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plot_clean_origin_peaks.m
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newdatadir = 'C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020_corrected\all_units\';
channelfilename = [newdatadir 'clean_origin_sup_50'];
data_file = load(channelfilename);
channelfilename = [newdatadir 'clean_SUA_sup_50'];
filt_data_file = load(channelfilename);
locsfilename = [newdatadir 'clean_SUA_locs'];
all_locsdSUA = load(locsfilename);
xabs = -199:1300;
nyq = 500;
channum = 1: length(data_file.clean_origin_data);
mean_origin_dSUA = struct();
mean_filtered_dSUA = struct();
suas_trials = struct();
up_dist = nan(1, length(channum),4);
max_low_dist = nan(1, length(channum));
all_locsdSUA_filtered = nan(1,length(channum),4);
for i = channum
if ~isempty(data_file.clean_origin_data(i).unit)
trialidx = 1:length(data_file.clean_origin_data(i).unit(1,:));
origin_dSUA = data_file.clean_origin_data(i).unit(401:1900,:); %-mean(data_file.clean_origin_data(i).unit(401:600,:),1);
filtered_dSUA = filt_data_file.clean_high_SUA(i).namelist;
%{
for tr = trialidx
lpc = 4.5; %low pass cutoff
lWn = lpc/nyq;
[bwb,bwa] = butter(4,lWn,'low');
origin_dSUA(:,tr) = filtfilt(bwb,bwa, origin_dSUA(:,tr));
end
%}
%determine the peak location of interest for each trial of a given single
%unit
all_locsdSUA_trials = all_locsdSUA.peaks_locs(i).locs;
up_dist_trials = nan(4,length(trialidx));
clear pn
for pn = 1:4
locs_peak = all_locsdSUA_trials(pn, :);
up_dist_trials(pn,:)= length(xabs)- locs_peak;
end
%get the max distance between the peakalign and the stimulus onset
max_low_dist_unit = max(all_locsdSUA_trials,[],'all');
%create new matrix with the length(max(d)+max(xabs - d))
new_dist_unit = max_low_dist_unit + max(up_dist_trials,[],'all');
fp_locked_trials = nan(new_dist_unit,length(origin_dSUA(1,:)),4);
filtered_fp_locked_trials = nan(new_dist_unit,length(filtered_dSUA(1,:)),4);
clear n pn
for pn =1:4
for n = 1:length(origin_dSUA(1,:))
lower_unit_bound =max_low_dist_unit-all_locsdSUA_trials(pn,n)+1;
upper_unit_bound =max_low_dist_unit-all_locsdSUA_trials(pn,n)+length(xabs);
fp_locked_trials(lower_unit_bound:upper_unit_bound,n,pn) = origin_dSUA(:,n);
filtered_fp_locked_trials(lower_unit_bound:upper_unit_bound,n,pn) = filtered_dSUA(:,n);
end
%{
%compute the mean single unit activity if more than 10 trials
filtered_mean = nanmean(filtered_fp_locked_trials(:,:,pn),2);
lpc = 4.5; %low pass cutoff
lWn = lpc/nyq;
[bwb,bwa] = butter(4,lWn,'low');
lpdSUA = filtfilt(bwb,bwa, filtered_mean(~isnan(filtered_mean)));
%}
eval(['mean_origin_dSUA(i).mean_peakaligned' num2str(pn) '= nanmean(fp_locked_trials(:,:,pn),2);'])
eval(['mean_filtered_dSUA(i).mean_peakaligned' num2str(pn) '= nanmean(filtered_fp_locked_trials(:,:,pn),2);']) % for nan - cols
end
%get the aligned data if it exists for the unit
suas_trials(i).aligned= fp_locked_trials;
max_low_dist(i) = max_low_dist_unit;
%{
figure();
x = 1:length(fp_locked_trials(:,1,4));
plot(x,fp_locked_trials(:,:,4))
hold on
plot(x, mean(fp_locked_trials(:,:,4),2),'LineWidth',1, 'Color', 'black')
%mean_aligned = mean(fp_locked_trials_out,2);
%nanmean_aligned = nanmean(fp_locked_trials_out,2);
%}
end
end
%%% plot aligned units to peak of interest
layer = {'K','M','P','K','K','K','M','P','P','','M','M','','','M','','','P','','M','','M','M','','P','M','','P', ...
'P','','','K','P','M','M','M','P','','P','K','P','P','','P','P','M','','P','M','P','M','P','','P','M','M','P','','M','M','P','M', ...
'','','M','M','M','P','M','M','M','M','P','P'};
layer([1,46,55]) = [];
for i = 1:length(suas_trials)
if ~isnan(max_low_dist(i))
figure();
mean_unit = squeeze(nanmean(suas_trials(i).aligned(max_low_dist(i)-1-124:max_low_dist(i)-1+125,:,:),2));
stdev = squeeze(std(suas_trials(i).aligned(max_low_dist(i)-1-124:max_low_dist(i)-1+125,:,:),[],2));
for pn =1:4
h = subplot(1,4,pn);
plot(-125:124, mean_unit(:,pn), 'color', 'k');
hold on
h1= ciplot( mean_unit(:,pn)+ stdev(:,pn)/sqrt(14), mean_unit(:,pn)-stdev(:,pn)/sqrt(14),[-125:124],[40/255 40/255 40/255],0.1);
set(h1, 'edgecolor','none')
set(h,'position',get(h,'position').*[1 1 1.15 1])
ylim([-40 160])
xlim([-125 125])
set(gca,'box','off')
set(gca, 'linewidth',2)
ylabel({'\fontsize{14}Spike Rate (spikes/s)'});
if pn > 1
ax1 = gca;
ax1.YAxis.Visible = 'off';
end
end
sgtitle({'Baseline corrected M cell trials'}, 'Interpreter', 'none')
xlabel('Resolution (ms)')
set(gcf,'Units','inches')
%set(gcf,'position',[1 1 8.5 11])
filename = strcat('C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020_corrected\plots\bscorr_aligned_trials_unit1');
saveas(gcf, strcat(filename, '.png'));
saveas(gcf, strcat(filename, '.svg'));
end
end
nnz(~isnan(max_low_dist))
%% plot mean cell class activity with data aligned to peak of interest
layer = {'K','M','P','K','K','K','M','P','P','','M','M','','','M','','','P','','M','','M','M','','P','M','','P', ...
