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259 lines
7.1 KiB
Python
259 lines
7.1 KiB
Python
"""
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Module of functions that are like ufuncs in acting on arrays and optionally
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storing results in an output array.
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"""
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from __future__ import division, absolute_import, print_function
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__all__ = ['fix', 'isneginf', 'isposinf']
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import numpy.core.numeric as nx
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from numpy.core.overrides import (
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array_function_dispatch, ARRAY_FUNCTION_ENABLED,
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)
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import warnings
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import functools
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def _deprecate_out_named_y(f):
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"""
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Allow the out argument to be passed as the name `y` (deprecated)
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In future, this decorator should be removed.
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"""
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@functools.wraps(f)
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def func(x, out=None, **kwargs):
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if 'y' in kwargs:
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if 'out' in kwargs:
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raise TypeError(
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"{} got multiple values for argument 'out'/'y'"
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.format(f.__name__)
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)
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out = kwargs.pop('y')
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# NumPy 1.13.0, 2017-04-26
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warnings.warn(
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"The name of the out argument to {} has changed from `y` to "
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"`out`, to match other ufuncs.".format(f.__name__),
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DeprecationWarning, stacklevel=3)
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return f(x, out=out, **kwargs)
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return func
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def _fix_out_named_y(f):
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"""
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Allow the out argument to be passed as the name `y` (deprecated)
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This decorator should only be used if _deprecate_out_named_y is used on
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a corresponding dispatcher function.
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"""
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@functools.wraps(f)
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def func(x, out=None, **kwargs):
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if 'y' in kwargs:
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# we already did error checking in _deprecate_out_named_y
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out = kwargs.pop('y')
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return f(x, out=out, **kwargs)
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return func
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def _fix_and_maybe_deprecate_out_named_y(f):
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"""
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Use the appropriate decorator, depending upon if dispatching is being used.
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"""
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if ARRAY_FUNCTION_ENABLED:
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return _fix_out_named_y(f)
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else:
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return _deprecate_out_named_y(f)
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@_deprecate_out_named_y
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def _dispatcher(x, out=None):
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return (x, out)
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@array_function_dispatch(_dispatcher, verify=False, module='numpy')
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@_fix_and_maybe_deprecate_out_named_y
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def fix(x, out=None):
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"""
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Round to nearest integer towards zero.
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Round an array of floats element-wise to nearest integer towards zero.
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The rounded values are returned as floats.
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Parameters
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----------
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x : array_like
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An array of floats to be rounded
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y : ndarray, optional
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Output array
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Returns
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-------
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out : ndarray of floats
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The array of rounded numbers
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See Also
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--------
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trunc, floor, ceil
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around : Round to given number of decimals
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Examples
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--------
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>>> np.fix(3.14)
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3.0
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>>> np.fix(3)
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3.0
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>>> np.fix([2.1, 2.9, -2.1, -2.9])
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array([ 2., 2., -2., -2.])
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"""
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# promote back to an array if flattened
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res = nx.asanyarray(nx.ceil(x, out=out))
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res = nx.floor(x, out=res, where=nx.greater_equal(x, 0))
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# when no out argument is passed and no subclasses are involved, flatten
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# scalars
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if out is None and type(res) is nx.ndarray:
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res = res[()]
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return res
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@array_function_dispatch(_dispatcher, verify=False, module='numpy')
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@_fix_and_maybe_deprecate_out_named_y
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def isposinf(x, out=None):
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"""
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Test element-wise for positive infinity, return result as bool array.
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Parameters
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----------
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x : array_like
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The input array.
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y : array_like, optional
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A boolean array with the same shape as `x` to store the result.
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Returns
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-------
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out : ndarray
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A boolean array with the same dimensions as the input.
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If second argument is not supplied then a boolean array is returned
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with values True where the corresponding element of the input is
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positive infinity and values False where the element of the input is
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not positive infinity.
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If a second argument is supplied the result is stored there. If the
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type of that array is a numeric type the result is represented as zeros
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and ones, if the type is boolean then as False and True.
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The return value `out` is then a reference to that array.
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See Also
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--------
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isinf, isneginf, isfinite, isnan
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Notes
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-----
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NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
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(IEEE 754).
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Errors result if the second argument is also supplied when x is a scalar
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input, if first and second arguments have different shapes, or if the
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first argument has complex values
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Examples
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--------
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>>> np.isposinf(np.PINF)
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True
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>>> np.isposinf(np.inf)
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True
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>>> np.isposinf(np.NINF)
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False
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>>> np.isposinf([-np.inf, 0., np.inf])
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array([False, False, True])
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>>> x = np.array([-np.inf, 0., np.inf])
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>>> y = np.array([2, 2, 2])
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>>> np.isposinf(x, y)
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array([0, 0, 1])
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>>> y
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array([0, 0, 1])
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"""
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is_inf = nx.isinf(x)
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try:
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signbit = ~nx.signbit(x)
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except TypeError:
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raise TypeError('This operation is not supported for complex values '
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'because it would be ambiguous.')
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else:
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return nx.logical_and(is_inf, signbit, out)
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@array_function_dispatch(_dispatcher, verify=False, module='numpy')
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@_fix_and_maybe_deprecate_out_named_y
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def isneginf(x, out=None):
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"""
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Test element-wise for negative infinity, return result as bool array.
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Parameters
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----------
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x : array_like
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The input array.
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out : array_like, optional
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A boolean array with the same shape and type as `x` to store the
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result.
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Returns
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-------
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out : ndarray
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A boolean array with the same dimensions as the input.
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If second argument is not supplied then a numpy boolean array is
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returned with values True where the corresponding element of the
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input is negative infinity and values False where the element of
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the input is not negative infinity.
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If a second argument is supplied the result is stored there. If the
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type of that array is a numeric type the result is represented as
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zeros and ones, if the type is boolean then as False and True. The
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return value `out` is then a reference to that array.
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See Also
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--------
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isinf, isposinf, isnan, isfinite
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Notes
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-----
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NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
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(IEEE 754).
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Errors result if the second argument is also supplied when x is a scalar
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input, if first and second arguments have different shapes, or if the
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first argument has complex values.
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Examples
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--------
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>>> np.isneginf(np.NINF)
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True
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>>> np.isneginf(np.inf)
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False
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>>> np.isneginf(np.PINF)
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False
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>>> np.isneginf([-np.inf, 0., np.inf])
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array([ True, False, False])
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>>> x = np.array([-np.inf, 0., np.inf])
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>>> y = np.array([2, 2, 2])
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>>> np.isneginf(x, y)
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array([1, 0, 0])
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>>> y
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array([1, 0, 0])
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"""
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is_inf = nx.isinf(x)
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try:
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signbit = nx.signbit(x)
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except TypeError:
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raise TypeError('This operation is not supported for complex values '
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'because it would be ambiguous.')
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else:
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return nx.logical_and(is_inf, signbit, out)
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