Interpolate regularly sampled data to increase its sampling rate and match its length to another variable.

This function is used to reduce the time span of data by cropping out any data that falls before and after two time cues.

interp2length(X, Z, fs_in = NULL, fs_out = NULL, n_out = NULL)

Arguments

X	A sensor list, vector, or matrix. If x is or contains matrix, each column is treated as an independent signal.
Z	is a sensor structure, vector or matrix whose sampling rate and length is to be matched.
fs_in	is the sampling rate in Hz of the data in X. This is only needed if X is not a sensor structure.
fs_out	is the required new sampling rate in Hz. This is only needed if Z is not given.
n_out	is an optional length for the output data. If n_out is not given, the output data length will be the input data length * fs_out/fs_in.

Value

Y is a sensor structure, vector or matrix of interpolated data with the same number of columns as X.

Examples

         plott(X = list(harbor_seal$P), fsx = 5) 
         # get an idea of what the data looks like
         P_dec <- decdc(harbor_seal$P, 5)
         
         # note: you would not really want to decimate and then linearly interpolate. 
         # only doing so here to create an example from existing datasets 
         # that have uniform sampling rates across sensors
         
         P_interp <- interp2length(X = P_dec, Z = harbor_seal$A)
         plott(X = list(P_interp$data), fsx = 1) 
         # compare to original plot. should be pretty close