/
sc_loadmuadata.m
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sc_loadmuadata.m
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function [features,mua]=sc_loadmuadata(muafile, dofeatures,s_opt)
skipsetup=0;
muafile
[ignore,ignoreb,muafile_ext] = fileparts(muafile);
muafile_ext = muafile_ext(2:end);
switch muafile_ext
case 'spikes' % open ephys data format
[data, timestamps, info] = load_open_ephys_data_faster(muafile);
% remove end of file?
% cutoff_t=3.9100e+03;
% cutoff=min(find(timestamps>cutoff_t));
% data=data(1:cutoff,:,:);
% timestamps=timestamps(1:cutoff);
features.chnumstr = info.header.electrode;
features.sourcechannel= str2num(info.header.electrode(end-1:end));
if numel( features.sourcechannel)==0
features.sourcechannel= str2num(info.header.electrode(end));
end;
sourcechannel=features.sourcechannel; % just so we dont overwrite it in 'features=sc_mua2features(mua,s_opt);'
mua.Nspikes = numel(timestamps);
% mua.ts=timestamps./info.header.sampleRate;
mua.ts=timestamps;
[pathstr, name, ext] = fileparts(muafile);
mua.fname=[name,ext];
mua.opt=[];
mua.header=info.header;
mua.ncontacts = info.header.num_channels;
mua.val2volt=1; % alreaddy done in load script
% flatten waveforms for display
mua.waveforms=reshape(data,size(data,1),size(data,2)*size(data,3),1);
mua.ts_spike=linspace(-.5,3.5,40* mua.ncontacts); % neuralynx saves 32 samples at 30303Hz, so its a 1.056ms window
if dofeatures
features=sc_mua2features(mua,s_opt);
features.sourcechannel=sourcechannel;
end;
case 'mat'
load(muafile);
if ~ exist('mua', 'var') % no mua var in there, try doreas format
if exist('times_all', 'var') % marker for doreas mat format
if dofeatures
if s_opt.auto_number==0
prompt = {['source channel nr for file ',muafile]};
dlg_title = 'channel nr';
num_lines = 1;
def = {''};
features.chnumstr = inputdlg(prompt,dlg_title,num_lines,def);
features.sourcechannel= str2num(features.chnumstr{1});
sourcechannel=features.sourcechannel; % just so we dont overwrite it in 'features=sc_mua2features(mua,s_opt);'
else
%do it automatically
[ignore,n,ignoreb]=fileparts(muafile)
disp('automatically detecting ch number for');
disp(muafile);
nind=find(ismember(n, '0':'9'));
if numel(nind)>2 || numel(nind)==0
error('for automatic channel numbering make sure the mua filenames contain just one number between 1 and 99 ');
end;
ch=str2num(n(nind));
disp(['-> ch ',num2str(ch)]);
features.chnumstr = ch;
features.sourcechannel= ch;
sourcechannel=features.sourcechannel; % just so we dont overwrite it in 'features=sc_mua2features(mua,s_opt);'
end;
end;
% load doreas format
mua.opt=[];
mua.fname=muafile;
mua.Nspikes = numel(times_all);
% concatenate all waveforms
mua.ncontacts = size(waveforms,2);
mua.waveforms = (reshape(waveforms,size(waveforms,1)*size(waveforms,2),size(waveforms,3) ))';
mua.ts = times_all;
mua.ts_spike=[1:size( mua.waveforms,2)];
if dofeatures
features=sc_mua2features(mua,s_opt);
features.sourcechannel=sourcechannel;
end;
end;
else % file has a variable mua in it, probably jakobs own format
if exist('features', 'var') %marker for simple_clust output format
if numel(mua.ts)<size(mua.waveforms,1)
warning('fewer spikes than waveforms, truncating!');
mua.waveforms = mua.waveforms(1:numel(mua.ts),:);
end;
% we just loaded previous simple_clust data
% in theory there should be nothing left to do here?
skipsetup=1;
features.skipsetup=1; % so the main program doesnt try to run scripts like auto noise rejection again
else
% parse jakobs ad hoc format here
% for now we only deal with simple electrodes and ones
% extracted from laminar recordings
if size(mua.waveforms,2)==3% extracted from laminar, 3 contacts!
