numpy.s_ = A nicer way to build up index tuples for arrays. Note

ndarray.sort(axis=-1, kind='quicksort', order=None) Sort an array, in-place.

numpy.polynomial.legendre.legmul(c1, c2)

numpy.nanmean(a, axis=None, dtype=None, out=None, keepdims=False)

numpy.random.f(dfnum, dfden, size=None) Draw samples from an F distribution. Samples are drawn from an F distribution

numpy.polynomial.legendre.leggrid2d(x, y, c)

Elementwise bit operations

numpy.nansum(a, axis=None, dtype=None, out=None, keepdims=0)

numpy.r_ = Translates slice objects to concatenation along the first axis. This is a simple way to build up arrays quickly. There

numpy.testing.assert_string_equal(actual, desired)