - "source": [ - "random_functions_df_grouped" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "RPOSZPp40EGN" - }, - "outputs": [], - "source": [ - "random_functions_df_grouped[\"covered_lines_percentage_\"] = random_functions_df_grouped[\"covered_lines\"] / [random_functions_total_lines_df['total_lines'].sum() for e in random_functions_df_grouped[\"covered_lines\"]]\n", - "random_functions_df_grouped[\"execution_time_percentage_\"] = random_functions_df_grouped[\"execution_time\"] / random_functions_df_grouped[\"covered_lines\"]\n", - "random_functions_df_grouped" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WbimjtWs0sY5" - }, - "source": [ - "### Main results" + "random_functions_df = random_functions_df.merge(random_functions_total_lines_df, how='left', on='file')\n", + "random_functions_df" ] }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 317, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 332 - }, - "id": "lT52PchhLIhQ", - "outputId": "d85a033a-ad68-4fb7-ed11-b443706ed3cd" + "id": "8nTLxCuLJRTC" }, "outputs": [ { "data": { "text/html": [ - "\n", - "
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filepredictorcovered_iidstotal_usesguided_usesexecuted_linescovered_linesexecution_timetotal_linescovered_lines_percentageline_execution_timecompletely_executed
0./popular_projects_snippets_dataset/black/bodi...FrequencyValuePredictor554110.007880170.0588240.0078800
1./popular_projects_snippets_dataset/black/bodi...FrequencyValuePredictor443000.00101320.0000000.0000000
2./popular_projects_snippets_dataset/black/bodi...FrequencyValuePredictor333000.00073180.0000000.0000000
3./popular_projects_snippets_dataset/black/bodi...FrequencyValuePredictor332000.00093320.0000000.0000000
4./popular_projects_snippets_dataset/black/bodi...FrequencyValuePredictor222110.00637180.1250000.0063710
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991./popular_projects_snippets_dataset/tensorflow...PynguinTests000000.000000210.000000NaN0
992./popular_projects_snippets_dataset/tensorflow...PynguinTests000000.00000060.000000NaN0
995./popular_projects_snippets_dataset/tensorflow...PynguinTests000000.00000020.000000NaN0
996./popular_projects_snippets_dataset/tensorflow...PynguinTests000000.00000010.000000NaN0
997./popular_projects_snippets_dataset/tensorflow...PynguinTests000000.00000070.000000NaN0
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8000 rows × 12 columns

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" ], "text/plain": [ - " covered_lines_percentage \\\n", - "predictor \n", - "AsIs 0.040729 \n", - "CodeT5ValuePredictor_coarse_grained_deterministic 0.500481 \n", - "CodeT5ValuePredictor_coarse_grained_randomized 0.493197 \n", - "CodeT5ValuePredictor_fine_grained 0.498525 \n", - "FrequencyValuePredictor 0.129595 \n", - "NaiveValuePredictor 0.127589 \n", - "PynguinTests 0.040725 \n", - "RandomPredictor 0.137774 \n", + " file \\\n", + "0 ./popular_projects_snippets_dataset/black/bodi... \n", + "1 ./popular_projects_snippets_dataset/black/bodi... \n", + "2 ./popular_projects_snippets_dataset/black/bodi... \n", + "3 ./popular_projects_snippets_dataset/black/bodi... \n", + "4 ./popular_projects_snippets_dataset/black/bodi... \n", + ".. ... \n", + "991 ./popular_projects_snippets_dataset/tensorflow... \n", + "992 ./popular_projects_snippets_dataset/tensorflow... \n", + "995 ./popular_projects_snippets_dataset/tensorflow... \n", + "996 ./popular_projects_snippets_dataset/tensorflow... \n", + "997 ./popular_projects_snippets_dataset/tensorflow... \n", "\n", - " completely_executed \\\n", - "predictor \n", - "AsIs 0.019 \n", - "CodeT5ValuePredictor_coarse_grained_deterministic 0.342 \n", - "CodeT5ValuePredictor_coarse_grained_randomized 0.338 \n", - "CodeT5ValuePredictor_fine_grained 0.340 \n", - "FrequencyValuePredictor 0.076 \n", - "NaiveValuePredictor 0.074 \n", - "PynguinTests 0.018 \n", - "RandomPredictor 0.080 \n", + " predictor covered_iids total_uses guided_uses \\\n", + "0 FrequencyValuePredictor 5 5 4 \n", + "1 FrequencyValuePredictor 4 4 3 \n", + "2 FrequencyValuePredictor 3 3 3 \n", + "3 FrequencyValuePredictor 3 3 2 \n", + "4 FrequencyValuePredictor 2 2 2 \n", + ".. ... ... ... ... \n", + "991 PynguinTests 0 0 0 \n", + "992 PynguinTests 0 0 0 \n", + "995 PynguinTests 0 0 0 \n", + "996 PynguinTests 0 0 0 \n", + "997 PynguinTests 0 0 0 \n", "\n", - " line_execution_time \n", - "predictor \n", - "AsIs 0.001498 \n", - "CodeT5ValuePredictor_coarse_grained_deterministic 0.185076 \n", - "CodeT5ValuePredictor_coarse_grained_randomized 0.167478 \n", - "CodeT5ValuePredictor_fine_grained 0.178694 \n", - "FrequencyValuePredictor 0.003613 \n", - "NaiveValuePredictor 0.003617 \n", - "PynguinTests 0.012964 \n", - "RandomPredictor 0.003940 " + " executed_lines covered_lines execution_time total_lines \\\n", + "0 1 1 0.007880 17 \n", + "1 0 0 0.001013 2 \n", + "2 0 0 0.000731 8 \n", + "3 0 0 0.000933 2 \n", + "4 1 1 0.006371 8 \n", + ".. ... ... ... ... \n", + "991 0 0 0.000000 21 \n", + "992 0 0 0.000000 6 \n", + "995 0 0 0.000000 2 \n", + "996 0 0 0.000000 1 \n", + "997 0 0 0.000000 7 \n", + "\n", + " covered_lines_percentage line_execution_time completely_executed \n", + "0 0.058824 0.007880 0 \n", + "1 0.000000 0.000000 0 \n", + "2 0.000000 0.000000 0 \n", + "3 0.000000 0.000000 0 \n", + "4 0.125000 0.006371 0 \n", + ".. ... ... ... \n", + "991 0.000000 NaN 0 \n", + "992 0.000000 NaN 0 \n", + "995 0.000000 NaN 0 \n", + "996 0.000000 NaN 0 \n", + "997 0.000000 NaN 0 \n", + "\n", + "[8000 rows x 12 columns]" ] }, - "execution_count": 156, + "execution_count": 318, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "random_functions_df_grouped = random_functions_df.groupby(by=\"predictor\")[[\"covered_lines_percentage\", \"completely_executed\", \"line_execution_time\"]].mean()\n", - "random_functions_df_grouped" + "random_functions_df['covered_lines_percentage'] = random_functions_df['covered_lines'] / random_functions_df['total_lines']\n", + "random_functions_df['line_execution_time'] = random_functions_df['execution_time'] / random_functions_df['executed_lines']\n", + "random_functions_df['completely_executed'] = random_functions_df['total_lines'] - random_functions_df['covered_lines']\n", + "random_functions_df['completely_executed'] = [1 if value == 0 else 0 for value in random_functions_df['completely_executed']]\n", + "random_functions_df.replace([np.inf, -np.inf], 0, inplace=True)\n", + "random_functions_df" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 468 - }, - "id": "CsmEpQK1ZDrr", - "outputId": "15f910c2-a562-4a3f-88b3-515bc4484b75" + "id": "7l4cf3NbK3jB" }, - "outputs": [], "source": [ - "random_functions_df_covered_lines_per_predictor = pd.DataFrame()\n", - "random_functions_df_covered_lines_per_predictor['As Is'] = [x * 100 for x in random_functions_df[random_functions_df['predictor'] == 'AsIs']['covered_lines_percentage'].tolist()]\n", - "random_functions_df_covered_lines_per_predictor['Pynguin Tests'] = [x * 100 for x in random_functions_df[random_functions_df['predictor'] == 'PynguinTests']['covered_lines_percentage'].tolist()]\n", - "random_functions_df_covered_lines_per_predictor['Naive'] = [x * 100 for x in random_functions_df[random_functions_df['predictor'] == 'NaiveValuePredictor']['covered_lines_percentage'].tolist()]\n", - "random_functions_df_covered_lines_per_predictor['Frequency'] = [x * 100 for x in random_functions_df[random_functions_df['predictor'] == 'FrequencyValuePredictor']['covered_lines_percentage'].tolist()]\n", - "random_functions_df_covered_lines_per_predictor['Random'] = [x * 100 for x in random_functions_df[random_functions_df['predictor'] == 'RandomPredictor']['covered_lines_percentage'].tolist()]\n", - "random_functions_df_covered_lines_per_predictor['Coarse-grained \\n(randomized)'] = [x * 100 for x in random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_randomized']['covered_lines_percentage'].tolist()]\n", - "random_functions_df_covered_lines_per_predictor['Coarse-grained \\n(deterministic)'] = [x * 100 for x in random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_deterministic']['covered_lines_percentage'].tolist()]\n", - "random_functions_df_covered_lines_per_predictor['Fine-grained'] = [x * 100 for x in random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_fine_grained']['covered_lines_percentage'].tolist()]\n", - "random_functions_df_covered_lines_per_predictor" + "Overall Comparisson" ] }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 319, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 1000 + "height": 430 }, - "id": "fDIoCk2M9TzG", - "outputId": "68af316b-f745-4209-91bc-facd68d2e108" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "
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9WOoQWF7RQPu4MbD8vrs/W1dBd18o6f8FNp1RV68ld98Wzc7dfbuk3wQ2XRDN68JucfdvI5QJDu+b6u47o2iTu3tlLU9dLKlneHmZpB/XUa6qnmJJ1wc2/TTWyZ3rcJakboH1nwSDwFrcpVBAFI9ojrGkmM75Mkl/CWyK5ZxH4/n6hreFhyR+Gtg0pI6i1ys0L5Qkfe7uv61vp+6+RtKtgU031ixjZrmSzg9sGuXui+qp9m6Fhh8CALDfuu6666o9AABoTJr6HEvB4S/xzJFTLzPrp9AcQFWimcj4WUn3KzSHj0n6vqoPNYtHsMfPsVG+Zr2kd6Iotz2w3N/MUuoLgyK4KrD8hLvviPQCd59iZksUmtvnIIXmlpof5/6rnBtYnhXuHVVfG7ab2XMKhRSxiPYYxyN4zo9LcN1PRlHmU+0eatqvjjLB8x3tBNpjFJqXLEvScWbWokbg9j2Fhn0qXO6Z+ipz9zIze1LSH6LcPwAATc7f/57okfEAACROUw+WMgLLEUOMOAwOLJcqijmcwiHFeIV679Sso1Zm1lehYXT9FRqS1Uq7v1zX1MbMsty9JEK1U9y9PNK+JU0PLB8u6VkzuzPcuyRq4Z5GwZ4t42N4+WztnjR6oPY+WAqGb/+J8jX/UezBUrTHuJrAsRqsUGiTo9AQtWBvrbaB5dxY91GPndo9b1d9ghOGt6n5pJm1VejzUuWjaHbu7jvNbKFCd+RLVWiS8s8DRYIh2qQoe3l9KIIlAAAAAEiKph4sbdbu4XAJm/g5oGdgeV4MIcJs7Q6WetZVyMyOVWh+nxNjbFe2Qr056rMkmorcfYWZva3d7f2hpBFm9qlCYcGnCgUoke681VXVA4iHzSzisLqwIwLLHeosFb1DAst1TjReQ7TlgqI6xkFmNkLSfao+VC+S7Fj3U4+NUX6Og5+vrFqeP1zV73D3agyjGIPvveb5Dv68fBNlfQsUmow8PdoGAAAAAAASo6kHS4Xa/cW0bX0F45QTWN4Yw+uCk3Hv0dtDkszsUkmvqu6eSfXJiFxE9c0pVNN1Ch3HYeH1VIUmbK6atHmHmU2S9IqkV+oY4lbzrlynxrD/oGohipk9r923qq/LH919XGA9eN421yxch3iGUsZyjGVmjyp0V71YRXO+oxVt2BdUW2IUPN8pkr4bX3P2CM2CPy9R/cy5e4WZFSm+CdgBAAAAAHuhqU/evTSwfHiCJn4OCn6hj+ULebDsHqGAmXVT6O5pVaHSBkkPSDpTobtwtZKU5u5W9Yip1SFRz5Pk7psUCoJGSJpUy2szJH1HofluFppZbaFRizjaWJuan8mTFQot6nskYqhYPPNKRf0aMxuu6qHSPEm3KdRbLVeh45cSON/xBnP7SkOd73h/5hpiKGyTkHfn+8luAgAAAIADWFPvsTRJ0tnh5RwlZuLnoGAvllYxvC5YtrYeM7/U7tupL5U0pL47i5lZLPuOS3jC7lckvRK+jfyw8OMUheY9qgq3DpH0oZkNC989rErNHj9dor1bWgMoktQxvBztMLJae5Yl0F2B5XclXeruZfWUb/BzvpeC53uHu2fWWTI2WwLL8f7MAQAAAAD2kabeY+mTGuuXJ7j+9YHlQ2N4XY/A8vpanj8zsHxPFAFM1xj2vdfcfbO7v+fut7n7MQqFSQ9IqggXyZD0PzVetrbGekclgLvnBXtu1fF4rsbLVgSW67qjWU19IxeJj5l1VGiy6iq/jBAqSfv4nMcheL4zzCxRc5wF6+0ezQvMrJ0aZo41AAAaBTOr9gAAoDFp0sFS+DbywQl+rzWz5gncxVeB5a5mFu2Qq+Cd4L6q5fng5NLToqgv1sm9E8rdV7r7XZLuDWw+2cwyAmXWq/rQxBP2VftqETym0Q4pa8ihZwcHlje4e34Ur0nqOY/CTIXulFglUed7RmD5uDpLVRdtOQAAAABAgjXpYCnsocByrvbsSRM1M+tVY9MXqj7Py5VR1NFX0qDApom1FIv17lXXxFi+obwTWE7TnhN2/zOwnMw2fxBYPsrMBtdZUlI4IBvZgO2J6XybWbakCxuoBMPDMgAAIABJREFULQnh7jslfRzYlKjzHfx56WtmR0bxmisStG8AAAAAQIz2h2DpZUkTAuu/MLMbY6nAzJqb2QuSqr3O3bdIeiuw6VfhYTf1+X1geb2k92opsyawfFKEtl0uaUiEfcYtxgnPaw432lRj/THtntB6sJldH3fD9s6HkgoC638zs5b1lL9PUrcGbE/wfLc3sz4Ryj+oxE2O3ZAeCSxfamZn11kyeuMlrQqs1xsUm1l/RRH4AgAAAAAaRpMPlty9QqEeC+vCm0zSX8zsKTPrXN9rzSzVzK5RaMLvq+oo9qCkqvlw2kv6h5m1raWuFDN7UNL5gc0PuXttd6v6JLD8WzM7uJYyMrNzFboTW0P6lZn91czqnYvIzFpI+u/ApqnuHhwKJXdfKOmvgU2Pm9nNZpaqephZWzP7hZm9HmvjaxP+TNwW2HSUpI/MLDjPkcysnZk9IelWhe7M1yDcfbmk/MCmvwSHEQbak2JmoyT9uKHakkjuPl5S1S3JUiS9aWYjIr3OzLqY2W/N7M+11Fmh6uHsuWb2P7UFoOG7K76j3XdXBAAAAADsY039rnCSJHdfY2anKPQlt2rC32sljTCzcfr/7N153G313Pj/17v5NGieaVBCSdSJJioqQ8YfIRXFXTLPFG6VsXCTO4mEELqlDIlQt0PdKU0aFA06RSrN83zevz8+6/pe69pnD2vvs69rd13n9Xw89uOstfZnfdZ7r7U3rrfP5/0poyCuA26jrBK2JmUU0IuBVXv0fXFEHEwZ1QKllsxlEfF14GzgAcpqdG8GZtdOPRP4Yodu/xt4IyUJthZwYUQcCfwfZdn0dYFXM56k+gawb7c4F8AsYH9g/4i4CDidUufmJuA+YCXK1L59mFgb6lO09z5KImdbyvfri8Dbq6TReZQEzhKUaXSbUO7njlXbc4b1oTLzRxGxM+V7AKUOz4URcRVlNNPywNMZ/w3sA5xc66Kfpe6bOLx6AexUxfJVSo2wxYCNKdPxnlm1mcxnPkx7UX4HG1FGWR0XER8ETgT+TBnVNouSlN2MMkJva0oiqlMi8UhKsnirav9AYMeI+BZwZXWd51K+t4+j/G7W5bFf8FySJEmSZpwZkVgCyMzLI2Ir4Gjg5dXhWcDLqlcvv63Obdf3Z6oROx+pDq0OfLxLX2cCL6lGX7Tr78KI+Ajw2erQyl36mwO8k6lJMmxWvXo5IDNPbvdGZj4UEbtQRlq9tjq8AeP3biq9BbgHeDcliQewYfUac1/V7rSWc+8ccixfoawG+OJq/6nAfCN2KocCv2YaJJYy8/aI2BY4AdihOtz0e9Spz0cj4qWUKa4bV4e3YjzRVHcdZSpcu1pmkiRJkqRJNu2nwtVl5r8z8xWUP0BPoHdy4E7g28B2mblLZl7Wpe+PUkaanNelvxuB9wDPy8yu187MQymjlm7o0ORW4GBgpw7T6YblBEqC46oe7ZJSrPk5mXlY14aZ92Xm64AXAWcwXnepU78XAB8DdmsadBOZOS8z30tZpe8Yyqp191NG0VxESeA8LTOPA1arnXp/Zt475FgeBV5BmeZ1f4dmVwG7VSvwTRuZeQvwfMooowt7NH+EMsLovZSEabc+twK+SvvRY49Q6p9tkZnXDRC2JEmSJGkIZsyIpbrMPAd4TVXbZzZletyqlGkzd1OmY10EXJ6Z3ZIerf2eDmxZ1XbZFliDMq3rZuAS4NzMzD76+25EHA88hzItbFlKrai5wO8z8+Fa255FtjNzb/pc3Swz/wK8CyAiVqdMD1ufMgVuUeCuKp5zM/PGPvs+FTi1qkm1HWXVvhUpNavuoExrujgzb+un335l5rnAuT2a1Zesv6hLX3sz4Apy1fP8cEQcSpn+tz7lHt8IXJaZ59XazmF8lFW3Pndo0KZRXy3nHAsc20f7ecDxwPERsSawDeX3sQJleuetwBWU5313wz7vpkyj/CiwM2UqZgL/BP5Q/z5m5npNY5UkSZIkDc+MTCyNqUaJnMMQa/dU/V4LXDukvh6i1DU6fRj9LWAsN1GmBA6739tovzreY8mba9tnTeaFMvN24KTJvMYoZeYNlBpLw+rvDsrIOkmSJEnSY8yMTixp4RYR0WQEWUS8CXhh7dCxkxaUppWI2A/YD2Cdddbp0Xp01jvglN6NJEmSJGkSmFjSTHZkRDxMmaJ1Tuu0x2pK4/uBd9QO/yQzL5nCGPUYlplHUxX1nz17duNprlNt7qG7jjqEacuknCRJkrRgTCxpJnscsAelhtR9EXEFcBuwOGVp+vVb2l9DWSFOkiRJkiQ1YGJJM1l9hNLSwDO6tD0N2CMzb57ckCRJkiRJmjlMLGkmewvwM+D5wBbAupRVyhYFbgeuB84ETsjMP4wqSEmSpG4233zzUYcgSVJHJpY0Y2Xm/ZTVyYa2QpkkSdJUO//880cdgiRJHS0y6gAkSZIkSZI0PZlYkiRJkiRJ0kBMLEmSJEmSJGkgJpYkSZIkSZI0EBNLDUXE7IiYFxEZEYcPsd+Dqz4zIo4dVr+aWhGxd+05zhnB9Y+tXf/gqb5+UxExtxbnDlNwvR1q1/vEZF9PkiRJkhY2rgrXQEQEcAQQwN3Ap0cbkaQmMnNORPwG2AX4QER8MzOvHXVckiT1Y4sttpiw7ypxkqTHEkcsNbMbsFW1fVRm3jzKYIZl1KNsNH1NlxFSlUOqf2cBnxplIJIkDeKCCy6Y8JIk6bHExFIPEbEIcHC1+xAwtGlwkiZfZp4FnFXt7h4RTx5lPJIkSZI0k5hY6u2VwFOr7RMz84ZRBqPHpsw8NjOjeu0wguvvXbv+wVN9/aYyc71anHOm8NJHVP8uChwwhdeddHMP3XXUIUiSJElaiJlY6u09te1vjCwKSQviJ8Bt1fbuEbHaKIORJEmSpJnCxFIXEbEJsF21ez0wZ3TRSBpUZj4InFjtLgnsPbpoJEmSJGnmMLHU3Z617Z9mZjY9MSIWj4j/iIjTI+LGiHggIq6JiJMj4lVV7aaBRMTSVd8/iYirI+Ke6nV1RHw/Il5ZrWTX6fw5EZHAt2uHt68VY2597dAjnh0i4oiIuDgibo6IByPiX9V1PhQRKzb4TPVl4efWjm8UEZ+JiAsi4t8RMS8i7qi9f3DtvGNrx58fET+IiKsi4r6IuDMi/lAVLJ/v3kfEEyLic9VnuLc65/KI+EJErNog/kaF0CNibut9jYglI+KNEXFaRPyzun83RsSvImLPbs+y1m/jYtoRsUpEvC8ifh0R10fE/RHxcETcERF/qb5XH46ITducm9V35421wwd1+u40+fwNPtuaEfH+Kt651fN5oIr9tIg4KCKe3qCrn9S29+zYSpIkSZLU2GKjDuAx7lW17VObnhQRGwInAM9oeWu96vUS4LcRsXu/AUXEa4AvAWu1efuJ1ev1wDkR8drJXFo9ItYHvg7s3ObtNavX9sCHI+KdmfmDPvv/APAZYPE+zlkKOBJ4U8tbs4DnVK+XRcRrMvOR6pw9q8+xdMs5T6lee0fEzpl5YT/xN4x3A8p35Zktb60OvLB6vSkiXpaZ9wzhertRPmu7ZN/y1Wtj4BXAoRHx7Mz804JedxARsSilcP77mP/ZQPkNrAU8Hzg4It6bmd2K6/8OeJAyYmnTiNgoM68YbtSSJEmStHAxsdRBlTR5UrU7Dzij4XlPAE4H1qkdvh/4S/Xvk4HVKMmYn1H+2G0a04GUREvdP4C51fZGlIQEwLOBsyLiOZn595Zz/gQ8AKwNPK06dnt1vJ3bWg9ExDMoybbVa4fvBS4D7gbWoBQ9D2Al4LiIWD4zj+ryEev9vx/4fLX7EHApcBclkdCtPs43KYk1gBuAK4ElKEm+parjr6Ss7veOKrn33SrOB4FLgHsoz37tqv3KwC8j4imZeWeT+BtaA/ge8Phq/yrgn8CywGaMJ9R2BL7DxERn3yJiF+B4Jo5U/BdwDeW7uSywLiUhOKZ1dNevq383ZTy5eXUV+9BExNKUqWsvbHnrxireBynfgydTCnIDrNCtz8x8ICLOpiQ7AV4AmFiSJEmSpAVgYqmz7WvbV/SRUPgW40mlpCSCDsvMuwGqaVivAo4CtqX8YdxTRLyaiUmlE4GDMvMvtTZB+UP8KEqCYC3gBxGx3djoHIDM/FDVfm/Gp8NdnJmtf8R3imVF4OeMJ5XmAh+kTBd8pNZuXeALwKspiZsvR8Q5mXlBj0usBhwKPAJ8Ajh87P5V/W7Q4byXUJJA1wD7A78dm74YESsARwO7VW3fEhEnAcdQEoefAL44NiqoupdvqN5fjJIEej/w8R6x9+MrVby/At6XmX+tfcZVgK8xnkz6/yJi+8z8/QJc7wuMJ4r+CLy93SisiFiDci/3a31v7DtSTTscmw533CSsRPdVJiaVTgc+0jp6qkpAvRjYl/J76+VPjP+2d2B8tThJkiRJ0gCssdTZFrXtS5ucEBEvB3aqHfpgZn6snhTJzHmZeQKwC2WUyCoN+l2BiSvSfTIzX11PKlV9Z2b+CtiaMhIFysil1zaJvw9fBJ5QbV8MbJ6ZP64nlap4rs3M3SjJNigjcA5r0P8sSjJn78z8ZP3+Vf1e3eG8lSmjlLbNzN/Ua2Jl5h3AHoyPrFmMktBZGnhzZn6iPtWsupffoSS4xryhQez9WBn4EfCSelKpuv4twOso93fMPoNeqBpJN1Yz6R5g105T+zLzxsw8JjOfBZw76DUHFREvZmINp28AO7ebkpeZ91XfvRcA/9Wg+0tq27MXLFJJkiRJkomlzjaubXdKZLR6a237PEoCpq1q1M7nGva7H+PTfM7KzK6jZjLzBsromjFvb3idniJibUqCBsqIot0z8/Yep70LuLXa3ikiNmpwqVMy8/sDhPj+6vPPJzMfZjzJBWWK3G+qBFInX6ttrxsRj+/Ysn93Avtl5rx2b1aJui/XDm27ANeqx/2XBs9sLIZHF+Cagzqgtn0xZWRVz9FIDWtQ1X/L60TEsv0GJ0mSJEkaZ2Kps3Vr29f3alz9gVovYn1kgz+GjwKa/OG+V237Sw3aA5wE3FdtPysilml4Xi+vY7z2z68z87JeJ2TmvcBPa4ee1+A6Rw8Q253Aj3u0aR31cky3xpl5PaXu0ZinDhBXJ8c3mGJ5Zm17g4hYYsBrPVDbflJEzBqwn0lVJS6fUzv0mSohOCytv+V12raSJEmSJDViYqmz+hS1JqM7tmDi/ey5ilxm3gR0rTcUESsBm9QOnd4gFjLzIcYLEy9KKQY9DPU/+k/r47z6FKTNG7Q/s3eT+ZzfIAnROprp7Ab91s/pWiC6T2c1aFNPagVl1bZBXM54onEl4KQutapGqf79eoRSy2uYWgvRrzrk/iVJkiRpoWLx7s7qI3zub9B+w9r2rZl5Y8PrXAps2eX9TSgJBShFpn9Y6ko3Uh91Naw/oDetbe8dEY0KfjO+wlqTWO7IzPlWomvgpgZt7mvZ7/ecdsveD6rndyQz72t53gNdv1oR7SjGp0i+ELgyIs6lJAjPoEyzvGuQ/oeoPiLsssxs8tvrR2t/wxrJJ0mSJEkLJRNLzTTJ5KxY2761Y6v59Wq7cm17EcoS6YMYdKRLq3o8mzHYSKhesdzd4/1OHur3hGpkVz8aZ/Ua6DveBbz+RyjJxlfX+npW9QJ4NCLOAY4Hjm0tmj5FVqpt/3sS+h/m89MMsd4Bp4w6BGmhsk/LZGx/g72t++FfTNj3nknSzLb8rMV7N3oMMbHU2b2MJ0Ca1KNZsrbdT8LgwR7vD2tExbCmPQ4jnl6xtC1mrQVTJdF2q1ZdewdlBcP6f2ItCmxTvQ6KiLdn5v9McZj131Gv38YgWn/L907CNTSNzD1011GHIC10DjnkvAn7/g4lSZreTCx1djPjiaUVuzWs1KcQLdfHdXq1rRd3fjAzl+qj78lwJ+OjlnbPzONHGYz6l5m/BH5ZFXTfFngusD2wNSW5BOUZ/zAiHszMn7bvaVLcUdseZj2rMa2/5Zsn4RqSpC4OOuigUYcgSZKGyOLdnV1b2167Y6tx9Vo9a/WxetcT++h3yYh4XMN+J0s9ntVGFoUWWGbem5m/ycyPZeZzgNWBDzNeUyqAL0xxWPW6Uxt2bDW41t/ydZNwDUmSJElaaJhY6uyy2naTP3AvrG0vDjyz1wlRqjLP7tHsIiYWHN66QSxN1aecNa09U19FbZixaMQy89bM/Bzw9trhDTqsHjfId6eJ+vdr9Yh48hD7hom/5esy854h9y9JkiRJCxUTS52dX9vetGOrcVcxcVn61zY4Z0dgjW4Nqro4v6sd2qdBv03V68s0qSMFcGpt+6URsXLHlpquWqe+rd6mzSDfnSbOBW6v7b9liH3DxN/y+R1bSZIkSZIaMbHU2R9q2xtGRNc6S5mZwHdrh94SEet0ah8RiwCfahjLl2rbu0XEixqe10t92tETo2Vd+w5+AsyttpcBjhhSLJpEDZ/tmNbplre1aTMpU9Yy82Hg67VD74iInqP/+rBlbfv3Q+xXkiRJkhZKJpY6yMxrgCuq3aAUOO7lCGBsifalgZ9HxHyjPSJiMeCrNJxKlpmnAWPryi4CnBARe/Q6LyLWjIiPR0Sn5M8lwCPV9srAGxrE8gjwodqh3SPiuxHRtQh5RCwREa+KiLMjYtQFyBdGe0TEDyJiy26Nqu/mYbVDNzD+O6i7oLa9S0RsPIQYx3yR8cTV4sCvI2K7bidExDYR8ZIebZYCtqod+vUCRSlJkiRJclW4Hk4EDqy2Xwj8rFvjzLw+Ig4AjqwObQZcGhFfA84CHgCeCuwLPIOynPqvgFc0iGUvSv2ZjSgjhY6LiA9WMf6ZMn1oFrBKdd3tKImrRYC2S8Zn5l0R8Yva9Y+NiI8AVwMP1Zp+LDMvrZ13QkR8HvhgLbaXRsQPgTMpyYh5lFW9NqKMEnkB84+E0dRZDNidkgi8ipJUOR/4F3AP5dk8HXgj5Ts65rOZOY/5nQ78m1LAfRng4oi4kJIQenSsUWY2+W5PkJk3V4nTUymJpVWBMyLil8DPgb9TfjurAZsDLwM2AQ4BftGl6+2BJavtyzLzr/3GJkmSJEmayMRSd99jPLH08oh4WzXlraPM/GpErMv4qJ5VgI+1afoo8DZgHRokljLz9ojYFjgB2KE6vFn1WhDvpRQQf3y1v1H1qju8TTwfiohbgM9SklcrAG+tXnps25Bm09eOBL7S7o3MfCgi3kz5Pi4FLErvQvSNZeb/VlM+TwSWrw6/uHoN6pW17eMWoB9JkiRJUsWpcF1k5uXAGdXumsDzGp73YcrokH92aHI58ILM/Faf8dwCPL/q+8IezR8B/o+SOHpnlz7nUpJTB1LqSv2biaOVusXzOUox5B9SRmN1cw1lquCWmdmrrYbvd8DngEuBrslRSgHtl2fmO7olUjPzF5TvzuGUqXF3UButtKAy83TgKcBRlFFVndxLGZV3YqcGEbEE8Kpq9yHg20MKU5IkSZIWatFjAM5CLyJeCZxU7R6fmbv3ce4iwHOAjSmjLm4CLsnM84YU25rANpSV5VagTA+6lVIT5+LMvLvL6UNV1a/ZBngipV7TIsCdlELfl2bmdVMVi7qrCtE/g/FntQQlcXMdcH5mXjvC8NqqEkPbUkZarUKZankL8FfgvMx8sMf5uwE/qna/l5k964m1mj17dp533lB+upIkSZI07UTE+Zk530wVE0s9VMmhiyk1XB4C1svMG0YblaR+RMQZlLpj84BNM/OyfvswsSRJkiRpYdYpseRUuB6qwsUHV7tLAO8ZXTSS+hURW1GSSlBGHfadVJIkSZIktWdiqZkTKSuyAbw1IlYdZTCS+nJw9e8DwEdHGIckSZIkzTgmlhqoChi/i1L0eDn841SaFiJie+AF1e7nq2L1kiRJkqQhWWzUAUwXmXkuJuKkaSUzfw/EqOOQJEmSpJnKRIkkSZIkSZIGYmJJkiRJkiRJAzGxJEmSJEmSpIGYWJIkSZIkSdJATCxJkiRJkiRpICaWJEmSJEmSNBATS5IkSZIkSRqIiSVJkiRJkiQNxMSSJEmSJEmSBmJiSZIkSZIkSQMxsSRJkiRJkqSBmFiSJEmSJEnSQEwsSZIkSZIkaSAmliRJkiRJkjQQE0uSJEmSJEkaiIklSZIkSZIkDcTEkiRJkiRJkgZiYkmSJEmSJEkDMbEkSZIkSZKkgZhYkiRJkiRJ0kBMLEmSJEmSJGkgJpYkSZIkSZI0EBNLkiRJkiRJGoiJJUmSJEmSJA3ExJIkSZIkSZIGYmJJkiRJkiRJAzGxJEmSJEmSpIGYWJIkSZIkSdJATCxJkiRJkiRpICaWJEmSJEmSNBATS9I0FhFzIiKr196jjmdUImJu7T7sMOp4JEmSJGlhsdioA5gsEbEx8EJgJ2B9YBVgBeAu4FbgYuBs4KTM/Puo4pQkSZIkSZquZlxiKSKeCXwKeHGHJitVrycBrwI+FxFnAAdl5pwpCVKSJGkhdcghh0zYP+igg0YUiSRJGoYZNRUuIj4AnMf8SaXbKCOUfgdcAPyrfhrwXOB3EfGhqYhTkiRJkiRpJpgxI5Yi4gjgHbVDDwFHAd8DLsjMbGm/PvBS4F3ABtXhtaYgVGloMnOHUccgTSebHfIb7rz/4VGHIS3U9pk1cX+9A04ZTSCPQdce9pIJ++t++BcjikSSNErLz1qciw7aZdRhNDYjEksR8R9MTCpdAeyamVd1OiczrwH+OyKOAvYDPje5UUqSRu3O+x9m7qG7jjoMaaF2yCHnTdj3NzkuDpu4772RpIXTdPs/Xab9VLiI2AD479qhvwPbdUsq1WXmw5l5JLA1cPUkhChJkiRJkjQjTfvEEvBBYGxQ9Txg78y8ud9OMvPizDyiV7uIeFFEfDMi/hoRd0TE/RFxbUT8IiL2j4ilm14zIh4XEbtFxFER8ceI+HdEPBQR91R9/iwi3h4RyzTs79jakusHV8ciIl4SEcdHxBURcXf1/uFtzn9CRPxnRPwuIm6MiAeqeG6LiIuqPt4TEU9sGM9iEbF7RPyguvadtft1UkS8ISImfdRcRMyOiK9HxJURcW9E3BoRF0bEpyJi3Vq7rL3W69DXMO7xfhHx/eqe3h4RD1f//i0ivhsR/19ERMPPNqcWz94d2uxQazO3dnzd6h78ubr+fRFxVUR8IyI2bXL9lussHRH/ERE/iYirq+/xPdX29yPilU0/V0uMh0bEpRFxV/Ud+ktEfDkiNuk3RkmSJEnScE3rqXARsQrwxtqhX2XmGZN0rXWB7wDbt3l7neq1K/DRiNgvM3/Vo78PAJ8Elmrz9uLAMlWfLwMOjog39OqzzTVWA74LvKBB23cChzGepKtbsXo9HXgt8KWIWD0z/92lv+dRalxt1Obtsfv1SuDAiNg9M//cK8Z+VUmMzwPvZWISdWnKyoDPAN4TEftm5g8HvEY/9/hEymdul1xZoXptBOwFXBQRr2468q5fEbEX5fm0Ji03qF77RMT7M/PLDft7DfAl2tcpe2L1ej1wTkS8NjOvbdDn3sARwLItb21cvd4WER/KzC81iVGSJEmSNHzTOrEE7MLExMw3JuMiEbEhZUW5x9cO3w/8BbiPkgxYozr+eODnEbFXZh7fpdunMDH2fwH/AO6hJD6eTEl+AKwCnBwRL8vMXzYMe0ngFGB2tf9v4EpKguVJLZ/vzUycTghwbfV6GFiOkhhYpfZ+x9FuVdLiW0z8ft1EmWr4cNXXE6rjTwF+HxE7Z+afGn62pr4K7N9y7GrKfV4e2JSSWPl+RNw5QP+N73FlM8aTSvMo0zZvAh6gJO6eynhibzPgjxHxzMz85wCxdRQRe1KSYVCK3F8C3EVJ9o0Vsl8UODwi/p6ZJ/fo70DgMy2H/wHMrbY3Alavtp8NnBURz8nMv3fpc2/Kd6iehPsXcBXlmW0KLAF8MSLu7xafJEmSJGnyTPepcM+pbScl+TNUEbE48D+MJ5UeAQ4CVs/MLTNze2Bt4OXADVWbxYBvRcSTu3Q9DzgVeAOwamaunZlbZeZOmblNZq5MGR01VuFyUeDYiGgdvdHJ2ykJj6uBFwFrZOZ2mbkNJQn2pdrnq5eKPBl4Umaul5nbV/E8OzNXBdYH3g1c3umiEbE1E5NKvwO2zsw1MnPbzNwhM9cBtgEuqto8Djg+Ih7X8LP1FBGvYmJS6Txg88zcMDN3zMzNgTWBr1OSF98Z4DKN7nHN/ZR782Jg2cx8UtV+p8zcgpJc2gu4sWq/CnD0AHF1swolAfswcCCwcmbOzsznZeaGwHaUBM6Yw7tNX4uIVzMxqXQi8LTMXCczn5uZz6Xc5xdTEpVQRjX9oNM0yCqRexTjSaXrKaMBH199J2dT7u/Y1NUvASs3+/iSJEmSpGGa7oml2bXtKzLzrkm4xluAzWv7+2XmJzLz7rEDmTkvM38O7ADcVh2exfyjgOrem5kvyszvZeYt7Rpk5h8oybNzqkOrUhJRTSxHGTWyXWaemplZ6/fR2lSkrRj/o/waoOP0q8ycm5n/DWxCGZ0zQUQsShkJM5Yw+A6wU2ae3aavP1KSGJdUh9YH3tnws3UVEYsA/1U7dDHwvMy8sCWGWzJzf+BwJo7GaqrpPR6zVWa+OTN/lZnzjbLJzAcz8zhK0u2O6vCLImLjAWLrZBnKSKtXZeahmXlPSwz/R5muN/ZZnkj76Z9ExApMHCX4ycx8dWb+paXPrKZxbs140urZlGmV7Xye8dF8twM7ZuYvW+7v7Zn5LkpSdCnmny4nSZIkSZoC0z2xtGpt+7pJusbba9unZOa3OzXMzCuAj9UO7dxp1FJm3tvk4pn5APDR2qGXNzmv8r7MvLFHm/r0vj9l5kMNYsrMnNfmrVcBG1bb1wBv6dBurJ97gP1qh97Wb3HnDl4IrFvb37+eCGzjQEqCaBBN7jHQ1zO/BvhK7VA/z7yJ73Sb3lZNSTyzdmjbDk33o9SFAjgrMz/e7aKZeQPw/tqht7eOHYqMAAAgAElEQVS2iYi1gZfWDh2UmVd26fY/KdMPJUma9vbdd98JL0mSpoPpXmOpPv1lkBo5XUXEUyk1gMY0KWT8beCzlBo+AbyCiVPNBlEf8bNlw3NuBn7aoN0Dte2nRcQi3ZJBPexV2/5qZj7Y64TMPDsirqbU9lmLUlvqrwNef8yute2Lq9FR3WJ4ICKOpSQp+tH0Hg+i/syfNeS+v96gzZmMTzV9aoc29efdtID2SZS6ZEsDz4qIZVoSbi+jTPukavetbp1l5sMR8XXgCw2vL0nSY9bRRw97BrwkSZNvuieWlqxt90xiDODZte37aVDDqUpSnEYZvdPaR1sR8RTKNLqnUaZkLcf4H9etVoyIpTPzvh7dnp2Zj/S6NnB+bXsT4NsRcUA1uqSxaqRRfWTLaX2cfgnjRaM3Z8ETS/Xk2/82POd/6T+x1PQeT1C7V8+mJG1WoExRq4/WWqm2vXa/1+jiIcbrdnVTLxi+YuubEbES5fsy5vQmF8/MhyLiCsqKfItSipSfVWtST6Kd0XCU168wsSRJkiRJIzHdE0t3MD4dbmiFn2s2rG1f3kcS4RLGE0sbdmoUEVtS6vts02dcy1NGc3RzdZOOMvO6iDiR8XjfAOwREWdSkgVnUhIovVbeejwTExBfjIie0+oqm9a2V+3Yqrl1atsdC423aNqurtE9rouIPYBPM3GqXi/L93udLm5t+D2uf7+WbvP+Jkxc4e6HfcxirH/21udd/71c2rC/v1GKkS/eNABJkiRJ0nBM98TSbYz/YbpSt4YDWqG2fWsf59WLcc832gMgInYDfkjnkUndLNm7Cd1qCrXal3Ifn1vtL0op2DxWtPnBiDgD+AHwgw5T3FpX5dqxj+vXTUiiRMR3GF+qvpP/yszf1vbrz+2O1sYdDDKVsp97TEQcTllVr19NnndTTZN9de0yRvXnvQjwgsHCmS9pVv+9NPrNZeajEXEngxVgn/bWO+AU5h66a++GkiRJkjQJpnti6e+UmjwAm0RE1FeOGoL6H/T9/EFebztfUiAi1qWsnjaWVLoFOIYyHesqSu2e+zPz0do5/X6uxnWSMvP2iNgReB2wP2WaVr2w+5LATtXr4IjYOzNbpwUu02d8nbQWlN+e3qN7jh/CdQepK9X4nIh4DROTSpcD36RMA7uWkgC7f+z7GxE70GDq5QhN1vMe9Dc3GVNhJUmSJEk9TPfE0hnAi6rtFRhO4ee6+iiW5fo4r9623YiZ9zC+nPrfgW27rSwWEf1ceyBVwe4fAD+olpF/bvXagVL3aGzUyjrAryLiudXqYWNaR/ys2XS1tElwJ7Batd10GlnbkWVDdGBt+2fAbpn5cJf2k/7MF1D9eT+YmUt1bNmfu2rbg/7mJEmSJElTpHW0wHQzp2X/dUPu/+ba9hP7OG+D2vbNbd7fpbb9yQYJmMf3ce0Flpl3ZObPM/MDmTmbkkw6FBgbQbUk8JmW025q2V+NIcjM9TIzeryObTntutp2pxXNWj2ld5PBRMRqlGLVY97TI6kEU/zMB1B/3ktGxLBqnNX7Xb/JCRGxMpNTY02SpCkVERNekiRNB9M6sVQtI18v8PsfETFriJe4oLb9+IhoujpXfSW4C9q8Xy8ufW6D/vot7j1UmfnPzDwQ+FTt8PYRsWStzc2U0Vdjtp6q+Nqo39OmtZ4GrQnVxBNq27dk5twG54z0mTdwEWWlxDHDet4X1raf1bHVRE3bSZIkSZKGbFonliqfr22vzfwjaRqLiCe1HDqHiXVeXt+gj6cAW9QO/aFNs35Xr9qnz/aT5ae17cWYv2D3qbXtUcb8y9r2MyLi2R1bAlWCbO9JjKev5x0RywOvnKRYhiIzH2JiDahhPe/67+UpEfH0BufsPqRrS5IkSZL6NBMSS98Hfl/bf3dEvL2fDiJiVkR8F5hwXmbeBfy4duhD1bSbbj5X274Z+HmbNjfUtrfrEdvrKMW0J0X0N866dbrR7S37X2a8oPWzI2K/gQNbML8C/lHb/1pELNul/afpXSB8QdSf9yoR8eSOLYvDGF5x7Mn0pdr2bhHxoo4tmzsNuL623zVRHBFPo0HCV5IkSZI0OaZ9YqlaOW134N/VoQC+EhHfiIg1up0bEYtGxD6Ugt97dWh2GDBWD2cV4OSIWKlNX4tExGHAS2uHP5+Z7VarmlPb/nhEPKFNGyJiV+Bb3T7DEHwoIo6KiK61iCJiGeATtUN/ysz6VCgy8wrgqNqhIyPivRGxKF1ExEoR8e6I+J9+g2+n+k58oHboGcDpEVGvc0RErBwRXwXeT1mZb1Jk5rXA3Nqhr9SnEdbiWSQiDgLeMlmxDFNmngacUu0uApwQEXv0Oi8i1oyIj0fEEW36fJSJydldI+Iz7RKg1eqKP2V8dUVJkiRJ0hSb7qvCAZCZN1TLs5/CeMHf/wD2iIjfUkZBXAfcRlklbE3KKKAXA6v26PviiDiYMqoFSi2ZyyLi68DZwAOU1ejeDMyunXom8MUO3f438EZKEmwt4MKIOBL4P8qy6esCr2Y8SfUNYN9ucS6AWcD+wP4RcRFwOqXOzU3AfcBKlKl9+zCxNtSnaO99lETOtpTv1xeBt1dJo/MoCZwlKNPoNqHczx2rtucM60Nl5o8iYmfK9wBKHZ4LI+Iqymim5YGnM/4b2Ac4udZFP0vdN3F49QLYqYrlq5QaYYsBG1Om4z2zajOZz3yY9qL8DjaijLI6LiI+CJwI/Jkyqm0WJSm7GWWE3taURFSnROKRlGTxVtX+gcCOEfEt4MrqOs+lfG8fR/ndrMtjv+C5JEmSJM04MyKxBJCZl0fEVsDRwMurw7OAl1WvXn5bnduu789UI3Y+Uh1aHfh4l77OBF5Sjb5o19+FEfER4LPVoZW79DcHeCdTk2TYrHr1ckBmntzujcx8KCJ2oYy0em11eAPG791UegtwD/BuShIPYMPqNea+qt1pLefeOeRYvkJZDfDF1f5TgflG7FQOBX7NNEgsZebtEbEtcAKwQ3W46feoU5+PRsRLKVNcN64Ob8V4oqnuOspUuHa1zCRJkiRJk2zaT4Wry8x/Z+YrKH+AnkDv5MCdwLeB7TJzl8y8rEvfH6WMNDmvS383Au8BnpeZXa+dmYdSRi3d0KHJrcDBwE4dptMNywmUBMdVPdolpVjzczLzsK4NM+/LzNcBLwLOYLzuUqd+LwA+BuzWNOgmMnNeZr6XskrfMZRV6+6njKK5iJLAeVpmHgesVjv1/sy8d8ixPAq8gjLN6/4Oza4CdqtW4Js2MvMW4PmUUUYX9mj+CGWE0XspCdNufW4FfJX2o8ceodQ/2yIzrxsgbEmSJEnSEMyYEUt1mXkO8Jqqts9syvS4VSnTZu6mTMe6CLg8M7slPVr7PR3Ysqrtsi2wBmVa183AJcC5mZl99PfdiDgeeA5lWtiylFpRc4HfZ+bDtbY9i2xn5t70ubpZZv4FeBdARKxOmR62PmUK3KLAXVU852bmjX32fSpwalWTajvKqn0rUmpW3UGZ1nRxZt7WT7/9ysxzgXN7NKsvWX9Rl772ZsAV5Krn+eGIOJQy/W99yj2+EbgsM8+rtZ3D+Cirbn3u0KBNo75azjkWOLaP9vOA44HjI2JNYBvK72MFyvTOW4ErKM/77oZ93k2ZRvlRYGfKVMwE/gn8of59zMz1msYqSZIkSRqeGZlYGlONEjmHIdbuqfq9Frh2SH09RKlrdPow+lvAWG6iTAkcdr+30X51vMeSN9e2z5rMC2Xm7cBJk3mNUcrMGyg1lobV3x2UkXWSJEmSpMeYGZ1Y0sItIqLJCLKIeBPwwtqhYyctKE0rEbEfsB/AOuus06P16Kx3wCm9G0mSJEnSJDCxpJnsyIh4mDJF65zWaY/VlMb3A++oHf5JZl4yhTHqMSwzj6Yq6j979uzG01yn2txDdx11CNOGSThJkiRpuEwsaSZ7HLAHpYbUfRFxBXAbsDhlafr1W9pfQ1khTpIkSZIkNWBiSTNZfYTS0sAzurQ9DdgjM2+e3JAkSZIkSZo5TCxpJnsL8DPg+cAWwLqUVcoWBW4HrgfOBE7IzD+MKkhJkiSAzTfffNQhSJLUNxNLmrEy837K6mRDW6FMkiRpspx//vmjDkGSpL4tMuoAJEmSJEmSND2ZWJIkSZIkSdJATCxJkiRJkiRpICaWJEmSJEmSNBATSyMWEbMjYl5EZEQcPup4ppuIOLa6dxkRB486nlGIiDm1e7D3qOOZKk2efUTsUGvziSkOUZIkSZJmPFeFG6GICOAIIIC7gU+PNiJpZsnMORHxG2AX4AMR8c3MvHbUcUmS1M4WW2wxYd9V4iRJ04GJpdHaDdiq2j4qM28eZTDSDHUIJbE0C/gUsNdow5Ekqb0LLrhg1CFIktQ3p8KNSEQsAhxc7T4EOA1OmgSZeRZwVrW7e0Q8eZTxSJIkSdJMYmJpdF4JPLXaPjEzbxhlMJq+MnOHzIzqdeyo43mMOqL6d1HggFEGMmxzD9111CFIkiRJWoiZWBqd99S2vzGyKKSFw0+A26rt3SNitVEGI0mSJEkzhYmlEYiITYDtqt3rgTmji0aa+TLzQeDEandJYO/RRSNJkiRJM4eJpdHYs7b908zMbo1blkyfWzu+UUR8JiIuiIh/R8S8iLijzflLRcRLIuJLEfGHiLghIh6IiPsi4p8R8euI+FBErNwk+Ig4uBbPsbXjz4qIb0bE3yLi3oi4IyIujIhPR8QqTfqu9bVMRLw3Is6KiJurWK+KiP+JiF366atN39tGxFci4tKIuK26F/+MiNMj4n0RsWLDfubW7sMO1bFlI+Kt1X3+V0Q8FBHXR8RxEfGMDv3sEhE/ioh/VO1vjYg5EbFPtXJgrzjm1OLYu837e9fe7/d1cI9rR0S8NCKOiYjLWu7nqRHxtoiY1eR+1vqcrGf/k9r2nh1bSZIkSZIac1W40XhVbfvUQTqIiA8AnwEW79Fud+DrwHIdmqxdvXYB/jMi3pWZ3+4zlsWBQ4H3tXn7GdXrbRHx0sw8s0F/WwL/A6zf8tYG1es1EXEc8JY+41wR+CalvlWrsfvwPOCjEfG+zPxOn/0/jTIqZqOWt9YC9gBeFxGvz8wfVe2XAr4DvKal/UrA9tVrt4h4ZTXi5jGlSpQdDWzZ5u2x+/kC4MCI2CczT2vQ56Q8+8rvgAcpI5Y2jYiNMvOKAfqRJEmSJFVMLE2xiFgfeFK1Ow84Y4A+3g98vtp9CLgUuIuSwGitHbMBE5NKNwNzq/ZLVe+vUb23LPCtiFgiM7/eR0hHAW+utm8H/go8TClOvmp1fAXglIjYODOv7/LZNgN+U7UfcxdwGeV+bVy9tyclqfZAkwAjYlXgdGDT2uGHGb936wPrVMdXAo6NiFUz8wtN+qckUX5E+bwJ/IVyr9cEnlK1WRQ4LiL+AZxDSUK9uHrvRuBKym/ymZRnA/Ai4EvA2xrG0c71wK8btp0FPLdXo4jYiTICaNna4duBvwH3A08ANqyOP57y7HfLzJ936XNSnv2YzHwgIs6mJOygJL1MLEmSJEnSAjCxNPW2r21fkZl39nn+apTRQY8AnwAOz8y7x96MiA1a2idlqfXvAqdk5j9bO4yIZ1Z9jk0z+lJEnJqZ1zaI5yXAysANwLuBkzLz0arfRYA3AV+lJAIeB3wK2KddRxGxBPADxhMLD1JW8PpaZj5Qa/Mm4L+A1wK3NIgRykilelLpSOCgzLy1dv3nUAqpjy1H/7mIODczf9+g/y9T7sOPgPfX73M1CuenlMTf4pR7cColqXQN8FbgN2NTIiNiBeAYxke2vSUivpSZVzb8rBNk5m+B3/ZqV027+5/aofuB+RJBEfFESlJsLKn0Z+BDwOmZOa/WbhPgK8AOwBLAdyLi6Zn5jzZ9Tuazr/sT47/BHRhfLU6SJEmSNABrLE29LWrblw5w/ixKQnDvzPxkPakEkJlXt7Q/PDO3zcyvt0sqVedcSBkZM1aDZhbw9obxrEz5A3+bzDxhLKlU9TsvM48BPlpr/5qIWLpDX2+ljEoZs2dmHj6WWKj6fCgzvwa8mpI061m7KSJeAry0dugTmfmOelKp6vsMymida8ZOBb7epM4R5T58OzNf23qfM/NcylS4Mc+jJJduALbNzF/X62xl5h3A64GxZ7lIy/mT5RBgt7EwgDdm5gVt2n2TkiSEkrDaKjN/W08qAWTmX4CdKaOQoCSNPt7h2pPy7Nu4pLY9e4DzJUmSJEk1JpamXv2P59YkUFOnZOb3mzTMzHsbtpsHfLB26OV9xPOBzJzb5f2jKKNfAJamTPVqZ//a9kmZ+eNOHWbmryijsJp4R237YspIr079/puJSbUnU5IjvdwGvKtLv3OYOO1qCcrIphs6tH8I+Fbt0Hbt2g1LRLwO+M/aoU9k5glt2m1JGekDcCewR7f6T5n5CLAvZdohwJ4R8bg2TSfr2beq/+bWiYhlO7aUJEmSJPVkYmnqrVvb7lhrqIejhxFIq2q009j0oidVU7J6uRv4YY9+76FMlxrz1NY2EfFUxmsRQbMpSj3bVImDnWqHvlIfVdVOlbj4W+1Qu2LfrY6vPmc3f6pt3wnMl7hpcU5te757NiwR8SygXrD9BMropXbeUNs+LjNv7tV/Zl4H/KHaXQrYpuX6k/LsO2j9za3TtpUkSZIkqRFrLE29+vSd2wfso+fKau1ExDqUJMvTKbWaHsf834GxERxBqQl0R49uz6tG1/RSnx62Ypv3n1Xbvo8GRc0z8/yIuJnxAuHtzKYUzR5zSq9+K79gvNbSsxu0P7tBm/ropPOr0TxN2zdJ8vUtIh4P/IzxYuEXUKZZZodTnlPb7rnKW80lwPOr7c2ZuBriZD37dm5r2e/3fEmSJElSjYmlqbdMbfv+jq06uyMzW/847ioiNgIOB15ISRg1tXyDNjc27Ou+2na7Gksb1rYv7zWqqOZSYMcu79f7vS0z/9Ww33otng07thp3U4M29XvQb/tOdakGFhHLUIpzj60KeAPw8sy8r0P7AJ5WO/ThiNi/Xds26vewNZkzWc++ndbf3DJtW0mSJEmSGjGxNFr9JHnG3N27Se0CEdtRRocM8gf0kg3aNBmt1Krd566PYrq1zfud9GpbH+nTT7/1FceWi4hFeyQ8+r0Pg9y3oamSRN9jvN7VA8ArOhV4ryzPxNFfWw14+daE5WQ9+3YG+c1phlnvgKYDFyVNhn1mTdz3Nzlu3Q//YsK+90aSFk7Lz1p81CH0xcTS1LuX8T+sZ3Vr2MG83k2KiFgO+DHjSaV7KLV0fkOpIXQjcH99SlZEzGViHaipUk9i9ZN06Vg4egH7bW27JBNHEE13n2Zi7ag3ZeafOjWuDGt0T2ttt8l69u20/uYaFbfXzDH30F1HHYK00DvkkPMm7Pu7lCRpejOxNPVuZjyx1K7W0DC9CVi92r6Dsiz837q0B1huckPq6K4BY+jV9s4h9Ptwp+lh01FE7AkcWDv06czsWoC9cmfL/taZ2aS2VC+T9ezbaf3N9Sw+LkkaroMOOmjUIUiSpCFyVbipd21te+1JvtYute0v90oqVTV3JqVIdAP1mkPr93HeE3u8X08crBURTab3AWzQoY9pLSK2Bo6pHfoJ8J9Nzq1Wvasn2FYbUliT9ezbaf3NXTdAH5IkSZKkiomlqXdZbbtJUegFUV9K/dwG7bdidN+JC2vb60TEGh1bViLicYyv3NbJBbXtxSirxDVRXwnugo6tppFqVcCfMD717CJgry4rwLVTH6G09ZBCm6xn3079N3ddlSyTJEmSJA3IxNLUO7+2vekkX6vfil/7TEoUzfyJiTVzXtPgnFfT4zNm5pWU1c7GvL5XpxGxIlAv+PCHBrE8pkXEssDJjE+NvAl4WWb2W2Po1Nr2HhExjKpyk/LsO6j/5s7v2EqSJEmS1IiJpalXT1JsWCUxJks9obJdt4YRsS2w+yTG0lVm3gn8tHbogGpUSlsRMYuGU7goBcvHvDkiNurR/hPAUtX2Q8B3G17nMSkiFgG+Dzy9OvQg8MrMHGQa2DGMr0z4BOCQBY1vkp99qy1r278fsA9JkiRJUsXE0hTLzGuAK6rdAJ47iZebU9t+R0Rs1q5RRDyL8of9qL8PhwGPVttrAj+uVrabICKWBo4H1mvY71coxcuhTAP7RTUtbD4R8Q7gHbVDx2TmTe3aTiOfAV5W298vM/84SEeZeTsl8TbmwIg4rFftqohYJiL2iYj/7dBksp59/dylKNM9x/y63z4kSZIkSRO5KtxonMj4qlwvBH42Sdc5GvgwZYn1ZYGzIuJrwP9SVuJaG3gJ8DpgUcof2htTRqJMucy8MCK+CHywOrQzcEkV8/lAApsBb6UU176VUp9npx793lAljI6rDj0JuDQijqYk3+6mFI3eE3h+7dQrgQ8t+CcbudfVtu8FXh8RPacEVo7LzOPqBzLzCxGxJeNT1j4E7BURP6TUYPo3JUm5IvBUSr2qnSjfw7ZJusl69i22Z7y+1GWZ+dc+zpUkSZIktWFiaTS+x3hi6eUR8bY+Cyg3UiVU/qO63iLA0sD7qlerS4E9GH3dmQOAxzM+LW9d4LNt2t1PSQS9rs1788nM71cjYI6k3IvlgPdXr3YuB3YeoAbRY90ywAv6aH92h+Ovp0y1fHe1vybtv1f9mJRnX/PK2vZxHVtJkiRJkhob9dSnhVJmXg6cUe2uCTxvEq/1A8qopKs7NLkHOAJ4VmbeOllxNJWZ8ygJrncBt3Vodi6wTWae2uH9Tn1/DXgWcDplBEw7dwCfBLbMzOv76X9hkpmPZuZ7KCvD/RJ4pMcplwGHUkYNdepz0p59RCwBvKrafYiJdbckSZIkSQOKSRgoowYi4pXASdXu8Zk5qYWzI2JRypSkZwLLU/5wvw6Yk5n3Tea1B1WtOPY8YCPKaKt/Aedn5mVD6Ht1Sn2rtaq+bwH+BpyVmb2SJGpRrTq3HbAOsDIwD7iTktC8JDNv7LO/oT77iNgN+FG1+73MfEO/fcyePTvPO++8QS4vSZIkSdNeRJyfmbPnO25iaTSqlbouBjahjKBYLzNv6H6WpEFExBmUxNc8YNNBElQmliRJkiQtzDollpwKNyLVtJ+Dq90lgPeMLhpp5oqIrShJJSijAxd4xJskSZIkqTCxNFonMl4c+a0Rseoog5FmqIOrfx8APjrCOCRJkiRpxjGxNELVSnDvohSSXg7/6JWGKiK2Z3wVvM9n5twRhiNJkiRJM85iow5gYZeZ52KCT5oUmfl7IEYdhyRJkiTNVCY0JEmSJEmSNBATS5IkSZIkSRqIiSVJkiRJkiQNxMSSJEmSJEmSBmJiSZIkSZIkSQMxsSRJkiRJkqSBmFiSJEmSJEnSQEwsSZIkSZIkaSAmliRJkiRJkjQQE0uSJEmSJEkaiIklSZIkSZIkDcTEkiRJkiRJkgZiYkmSJEmSJEkDMbEkSZIkSZKkgZhYkiRJkiRJ0kBMLEmSJEmSJGkgJpYkSZIkSZI0EBNLkiRJkiRJGoiJJUmSJEmSJA3ExJIkSZIkSZIGYmJJkiRJkiRJAzGxJEmSJEmSpIGYWJIkSZIkSdJATCxJkiRJkiRpICaWJEmSJEmSNBATS5IkSZIkSRqIiSVJkiRJkiQNxMSSJEmSJEmSBmJiSZIkSZIkSQMxsSRJkiRJkqSBmFiSJEmSJEnSQEwsSZIkSZIkaSCLjToASVL/DjnkkAn7Bx100IgikSRJkrQwW+DEUkQsDbwa2BZ4KrAisDQQDbvIzNxgQeOQJEmSJEnS1FqgxFJEvB34NLDcoF0AuSAxSJJgvQNOGXUIC53lZy3ORQft0vH9iIn//0qm/3UnSZKkmWfgxFJEHAZ8gOYjkyRJk2TuobuOOoSFjsk8SZIkacDEUkRsC3yQ8dFG9wM/Bs4E/gncN5ToJEmSJEmS9Jg16Iilt9a2/wLsmpnXDSEeSZIkSZIkTROLDHjedrXt15pUkiRJkiRJWvgMmlhanTIN7vLMvGyI8UiSJEmSJGmaGDSxdHf1743DCkSSJEmSJEnTy6CJpbmU1eBWGl4okiRJkiRJmk4GLd59EjAbeFpErJiZtw8xJkmSpr1999131CFIkiRJk27QxNIxwPuAlYEDgQ8NLSJJkmaAo48+etQhSJIkSZNuoKlwmXkLsCcwD3hfROw/1KgkSZIkSZL0mDdojSUy8zfAzsDtwJERcVpEvCYiHh8Rg46EkiRJkiRJ0jTRMQEUEY/20U8AO1avsfObnpuZaSJKkiRNa+sdcApzD9111GFIkiRNqW4jlqL2b7cXQLac1+9L0mNERKwXETn2GnU8kiRJkqTHrl5T4ZokfUwS6TEpIo6tJ0javB6OiFsi4pKI+E41lXOJUcctSZIkSdJ00W0K2vpTFoU0GotRVjZcGXga8AbgHxHxpsw8baSRSZr2WqeEZzoAUJIkSTNPx8RSZl47lYFIk+wB4PctxxYH1gCewvjovScAv4qIl2fmL6cwPkmSJEmSph2LZmthcVNmvrDdGxGxOnAQ8Nbq0GLAtyNig8y8Z6oClCRJkiRpuulVY0ma8TLzpsx8G/CV2uHVgD1HFJIkSZIkSdOCI5akcYcAb2M84fp84GujC0eStLD717/+xTe+8Y2O7++7776stdZak3b9efPm8ec//5k5c+Zw9913/7/jyy23HDvuuCObbbYZiyxS/mszM7n++uv54x//yJVXXsnDDz/M4osvzpOe9CS22WYbVl55Zb71rW9x8803z3ed1VZbjTe96U0sueSSfcfY67prrbXWfDXPpoObbrqJr32t8/8M2X///Vl99dWnMKL2Brn/k/HMHn30UebMmcPZZ5/NI4888v+OL7bYYmy99dZsv/32LLrookP5zJPt3nvv5YgjjuDBBx+c770ll1ySd77znSyzzDIjiEzSZDn55JO54IILOr6/+eab89KXvnQKI+pPDFpMNCK+CKwAPAq8OzR2Y34AACAASURBVDPv6+Pc3YGdq91jMvOsgYKQuoiIY4E3VrvXZuZ6Dc75C7BxtXt+Zs7u0nYj4IXAc6tz1gaWBu4CbgDOAn7UtBB4RNR/jOtn5tyIWI5SVHx3YANgReAW+P/Zu/Mwueoq4ePfQ3cnLAlxSUACYhAhwY0tAkHREB0BecGAG26IaFwaxXmR0TijE+OMDs6rOINDZgRZRHEbFYZMRFwggEMAg4AbhKiAShwgLmE1pJPz/nGr6ErT3VV1u6qrO/l+nqeevnXr3N89dW+l0336t3AjcEFmLm2k7ZpzHAK8A5gL7AI8CNwFfAs4PzPvi4gZwJ3VYzKz7k92EbEjxbX+P8A+FD2+HqW4DtcAX8vMqxpo5yTggsrTqzNzbmX/Cyp5HwbsVnn9NuBiYElmPjagnacA7wGOBfYGJgD3AN8DPpmZd9Kk2bNn58qVK5s9rG0WL1682fNFixZ1KJOt14yFy7jrjKOHfN3Ju7c+9T4Tzbr22mu58sor68bNmzePww47rGXnrXrsscdYsmQJ69atGzJmypQp9Pb20tXVxaWXXsqqVavo6+vb7PMeEQ1//nt7e5k2bVrDOW7cuHHY83Z3dzNz5kzmz58/booKADfccAPf+c536sYdeeSRHHzwwaOQ0eDKXP923LNHH32UM888c7OC0kDd3d2cdtppbLfddiXf7ei4++67ufDCC+vGnXTSSTzjGc9of0KS2u6MM84YtJA80MSJE1m4cOEoZDS0iBj0d+RSQ+EiYg7w1xS/SE5upqhU8RPgpMrxHyyTg9Qmf6rZnjJUUESsBFYB/wq8iqKYsiNFL8CnAM8BFgDfi4grI2KnZhOJiAOBWyiG6L2QYqLxiRQFrOOAyyLiyxFRt+dhRHRFxFkUxa63Uqz6uC0wDXgB8E/AzyJiXok8XwusBs4CXk4xAfpEisLzPsA7gSsj4tvNXoeI2CYiPg7cALwdmAnsUHnMBj4DLI+ISTXH/BVwB0UPtAOByZV8nlnJ5WcRMeh8W5I0VqxZs6ahohLAlVdeyZo1a1p6/k2bNtUtKgGsW7eOs88+m29961usWrWKDRs2PKGI1ExRdcmSJQ39cF1tt1qgGOq8GzZs4Pbbb+fSSy8dN8Xde++9t6GiEsB3vvMd7r333jZnNLgy178d92zjxo11i0oAfX19nHnmmWzcuLH5NztKHn744YaKSgAXXnghDz/8cHsTktR2S5cubfj/vfXr17N0aVP9CkZN2TmWjq3ZvmDIqCFk5s8pflEM4OURMbb/dKCtyVNrth8cMgr2q9nuo+g980PgSorC6Yaa1w8HfljpfdSoZwNXURRDEvhF5fmtwKaauNcDn2ygvXOB91L8m6taBSyv5A5FkenbwL6NJhkR7wa+StFDqWoNRS+llUDtd8mjgGsjopkxG/8I/G0l7z8A/0NxnWvvzZxKDkTEi4GlFPexj6IwdxXw65r47YFvVnpmSdKYNNzwt1bE13PLLbfULSpVPfDAA48XClrh/PPPbyjunnvuaei8fX19rFq1quXFt3YZbvhbK+Jbpcz1b8c9W758ed2iUm27V189cJHgseOzn/1sW+MljT3DDX9rRfxoKVtYOrTy9THgByXbuLzydQJFjwmpoyqrw+1ds+unw4T/maKHzlxgh8x8dmYelpkvzcx9KXotvZdiWBzAXjRWAKq6iKKnzReB3TPzOZk5LzP3oyg2XVsT+76IeOYw7+tNFL2Uqq4DnpOZszLz8Mx8NsX7voqiZ8/nG0kwIg4CPkt/seouiqGBu2XmSzLzBcDOFO+7+ifHvYGLorGJE54PLKQY+ncCsHNmvigzD6MoZJ1VE3t0RPwfigLTRIpeXk/LzP0r121P4BVAdZW/7YGPNfI+JWlrtHz58qbiW9kL5L777msobsWKFU0VFFasWDGStDRAmevfjnt2/fXXN9Re1Vj+HDTaa6FsvCS1S9nC0kyKXxRvz8zG/nd4olsHtCd12mI2/zfxjWFin5GZ78vMqwfO7wOQmQ9l5r9RzCVW/Tfy1oh46sDYITwV+FRmnpiZvxvQ9t0Ucxn9b2VXF8U8TE8QEROBT9Xs+hHwssz8xYA2V1MUha4BpjaY45LKuaHopXRYZl6RNf3WM3NdZi4EPlBz3EspCkX1PBl4BJibmV/LzMd/a8nMv2Tm+4DaPzt+k2LeqI9m5nsz8w8D3uPlFEN4q14VEc58KUmDqJ2oe6xavXp1w8PbMpM77rijzRltXcpc/3bcs0YLVWXjJUn1lV0V7smVr38YNmp4tcc+ecgoqc0i4mnARynm36m6BvjvoY7JzIYGtWfmjRHxVeBNFHMavRz4SgOHrgY+NEy7D0TEucBHKrteOETocRS9hqAoBi/IzEeHaPOxiFgA/AzoGS65yiTgB9bset/AAtgAn6aYi+qQyvP30th1+Hhl6OxQPge8pLI9gWJY3z8ME/8linmZJlP0WtqfYmjdoCLiHRSThrP77rs3kK62NjMWLmtLrKT6mh16Z0Ghtcpc/2bnufKeSdL4ULaw9BdgEsUEumVtX7M9PmZT1Hi2c0QMnAmzm2JC7H3YvKfSTcBrstmffoZ2PUVhCeAgGiuonN9Ab8Dagsg+Q8QcV7N9TWbeOkQcAJl5R0RcQdEjaji17f6OorfQcO1mRPwr/YWlORGxc2bWm3H0vDqv3zjg+QWZuWnQyCKP9RFxK/Ciyq59GKawlJnnAOdAsSpcnVy0FRp2VbgBg19buVqYxiaLh6Orp6enqeJGd3fZH3s1mLLX33smSVuest+t11L8xf9ZIzh37fC3+0fQjtSIbYEj6sTcA3wCODczG/qpp7Ii2+EUK5TtTbEy3PZsPkn2rkNsD+e6BmJqewgN1evvoJrtxpaYKeY/q1dYql3b+PIGi3DfpigiV6/NwcBlw8T/OjPrTbTx+wHPG5loofaYJzUQL0lbncmTJ4/54XB77bUXt912W0O9YCKCvffeu26cGlfm+mdmy+9Zd3d3Uz2bLFZJUuuVnWOpOqnxUyLi8JJtvLpm+7Yho6TRM51iwui6P+1ERFdEvI+iGPVdioLUScDxFHMVHVHzeG7NoVMazOV/64fwSM329gNfrBS9ZtTs+lmD524krraoPNwk54/LzAeAu4doYzB110/OzEcG7GpkzeVhr5skCebOndtUfFdXV/2gBu200071g4A5c+Y0XCTo7u5mzpw5I0lLA5S5/u24Z4ccckjdmFpj+XMwceLEtsZLUruULSxdUbP9yYgYdj6WgSLiOOCwytM/8sThLFKr3Z2ZUX1QTDq9G8WcR0srMUExz9IXh2uoUrD5GvAvFKuTNaPRnwCeMCF4CQN74zQ6J1ojcbVtNzPX2tqa7Xpzq5W5Bs0e08jqdFIpBxxwwGYPqRkLFixoa3w9++23H1OmNPa3kB133JGZM2e2rCfIySef3FDcrrvu2tB5u7u7mTlzJtOnT29Fem33rne9q63xrVLm+rfjns2dO7epYtVLXvKS+oEd8t73vret8ZLGnmZ/RhyrP1OWLSxdDPypsn0g8I2I2LGRAyPiKPp/cU/gcy2cy0ZqSGZuysx7MvN7mXks8OGal0+oTNo8lNMoJqKuuhE4BXgBxUTZ2wPb1BSx3tri9Bs1sIjVaNGlkbVra9tupphTG+uf2bRFu+mmmzZ7SM2YPn068+bNayh23rx5LS+abLPNNvT29tYtLk2ZMoVTTjmF448/nlmzZtHT00PE5jX7gc+H09vb23AvjIhg/vz5w563p6eHWbNmMX/+/Kby6KSdd96ZI488sqHYI488kp133rl+YBuUuf7tuGddXV2cdtppDRWrTjvttJb2rmu1HXbYgZNOOqmh2JNOOokddnBxW2m8O+aYYxr+f2/ixIkcc8wxbc6onFJ/WqqsSLWYosdGUszH8ouI+BfgW5n569r4iNiOYrLcd1JM+huV4+4BBkxvKo2+zPx4RLwQOKqy658j4huZ+cfauIjoAj5Qs2sJ8J46xdHJrc22YQ8MeN5oHo3ErQOe2mS7A2P/3MRxkrTVOeyww9hzzz0599xzh4xZtGhR284/YcIETj31VG699VauuuqqzeZc2nHHHTn88MPZb7/9Ht93/PHHs2bNGq677jpWr15NX18f3d3d7L333syZM4epU6dy/vnnc999T5w+b+eddy7V86arq6vueXfdtdHpDceOgw8+mBkzZvAf//EfQ8a08943qsz1b8c922677Vi4cCFXX301K1as2GzOpe7ubg499FAOP7zs7B2j6xnPeAann346n/3sZ1m//ol/69t222354Ac/2IHMJLXLwoULWbp0KT/+8Y+HjBkL3/OHEyPpLBQRFwBvoX9C3mpj6ygm5F5PMWTmaRRDj6B/6MkDwGGZ2dD8LFKzIuJCis8nFEPhZtSJfxbFfF/VguunMvNvBsS8gP6hm48A0waZ52dgu5+kvxh1dWbOHSKu9h/jHpl5V512ZwB3Vp9XekcNjHkE2K7y9OTMvGC4NivHvAr4Rp12bwNmVZ7+Y2Z+pIF2g+Lf/aTKrndk5rkDYk4CqjkOea0GHNPsdbuQ/s/F4sz8aL1zQLEq3MqVKxsJHRWLFy/e7PlY/89mSzRj4TJXetNm/ExIkqQtWUTclJmzB+4vOxSu6mTgY2w+2XFQFJOeBTyHYh6b7prXAH4BHGpRSWNJZv4SuLBm1ykRMbB/+e4127+oV1SqOHSkuY3AzTXbBw0ZtblG4mrL6Y3Omvkc+otKA9uQJEmSJI1DIyosZeGjwH7Al4BHa14ONp8YN4CfA+8CDszMX4zk3FKb/BNQ7T+9HZsPewNodqL6vYAXtiCvsq6p2T4uIiYMF1wZ6vfaJtudGxG7NHDMm2q21wG3NnCMJEmSJGkMG2mPJQAy82eZeSJFT6U5FD2ZTgf+jmJS41cBu2