Index: /issm/trunk-jpl/src/m/qmu/dakota_out_parse.m =================================================================== --- /issm/trunk-jpl/src/m/qmu/dakota_out_parse.m (revision 21661) +++ /issm/trunk-jpl/src/m/qmu/dakota_out_parse.m (revision 21662) @@ -1,2 +1,3 @@ +function [method,dresp,scm,pcm,srcm,prcm]=dakota_out_parse(filei) % {{{ % % read a Dakota .out or .dat output file and parse it. @@ -46,980 +47,950 @@ % to foreign countries or providing access to foreign persons." % -function [method,dresp,scm,pcm,srcm,prcm]=dakota_out_parse(filei) - -if ~nargin - help dakota_out_parse - return -end - -if ~exist('filei' ,'var') || isempty(filei) - filei=input('Input file? ','s'); -end -fidi=fopen(sprintf('%s',filei),'r'); -if (fidi < 0) - error('''%s'' could not be opened.',filei); -end - -%% check the first line for the Dakota tabular output file - -method=[]; -fline=fgetl(fidi); -if ~ischar(fline) - error('File ''%s'' is empty.',filei); -end - -if strncmpi(fline,'%eval_id',8) - method='unknown'; - [dresp]=dak_tab_out(fidi,fline); - return -else - fseek(fidi,0,'bof'); -end - -%% loop through the file to find the Dakota method name - -[fline]=findline(fidi,'methodName = '); -if ~ischar(fline) - %do nothing -else - % display([' ' deblank(fline)]); + + if ~nargin + help dakota_out_parse + return + end + + if ~exist('filei' ,'var') || isempty(filei) + filei=input('Input file? ','s'); + end + fidi=fopen(sprintf('%s',filei),'r'); + if (fidi < 0) + error('''%s'' could not be opened.',filei); + end + + %% check the first line for the Dakota tabular output file + + method=[]; + fline=fgetl(fidi); + if ~ischar(fline) + error('File ''%s'' is empty.',filei); + end + + if strncmpi(fline,'%eval_id',8) + method='unknown'; + [dresp]=dak_tab_out(fidi,fline); + return + else + fseek(fidi,0,'bof'); + end + + %% loop through the file to find the Dakota method name + + [fline]=findline(fidi,'methodName = '); + if ~ischar(fline) + %do nothing + else + % display([' ' deblank(fline)]); + + [ntokens,tokens]=fltokens(fline); + method=tokens{1}{3}; + display(sprintf('Dakota methodName=''%s''.',method)); + end + + dresp=struct([]); + scm =struct([]); + pcm =struct([]); + srcm=struct([]); + prcm=struct([]); + + %% loop through the file to find the function evaluation summary + + fline=''; + [nfeval]=nfeval_read(fidi,fline); + fline=fgetl(fidi); + + %% process each results section based on content of the file + + while ischar(fline) + % ipos=ftell(fidi); + if isempty(fline) + elseif strncmp(fline,'<<<<< Function evaluation summary',33) + [nfeval]=nfeval_read(fidi,fline); + elseif strncmp(fline,'Statistics based on ',20) + [nsamp]=nsamp_read(fidi,fline); + elseif strncmp(fline,'Moments for each response function',34) + [dresp]=moments_read(fidi,dresp,fline); + elseif strncmp(fline,'Moment-based statistics for each response function',50) + [dresp]=mbstats_read(fidi,dresp,fline); + elseif strncmp(fline,'95% confidence intervals for each response function',51) + [dresp]=cis_read(fidi,dresp,fline); + elseif strncmp(fline,'Probabilities for each response function',40) || ... + strncmp(fline,'Level mappings for each response function',41) + [dresp]=cdfs_read(fidi,dresp,fline); + elseif strncmp(fline,'Probability Density Function (PDF) histograms for each response function',72) + [dresp]=pdfs_read(fidi,dresp,fline); + elseif strncmp(fline,'Simple Correlation Matrix',25) + [scm]=corrmat_read(fidi,'Simple Correlation Matrix',fline); + elseif strncmp(fline,'Partial Correlation Matrix',26) + [pcm]=corrmat_read(fidi,'Partial Correlation Matrix',fline); + elseif strncmp(fline,'Simple Rank Correlation Matrix',30) + [srcm]=corrmat_read(fidi,'Simple Rank Correlation Matrix',fline); + elseif strncmp(fline,'Partial Rank Correlation Matrix',31) + [prcm]=corrmat_read(fidi,'Partial Rank Correlation Matrix',fline); + elseif strncmp(fline,'MV Statistics for ',18) + [dresp]=mvstats_read(fidi,dresp,fline); + elseif strncmp(fline,'<<<<< Best ',11) + [dresp]=best_read(fidi,dresp,fline); + elseif strncmp(fline,'The following lists volumetric uniformity measures',50) + [dresp]=vum_read(fidi,dresp,fline); + elseif strncmp(fline,'<<<<< Iterator ',15) && ... + (length(fline) > 26) && ... + ~isempty(strfind(fline(16:end),' completed.')) + [method]=itcomp_read(fidi,fline); + elseif strncmp(fline,'-----',5) + else + display(['Unexpected line: ' deblank(fline)]); + end + fline=fgetl(fidi); + % fseek(fidi,ipos,'bof'); + end + + %% loop through the file to verify the end + + % [fline]=findline(fidi,'<<<<< Single Method Strategy completed'); + % if ~ischar(fline) + % return + % end + display('End of file successfully reached.'); + fclose(fidi); + +end % }}} +function [dresp]=dak_tab_out(fidi,fline) % {{{ +%% function to parse the dakota tabular output file + + display('Reading Dakota tabular output file.'); + + % process column headings of matrix (skipping eval_id) + + [ntokens,tokens]=fltokens(fline); + + % New file DAKOTA versions>6 + if strncmpi(fline,'%eval_id interface',18) + offset=2; + else %DAKOTA versions<6 + offset=1; + end + desc=cell (1,ntokens-offset); + data=zeros(1,ntokens-offset); + + for i=1:ntokens-offset + desc(1,i)=cellstr(tokens{1}{i+offset}); + end + display(sprintf('Number of columns (Dakota V+R)=%d.',ntokens-2)); + + % process rows of matrix + + nrow=0; + while 1 + fline=fgetl(fidi); + if ~ischar(fline) || isempty(fline) + break; + end + [ntokens,tokens]=fltokens(fline); + + % add row values to matrix (skipping eval_id) + + nrow=nrow+1; + for i=1:ntokens-offset + data(nrow,i)=tokens{1}{i+offset}; + end + end + display(sprintf('Number of rows (Dakota func evals)=%d.',nrow)); + + % calculate statistics + + % since normfit doesn't have a dim argument, and matlab isvector