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drgCaImAn_analyze_batch_pre_per_to_decode_per_mouse.m
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%drgCaImAn_analyze_batch_pre_per_to_decode_per_mouse
close all
clear all
[choiceFileName,choiceBatchPathName] = uigetfile({'drgCaImAn_LDAfsdz_choices*.m'},'Select the .m file with all the choices for analysis');
fprintf(1, ['\ndrgCaImAn_analyze_batch_pre_per_to_decode_entire_session_fsdzv2 run for ' choiceFileName '\n\n']);
tempDirName=['temp' choiceFileName(12:end-2)];
addpath(choiceBatchPathName)
eval(['handles=' choiceFileName(1:end-2) ';'])
handles.choiceFileName=choiceFileName;
handles.choiceBatchPathName=choiceBatchPathName;
fileID = fopen([choiceBatchPathName 'drgCaImAn_analyze_batch_pre_per_to_decode_per_mouse.txt'],'w');
no_files=handles.no_files;
moving_mean_n=10;
time_periods_eu=[-5 -3;
-2.5 -1.5;
-1 0;
2 4.1];
%Which threshold value should we use?
ii_thr=4;
%Show the per mouse graphs?
show_per_mouse=0;
is_Fabio=0;
switch is_Fabio
case 0
%Choices for Ming's go-no go processing
no_pcorr=4;
%groups to be shown in the zoomed figures for Ming's data
grNo1=4; %Forward proficient
grNo1_label='forward proficient';
grNo2=8; %Forward proficient
grNo2_label='reversed proficient';
case 1
%Chaoices for Fabio
%groups to be shown in the zoomed figures for Fabio's data
% grNo1=1; %AAAP
% grNo1_label='AAAP';
grNo1=2; %female bedding
grNo1_label='female bedding';
grNo2=4; %male bedding
grNo2_label='male bedding';
%Choices for Fabio's passive odorant exposure processing
no_pcorr=1;
case 2
%Choices for Ming's passive
%groups to be shown in the zoomed figures for Fabio's data
% grNo1=1; %AAAP
% grNo1_label='AAAP';
grNo1=1; %passive
grNo1_label='passive';
grNo2=1; %passive
grNo2_label='passive';
%Choices for Fabio's passive odorant exposure processing
no_pcorr=1;
end
fprintf(1, ['\nData were processed with p value threshold = ' num2str(handles.p_threshold(ii_thr)) '\n\n']);
t_from=-10;
t_to=20;
%Now plot for each algorithm the prediction accuracy
handles_out2=[];
handles_out2.classifier_names{1}='Linear Discriminant';
handles_out2.classifier_names{2}='Support Vector Machine';
handles_out2.classifier_names{3}='Naive Bayes Classifier';
handles_out2.classifier_names{4}='Neural Network';
handles_out2.classifier_names{5}='Decision tree';
handles_out2.classifier_names{6}='Binomial glm';
delta_odor=4.127634e+00;
delta_odor_on_reinf_on=4.415787e+00;
delta_reinf=4.078266e-01;
%
% %First and last sessions per mouse
% first_last=[20 2;
% 5 15;
% 25 1;
% 13 11;
% 26 12];
%
% handles_out2.first_last=first_last;
%
% %Mouse names
% handles_out2.mouse_names{1}='GRIN1';
% handles_out2.mouse_names{2}='GRIN3';
% handles_out2.mouse_names{3}='GRIN4';
% handles_out2.mouse_names{4}='GRIN6';
% handles_out2.mouse_names{5}='GRIN7';
per_names{1}='<40%';
per_names{2}='40-65%';
per_names{3}='65-80%';
per_names{4}='>=80%';
fr_per_names{1}='Fwd <40%%';
fr_per_names{2}='Fwd 40-65%%';
fr_per_names{3}='Fwd 65-80%%';
fr_per_names{4}='Fwd >=80%%';
fr_per_names{5}='Rev <40%%';
fr_per_names{6}='Rev 40-65%%';
fr_per_names{7}='Rev 65-80%%';
fr_per_names{8}='Rev >=80%%';
if no_pcorr==1
per_names{1}='';
end
these_groups=unique(handles.group);
if exist([choiceBatchPathName choiceFileName(1:end-2) '.mat'])==0
%Process each file separately
for grNo=1:no_pcorr*length(these_groups)
handles_out2.group_no(grNo).ii_euclid=0;
for iiMLalgo=handles.MLalgo_to_use
for ii_out=1:length(handles.p_threshold)
handles_out2.group_no(grNo).ii_thr(ii_out).MLalgo(iiMLalgo).ii=0;
end
end
end
%Find the mouse numbers
handles_out2.mouseNo_per_file=[];
handles_out2.mouse_names=[];
for fileNo=1:no_files
if fileNo==1
