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github
|
P-Chao/acfdetect-master
|
checkNumArgs.m
|
.m
|
acfdetect-master/toolbox/matlab/checkNumArgs.m
| 3,796 |
utf_8
|
726c125c7dc994c4989c0e53ad4be747
|
function [ x, er ] = checkNumArgs( x, siz, intFlag, signFlag )
% Helper utility for checking numeric vector arguments.
%
% Runs a number of tests on the numeric array x. Tests to see if x has all
% integer values, all positive values, and so on, depending on the values
% for intFlag and signFlag. Also tests to see if the size of x matches siz
% (unless siz==[]). If x is a scalar, x is converted to a array simply by
% creating a matrix of size siz with x in each entry. This is why the
% function returns x. siz=M is equivalent to siz=[M M]. If x does not
% satisfy some criteria, an error message is returned in er. If x satisfied
% all the criteria er=''. Note that error('') has no effect, so can use:
% [ x, er ] = checkNumArgs( x, ... ); error(er);
% which will throw an error only if something was wrong with x.
%
% USAGE
% [ x, er ] = checkNumArgs( x, siz, intFlag, signFlag )
%
% INPUTS
% x - numeric array
% siz - []: does not test size of x
% - [if not []]: intended size for x
% intFlag - -1: no need for integer x
% 0: error if non integer x
% 1: error if non odd integers
% 2: error if non even integers
% signFlag - -2: entires of x must be strictly negative
% -1: entires of x must be negative
% 0: no contstraints on sign of entries in x
% 1: entires of x must be positive
% 2: entires of x must be strictly positive
%
% OUTPUTS
% x - if x was a scalar it may have been replicated into a matrix
% er - contains error msg if anything was wrong with x
%
% EXAMPLE
% a=1; [a, er]=checkNumArgs( a, [1 3], 2, 0 ); a, error(er)
%
% See also NARGCHK
%
% Piotr's Computer Vision Matlab Toolbox Version 2.0
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
xname = inputname(1); er='';
if( isempty(siz) ); siz = size(x); end;
if( length(siz)==1 ); siz=[siz siz]; end;
% first check that x is numeric
if( ~isnumeric(x) ); er = [xname ' not numeric']; return; end;
% if x is a scalar, simply replicate it.
xorig = x; if( length(x)==1); x = x(ones(siz)); end;
% regardless, must have same number of x as n
if( length(siz)~=ndims(x) || ~all(size(x)==siz) )
er = ['has size = [' num2str(size(x)) '], '];
er = [er 'which is not the required size of [' num2str(siz) ']'];
er = createErrMsg( xname, xorig, er ); return;
end
% check that x are the right type of integers (unless intFlag==-1)
switch intFlag
case 0
if( ~all(mod(x,1)==0))
er = 'must have integer entries';
er = createErrMsg( xname, xorig, er); return;
end;
case 1
if( ~all(mod(x,2)==1))
er = 'must have odd integer entries';
er = createErrMsg( xname, xorig, er); return;
end;
case 2
if( ~all(mod(x,2)==0))
er = 'must have even integer entries';
er = createErrMsg( xname, xorig, er ); return;
end;
end;
% check sign of entries in x (unless signFlag==0)
switch signFlag
case -2
if( ~all(x<0))
er = 'must have strictly negative entries';
er = createErrMsg( xname, xorig, er ); return;
end;
case -1
if( ~all(x<=0))
er = 'must have negative entries';
er = createErrMsg( xname, xorig, er ); return;
end;
case 1
if( ~all(x>=0))
er = 'must have positive entries';
er = createErrMsg( xname, xorig, er ); return;
end;
case 2
if( ~all(x>0))
er = 'must have strictly positive entries';
er = createErrMsg( xname, xorig, er ); return;
end
end
function er = createErrMsg( xname, x, er )
if(numel(x)<10)
er = ['Numeric input argument ' xname '=[' num2str(x) '] ' er '.'];
else
er = ['Numeric input argument ' xname ' ' er '.'];
end
|
github
|
P-Chao/acfdetect-master
|
fevalDistr.m
|
.m
|
acfdetect-master/toolbox/matlab/fevalDistr.m
| 11,227 |
utf_8
|
7e4d5077ef3d7a891b2847cb858a2c6c
|
function [out,res] = fevalDistr( funNm, jobs, varargin )
% Wrapper for embarrassingly parallel function evaluation.
%
% Runs "r=feval(funNm,jobs{i}{:})" for each job in a parallel manner. jobs
% should be a cell array of length nJob and each job should be a cell array
% of parameters to pass to funNm. funNm must be a function in the path and
% must return a single value (which may be a dummy value if funNm writes
% results to disk). Different forms of parallelization are supported
% depending on the hardware and Matlab toolboxes available. The type of
% parallelization is determined by the parameter 'type' described below.
%
% type='LOCAL': jobs are executed using a simple "for" loop. This implies
% no parallelization and is the default fallback option.
%
% type='PARFOR': jobs are executed using a "parfor" loop. This option is
% only available if the Matlab *Parallel Computing Toolbox* is installed.
% Make sure to setup Matlab workers first using "matlabpool open".
%
% type='DISTR': jobs are executed on the Caltech cluster. Distributed
% queuing system must be installed separately. Currently this option is
% only supported on the Caltech cluster but could easily be installed on
% any Linux cluster as it requires only SSH and a shared filesystem.
% Parameter pLaunch is used for controller('launchQueue',pLaunch{:}) and
% determines cluster machines used (e.g. pLaunch={48,401:408}).
%
% type='COMPILED': jobs are executed locally in parallel by first compiling
% an executable and then running it in background. This option requires the
% *Matlab Compiler* to be installed (but does NOT require the Parallel
% Computing Toolbox). Compiling can take 1-10 minutes, so use this option
% only for large jobs. (On Linux alter startup.m by calling addpath() only
% if ~isdeployed, otherwise will get error about "CTF" after compiling).
% Note that relative paths will not work after compiling so all paths used
% by funNm must be absolute paths.
%
% type='WINHPC': jobs are executed on a Windows HPC Server 2008 cluster.
% Similar to type='COMPILED', except after compiling, the executable is
% queued to the HPC cluster where all computation occurs. This option
% likewise requires the *Matlab Compiler*. Paths to data, etc., must be
% absolute paths and available from HPC cluster. Parameter pLaunch must
% have two fields 'scheduler' and 'shareDir' that define the HPC Server.
% Extra parameters in pLaunch add finer control, see fedWinhpc for details.
% For example, at MSR one possible cluster is defined by scheduler =
% 'MSR-L25-DEV21' and shareDir = '\\msr-arrays\scratch\msr-pool\L25-dev21'.
% Note call to 'job submit' from Matlab will hang unless pwd is saved
% (simply call 'job submit' from cmd prompt and enter pwd).
%
% USAGE
% [out,res] = fevalDistr( funNm, jobs, [varargin] )
%
% INPUTS
% funNm - name of function that will process jobs
% jobs - [1xnJob] cell array of parameters for each job
% varargin - additional params (struct or name/value pairs)
% .type - ['local'], 'parfor', 'distr', 'compiled', 'winhpc'
% .pLaunch - [] extra params for type='distr' or type='winhpc'
% .group - [1] send jobs in batches (only relevant if type='distr')
%
% OUTPUTS
% out - 1 if jobs completed successfully
% res - [1xnJob] cell array containing results of each job
%
% EXAMPLE
% % Note: in this case parallel versions are slower since conv2 is so fast
% n=16; jobs=cell(1,n); for i=1:n, jobs{i}={rand(500),ones(25)}; end
% tic, [out,J1] = fevalDistr('conv2',jobs,'type','local'); toc,
% tic, [out,J2] = fevalDistr('conv2',jobs,'type','parfor'); toc,
% tic, [out,J3] = fevalDistr('conv2',jobs,'type','compiled'); toc
% [isequal(J1,J2), isequal(J1,J3)], figure(1); montage2(cell2array(J1))
%
% See also matlabpool mcc
%
% Piotr's Computer Vision Matlab Toolbox Version 3.26
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
dfs={'type','local','pLaunch',[],'group',1};
[type,pLaunch,group]=getPrmDflt(varargin,dfs,1); store=(nargout==2);
if(isempty(jobs)), res=cell(1,0); out=1; return; end
switch lower(type)
case 'local', [out,res]=fedLocal(funNm,jobs,store);
case 'parfor', [out,res]=fedParfor(funNm,jobs,store);
case 'distr', [out,res]=fedDistr(funNm,jobs,pLaunch,group,store);
case 'compiled', [out,res]=fedCompiled(funNm,jobs,store);
case 'winhpc', [out,res]=fedWinhpc(funNm,jobs,pLaunch,store);
otherwise, error('unkown type: ''%s''',type);
end
end
function [out,res] = fedLocal( funNm, jobs, store )
% Run jobs locally using for loop.
nJob=length(jobs); res=cell(1,nJob); out=1;
tid=ticStatus('collecting jobs');
for i=1:nJob, r=feval(funNm,jobs{i}{:});
if(store), res{i}=r; end; tocStatus(tid,i/nJob); end
end
function [out,res] = fedParfor( funNm, jobs, store )
% Run jobs locally using parfor loop.
nJob=length(jobs); res=cell(1,nJob); out=1;
parfor i=1:nJob, r=feval(funNm,jobs{i}{:});
if(store), res{i}=r; end; end
end
function [out,res] = fedDistr( funNm, jobs, pLaunch, group, store )
% Run jobs using Linux queuing system.
if(~exist('controller.m','file'))
msg='distributed queuing not installed, switching to type=''local''.';
warning(msg); [out,res]=fedLocal(funNm,jobs,store); return; %#ok<WNTAG>
end
nJob=length(jobs); res=cell(1,nJob); controller('launchQueue',pLaunch{:});
if( group>1 )
nJobGrp=ceil(nJob/group); jobsGrp=cell(1,nJobGrp); k=0;
for i=1:nJobGrp, k1=min(nJob,k+group);
jobsGrp{i}={funNm,jobs(k+1:k1),'type','local'}; k=k1; end
nJob=nJobGrp; jobs=jobsGrp; funNm='fevalDistr';
end
jids=controller('jobsAdd',nJob,funNm,jobs); k=0;
fprintf('Sent %i jobs...\n',nJob); tid=ticStatus('collecting jobs');
while( 1 )
jids1=controller('jobProbe',jids);
if(isempty(jids1)), pause(.1); continue; end
jid=jids1(1); [r,err]=controller('jobRecv',jid);
if(~isempty(err)), disp('ABORTING'); out=0; break; end
k=k+1; if(store), res{jid==jids}=r; end
tocStatus(tid,k/nJob); if(k==nJob), out=1; break; end
end; controller('closeQueue');
end
function [out,res] = fedCompiled( funNm, jobs, store )
% Run jobs locally in background in parallel using compiled code.
nJob=length(jobs); res=cell(1,nJob); tDir=jobSetup('.',funNm,'',{});
cmd=[tDir 'fevalDistrDisk ' funNm ' ' tDir ' ']; i=0; k=0;
Q=feature('numCores'); q=0; tid=ticStatus('collecting jobs');
while( 1 )
% launch jobs until queue is full (q==Q) or all jobs launched (i==nJob)
while(q<Q && i<nJob), q=q+1; i=i+1; jobSave(tDir,jobs{i},i);
if(ispc), system2(['start /B /min ' cmd int2str2(i,10)],0);
else system2([cmd int2str2(i,10) ' &'],0); end
end
% collect completed jobs (k1 of them), release queue slots
done=jobFileIds(tDir,'done'); k1=length(done); k=k+k1; q=q-k1;
for i1=done, res{i1}=jobLoad(tDir,i1,store); end
pause(1); tocStatus(tid,k/nJob); if(k==nJob), out=1; break; end
end
for i=1:10, try rmdir(tDir,'s'); break; catch,pause(1),end; end %#ok<CTCH>
end
function [out,res] = fedWinhpc( funNm, jobs, pLaunch, store )
% Run jobs using Windows HPC Server.
nJob=length(jobs); res=cell(1,nJob);
dfs={'shareDir','REQ','scheduler','REQ','executable','fevalDistrDisk',...
'mccOptions',{},'coresPerTask',1,'minCores',1024,'priority',2000};
p = getPrmDflt(pLaunch,dfs,1);
tDir = jobSetup(p.shareDir,funNm,p.executable,p.mccOptions);
for i=1:nJob, jobSave(tDir,jobs{i},i); end
hpcSubmit(funNm,1:nJob,tDir,p); k=0;
ticId=ticStatus('collecting jobs');
while( 1 )
done=jobFileIds(tDir,'done'); k=k+length(done);
for i1=done, res{i1}=jobLoad(tDir,i1,store); end
pause(5); tocStatus(ticId,k/nJob); if(k==nJob), out=1; break; end
end
for i=1:10, try rmdir(tDir,'s'); break; catch,pause(5),end; end %#ok<CTCH>
end
function tids = hpcSubmit( funNm, ids, tDir, pLaunch )
% Helper: send jobs w given ids to HPC cluster.
n=length(ids); tids=cell(1,n); if(n==0), return; end;
scheduler=[' /scheduler:' pLaunch.scheduler ' '];
m=system2(['cluscfg view' scheduler],0);
minCores=(hpcParse(m,'total number of nodes',1) - ...
hpcParse(m,'Unreachable nodes',1) - 1)*8;
minCores=min([minCores pLaunch.minCores n*pLaunch.coresPerTask]);
m=system2(['job new /numcores:' int2str(minCores) '-*' scheduler ...
'/priority:' int2str(pLaunch.priority)],1);
jid=hpcParse(m,'created job, id',0);
s=min(ids); e=max(ids); p=n>1 && isequal(ids,s:e);
if(p), jid1=[jid '.1']; else jid1=jid; end
for i=1:n, tids{i}=[jid1 '.' int2str(i)]; end
cmd0=''; if(p), cmd0=['/parametric:' int2str(s) '-' int2str(e)]; end
cmd=@(id) ['job add ' jid scheduler '/workdir:' tDir ' /numcores:' ...
int2str(pLaunch.coresPerTask) ' ' cmd0 ' /stdout:stdout' id ...
'.txt ' pLaunch.executable ' ' funNm ' ' tDir ' ' id];
if(p), ids1='*'; n=1; else ids1=int2str2(ids); end
if(n==1), ids1={ids1}; end; for i=1:n, system2(cmd(ids1{i}),1); end
system2(['job submit /id:' jid scheduler],1); disp(repmat(' ',1,80));
end
function v = hpcParse( msg, key, tonum )
% Helper: extract val corresponding to key in hpc msg.
t=regexp(msg,': |\n','split'); t=strtrim(t(1:floor(length(t)/2)*2));
keys=t(1:2:end); vals=t(2:2:end); j=find(strcmpi(key,keys));
if(isempty(j)), error('key ''%s'' not found in:\n %s',key,msg); end
v=vals{j}; if(tonum==0), return; elseif(isempty(v)), v=0; return; end
if(tonum==1), v=str2double(v); return; end
v=regexp(v,' ','split'); v=str2double(regexp(v{1},':','split'));
if(numel(v)==4), v(5)=0; end; v=((v(1)*24+v(2))*60+v(3))*60+v(4)+v(5)/1000;
end
function tDir = jobSetup( rtDir, funNm, executable, mccOptions )
% Helper: prepare by setting up temporary dir and compiling funNm
t=clock; t=mod(t(end),1); t=round((t+rand)/2*1e15);
tDir=[rtDir filesep sprintf('fevalDistr-%015i',t) filesep]; mkdir(tDir);
if(~isempty(executable) && exist(executable,'file'))
fprintf('Reusing compiled executable...\n'); copyfile(executable,tDir);
else
t=clock; fprintf('Compiling (this may take a while)...\n');
[~,f,e]=fileparts(executable); if(isempty(f)), f='fevalDistrDisk'; end
mcc('-m','fevalDistrDisk','-d',tDir,'-o',f,'-a',funNm,mccOptions{:});
t=etime(clock,t); fprintf('Compile complete (%.1f seconds).\n',t);
if(~isempty(executable)), copyfile([tDir filesep f e],executable); end
end
end
function ids = jobFileIds( tDir, type )
% Helper: get list of job files ids on disk of given type
fs=dir([tDir '*-' type '*']); fs={fs.name}; n=length(fs);
ids=zeros(1,n); for i=1:n, ids(i)=str2double(fs{i}(1:10)); end
end
function jobSave( tDir, job, ind ) %#ok<INUSL>
% Helper: save job to temporary file for use with fevalDistrDisk()
save([tDir int2str2(ind,10) '-in'],'job');
end
function r = jobLoad( tDir, ind, store )
% Helper: load job and delete temporary files from fevalDistrDisk()
f=[tDir int2str2(ind,10)];
if(store), r=load([f '-out']); r=r.r; else r=[]; end
fs={[f '-done'],[f '-in.mat'],[f '-out.mat']};
delete(fs{:}); pause(.1);
for i=1:3, k=0; while(exist(fs{i},'file')==2) %#ok<ALIGN>
warning('Waiting to delete %s.',fs{i}); %#ok<WNTAG>
delete(fs{i}); pause(5); k=k+1; if(k>12), break; end;
end; end
end
function msg = system2( cmd, show )
% Helper: wraps system() call
if(show), disp(cmd); end
[status,msg]=system(cmd); msg=msg(1:end-1);
if(status), error(msg); end
if(show), disp(msg); end
end
|
github
|
P-Chao/acfdetect-master
|
medfilt1m.m
|
.m
|
acfdetect-master/toolbox/filters/medfilt1m.m
| 2,998 |
utf_8
|
a3733d27c60efefd57ada9d83ccbaa3d
|
function y = medfilt1m( x, r, z )
% One-dimensional adaptive median filtering with missing values.
%
% Applies a width s=2*r+1 one-dimensional median filter to vector x, which
% may contain missing values (elements equal to z). If x contains no
% missing values, y(j) is set to the median of x(j-r:j+r). If x contains
% missing values, y(j) is set to the median of x(j-R:j+R), where R is the
% smallest radius such that sum(valid(x(j-R:j+R)))>=s, i.e. the number of
% valid values in the window is at least s (a value x is valid x~=z). Note
% that the radius R is adaptive and can vary as a function of j.
%
% This function uses a modified version of medfilt1.m from Matlab's 'Signal
% Processing Toolbox'. Note that if x contains no missing values,
% medfilt1m(x) and medfilt1(x) are identical execpt at boundary regions.
%
% USAGE
% y = medfilt1m( x, r, [z] )
%
% INPUTS
% x - [nx1] length n vector with possible missing entries
% r - filter radius
% z - [NaN] element that represents missing entries
%
% OUTPUTS
% y - [nx1] filtered vector x
%
% EXAMPLE
% x=repmat((1:4)',1,5)'; x=x(:)'; x0=x;
% n=length(x); x(rand(n,1)>.8)=NaN;
% y = medfilt1m(x,2); [x0; x; y; x0-y]
%
% See also MODEFILT1, MEDFILT1
%
% Piotr's Computer Vision Matlab Toolbox Version 2.35
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
% apply medfilt1 (standard median filter) to valid locations in x
if(nargin<3 || isempty(z)), z=NaN; end; x=x(:)'; n=length(x);
if(isnan(z)), valid=~isnan(x); else valid=x~=z; end; v=sum(valid);
if(v==0), y=repmat(z,1,n); return; end
if(v<2*r+1), y=repmat(median(x(valid)),1,n); return; end
y=medfilt1(x(valid),2*r+1);
% get radius R needed at each location j to span s=2r+1 valid values
% get start (a) and end (b) locations and map back to location in y
C=[0 cumsum(valid)]; s=2*r+1; R=find(C==s); R=R(1)-2; pos=zeros(1,n);
for j=1:n, R0=R;
R=R0-1; a=max(1,j-R); b=min(n,j+R);
if(C(b+1)-C(a)<s), R=R0; a=max(1,j-R); b=min(n,j+R);
if(C(b+1)-C(a)<s), R=R0+1; a=max(1,j-R); b=min(n,j+R); end
end
pos(j)=(C(b+1)+C(a+1))/2;
end
y=y(floor(pos));
end
function y = medfilt1( x, s )
% standard median filter (copied from medfilt1.m)
n=length(x); r=floor(s/2); indr=(0:s-1)'; indc=1:n;
ind=indc(ones(1,s),1:n)+indr(:,ones(1,n));
x0=x(ones(r,1))*0; X=[x0'; x'; x0'];
X=reshape(X(ind),s,n); y=median(X,1);
end
% function y = medfilt1( x, s )
% % standard median filter (slow)
% % get unique values in x
% [vals,disc,inds]=unique(x); m=length(vals); n=length(x);
% if(m>256), warning('x takes on large number of diff vals'); end %#ok<WNTAG>
% % create quantized representation [H(i,j)==1 iff x(j)==vals(i)]
% H=zeros(m,n); H(sub2ind2([m,n],[inds; 1:n]'))=1;
% % create histogram [H(i,j) is count of x(j-r:j+r)==vals(i)]
% H=localSum(H,[0 s],'same');
% % compute median for each j and map inds back to original vals
% [disc,inds]=max(cumsum(H,1)>s/2,[],1); y=vals(inds);
% end
|
github
|
P-Chao/acfdetect-master
|
FbMake.m
|
.m
|
acfdetect-master/toolbox/filters/FbMake.m
| 6,692 |
utf_8
|
b625c1461a61485af27e490333350b4b
|
function FB = FbMake( dim, flag, show )
% Various 1D/2D/3D filterbanks (hardcoded).
%
% USAGE
% FB = FbMake( dim, flag, [show] )
%
% INPUTS
% dim - dimension
% flag - controls type of filterbank to create
% - if d==1
% 1: gabor filter bank for spatiotemporal stuff
% - if d==2
% 1: filter bank from Serge Belongie
% 2: 1st/2nd order DooG filters. Similar to Gabor filterbank.
% 3: similar to Laptev&Lindberg ICPR04
% 4: decent seperable steerable? filterbank
% 5: berkeley filterbank for textons papers
% 6: symmetric DOOG filters
% - if d==3
% 1: decent seperable steerable filterbank
% show - [0] figure to use for optional display
%
% OUTPUTS
%
% EXAMPLE
% FB = FbMake( 2, 1, 1 );
%
% See also FBAPPLY2D
%
% Piotr's Computer Vision Matlab Toolbox Version 2.0
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
if( nargin<3 || isempty(show) ); show=0; end
% create FB
switch dim
case 1
FB = FbMake1D( flag );
case 2
FB = FbMake2D( flag );
case 3
FB = FbMake3d( flag );
otherwise
error( 'dim must be 1 2 or 3');
end
% display
FbVisualize( FB, show );
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function FB = FbMake1D( flag )
switch flag
case 1 %%% gabor filter bank for spatiotemporal stuff
omegas = 1 ./ [3 4 5 7.5 11];
sigmas = [3 4 5 7.5 11];
FB = FbMakegabor1D( 15, sigmas, omegas );
otherwise
error('none created.');
end
function FB = FbMakegabor1D( r, sigmas, omegas )
for i=1:length(omegas)
[feven,fodd]=filterGabor1d(r,sigmas(i),omegas(i));
if( i==1 ); FB=repmat(feven,[2*length(omegas) 1]); end
FB(i*2-1,:)=feven; FB(i*2,:)=fodd;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function FB = FbMake2D( flag )
switch flag
case 1 %%% filter bank from Berkeley / Serge Belongie
r=15;
FB = FbMakegabor( r, 6, 3, 3, sqrt(2) );
FB2 = FbMakeDOG( r, .6, 2.8, 4);
FB = cat(3, FB, FB2);
%FB = FB(:,:,1:2:36); %include only even symmetric filters
%FB = FB(:,:,2:2:36); %include only odd symmetric filters
case 2 %%% 1st/2nd order DooG filters. Similar to Gabor filterbank.
FB = FbMakeDooG( 15, 6, 3, 5, .5) ;
case 3 %%% similar to Laptev&Lindberg ICPR04
% Wierd filterbank of Gaussian derivatives at various scales
% Higher order filters probably not useful.
r = 9; dims=[2*r+1 2*r+1];
sigs = [.5 1 1.5 3]; % sigs = [1,1.5,2];
derivs = [];
%derivs = [ derivs; 0 0 ]; % 0th order
%derivs = [ derivs; 1 0; 0 1 ]; % first order
%derivs = [ derivs; 2 0; 0 2; 1 1 ]; % 2nd order
%derivs = [ derivs; 3 0; 0 3; 1 2; 2 1 ]; % 3rd order
%derivs = [ derivs; 4 0; 0 4; 1 3; 3 1; 2 2 ]; % 4th order
derivs = [ derivs; 0 1; 0 2; 0 3; 0 4; 0 5]; % 0n order
derivs = [ derivs; 1 0; 2 0; 3 0; 4 0; 5 0]; % n0 order
cnt=1; nderivs = size(derivs,1);
for s=1:length(sigs)
for i=1:nderivs
dG = filterDoog( dims, [sigs(s) sigs(s)], derivs(i,:), 0 );
if(s==1 && i==1); FB=repmat(dG,[1 1 length(sigs)*nderivs]); end
FB(:,:,cnt) = dG; cnt=cnt+1;
%dG = filterDoog( dims, [sigs(s)*3 sigs(s)], derivs(i,:), 0 );
%FB(:,:,cnt) = dG; cnt=cnt+1;
%dG = filterDoog( dims, [sigs(s) sigs(s)*3], derivs(i,:), 0 );
%FB(:,:,cnt) = dG; cnt=cnt+1;
end
end
case 4 % decent seperable steerable? filterbank
r = 9; dims=[2*r+1 2*r+1];
sigs = [.5 1.5 3];
derivs = [1 0; 0 1; 2 0; 0 2];
cnt=1; nderivs = size(derivs,1);
for s=1:length(sigs)
for i=1:nderivs
dG = filterDoog( dims, [sigs(s) sigs(s)], derivs(i,:), 0 );
if(s==1 && i==1); FB=repmat(dG,[1 1 length(sigs)*nderivs]); end
FB(:,:,cnt) = dG; cnt=cnt+1;
end
end
FB2 = FbMakeDOG( r, .6, 2.8, 4);
FB = cat(3, FB, FB2);
case 5 %%% berkeley filterbank for textons papers
FB = FbMakegabor( 7, 6, 1, 2, 2 );
case 6 %%% symmetric DOOG filters
FB = FbMakeDooGSym( 4, 2, [.5 1] );
otherwise
error('none created.');
end
function FB = FbMakegabor( r, nOrient, nScales, lambda, sigma )
% multi-scale even/odd gabor filters. Adapted from code by Serge Belongie.
cnt=1;
for m=1:nScales
for n=1:nOrient
[F1,F2]=filterGabor2d(r,sigma^m,lambda,180*(n-1)/nOrient);
if(m==1 && n==1); FB=repmat(F1,[1 1 nScales*nOrient*2]); end
FB(:,:,cnt)=F1; cnt=cnt+1; FB(:,:,cnt)=F2; cnt=cnt+1;
end
end
function FB = FbMakeDooGSym( r, nOrient, sigs )
% Adds symmetric DooG filters. These are similar to gabor filters.
cnt=1; dims=[2*r+1 2*r+1];
for s=1:length(sigs)
Fodd = -filterDoog( dims, [sigs(s) sigs(s)], [1 0], 0 );
Feven = filterDoog( dims, [sigs(s) sigs(s)], [2 0], 0 );
if(s==1); FB=repmat(Fodd,[1 1 length(sigs)*nOrient*2]); end
for n=1:nOrient
theta = 180*(n-1)/nOrient;
FB(:,:,cnt) = imrotate( Feven, theta, 'bil', 'crop' ); cnt=cnt+1;
FB(:,:,cnt) = imrotate( Fodd, theta, 'bil', 'crop' ); cnt=cnt+1;
end
end
function FB = FbMakeDooG( r, nOrient, nScales, lambda, sigma )
% 1st/2nd order DooG filters. Similar to Gabor filterbank.
% Defaults: nOrient=6, nScales=3, lambda=5, sigma=.5,
cnt=1; dims=[2*r+1 2*r+1];
for m=1:nScales
sigma = sigma * m^.7;
Fodd = -filterDoog( dims, [sigma lambda*sigma^.6], [1,0], 0 );
Feven = filterDoog( dims, [sigma lambda*sigma^.6], [2,0], 0 );
if(m==1); FB=repmat(Fodd,[1 1 nScales*nOrient*2]); end
for n=1:nOrient
theta = 180*(n-1)/nOrient;
FB(:,:,cnt) = imrotate( Feven, theta, 'bil', 'crop' ); cnt=cnt+1;
FB(:,:,cnt) = imrotate( Fodd, theta, 'bil', 'crop' ); cnt=cnt+1;
end
end
function FB = FbMakeDOG( r, sigmaStr, sigmaEnd, n )
% adds a serires of difference of Gaussian filters.
sigs = sigmaStr:(sigmaEnd-sigmaStr)/(n-1):sigmaEnd;
for s=1:length(sigs)
FB(:,:,s) = filterDog2d(r,sigs(s),2); %#ok<AGROW>
if( s==1 ); FB=repmat(FB,[1 1 length(sigs)]); end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function FB = FbMake3d( flag )
switch flag
case 1 % decent seperable steerable filterbank
r = 25; dims=[2*r+1 2*r+1 2*r+1];
sigs = [.5 1.5 3];
derivs = [0 0 1; 0 1 0; 1 0 0; 0 0 2; 0 2 0; 2 0 0];
cnt=1; nderivs = size(derivs,1);
for s=1:length(sigs)
for i=1:nderivs
dG = filterDoog( dims, repmat(sigs(s),[1 3]), derivs(i,:), 0 );
if(s==1 && i==1); FB=repmat(dG,[1 1 1 nderivs*length(sigs)]); end
FB(:,:,:,cnt) = dG; cnt=cnt+1;
end
end
otherwise
error('none created.');
end
|
github
|
VGligorijevic/Patient-specific-DF-master
|
compute_clusters_ssnmtf.m
|
.m
|
Patient-specific-DF-master/code/compute_clusters_ssnmtf.m
| 1,509 |
utf_8
|
1c0d5ec6ded411b4117d1fdd63cb10b1
|
% Function for assigning entities to clusters
% -------------------------------------------------------------------------------------------------------------
% Vladimir Gligorijevic
% Imperial College London
% [email protected]
% Last updated: 5/07/2015
% --------------------------------------------------------------------------------------------------------------
% [Input]:
% G: <Cell list>, Cell list containing cluster indicator matrices
% label_list: <Cell string>, arrays of labels for each data source
% file_list: <Cell string>, list of filenames for exporting figures and
% clusters
% --------------------------------------------------------------------------------------------------------------
function compute_clusters_ssnmtf(G, label_list, file_list)
fprintf('################################\n');
fprintf('Computing cluster assignment....\n');
for i=1:length(G)
% Cluster indicator matrix
[n,k] = size(G{i});
if k > n
G{i} = G{i}';
end;
[y,index] = max(G{i},[],2); %find largest factor in column
% Computing connectivity matrix
C{i} = connectivity(G{i});
% Exporting cluster indices into file
fWrite = fopen([file_list{i} '_clust.txt'],'w');
for ii=1:length(index)
fprintf(fWrite,'%s %d\n',label_list{i}{ii},index(ii));
end;
fclose(fWrite);
% Writing connectivity matrix into file
dlmwrite([file_list{i} '.mtrx'],C{i},'delimiter',',');
fprintf('Dataset [%d] finished.\n',i);
end;
|
github
|
VGligorijevic/Patient-specific-DF-master
|
run_simNMTF.m
|
.m
|
Patient-specific-DF-master/code/run_simNMTF.m
| 783 |
utf_8
|
285dc66390576a205f7e8f85292d2b41
|
% Test ranks
function run_simNMTF(k, R, A, label_list, max_iter, initialization)
ext = [num2str(k(1)) '_' num2str(k(2)) '_' num2str(k(3))];
[S, G] = factorization_ssnmtf(R,A,k,max_iter,initialization);
file_list = {['./results/patients_final_' ext],...
['./results/genes_final_' ext],...
['./results/drugs_final_' ext]};
% Exporting clusters
compute_clusters_ssnmtf(G, label_list, file_list);
% Writing connectivity matrix into file
dlmwrite(['./results/gclust-gclust_' ext '.mtrx'], full(S{2,2}), 'delimiter', ',');
% Exporting predictions
export_significant_associations(G, S, label_list, [2,2], ['./results/gene-gene_pred_' ext '.txt'], 'mix')
export_significant_associations(G, S, label_list, [2,3], ['./results/gene-drug_pred_' ext '.txt'], 'mix')
|
github
|
VGligorijevic/Patient-specific-DF-master
|
export_significant_associations.m
|
.m
|
Patient-specific-DF-master/code/export_significant_associations.m
| 1,242 |
utf_8
|
67d20b3b1b17660dacf8b81935c247ef
|
% Reconstruct relation matrices -> only significant values
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function export_significant_associations(G, S, label_list, pair_ind, sim_file, exp_type)
tol = 1e-5; % scores below tol are considered zero
fprintf('############################################\n');
fprintf('---Creating reconstructed relation matrix...\n');
gene_labels = label_list{2};
Rapprox = G{pair_ind(1)}*S{pair_ind(1),pair_ind(2)}*G{pair_ind(2)}';
Rapprox = Rapprox.*(Rapprox > tol);
Rapprox = centric_rule(Rapprox,exp_type);
if ( pair_ind(1) == pair_ind(2) )
Rapprox = triu(Rapprox);
end;
fprintf('---Finished\n\n');
%%%%%%% New export way (combination of row- and column-centric rules)
[indx_i,indx_j] = find(Rapprox);
indx_val = find(Rapprox);
fprintf('--Exporting associations....\n');
fid = fopen(sim_file,'w');
fprintf(fid, 'Gene1 | Gene2 | Score\n');
for i=1:length(indx_val)
fprintf(fid,'%s %s %f\n',label_list{pair_ind(1)}{indx_i(i)},label_list{pair_ind(2)}{indx_j(i)},full(Rapprox(indx_val(i))));
if (mod(i,500)==0)
fprintf('Finished %d out of %d relations.\n',i,length(indx_val));
end;
end;
fprintf('--Writing significant associatons finished!');
|
github
|
HADESAngelia/Balance-Constraint-KMeans-master
|
munkres.m
|
.m
|
Balance-Constraint-KMeans-master/munkres.m
| 6,971 |
utf_8
|
d287696892e8ef857858223a49ad48fd
|
function [assignment,cost] = munkres(costMat)
% MUNKRES Munkres (Hungarian) Algorithm for Linear Assignment Problem.
%
% [ASSIGN,COST] = munkres(COSTMAT) returns the optimal column indices,
% ASSIGN assigned to each row and the minimum COST based on the assignment
% problem represented by the COSTMAT, where the (i,j)th element represents the cost to assign the jth
% job to the ith worker.
%
% Partial assignment: This code can identify a partial assignment is a full
% assignment is not feasible. For a partial assignment, there are some
% zero elements in the returning assignment vector, which indicate
% un-assigned tasks. The cost returned only contains the cost of partially
% assigned tasks.
% This is vectorized implementation of the algorithm. It is the fastest
% among all Matlab implementations of the algorithm.
% Examples
% Example 1: a 5 x 5 example
%{
[assignment,cost] = munkres(magic(5));
disp(assignment); % 3 2 1 5 4
disp(cost); %15
%}
% Example 2: 400 x 400 random data
%{
n=400;
A=rand(n);
tic
[a,b]=munkres(A);
toc % about 2 seconds
%}
% Example 3: rectangular assignment with inf costs
%{
A=rand(10,7);
A(A>0.7)=Inf;
[a,b]=munkres(A);
%}
% Example 4: an example of partial assignment
%{
A = [1 3 Inf; Inf Inf 5; Inf Inf 0.5];
[a,b]=munkres(A)
%}
% a = [1 0 3]
% b = 1.5
% Reference:
% "Munkres' Assignment Algorithm, Modified for Rectangular Matrices",
% http://csclab.murraystate.edu/bob.pilgrim/445/munkres.html
% version 2.3 by Yi Cao at Cranfield University on 11th September 2011
assignment = zeros(1,size(costMat,1));
cost = 0;
validMat = costMat == costMat & costMat < Inf;
bigM = 10^(ceil(log10(sum(costMat(validMat))))+1);
costMat(~validMat) = bigM;
% costMat(costMat~=costMat)=Inf;
% validMat = costMat<Inf;
validCol = any(validMat,1);
validRow = any(validMat,2);
nRows = sum(validRow);
nCols = sum(validCol);
n = max(nRows,nCols);
if ~n
return
end
maxv=10*max(costMat(validMat));
dMat = zeros(n) + maxv;
dMat(1:nRows,1:nCols) = costMat(validRow,validCol);
%*************************************************
% Munkres' Assignment Algorithm starts here
%*************************************************
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% STEP 1: Subtract the row minimum from each row.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
minR = min(dMat,[],2);
minC = min(bsxfun(@minus, dMat, minR));
%**************************************************************************
% STEP 2: Find a zero of dMat. If there are no starred zeros in its
% column or row start the zero. Repeat for each zero
%**************************************************************************
zP = dMat == bsxfun(@plus, minC, minR);
starZ = zeros(n,1);
while any(zP(:))
[r,c]=find(zP,1);
starZ(r)=c;
zP(r,:)=false;
zP(:,c)=false;
end
while 1
%**************************************************************************
% STEP 3: Cover each column with a starred zero. If all the columns are
% covered then the matching is maximum
%**************************************************************************
if all(starZ>0)
break
end
coverColumn = false(1,n);
coverColumn(starZ(starZ>0))=true;
coverRow = false(n,1);
primeZ = zeros(n,1);
[rIdx, cIdx] = find(dMat(~coverRow,~coverColumn)==bsxfun(@plus,minR(~coverRow),minC(~coverColumn)));
while 1
%**************************************************************************
% STEP 4: Find a noncovered zero and prime it. If there is no starred
% zero in the row containing this primed zero, Go to Step 5.
% Otherwise, cover this row and uncover the column containing
% the starred zero. Continue in this manner until there are no
% uncovered zeros left. Save the smallest uncovered value and
% Go to Step 6.
%**************************************************************************
cR = find(~coverRow);
cC = find(~coverColumn);
rIdx = cR(rIdx);
cIdx = cC(cIdx);
Step = 6;
while ~isempty(cIdx)
uZr = rIdx(1);
uZc = cIdx(1);
primeZ(uZr) = uZc;
stz = starZ(uZr);
if ~stz
Step = 5;
break;
end
coverRow(uZr) = true;
coverColumn(stz) = false;
z = rIdx==uZr;
rIdx(z) = [];
cIdx(z) = [];
cR = find(~coverRow);
z = dMat(~coverRow,stz) == minR(~coverRow) + minC(stz);
rIdx = [rIdx(:);cR(z)];
cIdx = [cIdx(:);stz(ones(sum(z),1))];
end
if Step == 6
% *************************************************************************
% STEP 6: Add the minimum uncovered value to every element of each covered
% row, and subtract it from every element of each uncovered column.
% Return to Step 4 without altering any stars, primes, or covered lines.
%**************************************************************************
[minval,rIdx,cIdx]=outerplus(dMat(~coverRow,~coverColumn),minR(~coverRow),minC(~coverColumn));
minC(~coverColumn) = minC(~coverColumn) + minval;
minR(coverRow) = minR(coverRow) - minval;
else
break
end
end
%**************************************************************************
% STEP 5:
% Construct a series of alternating primed and starred zeros as
% follows:
% Let Z0 represent the uncovered primed zero found in Step 4.
% Let Z1 denote the starred zero in the column of Z0 (if any).
% Let Z2 denote the primed zero in the row of Z1 (there will always
% be one). Continue until the series terminates at a primed zero
% that has no starred zero in its column. Unstar each starred
% zero of the series, star each primed zero of the series, erase
% all primes and uncover every line in the matrix. Return to Step 3.
%**************************************************************************
rowZ1 = find(starZ==uZc);
starZ(uZr)=uZc;
while rowZ1>0
starZ(rowZ1)=0;
uZc = primeZ(rowZ1);
uZr = rowZ1;
rowZ1 = find(starZ==uZc);
starZ(uZr)=uZc;
end
end
% Cost of assignment
rowIdx = find(validRow);
colIdx = find(validCol);
starZ = starZ(1:nRows);
vIdx = starZ <= nCols;
assignment(rowIdx(vIdx)) = colIdx(starZ(vIdx));
pass = assignment(assignment>0);
pass(~diag(validMat(assignment>0,pass))) = 0;
assignment(assignment>0) = pass;
cost = trace(costMat(assignment>0,assignment(assignment>0)));
function [minval,rIdx,cIdx]=outerplus(M,x,y)
ny=size(M,2);
minval=inf;
for c=1:ny
M(:,c)=M(:,c)-(x+y(c));
minval = min(minval,min(M(:,c)));
end
[rIdx,cIdx]=find(M==minval);
|
github
|
HADESAngelia/Balance-Constraint-KMeans-master
|
hungarian.m
|
.m
|
Balance-Constraint-KMeans-master/evaluation/hungarian.m
| 11,320 |
utf_8
|
f198a6fac77b9686b122d31e51cecc74
|
function [C,T]=hungarian(A)
%HUNGARIAN Solve the Assignment problem using the Hungarian method.
%
%[C,T]=hungarian(A)
%A - a square cost matrix.
%C - the optimal assignment.
%T - the cost of the optimal assignment.
%s.t. T = trace(A(C,:)) is minimized over all possible assignments.
% Adapted from the FORTRAN IV code in Carpaneto and Toth, "Algorithm 548:
% Solution of the assignment problem [H]", ACM Transactions on
% Mathematical Software, 6(1):104-111, 1980.
% v1.0 96-06-14. Niclas Borlin, [email protected].
% Department of Computing Science, Ume? University,
% Sweden.
% All standard disclaimers apply.
% A substantial effort was put into this code. If you use it for a
% publication or otherwise, please include an acknowledgement or at least
% notify me by email. /Niclas
[m,n]=size(A);
if (m~=n)
error('HUNGARIAN: Cost matrix must be square!');
end
% Save original cost matrix.
orig=A;
% Reduce matrix.
A=hminired(A);
% Do an initial assignment.
[A,C,U]=hminiass(A);
% Repeat while we have unassigned rows.
while (U(n+1))
% Start with no path, no unchecked zeros, and no unexplored rows.
LR=zeros(1,n);
LC=zeros(1,n);
CH=zeros(1,n);
RH=[zeros(1,n) -1];
% No labelled columns.
SLC=[];
% Start path in first unassigned row.
r=U(n+1);
% Mark row with end-of-path label.
LR(r)=-1;
% Insert row first in labelled row set.
SLR=r;
% Repeat until we manage to find an assignable zero.
while (1)
% If there are free zeros in row r
if (A(r,n+1)~=0)
% ...get column of first free zero.
l=-A(r,n+1);
% If there are more free zeros in row r and row r in not
% yet marked as unexplored..
if (A(r,l)~=0 & RH(r)==0)
% Insert row r first in unexplored list.
RH(r)=RH(n+1);
RH(n+1)=r;
% Mark in which column the next unexplored zero in this row
% is.
CH(r)=-A(r,l);
end
else
% If all rows are explored..
if (RH(n+1)<=0)
% Reduce matrix.
[A,CH,RH]=hmreduce(A,CH,RH,LC,LR,SLC,SLR);
end
% Re-start with first unexplored row.
r=RH(n+1);
% Get column of next free zero in row r.
l=CH(r);
% Advance "column of next free zero".
CH(r)=-A(r,l);
% If this zero is last in the list..
if (A(r,l)==0)
% ...remove row r from unexplored list.
RH(n+1)=RH(r);
RH(r)=0;
end
end
% While the column l is labelled, i.e. in path.
while (LC(l)~=0)
% If row r is explored..
if (RH(r)==0)
% If all rows are explored..
if (RH(n+1)<=0)
% Reduce cost matrix.
[A,CH,RH]=hmreduce(A,CH,RH,LC,LR,SLC,SLR);
end
% Re-start with first unexplored row.
r=RH(n+1);
end
% Get column of next free zero in row r.
l=CH(r);
% Advance "column of next free zero".
CH(r)=-A(r,l);
% If this zero is last in list..
if(A(r,l)==0)
% ...remove row r from unexplored list.
RH(n+1)=RH(r);
RH(r)=0;
end
end
% If the column found is unassigned..
if (C(l)==0)
% Flip all zeros along the path in LR,LC.
[A,C,U]=hmflip(A,C,LC,LR,U,l,r);
% ...and exit to continue with next unassigned row.
break;
else
% ...else add zero to path.
% Label column l with row r.
LC(l)=r;
% Add l to the set of labelled columns.
SLC=[SLC l];
% Continue with the row assigned to column l.
r=C(l);
% Label row r with column l.
LR(r)=l;
% Add r to the set of labelled rows.
SLR=[SLR r];
end
end
end
% Calculate the total cost.
T=sum(orig(logical(sparse(C,1:size(orig,2),1))));
function A=hminired(A)
%HMINIRED Initial reduction of cost matrix for the Hungarian method.
%
%B=assredin(A)
%A - the unreduced cost matris.
%B - the reduced cost matrix with linked zeros in each row.
% v1.0 96-06-13. Niclas Borlin, [email protected].
[m,n]=size(A);
% Subtract column-minimum values from each column.
colMin=min(A);
A=A-colMin(ones(n,1),:);
% Subtract row-minimum values from each row.
rowMin=min(A')';
A=A-rowMin(:,ones(1,n));
% Get positions of all zeros.
[i,j]=find(A==0);
% Extend A to give room for row zero list header column.
A(1,n+1)=0;
for k=1:n
% Get all column in this row.
cols=j(k==i)';
% Insert pointers in matrix.
A(k,[n+1 cols])=[-cols 0];
end
function [A,C,U]=hminiass(A)
%HMINIASS Initial assignment of the Hungarian method.
%
%[B,C,U]=hminiass(A)
%A - the reduced cost matrix.
%B - the reduced cost matrix, with assigned zeros removed from lists.
%C - a vector. C(J)=I means row I is assigned to column J,
% i.e. there is an assigned zero in position I,J.
%U - a vector with a linked list of unassigned rows.
% v1.0 96-06-14. Niclas Borlin, [email protected].
[n,np1]=size(A);
% Initalize return vectors.
C=zeros(1,n);
U=zeros(1,n+1);
% Initialize last/next zero "pointers".
LZ=zeros(1,n);
NZ=zeros(1,n);
for i=1:n
% Set j to first unassigned zero in row i.
lj=n+1;
j=-A(i,lj);
% Repeat until we have no more zeros (j==0) or we find a zero
% in an unassigned column (c(j)==0).
while (C(j)~=0)
% Advance lj and j in zero list.
lj=j;
j=-A(i,lj);
% Stop if we hit end of list.
if (j==0)
break;
end
end
if (j~=0)
% We found a zero in an unassigned column.
% Assign row i to column j.
C(j)=i;
% Remove A(i,j) from unassigned zero list.
A(i,lj)=A(i,j);
% Update next/last unassigned zero pointers.
NZ(i)=-A(i,j);
LZ(i)=lj;
% Indicate A(i,j) is an assigned zero.
A(i,j)=0;
else
% We found no zero in an unassigned column.
% Check all zeros in this row.
lj=n+1;
j=-A(i,lj);
% Check all zeros in this row for a suitable zero in another row.
while (j~=0)
% Check the in the row assigned to this column.
r=C(j);
% Pick up last/next pointers.
lm=LZ(r);
m=NZ(r);
% Check all unchecked zeros in free list of this row.
while (m~=0)
% Stop if we find an unassigned column.
if (C(m)==0)
break;
end
% Advance one step in list.
lm=m;
m=-A(r,lm);
end
if (m==0)
% We failed on row r. Continue with next zero on row i.
lj=j;
j=-A(i,lj);
else
% We found a zero in an unassigned column.
% Replace zero at (r,m) in unassigned list with zero at (r,j)
A(r,lm)=-j;
A(r,j)=A(r,m);
% Update last/next pointers in row r.
NZ(r)=-A(r,m);
LZ(r)=j;
% Mark A(r,m) as an assigned zero in the matrix . . .
A(r,m)=0;
% ...and in the assignment vector.
C(m)=r;
% Remove A(i,j) from unassigned list.
A(i,lj)=A(i,j);
% Update last/next pointers in row r.
NZ(i)=-A(i,j);
LZ(i)=lj;
% Mark A(r,m) as an assigned zero in the matrix . . .
A(i,j)=0;
% ...and in the assignment vector.
C(j)=i;
% Stop search.
break;
end
end
end
end
% Create vector with list of unassigned rows.
% Mark all rows have assignment.
r=zeros(1,n);
rows=C(C~=0);
r(rows)=rows;
empty=find(r==0);
% Create vector with linked list of unassigned rows.
U=zeros(1,n+1);
U([n+1 empty])=[empty 0];
function [A,C,U]=hmflip(A,C,LC,LR,U,l,r)
%HMFLIP Flip assignment state of all zeros along a path.
%
%[A,C,U]=hmflip(A,C,LC,LR,U,l,r)
%Input:
%A - the cost matrix.
%C - the assignment vector.
%LC - the column label vector.
%LR - the row label vector.
%U - the
%r,l - position of last zero in path.
%Output:
%A - updated cost matrix.
%C - updated assignment vector.
%U - updated unassigned row list vector.
% v1.0 96-06-14. Niclas Borlin, [email protected].
n=size(A,1);
while (1)
% Move assignment in column l to row r.
C(l)=r;
% Find zero to be removed from zero list..
% Find zero before this.
m=find(A(r,:)==-l);
% Link past this zero.
A(r,m)=A(r,l);
A(r,l)=0;
% If this was the first zero of the path..
if (LR(r)<0)
...remove row from unassigned row list and return.
U(n+1)=U(r);
U(r)=0;
return;
else
% Move back in this row along the path and get column of next zero.
l=LR(r);
% Insert zero at (r,l) first in zero list.
A(r,l)=A(r,n+1);
A(r,n+1)=-l;
% Continue back along the column to get row of next zero in path.
r=LC(l);
end
end
function [A,CH,RH]=hmreduce(A,CH,RH,LC,LR,SLC,SLR)
%HMREDUCE Reduce parts of cost matrix in the Hungerian method.
%
%[A,CH,RH]=hmreduce(A,CH,RH,LC,LR,SLC,SLR)
%Input:
%A - Cost matrix.
%CH - vector of column of 'next zeros' in each row.
%RH - vector with list of unexplored rows.
%LC - column labels.
%RC - row labels.
%SLC - set of column labels.
%SLR - set of row labels.
%
%Output:
%A - Reduced cost matrix.
%CH - Updated vector of 'next zeros' in each row.
%RH - Updated vector of unexplored rows.
% v1.0 96-06-14. Niclas Borlin, [email protected].
n=size(A,1);
% Find which rows are covered, i.e. unlabelled.
coveredRows=LR==0;
% Find which columns are covered, i.e. labelled.
coveredCols=LC~=0;
r=find(~coveredRows);
c=find(~coveredCols);
% Get minimum of uncovered elements.
m=min(min(A(r,c)));
% Subtract minimum from all uncovered elements.
A(r,c)=A(r,c)-m;
% Check all uncovered columns..
for j=c
% ...and uncovered rows in path order..
for i=SLR
% If this is a (new) zero..
if (A(i,j)==0)
% If the row is not in unexplored list..
if (RH(i)==0)
% ...insert it first in unexplored list.
RH(i)=RH(n+1);
RH(n+1)=i;
% Mark this zero as "next free" in this row.
CH(i)=j;
end
% Find last unassigned zero on row I.
row=A(i,:);
colsInList=-row(row<0);
if (length(colsInList)==0)
% No zeros in the list.
l=n+1;
else
l=colsInList(row(colsInList)==0);
end
% Append this zero to end of list.
A(i,l)=-j;
end
end
end
% Add minimum to all doubly covered elements.
r=find(coveredRows);
c=find(coveredCols);
% Take care of the zeros we will remove.
[i,j]=find(A(r,c)<=0);
i=r(i);
j=c(j);
for k=1:length(i)
% Find zero before this in this row.
lj=find(A(i(k),:)==-j(k));
% Link past it.
A(i(k),lj)=A(i(k),j(k));
% Mark it as assigned.
A(i(k),j(k))=0;
end
A(r,c)=A(r,c)+m;
|
github
|
BottjerLab/Acoustic_Similarity-master
|
loaderGUI.m
|
.m
|
Acoustic_Similarity-master/code/GUI/loaderGUI.m
| 6,021 |
utf_8
|
49a1b35d38cb2b26bbd2e1a53028094b
|
function varargout = loaderGUI(varargin)
% LOADERGUI MATLAB code for loaderGUI.fig
% LOADERGUI, by itself, creates a new LOADERGUI or raises the existing
% singleton*.
%
% H = LOADERGUI returns the handle to a new LOADERGUI or the handle to
% the existing singleton*.
%
% LOADERGUI('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in LOADERGUI.M with the given input arguments.
%
% LOADERGUI('Property','Value',...) creates a new LOADERGUI or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before loaderGUI_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to loaderGUI_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help loaderGUI
% Last Modified by GUIDE v2.5 12-Apr-2013 16:10:48
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @loaderGUI_OpeningFcn, ...
'gui_OutputFcn', @loaderGUI_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before loaderGUI is made visible.
function loaderGUI_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to loaderGUI (see VARARGIN)
% Choose default command line output for loaderGUI
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
%keyboard
% Update settings structure to default
setappdata(handles.settingsPanel, 'params', defaultParams);
% UIWAIT makes loaderGUI wait for user response (see UIRESUME)
% uiwait(handles.mainFigure);
% --- Outputs from this function are returned to the command line.
function varargout = loaderGUI_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
%keyboard
varargout{1} = handles.output;
% --- Executes on button press in LoadSpike.
function LoadSpike_Callback(hObject, eventdata, handles)
% hObject handle to LoadSpike (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
[matFile, matpath] = uigetfile('*.mat','Please choose the song Spike2 file','data');
birdID = regexp(matpath, '\w{1,2}\d{1,3}','match');
if ~isempty(birdID)
birdID = birdID{:};
else
birdID = '?';
end
sessionID = regexp(matFile, ...
sprintf('(?<=%s_)\\d{1,2}_\\d{1,2}_\\d{1,3}',birdID),'match');
if ~isempty(sessionID)
sessionID = sessionID{:};
else
sessionID = '?';
end
set(handles.birdField,'String', sprintf('Bird: %s', birdID));
set(handles.sessionField,'String', sprintf('Session: %s',sessionID));
% load file
songStruct = load([matpath matFile]);
% trick to get the main struct into a standard name, if there's only one
% variable in the file
fld=fieldnames(songStruct);
songStruct=songStruct.(fld{1});
fs = 1/songStruct.interval;
songStruct.fs = fs;
songStruct.title = matFile;
% set variables in the guiData
setappdata(handles.mainFigure, 'songStruct', songStruct);
% --- Executes on button press in FindSounds.
function FindSounds_Callback(hObject, eventdata, handles)
% hObject handle to FindSounds (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
hfig = figure;
sounds = stepSpectrogram(getappdata(handles.mainFigure, 'songStruct'), ...
getappdata(handles.settingsPanel, 'params'),...
'Nsplits',400,'plot',true);
close(hfig);
divs = guidata(handles.divisionsPanel);
setappdata(handles.divisionsPanel, 'sounds', divs);
% update workspace
updateWorkspace(hObject, eventdata, handles);
% --- Executes on button press in keyboardAccess.
function keyboardAccess_Callback(hObject, eventdata, handles)
% hObject handle to keyboardAccess (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
keyboard
% update workspace
updateWorkspace(hObject, eventdata, handles);
% --- Executes on button press in loadVariables.
function loadVariables_Callback(hObject, eventdata, handles)
% hObject handle to loadVariables (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
[tmpFile tmpPath] = uigetfile('*.mat','Select matfile to load');
tmpStruct = load([tmpPath filesep tmpFile]);
varNames = fieldnames(tmpStruct);
for ii = 1:numel(varNames);
setappdata(handles.divisionsPanel, varNames{ii}, tmpStruct.(varNames{ii}));
end
% update workspace
updateWorkspace(hObject, eventdata, handles);
function updateWorkspace(hObject, eventdata, handles)
values = getappdata(handles.divisionsPanel);
fNames = fieldnames(values);
fNames(strcmp('lastValidTag',fNames)) = [];
set(handles.divisionsPanel,'String', fNames);
|
github
|
BottjerLab/Acoustic_Similarity-master
|
standardDistance.m
|
.m
|
Acoustic_Similarity-master/code/clustering/standardDistance.m
| 3,471 |
utf_8
|
fabe0ae08aad5c902142064cb4c9e7f6
|
function [totalDists, indivDists] = standardDistance(spectrum1, spectrum2, params, varargin)
%STANDARDDISTANCE Running, sample by sample scan of two spectral feature sets
%
% COVAR = standardDistance(SPECTRUM1, SPECTRUM2) returns the similarity of two
% sounds, sample by sample, according to the standardized difference found between
% their features. The correlations are taken along ever calculated
% feature of the sample (power, entropy, average pitch, etc.) and is a
% VECTOR of values, one for each possible offset of the spectrum relative
% to the other one.
%
% See also TIMEWARPEDDISTANCE
%% argument handling
if nargin < 3
params = defaultParams;
end
params = processArgs(params,varargin{:});
%% order by length
% convert lengths
len1 = length(spectrum1.times);
len2 = length(spectrum2.times);
if(len1 > len2)
temp = spectrum1;
spectrum1 = spectrum2;
spectrum2 = temp;
len1 = length(spectrum1.times);
len2 = length(spectrum2.times);
end
%% name all features that should be compared
featCatalog = params.featureCatalog;
featureSel = false(1,numel(featCatalog));
for ii = 1:numel(featCatalog)
featureSel(ii) = any(strcmp(fieldnames(spectrum1), featCatalog(ii).name)) && ...
any(strcmp(fieldnames(spectrum2), featCatalog(ii).name));
end
featCatalog = featCatalog(featureSel);
nF = numel(featCatalog);
%% convert structure to array to speed indexing
% specArray1 = zeros(len1,nF);
% specArray2 = zeros(len2,nF);
% for ii = 1:nF
% if featCatalog(ii).doLog
% natVel1 = log(spectrum1.(featCatalog(ii).name));
% natVel2 = log(spectrum2.(featCatalog(ii).name));
% else
% natVel1 = spectrum1.(featCatalog(ii).name);
% natVel2 = spectrum2.(featCatalog(ii).name);
% end
% specArray1(:,ii) = (natVel1 - featCatalog(ii).median)/featCatalog(ii).MAD;
% specArray2(:,ii) = (natVel2 - featCatalog(ii).median)/featCatalog(ii).MAD;
% end
% clear natVel1 natVel2
%% score differences
% determine the time intervals that we want to run similarity
nSkipMs = 0.4; % in ms
nSkipSamples = ceil(nSkipMs/1000 / (spectrum1.times(2) - spectrum1.times(1)));
nComparisons = numel(1:nSkipSamples:len2 - len1 + 1);
indivDists = zeros(nComparisons, nF);
for jj = 1:nF % loop over the features
% get standardized vectors
fv1 = getStdVector(spectrum1, featCatalog(jj));
fullfv2 = getStdVector(spectrum2,featCatalog(jj));
% loop over every nSkipSamples of the longer sound
% (restrict to 1 for completeness)
ctr = 1;
for ii = 1:nSkipSamples:len2 - len1 + 1
indivDists(ctr,jj) = norm(fv1-fullfv2(ii:(ii + len1 - 1)))/len1;
ctr = ctr+1;
end
end
%% duration distance - append this to the distances
durDev = 40; % this is an empirical number based on Lb189_4_25_5
durationDist = abs(spectrum1.times(end) - spectrum2.times(end)) / (durDev / 1000);
indivDists(:,nF+1) = durationDist;
%% combine scores along features - where weighting can be directly controlled
weightVec = [params.featureCatalog.weights];
weightVec(end+1) = 0.5; %length is discouraged a little
weightVec = weightVec/sum(weightVec);
totalDists = weightVec * indivDists';
end
function fv = getStdVector(spectrum, featEntry, sampStart, sampEnd)
if nargin == 2
sampStart = 1;
sampEnd = numel(spectrum.(featEntry.name));
end
fv = spectrum.(featEntry.name)(sampStart:sampEnd);
if featEntry.doLog
fv = log(fv);
end
fv = (fv - featEntry.median) ./ featEntry.MAD;
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
DRcluster.m
|
.m
|
Acoustic_Similarity-master/code/clustering/DRcluster.m
| 6,704 |
utf_8
|
4ad08210957bd0416d5be7155c27ad78
|
function [clustIdxs, empMatrices, distMatrices, empDistrs] = DRcluster(DRsylls, featureTable, spectra, params, varargin)
timeFlag = ['T-' datestr(clock, 'mm_dd_HH_MM')];
featuresCached = (nargin >= 2);
specsCached = (nargin >= 3);
if nargin < 4
params = defaultParams;
end
params = processArgs(params, varargin{:});
% remove any syllables that are too short
isTooShort = (params.fine.windowSize / 1000 > [DRsylls.stop] - [DRsylls.start]);
DRsylls(isTooShort) = [];
fprintf('Removing %d syllables that are too short...\n', sum(isTooShort));
% sort
[DRsylls, sortedIdx] = sortBy(DRsylls, 'file');
N = numel(DRsylls);% = min(ceil(numel(allDRsylls)/2),1000);
NC2 = nchoosek(N,2);
if any(sortedIdx ~= 1:N) fprintf('NB: Check sorting...\n'); end
nEmpD = min(NC2,2e4);
distMatrices = struct(...
'warpedLocal' , zeros(1,NC2),...
'global' , zeros(1,NC2));
empMatrices = struct(...
'warpedLocal' , zeros(1,NC2),...
'global' , zeros(1,NC2));
empDistrs = struct(...,
'warpedLocal', zeros(2,nEmpD),...
'global' , zeros(2,nEmpD));
fieldsToKeep = {'AM','FM','pitchGoodness','wienerEntropy','fundamentalFreq','times'};
% store the feature-based spectra for all of them
params.fine.features = {'wienerEntropy','deriv','harmonicPitch','fundamentalFreq'};
% get sampling rate
[filePath, fileStem] = fileparts(DRsylls(1).file);
metaFile = [filePath filesep 'meta-' fileStem];
metaStruct = []; load(metaFile);
params.fine.fs = 1/metaStruct.interval;
% calculate spectra
if ~specsCached
spectra = initEmptyStructArray(fieldsToKeep, N);
if ~featuresCached
featureTable = cell(1,N);
end
progressbar(sprintf('Calculating spectra & features for regions (# = %d)',N));
for ii = 1:N
%get noisemask
if ii==1 || ~strcmp(DRsylls(ii-1).file, DRsylls(ii).file)
[filePath fileStem] = fileparts(DRsylls(ii).file);
nMFile = [filePath filesep 'noiseMask-' fileStem '.mat'];
if exist(nMFile, 'file')
fprintf('Loading noise mask from %s...\n',nMFile);
noiseMask = []; load(nMFile);
end
end
cl = getClipAndProcess([],DRsylls(ii), params, 'noroll','doFilterNoise',true,'noiseFilter', noiseMask);
tmpSpec = getMTSpectrumStats(cl, params.fine);
for jj = 1:numel(fieldsToKeep)
spectra(ii).(fieldsToKeep{jj}) = tmpSpec.(fieldsToKeep{jj});
end
if ~featuresCached
featureTable{ii} = extractFeatures(tmpSpec);
end
progressbar(ii/N);
end
end
if ~featuresCached
featureTable = [featureTable{:}];
save(['tmpFeatures-' timeFlag],'DRsylls','spectra','featureTable');
end
% convert features from struct array to 2D array
fn = fieldnames(featureTable);
featureTable = cellfun(@(x) [featureTable.(x)]', fn', 'UniformOutput',false);
featureTable = [featureTable{:}];
%% calculate local distances, fixed-time version, no
%{
innerIdx = 0;
progressbar('Unwarped Distance Calcs');
for ii = 1:N-1
for jj = ii+1:N
innerIdx = innerIdx + 1;
distMatrices.unwarpedLocal(innerIdx) = min(standardDistance(spectra(ii), spectra(jj), params));
progressbar(innerIdx/nchoosek(N,2));
end
end
save(['tmpDists-' timeFlag],'DRsylls','distMatrices');
%}
%% calculate local distances, TIME WARPED version
tic
innerIdx = 0;
progressbar('Saves','Time Warped Distance Calcs');
for ii = 1:N-1
iLen = DRsylls(ii).stop - DRsylls(ii).start;
for jj = ii+1:N
jLen = DRsylls(jj).stop - DRsylls(jj).start;
innerIdx = innerIdx + 1;
distMatrices.warpedLocal(innerIdx) = ...
timeWarpedDistance(spectra(ii), spectra(jj), params) / mean([iLen, jLen]);
progressbar([],innerIdx/nchoosek(N,2));
if rem(innerIdx, floor(sqrt(nchoosek(N,2)))) == 0
%save(['tmpDists-' timeFlag],'DRsylls','distMatrices');
progressbar(floor(innerIdx/floor(sqrt(nchoosek(N,2)))) / ...
floor(nchoosek(N,2)/floor(sqrt(nchoosek(N,2)))))
end
end
end
save(['tmpDists-' timeFlag],'DRsylls','distMatrices');
progressbar(1);
tt=toc;
fprintf('Time warping took %0.2f s...\n', tt);
%save([dataPath 'localSimTW-' birdID '.mat'],'clustSylls','twDistM');
%% step 5: measure global distances within pairs of syllables
%seldFeaturesTable = allFeaturesTable;
%clustSylls = allDRsylls(trainIdxs);
%featureTable = allFeaturesTable(trainIdxs,:);
% start with unnormalized table of features
% step 1: normalize to z-scores
fprintf('Calculating global dissimilarity scores...\n');
zNormedFeatures = zscore(featureTable);
distMatrices.global = pdist(zNormedFeatures);
save(['tmpDists-' timeFlag],'DRsylls','distMatrices');
%% get non-parametric (probability-rank) ordering of similarity scores
fprintf('Calculating empirical scores...\n');
scoreTypes = fieldnames(distMatrices);
for ii = 1:numel(scoreTypes)
fld = scoreTypes{ii};
arr = distMatrices.(fld);
[sArr,rord] = sort(arr);
empMatrices.(fld)(rord) = [1:numel(arr)] / numel(arr);
xx = linspace(0,1,nEmpD);
if nEmpD == numel(arr)
yy = sArr;
else
yy = interp1(linspace(0,1,numel(arr)), sArr, xx);
end
% prepare for interp1 by removing redundant entries
redun = [diff(yy)==0 false];
if any(redun)
xx = xx(~redun); % might be better to take a mean of the p-values instead of the max (as this implies)
yy = yy(~redun);
end
empDistrs.(fld) = zeros(2,numel(xx));
empDistrs.(fld)(1,:) = xx;
empDistrs.(fld)(2,:) = yy;
save(['tmpEmp-' timeFlag],'DRsylls','empMatrices', 'empDistrs');
end
%% construct co-similarity as fusion of local and global p-values
fprintf('Calculating co-dissimilarity (correlation of dissimilarities, which is a similarity score)...\n');
fusedPVals = sqrt(empMatrices.warpedLocal .* empMatrices.global);
distMatrices.cosim = pdist(squareform(fusedPVals), 'correlation');
% do the clustering - the easiest part
nClusters = 4:25;
pairLinks = linkage(distMatrices.cosim,'complete');
clustIdxs = cluster(pairLinks,'maxclust',nClusters);
% undo sorting step
clustIdxs(sortedIdx,:) = clustIdxs;
scoreTypes = fieldnames(distMatrices);
for ii = 1:numel(scoreTypes)
distMatrices.(scoreTypes{ii}) = squareform(unsort2D(squareform(distMatrices.(scoreTypes{ii})), sortedIdx));
if isfield(empMatrices, scoreTypes{ii})
empMatrices.(scoreTypes{ii}) = squareform(unsort2D(squareform( empMatrices.(scoreTypes{ii})), sortedIdx));
end
end
end
function mat = unsort2D(mat, sI)
[coordsi, coordsj] = meshgrid(sI,sI);
mat(sub2ind(size(mat),coordsi, coordsj)) = mat;
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
searchClusterOrder.m
|
.m
|
Acoustic_Similarity-master/code/clustering/searchClusterOrder.m
| 2,564 |
utf_8
|
e8ce8804d0b9575a2138d12120d95103
|
function [bestSeq, bestSeqFreq] = searchClusterOrder(events, links, params, varargin)
if nargin < 3; params = defaultParams; end;
params = processArgs(params,varargin{:});
% separate syllables into separate clusters,
% either clusters or number of possible clusters
distCutoff = 0.7;
clusterIdxs = cluster(links,'Cutoff', distCutoff,'criterion','Distance');
uClusters = unique(clusterIdxs);
nClusters = numel(uClusters) + 1;
transMatrix = zeros(nClusters); % let silence be the last transition possibility
% build up first-order transition matrix
nEvents = numel(events);
silenceIdx = nEvents;
silenceDuration = 2.0; % seconds, any
currSyllType = clusterIdxs(1);
seqList = currSyllType;
for ii = 1:nEvents-1 % list transition matrix between pairs of events
nextSyllType = clusterIdxs(ii+1);
if events(ii+1).start - events(ii).stop > silenceDuration
transMatrix(currSyllType, silenceIdx) = ...
transMatrix(currSyllType, silenceIdx) + 1;
transMatrix(silenceIdx, nextSyllType) = ...
transMatrix(silenceIdx, nextSyllType) + 1;
seqList = [seqList silenceIdx nextSyllType];
else
transMatrix(currSyllType, nextSyllType) = ...
transMatrix(currSyllType, nextSyllType) + 1;
seqList = [seqList silenceIdx nextSyllType];
end
currSyllType = nextSyllType;
end
% convert to probabilities
nEventsPerCluster = sum(transMatrix,1);
probMatrix = transMatrix;
for ii = 1:nClusters
probMatrix(:,ii) = probMatrix(:,ii) ./ nEventsPerCluster;
end
if params.plot
imagesc(probMatrix);
end
%% find the most common subsequences of a certain length -
% lookoup Algorithms on Strings, Trees and Sequences - Dan Gusfield
minimumLength = 3;
maximumArray = 1e5;
if nClusters^minimumLength > maximumArray,
error('TooManyPossibilities','We need to use a suffix tree...');
end;
nSeq = numel(seqList);
hashSubFreq = zeros(nClusters ^ minimumLength, 1);
% brute force: hash a subsequence to a key
for ii = 1:nSeq - minimumLength
hashedIdx = enBase(seqList(ii:ii+minLength - 1)-1, nClusters) + 1;
hashSubFreq(hashedIdx) = hashSubFreq(hashedIdx) + 1;
end
[sortFreq, idxFreq] = sort(hashSubFreq);
seqFreq = 10000;
ii = 1
bestSeq = zeros(10,minimumLength);
while seqFreq > 100 && ii <= 10
bestSeq(ii,:) = unBase(idxFreq(ii) - 1) + 1
bestSeqFreq(ii) = hashSubFreq(idxFreq(ii));
end
function ret = enBase(array, base) %
ret = nBase(array(1:end-1)) * base + nBase(end);
end
function arr = unBase(num, base) %
arr = [unBase(floor(num/base)) mod(num,base)];
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
DRclusterSaveOnDisk.m
|
.m
|
Acoustic_Similarity-master/code/clustering/DRclusterSaveOnDisk.m
| 7,006 |
utf_8
|
a2131d36c3d9304df43541f319f31267
|
function [clustIdxs, empMatricesFil, distMatricesFil, empDistrs] = ...
DRclusterSaveOnDisk(DRsylls, featureTable, spectra, params, varargin)
% we use this version of DRcluster if we have a large number of syllables
% (>5000) for memory constraints
% the two matrices are returned as files
timeFlag = ['T-' datestr(clock, 'mm_dd_HH_MM')];
tmpDir = ['tmp-' timeFlag filesep];
featuresCached = (nargin >= 2);
specsCached = (nargin >= 3);
if nargin < 4
params = defaultParams;
end
params = processArgs(params, varargin{:});
% remove any syllables that are too short
isTooShort = (params.fine.windowSize / 1000 > [DRsylls.stop] - [DRsylls.start]);
DRsylls(isTooShort) = [];
fprintf('Removing %d syllables that are too short...\n', sum(isTooShort));
% sort
[DRsylls, sortedIdx] = sortBy(DRsylls, 'file');
N = numel(DRsylls);% = min(ceil(numel(allDRsylls)/2),1000);
NC2 = nchoosek(N,2);
if any(sortedIdx ~= 1:N) fprintf('NB: Check sorting...\n'); end
nEmpD = min(NC2,2e4);
distMatricesFil = matfile([tmpDir 'distMatrices.mat'],'Writable', true);
distMatricesFil.warpedLocal = zeros(1,NC2);
distMatricesFil.global = zeros(1,NC2);
empMatricesFil = matfile([tmpDir 'empMatrices.mat'],'Writable', true);
empMatricesFil.warpedLocal = zeros(1,NC2);
empMatricesFil.global = zeros(1,NC2);
empDistrs = struct(...,
'warpedLocal', zeros(2,nEmpD),...
'global' , zeros(2,nEmpD));
fieldsToKeep = {'AM','FM','pitchGoodness','wienerEntropy','fundamentalFreq','times'};
% store the feature-based spectra for all of them
params.fine.features = {'wienerEntropy','deriv','harmonicPitch','fundamentalFreq'};
% get sampling rate
[filePath, fileStem] = fileparts(DRsylls(1).file);
metaFile = [filePath filesep 'meta-' fileStem];
metaStruct = []; load(metaFile);
params.fine.fs = 1/metaStruct.interval;
% calculate spectra
if ~specsCached
spectra = initEmptyStructArray(fieldsToKeep, N);
if ~featuresCached
featureTable = cell(1,N);
end
progressbar(sprintf('Calculating spectra & features for regions (# = %d)',N));
for ii = 1:N
%get noisemask
if ii==1 || ~strcmp(DRsylls(ii-1).file, DRsylls(ii).file)
[filePath fileStem] = fileparts(DRsylls(ii).file);
nMFile = [filePath filesep 'noiseMask-' fileStem '.mat'];
if exist(nMFile, 'file')
fprintf('Loading noise mask from %s...\n',nMFile);
noiseMask = []; load(nMFile);
end
end
cl = getClipAndProcess([],DRsylls(ii), params, 'noroll',...
'doFilterNoise',true,'noiseFilter', noiseMask);
tmpSpec = getMTSpectrumStats(cl, params.fine);
for jj = 1:numel(fieldsToKeep)
spectra(ii).(fieldsToKeep{jj}) = tmpSpec.(fieldsToKeep{jj});
end
if ~featuresCached
featureTable{ii} = extractFeatures(tmpSpec);
end
progressbar(ii/N);
end
end
if ~featuresCached
featureTable = [featureTable{:}];
save(['tmpFeatures-' timeFlag],'DRsylls','spectra','featureTable');
end
% convert features from struct array to 2D array
fn = fieldnames(featureTable);
featureTable = cellfun(@(x) [featureTable.(x)]', fn', 'UniformOutput',false);
featureTable = [featureTable{:}];
%% calculate local distances, fixed-time version, no
%{
innerIdx = 0;
progressbar('Unwarped Distance Calcs');
for ii = 1:N-1
for jj = ii+1:N
innerIdx = innerIdx + 1;
distMatrices.unwarpedLocal(innerIdx) = min(standardDistance(spectra(ii), spectra(jj), params));
progressbar(innerIdx/nchoosek(N,2));
end
end
save(['tmpDists-' timeFlag],'DRsylls','distMatrices');
%}
%% calculate local distances, TIME WARPED version
tic
innerIdx = 0;
progressbar('Saves','Time Warped Distance Calcs');
for ii = 1:N-1
iLen = DRsylls(ii).stop - DRsylls(ii).start;
for jj = ii+1:N
jLen = DRsylls(jj).stop - DRsylls(jj).start;
innerIdx = innerIdx + 1;
% distance is normalized by the length of the syllables
distMatricesFil.warpedLocal(innerIdx) = ...
timeWarpedDistance(spectra(ii), spectra(jj), params) / mean([iLen, jLen]);
progressbar([],innerIdx/nchoosek(N,2));
if rem(innerIdx, floor(sqrt(nchoosek(N,2)))) == 0
progressbar(floor(innerIdx/floor(sqrt(nchoosek(N,2)))) / ...
floor(nchoosek(N,2)/floor(sqrt(nchoosek(N,2)))))
end
end
end
save(['tmpDists-' timeFlag],'DRsylls','distMatrices');
progressbar(1);
tt=toc;
fprintf('Time warping took %0.2f s...\n', tt);
%save([dataPath 'localSimTW-' birdID '.mat'],'clustSylls','twDistM');
%% step 5: measure global distances within pairs of syllables
%seldFeaturesTable = allFeaturesTable;
%clustSylls = allDRsylls(trainIdxs);
%featureTable = allFeaturesTable(trainIdxs,:);
% start with unnormalized table of features
% step 1: normalize to z-scores
fprintf('Calculating global dissimilarity scores...\n');
zNormedFeatures = zscore(featureTable);
distMatricesFil.global = pdist(zNormedFeatures);
save(['tmpDists-' timeFlag],'DRsylls','distMatrices');
%% get non-parametric (probability-rank) ordering of similarity scores
fprintf('Calculating empirical scores...\n');
scoreTypes = fieldnames(distMatricesFil);
for ii = 1:numel(scoreTypes)
fld = scoreTypes{ii};
arr = distMatricesFil.(fld);
[sArr,rord] = sort(arr);
empMatricesFil.(fld)(rord) = [1:numel(arr)] / numel(arr);
xx = linspace(0,1,nEmpD);
if nEmpD == numel(arr)
yy = sArr;
else
yy = interp1(linspace(0,1,numel(arr)), sArr, xx);
end
% prepare for interp1 by removing redundant entries
redun = [diff(yy)==0 false];
if any(redun)
xx = xx(~redun); % might be better to take a mean of the p-values instead of the max (as this implies)
yy = yy(~redun);
end
empDistrs.(fld) = zeros(2,numel(xx));
empDistrs.(fld)(1,:) = xx;
empDistrs.(fld)(2,:) = yy;
end
%% construct co-similarity as fusion of local and global p-values
fprintf('Calculating co-dissimilarity (correlation of dissimilarities, which is a similarity score)...\n');
fusedPVals = sqrt(empMatricesFil.warpedLocal .* empMatricesFil.global);
distMatricesFil.cosim = pdist(squareform(fusedPVals), 'correlation');
% do the clustering - the easiest part
nClusters = 4:25;
pairLinks = linkage(distMatricesFil.cosim,'complete');
clustIdxs = cluster(pairLinks,'maxclust',nClusters);
% undo sorting step
clustIdxs(sortedIdx,:) = clustIdxs;
scoreTypes = fieldnames(distMatricesFil);
for ii = 1:numel(scoreTypes)
distMatricesFil.(scoreTypes{ii}) = squareform(unsort2D(squareform(distMatricesFil.(scoreTypes{ii})), sortedIdx));
if isfield(empMatricesFil, scoreTypes{ii})
empMatricesFil.(scoreTypes{ii}) = squareform(unsort2D(squareform( empMatricesFil.(scoreTypes{ii})), sortedIdx));
end
end
end
function mat = unsort2D(mat, sI)
[coordsi, coordsj] = meshgrid(sI,sI);
mat(sub2ind(size(mat),coordsi, coordsj)) = mat;
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
similarityRegions.m
|
.m
|
Acoustic_Similarity-master/code/clustering/similarityRegions.m
| 846 |
utf_8
|
a2fa57b6fa30d6e2a1c0dba7bd8209f3
|
function sim = similarityRegions(songStruct, keyreg, otherRegs, noiseGate, params, varargin)
%% parameter handling
if nargin < 5; params = defaultParams; end
params = processArgs(params, varargin{:});
fs = 1/songStruct.interval;
%% preprocessing
% filter out noise first
[keyclip,keyspec] = cleanClip(keyreg);
for ii = 1:numel(otherRegs)
[iclip,ispec] = cleanClip(otherRegs(ii));
sim{ii} = similarityScan(keyspec, ispec);
end
function [clip,spec] = cleanClip(region)
region = addPrePost(region,params);
if nargin >= 3
clip = noiseGate(songStruct, region, noiseProfile);
else
clip = getClip(region, songStruct);
end
if ~params.quiet, playSound(clip,fs); end
% high pass the result
params.fs=fs;
clip = highPassSample(clip,params);
%% spectral analysis
params.fine.fs = fs;
spec = getSpectrumStats(clip, params.fine);
end
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
drawClustersUpdated.m
|
.m
|
Acoustic_Similarity-master/code/clustering/drawClustersUpdated.m
| 8,383 |
utf_8
|
2a5608d992b05217c84073336bf22e21
|
function drawClustersUpdated(birdID, age, params, varargin)
if nargin < 3 || isempty(params)
params = defaultParams;
end
params = processArgs(params, varargin{:});
dataDir = [pwd filesep 'data' filesep birdID filesep];
% clusterDir = ['data' filesep 'cluster-' birdID filesep];
% get sessions for a certain age
rep = reportOnData(birdID);
birdSessions = {rep.sessionID};
sessAges = getAgeOfSession(birdSessions);
rep = rep(age == sessAges);
% get syllables for a certain age by concatenating across different groups
ageSylls = [];
for ii = 1:numel(rep)
mani = rep(ii).manifest;
[~,fil] = findInManifest(mani, 'sAudio');
theseSylls = loadFromManifest(mani, 'approvedSyllables');
% minimum length used in DRcluster would be smaller than the
% spectrogram window
% remove syllables that are too short
minLen = params.fine.windowSize / 1000;
isTooShort = minLen > [theseSylls.stop] - [theseSylls.start];
theseSylls(isTooShort) = [];
[theseSylls.file] = deal(fil);
% get the labels
labels = loadFromManifest(mani, 'acceptedLabels');
if isempty(labels)
fprintf('No acceptedLabels for session %s, not retrieving...\n', rep(ii).sessionID);
continue;
end
% apply labels
num2cell(labels); [theseSylls.type] = ans{:};
ageSylls = [ageSylls theseSylls];
end
% plot all syllables within each cluster as a mosaic
labels = [ageSylls.type];
nClusts = nanmax([ageSylls.type]);
clustLabels = unique(labels(~isnan(labels)));
if any(isnan(labels)), clustLabels(end+1) = NaN; end
mkdir('figures/',sprintf('%s-age%d', birdID, age));
redoneLabels = labels;
for ii = 1:nClusts
clustSylls = ageSylls(labels == ii);
if isempty(clustSylls), continue; end;
clustSylls = addPrePost(clustSylls, [], 'preroll', 5, 'postroll',5);
maxMosaicLength = 6.5;
[hfs, newLabels] = plotInPages(clustSylls, maxMosaicLength, ii, ...
clustLabels, birdID, age, params);
close(hfs)
% updating syllable labels
if params.manuallyUpdateClusters
redoneLabels(labels == ii) = newLabels;
redoneFile = sprintf('%sredoneLabels-%s-age%d.mat', dataDir, birdID, age, params);
fprintf('Intermediate saving to %s...\n', redoneFile);
save(redoneFile, 'redoneLabels');
end
end
unIDedSylls = ageSylls(isnan([ageSylls.type]));
unIDedSylls = addPrePost(unIDedSylls, [], 'preroll', 5, 'postroll',5);
maxMosaicLength = 6.5;
[hfs, newLabels] = plotInPages(unIDedSylls, maxMosaicLength, NaN, clustLabels, birdID, age, params);
% updating syllable labels
if params.manuallyUpdateClusters
redoneLabels(isnan(labels)) = newLabels;
redoneFile = sprintf('%sredoneLabels-%s-age%d.mat', dataDir, birdID, age);
fprintf('Final saving to %s...\n', redoneFile);
save(redoneFile, 'redoneLabels');
end
close(hfs)
end
function [hfs, newLabels] = plotInPages(sylls, pageLength, syllNum, clustLabels, birdID, age, params)
% pageLength in seconds, max amount to plot, accounting for wasted margins on the mosaic
% break down clustSylls into groups no longer than maxTime
lens = [sylls.stop] - [sylls.start];
grpStarts = 1;
grpEnds = numel(sylls);
while sum(lens(grpStarts(end):end)) > pageLength
newGrpStart = find(cumsum(lens) > pageLength, 1);
grpStarts(end+1) = newGrpStart;
grpEnds = [grpEnds(1:end-1) (newGrpStart-1) grpEnds(end)];
lens(1:newGrpStart-1) = 0; % zero out the lengths that are already accounted for
end
isClosed = false(1,numel(grpStarts));
hfs = zeros(1,numel(grpStarts));
newLabels = ones(1,numel(sylls)) * syllNum;
for jj = 1:numel(hfs)
figure;
% get the figure handle and image handles
[hfs(jj) hIms] = mosaicDRSpec(sylls(grpStarts(jj):grpEnds(jj)), [],...
'dgram.minContrast', 1e-11, 'doFilterNoise', false,...
'noroll', 'maxMosaicLength', Inf);
set(hfs(jj), 'Name', sprintf('%s, syllable #%d, instances #%d-%d', ...
birdID, syllNum, grpStarts(jj), grpEnds(jj)));
% interactive portion %
if params.manuallyUpdateClusters
% get the axes objects
hRowAxes = get(hIms,'Parent');
% de-cell
hRowAxes = [hRowAxes{:}];
for kk = 1:numel(hRowAxes)
hThisRowAxes = hRowAxes(kk);
hThisIm = hIms(kk);
sepH = findall(hThisRowAxes, 'Type','line','Color',[1 1 1]);
nSeps = numel(sepH);
% boundaries of the regions in figure x-coordinates
xSeps = zeros(1,nSeps + 2);
% ends of the separators
xl = xlim(hThisRowAxes);
xSeps(1:2) = xl;
for ll = 1:nSeps
xl = get(sepH(ll), 'XData');
xSeps(ll+2) = xl(1); % first x-coordinate of the line is x position
end
xSeps = sort(xSeps);
% make red text labels
axes(hThisRowAxes);
for ll = 1:nSeps+1 %each segment
hlabel = createTextLabel(mean(xSeps(ll:ll+1)), ...
num2str(syllNum));
set(hlabel, 'UserData', ll);
end
% save bounds and current labels in figure data structure
set(hThisIm,'UserData', ...
struct('bounds', xSeps, ...
'currLabels', syllNum * ones(1,nSeps + 1)));
hcmenu = uicontextmenu;
for ll = 1:numel(clustLabels)
uimenu(hcmenu,'Label',num2str(clustLabels(ll)),...
'Callback', @(hEntry, data) changeLabels(...
clustLabels(ll), hThisIm)); % both input variables are dummy variables
end
uimenu(hcmenu,'Label','Done',...
'Callback', @(hEntry, data) ...
stopLabeling(gcf)); %the data input dummy variables
set(hThisIm,'uicontextmenu', hcmenu);
end
% set the close request function and a cleanup in case of
% ctrl-c - this needs to be fixed so that c cleans up on ctrl-c
% instead of on request to delete hfs(jj)
set(hfs(jj),'CloseRequestFcn',@(hfig,event) set(hfig, 'Tag','Done'));
c = onCleanup(@() resetCloseFcn(hfs(jj)));
waitfor(hfs(jj),'Tag','Done')
% change the labels by grabbing userdata from the images
ptr = grpStarts(jj);
for kk = 1:numel(hIms)
foo = getfield(get(hIms(kk),'UserData'),'currLabels');
newLabels(ptr:ptr+numel(foo)-1) = foo;
ptr = ptr + numel(foo);
end
% todo: delete the labels
assert(ptr == grpEnds(jj)+1);
nChanged = sum(newLabels~=syllNum);
if isnan(syllNum)
nChanged = sum(isnan(newLabels));
end
fprintf('\tcluster #%d: %d labels changed...\n', syllNum, nChanged);
end
% end interactive portion %
if params.saveplot
fName = sprintf('figures/%s-age%d/%s-age%d-clust%02d-i%04d-%04d.jpg',...
birdID,age, birdID,age, syllNum,grpStarts(jj), grpEnds(jj));
saveCurrFigure(fName);
end
isClosed(jj) = true;
close(hfs(jj));
end
hfs(isClosed) = [];
end
function resetCloseFcn(hfig)
fprintf('Calling reset fxn\n');
if isvalid(hfig),
set(hfig,'CloseRequestFcn','closereq');
end
end
function changeLabels(seldLabel, hIm)
% seldLabel is the name of the label
% hIm is the image handle that contains the track of labels in userData
% get the current point of the click in normalized coordinates
hCurrAxes = get(hIm,'Parent');
hCurrFig = get(hCurrAxes, 'Parent');
currPt = get(hCurrFig, 'CurrentPoint');
figDims = get(hCurrFig, 'Position');
currPt = currPt ./ figDims([3 4]);
% bounds of array
userData = get(hIm,'UserData');
xb = userData.bounds;
% get the segment which was selected
xpos = currPt(1);
segNum = find(xb(1:end-1) < xpos & xb(2:end) >= xpos, 1);
% reassign the label from the segment which was clicked
userData.currLabels(segNum) = seldLabel;
set(hIm,'UserData', userData);
% rewrite the label
hCurrLabel = findall(hCurrAxes, 'Type', 'text', 'UserData', segNum);
set(hCurrLabel, 'String', num2str(seldLabel));
end
function stopLabeling(gCurrFig)
% set the done tag
set(gCurrFig,'Tag','Done');
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
similarityScan.m
|
.m
|
Acoustic_Similarity-master/code/clustering/similarityScan.m
| 2,368 |
utf_8
|
a3651e090a6d323689f5c986b206dc7f
|
function [covar, indivDists] = similarityScan(spectrum1, spectrum2, params, varargin)
%SIMILARITYSCAN Running, sample by sample scan of two spectral feature sets
% COVAR = similarityScan(SPECTRUM1, SPECTRUM2) returns the similarity of two
% sounds, sample by sample, according to the correlation found between
% their features. The correlations are taken along ever calculated
% feature of the sample (power, entropy, average pitch, etc.) and is a
% VECTOR of values, one for each possible offset of the spectrum relative
% to the other one.
%
%% argument handling
if nargin < 3
params = defaultParams;
end
params = processArgs(params,varargin{:});
%% order by length
% convert lengths
len1 = length(spectrum1.times);
len2 = length(spectrum2.times);
if(len1 > len2)
[covar, indivDists] = similarityScan(spectrum2, spectrum1, params);
return;
end
%% find all features that can be correlated
features = whichFeatures(spectrum1);
nF = numel(features);
%% look at x) / var(y)) over the different fields, for each matching window
% TODO: make a better way to weight features
%weightVec = [24.7557 629.1870 28.8207 24.3587 133.1410 679.5239 27.8380 63.5714 41.6093 55.1736];
% TODO: define similarity more robustly, with real euclidean distances and
% more weight on frequency measures
weightVec = ones(1,nF) / nF; % uniform weight, can be changed
%weightVec = weightVec/sum(weightVec);
% determine the time intervals that we want to run similarity
nSkipMs = 0.2; % in ms
nSkipSamples = floor(nSkipMs/1000 * params.fs);
nComparisons = numel(1:nSkipSamples:len2 - len1 + 1);
covar = zeros(1,nComparisons);
ctr = 1;
indivDists = zeros(nComparisons, nF);
for ii = 1:nSkipSamples:len2 - len1 + 1
segStart = ii;
segEnd = ii + len1 - 1;
for jj = 1:nF % loop over the features
fv1 = spectrum1.(features{jj});
fv2 = spectrum2.(features{jj})(segStart:segEnd);
indivDists(ctr,jj) = 1 - pdist([fv1;fv2],'cosine');
%corr=cc(2,1);
% this will happen when one of the vectors is constant
% if isnan(corr), corr = 0; end
covar(ctr) = covar(ctr) + indivDists(ctr,jj) * weightVec(jj);
end
ctr = ctr+1;
end
function ret = featsToArray(featSummary,features)
nF = numel(features);
nS = numel(featsToArray.times);
ret = zeros(nF,nS);
for ii = 1:nF
ret(ii,:) = featSummary.(features{ii});
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
browseAndAccept.m
|
.m
|
Acoustic_Similarity-master/code/clustering/browseAndAccept.m
| 18,753 |
utf_8
|
621019bb5bcc42a29f290a11c46a03b1
|
function [acceptedLabels, augmentedLabels] = browseAndAccept(birdID)
% interactive part of clustering post-machine step
% several prompts based on the output results from recalcClusters* family
%%
close all;
clusterDir = [pwd filesep 'data' filesep 'cluster-' birdID filesep];
dataDir = [pwd filesep 'data' filesep birdID filesep];
plotParams = processArgs(defaultParams,...
