617 lines
22 KiB
Python
617 lines
22 KiB
Python
import math
|
|
import sys
|
|
|
|
import py
|
|
|
|
from _pytest.compat import isclass, izip
|
|
from _pytest.outcomes import fail
|
|
import _pytest._code
|
|
|
|
|
|
def _cmp_raises_type_error(self, other):
|
|
"""__cmp__ implementation which raises TypeError. Used
|
|
by Approx base classes to implement only == and != and raise a
|
|
TypeError for other comparisons.
|
|
|
|
Needed in Python 2 only, Python 3 all it takes is not implementing the
|
|
other operators at all.
|
|
"""
|
|
__tracebackhide__ = True
|
|
raise TypeError('Comparison operators other than == and != not supported by approx objects')
|
|
|
|
|
|
# builtin pytest.approx helper
|
|
|
|
|
|
class ApproxBase(object):
|
|
"""
|
|
Provide shared utilities for making approximate comparisons between numbers
|
|
or sequences of numbers.
|
|
"""
|
|
|
|
def __init__(self, expected, rel=None, abs=None, nan_ok=False):
|
|
self.expected = expected
|
|
self.abs = abs
|
|
self.rel = rel
|
|
self.nan_ok = nan_ok
|
|
|
|
def __repr__(self):
|
|
raise NotImplementedError
|
|
|
|
def __eq__(self, actual):
|
|
return all(
|
|
a == self._approx_scalar(x)
|
|
for a, x in self._yield_comparisons(actual))
|
|
|
|
__hash__ = None
|
|
|
|
def __ne__(self, actual):
|
|
return not (actual == self)
|
|
|
|
if sys.version_info[0] == 2:
|
|
__cmp__ = _cmp_raises_type_error
|
|
|
|
def _approx_scalar(self, x):
|
|
return ApproxScalar(x, rel=self.rel, abs=self.abs, nan_ok=self.nan_ok)
|
|
|
|
def _yield_comparisons(self, actual):
|
|
"""
|
|
Yield all the pairs of numbers to be compared. This is used to
|
|
implement the `__eq__` method.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
|
|
class ApproxNumpy(ApproxBase):
|
|
"""
|
|
Perform approximate comparisons for numpy arrays.
|
|
"""
|
|
|
|
# Tell numpy to use our `__eq__` operator instead of its.
|
|
__array_priority__ = 100
|
|
|
|
def __repr__(self):
|
|
# It might be nice to rewrite this function to account for the
|
|
# shape of the array...
|
|
return "approx({0!r})".format(list(
|
|
self._approx_scalar(x) for x in self.expected))
|
|
|
|
if sys.version_info[0] == 2:
|
|
__cmp__ = _cmp_raises_type_error
|
|
|
|
def __eq__(self, actual):
|
|
import numpy as np
|
|
|
|
try:
|
|
actual = np.asarray(actual)
|
|
except:
|
|
raise TypeError("cannot compare '{0}' to numpy.ndarray".format(actual))
|
|
|
|
if actual.shape != self.expected.shape:
|
|
return False
|
|
|
|
return ApproxBase.__eq__(self, actual)
|
|
|
|
def _yield_comparisons(self, actual):
|
|
import numpy as np
|
|
|
|
# We can be sure that `actual` is a numpy array, because it's
|
|
# casted in `__eq__` before being passed to `ApproxBase.__eq__`,
|
|
# which is the only method that calls this one.
|
|
for i in np.ndindex(self.expected.shape):
|
|
yield actual[i], self.expected[i]
|
|
|
|
|
|
class ApproxMapping(ApproxBase):
|
|
"""
|
|
Perform approximate comparisons for mappings where the values are numbers
|
|
(the keys can be anything).
|
|
"""
|
|
|
|
def __repr__(self):
|
|
return "approx({0!r})".format(dict(
|
|
(k, self._approx_scalar(v))
|
|
for k, v in self.expected.items()))
|
|
|
|
def __eq__(self, actual):
|
|
if set(actual.keys()) != set(self.expected.keys()):
|
|
return False
|
|
|
|
return ApproxBase.__eq__(self, actual)
|
|
|
|
def _yield_comparisons(self, actual):
|
|
for k in self.expected.keys():
|
|
yield actual[k], self.expected[k]
|
|
|
|
|
|
class ApproxSequence(ApproxBase):
|
|
"""
|
|
Perform approximate comparisons for sequences of numbers.
