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