Source code for _pytest.python_api

import math
import pprint
from import Iterable
from import Mapping
from import Sized
from decimal import Decimal
from numbers import Complex
from types import TracebackType
from typing import Any
from typing import Callable
from typing import cast
from typing import Generic
from typing import Optional
from typing import overload
from typing import Pattern
from typing import Tuple
from typing import Type
from typing import TYPE_CHECKING
from typing import TypeVar
from typing import Union

    from numpy import ndarray

import _pytest._code
from _pytest.compat import final
from _pytest.compat import STRING_TYPES
from _pytest.outcomes import fail

def _non_numeric_type_error(value, at: Optional[str]) -> TypeError:
    at_str = f" at {at}" if at else ""
    return TypeError(
        "cannot make approximate comparisons to non-numeric values: {!r} {}".format(
            value, at_str

# builtin pytest.approx helper

class ApproxBase:
    """Provide shared utilities for making approximate comparisons between
    numbers or sequences of numbers."""

    # Tell numpy to use our `__eq__` operator instead of its.
    __array_ufunc__ = None
    __array_priority__ = 100

    def __init__(self, expected, rel=None, abs=None, nan_ok: bool = False) -> None:
        __tracebackhide__ = True
        self.expected = expected
        self.abs = abs
        self.rel = rel
        self.nan_ok = nan_ok

    def __repr__(self) -> str:
        raise NotImplementedError

    def __eq__(self, actual) -> bool:
        return all(
            a == self._approx_scalar(x) for a, x in self._yield_comparisons(actual)

    # Ignore type because of
    __hash__ = None  # type: ignore

    def __ne__(self, actual) -> bool:
        return not (actual == self)

    def _approx_scalar(self, x) -> "ApproxScalar":
        return ApproxScalar(x, rel=self.rel, abs=self.abs, nan_ok=self.nan_ok)

    def _yield_comparisons(self, actual):
        """Yield all the pairs of numbers to be compared.

        This is used to implement the `__eq__` method.
        raise NotImplementedError

    def _check_type(self) -> None:
        """Raise a TypeError if the expected value is not a valid type."""
        # This is only a concern if the expected value is a sequence.  In every
        # other case, the approx() function ensures that the expected value has
        # a numeric type.  For this reason, the default is to do nothing.  The
        # classes that deal with sequences should reimplement this method to
        # raise if there are any non-numeric elements in the sequence.

def _recursive_list_map(f, x):
    if isinstance(x, list):
        return list(_recursive_list_map(f, xi) for xi in x)
        return f(x)

class ApproxNumpy(ApproxBase):
    """Perform approximate comparisons where the expected value is numpy array."""

    def __repr__(self) -> str:
        list_scalars = _recursive_list_map(self._approx_scalar, self.expected.tolist())
        return f"approx({list_scalars!r})"

    def __eq__(self, actual) -> bool:
        import numpy as np

        # self.expected is supposed to always be an array here.

        if not np.isscalar(actual):
                actual = np.asarray(actual)
            except Exception as e:
                raise TypeError(f"cannot compare '{actual}' to numpy.ndarray") from e

        if not np.isscalar(actual) and actual.shape != self.expected.shape:
            return False

        return ApproxBase.__eq__(self, actual)

    def _yield_comparisons(self, actual):
        import numpy as np

        # `actual` can either be a numpy array or a scalar, it is treated in
        # `__eq__` before being passed to `ApproxBase.__eq__`, which is the
        # only method that calls this one.

        if np.isscalar(actual):
            for i in np.ndindex(self.expected.shape):
                yield actual, self.expected[i].item()
            for i in np.ndindex(self.expected.shape):
                yield actual[i].item(), self.expected[i].item()

class ApproxMapping(ApproxBase):
    """Perform approximate comparisons where the expected value is a mapping
    with numeric values (the keys can be anything)."""

