=============================
Details and advanced features
=============================

This is an account of slightly less common Hypothesis features that you don't need
to get started but will nevertheless make your life easier.


----------------------
Additional test output
----------------------

Normally the output of a failing test will look something like:

.. code::

    Falsifying example: test_a_thing(x=1, y="foo")

With the ``repr`` of each keyword argument being printed.

Sometimes this isn't enough, either because you have values with a ``repr`` that
isn't very descriptive or because you need to see the output of some
intermediate steps of your test. That's where the ``note`` function comes in:

.. autofunction:: hypothesis.note

.. code-block:: pycon

    >>> from hypothesis import given, note, strategies as st
    >>> @given(st.lists(st.integers()), st.randoms())
    ... def test_shuffle_is_noop(ls, r):
    ...     ls2 = list(ls)
    ...     r.shuffle(ls2)
    ...     note("Shuffle: %r" % (ls2))
    ...     assert ls == ls2
    ...
    >>> try:
    ...     test_shuffle_is_noop()
    ... except AssertionError:
    ...     print('ls != ls2')
    Falsifying example: test_shuffle_is_noop(ls=[0, 1], r=RandomWithSeed(1))
    Shuffle: [1, 0]
    ls != ls2

The note is printed in the final run of the test in order to include any
additional information you might need in your test.


.. _statistics:

---------------
Test Statistics
---------------

If you are using :pypi:`pytest` you can see a number of statistics about the executed tests
by passing the command line argument ``--hypothesis-show-statistics``. This will include
some general statistics about the test:

For example if you ran the following with ``--hypothesis-show-statistics``:

.. code-block:: python

  from hypothesis import given, strategies as st


  @given(st.integers())
  def test_integers(i):
      pass


You would see:

.. code-block:: none

  test_integers:

    - 100 passing examples, 0 failing examples, 0 invalid examples
    - Typical runtimes: ~ 1ms
    - Fraction of time spent in data generation: ~ 12%
    - Stopped because settings.max_examples=100

The final "Stopped because" line is particularly important to note: It tells you the
setting value that determined when the test should stop trying new examples. This
can be useful for understanding the behaviour of your tests. Ideally you'd always want
this to be :obj:`~hypothesis.settings.max_examples`.

In some cases (such as filtered and recursive strategies) you will see events mentioned
which describe some aspect of the data generation:

.. code-block:: python

  from hypothesis import given, strategies as st


  @given(st.integers().filter(lambda x: x % 2 == 0))
  def test_even_integers(i):
      pass

You would see something like:

.. code-block:: none

  test_even_integers:

      - 100 passing examples, 0 failing examples, 36 invalid examples
      - Typical runtimes: 0-1 ms
      - Fraction of time spent in data generation: ~ 16%
      - Stopped because settings.max_examples=100
      - Events:
        * 80.88%, Retried draw from integers().filter(lambda x: <unknown>) to satisfy filter
        * 26.47%, Aborted test because unable to satisfy integers().filter(lambda x: <unknown>)

You can also mark custom events in a test using the ``event`` function:

.. autofunction:: hypothesis.event

.. code:: python

  from hypothesis import given, event, strategies as st


  @given(st.integers().filter(lambda x: x % 2 == 0))
  def test_even_integers(i):
      event("i mod 3 = %d" % (i % 3,))


You will then see output like:

.. code-block:: none

  test_even_integers:

    - 100 passing examples, 0 failing examples, 38 invalid examples
    - Typical runtimes: 0-1 ms
    - Fraction of time spent in data generation: ~ 16%
    - Stopped because settings.max_examples=100
    - Events:
      * 80.43%, Retried draw from integers().filter(lambda x: <unknown>) to satisfy filter
      * 31.88%, i mod 3 = 0
      * 27.54%, Aborted test because unable to satisfy integers().filter(lambda x: <unknown>)
      * 21.74%, i mod 3 = 1
      * 18.84%, i mod 3 = 2

Arguments to ``event`` can be any hashable type, but two events will be considered the same
if they are the same when converted to a string with :obj:`python:str`.

