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dfs_reldist_analysis_allcombination.m
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dfs_reldist_analysis_allcombination.m
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%% dfs_reldist_analysis_allcombination.m
% -> dfa_ahead_behind_distance_muareldist.m
% -> dfa_ahead_behind_distance_muareldist_lintrack.m
% -> aj_get2dstate_dlc.m
% -> Figure23_plant_triggered_avgs.mat
% -> Figure2D_EDFigure7BC_decode_to_animal_dist.mat
%% CENTER ARM
% clear all; close all; clc;
% %animal filters | enter animal names to anlyse
% animals={'Jaq','Roqui', 'Lotus', 'Monty', 'Peanut'};
% for a = 1:length(animals)
%
% % epoch filters | select wtrack epochs
% epochfilter{1} = 'isequal($type, ''run'') && isequal($environment, ''wtrack'')';
%
% % iterators | these are goimg to define your base unit of analysis, such as per cell or per epoch
% iterator= 'epocheeganal';
%
% % goes into tetinfo and searches for the correct tetrodes to be included
% tetfilter = 'isequal($area,''ca1Rref'') '; %isequal($area,''ca1Lref'') |
%
% % timefilter is a filter that excludes a selected time window
% timefilter{1} = {'aj_get2dstate_dlc', '($mobility == 1)','mobility_velocity',[10 200], 'mobility_buffer',0, 'min_durations',0.75, 'vel2choose', 'nose'};
%
% f(a) = createfilter('animal',animals{a},'epochs',epochfilter,'excludetime', timefilter, 'eegtetrodes',tetfilter, 'iterator', iterator);
% f(a) = setfilterfunction(f(a), 'dfa_ahead_behind_distance_muareldist', {'eeg', 'eeggnd', 'theta' ,'posdlc'},'animal',animals{a},'out_min',60, 'out_max',100, 'a',a, 'ci_thresh', 50, 'ci_mean', 50, 'reldist_mean', 50, 'rel_dist_cutoff',10);
% f(a)= runfilter(f(a));
% end
%
% fname ='/';
% save(fname,'f', '-v7.3')
%% LINEARTRACK
% % % set filters
% clear all; close all; clc;
%
% %animal filters | enter animal names to anlyse
% animals={'Jaq','Roqui', 'Lotus'};
% %animals={'Roqui'};
%
% for a = 1:length(animals)
%
% % epoch filters | select wtrack epochs
% epochfilter{1} = 'isequal($type, ''run'') && isequal($environment, ''lineartrack'')';
%
% % iterators | these are goimg to define your base unit of analysis, such as per cell or per epoch
% iterator= 'epocheeganal';
%
% % goes into tetinfo and searches for the correct tetrodes to be included
% tetfilter = 'isequal($area,''ca1Rref'') '; %isequal($area,''ca1Lref'') |
%
% % timefilter is a filter that excludes a selected time window
% timefilter{1} = {'aj_get2dstate_dlc', '($mobility == 1)','mobility_velocity',[10 200], 'mobility_buffer',0.25 'min_durations',0.75, 'vel2choose', 'nose'};
%
% f(a) = createfilter('animal',animals{a},'epochs',epochfilter,'excludetime', timefilter, 'eegtetrodes',tetfilter, 'iterator', iterator);
% f(a) = setfilterfunction(f(a), 'dfa_ahead_behind_distance_muareldist_lintrack', {'eeg', 'eeggnd', 'theta' ,'posdlc'},'animal',animals{a},'out_min',40, 'out_max',80, 'a',a, 'ci_thresh', 50, 'ci_mean', 50, 'reldist_mean', 50, 'rel_dist_cutoff',10);
% f(a)= runfilter(f(a));
% end
%
% fname ='';
% save(fname,'f', '-v7.3')
%% initialization parameters
clear all;
mod_score_window=0.04; % corresponds to 80ms around the plant time
t_pre=0.07; t_post=0.07; % the usal window around which we compute one cycle of MUA fluctuations ~125 ms is a theta cycle
OffsetMin=-0.07; OffsetMax=0.07; % for shuffle analysis
number_of_shuffles=5000; % per epoch
savefigs=0; %saves the mua trace per wtrack portion for all aniamls
shuffles=0; n_samps=1000; %does the shuffles and saves them for each portion of the track in results_dir
resdir='';
%% OUTBOUND RELDIST
results_dir='/'; cd(results_dir);
results_filename=[''];
load([results_filename,'.mat']);
%clearvars -except f;
animals={'Jaq', 'Roqui', 'Lotus', 'Monty', 'Peanut'};
for a=[1]
ind = cell2mat(arrayfun(@(x) x.index',f(a).output{1},'Un',0))';
ind_select=((ind(:,1)==1)|(ind(:,1)==2)|(ind(:,1)==3)|(ind(:,1)==4)|(ind(:,1)==5)| (ind(:,1)==6)); counter=0; number_steps=0;
for i=rat1_idx % these epochs have good quality decoding
if size(f(a).output{1,1}(i).forelimb_outbound_reldist,2)>1
counter=[counter; size(i,1)];
animal=animals{1,a};
base_dir='/opt/stelmo/abhilasha/animals';
destdir=[base_dir,'/',animal,'/','filterframework', '/'];
index=f(a).output{1,1}(i).index;close all
d=index(1); e=index(2);
task = loaddatastruct(destdir,animal,'task',[d e]);
trials = loaddatastruct(destdir,animal,'trials',[d e]);
tetinfo = loaddatastruct(destdir,animal,'tetinfo',[d e]);
tets_ca1R = evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1R'')');
tets_ca1L=evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1L'')');
tets=vertcat(tets_ca1R,tets_ca1L);
eeg = loadeegstruct(destdir,animal,'eeggnd',d,e,tets);
eegtimes = geteegtimes(eeg{d}{e}{tets(1)});
marks = loaddatastruct(destdir, animal,'marks',[d e ]);
[muatimes, mua] = getMUAtrace(marks{d}{e}(tets),eeg{d}{e}{tets(1)});
ahead_behind_dist_smooth = f(a).output{1,1}(i).ahead_behind_smooth;
forelimb_outbound_reldist = f(a).output{1,1}(i).forelimb_outbound_longsweep;
forelimb_outbound_reldist(:,2)=forelimb_outbound_reldist;
limb_select_reldist=forelimb_outbound_reldist;
number_steps=[number_steps; size(limb_select_reldist,1)];
trig_plot_reldist=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist, 't_pre',t_pre, 't_post',t_post);
lags_reldist=trig_plot_reldist.lags;
mydata_reldist{a,i}=trig_plot_reldist.peritrig;
mod_score_perepoch_stdev{a,i}= sum(abs(mydata_reldist{a,i}(:)-mean(mydata_reldist{a,i})));
% keyboard
% plot(mydata_reldist{a,i}, 'k'); hold on;
% for n_rows=1:size(trig_plot_reldist.peritrig_data,1)
% if sum(isnan(trig_plot_reldist.peritrig_data(n_rows,:)))<2
% mydata_reldist_nonnan{n_rows}=trig_plot_reldist.peritrig_data(n_rows,:);
% end
% end
% plot(nanmean(cell2mat(mydata_reldist_nonnan(:))), 'r'); hold on;
% just for fun, remove those row with nans
% keyboard
if shuffles == 1
%% Iteration for shuffling analysis
%RandomOffsets=OffsetMin:(1/(number_of_shuffles*10)):OffsetMax;
RandomOffsets=OffsetMin+(OffsetMax-OffsetMin).*rand(number_of_shuffles,1); % in seconds
% this is my shuffled distribution
for i_shuff=1:size(RandomOffsets,1)
%number_of_shuffles
forelimb_outbound_reldist_shuff=limb_select_reldist(:,1)+RandomOffsets(i_shuff);
forelimb_outbound_reldist_shuff(:,2)=forelimb_outbound_reldist_shuff;
limb_select_reldist_shuff=forelimb_outbound_reldist_shuff;
trig_plot_reldist_shuff=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist_shuff, 't_pre',t_pre, 't_post',t_post);
mydata_reldist_shuff{a,i,i_shuff}=trig_plot_reldist_shuff.peritrig_data; % doing peritrig_data instead of peritrig ensures that all shuffled plants get to go in the massive shuffle
close all; clear limb_select_reldist_shuff trig_plot_reldist_shuff;
end
mydata_reldist_shuff_all=cell2mat(squeeze(((mydata_reldist_shuff(a,i,:)))));
% now create a null distribution based on the shuffles i.e from
% the shuffled relationships, sample the same number of plants
% that you have in the epoch a 1000 times
for n_shuff=1:n_samps
randIdcs=randperm(size(mydata_reldist_shuff_all,1),size(limb_select_reldist(:,1),1));
mydata_reldist_nulldist{a,i,n_shuff}=nanmean(mydata_reldist_shuff_all(randIdcs,:));
mod_score_perepoch_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_reldist_nulldist{a,i,n_shuff}(:)-mean(mydata_reldist_nulldist{a,i,n_shuff})));
clear randIdcs
end
end
clearvars -except animals results_dir lags_reldist mydata_reldist_nulldist a t_pre t_post shuffles number_of_shuffles n_samps OffsetMin OffsetMax f f1 f2 counter number_steps number_steps_correct number_steps_incorrect ...