    def __repr__(self) -> str:
        return "approx({!r})".format(
            {k: self._approx_scalar(v) for k, v in self.expected.items()}

    def __eq__(self, actual) -> bool:
            if set(actual.keys()) != set(self.expected.keys()):
                return False
        except AttributeError:
            return False

        return ApproxBase.__eq__(self, actual)

    def _yield_comparisons(self, actual):
        for k in self.expected.keys():
            yield actual[k], self.expected[k]

    def _check_type(self) -> None:
        __tracebackhide__ = True
        for key, value in self.expected.items():
            if isinstance(value, type(self.expected)):
                msg = "pytest.approx() does not support nested dictionaries: key={!r} value={!r}\n  full mapping={}"
                raise TypeError(msg.format(key, value, pprint.pformat(self.expected)))

class ApproxSequencelike(ApproxBase):
    """Perform approximate comparisons where the expected value is a sequence of numbers."""

    def __repr__(self) -> str:
        seq_type = type(self.expected)
        if seq_type not in (tuple, list, set):
            seq_type = list
        return "approx({!r})".format(
            seq_type(self._approx_scalar(x) for x in self.expected)

    def __eq__(self, actual) -> bool:
            if len(actual) != len(self.expected):
                return False
        except TypeError:
            return False
        return ApproxBase.__eq__(self, actual)

    def _yield_comparisons(self, actual):
        return zip(actual, self.expected)

    def _check_type(self) -> None:
        __tracebackhide__ = True
        for index, x in enumerate(self.expected):
            if isinstance(x, type(self.expected)):
                msg = "pytest.approx() does not support nested data structures: {!r} at index {}\n  full sequence: {}"
                raise TypeError(msg.format(x, index, pprint.pformat(self.expected)))

class ApproxScalar(ApproxBase):
    """Perform approximate comparisons where the expected value is a single number."""

    # Using Real should be better than this Union, but not possible yet:
    DEFAULT_ABSOLUTE_TOLERANCE: Union[float, Decimal] = 1e-12
    DEFAULT_RELATIVE_TOLERANCE: Union[float, Decimal] = 1e-6

    def __repr__(self) -> str:
        """Return a string communicating both the expected value and the
        tolerance for the comparison being made.

        For example, ``1.0 ± 1e-6``, ``(3+4j) ± 5e-6 ∠ ±180°``.

        # Don't show a tolerance for values that aren't compared using
        # tolerances, i.e. non-numerics and infinities. Need to call abs to
        # handle complex numbers, e.g. (inf + 1j).
        if (not isinstance(self.expected, (Complex, Decimal))) or math.isinf(
            abs(self.expected)  # type: ignore[arg-type]
            return str(self.expected)

        # If a sensible tolerance can't be calculated, self.tolerance will
        # raise a ValueError.  In this case, display '???'.
            vetted_tolerance = f"{self.tolerance:.1e}"
            if (
                isinstance(self.expected, Complex)
                and self.expected.imag
                and not math.isinf(self.tolerance)
                vetted_tolerance += " ∠ ±180°"
        except ValueError:
            vetted_tolerance = "???"

        return f"{self.expected} ± {vetted_tolerance}"

    def __eq__(self, actual) -> bool:
        """Return whether the given value is equal to the expected value
        within the pre-specified tolerance."""
        asarray = _as_numpy_array(actual)
        if asarray is not None:
            # Call ``__eq__()`` manually to prevent infinite-recursion with
            # numpy<1.13.  See #3748.
            return all(self.__eq__(a) for a in asarray.flat)

        # Short-circuit exact equality.
        if actual == self.expected:
            return True

        # If either type is non-numeric, fall back to strict equality.
        # NB: we need Complex, rather than just Number, to ensure that __abs__,
        # __sub__, and __float__ are defined.
        if not (
            isinstance(self.expected, (Complex, Decimal))
            and isinstance(actual, (Complex, Decimal))
            return False