'dgram.minContrast', 1e-11, 'doFilterNoise', false,...
'preroll', 3, 'postroll', 3);
% load the subset of syllables
fprintf('Loading spectra for bird %s...', birdID);
DRsylls = []; featureTable = []; spectra = [];
load([dataDir 'allSpecs-' birdID '.mat'], 'DRsylls','featureTable');
fprintf(' done loading.\n');
% choose the cluster file
[filName, pathName] = uigetfile([clusterDir '*.mat'],'Select the file to grab clusters from');
fprintf('Loading cluster file...');
%%
% parse the age, subselect the syllables to save memory
[~,foooo] = strtok(filName,'-');
thisAge = sscanf(foooo(2:end), '%d'); thisAge = thisAge(1); % make me less hacky.......... use a regexp
clustSession = strrep(filName(1:end-5), 'altClustDataAge-','');
%%
isAge = ([DRsylls.age] == thisAge);
DRsylls = DRsylls(isAge);
featureTable = featureTable(isAge);
%% load cluster file
clusterIdxs = [];
load([pathName filesep filName], 'clusterIdxs');
fprintf('Now loading distance matrix for additional refinement... ');
load([pathName filesep filName], 'distMats');
fprintf('done loading.\n');
%%
% pick the kind of clustering
if isstruct(clusterIdxs)
fn = fieldnames(clusterIdxs);
seldClusterType = fn{listdlg('ListString', fn, 'SelectionMode', 'single','Name', 'Which type of clustering?')};
if isempty(seldClusterType), error('Did not select option, buggin'' out.'); end
% cIdxs = clusterIdxs.(seldClusterType);
else
% cIdxs = clusterIdxs;
seldClusterType = 'cosim';
end
%maxes = max(cIdxs,[],1);
%%
dists = squareform(distMats.(seldClusterType)); clear distMats;
%% pick which level of clustering you want:
nDefClusts = 12;
maxes = 4:30;
%%
linktree = linkage(dists, 'complete');
cIdxs = cluster(linktree,'maxclust',maxes);
%%
nClusts = str2double(inputdlg(...
sprintf('How many clusters are you looking for? (%d-%d)',min(maxes),max(maxes)),...
'Number of clusters', 1, {num2str(nDefClusts)}));
thisSetPtr = find(maxes==nClusts);
thisSetIdxs = cIdxs(:,thisSetPtr);
counts = hist(thisSetIdxs,1:nClusts);
%% loop through the different clusters in order of decreasing size
[~, sortPerm] = sort(counts, 'descend');
acceptedLabels = -ones(size(thisSetIdxs));
isFused = false(1,nClusts);
%%
grpCtr = 1;
for ii = 1:nClusts
if isFused(sortPerm(ii)), continue; end;
%% inspect cluster
clf
mosaicDRSpec(DRsylls(thisSetIdxs == sortPerm(ii)), plotParams, 'maxMosaicLength', 4.5);
set(gcf,'Name',sprintf('Cluster #%d (%d/%d)',sortPerm(ii),ii,nClusts));
retry = true;
while retry
retry = false;
switch nm_questdlg('How to treat this cluster?', ...
sprintf('Cluster %d, # = %d', sortPerm(ii), sum(thisSetIdxs == sortPerm(ii))),...
'Accept', 'Reject', 'Edit', 'Accept')
case 'Accept'
acceptedLabels(thisSetIdxs == sortPerm(ii)) = grpCtr;
grpCtr = grpCtr + 1;
case 'Reject'
acceptedLabels(thisSetIdxs == sortPerm(ii)) = -1; % sentinel for rejection
continue;
case 'Edit'
% todo: report metrics of conformity/similarity?
[newLabelGroups, retry] = editSub(thisSetIdxs, sortPerm(ii));
% implement labeling groups
for jj = 1:numel(newLabelGroups)
acceptedLabels(newLabelGroups{jj}) = grpCtr;
grpCtr = grpCtr + 1;
end
end
end
end
acceptedLabels(acceptedLabels == -1) = NaN;
% save to an acceptedLabels file
clusterIdxs = struct('accepted', acceptedLabels);
saveFileName = [pwd filesep 'data' filesep birdID filesep 'acceptedLabels-' birdID '-age' num2str(thisAge) '.mat'];
fprintf('Saving cluster identifies to %s...', saveFileName)
save(saveFileName, 'clusterIdxs');
%% step 1.5: encourage review and merging of labels
% goal: make more representative syllables
regroupedLabels = mergeClustersByHand(DRsylls, dists, acceptedLabels);
%% second pass: try to match labels to establish groups
% find matches by looking at the distance
% loop through the unlabeled syllables
% look for matches within the range of acceptable distances
augmentedLabels = semiLabelSyllables(DRsylls, dists, regroupedLabels);
%% save to an acceptedLabels file
clusterIdxs = struct('accepted', acceptedLabels, 'regrouped', regroupedLabels, 'augmented', augmentedLabels);
saveFileName = [pwd filesep 'data' filesep birdID filesep 'acceptedLabels-' birdID '-age' num2str(thisAge) '.mat'];
fprintf('Saving cluster identifies to %s...', saveFileName);
save(saveFileName, 'clusterIdxs');
fprintf('done. hooray. \n');
%%
function [newLabelGrps, tryAgain] = editSub(labels, oldLabel)
nThisType = sum(labels == oldLabel);
ttl = sprintf('Cluster %d, # = %d', oldLabel, nThisType);
openFigures = [];
tryAgain = false;
newLabelGrps = {};
switch nm_questdlg('How to edit this cluster?', ttl, 'Merge', 'Split', 'Sort by Ear', 'Merge')
case 'Merge'
% follow the tree backwards
mergedCluster = findMerge(thisSetPtr, oldLabel, cIdxs);
% now presentIden and clustIden should fuse
fprintf('Fusing candidate...');
figureName = sprintf('%sexpClusters-%s-%s-c%d.jpg', pathName, clustSession, ...
seldClusterType, mergedCluster);
if exist(figureName,'file') == 2;
fOp = figure; openFigures = [openFigures fOp];
imshow(figureName);
set(gcf,'Name',[figureName ' - candidate cluster for merging']);
else
end
switch nm_questdlg('Fuse?',ttl,'Yes','No, reject all', 'Retry', 'Yes')
case 'Yes'
isFused(oldLabel) = true;
isFused(mergedCluster) = true;
newLabelGrps = [find(labels == oldLabel | labels == mergedCluster) newLabelGrps];
case 'Accept original'
newLabelGrps = [find(labels == oldLabel) newLabelGrps];
case 'No, reject'
% do nothing
case 'Retry'
tryAgain = true;
end
case 'Split'
% how do we split?
splitFeatOptions = ['tree'; fieldnames(featureTable)];
% follow the tree forwards
repeatSplit = true;
while repeatSplit % loop around try-catchtry for clustering robustness
repeatSplit = false;
[splitOpts, hitOk] = listdlg('ListString', splitFeatOptions, ...
'Name', 'Which features to split?');
if ~hitOk
tryAgain = true;
closeAll(openFigures);
return;
end
if any(splitOpts == 1)
[typeA, typeB] = findSplit(thisSetPtr, oldLabel, cIdxs);
else
% try a two-component mixture of gaussian clusters
nFeats = numel(splitOpts);
stats = zeros(nThisType, nFeats);
omitFeature = false(1, nFeats);
varStat = zeros(1,nFeats);
for kk = 1:nFeats
stats(:,kk) = [featureTable(labels == oldLabel).(splitFeatOptions{splitOpts(kk)})];
end
varStat = var(stats);
omitFeature = (varStat < eps(max(varStat))*nThisType);
if all(omitFeature), warning('All features are constant...'); end;
if any(omitFeature)
omitStr = '';
omitList = splitOpts(omitFeature);
% create the join string
for kk = 1:numel(omitList),
omitStr = strcat(omitStr , splitFeatOptions(omitList(kk)));
if kk < nFeats, omitStr = strcat(omitStr,', '); end
end
omitStr = omitStr{1};
fprintf('Removing features %s...\n', omitStr);
stats(:,omitFeature) = [];
splitOpts(omitFeature) = [];
nFeats = numel(splitOpts);
end
try
fitObj = gmdistribution.fit(stats,2,'Regularize',0.0001);
newIdxs = cluster(fitObj,stats);
% do some plotting
fTab = figure('Name','Clustergram');
openFigures = [openFigures fTab];
if size(stats,2) == 1
nBins = 40; bins = zeros(1,nBins);
bins(2:end-1) = linspace(min(stats),max(stats),nBins-2);
bins(end) = 2 * bins(end-1) - bins(end-2);
bins(1) = 2 * bins(2) - bins(3);
bar(bins,histc(stats, bins),1);
xlim(bins([1 end]));
hold on;
plot(bins, pdf(fitObj,bins'),'r-', 'LineWidth', 2);
hold off;
xlabel(splitFeatOptions{splitOpts}, 'Interpreter','none'); ylabel('Count');
else
% pick the two most diagnostic features
dprime = zeros(1,nFeats);
for kk = 1:size(stats,2)
dMu = diff(fitObj.mu(:,kk));
sumSigma = sqrt(sum(fitObj.Sigma(kk,kk,:)));
dprime(kk) = dMu/sumSigma;
end
[~,bestDims] = sort(dprime, 'descend'); bestDims = bestDims(1:2);
plot(stats(:,bestDims(1)), stats(:,bestDims(2)),'k.');
xlabel(splitFeatOptions(splitOpts(bestDims(1))), 'Interpreter','none');
ylabel(splitFeatOptions(splitOpts(bestDims(2))), 'Interpreter','none');
end
% split done
subset = find(labels == oldLabel);
typeA = subset(newIdxs == 1);
typeB = subset(newIdxs == 2);
catch err
repeatSplit = questdlg(['Error in clustering: [', err.message, ']; try again?'],...
'Clustering Error', ...
'Yes','No','Yes');
repeatSplit = strcmp('Yes', repeatSplit);
end
end
end
% give option to view/sing/approve blind
switch nm_questdlg(sprintf('How to review the split (A = %d,B = %d)?', ...
numel(typeA), numel(typeB)), ...
ttl,'View','Listen','Continue w/o review', 'View')
case 'View'
totalLenA = sum([DRsylls(typeA).stop] - [DRsylls(typeA).start]);
totalLenB = sum([DRsylls(typeB).stop] - [DRsylls(typeB).start]);
defaultVal = min(totalLenA, 4.0);
tPrev = nm_inputdlg(...
sprintf('Number of seconds to preview (max %.1fs): ', totalLenA),...
'Cluster A', 1, {num2str(defaultVal)});
if isempty(tPrev), tryAgain = true; return;
else tPrev = str2double(tPrev); end
fprintf('Plotting split cluster A (# = %d)...\n', numel(typeA));
figA = figure;
openFigures = [openFigures figA];
mosaicDRSpec(DRsylls(typeA), plotParams, 'maxMosaicLength', tPrev);
set(gcf,'Name',sprintf('Cluster A (# = %d)',numel(typeA)));
defaultVal = min(totalLenB, tPrev);
tPrev = nm_inputdlg(...
sprintf('Number of seconds to preview (max %.1fs): ', totalLenB),...
'Cluster B', 1, {num2str(defaultVal)});
if isempty(tPrev), tryAgain = true; return;
else tPrev = str2double(tPrev); end
fprintf('Plotting split cluster B (# = %d)...\n', numel(typeB));
figB = figure;
openFigures = [openFigures figB];
mosaicDRSpec(DRsylls(typeB), plotParams, 'maxMosaicLength', tPrev);
set(gcf,'Name',sprintf('Cluster B (# = %d)',numel(typeB)));
case 'Listen'
% simply play the sounds: it's faster to load but
% slower to be complete
fprintf('Splitting candidate... branch A audio: \n');
markRegions([],DRsylls(typeA));
fprintf('Splitting candidate... branch B audio: \n');
markRegions([],DRsylls(typeB));
case 'Accept w/o review'
end
choices = {'Both','Branch A', 'Branch B','None'};
[respStr, isAccept] = nm_listdlg('Name', 'Which branches to accept (none is an option)?', 'ListString',choices, ...
'SelectionMode', 'Single','OKString','Accept',...
'CancelString','Retry');
if ~isAccept,
tryAgain = true;
closeAll(openFigures);
return;
end;
respStr = choices{respStr};
switch respStr
case 'Both'
% will flatten these later
newLabelGrps = {typeA, typeB};
case 'Branch A'
newLabelGrps = {typeA};
case 'Branch B'
newLabelGrps = {typeB};
case 'None'
newLabelGrps= {};
end
case 'Sort by Ear'
fprintf('Split into no more than 10..., based on audio: \n');
newIdxs = multiMark([], DRsylls(labels == oldLabel));
if any(isnan(newIdxs))
if strcmp('Start over',questdlg(['Hand labeling is stopped early, accept or start over?'],...
'Labeling stopped', ...
'Accept','Start over','Start over'))
tryAgain = true;
closeAll(openFigures);
return
end
end
[~,~,rIdxs] = unique(newIdxs);
newLabelGrps = cell(1,numel(rIdxs));
for kk = 1:numel(rIdxs)
newLabelGrps{kk} = find(rIdxs == kk);
end
end
closeAll(openFigures);
end
end
function closeAll(figH)
for kk = 1:numel(figH)
close(figH(kk));
end
end
function [otherCluster, mergeDepth] = findMerge(grpPtr, origClust, clusterLabels)
isMerged = false;
origPtr = grpPtr;
clustTrav = origClust;
while ~isMerged && grpPtr > 1
ct = crosstab(clusterLabels(:,grpPtr), clusterLabels(:, grpPtr-1));
% the row that contains the current cluster - does it
% contain another cluster?
dstClusters = find(ct(clustTrav,:)>0);
if sum(ct(:,dstClusters) > 0) > 1
otherCluster = sum(find(ct(:,dstClusters))) - clustTrav;
isMerged = true;
else
clustTrav = dstClusters;
grpPtr = grpPtr - 1;
end
end
mergeDepth = grpPtr;
% follow the new cluster back to the current division? (we
% don't have to do this, it might split again)
for jj = grpPtr:origPtr-1
ct = crosstab(clusterLabels(:,jj), clusterLabels(:,jj+1));
[~, otherCluster] = max(ct(otherCluster,:));
end
end
function [clusterInds, splitInds, splitDepth] = findSplit(grpPtr, origClust, clusterLabels)
hasSplit = false;
clustTrav = origClust;
nClusterings = size(clusterLabels, 2);
while ~hasSplit && grpPtr < nClusterings
ct = crosstab(clusterLabels(:,grpPtr), clusterLabels(:, grpPtr+1));
% the row that contains the current cluster - does it
% contain another cluster?
dstClusters = find(ct(clustTrav,:) > 0);
if numel(dstClusters) == 2
clustTrav = dstClusters(1);
otherCluster = dstClusters(2);
hasSplit = true;
else
clustTrav = dstClusters;
end
grpPtr = grpPtr + 1;
end
if hasSplit
clusterInds = find(clusterLabels(:,grpPtr) == clustTrav);
splitInds = find(clusterLabels(:,grpPtr) == otherCluster);
splitDepth = grpPtr;
else
clusterInds = find(clusterLabels(:,grpPtr) == clustTrav);
splitInds = [];
splitDepth = nClusterings; % excepted
end
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
createAlphabet.m
|
.m
|
Acoustic_Similarity-master/code/clustering/createAlphabet.m
| 2,178 |
utf_8
|
36c3f166b9e69f24394fe2e97a135f24
|
function [vocString, clusterIdxs] = createAlphabet(syllables, distMatrix, songStruct, params, varargin)
%CREATEALPHABET given distance matrix, cluster into groups
%
if nargin < 4 || isempty(params)
params = defaultParams;
end
params = processArgs(params,varargin{:});
fs = 1/songStruct.interval;
% cluster the syllables by distance - assumes triangular matrix
%boutCorrVector = squareform(corrMatrix + corrMatrix' ...
% - 2 * diag(diag(corrMatrix)),'tovector');
% dendTree = clusterAll(vocalizations, boutCorrMatrix, songStruct);
%dendTree = linkage(1 - boutCorrVector, 'complete'); % TODO: try top-down instead of agglomerative?
% for correlation matrices
% dendTree = linkage(1 - corrMatrix, params.clusterMethod);
% for distance matrices
dendTree = linkage(distMatrix, params.clusterMethod);
%% create clusters
clusterIdxs = cluster(dendTree, 'maxClust', params.nClusters)';
%% reassign numbers to cluster letters - don't do this for now
alphaFreq = histc(clusterIdxs,1:params.nClusters);
% translate to string
vocString = numToAlpha(clusterIdxs);
freqs = hist(clusterIdxs, 1:params.nClusters)
if params.plot
asciied = double(vocString);
hist(asciied,min(asciied):1:max(asciied))
xlim([min(asciied)-0.5 max(asciied)+0.5])
set(gca,'XTick',min(asciied):1:max(asciied))
set(gca,'XTickLabels',char(min(asciied):1:max(asciied))');
title('Distribution of labels');
end
% sample the alphabet
if params.playsample
for ii = 1:params.nClusters
% pick at most 5 example syllables and more if we have more
nSamples = max(floor(sqrt(alphaFreq(ii))),min(5,alphaFreq(ii)));
fprintf('Playing syllable %s (# = %d)...\n',numToAlpha(ii), freqs(ii));
sampleIdxs = find(clusterIdxs==ii,nSamples);
for jj = 1:nSamples
playSound(getClip(syllables(sampleIdxs(jj)), songStruct),fs,true);
pause(0.25);
end
beep
pause(0.25);
end
end
end
function str = numToAlpha(vec)
vec(vec>= 27 & vec <=52) = vec(vec>= 27 & vec <=52) + double('A') - double('a') - 26;
str = char(double('a' - 1 + vec));
end
function vec = alphaToNum(str)
vec = str - 'A' + 1;
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
DRclusterMFCC.m
|
.m
|
Acoustic_Similarity-master/code/clustering/DRclusterMFCC.m
| 6,385 |
utf_8
|
e15c7f427391ef218dcc74553dfe1444
|
function [clustIdxs, empMatrices, distMatrices, empDistrs] = DRclusterMFCC(DRsylls, featureTable, spectra, params, varargin)
timeFlag = ['T-' datestr(clock, 'mm_dd_HH_MM')];
featuresCached = (nargin >= 2);
specsCached = (nargin >= 3);
if nargin < 4
params = defaultParams;
end
params = processArgs(params, varargin{:});
% remove any syllables that are too short
isTooShort = (params.fine.windowSize / 1000 > [DRsylls.stop] - [DRsylls.start]);
DRsylls(isTooShort) = [];
fprintf('Removing %d syllables that are too short...\n', sum(isTooShort));
% sort
[DRsylls, sortedIdx] = sortBy(DRsylls, 'file');
N = numel(DRsylls);% = min(ceil(numel(allDRsylls)/2),1000);
NC2 = nchoosek(N,2);
if any(sortedIdx ~= 1:N) fprintf('NB: Check sorting...\n'); end
nEmpD = min(NC2,2e4);
distMatrices = struct(...
'warpedLocal' , zeros(1,NC2),...
'global' , zeros(1,NC2));
empMatrices = struct(...
'warpedLocal' , zeros(1,NC2),...
'global' , zeros(1,NC2));
empDistrs = struct(...,
'warpedLocal', zeros(2,nEmpD),...
'global' , zeros(2,nEmpD));
fieldsToKeep = {'AM','FM','pitchGoodness','wienerEntropy','fundamentalFreq','times'};
% store the feature-based spectra for all of them
params.fine.features = {'wienerEntropy','deriv','harmonicPitch','fundamentalFreq'};
% get sampling rate
[filePath, fileStem] = fileparts(DRsylls(1).file);
metaFile = [filePath filesep 'meta-' fileStem];
metaStruct = []; load(metaFile);
params.fine.fs = 1/metaStruct.interval;
% calculate spectra
if ~specsCached
spectra = initEmptyStructArray(fieldsToKeep, N);
if ~featuresCached
featureTable = cell(1,N);
end
progressbar(sprintf('Calculating spectra & features for regions (# = %d)',N));
for ii = 1:N
%get noisemask
if ii==1 || ~strcmp(DRsylls(ii-1).file, DRsylls(ii).file)
[filePath fileStem] = fileparts(DRsylls(ii).file);
nMFile = [filePath filesep 'noiseMask-' fileStem '.mat'];
if exist(nMFile, 'file')
fprintf('Loading noise mask from %s...\n',nMFile);
noiseMask = []; load(nMFile);
end
end
cl = getClipAndProcess([],DRsylls(ii), params, 'noroll','doFilterNoise',true,'noiseFilter', noiseMask);
tmpSpec = getMTSpectrumStats(cl, params.fine);
for jj = 1:numel(fieldsToKeep)
spectra(ii).(fieldsToKeep{jj}) = tmpSpec.(fieldsToKeep{jj});
end
if ~featuresCached
featureTable{ii} = extractFeatures(tmpSpec);
end
progressbar(ii/N);
end
end
if ~featuresCached
featureTable = [featureTable{:}];
save(['tmpFeatures-' timeFlag],'DRsylls','spectra','featureTable');
end
% convert features from struct array to 2D array
fn = fieldnames(featureTable);
featureTable = cellfun(@(x) [featureTable.(x)]', fn', 'UniformOutput',false);
featureTable = [featureTable{:}];
%% calculate local distances, TIME WARPED version, on MFCC
tic
innerIdx = 0;
progressbar('Saves','Time Warped Distance Calcs');
for ii = 1:N-1
iLen = DRsylls(ii).stop - DRsylls(ii).start;
for jj = ii+1:N
jLen = DRsylls(jj).stop - DRsylls(jj).start;
innerIdx = innerIdx + 1;
% distance, normalized by the average length
distMatrices.warpedLocal(innerIdx) = ...
timeWarpedDistanceMFCC(spectra(ii), spectra(jj), params) / ...
((iLen + jLen) / 2);
progressbar([],innerIdx/nchoosek(N,2));
if rem(innerIdx, floor(sqrt(nchoosek(N,2)))) == 0
save(['tmpDists-' timeFlag],'DRsylls','distMatrices');
progressbar(floor(innerIdx/floor(sqrt(nchoosek(N,2)))) / ...
floor(nchoosek(N,2)/floor(sqrt(nchoosek(N,2)))))
end
end
end
save(['tmpDists-' timeFlag],'DRsylls','distMatrices');
progressbar(1);
tt=toc;
fprintf('Time warping took %0.2f s...\n', tt);
%save([dataPath 'localSimTW-' birdID '.mat'],'clustSylls','twDistM');
%% step 5: measure global distances within pairs of syllables
%seldFeaturesTable = allFeaturesTable;
%clustSylls = allDRsylls(trainIdxs);
%featureTable = allFeaturesTable(trainIdxs,:);
% start with unnormalized table of features
% step 1: normalize to z-scores
fprintf('Calculating global dissimilarity scores...\n');
zNormedFeatures = zscore(featureTable);
distMatrices.global = pdist(zNormedFeatures);
save(['tmpDists-' timeFlag],'DRsylls','distMatrices');
%% get non-parametric (probability-rank) ordering of similarity scores
fprintf('Calculating empirical scores...\n');
scoreTypes = fieldnames(distMatrices);
for ii = 1:numel(scoreTypes)
fld = scoreTypes{ii};
arr = distMatrices.(fld);
[sArr,rord] = sort(arr);
empMatrices.(fld)(rord) = [1:numel(arr)] / numel(arr);
xx = linspace(0,1,nEmpD);
if nEmpD == numel(arr)
yy = sArr;
else
yy = interp1(linspace(0,1,numel(arr)), sArr, xx);
end
% prepare for interp1 by removing redundant entries
redun = [diff(yy)==0 false];
if any(redun)
xx = xx(~redun); % might be better to take a mean of the p-values instead of the max (as this implies)
yy = yy(~redun);
end
empDistrs.(fld) = zeros(2,numel(xx));
empDistrs.(fld)(1,:) = xx;
empDistrs.(fld)(2,:) = yy;
save(['tmpEmp-' timeFlag],'DRsylls','empMatrices', 'empDistrs');
end
%% construct co-similarity as fusion of local and global p-values
fprintf('Calculating co-dissimilarity (correlation of dissimilarities, which is a similarity score)...\n');
fusedPVals = sqrt(empMatrices.warpedLocal .* empMatrices.global);
distMatrices.cosim = pdist(squareform(fusedPVals), 'correlation');
% do the clustering - the easiest part
nClusters = 4:10;
pairLinks = linkage(distMatrices.cosim,'complete');
clustIdxs = cluster(pairLinks,'maxclust',nClusters);
% undo sorting step
clustIdxs(sortedIdx,:) = clustIdxs;
scoreTypes = fieldnames(distMatrices);
for ii = 1:numel(scoreTypes)
distMatrices.(scoreTypes{ii}) = squareform(unsort2D(squareform(distMatrices.(scoreTypes{ii})), sortedIdx));
if isfield(empMatrices, scoreTypes{ii})
empMatrices.(scoreTypes{ii}) = squareform(unsort2D(squareform( empMatrices.(scoreTypes{ii})), sortedIdx));
end
end
end
function mat = unsort2D(mat, sI)
[coordsi, coordsj] = meshgrid(sI,sI);
mat(sub2ind(size(mat),coordsi, coordsj)) = mat;
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
getClipAndProcess.m
|
.m
|
Acoustic_Similarity-master/code/audioProc/getClipAndProcess.m
| 4,796 |
utf_8
|
30480d5c09cbb7de571c3d0a447cf8f6
|
function [clip, fs] = getClipAndProcess(songStruct, region, params, varargin)
% GETCLIPANDPROCESS add pre/post roll, bandpass, and filter noise
%
% function clip = getClipAndProcess(songStruct, region, params) returns the
% clean clip belonging to a specific region, as preprocessed by the
% default params.
%
% revision note: an empty songStruct first argument can be given if the
% region structure has a 'file' field which will access partial read data
if nargin < 3
params = defaultParams;
end
params = processArgs(params, varargin{:});
region = addPrePost(region, params);
% either filter noise or get raw values
[clip, fs] = getClip(region, songStruct);
params.fs = fs;
minFiltLength = (params.noiseReduce.windowSize * 2 - ...
params.noiseReduce.nOverlap) / 1000 + params.nps.attack; % minimum length in seconds
if params.doFilterNoise && length(clip) / fs > minFiltLength
if isempty(songStruct) && isempty(params.noiseFilter) && isfield(region,'file');
[filedir filestem] = fileparts(region.file);
% TODO: check here
manifest = struct('name','noiseMask','originalFile',[filedir filesep 'noiseMask-' filestem '.mat']);
params.noiseFilter = loadFromManifest(manifest,'noiseMask');
end
if isempty(params.noiseFilter)
warning('getClipAndProcess:noNoiseFilter','Cannot find noise filter, skipping filtering for clip...');
else
clip = noiseGate(clip, params.noiseFilter);
end
end
% band pass the result
clip = highPassSample(clip, params);
clip = lowPassSample(clip, params);
function reconstructClip = noiseGate(clip, noiseProfile)
%NOISEGATE Extracts a noise filtered version of a clip
% clip = noiseGate(songStruct, region, noiseProfile) returns a clip which
% has been cleaned of its noise, following certain parameters
% The spectral noise filter is equivalent to a bank of narrow band-passed
% limiters for each frequency band. This has the general advantage of
% precisely defining syllable boundaries in noisier recording conditions.
%
% noiseProfile is the 1D reduced FFT of noise given by noiseAnalysis.m
params.noiseReduce.fs = fs;
spec = getMTSpectrumStats(clip, params.noiseReduce);
nps = params.nps;
% define regions where gating is required -todo, apply hysteresis
gain = ones(size(spec.spectrum)) * nps.reduction;
for ifreq = 1:numel(spec.freqs)
gain(ifreq,spec.psd(ifreq,:) >= noiseProfile(ifreq)) = 0.0;
end
% smooth in frequency space
if nps.freqSmooth > 0.0
dF = spec.freqs(2) - spec.freqs(1);
fWindowSize = nps.freqSmooth * 8/dF; % 4 half widths in either direction
fWindow = (-fWindowSize / 2 : fWindowSize / 2) * dF;
fWindow = exp(- (fWindow / nps.freqSmooth).^2);
fWindow = fWindow ./ sum(fWindow) ; % normalize
gain = conv2(fWindow,[1],gain,'same');
end
% apply attack, hold, release
dT = spec.times(2) - spec.times(1);
attackRate = -nps.reduction * dT / nps.attack;
releaseRate = -nps.reduction * dT / nps.release;
holdSamples = floor(nps.hold / dT);
for ifreq = 1:numel(spec.freqs)
holdClock = 0;
for iTp = 1:numel(spec.times)-1
dG = gain(ifreq, iTp+1) - gain(ifreq, iTp);
if dG > attackRate % attack is on
gain(ifreq, iTp+1) = gain(ifreq, iTp) + attackRate;
elseif gain(ifreq,iTp + 1)== 0.0 % gate is open, hold is ready
holdClock = holdSamples;
elseif holdClock > 0 % hold is being used up
gain(ifreq,iTp+1) = 0.0;
holdClock = holdClock - 1;
elseif dG < -releaseRate
gain(ifreq, iTp+1) = gain(ifreq, iTp) - releaseRate;
end
end
end
% reduce volume in those bands
adjustedSpec = spec.spectrum .* ...
(10.^(gain / 10));
% the inverse spectrogram does not always reproduce the exact volume, but
% this is pretty close to about +/- %1
scaleFac = 2.195; % empirically found by running spectrogram and inverse
winSS = floor(params.noiseReduce.windowSize * params.noiseReduce.fs/1000);
overlapSS = floor(params.noiseReduce.nOverlap * params.noiseReduce.fs/1000);
reconstructClip = invspecgram(adjustedSpec, params.noiseReduce.NfreqBands, ...
params.noiseReduce.fs, winSS, overlapSS);
reconstructClip = reconstructClip / scaleFac;
% debug function to plot either the spectrogram or the shape of the
% spectral noise filter
function hndl = plotGram(mat)
mat = mat + eps;
hndl = surf(spec.times, spec.freqs, 10*log10(abs(mat)),'EdgeColor','none');
view(0,90);
xlim([min(spec.times) max(spec.times)]); xlabel('Time (s)');
ylim([min(spec.freqs) max(spec.freqs)]); ylabel('Frequency (Hz)');
colorbar;
title(sprintf('max=%0.3f,min=%0.3f',10*log10(abs(max(mat(:)))),...
10*log10(abs(min(mat(:))))));
end
end
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
checkForNoise.m
|
.m
|
Acoustic_Similarity-master/code/workflow/checkForNoise.m
| 620 |
utf_8
|
e971738b1c5c8e7b18833aea214510e8
|
for ii = 1:numel(qqq)
[newFeats,newSylls]=getFeatures(Lb277_3_27_4_Ch1,qqq(ii),noiseProfile,...
'plot',false,'verbose',true,'playsample',false);
if ~isempty(newFeats)
noisy = testIsNoise(forest, newFeats(1));
end
end
function isNoise = testIsNoise(Forest, featureStruct)
%% compile feature matrix
fields = fieldnames(featureStruct);
nF = numel(fields); % number of features
nE = numel(marks); % number of examples
featVecs = zeros(nF, nE); % columns-major
for ii = 1:nF
featVecs(ii,:) = [features.(fields{ii})];
end
isNoise = Classify(Forest.Learners, Forest.Weights, featVecs) > 0;
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
loadSessionDataFiles.m
|
.m
|
Acoustic_Similarity-master/code/workflow/loadSessionDataFiles.m
| 3,278 |
utf_8
|
552b52726f88b0358eba63af39b6071c
|
%startup a current session
function loadSessionDataFiles(session)
dataDir = 'data\';
%session = 'Lb277_3_27';
birdID = strtok(session, '_');
dataSubdir = [dataDir dataSubdir filesep];
% look at what files are there
dir([dataSubdir '*' session '*.mat']);
fil=[dataSubdir session '_voice.mat'];
if exist(fil,'file'), load(fil), else fprintf('%s not found...\n', fil); end
fil = [dataSubdir 'markedSongs-' session '_voice.mat'] ;
if exist(fil,'file'), load(fil), else fprintf('%s not found...\n', fil); end
fil = [dataSubdir 'spikeRates-' session '_ch13-16_times.mat'];
if exist(fil,'file'), load(fil), else fprintf('%s not found...\n', fil); end
fil = [dataSubdir 'noiseMask-' session '_voice.mat'];
if exist(fil,'file'), load(fil), else fprintf('%s not found...\n', fil); end
fil = [dataSubdir 'tutorSimilarity-' session '_voice.mat'];
if exist(fil,'file'), load(fil), else fprintf('%s not found...\n', fil); end
% rename the songStruct (it's the biggest one) with a little dirty eval
vars = whos;
[~,bigVarIdx] = max([vars.bytes]);
bigVarName = vars(bigVarIdx).name;
eval(sprintf('songStruct = %s; clear %s', bigVarName, bigVarName));
%% (5) LOAD the SPIKE-SORTED-FILE and calculate firing rate during syllables
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% All processing up to this point has been solely on the song file
%%%% To continue you should have:
%%%% (a) some measure of distance between syllables and different tutor
%%%% syllables (which we call stdDist),
%%%% (b) definitions of the tutor syllables (tutorSylls), and
%%%% (c) juvenileSylls with defined boundaries
%%%% AND types that correspond to the tutor syllables' types
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% this is where we LOAD the SPIKE-SORTED-FILE
[matFile, matSpikePath] = uigetfile('*.mat','Please choose the SPIKING Spike2 file','data');
spikes = loadSpikeData([matSpikePath matFile]);
% calculate spike data w.r.t segmented regions
syllLengths = [juvenileSylls.stop] - [juvenileSylls.start];
for ii = 1:numel(spikes)
[spikeCounts{ii}, spikeInternalTimes{ii}] = countSpikes(juvenileSylls, spikes{ii},'onset');
spikeRates{ii} = spikeCounts{ii} ./ syllLengths;
end
totalSpikeRates = sum(vertcat(spikeRates{:}));
%spikeTimes = sort(vertcat(spikes{:}));
%% (5.x) option: load ANOTHER spike file (make sure (5) is run first)
syllLengths = [juvenileSylls.stop] - [juvenileSylls.start];
while(strcmp(questdlg('Load another spike file?', 'Load more spikes', 'OK','No', 'No'), 'OK'))
[matFile, matpath] = uigetfile('*.mat','Please choose the SPIKING Spike2 file','data');
spikeData = load([matpath matFile]);
clusterFields = fieldnames(spikeData);
% append to spikes cell array and recount
newSpikes = loadSpikeData([matpath matFile]);
spikes = [spikes newSpikes];
for ii = 1:numel(newSpikes)
[spikeCounts{end+1}, spikeInternalTimes{end+1}] = countSpikes(juvenileSylls, newSpikes{ii},'onset');
spikeRates{end+1} = spikeCounts{end} ./ syllLengths;
end
clear spikeData clusterFields
end
if exist('spikeRates'), totalSpikeRates = sum(vertcat(spikeRates{:})); end;
%[spikeCounts, spikeInternalTimes] = countSpikes(juvenileSylls, spikeTimes,'onset');
|
github
|
BottjerLab/Acoustic_Similarity-master
|
examineClusterQuality.m
|
.m
|
Acoustic_Similarity-master/code/workflow/examineClusterQuality.m
| 6,435 |
utf_8
|
79b765aad674ddcef9542920f09210d6
|
%function examineClusterFull(birdID, sessFilter)
function clustQuality = examineClusterQuality(birdID, ages)
% calculate the objective cluster quality for the given ages of a bird
% TODO: test rewrite - make objective cluster score
% get the files
clusterDir = [pwd filesep 'data' filesep 'cluster-' birdID filesep];
dataDir = [pwd filesep 'data' filesep birdID filesep];
clustQuality = cell(1,numel(ages));
for ii = 1:numel(ages)
thisAge = ages(ii);
[fil, filExist] = getLatestFile([clusterDir 'altClustDataAge-' ...
num2str(thisAge) '*.mat']);
if ~filExist
error('examineClusterFull:missingDistances',...
'Missing distances file for bird %s, age %d', birdID, thisAge);
end
load(fil, 'distMats');
labelsFil = sprintf('%sacceptedLabels-%s-age%d.mat', dataDir, birdID, thisAge);
if ~exist(labelsFil,'file')
error('examineClusterFull:missingClusters',...
'Missing clusterfile for bird %s, age %d', birdID, thisAge);
end
syllLabels = loadAcceptedLabels(birdID, thisAge);
% no labels means no quality
if all(isnan(syllLabels))
clustQuality{ii} = [];
continue;
end
% assign the types
num2cell(syllLabels); [thisAgeSylls.type] = ans{:}; %#ok<NOANS>
[~,clustQuality{ii}] = clusterQuality(distMats.cosim, syllLabels);
end
%{
% cross tabulate / tree out types
fprintf('Cross-tabulation of syllables...\n');
nClusterings = size(syllLabels,2);
nMaxClust = max(syllLabels(:,end));
splitNodes = zeros(1,nClusterings);
parentRef = NaN(nMaxClust, nClusterings);
for jj = 1:nClusterings-1
ct = crosstab(syllLabels(:,jj), syllLabels(:,jj+1));
splitNodes(jj) = find(sum(ct > 0, 2)==2,1);
for kk = 1:size(ct,2)
parentRef(kk,jj+1) = find(ct(:, kk) > 0, 1);
end
end
nSylls = sum(isThisAge);
mConform = zeros(nSylls, nClusterings);
vConform = zeros(nSylls, nClusterings);
clustQuality = NaN(nMaxClust, nClusterings);
clustMConform = NaN(nMaxClust, nClusterings);
clustVConform = NaN(nMaxClust, nClusterings);
for jj = 1:nClusterings
thisIdxs = syllLabels(:,jj);
[mConform(:,jj), vConform] = conformity(distMats.cosim, thisIdxs);
nClusters = max(thisIdxs);
[~,clustQuality(1:nClusters, jj)] = clusterQuality(distMats.cosim, thisIdxs);
for kk = 1:nClusters
clustMConform(kk,jj) = mean(mConform(thisIdxs == kk));
clustVConform(kk,jj) = mean(vConform(thisIdxs == kk));
end
end
parentRef
clustQuality
clustMConform
clustVConform
%{
figureDir = [pwd filesep 'figures' filesep 'cluster-' birdID filesep clustSession{ii} filesep];
mkdir([pwd filesep 'figures' filesep 'cluster-' birdID filesep], clustSession{ii});
nClusts = max([thisAgeSylls.type]);
for jj = 1:nClusts
trainClust = thisAgeSylls([thisAgeSylls.type]==jj);
if ~isempty(trainClust),
figure
hf = mosaicDRSpec(trainClust, [], 'dgram.minContrast', 1e-10, 'maxMosaicLength', 5.5, 'noroll');
set(hf,'Name', sprintf('Trained cluster %d, age %d, bird %s', jj, currAge, birdID));
saveCurrFigure(sprintf('%s%s_a%d_c%d-train.jpg', figureDir, birdID, currAge, jj));
close(hf);
end
end
%}
end
%}
%{
% get all cluster files that match the pattern for
files = dir([clusterDir sessFilter]); files = {files.name}';
fPats = {'altClustDataAge-'};
isAssigned = strncmp(fPats{1}, files, length(fPats{1}));
clustSession = strrep(strrep(files(isAssigned), fPats{1}, ''),'.mat','');
fprintf('Loading library for bird %s...\n',birdID);
load([dataDir 'allSpecs-' birdID])
for ii = 1:numel(clustSession)
if ~exist([clusterDir fPats{1} clustSession{ii} '.mat'], 'file') %&& ...
%exist([clusterDir fPats{2} clustSession{ii}],'file'))
continue;
end
fprintf('Loading session %s...\n', clustSession{ii});
clusterIdxs = []; load([clusterDir fPats{1} clustSession{ii}]);
%load([clusterDir fPats{2} clustSession{ii}]);
currAge = str2double(clustSession{ii}(1:2));
isThisAge = [DRsylls.age]==currAge;
thisAgeSylls = DRsylls(isThisAge);
% assign the types
num2cell(clusterIdxs(:,end)); [thisAgeSylls.type] = ans{:}; %#ok<NOANS>
% cross tabulate / tree out types
fprintf('Cross-tabulation of syllables...\n');
nClusterings = size(clusterIdxs,2);
nMaxClust = max(clusterIdxs(:,end));
splitNodes = zeros(1,nClusterings);
parentRef = NaN(nMaxClust, nClusterings);
for jj = 1:nClusterings-1
ct = crosstab(clusterIdxs(:,jj), clusterIdxs(:,jj+1));
splitNodes(jj) = find(sum(ct > 0, 2)==2,1);
for kk = 1:size(ct,2)
parentRef(kk,jj+1) = find(ct(:, kk) > 0, 1);
end
end
nSylls = sum(isThisAge);
mConform = zeros(nSylls, nClusterings);
vConform = zeros(nSylls, nClusterings);
clustQuality = NaN(nMaxClust, nClusterings);
clustMConform = NaN(nMaxClust, nClusterings);
clustVConform = NaN(nMaxClust, nClusterings);
for jj = 1:nClusterings
thisIdxs = clusterIdxs(:,jj);
[mConform(:,jj), vConform] = conformity(distMats.cosim, thisIdxs);
nClusters = max(thisIdxs);
[~,clustQuality(1:nClusters, jj)] = clusterQuality(distMats.cosim, thisIdxs);
for kk = 1:nClusters
clustMConform(kk,jj) = mean(mConform(thisIdxs == kk));
clustVConform(kk,jj) = mean(vConform(thisIdxs == kk));
end
end
parentRef
clustQuality
clustMConform
clustVConform
figureDir = [pwd filesep 'figures' filesep 'cluster-' birdID filesep clustSession{ii} filesep];
mkdir([pwd filesep 'figures' filesep 'cluster-' birdID filesep], clustSession{ii});
nClusts = max([thisAgeSylls.type]);
for jj = 1:nClusts
trainClust = thisAgeSylls([thisAgeSylls.type]==jj);
if ~isempty(trainClust),
figure
hf = mosaicDRSpec(trainClust, [], 'dgram.minContrast', 1e-10, 'maxMosaicLength', 5.5, 'noroll');
set(hf,'Name', sprintf('Trained cluster %d, age %d, bird %s', jj, currAge, birdID));
saveCurrFigure(sprintf('%s%s_a%d_c%d-train.jpg', figureDir, birdID, currAge, jj));
close(hf);
end
end
end
%}
|
github
|
BottjerLab/Acoustic_Similarity-master
|
reportOnData.m
|
.m
|
Acoustic_Similarity-master/code/workflow/reportOnData.m
| 8,565 |
utf_8
|
0855589187bfce7342e1e3c70b7de51b
|
function database = reportOnData(birdIDs, sessions, params, varargin)
% reportOnData looks in the data folder and sees what is generated for what
% folder
% if birdIDs are provided, only looks at those birdIDs
% if sessions are provided, only looks at those sessions
% note: to clear old contents files, run clearSummaries.m
dataDir = [pwd filesep 'data'];
relDataDir = 'data';
% find all the bird IDs from directory
if nargin < 1 || isempty(birdIDs)
files = dir(dataDir);
birdIDs = {files([files.isdir]).name};
isID = ~cellfun('isempty',regexp(birdIDs, '^[A-Z][a-z]?\d{1,3}')); %could make tighter by using cap
birdIDs = birdIDs(isID);
end
if nargin < 2, sessions = ''; end
if nargin < 3 || isempty(params), params = defaultParams; end
if nargin > 3, params = processArgs(params,varargin{:}); end
if ~iscell(birdIDs)
birdIDs = {birdIDs};
end
summaryFields = {'sessionID','manifest','spikeFiles'};
database = cell(1,numel(birdIDs));
for ii = 1:numel(birdIDs) % for each bird
thisID = birdIDs{ii};
thisBirdPath = [dataDir filesep thisID];
relBirdPath = [relDataDir filesep thisID];
matFiles = dir([thisBirdPath filesep '*.mat']);
matFiles = {matFiles.name};
% %%%%%%%% look for spike-sorted neuron files:
% subdirectories in the bird directory
% should contain NEURONS ONLY
subDirs = dir(thisBirdPath); subDirs = {subDirs([subDirs.isdir]).name};
subDirs(1:2)=[]; % get rid of the first two . / .. directories
% get all the neurons in the bird directory subdirectories
birdNeuronFiles = '';
for jj = 1:numel(subDirs)
% get all related files in this subdir
% these are the neuron files
thisSubDir = [thisBirdPath filesep subDirs{jj}];
relSubDir = [relBirdPath filesep subDirs{jj}];
possSpikeFiles = dir([thisSubDir filesep thisID '*times.mat']);
if isempty(possSpikeFiles), continue; end
birdNeuronFiles = [birdNeuronFiles strcat([relSubDir filesep], {possSpikeFiles.name})];
if params.rejectSpikeFiles
filesToReject = ~cellfun('isempty', strfind(birdNeuronFiles,'REJECTME'));
birdNeuronFiles = birdNeuronFiles(~filesToReject);
end
end
isNFClaimed = false(1,numel(birdNeuronFiles));
% look for spike6 session files: they have the ID at the front
isAudioFile = strncmp(matFiles, thisID, numel(thisID));
audioFiles = matFiles(isAudioFile);
% get rid of the mat suffixes
audioFiles = strrep(audioFiles, '.mat','');
if ~isempty(sessions)
audioFiles = intersect(audioFiles, sessions);
end
sessionRecords = initEvents(numel(audioFiles),summaryFields);
for jj = 1:numel(audioFiles) % for each session
thisSession = audioFiles{jj};
thisSpikeStem = strtok(thisSession, '.');
isRelatedFile = ~cellfun('isempty',strfind(matFiles,thisSession));
relatedFiles = matFiles(isRelatedFile); % includes the original spikeFile
% summary file -> not in the data directory,
% but its parent directory
summaryDir = [dataDir filesep '..' filesep 'summaries'];
if ~exist(summaryDir, 'dir'), mkdir(summaryDir); end
summaryFile = [summaryDir filesep ...
'contents-' thisSession '.mat'];
% get a manifest of all the files
% first, check to see if the contents are updated
freshContents = false;
if exist(summaryFile,'file')
freshContents = true;
contentUpdate = getModifiedStamp(summaryFile);
for kk = 1:numel(relatedFiles)
if(getModifiedStamp([thisBirdPath filesep relatedFiles{kk}]) > contentUpdate)
freshContents = false;
break;
end
end
if ~isempty(birdNeuronFiles)
% are we related to this particular session?
isRelatedNeuronFile = ~cellfun('isempty',strfind(birdNeuronFiles,thisSpikeStem));
relatedNeuronFiles = birdNeuronFiles(isRelatedNeuronFile);
for kk = 1:numel(relatedNeuronFiles)
if(getModifiedStamp(relatedNeuronFiles{kk}) > contentUpdate)
freshContents = false;
break;
end
end
end
end
% if the summary file is fresher than its related data files
if freshContents
% read off the manifest from the previous file, converting the one-field
% structure to a struct.
tmp = load(summaryFile);
flds = fieldnames(tmp);
sessionRecords(jj) = tmp.(flds{1});
if ~isempty(birdNeuronFiles)
isNFClaimed = isNFClaimed | cellfun(...
@(x) any(strcmpi(x,sessionRecords(jj).spikeFiles)), birdNeuronFiles);
end
else
[~,idxShortest] = min(cellfun('length',relatedFiles));
[~,contents.sessionID,~] = fileparts(relatedFiles{idxShortest});
relatedFiles = strcat([relBirdPath filesep], relatedFiles);
contents.manifest = getManifest(relatedFiles);
contents.spikeFiles = [];
% find the neuron files that are linked with the session
if ~isempty(birdNeuronFiles)
isRelatedNeuronFile = ~cellfun('isempty',strfind(birdNeuronFiles,thisSpikeStem));
isNFClaimed = isNFClaimed | isRelatedNeuronFile;
contents.spikeFiles = birdNeuronFiles(isRelatedNeuronFile);
end
save(summaryFile,'contents');
sessionRecords(jj) = contents;
end
spikeData = loadSpikeData(sessionRecords(jj).spikeFiles);
nNeurons = numel(spikeData);
if params.verbose && ~params.quiet
fprintf('%s/%s with %d recorded variables, %d neurons...\n', thisID, thisSession, numel(sessionRecords(jj).manifest), nNeurons);
for kk = 1:numel(sessionRecords(jj).manifest)
fprintf('\t%s --> %s\n', sessionRecords(jj).manifest(kk).originalFile, ...
sessionRecords(jj).manifest(kk).name);
end
end
end
unclaimedSessionRecords = initEvents(0,summaryFields);
if any(~isNFClaimed) && isempty(sessions)
unclaimedNeuronFiles = birdNeuronFiles(~isNFClaimed);
% get the session IDs from the neuron files
sessionID = cell(1,numel(unclaimedNeuronFiles));
for jj = 1:numel(unclaimedNeuronFiles)
[~, birdFile] = fileparts(unclaimedNeuronFiles{jj});
sessionID{jj} = birdFile(1:strfind(birdFile, '_ch')-1);
end
[uniqIDs,~,crossIdxs] = unique(sessionID);
unclaimedSessionRecords = initEvents(numel(uniqIDs),summaryFields);
for jj = 1:numel(uniqIDs)
unclaimedSessionRecords(jj).sessionID = sessionID{jj};
iNeuronFiles = unclaimedNeuronFiles(crossIdxs==jj);
if params.verbose && ~params.quiet
fprintf('UNPROCESSED: %s - %d attached neurons...\n', uniqIDs{jj}, ...
numel(loadSpikeData(iNeuronFiles)));
end
% let's create an entry for these files now
unclaimedSessionRecords(jj).manifest=[];
unclaimedSessionRecords(jj).spikeFiles = iNeuronFiles;
end
end
database{ii} = [sessionRecords unclaimedSessionRecords];
if params.rejectNoNeuronSessions
sessionsWithNoNeurons = arrayfun(@(x) isempty(x.spikeFiles), database{ii});
database{ii}(sessionsWithNoNeurons) = [];
end
end
if numel(database) == 1
database = database{1};
end
function manifest = getManifest(files)
manifest = initEvents(0, {'name','originalFile'});
for iFile = 1:numel(files)
thisFile = files{iFile};
% this who step is slow, so we presave manifests
vars = who('-file', thisFile);
fileVars = struct('name',vars);
% make the path relative
[fileVars.originalFile] = deal(thisFile);
% we don't know how many variables each file has, hence no
% preallocation
manifest = [manifest; fileVars];
end
function timestamp = getModifiedStamp(filename)
if ~iscell(filename)
timestamp = getfield(dir(filename),'datenum');
else
timestamp = cellfun(@(x) getfield(dir(x),'datenum'), filename);
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
examineClusterFull.m
|
.m
|
Acoustic_Similarity-master/code/workflow/examineClusterFull.m
| 6,296 |
utf_8
|
de956fe651ecf344838022d04dae40fb
|
%function examineClusterFull(birdID, sessFilter)
function clustQuality = examineClusterFull(birdID, ages)
% get the objective cluster quality for the given ages of a bird
% TODO: test rewrite - make objective cluster score
% get the files
clusterDir = [pwd filesep 'data' filesep 'cluster-' birdID filesep];
dataDir = [pwd filesep 'data' filesep birdID filesep];
clustQuality = cell(1,numel(ages));
for ii = 1:numel(ages)
thisAge = ages(ii);
[fil, filExist] = getLatestFile([clusterDir 'altClustDataAge-' ...
num2str(thisAge) '*.mat']);
if ~filExist
error('examineClusterFull:missingDistances',...
'Missing distances file for bird %s, age %d', birdID, thisAge);
end
load(fil, 'distMats');
labelsFil = sprintf('%sacceptedLabels-%s-age%d.mat', dataDir, birdID, thisAge);
if ~exist(labelsFil,2)
error('examineClusterFull:missingClusters',...
'Missing clusterfile for bird %s, age %d', birdID, thisAge);
end
syllLabels = loadAcceptedLabels(birdID, thisAge);
% assign the types
num2cell(syllLabels); [thisAgeSylls.type] = ans{:}; %#ok<NOANS>
[~,clustQuality{ii}] = clusterQuality(distMats.cosim, syllLabels);
end
%{
% cross tabulate / tree out types
fprintf('Cross-tabulation of syllables...\n');
nClusterings = size(syllLabels,2);
nMaxClust = max(syllLabels(:,end));
splitNodes = zeros(1,nClusterings);
parentRef = NaN(nMaxClust, nClusterings);
for jj = 1:nClusterings-1
ct = crosstab(syllLabels(:,jj), syllLabels(:,jj+1));
splitNodes(jj) = find(sum(ct > 0, 2)==2,1);
for kk = 1:size(ct,2)
parentRef(kk,jj+1) = find(ct(:, kk) > 0, 1);
end
end
nSylls = sum(isThisAge);
mConform = zeros(nSylls, nClusterings);
vConform = zeros(nSylls, nClusterings);
clustQuality = NaN(nMaxClust, nClusterings);
clustMConform = NaN(nMaxClust, nClusterings);
clustVConform = NaN(nMaxClust, nClusterings);
for jj = 1:nClusterings
thisIdxs = syllLabels(:,jj);
[mConform(:,jj), vConform] = conformity(distMats.cosim, thisIdxs);
nClusters = max(thisIdxs);
[~,clustQuality(1:nClusters, jj)] = clusterQuality(distMats.cosim, thisIdxs);
for kk = 1:nClusters
clustMConform(kk,jj) = mean(mConform(thisIdxs == kk));
clustVConform(kk,jj) = mean(vConform(thisIdxs == kk));
end
end
parentRef
clustQuality
clustMConform
clustVConform
%{
figureDir = [pwd filesep 'figures' filesep 'cluster-' birdID filesep clustSession{ii} filesep];
mkdir([pwd filesep 'figures' filesep 'cluster-' birdID filesep], clustSession{ii});
nClusts = max([thisAgeSylls.type]);
for jj = 1:nClusts
trainClust = thisAgeSylls([thisAgeSylls.type]==jj);
if ~isempty(trainClust),
figure
hf = mosaicDRSpec(trainClust, [], 'dgram.minContrast', 1e-10, 'maxMosaicLength', 5.5, 'noroll');
set(hf,'Name', sprintf('Trained cluster %d, age %d, bird %s', jj, currAge, birdID));
saveCurrFigure(sprintf('%s%s_a%d_c%d-train.jpg', figureDir, birdID, currAge, jj));
close(hf);
end
end
%}
end
%{
% get all cluster files that match the pattern for
files = dir([clusterDir sessFilter]); files = {files.name}';
fPats = {'altClustDataAge-'};
isAssigned = strncmp(fPats{1}, files, length(fPats{1}));
clustSession = strrep(strrep(files(isAssigned), fPats{1}, ''),'.mat','');
fprintf('Loading library for bird %s...\n',birdID);
load([dataDir 'allSpecs-' birdID])
for ii = 1:numel(clustSession)
if ~exist([clusterDir fPats{1} clustSession{ii} '.mat'], 'file') %&& ...
%exist([clusterDir fPats{2} clustSession{ii}],'file'))
continue;
end
fprintf('Loading session %s...\n', clustSession{ii});
clusterIdxs = []; load([clusterDir fPats{1} clustSession{ii}]);
%load([clusterDir fPats{2} clustSession{ii}]);
currAge = str2double(clustSession{ii}(1:2));
isThisAge = [DRsylls.age]==currAge;
thisAgeSylls = DRsylls(isThisAge);
% assign the types
num2cell(clusterIdxs(:,end)); [thisAgeSylls.type] = ans{:}; %#ok<NOANS>
% cross tabulate / tree out types
fprintf('Cross-tabulation of syllables...\n');
nClusterings = size(clusterIdxs,2);
nMaxClust = max(clusterIdxs(:,end));
splitNodes = zeros(1,nClusterings);
parentRef = NaN(nMaxClust, nClusterings);
for jj = 1:nClusterings-1
ct = crosstab(clusterIdxs(:,jj), clusterIdxs(:,jj+1));
splitNodes(jj) = find(sum(ct > 0, 2)==2,1);
for kk = 1:size(ct,2)
parentRef(kk,jj+1) = find(ct(:, kk) > 0, 1);
end
end
nSylls = sum(isThisAge);
mConform = zeros(nSylls, nClusterings);
vConform = zeros(nSylls, nClusterings);
clustQuality = NaN(nMaxClust, nClusterings);
clustMConform = NaN(nMaxClust, nClusterings);
clustVConform = NaN(nMaxClust, nClusterings);
for jj = 1:nClusterings
thisIdxs = clusterIdxs(:,jj);
[mConform(:,jj), vConform] = conformity(distMats.cosim, thisIdxs);
nClusters = max(thisIdxs);
[~,clustQuality(1:nClusters, jj)] = clusterQuality(distMats.cosim, thisIdxs);
for kk = 1:nClusters
clustMConform(kk,jj) = mean(mConform(thisIdxs == kk));
clustVConform(kk,jj) = mean(vConform(thisIdxs == kk));
end
end
parentRef
clustQuality
clustMConform
clustVConform
figureDir = [pwd filesep 'figures' filesep 'cluster-' birdID filesep clustSession{ii} filesep];
mkdir([pwd filesep 'figures' filesep 'cluster-' birdID filesep], clustSession{ii});
nClusts = max([thisAgeSylls.type]);
for jj = 1:nClusts
trainClust = thisAgeSylls([thisAgeSylls.type]==jj);
if ~isempty(trainClust),
figure
hf = mosaicDRSpec(trainClust, [], 'dgram.minContrast', 1e-10, 'maxMosaicLength', 5.5, 'noroll');
set(hf,'Name', sprintf('Trained cluster %d, age %d, bird %s', jj, currAge, birdID));
saveCurrFigure(sprintf('%s%s_a%d_c%d-train.jpg', figureDir, birdID, currAge, jj));
close(hf);
end
end
end
%}
|
github
|
BottjerLab/Acoustic_Similarity-master
|
getSpectrumStats.m
|
.m
|
Acoustic_Similarity-master/code/obsoleted/getSpectrumStats.m
| 3,226 |
utf_8
|
9acb25dde98de1dc0bc1056aa718a6c2
|
function spectrumData = getSpectrumStats(sample, params)
% acquire spectrogram - TODO: use multitaper
% https://github.com/dmeliza/libtfr
winSS = floor(params.windowSize * params.fs/1000);
overlapSS = floor(params.nOverlap * params.fs/1000);
if ~isfield(params,'freqBands')
[spectrumData.spectrum, spectrumData.freqs, spectrumData.times, spectrumData.psd] = ...
spectrogram(sample, winSS, overlapSS, params.NfreqBands, params.fs);
else
[spectrumData.spectrum, spectrumData.freqs, spectrumData.times, spectrumData.psd] = ...
spectrogram(sample, winSS, overlapSS, params.freqBands, params.fs);
end
% get center frequency - simply weighted average by spectrogram
spectrumData.centerFreq = getCentroidFreq(spectrumData);
% get total power
spectrumData.totalPower = getTotalPower(spectrumData);
% get wiener entropy: high = noise, low = pure tone, mid = stack/chirp ramp
spectrumData.wienerEntropy = getEntropy(spectrumData);
% get derivative spectrogram
%spectrumData.deriv = getSpectralDerivative(spectrumData, params);
[spectrumData.deriv spectrumData.mTD, spectrumData.mFD, spectrumData.FM] ...
= getSpectralDerivative(spectrumData);
% get pitch goodness
[spectrumData.pitchGoodness, spectrumData.harmonicPitch] = ...
getHarmonicPitch(spectrumData, params);
end
function power = getTotalPower(spectrum)
dFreq = spectrum.freqs(2) - spectrum.freqs(1);
power = sqrt(dot(spectrum.psd,spectrum.psd)) * dFreq; % amp factor
end
function centerfreq = getCentroidFreq(spectrum)
centerfreq = spectrum.freqs' * spectrum.psd ./ sum(spectrum.psd);
end
function entropy = getEntropy(spectrum)
% Wiener entropy, defined as the log ratio of GM to AM of the power
% pure white noise means that Wiener entropy should be 0, and pure tone
% should have -inf Wiener entropy
AMpsd = mean(spectrum.psd,1);
logGMpsd = mean(log(spectrum.psd),1);
entropy = logGMpsd - log(AMpsd);
end
% get mag gradient of spectral derivative, as well as frequency Modulation
function [deriv, mTD, mFD, freqMod] = getSpectralDerivative(spectrum)
% and maximum derivatives in time and space
dt = spectrum.times(2) - spectrum.times(1);
df = spectrum.freqs(2) - spectrum.freqs(1);
freqDeriv = conv2([1 0 -1], [1], spectrum.psd, 'same') / df;
timeDeriv = conv2([1], [1 0 -1], spectrum.psd, 'same') / dt;
deriv = sqrt(timeDeriv.*timeDeriv + ...
freqDeriv .* freqDeriv) .* sign(freqDeriv);
mTD = max(abs(timeDeriv),[],1);
mFD = max(abs(freqDeriv),[],1);
%[foo,imax] = max(abs(deriv), [], 1);
% this definition doesn't seem to be that sensitive
freqMod = atan2(max(abs(freqDeriv),[],1),max(abs(timeDeriv / 1000),[],1)) * 180 / pi;
%{
tPart = sin(freqMod);
fPart = cos(freqMod);
deriv = timeDeriv .* (ones(size(spectrum.freqs)) * tPart) + ...
freqDeriv .* (ones(size(spectrum.freqs)) * fPart) ./ spectrum.psd;
%}
end
function [goodness, pitch] = getHarmonicPitch(spectrum, params)
dcepstrum = fft(spectrum.deriv,[],1);
dcepstrum = dcepstrum(1:ceil(end/2),:); % remove the non-symmetric part out
% pitch goodness is unscaled, calculated from deriv-cepstrum as in SAP
% 2011
% harmonic pitch is estimated as well - doesn't seem to work too well
[goodness, pitch] = max(abs(dcepstrum),[],1);
pitch = params.fs./pitch;
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
editEventsNoLabelOLD.m
|
.m
|
Acoustic_Similarity-master/code/obsoleted/editEventsNoLabelOLD.m
| 10,197 |
utf_8
|
99d7c2aaf98a6ba4f6f61bea18bafa43
|
afunction evsNew = editEvents(evs)
% Prereqs: active figure/axes that are appropriate to have marks
% evs is properly sorted
% NB: for resizing to work properly, the axes property 'Units' should be
% normalized
% housekeeping, removing warning
RGBWarnID = 'MATLAB:hg:patch:RGBColorDataNotSupported';
warnState = warning('query',RGBWarnID);
warning('off',RGBWarnID');
if isempty(evs),
evs = initEvents;
fs = 44100; % FIXME: a pure guess
warning('editEvents:InputUninitialized','Events uninitialized, sampling rate may be incorrect...');
else
fs = evs(1).idxStart/evs(1).start;
end
greycol = [0.75 0.75 0.75];
% inform user of termination behavior
oldTitle = get(get(gca,'Title'),'String');
newTitle = 'Click outside figure to exit and save';
title(newTitle);
% create patch handle
hp = plotAreaMarks(evs,greycol);
% prepare handle for axes
hax = gca;
set(gcf,'WindowButtonMotionFcn',{@mouseOverFcn, [hax hp]});
set(gcf,'WindowButtonUpFcn',{@buttonUpFcn, [hax hp]});
set(gcf,'WindowButtonDownFcn',{@buttonDownFcn, [hax hp]});
disp('Click outside to finish');
% exit is triggered when mouseUp occurs outside the axis window
waitfor(gcf,'WindowButtonMotionFcn','');
% clean up - restore any changed properties
title(oldTitle);
warning(warnState.state,RGBWarnID);
% read the events back from the edited patch handle
xdat = get(hp,'XData');
evsNew = initEvents(size(xdat,2));
if isempty(xdat), return; end; % return an empty event structure if no marks
starts = num2cell(xdat(2,:)); idxStarts = num2cell(floor(xdat(2,:) * fs));
stops = num2cell(xdat(3,:)); idxStops = num2cell( ceil(xdat(3,:) * fs));
[evsNew.start] = starts{:}; [evsNew.stop] = stops{:};
[evsNew.idxStart] = idxStarts{:}; [evsNew.idxStop] = idxStops{:};
[evsNew.type] = deal(NaN);
% get rid of the old patch
delete(hp);
end
%%%%%%%% begin callbacks %%%%%%%%%%%%%
function mouseOverFcn(gcbo, eventdata, handles)
% some default colors
greyCol = [0.75 0.75 0.75];
hiCol = [0.5 0.5 0.5];
lineCol = [0.8 0 0];
% unpack handles
hax = handles(1); hp = handles(2);
currPt = get(hax,'CurrentPoint'); currPt = currPt(1,1:2);
nFaces = size(get(hp,'XData'),2);
vertexColors = greyCol(ones(4 * nFaces,1),:);
[patchHover, lineHover] = clickStatus(currPt, handles);
if ~isempty(patchHover)
% highlight that patch
vertexColors(patchHover * 4 - 3,:) = hiCol;
if ~isempty(lineHover)
vertexColors(patchHover * 4 + [-2,0],:) = lineCol(ones(2,1),:);
end
end
set(hp,'FaceVertexCData',vertexColors);
end
function buttonUpFcn(gcbo, eventdata, handles)
hax = handles(1); hp = handles(2);
% get point relative to window to determine if click lies outside
currPt = get(gcbo,'CurrentPoint'); currPt(1,1:2);
oldUnits = get(gca,'Units');
set(gca,'Units','pixels');
axisWindow = get(hax,'Position');
set(gca,'Units',oldUnits);
% exiting function - did we click outside the figure and not as part of a drag?
if ~inRect(axisWindow, currPt) && isempty(get(hp,'UserData'));
% clearing the callbacks is the signal for the program to exit
set(gcbo,'WindowButtonMotionFcn','');
set(gcbo,'WindowButtonUpFcn','');
set(gcbo,'WindowButtonDownFcn','');
elseif ~isempty(get(hp,'UserData')) % finished editing drag
% clean up intervals
%(1) negative intervals - delete
%(2) overlapping intervals - merge
resolveOverlaps(hp);
set(hp,'UserData','');
set(gcbo,'WindowButtonMotionFcn',{@mouseOverFcn, [hax hp]});
disp('Letting go');
else
disp('Still holding on...');
end
end
function buttonDownFcn(gcbo, eventdata, handles)
hax = handles(1); hp = handles(2);
currPt = get(hax,'CurrentPoint'); currPt = currPt(1,1:2);
[patchClicked, lineClicked, hitWindow] = clickStatus(currPt, handles);
if ~hitWindow, return; end;
if isempty(patchClicked) % nothing, or create new event?
% create new event
currXData = get(hp, 'XData');
currYData = get(hp, 'YData');
currVertexColors = get(hp, 'FaceVertexCData');
% find where to insert new event
insertPt = find(currPt(1) <= [currXData(1,:) Inf], 1);
currXData = [currXData(:,1:insertPt-1) currPt(1)*ones(4,1) currXData(:,insertPt:end)];
currYData = currYData(:,[1 1:end]); %all columns are the same
currVertexColors = currVertexColors([ones(1,4) 1:end], :);% we need four more rows, but the colors are all the same
disp('Creating patch');
set(hp,'XData',currXData,'YData',currYData,'FaceVertexCData',currVertexColors);
% handle dragging
dragData = struct('lineHeld', [], ...
'startPt', currPt, ...
'patchHeld', insertPt, ...
'justCreated', true, ...
'origBounds', []);
set(hp,'UserData',dragData);
set(gcf,'WindowButtonMotionFcn',{@draggingFcn, [hax hp]});
else
xBounds = get(hp,'XData');
dragData = struct('lineHeld', lineClicked, ...
'startPt', currPt, ...
'patchHeld', patchClicked,...
'justCreated', false,...
'origBounds', xBounds(:,patchClicked));
set(hp,'UserData',dragData);
set(gcf,'WindowButtonMotionFcn',{@draggingFcn, [hax hp]});
end
end
function draggingFcn(gcbo, eventdata, handles)
hax = handles(1); hp = handles(2);
currPt = get(hax,'CurrentPoint');
currXData = get(hp,'XData');
userData = get(hp, 'UserData');
%nFaces = size(currXData,2);
if isempty(userData.patchHeld)
error('editEvents:PatchNotClicked','Patch not Clicked, drag callback should not be set');
end
if userData.justCreated
% if we just created an event, detect the drag motion
if currPt(1) ~= userData.startPt(1)
userData.justCreated = false;
userData.lineHeld = 1 + (currPt(1) > userData.startPt(1));
end
set(hp,'UserData',userData);
end
if ~isempty(userData.lineHeld) % moving one edge of the eventdata
rIdxs = 2 * userData.lineHeld + [-1 0];
currXData(rIdxs,userData.patchHeld) = currPt(1);
set(hp,'XData',currXData);
% detect collisions immediately and quit drag
if detectCollision(hp,userData.patchHeld,userData.lineHeld),
set(gcbo,'WindowButtonMotionFcn',{@mouseOverFcn, [hax hp]});
resolveOverlaps(hp);
end
else % moving whole event
currXData(:,userData.patchHeld) = userData.origBounds + currPt(1) - userData.startPt(1);
set(hp,'XData',currXData);
end
end
%%%%%%%% end callbacks %%%%%%%%%%%%%
function resolveOverlaps(patchHandle)
% cleaning up intervals,
%keeping colors the same
greyCol = [0.75 0.75 0.75];
currXData = get(patchHandle,'XData');
currYData = get(patchHandle,'YData');
%currColData = get(patchHandle,'FaceVertexCData');
if isempty(currXData), return; end; % nothing to resolve
borders = currXData(2:3,:);
% remove any negative-length intervals
isNonPosLength = (borders(1,:) >= borders(2,:));
borders(:,isNonPosLength) = [];
% merge overlapping regions
% since the number of regions is probably small (<10), we'll do this in a
% naive way (better is with interval trees)
ii = 1;
nFaces = size(borders,2);
while ii <= nFaces && nFaces > 1
toMerge = find(borders(1,ii) >= borders(1,:) & borders(1,ii) <= borders(2,:) | ...
borders(2,ii) >= borders(1,:) & borders(2,ii) <= borders(2,:));
if numel(toMerge) > 1
disp('Merging...')
borders(1,ii) = min(borders(1,toMerge));
borders(2,ii) = max(borders(2,toMerge));
toDelete = toMerge(toMerge ~= ii);
borders(:,toDelete) = [];
nFaces = size(borders,2);
else
ii = ii + 1;
end
end
currXData = borders([1 1 2 2],:);
currYData = currYData(:,ones(1,nFaces)); % just copy the first row
currColData = greyCol(ones(4*nFaces,1),:); % just copy the first color
set(patchHandle,'XData',currXData,'YData',currYData,'FaceVertexCData',currColData);
end
function didCollide = detectCollision(patchHandle,activePatch, boundarySide)
currXData = get(patchHandle,'XData');
if isempty(currXData), return; end; % nothing to collide
borders = currXData(2:3,:);
nFaces = size(borders,2);
adjBorder = NaN;
if activePatch > 1 && boundarySide == 1
adjBorder = borders(2,activePatch - 1);
elseif activePatch < nFaces && boundarySide == 2
adjBorder = borders(1,activePatch + 1);
end
didCollide = (borders(1,activePatch) >= borders(2,activePatch)) || ...
borders(boundarySide,activePatch) <= adjBorder && boundarySide == 1 || ...
borders(boundarySide,activePatch) >= adjBorder && boundarySide == 2;
end
function foo = inRect(win, pt)
foo = win(1) <= pt(1) && pt(1) < win(1) + win(3) && ...
win(2) <= pt(2) && pt(2) < win(2) + win(4);
end
function [patchSeld, lineSeld, hitWindow] = clickStatus(currPt, handles)
% returns empties on default
patchSeld = []; lineSeld = [];
hax = handles(1); hp = handles(2);
win([1 3]) = get(hax,'XLim'); win(3) = win(3) - win(1);
win([2 4]) = get(hax,'YLim'); win(4) = win(4) - win(2);
hitWindow = inRect(win, currPt); if ~hitWindow, return, end;
% how 'fat' should our edge be for us to highlight/grab it?
edgeFuzzFrac = 0.0035;
edgeFuzz = diff(get(hax,'XLim')) * edgeFuzzFrac;
yy = get(hax,'YLim');
if currPt(2) > yy(2) || currPt(2) < yy(1), return; end;
xBounds = get(hp,'XData');
if isempty(xBounds), return; end; % nothing to click
xBounds = xBounds(2:3,:);
patchSeld = ...
find(xBounds(1,:) - edgeFuzz <= currPt(1) & ...
xBounds(2,:) + edgeFuzz >= currPt(1));
if any(abs(xBounds(1,:) - currPt(1)) <= edgeFuzz), lineSeld = 1;
elseif any(abs(xBounds(2,:) - currPt(1)) <= edgeFuzz), lineSeld = 2;
end
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
editEventsOLD.m
|
.m
|
Acoustic_Similarity-master/code/obsoleted/editEventsOLD.m
| 10,556 |
utf_8
|
06bb207d15179346d7118826aa157710
|
function evsNew = editEvents(evs,fs)
% Prereqs: active figure/axes that are appropriate to have marks
% evs is properly sorted
% NB: for resizing to work properly, the axes property 'Units' should be
% normalized
% housekeeping, removing warning
RGBWarnID = 'MATLAB:hg:patch:RGBColorDataNotSupported';
warnState = warning('query',RGBWarnID);
warning('off',RGBWarnID');
if isempty(evs),
evs = initEvents;
if nargin < 2
fs = 44100; % FIXME: a pure guess
warning('editEvents:InputUninitialized','Events uninitialized, sampling rate may be incorrect...');
end
else
if nargin < 2
fs = evs(1).idxStart/evs(1).start;
end
end
greycol = [0.75 0.75 0.75];
% inform user of termination behavior
oldTitle = get(get(gca,'Title'),'String');
newTitle = 'Click outside figure to exit and save';
title(newTitle);
% create patch handle
if isempty(evs)
% create a fake event and then delete it later
hp = plotAreaMarks(initEvent,greycol);
else
hp = plotAreaMarks(evs,greycol);
end
% prepare handle for axes
hax = gca;
set(gcf,'WindowButtonMotionFcn',{@mouseOverFcn, [hax hp]});
set(gcf,'WindowButtonUpFcn',{@buttonUpFcn, [hax hp]});
set(gcf,'WindowButtonDownFcn',{@buttonDownFcn, [hax hp]});
disp('Click outside to finish');
% exit is triggered when mouseUp occurs outside the axis window
waitfor(gcf,'WindowButtonMotionFcn','');
% clean up - restore any changed properties
title(oldTitle);
warning(warnState.state,RGBWarnID);
% read the events back from the edited patch handle
xdat = get(hp,'XData');
evsNew = initEvents(size(xdat,2));
if isempty(xdat), return; end; % return an empty event structure if no marks
starts = num2cell(xdat(2,:)); idxStarts = num2cell(floor(xdat(2,:) * fs));
stops = num2cell(xdat(3,:)); idxStops = num2cell( ceil(xdat(3,:) * fs));
[evsNew.start] = starts{:}; [evsNew.stop] = stops{:};
[evsNew.idxStart] = idxStarts{:}; [evsNew.idxStop] = idxStops{:};
[evsNew.type] = deal(NaN);
% get rid of the old patch
delete(hp);
% if we had an empty event structure to begin with, remove the placeholder
% first structure
if isempty(evs)
evsNew(1) = [];
end
end
%%%%%%%% begin callbacks %%%%%%%%%%%%%
function mouseOverFcn(gcbo, eventdata, handles)
% some default colors
greyCol = [0.75 0.75 0.75];
hiCol = [0.5 0.5 0.5];
lineCol = [0.8 0 0];
% unpack handles
hax = handles(1); hp = handles(2);
currPt = get(hax,'CurrentPoint'); currPt = currPt(1,1:2);
nFaces = size(get(hp,'XData'),2);
vertexColors = greyCol(ones(4 * nFaces,1),:);
[patchHover, lineHover] = clickStatus(currPt, handles);
if ~isempty(patchHover)
% highlight that patch
vertexColors(patchHover * 4 - 3,:) = hiCol; % what if numel(patchHover) > 1
if ~isempty(lineHover)
vertexColors(patchHover * 4 + [-2,0],:) = lineCol(ones(2,1),:);
end
end
set(hp,'FaceVertexCData',vertexColors);
end
function buttonUpFcn(gcbo, eventdata, handles)
hax = handles(1); hp = handles(2);
% get point relative to window to determine if click lies outside
currPt = get(gcbo,'CurrentPoint'); currPt(1,1:2);
oldUnits = get(gca,'Units');
set(gca,'Units','pixels');
axisWindow = get(hax,'Position');
set(gca,'Units',oldUnits);
% exiting function - did we click outside the figure and not as part of a drag?
if ~inRect(axisWindow, currPt) && isempty(get(hp,'UserData'));
% clearing the callbacks is the signal for the program to exit
set(gcbo,'WindowButtonMotionFcn','');
set(gcbo,'WindowButtonUpFcn','');
set(gcbo,'WindowButtonDownFcn','');
elseif ~isempty(get(hp,'UserData')) % finished editing drag
% clean up intervals
%(1) negative intervals - delete
%(2) overlapping intervals - merge
resolveOverlaps(hp);
set(hp,'UserData','');
set(gcbo,'WindowButtonMotionFcn',{@mouseOverFcn, [hax hp]});
disp('Letting go');
else
disp('Still holding on...');
end
end
function buttonDownFcn(gcbo, eventdata, handles)
hax = handles(1); hp = handles(2);
currPt = get(hax,'CurrentPoint'); currPt = currPt(1,1:2);
[patchClicked, lineClicked, hitWindow] = clickStatus(currPt, handles);
if ~hitWindow, return; end;
if isempty(patchClicked) % nothing, or create new event?
% create new event
currXData = get(hp, 'XData');
currYData = get(hp, 'YData');
currVertexColors = get(hp, 'FaceVertexCData');
% find where to insert new event
insertPt = find(currPt(1) <= [currXData(1,:) Inf], 1);
currXData = [currXData(:,1:insertPt-1) currPt(1)*ones(4,1) currXData(:,insertPt:end)];
currYData = currYData(:,[1 1:end]); %all columns are the same
currVertexColors = currVertexColors([ones(1,4) 1:end], :);% we need four more rows, but the colors are all the same
disp('Creating patch');
set(hp,'XData',currXData,'YData',currYData,'FaceVertexCData',currVertexColors);
% handle dragging
dragData = struct('lineHeld', [], ...
'startPt', currPt, ...
'patchHeld', insertPt, ...
'justCreated', true, ...
'origBounds', []);
set(hp,'UserData',dragData);
set(gcf,'WindowButtonMotionFcn',{@draggingFcn, [hax hp]});
else
xBounds = get(hp,'XData');
dragData = struct('lineHeld', lineClicked, ...
'startPt', currPt, ...
'patchHeld', patchClicked,...
'justCreated', false,...
'origBounds', xBounds(:,patchClicked));
set(hp,'UserData',dragData);
set(gcf,'WindowButtonMotionFcn',{@draggingFcn, [hax hp]});
end
end
function draggingFcn(gcbo, eventdata, handles)
hax = handles(1); hp = handles(2);
currPt = get(hax,'CurrentPoint');
currXData = get(hp,'XData');
userData = get(hp, 'UserData');
%nFaces = size(currXData,2);
if isempty(userData.patchHeld)
error('editEvents:PatchNotClicked','Patch not Clicked, drag callback should not be set');
end
if userData.justCreated
% if we just created an event, detect the drag motion
if currPt(1) ~= userData.startPt(1)
userData.justCreated = false;
userData.lineHeld = 1 + (currPt(1) > userData.startPt(1));
end
set(hp,'UserData',userData);
end
if ~isempty(userData.lineHeld) % moving one edge of the eventdata
rIdxs = 2 * userData.lineHeld + [-1 0];
currXData(rIdxs,userData.patchHeld) = currPt(1);
set(hp,'XData',currXData);
% detect collisions immediately and quit drag
if detectCollision(hp,userData.patchHeld,userData.lineHeld),
set(gcbo,'WindowButtonMotionFcn',{@mouseOverFcn, [hax hp]});
resolveOverlaps(hp);
end
else % moving whole event
currXData(:,userData.patchHeld) = userData.origBounds + currPt(1) - userData.startPt(1);
set(hp,'XData',currXData);
end
end
%%%%%%%% end callbacks %%%%%%%%%%%%%
function resolveOverlaps(patchHandle)
% cleaning up intervals,
%keeping colors the same
greyCol = [0.75 0.75 0.75];
currXData = get(patchHandle,'XData');
currYData = get(patchHandle,'YData');
%currColData = get(patchHandle,'FaceVertexCData');
if isempty(currXData), return; end; % nothing to resolve
borders = currXData(2:3,:);
% remove any negative-length intervals
isNonPosLength = (borders(1,:) >= borders(2,:));
borders(:,isNonPosLength) = [];
% merge overlapping regions
% since the number of regions is probably small (<10), we'll do this in a
% naive way (better is with interval trees)
ii = 1;
nFaces = size(borders,2);
while ii <= nFaces && nFaces > 1
toMerge = find(borders(1,ii) >= borders(1,:) & borders(1,ii) <= borders(2,:) | ...
borders(2,ii) >= borders(1,:) & borders(2,ii) <= borders(2,:));
if numel(toMerge) > 1
disp('Merging...')
borders(1,ii) = min(borders(1,toMerge));
borders(2,ii) = max(borders(2,toMerge));
toDelete = toMerge(toMerge ~= ii);
borders(:,toDelete) = [];
nFaces = size(borders,2);
else
ii = ii + 1;
end
end
currXData = borders([1 1 2 2],:);
currYData = currYData(:,ones(1,nFaces)); % just copy the first row
currColData = greyCol(ones(4*nFaces,1),:); % just copy the first color
set(patchHandle,'XData',currXData,'YData',currYData,'FaceVertexCData',currColData);
end
function didCollide = detectCollision(patchHandle,activePatch, boundarySide)
currXData = get(patchHandle,'XData');
if isempty(currXData), return; end; % nothing to collide
borders = currXData(2:3,:);
nFaces = size(borders,2);
adjBorder = NaN;
if activePatch > 1 && boundarySide == 1
adjBorder = borders(2,activePatch - 1);
elseif activePatch < nFaces && boundarySide == 2
adjBorder = borders(1,activePatch + 1);
end
didCollide = (borders(1,activePatch) >= borders(2,activePatch)) || ...
borders(boundarySide,activePatch) <= adjBorder && boundarySide == 1 || ...
borders(boundarySide,activePatch) >= adjBorder && boundarySide == 2;
end
function foo = inRect(win, pt)
foo = win(1) <= pt(1) && pt(1) < win(1) + win(3) && ...
win(2) <= pt(2) && pt(2) < win(2) + win(4);
end
function [patchSeld, lineSeld, hitWindow] = clickStatus(currPt, handles)
% returns empties on default
patchSeld = []; lineSeld = [];
hax = handles(1); hp = handles(2);
win([1 3]) = get(hax,'XLim'); win(3) = win(3) - win(1);
win([2 4]) = get(hax,'YLim'); win(4) = win(4) - win(2);
hitWindow = inRect(win, currPt); if ~hitWindow, return, end;
% how 'fat' should our edge be for us to highlight/grab it?
edgeFuzzFrac = 0.0035;
edgeFuzz = diff(get(hax,'XLim')) * edgeFuzzFrac;
yy = get(hax,'YLim');
if currPt(2) > yy(2) || currPt(2) < yy(1), return; end;
xBounds = get(hp,'XData');
if isempty(xBounds), return; end; % nothing to click
xBounds = xBounds(2:3,:);
patchSeld = ...
find(xBounds(1,:) - edgeFuzz <= currPt(1) & ...
xBounds(2,:) + edgeFuzz >= currPt(1),1);
if any(abs(xBounds(1,:) - currPt(1)) <= edgeFuzz), lineSeld = 1;
elseif any(abs(xBounds(2,:) - currPt(1)) <= edgeFuzz), lineSeld = 2;
end
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
constructBaselineJenny.m
|
.m
|
Acoustic_Similarity-master/code/obsoleted/constructBaselineJenny.m
| 3,951 |
utf_8
|
105d1cfdf33ca3dbb3d59d1efadcff2b
|
%removing baseline periods that overlap with events---Jenny
function baselineEvents = constructBaselineJenny(events, keyName, startOffsets, stopOffsets, params)
% does automatic exclusion
% startOffsets is 2 x 1 vector in seconds, and is SIGNED (to go backwards
% in time, go negative)
% stopOffsets is similar
% if we only want one of st artOfsets/stopOffsets, blank the other ([])
fs = params.fs;
% allow overlap?
%allowSelfOverlap = false;
%allowOtherOverlap = true;
% dodgy: default behavior for keyName
isKeyEvent = true(numel(events),1);
if ~isempty(keyName)
isKeyEvent = strcmp(keyName, {events.type});
end
keyStarts = [events(isKeyEvent).start];
keyStops = [events(isKeyEvent).stop];
nKey = sum(isKeyEvent);
otherEvents = events(~isKeyEvent);
isUsablePreEvent = false(nKey,1);
isUsablePostEvent = false(nKey,1);
for j = 1:length(events)
isUsablePreEvent = isUsablePreEvent | (keyStarts + startOffsets(2) <
end
for i = 1:length(keyStarts); %go through each event of this type
for j = 1:length(events); %look for overlap with any event
preEventsToUse = [];
if (keyStarts(i) + startOffsets(2)) < events(j).start &...
(keyStarts(i)+ startOffsets(2)) > events(j).stop;
%keep track of the baseline periods that do not overlap
preEventsToUse(end+1) = i;
end
postEventsToUse = [];
if (keyStops(i)+ stopOffsets(2)) < events(j).start &...
(keyStops(i)+ stopOffsets(2)) > events(j).stop;
postEventsToUse(end+1) = i;
end
end
%save events for which baseline is usable
correctedOwnStarts = preEventsToUse(keyStarts);
correctedOwnStops = postEventsToUse(keyStops);
baselinePreEvents = initEvents(0);
if ~isempty(startOffsets)
baselinePreEvents = eventFromTimes(...
correctedOwnStarts + startOffsets(1), ...
correctedOwnStarts + startOffsets(2), fs);
end
baselinePostEvents = initEvents(0);
if ~isempty(stopOffsets)
baselinePostEvents = eventFromTimes(...
correctedOwnStops + stopOffsets(1), ...
correctedOwnStops + stopOffsets(2), fs);
end
end
%% Option 1:"timing", removing early
%{
for i = 1:length(ownStarts); %go through each event of this type
for j = 1:length(events.type); %look for overlap with any event
k = 1;
if (ownStarts(i) + startOffsets(2)) < events(j).start &...
(ownStarts(i)+ startOffsets(2)) > events(j).stop;
%keep track of the baseline periods that do not overlap
preEventsToUse(k) = i;
k = k + 1;
end
k = 1;
if (ownStops(i)+ stopOffsets(2)) < events(j).start &...
(ownStops(i)+ stopOffsets(2)) > events(j).stop;
postEventsToUse(k) = i;
k = k + 1;
end
end
%save events for which baseline is usable
correctedOwnStarts = preEventsToUse(ownStarts);
correctedOwnStops = postEventsToUse(ownStops);
baselinePreEvents = initEvents(0);
if ~isempty(startOffsets)
baselinePreEvents = eventFromTimes(...
correctedOwnStarts + startOffsets(1), ...
correctedOwnStarts + startOffsets(2), fs);
end
baselinePostEvents = initEvents(0);
if ~isempty(stopOffsets)
baselinePostEvents = eventFromTimes(...
correctedOwnStops + stopOffsets(1), ...
correctedOwnStops + stopOffsets(2), fs);
end
end
%}
%would need to run findUnion for only those baselines that have both
%pre and post?
%%Option 2: "length", removing later
%let everything go as usual through findUniqueAreas, then remove...
% pre-periods that are shorter in time than
% abs(startOffsets(1)) - abs(startOffsets(2))...
% or post-periods that are shorter than stopOffsets(2) - stopOffsets(1);
%Are the lengths of baselines preserved in findUniqueAreas?
|
github
|
BottjerLab/Acoustic_Similarity-master
|
findSilence.m
|
.m
|
Acoustic_Similarity-master/code/segmentation/findSilence.m
| 4,173 |
utf_8
|
65b47ba2d9790e7aba6aa48812df90f6
|
function [silentRegions, soundRegions, spectrum] = findSilence(songStruct, region, params, varargin)
% yet another segmentation method for detecting silence...
