ndgrid

Rectangular grid in N-D space

Syntax

[X1,X2,...,Xn] = ndgrid(x1,x2,...,xn)

[X1,X2,...,Xn] = ndgrid(xg)

Description

[X1,X2,...,Xn] = ndgrid(x1,x2,...,xn) replicates the grid vectors x1,x2,...,xn to produce an n-dimensional full grid.

[X1,X2,...,Xn] = ndgrid(xg) specifies a single grid vector xg to use for all dimensions. The number of output arguments you specify determines the dimensionality n of the output.

Examples

Create 2-D Grid

Create a 2-D grid from the vectors [1 3 5 7 9 11 13 15 17 19] and [2 4 6 8 10 12].

[X,Y] = ndgrid(1:2:19,2:2:12)

X = 10×6

     1     1     1     1     1     1
     3     3     3     3     3     3
     5     5     5     5     5     5
     7     7     7     7     7     7
     9     9     9     9     9     9
    11    11    11    11    11    11
    13    13    13    13    13    13
    15    15    15    15    15    15
    17    17    17    17    17    17
    19    19    19    19    19    19

Y = 10×6

     2     4     6     8    10    12
     2     4     6     8    10    12
     2     4     6     8    10    12
     2     4     6     8    10    12
     2     4     6     8    10    12
     2     4     6     8    10    12
     2     4     6     8    10    12
     2     4     6     8    10    12
     2     4     6     8    10    12
     2     4     6     8    10    12

Refine Grid Using Interpolation

Create a rectangular grid and calculate function values on the grid. Interpolate between the assigned values to refine the grid.

Create a coarse grid for $$(x,y)$$, where the range of $$x$$ is $$[-6,6]$$ and the range of $$y$$ is $$[-3,3]$$.

[X,Y] = ndgrid(-6:0.5:6,-3:0.5:3);

Evaluate the function at the locations defined in the grid. Then, visualize the function using a surface plot. Alternatively, since R2016b, you can use implicit expansion for this task.

f = sin(X.^2) .* cos(Y.^2);
surf(Y,X,f)

Interpolate between the points on a more refined grid (Xq,Yq). Then, visualize the interpolated values using a surface plot.

[Xq,Yq] = ndgrid(-6:0.125:6,-3:0.125:3);
F = interpn(X,Y,f,Xq,Yq,"spline");
surf(Yq,Xq,F)

Input Arguments

x1,x2,...,xn — Grid vectors (as separate arguments)

vectors

Grid vectors, specified as vectors containing grid coordinates for each dimension. The grid vectors implicitly define the grid. For example, in 2-D:

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64
Complex Number Support: Yes

xg — Grid vector for all dimensions

vectors

Grid vector for all dimensions, specified as a vector containing grid coordinates. ndgrid uses xg as the grid vector for each dimension.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64
Complex Number Support: Yes

Output Arguments

X1,X2,...,Xn — Full grid representation

array

Full grid representation, returned as separate arrays. For each output array Xi, the ith dimension contains copies of the grid vector xi.

More About

Convert Between meshgrid and ndgrid Formats

meshgrid and ndgrid create grids using different output formats. Specifically, the first two dimensions of a grid created using one of these functions are swapped when compared to the other grid format. Some MATLAB^® functions use grids in meshgrid format, while others use ndgrid format, so it is common to convert grids between the two formats.

You can convert between these grid formats using pagetranspose (as of R2020b) or permute to swap the first two dimensions of the grid arrays. For example, create a 3-D grid with meshgrid.

[X,Y,Z] = meshgrid(1:4,1:3,1:2);

Now transpose the first two dimensions of each grid array to convert the grid to ndgrid format, and compare the results against the outputs from ndgrid.

Xt = pagetranspose(X);
Yt = pagetranspose(Y);
Zt = pagetranspose(Z);
[Xn,Yn,Zn] = ndgrid(1:4,1:3,1:2);
isequal(Xt,Xn) & isequal(Yt,Yn) & isequal(Zt,Zn)

ans =

  logical

   1

Using pagetranspose is equivalent to permuting the first two dimensions while leaving other dimensions the same. You can also perform this operation using permute(X,[2 1 3:ndims(X)]).

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Thread-Based Environment

Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

The ndgrid function supports GPU array input with these usage notes and limitations:

• The 1-D syntax, X = ndgrid(x), returns a gpuArray column vector X that contains the elements of the input gpuArray x for use as a one-dimensional grid.

• The inputs must be floating-point double or single.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Distributed Arrays

Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

Usage notes and limitations:

• The 1-D syntax, X = ndgrid(x), returns a distributed array column vector X that contains the elements of the input distributed array x for use as a one-dimensional grid.

• The inputs must be floating-point double or single.

For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).

Version History

Introduced before R2006a