ii_mouse=1;
handles_out2.mouse_names{1}=handles.mouse{1};
handles_out2.mouseNo_per_file(1)=1;
else
%Find whether this mouse is already in the list
mouse_found=0;
for this_ii=1:length(handles_out2.mouse_names)
if strcmp(handles.mouse{fileNo},handles_out2.mouse_names{this_ii})
mouse_found=1;
mouse_found_ii=this_ii;
end
end
if mouse_found==0
%This mouse's name is not in the list
ii_mouse=ii_mouse+1;
handles_out2.mouse_names{ii_mouse}=handles.mouse{fileNo};
handles_out2.mouseNo_per_file(fileNo)=ii_mouse;
else
%This mouse's name is in the list
handles_out2.mouseNo_per_file(fileNo)=mouse_found_ii;
end
end
end
group_per_file=handles.group;
handles_out2.pcorr_per_file=zeros(1,length(group_per_file));
for fileNo=1:no_files
tic
if iscell(handles.PathName_out)
pre_per_outPathName=handles.PathName_out{fileNo};
else
pre_per_outPathName=handles.PathName_out;
end
pre_per_FileName=handles.FileName_pre_per{fileNo};
this_group=handles.group(fileNo);
this_grNo=find(these_groups==this_group);
if exist([pre_per_outPathName pre_per_FileName(1:end-4) handles.suffix_out])~=0
load([pre_per_outPathName pre_per_FileName(1:end-4) handles.suffix_out])
if isfield(handles_out.ii_out(1).handles_out,'percent_correct')
pCorr=handles_out.ii_out(1).handles_out.percent_correct;
handles_out2.pcorr_per_file(fileNo)=pCorr;
ii_pCorr=1;
if (pCorr>=40)&(pCorr<=65)
ii_pCorr=2;
else
if (pCorr>65)&(pCorr<80)
ii_pCorr=3;
else
if pCorr>=80
ii_pCorr=4;
end
end
end
else
ii_pCorr=1;
end
if (is_Fabio==1)||(is_Fabio==2)
ii_pCorr=1;
end
grNo=(this_grNo-1)*no_pcorr+ii_pCorr;
for iiMLalgo=handles.MLalgo_to_use
if iiMLalgo==handles.MLalgo_to_use(1)
handles_out2.group_no(grNo).ii_euclid=handles_out2.group_no(grNo).ii_euclid+1;
ii_euclid=handles_out2.group_no(grNo).ii_euclid;
handles_out2.group_no(grNo).dist_euclid(ii_euclid,1:length(handles_out.ii_out(1).handles_out.dist_euclid))=handles_out.ii_out(1).handles_out.dist_euclid-handles_out.ii_out(1).handles_out.dist_euclid_zero;
handles_out2.group_no(grNo).KLdivergence(ii_euclid,1:length(handles_out.ii_out(1).handles_out.KLdivergence))=handles_out.ii_out(1).handles_out.KLdivergence;
handles_out2.group_no(grNo).time_span_euclid(ii_euclid,1:length(handles_out.ii_out(1).handles_out.time_span))=handles_out.ii_out(1).handles_out.time_span;
handles_out2.group_no(grNo).ii_time_span(ii_euclid,1)=length(handles_out.ii_out(1).handles_out.time_span);
handles_out2.group_no(grNo).meandFFsp(ii_euclid,1:length(handles_out.ii_out(1).handles_out.time_span))=handles_out.ii_out(1).handles_out.meandFFsp;
handles_out2.group_no(grNo).meandFFsm(ii_euclid,1:length(handles_out.ii_out(1).handles_out.time_span))=handles_out.ii_out(1).handles_out.meandFFsm;
handles_out2.group_no(grNo).mouseNo_euclid(ii_euclid)= handles_out2.mouseNo_per_file(fileNo);
end
for ii_out=1:length(handles_out.ii_out)
handles_out2.group_no(grNo).ii_thr(ii_out).MLalgo(iiMLalgo).ii=handles_out2.group_no(grNo).ii_thr(ii_out).MLalgo(iiMLalgo).ii+1;
ii=handles_out2.group_no(grNo).ii_thr(ii_out).MLalgo(iiMLalgo).ii;
accuracy_tr=handles_out.ii_out(ii_out).handles_out.MLalgo(iiMLalgo).accuracy_tr;
handles_out2.group_no(grNo).ii_thr(ii_out).MLalgo(iiMLalgo).accuracy_tr(ii)=accuracy_tr;
accuracy_tr_sh=handles_out.ii_out(ii_out).handles_out.MLalgo(iiMLalgo).accuracy_tr_sh;
handles_out2.group_no(grNo).ii_thr(ii_out).MLalgo(iiMLalgo).accuracy_tr_sh(ii)=accuracy_tr_sh;
accuracy_tr_sh2=handles_out.ii_out(ii_out).handles_out.MLalgo(iiMLalgo).accuracy_tr_sh2;
handles_out2.group_no(grNo).ii_thr(ii_out).MLalgo(iiMLalgo).accuracy_tr_sh2(ii)=accuracy_tr_sh2;
this_mean_correct_predict=mean(handles_out.ii_out(ii_out).handles_out.MLalgo(iiMLalgo).this_correct_predict);
