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: SP_Summaries.m
    path of file: ./repos/hctsa/Operations
    
    the code of the file until where you have to start completion: function out = SP_Summaries(y,psdMeth,windowType,nf,doLogAbs)
% SP_Summaries  Statistics of the power spectrum of a time series
%
% The estimation can be done using a periodogram, using the periodogram code in
% Matlab's Signal Processing Toolbox, or a fast fourier transform, implemented
% using Matlab's fft code.
%
%---INPUTS:
% y, the input time series
%
% psdMeth, the method of obtaining the spectrum from the signal:
%               (i) 'periodogram': periodogram
%               (ii) 'fft': fast fourier transform
%               (iii) 'welch': Welch's method
%
% windowType, the window to use:
%               (i) 'boxcar'
%               (ii) 'rect'
%               (iii) 'bartlett'
%               (iv) 'hann'
%               (v) 'hamming'
%               (vi) 'none'
%
% nf, the number of frequency components to include. If
%           empty (default), it's approximately length(y).
%
% doLogAbs, if 1, takes log amplitude of the signal before
%           transforming to the frequency domain.
%
% doPower, analyzes the power spectr
    
function
out.spect_shann_ent = sum(Hshann);
out.spect_shann_ent_norm = mean(Hshann);

% ------------------------------------------------------------------------------
% "Spectral Flatness Measure"
% ------------------------------------------------------------------------------
% which is given in dB as 10 log_10(gm/am) where gm is the geometric mean and am
% is the arithmetic mean of the power spectral density
out.sfm = 10*log10(geomean(S)/mean(S));

% ------------------------------------------------------------------------------
% Areas under power spectrum
% ------------------------------------------------------------------------------
% Area up to peak: (may be more appropriate in squared log units?)
out.areatopeak = sum(S(1:i_maxS))*dw;
out.ylogareatopeak = sum(logS(1:i_maxS))*dw; % (semilogy)
% out.logareatopeak=sum(logS(1:i_maxS).*diff(logw(1:i_maxS+1)));

% ------------------------------------------------------------------------------
%% Robust (e.g., iteratively re-weighted least squares) linear fits to log-log
%   plots
% ------------------------------------------------------------------------------

% Use the local function, giveMeRobustStats, which adds robust
% stats to the out structure.

% Suppress rank deficient warnings for this section:
warning('off','stats:robustfit:RankDeficient')

% (1): Across full range
r_all = (w > 0); % avoid -Inf for log(0) when w = 0;
out = giveMeRobustStats(log(w(r_all)),log(S(r_all)),'linfitloglog_all',out);

% (2): First half (low frequency)
r_lf = (w > 0); % w(1) = 0 -> log(0) = -Inf
r_lf(floor(N/2)+1:end