numpy.core.defchararray.less(x1, x2) [source] Return (x1 < x2) element-wise. Unlike numpy.greater, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters: x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns: out : ndarray or bool Output array of bools, or a single bool if x1 and x2 are scalars. See also equal, not_equal, greater_equa

record.tostring() 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

matrix.swapaxes(axis1, axis2) Return a view of the array with axis1 and axis2 interchanged. Refer to numpy.swapaxes for full documentation. See also numpy.swapaxes equivalent function

MaskedArray.cumprod(axis=None, dtype=None, out=None) [source] Return the cumulative product of the elements along the given axis. The cumulative product is taken over the flattened array by default, otherwise over the specified axis. Masked values are set to 1 internally during the computation. However, their position is saved, and the result will be masked at the same locations. Parameters: axis : {None, -1, int}, optional Axis along which the product is computed. The default (axis = Non

numpy.polynomial.laguerre.lagmul(c1, c2) [source] Multiply one Laguerre series by another. Returns the product of two Laguerre series c1 * c2. The arguments are sequences of coefficients, from lowest order ?term? to highest, e.g., [1,2,3] represents the series P_0 + 2*P_1 + 3*P_2. Parameters: c1, c2 : array_like 1-D arrays of Laguerre series coefficients ordered from low to high. Returns: out : ndarray Of Laguerre series coefficients representing their product. See also lagadd, lag

ndarray.strides Tuple of bytes to step in each dimension when traversing an array. The byte offset of element (i[0], i[1], ..., i[n]) in an array a is: offset = sum(np.array(i) * a.strides) A more detailed explanation of strides can be found in the ?ndarray.rst? file in the NumPy reference guide. See also numpy.lib.stride_tricks.as_strided Notes Imagine an array of 32-bit integers (each 4 bytes): x = np.array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]], dtype=np.int32) This array i

numpy.polynomial.hermite.hermzero = array([0])

numpy.polynomial.hermite_e.hermevander3d(x, y, z, deg) [source] Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees deg and sample points (x, y, z). If l, m, n are the given degrees in x, y, z, then Hehe pseudo-Vandermonde matrix is defined by where 0 <= i <= l, 0 <= j <= m, and 0 <= j <= n. The leading indices of V index the points (x, y, z) and the last index encodes the degrees of the HermiteE polynomials. If V = hermevander3d

numpy.polynomial.hermite_e.hermegauss(deg) [source] Gauss-HermiteE quadrature. Computes the sample points and weights for Gauss-HermiteE quadrature. These sample points and weights will correctly integrate polynomials of degree or less over the interval with the weight function . Parameters: deg : int Number of sample points and weights. It must be >= 1. Returns: x : ndarray 1-D ndarray containing the sample points. y : ndarray 1-D ndarray containing the weights. Notes The re

Hermite.__call__(arg) [source]