% slower than stepSpectrogram but tends to be more accurate
% not as accurate/slow as segmentSyllables, especially in finding boundaries
% (can be tuned in the future)
% in general tends to have false negatives in finding silence
% (due to long smoothing)
% (which is to say, marks silence as sound)
% but has good false positive rate (doesn't mark sound as silence)
% i.e. is more robust to find silence (properly filtered)
if nargin < 3
params = defaultParams;
end
params = processArgs(params, varargin{:});
fs = 1/songStruct.interval;
params.fs = fs;
params.(params.editSpecType).fs = fs;
% get waveform and spectrum data
clip = getClipAndProcess(songStruct, region, params);
spectrum = getMTSpectrumStats(clip, params.(params.editSpecType));
% find amplitude modulation in different power bands
% modifiable parameters
nPowerBands = 7;
scale = 5:8:61;
masterThresh = params.syllable.minPower;
nScales = numel(scale);
powerBandBorders = logspace(log10(params.highPassFq), log10(params.lowPassFq), ...
nPowerBands + 1);
thresholds = ones(nPowerBands) * masterThresh;
powerPerBand = zeros(nPowerBands, numel(spectrum.times), nScales);
aboveT = zeros(nPowerBands, numel(spectrum.times), nScales);
cols = jet(nPowerBands);
for ii = 1:nPowerBands
% powerBand = band pass filtering
% todo: replace with MFCC power bands if necessary
includedBands = (powerBandBorders(ii) <= spectrum.freqs & spectrum.freqs < powerBandBorders(ii+1));
for jj = 1:nScales
% scale = amount of smoothing that the signal undergoes (similar to
% a low pass filter with large sidelobes)
% note: these low passes don't do very much
smoothedBandSignal = smoothSignal(sum(spectrum.psd(includedBands, :)),scale(jj));
powerPerBand(ii,:,jj) = smoothedBandSignal;
aboveT(ii,:,jj) = (smoothedBandSignal > thresholds(ii));
if params.plot
subplot(311);
plot(spectrum.times, smoothedBandSignal - thresholds(ii), '-', 'Color', cols(ii, :));
hold on;
end
end
end
% vote is sum over all scales
votePercent = sum(sum(aboveT,3),1) / (nScales * nPowerBands);
% what fraction of votes are needed
voteThresh = 1/(sqrt(nScales * nPowerBands));
if params.plot %&& params.verbose
hold off;
xlabel('time'); ylabel('power in band');
xlim([0 max(spectrum.times)]);
subplot(312);
plot(spectrum.times, votePercent, 'r-', ...
spectrum.times, voteThresh * ones(size(spectrum.times)), 'k-');
xlabel('time (s)'); ylabel('vote %');
xlim([0 max(spectrum.times)]);
subplot(313);
plot((1:numel(spectrum.waveform)) / fs, spectrum.waveform, 'b-');
xlim([0 numel(spectrum.waveform) / fs])
end
% detect silence based on the times that bands are above above threshold
diffAT = diff(votePercent > voteThresh);
ducksThresh = find(diffAT == -1);
jumpsThresh = find(diffAT == 1);
[soundOnsets soundOffsets] = regBounds(ducksThresh, jumpsThresh, ...
numel(spectrum.times));
[silOffsets silOnsets] = regBounds(jumpsThresh, ducksThresh, ...
numel(spectrum.times));
fSO = spectrum.times(1); % first sample offset
soundRegions = eventFromTimes(spectrum.times(soundOnsets)' - fSO, ...
spectrum.times(soundOffsets)', fs);
silentRegions = eventFromTimes(spectrum.times(silOnsets)' - fSO, ...
spectrum.times(silOffsets)', fs);
% adjust the timestamps before we leave
soundRegions = adjustTimeStamps(soundRegions, region.start, fs);
silentRegions = adjustTimeStamps(silentRegions, region.start, fs);
function [putStarts, putEnds] = regBounds(putStarts, putEnds, endNum)
if numel(putStarts) == 0 && numel(putEnds) == 0, return; end
% boundary conditions - will fix later
if numel(putStarts) == numel(putEnds) + 1
putEnds = [1 putEnds];
elseif numel(putEnds) > numel(putStarts),
putStarts = [putStarts endNum];
elseif numel(putEnds) == numel(putStarts) && ...
putEnds(1) > putStarts(1)
putEnds = [1 putEnds];
putStarts = [putStarts endNum];
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
plotNeuronCorrDataPairs.m
|
.m
|
Acoustic_Similarity-master/code/plotting/plotNeuronCorrDataPairs.m
| 27,362 |
utf_8
|
1bbbea4ea958b442a35dcf226fae963e
|
function plotNeuronCorrData(allNeuronCorrData, params, varargin)
if nargin < 2 || isempty(params)
params = defaultParams;
end
params = processArgs(params, varargin{:});
% plot difference between firing rates for near tutor/far from tutor
% and also p-values for correlations between neurons and firing rates
% this data is compiled in correlateDistanceToFiring
if nargin < 1 || isempty(allNeuronCorrData)
load('data/allNeuronCorrelations.mat');
end
%% here we load the cluster quality
% get birds and ages first
sessionIDs = {allNeuronCorrData.sessionID};
birdIDs = strtok(sessionIDs, '_');
[uSessions, ~, rIdxSession] = unique(sessionIDs); % index through ages can go back to sessions
uAges = getAgeOfSession(uSessions);
sessionAges = zeros(size(sessionIDs));
for ii = 1:numel(uAges)
sessionAges(rIdxSession == ii) = uAges(ii);
end
[sessionQ , allSubj] = getClusterQuality(birdIDs, sessionAges, [allNeuronCorrData.syllID]);
[sessionObjQ, allObj ] = getClusterQuality(birdIDs, sessionAges, [allNeuronCorrData.syllID], true);
foo = num2cell(sessionQ ); [allNeuronCorrData.clusterQ ] = foo{:};
foo = num2cell(sessionObjQ); [allNeuronCorrData.clusterObjQ] = foo{:};
foo = allSubj'; allSubj = [allSubj(:)];
foo = allObj' ; allObj = [allObj(:) ];
missingData = isnan(allSubj) | isnan(allObj);
qualityFit = polyfit(allSubj(~missingData), allObj(~missingData), 1);
plot(1:5, polyval(qualityFit,1:5),'r-');
hold on;
%boxplot(sessionObjQ', sessionQ', 'notch', 'on');
plot(allSubj, allObj, 'k.');
[rQualCorr, pQualCorr] = corrcoef(allSubj(~missingData), allObj(~missingData));
legend(sprintf('r^2 = %0.3f, p = %0.3g', rQualCorr(2,1), pQualCorr(2,1)));
xlim([0.5 5.5])
xlabel('Subjective Cluster Quality');
ylabel('Davies-Bouldin Index');
title('Subjective vs. objective cluster quality correlations');
if params.saveplot
saveCurrFigure('figures\A_keeper\objSubjClusterQuality.jpg');
end
%% flags
isCore = [allNeuronCorrData.isCore];
isMUA = [allNeuronCorrData.isMUA];
isPlastic = [allNeuronCorrData.isPlastic];
isSignificant = [allNeuronCorrData.sigResponse];
nSylls = [allNeuronCorrData.nSylls];
%isSignificant = true(1,numel(allNeuronCorrData));
isExcited = [allNeuronCorrData.isExcited];
%%
isSubjGood = [allNeuronCorrData.clusterQ] < 1.5; % < 2.5
isObjGood = [allNeuronCorrData.clusterObjQ] < 0.8; % < 1
%%
% criteria for cluster inclusion
% must have at least three syllables per quartile
isPresel = ~isMUA & nSylls >= 40 & isObjGood; %JMA
%isPresel = ~isMUA & isObjGood; %~isMUA & isObjGood
%%isPresel = isSignificant; % for mostresponsive neurons in todo0410.m
%isPresel = nSylls >= 12;
% subplot rows / columns
nR = 3; nC = 2;
% measures
distanceTypes = {'tutor', 'intra', 'inter', 'consensus', 'central','humanMatch'}';
distanceDescriptions = {'closest tutor', 'cluster center', 'normed center', ...
'closest tutor to cluster consensus', 'closest tutor to cluster center', 'expert-designated tutor'};
dFieldsRS = [strcat(distanceTypes, '_nearMeanRS') strcat(distanceTypes, '_farMeanRS')];
dFieldsSEM = [strcat(distanceTypes, '_nearMeanSEM') strcat(distanceTypes, '_farMeanSEM')];
eiTitle = {'Significantly inhibited single unit-syllable pairs', ...
'Significantly excited single unit-syllable pairs', ...
'All significant single unit-syllable pairs'};
xlabels = strcat({'RS for near - far to '}, distanceDescriptions);
filsuff = {'inh','exc','all'};
%%
%{
for hh = 1:3 % inhibited, excited, all
for ii = 1:numel(distanceTypes) % six of them
figure;
diffTutorMeanRS = [allNeuronCorrData.(dFieldsRS{ii,1})] - [allNeuronCorrData.(dFieldsRS{ii,2})];
nearMeanRS = [allNeuronCorrData.(dFieldsRS{ii,1})];
farMeanRS = [allNeuronCorrData.(dFieldsRS{ii,2})];
nearMeanSEM = [allNeuronCorrData.(dFieldsRS{ii,1})];
farMeanSEM = [allNeuronCorrData.(dFieldsRS{ii,2})];
if hh < 3
selHereCore = isPresel & isExcited == hh-1 & isCore;
selHereShell = isPresel & isExcited == hh-1 & ~isCore;
coreDiffRS = diffTutorMeanRS(selHereCore);
shellDiffRS = diffTutorMeanRS(selHereShell);
coreSubDiffRS = diffTutorMeanRS(selHereCore & ~isPlastic);
shellSubDiffRS = diffTutorMeanRS(selHereShell & ~isPlastic);
corePlastDiffRS = diffTutorMeanRS(selHereCore & isPlastic);
shellPlastDiffRS = diffTutorMeanRS(selHereShell & isPlastic);
else
coreDiffRS = diffTutorMeanRS(isPresel & isCore);
shellDiffRS = diffTutorMeanRS(isPresel & ~isCore);
coreSubDiffRS = diffTutorMeanRS(isPresel & isCore & ~isPlastic);
shellSubDiffRS = diffTutorMeanRS(isPresel & ~isCore & ~isPlastic);
corePlastDiffRS = diffTutorMeanRS(isPresel & isCore & isPlastic);
shellPlastDiffRS = diffTutorMeanRS(isPresel & ~isCore & isPlastic);
end
pc = signrank( coreDiffRS);
ps = signrank(shellDiffRS);
pMannU = ranksum(coreDiffRS(~isnan(coreDiffRS)), shellDiffRS(~isnan(shellDiffRS)));
fprintf(['%s-%s:\n\tsign-rank test p-value for core: %0.3f' ...
' \n\tsign-rank test p-value for shell: %0.3f',...
' \n\tMann-Whitney U test p-value for core v shell: %0.3f\n'],...
eiTitle{hh},xlabels{ii},pc,ps,pMannU);
% clunky way just to get the top histogram value
RSdiffBins = -10:0.2:10;
plotInterlaceBars(coreDiffRS, shellDiffRS, RSdiffBins);
ytop = ylim * [0 1]';
% todo: plot significance on graph
hold on;
plotSEMBar( coreDiffRS, ytop , [0.5 0.5 0.5]);
plotSEMBar( coreSubDiffRS, ytop+1, [0.5 0.5 0.5]);
plotSEMBar( corePlastDiffRS, ytop+2, [0.5 0.5 0.5]);
plotSEMBar( shellDiffRS, ytop+3, [ 1 0 0]);
plotSEMBar( shellSubDiffRS, ytop+4, [ 1 0 0]);
plotSEMBar(shellPlastDiffRS, ytop+5, [ 1 0 0]);
plot([0 0], ylim, 'k--');
hold off;
% redo y axis labels
yt = get(gca,'YTick');
yt = [yt(yt < ytop) ytop:ytop+5];
ytl = cellfun(@(x) sprintf('%d',x),num2cell(yt),'UniformOutput',false);
ytl(end-5:end) = {'Core','Core/Subsong','Core/Plastic','Shell','Shell/Subsong','Shell/Plastic'};
set(gca,'YTick',yt,'YTickLabel',ytl);
% figure formatting
xlabel(xlabels{ii});
ylabel('Count');
xlim([min(RSdiffBins) max(RSdiffBins)]);
set(gca,'Box','off');
set(gca, 'FontSize', 14);
set(get(gca,'XLabel'),'FontSize', 14);
set(get(gca,'YLabel'),'FontSize', 14);
set(get(gca,'Title' ),'FontSize', 14);
title(eiTitle{hh});
set(gcf,'Color',[1 1 1]);
if params.saveplot
saveCurrFigure(sprintf('figures/distanceCorrelations/RSdiffs-SUA-%s-%s.jpg', distanceTypes{ii}, filsuff{hh}));
end
end
end
%}
%% JMA added this section to compare neurons with compiled cluster data
for aa = 1: length(allNeuronCorrData)
allNeuronCorrData(aa).intra_DistanceAll = allNeuronCorrData(aa).intra_DistanceAll';%these were in columns
allNeuronCorrData(aa).inter_DistanceAll = allNeuronCorrData(aa).inter_DistanceAll';
allNeuronCorrData(aa).burstFraction = allNeuronCorrData(aa).burstFraction';
allNeuronCorrData(aa).quality = allNeuronCorrData(aa).clusterObjQ;
end
ccc = allNeuronCorrData(1);%trying to get unique syllable iterations to compare similarity to tutor with song stage goodness-of-fit coefficient
for aaa = 2: length(allNeuronCorrData)
ddd = strcmp(allNeuronCorrData(aaa).sessionID,{ccc.sessionID});
if ~any(ddd)
ccc = [ccc;allNeuronCorrData(aaa)];%I know, I know, I can't pre-allocate
else
eee = ccc(ddd);
bbb = ~ismember(allNeuronCorrData(aaa).syllID,[eee.syllID]);
if bbb
ccc = [ccc;allNeuronCorrData(aaa)];
end
end
end
load('goodnessOfFitCo.mat')
for aa = 1: length(ccc)
tt = zeros(ccc(aa).nSylls,1);%needed to get syllable type for every syllable
tt(:,1) = deal(ccc(aa).syllID);
ccc(aa).syllID = tt';
for bb = 1: length(goodness.fit)
sess(bb) = strcmp(ccc(aa).sessionID,goodness.sessionID(bb));
end
gof = cell2mat(goodness.fit(sess));
ttt = zeros(ccc(aa).nSylls,1);
ttt(:,1) = deal(gof);
ccc(aa).indGoF = ttt';
end
usablePairs = allNeuronCorrData(isPresel);
% usableClusterSessions = {usablePairs.sessionID};
% [uUCSessions, ~, ~] = unique(usableClusterSessions);
% corrByNeuron = struct([]);
% for mm = 1: length(uUCSessions)
% isCurrentSession = strcmp(uUCSessions(mm),{usablePairs.sessionID});
% currentSessionPairs = usablePairs(isCurrentSession);
% % [neuronsHere, ~, ~] = unique([currentSessionPairs.unitNum]);
% % for nn = 1: length(neuronsHere)
% % isCurrentNeuron = [currentSessionPairs.unitNum] == neuronsHere(nn);
% % currentNeuronPairs = currentSessionPairs(isCurrentNeuron);
% % compiledNeuron.isCore = currentNeuronPairs(1).isCore;
% % compiledNeuron.isMUA = currentNeuronPairs(1).isMUA;
% % compiledNeuron.isPlastic = currentNeuronPairs(1).isPlastic;
% % compiledNeuron.sessionID = currentNeuronPairs(1).sessionID;
% % compiledNeuron.unitNum = currentNeuronPairs(1).unitNum;
% % compiledNeuron.nSylls = sum([currentNeuronPairs.nSylls]);
% % compiledNeuron.sigSyll = sum([currentNeuronPairs.sigResponse]);
% % compiledNeuron.RSAll = horzcat([currentNeuronPairs.RSAll]);
% % compiledNeuron.FRSyll = horzcat([currentNeuronPairs.FRSyll]);
% % compiledNeuron.FRBase = horzcat([currentNeuronPairs.FRBase]);
% % compiledNeuron.burstFraction = horzcat([currentNeuronPairs.burstFraction]);
% % compiledNeuron.tutor_DistanceAll = horzcat([currentNeuronPairs.tutor_DistanceAll]);
% % compiledNeuron.consensus_DistanceAll = horzcat([currentNeuronPairs.consensus_DistanceAll]);
% % compiledNeuron.central_DistanceAll = horzcat([currentNeuronPairs.central_DistanceAll]);
% % compiledNeuron.intra_DistanceAll = horzcat([currentNeuronPairs.intra_DistanceAll]);
% % compiledNeuron.inter_DistanceAll = horzcat([currentNeuronPairs.inter_DistanceAll]);
% % compiledNeuron.quality = horzcat([currentNeuronPairs.clusterObjQ]);
% % compiledNeuron.syllID = horzcat([currentNeuronPairs.syllID]);
% % numClassSyll = length(unique(compiledNeuron.syllID));
% % compiledNeuron.classSyll = deal(numClassSyll);
% corrByNeuron = [corrByNeuron; compiledNeuron];
% % end
% end
% isenoughSyll = [corrByNeuron.nSylls] > 39; %want at least 10 syllables in each quartile
% usableNeuron = [corrByNeuron.sigSyll] > 0 & isenoughSyll; %neuron has to respond to at least one syllable cluster (but maybe shouldn't do this)
% usableNeuron = isenoughSyll;
% corrByNeuron = corrByNeuron(usableNeuron);
corrByNeuron = usablePairs;
%correlation of distance to response strength
for bb = 1: length(corrByNeuron)
yDist = corrByNeuron(bb).tutor_DistanceAll'; %can try other distances
xRS = corrByNeuron(bb).RSAll'; %response strength, does it make sense to use firing rate?
[linfit, ~,~,~, fitStats] = regress(yDist, [ones(numel(xRS),1) xRS]); %checked with corrcoef and gives same p value
%if params.plot
% figure
% plot(xRS, yDist, 'k.', 'HandleVisibility', 'off');
% hold on;
% plot(xRS, linfit(1) + xRS * linfit(2), '--','Color',[1 0 0]);
% legend(sprintf('r^2 = %0.3g, F = %0.3g, p = %0.3g\n',...
% fitStats(1), fitStats(2),fitStats(3)));
% xlabel('Response Strength'); ylabel('Matched distance');
%end
corrByNeuron(bb).linfit = linfit;
corrByNeuron(bb).fitStats = fitStats;
end
isCore2 = [corrByNeuron.isCore];
CorrPRS = zeros(length(corrByNeuron),1);
for cc = 1: length(corrByNeuron)
CorrPRS(cc) = corrByNeuron(cc).fitStats(3);
end
isCorrel = CorrPRS < 0.05;
cSC = isCore2 & isCorrel';
cSS = ~isCore2 & isCorrel';
fprintf('Number neurons with significant correlation of RS in core %s out of %s core neurons \n', num2str(sum(cSC)), num2str(sum(isCore2)))
fprintf('Number neurons with significant correlation of RS in shell %s out of %s shell neurons \n', num2str(sum(cSS)), num2str(sum(~isCore2)))
%correlation of distance to burst fraction
for bb = 1: length(corrByNeuron)
yDist = corrByNeuron(bb).tutor_DistanceAll';
xRS = corrByNeuron(bb).burstFraction';
[linfit, ~,~,~, fitStats] = regress(yDist, [ones(numel(xRS),1) xRS]); %checked with corrcoef and gives same p value
corrByNeuron(bb).linfitBF = linfit;
corrByNeuron(bb).fitStatsBF = fitStats;
end
isCore2 = [corrByNeuron.isCore];
CorrP = zeros(length(corrByNeuron),1);
for cc = 1: length(corrByNeuron)
CorrP(cc) = corrByNeuron(cc).fitStatsBF(3);
end
isCorrel = CorrP < 0.05;
cSC = isCore2 & isCorrel';
cSS = ~isCore2 & isCorrel';
fprintf('Number neurons with significant correlation of BF in core %s out of %s core neurons \n', num2str(sum(cSC)), num2str(sum(isCore2)))
fprintf('Number neurons with significant correlation of BF in shell %s out of %s shell neurons \n', num2str(sum(cSS)), num2str(sum(~isCore2)))
%compare population response, standardized response to top and bottom 50%
%similarity to tutor song
for dd = 1: length(corrByNeuron)
dists = corrByNeuron(dd).tutor_DistanceAll; %tutor_DistanceAll
iqDists = prctile(dists, 50);
near_medianFR = corrByNeuron(dd).FRSyll(dists < iqDists); nMedian = numel(near_medianFR);
far_medianFR = corrByNeuron(dd).FRSyll(dists > iqDists); fMedian = numel( far_medianFR);
near_medianFRBase = corrByNeuron(dd).FRBase(dists < iqDists);
far_medianFRBase = corrByNeuron(dd).FRBase(dists > iqDists);
near_medianBF = corrByNeuron(dd).burstFraction(dists < iqDists);
far_medianBF = corrByNeuron(dd).burstFraction(dists > iqDists);
near_medianDist = corrByNeuron(dd).tutor_DistanceAll(dists < iqDists);
far_medianDist = corrByNeuron(dd).tutor_DistanceAll(dists > iqDists);
allDist = corrByNeuron(dd).tutor_DistanceAll;
% near_quartileQual = corrByNeuron(dd).quality(dists < iqDists(1));
% far_quartileQual = corrByNeuron(dd).quality(dists > iqDists(2));
% near_quartileType = corrByNeuron(dd).syllID(dists < iqDists(1));
% far_quartileType = corrByNeuron(dd).syllID(dists > iqDists(2));
farMedian = nanmean(far_medianDist);
nearMedian = nanmean(near_medianDist);
meanDist = nanmean(allDist);
% farQual = nanmean(far_quartileQual);
% nearQual = nanmean(near_quartileQual);
% farTypes = length(unique(far_quartileType));
% nearTypes = length(unique(near_quartileType));
nq_meanFR = nanmean(near_medianFR); nq_varFR = nanvar(near_medianFR);
fq_meanFR = nanmean( far_medianFR); fq_varFR = nanvar( far_medianFR);
nq_meanBF = nanmean(near_medianBF);
fq_meanBF = nanmean(far_medianBF);
nq_CV = nanstd(near_medianFR)/nq_meanFR;
fq_CV = nanstd(far_medianFR)/fq_meanFR;
nq_meanFRBase = nanmean(near_medianFRBase); nq_varFRBase = nanvar(near_medianFRBase);
fq_meanFRBase = nanmean( far_medianFRBase); fq_varFRBase = nanvar( far_medianFRBase);
[~, pVal, ~, tValStruct] = ttest2(near_medianFR- near_medianFRBase, far_medianFR- far_medianFRBase);
tVal = tValStruct.tstat;
fcovar = nancov(far_medianFR,far_medianFRBase);if numel(fcovar) > 1, fcovar = fcovar(2,1); end;
ncovar = nancov(near_medianFR,near_medianFRBase);if numel(ncovar) > 1, ncovar = ncovar(2,1); end;
farZDenom = sqrt(fq_varFR + fq_varFRBase - 2*fcovar);
nearZDenom = sqrt(nq_varFR + nq_varFRBase - 2*ncovar);
farZ = ((fq_meanFR - fq_meanFRBase)* sqrt(fMedian))/farZDenom;
nearZ = ((nq_meanFR - nq_meanFRBase)* sqrt(nMedian))/nearZDenom;
corrByNeuron(dd).medianPValue = pVal;
corrByNeuron(dd).farZ = farZ;
corrByNeuron(dd).nearZ = nearZ;
corrByNeuron(dd).farCV = fq_CV;
corrByNeuron(dd).nearCV = nq_CV;
corrByNeuron(dd).nearBF = nq_meanBF;
corrByNeuron(dd).farBF = fq_meanBF;
corrByNeuron(dd).farQuart = farMedian;
corrByNeuron(dd).nearQuart = nearMedian;
corrByNeuron(dd).meanDist = meanDist;
corrByNeuron(dd).farRS = (fq_meanFR - fq_meanFRBase);
corrByNeuron(dd).nearRS = (nq_meanFR - nq_meanFRBase);
% corrByNeuron(dd).farQual = farQual;
% corrByNeuron(dd).nearQual = nearQual;
% corrByNeuron(dd).farTypes = farTypes;
% corrByNeuron(dd).nearTypes = nearTypes;
% corrByNeuron(dd).farTypeID = {unique(far_quartileType)};
% corrByNeuron(dd).nearTypeID = {unique(near_quartileType)};
end
isQS = [corrByNeuron.medianPValue] < 0.05;
SQC = isCore2 & isQS;
SQS = ~isCore2 & isQS;
fprintf('Number neurons with significant difference in RS to near vs far in core %s out of %s core neurons \n', num2str(sum(SQC)), num2str(sum(isCore2)))
fprintf('Number neurons with significant difference in RS to near vs far in shell %s out of %s shell neurons \n', num2str(sum(SQS)), num2str(sum(~isCore2)))
meanFarZC = nanmean([corrByNeuron(isCore2).farZ]);
meanFarZS = nanmean([corrByNeuron(~isCore2).farZ]);
SEMFarC = nanstd([corrByNeuron(isCore2).farZ])/sqrt(length(corrByNeuron(isCore2)));
SEMFarS = nanstd([corrByNeuron(~isCore2).farZ])/sqrt(length(corrByNeuron(~isCore2)));
meanNearZC = nanmean([corrByNeuron(isCore2).nearZ]);
meanNearZS = nanmean([corrByNeuron(~isCore2).nearZ]);
SEMNearC = nanstd([corrByNeuron(isCore2).nearZ])/sqrt(length(corrByNeuron(isCore2)));
SEMFarS = nanstd([corrByNeuron(~isCore2).nearZ])/sqrt(length(corrByNeuron(~isCore2)));
% normalized RS values--not using this part
dNormedRSFields = strcat(distanceTypes, '_dRSnorm');
for hh = 1:3 % inhibited, excited, all
for ii = 1:numel(distanceTypes) % six of them
figure;
dRSNormed = [allNeuronCorrData.(dNormedRSFields{ii})];
if hh < 3
coreDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & isCore);
shellDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & ~isCore);
coreSubDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & isCore & ~isPlastic);
shellSubDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & ~isCore & ~isPlastic);
corePlastDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & isCore & isPlastic);
shellPlastDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & ~isCore & isPlastic);
else
coreDiffRSNorm = dRSNormed(isPresel & isCore);
shellDiffRSNorm = dRSNormed(isPresel & ~isCore);
coreSubDiffRSNorm = dRSNormed(isPresel & isCore & ~isPlastic);
shellSubDiffRSNorm = dRSNormed(isPresel & ~isCore & ~isPlastic);
corePlastDiffRSNorm = dRSNormed(isPresel & isCore & isPlastic);
shellPlastDiffRSNorm = dRSNormed(isPresel & ~isCore & isPlastic);
end
fprintf('%s-normed %s: ', eiTitle{hh},xlabels{ii});
if ~(all(isnan( coreSubDiffRSNorm)) || ...
all(isnan( corePlastDiffRSNorm)) || ...
all(isnan( shellSubDiffRSNorm)) || ...
all(isnan(shellPlastDiffRSNorm)))
% significance tests: two-way anova, permuted
% this function is not consistent with matlab's anovan, so it
% won't be used until we can see why the inconsistency's there
%[stats, df, pvals] = statcond(...
% {noNaN( coreSubDiffRSNorm), noNaN( corePlastDiffRSNorm); ...
% noNaN(shellSubDiffRSNorm), noNaN(shellPlastDiffRSNorm)}, ...
%'mode','param');
% test against anova
xx = [coreSubDiffRSNorm corePlastDiffRSNorm shellSubDiffRSNorm shellPlastDiffRSNorm]';
grps = [zeros(size(coreSubDiffRSNorm)) zeros(size(corePlastDiffRSNorm)) ...
ones(size(shellSubDiffRSNorm)) ones(size(shellPlastDiffRSNorm)); ...
zeros(size(coreSubDiffRSNorm)) ones(size(corePlastDiffRSNorm)) ...
zeros(size(shellSubDiffRSNorm)) ones(size(shellPlastDiffRSNorm))]';
if isreal(xx) %JMA added
[pAnova, tAnova] = anovan(xx,grps,'model','interaction','display', 'off');
fprintf(['\n\tANOVA (fixed model), 2-way: effect of core/shell, p = %0.2f, '...
'effect of subsong/plastic, p = %0.2f, interaction, p = %0.2f'], ...
pAnova(1), pAnova(2), pAnova(3))
else
fprintf('Skipping two-way permutation ANOVA');
end
end
% significance tests: post-hoc, core vs shell
if ~isempty(coreDiffRSNorm) && ~isempty(shellDiffRSNorm)
pc = signrank( coreDiffRSNorm);
ps = signrank(shellDiffRSNorm);
pMannU = ranksum(coreDiffRSNorm(~isnan(coreDiffRSNorm)), shellDiffRSNorm(~isnan(shellDiffRSNorm)));
% no subsong/plastic significance tests
fprintf(['\n\tsign-rank test p-value for core: %0.3f' ...
'\n\tsign-rank test p-value for shell: %0.3f',...
'\n\tMann-Whitney U test p-value for core v shell: %0.3f\n'],...
pc,ps,pMannU);
end
% set bins for histogram
RSdiffBins = -10:0.5:10;
plotInterlaceBars(coreDiffRSNorm, shellDiffRSNorm, RSdiffBins);
ytop = ylim * [0 1]';
% todo: plot significance on graph
hold on;
plotSEMBar( coreDiffRSNorm, ytop , [0.5 0.5 0.5]);
plotSEMBar( shellDiffRSNorm, ytop+1, [ 1 0 0]);
plotSEMBar( coreSubDiffRSNorm, ytop+2, [0.5 0.5 0.5]);
plotSEMBar( shellSubDiffRSNorm, ytop+3, [ 1 0 0]);
plotSEMBar( corePlastDiffRSNorm, ytop+4, [0.5 0.5 0.5]);
plotSEMBar(shellPlastDiffRSNorm, ytop+5, [ 1 0 0]);
plot([0 0], ylim, 'k--');
hold off;
% redo y axis labels
yt = get(gca,'YTick');
yt = [yt(yt < ytop) ytop:ytop+5];
ytl = cellfun(@(x) sprintf('%d',x),num2cell(yt),'UniformOutput',false);
ytl(end-5:end) = {'Core','Shell','Core/Subsong','Shell/Subsong','Core/Plastic','Shell/Plastic'};
set(gca,'YTick',yt,'YTickLabel',ytl);
% figure formatting
xlabel(['normalized ' xlabels{ii}]);
ylabel('Count');
legend(sprintf('CORE: n = %d', numel( coreDiffRSNorm(~isnan( coreDiffRSNorm)))),...
sprintf('SHELL: n = %d', numel(shellDiffRSNorm(~isnan(shellDiffRSNorm)))));
xlim([min(RSdiffBins) max(RSdiffBins)]);
set(gca,'Box','off');
set(gca, 'FontSize', 14);
set(get(gca,'XLabel'),'FontSize', 14);
set(get(gca,'YLabel'),'FontSize', 14);
set(get(gca,'Title' ),'FontSize', 14);
title(sprintf('%s, core/shell diff p = %0.2g, core from zero p = %0.2g, shell from zero p = %0.2g', eiTitle{hh}, pMannU, pc, ps));
set(gcf,'Color',[1 1 1]);
%mean(coreDiffRSNorm)
%mean(shellDiffRSNorm)
%pause;
if params.saveplot
imFile = sprintf('figures/paper/distanceCorrelations-subjectiveScoreFilter/normedRSdiffs-SUA-%s-%s.pdf', distanceTypes{ii}, filsuff{hh});
fprintf('Writing image to %s', imFile);
scrsz = get(0,'ScreenSize');
set(gcf, 'Position', [1 1 scrsz(3) scrsz(4)]);
export_fig(imFile);
% saveCurrFigure(sprintf('figures/A_keeper/mostResponsive/normedRSdiffs-SUA-%s-%s.jpg', distanceTypes{ii}, filsuff{hh}));
end
end
end
%{
fprintf('\n\np-values of FR correlation to distance types');
pFields = strcat(distanceTypes, 'Distance_p');
R2Fields = strcat(distanceTypes, 'DistanceR2');
xlabels = strcat({'Linear trend p-values of FR to '}, distanceDescriptions);
for hh = 1:3 % inhibited, excited, all
figure;
for ii = 1:numel(distanceTypes)
subplot(nR,nC,ii)
corrPVals = [allNeuronCorrData.(pFields{ii})];
corrR2Vals = [allNeuronCorrData.(R2Fields{ii})];
if hh < 3
coreCPVs = corrPVals(isPresel & isExcited == hh-1 & isCore);
shellCPVs = corrPVals(isPresel & isExcited == hh-1 & ~isCore);
% get % variance explained
[ coreR2M, coreR2SEM] = meanSEM(corrR2Vals(isPresel & isExcited == hh-1 & isCore));
[shellR2M, shellR2SEM] = meanSEM(corrR2Vals(isPresel & isExcited == hh-1 & ~isCore));
%coreSubCPVs = corrPVals(isPresel & isExcited == hh-1 & isCore & ~isPlastic);
%shellSubCPVs = corrPVals(isPresel & isExcited == hh-1 & ~isCore & ~isPlastic);
%corePlastCPVs = corrPVals(isPresel & isExcited == hh-1 & isCore & isPlastic);
%shellPlastCPVs = corrPVals(isPresel & isExcited == hh-1 & ~isCore & isPlastic);
else
coreCPVs = corrPVals(isPresel & isCore);
shellCPVs = corrPVals(isPresel & ~isCore);
[ coreR2M, coreR2SEM] = meanSEM(corrR2Vals(isPresel & isCore));
[shellR2M, shellR2SEM] = meanSEM(corrR2Vals(isPresel & ~isCore));
%coreSubCPVs = corrPVals(isPresel & isCore & ~isPlastic);
%shellSubCPVs = corrPVals(isPresel & ~isCore & ~isPlastic);
%corePlastCPVs = corrPVals(isPresel & isCore & isPlastic);
%shellPlastCPVs = corrPVals(isPresel & ~isCore & isPlastic);
end
% test the difference between core and shell
pMannU = ranksum(coreCPVs(~isnan(coreCPVs)), shellCPVs(~isnan(shellCPVs)));
fprintf('%s - %s: Mann-Whitney U p-value for core v shell: %0.3f\n',...
eiTitle{hh}, xlabels{ii},pMannU);
fprintf(['\tCore variance explained: %0.2f +/- %0.2f, '...
'shell variance explained: %0.2f +/- %0.2f\n'], ...
coreR2M, coreR2SEM, shellR2M, shellR2SEM);
pBins = logspace(-3,0,30);
plotInterlaceBars(coreCPVs, shellCPVs, pBins);
legend(sprintf('CORE: n = %d', numel( coreCPVs(~isnan( coreCPVs)))),...
sprintf('SHELL: n = %d', numel(shellCPVs(~isnan(shellCPVs)))));
xlabel(xlabels{ii});
ylabel('Count');
xlim([0 1])
% figure formatting
set(gca, 'XTick', [0.05 0.1 0.2 0.4 0.6 0.8]);
set(gca, 'Box', 'off');
set(gca, 'FontSize', 12);
% xticklabel_rotate([],45); % this messes up the figure subplots
set(get(gca,'XLabel'),'FontSize', 14);
set(get(gca,'YLabel'),'FontSize', 14);
set(get(gca,'Title' ),'FontSize', 14);
% todo: plot the significance markers
ytop = ylim * [0 1]'; ylim([0 ytop+2]);
hold on;
plotSEMBar( coreCPVs, ytop-0.2, [0.5 0.5 0.5]);
plotSEMBar(shellCPVs, ytop-0.1, [1 0 0]);
hold off;
end
subplot(nR,nC,1);
title(eiTitle{hh});
set(gcf,'Color',[1 1 1]);
if params.saveplot
saveCurrFigure(sprintf('figures/distanceCorrelations/neuronDistanceCorr-SUA-%s.jpg', filsuff{hh}));
end
end
%}
end
function [m, v] = meanSEM(set1)
m = nanmean(set1);
if numel(set1) >= 2
v = nanstd(set1 )/sqrt(numel(set1)-1);
else
v = 0;
end
end
% plot the error bars w/ SEM on top
function plotSEMBar(set, y, col)
[m,v] = meanSEM(set);
plotHorzErrorBar(m,y,v,col);
end
function x = noNaN(x)
x(isnan(x)) = [];
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
plotNeuronCorrDataVert.m
|
.m
|
Acoustic_Similarity-master/code/plotting/plotNeuronCorrDataVert.m
| 9,117 |
utf_8
|
36d30b062aeb58c51826251d08b27b1b
|
function plotNeuronCorrDataVert(params, varargin)
if nargin < 1 || isempty(params)
params = defaultParams;
end
params = processArgs(params, varargin{:});
% plot excited difference between firing rates for near tutor/far from tutor
% and also p-values for correlations between neurons and firing rates
allNeuronCorrData = [];
load('data/allNeuronCorrelations.mat');
%% flags
isCore = [allNeuronCorrData.isCore];
isMUA = [allNeuronCorrData.isMUA];
isPlastic = [allNeuronCorrData.isPlastic];
isSignificant = [allNeuronCorrData.sigResponse];
isExcited = [allNeuronCorrData.isExcited];
isPresel = isSignificant & ~isMUA;
RSdiffBins = -10:0.2:10;
% subplot rows / columns
nR = 3; nC = 2;
% measures
distanceTypes = {'tutor', 'intra', 'inter', 'consensus', 'central','humanMatch'}';
distanceDescriptions = {'closest tutor', 'cluster center', 'normed center', ...
'closest tutor to cluster consensus', 'closest tutor to cluster center', 'expert-designated tutor'};
dFields = [strcat(distanceTypes, '_nearMeanRS') strcat(distanceTypes, '_farMeanRS')];
eiTitle = {'Significantly inhibited single unit-syllable pairs', ...
'Significantly excited single unit-syllable pairs', ...
'All significant single unit-syllable pairs'};
xlabels = strcat({'RS for near - far to '}, distanceDescriptions);
filsuff = {'inh','exc','all'};
%%
ycommlims = [-10 10];
for hh = 1:3 % inhibited, excited, all
for ii = 1:numel(distanceTypes) % six of them
diffTutorMeanRS = [allNeuronCorrData.(dFields{ii,1})] - [allNeuronCorrData.(dFields{ii,2})];
if hh < 3
coreDiffRS = diffTutorMeanRS(isPresel & isExcited == hh-1 & isCore);
shellDiffRS = diffTutorMeanRS(isPresel & isExcited == hh-1 & ~isCore);
coreSubDiffRS = diffTutorMeanRS(isPresel & isExcited == hh-1 & isCore & ~isPlastic);
shellSubDiffRS = diffTutorMeanRS(isPresel & isExcited == hh-1 & ~isCore & ~isPlastic);
corePlastDiffRS = diffTutorMeanRS(isPresel & isExcited == hh-1 & isCore & isPlastic);
shellPlastDiffRS = diffTutorMeanRS(isPresel & isExcited == hh-1 & ~isCore & isPlastic);
else
coreDiffRS = diffTutorMeanRS(isPresel & isCore);
shellDiffRS = diffTutorMeanRS(isPresel & ~isCore);
coreSubDiffRS = diffTutorMeanRS(isPresel & isCore & ~isPlastic);
shellSubDiffRS = diffTutorMeanRS(isPresel & ~isCore & ~isPlastic);
corePlastDiffRS = diffTutorMeanRS(isPresel & isCore & isPlastic);
shellPlastDiffRS = diffTutorMeanRS(isPresel & ~isCore & isPlastic);
end
pc = signrank( coreDiffRS);
ps = signrank(shellDiffRS);
pMannU = ranksum(coreDiffRS(~isnan(coreDiffRS)), shellDiffRS(~isnan(shellDiffRS)));
fprintf(['%s-%s:\n\tsign-rank test p-value for core: %0.3f' ...
' \n\tsign-rank test p-value for shell: %0.3f',...
' \n\tMann-Whitney U test p-value for core v shell: %0.3f\n'],...
eiTitle{hh},xlabels{ii},pc,ps,pMannU);
figure
% plot the core/shell contrast for single units for this
% RS contrast
subplot(1,2,1);
plotInterlaceBarsVert(coreDiffRS, shellDiffRS, RSdiffBins, pMannU < 0.05);
legend(sprintf('CORE: n = %d', numel( coreDiffRS(~isnan( coreDiffRS)))),...
sprintf('SHELL: n = %d', numel(shellDiffRS(~isnan(shellDiffRS)))))
xlabel('Count');
ylabel(xlabels{ii});
ylim(ycommlims);
set(gca,'Box','off');
set(gca, 'FontSize', 14);
set(get(gca,'XLabel'),'FontSize', 14);
set(get(gca,'YLabel'),'FontSize', 14);
set(get(gca,'Title' ),'FontSize', 14);
title(eiTitle{hh});
set(gcf,'Color',[1 1 1]);
subplot(1,2,2)
% plot the core/core subsong/core plastic//
% shell/shell subsong/shell plastic individual bars
[ coreMean, coreVar] = meanVar( coreDiffRS);
[ coreSubMean, coreSubVar] = meanVar( coreSubDiffRS);
[ corePlastMean, corePlastVar] = meanVar(corePlastDiffRS);
[ shellMean, shellVar] = meanVar( shellDiffRS);
[ shellSubMean, shellSubVar] = meanVar( shellSubDiffRS);
[shellPlastMean, shellPlastVar] = meanVar(shellPlastDiffRS);
xlim([0.5 6.5])
hold on;
plotHorzErrorBarVert(1, coreMean, coreVar, [0.5 0.5 0.5]);
plotHorzErrorBarVert(2, coreSubMean, coreSubVar, [0.5 0.5 0.5]);
plotHorzErrorBarVert(3, corePlastMean, corePlastVar, [0.5 0.5 0.5]);
plotHorzErrorBarVert(4, shellMean, shellVar, [0.5 0.5 0.5]);
plotHorzErrorBarVert(5, shellSubMean, shellSubVar, [0.5 0.5 0.5]);
plotHorzErrorBarVert(6, shellPlastMean, shellPlastVar, [0.5 0.5 0.5]);
ylim(ycommlims);
set(gca,'Box','off', 'XTick', 1:6,...
'XTickLabel',{'Core', 'Core/Subsong','Core/Plastic',...
'Shell','Shell/Subsong','Shell/Plastic'});
xticklabel_rotate([],45, [], 'FontSize', 14);
hold on;
keyboard
end
if params.saveplot
saveCurrFigure(sprintf('figures/distanceCorrelations/RSdiffs-SUA-%s.jpg', filsuff{hh}));
end
end
%{
pFields = strcat(distanceTypes, 'Distance_p');
xlabels = strcat({'Linear trend p-values of FR to '}, distanceDescriptions);
for hh = 1:3 % inhibited, excited, all
figure(hh+3);
for ii = 1:numel(distanceTypes)
subplot(nR,nC,ii)
corrPVals = [allNeuronCorrData.(pFields{ii})];
if hh < 3
coreCPVs = corrPVals(isPresel & isExcited == hh-1 & isCore);
shellCPVs = corrPVals(isPresel & isExcited == hh-1 & ~isCore);
else
coreCPVs = corrPVals(isPresel & isCore);
shellCPVs = corrPVals(isPresel & ~isCore);
end
pMannU = ranksum(coreCPVs(~isnan(coreCPVs)), shellCPVs(~isnan(shellCPVs)));
fprintf('%s - %s: Mann-Whitney U p-value for core v shell: %0.3f\n',...
eiTitle{hh}, xlabels{ii},pMannU);
pBins = logspace(-3,0,30);
plotInterlaceBarsVert(coreCPVs, shellCPVs, pBins, pMannU < 0.05);
legend(sprintf('CORE: n = %d', numel( coreCPVs(~isnan( coreCPVs)))),...
sprintf('SHELL: n = %d', numel(shellCPVs(~isnan(shellCPVs)))));
xlabel(xlabels{ii});
ylabel('Count');
xlim([0 1])
set(gca, 'XTick', [0.01 0.05 0.1 0.2 0.4 0.6 0.8]);
set(gca, 'Box', 'off');
set(gca, 'FontSize', 14);
set(get(gca,'XLabel'),'FontSize', 14);
set(get(gca,'YLabel'),'FontSize', 14);
set(get(gca,'Title' ),'FontSize', 14);
end
subplot(nR,nC,1);
title(eiTitle{hh});
set(gcf,'Color',[1 1 1]);
if params.saveplot
saveCurrFigure(sprintf('figures/distanceCorrelations/neuronDistanceCorr-SUA-%s.jpg', filsuff{hh}));
end
end
%}
end
function [m, v] = meanVar(set1)
m = nanmean(set1);
v = nanstd(set1 )/sqrt(numel(set1)-1);
end
function plotInterlaceBarsVert(setCore, setShell, bins, sigLevel)
% core plotted in gray, shell plotted in red
% run mann-whitney u test
[p,h] = ranksum(setCore, setShell);
binTol = 1e-5;
hCore = histc(setCore , bins);
hShell = histc(setShell, bins);
[m1 sem1] = meanVar(setCore );
[m2 sem2] = meanVar(setShell);
if all(diff(bins) - (bins(2) - bins(1)) < binTol)
bw = bins(2) - bins(1);
barh(bins, hCore, 0.5, 'FaceColor', [0.5 0.5 0.5]);
hold on;
barh(bins+bw/2, hShell, 0.5, 'r');
else
bw = diff(bins); bw = [bins(1)/2 bw bw(end)];
% make visible legend groups
ghCore = hggroup; ghShell = hggroup;
set(get(get(ghCore , 'Annotation'),'LegendInformation'), 'IconDisplayStyle','on');
set(get(get(ghShell, 'Annotation'),'LegendInformation'), 'IconDisplayStyle','on');
for ii = 1:numel(bins) % draw histogram bin by bin
% core bin
yd = bins(ii) - bw(ii)/2; yu = bins(ii);
xl = 0; xr = hCore(ii);
patch([xl xr; xr xr; xl xl], [yd yd; yd yu; yu yu], [0.5 0.5 0.5],...
'EdgeColor','none', 'Parent', ghCore);
hold on;
% shell bins
yd = bins(ii); yu = bins(ii) + bw(ii+1)/2;
xl = 0; xr = hShell(ii);
patch([xl xr; xr xr; xl xl], [yd yd; yd yu; yu yu], [1 0 0],...
'EdgeColor','none', 'Parent', ghShell);
end
end
xlims = xlim;
right = xlims(2) + 2;
xlim([0 right]);
plotHorzErrorBarVert(right - 0.8, m1, sem1, [0.5 0.5 0.5]);
hold on;
plotHorzErrorBarVert(right - 1.2, m2, sem2, [1 0 0]);
if sigLevel > 0
plot(right-1.0, mean([m1 m2]), 'k*','MarkerSize',6);
end
hold off;
end
function plotHorzErrorBarVert(x, y, yerr, col)
xw = 0.02*diff(xlim);
% the connecting line, and the bottom and top end lines
xx = [x x NaN x-xw x+xw NaN x-xw x+xw ];
yy = [y-yerr y+yerr NaN y-yerr y-yerr NaN y+yerr y+yerr];
plot(xx,yy, '-','Color', col, 'LineWidth', 1.5);
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
plotPSTHOld.m
|
.m
|
Acoustic_Similarity-master/code/plotting/plotPSTHOld.m
| 9,698 |
utf_8
|
5ec4c7c774dbbb5c775903bcb0904f2d
|
function eventSpikeTimes = plotPSTHOld(events, spikeTimes, subEvents, labelsOfInterest, prePostParams)
% function plotPSTH(events, spikeTimes) plot time-warped rasters/PSTH
% TODO: revise/refactor this - too specific
%
% This function plots the raster and PSTH for each event. Spike times
% should be input uncorrected, that is, raw times with respect to the same
% timeframe as that of the events.
%
% plotPSTH(events, spikeTimes, subEvents) plots the subEvents as gray
% areas. Tip: Use events as an expanded version of subEvents to plot a PSTH
% with baseline.
%
% plotPSTH(events, spikeTimes, subEvents, labelsOfInterest) plots a separate
% raster/PSTH with time warping for each event of type label of interest. Times are
% warped so that events of each label are warped.
%
% labelsOfInterest is a cell array of strings for the syllables that should
% be aligned. everything will be time warped to those syllables. (These
% should be inorder)
%
% spikeTimes is expected to be a column vector of spike times.
%
% if warping is requested (by adding labels)
if nargin < 3
subEvents = initEvents;
end
if nargin < 4
labelsOfInterest = {};
elseif ~iscell(labelsOfInterest)
labelsOfInterest = {labelsOfInterest};
end
% find which subevents belong to which events
% note: if the events and subevents are in one to one correspondence, (i.e.
% one is the pre/post rolled version of another, then the parentage is
% probably just one-to-one
if numel(events) == numel(subEvents)
parentage = 1:numel(subEvents);
for ii = 1:numel(subEvents)
eventsInContext(ii) = adjustTimeStamps(subEvents(ii), ...
-events(ii).start);
end
else
if nargin < 5
[parentage, eventsInContext] = findParent(events,subEvents);
else %nargin == 5
parentage = findParent(events,subEvents);
events = addPrePost(events, prePostParams);
% take out orphan events :[
subEvents(isnan(parentage)) = [];
parentage(isnan(parentage)) = [];
for ii = 1:numel(subEvents)
eventsInContext(ii) = adjustTimeStamps(subEvents(ii), ...
-events(parentage(ii)).start);
end
end
end
% take out orphan events :[
subEvents(isnan(parentage)) = [];
eventsInContext(isnan(parentage)) = [];
parentage(isnan(parentage)) = [];
% equalize outside events so that spikes are evenly counted
maxEvLength = max([events.stop] - [events.start]);
% now extend/alter the events so that they all have the same postroll /
% preroll
% if ~isempty(events)
% num2cell(maxEvLength + [events.start]); [events.stop] = ans{:};
% [events.length] = deal(maxEvLength);
% end
[counts, eventSpikeTimes] = countSpikes(events, spikeTimes,'onset');
syllableLabels = {subEvents.type};
syllableLengths = [subEvents.stop] - [subEvents.start];
[uLabels, foo, rLabelIdx] = unique(syllableLabels);
nAlign = numel(labelsOfInterest);
rAlignIdx = NaN(size(rLabelIdx));
% should we warp? (according to whether or not user gave syllables list)
doWarping = (nargin >= 4);
if doWarping
% set index of event types according to the align list
for ii = 1:nAlign
labelsIndex = find(strcmp(uLabels,labelsOfInterest{ii}));
if ~isempty(labelsIndex)
rAlignIdx(rLabelIdx == labelsIndex) = ii;
end
end
% get the average onset and length of each syllable
avgEnds = zeros(1,nAlign);
avgStarts = zeros(1,nAlign);
for ii = 1:nAlign
% get average length
isThisLabel = (rAlignIdx == ii);
avgEnds(ii) = mean([eventsInContext(isThisLabel).stop]);
avgStarts(ii) = mean([eventsInContext(isThisLabel).start]);
end
% repair cases where average events overlap for some strange reason
% (needs to be more reasonable)
if any(avgEnds(1:end-1) > avgStarts(2:end))
warning('distorted average event profile, repairing...');
defaultGap = 0.005;
overlaps = find(avgEnds(1:end-1) > avgStarts(2:end));
for ll = 1:numel(overlaps)
gapJump = avgEnds(ll) - avgStarts(ll+1) + defaultGap;
avgEnds((ll+1):end) = avgEnds((ll+1):end) + gapJump;
avgStarts((ll+1):end) = avgStarts((ll+1):end) + gapJump;
end
end
avgLengths = avgEnds - avgStarts;
warpedEventsInContext = eventsInContext;
for ii = 1:numel(events)
isChild = (parentage == ii);
childEvs = eventsInContext(isChild);
controlPoints = NaN(2*nAlign, 2); % first column is sample, second is standard;
controlSet = false(1,2*nAlign);
for jj = 1:nAlign
% NB: in case of multiply labeled syllables, just pick the first
% one for alignment purposes
matchAlign = find(strcmp({childEvs.type},labelsOfInterest{jj}),1);
if ~isempty(matchAlign)
controlPoints(2*jj-1:2*jj,:) = ...
[childEvs(matchAlign).start avgStarts(jj); ...
childEvs(matchAlign).stop avgStarts(jj) + avgLengths(jj)];
controlSet(2*jj-1) = true;
controlSet(2*jj) = true;
end
end
controlPoints(~controlSet,:) = []; % get rid of nan points
% now use control points to linearly interpolate times
adjEventSpikeTimes{ii} = interpLinearPoints(eventSpikeTimes{ii},...
controlPoints(:,2), controlPoints(:,1));
% for plotting purposes: warp event boundaries themselves
warpedStarts = interpLinearPoints([childEvs.start],...
controlPoints(:,2), controlPoints(:,1));
warpedStops = interpLinearPoints([childEvs.stop],...
controlPoints(:,2), controlPoints(:,1));
foo = num2cell(warpedStarts);
[warpedEventsInContext(isChild).start] = foo{:};
foo = num2cell(warpedStops);
[warpedEventsInContext(isChild).stop] = foo{:};
end
end
% prepare the bins - all important binning parameter
binSize = 1e-3; % in seconds
% concatenate the context spiketimes
if doWarping
eventSpikeTimes = adjEventSpikeTimes;
end
% these values are needed by both plotting subroutines
xcoords = vertcat(eventSpikeTimes{:});
maxTime = max(max(xcoords), maxEvLength);
% plot raster
subplot(3,1,1:2)
plotRasterInner;
title(sprintf('Raster for song'));
% plot average PSTH
subplot(3,1,3)
title(sprintf('PSTH'));
plotPeriHist
% for title positioning
subplot(3,1,1:2)
%%%%%%%%%%%%%%%%%%%%%%%% plotting functions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function plotRasterInner
plotColors = true;
% this code tells us how to sort out the y-coordinates so that
% each 'spike' lands on the correct row
ycoords = [];
for ii = 1:numel(counts)
ycoords = [ycoords; ii * ones(counts(ii), 1)];
end
% actually plot the spikes
plot(xcoords, ycoords, 'ks','MarkerSize',2,'MarkerFaceColor','k');
xlim([0 maxTime]);
ylim([0.5 numel(events) + 0.5])
% optional: plot gray/colored subEvent backgrounds behind
for kk = 1:numel(events)
%isKthChild = (parentage == kk);
% subEvs = eventsInContext(isKthChild);
% plot all gray/colored
isKthChild = isWithinEvent(subEvents, events(kk));
subEvs = adjustTimeStamps(subEvents(isKthChild), -events(kk).start);
if doWarping
subEvs = warpedEventsInContext(isKthChild);
end
% assign the new colors only to the labels that correspond to alignment ones
if plotColors
typeColors = autumn(nAlign); % color assignment for each label
evTypes = rAlignIdx(isKthChild);
markColors = 0.5*ones(numel(subEvs),3);
markColors(~isnan(evTypes),:) = typeColors(evTypes(~isnan(evTypes)),:);
else
markColors = [0.5 0.5 0.5];
end
plotAreaMarks(subEvs, markColors, false, [-0.5 0.5] + kk);
end
end
function plotPeriHist
% counts per bin
spikesPerBin = histc(xcoords, 0:binSize:maxTime);
spikeRate = spikesPerBin / (binSize * numel(events));
% smooth bins with gaussian curve:
tt = 0:binSize:maxTime;
smoothedSpikeCount = zeros(size(tt));
smoothWidth = 4e-3;
for ii = 1:numel(tt)
smoothedSpikeCount(ii) = 1/sqrt(2*pi) * sum(exp(-((xcoords-tt(ii))/smoothWidth).^2));
end
smoothedRate = smoothedSpikeCount / (binSize * numel(events));
% plot the raw PSTH
%bar(0:binSize:maxTime, spikeRate, 'histc');
%hold on
% plot the smoothed stuff
plot(tt,smoothedRate, 'r-');
xlim([0 maxTime]);
xlabel('Time(s)');
ylabel('Mean firing rate (Hz)');
%hold off;
end
end
function yy = interpLinearPoints(xx,y1,x1)
% interpolate linearly between each pair of points & interpolate, without
% warping before and after the control points
assert(numel(x1) == numel(y1))
if numel(x1) == 0, yy = xx; return; end;
[x1, idxs] = sort(x1);
y1 = y1(idxs);
intervalPtr = 1;
yy = zeros(size(xx));
for kk = 1:numel(xx)
intervalPtr = find(xx(kk) <= x1,1);
if isempty(intervalPtr)
yy(kk) = xx(kk) - x1(end) + y1(end); % no warping after last control point
elseif intervalPtr == 1
yy(kk) = xx(kk) - x1(1) + y1(1); % no warping before first control point
else
yy(kk) = interp1(x1(intervalPtr-1:intervalPtr),...
y1(intervalPtr-1:intervalPtr),xx(kk));
end
end
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
plotAllFigures.m
|
.m
|
Acoustic_Similarity-master/code/plotting/plotAllFigures.m
| 13,002 |
utf_8
|
f8ebba7b55bed1fb7bd5eaa079f21fb7
|
function [hax, optGraphs, hfig] = plotAllFigures(spec, regions, params, varargin)
% PLOTALLFIGURES main plotting function
% plotAllFigures(spec) plots the waveform in parallel with audio features
% handling bad arguments
% (1) empty spectrogram
if isempty(spec)
hax = 0;
optGraphs = params.optGraphs;
hfig = 0;
return;
end % returns axis handles
if nargin < 2, regions = []; end;
if nargin < 3 || isempty(params), params = defaultParams; end;
% plot only the graphs that you want and that have information provided by
% the spectrum data structure
params = processArgs(params, varargin{:});
optGraphs = params.optGraphs;
optGraphs = optGraphs(isfield(spec, optGraphs) | strcmp('spectrogram',optGraphs) | ...
strcmp('fracDiffPower',optGraphs));
% find the number of plots
nPlots = numel(optGraphs);
hax = zeros(1,nPlots);
% labels for events structure
toggleLabels = true;
cP = 0;
for ii = 1:nPlots
% plot in next window
cP = cP + 1;
hax(ii) = subplot(nPlots,1,cP);
switch(optGraphs{ii})
case 'spectrogram'
plotSpectrogram(spec);
% if isfield(spec,'fundamentalFreq')
% hold on; plotPitch('fundamentalFreq','r-'); hold off;
% end;
% if isfield(spec,'centerFreq')
% hold on; plotPitch('centerFreq','y-'); hold off;
% end;
for ii = 1:numel(regions),
regionsMark(ii).start = regions(ii).start * numel(spec.times) / max(spec.times);
regionsMark(ii).stop = regions(ii).stop * numel(spec.times) / max(spec.times);
end
%plotMarks(regions);
case 'deriv'
% if any(strcmp(optGraphs,'spectrogram'))
% freezeColors(hh); %seems not to work in 2011b =/
% end
% todo: it would be a very nice detail to change contrast based
% on the scroll wheel, but that would mean carrying large data
% amounts in the userData memory
plotDerivGram(spec,params);
%{
if isfield(spec,'fundamentalFreq')
hold on; plotPitch('fundamentalFreq','r-.', 'LineWidth',0.5); hold off;
end;
if isfield(spec,'centerFreq')
hold on; plotPitch('centerFreq','y-.', 'LineWidth',0.5); hold off;
end;
%}
switch params.showDgramRegionStyle
case 'lines' % non-fancy non-interactive method
plotMarks(regions);
plotLabels;
case 'fancy' % fancy interactive method, slows down interface some and
set(gcf,'Renderer','OpenGL');
plotAreaMarks(regions, [0 0.8 0.8 0.15]); % requires openGL
plotLabels;
% note: have to use opengl software to render, which is
% slower
end
case 'totalPower'
plotRMS;
plotAreaMarks(regions,[],params.showLabels && toggleLabels);
toggleLabels = false;
case 'fracDiffPower'
plotFracDiffPower;
plotAreaMarks(regions,[],params.showLabels && toggleLabels);
toggleLabels = false;
case 'wienerEntropy'
plotEntropy;
plotAreaMarks(regions,[],params.showLabels && toggleLabels);
toggleLabels = false;
case 'FM'
plotFM;
plotAreaMarks(regions,[],params.showLabels && toggleLabels);
toggleLabels = false;
case 'AM'
plotAM;
plotAreaMarks(regions,[],params.showLabels && toggleLabels);
toggleLabels = false;
case 'rawAM'
plotRawAM;
plotAreaMarks(regions,[],params.showLabels && toggleLabels);
toggleLabels = false;
case 'pitchGoodness'
plotPitchGoodness;
plotAreaMarks(regions,[],params.showLabels && toggleLabels);
toggleLabels = false;
case 'aperiodicity'
hold off; plotFracPeriodicPower;
plotAreaMarks(regions,[],params.showLabels && toggleLabels);
toggleLabels = false;
case 'waveform'
if ~isfield(params,'fs')
error('plotAllFigures:undefined','sampling rate not defined, pass parameters');
end;
hline = plotWaveform(spec.waveform,params.fs);
% magic user interface happens here
set(hline,'UserData',params.fs); % give playback clue
xlim([min(spec.times) max(spec.times)]);
hp = plotAreaMarks(regions,[],params.showLabels && toggleLabels);
% magic playback UI control happens here
set(gcf,'WindowButtonDownFcn',{@buttonDownFcn, [hax(ii) hp]});
toggleLabels = false;
case 'centerFreq'
hold off; plotPitch('centerFreq');
plotAreaMarks(regions,[],params.showLabels && toggleLabels);
toggleLabels = false;
case 'fundamentalFreq'
hold off; plotPitch('fundamentalFreq');
title('Fundamental frequency');
plotAreaMarks(regions,[],params.showLabels && toggleLabels);
toggleLabels = false;
case 'mTD'
hold off; plotPartialDerivs;
plotAreaMarks(regions,[],params.showLabels && toggleLabels);
toggleLabels = false;
case 'mfcc'
hold off; plotMFCC;
%plotAreaMarks(regions,[],params.showLabels && toggleLabels);
toggleLabels = false;
otherwise
error('plotAllFigures:UnspecifiedPlotting','Don''t know how to plot %s', optGraphs{ii});
end
end
subplot(nPlots,1,1); % for correct title positioning
% allow for linked scanning/zooming on segments
set(gcf,'UserData', struct('hlink', linkprop(hax, 'XLim')));
%drawnow
hfig = gcf;
% individual plotting for different acoustic features
function plotRMS
% technical note:
% openGL rendering does not support log scaling, which causes
% problems with highlighting
% plot unsmoothed power
semilogy(spec.times, spec.totalPower, 'm-');
% plot first derivative
smoothedPower = smoothSignal(spec.totalPower,17);
dt = spec.times(2) - spec.times(1);
dPdT = [diff(smoothedPower) / dt eps];
posTimes = spec.times; posTimes(dPdT<=0) = NaN;
negTimes = spec.times; negTimes(dPdT>=0) = NaN;
% plot negative and positive as diff signs
hold on;
semilogy(posTimes, dPdT,'g-')
semilogy(negTimes, -dPdT,'r-')
hold off;
% set plotting window
set(gca, 'YLimMode', 'manual')
minPower = min(spec.totalPower);
ylim([minPower/2,1e-1])
set(gca,'YTick',10.^(ceil(log10(minPower)):-1));
xlim([min(spec.times) max(spec.times)]);
ylabel('log RMS Power')
title('Total Power (Volume)')
end
function plotFracDiffPower
% plot first derivative
smoothedPower = smoothSignal(spec.totalPower,17);
dt = spec.times(2) - spec.times(1);
dPdT = [diff(smoothedPower) / dt eps];
dfPdT = dPdT./spec.totalPower;
posTimes = spec.times; posTimes(dPdT<=0) = NaN;
negTimes = spec.times; negTimes(dPdT>=0) = NaN;
semilogy(posTimes, dfPdT,'g-', negTimes, -dfPdT,'r-')
hold off;
% set plotting window
set(gca, 'YLimMode', 'manual')
xlim([min(spec.times) max(spec.times)]);
%minPower = min(spec.totalPower);
ylim([min(abs(dfPdT))/3,max(abs(dfPdT)) * 3])
%set(gca,'YTick',10.^(ceil(log10(minPower)):-1));
end
function plotEntropy
plot(spec.times, spec.wienerEntropy, 'r-');
xlim([min(spec.times) max(spec.times)]);
ylabel('Entropy')
title('Wiener entropy (high = noise, low = tone, mid = stack)')
end
function plotFM
%semilogy(spec.times, spec.mTD, 'r-',spec.times, spec.mFD,'b-');
plot(spec.times, spec.FM, 'r-');
xlim([min(spec.times) max(spec.times)]);
%ylim([0 90]);
ylabel('FM (deg)')
title('Frequency modulation')
end
function plotAM
plot(spec.times, spec.AM, 'r-');
xlim([min(spec.times) max(spec.times)]);
%ylim([0 90]);
ylabel('AM (1/ms)')
title('Amplitude modulation')
end
function plotRawAM
% plot first derivative
posTimes = spec.times; posTimes(spec.rawAM<=0) = NaN;
negTimes = spec.times; negTimes(spec.rawAM>=0) = NaN;
% plot negative and positive as diff signs
semilogy(posTimes, spec.rawAM,'g-')
hold on;
semilogy(negTimes, -spec.rawAM,'r-')
hold off;
xlim([min(spec.times) max(spec.times)]);
ylabel('raw AM (dB/ms)')
ylim([1e-5 1]);
title('Unnormalized amplitude modulation')
end
function plotPitchGoodness
semilogy(spec.times, spec.pitchGoodness, 'r-');
% plot(spec.times, log10(spec.pitchGoodness), 'r-');
xlim([min(spec.times) max(spec.times)]);
ylabel('log Cepstral strength');
title('Goodness of pitch');
end
function plotPitch(field, colStyle, varargin)
if nargin < 2, colStyle = 'r-'; end;
if nargin < 1, field = 'fundamentalFreq'; end;
plot(spec.times, spec.(field), colStyle,varargin{:});
xlim([min(spec.times) max(spec.times)]);
ylim([min(spec.freqs) max(spec.freqs)]);
ylabel('freq (Hz)');
% title(field);
end
function plotFracPeriodicPower
plot(spec.times, spec.aperiodicity, 'r-');
xlim([min(spec.times) max(spec.times)]);
ylim([0 1]);
ylabel('aper pow frac');
title('Fraction of Periodic power')
end
function plotPartialDerivs
semilogy(spec.times, spec.mTD, 'r-', spec.times, spec.mFD, 'g-');
%plot(spec.times, log10(spec.mTD), 'r-', ...
% spec.times, log10(spec.mFD), 'g-');
xlim([min(spec.times) max(spec.times)]);
ylabel('partial deriv amp (dB/msec)');
end
function plotMFCC
nColors=256;
% the first row of the MFCC is the volume
nBands = size(spec.mfcc, 1) - 1;
hs = imagesc([min(spec.times) max(spec.times)],...
[2 nBands],...
spec.mfcc(2:end,:));
xlabel('Time (s)');
ylabel('mel-freq cepstral coeff');
set(gca,'YDir','normal');
colormap(gray(nColors));
end
function plotLabels
for kk = 1:numel(regions)
if ~isempty(regions(kk).type)
xpos = (regions(kk).start + regions(kk).stop)/2;
createTextLabel(xpos, regions(kk).type);
end
end
end
end
function buttonDownFcn(gcbo, eventdata, handles)
hax = handles(1); hp = handles(2);
currPt = get(hax,'CurrentPoint'); currPt = currPt(1,1:2);
[patchClicked, lineClicked, hitWindow] = clickStatus(currPt, handles);
if ~hitWindow, return; end;
% if we are NOT double clicked, get out
if ~strcmp(get(gcbo,'SelectionType'),'open'), return; end;
% we are double clicked: play a sound
% get the clip data
hline = findobj(hax,'Type','line');
hline = hline(end); % it should be the one furthest back
xWave = get(hline,'XData'); yWave = get(hline,'YData');
fs = get(hline,'UserData');
if isempty(patchClicked) % what should we do if a patch is not clicked?
% play the whole clip
playSound(yWave, fs, true);
else
% get the borders
currXData = get(hp, 'XData');
patchBorders = currXData(2:3,patchClicked);
% cut the clip at the right points
clipStart = find(xWave >= patchBorders(1),1);
clipEnd = find(xWave >= patchBorders(2),1);
clip = yWave(clipStart:clipEnd);
% play the sound clip, while blocking
playSound(clip, fs, true);
end
end
function [patchSeld, lineSeld, hitWindow] = clickStatus(currPt, handles)
% returns empties on default
patchSeld = []; lineSeld = [];
hax = handles(1); hp = handles(2);
win([1 3]) = get(hax,'XLim'); win(3) = win(3) - win(1);
win([2 4]) = get(hax,'YLim'); win(4) = win(4) - win(2);
hitWindow = inRect(win, currPt); if ~hitWindow, return, end;
yy = get(hax,'YLim');
if currPt(2) > yy(2) || currPt(2) < yy(1), return; end;
xBounds = get(hp,'XData');
if isempty(xBounds), return; end; % nothing to click
xBounds = xBounds(2:3,:);
patchSeld = find(xBounds(1,:) <= currPt(1) & xBounds(2,:) >= currPt(1));
if numel(patchSeld) > 1
% find the one which is closer
distsToCursor = min(xBounds(1,patchSeld) - currPt(1));
[~,closest] = min(distsToCursor);
patchSeld = patchSeld(closest);
end
end
function foo = inRect(win, pt)
foo = win(1) <= pt(1) && pt(1) < win(1) + win(3) && ...
win(2) <= pt(2) && pt(2) < win(2) + win(4);
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
plotWaveform.m
|
.m
|
Acoustic_Similarity-master/code/plotting/plotWaveform.m
| 2,453 |
utf_8
|
d3bade9bec54e936a5bd0b02de9ae59a
|
function hline = plotWaveform(clip, fs)
%PLOTWAVEFORM plots waveform in the window, with downsampling if necessary
len = length(clip);
xx = 1:len;
% Plots downsampled data. This has much faster response time. This is
% based on dsplot by Jiro Doke in file exchange.
% Find outliers
filterWidth = min([50, ceil(length(xx)/10)]); % max window size of 50
a = clip - ...
filter(ones(filterWidth,1)/filterWidth, 1, clip);
this.iOutliers = ...
find(abs(a - repmat(mean(a), size(a, 1), 1)) > ...
repmat(4 * std(a), size(a, 1), 1));
range = floor([0 len]);
% Despite the spirit of this script (which is to keep memory down for
% large (1M #) arrays,
% We keep a large number of points in order to make the playback simpler -
% since the data needs to be stored anyway for playback in some
% applications (editEventLabels), we might as well keep them all.
% TODO: make this an option in the future
numPoints = 1e6;
if length(xx) > numPoints
idx = round(linspace(xx(1), xx(end), numPoints))';
idxi = unique([idx; this.iOutliers]);
hline = plot(idxi/fs,clip(idxi));
else
hline = plot(xx/fs,clip(xx));
end
%%%
maxNTicks = 25;
interval = max(0.05,numel(clip)/fs/maxNTicks);
interval = round(interval * 100)/100; % fix to two decimal points
set(gca,'XTick',0:interval:numel(clip)/fs);
xlim([0 len]/fs);
ylim([-1 1]);
end
% Plots downsampled data. This has much faster response time. This is
% based on dsplot by Jiro Doke in file exchange.
% TODO Add data cursor callbacks
function dsplot(clip)
len = length(clip);
x = 1:len;
% Find outliers
filterWidth = min([50, ceil(length(x)/10)]); % max window size of 50
a = clip - ...
filter(ones(filterWidth,1)/filterWidth, 1, clip);
[this.iOutliers, this.jOutliers] = ...
find(abs(a - repmat(mean(a), size(a, 1), 1)) > ...
repmat(4 * std(a), size(a, 1), 1));
range = floor([0 len]);
numPoints = 50000;
%len = length(this.ChannelHandles);
x = (1:size(clip, 1))';
id = find(x >= range(1) & x <= range(2));
if length(id) > numPoints/len
idx = round(linspace(id(1), id(end), round(numPoints/len)))';
ol = this.iOutliers > id(1) & this.iOutliers < id(end);
for i=1:len
idxi = unique([idx; this.iOutliers(ol & this.jOutliers == i)]);
set(this.ChannelHandles(i), 'XData', idxi/fs, ...
'YData', clip(idxi));
end
else
for i=1:len
set(this.ChannelHandles(i), 'XData', id/fs, ...
'YData', clip(id));
end
end
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
plotEvent.m
|
.m
|
Acoustic_Similarity-master/code/plotting/plotEvent.m
| 971 |
utf_8
|
5fe64e798dff955eb8852350c57d55ca
|
function plotEvent(songStruct, ev, regions)
cl = getClip(ev, songStruct);
plot( (1:numel(cl)) * songStruct.interval, cl);
plotMarks(0.95)
function plotMarks(yc)
hold on;
if ~isempty(regions)
plot([regions.start],yc, 'cv',...
'MarkerFaceColor','c', 'MarkerSize', 5);
plot([regions.stop],yc, 'mv',...
'MarkerFaceColor','m', 'MarkerSize', 5);
for jj = 1:numel(regions)
ymins = ylim;
if strcmp(get(gca,'YScale'),'log')
semilogy(repmat([regions.start],2,1), [ymins(1) yc],'c-');
semilogy(repmat([regions.stop],2,1), [ymins(1) yc],'m-');
else
plot(repmat([regions.start],2,1), [ymins(1) yc],'c-');
plot(repmat([regions.stop],2,1), [ymins(1) yc],'m-');
end
end
end
hold off;
end
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
plotFRERA.m
|
.m
|
Acoustic_Similarity-master/code/plotting/plotFRERA.m
| 2,754 |
utf_8
|
a0fba374e90a98c7ad40a0648beddb91
|
function plotFRERA(subset1, subset2, field, bins)
% plots a firing rate comparison
if ~isempty(subset1)
set1Hist = sum(horzcat(subset1.(field)),2)/length(subset1);
set1HistSem = std(horzcat(subset1.(field)),[],2)/sqrt(length(subset1));
plotLineTransparentSEM(bins(1:end-3), set1Hist, set1HistSem, [0.5 0.5 0.5]);%removing bins because removed last three points in eraFind
hold on;
end
if ~isempty(subset2)
set2Hist = sum(horzcat(subset2.(field)),2)/length(subset2);
set2HistSem = std(horzcat(subset2.(field)),[],2)/sqrt(length(subset2));
plotLineTransparentSEM(bins(1:end-3), set2Hist, set2HistSem, [1 0.2 0.2]); %removing bins because removed last three points in eraFind
end
if ~isempty(subset1)
hold off;
end
% let's look for significance
N1 = length(subset1);
N2 = length(subset2);
t_stat = tinv(0.975, N1-1); %0.05 two-tailed critical value
if any((set1Hist - set1HistSem * t_stat) > 0)
sig_set1IdxPos = find((set1Hist - set1HistSem * t_stat) > 0);
sig_set1xxPos = insertNonConsecNans(bins(sig_set1IdxPos), sig_set1IdxPos);
hold on;
ybar = ylim * [0.08 0.92]'; % 92% of the way to the top
plot(sig_set1xxPos, ybar * ones(1,numel(sig_set1xxPos)), '-', 'LineWidth', 2, 'Color', [0.5 0.5 0.5]);
end
if any((set1Hist + set1HistSem * t_stat) < 0)
sig_set1IdxNeg = find((set1Hist + set1HistSem * t_stat) < 0);
sig_set1xxNeg = insertNonConsecNans(bins(sig_set1IdxNeg), sig_set1IdxNeg);
hold on;
ybar = ylim * [0.92 0.08]'; % 92% of the way to the bottom
plot(sig_set1xxNeg, ybar * ones(1,numel(sig_set1xxNeg)), '-', 'LineWidth', 2, 'Color', [0.5 0.5 0.5]);
end
t_stat = tinv(0.975, N2-1); %0.05 two-tailed critical value
if any((set2Hist - set2HistSem * t_stat) > 0)
sig_set2IdxPos = find((set2Hist - set2HistSem * t_stat) > 0);
sig_set2xxPos = insertNonConsecNans(bins(sig_set2IdxPos), sig_set2IdxPos);
hold on;
ybar = ylim * [0.12 0.88]'; % 88% of the way to the top
plot(sig_set2xxPos, ybar * ones(1,numel(sig_set2xxPos)), 'r-', 'LineWidth', 2);
end
if any((set2Hist + set2HistSem * t_stat) < 0)
sig_set2IdxNeg = find((set2Hist + set2HistSem * t_stat) < 0);
sig_set2xxNeg = insertNonConsecNans(bins(sig_set2IdxNeg), sig_set2IdxNeg);
hold on;
ybar = ylim * [0.88 0.12]'; % 88% of the way to the bottom
plot(sig_set2xxNeg, ybar * ones(1,numel(sig_set2xxNeg)), 'r-', 'LineWidth', 2);
end
end
function ret = insertNonConsecNans(lookupvec, idxvec)
nonConsec = find(diff(idxvec)>1);
ret = lookupvec;
for jj = 1:numel(nonConsec)
ret = [ret(1:nonConsec(jj)); NaN; ...
ret(nonConsec(jj)+1:end)];
nonConsec = nonConsec+1;
end
xlim([-150 150]);
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
plotDerivGram.m
|
.m
|
Acoustic_Similarity-master/code/plotting/plotDerivGram.m
| 1,212 |
utf_8
|
55be1f678d3bab14683fd36a1e2f021c
|
function hs = plotDerivGram(spec, params, varargin)
if nargin < 2 || isempty(params)
params = defaultParams;
end
params = processArgs(params, varargin{:});
% arrange into log space
deps = params.dgram.minContrast;
lDeriv = spec.deriv;
lDeriv(abs(spec.deriv) < deps) = 0;
lDeriv(spec.deriv > deps) = -log(lDeriv(spec.deriv > deps)/deps);
lDeriv(spec.deriv < -deps) = log(-lDeriv(spec.deriv < -deps)/deps);
% convert to direct color mapping, for efficiency's sake
nColors = 256;
lmin = min(lDeriv(:)); lmax = max(lDeriv(:));
%lDerivMap = flipud(uint8(fix((lDeriv - lmin)/(lmax-lmin) * nColors) + 1));
hs = imagesc([min(spec.times) max(spec.times)],...
[min(spec.freqs) max(spec.freqs)],...
lDeriv);
colormap(gray(nColors));
% flip the y-axis
set(gca,'YDir','normal');
% a much slower way to do it, but imagesc may not be supported in all matlab
% versions
%hs = surf(spec.times, spec.freqs, lDerivMap,'EdgeColor','none',...
% 'CDataMapping','direct','facecolor','texturemap');%,...
% 'xlimmode','manual','ylimmode','manual','zlimmode','manual',...
% 'climmode','manual','alimmode','manual');
%view(0,90)
end
function ans = roundN(val,N)
round(val / N) * N;
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
plotNeuronCorrData.m
|
.m
|
Acoustic_Similarity-master/code/plotting/plotNeuronCorrData.m
| 29,105 |
utf_8
|
b64a370a4d3b4143c844755b92c7984d
|
function plotNeuronCorrData(allNeuronCorrData, params, varargin)
if nargin < 2 || isempty(params)
params = defaultParams;
end
params = processArgs(params, varargin{:});
% plot difference between firing rates for near tutor/far from tutor
% and also p-values for correlations between neurons and firing rates
% this data is compiled in correlateDistanceToFiring, but JMA edited so
% that main neural analysis is done here and ignores neural analysis stuff
% done in correlateDistanceToFiring
if nargin < 1 || isempty(allNeuronCorrData)
load('data/allNeuronCorrelations.mat');
end
%% here we load the cluster quality
% get birds and ages first
sessionIDs = {allNeuronCorrData.sessionID};
birdIDs = strtok(sessionIDs, '_');
[uSessions, ~, rIdxSession] = unique(sessionIDs); % index through ages can go back to sessions
uAges = getAgeOfSession(uSessions);
sessionAges = zeros(size(sessionIDs));
for ii = 1:numel(uAges)
sessionAges(rIdxSession == ii) = uAges(ii);
end
[sessionQ , allSubj] = getClusterQuality(birdIDs, sessionAges, [allNeuronCorrData.syllID]);
[sessionObjQ, allObj ] = getClusterQuality(birdIDs, sessionAges, [allNeuronCorrData.syllID], true);
foo = num2cell(sessionQ ); [allNeuronCorrData.clusterQ ] = foo{:};
foo = num2cell(sessionObjQ); [allNeuronCorrData.clusterObjQ] = foo{:};
foo = allSubj'; allSubj = [allSubj(:)];
foo = allObj' ; allObj = [allObj(:) ];
missingData = isnan(allSubj) | isnan(allObj);
qualityFit = polyfit(allSubj(~missingData), allObj(~missingData), 1);
plot(1:5, polyval(qualityFit,1:5),'r-');
hold on;
%boxplot(sessionObjQ', sessionQ', 'notch', 'on');
plot(allSubj, allObj, 'k.');
[rQualCorr, pQualCorr] = corrcoef(allSubj(~missingData), allObj(~missingData));
legend(sprintf('r^2 = %0.3f, p = %0.3g', rQualCorr(2,1), pQualCorr(2,1)));
xlim([0.5 5.5])
xlabel('Subjective Cluster Quality');
ylabel('Davies-Bouldin Index');
title('Subjective vs. objective cluster quality correlations');
if params.saveplot
saveCurrFigure('figures\A_keeper\objSubjClusterQuality.jpg');
end
%% flags
isCore = [allNeuronCorrData.isCore];
isMUA = [allNeuronCorrData.isMUA];
isPlastic = [allNeuronCorrData.isPlastic];
isSignificant = [allNeuronCorrData.sigResponse];
nSylls = [allNeuronCorrData.nSylls];
%isSignificant = true(1,numel(allNeuronCorrData));
isExcited = [allNeuronCorrData.isExcited];
%%
isSubjGood = [allNeuronCorrData.clusterQ] < 1.5; % < 2.5
isObjGood = [allNeuronCorrData.clusterObjQ] < 0.8; % < 1
%%
% criteria for cluster inclusion
% isPresel = isSignificant & ~isMUA & nSylls >= 12 & isSubjGood; %JMA changed this because don't need a certain #/cluster rather certain # overall all clusters for neurons correlation (as opposed to neuron/syllable pair)
isPresel = ~isMUA & isObjGood; %~isMUA & isObjGood
%%isPresel = isSignificant; % for mostresponsive neurons in todo0410.m
%isPresel = nSylls >= 12;
% subplot rows / columns
nR = 3; nC = 2;
% measures
distanceTypes = {'tutor', 'intra', 'inter', 'consensus', 'central','humanMatch'}';
distanceDescriptions = {'closest tutor', 'cluster center', 'normed center', ...
'closest tutor to cluster consensus', 'closest tutor to cluster center', 'expert-designated tutor'};
dFieldsRS = [strcat(distanceTypes, '_nearMeanRS') strcat(distanceTypes, '_farMeanRS')];
dFieldsSEM = [strcat(distanceTypes, '_nearMeanSEM') strcat(distanceTypes, '_farMeanSEM')];
eiTitle = {'Significantly inhibited single unit-syllable pairs', ...
'Significantly excited single unit-syllable pairs', ...
'All significant single unit-syllable pairs'};
xlabels = strcat({'RS for near - far to '}, distanceDescriptions);
filsuff = {'inh','exc','all'};
%%
%{
for hh = 1:3 % inhibited, excited, all
for ii = 1:numel(distanceTypes) % six of them
figure;
diffTutorMeanRS = [allNeuronCorrData.(dFieldsRS{ii,1})] - [allNeuronCorrData.(dFieldsRS{ii,2})];
nearMeanRS = [allNeuronCorrData.(dFieldsRS{ii,1})];
farMeanRS = [allNeuronCorrData.(dFieldsRS{ii,2})];
nearMeanSEM = [allNeuronCorrData.(dFieldsRS{ii,1})];
farMeanSEM = [allNeuronCorrData.(dFieldsRS{ii,2})];
if hh < 3
selHereCore = isPresel & isExcited == hh-1 & isCore;
selHereShell = isPresel & isExcited == hh-1 & ~isCore;
coreDiffRS = diffTutorMeanRS(selHereCore);
shellDiffRS = diffTutorMeanRS(selHereShell);
coreSubDiffRS = diffTutorMeanRS(selHereCore & ~isPlastic);
shellSubDiffRS = diffTutorMeanRS(selHereShell & ~isPlastic);
corePlastDiffRS = diffTutorMeanRS(selHereCore & isPlastic);
shellPlastDiffRS = diffTutorMeanRS(selHereShell & isPlastic);
else
coreDiffRS = diffTutorMeanRS(isPresel & isCore);
shellDiffRS = diffTutorMeanRS(isPresel & ~isCore);
coreSubDiffRS = diffTutorMeanRS(isPresel & isCore & ~isPlastic);
shellSubDiffRS = diffTutorMeanRS(isPresel & ~isCore & ~isPlastic);
corePlastDiffRS = diffTutorMeanRS(isPresel & isCore & isPlastic);
shellPlastDiffRS = diffTutorMeanRS(isPresel & ~isCore & isPlastic);
end
pc = signrank( coreDiffRS);
ps = signrank(shellDiffRS);
pMannU = ranksum(coreDiffRS(~isnan(coreDiffRS)), shellDiffRS(~isnan(shellDiffRS)));
fprintf(['%s-%s:\n\tsign-rank test p-value for core: %0.3f' ...
' \n\tsign-rank test p-value for shell: %0.3f',...
' \n\tMann-Whitney U test p-value for core v shell: %0.3f\n'],...
eiTitle{hh},xlabels{ii},pc,ps,pMannU);
% clunky way just to get the top histogram value
RSdiffBins = -10:0.2:10;
plotInterlaceBars(coreDiffRS, shellDiffRS, RSdiffBins);
ytop = ylim * [0 1]';
% todo: plot significance on graph
hold on;
plotSEMBar( coreDiffRS, ytop , [0.5 0.5 0.5]);
plotSEMBar( coreSubDiffRS, ytop+1, [0.5 0.5 0.5]);
plotSEMBar( corePlastDiffRS, ytop+2, [0.5 0.5 0.5]);
plotSEMBar( shellDiffRS, ytop+3, [ 1 0 0]);
plotSEMBar( shellSubDiffRS, ytop+4, [ 1 0 0]);
plotSEMBar(shellPlastDiffRS, ytop+5, [ 1 0 0]);
plot([0 0], ylim, 'k--');
hold off;
% redo y axis labels
yt = get(gca,'YTick');
yt = [yt(yt < ytop) ytop:ytop+5];
ytl = cellfun(@(x) sprintf('%d',x),num2cell(yt),'UniformOutput',false);
ytl(end-5:end) = {'Core','Core/Subsong','Core/Plastic','Shell','Shell/Subsong','Shell/Plastic'};
set(gca,'YTick',yt,'YTickLabel',ytl);
% figure formatting
xlabel(xlabels{ii});
ylabel('Count');
xlim([min(RSdiffBins) max(RSdiffBins)]);
set(gca,'Box','off');
set(gca, 'FontSize', 14);
set(get(gca,'XLabel'),'FontSize', 14);
set(get(gca,'YLabel'),'FontSize', 14);
set(get(gca,'Title' ),'FontSize', 14);
title(eiTitle{hh});
set(gcf,'Color',[1 1 1]);
if params.saveplot
saveCurrFigure(sprintf('figures/distanceCorrelations/RSdiffs-SUA-%s-%s.jpg', distanceTypes{ii}, filsuff{hh}));
end
end
end
%}
%% JMA added this section to compare neurons with compiled cluster data
for aa = 1: length(allNeuronCorrData)
allNeuronCorrData(aa).intra_DistanceAll = allNeuronCorrData(aa).intra_DistanceAll';%these were in columns
allNeuronCorrData(aa).inter_DistanceAll = allNeuronCorrData(aa).inter_DistanceAll';
allNeuronCorrData(aa).burstFraction = allNeuronCorrData(aa).burstFraction';
qq = zeros(allNeuronCorrData(aa).nSylls,1);%needed to do this to get cluster quality and syllable type for every syllable
tt = zeros(allNeuronCorrData(aa).nSylls,1);
qq(:,1) = deal(allNeuronCorrData(aa).clusterObjQ);
tt(:,1) = deal(allNeuronCorrData(aa).syllID);
allNeuronCorrData(aa).clusterObjQ = qq';
allNeuronCorrData(aa).syllID = tt';
end
usablePairs = allNeuronCorrData(isPresel);
usableClusterSessions = {usablePairs.sessionID};
[uUCSessions, ~, ~] = unique(usableClusterSessions);
corrByNeuron = struct([]);
for mm = 1: length(uUCSessions)
isCurrentSession = strcmp(uUCSessions(mm),{usablePairs.sessionID});
currentSessionPairs = usablePairs(isCurrentSession);
[neuronsHere, ~, ~] = unique([currentSessionPairs.unitNum]);
for nn = 1: length(neuronsHere)
isCurrentNeuron = [currentSessionPairs.unitNum] == neuronsHere(nn);
currentNeuronPairs = currentSessionPairs(isCurrentNeuron);
compiledNeuron.isCore = currentNeuronPairs(1).isCore;
compiledNeuron.isMUA = currentNeuronPairs(1).isMUA;
compiledNeuron.isPlastic = currentNeuronPairs(1).isPlastic;
compiledNeuron.sessionID = currentNeuronPairs(1).sessionID;
compiledNeuron.unitNum = currentNeuronPairs(1).unitNum;
compiledNeuron.nSylls = sum([currentNeuronPairs.nSylls]);
compiledNeuron.sigSyll = sum([currentNeuronPairs.sigResponse]);
compiledNeuron.RSAll = horzcat([currentNeuronPairs.RSAll]);
compiledNeuron.FRSyll = horzcat([currentNeuronPairs.FRSyll]);
compiledNeuron.FRBase = horzcat([currentNeuronPairs.FRBase]);
compiledNeuron.burstFraction = horzcat([currentNeuronPairs.burstFraction]);
compiledNeuron.tutor_DistanceAll = horzcat([currentNeuronPairs.tutor_DistanceAll]);
compiledNeuron.consensus_DistanceAll = horzcat([currentNeuronPairs.consensus_DistanceAll]);
compiledNeuron.central_DistanceAll = horzcat([currentNeuronPairs.central_DistanceAll]);
compiledNeuron.intra_DistanceAll = horzcat([currentNeuronPairs.intra_DistanceAll]);
compiledNeuron.inter_DistanceAll = horzcat([currentNeuronPairs.inter_DistanceAll]);
compiledNeuron.quality = horzcat([currentNeuronPairs.clusterObjQ]);
compiledNeuron.syllID = horzcat([currentNeuronPairs.syllID]);
numClassSyll = length(unique(compiledNeuron.syllID));
compiledNeuron.classSyll = deal(numClassSyll);
corrByNeuron = [corrByNeuron; compiledNeuron];
end
end
isenoughSyll = [corrByNeuron.nSylls] > 39; %want at least 10 syllables in each quartile
% usableNeuron = [corrByNeuron.sigSyll] > 0 & isenoughSyll; %neuron has to respond to at least one syllable cluster (but maybe shouldn't do this)
usableNeuron = isenoughSyll;
corrByNeuron = corrByNeuron(usableNeuron);
%correlation of distance to response strength
for bb = 1: length(corrByNeuron)
yDist = corrByNeuron(bb).tutor_DistanceAll'; %can try other distances
xRS = corrByNeuron(bb).RSAll';
[linfit, ~,~,~, fitStats] = regress(yDist, [ones(numel(xRS),1) xRS]); %checked with corrcoef and gives same p value
CC = corrcoef(yDist,xRS); %JMA added to get direction of correlation
%if params.plot
% figure
% plot(xRS, yDist, 'k.', 'HandleVisibility', 'off');
% hold on;
% plot(xRS, linfit(1) + xRS * linfit(2), '--','Color',[1 0 0]);
% legend(sprintf('r^2 = %0.3g, F = %0.3g, p = %0.3g\n',...