bm8zLznMxsZHJgadRV5gf7cs2ud0VE7cpvv6/Zfm5EDDmraWXo11l0duWxL9Rs7wycWid+ATCjgXa/SjEUEIoeiZ8YLjgidgfeU5tXZvYNFS9JkiRJGh9asy5sReUXxRsqD2m8+gRF75ptKFZ4+5vKA4r5lR6l6M20LfCvEXFyZm6qbaDSM+jfgMaWdWmTzLw9Ii6hmDQf4BMRsSozlw6MjYiXAZ9psN11EfFv9PfoOqnS7hmDtLsL8N9AdemShxs9jzSeHXjggZs9d2U4SZIkbYlaWliStgSZuSoi/hN4XWXXuyPinzPz/sx8NCI+B/x15bW3ALMi4vPAHRQFp/2At1PMM9YHfBF466i+ic2dCswFnkwxlO+/Ku/vWxQ9sHYCXgm8gaKY9lXghAbaXQS8Anhu5fk/RcTRwEXAaoqiXHU1yKfUHHdavQm2pS3BcCt7SJIkSVsKC0vS4D5OMddQUPS0OR2oru36YeDFwAGV5wdXHgNtBN5HMWSsY4WlzPxdRBwFfA+YTPGeXsvgcyl9D/gIDRSWMvMvEfFS4AqKYhoUhaQXDXPY6Zl5ThPpS5IkSZLGsJbMsSRtaSorFl5as+uUiJhaee1h4CXAefRP9D3QLcDLMnNJWxNtUGbeAOwPfHeIkAcohgAexdDvabB276OYV+3vgD8ME/pDipUgP91o25I03tx1xtGdTkGSJGnUtazHUmVZ9oOAXYEpwMRGj83Mj7UqD6kqM08CThrB8ccP89pDwNsj4u+Aw4HdgU0UQ8turl31MDMvBC5s4HxNTfJdGU7W8DGZ+SvgiIjYg6LH1S7Ag8DdwJWZWZ2Mu9l2/0Ixd9MnKXpuPRuYCqynuB7XZOaaBtu6kAau1YBjmr1uJzGCz4UkSZIkqd+IC0sR8WqKYUIvGEEzFpY0LmXmvRRzEo0bmXkncGcb2t0IXFd5SJIkSZK2AqULSxHRRTFJb3Uulnq9BnKIuBwYKEmSJEmSpLFvJD2WzgReX/P8NxRLsR8KTKcoGF1EMVnwbsC+FMPjqoWkbwNrR3B+SZIkSZIkdVCpwlJEzAROqTzdRLHS079UXrucorBEZr615pjtgDcCiynmdtkXeHVm3lg6e0mSJEmSJHVM2VXhTq4cm8BZ1aLScDLz0cz8PPBc4EcUvZiWRcSuJXOQJEmSJElSB5UtLL248jWBTzVzYGb+CTgWWAc8BRgTy7FLkiRJkiSpOWULSzMoikq/Gm4Z8YjoGWx/ZSWtz1NM5H1UROxUMg9JkiRJkiR1SNnC0lMqX+8Z5LX1NdvbD9PGNZWvXcCLSuYhSZIkSZKkDilbWNpQ+ZqDvPZAzfZw8yf9sWZ7esk8JEmSJEmS1CFlC0v3Vb4+aZDXflOzve8wbexSs71DyTwkSZIkSZLUId0lj7udYp6lvQZ57Zaa7fnAV4Zo41U12/cNESNJasCMhcs6ncJWZ8p2g04j+LjMwTr1SpIkSVuWsoWl64EjgR0i4tmZ+Yua164AHgW2BY6PiFdl5jdrD46ItwKvq9n1PyXzkCQBd51xdKdTkCRJkrQVKltY+h7w0cr2McDjhaXMfDAiLgB6KYbafT0irgZ+VAl5EXBINRy4OjPvKJmHJG2VFi1a1OkUJEmSJIko21U/In5LMTn3rzPzWQNe2xG4EdibwSf4BgiKCbwPzsxflUpCkkbJ7Nmzc+XKlZ1OQ5IkSZI6IiJuyszZA/eX7bEE8DJgaqXxiZm5vvpCZj4QES8BLqAYMjeYm4E3WlSSJEmSJEkan0oXljJzFbBqmNfvBV4REc8HXg7sDvQAvweWZ+Y1Zc8tSZIkSZKkzhtJj6WGZOZPgJ+0+zySJEmSJEkaXdt0OgFJkiRJkiSNTxaWJEmSJEmSVErLh8JVVoR7JvBkYCLwZ+B+J+mWJEmSJEnasrSksBQRuwHvAo4DZgIxSMwDwA+Bc4GlmZmtOLckSZIkSZI6Y0RD4SJim4j4MPBL4EPArEqbtYWlqDymAK8ALgFuiojnjuTckiRJkiRJ6qzShaWI6AK+ASwGJlR314bwxJ5L1X37ATdExGFlzy9JkiRJkqTOGkmPpU8C82uePwR8DjgGmAFMoig4PRU4CHgvcEMlNoHtgKWVYXSSJEmSJEkaZ0oVliJib+BUigIRwGXAXpn57sxclpm/ycxHMrMvM/+UmSsz8+zMnAO8Bniwcuxk4OMteB+SJEmSJEkaZWV7LJ1I/8Tf3waOy8z7GjkwM79JMdfSJophca+NiB1K5iFJkiRJkqQOKVtYennlawLvaXaFt8y8Drio8nQCMLdkHpIkSZIkSeqQsoWl3SmKSr/IzLtLtrFsQHuSJEmSJEkaR8oWlp5U+Xr/CM5de+yUEbQjSZIkSZKkDihbWPpT5esuIzh37bF/HkE7kiRJkiRJ6oCyhaW7KCbenhkRe5Vs45UD2pMkSZIkSdI4UrawdEXN9r9HRFczB0fEPOCEytO/AMtL5iFJkiRJkqQOKVtY+gLwWGX7cGBZROzayIER8Wbgvyh6PCXw5cz8S8k8JEmSJEmS1CHdZQ7KzDsj4lPA31IUh/4KWBURl1Cs9vZT4A8UxafJwDOBQ4DXA8+hKCpBMVfTh0fyBiRJkiRJktQZpQpLFR+hKBidQFFc2h54Q+XRiIeAV2TmvSPIQZIkSZIkSR1SdigcmZnAG4HTgfWV3VHzdbjH9cDszLyx7PklSZIkSZLUWaULS1AUlzLzTGB3imFxNwIbhghfA3wVeHlmHpqZd4zk3JIkSZIkSeqskQyFe1xmrgXOAM6IiAnA04EnAROBdcD9mXlfK84lSZIkSZKksaFUYSkizqx5+pnM/G31SWY+BvxqpIlJkiRJkiRpbCvbY+mvKSbsXgu8v3XpSJIkSZIkabwoO8fSQ5WvqyuTeEuSJEmSJGkrU7aw9PvK102tSkSSJEmSJEnjS9nC0o+BAPZqYS6SJEmSJEkaR8oWlr5U+bpTRPxVq5KRJEmSJEnS+FGqsJSZy4DvUvRa+reImNbSrCRJkiRJkjTmle2xBPBmYCXFcLgfRcRxETGS9iRJkiRJkjSOdJc5KCL+vrL5fWAmsDvwDeD+iLge+BXwIA1O7p2ZHyuThyRJkiRJkjqnVGEJ+CiQNc+TYljcTsAxJdqzsCRJkiRJkjTOlC0sQVFIamb/ULJ+iCRJkiRJksaasoWlL7Q0C0mSJEmSJI07pQpLmfnWViciSZIkSZKk8cVV3CRJkiRJklSKhSVJkiRJkiSVYmFJkiRJkiRJpYxkVbhBRcRuwF7Ak4CJwAPAfcBPM3N9q88nSZIkSZKkzmhJYSking+8EzgO2HmIsL6IWAlcAFycmY+24tySJEmSJEnqjBENhYuIyRHxOeDHwLuApwFReTDgaw9wCPA54LaIOHIk55YkSZIkSVJnlS4sRcRUYDnw9ko7MSCkD3h4wP7q9u7Af0fEO8qeX5IkSZIkSZ01kh5LXwX2r3m+BvhH4IXAlMycmJmTgS7gWcAbgKWV2Kyce0lEvGQEOUiSJEmSJKlDSs2xFBGvAeZRFIigGN72/sx8ZGBsZibw68rjqxExB/hPYBeK4tLZwHPL5CFJW6vFixdv9nzRokUdykSSJEnS1qxsj6U312xfkJnvHqyoNJjMXEFRlKpO3r1PRBxQMg9JkiRJkiR1SNlV4apD4PqADzZ7cGbeERHnAu+rae/HJXORpK3ejIXLOp3CuDBlux5uXfTylrUXsfn0gkUnXUmSJGnrUbawNJViGNxPM/MPJdv4Af2Fpakl25AkAXedcXSnUxgXLMBJkiRJrVV2KNz9la8PjODctcfeP2SUJEmSJEmSxqSyhaVVQADPHMG596zZvmME7UiSJEmSJKkDyhaWLq58fXpEzC3ZxomVr7/JzB+WbEOSJEmSJEkdUraw9GXgZopeS5+PiOnNHBwRHwBeQjFP08KSOUiSJEmSJKmDShWWMvMx4HjglxTD4X4UEW+IiGHbi4jdI+Ii4J8oikqLM/NrZXKQJEmSJElSZ5VaFS4iqsPYzgL+HtgF+CLwmYj4AfAz4A/AY8BkYA/gIOBgil5OAF8H7qppa1CZeVGZHCVJkiRJktRepQpLwIUUPY6qkqJgNA14XeUxmKiJf23lMZwELCxJksakBQsWdDoFSZIkqaPKFpagv0jU6P5mYyRJGtPOOeecTqcgSZIkdVTZwtI1bN5jSZIkSZIkSVuZUoWlzJzb4jwkSZIkSZI0zpRaFU6SJKlTZixc1ukUJEmSVGFhSZIkSZIkSaVYWFLLRMSFEZElH0/qdP6SJEmSJKk5I1kVblAR0QXsDjwJmAg8ANyXmWtbfS5JkjopYvNFTjNd10KSJElbl5YUliJiGnAScDywL0VBaWDMPcC1wIWZ+b1WnFdj2l+Aq5uI39CuRCRJkiRJUnuMqLBU6Z30AeDDwLbV3UOE7wacAJwQET8C3pGZPxnJ+TWm3ZuZR3Y6CUmSJEmS1D6l51iKiG2BpcA/AttVd9c7rPI4CLg+Io4ue35JkiRJkiR11kh6LF0AHAlUJ5R4FPgWcDnwM+APwHpgMvBMimLSCcDzK8dsC3wzIg6y55IkSWqlzOSee+5hxYoVrF69mg0bNtDV1cX222/PI488wsaNG+np6WGvvfbi0EMPZfr06UTEoMcNFjfUOYaKLWvt2rWcffbZQ75+yimnMHXq1BGdYzitfI/r16/nvPPO4/7773/CazvttBMnn3wyEyc+YTaFUc1xLFmzZg3nnnvukK8vWLCA6dOnj2JGrdOOe7YlfQ4eeughzjrrLDZseOJMET09PZx66qlMmjSpA5m136ZNm7jllltYvnw5Dz744OP7J0+ezOGHH86+++7LNtu4/pS2PJdffjk33njjkK8fdNBBHHXUUaOYUXOizESjEfFy4DsUBaIAlgHvzMw1DRx7AvA5YFLl2BWZ+cKmk9CYExEXAm+pPL07M2c0efxc4KqBx0fE3hRzeB1JMaRyKvBAZg66klxEHAi8DphXiX8K8Cfg1xSf289n5j1N5BXAa4ATgf0q578fWAV8EfhqZq6PiJMoCq4AV2fm3EHamgHcWX2emXV/whnqujSQ8/8BXgkcCjwN2B5YS1H4vQy4IDMfrdPOSQzyniLi2cA7gZcBT6f4t/xb4HvAZzLzrno5DjjPFOCNwBHA8yiu8bYU920VcB1wWWZeN+CYNZX3BfDKzLyswfPtCaymv5flQZn5o+GOmT17dq5cubLh99Ruixcv3uz5okWLOpTJ+DJj4TLuOqN1nWWdvLsz6t3HjRs3cumll7Jq1Sr6+vqGvS8RQXd3NzNnzuSYY45h6dKlgx5XGzd//nyAIc8xMLarq6vU+7z55pu57LL639aOPfZY9t9//1LnGM5w17HZ93j//fezZMmSuufs7e1l2rRpHclxLLn22mu58sor68bNmzePww47bBQyap123LMt6XNw5513ctFFF9WNO/HEE9ljjz1GIaPR89hjj7FkyRLWrVs3ZMyUKVPo7e1lwoQJo5iZ1F6f+tSnePjhh+vG7bDDDpx++umjkNHQIuKmzJw9cH/Zcu/JNdv/TfELXd2iEkBmfpWiQLCxsuuQiJhVMg9t4SLidIpCyIeA/YFpDDHkMiKeGhFfBVYCfwMcCOwM9AA7AYcAHwXuiIj/2+D5pwE/AL4GHA3sSjE5/W7AS4ELgesqBaMxISL2A26gKB69DdgHeDJF3rtSFG/OprgOLyvR/t8AtwKnAs+m6JU4qXKeU4HbIuLVTbR3KkWx7WzgWGCPSpvV+3YY8EHgfyLiX6rHZeY6ivtS9fYm3sbb6P8c3VqvqCRp/MjMx3/B3LBhQ91iX2ayYcMGbrvtNpYsWTLkcdW422+/nUsuuYRLLrmkodhLL720VMFx7dq1DRWVAC677DLWrm3t4rv1rmMz73H9+vUNFZUAlixZwvr160c9x7FkzZo1DRWVAK688krWrGnoR/AxoR33bEv6HDz00EMNFZUALrroIh566KE2ZzR6Nm3aVLeoBLBu3TqWLFnCpk2bRikzqb0uv/zyVm0UjQAAIABJREFUhopKAA8//DCXX355mzMqp2xh6ZDK1wROycym/mVn5gqg9rvmIUPFausVEe8H/h9FgeEx4MfAcuAO+odgVmOfQdGr5XU1u9dXjrmSohBSLWZuD5wZEWfUOf8U4LvA4TW7+4CbKVa8+11l3wEUPXUmN/P+2qFSKLoWeEHN7j8B11P0evplzf7dgGURcWwT7X8Y+GeKYbQPAzdS3JPf1YRtC3wlIob983lEbBMRFwD/SlH4qvpjpd2rgJ9Q3Puqgb3UPlez/YqI2KWB99BFf886gKHHGUgad+65557Hf8FsxsaNG1m3bl3d4/r6+rj99tsbOkdfXx+rVq0q9Yv/cMPfWhFfT6PXsZH3eN555zV17vPPP3/UcxxLhhv+1or4TmrHPduSPgdnnXVWW+PHsltuuaVuUalq3bp13HrrrW3OSBodww1/a0X8aClbWNqZ4hf7n2fmb0u2sWxAe1KtnYAzKAo5fw9MzcwDM/PwzJwJPN79LiImAJcAe1d2raXovfKkyjEvzcz9KIaD1f7J9IMRccwwOXyaYuhb1eeBXTLzgMycm5lPB/4KuAt4ViXPjomIZwLfpOg9BHAL8HKKazcnM+dl5l7AcymKQQATgC9ExNMbOMXzgI8BDwDvAJ6amQdX7snTKYbdVQfDdwNn1mnv7ymGOFb9mOJ6Tqu0Oy8z96Uo2L0C+E/6i4MAZOYNFEVDgC7grQ28j6OB6qQUjwIXN3CMpHFixYoV9PX1tfUcGzdubPgcfX19rFixoq35tEMz17HeexxsTqXh3HfffQ3FtTJHjY523LMt6XPQbEG82fixbPny5W2Nl9ReZQtLf6x8HUm/69pj/zSCdrRl2o6iOHFSZv5DZj5Y+2Jm/qrmaXWYHBQ9Zw7MzPMy8y8DjlmbmacAtZPRfCoGmcmx0tumdsjn2Zm5IDM3+8xn5veBucB9FPMCddJ5wI6V7e8Bh2Tm9wb2KMzMn1MUcL5b2fUkGiuKPYWiF9hLM/PczNxsrEJlfqO31ex6SUQMOvg/Ip4HfKRm1zLg0Mz8/iD5PpaZl2fma4H3DdJcba+lkwe7nwPUDpn7Rmb+uU68pHFk9erVY2qoS2Zyxx13dDqNpjVzHTv1HsdDjtpcO+6Zn4MtQ+1E3Y144IEH2pSJpDLKrgr3a2AXikl7y6o99ldDRmm8ekZENPK//LqhJuEGlmXmsL1JImI74L01u96Wmb+pc85/oFihcB+KXk61RZaqd9I/B8/vKOZsGlRm3h0RH6R/kutRFxEvoChwAawD3jiw8FMrM/siYgHF0Lge4E0R8f7MrPe/9BmZOdwM1t8AfgPsTnH9DqVmsvIaf0N/Yfv3wJuGy7cm78EmE7iYYsjkDsCeFEMXB52cojJU7hU1u4YdPxAR76DoncXuu+9eLz2NEzMWLqsfNAbbVmPG4l/w292Dqh2avY6deI/jIUdtrh33zM+BJHVe2cLS14AXAntGxL6ZWWaQ62srX++nf1iOVOucBmJeATy1sv3zzBxYIHqCzMyI+DJFgQmK1eMGHndczfZ59VZPoyhufJqiV08nnFiz/aXMrDvuIDN/ExHXUExCvi1FEeg7dQ773HAvVq7t/1AUlqAo3m0mInqAV9Xs+teR9BrKzAci4iv090R6O0MUliiGylWXg1mVmdfWafscKp/D2bNnj50uEBqRlq4K98n2ta2hDVfA6+npGXPFpe7usj9udU6z17ET73E85KjNteOe+TmQpM4rOxTuC/T3QjgvIpqatDgiXk+x+lMCn8jMjXUO0fjzF+CKBh7fH6aNHzZwnto1dodra6Cf1mwfUPtCZSLwnWp21Su2kJkbKFaP65SWX4dB3JmZ/9tAm7UTeT95kNcPpJhAveqbDbRZT20R8viIeEKBrzJErnZ44+dbcF5JY8xee+1F/RGxoyci2HvvvesHjjHNXMdOvcfxkKM214575udgyzB5cnNr4Oy44471gySNmlIl+8x8MCJeRTGPy/4Uy4C/KzOvG+64iNgeWEgxJw7AhZm55SxnoFr3ZuaRIzj+z5n5x/phPK9m+6iImNVg+7WFh2kDXnvWgOc/a7DNnwGvaTC2ZSoFk+fW7PpgRLyrwcNr3+vA6zBQI0UlgEdqtrcf5PXaXkzrMvOXg8Q0JTN/FBE3U3w/mgi8mWK1uVqHUwyVg2KluS+M9LySxp45c+awevXqtvZa6urqIiIaGlLT3d3NnDlz2pZLuzRzHeu9x2nTpjU1gfdOO+1UP6jFOWp0tOOebUmfg2Z7X/X09LQxm9E1d+5cli5d2lS8pLGjVGEpIl5c2fxr4DMUv9ReGxG3UvTu+BnwB4pf3iYDewAHUazGVC1Hf5ViNaoXM4zMvKZMjhr3Gp3B76k123vTvzJcM6YMeF7by+axIeb1GcwfSpy7FabQP7wL4JARtDOcx0q0OdifEGuLes0tFTS8zwH/Udl+G08sLNVOLH5ZI8MFpXoOOKBeRz+Ntl133ZWZM2dy++23NzWXSldXF5MmTeLhhx8e9rju7m5mzpwJwKpVqxqKnT59+pAxQznllFM4++yzm4pvpUavYyPv8W1vextnnHFGw+c++eST6we1OMexZMGCBZx77rBTAD4hfrxoxz3bkj4Hp556Kp/+9Kebit9S7LffflxzzTWsW7eubuyUKVPYd999RyErqf0OOuggbrzxxqbix6KyQ+GWA1cBF1H8Yp8Uv0DuB3wQ+CLwbYohOZdQLDt+AsWKVdVfNF9XaWO4x1DzpGjLt6l+CFBM2DxSA/8dTKzZbqaYUnfy6TZpxTWA8t8PmlV7fVt5zb4MVIuAz4uIg6svRMSTgeNrYhv/iV0axk033bTZQ50XEcyfP59Zs2bR09NTd4hMRNDT08M+++xDb2/vkMdV42bNmsVxxx3Hcccd11Ds/PnzSw3Nmzp1Kscee2xDscceeyxTp7Z2YdJ617GZ9zhx4kR6e3sbOm9vby8TJ06sH9jiHMeS6dOnM2/evIZi582bN6YLJQO1455tSZ+DSZMmceKJJ9YPBE488UQmTZrU5oxGzzbbbENvby9Tpgz/d84pU6bQ29vLNtuM1o+tUnsdddRR7LBDY7/O7bDDDhx11FFtzqickc5eFxRFJWq+Dvfdunby27H7XV3jSe2fNT6UmY3/SXRotSuj7RARkY2tY9vc4PDGddV5feCfduZk5vVtyqUVaifqHmpFwKZVhuh+mcoqbhSTeN9Q2X4TxQTlAHdRDOOVtIXq6uri+OOPZ82aNVx33XWsXr2avr4+urq62G677XjkkUfYtGkT3d3d7L333syZM4ddd90VYNDjBotrNraM/fffn6c//enD9lxatGjRiM4xnKGuY5n3OG3aNBYuXMj555/Pfffd94TXd955Z971rkZHcbcnx7HksMMOY8899xy251I77307teOebUmfgz322IP3v//9nHXWWYMOi5swYQIf+tCHBjly/JswYQKnnnoqt956K1dddRUPPtg/gGHHHXfk8MMPZ7/99utghlJ7nH766Vx++eXD9lwa69/zo7HflwccFHEXmxeJ2iYz9xiN82jkIuJC4C2Vp3dn5owmj59L0VOt4eMjYhn9y8d/JjNPa+acQ7R5EP0FCYBnZuadQ8XXHPdZ4D2Vp1dn5txBYqYD99TsmlCZ+Hu4dl8FfKPydNDrEhEP0z+f0Ssz87J6+TYiIk4CLqg8HfQ9DXLMR4Hqd74vZOZJA14/nv4JuzcCUzLz4RakS0QcAFS7jTwI7JKZD0fELUC1z/TfZ+Y/DNrAMGbPnp0rV65sRZotsXjx4s2ej/X/bMaKGQuXuXLbFsD7KEmSNPoi4qbMnD1wf9nJu2eMOCOpNa6nv7DUqtkYfwr00f/v4yD6V0EcTiMDXgfOHfUU4N46xzyvzutQXIdqv/k5QEsKS21S25uqC3gRxQqBI5aZP46IlcBsih5kJ0TET+gvKm0Ezm/FuSRJkiRJozenitQu36nZPjginj3SBjPzUaC2a8rr6h0TEXvSQGEpMx8E/lSz6/kNpDS/gZja6/DGiBizy4Rk5hrg5zW73tniU5xTs72AYkhc1eWZeQ+SJEmSpJboaGEpInaJiA90MgeNb5n5I+B/Kk8D+PcWFVVql6KfXxkeN5yPN9H2zTXbwxatIuK19Pe2Gc7n6e8N9XRg8TCxY8Fna7aPi4hWjmn5Cv3X4mCgdhbMz7fwPJIkSZK01Rv1wlJETIiI10bEt4HfAP802jloi/M3FEPXAF4M/HdE7DzcARHRFREvj4grImLmICFfBH5bDQe+GRHPGqKtv6OBXk01vlWz/ZaIeMkQ7R5Og6uXZeafgI/V7PpQRHwyIoZdWicidoiIt0bEaK/A+AXgFzXPv16Ze2lIEfHciHhDvYYz8yHg4ppd1Um7fw8sazZRaTgHHnjgZg9JkiRpazPSVeEaVunxcRLFL+DVlaBqV5WTSsnMFRHx18C/VXa9HLgzIr4OLAd+R7Gs/ZOAZwIHAkcC0yrxT1ihsDLh87uBpZXXdwNuiYhzKCYYfwDYk2Ky8hdTfI7/E3htAyl/Cfgw8DSKf4PfjYh/B74LPETR4+iVwKsr576IzXvdDHUdPhURL6jJ4QPAmyPiKxTzGt1HUUx+MrAPRW+elwHbUX+ep5bKzL9UemOtoJgLaXuK4t21FBOVrwIeAZ5K0WPrqEq+XwC+3MApPgcMXF7ogszsGyxYKuvHP/5xp1OQJEmSOqqthaWI2IXiF+K3ANVeIdViUvWXeQtLGrHMPDsi/kwx1GlbimLJW+hfpa5Mm8si4j1AdZ3nHYD/W3kMtBi4mwYKS5m5LiLeBlwK9AATgPdVHgOdQTGxdd3CUsUbKHrmVNvaBRjxSnntkJk/j4jDKHoRVdcAPqzyGGnbt0TEjfTPe5XAeSNtV5IkSZK0uZYPhasMdXtdRFxOMdTtE8AsikJSbc+Q6yh+QX9Gq3PQ1ikzL6YoYP4HT1x9baA1FKuDHU7RO2aoNpdQ9Oq5fYiQ3wBvyMym5jTKzG9T9Kwaqt27gddn5oeabHdjZv41xcpw36Z/iOBQfkFRvBp0OF67ZeatwHMohsT+cZjQxygKUM3MkfSTmu0rM/PXzWcoaSy664xWTssmSZKkkWhZj6WIOIRiqNtrgSnV3TUhSVFM+k/gm67MtOXJzJMoPgNlj1/OIMPSmmzjN8C7I+K9FL1VZgJTKXoGPUhRCPpFZq5uos0fAPtUhpntTzE8636KgtT/ZOamkrkur6xi9wLgAOApwFqKYtMPq+2WuS6ZeT1wdERMAl4E7F7JexOwDvgV8NPM/N867VwIXNjkuT8KfLSJ+HXA30bERyju2T4UwxSDYgW9VcCPMvPhRtuMiG2BV9XsamiuKkmSJElSc0ZUWIqI6fQPddu7ursmpDrkLYGnV5YZl9quMpfOdZVHq9r8EfCjVrVXaTOBGyuPlqtMZP2ddrTdapm5kWLOpRUtaO41FHNJQVGsu6QFbUqSJEmSBmi6sFRZZeo4ip4pL6V/OF1tQen3FBPsvptiUl4sKkkaDRGxDXB6za7PZ+ZjncpHkiRJkrZkDReWImIO/UPddqzurgl5mGIZ9S8BP8jMTRHx9hblKUl1RUQX8DHg+ZVdjwJndS4jSZIkSdqyDVtYiohd6R/qtld1d03IRuD7wBeBSzPzkXYkKUlDiYgTgBOAicCzKeaTqvp0Zv6+I4lJkiRJ0lagXo+lu3niam4AN1MUk76Smfe2IzFJatAs4JWD7L8W+MdRzkWSJEmStir1CkvbUEy8ncBvgYuBizPzF+1OTJJKWA+sBr5C0VtpfYfzkSRJkqQtWjOTdz9AsfT3n9uUizSuZeaFwIUdTmOrk5kfBT7a4TQkSZIkaau0Tf2Qx4fBPQf4JHB3RHwvIk6MiB3al5okSZIkSZLGsnqFpecBZwL30j/XUhcwD7gAuDciLo6IIytLfEuSJEmSJGkrMWwxKDN/npmnA08HjgG+CTxGf5Fpe4rVmJYB90TEmRFxYHtTliRJkiRJ0ljQ0BxLmbmRoni0LCKeDLwBeAswm/6hcjsD7wPeFxG3A19qtH1J0sjMWLis0ymMC1O262lpe5nZ0vYkSZKk8abpwk9m/gk4Gzg7Ip4NvBV4I/A0+otMsxiwzHdEPCUz/ziydCVJg7nrjKM7nYIkSZKkrVC04q+tlfmVjqToxXQsMLHyUrXxAPqA5cA3gG9l5toRn1iSRsns2bNz5cqVnU5DkiRJkjoiIm7KzNkD97dkwu3M3JSZ387M1wG7AO8BfkT/XExJ0TvqpcC/A2si4gcR8a6I2KkVOUiSJEmSJGl0tXwlt8z8c2YuycyDgecAnwL+t/JydahcNzCXYkjd71qdgyRJkiRJktqv5YWlWpl5W2Z+gGJVuVcAX6dYVQ76ezN1tTMHSZIkSZIktUdbC0tVlaFy38nMEygm+T4FuHE0zi1JkiRJkqT2GJXCUq3MXJeZ/56Zh1AMlfvn0c5BkiRJkiRJI9fdyZNn5m3AhzqZgyRJkiRJksoZ9R5LkiRJkiRJ2jJYWJIkSZIkSVIpFpYkSZIkSZJUioUlSZIkSZIklWJhSZIkSZIkSaVYWJIkSZIkSVIpFpYkSZIkSZJUioUlSZIkSZIklWJhSZIkSZIkSaVYWJIkSZIkSVIpFpYkSZIkSZJUioUlSZIkSZIklWJhSZIkSZIkSaVYWJIkSZIkSVIpFpYkSZIkSZJUioUlSZIkSZIklWJhSZIkSZIkSaVYWJIkSZIkSVIpFpYkSZIkSZJUioUlSZIkSZIklWJhSZIkSZIkSaVYWJIkSZIkSVIpFpYkSZIkSZJUioUlSZIkSZIklWJhSZIkSZIkSaVYWJIkSZIkSVIpFpYkSZIkSZJUioUlSZIkSZIklWJhSZIkSZIkSaVYWJIkSZIkSVIpFpYkSZIkSZJUioUlSZIkSZIklWJhSZIkSZIkSaVYWJIkSZIkSVIpFpYkSZIkSZJUioUlSZIkSZIklWJhSZIkSZIkSaVYWJIkSZIkSVIpFpYkSZIkSZJUioUlSZIkSZIkldLd6QQkSc1bvHjxZs8XLVrUoUwkSZIkbc3ssSRJkiRJkqRS7LEkSVuAGQuXdTqFcWHKdj3cuujlLWkrIjZ7npktaVeSJEkaTywsSdIW4K4zju50CuOCBThJkiSptRwKJ0mSJEmSpFIsLEmSJEmSJKkUC0uSJEmSJEkqxcKSJEmSJEmSSrGwJEmSJEmSpFJcFU6SpBIWLFjQ6RQkSZKkjrOwJElSCeecc06nU5AkSZI6zqFwkiRJkiRJKsXCkiRJkiRJkkqxsCRJkiRJkqRSLCxJkqRxZcbCZZ1OQZIkSRUWliSNWETMjYisPO7qdD6SJEmSpNFhYUkaQyLiwpoCTfUxr+Txl7YzV2lrFxGbPSRJkqStkYUlaez7eKcTkCRJkiRpMBaWpLHvkIg4ttNJSJIkSZI0kIUlaXz4hxjDY20yc3lmRuUxo9P5SJIkSZJGh4Ulaez6FZCV7ecDr+9gLpIkSZIkPUF3pxOQNKSfATcBr608XxwRX8/Mvg7mJEnjQmZyzz33sGLFClavXs2GDRvo6upi++2355FHHmHjxo309PSw1157ceihhzJ9+nQiYtDjBosb6hxDxZa1du1azj777CFfP+WUU5g6deqIzjGcVr7H9evXc95553H//fc/4bWddtqJk08+mYkTJ3Y0x7FkzZo1nHvuuUO+vmDBAqZPnz6KGbVOO+7ZlvQ5eOihhzjrrLPYsGHDE17r6enh1FNPZdKkSR3IrP02bdrELbfcwvLly3nwwQcf3z958mQOP/xw9t13X7bZxr4R2vJcccUVXH/99UO+fsghh3DEEUeMYkbNicysHyVpVETEhcBbKk//C/gA8Augq7LvnZl5TqPHZ+b8IeK6gZcALwNeAMwEnkrRi/GPwGpgOXBuZv6ugbznAldVnt49cDhcRHyZ/h5X38rMV9Vrs+bYbwNHVZ5ekJknDxPbDbwGOAaYDewMTADuoyjSXQp8uUxxbvbs2bly5cpmD2ubxYsXb/Z80aJFHcpkfJmxcBl3nXF0S9oa+AuK/5+Onnr3cePGjVx66aWsWrWKvr6+Ye9NRNDd3c3MmTM55phjWLp06aDH1cbNn198ax3qHANju7q6nnDeRtx8881cdtlldeOOPfZY9t9//1LnGM5w17HZ93j//fezZMmSuufs7e1l2rRpHclxLLn22mu58sor68bNmzePww47bBQyap123LMt6XNw5513ctFFF9WNO/HEE9ljjz1GIaPR89hjj7FkyRLWrVs3ZMyUKVPo7e1lwoQJo5iZ1F5nnnnmZoXUoUyePJnTTjttFDIaWkTclJmzB+633CuNYZl5B/CFml0fiYhtR9JmpQj0e+D7wELgpcBuwHbARGAX4MXA3wO/ioi/Hcn5Ki6u2T46IqY0mOs04K9qdn1pmNh5wM+BahFrL2BHYFtgd+A4imv504jYr6nsJY0bmfn4L5gbNmyoW/DLTDZs2MBtt93GkiVLhjyuGnf77bdzySWXcMkllzQUe+mll5YqOq5du7ahohLAZZddxtq1a5s+x3DqXcdm3uP69esbKioBLFmyhPXr1496jmPJmjVrGioqAVx55ZWsWbOmzRm1Tjvu2Zb0OXjooYcaKioBXHTRRTz00ENtzmj0bNq0qW5RCWDdunUsWbKETZs2jVJmUntdccUVDRWVAB588EGuuOKKNmdUjoUlaexbDDxW2d4NePcI29sNqB038WeK3jxXAtcCv6l5bQLw8Yj4pxGe8wqgOvZhIvDqBo97Hf1Ddn9H0YvqCSLizZVz7F2z+17gOuBq4Lc1+2cBV0fEQQ3mIGkcueeeex7/BbMZGzduZN26dXWP6+vr4/bbb2/oHH19faxatarUL/7DDX9rRXw9jV7HRt7jeeed19S5zz///FHPcSwZbvhbK+I7qR33bEv6HJx11lltjR/LbrnllrpFpap169Zx6623tjkjaXQMN/ytFfGjxcKSNMZl5m+Az9Xs+lBEjHRg/U+B/wvslZlPzszZmfnSzHxxZj6DokBT28vogxFxSNmTVYaefa1m15saPLQ27iuZ+YQ/T0XEHOB8+gtQVwFzMvNpmfnCzJybmbsDhwLVn0J2BL4aETs28z4kjX0rVqygr6+9U9Ft3Lix4XP09fWxYsWKtubTDs1cx3rvcbA5lYZz3333NRTXyhw1Otpxz7akz0GzBfFm48ey5cuXtzVeUntZWJLGh48DD1e2p1EUhcq6JDOfn5n/kpm/HCwgM1dn5puA6p/CAhjpgN7aYWwvjojdhguOiD2Bg4c4vhrTBVxEf1HpC8DLMvMJpfzMXAG8iKKoBrAH8N6Gs5c0LqxevXpMDXXJTO64445Op9G0Zq5jp97jeMhRm2vHPfNzsGVodChQ1QMPPNCmTCSV4apw0jiQmfdGxFnAhyq73h8RZ2fmH0u09XD9qMf9HfAOinmKjo6Irszc2Ow5K+e9ISJ+CTyLoqj9euD/DXPIG2u2f5aZPxkk5lWV9gDupJjcfMhB95n5UES8A6j+ubI3Ij6RQ/xEWol9B8Duu+8+TKoaT2YsXDau2lVzxuJf8Nvdg6odmr2OnXiP4yFHba4d98zPgSR1noUlafz4Z4r5lZ4ETAE+WHm0TaUQ8zOKFda2B55L/3CyMi4GqsuXvYnGC0tDTdr95prtJZlZd8bXzLw+In4F7AlMp1gR7/YhYs8BzoFiVbh6bWt8aNmqcJ9sT7uqb7giXk9Pz5grLnV3j78ft5q9jp14j+MhR22uHffMz4EkdZ5D4aRxIjP/zOaFmPdExNNG0mZETIuIN0XE/4uIiyPisoj4Tu2DogBTtetIzsfmBaLnR8Rzh8jrBfRPxJ0UK70NjAnghTW7vt9EHj+t2T6gieMkjXF77bUXxbeHsSEi2HvvvesHjjHNXMdOvcfxkKM214575udgyzB58uSm4nfc0WkypbHEkr00vvwrcCqwM0UPoo8ApzTbSETsAnwaeA3NfR+Y0uy5amXmLyPiBvrnTnoj/cP7atX2Vro6M387SMxuwJNrnp8ZEY8NEjeY59VsT2vwGGkzBxxgTXIsmjNnDqtXr25rr6Wuri4ioqEhNd3d3cyZM6dtubRLM9e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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanlineprops = dict(linestyle='--', linewidth=4, color='black')\n", - "medianlineprops = dict(linestyle='-', linewidth=4, color='gray')\n", - "flierprops = dict(marker='o',markerfacecolor='gray', markersize=12,\n", - " linestyle='none', markeredgecolor='gray')\n", - "\n", - "plt.figure(figsize=(36,6))\n", - "random_functions_df_covered_lines_per_predictor.plot.box(\n", - " showmeans=True, meanprops=meanlineprops, meanline=True, \n", - " medianprops=medianlineprops,\n", - " vert=0,\n", - " flierprops=flierprops);\n", - "\n", - "#plt.rcParams.update({'font.size': 35})\n", - "plt.rcParams[\"figure.figsize\"] = (15,15)\n", - "plt.xlabel(\"Line Coverage (%)\")\n", - "plt.ylabel(\"Approach\")\n", - "plt.plot([], [], '--', linewidth=4, color='black', label='mean')\n", - "plt.plot([], [], '-', linewidth=4, color='gray', label='median')\n", - "plt.plot([], [], 'o', markersize=12, color='gray', label='fliers')\n", - "plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.11), ncol=3)\n", - "plt.savefig(\"fcts_dataset_LC.pdf\")" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "metadata": { - "id": "l9rD6G55_sWf" - }, - "outputs": [], - "source": [ - "#files.download(\"fcts_dataset_LC.pdf\") " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ObZCAQT-HMna" - }, - "source": [ - "AsIs vs others" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "m1TVBIBnAjH2", - "outputId": "8fdb840c-4c1c-480c-8619-7a17a23c6a45" + "id": "QOvO3nmmJmYV", + "outputId": "bc6cebd1-2bcc-4ff7-d2f4-1087b6ba11ed" }, "outputs": [ { "data": { + "text/html": [ + "
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covered_iidstotal_usesguided_usescovered_linescompletely_executedexecution_timeApproach
predictor
AsIs121812180154192.414686Pynguin Tests
CodeT5ValuePredictor_coarse_grained_deterministic969018731108942034342469.546773Fine-grained
CodeT5ValuePredictor_coarse_grained_randomized93511131767301987338371.841410Coarse-grained \\n(randomized)
CodeT5ValuePredictor_fine_grained95911685997812019340439.997213Coarse-grained \\n(deterministic)
FrequencyValuePredictor422942312976428765.285355Frequency
NaiveValuePredictor422942312976426745.088755Naive
PynguinTests0002331867.778833As Is
RandomPredictor440744093060477805.626200Random
\n", + "
