=============================
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:

.. doctest::

    >>> 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, 0, 1], r=RandomWithSeed(0))
    Shuffle: [0, 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 py.test 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 ``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 ``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 200 examples per test (the default) and
it turns out 150 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()
  [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.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:

.. doctest::

    >>> integers(min_value=0, max_value=10).example()
    9

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:

.. doctest::

    >>> from hypothesis.strategies import tuples
    >>> tuples(integers(), integers()).example()
    (-85296636193678268231691518597782489127, 68871684356256783618296489618877951982)

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 or a mixture
of the two.

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 ``given``.

-------------------------
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.

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
    import inspect

    class TestRunTwice(TestCase):
        def execute_example(self, f):
            result = f()
            if inspect.isfunction(result):
                result = result()
            return result


-------------------------------
Using Hypothesis to find values
-------------------------------

You can use Hypothesis's data exploration features to find values satisfying
some predicate.  This is generally useful for exploring custom strategies
defined with :func:`@composite <hypothesis.strategies.composite>`, or
experimenting with conditions for filtering data.

.. autofunction:: hypothesis.find

.. doctest::

    >>> from hypothesis import find
    >>> from hypothesis.strategies import sets, lists, integers
    >>> find(lists(integers()), lambda x: sum(x) >= 10)
    [10]
    >>> find(lists(integers()), lambda x: sum(x) >= 10 and len(x) >= 3)
    [0, 0, 10]
    >>> find(sets(integers()), lambda x: sum(x) >= 10 and len(x) >= 3)
    {0, 1, 9}

The first argument to :func:`~hypothesis.find` describes data in the usual way for an argument to
:func:`~hypothesis.given`, and supports :doc:`all the same data types <data>`. The second is a
predicate it must satisfy.

Of course not all conditions are satisfiable. If you ask Hypothesis for an
example to a condition that is always false it will raise an error:

.. doctest::

    >>> find(integers(), lambda x: False)
    Traceback (most recent call last):
        ...
    hypothesis.errors.NoSuchExample: No examples of condition lambda x: <unknown>

(The ``lambda x: unknown`` is because Hypothesis can't retrieve the source code
of lambdas from the interactive python console. It gives a better error message
most of the time which contains the actual condition)


.. _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.

.. doctest::

    >>> def func(a: int, b: str):
    ...     return [a, b]
    >>> builds(func).example()
    [72627971792323936471739212691379790782, '']

: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.
