1014 lines
38 KiB
Python
1014 lines
38 KiB
Python
import math
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import pprint
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from collections.abc import Collection
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from collections.abc import Sized
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from decimal import Decimal
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from numbers import Complex
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from types import TracebackType
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from typing import Any
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from typing import Callable
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from typing import cast
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from typing import ContextManager
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from typing import final
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from typing import List
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from typing import Mapping
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from typing import Optional
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from typing import overload
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from typing import Pattern
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from typing import Sequence
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from typing import Tuple
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from typing import Type
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from typing import TYPE_CHECKING
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from typing import TypeVar
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from typing import Union
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import _pytest._code
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from _pytest.compat import STRING_TYPES
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from _pytest.outcomes import fail
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if TYPE_CHECKING:
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from numpy import ndarray
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def _compare_approx(
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full_object: object,
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message_data: Sequence[Tuple[str, str, str]],
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number_of_elements: int,
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different_ids: Sequence[object],
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max_abs_diff: float,
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max_rel_diff: float,
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) -> List[str]:
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message_list = list(message_data)
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message_list.insert(0, ("Index", "Obtained", "Expected"))
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max_sizes = [0, 0, 0]
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for index, obtained, expected in message_list:
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max_sizes[0] = max(max_sizes[0], len(index))
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max_sizes[1] = max(max_sizes[1], len(obtained))
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max_sizes[2] = max(max_sizes[2], len(expected))
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explanation = [
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f"comparison failed. Mismatched elements: {len(different_ids)} / {number_of_elements}:",
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f"Max absolute difference: {max_abs_diff}",
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f"Max relative difference: {max_rel_diff}",
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] + [
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f"{indexes:<{max_sizes[0]}} | {obtained:<{max_sizes[1]}} | {expected:<{max_sizes[2]}}"
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for indexes, obtained, expected in message_list
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]
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return explanation
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# builtin pytest.approx helper
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class ApproxBase:
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"""Provide shared utilities for making approximate comparisons between
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numbers or sequences of numbers."""
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# Tell numpy to use our `__eq__` operator instead of its.
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__array_ufunc__ = None
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__array_priority__ = 100
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def __init__(self, expected, rel=None, abs=None, nan_ok: bool = False) -> None:
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__tracebackhide__ = True
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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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self._check_type()
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def __repr__(self) -> str:
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raise NotImplementedError
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def _repr_compare(self, other_side: Any) -> List[str]:
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return [
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"comparison failed",
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f"Obtained: {other_side}",
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f"Expected: {self}",
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]
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def __eq__(self, actual) -> bool:
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return all(
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a == self._approx_scalar(x) for a, x in self._yield_comparisons(actual)
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)
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def __bool__(self):
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__tracebackhide__ = True
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raise AssertionError(
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"approx() is not supported in a boolean context.\nDid you mean: `assert a == approx(b)`?"
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)
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# Ignore type because of https://github.com/python/mypy/issues/4266.
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__hash__ = None # type: ignore
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def __ne__(self, actual) -> bool:
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return not (actual == self)
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def _approx_scalar(self, x) -> "ApproxScalar":
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if isinstance(x, Decimal):
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return ApproxDecimal(x, rel=self.rel, abs=self.abs, nan_ok=self.nan_ok)
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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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"""Yield all the pairs of numbers to be compared.
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This is used to implement the `__eq__` method.
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"""
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raise NotImplementedError
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def _check_type(self) -> None:
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"""Raise a TypeError if the expected value is not a valid type."""
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# This is only a concern if the expected value is a sequence. In every
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# other case, the approx() function ensures that the expected value has
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# a numeric type. For this reason, the default is to do nothing. The
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# classes that deal with sequences should reimplement this method to
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# raise if there are any non-numeric elements in the sequence.
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def _recursive_sequence_map(f, x):
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"""Recursively map a function over a sequence of arbitrary depth"""
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if isinstance(x, (list, tuple)):
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seq_type = type(x)
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return seq_type(_recursive_sequence_map(f, xi) for xi in x)
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else:
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return f(x)
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class ApproxNumpy(ApproxBase):
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"""Perform approximate comparisons where the expected value is numpy array."""
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def __repr__(self) -> str:
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list_scalars = _recursive_sequence_map(
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self._approx_scalar, self.expected.tolist()
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)
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return f"approx({list_scalars!r})"
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def _repr_compare(self, other_side: "ndarray") -> List[str]:
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import itertools
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import math
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def get_value_from_nested_list(
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nested_list: List[Any], nd_index: Tuple[Any, ...]
