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

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.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

RandomState.logseries(p, size=None) Draw samples from a logarithmic series distribution. Samples are drawn from a log series distribution with specified shape parameter, 0 < p < 1. Parameters: loc : float scale : float > 0. 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 a single value is returned. Returns: samples : ndarray or scalar where the values are all integers

numpy.polynomial.legendre.legval(x, c, tensor=True) [source] Evaluate a Legendre series at points x. If c is of length n + 1, this function returns the value: The parameter x is converted to an array only if it is a tuple or a list, otherwise it is treated as a scalar. In either case, either x or its elements must support multiplication and addition both with themselves and with the elements of c. If c is a 1-D array, then p(x) will have the same shape as x. If c is multidimensional, the

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

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

record.flat a 1-d view of scalar

matrix.put(indices, values, mode='raise') Set a.flat[n] = values[n] for all n in indices. Refer to numpy.put for full documentation. See also numpy.put equivalent function

generic.ravel() 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