618 lines
22 KiB
Python
618 lines
22 KiB
Python
import math
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import sys
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import py
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from _pytest.compat import isclass, izip
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from _pytest.outcomes import fail
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import _pytest._code
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def _cmp_raises_type_error(self, other):
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"""__cmp__ implementation which raises TypeError. Used
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by Approx base classes to implement only == and != and raise a
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TypeError for other comparisons.
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Needed in Python 2 only, Python 3 all it takes is not implementing the
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other operators at all.
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"""
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__tracebackhide__ = True
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raise TypeError('Comparison operators other than == and != not supported by approx objects')
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# builtin pytest.approx helper
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class ApproxBase(object):
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"""
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Provide shared utilities for making approximate comparisons between numbers
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or sequences of numbers.
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"""
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def __init__(self, expected, rel=None, abs=None, nan_ok=False):
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self.expected = expected
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self.abs = abs
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self.rel = rel
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self.nan_ok = nan_ok
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def __repr__(self):
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raise NotImplementedError
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def __eq__(self, actual):
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return all(
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a == self._approx_scalar(x)
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for a, x in self._yield_comparisons(actual))
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__hash__ = None
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def __ne__(self, actual):
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return not (actual == self)
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if sys.version_info[0] == 2:
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__cmp__ = _cmp_raises_type_error
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def _approx_scalar(self, x):
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return ApproxScalar(x, rel=self.rel, abs=self.abs, nan_ok=self.nan_ok)
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def _yield_comparisons(self, actual):
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"""
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Yield all the pairs of numbers to be compared. This is used to
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implement the `__eq__` method.
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"""
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raise NotImplementedError
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class ApproxNumpy(ApproxBase):
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"""
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Perform approximate comparisons for numpy arrays.
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"""
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# Tell numpy to use our `__eq__` operator instead of its.
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__array_priority__ = 100
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def __repr__(self):
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# It might be nice to rewrite this function to account for the
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# shape of the array...
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return "approx({0!r})".format(list(
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self._approx_scalar(x) for x in self.expected))
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if sys.version_info[0] == 2:
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__cmp__ = _cmp_raises_type_error
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def __eq__(self, actual):
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import numpy as np
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try:
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actual = np.asarray(actual)
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except:
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raise TypeError("cannot compare '{0}' to numpy.ndarray".format(actual))
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if actual.shape != self.expected.shape:
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return False
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return ApproxBase.__eq__(self, actual)
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def _yield_comparisons(self, actual):
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import numpy as np
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# We can be sure that `actual` is a numpy array, because it's
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# casted in `__eq__` before being passed to `ApproxBase.__eq__`,
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# which is the only method that calls this one.
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for i in np.ndindex(self.expected.shape):
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yield actual[i], self.expected[i]
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class ApproxMapping(ApproxBase):
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"""
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Perform approximate comparisons for mappings where the values are numbers
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(the keys can be anything).
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"""
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def __repr__(self):
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return "approx({0!r})".format(dict(
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(k, self._approx_scalar(v))
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for k, v in self.expected.items()))
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def __eq__(self, actual):
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if set(actual.keys()) != set(self.expected.keys()):
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return False
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return ApproxBase.__eq__(self, actual)
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def _yield_comparisons(self, actual):
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for k in self.expected.keys():
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yield actual[k], self.expected[k]
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class ApproxSequence(ApproxBase):
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"""
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Perform approximate comparisons for sequences of numbers.
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"""
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# Tell numpy to use our `__eq__` operator instead of its.
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__array_priority__ = 100
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def __repr__(self):
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seq_type = type(self.expected)
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if seq_type not in (tuple, list, set):
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seq_type = list
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return "approx({0!r})".format(seq_type(
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self._approx_scalar(x) for x in self.expected))
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def __eq__(self, actual):
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if len(actual) != len(self.expected):
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return False
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return ApproxBase.__eq__(self, actual)
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def _yield_comparisons(self, actual):
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return izip(actual, self.expected)
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class ApproxScalar(ApproxBase):
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"""
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Perform approximate comparisons for single numbers only.
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"""
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def __repr__(self):
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"""
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Return a string communicating both the expected value and the tolerance
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for the comparison being made, e.g. '1.0 +- 1e-6'. Use the unicode
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plus/minus symbol if this is python3 (it's too hard to get right for
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python2).
