Hypothesis

Latest version: v6.122.3

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4.11.1

Not secure
-------------------

This is a formatting-only patch, enabled by a new version of :pypi:`isort`.

4.11.0

Not secure
-------------------

This release deprecates :func:`~hypothesis.strategies.sampled_from` with empty
sequences. This returns :func:`~hypothesis.strategies.nothing`, which gives a
clear error if used directly... but simply vanishes if combined with another
strategy.

Tests that silently generate less than expected are a serious problem for
anyone relying on them to find bugs, and we think reliability more important
than convenience in this case.

4.10.0

Not secure
-------------------

This release improves Hypothesis's to detect flaky tests, by noticing when the behaviour of the test changes between runs.
In particular this will notice many new cases where data generation depends on external state (e.g. external sources of randomness) and flag those as flaky sooner and more reliably.

The basis of this feature is a considerable reengineering of how Hypothesis stores its history of test cases,
so on top of this its memory usage should be considerably reduced.

4.9.0

Not secure
------------------

This release adds the strategy :func:`~hypothesis.extra.numpy.valid_tuple_axes`,
which generates tuples of axis-indices that can be passed to the ``axis`` argument
in NumPy's sequential functions (e.g. :func:`numpy:numpy.sum`).

Thanks to Ryan Soklaski for this strategy.

4.8.0

Not secure
------------------

This release significantly tightens validation in :class:`hypothesis.settings`.
:obj:`~hypothesis.settings.max_examples`, ``buffer_size``,
and :obj:`~hypothesis.settings.stateful_step_count` must be positive integers;
:obj:`~hypothesis.settings.deadline` must be a positive number or ``None``; and
:obj:`~hypothesis.settings.derandomize` must be either ``True`` or ``False``.

As usual, this replaces existing errors with a more helpful error and starts new
validation checks as deprecation warnings.

4.7.19

Not secure
-------------------

This release makes some micro-optimisations to certain calculations performed in the shrinker.
These should particularly speed up large test cases where the shrinker makes many small changes.
It will also reduce the amount allocated, but most of this is garbage that would have been immediately thrown away,
so you probably won't see much effect specifically from that.

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