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: TS_CalculateFeatureVector.m
    path of file: ./repos/hctsa/Calculation
    
    the code of the file until where you have to start completion: function [featureVector,calcTimes,calcQuality] = TS_CalculateFeatureVector(tsStruct,doParallel,Operations,MasterOperations,codeSpecial,howVocal)
% TS_CalculateFeatureVector	Compute a feature vector from an input time series
%
%---INPUTS:
% tsStruct, either (i) a vector of time-series data to compute
% 				or (ii) an hctsa-style table of time-series data
% doParallel, (binary) whether to compute the features using parallel processing
% Operations, an hctsa-style table of Operations
% MasterOperations, an hctsa-style table of MasterOperations
% codeSpecial, whether to code special values with quality labels (for mySQL database)
% 				this makes sure the featureVector is all real numbers, and any
%				special values (like NaNs, Infs, etc.) are coded with corresponding
% 				labels in calcQuality.
% 				codeSpecial = false [default]: special values are kept in the
% 							feature vector, and any errors are coded as NaN.
% 				codeSpecial = true: featureVector is all real numbers, and is set to
% 							     zero where any special-valued outputs occur.
% howVocal, {'fast', 'minimal', or 'full'}: how to give user feedback on the computation.
%
%---OUTPUTS:
% featureVector, the feature vector obtained by running MasterOperations and
% 					retrieving all features defined in the Operations structure
% 					array on the time series data given in tsStruct.
% calcTimes, corresponding calculation times for each feature in featureVector
% calcQuality, quality labels of each calculation (e.g., coding for NaNs, Infs, etc.)
%
%---USAGE:
% Quickly compute a feature vector of a 500-long random time-series using the

    
function [featureVector,calcTimes,calcQuality] = TS_CalculateFeatureVector(tsStruct,doParallel,Operations,MasterOperations,codeSpecial,howVocal)
% TS_CalculateFeatureVector	Compute a feature vector from an input time series
%
%---INPUTS:
% tsStruct, either (i) a vector of time-series data to compute
% 				or (ii) an hctsa-style table of time-series data
% doParallel, (binary) whether to compute the features using parallel processing
% Operations, an hctsa-style table of Operations
% MasterOperations, an hctsa-style table of MasterOperations
% codeSpecial, whether to code special values with quality labels (for mySQL database)
% 				this makes sure the featureVector is all real numbers, and any
%				special values (like NaNs, Infs, etc.) are coded with corresponding
% 				labels in calcQuality.
% 				codeSpecial = false [default]: special values are kept in the
% 							feature vector, and any errors are coded as NaN.
% 				codeSpecial = true: featureVector is all real numbers, and is set to
% 							     zero where any special-valued outputs occur.
% howVocal, {'fast', 'minimal', or 'full'}: how to give user feedback on the computation.
%
%---OUTPUTS:
% featureVector, the feature vector obtained by running MasterOperations and
% 					retrieving all features defined in the Operations structure
% 					array on the time series data given in tsStruct.
% calcTimes, corresponding calculation times for each feature in featureVector
% calcQuality, quality labels of each calculation (e.g., coding for NaNs, Infs, etc.)
%
%---USAGE:
% Quickly compute a feature vector of a 500-long random time-series using the
% default operation library using parallel processing:
% >> features = TS_CalculateFeatureVector(randn(500,1));

% ------------------------------------------------------------------------------
% 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 work is licensed under the Creative Commons
% Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of
% this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or send