tand

Tangent of argument in degrees

Syntax

Y = tand(X)

Description

Y = tand(X) returns the tangent of the elements of X, which are expressed in degrees.

Examples

Tangent of 90 Degrees Compared to Tangent of π/2 Radians

tand(90)

ans = 
Inf

tan(pi/2)

ans = 
1.6331e+16

tand(90) is infinite, whereas tan(pi/2) is large but finite.

Tangent of Vector of Complex Angles, Specified in Degrees

z = [180+i 15+2i 10+3i];
y = tand(z)

y = 1×3 complex

   0.0000 + 0.0175i   0.2676 + 0.0374i   0.1758 + 0.0539i

Input Arguments

X — Angle in degrees

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

Angle in degrees, specified as a real-valued or complex-valued scalar, vector, matrix, multidimensional array, table, or timetable. The tand operation is element-wise when X is nonscalar.

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

Output Arguments

Y — Tangent of angle

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

Tangent of angle, 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 tand 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:

• In some cases, generated code returns -Inf when MATLAB^® returns Inf.

• In some cases, generated code returns Inf when MATLAB returns -Inf.

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 tand 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 tand 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.