% fitStats(1), fitStats(2),fitStats(3)));
% xlabel('Response Strength'); ylabel('Matched distance');
%end
corrByNeuron(bb).linfit = linfit;
corrByNeuron(bb).fitStats = fitStats;
corrByNeuron(bb).CorrCoef = CC; %JMA added
end
isCore2 = [corrByNeuron.isCore];
CorrPRS = zeros(length(corrByNeuron),1);
for cc = 1: length(corrByNeuron)
CorrPRS(cc) = corrByNeuron(cc).fitStats(3);
end
isCorrel = CorrPRS < 0.05;
cSC = isCore2 & isCorrel';
cSS = ~isCore2 & isCorrel';
fprintf('Number neurons with significant correlation of RS in core %s out of %s core neurons \n', num2str(sum(cSC)), num2str(sum(isCore2)))
fprintf('Number neurons with significant correlation of RS in shell %s out of %s shell neurons \n', num2str(sum(cSS)), num2str(sum(~isCore2)))
%correlation of distance to burst fraction; need to work out bugs if want
%to use this burst fraction analysis because just giving NaNs
% for bb = 1: length(corrByNeuron)
% yDist = corrByNeuron(bb).tutor_DistanceAll';
% xRS = corrByNeuron(bb).burstFraction';
% [linfit, ~,~,~, fitStats] = regress(yDist, [ones(numel(xRS),1) xRS]);
% corrByNeuron(bb).linfitBF = linfit;
% corrByNeuron(bb).fitStatsBF = fitStats;
% end
% isCore2 = [corrByNeuron.isCore];
% CorrP = zeros(length(corrByNeuron),1);
% for cc = 1: length(corrByNeuron)
% CorrP(cc) = corrByNeuron(cc).fitStatsBF(3);
% end
% isCorrel = CorrP < 0.05;
% cSC = isCore2 & isCorrel';
% cSS = ~isCore2 & isCorrel';
% fprintf('Number neurons with significant correlation of BF in core %s out of %s core neurons \n', num2str(sum(cSC)), num2str(sum(isCore2)))
% fprintf('Number neurons with significant correlation of BF in shell %s out of %s shell neurons \n', num2str(sum(cSS)), num2str(sum(~isCore2)))
%compare population response, standardized response to top and bottom 25%
%or 50%
%similarity to tutor song
for dd = 1: length(corrByNeuron)
dists = corrByNeuron(dd).tutor_DistanceAll; %tutor_DistanceAll
syllCV = nanstd(corrByNeuron(dd).FRSyll)/nanmean(corrByNeuron(dd).FRSyll);
iqDists = prctile(dists, 50); %iqDists = prctile(dists, [25 75]); %quartiles
near_quartileFR = corrByNeuron(dd).FRSyll(dists < iqDists); nQuartile = numel(near_quartileFR); %changing it to median instead of quartiles but not changing all the variable names JMA
far_quartileFR = corrByNeuron(dd).FRSyll(dists > iqDists); fQuartile = numel( far_quartileFR);
% near_quartileFR = corrByNeuron(dd).FRSyll(dists < iqDists(1)); nQuartile = numel(near_quartileFR); %changing it to median instead of quartiles but not changing all the variable names JMA
% far_quartileFR = corrByNeuron(dd).FRSyll(dists > iqDists(2)); fQuartile = numel( far_quartileFR);
% near_quartileFRBase = corrByNeuron(dd).FRBase(dists < iqDists(1));
% far_quartileFRBase = corrByNeuron(dd).FRBase(dists > iqDists(2));
% near_quartileBF = corrByNeuron(dd).burstFraction(dists < iqDists(1));
% far_quartileBF = corrByNeuron(dd).burstFraction(dists > iqDists(2));
% near_quartileDist = corrByNeuron(dd).tutor_DistanceAll(dists < iqDists(1));
% far_quartileDist = corrByNeuron(dd).tutor_DistanceAll(dists > iqDists(2));
% near_quartileQual = corrByNeuron(dd).quality(dists < iqDists(1));
% far_quartileQual = corrByNeuron(dd).quality(dists > iqDists(2));
% near_quartileType = corrByNeuron(dd).syllID(dists < iqDists(1));
% far_quartileType = corrByNeuron(dd).syllID(dists > iqDists(2));
near_quartileFRBase = corrByNeuron(dd).FRBase(dists < iqDists);
far_quartileFRBase = corrByNeuron(dd).FRBase(dists > iqDists);
near_quartileBF = corrByNeuron(dd).burstFraction(dists < iqDists);
far_quartileBF = corrByNeuron(dd).burstFraction(dists > iqDists);
near_quartileDist = corrByNeuron(dd).tutor_DistanceAll(dists < iqDists);
far_quartileDist = corrByNeuron(dd).tutor_DistanceAll(dists > iqDists);
near_quartileQual = corrByNeuron(dd).quality(dists < iqDists);
far_quartileQual = corrByNeuron(dd).quality(dists > iqDists);
near_quartileType = corrByNeuron(dd).syllID(dists < iqDists);
far_quartileType = corrByNeuron(dd).syllID(dists > iqDists);
farQuart = nanmean(far_quartileDist);
nearQuart = nanmean(near_quartileDist);
farQual = nanmean(far_quartileQual);
nearQual = nanmean(near_quartileQual);
farTypes = length(unique(far_quartileType));
nearTypes = length(unique(near_quartileType));
nq_meanFR = nanmean(near_quartileFR); nq_varFR = nanvar(near_quartileFR);
fq_meanFR = nanmean( far_quartileFR); fq_varFR = nanvar( far_quartileFR);
nq_meanBF = nanmean(near_quartileBF);
fq_meanBF = nanmean(far_quartileBF);
nq_CV = nanstd(near_quartileFR)/nq_meanFR;
fq_CV = nanstd(far_quartileFR)/fq_meanFR;
nq_meanFRBase = nanmean(near_quartileFRBase); nq_varFRBase = nanvar(near_quartileFRBase);
fq_meanFRBase = nanmean( far_quartileFRBase); fq_varFRBase = nanvar( far_quartileFRBase);
[~, pVal, ~, tValStruct] = ttest2(near_quartileFR- near_quartileFRBase, far_quartileFR- far_quartileFRBase);
tVal = tValStruct.tstat;
fcovar = nancov(far_quartileFR,far_quartileFRBase);if numel(fcovar) > 1, fcovar = fcovar(2,1); end;
ncovar = nancov(near_quartileFR,near_quartileFRBase);if numel(ncovar) > 1, ncovar = ncovar(2,1); end;
aCovar = nancov(corrByNeuron(dd).FRSyll,corrByNeuron(dd).FRBase);if numel(aCovar) > 1, aCovar = aCovar(2,1); end;
farZDenom = sqrt(fq_varFR + fq_varFRBase - 2*fcovar);
nearZDenom = sqrt(nq_varFR + nq_varFRBase - 2*ncovar);
farZ = ((fq_meanFR - fq_meanFRBase)* sqrt(fQuartile))/farZDenom;
nearZ = ((nq_meanFR - nq_meanFRBase)* sqrt(nQuartile))/nearZDenom;
allZDenom = sqrt(nanvar(corrByNeuron(dd).FRSyll) + nanvar(corrByNeuron(dd).FRBase) - 2*aCovar);
allZ = ((nanmean(corrByNeuron(dd).FRSyll) - nanmean(corrByNeuron(dd).FRBase)) *sqrt(corrByNeuron(dd).nSylls))/allZDenom;
corrByNeuron(dd).quartilePValue = pVal;
corrByNeuron(dd).farZ = farZ;
corrByNeuron(dd).nearZ = nearZ;
corrByNeuron(dd).farCV = fq_CV;
corrByNeuron(dd).nearCV = nq_CV;
corrByNeuron(dd).nearBF = nq_meanBF;
corrByNeuron(dd).farBF = fq_meanBF;
corrByNeuron(dd).farQuart = farQuart;
corrByNeuron(dd).nearQuart = nearQuart;
corrByNeuron(dd).farQual = farQual;
corrByNeuron(dd).nearQual = nearQual;
corrByNeuron(dd).farTypes = farTypes;
corrByNeuron(dd).nearTypes = nearTypes;
corrByNeuron(dd).farTypeID = {unique(far_quartileType)};
corrByNeuron(dd).nearTypeID = {unique(near_quartileType)};
corrByNeuron(dd).modeFarTypeID = mode(far_quartileType);
corrByNeuron(dd).modeNearTypeID = mode(near_quartileType);
corrByNeuron(dd).syllCV = syllCV;
corrByNeuron(dd).allZ = allZ;
corrByNeuron(dd).BLFR = nanmean(corrByNeuron(dd).FRBase);
corrByNeuron(dd).FR = nanmean(corrByNeuron(dd).FRSyll);
corrByNeuron(dd).meanDist = mean(dists);
corrByNeuron(dd).farRS = (fq_meanFR - fq_meanFRBase);
corrByNeuron(dd).nearRS = (nq_meanFR - nq_meanFRBase);
end
isQS = [corrByNeuron.quartilePValue] < 0.05;
SQC = isCore2 & isQS;
SQS = ~isCore2 & isQS;
fprintf('Number neurons with significant difference in RS to near vs far in core %s out of %s core neurons \n', num2str(sum(SQC)), num2str(sum(isCore2)))
fprintf('Number neurons with significant difference in RS to near vs far in shell %s out of %s shell neurons \n', num2str(sum(SQS)), num2str(sum(~isCore2)))
meanFarZC = nanmean([corrByNeuron(isCore2).farZ]);
meanFarZS = nanmean([corrByNeuron(~isCore2).farZ]);
SEMFarC = nanstd([corrByNeuron(isCore2).farZ])/sqrt(length(corrByNeuron(isCore2)));
SEMFarS = nanstd([corrByNeuron(~isCore2).farZ])/sqrt(length(corrByNeuron(~isCore2)));
meanNearZC = nanmean([corrByNeuron(isCore2).nearZ]);
meanNearZS = nanmean([corrByNeuron(~isCore2).nearZ]);
SEMNearC = nanstd([corrByNeuron(isCore2).nearZ])/sqrt(length(corrByNeuron(isCore2)));
SEMFarS = nanstd([corrByNeuron(~isCore2).nearZ])/sqrt(length(corrByNeuron(~isCore2)));
% % normalized RS values--JMA not using anything from this point on
% dNormedRSFields = strcat(distanceTypes, '_dRSnorm');
% for hh = 1:3 % inhibited, excited, all
% for ii = 1:numel(distanceTypes) % six of them
% figure;
% dRSNormed = [allNeuronCorrData.(dNormedRSFields{ii})];
% if hh < 3
% coreDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & isCore);
% shellDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & ~isCore);
%
% coreSubDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & isCore & ~isPlastic);
% shellSubDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & ~isCore & ~isPlastic);
%
% corePlastDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & isCore & isPlastic);
% shellPlastDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & ~isCore & isPlastic);
% else
% coreDiffRSNorm = dRSNormed(isPresel & isCore);
% shellDiffRSNorm = dRSNormed(isPresel & ~isCore);
%
% coreSubDiffRSNorm = dRSNormed(isPresel & isCore & ~isPlastic);
% shellSubDiffRSNorm = dRSNormed(isPresel & ~isCore & ~isPlastic);
%
% corePlastDiffRSNorm = dRSNormed(isPresel & isCore & isPlastic);
% shellPlastDiffRSNorm = dRSNormed(isPresel & ~isCore & isPlastic);
% end
% fprintf('%s-normed %s: ', eiTitle{hh},xlabels{ii});
% if ~(all(isnan( coreSubDiffRSNorm)) || ...
% all(isnan( corePlastDiffRSNorm)) || ...
% all(isnan( shellSubDiffRSNorm)) || ...
% all(isnan(shellPlastDiffRSNorm)))
%
% % significance tests: two-way anova, permuted
% % this function is not consistent with matlab's anovan, so it
% % won't be used until we can see why the inconsistency's there
% %[stats, df, pvals] = statcond(...
% % {noNaN( coreSubDiffRSNorm), noNaN( corePlastDiffRSNorm); ...
% % noNaN(shellSubDiffRSNorm), noNaN(shellPlastDiffRSNorm)}, ...
% %'mode','param');
% % test against anova
%
% xx = [coreSubDiffRSNorm corePlastDiffRSNorm shellSubDiffRSNorm shellPlastDiffRSNorm]';
% grps = [zeros(size(coreSubDiffRSNorm)) zeros(size(corePlastDiffRSNorm)) ...
% ones(size(shellSubDiffRSNorm)) ones(size(shellPlastDiffRSNorm)); ...
% zeros(size(coreSubDiffRSNorm)) ones(size(corePlastDiffRSNorm)) ...
% zeros(size(shellSubDiffRSNorm)) ones(size(shellPlastDiffRSNorm))]';
% if isreal(xx) %JMA added
% [pAnova, tAnova] = anovan(xx,grps,'model','interaction','display', 'off');
% fprintf(['\n\tANOVA (fixed model), 2-way: effect of core/shell, p = %0.2f, '...
% 'effect of subsong/plastic, p = %0.2f, interaction, p = %0.2f'], ...
% pAnova(1), pAnova(2), pAnova(3))
% else
% fprintf('Skipping two-way permutation ANOVA');
% end
% end
% % significance tests: post-hoc, core vs shell
% if ~isempty(coreDiffRSNorm) && ~isempty(shellDiffRSNorm)
% pc = signrank( coreDiffRSNorm);
% ps = signrank(shellDiffRSNorm);
% pMannU = ranksum(coreDiffRSNorm(~isnan(coreDiffRSNorm)), shellDiffRSNorm(~isnan(shellDiffRSNorm)));
% % no subsong/plastic significance tests
%
% fprintf(['\n\tsign-rank test p-value for core: %0.3f' ...
% '\n\tsign-rank test p-value for shell: %0.3f',...
% '\n\tMann-Whitney U test p-value for core v shell: %0.3f\n'],...
% pc,ps,pMannU);
% end
%
% % set bins for histogram
% RSdiffBins = -10:0.5:10;
% plotInterlaceBars(coreDiffRSNorm, shellDiffRSNorm, RSdiffBins);
% ytop = ylim * [0 1]';
%
% % todo: plot significance on graph
% hold on;
% plotSEMBar( coreDiffRSNorm, ytop , [0.5 0.5 0.5]);
% plotSEMBar( shellDiffRSNorm, ytop+1, [ 1 0 0]);
% plotSEMBar( coreSubDiffRSNorm, ytop+2, [0.5 0.5 0.5]);
% plotSEMBar( shellSubDiffRSNorm, ytop+3, [ 1 0 0]);
% plotSEMBar( corePlastDiffRSNorm, ytop+4, [0.5 0.5 0.5]);
% plotSEMBar(shellPlastDiffRSNorm, ytop+5, [ 1 0 0]);
% plot([0 0], ylim, 'k--');
% hold off;
% % redo y axis labels
% yt = get(gca,'YTick');
% yt = [yt(yt < ytop) ytop:ytop+5];
% ytl = cellfun(@(x) sprintf('%d',x),num2cell(yt),'UniformOutput',false);
% ytl(end-5:end) = {'Core','Shell','Core/Subsong','Shell/Subsong','Core/Plastic','Shell/Plastic'};
% set(gca,'YTick',yt,'YTickLabel',ytl);
%
% % figure formatting
% xlabel(['normalized ' xlabels{ii}]);
% ylabel('Count');
% legend(sprintf('CORE: n = %d', numel( coreDiffRSNorm(~isnan( coreDiffRSNorm)))),...
% sprintf('SHELL: n = %d', numel(shellDiffRSNorm(~isnan(shellDiffRSNorm)))));
% xlim([min(RSdiffBins) max(RSdiffBins)]);
% set(gca,'Box','off');
% set(gca, 'FontSize', 14);
% set(get(gca,'XLabel'),'FontSize', 14);
% set(get(gca,'YLabel'),'FontSize', 14);
% set(get(gca,'Title' ),'FontSize', 14);
% title(sprintf('%s, core/shell diff p = %0.2g, core from zero p = %0.2g, shell from zero p = %0.2g', eiTitle{hh}, pMannU, pc, ps));
% set(gcf,'Color',[1 1 1]);
%
% %mean(coreDiffRSNorm)
% %mean(shellDiffRSNorm)
% %pause;
% if params.saveplot
% imFile = sprintf('figures/paper/distanceCorrelations-subjectiveScoreFilter/normedRSdiffs-SUA-%s-%s.pdf', distanceTypes{ii}, filsuff{hh});
% fprintf('Writing image to %s', imFile);
% scrsz = get(0,'ScreenSize');
% set(gcf, 'Position', [1 1 scrsz(3) scrsz(4)]);
%
% export_fig(imFile);
%
% % saveCurrFigure(sprintf('figures/A_keeper/mostResponsive/normedRSdiffs-SUA-%s-%s.jpg', distanceTypes{ii}, filsuff{hh}));
% end
% end
% end
%{
fprintf('\n\np-values of FR correlation to distance types');
pFields = strcat(distanceTypes, 'Distance_p');
R2Fields = strcat(distanceTypes, 'DistanceR2');
xlabels = strcat({'Linear trend p-values of FR to '}, distanceDescriptions);
for hh = 1:3 % inhibited, excited, all
figure;
for ii = 1:numel(distanceTypes)
subplot(nR,nC,ii)
corrPVals = [allNeuronCorrData.(pFields{ii})];
corrR2Vals = [allNeuronCorrData.(R2Fields{ii})];
if hh < 3
coreCPVs = corrPVals(isPresel & isExcited == hh-1 & isCore);
shellCPVs = corrPVals(isPresel & isExcited == hh-1 & ~isCore);
% get % variance explained
[ coreR2M, coreR2SEM] = meanSEM(corrR2Vals(isPresel & isExcited == hh-1 & isCore));
[shellR2M, shellR2SEM] = meanSEM(corrR2Vals(isPresel & isExcited == hh-1 & ~isCore));
%coreSubCPVs = corrPVals(isPresel & isExcited == hh-1 & isCore & ~isPlastic);
%shellSubCPVs = corrPVals(isPresel & isExcited == hh-1 & ~isCore & ~isPlastic);
%corePlastCPVs = corrPVals(isPresel & isExcited == hh-1 & isCore & isPlastic);
%shellPlastCPVs = corrPVals(isPresel & isExcited == hh-1 & ~isCore & isPlastic);
else
coreCPVs = corrPVals(isPresel & isCore);
shellCPVs = corrPVals(isPresel & ~isCore);
[ coreR2M, coreR2SEM] = meanSEM(corrR2Vals(isPresel & isCore));
[shellR2M, shellR2SEM] = meanSEM(corrR2Vals(isPresel & ~isCore));
%coreSubCPVs = corrPVals(isPresel & isCore & ~isPlastic);
%shellSubCPVs = corrPVals(isPresel & ~isCore & ~isPlastic);
%corePlastCPVs = corrPVals(isPresel & isCore & isPlastic);
%shellPlastCPVs = corrPVals(isPresel & ~isCore & isPlastic);
end
% test the difference between core and shell
pMannU = ranksum(coreCPVs(~isnan(coreCPVs)), shellCPVs(~isnan(shellCPVs)));
fprintf('%s - %s: Mann-Whitney U p-value for core v shell: %0.3f\n',...
eiTitle{hh}, xlabels{ii},pMannU);
fprintf(['\tCore variance explained: %0.2f +/- %0.2f, '...
'shell variance explained: %0.2f +/- %0.2f\n'], ...
coreR2M, coreR2SEM, shellR2M, shellR2SEM);
pBins = logspace(-3,0,30);
plotInterlaceBars(coreCPVs, shellCPVs, pBins);
legend(sprintf('CORE: n = %d', numel( coreCPVs(~isnan( coreCPVs)))),...
sprintf('SHELL: n = %d', numel(shellCPVs(~isnan(shellCPVs)))));
xlabel(xlabels{ii});
ylabel('Count');
xlim([0 1])
% figure formatting
set(gca, 'XTick', [0.05 0.1 0.2 0.4 0.6 0.8]);
set(gca, 'Box', 'off');
set(gca, 'FontSize', 12);
% xticklabel_rotate([],45); % this messes up the figure subplots
set(get(gca,'XLabel'),'FontSize', 14);
set(get(gca,'YLabel'),'FontSize', 14);
set(get(gca,'Title' ),'FontSize', 14);
% todo: plot the significance markers
ytop = ylim * [0 1]'; ylim([0 ytop+2]);
hold on;
plotSEMBar( coreCPVs, ytop-0.2, [0.5 0.5 0.5]);
plotSEMBar(shellCPVs, ytop-0.1, [1 0 0]);
hold off;
end
subplot(nR,nC,1);
title(eiTitle{hh});
set(gcf,'Color',[1 1 1]);
if params.saveplot
saveCurrFigure(sprintf('figures/distanceCorrelations/neuronDistanceCorr-SUA-%s.jpg', filsuff{hh}));
end
end
%}
end
function [m, v] = meanSEM(set1)
m = nanmean(set1);
if numel(set1) >= 2
v = nanstd(set1 )/sqrt(numel(set1)-1);
else
v = 0;
end
end
% plot the error bars w/ SEM on top
function plotSEMBar(set, y, col)
[m,v] = meanSEM(set);
plotHorzErrorBar(m,y,v,col);
end
function x = noNaN(x)
x(isnan(x)) = [];
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
plotAlignedPSTH.m
|
.m
|
Acoustic_Similarity-master/code/plotting/plotAlignedPSTH.m
| 5,624 |
utf_8
|
ab6ae9432ab85c399e8892991cf10680
|
function eventSpikeTimes = plotAlignedPSTH(events, spikeTimes, subEvents, labelsOfInterest)
% function plotAlignedPSTH(events, spikeTimes) plot time-warped rasters/PSTH
%
%
% spikeTimes is expected to be a raw array of spike times
if nargin < 3
subEvents = initEvents(1);
end
if nargin < 4
labelsOfInterest = {};
elseif ~iscell(labelsOfInterest)
labelsOfInterest = {labelsOfInterest};
end
[counts, eventSpikeTimes] = countSpikes(events, spikeTimes,'onset');
syllableLabels = {subEvents.type};
syllableLengths = [subEvents.stop] - [subEvents.start];
[uLabels, foo, rLabelIdx] = unique(syllableLabels);
[parentage, eventsInContext] = findParent(events,subEvents);
nAlign = numel(labelsOfInterest);
rAlignIdx = NaN(size(rLabelIdx));
doWarping = (nargin == 4);
if doWarping
% set index of event types according to the align list
for ii = 1:nAlign
labelsIndex = find(strcmp(uLabels,labelsOfInterest{ii}));
if ~isempty(labelsIndex)
rAlignIdx(rLabelIdx == labelsIndex) = ii;
end
end
% get the average onset and length of each syllable
avgLengths = zeros(1,nAlign);
for ii = 1:nAlign
% get average length
isThisLabel = (rAlignIdx == ii);
avgLengths(ii) = mean(syllableLengths(isThisLabel));
avgStarts(ii) = mean([eventsInContext(isThisLabel).start]);
end
warpedEventsInContext = eventsInContext;
for ii = 1:numel(events)
isChild = (parentage == ii);
childEvs = eventsInContext(isChild);
controlPoints = NaN(2*nAlign, 2); % first column is sample, second is standard;
controlSet = false(1,2*nAlign);
for jj = 1:nAlign
% in case of multiply labeled syllables, just pick the first
% one for alignment purposes
matchAlign = find(strcmp({childEvs.type},labelsOfInterest{jj}),1);
if ~isempty(matchAlign)
controlPoints(2*jj-1:2*jj,:) = ...
[childEvs(matchAlign).start avgStarts(jj); ...
childEvs(matchAlign).stop avgStarts(jj) + avgLengths(jj)];
controlSet(2*jj-1) = true;
controlSet(2*jj) = true;
end
end
controlPoints(~controlSet,:) = []; % get rid of nan points
% now use control points to linearly interpolate times
adjEventSpikeTimes{ii} = interpLinearPoints(eventSpikeTimes{ii},...
controlPoints(:,2), controlPoints(:,1));
% for plotting purposes: warp event boundaries themselves
warpedStarts = interpLinearPoints([childEvs.start],...
controlPoints(:,2), controlPoints(:,1));
warpedStops = interpLinearPoints([childEvs.stop],...
controlPoints(:,2), controlPoints(:,1));
foo = num2cell(warpedStarts);
[warpedEventsInContext(isChild).start] = foo{:};
foo = num2cell(warpedStops);
[warpedEventsInContext(isChild).stop] = foo{:};
end
end
% prepare the bins - all important binning parameter
binSize = 1e-3; % in seconds
% concatenate the spiketimes
if doWarping
eventSpikeTimes = adjEventSpikeTimes;
end
xcoords = vertcat(eventSpikeTimes{:});
maxTime = max(xcoords);
% plot raster
subplot(3,1,1:2)
plotRaster;
title(sprintf('Raster for song'));
% plot average PSTH
subplot(3,1,3)
title(sprintf('PSTH'));
plotPeriHist
function plotRaster
% option
plotColors = true;
% this code tells us how to sort out the y-coordinates so that
% each 'spike' lands on the correct row
stepsUp = zeros(size(xcoords));
stepsUp(cumsum(counts(1:end-1))+1) = 1; stepsUp(1) = 1;
ycoords = cumsum(stepsUp);
% actually plot the spikes
plot(xcoords, ycoords, 'ks','MarkerSize',2,'MarkerFaceColor','k');
xlim([0 maxTime]);
ylim([0.5 numel(events) + 0.5])
% optional: plot marks behind
for kk = 1:numel(events)
isKthChild = (parentage == kk);
subEvs = eventsInContext(isKthChild);
if doWarping
subEvs = warpedEventsInContext(isKthChild);
end
% assign the new colors only to the labels that correspond to alignment ones
if plotColors
typeColors = autumn(nAlign); % color assignment for each label
evTypes = rAlignIdx(isKthChild);
markColors = 0.5*ones(numel(subEvs),3);
markColors(~isnan(evTypes),:) = typeColors(evTypes(~isnan(evTypes)),:);
else
markColors = [0.5 0.5 0.5];
end
plotAreaMarks(subEvs, markColors, false, [-0.5 0.5] + kk);
end
end
function plotPeriHist
histBar = histc(xcoords, 0:binSize:maxTime);
bar(0:binSize:maxTime, histBar, 'histc');
xlim([0 maxTime]);
end
end
function yy = interpLinearPoints(xx,y1,x1)
% interpolate linearly between each pair of points & interpolate
assert(numel(x1) == numel(y1))
if numel(x1) == 0, yy = xx; return; end;
[x1, idxs] = sort(x1);
y1 = y1(idxs);
intervalPtr = 1;
yy = zeros(size(xx));
for kk = 1:numel(xx)
intervalPtr = find(xx(kk) <= x1,1);
if isempty(intervalPtr)
yy(kk) = xx(kk) - x1(end) + y1(end); % no warping after last control point
elseif intervalPtr == 1
yy(kk) = xx(kk) - x1(1) + y1(1); % no warping before first control point
else
yy(kk) = interp1(x1(intervalPtr-1:intervalPtr),...
y1(intervalPtr-1:intervalPtr),xx(kk));
end
end
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
plotNeuronCorrDataNoCluster.m
|
.m
|
Acoustic_Similarity-master/code/plotting/plotNeuronCorrDataNoCluster.m
| 27,240 |
utf_8
|
854abe747d607ccc45ebd0bfeea55088
|
function plotNeuronCorrDataNoCluster(allNeuronCorrData, params, varargin)
if nargin < 2 || isempty(params)
params = defaultParams;
end
params = processArgs(params, varargin{:});
% plot difference between firing rates for near tutor/far from tutor
% and also p-values for correlations between neurons and firing rates
%This will be without regard classified syllables (i,e. all syllables will
%be included)
% this data is compiled in correlateDistanceToFiring
if nargin < 1 || isempty(allNeuronCorrData)
load('data/allNeuronCorrelations.mat');
end
%% here we load the cluster quality
% get birds and ages first
sessionIDs = {allNeuronCorrData.sessionID};
birdIDs = strtok(sessionIDs, '_');
[uSessions, ~, rIdxSession] = unique(sessionIDs); % index through ages can go back to sessions
uAges = getAgeOfSession(uSessions);
sessionAges = zeros(size(sessionIDs));
for ii = 1:numel(uAges)
sessionAges(rIdxSession == ii) = uAges(ii);
end
[sessionQ , allSubj] = getClusterQuality(birdIDs, sessionAges, [allNeuronCorrData.syllID]);
[sessionObjQ, allObj ] = getClusterQuality(birdIDs, sessionAges, [allNeuronCorrData.syllID], true);
foo = num2cell(sessionQ ); [allNeuronCorrData.clusterQ ] = foo{:};
foo = num2cell(sessionObjQ); [allNeuronCorrData.clusterObjQ] = foo{:};
foo = allSubj'; allSubj = [allSubj(:)];
foo = allObj' ; allObj = [allObj(:) ];
missingData = isnan(allSubj) | isnan(allObj);
qualityFit = polyfit(allSubj(~missingData), allObj(~missingData), 1);
plot(1:5, polyval(qualityFit,1:5),'r-');
hold on;
%boxplot(sessionObjQ', sessionQ', 'notch', 'on');
plot(allSubj, allObj, 'k.');
[rQualCorr, pQualCorr] = corrcoef(allSubj(~missingData), allObj(~missingData));
legend(sprintf('r^2 = %0.3f, p = %0.3g', rQualCorr(2,1), pQualCorr(2,1)));
xlim([0.5 5.5])
xlabel('Subjective Cluster Quality');
ylabel('Davies-Bouldin Index');
title('Subjective vs. objective cluster quality correlations');
if params.saveplot
saveCurrFigure('figures\A_keeper\objSubjClusterQuality.jpg');
end
%% flags
isCore = [allNeuronCorrData.isCore];
isMUA = [allNeuronCorrData.isMUA];
isPlastic = [allNeuronCorrData.isPlastic];
isSignificant = [allNeuronCorrData.sigResponse];
nSylls = [allNeuronCorrData.nSylls];
%isSignificant = true(1,numel(allNeuronCorrData));
isExcited = [allNeuronCorrData.isExcited];
%%
isSubjGood = [allNeuronCorrData.clusterQ] < 1.5; % < 2.5
isObjGood = [allNeuronCorrData.clusterObjQ] < 0.8; % < 1
%%
% criteria for cluster inclusion
% must have at least three syllables per quartile
% isPresel = isSignificant & ~isMUA & nSylls >= 12 & isSubjGood; %JMA changed this because don't need a certain #/cluster rather certain # overall all clusters for neurons correlation (as opposed to neuron/syllable pair)
isPresel = ~isMUA; %~isMUA & isObjGood
%%isPresel = isSignificant; % for mostresponsive neurons in todo0410.m
%isPresel = nSylls >= 12;
% subplot rows / columns
nR = 3; nC = 2;
% measures
distanceTypes = {'tutor', 'intra', 'inter', 'consensus', 'central','humanMatch'}';
distanceDescriptions = {'closest tutor', 'cluster center', 'normed center', ...
'closest tutor to cluster consensus', 'closest tutor to cluster center', 'expert-designated tutor'};
dFieldsRS = [strcat(distanceTypes, '_nearMeanRS') strcat(distanceTypes, '_farMeanRS')];
dFieldsSEM = [strcat(distanceTypes, '_nearMeanSEM') strcat(distanceTypes, '_farMeanSEM')];
eiTitle = {'Significantly inhibited single unit-syllable pairs', ...
'Significantly excited single unit-syllable pairs', ...
'All significant single unit-syllable pairs'};
xlabels = strcat({'RS for near - far to '}, distanceDescriptions);
filsuff = {'inh','exc','all'};
%%
%{
for hh = 1:3 % inhibited, excited, all
for ii = 1:numel(distanceTypes) % six of them
figure;
diffTutorMeanRS = [allNeuronCorrData.(dFieldsRS{ii,1})] - [allNeuronCorrData.(dFieldsRS{ii,2})];
nearMeanRS = [allNeuronCorrData.(dFieldsRS{ii,1})];
farMeanRS = [allNeuronCorrData.(dFieldsRS{ii,2})];
nearMeanSEM = [allNeuronCorrData.(dFieldsRS{ii,1})];
farMeanSEM = [allNeuronCorrData.(dFieldsRS{ii,2})];
if hh < 3
selHereCore = isPresel & isExcited == hh-1 & isCore;
selHereShell = isPresel & isExcited == hh-1 & ~isCore;
coreDiffRS = diffTutorMeanRS(selHereCore);
shellDiffRS = diffTutorMeanRS(selHereShell);
coreSubDiffRS = diffTutorMeanRS(selHereCore & ~isPlastic);
shellSubDiffRS = diffTutorMeanRS(selHereShell & ~isPlastic);
corePlastDiffRS = diffTutorMeanRS(selHereCore & isPlastic);
shellPlastDiffRS = diffTutorMeanRS(selHereShell & isPlastic);
else
coreDiffRS = diffTutorMeanRS(isPresel & isCore);
shellDiffRS = diffTutorMeanRS(isPresel & ~isCore);
coreSubDiffRS = diffTutorMeanRS(isPresel & isCore & ~isPlastic);
shellSubDiffRS = diffTutorMeanRS(isPresel & ~isCore & ~isPlastic);
corePlastDiffRS = diffTutorMeanRS(isPresel & isCore & isPlastic);
shellPlastDiffRS = diffTutorMeanRS(isPresel & ~isCore & isPlastic);
end
pc = signrank( coreDiffRS);
ps = signrank(shellDiffRS);
pMannU = ranksum(coreDiffRS(~isnan(coreDiffRS)), shellDiffRS(~isnan(shellDiffRS)));
fprintf(['%s-%s:\n\tsign-rank test p-value for core: %0.3f' ...
' \n\tsign-rank test p-value for shell: %0.3f',...
' \n\tMann-Whitney U test p-value for core v shell: %0.3f\n'],...
eiTitle{hh},xlabels{ii},pc,ps,pMannU);
% clunky way just to get the top histogram value
RSdiffBins = -10:0.2:10;
plotInterlaceBars(coreDiffRS, shellDiffRS, RSdiffBins);
ytop = ylim * [0 1]';
% todo: plot significance on graph
hold on;
plotSEMBar( coreDiffRS, ytop , [0.5 0.5 0.5]);
plotSEMBar( coreSubDiffRS, ytop+1, [0.5 0.5 0.5]);
plotSEMBar( corePlastDiffRS, ytop+2, [0.5 0.5 0.5]);
plotSEMBar( shellDiffRS, ytop+3, [ 1 0 0]);
plotSEMBar( shellSubDiffRS, ytop+4, [ 1 0 0]);
plotSEMBar(shellPlastDiffRS, ytop+5, [ 1 0 0]);
plot([0 0], ylim, 'k--');
hold off;
% redo y axis labels
yt = get(gca,'YTick');
yt = [yt(yt < ytop) ytop:ytop+5];
ytl = cellfun(@(x) sprintf('%d',x),num2cell(yt),'UniformOutput',false);
ytl(end-5:end) = {'Core','Core/Subsong','Core/Plastic','Shell','Shell/Subsong','Shell/Plastic'};
set(gca,'YTick',yt,'YTickLabel',ytl);
% figure formatting
xlabel(xlabels{ii});
ylabel('Count');
xlim([min(RSdiffBins) max(RSdiffBins)]);
set(gca,'Box','off');
set(gca, 'FontSize', 14);
set(get(gca,'XLabel'),'FontSize', 14);
set(get(gca,'YLabel'),'FontSize', 14);
set(get(gca,'Title' ),'FontSize', 14);
title(eiTitle{hh});
set(gcf,'Color',[1 1 1]);
if params.saveplot
saveCurrFigure(sprintf('figures/distanceCorrelations/RSdiffs-SUA-%s-%s.jpg', distanceTypes{ii}, filsuff{hh}));
end
end
end
%}
%% JMA added this section to compare neurons with compiled cluster data
for aa = 1: length(allNeuronCorrData)
allNeuronCorrData(aa).intra_DistanceAll = allNeuronCorrData(aa).intra_DistanceAll';%these were in columns
allNeuronCorrData(aa).inter_DistanceAll = allNeuronCorrData(aa).inter_DistanceAll';
allNeuronCorrData(aa).burstFraction = allNeuronCorrData(aa).burstFraction';
qq = zeros(allNeuronCorrData(aa).nSylls,1);%needed to do this to get cluster quality and syllable type for every syllable
tt = zeros(allNeuronCorrData(aa).nSylls,1);
qq(:,1) = deal(allNeuronCorrData(aa).clusterObjQ);
tt(:,1) = deal(allNeuronCorrData(aa).syllID);
allNeuronCorrData(aa).clusterObjQ = qq';
allNeuronCorrData(aa).syllID = tt';
end
usablePairs = allNeuronCorrData(isPresel);
usableClusterSessions = {usablePairs.sessionID};
[uUCSessions, ~, ~] = unique(usableClusterSessions);
corrByNeuron = struct([]);
for mm = 1: length(uUCSessions)
isCurrentSession = strcmp(uUCSessions(mm),{usablePairs.sessionID});
currentSessionPairs = usablePairs(isCurrentSession);
[neuronsHere, ~, ~] = unique([currentSessionPairs.unitNum]);
for nn = 1: length(neuronsHere)
isCurrentNeuron = [currentSessionPairs.unitNum] == neuronsHere(nn);
currentNeuronPairs = currentSessionPairs(isCurrentNeuron);
compiledNeuron.isCore = currentNeuronPairs(1).isCore;
compiledNeuron.isMUA = currentNeuronPairs(1).isMUA;
compiledNeuron.isPlastic = currentNeuronPairs(1).isPlastic;
compiledNeuron.sessionID = currentNeuronPairs(1).sessionID;
compiledNeuron.unitNum = currentNeuronPairs(1).unitNum;
compiledNeuron.nSylls = sum([currentNeuronPairs.nSylls]);
compiledNeuron.sigSyll = sum([currentNeuronPairs.sigResponse]);
compiledNeuron.RSAll = horzcat([currentNeuronPairs.RSAll]);
compiledNeuron.FRSyll = horzcat([currentNeuronPairs.FRSyll]);
compiledNeuron.FRBase = horzcat([currentNeuronPairs.FRBase]);
compiledNeuron.burstFraction = horzcat([currentNeuronPairs.burstFraction]);
compiledNeuron.tutor_DistanceAll = horzcat([currentNeuronPairs.tutor_DistanceAll]);
compiledNeuron.consensus_DistanceAll = horzcat([currentNeuronPairs.consensus_DistanceAll]);
compiledNeuron.central_DistanceAll = horzcat([currentNeuronPairs.central_DistanceAll]);
compiledNeuron.intra_DistanceAll = horzcat([currentNeuronPairs.intra_DistanceAll]);
compiledNeuron.inter_DistanceAll = horzcat([currentNeuronPairs.inter_DistanceAll]);
compiledNeuron.quality = horzcat([currentNeuronPairs.clusterObjQ]);
compiledNeuron.syllID = horzcat([currentNeuronPairs.syllID]);
numClassSyll = length(unique(compiledNeuron.syllID));
compiledNeuron.classSyll = deal(numClassSyll);
corrByNeuron = [corrByNeuron; compiledNeuron];
end
end
isenoughSyll = [corrByNeuron.nSylls] > 39; %want at least 10 syllables in each quartile
% usableNeuron = [corrByNeuron.sigSyll] > 0 & isenoughSyll; %neuron has to respond to at least one syllable cluster (but maybe shouldn't do this)
usableNeuron = isenoughSyll;
corrByNeuron = corrByNeuron(usableNeuron);
%correlation of distance to response strength
for bb = 1: length(corrByNeuron)
yDist = corrByNeuron(bb).tutor_DistanceAll'; %can try other distances
xRS = corrByNeuron(bb).RSAll'; %response strength, does it make sense to use firing rate?
[linfit, ~,~,~, fitStats] = regress(yDist, [ones(numel(xRS),1) xRS]); %checked with corrcoef and gives same p value
%if params.plot
% figure
% plot(xRS, yDist, 'k.', 'HandleVisibility', 'off');
% hold on;
% plot(xRS, linfit(1) + xRS * linfit(2), '--','Color',[1 0 0]);
% legend(sprintf('r^2 = %0.3g, F = %0.3g, p = %0.3g\n',...
% fitStats(1), fitStats(2),fitStats(3)));
% xlabel('Response Strength'); ylabel('Matched distance');
%end
corrByNeuron(bb).linfit = linfit;
corrByNeuron(bb).fitStats = fitStats;
end
isCore2 = [corrByNeuron.isCore];
CorrPRS = zeros(length(corrByNeuron),1);
for cc = 1: length(corrByNeuron)
CorrPRS(cc) = corrByNeuron(cc).fitStats(3);
end
isCorrel = CorrPRS < 0.05;
cSC = isCore2 & isCorrel';
cSS = ~isCore2 & isCorrel';
fprintf('Number neurons with significant correlation of RS in core %s out of %s core neurons \n', num2str(sum(cSC)), num2str(sum(isCore2)))
fprintf('Number neurons with significant correlation of RS in shell %s out of %s shell neurons \n', num2str(sum(cSS)), num2str(sum(~isCore2)))
%correlation of distance to burst fraction
for bb = 1: length(corrByNeuron)
yDist = corrByNeuron(bb).tutor_DistanceAll';
xRS = corrByNeuron(bb).burstFraction';
[linfit, ~,~,~, fitStats] = regress(yDist, [ones(numel(xRS),1) xRS]); %checked with corrcoef and gives same p value
corrByNeuron(bb).linfitBF = linfit;
corrByNeuron(bb).fitStatsBF = fitStats;
end
isCore2 = [corrByNeuron.isCore];
CorrP = zeros(length(corrByNeuron),1);
for cc = 1: length(corrByNeuron)
CorrP(cc) = corrByNeuron(cc).fitStatsBF(3);
end
isCorrel = CorrP < 0.05;
cSC = isCore2 & isCorrel';
cSS = ~isCore2 & isCorrel';
fprintf('Number neurons with significant correlation of BF in core %s out of %s core neurons \n', num2str(sum(cSC)), num2str(sum(isCore2)))
fprintf('Number neurons with significant correlation of BF in shell %s out of %s shell neurons \n', num2str(sum(cSS)), num2str(sum(~isCore2)))
%compare population response, standardized response to top and bottom 25%
%similarity to tutor song
for dd = 1: length(corrByNeuron)
dists = corrByNeuron(dd).tutor_DistanceAll; %tutor_DistanceAll
syllCV = nanstd(corrByNeuron(dd).FRSyll)/nanmean(corrByNeuron(dd).FRSyll);
iqDists = prctile(dists, [25 75]);
near_quartileFR = corrByNeuron(dd).FRSyll(dists < iqDists(1)); nQuartile = numel(near_quartileFR);
far_quartileFR = corrByNeuron(dd).FRSyll(dists > iqDists(2)); fQuartile = numel( far_quartileFR);
near_quartileFRBase = corrByNeuron(dd).FRBase(dists < iqDists(1));
far_quartileFRBase = corrByNeuron(dd).FRBase(dists > iqDists(2));
near_quartileBF = corrByNeuron(dd).burstFraction(dists < iqDists(1));
far_quartileBF = corrByNeuron(dd).burstFraction(dists > iqDists(2));
near_quartileDist = corrByNeuron(dd).tutor_DistanceAll(dists < iqDists(1));
far_quartileDist = corrByNeuron(dd).tutor_DistanceAll(dists > iqDists(2));
near_quartileQual = corrByNeuron(dd).quality(dists < iqDists(1));
far_quartileQual = corrByNeuron(dd).quality(dists > iqDists(2));
near_quartileType = corrByNeuron(dd).syllID(dists < iqDists(1));
far_quartileType = corrByNeuron(dd).syllID(dists > iqDists(2));
farQuart = nanmean(far_quartileDist);
nearQuart = nanmean(near_quartileDist);
farQual = nanmean(far_quartileQual);
nearQual = nanmean(near_quartileQual);
farTypes = length(unique(far_quartileType));
nearTypes = length(unique(near_quartileType));
nq_meanFR = nanmean(near_quartileFR); nq_varFR = nanvar(near_quartileFR);
fq_meanFR = nanmean( far_quartileFR); fq_varFR = nanvar( far_quartileFR);
nq_meanBF = nanmean(near_quartileBF);
fq_meanBF = nanmean(far_quartileBF);
nq_CV = nanstd(near_quartileFR)/nq_meanFR;
fq_CV = nanstd(far_quartileFR)/fq_meanFR;
nq_meanFRBase = nanmean(near_quartileFRBase); nq_varFRBase = nanvar(near_quartileFRBase);
fq_meanFRBase = nanmean( far_quartileFRBase); fq_varFRBase = nanvar( far_quartileFRBase);
[~, pVal, ~, tValStruct] = ttest2(near_quartileFR- near_quartileFRBase, far_quartileFR- far_quartileFRBase);
tVal = tValStruct.tstat;
fcovar = nancov(far_quartileFR,far_quartileFRBase);if numel(fcovar) > 1, fcovar = fcovar(2,1); end;
ncovar = nancov(near_quartileFR,near_quartileFRBase);if numel(ncovar) > 1, ncovar = ncovar(2,1); end;
aCovar = nancov(corrByNeuron(dd).FRSyll,corrByNeuron(dd).FRBase);if numel(aCovar) > 1, aCovar = aCovar(2,1); end;
farZDenom = sqrt(fq_varFR + fq_varFRBase - 2*fcovar);
nearZDenom = sqrt(nq_varFR + nq_varFRBase - 2*ncovar);
farZ = ((fq_meanFR - fq_meanFRBase)* sqrt(fQuartile))/farZDenom;
nearZ = ((nq_meanFR - nq_meanFRBase)* sqrt(nQuartile))/nearZDenom;
allZDenom = sqrt(nanvar(corrByNeuron(dd).FRSyll) + nanvar(corrByNeuron(dd).FRBase) - 2*aCovar);
allZ = ((nanmean(corrByNeuron(dd).FRSyll) - nanmean(corrByNeuron(dd).FRBase)) *sqrt(corrByNeuron(dd).nSylls))/allZDenom;
corrByNeuron(dd).quartilePValue = pVal;
corrByNeuron(dd).farZ = farZ;
corrByNeuron(dd).nearZ = nearZ;
corrByNeuron(dd).farCV = fq_CV;
corrByNeuron(dd).nearCV = nq_CV;
corrByNeuron(dd).nearBF = nq_meanBF;
corrByNeuron(dd).farBF = fq_meanBF;
corrByNeuron(dd).farQuart = farQuart;
corrByNeuron(dd).nearQuart = nearQuart;
corrByNeuron(dd).farQual = farQual;
corrByNeuron(dd).nearQual = nearQual;
corrByNeuron(dd).farTypes = farTypes;
corrByNeuron(dd).nearTypes = nearTypes;
corrByNeuron(dd).farTypeID = {unique(far_quartileType)};
corrByNeuron(dd).nearTypeID = {unique(near_quartileType)};
corrByNeuron(dd).syllCV = syllCV;
corrByNeuron(dd).allZ = allZ;
corrByNeuron(dd).BLFR = nanmean(corrByNeuron(dd).FRBase);
corrByNeuron(dd).FR = nanmean(corrByNeuron(dd).FRSyll);
end
isQS = [corrByNeuron.quartilePValue] < 0.05;
SQC = isCore2 & isQS;
SQS = ~isCore2 & isQS;
fprintf('Number neurons with significant difference in RS to near vs far in core %s out of %s core neurons \n', num2str(sum(SQC)), num2str(sum(isCore2)))
fprintf('Number neurons with significant difference in RS to near vs far in shell %s out of %s shell neurons \n', num2str(sum(SQS)), num2str(sum(~isCore2)))
meanFarZC = nanmean([corrByNeuron(isCore2).farZ]);
meanFarZS = nanmean([corrByNeuron(~isCore2).farZ]);
SEMFarC = nanstd([corrByNeuron(isCore2).farZ])/sqrt(length(corrByNeuron(isCore2)));
SEMFarS = nanstd([corrByNeuron(~isCore2).farZ])/sqrt(length(corrByNeuron(~isCore2)));
meanNearZC = nanmean([corrByNeuron(isCore2).nearZ]);
meanNearZS = nanmean([corrByNeuron(~isCore2).nearZ]);
SEMNearC = nanstd([corrByNeuron(isCore2).nearZ])/sqrt(length(corrByNeuron(isCore2)));
SEMFarS = nanstd([corrByNeuron(~isCore2).nearZ])/sqrt(length(corrByNeuron(~isCore2)));
% normalized RS values--not using this part
dNormedRSFields = strcat(distanceTypes, '_dRSnorm');
for hh = 1:3 % inhibited, excited, all
for ii = 1:numel(distanceTypes) % six of them
figure;
dRSNormed = [allNeuronCorrData.(dNormedRSFields{ii})];
if hh < 3
coreDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & isCore);
shellDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & ~isCore);
coreSubDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & isCore & ~isPlastic);
shellSubDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & ~isCore & ~isPlastic);
corePlastDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & isCore & isPlastic);
shellPlastDiffRSNorm = dRSNormed(isPresel & isExcited == hh-1 & ~isCore & isPlastic);
else
coreDiffRSNorm = dRSNormed(isPresel & isCore);
shellDiffRSNorm = dRSNormed(isPresel & ~isCore);
coreSubDiffRSNorm = dRSNormed(isPresel & isCore & ~isPlastic);
shellSubDiffRSNorm = dRSNormed(isPresel & ~isCore & ~isPlastic);
corePlastDiffRSNorm = dRSNormed(isPresel & isCore & isPlastic);
shellPlastDiffRSNorm = dRSNormed(isPresel & ~isCore & isPlastic);
end
fprintf('%s-normed %s: ', eiTitle{hh},xlabels{ii});
if ~(all(isnan( coreSubDiffRSNorm)) || ...
all(isnan( corePlastDiffRSNorm)) || ...
all(isnan( shellSubDiffRSNorm)) || ...
all(isnan(shellPlastDiffRSNorm)))
% significance tests: two-way anova, permuted
% this function is not consistent with matlab's anovan, so it
% won't be used until we can see why the inconsistency's there
%[stats, df, pvals] = statcond(...
% {noNaN( coreSubDiffRSNorm), noNaN( corePlastDiffRSNorm); ...
% noNaN(shellSubDiffRSNorm), noNaN(shellPlastDiffRSNorm)}, ...
%'mode','param');
% test against anova
xx = [coreSubDiffRSNorm corePlastDiffRSNorm shellSubDiffRSNorm shellPlastDiffRSNorm]';
grps = [zeros(size(coreSubDiffRSNorm)) zeros(size(corePlastDiffRSNorm)) ...
ones(size(shellSubDiffRSNorm)) ones(size(shellPlastDiffRSNorm)); ...
zeros(size(coreSubDiffRSNorm)) ones(size(corePlastDiffRSNorm)) ...
zeros(size(shellSubDiffRSNorm)) ones(size(shellPlastDiffRSNorm))]';
if isreal(xx) %JMA added
[pAnova, tAnova] = anovan(xx,grps,'model','interaction','display', 'off');
fprintf(['\n\tANOVA (fixed model), 2-way: effect of core/shell, p = %0.2f, '...
'effect of subsong/plastic, p = %0.2f, interaction, p = %0.2f'], ...
pAnova(1), pAnova(2), pAnova(3))
else
fprintf('Skipping two-way permutation ANOVA');
end
end
% significance tests: post-hoc, core vs shell
if ~isempty(coreDiffRSNorm) && ~isempty(shellDiffRSNorm)
pc = signrank( coreDiffRSNorm);
ps = signrank(shellDiffRSNorm);
pMannU = ranksum(coreDiffRSNorm(~isnan(coreDiffRSNorm)), shellDiffRSNorm(~isnan(shellDiffRSNorm)));
% no subsong/plastic significance tests
fprintf(['\n\tsign-rank test p-value for core: %0.3f' ...
'\n\tsign-rank test p-value for shell: %0.3f',...
'\n\tMann-Whitney U test p-value for core v shell: %0.3f\n'],...
pc,ps,pMannU);
end
% set bins for histogram
RSdiffBins = -10:0.5:10;
plotInterlaceBars(coreDiffRSNorm, shellDiffRSNorm, RSdiffBins);
ytop = ylim * [0 1]';
% todo: plot significance on graph
hold on;
plotSEMBar( coreDiffRSNorm, ytop , [0.5 0.5 0.5]);
plotSEMBar( shellDiffRSNorm, ytop+1, [ 1 0 0]);
plotSEMBar( coreSubDiffRSNorm, ytop+2, [0.5 0.5 0.5]);
plotSEMBar( shellSubDiffRSNorm, ytop+3, [ 1 0 0]);
plotSEMBar( corePlastDiffRSNorm, ytop+4, [0.5 0.5 0.5]);
plotSEMBar(shellPlastDiffRSNorm, ytop+5, [ 1 0 0]);
plot([0 0], ylim, 'k--');
hold off;
% redo y axis labels
yt = get(gca,'YTick');
yt = [yt(yt < ytop) ytop:ytop+5];
ytl = cellfun(@(x) sprintf('%d',x),num2cell(yt),'UniformOutput',false);
ytl(end-5:end) = {'Core','Shell','Core/Subsong','Shell/Subsong','Core/Plastic','Shell/Plastic'};
set(gca,'YTick',yt,'YTickLabel',ytl);
% figure formatting
xlabel(['normalized ' xlabels{ii}]);
ylabel('Count');
legend(sprintf('CORE: n = %d', numel( coreDiffRSNorm(~isnan( coreDiffRSNorm)))),...
sprintf('SHELL: n = %d', numel(shellDiffRSNorm(~isnan(shellDiffRSNorm)))));
xlim([min(RSdiffBins) max(RSdiffBins)]);
set(gca,'Box','off');
set(gca, 'FontSize', 14);
set(get(gca,'XLabel'),'FontSize', 14);
set(get(gca,'YLabel'),'FontSize', 14);
set(get(gca,'Title' ),'FontSize', 14);
title(sprintf('%s, core/shell diff p = %0.2g, core from zero p = %0.2g, shell from zero p = %0.2g', eiTitle{hh}, pMannU, pc, ps));
set(gcf,'Color',[1 1 1]);
%mean(coreDiffRSNorm)
%mean(shellDiffRSNorm)
%pause;
if params.saveplot
imFile = sprintf('figures/paper/distanceCorrelations-subjectiveScoreFilter/normedRSdiffs-SUA-%s-%s.pdf', distanceTypes{ii}, filsuff{hh});
fprintf('Writing image to %s', imFile);
scrsz = get(0,'ScreenSize');
set(gcf, 'Position', [1 1 scrsz(3) scrsz(4)]);
export_fig(imFile);
% saveCurrFigure(sprintf('figures/A_keeper/mostResponsive/normedRSdiffs-SUA-%s-%s.jpg', distanceTypes{ii}, filsuff{hh}));
end
end
end
%{
fprintf('\n\np-values of FR correlation to distance types');
pFields = strcat(distanceTypes, 'Distance_p');
R2Fields = strcat(distanceTypes, 'DistanceR2');
xlabels = strcat({'Linear trend p-values of FR to '}, distanceDescriptions);
for hh = 1:3 % inhibited, excited, all
figure;
for ii = 1:numel(distanceTypes)
subplot(nR,nC,ii)
corrPVals = [allNeuronCorrData.(pFields{ii})];
corrR2Vals = [allNeuronCorrData.(R2Fields{ii})];
if hh < 3
coreCPVs = corrPVals(isPresel & isExcited == hh-1 & isCore);
shellCPVs = corrPVals(isPresel & isExcited == hh-1 & ~isCore);
% get % variance explained
[ coreR2M, coreR2SEM] = meanSEM(corrR2Vals(isPresel & isExcited == hh-1 & isCore));
[shellR2M, shellR2SEM] = meanSEM(corrR2Vals(isPresel & isExcited == hh-1 & ~isCore));
%coreSubCPVs = corrPVals(isPresel & isExcited == hh-1 & isCore & ~isPlastic);
%shellSubCPVs = corrPVals(isPresel & isExcited == hh-1 & ~isCore & ~isPlastic);
%corePlastCPVs = corrPVals(isPresel & isExcited == hh-1 & isCore & isPlastic);
%shellPlastCPVs = corrPVals(isPresel & isExcited == hh-1 & ~isCore & isPlastic);
else
coreCPVs = corrPVals(isPresel & isCore);
shellCPVs = corrPVals(isPresel & ~isCore);
[ coreR2M, coreR2SEM] = meanSEM(corrR2Vals(isPresel & isCore));
[shellR2M, shellR2SEM] = meanSEM(corrR2Vals(isPresel & ~isCore));
%coreSubCPVs = corrPVals(isPresel & isCore & ~isPlastic);
%shellSubCPVs = corrPVals(isPresel & ~isCore & ~isPlastic);
%corePlastCPVs = corrPVals(isPresel & isCore & isPlastic);
%shellPlastCPVs = corrPVals(isPresel & ~isCore & isPlastic);
end
% test the difference between core and shell
pMannU = ranksum(coreCPVs(~isnan(coreCPVs)), shellCPVs(~isnan(shellCPVs)));
fprintf('%s - %s: Mann-Whitney U p-value for core v shell: %0.3f\n',...
eiTitle{hh}, xlabels{ii},pMannU);
fprintf(['\tCore variance explained: %0.2f +/- %0.2f, '...
'shell variance explained: %0.2f +/- %0.2f\n'], ...
coreR2M, coreR2SEM, shellR2M, shellR2SEM);
pBins = logspace(-3,0,30);
plotInterlaceBars(coreCPVs, shellCPVs, pBins);
legend(sprintf('CORE: n = %d', numel( coreCPVs(~isnan( coreCPVs)))),...
sprintf('SHELL: n = %d', numel(shellCPVs(~isnan(shellCPVs)))));
xlabel(xlabels{ii});
ylabel('Count');
xlim([0 1])
% figure formatting
set(gca, 'XTick', [0.05 0.1 0.2 0.4 0.6 0.8]);
set(gca, 'Box', 'off');
set(gca, 'FontSize', 12);
% xticklabel_rotate([],45); % this messes up the figure subplots
set(get(gca,'XLabel'),'FontSize', 14);
set(get(gca,'YLabel'),'FontSize', 14);
set(get(gca,'Title' ),'FontSize', 14);
% todo: plot the significance markers
ytop = ylim * [0 1]'; ylim([0 ytop+2]);
hold on;
plotSEMBar( coreCPVs, ytop-0.2, [0.5 0.5 0.5]);
plotSEMBar(shellCPVs, ytop-0.1, [1 0 0]);
hold off;
end
subplot(nR,nC,1);
title(eiTitle{hh});
set(gcf,'Color',[1 1 1]);
if params.saveplot
saveCurrFigure(sprintf('figures/distanceCorrelations/neuronDistanceCorr-SUA-%s.jpg', filsuff{hh}));
end
end
%}
end
function [m, v] = meanSEM(set1)
m = nanmean(set1);
if numel(set1) >= 2
v = nanstd(set1 )/sqrt(numel(set1)-1);
else
v = 0;
end
end
% plot the error bars w/ SEM on top
function plotSEMBar(set, y, col)
[m,v] = meanSEM(set);
plotHorzErrorBar(m,y,v,col);
end
function x = noNaN(x)
x(isnan(x)) = [];
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
mosaicDRSpec.m
|
.m
|
Acoustic_Similarity-master/code/plotting/mosaicDRSpec.m
| 3,635 |
utf_8
|
35303eb898e6dba8e08964640e85f3e2
|
function [hf, hims] = mosaicDRSpec(DRevents, params, varargin)
hf = [];
if isempty(DRevents)
warning('mosaicDRspec:noClips', 'No clips input or maxLength is smaller than clip');
return;
end
if nargin < 2 || isempty(params)
params = defaultParams;
end
params = processArgs(params,varargin{:});
% make sure we only have the clips we want
clLens = [DRevents.stop]-[DRevents.start];
cumLens = cumsum(clLens);
if ~isinf(params.maxMosaicLength) && cumLens(end) > params.maxMosaicLength
cutoff = find(cumLens < params.maxMosaicLength, 1, 'last');
cumLens = cumLens(1:cutoff);
clLens = clLens(1:cutoff);
DRevents = DRevents(1:cutoff);
end
% extend by any roll
DRevents = addPrePost(DRevents, params);
% make one long clip...
nEv = numel(DRevents);
cl = cell(1, nEv);
fs = zeros(1,nEv);
%params = processArgs(params, 'noroll'); % strict boundaries
for ii = 1:nEv
[cl{ii}, fs(ii)] = getClipAndProcess([],DRevents(ii), params, 'noroll');
% TEMP: readjust lengths (bugfix until we can fix noiseGate chopping
% problem)
clLens(ii) = numel(cl{ii}) / fs(ii);
end
cumLens = cumsum(clLens);
if isempty(fs)
warning('mosaicDRspec:noClips', 'No clips input or maxLength is smaller than clip');
return
end
if ~all(fs==fs(1))
error('mosaicDRSpec:variableSampling', ...
'Different sampling frequencies in each clip...');
end
fs = fs(1);
%%
maxClipLen = params.mosaicRowLength; %seconds
nLowPlots = floor(cumLens(end)/maxClipLen);
concatCl = cell(1,nLowPlots);
idxRange = 1:find(cumLens < maxClipLen, 1,'last');
params.fine.fs=fs;
refRanges = cell(1,nLowPlots);
ii = 1;
while ~isempty(idxRange) && (idxRange(end) <= nEv || isempty(concatCl))
% concatenate clip
concatCl{ii} = vertcat(cl{idxRange});
refRanges{ii} = idxRange;
idxRange = (idxRange(end)+1):find(cumLens < maxClipLen + cumLens(idxRange(end)), 1, 'last');
ii = ii+1;
end
% for display purposes - 1/3 rows is the most proportional for spectrogram
% display
nPlots = max(3,numel(concatCl));
%maxClipLen = max(cellfun(@numel, concatCl));
for ii = 1:min(nPlots, numel(concatCl));
% calculate and plot spectrogram - todo: more space, less ticks...
%subplot(nPlots,1,ii);
% handle all plots to scale - 3rd entry has normalized unit of length
hh(ii) = subplot('Position',[0 (nPlots-ii)/nPlots (length(concatCl{ii})/fs)/maxClipLen 1/nPlots]);
spectrum = getMTSpectrumStats(concatCl{ii}, params.fine);
hims(ii) = plotDerivGram(spectrum,params);
% plot vertical separators
yy = ylim;
xpts = cumsum(clLens(refRanges{ii}));
xpts(end) = []; % don't need the last one b/c it's already at the boundary
hold on;
for jj = 1:numel(xpts)
plot(xpts(jj) * [1 1], yy,'w-','LineWidth',2);
end
% plot a small time scale bar
if ii == 1
xx = xlim;
xSB = xx * [0.95 0.05]';
fracBar = 0.04;
xSBEnd = xx * [(0.95 - fracBar) (0.05 + fracBar)]';
ySB = yy * [0.15 0.85]';
textH = yy * [0.24 0.76]';
SBlen = roundn(maxClipLen * 1000 * fracBar, 10); % 1/25th of the bar in milliseconds, 10 ms length intervals;
plot([xSB, xSBEnd], [ySB, ySB], 'g-', 'LineWidth', 2);
text(xSB, textH, sprintf('%d ms', SBlen),'FontWeight','bold',...
'HorizontalAlignment','left','VerticalAlignment','bottom');
hold off;
end
% remove axes
set(gca,'YTickLabel',[],'YTick',[]);
set(gca,'XTickLabel',[],'XTick',[]);
end
% reset for title placement
axes(hh(1))
hf = gcf;
end
function x = roundn(x,y)
% round to the nearest y
x = round(x/y)*y;
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
editEventsLabel.m
|
.m
|
Acoustic_Similarity-master/code/plotting/editEventsLabel.m
| 19,214 |
utf_8
|
243ddcd2f4ac2fdfade6c2cc2032a578
|
function evsNew = editEventsLabel(evs,fs,doLabel)
% EDITEVENTSLABEL(EVS)
% This function allows the user to edit event boundaries and labels.
%
% The function plots a set of gray patches over each event in the active
% figure. Usually a waveform or some other line plot pertaining to
% the signal should be behind it.
% The user can left-click and drag on events to adjust either the onset,
% offset, or both. The user can also right-click on events to relabel
% them. Clicking and dragging on a space without an event will create
% a new event, while dragging the boundaries of an event past each other
% results in deletion.
%
% For the purpose of this
%
% Known issues:
% 1) Cursor may jump to the wrong side if patches are too close together
% 2) Can be slow if surface data is being displayed in same figure
% 3) If slow and you try to type a label too soon, focus moves to command window
% 4) Events cnnot be added when no events exist?
% Prereqs: active figure/axes that are appropriate to have marks
% evs is properly sorted
% TODO: link callbacks so that drags can be performed everywhere
% figure out a way to draw patches smartly over the spectrograms
% NB: for resizing to work properly, the axes property 'Units' should be
% normalized
% housekeeping, removing warning
RGBWarnID = 'MATLAB:hg:patch:RGBColorDataNotSupported';
warnState = warning('query',RGBWarnID);
warning('off',RGBWarnID');
if nargin < 3
doLabel = false;
end
% setting some defaults for sampling rate
if isempty(evs),
evs = initEvents;
if nargin < 2
fs = 44100; % FIXME: a pure guess
warning('editEvents:InputUninitialized','Events uninitialized, sampling rate may be incorrect...');
end
else
if nargin < 2
fs = evs(1).idxStart/evs(1).start;
end
end
greycol = [0.75 0.75 0.75];
% inform user of termination behavior
oldTitle = get(get(gca,'Title'),'String');
newTitle = [oldTitle ' - Double click outside figure to exit and save, double click to play sound'];
if doLabel, newTitle = [newTitle ', right click to relabel']; end;
title(newTitle);
% hide any other patch handles that are being used here
otherPatchesOnAxis = findobj('Type','patch','Parent',gca);
set(otherPatchesOnAxis,'visible','off');
% create patch handle
if isempty(evs)
% create a fake event and then delete it later
hp = plotAreaMarks(initEvents(1),greycol);
else
hp = plotAreaMarks(evs,greycol);
end
% prepare handle for axes
hax = gca;
% give the label information to the patch handles
set(hp,'UserData',struct('labels', [evs.type]));
% transform labels into text objects
if doLabel
drawLabelsInit(hp);
end
% some notes on action:
% when in drag mode, data gets added to the patch's userData field
% when changing labels, the data gets changed in the patch's userData.labels field
set(gcf,'WindowButtonMotionFcn',{@mouseOverFcn, [hax hp]});
set(gcf,'WindowButtonUpFcn',{@buttonUpFcn, [hax hp]});
set(gcf,'WindowButtonDownFcn',{@buttonDownFcn, [hax hp]});
% exit is triggered when mouseUp occurs outside the axis window
disp('Click outside to finish...');
waitfor(gcf,'WindowButtonMotionFcn','');
disp('Wrapping up...');
% clean up - restore any changed properties
title([oldTitle ' - Finishing']);
warning(warnState.state,RGBWarnID);
% read the events back from the edited patch handle
xdat = get(hp,'XData');
evsNew = initEvents(size(xdat,2));
if isempty(xdat), return; end; % return an empty event structure if no marks
starts = num2cell(xdat(2,:)); idxStarts = num2cell(floor(xdat(2,:) * fs));
stops = num2cell(xdat(3,:)); idxStops = num2cell( ceil(xdat(3,:) * fs));
[evsNew.start] = starts{:}; [evsNew.stop] = stops{:};
[evsNew.idxStart] = idxStarts{:}; [evsNew.idxStop] = idxStops{:};
[evsNew.type] = deal(NaN);
if doLabel
% retrieve the labels
labelHandles = getfield(get(hp,'UserData'),'labels');
textLabels = get(labelHandles,'String');
if ~iscell(textLabels), textLabels = {textLabels}; end;
[evsNew.type] = textLabels{:};
% get rid of the old text labels
delete(labelHandles);
set(otherPatchesOnAxis,'XData',get(hp,'XData'),'visible','on');
end
% get rid of the old patch
delete(hp);
% if we had an empty event structure to begin with, remove the placeholder
% first structure
if isempty(evs)
evsNew(1) = [];
end
title(oldTitle);
%%%%%%%% begin callbacks %%%%%%%%%%%%%
function mouseOverFcn(gcbo, eventdata, handles)
% some default colors
greyCol = [0.75 0.75 0.75];
hiCol = [0.5 0.5 0.5];
lineCol = [0.8 0 0];
% unpack handles
hax = handles(1); hp = handles(2);
currPt = get(hax,'CurrentPoint'); currPt = currPt(1,1:2);
nFaces = size(get(hp,'XData'),2);
vertexColors = greyCol(ones(4 * nFaces,1),:);
[patchHover, lineHover] = clickStatus(currPt, handles);
if ~isempty(patchHover)
% highlight that patch
vertexColors(patchHover * 4 - 3,:) = hiCol;
if ~isempty(lineHover)
%vertexColors(patchHover * 4 + [-2,0],:) = lineCol(ones(2,1),:);
currXData = get(hp,'XData');
cursor(currXData(1+lineHover,patchHover),'on');
else
cursor(0,'off');
end
else
cursor(0,'off');
end
set(hp,'FaceVertexCData',vertexColors);
end
function cursor(xpos, status)
lineHandle = findobj('Tag','cursor');
if isempty(lineHandle)
line([xpos xpos], ylim,'Color',lineCol,'Tag','cursor','LineWidth',1.5);
elseif strcmp(status, 'off') && strcmp(get(lineHandle,'visible'), 'on')
set(lineHandle,'visible','off');
elseif strcmp(status, 'on')
set(lineHandle,'XData',[xpos xpos],'visible',status);
end
end
function buttonUpFcn(gcbo, eventdata, handles)
hax = handles(1); hp = handles(2);
% get point relative to window to determine if click lies outside
currPt = get(gcbo,'CurrentPoint'); currPt(1,1:2);
oldUnits = get(gca,'Units');
set(gca,'Units','pixels');
axisWindow = get(hax,'Position');
set(gca,'Units',oldUnits);
% return cursor to original look
setptr(gcf,'arrow');
% exiting function - did we double-click outside the figure and
% not as part of a drag?
if ~inRect(axisWindow, currPt) && ~isfield(get(hp,'UserData'),'lineHeld') && ...
strcmp(get(gcbo,'SelectionType'),'open')
% clearing the callbacks is the signal for the program to exit
set(gcbo,'WindowButtonMotionFcn','');
set(gcbo,'WindowButtonUpFcn','');
set(gcbo,'WindowButtonDownFcn','');
elseif isfield(get(hp,'UserData'),'patchHeld') % finished clicking on an event
% remove the data not pertaining to labels
userData = get(hp,'UserData');
set(hp,'UserData',struct('labels',userData.labels,'lastClicked',userData.patchHeld));
%(1) negative intervals - delete
%(2) overlapping intervals - merge
resolveOverlaps(hp);
set(gcbo,'WindowButtonMotionFcn',{@mouseOverFcn, [hax hp]});
else
% clear last Clicked field
userData = get(hp,'UserData');
set(hp,'UserData',struct('labels',userData.labels)); % leave only the labels
end
% links patches from other plots - a bit hacky, assumes all plots
% hold same patch pattern
% note that a naive linkprop doesn't work because we need the size of
% yData/colorData to vary dynamically
% TODO: dynamically activate/deactivate linkprop
otherPatches = findobj(gcbo, 'Type', 'patch');
for ii = 1:numel(otherPatches)
otherYData = get(otherPatches(ii),'YData');
otherYData = otherYData(:,ones(1,size(get(hp,'YData'),2)));
set(otherPatches(ii),'XData',get(hp,'XData'),...
'YData',otherYData,...
'FaceVertexCData',get(hp,'FaceVertexCData'));
end
% refresh labels
if doLabel
repositionLabels(hp);
end
end
function buttonDownFcn(gcbo, eventdata, handles)
hax = handles(1); hp = handles(2);
currPt = get(hax,'CurrentPoint'); currPt = currPt(1,1:2);
[patchClicked, lineClicked, hitWindow] = clickStatus(currPt, handles);
if ~hitWindow, return; end;
if doLabel && strcmp(get(gcbo,'SelectionType'),'alt') % if a right click, rename
if ~isempty(patchClicked)
textHandles = getfield(get(hp,'UserData'),'labels');
set(textHandles(patchClicked),'Editing','on');
end
elseif strcmp(get(gcbo,'SelectionType'),'extend') % middle click
disp('Debug Mode:');
keyboard;
% profile viewer;
% profile on;
% pause;
elseif strcmp(get(gcbo,'SelectionType'),'open') % double click detection? play a sound
% let people know we're working while we load the clip
% set(gcf,'Pointer','watch');
% disp('Clock on');
%
% get the clip data
hline = findobj(hax,'Type','line');
hline = hline(end); % it should be the one furthest back
xWave = get(hline,'XData'); yWave = get(hline,'YData');
if isempty(patchClicked) % what should we do if a patch is not clicked?
% play the whole clip
playSound(yWave, fs, true);
else
% get the borders
currXData = get(hp, 'XData');
patchBorders = currXData(2:3,patchClicked);
% cut the clip at the right points
clipStart = find(xWave >= patchBorders(1),1);
clipEnd = find(xWave >= patchBorders(2),1);
clip = yWave(clipStart:clipEnd);
% play the sound clip, while blocking
playSound(clip, fs, true);
end
% ok, waiting's up
% disp('Clock off');
% set(gcf,'Pointer', 'arrow');
elseif isempty(patchClicked) % clicked on an empty space
% create new event
currXData = get(hp, 'XData');
currYData = get(hp, 'YData');
currVertexColors = get(hp, 'FaceVertexCData');
userData = get(hp,'UserData');
% find where to insert new event
if ~all(isnan(currXData))
insertPt = find(currPt(1) <= [currXData(1,:) Inf], 1);
currXData = [currXData(:,1:insertPt-1) currPt(1)*ones(4,1) currXData(:,insertPt:end)];
currYData = currYData(:,[1 1:end]); %all columns are the same
currVertexColors = currVertexColors([ones(1,4) 1:end], :);% we need four more rows, but the colors are all the same
if doLabel
newTextHandle = createTextLabel(currPt(1), ' ');
userData.labels = [userData.labels(:,1:insertPt-1) newTextHandle userData.labels(:,insertPt:end)];
end
else
% currXData is filled with a NaN box so that insertPt is not
% consistent with adding into an empty array
insertPt = 1;
currXData = currPt(1) * ones(4,1);
currYData = [ylim fliplr(ylim)]';
if doLabel
newTextHandle = createTextLabel(currPt(1), ' ');
userData.labels=newTextHandle;
end
end
set(hp,'XData',currXData,'YData',currYData,'FaceVertexCData',currVertexColors);
% handle dragging
dragData = struct('lineHeld', [], ...
'startPt', currPt, ...
'patchHeld', insertPt, ...
'justCreated', true, ...
'origBounds', currPt(1)*ones(4,1), ...
'labels', userData.labels);
% dragging data gets added to the patch userData
set(hp,'UserData',dragData);
set(gcf,'WindowButtonMotionFcn',{@draggingFcn, [hax hp]});
% set the cursor look
setptr(gcf, 'fullcrosshair');
else % we clicked on a patch
xBounds = get(hp,'XData');
userData = get(hp,'UserData');
dragData = struct('lineHeld', lineClicked, ...
'startPt', currPt, ...
'patchHeld', patchClicked,...
'justCreated', false,...
'origBounds', xBounds(:,patchClicked),...
'labels', userData.labels);
set(hp,'UserData',dragData);
set(gcf,'WindowButtonMotionFcn',{@draggingFcn, [hax hp]});
% set cursor look
if ~isempty(lineClicked)
setptr(gcf,'fullcrosshair');
else
setptr(gcf,'hand');
end
end
end
function draggingFcn(gcbo, eventdata, handles)
hax = handles(1); hp = handles(2);
currPt = get(hax,'CurrentPoint');
currXData = get(hp,'XData');
userData = get(hp, 'UserData');
%nFaces = size(currXData,2);
if ~isfield(userData,'patchHeld') || isempty(userData.patchHeld)
error('editEvents:PatchNotClicked','Patch not Clicked, drag callback should not be set');
end
if userData.justCreated
% if we just created an event, detect the drag motion
if currPt(1) ~= userData.startPt(1)
userData.justCreated = false;
userData.lineHeld = 1 + (currPt(1) > userData.startPt(1));
end
end
if ~isempty(userData.lineHeld) % moving one edge of the eventdata
rIdxs = 2 * userData.lineHeld + [-1 0];
currXData(rIdxs,userData.patchHeld) = currPt(1);
set(hp,'XData',currXData);
cursor(currPt(1),'on');
% detect collisions immediately and quit drag
if detectCollision(hp,userData.patchHeld,userData.lineHeld),
set(gcbo,'WindowButtonMotionFcn',{@mouseOverFcn, [hax hp]});
resolveOverlaps(hp);
cursor(0,'off');
end
else % moving whole event
currXData(:,userData.patchHeld) = userData.origBounds + currPt(1) - userData.startPt(1);
set(hp,'XData',currXData);
end
if doLabel
repositionLabels(hp);
end
end
%%%%%%%% end callbacks %%%%%%%%%%%%%
function didCollide = detectCollision(patchHandle,activePatch, boundarySide)
currXData = get(patchHandle,'XData');
if isempty(currXData), return; end; % nothing to collide
borders = currXData(2:3,:);
nFaces = size(borders,2);
adjBorder = NaN;
if activePatch > 1 && boundarySide == 1
adjBorder = borders(2,activePatch - 1);
elseif activePatch < nFaces && boundarySide == 2
adjBorder = borders(1,activePatch + 1);
end
didCollide = (borders(1,activePatch) >= borders(2,activePatch)) || ...
borders(boundarySide,activePatch) <= adjBorder && boundarySide == 1 || ...
borders(boundarySide,activePatch) >= adjBorder && boundarySide == 2;
end
function repositionLabels(hp)
userData = get(hp,'UserData');
currXData = get(hp,'XData');
nFaces = size(currXData,2);
for ii = 1:nFaces
labelPos = get(userData.labels(ii),'Position');
labelPos(1) = mean(currXData(:,ii));
set(userData.labels(ii),'Position', labelPos);
end
end
function resolveOverlaps(patchHandle)
% cleans up intervals
%keeping colors the same
greyCol = [0.75 0.75 0.75];
currXData = get(patchHandle,'XData');
currYData = get(patchHandle,'YData');
%currColData = get(patchHandle,'FaceVertexCData');
if isempty(currXData), return; end; % nothing to resolve
borders = currXData(2:3,:);
userData = get(patchHandle,'UserData');
% remove any negative-length intervals
isNonPosLength = (borders(1,:) >= borders(2,:));
borders(:,isNonPosLength) = [];
% remove their label
if doLabel
delete(userData.labels(isNonPosLength));
userData.labels(isNonPosLength) = [];
end
% merge overlapping regions
% since the number of regions is probably small (<10), we'll do this in a
% naive way (better is with interval trees)
ii = 1;
nFaces = size(borders,2);
while ii <= nFaces && nFaces > 1
toMerge = find(borders(1,ii) >= borders(1,:) & borders(1,ii) <= borders(2,:) | ...
borders(2,ii) >= borders(1,:) & borders(2,ii) <= borders(2,:));
if numel(toMerge) > 1
borders(1,ii) = min(borders(1,toMerge));
borders(2,ii) = max(borders(2,toMerge));
toDelete = toMerge(toMerge ~= ii);
% remove patch information
borders(:,toDelete) = [];
% remove labels
if doLabel
delete(userData.labels(toDelete));
userData.labels(toDelete) = [];
end
% get resized # of patches
nFaces = size(borders,2);
else
ii = ii + 1;
end
end
if ~isempty(borders)
currXData = borders([1 1 2 2],:);
currYData = currYData(:,ones(1,nFaces)); % just copy the first row
currColData = greyCol(ones(4*nFaces,1),:); % just copy the first color
else
currXData = NaN(4,1);
currColData = greyCol(ones(4,1),:);
currYData = NaN(4,1);
end
set(patchHandle,'XData',currXData,'YData',currYData,'FaceVertexCData',currColData,'UserData',userData);
end
function foo = inRect(win, pt)
foo = win(1) <= pt(1) && pt(1) < win(1) + win(3) && ...
win(2) <= pt(2) && pt(2) < win(2) + win(4);
end
function [patchSeld, lineSeld, hitWindow] = clickStatus(currPt, handles)
% returns empties on default
patchSeld = []; lineSeld = [];
hax = handles(1); hp = handles(2);
win([1 3]) = get(hax,'XLim'); win(3) = win(3) - win(1);
win([2 4]) = get(hax,'YLim'); win(4) = win(4) - win(2);
hitWindow = inRect(win, currPt); if ~hitWindow, return, end;
% how 'fat' should our edge be for us to highlight/grab it?
edgeFuzzFrac = 3e-3;
edgeFuzz = diff(get(hax,'XLim')) * edgeFuzzFrac;
yy = get(hax,'YLim');
if currPt(2) > yy(2) || currPt(2) < yy(1), return; end;
xBounds = get(hp,'XData');
if isempty(xBounds), return; end; % nothing to click
xBounds = xBounds(2:3,:);
patchSeld = ...
find(xBounds(1,:) - edgeFuzz <= currPt(1) & ...
xBounds(2,:) + edgeFuzz >= currPt(1));
if numel(patchSeld) > 1
% find the one which is closer
distsToCursor = min(xBounds(1,patchSeld) - currPt(1));
[~,closest] = min(distsToCursor);
patchSeld = patchSeld(closest);
end
if ~isempty(patchSeld)
if abs(xBounds(1,patchSeld) - currPt(1)) <= edgeFuzz, lineSeld = 1;
elseif abs(xBounds(2,patchSeld) - currPt(1)) <= edgeFuzz, lineSeld = 2;
end
end
end
function drawLabelsInit(patchHandle)
%prereq: userdata is already set with labels
%converts labels to a set of txthandles
currXData = get(patchHandle,'XData');
borders = currXData(2:3,:);
nFaces = size(borders,2);
userData = get(patchHandle,'UserData');
isString = ischar(userData.labels);
txthandles = zeros(1,nFaces);
for ii = 1:nFaces
% convert to string if necessary
thisLabel = userData.labels(ii);
if ~isString, thisLabel = num2str(thisLabel); end
txthandles(ii) = createTextLabel(mean(borders(:,ii)),thisLabel);
end
% labels is cell
userData.labels = txthandles;
set(patchHandle,'UserData',userData);
end
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
plotNeuronCorrDataBoxPlot.m
|
.m
|
Acoustic_Similarity-master/code/plotting/plotNeuronCorrDataBoxPlot.m
| 10,340 |
utf_8
|
ac60482f5dcd5bdd9734c2fa9c47fa86
|
function plotNeuronCorrData(params, varargin)
if nargin < 1 || isempty(params)
params = defaultParams;
end
params = processArgs(params, varargin{:});
% plot excited difference between firing rates for near tutor/far from tutor
% and also p-values for correlations between neurons and firing rates
allNeuronCorrData = [];
load('data/allNeuronCorrelations.mat');
%% flags
isCore = [allNeuronCorrData.isCore];
isMUA = [allNeuronCorrData.isMUA];
isPlastic = [allNeuronCorrData.isPlastic];
isSignificant = [allNeuronCorrData.sigResponse];
%isSignificant = true(1,numel(allNeuronCorrData));
isExcited = [allNeuronCorrData.isExcited];
isPresel = isSignificant & ~isMUA;
RSdiffBins = -10:0.2:10;
% subplot rows / columns
nR = 3; nC = 2;
% measures
distanceTypes = {'tutor', 'intra', 'inter', 'consensus', 'central','humanMatch'}';
distanceDescriptions = {'closest tutor', 'cluster center', 'normed center', ...
'closest tutor to cluster consensus', 'closest tutor to cluster center', 'expert-designated tutor'};
dFields = [strcat(distanceTypes, '_nearMeanRS') strcat(distanceTypes, '_farMeanRS')];
eiTitle = {'Significantly inhibited single unit-syllable pairs', ...
'Significantly excited single unit-syllable pairs', ...
'All significant single unit-syllable pairs'};
xlabels = strcat({'RS for near - far to '}, distanceDescriptions);
filsuff = {'inh','exc','all'};
%%
for hh = 1:3 % inhibited, excited, all
for ii = 1:numel(distanceTypes) % six of them
figure;
diffTutorMeanRS = [allNeuronCorrData.(dFields{ii,1})] - [allNeuronCorrData.(dFields{ii,2})];
if hh < 3
coreDiffRS = diffTutorMeanRS(isPresel & isExcited == hh-1 & isCore);
shellDiffRS = diffTutorMeanRS(isPresel & isExcited == hh-1 & ~isCore);
coreSubDiffRS = diffTutorMeanRS(isPresel & isExcited == hh-1 & isCore & ~isPlastic);
shellSubDiffRS = diffTutorMeanRS(isPresel & isExcited == hh-1 & ~isCore & ~isPlastic);
corePlastDiffRS = diffTutorMeanRS(isPresel & isExcited == hh-1 & isCore & isPlastic);
shellPlastDiffRS = diffTutorMeanRS(isPresel & isExcited == hh-1 & ~isCore & isPlastic);
else
coreDiffRS = diffTutorMeanRS(isPresel & isCore);
shellDiffRS = diffTutorMeanRS(isPresel & ~isCore);
coreSubDiffRS = diffTutorMeanRS(isPresel & isCore & ~isPlastic);
shellSubDiffRS = diffTutorMeanRS(isPresel & ~isCore & ~isPlastic);
corePlastDiffRS = diffTutorMeanRS(isPresel & isCore & isPlastic);
shellPlastDiffRS = diffTutorMeanRS(isPresel & ~isCore & isPlastic);
end
pc = signrank( coreDiffRS);
ps = signrank(shellDiffRS);
pMannU = ranksum(coreDiffRS(~isnan(coreDiffRS)), shellDiffRS(~isnan(shellDiffRS)));
fprintf(['%s-%s:\n\tsign-rank test p-value for core: %0.3f' ...
' \n\tsign-rank test p-value for shell: %0.3f',...
' \n\tMann-Whitney U test p-value for core v shell: %0.3f\n'],...
eiTitle{hh},xlabels{ii},pc,ps,pMannU);
% clunky way just to get the top histogram value
plotInterlaceBars(coreDiffRS, shellDiffRS, RSdiffBins, pMannU < 0.05);
ytop = ylim * [0 1]';
% plot boxplots above - b/c of boxplot behavior, this eliminates
% the histograms
grps = [ones(size(coreDiffRS)) 2*ones(size(coreSubDiffRS)) 3*ones(size(corePlastDiffRS)) ,...
4*ones(size(shellDiffRS)), 5*ones(size(shellSubDiffRS)) 6*ones(size(shellPlastDiffRS))];
dats = [coreDiffRS coreSubDiffRS corePlastDiffRS shellDiffRS shellSubDiffRS shellPlastDiffRS];
boxplot(gca, dats, grps, 'orientation', 'horizontal', 'notch','on','positions', ytop:ytop+5, ...
'colors', [0.5 * ones(3); [1 1 1]' * [1 0 0]]);
% plot histograms underneath again
hold on;
plotInterlaceBars(coreDiffRS, shellDiffRS, RSdiffBins, pMannU < 0.05);
ylim([0 ytop+6]);
plot([0 0], ylim,'k--','HandleVisibility','off');
legend(sprintf('CORE: n = %d', numel( coreDiffRS(~isnan( coreDiffRS)))),...
sprintf('SHELL: n = %d', numel(shellDiffRS(~isnan(shellDiffRS)))))
%{
plotSEMBar( coreDiffRS, ytop , [0.5 0.5 0.5]);
plotSEMBar( coreSubDiffRS, ytop+1, [0.5 0.5 0.5]);
plotSEMBar( corePlastDiffRS, ytop+2, [0.5 0.5 0.5]);
plotSEMBar( shellDiffRS, ytop+3, [ 1 0 0]);
plotSEMBar( shellSubDiffRS, ytop+4, [ 1 0 0]);
plotSEMBar(shellPlastDiffRS, ytop+5, [ 1 0 0]);
%}
% redo y axis labels
yt = get(gca,'YTick');
yt = [yt(yt < ytop) ytop:ytop+5];
ytl = cellfun(@(x) sprintf('%d',x),num2cell(yt),'UniformOutput',false);
ytl(end-5:end) = {'Core','Core/Subsong','Core/Plastic','Shell','Shell/Subsong','Shell/Plastic'};
set(gca,'YTick',yt,'YTickLabel',ytl);
% figure formatting
xlabel(xlabels{ii});
ylabel('Count');
xlim([-10 10]);
set(gca,'Box','off');
set(gca, 'FontSize', 14);
set(get(gca,'XLabel'),'FontSize', 14);
set(get(gca,'YLabel'),'FontSize', 14);
set(get(gca,'Title' ),'FontSize', 14);
title(eiTitle{hh});
set(gcf,'Color',[1 1 1]);
hold off;
if params.saveplot
saveCurrFigure(sprintf('figures/distanceCorrelations/RSdiffs-SUA-box-%s-%s.jpg', distanceTypes{ii}, filsuff{hh}));
end
end
end
pFields = strcat(distanceTypes, 'Distance_p');
xlabels = strcat({'Linear trend p-values of FR to '}, distanceDescriptions);
for hh = 1:3 % inhibited, excited, all
figure;
for ii = 1:numel(distanceTypes)
subplot(nR,nC,ii)
corrPVals = [allNeuronCorrData.(pFields{ii})];
if hh < 3
coreCPVs = corrPVals(isPresel & isExcited == hh-1 & isCore);
shellCPVs = corrPVals(isPresel & isExcited == hh-1 & ~isCore);
%coreSubCPVs = corrPVals(isPresel & isExcited == hh-1 & isCore & ~isPlastic);
%shellSubCPVs = corrPVals(isPresel & isExcited == hh-1 & ~isCore & ~isPlastic);
%corePlastCPVs = corrPVals(isPresel & isExcited == hh-1 & isCore & isPlastic);
%shellPlastCPVs = corrPVals(isPresel & isExcited == hh-1 & ~isCore & isPlastic);
else
coreCPVs = corrPVals(isPresel & isCore);
shellCPVs = corrPVals(isPresel & ~isCore);
%coreSubCPVs = corrPVals(isPresel & isCore & ~isPlastic);
%shellSubCPVs = corrPVals(isPresel & ~isCore & ~isPlastic);
%corePlastCPVs = corrPVals(isPresel & isCore & isPlastic);
%shellPlastCPVs = corrPVals(isPresel & ~isCore & isPlastic);
end
pMannU = ranksum(coreCPVs(~isnan(coreCPVs)), shellCPVs(~isnan(shellCPVs)));
fprintf('%s - %s: Mann-Whitney U p-value for core v shell: %0.3f\n',...
eiTitle{hh}, xlabels{ii},pMannU);
pBins = logspace(-3,0,30);
plotInterlaceBars(coreCPVs, shellCPVs, pBins, pMannU < 0.05);
legend(sprintf('CORE: n = %d', numel( coreCPVs(~isnan( coreCPVs)))),...
sprintf('SHELL: n = %d', numel(shellCPVs(~isnan(shellCPVs)))));
xlabel(xlabels{ii});
ylabel('Count');
xlim([0 1])
set(gca, 'XTick', [0.01 0.05 0.1 0.2 0.4 0.6 0.8]);
set(gca, 'Box', 'off');
set(gca, 'FontSize', 11);
set(get(gca,'XLabel'),'FontSize', 14);
set(get(gca,'YLabel'),'FontSize', 14);
set(get(gca,'Title' ),'FontSize', 14);
ytop = ylim * [0 1]'; ylim([0 ytop+2]);
plotSEMBar( coreCPVs, ytop, [0.5 0.5 0.5]);
plotSEMBar(shellCPVs, ytop, [1 0 0]);
end
subplot(nR,nC,1);
title(eiTitle{hh});
set(gcf,'Color',[1 1 1]);
if params.saveplot
saveCurrFigure(sprintf('figures/distanceCorrelations/neuronDistanceCorr-SUA-%s.jpg', filsuff{hh}));
end
end
end
function plotInterlaceBars(setCore, setShell, bins, sigLevel)
% core plotted in gray, shell plotted in red
% run mann-whitney u test
[p,h] = ranksum(setCore, setShell);
binTol = 1e-5;
hCore = histc(setCore , bins);
hShell = histc(setShell, bins);
%[m1 sem1] = meanSEM( setCore);
%[m2 sem2] = meanSEM(setShell);
holdState = ishold;
if all(diff(bins) - (bins(2) - bins(1)) < binTol)
bw = bins(2) - bins(1);
bar(bins, hCore, 0.5, 'FaceColor', [0.5 0.5 0.5]);
hold on;
bar(bins+bw/2, hShell, 0.5, 'r');
else
bw = diff(bins); bw = [bins(1)/2 bw bw(end)];
% make visible legend groups
ghCore = hggroup; ghShell = hggroup;
set(get(get(ghCore , 'Annotation'),'LegendInformation'), 'IconDisplayStyle','on');
set(get(get(ghShell, 'Annotation'),'LegendInformation'), 'IconDisplayStyle','on');
for ii = 1:numel(bins) % draw histogram bin by bin
% core bin
xl = bins(ii) - bw(ii)/2; xr = bins(ii);
yd = 0; yu = hCore(ii);
patch([xl xl; xl xr; xr xr], [yd yu; yu yu; yd yd], [0.5 0.5 0.5],...
'EdgeColor','none', 'Parent', ghCore);
hold on;
% shell bins
xl = bins(ii); xr = bins(ii) + bw(ii+1)/2;
yd = 0; yu = hShell(ii);
patch([xl xl; xl xr; xr xr], [yd yu; yu yu; yd yd], [1 0 0],...
'EdgeColor','none', 'Parent', ghShell);
end
end
% plot the error bars w/ SEM on top
%{
ylims = ylim;
top = ylims(2) + 2;
ylim([0 top]);
plotHorzErrorBar(m1, top - 0.8, sem1, [0.5 0.5 0.5]);
plotHorzErrorBar(m2, top - 1.2, sem2, [1 0 0]);
if sigLevel > 0
plot(mean([m1 m2]), top-1, 'k*','MarkerSize',8);
end
%}
if ~ishold
hold off
end
end
function [m, v] = meanSEM(set1)
m = nanmean(set1);
v = nanstd(set1 )/sqrt(numel(set1)-1);
end
function plotSEMBar(set, y, col)
[m,v] = meanSEM(set);
plotHorzErrorBar(m,y,v,col);
end
function plotHorzErrorBar(x, y, xwidth, col)
yh = 0.02*diff(ylim);
xx = [x-xwidth x+xwidth NaN x-xwidth x-xwidth NaN x+xwidth x+xwidth];
yy = [y y NaN y-yh y+yh NaN y-yh y+yh];
plot(xx,yy, '-','Color', col, 'LineWidth', 1.5);
%hold on;
%plot(x,y,'.','MarkerSize', 16, 'Color', col);
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
ksfitstat.m
|
.m
|
Acoustic_Similarity-master/code/metaAnalysis/ksfitstat.m
| 793 |
utf_8
|
9bea8df82fcafc068593eb078693ff26
|
% fit syllable distributions
% based on veit/aronov/fee papers on breaths/etc
function [kstat, nIncluded, tPrime] = ksfitstat(X, fitRange)
% default? fitRange = [0.02 0.5]; % seconds
%warning('off', 'stats:lillietest:OutOfRangePLow');
MLEfunc = @(t, sampMean) t + fitRange(1) - sampMean - ...