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) -> Any:
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"""
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Helper function to get the value out of a nested list, given an n-dimensional index.
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This mimics numpy's indexing, but for raw nested python lists.
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"""
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value: Any = nested_list
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for i in nd_index:
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value = value[i]
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return value
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np_array_shape = self.expected.shape
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approx_side_as_seq = _recursive_sequence_map(
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self._approx_scalar, self.expected.tolist()
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)
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if np_array_shape != other_side.shape:
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return [
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"Impossible to compare arrays with different shapes.",
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f"Shapes: {np_array_shape} and {other_side.shape}",
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]
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number_of_elements = self.expected.size
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max_abs_diff = -math.inf
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max_rel_diff = -math.inf
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different_ids = []
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for index in itertools.product(*(range(i) for i in np_array_shape)):
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approx_value = get_value_from_nested_list(approx_side_as_seq, index)
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other_value = get_value_from_nested_list(other_side, index)
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if approx_value != other_value:
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abs_diff = abs(approx_value.expected - other_value)
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max_abs_diff = max(max_abs_diff, abs_diff)
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if other_value == 0.0:
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max_rel_diff = math.inf
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else:
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max_rel_diff = max(max_rel_diff, abs_diff / abs(other_value))
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different_ids.append(index)
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message_data = [
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(
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str(index),
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str(get_value_from_nested_list(other_side, index)),
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str(get_value_from_nested_list(approx_side_as_seq, index)),
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)
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for index in different_ids
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]
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return _compare_approx(
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self.expected,
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message_data,
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number_of_elements,
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different_ids,
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max_abs_diff,
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max_rel_diff,
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)
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def __eq__(self, actual) -> bool:
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import numpy as np
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# self.expected is supposed to always be an array here.
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if not np.isscalar(actual):
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try:
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actual = np.asarray(actual)
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except Exception as e:
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raise TypeError(f"cannot compare '{actual}' to numpy.ndarray") from e
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if not np.isscalar(actual) and actual.shape != self.expected.shape:
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return False
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return super().__eq__(actual)
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def _yield_comparisons(self, actual):
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import numpy as np
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# `actual` can either be a numpy array or a scalar, it is treated in
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# `__eq__` before being passed to `ApproxBase.__eq__`, which is the
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# only method that calls this one.
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if np.isscalar(actual):
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for i in np.ndindex(self.expected.shape):
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yield actual, self.expected[i].item()
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else:
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for i in np.ndindex(self.expected.shape):
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yield actual[i].item(), self.expected[i].item()
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class ApproxMapping(ApproxBase):
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"""Perform approximate comparisons where the expected value is a mapping
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with numeric values (the keys can be anything)."""
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def __repr__(self) -> str:
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return "approx({!r})".format(
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{k: self._approx_scalar(v) for k, v in self.expected.items()}
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)
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def _repr_compare(self, other_side: Mapping[object, float]) -> List[str]:
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import math
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approx_side_as_map = {
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k: self._approx_scalar(v) for k, v in self.expected.items()
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}
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number_of_elements = len(approx_side_as_map)
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max_abs_diff = -math.inf
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max_rel_diff = -math.inf
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different_ids = []
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for (approx_key, approx_value), other_value in zip(
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approx_side_as_map.items(), other_side.values()
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):
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if approx_value != other_value:
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if approx_value.expected is not None and other_value is not None:
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max_abs_diff = max(
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max_abs_diff, abs(approx_value.expected - other_value)
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)
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if approx_value.expected == 0.0:
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max_rel_diff = math.inf
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else:
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max_rel_diff = max(
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max_rel_diff,
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abs(
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(approx_value.expected - other_value)
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/ approx_value.expected
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),
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)
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different_ids.append(approx_key)
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message_data = [
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(str(key), str(other_side[key]), str(approx_side_as_map[key]))
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for key in different_ids
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]
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return _compare_approx(
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self.expected,
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message_data,
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number_of_elements,
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different_ids,
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max_abs_diff,
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max_rel_diff,
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)
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def __eq__(self, actual) -> bool:
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try:
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if set(actual.keys()) != set(self.expected.keys()):
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return False
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except AttributeError:
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return False
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return super().__eq__(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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def _check_type(self) -> None:
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__tracebackhide__ = True
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for key, value in self.expected.items():
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if isinstance(value, type(self.expected)):
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msg = "pytest.approx() does not support nested dictionaries: key={!r} value={!r}\n full mapping={}"
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raise TypeError(msg.format(key, value, pprint.pformat(self.expected)))
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class ApproxSequenceLike(ApproxBase):
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"""Perform approximate comparisons where the expected value is a sequence of numbers."""