handles_out2.group_no(grNo).ii_thr(ii_out).MLalgo(iiMLalgo).mean_correct_predict(ii,1:length(this_mean_correct_predict))=this_mean_correct_predict;
this_mean_correct_predict=mean(handles_out.ii_out(ii_out).handles_out.MLalgo(iiMLalgo).this_correct_predict);
handles_out2.group_no(grNo).ii_thr(ii_out).MLalgo(iiMLalgo).mean_correct_predict(ii,1:length(this_mean_correct_predict))=this_mean_correct_predict;
this_mean_correct_predict_sh=mean(handles_out.ii_out(ii_out).handles_out.MLalgo(iiMLalgo).this_correct_predict_sh);
handles_out2.group_no(grNo).ii_thr(ii_out).MLalgo(iiMLalgo).mean_correct_predict_sh(ii,1:length(this_mean_correct_predict_sh))=this_mean_correct_predict_sh;
this_per_trial_sp_timecourse=handles_out.ii_out(ii_out).handles_out.MLalgo(iiMLalgo).per_trial_sp_timecourse;
this_per_trial_sm_timecourse=handles_out.ii_out(ii_out).handles_out.MLalgo(iiMLalgo).per_trial_sm_timecourse;
this_moving_mean_per_trial_sp_timecourse = movmean(this_per_trial_sp_timecourse',moving_mean_n)';
this_mean_moving_mean_per_trial_sp_timecourse=mean(this_moving_mean_per_trial_sp_timecourse);
handles_out2.group_no(grNo).ii_thr(ii_out).MLalgo(iiMLalgo).mean_moving_mean_per_trial_sp_timecourse(ii,1:length(this_mean_moving_mean_per_trial_sp_timecourse))=this_mean_moving_mean_per_trial_sp_timecourse;
this_moving_mean_per_trial_sm_timecourse = movmean(this_per_trial_sm_timecourse',moving_mean_n)';
this_mean_moving_mean_per_trial_sm_timecourse=mean(this_moving_mean_per_trial_sm_timecourse);
handles_out2.group_no(grNo).ii_thr(ii_out).MLalgo(iiMLalgo).mean_moving_mean_per_trial_sm_timecourse(ii,1:length(this_mean_moving_mean_per_trial_sm_timecourse))=this_mean_moving_mean_per_trial_sm_timecourse;
per_trial_scores_sp=handles_out.ii_out(ii_out).handles_out.MLalgo(iiMLalgo).per_trial_scores_sp;
mean_per_trial_scores_sp=zeros(size(per_trial_scores_sp,2),size(per_trial_scores_sp,3));
mean_per_trial_scores_sp(:,:)=mean(per_trial_scores_sp);
handles_out2.group_no(grNo).ii_thr(ii_out).MLalgo(iiMLalgo).mean_per_trial_scores_sp(ii,1:2,1:size(per_trial_scores_sp,3))=mean_per_trial_scores_sp;
per_trial_scores_sm=handles_out.ii_out(ii_out).handles_out.MLalgo(iiMLalgo).per_trial_scores_sm;
mean_per_trial_scores_sm=zeros(size(per_trial_scores_sm,2),size(per_trial_scores_sm,3));
mean_per_trial_scores_sm(:,:)=mean(per_trial_scores_sm);
handles_out2.group_no(grNo).ii_thr(ii_out).MLalgo(iiMLalgo).mean_per_trial_scores_sm(ii,1:2,1:size(per_trial_scores_sm,3))=mean_per_trial_scores_sm;
handles_out2.group_no(grNo).ii_thr(ii_out).MLalgo(iiMLalgo).mouseNo(ii)=handles_out2.mouseNo_per_file(fileNo);
end
end
fprintf(1, ['Import for file No ' num2str(fileNo) ' done in ' num2str(toc) ' sec\n'])
else
fprintf(1, ['Import for file No ' num2str(fileNo) ' failed\n'])
if fileNo==69
pffft=1;
end
end
end
else
load([choiceBatchPathName choiceFileName(1:end-2) '.mat'])
end
% % ii_thr=length(handles.p_threshold);
% ii_thr=1;
%Bar graph plot for accuracy
figureNo=0;
for iiMLalgo=handles.MLalgo_to_use
figureNo = figureNo + 1;
try
close(figureNo)
catch
end
hFig=figure(figureNo);
ax=gca;ax.LineWidth=3;
set(hFig, 'units','normalized','position',[.3 .3 .5 .25])
hold on
edges=[0:0.05:1];
rand_offset=0.8;
bar_offset=0;
for grNo=1:no_pcorr*length(these_groups)
if handles_out2.group_no(grNo).ii_thr(ii_thr).MLalgo(iiMLalgo).ii>0
%Shuffled
bar_offset=bar_offset+1;
these_accuracy_tr_sh2=handles_out2.group_no(grNo).ii_thr(ii_thr).MLalgo(iiMLalgo).accuracy_tr_sh2;
bar(bar_offset,mean(these_accuracy_tr_sh2),'LineWidth', 3,'EdgeColor','none','FaceColor',[80/255 194/255 255/255])
if length(these_accuracy_tr_sh2)>2
%Violin plot
[mean_out, CIout]=drgViolinPoint(these_accuracy_tr_sh2...
