ndims

Number of array dimensions

Syntax

N = ndims(A)

Description

N = ndims(A) returns the number of dimensions in the array A. The number of dimensions is always greater than or equal to 2. The function ignores trailing singleton dimensions, for which size(A,dim) = 1.

example

Examples

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Find Dimensions of Vector

Open Live Script

Create a row vector.

A = 1:5;

Find the number of dimensions in the vector.

ndims(A)

ans = 
2

The result is 2 because the vector has a size of 1-by-5.

Find Dimensions of Cell Array

Open Live Script

Create a cell array of character vectors.

A{1,1,1} = 'cell_1';
A{1,1,2} = 'cell_2';
A{1,1,3} = 'cell_3'

A = 1x1x3 cell array
A(:,:,1) = 

    {'cell_1'}


A(:,:,2) = 

    {'cell_2'}


A(:,:,3) = 

    {'cell_3'}

Find the number of dimensions of the cell array.

ndims(A)

ans = 
3

The result is 3 because the cell array has a size of 1-by-1-by-3.

Input Arguments

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`A` — Input array
scalar | vector | matrix | multidimensional array | table | timetable

Input array, specified as a scalar, vector, matrix, multidimensional array, table, or timetable.

Algorithms

The number of dimensions in an array is the same as the length of the size vector of the array. In other words, ndims(A) = length(size(A)).

Extended Capabilities

Tall Arrays
Calculate with arrays that have more rows than fit in memory.

The ndims function fully supports tall arrays. For more information, see Tall Arrays.

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

HDL Code Generation
Generate VHDL, Verilog and SystemVerilog code for FPGA and ASIC designs using HDL 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 ndims function fully supports GPU arrays. To run the function on a GPU, specify the input data as a gpuArray (Parallel Computing Toolbox). 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™.

This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).

Version History

Introduced before R2006a

ndims

Syntax

Description

Examples

Find Dimensions of Vector

Find Dimensions of Cell Array

Input Arguments

A — Input array scalar | vector | matrix | multidimensional array | table | timetable

Algorithms

Extended Capabilities

Tall Arrays Calculate with arrays that have more rows than fit in memory.

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

HDL Code Generation Generate VHDL, Verilog and SystemVerilog code for FPGA and ASIC designs using HDL Coder™.

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

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

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