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# Testing guidelines

## Introduction

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 `numpy.testing`, so all of the NumPy examples shown below are also applicable to SciPy

## Testing NumPy

NumPy can be tested in a number of ways, choose any way you feel comfortable.

### Running tests from inside Python

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()

### Running tests from the command line

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 Testing builds

### Running doctests

NumPy documentation contains code examples, “doctests”. To check that the examples are correct, install the `scipy-doctest` package:

    $ pip install scipy-doctest

and run one of:

    $ spin check-docs -v
    $ spin check-docs numpy/linalg
    $ spin check-docs -- -k 'det and not slogdet'

Note that the doctests are not run when you use `spin test`.

### Other methods of running tests

Run tests using your favourite IDE such as vscode or pycharm

## Writing your own tests

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:

- `numpy.testing.assert_equal` for testing exact elementwise equality between a result array and a reference,

- `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

- `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 `pytest.raises` and `pytest.warns` should be preferred over their legacy counterparts `numpy.testing.assert_raises` and `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.

### Using C code in tests

NumPy exposes a rich 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.

numpy.testing.extbuild.build_and_import_extension(modname, functions, *, prologue='', build_dir=None, include_dirs=None, more_init='')

  Build and imports a c-extension module modname from a list of function fragments functions.

  Parameters:
    functions list of fragments

      Each fragment is a sequence of func_name, calling convention, snippet.

    prologue string

      Code to precede the rest, usually extra `#include` or `#define` macros.

    build_dir pathlib.Path

      Where to build the module, usually a temporary directory

    include_dirs list

      Extra directories to find include files when compiling

    more_init string

      Code to appear in the module PyMODINIT_FUNC

  Returns:
    out: module

      The module will have been loaded and is ready for use

  Examples

      >>> functions = [("test_bytes", "METH_O", """
          if ( !PyBytesCheck(args)) {
              Py_RETURN_FALSE;
          }
          Py_RETURN_TRUE;
      """)]
      >>> mod = build_and_import_extension("testme", functions)
      >>> assert not mod.test_bytes('abc')
      >>> assert mod.test_bytes(b'abc')

### Labeling tests

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')

### Easier setup and teardown functions / methods

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.

### Parametric tests

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

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.

### `tests/`

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()

### `__init__.py` and `setup.py`

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!

## Tips & Tricks

### Known failures & skipping tests

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

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.

### Documentation for `numpy.test`

numpy.test(label='fast', verbose=1, extra_argv=None, doctests=False, coverage=False, durations=-1, tests=None)

  Pytest test runner.

  A test function is typically added to a package’s __init__.py like so:

      from numpy._pytesttester import PytestTester
      test = PytestTester(__name__).test
      del PytestTester

  Calling this test function finds and runs all tests associated with the module and all its sub-modules.

  Parameters:
    module_name module name

      The name of the module to test.

  Attributes:
    module_name str

      Full path to the package to test.

  Notes

  Unlike the previous `nose`-based implementation, this class is not publicly exposed as it performs some `numpy`-specific warning suppression.