Package: pypete
This plugin helps writing performance tests in traditional nosetests way. To
turn it use the--with-pypeteargument in running nosetests in command
line.Plugin run testsnumbertimes and this experiment is repeatedrepeattimes. So for measurement test will be rantimes x repeattimes. Ifnumberequals 0, plugin computes optimal number of tests so the time of each
experiment is bigger thanthreshold.I recommend to usePrettyTablefor better overview of test results. You can select file, where the results
will be stored in json format. With file and prettytable you can see
comparison of current, last, best and worst run of tests (best and worst
are according to avg value).You can access all source codes atmy Github.Installpip install pypeteExample of usage:$ nosetests --with-pypete --pypete-prettytable --pypete-file pypete.json tests/tests.py
F..F..
======================================================================
FAIL: test_fail (tests.BasicTest)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/psebek/projects/pypete/tests/tests.py", line 18, in test_fail
    self.assertTrue(False)
AssertionError: False is not true

======================================================================
FAIL: test_timed (tests.BasicTest)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.7/site-packages/nose/tools/nontrivial.py", line 100, in newfunc
    raise TimeExpired("Time limit (%s) exceeded" % limit)
TimeExpired: Time limit (0.001) exceeded

Pypete results:
repeat = 3 and number = 0
test_fail (tests.BasicTest):
+--------+-------------+----------+----------+-----------+
| Metric | current [s] | last [s] | best [s] | worst [s] |
+--------+-------------+----------+----------+-----------+
|  best  |   0.000034  | 0.000033 | 0.000033 |  0.000033 |
|  avg   |   0.000037  | 0.000033 | 0.000033 |  0.000036 |
| worst  |   0.000039  | 0.000034 | 0.000034 |  0.000040 |
+--------+-------------+----------+----------+-----------+
test_time (tests.BasicTest):
+--------+-------------+----------+----------+-----------+
| Metric | current [s] | last [s] | best [s] | worst [s] |
+--------+-------------+----------+----------+-----------+
|  best  |   0.001184  | 0.001174 | 0.001158 |  0.001193 |
|  avg   |   0.001186  | 0.001184 | 0.001176 |  0.001193 |
| worst  |   0.001189  | 0.001189 | 0.001196 |  0.001193 |
+--------+-------------+----------+----------+-----------+
test_time2 (tests.BasicTest):
+--------+-------------+----------+----------+-----------+
| Metric | current [s] | last [s] | best [s] | worst [s] |
+--------+-------------+----------+----------+-----------+
|  best  |   0.002216  | 0.002229 | 0.002144 |  0.002257 |
|  avg   |   0.002237  | 0.002238 | 0.002179 |  0.002276 |
| worst  |   0.002252  | 0.002245 | 0.002201 |  0.002302 |
+--------+-------------+----------+----------+-----------+
test_timed (tests.BasicTest):
+--------+-------------+----------+----------+-----------+
| Metric | current [s] | last [s] | best [s] | worst [s] |
+--------+-------------+----------+----------+-----------+
|  best  |   0.010448  | 0.010492 | 0.010442 |  0.010490 |
|  avg   |   0.010465  | 0.010541 | 0.010470 |  0.010645 |
| worst  |   0.010474  | 0.010621 | 0.010492 |  0.010751 |
+--------+-------------+----------+----------+-----------+
tests.test_arguments(0.001,):
+--------+-------------+----------+----------+-----------+
| Metric | current [s] | last [s] | best [s] | worst [s] |
+--------+-------------+----------+----------+-----------+
|  best  |   0.001188  | 0.001210 | 0.001150 |  0.001205 |
|  avg   |   0.001202  | 0.001213 | 0.001186 |  0.001219 |
| worst  |   0.001213  | 0.001217 | 0.001205 |  0.001227 |
+--------+-------------+----------+----------+-----------+
tests.test_arguments(0.002,):
+--------+-------------+----------+----------+-----------+
| Metric | current [s] | last [s] | best [s] | worst [s] |
+--------+-------------+----------+----------+-----------+
|  best  |   0.002274  | 0.002250 | 0.002183 |  0.002250 |
|  avg   |   0.002280  | 0.002262 | 0.002204 |  0.002286 |
| worst  |   0.002288  | 0.002273 | 0.002222 |  0.002318 |
+--------+-------------+----------+----------+-----------+

----------------------------------------------------------------------
Ran 6 tests in 1.619s

FAILED (failures=2)