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def __repr__(self) -> str:
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seq_type = type(self.expected)
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if seq_type not in (tuple, list):
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seq_type = list
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return "approx({!r})".format(
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seq_type(self._approx_scalar(x) for x in self.expected)
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)
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def _repr_compare(self, other_side: Sequence[float]) -> List[str]:
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import math
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if len(self.expected) != len(other_side):
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return [
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"Impossible to compare lists with different sizes.",
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f"Lengths: {len(self.expected)} and {len(other_side)}",
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]
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approx_side_as_map = _recursive_sequence_map(self._approx_scalar, self.expected)
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number_of_elements = len(approx_side_as_map)
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max_abs_diff = -math.inf
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max_rel_diff = -math.inf
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different_ids = []
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for i, (approx_value, other_value) in enumerate(
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zip(approx_side_as_map, other_side)
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):
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if approx_value != other_value:
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abs_diff = abs(approx_value.expected - other_value)
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max_abs_diff = max(max_abs_diff, abs_diff)
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if other_value == 0.0:
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max_rel_diff = math.inf
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else:
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max_rel_diff = max(max_rel_diff, abs_diff / abs(other_value))
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different_ids.append(i)
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message_data = [
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(str(i), str(other_side[i]), str(approx_side_as_map[i]))
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for i in different_ids
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]
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return _compare_approx(
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self.expected,
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message_data,
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number_of_elements,
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different_ids,
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max_abs_diff,
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max_rel_diff,
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)
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def __eq__(self, actual) -> bool:
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try:
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if len(actual) != len(self.expected):
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return False
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except TypeError:
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return False
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return super().__eq__(actual)
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def _yield_comparisons(self, actual):
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return zip(actual, self.expected)
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def _check_type(self) -> None:
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__tracebackhide__ = True
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for index, x in enumerate(self.expected):
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if isinstance(x, type(self.expected)):
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msg = "pytest.approx() does not support nested data structures: {!r} at index {}\n full sequence: {}"
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raise TypeError(msg.format(x, index, pprint.pformat(self.expected)))
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class ApproxScalar(ApproxBase):
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"""Perform approximate comparisons where the expected value is a single number."""
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# Using Real should be better than this Union, but not possible yet:
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# https://github.com/python/typeshed/pull/3108
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DEFAULT_ABSOLUTE_TOLERANCE: Union[float, Decimal] = 1e-12
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DEFAULT_RELATIVE_TOLERANCE: Union[float, Decimal] = 1e-6
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def __repr__(self) -> str:
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"""Return a string communicating both the expected value and the
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tolerance for the comparison being made.
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For example, ``1.0 ± 1e-6``, ``(3+4j) ± 5e-6 ∠ ±180°``.
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"""
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# Don't show a tolerance for values that aren't compared using
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# tolerances, i.e. non-numerics and infinities. Need to call abs to
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# handle complex numbers, e.g. (inf + 1j).
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if (not isinstance(self.expected, (Complex, Decimal))) or math.isinf(
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abs(self.expected) # type: ignore[arg-type]
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):
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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 = f"{self.tolerance:.1e}"
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if (
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isinstance(self.expected, Complex)
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and self.expected.imag
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and not math.isinf(self.tolerance)
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):
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vetted_tolerance += " ∠ ±180°"
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except ValueError:
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vetted_tolerance = "???"
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return f"{self.expected} ± {vetted_tolerance}"
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def __eq__(self, actual) -> bool:
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"""Return whether the given value is equal to the expected value
|
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within the pre-specified tolerance."""
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asarray = _as_numpy_array(actual)
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if asarray is not None:
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# Call ``__eq__()`` manually to prevent infinite-recursion with
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# numpy<1.13. See #3748.
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return all(self.__eq__(a) for a in asarray.flat)
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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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# If either type is non-numeric, fall back to strict equality.
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# NB: we need Complex, rather than just Number, to ensure that __abs__,
|
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# __sub__, and __float__ are defined.