is true + % for a 1xn matrix, handle the case of one row explicitly + if (size(data,1) > 1) + %dmean =mean (data); + %dstddev=std (data,0); + [dmean,dstddev,dmeanci,dstddevci]=... + normfit_issm(data,0.05); + else + dmean =zeros(1,size(data,2)); + dstddev =zeros(1,size(data,2)); + dmeanci =zeros(2,size(data,2)); + dstddevci=zeros(2,size(data,2)); + for i=1:size(data,2) + [dmean(1,i),dstddev(1,i),dmeanci(:,i),dstddevci(:,i)]=... + normfit_issm(data(:,i),0.05); + end + end + + dmin =min (data,[],1); + dquart1=prctile_issm(data,25,1); + dmedian=median (data,1); + dquart3=prctile_issm(data,75,1); + dmax =max (data,[],1); + dmin95=prctile_issm(data,5,1); + dmax95=prctile_issm(data,95,1); + + % same as Dakota scm, Excel correl + dcorrel=corrcoef(data); + + % divide the data into structures for consistency + + for i=1:length(desc) + dresp(i).descriptor=char(desc(i)); + dresp(i).sample =data(:,i); + dresp(i).mean =dmean(i); + dresp(i).stddev =dstddev(i); + dresp(i).meanci =dmeanci(:,i); + dresp(i).stddevci =dstddevci(:,i); + dresp(i).min =dmin(i); + dresp(i).quart1 =dquart1(i); + dresp(i).median =dmedian(i); + dresp(i).quart3 =dquart3(i); + dresp(i).max =dmax(i); + dresp(i).dmin95 =dmin95(i); + dresp(i).dmax95 =dmax95(i); + end + + % draw box plot + + % figure + % subplot(2,1,1) + % plot_boxplot(dresp); + + % draw normal probability plot + + % subplot(2,1,2) + % plot_normplot(dresp); + +end % }}} +function [nfeval]=nfeval_read(fidi,fline) % {{{ +%% function to find and read the number of function evaluations + + if ~exist('fline','var') || isempty(fline) || ~ischar(fline) + [fline]=findline(fidi,'<<<<< Function evaluation summary'); + if ~ischar(fline) + nfeval = 0; + return + end + end + + [ntokens,tokens]=fltokens(fline); + nfeval=tokens{1}{5}; + display(sprintf(' Dakota function evaluations=%d.',nfeval)); + +end % }}} +function [nsamp]=nsamp_read(fidi,fline) % {{{ +%% function to find and read the number of samples + + if ~exist('fline','var') || isempty(fline) || ~ischar(fline) + [fline]=findline(fidi,'Statistics based on '); + if ~ischar(fline) + return + end + end + + [ntokens,tokens]=fltokens(fline); + nsamp=tokens{1}{4}; + display(sprintf(' Dakota samples=%d.',nsamp)); + +end % }}} +function [dresp]=moments_read(fidi,dresp,fline) % {{{ +%% function to find and read the moments + + if ~exist('fline','var') || isempty(fline) || ~ischar(fline) + [fline]=findline(fidi,'Moments for each response function'); + if ~ischar(fline) + return + end + end + + display('Reading moments for response functions:'); + + while 1 + fline=fgetl(fidi); + if isempty(fline) + break; + end + [ntokens,tokens]=fltokens(fline); + + % add new response function and moments + + dresp(end+1).descriptor=tokens{1}{ 1}; + display(sprintf(' %s',dresp(end).descriptor)); + dresp(end ).mean =tokens{1}{ 4}; + dresp(end ).stddev =tokens{1}{ 8}; + dresp(end ).coefvar =tokens{1}{13}; + end + + display(sprintf(' Number of Dakota response functions=%d.',... + length(dresp))); + +end % }}} +function [dresp]=mbstats_read(fidi,dresp,fline) % {{{ +%% function to find and read the moment-based statistics + + if ~exist('fline','var') || isempty(fline) || ~ischar(fline) + [fline]=findline(fidi,'Moment-based statistics for each response function'); + if ~ischar(fline) + return + end + end + + display('Reading moment-based statistics for response functions:'); + + % skip column headings of moment-based statistics + + fline=fgetl(fidi); + + while 1 + fline=fgetl(fidi); + if isempty(fline) + break; + end + [ntokens,tokens]=fltokens(fline); + + % add new response function and moment-based statistics + + dresp(end+1).descriptor=tokens{1}{ 1}; + display(sprintf(' %s',dresp(end).descriptor)); + dresp(end ).mean =tokens{1}{ 2}; + dresp(end ).stddev =tokens{1}{ 3}; + dresp(end ).skewness =tokens{1}{ 4}; + dresp(end ).kurtosis =tokens{1}{ 5}; + end + + display(sprintf(' Number of Dakota response functions=%d.',... + length(dresp))); + +end % }}} +function [dresp]=cis_read(fidi,dresp,fline) % {{{ +%% function to find and read the confidence intervals + + if ~exist('fline','var') || isempty(fline) || ~ischar(fline) + [fline]=findline(fidi,... + '95% confidence intervals for each response function'); + if ~ischar(fline) + return + end + end + + display('Reading 95% confidence intervals for response functions:'); + + while 1 + fline=fgetl(fidi); + if isempty(fline) + break; + end + [ntokens,tokens]=fltokens(fline); + + % check for column headings in Dakota 5.2 + + if (ntokens == 4) + fline=fgetl(fidi); + if isempty(fline) + break; + end + [ntokens,tokens]=fltokens(fline); + end + + % find response function associated with confidence intervals + + idresp=0; + for i=1:length(dresp) + if strcmpi(tokens{1}{ 1},dresp(i).descriptor) + idresp=i; + break; + end + end + if ~idresp + idresp=length(dresp)+1; + dresp(idresp).descriptor=tokens{1}{ 1}; + display(sprintf(' %s',dresp(idresp).descriptor)); + end + + % add confidence intervals to response functions + + if (ntokens == 14) + dresp(i).meanci (1,1)=tokens{1}{ 5}; + dresp(i).meanci (2,1)=tokens{1}{ 6}; + dresp(i).stddevci(1,1)=tokens{1}{12}; + dresp(i).stddevci(2,1)=tokens{1}{13}; + else + dresp(i).meanci (1,1)=tokens{1}{ 2}; + dresp(i).meanci (2,1)=tokens{1}{ 3}; + dresp(i).stddevci(1,1)=tokens{1}{ 4}; + dresp(i).stddevci(2,1)=tokens{1}{ 5}; + end + end + + display(sprintf(' Number of Dakota response functions=%d.',... + length(dresp))); + +end % }}} +function [dresp]=cdfs_read(fidi,dresp,fline) % {{{ +%% function to find and read the cdf's + + if ~exist('fline','var') || isempty(fline) || ~ischar(fline) + [fline]=findline(fidi,'Probabilities for each response function'); + if ~ischar(fline) + [fline]=findline(fidi,'Level mappings for