FX = gradient(F)
[FX,FY] = gradient(F)
[FX,FY,FZ,...] = gradient(F)
[...] = gradient(F,h)
[...] = gradient(F,h1,h2,...)
The gradient of a function of two variables, F(x,y), is defined as
and can be thought of as a collection of vectors pointing in the direction of increasing values of F. In MATLAB® software, numerical gradients (differences) can be computed for functions with any number of variables. For a function of N variables, F(x,y,z, ...),
[FX,FY] = gradient(F), where F is a matrix, returns the x and y components of the two-dimensional numerical gradient. FX corresponds to ∂F/∂x, the differences in x (horizontal) direction. FY corresponds to ∂F/∂y, the differences in the y (vertical) direction. The spacing between points in each direction is assumed to be one.
A single spacing value, h, specifies the spacing between points in every direction.
N spacing values (h1,h2,...) specifies the spacing for each dimension of F. Scalar spacing parameters specify a constant spacing for each dimension. Vector parameters specify the coordinates of the values along corresponding dimensions of F. In this case, the length of the vector must match the size of the corresponding dimension.
Note The first output FX is always the gradient along the 2nd dimension of F, going across columns. The second output FY is always the gradient along the 1st dimension of F, going across rows. For the third output FZ and the outputs that follow, the Nth output is the gradient along the Nth dimension of F.
v = -2:0.2:2; [x,y] = meshgrid(v); z = x .* exp(-x.^2 - y.^2); [px,py] = gradient(z,.2,.2); contour(v,v,z), hold on, quiver(v,v,px,py), hold off
F(:,:,1) = magic(3); F(:,:,2) = pascal(3); gradient(F)
takes dx = dy = dz = 1.
[PX,PY,PZ] = gradient(F,0.2,0.1,0.2)
takes dx = 0.2, dy = 0.1, and dz = 0.2.
gradient calculates the central difference between data points. For an array, matrix, or vector with N values in each row, the ith value is defined by
The gradient at the end points, where i=1 and i=N, is calculated with a single-sided difference between the endpoint value and the next adjacent value within the row. If two or more outputs are specified, gradient also calculates central differences along other dimensions. Unlike the diff function, gradient returns an array with the same number of elements as the input.