,edges,bar_offset,rand_offset,'k','k',3);
end
%Accuracy
bar_offset=bar_offset+1;
these_accuracy_tr=handles_out2.group_no(grNo).ii_thr(ii_thr).MLalgo(iiMLalgo).accuracy_tr;
bar(bar_offset,mean(these_accuracy_tr),'LineWidth', 3,'EdgeColor','none','FaceColor',[0 114/255 178/255])
if length(these_accuracy_tr)>2
%Violin plot
[mean_out, CIout]=drgViolinPoint(these_accuracy_tr...
,edges,bar_offset,rand_offset,'k','k',3);
end
else
bar_offset=bar_offset+2;
end
bar_offset=bar_offset+1;
end
xticks([1.5:3:22.5])
labels='xticklabels({';
for ii_label=1:length(these_groups)
for ii_pcorr=1:no_pcorr
labels=[labels '''' handles.group_names{these_groups(ii_label)} per_names{ii_pcorr} ''', '];
end
end
labels=[labels(1:end-2) '})'];
eval(labels)
title(['Prediction accuracy for ' handles_out2.classifier_names{iiMLalgo}])
ylabel('Accuracy')
ylim([0 1])
xlim([0 24])
pffft=1;
end
%Plot timecourses
%Note: These data were acquired at different rates. Here they are all
%resampled to a dt of 0.03 sec
dt_res=0.03;
time_span=t_from:dt_res:t_to;
ii_tspan=length(time_span);
fprintf(1, ['Length of time_span ' num2str(length(time_span)) '\n'])
%Plot the timecourse of mean dFF
for grNo=1:no_pcorr*length(these_groups)
figureNo = figureNo + 1;
try
close(figureNo)
catch
end
hFig=figure(figureNo);
hold on
ax=gca;ax.LineWidth=3;
set(hFig, 'units','normalized','position',[.2 .2 .3 .3])
if ~isempty(handles_out2.group_no(grNo).ii_time_span)
%Extrapolate all points onto the longest ii_tspan
these_meandFFsp=zeros(size(handles_out2.group_no(grNo).ii_time_span,1),ii_tspan);
these_meandFFsm=zeros(size(handles_out2.group_no(grNo).ii_time_span,1),ii_tspan);
for ii_f=1:size(handles_out2.group_no(grNo).ii_time_span,1)
print_out=1;
this_ii_tspan=handles_out2.group_no(grNo).ii_time_span(ii_f);
this_time_span=handles_out2.group_no(grNo).time_span_euclid(ii_f,1:this_ii_tspan);
% this_meandFFsp=zeros(1,ii_tspan);
for ii_tsp=1:ii_tspan
if time_span(ii_tsp)<this_time_span(1)
these_meandFFsp(ii_f,ii_tsp)=handles_out2.group_no(grNo).meandFFsp(ii_f,1);
these_meandFFsm(ii_f,ii_tsp)=handles_out2.group_no(grNo).meandFFsm(ii_f,1);
else
if time_span(ii_tsp)>this_time_span(end)
these_meandFFsp(ii_f,ii_tsp)=handles_out2.group_no(grNo).meandFFsp(ii_f,end);
these_meandFFsm(ii_f,ii_tsp)=handles_out2.group_no(grNo).meandFFsm(ii_f,end);
if print_out==1
fprintf(1, ['Mouse No ' num2str(mouseNo) ' Group No ' num2str(grNo) ' File No ' num2str(ii_f) '\n'])
print_out=0;
end
else
ii_0=find(this_time_span<=time_span(ii_tsp),1,'last');
ii_1=find(this_time_span>time_span(ii_tsp),1,'first');
these_meandFFsp(ii_f,ii_tsp)=handles_out2.group_no(grNo).meandFFsp(ii_f,ii_0)+...
(handles_out2.group_no(grNo).meandFFsp(ii_f,ii_1)-handles_out2.group_no(grNo).meandFFsp(ii_f,ii_0))...
*(time_span(ii_tsp)-this_time_span(ii_0))/(this_time_span(ii_1)-this_time_span(ii_0));
these_meandFFsm(ii_f,ii_tsp)=handles_out2.group_no(grNo).meandFFsm(ii_f,ii_0)+...
(handles_out2.group_no(grNo).meandFFsm(ii_f,ii_1)-handles_out2.group_no(grNo).meandFFsm(ii_f,ii_0))...