mod_score_mua_perepoch_stdev_correct mod_score_mua_perepoch_stdev_incorrect mod_score_mua_perepoch_stdev lags_mua_correct ...
mod_score_mua_perepoch_pershuff_stdev mod_score_mua_perepoch_pershuff_stdev_incorrect_control ...
mydata_mua_correct mydata_mua_incorrect mydata_mua ...
mod_score_theta_perepoch_stdev_correct mod_score_theta_perepoch_stdev_incorrect mod_score_theta_perepoch_stdev lags_theta_correct ...
mod_score_theta_perepoch_pershuff_stdev mod_score_theta_perepoch_pershuff_stdev_incorrect_control ...
mydata_theta_correct mydata_theta_incorrect mydata_theta ...
mod_score_perepoch_stdev_correct mod_score_perepoch_stdev_incorrect mod_score_perepoch_stdev lags_reldist_correct ...
mod_score_perepoch_pershuff_stdev mod_score_perepoch_pershuff_stdev_incorrect_control ...
mydata_reldist_correct mydata_reldist_incorrect mydata_reldist ...
number_of_epochs_all number_steps_all number_steps_correct_all number_steps_incorrect_all
end
end
number_of_epochs_all(a)=sum(counter);
number_steps_all(a)=sum(number_steps);
clear counter number_steps
end
for a=[2]
ind= cell2mat(arrayfun(@(x) x.index',f(a).output{1},'Un',0))';
ind_select=((ind(:,1)==1)|(ind(:,1)==2)|(ind(:,1)==3)|(ind(:,1)==4)|(ind(:,1)==5)| (ind(:,1)==6)); counter=0; number_steps=0;
for i=rat2_idx
if size(f(a).output{1,1}(i).forelimb_outbound_reldist,2)>1
counter=[counter; size(i,1)];
animal=animals{1,a};
base_dir='/opt/stelmo/abhilasha/animals';
destdir=[base_dir,'/',animal,'/','filterframework', '/'];
index=f(a).output{1,1}(i).index;close all
d=index(1); e=index(2);
task = loaddatastruct(destdir,animal,'task',[d e]);
trials = loaddatastruct(destdir,animal,'trials',[d e]);
tetinfo = loaddatastruct(destdir,animal,'tetinfo',[d e]);
tets_ca1R = evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1R'')');
tets_ca1L=evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1L'')');
tets=vertcat(tets_ca1R,tets_ca1L);
eeg = loadeegstruct(destdir,animal,'eeggnd',d,e,tets);
eegtimes = geteegtimes(eeg{d}{e}{tets(1)});
marks = loaddatastruct(destdir, animal,'marks',[d e ]);
[muatimes, mua] = getMUAtrace(marks{d}{e}(tets),eeg{d}{e}{tets(1)});
ahead_behind_dist_smooth = f(a).output{1,1}(i).ahead_behind_smooth;
forelimb_outbound_reldist = f(a).output{1,1}(i).forelimb_outbound_longsweep;
forelimb_outbound_reldist(:,2)=forelimb_outbound_reldist;
limb_select_reldist=forelimb_outbound_reldist;
number_steps=[number_steps; size(limb_select_reldist,1)];
trig_plot_reldist=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist, 't_pre',t_pre, 't_post',t_post);
lags_reldist=trig_plot_reldist.lags;
mydata_reldist{a,i}=trig_plot_reldist.peritrig;
mod_score_perepoch_stdev{a,i}= sum(abs(mydata_reldist{a,i}(:)-mean(mydata_reldist{a,i})));
% keyboard
% plot(mydata_reldist{a,i}, 'k'); hold on;
% for n_rows=1:size(trig_plot_reldist.peritrig_data,1)
% if sum(isnan(trig_plot_reldist.peritrig_data(n_rows,:)))<2
% mydata_reldist_nonnan{n_rows}=trig_plot_reldist.peritrig_data(n_rows,:);
% end
% end
% plot(nanmean(cell2mat(mydata_reldist_nonnan(:))), 'r'); hold on;
% just for fun, remove those row with nans
% keyboard
if shuffles == 1
%% Iteration for shuffling analysis
%RandomOffsets=OffsetMin:(1/(number_of_shuffles*10)):OffsetMax;
RandomOffsets=OffsetMin+(OffsetMax-OffsetMin).*rand(number_of_shuffles,1); % in seconds
% this is my shuffled distribution
for i_shuff=1:size(RandomOffsets,1)
%number_of_shuffles
forelimb_outbound_reldist_shuff=limb_select_reldist(:,1)+RandomOffsets(i_shuff);
forelimb_outbound_reldist_shuff(:,2)=forelimb_outbound_reldist_shuff;
limb_select_reldist_shuff=forelimb_outbound_reldist_shuff;
trig_plot_reldist_shuff=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist_shuff, 't_pre',t_pre, 't_post',t_post);
mydata_reldist_shuff{a,i,i_shuff}=trig_plot_reldist_shuff.peritrig_data; % doing peritrig_data instead of peritrig ensures that all shuffled plants get to go in the massive shuffle
close all; clear limb_select_reldist_shuff trig_plot_reldist_shuff;
end
mydata_reldist_shuff_all=cell2mat(squeeze(((mydata_reldist_shuff(a,i,:)))));
% now create a null distribution based on the shuffles i.e from
% the shuffled relationships, sample the same number of plants
% that you have in the epoch a 1000 times
for n_shuff=1:n_samps
randIdcs=randperm(size(mydata_reldist_shuff_all,1),size(limb_select_reldist(:,1),1));
mydata_reldist_nulldist{a,i,n_shuff}=nanmean(mydata_reldist_shuff_all(randIdcs,:));
mod_score_perepoch_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_reldist_nulldist{a,i,n_shuff}(:)-mean(mydata_reldist_nulldist{a,i,n_shuff})));
clear randIdcs
end
end
clearvars -except animals results_dir lags_reldist mydata_reldist_nulldist a t_pre t_post shuffles number_of_shuffles n_samps OffsetMin OffsetMax f f1 f2 counter number_steps number_steps_correct number_steps_incorrect ...
