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AG_matching_cells_comptest.m
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figure(5); clf
all_animal_name = {'TestMouse1', 'TestMouse2'};%animals{animalLoop};
pop_sec_res = 60;
cell_sec_res = 3;
pall = cell(2,1);
rng('default')
for aLoop = 1:2
animal_name = all_animal_name{aLoop};%animals{animalLoop};
experiment_folder = 'C:/Users/gjb326/Desktop/RecordingData/AlejandroGrau/';
dir_list_fname = '_directory_list.csv';
dir_file = sprintf('%s%s/%s%s', experiment_folder, animal_name, animal_name, dir_list_fname);
processedDir = sprintf('%s%s/processed_files/', experiment_folder, animal_name);
contourDir = sprintf('%s%s/matching_contours/', experiment_folder, animal_name);
AnimalDir = setup_imaging_Sessionfiles(animal_name, dir_file, processedDir, contourDir);
datadir = 'C:\Users\gjb326\Desktop\RecordingData\AlejandroGrau\TestMouse2\processed_files\';
manmatchDir = 'C:\Users\gjb326\Desktop\RecordingData\AlejandroGrau\TestMouse2\matching_contours\TestMouse1\';
matchingfname = sprintf('%smatching_matrix.mat', processedDir);
load(matchingfname);
if strcmp(animal_name, 'TestMouse1')
AnimalDir.processedFile = AnimalDir.processedFile(5:9);
cellmap = cellmap(:,5:9);
sess_corr1 = NaN(5);
% subplot(1,3,1)
colors = viridis(20);
colors = colors(6:10,:);
light_colors = colors + (1-colors)/3;
else
sess_corr2 = NaN(5);
% subplot(1,3,2)
colors = plasma(20);
colors = colors(13:17,:);
light_colors = colors + (1-colors)/3;
end
numcells2use = 80;
sess_corr = NaN(5);
pall{aLoop} = NaN(5,30);
for s1 = 2:5
for s2 = s1:5
if s1~=s2
fname1 = AnimalDir.processedFile{s1};
fname2 = AnimalDir.processedFile{s2};
f1 = load(fname1);
f2 = load(fname2);
% matched = sum(cellmap>0,2)==size(cellmap,2); %
matched = cellmap(:,s1)>0 & cellmap(:,s2)>0;
% sum(matched)
spks1 = f1.ms.neuron.S_matw(cellmap(matched,s1), :);
spks2 = f2.ms.neuron.S_matw(cellmap(matched,s2), :);
spks1 = normalize_rows(spks1);
spks2 = normalize_rows(spks2);
nsamples = min(size(spks1,2), size(spks2,2));
spks1 = spks1(:, 1:nsamples);
spks2 = spks2(:, 1:nsamples);
if ~isempty(numcells2use)
[~, randord] = sort(rand(sum(matched), 1));
spks1 = spks1(randord(1:numcells2use), :);
spks2 = spks2(randord(1:numcells2use), :);
end
pop_dt1 = round(1/median(f1.ms.dt))*pop_sec_res;
pop_dt2 = round(1/median(f2.ms.dt))*pop_sec_res;
pcell_dt1 = round(1/median(f1.ms.dt))*cell_sec_res;
pcell_dt2 = round(1/median(f2.ms.dt))*cell_sec_res;
[popcorr1, ~, ~] = Fenton_pop_stability(spks1, pop_dt1, false);
[popcorr2, ~, ~] = Fenton_pop_stability(spks2, pop_dt2, false);
% [popcorr3, ~, cellcorr3, ~, ~, ~] = Fenton_pop_stability(cat(2, spks1, spks2), pop_dt1, false);
[cellcorr1, ~, ~] = Fenton_cell_corr(spks1, pcell_dt1, false);