'P','','','K','P','M','M','M','P','','P','K','P','P','','P','P','M','','P','M','P','M','P','','P','M','M','P','','M','M','P','M', ...
'','','M','M','M','P','M','M','M','M','P','P'};
layer([1,46,55]) = [];
layer_idx = find(strcmp(layer, 'K'));
%%store all trials of a given peak, in a matrix, across all units
aligned_trials = nan(1200, length(layer_idx));
clear i
for i = 1:length(layer_idx)
if ~isnan(max_low_dist(layer_idx(i)))
for pn = 1:4
aligned_trials(250*(pn-1)+1:250*pn,i)= mean(suas_trials(layer_idx(i)).aligned(max_low_dist(layer_idx(i))-1-124:max_low_dist(layer_idx(i))-1+125,:,pn),2);
end
end
end
aligned_trials = aligned_trials(:,~all(isnan(aligned_trials)));
figure();
mean_origin =nanmean(aligned_trials,2);
plot(1:250, mean_origin(1:250),'LineWidth',1, 'Color',[165/255 42/255 42/255])
hold on
plot(301:550, mean_origin(251:500),'LineWidth',1, 'Color',[165/255 42/255 42/255])
hold on
plot(601:850, mean_origin(501:750),'LineWidth',1, 'Color',[165/255 42/255 42/255])
hold on
plot(901:1150, mean_origin(751:1000),'LineWidth',1, 'Color',[165/255 42/255 42/255])
hold on
ci_low = mean_origin - 1.96*std(aligned_trials,0,2, 'omitnan')./sqrt(length(aligned_trials(1,:)));
plot(1:250, ci_low(1:250),':', 'LineWidth',.7,'Color', [165/255 42/255 42/255])
hold on
plot(301:550, ci_low(251:500),':', 'LineWidth',.7,'Color', [165/255 42/255 42/255])
hold on
plot(601:850, ci_low(501:750),':', 'LineWidth',.7,'Color', [165/255 42/255 42/255])
hold on
plot(901:1150, ci_low(751:1000),':', 'LineWidth',.7,'Color', [165/255 42/255 42/255])
hold on
ci_high = mean_origin + 1.96*std(aligned_trials,0,2, 'omitnan')./sqrt(length(aligned_trials(1,:)));
plot(1:250, ci_high(1:250),':', 'LineWidth',.7,'Color', [165/255 42/255 42/255])
hold on
plot(301:550, ci_high(251:500),':', 'LineWidth',.7,'Color', [165/255 42/255 42/255])
hold on
plot(601:850, ci_high(501:750),':', 'LineWidth',.7,'Color', [165/255 42/255 42/255])
hold on
plot(901:1150, ci_high(751:1000),':', 'LineWidth',.7,'Color', [165/255 42/255 42/255])
%blue [49/255 130/255 189/255]
%black [222/255 45/255 38/255]
%orange [253/255 174/255 107/255]
%brown [165/255 42/255 42/255]
%red [215/255 25/255 28/255]
%pink = [229/255, 49/255, 90/255])
set(gca, 'linewidth',2)
set(gca,'box','off')
title({'K mean responses'}, 'Interpreter', 'none', 'FontSize', 20)
xlabel('\fontsize{14}Resolution (ms)')
ylh = ylabel({'\fontsize{14}Spike Rate (spikes/s)'});
set(gcf,'Units','inches')
%set(gcf,'position',[1 1 8.5 11])
set(gcf,'position',[1 1 15 11])
filename = strcat('C:\Users\maier\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020\plots\',strcat('spaced_95ci_nanmean_bscorr_origin_data_aligned_Kcells_brown'));
saveas(gcf, strcat(filename, '.png'));
% h = subplot(1,4,1). then, set(h,'position',get(h,'position').*[1 1 1.25 1]);
%then for sublots 2,3,4, ... set(gca,'ycolor','w','yticklabel',[]); also for every subplot set ylims to the same values
%% plot mean cell class activity with data aligned to peak of interest using subplot
layer = {'K','M','P','K','K','K','M','P','P','','M','M','','','M','','','P','','M','','M','M','','P','M','','P', ...
'P','','','K','P','M','M','M','P','','P','K','P','P','','P','P','M','','P','M','P','M','P','','P','M','M','P','','M','M','P','M', ...
