It is extremely important that all code contributed to IPython has tests.
Tests should be written as unittests, doctests or other entities that the
IPython test system can detect. See below for more details on this.
Each subpackage in IPython should have its own tests directory that
contains all of the tests for that subpackage. All of the files in the
tests directory should have the word “tests” in them to enable
the testing framework to find them.
In docstrings, examples (either using IPython prompts like In : or
‘classic’ python >>> ones) can and should be included. The testing system
will detect them as doctests and will run them; it offers control to skip parts
or all of a specific doctest if the example is meant to be informative but
shows non-reproducible information (like filesystem data).
If a subpackage has any dependencies beyond the Python standard library, the
tests for that subpackage should be skipped if the dependencies are not found.
This is very important so users don’t get tests failing simply because they
don’t have dependencies.
The testing system we use is a hybrid of nose and Twisted’s trial test runner.
We use both because nose detects more things than Twisted and allows for more
flexible (and lighter-weight) ways of writing tests; in particular we’ve
developed a nose plugin that allows us to paste verbatim IPython sessions and
test them as doctests, which is extremely important for us. But the parts of
IPython that depend on Twisted must be tested using trial, because only trial
manages the Twisted reactor correctly.
For the impatient: running the tests
You can run IPython from the source download directory without even installing
it system-wide or having configure anything, by typing at the terminal:
In order to run the test suite, you must at least be able to import IPython,
even if you haven’t fully installed the user-facing scripts yet (common in a
development environment). You can then run the tests with:
python -c "import IPython; IPython.test()"
Once you have installed IPython either via a full install or using:
you will have available a system-wide script called iptest that runs
the full test suite. You can then run the suite with:
Regardless of how you run things, you should eventually see something like:
Test suite completed for system with the following information:
'sys_version': '2.6.6 (r266:84292, Sep 15 2010, 15:52:39) \n[GCC 4.4.5]'}
Tools and libraries available at test time:
curses foolscap gobject gtk pexpect twisted wx wx.aui zope.interface
Ran 9 test groups in 67.213s
If not, there will be a message indicating which test group failed and how to
rerun that group individually. For example, this tests the
IPython.utils subpackage, the -v option shows progress
$ iptest -v IPython.utils
Ran 125 tests in 0.119s
Because the IPython test machinery is based on nose, you can use all nose
options and syntax, typing iptest -h shows all available options. For
example, this lets you run the specific test test_rehashx() inside the
$ iptest -vv IPython.core.tests.test_magic:test_rehashx
IPython.core.tests.test_magic.test_rehashx(True,) ... ok
IPython.core.tests.test_magic.test_rehashx(True,) ... ok
Ran 2 tests in 0.100s
When developing, the --pdb and --pdb-failures of nose are
particularly useful, these drop you into an interactive pdb session at the
point of the error or failure respectively.
To run Twisted-using tests, use the trial command on a per file or
The system information summary printed above is accessible from the top
level package. If you encounter a problem with IPython, it’s useful to
include this information when reporting on the mailing list; use:
from IPython import sys_info
and include the resulting information in your query.
For developers: writing tests
By now IPython has a reasonable test suite, so the best way to see what’s
available is to look at the tests directory in most subpackages. But
here are a few pointers to make the process easier.
The IPython.testing package is where all of the machinery to test
IPython (rather than the tests for its various parts) lives. In particular,
the iptest module in there has all the smarts to control the test
process. In there, the make_exclude() function is used to build a
blacklist of exclusions, these are modules that do not get even imported for
tests. This is important so that things that would fail to even import because
of missing dependencies don’t give errors to end users, as we stated above.
The decorators module contains a lot of useful decorators, especially
useful to mark individual tests that should be skipped under certain conditions
(rather than blacklisting the package altogether because of a missing major
Our nose plugin for doctests
The plugin subpackage in testing contains a nose plugin called
ipdoctest that teaches nose about IPython syntax, so you can write
doctests with IPython prompts. You can also mark doctest output with #
random for the output corresponding to a single input to be ignored (stronger
than using ellipsis and useful to keep it as an example). If you want the
entire docstring to be executed but none of the output from any input to be
checked, you can use the # all-random marker. The
IPython.testing.plugin.dtexample module contains examples of how to use
these; for reference here is how to use # random:
"""A function with some random output.
Normal examples are verified as usual:
But if you put '# random' in the output, it is ignored:
junk goes here... # random
again, anything goes #random
if multiline, the random mark is only needed once.
You can also put the random marker at the end:
.. or at the beginning.
More correct input is properly verified:
and an example of # all-random:
"""A function where we ignore the output of ALL examples.
