When I apply or run the PowerBI Python script, I see two of the following:
Two Python instances executing the same script
Both codes utilizes all resources similarly
Both generate their own log files
This is redundant, takes double the time, uses all my resources, and is super annoying!
Why is this the case? Can we avoid this or fix this somehow so that only one instance of python executes the script to generate data for the data source?
I'm running a couple of instances of pytest.main() and once they are all complete I want to quickly see the failures across all the runs without rooting through all the individual reports. How can I do that?
Do I have to parse the textual reports or can I get py.test to return an object with failure data? (so far as I've seen it just returns an integer)
I use Allure reports (https://docs.qameta.io/allure/#_pytest) for that.
You can run each pytest.main() with option --alluredir= where each instance has different path, for example /path/to/reports/report1, /path/to/reports/report2.
After all runs are completed, you can generate one combined report by running command allure serve /path/to/reports. More about reports generating here: https://docs.qameta.io/allure/#_get_started
Will it is possible to run a small set of code automatically after a script was run?
I am asking this because for some reasons, if I added this set of code into the main script, though it works, it will displays a list of tab errors (its already there, but it is stating that it cannot find it some sort).
I realized that after running my script, Maya seems to 'load' its own setup of refreshing, along with some plugins done by my company. As such, if I am running the small set of code after my main script execution and the Maya/ plugins 'refresher', it works with no problem. I had like to make the process as automated as possible, all within a script if that is possible...
Thus is it possible to do so? Like a delayed sort of coding method?
FYI, the main script execution time depends on the number of elements in the scene. The more there are, it will takes longer...
Maya has a command Maya.cmds.evalDeferred that is meant for this purpose. It waits till no more Maya processing is pending and then evaluates itself.
You can also use Maya.cmds.scriptJob for the same purpose.
Note: While eval is considered dangerous and insecure in Maya context its really normal. Mainly because everything in Maya is inherently insecure as nearly all GUI items are just eval commands that the user may modify. So the second you let anybody use your Maya shell your security is breached.
I have a script that gets a file input plus some info, runs a couple of (possibly interdependent) programs on it using subprocess module, and distributes the output over the file-system.
Only a few parts can be tested in isolation by traditional unit-testing, so I'm searching a convenient way to automate the integration-testing (see if the output files exist in the right locations, in the right number, of the right size, etc).
I initially thought that setUp and tearDown methods from the default unittest module could help me, but they are re-run with each test, not once for the entire test suite, so it is not an option. Is there any way to make the unittest module run a global setUp and tearDown once? Or an alternative module/tool that I can use? Eclipse/PyDev integration would be a bonus.
What is the latest way to write Python tests? What modules/frameworks to use?
And another question: are doctest tests still of any value? Or should all the tests be written in a more modern testing framework?
Thanks, Boda Cydo.
The usual way is to use the builtin unittest module for creating unit tests and bundling them together to test suites which can be run independently. unittest is very similar to (and inspired by) jUnit and thus very easy to use.
If you're interested in the very latest changes, take a look at the new PyCon talk by Michael Foord:
PyCon 2010: New and Improved: Coming changes to unittest
Using the built-in unittest module is as relevant and easy as ever. The other unit testing options, py.test,nose, and twisted.trial are mostly compatible with unittest.
Doctests are of the same value they always were—they are great for testing your documentation, not your code. If you are going to put code examples in your docstrings, doctest can assure you keep them correct and up to date. There's nothing worse than trying to reproduce an example and failing, only to later realize it was actually the documentation's fault.
I don't know much about doctests, but at my university, nose testing is taught and encouraged.
Nose can be installed by following this procedure (I'm assuming you're using a PC - Windows OS):
install setuptools
Run DOS Command Prompt (Start -> All Programs -> Accessories -> Command Prompt)
For this step to work, you must be connected to the internet. In DOS, type: C:\Python25\Scripts\easy_install nose
If you are on a different OS, check this site
EDIT:
It's been two years since I originally wrote this post. Now, I've learned of this programming principle called Designing by Contract. This allows a programmer to define preconditions, postconditions and invariants (called contracts) for all functions in their code. The effect is that an error is raised if any of these contracts are violated.
