atan2d

Four-quadrant inverse tangent in degrees

Syntax

D = atan2d(Y,X)

Description

D = atan2d(Y,X) returns the four-quadrant inverse tangent (tan^-1) of Y and X, which must be real. The result, D, is expressed in degrees.

Examples

Inverse Tangent of Four Points on the Unit Circle

x = [1 0 -1 0];
y = [0 1 0 -1];
d = atan2d(y,x)

d = 1×4

     0    90   180   -90

Input Arguments

Y — y-coordinates

scalar | vector | matrix | multidimensional array

y-coordinates, specified as a scalar, vector, matrix, or multidimensional array. Inputs Y and X must either be the same size or have sizes that are compatible (for example, Y is an M-by-N matrix and X is a scalar or 1-by-N row vector). For more information, see Compatible Array Sizes for Basic Operations.

Data Types: single | double

X — x-coordinates

scalar | vector | matrix | multidimensional array

x-coordinates, specified as a scalar, vector, matrix, or multidimensional array. Inputs Y and X must either be the same size or have sizes that are compatible (for example, Y is an M-by-N matrix and X is a scalar or 1-by-N row vector). For more information, see Compatible Array Sizes for Basic Operations.

Data Types: single | double

More About

Four-Quadrant Inverse Tangent

The four-quadrant inverse tangent, atan2d(Y,X), returns values in the closed interval [-180,180] based on the values of Y and X as shown in the graphic.

In contrast, atand(Y/X) returns results that are limited to the interval [-90,90], shown on the right side of the diagram.

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™.

Usage notes and limitations:

• If you use atan2d with single type and double type operands, the generated code might not produce the same result as MATLAB^®. See Binary Element-Wise Operations with Single and Double Operands (MATLAB Coder).

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU 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).