# crop_to

Reduce the time span of data by cropping out any data that falls before and after two time cues.

#### Matlab & Octave

Y = crop_to(X,tcues)				% X is a sensor structure
or
Y = crop_to(X,fs,tcues)			% X is a regularly sampled vector or matrix
or
[Y,T] = crop_to(X,T,tcues)		% X is an irregularly sampled vector or matrix

#### R

list <- crop_to(X, tcues = tcues)          # X is a sensor list
or
list <- crop_to(X, sampling_rate, tcues)     # X is a regularly sampled vector or matrix
or
list <- crop_to(X, sampling_rate = T, tcues)   # X is an irregularly sampled vector or matrix

Reduce the time span of data by cropping out any data that falls before and after two time cues.

Input var Description Units
X is a sensor structure, vector or matrix. X can be regularly or irregularly sampled data in any frame and unit.N/A
fs/sampling_rate is the sampling rate of X in Hz. This is only needed if X is not a sensor structure and X is regularly sampled. If X is not a sensor structure/list and X is not regularly sampled, use the input “T”, a vector of sampling times for X.Hz
tcues is a two-element vector containing the start and end time cue in seconds of the data segment to keep, i.e., tcues = [start_time, end_time] (%Mat. or Oct.) tcues >- c(start_time, end_time) (#R). seconds
Output var Description Units
Y is a sensor structure/list, vector or matrix containing the cropped data segment. If the input is a sensor structure/list, the output will also be. The output has the same units, frame and sampling characteristics as the input.N/A
T is a vector of sampling times for Y. This is only returned if X is irregularly sampled and X is not a sensor structure/list. If X is a sensor structure/list, the sampling times are stored in the structure/list.Hz

### Matlab & Octave

load_nc('testset3')
d = find_dives(P,300) ;
P2 = crop_to(P,[d.start(2) d.end(2)]);	% crop to 2nd dive
plott(P2)
% plot shows the profile of the second dive

### R

data <- beaked_whale
d <- find_dives(data$P,300) P2 <- crop_to(data$P, tcues = c(d$start[2], d$end[2]))	#crop to 2nd dive
Xdata <- list(datatest = P2\$X)
plott(Xdata)
#plot shows the dive profile and acceleration of the second dive