mod_score_mua_perepoch_stdev_correct mod_score_mua_perepoch_stdev_incorrect mod_score_mua_perepoch_stdev lags_mua_correct ...
mod_score_mua_perepoch_pershuff_stdev mod_score_mua_perepoch_pershuff_stdev_incorrect_control ...
mydata_mua_correct mydata_mua_incorrect mydata_mua ...
mod_score_theta_perepoch_stdev_correct mod_score_theta_perepoch_stdev_incorrect mod_score_theta_perepoch_stdev lags_theta_correct ...
mod_score_theta_perepoch_pershuff_stdev mod_score_theta_perepoch_pershuff_stdev_incorrect_control ...
mydata_theta_correct mydata_theta_incorrect mydata_theta ...
mod_score_perepoch_stdev_correct mod_score_perepoch_stdev_incorrect mod_score_perepoch_stdev lags_reldist_correct ...
mod_score_perepoch_pershuff_stdev mod_score_perepoch_pershuff_stdev_incorrect_control ...
mydata_reldist_correct mydata_reldist_incorrect mydata_reldist ...
number_of_epochs_all number_steps_all number_steps_correct_all number_steps_incorrect_all
end
end
number_of_epochs_all(a)=sum(counter);
number_steps_all(a)=sum(number_steps);
clear counter number_steps
end
for a=[3]
ind = cell2mat(arrayfun(@(x) x.index',f(a).output{1},'Un',0))';
ind_select=((ind(:,1)==1)|(ind(:,1)==2)|(ind(:,1)==3)|(ind(:,1)==4)|(ind(:,1)==5)| (ind(:,1)==6)); counter=0; number_steps=0;
for i=rat3_idx
if size(f(a).output{1,1}(i).forelimb_outbound_reldist,2)>1
counter=[counter; size(i,1)];
animal=animals{1,a};
base_dir='/opt/stelmo/abhilasha/animals';
destdir=[base_dir,'/',animal,'/','filterframework', '/'];
index=f(a).output{1,1}(i).index;close all
d=index(1); e=index(2);
task = loaddatastruct(destdir,animal,'task',[d e]);
trials = loaddatastruct(destdir,animal,'trials',[d e]);
tetinfo = loaddatastruct(destdir,animal,'tetinfo',[d e]);
tets_ca1R = evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1R'')');
tets_ca1L=evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1L'')');
tets=vertcat(tets_ca1R,tets_ca1L);
eeg = loadeegstruct(destdir,animal,'eeggnd',d,e,tets);
eegtimes = geteegtimes(eeg{d}{e}{tets(1)});
marks = loaddatastruct(destdir, animal,'marks',[d e ]);
[muatimes, mua] = getMUAtrace(marks{d}{e}(tets),eeg{d}{e}{tets(1)});
ahead_behind_dist_smooth = f(a).output{1,1}(i).ahead_behind_smooth;
forelimb_outbound_reldist = f(a).output{1,1}(i).forelimb_outbound_longsweep;
forelimb_outbound_reldist(:,2)=forelimb_outbound_reldist;
limb_select_reldist=forelimb_outbound_reldist;
number_steps=[number_steps; size(limb_select_reldist,1)];
trig_plot_reldist=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist, 't_pre',t_pre, 't_post',t_post);
lags_reldist=trig_plot_reldist.lags;
mydata_reldist{a,i}=trig_plot_reldist.peritrig;
mod_score_perepoch_stdev{a,i}= sum(abs(mydata_reldist{a,i}(:)-mean(mydata_reldist{a,i})));
% keyboard
% plot(mydata_reldist{a,i}, 'k'); hold on;
% for n_rows=1:size(trig_plot_reldist.peritrig_data,1)
% if sum(isnan(trig_plot_reldist.peritrig_data(n_rows,:)))<2
% mydata_reldist_nonnan{n_rows}=trig_plot_reldist.peritrig_data(n_rows,:);
% end
% end
% plot(nanmean(cell2mat(mydata_reldist_nonnan(:))), 'r'); hold on;
% just for fun, remove those row with nans
% keyboard
if shuffles == 1
%% Iteration for shuffling analysis
%RandomOffsets=OffsetMin:(1/(number_of_shuffles*10)):OffsetMax;
RandomOffsets=OffsetMin+(OffsetMax-OffsetMin).*rand(number_of_shuffles,1); % in seconds
% this is my shuffled distribution
for i_shuff=1:size(RandomOffsets,1)
%number_of_shuffles
forelimb_outbound_reldist_shuff=limb_select_reldist(:,1)+RandomOffsets(i_shuff);
forelimb_outbound_reldist_shuff(:,2)=forelimb_outbound_reldist_shuff;
limb_select_reldist_shuff=forelimb_outbound_reldist_shuff;
trig_plot_reldist_shuff=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist_shuff, 't_pre',t_pre, 't_post',t_post);
mydata_reldist_shuff{a,i,i_shuff}=trig_plot_reldist_shuff.peritrig_data; % doing peritrig_data instead of peritrig ensures that all shuffled plants get to go in the massive shuffle
close all; clear limb_select_reldist_shuff trig_plot_reldist_shuff;
end
mydata_reldist_shuff_all=cell2mat(squeeze(((mydata_reldist_shuff(a,i,:)))));
% now create a null distribution based on the shuffles i.e from
% the shuffled relationships, sample the same number of plants
% that you have in the epoch a 1000 times
for n_shuff=1:n_samps
randIdcs=randperm(size(mydata_reldist_shuff_all,1),size(limb_select_reldist(:,1),1));
mydata_reldist_nulldist{a,i,n_shuff}=nanmean(mydata_reldist_shuff_all(randIdcs,:));
mod_score_perepoch_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_reldist_nulldist{a,i,n_shuff}(:)-mean(mydata_reldist_nulldist{a,i,n_shuff})));
clear randIdcs
end
end
clearvars -except animals results_dir lags_reldist mydata_reldist_nulldist a t_pre t_post shuffles number_of_shuffles n_samps OffsetMin OffsetMax f f1 f2 counter number_steps number_steps_correct number_steps_incorrect ...
mod_score_mua_perepoch_stdev_correct mod_score_mua_perepoch_stdev_incorrect mod_score_mua_perepoch_stdev lags_mua_correct ...
mod_score_mua_perepoch_pershuff_stdev mod_score_mua_perepoch_pershuff_stdev_incorrect_control ...
mydata_mua_correct mydata_mua_incorrect mydata_mua ...
mod_score_theta_perepoch_stdev_correct mod_score_theta_perepoch_stdev_incorrect mod_score_theta_perepoch_stdev lags_theta_correct ...