The DbC framework that I would recommend for python is called PyContract I have successfully used it in my evolutionary programming framework
In my current project I'm using unittest, minimock, nose. In the past I've made heavy use of doctests, but in a large projects some tests can get kinda unwieldy, so I tend to reserve usage of doctests for simpler functions.
If you are using setuptools or distribute (you should be switching to distribute), you can set up nose as the default test collector so that you can run your tests with "python setup.py test"
setup(name='foo',
...
test_suite='nose.collector',
...
Now running "python setup.py test" will invoke nose, which will crawl your project for things that look like tests and run them, accumulating the results. If you also have doctests in your project, you can run nosetests with the --with-doctest option to enable the doctest plugin.
nose also has integration with coverage
nosetests --with-coverage.
You can also use the --cover-html --cover-html-dir options to generate an HTML coverage report for each module, with each line of code that is not under test highlighted. I wouldn't get too obsessed with getting coverage to report 100% test coverage for all modules. Some code is better left for integration tests, which I'll cover at the end.
I have become a huge fan of minimock, as it makes testing code with a lot of external dependencies really easy. While it works really well when paired with doctest, it can be used with any testing framework using the unittest.TraceTracker class. I would encourage you to avoid using it to test all of your code though, since you should still try to write your code so that each translation unit can be tested in isolation without mocking. Sometimes that's not possible though.
Here is an (untested) example of such a test using minimock and unittest:
# tests/test_foo.py
import minimock
import unittest
import foo
class FooTest(unittest2.TestCase):
def setUp(self):
# Track all calls into our mock objects. If we don't use a TraceTracker
# then all output will go to stdout, but we want to capture it.
self.tracker = minimock.TraceTracker()
def tearDown(self):
# Restore all objects in global module state that minimock had
# replaced.
minimock.restore()
def test_bar(self):
# foo.bar invokes urllib2.urlopen, and then calls read() on the
# resultin file object, so we'll use minimock to create a mocked
# urllib2.
urlopen_result = minimock.Mock('urlobject', tracker=self.tracker)
urlopen_result.read = minimock.Mock(
'urlobj.read', tracker=self.tracker, returns='OMG')
foo.urllib2.urlopen = minimock.Mock(
'urllib2.urlopen', tracker=self.tracker, returns=urlopen_result)
# Now when we call foo.bar(URL) and it invokes
# *urllib2.urlopen(URL).read()*, it will not actually send a request
# to URL, but will instead give us back the dummy response body 'OMG',
# which it then returns.
self.assertEquals(foo.bar('http://example.com/foo'), 'OMG')
# Now we can get trace info from minimock to verify that our mocked
# urllib2 was used as intended. self.tracker has traced our calls to
# *urllib2.urlopen()*
minimock.assert_same_trace(self.tracker, """\
Called urllib2.urlopen('http://example.com/foo)
Called urlobj.read()
Called urlobj.close()""")
Unit tests shouldn't be the only kinds of tests you write though. They are certainly useful and IMO extremely important if you plan on maintaining this code for any extended period of time. They make refactoring easier and help catch regressions, but they don't really test the interaction between various components and how they interact (if you do it right).
When I start getting to the point where I have a mostly finished product with decent test coverage that I intend to release, I like to write at least one integration test that runs the complete program in an isolated environment.
I've had a lot of success with this on my current project. I had about 80% unit test coverage, and the rest of the code was stuff like argument parsing, command dispatch and top level application state, which is difficult to cover in unit tests. This program has a lot of external dependencies, hitting about a dozen different web services and interacting with about 6,000 machines in production, so running this in isolation proved kinda difficult.
I ended up writing an integration test which spawns a WSGI server written with eventlet and webob that simulates all of the services my program interacts with in production. Then the integration test monkey patches our web service client library to intercept all HTTP requests and send them to the WSGI application. After doing that, it loads a state file that contains a serialized snapshot of the state of the cluster, and invokes the application by calling it's main() function. Now all of the external services my program interacts with are simulated, so that I can run my program as it would be run in production in a repeatable manner.
The important thing to remember about doctests is that the tests are based on string comparisons, and the way that numbers are rendered as strings will vary on different platforms and even in different python interpreters.
Most of my work deals with computations, so I use doctests only to test my examples and my version string. I put a few in the __init__.py since that will show up as the front page of my epydoc-generated API documentation.
I use nose for testing, although I'm very interested in checking out the latest changes to py.test.