        # Allow the user to control whether NaNs are considered equal to each
        # other or not.  The abs() calls are for compatibility with complex
        # numbers.
        if math.isnan(abs(self.expected)):  # type: ignore[arg-type]
            return self.nan_ok and math.isnan(abs(actual))  # type: ignore[arg-type]

        # Infinity shouldn't be approximately equal to anything but itself, but
        # if there's a relative tolerance, it will be infinite and infinity
        # will seem approximately equal to everything.  The equal-to-itself
        # case would have been short circuited above, so here we can just
        # return false if the expected value is infinite.  The abs() call is
        # for compatibility with complex numbers.
        if math.isinf(abs(self.expected)):  # type: ignore[arg-type]
            return False

        # Return true if the two numbers are within the tolerance.
        result: bool = abs(self.expected - actual) <= self.tolerance
        return result

    # Ignore type because of
    __hash__ = None  # type: ignore

    def tolerance(self):
        """Return the tolerance for the comparison.

        This could be either an absolute tolerance or a relative tolerance,
        depending on what the user specified or which would be larger.

        def set_default(x, default):
            return x if x is not None else default

        # Figure out what the absolute tolerance should be.  ``self.abs`` is
        # either None or a value specified by the user.
        absolute_tolerance = set_default(self.abs, self.DEFAULT_ABSOLUTE_TOLERANCE)

        if absolute_tolerance < 0:
            raise ValueError(
                f"absolute tolerance can't be negative: {absolute_tolerance}"
        if math.isnan(absolute_tolerance):
            raise ValueError("absolute tolerance can't be NaN.")

        # If the user specified an absolute tolerance but not a relative one,
        # just return the absolute tolerance.
        if self.rel is None:
            if self.abs is not None:
                return absolute_tolerance

        # Figure out what the relative tolerance should be.  ``self.rel`` is
        # either None or a value specified by the user.  This is done after
        # we've made sure the user didn't ask for an absolute tolerance only,
        # because we don't want to raise errors about the relative tolerance if
        # we aren't even going to use it.
        relative_tolerance = set_default(
            self.rel, self.DEFAULT_RELATIVE_TOLERANCE
        ) * abs(self.expected)

        if relative_tolerance < 0:
            raise ValueError(
                f"relative tolerance can't be negative: {absolute_tolerance}"
        if math.isnan(relative_tolerance):
            raise ValueError("relative tolerance can't be NaN.")

        # Return the larger of the relative and absolute tolerances.
        return max(relative_tolerance, absolute_tolerance)

class ApproxDecimal(ApproxScalar):
    """Perform approximate comparisons where the expected value is a Decimal."""