mod_score_theta_perepoch_pershuff_stdev mod_score_theta_perepoch_pershuff_stdev_incorrect_control ...
mydata_theta_correct mydata_theta_incorrect mydata_theta ...
mod_score_perepoch_stdev_correct mod_score_perepoch_stdev_incorrect mod_score_perepoch_stdev lags_reldist_correct ...
mod_score_perepoch_pershuff_stdev mod_score_perepoch_pershuff_stdev_incorrect_control ...
mydata_reldist_correct mydata_reldist_incorrect mydata_reldist ...
number_of_epochs_all number_steps_all number_steps_correct_all number_steps_incorrect_all
end
end
number_of_epochs_all(a)=sum(counter);
number_steps_all(a)=sum(number_steps);
clear counter number_steps
end
for a=[5]
ind = cell2mat(arrayfun(@(x) x.index',f(a).output{1},'Un',0))';
ind_select=((ind(:,1)==1)|(ind(:,1)==2)|(ind(:,1)==3)|(ind(:,1)==4)|(ind(:,1)==5)| (ind(:,1)==6)); counter=0; number_steps=0;
for i=rat5_idx
if size(f(a).output{1,1}(i).forelimb_outbound_reldist,2)>1
counter=[counter; size(i,1)];
animal=animals{1,a};
base_dir='/opt/stelmo/abhilasha/animals';
destdir=[base_dir,'/',animal,'/','filterframework', '/'];
index=f(a).output{1,1}(i).index;close all
d=index(1); e=index(2);
task = loaddatastruct(destdir,animal,'task',[d e]);
trials = loaddatastruct(destdir,animal,'trials',[d e]);
tetinfo = loaddatastruct(destdir,animal,'tetinfo',[d e]);
tets_ca1R = evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1R'')');
tets_ca1L=evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1L'')');
tets=vertcat(tets_ca1R,tets_ca1L);
eeg = loadeegstruct(destdir,animal,'eeggnd',d,e,tets);
eegtimes = geteegtimes(eeg{d}{e}{tets(1)});
marks = loaddatastruct(destdir, animal,'marks',[d e ]);
[muatimes, mua] = getMUAtrace(marks{d}{e}(tets),eeg{d}{e}{tets(1)});
ahead_behind_dist_smooth = f(a).output{1,1}(i).ahead_behind_smooth;
forelimb_outbound_reldist = f(a).output{1,1}(i).forelimb_outbound_longsweep;
forelimb_outbound_reldist(:,2)=forelimb_outbound_reldist;
limb_select_reldist=forelimb_outbound_reldist;
number_steps=[number_steps; size(limb_select_reldist,1)];
trig_plot_reldist=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist, 't_pre',t_pre, 't_post',t_post);
lags_reldist=trig_plot_reldist.lags;
mydata_reldist{a,i}=trig_plot_reldist.peritrig;
mod_score_perepoch_stdev{a,i}= sum(abs(mydata_reldist{a,i}(:)-mean(mydata_reldist{a,i})));
% keyboard
% plot(mydata_reldist{a,i}, 'k'); hold on;
% for n_rows=1:size(trig_plot_reldist.peritrig_data,1)
% if sum(isnan(trig_plot_reldist.peritrig_data(n_rows,:)))<2
% mydata_reldist_nonnan{n_rows}=trig_plot_reldist.peritrig_data(n_rows,:);
% end
% end
% plot(nanmean(cell2mat(mydata_reldist_nonnan(:))), 'r'); hold on;
% just for fun, remove those row with nans
% keyboard
if shuffles == 1
%% Iteration for shuffling analysis
%RandomOffsets=OffsetMin:(1/(number_of_shuffles*10)):OffsetMax;
RandomOffsets=OffsetMin+(OffsetMax-OffsetMin).*rand(number_of_shuffles,1); % in seconds
% this is my shuffled distribution
for i_shuff=1:size(RandomOffsets,1)
%number_of_shuffles
forelimb_outbound_reldist_shuff=limb_select_reldist(:,1)+RandomOffsets(i_shuff);
forelimb_outbound_reldist_shuff(:,2)=forelimb_outbound_reldist_shuff;
limb_select_reldist_shuff=forelimb_outbound_reldist_shuff;
trig_plot_reldist_shuff=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist_shuff, 't_pre',t_pre, 't_post',t_post);
mydata_reldist_shuff{a,i,i_shuff}=trig_plot_reldist_shuff.peritrig_data; % doing peritrig_data instead of peritrig ensures that all shuffled plants get to go in the massive shuffle
close all; clear limb_select_reldist_shuff trig_plot_reldist_shuff;
end
mydata_reldist_shuff_all=cell2mat(squeeze(((mydata_reldist_shuff(a,i,:)))));
% now create a null distribution based on the shuffles i.e from
% the shuffled relationships, sample the same number of plants
% that you have in the epoch a 1000 times
for n_shuff=1:n_samps
randIdcs=randperm(size(mydata_reldist_shuff_all,1),size(limb_select_reldist(:,1),1));
mydata_reldist_nulldist{a,i,n_shuff}=nanmean(mydata_reldist_shuff_all(randIdcs,:));
mod_score_perepoch_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_reldist_nulldist{a,i,n_shuff}(:)-mean(mydata_reldist_nulldist{a,i,n_shuff})));
clear randIdcs
end
end
clearvars -except animals results_dir lags_reldist mydata_reldist_nulldist a t_pre t_post shuffles number_of_shuffles n_samps OffsetMin OffsetMax f f1 f2 counter number_steps number_steps_correct number_steps_incorrect ...
mod_score_mua_perepoch_stdev_correct mod_score_mua_perepoch_stdev_incorrect mod_score_mua_perepoch_stdev lags_mua_correct ...
mod_score_mua_perepoch_pershuff_stdev mod_score_mua_perepoch_pershuff_stdev_incorrect_control ...
mydata_mua_correct mydata_mua_incorrect mydata_mua ...
mod_score_theta_perepoch_stdev_correct mod_score_theta_perepoch_stdev_incorrect mod_score_theta_perepoch_stdev lags_theta_correct ...
mod_score_theta_perepoch_pershuff_stdev mod_score_theta_perepoch_pershuff_stdev_incorrect_control ...
mydata_theta_correct mydata_theta_incorrect mydata_theta ...
mod_score_perepoch_stdev_correct mod_score_perepoch_stdev_incorrect mod_score_perepoch_stdev lags_reldist_correct ...
mod_score_perepoch_pershuff_stdev mod_score_perepoch_pershuff_stdev_incorrect_control ...
mydata_reldist_correct mydata_reldist_incorrect mydata_reldist ...