'','','M','M','M','P','M','M','M','M','P','P'};
layer([1,46,55]) = [];
layer_idx = find(strcmp(layer, 'M'));
%%store all trials of a given peak, in a matrix, across all units
aligned_trials = nan(1204, length(layer_idx));
norm_aligned_trials = nan(1204, length(layer_idx));
clear i
for i = 1:length(layer_idx)
if ~isnan(max_low_dist(layer_idx(i)))
for pn = 1:4
aligned_trials(250*(pn-1)+1:250*pn+1,i)= mean(suas_trials(layer_idx(i)).aligned(max_low_dist(layer_idx(i))-125:max_low_dist(layer_idx(i))+125,:,pn),2);
end
norm_aligned_trials(:,i) = (aligned_trials(:,i) - min(aligned_trials(:,i)))/(max(aligned_trials(:,i))-min(aligned_trials(:,i)));
end
end
aligned_trials = aligned_trials(:,~all(isnan(aligned_trials)));
figure();
mean_origin =nanmean(norm_aligned_trials,2);
ci_low = mean_origin - 1.96*std(norm_aligned_trials,0,2, 'omitnan')./sqrt(length(aligned_trials(1,:)));
ci_high = mean_origin + 1.96*std(norm_aligned_trials,0,2, 'omitnan')./sqrt(length(aligned_trials(1,:)));
for pn = 1:4
h =subplot(1,4,pn);
plot(-125:125, mean_origin(250*(pn-1)+1:250*pn+1),'LineWidth',1, 'Color',[215/255 25/255 28/255])
hold on
plot(-125:125, ci_low(250*(pn-1)+1:250*pn+1),':', 'LineWidth',.7,'Color', [215/255 25/255 28/255])
hold on
plot(-125:125, ci_high(250*(pn-1)+1:250*pn+1),':', 'LineWidth',.7,'Color', [215/255 25/255 28/255])
set(h,'position',get(h,'position').*[1 1 1.15 1])
% P ylim([-20 85])
% M ylim([-35 135])
% K ylim([-20 200])
ylim([0 1])
xlim([-125 125])
set(gca,'box','off')
%set(gca, 'linewidth',2)
%hold on
%plot([0 0], ylim,'k')
if pn >1
ax1 = gca;
ax1.YAxis.Visible = 'off';
end
end
%red [215/255 25/255 28/255]
%orange [253/255 174/255 97/255]
%blue [44/255 123/255 182/255]
%brown [165/255 42/255 42/255]
currfig = gcf;
title(currfig.Children(end),{'M mean responses'}, 'Interpreter', 'none', 'FontSize', 20)
% xlabel('\fontsize{14}Resolution (ms)')
ylh = ylabel({'\fontsize{14}Spike Rate (spikes/s)'});
set(gcf,'Units','inches')
set(gcf,'position',[1 1 15 11])
filename = strcat('C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020\plots\',strcat('subplots_norm_95ci_nanmean_bscorr_origin_data_aligned_Mcells_red'));
saveas(gcf, strcat(filename, '.png'));
%% plot with significant adapting units
pvaluesdir = 'C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\lmer_results_peaks\';
pvalfilename = [pvaluesdir 'lmer_results_orig_03032020_corrected.csv'];
pvalues = dlmread(pvalfilename, ',', 1,1);
channeldir = 'C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020_corrected\orig_peak_values\all_units\';
peakvals = load([channeldir 'all_data_peaks']);
layer = {'K','M','P','K','K','K','M','P','P','','M','M','','','M','','','P','','M','','M','M','','P','M','','P', ...
'P','','','K','P','M','M','M','P','','P','K','P','P','','P','P','M','','P','M','P','M','P','','P','M','M','P','','M','M','P','M', ...
'','','M','M','M','P','M','M','M','M','P','P'};
layer([1,46,55]) = [];
cellclass = [ 'M', 'P', 'K'];
red = [215/255 25/255 28/255];
orange = [253/255 174/255 97/255];
blue =[44/255 123/255 182/255];
colors = [red; orange; blue];
% M ylim([0.05 .75])
% P ylim([.1 .8])
% K ylim([-0.01 1])
normylims = [[0.05 .75];[.1 .8];[-0.01 1]];
% P ylim([-20 85])
% M ylim([-35 135])
% K ylim([-20 200])
ylims = [[0 160];[0 100];[0 220]];
for nc = 1:3
clear layer_idx
layer_idx = find(strcmp(layer, cellclass(nc)));
%%store all trials of a given peak, in a matrix, across all units
%aligned_trials = nan(1204, length(layer_idx));
clear aligned_trials
aligned_trials = nan(1204, length(layer_idx));
%norm_aligned_trials = nan(1204, length(layer_idx));
%norm_aligned_trials = nan(1204, length(layer_idx));
clear i
for i = 1:length(layer_idx)
if ~isnan(max_low_dist(layer_idx(i)))
for pn = 1:4
% aligned_trials(250*(pn-1)+1:250*pn+1,i)= mean(suas_trials(layer_idx(i)).aligned(max_low_dist(layer_idx(i))-125:max_low_dist(layer_idx(i))+125,:,pn),2);
aligned_trials(250*(pn-1)+1:250*pn+1,i)= mean(suas_trials(layer_idx(i)).aligned(max_low_dist(layer_idx(i))-1-124:max_low_dist(layer_idx(i))+125,:,pn),2);