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if not (
|
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isinstance(self.expected, (Complex, Decimal))
|
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and isinstance(actual, (Complex, Decimal))
|
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):
|
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return False
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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)): # type: ignore[arg-type]
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return self.nan_ok and math.isnan(abs(actual)) # type: ignore[arg-type]
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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)): # type: ignore[arg-type]
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return False
|
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|
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# Return true if the two numbers are within the tolerance.
|
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result: bool = abs(self.expected - actual) <= self.tolerance
|
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return result
|
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|
|
# Ignore type because of https://github.com/python/mypy/issues/4266.
|
|
__hash__ = None # type: ignore
|
|
|
|
@property
|
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def tolerance(self):
|
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"""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.
|
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"""
|
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|
|
def set_default(x, default):
|
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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.
|
|
absolute_tolerance = set_default(self.abs, self.DEFAULT_ABSOLUTE_TOLERANCE)
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|
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if absolute_tolerance < 0:
|
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raise ValueError(
|
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f"absolute tolerance can't be negative: {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.
|
|
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
|
|
# 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
|
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# we aren't even going to use it.
|
|
relative_tolerance = set_default(
|
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self.rel, self.DEFAULT_RELATIVE_TOLERANCE
|
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) * abs(self.expected)
|
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|
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if relative_tolerance < 0:
|
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raise ValueError(
|
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f"relative tolerance can't be negative: {relative_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)
|
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|
|
|
|
class ApproxDecimal(ApproxScalar):
|
|
"""Perform approximate comparisons where the expected value is a Decimal."""
|
|
|
|
DEFAULT_ABSOLUTE_TOLERANCE = Decimal("1e-12")
|
|
DEFAULT_RELATIVE_TOLERANCE = Decimal("1e-6")
|
|
|
|
|
|
def approx(expected, rel=None, abs=None, nan_ok: bool = False) -> ApproxBase:
|
|
"""Assert that two numbers (or two ordered sequences of numbers) are equal to each other
|
|
within some tolerance.
|
|
|
|
Due to the :doc:`python:tutorial/floatingpoint`, numbers that we
|
|
would intuitively expect to be equal are not always so::
|
|
|
|
>>> 0.1 + 0.2 == 0.3
|
|
False
|
|
|
|
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 ordered sequences of numbers::
|
|
|
|
>>> (0.1 + 0.2, 0.2 + 0.4) == approx((0.3, 0.6))
|
|
True
|
|
|
|
``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
|
|
|
|
And for a ``numpy`` array against a scalar::
|
|
|
|
>>> import numpy as np # doctest: +SKIP
|
|
>>> np.array([0.1, 0.2]) + np.array([0.2, 0.1]) == approx(0.3) # doctest: +SKIP
|
|
True
|
|
|
|
Only ordered sequences are supported, because ``approx`` needs
|
|
to infer the relative position of the sequences without ambiguity. This means
|
|
``sets`` and other unordered sequences are not supported.
|
|
|
|
Finally, dictionary *values* can also be compared::
|
|
|
|
>>> {'a': 0.1 + 0.2, 'b': 0.2 + 0.4} == approx({'a': 0.3, 'b': 0.6})
|
|
True
|
|
|
|
The comparison will be true if both mappings have the same keys and their
|
|
respective values match the expected tolerances.
|
|
|
|
**Tolerances**
|
|
|
|
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
|
|
|
|
You can also use ``approx`` to compare nonnumeric types, or dicts and
|
|
sequences containing nonnumeric types, in which case it falls back to
|
|
strict equality. This can be useful for comparing dicts and sequences that
|
|
can contain optional values::
|
|
|
|
>>> {"required": 1.0000005, "optional": None} == approx({"required": 1, "optional": None})
|
|
True
|
|
>>> [None, 1.0000005] == approx([None,1])
|
|
True
|
|
>>> ["foo", 1.0000005] == approx([None,1])
|
|
False
|
|
|
|
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. More information: :py:func:`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 :py:func:`numpy.allclose`. More information:
|
|
:std:doc:`numpy:reference/generated/numpy.isclose`.
|
|
|
|
- ``unittest.TestCase.assertAlmostEqual(a, b)``: True if ``a`` and ``b``
|
|
are within an absolute tolerance of ``1e-7``. No relative tolerance is
|
|
considered , 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:
|
|
:py:meth:`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.
