RandomState.laplace(loc=0.0, scale=1.0, size=None) Draw samples from the Laplace or double exponential distribution

MaskedArray.data Return the current data, as a view of the original underlying data.

numpy.ma.array(data, dtype=None, copy=False, order=None, mask=False, fill_value=None, keep_mask=True, hard_mask=False, shrink=True, subok=True, ndmin=0)

numpy.less(x1, x2[, out]) = Return the truth value of (x1 < x2) element-wise.

numpy.outer(a, b, out=None)

numpy.core.defchararray.ljust(a, width, fillchar=' ')

numpy.ma.allclose(a, b, masked_equal=True, rtol=1e-05, atol=1e-08)

numpy.random.seed(seed=None) Seed the generator. This method is called when

numpy.random.negative_binomial(n, p, size=None) Draw samples from a negative binomial distribution. Samples

numpy.polynomial.hermite.hermdiv(c1, c2)