end
%normalizing with max and min of each unit
%norm_aligned_trials(:,i) = (aligned_trials(:,i) - min(aligned_trials(:,i)))/(max(aligned_trials(:,i))-min(aligned_trials(:,i)));
end
end
%normalizing with max and min across units
maximum = max(aligned_trials,[],'all');
minimum = min(aligned_trials,[],'all');
norm_aligned_trials = (aligned_trials - minimum)/(maximum-minimum);
%aligned_trials = aligned_trials(:,~all(isnan(aligned_trials)));
%figure(); plot(1:length(aligned_trials(:,4)), aligned_trials(:,:))
%figure(); plot(1:length(aligned_trials(:,4)), norm_aligned_trials(:,:))
clear sig_su mean_sig_su
cnt = 0;
all_mean_data = nan(4, length(layer_idx));
sig_adapsu = nan(length(aligned_trials(:,1)),length(layer_idx));
for nunit = 1:length(layer_idx)
if ~isempty(peakvals.peak_vals(layer_idx(nunit)).peak)
mean_data = nanmean(peakvals.peak_vals(layer_idx(nunit)).peak,2);
all_mean_data(:,nunit) = mean_data;
if all_mean_data(4,nunit) < all_mean_data(1,nunit) && pvalues(layer_idx(nunit),4) < .05
cnt= cnt+1;
sig_adapsu(:,cnt) = aligned_trials(:,nunit);
% plot(x_stim,norm_chan(:, nunit)')
%hold on
end
end
end
mean_sig_su = nanmean(sig_adapsu,2);
figure();
mean_origin =nanmean(aligned_trials,2);
ci_low = mean_origin - 1.96*std(aligned_trials,0,2, 'omitnan')./sqrt(length(aligned_trials(1,:)));
ci_high = mean_origin + 1.96*std(aligned_trials,0,2, 'omitnan')./sqrt(length(aligned_trials(1,:)));
for pn = 1:4
h =subplot(1,4,pn);
plot(-125:125, mean_origin(250*(pn-1)+1:250*pn+1),'LineWidth',1, 'Color',colors(nc,:) )
hold on
plot(-125:125, ci_low(250*(pn-1)+1:250*pn+1),':', 'LineWidth',.7,'Color',colors(nc,:) )
hold on
plot(-125:125, ci_high(250*(pn-1)+1:250*pn+1),':', 'LineWidth',.7,'Color', colors(nc,:))
hold on
plot(-125:125, mean_sig_su(250*(pn-1)+1:250*pn+1), 'LineWidth',1, 'Color',[141/255 140/255 140/255] )
set(h,'position',get(h,'position').*[1 1 1.15 1])
ylim(ylims(nc,:))
% K norm ylim([-0.02 1.1])
xlim([-125 125])
set(gca,'box','off')
%set(gca, 'linewidth',2)
%hold on
%plot([0 0], ylim,'k')
if pn >1
ax1 = gca;
ax1.YAxis.Visible = 'off';
end
end
%brown [165/255 42/255 42/255]
currfig = gcf;
title(currfig.Children(end),{sprintf('%s mean responses', cellclass(nc))}, 'Interpreter', 'none', 'FontSize', 20)
% xlabel('\fontsize{14}Resolution (ms)')
ylh = ylabel({'\fontsize{14}Spike Rate (spikes/s)'});
set(gcf,'Units','inches')
set(gcf,'position',[1 1 15 11])
filename = strcat('C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020_corrected\plots\',strcat(sprintf('sigadapt_95ci_origin_data_aligned_%s', cellclass(nc),'cells')));
%saveas(gcf, strcat(filename, '.svg'));
%saveas(gcf, strcat(filename, '.png'));
end
%% plot with significant adapting units and overall mean in two separate sublots, stacking all peaks together
pvaluesdir = 'C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\lmer_results_peaks\';
pvalfilename = [pvaluesdir 'lmer_results_orig_03032020_corrected.csv'];
pvalues = dlmread(pvalfilename, ',', 1,1);
channeldir = 'C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020_corrected\orig_peak_values\all_units\';
peakvals = load([channeldir 'all_data_peaks']);
layer = {'K','M','P','K','K','K','M','P','P','','M','M','','','M','','','P','','M','','M','M','','P','M','','P', ...
'P','','','K','P','M','M','M','P','','P','K','P','P','','P','P','M','','P','M','P','M','P','','P','M','M','P','','M','M','P','M', ...
'','','M','M','M','P','M','M','M','M','P','P'};
layer([1,46,55]) = [];
cellclass = [ 'M', 'P', 'K'];
%red [215/255 25/255 28/255]
%orange [253/255 174/255 97/255] %brown [121/255 96/255 76/255];
%blue [44/255 123/255 182/255]
brown = [121/255 96/255 76/255];
red = [215/255 25/255 28/255];
orange = [253/255 174/255 97/255];
blue =[44/255 123/255 182/255];
colors = [red; orange; blue];
% M ylim([0.05 .75])
% P ylim([.1 .8])
% K ylim([-0.01 1])
normylims = [[0.05 .75];[.1 .8];[-0.01 1]];
% P ylim([-20 85])
% M ylim([-35 135])
% K ylim([-20 200])
ylims = [[-30 135];[-20 85];[-20 200]];
for nc = 1:3
clear layer_idx
layer_idx = find(strcmp(layer, cellclass(nc)));