each response function'); + if ~ischar(fline) + return + end + end + end + + display('Reading CDF''s for response functions:'); + + while ischar(fline) && ~isempty(fline) + fline=fgetl(fidi); + if ~ischar(fline) + break; + end + + % process header line of cdf + + while ischar(fline) && ~isempty(fline) + [ntokens,tokens]=fltokens(fline); + + % find response function associated with cdf + + idresp=0; + for i=1:length(dresp) + if strcmpi(tokens{1}{ 6},dresp(i).descriptor) + idresp=i; + break; + end + end + if ~idresp + idresp=length(dresp)+1; + dresp(idresp).descriptor=tokens{1}{ 6}; + display(sprintf(' %s',dresp(idresp).descriptor)); + end + + % skip column headings of cdf + + fline=fgetl(fidi); + fline=fgetl(fidi); + + % read and add cdf table to response function + + fline=fgetl(fidi); + icdf=0; + while ischar(fline) && ~isempty(fline) && ... + ~strncmpi(fline,'Cumulative Distribution Function',32) + [ntokens,tokens]=fltokens(fline); + icdf=icdf+1; + dresp(idresp).cdf(icdf,1:4)=NaN; + % in later versions of Dakota, uncalculated columns are now blank + itoken=0; + for i=1:length(fline)/19 + if ~isempty(deblank(fline((i-1)*19+1:i*19))) + itoken=itoken+1; + dresp(idresp).cdf(icdf,i)=tokens{1}{itoken}; + end + end + fline=fgetl(fidi); + end + end + end + + display(sprintf(' Number of Dakota response functions=%d.',... + length(dresp))); + +end % }}} +function [dresp]=pdfs_read(fidi,dresp,fline) % {{{ +%% function to find and read the pdf's + + if ~exist('fline','var') || isempty(fline) || ~ischar(fline) + [fline]=findline(fidi,'Probability Density Function (PDF) histograms for each response function'); + if ~ischar(fline) + return + end + end + + display('Reading PDF''s for response functions:'); + + while ischar(fline) && ~isempty(fline) + fline=fgetl(fidi); + if ~ischar(fline) + break; + end + + % process header line of pdf + + while ischar(fline) && ~isempty(fline) + [ntokens,tokens]=fltokens(fline); + + % find response function associated with pdf + + idresp=0; + for i=1:length(dresp) + if strcmpi(tokens{1}{ 3},dresp(i).descriptor) + idresp=i; + break; + end + end + if ~idresp + idresp=length(dresp)+1; + dresp(idresp).descriptor=tokens{1}{ 3}; + display(sprintf(' %s',dresp(idresp).descriptor)); + end + + % skip column headings of pdf + + fline=fgetl(fidi); + fline=fgetl(fidi); + + % read and add pdf table to response function + + fline=fgetl(fidi); + ipdf=0; + while ischar(fline) && ~isempty(fline) && ... + ~strncmpi(fline,'PDF for', 7) + [ntokens,tokens]=fltokens(fline); + ipdf=ipdf+1; + dresp(idresp).pdf(ipdf,1:3)=NaN; + for i=1:3 + dresp(idresp).pdf(ipdf,i)=tokens{1}{i}; + end + fline=fgetl(fidi); + end + end + end + + display(sprintf(' Number of Dakota response functions=%d.',... + length(dresp))); + +end % }}} +function [cmat]=corrmat_read(fidi,cmstr,fline) % {{{ +%% function to find and read a correlation matrix + + if ~exist('fline','var') || isempty(fline) || ~ischar(fline) + [fline]=findline(fidi,cmstr); + if ~ischar(fline) + cmat=struct([]); + return + end + end + + display(['Reading ''' fline '''.']); + + cmat.title=fline; + + while ~isempty(fline) + fline=fgetl(fidi); + if ~ischar(fline) + break; + end + + % process column headings of matrix + + [ntokens,tokens]=fltokens(fline); + cmat.column=cell(1,ntokens); + cmat.row =cell(1,1); + cmat.matrix=zeros(1,ntokens); + + for i=1:ntokens + cmat.column(1,i)=cellstr(tokens{1}{i}); + end + + % process rows of matrix, reading until blank line + + nrow=0; + while 1 + fline=fgetl(fidi); + if isempty(fline) + break; + end + [ntokens,tokens]=fltokens(fline); + + % add row heading to matrix + + nrow=nrow+1; + cmat.row (nrow,1)=cellstr(tokens{1}{1}); + + % add row values to matrix + + for i=2:ntokens + cmat.matrix(nrow,i-1)=tokens{1}{i}; + end + end + end + +end % }}} +function [dresp]=mvstats_read(fidi,dresp,fline) % {{{ +%% function to find and read the MV statistics + + if ~exist('fline','var') || isempty(fline) || ~ischar(fline) + [fline]=findline(fidi,'MV Statistics for '); + if ~ischar(fline) + return + end + end + + display('Reading MV statistics for response functions:'); + ndresp=0; + + while ischar(fline) && ~isempty(fline) && ... + strncmpi(fline,'MV Statistics for ',18) + + % add new response function and moments + + [ntokens,tokens]=fltokens(fline); + dresp(end+1).descriptor=tokens{1}{4}; + display(sprintf(' %s',dresp(end).descriptor)); + fline=fgetl(fidi); + [ntokens,tokens]=fltokens(fline); + dresp(end ).mean =tokens{1}{5}; + fline=fgetl(fidi); + [ntokens,tokens]=fltokens(fline); + dresp(end ).stddev =tokens{1}{7}; + + % read and add importance factors to response function + + idvar=0; + fline=fgetl(fidi); + if ~ischar(fline) + break; + end + + while ischar(fline) && ~isempty(fline) && ... + strncmpi(fline,' Importance Factor for variable ',33) + [ntokens,tokens]=fltokens(fline); + idvar=idvar+1; + dresp(end).var (idvar,1)=cellstr(tokens{1}{ 5}); + dresp(end).impfac(idvar,1)= tokens{1}{ 7}; + if (ntokens >= 10) + dresp(end).sens (idvar,1)= tokens{1}{10}; + else + dresp(end).sens (idvar,1)=NaN; + end + + fline=fgetl(fidi); + end + + % if importance factors missing, skip to cdf + + if ~idvar + display(' Importance Factors not available.'); + dresp(end).var ={}; + dresp(end).impfac=[]; + dresp(end).sens =[]; + while ischar(fline) && ... + ~strncmpi(fline,'Cumulative Distribution Function',32) && ... + ~strncmpi(fline,'MV Statistics for ',18) && ... + ~strncmp (fline,'-',1) + fline=fgetl(fidi); + end + end + + % process header line of cdf + + icdf=0; + + while ischar(fline) && ~isempty(fline) && ... + strncmpi(fline,'Cumulative Distribution Function',32) + [ntokens,tokens]=fltokens(fline); + + % find response function associated with cdf + + idresp=0; + for