------------------
Making assumptions
------------------

Sometimes Hypothesis doesn't give you exactly the right sort of data you want - it's
mostly of the right shape, but some examples won't work and you don't want to care about
them. You *can* just ignore these by aborting the test early, but this runs the risk of
accidentally testing a lot less than you think you are. Also it would be nice to spend
less time on bad examples - if you're running 100 examples per test (the default) and
it turns out 70 of those examples don't match your needs, that's a lot of wasted time.

.. autofunction:: hypothesis.assume

For example suppose you had the following test:


.. code:: python

  @given(floats())
  def test_negation_is_self_inverse(x):
      assert x == -(-x)


Running this gives us:

.. code::

  Falsifying example: test_negation_is_self_inverse(x=float('nan'))
  AssertionError

This is annoying. We know about NaN and don't really care about it, but as soon as Hypothesis
finds a NaN example it will get distracted by that and tell us about it. Also the test will
fail and we want it to pass.

So lets block off this particular example:

.. code:: python

  from math import isnan


  @given(floats())
  def test_negation_is_self_inverse_for_non_nan(x):
      assume(not isnan(x))
      assert x == -(-x)

And this passes without a problem.

In order to avoid the easy trap where you assume a lot more than you intended, Hypothesis
will fail a test when it can't find enough examples passing the assumption.

If we'd written:

.. code:: python

  @given(floats())
  def test_negation_is_self_inverse_for_non_nan(x):
      assume(False)
      assert x == -(-x)

Then on running we'd have got the exception:

.. code::

  Unsatisfiable: Unable to satisfy assumptions of hypothesis test_negation_is_self_inverse_for_non_nan. Only 0 examples considered satisfied assumptions

~~~~~~~~~~~~~~~~~~~
How good is assume?
~~~~~~~~~~~~~~~~~~~

Hypothesis has an adaptive exploration strategy to try to avoid things which falsify
assumptions, which should generally result in it still being able to find examples in
hard to find situations.

Suppose we had the following:


.. code:: python

  @given(lists(integers()))
  def test_sum_is_positive(xs):
      assert sum(xs) > 0

Unsurprisingly this fails and gives the falsifying example ``[]``.

Adding ``assume(xs)`` to this removes the trivial empty example and gives us ``[0]``.

Adding ``assume(all(x > 0 for x in xs))`` and it passes: the sum of a list of
positive integers is positive.

The reason that this should be surprising is not that it doesn't find a
counter-example, but that it finds enough examples at all.

In order to make sure something interesting is happening, suppose we wanted to
try this for long lists. e.g. suppose we added an ``assume(len(xs) > 10)`` to it.
This should basically never find an example: a naive strategy would find fewer
than one in a thousand examples, because if each element of the list is
negative with probability one-half, you'd have to have ten of these go the right
way by chance. In the default configuration Hypothesis gives up long before
it's tried 1000 examples (by default it tries 200).

Here's what happens if we try to run this:


.. code:: python

  @given(lists(integers()))
  def test_sum_is_positive(xs):
      assume(len(xs) > 10)
      assume(all(x > 0 for x in xs))
      print(xs)
      assert sum(xs) > 0


In: ``test_sum_is_positive()``

.. code:: python

  [17, 12, 7, 13, 11, 3, 6, 9, 8, 11, 47, 27, 1, 31, 1]
  [6, 2, 29, 30, 25, 34, 19, 15, 50, 16, 10, 3, 16]
  [25, 17, 9, 19, 15, 2, 2, 4, 22, 10, 10, 27, 3, 1, 14, 17, 13, 8, 16, 9, 2, ...]
  [17, 65, 78, 1, 8, 29, 2, 79, 28, 18, 39]
  [13, 26, 8, 3, 4, 76, 6, 14, 20, 27, 21, 32, 14, 42, 9, 24, 33, 9, 5, 15, ...]
  [2, 1, 2, 2, 3, 10, 12, 11, 21, 11, 1, 16]

As you can see, Hypothesis doesn't find *many* examples here, but it finds some - enough to
keep it happy.

In general if you *can* shape your strategies better to your tests you should - for example
:py:func:`integers(1, 1000) <hypothesis.strategies.integers>` is a lot better than
``assume(1 <= x <= 1000)``, but ``assume`` will take you a long way if you can't.