%%store all trials of a given peak, in a matrix, across all units
clear aligned_trials
aligned_trials = nan(1204, length(layer_idx));
clear i
for i = 1:length(layer_idx)
if ~isnan(max_low_dist(layer_idx(i)))
for pn = 1:4
% aligned_trials(250*(pn-1)+1:250*pn+1,i)= mean(suas_trials(layer_idx(i)).aligned(max_low_dist(layer_idx(i))-125:max_low_dist(layer_idx(i))+125,:,pn),2);
aligned_trials(250*(pn-1)+1:250*pn+1,i)= mean(suas_trials(layer_idx(i)).aligned(max_low_dist(layer_idx(i))-1-124:max_low_dist(layer_idx(i))+125,:,pn),2);
end
%normalizing with max and min of each unit
%norm_aligned_trials(:,i) = (aligned_trials(:,i) - min(aligned_trials(:,i)))/(max(aligned_trials(:,i))-min(aligned_trials(:,i)));
end
end
%normalizing with max and min across units
maximum = max(aligned_trials,[],'all');
minimum = min(aligned_trials,[],'all');
norm_aligned_trials = (aligned_trials - minimum)/(maximum-minimum);
%aligned_trials = aligned_trials(:,~all(isnan(aligned_trials)));
%figure(); plot(1:length(aligned_trials(:,4)), aligned_trials(:,:))
%figure(); plot(1:length(aligned_trials(:,4)), norm_aligned_trials(:,:))
clear sig_su mean_sig_su
cnt = 0;
all_mean_data = nan(4, length(layer_idx));
sig_adapsu = nan(length(norm_aligned_trials(:,1)),length(layer_idx));
for nunit = 1:length(layer_idx)
if ~isempty(peakvals.peak_vals(layer_idx(nunit)).peak)
mean_data = nanmean(peakvals.peak_vals(layer_idx(nunit)).peak,2);
all_mean_data(:,nunit) = mean_data;
if all_mean_data(4,nunit) < all_mean_data(1,nunit) && pvalues(layer_idx(nunit),4) < .05
cnt= cnt+1;
sig_adapsu(:,cnt) = aligned_trials(:,nunit);
% plot(x_stim,norm_chan(:, nunit)')
%hold on
end
end
end
mean_sig_su = nanmean(sig_adapsu,2);
figure();
mean_origin =nanmean(aligned_trials,2);
col1 = [121/255 96/255 76/255];
col2 = colors(nc,:);
R = linspace(col1(1), col2(1),4);
G = linspace(col1(2), col2(2),4);
B = linspace(col1(3), col2(3),4);
cmap = [R' G' B'];
colormap(cmap);
h = subplot(1,2,1);
nlines = 4;
for nl = 1:nlines
%cmap = jet(4);
plot(-125:125, mean_origin(250*(nl-1)+1:250*nl+1), 'LineWidth',1,'color',cmap(nl,:));
hold on
end
set(h,'position',get(h,'position').*[1 1 1.15 1])
ylim(ylims(nc,:))
xlim([-125 125])
set(gca,'box','off')
ylabel({'\fontsize{14}Spike Rate (spikes/s)'});
title('Overall Mean')
h1 = subplot(1,2,2);
for nl = 1:nlines
plot(-125:125, mean_sig_su(250*(nl-1)+1:250*nl+1), 'LineWidth',1, 'Color',cmap(nl,:) )
hold on
end
set(h1,'position',get(h1,'position').*[1 1 1.15 1])
ylim(ylims(nc,:))
xlim([-125 125])
title('Adapting units mean')
set(gca,'box','off')
legend('peak 1', 'peak 2', 'peak 3', 'peak 4')
% K norm ylim([-0.02 1.1])
%set(gca, 'linewidth',2)
%hold on
%plot([0 0], ylim,'k')
% if pn >1
%ax1 = gca;
%ax1.YAxis.Visible = 'off';
% end
currfig = gcf;
title(currfig.Children(end),{sprintf('%s mean responses', cellclass(nc))}, 'Interpreter', 'none', 'FontSize', 20)
% xlabel('\fontsize{14}Resolution (ms)')
set(gcf,'Units','inches')
set(gcf,'position',[1 1 15 11])
filename = strcat('C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020_corrected\plots\',strcat(sprintf('sigadapt_stacked_common_nanmean_bscorr_origin_data_aligned_%s',cellclass(nc),'cells_cmap_corrected_scaled')));
% saveas(gcf, strcat(filename, '.svg'));
% saveas(gcf, strcat(filename, '.png'));
end
%% plot with significant adapting units, non significant units and overall mean in three separate subplots, stacking all peaks together
pvaluesdir = 'C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\lmer_results_peaks\';
pvalfilename = [pvaluesdir 'lmer_results_orig_03032020_corrected.csv'];
pvalues = dlmread(pvalfilename, ',', 1,1);
channeldir = 'C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020_corrected\orig_peak_values\all_units\';
peakvals = load([channeldir 'all_data_peaks']);
layer = {'K','M','P','K','K','K','M','P','P','','M','M','','','M','','','P','','M','','M','M','','P','M','','P', ...
'P','','','K','P','M','M','M','P','','P','K','P','P','','P','P','M','','P','M','P','M','P','','P','M','M','P','','M','M','P','M', ...
