Split a target date interval into seasons and find the percentile of days for each season

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I have the following seasons:
  • Spring: {01-March , 31-May}
  • Summer: {01-June, 31-August}
  • Autumn: {01-Sep, 30-Nov}
  • Winter: {01-Dec, 28-Feb}
and I also have as a data a reference period, e.g. 01-Jan until 15-Sep (254 days).
What I want is to compute what is the percentile of the days of reference period that belong to each season.
For example, in the present case, we have:
  • Winter: 01-Jan until 28-Feb --> 59 days / 254 days = 23.5%
  • Spring: 01-Mar until 31-May --> 90 days / 254 days = 35%
  • Summer: 01-Jun until 31-Aug --> 90 days / 254 days = 35%
  • Autumn: 01-Sep until 15-Sep --> 15 days /254 days = 6.5%
So the requested values would be {23.5, 35, 35, 6.5}.

Accepted Answer

Scott MacKenzie
Scott MacKenzie on 11 Jul 2021
There might be a way to shorten this, but I think it achieves what you are after. You didn't mention the year, so I set this up as a variable (change, as necessary). This script accommodates leap years. BTW, the number of reference days below is 259. Not sure why this differs from your count of 254.
yr = 2020; % change, as necessary
% reference days of interest
r1= datetime(yr,1,1):datetime(yr,2,eomday(yr,2));
r2= datetime(yr,3,1):datetime(yr,5,31);
r3= datetime(yr,6,1):datetime(yr,8,31);
r4= datetime(yr,9,1):datetime(yr,9,15);
n = length([r1 r2 r3 r4]) % number of reference days
n = 259
percentDays = [length(r1), length(r2), length(r3), length(r4)] /n * 100
percentDays = 1×4
23.1660 35.5212 35.5212 5.7915
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More Answers (2)

Seth Furman
Seth Furman on 14 Jul 2021
To add to Scott's answer, this kind of grouped calculation on timestamped data lends itself well to timetable and groupsummary.
isWinter = @(dt) dt.Month == 12 | dt.Month <= 2;
isSpring = @(dt) 3 <= dt.Month & dt.Month <= 5;
isSummer = @(dt) 6 <= dt.Month & dt.Month <= 8;
isAutumn = @(dt) 9 <= dt.Month & dt.Month <= 11;
referencePeriod = timetable('RowTimes',datetime(2020,1,1):caldays(1):datetime(2020,9,15));
referencePeriod.Season(isWinter(referencePeriod.Time)) = categorical("Winter");
referencePeriod.Season(isSpring(referencePeriod.Time)) = categorical("Spring");
referencePeriod.Season(isSummer(referencePeriod.Time)) = categorical("Summer");
referencePeriod.Season(isAutumn(referencePeriod.Time)) = categorical("Autumn");
counts = groupsummary(referencePeriod,"Season")
counts = 4×2 table
Season GroupCount ______ __________ Winter 60 Spring 92 Summer 92 Autumn 15
counts.Percentages = counts.GroupCount ./ sum(counts.GroupCount)
counts = 4×3 table
Season GroupCount Percentages ______ __________ ___________ Winter 60 0.23166 Spring 92 0.35521 Summer 92 0.35521 Autumn 15 0.057915
  2 Comments
Peter Perkins
Peter Perkins on 11 Apr 2022
The general idea in Seth's suggestion is a good one, and his solution works for multiple years of data. If the data are always within one year, discretize would make this even simpler. Obviously winter crosses a year boundary, but it's not hard to use discretize in a way that handles that:
dt = datetime(2022,1,1):datetime(2022,9,15);
edges = datetime(2022,[1,3,6,9,12,13],[1,1,1,1,1,1]); % winter at both ends
season = discretize(dt,edges,"categorical",["Winter" "Spring" "Summer" "Autumn" "Winter"])
season = 1×258 categorical array
Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Winter Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer Autumn Autumn Autumn Autumn Autumn Autumn Autumn Autumn Autumn Autumn Autumn Autumn Autumn Autumn Autumn
summary(season)
Winter Spring Summer Autumn 59 92 92 15
Using groupsummary gives you a nice output table; creating season would work as a lead-up to that too.

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Peter Perkins
Peter Perkins on 27 Jul 2021
Another possibility:
>> yr = 2020;
>> edges = datetime(yr,[1 3 6 9 12,12],[1 1 1 1 1 32])
edges =
1×6 datetime array
01-Jan-2020 01-Mar-2020 01-Jun-2020 01-Sep-2020 01-Dec-2020 01-Jan-2021
>> t = datetime(yr,1,1:259)';
>> tf = isbetween(t,edges(1:end-1),edges(2:end),'openright'); % uses implicit expansion
>> tf(:,1) = tf(:,1) + tf(:,end);
>> tf(:,end) = [];
>> 100*sum(tf,1)/length(t)
ans =
23.166 35.521 35.521 5.7915

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