海洋潮汐分析工具箱-t-tide-v1.3beta.zip

function [nameu,fu,tidecon,xout]=t_tide(xin,varargin); % T_TIDE Harmonic analysis of a time series % [NAME,FREQ,TIDECON,XOUT]=T_TIDE(XIN) computes the tidal analysis % of the (possibly complex) time series XIN. % % [TIDESTRUC,XOUT]=T_TIDE(XIN) returns the analysis information in % a structure formed of NAME, FREQ, and TIDECON. % % XIN can be scalar (e.g. for elevations), or complex ( =U+sqrt(-1)*V % for eastward velocity U and northward velocity V. % % Further inputs are optional, and are specified as property/value pairs % [...]=T_TIDE(XIN,property,value,property,value,...,etc.) % % These properties are: % % 'interval' Sampling interval (hours), default = 1. % (if interval is >1 then you *may* run into % problems with 'error','cboot' (the default). % Id this happens, pick a different error algorithm!) % % The next two are required if nodal corrections are to be computed, % otherwise not necessary. If they are not included then the reported % phases are raw constituent phases at the central time. % % If your time series is longer than 18.6 years then nodal corrections % are not made -instead we fit directly to all satellites (start time % is then just used to generate Greenwich phases). % % 'start time' [year,month,day,hour,min,sec] % - min,sec are optional OR % decimal day (matlab DATENUM scalar) % 'latitude' decimal degrees (+north) (default: none). % % Where to send the output. % 'output' where to send printed output: % 'none' (no printed output) % 'screen' (to screen) - default % FILENAME (to a file) % % Correction factor for prefiltering. % 'prefilt' FS,CORR % If the time series has been passed through % a pre-filter of some kind (say, to reduce the % low-frequency variability), then the analyzed % constituents will have to be corrected for % this. The correction transfer function % (1/filter transfer function) has (possibly % complex) magnitude CORR at frequency FS (cph). % Corrections of more than a factor of 100 are % not applied; it is assumed these refer to tidal % constituents that were intentionally filtered % out, e.g., the fortnightly components. % % Adjustment for long-term behavior ("secular" behavior). % 'secular' 'mean' - assume constant offset (default). % 'linear' - get linear trend. % % Inference of constituents. % 'inference' NAME,REFERENCE,AMPRAT,PHASE_OFFSET % where NAME is an array of the names of % constituents to be inferred, REFERENCE is an % array of the names of references, and AMPRAT % and PHASE_OFFSET are the amplitude factor and % phase offset (in degrees)from the references. % NAME and REFERENCE are Nx4 (max 4 characters % in name), and AMPRAT and PHASE_OFFSET are Nx1 % (for scalar time series) and Nx2 for vector % time series (column 1 is for + frequencies and % column 2 for - frequencies). % NB - you can only infer ONE unknown constituent % per known constituent (i.e. REFERENCE must not % contain multiple instances of the same name). % % Shallow water constituents % 'shallow' NAME % A matrix whose rows contain the names of % shallow-water constituents to analyze. % % Resolution criterions for least-squares fit. % 'rayleigh' scalar - Rayleigh criteria, default = 1. % Matrix of strings - names of constituents to % use (useful for testing purposes). % % Calculation of confidence limits. % 'error' 'wboot' - Boostrapped confidence intervals % based on a correlated bivariate % white-noise model. % 'cboot' - Boostrapped confidence intervals % based on an uncorrelated bivariate % coloured-noise model (default). % 'linear' - Linearized error analysis that % assumes an uncorrelated bivariate % coloured noise model. % % Computation of "predicted" tide (passed to t_predic, but note that % the default value is different). % 'synthesis' 0 - use all selected constituents % scalar>0 - use only those constituents with a % SNR greater than that given (1 or 2 % are good choices, 2 is the default). % <0 - return result of least-squares fit % (should be the same as using '0', % except that NaN-holes in original % time series will remain and mean/trend % are included). % % Least squares soln computational efficiency parameter % 'lsq' 'direct' - use A\x fit % 'normal' - use (A'A)\(A'x) (may be necessary % for very large input vectors since % A'A is much smaller than A) % 'best' - automatically choose based on % length of series (default). % % It is possible to call t_tide without using property names, % in which case the assumed calling sequence is % % T_TIDE(XIN,INTERVAL,START_TIME,LATITUDE,RAYLEIGH) % % % OUTPUT: % % nameu=list of constituents used % fu=frequency of tidal constituents (cycles/hr) % tidecon=[fmaj,emaj,fmin,emin,finc,einc,pha,epha] for vector xin % =[fmaj,emaj,pha,epha] for scalar (real) xin % fmaj,fmin - constituent major and minor axes (same units as xin) % emaj,emin - 95% confidence intervals for fmaj,fmin % finc - ellipse orientations (degrees) % einc - 95% confidence intervals for finc % pha - constituent phases (degrees relative to Greenwich) % epha - 95% confidence intervals for pha % xout=tidal prediction % % Note: Although missing data can be handled with NaN, it is wise not % to have too many of them. If your time series has a lot of % missing data at the beginning and/or end, then truncate the % input time series. The Rayleigh criterion is applied to % frequency intervals calculated as the inverse of the input % series length. % % A description of the theoretical basis of the analysis and some % implementation details can be found in: % % Pawlowicz, R., B. Beardsley, and S. Lentz, "Classical Tidal % "Harmonic Analysis Including Error Estimates in MATLAB % using T_TIDE", Computers and Geosciences, 28, 929-937 (2002). % % (citation of this article would be appreciated if you find the % toolbox useful). % R. Pawlowicz 11/8/99 - Completely rewritten from the transliterated- % to-matlab IOS/Foreman fortran code by S. Lentz % and B. Beardsley. % 3/3/00 - Redid errors