% readme.m
% DRAFT - January 23, 1999  
%
%
%                    Tidal Analysis Toolbox
%
% The tidal analysis toolbox uses harmonic analysis to estimate tidal
% consitutents and their uncertainities in scalar and vector time series. 
%
% This toolbox is based on the tidal analysis package developed by M. G.
% G. Foreman and coworkers at the Institute of Ocean Sciences (IOS). (The
% IOS tidal package and user manuals are available from Foreman at
% http://www.ios.bc.ca/ios/iap/pages/tidpack.htm.)  We have recoded the
% key IOS FORTRAN routines to take advantage of the Matlab built-in matrix
% operations useful in least-squares problems, plus improve the error
% estimation utilizing Matlab power spectrum code.
%
% The toolbox presently contains the following mfiles:
%
% 1. tide.m - computes the tidal analysis of the complex time series xin=x+i*y
%
% 2. constituents.m - loads 146 constituents, frequency and comparison 
%                     constituents
%
% 3. find_const.m - finds match of constituent name to list of   
%                   constituents
%
% 4. opnvuf.m - loads doodson numbers and satellite and shallow water   
%               constituent information
%
% 5. vuf.m - computes correction for phase relative to Greenwich and nodal 
%            amplitude and phase corrections
% 
% 6. astron.m - computes astronomical variables and their time derivatives 
%
% 7. errcoef - computes errors in the x (and y) cos,sin coefficients
%
% 8. errell - computes errors in ellipse parameters
% 
% 9. julian.m - converts Gregorian calendar time to decimal Julian day
%
% 10. constituents.dat - contains list of constituent names, frequencies and 
%                        comparison constituents (total presently 146)
%
% 11. doodson.dat - doodson numbers (6) for 45 major constituents
%
% 12. satellite.dat - satellite constituent information for major constituents
%
% 13. shallow.dat - shallow water constituent information 
%
% 14. mk_tide.m - creates simple tidal time series with white noise
%
% 15. readme.m - this file
%
% Limitations:
% 1. No constituent inference is done.
% 2. Input time series may have gaps but missing data must be set = NaN.
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This toolbox is being developed by Steve Lentz and Bob Beardsley with help from 
% Rich Signell and Mike Foreman. 
%
% PLEASE NOTE that this is version 0.0, and very much a work in progress! Please 
% report any  problems or suggestions for improvements to Steve (slentz@whoi.edu) 
% or Bob (rbeardsley@whoi.edu).                                                     
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