*(time_span(ii_tsp)-this_time_span(ii_0))/(this_time_span(ii_1)-this_time_span(ii_0));
end
end
end
end
if size(these_meandFFsm,1)>2
CIpv = bootci(1000, @mean, these_meandFFsm);
meanpv=mean(these_meandFFsm,1);
CIpv(1,:)=meanpv-CIpv(1,:);
CIpv(2,:)=CIpv(2,:)-meanpv;
[hlpvl, hppvl] = boundedline(time_span',mean(these_meandFFsm,1)', CIpv', 'b');
else
if size(these_meandFFsm,1)>0
plot(time_span',mean(these_meandFFsm,1)', 'b');
end
end
if size(these_meandFFsp,1)>2
CIpv = bootci(1000, @mean, these_meandFFsp);
meanpv=mean(these_meandFFsp,1);
CIpv(1,:)=meanpv-CIpv(1,:);
CIpv(2,:)=CIpv(2,:)-meanpv;
[hlpvl, hppvl] = boundedline(time_span',mean(these_meandFFsp,1)', CIpv', 'r');
else
if size(these_meandFFsp,1)>0
plot(time_span',mean(these_meandFFsp,1)', 'r');
end
end
end
xlim([-10 20])
xlabel('Time(sec)')
if no_pcorr==1
title(['Mean dFF ' handles.group_names{grNo}])
else
this_grNo=floor((grNo-1)/4)+1;
ii_pcorr=grNo-4*(this_grNo-1);
title(['Mean dFF ' handles.group_names{these_groups(this_grNo)} ' ' per_names{ii_pcorr}])
end
pffft=1;
end
%Now plot the timecourse for euclidean distance
for grNo=1:no_pcorr*length(these_groups)
figureNo = figureNo + 1;
try
close(figureNo)
catch
end
hFig=figure(figureNo);
hold on
ax=gca;ax.LineWidth=3;
set(hFig, 'units','normalized','position',[.2 .2 .3 .3])
if ~isempty(handles_out2.group_no(grNo).ii_time_span)
%Extrapolate all points onto time_span
these_dist_euclid=zeros(size(handles_out2.group_no(grNo).ii_time_span,1),ii_tspan);
for ii_f=1:size(handles_out2.group_no(grNo).ii_time_span,1)
this_ii_tspan=handles_out2.group_no(grNo).ii_time_span(ii_f);
this_time_span=handles_out2.group_no(grNo).time_span_euclid(ii_f,1:this_ii_tspan);
this_dist_euclid=zeros(1,ii_tspan);
for ii_tsp=1:ii_tspan
if time_span(ii_tsp)<this_time_span(1)
these_dist_euclid(ii_f,ii_tsp)=handles_out2.group_no(grNo).dist_euclid(ii_f,1);
else
if time_span(ii_tsp)>this_time_span(end)
these_dist_euclid(ii_f,ii_tsp)=handles_out2.group_no(grNo).dist_euclid(ii_f,end);
else
ii_0=find(this_time_span<=time_span(ii_tsp),1,'last');
ii_1=find(this_time_span>time_span(ii_tsp),1,'first');
these_dist_euclid(ii_f,ii_tsp)=handles_out2.group_no(grNo).dist_euclid(ii_f,ii_0)+...
(handles_out2.group_no(grNo).dist_euclid(ii_f,ii_1)-handles_out2.group_no(grNo).dist_euclid(ii_f,ii_0))...
*(time_span(ii_tsp)-this_time_span(ii_0))/(this_time_span(ii_1)-this_time_span(ii_0));
end
end
end
end
if size(these_dist_euclid,1)>2
CIpv = bootci(1000, @mean, these_dist_euclid);
meanpv=mean(these_dist_euclid,1);
CIpv(1,:)=meanpv-CIpv(1,:);
CIpv(2,:)=CIpv(2,:)-meanpv;
[hlpvl, hppvl] = boundedline(time_span',mean(these_dist_euclid,1)', CIpv', 'm');
else
if size(these_dist_euclid,1)>0
plot(time_span',mean(these_dist_euclid,1)', 'm');
end
end
this_ylim=ylim;
plot([0 0],this_ylim,'-k')
end
xlim([-10 20])
xlabel('Time(sec)')
if no_pcorr==1
title(['Euclidean distance ' handles.group_names{grNo}])
else
this_grNo=floor((grNo-1)/4)+1;
ii_pcorr=grNo-4*(this_grNo-1);
title(['Euclidean distance ' handles.group_names{these_groups(this_grNo)} ' ' per_names{ii_pcorr}])
end
end
delta_KLdiv_post=[];
for grNo=1:no_pcorr*length(these_groups)
figureNo = figureNo + 1;
try
close(figureNo)
catch
end
hFig=figure(figureNo);
hold on
ax=gca;ax.LineWidth=3;
set(hFig, 'units','normalized','position',[.2 .2 .3 .3])
if ~isempty(handles_out2.group_no(grNo).ii_time_span)
%Extrapolate all points onto time_span
for ii_f=1:size(handles_out2.group_no(grNo).ii_time_span,1)
this_ii_tspan=handles_out2.group_no(grNo).ii_time_span(ii_f);
this_time_span=handles_out2.group_no(grNo).time_span_euclid(ii_f,1:this_ii_tspan);
this_KLdivergence=zeros(1,ii_tspan);
for ii_tsp=1:ii_tspan
if time_span(ii_tsp)<this_time_span(1)
these_KLdivergence(ii_f,ii_tsp)=handles_out2.group_no(grNo).KLdivergence(ii_f,1);
else
if time_span(ii_tsp)>this_time_span(end)
these_KLdivergence(ii_f,ii_tsp)=handles_out2.group_no(grNo).KLdivergence(ii_f,end);
else
ii_0=find(this_time_span<=time_span(ii_tsp),1,'last');
ii_1=find(this_time_span>time_span(ii_tsp),1,'first');
these_KLdivergence(ii_f,ii_tsp)=handles_out2.group_no(grNo).KLdivergence(ii_f,ii_0)+...
