/
sc_parse_clickonwaveforms.m
234 lines (156 loc) · 10.2 KB
/
sc_parse_clickonwaveforms.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
function features= parse_clickonwaveforms(x,y,features,mua,s_opt)
psize=0.65;
xpos=[0 0 0 1 1 1 2 2 2];
ypos=[1 2 3 1 2 3 1 2 3];
labelpos=[linspace(0, psize-.3,5),linspace(0, psize-.3,10) ,linspace(0, psize-.3,10) ; zeros(1,5),ones(1,10).*.2,ones(1,10).*.3];
for i=1:features.Nclusters
xo=(xpos(i)*(psize+.01))+.05;
yo=-(ypos(i)*(psize+.01))+1;
if (x> 1+xo) && (x<1+xo+psize) && (y>yo) && (y<psize+yo) % find waveform display that click is in
% plot( [1 1.1]+xo , [psize-0.1 psize]+yo,'k');
%disp(((x-xo)-(y-yo)));
if ((x-xo)-(y-yo))<0.5 % click on label button
% better: do it in one click
% fill([1+xo+psize 1+xo 1+xo 1+xo+psize],[ yo yo yo+psize yo+psize],'c','facecolor',[.9 .9 .9]); % draw a box
% better: draw whitened out spike so user can still see it
im=-((features.clusterimages(:,:,i)./max(max(features.clusterimages(:,:,i))) ).^(.6));
imagesc( linspace(1,1+psize,features.imagesize)+xo , linspace(0,psize,features.imagesize)+yo , im/2 );
text(xo+1.01,yo+0.02,num2str(i),'color',[0 0 0]);
plot(xo+1.06,yo+0.03,features.clusterfstrs{i},'MarkerSize',22,'color',features.colors(i,:));
for j=1:features.nlabels
if features.clusterlabels(i)==j
text(labelpos(2,j)+xo+1.03,labelpos(1,j)+yo+.15,features.labelcategories{j},'color',[0 0 0],'BackgroundColor',[.7 .9 .7]);
else
text(labelpos(2,j)+xo+1.03,labelpos(1,j)+yo+.15,features.labelcategories{j},'color',[0 0 0]);
end;
%just click on nearest, not pretty but easy
lx(j)=labelpos(2,j)+xo+1.06;
ly(j)=labelpos(1,j)+yo+.15;
end;
c=features.colors(i,:);
plot( [1 1]+xo , [0 psize]+yo,'k','color',c);
plot( [1+psize 1+psize]+xo , [0 psize]+yo,'k','color',c);
plot( [1 1+psize]+xo , [0 0]+yo,'k','color',c);
plot( [1 1+psize]+xo , [psize psize]+yo,'k','color',c);
if i==1
text(xo+1.1 ,yo+0.02,['N: ',num2str(sum(features.clusters==i)),' (MUA/null cluster)'],'color',[0 0 0]);
else
text(xo+1.1 ,yo+0.02,['N: ',num2str(sum(features.clusters==i)),' ',features.labelcategories{features.clusterlabels(i)}],'color',[0 0 0]);
end;
text(labelpos(2,1)+xo+1.03,labelpos(1,1)+yo+.15,'none','color',[.4 .4 .4]);
[ix iy ib]=ginput(1);
if ib==1 % only left clicks, right cancels
d=(ix-lx).^2 +(iy-ly).^2;
[ignore,m]=min(d);
features.clusterlabels(i)=m;
end;
elseif ((x-xo)+(y-yo))>2.2 % click on +/options button
im=-((features.clusterimages(:,:,i)./max(max(features.clusterimages(:,:,i))) ).^(.6));
imagesc( linspace(1,1+psize,features.imagesize)+xo , linspace(0,psize,features.imagesize)+yo , im/2 );
text(xo+1.01,yo+0.02,num2str(i),'color',[0 0 0]);
plot(xo+1.06,yo+0.03,features.clusterfstrs{i},'MarkerSize',22,'color',features.colors(i,:));
c=features.colors(i,:);
plot( [1 1]+xo , [0 psize]+yo,'k','color',c);
plot( [1+psize 1+psize]+xo , [0 psize]+yo,'k','color',c);
plot( [1 1+psize]+xo , [0 0]+yo,'k','color',c);
plot( [1 1+psize]+xo , [psize psize]+yo,'k','color',c);
if i==1
text(xo+1.1 ,yo+0.02,['N: ',num2str(sum(features.clusters==i)),' (MUA/null cluster)'],'color',[0 0 0]);
else
text(xo+1.1 ,yo+0.02,['N: ',num2str(sum(features.clusters==i)),' ',features.labelcategories{features.clusterlabels(i)}],'color',[0 0 0]);
end;
% plot options
optlabels={'move cluster to noise', '[add P_{in cluster} feature]', 'add regression feature','merge with cluster'};
for j=1:numel(optlabels)
text(labelpos(2,j)+xo+1.03,labelpos(1,j)+yo+.15,optlabels{j},'color',[0 0 0],'BackgroundColor',[.9 .9 .9]);
%just click on nearest, not pretty but easy
lx(j)=labelpos(2,j)+xo+1.06;
ly(j)=labelpos(1,j)+yo+.15;
end;
[ix iy ib]=ginput(1);
d=(ix-lx).^2 +(iy-ly).^2;
[ignore,m]=min(d);
if ib==1
if m==1 % move tcluster to noise
incluster=find(features.clusters==i );
features.clusters_undo=features.clusters;
features.clusters(incluster)=2;
features=sc_updateclusterimages(features,mua,s_opt);
end;
if m==2 % add feature based on likelihood of any spikewaveform to be in cluster based on waveform dist.