|
|
|
|
.. note::
|
|
|
|
``approx`` can handle numpy arrays, but we recommend the
|
|
specialised test helpers in :std:doc:`numpy:reference/routines.testing`
|
|
if you need support for comparisons, NaNs, or ULP-based tolerances.
|
|
|
|
To match strings using regex, you can use
|
|
`Matches <https://github.com/asottile/re-assert#re_assertmatchespattern-str-args-kwargs>`_
|
|
from the
|
|
`re_assert package <https://github.com/asottile/re-assert>`_.
|
|
|
|
.. warning::
|
|
|
|
.. versionchanged:: 3.2
|
|
|
|
In order to avoid inconsistent behavior, :py:exc:`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: :py:meth:`object.__ge__`
|
|
|
|
.. versionchanged:: 3.7.1
|
|
``approx`` raises ``TypeError`` when it encounters a dict value or
|
|
sequence element of nonnumeric type.
|
|
|
|
.. versionchanged:: 6.1.0
|
|
``approx`` falls back to strict equality for nonnumeric types instead
|
|
of raising ``TypeError``.
|
|
"""
|
|
|
|
# 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).
|
|
#
|
|
# The primary responsibility of these classes is to implement ``__eq__()``
|
|
# and ``__repr__()``. The former is used to actually check if some
|
|
# "actual" value is equivalent to the given expected value within the
|
|
# allowed tolerance. The latter is used to show the user the expected
|
|
# value and tolerance, in the case that a test failed.
|
|
#
|
|
# The actual logic for making approximate comparisons can be found in
|
|
# ApproxScalar, which is used to compare individual numbers. All of the
|
|
# other Approx classes eventually delegate to this class. The ApproxBase
|
|
# class provides some convenient methods and overloads, but isn't really
|
|
# essential.
|
|
|
|
__tracebackhide__ = True
|
|
|
|
if isinstance(expected, Decimal):
|
|
cls: Type[ApproxBase] = ApproxDecimal
|
|
elif isinstance(expected, Mapping):
|
|
cls = ApproxMapping
|
|
elif _is_numpy_array(expected):
|
|
expected = _as_numpy_array(expected)
|
|
cls = ApproxNumpy
|
|
elif (
|
|
hasattr(expected, "__getitem__")
|
|
and isinstance(expected, Sized)
|
|
# Type ignored because the error is wrong -- not unreachable.
|
|
and not isinstance(expected, STRING_TYPES) # type: ignore[unreachable]
|
|
):
|
|
cls = ApproxSequenceLike
|
|
elif (
|
|
isinstance(expected, Collection)
|
|
# Type ignored because the error is wrong -- not unreachable.
|
|
and not isinstance(expected, STRING_TYPES) # type: ignore[unreachable]
|
|
):
|
|
msg = f"pytest.approx() only supports ordered sequences, but got: {repr(expected)}"
|
|
raise TypeError(msg)
|
|
else:
|
|
cls = ApproxScalar
|
|
|
|
return cls(expected, rel, abs, nan_ok)
|
|
|
|
|
|
def _is_numpy_array(obj: object) -> bool:
|
|
"""
|
|
Return true if the given object is implicitly convertible to ndarray,
|
|
and numpy is already imported.
|
|
"""
|
|
return _as_numpy_array(obj) is not None
|
|
|
|
|
|
def _as_numpy_array(obj: object) -> Optional["ndarray"]:
|
|
"""
|
|
Return an ndarray if the given object is implicitly convertible to ndarray,
|
|
and numpy is already imported, otherwise None.
|
|
"""
|
|
import sys
|
|
|
|
np: Any = sys.modules.get("numpy")
|
|
if np is not None:
|
|
# avoid infinite recursion on numpy scalars, which have __array__
|
|
if np.isscalar(obj):
|
|
return None
|
|
elif isinstance(obj, np.ndarray):
|
|
return obj
|
|
elif hasattr(obj, "__array__") or hasattr("obj", "__array_interface__"):
|
|
return np.asarray(obj)
|
|
return None
|
|
|
|
|
|
# builtin pytest.raises helper
|
|
|
|
E = TypeVar("E", bound=BaseException)
|
|
|
|
|
|
@overload
|
|
def raises(
|
|
expected_exception: Union[Type[E], Tuple[Type[E], ...]],
|
|
*,
|
|
match: Optional[Union[str, Pattern[str]]] = ...,
|
|
) -> "RaisesContext[E]":
|
|
...