[cellcorr2, ~, ~] = Fenton_cell_corr(spks2, pcell_dt2, false);
c1 = triu(cellcorr1,1);
c2 = triu(cellcorr2,1);
lowerinds = c1==0 & c2==0;
sess_corr(s1,s2) = corr(c1(~lowerinds), c2(~lowerinds));
else
fname1 = AnimalDir.processedFile{s1};
f1 = load(fname1);
matched = cellmap(:,s1)>0;
spks1 = f1.ms.neuron.S_matw(cellmap(matched,s1), :);
spks1 = normalize_rows(spks1);
nsamples = size(spks1,2);
spks1 = spks1(:, 1:nsamples);
if ~isempty(numcells2use)
[~, randord] = sort(rand(sum(matched), 1));
spks1 = spks1(randord(1:numcells2use), :);
end
pop_dt1 = round(1/median(f1.ms.dt))*pop_sec_res;
pcell_dt1 = round(1/median(f1.ms.dt))*cell_sec_res;
[popcorr1, ~, ~] = Fenton_pop_stability(spks1, pop_dt1, false);
popcorr1_within = popcorr1;
figure;
set(gcf, 'Name', sprintf('Animal - %s, s1=%d', animal_name, s1))
imagesc(popcorr1, [-.2 1]);
colorbar
end
p1 = nanmean(popcorr1_within,1);
pall{aLoop}(s1,:) = p1;
% p2 = nanmean(popcorr2,1);
% p3 = nanmean(popcorr3,1);
nt = length(p1);
% subplot(5,5, 5*(s1-1)+s2)
figure(5);
subplot(1,2,1)
hold on
plot(1:nt, p1, 'Color', colors(s1,:))
% plot(nt+1:2*nt, p2, 'Color', light_colors(s2,:))
drawnow
end
end
% imagesc(popcorr3, [-.1, 1])
% imagesc(cellcorr3, [-.1, 1])
if strcmp(animal_name, 'TestMouse1')
sess_corr1 = sess_corr;
else
sess_corr2 = sess_corr;
end
% plot(1:nt, nanmean(pall{aLoop},1), 'Color', mean(colors,1), 'LineWidth', 3)
end
subplot(1,2,1)
axis([-1, nt+1, -.1, 1.1])
xv = [0:15:30];
set(gca, 'XTick', xv, 'XTickLabel', xv)
xlabel('Session Time (min)')
ylabel('Population stability (corr)')
%%
% for i = 1:2
a = sess_corr1(~isnan(sess_corr1)); b = sess_corr2(~isnan(sess_corr2));
subplot(1,2,2); cla
hold on
aa = mean(a); bb = mean(b);
colors = viridis(20);
colors = colors(6:10,:);
c1 = mean(colors,1);
colors = plasma(20);
colors = colors(13:17,:);
c2 = mean(colors,1);
bar(0, aa, 'FaceColor', c1/1.5);
scatter(zeros(length(a),1), a, 'MarkerFaceColor', c1, 'MarkerEdgeColor', 'k');
bar(1, bb, 'FaceColor', c2/1.5);
scatter( ones(length(b),1), b, 'MarkerFaceColor', c2, 'MarkerEdgeColor', 'k')
set(gca, 'XTickLabel', {'Mouse1', 'Mouse2'}, 'XTick', [0 1])
axis([-1.5 2.5 0 1.1])
axis square
xlabel('')
ylabel('PCO (corr)')
%%
%%
% fdir = 'C:\Users\gjb326\Desktop\RecordingData\AlejandroGrau\datfiles\V4Min\';
% afname = sprintf('%s%s', 'GRAU_V4Miniscope_TestMouse2_CaImaging_Pretraining_Arena.dat');
% rfname = sprintf('%s%s', 'GRAU_V4Miniscope_TestMouse2_CaImaging_Pretraining_Room.dat');
clear
behav = [];
fdir = 'C:\Users\gjb326\Desktop\RecordingData\AlejandroGrau\datfiles\V4Min\';
% afname = sprintf('%s%s', fdir, 'GRAU_V4Miniscope_TestMouse1_CaImaging_Pretraining_Arena.dat');
% rfname = sprintf('%s%s', fdir, 'GRAU_V4Miniscope_TestMouse1_CaImaging_Pretraining_Room.dat');