i=1:length(dresp) + if strcmpi(tokens{1}{ 6},dresp(i).descriptor) + idresp=i; + break; + end + end + if ~idresp + idresp=length(dresp)+1; + dresp(idresp).descriptor=tokens{1}{ 6}; + display(sprintf(' %s',dresp(idresp).descriptor)); + end + + % skip column headings of cdf + + fline=fgetl(fidi); + fline=fgetl(fidi); + + % read and add cdf table to response function + + fline=fgetl(fidi); + while ~isempty(fline) && ... + ~strncmpi(fline,'MV Statistics for ',18) && ... + ~strncmp (fline,'-',1) + [ntokens,tokens]=fltokens(fline); + icdf=icdf+1; + dresp(idresp).cdf(icdf,1)=tokens{1}{1}; + dresp(idresp).cdf(icdf,2)=tokens{1}{2}; + if (ntokens == 4) + dresp(idresp).cdf(icdf,3)=tokens{1}{3}; + dresp(idresp).cdf(icdf,4)=tokens{1}{4}; + else + dresp(idresp).cdf(icdf,3)=NaN; + dresp(idresp).cdf(icdf,4)=NaN; + end + fline=fgetl(fidi); + end + end + + % if cdf missing, skip to end of response function + + if ~icdf + display(' Cumulative Distribution Function not available.'); + dresp(ndresp).cdf=[]; + while ischar(fline) && ... + ~strncmpi(fline,'MV Statistics for ',18) && ... + ~strncmp (fline,'-',1) + fline=fgetl(fidi); + end + end + + end + + display(sprintf(' Number of Dakota response functions=%d.',... + length(dresp))); + +end % }}} +function [dresp]=best_read(fidi,dresp,fline) % {{{ +%% function to find and read the best evaluation + + if ~exist('fline','var') || isempty(fline) || ~ischar(fline) + [fline]=findline(fidi,'<<<<< Best '); + if ~ischar(fline) + return + end + end + + if isempty(dresp) + dresp(end+1).best=[]; + end + display('Reading values for best function evaluation:'); + + while ischar(fline) && ~isempty(fline) && ... + strncmpi(fline,'<<<<< Best ',11) + [ntokens,tokens]=fltokens(fline); + + % read and add best parameter(s) + + if strncmpi(cellstr(tokens{1}{3}),'parameter', 9) + display([' ' deblank(fline)]); + + fline=fgetl(fidi); + dresp.best.param =[]; + dresp.best.descriptor={}; + + while ischar(fline) && ~isempty(fline) && ... + ~strncmpi(fline,'<<<<< Best ',11) + [ntokens,tokens]=fltokens(fline); + dresp.best.param (end+1,1)= tokens{1}{1}; + dresp.best.descriptor(end+1,1)=cellstr(tokens{1}{2}); + fline=fgetl(fidi); + end + + % read and add best objective function(s) + + elseif strncmpi(cellstr(tokens{1}{3}),'objective', 9) && ... + strncmpi(cellstr(tokens{1}{4}),'function' , 8) + display([' ' deblank(fline)]); + + fline=fgetl(fidi); + dresp.best.of=[]; + + while ischar(fline) && ~isempty(fline) && ... + ~strncmpi(fline,'<<<<< Best ',11) + [ntokens,tokens]=fltokens(fline); + dresp.best.of(end+1,1)= tokens{1}{1}; + fline=fgetl(fidi); + end + + % read and add best residual norms + + elseif strncmpi(cellstr(tokens{1}{3}),'residual', 8) && ... + strncmpi(cellstr(tokens{1}{4}),'norm' , 4) + display([' ' deblank(fline)]); + dresp.best.norm = tokens{1}{ 6}; + dresp.best.hnormsq= tokens{1}{11}; + + fline=fgetl(fidi); + + while ischar(fline) && ~isempty(fline) && ... + ~strncmpi(fline,'<<<<< Best ',11) + fline=fgetl(fidi); + end + + % read and add best residual term(s) + + elseif strncmpi(cellstr(tokens{1}{3}),'residual', 8) && ... + strncmpi(cellstr(tokens{1}{4}),'term' , 4) + display([' ' deblank(fline)]); + + fline=fgetl(fidi); + dresp.best.res=[]; + + while ischar(fline) && ~isempty(fline) && ... + ~strncmpi(fline,'<<<<< Best ',11) + [ntokens,tokens]=fltokens(fline); + dresp.best.res(end+1,1)= tokens{1}{1}; + fline=fgetl(fidi); + end + + % read and add best constraint value(s) + + elseif strncmpi(cellstr(tokens{1}{3}),'constraint',10) && ... + strncmpi(cellstr(tokens{1}{4}),'value' , 5) + display([' ' deblank(fline)]); + + fline=fgetl(fidi); + dresp.best.nc=[]; + + while ischar(fline) && ~isempty(fline) && ... + ~strncmpi(fline,'<<<<< Best ',11) + [ntokens,tokens]=fltokens(fline); + dresp.best.nc(end+1,1)= tokens{1}{1}; + fline=fgetl(fidi); + end + + % read and add best data captured + + elseif strncmpi(cellstr(tokens{1}{3}),'data' , 4) && ... + strncmpi(cellstr(tokens{1}{4}),'captured', 8) + display([' ' deblank(fline)]); + dresp.best.eval= tokens{1}{8}; + + fline=fgetl(fidi); + + while ischar(fline) && ~isempty(fline) && ... + ~strncmpi(fline,'<<<<< Best ',11) + fline=fgetl(fidi); + end + + % read until next best or blank or end + + else + display([' ' deblank(fline) ' (ignored)']); + + fline=fgetl(fidi); + + while ischar(fline) && ~isempty(fline) && ... + ~strncmpi(fline,'<<<<< Best ',11) + fline=fgetl(fidi); + end + end + end + +end % }}} +function [dresp]=vum_read(fidi,dresp,fline) % {{{ +%% function to find and read the volumetric uniformity measures + + if ~exist('fline','var') || isempty(fline) || ~ischar(fline) + [fline]=findline(fidi,'The following lists volumetric uniformity measures'); + if ~ischar(fline) + return + end + end + + if isempty(dresp) + dresp(end+1).vum=[]; + end + display('Reading measures for volumetric uniformity.'); + + fline=fgetl(fidi); + fline=fgetl(fidi); + + while ischar(fline) && ~isempty(fline) + [ntokens,tokens]=fltokens(fline); + switch lower(tokens{1}{1}) + case 'chi' + dresp.vum.chi=tokens{1}{4}; + case 'd' + dresp.vum.d =tokens{1}{4}; + case 'h' + dresp.vum.h =tokens{1}{4}; + case 'tau' + dresp.vum.tau=tokens{1}{4}; + end + fline=fgetl(fidi); + end + +end % }}} +function [method]=itcomp_read(fidi,fline) % {{{ +%% function to find and read the iterator completion + + if ~exist('fline','var') || isempty(fline) || ~ischar(fline) + while 1 + [fline]=findline(fidi,'<<<<< Iterator '); + if ~ischar(fline) + return + end + if (length(fline) > 26) && ... + ~isempty(strfind(fline(16:end),' completed.')) + break + end + end + end [ntokens,tokens]=fltokens(fline); method=tokens{1}{3}; - display(sprintf('Dakota methodName=''%s''.',method)); -end - -dresp=struct([]); -scm =struct([]); -pcm =struct([]); -srcm=struct([]); -prcm=struct([]); - -%% loop through the file to find the function evaluation summary - -fline=''; -[nfeval]=nfeval_read(fidi,fline); -fline=fgetl(fidi); - -%% process each results section based on content of the file - -while ischar(fline) -% ipos=ftell(fidi); - if isempty(fline) - elseif strncmp(fline,'<<<<< Function evaluation summary',33) - [nfeval]=nfeval_read(fidi,fline); - elseif strncmp(fline,'Statistics based on ',20) - [nsamp]=nsamp_read(fidi,fline); - elseif strncmp(fline,'Moments for each response function',34) - [dresp]=moments_read(fidi,dresp,fline); - elseif strncmp(fline,'Moment-based statistics for each response function',50) - [dresp]=mbstats_read(fidi,dresp,fline); - elseif strncmp(fline,'95% confidence intervals for each response function',51) - [dresp]=cis_read(fidi,dresp,fline); - elseif strncmp(fline,'Probabilities for each response function',40) || ... - strncmp(fline,'Level mappings for each response function',41) - [dresp]=cdfs_read(fidi,dresp,fline); - elseif strncmp(fline,'Probability Density Function (PDF) histograms for each response function',72) - [dresp]=pdfs_read(fidi,dresp,fline); - elseif strncmp(fline,'Simple Correlation Matrix',25) - [scm]=corrmat_read(fidi,'Simple Correlation Matrix',fline); - elseif strncmp(fline,'Partial Correlation Matrix',26) - [pcm]=corrmat_read(fidi,'Partial Correlation Matrix',fline); - elseif strncmp(fline,'Simple Rank Correlation Matrix',30) - [srcm]=corrmat_read(fidi,'Simple Rank Correlation Matrix',fline); - elseif strncmp(fline,'Partial Rank Correlation Matrix',31) - [prcm]=corrmat_read(fidi,'Partial Rank Correlation Matrix',fline); - elseif strncmp(fline,'MV Statistics for ',18) - [dresp]=mvstats_read(fidi,dresp,fline); - elseif strncmp(fline,'<<<<< Best ',11) - [dresp]=best_read(fidi,dresp,fline); - elseif strncmp(fline,'The following lists volumetric uniformity measures',50) - [dresp]=vum_read(fidi,dresp,fline); - elseif strncmp(fline,'<<<<< Iterator ',15) && ... - (length(fline) > 26) && ... - ~isempty(strfind(fline(16:end),' completed.')) - [method]=itcomp_read(fidi,fline); - elseif strncmp(fline,'-----',5) - else - display(['Unexpected line: ' deblank(fline)]); - end - fline=fgetl(fidi); -% fseek(fidi,ipos,'bof'); -end - -%% loop through the file to verify the end - -% [fline]=findline(fidi,'<<<<< Single Method Strategy completed'); -% if ~ischar(fline) -% return -% end -display('End of file successfully reached.'); -fclose(fidi); - -end - -%% function to parse the dakota tabular output file - -function [dresp]=dak_tab_out(fidi,fline) - -display('Reading Dakota tabular output file.'); - -% process column headings of matrix (skipping eval_id) - -[ntokens,tokens]=fltokens(fline); - -% New file DAKOTA versions>6 -if strncmpi(fline,'%eval_id interface',18) - offset=2; -else %DAKOTA versions<6 - offset=1; -end -desc=cell (1,ntokens-offset); -data=zeros(1,ntokens-offset); - -for i=1:ntokens-offset - desc(1,i)=cellstr(tokens{1}{i+offset}); -end -display(sprintf('Number of columns (Dakota V+R)=%d.',ntokens-2)); - -% process rows of matrix - -nrow=0; -while 1 - fline=fgetl(fidi); - if ~ischar(fline) || isempty(fline) - break; - end - [ntokens,tokens]=fltokens(fline); - -% add row values to matrix (skipping eval_id) - - nrow=nrow+1; - for i=1:ntokens-offset - data(nrow,i)=tokens{1}{i+offset}; - end -end -display(sprintf('Number of rows (Dakota func evals)=%d.',nrow)); - -% calculate statistics - -% since normfit doesn't have a dim argument, and matlab isvector is true -% for a 1xn matrix, handle the case of one row explicitly -if (size(data,1) > 1) - %dmean =mean (data); - %dstddev=std (data,0); - [dmean,dstddev,dmeanci,dstddevci]=... - normfit_issm(data,0.05); -else - dmean =zeros(1,size(data,2)); - dstddev =zeros(1,size(data,2)); - dmeanci =zeros(2,size(data,2)); - dstddevci=zeros(2,size(data,2)); - for i=1:size(data,2) - [dmean(1,i),dstddev(1,i),dmeanci(:,i),dstddevci(:,i)]=... - normfit_issm(data(:,i),0.05); - end -end - -dmin =min (data,[],1); -dquart1=prctile_issm(data,25,1); -dmedian=median (data,1); -dquart3=prctile_issm(data,75,1); -dmax =max (data,[],1); -dmin95=prctile_issm(data,5,1); -dmax95=prctile_issm(data,95,1); - -% same as Dakota scm, Excel correl -dcorrel=corrcoef(data); - -% divide the data into structures for consistency - -for i=1:length(desc) - dresp(i).descriptor=char(desc(i)); - dresp(i).sample =data(:,i); - dresp(i).mean =dmean(i); - dresp(i).stddev =dstddev(i); - dresp(i).meanci =dmeanci(:,i); - dresp(i).stddevci =dstddevci(:,i); - dresp(i).min =dmin(i); - dresp(i).quart1 =dquart1(i); - dresp(i).median =dmedian(i); - dresp(i).quart3 =dquart3(i); - dresp(i).max =dmax(i); - dresp(i).dmin95 =dmin95(i); - dresp(i).dmax95 =dmax95(i); -end - -% draw box plot - -% figure -% subplot(2,1,1) -% plot_boxplot(dresp); - -% draw normal probability plot - -% subplot(2,1,2) -% plot_normplot(dresp); - -end - -%% function to find and read the number of function evaluations - -function [nfeval]=nfeval_read(fidi,fline) - -if ~exist('fline','var') || isempty(fline) || ~ischar(fline) - [fline]=findline(fidi,'<<<<< Function evaluation summary'); - if ~ischar(fline) - return - end -end - -[ntokens,tokens]=fltokens(fline); -nfeval=tokens{1}{5}; -display(sprintf(' Dakota function evaluations=%d.',nfeval)); - -end - -%% function to find and read the number of samples - -function [nsamp]=nsamp_read(fidi,fline) - -if ~exist('fline','var') || isempty(fline) || ~ischar(fline) - [fline]=findline(fidi,'Statistics based on '); - if ~ischar(fline) - return - end -end - -[ntokens,tokens]=fltokens(fline); -nsamp=tokens{1}{4}; -display(sprintf(' Dakota samples=%d.',nsamp)); - -end - -%% function to find and read the moments - -function [dresp]=moments_read(fidi,dresp,fline) - -if ~exist('fline','var') || isempty(fline) || ~ischar(fline) - [fline]=findline(fidi,'Moments for each response function'); - if ~ischar(fline) - return - end -end - -display('Reading moments for response functions:'); - -while 1 - fline=fgetl(fidi); - if isempty(fline) - break; - end - [ntokens,tokens]=fltokens(fline); - -% add new response function and moments - - dresp(end+1).descriptor=tokens{1}{ 1}; - display(sprintf(' %s',dresp(end).descriptor)); - dresp(end ).mean =tokens{1}{ 4}; - dresp(end ).stddev =tokens{1}{ 8}; - dresp(end ).coefvar =tokens{1}{13}; -end - -display(sprintf(' Number of Dakota response functions=%d.',... - length(dresp))); - -end - -%% function to find and read the moment-based statistics - -function [dresp]=mbstats_read(fidi,dresp,fline) - -if ~exist('fline','var') || isempty(fline) || ~ischar(fline) - [fline]=findline(fidi,'Moment-based statistics for each response function'); - if ~ischar(fline) - return - end -end - -display('Reading moment-based statistics for response functions:'); - -% skip column headings of moment-based statistics - - fline=fgetl(fidi); - -while 1 - fline=fgetl(fidi); - if isempty(fline) - break; - end - [ntokens,tokens]=fltokens(fline); - -% add new response function and moment-based statistics - - dresp(end+1).descriptor=tokens{1}{ 1}; - display(sprintf(' %s',dresp(end).descriptor)); - dresp(end ).mean =tokens{1}{ 2}; - dresp(end ).stddev =tokens{1}{ 3}; - dresp(end ).skewness =tokens{1}{ 4}; - dresp(end ).kurtosis =tokens{1}{ 5}; -end - -display(sprintf(' Number of Dakota response functions=%d.',... - length(dresp))); - -end - -%% function to find and read the confidence intervals - -function [dresp]=cis_read(fidi,dresp,fline) - -if ~exist('fline','var') || isempty(fline) || ~ischar(fline) - [fline]=findline(fidi,... - '95% confidence intervals for each response function'); - if ~ischar(fline) - return - end -end - -display('Reading 95% confidence intervals for response functions:'); - -while 1 - fline=fgetl(fidi); - if isempty(fline) - break; - end - [ntokens,tokens]=fltokens(fline); - -% check for column headings in Dakota 5.2 - - if (ntokens == 4) - fline=fgetl(fidi); - if isempty(fline) - break; - end - [ntokens,tokens]=fltokens(fline); - end - -% find response function associated with confidence intervals - - idresp=0; - for i=1:length(dresp) - if strcmpi(tokens{1}{ 1},dresp(i).descriptor) - idresp=i; - break; - end - end - if ~idresp - idresp=length(dresp)+1; - dresp(idresp).descriptor=tokens{1}{ 1}; - display(sprintf(' %s',dresp(idresp).descriptor)); - end - -% add confidence intervals to response functions - - if (ntokens == 14) - dresp(i).meanci (1,1)=tokens{1}{ 5}; - dresp(i).meanci (2,1)=tokens{1}{ 6}; - dresp(i).stddevci(1,1)=tokens{1}{12}; - dresp(i).stddevci(2,1)=tokens{1}{13}; - else - dresp(i).meanci (1,1)=tokens{1}{ 2}; - dresp(i).meanci (2,1)=tokens{1}{ 3}; - dresp(i).stddevci(1,1)=tokens{1}{ 4}; - dresp(i).stddevci(2,1)=tokens{1}{ 5}; - end -end - -display(sprintf(' Number of Dakota response functions=%d.',... - length(dresp))); - -end - -%% function to find and read the cdf's - -function [dresp]=cdfs_read(fidi,dresp,fline) - -if ~exist('fline','var') || isempty(fline) || ~ischar(fline) - [fline]=findline(fidi,'Probabilities for each response function'); - if ~ischar(fline) - [fline]=findline(fidi,'Level mappings for each response function'); - if ~ischar(fline) - return - end - end -end - -display('Reading CDF''s for response functions:'); - -while ischar(fline) && ~isempty(fline) - fline=fgetl(fidi); - if ~ischar(fline) - break; - end - -% process header line of cdf - - while ischar(fline) && ~isempty(fline) - [ntokens,tokens]=fltokens(fline); - -% find response function associated with cdf - - idresp=0; - for i=1:length(dresp) - if strcmpi(tokens{1}{ 6},dresp(i).descriptor) - idresp=i; - break; - end - end - if ~idresp - idresp=length(dresp)+1; - dresp(idresp).descriptor=tokens{1}{ 6}; - display(sprintf(' %s',dresp(idresp).descriptor)); - end - -% skip column headings of cdf - - fline=fgetl(fidi); - fline=fgetl(fidi); - -% read and add cdf table to response function - - fline=fgetl(fidi); - icdf=0; - while ischar(fline) && ~isempty(fline) && ... - ~strncmpi(fline,'Cumulative Distribution Function',32) - [ntokens,tokens]=fltokens(fline); - icdf=icdf+1; - dresp(idresp).cdf(icdf,1:4)=NaN; -% in later versions of Dakota, uncalculated columns are now blank - itoken=0; - for i=1:length(fline)/19 - if ~isempty(deblank(fline((i-1)*19+1:i*19))) - itoken=itoken+1; - dresp(idresp).cdf(icdf,i)=tokens{1}{itoken}; - end - end - fline=fgetl(fidi); - end - end -end - -display(sprintf(' Number of Dakota response functions=%d.',... - length(dresp))); - -end - -%% function to find and read the pdf's - -function [dresp]=pdfs_read(fidi,dresp,fline) - -if ~exist('fline','var') || isempty(fline) || ~ischar(fline) - [fline]=findline(fidi,'Probability Density Function (PDF) histograms for each response function'); - if ~ischar(fline) - return - end -end - -display('Reading PDF''s for response functions:'); - -while ischar(fline) && ~isempty(fline) - fline=fgetl(fidi); - if ~ischar(fline) - break; - end - -% process header line of pdf - - while ischar(fline) && ~isempty(fline) - [ntokens,tokens]=fltokens(fline); - -% find response function associated with pdf - - idresp=0; - for i=1:length(dresp) - if strcmpi(tokens{1}{ 3},dresp(i).descriptor) - idresp=i; - break; - end - end - if ~idresp - idresp=length(dresp)+1; - dresp(idresp).descriptor=tokens{1}{ 3}; - display(sprintf(' %s',dresp(idresp).descriptor)); - end - -% skip column headings of pdf - - fline=fgetl(fidi); - fline=fgetl(fidi); - -% read and add pdf table to response