|
|
"""
|
|
|
|
# Tell numpy to use our `__eq__` operator instead of its.
|
|
__array_priority__ = 100
|
|
|
|
def __repr__(self):
|
|
seq_type = type(self.expected)
|
|
if seq_type not in (tuple, list, set):
|
|
seq_type = list
|
|
return "approx({0!r})".format(seq_type(
|
|
self._approx_scalar(x) for x in self.expected))
|
|
|
|
def __eq__(self, actual):
|
|
if len(actual) != len(self.expected):
|
|
return False
|
|
return ApproxBase.__eq__(self, actual)
|
|
|
|
def _yield_comparisons(self, actual):
|
|
return izip(actual, self.expected)
|
|
|
|
|
|
class ApproxScalar(ApproxBase):
|
|
"""
|
|
Perform approximate comparisons for single numbers only.
|
|
"""
|
|
|
|
def __repr__(self):
|
|
"""
|
|
Return a string communicating both the expected value and the tolerance
|
|
for the comparison being made, e.g. '1.0 +- 1e-6'. Use the unicode
|
|
plus/minus symbol if this is python3 (it's too hard to get right for
|
|
python2).
|
|
"""
|
|
if isinstance(self.expected, complex):
|
|
return str(self.expected)
|
|
|
|
# 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 = '???'
|
|
|
|
if sys.version_info[0] == 2:
|
|
return '{0} +- {1}'.format(self.expected, vetted_tolerance)
|
|
else:
|
|
return u'{0} \u00b1 {1}'.format(self.expected, vetted_tolerance)
|
|
|
|
def __eq__(self, actual):
|
|
"""
|
|
Return true if the given value is equal to the expected value within
|
|
the pre-specified tolerance.
|
|
"""
|
|
|
|
# Short-circuit exact equality.
|
|
if actual == self.expected:
|
|
return True
|
|
|
|
# Allow the user to control whether NaNs are considered equal to each
|
|
# other or not. The abs() calls are for compatibility with complex
|
|
# numbers.
|
|
if math.isnan(abs(self.expected)):
|
|
return self.nan_ok and math.isnan(abs(actual))
|
|
|
|
# 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
|
|
|
|
__hash__ = None
|
|
|
|
@property
|
|
def tolerance(self):
|
|
"""
|
|
Return the tolerance for the comparison. This could be either an
|
|
absolute tolerance or a relative tolerance, depending on what the user
|
|
specified or which would be larger.
|
|
"""
|
|
def set_default(x, default): return 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)
|
|
|
|
|
|
def approx(expected, rel=None, abs=None, nan_ok=False):
|
|
"""
|
|
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 for sequences of numbers::
|
|
|
|
>>> (0.1 + 0.2, 0.2 + 0.4) == approx((0.3, 0.6))
|
|
True
|
|
|
|
Dictionary *values*::
|
|
|
|
>>> {'a': 0.1 + 0.2, 'b': 0.2 + 0.4} == approx({'a': 0.3, 'b': 0.6})
|
|
True
|
|
|
|
And ``numpy`` arrays::
|
|
|
|
>>> import numpy as np # doctest: +SKIP
|
|
>>> np.array([0.1, 0.2]) + np.array([0.2, 0.4]) == approx(np.array([0.3, 0.6])) # doctest: +SKIP
|
|
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. Infinity and NaN are special cases. Infinity is only considered
|
|
equal to itself, regardless of the relative tolerance. NaN is not
|
|
considered equal to anything by default, but you can make it be equal to
|
|
itself by setting the ``nan_ok`` argument to True. (This is meant to
|
|
facilitate comparing arrays that use NaN to mean "no data".)
|
|
|
|
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.
|
|
|
|
.. warning::
|
|
|
|
.. versionchanged:: 3.2
|
|
|
|
In order to avoid inconsistent behavior, ``TypeError`` is
|
|
raised for ``>``, ``>=``, ``<`` and ``<=`` comparisons.