" + ], "text/plain": [ - "WilcoxonResult(statistic=0.0, pvalue=8.088766244530643e-128)" + " covered_iids total_uses \\\n", + "predictor \n", + "AsIs 1218 1218 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 9690 18731 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 9351 11317 \n", + "CodeT5ValuePredictor_fine_grained 9591 16859 \n", + "FrequencyValuePredictor 4229 4231 \n", + "NaiveValuePredictor 4229 4231 \n", + "PynguinTests 0 0 \n", + "RandomPredictor 4407 4409 \n", + "\n", + " guided_uses covered_lines \\\n", + "predictor \n", + "AsIs 0 154 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 10894 2034 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 6730 1987 \n", + "CodeT5ValuePredictor_fine_grained 9781 2019 \n", + "FrequencyValuePredictor 2976 428 \n", + "NaiveValuePredictor 2976 426 \n", + "PynguinTests 0 233 \n", + "RandomPredictor 3060 477 \n", + "\n", + " completely_executed \\\n", + "predictor \n", + "AsIs 19 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 342 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 338 \n", + "CodeT5ValuePredictor_fine_grained 340 \n", + "FrequencyValuePredictor 76 \n", + "NaiveValuePredictor 74 \n", + "PynguinTests 18 \n", + "RandomPredictor 80 \n", + "\n", + " execution_time \\\n", + "predictor \n", + "AsIs 2.414686 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 469.546773 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 371.841410 \n", + "CodeT5ValuePredictor_fine_grained 439.997213 \n", + "FrequencyValuePredictor 5.285355 \n", + "NaiveValuePredictor 5.088755 \n", + "PynguinTests 67.778833 \n", + "RandomPredictor 5.626200 \n", + "\n", + " Approach \n", + "predictor \n", + "AsIs Pynguin Tests \n", + "CodeT5ValuePredictor_coarse_grained_deterministic Fine-grained \n", + "CodeT5ValuePredictor_coarse_grained_randomized Coarse-grained \\n(randomized) \n", + "CodeT5ValuePredictor_fine_grained Coarse-grained \\n(deterministic) \n", + "FrequencyValuePredictor Frequency \n", + "NaiveValuePredictor Naive \n", + "PynguinTests As Is \n", + "RandomPredictor Random " ] }, - "execution_count": 160, + "execution_count": 319, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import scipy.stats as stats\n", - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'AsIs']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_randomized']['covered_lines_percentage'])" + "random_functions_df_grouped = random_functions_df.groupby(by=\"predictor\")[[\"covered_iids\",\"total_uses\",\"guided_uses\",\"covered_lines\",\"completely_executed\", \"execution_time\"]].sum()\n", + "random_functions_df_grouped['Approach'] = ['Pynguin Tests', 'Fine-grained', 'Coarse-grained \\n(randomized)', 'Coarse-grained \\n(deterministic)', 'Frequency', 'Naive', 'As Is', 'Random']\n", + "random_functions_df_grouped" ] }, { "cell_type": "code", - "execution_count": 161, + "execution_count": 320, "metadata": { "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/", + "height": 942 }, - "id": "YZPDKmCwHYV_", - "outputId": "db508b56-fd94-4795-9fc2-db9c42e101f3" + "id": "jdC6tHCpcIAe", + "outputId": "b1960172-2730-4970-8e85-b2e9155592cc" }, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "WilcoxonResult(statistic=0.0, pvalue=7.62364924293234e-129)" + "
" ] }, - "execution_count": 161, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'AsIs']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_deterministic']['covered_lines_percentage'])" + "random_functions_df_grouped = random_functions_df_grouped.sort_values(by=['completely_executed'])\n", + "random_functions_df_grouped['completely_executed'] = [x * 100 for x in random_functions_df_grouped['completely_executed'] / 1000]\n", + "\n", + "# plt.figure(figsize=(5,10))\n", + "plt.rcParams.update({'font.size':35})\n", + "random_functions_df_grouped.plot.barh(x='Approach', y=['completely_executed'], color='gray', legend=None)\n", + "plt.xlabel(\"Fully executed functions (%)\")\n", + "plt.gcf().set_size_inches(16, 10)\n", + "plt.savefig(\"fcts_dataset_full_LC.pdf\", bbox_inches = \"tight\")" ] }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 321, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "avfCGoHVHYrm", - "outputId": "a80cc5f3-7552-4e3f-b79b-9a51bb8c365b" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "WilcoxonResult(statistic=0.0, pvalue=1.7221400712579933e-128)" - ] - }, - "execution_count": 162, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'AsIs']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_fine_grained']['covered_lines_percentage'])" - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4-w03GTxHY4W", - "outputId": "69dca6e4-13ad-43ef-dde7-47c0e38dee5f" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "WilcoxonResult(statistic=0.0, pvalue=1.6889615447682844e-36)" - ] - }, - "execution_count": 163, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'AsIs']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'NaiveValuePredictor']['covered_lines_percentage'])" - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "CQntvCjqHrgm", - "outputId": "55aa81f1-fe82-4a36-ff17-cb6ee3f0aa7b" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "WilcoxonResult(statistic=0.0, pvalue=2.8585034424633626e-41)" - ] - }, - "execution_count": 164, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'AsIs']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'RandomPredictor']['covered_lines_percentage'])" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "qNUPLY5-Hro9", - "outputId": "d96c1d7b-5c87-4caa-d0eb-bf40ac147b9f" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "WilcoxonResult(statistic=0.0, pvalue=7.738873702395389e-37)" - ] - }, - "execution_count": 165, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'AsIs']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'FrequencyValuePredictor']['covered_lines_percentage'])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tK_q6RSrIdPo" - }, - "source": [ - "Naive vs others" - ] - }, - { - "cell_type": "code", - "execution_count": 166, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Iu2OmeYBHrwm", - "outputId": "3360de0c-3170-442d-d772-298b59545ea3" + "id": "oBVZ_epoq2qe" }, "outputs": [ { "data": { + "text/html": [ + "
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CodeT5ValuePredictor_coarse_grained_randomized9351113176730198733.8371.841410Coarse-grained \\n(randomized)0.2826060.187137
CodeT5ValuePredictor_fine_grained9591168599781201934.0439.997213Coarse-grained \\n(deterministic)0.2871570.217928
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" + ], "text/plain": [ - "WilcoxonResult(statistic=1823.5, pvalue=1.2685438739457949e-105)" - ] - }, - "execution_count": 167, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'NaiveValuePredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_deterministic']['covered_lines_percentage'])" - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Dfu04Gr1Isdl", - "outputId": "1a6c422f-40b3-4011-f12d-d61f6227ad36" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "WilcoxonResult(statistic=2833.0, pvalue=2.7649511566320577e-104)" - ] - }, - "execution_count": 168, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'NaiveValuePredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_fine_grained']['covered_lines_percentage'])" - ] - }, - { - "cell_type": "code", - "execution_count": 169, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "edw-K6UTIslB", - "outputId": "1737063a-40fe-4120-ae25-e0320333e6c4" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "WilcoxonResult(statistic=3008.0, pvalue=0.02174572458598923)" - ] - }, - "execution_count": 169, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'NaiveValuePredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'RandomPredictor']['covered_lines_percentage'])" - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "GFHBwgnOIsyx", - "outputId": "d5c3b4b1-af73-42b5-c298-67ebe062a688" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.8/dist-packages/scipy/stats/morestats.py:3155: UserWarning: Sample size too small for normal approximation.\n", - " warnings.warn(\"Sample size too small for normal approximation.\")\n" - ] - }, - { - "data": { - "text/plain": [ - "WilcoxonResult(statistic=0.0, pvalue=0.04216819709715596)" + " covered_iids total_uses \\\n", + "predictor \n", + "PynguinTests 0 0 \n", + "AsIs 1218 1218 \n", + "NaiveValuePredictor 4229 4231 \n", + "FrequencyValuePredictor 4229 4231 \n", + "RandomPredictor 4407 4409 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 9351 11317 \n", + "CodeT5ValuePredictor_fine_grained 9591 16859 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 9690 18731 \n", + "\n", + " guided_uses covered_lines \\\n", + "predictor \n", + "PynguinTests 0 233 \n", + "AsIs 0 154 \n", + "NaiveValuePredictor 2976 426 \n", + "FrequencyValuePredictor 2976 428 \n", + "RandomPredictor 3060 477 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 6730 1987 \n", + "CodeT5ValuePredictor_fine_grained 9781 2019 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 10894 2034 \n", + "\n", + " completely_executed \\\n", + "predictor \n", + "PynguinTests 1.8 \n", + "AsIs 1.9 \n", + "NaiveValuePredictor 7.4 \n", + "FrequencyValuePredictor 7.6 \n", + "RandomPredictor 8.0 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 33.8 \n", + "CodeT5ValuePredictor_fine_grained 34.0 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 34.2 \n", + "\n", + " execution_time \\\n", + "predictor \n", + "PynguinTests 67.778833 \n", + "AsIs 2.414686 \n", + "NaiveValuePredictor 5.088755 \n", + "FrequencyValuePredictor 5.285355 \n", + "RandomPredictor 5.626200 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 371.841410 \n", + "CodeT5ValuePredictor_fine_grained 439.997213 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 469.546773 \n", + "\n", + " Approach \\\n", + "predictor \n", + "PynguinTests As Is \n", + "AsIs Pynguin Tests \n", + "NaiveValuePredictor Naive \n", + "FrequencyValuePredictor Frequency \n", + "RandomPredictor Random \n", + "CodeT5ValuePredictor_coarse_grained_randomized Coarse-grained \\n(randomized) \n", + "CodeT5ValuePredictor_fine_grained Coarse-grained \\n(deterministic) \n", + "CodeT5ValuePredictor_coarse_grained_deterministic Fine-grained \n", + "\n", + " covered_lines_percentage_ \\\n", + "predictor \n", + "PynguinTests 0.033139 \n", + "AsIs 0.021903 \n", + "NaiveValuePredictor 0.060589 \n", + "FrequencyValuePredictor 0.060873 \n", + "RandomPredictor 0.067842 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 0.282606 \n", + "CodeT5ValuePredictor_fine_grained 0.287157 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 0.289290 \n", + "\n", + " execution_time_percentage_ \n", + "predictor \n", + "PynguinTests 0.290896 \n", + "AsIs 0.015680 \n", + "NaiveValuePredictor 0.011945 \n", + "FrequencyValuePredictor 0.012349 \n", + "RandomPredictor 0.011795 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 0.187137 \n", + "CodeT5ValuePredictor_fine_grained 0.217928 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 0.230849 " ] }, - "execution_count": 170, + "execution_count": 322, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'NaiveValuePredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'FrequencyValuePredictor']['covered_lines_percentage'])" + "random_functions_df_grouped[\"covered_lines_percentage_\"] = random_functions_df_grouped[\"covered_lines\"] / [random_functions_total_lines_df['total_lines'].sum() for e in random_functions_df_grouped[\"covered_lines\"]]\n", + "random_functions_df_grouped[\"execution_time_percentage_\"] = random_functions_df_grouped[\"execution_time\"] / random_functions_df_grouped[\"covered_lines\"]\n", + "random_functions_df_grouped" ] }, { "cell_type": "markdown", "metadata": { - "id": "wDVsbeK7Jpj8" - }, - "source": [ - "Random vs others" - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "g_ecXYEnJyQy", - "outputId": "90310c45-f81c-4896-8d2b-889047abe27e" + "id": "WbimjtWs0sY5" }, - "outputs": [ - { - "data": { - "text/plain": [ - "WilcoxonResult(statistic=2899.0, pvalue=4.852444571098655e-102)" - ] - }, - "execution_count": 171, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'RandomPredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_fine_grained']['covered_lines_percentage'])" + "### Main results" ] }, { "cell_type": "code", - "execution_count": 172, + "execution_count": 323, "metadata": { "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/", + "height": 332 }, - "id": "_AO5KY0LJyfw", - "outputId": "3720aeed-8415-49f2-d608-7393bc2b293a" + "id": "lT52PchhLIhQ", + "outputId": "d85a033a-ad68-4fb7-ed11-b443706ed3cd" }, "outputs": [ { "data": { + "text/html": [ + "
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" + ], "text/plain": [ - "WilcoxonResult(statistic=3159.5, pvalue=6.876581923137283e-102)" + " covered_lines_percentage \\\n", + "predictor \n", + "AsIs 0.040729 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 0.500481 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 0.493197 \n", + "CodeT5ValuePredictor_fine_grained 0.498525 \n", + "FrequencyValuePredictor 0.129595 \n", + "NaiveValuePredictor 0.127589 \n", + "PynguinTests 0.040725 \n", + "RandomPredictor 0.137774 \n", + "\n", + " completely_executed \\\n", + "predictor \n", + "AsIs 0.019 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 0.342 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 0.338 \n", + "CodeT5ValuePredictor_fine_grained 0.340 \n", + "FrequencyValuePredictor 0.076 \n", + "NaiveValuePredictor 0.074 \n", + "PynguinTests 0.018 \n", + "RandomPredictor 0.080 \n", + "\n", + " line_execution_time \n", + "predictor \n", + "AsIs 0.001498 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 0.185076 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 0.167478 \n", + "CodeT5ValuePredictor_fine_grained 0.178694 \n", + "FrequencyValuePredictor 0.003613 \n", + "NaiveValuePredictor 0.003617 \n", + "PynguinTests 0.012964 \n", + "RandomPredictor 0.003940 " ] }, - "execution_count": 172, + "execution_count": 323, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'RandomPredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_deterministic']['covered_lines_percentage'])" + "random_functions_df_grouped = random_functions_df.groupby(by=\"predictor\")[[\"covered_lines_percentage\", \"completely_executed\", \"line_execution_time\"]].mean()\n", + "random_functions_df_grouped" ] }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 324, "metadata": { "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/", + "height": 468 }, - "id": "mc47Rg82Jyz0", - "outputId": "e29a599a-ba97-463e-b2e9-e9ce51af8318" + "id": "CsmEpQK1ZDrr", + "outputId": "15f910c2-a562-4a3f-88b3-515bc4484b75" }, "outputs": [ { "data": { + "text/html": [ + "
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" + ], "text/plain": [ - "WilcoxonResult(statistic=4684.5, pvalue=1.3696867492228425e-98)" + " As Is Pynguin Tests Naive Frequency Random \\\n", + "0 0.0 0.0 5.882353 5.882353 5.882353 \n", + "1 0.0 0.0 0.000000 0.000000 0.000000 \n", + "2 0.0 0.0 0.000000 0.000000 0.000000 \n", + "3 0.0 0.0 0.000000 0.000000 0.000000 \n", + "4 12.5 0.0 12.500000 12.500000 12.500000 \n", + ".. ... ... ... ... ... \n", + "995 0.0 0.0 0.000000 0.000000 0.000000 \n", + "996 0.0 0.0 0.000000 0.000000 0.000000 \n", + "997 0.0 0.0 0.000000 0.000000 0.000000 \n", + "998 0.0 0.0 0.000000 0.000000 0.000000 \n", + "999 0.0 0.0 0.000000 0.000000 0.000000 \n", + "\n", + " Coarse-grained \\n(randomized) Coarse-grained \\n(deterministic) \\\n", + "0 35.294118 29.411765 \n", + "1 50.000000 50.000000 \n", + "2 0.000000 0.000000 \n", + "3 100.000000 100.000000 \n", + "4 12.500000 12.500000 \n", + ".. ... ... \n", + "995 0.000000 0.000000 \n", + "996 0.000000 0.000000 \n", + "997 0.000000 0.000000 \n", + "998 0.000000 0.000000 \n", + "999 0.000000 0.000000 \n", + "\n", + " Fine-grained \n", + "0 29.411765 \n", + "1 50.000000 \n", + "2 0.000000 \n", + "3 100.000000 \n", + "4 12.500000 \n", + ".. ... \n", + "995 0.000000 \n", + "996 0.000000 \n", + "997 0.000000 \n", + "998 0.000000 \n", + "999 0.000000 \n", + "\n", + "[1000 rows x 8 columns]" ] }, - "execution_count": 173, + "execution_count": 324, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'RandomPredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_randomized']['covered_lines_percentage'])" + "random_functions_df_covered_lines_per_predictor = pd.DataFrame()\n", + "random_functions_df_covered_lines_per_predictor['As Is'] = [x * 100 for x in random_functions_df[random_functions_df['predictor'] == 'AsIs']['covered_lines_percentage'].tolist()]\n", + "random_functions_df_covered_lines_per_predictor['Pynguin Tests'] = [x * 100 for x in random_functions_df[random_functions_df['predictor'] == 'PynguinTests']['covered_lines_percentage'].tolist()]\n", + "random_functions_df_covered_lines_per_predictor['Naive'] = [x * 100 for x in random_functions_df[random_functions_df['predictor'] == 'NaiveValuePredictor']['covered_lines_percentage'].tolist()]\n", + "random_functions_df_covered_lines_per_predictor['Frequency'] = [x * 100 for x in random_functions_df[random_functions_df['predictor'] == 'FrequencyValuePredictor']['covered_lines_percentage'].tolist()]\n", + "random_functions_df_covered_lines_per_predictor['Random'] = [x * 100 for x in random_functions_df[random_functions_df['predictor'] == 'RandomPredictor']['covered_lines_percentage'].tolist()]\n", + "random_functions_df_covered_lines_per_predictor['Coarse-grained \\n(randomized)'] = [x * 100 for x in random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_randomized']['covered_lines_percentage'].tolist()]\n", + "random_functions_df_covered_lines_per_predictor['Coarse-grained \\n(deterministic)'] = [x * 100 for x in random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_deterministic']['covered_lines_percentage'].tolist()]\n", + "random_functions_df_covered_lines_per_predictor['Fine-grained'] = [x * 100 for x in random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_fine_grained']['covered_lines_percentage'].tolist()]\n", + "random_functions_df_covered_lines_per_predictor" ] }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 325, "metadata": { "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/", + "height": 1000 }, - "id": "5IsudBpOJy_o", - "outputId": "f22eb18e-09f8-46c2-ed40-9ce9cf62f7c8" + "id": "fDIoCk2M9TzG", + "outputId": "68af316b-f745-4209-91bc-facd68d2e108" }, "outputs": [ { "data": { "text/plain": [ - "WilcoxonResult(statistic=3116.0, pvalue=0.042545325392512726)" + "
