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Merge pull request #2492 from kalekundert/features 

Add support for numpy arrays (and dicts) to approx.
This commit is contained in:
Ronny Pfannschmidt 2017-07-06 11:46:51 +02:00 committed by GitHub
parent b62aef3372 7a1a439049
commit ef62b86335
8 changed files with 464 additions and 184 deletions
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2
.travis.yml
View File

@ -20,9 +20,11 @@ env:
- TOXENV=py27-pexpect
- TOXENV=py27-xdist
- TOXENV=py27-trial
- TOXENV=py27-numpy
- TOXENV=py35-pexpect
- TOXENV=py35-xdist
- TOXENV=py35-trial
- TOXENV=py35-numpy
- TOXENV=py27-nobyte
- TOXENV=doctesting
- TOXENV=freeze

3
_pytest/compat.py
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@ -125,6 +125,7 @@ if sys.version_info[:2] == (2, 6):
if _PY3:
import codecs
imap = map
izip = zip
STRING_TYPES = bytes, str
UNICODE_TYPES = str,
@ -160,7 +161,7 @@ else:
STRING_TYPES = bytes, str, unicode
UNICODE_TYPES = unicode,
from itertools import imap # NOQA
from itertools import imap, izip # NOQA
def _escape_strings(val):
"""In py2 bytes and str are the same type, so return if it's a bytes

452
_pytest/python_api.py
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@ -3,13 +3,279 @@ import sys
import py
from _pytest.compat import isclass
from _pytest.compat import isclass, izip
from _pytest.runner import fail
import _pytest._code
# builtin pytest.approx helper
class ApproxBase(object):
"""
Provide shared utilities for making approximate comparisons between numbers
or sequences of numbers.
"""
class approx(object):
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)
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 ApproxNumpyBase(ApproxBase):
"""
Perform approximate comparisons for numpy arrays.
This class should not be used directly. Instead, the `inherit_ndarray()`
class method should be used to make a subclass that also inherits from
`np.ndarray`. This indirection is necessary because the object doing the
approximate comparison must inherit from `np.ndarray`, or it will only work
on the left side of the `==` operator. But importing numpy is relatively
expensive, so we also want to avoid that unless we actually have a numpy
array to compare.
The reason why the approx object needs to inherit from `np.ndarray` has to
do with how python decides whether to call `a.__eq__()` or `b.__eq__()`
when it parses `a == b`. If `a` and `b` are not related by inheritance,
`a` gets priority. So as long as `a.__eq__` is defined, it will be called.
Because most implementations of `a.__eq__` end up calling `b.__eq__`, this
detail usually doesn't matter. However, `np.ndarray.__eq__` treats the
approx object as a scalar and builds a new array by comparing it to each
item in the original array. `b.__eq__` is called to compare against each
individual element in the array, but it has no way (that I can see) to
prevent the return value from being an boolean array, and boolean arrays
can't be used with assert because "the truth value of an array with more
than one element is ambiguous."
The trick is that the priority rules change if `a` and `b` are related
by inheritance. Specifically, `b.__eq__` gets priority if `b` is a
subclass of `a`. So by inheriting from `np.ndarray`, we can guarantee that
`ApproxNumpy.__eq__` gets called no matter which side of the `==` operator
it appears on.
"""
subclass = None
@classmethod
def inherit_ndarray(cls):
import numpy as np
assert not isinstance(cls, np.ndarray)
if cls.subclass is None:
cls.subclass = type('ApproxNumpy', (cls, np.ndarray), {})
return cls.subclass
def __new__(cls, expected, rel=None, abs=None, nan_ok=False):
"""
Numpy uses __new__ (rather than __init__) to initialize objects.
The `expected` argument must be a numpy array. This should be
ensured by the approx() delegator function.
"""
obj = super(ApproxNumpyBase, cls).__new__(cls, ())
obj.__init__(expected, rel, abs, nan_ok)
return obj
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))
def __eq__(self, actual):
import numpy as np
try:
actual = np.asarray(actual)
except:
raise ValueError("cannot cast '{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.
"""
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.
"""
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)
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.
@ -45,21 +311,36 @@ class approx(object):
>>> 0.1 + 0.2 == approx(0.3)
True
The same syntax also works on sequences of numbers::
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. 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::
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
@ -123,138 +404,54 @@ class approx(object):
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
from collections import Mapping, Sequence
from _pytest.compat import STRING_TYPES as String
def __repr__(self):
return ', '.join(repr(x) for x in self.expected)
# 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.
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))
__hash__ = None
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]
if _is_numpy_array(expected):
# Create the delegate class on the fly. This allow us to inherit from
# ``np.ndarray`` while still not importing numpy unless we need to.
cls = ApproxNumpyBase.inherit_ndarray()
elif isinstance(expected, Mapping):
cls = ApproxMapping
elif isinstance(expected, Sequence) and not isinstance(expected, String):
cls = ApproxSequence
else:
return [approx_non_iter(self._expected)]
cls = ApproxScalar
@expected.setter
def expected(self, expected):
self._expected = expected
return cls(expected, rel, abs, nan_ok)
class ApproxNonIterable(object):
def _is_numpy_array(obj):
"""
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``.
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
def __init__(self, expected, rel=None, abs=None):
self.expected = expected
self.abs = abs
self.rel = rel
def __repr__(self):
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 '???'.
for cls in inspect.getmro(type(obj)):
if cls.__module__ == 'numpy':
try:
vetted_tolerance = '{:.1e}'.format(self.tolerance)
except ValueError:
vetted_tolerance = '???'
import numpy as np
return isinstance(obj, np.ndarray)
except ImportError:
pass
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):
# 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
__hash__ = None
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)
# builtin pytest.raises helper
@ -282,7 +479,6 @@ def raises(expected_exception, *args, **kwargs):
...
Failed: Expecting ZeroDivisionError
.. note::
When using ``pytest.raises`` as a context manager, it's worthwhile to
@ -315,7 +511,6 @@ def raises(expected_exception, *args, **kwargs):
>>> 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)
@ -398,7 +593,6 @@ def raises(expected_exception, *args, **kwargs):
raises.Exception = fail.Exception
class RaisesContext(object):
def __init__(self, expected_exception, message, match_expr):
self.expected_exception = expected_exception