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"""
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if isinstance(self.expected, complex):
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return str(self.expected)
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# Infinities aren't compared using tolerances, so don't show a
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# tolerance.
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if math.isinf(self.expected):
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return str(self.expected)
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# If a sensible tolerance can't be calculated, self.tolerance will
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# raise a ValueError. In this case, display '???'.
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try:
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vetted_tolerance = '{:.1e}'.format(self.tolerance)
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except ValueError:
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vetted_tolerance = '???'
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if sys.version_info[0] == 2:
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return '{0} +- {1}'.format(self.expected, vetted_tolerance)
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else:
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return u'{0} \u00b1 {1}'.format(self.expected, vetted_tolerance)
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def __eq__(self, actual):
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"""
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Return true if the given value is equal to the expected value within
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the pre-specified tolerance.
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"""
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# Short-circuit exact equality.
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if actual == self.expected:
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return True
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# Allow the user to control whether NaNs are considered equal to each
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# other or not. The abs() calls are for compatibility with complex
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# numbers.
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if math.isnan(abs(self.expected)):
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return self.nan_ok and math.isnan(abs(actual))
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# Infinity shouldn't be approximately equal to anything but itself, but
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# if there's a relative tolerance, it will be infinite and infinity
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# will seem approximately equal to everything. The equal-to-itself
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# case would have been short circuited above, so here we can just
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# return false if the expected value is infinite. The abs() call is
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# for compatibility with complex numbers.
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if math.isinf(abs(self.expected)):
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return False
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# Return true if the two numbers are within the tolerance.
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return abs(self.expected - actual) <= self.tolerance
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__hash__ = None
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@property
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def tolerance(self):
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"""
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Return the tolerance for the comparison. This could be either an
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absolute tolerance or a relative tolerance, depending on what the user
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specified or which would be larger.
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"""
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def set_default(x, default): return x if x is not None else default
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# Figure out what the absolute tolerance should be. ``self.abs`` is
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# either None or a value specified by the user.
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absolute_tolerance = set_default(self.abs, 1e-12)
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if absolute_tolerance < 0:
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raise ValueError("absolute tolerance can't be negative: {}".format(absolute_tolerance))
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if math.isnan(absolute_tolerance):
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raise ValueError("absolute tolerance can't be NaN.")
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# If the user specified an absolute tolerance but not a relative one,
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# just return the absolute tolerance.
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if self.rel is None:
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if self.abs is not None:
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return absolute_tolerance
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# Figure out what the relative tolerance should be. ``self.rel`` is
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# either None or a value specified by the user. This is done after
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# we've made sure the user didn't ask for an absolute tolerance only,
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# because we don't want to raise errors about the relative tolerance if
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# we aren't even going to use it.
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relative_tolerance = set_default(self.rel, 1e-6) * abs(self.expected)
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if relative_tolerance < 0:
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raise ValueError("relative tolerance can't be negative: {}".format(absolute_tolerance))
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if math.isnan(relative_tolerance):
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raise ValueError("relative tolerance can't be NaN.")
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# Return the larger of the relative and absolute tolerances.
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return max(relative_tolerance, absolute_tolerance)
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def approx(expected, rel=None, abs=None, nan_ok=False):
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"""
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Assert that two numbers (or two sets of numbers) are equal to each other
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within some tolerance.
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Due to the `intricacies of floating-point arithmetic`__, numbers that we
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would intuitively expect to be equal are not always so::
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>>> 0.1 + 0.2 == 0.3
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False
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__ https://docs.python.org/3/tutorial/floatingpoint.html
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This problem is commonly encountered when writing tests, e.g. when making
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sure that floating-point values are what you expect them to be. One way to
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deal with this problem is to assert that two floating-point numbers are
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equal to within some appropriate tolerance::
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>>> abs((0.1 + 0.2) - 0.3) < 1e-6
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True
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However, comparisons like this are tedious to write and difficult to
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understand. Furthermore, absolute comparisons like the one above are
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usually discouraged because there's no tolerance that works well for all
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situations. ``1e-6`` is good for numbers around ``1``, but too small for
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very big numbers and too big for very small ones. It's better to express
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the tolerance as a fraction of the expected value, but relative comparisons
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like that are even more difficult to write correctly and concisely.