(handles_out2.group_no(grNo).KLdivergence(ii_f,ii_1)-handles_out2.group_no(grNo).KLdivergence(ii_f,ii_0))...
*(time_span(ii_tsp)-this_time_span(ii_0))/(this_time_span(ii_1)-this_time_span(ii_0));
end
end
end
end
%Calculate the delta_KLdiv
delta_KLdiv=[];
these_delta_KLdiv_post=[];
for ii_file=1:ii_f
this_delta_KLdiv=[];
this_delta_KLdiv=these_KLdivergence(ii_file,:)-mean(these_KLdivergence(ii_file,(time_span'>-20)&(time_span'<-2)));
delta_KLdiv(ii_file,:)=this_delta_KLdiv;
these_delta_KLdiv_post=[these_delta_KLdiv_post mean(this_delta_KLdiv((time_span'>=0)&(time_span'<=handles.post_time)))];
end
delta_KLdiv_post.group(grNo).delta_KLdiv_post=these_delta_KLdiv_post;
if size(these_KLdivergence,1)>2
CIpv = bootci(1000, @mean, delta_KLdiv);
meanpv=mean(delta_KLdiv,1);
CIpv(1,:)=meanpv-CIpv(1,:);
CIpv(2,:)=CIpv(2,:)-meanpv;
delta_mean_KLdiv=mean(delta_KLdiv,1)';
[hlpvl, hppvl] = boundedline(time_span',delta_mean_KLdiv, CIpv', 'm');
else
if size(these_KLdivergence,1)>0
delta_mean_KLdiv=mean(delta_KLdiv,1)';
plot(time_span',delta_mean_KLdiv, 'm');
end
end
this_ylim=ylim;
plot([0 0],this_ylim,'-k')
end
xlim([-10 20])
xlabel('Time(sec)')
if no_pcorr==1
title(['KL divergence ' handles.group_names{grNo}])
else
this_grNo=floor((grNo-1)/4)+1;
ii_pcorr=grNo-4*(this_grNo-1);
title(['KL divergence ' handles.group_names{these_groups(this_grNo)} ' ' per_names{ii_pcorr}])
end
end
%Plot a bar graph for post_time KL divergence
figureNo = figureNo + 1;
try
close(figureNo)
catch
end
hFig=figure(figureNo);
ax=gca;ax.LineWidth=3;
set(hFig, 'units','normalized','position',[.3 .3 .5 .25])
hold on
edges=[0:0.05:1];
rand_offset=0.8;
bar_offset=0;
for grNo=1:no_pcorr*length(these_groups)
if length(delta_KLdiv_post.group(grNo).delta_KLdiv_post)>0
bar_offset=bar_offset+1;
these_delta_KLdiv=delta_KLdiv_post.group(grNo).delta_KLdiv_post;
bar(bar_offset,mean(these_delta_KLdiv),'LineWidth', 3,'EdgeColor','none','FaceColor',[0 114/255 178/255])
if length(these_delta_KLdiv)>2
%Violin plot
[mean_out, CIout]=drgViolinPoint(these_delta_KLdiv...
,edges,bar_offset,rand_offset,'k','k',3);
end
else
bar_offset=bar_offset+1;
end
bar_offset=bar_offset+1;
end
xticks([1:2:15])
labels='xticklabels({';
for ii_label=1:length(these_groups)
for ii_pcorr=1:no_pcorr
labels=[labels '''' handles.group_names{these_groups(ii_label)} per_names{ii_pcorr} ''', '];
end
end
labels=[labels(1:end-2) '})'];
eval(labels)
title('delta KL divergence ')
ylabel('delta KL div')
%Plot decoding accuracy for each group per mouse
per_mouse_acc=[];
for mouseNo=1:length(handles_out2.mouse_names)
for grNo=1:no_pcorr*length(these_groups)
per_mouse_acc.group(grNo).mouse(mouseNo).mean_accuracy=[];
per_mouse_acc.group(grNo).mouse(mouseNo).t_mean_accuracy=[];
per_mouse_acc.group(grNo).mouse(mouseNo).mean_accuracy_sh=[];
per_mouse_acc.group(grNo).mouse(mouseNo).t_mean_accuracy_sh=[];
if show_per_mouse==1
figureNo = figureNo + 1;
try
close(figureNo)
catch
end
hFig=figure(figureNo);
hold on
ax=gca;ax.LineWidth=3;
set(hFig, 'units','normalized','position',[.2 .2 .3 .3])
end
% handles_out2.group_no(grNo).ii_thr(ii_thr).MLalgo(iiMLalgo).mouseNo
if ~isempty(handles_out2.group_no(grNo).ii_time_span)
these_per_corr=[];
%Extrapolate all points onto the longest ii_tspan
these_mice=handles_out2.group_no(grNo).ii_thr(ii_thr).MLalgo(iiMLalgo).mouseNo;
ii_included=0;
for ii_f=1:size(handles_out2.group_no(grNo).ii_time_span,1)
if these_mice(ii_f)==mouseNo
ii_included=ii_included+1;
this_ii_tspan=handles_out2.group_no(grNo).ii_time_span(ii_f);
this_time_span=handles_out2.group_no(grNo).time_span_euclid(ii_f,1:this_ii_tspan);
for ii_tsp=1:ii_tspan
if time_span(ii_tsp)<this_time_span(1)
these_per_corr(ii_included,ii_tsp)=handles_out2.group_no(grNo).ii_thr(ii_thr).MLalgo(iiMLalgo).mean_correct_predict_sh(ii_f,1);
else
if time_span(ii_tsp)>this_time_span(end)
these_per_corr(ii_included,ii_tsp)=handles_out2.group_no(grNo).ii_thr(ii_thr).MLalgo(iiMLalgo).mean_correct_predict_sh(ii_f,end);
else
ii_0=find(this_time_span<=time_span(ii_tsp),1,'last');
ii_1=find(this_time_span>time_span(ii_tsp),1,'first');
these_per_corr(ii_included,ii_tsp)=handles_out2.group_no(grNo).ii_thr(ii_thr).MLalgo(iiMLalgo).mean_correct_predict_sh(ii_f,ii_0)+...
