subunit2sql/subunit2sql/analysis/run_time.py

85 lines
2.8 KiB
Python

# Copyright 2014 Hewlett-Packard Development Company, L.P.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import matplotlib
import matplotlib.dates as dates
import matplotlib.pyplot as plt
from oslo_config import cfg
import pandas as pd
from subunit2sql.analysis import utils
from subunit2sql.db import api
CONF = cfg.CONF
matplotlib.style.use('ggplot')
def set_cli_opts(parser):
parser.add_argument('test_id', nargs='?',
help='Test id to extract time series for. This '
'previously took a UUID from the tests.id '
'column, however this will no longer work. It '
'only works with a value from tests.test_id.')
def generate_series():
session = api.get_session()
test_id = api.get_id_from_test_id(CONF.command.test_id, session)
if not test_id:
print("The test_id %s was not found in the database" %
CONF.command.test_id)
exit(2)
run_times = api.get_test_run_time_series(test_id, session)
if not run_times:
print("There was no data found in the database")
exit(3)
if not CONF.title:
test = api.get_test_by_id(test_id, session)
session.close()
ts = pd.Series(run_times)
ts = utils.filter_dates(ts)
if ts.count() == 0:
print("No data available. Check your query and try again.")
exit(-1)
roll = ts.rolling(window=20, center=False)
mean = roll.mean()
rolling_std = roll.std()
plt.figure()
if not CONF.title:
plt.title(test.test_id)
else:
plt.title(CONF.title)
plt.ylabel('Time (sec.)')
# format x-axis with dates
fig, ax = plt.subplots(1)
fig.autofmt_xdate()
xfmt = dates.DateFormatter("%b %d %Y")
ax.xaxis_date()
ax.xaxis.set_major_formatter(xfmt)
plt.plot(ts.index, ts, 'ko', label='Run Time', markersize=0.45)
plt.plot(mean.index, mean, 'b', label='Avg. Run Time', linewidth=0.45)
upper_std_dev = mean + 2 * rolling_std
lower_std_dev = mean - 2 * rolling_std
# Set negative numbers to 0
lower_std_dev[lower_std_dev < 0] = 0
plt.fill_between(rolling_std.index, upper_std_dev,
lower_std_dev, color='b', alpha=0.2,
label='std dev')
plt.legend()
plt.savefig(CONF.output, dpi=CONF.dpi)
return ts