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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'RandomPredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'FrequencyValuePredictor']['covered_lines_percentage'])" + "meanlineprops = dict(linestyle='--', linewidth=4, color='black')\n", + "medianlineprops = dict(linestyle='-', linewidth=4, color='gray')\n", + "flierprops = dict(marker='o',markerfacecolor='gray', markersize=12,\n", + " linestyle='none', markeredgecolor='gray')\n", + "\n", + "plt.figure(figsize=(36,6))\n", + "random_functions_df_covered_lines_per_predictor.plot.box(\n", + " showmeans=True, meanprops=meanlineprops, meanline=True, \n", + " medianprops=medianlineprops,\n", + " vert=0,\n", + " flierprops=flierprops);\n", + "\n", + "#plt.rcParams.update({'font.size': 35})\n", + "plt.rcParams[\"figure.figsize\"] = (15,15)\n", + "plt.xlabel(\"Line Coverage (%)\")\n", + "plt.ylabel(\"Approach\")\n", + "plt.plot([], [], '--', linewidth=4, color='black', label='mean')\n", + "plt.plot([], [], '-', linewidth=4, color='gray', label='median')\n", + "plt.plot([], [], 'o', markersize=12, color='gray', label='fliers')\n", + "plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.18), ncol=3)\n", + "plt.gcf().set_size_inches(16, 10)\n", + "plt.savefig(\"fcts_dataset_LC.pdf\", bbox_inches = \"tight\")" + ] + }, + { + "cell_type": "code", + "execution_count": 326, + "metadata": { + "id": "l9rD6G55_sWf" + }, + "outputs": [], + "source": [ + "#files.download(\"fcts_dataset_LC.pdf\") " ] }, { "cell_type": "markdown", "metadata": { - "id": "3azgc-krKNow" + "id": "ObZCAQT-HMna" }, "source": [ - "Frequency vs others" + "AsIs vs others" ] }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 327, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "7nGacezBKQgJ", - "outputId": "bcd1e75a-3705-4966-8ac8-fab8dc5107b0" - }, + "id": "m1TVBIBnAjH2", + "outputId": "8fdb840c-4c1c-480c-8619-7a17a23c6a45" + }, "outputs": [ { "data": { "text/plain": [ - "WilcoxonResult(statistic=1821.5, pvalue=2.8940249868780098e-105)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 175, + "execution_count": 327, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'FrequencyValuePredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_deterministic']['covered_lines_percentage'])" + "import scipy.stats as stats\n", + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'AsIs']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_randomized']['covered_lines_percentage'])" ] }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 328, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "pI8kal-gKRw7", - "outputId": "a29cb302-eefb-4700-b097-53d899d32997" + "id": "YZPDKmCwHYV_", + "outputId": "db508b56-fd94-4795-9fc2-db9c42e101f3" }, "outputs": [ { "data": { "text/plain": [ - "WilcoxonResult(statistic=2293.0, pvalue=1.8599746900478619e-103)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 176, + "execution_count": 328, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'FrequencyValuePredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_randomized']['covered_lines_percentage'])" + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'AsIs']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_deterministic']['covered_lines_percentage'])" ] }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 329, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "nsOx5PneKR89", - "outputId": "0b11643f-9227-4509-d374-d37634e6f8c1" + "id": "avfCGoHVHYrm", + "outputId": "a80cc5f3-7552-4e3f-b79b-9a51bb8c365b" }, "outputs": [ { "data": { "text/plain": [ - "WilcoxonResult(statistic=2832.0, pvalue=6.412207233021935e-104)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 177, + "execution_count": 329, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'FrequencyValuePredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_fine_grained']['covered_lines_percentage'])" + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'AsIs']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_fine_grained']['covered_lines_percentage'])" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 330, "metadata": { - "id": "YKqXppM7KlQh" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4-w03GTxHY4W", + "outputId": "69dca6e4-13ad-43ef-dde7-47c0e38dee5f" }, + "outputs": [ + { + "data": { + "text/plain": [ + "WilcoxonResult(statistic=nan, pvalue=nan)" + ] + }, + "execution_count": 330, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "CodeT5ValuePredictor_coarse_grained_deterministic vs others" + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'AsIs']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'NaiveValuePredictor']['covered_lines_percentage'])" ] }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 331, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "5CqMoBLzGEX2", - "outputId": "0424c042-e9c1-4527-e437-25c220fe0424" + "id": "CQntvCjqHrgm", + "outputId": "55aa81f1-fe82-4a36-ff17-cb6ee3f0aa7b" }, "outputs": [ { "data": { "text/plain": [ - "WilcoxonResult(statistic=628.5, pvalue=0.0037049569713849964)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 178, + "execution_count": 331, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_deterministic']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_randomized']['covered_lines_percentage'])" + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'AsIs']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'RandomPredictor']['covered_lines_percentage'])" ] }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 332, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "2WfYuHf_GU_m", - "outputId": "ce14e5fe-09f2-4ae0-c994-bfa43525afdd" + "id": "qNUPLY5-Hro9", + "outputId": "d96c1d7b-5c87-4caa-d0eb-bf40ac147b9f" }, "outputs": [ { "data": { "text/plain": [ - "WilcoxonResult(statistic=281.0, pvalue=0.19416837666185682)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 179, + "execution_count": 332, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_deterministic']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_fine_grained']['covered_lines_percentage'])" + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'AsIs']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'FrequencyValuePredictor']['covered_lines_percentage'])" ] }, { "cell_type": "markdown", "metadata": { - "id": "cq3-p2MSK3l5" + "id": "tK_q6RSrIdPo" }, "source": [ - "CodeT5ValuePredictor_coarse_grained_randomized vs others" + "Naive vs others" ] }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 333, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "v7twmdeQGcww", - "outputId": "20bec486-5991-4fce-b5c6-edb68db752f8" + "id": "Iu2OmeYBHrwm", + "outputId": "3360de0c-3170-442d-d772-298b59545ea3" }, "outputs": [ { "data": { "text/plain": [ - "WilcoxonResult(statistic=836.5, pvalue=0.011389453463694492)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 180, + "execution_count": 333, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_randomized']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_fine_grained']['covered_lines_percentage'])" + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'NaiveValuePredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_randomized']['covered_lines_percentage'])" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 334, "metadata": { - "id": "BZ3YIIj_GPUv" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VUs2-1kvIsUg", + "outputId": "402ac0a3-540f-4717-fd06-ec56cb54ab47" }, + "outputs": [ + { + "data": { + "text/plain": [ + "WilcoxonResult(statistic=nan, pvalue=nan)" + ] + }, + "execution_count": 334, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## SO snippets" + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'NaiveValuePredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_deterministic']['covered_lines_percentage'])" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 335, "metadata": { - "id": "PmDuDEz_GcKK" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Dfu04Gr1Isdl", + "outputId": "1a6c422f-40b3-4011-f12d-d61f6227ad36" }, + "outputs": [ + { + "data": { + "text/plain": [ + "WilcoxonResult(statistic=nan, pvalue=nan)" + ] + }, + "execution_count": 335, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "Load Evaluation Data" + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'NaiveValuePredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_fine_grained']['covered_lines_percentage'])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 336, "metadata": { - "id": "JsvBNWzqGfJ5" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "edw-K6UTIslB", + "outputId": "1737063a-40fe-4120-ae25-e0320333e6c4" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "WilcoxonResult(statistic=nan, pvalue=nan)" + ] + }, + "execution_count": 336, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "so_snippets_df = pd.read_csv('/metrics_so_snippets_dataset.csv')\n", - "so_snippets_df" + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'NaiveValuePredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'RandomPredictor']['covered_lines_percentage'])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 337, "metadata": { - "id": "pCIhLI5lNX5Q" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GFHBwgnOIsyx", + "outputId": "d5c3b4b1-af73-42b5-c298-67ebe062a688" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "WilcoxonResult(statistic=nan, pvalue=nan)" + ] + }, + "execution_count": 337, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "so_snippets_total_lines_df = pd.read_csv('/total_lines_so_snippets_dataset.csv')" + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'NaiveValuePredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'FrequencyValuePredictor']['covered_lines_percentage'])" ] }, { - "cell_type": "code", - "execution_count": 183, + "cell_type": "markdown", "metadata": { - "id": "SGfn03vx8txo" + "id": "wDVsbeK7Jpj8" }, - "outputs": [], "source": [ - "so_snippets_As_Is_df = so_snippets_df[so_snippets_df['predictor'] == 'AsIs']\n", - "files_with_results_As_Is = so_snippets_As_Is_df.file.unique()\n", - "files_without_results_As_Is = so_snippets_total_lines_df[~so_snippets_total_lines_df['file'].isin(files_with_results_As_Is)]\n", - "\n", - "so_snippets_Naive_df = so_snippets_df[so_snippets_df['predictor'] == 'NaiveValuePredictor']\n", - "files_with_results_Naive = so_snippets_Naive_df.file.unique()\n", - "files_without_results_Naive = so_snippets_total_lines_df[~so_snippets_total_lines_df['file'].isin(files_with_results_Naive)]\n", - "\n", - "so_snippets_Random_df = so_snippets_df[so_snippets_df['predictor'] == 'RandomPredictor']\n", - "files_with_results_Random = so_snippets_Random_df.file.unique()\n", - "files_without_results_Random = so_snippets_total_lines_df[~so_snippets_total_lines_df['file'].isin(files_with_results_Random)]\n", - "\n", - "so_snippets_Frequency_df = so_snippets_df[so_snippets_df['predictor'] == 'FrequencyValuePredictor']\n", - "files_with_results_Frequency = so_snippets_Frequency_df.file.unique()\n", - "files_without_results_Frequency = so_snippets_total_lines_df[~so_snippets_total_lines_df['file'].isin(files_with_results_Frequency)]\n", - "\n", - "so_snippets_Fine_Grained_df = so_snippets_df[so_snippets_df['predictor'] == 'CodeT5ValuePredictor_fine_grained']\n", - "files_with_results_Fine_Grained = so_snippets_Fine_Grained_df.file.unique()\n", - "files_without_results_Fine_Grained = so_snippets_total_lines_df[~so_snippets_total_lines_df['file'].isin(files_with_results_Fine_Grained)]\n", - "\n", - "so_snippets_Deterministic_df = so_snippets_df[so_snippets_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_deterministic']\n", - "files_with_results_Deterministic = so_snippets_Deterministic_df.file.unique()\n", - "files_without_results_Deterministic = so_snippets_total_lines_df[~so_snippets_total_lines_df['file'].isin(files_with_results_Deterministic)]\n", - "\n", - "so_snippets_Randomized_df = so_snippets_df[so_snippets_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_randomized']\n", - "files_with_results_Randomized = so_snippets_Randomized_df.file.unique()\n", - "files_without_results_Randomized = so_snippets_total_lines_df[~so_snippets_total_lines_df['file'].isin(files_with_results_Randomized)]" + "Random vs others" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 338, "metadata": { - "id": "VQN6Dkhu-KZ8" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "g_ecXYEnJyQy", + "outputId": "90310c45-f81c-4896-8d2b-889047abe27e" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "WilcoxonResult(statistic=nan, pvalue=nan)" + ] + }, + "execution_count": 338, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "files_without_results_As_Is = files_without_results_As_Is\n", - "files_without_results_As_Is['predictor'] = 'AsIs'\n", - "files_without_results_As_Is['covered_iids'] = 0\n", - "files_without_results_As_Is['total_uses'] = 0\n", - "files_without_results_As_Is['guided_uses'] = 0\n", - "files_without_results_As_Is['executed_lines'] = 0\n", - "files_without_results_As_Is['covered_lines'] = 0\n", - "files_without_results_As_Is['execution_time'] = 0\n", - "\n", - "files_without_results_Naive = files_without_results_Naive\n", - "files_without_results_Naive['predictor'] = 'NaiveValuePredictor'\n", - "files_without_results_Naive['covered_iids'] = 0\n", - "files_without_results_Naive['total_uses'] = 0\n", - "files_without_results_Naive['guided_uses'] = 0\n", - "files_without_results_Naive['executed_lines'] = 0\n", - "files_without_results_Naive['covered_lines'] = 0\n", - "files_without_results_Naive['execution_time'] = 0\n", - "\n", - "files_without_results_Random = files_without_results_Random\n", - "files_without_results_Random['predictor'] = 'RandomPredictor'\n", - "files_without_results_Random['covered_iids'] = 0\n", - "files_without_results_Random['total_uses'] = 0\n", - "files_without_results_Random['guided_uses'] = 0\n", - "files_without_results_Random['executed_lines'] = 0\n", - "files_without_results_Random['covered_lines'] = 0\n", - "files_without_results_Random['execution_time'] = 0\n", - "\n", - "files_without_results_Frequency = files_without_results_Frequency\n", - "files_without_results_Frequency['predictor'] = 'FrequencyValuePredictor'\n", - "files_without_results_Frequency['covered_iids'] = 0\n", - "files_without_results_Frequency['total_uses'] = 0\n", - "files_without_results_Frequency['guided_uses'] = 0\n", - "files_without_results_Frequency['executed_lines'] = 0\n", - "files_without_results_Frequency['covered_lines'] = 0\n", - "files_without_results_Frequency['execution_time'] = 0\n", - "\n", - "files_without_results_Fine_Grained = files_without_results_Fine_Grained\n", - "files_without_results_Fine_Grained['predictor'] = 'CodeT5ValuePredictor_fine_grained'\n", - "files_without_results_Fine_Grained['covered_iids'] = 0\n", - "files_without_results_Fine_Grained['total_uses'] = 0\n", - "files_without_results_Fine_Grained['guided_uses'] = 0\n", - "files_without_results_Fine_Grained['executed_lines'] = 0\n", - "files_without_results_Fine_Grained['covered_lines'] = 0\n", - "files_without_results_Fine_Grained['execution_time'] = 0\n", - "\n", - "files_without_results_Deterministic = files_without_results_Deterministic\n", - "files_without_results_Deterministic['predictor'] = 'CodeT5ValuePredictor_coarse_grained_deterministic'\n", - "files_without_results_Deterministic['covered_iids'] = 0\n", - "files_without_results_Deterministic['total_uses'] = 0\n", - "files_without_results_Deterministic['guided_uses'] = 0\n", - "files_without_results_Deterministic['executed_lines'] = 0\n", - "files_without_results_Deterministic['covered_lines'] = 0\n", - "files_without_results_Deterministic['execution_time'] = 0\n", - "\n", - "files_without_results_Randomized = files_without_results_Randomized\n", - "files_without_results_Randomized['predictor'] = 'CodeT5ValuePredictor_coarse_grained_randomized'\n", - "files_without_results_Randomized['covered_iids'] = 0\n", - "files_without_results_Randomized['total_uses'] = 0\n", - "files_without_results_Randomized['guided_uses'] = 0\n", - "files_without_results_Randomized['executed_lines'] = 0\n", - "files_without_results_Randomized['covered_lines'] = 0\n", - "files_without_results_Randomized['execution_time'] = 0" + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'RandomPredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_fine_grained']['covered_lines_percentage'])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 339, "metadata": { - "id": "PSY1aElDNydw" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_AO5KY0LJyfw", + "outputId": "3720aeed-8415-49f2-d608-7393bc2b293a" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "WilcoxonResult(statistic=nan, pvalue=nan)" + ] + }, + "execution_count": 339, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'RandomPredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_deterministic']['covered_lines_percentage'])" + ] + }, + { + "cell_type": "code", + "execution_count": 340, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mc47Rg82Jyz0", + "outputId": "e29a599a-ba97-463e-b2e9-e9ce51af8318" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "WilcoxonResult(statistic=nan, pvalue=nan)" + ] + }, + "execution_count": 340, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'RandomPredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_randomized']['covered_lines_percentage'])" + ] + }, + { + "cell_type": "code", + "execution_count": 341, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5IsudBpOJy_o", + "outputId": "f22eb18e-09f8-46c2-ed40-9ce9cf62f7c8" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "WilcoxonResult(statistic=nan, pvalue=nan)" + ] + }, + "execution_count": 341, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'RandomPredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'FrequencyValuePredictor']['covered_lines_percentage'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3azgc-krKNow" + }, + "source": [ + "Frequency vs others" + ] + }, + { + "cell_type": "code", + "execution_count": 342, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7nGacezBKQgJ", + "outputId": "bcd1e75a-3705-4966-8ac8-fab8dc5107b0" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "WilcoxonResult(statistic=nan, pvalue=nan)" + ] + }, + "execution_count": 342, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'FrequencyValuePredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_deterministic']['covered_lines_percentage'])" + ] + }, + { + "cell_type": "code", + "execution_count": 343, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pI8kal-gKRw7", + "outputId": "a29cb302-eefb-4700-b097-53d899d32997" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "WilcoxonResult(statistic=nan, pvalue=nan)" + ] + }, + "execution_count": 343, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'FrequencyValuePredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_randomized']['covered_lines_percentage'])" + ] + }, + { + "cell_type": "code", + "execution_count": 344, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nsOx5PneKR89", + "outputId": "0b11643f-9227-4509-d374-d37634e6f8c1" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "WilcoxonResult(statistic=nan, pvalue=nan)" + ] + }, + "execution_count": 344, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'FrequencyValuePredictor']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_fine_grained']['covered_lines_percentage'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YKqXppM7KlQh" + }, + "source": [ + "CodeT5ValuePredictor_coarse_grained_deterministic vs others" + ] + }, + { + "cell_type": "code", + "execution_count": 345, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5CqMoBLzGEX2", + "outputId": "0424c042-e9c1-4527-e437-25c220fe0424" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "WilcoxonResult(statistic=nan, pvalue=nan)" + ] + }, + "execution_count": 345, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_deterministic']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_randomized']['covered_lines_percentage'])" + ] + }, + { + "cell_type": "code", + "execution_count": 346, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2WfYuHf_GU_m", + "outputId": "ce14e5fe-09f2-4ae0-c994-bfa43525afdd" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "WilcoxonResult(statistic=nan, pvalue=nan)" + ] + }, + "execution_count": 346, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_deterministic']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_fine_grained']['covered_lines_percentage'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cq3-p2MSK3l5" + }, + "source": [ + "CodeT5ValuePredictor_coarse_grained_randomized vs others" + ] + }, + { + "cell_type": "code", + "execution_count": 347, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "v7twmdeQGcww", + "outputId": "20bec486-5991-4fce-b5c6-edb68db752f8" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "WilcoxonResult(statistic=nan, pvalue=nan)" + ] + }, + "execution_count": 347, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.wilcoxon(random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_randomized']['covered_lines_percentage'], random_functions_df[random_functions_df['predictor'] == 'CodeT5ValuePredictor_fine_grained']['covered_lines_percentage'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BZ3YIIj_GPUv" + }, + "source": [ + "## SO snippets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PmDuDEz_GcKK" + }, + "source": [ + "Load Evaluation Data" + ] + }, + { + "cell_type": "code", + "execution_count": 348, + "metadata": { + "id": "JsvBNWzqGfJ5" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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filepredictorcovered_iidstotal_usesguided_usesexecuted_linescovered_linesexecution_time
