210 lines
6.2 KiB
Python
210 lines
6.2 KiB
Python
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"""
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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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__all__ = ['fix', 'isneginf', 'isposinf']
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import numpy._core.numeric as nx
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from numpy._core.overrides import array_function_dispatch
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import warnings
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import functools
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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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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 have the same data-type as the input.
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Parameters
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----------
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x : array_like
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An array to be rounded
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out : ndarray, optional
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A location into which the result is stored. If provided, it must have
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a shape that the input broadcasts to. If not provided or None, a
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freshly-allocated array is returned.
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Returns
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-------
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out : ndarray of floats
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An array with the same dimensions and data-type as the input.
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If second argument is not supplied then a new array is returned
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with the rounded values.
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If a second argument is supplied the result is stored there.
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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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rint, 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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>>> import numpy as np
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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
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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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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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out : array_like, optional
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A location into which the result is stored. If provided, it must have a
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shape that the input broadcasts to. If not provided or None, a
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freshly-allocated boolean array is returned.
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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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>>> import numpy as np
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>>> np.isposinf(np.inf)
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True
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>>> np.isposinf(-np.inf)
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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 as e:
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dtype = nx.asanyarray(x).dtype
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raise TypeError(f'This operation is not supported for {dtype} values '
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'because it would be ambiguous.') from e
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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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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 location into which the result is stored. If provided, it must have a
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shape that the input broadcasts to. If not provided or None, a
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freshly-allocated boolean array is returned.
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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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>>> import numpy as np
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>>> np.isneginf(-np.inf)
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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.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 as e:
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dtype = nx.asanyarray(x).dtype
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raise TypeError(f'This operation is not supported for {dtype} values '
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'because it would be ambiguous.') from e
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else:
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return nx.logical_and(is_inf, signbit, out)
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