import collections
import random
import bisect

def sample(xs,sample_size = None,replace=False,sample_probabilities = None):
    """Mimics the functionality of http://statistics.ats.ucla.edu/stat/r/library/bootstrap.htm sample()"""

    if not isinstance(xs,collections.Iterable):
        xs = range(xs)
    if not sample_size:
        sample_size = len(xs)            

    if not sample_probabilities:
        if replace:
            return [random.choice(xs) for _ in range(sample_size)]
        else:
            return random.sample(xs,sample_size)
    else:
        if replace:
            total,cdf = 0,[]
            for x,p in zip(xs,sample_probabilities):
                total += p
                cdf.append(total)

            return [ xs[ bisect.bisect(cdf,random.uniform(0,total)) ] 
                    for _ in range(sample_size) ]
        else:            
            assert len(sample_probabilities) == len(xs)
            xps = list(zip(xs,sample_probabilities))           
            total = sum(sample_probabilities)
            result = []
            for _ in range(sample_size):
                # choose an item based on weights,and remove it from future iterations.
                # this is slow (N^2),a tree structure for xps would be better (NlogN)
                target = random.uniform(0,total)
                current_total = 0                
                for index,(x,p) in enumerate(xps):
                    current_total += p
                    if current_total > target:
                        xps.pop(index)
                        result.append(x)
                        total -= p
                        break
            return result