Python core.all方法代码示例

# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import all [as 别名]
def rundocs(filename=None, raise_on_error=True):
    """
    Run doctests found in the given file.

    By default `rundocs` raises an AssertionError on failure.

    Parameters
    ----------
    filename : str
        The path to the file for which the doctests are run.
    raise_on_error : bool
        Whether to raise an AssertionError when a doctest fails. Default is
        True.

    Notes
    -----
    The doctests can be run by the user/developer by adding the ``doctests``
    argument to the ``test()`` call. For example, to run all tests (including
    doctests) for `numpy.lib`:

    >>> np.lib.test(doctests=True) #doctest: +SKIP
    """
    from numpy.compat import npy_load_module
    import doctest
    if filename is None:
        f = sys._getframe(1)
        filename = f.f_globals['__file__']
    name = os.path.splitext(os.path.basename(filename))[0]
    m = npy_load_module(name, filename)

    tests = doctest.DocTestFinder().find(m)
    runner = doctest.DocTestRunner(verbose=False)

    msg = []
    if raise_on_error:
        out = lambda s: msg.append(s)
    else:
        out = None

    for test in tests:
        runner.run(test, out=out)

    if runner.failures > 0 and raise_on_error:
        raise AssertionError("Some doctests failed:\n%s" % "\n".join(msg)) 

# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import all [as 别名]
def decorate_methods(cls, decorator, testmatch=None):
    """
    Apply a decorator to all methods in a class matching a regular expression.

    The given decorator is applied to all public methods of `cls` that are
    matched by the regular expression `testmatch`
    (``testmatch.search(methodname)``). Methods that are private, i.e. start
    with an underscore, are ignored.

    Parameters
    ----------
    cls : class
        Class whose methods to decorate.
    decorator : function
        Decorator to apply to methods
    testmatch : compiled regexp or str, optional
        The regular expression. Default value is None, in which case the
        nose default (``re.compile(r'(?:^|[\\b_\\.%s-])[Tt]est' % os.sep)``)
        is used.
        If `testmatch` is a string, it is compiled to a regular expression
        first.

    """
    if testmatch is None:
        testmatch = re.compile(r'(?:^|[\\b_\\.%s-])[Tt]est' % os.sep)
    else:
        testmatch = re.compile(testmatch)
    cls_attr = cls.__dict__

    # delayed import to reduce startup time
    from inspect import isfunction

    methods = [_m for _m in cls_attr.values() if isfunction(_m)]
    for function in methods:
        try:
            if hasattr(function, 'compat_func_name'):
                funcname = function.compat_func_name
            else:
                funcname = function.__name__
        except AttributeError:
            # not a function
            continue
        if testmatch.search(funcname) and not funcname.startswith('_'):
            setattr(cls, funcname, decorator(function))
    return 

# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import all [as 别名]
def assert_array_almost_equal_nulp(x, y, nulp=1):
    """
    Compare two arrays relatively to their spacing.

    This is a relatively robust method to compare two arrays whose amplitude
    is variable.

    Parameters
    ----------
    x, y : array_like
        Input arrays.
    nulp : int, optional
        The maximum number of unit in the last place for tolerance (see Notes).
        Default is 1.

    Returns
    -------
    None

    Raises
    ------
    AssertionError
        If the spacing between `x` and `y` for one or more elements is larger
        than `nulp`.

    See Also
    --------
    assert_array_max_ulp : Check that all items of arrays differ in at most
        N Units in the Last Place.
    spacing : Return the distance between x and the nearest adjacent number.

    Notes
    -----
    An assertion is raised if the following condition is not met::

        abs(x - y) <= nulps * spacing(maximum(abs(x), abs(y)))

    Examples
    --------
    >>> x = np.array([1., 1e-10, 1e-20])
    >>> eps = np.finfo(x.dtype).eps
    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x)

    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x)
    Traceback (most recent call last):
      ...
    AssertionError: X and Y are not equal to 1 ULP (max is 2)

    """
    __tracebackhide__ = True  # Hide traceback for py.test
    import numpy as np
    ax = np.abs(x)
    ay = np.abs(y)
    ref = nulp * np.spacing(np.where(ax > ay, ax, ay))
    if not np.all(np.abs(x-y) <= ref):
        if np.iscomplexobj(x) or np.iscomplexobj(y):
            msg = "X and Y are not equal to %d ULP" % nulp
        else:
            max_nulp = np.max(nulp_diff(x, y))
            msg = "X and Y are not equal to %d ULP (max is %g)" % (nulp, max_nulp)
        raise AssertionError(msg) 

# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import all [as 别名]
def assert_array_max_ulp(a, b, maxulp=1, dtype=None):
    """
    Check that all items of arrays differ in at most N Units in the Last Place.

    Parameters
    ----------
    a, b : array_like
        Input arrays to be compared.
    maxulp : int, optional
        The maximum number of units in the last place that elements of `a` and
        `b` can differ. Default is 1.
    dtype : dtype, optional
        Data-type to convert `a` and `b` to if given. Default is None.

