rad2deg

Convert angle from radians to degrees

Syntax

D = rad2deg(R)

Description

D = rad2deg(R) converts angle units from radians to degrees for each element of R.

Examples

pi in Degrees

Convert pi into degrees.

D = rad2deg(pi)

D = 
180

Spherical Distance

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

dist = 7194;
radEarth = 6371;
R = dist/radEarth;
D = rad2deg(R)

D = 
64.6972

Input Arguments

R — Angle in radians

scalar | vector | matrix | multidimensional array

Angle in radians, specified as a scalar, vector, matrix, or multidimensional array. If R contains complex elements, rad2deg converts the real and imaginary parts separately.

Data Types: single | double
Complex Number Support: Yes

Output Arguments

D — Angle in degrees

scalar | vector | matrix | multidimensional array

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

Extended Capabilities

Tall Arrays

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

The rad2deg 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 rad2deg 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