|
|
The example below illustrates the problem::
|
|
|
|
assert approx(0.1) > 0.1 + 1e-10 # calls approx(0.1).__gt__(0.1 + 1e-10)
|
|
assert 0.1 + 1e-10 > approx(0.1) # calls approx(0.1).__lt__(0.1 + 1e-10)
|
|
|
|
In the second example one expects ``approx(0.1).__le__(0.1 + 1e-10)``
|
|
to be called. But instead, ``approx(0.1).__lt__(0.1 + 1e-10)`` is used to
|
|
comparison. This is because the call hierarchy of rich comparisons
|
|
follows a fixed behavior. `More information...`__
|
|
|
|
__ https://docs.python.org/3/reference/datamodel.html#object.__ge__
|
|
"""
|
|
|
|
from collections import Mapping, Sequence
|
|
from _pytest.compat import STRING_TYPES as String
|
|
|
|
# Delegate the comparison to a class that knows how to deal with the type
|
|
# of the expected value (e.g. int, float, list, dict, numpy.array, etc).
|
|
#
|
|
# This architecture is really driven by the need to support numpy arrays.
|
|
# The only way to override `==` for arrays without requiring that approx be
|
|
# the left operand is to inherit the approx object from `numpy.ndarray`.
|
|
# But that can't be a general solution, because it requires (1) numpy to be
|
|
# installed and (2) the expected value to be a numpy array. So the general
|
|
# solution is to delegate each type of expected value to a different class.
|
|
#
|
|
# This has the advantage that it made it easy to support mapping types
|
|
# (i.e. dict). The old code accepted mapping types, but would only compare
|
|
# their keys, which is probably not what most people would expect.
|
|
|
|
if _is_numpy_array(expected):
|
|
cls = ApproxNumpy
|
|
elif isinstance(expected, Mapping):
|
|
cls = ApproxMapping
|
|
elif isinstance(expected, Sequence) and not isinstance(expected, String):
|
|
cls = ApproxSequence
|
|
else:
|
|
cls = ApproxScalar
|
|
|
|
return cls(expected, rel, abs, nan_ok)
|
|
|
|
|
|
def _is_numpy_array(obj):
|
|
"""
|
|
Return true if the given object is a numpy array. Make a special effort to
|
|
avoid importing numpy unless it's really necessary.
|
|
"""
|
|
import inspect
|
|
|
|
for cls in inspect.getmro(type(obj)):
|
|
if cls.__module__ == 'numpy':
|
|
try:
|
|
import numpy as np
|
|
return isinstance(obj, np.ndarray)
|
|
except ImportError:
|
|
pass
|
|
|
|
return False
|
|
|
|
|
|
# builtin pytest.raises helper
|
|
|
|
def raises(expected_exception, *args, **kwargs):
|
|
"""
|
|
Assert that a code block/function call raises ``expected_exception``
|
|
and raise a failure exception otherwise.
|
|
|
|
This helper produces a ``ExceptionInfo()`` object (see below).
|
|
|
|
If using Python 2.5 or above, you may use this function as a
|
|
context manager::
|
|
|
|
>>> with raises(ZeroDivisionError):
|
|
... 1/0
|
|
|
|
.. versionchanged:: 2.10
|
|
|
|
In the context manager form you may use the keyword argument
|
|
``message`` to specify a custom failure message::
|
|
|
|
>>> with raises(ZeroDivisionError, message="Expecting ZeroDivisionError"):
|
|
... pass
|
|
Traceback (most recent call last):
|
|
...
|
|
Failed: Expecting ZeroDivisionError
|
|
|
|
.. note::
|
|
|
|
When using ``pytest.raises`` as a context manager, it's worthwhile to
|
|
note that normal context manager rules apply and that the exception
|
|
raised *must* be the final line in the scope of the context manager.
|
|
Lines of code after that, within the scope of the context manager will
|
|
not be executed. For example::
|
|
|
|
>>> value = 15
|
|
>>> with raises(ValueError) as exc_info:
|
|
... if value > 10:
|
|
... raise ValueError("value must be <= 10")
|
|
... assert exc_info.type == ValueError # this will not execute
|
|
|
|
Instead, the following approach must be taken (note the difference in
|
|
scope)::
|
|
|
|
>>> with raises(ValueError) as exc_info:
|
|
... if value > 10:
|
|
... raise ValueError("value must be <= 10")
|
|
...
|
|
>>> assert exc_info.type == ValueError
|
|
|
|
Or you can use the keyword argument ``match`` to assert that the
|
|
exception matches a text or regex::
|
|
|
|
>>> with raises(ValueError, match='must be 0 or None'):
|
|
... raise ValueError("value must be 0 or None")
|
|
|
|
>>> with raises(ValueError, match=r'must be \d+$'):
|
|
... raise ValueError("value must be 42")
|
|
|
|
Or you can specify a callable by passing a to-be-called lambda::
|
|
|
|
>>> raises(ZeroDivisionError, lambda: 1/0)
|
|
<ExceptionInfo ...>
|
|
|
|
or you can specify an arbitrary callable with arguments::
|
|
|
|
>>> def f(x): return 1/x
|
|
...