(diff(fitRange) * exp(-(diff(fitRange)/t))) / (1 - exp(-(diff(fitRange)/t)));
isWithin = X > fitRange(1) & X < fitRange(2);
sample = X(isWithin);
nIncluded = sum(isWithin);
tBounds = [0 10*diff(fitRange)]; % seconds
tPrime = fzero(@(x) MLEfunc(x, mean(sample)), tBounds); % the sufficient statistic
if versionNumber > 8
[~,p,kstat] = lillietest(sample, 0.05, 'exp', 'MCTol', 1e-6);
else
[~,p,kstat] = lillietest(sample, 0.05, 'exp', 1e-3);
end
kstat = kstat * sqrt(numel(sample));
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
fitExponentialDuration.m
|
.m
|
Acoustic_Similarity-master/code/metaAnalysis/fitExponentialDuration.m
| 5,287 |
utf_8
|
3f37d7c17678f5d070489b56561fb55b
|
%% This script examines the syllable durations over sessions
function [kstatValues, ages] = fitExponentialDuration(birdID)
%birdID = 'Gy242';
report = reportOnData(birdID,'',[],'verbose',false);
% only look for sessions with approved syllables
nSessions = numel(report);
hasData = false(1,nSessions);
for ii = 1:nSessions
hasData(ii) = any(findInManifest(report(ii).manifest, {'approvedSyllables'})); %,'bosSyllables','manualSyllables','syllables'}));
end
report = report(hasData);
nSessions = numel(report);
%% bookkeeping
dateStr = cell(size(report));
for ii = 1:nSessions
% get the date for each session
sess = report(ii).sessionID;
delimIdx = strfind(sess, '_');
dateStr{ii} = sess((delimIdx(1)+1):(delimIdx(3) - 1));
end
% organize data - not every session has a date (in the excel file),
% but every session has a recording date embedded in the name
possAges = getAgeOfSession({report.sessionID});
[uDate, ~, dateIdx] = unique(dateStr);
nAges = numel(uDate);
% syllable Records
sR = initEmptyStructArray({'sessions', 'age', 'kstat', 'nIncluded', 'tPrime', 'adjKS', 'adjIncl'}, nAges);
for ii = 1:nAges
foo = possAges(dateIdx == ii); foo(isnan(foo)) = [];
if isempty(foo), error('sylDurationByBird:noAgeForSession', 'Age not found...'); end;
sR(ii).age = foo(1);
end
%% gather syllable length empirical distributions
fprintf('Session %s, %d sessions with data...\n', birdID, nSessions)
syllLensPerDate = cell(1,nAges);
for ii = 1:nSessions
sylls = loadFromManifest(report(ii).manifest, 'approvedSyllables');
syllLengths = [sylls.stop]-[sylls.start];
syllLensPerDate{dateIdx(ii)} = [syllLensPerDate{dateIdx(ii)} syllLengths];
sR(dateIdx(ii)).sessions = [sR(dateIdx(ii)).sessions report(ii).sessionID];
end
%% plotting, not always necessary
%{
nR = floor(sqrt(nSessions));
nC = ceil(nSessions / nR);
figure(1)
for ii = 1:nSessions
subplot(nR, nC, ii);
syllLengths = plotDurationDistr(sylls);
title(report(ii).sessionID,'interpreter', 'none');
xlim([0 0.5])
end
%}
%% do subsong/plastic song analysis with correction for sample size (exponential, maybe MOG/log-normals)
% distribution is roughly exponential from 25-400 ms
expRegion = [0.025 0.4]; % seconds
%progressbar(sprintf('%s: Ages', birdID), 'Trials');
for ii = 1:nAges
syllLens = syllLensPerDate{ii};
nPop = numel(syllLens);
[sR(ii).kstat, sR(ii).nIncluded, sR(ii).tPrime] ...
= ksfitstat(syllLens, expRegion);
%{
sampleNumbers = 100 * 2.^(0:10);
nTrials = 20;
ksTrial = zeros(nTrials, numel(sampleNumbers));
tTrial = zeros(nTrials, numel(sampleNumbers));
for jj = 1:nTrials
for kk = 1:numel(sampleNumbers)
oversamp = randi(nPop,1,sampleNumbers(kk));
bootstrapSample = syllLens(oversamp);
[ksTrial(jj,kk), ~, tTrial(jj,kk)] = ...
ksfitstat(bootstrapSample, expRegion);
end
progressbar([],jj/nTrials);
end
fitParams = polyfit(log(sampleNumbers), log(mean(ksTrial)),1);
surrogate = 0;
sR(ii).adjKS = exp(fitParams(2)); %exp(polyval(fitParams, log(surrogate)));
sR(ii).adjIncl = surrogate;
%keyboard
progressbar(ii/nAges,[]);
%}
end
%%
% take out day 48
%{
if strcmp(birdID,'Y231')
ageToEliminate = find(birdAge == 48);
uDate(ageToEliminate) = [];
syllLensPerDate(ageToEliminate) = [];
nInInterval(ageToEliminate) = [];
tPrime(ageToEliminate) = [];
kstat(ageToEliminate) = [];
birdAge(ageToEliminate) = [];
end
%}
%% plotting
%{
% histogram binning for display purposes
binWidth = 0.003;
syllBins = 0:binWidth:0.65;
% prep axes
hax = zeros(1,nAges);
nR = nAges; nC = 1;
for ii = 1:nAges
hax(ii) = subplot(nR,nC,ii);
hold off;
% make axis wider
pos = get(gca,'Position');
set(gca, 'Position', [0.1 pos(2) 0.8 pos(4)]);
% x-coordinates
binCenters = syllBins + binWidth/2;
% histogram
syllDatePDF = hist(syllLensPerDate{ii},syllBins);
bar(binCenters, syllDatePDF, 1, 'FaceColor', [0 0 0])
hold on;
% trend line
distrFunc = @(x,t) 1/t * (exp(-expRegion(1)/t) - exp(-expRegion(2)/t)).^(-1) * exp(-x/t);
plot(binCenters, sR(ii).nIncluded * binWidth * distrFunc(binCenters, sR(ii).tPrime), 'r-',...
'LineWidth',2);
% axis labels
fontN = 'Arial';
if ii == numel(uDate)
xlabel('syllable length (ms)','FontName', fontN, 'FontSize',14);
end
ylabel({'syllable count',sprintf('age %d dph', sR(ii).age)},'FontName', fontN, 'FontSize',14);
xlim([0 0.4]);
% text labels
text(0.32, ylim * [0.4 0.6]', ...
sprintf('Score = %0.2f, (# = %d)\nAdj score = %0.2f, (# = %d)', ...
sR(ii).kstat, sR(ii).nIncluded, sR(ii).adjKS, sR(ii).adjIncl),...
'HorizontalAlignment', 'right', 'VerticalAlignment','middle',...
'FontName', fontN, 'FontSize',14);
% appearance tweaks
set(gca,'TickLength',[0 0], 'FontName', fontN, 'FontSize',14, ...
'Box', 'off');
end
%subplot(hax(2)); ylim([0 150]); %?
set(gcf,'Color', [1 1 1]);
subplot(hax(1));
%}
%%
kstatValues = [sR.kstat];
ages = [sR.age];
%%+
%saveCurrFigure([pwd filesep 'figures' filesep 'dailySyllDur-' birdID '.pdf']);
|
github
|
BottjerLab/Acoustic_Similarity-master
|
oversampleTest.m
|
.m
|
Acoustic_Similarity-master/code/metaAnalysis/oversampleTest.m
| 2,319 |
utf_8
|
86835abc58823a3ff01a7c733f84fe07
|
%% This script examines the syllable durations over sessions
function [ksamples, sampNumbers] = oversampleTest(birdID, selAge)
%birdID = 'Gy242';
report = reportOnData(birdID,'',[],'verbose',false);
% only look for sessions with approved syllables
nSessions = numel(report);
hasData = false(1,nSessions);
for ii = 1:nSessions
hasData(ii) = any(findInManifest(report(ii).manifest, {'approvedSyllables'})); %,'bosSyllables','manualSyllables','syllables'}));
end
report = report(hasData);
nSessions = numel(report);
%% bookkeeping
dateStr = cell(size(report));
for ii = 1:nSessions
% get the date for each session
sess = report(ii).sessionID;
delimIdx = strfind(sess, '_');
dateStr{ii} = sess((delimIdx(1)+1):(delimIdx(3) - 1));
end
% organize data - not every session has a date (in the excel file),
% but every session has a recording date embedded in the name
possAges = getAgeOfSession({report.sessionID});
[uDate, ~, dateIdx] = unique(dateStr);
nAges = numel(uDate);
% syllable Records
sR = initEmptyStructArray({'sessions', 'age', 'kstat', 'nIncluded', 'tPrime'}, nAges);
for ii = 1:nAges
foo = possAges(dateIdx == ii); foo(isnan(foo)) = [];
if isempty(foo), error('sylDurationByBird:noAgeForSession', 'Age not found...'); end;
sR(ii).age = foo(1);
end
%% gather syllable length empirical distributions
fprintf('Session %s, %d sessions with data...\n', birdID, nSessions)
syllLensPerDate = cell(1,nAges);
for ii = 1:nSessions
sylls = loadFromManifest(report(ii).manifest, 'approvedSyllables');
syllLengths = [sylls.stop]-[sylls.start];
syllLensPerDate{dateIdx(ii)} = [syllLensPerDate{dateIdx(ii)} syllLengths];
sR(dateIdx(ii)).sessions = [sR(dateIdx(ii)).sessions report(ii).sessionID];
end
%% select only the age we want
syllLensPerDate = syllLensPerDate{[sR.age] == selAge};
sR([sR.age] ~= selAge) = [];
nAges = 1;
%% oversampling mini-experiment
expRegion = [0.025 0.4]; % seconds
sampNumbers = 100 * 2.^(0:15);
trials = 50;
ksamples = zeros(numel(sampNumbers), trials);
nPop = numel(syllLensPerDate);
progressbar(0)
for jj = 1:trials
for ii = 1:numel(sampNumbers)
oversamp = randi(nPop,1,sampNumbers(ii));
boots = syllLensPerDate(oversamp);
ksamples(ii,jj) = ksfitstat(boots, expRegion);
end
progressbar(jj/trials)
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
fitBimodalDuration.m
|
.m
|
Acoustic_Similarity-master/code/metaAnalysis/fitBimodalDuration.m
| 3,365 |
utf_8
|
663c9211895a201f0fdc7878d1b6db72
|
%% This script examines the syllable durations over sessions
function [peakSize, ages, fitQ] = fitBimodalDuration(birdID)
%birdID = 'Gy242';
report = reportOnData(birdID,'',[],'verbose',false);
% only look for sessions with approved syllables
nSessions = numel(report);
hasData = false(1,nSessions);
for ii = 1:nSessions
hasData(ii) = any(findInManifest(report(ii).manifest, {'approvedSyllables'})); %,'bosSyllables','manualSyllables','syllables'}));
end
report = report(hasData);
nSessions = numel(report);
%% bookkeeping
dateStr = cell(size(report));
for ii = 1:nSessions
% get the date for each session
sess = report(ii).sessionID;
delimIdx = strfind(sess, '_');
dateStr{ii} = sess((delimIdx(1)+1):(delimIdx(3) - 1));
end
% organize data - not every session has a date (in the excel file),
% but every session has a recording date embedded in the name
possAges = getAgeOfSession({report.sessionID});
[uDate, ~, dateIdx] = unique(dateStr);
nAges = numel(uDate);
% syllable Records
sR = initEmptyStructArray({'sessions', 'age', 'peakheight', 'peakfit'}, nAges);
for ii = 1:nAges
foo = possAges(dateIdx == ii); foo(isnan(foo)) = [];
if isempty(foo), error('sylDurationByBird:noAgeForSession', 'Age not found...'); end;
sR(ii).age = foo(1);
end
%% gather syllable length empirical distributions
fprintf('Session %s, %d sessions with data...\n', birdID, nSessions)
syllLensPerDate = cell(1,nAges);
for ii = 1:nSessions
sylls = loadFromManifest(report(ii).manifest, 'approvedSyllables');
syllLengths = [sylls.stop]-[sylls.start];
syllLensPerDate{dateIdx(ii)} = [syllLensPerDate{dateIdx(ii)} syllLengths];
sR(dateIdx(ii)).sessions = [sR(dateIdx(ii)).sessions report(ii).sessionID];
end
tau = zeros(1,nAges);
for ii = 1:nAges
syllLens = syllLensPerDate{ii};
binWidth = 0.003; % in seconds
syllBins = 0:binWidth:0.65;
% get the fit from 200-400 milliseconds
lensDistr = hist(syllLens, syllBins) / numel(syllLens) / binWidth;
[tau(ii), nFit] = fitExpOnInterval(syllLens, [0.2 0.4]);
expFit = exp(-syllBins/tau(ii)) / tau(ii);
% fit a gaussian to the residual
residDistr = lensDistr - expFit;
zeroedDistr = residDistr; zeroedDistr(zeroedDistr<0) = 0;
[cfun, opts] = fit(syllBins', zeroedDistr', 'gauss1');
sR(ii).peakheight = cfun.a1;
sR(ii).peakfit = opts.rsquare;
subplot(nAges, 3, 1 + 3*(ii-1));
plot(syllBins, lensDistr, 'k-', syllBins, expFit, 'r-');
ylabel('Prob. density (s^{-1})');
title(sprintf('%s, age %d (fit on %d)', birdID, sR(ii).age, nFit));
if ii == nAges, xlabel('Syllable duration (ms)'); end
set(gca,'Box', 'off');
ylim([0 15])
xlim([0 0.5]);
subplot(nAges, 3, 2 + 3*(ii-1));
semilogy(syllBins, lensDistr, 'k-', syllBins, expFit, 'r-');
set(gca,'Box', 'off');
xlim([0 0.5]);
set(gca,'YTick',[0.01 0.1 1 10]);
ylim([0.01 15]);
subplot(nAges, 3, 3*ii)
plot(syllBins, lensDistr, 'k-', syllBins, expFit'+cfun(syllBins), 'b-');
title(sprintf('Peak = %0.2f, fit = %0.2f', sR(ii).peakheight, sR(ii).peakfit));
if ii == nAges, xlabel('Syllable duration (ms)'); end
set(gca,'Box', 'off');
ylim([0 15])
xlim([0 0.5]);
% try to fit
end
set(gcf,'Color', [1 1 1]);
peakSize = [sR.peakheight];
ages = [sR.age];
fitQ = [sR.peakfit];
|
github
|
BottjerLab/Acoustic_Similarity-master
|
nm_inputdlg.m
|
.m
|
Acoustic_Similarity-master/code/interactive/nm_inputdlg.m
| 12,683 |
utf_8
|
0479860aeaed1cdb423e50e732f1bb77
|
function Answer=nm_inputdlg(Prompt, Title, NumLines, DefAns, Resize)
%INPUTDLG Input dialog box.
% ANSWER = INPUTDLG(PROMPT) creates a modal dialog box that returns user
% input for multiple prompts in the cell array ANSWER. PROMPT is a cell
% array containing the PROMPT strings.
%
% INPUTDLG uses UIWAIT to suspend execution until the user responds.
%
% ANSWER = INPUTDLG(PROMPT,NAME) specifies the title for the dialog.
%
% ANSWER = INPUTDLG(PROMPT,NAME,NUMLINES) specifies the number of lines for
% each answer in NUMLINES. NUMLINES may be a constant value or a column
% vector having one element per PROMPT that specifies how many lines per
% input field. NUMLINES may also be a matrix where the first column
% specifies how many rows for the input field and the second column
% specifies how many columns wide the input field should be.
%
% ANSWER = INPUTDLG(PROMPT,NAME,NUMLINES,DEFAULTANSWER) specifies the
% default answer to display for each PROMPT. DEFAULTANSWER must contain
% the same number of elements as PROMPT and must be a cell array of
% strings.
%
% ANSWER = INPUTDLG(PROMPT,NAME,NUMLINES,DEFAULTANSWER,OPTIONS) specifies
% additional options. If OPTIONS is the string 'on', the dialog is made
% resizable. If OPTIONS is a structure, the fields Resize, WindowStyle, and
% Interpreter are recognized. Resize can be either 'on' or
% 'off'. WindowStyle can be either 'normal' or 'modal'. Interpreter can be
% either 'none' or 'tex'. If Interpreter is 'tex', the prompt strings are
% rendered using LaTeX.
%
% Examples:
%
% prompt={'Enter the matrix size for x^2:','Enter the colormap name:'};
% name='Input for Peaks function';
% numlines=1;
% defaultanswer={'20','hsv'};
%
% answer=inputdlg(prompt,name,numlines,defaultanswer);
%
% options.Resize='on';
% options.WindowStyle='normal';
% options.Interpreter='tex';
%
% answer=inputdlg(prompt,name,numlines,defaultanswer,options);
%
% See also DIALOG, ERRORDLG, HELPDLG, LISTDLG, MSGBOX,
% QUESTDLG, TEXTWRAP, UIWAIT, WARNDLG .
% Copyright 1994-2010 The MathWorks, Inc.
% $Revision: 1.58.4.21 $
%%%%%%%%%%%%%%%%%%%%
%%% Nargin Check %%%
%%%%%%%%%%%%%%%%%%%%
error(nargchk(0,5,nargin));
error(nargoutchk(0,1,nargout));
%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Handle Input Args %%%
%%%%%%%%%%%%%%%%%%%%%%%%%
if nargin<1
Prompt=getString(message('MATLAB:uistring:popupdialogs:InputDlgInput'));
end
if ~iscell(Prompt)
Prompt={Prompt};
end
NumQuest=numel(Prompt);
if nargin<2,
Title=' ';
end
if nargin<3
NumLines=1;
end
if nargin<4
DefAns=cell(NumQuest,1);
for lp=1:NumQuest
DefAns{lp}='';
end
end
if nargin<5
Resize = 'off';
end
WindowStyle='normal';
Interpreter='none';
Options = struct([]); %#ok
if nargin==5 && isstruct(Resize)
Options = Resize;
Resize = 'off';
if isfield(Options,'Resize'), Resize=Options.Resize; end
if isfield(Options,'WindowStyle'), WindowStyle=Options.WindowStyle; end
if isfield(Options,'Interpreter'), Interpreter=Options.Interpreter; end
end
[rw,cl]=size(NumLines);
OneVect = ones(NumQuest,1);
if (rw == 1 & cl == 2) %#ok Handle []
NumLines=NumLines(OneVect,:);
elseif (rw == 1 & cl == 1) %#ok
NumLines=NumLines(OneVect);
elseif (rw == 1 & cl == NumQuest) %#ok
NumLines = NumLines';
elseif (rw ~= NumQuest | cl > 2) %#ok
error(message('MATLAB:inputdlg:IncorrectSize'))
end
if ~iscell(DefAns),
error(message('MATLAB:inputdlg:InvalidDefaultAnswer'));
end
%%%%%%%%%%%%%%%%%%%%%%%
%%% Create InputFig %%%
%%%%%%%%%%%%%%%%%%%%%%%
FigWidth=175;
FigHeight=100;
FigPos(3:4)=[FigWidth FigHeight]; %#ok
FigColor=get(0,'DefaultUicontrolBackgroundColor');
InputFig=dialog( ...
'Visible' ,'off' , ...
'KeyPressFcn' ,@doFigureKeyPress, ...
'Name' ,Title , ...
'Pointer' ,'arrow' , ...
'Units' ,'pixels' , ...
'UserData' ,'Cancel' , ...
'Tag' ,Title , ...
'HandleVisibility' ,'callback' , ...
'Color' ,FigColor , ...
'NextPlot' ,'new' , ...
'WindowStyle' ,WindowStyle, ...
'Resize' ,Resize ...
);
%%%%%%%%%%%%%%%%%%%%%
%%% Set Positions %%%
%%%%%%%%%%%%%%%%%%%%%
DefOffset = 5;
DefBtnWidth = 53;
DefBtnHeight = 23;
TextInfo.Units = 'pixels' ;
TextInfo.FontSize = get(0,'FactoryUicontrolFontSize');
TextInfo.FontWeight = get(InputFig,'DefaultTextFontWeight');
TextInfo.HorizontalAlignment= 'left' ;
TextInfo.HandleVisibility = 'callback' ;
StInfo=TextInfo;
StInfo.Style = 'text' ;
StInfo.BackgroundColor = FigColor;
EdInfo=StInfo;
EdInfo.FontWeight = get(InputFig,'DefaultUicontrolFontWeight');
EdInfo.Style = 'edit';
EdInfo.BackgroundColor = 'white';
BtnInfo=StInfo;
BtnInfo.FontWeight = get(InputFig,'DefaultUicontrolFontWeight');
BtnInfo.Style = 'pushbutton';
BtnInfo.HorizontalAlignment = 'center';
% Add VerticalAlignment here as it is not applicable to the above.
TextInfo.VerticalAlignment = 'bottom';
TextInfo.Color = get(0,'FactoryUicontrolForegroundColor');
% adjust button height and width
btnMargin=1.4;
ExtControl=uicontrol(InputFig ,BtnInfo , ...
'String' ,getString(message('MATLAB:uistring:popupdialogs:Cancel')) , ...
'Visible' ,'off' ...
);
% BtnYOffset = DefOffset;
BtnExtent = get(ExtControl,'Extent');
BtnWidth = max(DefBtnWidth,BtnExtent(3)+8);
BtnHeight = max(DefBtnHeight,BtnExtent(4)*btnMargin);
delete(ExtControl);
% Determine # of lines for all Prompts
TxtWidth=FigWidth-2*DefOffset;
ExtControl=uicontrol(InputFig ,StInfo , ...
'String' ,'' , ...
'Position' ,[ DefOffset DefOffset 0.96*TxtWidth BtnHeight ] , ...
'Visible' ,'off' ...
);
WrapQuest=cell(NumQuest,1);
QuestPos=zeros(NumQuest,4);
for ExtLp=1:NumQuest
if size(NumLines,2)==2
[WrapQuest{ExtLp},QuestPos(ExtLp,1:4)]= ...
textwrap(ExtControl,Prompt(ExtLp),NumLines(ExtLp,2));
else
[WrapQuest{ExtLp},QuestPos(ExtLp,1:4)]= ...
textwrap(ExtControl,Prompt(ExtLp),80);
end
end % for ExtLp
delete(ExtControl);
QuestWidth =QuestPos(:,3);
QuestHeight=QuestPos(:,4);
if ismac % Change Edit box height to avoid clipping on mac.
editBoxHeightScalingFactor = 1.4;
else
editBoxHeightScalingFactor = 1;
end
TxtHeight=QuestHeight(1)/size(WrapQuest{1,1},1) * editBoxHeightScalingFactor;
EditHeight=TxtHeight*NumLines(:,1);
EditHeight(NumLines(:,1)==1)=EditHeight(NumLines(:,1)==1)+4;
FigHeight=(NumQuest+2)*DefOffset + ...
BtnHeight+sum(EditHeight) + ...
sum(QuestHeight);
TxtXOffset=DefOffset;
QuestYOffset=zeros(NumQuest,1);
EditYOffset=zeros(NumQuest,1);
QuestYOffset(1)=FigHeight-DefOffset-QuestHeight(1);
EditYOffset(1)=QuestYOffset(1)-EditHeight(1);
for YOffLp=2:NumQuest,
QuestYOffset(YOffLp)=EditYOffset(YOffLp-1)-QuestHeight(YOffLp)-DefOffset;
EditYOffset(YOffLp)=QuestYOffset(YOffLp)-EditHeight(YOffLp);
end % for YOffLp
QuestHandle=[];
EditHandle=[];
AxesHandle=axes('Parent',InputFig,'Position',[0 0 1 1],'Visible','off');
inputWidthSpecified = false;
for lp=1:NumQuest,
if ~ischar(DefAns{lp}),
delete(InputFig);
error(message('MATLAB:inputdlg:InvalidInput'));
end
EditHandle(lp)=uicontrol(InputFig , ...
EdInfo , ...
'Max' ,NumLines(lp,1) , ...
'Position' ,[ TxtXOffset EditYOffset(lp) TxtWidth EditHeight(lp)], ...
'String' ,DefAns{lp} , ...
'Tag' ,'Edit' ...
);
QuestHandle(lp)=text('Parent' ,AxesHandle, ...
TextInfo , ...
'Position' ,[ TxtXOffset QuestYOffset(lp)], ...
'String' ,WrapQuest{lp} , ...
'Interpreter',Interpreter , ...
'Tag' ,'Quest' ...
);
MinWidth = max(QuestWidth(:));
if (size(NumLines,2) == 2)
% input field width has been specified.
inputWidthSpecified = true;
EditWidth = setcolumnwidth(EditHandle(lp), NumLines(lp,1), NumLines(lp,2));
MinWidth = max(MinWidth, EditWidth);
end
FigWidth=max(FigWidth, MinWidth+2*DefOffset);
end % for lp
% fig width may have changed, update the edit fields if they dont have user specified widths.
if ~inputWidthSpecified
TxtWidth=FigWidth-2*DefOffset;
for lp=1:NumQuest
set(EditHandle(lp), 'Position', [TxtXOffset EditYOffset(lp) TxtWidth EditHeight(lp)]);
end
end
FigPos=get(InputFig,'Position');
FigWidth=max(FigWidth,2*(BtnWidth+DefOffset)+DefOffset);
FigPos(1)=0;
FigPos(2)=0;
FigPos(3)=FigWidth;
FigPos(4)=FigHeight;
set(InputFig,'Position',getnicedialoglocation(FigPos,get(InputFig,'Units')));
OKHandle=uicontrol(InputFig , ...
BtnInfo , ...
'Position' ,[ FigWidth-2*BtnWidth-2*DefOffset DefOffset BtnWidth BtnHeight ] , ...
'KeyPressFcn',@doControlKeyPress , ...
'String' ,getString(message('MATLAB:uistring:popupdialogs:OK')) , ...
'Callback' ,@doCallback , ...
'Tag' ,'OK' , ...
'UserData' ,'OK' ...
);
setdefaultbutton(InputFig, OKHandle);
CancelHandle=uicontrol(InputFig , ...
BtnInfo , ...
'Position' ,[ FigWidth-BtnWidth-DefOffset DefOffset BtnWidth BtnHeight ] , ...
'KeyPressFcn',@doControlKeyPress , ...
'String' ,getString(message('MATLAB:uistring:popupdialogs:Cancel')) , ...
'Callback' ,@doCallback , ...
'Tag' ,'Cancel' , ...
'UserData' ,'Cancel' ...
); %#ok
handles = guihandles(InputFig);
handles.MinFigWidth = FigWidth;
handles.FigHeight = FigHeight;
handles.TextMargin = 2*DefOffset;
guidata(InputFig,handles);
set(InputFig,'ResizeFcn', {@doResize, inputWidthSpecified});
% make sure we are on screen
movegui(InputFig)
% if there is a figure out there and it's modal, we need to be modal too
if ~isempty(gcbf) && strcmp(get(gcbf,'WindowStyle'),'modal')
set(InputFig,'WindowStyle','modal');
end
set(InputFig,'Visible','on');
drawnow;
if ~isempty(EditHandle)
uicontrol(EditHandle(1));
end
if ishghandle(InputFig)
% Go into uiwait if the figure handle is still valid.
% This is mostly the case during regular use.
uiwait(InputFig);
end
% Check handle validity again since we may be out of uiwait because the
% figure was deleted.
if ishghandle(InputFig)
Answer={};
if strcmp(get(InputFig,'UserData'),'OK'),
Answer=cell(NumQuest,1);
for lp=1:NumQuest,
Answer(lp)=get(EditHandle(lp),{'String'});
end
end
delete(InputFig);
else
Answer={};
end
function doFigureKeyPress(obj, evd) %#ok
switch(evd.Key)
case {'return','space'}
set(gcbf,'UserData','OK');
uiresume(gcbf);
case {'escape'}
delete(gcbf);
end
function doControlKeyPress(obj, evd) %#ok
switch(evd.Key)
case {'return'}
if ~strcmp(get(obj,'UserData'),'Cancel')
set(gcbf,'UserData','OK');
uiresume(gcbf);
else
delete(gcbf)
end
case 'escape'
delete(gcbf)
end
function doCallback(obj, evd) %#ok
if ~strcmp(get(obj,'UserData'),'Cancel')
set(gcbf,'UserData','OK');
uiresume(gcbf);
else
delete(gcbf)
end
function doResize(FigHandle, evd, multicolumn) %#ok
% TBD: Check difference in behavior w/ R13. May need to implement
% additional resize behavior/clean up.
Data=guidata(FigHandle);
resetPos = false;
FigPos = get(FigHandle,'Position');
FigWidth = FigPos(3);
FigHeight = FigPos(4);
if FigWidth < Data.MinFigWidth
FigWidth = Data.MinFigWidth;
FigPos(3) = Data.MinFigWidth;
resetPos = true;
end
% make sure edit fields use all available space if
% number of columns is not specified in dialog creation.
if ~multicolumn
for lp = 1:length(Data.Edit)
EditPos = get(Data.Edit(lp),'Position');
EditPos(3) = FigWidth - Data.TextMargin;
set(Data.Edit(lp),'Position',EditPos);
end
end
if FigHeight ~= Data.FigHeight
FigPos(4) = Data.FigHeight;
resetPos = true;
end
if resetPos
set(FigHandle,'Position',FigPos);
end
% set pixel width given the number of columns
function EditWidth = setcolumnwidth(object, rows, cols)
% Save current Units and String.
old_units = get(object, 'Units');
old_string = get(object, 'String');
old_position = get(object, 'Position');
set(object, 'Units', 'pixels')
set(object, 'String', char(ones(1,cols)*'x'));
new_extent = get(object,'Extent');
if (rows > 1)
% For multiple rows, allow space for the scrollbar
new_extent = new_extent + 19; % Width of the scrollbar
end
new_position = old_position;
new_position(3) = new_extent(3) + 1;
set(object, 'Position', new_position);
% reset string and units
set(object, 'String', old_string, 'Units', old_units);
EditWidth = new_extent(3);
|
github
|
BottjerLab/Acoustic_Similarity-master
|
nm_listdlg.m
|
.m
|
Acoustic_Similarity-master/code/interactive/nm_listdlg.m
| 8,294 |
utf_8
|
54b220d4944ea9e2e83a641976b6b6f7
|
function [selection,value] = nm_listdlg(varargin)
%LISTDLG List selection dialog box.
% [SELECTION,OK] = LISTDLG('ListString',S) creates a modal dialog box
% which allows you to select a string or multiple strings from a list.
% SELECTION is a vector of indices of the selected strings (length 1 in
% the single selection mode). This will be [] when OK is 0. OK is 1 if
% you push the OK button, or 0 if you push the Cancel button or close the
% figure.
%
% Double-clicking on an item or pressing <CR> when multiple items are
% selected has the same effect as clicking the OK button. Pressing <CR>
% is the same as clicking the OK button. Pressing <ESC> is the same as
% clicking the Cancel button.
%
% Inputs are in parameter,value pairs:
%
% Parameter Description
% 'ListString' cell array of strings for the list box.
% 'SelectionMode' string; can be 'single' or 'multiple'; defaults to
% 'multiple'.
% 'ListSize' [width height] of listbox in pixels; defaults
% to [160 300].
% 'InitialValue' vector of indices of which items of the list box
% are initially selected; defaults to the first item.
% 'Name' String for the figure's title; defaults to ''.
% 'PromptString' string matrix or cell array of strings which appears
% as text above the list box; defaults to {}.
% 'OKString' string for the OK button; defaults to 'OK'.
% 'CancelString' string for the Cancel button; defaults to 'Cancel'.
%
% A 'Select all' button is provided in the multiple selection case.
%
% Example:
% d = dir;
% str = {d.name};
% [s,v] = listdlg('PromptString','Select a file:',...
% 'SelectionMode','single',...
% 'ListString',str)
%
% See also DIALOG, ERRORDLG, HELPDLG, INPUTDLG,
% MSGBOX, QUESTDLG, WARNDLG.
% Copyright 1984-2010 The MathWorks, Inc.
% $Revision: 1.20.4.14 $ $Date: 2011/09/08 23:36:08 $
% 'uh' uicontrol button height, in pixels; default = 22.
% 'fus' frame/uicontrol spacing, in pixels; default = 8.
% 'ffs' frame/figure spacing, in pixels; default = 8.
% simple test:
%
% d = dir; [s,v] = listdlg('PromptString','Select a file:','ListString',{d.name});
%
% Generate a warning in -nodisplay and -noFigureWindows mode.
warnfiguredialog('nm_listdlg');
error(nargchk(1,inf,nargin))
figname = '';
smode = 2; % (multiple)
promptstring = {};
liststring = [];
listsize = [160 300];
initialvalue = [];
okstring = getString(message('MATLAB:uistring:popupdialogs:OK'));
cancelstring = getString(message('MATLAB:uistring:popupdialogs:Cancel'));
fus = 8;
ffs = 8;
uh = 22;
if mod(length(varargin),2) ~= 0
% input args have not com in pairs, woe is me
error(message('MATLAB:listdlg:InvalidArgument'))
end
for i=1:2:length(varargin)
switch lower(varargin{i})
case 'name'
figname = varargin{i+1};
case 'promptstring'
promptstring = varargin{i+1};
case 'selectionmode'
switch lower(varargin{i+1})
case 'single'
smode = 1;
case 'multiple'
smode = 2;
end
case 'listsize'
listsize = varargin{i+1};
case 'liststring'
liststring = varargin{i+1};
case 'initialvalue'
initialvalue = varargin{i+1};
case 'uh'
uh = varargin{i+1};
case 'fus'
fus = varargin{i+1};
case 'ffs'
ffs = varargin{i+1};
case 'okstring'
okstring = varargin{i+1};
case 'cancelstring'
cancelstring = varargin{i+1};
otherwise
error(message('MATLAB:listdlg:UnknownParameter', varargin{ i }))
end
end
if ischar(promptstring)
promptstring = cellstr(promptstring);
end
if isempty(initialvalue)
initialvalue = 1;
end
if isempty(liststring)
error(message('MATLAB:listdlg:NeedParameter'))
end
ex = get(0,'DefaultUicontrolFontSize')*1.7; % height extent per line of uicontrol text (approx)
fp = get(0,'DefaultFigurePosition');
w = 2*(fus+ffs)+listsize(1);
h = 2*ffs+6*fus+ex*length(promptstring)+listsize(2)+uh+(smode==2)*(fus+uh);
fp = [fp(1) fp(2)+fp(4)-h w h]; % keep upper left corner fixed
fig_props = { ...
'name' figname ...
'color' get(0,'DefaultUicontrolBackgroundColor') ...
'resize' 'off' ...
'numbertitle' 'off' ...
'menubar' 'none' ...
'windowstyle' 'normal' ...
'visible' 'off' ...
'createfcn' '' ...
'position' fp ...
'closerequestfcn' 'delete(gcbf)', ...
'nextplot' 'new'...
};
liststring=cellstr(liststring);
fig = figure(fig_props{:});
if length(promptstring)>0
prompt_text = uicontrol('Style','text','String',promptstring,...
'HorizontalAlignment','left',...
'Position',[ffs+fus fp(4)-(ffs+fus+ex*length(promptstring)) ...
listsize(1) ex*length(promptstring)]); %#ok
end
btn_wid = (fp(3)-2*(ffs+fus)-fus)/2;
listbox = uicontrol('Style','listbox',...
'Position',[ffs+fus ffs+uh+4*fus+(smode==2)*(fus+uh) listsize],...
'String',liststring,...
'BackgroundColor','w',...
'Max',smode,...
'Tag','listbox',...
'Value',initialvalue, ...
'Callback', {@doListboxClick});
ok_btn = uicontrol('Style','pushbutton',...
'String',okstring,...
'Position',[ffs+fus ffs+fus btn_wid uh],...
'Tag','ok_btn',...
'Callback',{@doOK,listbox});
cancel_btn = uicontrol('Style','pushbutton',...
'String',cancelstring,...
'Position',[ffs+2*fus+btn_wid ffs+fus btn_wid uh],...
'Tag','cancel_btn',...
'Callback',{@doCancel,listbox});
if smode == 2
selectall_btn = uicontrol('Style','pushbutton',...
'String',getString(message('MATLAB:uistring:popupdialogs:SelectAll')),...
'Position',[ffs+fus 4*fus+ffs+uh listsize(1) uh],...
'Tag','selectall_btn',...
'Callback',{@doSelectAll, listbox});
if length(initialvalue) == length(liststring)
set(selectall_btn,'Enable','off')
end
set(listbox,'Callback',{@doListboxClick, selectall_btn})
end
set([fig, ok_btn, cancel_btn, listbox], 'KeyPressFcn', {@doKeypress, listbox});
set(fig,'Position',getnicedialoglocation(fp, get(fig,'Units')));
% Make ok_btn the default button.
setdefaultbutton(fig, ok_btn);
% make sure we are on screen
movegui(fig)
set(fig, 'Visible','on'); drawnow;
try
% Give default focus to the listbox *after* the figure is made visible
uicontrol(listbox);
uiwait(fig);
catch
if ishghandle(fig)
delete(fig)
end
end
if isappdata(0,'ListDialogAppData__')
ad = getappdata(0,'ListDialogAppData__');
selection = ad.selection;
value = ad.value;
rmappdata(0,'ListDialogAppData__')
else
% figure was deleted
selection = [];
value = 0;
end
%% figure, OK and Cancel KeyPressFcn
function doKeypress(src, evd, listbox) %#ok
switch evd.Key
case 'escape'
doCancel([],[],listbox);
end
%% OK callback
function doOK(ok_btn, evd, listbox) %#ok
if (~isappdata(0, 'ListDialogAppData__'))
ad.value = 1;
ad.selection = get(listbox,'Value');
setappdata(0,'ListDialogAppData__',ad);
delete(gcbf);
end
%% Cancel callback
function doCancel(cancel_btn, evd, listbox) %#ok
ad.value = 0;
ad.selection = [];
setappdata(0,'ListDialogAppData__',ad)
delete(gcbf);
%% SelectAll callback
function doSelectAll(selectall_btn, evd, listbox) %#ok
set(selectall_btn,'Enable','off')
set(listbox,'Value',1:length(get(listbox,'String')));
%% Listbox callback
function doListboxClick(listbox, evd, selectall_btn) %#ok
% if this is a doubleclick, doOK
if strcmp(get(gcbf,'SelectionType'),'open')
doOK([],[],listbox);
elseif nargin == 3
if length(get(listbox,'String'))==length(get(listbox,'Value'))
set(selectall_btn,'Enable','off')
else
set(selectall_btn,'Enable','on')
end
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
editEventsLabel.m
|
.m
|
Acoustic_Similarity-master/code/interactive/editEventsLabel.m
| 21,267 |
utf_8
|
2b2164914a00e4013ae1608717b153fb
|
function evsNew = editEventsLabel(evs,fs,doLabel)
% EDITEVENTSLABEL(EVS)
% This function allows the user to edit event boundaries and labels.
%
% The function plots a set of gray patches over each event in the active
% figure. Usually a waveform or some other line plot pertaining to
% the signal should be behind it.
% The user can left-click and drag on events to adjust either the onset,
% offset, or both. The user can also right-click on events to relabel
% them. Clicking and dragging on a space without an event will create
% a new event, while dragging the boundaries of an event past each other
% results in deletion.
%
% For the purpose of this
%
% Known issues:
% 1) Can be slow if surface data is being displayed in same figure or if
% transparency is being used
% 1b) If slow and you try to type a label too soon, focus moves to command window
% 2) Will cause harmless error is patch handle is not ready in time for
% mouse
%
% Prereqs: active figure/axes that are appropriate to have marks from the
% labels applied
% evs should be properly sorted
% TODO: link callbacks so that drags can be performed everywhere
% figure out a way to draw patches smartly over the spectrograms
% NB: for resizing to work properly, the axes property 'Units' should be
% normalized
evsNew = initEvents(0);
clearCallbacks(gcf);
% housekeeping, removing warning
RGBWarnID = 'MATLAB:hg:patch:RGBColorDataNotSupported';
warnState = warning('query',RGBWarnID);
warning('off',RGBWarnID');
if nargin < 3
doLabel = false;
end
% setting some defaults for sampling rate
if isempty(evs),
evs = initEvents;
if nargin < 2
fs = 44100; % FIXME: a pure guess
warning('editEvents:InputUninitialized','Events uninitialized, sampling rate may be incorrect...');
end
else
if nargin < 2
fs = evs(1).idxStart/evs(1).start;
end
end
greycol = [0.75 0.75 0.75];
% inform user of termination behavior
oldTitle = get(get(gca,'Title'),'String');
newTitle = [oldTitle ' - Double click outside figure to exit and save, double click to play sound'];
if doLabel, newTitle = [newTitle ', right click to relabel']; end;
title(newTitle);
% hide any other patch handles that are being used here
otherPatchesOnAxis = findobj('Type','patch','Parent',gca);
set(otherPatchesOnAxis,'visible','off');
% create patch handle
if isempty(evs)
% create a fake event and then delete it later
fakeEvent = eventFromTimes(NaN,NaN, 1);
hp = plotAreaMarks(fakeEvent,greycol);
else
hp = plotAreaMarks(evs,greycol);
end
% prepare handle for axes
hax = gca;
% give the label information to the patch handles
labelsCell = {evs.type};
labelsCell = {labelsCell};
set(hp,'UserData',struct('labels', labelsCell));
% transform labels into text objects
if doLabel
drawLabelsInit(hp);
end
% some notes on action:
% when in drag mode, data gets added to the patch's userData field
% when changing labels, the data gets changed in the patch's userData.labels field
set(gcf,'WindowButtonMotionFcn',{@mouseOverFcn, [hax hp]});
set(gcf,'WindowButtonUpFcn',{@buttonUpFcn, [hax hp]});
set(gcf,'WindowButtonDownFcn',{@buttonDownFcn, [hax hp]});
% the end of the function is called on cleanup, that is, when the figure is
% closed
set(gcf,'DeleteFcn',{@cleanup, [hax hp]});
% exit is triggered when mouseUp occurs outside the axis window
disp('Click outside to finish...');
waitfor(gcf,'WindowButtonMotionFcn','');
% NB: the termination of this routine, where evsNew is defined is in cleanup
%%%%%%%% begin callbacks %%%%%%%%%%%%%
function mouseOverFcn(gcbo, eventdata, handles)
% some default colors
greyCol = [0.75 0.75 0.75];
hiCol = [0.5 0.5 0.5];
lineCol = [0.8 0 0];
% unpack handles
hax = handles(1); hp = handles(2);
currPt = get(hax,'CurrentPoint'); currPt = currPt(1,1:2);
nFaces = size(get(hp,'XData'),2);
vertexColors = greyCol(ones(4 * nFaces,1),:);
[patchHover, lineHover] = clickStatus(currPt, handles);
if ~isempty(patchHover)
% highlight that patch
vertexColors(patchHover * 4 - 3,:) = hiCol;
if ~isempty(lineHover)
%vertexColors(patchHover * 4 + [-2,0],:) = lineCol(ones(2,1),:);
currXData = get(hp,'XData');
cursor(currXData(1+lineHover,patchHover),'on');
else
cursor(0,'off');
end
else
cursor(0,'off');
end
set(hp,'FaceVertexCData',vertexColors);
end
function cursor(xpos, status)
lineCol = [0.8 0 0];
lineHandle = findobj('Tag','cursor');
if isempty(lineHandle)
line([xpos xpos], ylim,'Color',lineCol,'Tag','cursor','LineWidth',1.5);
elseif strcmp(status, 'off') && strcmp(get(lineHandle,'visible'), 'on')
set(lineHandle,'visible','off');
elseif strcmp(status, 'on')
set(lineHandle,'XData',[xpos xpos],'visible',status);
end
end
function buttonUpFcn(gcbo, eventdata, handles)
hax = handles(1); hp = handles(2);
% get point relative to window to determine if click lies outside
currPt = get(gcbo,'CurrentPoint'); currPt(1,1:2);
oldUnits = get(gca,'Units');
set(gca,'Units','pixels');
axisWindow = get(hax,'Position');
set(gca,'Units',oldUnits);
% return cursor to original look
setptr(gcf,'arrow');
% exiting function - did we double-click outside the figure and
% not as part of a drag?
if ~inRect(axisWindow, currPt) && ~isfield(get(hp,'UserData'),'lineHeld') && ...
strcmp(get(gcbo,'SelectionType'),'open')
cleanup(gcbo);
return;
elseif isfield(get(hp,'UserData'),'patchHeld') % finished clicking on an event
% remove the data not pertaining to labels
userData = get(hp,'UserData');
set(hp,'UserData',struct('labels',userData.labels,'lastClicked',userData.patchHeld));
%(1) negative intervals - delete
%(2) overlapping intervals - merge
resolveOverlaps(hp);
set(gcbo,'WindowButtonMotionFcn',{@mouseOverFcn, [hax hp]});
else
% clear last Clicked field
userData = get(hp,'UserData');
set(hp,'UserData',struct('labels',userData.labels)); % leave only the labels
end
% links patches from other plots - a bit hacky, assumes all plots
% hold same patch pattern
% note that a naive linkprop doesn't work because we need the size of
% yData/colorData to vary dynamically
% TODO: dynamically activate/deactivate linkprop
otherPatches = findobj(gcbo, 'Type', 'patch');
for ii = 1:numel(otherPatches)
otherYData = get(otherPatches(ii),'YData');
otherYData = otherYData(:,ones(1,size(get(hp,'YData'),2)));
set(otherPatches(ii),'XData',get(hp,'XData'),...
'YData',otherYData,...
'FaceVertexCData',get(hp,'FaceVertexCData'));
end
% refresh labels
if doLabel
repositionLabels(hp);
end
end
function buttonDownFcn(gcbo, eventdata, handles)
hax = handles(1); hp = handles(2);
currPt = get(hax,'CurrentPoint'); currPt = currPt(1,1:2);
[patchClicked, lineClicked, hitWindow] = clickStatus(currPt, handles);
if ~hitWindow, return; end;
if doLabel && strcmp(get(gcbo,'SelectionType'),'alt') % if a right click, rename
if ~isempty(patchClicked)
textHandles = getfield(get(hp,'UserData'),'labels');
%oldLabel = get(textHandles(patchClicked),'Text');
% erase old label
set(textHandles(patchClicked),'String','');
set(textHandles(patchClicked),'Editing','on');
end
elseif strcmp(get(gcbo,'SelectionType'),'extend') % middle click
disp('Debug Mode (access variables through get(hp,''UserData'', \n and type ''dbcont'' to exit.');
% todo: allow different debug modes?
keyboard;
% profile viewer;
% profile on;
% pause;
elseif strcmp(get(gcbo,'SelectionType'),'open') % double click detection? play a sound
% let people know we're working while we load the clip
% set(gcf,'Pointer','watch');
% disp('Clock on');
%
% get the clip data
hline = findobj(hax,'Type','line');
hline = hline(end); % it should be the one furthest back
xWave = get(hline,'XData'); yWave = get(hline,'YData');
if isempty(patchClicked) % what should we do if a patch is not clicked?
% play the whole clip
playSound(yWave, fs, true);
else
% get the borders
currXData = get(hp, 'XData');
patchBorders = currXData(2:3,patchClicked);
% cut the clip at the right points
clipStart = find(xWave >= patchBorders(1),1);
clipEnd = find(xWave >= patchBorders(2),1);
clip = yWave(clipStart:clipEnd);
% play the sound clip, while blocking
playSound(clip, fs, true);
end
% ok, waiting's up
% disp('Clock off');
% set(gcf,'Pointer', 'arrow');
elseif isempty(patchClicked) % clicked on an empty space
% create new event
currXData = get(hp, 'XData');
currYData = get(hp, 'YData');
currVertexColors = get(hp, 'FaceVertexCData');
userData = get(hp,'UserData');
% find where to insert new event
if ~all(isnan(currXData))
insertPt = find(currPt(1) <= [currXData(1,:) Inf], 1);
currXData = [currXData(:,1:insertPt-1) currPt(1)*ones(4,1) currXData(:,insertPt:end)];
currYData = currYData(:,[1 1:end]); %all columns are the same
currVertexColors = currVertexColors([ones(1,4) 1:end], :);% we need four more rows, but the colors are all the same
if doLabel
newTextHandle = createTextLabel(currPt(1), ' ');
% move to front
ch = get(hax,'Children');
ch = [newTextHandle; ch(ch~=newTextHandle)];
set(hax,'Children', ch);
userData.labels = [userData.labels(:,1:insertPt-1) newTextHandle userData.labels(:,insertPt:end)];
end
else
% currXData is filled with a NaN box so that insertPt is not
% consistent with adding into an empty array
insertPt = 1;
currXData = currPt(1) * ones(4,1);
currYData = [ylim fliplr(ylim)]';
if doLabel
newTextHandle = createTextLabel(currPt(1), ' ');
% move to front
ch = get(hax,'Children');
ch = [newTextHandle; ch(ch~=newTextHandle)];
set(hax,'Children', ch);
userData.labels=newTextHandle;
end
end
set(hp,'XData',currXData,'YData',currYData,'FaceVertexCData',currVertexColors);
% handle dragging
dragData = struct('lineHeld', [], ...
'startPt', currPt, ...
'patchHeld', insertPt, ...
'justCreated', true, ...
'origBounds', currPt(1)*ones(4,1), ...
'labels', userData.labels);
% dragging data gets added to the patch userData
set(hp,'UserData',dragData);
set(gcf,'WindowButtonMotionFcn',{@draggingFcn, [hax hp]});
% set the cursor look
setptr(gcf, 'fullcrosshair');
else % we clicked on a patch
xBounds = get(hp,'XData');
userData = get(hp,'UserData');
dragData = struct('lineHeld', lineClicked, ...
'startPt', currPt, ...
'patchHeld', patchClicked,...
'justCreated', false,...
'origBounds', xBounds(:,patchClicked),...
'labels', userData.labels);
set(hp,'UserData',dragData);
set(gcf,'WindowButtonMotionFcn',{@draggingFcn, [hax hp]});
% set cursor look
if ~isempty(lineClicked)
setptr(gcf,'fullcrosshair');
else
setptr(gcf,'hand');
end
end
end
function draggingFcn(gcbo, eventdata, handles)
hax = handles(1); hp = handles(2);
currPt = get(hax,'CurrentPoint');
currXData = get(hp,'XData');
userData = get(hp, 'UserData');
%nFaces = size(currXData,2);
if ~isfield(userData,'patchHeld') || isempty(userData.patchHeld)
error('editEvents:PatchNotClicked','Patch not Clicked, drag callback should not be set');
end
if userData.justCreated
% if we just created an event, detect the drag motion
if currPt(1) ~= userData.startPt(1)
userData.justCreated = false;
userData.lineHeld = 1 + (currPt(1) > userData.startPt(1));
end
end
if ~isempty(userData.lineHeld) % moving one edge of the eventdata
rIdxs = 2 * userData.lineHeld + [-1 0];
currXData(rIdxs,userData.patchHeld) = currPt(1);
set(hp,'XData',currXData);
cursor(currPt(1),'on');
% detect collisions immediately and quit drag
if detectCollision(hp,userData.patchHeld,userData.lineHeld),
set(gcbo,'WindowButtonMotionFcn',{@mouseOverFcn, [hax hp]});
resolveOverlaps(hp);
cursor(0,'off');
end
else % moving whole event
currXData(:,userData.patchHeld) = userData.origBounds + currPt(1) - userData.startPt(1);
set(hp,'XData',currXData);
end
if doLabel
repositionLabels(hp);
end
end
%%%%%%%% end callbacks %%%%%%%%%%%%%
function didCollide = detectCollision(patchHandle,activePatch, boundarySide)
currXData = get(patchHandle,'XData');
if isempty(currXData), return; end; % nothing to collide
borders = currXData(2:3,:);
nFaces = size(borders,2);
adjBorder = NaN;
if activePatch > 1 && boundarySide == 1
adjBorder = borders(2,activePatch - 1);
elseif activePatch < nFaces && boundarySide == 2
adjBorder = borders(1,activePatch + 1);
end
didCollide = (borders(1,activePatch) >= borders(2,activePatch)) || ...
borders(boundarySide,activePatch) <= adjBorder && boundarySide == 1 || ...
borders(boundarySide,activePatch) >= adjBorder && boundarySide == 2;
end
function repositionLabels(hp)
userData = get(hp,'UserData');
currXData = get(hp,'XData');
nFaces = size(currXData,2);
% all NaNs is the placeholder for no regions
if all(isnan(currXData)), nFaces = 0; end;
for ii = 1:nFaces
labelPos = get(userData.labels(ii),'Position');
labelPos(1) = mean(currXData(:,ii));
set(userData.labels(ii),'Position', labelPos);
end
end
function resolveOverlaps(patchHandle)
% cleans up intervals
%keeping colors the same
greyCol = [0.75 0.75 0.75];
currXData = get(patchHandle,'XData');
currYData = get(patchHandle,'YData');
%currColData = get(patchHandle,'FaceVertexCData');
if isempty(currXData), return; end; % nothing to resolve
borders = currXData(2:3,:);
userData = get(patchHandle,'UserData');
% remove any negative-length intervals
isNonPosLength = (borders(1,:) >= borders(2,:));
borders(:,isNonPosLength) = [];
% remove their label
if doLabel
delete(userData.labels(isNonPosLength));
userData.labels(isNonPosLength) = [];
end
% merge overlapping regions
% since the number of regions is probably small (<10), we'll do this in a
% naive way (better is with interval trees)
ii = 1;
nFaces = size(borders,2);
while ii <= nFaces && nFaces > 1
toMerge = find(borders(1,ii) >= borders(1,:) & borders(1,ii) <= borders(2,:) | ...
borders(2,ii) >= borders(1,:) & borders(2,ii) <= borders(2,:));
if numel(toMerge) > 1
borders(1,ii) = min(borders(1,toMerge));
borders(2,ii) = max(borders(2,toMerge));
toDelete = toMerge(toMerge ~= ii);
% remove patch information
borders(:,toDelete) = [];
% remove labels
if doLabel
delete(userData.labels(toDelete));
userData.labels(toDelete) = [];
end
% get resized # of patches
nFaces = size(borders,2);
else
ii = ii + 1;
end
end
if ~isempty(borders)
currXData = borders([1 1 2 2],:);
currYData = currYData(:,ones(1,nFaces)); % just copy the first row
currColData = greyCol(ones(4*nFaces,1),:); % just copy the first color
else
currXData = NaN(4,1);
currColData = greyCol(ones(4,1),:);
currYData = NaN(4,1);
end
set(patchHandle,'XData',currXData,'YData',currYData,'FaceVertexCData',currColData,'UserData',userData);
end
function foo = inRect(win, pt)
foo = win(1) <= pt(1) && pt(1) < win(1) + win(3) && ...
win(2) <= pt(2) && pt(2) < win(2) + win(4);
end
function [patchSeld, lineSeld, hitWindow] = clickStatus(currPt, handles)
% returns empties on default
patchSeld = []; lineSeld = [];
hax = handles(1); hp = handles(2);
win([1 3]) = get(hax,'XLim'); win(3) = win(3) - win(1);
win([2 4]) = get(hax,'YLim'); win(4) = win(4) - win(2);
hitWindow = inRect(win, currPt); if ~hitWindow, return, end;
% how 'fat' should our edge be for us to highlight/grab it?
edgeFuzzFrac = 4.3e-3;
edgeFuzz = diff(get(hax,'XLim')) * edgeFuzzFrac;
yy = get(hax,'YLim');
if currPt(2) > yy(2) || currPt(2) < yy(1), return; end;
xBounds = get(hp,'XData');
if isempty(xBounds), return; end; % nothing to click
xBounds = xBounds(2:3,:);
patchSeld = ...
find(xBounds(1,:) - edgeFuzz <= currPt(1) & ...
xBounds(2,:) + edgeFuzz >= currPt(1));
if numel(patchSeld) > 1
% find the one which is closer
distsToCursor = min(xBounds(1,patchSeld) - currPt(1));
[~,closest] = min(distsToCursor);
patchSeld = patchSeld(closest);
end
if ~isempty(patchSeld)
if abs(xBounds(1,patchSeld) - currPt(1)) <= edgeFuzz, lineSeld = 1;
elseif abs(xBounds(2,patchSeld) - currPt(1)) <= edgeFuzz, lineSeld = 2;
end
end
end
function drawLabelsInit(patchHandle)
%prereq: userdata is already set with labels
%converts labels to a set of txthandles
currXData = get(patchHandle,'XData');
borders = currXData(2:3,:);
nFaces = size(borders,2);
userData = get(patchHandle,'UserData');
txthandles = zeros(nFaces,1);
chold = get(hax,'Children');
if numel(userData.labels) == 0, return, end;
for ii = 1:nFaces
thisLabel = userData.labels{ii};
% convert to string if necessary
if ~(ischar(thisLabel) || iscellstr(thisLabel) || isempty(thisLabel)) && isnumeric(thisLabel)
thisLabel = num2str(thisLabel);
elseif isempty(thisLabel)
thisLabel = '';
end
txthandles(ii) = createTextLabel(mean(borders(:,ii)),thisLabel);
end
% move to front - for some reason this doesn't work with openGL
ch = [txthandles; chold];
set(hax,'Children', ch);
% labels is cell
userData.labels = txthandles';
set(patchHandle,'UserData',userData);
end
function clearCallbacks(gcbo)
set(gcbo,'WindowButtonMotionFcn',''); % triggers the end of waitfor
set(gcbo,'WindowButtonUpFcn','');
set(gcbo,'WindowButtonDownFcn','');
set(gcbo,'DeleteFcn','');
end
function cleanup(gcbo, eventdata, handles)
% clearing the callbacks is the signal for the program to exit
% this also triggers the end of the function in normal operation,
% but cleanup is allowed to finish
clearCallbacks(gcbo);
disp('Wrapping up...');
% clean up - restore any changed properties
title([oldTitle ' - Finishing']);
warning(warnState.state,RGBWarnID);
% read the events back from the edited patch handle
xdat = [];
if ishandle(hp)
xdat = get(hp,'XData');
% get rid of any NaN columns that were placeholders
xdat(:,all(isnan(xdat))) = [];
% allocate the correct number of events
% evsNew = initEvents(size(xdat,2));
end
if isempty(xdat), return; end; % return an empty event structure if no marks
evsNew = eventFromTimes(xdat(2,:), xdat(3,:),fs);
%{
starts = num2cell(xdat(2,:)); idxStarts = num2cell(floor(xdat(2,:) * fs));
stops = num2cell(xdat(3,:)); idxStops = num2cell(floor(xdat(3,:) * fs));
[evsNew.start] = starts{:}; [evsNew.stop] = stops{:};
[evsNew.idxStart] = idxStarts{:}; [evsNew.idxStop] = idxStops{:};
[evsNew.type] = deal(NaN);
%}
if doLabel
% retrieve the labels
labelHandles = getfield(get(hp,'UserData'),'labels');
textLabels = get(labelHandles,'String');
if ~iscell(textLabels), textLabels = {textLabels}; end;
[evsNew.type] = textLabels{:};
% get rid of the editing text labels
delete(labelHandles);
set(otherPatchesOnAxis,'XData',get(hp,'XData'),'visible','on');
end
% get rid of the editing patch
delete(hp);
title(oldTitle);
end
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
uigetfile_deprecated.m
|
.m
|
Acoustic_Similarity-master/code/interactive/private/uigetfile_deprecated.m
| 39,745 |
utf_8
|
4a87e0c1ae0cc528d4f96bb00362a730
|
function [filename, pathname, filterindex] = uigetfile_deprecated(varargin)
% Copyright 1984-2012 The MathWorks, Inc.
% $Revision: 1.1.6.11 $ $Date: 2012/07/05 16:48:22 $
% Built-in function.
%%%%%%%%%%%%%%%%
% Error messages
%%%%%%%%%%%%%%%%
badLocMessage = 'The Location parameter value must be a 2 element vector.' ;
badMultiMessage = 'The MultiSelect parameter value must be a string specifying on/off.' ;
%badFirstMessage = 'Ill formed first argument to uigetfile' ;
badArgsMessage = 'Unrecognized input arguments.' ;
bad2ndArgMessage = 'Expecting a string as 2nd arg' ;
bad3rdArgMessage = 'Expecting a string as 3rd arg' ;
badFilterMessage = 'FILTERSPEC argument must be a string or an M by 1 or M by 2 cell array.' ;
badNumArgsMessage = 'Too many input arguments.' ;
badLastArgsMessage = 'MultiSelect and Location args must be the last args' ;
badMultiPosMessage = '''MultiSelect'' , ''on/off'' can only be followed by Location args' ;
badLocationPosMessage = '''Location'' , [ x y ] can only be followed by MultiSelect args' ;
caErr1Message = 'Illegal filespec';
caErr2Message = 'Illegal filespec - can have at most two cols';
caErr3Message = 'Illegal file extension - ''' ;
maxArgs = 7 ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% check the number of args - must be <= maxArgs
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
numArgs = nargin ;
if( numArgs > maxArgs )
error( badNumArgsMessage )
end
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Restrict new version to the mac
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% If we are on the mac, default to using non-native
% dialog. If the root property UseNativeSystemDialogs
% is false, use the non-native version instead.
useNative = true; %#ok<NASGU>
% If we are on the Mac & swing is available, set useNative to false,
% i.e., we are going to use Java dialogs not native dialogs.
% Comment the following line to disable java dialogs on Mac.
useNative = ~( ismac && usejava('awt') ) ;
% If the root appdata is set and swing is available,
% honor that overriding all other prefs.
%if isequal(-1, getappdata(0,'UseNativeSystemDialogs')) && isempty( javachk('swing') )
% useNative = false ;
%end
if useNative
try
if nargin == 0
[filename, pathname, filterindex] = native_uigetfile ;
else
[filename, pathname, filterindex] = native_uigetfile( varargin{:} ) ;
end
catch ex
rethrow(ex)
end
return
end % end useNative
%%%%%%%%%%%%%%%%%
% General globals
%%%%%%%%%%%%%%%%%
%multiPosition = '' ;
locationPosition = '' ;
%fileSeparator = filesep ;
%pathSeparator = pathsep ;
remainderArgs = '' ;
newFilter = '' ;
locationPosition = '' ; % position of 'Location' arg
multiSelectPosition = '' ; % position of 'MultiSelect' arg
theError = '' ;
userTitle = '' ;
extError = 'mExtError' ; % returned from dFilter if there is an error
%selectionFilter = '' ; % File Filter used by the user in the dialog
%%%%%%%
% Flags
%%%%%%%
filespecError = false ;
cellArrayOk = false ;
locationError = false ;
multiSelectOn = false ;
multiSelectError = false ;
%argUsedAsFileName = false ;
multiSelectFound = false ;
locationFound = false ;
%%%%%%%%%%%%%%%%%%%%%
% Filter descriptions
%%%%%%%%%%%%%%%%%%%%%
allMDesc = 'All MATLAB Files' ;
mDesc = 'MATLAB code (*.m)' ;
figDesc = 'Figures (*.fig)' ;
matDesc = 'MAT-files (*.mat)' ;
simDesc = 'Simulink Models (*.mdl, *.slx)' ;
staDesc = 'Stateflow Files (*.cdr)' ;
wksDesc = 'Code generation files (*.rtw,*.tmf,*.tlc,*.c,*.h)' ;
rptDesc = 'Report Generator Files (*.rpt)' ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Extension "specs" for our default filters
% These strings are used by filters to match file extensions
% NOTE that they do NOT contian a '.' character
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
mSpec = 'm' ;
figSpec = 'fig' ;
matSpec = 'mat' ;
simSpec = 'mdl' ;
sim2Spec = 'slx';
staSpec = 'cdr' ;
rtwSpec = 'rtw' ;
tmfSpec = 'tmf' ;
tlcSpec = 'tlc' ;
rptSpec = 'rpt' ;
cSpec = 'c' ;
hSpec = 'h' ;
%%%%%%%%%%%%%%%%%
% Default filters
%%%%%%%%%%%%%%%%%
% A filter for all Matlab files
allMatlabFilter = '' ;
% A filter for .m files
mFilter = '' ;
% A filter for .fig files
figFilter = '' ;
% A filter for .mat files
matFilter = '' ;
% A filter for Simulink files - .mdl
simFilter = '' ;
% A filter for Stateflow files - .cdr
staFilter = '' ;
% A filter for Code generation files - .rtw, .tmf, .tlc, .c, .h
wksFilter = '' ;
% A filter for Report Generator files - .rpt
rptFilter = '' ;
% A filter for all files - *.*
allFilter = '' ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% The dialog that holds our file chooser
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% jp = handle(javax.swing.JPanel) ;
jp = awtcreate('com.mathworks.mwswing.MJPanel', ...
'Ljava.awt.LayoutManager;', ...
java.awt.BorderLayout);
% Title is set later
d = mydialog( ...
'Visible','off', ...
'DockControls','off', ...
'Color',get(0,'DefaultUicontrolBackgroundColor'), ...
'Windowstyle','modal', ...
'Resize','on' ...
);
% Create a JPanel and put it into the dialog - this is for resizing
[panel, container] = javacomponent( jp,[10 10 20 20],d);
% Create a JFileChooser
sys = char( computer ) ;
jfc = awtcreate('com.mathworks.hg.util.dFileChooser');
awtinvoke( jfc , 'init(ZLjava/lang/String;)' , false , java.lang.String(sys) ) ;
%jfc.init( false , sys ) ;
% We're going to use our own "all" file filter
awtinvoke( jfc , 'setAcceptAllFileFilterUsed(Z)' , false ) ;
%jfc.setAcceptAllFileFilterUsed( false ) ;
% Set the chooser's current directory
awtinvoke( jfc , 'setCurrentDirectory(Ljava/io/File;)' , java.io.File(pwd) ) ;
%jfc.setCurrentDirectory( java.io.File(pwd) ) ;
% Make sure multi select is initially disabled
awtinvoke( jfc , 'setMultiSelectionEnabled(Z)' , false ) ;
%jfc.setMultiSelectionEnabled( false ) ;
awtinvoke( java(panel), 'add(Ljava.awt.Component;)', jfc );
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Eliminate "built-in" args such as 'multiselect', 'location' and
% their values. As a side effect, create the array remainderArgs.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
eliminateBuiltIns() ;
% Exit if there was an error with MultiSelect or Location
numArgs = nargin ;
if( multiSelectError || locationError )
error( theError ) ;
end
% Check to see that there are no extra args.
% 'MultiSelect', 'location' and their values
% must be the last args.
if( multiSelectFound && ~locationFound )
if( ~( multiSelectPosition == ( numArgs - 1 ) ) )
error( badMultiPosMessage ) ;
end % end if( ~( multiSelectPosition ...
end % end if( multiSelectFound )
if( locationFound && ~multiSelectFound )
if( ~( locationPosition == ( numArgs - 1 ) ) )
error( badLocationPosMessage ) ;
end % end if( ~( locationPosition ...
end % end if( locationFound )
if( multiSelectFound && locationFound )
if( ( ~( ( numArgs - 1 ) == multiSelectPosition ) && ~( ( numArgs - 3 ) == multiSelectPosition ) ) || ...
( ~( ( numArgs - 1 ) == locationPosition ) && ~( ( numArgs - 3 ) == locationPosition ) ) )
error( badLastArgsMessage ) ;
end
end % end if( multiSelectFound & locationFound )
% Set the chooser to multi select if required
% if( multiSelectFound )
% warning('MATLAB:UIGETFILE:MultiSelectIg','MultiSelect is being ignored temporally.');
% end
if( locationFound )
warning(message('MATLAB:UIGETFILE:LocationIgnore'));
end
if( multiSelectOn )
awtinvoke( jfc , 'setMultiSelectionEnabled(Z)' , true ) ;
% jfc.setMultiSelectionEnabled( true ) ;
end
% Reset the content of varargin & numArgs
varargin = remainderArgs ;
numArgs = numel( remainderArgs ) ;
% At this point we can have at most 3 remaining args
if( numArgs > 3 )
error( badArgsMessage )
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case of no args. In this case
% we load and use the default filters.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( 0 == numArgs )
buildDefaultFilters() ;
% Load our filters into the JFileChooser
loadDefaultFilters( jfc , 1 ) ;
% Set our "allMatlabFilter" to be the active filter
awtinvoke( jfc , 'setFileFilter(Ljavax/swing/filechooser/FileFilter;)' , allMatlabFilter ) ;
%jfc.setFileFilter( allMatlabFilter ) ;
end % end if( 0 == numArgs )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case of exactly one remaining arg.
% The argument must be a string or a cell array.
%
% If it's a cell array we try to use it as a filespec.
%
% If it's a string, there are 2 options:
%
% If it's a legit description of a file ext, we use it
% to create a filter and a description. We then add
% that filter and the "all" filter to the file chooser.
% Example - '*.txt' or '.txt'
%
% FOR COMPATIBILITY,
% if it's not an ext we understand, we use it as a
% "selected file name." We then set up the file chooser
% to use our default filters including the "all" filter.
% Example - 'x'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( 1 == numArgs )
spec = varargin{ 1 } ;
if ~( ischar( spec ) ) && ~( iscellstr( spec ) )
error( badFilterMessage )
end
% OK - the type is correct
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case where the arg is a string
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if isempty(spec) || ( ischar( spec ) && isvector( spec ) )
if( ~( 1 == size( spec , 1 ) ) )
spec = spec' ;
end
handleStringFilespec( jfc , spec , 1 ) ;
if( filespecError )
error( theError ) ;
end
end % end if( ischar( spec ) ) ...
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case where the arg is a cell array
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( iscellstr( spec ) )
cellArrayOk = false ;
handleCellArrayFilespec( spec , jfc ) ;
if ~cellArrayOk
error( theError ) ;
end
end % if( iscellstr( spec ) )
end % end if( 1 == numArgs )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case of two remaining args.
%
% If arg 1 is a good filespec, use it
% and interpret the 2nd arg as a title.
%
% FOR COMPATIBILITY,
% if arg1 is a string but not a legit
% filespec, we use it as a file name
% and interpret the 2nd arg as a title.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( 2 == numArgs )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Error check the types of the args.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% The 2nd arg must be a string
arg2 = varargin{ 2 } ;
if ~isempty(arg2) && ~( ischar( arg2 ) && isvector( arg2 ) )
% Not a string
error( bad2ndArgMessage )
end
% Transpose if necessary
if( ~( 1 == size( arg2 , 1 ) ) )
arg2 = arg2' ;
end
% Use arg2 as title
userTitle = char(arg2);
% Check out the first arg
spec = varargin{ 1 } ;
if ~( ischar( spec ) ) && ~( iscellstr( spec ) )
error( badFilterMessage )
end
% OK - the types are correct
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case where the 1st arg is a string
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if isempty(spec) || ( ischar( spec ) && isvector( spec ) )
if( ~( 1 == size( spec , 1 ) ) )
spec = spec' ;
end
handleStringFilespec( jfc, spec, '1' );
if( filespecError )
error( theError ) ;
end
else
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case where first arg is a cell array
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
handleCellArrayFilespec( spec , jfc ) ;
if( ~cellArrayOk )
error( theError ) ;
end
end %if( ischar( spec ) & isvector( spec ) )
end % if( 2 == numArgs )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case of three remaining arguments.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( 3 == numArgs )
% The 2nd and 3rd args must be strings
arg2 = varargin{ 2 } ;
if ~isempty(arg2) && ~( ischar( arg2 ) && isvector( arg2 ) )
% Not a string
error( bad2ndArgMessage )
end
if( ~( 1 == size( arg2 , 1 ) ) )
arg2 = arg2' ;
end
% Use arg2 as title
userTitle = char(arg2);
arg3 = varargin{ 3 } ;
if ~isempty(arg3) && ~( ischar( arg3 ) && isvector( arg3 ) )
% Not a string
error( bad3rdArgMessage )
end
if( ~( 1 == size( arg3 , 1 ) ) )
arg3 = arg3' ;
end
% Use arg3 as file after handling arg1
% Check out the first arg
spec = varargin{ 1 } ;
% The first arg must be a string or cell array
if ~( ischar( spec ) ) && ~( iscellstr( spec ) )
error( badFilterMessage )
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case where the 1st arg is a string
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if isempty(spec) || ( ischar( spec ) && isvector( spec ) )
if( ~( 1 == size( spec , 1 ) ) )
spec = spec' ;
end
handleStringFilespec( jfc , spec , '1' ) ;
if( filespecError )
error( theError ) ;
end
else
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle case where 1st arg is a cell array
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
handleCellArrayFilespec( spec , jfc ) ;
if( ~cellArrayOk )
error( theError ) ;
end
end % if( ischar( spec ) & isvector( spec ) )
% Use arg3 as file after handling arg1
awtinvoke( jfc , 'setSelectedFile(Ljava/io/File;)' , java.io.File( arg3 ) ) ;
end % if( 3 == numargs )
% Set the title of the dialog
if( ~( strcmp( char(userTitle) , '' ) ) )
set( d , 'Name' , userTitle )
else
set( d , 'Name' , char( jfc.getDefaultGetfileTitle() ) )
end
set(container,'Units','normalized','Position',[0 0 1 1]);
jfcHandle = handle(jfc , 'callbackproperties' );
% these will get set by the callback
filename = 0 ;
pathname = 0 ;
filterindex = 0 ;
set(jfcHandle,'PropertyChangeCallback', {@callbackHandler, d , multiSelectOn , sys })
figure(d)
refresh(d)
awtinvoke( jfc , 'listen()' ) ;
%jfc.listen() ;
waitfor(d);
% Retrieve the data stored by the callback
if isappdata( 0 , 'uigetfileData' )
uigetfileData = getappdata( 0 , 'uigetfileData' ) ;
filename = uigetfileData.filename ;
pathname = uigetfileData.pathname ;
filterindex = uigetfileData.filterindex ;
rmappdata( 0 , 'uigetfileData' ) ;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Build all the default filters. Each filter handles
% one or more extension. Each filter also has a
% description string which appears in the file selection
% dialog. Each filter can also be assigned a string "id."
% Filters are Java objects.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function buildDefaultFilters()
allMatlabFilter = com.mathworks.hg.util.dFilter ;
allMatlabFilter.setDescription( allMDesc ) ;
allMatlabFilter.addExtension( mSpec ) ;
allMatlabFilter.addExtension( figSpec ) ;
allMatlabFilter.addExtension( matSpec ) ;
% Filter for files
mFilter = com.mathworks.hg.util.dFilter ;
mFilter.setDescription( mDesc ) ;
mFilter.addExtension( mSpec ) ;
% Filter for .fig files
figFilter = com.mathworks.hg.util.dFilter ;
figFilter.setDescription( figDesc ) ;
figFilter.addExtension( figSpec ) ;
% Filter for MAT-files
matFilter = com.mathworks.hg.util.dFilter ;
matFilter.setDescription( matDesc ) ;
matFilter.addExtension( matSpec ) ;
% Filter for Simulink Models
simFilter = com.mathworks.hg.util.dFilter ;
simFilter.setDescription( simDesc ) ;
simFilter.addExtension( simSpec ) ;
simFilter.addExtension( sim2Spec ) ;
% Filter for Stateflow files
staFilter = com.mathworks.hg.util.dFilter ;
staFilter.setDescription( staDesc ) ;
staFilter.addExtension( staSpec ) ;
% Filter for Code generation files
wksFilter = com.mathworks.hg.util.dFilter ;
wksFilter.setDescription( wksDesc ) ;
wksFilter.addExtension( rtwSpec ) ;
wksFilter.addExtension( tmfSpec ) ;
wksFilter.addExtension( tlcSpec ) ;
wksFilter.addExtension( cSpec ) ;
wksFilter.addExtension( hSpec ) ;
% Filter for Report Generator files
rptFilter = com.mathworks.hg.util.dFilter ;
rptFilter.setDescription( rptDesc ) ;
rptFilter.addExtension( rptSpec ) ;
% Filter for "All Files"
allFilter = com.mathworks.hg.util.AllFileFilter ;
end % end buildDefaultFilters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Load all the default filters into the jFileChooser.
% Give each filter an id starting at "startId." We'll
% later use the id to determine which filter was active
% when the user made the selection.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function loadDefaultFilters( chooser , startId )
j = startId ;
allMatlabFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax/swing/filechooser/FileFilter;)' , allMatlabFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax/swing/filechooser/FileFilter;)' , allMatlabFilter ) ;
% chooser.addFileFilter( allMatlabFilter ) ;
% chooser.noteFilter( allMatlabFilter ) ;
j = j + 1 ;
mFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax/swing/filechooser/FileFilter;)' , mFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax/swing/filechooser/FileFilter;)' , mFilter ) ;
% chooser.addFileFilter( mFilter ) ;
% chooser.noteFilter( mFilter ) ;
j = j + 1 ;
figFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax/swing/filechooser/FileFilter;)' , figFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax/swing/filechooser/FileFilter;)' , figFilter ) ;
% chooser.addFileFilter( figFilter ) ;
% chooser.noteFilter( figFilter ) ;
j = j + 1 ;
matFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax/swing/filechooser/FileFilter;)' , matFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax/swing/filechooser/FileFilter;)' , matFilter ) ;
% chooser.addFileFilter( matFilter ) ;
% chooser.noteFilter( matFilter ) ;
j = j + 1 ;
simFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax/swing/filechooser/FileFilter;)' , simFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax/swing/filechooser/FileFilter;)' , simFilter ) ;
% chooser.addFileFilter( simFilter ) ;
% chooser.noteFilter( simFilter ) ;
j = j + 1 ;
staFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax/swing/filechooser/FileFilter;)' , staFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax/swing/filechooser/FileFilter;)' , staFilter ) ;
% chooser.addFileFilter( staFilter ) ;
% chooser.noteFilter( staFilter ) ;
j = j + 1 ;
wksFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax/swing/filechooser/FileFilter;)' , wksFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax/swing/filechooser/FileFilter;)' , wksFilter ) ;
% chooser.addFileFilter( wksFilter ) ;
% chooser.noteFilter( wksFilter ) ;
j = j + 1 ;
rptFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax/swing/filechooser/FileFilter;)' , rptFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax/swing/filechooser/FileFilter;)' , rptFilter ) ;
% chooser.addFileFilter( rptFilter ) ;
% chooser.noteFilter( rptFilter ) ;
j = j + 1 ;
allFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax/swing/filechooser/FileFilter;)' , allFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax/swing/filechooser/FileFilter;)' , allFilter ) ;
% chooser.addFileFilter( allFilter )
% chooser.noteFilter( allFilter ) ;
% We shouldn't need the pause or the following drawnow -
% But it doesn't work without them
pause(.5) ;
awtinvoke( chooser, 'setFileFilter(Ljavax/swing/filechooser/FileFilter;)' , allMatlabFilter ) ;
drawnow() ;
end % end loadDefaultFilters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Build a new filter containing an extension and a description
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function buildFilter( desc , ext )
newFilter = com.mathworks.hg.util.dFilter ;
newFilter.setDescription( desc ) ;
newFilter.addExtension( ext ) ;
end % buildFilter
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Add a filter to the indicated JFileChooser
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function addFilterToChooser( chooser , filter )
awtinvoke( chooser , 'addFileFilter(Ljavax/swing/filechooser/FileFilter;)' , filter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax/swing/filechooser/FileFilter;)' , filter ) ;
% chooser.addFileFilter( filter ) ;
% chooser.noteFilter( filter ) ;
end % end addFilterToChooser
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Set the indicated filter's identifier (must be a string)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function setFilterIdentifier( filter , id )
filter.setIdentifier( id ) ;
end % setFilterIdentifier
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Build a filter that "accepts" all files
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function buildAllFilter()
allFilter = com.mathworks.hg.util.AllFileFilter ;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This handles the case where the filespec is a string
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function handleStringFilespec( chooser , str , id )
%argUsedAsFileName = false ;
useDefaultFilter = false;
if( isempty( str ) )
% If the filter string is empty use defaults.
useDefaultFilter = true;
else
filterspec = str ;
%extStr = '' ;
extStr = char(com.mathworks.hg.util.dFilter.returnExtensionString( str )) ;
if( strcmpi( extStr , extError ) )
% This isn't a "legal" extension we know about.
% Treat it as the name of a file or a path with filter
% for COMPATIBILITY with the current release. So, it can be:
% 'filename.m'
% 'H:\filename.m' or '/home/user/filename.m'
% 'H:\*.m' or '/home/user/*.m'
% Do a fileparts to analyze the string. This returns us the
% path, file and ext.
[p, n, e] = fileparts(str);
filename = '';
if ~isempty(p)
filename = [p, filesep];
end
if isempty(strfind(n, '*'))
% There is no wildcard in the file name
filename = [filename, n, e];
end
% Set the file
% test = java.io.File( str ) ;
% if( test.isFile() )
awtinvoke( chooser , 'setSelectedFile(Ljava/io/File;)' , java.io.File( filename ) ) ;
if isempty(e)
% If no file extension was specified to be used as a
% filter, use defaults
useDefaultFilter = true;
else
% extStr is the extension w/o the '.'
extStr = e(2:end);
% filterspec is *.<ext>
filterspec = ['*', e];
end
end
end
if (useDefaultFilter)
buildDefaultFilters() ;
loadDefaultFilters( chooser , id ) ;
awtinvoke( chooser , 'setFileFilter(Ljavax/swing/filechooser/FileFilter;)' , allMatlabFilter ) ;
%argUsedAsFileName = true ;
% else
% theError = caErr1Message ;
% filespecError = true ;
% return ;
% end
else
% Build a new filter.
% Load the new filter and
% load the "all" filter if necessary
newFilter = [] ;
if ( strcmp( char( spec ) , '*.*' ) )
buildAllFilter() ;
setFilterIdentifier( allFilter , '1' ) ;
addFilterToChooser( chooser , allFilter ) ;
else
buildFilter( filterspec, extStr ) ;
setFilterIdentifier( newFilter , '1' ) ;
addFilterToChooser( chooser , newFilter ) ;
buildAllFilter() ;
setFilterIdentifier( allFilter , '2' ) ;
addFilterToChooser( chooser , allFilter ) ;
end
% don't know why the pause and drawnow are needed
% but things don't work properly if they are not there
pause(.5) ;
% If necessary, set the active filter to the new filter
if ~isempty( newFilter )
awtinvoke( chooser , 'setFileFilter(Ljavax/swing/filechooser/FileFilter;)' , newFilter ) ;
end
drawnow() ;
end % end if/else
end % end handleStringFilespec
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This handles the case where the filespec is a cell array
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function handleCellArrayFilespec( theCellArray , chooser )
cellArrayOk = false ;
t = '' ;
rows = '' ;
cols = '' ;
try
t = size( theCellArray ) ;
rows = t(1) ;
cols = t(2) ;
catch ex
theError = caErr1Message;
return
end
if( 2 < cols )
theError = caErr2Message ;
return
end
% The first col is supposed to hold an extension array
extArray = '' ;
descrArray = '' ;
%ext = '' ;
for i = 1:rows
ext = theCellArray{ i , 1 } ;
% Format the extension for our filter
s = com.mathworks.hg.util.dFilter.returnExtensionString( char(ext) ) ;
s = char( s ) ;
if~( strcmpi( s , extError ) )
extArray{i} = s ;
else
theError = strcat( caErr3Message , ext , '''' ) ;
cellArrayOk = false ;
return
end
end % end for
% If there are two cols, the 2nd col is
% supposed to be descriptions for 1st col
if( 2 == cols )
for i = 1 : rows
descrArray{ i } = theCellArray{ i , 2 } ;
end % end for i = 1 : rows
end % end if( 2 == cols )
% Create the filters for file selection
ii = 0 ;
firstFilter = '';
%theFilter = '';
allFound = false;
for i = 1:rows
if( strcmp( char( extArray{i} ) , '*.*' ))
% This corresponds to the All Files filter.
% buildAllFilter() ;
theFilter = com.mathworks.hg.util.AllFileFilter ;
theFilter.setIdentifier( int2str( i ) ) ;
addFilterToChooser(chooser, theFilter);
% awtinvoke( chooser , 'addFileFilter(Ljavax/swing/filechooser/FileFilter;)' , allFilter ) ;
% awtinvoke( chooser , 'noteFilter(Ljavax/swing/filechooser/FileFilter;)' , allFilter ) ;
allFound = true ;
else
theFilter = com.mathworks.hg.util.dFilter ;
theFilter.addExtension( extArray{i} ) ;
theFilter.setIdentifier( int2str(i) ) ;
if( 2 == cols )
theFilter.setDescription( descrArray{i} ) ;
else
theFilter.setDescription( theCellArray{i,1} ) ;
end % end if( 2 == cols )
% Add the filter
addFilterToChooser(chooser, theFilter);
% awtinvoke( chooser , 'addFileFilter(Ljavax/swing/filechooser/FileFilter;)' , usrFilter ) ;
% awtinvoke( chooser , 'noteFilter(Ljavax/swing/filechooser/FileFilter;)' , usrFilter ) ;
ii = i ;
end % if( strcmp( char( extArray{i} ) , '*.*' ))
% Set a current filter
if( 1 == i )
firstFilter = theFilter ;
end % end if( 1 == i )
end % end for i = 1:rows
% Add in the "all" filter
if( ~allFound )
% buildAllFilter() ;
theFilter = com.mathworks.hg.util.AllFileFilter ;
theFilter.setIdentifier( int2str( ii+1 ) ) ;
addFilterToChooser(chooser, theFilter);
% awtinvoke( chooser , 'addFileFilter(Ljavax/swing/filechooser/FileFilter;)' , allFilter ) ;
% awtinvoke( chooser , 'noteFilter(Ljavax/swing/filechooser/FileFilter;)' , allFilter ) ;
end
% Set the user's first filter as the active filter
% Shouldn't need the pause and drawnow, but things
% don't work without them.
pause(.5) ;
awtinvoke( chooser , 'setFileFilter(Ljavax/swing/filechooser/FileFilter;)' , firstFilter ) ;
drawnow() ;
cellArrayOk = true ;
end % end handleCellArrayFilespec
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Scan the input arguments for 'MultiSelect' and 'Location'.
% If either is present, check that the next arg has an
% appropriate value. If not, set an appropriate error flag.
%
% Also, store all the other arguments in the array "remainderArgs".
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function eliminateBuiltIns()
args = varargin ;
numberOfArgs = numel( args ) ;
remainderArgIndex = 1 ;
i = 1 ;
while( i <= numberOfArgs )
theArg = args{i} ;
% Add to remainder args if its not a string
if( ~( ischar( theArg ) ) || ~( isvector( theArg ) ) )
remainderArgs{ remainderArgIndex } = theArg ; %#ok<AGROW>
remainderArgIndex = remainderArgIndex + 1 ;
i = i + 1 ;
%end % end if( ~( ischar( theArg ) ) | ~( isvector( theArg ) ) )
if( i > numberOfArgs )
return
end
continue
end % end if( ~( ischar( theArg ) ) | ~( isvector( theArg ) ) )
% Transpose if necessary
if( ~( 1 == size( theArg , 1 ) ) )
theArg = theArg' ;
end
% Check to see if we have an interesting string
lowArg = lower( theArg ) ;
if( ~strcmp( 'multiselect' , lowArg ) && ~strcmp( 'location' , lowArg ) )
remainderArgs{ remainderArgIndex } = theArg ; %#ok<AGROW>
remainderArgIndex = remainderArgIndex + 1 ;
i = i + 1 ;
continue ;
end % end if( ~strcmp( 'multiselect' , lowArg ) ...
% Check the next arg - we have found 'multiselect' or 'location'
i = i + 1 ;
if( i > numberOfArgs )
% oops - missing arg
switch( lowArg )
case 'multiselect'
theError = badMultiMessage ;
multiSelectError = true ;
return
case 'location'
theError = badLocMessage ;
locationError = true ;
return
end % end switch
return
end % end if( i > numberOfArgs )
nextArg = args{ i } ;
switch( lowArg )
case 'multiselect'
% nextArg must be 'on' or 'off'
if( ~( ischar( nextArg ) ) || ~( isvector( nextArg ) ) )
theError = badMultiMessage ;
multiSelectError = true ;
return
end
if( ~( 1 == size( nextArg , 1 ) ) )
nextArg = nextArg' ;
end
if( ~( strcmpi( 'on',nextArg ) ) && ~( strcmpi( 'off' , nextArg) ) )
theError = badMultiMessage ;
multiSelectError = true ;
return
end
multiSelectFound = true ;
multiSelectPosition = i - 1 ;
if( strcmpi( 'on' , nextArg ) )
multiSelectOn = true ;
end
i = i + 1 ;
if( i > numberOfArgs )
return
end
case 'location'
% nextArg must be a numeric vector
if( ~( isvector( nextArg ) ) || ...
~( isnumeric( nextArg ) ) )
theError = badLocMessage ;
locationError = true ;
return
end
% Transpose if necessary
if( ~( 1 == size( nextArg , 1 ) ) )
nextArg = nextArg' ;
end
% Check size
if( ~( 1 == size( nextArg , 1 ) ) || ...
~( 2 == size( nextArg , 2 ) ) )
theError = badLocMessage ;
locationError = true ;
return
end
% Record the fact that we've found 'location'
locationFound = true ;
locationPosition = i - 1 ;
% skip to the next arg
i = i + 1 ;
if( i > numberOfArgs )
return
end
end % end switch
end % end while
end % eliminateBuiltIns
function out = mydialog(varargin)
out = [];
try
out = dialog(varargin{:}) ;
catch ex
rethrow(ex)
end
end % end myDialog
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the callback from the JFileChooser. If the user
% selected "Open", return the name of the selected file,
% the full pathname and the index of the current filter.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function callbackHandler(obj, evd, d , multiSelectOn , sys )
fileSeparator = filesep ;
%pathSeparator = pathsep ;
jfc = obj;
cmd = char(evd.getPropertyName()) ;
switch(cmd)
case 'mathworksHgCancel'
if ishandle(d)
close(d)
end
case 'mathworksHgOk'
selectionFilter = jfc.getFileFilter() ;
if( ~multiSelectOn )
[pathname, fn, ext ] = fileparts(char(jfc.getSelectedFile.toString));
filename = [fn ext];
pathname = strcat( pathname , fileSeparator ) ;
else % Multi Select is On
fileObjArray = jfc.getSelectedFiles ;
fileNames = jfc.getSelectedFileNames() ;
rows = size( fileNames , 1 ) ;
cols = size( fileNames , 2 ) ; %#ok<NASGU>
x = '' ;%( 1 , rows ) ;
j = 1 ;
for i = 1 : rows
%x{ 1 , i } = char( fileNames( i , 1 ) ) ;xxxxxx
if( ~( strncmp( 'MAC' , sys, 3 ) ) )
x{ 1 , i } = char( fileNames( i , 1 ) ) ;
else
name = char( fileNames( i , 1 ) ) ;
if( selectionFilter.accept( java.io.File( name ) ) )
x{ 1 , j } = name ;
j = j + 1 ;
end
end
end % end for i = 1 : rows
filename = x ;
fileObj = fileObjArray(1) ;
[pathname, fn, ext ] = fileparts(char(fileObj.getAbsolutePath())); %#ok<NASGU,NASGU>
pathname = strcat( pathname , fileSeparator ) ;
end % end if( ~multiSelectOn )
uigetfileData.filename = filename ;
uigetfileData.pathname = pathname ;
uigetfileData.filterindex = str2double( selectionFilter.getIdentifier() ) ;
setappdata( 0 , 'uigetfileData' , uigetfileData ) ;
close(d);
end % end switch
end % end callbackHandler
|
github
|
BottjerLab/Acoustic_Similarity-master
|
removeJavaCallbacks.m
|
.m
|
Acoustic_Similarity-master/code/interactive/private/removeJavaCallbacks.m
| 1,211 |
utf_8
|
9275dd0972c0c82aabff1abd170e22f5
|
% Copyright 2011 The MathWorks, Inc.
% This function is for internal use and will change in a future release
%-----------------------------------------------------
% We are giving ourselves a hook to run resource cleanup functions
% like freeing up callbacks. This is important for uitree because
% the expand and selectionchange callbacks need to be freed when
% the figure is destroyed. See G769077 for more information.