number_of_epochs_all number_steps_all number_steps_correct_all number_steps_incorrect_all
end
end
number_of_epochs_all(a)=sum(counter);
number_steps_all(a)=sum(number_steps);
clear counter number_steps
end
% degree of theta modulation Frank et al 2001
% theta_mod=maximum value -minimum value
% saveworkspace
if shuffles==1
cd(results_dir); save('');
end
%% INBOUND RELDIST
clearvars -except f t_pre t_post n_samps savefigs resdir OffsetMin OffsetMax number_of_shuffles results_dir shuffles
animals={'Jaq', 'Roqui', 'Lotus', 'Monty', 'Peanut'};
for a=[1]
ind = cell2mat(arrayfun(@(x) x.index',f(a).output{1},'Un',0))';
ind_select=((ind(:,1)==1)|(ind(:,1)==2)|(ind(:,1)==3)|(ind(:,1)==4)|(ind(:,1)==5)| (ind(:,1)==6)); counter=0; number_steps=0;
for i=rat1_idx
if size(f(a).output{1,1}(i).forelimb_inbound_reldist,2)>1
counter=[counter; size(i,1)];
animal=animals{1,a};
base_dir='/opt/stelmo/abhilasha/animals';
destdir=[base_dir,'/',animal,'/','filterframework', '/'];
index=f(a).output{1,1}(i).index;close all
d=index(1); e=index(2);
task = loaddatastruct(destdir,animal,'task',[d e]);
trials = loaddatastruct(destdir,animal,'trials',[d e]);
tetinfo = loaddatastruct(destdir,animal,'tetinfo',[d e]);
tets_ca1R = evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1R'')');
tets_ca1L=evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1L'')');
tets=vertcat(tets_ca1R,tets_ca1L);
eeg = loadeegstruct(destdir,animal,'eeggnd',d,e,tets);
eegtimes = geteegtimes(eeg{d}{e}{tets(1)});
marks = loaddatastruct(destdir, animal,'marks',[d e ]);
[muatimes, mua] = getMUAtrace(marks{d}{e}(tets),eeg{d}{e}{tets(1)});
ahead_behind_dist_smooth = f(a).output{1,1}(i).ahead_behind_smooth;
forelimb_inbound_reldist = f(a).output{1,1}(i).forelimb_inbound_longsweep;
forelimb_inbound_reldist(:,2)=forelimb_inbound_reldist;
limb_select_reldist=forelimb_inbound_reldist;
number_steps=[number_steps; size(limb_select_reldist,1)];
trig_plot_reldist=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist, 't_pre',t_pre, 't_post',t_post);
lags_reldist=trig_plot_reldist.lags;
mydata_reldist{a,i}=trig_plot_reldist.peritrig;
mod_score_perepoch_stdev{a,i}= sum(abs(mydata_reldist{a,i}(:)-mean(mydata_reldist{a,i})));
% keyboard
% plot(mydata_reldist{a,i}, 'k'); hold on;
% for n_rows=1:size(trig_plot_reldist.peritrig_data,1)
% if sum(isnan(trig_plot_reldist.peritrig_data(n_rows,:)))<2
% mydata_reldist_nonnan{n_rows}=trig_plot_reldist.peritrig_data(n_rows,:);
% end
% end
% plot(nanmean(cell2mat(mydata_reldist_nonnan(:))), 'r'); hold on;
% just for fun, remove those row with nans
% keyboard
if shuffles == 1
%% Iteration for shuffling analysis
%RandomOffsets=OffsetMin:(1/(number_of_shuffles*10)):OffsetMax;
RandomOffsets=OffsetMin+(OffsetMax-OffsetMin).*rand(number_of_shuffles,1); % in seconds
% this is my shuffled distribution
for i_shuff=1:size(RandomOffsets,1)
%number_of_shuffles
forelimb_inbound_reldist_shuff=limb_select_reldist(:,1)+RandomOffsets(i_shuff);
forelimb_inbound_reldist_shuff(:,2)=forelimb_inbound_reldist_shuff;
limb_select_reldist_shuff=forelimb_inbound_reldist_shuff;
trig_plot_reldist_shuff=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist_shuff, 't_pre',t_pre, 't_post',t_post);
mydata_reldist_shuff{a,i,i_shuff}=trig_plot_reldist_shuff.peritrig_data; % doing peritrig_data instead of peritrig ensures that all shuffled plants get to go in the massive shuffle
close all; clear limb_select_reldist_shuff trig_plot_reldist_shuff;
end
mydata_reldist_shuff_all=cell2mat(squeeze(((mydata_reldist_shuff(a,i,:)))));
% now create a null distribution based on the shuffles i.e from
% the shuffled relationships, sample the same number of plants
% that you have in the epoch a 1000 times
for n_shuff=1:n_samps
randIdcs=randperm(size(mydata_reldist_shuff_all,1),size(limb_select_reldist(:,1),1));
mydata_reldist_nulldist{a,i,n_shuff}=nanmean(mydata_reldist_shuff_all(randIdcs,:));
mod_score_perepoch_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_reldist_nulldist{a,i,n_shuff}(:)-mean(mydata_reldist_nulldist{a,i,n_shuff})));
clear randIdcs
end
end
clearvars -except animals results_dir lags_reldist mydata_reldist_nulldist a t_pre t_post shuffles number_of_shuffles n_samps OffsetMin OffsetMax f f1 f2 counter number_steps number_steps_correct number_steps_incorrect ...
mod_score_mua_perepoch_stdev_correct mod_score_mua_perepoch_stdev_incorrect mod_score_mua_perepoch_stdev lags_mua_correct ...
mod_score_mua_perepoch_pershuff_stdev mod_score_mua_perepoch_pershuff_stdev_incorrect_control ...
mydata_mua_correct mydata_mua_incorrect mydata_mua ...
mod_score_theta_perepoch_stdev_correct mod_score_theta_perepoch_stdev_incorrect mod_score_theta_perepoch_stdev lags_theta_correct ...
mod_score_theta_perepoch_pershuff_stdev mod_score_theta_perepoch_pershuff_stdev_incorrect_control ...
mydata_theta_correct mydata_theta_incorrect mydata_theta ...
mod_score_perepoch_stdev_correct mod_score_perepoch_stdev_incorrect mod_score_perepoch_stdev lags_reldist_correct ...
mod_score_perepoch_pershuff_stdev mod_score_perepoch_pershuff_stdev_incorrect_control ...
mydata_reldist_correct mydata_reldist_incorrect mydata_reldist ...
