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Merge remote-tracking branch 'upstream/features' into allow-none-as-parametrized-test-id

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palaviv
2016-03-20 18:57:17 +02:00
parent 32f44ce2fd 2cfcf12d09
commit 7885e43b78
13 changed files with 813 additions and 8 deletions
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275
_pytest/python.py
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@@ -1,10 +1,12 @@
""" Python test discovery, setup and run of test functions. """
import fnmatch
import functools
import inspect
import re
import types
import sys
import math
import py
import pytest
@@ -114,12 +116,13 @@ def safe_getattr(object, name, default):
class FixtureFunctionMarker:
def __init__(self, scope, params,
autouse=False, yieldctx=False, ids=None):
autouse=False, yieldctx=False, ids=None, name=None):
self.scope = scope
self.params = params
self.autouse = autouse
self.yieldctx = yieldctx
self.ids = ids
self.name = name
def __call__(self, function):
if isclass(function):
@@ -129,7 +132,7 @@ class FixtureFunctionMarker:
return function
def fixture(scope="function", params=None, autouse=False, ids=None):
def fixture(scope="function", params=None, autouse=False, ids=None, name=None):
""" (return a) decorator to mark a fixture factory function.
This decorator can be used (with or or without parameters) to define
@@ -155,14 +158,21 @@ def fixture(scope="function", params=None, autouse=False, ids=None):
so that they are part of the test id. If no ids are provided
they will be generated automatically from the params.
:arg name: the name of the fixture. This defaults to the name of the
decorated function. If a fixture is used in the same module in
which it is defined, the function name of the fixture will be
shadowed by the function arg that requests the fixture; one way
to resolve this is to name the decorated function
``fixture_<fixturename>`` and then use
``@pytest.fixture(name='<fixturename>')``.
"""
if callable(scope) and params is None and autouse == False:
# direct decoration
return FixtureFunctionMarker(
"function", params, autouse)(scope)
"function", params, autouse, name=name)(scope)
if params is not None and not isinstance(params, (list, tuple)):
params = list(params)
return FixtureFunctionMarker(scope, params, autouse, ids=ids)
return FixtureFunctionMarker(scope, params, autouse, ids=ids, name=name)
def yield_fixture(scope="function", params=None, autouse=False, ids=None):
""" (return a) decorator to mark a yield-fixture factory function
@@ -261,7 +271,8 @@ def pytest_namespace():
return {
'fixture': fixture,
'yield_fixture': yield_fixture,
'raises' : raises,
'raises': raises,
'approx': approx,
'collect': {
'Module': Module, 'Class': Class, 'Instance': Instance,
'Function': Function, 'Generator': Generator,
@@ -1206,7 +1217,8 @@ def getlocation(function, curdir):
# builtin pytest.raises helper
def raises(expected_exception, *args, **kwargs):
""" assert that a code block/function call raises ``expected_exception``
"""
Assert that a code block/function call raises ``expected_exception``
and raise a failure exception otherwise.
This helper produces a ``ExceptionInfo()`` object (see below).
@@ -1339,6 +1351,255 @@ class RaisesContext(object):
self.excinfo.__init__(tp)
return issubclass(self.excinfo.type, self.expected_exception)
# builtin pytest.approx helper
class approx(object):
"""
Assert that two numbers (or two sets of numbers) are equal to each other
within some tolerance.
Due to the `intricacies of floating-point arithmetic`__, numbers that we
would intuitively expect to be equal are not always so::
>>> 0.1 + 0.2 == 0.3
False
__ https://docs.python.org/3/tutorial/floatingpoint.html
This problem is commonly encountered when writing tests, e.g. when making
sure that floating-point values are what you expect them to be. One way to
deal with this problem is to assert that two floating-point numbers are
equal to within some appropriate tolerance::
>>> abs((0.1 + 0.2) - 0.3) < 1e-6
True
However, comparisons like this are tedious to write and difficult to
understand. Furthermore, absolute comparisons like the one above are
usually discouraged because there's no tolerance that works well for all
situations. ``1e-6`` is good for numbers around ``1``, but too small for
very big numbers and too big for very small ones. It's better to express
the tolerance as a fraction of the expected value, but relative comparisons
like that are even more difficult to write correctly and concisely.