'','','M','M','M','P','M','M','M','M','P','P'};
layer([1,46,55]) = [];
cellclass = [ 'M', 'P', 'K'];
%{
brown = [121/255 96/255 76/255];
red = [215/255 25/255 28/255];
orange = [253/255 174/255 97/255];
blue =[44/255 123/255 182/255];
colors = [red; orange; blue];
%}
%maps
nlines = 4;
cmaps = struct();
cmaps(1).map =cbrewer2('Reds', nlines);
cmaps(2).map =cbrewer2('YlOrBr', nlines);
cmaps(3).map =cbrewer2('Blues', nlines);
% M ylim([0.05 .75])
% P ylim([.1 .8])
% K ylim([-0.01 1])
normylims = [[0.05 .75];[.1 .8];[-0.01 1]];
% P ylim([-20 85])
% M ylim([-30 135])
% K ylim([-20 200])
%ylims = [[-30 135];[-20 85];[-20 200]];
ylims = [[0 160];[0 100];[0 220]];
for nc = 1:3
clear layer_idx
layer_idx = find(strcmp(layer, cellclass(nc)));
%%store all trials of a given peak, in a matrix, across all units
clear aligned_trials
aligned_trials = nan(1204, length(layer_idx));
clear i
for i = 1:length(layer_idx)
if ~isnan(max_low_dist(layer_idx(i)))
for pn = 1:4
% aligned_trials(250*(pn-1)+1:250*pn+1,i)= mean(suas_trials(layer_idx(i)).aligned(max_low_dist(layer_idx(i))-125:max_low_dist(layer_idx(i))+125,:,pn),2);
aligned_trials(250*(pn-1)+1:250*pn+1,i)= mean(suas_trials(layer_idx(i)).aligned(max_low_dist(layer_idx(i))-1-124:max_low_dist(layer_idx(i))+125,:,pn),2);
end
%normalizing with max and min of each unit
%norm_aligned_trials(:,i) = (aligned_trials(:,i) - min(aligned_trials(:,i)))/(max(aligned_trials(:,i))-min(aligned_trials(:,i)));
end
end
%normalizing with max and min across units
maximum = max(aligned_trials,[],'all');
minimum = min(aligned_trials,[],'all');
norm_aligned_trials = (aligned_trials - minimum)/(maximum-minimum);
%aligned_trials = aligned_trials(:,~all(isnan(aligned_trials)));
%figure(); plot(1:length(aligned_trials(:,4)), aligned_trials(:,:))
%figure(); plot(1:length(aligned_trials(:,4)), norm_aligned_trials(:,:))
clear sig_su non_sig_su mean_sig_su
cntsig = 0;
cntnotsig = 0;
all_mean_data = nan(4, length(layer_idx));
sig_adapsu = nan(length(norm_aligned_trials(:,1)),length(layer_idx));
non_sig_su =nan(length(norm_aligned_trials(:,1)),length(layer_idx));
for nunit = 1:length(layer_idx)
if ~isempty(peakvals.peak_vals(layer_idx(nunit)).peak)
mean_data = nanmean(peakvals.peak_vals(layer_idx(nunit)).peak,2);
all_mean_data(:,nunit) = mean_data;
if all_mean_data(4,nunit) < all_mean_data(1,nunit) && pvalues(layer_idx(nunit),4) < .05
cntsig= cntsig+1;
sig_adapsu(:,cntsig) = aligned_trials(:,nunit);
% plot(x_stim,norm_chan(:, nunit)')
%hold on
end
if pvalues(layer_idx(nunit),4) > .05
cntnotsig= cntnotsig+1;
non_sig_su(:,cntnotsig) = aligned_trials(:,nunit);
end
end
end
mean_sig_su = nanmean(sig_adapsu,2);
mean_not_sig_su = nanmean(non_sig_su,2);
mean_origin =nanmean(aligned_trials,2);
figure();
%{
col1 = [121/255 96/255 76/255];
col2 = colors(nc,:);
R = linspace(col1(1), col2(1),4);
G = linspace(col1(2), col2(2),4);
B = linspace(col1(3), col2(3),4);
cmap = [R' G' B'];
colormap(cmap);
%}
cmap = flip(cmaps(nc).map) ;
% axes('ColorOrder', cmap, 'NextPlot', 'ReplaceChildren');
colormap(cmap);
h1 = subplot(1,3,1);
for nl = 1:nlines
plot(-125:125, mean_not_sig_su(250*(nl-1)+1:250*nl+1), 'LineWidth',2,'color',cmap(nl,:));
hold on
end
set(h1,'position',get(h1,'position').*[1 1 1.15 1])
ylim(ylims(nc,:))
% ylim([-0.01 1])
xlim([-125 125])
set(gca,'box','off')
ylabel({'\fontsize{14}Spike Rate (spikes/s)'});
title('non sig')
h2 = subplot(1,3,2);
for nl = 1:nlines
plot(-125:125, mean_origin(250*(nl-1)+1:250*nl+1), 'LineWidth',2,'color',cmap(nl,:));
hold on
end
set(h2,'position',get(h2,'position').*[1 1 1.15 1])
ylim(ylims(nc,:))
%ylim([-0.01 1])
xlim([-125 125])
set(gca,'box','off')
ylabel({'\fontsize{14}Spike Rate (spikes/s)'});
title('Overall Mean')
ax1 = gca;
ax1.YAxis.Visible = 'off';
h3 = subplot(1,3,3);
for nl = 1:nlines
plot(-125:125, mean_sig_su(250*(nl-1)+1:250*nl+1), 'LineWidth',2, 'Color',cmap(nl,:) )
hold on
end
set(h3,'position',get(h3,'position').*[1 1 1.15 1])
ylim(ylims(nc,:))
% ylim([-0.01 1])
xlim([-125 125])
title('Adapting units mean')
set(gca,'box','off')
legend('peak 1', 'peak 2', 'peak 3', 'peak 4')
ax1 = gca;
ax1.YAxis.Visible = 'off';
% K norm ylim([-0.02 1.1])
%set(gca, 'linewidth',2)
%hold on
%plot([0 0], ylim,'k')
% if pn >1
%ax1 = gca;
%ax1.YAxis.Visible = 'off';
% end
currfig = gcf;
%currfig.Children(end),
sgtitle({sprintf('%s mean responses', cellclass(nc))}, 'Interpreter', 'none', 'FontSize', 20)
% xlabel('\fontsize{14}Resolution (ms)')
set(gcf,'Units','inches')
set(gcf,'position',[1 1 15 11])
filename = strcat('C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020_corrected\plots\',strcat(sprintf('signonsigadapt_origin_data_aligned_%s',cellclass(nc),'cells_cbrewer2_scaled')));
% saveas(gcf, strcat(filename, '.svg'));
% saveas(gcf, strcat(filename, '.png'));
end
%% Plot three subplots: nonsig| sig increase | sig adapt
pvaluesdir = 'C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\lmer_results_peaks\';
pvalfilename = [pvaluesdir 'lmer_results_orig_03032020_corrected.csv'];
pvalues = dlmread(pvalfilename, ',', 1,1);
channeldir = 'C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020_corrected\orig_peak_values\all_units\';
peakvals = load([channeldir 'all_data_peaks']);
layer = {'K','M','P','K','K','K','M','P','P','','M','M','','','M','','','P','','M','','M','M','','P','M','','P', ...