number_of_epochs_all number_steps_all number_steps_correct_all number_steps_incorrect_all
end
end
number_of_epochs_all(a)=sum(counter);
number_steps_all(a)=sum(number_steps);
clear counter number_steps
end
for a=[2]
ind = cell2mat(arrayfun(@(x) x.index',f(a).output{1},'Un',0))';
ind_select=((ind(:,1)==1)|(ind(:,1)==2)|(ind(:,1)==3)|(ind(:,1)==4)|(ind(:,1)==5)| (ind(:,1)==6)); counter=0; number_steps=0;
for i=rat2_idx
if size(f(a).output{1,1}(i).forelimb_inbound_reldist,2)>1
counter=[counter; size(i,1)];
animal=animals{1,a};
base_dir='/opt/stelmo/abhilasha/animals';
destdir=[base_dir,'/',animal,'/','filterframework', '/'];
index=f(a).output{1,1}(i).index;close all
d=index(1); e=index(2);
task = loaddatastruct(destdir,animal,'task',[d e]);
trials = loaddatastruct(destdir,animal,'trials',[d e]);
tetinfo = loaddatastruct(destdir,animal,'tetinfo',[d e]);
tets_ca1R = evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1R'')');
tets_ca1L=evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1L'')');
tets=vertcat(tets_ca1R,tets_ca1L);
eeg = loadeegstruct(destdir,animal,'eeggnd',d,e,tets);
eegtimes = geteegtimes(eeg{d}{e}{tets(1)});
marks = loaddatastruct(destdir, animal,'marks',[d e ]);
[muatimes, mua] = getMUAtrace(marks{d}{e}(tets),eeg{d}{e}{tets(1)});
ahead_behind_dist_smooth = f(a).output{1,1}(i).ahead_behind_smooth;
forelimb_inbound_reldist = f(a).output{1,1}(i).forelimb_inbound_longsweep;
forelimb_inbound_reldist(:,2)=forelimb_inbound_reldist;
limb_select_reldist=forelimb_inbound_reldist;
number_steps=[number_steps; size(limb_select_reldist,1)];
trig_plot_reldist=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist, 't_pre',t_pre, 't_post',t_post);
lags_reldist=trig_plot_reldist.lags;
mydata_reldist{a,i}=trig_plot_reldist.peritrig;
mod_score_perepoch_stdev{a,i}= sum(abs(mydata_reldist{a,i}(:)-mean(mydata_reldist{a,i})));
% keyboard
% plot(mydata_reldist{a,i}, 'k'); hold on;
% for n_rows=1:size(trig_plot_reldist.peritrig_data,1)
% if sum(isnan(trig_plot_reldist.peritrig_data(n_rows,:)))<2
% mydata_reldist_nonnan{n_rows}=trig_plot_reldist.peritrig_data(n_rows,:);
% end
% end
% plot(nanmean(cell2mat(mydata_reldist_nonnan(:))), 'r'); hold on;
% just for fun, remove those row with nans
% keyboard
if shuffles == 1
%% Iteration for shuffling analysis
%RandomOffsets=OffsetMin:(1/(number_of_shuffles*10)):OffsetMax;
RandomOffsets=OffsetMin+(OffsetMax-OffsetMin).*rand(number_of_shuffles,1); % in seconds
% this is my shuffled distribution
for i_shuff=1:size(RandomOffsets,1)
%number_of_shuffles
forelimb_inbound_reldist_shuff=limb_select_reldist(:,1)+RandomOffsets(i_shuff);
forelimb_inbound_reldist_shuff(:,2)=forelimb_inbound_reldist_shuff;
limb_select_reldist_shuff=forelimb_inbound_reldist_shuff;
trig_plot_reldist_shuff=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist_shuff, 't_pre',t_pre, 't_post',t_post);
mydata_reldist_shuff{a,i,i_shuff}=trig_plot_reldist_shuff.peritrig_data; % doing peritrig_data instead of peritrig ensures that all shuffled plants get to go in the massive shuffle
close all; clear limb_select_reldist_shuff trig_plot_reldist_shuff;
end
mydata_reldist_shuff_all=cell2mat(squeeze(((mydata_reldist_shuff(a,i,:)))));
% now create a null distribution based on the shuffles i.e from
% the shuffled relationships, sample the same number of plants
% that you have in the epoch a 1000 times
for n_shuff=1:n_samps
randIdcs=randperm(size(mydata_reldist_shuff_all,1),size(limb_select_reldist(:,1),1));
mydata_reldist_nulldist{a,i,n_shuff}=nanmean(mydata_reldist_shuff_all(randIdcs,:));
mod_score_perepoch_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_reldist_nulldist{a,i,n_shuff}(:)-mean(mydata_reldist_nulldist{a,i,n_shuff})));
clear randIdcs
end
end
clearvars -except animals results_dir lags_reldist mydata_reldist_nulldist a t_pre t_post shuffles number_of_shuffles n_samps OffsetMin OffsetMax f f1 f2 counter number_steps number_steps_correct number_steps_incorrect ...
mod_score_mua_perepoch_stdev_correct mod_score_mua_perepoch_stdev_incorrect mod_score_mua_perepoch_stdev lags_mua_correct ...
mod_score_mua_perepoch_pershuff_stdev mod_score_mua_perepoch_pershuff_stdev_incorrect_control ...
mydata_mua_correct mydata_mua_incorrect mydata_mua ...
mod_score_theta_perepoch_stdev_correct mod_score_theta_perepoch_stdev_incorrect mod_score_theta_perepoch_stdev lags_theta_correct ...
mod_score_theta_perepoch_pershuff_stdev mod_score_theta_perepoch_pershuff_stdev_incorrect_control ...
mydata_theta_correct mydata_theta_incorrect mydata_theta ...
mod_score_perepoch_stdev_correct mod_score_perepoch_stdev_incorrect mod_score_perepoch_stdev lags_reldist_correct ...
mod_score_perepoch_pershuff_stdev mod_score_perepoch_pershuff_stdev_incorrect_control ...
mydata_reldist_correct mydata_reldist_incorrect mydata_reldist ...
number_of_epochs_all number_steps_all number_steps_correct_all number_steps_incorrect_all
end
end
number_of_epochs_all(a)=sum(counter);
number_steps_all(a)=sum(number_steps);
clear counter number_steps
end
for a=[3]
ind = cell2mat(arrayfun(@(x) x.index',f(a).output{1},'Un',0))';
ind_select=((ind(:,1)==1)|(ind(:,1)==2)|(ind(:,1)==3)|(ind(:,1)==4)|(ind(:,1)==5)| (ind(:,1)==6)); counter=0; number_steps=0;
for i=rat3_idx
if size(f(a).output{1,1}(i).forelimb_inbound_reldist,2)>1
counter=[counter; size(i,1)];
animal=animals{1,a};
base_dir='/opt/stelmo/abhilasha/animals';
destdir=[base_dir,'/',animal,'/','filterframework', '/'];
index=f(a).output{1,1}(i).index;close all
d=index(1); e=index(2);
task = loaddatastruct(destdir,animal,'task',[d e]);
trials = loaddatastruct(destdir,animal,'trials',[d e]);
tetinfo = loaddatastruct(destdir,animal,'tetinfo',[d e]);
tets_ca1R = evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1R'')');
tets_ca1L=evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1L'')');
tets=vertcat(tets_ca1R,tets_ca1L);
eeg = loadeegstruct(destdir,animal,'eeggnd',d,e,tets);
eegtimes = geteegtimes(eeg{d}{e}{tets(1)});
marks = loaddatastruct(destdir, animal,'marks',[d e ]);
[muatimes, mua] = getMUAtrace(marks{d}{e}(tets),eeg{d}{e}{tets(1)});
ahead_behind_dist_smooth = f(a).output{1,1}(i).ahead_behind_smooth;
forelimb_inbound_reldist = f(a).output{1,1}(i).forelimb_inbound_longsweep;
forelimb_inbound_reldist(:,2)=forelimb_inbound_reldist;
limb_select_reldist=forelimb_inbound_reldist;
number_steps=[number_steps; size(limb_select_reldist,1)];
trig_plot_reldist=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist, 't_pre',t_pre, 't_post',t_post);
lags_reldist=trig_plot_reldist.lags;
mydata_reldist{a,i}=trig_plot_reldist.peritrig;
mod_score_perepoch_stdev{a,i}= sum(abs(mydata_reldist{a,i}(:)-mean(mydata_reldist{a,i})));
% keyboard
% plot(mydata_reldist{a,i}, 'k'); hold on;
% for n_rows=1:size(trig_plot_reldist.peritrig_data,1)
% if sum(isnan(trig_plot_reldist.peritrig_data(n_rows,:)))<2
% mydata_reldist_nonnan{n_rows}=trig_plot_reldist.peritrig_data(n_rows,:);
% end
% end
% plot(nanmean(cell2mat(mydata_reldist_nonnan(:))), 'r'); hold on;
% just for fun, remove those row with nans
% keyboard
if shuffles == 1
%% Iteration for shuffling analysis
%RandomOffsets=OffsetMin:(1/(number_of_shuffles*10)):OffsetMax;
RandomOffsets=OffsetMin+(OffsetMax-OffsetMin).*rand(number_of_shuffles,1); % in seconds
% this is my shuffled distribution
for i_shuff=1:size(RandomOffsets,1)
%number_of_shuffles
forelimb_inbound_reldist_shuff=limb_select_reldist(:,1)+RandomOffsets(i_shuff);
forelimb_inbound_reldist_shuff(:,2)=forelimb_inbound_reldist_shuff;
limb_select_reldist_shuff=forelimb_inbound_reldist_shuff;
trig_plot_reldist_shuff=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist_shuff, 't_pre',t_pre, 't_post',t_post);
mydata_reldist_shuff{a,i,i_shuff}=trig_plot_reldist_shuff.peritrig_data; % doing peritrig_data instead of peritrig ensures that all shuffled plants get to go in the massive shuffle
close all; clear limb_select_reldist_shuff trig_plot_reldist_shuff;
end
mydata_reldist_shuff_all=cell2mat(squeeze(((mydata_reldist_shuff(a,i,:)))));
% now create a null distribution based on the shuffles i.e from
% the shuffled relationships, sample the same number of plants
% that you have in the epoch a 1000 times
for n_shuff=1:n_samps
randIdcs=randperm(size(mydata_reldist_shuff_all,1),size(limb_select_reldist(:,1),1));
mydata_reldist_nulldist{a,i,n_shuff}=nanmean(mydata_reldist_shuff_all(randIdcs,:));
mod_score_perepoch_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_reldist_nulldist{a,i,n_shuff}(:)-mean(mydata_reldist_nulldist{a,i,n_shuff})));
clear randIdcs
end
end
clearvars -except animals results_dir lags_reldist mydata_reldist_nulldist a t_pre t_post shuffles number_of_shuffles n_samps OffsetMin OffsetMax f f1 f2 counter number_steps number_steps_correct number_steps_incorrect ...