    Returns
    -------
    ret : ndarray
        Array containing number of representable floating point numbers between
        items in `a` and `b`.

    Raises
    ------
    AssertionError
        If one or more elements differ by more than `maxulp`.

    See Also
    --------
    assert_array_almost_equal_nulp : Compare two arrays relatively to their
        spacing.

    Examples
    --------
    >>> a = np.linspace(0., 1., 100)
    >>> res = np.testing.assert_array_max_ulp(a, np.arcsin(np.sin(a)))

    """
    __tracebackhide__ = True  # Hide traceback for py.test
    import numpy as np
    ret = nulp_diff(a, b, dtype)
    if not np.all(ret <= maxulp):
        raise AssertionError("Arrays are not almost equal up to %g ULP" %
                             maxulp)
    return ret 

# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import all [as 别名]
def assert_warns(warning_class, *args, **kwargs):
    """
    Fail unless the given callable throws the specified warning.

    A warning of class warning_class should be thrown by the callable when
    invoked with arguments args and keyword arguments kwargs.
    If a different type of warning is thrown, it will not be caught, and the
    test case will be deemed to have suffered an error.

    If called with all arguments other than the warning class omitted, may be
    used as a context manager:

        with assert_warns(SomeWarning):
            do_something()

    The ability to be used as a context manager is new in NumPy v1.11.0.

    .. versionadded:: 1.4.0

    Parameters
    ----------
    warning_class : class
        The class defining the warning that `func` is expected to throw.
    func : callable
        The callable to test.
    \\*args : Arguments
        Arguments passed to `func`.
    \\*\\*kwargs : Kwargs
        Keyword arguments passed to `func`.

    Returns
    -------
    The value returned by `func`.

    """
    if not args:
        return _assert_warns_context(warning_class)

    func = args[0]
    args = args[1:]
    with _assert_warns_context(warning_class, name=func.__name__):
        return func(*args, **kwargs) 

# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import all [as 别名]
def rundocs(filename=None, raise_on_error=True):
    """
    Run doctests found in the given file.

    By default `rundocs` raises an AssertionError on failure.

    Parameters
    ----------
    filename : str
        The path to the file for which the doctests are run.
    raise_on_error : bool
        Whether to raise an AssertionError when a doctest fails. Default is
        True.

    Notes
    -----
    The doctests can be run by the user/developer by adding the ``doctests``
    argument to the ``test()`` call. For example, to run all tests (including
    doctests) for `numpy.lib`:

    >>> np.lib.test(doctests=True) #doctest: +SKIP
    """
    import doctest, imp
    if filename is None:
        f = sys._getframe(1)
        filename = f.f_globals['__file__']
    name = os.path.splitext(os.path.basename(filename))[0]
    path = [os.path.dirname(filename)]
    file, pathname, description = imp.find_module(name, path)
    try:
        m = imp.load_module(name, file, pathname, description)
    finally:
        file.close()

    tests = doctest.DocTestFinder().find(m)
    runner = doctest.DocTestRunner(verbose=False)

    msg = []
    if raise_on_error:
        out = lambda s: msg.append(s)
    else:
        out = None

    for test in tests:
        runner.run(test, out=out)

    if runner.failures > 0 and raise_on_error:
        raise AssertionError("Some doctests failed:\n%s" % "\n".join(msg)) 

# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import all [as 别名]
def assert_array_almost_equal_nulp(x, y, nulp=1):
    """
    Compare two arrays relatively to their spacing.

    This is a relatively robust method to compare two arrays whose amplitude
    is variable.

    Parameters
    ----------
    x, y : array_like
        Input arrays.
    nulp : int, optional
        The maximum number of unit in the last place for tolerance (see Notes).
        Default is 1.

    Returns
    -------
    None

    Raises
    ------
    AssertionError
        If the spacing between `x` and `y` for one or more elements is larger
        than `nulp`.

    See Also
    --------
    assert_array_max_ulp : Check that all items of arrays differ in at most
        N Units in the Last Place.
    spacing : Return the distance between x and the nearest adjacent number.

    Notes
    -----
    An assertion is raised if the following condition is not met::

        abs(x - y) <= nulps * spacing(max(abs(x), abs(y)))

    Examples
    --------
    >>> x = np.array([1., 1e-10, 1e-20])
    >>> eps = np.finfo(x.dtype).eps
    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x)

    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x)
    ------------------------------------------------------------
    Traceback (most recent call last):
      ...
    AssertionError: X and Y are not equal to 1 ULP (max is 2)

    """
    import numpy as np
    ax = np.abs(x)
    ay = np.abs(y)
    ref = nulp * np.spacing(np.where(ax > ay, ax, ay))
    if not np.all(np.abs(x-y) <= ref):
        if np.iscomplexobj(x) or np.iscomplexobj(y):
            msg = "X and Y are not equal to %d ULP" % nulp
        else:
            max_nulp = np.max(nulp_diff(x, y))
            msg = "X and Y are not equal to %d ULP (max is %g)" % (nulp, max_nulp)
        raise AssertionError(msg) 

# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import all [as 别名]
def assert_array_max_ulp(a, b, maxulp=1, dtype=None):
    """
    Check that all items of arrays differ in at most N Units in the Last Place.

