You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: timeseries4.m
    path of file: ./repos/CanlabCore/CanlabCore/Data_extraction/Grandfathered
    
    the code of the file until where you have to start completion: function [ts,vols,chunksize] = timeseries4(coords,P,varargin)
% function [ts,vols,chunksize] = timeseries4(coords,P,[chunksize],[nochk])
%
% Simple extraction from images named in str mtx P or vols
% from voxel coordinates (not mm!) listed in coords
%
% P can be filenames in str matrix (char array)
% or 4-D array of all volume info (vols)
% (i.e., put vols in output back in as P)
%
% ts is timeseries, with fields avg and indiv for average cluster
    
function chk = check_timeseries_vals(V,dat,coords)
% function chk = check_timeseries_vals(V,dat,coords)
%
% Checks a random subset of up to 5 images against extracted data values
% to make sure the right data is being extracted.
%
% V is memory-mapped volumes (see spm_vol)
% dat is extracted data
% coords is voxel coordinates, n rows x 3 columns
%
% tor wager

n = min(5,length(V));

% get random set of n images, up to 5
wh = randperm(length(V));
wh = wh(1:n);

% select these rows in V and dat
% dat is [images, coordinates]
V = V(wh);
dat = dat(wh,:);

% get random set of nn coordinates, up to 5

nc = size(coords,1);
nn = min(5,nc);
whc = randperm(nc);
whc = whc(1:nn);
coords = coords(whc,:);

% select these columns in dat
dat = dat(:,whc);

v = spm_read_vols(V);
for i = 1:nn    % for each coordinate
    dat2(:,i) = squeeze(v(coords(i,1),coords(i,2),coords(i,3),:));
end