hms

Hour, minute, and second numbers of datetime or duration

Syntax

[h,m,s] = hms(t)

Description

[h,m,s] = hms(t) returns the hour, minute, and second values of the datetime or duration values in t as separate numeric arrays. The h and m outputs contain integer values, and the s output can contain a fractional part. h, m, and s are the same size as t.

Calling hms on a datetime array is equivalent to calling the hour, minute, and second functions.

Examples

Find Hour, Minute, and Second Numbers of datetime Array

t1 = datetime('now','Format','HH:mm:ss.SSS');
t = t1 + minutes(0:45:135)

t = 1x4 datetime
   08:12:35.504   08:57:35.504   09:42:35.504   10:27:35.504

[h,m,s] = hms(t)

h = 1×4

     8     8     9    10

m = 1×4

    12    57    42    27

s = 1×4

   35.5043   35.5043   35.5043   35.5043

hms returns the hour, minute, and second numbers in separate arrays.

Input Arguments

t — Input dates and times

datetime or duration array

Input dates and times, specified as a datetime or duration array.

Data Types: datetime | duration

Output Arguments

h — Hour numbers

numeric array

Hour numbers, returned as a numeric array of integer values from 0 to 23. The h output is of type double and is the same size as t.

m — Minute numbers

numeric array

Minute numbers, returned as a numeric array of integer values from 0 to 59. The m output is of type double and is the same size as t.

s — Second numbers

numeric array

Second numbers, returned as a numeric array of values from 0 to less than 60, and can include a fractional part. For datetime values whose time zone is UTCLeapSeconds, the s output can contain a value between 60 and 61 for times that fall during a leap second occurrence. The s output is of type double and is the same size as t.

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™.

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.

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 R2014b