---------------------
Defining strategies
---------------------

The type of object that is used to explore the examples given to your test
function is called a :class:`~hypothesis.strategies.SearchStrategy`.
These are created using the functions
exposed in the :mod:`hypothesis.strategies` module.

Many of these strategies expose a variety of arguments you can use to customize
generation. For example for integers you can specify ``min`` and ``max`` values of
integers you want.
If you want to see exactly what a strategy produces you can ask for an example:

.. code-block:: pycon

    >>> integers(min_value=0, max_value=10).example()
    1

Many strategies are built out of other strategies. For example, if you want
to define a tuple you need to say what goes in each element:

.. code-block:: pycon

    >>> from hypothesis.strategies import tuples
    >>> tuples(integers(), integers()).example()
    (-24597, 12566)

Further details are :doc:`available in a separate document <data>`.

------------------------------------
The gory details of given parameters
------------------------------------

.. autofunction:: hypothesis.given

The :func:`@given <hypothesis.given>` decorator may be used to specify
which arguments of a function should be parametrized over. You can use
either positional or keyword arguments, but not a mixture of both.

For example all of the following are valid uses:

.. code:: python

  @given(integers(), integers())
  def a(x, y):
      pass


  @given(integers())
  def b(x, y):
      pass


  @given(y=integers())
  def c(x, y):
      pass


  @given(x=integers())
  def d(x, y):
      pass


  @given(x=integers(), y=integers())
  def e(x, **kwargs):
      pass


  @given(x=integers(), y=integers())
  def f(x, *args, **kwargs):
      pass


  class SomeTest(TestCase):
      @given(integers())
      def test_a_thing(self, x):
          pass

The following are not:

.. code:: python

  @given(integers(), integers(), integers())
  def g(x, y):
      pass


  @given(integers())
  def h(x, *args):
      pass


  @given(integers(), x=integers())
  def i(x, y):
      pass


  @given()
  def j(x, y):
      pass


The rules for determining what are valid uses of ``given`` are as follows:

1. You may pass any keyword argument to ``given``.
2. Positional arguments to ``given`` are equivalent to the rightmost named
   arguments for the test function.
3. Positional arguments may not be used if the underlying test function has
   varargs, arbitrary keywords, or keyword-only arguments.
4. Functions tested with ``given`` may not have any defaults.

The reason for the "rightmost named arguments" behaviour is so that
using :func:`@given <hypothesis.given>` with instance methods works: ``self``
will be passed to the function as normal and not be parametrized over.

The function returned by given has all the same arguments as the original
test, minus those that are filled in by :func:`@given <hypothesis.given>`.
Check :ref:`the notes on framework compatibility <framework-compatibility>`
to see how this affects other testing libraries you may be using.


.. _targeted-search:

---------------------------
Targeted example generation
---------------------------

Targeted property-based testing combines the advantages of both search-based
and property-based testing.  Instead of being completely random, T-PBT uses
a search-based component to guide the input generation towards values that
have a higher probability of falsifying a property.  This explores the input
space more effectively and requires fewer tests to find a bug or achieve a
high confidence in the system being tested than random PBT.
(`Löscher and Sagonas <http://proper.softlab.ntua.gr/Publications.html>`__)

This is not *always* a good idea - for example calculating the search metric
might take time better spent running more uniformly-random test cases - but
Hypothesis has **experimental** support for targeted PBT you may wish to try.

.. autofunction:: hypothesis.target

We recommend that users also skim the papers introducing targeted PBT;
from `ISSTA 2017 <http://proper.softlab.ntua.gr/papers/issta2017.pdf>`__
and `ICST 2018 <http://proper.softlab.ntua.gr/papers/icst2018.pdf>`__.
For the curious, the initial implementation in Hypothesis uses hill-climbing
search via a mutating fuzzer, with some tactics inspired by simulated
annealing to avoid getting stuck and endlessly mutating a local maximum.


.. _custom-function-execution:

-------------------------
Custom function execution
-------------------------

Hypothesis provides you with a hook that lets you control how it runs
examples.

This lets you do things like set up and tear down around each example, run
examples in a subprocess, transform coroutine tests into normal tests, etc.
For example, :class:`~hypothesis.extra.django.TransactionTestCase` in the
Django extra runs each example in a separate database transaction.

