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authoritraviv <itraviv@cisco.com>2017-03-05 14:30:08 +0200
committeritraviv <itraviv@cisco.com>2017-03-05 14:30:08 +0200
commite48eee124239f433d28b53eef5d4c41746ddd18f (patch)
tree07d32b983b2dade8529152d817051d3e0e370e94
parent02b2d82180e957fd058d2b944d6ce2bd3de08e49 (diff)
removed old DataAnalysis module
Signed-off-by: itraviv <itraviv@cisco.com>
-rwxr-xr-xdoc/TRexDataAnalysis.py171
1 files changed, 0 insertions, 171 deletions
diff --git a/doc/TRexDataAnalysis.py b/doc/TRexDataAnalysis.py
deleted file mode 100755
index 3f6dc170..00000000
--- a/doc/TRexDataAnalysis.py
+++ /dev/null
@@ -1,171 +0,0 @@
-#!/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')