how to fill a time series from burst sampled data?

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I have data recorded in ~3 minute bursts at a frequency of 4hz and taken every ~13 minutes. On plotting, therefore, a line is created connecting each of these bursts [see image attached]. To remove this and also to make a complete timeseries with time intervals running complete from the start to end [rather than a table with only the times at which data was recorded], I figured I could create a new table with a complete timeseries with appropriate time interval, merge it with the 'burst time series' and then pack the missing data with NaNs.
I have created the new timeseries
dtn = datenum(['9/11/2016 08:29:11:000';'9/11/2016 17:02:09:000'],'dd/mm/yyyy HH:MM:SS.FFF');
dts = datestr(t(1):1/24/60/60/4:t(2),'dd/mm/yyyy HH:MM:SS'); %create [char] time series 08:29:11-17:02:09 for 9/11/2016
dts=datetime(dts,'InputFormat','dd/MM/yyyy HH:mm:ss');
and with a table of both sets of data, thought I could then use a 'join' to merge them inserting data at appropraite times into 'dts' with gaps to be subsequently filled by NaNs at other times, but haven't had any success in this.
A=table(vpdtime,vppres,...
'VariableNames',{'Time' 'Pressure'});
B=table(dts,...
'VariableNames',{'Time'});
  2 个评论
Peter Perkins
Peter Perkins 2017-1-24
I think the three lines you have to construct a datetime vector would be more succintly written as
dts = datetime('9/11/2016 08:29:11'):seconds(.25):datetime('9/11/2016 17:02:09');
Generally speaking, for both performance and clarity, it's best to avoid mixing datenum/datestr and datetime.
Dom Smith
Dom Smith 2017-1-25
Thanks, I do manage to get endlessly confused on converting times!

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采纳的回答

Peter Perkins
Peter Perkins 2017-1-24
Dom, since it appears you have R2016b, here's what I came up with using a timetable:
>> % t0 = datetime(2016,9,11,8,29,11,'Format','yyyy-MM-dd HH:mm:ss.SSS');
Could have done it with datetimes, like the above, but since it's all in one day, maybe a duration makes more sense?
>> t0 = duration(8,29,11,'Format','hh:mm:ss.SSS');
>> burst = (0:seconds(.25):minutes(3))';
>> time = [t0 + burst; t0 + minutes(13) + burst; t0 + minutes(26) + burst];
>> signal = seconds(time - t0) + 100*randn(size(time)); % fake data
>> t1 = time(end);
>> tt = timetable(time,signal);
>> tt(1:1000,:)
tt =
time signal
____________ ________
08:29:11.000 57.136
08:29:11.250 -50.832
08:29:11.500 -33.331
[snip]
08:32:10.500 545.13
08:32:10.750 5.3862
08:32:11.000 352.54
08:42:11.000 764.23
08:42:11.250 657.03
08:42:11.500 740.39
08:42:11.750 888.41
[snip]
and so on. This
plot(tt.time,tt.signal)
creates the same plot you already made, with lines that span the empty spaces. As you say, NaNs will prevent that in the plot. I'm not sure what you would then do with all those NaNs, but I think this is what you are asking for:
tt2 = retime(tt,[t0:seconds(.25):t1]);
So now this
plot(tt2.time,tt2.signal)
will show only the measured data. It seems like maybe you'd then do some kind of smoothing/interpolation to fill in the gaps that are now filled with NaNs.
Hope this helps.

更多回答(1 个)

Walter Roberson
Walter Roberson 2017-1-23
Have you considered using a timetable() ?
  2 个评论
Dom Smith
Dom Smith 2017-1-24
I have yeh, I can do it using synchronise making A and B a timetable and then synchronising to new 'secondly' timestep... but then I loose some of the data's resolution.
C=synchronize(Att,Btt,'secondly');
Is there any way of synchronising to timesteps smaller than a second with the new timetable function?
Walter Roberson
Walter Roberson 2017-1-24
Instead of 'secondly' pass in a vector of the times to synchronize against.

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