mod_score_mua_perepoch_stdev_correct mod_score_mua_perepoch_stdev_incorrect mod_score_mua_perepoch_stdev lags_mua_correct ...
mod_score_mua_perepoch_pershuff_stdev mod_score_mua_perepoch_pershuff_stdev_incorrect_control ...
mydata_mua_correct mydata_mua_incorrect mydata_mua ...
mod_score_theta_perepoch_stdev_correct mod_score_theta_perepoch_stdev_incorrect mod_score_theta_perepoch_stdev lags_theta_correct ...
mod_score_theta_perepoch_pershuff_stdev mod_score_theta_perepoch_pershuff_stdev_incorrect_control ...
mydata_theta_correct mydata_theta_incorrect mydata_theta ...
mod_score_perepoch_stdev_correct mod_score_perepoch_stdev_incorrect mod_score_perepoch_stdev lags_reldist_correct ...
mod_score_perepoch_pershuff_stdev mod_score_perepoch_pershuff_stdev_incorrect_control ...
mydata_reldist_correct mydata_reldist_incorrect mydata_reldist ...
number_of_epochs_all number_steps_all number_steps_correct_all number_steps_incorrect_all
end
end
number_of_epochs_all(a)=sum(counter);
number_steps_all(a)=sum(number_steps);
clear counter number_steps
end
for a=[5]
ind = cell2mat(arrayfun(@(x) x.index',f(a).output{1},'Un',0))';
ind_select=((ind(:,1)==1)|(ind(:,1)==2)|(ind(:,1)==3)|(ind(:,1)==4)|(ind(:,1)==5)| (ind(:,1)==6)); counter=0; number_steps=0;
for i=rat5_idx
if size(f(a).output{1,1}(i).forelimb_inbound_reldist,2)>1
counter=[counter; size(i,1)];
animal=animals{1,a};
base_dir='/opt/stelmo/abhilasha/animals';
destdir=[base_dir,'/',animal,'/','filterframework', '/'];
index=f(a).output{1,1}(i).index;close all
d=index(1); e=index(2);
task = loaddatastruct(destdir,animal,'task',[d e]);
trials = loaddatastruct(destdir,animal,'trials',[d e]);
tetinfo = loaddatastruct(destdir,animal,'tetinfo',[d e]);
tets_ca1R = evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1R'')');
tets_ca1L=evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1L'')');
tets=vertcat(tets_ca1R,tets_ca1L);
eeg = loadeegstruct(destdir,animal,'eeggnd',d,e,tets);
eegtimes = geteegtimes(eeg{d}{e}{tets(1)});
marks = loaddatastruct(destdir, animal,'marks',[d e ]);
[muatimes, mua] = getMUAtrace(marks{d}{e}(tets),eeg{d}{e}{tets(1)});
ahead_behind_dist_smooth = f(a).output{1,1}(i).ahead_behind_smooth;
forelimb_inbound_reldist = f(a).output{1,1}(i).forelimb_inbound_longsweep;
forelimb_inbound_reldist(:,2)=forelimb_inbound_reldist;
limb_select_reldist=forelimb_inbound_reldist;
number_steps=[number_steps; size(limb_select_reldist,1)];
trig_plot_reldist=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist, 't_pre',t_pre, 't_post',t_post);
lags_reldist=trig_plot_reldist.lags;
mydata_reldist{a,i}=trig_plot_reldist.peritrig;
mod_score_perepoch_stdev{a,i}= sum(abs(mydata_reldist{a,i}(:)-mean(mydata_reldist{a,i})));
% keyboard
% plot(mydata_reldist{a,i}, 'k'); hold on;
% for n_rows=1:size(trig_plot_reldist.peritrig_data,1)
% if sum(isnan(trig_plot_reldist.peritrig_data(n_rows,:)))<2
% mydata_reldist_nonnan{n_rows}=trig_plot_reldist.peritrig_data(n_rows,:);
% end
% end
% plot(nanmean(cell2mat(mydata_reldist_nonnan(:))), 'r'); hold on;
% just for fun, remove those row with nans
% keyboard
if shuffles == 1
%% Iteration for shuffling analysis
%RandomOffsets=OffsetMin:(1/(number_of_shuffles*10)):OffsetMax;
RandomOffsets=OffsetMin+(OffsetMax-OffsetMin).*rand(number_of_shuffles,1); % in seconds
% this is my shuffled distribution
for i_shuff=1:size(RandomOffsets,1)
%number_of_shuffles
forelimb_inbound_reldist_shuff=limb_select_reldist(:,1)+RandomOffsets(i_shuff);
forelimb_inbound_reldist_shuff(:,2)=forelimb_inbound_reldist_shuff;
limb_select_reldist_shuff=forelimb_inbound_reldist_shuff;
trig_plot_reldist_shuff=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist_shuff, 't_pre',t_pre, 't_post',t_post);
mydata_reldist_shuff{a,i,i_shuff}=trig_plot_reldist_shuff.peritrig_data; % doing peritrig_data instead of peritrig ensures that all shuffled plants get to go in the massive shuffle
close all; clear limb_select_reldist_shuff trig_plot_reldist_shuff;
end
mydata_reldist_shuff_all=cell2mat(squeeze(((mydata_reldist_shuff(a,i,:)))));
% now create a null distribution based on the shuffles i.e from
% the shuffled relationships, sample the same number of plants
% that you have in the epoch a 1000 times
for n_shuff=1:n_samps
randIdcs=randperm(size(mydata_reldist_shuff_all,1),size(limb_select_reldist(:,1),1));
mydata_reldist_nulldist{a,i,n_shuff}=nanmean(mydata_reldist_shuff_all(randIdcs,:));
mod_score_perepoch_pershuff_stdev{a,i,n_shuff}= sum(abs(mydata_reldist_nulldist{a,i,n_shuff}(:)-mean(mydata_reldist_nulldist{a,i,n_shuff})));
clear randIdcs
end
end
clearvars -except animals results_dir lags_reldist mydata_reldist_nulldist a t_pre t_post shuffles number_of_shuffles n_samps OffsetMin OffsetMax f f1 f2 counter number_steps number_steps_correct number_steps_incorrect ...