2
appveyor.yml
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@ -20,9 +20,11 @@ environment:
- TOXENV: "py27-pexpect"
- TOXENV: "py27-xdist"
- TOXENV: "py27-trial"
- TOXENV: "py27-numpy"
- TOXENV: "py35-pexpect"
- TOXENV: "py35-xdist"
- TOXENV: "py35-trial"
- TOXENV: "py35-numpy"
- TOXENV: "py27-nobyte"
- TOXENV: "doctesting"
- TOXENV: "freeze"

1
changelog/1994.feature Normal file
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@ -0,0 +1 @@
Add support for numpy arrays (and dicts) to approx.

2
doc/en/builtin.rst
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@ -38,7 +38,7 @@ Examples at :ref:`assertraises`.
Comparing floating point numbers
--------------------------------
.. autoclass:: approx
.. autofunction:: approx
Raising a specific test outcome
--------------------------------------

168
testing/python/approx.py
View File

@ -9,7 +9,6 @@ from decimal import Decimal
from fractions import Fraction
inf, nan = float('inf'), float('nan')
class MyDocTestRunner(doctest.DocTestRunner):
def __init__(self):
@ -29,12 +28,19 @@ class TestApprox(object):
if sys.version_info[:2] == (2, 6):
tol1, tol2, infr = '???', '???', '???'
assert repr(approx(1.0)) == '1.0 {pm} {tol1}'.format(pm=plus_minus, tol1=tol1)
assert repr(approx([1.0, 2.0])) == '1.0 {pm} {tol1}, 2.0 {pm} {tol2}'.format(pm=plus_minus, tol1=tol1, tol2=tol2)
assert repr(approx([1.0, 2.0])) == 'approx([1.0 {pm} {tol1}, 2.0 {pm} {tol2}])'.format(pm=plus_minus, tol1=tol1, tol2=tol2)
assert repr(approx((1.0, 2.0))) == 'approx((1.0 {pm} {tol1}, 2.0 {pm} {tol2}))'.format(pm=plus_minus, tol1=tol1, tol2=tol2)
assert repr(approx(inf)) == 'inf'
assert repr(approx(1.0, rel=nan)) == '1.0 {pm} ???'.format(pm=plus_minus)
assert repr(approx(1.0, rel=inf)) == '1.0 {pm} {infr}'.format(pm=plus_minus, infr=infr)
assert repr(approx(1.0j, rel=inf)) == '1j'
# Dictionaries aren't ordered, so we need to check both orders.
assert repr(approx({'a': 1.0, 'b': 2.0})) in (
"approx({{'a': 1.0 {pm} {tol1}, 'b': 2.0 {pm} {tol2}}})".format(pm=plus_minus, tol1=tol1, tol2=tol2),
"approx({{'b': 2.0 {pm} {tol2}, 'a': 1.0 {pm} {tol1}}})".format(pm=plus_minus, tol1=tol1, tol2=tol2),
)
def test_operator_overloading(self):
assert 1 == approx(1, rel=1e-6, abs=1e-12)
assert not (1 != approx(1, rel=1e-6, abs=1e-12))
@ -212,34 +218,51 @@ class TestApprox(object):
def test_expecting_nan(self):
examples = [
(nan, nan),
(-nan, -nan),
(nan, -nan),
(0.0, nan),
(inf, nan),
(eq, nan, nan),
(eq, -nan, -nan),
(eq, nan, -nan),
(ne, 0.0, nan),
(ne, inf, nan),
]
for a, x in examples:
# If there is a relative tolerance and the expected value is NaN,
# the actual tolerance is a NaN, which should be an error.
with pytest.raises(ValueError):