'P','','','K','P','M','M','M','P','','P','K','P','P','','P','P','M','','P','M','P','M','P','','P','M','M','P','','M','M','P','M', ...
'','','M','M','M','P','M','M','M','M','P','P'};
layer([1,46,55]) = [];
cellclass = [ 'M', 'P', 'K'];
%maps
nlines = 4;
cmaps = struct();
cmaps(1).map =cbrewer2('Reds', nlines);
cmaps(2).map =cbrewer2('Oranges', nlines);
cmaps(3).map =cbrewer2('Blues', nlines);
% M ylim([0.05 .75])
% P ylim([.1 .8])
% K ylim([-0.01 1])
normylims = [[0.05 .75];[.1 .8];[-0.01 1]];
% P ylim([-20 85])
% M ylim([-30 135])
% K ylim([-20 200])
ylims = [[-30 135];[-20 85];[-20 200]];
for nc = 1:3
clear layer_idx
layer_idx = find(strcmp(layer, cellclass(nc)));
%%store all trials of a given peak, in a matrix, across all units
clear aligned_trials
aligned_trials = nan(1204, length(layer_idx));
clear i
for i = 1:length(layer_idx)
if ~isnan(max_low_dist(layer_idx(i)))
for pn = 1:4
% aligned_trials(250*(pn-1)+1:250*pn+1,i)= mean(suas_trials(layer_idx(i)).aligned(max_low_dist(layer_idx(i))-125:max_low_dist(layer_idx(i))+125,:,pn),2);
aligned_trials(250*(pn-1)+1:250*pn+1,i)= mean(suas_trials(layer_idx(i)).aligned(max_low_dist(layer_idx(i))-1-124:max_low_dist(layer_idx(i))+125,:,pn),2);
end
%normalizing with max and min of each unit
%norm_aligned_trials(:,i) = (aligned_trials(:,i) - min(aligned_trials(:,i)))/(max(aligned_trials(:,i))-min(aligned_trials(:,i)));
end
end
%normalizing with max and min across units
maximum = max(aligned_trials,[],'all');
minimum = min(aligned_trials,[],'all');
norm_aligned_trials = (aligned_trials - minimum)/(maximum-minimum);
%aligned_trials = aligned_trials(:,~all(isnan(aligned_trials)));
%figure(); plot(1:length(aligned_trials(:,4)), aligned_trials(:,:))
%figure(); plot(1:length(aligned_trials(:,4)), norm_aligned_trials(:,:))
clear sig_adapsu sig_incsu non_sig_su mean_sig_su
cntsigadapt = 0;
cntsiginc = 0;
cntnotsig = 0;
all_mean_data = nan(4, length(layer_idx));
sig_adapsu = nan(length(norm_aligned_trials(:,1)),length(layer_idx));
sig_incsu = nan(length(norm_aligned_trials(:,1)),length(layer_idx));
non_sig_su =nan(length(norm_aligned_trials(:,1)),length(layer_idx));
for nunit = 1:length(layer_idx)
if ~isempty(peakvals.peak_vals(layer_idx(nunit)).peak)
mean_data = nanmean(peakvals.peak_vals(layer_idx(nunit)).peak,2);
all_mean_data(:,nunit) = mean_data;
if all_mean_data(4,nunit) < all_mean_data(1,nunit) && pvalues(layer_idx(nunit),4) < .05
cntsigadapt= cntsigadapt+1;
sig_adapsu(:,cntsigadapt) = norm_aligned_trials(:,nunit);
% plot(x_stim,norm_chan(:, nunit)')
%hold on
end
if all_mean_data(4,nunit) > all_mean_data(1,nunit) && pvalues(layer_idx(nunit),4) < .05
cntsiginc= cntsiginc+1;
sig_incsu(:,cntsiginc) = norm_aligned_trials(:,nunit);
% plot(x_stim,norm_chan(:, nunit)')
%hold on
end
if pvalues(layer_idx(nunit),4) > .05
cntnotsig= cntnotsig+1;
non_sig_su(:,cntnotsig) = norm_aligned_trials(:,nunit);
end
end
end
mean_not_sig_su = nanmean(non_sig_su,2);
mean_sig_incsu = nanmean(sig_incsu,2);
mean_sig_adapsu = nanmean(sig_adapsu,2);
mean_origin =nanmean(norm_aligned_trials,2);
figure();
cmap = flip(cmaps(nc).map) ;
% axes('ColorOrder', cmap, 'NextPlot', 'ReplaceChildren');
colormap(cmap);
h1 = subplot(1,3,1);
for nl = 1:nlines
plot(-125:125, mean_not_sig_su(250*(nl-1)+1:250*nl+1), 'LineWidth',2,'color',cmap(nl,:));
hold on
end
set(h1,'position',get(h1,'position').*[1 1 1.15 1])
ylim(normylims(nc,:))
% ylim([-0.01 1])
xlim([-125 125])
set(gca,'box','off')
ylabel({'\fontsize{14}Spike Rate (spikes/s)'});
title('non sig')
h2 = subplot(1,3,2);
for nl = 1:nlines
plot(-125:125, mean_sig_incsu(250*(nl-1)+1:250*nl+1), 'LineWidth',2,'color',cmap(nl,:));
hold on
end
set(h2,'position',get(h2,'position').*[1 1 1.15 1])
ylim(normylims(nc,:))
%ylim([-0.01 1])
xlim([-125 125])
set(gca,'box','off')
ylabel({'\fontsize{14}Spike Rate (spikes/s)'});
title('sig increase')
h3 = subplot(1,3,3);
for nl = 1:nlines
plot(-125:125, mean_sig_adapsu(250*(nl-1)+1:250*nl+1), 'LineWidth',2, 'Color',cmap(nl,:) )
hold on
end