The way this works is by introducing the concept of an executor. An executor
is essentially a function that takes a block of code and run it. The default
executor is:

.. code:: python

    def default_executor(function):
        return function()

You define executors by defining a method ``execute_example`` on a class. Any
test methods on that class with :func:`@given <hypothesis.given>` used on them will use
``self.execute_example`` as an executor with which to run tests. For example,
the following executor runs all its code twice:

.. code:: python

    from unittest import TestCase


    class TestTryReallyHard(TestCase):
        @given(integers())
        def test_something(self, i):
            perform_some_unreliable_operation(i)

        def execute_example(self, f):
            f()
            return f()

Note: The functions you use in map, etc. will run *inside* the executor. i.e.
they will not be called until you invoke the function passed to ``execute_example``.

An executor must be able to handle being passed a function which returns None,
otherwise it won't be able to run normal test cases. So for example the following
executor is invalid:

.. code:: python

    from unittest import TestCase


    class TestRunTwice(TestCase):
        def execute_example(self, f):
            return f()()

and should be rewritten as:

.. code:: python

    from unittest import TestCase


    class TestRunTwice(TestCase):
        def execute_example(self, f):
            result = f()
            if callable(result):
                result = result()
            return result


An alternative hook is provided for use by test runner extensions such as
:pypi:`pytest-trio`, which cannot use the ``execute_example`` method.
This is **not** recommended for end-users - it is better to write a complete
test function directly, perhaps by using a decorator to perform the same
transformation before applying :func:`@given <hypothesis.given>`.

.. code:: python

    @given(x=integers())
    @pytest.mark.trio
    async def test(x):
        ...


    # Illustrative code, inside the pytest-trio plugin
    test.hypothesis.inner_test = lambda x: trio.run(test, x)

For authors of test runners however, assigning to the ``inner_test`` attribute
of the ``hypothesis`` attribute of the test will replace the interior test.

.. note::
    The new ``inner_test`` must accept and pass through all the ``*args``
    and ``**kwargs`` expected by the original test.

If the end user has also specified a custom executor using the
``execute_example`` method, it - and all other execution-time logic - will
be applied to the *new* inner test assigned by the test runner.


--------------------------------
Making random code deterministic
--------------------------------

While Hypothesis' example generation can be used for nondeterministic tests,
debugging anything nondeterministic is usually a very frustrating exercise.
To make things worse, our example *shrinking* relies on the same input
causing the same failure each time - though we show the un-shrunk failure
and a decent error message if it doesn't.

By default, Hypothesis will handle the global ``random`` and ``numpy.random``
random number generators for you, and you can register others:

.. autofunction:: hypothesis.register_random


.. _type-inference:

-------------------
Inferred Strategies
-------------------

In some cases, Hypothesis can work out what to do when you omit arguments.
This is based on introspection, *not* magic, and therefore has well-defined
limits.

:func:`~hypothesis.strategies.builds` will check the signature of the
``target`` (using :func:`~python:inspect.getfullargspec`).
If there are required arguments with type annotations and
no strategy was passed to :func:`~hypothesis.strategies.builds`,
:func:`~hypothesis.strategies.from_type` is used to fill them in.
You can also pass the special value :const:`hypothesis.infer` as a keyword
argument, to force this inference for arguments with a default value.

.. code-block:: pycon

    >>> def func(a: int, b: str):
    ...     return [a, b]
    >>> builds(func).example()
    [-6993, '']

.. data:: hypothesis.infer

:func:`@given <hypothesis.given>` does not perform any implicit inference
for required arguments, as this would break compatibility with pytest fixtures.
:const:`~hypothesis.infer` can be used as a keyword argument to explicitly
fill in an argument from its type annotation.

.. code:: python

    @given(a=infer)
    def test(a: int):
        pass


    # is equivalent to
    @given(a=integers())
    def test(a):
        pass

~~~~~~~~~~~
Limitations
~~~~~~~~~~~

:pep:`3107` type annotations are not supported on Python 2, and Hypothesis
does not inspect :pep:`484` type comments at runtime.  While
:func:`~hypothesis.strategies.from_type` will work as usual, inference in
:func:`~hypothesis.strategies.builds` and :func:`@given <hypothesis.given>`
will only work if you manually create the ``__annotations__`` attribute
(e.g. by using ``@annotations(...)`` and ``@returns(...)`` decorators).
The :mod:`python:typing` module is fully supported on Python 2 if you have
the backport installed.

