Until the 1.15 release, NumPy used the nose testing framework, it now uses the pytest framework. The older framework is still maintained in order to support downstream projects that use the old numpy framework, but all tests for NumPy should use pytest.
Our goal is that every module and package in NumPy should have a thorough set of unit tests. These tests should exercise the full functionality of a given routine as well as its robustness to erroneous or unexpected input arguments. Well-designed tests with good coverage make an enormous difference to the ease of refactoring. Whenever a new bug is found in a routine, you should write a new test for that specific case and add it to the test suite to prevent that bug from creeping back in unnoticed.
Note
SciPy uses the testing framework from :mod:`numpy.testing`, so all of the NumPy examples shown below are also applicable to SciPy
NumPy can be tested in a number of ways, choose any way you feel comfortable.
You can test an installed NumPy by numpy.test, for example, To run NumPy's full test suite, use the following:
>>> import numpy >>> numpy.test(label='slow')
The test method may take two or more arguments; the first label is a
string specifying what should be tested and the second verbose is an
integer giving the level of output verbosity. See the docstring
numpy.test
for details. The default value for label is 'fast' - which
will run the standard tests. The string 'full' will run the full battery
of tests, including those identified as being slow to run. If verbose
is 1 or less, the tests will just show information messages about the tests
that are run; but if it is greater than 1, then the tests will also provide
warnings on missing tests. So if you want to run every test and get
messages about which modules don't have tests:
>>> numpy.test(label='full', verbose=2) # or numpy.test('full', 2)
Finally, if you are only interested in testing a subset of NumPy, for
example, the _core module, use the following:
>>> numpy._core.test()
If you want to build NumPy in order to work on NumPy itself, use the spin
utility. To run NumPy's full test suite:
$ spin test -m full
Testing a subset of NumPy:
$ spin test -t numpy/_core/tests
For detailed info on testing, see :ref:`testing-builds`
Run tests using your favourite IDE such as vscode or pycharm
If you are writing code that you'd like to become part of NumPy,
please write the tests as you develop your code.
Every Python module, extension module, or subpackage in the NumPy
package directory should have a corresponding test_<name>.py file.
Pytest examines these files for test methods (named test*) and test
classes (named Test*).
Suppose you have a NumPy module numpy/xxx/yyy.py containing a
function zzz(). To test this function you would create a test
module called test_yyy.py. If you only need to test one aspect of
zzz, you can simply add a test function:
def test_zzz():
assert zzz() == 'Hello from zzz'
More often, we need to group a number of tests together, so we create a test class:
import pytest
# import xxx symbols
from numpy.xxx.yyy import zzz
import pytest
class TestZzz:
def test_simple(self):
assert zzz() == 'Hello from zzz'
def test_invalid_parameter(self):
with pytest.raises(ValueError, match='.*some matching regex.*'):
...
Within these test methods, the assert statement or a specialized assertion
function is used to test whether a certain assumption is valid. If the
assertion fails, the test fails. Common assertion functions include:
- :func:`numpy.testing.assert_equal` for testing exact elementwise equality between a result array and a reference,
- :func:`numpy.testing.assert_allclose` for testing near elementwise equality between a result array and a reference (i.e. with specified relative and absolute tolerances), and
- :func:`numpy.testing.assert_array_less` for testing (strict) elementwise ordering between a result array and a reference.
By default, these assertion functions only compare the numerical values in the
arrays. Consider using the strict=True option to check the array dtype
and shape, too.
When you need custom assertions, use the Python assert statement. Note that
pytest internally rewrites assert statements to give informative
output when it fails, so it should be preferred over the legacy variant
numpy.testing.assert_. Whereas plain assert statements are ignored
when running Python in optimized mode with -O, this is not an issue when
running tests with pytest.
Similarly, the pytest functions :func:`pytest.raises` and :func:`pytest.warns`
should be preferred over their legacy counterparts
:func:`numpy.testing.assert_raises` and :func:`numpy.testing.assert_warns`,
which are more broadly used. These versions also accept a match
parameter, which should always be used to precisely target the intended
warning or error.
Note that test_ functions or methods should not have a docstring, because
that makes it hard to identify the test from the output of running the test
suite with verbose=2 (or similar verbosity setting). Use plain comments
(#) to describe the intent of the test and help the unfamiliar reader to
interpret the code.
Also, since much of NumPy is legacy code that was originally written without unit tests, there are still several modules that don't have tests yet. Please feel free to choose one of these modules and develop tests for it.
NumPy exposes a rich :ref:`C-API<c-api>` . These are tested using c-extension
modules written "as-if" they know nothing about the internals of NumPy, rather
using the official C-API interfaces only. Examples of such modules are tests
for a user-defined rational dtype in _rational_tests or the ufunc
machinery tests in _umath_tests which are part of the binary distribution.
Starting from version 1.21, you can also write snippets of C code in tests that
will be compiled locally into c-extension modules and loaded into python.