0./so_snippets_dataset/snippet_100.pyAsIs1.01.00.00.00.00.000639
1./so_snippets_dataset/snippet_102.pyAsIs2.02.00.00.00.00.000689
2./so_snippets_dataset/snippet_105.pyAsIs1.01.00.00.00.00.001339
3./so_snippets_dataset/snippet_107.pyAsIs1.01.00.00.00.00.001344
4./so_snippets_dataset/snippet_108.pyAsIs15.0309.00.0202.06.00.668670
...........................
3130./so_snippets_dataset/snippet_94.pyCodeT5ValuePredictor_coarse_grained_randomized2.02.01.01.01.00.122726
3131./so_snippets_dataset/snippet_95.pyCodeT5ValuePredictor_coarse_grained_randomized3.03.00.02.02.00.010906
3132./so_snippets_dataset/snippet_98.pyCodeT5ValuePredictor_coarse_grained_randomized3.03.02.00.00.00.137248
3133./so_snippets_dataset/snippet_99.pyCodeT5ValuePredictor_coarse_grained_randomized17.017.00.06.06.00.022246
3134./so_snippets_dataset/snippet_9.pyCodeT5ValuePredictor_coarse_grained_randomized21.047.00.014.06.00.044809
\n", + "

3135 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " file \\\n", + "0 ./so_snippets_dataset/snippet_100.py \n", + "1 ./so_snippets_dataset/snippet_102.py \n", + "2 ./so_snippets_dataset/snippet_105.py \n", + "3 ./so_snippets_dataset/snippet_107.py \n", + "4 ./so_snippets_dataset/snippet_108.py \n", + "... ... \n", + "3130 ./so_snippets_dataset/snippet_94.py \n", + "3131 ./so_snippets_dataset/snippet_95.py \n", + "3132 ./so_snippets_dataset/snippet_98.py \n", + "3133 ./so_snippets_dataset/snippet_99.py \n", + "3134 ./so_snippets_dataset/snippet_9.py \n", + "\n", + " predictor covered_iids \\\n", + "0 AsIs 1.0 \n", + "1 AsIs 2.0 \n", + "2 AsIs 1.0 \n", + "3 AsIs 1.0 \n", + "4 AsIs 15.0 \n", + "... ... ... \n", + "3130 CodeT5ValuePredictor_coarse_grained_randomized 2.0 \n", + "3131 CodeT5ValuePredictor_coarse_grained_randomized 3.0 \n", + "3132 CodeT5ValuePredictor_coarse_grained_randomized 3.0 \n", + "3133 CodeT5ValuePredictor_coarse_grained_randomized 17.0 \n", + "3134 CodeT5ValuePredictor_coarse_grained_randomized 21.0 \n", + "\n", + " total_uses guided_uses executed_lines covered_lines execution_time \n", + "0 1.0 0.0 0.0 0.0 0.000639 \n", + "1 2.0 0.0 0.0 0.0 0.000689 \n", + "2 1.0 0.0 0.0 0.0 0.001339 \n", + "3 1.0 0.0 0.0 0.0 0.001344 \n", + "4 309.0 0.0 202.0 6.0 0.668670 \n", + "... ... ... ... ... ... \n", + "3130 2.0 1.0 1.0 1.0 0.122726 \n", + "3131 3.0 0.0 2.0 2.0 0.010906 \n", + "3132 3.0 2.0 0.0 0.0 0.137248 \n", + "3133 17.0 0.0 6.0 6.0 0.022246 \n", + "3134 47.0 0.0 14.0 6.0 0.044809 \n", + "\n", + "[3135 rows x 8 columns]" + ] + }, + "execution_count": 348, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "so_snippets_df = pd.read_csv(home_dir+'/metrics_so_snippets_dataset.csv')\n", + "so_snippets_df" + ] + }, + { + "cell_type": "code", + "execution_count": 349, + "metadata": { + "id": "pCIhLI5lNX5Q" }, "outputs": [], "source": [ - "so_snippets_df = so_snippets_df.merge(so_snippets_total_lines_df, how='left', on='file')\n", + "so_snippets_total_lines_df = pd.read_csv(home_dir+'/total_lines_so_snippets_dataset.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 350, + "metadata": { + "id": "SGfn03vx8txo" + }, + "outputs": [], + "source": [ + "so_snippets_As_Is_df = so_snippets_df[so_snippets_df['predictor'] == 'AsIs']\n", + "files_with_results_As_Is = so_snippets_As_Is_df.file.unique()\n", + "files_without_results_As_Is = so_snippets_total_lines_df[~so_snippets_total_lines_df['file'].isin(files_with_results_As_Is)]\n", + "\n", + "so_snippets_Naive_df = so_snippets_df[so_snippets_df['predictor'] == 'NaiveValuePredictor']\n", + "files_with_results_Naive = so_snippets_Naive_df.file.unique()\n", + "files_without_results_Naive = so_snippets_total_lines_df[~so_snippets_total_lines_df['file'].isin(files_with_results_Naive)]\n", + "\n", + "so_snippets_Random_df = so_snippets_df[so_snippets_df['predictor'] == 'RandomPredictor']\n", + "files_with_results_Random = so_snippets_Random_df.file.unique()\n", + "files_without_results_Random = so_snippets_total_lines_df[~so_snippets_total_lines_df['file'].isin(files_with_results_Random)]\n", + "\n", + "so_snippets_Frequency_df = so_snippets_df[so_snippets_df['predictor'] == 'FrequencyValuePredictor']\n", + "files_with_results_Frequency = so_snippets_Frequency_df.file.unique()\n", + "files_without_results_Frequency = so_snippets_total_lines_df[~so_snippets_total_lines_df['file'].isin(files_with_results_Frequency)]\n", + "\n", + "so_snippets_Fine_Grained_df = so_snippets_df[so_snippets_df['predictor'] == 'CodeT5ValuePredictor_fine_grained']\n", + "files_with_results_Fine_Grained = so_snippets_Fine_Grained_df.file.unique()\n", + "files_without_results_Fine_Grained = so_snippets_total_lines_df[~so_snippets_total_lines_df['file'].isin(files_with_results_Fine_Grained)]\n", + "\n", + "so_snippets_Deterministic_df = so_snippets_df[so_snippets_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_deterministic']\n", + "files_with_results_Deterministic = so_snippets_Deterministic_df.file.unique()\n", + "files_without_results_Deterministic = so_snippets_total_lines_df[~so_snippets_total_lines_df['file'].isin(files_with_results_Deterministic)]\n", + "\n", + "so_snippets_Randomized_df = so_snippets_df[so_snippets_df['predictor'] == 'CodeT5ValuePredictor_coarse_grained_randomized']\n", + "files_with_results_Randomized = so_snippets_Randomized_df.file.unique()\n", + "files_without_results_Randomized = so_snippets_total_lines_df[~so_snippets_total_lines_df['file'].isin(files_with_results_Randomized)]" + ] + }, + { + "cell_type": "code", + "execution_count": 351, + "metadata": { + "id": "VQN6Dkhu-KZ8" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_49244/119026422.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_As_Is['predictor'] = 'AsIs'\n", + "/tmp/ipykernel_49244/119026422.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_As_Is['covered_iids'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_As_Is['total_uses'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:5: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_As_Is['guided_uses'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_As_Is['executed_lines'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:7: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_As_Is['covered_lines'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:8: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_As_Is['execution_time'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:11: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Naive['predictor'] = 'NaiveValuePredictor'\n", + "/tmp/ipykernel_49244/119026422.py:12: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Naive['covered_iids'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:13: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Naive['total_uses'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:14: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Naive['guided_uses'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:15: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Naive['executed_lines'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:16: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Naive['covered_lines'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Naive['execution_time'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:20: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Random['predictor'] = 'RandomPredictor'\n", + "/tmp/ipykernel_49244/119026422.py:21: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Random['covered_iids'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:22: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Random['total_uses'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:23: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Random['guided_uses'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:24: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Random['executed_lines'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:25: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Random['covered_lines'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:26: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Random['execution_time'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:29: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Frequency['predictor'] = 'FrequencyValuePredictor'\n", + "/tmp/ipykernel_49244/119026422.py:30: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Frequency['covered_iids'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:31: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Frequency['total_uses'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:32: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Frequency['guided_uses'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:33: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Frequency['executed_lines'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:34: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Frequency['covered_lines'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:35: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Frequency['execution_time'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:38: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Fine_Grained['predictor'] = 'CodeT5ValuePredictor_fine_grained'\n", + "/tmp/ipykernel_49244/119026422.py:39: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Fine_Grained['covered_iids'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:40: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Fine_Grained['total_uses'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:41: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Fine_Grained['guided_uses'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:42: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Fine_Grained['executed_lines'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:43: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Fine_Grained['covered_lines'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:44: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Fine_Grained['execution_time'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:47: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Deterministic['predictor'] = 'CodeT5ValuePredictor_coarse_grained_deterministic'\n", + "/tmp/ipykernel_49244/119026422.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Deterministic['covered_iids'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:49: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Deterministic['total_uses'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:50: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Deterministic['guided_uses'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:51: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Deterministic['executed_lines'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:52: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Deterministic['covered_lines'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:53: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Deterministic['execution_time'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:56: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Randomized['predictor'] = 'CodeT5ValuePredictor_coarse_grained_randomized'\n", + "/tmp/ipykernel_49244/119026422.py:57: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Randomized['covered_iids'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:58: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Randomized['total_uses'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:59: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Randomized['guided_uses'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:60: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Randomized['executed_lines'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:61: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Randomized['covered_lines'] = 0\n", + "/tmp/ipykernel_49244/119026422.py:62: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " files_without_results_Randomized['execution_time'] = 0\n" + ] + } + ], + "source": [ + "files_without_results_As_Is = files_without_results_As_Is\n", + "files_without_results_As_Is['predictor'] = 'AsIs'\n", + "files_without_results_As_Is['covered_iids'] = 0\n", + "files_without_results_As_Is['total_uses'] = 0\n", + "files_without_results_As_Is['guided_uses'] = 0\n", + "files_without_results_As_Is['executed_lines'] = 0\n", + "files_without_results_As_Is['covered_lines'] = 0\n", + "files_without_results_As_Is['execution_time'] = 0\n", + "\n", + "files_without_results_Naive = files_without_results_Naive\n", + "files_without_results_Naive['predictor'] = 'NaiveValuePredictor'\n", + "files_without_results_Naive['covered_iids'] = 0\n", + "files_without_results_Naive['total_uses'] = 0\n", + "files_without_results_Naive['guided_uses'] = 0\n", + "files_without_results_Naive['executed_lines'] = 0\n", + "files_without_results_Naive['covered_lines'] = 0\n", + "files_without_results_Naive['execution_time'] = 0\n", + "\n", + "files_without_results_Random = files_without_results_Random\n", + "files_without_results_Random['predictor'] = 'RandomPredictor'\n", + "files_without_results_Random['covered_iids'] = 0\n", + "files_without_results_Random['total_uses'] = 0\n", + "files_without_results_Random['guided_uses'] = 0\n", + "files_without_results_Random['executed_lines'] = 0\n", + "files_without_results_Random['covered_lines'] = 0\n", + "files_without_results_Random['execution_time'] = 0\n", + "\n", + "files_without_results_Frequency = files_without_results_Frequency\n", + "files_without_results_Frequency['predictor'] = 'FrequencyValuePredictor'\n", + "files_without_results_Frequency['covered_iids'] = 0\n", + "files_without_results_Frequency['total_uses'] = 0\n", + "files_without_results_Frequency['guided_uses'] = 0\n", + "files_without_results_Frequency['executed_lines'] = 0\n", + "files_without_results_Frequency['covered_lines'] = 0\n", + "files_without_results_Frequency['execution_time'] = 0\n", + "\n", + "files_without_results_Fine_Grained = files_without_results_Fine_Grained\n", + "files_without_results_Fine_Grained['predictor'] = 'CodeT5ValuePredictor_fine_grained'\n", + "files_without_results_Fine_Grained['covered_iids'] = 0\n", + "files_without_results_Fine_Grained['total_uses'] = 0\n", + "files_without_results_Fine_Grained['guided_uses'] = 0\n", + "files_without_results_Fine_Grained['executed_lines'] = 0\n", + "files_without_results_Fine_Grained['covered_lines'] = 0\n", + "files_without_results_Fine_Grained['execution_time'] = 0\n", + "\n", + "files_without_results_Deterministic = files_without_results_Deterministic\n", + "files_without_results_Deterministic['predictor'] = 'CodeT5ValuePredictor_coarse_grained_deterministic'\n", + "files_without_results_Deterministic['covered_iids'] = 0\n", + "files_without_results_Deterministic['total_uses'] = 0\n", + "files_without_results_Deterministic['guided_uses'] = 0\n", + "files_without_results_Deterministic['executed_lines'] = 0\n", + "files_without_results_Deterministic['covered_lines'] = 0\n", + "files_without_results_Deterministic['execution_time'] = 0\n", + "\n", + "files_without_results_Randomized = files_without_results_Randomized\n", + "files_without_results_Randomized['predictor'] = 'CodeT5ValuePredictor_coarse_grained_randomized'\n", + "files_without_results_Randomized['covered_iids'] = 0\n", + "files_without_results_Randomized['total_uses'] = 0\n", + "files_without_results_Randomized['guided_uses'] = 0\n", + "files_without_results_Randomized['executed_lines'] = 0\n", + "files_without_results_Randomized['covered_lines'] = 0\n", + "files_without_results_Randomized['execution_time'] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 352, + "metadata": { + "id": "PSY1aElDNydw" + }, + "outputs": [ + { + "data": { + 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filepredictorcovered_iidstotal_usesguided_usesexecuted_linescovered_linesexecution_timetotal_lines
0./so_snippets_dataset/snippet_100.pyAsIs1.01.00.00.00.00.0006395
1./so_snippets_dataset/snippet_102.pyAsIs2.02.00.00.00.00.0006894
2./so_snippets_dataset/snippet_105.pyAsIs1.01.00.00.00.00.0013392
3./so_snippets_dataset/snippet_107.pyAsIs1.01.00.00.00.00.0013445
4./so_snippets_dataset/snippet_108.pyAsIs15.0309.00.0202.06.00.6686706
..............................