% behav.msFileName = "C:\Users\gjb326\Desktop\RecordingData\AlejandroGrau\TestMouse1\processed_files\2022_07_05___17_09_41_ms_placecells_data.mat";
% afname = sprintf('%s%s', fdir, 'GRAU_V4Miniscope_TestMouse1_CaImaging_TR2-1_Arena.dat');
% rfname = sprintf('%s%s', fdir, 'GRAU_V4Miniscope_TestMouse1_CaImaging_TR2-1_Room.dat');
% behav.msFileName = "C:\Users\gjb326\Desktop\RecordingData\AlejandroGrau\TestMouse1\processed_files\2022_07_13___15_12_07_ms_placecells_data.mat";
afname = sprintf('%s%s', fdir, 'GRAU_V4Miniscope_TestMouse1_CaImaging_TR2-RET_Arena.dat');
rfname = sprintf('%s%s', fdir, 'GRAU_V4Miniscope_TestMouse1_CaImaging_TR2-RET_Room.dat');
behav.msFileName = "C:\Users\gjb326\Desktop\RecordingData\AlejandroGrau\TestMouse1\processed_files\2022_07_18___09_27_18_ms_placecells_data.mat";
% afname = sprintf('%s%s', fdir, 'GRAU_V4Miniscope_TestMouse2_CaImaging_RET_Arena.dat');
% rfname = sprintf('%s%s', fdir, 'GRAU_V4Miniscope_TestMouse2_CaImaging_RET_Room.dat');
% behav.msFileName = "C:\Users\gjb326\Desktop\RecordingData\AlejandroGrau\TestMouse2\processed_files\2022_07_18___08_51_01_ms_placecells_data.mat";
% afname = sprintf('%s%s', fdir, 'GRAU_V4Miniscope_TestMouse2_CaImaging_TR1_Arena.dat');
% rfname = sprintf('%s%s', fdir, 'GRAU_V4Miniscope_TestMouse2_CaImaging_TR1_Room.dat');
% behav.msFileName = "C:\Users\gjb326\Desktop\RecordingData\AlejandroGrau\TestMouse2\processed_files\2022_07_12___11_15_53_ms_placecells_data.mat";
behav.roomFname = rfname;
behav.arenaFname = afname;
%%
[arena, arena_params] = read_APA_csv(afname, []);
[room , room_params] = read_APA_csv(rfname, []);
behav.timestamps = arena.timestamps;
behav_params.arena_radius = 20;
% % % [xx,yy] = ginput(3);
% % % [R,xcyc] = fit_circle_through_3_points([xx yy])
behav_params.center = [130 130];
behav_params.radius = 112;
behav_params.behav_fps = round(1/median(abs(diff(behav.timestamps./1000))));
behav_params.behav_smoothing_interval = .5;
behav_params.num_partitions = 2;
behav_params.max_spd_thresh = 50;
behav_params.min_spd_thresh = 2;
behav_params.min_samples = 10;
% ARENA FRAME
nanind = arena.rawx==0 & arena.rawy == 0;
x = arena.rawx;
y = arena.rawy;
x(nanind) = NaN;
y(nanind) = NaN;
t = arena.timestamps;
if length(unique(t)) ~= length(t)
ind = find(diff(t)==0)+1;
t(ind) = t(ind)+1;
end
ts = t./1000; % convert from ms to seconds
nanind = (isnan(x) & isnan(y));
xn = interp1(ts(~nanind), x(~nanind), ts(nanind), 'linear');
yn = interp1(ts(~nanind), y(~nanind), ts(nanind), 'linear');
x(nanind) = xn; y(nanind) = yn;
x = behav_params.arena_radius*(x-behav_params.center(1))./behav_params.radius;
y = behav_params.arena_radius*(y-behav_params.center(2))./behav_params.radius;