.. currentmodule:: numpy.testing.extbuild
.. autofunction:: build_and_import_extension
Unlabeled tests like the ones above are run in the default
numpy.test() run. If you want to label your test as slow - and
therefore reserved for a full numpy.test(label='full') run, you
can label it with pytest.mark.slow:
import pytest
@pytest.mark.slow
def test_big(self):
print('Big, slow test')
Similarly for methods:
class test_zzz:
@pytest.mark.slow
def test_simple(self):
assert_(zzz() == 'Hello from zzz')
Testing looks for module-level or class method-level setup and teardown functions by name; thus:
def setup_module():
"""Module-level setup"""
print('doing setup')
def teardown_module():
"""Module-level teardown"""
print('doing teardown')
class TestMe:
def setup_method(self):
"""Class-level setup"""
print('doing setup')
def teardown_method():
"""Class-level teardown"""
print('doing teardown')
Setup and teardown functions to functions and methods are known as "fixtures",
and they should be used sparingly.
pytest supports more general fixture at various scopes which may be used
automatically via special arguments. For example, the special argument name
tmpdir is used in test to create a temporary directory.
One very nice feature of pytest is the ease of testing across a range
of parameter values using the pytest.mark.parametrize decorator. For example,
suppose you wish to test linalg.solve for all combinations of three
array sizes and two data types:
@pytest.mark.parametrize('dimensionality', [3, 10, 25])
@pytest.mark.parametrize('dtype', [np.float32, np.float64])
def test_solve(dimensionality, dtype):
np.random.seed(842523)
A = np.random.random(size=(dimensionality, dimensionality)).astype(dtype)
b = np.random.random(size=dimensionality).astype(dtype)
x = np.linalg.solve(A, b)
eps = np.finfo(dtype).eps
assert_allclose(A @ x, b, rtol=eps*1e2, atol=0)
assert x.dtype == np.dtype(dtype)
Doctests are a convenient way of documenting the behavior of a function and allowing that behavior to be tested at the same time. The output of an interactive Python session can be included in the docstring of a function, and the test framework can run the example and compare the actual output to the expected output.
The doctests can be run by adding the doctests argument to the
test() call; for example, to run all tests (including doctests)
for numpy.lib:
>>> import numpy as np >>> np.lib.test(doctests=True)
The doctests are run as if they are in a fresh Python instance which
has executed import numpy as np. Tests that are part of a NumPy
subpackage will have that subpackage already imported. E.g. for a test
in numpy/linalg/tests/, the namespace will be created such that
from numpy import linalg has already executed.
Rather than keeping the code and the tests in the same directory, we
put all the tests for a given subpackage in a tests/
subdirectory. For our example, if it doesn't already exist you will
need to create a tests/ directory in numpy/xxx/. So the path
for test_yyy.py is numpy/xxx/tests/test_yyy.py.
Once the numpy/xxx/tests/test_yyy.py is written, its possible to
run the tests by going to the tests/ directory and typing:
python test_yyy.py
Or if you add numpy/xxx/tests/ to the Python path, you could run
the tests interactively in the interpreter like this:
>>> import test_yyy >>> test_yyy.test()
Usually, however, adding the tests/ directory to the python path
isn't desirable. Instead it would better to invoke the test straight
from the module xxx. To this end, simply place the following lines
at the end of your package's __init__.py file:
...
def test(level=1, verbosity=1):
from numpy.testing import Tester
return Tester().test(level, verbosity)
You will also need to add the tests directory in the configuration section of your setup.py:
...
def configuration(parent_package='', top_path=None):
...
config.add_subpackage('tests')
return config
...
Now you can do the following to test your module:
>>> import numpy >>> numpy.xxx.test()
Also, when invoking the entire NumPy test suite, your tests will be found and run:
>>> import numpy >>> numpy.test() # your tests are included and run automatically!
Sometimes you might want to skip a test or mark it as a known failure, such as when the test suite is being written before the code it's meant to test, or if a test only fails on a particular architecture.
To skip a test, simply use skipif:
import pytest
@pytest.mark.skipif(SkipMyTest, reason="Skipping this test because...")
def test_something(foo):
...
The test is marked as skipped if SkipMyTest evaluates to nonzero,
and the message in verbose test output is the second argument given to
skipif. Similarly, a test can be marked as a known failure by
using xfail:
import pytest
@pytest.mark.xfail(MyTestFails, reason="This test is known to fail because...")
def test_something_else(foo):
...
Of course, a test can be unconditionally skipped or marked as a known
failure by using skip or xfail without argument, respectively.
A total of the number of skipped and known failing tests is displayed
at the end of the test run. Skipped tests are marked as 'S' in
the test results (or 'SKIPPED' for verbose > 1), and known
failing tests are marked as 'x' (or 'XFAIL' if verbose >
1).
Tests on random data are good, but since test failures are meant to expose
new bugs or regressions, a test that passes most of the time but fails
occasionally with no code changes is not helpful. Make the random data
deterministic by setting the random number seed before generating it. Use
either Python's random.seed(some_number) or NumPy's
numpy.random.seed(some_number), depending on the source of random numbers.
Alternatively, you can use Hypothesis to generate arbitrary data.
Hypothesis manages both Python's and Numpy's random seeds for you, and
provides a very concise and powerful way to describe data (including
hypothesis.extra.numpy, e.g. for a set of mutually-broadcastable shapes).
The advantages over random generation include tools to replay and share failures without requiring a fixed seed, reporting minimal examples for each failure, and better-than-naive-random techniques for triggering bugs.
.. autofunction:: numpy.test