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: SY_LocalGlobal.m
    path of file: ./repos/hctsa/Operations
    
    the code of the file until where you have to start completion: function out = SY_LocalGlobal(y,subsetHow,n,randomSeed)
% SY_LocalGlobal  Compares local statistics to global statistics of a time series.
%
%---INPUTS:
% y, the time series to analyze
%
% subsetHow, the local subset of time series to study:
%             (i) 'l': the first n points in a time series,
%             (ii) 'p': an initial proportion of the full time series, n
%             (iii) 'unicg': n evenly-spaced points throughout the time series
%             (iv) 'randcg': n randomly-chosen points from the time series
%                               (chosen with replacement)
%
% n, the parameter for the method specified above
%
% randomSeed, an option for whether (and how) to reset the random seed, for the
% 'randcg' input
%
%---OUTPUTS: the mean, standard deviation, median, interquartile range,
% skewness, kurtosis, AC(1), and SampEn(1,0.1).
%
% This is not the most reliable or systematic operation because only a single
% sample is taken from the time series and compared to the full time series.
% A better approach would be to repeat over many local subsets and compare the
% statistics of these local regions to the full time series,
% cf. SY_SpreadRandomLocal

% ------------------------------------------------------------------------------
% Copyright (C) 2020, Ben D. Fulcher <ben.d.fulcher@gmail.com>,
% <http://www.benfulcher.com>
%
% If you use this code for your research, please cite the following two papers:
%
% (1) B.D. Fulcher and N.S. Jones, "hctsa: A Computational Framework for Automated
% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).
% DOI: 10.1016/j.cels.2017.10.001
%
% (2) B.D. Fulcher, M.A. Little, N.S. Jones, "Highly comparative time-series
% analysis: the empirical structure of time series and their methods",
% J. Roy. Soc. Interface 10(83) 20130048 (2013).
% DOI: 10.1098/rsif.2013.0048
%
% This function is free software: you can redistribute it and/or modify it under
% the terms of the GNU General Public License as published by the Free Software
% Foundation, either version 3 of the License, or (at your option) any later
% version.
%
% This program is distributed in the hope that it will be useful, but WITHOUT
% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more

    
function out = SY_LocalGlobal(y,subsetHow,n,randomSeed)
% SY_LocalGlobal  Compares local statistics to global statistics of a time series.
%
%---INPUTS:
% y, the time series to analyze
%
% subsetHow, the local subset of time series to study:
%             (i) 'l': the first n points in a time series,
%             (ii) 'p': an initial proportion of the full time series, n
%             (iii) 'unicg': n evenly-spaced points throughout the time series
%             (iv) 'randcg': n randomly-chosen points from the time series
%                               (chosen with replacement)
%
% n, the parameter for the method specified above
%
% randomSeed, an option for whether (and how) to reset the random seed, for the
% 'randcg' input
%
%---OUTPUTS: the mean, standard deviation, median, interquartile range,
% skewness, kurtosis, AC(1), and SampEn(1,0.1).
%
% This is not the most reliable or systematic operation because only a single
% sample is taken from the time series and compared to the full time series.
% A better approach would be to repeat over many local subsets and compare the
% statistics of these local regions to the full time series,
% cf. SY_SpreadRandomLocal

% ------------------------------------------------------------------------------
% Copyright (C) 2020, Ben D. Fulcher <ben.d.fulcher@gmail.com>,
% <http://www.benfulcher.com>
%
% If you use this code for your research, please cite the following two papers:
%
% (1) B.D. Fulcher and N.S. Jones, "hctsa: A Computational Framework for Automated
% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).
% DOI: 10.1016/j.cels.2017.10.001
%
% (2) B.D. Fulcher, M.A. Little, N.S. Jones, "Highly comparative time-series
% analysis: the empirical structure of time series and their methods",
% J. Roy. Soc. Interface 10(83) 20130048 (2013).
% DOI: 10.1098/rsif.2013.0048
%
% This function is free software: you can redistribute it and/or modify it under
% the terms of the GNU General Public License as published by the Free Software
% Foundation, either version 3 of the License, or (at your option) any later
% version.
%
% This program is distributed in the hope that it will be useful, but WITHOUT
% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
% details.
%
% You should have received a copy of the GNU General Public License along with
% this program. If not, see <http://www.gnu.org/licenses/>.
% ------------------------------------------------------------------------------

% ------------------------------------------------------------------------------
% Preliminaries
% ------------------------------------------------------------------------------

% Check input time series is z-scored:
if ~BF_iszscored(y)
    warning('The input time series should be z-scored (mean=%g and std-1=%g)', ...
                                            mean(y),std(y)-1)
end