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The ``approx`` class performs floating-point comparisons using a syntax
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that's as intuitive as possible::
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>>> from pytest import approx
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>>> 0.1 + 0.2 == approx(0.3)
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True
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The same syntax also works for sequences of numbers::
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>>> (0.1 + 0.2, 0.2 + 0.4) == approx((0.3, 0.6))
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True
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Dictionary *values*::
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>>> {'a': 0.1 + 0.2, 'b': 0.2 + 0.4} == approx({'a': 0.3, 'b': 0.6})
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True
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And ``numpy`` arrays::
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>>> import numpy as np # doctest: +SKIP
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>>> np.array([0.1, 0.2]) + np.array([0.2, 0.4]) == approx(np.array([0.3, 0.6])) # doctest: +SKIP
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True
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By default, ``approx`` considers numbers within a relative tolerance of
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``1e-6`` (i.e. one part in a million) of its expected value to be equal.
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This treatment would lead to surprising results if the expected value was
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``0.0``, because nothing but ``0.0`` itself is relatively close to ``0.0``.
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To handle this case less surprisingly, ``approx`` also considers numbers
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within an absolute tolerance of ``1e-12`` of its expected value to be
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equal. Infinity and NaN are special cases. Infinity is only considered
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equal to itself, regardless of the relative tolerance. NaN is not
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considered equal to anything by default, but you can make it be equal to
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itself by setting the ``nan_ok`` argument to True. (This is meant to
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facilitate comparing arrays that use NaN to mean "no data".)
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Both the relative and absolute tolerances can be changed by passing
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arguments to the ``approx`` constructor::
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>>> 1.0001 == approx(1)
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False
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>>> 1.0001 == approx(1, rel=1e-3)
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True
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>>> 1.0001 == approx(1, abs=1e-3)
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True
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If you specify ``abs`` but not ``rel``, the comparison will not consider
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the relative tolerance at all. In other words, two numbers that are within
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the default relative tolerance of ``1e-6`` will still be considered unequal
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if they exceed the specified absolute tolerance. If you specify both
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``abs`` and ``rel``, the numbers will be considered equal if either
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tolerance is met::
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>>> 1 + 1e-8 == approx(1)
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True
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>>> 1 + 1e-8 == approx(1, abs=1e-12)
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False
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>>> 1 + 1e-8 == approx(1, rel=1e-6, abs=1e-12)
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True
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If you're thinking about using ``approx``, then you might want to know how
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it compares to other good ways of comparing floating-point numbers. All of
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these algorithms are based on relative and absolute tolerances and should
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agree for the most part, but they do have meaningful differences:
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- ``math.isclose(a, b, rel_tol=1e-9, abs_tol=0.0)``: True if the relative
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tolerance is met w.r.t. either ``a`` or ``b`` or if the absolute
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tolerance is met. Because the relative tolerance is calculated w.r.t.
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both ``a`` and ``b``, this test is symmetric (i.e. neither ``a`` nor
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``b`` is a "reference value"). You have to specify an absolute tolerance
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if you want to compare to ``0.0`` because there is no tolerance by
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default. Only available in python>=3.5. `More information...`__
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__ https://docs.python.org/3/library/math.html#math.isclose
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- ``numpy.isclose(a, b, rtol=1e-5, atol=1e-8)``: True if the difference
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between ``a`` and ``b`` is less that the sum of the relative tolerance
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w.r.t. ``b`` and the absolute tolerance. Because the relative tolerance
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is only calculated w.r.t. ``b``, this test is asymmetric and you can
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think of ``b`` as the reference value. Support for comparing sequences
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is provided by ``numpy.allclose``. `More information...`__
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__ http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.isclose.html
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- ``unittest.TestCase.assertAlmostEqual(a, b)``: True if ``a`` and ``b``
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are within an absolute tolerance of ``1e-7``. No relative tolerance is
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considered and the absolute tolerance cannot be changed, so this function
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is not appropriate for very large or very small numbers. Also, it's only
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available in subclasses of ``unittest.TestCase`` and it's ugly because it
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doesn't follow PEP8. `More information...`__
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__ https://docs.python.org/3/library/unittest.html#unittest.TestCase.assertAlmostEqual
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- ``a == pytest.approx(b, rel=1e-6, abs=1e-12)``: True if the relative
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tolerance is met w.r.t. ``b`` or if the absolute tolerance is met.