function - - fline=fgetl(fidi); - ipdf=0; - while ischar(fline) && ~isempty(fline) && ... - ~strncmpi(fline,'PDF for', 7) - [ntokens,tokens]=fltokens(fline); - ipdf=ipdf+1; - dresp(idresp).pdf(ipdf,1:3)=NaN; - for i=1:3 - dresp(idresp).pdf(ipdf,i)=tokens{1}{i}; - end - fline=fgetl(fidi); - end - end -end - -display(sprintf(' Number of Dakota response functions=%d.',... - length(dresp))); - -end - -%% function to find and read a correlation matrix - -function [cmat]=corrmat_read(fidi,cmstr,fline) - -if ~exist('fline','var') || isempty(fline) || ~ischar(fline) - [fline]=findline(fidi,cmstr); - if ~ischar(fline) - cmat=struct([]); - return - end -end - -display(['Reading ''' fline '''.']); - -cmat.title=fline; - -while ~isempty(fline) - fline=fgetl(fidi); - if ~ischar(fline) - break; - end - -% process column headings of matrix - - [ntokens,tokens]=fltokens(fline); - cmat.column=cell(1,ntokens); - cmat.row =cell(1,1); - cmat.matrix=zeros(1,ntokens); - - for i=1:ntokens - cmat.column(1,i)=cellstr(tokens{1}{i}); - end - -% process rows of matrix, reading until blank line - - nrow=0; - while 1 - fline=fgetl(fidi); - if isempty(fline) - break; - end - [ntokens,tokens]=fltokens(fline); - -% add row heading to matrix - - nrow=nrow+1; - cmat.row (nrow,1)=cellstr(tokens{1}{1}); - -% add row values to matrix - - for i=2:ntokens - cmat.matrix(nrow,i-1)=tokens{1}{i}; - end - end -end - -end - -%% function to find and read the MV statistics - -function [dresp]=mvstats_read(fidi,dresp,fline) - -if ~exist('fline','var') || isempty(fline) || ~ischar(fline) - [fline]=findline(fidi,'MV Statistics for '); - if ~ischar(fline) - return - end -end - -display('Reading MV statistics for response functions:'); -ndresp=0; - -while ischar(fline) && ~isempty(fline) && ... - strncmpi(fline,'MV Statistics for ',18) - -% add new response function and moments - - [ntokens,tokens]=fltokens(fline); - dresp(end+1).descriptor=tokens{1}{4}; - display(sprintf(' %s',dresp(end).descriptor)); - fline=fgetl(fidi); - [ntokens,tokens]=fltokens(fline); - dresp(end ).mean =tokens{1}{5}; - fline=fgetl(fidi); - [ntokens,tokens]=fltokens(fline); - dresp(end ).stddev =tokens{1}{7}; - -% read and add importance factors to response function - - idvar=0; - fline=fgetl(fidi); - if ~ischar(fline) - break; - end - - while ischar(fline) && ~isempty(fline) && ... - strncmpi(fline,' Importance Factor for variable ',33) - [ntokens,tokens]=fltokens(fline); - idvar=idvar+1; - dresp(end).var (idvar,1)=cellstr(tokens{1}{ 5}); - dresp(end).impfac(idvar,1)= tokens{1}{ 7}; - if (ntokens >= 10) - dresp(end).sens (idvar,1)= tokens{1}{10}; - else - dresp(end).sens (idvar,1)=NaN; - end - - fline=fgetl(fidi); - end - -% if importance factors missing, skip to cdf - - if ~idvar - display(' Importance Factors not available.'); - dresp(end).var ={}; - dresp(end).impfac=[]; - dresp(end).sens =[]; - while ischar(fline) && ... - ~strncmpi(fline,'Cumulative Distribution Function',32) && ... - ~strncmpi(fline,'MV Statistics for ',18) && ... - ~strncmp (fline,'-',1) - fline=fgetl(fidi); - end - end - -% process header line of cdf - - icdf=0; - - while ischar(fline) && ~isempty(fline) && ... - strncmpi(fline,'Cumulative Distribution Function',32) - [ntokens,tokens]=fltokens(fline); - -% find response function associated with cdf - - idresp=0; - for i=1:length(dresp) - if strcmpi(tokens{1}{ 6},dresp(i).descriptor) - idresp=i; - break; - end - end - if ~idresp - idresp=length(dresp)+1; - dresp(idresp).descriptor=tokens{1}{ 6}; - display(sprintf(' %s',dresp(idresp).descriptor)); - end - -% skip column headings of cdf - - fline=fgetl(fidi); - fline=fgetl(fidi); - -% read and add cdf table to response function - - fline=fgetl(fidi); - while ~isempty(fline) && ... - ~strncmpi(fline,'MV Statistics for ',18) && ... - ~strncmp (fline,'-',1) - [ntokens,tokens]=fltokens(fline); - icdf=icdf+1; - dresp(idresp).cdf(icdf,1)=tokens{1}{1}; - dresp(idresp).cdf(icdf,2)=tokens{1}{2}; - if (ntokens == 4) - dresp(idresp).cdf(icdf,3)=tokens{1}{3}; - dresp(idresp).cdf(icdf,4)=tokens{1}{4}; - else - dresp(idresp).cdf(icdf,3)=NaN; - dresp(idresp).cdf(icdf,4)=NaN; - end - fline=fgetl(fidi); - end - end - -% if cdf missing, skip to end of response function - - if ~icdf - display(' Cumulative Distribution Function not available.'); - dresp(ndresp).cdf=[]; - while ischar(fline) && ... - ~strncmpi(fline,'MV Statistics for ',18) && ... - ~strncmp (fline,'-',1) - fline=fgetl(fidi); - end - end - -end - -display(sprintf(' Number of Dakota response functions=%d.',... - length(dresp))); - -end - -%% function to find and read the best evaluation - -function [dresp]=best_read(fidi,dresp,fline) - -if ~exist('fline','var') || isempty(fline) || ~ischar(fline) - [fline]=findline(fidi,'<<<<< Best '); - if ~ischar(fline) - return - end -end - -if isempty(dresp) - dresp(end+1).best=[]; -end -display('Reading values for best function evaluation:'); - -while ischar(fline) && ~isempty(fline) && ... - strncmpi(fline,'<<<<< Best ',11) - [ntokens,tokens]=fltokens(fline); - -% read and add best parameter(s) - - if strncmpi(cellstr(tokens{1}{3}),'parameter', 9) - display([' ' deblank(fline)]); - - fline=fgetl(fidi); - dresp.best.param =[]; - dresp.best.descriptor={}; - - while ischar(fline) && ~isempty(fline) && ... - ~strncmpi(fline,'<<<<< Best ',11) - [ntokens,tokens]=fltokens(fline); - dresp.best.param (end+1,1)= tokens{1}{1}; - dresp.best.descriptor(end+1,1)=cellstr(tokens{1}{2}); - fline=fgetl(fidi); - end - -% read and add best objective function(s) - - elseif strncmpi(cellstr(tokens{1}{3}),'objective', 9) && ... - strncmpi(cellstr(tokens{1}{4}),'function' , 8) - display([' ' deblank(fline)]); - - fline=fgetl(fidi); - dresp.best.of=[]; - - while