#------------------------------------------------------------------------------
# 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))