3130./so_snippets_dataset/snippet_94.pyCodeT5ValuePredictor_coarse_grained_randomized2.02.01.01.01.00.1227262
3131./so_snippets_dataset/snippet_95.pyCodeT5ValuePredictor_coarse_grained_randomized3.03.00.02.02.00.01090615
3132./so_snippets_dataset/snippet_98.pyCodeT5ValuePredictor_coarse_grained_randomized3.03.02.00.00.00.1372482
3133./so_snippets_dataset/snippet_99.pyCodeT5ValuePredictor_coarse_grained_randomized17.017.00.06.06.00.0222466
3134./so_snippets_dataset/snippet_9.pyCodeT5ValuePredictor_coarse_grained_randomized21.047.00.014.06.00.0448096
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.......................................
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covered_iidstotal_usesguided_usescovered_linesexecuted_linesexecution_timecompletely_executedApproach
predictor
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CodeT5ValuePredictor_coarse_grained_randomized3976.042026.04258.01648.08451.0163.063255221Coarse-grained \\n(randomized)
CodeT5ValuePredictor_fine_grained4036.044740.04127.01660.09030.0187.429917225Fine-grained
FrequencyValuePredictor3228.016533.0510.01354.05481.065.053628149Naive
NaiveValuePredictor3218.011585.0510.01353.04249.047.802054149Frequency
RandomPredictor3244.011804.0949.01351.04185.047.210649150Random
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"CodeT5ValuePredictor_coarse_grained_randomized 8451.0 \n", + "CodeT5ValuePredictor_fine_grained 9030.0 \n", + "FrequencyValuePredictor 5481.0 \n", + "NaiveValuePredictor 4249.0 \n", + "RandomPredictor 4185.0 \n", + "\n", + " execution_time \\\n", + "predictor \n", + "AsIs 72.370769 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 160.959619 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 163.063255 \n", + "CodeT5ValuePredictor_fine_grained 187.429917 \n", + "FrequencyValuePredictor 65.053628 \n", + "NaiveValuePredictor 47.802054 \n", + "RandomPredictor 47.210649 \n", + "\n", + " completely_executed \\\n", + "predictor \n", + "AsIs 140 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 225 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 221 \n", + "CodeT5ValuePredictor_fine_grained 225 \n", + "FrequencyValuePredictor 149 \n", + "NaiveValuePredictor 149 \n", + "RandomPredictor 150 \n", + "\n", + " Approach \n", + "predictor \n", + "AsIs As Is \n", + "CodeT5ValuePredictor_coarse_grained_deterministic Coarse-grained \\n(deterministic) \n", + "CodeT5ValuePredictor_coarse_grained_randomized Coarse-grained \\n(randomized) \n", + "CodeT5ValuePredictor_fine_grained Fine-grained \n", + "FrequencyValuePredictor Naive \n", + "NaiveValuePredictor Frequency \n", + "RandomPredictor Random " + ] + }, + "execution_count": 355, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "so_snippets_df = pd.concat([so_snippets_df, files_without_results_As_Is])\n", - "so_snippets_df = pd.concat([so_snippets_df, files_without_results_Naive])\n", - "so_snippets_df = pd.concat([so_snippets_df, files_without_results_Random])\n", - "so_snippets_df = pd.concat([so_snippets_df, files_without_results_Frequency])\n", - "so_snippets_df = pd.concat([so_snippets_df, files_without_results_Fine_Grained])\n", - "so_snippets_df = pd.concat([so_snippets_df, files_without_results_Deterministic])\n", - "so_snippets_df = pd.concat([so_snippets_df, files_without_results_Randomized])\n", - "so_snippets_df" + "so_snippets_df_grouped = so_snippets_df.groupby(by=\"predictor\")[[\"covered_iids\",\"total_uses\",\"guided_uses\",\"covered_lines\",\"executed_lines\", \"execution_time\", \"completely_executed\"]].sum()\n", + "so_snippets_df_grouped['Approach'] = ['As Is', 'Coarse-grained \\n(deterministic)', 'Coarse-grained \\n(randomized)', 'Fine-grained', 'Naive', 'Frequency', 'Random']\n", + "so_snippets_df_grouped" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "LbqDNojJOLwc" - }, - "outputs": [], - "source": [ - "so_snippets_df['covered_lines_percentage'] = so_snippets_df['covered_lines'] / so_snippets_df['total_lines']\n", - "so_snippets_df['line_execution_time'] = so_snippets_df['execution_time'] / so_snippets_df['executed_lines']\n", - "so_snippets_df['completely_executed'] = so_snippets_df['total_lines'] - so_snippets_df['covered_lines']\n", - "so_snippets_df['completely_executed'] = [1 if value == 0 else 0 for value in so_snippets_df['completely_executed']]\n", - "so_snippets_df.replace([np.inf, -np.inf], 0, inplace=True)\n", - "so_snippets_df" - ] - }, - { - "cell_type": "markdown", + "execution_count": 356, "metadata": { - "id": "sji_UfoGGovq" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 942 + }, + "id": "hdLy2Wb5ixn2", + "outputId": "8e2b16e4-d4a4-4603-a25a-10126f32dfca" }, + "outputs": [ + { + "data": { + "image/png": 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cGqOnzJkzRz179lRMTEy29/3jjz/q/vvvV2xsrNN1Nm7cqI4dO+r69etZODLH1q9fr44dOyo6OtrpOrt27VK7du3cnrc3zZ8/X7169VJ8fLzTdVatWqU2bdooKirK5f7Gjx+vLl26aPfu3S7XjYyMVIsWLfTHH3+4XDez9u7dq9atW2v79u3Z3rcrlixZYioXKlRINWrUcLp+2bJlTUvFbty4Md3jExMTTY9JrVq1HH4XSE5O1uDBg1PLJUqU0IgRI5wel7v8/PzUvHlzU8z+MQKA2wF7YgEAAAC44/Xp00cvvfSSzp8/nxr73//+p2bNmrnc1sSJE00bv/v5+WnAgAEeGWdann/+eY0dO9YSL126tDp06KB69eqpcOHC8vHxUUxMjNavX69FixaZTsyePn1a3bp1U0REhIoWLZpmX++++65WrFih1atXp8amT5+uDh06KDw8PN1xPvPMMzpy5Igp9tZbb6lVq1bp1pszZ4769etnSVAEBQWpc+fOatiwoYoXL67AwEBduHBBERERWrRokSmhl5SUpAEDBqhs2bJq06ZNuv3d9P777+udd95xeFvt2rXVuXNnlS9fXoULF1ZCQoLOnTun7du3a+3atTp48KBTfWS3SpUqpe7rEh0drTNnzqTelitXrgxP6Ka3z9i5c+fUrFkzy3MsSU2bNlWLFi1UuXJl5c+fX1evXtWxY8f0559/atWqVaZjf/75Zw0ePFjfffedU/dp6NChmjlzpiVeuHBh9erVS/fcc48KFiyomJgYbdy4UT///LMuX74s6Z8kZYkSJZzqx1Oio6P1zDPPKCUlxRQPCgpSp06dVL9+fZUpU0aBgYFKSEhQXFycDh8+nJpcv3Tpktt9b926VcOHD0/tOyQkRPfee6+aNm2qokWLKjExUQcOHNBPP/2kvXv3WuqOHj06W06u33Tx4kVTUtNms6l58+bq2rWrypQpo5SUFB09elQLFiywJOFjY2PVoUMH7dixQ8WKFXO57yNHjujzzz9Pfd/x8/NT+/bt1aFDB5UqVUpXr17VoUOH9NNPP2n//v2Wuh07dtTWrVvT3P/I3ueff65XX33VEi9UqJA6deqkBg0aqGjRosqdO7fOnTunzZs3a+HChTp79mzqsfHx8XrooYe0adMmVa9e3dJWwYIFVadOHUn/JI337Nljur1ixYoKCgpKd5z2txuGoQEDBpjGIf3zA4VWrVqpefPmKl++vIKDg5WSkqL4+HidOnVKO3fu1Pr163Xq1Kn0HxgPsn+vqVevnkv78fn4+Khly5ZauHChJGn//v3auHGjGjVq5PD4n376KfW9RpJat27t8Livv/5au3btSi1/9tln2bZnZf369fX777+nllevXq0bN25k+Q9vAMCjcvhKMAAAAAAZYDlBx1xZTtAwDOPll182HZ8nTx7j4sWLzt2x/3P9+nXL8mAPPPCAS224avbs2ZbHp0SJEsasWbOM5OTkNOudP3/eGDx4sKVux44dM+zz+PHjln1FgoKCjH379qVZZ+LEiZa+WrVqle4YDeOffZXslzvy9/c3PvjgAyM+Pj7NeklJScZ///tfI0+ePKa6xYsXN86dO5fhffz9998dLk3XvHlzY8OGDRnWj4iIMF555RUjODjY2LZtW5rHZfdygrfy1Lw2jH/2DOvWrZvl8erSpYuxe/fudOtu2bLF4XKNM2bMyLDf9evXO3yeXn75ZePq1asO65w7d854+OGHU48NCAhwesk0T7B/3CUZAwYMMM6fP59h3cTERGPJkiVGr169MnxvcbSk36339YUXXjAuXLjgsG5ycrIxatQoS/2goCCnlg/11HKCt463UqVKxpo1a9Ksu2DBAofLM/bu3TvDfg3DupzgrX3Xr18/3f2JJk6caAQFBVnaGDp0qFN9r1mzxrKPW758+Yzx48enu6ff1atXjZEjRxq+vr6mujVr1jQSExPT7dNTy2g6+oxu1KiRsX///gzr3rhxw9i8ebPx4osvGiVLlnS5b1fZ76f24osvutzGggULTG00bNjQ4XvNmTNnLMv7Olq68PTp06blKlu1auXWfXOXo+8QGb1nA4C3IYkFAAAAeDmSWI65elJ///79lo3ov/76a+fu2P+ZM2eOZayLFi1yqQ1XnDlzxggJCTH1d8899xhnz551uo3PPvvMMmZn9o/6+eefLfXq1q3r8ITr7t27jcDAQNOxBQsWNE6cOJFhP/Z7YIWEhDiVRLpp9erVhr+/v6mNd955J906ly9fdngyfMCAARkm3eydP3/eiIuLS/P2OyWJNWHCBMvjNXz4cKfrX7lyxWjcuLGpfoUKFTJ8vMPCwiz9fvTRR071OXDgQEvdzJzMd1b9+vVNfXXo0MGtdq5cuZLu7Y6SFDf/xo0b51QfL774oqXud999l2E9TyWxbv6VL1/eOHXqVIb1d+3aZRQoUMBSf+nSpRnWTeuxatCggVOJu+XLl1vea3x9fdPdE88w/klMhoaGmuqVKVPGOHjwYIZ93vTjjz9aPr8yep48lcR65ZVXTG0UKVLEqYSsvYxez5l16NAhy/2dOHGiy+2kpKSY9rmS/kna/fXXX8bly5eNixcvGvPmzTMqVapkOiat/bMeffTR1GP8/PyMnTt3ZvauuiQyMtLyuEybNi1bxwAAmcW1owAAAMBt6JtvvlHdunVd/ktr6bS7QeXKldW+fXtTbOLEiS618b///c9ULlu2rLp06ZLpsaVl3LhxiouLSy2HhIRo8eLFKly4sNNtvPLKK+rWrZsp9tlnn2VY74EHHtBzzz1nikVERGjo0KGmWEJCgvr27aurV6+a4t99951Kly6dbh/Lly+37IE1Y8aMNJducqR58+b64IMPTLFx48alu+fSxIkTLXsVderUSZMmTZKvr6/TfUtSgQIFlC9fPpfq3G5SUlL00UcfmWK9evXSqFGjnG4jMDBQc+fOVUBAQGrs8OHDmjdvXpp11q1bp61bt5piXbt21euvv+5Un998841L++F4iv1yi08++aRb7QQGBrpV7/HHH9e///1vp44dOXKkpZ+lS5e61a+7bDabfvjhB6eWfaxZs6YmTJhgiX/99ddu9Z0nTx7NnTs3wyX2JKlNmzZ6//33TbGUlBSNHz8+3XozZszQ0aNHU8t+fn769ddfVbFiRafH2atXL8tz6sz7uCfYv5579OihAgUKuNyOu69nZx04cMASK1u2rMvt+Pj4aPr06abX48aNG9W+fXsFBQUpf/78euihh0xLytaqVUvjxo2ztLVq1SrNmDEjtfzcc8+pVq1aLo8pMxw9Bo4eKwDwZiSxAAAAgNvQmTNntH37dpf/bt1D6G70r3/9y1TesWOHNmzY4FTdw4cP66+//jLFnnrqqSzbVyIhIUH//e9/TbFhw4alu2dRWt58801TeenSpekmeW767LPPVLduXVPs66+/1i+//JJafvnll7Vjxw7TMc8995weeOCBDNv//PPPTeXOnTtbEm7OeO655xQSEpJajo2N1Zo1axwee+PGDf3nP/8xxQICAjR58mSX9k65m8yfP990ItvX11dffPGFy+2ULl1aTzzxhCm2YMGCNI+fNGmSqWyz2SzPXXr8/Pyy7UT/rez3tCpUqFC29e3r62tJ6qYnX758ljlnnzjMav369VPjxo2dPr53795q2rSpKbZo0SK39l566aWXVK5cOaePHzJkiOX4adOmKSEhIc069q/ZgQMHpu5b5Yrhw4eb3qN2796dLXvz5eTr2RXHjh2zxEqVKuVWW6GhoVq7dq3q16+f4bFdu3bVqlWrLD9mSElJ0eDBg1PLxYsX18iRI90aT2YEBQVZ9t9y9FgBgDcjiQUAAADgrtG9e3dLEsj+6qq0TJo0SYZhpJZ9fX01cOBAj47vVitXrtS5c+dSyzabTeHh4W611bhxY9Mv55OSkixXQDni7++v2bNnK2/evKb4gAEDdPLkSc2fP99yFUKdOnWcShxcuXJFS5YsMcX69++fYT1HAgIC1KZNG1Ns5cqVDo/duXOnTpw4YYr169fPreTg3cL+aqkOHTpkeJVdWu69915TedWqVWkea//6aNGihapWrepSf507d3Z7rO6yP8m/du3abOu7Y8eOLl990rBhQ1M5OxIjt3LnfXTAgAGmckpKiuVHBu60k5HcuXPrscceM8UuXLigTZs2OTx+//792rVrlynm7vtcyZIldc8995hiab3PeVJOvp5dcfLkSUusWLFibrcXGhqqTZs2ac6cOerdu7fKlSunPHnyKG/evKpUqZKefPJJ/fnnn1q0aJHy589vqT927Fjt3Lkztfzxxx87vGr37Nmz+uCDD9S0aVMVLVpU/v7+KlWqlDp37qwJEyYoMTHR7ftwk/1VjlFRUZluEwCyE0ksAAAAAHcNPz8/PfXUU6bY7NmzFR8fn2695ORkTZ482RTr1q1bliY+7E/ulytXzqnlthzx8fGxXD0QERHhVN0qVapYlkmKjY1Vz549LSef8+bNq1mzZsnf3z/DdtevX6/k5GRTzP7qCleUL1/eVE7r/v3999+WmP1JaZjZvxY9+TwdP35c58+ftxx3+vRpy0lpZ67us+fj46Pu3bu7XC8z7JNCn376qX799dds6btly5Yu16lQoYKpnJKSosuXL3tqSOnKly+fJQHtjB49elhiGzdudKmNWrVqubSknzt928+d3LlzO3V1T1qcfZ/zJPvX88qVKzVy5EilpKRked+ucPQ5ntklDG02m3r37q05c+bo6NGjunr1qi5fvqwDBw7ou+++syxRfNOZM2c0YsSI1HLz5s31+OOPW46bNm2aKleurLffflvr16/X2bNndf36dZ06dUp//PGHnn32WdWuXVtbtmzJ1P2wfxzsr64DAG/nl9MDAAAAAOC6ESNG6N13383pYdyWnn76aY0aNSr1BNyVK1c0Y8YMy1KDt/rll18UHR1tij3zzDNZOk77X7ufPXvWsrSfK+yvroiNjXW6bnh4uP78809Nnz49NeZoGcavv/5a1apVc6pNR7/mdydJcZP985PW/bMft6+vr0t7cN1tTpw4YfnV/rfffqv58+e71d7169ctsdjYWBUsWNAUc7SkXb169dzq09167goPDzclra5du6bu3burVatWCg8P13333aeiRYtmSd+VKlVyuY6jq0Pi4uKc2icqs+rUqePWkqyFCxdWqVKlTIlOV5dBdPd1UatWLfn5+ZmS8Gn1bf8+ZxiGJSnkCvslgV15H3fXww8/rOHDh5uuCHr33Xc1Y8YMDRw4UD169HD5CsmsYL8voyTTHnzZaejQoan7Wfr6+mrcuHGW5WonTpyoQYMGma7wduTAgQNq3769/vrrL7cToHny5DGVr1y54lY7AJBTSGIBAAAAuKuULl1a999/v37++efU2MSJE9NNYtkvOVimTBnLsmieZn8VypUrV7R9+3aPte/qyc/x48drw4YNaW4I369fPz355JNOt+do6afsuH8xMTGmcrly5bLlZP3tytHzdOLECcuSjJkRGxurypUrm2Jnz561HFelShW32s/uE+wPPfSQunTpot9//90UX7lypVauXCmbzaaaNWuqefPmatq0qdq0aePSvkzpuXXZUGflypXLEktKSvLEcDLk7nMq/fO83vr6tJ/bWdV3QECAypYtq8OHD2fYt/38SUpKytH3cXeUKlVKI0aM0PDhw03xAwcOaNiwYRo2bJhKly6tli1bqkmTJmrdurXuueeebN9j0P7KXpvNlmV7VqZn9erVmjZtWmr5X//6l2UPtN27d+v5559PTWD5+Pho6NChGjRokEqUKKG9e/fqvffeS/2xQFxcnB599FFFRES4lZjz8zOf/rV/rADA27GcIAAAAIC7jn3Catu2bWnuaXL06FEtXbrUFBs4cGCWnxxztMSaJyUkJLh0fFBQkGbPnm05GSb9sxzZN99841J7OXX/7E/6unPS/26S1c+T5Pi5unjxoiUWEhLiVvuOrjTKSjabTXPmzElzGUPDMLRr1y5NmDBB/fv3V2hoqCpVqqRhw4Zpz549merbUULKm7n7nErW59XRayan+/a293F3vfHGGxoxYkSaiamoqCj98MMPGjJkiOrWrasiRYroySef1LJly7JlfJJ1yTzDMBxe+ZmVUlJSNHjw4NRykSJF9P7771uOe+ONN0xXtn311Vf66KOPVL58eQUEBKhu3bqaN2+eevfunXrMvn37XP6cvenatWumcmaXWQSA7EYSCwAAAMBdp2PHjpa9UOyvtrpp0qRJunHjRmrZ19fXshdUVnD1hGx22Lt3r8NfcFeqVEnBwcEutZVT989+LxCuwkpfTj1PjvZkcvfEa968eTM7HJcFBwdrwYIFmjt3rsLCwjI8/tChQ/r4449Vs2ZN9e7d27Js3J0qMyfT7Z9XV/fxyo6+vfF93F3vvvuuNm/erPvvv1++vr7pHhsbG6spU6aoffv2atiwoWVvsKzgaJ7bJ2+y2rhx47Rjx47U8scff6z8+fObjomKitLChQtTy2FhYabE162++uor05VXnkpi5cR7IgBkBkksAAAAAHcdm82mQYMGmWKzZs2ynIhMSUnR5MmTTbF7771XpUuXzvIx2u9h0bhxYxmG4bG/KVOmuDSew4cP69lnn3V42x9//KExY8Zk6v5J/5xo89T9O3r0qMN+7ZNtrp74vts4ep7Gjx/v0ddimzZtLH04Si462vPGGTm5/0vPnj21ZcsW7dixQ6NHj1anTp3STfgahqG5c+eqXr16WrNmTTaONGe4+5xK1ufV1YR0dvRtP3+KFSvm0bmzYsUKt++DO8LCwvTLL7/oxIkT+uabb9SnTx+VKFEi3TqbN29W27ZtNXbs2CwdW5EiRSyxCxcuZGmft4qJidE777yTWm7SpIn69+9vOe6vv/4y/TDG0TE3FS9eXF26dEkt79u3z62lXO0fB0ePFQB4M5JYAAAAADwmu/ZR8YQnn3xS/v7+qeXLly9r5syZpmMWLlyoU6dOmWL2ya+sUqhQIVM5O5Z1S0tSUpL69eun+Pj4NI8ZNmyYtm7d6nSb9vdPyp77WLBgQVM5O09yZkZOza2cep7sr16Q/tkXxh3pvW6zS+3atTVs2DAtWbJEFy9e1LZt2zRmzBh169bN4R4358+f1wMPPKAzZ87kwGizj7vPqWR9Xh29ZnK6b/v5c7u832SkRIkSGjRokGbPnq1Tp07p8OHDmjJlip544gmH7xkpKSl64YUXLEvzepKjfeXsP7+z0tChQ1NfUz4+Pho3bpzD5Rc3b95sKjdv3jzddu1vT2vp47SkpKRY3kc8tQcfAGQXklgAAAAALPuouHvCPCcTLa4qXLiwab8JybqkoH25TJkyuvfee7N8bJJUtGhRU/nUqVNKSUnJlr7tDR8+XBs3bjTF2rVrZ9oX7Pr16+rbt6/TVzbZ3z9J2bKEWvHixU3lY8eOZenVWLf73Mqp58nRlQL79+93q619+/Zldjge5ePjo7p162rIkCFauHChzpw5oy+//NJy8j82NlaffPJJDo0ye7j7nErW59XRazUr+k5ISLDMgbT6to9fv35d0dHRbvXrzcqXL6/w8HB9//33OnPmjObNm6eaNWuajjEMQ8OGDcuyMYSGhlpiUVFRWdbfrdasWaNp06allgcNGpTmMqKnT582lStUqJBu2/ZLH7v6+omOjrZ8dyCJBeB2QxILAAAAgGV5K/t9g5x16NAhTwwn29gvj7dlyxZt27ZNknTixAn9/vvvptsHDBiQ4V4gntKwYUNT+cqVK4qIiMiWvm+1ZMkSff7556ZYrVq1tHDhQssJyQMHDqS5t4c9+/snKVuWT2vcuLGpnJKSYknQeZIn5lZSUlKO7ZFUuXJlhYSEmGLZ8TzVq1fPErs5N13lbr3ski9fPr3wwgtav3698uXLZ7pt3rx5OTSq7LF9+3bT0mrOOnfunE6ePGmKObP32K3cfV3s2rXLsjdgWn3n1PtcTvL19dWDDz6ojRs3qkGDBqbbtm7dqmPHjmVJvzVq1LB8PmcmSeqslJQUDR48WIZhSPrnBzKjRo1K83j7KwAz2k/S/nZX91lzlMSvU6eOS20AQE4jiQUAAADAcpL60qVLOnfunEttREdH68CBA54cVpZr3ry5ateubYpNmDBBkjRp0iTTyVVfX18NHDgw28bWrl07S+znn3/Otv6lf57TJ554IvXknPTPHi+zZs1Snjx5NHLkSDVt2tRUZ+rUqZo+fXqGbbdt29ay1FJ23L/WrVtbYjNmzMiy/uzn1tGjR02PpzO2bt3q1v49fn5+prI7V/L5+vpaHrPIyEgdPHjQ5bZcUbJkSZUqVcoUW7BggcvtGIahX3/91VPDylKVKlWyvMccPXo0U3s3ebv4+Hi39nVy9Fpo1KiRS23s2rVLhw8fztK+c/J93H7+S+69B7grMDBQb7/9tiUeGRmZZf1Vr17dFNu1a1eW9HWr8ePHa/v27anl0aNHq0CBAmkef+syxlLGV+fa325fPyM7d+40lW02m8sJXwDIaSSxAAAAAKhKlSqWmKv7Lnz33XeeGk62+te//mUqz5w5U/Hx8Zb706VLF5UpUybbxtWuXTvlzZvXFBs/fryuXLmSLf0bhqEnnnhCMTExpviYMWNSl4ny8/PTzJkzLYmaf//73xkmOQoWLKhmzZqZYqtXr9b69es9MPq01a5dW+XLlzfFfvjhB8sST55iP7cuXbqkvXv3utTGt99+61bfQUFBprK7yybef//9prJhGPrss8/cassVnTt3NpVXr17tcqL8jz/+0IkTJzw5rCxVtWpVSywzezfdDtz57LCv4+vrq/bt22d530lJSZYkfYECBRxecSVJdevWtXxuzJ49O1tek/bzX3L/PcBd2f16tk8m2idwPC0mJsaUqGvUqFGGP3axT3CdPXs23ePtb08vQebIjh07TOUqVapYPrMBwNuRxAIAAACgOnXqWJbhmTVrltP1o6Oj9cUXX3h6WNniscceM53su3TpksLDwy17aTzzzDPZOq6QkBDLcoexsbH697//nS39f/LJJ1q6dKkp1qtXL8vjEBoaatk77NKlS+rXr1+GvzB3tD/KwIEDszRRZ7PZ9NJLL5li165d04ABA7KkP0e/eHdlbkVGRmrq1Klu9W1/svPixYsuL0UlSY8//rjlqqiJEyfqzz//dGtcznrqqadMZcMw9PLLLztdPyUlRa+++qqnh5Wl7Pe7sdlslr2y7jQzZ8506UcTP/30k9auXWuKdevWTSVLlnS57y+++MKlhNJXX32lo0ePmmKPP/64AgICHB5vs9n02muvmWJJSUnq379/ll8VFRwcbLkay37sWc3R/k2O9rvzlI4dO5rKe/bsydL9BF9//fXU91QfHx+NGzfOcoWxPfvEnn2SyZ59Iq5atWoujXH16tWmsv1jBAC3A5JYAAAAABQYGKgWLVqYYjNnzjQtkZOWK1euqG/fvi4vP+gtgoOD9dhjj5li9ss9lSpVSt26dcvGUf1j6NChlquxpk6dqtdff92tfWSSk5P1/fff65NPPkn3uA0bNuitt94yxcqVK6eJEyc6PL5Pnz6WhMPmzZv1xhtvpNvPfffdZ9kzZffu3XrwwQfdSrZI0vLlyzVo0KB0jxk4cKCKFy9uiv3+++8aNGiQyyeWL1y4oPj4+DRvr1KlikJDQ02xMWPG6NSpUxm2HRMToz59+igxMdGlMd1084q5Wy1ZssTldvz9/S3P5Y0bN/Twww9r5cqVbo3t1KlTeuWVV7R169Y0j2natKllb6yFCxda9mhLy+DBg7NlObFbHTx4UKNGjXLrxPmVK1f0/fffm2JVqlRR7ty5PTU8r2QYhvr166czZ85keOzevXsdzu/nnnvOrb6vXr2q3r17O5U4X7lypeV90dfX1/JjA3tPP/20SpcubYotW7ZM/fv3V0JCgstjNgxDP//8s15//fV0j/P19bUkTOz3eXTG0KFD3d5baty4caayzWZTjRo13GrLGR07dpSPz/8/1WkYhlatWpUlfa1bt840X5966inL55kj9lcgL1y4MN3jb10ONXfu3C4tBRgdHW157rp06eJ0fQDwGgYAAAAAr7Z8+XJDkulvxIgRHu9n8uTJln5KlixpbNmyJc06mzZtMsLCwlKPDwgIMNVv3bp1hv06un9HjhzJsF7r1q1NdcLDw52/s3YiIiIsY7j17+2333a77cyaNWuWwzG1adPGWLNmjVNt7Nixwxg+fLhRtmxZQ5IxcODANI+9ePGiERoaaurLz8/PWLt2bbp9XLlyxahevbqpns1mMxYvXpxuvX379hkhISGW+1ehQgVj5syZRkpKSob379SpU8aYMWOMBg0aGJKMihUrZlhn6dKlho+Pj6XfVq1aGZs2bcqw/q5du4xXX33VCA4ONrZt25busSNGjLD0U7NmTePgwYPpjq9ChQppzi1nXu8JCQlGUFCQqV6pUqWMxYsXG8nJyRnWv1VKSorRrVs3y/3w8/MzXn/9dSMmJibDNhITE40FCxYY/fr1M/z9/Q1JxqpVq9Kts27dOsvzZLPZjKFDhxrXrl1zWCc2NtZ45JFH0nzsJBnLly936f47a9u2bYYkI2/evMbAgQONJUuWGElJSRnWO3DggNGiRQvLOEeOHJlmnSNHjnjkfrnzHuxu3+XKlTPVufW5qVKlSrrvM7/++qtRvHhxS7+9evVy6n7a17u174YNGxq7d+9Os+53331nBAcHW9p49dVXnep71apVRq5cuSz169ata/z2229OtXHo0CFj1KhRRrVq1QxJRvv27TOs89RTT1n6fPfdd43z58871adhGEZISIjh4+NjdOvWzZg+fboRFxeXYZ0LFy4YgwYNsvTdtm1bp/t1l/08ev755z3eR3JyslGvXr3UPgoWLGicO3fOqbpJSUlGyZIlU+vmyZPHOH78uMNjf/zxR9N96devn0vjnDFjhql+3rx5jStXrrjUBgB4A5thuLijLAAAAIBstWLFCrVt29YUGzFihN59912P9pOYmKi6deta9uvx8fHRfffdp/bt26t48eK6du2ajh07pqVLl2rNmjW6+V+Kpk2bqkKFCpoxY0Zq3datW2vFihXp9uvo/h05csRy9Yq9Nm3a6O+//04th4eHa8qUKRnf0TQ0a9ZM69ats8R9fHx05MgRlS1b1u22M2v48OEaPXq0w9tq1Kihdu3aqVq1aipYsKBsNpsuXryomJgYRUREaNOmTZalEQcOHKhJkyY5bO/hhx/WnDlzTLFRo0Zp+PDhGY5zx44daty4senqgqJFi2r79u2WK59utWjRIvXo0UPJycmW20qWLKl27dopLCxMhQsXVkBAgOLi4nT+/HlFRkZq06ZN2rdvn+nKtIoVK2a4J5ckvf/++3rnnXcc3lanTh116tRJFSpUUKFChZSQkKBz585p586dWrdunWmebNu2TXXr1k2zn9jYWFWvXt2yt4m/v78eeughtWzZUoULF9alS5d0+PBhLV682HSFUq9evXT27Fm3Xu9PPfWUwz21AgICVKZMGQUGBpriJUuW1G+//eawrbi4ODVp0sThnl4BAQFq3ry5WrZsqVKlSil//vy6evWqLly4oGPHjmnz5s3aunWr5YqXVatWWa4CtffKK6/oP//5jyVepEgR9e7dW7Vr11bBggUVExOjTZs2af78+bp06VLqcaNHj7ZcSbZ8+XK1adMm3X7dERERYbl6LCQkRPXr11dYWJjKlSunAgUKKCAgQJcvX9aRI0e0evVq/f3335bXf9myZbVr1y4FBwc77Ovo0aOW/d3cuV/uvAe723doaKiOHTuWWn7hhRc0Z86c1GXnbDabWrZsqXvvvVdlypRRSkqKjh49ql9++UVbtmyxtOfM+8tN9su8vf322/r888919epVSf/s8dexY0e1b99epUqV0rVr13Tw4EHNmzfP4Wu+WrVq2rJli2UOpWXChAlpXrVVvnx5tW/fXrVr11ahQoWUK1cuXbx4UefOndOOHTu0efNmHTp0yFSnffv2GS7puXr1arVs2dISt9lsKl26tEJCQixLCU+aNMl0RVH+/PlN+1jlzp1bderUUVhYmKpWraqCBQsqODhYCQkJioqK0pYtW7R48WLTHJT+eXzXr1+v+vXrpzvmzBo/frxp2d1y5cp5fBnFcePGma7+++abbzK8AvhWn376qWmZyQYNGmjhwoUqVqxYamzz5s3q1KmTLly4IOmf7yIbNmxw6mqvm/r06aMff/wxtfzYY49p2rRpTtcHAK+Rw0k0AAAAABnIriuxDMMw1q5dm3qFhCt/lSpVMqKjo43w8HBT/Ha5EsswDGPq1KkO71vXrl0z1a6nfPHFF4afn5/Lz42jv7SuxJo4caLl2Hbt2jl1NdRNY8eOtbTRsWNH48aNG+nWW7ZsmVG4cGGP3D9nrsS66bPPPnN4RZYrfxldiWUYhjF37lzDZrO53HaTJk2MK1euuP16P378uFGwYEGn+ytXrly67cXGxhodO3b0yPMkZXwllmH8cxVYv3793Gr/ueee89gVS864eSVWZv8KFy5sbN68Od2+7oQrsUaMGGGsWbPG4dVyGf0VLFjQ2L59u9P3077+5MmTjR9//NGt+R8aGmqcOHHC6b5vmjVrlhEYGOiR14gzV2IZhmH07t3bpXbtn0dHV8q6+ufr62t8++23Lj9e7jh37pzlqreM5pIrYmJijAIFCqS2Xb9+fZc+Iw3jn6uxbl45fPMvJCTECA8PN4YNG2b06NHDch+cvervJkdX4mZ0ZTQAeCv2xAIAAACQqmnTplq0aJGCgoKcrtOsWTOtXr3a9Avi21Hv3r1VqFAhS/yZZ57JgdFYvfjii/rrr78y/Sv2OnXq6P7777fE9+zZoyFDhphiRYoU0fTp0017jGRk8ODBeuCBB0yxpUuXZrgPV9u2bbVlyxY99NBDlismXFGyZEkNGDDA6eNfeeUVLVy40LJ3jLMCAwMt+5Y50rNnT02dOlW5cuVyuu0HHnhAS5cudfpKD0fKlCmjpUuXun3/7BUsWFCLFy/Whx9+qAIFCrjdTu7cudWzZ09VqFAhw2N9fHw0depUDRkyxOnXos1m06uvvqqvvvrK7TG6IzAw0KX3T0datmyptWvXZvkVK96iWbNmWrJkiYoUKeJ0nZo1a2rZsmW65557MtV3r169NGvWLJeesxYtWmjFihWWfa6c8fDDD2v9+vVq166dy3VvValSJfXr18+pYydPnqz+/fu73VdmP9tDQ0O1cOFCl96XM6NQoUJ66KGHTLFZs2Z5rP3XX3899eoom82mcePGufQZKf1zVdrChQtVq1at1FhcXJy+//57ffTRR/r555+VlJSUetsTTzyhjz76yKU+Fi1apMuXL6eWQ0ND1bFjR5faAABvQRILAAAAgEn79u21b98+DRgwIN2T5xUrVtT48eO1atWq2z6BJf2zHJr9hvMlS5bUfffdl0MjsmrVqpU2b96sRYsW6cEHH3QqieDr66smTZronXfe0ZYtWxQREWFJMiUkJKhv376py2pJ/5ycmzJlikqUKOHyOL/77jvLCd633npLGzZsSLde2bJl9dNPP2nHjh166qmnVK5cOaf6q1q1qp5//nktXrxYx48fd2rpw1vde++9ioyM1Lfffqs2bdpkmGjKlSuX2rRpoy+//FInT55U5cqVnernscce065du/TQQw8pd+7caR5Xp04dzZo1Sz///HOmEyKSFBYWpsjISP3666965pln1LhxYxUrVszt5Jivr6/eeOMNHTt2TB9//LEaN25sWZLMkfz58+uhhx7ShAkTdPLkSc2dO1clS5Z0qk8/Pz+NGTNGq1evVpcuXeTn5+fwOJvNpnbt2mnFihX69NNPM5UQdUeVKlV07tw5/fbbb3r++edVr149px4bf39/9ejRQwsWLNDKlSudfk3dKVq1aqXdu3dryJAhCgkJSfO4ihUravTo0dq6davq1Knjkb579+6tnTt36sknn1SePHnSPK527dr65ptvtHLlSqffm9Jq56+//tKaNWv0yCOPOPX5abPZVKdOHQ0dOlQrV67UgQMHNHDgQKf6y5s3ryZPnqy9e/fq7bffVpcuXRQaGupwKUFH9u7dq40bN+qdd95RmzZtnHrfsNlsatq0qcaNG6c9e/aoS5cuTo3VU+x/kDF16lRTUshd69evNy3jOmDAADVu3NittooVK6Z169bptddeS/OHECVLltS3336rKVOmOPVc3cp+yeDnnnvO5TYAwFuwJxYAAACANCUmJmr16tU6cuSIzp07J5vNphIlSigsLMz0C+I7wZEjR1SxYkXd+l+kt956S++//34Ojip9N27cUEREhA4fPqzY2FidP39ePj4+ypcvnwoXLqxq1aqpSpUq8vf3z+mhuu3QoUOKjIzUuXPndO7cOaWkpCg4OFj58+dX5cqVVb16deXLl8+jfV6+fFkbNmzQ6dOndfbsWV29elVBQUEqVKiQqlatqho1ajh19VVGfaxatUrHjx9XbGyscuXKpdKlS6tRo0aqWLGih+5J9omPj9emTZsUHR2tc+fO6dKlSwoMDFS+fPlUpkwZVa9eXWXKlPFYUuns2bNat26dTp48qfPnzytv3rwqX768mjZtqqJFi3qkD0+5cuWK9u/fr0OHDik6OlqXL1/WjRs3FBwcrIIFC6p69eqqVatWuonNO4X9nliO9ndMSkrSxo0bFRkZqdjYWOXOnVslSpRQtWrVFBYW5nbf9q89R1coXbt2LXXPu4sXLypPnjwqVaqUateurerVq7vdd0YiIyO1f/9+nTt3TrGxsZKk4OBgFSpUSFWqVFG1atUydUWmJyUnJ+vgwYM6ePCgoqKidOnSJSUmJipv3rwKCQlRpUqVVLduXY+/L7uqcePG2rhxY2p57ty56tmzZ6ba/PHHHxUZGZlaHjx4sEtXEKYlISFBy5cv1+HDhxUXF6fChQurdu3aaty4sctXeUlSVFSUypU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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "Overall