[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.