The :mod:`python:typing` module is provisional and has a number of internal
changes between Python 3.5.0 and 3.6.1, including at minor versions.  These
are all supported on a best-effort basis, but you may encounter problems with
an old version of the module.  Please report them to us, and consider
updating to a newer version of Python as a workaround.


.. _our-type-hints:

------------------------------
Type Annotations in Hypothesis
------------------------------

If you install Hypothesis and use :pypi:`mypy` 0.590+, or another
:PEP:`561`-compatible tool, the type checker should automatically pick
up our type hints.

.. note::
    Hypothesis' type hints may make breaking changes between minor releases.

    Upstream tools and conventions about type hints remain in flux - for
    example the :mod:`python:typing` module itself is provisional, and Mypy
    has not yet reached version 1.0 - and we plan to support the latest
    version of this ecosystem, as well as older versions where practical.

    We may also find more precise ways to describe the type of various
    interfaces, or change their type and runtime behaviour togther in a way
    which is otherwise backwards-compatible.  We often omit type hints for
    deprecated features or arguments, as an additional form of warning.

There are known issues inferring the type of examples generated by
:func:`~hypothesis.strategies.deferred`, :func:`~hypothesis.strategies.recursive`,
:func:`~hypothesis.strategies.one_of`, :func:`~hypothesis.strategies.dictionaries`,
and :func:`~hypothesis.strategies.fixed_dictionaries`.
We will fix these, and require correspondingly newer versions of Mypy for type
hinting, as the ecosystem improves.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Writing downstream type hints
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Projects that :doc:`provide Hypothesis strategies <strategies>` and use
type hints may wish to annotate their strategies too.  This *is* a
supported use-case, again on a best-effort provisional basis.  For example:

.. code:: python

    def foo_strategy() -> SearchStrategy[Foo]:
        ...

.. class:: hypothesis.strategies.SearchStrategy

:class:`~hypothesis.strategies.SearchStrategy` is the type of all strategy
objects.  It is a generic type, and covariant in the type of the examples
it creates.  For example:

- ``integers()`` is of type ``SearchStrategy[int]``.
- ``lists(integers())`` is of type ``SearchStrategy[List[int]]``.
- ``SearchStrategy[Dog]`` is a subtype of ``SearchStrategy[Animal]``
  if ``Dog`` is a subtype of ``Animal`` (as seems likely).

.. warning::
    :class:`~hypothesis.strategies.SearchStrategy` **should only be used
    in type hints.**  Please do not inherit from, compare to, or otherwise
    use it in any way outside of type hints.  The only supported way to
    construct objects of this type is to use the functions provided by the
    :mod:`hypothesis.strategies` module!


.. _pytest-plugin:

----------------------------
The Hypothesis pytest Plugin
----------------------------

Hypothesis includes a tiny plugin to improve integration with :pypi:`pytest`,
which is activated by default (but does not affect other test runners).
It aims to improve the integration between Hypothesis and Pytest by
providing extra information and convenient access to config options.

- ``pytest --hypothesis-show-statistics`` can be used to
  :ref:`display test and data generation statistics <statistics>`.
- ``pytest --hypothesis-profile=<profile name>`` can be used to
  :ref:`load a settings profile <settings_profiles>`.
  ``pytest --hypothesis-verbosity=<level name>`` can be used to
  :ref:`override the current verbosity level <verbose-output>`.
- ``pytest --hypothesis-seed=<an int>`` can be used to
  :ref:`reproduce a failure with a particular seed <reproducing-with-seed>`.

Finally, all tests that are defined with Hypothesis automatically have
``@pytest.mark.hypothesis`` applied to them.  See :ref:`here for information
on working with markers <pytest:mark examples>`.

.. note::
    Pytest will load the plugin automatically if Hypothesis is installed.
    You don't need to do anything at all to use it.
