Python numeric.bool_方法代碼示例

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import bool_ [as 別名]
def stdout_automatic_parser(result):
    """
    Try and automatically convert strings formatted as tables into a matrix.

    Under the hood, this function essentially applies the genfromtxt function
    to the stdout.

    Args:
      result (dict): the result to parse.
    """
    np.seterr(all='raise')
    parsed = {}

    # By default, if dtype is None, the order Numpy tries to convert a string
    # to a value is: bool, int, float. We don't like this, since it would give
    # us a mixture of integers and doubles in the output, if any integers
    # existed in the data. So, we modify the StringMapper's default mapper to
    # skip the int check and directly convert numbers to floats.
    oldmapper = np.lib._iotools.StringConverter._mapper
    np.lib._iotools.StringConverter._mapper = [(nx.bool_, np.lib._iotools.str2bool, False),
                                               (nx.floating, float, nx.nan),
                                               (nx.complexfloating, complex, nx.nan + 0j),
                                               (nx.longdouble, nx.longdouble, nx.nan)]

    file_contents = result['output']['stdout']

    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        parsed = np.genfromtxt(io.StringIO(file_contents))

    # Here we restore the original mapper, so no side-effects remain.
    np.lib._iotools.StringConverter._mapper = oldmapper

    return parsed 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import bool_ [as 別名]
def isposinf(x, y=None):
    """
    Test element-wise for positive infinity, return result as bool array.

    Parameters
    ----------
    x : array_like
        The input array.
    y : array_like, optional
        A boolean array with the same shape as `x` to store the result.

    Returns
    -------
    y : ndarray
        A boolean array with the same dimensions as the input.
        If second argument is not supplied then a boolean array is returned
        with values True where the corresponding element of the input is
        positive infinity and values False where the element of the input is
        not positive infinity.

        If a second argument is supplied the result is stored there. If the
        type of that array is a numeric type the result is represented as zeros
        and ones, if the type is boolean then as False and True.
        The return value `y` is then a reference to that array.

    See Also
    --------
    isinf, isneginf, isfinite, isnan

    Notes
    -----
    NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).

    Errors result if the second argument is also supplied when `x` is a
    scalar input, or if first and second arguments have different shapes.

    Examples
    --------
    >>> np.isposinf(np.PINF)
    array(True, dtype=bool)
    >>> np.isposinf(np.inf)
    array(True, dtype=bool)
    >>> np.isposinf(np.NINF)
    array(False, dtype=bool)
    >>> np.isposinf([-np.inf, 0., np.inf])
    array([False, False,  True], dtype=bool)

    >>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isposinf(x, y)
    array([0, 0, 1])
    >>> y
    array([0, 0, 1])

    """
    if y is None:
        x = nx.asarray(x)
        y = nx.empty(x.shape, dtype=nx.bool_)
    nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
    return y 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import bool_ [as 別名]
def ix_(*args):
    """
    Construct an open mesh from multiple sequences.

    This function takes N 1-D sequences and returns N outputs with N
    dimensions each, such that the shape is 1 in all but one dimension
    and the dimension with the non-unit shape value cycles through all
    N dimensions.

    Using `ix_` one can quickly construct index arrays that will index
    the cross product. ``a[np.ix_([1,3],[2,5])]`` returns the array
    ``[[a[1,2] a[1,5]], [a[3,2] a[3,5]]]``.

    Parameters
    ----------
    args : 1-D sequences

    Returns
    -------
    out : tuple of ndarrays
        N arrays with N dimensions each, with N the number of input
        sequences. Together these arrays form an open mesh.

    See Also
    --------
    ogrid, mgrid, meshgrid

    Examples
    --------
    >>> a = np.arange(10).reshape(2, 5)
    >>> a
    array([[0, 1, 2, 3, 4],
           [5, 6, 7, 8, 9]])
    >>> ixgrid = np.ix_([0,1], [2,4])
    >>> ixgrid
    (array([[0],
           [1]]), array([[2, 4]]))
    >>> ixgrid[0].shape, ixgrid[1].shape
    ((2, 1), (1, 2))
    >>> a[ixgrid]
    array([[2, 4],
           [7, 9]])

    """
    out = []
    nd = len(args)
    for k, new in enumerate(args):
        new = asarray(new)
        if new.ndim != 1:
            raise ValueError("Cross index must be 1 dimensional")
        if new.size == 0:
            # Explicitly type empty arrays to avoid float default
            new = new.astype(_nx.intp)
        if issubdtype(new.dtype, _nx.bool_):
            new, = new.nonzero()
        new = new.reshape((1,)*k + (new.size,) + (1,)*(nd-k-1))
        out.append(new)
    return tuple(out) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import bool_ [as 別名]
def isposinf(x, y=None):
    """
    Test element-wise for positive infinity, return result as bool array.