|
|
|
|
|
|
@overload
|
|
def raises( # noqa: F811
|
|
expected_exception: Union[Type[E], Tuple[Type[E], ...]],
|
|
func: Callable[..., Any],
|
|
*args: Any,
|
|
**kwargs: Any,
|
|
) -> _pytest._code.ExceptionInfo[E]:
|
|
...
|
|
|
|
|
|
def raises( # noqa: F811
|
|
expected_exception: Union[Type[E], Tuple[Type[E], ...]], *args: Any, **kwargs: Any
|
|
) -> Union["RaisesContext[E]", _pytest._code.ExceptionInfo[E]]:
|
|
r"""Assert that a code block/function call raises an exception type, or one of its subclasses.
|
|
|
|
:param expected_exception:
|
|
The expected exception type, or a tuple if one of multiple possible
|
|
exception types are expected. Note that subclasses of the passed exceptions
|
|
will also match.
|
|
|
|
:kwparam str | re.Pattern[str] | None match:
|
|
If specified, a string containing a regular expression,
|
|
or a regular expression object, that is tested against the string
|
|
representation of the exception and its :pep:`678` `__notes__`
|
|
using :func:`re.search`.
|
|
|
|
To match a literal string that may contain :ref:`special characters
|
|
<re-syntax>`, the pattern can first be escaped with :func:`re.escape`.
|
|
|
|
(This is only used when ``pytest.raises`` is used as a context manager,
|
|
and passed through to the function otherwise.
|
|
When using ``pytest.raises`` as a function, you can use:
|
|
``pytest.raises(Exc, func, match="passed on").match("my pattern")``.)
|
|
|
|
Use ``pytest.raises`` as a context manager, which will capture the exception of the given
|
|
type, or any of its subclasses::
|
|
|
|
>>> import pytest
|
|
>>> with pytest.raises(ZeroDivisionError):
|
|
... 1/0
|
|
|
|
If the code block does not raise the expected exception (:class:`ZeroDivisionError` in the example
|
|
above), or no exception at all, the check will fail instead.
|
|
|
|
You can also use the keyword argument ``match`` to assert that the
|
|
exception matches a text or regex::
|
|
|
|
>>> with pytest.raises(ValueError, match='must be 0 or None'):
|
|
... raise ValueError("value must be 0 or None")
|
|
|
|
>>> with pytest.raises(ValueError, match=r'must be \d+$'):
|
|
... raise ValueError("value must be 42")
|
|
|
|
The ``match`` argument searches the formatted exception string, which includes any
|
|
`PEP-678 <https://peps.python.org/pep-0678/>`__ ``__notes__``:
|
|
|
|
>>> with pytest.raises(ValueError, match=r'had a note added'): # doctest: +SKIP
|
|
... e = ValueError("value must be 42")
|
|
... e.add_note("had a note added")
|
|
... raise e
|
|
|
|
The context manager produces an :class:`ExceptionInfo` object which can be used to inspect the
|
|
details of the captured exception::
|
|
|
|
>>> with pytest.raises(ValueError) as exc_info:
|
|
... raise ValueError("value must be 42")
|
|
>>> assert exc_info.type is ValueError
|
|
>>> assert exc_info.value.args[0] == "value must be 42"
|
|
|
|
.. warning::
|
|
|
|
Given that ``pytest.raises`` matches subclasses, be wary of using it to match :class:`Exception` like this::
|
|
|
|
with pytest.raises(Exception): # Careful, this will catch ANY exception raised.
|
|
some_function()
|
|
|
|
Because :class:`Exception` is the base class of almost all exceptions, it is easy for this to hide
|
|
real bugs, where the user wrote this expecting a specific exception, but some other exception is being
|
|
raised due to a bug introduced during a refactoring.
|
|
|
|
Avoid using ``pytest.raises`` to catch :class:`Exception` unless certain that you really want to catch
|
|
**any** exception raised.
|
|
|
|
.. 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 pytest.raises(ValueError) as exc_info:
|
|
... if value > 10:
|
|
... raise ValueError("value must be <= 10")
|
|
... assert exc_info.type is ValueError # This will not execute.
|
|
|
|
Instead, the following approach must be taken (note the difference in
|
|
scope)::
|
|
|
|
>>> with pytest.raises(ValueError) as exc_info:
|
|
... if value > 10:
|
|
... raise ValueError("value must be <= 10")
|
|
...
