acotd

Inverse cotangent in degrees

Syntax

Y = acotd(X)

Description

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

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

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

Examples

Inverse Cotangent of Vector

x = [0 20 Inf];
y = acotd(x)

y = 1×3

   90.0000    2.8624         0

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

Inverse Cotangent of Complex Value

acotd(1+i)

ans = 
31.7175 -23.0535i

Input Arguments

X — Cotangent of angle

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

Cotangent of angle, specified as a real-valued or complex-valued scalar, vector, matrix, multidimensional array, table, or timetable. The acotd operation is element-wise when X is non-scalar.

Data Types: single | double | table | timetable
Complex Number Support: Yes

Output Arguments

Y — Angle in degrees

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

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

Extended Capabilities

Tall Arrays

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

The acotd 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 acotd 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 before R2006a

R2023a: Perform calculations directly on tables and timetables

The acotd function can calculate on all variables within a table or timetable without indexing to access those variables. All variables must have data types that support the calculation. For more information, see Direct Calculations on Tables and Timetables.