chararray.size Number of elements in the array. Equivalent to np.prod(a.shape), i.e., the product of the array?s dimensions. Examples >>> x = np.zeros((3, 5, 2), dtype=np.complex128) >>> x.size 30 >>> np.prod(x.shape) 30

numpy.ma.sum(self, axis=None, dtype=None, out=None) = Return the sum of the array elements over the given axis. Masked elements are set to 0 internally. Parameters: axis : {None, -1, int}, optional Axis along which the sum is computed. The default (axis = None) is to compute over the flattened array. dtype : {None, dtype}, optional Determines the type of the returned array and of the accumulator where the elements are summed. If dtype has the value None and the type of a is an integer

record.any() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

RandomState.poisson(lam=1.0, size=None) Draw samples from a Poisson distribution. The Poisson distribution is the limit of the binomial distribution for large N. Parameters: lam : float or sequence of float Expectation of interval, should be >= 0. A sequence of expectation intervals must be broadcastable over the requested size. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case

MaskedArray.__nonzero__ x.__nonzero__() <==> x != 0

flatiter.copy() Get a copy of the iterator as a 1-D array. Examples >>> x = np.arange(6).reshape(2, 3) >>> x array([[0, 1, 2], [3, 4, 5]]) >>> fl = x.flat >>> fl.copy() array([0, 1, 2, 3, 4, 5])

numpy.ma.clump_unmasked(a) [source] Return list of slices corresponding to the unmasked clumps of a 1-D array. (A ?clump? is defined as a contiguous region of the array). Parameters: a : ndarray A one-dimensional masked array. Returns: slices : list of slice The list of slices, one for each continuous region of unmasked elements in a. See also flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges, notmasked_contiguous, clump_masked Notes New in version 1.4.0. Examples &

numpy.polynomial.legendre.leggrid2d(x, y, c) [source] Evaluate a 2-D Legendre series on the Cartesian product of x and y. This function returns the values: where the points (a, b) consist of all pairs formed by taking a from x and b from y. The resulting points form a grid with x in the first dimension and y in the second. The parameters x and y are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars. In either case, either x and y or their ele

MaskedArray.__contains__ x.__contains__(y) <==> y in x

ndarray.__str__() <==> str(x)