(handles_out2.group_no(grNo).ii_thr(ii_thr).MLalgo(iiMLalgo).mean_correct_predict_sh(ii_f,ii_1)-handles_out2.group_no(grNo).ii_thr(ii_thr).MLalgo(iiMLalgo).mean_correct_predict_sh(ii_f,ii_0))...
*(time_span(ii_tsp)-this_time_span(ii_0))/(this_time_span(ii_1)-this_time_span(ii_0));
end
end
end
end
end
%For some reason whe have a subset of sessions with zero accuracy
vetted_these_per_corr=[];
ii_s=0;
for ii_sessions=1:size(these_per_corr,1)
if sum(these_per_corr(ii_sessions,:)==0)<200
ii_s=ii_s+1;
vetted_these_per_corr(ii_s,:)=these_per_corr(ii_sessions,:);
end
end
these_per_corr=[];
these_per_corr=vetted_these_per_corr;
if show_per_mouse==1
if size(these_per_corr,1)>0
if size(these_per_corr,1)>2
CIpv = bootci(1000, @mean, these_per_corr);
meanpv=mean(these_per_corr,1);
CIpv(1,:)=meanpv-CIpv(1,:);
CIpv(2,:)=CIpv(2,:)-meanpv;
[hlpvl, hppvl] = boundedline(time_span',mean(these_per_corr,1)', CIpv', 'r');
else
if size(these_per_corr,1)>1
plot(time_span',mean(these_per_corr,1)', 'r');
else
plot(time_span',these_per_corr', 'r');
end
end
this_ylim=ylim;
plot([0 0],this_ylim,'-k')
end
end
per_mouse_acc.group(grNo).mouse(mouseNo).mean_accuracy_sh=mean(these_per_corr,1)';
per_mouse_acc.group(grNo).mouse(mouseNo).accuracy_sh=these_per_corr;
per_mouse_acc.group(grNo).mouse(mouseNo).t_mean_accuracy_sh=time_span';
these_per_corr=[];
ii_included=0;
for ii_f=1:size(handles_out2.group_no(grNo).ii_time_span,1)
if these_mice(ii_f)==mouseNo
ii_included=ii_included+1;
this_ii_tspan=handles_out2.group_no(grNo).ii_time_span(ii_f);
this_time_span=handles_out2.group_no(grNo).time_span_euclid(ii_f,1:this_ii_tspan);
for ii_tsp=1:ii_tspan
if time_span(ii_tsp)<this_time_span(1)
these_per_corr(ii_included,ii_tsp)=handles_out2.group_no(grNo).ii_thr(ii_thr).MLalgo(iiMLalgo).mean_correct_predict(ii_f,1);
else
if time_span(ii_tsp)>this_time_span(end)
these_per_corr(ii_included,ii_tsp)=handles_out2.group_no(grNo).ii_thr(ii_thr).MLalgo(iiMLalgo).mean_correct_predict(ii_f,end);
else
ii_0=find(this_time_span<=time_span(ii_tsp),1,'last');
ii_1=find(this_time_span>time_span(ii_tsp),1,'first');
these_per_corr(ii_included,ii_tsp)=handles_out2.group_no(grNo).ii_thr(ii_thr).MLalgo(iiMLalgo).mean_correct_predict(ii_f,ii_0)+...
(handles_out2.group_no(grNo).ii_thr(ii_thr).MLalgo(iiMLalgo).mean_correct_predict(ii_f,ii_1)-handles_out2.group_no(grNo).ii_thr(ii_thr).MLalgo(iiMLalgo).mean_correct_predict(ii_f,ii_0))...
