numpy.polynomial.laguerre.poly2lag(pol) [source] Convert a polynomial to a Laguerre series. Convert an array representing the coefficients of a polynomial (relative to the ?standard? basis) ordered from lowest degree to highest, to an array of the coefficients of the equivalent Laguerre series, ordered from lowest to highest degree. Parameters: pol : array_like 1-D array containing the polynomial coefficients Returns: c : ndarray 1-D array containing the coefficients of the equivalent

numpy.polynomial.laguerre.lagzero = array([0])

numpy.polynomial.laguerre.lagx = array([ 1, -1])

numpy.polynomial.laguerre.lagweight(x) [source] Weight function of the Laguerre polynomials. The weight function is and the interval of integration is . The Laguerre polynomials are orthogonal, but not normalized, with respect to this weight function. Parameters: x : array_like Values at which the weight function will be computed. Returns: w : ndarray The weight function at x. Notes

numpy.polynomial.laguerre.lagvander3d(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 The 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 Laguerre polynomials. If V = lagvander3d(x, y,

numpy.polynomial.laguerre.lagvander2d(x, y, deg) [source] Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees deg and sample points (x, y). The pseudo-Vandermonde matrix is defined by where 0 <= i <= deg[0] and 0 <= j <= deg[1]. The leading indices of V index the points (x, y) and the last index encodes the degrees of the Laguerre polynomials. If V = lagvander2d(x, y, [xdeg, ydeg]), then the columns of V correspond to the elements of a

numpy.polynomial.laguerre.lagvander(x, deg) [source] Pseudo-Vandermonde matrix of given degree. Returns the pseudo-Vandermonde matrix of degree deg and sample points x. The pseudo-Vandermonde matrix is defined by where 0 <= i <= deg. The leading indices of V index the elements of x and the last index is the degree of the Laguerre polynomial. If c is a 1-D array of coefficients of length n + 1 and V is the array V = lagvander(x, n), then np.dot(V, c) and lagval(x, c) are the same up

numpy.polynomial.laguerre.lagval3d(x, y, z, c) [source] Evaluate a 3-D Laguerre series at points (x, y, z). This function returns the values: The parameters x, y, and z are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x, y, and z or their elements must support multiplication and addition both with themselves and with the elements of c. If c has fewer than 3 dimens

numpy.polynomial.laguerre.lagval2d(x, y, c) [source] Evaluate a 2-D Laguerre series at points (x, y). This function returns the values: The parameters x and y are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x and y or their elements must support multiplication and addition both with themselves and with the elements of c. If c is a 1-D array a one is implicitly ap

numpy.polynomial.laguerre.lagval(x, c, tensor=True) [source] Evaluate a Laguerre 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