The ``approx`` class performs floating-point comparisons using a syntax
that's as intuitive as possible::
>>> from pytest import approx
>>> 0.1 + 0.2 == approx(0.3)
True
The same syntax also works on sequences of numbers::
>>> (0.1 + 0.2, 0.2 + 0.4) == approx((0.3, 0.6))
True
By default, ``approx`` considers numbers within a relative tolerance of
``1e-6`` (i.e. one part in a million) of its expected value to be equal.
This treatment would lead to surprising results if the expected value was
``0.0``, because nothing but ``0.0`` itself is relatively close to ``0.0``.
To handle this case less surprisingly, ``approx`` also considers numbers
within an absolute tolerance of ``1e-12`` of its expected value to be
equal. Infinite numbers are another special case. They are only
considered equal to themselves, regardless of the relative tolerance. Both
the relative and absolute tolerances can be changed by passing arguments to
the ``approx`` constructor::
>>> 1.0001 == approx(1)
False
>>> 1.0001 == approx(1, rel=1e-3)
True
>>> 1.0001 == approx(1, abs=1e-3)
True
If you specify ``abs`` but not ``rel``, the comparison will not consider
the relative tolerance at all. In other words, two numbers that are within
the default relative tolerance of ``1e-6`` will still be considered unequal
if they exceed the specified absolute tolerance. If you specify both
``abs`` and ``rel``, the numbers will be considered equal if either
tolerance is met::
>>> 1 + 1e-8 == approx(1)
True
>>> 1 + 1e-8 == approx(1, abs=1e-12)
False
>>> 1 + 1e-8 == approx(1, rel=1e-6, abs=1e-12)
True
If you're thinking about using ``approx``, then you might want to know how
it compares to other good ways of comparing floating-point numbers. All of
these algorithms are based on relative and absolute tolerances and should
agree for the most part, but they do have meaningful differences:
- ``math.isclose(a, b, rel_tol=1e-9, abs_tol=0.0)``: True if the relative
tolerance is met w.r.t. either ``a`` or ``b`` or if the absolute
tolerance is met. Because the relative tolerance is calculated w.r.t.
both ``a`` and ``b``, this test is symmetric (i.e. neither ``a`` nor
``b`` is a "reference value"). You have to specify an absolute tolerance
if you want to compare to ``0.0`` because there is no tolerance by
default. Only available in python>=3.5. `More information...`__
__ https://docs.python.org/3/library/math.html#math.isclose
- ``numpy.isclose(a, b, rtol=1e-5, atol=1e-8)``: True if the difference
between ``a`` and ``b`` is less that the sum of the relative tolerance
w.r.t. ``b`` and the absolute tolerance. Because the relative tolerance
is only calculated w.r.t. ``b``, this test is asymmetric and you can
think of ``b`` as the reference value. Support for comparing sequences
is provided by ``numpy.allclose``. `More information...`__
__ http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.isclose.html
- ``unittest.TestCase.assertAlmostEqual(a, b)``: True if ``a`` and ``b``
are within an absolute tolerance of ``1e-7``. No relative tolerance is
considered and the absolute tolerance cannot be changed, so this function
is not appropriate for very large or very small numbers. Also, it's only
available in subclasses of ``unittest.TestCase`` and it's ugly because it
doesn't follow PEP8. `More information...`__
__ https://docs.python.org/3/library/unittest.html#unittest.TestCase.assertAlmostEqual
- ``a == pytest.approx(b, rel=1e-6, abs=1e-12)``: True if the relative
tolerance is met w.r.t. ``b`` or if the absolute tolerance is met.
Because the relative tolerance is only calculated w.r.t. ``b``, this test
is asymmetric and you can think of ``b`` as the reference value. In the
special case that you explicitly specify an absolute tolerance but not a
relative tolerance, only the absolute tolerance is considered.
