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%this function does a running mean along dimension dim_cur, |
function [fldOut]=runmean(fldIn,halfWindow,dim,varargin); |
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%averaging over indices [-nb_cur:nb_cur] |
%object: compute running mean window ('rmw') over a dimension |
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%input: fldIn is the field to which the rmw will be applied |
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% halfWindow is the half width of the rmw |
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% dim is the dimension over which the rmw will be applied |
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%optional: doCycle states whether the boundary condition is cyclic (1) or |
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% not (0; default). If doCycle==0, the no. of averaged points |
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% decreases from 1+2*halfWindow to halfWindow at the edges, |
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% and we mask all of those edge points with NaNs. |
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%output: fldOut is the resulting field |
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% |
% |
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function [field_out]=runmean(field_in,nb_cur,dim_cur); |
%notes: - NaNs are discarded in the rmw, implying that an average is |
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% computed if the rmw contains at least 1 valid point. |
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% - setting halfWindow to 0 implies fldOut=fldIn. |
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%determine and check a couple things |
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fld_isa_gcmfaces=isa(fldIn,'gcmfaces'); |
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if fld_isa_gcmfaces; |
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if dim<3; |
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error('for gcmfaces objects runmean excludes dim=1 or 2'); |
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end; |
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end; |
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if nargin==3; doCycle=0; else; doCycle=varargin{1}; end; |
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%switch to array format if needed |
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if fld_isa_gcmfaces; fldIn=convert2array(fldIn); end; |
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%switch dim to first dimension |
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sizeIn=size(fldIn); |
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perm1to2=[1:length(sizeIn)]; |
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perm1to2=[dim perm1to2(find(perm1to2~=dim))]; |
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perm2to1=[[1:dim-1]+1 1 [dim+1:length(sizeIn)]]; |
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sizeIn2=sizeIn(perm1to2); |
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fldIn=permute(fldIn,perm1to2); |
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sizeCur=size(fldIn); |
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if ~doCycle; |
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%add NaNs at both edges |
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sizeCur(1)=sizeCur(1)+2*halfWindow; |
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fldIn2=NaN*ones(sizeCur); |
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fldIn2(halfWindow+1:end-halfWindow,:,:,:)=fldIn; |
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fldIn=fldIn2; clear fldIn2; |
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end; |
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%create mask and remove NaNs: |
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fldMsk=~isnan(fldIn); |
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fldIn(isnan(fldIn))=0; |
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fldCnt=0*fldIn; |
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%apply the running mean |
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fldOut=zeros(sizeCur); |
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for tcur=-halfWindow:halfWindow |
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%To have halfWindow*2 coeffs rather than halfWindow*2+1, centered to the current |
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%point, it is convenient to reduce the weight of the farthest points to 1/2. |
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%This became necessary to get proper annual means, from monthly data, with halfWindow=6. |
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if abs(tcur)==halfWindow; fac=1/2; else; fac=1; end; |
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tmp1=circshift(fldIn,[tcur zeros(1,length(sizeCur)-1)]); |
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fldOut=fldOut+fac*tmp1; |
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tmp1=circshift(fldMsk,[tcur zeros(1,length(sizeCur)-1)]); |
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fldCnt=fldCnt+fac*tmp1; |
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end |
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if nb_cur~=0 |
fldCnt(fldCnt<2*halfWindow)=NaN; |
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fldOut=fldOut./fldCnt; |
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size_cur=size(field_in); |
if ~doCycle; |
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fldOut=fldOut(halfWindow+1:end-halfWindow,:,:,:); |
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%consistent with old version bug (one point offset) |
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% fldOut=fldOut(halfWindow:end-halfWindow,:,:,:); |
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end; |
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perm1to2=[1:length(size_cur)]; |
%switch dimensions order back to original order |
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perm1to2=[dim_cur perm1to2(find(perm1to2~=dim_cur))]; |
fldOut=permute(fldOut,perm2to1); |
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perm2to1=[[1:dim_cur-1]+1 1 [dim_cur+1:length(size_cur)]]; |
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size_cur2=size_cur(perm1to2); |
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field_in2=permute(field_in,perm1to2); |
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field_out2=zeros(size_cur2); |
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count_out2=zeros(size_cur2(1),2); |
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for tcur=-nb_cur:nb_cur |
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tmp1=[tcur:tcur-1+size_cur2(1)]; |
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tmp2=find(tmp1>=1&tmp1<=size_cur2(1)); |
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field_out2(tmp2,:)=field_out2(tmp2,:)+field_in2(tmp1(tmp2),:); |
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count_out2(tmp2,:)=count_out2(tmp2,:)+1; |
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end |
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for tcur=1:size_cur2(1); |
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field_out2(tcur,:)=field_out2(tcur,:)/count_out2(tcur); |
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end |
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%field_out=field_in-permute(field_out2,perm2to1); |
%switch back to gcmfaces format if needed |
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field_out=permute(field_out2,perm2to1); |
if fld_isa_gcmfaces; fldOut=convert2array(fldOut); end; |
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else |
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field_out=field_in; |
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end%if nb_cur~=0 |
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