mua.ncontacts = 3;
% reformat wavewforms, flatten for display
%D= (squeeze(reshape(mua.waveforms,1,128,size(mua.waveforms,3))));
D=[squeeze(mua.waveforms(:,1,:))',squeeze(mua.waveforms(:,2,:))',squeeze(mua.waveforms(:,3,:))'];
mua.waveforms=D;
mua.ts_spike=linspace(-.5,2.5,93); % we do 31 samples at 30303Hz, so its a 1.056ms window
if dofeatures
features=sc_mua2features(mua,s_opt);
sourcechannel=mua.sourcechannel;
features.sourcechannel=sourcechannel;
end;
elseif size(mua.waveforms,2)==4 %tetrode recording
mua.ncontacts = 4;
D=[squeeze(mua.waveforms(:,1,:))',squeeze(mua.waveforms(:,2,:))',squeeze(mua.waveforms(:,3,:))',squeeze(mua.waveforms(:,4,:))'];
mua.waveforms=D;
mua.ts_spike=linspace(-.5,2.5,93); % we do 31 samples at 30303Hz, so its a 1.056ms window
mua.ts_spike=linspace(0,4,size(mua.waveforms,2));
if dofeatures
features=sc_mua2features(mua,s_opt);
sourcechannel=mua.sourcechannel;
features.sourcechannel=sourcechannel;
end;
else
mua.ncontacts = 1;
if dofeatures
features=sc_mua2features(mua,s_opt);
sourcechannel=mua.sourcechannel;;
features.sourcechannel=sourcechannel;
end;
end;
end;
end;
case 'nse'
if dofeatures
if s_opt.auto_number==0
prompt = {['source channel nr for file ',muafile]};
dlg_title = 'channel nr';
num_lines = 1;
def = {''};
features.chnumstr = inputdlg(prompt,dlg_title,num_lines,def);
features.sourcechannel= str2num(features.chnumstr{1});
sourcechannel=features.sourcechannel; % just so we dont overwrite it in 'features=sc_mua2features(mua,s_opt);'
else
%do it automatically
[ignore,n,ignoreb]=fileparts(muafile)
disp('automatically detecting ch number for');
disp(muafile);
nind=find(ismember(n, '0':'9'));
if numel(nind)>2 || numel(nind)==0
error('for automatic channel numbering make sure the mua filenames contain just one number between 1 and 99 ');
end;
ch=str2num(n(nind));
disp(['-> ch ',num2str(ch)]);
features.chnumstr = ch;
features.sourcechannel= ch;
sourcechannel=features.sourcechannel; % just so we dont overwrite it in 'features=sc_mua2features(mua,s_opt);'
end;
end;
cdata = read_cheetah_data(muafile);
%mua = load_neuralynx_mua(muafile);
clear mua;
mua=[];
mua.Nspikes = size(cdata.ts,1);
mua.waveforms=cdata.waveforms;
mua.ts=cdata.ts;
%identify bits2volt in header
vstart=strfind(cdata.header,'ADBitVolts');
mua.val2volt=str2num(cdata.header(vstart+10:vstart+24));
[pathstr, name, ext] = fileparts(muafile);
mua.fname=[name,ext];
mua.opt=[];
mua.header=cdata.header;
mua.ncontacts = size(mua.waveforms,1);
% flatten waveforms for display
D= (squeeze(reshape(mua.waveforms,1,32,size(mua.waveforms,3))));
mua.waveforms=[D(1:end,:)]';
mua.ts_spike=linspace(-.5,0.5,32); % neuralynx saves 32 samples at 30303Hz, so its a 1.056ms window
if dofeatures
features=sc_mua2features(mua,s_opt);
features.sourcechannel=sourcechannel;
end;
case 'nst'
if dofeatures
if s_opt.auto_number==0
prompt = {['source channel nr for file ',muafile]};
dlg_title = 'channel nr';
num_lines = 1;
def = {''};
features.chnumstr = inputdlg(prompt,dlg_title,num_lines,def);
features.sourcechannel= str2num(features.chnumstr{1});
sourcechannel=features.sourcechannel; % just so we dont overwrite it in 'features=sc_mua2features(mua,s_opt);'
else
%do it automatically
[ignore,n,ignoreb]=fileparts(muafile)
disp('automatically detecting ch number for');
disp(muafile);
nind=find(ismember(n, '0':'9'));
