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One of the most common tasks in data analysis is the preparing of the data
segments to analyse. LTPDA offers various tools for preprocessing data. Here
we will review the splitting of data segments using the `ao/split` method.

Suppose you have a segment of data and you want to split that up. Here are various examples showing how you might do that.

To split the data in to `N` parts you can do:

% Generate a time-series waveformPlist = plist(... 'waveform', 'square wave', ... % Generate a square wave 'f', 0.1, ... % with 0.1Hz frequency 'fs', 10, ... % sampled at 10Hz 'nsecs', 100, ... % lasting for 100s 't0', '2012-03-10 12:00:00', ... % with the specified reference time 'toffset', 10 ... % and the first sample starts 10s after the reference time ); a = ao(waveformPlist); % Split into 5 parts splitPlist1 = plist('N', 5); % Split the time-series parts = split(a, splitPlist1); % Plot the result iplot(parts)

To split the data by elapsed time since the reference time t0 you can do

% Split a segment starting a t=20 and finishing at t=75 splitPlist2 = plist('times', [20 75]); % Split the time-series timeSegment = split(a, splitPlist2); % Plot the result iplot(timeSegment)

To split the data by an offset in seconds relative to the first and last samples, you do

% Split a segment starting 10s from the start to 40s from the start splitPlist3 = plist('offsets', [10 40]); % Split the time-series offsetSegment = split(a, splitPlist3); % Plot the result iplot(offsetSegment)

% Make a segment which drops 10s from the beginning and end of the original splitPlist4 = plist('offsets', [10 -10]); % Split the time-series dropSegment = split(a, splitPlist4); % Plot the result iplot(dropSegment)

To split the data by absolute times, you do

% Split a segment starting and ending at particular times splitPlist4 = plist('start_time', '2012-03-10 12:00:15', 'end_time', '2012-03-10 12:00:36'); % Split the time-series absTimeSegment = split(a, splitPlist4); % Plot the result iplot(absTimeSegment)

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