ksize = round(behav_params.behav_fps*behav_params.behav_smoothing_interval);
kern = ones(ksize, 1); kern = kern./sum(kern(:));
arena.x = conv(x, kern, 'same');
arena.y = conv(y, kern, 'same');
% ROOM FRAME
nanind = room.rawx==0 & room.rawy == 0;
x = room.rawx;
y = room.rawy;
x(nanind) = NaN;
y(nanind) = NaN;
t = room.timestamps;
if length(unique(t)) ~= length(t)
ind = find(diff(t)==0)+1;
t(ind) = t(ind)+1;
end
ts = t./1000; % convert from ms to seconds
nanind = (isnan(x) & isnan(y));
xn = interp1(ts(~nanind), x(~nanind), ts(nanind), 'linear');
yn = interp1(ts(~nanind), y(~nanind), ts(nanind), 'linear');
x(nanind) = xn; y(nanind) = yn;
x = behav_params.arena_radius*(x-behav_params.center(1))./behav_params.radius;
y = behav_params.arena_radius*(y-behav_params.center(2))./behav_params.radius;
ksize = round(behav_params.behav_fps*behav_params.behav_smoothing_interval);
kern = ones(ksize, 1); kern = kern./sum(kern(:));
room.x = conv(x, kern, 'same');
room.y = conv(y, kern, 'same');
%
behav_dt = [median(diff(behav.timestamps)); diff([behav.timestamps])]/1000;
behav.dt = behav_dt;
room.speed = sqrt(diff([room.x(1); room.x]).^2 + diff([room.y(1); room.y]).^2)./behav_dt;
arena.speed = sqrt(diff([arena.x(1); arena.x]).^2 + diff([arena.y(1); arena.y]).^2)./behav_dt;
behav.room = room; behav.arena = arena;
temp = load(behav.msFileName);
ms = temp.ms;
params = temp.params;
ms.room.x = interp1(behav.timestamps, behav.room.x, ms.timestamps, 'linear');
ms.room.y = interp1(behav.timestamps, behav.room.y, ms.timestamps, 'linear');
ms.arena.x = interp1(behav.timestamps, behav.arena.x, ms.timestamps, 'linear');
ms.arena.y = interp1(behav.timestamps, behav.arena.y, ms.timestamps, 'linear');
dt = ms.dt_corrected;
ms.room.speed = sqrt(diff([ms.room.x(1); ms.room.x]).^2 + diff([ms.room.y(1); ms.room.y]).^2)./dt;
ms.arena.speed = sqrt(diff([ms.arena.x(1); ms.arena.x]).^2 + diff([ms.arena.y(1); ms.arena.y]).^2)./dt;
ms.room.speed_smooth = conv(ms.room.speed, kern, 'same');
ms.arena.speed_smooth = conv(ms.arena.speed, kern, 'same');
% is_moving = ms.arena.speed_smooth>params_sub.min_spd_thresh;
[~, speed_epochs] = get_speed_epochs(ms.arena.speed_smooth, behav_params);
ms.speed_epochs = speed_epochs;
is_moving = speed_epochs;
%
params.pos_bins = [-20:2:20];
params.occupancy_thresh = .00; % in seconds
N = params.pfield_kernel_radius*2+1; alpha = 2.5; % default
params.pfield_gauss_std = ((N-1)/(2*alpha))*abs(params.pos_bins(1)-params.pos_bins(2));
spks = normalize_rows(ms.neuron.S_matw);
[ms.room] = construct_place_maps_2D(ms.room, ms.room.x(is_moving), ms.room.y(is_moving), ms.dt(is_moving), spks(:, is_moving), params.pos_bins, params);