if numel(nind)>2 || numel(nind)==0
error('for automatic channel numbering make sure the mua filenames contain just one number between 1 and 99 ');
end;
ch=str2num(n(nind));
disp(['-> ch ',num2str(ch)]);
features.chnumstr = ch;
features.sourcechannel= ch;
sourcechannel=features.sourcechannel; % just so we dont overwrite it in 'features=sc_mua2features(mua,s_opt);'
end;
end;
cdata = read_cheetah_data(muafile);
%mua = load_neuralynx_mua(muafile);
clear mua;
mua=[];
mua.Nspikes = size(cdata.ts,1);
mua.waveforms=cdata.waveforms;
mua.ts=cdata.ts;
[pathstr, name, ext] = fileparts(muafile);
mua.fname=[name,ext];
mua.opt=[];
mua.header=cdata.header;
%identify bits2volt in header
vstart=strfind(cdata.header,'ADBitVolts');
mua.val2volt=str2num(cdata.header(vstart+10:vstart+24));
mua.ncontacts = size(mua.waveforms,1);
% flatten waveforms for display
D= (squeeze(reshape(mua.waveforms,1,64,size(mua.waveforms,3))));
mua.waveforms=[D(1:2:end,:);D(2:2:end,:)]';
mua.ts_spike=linspace(-.5,1.5,64); % neuralynx saves 32 samples at 30303Hz, so its a 1.056ms window
if dofeatures
features=sc_mua2features(mua,s_opt);
features.sourcechannel=sourcechannel;
end;
case 'ntt'
if dofeatures
if s_opt.auto_number==0
prompt = {['source channel nr for file ',muafile]};
dlg_title = 'channel nr';
num_lines = 1;
def = {''};
features.chnumstr = inputdlg(prompt,dlg_title,num_lines,def);
features.sourcechannel= str2num(features.chnumstr{1});
sourcechannel=features.sourcechannel; % just so we dont overwrite it in 'features=sc_mua2features(mua,s_opt);'
else
%do it automatically
[ignore,n,ignoreb]=fileparts(muafile)
disp('automatically detecting ch number for');
disp(muafile);
nind=find(ismember(n, '0':'9'));
if numel(nind)>2 || numel(nind)==0
error('for automatic channel numbering make sure the mua filenames contain just one number between 1 and 99 ');
end;
ch=str2num(n(nind));
disp(['-> ch ',num2str(ch)]);
features.chnumstr = ch;
features.sourcechannel= ch;
sourcechannel=features.sourcechannel; % just so we dont overwrite it in 'features=sc_mua2features(mua,s_opt);'
end;
end;
cdata = read_cheetah_data(muafile);
%mua = load_neuralynx_mua(muafile);
clear mua;
mua=[];
mua.Nspikes = size(cdata.ts,1);
mua.waveforms=cdata.waveforms;
mua.ts=cdata.ts;
[pathstr, name, ext] = fileparts(muafile);
mua.fname=[name,ext];
mua.opt=[];
mua.header=cdata.header;
mua.ncontacts = size(mua.waveforms,1);
%identify bits2volt in header
vstart=strfind(cdata.header,'ADBitVolts');
mua.val2volt=str2num(cdata.header(vstart+10:vstart+24));
% flatten waveforms for display
D= (squeeze(reshape(mua.waveforms,1,128,size(mua.waveforms,3))));
mua.waveforms=[D(1:4:end,:);D(2:4:end,:);D(3:4:end,:);D(4:4:end,:)]';
mua.ts_spike=linspace(-.5,3.5,128); % neuralynx saves 32 samples at 30303Hz, so its a 1.056ms window
if dofeatures
features=sc_mua2features(mua,s_opt);
features.sourcechannel=sourcechannel;
end;
case 'wf'
if dofeatures
if s_opt.auto_number==0
prompt = {['source channel nr for file ',muafile]};
dlg_title = 'channel nr';
num_lines = 1;
def = {''};
features.chnumstr = inputdlg(prompt,dlg_title,num_lines,def);
features.sourcechannel= str2num(features.chnumstr{1});
sourcechannel=features.sourcechannel; % just so we dont overwrite it in 'features=sc_mua2features(mua,s_opt);'
else
%do it automatically
[ignore,n,ignoreb]=fileparts(muafile);
