MATLAB: How to create an index that keep tracking the hours in the data

for loop

Is anyone helping me with a simple code ???

In the code below I want to find the peak load and average peak price in peak hour from 7-10 and then do it the same for each peak hours (11-15, 16-21 and 23-6am. I write the code below but I couldn't figure out how to calculate the peak load and average peak price for each peak with the same code

h=twomonth(:,1)
 p1= 7 <= h & h < 10
 price=twomonth(:,2);
 price=price.*p1  
 demand=twomonth(:,3);
 demand=demand.*p1
 Index1=(7:24:N)'; ss1=10-1
N1=length( Index1);
Data1=zeros(N1,2);
for k=1:N1
    kk=Index1(k); kkk=kk+ss1-1;
    dd=sum(d(kk:kkk));pp=sum(p(kk:kkk).*d(kk:kkk)); pp=pp/dd;
    Data1(k,:)=[pp dd];
end

Best Answer

Here's how I'd do it:

%twomonth: a Nx3 matrix, whose first column is hour, 2nd is price, 3rd is demand
bin = discretize(mod(twomonth(:, 1)-7, 24), [7, 11, 16, 22, 23, Inf]-7);  %offset bin edges by 7 in order to put the 23-6 in the same bin (5)
%bin == 1 is [7-11), bin == 2 is [11-16), bin == 3 is [16-22), bin == 4 is [22], bin == 5 is [23-6]
avgprice = splitapply(@mean, twomonth(:, 2), bin);
peakdemand = splitapply(@max, twomonth(:, 3), bin);
result = table({'7-10'; '11-15'; '16-21'; '22'; '23-6'}, avgprice, peakdemand, 'VariableNames', {'period', 'avgprice', 'peakdemand'})

I've included the lone 22 in the result, you can always remove row 4 if you don't want it.

You have to do a little bit of arithmetic as discretize can't give you the same bin number for the start and end of a range, but once you've got your bin numbers it's trivial to calculate your mean and peak with splitapply.

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It's a bit puzzling that you are aware of timetables and yet resort to deprecated functions such as histc (which has no concept of dates) for the rest of your code, instead of using timetables functions which would make your task trivially easy.

Even the way you construct your timetable is more complicated than it should:

%Data1: a 2 column matrix of price and demand:
DT = array2timetable(Data1, 'VariableNames', {'Price', 'Demand'}, 'RowTimes', datetime(2006, 1, 1):dateime(2018, 12, 31));

There is no point in storing Ye, M, D separately. It's already stored as part of the time.

Once you have a timetable, you can easily resample it at regular or irregular intervals using retime. However retime doesn't do

%DT: a timetable
meanDT = retime(DT, 'regular', 'mean', 'TimeStep', caldays(14));  %discussion about timestep coming up
stdDT = retime(DT, 'regular', @std, 'TimeStep', caldays(14));
meanDT.Properties.VariableNames = {'MeanPrice', 'MeanDemand'};
stdDT.Properties.VariableNames = {'StdPrice', 'StdDemand'};
meanstdDT = [meanDT, stdDT]

Instead of using two retime, you could use groupsummary instead:

meanstdDT = groupsummary(DT, 'Time', caldays(14), {'mean', 'std'})

Now you say you want 4 bins per month. What does that mean exactly. 4 equal bins per month means that the edges fall at odd times during the days instead of falling at the end of days. That doesn't sound like something you'd want. In the above, I've used bins of 14 days but both retime and groupsummary support a bin vector instead. To construct a bin vector with 4 equal bins per month, the simplest is probably:

monthvector = DT.Time(1) : calmonths(1) : DT.Time(end) + calmonths(1);
binvector = fillmissing(reshape([monthvector; NaT(3, numel(monthvector))], [], 1), 'linear')

and then

meanstd = groupsummary(DT, 'Time', binvector, {'mean', 'std'})

MATLAB: Output score which is nearest to image date

The second output table is easy:

%demo data:
source_table = table([4270;4999], datetime({'17/11/2011';'02/04/2014'}, 'InputFormat', 'dd/MM/yyyy', 'Format', 'dd/MM/yyyy'), 'VariableNames', {'ID', 'Image_date'})
score_table = table([4270;4270;4270;4999;4999], datetime({'21/09/2011';'01/08/2012';'15/03/2014';'01/01/2011';'01/01/2014'}, 'InputFormat', 'dd/MM/yyyy', 'Format', 'dd/MM/yyyy'), [30;29;27;24;20], 'VariableNames', {'ID', 'Score_date', 'Score'})

%actual processing
distance_table = join(score_table, source_table)
distance_table.Months_from_image = between(distance_table.Score_date, distance_table.Image_date)

If you just want just the number of months without the days, then:

distance_table.Months_from_image = calmonths(distance_table.Months_from_image)

However, you lose the number of days (which may be important if two entries for the same ID have the name number of months).

For your first output, there are several ways to do this, either with rowfun, splitapply or groupsummary. First, you need an aggregation function. For splitapply I'd use:

function rowindex = nearest(rows, daysdiff) %using difference in days instead of months
    [~, idx] = min(abs(daysdiff));
    rowindex = rows(idx);
end

then:

group = findgroups(distance_table.ID);
rows = (1:height(distance_table))';
selectedrows = splitapply(@nearest, rows, days(distance_table.Score_date - distance_table.Image_date), group);
result = distance_table(selectedrows, :)

Note that with datetime, you use between to get a calduration (which can be expressed in months), but normal subtraction to get a duration which can be expressed in days

Best Answer

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