%-----------------------------------------------------
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Empties all callbacks on the given java UDD handle
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function removeJavaCallbacks(jh)
c = classhandle(jh);
if (~isJavaHandleWithCallbacks(c))
return;
end
E = c.Events;
for k = 1:length(E)
set(jh, [E(k).Name 'Callback'] , []);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Helper that determines if a given class handle
% is a java handle with callbacks or not
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function withCbks = isJavaHandleWithCallbacks(metacls)
assert(isa(metacls,'schema.class'));
pkgName = metacls.Package.Name;
withCbks = strcmp(pkgName,'javahandle_withcallbacks');
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
uisetcolor_helper.m
|
.m
|
Acoustic_Similarity-master/code/interactive/private/uisetcolor_helper.m
| 2,450 |
utf_8
|
3198fc328516b1eb1e94d7d5383ec49f
|
function selectedColor = uisetcolor_helper(varargin)
% Copyright 2007-2010 The MathWorks, Inc.
% $Revision: 1.1.6.9 $ $Date: 2011/09/23 19:13:38 $
[rgbColorVector,title,fhandle] = parseArgs(varargin{:});
ccDialog = matlab.ui.internal.dialog.ColorChooser;
ccDialog.Title = title;
if ~isempty(rgbColorVector)
ccDialog.InitialColor = rgbColorVector;
end
selectedColor = showDialog(ccDialog);
if (~isempty(rgbColorVector) && ~(size(selectedColor,2)==3))
selectedColor = rgbColorVector;
end
if ~isempty(fhandle)
try
set(fhandle,'Color',selectedColor);
catch
try
set(fhandle,'ForegroundColor',selectedColor);
catch
try
set(fhandle,'BackgroundColor',selectedColor);
catch
end
end
end
end
% Done. MCOS Object ccDialog cleans up and its java peer at the end of its
% scope(AbstractDialog has a destructor that every subclass
% inherits)
function [rgbColorVector,title,handle] = parseArgs(varargin)
handle = [];
rgbColorVector = [];
title = getString(message('MATLAB:uistring:uisetcolor:TitleColor'));
if nargin>2
error(message('MATLAB:uisetcolor:TooManyInputs')) ;
end
if (nargin==2)
if ~ischar(varargin{2})
error(message('MATLAB:uisetcolor:InvalidSecondParameter'));
end
title = varargin{2};
end
if (nargin>=1)
if (isscalar(varargin{1}) && ishghandle(varargin{1}))
handle = varargin{1};
rgbColorVector = getrgbColorVectorFromHandle(handle);
elseif (~ischar(varargin{1}) && isnumeric(varargin{1}))
rgbColorVector = varargin{1};
elseif ischar(varargin{1})
if (nargin > 1)
error(message('MATLAB:uisetcolor:InvalidParameterList'));
end
title = varargin{1};
else
error(message('MATLAB:uisetcolor:InvalidFirstParameter'));
end
end
%Given the dialog, user chooses to select or not select
function rgbColorVector = showDialog(ccDialog)
ccDialog.show;
rgbColorVector = ccDialog.SelectedColor;
if isempty(rgbColorVector)
rgbColorVector = 0;
end
%Helper functions to extract color(rgbColorVector) from the given handle
function rgbValue = getrgbColorVectorFromHandle(fhandle)
rgbValue = [0 0 0];
try
rgbValue = get(fhandle,'Color');
catch
try
rgbValue = get(fhandle,'ForegroundColor');
catch
try
rgbValue = get(fhandle,'BackgroundColor');
catch
end
end
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
javaaddlistener.m
|
.m
|
Acoustic_Similarity-master/code/interactive/private/javaaddlistener.m
| 1,648 |
utf_8
|
b588fa2b4b900654813b0c5e822813c7
|
function hdl=javaaddlistener(jobj, eventName, response)
% ADDLISTENER Add a listener to a Java object.
%
% L=ADDLISTENER(JObject, EventName, Callback)
% Adds a listener to the specified Java object for the given
% event name. The listener will stop firing when the return
% value L goes out of scope.
%
% ADDLISTENER(JObject)
% Lists all the available event names.
%
% Examples:
%
% jf = javaObjectEDT('javax.swing.JFrame');
% addlistener(jf) % list all events
%
% % Basic string eval callback:
% addlistener(jf,'WindowClosing','disp closing')
%
% % Function handle callback
% addlistener(jf,'WindowClosing',@(o,e) disp(e))
% Copyright 2003-2010 The MathWorks, Inc.
% make sure we have a Java objects
if ~isjava(jobj)
error(message('MATLAB:addlistener:InvalidNonJavaObject'))
end
if nargin == 1
if nargout
error(message('MATLAB:addlistener:InvalidNumberOfInputArguments'))
end
% just display the possible events
hSrc = handle(jobj,'callbackproperties');
allfields = sortrows(fields(set(hSrc)));
for i = 1:length(allfields)
fn = allfields{i};
if ~isempty(strfind(fn,'Callback'))
fn = strrep(fn,'Callback','');
disp(fn)
end
end
return;
end
hdl = handle.listener(handle(jobj), eventName, ...
@(o,e) cbBridge(o,e,response));
set(hdl,'RecursionLimit',255); % Allow nested callbacks g681014
end
function cbBridge(o,e,response)
hgfeval(response, java(o), e.JavaEvent)
end
% LocalWords: JObject jf invalidinput callbackproperties
|
github
|
BottjerLab/Acoustic_Similarity-master
|
uitable_deprecated.m
|
.m
|
Acoustic_Similarity-master/code/interactive/private/uitable_deprecated.m
| 7,741 |
utf_8
|
d10f44f75391f13c0bddf79d7d3ee0a3
|
function [table, container] = uitable_deprecated(varargin)
% This function is undocumented and has been replaced
% See also UITABLE
% Copyright 2002-2012 The MathWorks, Inc.
% $Revision: 1.1.6.9 $ $Date: 2012/07/05 16:48:24 $
% Warn that this old code path is no longer supported.
warn = warning('query', 'MATLAB:uitable:DeprecatedFunction');
if isequal(warn.state, 'on')
warning(message('MATLAB:uitable:DeprecatedFunction'));
end
% Setup and P-V parsing
% Change to if ~(usejavacomponent) when we find the appropriate error
% message.
error(javachk('awt'));
error(nargoutchk(0,2,nargout));
parent = [];
numargs = nargin;
% The rest of this code to be moved to private\uitable_deprecated.m.
datastatus=false; columnstatus=false;
rownum = 1; colnum = 1; % Default to a 1x1 table.
position = [20 20 200 200];
combo_box_found = false;
check_box_found = false;
import com.mathworks.hg.peer.UitablePeer;
if (numargs > 0 && isscalar(varargin{1}) && ...
ishghandle(varargin{1}, 'figure'))
parent = varargin{1};
varargin = varargin(2:end);
numargs = numargs - 1;
end
if (numargs > 0 && isscalar(varargin{1}) && ishandle(varargin{1}))
if ~isa(varargin{1}, 'javax.swing.table.DefaultTableModel')
error('MATLAB:uitable:UnrecognizedParameter', ['Unrecognized parameter: ', varargin{1}]);
end
data_model = varargin{1};
varargin = varargin(2:end);
numargs = numargs - 1;
elseif ((numargs > 1) && isscalar(varargin{1}) && isscalar(varargin{2}))
if(isnumeric(varargin{1}) && isnumeric(varargin{2}))
rownum = varargin{1};
colnum = varargin{2};
varargin = varargin(3:end);
numargs = numargs-2;
else
error(message('MATLAB:uitable:InputMustBeScalar'))
end
elseif ((numargs > 1) && isequal(size(varargin{2},1), 1) && iscell(varargin{2}))
if (size(varargin{1},2) == size(varargin{2},2))
if (isnumeric(varargin{1}))
varargin{1} = num2cell(varargin{1});
end
else
error(message('MATLAB:uitable:MustMatchInfo'));
end
data = varargin{1}; datastatus = true;
coln = varargin{1+1}; columnstatus = true;
varargin = varargin(3:end);
numargs = numargs-2;
end
for i = 1:2:numargs-1
if (~ischar(varargin{i}))
error('MATLAB:uitable:UnrecognizedParameter', ['Unrecognized parameter: ', varargin{i}]);
end
switch lower(varargin{i})
case 'data'
if (isnumeric(varargin{i+1}))
varargin{i+1} = num2cell(varargin{i+1});
end
data = varargin{i+1};
datastatus = true;
case 'columnnames'
if(iscell(varargin{i+1}))
coln = varargin{i+1};
columnstatus = true;
else
error(message('MATLAB:uitable:InvalidCellArray'))
end
case 'numrows'
if (isnumeric(varargin{i+1}))
rownum = varargin{i+1};
else
error(message('MATLAB:uitable:NumrowsMustBeScalar'))
end
case 'numcolumns'
if (isnumeric(varargin{i+1}))
colnum = varargin{i+1};
else
error(message('MATLAB:uitable:NumcolumnsMustBeScalar'))
end
case 'gridcolor'
if (ischar(varargin{i+1}))
gridcolor = varargin{i+1};
else if (isnumeric(varargin{i+1}) && (numel(varargin{i+1}) == 3))
gridcolor = varargin{i+1};
else
error(message('MATLAB:uitable:InvalidString'))
end
end
case 'rowheight'
if (isnumeric(varargin{i+1}))
rowheight = varargin{i+1};
else
error(message('MATLAB:uitable:RowheightMustBeScalar'))
end
case 'parent'
if ishandle(varargin{i+1})
parent = varargin{i+1};
else
error(message('MATLAB:uitable:InvalidParent'))
end
case 'position'
if (isnumeric(varargin{i+1}))
position = varargin{i+1};
else
error(message('MATLAB:uitable:InvalidPosition'))
end
case 'columnwidth'
if (isnumeric(varargin{i+1}))
columnwidth = varargin{i+1};
else
error(message('MATLAB:uitable:ColumnwidthMustBeScalar'))
end
otherwise
error('MATLAB:uitable:UnrecognizedParameter', ['Unrecognized parameter: ', varargin{i}]);
end
end
% ---combo/check box detection--- %
if (datastatus)
if (iscell(data))
rownum = size(data,1);
colnum = size(data,2);
combo_count =0;
check_count = 0;
combo_box_data = num2cell(zeros(1, colnum));
combo_box_column = zeros(1, colnum);
check_box_column = zeros(1, colnum);
for j = 1:rownum
for k = 1:colnum
if (iscell(data{j,k}))
combo_box_found = true;
combo_count = combo_count + 1;
combo_box_data{combo_count} = data{j,k};
combo_box_column(combo_count ) = k;
dc = data{j,k};
data{j,k} = dc{1};
else
if(islogical(data{j,k}))
check_box_found = true;
check_count = check_count + 1;
check_box_column(check_count) = k;
end
end
end
end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Check the validity of the parent and/or create a figure.
if isempty(parent)
parent = gcf; % Get the current figure. Create one if not available
end
if ( columnstatus && datastatus )
if(size(data,2) ~= size(coln,2))
error(message('MATLAB:uitable:NeedSameNumberColumns'));
end
elseif ( ~columnstatus && datastatus )
for i=1:size(data,2)
coln{i} = num2str(i);
end
columnstatus = true;
elseif ( columnstatus && ~datastatus)
error(message('MATLAB:uitable:NoDataProvided'));
end
if (~exist('data_model', 'var'))
data_model = javax.swing.table.DefaultTableModel;
end
if exist('rownum', 'var')
data_model.setRowCount(rownum);
end
if exist('colnum', 'var')
data_model.setColumnCount(colnum);
end
table_h= UitablePeer(data_model);
% We should have valid data and column names here.
if (datastatus), table_h.setData(data); end;
if (columnstatus), table_h.setColumnNames(coln); end;
if (combo_box_found),
for i=1:combo_count
table_h.setComboBoxEditor(combo_box_data(i), combo_box_column(i));
end
end
if (check_box_found),
for i = 1: check_count
table_h.setCheckBoxEditor(check_box_column(i));
end
end
[table, container] = javacomponentfigurechild_helper(table_h, position, parent);
% Have to do a drawnow here to make the properties stick. Try to restrict
% the drawnow call to only when it is absolutely required.
flushed = false;
if exist('gridcolor', 'var')
pause(.1); drawnow;
flushed = true;
table_h.setGridColor(gridcolor);
end
if exist('rowheight', 'var')
if (~flushed)
drawnow;
end
table_h.setRowHeight(rowheight);
end
if exist('columnwidth', 'var')
table_h.setColumnWidth(columnwidth);
end;
% Add a predestroy listener so we can call cleanup on the table.
temp = handle.listener(table, 'ObjectBeingDestroyed', @componentDelete);
save__listener__(table,temp);
function componentDelete(src, evd) %#ok
% Clean up the table here so it disengages all its internal listeners.
src.cleanup;
|
github
|
BottjerLab/Acoustic_Similarity-master
|
uitree_deprecated.m
|
.m
|
Acoustic_Similarity-master/code/interactive/private/uitree_deprecated.m
| 7,914 |
utf_8
|
6958f7f99706f031c84da06056614a35
|
function [tree, container] = uitree_deprecated(varargin)
% This function is undocumented and will change in a future release
% Copyright 2003-2012 The MathWorks, Inc.
% Release: R14. This feature will not work in previous versions of MATLAB.
% Warn that this old code path is no longer supported.
warn = warning('query', 'MATLAB:uitree:DeprecatedFunction');
if isequal(warn.state, 'on')
warning(message('MATLAB:uitree:DeprecatedFunction'));
end
%% Setup and P-V parsing.
error(javachk('jvm'));
error(nargoutchk(0, 2, nargout));
fig = [];
numargs = nargin;
if (nargin > 0 && isscalar(varargin{1}) && ishandle(varargin{1}))
if ~ishghandle(handle(varargin{1}), 'figure')
error(message('MATLAB:uitree:InvalidFigureHandle'));
end
fig = varargin{1};
varargin = varargin(2:end);
numargs = numargs - 1;
end
% RootFound = false;
root = [];
expfcn = [];
selfcn = [];
pos = [];
% parent = [];
if (numargs == 1)
error(message('MATLAB:uitree:InvalidNumInputs'));
end
for i = 1:2:numargs-1
if ~ischar(varargin{i})
error(message('MATLAB:uitree:UnrecognizedParameter'));
end
switch lower(varargin{i})
case 'root'
root = varargin{i+1};
case 'expandfcn'
expfcn = varargin{i+1};
case 'selectionchangefcn'
selfcn = varargin{i+1};
case 'parent'
if ishandle(varargin{i+1})
f = varargin{i+1};
if ishghandle(handle(f), 'figure')
fig = f;
end
end
case 'position'
p = varargin{i+1};
if isnumeric(p) && (length(p) == 4)
pos = p;
end
otherwise
error('MATLAB:uitree:UnknownParameter', ['Unrecognized parameter: ', varargin{i}]);
end
end
if isempty(expfcn)
[root, expfcn] = processNode(root);
else
root = processNode(root);
end
% This should not be tagged for EDT invocation. This is a FigureChild.
% Its public methods expect main thread invocation which subsequently post
% stuff to EDT.
tree_h = com.mathworks.hg.peer.UITreePeer;
tree_h.setRoot(root);
if isempty(fig)
fig = gcf;
end
if isempty(pos)
figpos = get(fig, 'Position');
pos = [0 0 min(figpos(3), 200) figpos(4)];
end
[tree, container] = javacomponentfigurechild_helper(tree_h,pos, fig);
if ~isempty(expfcn)
set(tree, 'NodeExpandedCallback', {@nodeExpanded, tree, expfcn});
end
if ~isempty(selfcn)
set(tree, 'NodeSelectedCallback', {@nodeSelected, tree, selfcn});
end
end
%% -----------------------------------------------------
function nodeExpanded(src, evd, tree, expfcn) %#ok
% tree = handle(src);
% evdsrc = evd.getSource;
evdnode = evd.getCurrentNode;
% indices = [];
if ~tree.isLoaded(evdnode)
value = evdnode.getValue;
% <call a user function(value) which returns uitreenodes>;
cbk = expfcn;
if iscell(cbk)
childnodes = feval(cbk{1}, tree, value, cbk{2:end});
else
childnodes = feval(cbk, tree, value);
end
if (length(childnodes) == 1)
% Then we dont have an array of nodes. Create an array.
chnodes = childnodes;
childnodes = javaArray('com.mathworks.hg.peer.UITreeNode', 1);
childnodes(1) = java(chnodes);
end
tree.add(evdnode, childnodes);
tree.setLoaded(evdnode, true);
end
end
%% -----------------------------------------------------
function nodeSelected(src, evd, tree, selfcn) %#ok
cbk = selfcn;
hgfeval(cbk, tree, evd);
end
%% -----------------------------------------------------
function [node, expfcn] = processNode(root)
expfcn = [];
if isempty(root) || isa(root, 'com.mathworks.hg.peer.UITreeNode') || ...
isa(root, 'javahandle.com.mathworks.hg.peer.UITreeNode')
node = root;
elseif ishghandle(root)
% Try to process as an HG object.
try
%In the HG tree rendering, the java side is intelligent enough to
%pick an icon based on the class of the object that we are sending
%in. It is absolutely essential that we send in the handle to java
%because the icon picking is based on the bean adapter's display
%name.
node = uitreenode_deprecated(handle(root), get(root, 'Type'), ...
[], isempty(get(0, 'Children')));
catch ex %#ok mlint
node = [];
end
expfcn = @hgBrowser;
elseif ismodel(root)
% Try to process as an open Simulink system
% TODO if there is an open simulink system and a directory on the path with
% the same name, the system will hide the directory. Perhaps we should
% warn about this.
try
h = handle(get_param(root,'Handle'));
% TODO we pass root to the tree as a string,
% it would be better if we could just pass the
% handle up
node = uitreenode_deprecated(root, get(h, 'Name'), ...
[], isempty(h.getHierarchicalChildren));
catch ex %#ok mlint
node = [];
end
expfcn = @mdlBrowser;
elseif ischar(root)
% Try to process this as a directory structure.
try
iconpath = [matlabroot, '/toolbox/matlab/icons/foldericon.gif'];
node = uitreenode_deprecated(root, root, iconpath, ~isdir(root));
catch ex %#ok mlint
node = [];
end
expfcn = @dirBrowser;
else
node = [];
end
end
%% -----------------------------------------------------
function nodes = hgBrowser(tree, value) %#ok
try
count = 0;
parent = handle(value);
ch = parent.children;
for i=1:length(ch)
count = count+1;
nodes(count) = uitreenode_deprecated(handle(ch(i)), get(ch(i), 'Type'), [], ...
isempty(get(ch(i), 'Children')));
end
catch
error(message('MATLAB:uitree:UnknownNodeType'));
end
if (count == 0)
nodes = [];
end
end
%% -----------------------------------------------------
function nodes = mdlBrowser(tree, value) %#ok
try
count = 0;
parent = handle(get_param(value,'Handle'));
ch = parent.getHierarchicalChildren;
for i=1:length(ch)
if isempty(findstr(class(ch(i)),'SubSystem'))
% not a subsystem
else
% is a subsystem
count = count+1;
descr = get(ch(i),'Name');
isleaf = true;
cch = ch(i).getHierarchicalChildren;
if ~isempty(cch)
for j = 1:length(cch)
if ~isempty(findstr(class(cch(j)),'SubSystem'))
isleaf = false;
break;
end
end
end
nodes(count) = uitreenode_deprecated([value '/' descr], descr, [], ...
isleaf);
end
end
catch
error(message('MATLAB:uitree:UnknownNodeType'));
end
if (count == 0)
nodes = [];
end
end
%% -----------------------------------------------------
function nodes = dirBrowser(tree, value) %#ok
try
count = 0;
ch = dir(value);
for i=1:length(ch)
if (any(strcmp(ch(i).name, {'.', '..', ''})) == 0)
count = count + 1;
if ch(i).isdir
iconpath = [matlabroot, '/toolbox/matlab/icons/foldericon.gif'];
else
iconpath = [matlabroot, '/toolbox/matlab/icons/pageicon.gif'];
end
nodes(count) = uitreenode_deprecated([value, ch(i).name, filesep], ...
ch(i).name, iconpath, ~ch(i).isdir);
end
end
catch
error(message('MATLAB:uitree:UnknownNodeType'));
end
if (count == 0)
nodes = [];
end
end
%% -----------------------------------------------------
function yesno = ismodel(input)
yesno = false;
try
if is_simulink_loaded
get_param(input,'handle');
yesno = true;
end
end
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
uisetcolor_deprecated.m
|
.m
|
Acoustic_Similarity-master/code/interactive/private/uisetcolor_deprecated.m
| 14,563 |
utf_8
|
b29ebe5b5c83b36a119bca02eb0be77f
|
function [selectedColor] = uisetcolor_deprecated(varargin)
% Copyright 1984-2010 The MathWorks, Inc.
% $Revision: 1.1.6.9 $ $Date: 2011/03/09 07:07:39 $
%%%%%%%%%%%%%%%%
% Error messages
%%%%%%%%%%%%%%%%
%bad1ArgMessage = 'First of two args cannot be a string' ;
bad2ArgMessage = 'Second argument (dialog title) must be a string.' ;
badTitleMessage = 'title must be the last parameter passed to uisetcolor.' ;
badNumArgMessage = 'Too many input arguments.' ;
badRgbAryMessage = 'Color value contains NaN, or element out of range 0.0 <= value <= 1.0.' ;
badObjTypMessage = 'Color selection is not supported for light objects.' ;
badPropertyMessage = 'Color selection is only supported for objects with Color or ForeGroundColor properties.' ;
badColorValMessage = 'Color value must be a 3 element numeric vector.' ;
badColorSelMessage1 = 'Color selection is not supported for' ;
badColorSelMessage2 = ' objects, but only for objects with Color or ForeGroundColor properties.' ;
maxArgs = 2 ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% check the number of args - must be <= maxArgs
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
numArgs = nargin ;
if( numArgs > maxArgs )
error( badNumArgMessage )
end
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Restrict new version to the mac
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% If we are on the mac, default to using non-native
% dialog. If the root property UseNativeSystemDialogs
% is false, use the non-native version instead.
useNative = true; %#ok<NASGU>
% If we are on the Mac & swing is available, set useNative to false,
% i.e., we are going to use Java dialogs not native dialogs.
% Comment the following line to disable java dialogs on Mac.
useNative = ~( ismac && usejava('awt') ) ;
% If the root appdata is set and swing is available,
% honor that overriding all other prefs.
% if isequal(0, getappdata(0,'UseNativeSystemDialogs')) && usejava('awt')
% useNative = false ;
% end
if useNative
try
if nargin == 0
[selectedColor] = native_uisetcolor ;
else
[selectedColor] = native_uisetcolor( varargin{:} ) ;
end
catch ex
rethrow(ex)
end
return
end % end useNative
%%%%%%%%%%%%%%%%%
% General globals
%%%%%%%%%%%%%%%%%
%dcc = '' ; % dColorChooser - our Java ColorChooser
rgbArray = '' ;
%sz = '' ;
objectType = '' ;
%propertyName = '' ;
firstArg = '' ;
propUsed = '' ;
userTitle = '' ;
sys = char( computer ) ; % String the type of computer - PCWIN, MAC, ...ui
%%%%%%%
% Flags
%%%%%%%
typeFound = false ;
rgbHandedIn = false ;
objectHandedIn = false ;
arg1IsBad = false ;
arg1IsRGB = false ;
arg1IsChar = false ;
arg1IsHgHandle = false ;
%rgbError = false ;
%%%%%%%%%%%%%%%%%%%%%%
% Default color values
%%%%%%%%%%%%%%%%%%%%%%
red = 1.0 ;
green = 1.0 ;
blue = 1.0 ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Check the first arg if there is one
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( 1 <= numArgs )
firstArg = varargin{1} ;
getArg1Type() ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Bail out if the first arg is not the correct type
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( arg1IsBad )
if( ishandle( firstArg ) )
try
objectType = get( firstArg , 'Type' ) ;
typeFound = true ;
catch
end
end % end if( ishandle( firstArg ) )
if( ~typeFound )
error( badPropertyMessage ) ;
else
objectType = strcat( ' ' , objectType ) ;
error( strcat( badColorSelMessage1 , objectType , badColorSelMessage2 ) ) ;
end
return ;
% end if( arg1IsBad )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Check to see that we really do have an RGB arg
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
elseif( arg1IsRGB )
% Check for 3 vals
if ~( 3 == length( firstArg ) )
error( badColorValMessage ) ;
end
rgbArray = firstArg;
% Check that vals are in range
rgbError = checkRGB() ;
if( rgbError )
error( badRgbAryMessage )
end
% Overwrite the default rgb values
red = rgbArray( 1 , 1 ) ;
green = rgbArray( 1 , 2 ) ;
blue = rgbArray( 1 , 3 ) ;
rgbHandedIn = true ;
% end if( arg1IsRGB )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Make sure that if first arg is a string, then it's the only arg
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
elseif( arg1IsChar )
if~( 1 == numArgs )
error( badTitleMessage )
end
% We'll use the string as a title
userTitle = firstArg ;
%end if( arg1IsChar )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Make sure the object is not of type 'light' (cpmpat)
% and that it has the Color or ForegroundColor property
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
else
if( arg1IsHgHandle )
objectType = get( firstArg , 'Type' ) ;
% The current uisetcolor does not support "light" objects
%if( strcmp( 'light' , lower( objectType ) ) )
if (strcmpi('light',objectType))
error( badObjTypMessage )
end
hasColorProperty = false ;
hasForegroundProperty = false ;
% Make sure the object has either the
% Color or ForegroundColor property.
propFound = false ;
try
rgbArray = get( firstArg , 'Color' ) ;
hasColorProperty = true ;
propUsed = 'Color' ;
propFound = true ;
catch
end
if( ~propFound )
try
rgbArray = get( firstArg , 'ForegroundColor' ) ;
hasForegroundProperty = true ;
propUsed = 'ForegroundColor' ;
catch
end
end % end if( ~propFound )
if( ~hasColorProperty && ~hasForegroundProperty )
error( badPropertyMessage )
end
% Overwrite the default rgb values
red = rgbArray( 1 , 1 ) ;
green = rgbArray( 1 , 2 ) ;
blue = rgbArray( 1 , 3 ) ;
objectHandedIn = true ;
end % end if( arg1IsHgHandle )
end % end of if/elseif
end % end if( 1 <= numArgs )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% If there are two args, the second must be a character string
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( 2 == numArgs )
arg2 = varargin{ 2 } ;
if( ~( ischar( arg2 ) ) )
error( bad2ArgMessage )
end
if( ~( 1 == size( arg2 , 1 ) ) )
arg2 = arg2';
if( ~( ischar( arg2 ) ) )
error( bad2ArgMessage )
end
end % end if( ~( 1 == size( arg2 , 1 ) ) )
userTitle = arg2 ;
end % end if( 2 == numArgs )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Create a dialog to hold our ColorChooser
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% jp = handle(javax.swing.JPanel) ;
jp = awtcreate('com.mathworks.mwswing.MJPanel', ...
'Ljava.awt.LayoutManager;', ...
java.awt.BorderLayout);
% Dialog title is set later
d = mydialog( ...
'Visible','off', ...
'Color',get(0,'DefaultUicontrolBackgroundColor'), ...
'Windowstyle','modal', ...
'Resize','on' ) ;
% Create an MJPanel and put it into the dialog - this is for resizing
[panel, container] = javacomponent(handle(jp),[10 10 20 20],d);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Create a chooser with the appropriate initial setting
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( objectHandedIn )
% Some object was handed in. Find its Color-related properties.
s = set( firstArg ) ;
flds = fields( s ) ;
propArray = '' ;
j = 1 ;
% Create an array of "Color-related" properties for this figure
for i = 1 : length( flds )
if ~( isempty( findstr( flds{i} , 'Color' ) ) )
propArray{ 1 , j } = flds{i} ;
j = j + 1 ;
end % end if
end % end for
jcc = handle(com.mathworks.hg.util.dColorChooser( java.awt.Color( red , green , blue ) , propArray , propUsed , sys ));
% Make the dialog large enough
% to show the Color-related properties combo box
set( d , 'Position' , [232 246 145 270] ) ;
else
% No object was handed in.
% No combo box - figure can be smaller
set( d , 'Position' , [232 246 145 230] ) ;
jcc = handle(com.mathworks.hg.util.dColorChooser( java.awt.Color( red , green , blue ) , [] , [] , sys ));
end % end if( objectHandedIn )
% Use supplied title if one was given else use default
if ~( strcmp( '' , userTitle ) )
set( d , 'Name' , userTitle ) ;
else
set( d , 'Name' , char( jcc.getDefaultTitle() ) )
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Add the panel holding the actual chooser to the panel in the dialog
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
dcc = jcc.getContentPanel() ;
awtinvoke(java(panel), 'add(Ljava.awt.Component;)', dcc);
set(container,'Units','normalized','Position',[0 0 1 1]);
%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%
% Set up callbacks
%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%
% jbOK = javax.swing.JButton() ;
jbOK = handle( jcc.getOkButton(), 'callbackproperties') ;
set(jbOK ,'ActionPerformedCallback', {@callbackHandler , firstArg , d , jcc , objectHandedIn , rgbHandedIn , rgbArray})
% jbCancel = javax.swing.JButton() ;
jbCancel = handle(jcc.getCancelButton(), 'callbackproperties') ;
set(jbCancel,'ActionPerformedCallback', {@callbackHandler , firstArg , d , jcc , objectHandedIn , rgbHandedIn , rgbArray})
%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%
% Display everything
%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%
figure(d)
refresh(d)
% Set the default return value to 0
selectedColor = 0 ;
% If RGB or object handle is passed in, set default return value to the
% specified color. This is used if an error occurs or the user hits
% Cancel.
if( rgbHandedIn || objectHandedIn ) % changed 12-30-04 Dave Oppenheim
selectedColor = rgbArray ; % See comment at line 283
end
waitfor(d);
if isappdata( 0 , 'uisetcolorData' )
selectedColor = getappdata( 0 , 'uisetcolorData' ) ;
rmappdata( 0 , 'uisetcolorData' ) ;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Discover the type of the first arg
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function getArg1Type()
if( ishghandle( firstArg ) & ( 1 == length( firstArg ) ) )
arg1IsHgHandle = true ;
elseif ( ischar( firstArg ) )
arg1IsChar = true ;
elseif( isnumeric( firstArg ) && isvector( firstArg ) )
arg1IsRGB = true ;
else
arg1IsBad = true ;
end
end % end getArg1Type()
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Check the values in an array
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function rgbErr = checkRGB()
rgbErr = false;
% Transpose if necessary ( maybe user handed in 3 by 1 array )
if( ~( 1 == size( rgbArray , 1) ) )
rgbArray = rgbArray' ;
end
% Validate size and component values OF rgbArray
if( ~( 1 == size( rgbArray , 1 ) ) | ~( 3 == size( rgbArray , 2 ) ) | ...
( 1.0 < rgbArray(1) ) | ( rgbArray(1) < 0.0 ) | ...
( 1.0 < rgbArray(2) ) | ( rgbArray(2) < 0.0 ) | ...
( 1.0 < rgbArray(3) ) | ( rgbArray(3) < 0.0 ) )
rgbErr = true ;
end
end % end function checkRGB()
function out = mydialog(varargin)
out = [];
try
out = dialog(varargin{:}) ;
catch ex
rethrow(ex)
end
end % end myDialog
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% The callback function for the chooser
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function callbackHandler( obj , evd , firstArg , d , jcc , objectHandedIn , rgbHandedIn , rgbArg )
cmd = char(evd.getActionCommand());
switch(cmd)
case 'dColorChooserOK'
red = jcc.getColor.getRed/255 ;
green = jcc.getColor.getGreen/255 ;
blue = jcc.getColor.getBlue/255 ;
% If an object was handed in (via its handle),
% set the appropriate color-related property
% to the value selected by the user.
if( objectHandedIn )
propertyName = char(jcc.getProperty()) ;
z = [ red green blue ] ;
if ~( strcmp( propertyName , 'None' ) )
set( firstArg , propertyName ,z ) ;
end
end % end if( objectHandedIn )
% Set the return value
selectedColor = [ red green blue ] ;
setappdata( 0 , 'uisetcolorData' , selectedColor ) ;
% Cleanup
jcc.cleanup();
if ishandle(d)
close(d)
end
case 'dColorChooserCancel'
% If user hits Cancel, we use the default values that are set up
% accordingly.
% Cleanup
jcc.cleanup();
if ishandle(d)
close(d)
end
otherwise
error(message('MATLAB:uisetcolor_deprecated:UnimplementedOption', cmd))
end
end % end callbackHandler
|
github
|
BottjerLab/Acoustic_Similarity-master
|
javacomponentundoc_helper.m
|
.m
|
Acoustic_Similarity-master/code/interactive/private/javacomponentundoc_helper.m
| 20,472 |
utf_8
|
1d9e111d2f81fc5e516d0bfa653fae2f
|
function [hcomponent, hcontainer] = javacomponentundoc_helper(varargin)
% Copyright 2010-2012 The MathWorks, Inc.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Old javacomponent implementation.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if (nargin>=1)
component = varargin{1};
end
if (nargin>=2)
position = varargin{2};
end
if (nargin>=3)
parent = varargin{3};
end
if (nargin==4)
callback = varargin{4};
end
if (usejavacomponent == 0)
error(message('MATLAB:javacomponent:FeatureNotSupported'));
end
if ~isempty(nargchk(1,4,nargin)) %#ok
error('MATLAB:javacomponent',getString(message('MATLAB:javacomponent:IncorrectUsage')));
end
if nargin < 4
callback = '';
else
if ~iscell(callback)
error('MATLAB:javacomponent',getString(message('MATLAB:javacomponent:IncorrectUsage')));
end
end
if nargin < 3
parent = gcf;
end
if nargin < 2
position = [20 20 60 20];
end
parentIsFigure = false;
hParent = handle(parent);
% g500548 - changed to use ishghandle.
if ( ishghandle(hParent, 'figure') || ...
ishghandle(hParent, 'uicontainer') || ...
ishghandle(hParent, 'uiflowcontainer') || ...
ishghandle(hParent, 'uigridcontainer'))
parentIsFigure = true;
peer = getJavaFrame(ancestor(parent,'figure'));
elseif ishghandle(hParent, 'uitoolbar')
peer = get(parent,'JavaContainer');
if isempty(peer)
drawnow;
peer = get(parent,'JavaContainer');
end
elseif (ishghandle(hParent, 'uisplittool') || ...
ishghandle(hParent, 'uitogglesplittool'))
parPeer = get(get(hParent,'Parent'),'JavaContainer');
if isempty(parPeer)
drawnow;
end
peer = get(parent,'JavaContainer');
else
error(message('MATLAB:javacomponent:InvalidParentHandle', getString(message('MATLAB:javacomponent:IncorrectUsage'))))
end
if isempty(peer)
error(message('MATLAB:javacomponent:JavaFigsNotEnabled'))
end
hUicontainer = [];
hgp = [];
returnContainer = true;
if ischar(component)
% create from class name
component = javaObjectEDT(component);
elseif iscell(component)
% create from class name and input args
component = javaObjectEDT(component{:});
elseif ~isa(component,'com.mathworks.hg.peer.FigureChild')
% tag existing object for auto-delegation - unless it is a FigureChild
javaObjectEDT(component);
end
% Promote the component to a handle object first. It seems once a java
% object is cast to a handle, you cannot get another handle with
% 'callbackproperties'.
if ~isjava(component)
component = java(component);
end
hcomponent = handle(component,'callbackProperties');
if nargin == 1
hgp = handle(peer.addchild(component));
% parent must be a figure, we default to gcf upstairs
createPanel;
hgp.setUIContainer(double(hUicontainer));
else
if parentIsFigure
if isnumeric(position)
if isempty(position)
position = [20 20 60 20];
end
% numeric position is not set here, rely on the uicontainer
% listeners below.
hgp = handle(peer.addchild(component));
createPanel;
hgp.setUIContainer(double(hUicontainer));
elseif ...
isequal(char(position),char(java.awt.BorderLayout.NORTH)) || ...
isequal(char(position),char(java.awt.BorderLayout.SOUTH)) || ...
isequal(char(position),char(java.awt.BorderLayout.EAST)) || ...
isequal(char(position),char(java.awt.BorderLayout.WEST)) || ...
isequal(char(position),char('Overlay'))
hgp = handle(peer.addchild(component, position));
returnContainer = false;
else
error(message('MATLAB:javacomponent:InvalidPosition', getString(message('MATLAB:javacomponent:IncorrectUsage'))))
end
else
% Adding component to the toolbar.
% component position is ignored
peer.add(component);
hUicontainer = parent; % toolbar.
handles = getappdata(hUicontainer, 'childhandles');
handles = [handles, hcomponent];
setappdata(hUicontainer, 'childhandles', handles);
end
% make sure the component is on the screen so the
% caller can interact with it right now.
% drawnow;
end
if returnContainer
configureComponent();
end
% If asked for callbacks, add them now.
if ~isempty(callback)
% The hg panel is the best place to store the listeners so they get
% cleaned up asap. We can't do that if the parent is a uitoolbar so we
% just put them on the toolbar itself.
lsnrParent = hgp;
if isempty(lsnrParent)
lsnrParent = hParent;
end
if mod(length(callback),2)
error('MATLAB:javacomponent',usage);
end
for i = 1:2:length(callback)
lsnrs = getappdata(lsnrParent,'JavaComponentListeners');
l = javalistener(component, callback{i}, callback{i+1});
setappdata(lsnrParent,'JavaComponentListeners',[l lsnrs]);
end
end
if returnContainer
hcontainer = hUicontainer;
else
hcontainer = [];
end
function createPanel
% add delete listener
hUicontainer = hgjavacomponent('Parent',parent,'Units', 'Pixels','Serializable','off');
set(hUicontainer, 'UserData', char(component.getClass.getName)); % For findobj queries.
if isa(java(hgp), 'com.mathworks.hg.peer.FigureChild')
set(hUicontainer, 'FigureChild', hgp);
end
if isa(java(hcomponent), 'javax.swing.JComponent')
% force component to be opaque if it's a JComponent. This prevents
% lightweight component from showing the figure background (which
% may never get a paint event)
hcomponent.setOpaque(true);
end
set(hUicontainer, 'JavaPeer', hcomponent);
if returnContainer
% add move/resize listener to the hgjavacomponent
addlistener(hUicontainer, 'PixelBounds', 'PostSet', @handleResize);
% add visible listener
addlistener(hUicontainer, 'Visible', 'PostSet', @handleVisible);
%Parent was set before we get here. Hence we need to explicitly
%walk up and attach listeners. For subsequent parent changes,
%the parent property postset listener callback will take care of setting
%up the hierarchy listeners to listen for position and visible changes
createHierarchyListeners(hUicontainer, @handleVisible);
% add parent listener
addlistener(hUicontainer, 'Parent', 'PreSet', @handlePreParent);
% add parent listener
addlistener(hUicontainer, 'Parent', 'PostSet', @(o,e) handlePostParent(o,e,@handleVisible));
% force though 1st resize event
set(hUicontainer,'Position', position);
else
% For the BorderLayout components, we don't really want the
% hUicontainer to show. But, it provides a nice place for cleanup.
set(hUicontainer,'Visible', 'off', 'HandleVisibility', 'off');
% Set position out of the figure to work around a current bug
% due to which invisible uicontainers show up when renderer is
% OpenGL (G.
set(hUicontainer, 'Position', [1 1 0.01 0.01]);
end
if isa(component,'com.mathworks.hg.peer.FigureChild')
component.setUIContainer(double(hUicontainer));
end
%Handles move or resize of the hgjavacomponent.
function handleResize(obj, evd) %#ok - mlint
%Deal with early warnings
[lastWarnMsg, lastWarnId] = lastwarn;
oldPBWarning = warning('off','MATLAB:HandleGraphics:ObsoletedProperty:PixelBounds');
pb = get(hUicontainer,'PixelBounds');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% HACK FOR HMI - When X-location is negative, we are
% messing up the width of the component because we are
% going from pixel bounds (xmin,ymin,xmax,ymax) to actual
% bounds on the screen (width and height calculated). There is
% a floor/ceil issue with the X-min not quite adjusted by
% the X-max so as to keep the width correct. I am forcing it
% here. An ideal fix would have been in the pixelbounds
% calculation but it seems like too much work in HG1 and hence
% I am using a narrow scoped fix for now - 815546
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
jcPos = getpixelposition(hUicontainer,true);
if (jcPos(1) < 0)
pb(3) = pb(1) + jcPos(3);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
hgp.setPixelBounds(pb);
warning(oldPBWarning.state, 'MATLAB:HandleGraphics:ObsoletedProperty:PixelBounds');
% restore the last warning thrown
lastwarn(lastWarnMsg, lastWarnId);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% g482174 - Bandage solution to support hierarchy visibility changes
% We will look for any invisible parent container to see if we can
% show the javacomponent or not.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function handleVisible(obj, evd) %#ok - mlint
source = evd.AffectedObject;
if ishghandle(source,'hgjavacomponent')
hgp.setVisible(strcmp(get(source,'Visible'),'on'));
else
if (strcmp(get(source,'Visible'),'off'))
setInternalVisible(hUicontainer, component, false);
else
setInternalVisible(hUicontainer, component, isVisibleInCurrentLineage(hUicontainer));
end
end
drawnow update;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% isVisibleInCurrentLineage - returns whether the javacomponent can
% be visible or not in its current hierarchy.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function visible = isVisibleInCurrentLineage(hUicontainer)
stParent = get(hUicontainer,'Parent');
hgjcompVisible = strcmp(get(hUicontainer,'Visible'),'on');
visible = hgjcompVisible && strcmp(get(stParent,'Visible'),'on');
while (~ishghandle(stParent,'root') && visible)
stParent = get(stParent,'Parent');
visible = visible && strcmp(get(stParent,'Visible'),'on');
end
end
function handlePreParent(obj, evd) %#ok - mlint
oldfig = ancestor(hUicontainer, 'figure');
removecomponent = true;
% NewValue field is absent in MCOS and hence we need to do the
% following safely.
if ~isempty(findprop(evd,'NewValue'))
newfig = ancestor(evd.NewValue, 'figure');
removecomponent = ~isempty(newfig) && ~isequal(oldfig, newfig);
end
%We are losing on this optimization(event may not have NewValue). We always
%remove and add upon reparenting. We do not have the context of
%the new parent in the preset to do a compare to see if we are
%being parented to the same parent again. We hope that this is
%not done often.
if (removecomponent)
peer = getJavaFrame(oldfig);
peer.remove(component);
end
end
function handlePostParent(obj, evd, visibleCbk) %#ok - mlint
createHierarchyListeners(hUicontainer, visibleCbk);
oldfig = ancestor(parent, 'figure');
newfig = ancestor(evd.AffectedObject,'figure');
addcomponent = true;
%Before, we could decide if we want to re-add the
%javacomponent. Now we have to always add.
if ~isempty(findprop(evd,'NewValue'))
newfig = ancestor(evd.NewValue, 'figure');
addcomponent = ~isempty(newfig) && ~isequal(oldfig, newfig);
end
if addcomponent
peer = getJavaFrame(newfig);
hgp= handle(peer.addchild(component));
%When we reparent ourself (javacomponent), the truth about
%whether we are visible or not needs to be queried from
%the proxy and its current lineage. Change visibility
%without changing state on the hUicontainer
setInternalVisible(hUicontainer, component, isVisibleInCurrentLineage(hUicontainer));
if isa(java(hgp), 'com.mathworks.hg.peer.FigureChild')
% used by the uicontainer C-code
setappdata(hUicontainer, 'FigureChild', java(hgp));
end
parent = newfig;
end
handleResize([],[]);
end
end
function configureComponent
set(hUicontainer,'DeleteFcn', {@containerDelete, hcomponent});
% addlistener(java(hcomponent), 'ObjectBeingDestroyed', @(o,e)componentDelete(o,e,hUicontainer, parentIsFigure));
temp = handle.listener(hcomponent, 'ObjectBeingDestroyed', @(o,e)componentDelete(o,e,hUicontainer, parentIsFigure));
save__listener__(hcomponent,temp);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% g637916 - Visibility of the hgjavacomponent is posing issues due to the
% fact that the state is being used to control visibility and hence when
% the parent's are turned visible off, we need an alternate api to make it
% go away from the screen.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function setInternalVisible(hUicontainer, component ,vis)
cleaner = warnSuppressAndSetInternalVisible(hUicontainer, component ,vis);
delete(cleaner);
end
function cleaner = warnSuppressAndSetInternalVisible(hUicontainer, component ,vis)
[ state.lastWarnMsg, state.lastWarnId ] = lastwarn;
state.usagewarning = warning('off','MATLAB:hg:javacomponent');
cleaner = onCleanup(@() warnRestore(state));
setVisibility(handle(hUicontainer),vis);
if (isa(component,'com.mathworks.hg.peer.FigureChild'))
component = component.getFigureComponent;
end
setVisible(component, vis);
end
function warnRestore(state)
lastwarn(state.lastWarnMsg, state.lastWarnId);
warning(state.usagewarning);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function containerDelete(obj, evd, hc) %#ok - mlint
obj = handle(obj);
if ishghandle(handle(obj), 'uitoolbar') || ...
ishghandle(handle(obj),'uisplittool') || ...
ishghandle(handle(obj),'uitogglesplittool');
childHandles = getappdata(obj, 'childhandles');
delete(childHandles(ishandle(childHandles)));
else
if ishandle(hc)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% We are giving ourselves a hook to run resource cleanup functions
% like freeing up callbacks. This is important for uitree because
% the expand and selectionchange callbacks need to be freed when
% the figure is destroyed. See G769077 for more information.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
removeJavaCallbacks(hc);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
delete(hc);
end
end
end
function componentDelete(obj, evd, hUicontainer, parentIsFigure) %#ok - mlint
if (parentIsFigure)
% This java component is always deleted before hUicontainer. It is
% ensured by calling component deletion in function containerDelete.
% hUicontainer becomes invalid when delete(hUicontainer) below is run.
parent = ancestor(hUicontainer,'figure');
peer = getJavaFrame(parent);
if any(ishandle(obj))
removeobj = java(obj);
if ~isempty(get(hUicontainer,'FigureChild'))
removeobj = get(hUicontainer,'FigureChild');
end
peer.remove(removeobj);
end
% delete container if it exists
if any(ishghandle(hUicontainer))
delete(hUicontainer);
end
else
parent = hUicontainer; % toolbar or split tool
if ~ishandle(parent) || ~ishandle(obj)
% The toolbar parent or the component has been deleted. Bail out.
% Toolbar clears all javacomponents after itself.
return;
end
% For uisplittool and uitogglesplittool objects
% The parent may have done this deletion for us
% already.
hPar = get(parent,'Parent');
if ishghandle(handle(hPar),'uitoolbar')
parPeer = get(hPar,'JavaContainer');
if isempty(parPeer)
return;
end
end
peer = get(parent, 'JavaContainer');
if ~isempty(peer)
peer.remove(java(obj));
end
end
end
function hdl=javalistener(jobj, eventName, response)
try
jobj = java(jobj);
catch ex %#ok
end
% make sure we have a Java objects
if ~ishandle(jobj) || ~isjava(jobj)
error(message('MATLAB:javacomponent:invalidinput'))
end
hSrc = handle(jobj,'callbackproperties');
allfields = sortrows(fields(set(hSrc)));
alltypes = cell(length(allfields),1);
j = 1;
for i = 1:length(allfields)
fn = allfields{i};
if ~isempty(strfind(fn,'Callback'))
fn = strrep(fn,'Callback','');
alltypes{j} = fn;
j = j + 1;
end
end
alltypes = alltypes(~cellfun('isempty',alltypes));
if nargin == 1
% show or return the possible events
if nargout
hdl = alltypes;
else
disp(alltypes)
end
return;
end
% validate event name
valid = any(cellfun(@(x) isequal(x,eventName), alltypes));
if ~valid
error(message('MATLAB:javacomponent:invalidevent', class( jobj ), char( cellfun( @(x) sprintf( '\t%s', x ), alltypes, 'UniformOutput', false ) )'))
end
hdl = handle.listener(handle(jobj), eventName, ...
@(o,e) cbBridge(o,e,response));
function cbBridge(o,e,response)
hgfeval(response, java(o), e.JavaEvent)
end
end
function createHierarchyListeners(hUicontainer, visCbk)
deleteExistingHierarchyListeners(hUicontainer, []);
hUicontainer = handle(hUicontainer);
parent = get(hUicontainer,'Parent');
% Walk up instance hierarchy and put a listener on all the
% containers. We don't need a listener on the figure.
while ~ishghandle(parent,'root')
%Set up all the visible listeners
createVisibleListener(parent, hUicontainer, visCbk);
%Keep walking up
parent = get(parent,'Parent');
end
% When the hgjavacomponent goes away, clean all listeners
addlistener(hUicontainer, 'ObjectBeingDestroyed', @(o,e) deleteExistingHierarchyListeners(o,e));
end
function deleteExistingHierarchyListeners(src,~)
hUicontainer = handle(src);
%Delete all the visibility listeners
if isListenerData(hUicontainer, 'VisiblilityListeners')
appdata = getListenerData(hUicontainer, 'VisiblilityListeners');
cellfun(@(x) delete(x), appdata, 'UniformOutput',false);
setListenerData(hUicontainer, 'VisiblilityListeners',{});
end
end
function createVisibleListener(object, hUicontainer, visCbk)
%Attach visibility listeners
visContainerListnr = addlistener(object, 'Visible','PostSet', visCbk);
if (~isListenerData(hUicontainer, 'VisiblilityListeners'))
setListenerData(hUicontainer, 'VisiblilityListeners',{});
end
visibilityAppdata = getListenerData(hUicontainer, 'VisiblilityListeners');
visibilityAppdata{end+1} = visContainerListnr;
setListenerData(hUicontainer, 'VisiblilityListeners', visibilityAppdata);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function javaFrame = getJavaFrame(f)
% store the last warning thrown
[ lastWarnMsg, lastWarnId ] = lastwarn;
% disable the warning when using the 'JavaFrame' property
% this is a temporary solution
oldJFWarning = warning('off','MATLAB:HandleGraphics:ObsoletedProperty:JavaFrame');
javaFrame = get(f,'JavaFrame');
warning(oldJFWarning.state, 'MATLAB:HandleGraphics:ObsoletedProperty:JavaFrame');
% restore the last warning thrown
lastwarn(lastWarnMsg, lastWarnId);
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
uigetdir_deprecated.m
|
.m
|
Acoustic_Similarity-master/code/interactive/private/uigetdir_deprecated.m
| 10,331 |
utf_8
|
a785775f5575f2c0d602724694ad0687
|
function [directoryname] = uigetdir_deprecated(varargin)
% $Revision: 1.1.6.7 $ $Date: 2010/07/02 16:17:19 $
% Copyright 2006-2010 The MathWorks, Inc.
%UIGETDIR Standard open directory dialog box
% DIRECTORYNAME = UIGETDIR(STARTPATH, TITLE)
% displays a dialog box for the user to browse through the directory
% structure and select a directory, and returns the directory name
% as a string. A successful return occurs if the directory exists.
%
% The STARTPATH parameter determines the initial display of directories
% and files in the dialog box.
%
% When STARTPATH is empty the dialog box opens in the current directory.
%
% When STARTPATH is a string representing a valid directory path, the
% dialog box opens in the specified directory.
%
% When STARTPATH is not a valid directory path, the dialog box opens
% in the base directory.
%
% Windows:
% Base directory is the Windows Desktop directory.
%
% UNIX:
% Base directory is the directory from which MATLAB is started.
% The dialog box displays all filetypes by default. The type
% of files that are displayed can be changed by changing the filter
% string in the Selected Directory field of the dialog box. If the
% user selects a file instead of a directory, then the directory
% containing the file is returned.
%
% Parameter TITLE is a string containing a title for the dialog box.
% When TITLE is empty, a default title is assigned to the dialog box.
%
% Windows:
% The TITLE string replaces the default caption inside the
% dialog box for specifying instructions to the user.
%
% UNIX:
% The TITLE string replaces the default title of the dialog box.
%
% When no input parameters are specified, the dialog box opens in the
% current directory with the default dialog title.
%
% The output parameter DIRECTORYNAME is a string containing the
% directory selected in the dialog box. If the user presses the Cancel
% button it is set to 0.
%
% Examples:
%
% directoryname = uigetdir;
%
% Windows:
% directoryname = uigetdir('D:\APPLICATIONS\MATLAB');
% directoryname = uigetdir('D:\APPLICATIONS\MATLAB', 'Pick a Directory');
%
% UNIX:
% directoryname = uigetdir('/home/matlab/work');
% directoryname = uigetdir('/home/matlab/work', 'Pick a Directory');
%
% See also UIGETFILE, UIPUTFILE.
% Copyright 1984-2009 The MathWorks, Inc.
% $Revision: 1.1.6.7 $ $Date: 2010/07/02 16:17:19 $
% Built-in function.
%%%%%%%%%%%%%%%%
% Error messages
%%%%%%%%%%%%%%%%
badNumArgsMessage = 'Too many input arguments.' ;
badTitleMessage = 'TITLE argument must be a string.' ;
badStartPathMessage = 'STARTPATH argument must be a string.';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% check the number of args - must be 0 , 1 , or 2
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
maxArgs = 2 ;
numArgs = nargin ;
if( numArgs > maxArgs )
error( badNumArgsMessage )
return
end
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Restrict new version to the mac
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% If we are on the mac, default to using non-native
% dialog. If the root property UseNativeSystemDialogs
% is false, use the non-native version instead.
useNative = true; %#ok
% If we are on the Mac & swing is available, set useNative to false,
% i.e., we are going to use Java dialogs not native dialogs.
% Comment the following line to disable java dialogs on Mac.
useNative = ~( ismac && usejava('awt') ) ;
% If the root appdata is set and swing is available,
% honor that overriding all other prefs.
%if isequal(0, getappdata(0,'UseNativeSystemDialogs')) && isempty( javachk('swing') )
% useNative = false ;
%end
if useNative
try
if nargin == 0
[directoryname] = native_uigetdir ;
else
[directoryname] = native_uigetdir( varargin{:} ) ;
end
catch ex
rethrow(ex)
end
return
end % end useNative
%%%%%%%%%%%%%%%%%
% General globals
%%%%%%%%%%%%%%%%%
dirName = '' ;
userTitle = '' ;
% fileSeparator = filesep ;
% pathSeparator = pathsep ;
directoryname = '' ; %#ok
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case of exactly one argument.
% The argument must be a string specifying a directory.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( 1 <= numArgs )
dirName = varargin{ 1 } ;
% First arg must be a string
if ~( ischar( dirName ) ) || ~( isvector( dirName ) )
error( badStartPathMessage ) ;
end
if~( 1 == size( dirName , 1 ) )
dirName = dirName' ;
end
% If the string is not a directory name,
% dirName to the "base" directory. On
% Windows, it's the Windows Desktop dir.
% On UNIX systems, it's the MATLAB dir.
if ~( isdir( dirName ) )
if ispc
dirName = char( com.mathworks.hg.util.dFileChooser.getUserHome() ) ;
dirName = strcat( dirName , '\Desktop' ) ;
else
dirname = char( matlabroot ) ; %#ok
end
end
end % end if( 1 <= numArgs )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case of two args.
% The second arg must be a title string.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( 2 == numArgs )
% The 2nd arg must be a string
title = varargin{ 2 } ;
if~( ischar( title ) && isvector( title ) )
% Not a string
error( badTitleMessage ) ;
end
% Transpose if necessary
if( ~( 1 == size( title , 1 ) ) )
title = title' ;
end
userTitle = title ;
end % if( 2 == numArgs )
directoryname = 0 ; %#ok
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Build the dialog that holds our file chooser and add the chooser
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% jp = handle(javax.swing.JPanel) ;
jp = awtcreate('com.mathworks.mwswing.MJPanel', ...
'Ljava.awt.LayoutManager;', ...
java.awt.BorderLayout);
% Title is set later
d = mydialog( ...
'Visible','off', ...
'DockControls','off', ...
'Color',get(0,'DefaultUicontrolBackgroundColor'), ...
'Windowstyle','modal', ...
'Resize','on' ...
);
% Create a JPanel and put it into the dialog - this is for resizing
[panel, container] = javacomponent(jp,[10 10 20 20],d);
% Create a JFileChooser - 'false' means do not show as 'Save' dialog
sys = char( computer ) ;
stringSys = java.lang.String( sys ) ;
jfc = awtcreate('com.mathworks.hg.util.dFileChooser');
% Set the dialog's title
if ~( strcmp( userTitle , char('') ) )
set( d , 'Name' , userTitle ) ;
else
set( d , 'Name' , char( jfc.getDefaultGetdirTitle() ) )
end
awtinvoke( jfc , 'init(ZLjava/lang/String;)' , false , stringSys ) ;
%jfc.init( false , sys ) ;
awtinvoke( jfc , 'setFileSelectionMode(I)' , javax.swing.JFileChooser.DIRECTORIES_ONLY) ;
%jfc.setFileSelectionMode(javax.swing.JFileChooser.DIRECTORIES_ONLY) ;
% file = java.io.File(dirName) ;
if ~( strcmp( dirName , char('') ) )
awtinvoke( jfc , 'setCurrentDirectory(Ljava/io/File;)' , java.io.File(dirName) ) ;
%jfc.setCurrentDirectory( java.io.File(dirName) ) ;
end
awtinvoke( java(panel), 'add(Ljava.awt.Component;)', jfc );
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case of no args. In this case
% open in the user's current working dir.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( 0 == numArgs )
% Set the chooser's current directory
if ispc
awtinvoke( jfc , 'setCurrentDirectory(Ljava/io/File;)' , java.io.File(pwd) ) ;
% jfc.setCurrentDirectory( java.io.File(pwd) ) ;
else
awtinvoke( jfc , 'setCurrentDirectory(Ljava/io/File;)' , java.io.File(matlabroot) ) ;
%jfc.setCurrentDirectory( java.io.File(matlabroot) ) ;
end % end if( 0 == numArgs )
end % end if( 0 == numArgs )
set(container,'Units','normalized','Position',[0 0 1 1]);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Set up a callback to this MATLAB file and show the dialog
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
jfcHandle = handle(jfc , 'callbackproperties' );
set(jfcHandle,'PropertyChangeCallback',{ @callbackHandler , d })
figure(d)
refresh(d)
awtinvoke( jfc , 'listen()' ) ;
waitfor(d);
directoryname = 0 ;
% Get the data stored by the callback
if( isappdata( 0 , 'uigetdirData' ) )
directoryname = getappdata( 0 , 'uigetdirData' ) ;
rmappdata( 0 , 'uigetdirData' ) ;
end
function out = mydialog(varargin)
out = [];
try
out = dialog(varargin{:}) ;
catch ex
rethrow(ex)
end
end % end myDialog
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the callback from the JFileChooser. If the user
% selected "Open", return the name of the selected file,
% the full pathname and the index of the current filter.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function callbackHandler(obj , evd , d )
jfc = obj ;
directoryname = '' ; %#ok
cmd = char(evd.getPropertyName());
switch(cmd)
%case 'CancelSelection'
case 'mathworksHgCancel'
if ishandle(d)
directoryname = 0 ;
setappdata( 0 , 'uigetdirData' , directoryname ) ;
close(d) ;
end
case 'mathworksHgOk'
directoryname = char(jfc.getSelectedFile.toString) ;
setappdata( 0 , 'uigetdirData' , directoryname ) ;
close(d) ;
end % end switch
end % end callbackHandler
|
github
|
BottjerLab/Acoustic_Similarity-master
|
uisetfont_helper.m
|
.m
|
Acoustic_Similarity-master/code/interactive/private/uisetfont_helper.m
| 2,310 |
utf_8
|
6541f09d1a3b5296c8b737665e6b6beb
|
function fontstruct = uisetfont_helper(varargin)
% Copyright 2007-2008 The MathWorks, Inc.
% $Revision: 1.1.8.10 $ $Date: 2011/09/08 23:36:53 $
[fontstruct,title,fhandle] = parseArgs(varargin{:});
fcDialog = matlab.ui.internal.dialog.FontChooser;
fcDialog.Title = title;
if ~isempty(fontstruct)
fcDialog.InitialFont = fontstruct;
end
fontstruct = showDialog(fcDialog);
if ~isempty(fhandle)
setPointFontOnHandle(fhandle,fontstruct);
end
% Done. MCOS Object fcDialog cleans up and its java peer at the end of its
% scope(AbstractDialog has a destructor that every subclass
% inherits)
function [fontstruct,title,handle] = parseArgs(varargin)
handle = [];
fontstruct = [];
title = getString(message('MATLAB:uistring:uisetfont:TitleFont'));
if nargin>2
error(message('MATLAB:uisetfont:TooManyInputs')) ;
end
if (nargin==2)
if ~ischar(varargin{2})
error(message('MATLAB:uisetfont:InvalidTitleType'));
end
title = varargin{2};
end
if (nargin>=1)
if ishghandle(varargin{1})
handle = varargin{1};
fontstruct = getPointFontFromHandle(handle);
elseif isstruct(varargin{1})
fontstruct = varargin{1};
elseif ischar(varargin{1})
if (nargin > 1)
error(message('MATLAB:uisetfont:InvalidParameterList'));
end
title = varargin{1};
else
error(message('MATLAB:uisetfont:InvalidFirstParameter'));
end
end
%Given the dialog, user chooses to select or not select
function fontstruct = showDialog(fcDialog)
fcDialog.show;
fontstruct = fcDialog.SelectedFont;
if isempty(fontstruct)
fontstruct = 0;
end
%Helper functions to convert font sizes based on the font units of the
%handle object
function setPointFontOnHandle(fhandle,fontstruct)
tempunits = get(fhandle,'FontUnits');
try
set(fhandle,fontstruct);
catch ex %#ok<NASGU>
end
set(fhandle,'FontUnits',tempunits);
function fs = getPointFontFromHandle(fhandle)
tempunits = get(fhandle,'FontUnits');
set(fhandle, 'FontUnits', 'points');
fs = [];
try
fs.FontName = get(fhandle, 'FontName');
fs.FontWeight = get(fhandle, 'FontWeight');
fs.FontAngle = get(fhandle, 'FontAngle');
fs.FontUnits = get(fhandle, 'FontUnits');
fs.FontSize = get(fhandle, 'FontSize');
catch ex %#ok<NASGU>
end
set(fhandle, 'FontUnits', tempunits);
|
github
|
BottjerLab/Acoustic_Similarity-master
|
uisetfont_deprecated.m
|
.m
|
Acoustic_Similarity-master/code/interactive/private/uisetfont_deprecated.m
| 15,770 |
utf_8
|
beff9a38d9734c348ddc6ef7fc211731
|
function [fontStruct] = uisetfont_deprecated(varargin)
% Copyright 1984-2010 The MathWorks, Inc.
% $Revision: 1.1.8.6 $ $Date: 2010/07/02 16:17:24 $
%%%%%%%%%%%%%%%%
% Error Messages
%%%%%%%%%%%%%%%%
badNumArgsMessage = 'Too many input arguments.' ;
badObjTypeMessage1 = 'Font selection is not supported for ';
badObjTypeMessage2 = ' objects, but only for axes, text, and uicontrols.' ;
% badHandleMessage = 'Invalid object handle.';
badTitleLocMessage = 'title must be the last parameter passed to uisetfont.' ;
badParamMessage = 'Invalid first parameter - please check usage' ;
% badTitleMessage = 'Second argument (dialog title) must be a string.' ;
badFontsizeMessage = 'Font size must be an integer.'; %#ok
badFontSize = false;
maxArgs = 2 ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Check the number of args - must <= maxArgs
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
numArgs = nargin ;
if( numArgs > maxArgs )
error('MATLAB:uisetfont:TooManyInputs', badNumArgsMessage ) ;
end % end if( numArgs > maxArgs )
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Restrict new version to the mac
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% If we are on the mac, default to using non-native
% dialog. If the root property UseNativeSystemDialogs
% is false, use the non-native version instead.
useNative = true; %#ok<NASGU>
% If we are on the Mac & swing is available, set useNative to false,
% i.e., we are going to use Java dialogs not native dialogs.
% Comment the following line to disable java dialogs on Mac.
useNative = ~( isequal( 'MAC' , computer ) && usejava('awt') ) ;
% % If the root appdata is set and swing is available,
% % honor that overriding all other prefs.
% if isequal(0, getappdata(0,'UseNativeSystemDialogs')) && usejava('awt')
% useNative = false ;
% end
if useNative
try
if nargin == 0
[fontStruct] = native_uisetfont ;
else
[fontStruct] = native_uisetfont( varargin{:} ) ;
end
catch ex
rethrow(ex)
end
return
end % end useNative
%%%%%%%%%%%%%%%%%
% General Globals
%%%%%%%%%%%%%%%%%
arg1 = '' ;
arg2 = '' ; %#ok
jfc = '' ;
arg1Type = '' ;
suppliedType = '' ;
suppliedFieldNames = '' ;
title = '' ;
numberAppropriateFields = 0 ;
badType = 'badType';
structType = 'struct' ;
objectType = 'object' ;
% stringType = 'string' ;
allowedObjectTypes = { 'axes' ; 'text' ; 'uicontrol' } ;
structFields = { 'FontName' ;...
'FontUnits' ; ...
'FontSize' ; ...
'FontWeight' ; ...
'FontAngle' } ;
%%%%%%%
% Flags
%%%%%%%
arg1OK = false ;
% titleFound = false ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Default font values - used when there are 0 args
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
fontStruct.FontName = 'Arial' ;
fontStruct.FontUnits = 'points' ;
fontStruct.FontSize = 10 ;
fontStruct.FontWeight = 'normal' ;
fontStruct.FontAngle = 'normal' ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% The dialog that will hold our chooser
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
d = '' ; %#ok
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case of exactly one arg
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( numArgs == 1 )
arg1 = varargin{1} ;
% Check out arg1 - it should be a struct having some specific
% fields or a handle to an object of type text , uicontrol or axes.
% FOR COMPATIBILITY,
% It might also be a string we are going to use as a title.
if( ischar( arg1 ) && isvector( arg1 ) )
% Save dialog title
if( 1 == size( arg1 , 1 ) )
title = char( arg1 ) ;
else
title = char( arg1' ) ;
end
% Handle the case where arg1 is a struct or handle here
elseif isstruct(arg1) || (isscalar(arg1) && ishandle(arg1))
% Process arg1 - return if there's an error
checkArg1() ;
% Return if there's an error with an object (illegal type)
% No errors are returned if a struct has been handed in
if( ~arg1OK || ( strcmp( suppliedType , badType ) ) )
if (badFontSize)
error('MATLAB:uisetfont:InvalidParameter', badFontsizeMessage);
else
if ~( strcmp( suppliedType , char('') ) )
error('MATLAB:uisetfont:InvalidObjectType', [ badObjTypeMessage1 suppliedType badObjTypeMessage2 ] ) ;
else
error('MATLAB:uisetfont:InvalidParameter', badParamMessage );
end
end
end % if( ~arg1OK & ( strcmp( arg1Type , objectType ) ) )
% Handle error of param type here
else
error( badParamMessage ) ;
end % end if/elseif/else
end % if( numArgs == 1 )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case of exactly two args
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( numArgs == 2 )
arg1 = varargin{1} ;
arg2 = varargin{2} ;
if ~( isstruct( arg1 ) || ishandle( arg1 ) )
error('MATLAB:uisetfont:InvalidParameter', badParamMessage ) ;
end
% The only "legit" combination here is to have
% the first arg be a struct or a handle and to
% have the second arg be a string (title).
if( ~( ischar( arg2 ) && isvector( arg2 ) ) )
error('MATLAB:uisetfont:TitleMustBeLastInput', badTitleLocMessage ) ;
end % end if ...
checkArg1() ;
% Return if arg1 is a handle to a type we don't support
if( ~arg1OK || ( strcmp( arg1Type , objectType ) ) )
if ~( strcmp( suppliedType , char('') ) )
error('MATLAB:uisetfont:InvalidObjectType', [ badObjTypeMessage1 suppliedType badObjTypeMessage2 ] ) ;
else
if (badFontSize)
error('MATLAB:uisetfont:InvalidParameter', badFontsizeMessage);
else
error('MATLAB:uisetfont:InvalidParameter', badParamMessage )
end
end
end % if( ~arg1OK & ( strcmp( arg1Type , objectType ) ) )
% Set op the title for in the dialog
if( 1 == size( arg2 , 1 ) )
title = char( arg2 ) ;
else
title = char( arg2' ) ;
end
end % end if( numArgs == 2 )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% OK - set up and display the dialog.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Dialog title is set later
d = mydialog( ...
'Visible','off', ...
'DockControls','off', ...
'Color',get(0,'DefaultUicontrolBackgroundColor'), ...
'Windowstyle','modal', ...
'Resize','on' ...
);
set( d , 'Position' , [232 246 345 390] ) ;
% Create a chooser (JPanel) and put it into the dialog - this is for resizing
sys = char( computer ) ;
lf = listfonts;
fonts = javaArray('java.lang.String', length(lf));
for i=1:length(lf) %#ok<FXUP>
fonts(i) = java.lang.String(lf{i});
end
jfc = awtcreate('com.mathworks.hg.util.FontChooser', ...
'[Ljava/lang/String;Ljava/lang/String;', ...
fonts , sys );
[jfc,container] = javacomponent( jfc,[10 10 20 20],d );
% Use supplied title if one was given else use default
if ~( strcmp( '' , title ) )
set( d , 'Name' , title ) ;
else
set( d , 'Name' , char( jfc.getDefaultTitle() ) )
end
% Initialize the font chooser using the fontStruct struct
initFontChooser() ;
awtinvoke( java(jfc), 'setUpAttributes()');
awtinvoke( java(jfc), 'updatePreviewFont()');
% Add some control buttons
jbSet = handle( awtcreate('com.mathworks.mwswing.MJButton', 'Ljava/lang/String;', 'OK'), ...
'callbackproperties' ) ;
jbCancel = handle( awtcreate('com.mathworks.mwswing.MJButton', 'Ljava/lang/String;', 'Cancel'), ...
'callbackproperties' ) ;
buttonPanel = handle( awtcreate('com.mathworks.mwswing.MJPanel') ) ;
awtinvoke(java(buttonPanel), 'add(Ljava.awt.Component;)', java(jbSet)) ;
awtinvoke(java(buttonPanel), 'add(Ljava.awt.Component;)', java(jbCancel)) ;
awtinvoke( java(jfc), 'add(Ljava.awt.Component;Ljava.lang.Object;)',...
java(buttonPanel), java.awt.BorderLayout.SOUTH ) ;
set(container,'Units','normalized','Position',[0 0 1 1]);
set(jbSet,'ActionPerformedCallback', {@callbackHandler, jfc , d , arg1})
set(jbCancel,'ActionPerformedCallback', {@callbackHandler, jfc , d , arg1})
% Go
fontStruct = 0 ;
figure(d)
refresh(d)
waitfor(d);
if isappdata( 0 , 'uisetfontData' )
fontStruct = getappdata( 0 , 'uisetfontData' ) ;
rmappdata( 0 , 'uisetfontData' ) ;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Error check the first arg. As a side effect, if
% arg1 is a struct, return the set of field names
% matching the "font-related set" ( 'FontName', 'FontSize', ... )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function checkArg1()
% Cover the case where arg1 is a handle
if( isscalar(arg1) && ishandle( arg1 ) )
suppliedType = '' ;
try
%arg1Type = objectType ;
suppliedType = get( arg1 , 'Type' ) ;
catch
arg1OK = false ;
suppliedType = badType ;
return ;
end
% Check type of supplied obj against allowed obj types (axes,text,...)
len = size( allowedObjectTypes , 1 ) ;
for i = 1 : len %#ok<FXUP>
if( strcmp( allowedObjectTypes( i , 1 ) , suppliedType ) )
arg1OK = true ;
return ;
end % end if( strcmp( allowedObjectTypes( i , 1 ) , arg1 ) )
end % end for i = 1 : len
% Bad object type
arg1OK = false ;
return ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% End processing for handle
% Do the case where arg1 is a struct
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
elseif( isstruct( arg1 ) )
arg1Type = structType ;
% Check the structure to see if has font-related fields
len = size( structFields , 1 );
numberAppropriateFields = 0 ;
for j = 1 : len
% Get next font-related field name
fieldName = char( structFields( j , 1 ) ) ;
% If arg1 has a field of this name, store the field name
if ( isfield( arg1 , fieldName ) )
if (strcmp(fieldName, 'FontSize'))
fieldVal = arg1.(fieldName);
% FontSize should be a number.
if ~isnumeric(fieldVal)
badFontSize = true;
arg1OK = false;
break;
end
end
arg1OK = true ;
numberAppropriateFields = numberAppropriateFields + 1 ;
suppliedFieldNames{ 1 , numberAppropriateFields } = char( fieldName ) ; %#ok<AGROW>
end % end if j = 1 : size( structFields , 1 )
end % end for
else
arg1OK = false ;
end % end if/else
end % end function checkArg1()
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Initialize the FontChooser with values given by the user or defaults
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function initFontChooser()
% If arg1 is a handle to an object, get the font values of the object
if( ishandle( arg1 ) )
%%%%%%%%%%%%%%%%%%%%??????? should be char( get( arg1 , 'FontName' )
%%%%%%%%%%%%%%%%%%%%)???????????????????????????????????????????????
fontStruct.FontName = get( arg1 , 'FontName' ) ;
fontStruct.FontUnits = get( arg1 , 'FontUnits' ) ;
fontStruct.FontSize = get( arg1 , 'FontSize' ) ;
fontStruct.FontWeight = get( arg1 , 'FontWeight' ) ;
fontStruct.FontAngle = get( arg1 , 'FontAngle' ) ;
end % end if( ishandle( arg1 ) )
% If arg1 is a struct, use it's font-related field values
if( isstruct( arg1 ) )
if( numberAppropriateFields > 0 )
% Get vals and set fields of the fontStruct structure
for i = 1 : numberAppropriateFields %#ok<FXUP>
fieldName = char( suppliedFieldNames( 1 , i )) ;
val = arg1.(fieldName);
fontStruct.(fieldName) = val ;
end % for i = 1 : numberAppropriateFields
end % if( numberAppropriateFields > 0 )
end % if( isStruct( arg1 ) )
%disp( fontStruct ) ;
% Now actually set the selections in the FileChooser
awtinvoke(java(jfc), 'selectFontName(Ljava/lang/String;)', fontStruct.('FontName')) ;
awtinvoke(java(jfc), 'selectFontSize(I)', fontStruct.('FontSize' )) ;
% fontAngle = '' ;
% fontWeight = '' ;
fw = fontStruct.('FontWeight') ;
fontStyle = 'Regular' ;
fa = fontStruct.('FontAngle');
if( strcmpi( fw , 'bold' ) )
if( strcmpi( fa , 'italic' ) )
fontStyle = 'Bold Italic';
else
fontStyle = 'Bold';
end
else
if( strcmpi( fa , 'italic' ) )
fontStyle = 'Italic';
end
end % end if( strcmp( fw , 'bold' )
awtinvoke(java(jfc), 'selectFontStyle(Ljava/lang/String;)', char(fontStyle) ) ;
jfc.addSampleTextActionListeners();
awtinvoke( java(jfc), 'setUpAttributes()');
awtinvoke( java(jfc), 'updatePreviewFont()');
end % end function initFontChooser
function out = mydialog(varargin)
out = [];
try
out = dialog(varargin{:}) ;
catch ex
rethrow(ex)
end
end % end myDialog
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% The callback from the chooser
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function callbackHandler( obj , evd , jfc , d , arg1 ) %#ok
cmd = char(evd.getActionCommand());
switch(cmd)
case 'Cancel'
if ishandle(d)
close(d)
end
case 'OK'
% Set up the output variable
fontStruct.FontName = char( jfc.getFontName() ) ;
fontStruct.FontSize = jfc.getFontSize() ;
fontStruct.FontWeight = char( jfc.getFontWeight() ) ;
fontStruct.FontAngle = char( jfc.getFontAngle() ) ;
setappdata( 0 , 'uisetfontData' , fontStruct ) ;
% If necessary, set the input obj's properties
if( isscalar(arg1) && ishandle( arg1 ) )
set( arg1 , 'FontName' , char( jfc.getFontName() ) ) ;
set( arg1 , 'FontSize' , jfc.getFontSize() ) ;
set( arg1 , 'FontWeight' , char( jfc.getFontWeight() ) ) ;
set( arg1 , 'FontAngle' , char( jfc.getFontAngle() ) ) ;
end % end if( ishandle( arg1 ) )
if ishandle(d)
close(d)
end
otherwise
disp([cmd ' Unimplemented'])
end % end switch
end % end callbackHandler
|
github
|
BottjerLab/Acoustic_Similarity-master
|
uiputfile_deprecated.m
|
.m
|
Acoustic_Similarity-master/code/interactive/private/uiputfile_deprecated.m
| 41,884 |
utf_8
|
c7e9f70a660e10105e4c07ebca2d2900
|
function [filename, pathname, filterindex] = uiputfile_deprecated(varargin)
% $Revision: 1.1.6.11 $ $Date: 2011/09/23 19:13:36 $
% Copyright 2006-2011 The MathWorks, Inc.
%UIPUTFILE Standard save file dialog box.
% [FILENAME, PATHNAME, FILTERINDEX] = UIPUTFILE(FILTERSPEC, TITLE)
% displays a dialog box for the user to fill in and returns the
% filename and path strings and the index of the selected filter.
% A successful return occurs if a valid filename is specified. If a
% exisiting filename is specified or selected, a warning message is
% displayed. The user may select Yes to use the filename or No to
% return to the dialog to select another filename.
%
% The FILTERSPEC parameter determines the initial display of files in
% the dialog box. For example '*.m' lists all MATLAB code files. If
% FILTERSPEC is a cell array, the first column is used as the list of
% extensions, and the second column is used as the list of descriptions.
%
% When FILTERSPEC is a string or a cell array, "All files" is appended
% to the list.
%
% When FILTERSPEC is empty the default list of file types is used.
%
% When FILTERSPEC is a filename, it is used as the default filename and
% the file's extension is used as the default filter.
%
% Parameter TITLE is a string containing the title of the dialog
% box.
%
% The output variable FILENAME is a string containing the name of the file
% selected in the dialog box. If the user presses Cancel, it is set to 0.
%
% The output variable PATH is a string containing the name of the path
% selected in the dialog box. If the user presses Cancel, it is set to 0.
%
% The output variable FILTERINDEX returns the index of the filter selected
% in the dialog box. The indexing starts at 1. If the user presses Cancel,
% it is set to 0.
%
% [FILENAME, PATHNAME, FILTERINDEX] = UIPUTFILE(FILTERSPEC, TITLE, FILE)
% FILE is a string containing the name to use as the default selection.
%
% [FILENAME, PATHNAME] = UIPUTFILE(..., 'Location', [X Y])
% places the dialog box at screen position [X,Y] in pixel units.
% This option is supported on UNIX platforms only.
%
% [FILENAME, PATHNAME] = UIPUTFILE(..., X, Y)
% places the dialog box at screen position [X,Y] in pixel units.
% This option is supported on UNIX platforms only.
% THIS SYNTAX IS OBSOLETE AND WILL BE REMOVED. PLEASE USE THE FOLLOWING
% SYNTAX INSTEAD:
% [FILENAME, PATHNAME] = UIPUTFILE(..., 'Location', [X Y])
%
%
% Examples:
%
% [filename, pathname] = uiputfile('matlab.mat', 'Save Workspace as');
%
% [filename, pathname] = uiputfile('*.mat', 'Save Workspace as');
%
% [filename, pathname, filterindex] = uiputfile( ...
% {'*.m;*.fig;*.mat;*.mdl', 'All MATLAB Files (*.m, *.fig, *.mat, *.mdl)';
% '*.m', 'MATLAB Code (*.m)'; ...
% '*.fig','Figures (*.fig)'; ...
% '*.mat','MAT-files (*.mat)'; ...
% '*.mdl','Models (*.mdl)'; ...
% '*.*', 'All Files (*.*)'}, ...
% 'Save as');
%
% [filename, pathname, filterindex] = uiputfile( ...
% {'*.mat','MAT-files (*.mat)'; ...
% '*.mdl','Models (*.mdl)'; ...
% '*.*', 'All Files (*.*)'}, ...
% 'Save as', 'Untitled.mat');
%
% Note, multiple extensions with no descriptions must be separated by semi-
% colons.
%
% [filename, pathname] = uiputfile( ...
% {'*.m';'*.mdl';'*.mat';'*.*'}, ...
% 'Save as');
%
% Associate multiple extensions with one description like this:
%
% [filename, pathname] = uiputfile( ...
% {'*.m;*.fig;*.mat;*.mdl', 'All MATLAB Files (*.m, *.fig, *.mat, *.mdl)'; ...
% '*.*', 'All Files (*.*)'}, ...
% 'Save as');
%
% This code checks if the user pressed cancel on the dialog.
%
% [filename, pathname] = uiputfile('*.m', 'Pick a MATLAB code file');
% if isequal(filename,0) || isequal(pathname,0)
% disp('User pressed cancel')
% else
% disp(['User selected ', fullfile(pathname, filename)])
% end
%
%
% See also UIGETDIR, UIGETFILE.
% Copyright 1984-2005 The MathWorks, Inc.
% $Revision: 1.1.6.11 $ $Date: 2011/09/23 19:13:36 $
% Built-in function.
%%%%%%%%%%%%%%%%
% Error messages
%%%%%%%%%%%%%%%%
badLocMessage = 'The Location parameter value must be a 2 element vector.' ;
% badMultiMessage = 'The MultiSelect parameter value must be a string specifying on/off.' ;
% badFirstMessage = 'Ill formed first argument to uigetfile' ;
badArgsMessage = 'Unrecognized input arguments.' ;
bad2ndArgMessage = 'Expecting a string as 2nd arg' ;
bad3rdArgMessage = 'Expecting a string as 3rd arg' ;
badFilterMessage = 'FILTERSPEC argument must be a string or an M by 1 or M by 2 cell array.' ;
badNumArgsMessage = 'Too many input arguments.' ;
badLastArgsMessage = 'MultiSelect and Location args must be the last args' ;
badMultiPosMessage = '''MultiSelect'' , ''on/off'' can only be followed by Location args' ;
badLocationPosMessage = '''Location'' , [ x y ] can only be followed by MultiSelect args' ;
caErr1Message = 'Illegal filespec';
caErr2Message = 'Illegal filespec - can have at most two cols';
caErr3Message = 'Illegal file extension - ''' ;
maxArgs = 5 ;
numArgs = nargin ; %#ok
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% check the number of args - must be <= maxArgs
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
numArgs = nargin ;
if( numArgs > maxArgs )
error( badNumArgsMessage ) ;
end
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Restrict new version to the mac
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% If we are on the mac, default to using non-native
% dialog. If the root property UseNativeSystemDialogs
% is false, use the non-native version instead.
useNative = true; %#ok
% If we are on the Mac & swing is available, set useNative to false,
% i.e., we are going to use Java dialogs not native dialogs.
% Comment the following line to disable java dialogs on Mac.
useNative = ~( ismac && usejava('awt') ) ;
% If the root appdata is set and swing is available,
% honor that overriding all other prefs.
%if isequal(0, getappdata(0,'UseNativeSystemDialogs')) && isempty( javachk('swing') )
% useNative = false ;
%end
if useNative
try
if nargin == 0
[filename, pathname, filterindex] = native_uiputfile ;
else
[filename, pathname, filterindex] = native_uiputfile( varargin{:} ) ;
end
catch ex
rethrow(ex)
end
return
end % end useNative
%%%%%%%%%%%%%%%%%
% General globals
%%%%%%%%%%%%%%%%%
selectedName = '' ;
% multiPosition = '' ;
locationPosition = '' ;
fileSeparator = filesep ; %#ok
%pathSeparator = pathsep ;
remainderArgs = '' ;
newFilter = '' ;
locationPosition = '' ; % position of 'Location' arg
multiSelectPosition = '' ; % position of 'MultiSelect' arg
extError = 'mExtError' ;
theError = '' ;
%%%%%%%
% Flags
%%%%%%%
filespecError = false ;
allFound = false ;
cellArrayOk = false ;
locationError = false ;
multiSelectOn = false ;
multiSelectError = false ;
%argUsedAsFileName = false ;
multiSelectFound = false ;
locationFound = false ;
%%%%%%%%%%%%%%%%%%%%%
% Filter descriptions
%%%%%%%%%%%%%%%%%%%%%
allMDesc = 'All MATLAB Files' ;
mDesc = 'MATLAB Code (*.m)' ;
figDesc = 'Figures (*.fig)' ;
matDesc = 'MAT-files (*.mat)' ;
simDesc = 'Simulink Models (*.mdl,*.slx)' ;
staDesc = 'Stateflow Files (*.cdr)' ;
wksDesc = 'Code generation files (*.rtw,*.tmf,*.tlc,*.c,*.h)' ;
rptDesc = 'Report Generator Files (*.rpt)' ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Extension "specs" for our default filters
% These strings are used by filters to match file extensions
% NOTE that they do NOT contian a '.' character
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
mSpec = 'm' ;
figSpec = 'fig' ;
matSpec = 'mat' ;
simSpec = 'mdl' ;
sim2Spec = 'slx';
staSpec = 'cdr' ;
rtwSpec = 'rtw' ;
tmfSpec = 'tmf' ;
tlcSpec = 'tlc' ;
rptSpec = 'rpt' ;
cSpec = 'c' ;
hSpec = 'h' ;
% allSpec = 'all' ;
%%%%%%%%%%%%%%%%%
% Default filters
%%%%%%%%%%%%%%%%%
% A filter for all Matlab files
allMatlabFilter = '' ;
% A filter for .m files
mFilter = '' ;
% A filter for .fig files
figFilter = '' ;
% A filter for .mat files
matFilter = '' ;
% A filter for Simulink files - .mdl
simFilter = '' ;
% A filter for Stateflow files - .cdr
staFilter = '' ;
% A filter for Code generation files - .rtw, .tmf, .tlc, .c, .h
wksFilter = '' ;
% A filter for Report Generator files - .rpt
rptFilter = '' ;
% A filter for all files - *.*
allFilter = '' ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% The dialog that holds our file chooser
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% jp = handle(javax.swing.JPanel) ;
jp = awtcreate('com.mathworks.mwswing.MJPanel', ...
'Ljava.awt.LayoutManager;', ...
java.awt.BorderLayout);
d = mydialog( ...
'Visible','off', ...
'DockControls','off', ...
'Color',get(0,'DefaultUicontrolBackgroundColor'), ...
'Windowstyle','modal', ...
'Resize','on' ...
);
% Create a JPanel and put it into the dialog - this is for resizing
[panel, container] = javacomponent(jp,[10 10 20 20],d);
% Create a JFileChooser
sys = char( computer ) ;
jfc = awtcreate('com.mathworks.hg.util.dFileChooser');
set( d , 'Name' , char( jfc.getDefaultPutfileTitle() ) )
awtinvoke( jfc , 'init(ZLjava.lang/String;)' , true , java.lang.String(sys) ) ;
%jfc.init( true , sys ) ;
% We're going to use our own "all" file filter
awtinvoke( jfc , 'setAcceptAllFileFilterUsed(Z)' , false ) ;
%jfc.setAcceptAllFileFilterUsed( false ) ;
% Set the chooser's current directory
awtinvoke( jfc , 'setCurrentDirectory(Ljava/io/File;)' , java.io.File(pwd) ) ;
%jfc.setCurrentDirectory( java.io.File(pwd) ) ;
% Make sure multi select is initially disabled
awtinvoke( jfc , 'setMultiSelectionEnabled(Z)' , false ) ;
awtinvoke( jfc , 'setDialogType(I)' , javax.swing.JFileChooser.SAVE_DIALOG ) ;
awtinvoke( java(panel), 'add(Ljava.awt.Component;)', jfc );
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Eliminate "built-in" args such as 'multiselect', 'location' and
% their values. As a side effect, create the array remainderArgs.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
eliminateBuiltIns() ;
% Exit if there was an error with MultiSelect or Location
if( multiSelectError || locationError )
error( theError ) ;
end
if( locationFound )
warning(message('MATLAB:UIPUTFILE:LocationIgnore'));
end
% Check to see that there are no extra args.
% 'MultiSelect', 'location' and their values
% must be the last args.
if( multiSelectFound && ~locationFound )
if( ~( multiSelectPosition == ( numArgs - 1 ) ) )
error( badMultiPosMessage ) ;
end % end if( ~( multiSelectPosition ...
end % end if( multiSelectFound )
if( locationFound && ~multiSelectFound )
if( ~( locationPosition == ( numArgs - 1 ) ) )
error( badLocationPosMessage ) ;
end % end if( ~( locationPosition ...
end % end if( locationFound )
if( multiSelectFound && locationFound )
if( ( ~( ( numArgs - 1 ) == multiSelectPosition ) && ~( ( numArgs - 3 ) == multiSelectPosition ) ) || ...
( ~( ( numArgs - 1 ) == locationPosition ) && ~( ( numArgs - 3 ) == locationPosition ) ) )
error( badLastArgsMessage ) ;
end
end % end if( multiSelectFound & locationFound )
% Set the chooser to multi select if required
if( multiSelectOn )
awtinvoke( jfc , 'setMultiSelectionEnabled(Z)' , true ) ;
% jfc.setMultiSelectionEnabled( true ) ;
end
% Reset the content of varargin & numArgs
varargin = remainderArgs ;
numArgs = numel( remainderArgs ) ;
% At this point we can have at most 3 remaining args
if( numArgs > 3 )
error( badArgsMessage )
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case of no args. In this case
% we load and use the default filters.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( 0 == numArgs )
buildDefaultFilters() ;
% Load our filters into the JFileChooser
loadDefaultFilters( jfc , 1 ) ;
% Set our "allMatlabFilter" to be the active filter
awtinvoke( jfc , 'setFileFilter(Ljavax/swing/filechooser/FileFilter;)' , allMatlabFilter ) ;
end % end if( 0 == numArgs )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case of exactly one remaining arg.
% The argument must be a string or a cell array.
%
% If it's a cell array we try to use it as a filespec.
%
% If it's a string, there are 2 options:
%
% If it's a legit description of a file ext, we use it
% to create a filter and a description. We then add
% that filter and the "all" filter to the file chooser.
% Example - '*.txt' or '.txt'
%
% FOR COMPATIBILITY,
% if it's not an ext we understand, we use it as a
% "selected file name." We then set up the file chooser
% to use our default filters including the "all" filter.
% Example - 'x'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( 1 == numArgs )
spec = varargin{ 1 } ;
if ~( ischar( spec ) ) && ~( iscellstr( spec ) )
error( badFilterMessage );
end
% OK - the type is correct
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case where the arg is a string
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( ischar( spec ) && isvector( spec ) )
if( ~( 1 == size( spec , 1 ) ) )
spec = spec' ;
end
handleStringFilespec( jfc , spec , 1 ) ;
if( filespecError )
error( theError ) ;
end
end % end if( ischar( spec ) ) ...
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case where the arg is a cell array
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( iscellstr( spec ) )
cellArrayOk = false ;
handleCellArrayFilespec( spec , jfc ) ;
if ~cellArrayOk
error( theError ) ;
end
end % if( iscellstr( spec ) )
end % end if( 1 == numArgs )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case of two remaining args.
%
% If arg 1 is a good filespec, use it
% and interpret the 2nd arg as a title.
%
% FOR COMPATIBILITY,
% if arg1 is a string but not a legit
% filespec, we use it as a file name
% and interpret the 2nd arg as a title.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( 2 == numArgs )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Error check the types of the args.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% The 2nd arg must be a string
arg2 = varargin{ 2 } ;
if~( ischar( arg2 ) && isvector( arg2 ) )
% Not a string
error( bad2ndArgMessage ) ;
end
% Transpose if necessary
if( ~( 1 == size( arg2 , 1 ) ) )
arg2 = arg2' ;
end
% Check out the first arg
spec = varargin{ 1 } ;
if ~( ischar( spec ) ) && ~( iscellstr( spec ) )
error( badFilterMessage );
end
% OK - the types are correct
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case where the 1st arg is a string
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( ischar( spec ) && isvector( spec ) )
if( ~( 1 == size( spec , 1 ) ) )
spec = spec' ;
end
% See if the 1st arg is a legit extension
extStr = char(com.mathworks.hg.util.dFilter.returnExtensionString( spec )) ;
if( strcmpi( extStr , extError ) )
% The 1st arg is a NOT legit extension.