a != approx(x, rel=inf)
for op, a, x in examples:
# Nothing is equal to NaN by default.
assert a != approx(x)
# You can make comparisons against NaN by not specifying a relative
# tolerance, so only an absolute tolerance is calculated.
assert a != approx(x, abs=inf)
# If ``nan_ok=True``, then NaN is equal to NaN.
assert op(a, approx(x, nan_ok=True))
def test_expecting_sequence(self):
within_1e8 = [
(1e8 + 1e0, 1e8),
(1e0 + 1e-8, 1e0),
(1e-8 + 1e-16, 1e-8),
def test_int(self):
within_1e6 = [
(1000001, 1000000),
(-1000001, -1000000),
]
actual, expected = zip(*within_1e8)
assert actual == approx(expected, rel=5e-8, abs=0.0)
for a, x in within_1e6:
assert a == approx(x, rel=5e-6, abs=0)
assert a != approx(x, rel=5e-7, abs=0)
assert approx(x, rel=5e-6, abs=0) == a
assert approx(x, rel=5e-7, abs=0) != a
def test_expecting_sequence_wrong_len(self):
assert [1, 2] != approx([1])
assert [1, 2] != approx([1,2,3])
def test_decimal(self):
within_1e6 = [
(Decimal('1.000001'), Decimal('1.0')),
(Decimal('-1.000001'), Decimal('-1.0')),
]
for a, x in within_1e6:
assert a == approx(x, rel=Decimal('5e-6'), abs=0)
assert a != approx(x, rel=Decimal('5e-7'), abs=0)
assert approx(x, rel=Decimal('5e-6'), abs=0) == a
assert approx(x, rel=Decimal('5e-7'), abs=0) != a
def test_fraction(self):
within_1e6 = [
(1 + Fraction(1, 1000000), Fraction(1)),
(-1 - Fraction(-1, 1000000), Fraction(-1)),
]
for a, x in within_1e6:
assert a == approx(x, rel=5e-6, abs=0)
assert a != approx(x, rel=5e-7, abs=0)
assert approx(x, rel=5e-6, abs=0) == a
assert approx(x, rel=5e-7, abs=0) != a
def test_complex(self):
within_1e6 = [
@ -251,33 +274,80 @@ class TestApprox(object):
for a, x in within_1e6:
assert a == approx(x, rel=5e-6, abs=0)
assert a != approx(x, rel=5e-7, abs=0)
assert approx(x, rel=5e-6, abs=0) == a
assert approx(x, rel=5e-7, abs=0) != a
def test_int(self):
within_1e6 = [
(1000001, 1000000),
(-1000001, -1000000),
]
for a, x in within_1e6:
assert a == approx(x, rel=5e-6, abs=0)
assert a != approx(x, rel=5e-7, abs=0)
def test_list(self):
actual = [1 + 1e-7, 2 + 1e-8]
expected = [1, 2]
def test_decimal(self):
within_1e6 = [
(Decimal('1.000001'), Decimal('1.0')),
(Decimal('-1.000001'), Decimal('-1.0')),
]
for a, x in within_1e6:
assert a == approx(x, rel=Decimal('5e-6'), abs=0)
assert a != approx(x, rel=Decimal('5e-7'), abs=0)
# Return false if any element is outside the tolerance.
assert actual == approx(expected, rel=5e-7, abs=0)
assert actual != approx(expected, rel=5e-8, abs=0)
assert approx(expected, rel=5e-7, abs=0) == actual
assert approx(expected, rel=5e-8, abs=0) != actual
def test_fraction(self):
within_1e6 = [
(1 + Fraction(1, 1000000), Fraction(1)),
(-1 - Fraction(-1, 1000000), Fraction(-1)),
