yt includes a testing suite which one can run on the codebase to assure that no breaks in functionality have occurred. This testing suite is based on the Nose testing framework. The suite consists of two components, unit tests and answer tests. Unit tests confirm that an isolated piece of functionality runs without failure for inputs with known correct outputs. Answer tests verify the integration and compatibility of the individual code unit by generating output from user-visible yt functions and comparing and matching the results against outputs of the same function produced using older versions of the yt codebase. This ensures consistency in results as development proceeds.
The testing suite should be run locally by developers to make sure they aren’t checking in any code that breaks existing functionality. To further this goal, an automatic buildbot runs the test suite after each code commit to confirm that yt hasn’t broken recently. To supplement this effort, we also maintain a continuous integration server that runs the tests with each commit to the yt version control repository.
Unit tests are tests that operate on some small set of machinery, and verify
that the machinery works. yt uses the Nose framework for running unit tests.
In practice, what this means is that we write scripts that yield
assertions, and Nose identifies those scripts, runs them, and verifies that the
assertions are true.
One can run the unit tests very straightforwardly from any python interpreter that can import the yt module:
import yt
yt.run_nose()
If you are developing new functionality, it is sometimes more convenient to use
the Nose command line interface, nosetests
. You can run the unit tests
using nose
by navigating to the base directory of the yt mercurial
repository and invoking nosetests
:
$ cd $YT_HG
$ nosetests
where $YT_HG
is the path to the root of the yt mercurial repository.
If you want to specify a specific unit test to run (and not run the entire
suite), you can do so by specifying the path of the test relative to the
$YT_HG/yt
directory – note that you strip off one yt more than you
normally would! For example, if you want to run the plot_window tests, you’d
run:
$ nosetests visualization/tests/test_plotwindow.py
yt provides several pieces of testing assistance, all in the yt.testing
module. Describing them in detail is somewhat outside the scope of this
document, as in some cases they belong to other packages. However, a few come
in handy:
fake_random_ds()
provides the ability to create a random
dataset, with several fields and divided into several different
grids, that can be operated on.assert_equal()
can operate on arrays.assert_almost_equal()
can operate on arrays and accepts a
relative allowable difference.amrspace()
provides the ability to create AMR grid
structures.expand_keywords()
provides the ability to iterate over
many values for keywords.To create new unit tests:
tests/
directory next to the file containing the
functionality you want to test. Be sure to add this new directory as a
subpackage in the setup.py script located in the directory you’re adding a
new tests/
folder to. This ensures that the tests will be deployed in
yt source and binary distributions.test_
and
including the name of the functionality.test_
that
accept no arguments. These should yield
a set of values of the form
function
, arguments
. For example yield assert_equal, 1.0, 1.0
would evaluate that 1.0 equaled 1.0.fake_random_ds
to test on datasets, and be sure to test for
several combinations of nproc
, so that domain decomposition can be
tested as well.expand_keywords()
function, which will enable much
easier iteration over options.For an example of how to write unit tests, look at the file
yt/data_objects/tests/test_covering_grid.py
, which covers a great deal of
functionality.
Answer tests test actual data, and many operations on that data, to make sure that answers don’t drift over time. This is how we will be testing frontends, as opposed to operations, in yt.
The very first step is to make a directory and copy over the data against which you want to test. Currently, we test:
DD0010/moving7_0010
(available in tests/
in the yt distribution)IsolatedGalaxy/galaxy0030/galaxy0030
WindTunnel/windtunnel_4lev_hdf5_plt_cnt_0030
GasSloshingLowRes/sloshing_low_res_hdf5_plt_cnt_0300
TurbBoxLowRes/data.0005.3d.hdf5
GaussianCloud/data.0077.3d.hdf5
RadAdvect/plt00000
RadTube/plt00500
These datasets are available at http://yt-project.org/data/.
