# Friday the 13th and the Datetime Method

Today is Friday, the 13th. In many parts of the world, today is regarded as *unlucky*. But I want to revisit an old question: is today *unlikely*? What are the chances that the 13th of any month falls on a Friday? Computing the answer makes use of a new MATLAB® feature, the `datetime` method.

I wrote about Friday the 13th in a blog post several years ago, Cleve's Corner, Friday the 13th. I am reusing a portion of that post today.

### Contents

#### Leap Years

Leap years make our calendar a nontrivial mathematical object. The leap year rule can be implemented by this anonymous function.

leapyear = @(y) mod(y,4)==0 & mod(y,100)~=0 | mod(y,400)==0;

This says that leap years happen every four years, except for the turn of centuries not divisible by 400. Try a few year numbers.

y = [2018 2020 2000 2100]'; isleap = [y leapyear(y)]

isleap = 2018 0 2020 1 2000 1 2100 0

So, this year is not a leap year, the next one is in 2020. The turn of the century 2000 was a leap year, but 2100 will not be.

#### Calendars

The leap year rule implies that our calendar has a period of 400 years. The calendar for years 2000 to 2399 will be reused for 2400 to 2799. In a 400 year period, there are 97 leap years, 4800 months, 20871 weeks, and 146097 days. So the average number of days in a calendar year is not 365.25, but

dpy = 365+97/400

dpy = 365.2425

We can compute the probability that the 13th of a month is a Friday by counting how many times that happens in 4800 months. The correct probability is then that count divided by 4800. Since 4800 is not a multiple of 7, the probability does not reduce to 1/7.

#### Datenum

For many years, computations involving dates and time have been done using a whole bunch of functions.

`clock``datenum``datevec``datestr``dateticks``now``weekday`

Those functions have served us well, but they have some significant shortcomings. They are not particularly easy to use. The display formatting is limited. The floating point precision of `datenum` effectively limits the calculation of durations. There are no immediate plans to retire `datenum` and its friends, but something preferable is now available.

#### Datetime

The `datetime` object, introduced in R2014b, combines and extends these functions into one.

`datetime`

The documentation for `datetime` is available online, or, from within MATLAB, with the command

doc datetime

For example, here is the date and time when I am publishing this post.

```
d = datetime('now')
```

d = datetime 13-Apr-2018 21:21:19

I can specify a custom display format with

d = datetime(d,'Format','eeee, MMMM d, yyyy HH:mm:ss')

d = datetime Friday, April 13, 2018 21:21:19

The available display formats include support for an international standard, ISO 8601.

#### Nanoseconds

`datetime`'s are much more precise than `datenum`'s can ever hope to be. Try this

hms = datetime(d,'Format','HH:mm:ss.SSS')

hms = datetime 21:21:19.502

We see that `d` is carrying fractional seconds to at least three places beyond the decimal point. How about nanoseconds?

secperday = 24*60*60 nano = 1.e-9/secperday hms = datetime(d,'Format','HH:mm:ss.SSSSSSSSS') hmsplusnano = hms + nano

secperday = 86400 nano = 1.1574e-14 hms = datetime 21:21:19.502000000 hmsplusnano = datetime 21:21:19.502000001

A nanosecond is four orders of magnitude smaller than roundoff error of `datenum`'s in this era. Compare

nano epsofdatenum = eps(datenum(d))

nano = 1.1574e-14 epsofdatenum = 1.1642e-10

I am pleased to reveal some details about the arithmetic in `datetime`. The object is using a form of quadruple precision known as double-double. In fact, the second half of the 112-bit format is stored as the imaginary part of the `data` field, as you see in the following.

disp(struct(hmsplusnano))

Warning: Calling STRUCT on an object prevents the object from hiding its implementation details and should thus be avoided. Use DISP or DISPLAY to see the visible public details of an object. See 'help struct' for more information. UTCZoneID: 'UTC' UTCLeapSecsZoneID: 'UTCLeapSeconds' ISO8601Format: 'uuuu-MM-dd'T'HH:mm:ss.SSS'Z'' epochDN: 719529 MonthsOfYear: [1×1 struct] DaysOfWeek: [1×1 struct] dateFields: [1×1 struct] noConstructorParamsSupplied: [1×1 struct] data: 1.5237e+12 + 1.0000e-06i fmt: 'HH:mm:ss.SSSSSSSSS' tz: '' isDateOnly: 0

#### Proleptic

The summary description in the `datetime` documentation describes some important features of the object, which has nearly 100 methods defined.

`datetime` arrays represent points in time using the proleptic
ISO calendar. `datetime` values have flexible display formats
up to nanosecond precision and can account for time zones,
daylight saving time, and leap seconds.

I had to look up the definition of "proleptic". In this context it means the calendar can be extended backwards in time to apply to dates even before it was adopted.

#### Vectorize

Let's get on with Friday the 13th. Like most things in MATLAB `datetime` works with vectors. The statements

y = 2000; m = 1:4800; t = 13; v = datetime(y,m,t);

produce a row vector of 4800 `datetime`'s for the 13th of every month. The first few and last few are

fprintf(' %s',v(1:4)) fprintf(' ...\n') fprintf(' %s',v(end-3:end))

13-Jan-2000 13-Feb-2000 13-Mar-2000 13-Apr-2000 ... 13-Sep-2399 13-Oct-2399 13-Nov-2399 13-Dec-2399

The statement

w = weekday(v);

produces a row vector of 4800 flints between 1 (for Sunday) and 7 (for Saturday). The first few and last few are

fprintf('%3d',w(1:4)) fprintf(' ...') fprintf('%3d',w(end-3:end))

5 1 2 5 ... 2 4 7 2

Now to get the counts of weekdays, all we need is

c = histcounts(w)

c = 687 685 685 687 684 688 684

There you have it. The count for Friday, 688, is higher than for any other day of the week. The 13th of any month is slightly more likely to fall on a Friday.

The probabilities are

p = c/4800

p = 0.1431 0.1427 0.1427 0.1431 0.1425 0.1433 0.1425

Compare these values with their mean, `1/7 = 0.1429`. Four probabilities are below the mean, three are above.

#### Categorize

Let's pair the counts with the days of the week, using a `categorical` variable, introduced in R2013b.

c = categorical(w,1:7,{'Sun' 'Mon' 'Tue' 'Wed' 'Thu' 'Fri' 'Sat'}); summary(c)

Sun Mon Tue Wed Thu Fri Sat 687 685 685 687 684 688 684

When we plot a histogram the appropriate labels are provided on the x-axis. Friday the 13th is the winner.

histogram(c) set(gca,'ylim',[680 690]) title('Weekday counts, 400 years')

#### Thanks

Thanks to Peter Perkins at MathWorks. I learned a lot from him while working on this post.

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