set(h3,'position',get(h3,'position').*[1 1 1.15 1])
ylim(normylims(nc,:))
% ylim([-0.01 1])
xlim([-125 125])
title('Adapting units mean')
set(gca,'box','off')
legend('peak 1', 'peak 2', 'peak 3', 'peak 4')
% K norm ylim([-0.02 1.1])
%set(gca, 'linewidth',2)
%hold on
%plot([0 0], ylim,'k')
% if pn >1
%ax1 = gca;
%ax1.YAxis.Visible = 'off';
% end
currfig = gcf;
%currfig.Children(end),
sgtitle({sprintf('%s mean responses', cellclass(nc))}, 'Interpreter', 'none', 'FontSize', 20)
% xlabel('\fontsize{14}Resolution (ms)')
set(gcf,'Units','inches')
set(gcf,'position',[1 1 15 11])
filename = strcat('C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020_corrected\plots\',strcat(sprintf('signonsigadaptinc_stacked_commonnorm_nanmean_bscorr_origin_data_aligned_%s',cellclass(nc),'cells_cbrewer2_scaled')));
saveas(gcf, strcat(filename, '.svg'));
saveas(gcf, strcat(filename, '.png'));
end
%% Plot two subplots: sig increase | sig adapt
pvaluesdir = 'C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\lmer_results_peaks\';
pvalfilename = [pvaluesdir 'lmer_results_orig_03032020_corrected.csv'];
pvalues = dlmread(pvalfilename, ',', 1,1);
channeldir = 'C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020_corrected\orig_peak_values\all_units\';
peakvals = load([channeldir 'all_data_peaks']);
layer = {'K','M','P','K','K','K','M','P','P','','M','M','','','M','','','P','','M','','M','M','','P','M','','P', ...
'P','','','K','P','M','M','M','P','','P','K','P','P','','P','P','M','','P','M','P','M','P','','P','M','M','P','','M','M','P','M', ...
'','','M','M','M','P','M','M','M','M','P','P'};
layer([1,46,55]) = [];
cellclass = [ 'M', 'P', 'K'];
%maps
nlines = 4;
cmaps = struct();
cmaps(1).map =cbrewer2('Reds', nlines);
cmaps(2).map =cbrewer2('YlOrBr', nlines);
cmaps(3).map =cbrewer2('Blues', nlines);
% M ylim([0.05 .75])
% P ylim([.1 .8])
% K ylim([-0.01 1])
normylims = [[0.05 .75];[.1 .8];[-0.01 1]];
% P ylim([-20 85])
% M ylim([-30 135])
% K ylim([-20 200])
%ylims = [[-30 135];[-20 85];[-20 200]];
ylims = [[0 160];[0 100];[0 220]];
for nc = 1:3
clear layer_idx
layer_idx = find(strcmp(layer, cellclass(nc)));
%%store all trials of a given peak, in a matrix, across all units
clear aligned_trials
aligned_trials = nan(1204, length(layer_idx));
clear i
for i = 1:length(layer_idx)
if ~isnan(max_low_dist(layer_idx(i)))
for pn = 1:4
% aligned_trials(250*(pn-1)+1:250*pn+1,i)= mean(suas_trials(layer_idx(i)).aligned(max_low_dist(layer_idx(i))-125:max_low_dist(layer_idx(i))+125,:,pn),2);
aligned_trials(250*(pn-1)+1:250*pn+1,i)= mean(suas_trials(layer_idx(i)).aligned(max_low_dist(layer_idx(i))-1-124:max_low_dist(layer_idx(i))+125,:,pn),2);
end
%normalizing with max and min of each unit
%norm_aligned_trials(:,i) = (aligned_trials(:,i) - min(aligned_trials(:,i)))/(max(aligned_trials(:,i))-min(aligned_trials(:,i)));
end
end
%normalizing with max and min across units
maximum = max(aligned_trials,[],'all');
minimum = min(aligned_trials,[],'all');
norm_aligned_trials = (aligned_trials - minimum)/(maximum-minimum);
%aligned_trials = aligned_trials(:,~all(isnan(aligned_trials)));
%figure(); plot(1:length(aligned_trials(:,4)), aligned_trials(:,:))
%figure(); plot(1:length(aligned_trials(:,4)), norm_aligned_trials(:,:))
clear sig_adapsu sig_incsu non_sig_su mean_sig_su
cntsigadapt = 0;
cntsiginc = 0;
cntnotsig = 0;
all_mean_data = nan(4, length(layer_idx));
sig_adapsu = nan(length(norm_aligned_trials(:,1)),length(layer_idx));
sig_incsu = nan(length(norm_aligned_trials(:,1)),length(layer_idx));
for nunit = 1:length(layer_idx)
if ~isempty(peakvals.peak_vals(layer_idx(nunit)).peak)
mean_data = nanmean(peakvals.peak_vals(layer_idx(nunit)).peak,2);
all_mean_data(:,nunit) = mean_data;
if all_mean_data(4,nunit) < all_mean_data(1,nunit) && pvalues(layer_idx(nunit),4) < .05
cntsigadapt= cntsigadapt+1;
sig_adapsu(:,cntsigadapt) = aligned_trials(:,nunit);
% plot(x_stim,norm_chan(:, nunit)')
%hold on
end
if all_mean_data(4,nunit) > all_mean_data(1,nunit) && pvalues(layer_idx(nunit),4) < .05