disp('automatically detecting ch number for');
disp(muafile);
chstr = regexpi(n, '_Ch(\d)', 'tokens');
if isempty(chstr),
ch = 0;
else
%We'll take the first channel as our channel
ch = str2num(chstr{1}{1});
end
disp(['-> ch ',num2str(ch)]);
features.chnumstr = ch;
features.sourcechannel= ch;
sourcechannel=features.sourcechannel; % just so we dont overwrite it in 'features=sc_mua2features(mua,s_opt);'
end;
end;
% load tim's format
mua.opt = LoadSpikeWF(muafile, [], 6);
mua.fname=muafile;
mua.Nspikes = LoadSpikeWF(muafile, [], 5);
%Load waveforms
[mua.ts, wv] = LoadSpikeWF(muafile, [1 mua.Nspikes], 4);
% concatenate all waveforms
mua.ncontacts = size(wv,2);
%Copy waveforms over
size(wv)
mua.waveforms = zeros(size(wv, 1), size(wv, 2)*size(wv, 3));
for i = 1:size(wv, 1),
mua.waveforms(i, :) = reshape(squeeze(wv(i, :, :))', [1 size(wv, 2)*size(wv, 3)]);
end
size(mua.waveforms)
mua.ts_spike = linspace(mua.opt.SpikeExtract_WFRange(1), mua.opt.SpikeExtract_WFRange(2), mua.opt.NumPointsInWF)./mua.opt.SampleFrequency;
if dofeatures
features=sc_mua2features(mua,s_opt);
features.sourcechannel=sourcechannel;
end;
otherwise
error('unrecognized file format');
end;
if ~dofeatures
skipsetup=1;
features=[]; % in this case just return the mua
end;
if ~skipsetup
%% config and setup
features.clusters=ones(size(features.id));
features.clusters_undo=features.clusters;
features.clustervisible=ones(1,12);
if ~isfield(s_opt,'skipevery_wf_display') % bkwrds comp.
s_opt.skipevery_wf_display=8; % if it's not specified, use a pretty conservative number so that things look the same
end;
%features.clusters(1:100)=2;
features.labelcategories = {' ','noise','neg','unit','unit_{FS}','unit_{RS}','unit_{huge}','mua big','mua wide','mua thin','mua small','negative','artefact',};
features.clusterfstrs={'k.','b.','r.','g.','c.','r.','k.','r.','b.'};
features.colors=[.7 .7 .7; 1 0 0; 0 1 0; 0 0 1; 1 .7 0; 1 .2 1; 0 1 1; 1 .5 0; .5 1 0; 1 0 .5];
features.Nclusters=2;
features.imagesize=100;
features.waveformscale=0.0001;
mua.waveforms( find(abs(mua.waveforms) > 10000) ) =0;
mua.waveforms( isnan(mua.waveforms) ) =0;
% find appropriate scale for plotting waveforms
features.waveformscale=0.1 ./ quantile(mua.waveforms(1:100:end)-mean(mua.waveforms(1:100:end)),.95);
features.range=zeros(size(features.data,1),2); % for x/y range display
features.zoomrange=zeros(size(features.data,1),2); % where do we display right now
features.numextrafeaatures=0;
features.highlight = 0;
features.clusterimages=ones(features.imagesize,features.imagesize,12);
features.selected=0;
features.plotsize=0;
features.timeselectwidth=200;
features.timeselection=0;
features.Ndisplay=25000;
for i=1:1
features.isioptions(1).tmax=10;
features.isioptions(1).nbins=50;
end;
features.plotgroup=1;
features.highlight_multiple(1:10)=1;
features.clusterlabels(1:10)=1;
features.nlabels=numel(features.labelcategories );
% preprocess features to fit 1 1 -1 -1 box
features=sc_scale_features(features);
features=sc_addnoisetoquantiledfeatures(features);
% laod icons
%features.eye_icon_o=imread('eye.png');
%features.eye_icon_x=imread('eye_closed.png');
features.timevisible=ones(1,numel(features.ts));
features.randperm = randperm(numel(features.ts));
features.featureselects=[2 3];
features=sc_updateclusterimages(features,mua, s_opt);
end;
run=1;