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.

Examples

Find Dimensions of Vector

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

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

A — Input array

scalar | vector | matrix | multidimensional array | table | timetable

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

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | char | string | struct | table | timetable | cell | categorical | datetime | duration | calendarDuration

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.

This 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 Verilog and VHDL code for FPGA and ASIC designs using HDL Coder™.

GPU Arrays

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

This function fully supports GPU arrays. 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).