Comparisson" + "so_snippets_df_grouped = so_snippets_df_grouped.sort_values(by=['completely_executed'])\n", + "so_snippets_df_grouped['completely_executed'] = [x * 100 for x in so_snippets_df_grouped['completely_executed'] / 462]\n", + "\n", + "plt.rcParams.update({'font.size': 35})\n", + "so_snippets_df_grouped.plot.barh(x='Approach', y=['completely_executed'], color='gray', legend=None)\n", + "plt.xlabel(\"Fully executed snippets (%)\")\n", + "plt.gcf().set_size_inches(16, 10)\n", + "plt.savefig(\"snippets_dataset_full_LC.pdf\", bbox_inches = \"tight\")" ] }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 357, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 399 - }, - "id": "2HkXhY1JGpXE", - "outputId": "2268eba2-3b5d-4e44-9b89-126f33f5827f" + "id": "TSErS9oKr8QA" }, "outputs": [ { "data": { "text/html": [ - "\n", - "
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covered_iidstotal_usesguided_usescovered_linesexecuted_linesexecution_timecompletely_executedApproachcovered_lines_percentage_line_execution_time_
predictor
AsIs2722.019208.00.01293.06372.072.37076930.303030As Is0.4038100.011358
FrequencyValuePredictor3228.01354.05481.065.05362814932.251082Naive0.4228610.011869
NaiveValuePredictor1353.04249.047.80205414932.251082Frequency0.4225480.011250
RandomPredictor1351.04185.047.21064915032.467532Random0.4219240.011281
CodeT5ValuePredictor_coarse_grained_randomized3976.042026.04258.01648.08451.0163.06325547.835498Coarse-grained \\n(randomized)0.5146780.019295
CodeT5ValuePredictor_coarse_grained_deterministic4036.041608.04267.01659.08323.0160.95961948.701299Coarse-grained \\n(deterministic)0.5181140.019339
CodeT5ValuePredictor_fine_grained4036.044740.04127.01660.09030.0187.42991748.701299Fine-grained0.5184260.020756
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" ], "text/plain": [ " covered_iids total_uses \\\n", "predictor \n", "AsIs 2722.0 19208.0 \n", - "CodeT5ValuePredictor_coarse_grained_deterministic 4036.0 41608.0 \n", - "CodeT5ValuePredictor_coarse_grained_randomized 3976.0 42026.0 \n", - "CodeT5ValuePredictor_fine_grained 4036.0 44740.0 \n", "FrequencyValuePredictor 3228.0 16533.0 \n", "NaiveValuePredictor 3218.0 11585.0 \n", "RandomPredictor 3244.0 11804.0 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 3976.0 42026.0 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 4036.0 41608.0 \n", + "CodeT5ValuePredictor_fine_grained 4036.0 44740.0 \n", "\n", " guided_uses covered_lines \\\n", "predictor \n", "AsIs 0.0 1293.0 \n", - "CodeT5ValuePredictor_coarse_grained_deterministic 4267.0 1659.0 \n", - "CodeT5ValuePredictor_coarse_grained_randomized 4258.0 1648.0 \n", - "CodeT5ValuePredictor_fine_grained 4127.0 1660.0 \n", "FrequencyValuePredictor 510.0 1354.0 \n", "NaiveValuePredictor 510.0 1353.0 \n", "RandomPredictor 949.0 1351.0 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 4258.0 1648.0 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 4267.0 1659.0 \n", + "CodeT5ValuePredictor_fine_grained 4127.0 1660.0 \n", "\n", " executed_lines \\\n", "predictor \n", "AsIs 6372.0 \n", - "CodeT5ValuePredictor_coarse_grained_deterministic 8323.0 \n", - "CodeT5ValuePredictor_coarse_grained_randomized 8451.0 \n", - "CodeT5ValuePredictor_fine_grained 9030.0 \n", "FrequencyValuePredictor 5481.0 \n", "NaiveValuePredictor 4249.0 \n", "RandomPredictor 4185.0 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 8451.0 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 8323.0 \n", + "CodeT5ValuePredictor_fine_grained 9030.0 \n", "\n", " execution_time \\\n", "predictor \n", "AsIs 72.370769 \n", - "CodeT5ValuePredictor_coarse_grained_deterministic 160.959619 \n", - "CodeT5ValuePredictor_coarse_grained_randomized 163.063255 \n", - "CodeT5ValuePredictor_fine_grained 187.429917 \n", "FrequencyValuePredictor 65.053628 \n", "NaiveValuePredictor 47.802054 \n", "RandomPredictor 47.210649 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 163.063255 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 160.959619 \n", + "CodeT5ValuePredictor_fine_grained 187.429917 \n", "\n", " completely_executed \\\n", "predictor \n", - "AsIs 140 \n", - "CodeT5ValuePredictor_coarse_grained_deterministic 225 \n", - "CodeT5ValuePredictor_coarse_grained_randomized 221 \n", - "CodeT5ValuePredictor_fine_grained 225 \n", - "FrequencyValuePredictor 149 \n", - "NaiveValuePredictor 149 \n", - "RandomPredictor 150 \n", + "AsIs 30.303030 \n", + "FrequencyValuePredictor 32.251082 \n", + "NaiveValuePredictor 32.251082 \n", + "RandomPredictor 32.467532 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 47.835498 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 48.701299 \n", + "CodeT5ValuePredictor_fine_grained 48.701299 \n", "\n", - " Approach \n", - "predictor \n", - "AsIs As Is \n", - "CodeT5ValuePredictor_coarse_grained_deterministic Coarse-grained \\n(deterministic) \n", - "CodeT5ValuePredictor_coarse_grained_randomized Coarse-grained \\n(randomized) \n", - "CodeT5ValuePredictor_fine_grained Fine-grained \n", - "FrequencyValuePredictor Naive \n", - "NaiveValuePredictor Frequency \n", - "RandomPredictor Random " - ] - }, - "execution_count": 188, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "so_snippets_df_grouped = so_snippets_df.groupby(by=\"predictor\")[[\"covered_iids\",\"total_uses\",\"guided_uses\",\"covered_lines\",\"executed_lines\", \"execution_time\", \"completely_executed\"]].sum()\n", - "so_snippets_df_grouped['Approach'] = ['As Is', 'Coarse-grained \\n(deterministic)', 'Coarse-grained \\n(randomized)', 'Fine-grained', 'Naive', 'Frequency', 'Random']\n", - "so_snippets_df_grouped" - ] - }, - { - "cell_type": "code", - "execution_count": 189, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 942 - }, - "id": "hdLy2Wb5ixn2", - "outputId": "8e2b16e4-d4a4-4603-a25a-10126f32dfca" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0, 'Fully executed snippets (%)')" + " Approach \\\n", + "predictor \n", + "AsIs As Is \n", + "FrequencyValuePredictor Naive \n", + "NaiveValuePredictor Frequency \n", + "RandomPredictor Random \n", + "CodeT5ValuePredictor_coarse_grained_randomized Coarse-grained \\n(randomized) \n", + "CodeT5ValuePredictor_coarse_grained_deterministic Coarse-grained \\n(deterministic) \n", + "CodeT5ValuePredictor_fine_grained Fine-grained \n", + "\n", + " covered_lines_percentage_ \\\n", + "predictor \n", + "AsIs 0.403810 \n", + "FrequencyValuePredictor 0.422861 \n", + "NaiveValuePredictor 0.422548 \n", + "RandomPredictor 0.421924 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 0.514678 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 0.518114 \n", + "CodeT5ValuePredictor_fine_grained 0.518426 \n", + "\n", + " line_execution_time_ \n", + "predictor \n", + "AsIs 0.011358 \n", + "FrequencyValuePredictor 0.011869 \n", + "NaiveValuePredictor 0.011250 \n", + "RandomPredictor 0.011281 \n", + "CodeT5ValuePredictor_coarse_grained_randomized 0.019295 \n", + "CodeT5ValuePredictor_coarse_grained_deterministic 0.019339 \n", + "CodeT5ValuePredictor_fine_grained 0.020756 " ] }, - "execution_count": 189, + "execution_count": 358, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" + ], + "text/plain": [ + " As Is Naive Frequency Random Coarse-grained \\n(randomized) \\\n", + "0 0.0 0.0 0.0 0.0 20.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 50.0 \n", + "3 0.0 0.0 0.0 0.0 0.0 \n", + "4 100.0 100.0 100.0 100.0 100.0 \n", + ".. ... ... ... ... ... \n", + "457 0.0 0.0 0.0 0.0 0.0 \n", + "458 0.0 0.0 0.0 0.0 0.0 \n", + "459 0.0 0.0 0.0 0.0 0.0 \n", + "460 0.0 0.0 0.0 0.0 0.0 \n", + "461 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " Coarse-grained \\n(deterministic) Fine-grained \n", + "0 20.0 20.0 \n", + "1 0.0 0.0 \n", + "2 50.0 50.0 \n", + "3 0.0 0.0 \n", + "4 100.0 100.0 \n", + ".. ... ... \n", + "457 0.0 0.0 \n", + "458 0.0 0.0 \n", + "459 0.0 0.0 \n", + "460 0.0 0.0 \n", + "461 0.0 0.0 \n", + "\n", + "[462 rows x 7 columns]" + ] + }, + "execution_count": 360, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "so_snippets_df_covered_lines_per_predictor = pd.DataFrame()\n", "so_snippets_df_covered_lines_per_predictor['As Is'] = [x * 100 for x in so_snippets_df[so_snippets_df['predictor'] == 'AsIs']['covered_lines_percentage'].tolist()]\n", @@ -2326,7 +5785,7 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 361, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -2338,14 +5797,12 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -2369,8 +5826,9 @@ "plt.plot([], [], '--', linewidth=4, color='black', label='mean')\n", "plt.plot([], [], '-', linewidth=4, color='gray', label='median')\n", "#plt.plot([], [], 'o', markersize=12, color='gray', label='fliers')\n", - "plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.11), ncol=2)\n", - "plt.savefig(\"snippets_dataset_LC.pdf\")" + "plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.18), ncol=2)\n", + "plt.gcf().set_size_inches(16, 10)\n", + "plt.savefig(\"snippets_dataset_LC.pdf\", bbox_inches = \"tight\")" ] }, { @@ -2384,7 +5842,7 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 362, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2396,10 +5854,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=99.5, pvalue=1.6882223141716426e-24)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 195, + "execution_count": 362, "metadata": {}, "output_type": "execute_result" } @@ -2410,7 +5868,7 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 363, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2422,10 +5880,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=101.5, pvalue=3.224087395954022e-25)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 196, + "execution_count": 363, "metadata": {}, "output_type": "execute_result" } @@ -2436,7 +5894,7 @@ }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 364, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2448,10 +5906,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=103.5, pvalue=2.2850541141512903e-25)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 197, + "execution_count": 364, "metadata": {}, "output_type": "execute_result" } @@ -2462,7 +5920,7 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 365, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2474,10 +5932,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=0.0, pvalue=2.4562426093140745e-06)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 198, + "execution_count": 365, "metadata": {}, "output_type": "execute_result" } @@ -2488,7 +5946,7 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 366, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2500,10 +5958,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=12617.0, pvalue=0.15933092253243653)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 199, + "execution_count": 366, "metadata": {}, "output_type": "execute_result" } @@ -2514,7 +5972,7 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 367, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2526,10 +5984,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=0.0, pvalue=1.666775499957924e-06)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 200, + "execution_count": 367, "metadata": {}, "output_type": "execute_result" } @@ -2549,7 +6007,7 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": 368, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2561,10 +6019,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=101.5, pvalue=3.224087395954022e-25)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 201, + "execution_count": 368, "metadata": {}, "output_type": "execute_result" } @@ -2575,7 +6033,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 369, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2587,10 +6045,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=135.5, pvalue=6.198238582769491e-22)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 202, + "execution_count": 369, "metadata": {}, "output_type": "execute_result" } @@ -2601,7 +6059,7 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 370, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2613,10 +6071,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=142.5, pvalue=4.92888789372186e-22)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 203, + "execution_count": 370, "metadata": {}, "output_type": "execute_result" } @@ -2627,7 +6085,7 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 371, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2639,10 +6097,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=9490.5, pvalue=9.286193442591195e-13)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 204, + "execution_count": 371, "metadata": {}, "output_type": "execute_result" } @@ -2662,7 +6120,7 @@ }, { "cell_type": "code", - "execution_count": 205, + "execution_count": 372, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2674,10 +6132,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=99.5, pvalue=1.6882223141716426e-24)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 205, + "execution_count": 372, "metadata": {}, "output_type": "execute_result" } @@ -2688,7 +6146,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": 373, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2700,10 +6158,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=154.5, pvalue=3.73266376989976e-21)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 206, + "execution_count": 373, "metadata": {}, "output_type": "execute_result" } @@ -2714,7 +6172,7 @@ }, { "cell_type": "code", - "execution_count": 207, + "execution_count": 374, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2726,10 +6184,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=9557.0, pvalue=5.669727473165634e-12)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 207, + "execution_count": 374, "metadata": {}, "output_type": "execute_result" } @@ -2740,7 +6198,7 @@ }, { "cell_type": "code", - "execution_count": 208, + "execution_count": 375, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2752,10 +6210,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=162.5, pvalue=3.0253487560082597e-21)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 208, + "execution_count": 375, "metadata": {}, "output_type": "execute_result" } @@ -2775,7 +6233,7 @@ }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 376, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2787,10 +6245,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=103.5, pvalue=2.2850541141512903e-25)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 209, + "execution_count": 376, "metadata": {}, "output_type": "execute_result" } @@ -2801,7 +6259,7 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 377, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2813,10 +6271,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=162.5, pvalue=5.221743397796789e-22)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 210, + "execution_count": 377, "metadata": {}, "output_type": "execute_result" } @@ -2827,7 +6285,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 378, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2839,10 +6297,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=170.5, pvalue=4.213696075239535e-22)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 211, + "execution_count": 378, "metadata": {}, "output_type": "execute_result" } @@ -2853,7 +6311,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 379, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2865,10 +6323,10 @@ { "data": { "text/plain": [ - "WilcoxonResult(statistic=9569.5, pvalue=8.913442560903327e-13)" + "WilcoxonResult(statistic=nan, pvalue=nan)" ] }, - "execution_count": 212, + "execution_count": 379, "metadata": {}, "output_type": "execute_result" } @@ -2883,11 +6341,21 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3", - "name": "python3" + "display_name": "SemSeedMP", + "language": "python", + "name": "semseedmp" }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" } }, "nbformat": 4,