Next, modify the file ~/.yt/config
to include a section [yt]
with the parameter test_data_dir
. Set this to point to the
directory with the test data you want to compare. Here is an example
config file:
[yt]
test_data_dir = /Users/tomservo/src/yt-data
More data will be added over time. To run the tests, you can import the yt
module and invoke yt.run_nose()
with a new keyword argument:
import yt
yt.run_nose(run_answer_tests=True)
If you have installed yt using python setup.py develop
you can also
optionally invoke nose using the nosetests
command line interface:
$ cd $YT_HG
$ nosetests --with-answer-testing
In either case, the current gold standard results will be downloaded from the
rackspace cloud and compared to what is generated locally. The results from a
nose testing session are pretty straightforward to understand, the results for
each test are printed directly to STDOUT. If a test passes, nose prints a
period, F if a test fails, and E if the test encounters an exception or errors
out for some reason. If you want to also run tests for the ‘big’ datasets,
then you can use the answer_big_data
keyword argument:
import yt
yt.run_nose(run_answer_tests=True, answer_big_data=True)
or, in the base directory of the yt mercurial repository:
$ nosetests --with-answer-testing --answer-big-data
It’s also possible to only run the answer tests for one frontend. For example, to run only the enzo answers tests, one can do,
$ nosetests --with-answer-testing yt.frontends.enzo
Tests can be added in the file yt/utilities/answer_testing/framework.py
.
You can find examples there of how to write a test. Here is a trivial example:
#!python
class MaximumValue(AnswerTestingTest):
_type_name = "ParentageRelationships"
_attrs = ("field",)
def __init__(self, ds_fn, field):
super(MaximumValue, self).__init__(ds_fn)
self.field = field
def run(self):
v, c = self.ds.find_max(self.field)
result = np.empty(4, dtype="float64")
result[0] = v
result[1:] = c
return result
def compare(self, new_result, old_result):
assert_equal(new_result, old_result)
What this does is calculate the location and value of the maximum of a
field. It then puts that into the variable result, returns that from
run
and then in compare
makes sure that all are exactly equal.
To write a new test:
AnswerTestingTest
_type_name
(a string) and _attrs
(a tuple of strings, one for each attribute that defines the test –
see how this is done for projections, for instance)run
and compare
The first
should return a result and the second should compare a result to an old
result. Neither should yield, but instead actually return. If you need
additional arguments to the test, implement an __init__
routine.run
will be stored. So if
you are going to return a huge amount of data, please ensure that the test
only gets run for small data. If you want a fast way to measure something as
being similar or different, either an md5 hash (see the grid values test) or
a sum and std of an array act as good proxies. If you must store a large
amount of data for some reason, try serializing the data to a string
(e.g. using numpy.ndarray.dumps
), and then compressing the data stream
using zlib.compress
.To add data to the testing suite, first write a new set of tests for the data.
The Enzo example in yt/frontends/enzo/tests/test_outputs.py
is
considered canonical. Do these things:
tests
inside the frontend’s directory.test_outputs.py
in the frontend’s tests
directory.@requires_ds(file_name)
This decorate can accept the argument
big_data
for if this data is too big to run all the time.small_patch_amr
and big_patch_amr
routines that
you can yield from to execute a bunch of standard tests. This is where
you should start, and then yield additional tests that stress the
outputs in whatever ways are necessary to ensure functionality.If you are adding to a frontend that has a few tests already, skip the first two steps.
To upload answers you can execute this command:
$ nosetests --with-answer-testing frontends/enzo/ --answer-store --answer-name=whatever
The current version of the gold standard can be found in the variable
_latest
inside yt/utilities/answer_testing/framework.py
As of
the time of this writing, it is gold007
Note that the name of the
suite of results is now disconnected from the dataset’s name, so you
can upload multiple outputs with the same name and not collide.
To upload answers, you must have the package boto installed, and you must have an Amazon key provided by Matt. Contact Matt for these keys.