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
|
#------------------------------------------------------------------------------
# Copyright (c) 2005, Enthought, Inc.
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD
# license included in enthought/LICENSE.txt and may be redistributed only
# under the conditions described in the aforementioned license. The license
# is also available online at http://www.enthought.com/licenses/BSD.txt
# Thanks for using Enthought open source!
#
# Author: Enthought, Inc.
# Description: <Enthought util package component>
#------------------------------------------------------------------------------
""" A placeholder for math functionality that is not implemented in SciPy.
"""
from scipyx import *
def is_monotonic(array):
""" Does the array increase monotonically?
>>> is_monotonic(array((1,2,3,4))
True
>>> is_monotonic(array((1,2,3,0,5))
False
This may not be the desired response but:
>>> is_monotonic(array((1))
False
"""
try:
min_increment = amin(array[1:] - array[:-1])
if min_increment >= 0:
return True
except Exception:
return False
return False;
def brange(min_value, max_value, increment):
""" Returns an inclusive version of arange().
The usual arange() gives:
>>> arange(1,4,1)
array([1, 2, 3])
However brange() returns:
>>> brange(1,4,1)
array([1, 2, 3, 4])
"""
return arange(min_value, max_value + increment / 2.0, increment)
def norm(mean, std):
""" Returns a single value from a normal distribution. """
return stats.norm(mean, std)[0]
def discrete_std (counts, bin_centers):
""" Returns a standard deviation from binned data. """
mean = stats.sum(counts * bin_centers)/stats.sum(counts)
return sqrt((stats.sum((counts-mean)**2))/len(counts))
|
