[PX,PY] = gradient(Z)[PX,PY] = gradient(Z,x,y)[PX,PY] = gradient(Z,dx,dy)p = gradient(Z,...)d = gradient(y)d = gradient(y,x)d = gradient(y,dx)
[PX,PY] = gradient(Z), with a single matrix argument, computes a numerical approximation to the gradient field of the function tabulated in Z. The result is ordinarily two matrices, the same size as Z, containing horizontal and vertical first differences. One-sided differences are used at the edges of the matrix and centered differences are used in the interior.
[PX,PY] = gradient(Z,x,y), with one matrix and two vector arguments, uses divided differences involving the vector x in the horizontal direction and the vector y in the vertical direction.
[PX,PY] = gradient(Z,dx,dy), with one matrix and two scalar arguments, divides the horizontal difference by the scalar dx and the vertical difference by the scalar dy.
P = gradient(Z,...), with one output argument, returns a complex result, P = PX + i*PY.
d = gradient(y), with a single vector argument, computes a numerical approximation to the first derivative of the function tabulated in y. The result is a vector, the same size as y, containing first differences. One-sided differences are used at the ends of the vector and centered differences are used in the interior.
d = gradient(y,x), with two vector arguments, uses divided differences to approximate the derivative.
d = gradient(y,dx), with one vector and one scalar argument, divides the first difference by the scalar dx.
produce a matrixx = -pi:pi/20:pi;y = -1:.05:1;[X,Y] = meshgrid(x,y);Z = sin(X) + Y.^3;[PX,PY] = gradient(Z,x,y);
PX approximating the partial derivative with respect to x, which is cos(X), and a matrix PY approximating the partial derivative with respect to y, which is 3*Y.^2.The statements
produce a vector which approximates the derivative dy/dx.x = -pi:pi/500:pi;y = tan(sin(x)) - sin(tan(x));d = gradient(y,x);
contour,del2,diff,quiver
(c) Copyright 1994 by The MathWorks, Inc.