[ms.arena] = construct_place_maps_2D(ms.arena, ms.arena.x(is_moving), ms.arena.y(is_moving), ms.dt(is_moving), spks(:, is_moving), params.pos_bins, params);
%
[ms.arena.pcell_stats] = place_cell_stats(spks, ms.arena.pfields, ms.arena.spkmap, ms.arena.vmap);
[ms.room.pcell_stats] = place_cell_stats(spks, ms.room.pfields, ms.room.spkmap, ms.room.vmap);
ms.arena.pfield_alpha = ~isnan(ms.arena.vmap);
ms.room.pfield_alpha = ~isnan(ms.room.vmap);
%%
rng(100);
nsegs = size(spks,1);
segs = 1:nsegs;
ds = 30;
[~, ord] = sort(rand(1,nsegs));
segs2use = segs(ord(1:ds));
% [~, spord] = sort(ms.arena.pcell_stats.spkRate, 'descend');
% segs2use = segs(spord(1:ds));
% as = ms.arena.pcell_stats.infoPerSpike(segs2use);
infspk = ms.arena.pcell_stats.infoPerSpike(segs2use);
% spkr = ms.arena.pcell_stats.spkRate(segs2use(1:ds));
as = infspk;
[~, arena_info_ord] = sort(as, 'descend');
p_a = ms.arena.pfields_smooth(segs2use,:,:);
p_a = p_a(arena_info_ord,:,:);
s_sub = spks(segs2use,:);
s_sub = s_sub(arena_info_ord,:);
% c_sub = ms.neuron.C(segs2use,:);
c_sub = ms.neuron.C(segs2use,:) + ms.neuron.YrA(segs2use,:);
c_sub = c_sub(arena_info_ord,:);
c_sub = normalize_rows(c_sub);
p_r = ms.room.pfields_smooth(segs2use,:,:);
p_r = p_r(arena_info_ord,:,:);
% ss = get(0,'screensize');
% ar = mean([1, ss(3)/ss(4)])-1;
% n = ceil(sqrt(nsegs/ds));
nc = 2; % round(n*(1-ar))+1;
nr = ds; % round(n*(1+ar));
smoothing_kern = gausswin(7);
% smoothing_kern = gausswin(ksize+1);
smoothing_kern = smoothing_kern*smoothing_kern';
smoothing_kern = smoothing_kern./(sum(smoothing_kern(:)));
%%
figure(10); clf;
set(gcf, 'Name', 'ARENA FRAME / ROOM FRAME')
% colormap(plasma)
% figure(11); clf
% set(gcf, 'Name', 'ROOM FRAME')
colormap(viridis)
figure(10);
for i = 1:ds
subplot_tight(2, ds+1, i, [.001 .001])
p = squeeze(p_a(i,:,:));
% p = squeeze(ms.arena.pfields(i,:,:));
p = p./max(p(:));
ii = imagesc(p, [0 .5]);
ii.AlphaData = ms.arena.pfield_alpha;
axis square off
end
% figure(11);
for i = 1:ds
subplot_tight(2, ds+1, ds+i+1, [.001 .001])
p = squeeze(p_r(i,:,:));
% p = squeeze(ms.room.pfields(i,:,:));
p = p./max(p(:));
ii = imagesc(p, [0 .5]);
ii.AlphaData = ms.room.pfield_alpha;
% ii = imagesc(p, 'AlphaData', ms.room.pfield_alpha);
% ii.Parent.ALim = [0 .5];
axis square off
end
p = p*NaN;
subplot_tight(2, ds+1, [ds+1, 2*(ds+1)], [.1 .001]); cla
ii = imagesc(p, [0 .5]);
ii.AlphaData = NaN*ms.arena.pfield_alpha;
colorbar
axis off
figure(11); clf;
set(gcf, 'Name', 'ca traces (blk) and inferred spks (red)')
hold on;
stacked_traces(c_sub, .9, {'k-'})
stacked_traces(s_sub, .9, {'r-'})
ts = ms.timestamps./1000;
mts = mod(ts, 60.001);
xd = find(mts(1:end-1) > mts(2:end));
xl = abs(round(ts(xd)));
set(gca, 'XTickLabel', xl, 'XTick', xd);
xlabel('Time (sec)')