Help extracting rows based on unique column 1 data

I have a large MxN matrix of data from which i would like to extract specific rows of data.

The first column of the matrix contains a chronological timebase in seconds, in ascending order.

t1 a1 b1 .. n1

t2 a2 b2 .. n2

:

talpha ..

:

tbeta ..

:

tgamma ..

:

tm am bm .. nm

I know the unique times of 3 events (talpha, tbeta, tgamma) that occur in column 1.

How do i extract the rows in chronological order based on the knowledge of their unique times?

Tried categorical with valueset and ordinal but no luck!

[Image Analyst]

How large is large? How many GB or rows? If it really is large, like hundreds of millions of rows or several GB of data then you may need to use memmapfile. If you just have a few million rows or less, that's not large.

[Brantosaurus]

Not THAT large!!!

Maybe 10,000 x 20 at most

Answer 1

[Star Strider]

There is a lot I don’t unerstand about this problem.

However, if you want to find the unique occurrences of each of the ‘talpha’ , ‘tbeta’ and ‘tgamma’ so that they return the indices of the first incidence of each value, use the unique function with the first two outputs (the unique values, and the indices of the first instance of each value) with the 'stable' argument (to prvent sorting the result).

[Brantosaurus]

Apologies for the vagueness. Had a play around and came up with something following your excelent advice. Thanks for mentioning the unique function and indices. Not heard of those before. Best regards.

[Star Strider]

As always, my pleasure!

Answer 2

[Campion Loong]

I would first put my data in a timetable. It is specifically built for time based data like yours.

% Make some pretend data for your a#/b#/c# numbers
A = rand(10000,1); B = rand(10000,1); C = rand(10000,1);

% Use timetable. Here I assume regular timesteps. If not, use the
% 'RowTimes' N/V pair instead
tt = timetable(A, B, C, 'TimeStep', seconds(1))

tt = 10000×3 timetable
      Time         A           B          C    
    ________    ________    _______    ________

    00:00:00    0.046518    0.25765     0.11216
    00:00:01    0.021558    0.19586    0.078626
    00:00:02       0.325    0.81709      0.9364
    00:00:03     0.11313    0.10249     0.36468
    00:00:04     0.65928    0.43818      0.4145
    00:00:05     0.59172    0.30821     0.52174
    00:00:06     0.39627    0.23423     0.42336
    00:00:07     0.42042    0.62615      0.8902
    00:00:08     0.62093    0.60716    0.098711
    00:00:09    0.030026    0.18906     0.49313
    00:00:10      0.5556    0.51185      0.6461
    00:00:11     0.65488    0.38215     0.59968
    00:00:12     0.48795    0.79178     0.69614
    00:00:13    0.040898    0.99794     0.59517
    00:00:14     0.17782    0.81708     0.12582
    00:00:15     0.64031    0.78373     0.81385

If you know the exact time of your unique events

t_alpha = seconds(10);
t_beta  = minutes(150);
t_gamma = hours(2);

% Index directly with time. Get all the rows in one go like below, or
% separately as your preferred:
tt([t_alpha; t_beta; t_gamma], :)

ans = 3×3 timetable
      Time         A          B          C   
    ________    _______    _______    _______

    00:00:10     0.5556    0.51185     0.6461
    02:30:00    0.44544    0.32722    0.23712
    02:00:00    0.48217    0.21062    0.37704

Now, if you are not sure the 'exact' time (e.g. your data has some noise)

tt.Time = tt.Time + milliseconds(randn(10000,1)); % mix in some made-up noise

% Exact time index will turn up nothing ...
tt([t_alpha; t_beta; t_gamma], :)

ans =

  0×3 empty timetable

% Use withtol to index with time tolerance:
wt = withtol([t_alpha; t_beta; t_gamma],seconds(0.5))

wt = 
	timetable withtol subscript:

		Select timetable rows matching the following times:

		  10 sec
		9000 sec
		7200 sec
		with tolerance of +/- 0.5 sec

	See Select Times in Timetable.

tt(wt, :)

ans = 3×3 timetable
      Time         A          B          C   
    ________    _______    _______    _______

    00:00:10     0.5556    0.51185     0.6461
    02:30:00    0.44544    0.32722    0.23712
    02:00:00    0.48217    0.21062    0.37704