    Parameters
    ----------
    a, b : array_like
        Input arrays to be compared.
    maxulp : int, optional
        The maximum number of units in the last place that elements of `a` and
        `b` can differ. Default is 1.
    dtype : dtype, optional
        Data-type to convert `a` and `b` to if given. Default is None.

    Returns
    -------
    ret : ndarray
        Array containing number of representable floating point numbers between
        items in `a` and `b`.

    Raises
    ------
    AssertionError
        If one or more elements differ by more than `maxulp`.

    See Also
    --------
    assert_array_almost_equal_nulp : Compare two arrays relatively to their
        spacing.

    Examples
    --------
    >>> a = np.linspace(0., 1., 100)
    >>> res = np.testing.assert_array_max_ulp(a, np.arcsin(np.sin(a)))

    """
    import numpy as np
    ret = nulp_diff(a, b, dtype)
    if not np.all(ret <= maxulp):
        raise AssertionError("Arrays are not almost equal up to %g ULP" % \
                             maxulp)
    return ret 

# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import all [as 别名]
def assert_array_almost_equal_nulp(x, y, nulp=1):
    """
    Compare two arrays relatively to their spacing.

    This is a relatively robust method to compare two arrays whose amplitude
    is variable.

    Parameters
    ----------
    x, y : array_like
        Input arrays.
    nulp : int, optional
        The maximum number of unit in the last place for tolerance (see Notes).
        Default is 1.

    Returns
    -------
    None

    Raises
    ------
    AssertionError
        If the spacing between `x` and `y` for one or more elements is larger
        than `nulp`.

    See Also
    --------
    assert_array_max_ulp : Check that all items of arrays differ in at most
        N Units in the Last Place.
    spacing : Return the distance between x and the nearest adjacent number.

    Notes
    -----
    An assertion is raised if the following condition is not met::

        abs(x - y) <= nulps * spacing(max(abs(x), abs(y)))

    Examples
    --------
    >>> x = np.array([1., 1e-10, 1e-20])
    >>> eps = np.finfo(x.dtype).eps
    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x)

    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x)
    Traceback (most recent call last):
      ...
    AssertionError: X and Y are not equal to 1 ULP (max is 2)

    """
    import numpy as np
    ax = np.abs(x)
    ay = np.abs(y)
    ref = nulp * np.spacing(np.where(ax > ay, ax, ay))
    if not np.all(np.abs(x-y) <= ref):
        if np.iscomplexobj(x) or np.iscomplexobj(y):
            msg = "X and Y are not equal to %d ULP" % nulp
        else:
            max_nulp = np.max(nulp_diff(x, y))
            msg = "X and Y are not equal to %d ULP (max is %g)" % (nulp, max_nulp)
        raise AssertionError(msg) 

# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import all [as 别名]
def assert_array_almost_equal_nulp(x, y, nulp=1):
    """
    Compare two arrays relatively to their spacing.

    This is a relatively robust method to compare two arrays whose amplitude
    is variable.

    Parameters
    ----------
    x, y : array_like
        Input arrays.
    nulp : int, optional
        The maximum number of unit in the last place for tolerance (see Notes).
        Default is 1.

    Returns
    -------
    None

    Raises
    ------
    AssertionError
        If the spacing between `x` and `y` for one or more elements is larger
        than `nulp`.

    See Also
    --------
    assert_array_max_ulp : Check that all items of arrays differ in at most
        N Units in the Last Place.
    spacing : Return the distance between x and the nearest adjacent number.

    Notes
    -----
    An assertion is raised if the following condition is not met::

        abs(x - y) <= nulps * spacing(maximum(abs(x), abs(y)))

    Examples
    --------
    >>> x = np.array([1., 1e-10, 1e-20])
    >>> eps = np.finfo(x.dtype).eps
    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x)

    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x)
    Traceback (most recent call last):
      ...
    AssertionError: X and Y are not equal to 1 ULP (max is 2)

    """
    import numpy as np
    ax = np.abs(x)
    ay = np.abs(y)
    ref = nulp * np.spacing(np.where(ax > ay, ax, ay))
    if not np.all(np.abs(x-y) <= ref):
        if np.iscomplexobj(x) or np.iscomplexobj(y):
            msg = "X and Y are not equal to %d ULP" % nulp
        else:
            max_nulp = np.max(nulp_diff(x, y))
            msg = "X and Y are not equal to %d ULP (max is %g)" % (nulp, max_nulp)
        raise AssertionError(msg)