l_average.m

function wlog=l_average(wlog,varargin)
% Function computes running average over specified log curves
% The function needs equidistantly sampled data; no null values
% Written by: E. R., June 7, 2001
% Last updated: March 11, 2002. Remove pre-existing averaged curves before recreating them
%                               Avoids having more than one curve with the same mnemonic. 
%
%         wlog=l_average(wlog,varargin)
% INPUT
% wlog    log structure
% varargin one or more cell arrays; the first element of each cell array is a keyword,
%         the following arguments contains a parameter(s). Accepted keywords are         
%         'curves'   mnemonics of curves to average
%               Default: {'curves','*'}   meaning all curves
%         'length'   length of averaging filter and units of measurement
%               Default: {'length',wlog.step,wlog.curve_info{1,2}}   no averaging (average over one sample)
%         'mnem' character string to append to mnemonic of original data
%               if "mnem" is empty (e.g. {'mnem',''}), then the original curves are replaced 
%               by the averaged curves
%               Default: {'mnem','mean'}, i.e. the string "mean" is appended to the mnemonics
%                    Example DTp ==> DTp_mean.
%         'weight' type of smoothing; possible values are: 'constant' (weight is 
%               constant) and 'binom' (binomial weight')
%               Default: {'weight','constant')
%
% OUTPUT
% wlog     log structure with averaged curves

%       Set defaults of input arguments
param.curves='*';
param.length={wlog.step,wlog.curve_info{2,1}};
param.mnem='mean';
param.weight='constant';

%       Decode and assign input arguments
param=assign_input(param,varargin);

if ~iscell(param.length)
   param.length={param.length,wlog.curve_info{1,2}};
end
if ~iscell(param.curves)
  if strcmp(param.curves,'*')
    param.curves=wlog.curve_info(2:end,1);
  else
    param.curves={param.curves};
  end
end

if wlog.step == 0
  error(' Log must be uniformly sampled')
end

[nsamp,ncurves]=size(wlog.curves);

nnew=length(param.curves);

%       Compute averaging distance in samples
dunits1=param.length{2};
dunits2=wlog.curve_info{1,2};
distance=param.length{1}/wlog.step;
if ~strcmp(dunits1,dunits2)
  if strcmp(dunits1,'m') & strcmp(dunits2,'ft')
     distance=distance/0.3048;
  elseif strcmp(dunits1,'ft') & strcmp(dunits2,'m')
     distance=distance*0.3048;
  elseif strcmp(dunits1,'s') & strcmp(dunits2,'ms')
    distance=distance*1000;
  elseif strcmp(dunits1,'ms') & strcmp(dunits2,'s')
    distance=distance/1000;
  else
    error([' Keyword "length" and depth curve have incompatible units:', dunits1,', ',dunits2])
  end
end

if isempty(param.mnem)
  for ii=1:nnew
    iloc=curve_index1(wlog,param.curves{ii});
    temp=wlog.curves(:,iloc);
    idx=find(~isnan(temp));
    ia=idx(1);
    ie=idx(end);
    wlog.curves(ia:ie,iloc)=smooth(temp(ia:ie),distance);
  end  

else
%	Check if curves already exist and delete those that do
  temss_curves=param.curves;
  for ii=1:length(param.curves)
    temss_curves{ii}=[temss_curves{ii},'_',param.mnem];
  end
  wlog=l_curve(wlog,'delete_ne',temss_curves);

%	 Reserve space for new curves and curve information
  wlog.curves=[wlog.curves,NaN*zeros(nsamp,nnew)];
  curve_info=[wlog.curve_info;cell(nnew,3)];
  index=ncurves+(1:nnew);

  for ii=1:nnew
    iloc=curve_index1(wlog,param.curves{ii});
    temp=wlog.curves(:,iloc);
    idx=find(~isnan(temp));
    ia=idx(1);
    ie=idx(end);
    if strcmpi(param.weight,'constant')
       wlog.curves(ia:ie,index(ii))=smooth(temp(ia:ie),distance);
    elseif strcmpi(param.weight,'binom')
       wlog.curves(ia:ie,index(ii))=binsmooth(temp(ia:ie),distance);
    else
       error([' Unknown weight of smoothing: ',param.weight])
    end
    curve_info(index(ii),:)=[{[param.curves{ii},'_',param.mnem]}, ...
       wlog.curve_info(iloc,2),{[wlog.curve_info{iloc,3},' (averaged)']}];
  end
  wlog.curve_info=curve_info;
end