%{
figure(4); clf; % debug
imagesc(-features.clusterimages(:,:,3)); hold on;
plot(round((features.waveforms_hi(find(1),:).*features.waveformscale*features.imagesize)+(features.imagesize/2)));
%}
P_in=zeros(size(mua.ts));
P_this=features.clusterimages(:,:,i)./sum(sum(features.clusterimages(:,:,i))); % we dont really care about correct normalization here
excl=[1:features.Nclusters]; excl(i)=[];
P_all=mean(features.clusterimages(:,:,excl),3)./sum(sum(mean(features.clusterimages(:,:,excl),3))); % we dont really care about correct normalization here
parfor s=1:numel(features.ts)
yc=round((features.waveforms_hi(s,:).*features.waveformscale*features.imagesize)+(features.imagesize/2));
yc=min(max(yc,1),features.imagesize);
iii=sub2ind(size(P_this),yc,[1:features.imagesize]);
P_in(s)=(sum(P_this(iii)./max(P_all(iii),0.0001) )); % P of this spike to be from this cluster
if mod(s,1000)==0
text(0,0,['making P_{in cluster} feature, (',num2str(round( 100*(s/numel(features.ts)) )),'%)'],'color',[0 0 0],'BackgroundColor',[.9 .9 .9]);
drawnow;
end;
end;
features.data(end+1,:)= P_in';
features.name{size(features.data,1)}=['P_{in ',num2str(i),'}'];
features=sc_scale_features(features);
end;
if m==3 % add feature based on regression on waveforms
visible = find(ismember(features.clusters, find(features.clustervisible)));
Nmaxregress=100000;
while numel(visible)>Nmaxregress
visible=visible(1:2:end);
end;
visible=logical(visible);
fy=(features.clusters(visible)'==i); % only run on visible ones
b=regress(fy,mua.waveforms(visible,:));
feat=mua.waveforms*b; % do prediction on all, why not
features.data(end+1,:)= feat';
features.name{size(features.data,1)}=['regr_{in ',num2str(i),'}'];
%features=sc_scale_features(features);
% select that feature
features.featureselects(2)=size(features.data,1);
features=sc_zoom_all(features);
end;
if m==4 % merge cluster with other cluster
incluster=find(features.clusters==i );
features.clusters_undo=features.clusters;
% select target cluster
text(0,0,['select target cluster'],'color',[0 0 0],'BackgroundColor',[.9 .9 .9]);
[x,y]=ginput(1);
targetcluster=[];
for j=1:features.Nclusters
xoo=(xpos(j)*(psize+.01))+.05;
yoo=-(ypos(j)*(psize+.01))+1;
if (x> 1+xoo) && (x<1+xoo+psize) && (y>yoo) && (y<psize+yoo) % find waveform display that click is in
targetcluster=j;
end ;
end;
if numel(targetcluster)>0
features.clusters(incluster)=targetcluster;
features=sc_updateclusterimages(features,mua,s_opt);
end;
end;
end; %left button?
else % click on actual waveform
% make new feature with amplitude at that point
npoints=numel(mua.ts_spike);
%xa= (linspace(0,psize,npoints));
samples=[-1:1]+((x-(1+xo))/psize)*npoints;
samples=max(min(round(samples),npoints),1);
% calculate new feature from avg value at that sample
%features.numextrafeaatures=features.numextrafeaatures+1;
features.data(end+1,:)= mean(mua.waveforms(:,samples)')';
features.name{size(features.data,1)}=['amp@',num2str(round(((x-(1+xo))/psize)*npoints))];
features=sc_scale_features(features);
% select that feature
features.featureselects(2)=size(features.data,1);
end;
end;
end;