mod_score_mua_perepoch_stdev_correct mod_score_mua_perepoch_stdev_incorrect mod_score_mua_perepoch_stdev lags_mua_correct ...
mod_score_mua_perepoch_pershuff_stdev mod_score_mua_perepoch_pershuff_stdev_incorrect_control ...
mydata_mua_correct mydata_mua_incorrect mydata_mua ...
mod_score_theta_perepoch_stdev_correct mod_score_theta_perepoch_stdev_incorrect mod_score_theta_perepoch_stdev lags_theta_correct ...
mod_score_theta_perepoch_pershuff_stdev mod_score_theta_perepoch_pershuff_stdev_incorrect_control ...
mydata_theta_correct mydata_theta_incorrect mydata_theta ...
mod_score_perepoch_stdev_correct mod_score_perepoch_stdev_incorrect mod_score_perepoch_stdev lags_reldist_correct ...
mod_score_perepoch_pershuff_stdev mod_score_perepoch_pershuff_stdev_incorrect_control ...
mydata_reldist_correct mydata_reldist_incorrect mydata_reldist ...
number_of_epochs_all number_steps_all number_steps_correct_all number_steps_incorrect_all
end
end
number_of_epochs_all(a)=sum(counter);
number_steps_all(a)=sum(number_steps);
clear counter number_steps
end
% saveworkspace
if shuffles ==1
cd(results_dir); save('');
end
%% INBOUND outer arms RELDIST
clearvars -except t_pre t_post n_samps savefigs resdir OffsetMin OffsetMax number_of_shuffles results_dir shuffles
results_dir='/'; cd(results_dir);
results_filename=[''];
load([results_filename,'.mat']);
f1=f; clear f;
results_filename=[''];
load([results_filename,'.mat']);
f2=f; clear f;
animals={'Jaq', 'Roqui', 'Lotus', 'Monty', 'Peanut'};
f=f1;
for a=[1]
ind = cell2mat(arrayfun(@(x) x.index',f(a).output{1},'Un',0))';
ind_select=((ind(:,1)==1)|(ind(:,1)==2)|(ind(:,1)==3)|(ind(:,1)==4)|(ind(:,1)==5)| (ind(:,1)==6)); counter=0; number_steps=0;
for i=rat1_idx
if size(f(a).output{1,1}(i).forelimb_outbound_reldist,2)>1
counter=[counter; size(i,1)];
animal=animals{1,a};
base_dir='/opt/stelmo/abhilasha/animals';
destdir=[base_dir,'/',animal,'/','filterframework', '/'];
index=f(a).output{1,1}(i).index;close all
d=index(1); e=index(2);
task = loaddatastruct(destdir,animal,'task',[d e]);
trials = loaddatastruct(destdir,animal,'trials',[d e]);
tetinfo = loaddatastruct(destdir,animal,'tetinfo',[d e]);
tets_ca1R = evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1R'')');
tets_ca1L=evaluatefilter(tetinfo{d}{e},'isequal($area,''ca1L'')');
tets=vertcat(tets_ca1R,tets_ca1L);
eeg = loadeegstruct(destdir,animal,'eeggnd',d,e,tets);
eegtimes = geteegtimes(eeg{d}{e}{tets(1)});
marks = loaddatastruct(destdir, animal,'marks',[d e ]);
[muatimes, mua] = getMUAtrace(marks{d}{e}(tets),eeg{d}{e}{tets(1)});
ahead_behind_dist_smooth = f(a).output{1,1}(i).ahead_behind_smooth;
forelimb_inbound_reldist_outer1 = f1(a).output{1,1}(i).forelimb_inbound_longsweep;
forelimb_inbound_reldist_outer2 = f2(a).output{1,1}(i).forelimb_inbound_longsweep;
forelimb_inbound_reldist=[forelimb_inbound_reldist_outer1;forelimb_inbound_reldist_outer2];
forelimb_inbound_reldist(:,2)=forelimb_inbound_reldist;
limb_select_reldist=forelimb_inbound_reldist;
number_steps=[number_steps; size(limb_select_reldist,1)];
trig_plot_reldist=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist, 't_pre',t_pre, 't_post',t_post);
lags_reldist=trig_plot_reldist.lags;
mydata_reldist{a,i}=trig_plot_reldist.peritrig;
mod_score_perepoch_stdev{a,i}= sum(abs(mydata_reldist{a,i}(:)-mean(mydata_reldist{a,i})));
% keyboard
% plot(mydata_reldist{a,i}, 'k'); hold on;
% for n_rows=1:size(trig_plot_reldist.peritrig_data,1)
% if sum(isnan(trig_plot_reldist.peritrig_data(n_rows,:)))<2
% mydata_reldist_nonnan{n_rows}=trig_plot_reldist.peritrig_data(n_rows,:);
% end
% end
% plot(nanmean(cell2mat(mydata_reldist_nonnan(:))), 'r'); hold on;
% just for fun, remove those row with nans
% keyboard
if shuffles == 1
%% Iteration for shuffling analysis
%RandomOffsets=OffsetMin:(1/(number_of_shuffles*10)):OffsetMax;
RandomOffsets=OffsetMin+(OffsetMax-OffsetMin).*rand(number_of_shuffles,1); % in seconds
% this is my shuffled distribution
for i_shuff=1:size(RandomOffsets,1)
%number_of_shuffles
forelimb_outbound_reldist_shuff=limb_select_reldist(:,1)+RandomOffsets(i_shuff);
forelimb_outbound_reldist_shuff(:,2)=forelimb_outbound_reldist_shuff;
limb_select_reldist_shuff=forelimb_outbound_reldist_shuff;
trig_plot_reldist_shuff=periseg('cdat', contchans(ahead_behind_dist_smooth, 'chans', 1), 'segs', limb_select_reldist_shuff, 't_pre',t_pre, 't_post',t_post);
mydata_reldist_shuff{a,i,i_shuff}=trig_plot_reldist_shuff.peritrig_data; % doing peritrig_data instead of peritrig ensures that all shuffled plants get to go in the massive shuffle
close all; clear limb_select_reldist_shuff trig_plot_reldist_shuff;
end
mydata_reldist_shuff_all=cell2mat(squeeze(((mydata_reldist_shuff(a,i,:)))));
% now create a null distribution based on the shuffles i.e from
% the shuffled relationships, sample the same number of plants
% that you have in the epoch a 1000 times