deg2rad

Convert angle from degrees to radians

Syntax

R = deg2rad(D)

Description

R = deg2rad(D) converts angle units from degrees to radians for each element of D.

Examples

Right Angle in Radians

Convert a 90 degree angle into radians.

R = deg2rad(90)

R = 
1.5708

Surface Distance

Specify the spherical distance between Munich and Bangalore in degrees and the mean radius of Earth in kilometers. Compute the distance (measured along the Earth's surface) between Munich and Bangalore, in kilometers.

D = 64.7;
radEarth = 6371;
R = deg2rad(D);
dist = radEarth*R

dist = 
7.1943e+03

Input Arguments

D — Angle in degrees

scalar | vector | matrix | multidimensional array

Angle in degrees, specified as a scalar, vector, matrix, or multidimensional array. If D contains complex elements, deg2rad converts the real and imaginary parts separately.

Data Types: single | double
Complex Number Support: Yes

Output Arguments

R — Angle in radians

scalar | vector | matrix | multidimensional array

Angle in radians, returned as a scalar, vector, matrix, or multidimensional array. R is the same size as D.

Extended Capabilities

Tall Arrays

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

The deg2rad function fully supports tall arrays. For more information, see Tall Arrays.

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

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU 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 deg2rad 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 in R2015b