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""" 

Module of functions that are like ufuncs in acting on arrays and optionally 

storing results in an output array. 

 

""" 

__all__ = ['fix', 'isneginf', 'isposinf'] 

 

import numpy.core.numeric as nx 

from numpy.core.overrides import ( 

array_function_dispatch, ARRAY_FUNCTION_ENABLED, 

) 

import warnings 

import functools 

 

 

def _deprecate_out_named_y(f): 

""" 

Allow the out argument to be passed as the name `y` (deprecated) 

 

In future, this decorator should be removed. 

""" 

@functools.wraps(f) 

def func(x, out=None, **kwargs): 

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) 

 

return func 

 

 

def _fix_out_named_y(f): 

""" 

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 function. 

""" 

@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 

 

 

def _fix_and_maybe_deprecate_out_named_y(f): 

""" 

Use the appropriate decorator, depending upon if dispatching is being used. 

""" 

if ARRAY_FUNCTION_ENABLED: 

return _fix_out_named_y(f) 

else: 

return _deprecate_out_named_y(f) 

 

 

@_deprecate_out_named_y 

def _dispatcher(x, out=None): 

return (x, out) 

 

 

@array_function_dispatch(_dispatcher, verify=False, module='numpy') 

@_fix_and_maybe_deprecate_out_named_y 

def fix(x, out=None): 

""" 

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 

out : ndarray, optional 

A location into which the result is stored. If provided, it must have 

a shape that the input broadcasts to. If not provided or None, a 

freshly-allocated array is returned. 

 

Returns 

------- 

out : ndarray of floats 

A float array with the same dimensions as the input. 

If second argument is not supplied then a float array is returned 

with the rounded values. 

 

If a second argument is supplied the result is stored there. 

The return value `out` is then a reference to that array. 

 

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 

 

 

@array_function_dispatch(_dispatcher, verify=False, module='numpy') 

@_fix_and_maybe_deprecate_out_named_y 

def isposinf(x, out=None): 

""" 

Test element-wise for positive infinity, return result as bool array. 

 

Parameters 

---------- 

x : array_like 

The input array. 

out : array_like, optional 

A location into which the result is stored. If provided, it must have a 

shape that the input broadcasts to. If not provided or None, a 

freshly-allocated boolean array is returned. 

 

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) 

True 

>>> np.isposinf(np.inf) 

True 

>>> np.isposinf(np.NINF) 

False 

>>> 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 as e: 

raise TypeError('This operation is not supported for complex values ' 

'because it would be ambiguous.') from e 

else: 

return nx.logical_and(is_inf, signbit, out) 

 

 

@array_function_dispatch(_dispatcher, verify=False, module='numpy') 

@_fix_and_maybe_deprecate_out_named_y 

def isneginf(x, out=None): 

""" 

Test element-wise for negative infinity, return result as bool array. 

 

Parameters 

---------- 

x : array_like 

The input array. 

out : array_like, optional 

A location into which the result is stored. If provided, it must have a 

shape that the input broadcasts to. If not provided or None, a 

freshly-allocated boolean array is returned. 

 

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) 

True 

>>> np.isneginf(np.inf) 

False 

>>> np.isneginf(np.PINF) 

False 

>>> 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 as e: 

raise TypeError('This operation is not supported for complex values ' 

'because it would be ambiguous.') from e 

else: 

return nx.logical_and(is_inf, signbit, out)