% Use first arg as file name and 2nd arg as title
set( d , 'Name' , arg2 ) ;
awtinvoke( jfc , 'setSelectedFile(Ljava/io/File;)' , java.io.File( spec ) ) ;
%jfc.setSelectedFile( java.io.File( spec ) ) ;
% Load and use the default filters
buildDefaultFilters() ;
loadDefaultFilters( jfc , 1 ) ;
else
% The 1st arg IS a legit extension.
% Use arg2 as a title string.
handleStringFilespec( jfc , spec , '1' );
if( filespecError )
error( theError ) ;
end
set( d , 'Name' , arg2 ) ;
end % if( ' ' == extStr )
else
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case where first arg is a cell array
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
handleCellArrayFilespec( spec , jfc ) ;
if( ~cellArrayOk )
error( theError ) ;
end
set( d , 'Name' , arg2 ) ;
end %if( ischar( spec ) & isvector( spec ) )
end % if( 2 == numArgs )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case of three remaining arguments.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( 3 == numArgs )
% The 2nd and 3rd args must be strings
arg2 = varargin{ 2 } ;
if~( ischar( arg2 ) && isvector( arg2 ) )
% Not a string
error( bad2ndArgMessage )
end
if( ~( 1 == size( arg2 , 1 ) ) )
arg2 = arg2' ;
end
arg3 = varargin{ 3 } ;
if~( ischar( arg3 ) && isvector( arg3 ) )
% Not a string
error( bad3rdArgMessage )
end
if( ~( 1 == size( arg3 , 1 ) ) )
arg3 = arg3' ;
end
% Use arg2 as title and arg3 as file
set( d , 'Name' , arg2 ) ;
awtinvoke( jfc , 'setSelectedFile(Ljava.io.File;)' , java.io.File( char(arg3) ) ) ;
% jfc.setSelectedFile( java.io.File( char(arg3) ) ) ;
selectedName = char( arg3 ) ;
spec = varargin{ 1 } ;
% The first arg must be a string or cell array
if ~( ischar( spec ) ) && ~( iscellstr( spec ) )
error( badFilterMessage );
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the case where the 1st arg is a string
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if( ischar( spec ) && isvector( spec ) )
if( ~( 1 == size( spec , 1 ) ) )
spec = spec' ;
end
% See if the 1st arg is a legit extension
extStr = com.mathworks.hg.util.dFilter.returnExtensionString( spec ) ;
if( strcmpi( extStr , extError ) )
% The 1st arg is a NOT legit extension.
% Ignore it FOR COMPATIBILITY WITH EXISTING RELEASE.
buildDefaultFilters() ;
loadDefaultFilters( jfc , 1 ) ;
else
% The 1st arg IS legit extension.
handleStringFilespec( jfc , spec , '1' ) ;
if( filespecError )
error( theError ) ;
end
end % if( ' ' == extStr )
else
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle case where 1st arg is a cell array
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
handleCellArrayFilespec( spec , jfc ) ;
if( ~cellArrayOk )
error( theError ) ;
end
end % if( ischar( spec ) & isvector( spec ) )
end % if( 3 == numargs )
if( ~strcmp( selectedName , char('') ) )
awtinvoke( jfc , 'noteName(Ljava/lang/String;)' , java.lang.String( selectedName ) ) ;
end
set(container,'Units','normalized','Position',[0 0 1 1]);
jfcHandle = handle(jfc , 'callbackproperties' );
set(jfcHandle,'PropertyChangeCallback', { @callbackHandler , d } );
figure(d)
refresh(d)
% these will get set by data from the callback
filename = 0 ;
pathname = 0 ;
filterindex = 0 ;
awtinvoke( jfc , 'listen()' ) ;
waitfor(d);
% Retrieve the data stored by the callback
if isappdata( 0 , 'uiputfileData' )
uiputfileData = getappdata( 0 , 'uiputfileData' ) ;
filename = uiputfileData.filename ;
pathname = uiputfileData.pathname ;
filterindex = uiputfileData.filterindex ;
rmappdata( 0 , 'uiputfileData' ) ;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Build all the default filters. Each filter handles
% one or more extension. Each filter also has a
% description string which appears in the file selection
% dialog. Each filter can also be assigned a string "id."
% Filters are Java objects.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function buildDefaultFilters()
allMatlabFilter = com.mathworks.hg.util.dFilter ;
allMatlabFilter.setDescription( allMDesc ) ;
allMatlabFilter.addExtension( mSpec ) ;
allMatlabFilter.addExtension( figSpec ) ;
allMatlabFilter.addExtension( matSpec ) ;
% Filter for files
mFilter = com.mathworks.hg.util.dFilter ;
mFilter.setDescription( mDesc ) ;
mFilter.addExtension( mSpec ) ;
% Filter for .fig files
figFilter = com.mathworks.hg.util.dFilter ;
figFilter.setDescription( figDesc ) ;
figFilter.addExtension( figSpec ) ;
% Filter for MAT-files
matFilter = com.mathworks.hg.util.dFilter ;
matFilter.setDescription( matDesc ) ;
matFilter.addExtension( matSpec ) ;
% Filter for Simulink Models
simFilter = com.mathworks.hg.util.dFilter ;
simFilter.setDescription( simDesc ) ;
simFilter.addExtension( simSpec ) ;
simFilter.addExtension( sim2Spec ) ;
% Filter for Stateflow files
staFilter = com.mathworks.hg.util.dFilter ;
staFilter.setDescription( staDesc ) ;
staFilter.addExtension( staSpec ) ;
% Filter for Real-Time Workshop files
wksFilter = com.mathworks.hg.util.dFilter ;
wksFilter.setDescription( wksDesc ) ;
wksFilter.addExtension( rtwSpec ) ;
wksFilter.addExtension( tmfSpec ) ;
wksFilter.addExtension( tlcSpec ) ;
wksFilter.addExtension( cSpec ) ;
wksFilter.addExtension( hSpec ) ;
% Filter for Report Generator files
rptFilter = com.mathworks.hg.util.dFilter ;
rptFilter.setDescription( rptDesc ) ;
rptFilter.addExtension( rptSpec ) ;
% Filter for "All Files"
allFilter = com.mathworks.hg.util.AllFileFilter ;
end % end buildDefaultFilters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Load all the default filters into the jFileChooser.
% Give each filter an id starting at "startId." We'll
% later use the id to determine which filter was active
% when the user made the selection.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function loadDefaultFilters( chooser , startId )
j = startId ;
allMatlabFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax.swing.filechooser.FileFilter;)' , allMatlabFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax.swing.filechooser.FileFilter;)' , allMatlabFilter ) ;
% chooser.addFileFilter( allMatlabFilter ) ;
% chooser.noteFilter( allMatlabFilter ) ;
j = j + 1 ;
mFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax.swing.filechooser.FileFilter;)' , mFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax.swing.filechooser.FileFilter;)' , mFilter ) ;
% chooser.addFileFilter( mFilter ) ;
% chooser.noteFilter( mFilter ) ;
j = j + 1 ;
figFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax.swing.filechooser.FileFilter;)' , figFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax.swing.filechooser.FileFilter;)' , figFilter ) ;
% chooser.addFileFilter( figFilter ) ;
% chooser.noteFilter( figFilter ) ;
j = j + 1 ;
matFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax.swing.filechooser.FileFilter;)' , matFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax.swing.filechooser.FileFilter;)' , matFilter ) ;
% chooser.addFileFilter( matFilter ) ;
% chooser.noteFilter( matFilter ) ;
j = j + 1 ;
simFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax.swing.filechooser.FileFilter;)' , simFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax.swing.filechooser.FileFilter;)' , simFilter ) ;
% chooser.addFileFilter( simFilter ) ;
% chooser.noteFilter( simFilter ) ;
j = j + 1 ;
staFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax.swing.filechooser.FileFilter;)' , staFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax.swing.filechooser.FileFilter;)' , staFilter ) ;
% chooser.addFileFilter( staFilter ) ;
% chooser.noteFilter( staFilter ) ;
j = j + 1 ;
wksFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax.swing.filechooser.FileFilter;)' , wksFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax.swing.filechooser.FileFilter;)' , wksFilter ) ;
% chooser.addFileFilter( wksFilter ) ;
% chooser.noteFilter( wksFilter ) ;
j = j + 1 ;
rptFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax.swing.filechooser.FileFilter;)' , rptFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax.swing.filechooser.FileFilter;)' , rptFilter ) ;
% chooser.addFileFilter( rptFilter ) ;
% chooser.noteFilter( rptFilter ) ;
j = j + 1 ;
allFilter.setIdentifier( int2str( j ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax.swing.filechooser.FileFilter;)' , allFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax.swing.filechooser.FileFilter;)' , allFilter ) ;
% chooser.addFileFilter( allFilter )
% chooser.noteFilter( allFilter ) ;
% We shouldn't need the pause or the following drawnow -
% But it doesn't work without them
pause(.5) ;
awtinvoke( chooser, 'setFileFilter(Ljavax/swing/filechooser/FileFilter;)' , allMatlabFilter ) ;
drawnow() ;
end % end loadDefaultFilters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Build a new filter containing an extension and a description
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function buildFilter( desc , ext )
newFilter = com.mathworks.hg.util.dFilter ;
newFilter.setDescription( desc ) ;
newFilter.addExtension( ext ) ;
end % buildFilter
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Add a filter to the indicated JFileChooser
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function addFilterToChooser( chooser , filter )
awtinvoke( chooser , 'addFileFilter(Ljavax.swing.filechooser.FileFilter;)' , filter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax.swing.filechooser.FileFilter;)' , filter ) ;
% chooser.addFileFilter( filter ) ;
% chooser.noteFilter( filter ) ;
end % end addFilterToChooser
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Set the indicated filter's identifier (must be a string)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function setFilterIdentifier( filter , id )
filter.setIdentifier( id ) ;
end % setFilterIdentifier
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Build a filter that "accepts" all files
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function buildAllFilter()
allFilter = com.mathworks.hg.util.AllFileFilter ;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This handles the case where the filespec is a string
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function handleStringFilespec( chooser , str , id )
%argUsedAsFileName = false ;
extStr = char(com.mathworks.hg.util.dFilter.returnExtensionString( str )) ;
if( strcmpi( extStr , extError ) )
% This isn't a "legal" extension we know about.
% Treat it as the name of a file for COMPATIBILITY
% with the current release.
% test = java.io.File( str ) ;
%if( test.isFile() )
awtinvoke( chooser , 'setSelectedFile(Ljava/io/File;)' , java.io.File( str ) ) ;
%chooser.setSelectedFile( java.io.File( str ) ) ;
buildDefaultFilters() ;
loadDefaultFilters( chooser , id ) ;
awtinvoke( chooser , 'setFileFilter(Ljavax/swing/filechooser/FileFilter;)' , allMatlabFilter ) ;
%chooser.setFileFilter( allMatlabFilter ) ;
%argUsedAsFileName = true ;
% else
% theError = caErr1Message ;
% filespecError = true ;
% return ;
% end
else
% Build a new filter.
% Load the new filter and the "all" filter
buildFilter( spec , extStr ) ;
setFilterIdentifier( newFilter , '1' ) ;
addFilterToChooser( chooser , newFilter ) ;
if ~( strcmp( char( spec ) , '*.*' ) )
buildAllFilter() ;
setFilterIdentifier( allFilter , '2' ) ;
addFilterToChooser( chooser , allFilter ) ;
end
% We shouldn't need the pause or the following drawnow -
% But it doesn't work without them
pause(.5) ;
awtinvoke( chooser , 'setFileFilter(Ljavax/swing/filechooser/FileFilter;)' , newFilter ) ;
%chooser.setFileFilter( newFilter ) ;
drawnow() ;
end % end if/else
end % end handleStringFilespec
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This handles the case where the filespec is a cell array
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function handleCellArrayFilespec( theCellArray , chooser )
cellArrayOk = false ;
t = '' ;
rows = '' ;
cols = '' ;
firstFilter = '' ;
try
t = size( theCellArray ) ;
rows = t(1) ;
cols = t(2) ;
catch
theError = caErr1Message ;
end
if( 2 < cols )
theError = caErr2Message ;
end
% The first col is supposed to hold an extension array
extArray = '' ;
descrArray = '' ;
ext = '' ; %#ok
for i = 1:rows
ext = theCellArray{ i , 1 } ;
% Format the extension for our filter
s = com.mathworks.hg.util.dFilter.returnExtensionString( char(ext) ) ;
s = char( s ) ;
if~( strcmpi( s , extError ) )
extArray{i} = s ;
else
theError = strcat( caErr3Message , ext , '''' ) ;
cellArrayOk = false ;
end
end % end for
% If there are two cols, the 2nd col is
% supposed to be descriptions for 1st col
if( 2 == cols )
for i = 1 : rows
descrArray{ i } = theCellArray{ i , 2 } ;
end % end for i = 1 : rows
end % end if( 2 == cols )
% Create the filters for file selection
ii = 0 ;
for i = 1:rows
%disp( extArray{i} )
if( strcmp( char( extArray{i} ) , '*.*' ))
buildAllFilter() ;
allFilter.setIdentifier( int2str( i ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax/swing/filechooser/FileFilter;)' , allFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax/swing/filechooser/FileFilter;)' , allFilter ) ;
% chooser.addFileFilter( allFilter ) ;
% chooser.noteFilter( allFilter ) ;
allFound = true ;
end
if ~( strcmp( char( extArray{i} ) , '*.*' ) )
usrFilter = com.mathworks.hg.util.dFilter ;
usrFilter.addExtension( extArray{i} ) ;
usrFilter.setIdentifier( int2str(i) ) ;
if( 2 == cols )
usrFilter.setDescription( descrArray{i} ) ;
else
usrFilter.setDescription( theCellArray{i,1} ) ;
end % end if( 2 == cols )
% Add the filter
awtinvoke( chooser , 'addFileFilter(Ljavax/swing/filechooser/FileFilter;)' , usrFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax/swing/filechooser/FileFilter;)' , usrFilter ) ;
% chooser.addFileFilter( usrFilter ) ;
% chooser.noteFilter( usrFilter ) ;
% Set a current filter
if( 1 == i )
firstFilter = usrFilter ;
%awtinvoke( chooser , 'setFileFilter(Ljavax/swing/filechooser/FileFilter;)' , usrFilter ) ;
%chooser.setFileFilter( usrFilter ) ;
end % end if( 1 == i )
ii = i ;
end % end if ~( strcmp( char( extArray{i} ) , '*.*' ) )
end % end for i = 1:rows
% Add in the "all" filter
if( ~allFound )
buildAllFilter() ;
allFilter.setIdentifier( int2str( ii+1 ) ) ;
awtinvoke( chooser , 'addFileFilter(Ljavax/swing/filechooser/FileFilter;)' , allFilter ) ;
awtinvoke( chooser , 'noteFilter(Ljavax/swing/filechooser/FileFilter;)' , allFilter ) ;
% chooser.addFileFilter( allFilter ) ;
% chooser.noteFilter( allFilter ) ;
end
% Set the user's first filter as the active filter
% Shouldn't need the pause and drawnow, but things
% don't work without them.
pause(.5) ;
awtinvoke( chooser , 'setFileFilter(Ljavax/swing/filechooser/FileFilter;)' , firstFilter ) ;
drawnow() ;
cellArrayOk = true ;
end % end handleCellArrayFilespec
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Scan the input arguments for 'Location'.
% If either is present, check that the next arg has an
% appropriate value. If not, set an appropriate error flag.
%
% Also, store all the other arguments in the array "remaining args".
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function eliminateBuiltIns()
args = varargin ;
numberOfArgs = numel( args ) ;
remainderArgIndex = 1 ;
i = 1 ;
while( i <= numberOfArgs )
theArg = args{i} ;
% Add to remainder args if its not a string
if( ~( ischar( theArg ) ) || ~( isvector( theArg ) ) )
remainderArgs{ remainderArgIndex } = theArg ; %#ok<AGROW>
remainderArgIndex = remainderArgIndex + 1 ;
i = i + 1 ;
%end % end if( ~( ischar( theArg ) ) | ~( isvector( theArg ) ) )
if( i > numberOfArgs )
return
end
continue
end % end if( ~( ischar( theArg ) ) | ~( isvector( theArg ) ) )
% Transpose if necessary
if( ~( 1 == size( theArg , 1 ) ) )
theArg = theArg' ;
end
% Check to see if we have an interesting string
lowArg = lower( theArg ) ;
%if( ~strcmp( 'multiselect' , lowArg ) & ~strcmp( 'location' , lowArg ) )
if( ~strcmp( 'location' , lowArg ) )
remainderArgs{ remainderArgIndex } = theArg ; %#ok<AGROW>
remainderArgIndex = remainderArgIndex + 1 ;
i = i + 1 ;
continue ;
end % end if( ~strcmp( 'multiselect' , lowArg ) ...
% Check the next arg - we have found x'location'
i = i + 1 ;
if( i > numberOfArgs )
% oops - missing arg
switch( lowArg )
% case 'multiselect'
% theError = badMultiMessage ;
% multiSelectError = true ;
% return
case 'location'
theError = badLocMessage ;
locationError = true ;
return
end % end switch
return
end % end if( i > numberOfArgs )
nextArg = args{ i } ;
switch( lowArg )
case 'location'
% nextArg must be a numeric vector
if( ~( isvector( nextArg ) ) || ...
~( isnumeric( nextArg ) ) )
theError = badLocMessage ;
locationError = true ;
return
end
% Transpose if necessary
if( ~( 1 == size( nextArg , 1 ) ) )
nextArg = nextArg' ;
end
% Check size
if( ~( 1 == size( nextArg , 1 ) ) || ...
~( 2 == size( nextArg , 2 ) ) )
theError = badLocMessage ;
locationError = true ;
return
end
% Record the fact that we've found 'location'
locationFound = true ;
locationPosition = i - 1 ;
% skip to the next arg
i = i + 1 ;
if( i > numberOfArgs )
return
end
end % end switch
end % end while
end % eliminateBuiltIns
function out = mydialog(varargin)
out = [];
try
out = dialog(varargin{:}) ;
catch ex
rethrow(ex)
end
end % end myDialog
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle the callback from the JFileChooser. If the user
% selected "Open", return the name of the selected file,
% the full pathname and the index of the current filter.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function callbackHandler( obj, evd , d )
fileSeparator = filesep ;
jfc = obj ;
cmd = char(evd.getPropertyName());
switch(cmd)
case 'mathworksHgCancel'
if ishandle(d)
close(d)
end
case 'mathworksHgOk'
[pathname, fn, ext ] = fileparts(char(jfc.getSelectedFile.toString));
if (isempty(ext))
% If the file name did not have any extension specified, try to
% guess from the filter selection (only if its not a compound
% filter).
filt_desc = char(jfc.getFileFilter().getDescription());
if (length(strfind(filt_desc, '.')) == 1)
ext = strtrim(filt_desc(strfind(filt_desc, '.') : ...
strfind(filt_desc, ')')-1));
end
end
uiputfileData.filename = [fn ext]; % concatenate filename and ext.
uiputfileData.pathname = strcat( pathname , fileSeparator ) ; % add trailing sep
uiputfileData.filterindex = str2double( jfc.getFileFilter().getIdentifier() ) ;
setappdata( 0 , 'uiputfileData' , uiputfileData ) ;
close(d);
end % end switch
end % callbackHandler
|
github
|
BottjerLab/Acoustic_Similarity-master
|
prefutils.m
|
.m
|
Acoustic_Similarity-master/code/interactive/private/prefutils.m
| 3,035 |
utf_8
|
77563c8b2ed8cde2667908a0fdf1a29e
|
function varargout = prefutils(varargin)
% PREFUTILS Utilities used by set/get/is/add/rmpref
% $Revision: 1.7.4.3 $ $Date: 2011/03/09 07:07:28 $
% Copyright 1984-2005 The MathWorks, Inc.
% Switchyard: call the subfunction named by the first input
% argument, passing it the remaning input arguments, and returning
% any return arguments from it.
[varargout{1:nargout}] = feval(varargin{:});
function prefName = truncToMaxLength(prefName)
% This is necessary because SETFIELD/GETFIELD/ISFIELD/RMFIELD do
% not operate the same as dotref and dotassign when it comes to
% variable names longer than 'namelengthmax'. Dotref/dotassign
% do an implicit truncation, so both operations appear to work
% fine with longer names, even though they're really paying
% attention only to the first 31 characters. But the *field
% functions don't do the truncation, so GETFIELD and ISFIELD
% and RMFIELD report errors when you pass them a longer name that
% you've just used with SETFIELD. So the suite of pref functions
% are using truncToMaxLength until that bug is fixed - when it is,
% just remove this.
prefName = prefName(1:min(end, namelengthmax));
function prefFile = getPrefFile
% return name of preferences file, create pref dir if it does not exist
prefFile = [prefdir(1) filesep 'matlabprefs.mat'];
function Preferences = loadPrefs
% return ALL preferences in the file. Return empty matrix if file
% doesn't exist, or it is empty.
prefFile = getPrefFile;
Preferences = [];
if exist(prefFile)
fileContents = load(prefFile);
if isfield(fileContents, 'Preferences')
Preferences = fileContents.Preferences;
end
end
function savePrefs(Preferences)
prefFile = getPrefFile;
save(prefFile, 'Preferences');
function [val, existed_out] = getFieldOptional(s, f)
fMax = truncToMaxLength(f);
existed = isfield(s, fMax);
if existed == 1
val = s.(fMax);
else
val = [];
end
if nargout == 2
existed_out = existed;
end
function val = getFieldRequired(s, f, e)
[val, existed] = getFieldOptional(s, f);
if ~existed
error(e);
end
function [p_out, v_out] = checkAndConvertToCellVector(pref, value)
% Pref must be a string or cell array of strings.
% return it as a cell vector.
% Value (if passed in) must be the same length as Pref.
% return it as a cell vector (only convert it to cell if we
% converted Pref to cell)
if ischar(pref)
p_out = {pref};
elseif iscell(pref)
p_out = {pref{:}};
for i = 1:length(p_out)
if ~ischar(p_out{i})
error(message('MATLAB:prefutils:InvalidCellArray'));
end
end
else
error(message('MATLAB:prefutils:InvalidPREFinput'));
end
if nargin == 2
if ischar(pref)
v_out = {value};
elseif iscell(value)
v_out = {value{:}};
else
error(message('MATLAB:prefutils:InvalidValueType'));
end
if length(v_out) ~= length(p_out)
error(message('MATLAB:prefutils:InvalidValueType'));
end
end
function checkGroup(group)
% Error out if group is not a string:
if ~ischar(group)
error(message('MATLAB:prefutils:InvalidGroupInput'));
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
chkmem.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/chkmem.m
| 8,064 |
utf_8
|
cdbeeb4199d2ee2ca04d008c7a5de7be
|
function chkmem
% CHKMEM
% This utility measures the size of the largest contiguous block of virtual
% memory available to MATLAB, then recommends solutions to increase its
% size should it be smaller than typical. This is only for the diagnosis of
% memory fragmentation problems just after MATLAB startup.
%
% The recommended solutions can help to reduce the occurence of "Out of Memory"
% errors. See MEMORY function in MATLAB 7.6 and later.
%
% Example:
% chkmem
% MATLAB Memory Fragmentation Detection Utility
% ---------------------------------------------
% Measurement of largest block size:
% Largest free block is 1157MB, which is close to typical (1200MB) but less than
% best case (1500MB), therefore, its size **CAN BE INCREASED** and may reduce the
% occurrence of "Out of Memory" errors if you are experiencing them.
%
% Possible software modules fragmenting MATLAB's memory space:
% c:\winnt\system32\uxtheme.dll
%
% Recommendations to increase size of largest block:
% * The location of uxtheme.dll may be causing problems. Consider solution 1-1HE4G5,
% which rebases a group of Windows DLLs. This has some known side effects which you
% should consider. If you choose to make the changes, restart MATLAB and run
% this utility again.
% * If the list of software modules above includes DLLs located in the MATLAB
% installation directory, check your startup.m or matlabrc.m files to see if
% they are running code that is not essential.
% * If the list of software modules above includes DLLs from applications other than
% Windows or MATLAB, consider uninstalling the application if it is not essential,
% then restart MATLAB and run this utility again. A tool such as Process Explorer
% can help identify the source and manufacturer of DLLs MATLAB is using. You could
% also contact the software manufacturer to ask if the DLL can be safely rebased.
%
% 3GB switch status:
% The MATLAB process limit has been detected as is 3071MB, therefore the 3GB switch
% in your system's boot.ini is SET.
%
% Copyright 2007-2009 The MathWorks, Inc
%% Test platform
if ~strcmp(computer,'PCWIN') % Check for 32-bit Windows
error('This utility is supported on 32-bit Windows only.');
else
fprintf('\n');
fprintf('MATLAB Memory Fragmentation Detection Utility\n');
fprintf('---------------------------------------------\n');
str = evalc('feature(''dumpmem'')');
newlines = regexp(str,'\n');
memmap=cell(size(newlines,2)-2,4); % Preallocate
%% Extract info about s/w modules
for i=4:size(newlines,2)-5
memmap{i-3,1}=deblank(str(newlines(i-1)+1:newlines(i)-36));
memmap{i-3,2}=hex2dec(str(newlines(i)-35:newlines(i)-28));
memmap{i-3,3}=hex2dec(str(newlines(i)-23:newlines(i)-16));
memmap{i-3,4}=hex2dec(str(newlines(i)-11:newlines(i)-4));
end
%% Analyze Largest Block
fprintf('Measurement of largest block size:\n')
free=cell2mat(memmap(:,4));
[largest index]=max(free);
fprintf(' Largest free block is %dMB, ',floor(largest/2^20));
if largest >= 1.4e9
fprintf('which is close to best case (1500MB), therefore\n cannot be improved.\n');
else
if largest >= 1.2e9
fprintf('which is close to typical (1200MB) but less than\n');
fprintf(' best case (1500MB), therefore its size **CAN BE INCREASED** and may reduce the\n');
fprintf(' occurrence of "Out of Memory" errors if you are experiencing them.\n');
else
fprintf('which is less than typical (1100MB) and much less than\n');
fprintf(' best case (1500MB), therefore its size **CAN BE INCREASED** and may reduce the\n');
fprintf(' occurrence of "Out of Memory" errors if you are experiencing them.\n');
end
%% Cause of fragmentation
modules=memmap([index-1:index+1],1);
found=~strcmp(modules,' <anonymous>');
fprintf('\nPossible software modules fragmenting MATLAB''s memory space:\n');
foundOnes=modules(found);
for i=1:length(foundOnes)
disp(foundOnes{i});
end
if isempty(foundOnes)
fprintf(' No software modules found. It is likely that chkmem is not being run\n')
fprintf(' immediately after startup. Restart MATLAB and run it again.\n');
else
%% Fragmentation Solutions
fprintf('\nRecommendations to increase size of largest block:\n');
if ~isempty(cell2mat(strfind(modules,'uxtheme.dll')))
fprintf('* The location of uxtheme.dll may be causing problems. Consider solution <a href="http://www.mathworks.com/support/solutions/data/1-1HE4G5.html?solution=1-1HE4G5">1-1HE4G5</a>,\n');
fprintf(' which rebases a group of Windows DLLs. This has some known side effects which you\n');
fprintf(' should consider. If you choose to make the changes, restart MATLAB and run\n');
fprintf(' this utility again.\n');
end
if ~isempty(cell2mat(strfind(modules,'NETAPI32.dll')))
fprintf('* The location of NETAPI32.dll may be causing problems. Consider solution <a href="http://www.mathworks.com/support/solutions/data/1-1HE4G5.html?solution=1-1HE4G5">1-1HE4G5</a>,\n');
fprintf(' which rebases a group of windows DLLs. This has some known side effects which you\n');
fprintf(' should consider. If you choose to make the changes, restart MATLAB and run\n');
fprintf(' this utility again.\n');
end
if ~isempty(cell2mat(strfind(modules,'wr_nspr4')));
fprintf('* The location of wr_nspr4.dll may be causing problems, consider <a href="http://www.mathworks.com/support/bugreports/details.html?rp=334120">Bug Report 334120</a>.\n');
end
fprintf('* If the list of software modules above includes DLLs located in the MATLAB\n');
fprintf(' installation directory, check your startup.m or matlabrc.m files to see if\n');
fprintf(' they are running code that is not essential.\n');
fprintf('* If the list of software modules above includes DLLs from applications other than\n');
fprintf(' Windows or MATLAB, consider uninstalling the application if it is not essential,\n');
fprintf(' then restart MATLAB and run this utility again. A tool such as <a href="http://www.microsoft.com/technet/sysinternals/utilities/ProcessExplorer.mspx">Process Explorer</a>\n');
fprintf(' can help identify the source and manufacturer of DLLs MATLAB is using. You could\n');
fprintf(' also contact the software manufacturer to ask if the DLL can be safely rebased.\n');
end
end
end
%% 3GB Switch setting information
virtual=virtinfo;
fprintf('\n3GB switch status:\n')
if virtual ==2047
fprintf(' The MATLAB process limit has been detected as %dMB, therefore the 3GB switch\n',virtual);
fprintf(' in your system''s boot.ini file is NOT SET. If you add this switch you can gain\n');
fprintf(' an additional 1GB of memory space for MATLAB. See this <a href="http://www.microsoft.com/whdc/system/platform/server/PAE/PAEmem.mspx">Microsoft Web Page</a>\n');
fprintf(' describing the /3GB switch (or <a href="http://www.google.com/search?hl=en&q=3gb&btnG=Google+Search">search Google</a>).\n');
else
fprintf(' The MATLAB process limit has been detected as is %dMB, therefore the 3GB switch\n in your system''s boot.ini is SET.\n',virtual);
end
function virtualTotal = virtinfo
% Calculates total memory for MATLAB process
str = evalc('feature memstats');
ind = findstr(str,'MB');
LEN = 20;
% Virtual Memory (Address Space):
% In Use: 536 MB (21851000)
% Free: 1511 MB (5e78f000)
% Total: 2047 MB (7ffe0000)
retval = str((ind(9)-2):-1:ind(9)-LEN);
virtualTotal = str2double(fliplr(retval));
|
github
|
BottjerLab/Acoustic_Similarity-master
|
xlswrite1.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/xlswrite1.m
| 10,438 |
utf_8
|
ef574cfee4f63e3d03d942a2194061cc
|
function xlswrite1(file,data,sheet,range)
Excel=evalin('base','Excel');
% Set default values.
Sheet1 = 1;
if nargin < 3
sheet = Sheet1;
range = '';
elseif nargin < 4
range = '';
end
if nargout > 0
success = true;
message = struct('message',{''},'identifier',{''});
end
% Handle input.
try
% handle requested Excel workbook filename.
if ~isempty(file)
if ~ischar(file)
error('MATLAB:xlswrite:InputClass','Filename must be a string');
end
% check for wildcards in filename
if any(findstr('*', file))
error('MATLAB:xlswrite:FileName', 'Filename must not contain *');
end
[Directory,file,ext]=fileparts(file);
if isempty(ext) % add default Excel extension;
ext = '.xls';
end
file = abspath(fullfile(Directory,[file ext]));
[a1 a2 a3] = fileattrib(file);
if a1 && ~(a2.UserWrite == 1)
error('MATLAB:xlswrite:FileReadOnly', 'File can not be read only.');
end
else % get workbook filename.
error('MATLAB:xlswrite:EmptyFileName','Filename is empty.');
end
% Check for empty input data
if isempty(data)
error('MATLAB:xlswrite:EmptyInput','Input array is empty.');
end
% Check for N-D array input data
if ndims(data)>2
error('MATLAB:xlswrite:InputDimension',...
'Dimension of input array cannot be higher than two.');
end
% Check class of input data
if ~(iscell(data) || isnumeric(data) || ischar(data)) && ~islogical(data)
error('MATLAB:xlswrite:InputClass',...
'Input data must be a numeric, cell, or logical array.');
end
% convert input to cell array of data.
if iscell(data)
A=data;
else
A=num2cell(data);
end
if nargin > 2
% Verify class of sheet parameter.
if ~(ischar(sheet) || (isnumeric(sheet) && sheet > 0))
error('MATLAB:xlswrite:InputClass',...
'Sheet argument must a string or a whole number greater than 0.');
end
if isempty(sheet)
sheet = Sheet1;
end
% parse REGION into sheet and range.
% Parse sheet and range strings.
if ischar(sheet) && ~isempty(strfind(sheet,':'))
range = sheet; % only range was specified.
sheet = Sheet1;% Use default sheet.
elseif ~ischar(range)
error('MATLAB:xlswrite:InputClass',...
'Range argument must a string of Excel A1 notation.');
end
end
catch
if ~isempty(nargchk(2,4,nargin))
error('MATLAB:xlswrite:InputArguments',nargchk(2,4,nargin));
elseif nargout == 0
rethrow(lasterror); % Display last error.
else
success = false;
message = lasterror; % Return last error.
end
return;
end
%------------------------------------------------------------------------------
try
% Construct range string
if isempty(strfind(range,':'))
% Range was partly specified or not at all. Calculate range.
[m,n] = size(A);
range = calcrange(range,m,n);
end
catch
if nargout == 0
rethrow(lasterror); % Display last error.
else
success = false;
message = lasterror; % Return last error.
end
return;
end
%------------------------------------------------------------------------------
try
if ~exist(file,'file')
% Create new workbook.
%This is in place because in the presence of a Google Desktop
%Search installation, calling Add, and then SaveAs after adding data,
%to create a new Excel file, will leave an Excel process hanging.
%This workaround prevents it from happening, by creating a blank file,
%and saving it. It can then be opened with Open.
ExcelWorkbook = Excel.workbooks.Add;
ExcelWorkbook.SaveAs(file,1);
ExcelWorkbook.Close(false);
end
%Open file
%ExcelWorkbook = Excel.workbooks.Open(file);
try % select region.
% Activate indicated worksheet.
message = activate_sheet(Excel,sheet);
% Select range in worksheet.
Select(Range(Excel,sprintf('%s',range)));
catch % Throw data range error.
error('MATLAB:xlswrite:SelectDataRange',lasterr);
end
% Export data to selected region.
set(Excel.selection,'Value',A);
%ExcelWorkbook.Save
%ExcelWorkbook.Close(false) % Close Excel workbook.
%Excel.Quit;
catch
try
%ExcelWorkbook.Close(false); % Close Excel workbook.
end
%Excel.Quit;
%delete(Excel); % Terminate Excel server.
if nargout == 0
rethrow(lasterror); % Display last error.
else
success = false;
message = lasterror; % Return last error.
end
end
%--------------------------------------------------------------------------
function message = activate_sheet(Excel,Sheet)
% Activate specified worksheet in workbook.
% Initialise worksheet object
WorkSheets = Excel.sheets;
message = struct('message',{''},'identifier',{''});
% Get name of specified worksheet from workbook
try
TargetSheet = get(WorkSheets,'item',Sheet);
catch
% Worksheet does not exist. Add worksheet.
TargetSheet = addsheet(WorkSheets,Sheet);
warning('MATLAB:xlswrite:AddSheet','Added specified worksheet.');
if nargout > 0
[message.message,message.identifier] = lastwarn;
end
end
% activate worksheet
Activate(TargetSheet);
%------------------------------------------------------------------------------
function newsheet = addsheet(WorkSheets,Sheet)
% Add new worksheet, Sheet into worsheet collection, WorkSheets.
if isnumeric(Sheet)
% iteratively add worksheet by index until number of sheets == Sheet.
while WorkSheets.Count < Sheet
% find last sheet in worksheet collection
lastsheet = WorkSheets.Item(WorkSheets.Count);
newsheet = WorkSheets.Add([],lastsheet);
end
else
% add worksheet by name.
% find last sheet in worksheet collection
lastsheet = WorkSheets.Item(WorkSheets.Count);
newsheet = WorkSheets.Add([],lastsheet);
end
% If Sheet is a string, rename new sheet to this string.
if ischar(Sheet)
set(newsheet,'Name',Sheet);
end
%------------------------------------------------------------------------------
function [absolutepath]=abspath(partialpath)
% parse partial path into path parts
[pathname filename ext] = fileparts(partialpath);
% no path qualification is present in partial path; assume parent is pwd, except
% when path string starts with '~' or is identical to '~'.
if isempty(pathname) && isempty(strmatch('~',partialpath))
Directory = pwd;
elseif isempty(regexp(partialpath,'(.:|\\\\)')) && ...
isempty(strmatch('/',partialpath)) && ...
isempty(strmatch('~',partialpath));
% path did not start with any of drive name, UNC path or '~'.
Directory = [pwd,filesep,pathname];
else
% path content present in partial path; assume relative to current directory,
% or absolute.
Directory = pathname;
end
% construct absulute filename
absolutepath = fullfile(Directory,[filename,ext]);
%------------------------------------------------------------------------------
function range = calcrange(range,m,n)
% Calculate full target range, in Excel A1 notation, to include array of size
% m x n
range = upper(range);
cols = isletter(range);
rows = ~cols;
% Construct first row.
if ~any(rows)
firstrow = 1; % Default row.
else
firstrow = str2double(range(rows)); % from range input.
end
% Construct first column.
if ~any(cols)
firstcol = 'A'; % Default column.
else
firstcol = range(cols); % from range input.
end
try
lastrow = num2str(firstrow+m-1); % Construct last row as a string.
firstrow = num2str(firstrow); % Convert first row to string image.
lastcol = dec2base27(base27dec(firstcol)+n-1); % Construct last column.
range = [firstcol firstrow ':' lastcol lastrow]; % Final range string.
catch
error('MATLAB:xlswrite:CalculateRange',...
'Data range must be between A1 and IV65536.');
end
%------------------------------------------------------------------------------
function s = dec2base27(d)
% DEC2BASE27(D) returns the representation of D as a string in base 27,
% expressed as 'A'..'Z', 'AA','AB'...'AZ', until 'IV'. Note, there is no zero
% digit, so strictly we have hybrid base26, base27 number system. D must be a
% negative integer bigger than 0 and smaller than 2^52, which is the maximum
% number of columns in an Excel worksheet.
%
% Examples
% dec2base(1) returns 'A'
% dec2base(26) returns 'Z'
% dec2base(27) returns 'AA'
%-----------------------------------------------------------------------------
b = 26;
symbols = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ';
d = d(:);
if d ~= floor(d) | any(d < 0) | any(d > 1/eps)
error('MATLAB:xlswrite:Dec2BaseInput',...
'D must be an integer, 0 <= D <= 2^52.');
end
% find the number of columns in new base
n = max(1,round(log2(max(d)+1)/log2(b)));
while any(b.^n <= d)
n = n + 1;
end
% set b^0 column
s(:,n) = rem(d,b);
while n > 1 && any(d)
if s(:,n) == 0
s(:,n) = b;
end
if d > b
% after the carry-over to the b^(n+1) column
if s(:,n) == b
% for the b^n digit at b, set b^(n+1) digit to b
s(:,n-1) = floor(d/b)-1;
else
% set the b^(n+1) digit to the new value after the last carry-over.
s(:,n-1) = rem(floor(d/b),b);
end
else
s(:,n-1) = []; % remove b^(n+1) digit.
end
n = n - 1;
end
s = symbols(s);
%------------------------------------------------------------------------------
function d = base27dec(s)
% BASE27DEC(S) returns the decimal of string S which represents a number in
% base 27, expressed as 'A'..'Z', 'AA','AB'...'AZ', until 'IV'. Note, there is
% no zero so strictly we have hybrid base26, base27 number system.
%
% Examples
% base27dec('A') returns 1
% base27dec('Z') returns 26
% base27dec('IV') returns 256
%-----------------------------------------------------------------------------
d = 0;
b = 26;
n = numel(s);
for i = n:-1:1
d = d+(s(i)-'A'+1)*(b.^(n-i));
end
%-------------------------------------------------------------------------------
|
github
|
BottjerLab/Acoustic_Similarity-master
|
pdftops.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/export_fig/pdftops.m
| 2,962 |
utf_8
|
fc695d9dae7025244e0f02be5116be46
|
function varargout = pdftops(cmd)
%PDFTOPS Calls a local pdftops executable with the input command
%
% Example:
% [status result] = pdftops(cmd)
%
% Attempts to locate a pdftops executable, finally asking the user to
% specify the directory pdftops was installed into. The resulting path is
% stored for future reference.
%
% Once found, the executable is called with the input command string.
%
% This function requires that you have pdftops (from the Xpdf package)
% installed on your system. You can download this from:
% http://www.foolabs.com/xpdf
%
% IN:
% cmd - Command string to be passed into pdftops.
%
% OUT:
% status - 0 iff command ran without problem.
% result - Output from pdftops.
% Copyright: Oliver Woodford, 2009-2010
% Thanks to Jonas Dorn for the fix for the title of the uigetdir window on
% Mac OS.
% Thanks to Christoph Hertel for pointing out a bug in check_xpdf_path
% under linux.
% Call pdftops
[varargout{1:nargout}] = system(sprintf('"%s" %s', xpdf_path, cmd));
return
function path_ = xpdf_path
% Return a valid path
% Start with the currently set path
path_ = user_string('pdftops');
% Check the path works
if check_xpdf_path(path_)
return
end
% Check whether the binary is on the path
if ispc
bin = 'pdftops.exe';
else
bin = 'pdftops';
end
if check_store_xpdf_path(bin)
path_ = bin;
return
end
% Search the obvious places
if ispc
path_ = 'C:\Program Files\xpdf\pdftops.exe';
else
path_ = '/usr/local/bin/pdftops';
end
if check_store_xpdf_path(path_)
return
end
% Ask the user to enter the path
while 1
if strncmp(computer,'MAC',3) % Is a Mac
% Give separate warning as the uigetdir dialogue box doesn't have a
% title
uiwait(warndlg('Pdftops not found. Please locate the program, or install xpdf-tools from http://users.phg-online.de/tk/MOSXS/.'))
end
base = uigetdir('/', 'Pdftops not found. Please locate the program.');
if isequal(base, 0)
% User hit cancel or closed window
break;
end
base = [base filesep];
bin_dir = {'', ['bin' filesep], ['lib' filesep]};
for a = 1:numel(bin_dir)
path_ = [base bin_dir{a} bin];
if exist(path_, 'file') == 2
break;
end
end
if check_store_xpdf_path(path_)
return
end
end
error('pdftops executable not found.');
function good = check_store_xpdf_path(path_)
% Check the path is valid
good = check_xpdf_path(path_);
if ~good
return
end
% Update the current default path to the path found
if ~user_string('pdftops', path_)
warning('Path to pdftops executable could not be saved. Enter it manually in pdftops.txt.');
return
end
return
function good = check_xpdf_path(path_)
% Check the path is valid
[good message] = system(sprintf('"%s" -h', path_));
% system returns good = 1 even when the command runs
% Look for something distinct in the help text
good = ~isempty(strfind(message, 'PostScript'));
return
|
github
|
BottjerLab/Acoustic_Similarity-master
|
isolate_axes.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/export_fig/isolate_axes.m
| 3,447 |
utf_8
|
c6ed56010868279f32b86dfa9815d524
|
%ISOLATE_AXES Isolate the specified axes in a figure on their own
%
% Examples:
% fh = isolate_axes(ah)
% fh = isolate_axes(ah, vis)
%
% This function will create a new figure containing the axes/uipanels
% specified, and also their associated legends and colorbars. The objects
% specified must all be in the same figure, but they will generally only be
% a subset of the objects in the figure.
%
% IN:
% ah - An array of axes and uipanel handles, which must come from the
% same figure.
% vis - A boolean indicating whether the new figure should be visible.
% Default: false.
%
% OUT:
% fh - The handle of the created figure.
% Copyright (C) Oliver Woodford 2011-2012
% Thank you to Rosella Blatt for reporting a bug to do with axes in GUIs
% 16/3/2012 Moved copyfig to its own function. Thanks to Bob Fratantonio
% for pointing out that the function is also used in export_fig.m.
% 12/12/12 - Add support for isolating uipanels. Thanks to michael for
% suggesting it.
% 08/10/13 - Bug fix to allchildren suggested by Will Grant (many thanks!).
function fh = isolate_axes(ah, vis)
% Make sure we have an array of handles
if ~all(ishandle(ah))
error('ah must be an array of handles');
end
% Check that the handles are all for axes or uipanels, and are all in the same figure
fh = ancestor(ah(1), 'figure');
nAx = numel(ah);
for a = 1:nAx
if ~ismember(get(ah(a), 'Type'), {'axes', 'uipanel'})
error('All handles must be axes or uipanel handles.');
end
if ~isequal(ancestor(ah(a), 'figure'), fh)
error('Axes must all come from the same figure.');
end
end
% Tag the objects so we can find them in the copy
old_tag = get(ah, 'Tag');
if nAx == 1
old_tag = {old_tag};
end
set(ah, 'Tag', 'ObjectToCopy');
% Create a new figure exactly the same as the old one
fh = copyfig(fh); %copyobj(fh, 0);
if nargin < 2 || ~vis
set(fh, 'Visible', 'off');
end
% Reset the object tags
for a = 1:nAx
set(ah(a), 'Tag', old_tag{a});
end
% Find the objects to save
ah = findall(fh, 'Tag', 'ObjectToCopy');
if numel(ah) ~= nAx
close(fh);
error('Incorrect number of objects found.');
end
% Set the axes tags to what they should be
for a = 1:nAx
set(ah(a), 'Tag', old_tag{a});
end
% Keep any legends and colorbars which overlap the subplots
lh = findall(fh, 'Type', 'axes', '-and', {'Tag', 'legend', '-or', 'Tag', 'Colorbar'});
nLeg = numel(lh);
if nLeg > 0
ax_pos = get(ah, 'OuterPosition');
if nAx > 1
ax_pos = cell2mat(ax_pos(:));
end
ax_pos(:,3:4) = ax_pos(:,3:4) + ax_pos(:,1:2);
leg_pos = get(lh, 'OuterPosition');
if nLeg > 1;
leg_pos = cell2mat(leg_pos);
end
leg_pos(:,3:4) = leg_pos(:,3:4) + leg_pos(:,1:2);
for a = 1:nAx
% Overlap test
ah = [ah; lh(leg_pos(:,1) < ax_pos(a,3) & leg_pos(:,2) < ax_pos(a,4) &...
leg_pos(:,3) > ax_pos(a,1) & leg_pos(:,4) > ax_pos(a,2))];
end
end
% Get all the objects in the figure
axs = findall(fh);
% Delete everything except for the input objects and associated items
delete(axs(~ismember(axs, [ah; allchildren(ah); allancestors(ah)])));
return
function ah = allchildren(ah)
ah = findall(ah);
if iscell(ah)
ah = cell2mat(ah);
end
ah = ah(:);
return
function ph = allancestors(ah)
ph = [];
for a = 1:numel(ah)
h = get(ah(a), 'parent');
while h ~= 0
ph = [ph; h];
h = get(h, 'parent');
end
end
return
|
github
|
BottjerLab/Acoustic_Similarity-master
|
pdf2eps.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/export_fig/pdf2eps.m
| 1,473 |
utf_8
|
cb8bb442a3d65a64025c32693704d89e
|
%PDF2EPS Convert a pdf file to eps format using pdftops
%
% Examples:
% pdf2eps source dest
%
% This function converts a pdf file to eps format.
%
% This function requires that you have pdftops, from the Xpdf suite of
% functions, installed on your system. This can be downloaded from:
% http://www.foolabs.com/xpdf
%
%IN:
% source - filename of the source pdf file to convert. The filename is
% assumed to already have the extension ".pdf".
% dest - filename of the destination eps file. The filename is assumed to
% already have the extension ".eps".
% Copyright (C) Oliver Woodford 2009-2010
% Thanks to Aldebaro Klautau for reporting a bug when saving to
% non-existant directories.
function pdf2eps(source, dest)
% Construct the options string for pdftops
options = ['-q -paper match -eps -level2 "' source '" "' dest '"'];
% Convert to eps using pdftops
[status message] = pdftops(options);
% Check for error
if status
% Report error
if isempty(message)
error('Unable to generate eps. Check destination directory is writable.');
else
error(message);
end
end
% Fix the DSC error created by pdftops
fid = fopen(dest, 'r+');
if fid == -1
% Cannot open the file
return
end
fgetl(fid); % Get the first line
str = fgetl(fid); % Get the second line
if strcmp(str(1:min(13, end)), '% Produced by')
fseek(fid, -numel(str)-1, 'cof');
fwrite(fid, '%'); % Turn ' ' into '%'
end
fclose(fid);
return
|
github
|
BottjerLab/Acoustic_Similarity-master
|
print2array.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/export_fig/print2array.m
| 6,276 |
utf_8
|
259fd52e4431efae4d76ca22d1d8dac8
|
%PRINT2ARRAY Exports a figure to an image array
%
% Examples:
% A = print2array
% A = print2array(figure_handle)
% A = print2array(figure_handle, resolution)
% A = print2array(figure_handle, resolution, renderer)
% [A bcol] = print2array(...)
%
% This function outputs a bitmap image of the given figure, at the desired
% resolution.
%
% If renderer is '-painters' then ghostcript needs to be installed. This
% can be downloaded from: http://www.ghostscript.com
%
% IN:
% figure_handle - The handle of the figure to be exported. Default: gcf.
% resolution - Resolution of the output, as a factor of screen
% resolution. Default: 1.
% renderer - string containing the renderer paramater to be passed to
% print. Default: '-opengl'.
%
% OUT:
% A - MxNx3 uint8 image of the figure.
% bcol - 1x3 uint8 vector of the background color
% Copyright (C) Oliver Woodford 2008-2012
% 05/09/11: Set EraseModes to normal when using opengl or zbuffer
% renderers. Thanks to Pawel Kocieniewski for reporting the
% issue.
% 21/09/11: Bug fix: unit8 -> uint8! Thanks to Tobias Lamour for reporting
% the issue.
% 14/11/11: Bug fix: stop using hardcopy(), as it interfered with figure
% size and erasemode settings. Makes it a bit slower, but more
% reliable. Thanks to Phil Trinh and Meelis Lootus for reporting
% the issues.
% 09/12/11: Pass font path to ghostscript.
% 27/01/12: Bug fix affecting painters rendering tall figures. Thanks to
% Ken Campbell for reporting it.
% 03/04/12: Bug fix to median input. Thanks to Andy Matthews for reporting
% it.
% 26/10/12: Set PaperOrientation to portrait. Thanks to Michael Watts for
% reporting the issue.
function [A, bcol] = print2array(fig, res, renderer)
% Generate default input arguments, if needed
if nargin < 2
res = 1;
if nargin < 1
fig = gcf;
end
end
% Warn if output is large
old_mode = get(fig, 'Units');
set(fig, 'Units', 'pixels');
px = get(fig, 'Position');
set(fig, 'Units', old_mode);
npx = prod(px(3:4)*res)/1e6;
if npx > 30
% 30M pixels or larger!
warning('MATLAB:LargeImage', 'print2array generating a %.1fM pixel image. This could be slow and might also cause memory problems.', npx);
end
% Retrieve the background colour
bcol = get(fig, 'Color');
% Set the resolution parameter
res_str = ['-r' num2str(ceil(get(0, 'ScreenPixelsPerInch')*res))];
% Generate temporary file name
tmp_nam = [tempname '.tif'];
if nargin > 2 && strcmp(renderer, '-painters')
% Print to eps file
tmp_eps = [tempname '.eps'];
print2eps(tmp_eps, fig, renderer, '-loose');
try
% Initialize the command to export to tiff using ghostscript
cmd_str = ['-dEPSCrop -q -dNOPAUSE -dBATCH ' res_str ' -sDEVICE=tiff24nc'];
% Set the font path
fp = font_path();
if ~isempty(fp)
cmd_str = [cmd_str ' -sFONTPATH="' fp '"'];
end
% Add the filenames
cmd_str = [cmd_str ' -sOutputFile="' tmp_nam '" "' tmp_eps '"'];
% Execute the ghostscript command
ghostscript(cmd_str);
catch me
% Delete the intermediate file
delete(tmp_eps);
rethrow(me);
end
% Delete the intermediate file
delete(tmp_eps);
% Read in the generated bitmap
A = imread(tmp_nam);
% Delete the temporary bitmap file
delete(tmp_nam);
% Set border pixels to the correct colour
if isequal(bcol, 'none')
bcol = [];
elseif isequal(bcol, [1 1 1])
bcol = uint8([255 255 255]);
else
for l = 1:size(A, 2)
if ~all(reshape(A(:,l,:) == 255, [], 1))
break;
end
end
for r = size(A, 2):-1:l
if ~all(reshape(A(:,r,:) == 255, [], 1))
break;
end
end
for t = 1:size(A, 1)
if ~all(reshape(A(t,:,:) == 255, [], 1))
break;
end
end
for b = size(A, 1):-1:t
if ~all(reshape(A(b,:,:) == 255, [], 1))
break;
end
end
bcol = uint8(median(single([reshape(A(:,[l r],:), [], size(A, 3)); reshape(A([t b],:,:), [], size(A, 3))]), 1));
for c = 1:size(A, 3)
A(:,[1:l-1, r+1:end],c) = bcol(c);
A([1:t-1, b+1:end],:,c) = bcol(c);
end
end
else
if nargin < 3
renderer = '-opengl';
end
err = false;
% Set paper size
old_pos_mode = get(fig, 'PaperPositionMode');
old_orientation = get(fig, 'PaperOrientation');
set(fig, 'PaperPositionMode', 'auto', 'PaperOrientation', 'portrait');
try
% Print to tiff file
print(fig, renderer, res_str, '-dtiff', tmp_nam);
% Read in the printed file
A = imread(tmp_nam);
% Delete the temporary file
delete(tmp_nam);
catch ex
err = true;
end
% Reset paper size
set(fig, 'PaperPositionMode', old_pos_mode, 'PaperOrientation', old_orientation);
% Throw any error that occurred
if err
rethrow(ex);
end
% Set the background color
if isequal(bcol, 'none')
bcol = [];
else
bcol = bcol * 255;
if isequal(bcol, round(bcol))
bcol = uint8(bcol);
else
bcol = squeeze(A(1,1,:));
end
end
end
% Check the output size is correct
if isequal(res, round(res))
px = [px([4 3])*res 3];
if ~isequal(size(A), px)
% Correct the output size
A = A(1:min(end,px(1)),1:min(end,px(2)),:);
end
end
return
% Function to return (and create, where necessary) the font path
function fp = font_path()
fp = user_string('gs_font_path');
if ~isempty(fp)
return
end
% Create the path
% Start with the default path
fp = getenv('GS_FONTPATH');
% Add on the typical directories for a given OS
if ispc
if ~isempty(fp)
fp = [fp ';'];
end
fp = [fp getenv('WINDIR') filesep 'Fonts'];
else
if ~isempty(fp)
fp = [fp ':'];
end
fp = [fp '/usr/share/fonts:/usr/local/share/fonts:/usr/share/fonts/X11:/usr/local/share/fonts/X11:/usr/share/fonts/truetype:/usr/local/share/fonts/truetype'];
end
user_string('gs_font_path', fp);
return
|
github
|
BottjerLab/Acoustic_Similarity-master
|
eps2pdf.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/export_fig/eps2pdf.m
| 5,017 |
utf_8
|
bc81caea32035f06d8cb08ea1ccdc81f
|
%EPS2PDF Convert an eps file to pdf format using ghostscript
%
% Examples:
% eps2pdf source dest
% eps2pdf(source, dest, crop)
% eps2pdf(source, dest, crop, append)
% eps2pdf(source, dest, crop, append, gray)
% eps2pdf(source, dest, crop, append, gray, quality)
%
% This function converts an eps file to pdf format. The output can be
% optionally cropped and also converted to grayscale. If the output pdf
% file already exists then the eps file can optionally be appended as a new
% page on the end of the eps file. The level of bitmap compression can also
% optionally be set.
%
% This function requires that you have ghostscript installed on your
% system. Ghostscript can be downloaded from: http://www.ghostscript.com
%
%IN:
% source - filename of the source eps file to convert. The filename is
% assumed to already have the extension ".eps".
% dest - filename of the destination pdf file. The filename is assumed to
% already have the extension ".pdf".
% crop - boolean indicating whether to crop the borders off the pdf.
% Default: true.
% append - boolean indicating whether the eps should be appended to the
% end of the pdf as a new page (if the pdf exists already).
% Default: false.
% gray - boolean indicating whether the output pdf should be grayscale or
% not. Default: false.
% quality - scalar indicating the level of image bitmap quality to
% output. A larger value gives a higher quality. quality > 100
% gives lossless output. Default: ghostscript prepress default.
% Copyright (C) Oliver Woodford 2009-2011
% Suggestion of appending pdf files provided by Matt C at:
% http://www.mathworks.com/matlabcentral/fileexchange/23629
% Thank you to Fabio Viola for pointing out compression artifacts, leading
% to the quality setting.
% Thank you to Scott for pointing out the subsampling of very small images,
% which was fixed for lossless compression settings.
% 9/12/2011 Pass font path to ghostscript.
function eps2pdf(source, dest, crop, append, gray, quality)
% Intialise the options string for ghostscript
options = ['-q -dNOPAUSE -dBATCH -sDEVICE=pdfwrite -dPDFSETTINGS=/prepress -sOutputFile="' dest '"'];
% Set crop option
if nargin < 3 || crop
options = [options ' -dEPSCrop'];
end
% Set the font path
fp = font_path();
if ~isempty(fp)
options = [options ' -sFONTPATH="' fp '"'];
end
% Set the grayscale option
if nargin > 4 && gray
options = [options ' -sColorConversionStrategy=Gray -dProcessColorModel=/DeviceGray'];
end
% Set the bitmap quality
if nargin > 5 && ~isempty(quality)
options = [options ' -dAutoFilterColorImages=false -dAutoFilterGrayImages=false'];
if quality > 100
options = [options ' -dColorImageFilter=/FlateEncode -dGrayImageFilter=/FlateEncode -c ".setpdfwrite << /ColorImageDownsampleThreshold 10 /GrayImageDownsampleThreshold 10 >> setdistillerparams"'];
else
options = [options ' -dColorImageFilter=/DCTEncode -dGrayImageFilter=/DCTEncode'];
v = 1 + (quality < 80);
quality = 1 - quality / 100;
s = sprintf('<< /QFactor %.2f /Blend 1 /HSample [%d 1 1 %d] /VSample [%d 1 1 %d] >>', quality, v, v, v, v);
options = sprintf('%s -c ".setpdfwrite << /ColorImageDict %s /GrayImageDict %s >> setdistillerparams"', options, s, s);
end
end
% Check if the output file exists
if nargin > 3 && append && exist(dest, 'file') == 2
% File exists - append current figure to the end
tmp_nam = tempname;
% Copy the file
copyfile(dest, tmp_nam);
% Add the output file names
options = [options ' -f "' tmp_nam '" "' source '"'];
try
% Convert to pdf using ghostscript
[status message] = ghostscript(options);
catch
% Delete the intermediate file
delete(tmp_nam);
rethrow(lasterror);
end
% Delete the intermediate file
delete(tmp_nam);
else
% File doesn't exist or should be over-written
% Add the output file names
options = [options ' -f "' source '"'];
% Convert to pdf using ghostscript
[status message] = ghostscript(options);
end
% Check for error
if status
% Report error
if isempty(message)
error('Unable to generate pdf. Check destination directory is writable.');
else
error(message);
end
end
return
% Function to return (and create, where necessary) the font path
function fp = font_path()
fp = user_string('gs_font_path');
if ~isempty(fp)
return
end
% Create the path
% Start with the default path
fp = getenv('GS_FONTPATH');
% Add on the typical directories for a given OS
if ispc
if ~isempty(fp)
fp = [fp ';'];
end
fp = [fp getenv('WINDIR') filesep 'Fonts'];
else
if ~isempty(fp)
fp = [fp ':'];
end
fp = [fp '/usr/share/fonts:/usr/local/share/fonts:/usr/share/fonts/X11:/usr/local/share/fonts/X11:/usr/share/fonts/truetype:/usr/local/share/fonts/truetype'];
end
user_string('gs_font_path', fp);
return
|
github
|
BottjerLab/Acoustic_Similarity-master
|
copyfig.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/export_fig/copyfig.m
| 814 |
utf_8
|
1844a9d51dbe52ce3927c9eac5ee672e
|
%COPYFIG Create a copy of a figure, without changing the figure
%
% Examples:
% fh_new = copyfig(fh_old)
%
% This function will create a copy of a figure, but not change the figure,
% as copyobj sometimes does, e.g. by changing legends.
%
% IN:
% fh_old - The handle of the figure to be copied. Default: gcf.
%
% OUT:
% fh_new - The handle of the created figure.
% Copyright (C) Oliver Woodford 2012
function fh = copyfig(fh)
% Set the default
if nargin == 0
fh = gcf;
end
% Is there a legend?
if isempty(findobj(fh, 'Type', 'axes', 'Tag', 'legend'))
% Safe to copy using copyobj
fh = copyobj(fh, 0);
else
% copyobj will change the figure, so save and then load it instead
tmp_nam = [tempname '.fig'];
hgsave(fh, tmp_nam);
fh = hgload(tmp_nam);
delete(tmp_nam);
end
return
|
github
|
BottjerLab/Acoustic_Similarity-master
|
user_string.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/export_fig/user_string.m
| 2,462 |
utf_8
|
dd1a7fa5b4f2be6320fc2538737a2f3e
|
%USER_STRING Get/set a user specific string
%
% Examples:
% string = user_string(string_name)
% saved = user_string(string_name, new_string)
%
% Function to get and set a string in a system or user specific file. This
% enables, for example, system specific paths to binaries to be saved.
%
% IN:
% string_name - String containing the name of the string required. The
% string is extracted from a file called (string_name).txt,
% stored in the same directory as user_string.m.
% new_string - The new string to be saved under the name given by
% string_name.
%
% OUT:
% string - The currently saved string. Default: ''.
% saved - Boolean indicating whether the save was succesful
% Copyright (C) Oliver Woodford 2011-2013
% This method of saving paths avoids changing .m files which might be in a
% version control system. Instead it saves the user dependent paths in
% separate files with a .txt extension, which need not be checked in to
% the version control system. Thank you to Jonas Dorn for suggesting this
% approach.
% 10/01/2013 - Access files in text, not binary mode, as latter can cause
% errors. Thanks to Christian for pointing this out.
function string = user_string(string_name, string)
if ~ischar(string_name)
error('string_name must be a string.');
end
% Create the full filename
string_name = fullfile(fileparts(mfilename('fullpath')), '.ignore', [string_name '.txt']);
if nargin > 1
% Set string
if ~ischar(string)
error('new_string must be a string.');
end
% Make sure the save directory exists
dname = fileparts(string_name);
if ~exist(dname, 'dir')
% Create the directory
try
if ~mkdir(dname)
string = false;
return
end
catch
string = false;
return
end
% Make it hidden
try
fileattrib(dname, '+h');
catch
end
end
% Write the file
fid = fopen(string_name, 'wt');
if fid == -1
string = false;
return
end
try
fprintf(fid, '%s', string);
catch
fclose(fid);
string = false;
return
end
fclose(fid);
string = true;
else
% Get string
fid = fopen(string_name, 'rt');
if fid == -1
string = '';
return
end
string = fgetl(fid);
fclose(fid);
end
return
|
github
|
BottjerLab/Acoustic_Similarity-master
|
export_fig.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/export_fig/export_fig.m
| 29,671 |
utf_8
|
c5d72abc2018b55d22123e68214518e4
|
%EXPORT_FIG Exports figures suitable for publication
% NB: patch objects have to be independently exported - in the future,
% write a function that programatically extracts them
% Examples:
% im = export_fig
% [im alpha] = export_fig
% export_fig filename
% export_fig filename -format1 -format2
% export_fig ... -nocrop
% export_fig ... -transparent
% export_fig ... -native
% export_fig ... -m<val>
% export_fig ... -r<val>
% export_fig ... -a<val>
% export_fig ... -q<val>
% export_fig ... -<renderer>
% export_fig ... -<colorspace>
% export_fig ... -append
% export_fig ... -bookmark
% export_fig(..., handle)
%
% This function saves a figure or single axes to one or more vector and/or
% bitmap file formats, and/or outputs a rasterized version to the
% workspace, with the following properties:
% - Figure/axes reproduced as it appears on screen
% - Cropped borders (optional)
% - Embedded fonts (vector formats)
% - Improved line and grid line styles
% - Anti-aliased graphics (bitmap formats)
% - Render images at native resolution (optional for bitmap formats)
% - Transparent background supported (pdf, eps, png)
% - Semi-transparent patch objects supported (png only)
% - RGB, CMYK or grayscale output (CMYK only with pdf, eps, tiff)
% - Variable image compression, including lossless (pdf, eps, jpg)
% - Optionally append to file (pdf, tiff)
% - Vector formats: pdf, eps
% - Bitmap formats: png, tiff, jpg, bmp, export to workspace
%
% This function is especially suited to exporting figures for use in
% publications and presentations, because of the high quality and
% portability of media produced.
%
% Note that the background color and figure dimensions are reproduced
% (the latter approximately, and ignoring cropping & magnification) in the
% output file. For transparent background (and semi-transparent patch
% objects), use the -transparent option or set the figure 'Color' property
% to 'none'. To make axes transparent set the axes 'Color' property to
% 'none'. Pdf, eps and png are the only file formats to support a
% transparent background, whilst the png format alone supports transparency
% of patch objects.
%
% The choice of renderer (opengl, zbuffer or painters) has a large impact
% on the quality of output. Whilst the default value (opengl for bitmaps,
% painters for vector formats) generally gives good results, if you aren't
% satisfied then try another renderer. Notes: 1) For vector formats (eps,
% pdf), only painters generates vector graphics. 2) For bitmaps, only
% opengl can render transparent patch objects correctly. 3) For bitmaps,
% only painters will correctly scale line dash and dot lengths when
% magnifying or anti-aliasing. 4) Fonts may be substitued with Courier when
% using painters.
%
% When exporting to vector format (pdf & eps) and bitmap format using the
% painters renderer, this function requires that ghostscript is installed
% on your system. You can download this from:
% http://www.ghostscript.com
% When exporting to eps it additionally requires pdftops, from the Xpdf
% suite of functions. You can download this from:
% http://www.foolabs.com/xpdf
%
%IN:
% filename - string containing the name (optionally including full or
% relative path) of the file the figure is to be saved as. If
% a path is not specified, the figure is saved in the current
% directory. If no name and no output arguments are specified,
% the default name, 'export_fig_out', is used. If neither a
% file extension nor a format are specified, a ".png" is added
% and the figure saved in that format.
% -format1, -format2, etc. - strings containing the extensions of the
% file formats the figure is to be saved as.
% Valid options are: '-pdf', '-eps', '-png',
% '-tif', '-jpg' and '-bmp'. All combinations
% of formats are valid.
% -nocrop - option indicating that the borders of the output are not to
% be cropped.
% -transparent - option indicating that the figure background is to be
% made transparent (png, pdf and eps output only).
% -m<val> - option where val indicates the factor to magnify the
% on-screen figure pixel dimensions by when generating bitmap
% outputs. Default: '-m1'.
% -r<val> - option val indicates the resolution (in pixels per inch) to
% export bitmap and vector outputs at, keeping the dimensions
% of the on-screen figure. Default: '-r864' (for vector output
% only). Note that the -m option overides the -r option for
% bitmap outputs only.
% -native - option indicating that the output resolution (when outputting
% a bitmap format) should be such that the vertical resolution
% of the first suitable image found in the figure is at the
% native resolution of that image. To specify a particular
% image to use, give it the tag 'export_fig_native'. Notes:
% This overrides any value set with the -m and -r options. It
% also assumes that the image is displayed front-to-parallel
% with the screen. The output resolution is approximate and
% should not be relied upon. Anti-aliasing can have adverse
% effects on image quality (disable with the -a1 option).
% -a1, -a2, -a3, -a4 - option indicating the amount of anti-aliasing to
% use for bitmap outputs. '-a1' means no anti-
% aliasing; '-a4' is the maximum amount (default).
% -<renderer> - option to force a particular renderer (painters, opengl
% or zbuffer) to be used over the default: opengl for
% bitmaps; painters for vector formats.
% -<colorspace> - option indicating which colorspace color figures should
% be saved in: RGB (default), CMYK or gray. CMYK is only
% supported in pdf, eps and tiff output.
% -q<val> - option to vary bitmap image quality (in pdf, eps and jpg
% files only). Larger val, in the range 0-100, gives higher
% quality/lower compression. val > 100 gives lossless
% compression. Default: '-q95' for jpg, ghostscript prepress
% default for pdf & eps. Note: lossless compression can
% sometimes give a smaller file size than the default lossy
% compression, depending on the type of images.
% -append - option indicating that if the file (pdfs only) already
% exists, the figure is to be appended as a new page, instead
% of being overwritten (default).
% -bookmark - option to indicate that a bookmark with the name of the
% figure is to be created in the output file (pdf only).
% handle - The handle of the figure, axes or uipanels (can be an array of
% handles, but the objects must be in the same figure) to be
% saved. Default: gcf.
%
%OUT:
% im - MxNxC uint8 image array of the figure.
% alpha - MxN single array of alphamatte values in range [0,1], for the
% case when the background is transparent.
%
% Some helpful examples and tips can be found at:
% http://sites.google.com/site/oliverwoodford/software/export_fig
%
% See also PRINT, SAVEAS.
% Copyright (C) Oliver Woodford 2008-2012
% The idea of using ghostscript is inspired by Peder Axensten's SAVEFIG
% (fex id: 10889) which is itself inspired by EPS2PDF (fex id: 5782).
% The idea for using pdftops came from the MATLAB newsgroup (id: 168171).
% The idea of editing the EPS file to change line styles comes from Jiro
% Doke's FIXPSLINESTYLE (fex id: 17928).
% The idea of changing dash length with line width came from comments on
% fex id: 5743, but the implementation is mine :)
% The idea of anti-aliasing bitmaps came from Anders Brun's MYAA (fex id:
% 20979).
% The idea of appending figures in pdfs came from Matt C in comments on the
% FEX (id: 23629)
% Thanks to Roland Martin for pointing out the colour MATLAB
% bug/feature with colorbar axes and transparent backgrounds.
% Thanks also to Andrew Matthews for describing a bug to do with the figure
% size changing in -nodisplay mode. I couldn't reproduce it, but included a
% fix anyway.
% Thanks to Tammy Threadgill for reporting a bug where an axes is not
% isolated from gui objects.
% 23/02/12: Ensure that axes limits don't change during printing
% 14/03/12: Fix bug in fixing the axes limits (thanks to Tobias Lamour for
% reporting it).
% 02/05/12: Incorporate patch of Petr Nechaev (many thanks), enabling
% bookmarking of figures in pdf files.
% 09/05/12: Incorporate patch of Arcelia Arrieta (many thanks), to keep
% tick marks fixed.
% 12/12/12: Add support for isolating uipanels. Thanks to michael for
% suggesting it.
% 25/09/13: Add support for changing resolution in vector formats. Thanks
% to Jan Jaap Meijer for suggesting it.
function [im, alpha] = export_fig(varargin)
% Make sure the figure is rendered correctly _now_ so that properties like
% axes limits are up-to-date.
drawnow;
% Parse the input arguments
[fig, options] = parse_args(nargout, varargin{:});
% Isolate the subplot, if it is one
cls = all(ismember(get(fig, 'Type'), {'axes', 'uipanel'}));
if cls
% Given handles of one or more axes, so isolate them from the rest
fig = isolate_axes(fig);
else
% Check we have a figure
if ~isequal(get(fig, 'Type'), 'figure');
error('Handle must be that of a figure, axes or uipanel');
end
% Get the old InvertHardcopy mode
old_mode = get(fig, 'InvertHardcopy');
end
% Hack the font units where necessary (due to a font rendering bug in
% print?). This may not work perfectly in all cases. Also it can change the
% figure layout if reverted, so use a copy.
magnify = options.magnify * options.aa_factor;
if isbitmap(options) && magnify ~= 1
fontu = findobj(fig, 'FontUnits', 'normalized');
if ~isempty(fontu)
% Some normalized font units found
if ~cls
fig = copyfig(fig);
set(fig, 'Visible', 'off');
fontu = findobj(fig, 'FontUnits', 'normalized');
cls = true;
end
set(fontu, 'FontUnits', 'points');
end
end
% MATLAB "feature": axes limits and tick marks can change when printing
Hlims = findall(fig, 'Type', 'axes');
if ~cls
% Record the old axes limit and tick modes
Xlims = make_cell(get(Hlims, 'XLimMode'));
Ylims = make_cell(get(Hlims, 'YLimMode'));
Zlims = make_cell(get(Hlims, 'ZLimMode'));
Xtick = make_cell(get(Hlims, 'XTickMode'));
Ytick = make_cell(get(Hlims, 'YTickMode'));
Ztick = make_cell(get(Hlims, 'ZTickMode'));
end
% Set all axes limit and tick modes to manual, so the limits and ticks can't change
set(Hlims, 'XLimMode', 'manual', 'YLimMode', 'manual', 'ZLimMode', 'manual', 'XTickMode', 'manual', 'YTickMode', 'manual', 'ZTickMode', 'manual');
% Set to print exactly what is there
set(fig, 'InvertHardcopy', 'off');
% Set the renderer
switch options.renderer
case 1
renderer = '-opengl';
case 2
renderer = '-zbuffer';
case 3
renderer = '-painters';
otherwise
renderer = '-opengl'; % Default for bitmaps
end
% Do the bitmap formats first
if isbitmap(options)
% Get the background colour
if options.transparent && (options.png || options.alpha)
% Get out an alpha channel
% MATLAB "feature": black colorbar axes can change to white and vice versa!
hCB = findobj(fig, 'Type', 'axes', 'Tag', 'Colorbar');
if isempty(hCB)
yCol = [];
xCol = [];
else
yCol = get(hCB, 'YColor');
xCol = get(hCB, 'XColor');
if iscell(yCol)
yCol = cell2mat(yCol);
xCol = cell2mat(xCol);
end
yCol = sum(yCol, 2);
xCol = sum(xCol, 2);
end
% MATLAB "feature": apparently figure size can change when changing
% colour in -nodisplay mode
pos = get(fig, 'Position');
% Set the background colour to black, and set size in case it was
% changed internally
tcol = get(fig, 'Color');
set(fig, 'Color', 'k', 'Position', pos);
% Correct the colorbar axes colours
set(hCB(yCol==0), 'YColor', [0 0 0]);
set(hCB(xCol==0), 'XColor', [0 0 0]);
% Print large version to array
B = print2array(fig, magnify, renderer);
% Downscale the image
B = downsize(single(B), options.aa_factor);
% Set background to white (and set size)
set(fig, 'Color', 'w', 'Position', pos);
% Correct the colorbar axes colours
set(hCB(yCol==3), 'YColor', [1 1 1]);
set(hCB(xCol==3), 'XColor', [1 1 1]);
% Print large version to array
A = print2array(fig, magnify, renderer);
% Downscale the image
A = downsize(single(A), options.aa_factor);
% Set the background colour (and size) back to normal
set(fig, 'Color', tcol, 'Position', pos);
% Compute the alpha map
alpha = round(sum(B - A, 3)) / (255 * 3) + 1;
A = alpha;
A(A==0) = 1;
A = B ./ A(:,:,[1 1 1]);
clear B
% Convert to greyscale
if options.colourspace == 2
A = rgb2grey(A);
end
A = uint8(A);
% Crop the background
if options.crop
[alpha, v] = crop_background(alpha, 0);
A = A(v(1):v(2),v(3):v(4),:);
end
if options.png
% Compute the resolution
res = options.magnify * get(0, 'ScreenPixelsPerInch') / 25.4e-3;
% Save the png
imwrite(A, [options.name '.png'], 'Alpha', double(alpha), 'ResolutionUnit', 'meter', 'XResolution', res, 'YResolution', res);
% Clear the png bit
options.png = false;
end
% Return only one channel for greyscale
if isbitmap(options)
A = check_greyscale(A);
end
if options.alpha
% Store the image
im = A;
% Clear the alpha bit
options.alpha = false;
end
% Get the non-alpha image
if isbitmap(options)
alph = alpha(:,:,ones(1, size(A, 3)));
A = uint8(single(A) .* alph + 255 * (1 - alph));
clear alph
end
if options.im
% Store the new image
im = A;
end
else
% Print large version to array
if options.transparent
% MATLAB "feature": apparently figure size can change when changing
% colour in -nodisplay mode
pos = get(fig, 'Position');
tcol = get(fig, 'Color');
set(fig, 'Color', 'w', 'Position', pos);
A = print2array(fig, magnify, renderer);
set(fig, 'Color', tcol, 'Position', pos);
tcol = 255;
else
[A, tcol] = print2array(fig, magnify, renderer);
end
% Crop the background
if options.crop
A = crop_background(A, tcol);
end
% Downscale the image
A = downsize(A, options.aa_factor);
if options.colourspace == 2
% Convert to greyscale
A = rgb2grey(A);
else
% Return only one channel for greyscale
A = check_greyscale(A);
end
% Outputs
if options.im
im = A;
end
if options.alpha
im = A;
alpha = zeros(size(A, 1), size(A, 2), 'single');
end
end
% Save the images
if options.png
res = options.magnify * get(0, 'ScreenPixelsPerInch') / 25.4e-3;
imwrite(A, [options.name '.png'], 'ResolutionUnit', 'meter', 'XResolution', res, 'YResolution', res);
end
if options.bmp
imwrite(A, [options.name '.bmp']);
end
% Save jpeg with given quality
if options.jpg
quality = options.quality;
if isempty(quality)
quality = 95;
end
if quality > 100
imwrite(A, [options.name '.jpg'], 'Mode', 'lossless');
else
imwrite(A, [options.name '.jpg'], 'Quality', quality);
end
end
% Save tif images in cmyk if wanted (and possible)
if options.tif
if options.colourspace == 1 && size(A, 3) == 3
A = double(255 - A);
K = min(A, [], 3);
K_ = 255 ./ max(255 - K, 1);
C = (A(:,:,1) - K) .* K_;
M = (A(:,:,2) - K) .* K_;
Y = (A(:,:,3) - K) .* K_;
A = uint8(cat(3, C, M, Y, K));
clear C M Y K K_
end
append_mode = {'overwrite', 'append'};
imwrite(A, [options.name '.tif'], 'Resolution', options.magnify*get(0, 'ScreenPixelsPerInch'), 'WriteMode', append_mode{options.append+1});
end
end
% Now do the vector formats
if isvector(options)
% Set the default renderer to painters
if ~options.renderer
renderer = '-painters';
end
% Generate some filenames
tmp_nam = [tempname '.eps'];
if options.pdf
pdf_nam = [options.name '.pdf'];
else
pdf_nam = [tempname '.pdf'];
end
% Generate the options for print
p2eArgs = {renderer, sprintf('-r%d', options.resolution)};
if options.colourspace == 1
p2eArgs = [p2eArgs {'-cmyk'}];
end
if ~options.crop
p2eArgs = [p2eArgs {'-loose'}];
end
try
% Generate an eps
print2eps(tmp_nam, fig, p2eArgs{:});
% Remove the background, if desired
if options.transparent && ~isequal(get(fig, 'Color'), 'none')
eps_remove_background(tmp_nam);
end
% Add a bookmark to the PDF if desired
if options.bookmark
fig_nam = get(fig, 'Name');
if isempty(fig_nam)
warning('export_fig:EmptyBookmark', 'Bookmark requested for figure with no name. Bookmark will be empty.');
end
add_bookmark(tmp_nam, fig_nam);
end
% Generate a pdf
eps2pdf(tmp_nam, pdf_nam, 1, options.append, options.colourspace==2, options.quality);
catch ex
% Delete the eps
delete(tmp_nam);
rethrow(ex);
end
% Delete the eps
delete(tmp_nam);
if options.eps
try
% Generate an eps from the pdf
pdf2eps(pdf_nam, [options.name '.eps']);
catch ex
if ~options.pdf
% Delete the pdf
delete(pdf_nam);
end
rethrow(ex);
end
if ~options.pdf
% Delete the pdf
delete(pdf_nam);
end
end
end
if cls
% Close the created figure
close(fig);
else
% Reset the hardcopy mode
set(fig, 'InvertHardcopy', old_mode);
% Reset the axes limit and tick modes
for a = 1:numel(Hlims)
set(Hlims(a), 'XLimMode', Xlims{a}, 'YLimMode', Ylims{a}, 'ZLimMode', Zlims{a}, 'XTickMode', Xtick{a}, 'YTickMode', Ytick{a}, 'ZTickMode', Ztick{a});
end
end
return
function [fig, options] = parse_args(nout, varargin)
% Parse the input arguments
% Set the defaults
fig = get(0, 'CurrentFigure');
options = struct('name', 'export_fig_out', ...
'crop', true, ...
'transparent', false, ...
'renderer', 0, ... % 0: default, 1: OpenGL, 2: ZBuffer, 3: Painters
'pdf', false, ...
'eps', false, ...
'png', false, ...
'tif', false, ...
'jpg', false, ...
'bmp', false, ...
'colourspace', 0, ... % 0: RGB/gray, 1: CMYK, 2: gray
'append', false, ...
'im', nout == 1, ...
'alpha', nout == 2, ...
'aa_factor', 3, ...
'magnify', [], ...
'resolution', [], ...
'bookmark', false, ...
'quality', []);
native = false; % Set resolution to native of an image
% Go through the other arguments
for a = 1:nargin-1
if all(ishandle(varargin{a}))
fig = varargin{a};
elseif ischar(varargin{a}) && ~isempty(varargin{a})
if varargin{a}(1) == '-'
switch lower(varargin{a}(2:end))
case 'nocrop'
options.crop = false;
case {'trans', 'transparent'}
options.transparent = true;
case 'opengl'
options.renderer = 1;
case 'zbuffer'
options.renderer = 2;
case 'painters'
options.renderer = 3;
case 'pdf'
options.pdf = true;
case 'eps'
options.eps = true;
case 'png'
options.png = true;
case {'tif', 'tiff'}
options.tif = true;
case {'jpg', 'jpeg'}
options.jpg = true;
case 'bmp'
options.bmp = true;
case 'rgb'
options.colourspace = 0;
case 'cmyk'
options.colourspace = 1;
case {'gray', 'grey'}
options.colourspace = 2;
case {'a1', 'a2', 'a3', 'a4'}
options.aa_factor = str2double(varargin{a}(3));
case 'append'
options.append = true;
case 'bookmark'
options.bookmark = true;
case 'native'
native = true;
otherwise
val = str2double(regexp(varargin{a}, '(?<=-(m|M|r|R|q|Q))(\d*\.)?\d+(e-?\d+)?', 'match'));
if ~isscalar(val)
error('option %s not recognised', varargin{a});
end
switch lower(varargin{a}(2))
case 'm'
options.magnify = val;
case 'r'
options.resolution = val;
case 'q'
options.quality = max(val, 0);
end
end
else
[p, options.name, ext] = fileparts(varargin{a});
if ~isempty(p)
options.name = [p filesep options.name];
end
switch lower(ext)
case {'.tif', '.tiff'}
options.tif = true;
case {'.jpg', '.jpeg'}
options.jpg = true;
case '.png'
options.png = true;
case '.bmp'
options.bmp = true;
case '.eps'
options.eps = true;
case '.pdf'
options.pdf = true;
otherwise
options.name = varargin{a};
end
end
end
end
% Compute the magnification and resolution
if isempty(options.magnify)
if isempty(options.resolution)
options.magnify = 1;
options.resolution = 864;
else
options.magnify = options.resolution ./ get(0, 'ScreenPixelsPerInch');
end
elseif isempty(options.resolution)
options.resolution = 864;
end
% Check we have a figure handle
if isempty(fig)
error('No figure found');
end
% Set the default format
if ~isvector(options) && ~isbitmap(options)
options.png = true;
end
% Check whether transparent background is wanted (old way)
if isequal(get(ancestor(fig, 'figure'), 'Color'), 'none')
options.transparent = true;
end
% If requested, set the resolution to the native vertical resolution of the
% first suitable image found
if native && isbitmap(options)
% Find a suitable image
list = findobj(fig, 'Type', 'image', 'Tag', 'export_fig_native');
if isempty(list)
list = findobj(fig, 'Type', 'image', 'Visible', 'on');
end
for hIm = list(:)'
% Check height is >= 2
height = size(get(hIm, 'CData'), 1);
if height < 2
continue
end
% Account for the image filling only part of the axes, or vice
% versa
yl = get(hIm, 'YData');
if isscalar(yl)
yl = [yl(1)-0.5 yl(1)+height+0.5];
else
if ~diff(yl)
continue
end
yl = yl + [-0.5 0.5] * (diff(yl) / (height - 1));
end
hAx = get(hIm, 'Parent');
yl2 = get(hAx, 'YLim');
% Find the pixel height of the axes
oldUnits = get(hAx, 'Units');
set(hAx, 'Units', 'pixels');
pos = get(hAx, 'Position');
set(hAx, 'Units', oldUnits);
if ~pos(4)
continue
end
% Found a suitable image
% Account for stretch-to-fill being disabled
pbar = get(hAx, 'PlotBoxAspectRatio');
pos = min(pos(4), pbar(2)*pos(3)/pbar(1));
% Set the magnification to give native resolution
options.magnify = (height * diff(yl2)) / (pos * diff(yl));
break
end
end
return
function A = downsize(A, factor)
% Downsample an image
if factor == 1
% Nothing to do
return
end
try
% Faster, but requires image processing toolbox
A = imresize(A, 1/factor, 'bilinear');
catch
% No image processing toolbox - resize manually
% Lowpass filter - use Gaussian as is separable, so faster
% Compute the 1d Gaussian filter
filt = (-factor-1:factor+1) / (factor * 0.6);
filt = exp(-filt .* filt);
% Normalize the filter
filt = single(filt / sum(filt));
% Filter the image
padding = floor(numel(filt) / 2);
for a = 1:size(A, 3)
A(:,:,a) = conv2(filt, filt', single(A([ones(1, padding) 1:end repmat(end, 1, padding)],[ones(1, padding) 1:end repmat(end, 1, padding)],a)), 'valid');
end
% Subsample
A = A(1+floor(mod(end-1, factor)/2):factor:end,1+floor(mod(end-1, factor)/2):factor:end,:);
end
return
function A = rgb2grey(A)
A = cast(reshape(reshape(single(A), [], 3) * single([0.299; 0.587; 0.114]), size(A, 1), size(A, 2)), class(A));
return
function A = check_greyscale(A)
% Check if the image is greyscale
if size(A, 3) == 3 && ...
all(reshape(A(:,:,1) == A(:,:,2), [], 1)) && ...
all(reshape(A(:,:,2) == A(:,:,3), [], 1))
A = A(:,:,1); % Save only one channel for 8-bit output
end
return
function [A, v] = crop_background(A, bcol)
% Map the foreground pixels
[h, w, c] = size(A);
if isscalar(bcol) && c > 1
bcol = bcol(ones(1, c));
end
bail = false;
for l = 1:w
for a = 1:c
if ~all(A(:,l,a) == bcol(a))
bail = true;
break;
end
end
if bail
break;
end
end
bail = false;
for r = w:-1:l
for a = 1:c
if ~all(A(:,r,a) == bcol(a))
bail = true;
break;
end
end
if bail
break;
end
end
bail = false;
for t = 1:h
for a = 1:c
if ~all(A(t,:,a) == bcol(a))
bail = true;
break;
end
end
if bail
break;
end
end
bail = false;
for b = h:-1:t
for a = 1:c
if ~all(A(b,:,a) == bcol(a))
bail = true;
break;
end
end
if bail
break;
end
end
% Crop the background, leaving one boundary pixel to avoid bleeding on
% resize
v = [max(t-1, 1) min(b+1, h) max(l-1, 1) min(r+1, w)];
A = A(v(1):v(2),v(3):v(4),:);
return
function eps_remove_background(fname)
% Remove the background of an eps file
% Open the file
fh = fopen(fname, 'r+');
if fh == -1
error('Not able to open file %s.', fname);
end
% Read the file line by line
while true
% Get the next line
l = fgets(fh);
if isequal(l, -1)
break; % Quit, no rectangle found
end
% Check if the line contains the background rectangle
if isequal(regexp(l, ' *0 +0 +\d+ +\d+ +rf *[\n\r]+', 'start'), 1)
% Set the line to whitespace and quit
l(1:regexp(l, '[\n\r]', 'start', 'once')-1) = ' ';
fseek(fh, -numel(l), 0);
fprintf(fh, l);
break;
end
end
% Close the file
fclose(fh);
return
function b = isvector(options)
b = options.pdf || options.eps;
return
function b = isbitmap(options)
b = options.png || options.tif || options.jpg || options.bmp || options.im || options.alpha;
return
% Helper function
function A = make_cell(A)
if ~iscell(A)
A = {A};
end
return
function add_bookmark(fname, bookmark_text)
% Adds a bookmark to the temporary EPS file after %%EndPageSetup
% Read in the file
fh = fopen(fname, 'r');
if fh == -1
error('File %s not found.', fname);
end
try
fstrm = fread(fh, '*char')';
catch ex
fclose(fh);
rethrow(ex);
end
fclose(fh);
% Include standard pdfmark prolog to maximize compatibility
fstrm = strrep(fstrm, '%%BeginProlog', sprintf('%%%%BeginProlog\n/pdfmark where {pop} {userdict /pdfmark /cleartomark load put} ifelse'));
% Add page bookmark
fstrm = strrep(fstrm, '%%EndPageSetup', sprintf('%%%%EndPageSetup\n[ /Title (%s) /OUT pdfmark',bookmark_text));
% Write out the updated file
fh = fopen(fname, 'w');
if fh == -1
error('Unable to open %s for writing.', fname);
end
try
fwrite(fh, fstrm, 'char*1');
catch ex
fclose(fh);
rethrow(ex);
end
fclose(fh);
return
|
github
|
BottjerLab/Acoustic_Similarity-master
|
ghostscript.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/export_fig/ghostscript.m
| 4,505 |
utf_8
|
18a672bb6982a1fbc6b21b3ab52b0fc9
|
%GHOSTSCRIPT Calls a local GhostScript executable with the input command
%
% Example:
% [status result] = ghostscript(cmd)
%
% Attempts to locate a ghostscript executable, finally asking the user to
% specify the directory ghostcript was installed into. The resulting path
% is stored for future reference.
%
% Once found, the executable is called with the input command string.
%
% This function requires that you have Ghostscript installed on your
% system. You can download this from: http://www.ghostscript.com
%
% IN:
% cmd - Command string to be passed into ghostscript.
%
% OUT:
% status - 0 iff command ran without problem.
% result - Output from ghostscript.
% Copyright: Oliver Woodford, 2009-2013
% Thanks to Jonas Dorn for the fix for the title of the uigetdir window on
% Mac OS.
% Thanks to Nathan Childress for the fix to the default location on 64-bit
% Windows systems.
% 27/4/11 - Find 64-bit Ghostscript on Windows. Thanks to Paul Durack and
% Shaun Kline for pointing out the issue
% 4/5/11 - Thanks to David Chorlian for pointing out an alternative
% location for gs on linux.
% 12/12/12 - Add extra executable name on Windows. Thanks to Ratish
% Punnoose for highlighting the issue.
