JLAB_HIGHLIGHTS is in the main directory of jLab.

 JLAB_HIGHLIGHTS  Introduction to some of the most useful routines.
    JLAB_HIGHLIGHTS  Highlights from the JLAB toolbox.
    TWODSTATS, TWODMED, TWODHIST.  Examining temperature mean and standard
    deviation over a lat/lon grid, or current speed as a function of say
    temperature and salinity, or any analysis of multivariate datasets, is
    faster and easier than you ever imagined.  If you are working with 
    data and not using these routines, you should seriously take five 
    minutes right now and try them out.  As of Matlab 2015b, these take 
    advantage of improvements in Matlab and are even faster than before, 
    and now support parallelization as well. 
    MSPEC.  If you analyze time series, you need a method you can rely on.
    There are many compelling reasons to prefer the "multitaper" method.
    In this method, a set of estimates from optimally concentrated tapers
    or window functions are averaged together, reducing variance while 
    minimizing bias.  MSPEC makes using the multitaper straightforward.  
    Just call SLEPTAP first to generate the tapers.  MSPEC features support
    for cross spectra and rotary spectra, as well as 'adaptive' estimation.
    WAVETRANS.  A lot of work and original research has gone into this 
    continuous wavelet transform routine, which I believe is the best you 
    will find anywhere, and it's free.  Features natural integration with
    the generalized Morse wavelets (see below), choice of conditions at 
    the signal endpoints, support for multiple wavelets or mulit-component
    data, and convenient handling of real-valued or complex-valued data.
    MORSEWAVE.  A detailed analysis of types of continuous wavelets 
    (Lilly and Olhede, 2009) points to a super-family, the generalized 
    Morse wavelets, that should answer your questions about which wavelet
    to employ. Pick the generalized Morse wavelet with parameter gamma=3 
    unless you have a very good reason to do otherwise.  This wavelet is
    'like' the Morlet in spirit, but without the Morlet's serious flaws at
    narrow time-domain settings---which is when you most want to employ a 
    wavelet anyway.  Use MORSESPACE to easily determine frequency bins.
    RIDGEWALK.  If your time series data contains signals that oscillate
    but also change in time, the wavelet transform can be used as the basis
    for a remarkably powerful analysis that connects time-varying 
    properties to spectral structure.  The starting place for this analysis
    is RIDGEWALK.  This advanced algorithm incorporates original research 
    from Lilly & Olhede (2010a+b, 2011).  A key innovation is the ability 
    to view *multivariate* and univariate time series within the same 
    framework as modulated oscillations.
    POLYSMOOTH.  Need to map scattered data, for example, temperature as a 
    function of X and Y?  POLYSMOOTH, short for "Polynomial Smoothing", 
    is a powerful and quite general mapping algorithm.  Features choice of 
    order of the fit, choice of local weighting function, support for 
    spherical geometry, options for constant fit radius or constant number
    of data points with variable radius.  This algorithm was implemented
    for the forthcoming Aquarius Sea Surface Salinity satellite.
    JGRAPH.  Finessing figures in Matlab can be a total hassle.  Which
    would you prefer to type, version (a) or  version (b) ? 
       (a)  z=peaks;
            plot(z(:,1),'linewidth',3,'linestyle','-','color','b'),hold on
       (b)  z=peaks;plot(z(:,1:2)), linestyle 3b- 2r--
    They are identical.  JLAB's LINESTYLE will save you a lot of time.  
    It includes natural syntax for grayscale, as in "linestyle 3k- 2G-.",
    where the letter G stands for the shade of gray, between A and K. 
    The JGRAPH module contains many similar time-saving functions, such as
    PACKFIG.  Are your multiple subplots too far away from each other?  
    Don't like having to set the axes properties by hand each time?  Try  
    this function.  For example, "packfig(3,4)" will squish your 3 rows and 
    4 columns so they are right next to each other, removing the redundant
    x- and y-axes labels.
    This is part of JLAB --- type 'help jlab' for more information
    (C) 2011--2015 J.M. Lilly --- type 'help jlab_license' for details

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