# Source code for _pytest.python_api

```
import math
import sys
import py
from six.moves import zip
from _pytest.compat import isclass
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: # noqa
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 zip(actual, self.expected)
class ApproxScalar(ApproxBase):
"""
Perform approximate comparisons for single numbers only.
"""
DEFAULT_ABSOLUTE_TOLERANCE = 1e-12
DEFAULT_RELATIVE_TOLERANCE = 1e-6
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, self.DEFAULT_ABSOLUTE_TOLERANCE)
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, self.DEFAULT_RELATIVE_TOLERANCE) * 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)
class ApproxDecimal(ApproxScalar):
from decimal import Decimal
DEFAULT_ABSOLUTE_TOLERANCE = Decimal('1e-12')
DEFAULT_RELATIVE_TOLERANCE = Decimal('1e-6')
[docs]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
from decimal import Decimal
# 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
elif isinstance(expected, Decimal):
cls = ApproxDecimal
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
[docs]def raises(expected_exception, *args, **kwargs):
"""
Assert that a code block/function call raises ``expected_exception``
and raise a failure exception otherwise.
:arg message: if specified, provides a custom failure message if the
exception is not raised
:arg match: if specified, asserts that the exception matches a text or regex
This helper produces a ``ExceptionInfo()`` object (see below).
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
Since version ``3.1`` 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")
**Legacy forms**
The forms below are fully supported but are discouraged for new code because the
context manager form is regarded as more readable and less error-prone.
It is possible to 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 ...>
It is also possible to pass a string to be evaluated at runtime::
>>> raises(ZeroDivisionError, "f(0)")
<ExceptionInfo ...>
The string will be evaluated using the same ``locals()`` and ``globals()``
at the moment of the ``raises`` call.
.. currentmodule:: _pytest._code
Consult the API of ``excinfo`` objects: :class:`ExceptionInfo`.
.. 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")
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)
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 and suppress_exception:
self.excinfo.match(self.match_expr)
return suppress_exception
```