|
|
>>> raises(ZeroDivisionError, f, 0)
|
|
<ExceptionInfo ...>
|
|
>>> raises(ZeroDivisionError, f, x=0)
|
|
<ExceptionInfo ...>
|
|
|
|
A third possibility is to use a string to be executed::
|
|
|
|
>>> raises(ZeroDivisionError, "f(0)")
|
|
<ExceptionInfo ...>
|
|
|
|
.. autoclass:: _pytest._code.ExceptionInfo
|
|
:members:
|
|
|
|
.. note::
|
|
Similar to caught exception objects in Python, explicitly clearing
|
|
local references to returned ``ExceptionInfo`` objects can
|
|
help the Python interpreter speed up its garbage collection.
|
|
|
|
Clearing those references breaks a reference cycle
|
|
(``ExceptionInfo`` --> caught exception --> frame stack raising
|
|
the exception --> current frame stack --> local variables -->
|
|
``ExceptionInfo``) which makes Python keep all objects referenced
|
|
from that cycle (including all local variables in the current
|
|
frame) alive until the next cyclic garbage collection run. See the
|
|
official Python ``try`` statement documentation for more detailed
|
|
information.
|
|
|
|
"""
|
|
__tracebackhide__ = True
|
|
msg = ("exceptions must be old-style classes or"
|
|
" derived from BaseException, not %s")
|
|
if isinstance(expected_exception, tuple):
|
|
for exc in expected_exception:
|
|
if not isclass(exc):
|
|
raise TypeError(msg % type(exc))
|
|
elif not isclass(expected_exception):
|
|
raise TypeError(msg % type(expected_exception))
|
|
|
|
message = "DID NOT RAISE {0}".format(expected_exception)
|
|
match_expr = None
|
|
|
|
if not args:
|
|
if "message" in kwargs:
|
|
message = kwargs.pop("message")
|
|
if "match" in kwargs:
|
|
match_expr = kwargs.pop("match")
|
|
message += " matching '{0}'".format(match_expr)
|
|
return RaisesContext(expected_exception, message, match_expr)
|
|
elif isinstance(args[0], str):
|
|
code, = args
|
|
assert isinstance(code, str)
|
|
frame = sys._getframe(1)
|
|
loc = frame.f_locals.copy()
|
|
loc.update(kwargs)
|
|
# print "raises frame scope: %r" % frame.f_locals
|
|
try:
|
|
code = _pytest._code.Source(code).compile()
|
|
py.builtin.exec_(code, frame.f_globals, loc)
|
|
# XXX didn'T mean f_globals == f_locals something special?
|
|
# this is destroyed here ...
|
|
except expected_exception:
|
|
return _pytest._code.ExceptionInfo()
|
|
else:
|
|
func = args[0]
|
|
try:
|
|
func(*args[1:], **kwargs)
|
|
except expected_exception:
|
|
return _pytest._code.ExceptionInfo()
|
|
fail(message)
|
|
|
|
|
|
raises.Exception = fail.Exception
|
|
|
|
|
|
class RaisesContext(object):
|
|
def __init__(self, expected_exception, message, match_expr):
|
|
self.expected_exception = expected_exception
|
|
self.message = message
|
|
self.match_expr = match_expr
|
|
self.excinfo = None
|
|
|
|
def __enter__(self):
|
|
self.excinfo = object.__new__(_pytest._code.ExceptionInfo)
|
|
return self.excinfo
|
|
|
|
def __exit__(self, *tp):
|
|
__tracebackhide__ = True
|
|
if tp[0] is None:
|
|
fail(self.message)
|
|
if sys.version_info < (2, 7):
|
|
# py26: on __exit__() exc_value often does not contain the
|
|
# exception value.
|
|
# http://bugs.python.org/issue7853
|
|
if not isinstance(tp[1], BaseException):
|
|
exc_type, value, traceback = tp
|
|
tp = exc_type, exc_type(value), traceback
|
|
self.excinfo.__init__(tp)
|
|
suppress_exception = issubclass(self.excinfo.type, self.expected_exception)
|
|
if sys.version_info[0] == 2 and suppress_exception:
|
|
sys.exc_clear()
|
|
if self.match_expr:
|
|
self.excinfo.match(self.match_expr)
|
|
return suppress_exception
|