[docs]def approx(expected, rel=None, abs=None, nan_ok: bool = False) -> ApproxBase: """Assert that two numbers (or two sets of numbers) are equal to each other within some tolerance. Due to the `intricacies of floating-point arithmetic`__, numbers that we would intuitively expect to be equal are not always so:: >>> 0.1 + 0.2 == 0.3 False __ This problem is commonly encountered when writing tests, e.g. when making sure that floating-point values are what you expect them to be. One way to deal with this problem is to assert that two floating-point numbers are equal to within some appropriate tolerance:: >>> abs((0.1 + 0.2) - 0.3) < 1e-6 True However, comparisons like this are tedious to write and difficult to understand. Furthermore, absolute comparisons like the one above are usually discouraged because there's no tolerance that works well for all situations. ``1e-6`` is good for numbers around ``1``, but too small for very big numbers and too big for very small ones. It's better to express the tolerance as a fraction of the expected value, but relative comparisons like that are even more difficult to write correctly and concisely. The ``approx`` class performs floating-point comparisons using a syntax that's as intuitive as possible:: >>> from pytest import approx >>> 0.1 + 0.2 == approx(0.3) True The same syntax also works for sequences of numbers:: >>> (0.1 + 0.2, 0.2 + 0.4) == approx((0.3, 0.6)) True Dictionary *values*:: >>> {'a': 0.1 + 0.2, 'b': 0.2 + 0.4} == approx({'a': 0.3, 'b': 0.6}) True ``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 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...`__ __ - ``numpy.isclose(a, b, rtol=1e-5, atol=1e-8)``: True if the difference between ``a`` and ``b`` is less that the sum of the relative tolerance w.r.t. ``b`` and the absolute tolerance. Because the relative tolerance is only calculated w.r.t. ``b``, this test is asymmetric and you can think of ``b`` as the reference value. Support for comparing sequences is provided by ``numpy.allclose``. `More information...`__ __ - ``unittest.TestCase.assertAlmostEqual(a, b)``: True if ``a`` and ``b`` are within an absolute tolerance of ``1e-7``. No relative tolerance is considered and the absolute tolerance cannot be changed, so this function is not appropriate for very large or very small numbers. Also, it's only available in subclasses of ``unittest.TestCase`` and it's ugly because it doesn't follow PEP8. `More information...`__ __ - ``a == pytest.approx(b, rel=1e-6, abs=1e-12)``: True if the relative tolerance is met w.r.t. ``b`` or if the absolute tolerance is met. Because the relative tolerance is only calculated w.r.t. ``b``, this test is asymmetric and you can think of ``b`` as the reference value. In the special case that you explicitly specify an absolute tolerance but not a relative tolerance, only the absolute tolerance is considered. .. warning:: .. versionchanged:: 3.2 In order to avoid inconsistent behavior, ``TypeError`` is raised for ``>``, ``>=``, ``<`` and ``<=`` comparisons. The example below illustrates the problem:: assert approx(0.1) > 0.1 + 1e-10 # calls approx(0.1).__gt__(0.1 + 1e-10) assert 0.1 + 1e-10 > approx(0.1) # calls approx(0.1).__lt__(0.1 + 1e-10) In the second example one expects ``approx(0.1).__le__(0.1 + 1e-10)`` to be called. But instead, ``approx(0.1).__lt__(0.1 + 1e-10)`` is used to comparison. This is because the call hierarchy of rich comparisons follows a fixed behavior. `More information...`__ __ .. 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 ( isinstance(expected, Iterable) and isinstance(expected, Sized) # Type ignored because the error is wrong -- not unreachable. and not isinstance(expected, STRING_TYPES) # type: ignore[unreachable] ): cls = ApproxSequencelike 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( expected_exception: Union[Type[_E], Tuple[Type[_E], ...]], func: Callable[..., Any], *args: Any, **kwargs: Any, ) -> _pytest._code.ExceptionInfo[_E]: ...
[docs]def raises( 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 ``expected_exception`` or raise a failure exception otherwise. :kwparam match: If specified, a string containing a regular expression, or a regular expression object, that is tested against the string representation of the exception using ````. To match a literal string that may contain `special characters`__, the pattern can first be escaped with ``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")``.) __ .. currentmodule:: _pytest._code Use ``pytest.raises`` as a context manager, which will capture the exception of the given type:: >>> import pytest >>> with pytest.raises(ZeroDivisionError): ... 1/0 If the code block does not raise the expected exception (``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 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" .. 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. **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 isinstance(expected_exception, type): excepted_exceptions: Tuple[Type[_E], ...] = (expected_exception,) else: excepted_exceptions = expected_exception for exc in excepted_exceptions: if not isinstance(exc, type) or not issubclass(exc, BaseException): # type: ignore[unreachable] 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( "{!r} object (type: {}) must be callable".format(func, type(func)) ) try: func(*args[1:], **kwargs) except expected_exception as e: # We just caught the exception - there is a traceback. assert e.__traceback__ is not None return _pytest._code.ExceptionInfo.from_exc_info( (type(e), e, e.__traceback__) ) fail(message)
# This doesn't work with mypy for now. Use fail.Exception instead. raises.Exception = fail.Exception # type: ignore @final class RaisesContext(Generic[_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