ischar(fline) && ~isempty(fline) && ... - ~strncmpi(fline,'<<<<< Best ',11) - [ntokens,tokens]=fltokens(fline); - dresp.best.of(end+1,1)= tokens{1}{1}; - fline=fgetl(fidi); - end - -% read and add best residual norms - - elseif strncmpi(cellstr(tokens{1}{3}),'residual', 8) && ... - strncmpi(cellstr(tokens{1}{4}),'norm' , 4) - display([' ' deblank(fline)]); - dresp.best.norm = tokens{1}{ 6}; - dresp.best.hnormsq= tokens{1}{11}; - - fline=fgetl(fidi); - - while ischar(fline) && ~isempty(fline) && ... - ~strncmpi(fline,'<<<<< Best ',11) - fline=fgetl(fidi); - end - -% read and add best residual term(s) - - elseif strncmpi(cellstr(tokens{1}{3}),'residual', 8) && ... - strncmpi(cellstr(tokens{1}{4}),'term' , 4) - display([' ' deblank(fline)]); - - fline=fgetl(fidi); - dresp.best.res=[]; - - while ischar(fline) && ~isempty(fline) && ... - ~strncmpi(fline,'<<<<< Best ',11) - [ntokens,tokens]=fltokens(fline); - dresp.best.res(end+1,1)= tokens{1}{1}; - fline=fgetl(fidi); - end - -% read and add best constraint value(s) - - elseif strncmpi(cellstr(tokens{1}{3}),'constraint',10) && ... - strncmpi(cellstr(tokens{1}{4}),'value' , 5) - display([' ' deblank(fline)]); - - fline=fgetl(fidi); - dresp.best.nc=[]; - - while ischar(fline) && ~isempty(fline) && ... - ~strncmpi(fline,'<<<<< Best ',11) - [ntokens,tokens]=fltokens(fline); - dresp.best.nc(end+1,1)= tokens{1}{1}; - fline=fgetl(fidi); - end - -% read and add best data captured - - elseif strncmpi(cellstr(tokens{1}{3}),'data' , 4) && ... - strncmpi(cellstr(tokens{1}{4}),'captured', 8) - display([' ' deblank(fline)]); - dresp.best.eval= tokens{1}{8}; - - fline=fgetl(fidi); - - while ischar(fline) && ~isempty(fline) && ... - ~strncmpi(fline,'<<<<< Best ',11) - fline=fgetl(fidi); - end - -% read until next best or blank or end - - else - display([' ' deblank(fline) ' (ignored)']); - - fline=fgetl(fidi); - - while ischar(fline) && ~isempty(fline) && ... - ~strncmpi(fline,'<<<<< Best ',11) - fline=fgetl(fidi); - end - end -end - -end - -%% function to find and read the volumetric uniformity measures - -function [dresp]=vum_read(fidi,dresp,fline) - -if ~exist('fline','var') || isempty(fline) || ~ischar(fline) - [fline]=findline(fidi,'The following lists volumetric uniformity measures'); - if ~ischar(fline) - return - end -end - -if isempty(dresp) - dresp(end+1).vum=[]; -end -display('Reading measures for volumetric uniformity.'); - -fline=fgetl(fidi); -fline=fgetl(fidi); - -while ischar(fline) && ~isempty(fline) - [ntokens,tokens]=fltokens(fline); - switch lower(tokens{1}{1}) - case 'chi' - dresp.vum.chi=tokens{1}{4}; - case 'd' - dresp.vum.d =tokens{1}{4}; - case 'h' - dresp.vum.h =tokens{1}{4}; - case 'tau' - dresp.vum.tau=tokens{1}{4}; - end - fline=fgetl(fidi); -end - -end - -%% function to find and read the iterator completion - -function [method]=itcomp_read(fidi,fline) - -if ~exist('fline','var') || isempty(fline) || ~ischar(fline) - while 1 - [fline]=findline(fidi,'<<<<< Iterator '); - if ~ischar(fline) - return - end - if (length(fline) > 26) && ... - ~isempty(strfind(fline(16:end),' completed.')) - break - end - end -end - -[ntokens,tokens]=fltokens(fline); -method=tokens{1}{3}; -display(sprintf('Dakota iterator ''%s'' completed.',method)); - -end - + display(sprintf('Dakota iterator ''%s'' completed.',method)); + +end % }}} +function [fline]=findline(fidi,string) % {{{ %% function to find a file line starting with a specified string -function [fline]=findline(fidi,string) - -ipos=ftell(fidi); - -while 1 - fline=fgetl(fidi); - if ~ischar(fline) - break; - else - if (strncmpi(fline,string,length(string))) - return; - end - end -end - -% issue warning and reset file position - -warning('findline:str_not_found',... - 'String ''%s'' not found in file.',string); -fseek(fidi,ipos,'bof'); - -end - + ipos=ftell(fidi); + + while 1 + fline=fgetl(fidi); + if ~ischar(fline) + break; + else + if (strncmpi(fline,string,length(string))) + return; + end + end + end + + % issue warning and reset file position + + warning('findline:str_not_found',... + 'String ''%s'' not found in file.',string); + fseek(fidi,ipos,'bof'); + +end % }}} +function [ntokens,tokens]=fltokens(fline) % {{{ %% function to parse a file line into tokens -function [ntokens,tokens]=fltokens(fline) - -if ~ischar(fline) - ntokens=-1; - tokens={}; - return; -end -if isempty(fline) - ntokens=0; - tokens={}; - return; -end - -strings=textscan(fline,'%s','delimiter',' :'); -%for i=1:length(strings{1}) -% display(sprintf('i=%d; strings{1}{%d}=%s',i,i,strings{1}{i})) -%end -ntokens=0; -tokens{1}{length(strings)}=''; - -for i=1:length(strings{1}) - if isempty(strings{1}{i}) - continue - end - ntokens=ntokens+1; - inum=sscanf(strings{1}{i},'%f'); - if isempty(inum) - tokens{1}{ntokens}=strings{1}{i}; -% display(sprintf('i=%d; tokens{1}{%d}=%s',... -% i,ntokens,tokens{1}{ntokens})) - else - tokens{1}{ntokens}=inum; -% display(sprintf('i=%d; tokens{1}{%d}=%f',... -% i,ntokens,tokens{1}{ntokens})) - end -end - -end + if ~ischar(fline) + ntokens=-1; + tokens={}; + return; + end + if isempty(fline) + ntokens=0; + tokens={}; + return; + end + + strings=textscan(fline,'%s','delimiter',' :'); + %for i=1:length(strings{1}) + % display(sprintf('i=%d; strings{1}{%d}=%s',i,i,strings{1}{i})) + %end + ntokens=0; + tokens{1}{length(strings)}=''; + + for i=1:length(strings{1}) + if isempty(strings{1}{i}) + continue + end + ntokens=ntokens+1; + inum=sscanf(strings{1}{i},'%f'); + if isempty(inum) + tokens{1}{ntokens}=strings{1}{i}; + % display(sprintf('i=%d; tokens{1}{%d}=%s',... + % i,ntokens,tokens{1}{ntokens})) + else + tokens{1}{ntokens}=inum; + % display(sprintf('i=%d; tokens{1}{%d}=%f',... + % i,ntokens,tokens{1}{ntokens})) + end + end + +end % }}}