|
|
>>> assert exc_info.type is ValueError
|
|
|
|
**Using with** ``pytest.mark.parametrize``
|
|
|
|
When using :ref:`pytest.mark.parametrize ref`
|
|
it is possible to parametrize tests such that
|
|
some runs raise an exception and others do not.
|
|
|
|
See :ref:`parametrizing_conditional_raising` for an example.
|
|
|
|
.. seealso::
|
|
|
|
:ref:`assertraises` for more examples and detailed discussion.
|
|
|
|
**Legacy form**
|
|
|
|
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 ...>
|
|
|
|
The form above is fully supported but discouraged for new code because the
|
|
context manager form is regarded as more readable and less error-prone.
|
|
|
|
.. 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.
|
|
More detailed information can be found in the official Python
|
|
documentation for :ref:`the try statement <python:try>`.
|
|
"""
|
|
__tracebackhide__ = True
|
|
|
|
if not expected_exception:
|
|
raise ValueError(
|
|
f"Expected an exception type or a tuple of exception types, but got `{expected_exception!r}`. "
|
|
f"Raising exceptions is already understood as failing the test, so you don't need "
|
|
f"any special code to say 'this should never raise an exception'."
|
|
)
|
|
if isinstance(expected_exception, type):
|
|
expected_exceptions: Tuple[Type[E], ...] = (expected_exception,)
|
|
else:
|
|
expected_exceptions = expected_exception
|
|
for exc in expected_exceptions:
|
|
if not isinstance(exc, type) or not issubclass(exc, BaseException):
|
|
msg = "expected exception must be a BaseException type, not {}" # type: ignore[unreachable]
|
|
not_a = exc.__name__ if isinstance(exc, type) else type(exc).__name__
|
|
raise TypeError(msg.format(not_a))
|
|
|
|
message = f"DID NOT RAISE {expected_exception}"
|
|
|
|
if not args:
|
|
match: Optional[Union[str, Pattern[str]]] = kwargs.pop("match", None)
|
|
if kwargs:
|
|
msg = "Unexpected keyword arguments passed to pytest.raises: "
|
|
msg += ", ".join(sorted(kwargs))
|
|
msg += "\nUse context-manager form instead?"
|
|
raise TypeError(msg)
|
|
return RaisesContext(expected_exception, message, match)
|
|
else:
|
|
func = args[0]
|
|
if not callable(func):
|
|
raise TypeError(f"{func!r} object (type: {type(func)}) must be callable")
|
|
try:
|
|
func(*args[1:], **kwargs)
|
|
except expected_exception as e:
|
|
return _pytest._code.ExceptionInfo.from_exception(e)
|
|
fail(message)
|
|
|
|
|
|
# This doesn't work with mypy for now. Use fail.Exception instead.
|
|
raises.Exception = fail.Exception # type: ignore
|
|
|
|
|
|
@final
|
|
class RaisesContext(ContextManager[_pytest._code.ExceptionInfo[E]]):
|
|
def __init__(
|
|
self,
|
|
expected_exception: Union[Type[E], Tuple[Type[E], ...]],
|
|
message: str,
|
|
match_expr: Optional[Union[str, Pattern[str]]] = None,
|
|
) -> None:
|
|
self.expected_exception = expected_exception
|
|
self.message = message
|
|
self.match_expr = match_expr
|
|
self.excinfo: Optional[_pytest._code.ExceptionInfo[E]] = None
|
|
|
|
def __enter__(self) -> _pytest._code.ExceptionInfo[E]:
|
|
self.excinfo = _pytest._code.ExceptionInfo.for_later()
|
|
return self.excinfo
|
|
|
|
def __exit__(
|
|
self,
|
|
exc_type: Optional[Type[BaseException]],
|
|
exc_val: Optional[BaseException],
|
|
exc_tb: Optional[TracebackType],
|
|
) -> bool:
|
|
__tracebackhide__ = True
|
|
if exc_type is None:
|
|
fail(self.message)
|
|
assert self.excinfo is not None
|
|
if not issubclass(exc_type, self.expected_exception):
|
|
return False
|
|
# Cast to narrow the exception type now that it's verified.
|
|
exc_info = cast(Tuple[Type[E], E, TracebackType], (exc_type, exc_val, exc_tb))
|
|
self.excinfo.fill_unfilled(exc_info)
|
|
if self.match_expr is not None:
|
|
self.excinfo.match(self.match_expr)
|
|
return True
|