def sample(self, population, k):
        """Chooses k unique random elements from a population sequence.

        Returns a new list containing elements from the population while
        leaving the original population unchanged.  The resulting list is
        in selection order so that all sub-slices will also be valid random
        samples.  This allows raffle winners (the sample) to be partitioned
        into grand prize and second place winners (the subslices).

        Members of the population need not be hashable or unique.  If the
        population contains repeats, then each occurrence is a possible
        selection in the sample.

        To choose a sample in a range of integers, use xrange as an argument.
        This is especially fast and space efficient for sampling from a
        large population:   sample(xrange(10000000), 60)
        """

        # Sampling without replacement entails tracking either potential
        # selections (the pool) in a list or previous selections in a
        # dictionary.

        # When the number of selections is small compared to the population,
        # then tracking selections is efficient, requiring only a small
        # dictionary and an occasional reselection.  For a larger number of
        # selections, the pool tracking method is preferred since the list takes
        # less space than the dictionary and it doesn't suffer from frequent
        # reselections.

        n = len(population)
        if not 0 <= k <= n:
            raise ValueError, "sample larger than population"
        random = self.random
        _int = int
        result = [None] * k
        if n < 6 * k:     # if n len list takes less space than a k len dict
            pool = list(population)
            for i in xrange(k):         # invariant:  non-selected at [0,n-i)
                j = _int(random() * (n-i))
                result[i] = pool[j]
                pool[j] = pool[n-i-1]   # move non-selected item into vacancy
        else:
            try:
                n < 0 and (population[0], population[n//2], population[n-1])
            except (TypeError, KeyError):   # handle sets and dictionaries
                population = tuple(population)
            selected = {}
            for i in xrange(k):
                j = _int(random() * n)
                while j in selected:
                    j = _int(random() * n)
                result[i] = selected[j] = population[j]
        return result

## -------------------- real-valued distributions  -------------------

## -------------------- uniform distribution -------------------
dataset/ETHPy150Open babble/babble/include/jython/Lib/random.py/Random.sample/operator `>` -> `<`