    Parameters
    ----------
    x : array_like
        The input array.
    y : array_like, optional
        A boolean array with the same shape as `x` to store the result.

    Returns
    -------
    y : ndarray
        A boolean array with the same dimensions as the input.
        If second argument is not supplied then a boolean array is returned
        with values True where the corresponding element of the input is
        positive infinity and values False where the element of the input is
        not positive infinity.

        If a second argument is supplied the result is stored there. If the
        type of that array is a numeric type the result is represented as zeros
        and ones, if the type is boolean then as False and True.
        The return value `y` is then a reference to that array.

    See Also
    --------
    isinf, isneginf, isfinite, isnan

    Notes
    -----
    Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).

    Errors result if the second argument is also supplied when `x` is a
    scalar input, or if first and second arguments have different shapes.

    Examples
    --------
    >>> np.isposinf(np.PINF)
    array(True, dtype=bool)
    >>> np.isposinf(np.inf)
    array(True, dtype=bool)
    >>> np.isposinf(np.NINF)
    array(False, dtype=bool)
    >>> np.isposinf([-np.inf, 0., np.inf])
    array([False, False,  True], dtype=bool)

    >>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isposinf(x, y)
    array([0, 0, 1])
    >>> y
    array([0, 0, 1])

    """
    if y is None:
        x = nx.asarray(x)
        y = nx.empty(x.shape, dtype=nx.bool_)
    nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
    return y 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import bool_ [as 別名]
def ix_(*args):
    """
    Construct an open mesh from multiple sequences.

    This function takes N 1-D sequences and returns N outputs with N
    dimensions each, such that the shape is 1 in all but one dimension
    and the dimension with the non-unit shape value cycles through all
    N dimensions.

    Using `ix_` one can quickly construct index arrays that will index
    the cross product. ``a[np.ix_([1,3],[2,5])]`` returns the array
    ``[[a[1,2] a[1,5]], [a[3,2] a[3,5]]]``.

    Parameters
    ----------
    args : 1-D sequences

    Returns
    -------
    out : tuple of ndarrays
        N arrays with N dimensions each, with N the number of input
        sequences. Together these arrays form an open mesh.

    See Also
    --------
    ogrid, mgrid, meshgrid

    Examples
    --------
    >>> a = np.arange(10).reshape(2, 5)
    >>> a
    array([[0, 1, 2, 3, 4],
           [5, 6, 7, 8, 9]])
    >>> ixgrid = np.ix_([0,1], [2,4])
    >>> ixgrid
    (array([[0],
           [1]]), array([[2, 4]]))
    >>> ixgrid[0].shape, ixgrid[1].shape
    ((2, 1), (1, 2))
    >>> a[ixgrid]
    array([[2, 4],
           [7, 9]])

    """
    out = []
    nd = len(args)
    baseshape = [1]*nd
    for k in range(nd):
        new = _nx.asarray(args[k])
        if (new.ndim != 1):
            raise ValueError("Cross index must be 1 dimensional")
        if issubclass(new.dtype.type, _nx.bool_):
            new = new.nonzero()[0]
        baseshape[k] = len(new)
        new = new.reshape(tuple(baseshape))
        out.append(new)
        baseshape[k] = 1
    return tuple(out) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import bool_ [as 別名]
def automatic_parser(result, dtypes={}, converters={}):
    """
    Try and automatically convert strings formatted as tables into nested
    list structures.

    Under the hood, this function essentially applies the genfromtxt function
    to all files in the output, and passes it the additional kwargs.

    Args:
      result (dict): the result to parse.
      dtypes (dict): a dictionary containing the dtype specification to perform
        parsing for each available filename. See the numpy genfromtxt
        documentation for more details on how to format these.
    """
    np.seterr(all='raise')
    parsed = {}

    # By default, if dtype is None, the order Numpy tries to convert a string
    # to a value is: bool, int, float. We don't like this, since it would give
    # us a mixture of integers and doubles in the output, if any integers
    # existed in the data. So, we modify the StringMapper's default mapper to
    # skip the int check and directly convert numbers to floats.
    oldmapper = np.lib._iotools.StringConverter._mapper
    np.lib._iotools.StringConverter._mapper = [(nx.bool_, np.lib._iotools.str2bool, False),
                                               (nx.floating, float, nx.nan),
                                               (nx.complexfloating, complex, nx.nan + 0j),
                                               (nx.longdouble, nx.longdouble, nx.nan)]

    for filename, contents in result['output'].items():
        if dtypes.get(filename) is None:
            dtypes[filename] = None
        if converters.get(filename) is None:
            converters[filename] = None

        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            parsed[filename] = np.genfromtxt(io.StringIO(contents),
                                             dtype=dtypes[filename],
                                             converters=converters[filename]
                                             ).tolist()

    # Here we restore the original mapper, so no side-effects remain.
    np.lib._iotools.StringConverter._mapper = oldmapper

    return parsed