atand

Inverse tangent in degrees

Syntax

Y = atand(X)

Description

Y = atand(X) returns the inverse tangent (tan^-1) of the elements of X in degrees. The function accepts both real and complex inputs.

• For real values of X, atand(X) returns values in the interval [-90, 90].

• For complex values of X, atand(X) returns complex values.

Examples

Inverse Tangent of Vector

x = [-50 -20 0 20 50];
y = atand(x)

y = 1×5

  -88.8542  -87.1376         0   87.1376   88.8542

The atand operation is element-wise when you pass a vector, matrix, or N-D array.

Inverse Tangent of Complex Value

atand(10+i)

ans = 84.3450 + 0.5618i

Input Arguments

X — Tangent of angle

scalar value | vector | matrix | N-D array

Tangent of angle, specified as a real-valued or complex-valued scalar, vector, matrix, or N-D array. The atand operation is element-wise when X is non-scalar.

Data Types: single | double
Complex Number Support: Yes

Output Arguments

Y — Angle in degrees

scalar value | vector | matrix | N-D array

Angle in degrees, returned as a real-valued or complex-valued scalar, vector, matrix, or N-D array of the same size as X.

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™.

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™.

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).

Version History

Introduced before R2006a