#!/scratch/Anaconda2.4.0/bin/python import pandas as pd import numpy as np import matplotlib matplotlib.use('Agg') from matplotlib import pyplot as plt import os import time def generate_dframe_for_test(setup_name, test_name, test_data): test_results = [] test_dates = [] test_build_ids = [] test_mins = set() test_maxs = set() for query in test_data: test_results.append(float(query[5])) date_formatted = time.strftime("%d-%m-%Y", time.strptime(query[2], "%Y%m%d")) time_of_res = date_formatted + '-' + query[3] + ':' + query[4] test_dates.append(time_of_res) test_build_ids.append(query[8]) test_mins.add(float(query[6])) test_maxs.add(float(query[7])) df = pd.DataFrame({test_name: test_results}) df_detailed = pd.DataFrame({(test_name + ' Results'): test_results, (test_name + ' Date'): test_dates, "Setup": ([setup_name] * len(test_results)), "Build Id": test_build_ids}) stats = tuple([float(df.mean()), min(test_mins), max(test_maxs)]) # stats = (avg_mpps,min,max) return df, stats, df_detailed def generate_dframe_arr_and_stats_of_tests_per_setup(date, setup_name, setup_dict): dframe_arr_trend = [] stats_arr = [] dframe_arr_latest = [] dframe_arr_detailed = [] test_names = setup_dict.keys() for test in test_names: df, stats, df_detailed = generate_dframe_for_test(setup_name, test, setup_dict[test]) dframe_arr_detailed.append(df_detailed) dframe_arr_trend.append(df) stats_arr.append(stats) df_latest = float(setup_dict[test][-1][5]) dframe_arr_latest.append(df_latest) dframe_arr_latest = pd.DataFrame({'Date': [date] * len(dframe_arr_latest), 'Setup': [setup_name], 'Test Name': test_names, 'MPPS': dframe_arr_latest}, index=range(1, len(dframe_arr_latest) + 1)) stats_df = pd.DataFrame(stats_arr, index=setup_dict.keys(), columns=['Avg MPPS', 'Golden Min', 'Golden Max']) stats_df.index.name = 'Test Name' return dframe_arr_trend, stats_df, dframe_arr_latest, dframe_arr_detailed def create_plot_for_dframe_arr(dframe_arr, setup_name, start_date, end_date, show='no', save_path='', file_name='_trend_graph'): dframe_all = pd.concat(dframe_arr, axis=1) dframe_all = dframe_all.astype(float) dframe_all.plot() plt.legend(fontsize='small', loc='best') plt.ylabel('MPPS/Core (Norm)') plt.title('Setup: ' + setup_name) plt.tick_params( axis='x', which='both', bottom='off', top='off', labelbottom='off') plt.xlabel('Time Period: ' + start_date + ' - ' + end_date) if save_path: plt.savefig(os.path.join(save_path, setup_name + file_name + '.png')) if show == 'yes': plt.show() def create_bar_plot_for_latest_runs_per_setup(dframe_all_tests_latest, setup_name, show='no', save_path=''): plt.figure() colors_for_bars = ['b', 'g', 'r', 'c', 'm', 'y'] dframe_all_tests_latest['MPPS'].plot(kind='bar', legend=False, color = colors_for_bars) dframe_all_tests_latest = dframe_all_tests_latest[['Test Name', 'Setup', 'Date', 'MPPS']] plt.xticks(rotation='horizontal') plt.xlabel('Index of Tests') plt.ylabel('MPPS/Core (Norm)') plt.title("Test Runs for Setup: " + setup_name) if save_path: plt.savefig(os.path.join(save_path, setup_name + '_latest_test_runs.png')) dframe_all_tests_latest = dframe_all_tests_latest.round(2) dframe_all_tests_latest.to_csv(os.path.join(save_path, setup_name + '_latest_test_runs_stats.csv')) if show == 'yes': plt.show() def create_all_data_per_setup(setup_dict, setup_name, start_date, end_date, show='no', save_path='', add_stats='', detailed_test_stats=''): dframe_arr, stats_arr, dframe_latest_arr, dframe_detailed = generate_dframe_arr_and_stats_of_tests_per_setup( end_date, setup_name, setup_dict) if detailed_test_stats: detailed_table = create_detailed_table(dframe_detailed) else: detailed_table = [] create_bar_plot_for_latest_runs_per_setup(dframe_latest_arr, setup_name, show=show, save_path=save_path) create_plot_for_dframe_arr(dframe_arr, setup_name, start_date, end_date, show, save_path) if add_stats: stats_arr = stats_arr.round(2) stats_arr.to_csv(os.path.join(save_path, setup_name + '_trend_stats.csv')) plt.close('all') return detailed_table, dframe_latest_arr def create_detailed_table(dframe_arr_detailed): result = reduce(lambda x, y: pd.merge(x, y, how='outer'), dframe_arr_detailed) return result def latest_runs_comparison_bar_chart(setup_name1, setup_name2, setup1_latest_result, setup2_latest_result, save_path='', show='no'): s1_res = setup1_latest_result[['Test Name', 'MPPS']] s2_res = setup2_latest_result[['Test Name', 'MPPS','Date']] s1_res.columns = ['Test Name', setup_name1] s2_res.columns = ['Test Name', setup_name2, 'Date'] compare_dframe = pd.merge(s1_res, s2_res, on='Test Name') compare_dframe.plot(kind='bar') plt.legend(fontsize='small', loc='best') plt.xticks(rotation='horizontal') plt.xlabel('Index of Tests') plt.ylabel('MPPS') plt.title("Comparison between " + setup_name1 + " and " + setup_name2) if save_path: plt.savefig(os.path.join(save_path, "_comparison.png")) compare_dframe = compare_dframe.round(2) compare_dframe.to_csv(os.path.join(save_path, '_comparison_stats_table.csv')) if show == 'yes': plt.show() # WARNING: if the file _all_stats.csv already exists, this script deletes it, to prevent overflowing of data # since data is appended to the file def create_all_data(ga_data, setup_names, start_date, end_date, save_path='', add_stats='', detailed_test_stats=''): total_detailed_data = [] trex07_latest = [] trex08_latest = [] if detailed_test_stats: if os.path.exists(os.path.join(save_path, '_detailed_table.csv')): os.remove(os.path.join(save_path, '_detailed_table.csv')) for setup_name in setup_names: if setup_name == 'trex07': detailed_setup_data, trex07_latest = create_all_data_per_setup(ga_data[setup_name], setup_name, start_date, end_date, show='no', save_path=save_path, add_stats=add_stats, detailed_test_stats=detailed_test_stats) elif setup_name == 'trex08': detailed_setup_data, trex08_latest = create_all_data_per_setup(ga_data[setup_name], setup_name, start_date, end_date, show='no', save_path=save_path, add_stats=add_stats, detailed_test_stats=detailed_test_stats) else: detailed_setup_data = create_all_data_per_setup(ga_data[setup_name], setup_name, start_date, end_date, show='no', save_path=save_path, add_stats=add_stats, detailed_test_stats=detailed_test_stats)[0] total_detailed_data.append(detailed_setup_data) if detailed_test_stats: total_detailed_dframe = pd.DataFrame().append(total_detailed_data) total_detailed_dframe.to_csv(os.path.join(save_path, '_detailed_table.csv')) latest_runs_comparison_bar_chart('Mellanox ConnectX-4', 'Intel XL710', trex07_latest, trex08_latest, save_path=save_path, show='no')