May be it is so simple but I'm new to Matlab and not good in Timestamps issues in general. Sorry!
I have two different cameras each contains timestamps of frames. I read them to two arrays TimestampsCam1 and TimestampsCam2:
TimestampsCam1 contains 1500 records and the timestamps are in Microseconds as follows:
1 20931160389
2 20931180407
3 20931200603
4 20931220273
5 20931240360 ...
and TimestampsCam2 contains 1000 records and the timestamps are in Milliseconds as follows:
1 28275280
2 28315443
3 28355607
4 28395771
5 28435935 ...
The first camera starts capturing first and ends a bit later than the second camera. So what I need to do is to know exactly where a frame from first camera is captured at the same time (or nearly the same time) by the other camera. In other words, I want to align the two arrays(cameras) in time according to the timestamps. I want to get at the end two arrays of same size where each record is tempo-aligned to the corresponding record in the other array.
Many thanks to all!
Sam
Make sure they are in the same unit of measurement, e.g. microseconds
Create an index which contains all values, except duplicates, suppose this one is 2400 records long
Create two NaN vectors of length 2400 by putting the value (for example the framenumber) at the place where the index matches the timestamp
Now you have two aligned vectors with NaNs to pad them where required.
Related
I am wondering if anyone can explain the interpretation of the size (number of feature) in a time series? For example consider a simple script in Matlab
X= randn(2,5,2)
X(:,:,1) =
-0.5530 0.4291 0.3937 -1.2534 0.2811
-1.4926 -0.7019 -0.8305 -1.4034 1.9545
X(:,:,2) =
0.2004 0.1438 2.3655 -0.1589 0.7140
0.4905 0.2301 -0.7813 -0.6737 0.2552
Assume X is a time series with the following output
This generates 2 vectors of length 5 each has 2 rows. Can anyone tell me what is exactly the meaning of first 2 and 5?
In some websites it says a creating 5 vectors of length 5 and size 2. What does size mean here?
Is 2 like number of features and 5 is like number of time series. The reason for this confusion is because I do not understand how to interpret following sentence:
"Generate 2 vector-valued sequences of length 5; each vector has size
2."
What do size 2 and length 5 mean here?
This entirely depends on your data, and how you want to store this. If you have some 2D data over time, I find it convenient to have a data matrix with in the 1st and 2nd dimension the 2D data per time step, and in the 3rd dimension time.
Say I have a movie of 1920 by 1080 pixels with 100 frames, I'd store this as mov = rand(1080,1920,100) (1080 and 1920 swapped because of row, col order of indexing). So now mov(:,:,1) would give me the first frame etc.
BTW, your X is a normal array, not to be confused with the timeseries object.
I have daily time series data and I want to calculate 5-day averages of that data while also retrieving the corresponding start date for each of the 5-day averages. For example:
x = [732099 732100 732101 732102 732103 732104 732105 732106 732107 732108];
y= [1 5 3 4 6 2 3 5 6 8];
Where x and y are actually size 92x1.
Firstly, how do I compute the 5-day mean when this time series data is not divisible by 5? Ultimately, I want to compute the 'jumping mean', where the average is not computed continuously (e.g., June 1-5, June 6-10, and so on).
I've tried doing the following:
Pentad_avg = mean(reshape(y(1:90),5,[]))'; %manually adjusted to be divisible by 5
Pentad_dt = x(1:5:90); %select every 5th day for time
However, Pentad_dt gives me dates 01-Jun-2004 and 06-Jun-2004 as output. And, that brings me to my second point.
I am looking to find 5-day averages for x and y that correspond to 5-day averages of another time series. This second time series has 5-day averaged data starting from 15-Jun-2004 until 29-Aug-2004 (instead of starting at 01-Jun-2004). Ultimately, how do I align the dates and 5-day averages between these two time series?
Synchronization between two time series can be accomplished using the timeseries object. Placing your data into an object allows Matlab to intelligently process it. The most useful thing is adds for your usage is the synchronize method.
You'll want to make sure to properly set the time vector on each of the timeseries objects.
An example of what this might look like is as follows:
ts1 = timeseries(y,datestr(x));
ts2 = timeseries(OtherData,OtherTimes);
[ts1 ts2] = synchronize(ts1,ts2,'Uniform','Interval',5);
This should return to you each timeseries aligned to be with the same times. You could also specify a specific time vector to align a timeseries to using the resample method.
I have large data files with 10Hz resolution which have been split up into half hour files. Each half hour file should contain 18000 rows. However, this is not usually the case and there are gaps in the time stamps due to datalogging errors.
In order to be able to process this data further, I need uniform data files with exactly 18000 rows each. I have written a script in MATLAB which solves this problem by using a generated full timestamp for each half hour period. Where there are gaps in the original timestamp, I fill the rows with NaNs (except the timestamp row).
Using a simple example, here is the code:
Fs=1:10; % uniform timestamp
Fs=Fs'; %'//<-- prevents string markdown
Ts=[1 3 7 2 1; 3 4 3 3 2;6 5 2 1 3]; %Ts is the original data with the timestamp in the first column
Corrected=zeros(length(Fs),6); % corrected is the data after applying uniform timestamp
for ii=1:length(Fs)
for jj=1:length (Ts(:,1))
if Fs(ii)==Ts(jj)
Corrected(ii,:)=[Fs(ii) Ts(jj,:)];
break
else
Corrected(ii,:)=[Fs(ii), NaN*[ii,1:4]];
continue
end
end
end
The code works well enough but when I apply it to the 10Hz data, it is extremely slow. Any ideas on how I can improve this code?
Note that in the actual code I compare date strings:
if sum(Fs_str(ii,:)==Ts_str(jj,:))==23
Corrected(ii,:)=[Fs(ii) Ts(jj,:)];
Suppose I have 121 elements and want to get all combinations of 4 elements taken at a time, i.e. 121c4.
Since combnk(1:121, 4) takes a lot of time, I want to go for 2% of that combination by providing:
z = 1:50:length(121c4(:, 1))
For example: 1st row, 5th row, 100th row and so on, up to 121c4, picking only those rows from a 121c4 matrix without generating the complete combination (it's consuming too much for large numbers like 625c4).
If you haven't defined an ordering on the combinations, why not just use
randi(121,p,4)
where p is the number of combinations you want in your set ? With this approach you may, or may not, want to replace duplicates.
If you have defined an ordering on the combinations, tell us what it is.
can I do this with the standard SQL or I need to create a function for the following problem?
I have 14 columns, which represent 2 properties of 7 consecutive objects (the order from 1 to 7 is important), so
table.object1prop1, ...,table.object1prop7,table.objects2prop2, ..., table.objects2prop7.
I need compute the minimum value of the property 2 of the 7 objects that have smaller values than a specific threshold for property 1.
The values of the property 1 of the 7 objects take values on a ascending arithmetic scale. So property 1 of the object 1 will ever be smaller than property 2 of the objects 1.
Thanks in advance for any clue!
This would be easier if the data were normalized. (Hint, any time you find a column name with a number in it, you are looking at a big red flag that the schema is not in 3rd normal form.) With the table as you describe, it will take a fair amount of code, but the greatest() and least() functions might be your best friends.
http://www.postgresql.org/docs/current/interactive/functions-conditional.html#FUNCTIONS-GREATEST-LEAST
If I had to write code for this, I would probably feed the values into a CTE and work from there.