"""
def __init__(self, expected, rel=None, abs=None):
self.expected = expected
self.abs = abs
self.rel = rel
def __repr__(self):
return ', '.join(repr(x) for x in self.expected)
def __eq__(self, actual):
from collections import Iterable
if not isinstance(actual, Iterable):
actual = [actual]
if len(actual) != len(self.expected):
return False
return all(a == x for a, x in zip(actual, self.expected))
def __ne__(self, actual):
return not (actual == self)
@property
def expected(self):
# Regardless of whether the user-specified expected value is a number
# or a sequence of numbers, return a list of ApproxNotIterable objects
# that can be compared against.
from collections import Iterable
approx_non_iter = lambda x: ApproxNonIterable(x, self.rel, self.abs)
if isinstance(self._expected, Iterable):
return [approx_non_iter(x) for x in self._expected]
else:
return [approx_non_iter(self._expected)]
@expected.setter
def expected(self, expected):
self._expected = expected
class ApproxNonIterable(object):
"""
Perform approximate comparisons for single numbers only.
In other words, the ``expected`` attribute for objects of this class must
be some sort of number. This is in contrast to the ``approx`` class, where
the ``expected`` attribute can either be a number of a sequence of numbers.
This class is responsible for making comparisons, while ``approx`` is
responsible for abstracting the difference between numbers and sequences of
numbers. Although this class can stand on its own, it's only meant to be
used within ``approx``.
"""
def __init__(self, expected, rel=None, abs=None):
self.expected = expected
self.abs = abs
self.rel = rel
def __repr__(self):
# Infinities aren't compared using tolerances, so don't show a
# tolerance.
if math.isinf(self.expected):
return str(self.expected)
# If a sensible tolerance can't be calculated, self.tolerance will
# raise a ValueError. In this case, display '???'.
try:
vetted_tolerance = '{:.1e}'.format(self.tolerance)
except ValueError:
vetted_tolerance = '???'
plus_minus = u'{0} \u00b1 {1}'.format(self.expected, vetted_tolerance)
# In python2, __repr__() must return a string (i.e. not a unicode
# object). In python3, __repr__() must return a unicode object
# (although now strings are unicode objects and bytes are what
# strings were).
if sys.version_info[0] == 2:
return plus_minus.encode('utf-8')
else:
return plus_minus
def __eq__(self, actual):
# Short-circuit exact equality.
if actual == self.expected:
return True
# Infinity shouldn't be approximately equal to anything but itself, but
# if there's a relative tolerance, it will be infinite and infinity
# will seem approximately equal to everything. The equal-to-itself
# case would have been short circuited above, so here we can just
# return false if the expected value is infinite. The abs() call is
# for compatibility with complex numbers.
if math.isinf(abs(self.expected)):
return False
# Return true if the two numbers are within the tolerance.
return abs(self.expected - actual) <= self.tolerance
def __ne__(self, actual):
return not (actual == self)
@property
def tolerance(self):
set_default = lambda x, default: x if x is not None else default
# Figure out what the absolute tolerance should be. ``self.abs`` is
# either None or a value specified by the user.
absolute_tolerance = set_default(self.abs, 1e-12)
if absolute_tolerance < 0:
raise ValueError("absolute tolerance can't be negative: {}".format(absolute_tolerance))
if math.isnan(absolute_tolerance):
raise ValueError("absolute tolerance can't be NaN.")
# If the user specified an absolute tolerance but not a relative one,
# just return the absolute tolerance.
if self.rel is None:
if self.abs is not None:
return absolute_tolerance
# Figure out what the relative tolerance should be. ``self.rel`` is
# either None or a value specified by the user. This is done after
# we've made sure the user didn't ask for an absolute tolerance only,
# because we don't want to raise errors about the relative tolerance if
# we aren't even going to use it.
relative_tolerance = set_default(self.rel, 1e-6) * abs(self.expected)
if relative_tolerance < 0:
raise ValueError("relative tolerance can't be negative: {}".format(absolute_tolerance))
if math.isnan(relative_tolerance):
raise ValueError("relative tolerance can't be NaN.")
# Return the larger of the relative and absolute tolerances.
return max(relative_tolerance, absolute_tolerance)
#
# the basic pytest Function item
#
@@ -1992,6 +2253,8 @@ class FixtureManager:
# fixture attribute
continue
else:
if marker.name:
name = marker.name
assert not name.startswith(self._argprefix)
fixturedef = FixtureDef(self, nodeid, name, obj,
marker.scope, marker.params,