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#------------------------------------------------------------------------------
# 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.
"""
import numpy
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 = numpy.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 numpy.arange(min_value, max_value + increment / 2.0, increment)
def norm(mean, std):
""" Returns a single random value from a normal distribution. """
return numpy.random.normal(mean, std)
def discrete_std (counts, bin_centers):
""" Returns a standard deviation from binned data. """
mean = numpy.sum(counts * bin_centers)/numpy.sum(counts)
return numpy.sqrt((numpy.sum((counts-mean)**2))/len(counts))
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