% 28/6/13 - Fix error using GS 9.07 in Linux. Many thanks to Jannick
% Steinbring for proposing the fix.
function varargout = ghostscript(cmd)
% Initialize any required system calls before calling ghostscript
shell_cmd = '';
if isunix
shell_cmd = 'export LD_LIBRARY_PATH=""; '; % Avoids an error on Linux with GS 9.07
end
% Call ghostscript
[varargout{1:nargout}] = system(sprintf('%s"%s" %s', shell_cmd, gs_path, cmd));
return
function path_ = gs_path
% Return a valid path
% Start with the currently set path
path_ = user_string('ghostscript');
% Check the path works
if check_gs_path(path_)
return
end
% Check whether the binary is on the path
if ispc
bin = {'gswin32c.exe', 'gswin64c.exe', 'gs'};
else
bin = {'gs'};
end
for a = 1:numel(bin)
path_ = bin{a};
if check_store_gs_path(path_)
return
end
end
% Search the obvious places
if ispc
default_location = 'C:\Program Files\gs\';
dir_list = dir(default_location);
if isempty(dir_list)
default_location = 'C:\Program Files (x86)\gs\'; % Possible location on 64-bit systems
dir_list = dir(default_location);
end
executable = {'\bin\gswin32c.exe', '\bin\gswin64c.exe'};
ver_num = 0;
% If there are multiple versions, use the newest
for a = 1:numel(dir_list)
ver_num2 = sscanf(dir_list(a).name, 'gs%g');
if ~isempty(ver_num2) && ver_num2 > ver_num
for b = 1:numel(executable)
path2 = [default_location dir_list(a).name executable{b}];
if exist(path2, 'file') == 2
path_ = path2;
ver_num = ver_num2;
end
end
end
end
if check_store_gs_path(path_)
return
end
else
bin = {'/usr/bin/gs', '/usr/local/bin/gs'};
for a = 1:numel(bin)
path_ = bin{a};
if check_store_gs_path(path_)
return
end
end
end
% Ask the user to enter the path
while 1
if strncmp(computer, 'MAC', 3) % Is a Mac
% Give separate warning as the uigetdir dialogue box doesn't have a
% title
uiwait(warndlg('Ghostscript not found. Please locate the program.'))
end
base = uigetdir('/', 'Ghostcript not found. Please locate the program.');
if isequal(base, 0)
% User hit cancel or closed window
break;
end
base = [base filesep];
bin_dir = {'', ['bin' filesep], ['lib' filesep]};
for a = 1:numel(bin_dir)
for b = 1:numel(bin)
path_ = [base bin_dir{a} bin{b}];
if exist(path_, 'file') == 2
if check_store_gs_path(path_)
return
end
end
end
end
end
error('Ghostscript not found. Have you installed it from www.ghostscript.com?');
function good = check_store_gs_path(path_)
% Check the path is valid
good = check_gs_path(path_);
if ~good
return
end
% Update the current default path to the path found
if ~user_string('ghostscript', path_)
warning('Path to ghostscript installation could not be saved. Enter it manually in ghostscript.txt.');
return
end
return
function good = check_gs_path(path_)
% Check the path is valid
[good, message] = system(sprintf('"%s" -h', path_));
good = good == 0;
return
|
github
|
BottjerLab/Acoustic_Similarity-master
|
freezeColors.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/freezeColors/freezeColors.m
| 9,815 |
utf_8
|
2068d7a4f7a74d251e2519c4c5c1c171
|
function freezeColors(varargin)
% freezeColors Lock colors of plot, enabling multiple colormaps per figure. (v2.3)
%
% Problem: There is only one colormap per figure. This function provides
% an easy solution when plots using different colomaps are desired
% in the same figure.
%
% freezeColors freezes the colors of graphics objects in the current axis so
% that subsequent changes to the colormap (or caxis) will not change the
% colors of these objects. freezeColors works on any graphics object
% with CData in indexed-color mode: surfaces, images, scattergroups,
% bargroups, patches, etc. It works by converting CData to true-color rgb
% based on the colormap active at the time freezeColors is called.
%
% The original indexed color data is saved, and can be restored using
% unfreezeColors, making the plot once again subject to the colormap and
% caxis.
%
%
% Usage:
% freezeColors applies to all objects in current axis (gca),
% freezeColors(axh) same, but works on axis axh.
%
% Example:
% subplot(2,1,1); imagesc(X); colormap hot; freezeColors
% subplot(2,1,2); imagesc(Y); colormap hsv; freezeColors etc...
%
% Note: colorbars must also be frozen. Due to Matlab 'improvements' this can
% no longer be done with freezeColors. Instead, please
% use the function CBFREEZE by Carlos Adrian Vargas Aguilera
% that can be downloaded from the MATLAB File Exchange
% (http://www.mathworks.com/matlabcentral/fileexchange/24371)
%
% h=colorbar; cbfreeze(h), or simply cbfreeze(colorbar)
%
% For additional examples, see test/test_main.m
%
% Side effect on render mode: freezeColors does not work with the painters
% renderer, because Matlab doesn't support rgb color data in
% painters mode. If the current renderer is painters, freezeColors
% changes it to zbuffer. This may have unexpected effects on other aspects
% of your plots.
%
% See also unfreezeColors, freezeColors_pub.html, cbfreeze.
%
%
% John Iversen ([email protected]) 3/23/05
%
% Changes:
% JRI ([email protected]) 4/19/06 Correctly handles scaled integer cdata
% JRI 9/1/06 should now handle all objects with cdata: images, surfaces,
% scatterplots. (v 2.1)
% JRI 11/11/06 Preserves NaN colors. Hidden option (v 2.2, not uploaded)
% JRI 3/17/07 Preserve caxis after freezing--maintains colorbar scale (v 2.3)
% JRI 4/12/07 Check for painters mode as Matlab doesn't support rgb in it.
% JRI 4/9/08 Fix preserving caxis for objects within hggroups (e.g. contourf)
% JRI 4/7/10 Change documentation for colorbars
% Hidden option for NaN colors:
% Missing data are often represented by NaN in the indexed color
% data, which renders transparently. This transparency will be preserved
% when freezing colors. If instead you wish such gaps to be filled with
% a real color, add 'nancolor',[r g b] to the end of the arguments. E.g.
% freezeColors('nancolor',[r g b]) or freezeColors(axh,'nancolor',[r g b]),
% where [r g b] is a color vector. This works on images & pcolor, but not on
% surfaces.
% Thanks to Fabiano Busdraghi and Jody Klymak for the suggestions. Bugfixes
% attributed in the code.
% Free for all uses, but please retain the following:
% Original Author:
% John Iversen, 2005-10
% [email protected]
appdatacode = 'JRI__freezeColorsData';
[h, nancolor] = checkArgs(varargin);
%gather all children with scaled or indexed CData
cdatah = getCDataHandles(h);
%current colormap
cmap = colormap;
nColors = size(cmap,1);
cax = caxis;
% convert object color indexes into colormap to true-color data using
% current colormap
for hh = cdatah',
g = get(hh);
%preserve parent axis clim
parentAx = getParentAxes(hh);
originalClim = get(parentAx, 'clim');
% Note: Special handling of patches: For some reason, setting
% cdata on patches created by bar() yields an error,
% so instead we'll set facevertexcdata instead for patches.
if ~strcmp(g.Type,'patch'),
cdata = g.CData;
else
cdata = g.FaceVertexCData;
end
%get cdata mapping (most objects (except scattergroup) have it)
if isfield(g,'CDataMapping'),
scalemode = g.CDataMapping;
else
scalemode = 'scaled';
end
%save original indexed data for use with unfreezeColors
siz = size(cdata);
setappdata(hh, appdatacode, {cdata scalemode});
%convert cdata to indexes into colormap
if strcmp(scalemode,'scaled'),
%4/19/06 JRI, Accommodate scaled display of integer cdata:
% in MATLAB, uint * double = uint, so must coerce cdata to double
% Thanks to O Yamashita for pointing this need out
idx = ceil( (double(cdata) - cax(1)) / (cax(2)-cax(1)) * nColors);
else %direct mapping
idx = cdata;
%10/8/09 in case direct data is non-int (e.g. image;freezeColors)
% (Floor mimics how matlab converts data into colormap index.)
% Thanks to D Armyr for the catch
idx = floor(idx);
end
%clamp to [1, nColors]
idx(idx<1) = 1;
idx(idx>nColors) = nColors;
%handle nans in idx
nanmask = isnan(idx);
idx(nanmask)=1; %temporarily replace w/ a valid colormap index
%make true-color data--using current colormap
realcolor = zeros(siz);
for i = 1:3,
c = cmap(idx,i);
c = reshape(c,siz);
c(nanmask) = nancolor(i); %restore Nan (or nancolor if specified)
realcolor(:,:,i) = c;
end
%apply new true-color color data
%true-color is not supported in painters renderer, so switch out of that
if strcmp(get(gcf,'renderer'), 'painters'),
set(gcf,'renderer','zbuffer');
end
%replace original CData with true-color data
if ~strcmp(g.Type,'patch'),
set(hh,'CData',realcolor);
else
set(hh,'faceVertexCData',permute(realcolor,[1 3 2]))
end
%restore clim (so colorbar will show correct limits)
if ~isempty(parentAx),
set(parentAx,'clim',originalClim)
end
end %loop on indexed-color objects
% ============================================================================ %
% Local functions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% getCDataHandles -- get handles of all descendents with indexed CData
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function hout = getCDataHandles(h)
% getCDataHandles Find all objects with indexed CData
%recursively descend object tree, finding objects with indexed CData
% An exception: don't include children of objects that themselves have CData:
% for example, scattergroups are non-standard hggroups, with CData. Changing
% such a group's CData automatically changes the CData of its children,
% (as well as the children's handles), so there's no need to act on them.
error(nargchk(1,1,nargin,'struct'))
hout = [];
if isempty(h),return;end
ch = get(h,'children');
for hh = ch'
g = get(hh);
if isfield(g,'CData'), %does object have CData?
%is it indexed/scaled?
if ~isempty(g.CData) && isnumeric(g.CData) && size(g.CData,3)==1,
hout = [hout; hh]; %#ok<AGROW> %yes, add to list
end
else %no CData, see if object has any interesting children
hout = [hout; getCDataHandles(hh)]; %#ok<AGROW>
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% getParentAxes -- return handle of axes object to which a given object belongs
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function hAx = getParentAxes(h)
% getParentAxes Return enclosing axes of a given object (could be self)
error(nargchk(1,1,nargin,'struct'))
%object itself may be an axis
if strcmp(get(h,'type'),'axes'),
hAx = h;
return
end
parent = get(h,'parent');
if (strcmp(get(parent,'type'), 'axes')),
hAx = parent;
else
hAx = getParentAxes(parent);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% checkArgs -- Validate input arguments
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [h, nancolor] = checkArgs(args)
% checkArgs Validate input arguments to freezeColors
nargs = length(args);
error(nargchk(0,3,nargs,'struct'))
%grab handle from first argument if we have an odd number of arguments
if mod(nargs,2),
h = args{1};
if ~ishandle(h),
error('JRI:freezeColors:checkArgs:invalidHandle',...
'The first argument must be a valid graphics handle (to an axis)')
end
% 4/2010 check if object to be frozen is a colorbar
if strcmp(get(h,'Tag'),'Colorbar'),
if ~exist('cbfreeze.m'),
warning('JRI:freezeColors:checkArgs:cannotFreezeColorbar',...
['You seem to be attempting to freeze a colorbar. This no longer'...
'works. Please read the help for freezeColors for the solution.'])
else
cbfreeze(h);
return
end
end
args{1} = [];
nargs = nargs-1;
else
h = gca;
end
%set nancolor if that option was specified
nancolor = [nan nan nan];
if nargs == 2,
if strcmpi(args{end-1},'nancolor'),
nancolor = args{end};
if ~all(size(nancolor)==[1 3]),
error('JRI:freezeColors:checkArgs:badColorArgument',...
'nancolor must be [r g b] vector');
end
nancolor(nancolor>1) = 1; nancolor(nancolor<0) = 0;
else
error('JRI:freezeColors:checkArgs:unrecognizedOption',...
'Unrecognized option (%s). Only ''nancolor'' is valid.',args{end-1})
end
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
ModestAdaBoost.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/ModestAdaBoost.m
| 3,156 |
utf_8
|
c030e77a7bae187a1e6fd5ec623874eb
|
% The algorithms developed and implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
%
% ModestAdaBoost Implements boosting process based on "Modest AdaBoost"
% algorithm
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% [Learners, Weights, final_hyp] = ModestAdaBoost(WeakLrn, Data, Labels,
% Max_Iter, OldW, OldLrn, final_hyp)
% ---------------------------------------------------------------------------------
% Arguments:
% WeakLrn - weak learner
% Data - training data. Should be DxN matrix, where D is the
% dimensionality of data, and N is the number of
% training samples.
% Labels - training labels. Should be 1xN matrix, where N is
% the number of training samples.
% Max_Iter - number of iterations
% OldW - weights of already built commitee (used for training
% of already built commitee)
% OldLrn - learnenrs of already built commitee (used for training
% of already built commitee)
% final_hyp - output for training data of already built commitee
% (used to speed up training of already built commitee)
% Return:
% Learners - cell array of constructed learners
% Weights - weights of learners
% final_hyp - output for training data
function [Learners, Weights, final_hyp] = ModestAdaBoost(WeakLrn, Data, Labels, Max_Iter, OldW, OldLrn, final_hyp)
global ME_min;
if( nargin < 7)
alpha = 1;
end
if( nargin == 4)
Learners = {};
Weights = [];
distr = ones(1, size(Data,2)) / size(Data,2);
final_hyp = zeros(1, size(Data,2));
elseif( nargin > 5)
Learners = OldLrn;
Weights = OldW;
if(nargin < 7)
final_hyp = Classify(Learners, Weights, Data);
end
distr = exp(- (Labels .* final_hyp));
distr = distr / sum(distr);
else
error('Fuck');
end
L = length(Learners);
for It = 1 : Max_Iter
%chose best learner
nodes = train(WeakLrn, Data, Labels, distr);
% calc error
rev_distr = ((1 ./ distr)) / sum ((1 ./ distr));
for i = 1:length(nodes)
curr_tr = nodes{i};
step_out = calc_output(curr_tr, Data);
s1 = sum( (Labels == 1) .* (step_out) .* distr);
s2 = sum( (Labels == -1) .* (step_out) .* distr);
s1_rev = sum( (Labels == 1) .* (step_out) .* rev_distr);
s2_rev = sum( (Labels == -1) .* (step_out) .* rev_distr);
Alpha = s1 * (1 - s1_rev) - s2 * (1 - s2_rev);
if(sign(Alpha) ~= sign(s1 - s2) || (s1 + s2) == 0)
continue;
end
Weights(end+1) = Alpha;
Learners{end+1} = curr_tr;
final_hyp = final_hyp + step_out .* Alpha;
end
distr = exp(- 1 * (Labels .* final_hyp));
Z = sum(distr);
distr = distr / Z;
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
RealAdaBoost.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/RealAdaBoost.m
| 2,840 |
utf_8
|
2666cf47840c3fe72ded547d5b355335
|
% The algorithms implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
%
% RealAdaBoost Implements boosting process based on "Real AdaBoost"
% algorithm
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% [Learners, Weights, final_hyp] = RealAdaBoost(WeakLrn, Data, Labels,
% Max_Iter, OldW, OldLrn, final_hyp)
% ---------------------------------------------------------------------------------
% Arguments:
% WeakLrn - weak learner
% Data - training data. Should be DxN matrix, where D is the
% dimensionality of data, and N is the number of
% training samples.
% Labels - training labels. Should be 1xN matrix, where N is
% the number of training samples.
% Max_Iter - number of iterations
% OldW - weights of already built commitee (used for training
% of already built commitee)
% OldLrn - learnenrs of already built commitee (used for training
% of already built commitee)
% final_hyp - output for training data of already built commitee
% (used to speed up training of already built commitee)
% Return:
% Learners - cell array of constructed learners
% Weights - weights of learners
% final_hyp - output for training data
function [Learners, Weights, final_hyp] = RealAdaBoost(WeakLrn, Data, Labels, Max_Iter, OldW, OldLrn, final_hyp)
if( nargin == 4)
Learners = {};
Weights = [];
distr = ones(1, size(Data,2)) / size(Data,2);
final_hyp = zeros(1, size(Data,2));
elseif( nargin > 5)
Learners = OldLrn;
Weights = OldW;
if(nargin < 7)
final_hyp = Classify(Learners, Weights, Data);
end
distr = exp(- (Labels .* final_hyp));
distr = distr / sum(distr);
else
error('Function takes eather 4 or 6 arguments');
end
for It = 1 : Max_Iter
%chose best learner
nodes = train(WeakLrn, Data, Labels, distr);
for i = 1:length(nodes)
curr_tr = nodes{i};
step_out = calc_output(curr_tr, Data);
s1 = sum( (Labels == 1) .* (step_out) .* distr);
s2 = sum( (Labels == -1) .* (step_out) .* distr);
if(s1 == 0 && s2 == 0)
continue;
end
Alpha = 0.5*log((s1 + eps) / (s2+eps));
Weights(end+1) = Alpha;
Learners{end+1} = curr_tr;
final_hyp = final_hyp + step_out .* Alpha;
end
distr = exp(- 1 * (Labels .* final_hyp));
Z = sum(distr);
distr = distr / Z;
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
GentleAdaBoost.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/GentleAdaBoost.m
| 2,913 |
utf_8
|
f5ca8646cb65ff5571b95135827f14b3
|
% The algorithms implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
%
% GentleAdaBoost Implements boosting process based on "Gentle AdaBoost"
% algorithm
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% [Learners, Weights, final_hyp] = GentleAdaBoost(WeakLrn, Data, Labels,
% Max_Iter, OldW, OldLrn, final_hyp)
% ---------------------------------------------------------------------------------
% Arguments:
% WeakLrn - weak learner
% Data - training data. Should be DxN matrix, where D is the
% dimensionality of data, and N is the number of
% training samples.
% Labels - training labels. Should be 1xN matrix, where N is
% the number of training samples.
% Max_Iter - number of iterations
% OldW - weights of already built commitee (used for training
% of already built commitee)
% OldLrn - learnenrs of already built commitee (used for training
% of already built commitee)
% final_hyp - output for training data of already built commitee
% (used to speed up training of already built commitee)
% Return:
% Learners - cell array of constructed learners
% Weights - weights of learners
% final_hyp - output for training data
function [Learners, Weights, final_hyp] = GentleAdaBoost(WeakLrn, Data, Labels, Max_Iter, OldW, OldLrn, final_hyp)
global GE_min;
if( nargin == 4)
Learners = {};
Weights = [];
distr = ones(1, size(Data,2)) / size(Data,2);
final_hyp = zeros(1, size(Data,2));
elseif( nargin > 5)
Learners = OldLrn;
Weights = OldW;
if(nargin < 7)
final_hyp = Classify(Learners, Weights, Data);
end
distr = exp(- (Labels .* final_hyp));
distr = distr / sum(distr);
else
error('Function takes eather 4 or 6 arguments');
end
for It = 1 : Max_Iter
fprintf('.');
if mod(It,50) == 0, fprintf('\n'),end;
%chose best learner
nodes = train(WeakLrn, Data, Labels, distr);
for i = 1:length(nodes)
curr_tr = nodes{i};
step_out = calc_output(curr_tr, Data);
s1 = sum( (Labels == 1) .* (step_out) .* distr);
s2 = sum( (Labels == -1) .* (step_out) .* distr);
if(s1 == 0 && s2 == 0)
continue;
end
Alpha = (s1 - s2) / (s1 + s2);
Weights(end+1) = Alpha;
Learners{end+1} = curr_tr;
final_hyp = final_hyp + step_out .* Alpha;
end
distr = exp(- 1 * (Labels .* final_hyp));
Z = sum(distr);
distr = distr / Z;
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
Classify.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/Classify.m
| 1,238 |
utf_8
|
ceb50fd41859da083c242df1896f71d9
|
% The algorithms implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
%
% Classify Implements classification data samples by already built
% boosted commitee
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% Result = Classify(Learners, Weights, Data)
% ---------------------------------------------------------------------------------
% Arguments:
% Learners - cell array of weak learners
% Weights - vector of learners weights
% Data - Data to be classified. Should be DxN matrix,
% where D is the dimensionality of data, and N
% is the number of data samples.
% Return:
% Result - vector of real valued commitee outputs for Data.
function Result = Classify(Learners, Weights, Data)
Result = zeros(1, size(Data, 2));
for i = 1 : length(Weights)
lrn_out = calc_output(Learners{i}, Data);
Result = Result + lrn_out * Weights(i);
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
TranslateToC.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/TranslateToC.m
| 1,339 |
utf_8
|
92e85b9a48079bf98fdb8f3d00a7fecf
|
% The algorithms implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
%
% TranslateToC Implements procedure of saving trained classifier to file,
% that can be further used in C++ programm
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% code = TranslateToC (Learners, Weights, fid)
% ---------------------------------------------------------------------------------
% Arguments:
% Learners - learners of commitee
% Weights - weights of commitee
% fid - opened file id (use fopen to make one)
% Return:
% code - equals 1 if everything was alright
function code = TranslateToC (Learners, Weights, fid)
Weights = Weights ./ (sum(abs(Weights)));
fprintf(fid, ' %d\r\n ', length(Weights));
for i = 1 : length(Weights)
Curr_Result = get_dim_and_tr(Learners{i});
fprintf(fid, ' %f ', Weights(i));
fprintf(fid, ' %d ', length(Curr_Result) / 3);
for j = 1 : length(Curr_Result)
fprintf(fid, ' %f ', Curr_Result(j));
end
fprintf(fid, '\r\n');
end
code = 1;
|
github
|
BottjerLab/Acoustic_Similarity-master
|
get_threshold_and_dim.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/@stump_w/get_threshold_and_dim.m
| 427 |
utf_8
|
786dd5f266e245d713c0e55ea0fd3878
|
% The algorithms implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
function [thr, dim] = get_threshold_and_dim(stump)
thr = stump.threshold;
dim = stump.t_dim;
|
github
|
BottjerLab/Acoustic_Similarity-master
|
stump_w.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/@stump_w/stump_w.m
| 512 |
utf_8
|
12cee278ab5b85d39a91ec8a6e578e65
|
% The algorithms implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
function stump = stump_w
stump.threshold = 0;
stump.signum = 1;
stump.t_dim = 1;
stump=class(stump, 'stump_w') ;
%tr=class(tr, 'threshold_w', learner(idim, odim), learner_w) ;
|
github
|
BottjerLab/Acoustic_Similarity-master
|
calc_output.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/@stump_w/calc_output.m
| 500 |
utf_8
|
1f8a7dbbe5dca5881c29affef02ecdd9
|
% The algorithms implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
function y = calc_output(stump, XData)
y = (XData(stump.t_dim, :) <= stump.threshold) * (stump.signum) + (XData(stump.t_dim, :) > stump.threshold) * (-stump.signum);
|
github
|
BottjerLab/Acoustic_Similarity-master
|
do_learn_nu.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/@stump_w/do_learn_nu.m
| 2,648 |
utf_8
|
a07989e9cb721ab7c2cad9e2629dac17
|
% The algorithms implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
function stump = do_learn_nu(stump, dataset, labels, weights)
%dataset=data_w(dataset) ;
%stump = set_distr(stump, get_sampl_weights(dataset)) ;
Distr = weights;
%[trainpat, traintarg] = get_train( dataset);
trainpat = dataset;
traintarg = labels;
tr_size = size(trainpat, 2);
T_MIN = zeros(3,size(trainpat,1));
for d = 1 : size(trainpat,1);
[DS, IX] = sort(trainpat(d,:));
TS = traintarg(IX);
DiS = Distr(IX);
lDS = length(DS);
vPos = 0 * TS;
vNeg = vPos;
i = 1;
j = 1;
while i <= lDS
k = 0;
while i + k <= lDS && DS(i) == DS(i+k)
if(TS(i+k) > 0)
vPos(j) = vPos(j) + DiS(i+k);
else
vNeg(j) = vNeg(j) + DiS(i+k);
end
k = k + 1;
end
i = i + k;
j = j + 1;
end
vNeg = vNeg(1:j-1);
vPos = vPos(1:j-1);
Error = zeros(1, j - 1);
InvError = Error;
IPos = vPos;
INeg = vNeg;
for i = 2 : length(IPos)
IPos(i) = IPos(i-1) + vPos(i);
INeg(i) = INeg(i-1) + vNeg(i);
end
Ntot = INeg(end);
Ptot = IPos(end);
for i = 1 : j - 1
Error(i) = IPos(i) + Ntot - INeg(i);
InvError(i) = INeg(i) + Ptot - IPos(i);
end
idx_of_err_min = find(Error == min(Error));
if(length(idx_of_err_min) < 1)
idx_of_err_min = 1;
end
if(length(idx_of_err_min) <1)
idx_of_err_min = idx_of_err_min;
end
idx_of_err_min = idx_of_err_min(1);
idx_of_inv_err_min = find(InvError == min(InvError));
if(length(idx_of_inv_err_min) < 1)
idx_of_inv_err_min = 1;
end
idx_of_inv_err_min = idx_of_inv_err_min(1);
if(Error(idx_of_err_min) < InvError(idx_of_inv_err_min))
T_MIN(1,d) = Error(idx_of_err_min);
T_MIN(2,d) = idx_of_err_min;
T_MIN(3,d) = -1;
else
T_MIN(1,d) = InvError(idx_of_inv_err_min);
T_MIN(2,d) = idx_of_inv_err_min;
T_MIN(3,d) = 1;
end
end
best_dim = find(T_MIN(1,:) == min(T_MIN(1,:)));
stump.t_dim = best_dim(1);
TDS = sort(trainpat(stump.t_dim,:));
lDS = length(TDS);
DS = TDS * 0;
i = 1;
j = 1;
while i <= lDS
k = 0;
while i + k <= lDS && TDS(i) == TDS(i+k)
DS(j) = TDS(i);
k = k + 1;
end
i = i + k;
j = j + 1;
end
DS = DS(1:j-1);
stump.threshold = (DS(T_MIN(2,stump.t_dim)) + DS(min(T_MIN(2,stump.t_dim) + 1, length(DS)))) / 2;
stump.signum = T_MIN(3,stump.t_dim);
|
github
|
BottjerLab/Acoustic_Similarity-master
|
get_dim_and_tr.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/@tree_node_w/get_dim_and_tr.m
| 1,905 |
utf_8
|
128d5e5a782cf045502564dba3c897eb
|
% The algorithms implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
%
% get_dim_and_tr is the function, that returns dimension and threshold of
% tree node
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% output = get_dim_and_tr(tree_node, output)
% ---------------------------------------------------------------------------------
% Arguments:
% tree_node - a node of classification tree
% output - vector of dimensions and thresholds. Result fo
% current node would be concatinated to it
% Return:
% output - a vector of thresholds and dimensions. It has the
% following format:
% [dimension threshold left/right ...]
% left/right is [-1, +1] number, wich signifies if
% current threshold is eather left or right
function output = get_dim_and_tr(tree_node, output)
if(nargin < 2)
output = [];
end
if(length(tree_node.parent) > 0)
output = get_dim_and_tr(tree_node.parent, output);
end
output(end+1) = tree_node.dim;
if( length(tree_node.right_constrain) > 0)
output(end+1) = tree_node.right_constrain;
output(end+1) = -1;
elseif( length(tree_node.left_constrain) > 0)
output(end+1) = tree_node.left_constrain;
output(end+1) = +1;
end
% function [dim, tr, signum] = get_dim_and_tr(tree_node)
%
% dim = tree_node.dim;
%
%
%
% if( length(tree_node.right_constrain) > 0)
% tr = tree_node.right_constrain;
% signum = -1;
% end
% if( length(tree_node.left_constrain) > 0)
% tr = tree_node.left_constrain;
% signum = +1;
% end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
calc_output.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/@tree_node_w/calc_output.m
| 1,236 |
utf_8
|
87fb96d3f77a0ac11a5430a6a3883aa6
|
% The algorithms implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
%
% calc_output Implements classification of input by a classification tree node
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% y = calc_output(tree_node, XData)
% ---------------------------------------------------------------------------------
% Arguments:
% tree_node - classification tree node
% XData - data, that will be classified
% Return:
% y - +1, if XData belongs to tree node, -1 otherwise (y is a vector)
function y = calc_output(tree_node, XData)
y = XData(tree_node.dim, :) * 0 + 1;
for i = 1 : length(tree_node.parent)
y = y .* calc_output(tree_node.parent, XData);
end
if( length(tree_node.right_constrain) > 0)
y = y .* ((XData(tree_node.dim, :) < tree_node.right_constrain));
end
if( length(tree_node.left_constrain) > 0)
y = y .* ((XData(tree_node.dim, :) > tree_node.left_constrain));
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
do_learn_nu.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/@tree_node_w/do_learn_nu.m
| 4,266 |
utf_8
|
5be6c419f73699a7df841d2736111631
|
% The algorithms implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
%
% do_learn_nu Implements splitting of tree node
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% [tree_node_left, tree_node_right, split_error] =
% do_learn_nu(tree_node, dataset, labels, weights, papa)
% ---------------------------------------------------------------------------------
% Arguments:
% tree_node - object of tree_node_w class
% dataset - training data
% labels - training labels
% weights - weights of training data
% papa - parent node (the one being split)
% Return:
% tree_node_left - left node (result of splitting)
% tree_node_right - right node (result of splitting)
% split_error - error of splitting
function [tree_node_left, tree_node_right, split_error] = do_learn_nu(tree_node, dataset, labels, weights, papa)
tree_node_left = tree_node;
tree_node_right = tree_node;
if(nargin > 4)
tree_node_left.parent = papa;
tree_node_right.parent = papa;
end
% [i,t] = weakLearner(weights,dataset', labels');
%
% tree_node_left.right_constrain = t;
% tree_node_right.left_constrain = t;
%
% tree_node_left.dim = i;
% tree_node_right.dim = i;
%
% return;
%dataset=data_w(dataset) ;
%stump = set_distr(stump, get_sampl_weights(dataset)) ;
Distr = weights;
%[trainpat, traintarg] = get_train( dataset);
trainpat = dataset;
traintarg = labels;
tr_size = size(trainpat, 2);
T_MIN = zeros(3,size(trainpat,1));
d_min = 1;
d_max = size(trainpat,1);
for d = d_min : d_max;
[DS, IX] = sort(trainpat(d,:));
TS = traintarg(IX);
DiS = Distr(IX);
lDS = length(DS);
vPos = 0 * TS;
vNeg = vPos;
i = 1;
j = 1;
while i <= lDS
k = 0;
while i + k <= lDS && DS(i) == DS(i+k)
if(TS(i+k) > 0)
vPos(j) = vPos(j) + DiS(i+k);
else
vNeg(j) = vNeg(j) + DiS(i+k);
end
k = k + 1;
end
i = i + k;
j = j + 1;
end
vNeg = vNeg(1:j-1);
vPos = vPos(1:j-1);
Error = zeros(1, j - 1);
InvError = Error;
IPos = vPos;
INeg = vNeg;
for i = 2 : length(IPos)
IPos(i) = IPos(i-1) + vPos(i);
INeg(i) = INeg(i-1) + vNeg(i);
end
Ntot = INeg(end);
Ptot = IPos(end);
for i = 1 : j - 1
Error(i) = IPos(i) + Ntot - INeg(i);
InvError(i) = INeg(i) + Ptot - IPos(i);
end
idx_of_err_min = find(Error == min(Error));
if(length(idx_of_err_min) < 1)
idx_of_err_min = 1;
end
if(length(idx_of_err_min) <1)
idx_of_err_min = idx_of_err_min;
end
idx_of_err_min = idx_of_err_min(1);
idx_of_inv_err_min = find(InvError == min(InvError));
if(length(idx_of_inv_err_min) < 1)
idx_of_inv_err_min = 1;
end
idx_of_inv_err_min = idx_of_inv_err_min(1);
if(Error(idx_of_err_min) < InvError(idx_of_inv_err_min))
T_MIN(1,d) = Error(idx_of_err_min);
T_MIN(2,d) = idx_of_err_min;
T_MIN(3,d) = -1;
else
T_MIN(1,d) = InvError(idx_of_inv_err_min);
T_MIN(2,d) = idx_of_inv_err_min;
T_MIN(3,d) = 1;
end
end
dim = [];
best_dim = find(T_MIN(1,:) == min(T_MIN(1,:)));
dim = best_dim(1);
tree_node_left.dim = dim;
tree_node_right.dim = dim;
TDS = sort(trainpat(dim,:));
lDS = length(TDS);
DS = TDS * 0;
i = 1;
j = 1;
while i <= lDS
k = 0;
while i + k <= lDS && TDS(i) == TDS(i+k)
DS(j) = TDS(i);
k = k + 1;
end
i = i + k;
j = j + 1;
end
DS = DS(1:j-1);
split = (DS(T_MIN(2,dim)) + DS(min(T_MIN(2,dim) + 1, length(DS)))) / 2;
split_error = T_MIN(1,dim);
tree_node_left.right_constrain = split;
tree_node_right.left_constrain = split;
function [i,t] = weakLearner(distribution,train,label)
%disp('run weakLearner');
for tt = unique(train)%1:(16*256-1)
error(tt)=(label .* distribution) * ((train(:,floor(tt/16)+1)>=16*(mod(tt,16)+1)));
end
[val,tt]=max(abs(error-0.5));
i=floor(tt/16)+1;
t=16*(mod(tt,16)+1);
return;
|
github
|
BottjerLab/Acoustic_Similarity-master
|
train.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/@tree_node_w/train.m
| 3,474 |
utf_8
|
4612a951935a7047eb5e580691cb7d0b
|
% The algorithms implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
%
% train Implements training of a classification tree
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% nodes = train(node, dataset, labels, weights)
% ---------------------------------------------------------------------------------
% Arguments:
% node - object of tree_node_w class (initialized properly)
% dataset - training data
% labels - training labels
% weights - weights of training data
% Return:
% nodes - tree is represented as a cell array of its nodes
function nodes = train(node, dataset, labels, weights)
max_split = node.max_split;
[left right spit_error] = do_learn_nu(node, dataset, labels, weights);
nodes = {left, right};
left_pos = sum((calc_output(left , dataset) == labels) .* weights);
left_neg = sum((calc_output(left , dataset) == -labels) .* weights);
right_pos = sum((calc_output(right, dataset) == labels) .* weights);
right_neg = sum((calc_output(right, dataset) == -labels) .* weights);
errors = [min(left_pos, left_neg), min(right_pos, right_neg)];
if(right_pos == 0 && right_neg == 0)
return;
end
if(left_pos == 0 && left_neg == 0)
return;
end
[errors, IDX] = sort(errors);
errors = flipdim(errors,2);
IDX = flipdim(IDX,2);
nodes = nodes(IDX);
splits = [];
split_errors = [];
deltas = [];
for i = 2 : max_split
for j = 1 : length(errors)
if(length(deltas) >= j)
continue;
end
max_node = nodes{j};
max_node_out = calc_output(max_node, dataset);
mask = find(max_node_out == 1);
[left right spit_error] = do_learn_nu(node, dataset(:,mask), labels(mask), weights(mask), max_node);
left_pos = sum((calc_output(left , dataset) == labels) .* weights);
left_neg = sum((calc_output(left , dataset) == -labels) .* weights);
right_pos = sum((calc_output(right, dataset) == labels) .* weights);
right_neg = sum((calc_output(right, dataset) == -labels) .* weights);
splits{end+1} = left;
splits{end+1} = right;
if( (right_pos + right_neg) == 0 || (left_pos + left_neg) == 0)
deltas(end+1) = 0;
else
deltas(end+1) = errors(j) - spit_error;
end
split_errors(end+1) = min(left_pos, left_neg);
split_errors(end+1) = min(right_pos, right_neg);
end
if(max(deltas) == 0)
return;
end
best_split = find(deltas == max(deltas));
best_split = best_split(1);
cut_vec = [1 : (best_split-1) (best_split + 1) : length(errors)];
nodes = nodes(cut_vec);
errors = errors(cut_vec);
deltas = deltas(cut_vec);
nodes{end+1} = splits{2 * best_split - 1};
nodes{end+1} = splits{2 * best_split};
errors(end+1) = split_errors(2 * best_split - 1);
errors(end+1) = split_errors(2 * best_split);
cut_vec = [1 : 2 * (best_split-1) 2 * (best_split)+1 : length(split_errors)];
split_errors = split_errors(cut_vec);
splits = splits(cut_vec);
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
crossvalidation.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/@crossvalidation/crossvalidation.m
| 967 |
utf_8
|
5885782c13c91dcbd80398a1bc3bc552
|
% The algorithms implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
%
% crossvalidation Implements the constructor for crossvalidation class
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% this = crossvalidation(folds)
% ---------------------------------------------------------------------------------
% Arguments:
% folds - number of cross-validation folds
% Return:
% this - object of crossvalidation class
function this = crossvalidation(folds)
if( folds == 1)
error('folds should be >= 2');
end
this.folds = folds;
this.CrossDataSets = cell(folds, 1);
this.CrossLabelsSets = cell(folds, 1);
this=class(this, 'crossvalidation') ;
|
github
|
BottjerLab/Acoustic_Similarity-master
|
CatFold.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/@crossvalidation/CatFold.m
| 1,132 |
utf_8
|
14bc637af960adb6280c6916fb2cb3fd
|
% The algorithms implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
%
% CatFold cancatinates cross-validation fold N to passed Data and Labels
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% [Data, Labels] = CatFold(this, Data, Labels, N)
% ---------------------------------------------------------------------------------
% Arguments:
% this - crossvalidation object
% Data - Data matrix
% Labels - Labels matrix
% N - number of fold
% Return:
% Data - Data with fold N concatinated
% Labels - Labels with fold N concatinated
function [Data, Labels] = CatFold(this, Data, Labels, N)
if(N > this.folds)
error('N > total folds');
end
Data = cat(2, Data, this.CrossDataSets{N}{1,1});
Labels = cat(2, Labels, this.CrossLabelsSets{N}{1,1});
|
github
|
BottjerLab/Acoustic_Similarity-master
|
Initialize.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/@crossvalidation/Initialize.m
| 2,347 |
utf_8
|
b3501dc177ff753b917d1f464e85f1e2
|
% The algorithms implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
%
% Initialize initializes crossvalidation object with data
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% this = Initialize(this, Data, Labels)
% ---------------------------------------------------------------------------------
% Arguments:
% this - crossvalidation object
% Data - Should be DxN matrix, where D is the
% dimensionality of data, and N is the number of
% samples.
% Labels - Should be 1xN matrix, where N is
% the number of samples.
% Return:
% this - crossvalidation object, initialized with data and
% labels
function this = Initialize(this, Data, Labels)
if( size(Labels,1) ~= 1 || length(size(Labels)) ~= 2)
error('Labels should be a (1,N) matrix.');
end
if( size(Labels,2) ~= size(Data,2))
error('Data should be (M,N) matrix and Labels (1,N)');
end
for i = 1 : length(Data)
fold_i = floor(rand * (this.folds - eps) + 1);
% fold_i = mod(i,this.folds) + 1;
if( i > this.folds )
this.CrossDataSets{fold_i} = {cat(2, Data(:, i), this.CrossDataSets{fold_i}{1,1})};
this.CrossLabelsSets{fold_i} = {cat(2, Labels(:, i), this.CrossLabelsSets{fold_i}{1,1})};
else
this.CrossDataSets{i} = {cat(2, Data(:, i), this.CrossDataSets{i})};
this.CrossLabelsSets{i} = {cat(2, Labels(:, i), this.CrossLabelsSets{i})};
end
% if( i > this.folds )
% this.CrossDataSets{mod(i,this.folds) + 1} = {cat(2, Data(:, i), this.CrossDataSets{mod(i,this.folds) + 1}{1,1})};
% this.CrossLabelsSets{mod(i,this.folds) + 1} = {cat(2, Labels(:, i), this.CrossLabelsSets{mod(i,this.folds) + 1}{1,1})};
% else
% this.CrossDataSets{mod(i,this.folds) + 1} = {cat(2, Data(:, i), this.CrossDataSets{mod(i,this.folds) + 1})};
% this.CrossLabelsSets{mod(i,this.folds) + 1} = {cat(2, Labels(:, i), this.CrossLabelsSets{mod(i,this.folds) + 1})};
% end
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
GetFold.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/boosting/@crossvalidation/GetFold.m
| 976 |
utf_8
|
a33135611bfd93f1eca9ba3999e2d7b0
|
% The algorithms implemented by Alexander Vezhnevets aka Vezhnick
% <a>href="mailto:[email protected]">[email protected]</a>
%
% Copyright (C) 2005, Vezhnevets Alexander
% [email protected]
%
% This file is part of GML Matlab Toolbox
% For conditions of distribution and use, see the accompanying License.txt file.
%
% GetFold returns Nth fold
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% [Data, Labels] = GetFold(this, N)
% ---------------------------------------------------------------------------------
% Arguments:
% this - crossvalidation object
% N - number of fold
% Return:
% Data - fold N data
% Labels - fold N labels
function [Data, Labels] = GetFold(this, N)
Data = [];
Labels = [];
if(N > this.folds)
error('N > total folds');
end
Data = cat(2, Data, this.CrossDataSets{N}{1,1});
Labels = cat(2, Labels, this.CrossLabelsSets{N}{1,1});
|
github
|
BottjerLab/Acoustic_Similarity-master
|
progressbar.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/progressbar/progressbar.m
| 11,767 |
utf_8
|
06705e480618e134da62478338e8251c
|
function progressbar(varargin)
% Description:
% progressbar() provides an indication of the progress of some task using
% graphics and text. Calling progressbar repeatedly will update the figure and
% automatically estimate the amount of time remaining.
% This implementation of progressbar is intended to be extremely simple to use
% while providing a high quality user experience.
%
% Features:
% - Can add progressbar to existing m-files with a single line of code.
% - Supports multiple bars in one figure to show progress of nested loops.
% - Optional labels on bars.
% - Figure closes automatically when task is complete.
% - Only one figure can exist so old figures don't clutter the desktop.
% - Remaining time estimate is accurate even if the figure gets closed.
% - Minimal execution time. Won't slow down code.
% - Randomized color. When a programmer gets bored...
%
% Example Function Calls For Single Bar Usage:
% progressbar % Initialize/reset
% progressbar(0) % Initialize/reset
% progressbar('Label') % Initialize/reset and label the bar
% progressbar(0.5) % Update
% progressbar(1) % Close
%
% Example Function Calls For Multi Bar Usage:
% progressbar(0, 0) % Initialize/reset two bars
% progressbar('A', '') % Initialize/reset two bars with one label
% progressbar('', 'B') % Initialize/reset two bars with one label
% progressbar('A', 'B') % Initialize/reset two bars with two labels
% progressbar(0.3) % Update 1st bar
% progressbar(0.3, []) % Update 1st bar
% progressbar([], 0.3) % Update 2nd bar
% progressbar(0.7, 0.9) % Update both bars
% progressbar(1) % Close
% progressbar(1, []) % Close
% progressbar(1, 0.4) % Close
%
% Notes:
% For best results, call progressbar with all zero (or all string) inputs
% before any processing. This sets the proper starting time reference to
% calculate time remaining.
% Bar color is choosen randomly when the figure is created or reset. Clicking
% the bar will cause a random color change.
%
% Demos:
% % Single bar
% m = 500;
% progressbar % Init single bar
% for i = 1:m
% pause(0.01) % Do something important
% progressbar(i/m) % Update progress bar
% end
%
% % Simple multi bar (update one bar at a time)
% m = 4;
% n = 3;
% p = 100;
% progressbar(0,0,0) % Init 3 bars
% for i = 1:m
% progressbar([],0) % Reset 2nd bar
% for j = 1:n
% progressbar([],[],0) % Reset 3rd bar
% for k = 1:p
% pause(0.01) % Do something important
% progressbar([],[],k/p) % Update 3rd bar
% end
% progressbar([],j/n) % Update 2nd bar
% end
% progressbar(i/m) % Update 1st bar
% end
%
% % Fancy multi bar (use labels and update all bars at once)
% m = 4;
% n = 3;
% p = 100;
% progressbar('Monte Carlo Trials','Simulation','Component') % Init 3 bars
% for i = 1:m
% for j = 1:n
% for k = 1:p
% pause(0.01) % Do something important
% % Update all bars
% frac3 = k/p;
% frac2 = ((j-1) + frac3) / n;
% frac1 = ((i-1) + frac2) / m;
% progressbar(frac1, frac2, frac3)
% end
% end
% end
%
% Author:
% Steve Hoelzer
%
% Revisions:
% 2002-Feb-27 Created function
% 2002-Mar-19 Updated title text order
% 2002-Apr-11 Use floor instead of round for percentdone
% 2002-Jun-06 Updated for speed using patch (Thanks to waitbar.m)
% 2002-Jun-19 Choose random patch color when a new figure is created
% 2002-Jun-24 Click on bar or axes to choose new random color
% 2002-Jun-27 Calc time left, reset progress bar when fractiondone == 0
% 2002-Jun-28 Remove extraText var, add position var
% 2002-Jul-18 fractiondone input is optional
% 2002-Jul-19 Allow position to specify screen coordinates
% 2002-Jul-22 Clear vars used in color change callback routine
% 2002-Jul-29 Position input is always specified in pixels
% 2002-Sep-09 Change order of title bar text
% 2003-Jun-13 Change 'min' to 'm' because of built in function 'min'
% 2003-Sep-08 Use callback for changing color instead of string
% 2003-Sep-10 Use persistent vars for speed, modify titlebarstr
% 2003-Sep-25 Correct titlebarstr for 0% case
% 2003-Nov-25 Clear all persistent vars when percentdone = 100
% 2004-Jan-22 Cleaner reset process, don't create figure if percentdone = 100
% 2004-Jan-27 Handle incorrect position input
% 2004-Feb-16 Minimum time interval between updates
% 2004-Apr-01 Cleaner process of enforcing minimum time interval
% 2004-Oct-08 Seperate function for timeleftstr, expand to include days
% 2004-Oct-20 Efficient if-else structure for sec2timestr
% 2006-Sep-11 Width is a multiple of height (don't stretch on widescreens)
% 2010-Sep-21 Major overhaul to support multiple bars and add labels
%
persistent progfig progdata lastupdate
% Get inputs
if nargin > 0
input = varargin;
ninput = nargin;
else
% If no inputs, init with a single bar
input = {0};
ninput = 1;
end
% If task completed, close figure and clear vars, then exit
if input{1} == 1
if ishandle(progfig)
delete(progfig) % Close progress bar
end
clear progfig progdata lastupdate % Clear persistent vars
drawnow
return
end
% Init reset flag
resetflag = false;
% Set reset flag if first input is a string
if ischar(input{1})
resetflag = true;
end
% Set reset flag if all inputs are zero
if input{1} == 0
% If the quick check above passes, need to check all inputs
if all([input{:}] == 0) && (length([input{:}]) == ninput)
resetflag = true;
end
end
% Set reset flag if more inputs than bars
if ninput > length(progdata)
resetflag = true;
end
% If reset needed, close figure and forget old data
if resetflag
if ishandle(progfig)
delete(progfig) % Close progress bar
end
progfig = [];
progdata = []; % Forget obsolete data
end
% Create new progress bar if needed
if ishandle(progfig)
else % This strange if-else works when progfig is empty (~ishandle() does not)
% Define figure size and axes padding for the single bar case
height = 0.03;
width = height * 8;
hpad = 0.02;
vpad = 0.25;
% Figure out how many bars to draw
nbars = max(ninput, length(progdata));
% Adjust figure size and axes padding for number of bars
heightfactor = (1 - vpad) * nbars + vpad;
height = height * heightfactor;
vpad = vpad / heightfactor;
% Initialize progress bar figure
left = (1 - width) / 2;
bottom = (1 - height) / 2;
progfig = figure(...
'Units', 'normalized',...
'Position', [left bottom width height],...
'NumberTitle', 'off',...
'Resize', 'off',...
'MenuBar', 'none' );
% Initialize axes, patch, and text for each bar
left = hpad;
width = 1 - 2*hpad;
vpadtotal = vpad * (nbars + 1);
height = (1 - vpadtotal) / nbars;
for ndx = 1:nbars
% Create axes, patch, and text
bottom = vpad + (vpad + height) * (nbars - ndx);
progdata(ndx).progaxes = axes( ...
'Position', [left bottom width height], ...
'XLim', [0 1], ...
'YLim', [0 1], ...
'Box', 'on', ...
'ytick', [], ...
'xtick', [] );
progdata(ndx).progpatch = patch( ...
'XData', [0 0 0 0], ...
'YData', [0 0 1 1] );
progdata(ndx).progtext = text(0.99, 0.5, '', ...
'HorizontalAlignment', 'Right', ...
'FontUnits', 'Normalized', ...
'FontSize', 0.7 );
progdata(ndx).proglabel = text(0.01, 0.5, '', ...
'HorizontalAlignment', 'Left', ...
'FontUnits', 'Normalized', ...
'FontSize', 0.7 );
if ischar(input{ndx})
set(progdata(ndx).proglabel, 'String', input{ndx})
input{ndx} = 0;
end
% Set callbacks to change color on mouse click
set(progdata(ndx).progaxes, 'ButtonDownFcn', {@changecolor, progdata(ndx).progpatch})
set(progdata(ndx).progpatch, 'ButtonDownFcn', {@changecolor, progdata(ndx).progpatch})
set(progdata(ndx).progtext, 'ButtonDownFcn', {@changecolor, progdata(ndx).progpatch})
set(progdata(ndx).proglabel, 'ButtonDownFcn', {@changecolor, progdata(ndx).progpatch})
% Pick a random color for this patch
changecolor([], [], progdata(ndx).progpatch)
% Set starting time reference
if ~isfield(progdata(ndx), 'starttime') || isempty(progdata(ndx).starttime)
progdata(ndx).starttime = clock;
end
end
% Set time of last update to ensure a redraw
lastupdate = clock - 1;
end
% Process inputs and update state of progdata
for ndx = 1:ninput
if ~isempty(input{ndx})
progdata(ndx).fractiondone = input{ndx};
progdata(ndx).clock = clock;
end
end
% Enforce a minimum time interval between graphics updates
myclock = clock;
if abs(myclock(6) - lastupdate(6)) < 0.01 % Could use etime() but this is faster
return
end
% Update progress patch
for ndx = 1:length(progdata)
set(progdata(ndx).progpatch, 'XData', ...
[0, progdata(ndx).fractiondone, progdata(ndx).fractiondone, 0])
end
% Update progress text if there is more than one bar
if length(progdata) > 1
for ndx = 1:length(progdata)
set(progdata(ndx).progtext, 'String', ...
sprintf('%1d%%', floor(100*progdata(ndx).fractiondone)))
end
end
% Update progress figure title bar
if progdata(1).fractiondone > 0
runtime = etime(progdata(1).clock, progdata(1).starttime);
timeleft = runtime / progdata(1).fractiondone - runtime;
timeleftstr = sec2timestr(timeleft);
titlebarstr = sprintf('%2d%% %s remaining', ...
floor(100*progdata(1).fractiondone), timeleftstr);
else
titlebarstr = ' 0%';
end
set(progfig, 'Name', titlebarstr)
% Force redraw to show changes
drawnow
% Record time of this update
lastupdate = clock;
% ------------------------------------------------------------------------------
function changecolor(h, e, progpatch) %#ok<INUSL>
% Change the color of the progress bar patch
% Prevent color from being too dark or too light
colormin = 1.5;
colormax = 2.8;
thiscolor = rand(1, 3);
while (sum(thiscolor) < colormin) || (sum(thiscolor) > colormax)
thiscolor = rand(1, 3);
end
set(progpatch, 'FaceColor', thiscolor)
% ------------------------------------------------------------------------------
function timestr = sec2timestr(sec)
% Convert a time measurement from seconds into a human readable string.
% Convert seconds to other units
w = floor(sec/604800); % Weeks
sec = sec - w*604800;
d = floor(sec/86400); % Days
sec = sec - d*86400;
h = floor(sec/3600); % Hours
sec = sec - h*3600;
m = floor(sec/60); % Minutes
sec = sec - m*60;
s = floor(sec); % Seconds
% Create time string
if w > 0
if w > 9
timestr = sprintf('%d week', w);
else
timestr = sprintf('%d week, %d day', w, d);
end
elseif d > 0
if d > 9
timestr = sprintf('%d day', d);
else
timestr = sprintf('%d day, %d hr', d, h);
end
elseif h > 0
if h > 9
timestr = sprintf('%d hr', h);
else
timestr = sprintf('%d hr, %d min', h, m);
end
elseif m > 0
if m > 9
timestr = sprintf('%d min', m);
else
timestr = sprintf('%d min, %d sec', m, s);
end
else
timestr = sprintf('%d sec', s);
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
yin2.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/yin/junk/yin2.m
| 2,235 |
utf_8
|
b91a6e57061458dd6bd01fc6a7162a27
|
function r=yin2(p,fileinfo)
% YIN2 - fundamental frequency estimator
% new version (feb 2003)
%
%
% process signal a chunk at a time
idx=0;
totalhops=round(fileinfo.nsamples / p.hop);
r1=nan*zeros(1,totalhops);r2=nan*zeros(1,totalhops);
r3=nan*zeros(1,totalhops);r4=nan*zeros(1,totalhops);
idx2=0+round(p.wsize/2/p.hop);
while (1)
xx=sf_wave(fileinfo, [idx+1, idx+p.bufsize], []);
xx=xx(:,1); % first channel if multichannel
[prd,ap0,ap,pwr]=yin_helper(xx,p);
n=size(prd ,2);
if (~n) break; end;
idx=idx+n*p.hop;
r1(idx2+1:idx2+n)= prd;
r2(idx2+1:idx2+n)= ap0;
r3(idx2+1:idx2+n)= ap;
r4(idx2+1:idx2+n)= pwr;
idx2=idx2+n;
end
size(r1)
r.r1=r1; % period estimate
r.r2=r2; % gross aperiodicity measure
r.r3=r3; % fine aperiodicity measure
r.r4=r4; % power
sf_cleanup(fileinfo);
% end of program
% Estimate F0 of a chunk of signal
function [prd,ap0,ap,pwr]=yin_helper(x,p,dd)
[m,n]=size(x);
smooth=ceil(p.sr/p.lpf);
x=rsmooth(x,smooth); % light low-pass smoothing
x=x(smooth+1:end);
maxlag = ceil(p.sr/p.minf0);
minlag = floor(p.sr/p.maxf0);
hops=floor((m-maxlag-p.wsize)/p.hop);
prd=zeros(1,hops);
ap0=zeros(1,hops);
ap=zeros(1,hops);
pwr=zeros(1,hops);
if hops<1; return; end
% difference function matrix
dd=zeros(ceil((m-maxlag-2)/p.hop),maxlag+2); % +2 to improve interp near maxlag
lags=[zeros(1,maxlag+2); 1:maxlag+2];
rdiff_inplace(x,x,dd,lags,p.hop);
rsum_inplace(dd,round(p.wsize/p.hop));
dd=dd';
% parabolic interpolation near min, then cumulative mean normalization
[dd,ddx]=minparabolic(dd);
cumnorm_inplace(dd);
for j=0:hops-1
idx=j*p.hop;
d=dd(:,j+1);
dx=ddx(:,j+1);
% estimate period
pd=dftoperiod(d,[minlag,maxlag],p.thresh*2);
% gross aperiodicity is value of cumnormed df at minimum:
ap0(j+1)=d(pd)/2;
% fine tune period based on parabolic interpolation
pd=pd+dx(pd+1)+1;
% power estimates
k=(1:ceil(pd))';
x1=x(k+idx);
x2=k+idx+pd-1;
interp_inplace(x,x2);
x3=x2-x1;
x4=x2+x1;
x1=x1.^2; rsum_inplace(x1,pd);
x3=x3.^2; rsum_inplace(x3,pd);
x4=x4.^2; rsum_inplace(x4,pd);
x1=x1(1)/pd;
x2=x2(1)/pd;
x3=x3(1)/pd;
x4=x4(1)/pd;
% total power
pwr(j+1)=x1;
% fine aperiodicity
ap(j+1)=(eps+x3)/(eps+(x3+x4)); % accurate, only for valid min
prd(j+1)=pd;
end
|
github
|
BottjerLab/Acoustic_Similarity-master
|
yink.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/yin/private/yink.m
| 3,440 |
utf_8
|
77a95ea21aeeda525ec337a2f6545d95
|
function r=yink(p,fileinfo)
% YINK - fundamental frequency estimator
% new version (feb 2003)
%
%
%global jj;
%jj=0;
% process signal a chunk at a time
idx=p.range(1)-1;
totalhops=round((p.range(2)-p.range(1)+1) / p.hop);
r1=nan*zeros(1,totalhops);r2=nan*zeros(1,totalhops);
r3=nan*zeros(1,totalhops);r4=nan*zeros(1,totalhops);
idx2=0+round(p.wsize/2/p.hop);
while (1)
start = idx+1;
stop = idx+p.bufsize;
stop=min(stop, p.range(2));
xx=sf_wave(fileinfo, [start, stop], []);
% if size(xx,1) == 1; xx=xx'; end
xx=xx(:,1); % first channel if multichannel
[prd,ap0,ap,pwr]=yin_helper(xx,p);
n=size(prd ,2);
if (~n) break; end;
idx=idx+n*p.hop;
r1(idx2+1:idx2+n)= prd;
r2(idx2+1:idx2+n)= ap0;
r3(idx2+1:idx2+n)= ap;
r4(idx2+1:idx2+n)= pwr;
idx2=idx2+n;
end
r.r1=r1; % period estimate
r.r2=r2; % gross aperiodicity measure
r.r3=r3; % fine aperiodicity measure
r.r4=r4; % power
sf_cleanup(fileinfo);
% end of program
% Estimate F0 of a chunk of signal
function [prd,ap0,ap,pwr]=yin_helper(x,p,dd)
smooth=ceil(p.sr/p.lpf);
x=rsmooth(x,smooth); % light low-pass smoothing
x=x(smooth:end-smooth+1);
[m,n]=size(x);
maxlag = ceil(p.sr/p.minf0);
minlag = floor(p.sr/p.maxf0);
mxlg = maxlag+2; % +2 to improve interp near maxlag
hops=floor((m-mxlg-p.wsize)/p.hop);
prd=zeros(1,hops);
ap0=zeros(1,hops);
ap=zeros(1,hops);
pwr=zeros(1,hops);
if hops<1; return; end
% difference function matrix
dd=zeros(floor((m-mxlg-p.hop)/p.hop),mxlg);
if p.shift == 0 % windows shift both ways
lags1=round(mxlg/2) + round((0:mxlg-1)/2);
lags2=round(mxlg/2) - round((1:mxlg)/2);
lags=[lags1; lags2];
elseif p.shift == 1 % one window fixed, other shifts right
lags=[zeros(1,mxlg); 1:mxlg];
elseif p.shift == -1 % one window fixed, other shifts right
lags=[mxlg-1:-1:0; mxlg*ones(1,mxlg)];
else
error (['unexpected shift flag: ', num2str(p.shift)]);
end
rdiff_inplace(x,x,dd,lags,p.hop);
rsum_inplace(dd,min(round(p.wsize/p.hop),size(dd,1)));
dd=dd';
[dd,ddx]=minparabolic(dd); % parabolic interpolation near min
cumnorm_inplace(dd);; % cumulative mean-normalize
% first period estimate
%global jj;
for j=1:hops
d=dd(:,j);
if p.relflag
pd=dftoperiod2(d,[minlag,maxlag],p.thresh);
else
pd=dftoperiod(d,[minlag,maxlag],p.thresh);
end
ap0(j)=d(pd+1);
prd(j)=pd;
end
% replace each estimate by best estimate in range
range = 2*round(maxlag/p.hop);
if hops>1; prd=prd(mininrange(ap0,range*ones(1,hops))); end
%prd=prd(mininrange(ap0,prd));
% refine estimate by constraining search to vicinity of best local estimate
margin1=0.6;
margin2=1.8;
for j=1:hops
d=dd(:,j);
dx=ddx(:,j);
pd=prd(j);
lo=floor(pd*margin1); lo=max(minlag,lo);
hi=ceil(pd*margin2); hi=min(maxlag,hi);
pd=dftoperiod(d,[lo,hi],0);
ap0(j)=d(pd+1);
pd=pd+dx(pd+1)+1; % fine tune based on parabolic interpolation
prd(j)=pd;
% power estimates
idx=(j-1)*p.hop;
k=(1:ceil(pd))';
x1=x(k+idx);
x2=k+idx+pd-1;
interp_inplace(x,x2);
x3=x2-x1;
x4=x2+x1;
x1=x1.^2; rsum_inplace(x1,pd);
x3=x3.^2; rsum_inplace(x3,pd);
x4=x4.^2; rsum_inplace(x4,pd);
x1=x1(1)/pd;
x2=x2(1)/pd;
x3=x3(1)/pd;
x4=x4(1)/pd;
% total power
pwr(j)=x1;
% fine aperiodicity
ap(j)=(eps+x3)/(eps+(x3+x4)); % accurate, only for valid min
%ap(j)
%plot(min(1, d)); pause
prd(j)=pd;
end
%cumulative mean-normalize
function y=cumnorm(x)
[m,n]=size(x);
y = cumsum(x);
y = (y)./ (eps+repmat((1:m)',1,n)); % cumulative mean
y = (eps+x) ./ (eps+y);
|
github
|
BottjerLab/Acoustic_Similarity-master
|
anova2rm_cell.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/resampling_statistical_toolkit/statistics/anova2rm_cell.m
| 6,774 |
utf_8
|
37ad08d0dfb97a5ae59971a6becc90b7
|
% anova2rm_cell() - compute F-values in cell array using repeated measure
% ANOVA.
%
% Usage:
% >> [FC FR FI dfc dfr dfi] = anova2rm_cell( data );
%
% Inputs:
% data = data consisting of PAIRED arrays to be compared. The last
% dimension of the data array is used to compute ANOVA.
% Outputs:
% FC - F-value for columns.
% FR - F-value for rows.
% FI - F-value for interaction.
% dfc - degree of freedom for columns.
% dfr - degree of freedom for rows.
% dfi - degree of freedom for interaction.
%
% Note: this function is inspired from rm_anova available at
% http://www.mathworks.se/matlabcentral/fileexchange/6874-two-way-rep
% eated-measures-anova
% It allows for fast computation of about 20 thousands ANOVA per
% second. It is different from anova2_cell which mimics the ANOVA
% fonction from the Matlab statistical toolbox. This function
% computes true repeated measure ANOVA.
%
% Example:
% a = { rand(1,10) rand(1,10) rand(1,10); rand(1,10) rand(1,10) rand(1,10) }
% [FC FR FI dfc dfr dfi] = anova2rm_cell(a)
% signifC = 1-fcdf(FC, dfc(1), dfc(2))
% signifR = 1-fcdf(FR, dfr(1), dfr(2))
% signifI = 1-fcdf(FI, dfi(1), dfi(2))
%
% % for comparison
% z = zeros(10,1); o = ones(10,1); t = ones(10,1)*2;
% rm_anova2( [ a{1,1}';a{1,2}';a{1,3}';a{2,1}';a{2,2}';a{2,3}' ], ...
% repmat([1:10]', [6 1]), [o;o;o;z;z;z], [z;o;t;z;o;t], {'a','b'})
%
% c = { rand(200,400,10) rand(200,400,10); ...
% rand(200,400,10) rand(200,400,10)};
% [FC FR FI dfc dfr dfi] = anova2rm_cell(c) % computes 200x400 ANOVAs
%
% Author: Arnaud Delorme, SCCN/INC/UCSD, La Jolla, 2010
% Copyright (C) Arnaud Delorme
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program; if not, write to the Free Software
% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
function [fA fB fAB dfApair dfBpair dfABpair] = anova2rm_cell(data)
% compute all means and all std
% -----------------------------
a = size(data,1);
b = size(data,2);
nd = myndims( data{1} );
n = size( data{1} ,nd);
% only for paired stats
% ---------------------
if nd == 1
AB = zeros(a,b,'single');
AS = zeros(a,n,'single');
BS = zeros(b,n,'single');
sq = single(0);
for ind1 = 1:a
for ind2 = 1:b
AB(ind1,ind2) = sum(data{ind1,ind2});
AS(ind1,:) = AS(ind1,:) + data{ind1,ind2}';
BS(ind2,:) = BS(ind2,:) + data{ind1,ind2}';
sq = sq + sum(data{ind1,ind2}.^2);
end;
end;
dimA = 2;
dimB = 1;
elseif nd == 2
AB = zeros(size(data{1},1),a,b,'single');
AS = zeros(size(data{1},1),a,n,'single');
BS = zeros(size(data{1},1),b,n,'single');
sq = zeros(size(data{1},1),1,'single');
for ind1 = 1:a
for ind2 = 1:b
AB(:,ind1,ind2) = sum(data{ind1,ind2},nd);
AS(:,ind1,:) = AS(:,ind1,:) + reshape(data{ind1,ind2},size(data{1},1),1,n);
BS(:,ind2,:) = BS(:,ind2,:) + reshape(data{ind1,ind2},size(data{1},1),1,n);
sq = sq + sum(data{ind1,ind2}.^2,nd);
end;
end;
dimA = 3;
dimB = 2;
elseif nd == 3
AB = zeros(size(data{1},1),size(data{1},2),a,b,'single');
AS = zeros(size(data{1},1),size(data{1},2),a,n,'single');
BS = zeros(size(data{1},1),size(data{1},2),b,n,'single');
sq = zeros(size(data{1},1),size(data{1},2),'single');
for ind1 = 1:a
for ind2 = 1:b
AB(:,:,ind1,ind2) = sum(data{ind1,ind2},nd);
AS(:,:,ind1,:) = AS(:,:,ind1,:) + reshape(data{ind1,ind2},size(data{1},1),size(data{1},2),1,n);
BS(:,:,ind2,:) = BS(:,:,ind2,:) + reshape(data{ind1,ind2},size(data{1},1),size(data{1},2),1,n);
sq = sq + sum(data{ind1,ind2}.^2,nd);
end;
end;
dimA = 4;
dimB = 3;
elseif nd == 4
AB = zeros(size(data{1},1),size(data{1},2),size(data{1},3),a,b,'single');
AS = zeros(size(data{1},1),size(data{1},2),size(data{1},3),a,n,'single');
BS = zeros(size(data{1},1),size(data{1},2),size(data{1},3),b,n,'single');
sq = zeros(size(data{1},1),size(data{1},2),size(data{1},3),'single');
for ind1 = 1:a
for ind2 = 1:b
AB(:,:,:,ind1,ind2) = sum(data{ind1,ind2},nd);
AS(:,:,:,ind1,:) = AS(:,:,:,ind1,:) + reshape(data{ind1,ind2},size(data{1},1),size(data{1},2),size(data{1},3),1,n);
BS(:,:,:,ind2,:) = BS(:,:,:,ind2,:) + reshape(data{ind1,ind2},size(data{1},1),size(data{1},2),size(data{1},3),1,n);
sq = sq + sum(data{ind1,ind2}.^2,nd);
end;
end;
dimA = 5;
dimB = 4;
end;
A = sum(AB,dimA); % sum across columns, so result is ax1 column vector
B = sum(AB,dimB); % sum across rows, so result is 1xb row vector
S = sum(AS,dimB); % sum across columns, so result is 1xs row vector
T = sum(sum(A,dimB),dimA); % could sum either A or B or S, choice is arbitrary
% degrees of freedom
dfA = a-1;
dfB = b-1;
dfAB = (a-1)*(b-1);
dfS = n-1;
dfAS = (a-1)*(n-1);
dfBS = (b-1)*(n-1);
dfABS = (a-1)*(b-1)*(n-1);
% bracket terms (expected value)
expA = sum(A.^2,dimB)./(b*n);
expB = sum(B.^2,dimA)./(a*n);
expAB = sum(sum(AB.^2,dimA),dimB)./n;
expS = sum(S.^2,dimA)./(a*b);
expAS = sum(sum(AS.^2,dimB),dimA)./b;
expBS = sum(sum(BS.^2,dimB),dimA)./a;
expY = sq; %sum(Y.^2);
expT = T.^2 / (a*b*n);
% sums of squares
ssA = expA - expT;
ssB = expB - expT;
ssAB = expAB - expA - expB + expT;
ssS = expS - expT;
ssAS = expAS - expA - expS + expT;
ssBS = expBS - expB - expS + expT;
ssABS = expY - expAB - expAS - expBS + expA + expB + expS - expT;
ssTot = expY - expT;
% mean squares
msA = ssA / dfA;
msB = ssB / dfB;
msAB = ssAB / dfAB;
msS = ssS / dfS;
msAS = ssAS / dfAS;
msBS = ssBS / dfBS;
msABS = ssABS / dfABS;
% f statistic
fA = msA ./ msAS;
fB = msB ./ msBS;
fAB = msAB ./ msABS;
dfApair = [dfA dfAS];
dfBpair = [dfB dfBS];
dfABpair = [dfAB dfABS];
function val = myndims(a)
if ndims(a) > 2
val = ndims(a);
else
if size(a,1) == 1,
val = 2;
elseif size(a,2) == 1,
val = 1;
else
val = 2;
end;
end;
|
github
|
BottjerLab/Acoustic_Similarity-master
|
statcond.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/resampling_statistical_toolkit/statistics/statcond.m
| 22,523 |
utf_8
|
4da6856416931e3152c3292a592259a0
|
% statcond() - compare two or more data conditions statistically using
% standard parametric or nonparametric permutation-based ANOVA
% (1-way or 2-way) or t-test methods. Parametric testing uses
% fcdf() from the Matlab Statistical Toolbox. Use of up to
% 4-D data matrices speeds processing.
% Usage:
% >> [stats, df, pvals, surrog] = statcond( data, 'key','val'... );
% Inputs:
% data = one-or two-dimensional cell array of data matrices.
% For nonparametric, permutation-based testing, the
% last dimension of the data arrays (which may be of up to
% 4 dimensions) is permuted across conditions, either in
% a 'paired' fashion (not changing the, e.g., subject or
% trial order in the last dimension) or in an umpaired
% fashion (not respecting this order). If the number of
% elements in the last dimension is not the same across
% conditions, the 'paired' option is turned 'off'. Note:
% All other dimensions MUST be constant across conditions.
% For example, consider a (1,3) cell array of matrices
% of size (100,20,x) each holding a (100,20) time/frequency
% transform from each of x subjects. Only the last dimension
% (here x, the number of subjects) may differ across the
% three conditions.
% The test used depends on the size of the data array input.
% When the data cell array has 2 columns and the data are
% paired, a paired t-test is performed; when the data are
% unpaired, an unpaired t-test is performed. If 'data'
% has only one row (paired or unpaired) and more than 2
% columns, a one-way ANOVA is performed. If the data cell
% array contains several rows and columns, and the data is
% paired, a two-way repeated measure ANOVA is performed.
% NOTE THAT IF THE DATA is unpaired, EEGLAB will use a
% balanced 1 or 2 way ANOVA and parametric results might not
% be meaningful (bootstrap and permutation should be fine).
%
% Optional inputs:
% 'paired' = ['on'|'off'] pair the data array {default: 'on' unless
% the last dimension of data array is of different lengths}.
% 'mode' = ['perm'|'bootstrap'|'param'] mode for computing the p-values:
% 'param' = parametric testing (standard ANOVA or t-test);
% 'perm' = non-parametric testing using surrogate data
% 'bootstrap' = non-parametric bootstrap
% made by permuting the input data {default: 'param'}
% 'naccu' = [integer] Number of surrogate data copies to use in 'perm'
% or 'bootstrap' mode estimation (see above) {default: 200}.
% 'verbose' = ['on'|'off'] print info on the command line {default: 'on'}.
% 'variance' = ['homegenous'|'inhomogenous'] this option is exclusively
% for parametric statistics using unpaired t-test. It allows
% to compute a more accurate value for the degree of freedom
% using the formula for inhomogenous variance (see
% ttest2_cell function). Default is 'homegenous'.
%
% Outputs:
% stats = F- or T-value array of the same size as input data without
% the last dimension. A T value is returned only when the data
% includes exactly two conditions.
% df = degrees of freedom, a (2,1) vector, when F-values are returned
% pvals = array of p-values. Same size as input data without the last
% data dimension. All returned p-values are two-tailed.
% surrog = surrogate data array (same size as input data with the last
% dimension filled with a number ('naccu') of surrogate data sets.
%
% Important note: When a two-way ANOVA is performed, outputs are cell arrays
% with three elements: output(1) = column effects;
% output(2) = row effects; output(3) = interactions
% between rows and columns.
% Examples:
% >> a = { rand(1,10) rand(1,10)+0.5 }; % pseudo 'paired' data vectors
% [t df pvals] = statcond(a); % perform paired t-test
% pvals =
% 5.2807e-04 % standard t-test probability value
% % Note: for different rand() outputs, results will differ.
%
% [t df pvals surog] = statcond(a, 'mode', 'perm', 'naccu', 2000);
% pvals =
% 0.0065 % nonparametric t-test using 2000 permuted data sets
%
% a = { rand(2,11) rand(2,10) rand(2,12)+0.5 }; % pseudo 'unpaired'
% [F df pvals] = statcond(a); % perform an unpaired ANOVA
% pvals =
% 0.00025 % p-values for difference between columns
% 0.00002 % for each data row
%
% a = { rand(3,4,10) rand(3,4,10) rand(3,4,10); ...
% rand(3,4,10) rand(3,4,10) rand(3,4,10)+0.5 };
% % pseudo (2,3)-condition data array, each entry containing
% % ten (3,4) data matrices
% [F df pvals] = statcond(a); % perform a paired 2-way ANOVA
% % Output:
% pvals{1} % a (3,4) matrix of p-values; effects across columns
% pvals{2} % a (3,4) matrix of p-values; effects across rows
% pvals{3} % a (3,4) matrix of p-values; interaction effects
% % across rows and columns
%
% Author: Arnaud Delorme, SCCN/INC/UCSD, La Jolla, 2005-
% With rhanks to Robert Oostenveld for fruitful discussions
% and advice on this function.
%
% See also: anova1_cell(), anova2_cell(), anova2rm_cell, fcdf()
% perform a paired t-test
% -----------------------
% a = { rand(2,10) rand(2,10) };
% [t df pval] = statcond(a); pval
% [h p t stat] = ttest( a{1}(1,:), a{2}(1,:)); p
% [h p t stat] = ttest( a{1}(2,:), a{2}(2,:)); p
%
% compare significance levels
% --------------------------
% a = { rand(1,10) rand(1,10) };
% [F df pval] = statcond(a, 'mode', 'perm', 'naccu', 200); pval
% [h p t stat] = ttest( a{1}(1,:), a{2}(1,:)); p
% Copyright (C) Arnaud Delorme
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program; if not, write to the Free Software
% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
function [ ori_vals, df, pvals, surrogval ] = statcond( data, varargin );
if nargin < 1
help statcond;
return;
end;
try, warning('off', 'MATLAB:divideByZero'); catch, end;
if exist('finputcheck')
g = finputcheck( varargin, { 'naccu' 'integer' [1 Inf] 200;
'mode' 'string' { 'param' 'perm' 'bootstrap' } 'param';
'paired' 'string' { 'on' 'off' } 'on';
'arraycomp' 'string' { 'on' 'off' } 'on';
'variance' 'string' { 'homogenous' 'inhomogenous' } 'homogenous';
'returnresamplingarray' 'string' { 'on' 'off' } 'off';
'verbose' 'string' { 'on' 'off' } 'on' }, 'statcond');
if isstr(g), error(g); end;
else
g = struct(varargin{:});
if ~isfield(g, 'naccu'), g.naccu = 200; end;
if ~isfield(g, 'mode'), g.mode = 'param'; end;
if ~isfield(g, 'paired'), g.paired = 'on'; end;
if ~isfield(g, 'arraycomp'), g.arraycomp = 'on'; end;
if ~isfield(g, 'verbose'), g.verbose = 'on'; end;
if ~isfield(g, 'variance'), g.variance = 'homogenous'; end;
if ~isfield(g, 'returnresamplingarray'), g.returnresamplingarray = 'off'; end;
end;
if strcmpi(g.verbose, 'on'), verb = 1; else verb = 0; end;
if strcmp(g.mode, 'param' ) & exist('fcdf') ~= 2
myfprintf(verb,['statcond(): parametric testing requires fcdf() \n' ...
' from the Matlab StatsticaL Toolbox.\n' ...
' Running nonparametric permutation tests\n.']);
g.mode = 'perm';
end
if size(data,2) == 1, data = transpose(data); end; % cell array transpose
tmpsize = size(data{1});
if ~strcmpi(g.mode, 'param')
surrogval = zeros([ tmpsize(1:end-1) g.naccu ], 'single');
else surrogval = [];
end;
% bootstrap flag
% --------------
if strcmpi(g.mode, 'bootstrap'), bootflag = 1;
else bootflag = 0;
end;
% concatenate all data arrays
% ---------------------------
[ datavals datalen datadims ] = concatdata( data );
% test if data can be paired
% --------------------------
if length(unique(cellfun('size', data, ndims(data{1}) ))) > 1
g.paired = 'off';
end;
if strcmpi(g.paired, 'on')
pairflag = 1;
else pairflag = 0;
end;
% return resampling array
% -----------------------
if strcmpi(g.returnresamplingarray, 'on')
if strcmpi(g.arraycomp, 'on')
ori_vals = supersurrogate( datavals, datalen, datadims, bootflag, pairflag, g.naccu);
else
ori_vals = surrogate( datavals, datalen, datadims, bootflag, pairflag);
end;
return;
end;
% text output
% -----------
myfprintf(verb,'%d x %d, ', size(data,1), size(data,2));
if strcmpi(g.paired, 'on')
myfprintf(verb,'paired data, ');
else myfprintf(verb,'unpaired data, ');
end;
if size(data,1) == 1 && size(data,2) == 2
myfprintf(verb,'computing T values\n');
else myfprintf(verb,'computing F values\n');
end;
if size(data,1) > 1
if strcmpi(g.paired, 'on')
myfprintf(verb,'Using 2-way repeated measure ANOVA\n');
else myfprintf(verb,'Using balanced 2-way ANOVA (not suitable for parametric testing, only bootstrap)\n');
end;
elseif size(data,2) > 2
if strcmpi(g.paired, 'on')
myfprintf(verb,'Using 1-way repeated measure ANOVA\n');
else myfprintf(verb,'Using balanced 1-way ANOVA (equivalent to Matlab anova1)\n');
end;
else
if strcmpi(g.paired, 'on')
myfprintf(verb,'Using paired t-test\n');
else myfprintf(verb,'Using unpaired t-test\n');
end;
end;
if ~strcmpi(g.mode, 'param')
if bootflag, myfprintf(verb,'Bootstraps (of %d):', g.naccu);
else myfprintf(verb,'Permutations (of %d):', g.naccu);
end;
end;
if size(data,1) == 1, % only one row
if size(data,2) == 2
% paired t-test (very fast)
% -------------
tail = 'both';
[ori_vals df] = ttest_cell_select(data, g.paired, g.variance);
if strcmpi(g.mode, 'param')
pvals = 2*tcdf(-abs(ori_vals), df);
pvals = reshape(pvals, size(pvals));
return;
else
if strcmpi(g.arraycomp, 'on')
try
myfprintf(verb,'...');
res = supersurrogate( datavals, datalen, datadims, bootflag, pairflag, g.naccu);
surrogval = ttest_cell_select( res, g.paired, g.variance);
catch,
lasterr
myfprintf(verb,'\nSuperfast array computation failed because of memory limitation, reverting to standard computation');
g.arraycomp = 'off';
end;
end;
if strcmpi(g.arraycomp, 'off')
for index = 1:g.naccu
res = surrogate( datavals, datalen, datadims, bootflag, pairflag);
if mod(index, 10) == 0, myfprintf(verb,'%d ', index); end;
if mod(index, 100) == 0, myfprintf(verb,'\n'); end;
switch myndims(res{1})
case 1 , surrogval(index) = ttest_cell_select(res, g.paired, g.variance);
case 2 , surrogval(:,index) = ttest_cell_select(res, g.paired, g.variance);
otherwise, surrogval(:,:,index) = ttest_cell_select(res, g.paired, g.variance);
end;
end;
end;
end;
else
% one-way ANOVA (paired) this is equivalent to unpaired t-test
% -------------
tail = 'one';
[ori_vals df] = anova1_cell_select( data, g.paired );
if strcmpi(g.mode, 'param')
pvals = 1-fcdf(ori_vals, df(1), df(2)); return;
else
if strcmpi(g.arraycomp, 'on')
try
myfprintf(verb,'...');
res = supersurrogate( datavals, datalen, datadims, bootflag, pairflag, g.naccu);
surrogval = anova1_cell( res );
catch,
myfprintf(verb,'\nSuperfast array computation failed because of memory limitation, reverting to standard computing');
g.arraycomp = 'off';
end;
end;
if strcmpi(g.arraycomp, 'off')
for index = 1:g.naccu
if mod(index, 10) == 0, myfprintf(verb,'%d ', index); end;
if mod(index, 100) == 0, myfprintf(verb,'\n'); end;
res = surrogate( datavals, datalen, datadims, bootflag, pairflag);
switch myndims(data{1})
case 1 , surrogval(index) = anova1_cell_select( res, g.paired );
case 2 , surrogval(:,index) = anova1_cell_select( res, g.paired );
otherwise, surrogval(:,:,index) = anova1_cell_select( res, g.paired );
end;
end;
end;
end;
end;
else
% two-way ANOVA (paired or unpaired)
% ----------------------------------
tail = 'one';
[ ori_vals{1} ori_vals{2} ori_vals{3} df{1} df{2} df{3} ] = anova2_cell_select( data, g.paired );
if strcmpi(g.mode, 'param')
pvals{1} = 1-fcdf(ori_vals{1}, df{1}(1), df{1}(2));
pvals{2} = 1-fcdf(ori_vals{2}, df{2}(1), df{2}(2));
pvals{3} = 1-fcdf(ori_vals{3}, df{3}(1), df{3}(2));
return;
else
surrogval = { surrogval surrogval surrogval };
dataori = data;
if strcmpi(g.arraycomp, 'on')
try
myfprintf(verb,'...');
res = supersurrogate( datavals, datalen, datadims, bootflag, pairflag, g.naccu);
[ surrogval{1} surrogval{2} surrogval{3} ] = anova2_cell_select( res, g.paired );
catch,
myfprintf(verb,'\nSuperfast array computation failed because of memory limitation, reverting to standard computing');
g.arraycomp = 'off';
end;
end;
if strcmpi(g.arraycomp, 'off')
for index = 1:g.naccu
if mod(index, 10) == 0, myfprintf(verb,'%d ', index); end;
if mod(index, 100) == 0, myfprintf(verb,'\n'); end;
res = surrogate( datavals, datalen, datadims, bootflag, pairflag);
switch myndims(data{1})
case 1 , [ surrogval{1}(index) surrogval{2}(index) surrogval{3}(index) ] = anova2_cell_select( res, g.paired );
case 2 , [ surrogval{1}(:,index) surrogval{2}(:,index) surrogval{3}(:,index) ] = anova2_cell_select( res, g.paired );
otherwise, [ surrogval{1}(:,:,index) surrogval{2}(:,:,index) surrogval{3}(:,:,index) ] = anova2_cell_select( res, g.paired );
end;
end;
end;
end;
end;
myfprintf(verb,'\n');
% compute p-values
% ----------------
if iscell( surrogval )
pvals{1} = compute_pvals(surrogval{1}, ori_vals{1}, tail);
pvals{2} = compute_pvals(surrogval{2}, ori_vals{2}, tail);
pvals{3} = compute_pvals(surrogval{3}, ori_vals{3}, tail);
else
pvals = compute_pvals(surrogval, ori_vals, tail);
end;
try, warning('on', 'MATLAB:divideByZero'); catch, end;
% compute p-values
% ----------------
function pvals = compute_pvals(surrog, oridat, tail)
surrog = sort(surrog, myndims(surrog)); % sort last dimension
if myndims(surrog) == 1
surrog(end+1) = oridat;
elseif myndims(surrog) == 2
surrog(:,end+1) = oridat;
elseif myndims(surrog) == 3
surrog(:,:,end+1) = oridat;
elseif myndims(surrog) == 4
surrog(:,:,:,end+1) = oridat;
else
surrog(:,:,:,:,end+1) = oridat;
end;
[tmp idx] = sort( surrog, myndims(surrog) );
[tmp mx] = max( idx,[], myndims(surrog));
len = size(surrog, myndims(surrog) );
pvals = 1-(mx-0.5)/len;
if strcmpi(tail, 'both')
pvals = min(pvals, 1-pvals);
pvals = 2*pvals;
end;
function res = supersurrogate(dat, lens, dims, bootstrapflag, pairedflag, naccu); % for increased speed only shuffle half the indices
% recompute indices in set and target cell indices
% ------------------------------------------------
ncond = length(lens)-1;
nsubj = lens(2);
if bootstrapflag
if pairedflag
indswap = mod( repmat([1:lens(end)],[naccu 1]) + ceil(rand(naccu,lens(end))*length(lens))*lens(2)-1, lens(end) )+1;
else indswap = ceil(rand(naccu,lens(end))*lens(end));
end;
else
if pairedflag
[tmp idx] = sort(rand(naccu,nsubj,ncond),3);
indswap = ((idx)-1)*nsubj + repmat( repmat([1:nsubj], [naccu 1 1]),[1 1 ncond]);
indswap = reshape(indswap, [naccu lens(end)]);
else
[tmp indswap] = sort(rand(naccu, lens(end)),2);
end;
end;
for i = 1:length(lens)-1
switch myndims(dat)
case 1, res{i} = reshape(dat(indswap(:,lens(i)+1:lens(i+1))), naccu, lens(i+1)-lens(i));
case 2, res{i} = reshape(dat(:,indswap(:,lens(i)+1:lens(i+1))), size(dat,1), naccu, lens(i+1)-lens(i));
case 3, res{i} = reshape(dat(:,:,indswap(:,lens(i)+1:lens(i+1))), size(dat,1), size(dat,2), naccu, lens(i+1)-lens(i));
case 4, res{i} = reshape(dat(:,:,:,indswap(:,lens(i)+1:lens(i+1))), size(dat,1), size(dat,2), size(dat,3), naccu, lens(i+1)-lens(i));
case 5, res{i} = reshape(dat(:,:,:,:,indswap(:,lens(i)+1:lens(i+1))), size(dat,1), size(dat,2), size(dat,3), size(dat,4), naccu, lens(i+1)-lens(i));
end;
end;
res = reshape(res, dims);
function res = surrogate(dataconcat, lens, dims, bootstrapflag, pairedflag); % for increased speed only shuffle half the indices
% recompute indices in set and target cell indices
% ------------------------------------------------
if bootstrapflag
if pairedflag
indswap = mod( [1:lens(end)]+ ceil(rand(1,lens(end))*length(lens))*lens(2)-1, lens(end) )+1;
else indswap = ceil(rand(1,lens(end))*lens(end));
end;
else
if pairedflag
indswap = [1:lens(end)];
indswap = reshape(indswap, [lens(2) length(lens)-1]);
for i = 1:size(indswap,1) % shuffle each row
[tmp idx] = sort(rand(1,size(indswap,2)));
indswap(i,:) = indswap(i,idx);
end;
indswap = reshape(indswap, [1 lens(2)*(length(lens)-1)]);
else
oriindices = [1:lens(end)]; % just shuffle indices
[tmp idx] = sort(rand(1,length(oriindices)));
indswap = oriindices(idx);
end;
end;
res = {};
for i = 1:length(lens)-1
switch myndims(dataconcat)
case 1, res{i} = dataconcat(indswap(lens(i)+1:lens(i+1)));
case 2, res{i} = dataconcat(:,indswap(lens(i)+1:lens(i+1)));
case 3, res{i} = dataconcat(:,:,indswap(lens(i)+1:lens(i+1)));
case 4, res{i} = dataconcat(:,:,:,indswap(lens(i)+1:lens(i+1)));
case 4, res{i} = dataconcat(:,:,:,:,indswap(lens(i)+1:lens(i+1)));
end;
end;
res = reshape(res, dims);
% compute ANOVA 2-way
% -------------------
function [f1 f2 f3 df1 df2 df3] = anova2_cell_select( res, paired);
if strcmpi(paired,'on')
[f1 f2 f3 df1 df2 df3] = anova2rm_cell( res );
else
[f1 f2 f3 df1 df2 df3] = anova2_cell( res );
end;
% compute ANOVA 1-way
% -------------------
function [f df] = anova1_cell_select( res, paired);
if strcmpi(paired,'on')
[f df] = anova1rm_cell( res );
else
[f df] = anova1_cell( res );
end;
% compute t-test
% -------------------
function [t df] = ttest_cell_select( res, paired, homogenous);
if strcmpi(paired,'on')
[t df] = ttest_cell( res{1}, res{2});
else
[t df] = ttest2_cell( res{1}, res{2}, homogenous);
end;
function val = myndims(a)
if ndims(a) > 2
val = ndims(a);
else
if size(a,1) == 1,
val = 2;
elseif size(a,2) == 1,
val = 1;
else
val = 2;
end;
end;
function myfprintf(verb, varargin)
if verb
fprintf(varargin{:});
end;
|
github
|
BottjerLab/Acoustic_Similarity-master
|
anova1rm_cell.m
|
.m
|
Acoustic_Similarity-master/code/fileExchange/resampling_statistical_toolkit/statistics/anova1rm_cell.m
| 4,008 |
utf_8
|
6d041396c55955b021a3bd629387ab33
|
% anova1rm_cell() - compute F-values in cell array using repeated measure
% ANOVA.
%
% Usage:
% >> [FC dfc] = anova2rm_cell( data );
%
% Inputs:
% data = data consisting of PAIRED arrays to be compared. The last
% dimension of the data array is used to compute ANOVA.
% Outputs:
% FC - F-value for columns
% dfc - degree of freedom for columns
%
% Note: this function is inspired from rm_anova available at
% http://www.mathworks.se/matlabcentral/fileexchange/6874-two-way-rep
% eated-measures-anova
%
% Example:
% a = { rand(1,10) rand(1,10) rand(1,10) }
% [FC dfc] = anova1rm_cell(a)
% signifC = 1-fcdf(FC, dfc(1), dfc(2))
%
% % for comparison
% [F1 F2 FI df1 df2 dfi] = anova1rm_cell(a);
% F2
%
% c = { rand(200,400,10) rand(200,400,10) };
% [FC dfc] = anova2rm_cell(c) % computes 200x400 ANOVAs
%
% Author: Arnaud Delorme, SCCN/INC/UCSD, La Jolla, 2010
% Copyright (C) Arnaud Delorme
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program; if not, write to the Free Software
% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
function [fA dfApair] = anova1rm_cell(data)
% compute all means and all std
% -----------------------------
a = length(data);
nd = myndims( data{1} );
n = size( data{1} ,nd);
% only for paired stats
% ---------------------
if nd == 1
AS = zeros(a,n,'single');
sq = single(0);
for ind1 = 1:a
AS(ind1,:) = AS(ind1,:) + data{ind1}';
sq = sq + sum(data{ind1}.^2);
end;
dimA = 2;
dimB = 1;
elseif nd == 2
AS = zeros(size(data{1},1),a,n,'single');
sq = zeros(size(data{1},1),1,'single');
for ind1 = 1:a
AS(:,ind1,:) = AS(:,ind1,:) + reshape(data{ind1},size(data{1},1),1,n);
sq = sq + sum(data{ind1}.^2,nd);
end;
dimA = 3;
dimB = 2;
elseif nd == 3
AS = zeros(size(data{1},1),size(data{1},2),a,n,'single');
sq = zeros(size(data{1},1),size(data{1},2),'single');
for ind1 = 1:a
AS(:,:,ind1,:) = AS(:,:,ind1,:) + reshape(data{ind1},size(data{1},1),size(data{1},2),1,n);
sq = sq + sum(data{ind1}.^2,nd);
end;
dimA = 4;
dimB = 3;
elseif nd == 4
AS = zeros(size(data{1},1),size(data{1},2),size(data{1},3),a,n,'single');
sq = zeros(size(data{1},1),size(data{1},2),size(data{1},3),'single');
for ind1 = 1:a
AS(:,:,:,ind1,:) = AS(:,:,:,ind1,:) + reshape(data{ind1},size(data{1},1),size(data{1},2),size(data{1},3),1,n);
sq = sq + sum(data{ind1}.^2,nd);
end;
dimA = 5;
dimB = 4;
end;
A = sum(AS,dimA); % sum across columns, so result is 1xs row vector
S = sum(AS,dimB); % sum across columns, so result is 1xs row vector
T = sum(sum(S,dimB),dimA); % could sum either A or B or S, choice is arbitrary
% degrees of freedom
dfA = a-1;
dfAS = (a-1)*(n-1);
% bracket terms (expected value)
expA = sum(A.^2,dimB)./n;
expS = sum(S.^2,dimA)./a;
expAS = sum(sum(AS.^2,dimB),dimA);
expT = T.^2 / (a*n);
% sums of squares
ssA = expA - expT;
ssAS = expAS - expA - expS + expT;
% mean squares
msA = ssA / dfA;
msAS = ssAS / dfAS;
% f statistic
fA = msA ./ msAS;
dfApair = [dfA dfAS];
function val = myndims(a)
if ndims(a) > 2
val = ndims(a);
else
if size(a,1) == 1,
val = 2;
elseif size(a,2) == 1,
val = 1;
else
val = 2;
end;
end;
|
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