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#!/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][6])
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')
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()
dframe_all_tests_latest['MPPS'].plot(kind='bar', legend=False)
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')
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, setup_name, save_path)
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
def create_detailed_table(dframe_arr_detailed, setup_name, save_path=''):
result = reduce(lambda x, y: pd.merge(x, y, on=('Build Id', 'Setup')), dframe_arr_detailed)
return result
# 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 = []
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 == 'trex11':
continue
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)
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'))
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