This mark tells the testing machinery that all subsequent examples
should be treated as random (ignoring their output). They are still
executed, so if a they raise an error, it will be detected as such,
but their output is completely ignored.
junk goes here...
In : print 'hello'
world # random
In : iprand()
When writing docstrings, you can use the @skip_doctest decorator to
indicate that a docstring should not be treated as a doctest at all. The
difference between # all-random and @skip_doctest is that the former
executes the example but ignores output, while the latter doesn’t execute any
code. @skip_doctest should be used for docstrings whose examples are
If a given docstring fails under certain conditions but otherwise is a good
doctest, you can use code like the following, that relies on the ‘null’
decorator to leave the docstring intact where it works as a test:
# The docstring for full_path doctests differently on win32 (different path
# separator) so just skip the doctest there, and use a null decorator
doctest_deco = dec.skip_doctest if sys.platform == 'win32' else dec.null_deco
"""Make full paths for all the listed files, based on startPath..."""
# function body follows...
With our nose plugin that understands IPython syntax, an extremely effective
way to write tests is to simply copy and paste an interactive session into a
docstring. You can writing this type of test, where your docstring is meant
only as a test, by prefixing the function name with doctest_ and leaving
its body absolutely empty other than the docstring. In
IPython.core.tests.test_magic you can find several examples of this, but
for completeness sake, your code should look like this (a simple case):
In : %time None
CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s
Wall time: 0.00 s
This function is only analyzed for its docstring but it is not considered a
separate test, which is why its body should be empty.
Parametric tests done right
If you need to run multiple tests inside the same standalone function or method
of a unittest.TestCase subclass, IPython provides the parametric
decorator for this purpose. This is superior to how test generators work in
nose, because IPython’s keeps intact your stack, which makes debugging vastly
easier. For example, these are some parametric tests both in class form and as
a standalone function (choose in each situation the style that best fits the
problem at hand, since both work):
from IPython.testing import decorators as dec
assert i<j,"%s !< %s" % (i,j)
yield is_smaller(3, 4)
x, y = 1, 2
yield is_smaller(x, y)
yield is_smaller(3, 4)
x, y = 1, 2
yield is_smaller(x, y)
Writing tests for Twisted-using code
Tests of Twisted [Twisted] using code should be written by subclassing the
TestCase class that comes with twisted.trial.unittest. Furthermore, all
Deferred instances that are created in the test must be properly
chained and the final one must be the return value of the test method.
The best place to see how to use the testing tools, are the tests for these
tools themselves, which live in IPython.testing.tests.
This section is a set of notes on the key points of the IPython testing needs,
that were used when writing the system and should be kept for reference as it
Testing IPython in full requires modifications to the default behavior of nose
and doctest, because the IPython prompt is not recognized to determine Python
input, and because IPython admits user input that is not valid Python (things
like %magics and !system commands.
We basically need to be able to test the following types of code:
Pure Python files containing normal tests. These are not a problem, since
Nose will pick them up as long as they conform to the (flexible) conventions
used by nose to recognize tests.
Python files containing doctests. Here, we have two possibilities:
- The prompts are the usual >>> and the input is pure Python.
- The prompts are of the form In : and the input can contain extended
In the first case, Nose will recognize the doctests as long as it is called
with the --with-doctest flag. But the second case will likely require
modifications or the writing of a new doctest plugin for Nose that is
ReStructuredText files that contain code blocks. For this type of file, we
have three distinct possibilities for the code blocks:
- They use >>> prompts.
- They use In : prompts.
- They are standalone blocks of pure Python code without any prompts.
The first two cases are similar to the situation #2 above, except that in
this case the doctests must be extracted from input code blocks using
docutils instead of from the Python docstrings.
In the third case, we must have a convention for distinguishing code blocks
that are meant for execution from others that may be snippets of shell code
or other examples not meant to be run. One possibility is to assume that
all indented code blocks are meant for execution, but to have a special
docutils directive for input that should not be executed.
For those code blocks that we will execute, the convention used will simply
be that they get called and are considered successful if they run to
completion without raising errors. This is similar to what Nose does for
standalone test functions, and by putting asserts or other forms of
exception-raising statements it becomes possible to have literate examples
that double as lightweight tests.
Extension modules with doctests in function and method docstrings.
Currently Nose simply can’t find these docstrings correctly, because the
underlying doctest DocTestFinder object fails there. Similarly to #2 above,
the docstrings could have either pure python or IPython prompts.
Of these, only 3-c (reST with standalone code blocks) is not implemented at