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Because the relative tolerance is only calculated w.r.t. ``b``, this test
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is asymmetric and you can think of ``b`` as the reference value. In the
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special case that you explicitly specify an absolute tolerance but not a
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relative tolerance, only the absolute tolerance is considered.
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.. warning::
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.. versionchanged:: 3.2
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In order to avoid inconsistent behavior, ``TypeError`` is
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raised for ``>``, ``>=``, ``<`` and ``<=`` comparisons.
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The example below illustrates the problem::
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assert approx(0.1) > 0.1 + 1e-10 # calls approx(0.1).__gt__(0.1 + 1e-10)
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assert 0.1 + 1e-10 > approx(0.1) # calls approx(0.1).__lt__(0.1 + 1e-10)
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In the second example one expects ``approx(0.1).__le__(0.1 + 1e-10)``
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to be called. But instead, ``approx(0.1).__lt__(0.1 + 1e-10)`` is used to
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comparison. This is because the call hierarchy of rich comparisons
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follows a fixed behavior. `More information...`__
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__ https://docs.python.org/3/reference/datamodel.html#object.__ge__
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"""
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from collections import Mapping, Sequence
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from _pytest.compat import STRING_TYPES as String
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# Delegate the comparison to a class that knows how to deal with the type
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# of the expected value (e.g. int, float, list, dict, numpy.array, etc).
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#
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# This architecture is really driven by the need to support numpy arrays.
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# The only way to override `==` for arrays without requiring that approx be
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# the left operand is to inherit the approx object from `numpy.ndarray`.
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# But that can't be a general solution, because it requires (1) numpy to be
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# installed and (2) the expected value to be a numpy array. So the general
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# solution is to delegate each type of expected value to a different class.
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#
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# This has the advantage that it made it easy to support mapping types
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# (i.e. dict). The old code accepted mapping types, but would only compare
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# their keys, which is probably not what most people would expect.
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if _is_numpy_array(expected):
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cls = ApproxNumpy
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elif isinstance(expected, Mapping):
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cls = ApproxMapping
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elif isinstance(expected, Sequence) and not isinstance(expected, String):
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cls = ApproxSequence
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else:
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cls = ApproxScalar
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return cls(expected, rel, abs, nan_ok)
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def _is_numpy_array(obj):
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"""
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Return true if the given object is a numpy array. Make a special effort to
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avoid importing numpy unless it's really necessary.
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"""
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import inspect
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for cls in inspect.getmro(type(obj)):
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if cls.__module__ == 'numpy':
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try:
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import numpy as np
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return isinstance(obj, np.ndarray)
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except ImportError:
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pass
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return False
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# builtin pytest.raises helper
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def raises(expected_exception, *args, **kwargs):
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"""
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Assert that a code block/function call raises ``expected_exception``
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and raise a failure exception otherwise.
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This helper produces a ``ExceptionInfo()`` object (see below).
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You may use this function as a context manager::
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>>> with raises(ZeroDivisionError):
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... 1/0
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.. versionchanged:: 2.10
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In the context manager form you may use the keyword argument
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``message`` to specify a custom failure message::
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>>> with raises(ZeroDivisionError, message="Expecting ZeroDivisionError"):
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... pass
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Traceback (most recent call last):
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...
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Failed: Expecting ZeroDivisionError
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.. note::
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When using ``pytest.raises`` as a context manager, it's worthwhile to
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note that normal context manager rules apply and that the exception
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raised *must* be the final line in the scope of the context manager.
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Lines of code after that, within the scope of the context manager will
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not be executed. For example::
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>>> value = 15
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>>> with raises(ValueError) as exc_info:
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... if value > 10:
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... raise ValueError("value must be <= 10")
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... assert exc_info.type == ValueError # this will not execute
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Instead, the following approach must be taken (note the difference in
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scope)::
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>>> with raises(ValueError) as exc_info:
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... if value > 10:
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... raise ValueError("value must be <= 10")
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...
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>>> assert exc_info.type == ValueError
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Since version ``3.1`` you can use the keyword argument ``match`` to assert that the
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exception matches a text or regex::
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>>> with raises(ValueError, match='must be 0 or None'):
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... raise ValueError("value must be 0 or None")
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>>> with raises(ValueError, match=r'must be \d+$'):
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... raise ValueError("value must be 42")
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**Legacy forms**
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The forms below are fully supported but are discouraged for new code because the
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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.
|
|
|
|
.. 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)
|
|
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
|