1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
|
"""
Module of functions that are like ufuncs in acting on arrays and optionally
storing results in an output array.
"""
__all__ = ['fix', 'isneginf', 'isposinf', 'log2']
import numpy.core.numeric as nx
def fix(x, y=None):
""" Round x to nearest integer towards zero.
"""
x = nx.asanyarray(x)
if y is None:
y = nx.zeros_like(x)
y1 = nx.floor(x)
y2 = nx.ceil(x)
y[...] = nx.where(x >= 0, y1, y2)
return y
def isposinf(x, y=None):
"""
Return True where x is +infinity, and False otherwise.
Parameters
----------
x : array_like
The input array.
y : array_like
A boolean array with the same shape as `x` to store the result.
Returns
-------
y : ndarray
A boolean array where y[i] = True only if x[i] = +Inf.
See Also
--------
isneginf, isfinite
Examples
--------
>>> np.isposinf([-np.inf, 0., np.inf])
array([ False, False, True], dtype=bool)
"""
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
def isneginf(x, y=None):
"""
Return True where x is -infinity, and False otherwise.
Parameters
----------
x : array_like
The input array.
y : array_like
A boolean array with the same shape as `x` to store the result.
Returns
-------
y : ndarray
A boolean array where y[i] = True only if x[i] = -Inf.
See Also
--------
isposinf, isfinite
Examples
--------
>>> np.isneginf([-np.inf, 0., np.inf])
array([ True, False, False], dtype=bool)
"""
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
_log2 = nx.log(2)
def log2(x, y=None):
"""
Return the base 2 logarithm.
Parameters
----------
x : array_like
Input array.
y : array_like
Optional output array with the same shape as `x`.
Returns
-------
y : {ndarray, scalar}
The logarithm to the base 2 of `x` elementwise.
NaNs are returned where `x` is negative.
See Also
--------
log, log1p, log10
Examples
--------
>>> np.log2([-1,2,4])
array([ NaN, 1., 2.])
"""
x = nx.asanyarray(x)
if y is None:
y = nx.log(x)
else:
nx.log(x, y)
y /= _log2
return y
|