]
for a, x in within_1e6:
assert a == approx(x, rel=5e-6, abs=0)
assert a != approx(x, rel=5e-7, abs=0)
def test_list_wrong_len(self):
assert [1, 2] != approx([1])
assert [1, 2] != approx([1,2,3])
def test_tuple(self):
actual = (1 + 1e-7, 2 + 1e-8)
expected = (1, 2)
# Return false if any element is outside the tolerance.
assert actual == approx(expected, rel=5e-7, abs=0)
assert actual != approx(expected, rel=5e-8, abs=0)
assert approx(expected, rel=5e-7, abs=0) == actual
assert approx(expected, rel=5e-8, abs=0) != actual
def test_tuple_wrong_len(self):
assert (1, 2) != approx((1,))
assert (1, 2) != approx((1,2,3))
def test_dict(self):
actual = {'a': 1 + 1e-7, 'b': 2 + 1e-8}
# Dictionaries became ordered in python3.6, so switch up the order here
# to make sure it doesn't matter.
expected = {'b': 2, 'a': 1}
# Return false if any element is outside the tolerance.
assert actual == approx(expected, rel=5e-7, abs=0)
assert actual != approx(expected, rel=5e-8, abs=0)
assert approx(expected, rel=5e-7, abs=0) == actual
assert approx(expected, rel=5e-8, abs=0) != actual
def test_dict_wrong_len(self):
assert {'a': 1, 'b': 2} != approx({'a': 1})
assert {'a': 1, 'b': 2} != approx({'a': 1, 'c': 2})
assert {'a': 1, 'b': 2} != approx({'a': 1, 'b': 2, 'c': 3})
def test_numpy_array(self):
np = pytest.importorskip('numpy')
actual = np.array([1 + 1e-7, 2 + 1e-8])
expected = np.array([1, 2])
# Return false if any element is outside the tolerance.
assert actual == approx(expected, rel=5e-7, abs=0)
assert actual != approx(expected, rel=5e-8, abs=0)
assert approx(expected, rel=5e-7, abs=0) == expected
assert approx(expected, rel=5e-8, abs=0) != actual
# Should be able to compare lists with numpy arrays.
assert list(actual) == approx(expected, rel=5e-7, abs=0)
assert list(actual) != approx(expected, rel=5e-8, abs=0)
assert actual == approx(list(expected), rel=5e-7, abs=0)
assert actual != approx(list(expected), rel=5e-8, abs=0)
def test_numpy_array_wrong_shape(self):
np = pytest.importorskip('numpy')
a12 = np.array([[1, 2]])
a21 = np.array([[1],[2]])
assert a12 != approx(a21)
assert a21 != approx(a12)
def test_doctests(self):
parser = doctest.DocTestParser()

12
tox.ini
View File

@ -12,7 +12,7 @@ envlist=
py36
py37
pypy
{py27,py35}-{pexpect,xdist,trial}
{py27,py35}-{pexpect,xdist,trial,numpy}
py27-nobyte
doctesting
freeze
@ -108,6 +108,16 @@ deps={[testenv:py27-trial]deps}
commands=
pytest -ra {posargs:testing/test_unittest.py}
[testenv:py27-numpy]
deps=numpy
commands=
pytest -rfsxX {posargs:testing/python/approx.py}
[testenv:py35-numpy]
deps=numpy
commands=
pytest -rfsxX {posargs:testing/python/approx.py}
[testenv:docs]
skipsdist=True
usedevelop=True