*(time_span(ii_tsp)-this_time_span(ii_0))/(this_time_span(ii_1)-this_time_span(ii_0));
end
end
end
end
end
%For some reason whe have a subset of sessions with zero accuracy
vetted_these_per_corr=[];
ii_s=0;
for ii_sessions=1:size(these_per_corr,1)
if sum(these_per_corr(ii_sessions,:)==0)<200
ii_s=ii_s+1;
vetted_these_per_corr(ii_s,:)=these_per_corr(ii_sessions,:);
end
end
these_per_corr=[];
these_per_corr=vetted_these_per_corr;
if show_per_mouse==1
if size(these_per_corr,1)>0
if size(these_per_corr,1)>2
CIpv = bootci(1000, @mean, these_per_corr);
meanpv=mean(these_per_corr,1);
CIpv(1,:)=meanpv-CIpv(1,:);
CIpv(2,:)=CIpv(2,:)-meanpv;
[hlpvl, hppvl] = boundedline(time_span',mean(these_per_corr,1)', CIpv', 'k');
else
if size(these_per_corr,1)>1
plot(time_span',mean(these_per_corr,1)', 'k');
else
plot(time_span',these_per_corr', 'k');
end
end
end
end
per_mouse_acc.group(grNo).mouse(mouseNo).mean_accuracy=mean(these_per_corr,1)';
per_mouse_acc.group(grNo).mouse(mouseNo).accuracy=these_per_corr;
per_mouse_acc.group(grNo).mouse(mouseNo).t_mean_accuracy=time_span';
if show_per_mouse==1
for ii_session=1:size(these_per_corr,1)
plot(time_span',smoothdata(these_per_corr(ii_session,:)','gaussian',100),'Color',[100/255 100/255 100/255],'LineWidth',1)
end
plot(time_span',mean(these_per_corr,1)', 'k','LineWidth',1.5);
ylim([0.3 1.2])
this_ylim=ylim;
plot([0 0],this_ylim,'-k')
end
end
if no_pcorr==1
xlim([-20 30])
else
xlim([-10 20])
end
xlabel('Time(sec)')
if no_pcorr==1
title(['Decoding accuracy for ' handles.group_names{grNo}])
else
this_grNo=floor((grNo-1)/4)+1;
ii_pcorr=grNo-4*(this_grNo-1);
title(['Decoding accuracy for ' handles.group_names{these_groups(this_grNo)} ' mouse ' handles_out2.mouse_names{mouseNo} ' ' per_names{ii_pcorr}])
end
end
end
%Plot the overall accuracy calculated from per mouse decoding
for grNo=1:no_pcorr*length(these_groups)
ii_m_included=0;
these_mean_accuracy=[];
these_mean_accuracy_sh=[];
for mouseNo=1:length(handles_out2.mouse_names)
if ~isempty(per_mouse_acc.group(grNo).mouse(mouseNo).mean_accuracy)
ii_m_included=ii_m_included+1;
these_mean_accuracy(ii_m_included,:)=per_mouse_acc.group(grNo).mouse(mouseNo).mean_accuracy;
these_mean_accuracy_sh(ii_m_included,:)=per_mouse_acc.group(grNo).mouse(mouseNo).mean_accuracy_sh;
end
end
if size(these_mean_accuracy,1)>2
figureNo = figureNo + 1;
try
close(figureNo)
catch
end
hFig=figure(figureNo);
hold on
ax=gca;ax.LineWidth=3;
set(hFig, 'units','normalized','position',[.2 .2 .3 .3])
CIpv = bootci(1000, @mean, these_mean_accuracy_sh);
meanpv=mean(these_mean_accuracy_sh,1);
CIpv(1,:)=meanpv-CIpv(1,:);
CIpv(2,:)=CIpv(2,:)-meanpv;
[hlpvl, hppvl] = boundedline(time_span',mean(these_mean_accuracy_sh,1)', CIpv', 'r');
CIpv = bootci(1000, @mean, these_mean_accuracy);
meanpv=mean(these_mean_accuracy,1);
CIpv(1,:)=meanpv-CIpv(1,:);
CIpv(2,:)=CIpv(2,:)-meanpv;
[hlpvl, hppvl] = boundedline(time_span',mean(these_mean_accuracy,1)', CIpv', 'k');
for ii_session=1:size(these_mean_accuracy,1)
plot(time_span',smoothdata(these_mean_accuracy(ii_session,:)','gaussian',100),'Color',[100/255 100/255 100/255],'LineWidth',1)
end
plot(time_span',mean(these_mean_accuracy,1)', 'k','LineWidth',1.5);
ylim([0.3 1.2])
this_ylim=ylim;
%Odor on markers
plot([0 0],this_ylim,'-k')
odorhl=plot([0 delta_odor],[this_ylim(1)+0.1*(this_ylim(2)-this_ylim(1)) this_ylim(1)+0.1*(this_ylim(2)-this_ylim(1))],'-k','LineWidth',5);
plot([delta_odor delta_odor],this_ylim,'-k')
%Reinforcement markers
plot([delta_odor_on_reinf_on delta_odor_on_reinf_on],this_ylim,'-r')
reinfhl=plot([delta_odor_on_reinf_on delta_odor_on_reinf_on+delta_reinf],[this_ylim(1)+0.1*(this_ylim(2)-this_ylim(1)) this_ylim(1)+0.1*(this_ylim(2)-this_ylim(1))],'-r','LineWidth',5);
plot([delta_odor_on_reinf_on+delta_reinf delta_odor_on_reinf_on+delta_reinf],this_ylim,'-r')
xlim([-10 20])
this_grNo=floor((grNo-1)/4)+1;
ii_pcorr=grNo-4*(this_grNo-1);