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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
PATH_FOR_GRAPHS = 'Z:/trex/trex-doc/images/'
def convert_dict_to_dframe(data, categories, index=''):
data_input = {}
for category in categories:
data_input[category] = data[category]
if index:
df = pd.DataFrame(data_input, index=data[index])
else:
df = pd.DataFrame(data_input)
return df
def plot_bar_by_category(data_frame, category, index='', graph_name='graph.png', show='', gtitle='', save_path=''):
if index:
data_frame = data_frame.sort_index(by=index)
print data_frame[index]
else:
print data_frame
data_frame = pd.DataFrame(data_frame[category], columns=category).astype(float)
data_frame.plot(kind='bar')
plt.xticks(rotation='horizontal')
plt.title(gtitle)
if save_path:
plt.savefig(save_path + graph_name)
if show:
plt.show()
def generate_csv(data_frame, file_name, save_path=(os.getcwd() + "/")):
f = open(save_path + file_name, 'w')
data_frame.to_csv(f)
f.close()
# category is an array of category names that will appear as metrics
def plot_bar_by_test_name(data_frame, test_name, category, graph_name='graph.png', show='', gtitle='', save_path=''):
data_frame = data_frame[data_frame['Test_name'] == test_name]
plot_bar_by_category(data_frame, category, 'Test_name', graph_name, show, gtitle=test_name, save_path=save_path)
def generate_dframe_for_test(test_name, test_data):
test_results = []
test_mins = set()
test_maxs = set()
for query in test_data:
test_results.append(float(query[3]))
test_mins.add(float(query[4]))
test_maxs.add(float(query[5]))
df = pd.DataFrame({test_name: test_results})
stats = tuple([float(df.mean()), min(test_mins), max(test_maxs)]) # stats = (avg_mpps,min,max)
return df, stats
def generate_dframe_arr_and_stats_of_tests_per_setup(date, setup_name, setup_dict):
dframe_arr_trend = []
stats_arr = []
dframe_arr_latest = []
test_names = setup_dict.keys()
for test in test_names:
df, stats = generate_dframe_for_test(test, setup_dict[test])
dframe_arr_trend.append(df)
stats_arr.append(stats)
df_latest = float(setup_dict[test][-1][3])
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
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(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(save_path + setup_name + '_latest_test_runs.png')
dframe_all_tests_latest = dframe_all_tests_latest.round(2)
dframe_all_tests_latest.to_csv(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=''):
dframe_arr, stats_arr, dframe_latest_arr = generate_dframe_arr_and_stats_of_tests_per_setup(end_date, setup_name,
setup_dict)
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(save_path + setup_name + '_trend_stats.csv')
plt.close('all')
def create_all_data(ga_data, setup_names, start_date, end_date, save_path='', add_stats=''):
for setup_name in setup_names:
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)
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