Global Index (all files) (short | long) | Local Index (files in subdir) (short | long)
| Name | Description |
|---|---|
A | |
| acosweight | x=cosweight(sst, nya, oro); |
| annave | [x, clim] = annave(y); |
| autocorr | a = autocorr(x, lags); |
C | |
| climave | [x, clim] = climave(y); |
| complex_eof | Complex (Hilbert) EOF: |
| corr | r=corr(vect1, vect2, lag); |
| corr2 | r=corr(vect1, data, lag); |
| corr_sig | [ccoef, sig_pat] = corr_sig(x, y, sig_lev); |
| cosp | function prod=cosp(x,y,nya,minperc) |
| cosweight | x=cosweight(sst, nya); |
| cov5 | For the 2x2 matrix, only returns a single value... |
| cross_corr | a = cross_corr(x1, x2, lags); |
D | |
| detrend | DETREND Remove a linear trend from a vector, usually for FFT processing. |
| dof_corr | dof = dof_corr(y, x); |
E | |
| eof2 | function [pc,eof_reg,sing]=eof2(input_array,n,nya) |
| eof_dan | [lam, lds, pcs, per] = eof_dan(data); |
| eof_dan2 | [lam, lds, per] = eof_dan(c, isrot, nkp); |
| eof_dan_noweight | [lam, lds, pcs, per] = eof_dan(data); |
| eof_nan | function [pc,eof_reg,sing]=eof2(input_array,n,nya) |
| eof_routine | Normalized eof routine: |
F | |
| filter_nr | dout = filter_nr(weights, data); |
G | |
| get_dof | dofx = dof(data); |
L | |
| lin_remove | lin_remove: linearly remove a time series from data |
M | |
| myrunning_ave | function y = running_ave ( x, n, usenan ) ; |
| myrunning_sum | function [y, nsum] = running_sum ( x, n, usenan ) ; |
N | |
| north | er=north(s,dat); |
P | |
| partial_autocorr | alpha = partial_autocorr(x, lags); |
R | |
| running_average | function y = running_ave ( x, n, usenan ) ; |
| rave | function y = rave ( x, n, usenan ) ; |
| regress_dan | fld=regress(tm,dat); |
| regress_eof | [out, ccoef] = regress_eof(dat, pcs, lags); |
| regress_map | [out, ccoef] = regress_map(dat, pcs, lags); |
| regress_out | DETREND Linearly remove time series from data. |
| remove_mean | [x, clim] = annave(y); |
| rsum | function [y, nsum] = rsum ( x, n, usenan ) ; |
S | |
| seasave | av = seasave(decx, x); |
| smooth | dout = smooth(vect, data); |
| standardize | function y = standardize ( x ) |
| std_NaN | stdx = std_NaN(x); |
| SVD_routine | Weight data accordingly |
T | |
| tscore_dan | score = tscore(dof, tail_per); |
| ttest_dan | [tstat, dof] = ttest(x1, x2); |
U | |
| uncosweight | x=cosweight(sst, nya); |
V | |
| varmaxt | USE: [Frot,AT,Cscor,Vrot,h] = varmaxt(Fm,L,norm,A) |
| vectsig | [fval, dof1, dof2] = vectsig(c1, c2); |
W | |
| where_sig | [locmax, locmin] = where_sig(x); |